{"slug":"1rm-estimation","name":"1RM Estimation","fullName":"One-Repetition Maximum Estimation from Submaximal Loads","aliases":["one-rep max prediction","estimated 1RM","strength prediction","maximal strength assessment"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Strength Testing","year":"1993","originator":"Matt Brzycki","url":"https://scholargate.app/en/sports-science/1rm-estimation","markdownUrl":"https://scholargate.app/en/sports-science/1rm-estimation.md","definition":"One-repetition maximum (1RM) estimation is a method to predict an athlete's maximum strength in a given lift without performing an actual maximal single repetition. Developed systematically by Matt Brzycki (1993) and refined by numerous researchers, 1RM estimation uses submaximal loads and repetition performance to extrapolate a strength ceiling. Rather than exposing untrained individuals, older adults, or post-injury athletes to the stress and injury risk of true 1RM testing, estimation provides a safer, time-efficient alternative. Multiple prediction equations exist, with varying accuracy depending on population and lift type.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matt Brzycki","subfamily":"Strength Testing","year":"1993","type":"submaximal prediction"},"citations":[{"ref":"Brzycki, M. (1993). Strength testing: predicting a one-rep max from reps-to-fatigue. Journal of Physical Education, Recreation and Dance, 64(1), 88-90.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/25068033/"},{"ref":"Reynolds, J. M., Gordon, T. J., & Robergs, R. A. (2006). Prediction of one repetition maximum strength from multiple repetition maximum testing and anthropometry. Journal of Strength and Conditioning Research, 20(3), 584-592.","type":"article","doi":"10.1519/00124278-200608000-00020","isbn":null,"url":null},{"ref":"Mayhew, J. L., Johnson, B. D., LaMonte, M. J., Laubach, L. L., & Karg, K. (2008). Accuracy of prediction equations for determining 1-RM strength in apparently healthy adults. Journal of Sports Medicine and Physical Fitness, 48(4), 418-424.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/18832587/"}],"related":["force-velocity-profile","rate-of-force-development","reactive-strength-index","isokinetic-dynamometry","counter-movement-jump"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"a-star-search-algorithm","name":"A-star Search Algorithm","fullName":"A* Search Algorithm","aliases":["A* algorithm","A-star algorithm","A* search"],"domain":"operations-research","family":"ml-model","subfamily":"Graph Algorithms","year":"1968","originator":"Peter E. Hart, Nils J. Nilsson, and Bertram Raphael","url":"https://scholargate.app/en/operations-research/a-star-search-algorithm","markdownUrl":"https://scholargate.app/en/operations-research/a-star-search-algorithm.md","definition":"The A* Search Algorithm, developed by Peter E. Hart, Nils J. Nilsson, and Bertram Raphael in 1968, is an optimal path-finding algorithm that combines the benefits of Dijkstra's algorithm with heuristic guidance. It efficiently finds the shortest path by balancing actual distance from the start with estimated distance to the goal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter E. Hart, Nils J. Nilsson, and Bertram Raphael","subfamily":"Graph Algorithms","year":"1968","type":"algorithm"},"citations":[{"ref":"Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107.","type":"article","doi":"10.1109/TSSC.1968.300136","isbn":null,"url":null},{"ref":"Russell, S. J., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0-13-604259-4","url":null}],"related":["dijkstra-algorithm","bellman-ford-algorithm","breadth-first-search"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ab-design","name":"AB Design","fullName":"AB Single-Subject Experimental Design","aliases":["baseline-intervention design","AB single-case design","AB phase design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s","originator":"Murray Sidman; Baer, Wolf & Risley","url":"https://scholargate.app/en/experimental-design/ab-design","markdownUrl":"https://scholargate.app/en/experimental-design/ab-design.md","definition":"The AB design is the simplest single-subject experimental design, consisting of two sequential phases: a baseline phase (A) in which the target behavior is observed under natural conditions without intervention, followed by an intervention phase (B) in which the treatment or manipulation is introduced. Changes in the behavior's level, trend, or variability between phases are used to infer the effect of the intervention on the individual participant.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray Sidman; Baer, Wolf & Risley","year":"1960s","type":"Single-subject experimental design","dataType":"Repeatedly measured behavioral or outcome data over time","subfamily":"Deneysel desen"},"citations":[{"ref":"Sidman, M. (1960). Tactics of Scientific Research: Evaluating Experimental Data in Psychology. Basic Books.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tactics+of+Scientific+Research+Sidman+1960"},{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91-97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null}],"related":["aba-design","abab-design","multiple-baseline-design","single-subject-experimental-design","changing-criterion-design","alternating-treatments-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ab-testing","name":"A/B Test","fullName":"A/B Test (Online Controlled Experiment)","aliases":["split test","controlled experiment","two-variant test","A/B Testi (Online Kontrollü Deney)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1935,"originator":"Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935)","url":"https://scholargate.app/en/experimental-design/ab-testing","markdownUrl":"https://scholargate.app/en/experimental-design/ab-testing.md","definition":"An A/B test is a randomized controlled experiment that simultaneously exposes two groups of users to a control variant (A) and a treatment variant (B) in order to determine whether a measured outcome differs significantly between them. The modern online controlled experiment framework was systematized by Ron Kohavi and colleagues at Microsoft in the early 2000s, building on R. A. Fisher's classical randomization principles from 1935. It is the dominant causal inference tool in web product development, digital marketing, and experimentation platforms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935)","year":1935,"popularized":2000,"family":"Hypothesis test","type":"Parametric comparison (frequentist or Bayesian)","variants":"frequentist z-test / t-test, Bayesian A/B, SPRT (Sequential Probability Ratio Test)","groups":2,"outcome":"binary or continuous","parametric":true,"minSamplePerGroup":100,"distribution":"Normal (z) or Student t"},"citations":[{"ref":"Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.","type":"book","doi":null,"isbn":"9781108724265","url":null},{"ref":"Deng, A., Xu, Y., Kohavi, R., & Walker, T. (2013). Improving the Sensitivity of Online Controlled Experiments by Utilizing Pre-Experiment Data. KDD '13.","type":"inproceedings","doi":null,"isbn":null,"url":"https://dl.acm.org/doi/10.1145/2487575.2488217"}],"related":["independent-t-test","chi-square-test","proportions-z-test","bayesian-ab-test","multiarm-bandit","randomized-controlled-trial"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aba-design","name":"ABA Design","fullName":"ABA Reversal Design","aliases":["reversal design","withdrawal design","ABA withdrawal design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968","originator":"Montrose Wolf, Donald Baer, Todd Risley (applied behavior analysis tradition)","url":"https://scholargate.app/en/experimental-design/aba-design","markdownUrl":"https://scholargate.app/en/experimental-design/aba-design.md","definition":"The ABA design is a single-subject experimental design that demonstrates experimental control through three sequential phases: a baseline phase (A1), an intervention phase (B), and a return-to-baseline withdrawal phase (A2). By removing the intervention in the final phase and observing whether behavior reverts toward baseline levels, researchers establish a functional relationship between the treatment and the target behavior for an individual participant.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Montrose Wolf, Donald Baer, Todd Risley (applied behavior analysis tradition)","year":"1968","type":"Single-subject experimental design","dataType":"Continuous behavioral observation data (frequency, rate, duration, intensity)","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134752556","url":null}],"related":["ab-design","abab-design","multiple-baseline-design","single-subject-experimental-design","alternating-treatments-design","changing-criterion-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"abab-design","name":"ABAB design","fullName":"ABAB Reversal Design","aliases":["reversal design","withdrawal design","ABAB reversal","operant reversal design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s (Sidman 1960; Baer et al. 1968)","originator":"Murray Sidman; Baer, Wolf & Risley (applied behavior analysis formalization)","url":"https://scholargate.app/en/experimental-design/abab-design","markdownUrl":"https://scholargate.app/en/experimental-design/abab-design.md","definition":"The ABAB design is a single-subject experimental methodology that establishes causal control by repeatedly introducing and removing an intervention. A baseline phase (A) is followed by an intervention phase (B), then a return to baseline (A), and a second intervention phase (B), allowing the researcher to demonstrate that observed behavior changes are produced by the intervention rather than by coincidental factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray Sidman; Baer, Wolf & Risley (applied behavior analysis formalization)","year":"1960s (Sidman 1960; Baer et al. 1968)","type":"Single-subject experimental design","dataType":"Repeated measures of a behavioral or performance outcome over time","subfamily":"Deneysel desen"},"citations":[{"ref":"Sidman, M. (1960). Tactics of Scientific Research: Evaluating Experimental Data in Psychology. Basic Books.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tactics+of+Scientific+Research+Sidman+1960"},{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null}],"related":["ab-design","aba-design","multiple-baseline-design","single-subject-experimental-design","alternating-treatments-design","changing-criterion-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"abbreviated-mental-test","name":"Abbreviated Mental Test Score","fullName":"Abbreviated Mental Test","aliases":["AMT","AMT4","Abbreviated Mental Test Score"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"cognitive screening","year":"1972","originator":"H. Mark Hodkinson","url":"https://scholargate.app/en/neuropsychology/abbreviated-mental-test","markdownUrl":"https://scholargate.app/en/neuropsychology/abbreviated-mental-test.md","definition":"The Abbreviated Mental Test (AMT) is a brief, 10-item cognitive screening instrument developed by Hodkinson in 1972 and originally published in Age and Ageing. It was specifically designed to quickly assess cognitive function in older hospitalized patients, detecting delirium and dementia in acute hospital settings. The AMT is valued for its simplicity, brevity (2–3 minutes), and utility in fast-paced clinical environments where quick cognitive triage is essential.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"H. Mark Hodkinson","subfamily":"cognitive screening","year":"1972","type":"Brief clinician-administered cognitive screening instrument"},"citations":[{"ref":"Hodkinson, H. M. (1972). Evaluation of a mental test score for assessment of mental impairment in the elderly. Age and Ageing, 1(4), 233-238.","type":"article","doi":"10.1093/ageing/1.4.233","isbn":null,"url":null},{"ref":"Swain, D. G., Nightingale, P. G., Constable, S. H., & Nightingale, J. M. (2007). Value of the Abbreviated Mental Test in screening for dementia and delirium among older people in the acute hospital setting. International Journal of Geriatric Psychiatry, 17(1), 63-69.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Value+of+the+Abbreviated+Mental+Test+in+screening+for+dementia+and+delirium+among+older+people+in+the+acute+hospital+setting+Swain"},{"ref":"Bellelli, G., Nobili, A., Annoni, G., et al. (2014). Under-reporting of cognitive impairment in older hospitalized patients: The role of cognitive reserve. Journal of the American Geriatrics Society, 56(12), 2271-2276.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/19093923"}],"related":["mmse","saint-louis-mental-status","dementia-rating-scale","addenbrookes-cognitive-examination","frontal-assessment-battery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"abbreviation-expansion","name":"Abbreviation Expansion","fullName":"Abbreviation and Acronym Resolution","aliases":["acronym resolution","abbreviation disambiguation","short-form expansion","Kısaltma ve Akronim Çözümleme"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2003","originator":"Schwartz & Hearst (2003) — seminal algorithm for biomedical abbreviation detection","url":"https://scholargate.app/en/text-mining/abbreviation-expansion","markdownUrl":"https://scholargate.app/en/text-mining/abbreviation-expansion.md","definition":"Abbreviation and acronym resolution is a natural-language-processing pipeline that maps each short form in a text to its full-length definition using contextual cues from the surrounding text. It is especially important in medical, legal, and technical documents, where the same acronym may carry entirely different meanings across domains. The field's foundational algorithm was published by Schwartz and Hearst (2003) for biomedical literature and has since been extended by neural and transformer-based approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schwartz & Hearst (2003) — seminal algorithm for biomedical abbreviation detection","year":"2003","type":"NLP disambiguation pipeline","inputData":"Text corpus (medical, legal, technical, or general domain)","output":"Short-form → long-form mapping per occurrence","requiresNormality":false,"minimumSample":10,"difficulty":2,"domainStrength":"Health (1.6), Natural Language (1.4), Social (1.3), Education (1.3), Business (1.3)"},"citations":[{"ref":"Schwartz, A.S. & Hearst, M.A. (2003). A Simple Algorithm for Identifying Abbreviation Definitions in Biomedical Text. Pacific Symposium on Biocomputing (PSB), 8, 451-462.","type":"inproceedings","doi":null,"isbn":null,"url":"https://psb.stanford.edu/psb-online/proceedings/psb03/schwartz.pdf"},{"ref":"Veyseh, A.P.B. et al. (2022). MACRONYM: A Large-Scale Dataset for Macroeconomic Acronym Understanding. Findings of NAACL 2022.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2022.findings-naacl.12"}],"related":["named-entity-recognition","word-sense-disambiguation","text-normalization","information-extraction","biomedical-text-mining"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"abc-analysis","name":"ABC Analysis","fullName":"ABC Inventory Classification","aliases":["Pareto Inventory Classification","80-20 Inventory Rule","ABC Classification","ABC Stok Analizi"],"domain":"operations-research","family":"process-pipeline","subfamily":"Inventory control","year":1998,"originator":"Pareto principle; Silver, Pyke & Peterson","url":"https://scholargate.app/en/operations-research/abc-analysis","markdownUrl":"https://scholargate.app/en/operations-research/abc-analysis.md","definition":"ABC Analysis is a demand-value segmentation technique that divides inventory items into three classes — A, B, and C — based on their annual usage value (unit cost multiplied by annual demand). Rooted in the Pareto principle and codified for inventory management by Silver, Pyke, and Peterson (1998), it guides managers to concentrate control resources on the small fraction of items that drive the vast majority of total inventory spend.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pareto principle; Silver, Pyke & Peterson","year":1998,"type":"Inventory segmentation technique","subfamily":"Inventory control","input":"Annual usage value per SKU","output":"A / B / C tier assignment per item"},"citations":[{"ref":"Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory Management and Production Planning and Scheduling (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0-471-11947-0","url":null}],"related":["economic-order-quantity","safety-stock"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"abcd-matrix","name":"ABCD Matrix","fullName":"ABCD Matrix Method","aliases":["ray transfer matrix","ABCD method","system matrix"],"domain":"optics","family":"process-pipeline","subfamily":"Matrix method","year":"1966","originator":"Herwig Kogelnik and Tingye Li","url":"https://scholargate.app/en/optics/abcd-matrix","markdownUrl":"https://scholargate.app/en/optics/abcd-matrix.md","definition":"The ABCD matrix, or ray transfer matrix method, is a compact algebraic framework for analyzing optical systems. Introduced by Kogelnik and Li in 1966, it represents the linear transformation of ray position and angle (or Gaussian beam parameters) through optical elements. This method is foundational in laser physics, Gaussian optics, and optical design, enabling rapid calculation of resonator stability, beam propagation, and system performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herwig Kogelnik and Tingye Li","subfamily":"Matrix method","year":"1966","type":"Ray optics formalism"},"citations":[{"ref":"Kogelnik, H., & Li, T. (1966). Laser beams and resonators. Applied Optics, 5(10), 1550-1567.","type":"article","doi":"10.1364/AO.5.001550","isbn":null,"url":null},{"ref":"Siegman, A. E. (1986). Lasers. University Science Books.","type":"book","doi":null,"isbn":null,"url":"https://www.uscibooks.com/"},{"ref":"Gerrard, A., & Burch, J. M. (1974). Introduction to Matrix Methods in Optics. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/"}],"related":["beam-propagation-method","jones-calculus","fourier-optics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"absorptive-capacity-scale","name":"Absorptive Capacity Scale","fullName":"Absorptive Capacity (ACAP) Measurement Scale","aliases":["ACAP","Zahra-George Scale"],"domain":"strategic-management","family":"process-pipeline","subfamily":"organizational-learning","year":"2002","originator":"Shaker Zahra and Gerard George","url":"https://scholargate.app/en/strategic-management/absorptive-capacity-scale","markdownUrl":"https://scholargate.app/en/strategic-management/absorptive-capacity-scale.md","definition":"Absorptive Capacity (ACAP) refers to an organization's ability to acquire, assimilate, transform, and exploit external knowledge to enhance innovation and performance. Zahra and George (2002) reconceptualized absorptive capacity into four distinct but interrelated processes in their foundational Academy of Management Review article. This measurement scale captures organizational learning dynamics and knowledge-based competitive advantage, making it essential for assessing innovation capability and knowledge management effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shaker Zahra and Gerard George","subfamily":"organizational-learning","year":"2002","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203.","type":"article","doi":"10.5465/amr.2002.6587995","isbn":null,"url":null},{"ref":"Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.","type":"article","doi":"10.2307/2393553","isbn":null,"url":null},{"ref":"Jansen, J. J. P., Van Den Bosch, F. A. J., & Volberda, H. W. (2005). Managing potential and realized absorptive capacity: How do organizational antecedents matter? Academy of Management Journal, 48(6), 999–1015.","type":"article","doi":"10.5465/amj.2005.19573106","isbn":null,"url":null}],"related":["dynamic-capabilities-scale","knowledge-management-scale","organizational-resilience-scale","entrepreneurial-orientation-scale","strategic-orientation-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"abstract-writing","name":"Abstract Writing","fullName":"Academic Abstract Composition and Structure","aliases":["abstract","structured abstract","unstructured abstract"],"domain":"academic-writing","family":"process-pipeline","subfamily":"manuscript-component","year":"1950","originator":"Scientific publishing community; formalized by ICMJE and indexing services (MEDLINE, Web of Science)","url":"https://scholargate.app/en/academic-writing/abstract-writing","markdownUrl":"https://scholargate.app/en/academic-writing/abstract-writing.md","definition":"An abstract is a self-contained, concise summary of a research article that enables readers to quickly understand the study's purpose, methods, results, and conclusions without reading the full paper. Abstracts are the primary gateway to published literature: they appear in journal issues, bibliographic databases (MEDLINE, Web of Science, Scopus), and search engine results. Well-written abstracts increase citation rates and visibility; poorly written ones obscure important research. The ICMJE and major journals mandate abstracts for original research, with structured formats (Background, Methods, Results, Conclusions) becoming increasingly standard.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scientific publishing community; formalized by ICMJE and indexing services (MEDLINE, Web of Science)","subfamily":"manuscript-component","year":"1950","type":"Guideline"},"citations":[{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.","type":"guideline","doi":null,"isbn":null,"url":"https://www.icmje.org/"},{"ref":"Greenhalgh, T. (1997). How to read a paper: The basics of evidence based medicine. British Medical Journal, 315(7112), 180–184.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+to+read+a+paper%3A+The+basics+of+evidence+based+medicine+Greenhalgh"}],"related":["imrad-structure","scientific-writing-clarity","statistical-reporting-standards","journal-submission-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"academic-burnout-scale","name":"Academic Burnout Scale","fullName":"Academic Burnout Scale (ABS)","aliases":["ABS"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"occupational-stress-academic","year":"2002","originator":"Schaufeli, Martínez-Martínez, Marqués Pinto, Salanova","url":"https://scholargate.app/en/educational-psychology/academic-burnout-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/academic-burnout-scale.md","definition":"The Academic Burnout Scale measures three dimensions of student burnout: emotional exhaustion, cynicism toward studies, and reduced academic efficacy. Developed by Schaufeli and colleagues in 2002, it adapts the Maslach Burnout Inventory framework to the academic context, providing researchers and educators with a validated tool to assess psychological distress in higher education settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schaufeli, Martínez-Martínez, Marqués Pinto, Salanova","subfamily":"occupational-stress-academic","year":"2002","type":"Self-report questionnaire"},"citations":[{"ref":"Schaufeli, W. B., Martinez, I. M., Marqués Pinto, A., Salanova, M., & Bakker, A. B. (2002). Burnout and engagement in university students: A cross-national study. Journal of Cross-Cultural Psychology, 33(5), 464-481.","type":"article","doi":"10.1177/0022022102033005003","isbn":null,"url":null},{"ref":"Maslach, C., Jackson, S. E., & Leiter, M. P. (2001). Maslach Burnout Inventory – Student Survey (MBI-SS). Consulting Psychologists Press.","type":"article","doi":null,"isbn":null,"url":"https://www.mindgarden.com/"}],"related":["procrastination-assessment-scale","test-anxiety-inventory","academic-resilience-scale","study-skills-assessment","academic-help-seeking-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"academic-help-seeking-scale","name":"Academic Help-Seeking Scale","fullName":"Academic Help-Seeking Scale (AHSS)","aliases":["AHSS"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"adaptive-seeking-behavior","year":"1990s-2000s","originator":"Karabenick, S.A.; colleagues","url":"https://scholargate.app/en/educational-psychology/academic-help-seeking-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/academic-help-seeking-scale.md","definition":"The Academic Help-Seeking Scale measures students' inclination to seek academic help, their preferred sources of assistance (instructors, peers, tutors), and barriers that inhibit help-seeking (fear of judgment, embarrassment, preference for independence). Developed by Karabenick and colleagues in the 1990s, the AHSS recognizes that seeking help when confused or struggling is not a sign of weakness but a critical academic skill that separates successful from struggling students. By identifying whether students avoid help due to shame, lack of awareness, or other barriers, this scale enables targeted interventions promoting adaptive help-seeking.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karabenick, S.A.; colleagues","subfamily":"adaptive-seeking-behavior","year":"1990s-2000s","type":"Self-report questionnaire"},"citations":[{"ref":"Karabenick, S. A., & Knapp, J. R. (2005). Help seeking in learning. In C. E. Spielberger (Ed.), Encyclopedia of applied psychology (Vol. 2, pp. 149–160). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Help+seeking+in+learning+Karabenick"},{"ref":"Arbreton, A. J. A. (1998). Student goal orientation and help-seeking strategy use. In S. A. Karabenick (Ed.), Strategic help seeking: Implications for learning and teaching (pp. 95–120). Lawrence Erlbaum.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=help-seeking+behavior+academic"}],"related":["academic-resilience-scale","study-skills-assessment","test-anxiety-inventory","peer-learning-scale","procrastination-assessment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"academic-integrity-scale","name":"Academic Integrity Scale","fullName":"Academic Integrity Scale (AIS)","aliases":["AIS"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"ethical-academic-behavior","year":"2000s","originator":"Various authors; instrument varies by version","url":"https://scholargate.app/en/educational-psychology/academic-integrity-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/academic-integrity-scale.md","definition":"The Academic Integrity Scale measures students' attitudes, values, and likelihood of engaging in academic dishonesty including cheating, plagiarism, and unauthorized collaboration. Multiple validated versions exist, each assessing different facets of academic integrity such as personal integrity commitment, perceived cheating prevalence, institutional support for honesty, and personal susceptibility to cheating. This instrument is essential for understanding integrity culture in educational settings and evaluating interventions promoting academic honesty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various authors; instrument varies by version","subfamily":"ethical-academic-behavior","year":"2000s","type":"Self-report questionnaire"},"citations":[{"ref":"Cizek, G. J. (2003). Detecting and preventing classroom cheating: Practical strategies for teachers and professors. Corwin Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=academic+integrity+scale+cheating"},{"ref":"Steneck, N. H. (2007). Introduction to the Responsible Conduct of Research. US Department of Health and Human Services.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products/rcrinstitutions"}],"related":["academic-burnout-scale","procrastination-assessment-scale","university-student-satisfaction","study-skills-assessment","academic-help-seeking-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"academic-motivation-scale","name":"Academic Motivation Scale","fullName":"Academic Motivation Scale (AMS)","aliases":["AMS","Intrinsic Motivation Scale"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Academic motivation","year":"1992","originator":"Robert J. Vallerand","url":"https://scholargate.app/en/educational-psychology/academic-motivation-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/academic-motivation-scale.md","definition":"The Academic Motivation Scale (AMS) is a 28-item self-report instrument developed by Vallerand et al. (1992) to assess the quality of students' academic motivation. It distinguishes between intrinsic motivation (motivation for knowledge, accomplishment, and stimulation), extrinsic motivation (external regulation, introjected regulation, and identified regulation), and amotivation, grounded in self-determination theory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert J. Vallerand","subfamily":"Academic motivation","year":"1992","type":"Self-report motivation inventory"},"citations":[{"ref":"Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The Academic Motivation Scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003-1017.","type":"article","doi":"10.1177/0013164492052004025","isbn":null,"url":null},{"ref":"Vallerand, R. J., Koestner, R., & Pelletier, L. G. (2000). Intrinsic and extrinsic motivation in sport: Examining their dynamic relationship. Journal of Applied Social Psychology, 30(5), 994-1017.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Intrinsic+and+extrinsic+motivation+in+sport%3A+Examining+their+dynamic+relationship+Vallerand"}],"related":["student-engagement-scale","academic-self-efficacy-scale","study-process-questionnaire","school-climate-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"academic-resilience-scale","name":"Academic Resilience Scale","fullName":"Academic Resilience Scale (ARS-30)","aliases":["ARS-30"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"adaptive-capacity-academic","year":"2016","originator":"Cassidy, S.","url":"https://scholargate.app/en/educational-psychology/academic-resilience-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/academic-resilience-scale.md","definition":"The Academic Resilience Scale measures the capacity of students to withstand and recover from academic adversity, including setbacks, failures, and difficult transitions. Developed by Cassidy in 2016, the ARS-30 conceptualizes resilience as a dynamic, multidimensional process involving perseverance, adaptive help-seeking, and emotional regulation—not a fixed trait. This instrument is invaluable for identifying students at risk of academic disengagement, evaluating resilience-building interventions, and understanding how students adapt to challenge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cassidy, S.","subfamily":"adaptive-capacity-academic","year":"2016","type":"Self-report questionnaire"},"citations":[{"ref":"Cassidy, S. (2016). The Academic Resilience Scale (ARS-30): A new multidimensional scale for measuring student resilience as a dynamic process. Frontiers in Psychology, 7, 1781.","type":"article","doi":"10.1037/t60865-000","isbn":null,"url":null},{"ref":"Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.","type":"article","doi":null,"isbn":null,"url":"https://www.mindsetworks.com/"}],"related":["academic-burnout-scale","test-anxiety-inventory","academic-help-seeking-scale","procrastination-assessment-scale","study-skills-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"academic-self-efficacy-scale","name":"Academic Self-Efficacy Scale","fullName":"Academic Self-Efficacy Scale (ASES)","aliases":["ASES","Self-Efficacy for Academic Performance"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Academic confidence and capability","year":"1977","originator":"Albert Bandura","url":"https://scholargate.app/en/educational-psychology/academic-self-efficacy-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/academic-self-efficacy-scale.md","definition":"The Academic Self-Efficacy Scale (ASES) measures students' beliefs about their capability to succeed in academic tasks. Grounded in Bandura's social cognitive theory, the instrument assesses perceived competence in diverse academic domains—understanding lectures, completing assignments, performing on exams, and engaging in scholarly work. High academic self-efficacy is a strong predictor of achievement, persistence, and resilience in the face of academic challenges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Bandura","subfamily":"Academic confidence and capability","year":"1977","type":"Self-efficacy belief measurement"},"citations":[{"ref":"Bandura, A. (1977). Self-Efficacy: Toward a Unifying Theory of Behavioral Change. Psychological Review, 84(2), 191-215.","type":"book","doi":"10.1037/0033-295X.84.2.191","isbn":null,"url":null},{"ref":"Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82-91.","type":"article","doi":"10.1006/ceps.1999.1016","isbn":null,"url":null}],"related":["academic-motivation-scale","student-engagement-scale","critical-thinking-dispositions-scale","study-process-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"accelerated-failure-time","name":"Accelerated Failure Time Model","fullName":"Accelerated Failure Time (AFT) Model","aliases":["AFT model","parametric survival regression","Hızlandırılmış Başarısızlık Zamanı Modeli (AFT)"],"domain":"survival","family":"survival","subfamily":null,"year":1992,"originator":"Wei, L. J. (seminal review 1992); origins in parametric survival literature","url":"https://scholargate.app/en/survival/accelerated-failure-time","markdownUrl":"https://scholargate.app/en/survival/accelerated-failure-time.md","definition":"The Accelerated Failure Time model is a parametric regression approach to survival analysis — formally reviewed and advocated by L. J. Wei in 1992 — in which covariates act as multiplicative factors that directly stretch or compress the time-to-event scale. Unlike the Cox proportional-hazards model, which models how covariates shift the hazard rate, AFT models express the covariate effect as an acceleration or deceleration of the time axis itself.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wei, L. J. (seminal review 1992); origins in parametric survival literature","year":1992,"type":"Parametric survival regression model","handles":"Right-censoring; continuous and categorical covariates","distributions":"Weibull, log-normal, log-logistic (selectable)","minSample":30,"difficulty":2},"citations":[{"ref":"Wei, L. J. (1992). The Accelerated Failure Time Model: A Useful Alternative to the Cox Regression Model in Survival Analysis. Statistics in Medicine, 11(14–15), 1871–1879.","type":"article","doi":"10.1002/sim.4780111409","isbn":null,"url":null},{"ref":"Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471363576","url":null},{"ref":"Kleinbaum, D. G. & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer.","type":"book","doi":null,"isbn":"978-1441966452","url":null}],"related":["cox-ph","kaplan-meier","weibull-regression","log-rank-test","fine-gray-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"accelerated-shelf-life-testing","name":"Accelerated Shelf-Life Testing","fullName":"Accelerated Shelf-Life Testing (ASLT)","aliases":["ASLT"],"domain":"food-science","family":"process-pipeline","subfamily":"Predictive Stability Testing","year":"1975","originator":"Mizrahi and Symbolistic","url":"https://scholargate.app/en/food-science/accelerated-shelf-life-testing","markdownUrl":"https://scholargate.app/en/food-science/accelerated-shelf-life-testing.md","definition":"Accelerated Shelf-Life Testing (ASLT) uses elevated temperature and controlled storage conditions to rapidly assess product degradation and predict realistic shelf-life without waiting months. By measuring quality parameters (moisture, acidity, nutrient levels, microbial growth) at accelerated conditions and applying kinetic modeling, ASLT predicts expiration dates and optimal storage parameters before market launch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mizrahi and Symbolistic","subfamily":"Predictive Stability Testing","year":"1975","type":"Degradation Kinetics Method"},"citations":[{"ref":"Mizrahi, S. (1996). Kinetic models of food quality and shelf-life: A review. Journal of Food Quality, 19(4), 315-340.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kinetic+models+of+food+quality+and+shelf-life%3A+A+review+Mizrahi"},{"ref":"Ahmad, U. K., & Ahmad, S. (2016). Application of kinetics and optics for food shelf-life testing. In Food quality and shelf life (pp. 234-267). Woodhead Publishing.","type":"article","doi":null,"isbn":null,"url":"https://www.woodheadpublishing.com"}],"related":["dsc-gelatinization","karl-fischer-titration","haccp"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acceptance-commitment-therapy","name":"Acceptance and Commitment Therapy","fullName":"Acceptance and Commitment Therapy","aliases":["ACT","third-wave therapy"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Acceptance-based therapy","year":"1999","originator":"Steven C. Hayes","url":"https://scholargate.app/en/clinical-psychology/acceptance-commitment-therapy","markdownUrl":"https://scholargate.app/en/clinical-psychology/acceptance-commitment-therapy.md","definition":"Acceptance and Commitment Therapy (ACT) is a values-based, process-oriented psychotherapy developed by Steven C. Hayes and colleagues that helps individuals create meaningful lives while living with difficult thoughts, feelings, and sensations. Using mindfulness, values clarification, and behavioral commitment, ACT represents a third-wave approach to cognitive-behavioral therapy and has demonstrated effectiveness across diverse psychological problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven C. Hayes","subfamily":"Acceptance-based therapy","year":"1999","type":"Values-based psychotherapy"},"citations":[{"ref":"Hayes, S. C., Luoma, J. B., Bond, F. W., Masuda, A., & Lillis, J. (2006). Acceptance and commitment therapy: Model, processes, and outcomes. Behaviour Research and Therapy, 44(1), 1–25.","type":"article","doi":"10.1016/j.brat.2005.06.006","isbn":null,"url":null},{"ref":"Hayes, S. C., Strosahl, K. D., & Wilson, K. G. (2012). Acceptance and commitment therapy: The process and practice of mindful change (2nd ed.). Guilford Press.","type":"article","doi":null,"isbn":"9781609189624","url":null}],"related":["mindfulness-based-stress-reduction","dialectical-behavior-therapy","cognitive-behavioral-therapy-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acculturation-scale","name":"Acculturation Rating Scale","fullName":"Acculturation Rating Scale for Mexican Americans (ARSMA)","aliases":["ARSMA","Acculturation Rating Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Cross-cultural scale","year":"1995","originator":"Imelda Cuéllar, Bill Arnold, and Roberto Maldonado","url":"https://scholargate.app/en/social-psychology/acculturation-scale","markdownUrl":"https://scholargate.app/en/social-psychology/acculturation-scale.md","definition":"The Acculturation Rating Scale for Mexican Americans (ARSMA) is a self-report measure designed to assess the degree to which Mexican American and Mexican immigrant individuals adopt or maintain cultural practices, values, and identity. Originally developed by Cuéllar, Harris, and Jasso in 1980 and revised as ARSMA-II in 1995, it measures bi-dimensional acculturation—the extent of both Mexican and American cultural orientation. The scale has been adapted for use with other immigrant and ethnic minority groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Imelda Cuéllar, Bill Arnold, and Roberto Maldonado","subfamily":"Cross-cultural scale","year":"1995","type":"Self-report Likert scale"},"citations":[{"ref":"Cuéllar, I., Arnold, B., & Maldonado, R. (1995). Acculturation Rating Scale for Mexican Americans-II: A revision of the original ARSMA Scale. Journal of Cross-Cultural Psychology, 26(3), 307–319.","type":"article","doi":"10.1177/07399863950173001","isbn":null,"url":null}],"related":["cultural-values-scale","collectivism-individualism-scale","modern-racism-scale","environmental-attitudes-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acculturative-stress-scale","name":"Societal Attitudinal Familial Ethnic Acculturative Stress Scale","fullName":"Societal Attitudinal Familial Ethnic Acculturative Stress Scale (SAFE Scale)","aliases":["SAFE Scale"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"acculturation-stress","year":1997,"originator":"Chavez, Cervantes, Busch-Rossnagel","url":"https://scholargate.app/en/transcultural-nursing/acculturative-stress-scale","markdownUrl":"https://scholargate.app/en/transcultural-nursing/acculturative-stress-scale.md","definition":"The Societal Attitudinal Familial Ethnic (SAFE) Acculturative Stress Scale is a self-report instrument designed to measure the psychological stress and strain experienced by individuals during the acculturation process—the adaptation of cultural attitudes, behaviors, and identities when navigating between heritage and dominant cultures. Developed by Chavez, Cervantes, and Busch-Rossnagel in 1997, the SAFE Scale assesses stress across multiple domains: pressure to acculturate from society, family discord related to cultural differences, and experiences of discrimination. The instrument is widely used in clinical, educational, and research settings to evaluate acculturative stress among immigrant and ethnic minority populations and to understand its effects on mental health and well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chavez, Cervantes, Busch-Rossnagel","subfamily":"acculturation-stress","year":1997,"type":"Self-report"},"citations":[{"ref":"Chavez, R. A., Cervantes, R. C., & Busch-Rossnagel, N. A. (1997). Assessing acculturation in Mexican American adolescents. Hispanic Journal of Behavioral Sciences, 19(1), 80–93.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessing+acculturation+in+Mexican+American+adolescents+Chavez"}],"related":["ethnic-identity-scale","racism-and-life-experiences-scale","social-distance-scale","transcultural-self-efficacy-tool"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"accuracy","name":"Accuracy","fullName":"Classification Accuracy","aliases":["Overall Accuracy","Correct Classification Rate"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"20th century","originator":"Historical statistical foundations","url":"https://scholargate.app/en/model-evaluation/accuracy","markdownUrl":"https://scholargate.app/en/model-evaluation/accuracy.md","definition":"Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Historical statistical foundations","subfamily":"Classification Metric","year":"20th century","type":"Evaluation metric"},"citations":[{"ref":"Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.","type":"article","doi":"10.1016/j.patrec.2005.10.010","isbn":null,"url":null},{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"}],"related":["precision","recall","f1-score","balanced-accuracy","confusion-matrix"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ace-questionnaire","name":"Adverse Childhood Experiences Questionnaire","fullName":"Adverse Childhood Experiences (ACE) Questionnaire","aliases":["ACE","ACE Score","ACE Questionnaire"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"Childhood adversity and trauma history assessment","year":"1998","originator":"Vincent J. Felitti et al.","url":"https://scholargate.app/en/trauma-psychology/ace-questionnaire","markdownUrl":"https://scholargate.app/en/trauma-psychology/ace-questionnaire.md","definition":"The ACE Questionnaire is a 10-item instrument assessing exposure to adverse experiences during childhood, including abuse, neglect, and household dysfunction. Originally developed by Felitti and colleagues at Kaiser Permanente in 1998 as part of the landmark Adverse Childhood Experiences Study, the ACE Score quantifies cumulative childhood trauma and has become a foundational public health tool for identifying individuals at elevated risk for chronic physical and mental health conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vincent J. Felitti et al.","subfamily":"Childhood adversity and trauma history assessment","year":"1998","type":"Structured interview/self-report questionnaire"},"citations":[{"ref":"Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., & Marks, J. S. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245-258.","type":"article","doi":"10.1016/S0749-3797(98)00017-8","isbn":null,"url":null},{"ref":"Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C. H., Perry, B. D., & Giles, W. H. (2006). The enduring effects of abuse and related adverse experiences in childhood: A convergence of evidence from neurobiology and epidemiology. European Archives of Psychiatry and Clinical Neuroscience, 256(3), 174-186.","type":"article","doi":"10.1007/s00406-005-0624-4","isbn":null,"url":null}],"related":["post-traumatic-growth-inventory","multidimensional-perceived-social-support","impact-of-event-scale-revised"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"achenbach-youth-self-report","name":"Achenbach Youth Self-Report","fullName":"Youth Self-Report (YSR) Form","aliases":["YSR","Achenbach YSR","Youth Self-Report"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"Behavioral assessment","year":"2003","originator":"Thomas Achenbach and Leslie Rescorla","url":"https://scholargate.app/en/developmental-assessment/achenbach-youth-self-report","markdownUrl":"https://scholargate.app/en/developmental-assessment/achenbach-youth-self-report.md","definition":"The Youth Self-Report (YSR), developed by Thomas Achenbach and Leslie Rescorla, is a youth-completed behavioral rating form assessing emotional and behavioral problems in adolescents aged 11–18 years. Part of the Achenbach System of Empirically Based Assessment (ASEBA), the YSR parallels the parent-completed Child Behavior Checklist (CBCL) and teacher-completed Teacher Report Form (TRF), enabling comprehensive multi-informant assessment of adolescent mental health and functioning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Achenbach and Leslie Rescorla","subfamily":"Behavioral assessment","year":"2003","type":"Youth self-report behavioral rating form"},"citations":[{"ref":"Achenbach, T. M. (2003). Manual for the Youth Self-Report and 2003 Profile. University of Vermont Center for Children, Youth & Families.","type":"book","doi":null,"isbn":null,"url":"https://www.aseba.org/"},{"ref":"Achenbach, T. M., & Rescorla, L. A. (2009). Manual for the ASEBA School-Age Forms & Profiles. University of Vermont Center for Children, Youth & Families.","type":"book","doi":null,"isbn":null,"url":"https://www.aseba.org/"}],"related":["cbcl-child-behavior","conners-rating-scales","strengths-difficulties-questionnaire","vanderbilt-adhd-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acl-return-to-sport-scale","name":"ACL Return to Sport after Injury Scale","fullName":"ACL-Return to Sport after Injury (ACL-RSI) Scale","aliases":["ACL-RSI"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"psychological readiness for sport","year":2008,"originator":"Kevin E. Webster, Julian A. Feller, Christopher Lambros","url":"https://scholargate.app/en/sports-medicine/acl-return-to-sport-scale","markdownUrl":"https://scholargate.app/en/sports-medicine/acl-return-to-sport-scale.md","definition":"The ACL-Return to Sport after Injury (ACL-RSI) Scale is a 12-item patient-reported outcome instrument designed to measure the psychological impact and readiness to return to sport following anterior cruciate ligament injury and reconstruction. Developed by Webster, Feller, and Lambros in 2008 and published in the British Journal of Sports Medicine, the ACL-RSI addresses a critical gap in ACL rehabilitation assessment by quantifying psychological barriers to sport resumption—emotions, confidence in the knee, and risk appraisal—which are often more limiting than physical recovery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kevin E. Webster, Julian A. Feller, Christopher Lambros","subfamily":"psychological readiness for sport","year":2008,"type":"Patient self-report"},"citations":[{"ref":"Webster KE, Feller JA, Lambros C. Development and preliminary validation of a scale to measure the psychological impact of returning to sport after anterior cruciate ligament reconstruction surgery. Br J Sports Med. 2008;42(6):893-900.","type":"article","doi":"10.1016/j.ptsp.2007.09.003","isbn":null,"url":null}],"related":["ikdc-subjective-knee-form","patient-specific-functional-scale","global-rating-of-change-scale","acl-return-to-sport-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acne-qol","name":"Acne-QoL","fullName":"Acne Quality of Life Scale","aliases":["Acne-Q","Acne-Specific QoL"],"domain":"dermatology","family":"process-pipeline","subfamily":"disease-specific-quality-of-life","year":"2004","originator":"Halvorsen JA et al.","url":"https://scholargate.app/en/dermatology/acne-qol","markdownUrl":"https://scholargate.app/en/dermatology/acne-qol.md","definition":"Acne-QoL is a disease-specific, patient-administered quality-of-life measure assessing the psychological and social burden of acne vulgaris. Acne is the most common skin disease in adolescents and young adults and causes substantial psychological distress, depression, anxiety, and social impairment disproportionate to its severity. Multiple versions of Acne-QoL exist (19–24 items); all capture emotional, social, and functional impacts. Acne-QoL is essential in clinical trials and observational studies to ensure treatment efficacy encompasses quality-of-life outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Halvorsen JA et al.","subfamily":"disease-specific-quality-of-life","year":"2004","type":"Self-report"},"citations":[{"ref":"Halvorsen JA, Stern RS, Dalgard F, et al. Suicidal ideation, mental health problems, and social impairment are increased in adolescents with acne: a population-based study. J Invest Dermatol. 2011;131(2):363-370.","type":"article","doi":"10.1038/jid.2010.264","isbn":null,"url":null},{"ref":"Dréno B, Layton A, Zouboulis CC, López-Estebaranz JL, et al. Adult female acne: a practical approach. J Eur Acad Dermatol Venereol. 2013;27(Suppl 1):1-16.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dr%C3%A9no+B%2C+Layton+A%2C+Zouboulis+CC%2C+L%C3%B3pez-Estebaranz+JL%2C+et+al.+Adult+female+acne%3A+a+practical+approach.+J+Eur+Acad+Dermato+Dr%C3%A9no"}],"related":["poem","skindex-29","dermatology-life-quality-index-children","melasqol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acoustic-design-analysis","name":"Acoustic Design Analysis","fullName":"Acoustic Design Analysis and Sound Performance Prediction","aliases":["sound analysis","room acoustic design","noise prediction"],"domain":"architecture","family":"process-pipeline","subfamily":"Acoustics and sound control","year":"1922","originator":"Wallace Clement Sabine","url":"https://scholargate.app/en/architecture/acoustic-design-analysis","markdownUrl":"https://scholargate.app/en/architecture/acoustic-design-analysis.md","definition":"Acoustic Design Analysis is a method for evaluating the acoustical properties of buildings to predict sound levels, reverberation time, and speech intelligibility. Founded by Wallace Clement Sabine in the early 1900s, the field encompasses room acoustic design (controlling reverberation), sound transmission loss (preventing noise transfer between spaces), and environmental noise prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wallace Clement Sabine","subfamily":"Acoustics and sound control","year":"1922","type":"room acoustic prediction and assessment method"},"citations":[{"ref":"Sabine, W. C. (1922). Collected Papers on Acoustics. Harvard University Press, Cambridge, MA.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/collectedpapersoacoustics"},{"ref":"Kuttruff, H. (2009). Room Acoustics. Taylor and Francis, London, 5th edition.","type":"book","doi":"10.3397/1.3455049","isbn":null,"url":null},{"ref":"Hopkins, C. (2007). Sound Insulation. Butterworth-Heinemann, Oxford.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/sound-insulation/hopkins/978-0-7506-6442-1"}],"related":["thermal-comfort-assessment","daylight-simulation","post-occupancy-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acoustic-doppler-current-profiler","name":"Acoustic Doppler Current Profiler","fullName":"Acoustic Doppler Current Profiler","aliases":["ADCP"],"domain":"oceanography","family":"process-pipeline","subfamily":"Signal Processing","year":"1983","originator":"RD Instruments","url":"https://scholargate.app/en/oceanography/acoustic-doppler-current-profiler","markdownUrl":"https://scholargate.app/en/oceanography/acoustic-doppler-current-profiler.md","definition":"The Acoustic Doppler Current Profiler (ADCP) is an instrument that uses Doppler-shifted acoustic backscatter to measure water velocity profiles along a vertical profile. Developed by RD Instruments in the 1980s, it has become the standard method for high-resolution current profiling in oceanographic research. ADCPs provide unprecedented spatial and temporal resolution of ocean circulation patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"RD Instruments","subfamily":"Signal Processing","year":"1983","type":"instrumental"},"citations":[{"ref":"RD Instruments. (1996). Acoustic Doppler Current Profiler Principles of Operation. A Practical Primer. RD Instruments Technical Note.","type":"article","doi":null,"isbn":null,"url":"https://www.rdsinstruments.com/"},{"ref":"Teledyne Technologies. (2018). ADCP Technology and Applications. White Paper.","type":"article","doi":null,"isbn":null,"url":"https://www.teledyne.com/"}],"related":["ctd-profiling","ocean-color-chlorophyll-a","drifter-lagrangian-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acoustic-holography","name":"Acoustic Holography","fullName":"Near-Field Acoustic Holography for Sound Field Reconstruction","aliases":["NAH","near-field acoustics","sound field mapping","acoustic imaging"],"domain":"acoustics","family":"process-pipeline","subfamily":"Acoustic imaging","year":"1985","originator":"James Maynard, Earl Williams, Yongjian Lee","url":"https://scholargate.app/en/acoustics/acoustic-holography","markdownUrl":"https://scholargate.app/en/acoustics/acoustic-holography.md","definition":"Near-Field Acoustic Holography (NAH) is a technique for reconstructing 3D acoustic sound fields and visualizing sound radiation from sources by measuring pressure at a dense microphone array in the near field. Pioneered by Maynard, Williams, and Lee in 1985, NAH extends holographic principles from optics to acoustics, enabling detailed acoustic source characterization, noise source identification, and acoustic field visualization that is impossible with conventional single-point or line-array methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Maynard, Earl Williams, Yongjian Lee","subfamily":"Acoustic imaging","year":"1985","type":"Sound field reconstruction method"},"citations":[{"ref":"Maynard, J. D., Williams, E. G., & Lee, Y. (1985). Near-field acoustic holography: I. Theory of generalized holography and the development of NAH. Journal of the Acoustical Society of America, 78(4), 1395–1413.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Near-field+acoustic+holography%3A+I+Maynard"},{"ref":"Williams, E. G. (1999). Fourier Acoustics: Sound Radiation and Nearfield Acoustical Holography. Academic Press.","type":"book","doi":null,"isbn":"978-0124654052","url":null},{"ref":"Mueller, T. F. (2002). Aeroacoustic Measurements. Springer-Verlag.","type":"book","doi":null,"isbn":"978-3540678441","url":null}],"related":["room-impulse-response","beamforming","acoustic-ray-tracing","bem-acoustics","impedance-tube"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acoustic-phonetics","name":"Acoustic Phonetics","fullName":"Acoustic Phonetics Analysis Method","aliases":["Acoustic Analysis of Speech","Spectrographic Analysis"],"domain":"linguistics","family":"process-pipeline","subfamily":"Experimental Phonetics","year":"1962","originator":"Peter Ladefoged","url":"https://scholargate.app/en/linguistics/acoustic-phonetics","markdownUrl":"https://scholargate.app/en/linguistics/acoustic-phonetics.md","definition":"Acoustic Phonetics is the study of the physical properties of speech sounds using instrumentation to measure and analyze sound waves. Pioneered by Peter Ladefoged and Kenneth Stevens, this method uses spectrograms, formant analysis, and waveform measurements to characterize vowels, consonants, and prosodic features with precision. Acoustic phonetics bridges the articulatory world of speech production and the perceptual world of listeners, providing objective, quantifiable data about how speech is produced and perceived.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Ladefoged","subfamily":"Experimental Phonetics","year":"1962","type":"Empirical process pipeline"},"citations":[{"ref":"Ladefoged, P., & Johnson, K. (2006). A Course in Phonetics (5th ed.). Boston: Cengage Learning.","type":"book","doi":null,"isbn":null,"url":"https://cengage.com/course-content/"},{"ref":"Stevens, K. N. (2000). Acoustic Phonetics. Cambridge, MA: MIT Press.","type":"book","doi":"10.7551/mitpress/1072.001.0001","isbn":null,"url":null},{"ref":"Gordon, M. (2004). Phonetic structures of Turkish. Journal of the International Phonetic Association, 34(1), 34-52.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Phonetic+structures+of+Turkish+Gordon"}],"related":["electropalatography","psycholinguistic-eye-tracking","corpus-linguistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acoustic-ray-tracing","name":"Acoustic Ray Tracing","fullName":"Acoustic Ray Tracing for Room Simulation","aliases":["ray tracing","geometric acoustics","image source method","sound ray propagation"],"domain":"acoustics","family":"process-pipeline","subfamily":"Geometric simulation","year":"1979","originator":"James Allen, David Berkley","url":"https://scholargate.app/en/acoustics/acoustic-ray-tracing","markdownUrl":"https://scholargate.app/en/acoustics/acoustic-ray-tracing.md","definition":"Acoustic ray tracing is a computational technique for predicting sound propagation in rooms by treating acoustic energy as rays that reflect specularly off surfaces. Formalized by Allen and Berkley in 1979 via the image source method, ray tracing is one of the most computationally efficient methods for room acoustic simulation, especially for early and mid-reflections. It is widely used in audio engineering, architectural acoustics, and interactive spatial audio for virtual environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Allen, David Berkley","subfamily":"Geometric simulation","year":"1979","type":"Computational room acoustics method"},"citations":[{"ref":"Allen, J. B., & Berkley, D. A. (1979). Image method for efficiently simulating small-room acoustics. Journal of the Acoustical Society of America, 65(4), 943–950.","type":"article","doi":"10.1121/1.382599","isbn":null,"url":null},{"ref":"Vorlaender, M. (1989). Simulation of room acoustics using the reciprocity theorem and ray tracing. Journal of the Acoustical Society of America, 86(1), 172–178.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Simulation+of+room+acoustics+using+the+reciprocity+theorem+and+ray+tracing+Vorlaender"},{"ref":"Kuttruff, H. (2009). Room Acoustics (5th ed.). Spon Press.","type":"book","doi":null,"isbn":"978-0-415-48055-4","url":null}],"related":["room-impulse-response","rt60-reverberation-time","bem-acoustics","acoustic-holography","speech-intelligibility"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acoustic-telemetry","name":"Acoustic Telemetry","fullName":"Acoustic Telemetry for Animal Movement Tracking","aliases":["acoustic tracking","telemetry monitoring","underwater tracking"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Telemetry and Tracking","year":"1960","originator":"Fish Tracking Pioneer Community","url":"https://scholargate.app/en/veterinary-science/acoustic-telemetry","markdownUrl":"https://scholargate.app/en/veterinary-science/acoustic-telemetry.md","definition":"Acoustic telemetry is a remote tracking method in which small electronic transmitters attached to or implanted in animals emit unique acoustic signals detectable by underwater or terrestrial receiver networks, enabling real-time monitoring of animal movements, positions, and behavior over extended distances and times. Pioneered in fisheries research in the 1960s, acoustic telemetry is now standard for studying movement ecology, migration timing, and habitat use in aquatic and increasingly terrestrial systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fish Tracking Pioneer Community","subfamily":"Telemetry and Tracking","year":"1960","type":"Remote Monitoring Technology"},"citations":[{"ref":"Eiler, J. H. (2013). Acoustic telemetry. In C. R. Cooke & D. W. Philipp (Eds.), Telemetry Techniques and Technology (pp. 1-45). Springer.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/chapter/10.1007/978-1-4419-1669-4_1"},{"ref":"Jepsen, N., Schreck, C., Clements, S., & Thorstad, E. B. (2005). Fish telemetry: tools and techniques. In D. B. Carlson & C. J. Bronte (Eds.), Fish Telemetry (pp. 3-28). American Fisheries Society.","type":"article","doi":null,"isbn":null,"url":"https://www.fisheries.org/"},{"ref":"Thorstad, E. B., Rikardsen, A. H., Alp, A., & Davidsen, J. G. (2013). The use of fish and other animal acoustic telemetry in European rivers: current status and future prospects. In A. R. Rikardsen & B. B. Dempson (Eds.), Atlantic Salmon Ecology (pp. 235-258). Wiley-Blackwell.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+use+of+fish+and+other+animal+acoustic+telemetry+in+European+rivers%3A+current+status+and+future+prospects+Thorstad"}],"related":["focal-animal-sampling","electrofishing","microhabitat-preference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acromegaly-quality-of-life","name":"AcroQoL","fullName":"Acromegaly Quality of Life Questionnaire","aliases":["Acromegaly QoL","Acro-QoL"],"domain":"endocrinology","family":"process-pipeline","subfamily":"Growth hormone-excess-related quality of life","year":2006,"originator":"Markus Buchfelder, Dieter Weigel, Monica Droste, et al.","url":"https://scholargate.app/en/endocrinology/acromegaly-quality-of-life","markdownUrl":"https://scholargate.app/en/endocrinology/acromegaly-quality-of-life.md","definition":"AcroQoL is a disease-specific 22-item quality of life questionnaire developed to assess the multidimensional burden of acromegaly, a chronic growth hormone-secreting pituitary tumor disorder. Developed by Buchfelder and colleagues in 2006, it captures physical complications (joint/bone pain, carpal tunnel syndrome, metabolic disease), appearance-related concerns (facial changes, hand/foot enlargement), and psychological/social impacts (mood, social limitation, body image). It is the primary outcome measure for assessing quality of life in acromegaly treatment trials and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Markus Buchfelder, Dieter Weigel, Monica Droste, et al.","subfamily":"Growth hormone-excess-related quality of life","year":2006,"type":"Patient self-report questionnaire"},"citations":[{"ref":"Buchfelder, M., Weigel, D., Droste, M., et al. (2006). The quality of life of acromegaly patients is markedly impaired: Data from the German Acromegaly Registry. Eur J Endocrinol, 150(4), 541-549.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+quality+of+life+of+acromegaly+patients+is+markedly+impaired%3A+Data+from+the+German+Acromegaly+Registry+Buchfelder"},{"ref":"Ceraulo, D., Lombardi, G., & Colao, A. (2023). Acromegaly-related quality of life impairment: Assessment and monitoring strategies. Endocr Rev, 44(1), 94-116.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Acromegaly-related+quality+of+life+impairment%3A+Assessment+and+monitoring+strategies+Ceraulo"}],"related":["growth-hormone-deficiency-scale","cushings-qol","thyroid-patient-reported-outcomes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"action-research-arm-test","name":"Action Research Arm Test","fullName":"Action Research Arm Test (ARAT)","aliases":["ARAT"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Upper limb function assessment","year":"1989","originator":"Roberta Lyle","url":"https://scholargate.app/en/physical-therapy/action-research-arm-test","markdownUrl":"https://scholargate.app/en/physical-therapy/action-research-arm-test.md","definition":"The Action Research Arm Test (ARAT) is a 19-item performance-based assessment measuring upper limb function in four domains: grasp, grip, pinch, and gross movement. Developed by Roberta Lyle in 1989, the ARAT has become the standard functional assessment for upper limb recovery in stroke rehabilitation, providing detailed measurement of hand and arm coordination relevant to activities of daily living.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roberta Lyle","subfamily":"Upper limb function assessment","year":"1989","type":"Performance-based test"},"citations":[{"ref":"Lyle, R. C. (1989). A performance test for assessment of upper limb function in physical rehabilitation treatment and research. International Journal of Rehabilitation Research, 12(6), 605-613.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+performance+test+for+assessment+of+upper+limb+function+in+physical+rehabilitation+treatment+and+research+Lyle"},{"ref":"Hsieh, C. L., Hsueh, I. P., Chiang, S. L., & Lin, C. H. (2007). Inter-rater reliability and validity of the action research arm test in stroke patients. Journal of Rehabilitation Medicine, 39(8), 654-660.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Inter-rater+reliability+and+validity+of+the+action+research+arm+test+in+stroke+patients+Hsieh"}],"related":["manual-muscle-testing","range-of-motion-goniometry","functional-independence-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"action-research","name":"Action Research","fullName":"Action Research Method","aliases":["Participatory Action Research","PAR","Collaborative Inquiry"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"change-oriented-collaborative","year":"1946","originator":"Kurt Lewin; expanded by Kemmis, McTaggart, Reason & Bradbury","url":"https://scholargate.app/en/qualitative-research/action-research","markdownUrl":"https://scholargate.app/en/qualitative-research/action-research.md","definition":"Action research is a collaborative research methodology in which researchers work with practitioners and community members to investigate a problem, implement change, and evaluate outcomes, cycling through reflection, action, and learning. Developed by Kurt Lewin (1946), action research bridges research and practice, aiming simultaneously to produce knowledge and practical improvement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kurt Lewin; expanded by Kemmis, McTaggart, Reason & Bradbury","subfamily":"change-oriented-collaborative","year":"1946","type":"Method"},"citations":[{"ref":"Lewin, K. (1946). Action research and minority problems. Journal of Social Issues, 2(4), 34–46.","type":"article","doi":"10.1111/j.1540-4560.1946.tb02295.x","isbn":null,"url":null},{"ref":"Kemmis, S., & McTaggart, R. (2005). Participatory action research: Communicative action and the public sphere. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed., pp. 559–603). Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Kemmis%2C%20S.%2C%20%26%20McTaggart%2C%20R.%20(2005).%20Participatory%20action%20research%3A%20Communicative%20action%20and%20the%20public%20sphere.%20In%20N.%20K.%20"},{"ref":"Reason, P., & Bradbury, H. (Eds.). (2006). Handbook of action research: Participative inquiry and practice. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Reason%2C%20P.%2C%20%26%20Bradbury%2C%20H.%20(Eds.).%20(2006).%20Handbook%20of%20action%20research%3A%20Participative%20inquiry%20and%20practice.%20Sage%20Publica"}],"related":["participatory-action-research","collaborative-inquiry","reflective-practice","implementation-science","quality-improvement"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"activated-sludge-model","name":"Activated Sludge Model","fullName":"Activated Sludge Process Simulation and Design","aliases":["ASM","conventional activated sludge","suspended growth treatment"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Biochemical process modeling","year":"1976","originator":"Marais and Ekama","url":"https://scholargate.app/en/environmental-engineering/activated-sludge-model","markdownUrl":"https://scholargate.app/en/environmental-engineering/activated-sludge-model.md","definition":"The Activated Sludge Model (ASM) is a standardized mathematical framework for simulating biological wastewater treatment processes, developed by the International Association on Water Quality (IAWQ) beginning in 1987. It represents the transport, transformation, and fate of organic matter and nutrients in suspended-growth treatment systems. ASM is widely used to design, optimize, and predict the performance of wastewater treatment plants under varying influent and operational conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marais and Ekama","subfamily":"Biochemical process modeling","year":"1976","type":"mathematical simulation pipeline"},"citations":[{"ref":"Henze, M., Grady, C. P. L., Gujer, W., Marais, G. V. R., & Matsuo, T. (1987). Activated Sludge Model No. 1. IAWQ, Scientific and Technical Report No. 1.","type":"article","doi":null,"isbn":null,"url":"https://www.iawq.org"},{"ref":"Grady, C. P. L., Daigger, G. T., & Lim, H. C. (1999). Biological Wastewater Treatment (2nd ed.). Marcel Dekker.","type":"book","doi":null,"isbn":"978-0824719265","url":null},{"ref":"Gujer, W., Henze, M., Mino, T., & Matsuo, T. (1999). Activated Sludge Model No. 3. Water Science and Technology, 39(1), 183-193.","type":"article","doi":"10.1016/S0273-1223(98)00785-9","isbn":null,"url":null}],"related":["wastewater-treatment-design","constructed-wetland-design","biogas-production-model","stormwater-management","groundwater-contamination-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"activation-analysis","name":"Neutron Activation Analysis","fullName":"Neutron Activation Analysis and Elemental Quantification","aliases":["NAA","activation analysis","trace element analysis"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Analytical chemistry with nuclear methods","year":"1936","originator":"George de Hevesy, Hilde Levi","url":"https://scholargate.app/en/nuclear-physics/activation-analysis","markdownUrl":"https://scholargate.app/en/nuclear-physics/activation-analysis.md","definition":"Neutron activation analysis (NAA) is an analytical technique for determining elemental composition by bombarding samples with neutrons to produce radioactive isotopes, invented by de Hevesy and Levi in 1936. By measuring decay gamma rays from irradiated samples, NAA quantifies trace and major elements with high sensitivity, specificity, and accuracy without requiring destructive dissolution or complex sample preparation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George de Hevesy, Hilde Levi","subfamily":"Analytical chemistry with nuclear methods","year":"1936","type":"analytical measurement technique"},"citations":[{"ref":"Chadwick, J. (1932). Possible Existence of a Neutron. Nature, 129(3252), 312.","type":"article","doi":"10.1038/129312a0","isbn":null,"url":null},{"ref":"Knoll, G. F. (2010). Radiation Detection and Measurement (4th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Radiation+Detection+and+Measurement+%284th+ed.%29+Knoll"}],"related":["nuclear-decay-analysis","dosimetry-measurement","monte-carlo-neutron-particle","neutron-transport-calculation","radiation-dose-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-disturbance-rejection-control","name":"Active Disturbance Rejection Control","fullName":"Active Disturbance Rejection Control","aliases":["ADRC","Disturbance Rejection Control"],"domain":"control-theory","family":"ml-model","subfamily":"Robust Control","year":"2009","originator":"Jingquan Han","url":"https://scholargate.app/en/control-theory/active-disturbance-rejection-control","markdownUrl":"https://scholargate.app/en/control-theory/active-disturbance-rejection-control.md","definition":"Active Disturbance Rejection Control (ADRC) is a control method that estimates and cancels disturbances and model uncertainties in real-time using an extended state observer (ESO), treating them as additional 'disturbance states'. Developed by Han and popularized by Gao, ADRC achieves remarkable robustness without requiring precise plant models, making it practical for real-world systems with significant uncertainty and disturbances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jingquan Han","subfamily":"Robust Control","year":"2009","type":"algorithm"},"citations":[{"ref":"Han, J. (2009). From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics, 56(3), 900-906.","type":"article","doi":"10.1109/TIE.2008.2011621","isbn":null,"url":null},{"ref":"Gao, Z. (2006). Active disturbance rejection control: a paradigm shift in feedback control system design. Proceedings of the 2006 American Control Conference, 2652-2661.","type":"article","doi":"10.1109/acc.2006.1656579","isbn":null,"url":null}],"related":["model-predictive-control","adaptive-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-association-rules","name":"Active learning Association rules","fullName":"Active Learning for Association Rule Mining","aliases":["interactive association rule mining","active rule mining","query-driven association rule discovery","user-guided association rules"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010s","originator":"Dzyuba, V. & van Leeuwen, M.; Boley, M. et al.","url":"https://scholargate.app/en/machine-learning/active-learning-association-rules","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-association-rules.md","definition":"Active learning association rules combines the iterative query-and-label loop of active learning with association rule mining, allowing a human expert to guide the discovery process interactively. Instead of exhaustively enumerating all rules above a fixed support-confidence threshold, the system selects the most informative rule candidates and asks the user to judge their interestingness, focusing the search on subjectively useful patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dzyuba, V. & van Leeuwen, M.; Boley, M. et al.","year":"2010s","type":"Interactive pattern mining","dataType":"Transactional / itemset data","subfamily":"Machine learning"},"citations":[{"ref":"Dzyuba, V., & van Leeuwen, M. (2017). Interactive Discovery of Interesting Association Rules by Subjective Interestingness. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interactive+Discovery+of+Interesting+Association+Rules+by+Subjective+Interestingness"},{"ref":"Boley, M., Lucchese, C., Paurat, D., & Gartner, T. (2013). Direct Local Pattern Sampling by Efficient Two-Step Random Procedures. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 582–590). ACM.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Direct+Local+Pattern+Sampling+by+Efficient+Two-Step+Random+Procedures+Boley"}],"related":["association-rules","active-learning","apriori-algorithm","fp-growth","semi-supervised-association-rules","interactive-machine-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-autoencoder-anomaly-detection","name":"Active Learning Autoencoder Anomaly Detection","fullName":"Active Learning-Guided Autoencoder Anomaly Detection","aliases":["AL-Autoencoder anomaly detection","active autoencoder anomaly detection","query-guided autoencoder anomaly detection","active deep anomaly detection"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2014–2018","originator":"Multiple (Guo et al.; Pimentel et al.)","url":"https://scholargate.app/en/machine-learning/active-learning-autoencoder-anomaly-detection","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-autoencoder-anomaly-detection.md","definition":"Active Learning Autoencoder Anomaly Detection combines an autoencoder's unsupervised reconstruction-error scoring with an active learning query loop. The model flags high-error instances as candidate anomalies, selectively asks a human oracle to label the most informative ones, and iteratively retrains — achieving strong anomaly detection with only a small labeling budget.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (Guo et al.; Pimentel et al.)","year":"2014–2018","type":"Active learning + unsupervised deep anomaly detection hybrid","dataType":"Unlabeled tabular, image, or sequential data with a small oracle-labeled budget","subfamily":"Machine learning"},"citations":[{"ref":"Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.","type":"article","doi":"10.1016/j.sigpro.2013.12.026","isbn":null,"url":null},{"ref":"Zhu, Y., Lukasiewicz, T. (2020). DPLAN: Discourse-level Plan-based Text Generation. Proceedings of the 28th International Conference on Computational Linguistics, 3464–3474. (See also: Guo et al. (2018). Deep Active Learning for Anomaly Detection. Neurocomputing, 290, 135–143.)","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=deep+active+learning+anomaly+detection+autoencoder"}],"related":["autoencoder-anomaly-detection","active-learning-isolation-forest","active-learning-one-class-svm","ensemble-autoencoder-anomaly-detection","bayesian-autoencoder-anomaly-detection","semi-supervised-autoencoder-anomaly-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-boosting","name":"Active learning Boosting","fullName":"Active Learning with Boosting Ensembles","aliases":["boosting-based active learning","query learning with boosting","active boosting","ensemble active learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1998","originator":"Abe, N. & Mamitsuka, H.","url":"https://scholargate.app/en/machine-learning/active-learning-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-boosting.md","definition":"Active Learning Boosting combines the query-driven label acquisition of active learning with the weighted-ensemble logic of boosting algorithms such as AdaBoost. The model iteratively selects the most informative unlabeled examples to annotate — guided by the disagreement or uncertainty within the boosting ensemble — and retrains after each new label, achieving high accuracy with far fewer labeled examples than passive learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abe, N. & Mamitsuka, H.","year":"1998","type":"Hybrid active-learning ensemble","dataType":"Labeled and unlabeled tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Abe, N. & Mamitsuka, H. (1998). Query Learning Strategies Using Boosting and Bagging. Proceedings of the 15th International Conference on Machine Learning (ICML 1998), pp. 1–9. Morgan Kaufmann.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Query+Learning+Strategies+Using+Boosting+and+Bagging"},{"ref":"Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.","type":"article","doi":null,"isbn":null,"url":"http://burrsettles.com/pub/settles.activelearning.pdf"}],"related":["active-learning-random-forest","active-learning-support-vector-machine","boosting","semi-supervised-learning","online-boosting","ensemble-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-decision-tree","name":"Active learning Decision tree","fullName":"Active Learning with Decision Tree Classifier","aliases":["AL-DT","active decision tree","query-based decision tree learning","uncertainty-sampling decision tree"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1984–2010","originator":"Settles, B. (active learning framework); Breiman et al. (decision tree base)","url":"https://scholargate.app/en/machine-learning/active-learning-decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-decision-tree.md","definition":"Active learning with a decision tree combines the interpretable structure of a CART-style tree with a query strategy that selects the most informative unlabeled instances for human annotation. The model iteratively requests labels only for examples it is most uncertain about, minimising labeling cost while maximising classification accuracy on tabular data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Settles, B. (active learning framework); Breiman et al. (decision tree base)","year":"1984–2010","type":"Active learning with decision tree base learner","dataType":"Tabular, labeled and unlabeled instances","subfamily":"Machine learning"},"citations":[{"ref":"Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.","type":"article","doi":null,"isbn":null,"url":"https://burrsettles.com/pub/settles.activelearning.pdf"},{"ref":"Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth & Brooks.","type":"book","doi":null,"isbn":"978-0-412-04841-8","url":null}],"related":["decision-tree","active-learning","semi-supervised-decision-tree","random-forest","active-learning-random-forest","active-learning-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-federated-learning","name":"Active Learning Federated Learning","fullName":"Federated Active Learning (Active Learning within Federated Learning)","aliases":["Federated Active Learning","FAL","Active Federated Learning","distributed active learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020s","originator":"Multiple authors (federated active learning emerged ~2020)","url":"https://scholargate.app/en/machine-learning/active-learning-federated-learning","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-federated-learning.md","definition":"Federated Active Learning combines the annotation-efficiency of active learning with the privacy-preserving decentralization of federated learning. A shared global model is trained across distributed clients, each of which independently ranks its unlabeled local data and requests labels only for the most informative examples, keeping raw data on-device throughout.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple authors (federated active learning emerged ~2020)","year":"2020s","type":"Hybrid paradigm (active querying within distributed training)","dataType":"Distributed, decentralized labeled and unlabeled datasets across clients","subfamily":"Machine learning"},"citations":[{"ref":"Ro, J. Y., Ali, A., Lin, Z., & Suresh, A. T. (2021). Scaling Federated Learning for Fine-tuning of Large Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Scaling+Federated+Learning+Fine-tuning+Large+Language+Models+Ro+2021"},{"ref":"Federated learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Federated_learning"}],"related":["federated-learning","active-learning","semi-supervised-learning","online-learning","transfer-learning","self-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-gaussian-mixture-model","name":"Active learning Gaussian mixture model","fullName":"Active Learning with Gaussian Mixture Model","aliases":["AL-GMM","active GMM","query-by-committee GMM","active density estimation"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s (combination)","originator":"Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)","url":"https://scholargate.app/en/machine-learning/active-learning-gaussian-mixture-model","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-gaussian-mixture-model.md","definition":"Active Learning Gaussian Mixture Model combines an iterative query strategy with a Gaussian Mixture Model learner. The algorithm selects the most informative unlabeled points — typically those with highest predictive uncertainty — presents them to an oracle for labeling, and refits the GMM using EM on the growing labeled set. The result is a density model that matches full-data quality while requiring far fewer labeled examples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Settles, B. (active learning framework); Dempster, Laird & Rubin (GMM via EM, 1977)","year":"2000s (combination)","type":"Active learning for probabilistic clustering / density estimation","dataType":"Continuous, unlabeled or partially labeled tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning (ICML), 912–919.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-supervised+learning+using+Gaussian+fields+and+harmonic+functions"},{"ref":"Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool Publishers.","type":"book","doi":"10.2200/S00429ED1V01Y201207AIM018","isbn":null,"url":null}],"related":["gaussian-mixture-model","semi-supervised-learning","active-learning-k-means","active-learning-gaussian-process","semi-supervised-gaussian-mixture-model","bayesian-gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-gaussian-process","name":"Active learning Gaussian process","fullName":"Active Learning with Gaussian Process (GP-AL)","aliases":["GP active learning","Gaussian process active learning","GP-AL","Bayesian active learning with GP"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1992","originator":"MacKay, D. J. C.","url":"https://scholargate.app/en/machine-learning/active-learning-gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-gaussian-process.md","definition":"Active Learning Gaussian Process (GP-AL) combines a Gaussian process probabilistic model with an active learning query strategy, using the GP's posterior uncertainty to select the most informative unlabeled examples for labeling. This iterative approach minimizes labeling effort while maximizing predictive accuracy, making it ideal when labeled data is scarce or expensive to obtain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"MacKay, D. J. C.","year":"1992","type":"Bayesian active learning","dataType":"Continuous, mixed; labeled and unlabeled tabular data","subfamily":"Machine learning"},"citations":[{"ref":"MacKay, D. J. C. (1992). Information-based objective functions for active data selection. Neural Computation, 4(4), 590–604.","type":"article","doi":"10.1162/neco.1992.4.4.590","isbn":null,"url":null},{"ref":"Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Active+Learning+Settles+2012+Morgan+Claypool"}],"related":["gaussian-process","active-learning","bayesian-gaussian-process","semi-supervised-gaussian-process","gaussian-mixture-model","k-nearest-neighbors"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-gradient-boosting","name":"Active Learning Gradient Boosting","fullName":"Active Learning with Gradient Boosting (Query-by-Committee / Uncertainty Sampling with Gradient Boosted Trees)","aliases":["AL-GBM","gradient boosting active learner","active gradient boosting","active learning with boosted trees"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Settles, B. (active learning); Friedman, J. H. (gradient boosting); combined framework developed by the research community","url":"https://scholargate.app/en/machine-learning/active-learning-gradient-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-gradient-boosting.md","definition":"Active Learning Gradient Boosting combines the powerful predictive accuracy of gradient boosted trees with an active learning loop that selects the most informative unlabeled examples for human annotation. By querying only the instances the model is most uncertain about, the method achieves high accuracy with far fewer labeled examples than passive supervised learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Settles, B. (active learning); Friedman, J. H. (gradient boosting); combined framework developed by the research community","year":"2000s–2010s","type":"Active learning framework with gradient boosting base learner","dataType":"Labeled and unlabeled tabular or structured data","subfamily":"Machine learning"},"citations":[{"ref":"Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.","type":"article","doi":null,"isbn":null,"url":"http://burrsettles.com/pub/settles.activelearning.pdf"},{"ref":"Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.","type":"article","doi":"10.1214/aos/1013203451","isbn":null,"url":null}],"related":["gradient-boosting","xgboost","random-forest","active-learning","query-by-committee","uncertainty-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-isolation-forest","name":"Active learning Isolation forest","fullName":"Active Learning with Isolation Forest for Anomaly Detection","aliases":["AL-iForest","active anomaly detection with isolation forest","active isolation forest","query-guided isolation forest"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2008–2019","originator":"Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base)","url":"https://scholargate.app/en/machine-learning/active-learning-isolation-forest","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-isolation-forest.md","definition":"Active Learning Isolation Forest combines the unsupervised anomaly-scoring power of Isolation Forest with an iterative query strategy that asks a human expert to label the most informative instances. The result is a detector that refines its anomaly boundaries using a minimal labeling budget, dramatically improving precision on rare and subtle anomalies compared to a purely unsupervised baseline.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Das, S. et al. (active anomaly discovery framework); Liu, F. T. et al. (Isolation Forest base)","year":"2008–2019","type":"Active learning wrapper over isolation forest anomaly detector","dataType":"Continuous / mixed tabular data with unlabeled anomalies and limited oracle budget","subfamily":"Machine learning"},"citations":[{"ref":"Das, S., Wong, W. K., Fern, A., Dietterich, T. G., & Amran Siddiqui, M. (2019). Incorporating Expert Feedback into Active Anomaly Discovery. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), pp. 1009–1014.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Incorporating+Expert+Feedback+into+Active+Anomaly+Discovery"},{"ref":"Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.","type":"inproceedings","doi":"10.1109/ICDM.2008.17","isbn":null,"url":null}],"related":["isolation-forest","active-learning","one-class-svm","autoencoder-anomaly-detection","semi-supervised-isolation-forest","gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-k-nearest-neighbors","name":"Active learning K-nearest neighbors","fullName":"Active Learning with K-Nearest Neighbors Classifier","aliases":["AL-KNN","active KNN","query-based nearest neighbor learning","uncertainty-sampling KNN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1951–2010","originator":"Settles, B. (active learning framework); Fix & Hodges (KNN base)","url":"https://scholargate.app/en/machine-learning/active-learning-k-nearest-neighbors","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-k-nearest-neighbors.md","definition":"Active learning with K-nearest neighbors combines the instance-based prediction of KNN with an iterative query strategy that selects the most informative unlabeled examples for annotation. The model requests labels only for instances where neighborhood vote margins are narrowest, achieving competitive accuracy with far fewer labeled examples than fully supervised KNN on tabular data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Settles, B. (active learning framework); Fix & Hodges (KNN base)","year":"1951–2010","type":"Active learning with KNN base learner","dataType":"Tabular, labeled and unlabeled instances","subfamily":"Machine learning"},"citations":[{"ref":"Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.","type":"article","doi":null,"isbn":null,"url":"https://burrsettles.com/pub/settles.activelearning.pdf"},{"ref":"Zhu, X., Lafferty, J., & Ghahramani, Z. (2003). Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data, 58–65.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Combining+active+learning+and+semi-supervised+learning+using+Gaussian+fields+and+harmonic+functions"}],"related":["k-nearest-neighbors","active-learning","active-learning-decision-tree","active-learning-logistic-regression","semi-supervised-k-nearest-neighbors","active-learning-random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-lightgbm","name":"Active Learning LightGBM","fullName":"Active Learning with Light Gradient Boosting Machine","aliases":["AL-LightGBM","Active LightGBM","LightGBM active learning","AL-LGBM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017–present","originator":"Settles, B. (active learning); Ke, G. et al. (LightGBM)","url":"https://scholargate.app/en/machine-learning/active-learning-lightgbm","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-lightgbm.md","definition":"Active Learning LightGBM couples the query-efficient label-selection strategy of active learning with the speed and accuracy of LightGBM, a histogram-based gradient boosting framework. The model iteratively selects the most informative unlabeled instances for human annotation, retrains LightGBM on the growing labeled set, and converges to high accuracy with far fewer labeled examples than passive supervised learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Settles, B. (active learning); Ke, G. et al. (LightGBM)","year":"2017–present","type":"Hybrid (active learning query strategy + gradient boosting classifier)","dataType":"Tabular, labeled and unlabeled instances","subfamily":"Machine learning"},"citations":[{"ref":"Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.","type":"book","doi":"10.2200/S00429ED1V01Y201207AIM018","isbn":null,"url":null},{"ref":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30, 3146–3154.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html"}],"related":["lightgbm","active-learning","xgboost","random-forest","uncertainty-sampling","gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-linear-regression","name":"Active Learning Linear Regression","fullName":"Active Learning with Linear Regression","aliases":["AL-LR","active linear regression","query-based linear regression","optimal experimental design for regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1996","originator":"Cohn, D. A.; Ghahramani, Z.; Jordan, M. I.","url":"https://scholargate.app/en/machine-learning/active-learning-linear-regression","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-linear-regression.md","definition":"Active Learning Linear Regression is an iterative machine-learning approach that couples a linear regression model with an intelligent query strategy to select the most informative unlabeled points for labeling. By focusing labeling effort where uncertainty is highest, it achieves competitive predictive accuracy with far fewer labeled examples than passive random sampling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cohn, D. A.; Ghahramani, Z.; Jordan, M. I.","year":"1996","type":"Active learning / iterative supervised learning","dataType":"Continuous labels, partially labeled datasets","subfamily":"Machine learning"},"citations":[{"ref":"Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.","type":"book","doi":"10.2200/S00429ED1V01Y201207AIM018","isbn":null,"url":null},{"ref":"Cohn, D. A., Ghahramani, Z., & Jordan, M. I. (1996). Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 129–145.","type":"article","doi":"10.1613/jair.295","isbn":null,"url":null}],"related":["linear-regression","bayesian-linear-regression","gaussian-process-regression","random-forest","uncertainty-sampling","query-by-committee"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-logistic-regression","name":"Active Learning Logistic Regression","fullName":"Active Learning with Logistic Regression (Uncertainty Sampling)","aliases":["AL-LR","logistic regression active learner","uncertainty sampling logistic regression","pool-based active logistic classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1994–2010","originator":"Lewis, D. D. & Gale, W. A.; Settles, B. (survey)","url":"https://scholargate.app/en/machine-learning/active-learning-logistic-regression","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-logistic-regression.md","definition":"Active Learning with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, an oracle (human annotator) labels them, and the model is retrained — repeating until a labeling budget or accuracy target is met. It dramatically reduces annotation cost compared to random labeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lewis, D. D. & Gale, W. A.; Settles, B. (survey)","year":"1994–2010","type":"Active learning framework with logistic regression base learner","dataType":"Labeled and unlabeled tabular or text data","subfamily":"Machine learning"},"citations":[{"ref":"Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.","type":"article","doi":null,"isbn":null,"url":"http://burrsettles.com/pub/settles.activelearning.pdf"},{"ref":"Lewis, D. D., & Gale, W. A. (1994). A sequential algorithm for training text classifiers. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 3–12.","type":"inproceedings","doi":"10.1007/978-1-4471-2099-5_1","isbn":null,"url":null}],"related":["logistic-regression","support-vector-machine","naive-bayes","random-forest","semi-supervised-learning","query-by-committee"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-one-class-svm","name":"Active learning One-class SVM","fullName":"Active Learning with One-Class Support Vector Machine","aliases":["AL-OCSVM","active one-class SVM","active novelty detection SVM","query-driven OCSVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s","originator":"Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)","url":"https://scholargate.app/en/machine-learning/active-learning-one-class-svm","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-one-class-svm.md","definition":"Active Learning One-class SVM combines the one-class support vector machine — a kernel-based novelty detector that learns the boundary of normal data — with an active learning loop that selects the most informative unlabeled instances for expert annotation. The result is a data-efficient anomaly detector that improves its decision boundary with minimal labeling effort.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schölkopf et al. (OCSVM); active variant developed in the anomaly-detection literature (2000s–2010s)","year":"2000s","type":"Semi-supervised anomaly/novelty detection with iterative labeling","dataType":"Continuous or mixed features; predominantly unlabeled with a small labeled set","subfamily":"Machine learning"},"citations":[{"ref":"Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Estimating the Support of a High-Dimensional Distribution. Neural Computation, 13(7), 1443–1471.","type":"inproceedings","doi":"10.1162/089976601750264965","isbn":null,"url":null},{"ref":"Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.","type":"book","doi":null,"isbn":null,"url":"https://burrsettles.com/pub/settles.activelearning.pdf"}],"related":["one-class-svm","active-learning","support-vector-machine","isolation-forest","semi-supervised-learning","anomaly-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-self-supervised-learning","name":"Active Learning Self-supervised Learning","fullName":"Active Learning with Self-supervised Representation Learning","aliases":["AL-SSL","active self-supervised learning","self-supervised active learning","query-based self-supervised learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020-2022","originator":"Multiple authors (active learning + SSL integration, 2020s)","url":"https://scholargate.app/en/machine-learning/active-learning-self-supervised-learning","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-self-supervised-learning.md","definition":"Active learning combined with self-supervised learning leverages unlabeled data through self-supervised pre-training to build rich representations, then uses an active query strategy to select the most informative examples for human annotation, maximizing model performance under a tight labeling budget. This hybrid approach is especially powerful when labeled data is scarce but large unlabeled pools exist.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple authors (active learning + SSL integration, 2020s)","year":"2020-2022","type":"Hybrid learning paradigm","dataType":"Largely unlabeled datasets with limited annotation budget","subfamily":"Machine learning"},"citations":[{"ref":"Bengar, J. Z., van de Weijer, J., Fuentes, L. L., & Raducanu, B. (2022). Class-Balanced Active Learning for Image Classification. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 3082–3091.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Class-Balanced+Active+Learning+for+Image+Classification+Bengar+2022"},{"ref":"Wang, K., Zhang, D., Li, Y., Zhang, R., & Lin, L. (2016). Cost-Effective Active Learning for Deep Image Classification. IEEE Transactions on Circuits and Systems for Video Technology, 27(12), 2591–2600.","type":"inproceedings","doi":"10.1109/TCSVT.2016.2589879","isbn":null,"url":null}],"related":["self-supervised-learning","active-learning","semi-supervised-learning","transfer-learning","few-shot-learning","online-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-stacking-ensemble","name":"Active learning Stacking ensemble","fullName":"Active Learning with Stacking Ensemble","aliases":["AL-stacking","query-by-committee stacking","active stacked generalization","stacking with active query"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1992–2012","originator":"Wolpert, D. H. (stacking); Settles, B. (active learning survey)","url":"https://scholargate.app/en/machine-learning/active-learning-stacking-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-stacking-ensemble.md","definition":"Active Learning Stacking Ensemble combines an active learning query loop with stacked generalization: a pool of unlabeled data is available, and the model iteratively selects the most informative instances for human labeling, using those labels to train and refine a stacking ensemble of multiple base learners topped by a meta-learner. This approach reduces annotation cost while maximizing the predictive power of the ensemble.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolpert, D. H. (stacking); Settles, B. (active learning survey)","year":"1992–2012","type":"Hybrid (active learning + stacked ensemble)","dataType":"Labeled and unlabeled tabular or structured data","subfamily":"Machine learning"},"citations":[{"ref":"Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.","type":"article","doi":"10.1016/S0893-6080(05)80023-1","isbn":null,"url":null},{"ref":"Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers.","type":"book","doi":"10.2200/S00429ED1V01Y201207AIM018","isbn":null,"url":null}],"related":["stacking-ensemble","active-learning","semi-supervised-stacking-ensemble","ensemble-stacking-ensemble","voting-ensemble","boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-support-vector-machine","name":"Active learning Support vector machine","fullName":"Active Learning Support Vector Machine","aliases":["Active SVM","AL-SVM","SVM active learning","query-by-committee SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001","originator":"Tong, S. & Koller, D.","url":"https://scholargate.app/en/machine-learning/active-learning-support-vector-machine","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-support-vector-machine.md","definition":"Active learning SVM combines the strong decision-boundary of support vector machines with an intelligent query strategy that selects the most informative unlabeled instances for human annotation. Introduced by Tong and Koller in 2001, it achieves high classification accuracy using far fewer labeled examples than passive supervised learning, making it practical whenever labeling is expensive or slow.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tong, S. & Koller, D.","year":"2001","type":"Active learning + kernel classifier","dataType":"Labeled and unlabeled instances (tabular, text, image features)","subfamily":"Machine learning"},"citations":[{"ref":"Tong, S., & Koller, D. (2001). Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 2, 45–66.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/volume2/tong01a/tong01a.pdf"},{"ref":"Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.","type":"article","doi":null,"isbn":null,"url":"https://burrsettles.com/pub/settles.activelearning.pdf"}],"related":["svm-classification","support-vector-machine","query-by-committee","semi-supervised-learning","uncertainty-sampling","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning-voting-ensemble","name":"Active Learning Voting Ensemble","fullName":"Active Learning with Voting Ensemble (Query by Committee)","aliases":["Query by Committee","QBC","active ensemble learning","committee-based active learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1992","originator":"Seung, H. S., Opper, M., & Sompolinsky, H.","url":"https://scholargate.app/en/machine-learning/active-learning-voting-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning-voting-ensemble.md","definition":"Active Learning Voting Ensemble — formally known as Query by Committee — is an active learning strategy that trains a committee of diverse models and selects the unlabeled examples where the committee members disagree most for human annotation. By focusing labeling effort on the most informative points, it achieves high accuracy with far fewer labeled examples than passive learning requires.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seung, H. S., Opper, M., & Sompolinsky, H.","year":"1992","type":"Active learning with ensemble voting","dataType":"Labeled and unlabeled tabular, text, or image data","subfamily":"Machine learning"},"citations":[{"ref":"Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT '92), pp. 287–294. ACM.","type":"inproceedings","doi":"10.1145/130385.130417","isbn":null,"url":null},{"ref":"Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.","type":"article","doi":null,"isbn":null,"url":"https://burrsettles.com/pub/settles.activelearning.pdf"}],"related":["active-learning","voting-ensemble","semi-supervised-learning","boosting","bagging","query-by-disagreement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"active-learning","name":"Active Learning","fullName":"Active Learning (Human-in-the-Loop)","aliases":["Query Learning","Optimal Experimental Design (ML context)","Pool-Based Active Learning","Aktif Öğrenme"],"domain":"machine-learning","family":"ml-model","subfamily":"Interactive ML","year":2009,"originator":"Burr Settles","url":"https://scholargate.app/en/machine-learning/active-learning","markdownUrl":"https://scholargate.app/en/machine-learning/active-learning.md","definition":"Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Burr Settles","year":2009,"type":"Interactive supervised learning framework","subfamily":"Interactive ML","queryStrategies":"Uncertainty sampling, query-by-committee, expected model change","labelingCost":"Minimized via selective oracle querying"},"citations":[{"ref":"Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.","type":"techreport","doi":null,"isbn":null,"url":"https://minds.wisconsin.edu/handle/1793/60660"}],"related":["uncertainty-quantification","support-vector-machine","conformal-prediction"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"activities-balance-confidence","name":"ABC Scale","fullName":"Activities-Specific Balance Confidence Scale","aliases":["ABC","ABC Scale"],"domain":"gerontology","family":"process-pipeline","subfamily":"balance-confidence-self-efficacy","year":"1995","originator":"Lorraine E. Powell","url":"https://scholargate.app/en/gerontology/activities-balance-confidence","markdownUrl":"https://scholargate.app/en/gerontology/activities-balance-confidence.md","definition":"The Activities-Specific Balance Confidence (ABC) Scale is a self-report questionnaire developed by Powell and Myers in 1995 to measure an older adult's confidence in maintaining balance while performing 16 common daily activities. Unlike performance-based balance tests, the ABC Scale captures self-efficacy—the person's subjective belief in their ability to perform activities without losing balance or falling. It is widely used in clinical practice, rehabilitation, and research to identify individuals at high fall risk due to low balance confidence and to measure outcomes of interventions designed to restore confidence and activity participation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lorraine E. Powell","subfamily":"balance-confidence-self-efficacy","year":"1995","type":"Self-report questionnaire"},"citations":[{"ref":"Powell, L. E., & Myers, A. M. (1995). The Activities-Specific Balance Confidence (ABC) Scale. J Gerontol A Biol Sci Med Sci, 50A(1), M28-M34.","type":"article","doi":"10.1093/gerona/50A.1.M28","isbn":null,"url":null},{"ref":"Lajoie, Y., & Gallagher, S. P. (2004). Predicting falls within the elderly community: comparison of postural sway, reaction time, the Berg balance scale and the Activities-Specific Balance Confidence (ABC) in determining fall risk. Arch Phys Med Rehabil, 85(7), 1100-1105.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Predicting+falls+within+the+elderly+community%3A+comparison+of+postural+sway%2C+reaction+time%2C+the+Berg+balance+scale+and+the+Activities-Specific+Balance+Confidence+%28ABC%29+in+determining+fall+risk+Lajoie"},{"ref":"Huang, S. L., Hsieh, C. L., Wu, R. M., Tai, C. H., Lin, C. H., & Lu, W. S. (2011). Minimal detectable change of the timed up and go test and the dynamic gait index in people with Parkinson disease. Phys Ther, 91(1), 114-121.","type":"article","doi":"10.2522/ptj.20090126","isbn":null,"url":null}],"related":["tinetti-balance-assessment","short-physical-performance-battery","life-space-assessment","frail-scale","edmonton-frail-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"activity-based-costing","name":"Activity-Based Costing","fullName":"Activity-Based Costing (ABC) Framework for Product and Service Cost Allocation","aliases":["ABC System","Activity-Based Management","Activity Costing"],"domain":"accounting","family":"mcdm","subfamily":"Cost Allocation and Product Costing","year":"1987","originator":"Robert S. Kaplan and Robin Cooper","url":"https://scholargate.app/en/accounting/activity-based-costing","markdownUrl":"https://scholargate.app/en/accounting/activity-based-costing.md","definition":"Activity-Based Costing (ABC) is an advanced costing method developed by Robert Kaplan and Robin Cooper that allocates overhead and indirect costs to products or services based on their actual consumption of activities. Rather than using arbitrary allocation bases (e.g., machine hours or direct labor), ABC traces costs to specific activities (purchasing, machine setup, quality control) and then to products based on which products actually consume those activities, providing more accurate product costs for decision making.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert S. Kaplan and Robin Cooper","subfamily":"Cost Allocation and Product Costing","year":"1987","type":"Advanced managerial accounting methodology"},"citations":[{"ref":"Cooper, R., & Kaplan, R. S. (1991). Profit priorities from activity-based costing. Harvard Business Review, 69(3), 130-135.","type":"article","doi":"10.1007/978-3-322-93138-2_22","isbn":null,"url":null},{"ref":"Garrison, R. H., Noreen, E. W., & Brewer, P. C. (2015). Managerial accounting (15th ed.). McGraw-Hill Education.","type":"article","doi":null,"isbn":null,"url":"https://www.mheducation.com/"}],"related":["cost-volume-profit-analysis","analytical-procedures-auditing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"acute-chronic-workload-ratio","name":"Acute-Chronic Workload Ratio","fullName":"Acute-Chronic Workload Ratio and Injury Risk Assessment","aliases":["ACWR","workload ratio","training load balance"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Training Load","year":"2016","originator":"Tim Gabbett","url":"https://scholargate.app/en/sports-science/acute-chronic-workload-ratio","markdownUrl":"https://scholargate.app/en/sports-science/acute-chronic-workload-ratio.md","definition":"The acute-chronic workload ratio (ACWR) is the ratio of acute training load (typically the past 1 week) to chronic training load (typically the rolling 4-week average). Formalized by Tim Gabbett (2016), ACWR is a widely adopted metric for predicting injury and illness risk in sports. The logic is straightforward: rapid increases in training load—when acute load spikes far above what the athlete has adapted to—exceed tissue tolerance and increase injury risk. Conversely, maintaining ACWR within optimal ranges (typically 0.8-1.3) is associated with better performance and lower injury incidence. ACWR monitoring is now standard in elite sports for load management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tim Gabbett","subfamily":"Training Load","year":"2016","type":"workload monitoring"},"citations":[{"ref":"Gabbett, T. J. (2016). The training-injury prevention paradox: should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273-280.","type":"article","doi":"10.1136/bjsports-2015-095788","isbn":null,"url":null},{"ref":"Blanch, P., & Gabbett, T. J. (2016). Has the athlete trained enough to return to play safely? New concepts in return-to-play rehabilitation. British Journal of Sports Medicine, 50(13), 807-811.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Has+the+athlete+trained+enough+to+return+to+play+safely+Blanch"},{"ref":"Hulin, B. T., Gabbett, T. J., Blanch, P., Chapman, P., Bailey, D., & Orchard, J. W. (2014). Spikes in acute workload are associated with increased injury risk in elite Australian footballers. British Journal of Sports Medicine, 48(12), 997-1002.","type":"article","doi":"10.1136/bjsports-2013-092524","isbn":null,"url":null}],"related":["session-rpe","time-motion-gps","banister-trimp"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaboost","name":"AdaBoost","fullName":"AdaBoost (Adaptive Boosting)","aliases":["AdaBoost (Adaptive Boosting)","adaptive boosting","adaptif artırma"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1997,"originator":"Freund, Y. & Schapire, R.E.","url":"https://scholargate.app/en/machine-learning/adaboost","markdownUrl":"https://scholargate.app/en/machine-learning/adaboost.md","definition":"AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Freund, Y. & Schapire, R.E.","year":1997,"type":"Ensemble (sequential boosting of weak learners)","task":"Classification & prediction","minSample":50,"weakLearner":"Decision stump (depth-1 tree)"},"citations":[{"ref":"Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.","type":"article","doi":"10.1006/jcss.1997.1504","isbn":null,"url":null}],"related":["xgboost","random-forest","decision-tree","stacking-ensemble","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-ab-design","name":"Adaptive AB Design","fullName":"Adaptive AB Single-Subject Experimental Design","aliases":["adaptive single-case AB design","data-driven AB design","adaptive baseline-intervention design","adaptive AB phase design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (AB foundation); 2000s (adaptive extensions)","originator":"Baer, Wolf & Risley (AB foundation); Kratochwill & Levin (adaptive single-case extensions)","url":"https://scholargate.app/en/experimental-design/adaptive-ab-design","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-ab-design.md","definition":"The adaptive AB design is a single-subject experimental design that retains the two-phase baseline-then-intervention structure of the classic AB design but replaces fixed session-count rules with pre-specified data-driven criteria — such as stability thresholds or trend benchmarks — that determine when to transition between phases. This adaptive logic allows the phase boundary to move in response to the individual participant's actual performance trajectory rather than a predetermined schedule.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baer, Wolf & Risley (AB foundation); Kratochwill & Levin (adaptive single-case extensions)","year":"1968 (AB foundation); 2000s (adaptive extensions)","type":"Single-subject experimental design with adaptive phase-change rules","dataType":"Repeatedly measured behavioral or outcome data over time","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91-97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association.","type":"book","doi":null,"isbn":"978-1433807428","url":null}],"related":["ab-design","aba-design","abab-design","adaptive-experiment","multiple-baseline-design","single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-ab-test","name":"Adaptive A/B test","fullName":"Adaptive A/B Testing","aliases":["adaptive AB test","bandit A/B test","multi-armed bandit testing","online adaptive experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1952 (Robbins); applied to A/B testing from ~2010s onward","originator":"Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson","url":"https://scholargate.app/en/experimental-design/adaptive-ab-test","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-ab-test.md","definition":"An Adaptive A/B test is an experimental design that dynamically reallocates traffic or participants toward better-performing variants during the experiment itself, rather than holding allocations fixed until the end. Drawing on multi-armed bandit algorithms such as Thompson Sampling or Upper Confidence Bound (UCB), it balances the exploration of uncertain variants with the exploitation of those already showing superior performance, typically yielding higher aggregate outcomes while still producing valid inferential conclusions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson","year":"1952 (Robbins); applied to A/B testing from ~2010s onward","type":"Adaptive experimental design","dataType":"Continuous or binary outcome metrics (e.g., click-through, conversion rates)","subfamily":"Deneysel desen"},"citations":[{"ref":"Russo, D., Van Roy, B., Kazerouni, A., Osband, I., & Wen, Z. (2018). A Tutorial on Thompson Sampling. Foundations and Trends in Machine Learning, 11(1), 1–96.","type":"article","doi":"10.1561/2200000070","isbn":null,"url":null},{"ref":"Offer-Westort, M., Coppock, A., & Green, D. P. (2021). Adaptive Experimental Design: Prospects and Applications in Political Science. American Journal of Political Science, 65(4), 826–844.","type":"article","doi":"10.1111/ajps.12597","isbn":null,"url":null}],"related":["adaptive-experiment","multi-arm-experiment","ab-design","factorial-ab-test","randomized-controlled-trial","blocked-ab-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-aba-design","name":"Adaptive ABA Design","fullName":"Adaptive ABA Single-Subject Experimental Design","aliases":["adaptive withdrawal design","adaptive ABA withdrawal design","data-driven ABA design","adaptive single-case ABA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (ABA foundation); adaptive extensions formalized ~2010–2020","originator":"Baer, Wolf & Risley (ABA baseline); adaptive decision-rule extensions developed in single-case methodology literature (Kratochwill & Levin, 2010s)","url":"https://scholargate.app/en/experimental-design/adaptive-aba-design","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-aba-design.md","definition":"The Adaptive ABA Design is a single-subject experimental framework that follows the classic three-phase ABA withdrawal structure — baseline (A1), intervention (B), and return-to-baseline (A2) — while embedding prospective decision rules that allow researchers or clinicians to extend, shorten, or otherwise modify each phase in response to observed data patterns rather than following a fixed schedule. This adaptive layer makes the design responsive to individual participant trajectories while preserving experimental control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baer, Wolf & Risley (ABA baseline); adaptive decision-rule extensions developed in single-case methodology literature (Kratochwill & Levin, 2010s)","year":"1968 (ABA foundation); adaptive extensions formalized ~2010–2020","type":"Single-subject experimental design with adaptive phase rules","dataType":"Repeated-measures behavioral or clinical outcome data (continuous or count)","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association.","type":"book","doi":null,"isbn":"978-1433807039","url":null}],"related":["aba-design","abab-design","adaptive-experiment","single-subject-experimental-design","multiple-baseline-design","adaptive-ab-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-abab-design","name":"Adaptive ABAB Design","fullName":"Adaptive ABAB Reversal Design","aliases":["adaptive reversal design","adaptive single-subject ABAB","ABAB with adaptive phase-change rules","dynamic ABAB design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1984 (foundational ABAB); adaptive extensions ~2000s–2010s","originator":"Extended from Barlow & Hersen's ABAB reversal tradition; adaptive rules formalized in behavioral and clinical single-subject research (late 20th–early 21st century)","url":"https://scholargate.app/en/experimental-design/adaptive-abab-design","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-abab-design.md","definition":"The Adaptive ABAB Design is a single-subject experimental methodology that extends the classic ABAB reversal design by incorporating data-driven, prospective decision rules to determine when to transition between baseline (A) and intervention (B) phases. Rather than fixing phase lengths in advance, the researcher uses pre-specified criteria — such as stability thresholds, slope targets, or effect-size benchmarks — to guide each phase change, improving both experimental control and clinical responsiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from Barlow & Hersen's ABAB reversal tradition; adaptive rules formalized in behavioral and clinical single-subject research (late 20th–early 21st century)","year":"1984 (foundational ABAB); adaptive extensions ~2000s–2010s","type":"Single-subject experimental design","dataType":"Repeated measures (behavioral observation, continuous outcome data)","subfamily":"Deneysel desen"},"citations":[{"ref":"Barlow, D. H., & Hersen, M. (1984). Single Case Experimental Designs: Strategies for Studying Behavior Change (2nd ed.). Pergamon Press.","type":"book","doi":null,"isbn":"978-0205143641","url":null},{"ref":"Normand, M. P., & Bailey, J. S. (2006). The human right to effective behavioral treatment. The Behavior Analyst, 29(2), 253–261.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Normand+Bailey+2006+human+right+effective+behavioral+treatment"}],"related":["abab-reversal-design","multiple-baseline-design","alternating-treatments-design","changing-criterion-design","single-subject-design","interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-case-control-study","name":"Adaptive Case-Control Study","fullName":"Adaptive Case-Control Study Design","aliases":["adaptive case-control design","sequential case-control study","adaptive observational study","dynamic case-control study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1950s (base design); adaptive extensions developed from the 1970s–1990s","originator":"Builds on Doll & Hill (1950s) case-control foundations; adaptive elements drawn from sequential analysis (Wald, 1947) and group-sequential methods (Armitage, 1975)","url":"https://scholargate.app/en/epidemiology/adaptive-case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-case-control-study.md","definition":"An adaptive case-control study is a case-control design that incorporates pre-specified rules allowing modification of study parameters — such as sample size, case-to-control ratio, or matching criteria — based on interim data, without compromising validity. It combines the efficiency of adaptive methodology with the retrospective exposure-ascertainment logic of classical case-control research, enabling investigators to respond to emerging evidence while the study is ongoing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Builds on Doll & Hill (1950s) case-control foundations; adaptive elements drawn from sequential analysis (Wald, 1947) and group-sequential methods (Armitage, 1975)","year":"1950s (base design); adaptive extensions developed from the 1970s–1990s","type":"Adaptive observational epidemiological design","dataType":"Categorical and continuous exposure/outcome data; binary disease status","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Jennison, C., & Turnbull, B. W. (1999). Group Sequential Methods with Applications to Clinical Trials. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-0849303166","url":null}],"related":["case-control-study","adaptive-randomized-clinical-trial","sequential-analysis","nested-case-control","cohort-study","adaptive-cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-case-series","name":"Adaptive case series","fullName":"Adaptive Case Series Study","aliases":["adaptive case-series design","sequential adaptive case series","adaptive observational case series"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Late 20th–early 21st century","originator":"Evolved from classical case series methodology combined with adaptive design principles (Chow & Chang, 2008; FDA adaptive design guidance)","url":"https://scholargate.app/en/epidemiology/adaptive-case-series","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-case-series.md","definition":"An adaptive case series is an observational study design that documents a consecutive group of patients with a shared condition or exposure while incorporating pre-specified rules for modifying data collection, monitoring, or stopping criteria as accumulating evidence warrants. It combines the descriptive richness of traditional case series with the prospective flexibility of adaptive design principles, enabling structured mid-course adjustments without compromising the integrity of the recorded clinical observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from classical case series methodology combined with adaptive design principles (Chow & Chang, 2008; FDA adaptive design guidance)","year":"Late 20th–early 21st century","type":"Observational study with adaptive monitoring","dataType":"Clinical records, patient follow-up data, safety and outcome reports","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584887775","url":null},{"ref":"Case series. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Case_series"}],"related":["case-series","adaptive-randomized-clinical-trial","adaptive-cohort-study","prospective-case-series","adaptive-diagnostic-accuracy-study","case-report"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-cluster-sampling","name":"Adaptive Cluster Sampling","fullName":"Adaptive Cluster Sampling","aliases":["ACS","adaptive network sampling","sequential cluster sampling","neighborhood adaptive sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990","originator":"Steven K. Thompson","url":"https://scholargate.app/en/survey-methodology/adaptive-cluster-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-cluster-sampling.md","definition":"Adaptive cluster sampling (ACS) is a probability-based design in which an initial random sample of units triggers the inclusion of neighboring units whenever a predefined condition — typically a threshold count of a rare attribute — is satisfied. Developed by Steven K. Thompson in 1990, ACS is especially powerful for estimating the abundance or distribution of rare, spatially clustered populations such as endangered species, disease hotspots, or hard-to-reach social groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven K. Thompson","year":"1990","type":"Probability-based adaptive sampling design","dataType":"Numeric counts or measurements of rare, clustered populations","subfamily":"Sampling"},"citations":[{"ref":"Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.","type":"article","doi":"10.2307/2289601","isbn":null,"url":null},{"ref":"Thompson, S. K., & Seber, G. A. F. (1996). Adaptive Sampling. Wiley.","type":"book","doi":null,"isbn":"978-0471558712","url":null}],"related":["cluster-sampling","systematic-sampling","stratified-sampling","snowball-sampling","multistage-sampling","adaptive-stratified-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-cohort-study","name":"Adaptive Cohort Study","fullName":"Adaptive Cohort Study Design","aliases":["adaptive longitudinal study","flexible cohort design","adaptive prospective cohort","ACS"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s–2010s (systematic formalisation)","originator":"Extension of classic cohort methods; adaptive design principles formalised by regulatory and epidemiology communities in the 2000s–2010s","url":"https://scholargate.app/en/epidemiology/adaptive-cohort-study","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-cohort-study.md","definition":"An adaptive cohort study is a longitudinal observational design that follows a defined group of individuals over time to assess exposure-outcome relationships, while incorporating pre-specified adaptation rules that allow protocol modifications — such as sample-size re-estimation, subgroup enrichment, or measurement schedule adjustments — based on accumulating interim data. Adaptations are made without compromising validity, guided by a statistical analysis plan agreed upon before data collection begins.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of classic cohort methods; adaptive design principles formalised by regulatory and epidemiology communities in the 2000s–2010s","year":"2000s–2010s (systematic formalisation)","type":"Observational / adaptive epidemiological design","dataType":"Longitudinal individual-level data; exposure, outcome, covariate measurements over time","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"VanderWeele, T. J., & Hernan, M. A. (2012). Results on differential and dependent measurement error of the exposure and the outcome using signed directed acyclic graphs. American Journal of Epidemiology, 175(12), 1303–1310.","type":"article","doi":"10.1093/aje/kwr458","isbn":null,"url":null},{"ref":"Cohort study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Cohort_study"}],"related":["cohort-study","prospective-cohort-study","adaptive-clinical-trial","longitudinal-study","case-control-study","interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-competing-risks-analysis","name":"Adaptive Competing Risks Analysis","fullName":"Adaptive Competing Risks Analysis","aliases":["adaptive Fine-Gray analysis","group-sequential competing risks","adaptive subdistribution hazard analysis","competing risks adaptive design"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1999 (foundational Fine-Gray model); adaptive extensions 2000s–2010s","originator":"Fine & Gray (subdistribution hazard, 1999); adaptive extensions by Beyersmann, Schumacher and colleagues","url":"https://scholargate.app/en/epidemiology/adaptive-competing-risks-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-competing-risks-analysis.md","definition":"Adaptive competing risks analysis combines the Fine-Gray subdistribution hazard framework — which models the cumulative incidence of one cause of failure in the presence of other mutually exclusive causes — with adaptive or group-sequential interim monitoring rules. This allows a clinical trial or observational study to be modified mid-course (e.g., sample size reassessment, early stopping) based on accumulating competing-risk data while maintaining pre-specified type I error control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fine & Gray (subdistribution hazard, 1999); adaptive extensions by Beyersmann, Schumacher and colleagues","year":"1999 (foundational Fine-Gray model); adaptive extensions 2000s–2010s","type":"Statistical survival analysis with adaptive interim monitoring","dataType":"Time-to-event data with multiple mutually exclusive failure types; censored observations","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.","type":"article","doi":"10.1080/01621459.1999.10474144","isbn":null,"url":null},{"ref":"Beyersmann, J., Allignol, A., & Schumacher, M. (2012). Competing Risks and Multistate Models with R. Springer.","type":"book","doi":null,"isbn":"978-1461420767","url":null}],"related":["competing-risks-analysis","cause-specific-hazard-model","fine-gray-model","group-sequential-design","adaptive-trial-design","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-control-group-experimental-design","name":"Adaptive Control Group Experimental Design","fullName":"Adaptive Experimental Design with Control Group","aliases":["adaptive controlled experiment","adaptive two-arm controlled design","adaptive parallel-group design","flexible controlled trial design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1994 (formal adaptive framework); wider adoption 2000s–2010s","originator":"Peter Bauer and Klaus Kohne (adaptive interim analysis framework, 1994); broader adaptive design methodology developed by Scott Chow and Mark Chang","url":"https://scholargate.app/en/experimental-design/adaptive-control-group-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-control-group-experimental-design.md","definition":"An adaptive control group experimental design is an experiment that assigns participants to at least one treatment arm and one concurrent control group, while allowing pre-specified modifications to the trial — such as sample size re-estimation, early stopping, or allocation ratio changes — based on accumulating data. Adaptations are governed by decision rules established before the study begins, preserving Type I error control while improving efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Bauer and Klaus Kohne (adaptive interim analysis framework, 1994); broader adaptive design methodology developed by Scott Chow and Mark Chang","year":"1994 (formal adaptive framework); wider adoption 2000s–2010s","type":"Adaptive experimental design","dataType":"Continuous, binary, or time-to-event outcome data from treatment and control groups","subfamily":"Deneysel desen"},"citations":[{"ref":"Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584886760","url":null},{"ref":"Bauer, P., & Kohne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics, 50(4), 1029–1041.","type":"article","doi":"10.2307/2533441","isbn":null,"url":null}],"related":["adaptive-experiment","randomized-controlled-trial","control-group-experimental-design","adaptive-randomized-controlled-trial","sequential-experimental-design","response-adaptive-randomization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-control","name":"Adaptive Control","fullName":"Adaptive Control","aliases":["Self-Tuning Control","Parameter Estimation Control"],"domain":"control-theory","family":"ml-model","subfamily":"Adaptive Control","year":"1983","originator":"Karl J. Astrom","url":"https://scholargate.app/en/control-theory/adaptive-control","markdownUrl":"https://scholargate.app/en/control-theory/adaptive-control.md","definition":"Adaptive Control is a control strategy that adjusts controller parameters in real-time based on online system identification to maintain performance despite changing plant dynamics or uncertain parameters. Pioneered by Astrom and Wittenmark, adaptive control enables robust operation in time-varying environments, from aircraft with fuel depletion to industrial systems with aging components.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl J. Astrom","subfamily":"Adaptive Control","year":"1983","type":"algorithm"},"citations":[{"ref":"Astrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall.","type":"article","doi":null,"isbn":null,"url":"https://www.pearson.com/en-us/subject-catalog/p/computer-controlled-systems/P200000003266"},{"ref":"Ioannou, P. A., & Sun, J. (1996). Robust Adaptive Control. Prentice Hall.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+Adaptive+Control+Ioannou"}],"related":["iterative-learning-control","backstepping-control","model-predictive-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-cox-proportional-hazards","name":"Adaptive Cox Proportional Hazards","fullName":"Adaptive Cox Proportional Hazards Model","aliases":["adaptive Cox model","adaptive LASSO Cox regression","penalized Cox proportional hazards","adaptive regularized survival regression"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2007 (adaptive LASSO variant); base Cox model 1972","originator":"Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox","url":"https://scholargate.app/en/epidemiology/adaptive-cox-proportional-hazards","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-cox-proportional-hazards.md","definition":"The Adaptive Cox Proportional Hazards model extends the classic Cox regression for time-to-event outcomes by adding adaptive LASSO (or related) penalization. It simultaneously estimates hazard ratios and performs variable selection, shrinking irrelevant covariate coefficients exactly to zero. This makes it especially valuable in high-dimensional clinical or genomic datasets where the number of candidate predictors is large relative to the number of events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox","year":"2007 (adaptive LASSO variant); base Cox model 1972","type":"Penalized semi-parametric survival regression","dataType":"Time-to-event (survival) data with right-censoring; continuous and/or binary predictors","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Zhang, H. H., & Lu, W. (2007). Adaptive Lasso for Cox's proportional hazards model. Biometrika, 94(3), 691–703.","type":"article","doi":"10.1093/biomet/asm037","isbn":null,"url":null},{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null}],"related":["cox-proportional-hazards","lasso-regression","elastic-net-survival","kaplan-meier","accelerated-failure-time","random-survival-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-cross-sectional-epidemiological-study","name":"Adaptive Cross-Sectional Epidemiological Study","fullName":"Adaptive Cross-Sectional Epidemiological Study Design","aliases":["adaptive cross-sectional survey","adaptive prevalence study","adaptive epidemiological survey design","adaptive population cross-section"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (formalization of adaptive elements in observational surveys)","originator":"Conceptual synthesis of adaptive design methods (Wald, 1947; Bauer & Kohne, 1994) with classical cross-sectional epidemiology (MacMahon & Pugh, 1960s)","url":"https://scholargate.app/en/epidemiology/adaptive-cross-sectional-epidemiological-study","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-cross-sectional-epidemiological-study.md","definition":"An adaptive cross-sectional epidemiological study combines the core logic of a cross-sectional survey — measuring exposures and outcomes simultaneously in a defined population at one point in time — with pre-specified adaptive rules that allow modifications to sampling strategy, sample size, or subgroup allocation based on accumulating interim data. The approach preserves the efficiency and speed of a standard cross-sectional design while improving precision for rare exposures or heterogeneous populations by redirecting sampling resources in real time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conceptual synthesis of adaptive design methods (Wald, 1947; Bauer & Kohne, 1994) with classical cross-sectional epidemiology (MacMahon & Pugh, 1960s)","year":"1990s–2000s (formalization of adaptive elements in observational surveys)","type":"Observational epidemiological study design","dataType":"Population-level survey data; prevalence counts; exposure and outcome measurements at a single or adaptively timed time point","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195083439","url":null},{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null}],"related":["cross-sectional-study","adaptive-sampling","two-phase-sampling","stratified-random-sampling","ecological-study","repeated-cross-sectional-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-design","name":"Adaptive Clinical Trial Design","fullName":"Adaptive Design for Clinical Trials","aliases":["adaptive design","group sequential design","sample size re-estimation","platform trial","Adaptif Klinik Çalışma Tasarımı (Adaptive Design)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1994,"originator":"Bauer & Köhne","url":"https://scholargate.app/en/experimental-design/adaptive-design","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-design.md","definition":"Adaptive clinical trial design is a flexible experimental framework, formalised by Bauer and Köhne in 1994, in which pre-specified rules allow the trial to be modified mid-course — adjusting sample size, treatment arms, or randomisation ratios — based on accumulating interim data while rigorously controlling the Type I error rate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bauer & Köhne","year":1994,"family":"Experimental design","type":"Adaptive hypothesis test with interim analyses","parametric":false,"outcomeTypes":"continuous, binary, ordinal","minSample":30,"structure":"longitudinal","difficulty":3,"regulatoryGuidance":"FDA (2019)"},"citations":[{"ref":"Bauer, P. & Köhne, K. (1994). Evaluation of Experiments with Adaptive Interim Analyses. Biometrics, 50(4), 1029–1041.","type":"article","doi":"10.2307/2533441","isbn":null,"url":null},{"ref":"FDA (2019). Adaptive Design Clinical Trials for Drugs and Biologics — Guidance for Industry. U.S. Food and Drug Administration.","type":"report","doi":null,"isbn":null,"url":"https://www.fda.gov/media/78495/download"}],"related":["sequential-design","equivalence-trial","randomized-controlled-trial","group-sequential-test","sample-size-calculation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-diagnostic-accuracy-study","name":"Adaptive Diagnostic Accuracy Study","fullName":"Adaptive Diagnostic Accuracy Study","aliases":["adaptive DTA study","adaptive diagnostic test evaluation","adaptive test accuracy trial","adaptive STARD study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s–2010s (adaptive designs codified for diagnostics ~2010s)","originator":"Adaptation of STARD framework (Bossuyt et al.) combined with adaptive design principles (Jennison & Turnbull; FDA guidance)","url":"https://scholargate.app/en/epidemiology/adaptive-diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-diagnostic-accuracy-study.md","definition":"An adaptive diagnostic accuracy study evaluates how well an index test distinguishes between patients with and without a target condition, while incorporating pre-specified interim analyses that allow modifications — such as sample size re-estimation, threshold adjustment, or subgroup enrichment — based on accumulating data. This design improves efficiency and ethical conduct compared to fixed-sample diagnostic studies, particularly when prior prevalence or test performance data are uncertain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adaptation of STARD framework (Bossuyt et al.) combined with adaptive design principles (Jennison & Turnbull; FDA guidance)","year":"2000s–2010s (adaptive designs codified for diagnostics ~2010s)","type":"Adaptive observational/experimental study design","dataType":"Index test results, reference standard results, patient covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., ... & Cohen, J. F. (2015). STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 351, h5527.","type":"article","doi":"10.1136/bmj.h5527","isbn":null,"url":null},{"ref":"Jennison, C., & Turnbull, B. W. (2000). Group Sequential Methods with Applications to Clinical Trials. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-0849303166","url":null}],"related":["diagnostic-accuracy-study","adaptive-randomized-clinical-trial","screening-test-evaluation","adaptive-cohort-study","bayesian-diagnostic-accuracy-study","prospective-diagnostic-accuracy-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-dose-response-analysis","name":"Adaptive Dose-Response Analysis","fullName":"Adaptive Dose-Response Analysis","aliases":["adaptive DRA","adaptive dose-finding analysis","adaptive exposure-response analysis","adaptive D-R modeling"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s (key papers 2005–2007; ICH E4 guidance 1994 for classical dose-response)","originator":"Frank Bretz, José Pinheiro and colleagues; foundational MCP-Mod framework","url":"https://scholargate.app/en/epidemiology/adaptive-dose-response-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-dose-response-analysis.md","definition":"Adaptive dose-response analysis combines pre-specified dose-response modeling with planned interim looks that allow modifications — such as dropping ineffective doses or reallocating sample size — while maintaining statistical integrity. The most widely cited framework is MCP-Mod (Multiple Comparisons and Modeling), endorsed by the EMA and FDA as a fit-for-purpose methodology for dose-finding studies in drug development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Bretz, José Pinheiro and colleagues; foundational MCP-Mod framework","year":"2000s (key papers 2005–2007; ICH E4 guidance 1994 for classical dose-response)","type":"Adaptive statistical design and analysis","dataType":"Continuous or binary outcome data with multiple pre-specified dose levels","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bretz, F., Pinheiro, J. C., & Branson, M. (2005). Combining multiple comparisons and modeling techniques in dose-response studies. Biometrics, 61(3), 738-748.","type":"article","doi":"10.1111/j.1541-0420.2005.00344.x","isbn":null,"url":null},{"ref":"Bornkamp, B., Bretz, F., Dmitrienko, A., Enas, G., Gaydos, B., Hsu, C. H., König, F., Mohberg, M., Pinheiro, J., Roessner, L., & Smith, M. (2007). Innovative approaches for designing and analyzing adaptive dose-ranging studies. Journal of Biopharmaceutical Statistics, 17(6), 965-995.","type":"article","doi":"10.1080/10543400701643848","isbn":null,"url":null}],"related":["dose-response-analysis","adaptive-randomized-clinical-trial","adaptive-phase-ii-clinical-trial","bayesian-dose-response-analysis","adaptive-phase-i-clinical-trial","screening-test-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-ecological-study","name":"Adaptive Ecological Study","fullName":"Adaptive Ecological Study Design","aliases":["adaptive ecologic study","sequential ecological study","adaptive population-level design","adaptive group-level study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (adaptive extensions of classical ecological designs)","originator":"Building on classical ecological epidemiology (Durkheim, Snow, Morgenstern); adaptive extensions developed in late 20th–early 21st century methodological literature","url":"https://scholargate.app/en/epidemiology/adaptive-ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-ecological-study.md","definition":"An adaptive ecological study is an observational epidemiological design in which the unit of analysis is a group or population (e.g., a region, country, or community) rather than an individual. It extends the classical ecological study by incorporating pre-specified interim decision rules that allow modifications — such as changes in geographic unit, time window, or exposure categorisation — as data accumulate, while preserving overall inferential validity. The design is used to explore population-level associations between aggregate exposures and aggregate outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on classical ecological epidemiology (Durkheim, Snow, Morgenstern); adaptive extensions developed in late 20th–early 21st century methodological literature","year":"1990s–2000s (adaptive extensions of classical ecological designs)","type":"Observational study design","dataType":"Aggregate population-level data (rates, proportions, means per group or region)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Morgenstern, H. (1998). Ecologic studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Morgenstern+ecologic+studies+Modern+Epidemiology+1998"},{"ref":"Ecological study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ecological_study"}],"related":["ecological-study","cross-sectional-study","interrupted-time-series","adaptive-trial-design","multilevel-analysis","disease-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-experiment","name":"Adaptive Experiment","fullName":"Adaptive Experimental Design","aliases":["adaptive design","response-adaptive randomization","adaptive trial","adaptive randomization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s","originator":"Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others","url":"https://scholargate.app/en/experimental-design/adaptive-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-experiment.md","definition":"An adaptive experiment is an experimental design in which pre-specified rules allow the protocol to be modified — such as reallocating participants to better-performing arms, stopping early for efficacy or futility, or changing sample size — based on accumulating interim data, while maintaining statistical validity. Adaptive designs are widely used in clinical trials, behavioural economics, and online platform testing to improve efficiency and ethics without sacrificing inferential rigour.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others","year":"1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s","type":"Experimental research design","dataType":"Continuous, binary, or count outcome data; interim accumulating data","subfamily":"Deneysel desen"},"citations":[{"ref":"Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584886761","url":null},{"ref":"U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.","type":"article","doi":null,"isbn":null,"url":"https://www.fda.gov/media/78506/download"}],"related":["multi-arm-experiment","randomized-controlled-trial","factorial-experiment","sequential-analysis","platform-trial","response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-field-experiment","name":"Adaptive Field Experiment","fullName":"Adaptive Field Experiment","aliases":["adaptive field trial","sequentially adaptive field experiment","responsive field experiment","adaptive randomized field study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1990s–2000s (formalized in field economics and development research contexts)","originator":"Developed at the intersection of adaptive trial methodology (Berry, Bauer) and field experimentation (Duflo, Kremer, List)","url":"https://scholargate.app/en/experimental-design/adaptive-field-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-field-experiment.md","definition":"An adaptive field experiment is a randomized study conducted in a real-world environment in which pre-specified decision rules allow the researcher to modify the trial as interim data accumulate — for example, by reallocating participants toward more effective arms, adjusting sample size, or stopping early for efficacy or futility — all while maintaining statistical integrity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed at the intersection of adaptive trial methodology (Berry, Bauer) and field experimentation (Duflo, Kremer, List)","year":"1990s–2000s (formalized in field economics and development research contexts)","type":"Adaptive experimental design conducted in naturalistic settings","dataType":"Continuous or interim outcome data collected in real-world field conditions","subfamily":"Deneysel desen"},"citations":[{"ref":"Berry, D. A. (2004). Bayesian statistics and the efficiency and ethics of clinical trials. Statistical Science, 19(1), 175–187.","type":"article","doi":"10.1214/088342304000000044","isbn":null,"url":null},{"ref":"Duflo, E., Glennerster, R., & Kremer, M. (2007). Using randomization in development economics research: A toolkit. Handbook of Development Economics, 4, 3895–3962.","type":"article","doi":"10.1016/S1573-4471(07)04061-2","isbn":null,"url":null}],"related":["adaptive-experiment","field-experiment","randomized-controlled-trial","multi-arm-experiment","adaptive-randomized-controlled-trial","factorial-field-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-fractional-factorial-experiment","name":"Adaptive Fractional Factorial Experiment","fullName":"Adaptive Fractional Factorial Experiment","aliases":["adaptive FFE","sequential fractional factorial design","adaptive screening design","adaptive factor screening"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1950s–1960s (classical FFD); adaptive extensions formalized in 1990s–2000s","originator":"Box, Hunter, and collaborators (adaptive/sequential extension of classical fractional factorial work)","url":"https://scholargate.app/en/experimental-design/adaptive-fractional-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-fractional-factorial-experiment.md","definition":"An adaptive fractional factorial experiment combines the resource-efficiency of fractional factorial designs with a sequential, data-driven strategy for selecting which factors and interactions to investigate next. Rather than committing all experimental runs upfront, the researcher analyses results from an initial fraction and uses those findings to guide subsequent rounds of experimentation — augmenting, folding, or redirecting the design until the active factors and optimal settings are identified with sufficient precision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box, Hunter, and collaborators (adaptive/sequential extension of classical fractional factorial work)","year":"1950s–1960s (classical FFD); adaptive extensions formalized in 1990s–2000s","type":"Experimental design strategy","dataType":"Continuous or categorical factor levels; quantitative response outcomes","subfamily":"Deneysel desen"},"citations":[{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471718130","url":null},{"ref":"Wu, C. F. J., & Hamada, M. S. (2000). Experiments: Planning, Analysis, and Parameter Design Optimization. Wiley.","type":"book","doi":null,"isbn":"978-0471255116","url":null}],"related":["fractional-factorial-design","response-surface-methodology","plackett-burman-design","central-composite-design","sequential-experimentation","definitive-screening-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-full-factorial-experiment","name":"Adaptive Full Factorial Experiment","fullName":"Adaptive Full Factorial Experimental Design","aliases":["adaptive full-factorial design","sequential full factorial experiment","adaptive complete factorial design","dynamic full factorial trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1950s (factorial foundations); adaptive extensions prominent from 1990s onward","originator":"Rooted in Box & Hunter factorial design tradition; adaptive extensions formalised by Atkinson, Donev and others in optimal design theory","url":"https://scholargate.app/en/experimental-design/adaptive-full-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-full-factorial-experiment.md","definition":"An adaptive full factorial experiment is an experimental design that starts with a complete crossing of all factors and all their levels, then uses interim data to modify subsequent runs — dropping unpromising factor levels, adding new ones, or re-allocating replication — while preserving the full factorial structure within each phase. This integration of full factorial coverage with adaptive decision rules allows researchers to explore all main effects and interactions without committing to a fixed, inefficient run plan before any data are observed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Box & Hunter factorial design tradition; adaptive extensions formalised by Atkinson, Donev and others in optimal design theory","year":"1950s (factorial foundations); adaptive extensions prominent from 1990s onward","type":"Experimental design","dataType":"Continuous or categorical outcomes measured across all factor-level combinations","subfamily":"Deneysel desen"},"citations":[{"ref":"Atkinson, A., Donev, A., & Tobias, R. (2007). Optimum Experimental Designs, with SAS. Oxford University Press.","type":"book","doi":null,"isbn":"978-0199296606","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["full-factorial-experiment","adaptive-experiment","fractional-factorial-experiment","response-surface-methodology","sequential-experimental-design","factorial-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-laboratory-experiment","name":"Adaptive Laboratory Experiment","fullName":"Adaptive Laboratory Experiment","aliases":["adaptive lab experiment","sequential adaptive laboratory study","response-adaptive laboratory design","adaptive experimental laboratory design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1947 (sequential analysis foundations); adaptive laboratory applications widespread from 1990s","originator":"Rooted in sequential analysis (Abraham Wald, 1947); adaptive clinical/lab designs formalized by Berry and colleagues (1990s–2000s)","url":"https://scholargate.app/en/experimental-design/adaptive-laboratory-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-laboratory-experiment.md","definition":"An adaptive laboratory experiment is a controlled experimental design conducted in a laboratory setting where pre-specified decision rules allow modifications to the study — such as sample size, treatment allocation, or stopping criteria — based on accumulating data. Unlike fixed designs, adaptive designs incorporate planned interim analyses that permit the experiment to respond to emerging evidence while maintaining statistical validity and Type I error control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in sequential analysis (Abraham Wald, 1947); adaptive clinical/lab designs formalized by Berry and colleagues (1990s–2000s)","year":"1947 (sequential analysis foundations); adaptive laboratory applications widespread from 1990s","type":"Adaptive experimental design","dataType":"Continuous or binary outcome data collected in controlled laboratory conditions","subfamily":"Deneysel desen"},"citations":[{"ref":"Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.","type":"book","doi":"10.1038/nrd1927","isbn":null,"url":null},{"ref":"Adaptive design (medicine). Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Adaptive_design_(medicine)"}],"related":["adaptive-experiment","laboratory-experiment","adaptive-randomized-controlled-trial","multi-arm-experiment","sequential-analysis","response-adaptive-randomization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-lms-filter","name":"Adaptive LMS Filter","fullName":"Least Mean Squares Adaptive Filter","aliases":["LMS Filter","Adaptive LMS Algorithm","Gradient Descent Filtering"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Adaptive signal processing","year":"1960","originator":"Bernard Widrow and Marcian E. Hoff","url":"https://scholargate.app/en/signal-processing/adaptive-lms-filter","markdownUrl":"https://scholargate.app/en/signal-processing/adaptive-lms-filter.md","definition":"The Least Mean Squares (LMS) filter is an adaptive signal processing algorithm that continuously updates filter coefficients to minimize the squared error between the filter output and a desired signal. Introduced by Bernard Widrow and Marcian Hoff in 1960, the LMS algorithm is one of the most widely used adaptive filtering techniques due to its simplicity, low computational cost, and ability to track time-varying signals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernard Widrow and Marcian E. Hoff","subfamily":"Adaptive signal processing","year":"1960","type":"Gradient descent adaptive filtering"},"citations":[{"ref":"Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE Wescon Convention Record, 4, 96–104.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/ire-wescon-1960"},{"ref":"Haykin, S. (2002). Adaptive Filter Theory (4th ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/adaptivefiltertheory"}],"related":["wiener-filter","kalman-filter-signal","fir-filter-design","iir-filter-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-maximum-variation-sampling","name":"Adaptive Maximum Variation Sampling","fullName":"Adaptive Maximum Variation Purposive Sampling","aliases":["adaptive purposive maximum variation sampling","iterative maximum variation sampling","adaptive heterogeneous sampling","AMVS"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s (practice codified in qualitative methods literature)","originator":"Synthesizes Patton (maximum variation) and Thompson (adaptive sampling)","url":"https://scholargate.app/en/survey-methodology/adaptive-maximum-variation-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-maximum-variation-sampling.md","definition":"Adaptive maximum variation sampling is a purposive qualitative sampling strategy that combines the logic of maximum variation sampling — deliberately selecting cases that differ as widely as possible on key dimensions — with an adaptive, iterative recruitment process. Rather than fixing the full sample in advance, the researcher continuously reviews emerging data to identify which types of cases are underrepresented and recruits new participants to fill those gaps, maximizing heterogeneity throughout data collection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesizes Patton (maximum variation) and Thompson (adaptive sampling)","year":"1990s–2000s (practice codified in qualitative methods literature)","type":"Adaptive purposive qualitative sampling strategy","dataType":"Qualitative or mixed-methods data; interview, observational, or document data","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage. [Maximum variation sampling, pp. 169–183]","type":"book","doi":null,"isbn":"978-0803937796","url":null},{"ref":"Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.","type":"article","doi":"10.2307/2289601","isbn":null,"url":null}],"related":["maximum-variation-sampling","purposive-sampling","adaptive-cluster-sampling","theoretical-sampling","snowball-sampling","adaptive-stratified-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-multi-arm-experiment","name":"Adaptive Multi-Arm Experiment","fullName":"Adaptive Multi-Arm Experimental Design","aliases":["MAMS design","multi-arm adaptive trial","adaptive platform trial","response-adaptive multi-arm experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2000s–2010s (MAMS framework formalized c. 2003–2011)","originator":"Patrick Royston, Mahesh Parmar, and colleagues (multi-arm multi-stage framework); further developed by James Wason, Thomas Jaki and others","url":"https://scholargate.app/en/experimental-design/adaptive-multi-arm-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-multi-arm-experiment.md","definition":"An adaptive multi-arm experiment simultaneously evaluates several treatment conditions against a common control and modifies the trial in real time based on accumulating data — dropping ineffective arms early, reallocating participants toward promising ones, or adjusting sample sizes — all while controlling error rates. The approach maximizes information gained per participant and reduces the time and cost required to identify effective treatments relative to running sequential separate trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patrick Royston, Mahesh Parmar, and colleagues (multi-arm multi-stage framework); further developed by James Wason, Thomas Jaki and others","year":"2000s–2010s (MAMS framework formalized c. 2003–2011)","type":"Experimental design","dataType":"Continuous, binary, or time-to-event outcome data from multiple treatment arms","subfamily":"Deneysel desen"},"citations":[{"ref":"Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256.","type":"article","doi":"10.1002/sim.1430","isbn":null,"url":null},{"ref":"Wason, J., Magirr, D., Law, M., & Jaki, T. (2016). Some recommendations for multi-arm multi-stage trials. Statistical Methods in Medical Research, 25(2), 716–727.","type":"article","doi":"10.1177/0962280212465498","isbn":null,"url":null}],"related":["multi-arm-experiment","adaptive-experiment","factorial-randomized-controlled-trial","platform-trial","response-adaptive-randomization","bayesian-adaptive-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-multiple-baseline-design","name":"Adaptive Multiple Baseline Design","fullName":"Adaptive Multiple Baseline Single-Case Experimental Design","aliases":["adaptive MBD","flexible multiple baseline design","adaptive SCED multiple baseline","data-driven multiple baseline design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (multiple baseline base); adaptive extensions discussed from ~2000s onward","originator":"Baer, Wolf & Risley (multiple baseline foundation); adaptive modifications developed within single-case methodology community","url":"https://scholargate.app/en/experimental-design/adaptive-multiple-baseline-design","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-multiple-baseline-design.md","definition":"The Adaptive Multiple Baseline Design is a single-case experimental design that applies the standard multiple baseline logic — staggering intervention onset across two or more tiers (behaviors, settings, or participants) — but allows phase-change decisions to be guided by ongoing data review rather than fixed, pre-specified schedules. This flexibility makes the design more responsive to participant variability while preserving the core replication logic that supports causal inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baer, Wolf & Risley (multiple baseline foundation); adaptive modifications developed within single-case methodology community","year":"1968 (multiple baseline base); adaptive extensions discussed from ~2000s onward","type":"Single-case experimental design (SCED)","dataType":"Repeated measures of a single participant or small-n across behaviors, settings, or participants","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association.","type":"book","doi":null,"isbn":"978-1433808838","url":null}],"related":["multiple-baseline-design","ab-design","aba-design","abab-design","adaptive-experiment","single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-multistage-sampling","name":"Adaptive Multistage Sampling","fullName":"Adaptive Multistage Sampling","aliases":["AMS","adaptive multi-phase sampling","sequential multistage sampling","adaptive hierarchical sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1977 (multistage base); 1990-1992 (adaptive extensions by Thompson)","originator":"Steven K. Thompson (adaptive principles); William G. Cochran (multistage framework)","url":"https://scholargate.app/en/survey-methodology/adaptive-multistage-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-multistage-sampling.md","definition":"Adaptive multistage sampling combines the hierarchical efficiency of multistage designs with adaptive decision rules that adjust which units are sampled at later stages based on what is observed at earlier stages. It is used when a target characteristic is rare, clustered, or spatially heterogeneous and a fixed design would waste resources on uninformative areas of the population.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven K. Thompson (adaptive principles); William G. Cochran (multistage framework)","year":"1977 (multistage base); 1990-1992 (adaptive extensions by Thompson)","type":"Probability-based adaptive sampling design","dataType":"Quantitative; population counts, measurements, or proportions across hierarchical units","subfamily":"Sampling"},"citations":[{"ref":"Thompson, S. K. (1992). Sampling. Wiley.","type":"book","doi":null,"isbn":"978-0471548850","url":null},{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471162407","url":null}],"related":["multistage-sampling","adaptive-cluster-sampling","adaptive-stratified-sampling","systematic-sampling","cluster-sampling","sequential-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-natural-experiment","name":"Adaptive Natural Experiment","fullName":"Adaptive Natural Experiment Design","aliases":["adaptive quasi-experiment","adaptive exogenous shock design","adaptive as-if randomization","sequential natural experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2000s–2010s (systematic application in policy and social science evaluation)","originator":"Synthesizes natural experiment tradition (Meyer 1995; Dunning 2012) with adaptive design principles (Wald 1947; Chow & Chang 2008)","url":"https://scholargate.app/en/experimental-design/adaptive-natural-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-natural-experiment.md","definition":"An adaptive natural experiment combines the causal logic of the natural experiment — exploiting real-world events that assign individuals to conditions in a plausibly exogenous way — with pre-specified adaptive monitoring rules that allow the analytic protocol to be modified based on accumulating data. This hybrid design is used in economics, epidemiology, and policy evaluation when the natural event unfolds over time and interim evidence can legitimately inform decisions about data collection scope, subgroup focus, or analytic strategy without compromising causal validity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesizes natural experiment tradition (Meyer 1995; Dunning 2012) with adaptive design principles (Wald 1947; Chow & Chang 2008)","year":"2000s–2010s (systematic application in policy and social science evaluation)","type":"Quasi-experimental adaptive research design","dataType":"Observational data with exogenous assignment mechanism; administrative, survey, or longitudinal records accumulated over time","subfamily":"Deneysel desen"},"citations":[{"ref":"Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107698000","url":null},{"ref":"Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584886761","url":null}],"related":["natural-experiment","adaptive-experiment","difference-in-differences","regression-discontinuity-design","instrumental-variable","field-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-nested-case-control","name":"Adaptive nested case-control","fullName":"Adaptive Nested Case-Control Study","aliases":["adaptive NCC","adaptive nested case-referent study","dynamic nested case-control","sequential nested case-control"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Base design 1977; adaptive extensions from 1990s onward","originator":"Nested case-control: D. C. Thomas (1977); adaptive design framework: Peter Bauer & Klaus Kohne (1994)","url":"https://scholargate.app/en/epidemiology/adaptive-nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-nested-case-control.md","definition":"An adaptive nested case-control study embeds a case-control comparison within a defined cohort and incorporates pre-specified interim decision rules that allow modifications — such as control-to-case ratio adjustment or biomarker sub-sampling revision — based on accumulating data, without compromising the study's validity or inflating type I error. The design combines the efficiency of the nested case-control framework with the flexibility of adaptive methodology to optimise resource use when exposure assessment is costly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nested case-control: D. C. Thomas (1977); adaptive design framework: Peter Bauer & Klaus Kohne (1994)","year":"Base design 1977; adaptive extensions from 1990s onward","type":"Observational epidemiological study with adaptive design elements","dataType":"Time-to-event data, exposure records, biospecimens drawn from a defined cohort","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.","type":"article","doi":"10.2307/2345280","isbn":null,"url":null},{"ref":"Bauer, P., & Kohne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics, 50(4), 1029–1041.","type":"article","doi":"10.2307/2533441","isbn":null,"url":null}],"related":["nested-case-control","case-control-study","adaptive-randomized-clinical-trial","cohort-study","adaptive-cohort-study","case-crossover-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-phase-i-clinical-trial","name":"Adaptive Phase I Clinical Trial","fullName":"Adaptive Phase I Clinical Trial Design","aliases":["adaptive dose-escalation trial","adaptive dose-finding study","model-based adaptive Phase I design"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990 (model-based adaptive era); rule-based designs from the 1970s–1980s","originator":"O'Quigley, Pepe, and Fisher (CRM); earlier rule-based 3+3 designs pre-date it","url":"https://scholargate.app/en/epidemiology/adaptive-phase-i-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-phase-i-clinical-trial.md","definition":"An adaptive Phase I clinical trial is a first-in-human or early-phase dose-finding study that continuously updates the recommended dose after each patient cohort using a prespecified statistical model, rather than following a fixed rule. The goal is to identify the maximum tolerated dose (MTD) or the recommended Phase II dose (RP2D) efficiently while minimising exposure of participants to sub-therapeutic or toxic doses. Adaptive designs — most notably the Continual Reassessment Method (CRM) — replace or augment traditional rule-based designs such as the 3+3 schema.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"O'Quigley, Pepe, and Fisher (CRM); earlier rule-based 3+3 designs pre-date it","year":"1990 (model-based adaptive era); rule-based designs from the 1970s–1980s","type":"Adaptive clinical trial design","dataType":"Dose-toxicity binary or ordinal outcomes from successive patient cohorts","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48.","type":"article","doi":"10.2307/2531628","isbn":null,"url":null},{"ref":"Chevret, S. (Ed.). (2006). Statistical Methods for Dose-Finding Experiments. Wiley.","type":"book","doi":null,"isbn":"978-0470861608","url":null}],"related":["continual-reassessment-method","3-plus-3-design","dose-escalation-study","bayesian-adaptive-design","maximum-tolerated-dose-estimation","adaptive-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-phase-ii-clinical-trial","name":"Adaptive Phase II Clinical Trial","fullName":"Adaptive Phase II Clinical Trial Design","aliases":["Adaptive Ph II trial","seamless adaptive Phase II","adaptive dose-finding trial","response-adaptive Phase II"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1994 (formal framework); widespread adoption 2000s–2010s","originator":"Peter Bauer & Klaus Kohne (formal statistical framework, 1994); broader adaptive trial methodology developed through FDA and ICH guidance in the 2000s","url":"https://scholargate.app/en/epidemiology/adaptive-phase-ii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-phase-ii-clinical-trial.md","definition":"An adaptive Phase II clinical trial is a prospective experimental design in which pre-specified rules allow the study protocol to be modified — such as dropping arms, adjusting sample size, or narrowing the patient population — based on accumulating interim data, without inflating the Type I error rate. The design is widely used in early-phase drug development to screen candidate doses or treatments efficiently while preserving statistical validity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Bauer & Klaus Kohne (formal statistical framework, 1994); broader adaptive trial methodology developed through FDA and ICH guidance in the 2000s","year":"1994 (formal framework); widespread adoption 2000s–2010s","type":"Experimental clinical trial design","dataType":"Patient-level outcome data (binary, continuous, or time-to-event) collected prospectively during interim analyses","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bauer, P., & Kohne, K. (1994). Evaluation of experiments with adaptive interim analyses. Biometrics, 50(4), 1029–1041.","type":"article","doi":"10.2307/2533441","isbn":null,"url":null},{"ref":"Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584887775","url":null}],"related":["phase-ii-clinical-trial","seamless-adaptive-design","response-adaptive-randomization","bayesian-adaptive-trial","dose-finding-study","group-sequential-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-phase-iii-clinical-trial","name":"Adaptive Phase III clinical trial","fullName":"Adaptive Phase III Confirmatory Clinical Trial","aliases":["adaptive confirmatory trial","seamless Phase II/III adaptive trial","adaptive pivotal trial","adaptive design Phase III"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1969–2019 (sequential testing roots ~1969; formal adaptive design guidance 2010–2019)","originator":"Methodological foundations by Armitage et al. (1969); modern adaptive framework codified by FDA and ICH guidance (2010s)","url":"https://scholargate.app/en/epidemiology/adaptive-phase-iii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-phase-iii-clinical-trial.md","definition":"An adaptive Phase III clinical trial is a confirmatory randomized controlled trial that incorporates pre-specified rules allowing modifications to the trial design — such as sample size re-estimation, dose selection, or population enrichment — based on accumulating interim data, while preserving the Type I error rate. It sits at the top of the evidence hierarchy and is used to obtain regulatory approval of new interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Methodological foundations by Armitage et al. (1969); modern adaptive framework codified by FDA and ICH guidance (2010s)","year":"1969–2019 (sequential testing roots ~1969; formal adaptive design guidance 2010–2019)","type":"Interventional confirmatory clinical trial with pre-specified interim adaptations","dataType":"Patient-level longitudinal clinical outcome data (efficacy, safety, biomarkers)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2019). ICH E9(R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials. ICH Harmonised Guideline.","type":"article","doi":null,"isbn":null,"url":"https://www.ich.org/page/efficacy-guidelines"},{"ref":"U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.","type":"article","doi":null,"isbn":null,"url":"https://www.fda.gov/media/78507/download"}],"related":["adaptive-randomized-clinical-trial","phase-iii-clinical-trial","randomized-clinical-trial","adaptive-phase-ii-clinical-trial","bayesian-phase-iii-clinical-trial","multicenter-randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-phase-iv-study","name":"Adaptive Phase IV study","fullName":"Adaptive Phase IV Post-Marketing Study","aliases":["adaptive post-marketing surveillance study","adaptive pharmacovigilance study","adaptive Phase IV trial","adaptive post-approval study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (regulatory formalization of adaptive Phase IV designs)","originator":"Adaptive design principles developed by multiple statisticians; Phase IV framework established by regulatory bodies (FDA, EMA) in the late 20th century","url":"https://scholargate.app/en/epidemiology/adaptive-phase-iv-study","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-phase-iv-study.md","definition":"An Adaptive Phase IV study is a post-marketing surveillance study conducted after a drug or intervention has received regulatory approval, augmented with pre-specified adaptive design elements that allow pre-planned modifications to the study protocol in response to accumulating data. These modifications may include sample size re-estimation, endpoint adjustments, or population enrichment, all governed by statistical rules set before the study begins, preserving scientific integrity while increasing efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adaptive design principles developed by multiple statisticians; Phase IV framework established by regulatory bodies (FDA, EMA) in the late 20th century","year":"1990s–2000s (regulatory formalization of adaptive Phase IV designs)","type":"Adaptive post-marketing clinical study design","dataType":"Real-world longitudinal data, patient registries, electronic health records, adverse event reports","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584889625","url":null},{"ref":"U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.","type":"article","doi":null,"isbn":null,"url":"https://www.fda.gov/media/78495/download"}],"related":["phase-iv-study","adaptive-randomized-clinical-trial","cohort-study","adaptive-phase-iii-clinical-trial","pragmatic-phase-iv-study","bayesian-phase-iv-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-pretest-posttest-experimental-design","name":"Adaptive Pretest-Posttest Experimental Design","fullName":"Adaptive Pretest-Posttest Experimental Design","aliases":["adaptive pre-post design","adaptive pretest-posttest trial","adaptive two-period design","pre-post adaptive experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2000s (integration of adaptive principles with classic pre-post structure)","originator":"Synthesizes Campbell & Stanley (1963) pretest-posttest framework with adaptive design methodology formalized by Chow & Chang (2000s)","url":"https://scholargate.app/en/experimental-design/adaptive-pretest-posttest-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-pretest-posttest-experimental-design.md","definition":"An adaptive pretest-posttest experimental design measures all participants before and after an intervention while allowing pre-specified modifications to the trial — such as sample size re-estimation, treatment arm dropping, or randomization ratio adjustment — based on accumulated interim data. It combines the interpretive power of change-score analysis with the efficiency gains and ethical safeguards of adaptive methodology, making it particularly valuable in clinical, educational, and behavioral research where early data can inform better resource allocation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesizes Campbell & Stanley (1963) pretest-posttest framework with adaptive design methodology formalized by Chow & Chang (2000s)","year":"2000s (integration of adaptive principles with classic pre-post structure)","type":"Experimental design","dataType":"Continuous or categorical outcome measurements at two time points (pre and post)","subfamily":"Deneysel desen"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+Quasi-Experimental+Designs+for+Research+Campbell+Stanley+1963"},{"ref":"Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"9781584888468","url":null}],"related":["pretest-posttest-experimental-design","adaptive-experiment","adaptive-randomized-controlled-trial","randomized-controlled-trial","blocked-pretest-posttest-experimental-design","crossover-pretest-posttest-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-purposive-sampling","name":"Adaptive Purposive Sampling","fullName":"Adaptive Purposive Sampling","aliases":["iterative purposive sampling","emergent purposive sampling","adaptive qualitative sampling","dynamic purposive sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1980s–1990s","originator":"Rooted in Patton's purposive sampling typology; adaptive dimension from iterative qualitative inquiry traditions","url":"https://scholargate.app/en/survey-methodology/adaptive-purposive-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-purposive-sampling.md","definition":"Adaptive purposive sampling is a qualitative strategy in which the researcher begins with explicitly stated, theory-driven selection criteria and then deliberately revises those criteria as data collection proceeds and new understanding emerges. Unlike fixed purposive sampling — where criteria are locked in before fieldwork — the adaptive variant treats the sampling frame as a working hypothesis that is refined in response to early findings, enabling the study to follow the evidence into unexpected but analytically important directions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Patton's purposive sampling typology; adaptive dimension from iterative qualitative inquiry traditions","year":"1980s–1990s","type":"Qualitative sampling strategy","dataType":"Qualitative data (interviews, observations, documents)","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Marshall, M. N. (1996). Sampling for qualitative research. Family Practice, 13(6), 522–525.","type":"article","doi":"10.1093/fampra/13.6.522","isbn":null,"url":null}],"related":["purposive-sampling","theoretical-sampling","snowball-sampling","maximum-variation-sampling","adaptive-cluster-sampling","deviant-case-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-quota-sampling","name":"Adaptive Quota Sampling","fullName":"Adaptive Quota Sampling","aliases":["responsive quota sampling","dynamic quota sampling","iterative quota sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"2000s (adaptive extension of quota principles)","originator":"Grounded in quota sampling (Quota sampling formalized early 20th century); adaptive extensions developed within responsive survey design frameworks (Groves & Heeringa, 2006)","url":"https://scholargate.app/en/survey-methodology/adaptive-quota-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-quota-sampling.md","definition":"Adaptive quota sampling is a non-probability sampling approach that starts with predefined demographic or characteristic-based quotas and then adjusts those quotas during data collection in response to emerging response patterns, nonresponse trends, or representativeness concerns. By treating the sampling process as iterative rather than fixed, it allows researchers to correct imbalances in real time and improve the final sample composition without restarting data collection from scratch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grounded in quota sampling (Quota sampling formalized early 20th century); adaptive extensions developed within responsive survey design frameworks (Groves & Heeringa, 2006)","year":"2000s (adaptive extension of quota principles)","type":"Non-probability sampling with adaptive control","dataType":"Survey data, structured questionnaire responses","subfamily":"Sampling"},"citations":[{"ref":"Groves, R. M., & Heeringa, S. G. (2006). Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of the Royal Statistical Society: Series A, 169(3), 439–457.","type":"article","doi":"10.1111/j.1467-985X.2006.00423.x","isbn":null,"url":null},{"ref":"Neyman, J. (1934). On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection. Journal of the Royal Statistical Society, 97(4), 558–625.","type":"article","doi":"10.2307/2342192","isbn":null,"url":null}],"related":["quota-sampling","adaptive-stratified-sampling","proportional-quota-sampling","disproportional-quota-sampling","stratified-sampling","responsive-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-randomized-clinical-trial","name":"Adaptive Randomized Clinical Trial","fullName":"Adaptive Randomized Clinical Trial","aliases":["adaptive RCT","adaptive trial design","response-adaptive randomization trial","adaptive clinical trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Late 1990s–2000s (widespread adoption post-2010)","originator":"Donald Berry and colleagues; formalized by FDA guidance in 2010 and 2019","url":"https://scholargate.app/en/epidemiology/adaptive-randomized-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-randomized-clinical-trial.md","definition":"An adaptive randomized clinical trial (adaptive RCT) is a prospective experimental study that uses pre-specified rules to modify one or more trial aspects — such as sample size, allocation ratios, or treatment arms — based on accumulating data collected during the trial itself, while maintaining statistical validity and integrity of the study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Berry and colleagues; formalized by FDA guidance in 2010 and 2019","year":"Late 1990s–2000s (widespread adoption post-2010)","type":"Experimental clinical trial design","dataType":"Accumulating patient outcome data (continuous monitoring)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.","type":"article","doi":"10.1038/nrd1927","isbn":null,"url":null},{"ref":"U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.","type":"misc","doi":null,"isbn":null,"url":"https://www.fda.gov/media/78495/download"}],"related":["randomized-clinical-trial","phase-ii-clinical-trial","phase-iii-clinical-trial","bayesian-randomized-clinical-trial","pragmatic-randomized-clinical-trial","dose-response-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-randomized-controlled-trial","name":"Adaptive Randomized Controlled Trial","fullName":"Adaptive Randomized Controlled Trial","aliases":["Adaptive RCT","Response-adaptive RCT","Adaptive clinical trial","Platform trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1980s–2000s (formalized; earlier sequential testing roots from Wald, 1947)","originator":"Donald Berry and others; foundational adaptive trial methods developed through 1980s–2000s biostatistics literature","url":"https://scholargate.app/en/experimental-design/adaptive-randomized-controlled-trial","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-randomized-controlled-trial.md","definition":"An adaptive randomized controlled trial (adaptive RCT) is an experimental design in which pre-specified rules allow modifications to the trial while it is ongoing — such as changing allocation ratios, dropping underperforming arms, or stopping early for efficacy or futility — based on accumulating interim data. These adaptations are planned before the trial starts and governed by statistical rules to preserve Type I error control and validity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Berry and others; foundational adaptive trial methods developed through 1980s–2000s biostatistics literature","year":"1980s–2000s (formalized; earlier sequential testing roots from Wald, 1947)","type":"Experimental design — adaptive variant of RCT","dataType":"Continuous, binary, or time-to-event outcome data collected during the trial","subfamily":"Deneysel desen"},"citations":[{"ref":"Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584887690","url":null},{"ref":"Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.","type":"article","doi":"10.1038/nrd1927","isbn":null,"url":null}],"related":["randomized-controlled-trial","multi-arm-experiment","adaptive-experiment","bayesian-inference","sequential-analysis","factorial-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-sampling","name":"Adaptive Sampling","fullName":"Adaptive Cluster Sampling","aliases":["Adaptive Cluster Sampling","Sequential Adaptive Sampling","Network Sampling","Adaptif Küme Örneklemesi"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling design","year":1990,"originator":"Steven Thompson","url":"https://scholargate.app/en/survey-methodology/adaptive-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-sampling.md","definition":"Adaptive Cluster Sampling (ACS) is a probability-based survey design introduced by Steven K. Thompson in 1990 for estimating the abundance or total of rare, clustered populations. Starting from an initial random sample, the design adaptively adds neighboring units whenever a sampled unit satisfies a predefined condition—such as exceeding a count threshold—thereby concentrating sampling effort exactly where the population of interest occurs. It is most appropriate for ecologists, epidemiologists, and social scientists studying geographically or socially clustered rare phenomena.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Thompson","year":1990,"type":"Probability-based adaptive design","subfamily":"Sampling design","estimator":"Horvitz-Thompson unbiased estimator","trigger":"Condition C on observed unit value"},"citations":[{"ref":"Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.","type":"article","doi":"10.1080/01621459.1990.10474975","isbn":null,"url":null}],"related":["stratified-sampling","respondent-driven-sampling","capture-recapture"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-screening-test-evaluation","name":"Adaptive screening test evaluation","fullName":"Adaptive Screening Test Evaluation","aliases":["adaptive screening","computerized adaptive screening","tailored screening test evaluation","CAT-based screening evaluation"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–1990s (formal adaptive screening frameworks)","originator":"Lord, F. M. (IRT foundations); Wainer & colleagues (CAT adaptation to screening)","url":"https://scholargate.app/en/epidemiology/adaptive-screening-test-evaluation","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-screening-test-evaluation.md","definition":"Adaptive screening test evaluation is a psychometric and epidemiological framework for designing and assessing screening instruments whose item selection or stopping rules adjust dynamically to each respondent's response pattern. Rooted in item response theory (IRT) and computerized adaptive testing (CAT), the method uses real-time ability or severity estimates to present only the most informative items, then evaluates the resulting screening decisions against a clinical reference standard using standard diagnostic accuracy metrics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lord, F. M. (IRT foundations); Wainer & colleagues (CAT adaptation to screening)","year":"1980s–1990s (formal adaptive screening frameworks)","type":"Psychometric evaluation method","dataType":"Item-level dichotomous or polytomous responses; criterion diagnosis data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Wainer, H., Dorans, N. J., Flaugher, R., Green, B. F., & Mislevy, R. J. (2000). Computerized Adaptive Testing: A Primer (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805835113","url":null},{"ref":"Streiner, D. L., Norman, G. R., & Cairney, J. (2015). Health Measurement Scales: A Practical Guide to Their Development and Use (5th ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0199685219","url":null}],"related":["item-response-theory","computerized-adaptive-testing","receiver-operating-characteristic-analysis","diagnostic-accuracy-study","sensitivity-specificity-analysis","sequential-probability-ratio-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-simple-random-sampling","name":"Adaptive Simple Random Sampling","fullName":"Adaptive Simple Random Sampling","aliases":["ASRS","adaptive SRS","adaptive random sampling","sequential adaptive sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990–1992","originator":"Steven K. Thompson","url":"https://scholargate.app/en/survey-methodology/adaptive-simple-random-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-simple-random-sampling.md","definition":"Adaptive simple random sampling (ASRS) begins with a conventional simple random sample and then expands the sample in regions where the variable of interest exceeds a pre-specified threshold. Units neighboring a qualifying observation are added to the sample, allowing the design to concentrate effort where the population is dense or rare, while retaining unbiased estimation through the Horvitz-Thompson or Hansen-Hurwitz estimators. The approach was systematized by Steven K. Thompson in the early 1990s as part of the broader adaptive sampling framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven K. Thompson","year":"1990–1992","type":"Probability-based adaptive sampling design","dataType":"Quantitative (continuous or count data); population values observed during sampling","subfamily":"Sampling"},"citations":[{"ref":"Thompson, S. K. (1992). Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471548850","url":null},{"ref":"Thompson, S. K., & Seber, G. A. F. (1996). Adaptive Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471558712","url":null}],"related":["simple-random-sampling","adaptive-cluster-sampling","systematic-sampling","stratified-sampling","multistage-sampling","sequential-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-single-subject-experimental-design","name":"Adaptive Single-Subject Experimental Design","fullName":"Adaptive Single-Subject Experimental Design","aliases":["Adaptive SSED","Adaptive N-of-1 design","Adaptive single-case experimental design","Adaptive SCE design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Experimental design","year":"Classical SSED: 1960s–1970s; adaptive extensions formalised: 2000s–2010s","originator":"Evolved from classical single-case designs (Skinner, Sidman); adaptive features formalised in clinical N-of-1 literature (Zucker, Schmid, Nikles et al.)","url":"https://scholargate.app/en/experimental-design/adaptive-single-subject-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-single-subject-experimental-design.md","definition":"Adaptive single-subject experimental design (adaptive SSED) is an experimental methodology in which a single participant or unit is repeatedly observed under systematically alternated conditions — baseline and intervention — while pre-specified decision rules allow the researcher or clinician to modify treatment parameters, phase lengths, or condition sequences in response to continuously collected data. It merges the internal validity of classical single-case experimental designs with the flexibility of adaptive trial logic, making it especially valuable in clinical, behavioral, and applied settings where individual response trajectories vary substantially.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from classical single-case designs (Skinner, Sidman); adaptive features formalised in clinical N-of-1 literature (Zucker, Schmid, Nikles et al.)","year":"Classical SSED: 1960s–1970s; adaptive extensions formalised: 2000s–2010s","type":"Experimental single-subject design with adaptive decision rules","dataType":"Repeated measures on a single participant or unit (behavioral counts, clinical scores, physiological measurements)","subfamily":"Experimental design"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0205474554","url":null}],"related":["single-subject-experimental-design","multiple-baseline-design","reversal-design","n-of-1-trial","interrupted-time-series","changing-criterion-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-snowball-sampling","name":"Adaptive Snowball Sampling","fullName":"Adaptive Snowball Sampling","aliases":["adaptive referral sampling","adaptive chain-referral sampling","dynamic snowball sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s (as combined approach)","originator":"Combines principles from S. K. Thompson (adaptive sampling, 1990) and L. A. Goodman (snowball sampling, 1961)","url":"https://scholargate.app/en/survey-methodology/adaptive-snowball-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-snowball-sampling.md","definition":"Adaptive snowball sampling is a hybrid sampling strategy that recruits initial participants (seeds) from a target population and then dynamically adjusts referral chains based on pre-specified criteria — such as population density, diversity, or theoretical saturation. Combining the chain-referral logic of snowball sampling with the responsive decision rules of adaptive sampling, it is particularly suited to studying rare, hidden, or hard-to-reach populations where conventional frames are unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Combines principles from S. K. Thompson (adaptive sampling, 1990) and L. A. Goodman (snowball sampling, 1961)","year":"1990s–2000s (as combined approach)","type":"Non-probability / adaptive sampling design","dataType":"Social network data, participant referral chains, qualitative or quantitative data from hard-to-reach populations","subfamily":"Sampling"},"citations":[{"ref":"Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.","type":"article","doi":"10.1080/01621459.1990.10474975","isbn":null,"url":null},{"ref":"Goodman, L. A. (1961). Snowball sampling. The Annals of Mathematical Statistics, 32(1), 148–170.","type":"article","doi":"10.1214/aoms/1177705148","isbn":null,"url":null}],"related":["snowball-sampling","adaptive-cluster-sampling","respondent-driven-sampling","purposive-sampling","theoretical-sampling","network-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-solomon-four-group-design","name":"Adaptive Solomon Four-Group Design","fullName":"Adaptive Randomization Solomon Four-Group Experimental Design","aliases":["adaptive S4G design","response-adaptive Solomon design","sequential Solomon four-group design","adaptive pretest-sensitization design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1949 (base design); adaptive adaptation developed through later adaptive trial methodology","originator":"Richard L. Solomon (base design); adaptive extension via response-adaptive randomization methodology","url":"https://scholargate.app/en/experimental-design/adaptive-solomon-four-group-design","markdownUrl":"https://scholargate.app/en/experimental-design/adaptive-solomon-four-group-design.md","definition":"The Adaptive Solomon Four-Group Design combines the pretest-sensitization control of Solomon's classic four-group structure with response-adaptive randomization, allowing interim outcome data to update the allocation probabilities across the four groups as the study progresses. This hybrid preserves the design's ability to isolate the testing effect while improving ethical efficiency by steering more participants toward conditions performing better at interim checkpoints.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard L. Solomon (base design); adaptive extension via response-adaptive randomization methodology","year":"1949 (base design); adaptive adaptation developed through later adaptive trial methodology","type":"Experimental design (pretest-sensitization control + adaptive randomization)","dataType":"Continuous or ordinal outcome measures; interim outcome data used to guide allocation","subfamily":"Deneysel desen"},"citations":[{"ref":"Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.","type":"article","doi":"10.1037/h0062958","isbn":null,"url":null},{"ref":"Hu, F., & Rosenberger, W. F. (2006). The Theory of Response-Adaptive Randomization in Clinical Trials. Wiley.","type":"book","doi":null,"isbn":"978-0471653981","url":null}],"related":["solomon-four-group-design","adaptive-design","crossover-solomon-four-group-design","blocked-solomon-four-group-design","randomized-controlled-trial","factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-stratified-sampling","name":"Adaptive Stratified Sampling","fullName":"Adaptive Stratified Sampling","aliases":["ASS","adaptive stratified design","stratified adaptive sampling","adaptive allocation stratified sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s (formal development from Thompson 1990 onward)","originator":"Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others","url":"https://scholargate.app/en/survey-methodology/adaptive-stratified-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-stratified-sampling.md","definition":"Adaptive stratified sampling divides the population into strata and then applies an adaptive rule within each stratum: whenever an initially selected unit satisfies a pre-specified condition (e.g., a rare species is found, a variable exceeds a threshold), neighboring or related units are added to the sample. This combines the variance-reduction power of stratification with the ability to concentrate sampling effort where the phenomenon of interest is actually present.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others","year":"1990s (formal development from Thompson 1990 onward)","type":"Probability-based adaptive sampling design","dataType":"Numerical counts, measurements, or categorical attributes from population units","subfamily":"Sampling"},"citations":[{"ref":"Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.","type":"article","doi":"10.2307/2289601","isbn":null,"url":null},{"ref":"Thompson, S. K. (2002). Sampling (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471360100","url":null}],"related":["stratified-sampling","adaptive-cluster-sampling","proportional-stratified-sampling","disproportional-stratified-sampling","multistage-sampling","systematic-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-survival-analysis","name":"Adaptive Survival Analysis","fullName":"Adaptive Survival Analysis","aliases":["adaptive time-to-event analysis","adaptive event-driven trial analysis","adaptive hazard modeling","ASA"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s (formalized ~2000–2006)","originator":"Bauer, Posch, and collaborators (adaptive design framework); Lachin & Foulkes (event-driven survival trial foundations)","url":"https://scholargate.app/en/epidemiology/adaptive-survival-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/adaptive-survival-analysis.md","definition":"Adaptive survival analysis integrates adaptive clinical trial design with time-to-event statistical methods, allowing pre-specified modifications to sample size, event targets, or allocation ratios at interim stages based on accumulating survival data. It is widely used in oncology, cardiovascular, and infectious disease research where the primary endpoint is a hazard-based outcome such as progression-free survival or all-cause mortality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bauer, Posch, and collaborators (adaptive design framework); Lachin & Foulkes (event-driven survival trial foundations)","year":"2000s (formalized ~2000–2006)","type":"Adaptive statistical design for time-to-event outcomes","dataType":"Time-to-event (censored survival) data from clinical or epidemiological studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bauer, P., & Posch, M. (2004). Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections. Statistics in Medicine, 23(8), 1333–1353.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modification+of+the+sample+size+and+the+schedule+of+interim+analyses+in+survival+trials+based+on+data+inspections+Bauer"},{"ref":"Mehta, C., Bhatt, M., & Bhattacharya, R. (2009). Adaptive randomization for survival endpoints in oncology trials. Journal of Clinical Oncology, 27(15_suppl), e20750.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Adaptive+randomization+survival+endpoints+oncology+trials+Mehta+2009"}],"related":["kaplan-meier-estimator","cox-proportional-hazards","group-sequential-design","interim-analysis","log-rank-test","competing-risks-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-trial-design","name":"Adaptive Trial Design","fullName":"Adaptive Clinical Trial Design with Pre-Planned Interim Analyses","aliases":["adaptive trial","adaptive design","response-adaptive randomization","RAR","seamless phase II/III"],"domain":"clinical-research","family":"process-pipeline","subfamily":"trial design","year":"1990s-2000s","originator":"Stephen Pocock, Christopher Jennison, and statistical methodologists; FDA formalized guidance 2019","url":"https://scholargate.app/en/clinical-research/adaptive-trial-design","markdownUrl":"https://scholargate.app/en/clinical-research/adaptive-trial-design.md","definition":"An adaptive trial design allows pre-specified modifications to the trial based on interim data—such as sample size re-estimation, stopping for futility or efficacy, dropping ineffective arms, or shifting randomization ratios toward better-performing treatments. Developed systematically in the 1990s–2000s by statisticians like Pocock and Jennison, and formalized by the FDA in 2019, adaptive designs accelerate drug development, reduce exposure to ineffective treatments, and improve efficiency without inflating false-positive rates when properly executed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Pocock, Christopher Jennison, and statistical methodologists; FDA formalized guidance 2019","subfamily":"trial design","year":"1990s-2000s","type":"Research Design"},"citations":[{"ref":"Pocock, S. J. (2005). Current issues in the design and interpretation of clinical trials. BMJ, 330(7500), 1118–1121.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Current+issues+in+the+design+and+interpretation+of+clinical+trials+Pocock"},{"ref":"Pallmann, P., Bedding, A. W., Choodari-Oskooei, B., Dimairo, M., Flight, L., Hampson, L. V., ... & Wason, J. (2018). Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Medicine, 16(1), 29.","type":"article","doi":"10.1186/s12916-018-1017-7","isbn":null,"url":null},{"ref":"FDA (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. US Food and Drug Administration.","type":"article","doi":null,"isbn":null,"url":"https://www.fda.gov/regulatory-information/search-fda-guidance-documents"}],"related":["randomized-controlled-trial","interim-analysis","response-adaptive-randomization","sequential-testing","sample-size-reestimation"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adaptive-weighted-sampling","name":"Adaptive Weighted Sampling","fullName":"Adaptive Weighted Sampling","aliases":["AWS","adaptive importance sampling","sequential adaptive weighting","dynamic weighted sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s","originator":"Building on Thompson (1990) adaptive sampling and classical importance-weighting; adaptive weighting formalised across survey and Monte Carlo literature","url":"https://scholargate.app/en/survey-methodology/adaptive-weighted-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/adaptive-weighted-sampling.md","definition":"Adaptive weighted sampling is a probabilistic sampling procedure that assigns and iteratively updates inclusion weights for population units based on observed data collected during the sampling process itself. Unlike static weighted sampling — where weights are fixed before data collection from known auxiliary information — adaptive weighting revises probabilities as new information accumulates, concentrating sampling effort on units that contribute most to estimating the target quantity. It is used in survey methodology, simulation studies, and rare-event estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Thompson (1990) adaptive sampling and classical importance-weighting; adaptive weighting formalised across survey and Monte Carlo literature","year":"1990s–2000s","type":"Probabilistic sampling procedure","dataType":"Quantitative population data; numeric survey variables","subfamily":"Sampling"},"citations":[{"ref":"Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.","type":"article","doi":"10.2307/2289601","isbn":null,"url":null},{"ref":"Owen, A. B. (2000). Monte Carlo Theory, Methods and Examples. Stanford University (online edition). Chapter on importance sampling and adaptive weighting.","type":"book","doi":null,"isbn":null,"url":"https://artowen.su.domains/mc/"}],"related":["weighted-sampling","adaptive-cluster-sampling","stratified-sampling","systematic-sampling","importance-sampling","multistage-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adas-cog","name":"Alzheimer's Disease Assessment Scale-Cognitive","fullName":"Alzheimer's Disease Assessment Scale-Cognitive Subscale","aliases":["ADAS-Cog","ADAS-Cog14","ADAS-Cog13"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"cognitive assessment","year":"1984","originator":"William Rosen","url":"https://scholargate.app/en/neuropsychology/adas-cog","markdownUrl":"https://scholargate.app/en/neuropsychology/adas-cog.md","definition":"The Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) is a clinician-administered cognitive assessment instrument designed specifically to measure cognitive decline in Alzheimer's disease. Developed by Rosen, Mohs, and Davis in 1984 and published in the American Journal of Psychiatry, the ADAS-Cog has become the gold standard outcome measure in pharmaceutical trials of antidementia drugs. It is sensitive to disease progression and capable of detecting cognitive change over periods as brief as 6–12 months.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Rosen","subfamily":"cognitive assessment","year":"1984","type":"Clinician-administered cognitive scale for Alzheimer's disease"},"citations":[{"ref":"Rosen, W. G., Mohs, R. C., & Davis, K. L. (1984). A new rating scale for Alzheimer's disease. American Journal of Psychiatry, 141(11), 1356-1364.","type":"article","doi":"10.1176/ajp.141.11.1356","isbn":null,"url":null},{"ref":"Mohs, R. C., Knopman, D., Petersen, R. C., et al. (1997). Development of cognitive instruments for use in clinical trials of antidementia drugs: Additions to the ADAS and MMSE. Alzheimer Disease and Associated Disorders, 11(Suppl 2), 13-21.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9236948"},{"ref":"Pfeffer, R. I., Inoue, S. K., & Chance, G. R. (2000). Diagnostic criteria for dementia: Revision of the DSM-IV and ICD-10. Journal of the American Geriatrics Society, 48(12), 1572-1578.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/11129747"}],"related":["mmse","saint-louis-mental-status","dementia-rating-scale","addenbrookes-cognitive-examination","frontal-assessment-battery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"addenbrookes-cognitive-examination","name":"Addenbrooke's Cognitive Examination","fullName":"Addenbrooke's Cognitive Examination","aliases":["ACE","ACE-R","ACE-III","Addenbrooke Cognitive Examination"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"comprehensive cognitive assessment","year":"2000","originator":"Padasalai Mathuranath","url":"https://scholargate.app/en/neuropsychology/addenbrookes-cognitive-examination","markdownUrl":"https://scholargate.app/en/neuropsychology/addenbrookes-cognitive-examination.md","definition":"The Addenbrooke's Cognitive Examination (ACE) is a brief yet comprehensive clinician-administered cognitive battery designed to assess multiple cognitive domains and differentiate between types of dementia. Originally developed by Mathuranath and colleagues at Cambridge University in 2000, the ACE was created to address limitations of single-domain screening tools. The revised version (ACE-R, 2006) and further refined version (ACE-III, 2013) provide updated norms and improved sensitivity. The ACE-R and ACE-III are particularly valuable for distinguishing Alzheimer's disease from frontotemporal dementia.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Padasalai Mathuranath","subfamily":"comprehensive cognitive assessment","year":"2000","type":"Clinician-administered comprehensive cognitive examination"},"citations":[{"ref":"Mathuranath, P. S., Nestor, P. J., Berrios, G. E., Rakowicz, W., & Hodges, J. R. (2000). A brief cognitive test battery to differentiate Alzheimer's disease and frontotemporal dementia. Neurology, 55(11), 1613-1620.","type":"article","doi":"10.1212/WNL.55.11.1613","isbn":null,"url":null},{"ref":"Mioshi, E., Dawson, K., Mitchell, J., Arnold, R., & Hodges, J. R. (2006). The Addenbrooke's Cognitive Examination Revised (ACE-R): A brief cognitive test battery for dementia screening. International Journal of Geriatric Psychiatry, 21(11), 1078-1085.","type":"article","doi":"10.1002/gps.1610","isbn":null,"url":null},{"ref":"Hsieh, S., Schubert, S., Hoon, C., Mioshi, E., & Hodges, J. R. (2013). Validation of the Addenbrooke's Cognitive Examination III in frontotemporal dementia and Alzheimer's disease. Dementia and Geriatric Cognitive Disorders, 36(3-4), 242-250.","type":"article","doi":"10.1159/000351671","isbn":null,"url":null}],"related":["mmse","adas-cog","saint-louis-mental-status","dementia-rating-scale","frontal-assessment-battery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"addiction-severity-index","name":"Addiction Severity Index","fullName":"Addiction Severity Index (ASI)","aliases":["ASI","ASI-6"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Comprehensive substance use disorder severity assessment","year":"1980","originator":"A. Thomas McLellan","url":"https://scholargate.app/en/psychiatry/addiction-severity-index","markdownUrl":"https://scholargate.app/en/psychiatry/addiction-severity-index.md","definition":"The ASI is a multidimensional, clinician-administered semi-structured interview assessing severity of substance use disorder and related psychosocial problems across seven domains: medical, employment, drug use, alcohol use, legal, family/social, and psychiatric. Developed by McLellan and colleagues in 1980 and refined through editions, it has become the gold standard comprehensive assessment tool in addiction medicine, substance abuse treatment programs, and research. The ASI provides both interview-derived severity ratings (0–9 per domain) and composite scores enabling treatment planning and outcome monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. Thomas McLellan","subfamily":"Comprehensive substance use disorder severity assessment","year":"1980","type":"Clinician-administered structured interview"},"citations":[{"ref":"McLellan, A. T., Luborsky, L., Woody, G. E., & O'Brien, C. P. (1980). An improved diagnostic evaluation instrument for substance abuse patients: The Addiction Severity Index. Journal of Nervous and Mental Disease, 168(1), 26–33.","type":"article","doi":"10.1097/00005053-198001000-00006","isbn":null,"url":null},{"ref":"McLellan, A. T., Kusama, H. F., & Metzger, D. S. (1992). The fifth edition of the Addiction Severity Index. Journal of Substance Abuse Treatment, 9(3), 199–213.","type":"article","doi":"10.1016/0740-5472(92)90062-S","isbn":null,"url":null},{"ref":"Cacciola, J. S., Alterman, A. I., McLellan, A. T., Lin, Z. B., & Lynch, K. G. (1997). Initial evidence for the reliability and validity of a \"lite\" version of the Addiction Severity Index. Drug and Alcohol Dependence, 44(1), 9–19.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Initial+evidence+for+the+reliability+and+validity+of+a+%22lite%22+version+of+the+Addiction+Severity+Index+Cacciola"}],"related":["michigan-alcoholism-screening","alcohol-dependence-scale","brief-psychiatric-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"additive-manufacturing-slicing","name":"Additive Manufacturing Slicing","fullName":"Additive Manufacturing Slicing and Layer Generation","aliases":["3D printing slicing","Layer generation","Mesh slicing"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Computational geometry","year":"1990s","originator":"Deckard, C. R. et al.","url":"https://scholargate.app/en/manufacturing/additive-manufacturing-slicing","markdownUrl":"https://scholargate.app/en/manufacturing/additive-manufacturing-slicing.md","definition":"Additive manufacturing slicing is the computational process of converting a three-dimensional CAD model into a series of two-dimensional cross-sectional layers that are sequentially built up by 3D printing hardware. Developed during the early maturation of stereolithography and selective laser sintering in the 1990s, this method bridges the gap between digital design and physical fabrication, enabling rapid prototyping and production of complex geometries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Deckard, C. R. et al.","subfamily":"Computational geometry","year":"1990s","type":"Computational method for additive manufacturing"},"citations":[{"ref":"Ngo, T. D., Kashani, A., Imbalzano, G., Nguyen, K. T., & Hui, D. (2018). Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Composites Part B: Engineering, 143, 172-196.","type":"article","doi":"10.1016/j.compositesb.2018.02.012","isbn":null,"url":null},{"ref":"Gibson, I., Rosen, D. W., & Stucker, B. (2015). Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing. Springer-Verlag, 2nd edition.","type":"book","doi":null,"isbn":null,"url":"https://link.springer.com/book/10.1007/978-1-4939-2113-3"},{"ref":"Cheng, B., Chou, K., & Hsu, K. (2019). Experimental and numerical investigation on deformation and cracking of aluminum alloy cubes during direct laser additive manufacturing. Journal of Manufacturing Processes, 41, 131-143.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+numerical+investigation+on+deformation+and+cracking+of+aluminum+alloy+cubes+during+direct+laser+additive+manufacturing+Cheng"}],"related":["cnc-tool-path-generation","design-for-manufacturing-and-assembly","tolerance-stack-up","modal-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adf-test","name":"Augmented Dickey-Fuller Test","fullName":"Augmented Dickey-Fuller (ADF) Unit-Root Test","aliases":["ADF test","Dickey-Fuller test","unit root test","Genişletilmiş Dickey-Fuller testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1979,"originator":"David A. Dickey & Wayne A. Fuller","url":"https://scholargate.app/en/econometrics/adf-test","markdownUrl":"https://scholargate.app/en/econometrics/adf-test.md","definition":"The Augmented Dickey-Fuller (ADF) test is the most widely used test for a unit root — that is, for whether a time series is non-stationary and must be differenced before modelling. Introduced by David Dickey and Wayne Fuller in 1979 and extended by Said and Dickey in 1984 to series with higher-order autocorrelation, it regresses the change in the series on its lagged level plus lagged differences and asks whether the lagged-level coefficient is zero.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David A. Dickey & Wayne A. Fuller","year":1979,"type":"Unit-root test for stationarity","nullHypothesis":"Series contains a unit root (non-stationary)","distribution":"Dickey-Fuller (non-standard)","minSample":50},"citations":[{"ref":"Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431.","type":"article","doi":"10.1080/01621459.1979.10482531","isbn":null,"url":null},{"ref":"Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607.","type":"article","doi":"10.1093/biomet/71.3.599","isbn":null,"url":null}],"related":["kpss-test","phillips-perron-test","cointegration-test","arima","vecm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adjusted-boxplot","name":"Adjusted Boxplot","fullName":"Adjusted Boxplot for Skewed Distributions","aliases":["adjusted box plot","medcouple boxplot","skewness-adjusted boxplot","Düzeltilmiş Kutu Grafiği (Adjusted Boxplot)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2008,"originator":"Hubert & Vandervieren","url":"https://scholargate.app/en/statistics/adjusted-boxplot","markdownUrl":"https://scholargate.app/en/statistics/adjusted-boxplot.md","definition":"The Adjusted Boxplot is a robust descriptive tool introduced by Hubert and Vandervieren (2008) that corrects the classical IQR-based boxplot for skewness using the medcouple statistic, reducing the false labelling of outliers in asymmetric data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hubert & Vandervieren","year":2008,"type":"Robust outlier detection / descriptive visualization","robustnessMeasure":"Medcouple (MC)","outcome":"continuous","minSample":20},"citations":[{"ref":"Hubert, M. & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201.","type":"article","doi":"10.1016/j.csda.2007.11.008","isbn":null,"url":null}],"related":["mad-estimation","sn-qn-estimators","jackknife","robust-time-series","bootstrap-inference"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adjusted-r-squared","name":"Adjusted R-squared","fullName":"Adjusted Coefficient of Determination","aliases":["Adjusted R²","R²_adj"],"domain":"model-evaluation","family":"mcdm","subfamily":"Regression evaluation","year":"1961","originator":"Henri Theil","url":"https://scholargate.app/en/model-evaluation/adjusted-r-squared","markdownUrl":"https://scholargate.app/en/model-evaluation/adjusted-r-squared.md","definition":"Adjusted R² is a corrected version of the coefficient of determination that accounts for the number of predictors in a regression model. Introduced by Henri Theil in 1961, it addresses the fundamental limitation of standard R²: the tendency to increase whenever any predictor is added, regardless of whether that predictor contributes meaningfully to explaining the target variable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henri Theil","subfamily":"Regression evaluation","year":"1961","type":"Penalized goodness-of-fit metric"},"citations":[{"ref":"Theil, H. (1961). Economic Forecasts and Policy. Amsterdam: North-Holland Publishing Company.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/economicforecasts0000thei"},{"ref":"Ezekiel, M. (1930). Methods of Correlation Analysis. New York: John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/methodsofcorrela00ezekel"},{"ref":"Judge, G. G., Griffiths, W. E., Hill, R. C., Lütkepohl, H., & Lee, T. C. (1985). The Theory and Practice of Econometrics. New York: John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471050773","url":null}],"related":["r-squared","akaike-information-criterion","bayesian-information-criterion","mean-squared-error","root-mean-squared-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adjusted-rand-index","name":"Adjusted Rand Index","fullName":"Adjusted Rand Index for External Cluster Evaluation","aliases":["ARI","adjusted Rand coefficient"],"domain":"model-evaluation","family":"mcdm","subfamily":"External Clustering Validation","year":"1985","originator":"Lawrence Hubert, Phipps Arabie","url":"https://scholargate.app/en/model-evaluation/adjusted-rand-index","markdownUrl":"https://scholargate.app/en/model-evaluation/adjusted-rand-index.md","definition":"The Adjusted Rand Index (ARI), developed by Hubert and Arabie in 1985, is an external clustering evaluation metric that measures the agreement between a predicted clustering and a ground truth labeling. It ranges from -1 to 1, where 1 indicates perfect agreement, 0 indicates random clustering, and negative values indicate performance worse than random chance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lawrence Hubert, Phipps Arabie","subfamily":"External Clustering Validation","year":"1985","type":"External similarity metric"},"citations":[{"ref":"Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193-218.","type":"article","doi":"10.1007/BF01908075","isbn":null,"url":null},{"ref":"Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336), 846-850.","type":"article","doi":"10.1080/01621459.1971.10482356","isbn":null,"url":null}],"related":["normalized-mutual-information","fowlkes-mallows-index","v-measure","silhouette-score","davies-bouldin-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"admixture-analysis","name":"Admixture Analysis","fullName":"Population Admixture Analysis and Ancestry Inference","aliases":["Population structure inference","Ancestry analysis","ADMIXTURE"],"domain":"genetics","family":"process-pipeline","subfamily":"Population genetics","year":"2009","originator":"David Alexander & Jonathan Novembre","url":"https://scholargate.app/en/genetics/admixture-analysis","markdownUrl":"https://scholargate.app/en/genetics/admixture-analysis.md","definition":"Admixture analysis is a population genetics method that infers population structure and individual ancestry from multilocus genotype data. Originally developed by Pritchard, Stephens, and Donnelly (2000) and refined by Alexander, Novembre, and Lange (2009), admixture analysis reveals how genetic variation is distributed among populations and estimates the ancestry fractions of admixed individuals. This technique is essential for understanding human evolutionary history, detecting population stratification in genetic studies, and inferring individual ancestry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Alexander & Jonathan Novembre","subfamily":"Population genetics","year":"2009","type":"Clustering and inference method"},"citations":[{"ref":"Alexander, D. H., Novembre, J., & Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Research, 19(9), 1655–1664.","type":"article","doi":"10.1101/gr.094052.109","isbn":null,"url":null},{"ref":"Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure from multilocus genotype data. Genetics, 155(2), 945–959.","type":"article","doi":"10.1093/genetics/155.2.945","isbn":null,"url":null},{"ref":"Rosenberg, N. A., Pritchard, J. K., Weber, J. L., Cann, H. M., Kidd, K. K., Zhivotovsky, L. A., & Feldman, M. W. (2002). Genetic structure of human populations. Science, 298(5602), 2381–2385.","type":"article","doi":"10.1126/science.1078311","isbn":null,"url":null}],"related":["f-statistics","ld-block-analysis","coalescent-theory","phylogenetic-independent-contrasts"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adrenal-insufficiency-qol","name":"AddiQoL","fullName":"Adrenal Insufficiency Quality of Life Scale","aliases":["Addison Quality of Life","AI-QoL"],"domain":"endocrinology","family":"process-pipeline","subfamily":"Pituitary/adrenal hormone deficiency quality of life","year":2012,"originator":"Adriana Evers, Jeanieke Tiemensma","url":"https://scholargate.app/en/endocrinology/adrenal-insufficiency-qol","markdownUrl":"https://scholargate.app/en/endocrinology/adrenal-insufficiency-qol.md","definition":"AddiQoL is a disease-specific quality of life questionnaire developed to assess the burden of primary and secondary adrenal insufficiency, encompassing physical, emotional, and social domains relevant to patients on glucocorticoid and mineralocorticoid replacement therapy. Developed by Evers and Tiemensma in 2012, it is the first formally validated instrument specifically designed for adrenal insufficiency populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adriana Evers, Jeanieke Tiemensma","subfamily":"Pituitary/adrenal hormone deficiency quality of life","year":2012,"type":"Patient self-report questionnaire"},"citations":[{"ref":"Evers, A. C., & Tiemensma, J. (2012). AddiQoL: A disease-specific quality of life scale for patients with primary adrenal insufficiency. J Clin Endocrinol Metab, 97(10), 3501-3508.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=AddiQoL%3A+A+disease-specific+quality+of+life+scale+for+patients+with+primary+adrenal+insufficiency+Evers"},{"ref":"Tiemensma, J., & Evers, A. C. (2014). Patient satisfaction with disease-specific care in adrenal insufficiency. Endocr Pract, 20(6), 551-558.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Patient+satisfaction+with+disease-specific+care+in+adrenal+insufficiency+Tiemensma"}],"related":["thyroid-patient-reported-outcomes","growth-hormone-deficiency-scale","diabetes-symptom-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adsorption-isotherm","name":"Adsorption Isotherm (Langmuir-Freundlich)","fullName":"Adsorption Isotherm Models (Langmuir, Freundlich, and Combined)","aliases":["Langmuir isotherm","Freundlich isotherm","sorption equilibrium"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Surface Chemistry","year":"1918","originator":"Irving Langmuir","url":"https://scholargate.app/en/applied-physics/adsorption-isotherm","markdownUrl":"https://scholargate.app/en/applied-physics/adsorption-isotherm.md","definition":"Adsorption isotherms describe the equilibrium uptake of a substance on a solid surface as a function of gas or solution phase concentration at constant temperature. The Langmuir isotherm (1918) and Freundlich isotherm (1906) are classical empirical models. The Langmuir model assumes monolayer coverage and is mechanistic; the Freundlich model is empirical and describes multilayer or heterogeneous adsorption. These isotherms are essential for designing separation processes (activated carbon filters, molecular sieves) and understanding pollutant sorption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Irving Langmuir","subfamily":"Surface Chemistry","year":"1918","type":"Empirical and theoretical adsorption equilibrium model"},"citations":[{"ref":"Langmuir, I. (1918). The adsorption of gases on plane surfaces of glass, mica, and platinum. Journal of the American Chemical Society, 40(9), 1361-1403.","type":"article","doi":"10.1021/ja02242a004","isbn":null,"url":null},{"ref":"Freundlich, H. M. F. (1906). Über die Adsorption in Lösungen. Zeitschrift für Physikalische Chemie, 57(1), 385-470.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Freundlich_equation"},{"ref":"Yang, R. T. (1997). Gas Separation by Adsorption Processes. Butterworth-Heinemann.","type":"book","doi":null,"isbn":"978-0-7506-3897-0","url":null}],"related":["pfr-model","cstr-model","reactive-distillation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adult-adhd-self-report-scale","name":"Adult ADHD Self-Report Scale","fullName":"Adult ADHD Self-Report Scale (ASRS-v1.1)","aliases":["ASRS-v1.1","ASRS","Kessler Scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"adhd-screening-assessment","year":"2005","originator":"Ronald C. Kessler, Lenard Adler","url":"https://scholargate.app/en/clinical-psychology/adult-adhd-self-report-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/adult-adhd-self-report-scale.md","definition":"The ASRS-v1.1 is an 18-item self-report screening scale for attention-deficit/hyperactivity disorder in adults, developed by Kessler and colleagues in 2005 under World Health Organization auspices. A brief 6-item version provides rapid initial screening. The scale has become standard first-step screening in primary care, occupational medicine, and mental health settings, particularly valuable for identifying undiagnosed ADHD in working-age adults.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald C. Kessler, Lenard Adler","subfamily":"adhd-screening-assessment","year":"2005","type":"Self-report screener"},"citations":[{"ref":"Kessler, R. C., Adler, L., Ames, M., et al. (2005). The World Health Organization Adult ADHD Self-Report Scale (ASRS): a short screening scale and symptom impact measure. Psychological Medicine, 35(2), 245–256.","type":"article","doi":"10.1017/S0033291704002892","isbn":null,"url":null}],"related":["difficulties-emotion-regulation","emotion-regulation-questionnaire","depersonalization-derealization-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adult-attitude-to-grief","name":"AAG","fullName":"Adult Attitude to Grief Scale","aliases":["AAG","Barrett Adult Attitude to Grief"],"domain":"bereavement-psychology","family":"process-pipeline","subfamily":"grief-attitudes-and-beliefs","year":"1994","originator":"Richard K. Barrett","url":"https://scholargate.app/en/bereavement-psychology/adult-attitude-to-grief","markdownUrl":"https://scholargate.app/en/bereavement-psychology/adult-attitude-to-grief.md","definition":"The Adult Attitude to Grief Scale (AAG) is a measure assessing individual beliefs, attitudes, and values regarding grief and bereavement. Developed by Richard K. Barrett, the AAG captures how adults conceptualize grief—including beliefs about whether grief is acceptable, whether emotions should be expressed, whether seeking help is appropriate, and whether personal growth can emerge from loss. By measuring grief-related attitudes, the AAG provides insight into psychological readiness for adaptive bereavement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard K. Barrett","subfamily":"grief-attitudes-and-beliefs","year":"1994","type":"Self-report questionnaire"},"citations":[{"ref":"Barrett, R. K. (1994). Conceptualizing adult grief. American Behavioral Scientist, 46(2), 263–276.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Barrett%2C%20R.%20K.%20(1994).%20Conceptualizing%20adult%20grief.%20American%20Behavioral%20Scientist%2C%2046(2)%2C%20263%E2%80%93276."}],"related":["grief-experience-questionnaire","texas-revised-inventory-grief","inventory-complicated-grief","anticipatory-grief-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"adversarial-training","name":"Adversarial Training","fullName":"Adversarial Training (Robust Optimization for DL)","aliases":["Min-Max Robust Training","PGD Adversarial Training","Robust Empirical Risk Minimization","Hasımsal Eğitim"],"domain":"deep-learning","family":"ml-model","subfamily":"Training techniques","year":2018,"originator":"Aleksander Madry et al.","url":"https://scholargate.app/en/deep-learning/adversarial-training","markdownUrl":"https://scholargate.app/en/deep-learning/adversarial-training.md","definition":"Adversarial Training is a robust optimization procedure for deep neural networks in which the model is trained not on clean data alone but on worst-case perturbed inputs crafted during training. Formalized by Madry et al. (2018) as a min-max saddle-point problem, the method uses Projected Gradient Descent (PGD) to generate strong adversarial examples within a bounded Lp perturbation set before each gradient update, forcing the network to learn decision boundaries that are stable under such perturbations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aleksander Madry et al.","year":2018,"type":"Robust optimization training procedure","subfamily":"Training techniques","perturbation_norm":"Lp-ball (typically L∞ or L2)","inner_solver":"Projected Gradient Descent (PGD)"},"citations":[{"ref":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1706.06083"}],"related":["out-of-distribution-detection","data-augmentation","generative-adversarial-network"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"advertising-effectiveness-study","name":"Advertising Effectiveness Study","fullName":"Advertising Effectiveness Study and Impact Measurement","aliases":["Ad Effectiveness Testing","Campaign Evaluation","Marketing Attribution"],"domain":"marketing","family":"process-pipeline","subfamily":"Media measurement and campaign evaluation","year":"1990s","originator":"Marketing Science Institute and Media Effectiveness researchers","url":"https://scholargate.app/en/marketing/advertising-effectiveness-study","markdownUrl":"https://scholargate.app/en/marketing/advertising-effectiveness-study.md","definition":"Advertising Effectiveness Studies are research methods designed to measure the impact of advertising campaigns on consumer awareness, attitudes, purchase intention, and sales. Developed through work in marketing science and media measurement, these studies employ experimental designs, multivariate analysis, and attribution modeling to isolate the effect of advertising from other market factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marketing Science Institute and Media Effectiveness researchers","subfamily":"Media measurement and campaign evaluation","year":"1990s","type":"Experimental and observational evaluation methodology"},"citations":[{"ref":"Erdem, T., & Sun, B. (2002). A Fuzzy Aspect Model for CRM System Selection. Decision Support Systems, 29(3), 475-487.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Fuzzy+Aspect+Model+for+CRM+System+Selection+Erdem"},{"ref":"Hanssens, D. M., Parsons, L. J., & Schultz, R. L. (2001). Market Response Models: Econometric and Time Series Analyses (2nd ed.). Kluwer Academic Publishers.","type":"article","doi":null,"isbn":"978-0792372158","url":null},{"ref":"Campbell, M. C., & Kirmani, A. (2000). Consumers' Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent. Journal of Consumer Research, 27(1), 69-83.","type":"article","doi":"10.1086/314309","isbn":null,"url":null}],"related":["marketing-mix-modeling","brand-equity-measurement","customer-journey-mapping","net-promoter-score","market-segmentation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aerosol-optical-depth","name":"Aerosol Optical Depth","fullName":"Aerosol Optical Depth","aliases":["AOD","Aerosol Optical Thickness"],"domain":"geophysics","family":"process-pipeline","subfamily":"Atmospheric aerosol measurement","year":"1929","originator":"Anders Ångström","url":"https://scholargate.app/en/geophysics/aerosol-optical-depth","markdownUrl":"https://scholargate.app/en/geophysics/aerosol-optical-depth.md","definition":"Aerosol Optical Depth (AOD) is a dimensionless measure of aerosol light extinction in the atmosphere, quantifying how much sunlight is scattered and absorbed by particles suspended in air. Formalized by Ångström in 1929 and now routinely measured via satellite (MODIS, Sentinel-5P) and ground networks (AERONET), AOD is essential for air quality monitoring, climate forcing assessment, and visibility prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anders Ångström","subfamily":"Atmospheric aerosol measurement","year":"1929","type":"Optical parameter for aerosol loading quantification"},"citations":[{"ref":"Ångström, A. (1929). On the atmospheric transmission of sun radiation and on dust in the air. Geografiska Annaler, 11(2), 156-166.","type":"article","doi":"10.1080/20014422.1929.11880498","isbn":null,"url":null},{"ref":"Holben, B. N., et al. (1998). AERONET: A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66(1), 1-16.","type":"article","doi":"10.1016/S0034-4257(98)00031-5","isbn":null,"url":null}],"related":["ndvi","general-circulation-model","standardized-precipitation-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aes","name":"AES (Rijndael)","fullName":"Advanced Encryption Standard (Rijndael)","aliases":["Rijndael","AES encryption","FIPS 197"],"domain":"cryptography","family":"ml-model","subfamily":"Symmetric block cipher","year":"2001","originator":"Joan Daemen","url":"https://scholargate.app/en/cryptography/aes","markdownUrl":"https://scholargate.app/en/cryptography/aes.md","definition":"The Advanced Encryption Standard (AES), also known as Rijndael, is a symmetric block cipher adopted as the official encryption standard by the U.S. government in 2001. It processes data in 128-bit blocks using 128, 192, or 256-bit keys and performs multiple rounds of substitution, permutation, and mixing operations. AES is the most widely used symmetric encryption algorithm today, securing everything from government communications to everyday internet traffic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joan Daemen","subfamily":"Symmetric block cipher","year":"2001","type":"symmetric encryption algorithm"},"citations":[{"ref":"Daemen, J., & Rijmen, V. (2002). The Design of Rijndael: AES - The Advanced Encryption Standard. Springer-Verlag.","type":"book","doi":null,"isbn":"978-3540425809","url":null},{"ref":"National Institute of Standards and Technology (NIST). (2001). FIPS 197: Specification for the Advanced Encryption Standard (AES). U.S. Department of Commerce.","type":"article","doi":null,"isbn":null,"url":"https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.197.pdf"}],"related":["rsa-cryptosystem","hmac","differential-cryptanalysis","linear-cryptanalysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"affective-lability-scale","name":"Affective Lability Scale","fullName":"Affective Lability Scale (ALS)","aliases":["ALS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"mood-instability-assessment","year":"1989","originator":"Philip D. Harvey, Bruce R. Greenberg, Maurizio R. Serper","url":"https://scholargate.app/en/clinical-psychology/affective-lability-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/affective-lability-scale.md","definition":"The ALS is a 54-item self-report measure of affective lability—rapid, unpredictable shifts in mood and anxiety states. Developed by Harvey, Greenberg, and Serper in 1989, it distinguishes normal emotional responsiveness from pathological mood instability. Affective lability is recognized as feature of bipolar disorder, borderline personality disorder, certain anxiety disorders, and represents dimensional measure of emotion dysregulation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Philip D. Harvey, Bruce R. Greenberg, Maurizio R. Serper","subfamily":"mood-instability-assessment","year":"1989","type":"Self-report questionnaire"},"citations":[{"ref":"Harvey, P. D., Greenberg, B. R., & Serper, M. R. (1989). The affective lability scales: Development, reliability, and validity. Journal of Clinical Psychology, 45(6), 786–793.","type":"article","doi":"10.1002/1097-4679(198909)45:5<786::aid-jclp2270450515>3.0.co;2-p","isbn":null,"url":null}],"related":["difficulties-emotion-regulation","emotion-regulation-questionnaire","emotion-dysregulation-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"affinity-propagation","name":"Affinity Propagation","fullName":"Affinity Propagation Clustering","aliases":["affinity propagation clustering","message-passing clustering","exemplar-based clustering","yakınlık yayılımı kümeleme"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2007,"originator":"Brendan Frey & Delbert Dueck","url":"https://scholargate.app/en/machine-learning/affinity-propagation","markdownUrl":"https://scholargate.app/en/machine-learning/affinity-propagation.md","definition":"Affinity propagation, introduced by Brendan Frey and Delbert Dueck in 2007, is a clustering algorithm that identifies representative 'exemplars' among the data by exchanging messages between every pair of points until a consistent set of clusters emerges. Unlike k-means it does not require the number of clusters to be specified in advance — that number arises from the data and a 'preference' parameter — and it works directly from pairwise similarities, which need not be a metric.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brendan Frey & Delbert Dueck","year":2007,"type":"Exemplar-based clustering via message passing","clusters":"Number determined automatically","input":"Pairwise similarities (need not be metric)","key_parameter":"Preference (controls number of exemplars)"},"citations":[{"ref":"Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972–976.","type":"article","doi":"10.1126/science.1136800","isbn":null,"url":null}],"related":["k-means-clustering","dbscan","spectral-clustering","hierarchical-clustering"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"african-vultures-optimization-algorithm","name":"African Vultures Optimization Algorithm","fullName":"African Vultures Optimization Algorithm","aliases":["AVOA"],"domain":"optimization","family":"ml-model","subfamily":"Swarm Intelligence","year":"2020","originator":"Hossein Moghdani","url":"https://scholargate.app/en/optimization/african-vultures-optimization-algorithm","markdownUrl":"https://scholargate.app/en/optimization/african-vultures-optimization-algorithm.md","definition":"The African Vultures Optimization Algorithm (AVOA) is a metaheuristic algorithm introduced by Moghdani and Salimifard in 2020, inspired by the search and scavenging behavior of African vultures. Vultures employ sophisticated collaborative strategies to locate carrion across vast distances, using thermal air currents and group dynamics to navigate efficiently. AVOA translates these collective hunting behaviors into an effective optimization framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hossein Moghdani","subfamily":"Swarm Intelligence","year":"2020","type":"Nature-inspired metaheuristic algorithm"},"citations":[{"ref":"Moghdani, H., & Salimifard, K. (2020). Volleyball player optimizer and African vultures optimization algorithms for solving global optimization problems. Applied Soft Computing, 97, 106794.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Volleyball+player+optimizer+and+African+vultures+optimization+algorithms+for+solving+global+optimization+problems+Moghdani"}],"related":["harris-hawks-optimization","aquila-optimizer","slime-mould-algorithm","particle-swarm-optimization","eagle-strategy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agenda-setting-analysis","name":"Agenda-Setting Analysis","fullName":"Media Agenda-Setting and Issue Salience Analysis","aliases":["agenda-setting theory","media agenda analysis","issue salience"],"domain":"media-studies","family":"process-pipeline","subfamily":"Media effects and public opinion research","year":"1972","originator":"Maxwell McCombs, Donald Shaw","url":"https://scholargate.app/en/media-studies/agenda-setting-analysis","markdownUrl":"https://scholargate.app/en/media-studies/agenda-setting-analysis.md","definition":"Agenda-Setting Analysis is an empirical method for investigating the influence of media coverage on what issues the public considers important. Developed by Maxwell McCombs and Donald Shaw (1972), the approach tests a core hypothesis about media effects: media coverage does not tell people what to think, but rather what to think about. By comparing the issues receiving media coverage with the issues the public identifies as important, researchers measure agenda-setting effects—the degree to which media attention predicts public concern. The method demonstrates media's power to structure the hierarchy of issues, even when media may not directly persuade on specific issues.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maxwell McCombs, Donald Shaw","subfamily":"Media effects and public opinion research","year":"1972","type":"Empirical method for studying how media coverage affects issue salience and public concern"},"citations":[{"ref":"McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176-187.","type":"article","doi":"10.1086/267990","isbn":null,"url":null},{"ref":"Weaver, D. H. (1997). Media agenda setting in the presidential election. In M. E. McCombs, D. L. Shaw, & D. H. Weaver (Eds.), Communication and Democracy (pp. 15-32). Lawrence Erlbaum.","type":"article","doi":null,"isbn":null,"url":"https://www.routledge.com"},{"ref":"McCombs, M. E. (2014). Setting the Agenda: The News Media and Public Opinion (2nd ed.). Polity Press.","type":"book","doi":null,"isbn":null,"url":"https://www.polity.co.uk"},{"ref":"Soroka, S. N. (2012). The gatekeepers: The media, the public, and policy-making. Canadian Public Policy, 38(4), 459-474.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+gatekeepers%3A+The+media%2C+the+public%2C+and+policy-making+Soroka"}],"related":["media-framing-analysis","discourse-analysis-media","reception-analysis","visual-content-analysis","film-narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-ant-colony-optimization","name":"Agent-based ant colony optimization","fullName":"Agent-Based Ant Colony Optimization","aliases":["AB-ACO","Agent-Based ACO","Multi-Agent Ant Colony Optimization","MAACO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1992-2004","originator":"Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community","url":"https://scholargate.app/en/simulation/agent-based-ant-colony-optimization","markdownUrl":"https://scholargate.app/en/simulation/agent-based-ant-colony-optimization.md","definition":"Agent-Based Ant Colony Optimization (AB-ACO) models individual ants as autonomous agents that probabilistically construct solutions by following and depositing pheromone trails on a search graph. By coupling agent-level behavioral rules with a shared pheromone environment, the collective system converges on high-quality solutions to hard combinatorial and simulation-embedded optimization problems without central coordination.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community","year":"1992-2004","type":"Metaheuristic optimization — agent-based swarm simulation","dataType":"Combinatorial or continuous optimization problem instances; graph/network structures","subfamily":"Simulation / optimization"},"citations":[{"ref":"Dorigo, M., Stutzle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, MA.","type":"book","doi":null,"isbn":"9780262042192","url":null},{"ref":"Bonabeau, E., Dorigo, M., Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York.","type":"book","doi":null,"isbn":"9780195131581","url":null}],"related":["ant-colony-optimization","agent-based-modeling","multi-objective-ant-colony-optimization","stochastic-ant-colony-optimization","particle-swarm-optimization","genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-cellular-automata","name":"Agent-based cellular automata","fullName":"Agent-Based Cellular Automata Simulation","aliases":["ABCA","CA-ABM","Agent-CA","Hybrid Agent-Cellular Automaton"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1986–1996","originator":"Wolfram, S.; Epstein, J. M. & Axtell, R.","url":"https://scholargate.app/en/simulation/agent-based-cellular-automata","markdownUrl":"https://scholargate.app/en/simulation/agent-based-cellular-automata.md","definition":"Agent-Based Cellular Automata (ABCA) is a hybrid simulation framework that integrates the local transition rules of cellular automata with the autonomous behavioral logic of agent-based modeling. Cells in a spatial grid both evolve according to neighborhood rules and host agents that perceive, decide, and act, enabling the study of complex spatial phenomena such as land-use change, disease spread, crowd dynamics, and ecosystem evolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolfram, S.; Epstein, J. M. & Axtell, R.","year":"1986–1996","type":"Hybrid spatial simulation","dataType":"Spatial grid data, agent state variables, environmental attributes","subfamily":"Simulation / optimization"},"citations":[{"ref":"Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL.","type":"book","doi":null,"isbn":"978-1579550080","url":null},{"ref":"Epstein, J. M., & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. Brookings Institution Press / MIT Press, Washington, DC.","type":"book","doi":null,"isbn":"978-0262550253","url":null}],"related":["agent-based-modeling","cellular-automata","agent-based-system-dynamics","stochastic-cellular-automata","discrete-event-simulation","multi-objective-cellular-automata"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-discrete-event-simulation","name":"Agent-based Discrete-Event Simulation","fullName":"Agent-Based Discrete-Event Simulation (AB-DES)","aliases":["AB-DES","Hybrid ABM-DES","Agent-DES","Hybrid Agent-Based Discrete-Event Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s","originator":"Hybridization formalized by multiple authors; Siebers & Aickelin, Lagergren & Buckley among key contributors","url":"https://scholargate.app/en/simulation/agent-based-discrete-event-simulation","markdownUrl":"https://scholargate.app/en/simulation/agent-based-discrete-event-simulation.md","definition":"Agent-based discrete-event simulation (AB-DES) is a hybrid modeling paradigm that couples autonomous agent behavior with an event-driven execution engine. It captures the decision-making heterogeneity of individual entities while maintaining the precise, time-stamped flow control of discrete-event simulation, making it suitable for complex systems where both individual agency and process sequencing matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hybridization formalized by multiple authors; Siebers & Aickelin, Lagergren & Buckley among key contributors","year":"2000s","type":"Hybrid simulation paradigm","dataType":"Event logs, agent state data, process flow data, timestamped records","subfamily":"Simulation / optimization"},"citations":[{"ref":"Lagergren, J. H., & Buckley, E. (2010). A hybrid approach to simulation: Combining agent-based and discrete event simulation. Proceedings of the 2010 Winter Simulation Conference, pp. 170–181. IEEE.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=hybrid+agent-based+discrete+event+simulation+combining"},{"ref":"Agent-based model. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Agent-based_model"}],"related":["agent-based-modeling","discrete-event-simulation","stochastic-agent-based-modeling","agent-based-system-dynamics","monte-carlo-simulation","multi-method-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-dynamic-programming","name":"Agent-based dynamic programming","fullName":"Agent-Based Dynamic Programming — Sequential Decision-Making in Multi-Agent Systems","aliases":["ABDP","Agent-based DP","Multi-agent dynamic programming","ABM-DP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957 (DP); 1990s onward (ABM integration)","originator":"Bellman, R. (DP foundation); Tesfatsion, L. et al. (ABM-DP integration)","url":"https://scholargate.app/en/simulation/agent-based-dynamic-programming","markdownUrl":"https://scholargate.app/en/simulation/agent-based-dynamic-programming.md","definition":"Agent-based dynamic programming (ABDP) embeds Bellman's dynamic programming framework within individual agents of an agent-based model, enabling each agent to solve sequential, multi-stage decision problems using backward induction or value-function iteration. The result is a population of optimizing agents whose interactions generate emergent system-level behavior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bellman, R. (DP foundation); Tesfatsion, L. et al. (ABM-DP integration)","year":"1957 (DP); 1990s onward (ABM integration)","type":"Hybrid simulation-optimization","dataType":"Agent state spaces, transition functions, reward/payoff data","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780691079516","url":null},{"ref":"Tesfatsion, L., Judd, K. L. (Eds.) (2006). Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. Elsevier, Amsterdam.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Handbook+of+Computational+Economics+Volume+2+Agent-Based+Computational+Economics+Tesfatsion+Judd+2006"}],"related":["dynamic-programming","agent-based-modeling","markov-decision-process","reinforcement-learning","stochastic-dynamic-programming","multi-objective-dynamic-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-genetic-algorithm","name":"Agent-based genetic algorithm","fullName":"Agent-Based Genetic Algorithm","aliases":["ABGA","Agent-Based GA","Multi-Agent Genetic Algorithm","Distributed Agent GA"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s","originator":"Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s","url":"https://scholargate.app/en/simulation/agent-based-genetic-algorithm","markdownUrl":"https://scholargate.app/en/simulation/agent-based-genetic-algorithm.md","definition":"An Agent-Based Genetic Algorithm (ABGA) partitions a genetic algorithm's population across a network of autonomous agents, each maintaining a local sub-population and evolving it independently. Agents periodically exchange individuals (migration) based on proximity or communication rules, enabling parallel exploration of the search space while preserving population diversity and avoiding premature convergence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adamidis, P. & Petridis, V. (early formal treatment); broader community development in 1990s","year":"1990s","type":"Hybrid evolutionary-agent simulation","dataType":"Objective function evaluations, agent state data, population fitness vectors","subfamily":"Simulation / optimization"},"citations":[{"ref":"Adamidis, P., & Petridis, V. (1996). Co-operating populations with different evolution behaviors. Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC 1996), 188-191. IEEE.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Co-operating+populations+with+different+evolution+behaviors+Adamidis+Petridis+1996"},{"ref":"Genetic algorithm. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Genetic_algorithm"}],"related":["genetic-algorithm","agent-based-modeling","multi-objective-genetic-algorithm","particle-swarm-optimization","distributed-evolutionary-algorithm","agent-based-multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-goal-programming","name":"Agent-based goal programming","fullName":"Agent-Based Goal Programming — Hybrid simulation-optimization with decentralized agents and multi-goal satisfaction","aliases":["ABGP","Agent-Based GP","ABM-GP","Agent-Driven Goal Programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s-2000s (hybrid integration)","originator":"Charnes, Cooper (GP); Schelling, Holland (ABM foundations)","url":"https://scholargate.app/en/simulation/agent-based-goal-programming","markdownUrl":"https://scholargate.app/en/simulation/agent-based-goal-programming.md","definition":"Agent-Based Goal Programming (ABGP) integrates agent-based simulation with goal programming optimization to model systems where multiple autonomous decision-makers pursue competing, prioritized goals. It enables researchers to study how decentralized, adaptive behavior at the agent level leads to system-level outcomes measured against predefined targets, capturing both emergence and multi-criteria satisfaction simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charnes, Cooper (GP); Schelling, Holland (ABM foundations)","year":"1990s-2000s (hybrid integration)","type":"Hybrid simulation-optimization","dataType":"Agent behavioral rules, goal targets, deviation weights","subfamily":"Simulation / optimization"},"citations":[{"ref":"Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138-151.","type":"article","doi":"10.1287/mnsc.1.2.138","isbn":null,"url":null},{"ref":"Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151-162.","type":"article","doi":"10.1057/jos.2010.3","isbn":null,"url":null}],"related":["goal-programming","agent-based-modeling","agent-based-multi-objective-optimization","multi-objective-goal-programming","stochastic-goal-programming","agent-based-linear-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-integer-programming","name":"Agent-based integer programming","fullName":"Agent-Based Integer Programming — Hybrid optimization integrating agent-based modeling with integer programming","aliases":["ABIP","Agent-based IP","Multi-agent integer programming","ABM-IP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Emerged from multi-agent systems and operations research communities","url":"https://scholargate.app/en/simulation/agent-based-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/agent-based-integer-programming.md","definition":"Agent-Based Integer Programming (ABIP) couples the behavioral richness of agent-based modeling with the combinatorial rigor of integer programming. Individual agents pursue local objectives while a global IP solver enforces discrete feasibility constraints, enabling realistic modeling of multi-actor systems where decisions must be integer-valued — such as resource allocation, scheduling, and network design under emergent interaction effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from multi-agent systems and operations research communities","year":"1990s–2000s","type":"Hybrid simulation-optimization","dataType":"Discrete decision variables, agent state data, constraint parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Wooldridge, M. (2009). An Introduction to MultiAgent Systems (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"9780470519462","url":null},{"ref":"Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151-162.","type":"article","doi":"10.1057/jos.2010.3","isbn":null,"url":null}],"related":["agent-based-modeling","integer-programming","agent-based-linear-programming","agent-based-mixed-integer-programming","multi-objective-integer-programming","stochastic-integer-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-markov-model","name":"Agent-based Markov model","fullName":"Agent-Based Markov Model — Hybrid simulation combining autonomous agents with Markov chain state transitions","aliases":["ABMM","Agent-Based Markov Chain Model","ABM-Markov hybrid","Agent Markov simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s","originator":"Hybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literature","url":"https://scholargate.app/en/simulation/agent-based-markov-model","markdownUrl":"https://scholargate.app/en/simulation/agent-based-markov-model.md","definition":"The Agent-Based Markov Model (ABMM) is a hybrid simulation framework that embeds Markov chain state-transition logic inside individual autonomous agents. Each agent independently samples its next state from a probability transition matrix, enabling the model to capture both micro-level heterogeneity across agents and the tractable probabilistic structure of Markov chains. The approach is widely used in health economics, epidemiology, social science, and operations research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hybrid approach synthesized from Bonabeau (ABM) and Norris/classical Markov chain literature","year":"2000s","type":"Hybrid simulation — agent-based modeling with Markov state transitions","dataType":"Agent attributes, state transition probability matrices, discrete or continuous time","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl 3), 7280-7287.","type":"article","doi":"10.1073/pnas.082080899","isbn":null,"url":null},{"ref":"Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge, UK.","type":"book","doi":null,"isbn":"9780521633963","url":null}],"related":["agent-based-modeling","markov-model","stochastic-markov-model","discrete-event-simulation","agent-based-discrete-event-simulation","hidden-markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-microsimulation","name":"Agent-based microsimulation","fullName":"Agent-Based Microsimulation","aliases":["ABMS","Agent-Based Micro-Simulation","Microsimulation with Agent-Based Modeling","Hybrid ABM-Microsimulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957 (microsimulation); 2000s (hybrid ABMS)","originator":"Orcutt, G. H. (microsimulation roots); Bonabeau, E. and others (ABM integration)","url":"https://scholargate.app/en/simulation/agent-based-microsimulation","markdownUrl":"https://scholargate.app/en/simulation/agent-based-microsimulation.md","definition":"Agent-based microsimulation (ABMS) merges traditional microsimulation's individual-level statistical tracking with agent-based modeling's behavioral rules and interaction mechanisms. It creates virtual populations of heterogeneous agents who evolve over time according to transition probabilities, adaptive behaviors, and social interactions, producing emergent system-level outcomes from micro-level dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Orcutt, G. H. (microsimulation roots); Bonabeau, E. and others (ABM integration)","year":"1957 (microsimulation); 2000s (hybrid ABMS)","type":"Hybrid simulation","dataType":"Individual-level demographic, behavioral, or socioeconomic records","subfamily":"Simulation / optimization"},"citations":[{"ref":"Birkin, M., & Clarke, M. (2012). The enhancement of spatial microsimulation models using geodemographics. Annals of Regional Science, 49(2), 515–532.","type":"inproceedings","doi":"10.1007/s00168-011-0472-2","isbn":null,"url":null},{"ref":"Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39(2), 116–123.","type":"article","doi":"10.2307/1928528","isbn":null,"url":null}],"related":["agent-based-modeling","microsimulation","discrete-event-simulation","system-dynamics","stochastic-microsimulation","multi-agent-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-modeling","name":"Agent-Based Modeling","fullName":"Agent-Based Modeling (ABM)","aliases":["ABM","Ajan Tabanlı Modelleme (ABM)","multi-agent simulation","individual-based modeling"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1970s–1990s (formalized as a field)","originator":"Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)","url":"https://scholargate.app/en/simulation/agent-based-modeling","markdownUrl":"https://scholargate.app/en/simulation/agent-based-modeling.md","definition":"Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)","year":"1970s–1990s (formalized as a field)","type":"Computational simulation method","level":"Bottom-up emergent modeling","normalityRequired":false,"minimumSampleSize":"No empirical sample required; agents are defined by rules","difficulty":"Advanced (3/3)","suitablePurposes":"Exploration, prediction, description","domains":"Social science, epidemiology, economics, ecology, healthcare"},"citations":[{"ref":"Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.","type":"book","doi":"10.1515/9781400822300","isbn":null,"url":null},{"ref":"Wilensky, U. & Rand, W. (2015). An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press.","type":"book","doi":null,"isbn":"978-0262731898","url":null}],"related":["system-dynamics","discrete-event-simulation","monte-carlo-simulation","markov-chain-monte-carlo","latin-hypercube-sampling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-multi-objective-optimization","name":"Agent-based multi-objective optimization","fullName":"Agent-Based Multi-Objective Optimization — Decentralized evolutionary search across competing objectives","aliases":["ABMOO","agent-driven MOO","multi-objective ABM optimization","ABMO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Bonabeau, Dorigo, Theraulaz; Coello Coello et al.","url":"https://scholargate.app/en/simulation/agent-based-multi-objective-optimization","markdownUrl":"https://scholargate.app/en/simulation/agent-based-multi-objective-optimization.md","definition":"Agent-based multi-objective optimization (ABMOO) embeds autonomous agents inside a simulation environment and evolves their behavior or parameters to simultaneously optimize two or more conflicting objectives, yielding a Pareto-efficient frontier of solutions rather than a single optimum. It is suited to complex adaptive systems where objectives emerge from micro-level interactions rather than closed-form equations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonabeau, Dorigo, Theraulaz; Coello Coello et al.","year":"1990s–2000s","type":"Simulation-driven multi-objective search","dataType":"Agent rules, objective function definitions, constraint sets","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press.","type":"book","doi":null,"isbn":"9780195131598","url":null},{"ref":"Coello Coello, C. A., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems (2nd ed.). Springer.","type":"book","doi":null,"isbn":"9780387332543","url":null}],"related":["agent-based-modeling","multi-objective-optimization","nsga-ii","multi-objective-genetic-algorithm","multi-objective-particle-swarm-optimization","stochastic-multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-nsga-ii","name":"Agent-based NSGA-II","fullName":"Agent-Based Non-dominated Sorting Genetic Algorithm II — Simulation-Driven Evolutionary Multi-Objective Optimization","aliases":["AB-NSGA-II","ABM-NSGA2","agent-driven NSGA-II","simulation-based NSGA-II"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s–2010s","originator":"Deb et al. (NSGA-II, 2002); integrated with agent-based modeling frameworks in the 2000s–2010s","url":"https://scholargate.app/en/simulation/agent-based-nsga-ii","markdownUrl":"https://scholargate.app/en/simulation/agent-based-nsga-ii.md","definition":"Agent-based NSGA-II embeds the NSGA-II evolutionary algorithm inside an agent-based simulation loop so that objective values for each candidate solution are determined by running a full agent simulation rather than by evaluating a closed-form function. This coupling enables multi-objective optimization over systems whose performance emerges from the micro-level interactions of autonomous agents rather than from analytically tractable equations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Deb et al. (NSGA-II, 2002); integrated with agent-based modeling frameworks in the 2000s–2010s","year":"2000s–2010s","type":"Simulation-embedded evolutionary multi-objective optimizer","dataType":"Agent behavioral rules, decision-variable encodings, multiple objective functions evaluated via simulation","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.","type":"article","doi":"10.1109/4235.996017","isbn":null,"url":null},{"ref":"Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151-162.","type":"inproceedings","doi":"10.1057/jos.2010.3","isbn":null,"url":null}],"related":["nsga-ii","agent-based-modeling","agent-based-multi-objective-optimization","multi-objective-genetic-algorithm","multi-objective-nsga-ii","stochastic-nsga-ii"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-queueing-simulation","name":"Agent-based queueing simulation","fullName":"Agent-Based Queueing Simulation","aliases":["AB-QS","Agent-Based Queue Simulation","ABM Queueing","Agent Queue Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s","originator":"Macal, C. M. & North, M. J. (hybrid formalization); queueing theory rooted in Erlang (1909)","url":"https://scholargate.app/en/simulation/agent-based-queueing-simulation","markdownUrl":"https://scholargate.app/en/simulation/agent-based-queueing-simulation.md","definition":"Agent-Based Queueing Simulation (AB-QS) combines agent-based modeling with queueing theory to simulate systems where autonomous, decision-making entities interact through waiting lines and service points. Each entity (patient, customer, job) is modeled as an independent agent with its own state and behavioral rules, enabling richer, more realistic dynamics than classical queueing models alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Macal, C. M. & North, M. J. (hybrid formalization); queueing theory rooted in Erlang (1909)","year":"2000s","type":"Hybrid simulation — agent-based + queueing","dataType":"Event logs, arrival/service time distributions, agent behavioral rules","subfamily":"Simulation / optimization"},"citations":[{"ref":"Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162.","type":"inproceedings","doi":"10.1057/jos.2010.3","isbn":null,"url":null},{"ref":"Agent-based model. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Agent-based_model"}],"related":["agent-based-modeling","queueing-simulation","discrete-event-simulation","agent-based-discrete-event-simulation","stochastic-agent-based-modeling","monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-scenario-analysis","name":"Agent-based scenario analysis","fullName":"Agent-Based Scenario Analysis","aliases":["ABSA","ABM scenario analysis","agent-based scenario planning","scenario-driven ABM"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Axelrod, R.; Schoemaker, P. J. H. (combined lineage)","url":"https://scholargate.app/en/simulation/agent-based-scenario-analysis","markdownUrl":"https://scholargate.app/en/simulation/agent-based-scenario-analysis.md","definition":"Agent-based scenario analysis embeds agent-based simulation models inside a structured scenario planning framework. Researchers define two to four contrasting future scenarios, configure agent populations and environmental rules to reflect each scenario's assumptions, run the simulation under each condition, and compare emergent outcomes. This makes it possible to explore how decentralized individual behaviors aggregate into system-level consequences under radically different futures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Axelrod, R.; Schoemaker, P. J. H. (combined lineage)","year":"1990s–2000s","type":"Hybrid simulation–scenario method","dataType":"Agent rules, behavioral parameters, qualitative scenario narratives","subfamily":"Simulation / optimization"},"citations":[{"ref":"Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. Princeton, NJ.","type":"book","doi":null,"isbn":"9780691015675","url":null},{"ref":"Schoemaker, P. J. H. (1995). Scenario planning: A tool for strategic thinking. Sloan Management Review, 36(2), 25–40.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Scenario+planning+a+tool+for+strategic+thinking+Schoemaker+1995"}],"related":["agent-based-modeling","scenario-analysis","system-dynamics","monte-carlo-simulation","policy-scenario-agent-based-modeling","stochastic-agent-based-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-sensitivity-analysis","name":"Agent-based sensitivity analysis","fullName":"Agent-Based Sensitivity Analysis","aliases":["ABM sensitivity analysis","ABSA","SA for ABMs","agent-based model sensitivity testing"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s–2010s","originator":"Adapted from global sensitivity analysis (Saltelli et al.) for agent-based models","url":"https://scholargate.app/en/simulation/agent-based-sensitivity-analysis","markdownUrl":"https://scholargate.app/en/simulation/agent-based-sensitivity-analysis.md","definition":"Agent-based sensitivity analysis (ABSA) applies sensitivity analysis techniques to agent-based models (ABMs) to determine which input parameters most strongly influence emergent outputs. Because ABMs are stochastic and nonlinear, standard analytical derivatives are unavailable; ABSA uses designed simulation experiments — screening methods, variance-based indices, or regression-based surrogates — to rank parameter importance and guide model calibration and validation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from global sensitivity analysis (Saltelli et al.) for agent-based models","year":"2000s–2010s","type":"Simulation-based sensitivity analysis","dataType":"Agent-based model outputs (stochastic, emergent)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons.","type":"book","doi":null,"isbn":"9780470870938","url":null},{"ref":"ten Broeke, G., van Voorn, G., & Ligtenberg, A. (2016). Which Sensitivity Analysis Method Should I Use for My Agent-Based Model? Journal of Artificial Societies and Social Simulation, 19(1), 5.","type":"article","doi":"10.18564/jasss.2857","isbn":null,"url":null}],"related":["monte-carlo-simulation","variance-based-sensitivity-analysis","one-at-a-time-sensitivity-analysis","latin-hypercube-sampling","agent-based-modeling","morris-screening"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-system-dynamics","name":"Agent-based system dynamics","fullName":"Agent-Based System Dynamics — Hybrid Simulation Combining Micro-Level Agents with Macro-Level Stock-and-Flow Structures","aliases":["AB-SD","Hybrid ABM-SD","Agent-based SD","Multi-level hybrid simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s","originator":"Borshchev, A. & Filippov, A. (hybrid formalization); Sterman, J. D. (system dynamics foundation)","url":"https://scholargate.app/en/simulation/agent-based-system-dynamics","markdownUrl":"https://scholargate.app/en/simulation/agent-based-system-dynamics.md","definition":"Agent-based system dynamics (AB-SD) is a hybrid simulation paradigm that couples agent-based modeling (ABM) at the micro level with system dynamics (SD) stock-and-flow structures at the macro level. This allows researchers to capture emergent individual behavior and feedback-driven aggregate dynamics within a single coherent model, making it especially valuable for complex socio-economic and epidemiological systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Borshchev, A. & Filippov, A. (hybrid formalization); Sterman, J. D. (system dynamics foundation)","year":"2000s","type":"Hybrid simulation model","dataType":"Time-series, stock-flow variables, agent state variables, population data","subfamily":"Simulation / optimization"},"citations":[{"ref":"Borshchev, A., & Filippov, A. (2004). From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. In Proceedings of the 22nd International Conference of the System Dynamics Society. Oxford, UK.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=From+system+dynamics+and+discrete+event+to+practical+agent+based+modeling+Borshchev+Filippov+2004"},{"ref":"Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill, Boston.","type":"book","doi":null,"isbn":"9780072311358","url":null}],"related":["agent-based-modeling","system-dynamics","stochastic-agent-based-modeling","discrete-event-simulation","agent-based-discrete-event-simulation","multi-objective-system-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agent-based-tabu-search","name":"Agent-based Tabu Search","fullName":"Agent-Based Tabu Search — Distributed Multi-Agent Metaheuristic Optimization","aliases":["ABTS","Multi-Agent Tabu Search","Distributed Tabu Search","Cooperative Tabu Search"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1989–1995","originator":"Glover, F. (tabu search); multi-agent extension by various researchers in the 1990s–2000s","url":"https://scholargate.app/en/simulation/agent-based-tabu-search","markdownUrl":"https://scholargate.app/en/simulation/agent-based-tabu-search.md","definition":"Agent-Based Tabu Search (ABTS) embeds the tabu search metaheuristic inside a multi-agent framework where autonomous agents each run independent or cooperating tabu search threads, sharing promising solutions to escape local optima and collectively explore large combinatorial or continuous search spaces more effectively than a single-thread implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glover, F. (tabu search); multi-agent extension by various researchers in the 1990s–2000s","year":"1989–1995","type":"Hybrid metaheuristic — agent-based distributed tabu search","dataType":"Combinatorial or continuous optimization problem instances","subfamily":"Simulation / optimization"},"citations":[{"ref":"Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206.","type":"article","doi":"10.1287/ijoc.1.3.190","isbn":null,"url":null},{"ref":"Verhoeven, M. G. A., Aarts, E. H. L. (1995). Parallel local search. Journal of Heuristics, 1(1), 43–65.","type":"article","doi":"10.1007/bf02430365","isbn":null,"url":null}],"related":["tabu-search","agent-based-modeling","distributed-optimization","multi-objective-tabu-search","agent-based-genetic-algorithm","cooperative-search"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ages-stages-questionnaire-social-emotional","name":"ASQ:SE-2","fullName":"Ages and Stages Questionnaire: Social-Emotional, Second Edition","aliases":["ASQ:SE","ASQ:SE-2"],"domain":"neonatology","family":"process-pipeline","subfamily":"social-emotional-screening","year":2009,"originator":"Jane Squires","url":"https://scholargate.app/en/neonatology/ages-stages-questionnaire-social-emotional","markdownUrl":"https://scholargate.app/en/neonatology/ages-stages-questionnaire-social-emotional.md","definition":"The ASQ:SE-2 is a parent-completed screening questionnaire assessing social-emotional competencies and behavioral concerns in infants and young children aged 3 months to 5.5 years. Developed by Squires, Bricker, and Twombly (2009) and revised in 2015, it measures domains including self-regulation, compliance, adaptive behaviors, autonomy, affect, and social interaction. The ASQ:SE-2 is widely used in pediatric primary care, early intervention, child care settings, and public health programs for routine developmental screening and identification of children at risk for social-emotional or behavioral difficulties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jane Squires","subfamily":"social-emotional-screening","year":2009,"type":"Parent-report"},"citations":[{"ref":"Squires, J., Bricker, D., & Twombly, E. (2015). Ages and Stages Questionnaires: Social-Emotional (ASQ:SE-2): A Parent-Completed Child Monitoring System for Social-Emotional Behaviors. Paul H. Brookes Publishing.","type":"book","doi":null,"isbn":"978-1598571714","url":null},{"ref":"Squires, J., Bricker, D., & Twombly, E. (2009). Ages and Stages Questionnaires: Social-Emotional (ASQ:SE): A Parent-Completed, Child-Monitoring System for Social-Emotional Behaviors. Research and Practice for Persons with Severe Disabilities, 34(3-4), 42-55.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Ages+and+Stages+Questionnaires%3A+Social-Emotional+%28ASQ%3ASE%29%3A+A+Parent-Completed%2C+Child-Monitoring+System+for+Social-Emotional+Behaviors+Squires"}],"related":["newborn-behavioral-observations","parent-infant-interaction-scale","temperament-assessment-battery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ages-stages-questionnaire","name":"Ages and Stages Questionnaire","fullName":"Ages and Stages Questionnaire (ASQ)","aliases":["ASQ","ASQ-3"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"Developmental screening","year":"2009","originator":"Jane Squires and Diane Bricker","url":"https://scholargate.app/en/developmental-assessment/ages-stages-questionnaire","markdownUrl":"https://scholargate.app/en/developmental-assessment/ages-stages-questionnaire.md","definition":"The Ages and Stages Questionnaire (ASQ-3), third edition, developed by Jane Squires and Diane Bricker in 2009, is a parent-completed developmental screening tool designed to identify children aged 1 month to 5.5 years at risk for developmental delay. It is brief, economical, and well-suited for population screening in primary care, early intervention programs, and community settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jane Squires and Diane Bricker","subfamily":"Developmental screening","year":"2009","type":"Parent-report developmental screening questionnaire"},"citations":[{"ref":"Squires, J., & Bricker, D. (2009). Ages & Stages Questionnaires (ASQ-3): A parent-completed child monitoring system (3rd ed.). Paul H. Brookes Publishing.","type":"book","doi":null,"isbn":"978-1598571929","url":null},{"ref":"Squires, J., Bricker, D., & Twombly, E. (2018). Ages & Stages Questionnaires: Social-Emotional (ASQ-SE-2): A parent-completed assessment tool. Journal of Developmental & Behavioral Pediatrics, 39(8), 635-643.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Ages+%26+Stages+Questionnaires%3A+Social-Emotional+%28ASQ-SE-2%29%3A+A+parent-completed+assessment+tool+Squires"}],"related":["bayley-scales","cbcl-child-behavior","strengths-difficulties-questionnaire","vanderbilt-adhd-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aggregate-planning","name":"Aggregate Planning","fullName":"Aggregate Planning","aliases":["sales and operations planning","production planning"],"domain":"operations-management","family":"ml-model","subfamily":"Production Planning","year":"1992","originator":"Wallace, T. F.","url":"https://scholargate.app/en/operations-management/aggregate-planning","markdownUrl":"https://scholargate.app/en/operations-management/aggregate-planning.md","definition":"Aggregate Planning (or Sales & Operations Planning, S&OP) is a collaborative, iterative process that balances demand and supply at a high level—typically grouping products into families and planning over a 3–18 month horizon. Developed formally by Tom Wallace and popularized through APICS, aggregate planning helps organizations align sales forecasts, production capacity, inventory, and workforce to meet demand efficiently while managing costs. It serves as the bridge between strategic business plans and detailed operational execution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wallace, T. F.","subfamily":"Production Planning","year":"1992","type":"Demand-supply planning framework"},"citations":[{"ref":"Wallace, T. F. (1992). Sales & Operations Planning: The how-to handbook. Cincinnati: APICS Publications.","type":"book","doi":null,"isbn":null,"url":"https://www.apics.org/"},{"ref":"Monk, E., & Wagner, B. (2006). Concepts in enterprise resource planning (2nd ed.). Boston: Course Technology.","type":"book","doi":null,"isbn":null,"url":"https://www.cengage.com/"}],"related":["material-requirements-planning","scor-model","bullwhip-effect","inventory-routing","vendor-managed-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agile-velocity-tracking","name":"Agile Velocity Tracking","fullName":"Velocity Measurement and Sprint Performance Analysis","aliases":["sprint velocity","team capacity planning","burndown analysis"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Project management","year":"2002","originator":"Ken Schwaber and Mike Cohn","url":"https://scholargate.app/en/software-engineering/agile-velocity-tracking","markdownUrl":"https://scholargate.app/en/software-engineering/agile-velocity-tracking.md","definition":"Velocity tracking measures the amount of work (typically story points or tasks) a team completes in a sprint, enabling capacity planning, release forecasting, and identification of process improvements. Introduced in Scrum methodology by Schwaber (2002), velocity provides empirical data for realistic sprint planning and project timeline prediction. Teams use velocity trends to identify bottlenecks and validate process improvements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ken Schwaber and Mike Cohn","subfamily":"Project management","year":"2002","type":"measurement metric"},"citations":[{"ref":"Schwaber, K., & Beedle, M. (2002). Agile Software Development with Scrum. Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/agilesoftwaredevelopmentwithscrum"},{"ref":"Cohn, M. (2005). Agile Estimating and Planning. Prentice Hall PTR.","type":"book","doi":null,"isbn":null,"url":"https://www.mountaingoatsoftware.com/books/agile-estimating-and-planning"},{"ref":"McCartney, P., & Hough, R. (2008). Velocity tracking in agile software development. Journal of Software Engineering Research and Development, 1(2), 25–40.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Velocity+tracking+in+agile+software+development"}],"related":["technical-debt-measurement","code-coverage-analysis","software-complexity-metrics","defect-prediction-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agoraphobia-cognitions-questionnaire","name":"Agoraphobia Cognitions Questionnaire","fullName":"Agoraphobia Cognitions Questionnaire (ACQ)","aliases":["ACQ"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"agoraphobia-cognitions","year":1984,"originator":"Dianne L. Chambless and colleagues","url":"https://scholargate.app/en/anxiety-disorders/agoraphobia-cognitions-questionnaire","markdownUrl":"https://scholargate.app/en/anxiety-disorders/agoraphobia-cognitions-questionnaire.md","definition":"The Agoraphobia Cognitions Questionnaire (ACQ) is a 14-item self-report instrument that assesses catastrophic and safety-related thoughts in individuals with agoraphobia and panic disorder. Developed by Chambless and colleagues in 1984, it measures two domains: fear of loss of control and worry about social consequences. The ACQ is a cornerstone measure in clinical research and practice for understanding the cognitive mechanisms that maintain agoraphobic avoidance and panic-related anxiety.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dianne L. Chambless and colleagues","subfamily":"agoraphobia-cognitions","year":1984,"type":"Self-report"},"citations":[{"ref":"Chambless, D. L., Caputo, G. C., Bright, P., & Gallagher, R. (1984). Assessment of fear in agoraphobics: The Body Sensations Questionnaire and the Agoraphobia Cognitions Questionnaire. Journal of Consulting and Clinical Psychology, 52(6), 1090–1097.","type":"article","doi":"10.1037/0022-006X.52.6.1090","isbn":null,"url":null}],"related":["body-sensations-questionnaire","anxiety-sensitivity-index","contamination-obsessions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"agrometeorological-yield-model","name":"Agrometeorological Yield Model","fullName":"Agrometeorological Crop Yield Prediction Model","aliases":["crop yield model","agroclimatic yield model","weather-based yield model","meteorological crop model"],"domain":"agronomy","family":"process-pipeline","subfamily":"Crop science / agricultural meteorology","year":"1960s–1980s (systematic development; FAO frameworks 1979)","originator":"Multiple contributors (FAO, USDA, Wageningen University researchers)","url":"https://scholargate.app/en/agronomy/agrometeorological-yield-model","markdownUrl":"https://scholargate.app/en/agronomy/agrometeorological-yield-model.md","definition":"An agrometeorological yield model is a quantitative framework that relates observed or forecasted weather variables — temperature, precipitation, solar radiation, humidity — to the final grain or biomass yield of a crop. Grounded in plant physiology and agricultural climatology, the approach is used worldwide in food security monitoring, insurance underwriting, irrigation planning, and climate-change impact assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (FAO, USDA, Wageningen University researchers)","year":"1960s–1980s (systematic development; FAO frameworks 1979)","type":"Quantitative predictive modelling","dataType":"Meteorological observations, crop phenology records, soil data, remote-sensing indices","subfamily":"Crop science / agricultural meteorology"},"citations":[{"ref":"Doorenbos, J., & Kassam, A. H. (1979). Yield Response to Water. FAO Irrigation and Drainage Paper No. 33. Food and Agriculture Organization of the United Nations, Rome.","type":"report","doi":null,"isbn":null,"url":"https://www.fao.org/3/Y4816E/y4816e00.htm"},{"ref":"Lobell, D. B., & Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150(11), 1443-1452.","type":"article","doi":"10.1016/j.agrformet.2010.07.008","isbn":null,"url":null}],"related":["crop-simulation-model","remote-sensing-vegetation-index","soil-water-balance-model","regression-analysis","time-series-analysis","climate-impact-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ahp-bocr","name":"AHP-BOCR","fullName":"Analytical Hierarchy Process with Benefits, Opportunities, Costs, and Risks (AHP-BOCR)","aliases":["AHP-BOCR","Strategic Criteria AHP"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2008","originator":"Thomas L. Saaty (AHP); BOCR framework development extended AHP","url":"https://scholargate.app/en/decision-making/ahp-bocr","markdownUrl":"https://scholargate.app/en/decision-making/ahp-bocr.md","definition":"AHP-BOCR is an extension of the Analytic Hierarchy Process that incorporates strategic perspectives through the BOCR framework: Benefits, Opportunities, Costs, and Risks. Instead of optimizing a single objective, AHP-BOCR decomposes decisions into four strategic dimensions and uses a formula (Benefits × Opportunities) / (Costs × Risks) to synthesize a strategic priority. This approach is particularly suited to long-term, complex decisions with multiple stakeholder perspectives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas L. Saaty (AHP); BOCR framework development extended AHP","subfamily":"Ranking","year":"2008","type":"Hierarchical pairwise comparison with strategic perspective"},"citations":[{"ref":"Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98.","type":"article","doi":"10.1504/ijssci.2008.017590","isbn":null,"url":null},{"ref":"Subagyo, B., & Tjahyono, T. (2010). Multiple criteria decision making in selecting strategic decisions. Journal of Organizational Computing and Electronic Commerce, 20(2), 200-216.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1080/10919392.2010.481903"}],"related":["ahp","fuzzy-ahp","bwm","maut","promethee"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ahp","name":"AHP","fullName":"Analytic Hierarchy Process","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1980","originator":"Saaty, T. L.","url":"https://scholargate.app/en/decision-making/ahp","markdownUrl":"https://scholargate.app/en/decision-making/ahp.md","definition":"AHP (Analytic Hierarchy Process) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Saaty, T. L. in 1980. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saaty, T. L.","subfamily":"Weight_Subjective","year":"1980","type":"Pairwise comparison (eigenvalue)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":true},"citations":[{"ref":"Saaty, T. L. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York","type":"article","doi":null,"isbn":"978-0070543713","url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ahpsort","name":"AHPSORT","fullName":"AHPSort — AHP-based classification of alternatives into ordered categories","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"2012","originator":"Ishizaka, A., Nemery, P., Pearman, C.","url":"https://scholargate.app/en/decision-making/ahpsort","markdownUrl":"https://scholargate.app/en/decision-making/ahpsort.md","definition":"AHPSORT (AHPSort — AHP-based classification of alternatives into ordered categories) is a sorting multi-criteria decision-making (MCDM) method introduced by Ishizaka, A., Nemery, P., Pearman, C. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ishizaka, A., Nemery, P., Pearman, C.","subfamily":"Sorting","year":"2012","type":"AHP-based sorting with limiting or central profiles","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":true},"citations":[{"ref":"Ishizaka, A., Nemery, P., Pearman, C. (2012). AHPSort: An AHP-based method for sorting problems. International Journal of Production Research","type":"article","doi":"10.1080/00207543.2012.657966","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ahrs","name":"AHRS","fullName":"Attitude Heading Reference System","aliases":["AHRS system","attitude reference","heading sensor"],"domain":"aerospace","family":"process-pipeline","subfamily":"Attitude Estimation","year":"1940s","originator":"Aviation heritage","url":"https://scholargate.app/en/aerospace/ahrs","markdownUrl":"https://scholargate.app/en/aerospace/ahrs.md","definition":"An Attitude Heading Reference System (AHRS) is a complete inertial navigation subsystem that estimates and outputs the three-dimensional orientation (attitude) and heading of a vehicle or platform. AHRS combines measurements from accelerometers, gyroscopes, and often magnetometers through sensor fusion algorithms (typically Kalman filters or complementary filters) to provide a drift-free, fast attitude estimate. AHRS is standard in aviation, marine navigation, and modern autonomous systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aviation heritage","subfamily":"Attitude Estimation","year":"1940s","type":"System"},"citations":[{"ref":"Savage, P. G. (2007). Strapdown Inertial Integration Technology (2nd ed.). Strapdown Associates.","type":"book","doi":null,"isbn":null,"url":"https://www.strapdownassociates.com"},{"ref":"Titterton, D. H., & Weston, J. L. (2004). Strapdown Inertial Navigation Technology (2nd ed.). Institution of Engineering and Technology.","type":"book","doi":"10.1049/PBRA017E","isbn":null,"url":null},{"ref":"Madgwick, S. O. H., Harrison, A. J. L., & Vaidyanathan, R. (2011). Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE International Conference on Rehabilitation Robotics (ICORR), 1–7.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/5975346"}],"related":["madgwick-filter","mahony-filter","quaternion-attitude"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ahspr","name":"AHSPR","fullName":"Asymmetric Hesitant Fuzzy Sigmoid Preference Relations (Zhou-Xu 2016)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Preference_Relations","year":"2016","originator":"Zhou, W. Xu, Z.","url":"https://scholargate.app/en/decision-making/ahspr","markdownUrl":"https://scholargate.app/en/decision-making/ahspr.md","definition":"AHSPR (Asymmetric Hesitant Fuzzy Sigmoid Preference Relations (Zhou-Xu 2016)) is a preference relations multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, W. Xu, Z.","subfamily":"Preference_Relations","year":"2016","type":"Hesitant fuzzy AHP-style method. Input is a pairwise hesitant fuzzy continuous preference term matrix (HFCPT) per criterion — NOT a standard decision matrix. An asymmetric sigmoid numerical scale (ASNS) parametrised by risk-appetite parameters h1 (preference) and h2 (non-preference) maps raw preference terms to [0,1] scores. Priority vectors are derived via the closed-form Approximate Translation Method (ATM, Theorem 2.5). Final ranking aggregates per-criterion priority vectors via hesitant fuzzy weighted operators.","value_space":"hesitant_fuzzy","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Zhou, W., Xu, Z. (2016). Asymmetric hesitant fuzzy sigmoid preference relations in the analytic hierarchy process. Information Sciences","type":"article","doi":"10.1016/j.ins.2016.04.003","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"air-dispersion-modeling","name":"Air Dispersion Modeling","fullName":"Atmospheric Dispersion and Transport of Air Pollutants","aliases":["air quality modeling","plume modeling","atmospheric transport","emission dispersion"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Atmospheric transport modeling","year":"1961","originator":"Pasquill and Gifford","url":"https://scholargate.app/en/environmental-engineering/air-dispersion-modeling","markdownUrl":"https://scholargate.app/en/environmental-engineering/air-dispersion-modeling.md","definition":"Air dispersion modeling is a quantitative method to predict the concentration and deposition of air pollutants (dust, gases, particulates) released from industrial sources, traffic, or combustion. Developed empirically by Pasquill and Gifford in the 1960s and formalized into the Gaussian plume model, these methods predict ground-level concentration downwind of a source using wind speed, stability class, source height, and meteorological data. Air dispersion models are essential tools for regulatory compliance, emission permitting, and exposure assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pasquill and Gifford","subfamily":"Atmospheric transport modeling","year":"1961","type":"mathematical simulation pipeline"},"citations":[{"ref":"Pasquill, F. (1974). Atmospheric Diffusion: The Dispersion of Windborne Material from Industrial and Other Sources (2nd ed.). Ellis Horwood Limited.","type":"article","doi":null,"isbn":"978-0470657034","url":null},{"ref":"Turner, D. B. (1994). Workbook of Atmospheric Dispersion Estimates (2nd ed.). US EPA Office of Air Quality Planning and Standards.","type":"article","doi":null,"isbn":null,"url":"https://www.epa.gov/scram"},{"ref":"Seinfeld, J. H., & Pandis, S. N. (2016). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-1118947401","url":null}],"related":["noise-mapping","environmental-impact-assessment","green-infrastructure-design","carbon-footprint-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"akaike-information-criterion","name":"Akaike Information Criterion","fullName":"Akaike Information Criterion","aliases":["AIC"],"domain":"model-evaluation","family":"mcdm","subfamily":"Information-theoretic criterion","year":"1974","originator":"Hirotugu Akaike","url":"https://scholargate.app/en/model-evaluation/akaike-information-criterion","markdownUrl":"https://scholargate.app/en/model-evaluation/akaike-information-criterion.md","definition":"The Akaike Information Criterion is an information-theoretic measure for model selection that balances goodness of fit against model complexity. Introduced by Hirotugu Akaike in 1974, AIC estimates the relative quality of models for a given dataset, penalizing additional parameters to prevent overfitting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hirotugu Akaike","subfamily":"Information-theoretic criterion","year":"1974","type":"Model selection metric"},"citations":[{"ref":"Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.","type":"article","doi":"10.1109/TAC.1974.1100705","isbn":null,"url":null},{"ref":"Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.). New York: Springer.","type":"book","doi":"10.2307/3802723","isbn":null,"url":null},{"ref":"Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.","type":"article","doi":"10.1214/aoms/1177729694","isbn":null,"url":null}],"related":["bayesian-information-criterion","r-squared","adjusted-r-squared","mean-squared-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"alamouti-code","name":"Alamouti Code","fullName":"Alamouti Space-Time Block Code","aliases":["space-time coding","transmit diversity"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Signal processing","year":"1998","originator":"Siavash Alamouti","url":"https://scholargate.app/en/telecommunications/alamouti-code","markdownUrl":"https://scholargate.app/en/telecommunications/alamouti-code.md","definition":"The Alamouti code is an elegant space-time coding scheme that provides full transmit diversity using two antennas and a simple linear receiver. Introduced by Siavash Alamouti in 1998, it requires no channel state information at the transmitter, achieves the same bit-error rate as a single-antenna system with receiver diversity, and uses linear processing for decoding. The Alamouti code has become the de facto standard for transmit diversity in cellular systems and is adopted in LTE, WiFi, and many 5G protocols.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Siavash Alamouti","subfamily":"Signal processing","year":"1998","type":"space-time coding scheme"},"citations":[{"ref":"Alamouti, S. M. (1998). A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications, 16(8), 1451-1458.","type":"article","doi":"10.1109/49.730453","isbn":null,"url":null},{"ref":"Tarokh, V., Jafarkhani, H., & Calderbank, A. R. (1999). Space-time block codes from orthogonal designs. IEEE Transactions on Information Theory, 45(5), 1456-1467.","type":"article","doi":"10.1109/18.771146","isbn":null,"url":null}],"related":["mimo","ofdm","turbo-code","ldpc-codes","shannon-capacity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"alcohol-dependence-scale","name":"Alcohol Dependence Scale","fullName":"Alcohol Dependence Scale (ADS)","aliases":["ADS"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Alcohol dependence severity assessment","year":"1982","originator":"Harvey A. Skinner","url":"https://scholargate.app/en/psychiatry/alcohol-dependence-scale","markdownUrl":"https://scholargate.app/en/psychiatry/alcohol-dependence-scale.md","definition":"The ADS is a 25-item self-report scale designed to measure the severity of alcohol dependence symptoms according to the alcohol dependence syndrome concept. Developed by Skinner and Allen in 1982, it focuses on dependence-specific features (withdrawal, tolerance, loss of control, continued use despite harm) rather than social consequences alone. The ADS is widely used in addiction medicine, treatment outcome research, and clinical settings to assess dependence severity, guide detoxification planning, and track treatment response in individuals with alcohol use disorder.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey A. Skinner","subfamily":"Alcohol dependence severity assessment","year":"1982","type":"Self-report questionnaire"},"citations":[{"ref":"Skinner, H. A., & Allen, B. A. (1982). Alcohol dependence syndrome: measurement and validation. Journal of Abnormal Psychology, 91(3), 199–209.","type":"article","doi":"10.1037/0021-843X.91.3.199","isbn":null,"url":null},{"ref":"Skinner, H. A. (1984). The Drug Abuse Screening Test. Addictive Behaviors, 9(4), 385–391.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Drug+Abuse+Screening+Test+Skinner"},{"ref":"Kivlahan, D. R., Sher, K. J., & Donovan, D. M. (1989). The Alcohol Dependence Scale: A measure of the severity of alcohol dependence syndrome. Journal of Studies on Alcohol, 50(2), 131–139.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Alcohol+Dependence+Scale%3A+A+measure+of+the+severity+of+alcohol+dependence+syndrome+Kivlahan"}],"related":["michigan-alcoholism-screening","addiction-severity-index","brief-psychiatric-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"alcohol-urge-questionnaire","name":"AUQ","fullName":"Alcohol Urge Questionnaire","aliases":["AUQ"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"craving-assessment","year":"1995","originator":"Bohn, Krahn, Staehler","url":"https://scholargate.app/en/addiction-medicine/alcohol-urge-questionnaire","markdownUrl":"https://scholargate.app/en/addiction-medicine/alcohol-urge-questionnaire.md","definition":"The AUQ is an 8-item self-report instrument that measures the intensity of urges and desire to drink alcohol. Developed by Bohn, Krahn, and Staehler in 1995, it is designed to assess craving in individuals with alcohol use disorder who are abstaining or attempting to reduce drinking. The AUQ is a brief, validated tool useful in alcohol treatment and research settings to predict relapse risk and monitor treatment progress.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bohn, Krahn, Staehler","subfamily":"craving-assessment","year":"1995","type":"Self-report"},"citations":[{"ref":"Bohn, M. J., Krahn, D. D., & Staehler, B. A. (1995). Development and initial validation of a measure of drinking urges in abstinent men. Journal of Studies on Alcohol, 56(2), 168–173.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+initial+validation+of+a+measure+of+drinking+urges+in+abstinent+men+Bohn"}],"related":["sadq","craving-questionnaire","brief-addiction-monitor","readiness-to-change-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"alexithymia-scale","name":"Toronto Alexithymia Scale","fullName":"Toronto Alexithymia Scale (TAS-20)","aliases":["TAS-20","TAS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"emotion-identification-assessment","year":"1994","originator":"R. Michael Bagby, James D. A. Parker, Graeme J. Taylor","url":"https://scholargate.app/en/clinical-psychology/alexithymia-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/alexithymia-scale.md","definition":"The TAS-20 is a 20-item self-report measure of alexithymia, the difficulty identifying and describing emotions. Developed by Bagby, Parker, and Taylor in 1994, it is the most widely used alexithymia measure in clinical and research practice. Alexithymia is recognized as a transdiagnostic feature across substance use, eating disorders, depression, anxiety, and somatic symptom disorders, making the TAS-20 valuable for identifying emotion processing deficits that complicate treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. Michael Bagby, James D. A. Parker, Graeme J. Taylor","subfamily":"emotion-identification-assessment","year":"1994","type":"Self-report questionnaire"},"citations":[{"ref":"Bagby, R. M., Parker, J. D., & Taylor, G. J. (1994). The twenty-item Toronto Alexithymia Scale: I. Item selection and cross-validation of the factor structure. Journal of Psychosomatic Research, 38(1), 23–32.","type":"article","doi":"10.1016/0022-3999(94)90005-1","isbn":null,"url":null}],"related":["emotion-regulation-questionnaire","difficulties-emotion-regulation","depersonalization-derealization-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"alexnet","name":"AlexNet","fullName":"AlexNet (Krizhevsky–Sutskever–Hinton Deep Convolutional Neural Network)","aliases":["AlexNet","Krizhevsky net","SuperVision CNN","ImageNet CNN 2012","deep convolutional neural network (AlexNet)"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2012,"originator":"Krizhevsky, A.; Sutskever, I.; Hinton, G. E.","url":"https://scholargate.app/en/deep-learning/alexnet","markdownUrl":"https://scholargate.app/en/deep-learning/alexnet.md","definition":"AlexNet is a deep convolutional neural network (CNN) introduced by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) with a top-5 error rate of 15.3%, outstripping the runner-up by more than 10 percentage points and reigniting broad interest in deep learning. The architecture introduced or popularised several techniques — ReLU activations, dropout regularisation, and multi-GPU training — that became standard practice across the field.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Krizhevsky, A.; Sutskever, I.; Hinton, G. E.","year":2012,"type":"Deep Convolutional Neural Network (CNN)","task":"Image classification (and general visual recognition)","layers":8,"trainableParams":"~60 million","datasetUsed":"ImageNet LSVRC-2012 (1.2 million images, 1000 classes)","top5ErrorRate":"15.3% (vs. 26.2% runner-up at ILSVRC 2012)","keyInnovations":"ReLU activation, dropout regularisation, GPU-accelerated training, data augmentation, local response normalisation"},"citations":[{"ref":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1097–1105. (Republished: Communications of the ACM, 60(6), 84–90, 2017.)","type":"article","doi":"10.1145/3065386","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null},{"ref":"LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep Learning. Nature, 521, 436–444.","type":"article","doi":"10.1038/nature14539","isbn":null,"url":null}],"related":["vgg-net","googlenet","resnet","lenet","convolutional-neural-network","dropout","batch-normalization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aligned-ranks-transform-anova","name":"Aligned Rank Transform ANOVA","fullName":"Aligned Rank Transform ANOVA (ART-ANOVA)","aliases":["ART-ANOVA","aligned ranks ANOVA","nonparametric factorial ANOVA","Hizalanmış Sıra Dönüşümü ANOVA (ART-ANOVA)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":2011,"originator":"Wobbrock, Findlater, Gergle & Higgins","url":"https://scholargate.app/en/statistics/aligned-ranks-transform-anova","markdownUrl":"https://scholargate.app/en/statistics/aligned-ranks-transform-anova.md","definition":"The Aligned Rank Transform ANOVA (ART-ANOVA) is a nonparametric factorial hypothesis test that detects main effects and interactions in designs with two or more independent variables, without requiring normality. The procedure was formalized by Wobbrock, Findlater, Gergle, and Higgins in their 2011 CHI paper and operates by separately aligning each effect before ranking, so that standard ANOVA machinery can be applied to nonparametric data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wobbrock, Findlater, Gergle & Higgins","year":2011,"family":"Hypothesis test","type":"Nonparametric factorial hypothesis test","minGroups":3,"minFactors":2,"minSample":20,"parametric":false,"outcome":"continuous or ordinal","requiresNormality":false,"handlesInteractions":true},"citations":[{"ref":"Wobbrock, J. O., Findlater, L., Gergle, D., & Higgins, J. J. (2011). The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2011), 143–146.","type":"article","doi":"10.1145/1978942.1978963","isbn":null,"url":null}],"related":["friedman-test","kruskal-wallis","two-way-anova","factorial-design","repeated-measures-anova","welch-anova","robust-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"alizarin-red-staining","name":"Alizarin Red Staining","fullName":"Alizarin Red S Mineralization Assay","aliases":["alizarin red-S","calcium staining","bone mineralization assay"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Mineralization assessment","year":"2004","originator":"Gregory, Gunn, Peister","url":"https://scholargate.app/en/biomaterials/alizarin-red-staining","markdownUrl":"https://scholargate.app/en/biomaterials/alizarin-red-staining.md","definition":"Alizarin red-S (1,2-dihydroxyanthraquinone-3-sulfonic acid) is a calcium-binding dye that forms a colored complex with mineralized deposits, enabling direct visualization and quantification of bone matrix mineralization. Developed as a standard assay by Gregory and colleagues in 2004, alizarin red staining is widely used to evaluate osteogenic differentiation of stem cells, assess the mineralization-promoting effects of biomaterial scaffolds and growth factors, and measure the calcium content of bone tissue and engineered constructs. The assay is rapid, quantitative, and provides both visual and colorimetric readout.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory, Gunn, Peister","subfamily":"Mineralization assessment","year":"2004","type":"Staining assay"},"citations":[{"ref":"Gregory, C. A., Gunn, W. G., Peister, A., & Prockop, D. J. (2004). An Alizarin red-based assay of mineralization by adherent cells in culture: comparison with cetylpyridinium chloride extraction. Analytical Biochemistry, 329(1), 77-84.","type":"article","doi":"10.1016/j.ab.2004.02.002","isbn":null,"url":null},{"ref":"Langenbach, F., & Handschel, J. (2016). Effects of dexamethasone, ascorbic acid and beta-glycerophosphate on the osteogenic differentiation of stem cells in vitro. Stem Cell Reviews and Reports, 9(3), 355-365.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Effects+of+dexamethasone%2C+ascorbic+acid+and+beta-glycerophosphate+on+the+osteogenic+differentiation+of+stem+cells+in+vitro+Langenbach"},{"ref":"Ozdamar, U., Kutlu, A., Aydin, E., et al. (2011). Comparison of osteogenic gene expression and mineralization of primary and precursor osteoblasts. Biochemistry and Biophysics Reports, 52(4), 565-571.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparison+of+osteogenic+gene+expression+and+mineralization+of+primary+and+precursor+osteoblasts+Ozdamar"}],"related":["picrosirius-red-staining","bmp-release","live-dead-assay","mtt-mts-assay"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"allometric-biomass-equation","name":"Allometric Biomass Equation","fullName":"Tree Biomass Estimation and Allometric Model Development","aliases":["Biomass allometry","Regression-based biomass prediction","Diameter-to-biomass conversion"],"domain":"forestry","family":"process-pipeline","subfamily":"Forest biomass quantification and allometry","year":"1990s–2010s","originator":"Chave, Niklas, and forest biometricians","url":"https://scholargate.app/en/forestry/allometric-biomass-equation","markdownUrl":"https://scholargate.app/en/forestry/allometric-biomass-equation.md","definition":"Allometric equations predict tree above-ground or total biomass from easily measured tree dimensions—typically diameter at breast height (DBH), height, and wood density. Grounded in biological allometry (scaling laws) and codified by Chave, Niklas, and others, allometric equations are essential tools for rapid biomass assessment without tree harvesting. Used globally for carbon accounting, yield estimation, and ecosystem characterization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chave, Niklas, and forest biometricians","subfamily":"Forest biomass quantification and allometry","year":"1990s–2010s","type":"Model development and application pipeline"},"citations":[{"ref":"Chave, J., Andalo, C., Brown, S., et al. (2005). Tree Allometry and Improved Estimation of Carbon-Stock and Density in Tropical Forests. Oecologia, 145(1), 87–99.","type":"article","doi":"10.1007/s00442-005-0100-x","isbn":null,"url":null},{"ref":"Niklas, K. J. (1994). Plant Allometry: The Scaling of Form and Process. University of Chicago Press.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/plantallometry"},{"ref":"West, G. B., Woodruff, W. H., & Brown, J. H. (2002). Allometric Scaling of Metabolic Rate from Molecules and Mitochondria to Cells and Mammals. Proceedings of the National Academy of Sciences, 99(Supplement 1), 2473–2478.","type":"article","doi":"10.1073/pnas.012579799","isbn":null,"url":null},{"ref":"Feldpausch, T. R., Lloyd, J., Lewis, S. L., et al. (2012). Tree Height Integrated into Pantropical Forest Biomass Estimates. Biogeosciences, 9(8), 3381–3403.","type":"article","doi":"10.5194/bg-9-3381-2012","isbn":null,"url":null}],"related":["carbon-stock-estimation-forest","tree-height-measurement","stand-basal-area-measurement","forest-inventory-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"allometric-pk-scaling","name":"Allometric PK Scaling","fullName":"Allometric Pharmacokinetic Scaling","aliases":["allometric scaling","inter-species extrapolation","FIH dose prediction"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Translational Pharmacokinetics","year":"1989","originator":"John Mordenti","url":"https://scholargate.app/en/pharmacology/allometric-pk-scaling","markdownUrl":"https://scholargate.app/en/pharmacology/allometric-pk-scaling.md","definition":"Allometric scaling is a mathematical approach for predicting human pharmacokinetics from preclinical animal data using body weight relationships. Developed systematically by Mordenti and colleagues in the late 1980s, it enables rational first-in-human dose prediction without assuming species-specific metabolic differences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Mordenti","subfamily":"Translational Pharmacokinetics","year":"1989","type":"inter-species extrapolation"},"citations":[{"ref":"Mordenti, J., & Chappell, W. (1989). The use of allometric scaling in toxicokinetic studies. Fundamental and Applied Toxicology, 13(2), 335-346.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+use+of+allometric+scaling+in+toxicokinetic+studies+Mordenti"},{"ref":"Feng, M. R., Chiang, S. T., & Grammatoglou, G. (2011). Allometric scaling of blood clearance from preclinical species to humans. Journal of Pharmaceutical and Biomedical Analysis, 21(2), 195-205.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Allometric+scaling+of+blood+clearance+from+preclinical+species+to+humans+Feng"}],"related":["physiologically-based-pharmacokinetics","michaelis-menten-kinetics","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"alsfrs-r","name":"ALSFRS-R","fullName":"ALS Functional Rating Scale Revised","aliases":["ALS FRS-R","Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised"],"domain":"neurology","family":"process-pipeline","subfamily":"disease-specific functional rating","year":"1999","originator":"James M. Cedarbaum, NIH/NINDS","url":"https://scholargate.app/en/neurology/alsfrs-r","markdownUrl":"https://scholargate.app/en/neurology/alsfrs-r.md","definition":"The ALSFRS-R is a 12-item clinician-administered functional rating scale designed to assess disease progression and functional status in amyotrophic lateral sclerosis (ALS). Introduced by Cedarbaum and colleagues in 1999, it expands upon the original ALSFRS by incorporating respiratory function assessment. It is the primary outcome measure in ALS clinical trials and routine clinical monitoring, providing quantitative tracking of disease decline that predicts survival and guides management decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James M. Cedarbaum, NIH/NINDS","subfamily":"disease-specific functional rating","year":"1999","type":"Clinician-rated and patient-reported hybrid"},"citations":[{"ref":"Cedarbaum, J. M., Stambler, N., Malta, E., Fuller, C., Hilt, D., Thurmond, B., & Nakanishi, A. (1999). The ALSFRS-R: A revised ALS functional rating scale that incorporates assessments of respiratory function. Journal of the Neurological Sciences, 169(1-2), 13-21.","type":"article","doi":"10.1016/S0022-510X(99)00210-5","isbn":null,"url":null}],"related":["modified-rankin-scale","msws-12","stroke-specific-qol","msqol-54"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"altman-z-score","name":"Altman Z-Score","fullName":"Altman Z-Score Bankruptcy Prediction","aliases":["Altman's Z-Score Model","Multiple Discriminant Analysis Bankruptcy Model","Z-Score Financial Distress Model","Altman Z-Skoru"],"domain":"finance","family":"regression-model","subfamily":"Financial distress","year":1968,"originator":"Edward Altman","url":"https://scholargate.app/en/finance/altman-z-score","markdownUrl":"https://scholargate.app/en/finance/altman-z-score.md","definition":"The Altman Z-Score is a linear discriminant model developed by Edward I. Altman in 1968 to predict corporate bankruptcy using five accounting-based financial ratios. Derived through multiple discriminant analysis on a matched sample of 66 US manufacturing firms, the model combines liquidity, profitability, leverage, solvency, and activity ratios into a single composite score that classifies firms as financially sound, distressed, or in a grey zone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward Altman","year":1968,"type":"Multiple discriminant analysis scoring model","subfamily":"Financial distress","originalSample":"66 US manufacturing firms (33 bankrupt, 33 non-bankrupt)","predictionHorizon":"Up to 2 years prior to bankruptcy"},"citations":[{"ref":"Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.","type":"article","doi":"10.1111/j.1540-6261.1968.tb00843.x","isbn":null,"url":null}],"related":["linear-discriminant-analysis","credit-scoring","beneish-m-score"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"altmetrics","name":"Altmetrics and Article-Level Metrics","fullName":"Alternative Metrics for Assessing Research Impact Beyond Citations","aliases":["altmetrics","article-level metrics","alternative impact metrics"],"domain":"research-skills","family":"process-pipeline","subfamily":"research-impact-assessment","year":"2010 (concept manifesto); 2011 (Altmetric.com platform launch)","originator":"Jason Priem and the altmetrics community (2010)","url":"https://scholargate.app/en/research-skills/altmetrics","markdownUrl":"https://scholargate.app/en/research-skills/altmetrics.md","definition":"Altmetrics (alternative metrics) measure the online attention and societal impact of research by tracking mentions in social media (Twitter), news outlets, policy documents, blogs, videos, and other online sources. Introduced formally in 2010 by Jason Priem and colleagues, altmetrics address limitations of citation-based assessment: citation counts accumulate slowly (taking years for impact to register), do not capture policy influence, and are biased toward certain fields (biomedicine receives more citations than social sciences). Altmetric.com, PlumX, and other platforms now provide real-time data on research reach, complementing traditional journal impact factors and H-indices. While altmetrics should not replace peer-reviewed citations for tenure and promotion, they offer valuable insight into public engagement with research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jason Priem and the altmetrics community (2010)","subfamily":"research-impact-assessment","year":"2010 (concept manifesto); 2011 (Altmetric.com platform launch)","type":"Tool"},"citations":[{"ref":"Priem, J., Taraborelli, D., Groth, P., & Neylon, C. (2010). Altmetrics: A manifesto. http://altmetrics.org/manifesto/","type":"article","doi":null,"isbn":null,"url":"http://altmetrics.org/manifesto/"},{"ref":"Piwowar, H., Priem, J., & Larivière, V. (2018). The state of OA: a large-scale analysis of the prevalence and impact of open access articles. PeerJ, 6, e4375.","type":"article","doi":"10.7717/peerj.4375","isbn":null,"url":null},{"ref":"Larivière, V., Gingras, Y., & Archambault, E. (2009). The decline in the concentration of citations, 1900–2007. Journal of the American Society for Information Science and Technology, 60(4), 858–862.","type":"article","doi":"10.1002/asi.21011","isbn":null,"url":null}],"related":["citation-analysis","doi-system","orcid-researcher-id","citation-management-tools"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ambient-noise-tomography","name":"Ambient Noise Tomography","fullName":"Ambient Noise Tomography","aliases":["ANT"],"domain":"geophysics","family":"process-pipeline","subfamily":"Seismic surface wave imaging","year":"2005","originator":"Shapiro, Campillo, Stehly, and Ritzwoller","url":"https://scholargate.app/en/geophysics/ambient-noise-tomography","markdownUrl":"https://scholargate.app/en/geophysics/ambient-noise-tomography.md","definition":"Ambient Noise Tomography (ANT) is a seismic imaging method that extracts surface wave information from long-term records of seismic background noise, enabling high-resolution imaging of crustal and upper mantle structure. Developed by Shapiro, Campillo, and colleagues in 2005, ANT has revolutionized seismic imaging by enabling detailed crustal velocity maps at minimal cost without requiring earthquakes or active sources.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shapiro, Campillo, Stehly, and Ritzwoller","subfamily":"Seismic surface wave imaging","year":"2005","type":"Passive seismic imaging via correlation of ambient noise"},"citations":[{"ref":"Shapiro, N. M., Campillo, M., Stehly, L., & Ritzwoller, M. H. (2005). High-resolution surface-wave tomography from ambient seismic noise. Science, 307(5715), 1615-1618.","type":"article","doi":"10.1126/science.1108339","isbn":null,"url":null},{"ref":"Bensen, G. D., Ritzwoller, M. H., Barmin, M. P., et al. (2008). Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements. Geophysical Journal International, 169(3), 1239-1260.","type":"article","doi":"10.1111/j.1365-246X.2007.03374.x","isbn":null,"url":null}],"related":["receiver-function-analysis","seismic-full-waveform-inversion","ground-penetrating-radar"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ambisonics","name":"Ambisonics","fullName":"Ambisonics: Spatial Audio Encoding","aliases":["spatial audio","B-format","ambisonic recording"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Spatial Audio","year":"1973","originator":"Michael Gerzon","url":"https://scholargate.app/en/applied-physics/ambisonics","markdownUrl":"https://scholargate.app/en/applied-physics/ambisonics.md","definition":"Ambisonics is a full-sphere spatial audio encoding and reproduction technique that captures and reproduces three-dimensional sound fields. Developed by Michael Gerzon in the 1970s, it uses spherical harmonics to represent sound at all directions around a central point. Unlike surround systems that use discrete channels, Ambisonics provides a format-agnostic spatial representation that can be rotated, translated, and rendered to any speaker configuration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Gerzon","subfamily":"Spatial Audio","year":"1973","type":"Spatial audio encoding and reproduction technique"},"citations":[{"ref":"Gerzon, M. A. (1973). Periphony: with-height sound reproduction. Journal of the Audio Engineering Society, 21(1), 2-10.","type":"article","doi":null,"isbn":null,"url":"https://www.aes.org/e-lib/browse.cfm?elib=2031"},{"ref":"Rafaely, B. (2015). Fundamentals of Spherical Array Processing. Springer.","type":"book","doi":null,"isbn":"978-3-662-45664-4","url":null},{"ref":"Heller, A. J., Benjamin, E., & Lee, R. (2012). Is My Decoder Ambisonic? In Proceedings of the 125th AES Convention, San Francisco.","type":"article","doi":null,"isbn":null,"url":"https://www.aes.org/publications/elib/"}],"related":["mfcc","independent-vector-analysis","head-related-transfer-function"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ambivalent-sexism-inventory","name":"Ambivalent Sexism Inventory","fullName":"Ambivalent Sexism Inventory (ASI)","aliases":["ASI"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Social cognition","year":"1996","originator":"Peter Glick and Susan T. Fiske","url":"https://scholargate.app/en/social-psychology/ambivalent-sexism-inventory","markdownUrl":"https://scholargate.app/en/social-psychology/ambivalent-sexism-inventory.md","definition":"The Ambivalent Sexism Inventory (ASI) is a 22-item self-report measure developed by Peter Glick and Susan T. Fiske in 1996 to assess both hostile and benevolent sexism toward women. The scale captures the dual nature of sexism: overtly antagonistic attitudes and paternalistic but ultimately restrictive attitudes that present themselves as protective. It has become widely used in gender studies and organizational research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Glick and Susan T. Fiske","subfamily":"Social cognition","year":"1996","type":"Self-report Likert scale"},"citations":[{"ref":"Glick, P., & Fiske, S. T. (1996). The Ambivalent Sexism Inventory: Differentiating hostile and benevolent sexism. Journal of Personality and Social Psychology, 70(3), 491–512.","type":"article","doi":"10.1037/0022-3514.70.3.491","isbn":null,"url":null}],"related":["modern-racism-scale","social-dominance-orientation-scale","right-wing-authoritarianism-scale","cultural-values-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"amd-quality-of-life","name":"AMD-QoL","fullName":"Age-Related Macular Degeneration Quality of Life Scale","aliases":["AMD-QoL","Macular QoL"],"domain":"ophthalmology","family":"process-pipeline","subfamily":"age-related macular degeneration quality of life","year":"2005","originator":"Mitchell J, Bradley C et al.","url":"https://scholargate.app/en/ophthalmology/amd-quality-of-life","markdownUrl":"https://scholargate.app/en/ophthalmology/amd-quality-of-life.md","definition":"The AMD Quality of Life (AMD-QoL) scale is a disease-specific instrument designed to measure the impact of age-related macular degeneration on patient-reported health-related quality of life. Developed by Mitchell, Bradley, and colleagues (2005), the AMD-QoL addresses concerns unique to macular disease: central vision loss, reading difficulty, facial recognition, driving impairment, and emotional burden. It serves as a primary or secondary outcome in AMD clinical trials and informs patient-centered care decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mitchell J, Bradley C et al.","subfamily":"age-related macular degeneration quality of life","year":"2005","type":"Self-report"},"citations":[{"ref":"Mitchell, J., Bradley, C., Anderson, S. J., et al. (2005). Perceived impact of retinal impairment on quality of life: the AMD and Cataract Symptom Effect Questionnaire. Health Qual Life Outcomes, 3(1), 12.","type":"article","doi":"10.1186/1477-7525-3-25","isbn":null,"url":null},{"ref":"Submacular Surgery Trials (SST) Research Group. (2004). Quality of life in patients with subfoveal choroidal neovascularization due to age-related macular degeneration. Arch Ophthalmol, 122(11), 1635-1645.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quality+of+life+in+patients+with+subfoveal+choroidal+neovascularization+due+to+age-related+macular+degeneration+Submacular"}],"related":["nei-vfq-25","low-vision-quality-of-life","impact-vision-impairment","visual-function-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"amg-estimator","name":"Augmented Mean Group Estimator","fullName":"Augmented Mean Group (AMG) Estimator","aliases":["AMG estimator","augmented mean group","Artırılmış Ortalama Grup Tahmincisi (AMG)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2010,"originator":"Eberhardt & Teal; Bond & Eberhardt","url":"https://scholargate.app/en/econometrics/amg-estimator","markdownUrl":"https://scholargate.app/en/econometrics/amg-estimator.md","definition":"The Augmented Mean Group estimator, developed by Eberhardt and Teal (2010), is a panel data method for estimating heterogeneous slope coefficients in the presence of cross-sectional dependence. It approximates the unobserved common dynamic process driving all units and folds it into unit-by-unit regressions, then averages the results.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eberhardt & Teal; Bond & Eberhardt","year":2010,"type":"Heterogeneous panel data estimator","estimator":"Two-stage mean of unit-specific regressions augmented with a common dynamic process","outcome":"continuous","dataStructure":"panel (long T, large N)","minSample":50},"citations":[{"ref":"Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford.","type":"report","doi":null,"isbn":null,"url":"https://ora.ox.ac.uk/objects/uuid:ea831625-9014-40ec-abc5-516ecfbd2118"},{"ref":"Bond, S. & Eberhardt, M. (2013). Accounting for Unobserved Heterogeneity in Panel Time Series Models. Nuffield College Discussion Paper.","type":"report","doi":null,"isbn":null,"url":"https://www.nuffield.ox.ac.uk/economics/papers/2013/Bond%20and%20Eberhardt%202013.pdf"}],"related":["panel-fixed-effects","ols-regression","mean-group-estimator","ccemg-estimator","panel-random-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"amplitude-of-low-frequency-fluctuation","name":"Amplitude of Low-Frequency Fluctuation","fullName":"Amplitude of Low-Frequency Fluctuation (ALFF)","aliases":["ALFF","low-frequency oscillation amplitude"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Spectral power analysis","year":"2007","originator":"Long Xiao-Yan","url":"https://scholargate.app/en/neuroimaging/amplitude-of-low-frequency-fluctuation","markdownUrl":"https://scholargate.app/en/neuroimaging/amplitude-of-low-frequency-fluctuation.md","definition":"Amplitude of Low-Frequency Fluctuation (ALFF) is a resting-state fMRI metric that quantifies the strength of spontaneous low-frequency oscillations (typically 0.01–0.1 Hz) in the brain. Introduced by Yang and colleagues in 2007, ALFF provides a voxel-wise measure of local brain activity, reflecting the amplitude of spontaneous fluctuations in blood oxygen levels at rest.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Long Xiao-Yan","subfamily":"Spectral power analysis","year":"2007","type":"Resting-state fMRI amplitude analysis"},"citations":[{"ref":"Yang, H., Long, X. Y., Yang, Y., et al. (2007). Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. NeuroImage, 36(4), 773–781.","type":"article","doi":"10.1016/j.neuroimage.2007.01.054","isbn":null,"url":null},{"ref":"Zou, Q. H., Zhu, C. Z., Yang, Y., et al. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. Journal of Neuroscience Methods, 172(1), 137–141.","type":"article","doi":"10.1016/j.jneumeth.2008.04.012","isbn":null,"url":null}],"related":["regional-homogeneity","dynamic-functional-connectivity","voxel-based-morphometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"analysis-of-variance","name":"Analysis of Variance (ANOVA)","fullName":"Analysis of Variance","aliases":["ANOVA","F-test"],"domain":"research-statistics","family":"process-pipeline","subfamily":"parametric-hypothesis-testing","year":"1925","originator":"Ronald A. Fisher","url":"https://scholargate.app/en/research-statistics/analysis-of-variance","markdownUrl":"https://scholargate.app/en/research-statistics/analysis-of-variance.md","definition":"ANOVA is a parametric statistical method developed by Ronald A. Fisher in 1925 that tests whether means differ significantly across three or more independent groups. By partitioning total variance into between-group and within-group components, ANOVA determines whether observed differences are likely due to treatment effects or random variation, making it fundamental to comparative research across medicine, psychology, agriculture, and engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","subfamily":"parametric-hypothesis-testing","year":"1925","type":"Method"},"citations":[{"ref":"Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Statistical+Methods+for+Research+Workers+Fisher"},{"ref":"Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621.","type":"article","doi":"10.1080/01621459.1952.10483441","isbn":null,"url":null}],"related":["multiple-regression-analysis","nonparametric-tests","factor-analysis","multilevel-modeling"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"analytical-procedures-auditing","name":"Analytical Procedures in Auditing","fullName":"Analytical Procedures Framework for Audit Testing and Risk Assessment","aliases":["Analytical Review","Ratio Analysis","Trend Analysis"],"domain":"accounting","family":"mcdm","subfamily":"Audit Testing Techniques","year":"1983","originator":"American Institute of Certified Public Accountants (AICPA)","url":"https://scholargate.app/en/accounting/analytical-procedures-auditing","markdownUrl":"https://scholargate.app/en/accounting/analytical-procedures-auditing.md","definition":"Analytical procedures are evaluations of financial information made by studying plausible relationships among both financial and non-financial data. Rather than testing individual transactions, auditors develop expectations about what numbers should be and compare them to actual results, investigating significant differences. This approach is both required during audit planning and is often more cost-effective than detailed transaction testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Institute of Certified Public Accountants (AICPA)","subfamily":"Audit Testing Techniques","year":"1983","type":"Audit procedure methodology"},"citations":[{"ref":"American Institute of Certified Public Accountants (AICPA). (2015). Analytical Procedures. AU-C Section 520. AICPA Professional Standards.","type":"article","doi":null,"isbn":null,"url":"https://www.aicpa.org/resources/download/audit-standards-codification"},{"ref":"Arens, A. A., Elder, R. J., & Beasley, M. S. (2014). Auditing and assurance services (15th ed.). Pearson Education.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com/"}],"related":["audit-risk-model","jones-accrual-model","fraud-risk-assessment","internal-control-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ancestral-state-reconstruction","name":"Ancestral State Reconstruction","fullName":"Ancestral State Reconstruction using Phylogenetic Methods","aliases":["ASR","Ancestral character reconstruction","Trait reconstruction"],"domain":"genetics","family":"process-pipeline","subfamily":"Phylogenetic reconstruction","year":"1991","originator":"Wayne Maddison","url":"https://scholargate.app/en/genetics/ancestral-state-reconstruction","markdownUrl":"https://scholargate.app/en/genetics/ancestral-state-reconstruction.md","definition":"Ancestral state reconstruction (ASR) is a phylogenetic method that infers the character states (trait values or evolutionary features) of extinct ancestors by analyzing patterns of variation in extant (living) species. Developed by Wayne Maddison and colleagues in the 1990s, ASR uses the phylogenetic tree and observed trait variation in living species to estimate what ancestors possessed, enabling researchers to trace the evolutionary history of morphological, behavioral, ecological, and genomic traits.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wayne Maddison","subfamily":"Phylogenetic reconstruction","year":"1991","type":"Inference method"},"citations":[{"ref":"Maddison, W. P. (1991). Squared-change parsimony reconstructions of ancestral states for continuous-valued characters on a phylogenetic tree. Systematic Zoology, 40(3), 308–314.","type":"article","doi":"10.2307/2992324","isbn":null,"url":null},{"ref":"Schluter, D., Price, T., Mooers, A. O., & Ludwig, D. (1995). Likelihood of ancestor states in adaptive radiation. Evolution, 51(6), 1699–1711.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Likelihood+of+ancestor+states+in+adaptive+radiation+Schluter"},{"ref":"Pagel, M. (1999). Inferring the historical patterns of biological evolution. Nature, 401(6756), 877–884.","type":"article","doi":"10.1038/44766","isbn":null,"url":null}],"related":["phylogenetic-independent-contrasts","coalescent-theory","f-statistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"anchor-based-minimal-important-difference","name":"Anchor-Based Minimal Important Difference","fullName":"Anchor-Based Method for Establishing Minimal Important Difference (Minimal Clinically Important Difference) in Patient-Reported Outcomes","aliases":["MCID","Minimal clinically important difference","Anchor-based MCID","Minimal important change"],"domain":"psychometrics","family":"process-pipeline","subfamily":"Patient-reported outcome","year":"1989","originator":"Guyatt, Jaeschke, and Singer","url":"https://scholargate.app/en/psychometrics/anchor-based-minimal-important-difference","markdownUrl":"https://scholargate.app/en/psychometrics/anchor-based-minimal-important-difference.md","definition":"The anchor-based method for establishing Minimal Clinically Important Difference (MCID) is a technique for determining the smallest change in a patient-reported outcome (PRO) that patients or clinicians perceive as meaningful or important. Pioneered by Guyatt, Jaeschke, and Singer in 1989, this approach anchors changes in outcome scores to external clinically meaningful events or judgments, enabling researchers and clinicians to interpret whether treatment effects represent real, patient-relevant improvements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guyatt, Jaeschke, and Singer","subfamily":"Patient-reported outcome","year":"1989","type":"Minimal clinically important difference estimation"},"citations":[{"ref":"Jaeschke, R., Singer, J., & Guyatt, G. H. (1989). Measurement of health status: Ascertaining the minimal clinically important difference. Controlled Clinical Trials, 10(4), 407-415.","type":"article","doi":"10.1016/0197-2456(89)90005-6","isbn":null,"url":null},{"ref":"Revicki, D., Hays, R. D., Cella, D., & Sloan, J. (2008). Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. Journal of Clinical Epidemiology, 61(2), 102-109.","type":"article","doi":"10.1016/j.jclinepi.2007.03.012","isbn":null,"url":null},{"ref":"Copay, A. G., Chung, A. S., Pfeiffer, T., Borframes, R., Braswell, K., Chou, L. C., & Spangehl, M. J. (2007). Minimum clinically important difference: a review of nomenclature, methods, and applications in speech-language pathology. Journal of Medical Speech-Language Pathology, 15(4), xlii-xliii.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/29557987"}],"related":["likert-scale-construction","factor-analysis-scale","floor-ceiling-effect","content-validity-ratio"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ancova","name":"ANCOVA","fullName":"Analysis of Covariance","aliases":["analysis of covariance","covariance analysis","ANCOVA (Kovaryans Analizi)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1932,"originator":"Ronald A. Fisher","url":"https://scholargate.app/en/statistics/ancova","markdownUrl":"https://scholargate.app/en/statistics/ancova.md","definition":"ANCOVA is a parametric hypothesis test that compares the adjusted means of two or more independent groups while statistically controlling for one or more continuous covariates. By removing the portion of outcome variance explained by the covariate, ANCOVA increases statistical precision and produces fairer group comparisons. The method builds on the general linear model framework consolidated by Fisher in the early 1930s and is described comprehensively by Tabachnick and Fidell (2013).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":1932,"family":"Hypothesis test","type":"Parametric group comparison with covariate control","groups":"2+","outcome":"continuous","parametric":true,"distribution":"F","covariates":"1+ continuous","minSample":30},"citations":[{"ref":"Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0205849574","url":null}],"related":["one-way-anova","mancova","multiple-regression","welch-t-test","kruskal-wallis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"anderson-darling-test","name":"Anderson-Darling Test","fullName":"Anderson-Darling Normality (Goodness-of-Fit) Test","aliases":["Anderson-Darling Normallik Testi","A-squared test","AD test","Anderson-Darling goodness-of-fit test"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1952,"originator":"Anderson & Darling (1952); EDF tables by Stephens (1974)","url":"https://scholargate.app/en/statistics/anderson-darling-test","markdownUrl":"https://scholargate.app/en/statistics/anderson-darling-test.md","definition":"The Anderson-Darling test is an empirical distribution function (EDF) goodness-of-fit test, introduced by Anderson and Darling in 1952, that checks whether a continuous sample comes from a specified distribution such as the normal, exponential, or Weibull. By weighting deviations more heavily in the tails, it detects departures in the distribution's extremes more powerfully than the Kolmogorov-Smirnov test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anderson & Darling (1952); EDF tables by Stephens (1974)","year":1952,"type":"Empirical distribution function (EDF) goodness-of-fit test","estimator":"A² statistic weighting the squared CDF deviation by 1/[F(x)(1−F(x))]","minSample":8,"outcome":"continuous"},"citations":[{"ref":"Anderson, T. W., & Darling, D. A. (1952). Asymptotic Theory of Certain 'Goodness of Fit' Criteria Based on Stochastic Processes. The Annals of Mathematical Statistics, 23(2), 193-212.","type":"article","doi":"10.1214/aoms/1177729437","isbn":null,"url":null},{"ref":"Stephens, M. A. (1974). EDF Statistics for Goodness of Fit and Some Comparisons. Journal of the American Statistical Association, 69(347), 730-737.","type":"article","doi":"10.1080/01621459.1974.10480196","isbn":null,"url":null}],"related":["lilliefors-test","kolmogorov-smirnov-2sample","shapiro-wilk-test","fligner-killeen-test","mood-median-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"anderson-hsiao","name":"Anderson-Hsiao IV","fullName":"Anderson-Hsiao Instrumental Variables Estimator","aliases":["Anderson-Hsiao Estimator","AH IV Estimator","Dynamic Panel IV Estimator","Anderson-Hsiao Araçsal Değişken Tahmincisi"],"domain":"econometrics","family":"regression-model","subfamily":"Dynamic panel","year":1981,"originator":"Theodore Anderson & Cheng Hsiao","url":"https://scholargate.app/en/econometrics/anderson-hsiao","markdownUrl":"https://scholargate.app/en/econometrics/anderson-hsiao.md","definition":"The Anderson-Hsiao IV estimator is a method for consistently estimating dynamic panel data models that include a lagged dependent variable as a regressor. Proposed by Theodore Anderson and Cheng Hsiao in 1981, it resolves the Nickell bias that arises when fixed effects are eliminated by first-differencing, by instrumenting the differenced lagged dependent variable with its own second lag in levels or differences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Theodore Anderson & Cheng Hsiao","year":1981,"type":"Instrumental variables estimator for dynamic panel data","subfamily":"Dynamic panel","instrument_type":"Lagged levels or lagged differences of the dependent variable","consistency":"Consistent but not efficient relative to GMM"},"citations":[{"ref":"Anderson, T. W., & Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76(375), 598–606.","type":"article","doi":"10.1080/01621459.1981.10477691","isbn":null,"url":null}],"related":["arellano-bond-difference-gmm","system-gmm","instrumental-variables"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"anesthesia-risk-scoring-vet","name":"Anesthesia Risk Scoring in Veterinary Medicine","fullName":"Systematic Anesthesia Risk Stratification and Perioperative Assessment in Veterinary Surgery","aliases":["surgical risk scoring","preoperative assessment","ASA scoring"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Perioperative assessment","year":"1941-present","originator":"American Society of Anesthesiologists (ASA)","url":"https://scholargate.app/en/veterinary-medicine/anesthesia-risk-scoring-vet","markdownUrl":"https://scholargate.app/en/veterinary-medicine/anesthesia-risk-scoring-vet.md","definition":"Anesthesia risk scoring is a systematic preoperative assessment method that stratifies patient risk based on medical history, physical findings, and health status. Adapted from the American Society of Anesthesiologists Physical Status classification (developed for humans in 1941) and refined for veterinary species through confidential enquiry data and clinical research, it guides anesthetic technique selection, identifies high-risk patients requiring optimization, and predicts perioperative morbidity and mortality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Society of Anesthesiologists (ASA)","subfamily":"Perioperative assessment","year":"1941-present","type":"Risk assessment and stratification"},"citations":[{"ref":"American Society of Anesthesiologists (ASA) House of Delegates. (2020). ASA Physical Status Classification System. Retrieved from ASA official website: https://www.asahq.org/standards-and-guidelines/asa-physical-status-classification-system","type":"article","doi":null,"isbn":null,"url":"https://www.asahq.org"},{"ref":"Brodbelt, D. C., Pfeiffer, D. U., Young, L. E., Wood, J. L. (2007). Risk factors for anaesthetic-related death in cats: Results from the confidential enquiry into perioperative small animal fatalities (CEPSAF). British Journal of Anaesthesia, 99(5), 617-623.","type":"article","doi":"10.1093/bja/aem229","isbn":null,"url":null},{"ref":"Brodbelt, D. C., Pfeiffer, D. U., Young, L. E., Wood, J. L. (2008). Risk factors for anaesthetic-related death in dogs: Results from the confidential enquiry into perioperative small animal fatalities (CEPSAF). British Journal of Anaesthesia, 100(3), 320-329.","type":"article","doi":"10.1111/j.1467-2995.2008.00397.x","isbn":null,"url":null}],"related":["clinical-scoring-system-veterinary","body-condition-score-dog-cat","blood-gas-analysis-veterinary"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"animal-blup","name":"Animal BLUP","fullName":"Best Linear Unbiased Predictor for Livestock Breeding","aliases":["BLUP","breeding value prediction","genetic merit estimation"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Quantitative Genetics","year":"1949","originator":"Charles R. Henderson","url":"https://scholargate.app/en/veterinary-science/animal-blup","markdownUrl":"https://scholargate.app/en/veterinary-science/animal-blup.md","definition":"Animal BLUP (Best Linear Unbiased Predictor) is a statistical method for estimating the genetic merit (breeding values) of livestock based on their own performance and the performance of their relatives. Developed by Charles R. Henderson in 1949 and refined continuously since, Animal BLUP accounts for pedigree relationships, environmental effects, and non-additive genetic variance, providing accurate predictions of an animal's ability to transmit desirable traits to offspring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles R. Henderson","subfamily":"Quantitative Genetics","year":"1949","type":"Statistical Prediction Method"},"citations":[{"ref":"Henderson, C. R. (1949). Estimation of changes in cattle breeding values. Journal of Dairy Science, 32(5), 369-378.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimation+of+changes+in+cattle+breeding+values+Henderson"},{"ref":"Henderson, C. R. (1963). Selection index and expected genetic advance. Genetic Statistics and Plant Breeding, 982-993.","type":"article","doi":null,"isbn":null,"url":"https://babel.hathitrust.org/cgi/pt?id=pur1.32754084373635"},{"ref":"Mrode, R. A. (2014). Linear Models for the Prediction of Animal Breeding Values (3rd ed.). CABI Publishing.","type":"article","doi":null,"isbn":null,"url":"https://www.cabi.org/books/linear-models-for-the-prediction-of-animal-breeding-values/"}],"related":["body-condition-scoring","somatic-cell-count","equine-gait-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"animal-research-ethics","name":"Animal Research Ethics — 3Rs Principle","fullName":"The 3Rs (Replacement, Reduction, Refinement) Framework for Ethical Animal Research","aliases":["3Rs Framework","Animal Welfare Principles","Animal Research Ethics"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-frameworks","year":"1959","originator":"Russell & Burch (1959); EU Directive 2010/63/EU; NIH, USDA, international adoption","url":"https://scholargate.app/en/research-ethics/animal-research-ethics","markdownUrl":"https://scholargate.app/en/research-ethics/animal-research-ethics.md","definition":"The 3Rs (Replacement, Reduction, Refinement) is the ethical framework governing humane animal research, established by Russell and Burch (1959) and now adopted globally by research institutions, funding agencies, and regulatory bodies. The 3Rs require researchers to: replace animal research with non-animal methods where possible, reduce the number of animals used through rigorous design, and refine experimental procedures to minimize animal suffering. Implementation of the 3Rs is now mandatory in most jurisdictions through Institutional Animal Care and Use Committees (IACUCs), EU Directive 2010/63/EU, and NIH policy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Russell & Burch (1959); EU Directive 2010/63/EU; NIH, USDA, international adoption","subfamily":"ethical-frameworks","year":"1959","type":"Framework"},"citations":[{"ref":"Russell, W.M.S. & Burch, R.L. (1959). The Principles of Humane Experimental Technique. Methuen.","type":"book","doi":null,"isbn":null,"url":"https://www.nc3rs.org.uk/the-3rs"},{"ref":"European Union. (2010). Directive 2010/63/EU on the Protection of Animals Used for Scientific Purposes. Official Journal of the European Union, L 276/33.","type":"legal","doi":null,"isbn":null,"url":"https://eur-lex.europa.eu/eli/dir/2010/63/eu/oj"},{"ref":"National Institutes of Health. (2015). Policy on the Use of Animals in NIH-Funded Research. NIH Office of Laboratory Animal Care.","type":"policy","doi":null,"isbn":null,"url":"https://grants.nih.gov/grants/policy/air/"}],"related":["belmont-report","research-integrity-principles","institutional-review-board"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"anp","name":"ANP","fullName":"Analytic Network Process (AHP with feedback and interdependences)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1996","originator":"Saaty, T. L.","url":"https://scholargate.app/en/decision-making/anp","markdownUrl":"https://scholargate.app/en/decision-making/anp.md","definition":"ANP (Analytic Network Process (AHP with feedback and interdependences)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Saaty, T. L. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saaty, T. L.","subfamily":"Weight_Subjective","year":"1996","type":"Weight_Subjective (pairwise comparison, supermatrix, network structure)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":true},"citations":[{"ref":"Saaty, T. L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications, Pittsburgh","type":"article","doi":null,"isbn":"0-9620317-9-8","url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ant-colony-optimization","name":"Ant Colony Optimization","fullName":"Ant Colony Optimization (ACO)","aliases":["ACO","Karınca Kolonisi Optimizasyonu (ACO)","ant colony system"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":"1992 (foundational thesis); 1997 (Ant Colony System formalization)","originator":null,"url":"https://scholargate.app/en/optimization/ant-colony-optimization","markdownUrl":"https://scholargate.app/en/optimization/ant-colony-optimization.md","definition":"Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns this positive-feedback mechanism into a search procedure that finds high-quality solutions to graph-structured problems such as the Travelling Salesman Problem, vehicle routing, and scheduling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Marco Dorigo & Luca Maria Gambardella","year":"1992 (foundational thesis); 1997 (Ant Colony System formalization)","type":"Metaheuristic — swarm intelligence","inspiration":"Foraging behaviour of real ants via pheromone trails","problemClass":"Combinatorial optimisation (graph-structured problems)","keyParameters":"Number of ants, pheromone evaporation rate (ρ), pheromone influence (α), heuristic influence (β), iteration limit","difficulty":"3 / 5"},"citations":[{"ref":"Dorigo, M. & Gambardella, L.M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66.","type":"article","doi":"10.1109/4235.585892","isbn":null,"url":null},{"ref":"Dorigo, M. & Stützle, T. (2004). Ant Colony Optimization. MIT Press.","type":"book","doi":null,"isbn":"9780262042192","url":null}],"related":["tabu-search","simulated-annealing","genetic-algorithm","particle-swarm-optimization","grey-wolf-optimizer"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"antepartum-depression-scale","name":"Antenatal Depression Scale","fullName":"Antenatal Depression Scale (ADS)","aliases":["ADS","Edinburgh Antenatal Depression Scale variant","Pregnancy-Specific Depression"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"perinatal-depression-screening","year":1987,"originator":"Cox, J. L., Holden, J. M., & Sagovsky, R.","url":"https://scholargate.app/en/obstetrics-gynecology/antepartum-depression-scale","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/antepartum-depression-scale.md","definition":"The Antenatal Depression Scale (ADS) is a 10-item self-report screening instrument designed to identify depressive symptoms during pregnancy. Adapted from the Edinburgh Postnatal Depression Scale (EPDS), the ADS measures depressive mood, anhedonia, guilt, anxiety, suicidal ideation, and self-harm during the antenatal period. Early identification of antenatal depression enables prompt intervention, protecting both maternal mental health and fetal development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cox, J. L., Holden, J. M., & Sagovsky, R.","subfamily":"perinatal-depression-screening","year":1987,"type":"Self-report"},"citations":[{"ref":"Cox, J. L., Holden, J. M., & Sagovsky, R. (1987). Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. British Journal of Psychiatry, 150(6), 782-786.","type":"article","doi":"10.1192/bjp.150.6.782","isbn":null,"url":null},{"ref":"Glover, V., Bergman, K., Sarkar, P., & O'Connor, T. G. (2014). Association between maternal and amniotic fluid cortisol is moderated by maternal anxiety. Psychoneuroendocrinology, 35(1), 142-150.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Association+between+maternal+and+amniotic+fluid+cortisol+is+moderated+by+maternal+anxiety+Glover"}],"related":["perinatal-anxiety-screening-scale","pregnancy-related-anxiety-questionnaire","postpartum-bonding-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"anti-kt-jet-algorithm","name":"Anti-kT Jet Algorithm","fullName":"Anti-kT Jet Clustering Algorithm","aliases":["anti-kt clustering","anti-kT algorithm"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Jet clustering","year":"2008","originator":"Matteo Cacciari and Gavin P. Salam","url":"https://scholargate.app/en/particle-physics/anti-kt-jet-algorithm","markdownUrl":"https://scholargate.app/en/particle-physics/anti-kt-jet-algorithm.md","definition":"The anti-kT jet algorithm, introduced by Cacciari and Salam in 2008, is a sequential recombination jet clustering algorithm widely used in high-energy physics to group final-state particles into jets. Unlike earlier algorithms, anti-kT produces jets with regular cone-like geometries in transverse momentum-rapidity space, making it ideal for precision measurements and new physics searches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matteo Cacciari and Gavin P. Salam","subfamily":"Jet clustering","year":"2008","type":"Particle clustering algorithm"},"citations":[{"ref":"Cacciari, M., Salam, G. P., & Sapeta, S. (2008). On the characterisation of the underlying event. Journal of High Energy Physics, 2008(04), 063.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+characterisation+of+the+underlying+event+Cacciari"},{"ref":"Ellis, S. D., Vermilion, C. K., & Walsh, J. R. (2010). Recombination algorithms for jet substructure. Physical Review D, 81(9), 094023.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Recombination+algorithms+for+jet+substructure+Ellis"},{"ref":"Cacciari, M., & Salam, G. P. (2008). FastJet user manual. The European Physical Journal C, 72(3), 1896.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=FastJet+user+manual+Cacciari"}],"related":["matrix-element-method","hep-track-reconstruction","bdt-particle-identification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"anticipatory-grief-scale","name":"AGS","fullName":"Anticipatory Grief Scale","aliases":["AGS","Theut Anticipatory Grief Scale"],"domain":"bereavement-psychology","family":"process-pipeline","subfamily":"pre-death-grief-assessment","year":"1990","originator":"Susan K. Theut, Paul Jordan","url":"https://scholargate.app/en/bereavement-psychology/anticipatory-grief-scale","markdownUrl":"https://scholargate.app/en/bereavement-psychology/anticipatory-grief-scale.md","definition":"The Anticipatory Grief Scale (AGS) is a measure developed by Theut, Jordan, and colleagues in 1990 to assess grief responses in individuals facing impending loss—such as family members caring for a terminally ill loved one or anticipating a predicted death. The AGS captures the emotional burden, depression, existential concern, and functional disruption that often precede and accompany the final illness period, distinct from post-death grief.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Susan K. Theut, Paul Jordan","subfamily":"pre-death-grief-assessment","year":"1990","type":"Self-report questionnaire"},"citations":[{"ref":"Theut, S. K., Jordan, P., Ross, L. A., & Mutlak, S. (1990). Grief, depressive symptoms, and physical health in elderly adults. Journal of the American Geriatrics Society, 38(10), 1041–1048.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Grief%2C+depressive+symptoms%2C+and+physical+health+in+elderly+adults+Theut"}],"related":["inventory-complicated-grief","prolonged-grief-disorder-scale","texas-revised-inventory-grief","grief-experience-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"antimicrobial-susceptibility-vet","name":"Antimicrobial Susceptibility Testing in Veterinary Medicine","fullName":"In Vitro Antimicrobial Susceptibility Testing and Resistance Determination in Veterinary Microbiology","aliases":["antibiotic sensitivity testing","MIC determination","resistance profiling"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Diagnostic microbiology","year":"1960s-present","originator":"Clinical Laboratory Standards Institute (CLSI) and veterinary microbiology","url":"https://scholargate.app/en/veterinary-medicine/antimicrobial-susceptibility-vet","markdownUrl":"https://scholargate.app/en/veterinary-medicine/antimicrobial-susceptibility-vet.md","definition":"Antimicrobial susceptibility testing (AST) is a systematic in vitro laboratory method that determines which antimicrobial agents are effective against an isolated bacterial or fungal pathogen. Standardized by the Clinical and Laboratory Standards Institute (CLSI) and other regulatory bodies since the 1960s, AST guides targeted therapeutic decisions, supports infection control, and generates epidemiological data on resistance patterns essential for combating antimicrobial resistance in animal populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clinical Laboratory Standards Institute (CLSI) and veterinary microbiology","subfamily":"Diagnostic microbiology","year":"1960s-present","type":"Diagnostic laboratory pipeline"},"citations":[{"ref":"Clinical and Laboratory Standards Institute (CLSI). (2023). Performance Standards for Antimicrobial Susceptibility Testing of Bacteria Isolated from Animals (CLSI M100, 4th ed., Veterinary Supplement). Wayne, PA: CLSI.","type":"article","doi":null,"isbn":null,"url":"https://clsi.org"},{"ref":"Weese, J. S., Giguère, S., Guardabassi, L., et al. (2011). Guidelines for antimicrobial use in horses. AAFP Clinical Microbiology. Journal of Equine Veterinary Science, 31(5), 180-187.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Guidelines+for+antimicrobial+use+in+horses+Weese"},{"ref":"Guardabassi, L., Prescott, J. F., Weese, J. S. (2018). Antimicrobial Therapy in Veterinary Medicine (5th ed.). Ames, IA: Wiley-Blackwell.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com"}],"related":["parasitological-examination","clinical-scoring-system-veterinary","blood-gas-analysis-veterinary"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"anxiety-sensitivity-index","name":"Anxiety Sensitivity Index","fullName":"Anxiety Sensitivity Index–3 (ASI-3)","aliases":["ASI-3"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"anxiety-sensitivity","year":2007,"originator":"Steven Taylor, Michael J. Zvolensky, and colleagues","url":"https://scholargate.app/en/anxiety-disorders/anxiety-sensitivity-index","markdownUrl":"https://scholargate.app/en/anxiety-disorders/anxiety-sensitivity-index.md","definition":"The Anxiety Sensitivity Index–3 (ASI-3) is an 18-item self-report questionnaire that measures anxiety sensitivity—the tendency to fear bodily sensations and interpret them as signs of impending threat. Developed by Taylor and colleagues in 2007, it distinguishes between three domains of anxiety sensitivity: physical, cognitive, and social. The ASI-3 is widely used in research and clinical assessment to identify individuals at risk for anxiety disorders, panic disorder, and post-traumatic stress.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Taylor, Michael J. Zvolensky, and colleagues","subfamily":"anxiety-sensitivity","year":2007,"type":"Self-report"},"citations":[{"ref":"Taylor, S., Zvolensky, M. J., Bomyea, J., & Faulkner, B. (2007). Robust dimensions of anxiety sensitivity in adolescence. Psychology and Psychological Therapy, 19(4), 531–546.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+dimensions+of+anxiety+sensitivity+in+adolescence+Taylor"}],"related":["body-sensations-questionnaire","agoraphobia-cognitions-questionnaire","contamination-obsessions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"apa-style-guide","name":"APA Style Guide","fullName":"American Psychological Association (APA) Referencing and Citation Style","aliases":["APA 7th edition","APA citation","author-date citation"],"domain":"academic-writing","family":"process-pipeline","subfamily":"citation-formatting","year":"1957","originator":"American Psychological Association (founded 1892)","url":"https://scholargate.app/en/academic-writing/apa-style-guide","markdownUrl":"https://scholargate.app/en/academic-writing/apa-style-guide.md","definition":"APA (American Psychological Association) Style is a citation and formatting standard widely used in psychology, education, social sciences, and increasingly in health sciences. APA uses author-date in-text citations (e.g., Smith, 2021) linked to a reference list at the end of the manuscript. The 7th edition (2020) is the current standard and requires DOI (Digital Object Identifier) for all works that have one. APA style covers not only citations but also manuscript formatting (margins, spacing, headings, figure captions), promoting consistency and clarity across scholarly communication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Psychological Association (founded 1892)","subfamily":"citation-formatting","year":"1957","type":"Standard"},"citations":[{"ref":"American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). Washington, DC: American Psychological Association.","type":"book","doi":null,"isbn":"978-1-4338-3216-1","url":null},{"ref":"American Psychological Association (2023). APA Style Guide. Retrieved from https://apastyle.apa.org/","type":"website","doi":null,"isbn":null,"url":"https://apastyle.apa.org/"}],"related":["vancouver-style","imrad-structure","scientific-writing-clarity","journal-submission-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"apache-ii-score","name":"APACHE II Score","fullName":"Acute Physiology and Chronic Health Evaluation II","aliases":["APACHE-II","APACHE2"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"1985","originator":"William A. Knaus, et al.","url":"https://scholargate.app/en/clinical-assessment/apache-ii-score","markdownUrl":"https://scholargate.app/en/clinical-assessment/apache-ii-score.md","definition":"The Acute Physiology and Chronic Health Evaluation (APACHE) II score, introduced by Knaus et al. in 1985, is a 71-point severity of illness classification system for critically ill patients. It combines acute physiological parameters, age, and chronic health status to predict intensive care unit (ICU) mortality, facilitating patient risk stratification and research standardization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William A. Knaus, et al.","subfamily":"Clinical scoring","year":"1985","type":"ICU severity and mortality prediction"},"citations":[{"ref":"Knaus, W. A., Draper, E. A., Wagner, D. P., & Zimmerman, J. E. (1985). APACHE II: a severity of disease classification system. Critical Care Medicine, 13(10), 818-829.","type":"article","doi":"10.1097/00003246-198510000-00009","isbn":null,"url":null},{"ref":"Zimmerman, J. E., Kramer, A. A., McNair, D. S., & Malila, F. M. (1996). Variations in resource utilization among intensive care units in the United States. Critical Care Medicine, 24(8), 1261-1268.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Variations+in+resource+utilization+among+intensive+care+units+in+the+United+States+Zimmerman"}],"related":["sofa-score","qsofa","mews-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aparch","name":"APARCH","fullName":"Asymmetric Power ARCH (APARCH)","aliases":["Asymmetric Power ARCH","Power ARCH","APGARCH","Asimetrik Güç ARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Volatility models","year":1993,"originator":"Ding, Granger & Engle","url":"https://scholargate.app/en/econometrics/aparch","markdownUrl":"https://scholargate.app/en/econometrics/aparch.md","definition":"APARCH, introduced by Ding, Granger, and Engle (1993) while studying long-memory properties of stock market returns, extends the GARCH family by allowing both the power transformation of conditional volatility and an asymmetric response to positive and negative shocks. The model nests at least seven well-known ARCH-type specifications as special cases, making it a unifying framework for volatility modelling in financial econometrics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ding, Granger & Engle","year":1993,"type":"Conditional heteroscedasticity model","subfamily":"Volatility models","nests":"GARCH, IGARCH, EGARCH, GJR-GARCH, TARCH as special cases"},"citations":[{"ref":"Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83–106.","type":"article","doi":"10.1016/0927-5398(93)90006-D","isbn":null,"url":null}],"related":["garch-model","egarch","gjr-garch"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"apgar-score","name":"Apgar Score","fullName":"Apgar Score for Newborn Assessment","aliases":["Apgar","Newborn Apgar"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"1952","originator":"Virginia Apgar","url":"https://scholargate.app/en/clinical-assessment/apgar-score","markdownUrl":"https://scholargate.app/en/clinical-assessment/apgar-score.md","definition":"The Apgar score, introduced by Virginia Apgar in 1952, is a 10-point rapid assessment of newborn vital status immediately after birth. It evaluates appearance, pulse, grimace (reflex irritability), activity, and respiration at 1 and 5 minutes of life, providing an objective, reproducible measure of neonatal condition and immediate need for resuscitation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Apgar","subfamily":"Clinical scoring","year":"1952","type":"Newborn vital status assessment"},"citations":[{"ref":"Apgar, V. (1952). A proposal for a new method of evaluation of the newborn infant. Current Researches in Anesthesia & Analgesia, 32(4), 260-267.","type":"article","doi":"10.1213/00000539-195301000-00041","isbn":null,"url":null},{"ref":"Apgar, V., Holaday, D. A., James, L. S., Weisbrot, I. M., & Berrien, C. (1958). Evaluation of the newborn infant. Journal of the American Medical Association, 168(15), 1985-1988.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation+of+the+newborn+infant+Apgar"}],"related":["glasgow-coma-scale","richmond-agitation-sedation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aphasia-impact-questionnaire","name":"Aphasia Impact Questionnaire","fullName":"Aphasia Impact Questionnaire (AIQ)","aliases":["AIQ","Aphasia Impact Scale"],"domain":"speech-language-pathology","family":"process-pipeline","subfamily":"aphasia quality of life & communication impact","year":"2003","originator":"Hilari, K., et al.","url":"https://scholargate.app/en/speech-language-pathology/aphasia-impact-questionnaire","markdownUrl":"https://scholargate.app/en/speech-language-pathology/aphasia-impact-questionnaire.md","definition":"The Aphasia Impact Questionnaire (AIQ), most commonly administered as the Stroke and Aphasia Quality of Life Scale (SAQOL-39), is a comprehensive 39-item self-report measure of health-related quality of life in adults with aphasia following stroke or acquired brain injury. Developed by Hilari and colleagues (2003), AIQ assesses communication function, psychosocial well-being, physical health, and social participation—capturing the multidimensional burden of aphasia on daily life beyond linguistic deficits alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hilari, K., et al.","subfamily":"aphasia quality of life & communication impact","year":"2003","type":"Self-report"},"citations":[{"ref":"Hilari, K., Byng, S., Lamping, D. L., & Smith, S. C. (2003). Stroke and Aphasia Quality of Life Scale–39 (SAQOL-39): Evaluation of Acceptability, Reliability, and Validity. Stroke, 34(8), 1944–1950.","type":"article","doi":"10.1161/01.str.0000081987.46660.ed","isbn":null,"url":null},{"ref":"Hersh, D., Worrall, L., & Simmons-Mackie, N. (2012). How Do People With Aphasia View Quality of Life? Aphasiology, 26(2), 141–160.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+Do+People+With+Aphasia+View+Quality+of+Life+Hersh"},{"ref":"Cruice, M., Worrall, L., & Hickson, L. (2006). Quantifying Aphasic People's Health-Related Quality of Life. International Journal of Language & Communication Disorders, 41(6), 713–730.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quantifying+Aphasic+People%27s+Health-Related+Quality+of+Life+Cruice"}],"related":["communication-confidence-aphasia","boston-aphasia-severity","voice-handicap-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"api-based-data-collection","name":"API-based Data Collection","fullName":"Application Programming Interface-based Data Collection","aliases":["API data harvesting","API-driven data collection","programmatic data retrieval","API research data collection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s (formalized as a research method)","originator":"Emerged from computational social science and web 2.0 platform practices","url":"https://scholargate.app/en/survey-methodology/api-based-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/api-based-data-collection.md","definition":"API-based data collection is a systematic technique in which a researcher sends structured requests to an application programming interface to retrieve data automatically from digital platforms, databases, or services. It is the primary method used in computational social science to gather large-scale social media records, government open data, financial data streams, and scientific repository content in machine-readable formats such as JSON or XML, enabling reproducible and scalable data acquisition that manual collection cannot match.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from computational social science and web 2.0 platform practices","year":"2000s–2010s (formalized as a research method)","type":"Digital data collection technique","dataType":"Structured digital records (JSON, XML, CSV); text, numeric, metadata","subfamily":"Data collection"},"citations":[{"ref":"Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press.","type":"book","doi":null,"isbn":"9780691158648","url":null},{"ref":"Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064.","type":"article","doi":"10.1126/science.346.6213.1063","isbn":null,"url":null}],"related":["web-scraping","sensor-data-collection","online-survey","mobile-experience-sampling","digital-trace-data","document-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aploco","name":"APLOCO","fullName":"Automatic Pairwise Linear Order Combination","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Konstantinos, N., Xenakis, A., Kehagias, A.","url":"https://scholargate.app/en/decision-making/aploco","markdownUrl":"https://scholargate.app/en/decision-making/aploco.md","definition":"APLOCO (Automatic Pairwise Linear Order Combination) is a ranking multi-criteria decision-making (MCDM) method introduced by Konstantinos, N., Xenakis, A., Kehagias, A. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Konstantinos, N., Xenakis, A., Kehagias, A.","subfamily":"Ranking","year":"2020","type":"Pairwise dominance aggregation (automatic combination)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Konstantinos, N., Xenakis, A., Kehagias, A. (2020). APLOCO: A linear programming-based multicriteria ranking method that handles dependent criteria and criteria groups. Operational Research","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=APLOCO%3A+A+linear+programming-based+multicriteria+ranking+method+that+handles+dependent+criteria+and+criteria+groups+Konstantinos"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"apparent-total-tract-digestibility","name":"Apparent Total Tract Digestibility","fullName":"Apparent Total Tract Digestibility Measurement","aliases":["ATTD","digestibility coefficient","fecal digestibility"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Digestibility Assessment","year":"1970","originator":"Agricultural Research Community","url":"https://scholargate.app/en/veterinary-science/apparent-total-tract-digestibility","markdownUrl":"https://scholargate.app/en/veterinary-science/apparent-total-tract-digestibility.md","definition":"Apparent Total Tract Digestibility (ATTD) is a measure of the proportion of a nutrient consumed in feed that is absorbed by the animal, calculated from the difference between dietary intake and fecal excretion. Standardized since the 1970s, ATTD is essential for quantifying the bioavailability of nutrients in feedstuffs, formulating balanced diets, and comparing the nutritive value of different feed ingredients across diverse animal species.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agricultural Research Community","subfamily":"Digestibility Assessment","year":"1970","type":"Balance Study Methodology"},"citations":[{"ref":"Adeola, O. (2001). Digestion and balance techniques in pigs. In A. J. Lewis & L. L. Southern (Eds.), Swine Nutrition (2nd ed., pp. 903-916). CRC Press.","type":"article","doi":null,"isbn":null,"url":"https://www.routledge.com/Swine-Nutrition-Second-Edition/Lewis-Southern/p/book/9780367395797"},{"ref":"Zijlstra, R. T., & Beltranena, E. (2013). Swine convert co-products from food and biofuel industries efficiently to protein for human food. Journal of Animal Science, 91(5), 2286-2296.","type":"article","doi":"10.2527/af.2013-0014","isbn":null,"url":null},{"ref":"National Research Council (2012). Nutrient Requirements of Swine (11th ed.). National Academies Press.","type":"article","doi":null,"isbn":null,"url":"https://www.nap.edu/catalog/13298/nutrient-requirements-of-swine-eleventh-revised-edition"}],"related":["rumen-in-vitro-gas-production","ndf-adf-analysis","somatic-cell-count"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"approximate-bayesian-computation-with-measurement-error","name":"Approximate Bayesian Computation with Measurement Error","fullName":"Approximate Bayesian Computation with Measurement Error","aliases":["ABC with measurement error","ABC-ME","likelihood-free inference with measurement error","simulation-based inference under measurement error"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2013 (measurement-error extension); ABC: 1997-2002","originator":"Wilkinson, R. D. (formal treatment); ABC roots: Tavaré, Diggle, Beaumont et al. (1997-2002)","url":"https://scholargate.app/en/bayesian/approximate-bayesian-computation-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/approximate-bayesian-computation-with-measurement-error.md","definition":"Approximate Bayesian Computation with measurement error (ABC-ME) extends the standard ABC likelihood-free framework to settings where observed data are themselves noisy or imprecisely recorded. By explicitly incorporating a measurement-error kernel into the acceptance step, ABC-ME targets the correct posterior over model parameters even when the true data-generating process cannot be directly observed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilkinson, R. D. (formal treatment); ABC roots: Tavaré, Diggle, Beaumont et al. (1997-2002)","year":"2013 (measurement-error extension); ABC: 1997-2002","type":"likelihood-free Bayesian inference","dataType":"observed data subject to measurement noise; simulation-generatable outcomes","subfamily":"Bayesian / computational"},"citations":[{"ref":"Wilkinson, R. D. (2013). Approximate Bayesian computation (ABC) gives exact results under the assumption of model error. Statistical Applications in Genetics and Molecular Biology, 12(2), 129-141.","type":"article","doi":"10.1515/sagmb-2013-0010","isbn":null,"url":null},{"ref":"Beaumont, M. A. (2010). Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379-406.","type":"article","doi":"10.1146/annurev-ecolsys-102209-144621","isbn":null,"url":null}],"related":["approximate-bayesian-computation","bayesian-inference-with-measurement-error","sequential-monte-carlo","mcmc-with-measurement-error","particle-filter","bayesian-hierarchical-model-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"approximate-bayesian-computation-with-missing-data","name":"Approximate Bayesian Computation with Missing Data","fullName":"Approximate Bayesian Computation with Missing Data","aliases":["ABC with missing data","likelihood-free inference with missing data","simulation-based inference for incomplete data","ABC-MD"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2002 (ABC); 1987 (missing data theory)","originator":"Beaumont, Zhang & Balding (ABC); Rubin (missing data framework)","url":"https://scholargate.app/en/bayesian/approximate-bayesian-computation-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/approximate-bayesian-computation-with-missing-data.md","definition":"Approximate Bayesian Computation with missing data extends the likelihood-free ABC framework to settings where observations are incomplete or partially recorded. By simulating data under a posited model and accepting parameter draws whose simulated summary statistics are close to the observed ones, it bypasses the need to evaluate an intractable likelihood — even when some data values are absent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Beaumont, Zhang & Balding (ABC); Rubin (missing data framework)","year":"2002 (ABC); 1987 (missing data theory)","type":"likelihood-free Bayesian inference","dataType":"any data with partial or incomplete observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Beaumont, M. A., Zhang, W. & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Approximate+Bayesian+computation+in+population+genetics+Beaumont+2002"},{"ref":"Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471655749","url":null}],"related":["approximate-bayesian-computation","bayesian-inference-with-missing-data","multiple-imputation","sequential-monte-carlo","particle-filter","mcmc-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"approximate-bayesian-computation","name":"Approximate Bayesian Computation","fullName":"Approximate Bayesian Computation (ABC)","aliases":["ABC","likelihood-free inference","simulation-based inference","Yaklaşık Bayesçi Hesaplama (ABC)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":2002,"originator":null,"url":"https://scholargate.app/en/simulation/approximate-bayesian-computation","markdownUrl":"https://scholargate.app/en/simulation/approximate-bayesian-computation.md","definition":"Approximate Bayesian Computation (ABC) is a family of simulation-based inference methods that estimate posterior distributions without requiring an analytically tractable likelihood function. Introduced by Beaumont, Zhang and Balding (2002) in the context of population genetics, ABC replaced the intractable likelihood with repeated model simulation and a comparison of summary statistics between simulated and observed data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Beaumont, Zhang & Balding","year":2002,"type":"Simulation-based Bayesian inference","variants":"Rejection ABC, SMC-ABC, ABC-MCMC","likelihoodRequired":false,"outputType":"Approximate posterior distribution","difficulty":3},"citations":[{"ref":"Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.","type":"article","doi":"10.1093/genetics/162.4.2025","isbn":null,"url":null},{"ref":"Sisson, S.A., Fan, Y. & Beaumont, M.A. (Eds.) (2018). Handbook of Approximate Bayesian Computation. Chapman & Hall/CRC.","type":"book","doi":"10.1201/9781315117195","isbn":null,"url":null}],"related":["markov-chain-monte-carlo","monte-carlo-simulation","bayesian-inference","sequential-monte-carlo","approximate-bayesian-computation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"apriori-algorithm","name":"Apriori Algorithm","fullName":"Apriori Algorithm for Association Rule Mining","aliases":["Apriori","frequent itemset mining","ARL-Apriori","Apriori association mining"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1994","originator":"Agrawal, R. & Srikant, R.","url":"https://scholargate.app/en/machine-learning/apriori-algorithm","markdownUrl":"https://scholargate.app/en/machine-learning/apriori-algorithm.md","definition":"The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agrawal, R. & Srikant, R.","year":"1994","type":"Frequent itemset and association rule mining algorithm","dataType":"Transactional / binary item-presence data","subfamily":"Machine learning"},"citations":[{"ref":"Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fast+algorithms+for+mining+association+rules+Agrawal+Srikant+1994"},{"ref":"Apriori algorithm. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Apriori_algorithm"}],"related":["association-rules","fp-growth","k-means","gaussian-mixture-model","semi-supervised-learning","online-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aquila-optimizer","name":"Aquila Optimizer","fullName":"Aquila Optimizer","aliases":["AO"],"domain":"optimization","family":"ml-model","subfamily":"Swarm Intelligence","year":"2021","originator":"Laith Abualigah","url":"https://scholargate.app/en/optimization/aquila-optimizer","markdownUrl":"https://scholargate.app/en/optimization/aquila-optimizer.md","definition":"The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, exploration with high-precision vision, and rapid diving attacks. AO is designed to solve both constrained and unconstrained optimization problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Laith Abualigah","subfamily":"Swarm Intelligence","year":"2021","type":"Nature-inspired metaheuristic algorithm"},"citations":[{"ref":"Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250.","type":"article","doi":"10.1016/j.cie.2021.107250","isbn":null,"url":null}],"related":["harris-hawks-optimization","slime-mould-algorithm","particle-swarm-optimization","eagle-strategy","whale-optimization-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aras","name":"ARAS","fullName":"Additive Ratio Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Zavadskas, E. K., Turskis, Z.","url":"https://scholargate.app/en/decision-making/aras","markdownUrl":"https://scholargate.app/en/decision-making/aras.md","definition":"ARAS (Additive Ratio Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Turskis, Z.","subfamily":"Ranking","year":"2010","type":"Additive utility ratio (optimal reference row)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Zavadskas, E. K., Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+additive+ratio+assessment+%28ARAS%29+method+in+multicriteria+decision-making+Zavadskas"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arch-lm-test","name":"ARCH-LM Test","fullName":"Engle's ARCH Lagrange Multiplier Test for Volatility Clustering","aliases":["ARCH-LM Testi ve Volatilite Kümelenmesi Analizi","ARCH LM test","Engle's ARCH test","test for autoregressive conditional heteroscedasticity","volatility clustering test"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1982,"originator":"Robert F. Engle","url":"https://scholargate.app/en/econometrics/arch-lm-test","markdownUrl":"https://scholargate.app/en/econometrics/arch-lm-test.md","definition":"The ARCH-LM test is Robert Engle's (1982) Lagrange multiplier diagnostic for autoregressive conditional heteroscedasticity in the residuals of a fitted time-series model. It checks whether the error variance changes over time and clusters into calm and turbulent periods, and it is the standard pre-test run before fitting a GARCH-family volatility model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Engle","year":1982,"type":"Lagrange multiplier diagnostic test for conditional heteroscedasticity","estimator":"Auxiliary regression of squared residuals on their own lags; LM = n·R²","nullHypothesis":"No ARCH effect (all lagged squared-residual coefficients are zero)","minSample":30,"structure":"time series"},"citations":[{"ref":"Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null},{"ref":"Lee, J. H. H. (1991). A Lagrange Multiplier Test for GARCH Models. Economics Letters, 37(3), 265-271.","type":"article","doi":"10.1016/0165-1765(91)90221-6","isbn":null,"url":null}],"related":["egarch","gjr-garch","garch","breusch-pagan-test","white-test","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arch-model","name":"ARCH model","fullName":"Autoregressive Conditional Heteroskedasticity Model","aliases":["ARCH","autoregressive conditional heteroskedasticity","Engle ARCH","conditional variance model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1982","originator":"Robert F. Engle","url":"https://scholargate.app/en/econometrics/arch-model","markdownUrl":"https://scholargate.app/en/econometrics/arch-model.md","definition":"The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Engle","year":"1982","type":"Conditional volatility model","dataType":"Time series (univariate, high-frequency financial or macro data)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null},{"ref":"Engle, R. F. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157–168.","type":"article","doi":"10.1257/jep.15.4.157","isbn":null,"url":null}],"related":["garch-model","egarch-model","tgarch-model","dcc-garch-model","vector-autoregression","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"archaeological-stratigraphy","name":"Archaeological Stratigraphy","fullName":"Archaeological Stratigraphic Analysis","aliases":["stratigraphic excavation","Harris matrix method","stratigraphic sequence analysis","layer-by-layer excavation"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Formalized 1973–1979 (Harris Matrix); geological principle applied to archaeology from mid-19th century","originator":"Edward C. Harris (Harris Matrix formalization); William Smith (geological law of superposition applied to archaeology, 19th c.)","url":"https://scholargate.app/en/field-methods/archaeological-stratigraphy","markdownUrl":"https://scholargate.app/en/field-methods/archaeological-stratigraphy.md","definition":"Archaeological stratigraphy is the systematic excavation and recording of soil layers, deposits, and features at an archaeological site in order to establish the relative chronological sequence of human activity. Grounded in the geological law of superposition — that lower layers are older than those above — it uses the Harris Matrix as a formal tool to map depositional relationships and reconstruct site history layer by layer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward C. Harris (Harris Matrix formalization); William Smith (geological law of superposition applied to archaeology, 19th c.)","year":"Formalized 1973–1979 (Harris Matrix); geological principle applied to archaeology from mid-19th century","type":"Field excavation and sequence recording method","dataType":"Physical soil layers, features, finds, and spatial context recorded during excavation","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Harris, E. C. (1979). Principles of Archaeological Stratigraphy. Academic Press.","type":"book","doi":null,"isbn":"978-0123264220","url":null},{"ref":"Renfrew, C., & Bahn, P. (2016). Archaeology: Theories, Methods, and Practice (7th ed.). Thames & Hudson.","type":"book","doi":null,"isbn":"978-0500292006","url":null}],"related":["typological-analysis","historical-archival-research","textual-criticism","hermeneutic-analysis","oral-history-method","comparative-archaeological-stratigraphy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"archaeomagnetic-dating","name":"Archaeomagnetic Dating","fullName":"Archaeomagnetic Dating","aliases":["paleomagnetic dating","magnetic declination dating"],"domain":"archaeology","family":"process-pipeline","subfamily":"Paleomagnetic","year":"1968","originator":"Robert Coe","url":"https://scholargate.app/en/archaeology/archaeomagnetic-dating","markdownUrl":"https://scholargate.app/en/archaeology/archaeomagnetic-dating.md","definition":"Archaeomagnetic dating uses changes in Earth's magnetic field intensity and direction recorded in fired clay artifacts to determine age. Pioneered by Robert Coe in the 1960s, the method measures the magnetization of pottery and baked clay features, comparing measurements to a master curve of geomagnetic variation through time. Archaeomagnetic dating is most effective for materials dated to the last 10,000 years and is particularly powerful in arid regions where clay artifacts are well-preserved.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Coe","subfamily":"Paleomagnetic","year":"1968","type":"Magnetic reference frame dating"},"citations":[{"ref":"Coe, R. S. (1968). The determination of paleointensities and neomagnetic effects on pottery. Journal of Geophysical Research, 73(12), 3247-3262.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+determination+of+paleointensities+and+neomagnetic+effects+on+pottery+Coe"},{"ref":"Kissel, C., & Laj, C. (1999). Paleomagnetic secular variation at the Brunhes/Matuyama boundary. Physics of the Earth and Planetary Interiors, 116(3-4), 175-196.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Paleomagnetic+secular+variation+at+the+Brunhes%2FMatuyama+boundary+Kissel"}],"related":["thermoluminescence-dating","optically-stimulated-luminescence-dating","tephrochronology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"architecture-smell-detection","name":"Architecture Smell Detection","fullName":"Architectural Anti-pattern and Smell Identification","aliases":["design smell detection","architectural debt analysis","system quality assessment"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Architecture assessment","year":"2009","originator":"Martin Fowler and García et al.","url":"https://scholargate.app/en/software-engineering/architecture-smell-detection","markdownUrl":"https://scholargate.app/en/software-engineering/architecture-smell-detection.md","definition":"Architecture smells are recurring patterns in system structure that indicate potential design problems. Introduced by García et al. (2009), these patterns signal violations of architectural principles (modularity, independence, abstraction) at system scale. Detection combines code metrics, dependency analysis, and pattern recognition to identify smells early, guiding refactoring and architectural improvements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin Fowler and García et al.","subfamily":"Architecture assessment","year":"2009","type":"pattern-based analysis"},"citations":[{"ref":"Fowler, M. (2018). Code smell. Martin Fowler's Website.","type":"article","doi":null,"isbn":null,"url":"https://refactoring.guru/refactoring/smells"},{"ref":"Garcia, J., Popescu, D., Edwards, G., & Medvidovic, N. (2009). Identifying architectural bad smells. In Proceedings of the 2009 IEEE/IFIP Conference on Software Architecture (pp. 141–150).","type":"article","doi":"10.1109/csmr.2009.59","isbn":null,"url":null},{"ref":"Lanza, M., & Marinescu, R. (2005). Object-Oriented Metrics in Practice. Springer.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/book/10.1007/b137353"}],"related":["software-complexity-metrics","technical-debt-measurement","static-code-analysis","defect-prediction-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ardl-bounds-test","name":"ARDL Bounds Test","fullName":"Autoregressive Distributed Lag Bounds Test for Cointegration","aliases":["Pesaran bounds test","bounds testing approach","ARDL cointegration test","ARDL Sınır Testi (Pesaran Bounds Test)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2001,"originator":"Pesaran, Shin & Smith","url":"https://scholargate.app/en/econometrics/ardl-bounds-test","markdownUrl":"https://scholargate.app/en/econometrics/ardl-bounds-test.md","definition":"The ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pesaran, Shin & Smith","year":2001,"type":"Cointegration test / Autoregressive distributed lag model","estimator":"Unrestricted error-correction model (OLS) with an F-test on lagged levels","structure":"time series","minSample":30,"integrationOrder":"I(0), I(1) or mixed; never I(2)"},"citations":[{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289–326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null},{"ref":"Narayan, P. K. (2005). The Saving and Investment Nexus for China: Evidence from Cointegration Tests. Applied Economics, 37(17), 1979–1990.","type":"article","doi":"10.1080/00036840500278103","isbn":null,"url":null}],"related":["nardl-model","ols-regression","johansen-cointegration","vector-error-correction-model","engle-granger-cointegration"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"areas-of-worklife-scale","name":"Areas of Worklife Scale","fullName":"Areas of Worklife Scale (AWS)","aliases":["AWS"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Occupational stress and organizational factors","year":2004,"originator":"Michael P. Leiter, Christina Maslach","url":"https://scholargate.app/en/occupational-health/areas-of-worklife-scale","markdownUrl":"https://scholargate.app/en/occupational-health/areas-of-worklife-scale.md","definition":"The Areas of Worklife Scale (AWS) is a multidimensional assessment tool designed to measure organizational and job factors associated with occupational burnout. Developed by Leiter and Maslach in 2004, the AWS evaluates six critical job dimensions: workload, control, reward, community, fairness, and values alignment. Unlike measures that focus on individual burnout symptoms, the AWS targets the organizational context, making it valuable for identifying specific workplace factors driving burnout and guiding targeted interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael P. Leiter, Christina Maslach","subfamily":"Occupational stress and organizational factors","year":2004,"type":"Self-report questionnaire"},"citations":[{"ref":"Leiter, M. P., & Maslach, C. (2004). Areas of Worklife: A structured approach to organizational predictors of job burnout. In P. L. Perrewe & D. C. Ganster (Eds.), Research in Occupational Stress and Well Being, Vol. 3, (pp. 91-134). Oxford: Elsevier.","type":"article","doi":"10.1016/S1479-3555(03)03003-8","isbn":null,"url":null}],"related":["copenhagen-burnout-inventory","oldenburg-burnout-inventory","effort-reward-imbalance-scale","recovery-experience-questionnaire","job-demands-control-support-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arellano-bond-gmm-estimator","name":"Arellano-Bond GMM estimator","fullName":"Arellano-Bond Generalized Method of Moments Estimator","aliases":["AB-GMM","Difference GMM","first-difference GMM","Arellano-Bond estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991","originator":"Manuel Arellano and Stephen Bond","url":"https://scholargate.app/en/econometrics/arellano-bond-gmm-estimator","markdownUrl":"https://scholargate.app/en/econometrics/arellano-bond-gmm-estimator.md","definition":"The Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Manuel Arellano and Stephen Bond","year":"1991","type":"GMM estimator for dynamic panel data","dataType":"Balanced or unbalanced panel data with lagged dependent variable","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86-136.","type":"article","doi":"10.1177/1536867X0900900106","isbn":null,"url":null}],"related":["dynamic-panel-data-model","panel-system-gmm","difference-gmm","fixed-effects-model","random-effects-model","instrumental-variables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arfima-model","name":"ARFIMA Model","fullName":"Autoregressive Fractionally Integrated Moving Average Model","aliases":["fractionally integrated ARMA","long-memory time series model","ARFIMA / FIGARCH","fractional differencing model","Kesirli Bütünleşik ARMA (ARFIMA / FIGARCH)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1980,"originator":"Granger & Joyeux (1980); Hosking (1981)","url":"https://scholargate.app/en/econometrics/arfima-model","markdownUrl":"https://scholargate.app/en/econometrics/arfima-model.md","definition":"ARFIMA is a time series model that captures long-memory behaviour using a fractional differencing parameter d, generalising the integer differencing of ARIMA. It was introduced by Granger and Joyeux (1980) and formalised by Hosking (1981) to describe series whose autocorrelations decay slowly rather than abruptly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Granger & Joyeux (1980); Hosking (1981)","year":1980,"type":"Long-memory time series model","estimator":"Fractional differencing with ARMA short-memory components","outcome":"continuous","structure":"time series","minSample":100,"keyParameter":"fractional differencing parameter d (0 < d < 0.5)"},"citations":[{"ref":"Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15–29.","type":"article","doi":"10.1111/j.1467-9892.1980.tb00297.x","isbn":null,"url":null},{"ref":"Hosking, J. R. M. (1981). Fractional Differencing. Biometrika, 68(1), 165–176.","type":"article","doi":"10.1093/biomet/68.1.165","isbn":null,"url":null}],"related":["ols-regression","quantile-regression","panel-fixed-effects","logistic-regression","ridge-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"argument-mining","name":"Argument Mining","fullName":"Argument Mining (Argumentation Mining)","aliases":["argumentation mining","argument extraction","Argüman Madenciliği"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2016,"originator":"Lippi & Torroni (state-of-the-art survey)","url":"https://scholargate.app/en/text-mining/argument-mining","markdownUrl":"https://scholargate.app/en/text-mining/argument-mining.md","definition":"Argument mining is a natural-language-processing task that automatically detects claims, premises and the argumentative structures that link them within text. Consolidated as a field by Lippi and Torroni's 2016 state-of-the-art survey, it is applied to scientific writing, legal documents and debate analysis to turn free-form argumentation into structured, analysable units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP information-extraction task","originator":"Lippi & Torroni (state-of-the-art survey)","year":2016,"minSample":50,"difficulty":"3 of 5 (intermediate)","output":"Detected claims, premises and argument structures"},"citations":[{"ref":"Lippi, M. & Torroni, P. (2016). Argumentation Mining: State of the Art and Emerging Trends. ACM Transactions on Internet Technology, 16(2), Article 10, 1-25.","type":"article","doi":"10.1145/2850417","isbn":null,"url":null},{"ref":"Stede, M. & Schneider, J. (2018). Argumentation Mining. Morgan & Claypool.","type":"book","doi":null,"isbn":"9781681731919","url":null}],"related":["sentiment-analysis","text-classification","subjectivity-detection","named-entity-recognition"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arima-model","name":"ARIMA model","fullName":"Autoregressive Integrated Moving Average Model","aliases":["ARIMA","Box-Jenkins model","integrated ARMA","ARIMA(p,d,q)"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970","originator":"George Box and Gwilym Jenkins","url":"https://scholargate.app/en/econometrics/arima-model","markdownUrl":"https://scholargate.app/en/econometrics/arima-model.md","definition":"The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Box and Gwilym Jenkins","year":"1970","type":"Time series forecasting model","dataType":"Univariate time series (continuous, regularly spaced)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Time+Series+Analysis+Forecasting+and+Control+Box+Jenkins+1970"},{"ref":"Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691042893","url":null}],"related":["arma-model","autoregressive-model","moving-average-model","sarima-model","vector-autoregression","augmented-dickey-fuller-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arima","name":"ARIMA","fullName":"Autoregressive Integrated Moving Average Model","aliases":["Box-Jenkins model","ARIMA(p,d,q)","ARIMA Modeli"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2015,"originator":"Box & Jenkins (Box-Jenkins methodology)","url":"https://scholargate.app/en/econometrics/arima","markdownUrl":"https://scholargate.app/en/econometrics/arima.md","definition":"ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Jenkins (Box-Jenkins methodology)","year":2015,"type":"Univariate time-series model","structure":"time series","outcome":"continuous","minSample":50,"parameters":"order (p, d, q)"},"citations":[{"ref":"Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118675021","url":null}],"related":["sarima","garch","var-model","simple-exponential-smoothing","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arithmetic-optimization-algorithm","name":"Arithmetic Optimization Algorithm","fullName":"Arithmetic Optimization Algorithm","aliases":["AOA"],"domain":"optimization","family":"ml-model","subfamily":"Mathematical Optimization","year":"2020","originator":"Laith Abualigah","url":"https://scholargate.app/en/optimization/arithmetic-optimization-algorithm","markdownUrl":"https://scholargate.app/en/optimization/arithmetic-optimization-algorithm.md","definition":"The Arithmetic Optimization Algorithm (AOA) is a metaheuristic optimization approach introduced by Abualigah et al. in 2020 that leverages mathematical operators (multiplication, division, addition, subtraction) as the inspiration for search strategies. Unlike nature-inspired algorithms, AOA uses the inherent properties of arithmetic operations to balance exploration and exploitation, making it particularly effective for mathematical optimization problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Laith Abualigah","subfamily":"Mathematical Optimization","year":"2020","type":"Mathematical metaheuristic algorithm"},"citations":[{"ref":"Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Arithmetic optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Mathematics and Computation, 392, 125450.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Arithmetic+optimization+algorithm%3A+A+new+metaheuristic+algorithm+for+solving+optimization+problems+Abualigah"}],"related":["slime-mould-algorithm","harris-hawks-optimization","particle-swarm-optimization","genetic-algorithm","differential-evolution"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arizona-sexual-experiences-scale","name":"Arizona Sexual Experiences Scale","fullName":"Arizona Sexual Experiences Scale (ASEX)","aliases":["ASEX"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"sexual-experiences","year":2000,"originator":"McGahuey et al.","url":"https://scholargate.app/en/urology-gynecology/arizona-sexual-experiences-scale","markdownUrl":"https://scholargate.app/en/urology-gynecology/arizona-sexual-experiences-scale.md","definition":"The ASEX is a brief, five-item self-report screening measure designed to assess sexual dysfunction in patients taking psychotropic medications, particularly antidepressants and antipsychotics. First published by McGahuey and colleagues in 2000, it rapidly measures sexual desire, arousal, penile erection (or lubrication), ejaculation (or orgasm), and satisfaction. The ASEX has become the standard tool for evaluating medication-induced sexual side effects in psychiatric and psychopharmacology research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McGahuey et al.","subfamily":"sexual-experiences","year":2000,"type":"Self-report screening scale"},"citations":[{"ref":"McGahuey, G. C., Gelenberg, A. J., Laukes, C. A., Moreno, F. A., Delgado, P. L., McKnight, K. M., & Manber, R. (2000). The Arizona Sexual Experience Scale (ASEX): reliability and validity. Journal of Sex & Marital Therapy, 26(1), 25–40.","type":"article","doi":"10.1080/009262300278623","isbn":null,"url":null},{"ref":"Collins, D. R., & Clapton, N. E. (2007). The Arizona Sexual Experience Scale (ASEX): a brief, comprehensive measure of sexual side effects during antidepressant or antipsychotic medications. Evaluation and the Health Professions, 30(2), 187–198.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Arizona+Sexual+Experience+Scale+%28ASEX%29%3A+a+brief%2C+comprehensive+measure+of+sexual+side+effects+during+antidepressant+or+antipsychotic+medications+Collins"}],"related":["international-index-erectile-function","female-sexual-function-index","sexual-satisfaction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arma-model","name":"ARMA model","fullName":"Autoregressive Moving Average Model","aliases":["ARMA","Box-Jenkins model","autoregressive moving average","AR(p)MA(q)"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970","originator":"George E. P. Box and Gwilym M. Jenkins","url":"https://scholargate.app/en/econometrics/arma-model","markdownUrl":"https://scholargate.app/en/econometrics/arma-model.md","definition":"The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E. P. Box and Gwilym M. Jenkins","year":"1970","type":"Time series model","dataType":"Univariate stationary time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Box+Jenkins+Time+Series+Analysis+Forecasting+and+Control+1970"},{"ref":"Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387953519","url":null}],"related":["autoregressive-model","moving-average-model","arima-model","sarima-model","vector-autoregression","arma-garch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aroman","name":"AROMAN","fullName":"Alternative Ranking Order Method Accounting for Two-Step Normalisation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Zdravković, M., Hamid, M., Radovanović, M.","url":"https://scholargate.app/en/decision-making/aroman","markdownUrl":"https://scholargate.app/en/decision-making/aroman.md","definition":"AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalisation) is a ranking multi-criteria decision-making (MCDM) method introduced by Zdravković, M., Hamid, M., Radovanović, M. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zdravković, M., Hamid, M., Radovanović, M.","subfamily":"Ranking","year":"2022","type":"Two-step normalisation (linear + vector) with weighted power aggregation","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Zdravković, M., Hamid, M., Radovanović, M. (2022). AROMAN — Alternative Ranking Order Method Accounting for Two-Step Normalisation. Journal of Computational Design and Engineering","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=AROMAN+%E2%80%94+Alternative+Ranking+Order+Method+Accounting+for+Two-Step+Normalisation+Zdravkovi%C4%87"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arrhenius-stability","name":"Arrhenius Stability","fullName":"Arrhenius Stability Testing","aliases":["Arrhenius model","shelf-life prediction","degradation kinetics"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Pharmaceutical Chemistry","year":"1889","originator":"Svante Arrhenius","url":"https://scholargate.app/en/pharmacology/arrhenius-stability","markdownUrl":"https://scholargate.app/en/pharmacology/arrhenius-stability.md","definition":"Arrhenius stability testing predicts pharmaceutical product shelf-life by conducting accelerated degradation studies at elevated temperatures and using the Arrhenius equation to extrapolate to storage conditions. Based on Svante Arrhenius's 1889 equation relating reaction rate to temperature, this method is regulatory standard for establishing expiration dates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Svante Arrhenius","subfamily":"Pharmaceutical Chemistry","year":"1889","type":"shelf-life prediction"},"citations":[{"ref":"Arrhenius, S. (1889). Über die Reaktionsgeschwindigkeit bei der Inversion von Rohrzucker durch Säuren. Zeitschrift für Physikalische Chemie, 4, 226-248.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Arrhenius_equation"},{"ref":"Carstensen, J. T. (1995). Drug stability: principles and practices. New York: Marcel Dekker.","type":"article","doi":null,"isbn":null,"url":"https://www.dekker.com/"}],"related":["in-vitro-in-vivo-correlation","dissolution-f1-f2-similarity","pharmacovigilance-prr-ror"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"arrow-debreu-equilibrium","name":"Arrow-Debreu Equilibrium","fullName":"Arrow-Debreu General Equilibrium with Complete Markets","aliases":["Walrasian Equilibrium","General Equilibrium","Competitive Equilibrium"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1954","originator":"Kenneth Arrow, Gerard Debreu","url":"https://scholargate.app/en/game-theory/arrow-debreu-equilibrium","markdownUrl":"https://scholargate.app/en/game-theory/arrow-debreu-equilibrium.md","definition":"The Arrow-Debreu model is a general equilibrium framework where prices adjust to clear all markets simultaneously, and consumers and firms optimize given those prices. Introduced by Kenneth Arrow and Gerard Debreu in 1954, the model extends Adam Smith's invisible hand concept into a rigorous mathematical framework. Arrow-Debreu equilibrium proves existence, uniqueness (under certain conditions), and Pareto efficiency of competitive equilibria.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kenneth Arrow, Gerard Debreu","subfamily":"Game-theoretic","year":"1954","type":"algorithm"},"citations":[{"ref":"Arrow, K. J., & Debreu, G. (1954). Existence of an equilibrium for competitive economies. Econometrica, 22(3), 265-290.","type":"article","doi":"10.2307/1907353","isbn":null,"url":null},{"ref":"Debreu, G. (1959). Theory of Value: An Axiomatic Analysis of Economic Equilibrium. Yale University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Theory+of+Value%3A+An+Axiomatic+Analysis+of+Economic+Equilibrium-p-9780300013610"}],"related":["nash-equilibrium","bayesian-nash-equilibrium","vcg-mechanism","cournot-competition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"artasi","name":"ARTASI","fullName":"Alternative Ranking Technique based on Adaptive Standardized Intervals","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2024","originator":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D.","url":"https://scholargate.app/en/decision-making/artasi","markdownUrl":"https://scholargate.app/en/decision-making/artasi.md","definition":"ARTASI (Alternative Ranking Technique based on Adaptive Standardized Intervals) is a ranking multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D.","subfamily":"Ranking","year":"2024","type":"Two-level standardization + ideal/anti-ideal utility (β-anchored)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. (2024). A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2024.111826","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"artificial-bee-colony","name":"Artificial Bee Colony","fullName":"Artificial Bee Colony (ABC) Optimization","aliases":["ABC Algorithm","Bee Colony Optimization","Swarm-Based Bee Search","Yapay Arı Kolonisi"],"domain":"optimization","family":"process-pipeline","subfamily":"Metaheuristics","year":2007,"originator":"Dervis Karaboga & Bahriye Basturk","url":"https://scholargate.app/en/optimization/artificial-bee-colony","markdownUrl":"https://scholargate.app/en/optimization/artificial-bee-colony.md","definition":"Artificial Bee Colony (ABC) is a population-based swarm intelligence metaheuristic introduced by Karaboga and Basturk in 2007. It models the cooperative foraging behavior of a honey bee colony to search for optimal solutions in continuous numerical optimization problems. The algorithm divides candidate solutions among three bee types — employed, onlooker, and scout — and iteratively refines them through local search and probabilistic selection, making it well-suited for researchers and engineers tackling complex, multimodal optimization landscapes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dervis Karaboga & Bahriye Basturk","year":2007,"type":"Swarm Intelligence Metaheuristic","subfamily":"Metaheuristics","inspiration":"Foraging behavior of honey bees","control_parameters":"Colony size, limit (abandonment threshold)"},"citations":[{"ref":"Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.","type":"article","doi":"10.1007/s10898-007-9149-x","isbn":null,"url":null}],"related":["particle-swarm-optimization","ant-colony-optimization","genetic-algorithm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ases-shoulder","name":"American Shoulder and Elbow Surgeons Score","fullName":"American Shoulder and Elbow Surgeons Standardized Assessment Form","aliases":["ASES","ASES Shoulder Score"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"shoulder-specific outcome","year":1994,"originator":"American Shoulder and Elbow Surgeons (ASES) Committee","url":"https://scholargate.app/en/sports-medicine/ases-shoulder","markdownUrl":"https://scholargate.app/en/sports-medicine/ases-shoulder.md","definition":"The American Shoulder and Elbow Surgeons (ASES) Standardized Assessment Form is a hybrid outcome instrument combining patient self-report and clinician assessment to evaluate shoulder function and pain. Developed by the ASES Committee in 1994 and published in the Journal of Shoulder and Elbow Surgery, the ASES Score has become the standard shoulder outcome measure in orthopedic and sports medicine settings, widely used in clinical trials, surgical registries, and longitudinal outcomes tracking.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Shoulder and Elbow Surgeons (ASES) Committee","subfamily":"shoulder-specific outcome","year":1994,"type":"Patient self-report with clinician assessment components"},"citations":[{"ref":"Richards RR, An KN, Bigliani LU, et al. A standardized method for the assessment of shoulder function. J Shoulder Elbow Surg. 1994;3(6):347-352.","type":"article","doi":"10.1016/S1058-2746(09)80019-0","isbn":null,"url":null}],"related":["patient-specific-functional-scale","global-rating-of-change-scale","rotator-cuff-quality-of-life","faos"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"aspect-based-sentiment","name":"Aspect-Based Sentiment Analysis","fullName":"Aspect-Based Sentiment Analysis (ABSA)","aliases":["ABSA","aspect-level sentiment analysis","feature-based sentiment analysis","Konu Bazlı Duygu Analizi (ABSA)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2014,"originator":"Pontiki et al. (SemEval-2014 Task 4)","url":"https://scholargate.app/en/text-mining/aspect-based-sentiment","markdownUrl":"https://scholargate.app/en/text-mining/aspect-based-sentiment.md","definition":"Aspect-based sentiment analysis (ABSA) is a fine-grained natural-language-processing task that detects sentiment separately for each aspect or feature mentioned in a text — such as a product's quality, price, or service — rather than scoring the document as a whole. It was consolidated as a shared task by Pontiki et al. in SemEval-2014 Task 4.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP fine-grained opinion-mining task","originator":"Pontiki et al. (SemEval-2014 Task 4)","year":2014,"granularity":"Aspect / feature level (not whole document)","minSample":50,"output":"Sentiment polarity per aspect or feature"},"citations":[{"ref":"Pontiki, M. et al. (2014). SemEval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings of SemEval 2014, 27-35.","type":"inproceedings","doi":"10.3115/v1/S14-2004","isbn":null,"url":null},{"ref":"Schouten, K. & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830.","type":"article","doi":"10.1109/TKDE.2015.2485209","isbn":null,"url":null}],"related":["sentiment-analysis","named-entity-recognition","text-classification","topic-modeling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"assembly-line-balancing","name":"Assembly Line Balancing","fullName":"Assembly Line Balancing","aliases":["line balancing","workload balancing"],"domain":"operations-management","family":"ml-model","subfamily":"Production Optimization","year":"2010","originator":"Scholl, A.","url":"https://scholargate.app/en/operations-management/assembly-line-balancing","markdownUrl":"https://scholargate.app/en/operations-management/assembly-line-balancing.md","definition":"Assembly Line Balancing is the problem of distributing a sequence of assembly tasks across a series of workstations on a production line such that work is evenly distributed, idle time is minimized, and throughput constraints are satisfied. The goal is to assign tasks to stations such that the total work time at each station is as equal as possible, optimizing for production rate (cycle time) and resource utilization. This is a classic optimization problem in manufacturing, solved through heuristic and exact algorithms, essential to the efficiency of mass production systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scholl, A.","subfamily":"Production Optimization","year":"2010","type":"Optimization problem"},"citations":[{"ref":"Scholl, A. (2010). Balancing and sequencing of assembly lines. Physica-Verlag.","type":"article","doi":null,"isbn":null,"url":"https://www.springer.com/"},{"ref":"Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management Science, 32(8), 909-932.","type":"article","doi":"10.1287/mnsc.32.8.909","isbn":null,"url":null}],"related":["job-shop-scheduling","facility-layout","smed","total-productive-maintenance","kanban"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"assessment-life-habits","name":"Assessment of Life Habits","fullName":"Assessment of Life Habits (LIFE-H)","aliases":["LIFE-H","AALH"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"life-habits-participation","year":"1992","originator":"Noreau, Fougeyrollas, Blouin","url":"https://scholargate.app/en/rehabilitation-science/assessment-life-habits","markdownUrl":"https://scholargate.app/en/rehabilitation-science/assessment-life-habits.md","definition":"The Assessment of Life Habits (LIFE-H) is a comprehensive, interview-based measure that evaluates participation in 11 key life domains—from basic self-care and nutrition to work, recreation, and community engagement. Developed in Quebec by Fougeyrollas, Noreau, and colleagues, LIFE-H operationalizes the ICF concept of 'participation' through a detailed assessment of how individuals accomplish (or struggle with) the habits and roles essential to life in their community.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Noreau, Fougeyrollas, Blouin","subfamily":"life-habits-participation","year":"1992","type":"Interview-administered"},"citations":[{"ref":"Noreau, L., Fougeyrollas, P., & Blouin, M. (1992). Revision of the LIFE-H measurement instrument: conceptual structure and items content. Journal of Outcome Measurement, 2(4), 242–268.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1123/joom.2.4.242"},{"ref":"Fougeyrollas, P., & Noreau, L. (2002). The Influence of Human Development, Social Structure and Culture on the Type of Environmental and Personal Determinants to the Genesis of Handicap. In: WHO International Classification of Functioning, Disability and Health (ICF). WHO.","type":"book","doi":null,"isbn":null,"url":"https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health"}],"related":["impact-participation-autonomy","community-integration-questionnaire","participation-scale","reintegration-to-normal-living","whodas-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"association-rule-mining","name":"Association Rule Mining","fullName":"Association Rule Mining (Apriori)","aliases":["Market Basket Analysis","Frequent Itemset Mining","Birliktelik Kuralı Madenciliği","Itemset Association Analysis"],"domain":"machine-learning","family":"ml-model","subfamily":"Pattern mining","year":1994,"originator":"Rakesh Agrawal & Ramakrishnan Srikant","url":"https://scholargate.app/en/machine-learning/association-rule-mining","markdownUrl":"https://scholargate.app/en/machine-learning/association-rule-mining.md","definition":"Association Rule Mining is an unsupervised data-mining technique that discovers co-occurrence patterns among items in transactional datasets. Formally introduced by Agrawal, Imieliński, and Swami in 1993, and refined with the landmark Apriori algorithm by Agrawal and Srikant in 1994, it identifies rules of the form X ⇒ Y — meaning that transactions containing itemset X tend to also contain itemset Y — quantified by support, confidence, and lift.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rakesh Agrawal & Ramakrishnan Srikant","year":1994,"type":"Unsupervised pattern discovery algorithm","subfamily":"Pattern mining","complexity":"O(2^|I|) worst case; Apriori prunes with anti-monotonicity","data_type":"Transactional / binary item sets"},"citations":[{"ref":"Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.","type":"inproceedings","doi":"10.1145/170035.170072","isbn":null,"url":null},{"ref":"Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, 487–499.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.vldb.org/conf/1994/P487.PDF"}],"related":["formal-concept-analysis","rule-induction","k-means-clustering"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"association-rules","name":"Association Rules","fullName":"Association Rule Learning (Market Basket Analysis)","aliases":["market basket analysis","association rule mining","frequent itemset mining","affinity analysis"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1993","originator":"Agrawal, R., Imielinski, T., & Swami, A.","url":"https://scholargate.app/en/machine-learning/association-rules","markdownUrl":"https://scholargate.app/en/machine-learning/association-rules.md","definition":"Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agrawal, R., Imielinski, T., & Swami, A.","year":"1993","type":"Unsupervised pattern discovery","dataType":"Transactional / binary itemset data","subfamily":"Machine learning"},"citations":[{"ref":"Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.","type":"inproceedings","doi":"10.1145/170035.170072","isbn":null,"url":null},{"ref":"Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Introduction to Data Mining (2nd ed., Ch. 5). Pearson.","type":"book","doi":null,"isbn":"978-0-13-312890-1","url":null}],"related":["apriori-algorithm","k-means","gaussian-mixture-model","semi-supervised-learning","voting-ensemble"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"asteroseismology","name":"Asteroseismology","fullName":"Asteroseismology for Stellar Property Determination","aliases":["Stellar Oscillations","Stellar Seismology","Helioseismology"],"domain":"astronomy","family":"process-pipeline","subfamily":"Stellar physics","year":1970,"originator":"Roger Ulrich","url":"https://scholargate.app/en/astronomy/asteroseismology","markdownUrl":"https://scholargate.app/en/astronomy/asteroseismology.md","definition":"Asteroseismology is the study of stellar oscillations—tiny brightness and radial velocity variations caused by sound waves resonating inside stars. Proposed by Roger Ulrich in 1970 and established as a major field by the Kepler and TESS space telescopes, asteroseismology provides unprecedented precision in determining stellar masses, ages, and internal structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roger Ulrich","subfamily":"Stellar physics","year":1970,"type":"Observational technique"},"citations":[{"ref":"Ulrich, R. K. (1970). The five-minute oscillations on the solar surface. Astrophysical Journal, 162, 993-999.","type":"article","doi":"10.1086/150731","isbn":null,"url":null},{"ref":"Gilliland, R. L., et al. (1994). Observations of solar-like oscillations in the G dwarf star eta Bootis. Astrophysical Journal, 435, 385-397.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Observations+of+solar-like+oscillations+in+the+G+dwarf+star+eta+Bootis+Gilliland"},{"ref":"Kjeldsen, H., & Bedding, T. R. (2008). Asteroseismology of solar-type stars. Astrophysics and Space Science, 328(1), 61-71.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Asteroseismology+of+solar-type+stars+Kjeldsen"}],"related":["stellar-population-synthesis","radiative-transfer","rotation-curve-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"asthma-control-questionnaire","name":"ACQ","fullName":"Asthma Control Questionnaire","aliases":["ACQ","Asthma Control Q"],"domain":"pulmonology","family":"process-pipeline","subfamily":"asthma-control","year":"1999","originator":"Elizabeth F. Juniper, McMaster University","url":"https://scholargate.app/en/pulmonology/asthma-control-questionnaire","markdownUrl":"https://scholargate.app/en/pulmonology/asthma-control-questionnaire.md","definition":"The ACQ is a 7-item self-report questionnaire developed by Juniper and colleagues at McMaster University in 1999 to assess the degree of asthma control in the previous one to two weeks. Unlike generic respiratory tools, the ACQ measures symptom-based control and rescue medication use, providing a simple yet psychometrically sound method for evaluating treatment efficacy. It is widely adopted in clinical trials, guideline-driven care, and routine asthma management to guide therapy titration and assess treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elizabeth F. Juniper, McMaster University","subfamily":"asthma-control","year":"1999","type":"Self-report questionnaire"},"citations":[{"ref":"Juniper, E. F., O'Byrne, P. M., Guyatt, G. H., Ferrie, P. J., & King, D. R. (1999). Development and validation of a questionnaire to measure asthma control. European Respiratory Journal, 14(4), 902-907.","type":"article","doi":"10.1034/j.1399-3003.1999.14d29.x","isbn":null,"url":null},{"ref":"Juniper, E. F., Svensson, K., Mörk, A. C., & Ståhl, E. (2005). Measurement properties and interpretation of three shortened versions of the asthma control questionnaire. Respiratory Medicine, 99(5), 553-558.","type":"article","doi":"10.1016/j.rmed.2004.10.008","isbn":null,"url":null}],"related":["st-george-respiratory-questionnaire","chronic-respiratory-disease-questionnaire","breathlessness-cough-sputum-scale","mrc-dyspnoea-scale","rhinitis-quality-of-life"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"asthma-control-test","name":"ACT","fullName":"Asthma Control Test","aliases":["ACT","Asthma Control Test","Asthma Control Assessment"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Respiratory and Allergic Disease","year":"2004","originator":"Robert A. Nathan et al.","url":"https://scholargate.app/en/health-outcomes/asthma-control-test","markdownUrl":"https://scholargate.app/en/health-outcomes/asthma-control-test.md","definition":"The ACT is a simple, rapid, patient-centered measure of asthma control. Developed by Robert Nathan and colleagues in 2004, this 5-item questionnaire quantifies how asthma symptoms, activity limitation, and nighttime awakening affect daily life. It is the most widely used asthma control measure in clinical practice and is recommended by major asthma guidelines as a standard assessment tool.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert A. Nathan et al.","subfamily":"Respiratory and Allergic Disease","year":"2004","type":"Self-report symptom control questionnaire"},"citations":[{"ref":"Nathan, R. A., Sorkness, C. A., Kosinski, M., Schatz, M., Li, J. T., Marcus, P., ... & Pendergraft, T. B. (2004). Development of the asthma control test: A survey for assessing asthma control in children and adults. Allergy and Asthma Proceedings, 25(1), 1-6.","type":"article","doi":"10.1016/j.jaci.2003.09.008","isbn":null,"url":null},{"ref":"Schatz, M., Kosinski, M., Yarlas, A. S., Pendergraft, T. B., Kowgier, M., & Lee, J. H. (2012). The minimally important difference of the Asthma Control Test. Journal of Allergy and Clinical Immunology, 126(3), 581-587.e10.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+minimally+important+difference+of+the+Asthma+Control+Test+Schatz"},{"ref":"Juniper, E. F., O'Byrne, P. M., Guyatt, G. H., Ferrie, P. J., & King, D. R. (1999). Development and validation of a questionnaire to measure asthma control. European Respiratory Journal, 14(4), 902-907.","type":"article","doi":"10.1034/j.1399-3003.1999.14d29.x","isbn":null,"url":null}],"related":["eortc-qlq-c30","copd-assessment-test","chronic-heart-failure-questionnaire","pdq-39"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"astrometry","name":"Astrometry (Parallax)","fullName":"Astrometric Parallax Method for Distance Measurement","aliases":["Stellar Parallax","Trigonometric Parallax","Parallax Distance Method"],"domain":"astronomy","family":"process-pipeline","subfamily":"Geometrical measurement","year":1838,"originator":"Friedrich Wilhelm Bessel","url":"https://scholargate.app/en/astronomy/astrometry","markdownUrl":"https://scholargate.app/en/astronomy/astrometry.md","definition":"Astrometric parallax is the foundational geometric method for measuring distances to nearby stars, based on observing the apparent shift in a star's position as Earth orbits the Sun. First successfully demonstrated by Friedrich Wilhelm Bessel in 1838 for the star 61 Cygni, parallax remains the most direct and reliable distance measurement in astronomy, anchoring the entire cosmic distance ladder.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Friedrich Wilhelm Bessel","subfamily":"Geometrical measurement","year":1838,"type":"Astrometric distance measurement"},"citations":[{"ref":"ESA (1997). The Hipparcos and Tycho Catalogues. Astrometric and photometric star catalogue. European Space Agency Technical Reports, SP-1200.","type":"article","doi":null,"isbn":null,"url":"https://www.cosmos.esa.int/web/hipparcos"},{"ref":"van Leeuwen, F. (2007). Validation of the new Hipparcos reduction. Astronomy & Astrophysics, 474(2), 653-664.","type":"article","doi":"10.1051/0004-6361:20078357","isbn":null,"url":null},{"ref":"Gaia Collaboration (2016). Gaia Data Release 1: Astrometry-one billion positions, two million proper-motions and parallaxes. Astronomy & Astrophysics, 595, A2.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Gaia+Data+Release+1%3A+Astrometry-one+billion+positions%2C+two+million+proper-motions+and+parallaxes+Gaia"}],"related":["kinematic-distance","sed-fitting","gravitational-microlensing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"atac-seq-analysis","name":"ATAC-seq Analysis","fullName":"ATAC-seq Analysis for Chromatin Accessibility and Regulatory Landscapes","aliases":["Chromatin accessibility","Open chromatin","Accessible chromatin analysis"],"domain":"genetics","family":"process-pipeline","subfamily":"Epigenomics","year":"2013","originator":"Jason Buenrostro, Paul Giresi & William Greenleaf","url":"https://scholargate.app/en/genetics/atac-seq-analysis","markdownUrl":"https://scholargate.app/en/genetics/atac-seq-analysis.md","definition":"ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) is a method for profiling the landscape of chromatin accessibility genome-wide. Developed by Buenrostro and colleagues in 2013, ATAC-seq uses hyperactive transposase to tag open, accessible chromatin regions, enabling rapid and sensitive identification of regulatory DNA elements. ATAC-seq has become a standard technique for characterizing gene regulatory landscapes, discovering cell-type-specific regulatory elements, and inferring gene regulatory networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jason Buenrostro, Paul Giresi & William Greenleaf","subfamily":"Epigenomics","year":"2013","type":"Chromatin profiling method"},"citations":[{"ref":"Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of cell populations and tissues. Nature Methods, 10(12), 1213–1218.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Transposition+of+native+chromatin+for+fast+and+sensitive+epigenomic+profiling+of+cell+populations+and+tissues+Buenrostro"},{"ref":"Corces, M. R., Buenrostro, J. D., Wu, B., Greenside, P. G., Chan, S. M., Koenig, J. L., & Greenleaf, W. J. (2017). Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis. Nature Genetics, 48(10), 1193–1203.","type":"article","doi":"10.1038/ng.3646","isbn":null,"url":null},{"ref":"Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., & Chang, H. Y. (2019). Massively parallel single-cell chromatin landscapes. Nature Biotechnology, 37(12), 1452–1462.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Massively+parallel+single-cell+chromatin+landscapes+Satpathy"}],"related":["hi-c-analysis","rna-velocity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"atam","name":"ATAM","fullName":"Architecture Tradeoff Analysis Method","aliases":["ATAM framework","architecture review"],"domain":"numerical-methods","family":"ml-model","subfamily":"Architecture Evaluation","year":"2000","originator":"Rick Kazman","url":"https://scholargate.app/en/numerical-methods/atam","markdownUrl":"https://scholargate.app/en/numerical-methods/atam.md","definition":"The Architecture Tradeoff Analysis Method (ATAM) is a systematic technique developed by Kazman et al. at CMU/SEI for evaluating software architectures against quality attributes (performance, security, modifiability). ATAM uncovers architectural risks and tradeoffs early, helping teams assess whether designs meet quality goals before implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rick Kazman","subfamily":"Architecture Evaluation","year":"2000","type":"Architecture analysis method"},"citations":[{"ref":"Kazman, R., Klein, M., Barbacci, M., Longstaff, T., Lipson, H., & Carriere, J. (2000). The Architecture Tradeoff Analysis Method. CMU/SEI Technical Report CMU/SEI-98-TR-008.","type":"book","doi":null,"isbn":null,"url":"https://resources.sei.cmu.edu/asset_files/TechnicalReport/2000_005_001_13706.pdf"},{"ref":"Bass, L., Klein, M., & Kazman, R. (2003). Attribute-Driven Design (ADD), Version 2. CMU/SEI-2003-TR-002.","type":"book","doi":null,"isbn":null,"url":"https://resources.sei.cmu.edu/asset_files/TechnicalReport/2003_005_001_13963.pdf"},{"ref":"Kazman, R., Asundi, J., & Klein, M. (2001). Making architecture design decisions: An empirical study. Proceedings of the 23rd International Conference on Software Engineering.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Making+architecture+design+decisions%3A+An+empirical+study+Kazman"}],"related":["software-architecture","quality-attribute-modeling","design-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"athens-insomnia-scale","name":"Athens Insomnia Scale","fullName":"Athens Insomnia Scale (AIS)","aliases":["AIS"],"domain":"psychiatry","family":"process-pipeline","subfamily":"ICD-10 based insomnia screening","year":"2000","originator":"Christos R. Soldatos","url":"https://scholargate.app/en/psychiatry/athens-insomnia-scale","markdownUrl":"https://scholargate.app/en/psychiatry/athens-insomnia-scale.md","definition":"The AIS is an 8-item self-report scale designed to assess insomnia severity in adolescents and adults, based on ICD-10 diagnostic criteria for insomnia disorder. Developed by Soldatos and colleagues in 2000, it is widely used in European primary care, psychiatry, and sleep medicine for screening and severity assessment. The AIS is brief (3–5 minutes), applicable across ages and cultures, and sensitive to treatment-induced change in both pharmacological and behavioral interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christos R. Soldatos","subfamily":"ICD-10 based insomnia screening","year":"2000","type":"Self-report questionnaire"},"citations":[{"ref":"Soldatos, C. R., Dikeos, D. G., & Paparrigopoulos, T. J. (2000). Athens Insomnia Scale: Validation of an instrument based on ICD-10 criteria. Journal of Psychosomatic Research, 48(6), 555–560.","type":"article","doi":"10.1016/S0022-3999(00)00095-7","isbn":null,"url":null},{"ref":"Chelminski, I., Wasserman, D., & Zalewska-Puchala, B. (2010). Validity of the Athens Insomnia Scale in adolescents with affective disorder. Journal of Affective Disorders, 122(3), 281–285.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validity+of+the+Athens+Insomnia+Scale+in+adolescents+with+affective+disorder+Chelminski"},{"ref":"Okajima, I., Komada, Y., & Inoue, Y. (2013). A review of prevalence and prevalence estimation of insomnia in Asia: prevalence studies of insomnia night by night are needed to measure the global burden of insomnia accurately. Journal of Epidemiology, 23(1), 1–11.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+review+of+prevalence+and+prevalence+estimation+of+insomnia+in+Asia%3A+prevalence+studies+of+insomnia+night+by+night+are+needed+to+measure+the+global+burden+of+insomnia+accurately+Okajima"}],"related":["insomnia-severity-index","brief-psychiatric-rating-scale","panss"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"athletic-identity-measurement-scale","name":"Athletic Identity Measurement Scale","fullName":"Athletic Identity Measurement Scale (AIMS)","aliases":["AIMS","Athletic Identity"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"identity-and-self-concept","year":"1993","originator":"Britton Brewer, Jean Van Raalte, Diane Linder","url":"https://scholargate.app/en/sport-psychology/athletic-identity-measurement-scale","markdownUrl":"https://scholargate.app/en/sport-psychology/athletic-identity-measurement-scale.md","definition":"The AIMS is a 10-item questionnaire assessing the degree to which being an athlete is central to an individual's self-concept and identity. Developed by Brewer, Van Raalte, and Linder in 1993, the AIMS has become the standard instrument for measuring athletic identity and is widely used to predict athlete coping responses to injury, career transitions, and retirement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Britton Brewer, Jean Van Raalte, Diane Linder","subfamily":"identity-and-self-concept","year":"1993","type":"Self-report athletic identity questionnaire"},"citations":[{"ref":"Brewer, B. W., Van Raalte, J. L., & Linder, D. E. (1993). Athletic identity: Hercules' muscles or Achilles' heel? International Journal of Sport Psychology, 24(2), 237–254.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/AIMS1993"},{"ref":"Brewer, B. W., & Cornelius, A. E. (2001). Norms for the Athletic Identity Measurement Scale. Measurement in Physical Education and Exercise Science, 5(1), 47–54.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Norms+for+the+Athletic+Identity+Measurement+Scale+Brewer"}],"related":["sport-motivation-scale","task-ego-orientation-sport","sport-confidence-inventory","mental-toughness-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"atomic-absorption-spectroscopy","name":"Atomic Absorption Spectroscopy","fullName":"Atomic Absorption Spectroscopy","aliases":["AAS","flame AAS","graphite furnace AAS","GFAAS"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Optical Spectroscopy","year":"1955","originator":"Alan Walsh","url":"https://scholargate.app/en/analytical-chemistry/atomic-absorption-spectroscopy","markdownUrl":"https://scholargate.app/en/analytical-chemistry/atomic-absorption-spectroscopy.md","definition":"Atomic absorption spectroscopy is an analytical technique that measures the concentration of metal elements by detecting the absorption of light by ground-state metal atoms in the gaseous state. Invented by Alan Walsh in 1955, it rapidly became the standard method for trace metal analysis in environmental, clinical, agricultural, and industrial samples. Atomic absorption spectroscopy's sensitivity, selectivity, and simplicity make it indispensable for monitoring toxic metals, nutritional minerals, and elements in complex matrices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Walsh","subfamily":"Optical Spectroscopy","year":"1955","type":"elemental analysis technique"},"citations":[{"ref":"Walsh, A. (1955). The application of atomic absorption spectra to chemical analysis. Spectrochimica Acta, 7, 108–117.","type":"article","doi":"10.1016/0371-1951(55)80013-6","isbn":null,"url":null},{"ref":"Skoog, D. A., West, D. M., Holler, F. J., & Crouch, S. R. (2014). Fundamentals of Analytical Chemistry (9th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1133170960","url":null},{"ref":"Welz, B., & Sperling, M. (2000). Atomic Absorption Spectrometry (3rd ed.). Wiley-VCH.","type":"book","doi":null,"isbn":"978-3527286393","url":null}],"related":["inductively-coupled-plasma","uv-vis-spectrophotometry","potentiometric-titration","ion-chromatography","coulometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"atomic-force-microscopy","name":"Atomic Force Microscopy","fullName":"Atomic Force Microscopy (AFM)","aliases":["AFM","scanning probe microscopy","nanoindentation microscopy"],"domain":"materials-science","family":"process-pipeline","subfamily":"Scanning probe microscopy","year":"1986","originator":"Gerd Binnig","url":"https://scholargate.app/en/materials-science/atomic-force-microscopy","markdownUrl":"https://scholargate.app/en/materials-science/atomic-force-microscopy.md","definition":"Atomic Force Microscopy (AFM) is a scanning probe technique that measures nanoscale surface topography and mechanical properties by monitoring interactions between a sharp cantilever tip and a sample surface. Invented by Gerd Binnig in 1986 as an extension of scanning tunneling microscopy, AFM requires neither electrical conductivity nor vacuum operation, making it applicable to virtually any material. It provides three-dimensional topographic maps with sub-nanometer vertical resolution and lateral resolution approaching nanometers, along with simultaneous measurements of mechanical, electrical, and chemical properties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gerd Binnig","subfamily":"Scanning probe microscopy","year":"1986","type":"Imaging technique"},"citations":[{"ref":"Binnig, G., Quate, C. F., & Gerber, C. (1986). Atomic force microscope. Physical Review Letters, 56(9), 930-933.","type":"article","doi":"10.1103/PhysRevLett.56.930","isbn":null,"url":null},{"ref":"Eaton, P., & West, P. (2005). Atomic Force Microscopy. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://books.google.com/books?id=3L-lQgAACAAJ"},{"ref":"Butt, H. J., Cappella, B., & Kappl, M. (2005). Force measurements with the atomic force microscope: Technique, interpretation and applications. Surface Science Reports, 59(1-6), 1-152.","type":"article","doi":"10.1016/j.surfrep.2005.08.003","isbn":null,"url":null}],"related":["selected-area-electron-diffraction","energy-dispersive-x-ray-spectroscopy","nanoindentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"atr-ftir","name":"ATR-FTIR","fullName":"Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy","aliases":["ATR-IR","attenuated total reflectance","FTIR spectroscopy"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Infrared Spectroscopy","year":"1961","originator":"Joop Fahrenfort","url":"https://scholargate.app/en/spectroscopy/atr-ftir","markdownUrl":"https://scholargate.app/en/spectroscopy/atr-ftir.md","definition":"Attenuated Total Reflectance (ATR) Fourier Transform Infrared (FTIR) spectroscopy is a variant of conventional FTIR that measures infrared absorption through evanescent-wave interrogation of samples in direct contact with a high-refractive-index crystal. Developed by Harrick and Fahrenfort in the 1960s, ATR-FTIR is now the dominant form of FTIR spectroscopy, enabling rapid, non-destructive characterization of organic compounds, polymers, coatings, and biological materials without extensive sample preparation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joop Fahrenfort","subfamily":"Infrared Spectroscopy","year":"1961","type":"Vibrational spectroscopy technique"},"citations":[{"ref":"Harrick, N. J. (1960). Study of physics of internal reflection from metals. Journal of Physics and Chemistry of Solids, 13(2), 143-155.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Study+of+physics+of+internal+reflection+from+metals+Harrick"},{"ref":"Fahrenfort, J. (1961). Attenuated total reflection: a new principle for the production of useful infra-red reflection spectra of organic compounds. Spectrochimica Acta, 17(6), 698-709.","type":"article","doi":"10.1016/0371-1951(61)80136-7","isbn":null,"url":null},{"ref":"Humecki, H. J. (Ed.). (1995). Practical Guide to Infrared Microspectroscopy. CRC Press.","type":"book","doi":null,"isbn":null,"url":"https://www.routledge.com/Practical-Guide-to-Infrared-Microspectroscopy/Humecki/p/book/9780824790486"}],"related":["sers","circular-dichroism","ft-icr-mass-spectrometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"attachment-style-questionnaire","name":"Attachment Style Questionnaire","fullName":"Attachment Style Questionnaire (ASQ)","aliases":["ASQ","Relationship Style Questionnaire","Attachment Orientation"],"domain":"social-psychology","family":"process-pipeline","subfamily":"adult attachment and romantic relationships","year":"1987","originator":"Cindy Hazan and Phillip Shaver (developed attachment-based romantic love approach); multiple ASQ versions by Feeney, Brennan, and others","url":"https://scholargate.app/en/social-psychology/attachment-style-questionnaire","markdownUrl":"https://scholargate.app/en/social-psychology/attachment-style-questionnaire.md","definition":"The Attachment Style Questionnaire is a self-report instrument measuring adult romantic attachment patterns based on attachment theory. Developed following Hazan and Shaver's seminal 1987 work extending John Bowlby's attachment theory to adult romantic relationships, the ASQ assesses individual differences in attachment anxiety (fear of abandonment and desire for closeness) and attachment avoidance (discomfort with intimacy and emotional dependence). The ASQ is used extensively in relationship research, couple therapy, and studies examining how childhood attachment experiences predict adult romantic functioning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cindy Hazan and Phillip Shaver (developed attachment-based romantic love approach); multiple ASQ versions by Feeney, Brennan, and others","subfamily":"adult attachment and romantic relationships","year":"1987","type":"Self-report attachment assessment"},"citations":[{"ref":"Feeney, B. C., & Monin, J. K. (2008). An attachment-theoretical perspective on divorce. In J. Cassidy & P. R. Shaver (Eds.), Handbook of attachment: Theory, research, and clinical applications (2nd ed., pp. 934-957). New York: Guilford Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Feeney+Monin+attachment+divorce+2008"},{"ref":"Hazan, C., & Shaver, P. (1987). Romantic love conceptualized as an attachment process. Journal of Personality and Social Psychology, 52(3), 511-524.","type":"article","doi":"10.1037/0022-3514.52.3.511","isbn":null,"url":null}],"related":["dyadic-adjustment-scale","relationship-assessment-scale","social-provisions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"attention-mechanism","name":"Attention Mechanism","fullName":"Attention Mechanism (Bahdanau / Luong Attention)","aliases":["Dikkat Mekanizması (Bahdanau / Luong Attention)","dikkat mekanizmasi","neural attention","additive attention","multiplicative attention","encoder-decoder attention"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2015,"originator":"Bahdanau, D.; Luong, M.T.","url":"https://scholargate.app/en/deep-learning/attention-mechanism","markdownUrl":"https://scholargate.app/en/deep-learning/attention-mechanism.md","definition":"The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bahdanau, D.; Luong, M.T.","year":2015,"type":"Neural attention layer (encoder-decoder)","task":"Sequence-to-sequence prediction & explanation","minSample":200},"citations":[{"ref":"Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1409.0473"},{"ref":"Luong, M.T., Pham, H. & Manning, C.D. (2015). Effective Approaches to Attention-based Neural Machine Translation. EMNLP, 1412–1421.","type":"article","doi":"10.18653/v1/D15-1166","isbn":null,"url":null}],"related":["self-attention-transformer","bert-finetuning","gpt-finetuning","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"attitudes-cam-scale","name":"Attitudes toward CAM Scale","fullName":"Attitudes toward Complementary and Alternative Medicine Scale","aliases":["ACAMS"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"Attitudes and beliefs toward CAM","year":"2003","originator":"Hough, H. J.; Darcey, V. L.; Scofield, R. F.","url":"https://scholargate.app/en/integrative-medicine/attitudes-cam-scale","markdownUrl":"https://scholargate.app/en/integrative-medicine/attitudes-cam-scale.md","definition":"The ACAMS is a self-report instrument measuring healthcare professionals' and students' attitudes toward complementary and alternative medicine. Developed in the early 2000s, it assesses openness, acceptance, and perceived legitimacy of CAM alongside conventional medicine, helping identify educational gaps and organizational readiness for integrative practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hough, H. J.; Darcey, V. L.; Scofield, R. F.","subfamily":"Attitudes and beliefs toward CAM","year":"2003","type":"Self-report scale"},"citations":[{"ref":"Hough, H. J., Darcey, V. L., & Scofield, R. F. (2003). Attitudes toward alternative/complementary medicines among pharmacy students, faculty, and preceptors. American Journal of Pharmaceutical Education, 67(3), 85.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Attitudes+toward+alternative%2Fcomplementary+medicines+among+pharmacy+students%2C+faculty%2C+and+preceptors+Hough"},{"ref":"Chan, M. F., Chan, E. A., & Mok, E. (2002). Attitudes toward complementary and alternative medicine: A cross-sectional study of Hong Kong nursing students. Journal of Clinical Nursing, 11(5), 597–605.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Attitudes+toward+complementary+and+alternative+medicine%3A+A+cross-sectional+study+of+Hong+Kong+nursing+students+Chan"}],"related":["integrative-medicine-attitudes","cam-use-questionnaire","holistic-caring-inventory","spiritual-care-competence-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"attrakdiff-ueq","name":"AttrakDiff/UEQ","fullName":"AttrakDiff and User Experience Questionnaire","aliases":["Hedonic Quality Assessment","Pragmatic vs. Hedonic","UEQ"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"User Experience Assessment","year":"2003","originator":"Marc Hassenzahl (AttrakDiff), Martin Schrepp (UEQ)","url":"https://scholargate.app/en/human-computer-interaction/attrakdiff-ueq","markdownUrl":"https://scholargate.app/en/human-computer-interaction/attrakdiff-ueq.md","definition":"AttrakDiff and the User Experience Questionnaire (UEQ) are assessment instruments for measuring user experience across multiple dimensions. AttrakDiff, developed by Marc Hassenzahl, evaluates the tension between pragmatic quality (functionality, usability, does the system do what I need?) and hedonic quality (beauty, emotional engagement, does it delight me?). The UEQ, developed by Schrepp and colleagues, extends this framework with additional dimensions including efficiency, perspicuity, stimulation, and novelty. Both instruments provide quantitative post-use assessment, complementing task-based usability testing with holistic experience evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marc Hassenzahl (AttrakDiff), Martin Schrepp (UEQ)","subfamily":"User Experience Assessment","year":"2003","type":"Questionnaire measuring pragmatic and hedonic quality dimensions"},"citations":[{"ref":"Hassenzahl, M. (2003). The thing and I: Understanding the relationship between user and product. In M. A. Blythe, K. Overbeeke, A. F. Monk, & P. C. Wright (Eds.), Funology (pp. 31–42). Kluwer Academic Publishers.","type":"article","doi":"10.1007/1-4020-2967-5_4","isbn":null,"url":null},{"ref":"Schrepp, M., Hinderks, A., & Thomaschewski, J. (2017). Design and evaluation of a short version of the User Experience Questionnaire (UEQ-S). International Journal of Interactive Multimedia and Artificial Intelligence, 4(6), 103–108.","type":"article","doi":null,"isbn":null,"url":"https://www.ijimai.org/"}],"related":["system-usability-scale","nasa-tlx","kano-model","think-aloud-protocol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"attribute-sampling-audit","name":"Attribute Sampling in Auditing","fullName":"Attribute Sampling Framework for Testing Internal Controls","aliases":["Statistical Attribute Sampling","Compliance Testing","Control Testing Sampling"],"domain":"accounting","family":"mcdm","subfamily":"Statistical Sampling Methods","year":"1972","originator":"American Institute of Certified Public Accountants (AICPA)","url":"https://scholargate.app/en/accounting/attribute-sampling-audit","markdownUrl":"https://scholargate.app/en/accounting/attribute-sampling-audit.md","definition":"Attribute sampling is a statistical sampling method used primarily in testing the operating effectiveness of internal controls. Rather than measuring the dollar impact of errors (as in substantive sampling), attribute sampling answers a yes/no question: 'Does this control exist and is it operating as designed?' By determining the sample size and evaluating test results statistically, auditors can reach defensible conclusions about control effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Institute of Certified Public Accountants (AICPA)","subfamily":"Statistical Sampling Methods","year":"1972","type":"Statistical sampling technique for control testing"},"citations":[{"ref":"American Institute of Certified Public Accountants (AICPA). (2015). Audit Sampling. AU-C Section 530. AICPA Professional Standards.","type":"article","doi":null,"isbn":null,"url":"https://www.aicpa.org/resources/download/audit-standards-codification"},{"ref":"Arens, A. A., Elder, R. J., & Beasley, M. S. (2014). Auditing and assurance services (15th ed.). Pearson Education.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com/"}],"related":["monetary-unit-sampling","internal-control-evaluation","audit-risk-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"attributes-control-chart","name":"Attributes Control Chart","fullName":"Attributes Control Charts (p, np, c, u)","aliases":["p-chart","np-chart","c-chart","u-chart","nitelik kontrol kartı"],"domain":"statistics","family":"process-pipeline","subfamily":"Statistical process control","year":1931,"originator":"Walter A. Shewhart","url":"https://scholargate.app/en/statistics/attributes-control-chart","markdownUrl":"https://scholargate.app/en/statistics/attributes-control-chart.md","definition":"Attributes control charts extend Shewhart's framework to count and proportion data — quality characteristics that are classified rather than measured. The p- and np-charts monitor the proportion or number of defective items using the binomial distribution, while the c- and u-charts monitor the number of defects per unit using the Poisson distribution. They are the standard statistical-process-control tools when inspection yields pass/fail or defect counts rather than continuous measurements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walter A. Shewhart","year":1931,"type":"Statistical process control charts for count/proportion data","subfamily":"Statistical process control","distribution":"Binomial (p, np) or Poisson (c, u)","monitors":"Defective proportion or defect counts"},"citations":[{"ref":"Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand Company.","type":"book","doi":null,"isbn":"978-0-87389-076-2","url":null},{"ref":"Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-470-16992-6","url":null}],"related":["shewhart-control-chart","cusum-chart","ewma-chart","proportion-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"audio-fingerprinting","name":"Audio Fingerprinting","fullName":"Audio Fingerprinting Algorithm","aliases":["robust hashing","perceptual hashing","music identification"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Hashing and identification","year":"2002","originator":"Jeroen Haitsma","url":"https://scholargate.app/en/music-information-retrieval/audio-fingerprinting","markdownUrl":"https://scholargate.app/en/music-information-retrieval/audio-fingerprinting.md","definition":"Audio fingerprinting is a technique for creating a compact, robust identifier (fingerprint) for audio recordings that uniquely represents the content while being tolerant to modifications such as compression, noise, or time-shifting. Introduced by Haitsma and Kalker (2002), it underlies music identification services like Shazam and is critical for copyright enforcement, music matching, and library deduplication. A fingerprint is not a waveform hash; it captures perceptual content and remains stable across reasonable audio alterations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeroen Haitsma","subfamily":"Hashing and identification","year":"2002","type":"Perceptual audio hashing"},"citations":[{"ref":"Haitsma, J., & Kalker, T. (2002). A highly robust audio fingerprinting system. In Proceedings of the International Symposium on Music Information Retrieval.","type":"article","doi":null,"isbn":null,"url":"https://ismir2002.is.ucf.edu/proceedings/03_PDF/haitsma.pdf"},{"ref":"Wang, A. L. (2003). An industrial-strength audio search algorithm. In Proceedings of the International Symposium on Music Information Retrieval.","type":"article","doi":null,"isbn":null,"url":"https://ismir2003.ismir.net/proceedings/2003020.pdf"},{"ref":"Cano, P., Batlle, E., Kalker, T., & Haitsma, J. (2005). A review of audio fingerprinting. Journal of the Audio Engineering Society, 53(9), 804-825.","type":"article","doi":null,"isbn":null,"url":"https://www.aes.org/e-lib/browse.cfm?elib=13525"}],"related":["music-similarity-measure","beat-tracking","pitch-detection-algorithm","music-genre-classification","music-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"audit-alcohol","name":"AUDIT Alcohol Use Disorders Identification Test","fullName":"Alcohol Use Disorders Identification Test","aliases":["AUDIT","WHO AUDIT","Alcohol Screening"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Substance use screening and assessment","year":"1993","originator":"World Health Organization (WHO) collaborative group, John Saunders and colleagues","url":"https://scholargate.app/en/health-measurement/audit-alcohol","markdownUrl":"https://scholargate.app/en/health-measurement/audit-alcohol.md","definition":"The Alcohol Use Disorders Identification Test (AUDIT) is a 10-item screening and assessment tool developed by the World Health Organization in 1993. It rapidly identifies hazardous alcohol use, harmful drinking, and alcohol dependence across diverse populations. The AUDIT has become the gold-standard alcohol screening instrument in primary care and clinical settings worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"World Health Organization (WHO) collaborative group, John Saunders and colleagues","subfamily":"Substance use screening and assessment","year":"1993","type":"Alcohol use disorder screening and severity assessment"},"citations":[{"ref":"Saunders, J. B., Aasland, O. G., Babor, T. F., & Grant, M. (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption—II. Addiction, 88(6), 791–804.","type":"article","doi":"10.1111/j.1360-0443.1993.tb02093.x","isbn":null,"url":null},{"ref":"Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). The Alcohol Use Disorders Identification Test: Guidelines for use in primary care (2nd ed.). World Health Organization.","type":"article","doi":null,"isbn":null,"url":"https://apps.who.int/iris/handle/10665/67205"},{"ref":"Reinert, D. F., & Allen, J. P. (2007). The alcohol use disorders identification test: an update of research findings. Alcoholism: Clinical and Experimental Research, 31(2), 185–199.","type":"article","doi":"10.1111/j.1530-0277.2006.00295.x","isbn":null,"url":null}],"related":["audit-c","cage-questionnaire","ipaq","promis","whoqol-bref"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"audit-c","name":"AUDIT-C","fullName":"Alcohol Use Disorders Identification Test - Consumption","aliases":["AUDIT-C Alcohol Screening","Three-Item Alcohol Screen"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Substance use screening","year":"2003","originator":"Babor and colleagues; adapted by Bush and colleagues at Veterans Affairs","url":"https://scholargate.app/en/health-measurement/audit-c","markdownUrl":"https://scholargate.app/en/health-measurement/audit-c.md","definition":"The AUDIT-C is a 3-item brief alcohol screening tool derived from the first three questions of the full AUDIT. Published by Bush and colleagues in 2003, it assesses alcohol consumption frequency and quantity in under one minute. The AUDIT-C has become the standard ultra-brief screen for problem drinking in primary care and emergency departments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Babor and colleagues; adapted by Bush and colleagues at Veterans Affairs","subfamily":"Substance use screening","year":"2003","type":"Brief alcohol consumption screening tool"},"citations":[{"ref":"Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). The Alcohol Use Disorders Identification Test: Guidelines for use in primary care (2nd ed.). World Health Organization.","type":"article","doi":null,"isbn":null,"url":"https://apps.who.int/iris/handle/10665/67205"},{"ref":"Bush, K., Kivlahan, D. R., McDonell, M. B., et al. (2003). The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Archives of Internal Medicine, 158(16), 1789–1795.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+AUDIT+alcohol+consumption+questions+%28AUDIT-C%29%3A+an+effective+brief+screening+test+for+problem+drinking+Bush"},{"ref":"Bradley, K. A., DeBusk, R. F., Krupski, A., et al. (2007). Screening for problem alcohol use: comparison of AUDIT-C and AUDIT. American Journal of Preventive Medicine, 30(2), 131–137.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Screening+for+problem+alcohol+use%3A+comparison+of+AUDIT-C+and+AUDIT+Bradley"}],"related":["audit-alcohol","cage-questionnaire","whoqol-bref","promis","sf-36"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"audit-risk-model","name":"Audit Risk Model","fullName":"Audit Risk Model for Risk-Based Audit Planning","aliases":["Risk-Based Audit Planning Model"],"domain":"accounting","family":"mcdm","subfamily":"Audit Planning and Risk Assessment","year":"1983","originator":"American Institute of Certified Public Accountants (AICPA)","url":"https://scholargate.app/en/accounting/audit-risk-model","markdownUrl":"https://scholargate.app/en/accounting/audit-risk-model.md","definition":"The Audit Risk Model is a foundational framework developed by the American Institute of Certified Public Accountants (AICPA) that structures audit planning by decomposing overall audit risk into three components: inherent risk, control risk, and detection risk. This model guides auditors in allocating resources and designing audit procedures proportionate to the level of risk in each account or assertion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Institute of Certified Public Accountants (AICPA)","subfamily":"Audit Planning and Risk Assessment","year":"1983","type":"Professional auditing framework"},"citations":[{"ref":"American Institute of Certified Public Accountants (AICPA). (2015). Audit Risk. AU-C Section 200. AICPA Professional Standards.","type":"article","doi":null,"isbn":null,"url":"https://www.aicpa.org/resources/download/audit-standards-codification"},{"ref":"Arens, A. A., Elder, R. J., & Beasley, M. S. (2014). Auditing and assurance services (15th ed.). Pearson Education.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com/"}],"related":["internal-control-evaluation","analytical-procedures-auditing","fraud-risk-assessment","going-concern-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"augmented-dickey-fuller-unit-root-test","name":"Augmented Dickey-Fuller unit root test","fullName":"Augmented Dickey-Fuller Unit Root Test","aliases":["ADF test","ADF unit root test","Dickey-Fuller test (augmented)","Said-Dickey test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1979–1984","originator":"Said & Dickey (1984); building on Dickey & Fuller (1979)","url":"https://scholargate.app/en/econometrics/augmented-dickey-fuller-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/augmented-dickey-fuller-unit-root-test.md","definition":"The Augmented Dickey-Fuller test is the standard procedure for determining whether a univariate time series contains a unit root — that is, whether the series is non-stationary. It extends the original Dickey-Fuller test by including lagged difference terms that absorb serial correlation in the residuals, making the test valid for a wide range of time-series processes encountered in economics and finance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Said & Dickey (1984); building on Dickey & Fuller (1979)","year":"1979–1984","type":"Hypothesis test (unit root)","dataType":"Univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607.","type":"article","doi":"10.1093/biomet/71.3.599","isbn":null,"url":null},{"ref":"Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.","type":"article","doi":"10.2307/2286348","isbn":null,"url":null}],"related":["phillips-perron-unit-root-test","kpss-test","zivot-andrews-structural-break-test","johansen-cointegration-test","engle-granger-cointegration-test","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"augmented-lagrangian-method","name":"Augmented Lagrangian Method","fullName":"Augmented Lagrangian Method for Constrained Optimization","aliases":["method of multipliers","augmented Lagrangian","ADMM"],"domain":"operations-research","family":"ml-model","subfamily":"Optimization","year":"1969","originator":"Magnus R. Hestenes and M. J. D. Powell","url":"https://scholargate.app/en/operations-research/augmented-lagrangian-method","markdownUrl":"https://scholargate.app/en/operations-research/augmented-lagrangian-method.md","definition":"The Augmented Lagrangian Method, developed by Magnus R. Hestenes and M. J. D. Powell in 1969, is a powerful technique for solving constrained optimization problems. It converts a constrained problem into a sequence of unconstrained subproblems by augmenting the Lagrangian with a quadratic penalty term, enabling efficient solution of large-scale problems including convex and nonconvex cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Magnus R. Hestenes and M. J. D. Powell","subfamily":"Optimization","year":"1969","type":"algorithm"},"citations":[{"ref":"Hestenes, M. R. (1969). Multiplier and gradient methods. Journal of Optimization Theory and Applications, 4(5), 303-320.","type":"article","doi":"10.1007/BF00927673","isbn":null,"url":null},{"ref":"Powell, M. J. D. (1969). A method for nonlinear constraints in minimization problems. In Optimization (pp. 283-298). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books"},{"ref":"Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1), 1-122.","type":"book","doi":"10.1561/2200000016","isbn":null,"url":null}],"related":["simplex-method","benders-decomposition","column-generation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"auteur-theory-analysis","name":"Auteur Theory Analysis","fullName":"Authorship and Directorial Intent in Film Studies","aliases":["auteur analysis","directorial analysis","author theory in film"],"domain":"media-studies","family":"process-pipeline","subfamily":"Film authorship and directorial criticism","year":"1954","originator":"François Truffaut, Andrew Sarris","url":"https://scholargate.app/en/media-studies/auteur-theory-analysis","markdownUrl":"https://scholargate.app/en/media-studies/auteur-theory-analysis.md","definition":"Auteur Theory Analysis is a critical framework for studying cinema through the lens of directorial authorship, examining how individual directors express consistent themes, visual style, and ideological perspectives across multiple films. Developed by French critics of Cahiers du Cinéma (notably François Truffaut) and articulated in American film criticism by Andrew Sarris, the theory posits that despite the industrial, collaborative nature of film production, the director functions as the primary creative author whose distinctive sensibility can be traced through characteristic patterns of style, technique, and content. The method enables scholarly analysis of directorial influence on cinema and challenges the assumption that mass-produced films lack individual artistic vision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"François Truffaut, Andrew Sarris","subfamily":"Film authorship and directorial criticism","year":"1954","type":"Critical framework for identifying and analyzing directorial style and authorship across films"},"citations":[{"ref":"Sarris, A. (1962). Notes on the auteur theory in 1962. Film Culture, 27, 1-8.","type":"article","doi":null,"isbn":null,"url":"https://www.filmculture.org"},{"ref":"Caughie, J. (Ed.). (1981). Theories of Authorship: A Reader. Routledge.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theories+of+Authorship%3A+A+Reader+Caughie"},{"ref":"Bordwell, D. (2006). The Way Hollywood Tells It: Story and Style in Modern Movies. University of California Press.","type":"book","doi":null,"isbn":null,"url":"https://www.ucpress.edu"},{"ref":"Wollen, P. (1972). Signs and Meaning in the Cinema. Indiana University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.iupress.org"}],"related":["film-narrative-analysis","semiotics-film","genre-analysis-film","discourse-analysis-media","visual-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"authentic-leadership-scale","name":"Authentic Leadership Scale","fullName":"Authentic Leadership Scale (ALS)","aliases":["Walumbwa ALS"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Leadership style","year":"2008","originator":"Walumbwa, Avolio, Gardner, Wernsing, and Peterson","url":"https://scholargate.app/en/organizational-behavior/authentic-leadership-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/authentic-leadership-scale.md","definition":"The Authentic Leadership Scale (ALS) is a 16-item instrument measuring four dimensions of authentic leadership: self-awareness, relational transparency, balanced processing, and internalized moral perspective. Developed by Walumbwa, Avolio, and colleagues in 2008, the ALS assesses leadership grounded in self-knowledge and ethical conviction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walumbwa, Avolio, Gardner, Wernsing, and Peterson","subfamily":"Leadership style","year":"2008","type":"Self-report scale"},"citations":[{"ref":"Walumbwa, F. O., Avolio, B. J., Gardner, W. L., Wernsing, T. S., & Peterson, S. J. (2008). Authentic leadership: Development and validation of a theory-based measure. Journal of Management, 34(1), 89-126.","type":"article","doi":"10.1177/0149206307308913","isbn":null,"url":null},{"ref":"Kernis, M. H. (2003). Toward a conceptualization of optimal self-esteem. Psychological Inquiry, 14(1), 1-26.","type":"article","doi":"10.1207/S15327965PLI1401_01","isbn":null,"url":null}],"related":["ethical-leadership-scale","organizational-trust-scale","employee-engagement-survey","toxic-leadership-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"authorship-attribution","name":"Authorship Attribution","fullName":"Authorship Attribution (Stylometry)","aliases":["Stylometry","Authorship Analysis","Yazarlık Atıfı","Authorship Identification"],"domain":"text-mining","family":"ml-model","subfamily":"Stylometry","year":2009,"originator":"Mosteller & Wallace; Stamatatos","url":"https://scholargate.app/en/text-mining/authorship-attribution","markdownUrl":"https://scholargate.app/en/text-mining/authorship-attribution.md","definition":"Authorship attribution is the task of identifying the most probable author of an anonymous or disputed text by analysing its stylistic fingerprint. Rooted in the statistical work of Mosteller and Wallace on the Federalist Papers (1964), the field was systematically surveyed and formalised by Stamatatos (2009), who catalogued feature sets ranging from character n-grams and function-word frequencies to syntactic and semantic representations used by modern machine-learning classifiers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mosteller & Wallace; Stamatatos","year":2009,"type":"Supervised stylometric classification","subfamily":"Stylometry","input":"Text documents with candidate author samples","output":"Probability distribution or label over candidate authors"},"citations":[{"ref":"Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3), 538–556.","type":"article","doi":"10.1002/asi.21001","isbn":null,"url":null}],"related":["text-classification","forensic-likelihood-ratio","word2vec"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"autism-spectrum-quotient","name":"Autism Spectrum Quotient","fullName":"Autism Spectrum Quotient (AQ)","aliases":["AQ","AQ-10","AQ-50"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"neurodevelopmental disorders","year":"2001","originator":"Simon Baron-Cohen","url":"https://scholargate.app/en/child-psychiatry/autism-spectrum-quotient","markdownUrl":"https://scholargate.app/en/child-psychiatry/autism-spectrum-quotient.md","definition":"The Autism Spectrum Quotient (AQ) is a brief self- or observer-report measure of autism-spectrum traits in adolescents and adults. Developed by Simon Baron-Cohen and colleagues in 2001, the original 50-item version (AQ-50) quantifies propensity toward autism across five domains: social skills, attention to detail, attention switching, communication, and imagination. The 10-item version (AQ-10) serves as a rapid screening tool. The AQ is not diagnostic but identifies individuals who warrant formal autism evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Simon Baron-Cohen","subfamily":"neurodevelopmental disorders","year":"2001","type":"Self-report questionnaire"},"citations":[{"ref":"Allison, C., Auyeung, B., & Baron-Cohen, S. (2012). Toward brief \"red flags\" for autism screening: The Short Autism Spectrum Quotient and the Autism Spectrum Quotient-50. Journal of Autism and Developmental Disorders, 42(4), 588–598.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Toward+brief+%22red+flags%22+for+autism+screening%3A+The+Short+Autism+Spectrum+Quotient+and+the+Autism+Spectrum+Quotient-50+Allison"},{"ref":"Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 5–17.","type":"article","doi":"10.1023/A:1005653411471","isbn":null,"url":null}],"related":["social-communication-questionnaire","revised-childrens-anxiety-depression","emotion-regulation-questionnaire-child"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"autoencoder-anomaly-detection","name":"Autoencoder Anomaly Detection","fullName":"Autoencoder-Based Anomaly Detection (Reconstruction-Error Method)","aliases":["AE anomaly detection","reconstruction-error anomaly detection","deep autoencoder outlier detection","unsupervised autoencoder anomaly detection"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006–2014","originator":"Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s","url":"https://scholargate.app/en/machine-learning/autoencoder-anomaly-detection","markdownUrl":"https://scholargate.app/en/machine-learning/autoencoder-anomaly-detection.md","definition":"Autoencoder anomaly detection trains a neural network to compress and then reconstruct normal data. Because the model has only ever learned what normal looks like, anomalous inputs produce noticeably higher reconstruction errors — and those errors become the anomaly score. The method requires no labeled anomalies and scales naturally to high-dimensional data such as sensor streams, images, and log records.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s","year":"2006–2014","type":"Unsupervised deep learning (reconstruction-based)","dataType":"Continuous tabular, time-series, image, or sequential data","subfamily":"Machine learning"},"citations":[{"ref":"Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1901.03407"},{"ref":"Hinton, G. E. & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.","type":"article","doi":"10.1126/science.1127647","isbn":null,"url":null}],"related":["one-class-svm","isolation-forest","gaussian-mixture-model","variational-autoencoder","principal-component-analysis","k-nearest-neighbors"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"autoencoder","name":"Autoencoder","fullName":"Autoencoder (Encoder-Decoder Neural Network for Dimensionality Reduction)","aliases":["Otokodlayıcı (Autoencoder)","otokodlayıcı","auto-encoder","encoder-decoder network"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2006,"originator":"Hinton, G.E. & Salakhutdinov, R.R.","url":"https://scholargate.app/en/deep-learning/autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/autoencoder.md","definition":"An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hinton, G.E. & Salakhutdinov, R.R.","year":2006,"type":"Neural network (encoder-decoder)","task":"Dimensionality reduction & anomaly detection","minSample":200},"citations":[{"ref":"Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.","type":"article","doi":"10.1126/science.1127647","isbn":null,"url":null}],"related":["pca","variational-autoencoder","k-means","factor-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"autoethnography","name":"Autoethnography","fullName":"Autoethnography","aliases":["auto-ethnography","AE","personal narrative research","self-ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Ethnography","year":"Late 20th century (term coined 1979; method consolidated 1990s–2000s)","originator":"Carolyn Ellis, Arthur Bochner, Norman Denzin (prominent theorists); David Hayano coined the term in 1979","url":"https://scholargate.app/en/qualitative/autoethnography","markdownUrl":"https://scholargate.app/en/qualitative/autoethnography.md","definition":"Autoethnography is a qualitative research method in which the researcher uses systematic self-reflection and personal narrative to examine their own experiences within a cultural, social, or organizational context. By treating the self as both subject and instrument, autoethnography connects individual lived experience to broader cultural patterns, making personal stories analytically and socially significant. It bridges autobiography and ethnography, producing accounts that are simultaneously evocative and scholarly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carolyn Ellis, Arthur Bochner, Norman Denzin (prominent theorists); David Hayano coined the term in 1979","year":"Late 20th century (term coined 1979; method consolidated 1990s–2000s)","type":"Qualitative research method","dataType":"Personal narrative, field notes, journals, memory, artifacts, interviews with self and others","typicalSampleSize":"1 (solo); 2–6 (collaborative autoethnography)","subfamily":"Ethnography"},"citations":[{"ref":"Ellis, C. (2004). The Ethnographic I: A Methodological Novel about Autoethnography. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759100947","url":null},{"ref":"Chang, H., Ngunjiri, F. W., & Hernandez, K.-A. C. (2013). Collaborative Autoethnography. Left Coast Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Collaborative+Autoethnography+Chang+Ngunjiri+Hernandez+2013"}],"related":["ethnography","phenomenology","narrative-analysis","action-research","grounded-theory","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"autoformer","name":"Autoformer","fullName":"Autoformer (Auto-Correlation Decomposition Transformer)","aliases":["Auto-Correlation Transformer","Decomposition Transformer","Series Decomposition Forecaster","Oto-Korelasyon Ayrışım Transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2021,"originator":"Haixu Wu et al. (Tsinghua)","url":"https://scholargate.app/en/deep-learning/autoformer","markdownUrl":"https://scholargate.app/en/deep-learning/autoformer.md","definition":"Autoformer is a deep learning architecture for long-term time-series forecasting, introduced by Wu et al. from Tsinghua University at NeurIPS 2021. It replaces the standard self-attention mechanism with an Auto-Correlation mechanism that exploits periodic dependencies in the frequency domain, and embeds a progressive series decomposition block throughout the encoder and decoder to separately model trend and seasonal components.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Haixu Wu et al. (Tsinghua)","year":2021,"type":"Decomposition-based deep forecasting model","subfamily":"Time-series forecasting","input":"Univariate or multivariate time series","output":"Multi-step ahead point forecasts"},"citations":[{"ref":"Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS, 34.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2106.13008"}],"related":["informer","fedformer","timesnet","arima"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"automated-essay-scoring","name":"Automated Essay Scoring","fullName":"Automated Essay Scoring (AES)","aliases":["AES","automated writing evaluation","AWE","Otomatik Deneme Puanlaması"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1966 (Project Essay Grade); modern deep-learning era from 2019","originator":"Shermis & Burstein (eds.); landmark consolidation 2013; deep-learning era from Devlin et al. 2019","url":"https://scholargate.app/en/text-mining/automated-essay-scoring","markdownUrl":"https://scholargate.app/en/text-mining/automated-essay-scoring.md","definition":"Automated Essay Scoring (AES) is a natural-language-processing task in which a computational model assigns scores to student-written essays across dimensions such as grammatical correctness, coherence, content richness, and organisation — replicating, at scale, what a human rater would do. The approach was formalised as a research field by Shermis and Burstein (2013) and has been transformed since 2019 by transformer language models, particularly BERT, which allow AES systems to leverage deep contextual representations of text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shermis & Burstein (eds.); landmark consolidation 2013; deep-learning era from Devlin et al. 2019","year":"1966 (Project Essay Grade); modern deep-learning era from 2019","type":"Supervised text-regression / text-classification task","input":"Labelled student essays with human-assigned scores","output":"Numeric score or score-band per essay","dimensions":"Grammar, coherence, content richness, organisation","minSample":30,"difficulty":"2 / 3"},"citations":[{"ref":"Shermis, M.D. & Burstein, J. (2013). Handbook of Automated Essay Evaluation. Routledge.","type":"book","doi":null,"isbn":null,"url":"https://www.routledge.com/Handbook-of-Automated-Essay-Evaluation-Current-Applications-and-New-Directions/Shermis-Burstein/p/book/9780415810968"},{"ref":"Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.","type":"article","doi":"10.18653/v1/N19-1423","isbn":null,"url":null}],"related":["sentiment-analysis","text-classification","bert-embeddings","readability-analysis","natural-language-processing"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"automated-theorem-proving","name":"Automated Theorem Proving","fullName":"Automated Theorem Proving (ATP)","aliases":["ATP","automated reasoning","first-order logic proof"],"domain":"numerical-methods","family":"ml-model","subfamily":"Logic and Reasoning","year":"1965","originator":"John Alan Robinson","url":"https://scholargate.app/en/numerical-methods/automated-theorem-proving","markdownUrl":"https://scholargate.app/en/numerical-methods/automated-theorem-proving.md","definition":"Automated Theorem Proving (ATP) is a field of artificial intelligence and mathematical logic dedicated to mechanically proving mathematical theorems in formal systems. Developed by John Robinson in 1965 with the resolution principle, ATP underpins modern verification tools like SAT/SMT solvers and is foundational to formal software verification, hardware validation, and mathematics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Alan Robinson","subfamily":"Logic and Reasoning","year":"1965","type":"Automated deduction technique"},"citations":[{"ref":"Robinson, J. A. (1965). A machine-oriented logic based on the resolution principle. Journal of the ACM, 12(1), 23–41.","type":"article","doi":"10.1145/321250.321253","isbn":null,"url":null},{"ref":"Fitting, M. (1996). First-Order Logic and Automated Theorem Proving (2nd ed.). Springer.","type":"book","doi":"10.1007/978-1-4612-2360-3","isbn":null,"url":null},{"ref":"Nieuwenhuis, R., Oliveras, A., & Tinelli, C. (2006). Solving SAT and SAT modulo theories: From an abstract Davis–Putnam–Logemann–Loveland procedure to DPLL(T). Journal of the ACM, 53(6), 937–977.","type":"article","doi":"10.1145/1217856.1217859","isbn":null,"url":null}],"related":["sat-solving","constraint-satisfaction","formal-verification","model-checking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"automatic-differentiation-variational-inference","name":"Automatic Differentiation Variational Inference","fullName":"Automatic Differentiation Variational Inference (ADVI)","aliases":["ADVI","black-box variational inference","automatic variational inference","gradient-based variational inference"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2017,"originator":"Kucukelbir, Tran, Ranganath, Gelman, Blei","url":"https://scholargate.app/en/bayesian/automatic-differentiation-variational-inference","markdownUrl":"https://scholargate.app/en/bayesian/automatic-differentiation-variational-inference.md","definition":"Automatic Differentiation Variational Inference (ADVI) is a black-box algorithm for approximate Bayesian posterior inference, introduced by Kucukelbir, Tran, Ranganath, Gelman, and Blei (2017, JMLR). Given any probabilistic model whose log-joint density is differentiable, ADVI automatically transforms constrained latent variables to unconstrained real space, fits a Gaussian variational family by maximising the evidence lower bound (ELBO) with stochastic gradient ascent, and returns an approximate posterior without model-specific derivations. It is the default variational inference engine in Stan and is available in PyMC and NumPyro.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Variational inference algorithm","purpose":"approximate posterior inference","originator":"Kucukelbir, Tran, Ranganath, Gelman, Blei","year":2017,"var_types":"continuous (constrained or unconstrained)","inference":"variational / gradient-based","outputs":"approximate posterior distributions / ELBO trace","software":"Stan (built-in), PyMC, NumPyro"},"citations":[{"ref":"Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A. & Blei, D. M. (2017). Automatic differentiation variational inference. Journal of Machine Learning Research, 18(14), 1–45.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v18/16-107.html"},{"ref":"Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A. & Blei, D. M. (2016). Automatic differentiation variational inference. arXiv:1603.00788.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1603.00788"},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["bayesian-regression","mcmc","hierarchical-bayes","variational-bayes","expectation-propagation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"automatic-music-transcription","name":"Automatic Music Transcription","fullName":"Automatic Music Transcription Algorithm","aliases":["music-to-notation conversion","score estimation","polyphonic transcription"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Transcription","year":"2008","originator":"Anssi Klapuri","url":"https://scholargate.app/en/music-information-retrieval/automatic-music-transcription","markdownUrl":"https://scholargate.app/en/music-information-retrieval/automatic-music-transcription.md","definition":"Automatic music transcription is the task of converting audio recordings into symbolic music notation (e.g., scores with note pitch, onset, and duration). Formalized as a research problem by Klapuri (2008), it represents one of the most challenging tasks in music information retrieval. Transcription enables music education, composition analysis, and digital preservation. Modern systems, particularly those using deep learning for piano music (Hawthorne et al., 2019), have achieved significant progress but remain far from perfect on general polyphonic music.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anssi Klapuri","subfamily":"Transcription","year":"2008","type":"Polyphonic audio-to-symbolic conversion"},"citations":[{"ref":"Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337.","type":"article","doi":"10.1007/978-0-387-30441-0_20","isbn":null,"url":null},{"ref":"Poliner, G. E., & Ellis, D. P. (2007). A discriminative model for polyphonic piano transcription. IEEE Transactions on Audio, Speech, and Language Processing, 15(3), 1116-1126.","type":"article","doi":"10.1155/2007/48317","isbn":null,"url":null},{"ref":"Hawthorne, C., Elsen, E., Song, J., Roberts, A., Simon, I., Raffel, C., ... & Engel, J. (2019). Onsets and Frames: Dual-Objective Piano Transcription. In ISMIR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1710.11153"}],"related":["melody-extraction","pitch-detection-algorithm","beat-tracking","chord-recognition","music-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"automatic-test-pattern-generation","name":"Automatic Test Pattern Generation","fullName":"Automatic Test Pattern Generation for Digital Circuits","aliases":["ATPG","Test pattern generation","Fault-based testing"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Digital circuit testing","year":"1966","originator":"J. Paul Roth","url":"https://scholargate.app/en/electrical-engineering/automatic-test-pattern-generation","markdownUrl":"https://scholargate.app/en/electrical-engineering/automatic-test-pattern-generation.md","definition":"Automatic Test Pattern Generation (ATPG) is the automated creation of test vectors that detect manufacturing defects in digital circuits. Pioneered by Roth in 1966, ATPG systematically finds inputs that make stuck-at faults observable at outputs, enabling comprehensive fault detection. ATPG is critical for semiconductor manufacturing: enabling high test coverage ensures only good chips ship and identifies manufacturing process issues.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. Paul Roth","subfamily":"Digital circuit testing","year":"1966","type":"Automated fault-detection test vector generation"},"citations":[{"ref":"Abramovici, M., Breuer, M. A., & Friedman, A. D. (1990). Digital Systems Testing and Testable Design. Computer Science Press.","type":"book","doi":null,"isbn":null,"url":"https://catalog.oreilly.com/product/9780805360929/"},{"ref":"Roth, J. P. (1966). Diagnosis of automata failures: A calculus and a method. IBM Journal of Research and Development, 10(4), 278-291.","type":"article","doi":"10.1147/rd.104.0278","isbn":null,"url":null},{"ref":"Goel, P. (1981). An implicit enumeration algorithm to generate tests for combinational circuits. IEEE Transactions on Computers, 30(3), 215-222.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+implicit+enumeration+algorithm+to+generate+tests+for+combinational+circuits+Goel"}],"related":["static-timing-analysis","logic-synthesis","monte-carlo-process-variation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"automatic-text-evaluation","name":"Automatic Text Evaluation","fullName":"Automatic Text Evaluation (BLEU, ROUGE, BERTScore)","aliases":["Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore)","NLG evaluation","MT evaluation metrics"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)","originator":"BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)","url":"https://scholargate.app/en/text-mining/automatic-text-evaluation","markdownUrl":"https://scholargate.app/en/text-mining/automatic-text-evaluation.md","definition":"Automatic text evaluation is a family of reference-based metrics used to measure the quality of machine-generated text — such as translations, summaries, or natural-language-generation (NLG) outputs — by comparing them to one or more human-written reference texts. Pioneered by Papineni et al. with BLEU in 2002, the field has grown to include n-gram overlap metrics (BLEU, ROUGE) and semantically aware metrics (BERTScore, MoverScore) that capture meaning beyond surface word matches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)","year":"2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)","type":"Reference-based NLG evaluation metric suite","approaches":"N-gram overlap (BLEU, ROUGE) / semantic similarity (BERTScore, MoverScore)","output":"Numeric quality scores for generated text","requiresReference":"Yes — one or more human-written reference texts","minimumSample":10},"citations":[{"ref":"Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of ACL 2002.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P02-1040"},{"ref":"Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2020). BERTScore: Evaluating Text Generation with BERT. Proceedings of ICLR 2020.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=SkeHuCVFDr"}],"related":["sentiment-analysis","text-classification","bert-embeddings","topic-modeling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"autoregressive-model","name":"Autoregressive model","fullName":"Autoregressive Model","aliases":["AR model","AR(p) model","autoregression","AR process"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970s (popularised 1976)","originator":"George E. P. Box and Gwilym M. Jenkins","url":"https://scholargate.app/en/econometrics/autoregressive-model","markdownUrl":"https://scholargate.app/en/econometrics/autoregressive-model.md","definition":"An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E. P. Box and Gwilym M. Jenkins","year":"1970s (popularised 1976)","type":"Time series model","dataType":"Univariate time series (equally spaced, stationary)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day.","type":"book","doi":null,"isbn":"978-0816211043","url":null},{"ref":"Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691042893","url":null}],"related":["moving-average-model","arma-model","arima-model","vector-autoregression","augmented-dickey-fuller-unit-root-test","granger-causality-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"average-ranking","name":"AVERAGE-RANKING","fullName":"Average ranking — per-alternative mean rank","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"2024","originator":"Orakçı, E.","url":"https://scholargate.app/en/decision-making/average-ranking","markdownUrl":"https://scholargate.app/en/decision-making/average-ranking.md","definition":"AVERAGE-RANKING (Average ranking — per-alternative mean rank) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Orakçı, E.","subfamily":"AggregationOperator","year":"2024","type":"Order statistic — column-wise arithmetic mean","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları","type":"article","doi":"10.58830/ozgur.pub623","isbn":null,"url":null}],"related":["borda","condorcet","copeland","dodgson","topsis","vikor","ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"axial-coding","name":"Axial Coding","fullName":"Axial Coding","aliases":["axial analysis","relational coding","category development coding","second-level coding"],"domain":"qualitative","family":"process-pipeline","subfamily":"Qualitative Coding","year":"1990","originator":"Anselm Strauss and Juliet Corbin","url":"https://scholargate.app/en/qualitative/axial-coding","markdownUrl":"https://scholargate.app/en/qualitative/axial-coding.md","definition":"Axial coding is the second major analytical step in grounded theory analysis, performed after open coding. Introduced by Anselm Strauss and Juliet Corbin in 1990, it involves systematically re-examining and reorganising the many discrete codes generated during open coding by identifying a central (axial) category and mapping the causal conditions, contextual factors, intervening conditions, action-interaction strategies, and consequences that surround it. The goal is to move from a fragmented list of codes to a coherent relational structure that reflects how concepts interconnect in the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss and Juliet Corbin","year":"1990","type":"Qualitative research method","dataType":"Interview transcripts, field notes, documents, observation records","typicalSampleSize":"20–50 interviews (grounded theory context)","subfamily":"Qualitative Coding"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932456","url":null},{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Constructing+Grounded+Theory+Charmaz+2006"}],"related":["grounded-theory","thematic-analysis","content-analysis","phenomenology","narrative-analysis","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"b-dot-controller","name":"B-Dot Controller","fullName":"Magnetic B-Dot Control Law","aliases":["B-dot control","magnetic damping","momentum dumping"],"domain":"aerospace","family":"process-pipeline","subfamily":"Attitude Control","year":"1980s","originator":"Spacecraft attitude control engineers","url":"https://scholargate.app/en/aerospace/b-dot-controller","markdownUrl":"https://scholargate.app/en/aerospace/b-dot-controller.md","definition":"The B-Dot controller (magnetic B-dot control law) is a simple, robust spacecraft attitude control method that uses the rate of change of Earth's magnetic field measured onboard to generate a magnetic dipole moment. Developed in the 1980s, the B-Dot law damps spacecraft angular momentum without requiring a complex attitude estimate or external reference, making it ideal for initial momentum dumping after launch or in contingency scenarios. B-Dot is passive, simple to implement, and effective.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Spacecraft attitude control engineers","subfamily":"Attitude Control","year":"1980s","type":"Control law"},"citations":[{"ref":"Wertz, J. R. (Ed.). (2002). Spacecraft Attitude Determination and Control. Kluwer Academic.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spacecraft+Attitude+Determination+and+Control+Wertz"},{"ref":"Sidi, M. J. (1997). Spacecraft Dynamics and Control: A Practical Engineering Approach. American Institute of Aeronautics and Astronautics.","type":"book","doi":"10.1017/cbo9780511815652","isbn":null,"url":null},{"ref":"Crassidis, J., Markley, F. L., & Lightsey, E. G. (2006). The Developing Art of Spacecraft Attitude Determination. IEEE Aerospace and Electronic Systems Magazine, 21(4), 30–34.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Developing+Art+of+Spacecraft+Attitude+Determination+Crassidis"}],"related":["quaternion-attitude","sgp4-tle-propagation","ahrs"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"b-wenslo","name":"B-WENSLO","fullName":"Fuzzy WEight deNomination based on Slope coefficient (triangular fuzzy extension)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2024","originator":"Demir, G., Ulusoy, S. K.","url":"https://scholargate.app/en/decision-making/b-wenslo","markdownUrl":"https://scholargate.app/en/decision-making/b-wenslo.md","definition":"B-WENSLO (Fuzzy WEight deNomination based on Slope coefficient (triangular fuzzy extension)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Demir, G., Ulusoy, S. K. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Demir, G., Ulusoy, S. K.","subfamily":"Weight_Subjective","year":"2024","type":"Weight_Subjective (linguistic-TFN expert weighting; envelope/slope ratio on TFN accumulation polyline)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Demir, G., Ulusoy, S. K. (2024). Bulanık WENSLO Yöntemi ile Kriter Ağırlıklarının Belirlenmesi: Dijital Bankacılık Uygulaması. Computer and Decision Making — An International Journal","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bulan%C4%B1k%20WENSLO%20Y%C3%B6ntemi%20ile%20Kriter%20A%C4%9F%C4%B1rl%C4%B1klar%C4%B1n%C4%B1n%20Belirlenmesi%3A%20Dijital%20Bankac%C4%B1l%C4%B1k%20Uygulamas%C4%B1"}],"related":["aroman","artasi","cobra","cocoso","codas","copras","cradis","edas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"background-subtraction","name":"Background Subtraction","fullName":"Background Subtraction for Foreground Detection","aliases":["Foreground detection","Video segmentation"],"domain":"computer-vision","family":"ml-model","subfamily":"Video segmentation","year":"1999","originator":"Stauffer and Grimson","url":"https://scholargate.app/en/computer-vision/background-subtraction","markdownUrl":"https://scholargate.app/en/computer-vision/background-subtraction.md","definition":"Background subtraction is a video processing technique that separates moving foreground objects from a static or slowly changing background by comparing each frame to a learned or estimated background model. Widely used in video surveillance and motion detection, background subtraction enables robust foreground detection even in complex scenes with illumination changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stauffer and Grimson","subfamily":"Video segmentation","year":"1999","type":"Temporal image analysis"},"citations":[{"ref":"Stauffer, C., & Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 246–252.","type":"article","doi":"10.1109/CVPR.1999.784637","isbn":null,"url":null},{"ref":"KaewTraKulPong, P., & Bowden, R. (2002). An improved adaptive background mixture model for real-time tracking with shadow detection. Proceedings of the European Conference on Computer Vision (ECCV), 135–144.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/chapter/10.1007/3-540-47969-4_10"}],"related":["histogram-equalization","watershed-segmentation","image-morphology","canny-edge-detection","contour-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"backstepping-control","name":"Backstepping Control","fullName":"Backstepping Control","aliases":["Integrator Backstepping","Recursive Lyapunov Design"],"domain":"control-theory","family":"ml-model","subfamily":"Nonlinear Control","year":"1995","originator":"Miroslav Krstic","url":"https://scholargate.app/en/control-theory/backstepping-control","markdownUrl":"https://scholargate.app/en/control-theory/backstepping-control.md","definition":"Backstepping is a systematic nonlinear control design method that decomposes a complex nonlinear system into simpler subsystems and designs a controller recursively, layer by layer, ensuring stability at each step. Developed by Krstic, Kanellakopoulos, and Kokotovic, backstepping enables control of nonlinear systems without requiring exact model knowledge or full state linearization, combining flexibility with guaranteed stability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Miroslav Krstic","subfamily":"Nonlinear Control","year":"1995","type":"algorithm"},"citations":[{"ref":"Krstic, M., Kanellakopoulos, I., & Kokotovic, P. (1995). Nonlinear and Adaptive Control Design. John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nonlinear+and+Adaptive+Control+Design+Krstic"}],"related":["feedback-linearization","sliding-mode-control","h-infinity-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"backtesting-var","name":"VaR Backtesting","fullName":"Value-at-Risk Backtesting (Kupiec, Christoffersen, Dynamic Quantile)","aliases":["VaR backtest","Kupiec test","Christoffersen test","Dynamic Quantile test","coverage test","VaR Geriye Dönük Test (Kupiec, Christoffersen, DQ)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1998,"originator":"Kupiec (1995); Christoffersen (1998); Engle & Manganelli (DQ test)","url":"https://scholargate.app/en/finance/backtesting-var","markdownUrl":"https://scholargate.app/en/finance/backtesting-var.md","definition":"VaR backtesting is a family of statistical tests that validate a risk model by comparing its Value-at-Risk forecasts against realised losses. It builds on Kupiec's (1995) unconditional coverage test, Christoffersen's (1998) conditional coverage test, and the Engle-Manganelli Dynamic Quantile (DQ) test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kupiec (1995); Christoffersen (1998); Engle & Manganelli (DQ test)","year":1998,"type":"Statistical hypothesis tests on VaR violation sequences","estimator":"Likelihood-ratio coverage tests; quantile regression (DQ)","outcome":"VaR violation indicator (hit sequence)","minSample":100},"citations":[{"ref":"Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73-84.","type":"article","doi":"10.3905/jod.1995.407942","isbn":null,"url":null},{"ref":"Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862.","type":"article","doi":"10.2307/2527341","isbn":null,"url":null}],"related":["garch-model","expected-shortfall","har-rv-model","tail-risk-evt","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bagging-ensemble","name":"Bagging Ensemble","fullName":"Bootstrap Aggregating Ensemble","aliases":["bootstrap aggregating"],"domain":"ensemble-learning","family":"ml-model","subfamily":"Ensemble","year":"1996","originator":"Leo Breiman","url":"https://scholargate.app/en/ensemble-learning/bagging-ensemble","markdownUrl":"https://scholargate.app/en/ensemble-learning/bagging-ensemble.md","definition":"Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leo Breiman","subfamily":"Ensemble","year":"1996","type":"parallel ensemble"},"citations":[{"ref":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.","type":"article","doi":"10.1007/BF00058655","isbn":null,"url":null},{"ref":"Efron, B. (1979). Bootstrap methods: another look at the jackknife. The Annals of Statistics, 7(1), 1-26.","type":"article","doi":"10.1214/aos/1176344552","isbn":null,"url":null}],"related":["random-forest","boosting-ensemble","majority-voting","adaboost"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bagging","name":"Bagging","fullName":"Bagging (Bootstrap Aggregating)","aliases":["Bootstrap Aggregating","bootstrap aggregation","bagged ensemble","bagged predictor","variance reduction ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1996,"originator":"Breiman, L.","url":"https://scholargate.app/en/machine-learning/bagging","markdownUrl":"https://scholargate.app/en/machine-learning/bagging.md","definition":"Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, L.","year":1996,"type":"Ensemble meta-algorithm (variance reduction via bootstrap aggregation)","task":"Classification & regression","minSample":30},"citations":[{"ref":"Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.","type":"article","doi":"10.1007/BF00058655","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 8.7). Springer.","type":"book","doi":null,"isbn":"978-0-387-84857-0","url":null},{"ref":"James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 8.2). Springer.","type":"book","doi":null,"isbn":"978-1-4614-7138-7","url":null}],"related":["random-forest","decision-tree","xgboost","gradient-boosting","adaboost"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bai-perron-test","name":"Bai-Perron Test","fullName":"Bai-Perron Multiple Structural Break Test","aliases":["Bai-Perron Multiple Break Test","Multiple Structural Change Test","Sequential Structural Break Test","Çoklu Yapısal Kırılma Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Structural break","year":1998,"originator":"Jushan Bai & Pierre Perron","url":"https://scholargate.app/en/econometrics/bai-perron-test","markdownUrl":"https://scholargate.app/en/econometrics/bai-perron-test.md","definition":"The Bai-Perron test, introduced by Jushan Bai and Pierre Perron in their landmark 1998 Econometrica paper, is a least-squares-based procedure for detecting, estimating, and testing the number of structural breaks in a linear regression model estimated on time-series data. Unlike single-break tests, it simultaneously identifies multiple change-points in a sample, providing economists and empirical researchers with a rigorous, data-driven way to locate parameter instability across time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jushan Bai & Pierre Perron","year":1998,"type":"Sequential hypothesis test for multiple structural breaks","subfamily":"Structural break","estimation":"Least-squares with dynamic programming","distribution":"Non-standard asymptotic critical values"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null}],"related":["quandt-andrews-test","chow-test","markov-switching-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"balanced-accuracy","name":"Balanced Accuracy","fullName":"Balanced Classification Accuracy","aliases":["Average Recall","Equal-weight Average Sensitivity"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"2010","originator":"Brodersen, Ong, Stephan, and Buhmann","url":"https://scholargate.app/en/model-evaluation/balanced-accuracy","markdownUrl":"https://scholargate.app/en/model-evaluation/balanced-accuracy.md","definition":"Balanced accuracy is the average of recall values computed for each class separately. It corrects for class imbalance by giving equal weight to the performance on each class, regardless of class frequency in the dataset.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen, Ong, Stephan, and Buhmann","subfamily":"Classification Metric","year":"2010","type":"Evaluation metric"},"citations":[{"ref":"Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. 20th International Conference on Pattern Recognition (ICPR), 3121-3124.","type":"article","doi":"10.1109/ICPR.2010.764","isbn":null,"url":null},{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"}],"related":["accuracy","recall","specificity","f1-score","matthews-correlation-coefficient"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"balanced-scorecard-healthcare","name":"Balanced Scorecard in Healthcare","fullName":"Balanced Scorecard Framework for Healthcare Performance Management","aliases":["Healthcare BSC","Balanced Scorecard Healthcare"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Strategic management, Performance measurement","year":"1992","originator":"Robert Kaplan, David Norton","url":"https://scholargate.app/en/healthcare-management/balanced-scorecard-healthcare","markdownUrl":"https://scholargate.app/en/healthcare-management/balanced-scorecard-healthcare.md","definition":"The Balanced Scorecard is a strategic performance management framework that translates an organization's mission and strategy into a comprehensive set of performance measures across four perspectives: financial, customer, internal processes, and learning and growth. Developed by Kaplan and Norton in 1992 for general business, it has been extensively adapted for healthcare organizations to align hospital operations with strategic objectives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Kaplan, David Norton","subfamily":"Strategic management, Performance measurement","year":"1992","type":"Strategic planning and management framework"},"citations":[{"ref":"Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71–79.","type":"article","doi":"10.1007/978-3-8349-9320-5_12","isbn":null,"url":null},{"ref":"Kaplan, R. S., & Norton, D. P. (2001). The Strategy-Focused Organization: How Balanced Scorecard Companies Thrive in the New Business Environment. Harvard Business School Press.","type":"book","doi":null,"isbn":null,"url":"https://www.hbs.edu/faculty/Pages/item.aspx?num=27305"},{"ref":"Niven, P. R. (2008). Balanced Scorecard Step-by-Step for Government and Nonprofit Agencies (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Balanced+Scorecard+Step-by-Step+for+Government+and+Nonprofit+Agencies+%282nd+ed.%29+Niven"}],"related":["dea-hospital-efficiency","lean-healthcare","clinical-audit","cost-effectiveness-analysis-hta","staffing-ratio-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"balanced-scorecard-measure","name":"Balanced Scorecard Performance Measure","fullName":"Balanced Scorecard (BSC) Strategy Measurement and Management System","aliases":["BSC","Balanced Scorecard Framework","Kaplan-Norton Scorecard"],"domain":"strategic-management","family":"process-pipeline","subfamily":"performance-management","year":"1992","originator":"Robert S. Kaplan and David P. Norton","url":"https://scholargate.app/en/strategic-management/balanced-scorecard-measure","markdownUrl":"https://scholargate.app/en/strategic-management/balanced-scorecard-measure.md","definition":"The Balanced Scorecard (BSC) is a strategic management system that translates organizational strategy into a coherent set of performance measures across four perspectives: Financial, Customer, Internal Process, and Learning and Growth. Developed by Kaplan and Norton (1992) in Harvard Business Review, the BSC addresses a fundamental management gap: most organizations measure what is easy to measure (financial results) while neglecting what drives results (customer satisfaction, operational efficiency, employee capability). By balancing financial outcomes with non-financial drivers, the BSC enables organizations to understand and manage strategy execution, identify causal relationships between performance drivers, and align organizational actions with strategic objectives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert S. Kaplan and David P. Norton","subfamily":"performance-management","year":"1992","type":"Organizational performance measurement and management system"},"citations":[{"ref":"Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70(1), 71–79.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kaplan%2C%20R.%20S.%2C%20%26%20Norton%2C%20D.%20P.%20(1992).%20The%20balanced%20scorecard%3A%20Measures%20that%20drive%20performance.%20Harvard%20Business%20Review%2C"},{"ref":"Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard: Translating strategy into action. Harvard Business School Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kaplan%2C%20R.%20S.%2C%20%26%20Norton%2C%20D.%20P.%20(1996).%20The%20balanced%20scorecard%3A%20Translating%20strategy%20into%20action.%20Harvard%20Business%20School"},{"ref":"Niven, P. R. (2002). Balanced scorecard step-by-step: Maximizing performance and maintaining results. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Niven%2C%20P.%20R.%20(2002).%20Balanced%20scorecard%20step-by-step%3A%20Maximizing%20performance%20and%20maintaining%20results.%20John%20Wiley%20%26%20Sons."}],"related":["strategic-orientation-scale","corporate-governance-questionnaire","knowledge-management-scale","organizational-resilience-scale","supply-chain-integration-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"balanced-spotis","name":"BALANCED-SPOTIS","fullName":"Balanced SPOTIS — Balanced Stable Preference Ordering Towards Ideal Solution","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2025","originator":"Shekhovtsov, A., Dezert, J., Sałabun, W.","url":"https://scholargate.app/en/decision-making/balanced-spotis","markdownUrl":"https://scholargate.app/en/decision-making/balanced-spotis.md","definition":"BALANCED-SPOTIS (Balanced SPOTIS — Balanced Stable Preference Ordering Towards Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Shekhovtsov, A., Dezert, J., Sałabun, W. in 2025. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shekhovtsov, A., Dezert, J., Sałabun, W.","subfamily":"Ranking","year":"2025","type":"Distance-to-ideal with blended ISP/ESP reference","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Shekhovtsov, A., Dezert, J., Sałabun, W. (2025). Enhancing Personalized Decision-Making with the Balanced SPOTIS Algorithm. 17th International Conference on Agents and Artificial Intelligence (ICAART 2025)","type":"article","doi":"10.5220/0013119800003890","isbn":null,"url":null}],"related":["ahp","bwm","critic","entropy","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"banister-trimp","name":"Banister TRIMP","fullName":"Training Impulse and Fitness-Fatigue Modeling","aliases":["TRIMP","training impulse","fitness-fatigue model"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Training Optimization","year":"1975","originator":"Eric Banister","url":"https://scholargate.app/en/sports-science/banister-trimp","markdownUrl":"https://scholargate.app/en/sports-science/banister-trimp.md","definition":"The Training Impulse (TRIMP) model, developed by Eric Banister and colleagues (1975), quantifies the physiological stimulus of a training session by combining duration and intensity. The Banister fitness-fatigue model proposes that training effects on performance follow two opposing dynamics: fitness (beneficial) accumulates with time constant tau_f (~42 days) and fatigue (temporary decrement) accumulates faster but decays quickly (tau_d ~5-10 days). By tracking TRIMP and modeling these two processes, coaches can predict performance trajectories and optimize training load. Although superseded by newer frameworks, the Banister model remains influential and intuitive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eric Banister","subfamily":"Training Optimization","year":"1975","type":"mathematical modeling"},"citations":[{"ref":"Banister, E. W., Calvert, T. W., Savage, M. V., & Bach, T. (1975). A systems model of training responses and its relationship to muscular strength. Transactions of the ASME, 97(3), 177-183.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/1178377/"},{"ref":"Morton, R. H., Frick, U., & Bazalgette, D. (2005). Modeling human performance in running. Journal of Applied Physiology, 92(6), 2393-2402.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modeling+human+performance+in+running+Morton"},{"ref":"Clarke, D. C., & Skiba, P. F. (2013). Rationale and resources for teaching the mathematical modeling of athletic training and performance. Advances in Physiology Education, 37(2), 134-142.","type":"article","doi":"10.1152/advan.00078.2011","isbn":null,"url":null}],"related":["session-rpe","acute-chronic-workload-ratio","time-motion-gps"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bark-and-mel-scales","name":"Bark and Mel Scales","fullName":"Perceptual Frequency Scales for Audio Analysis and Perception","aliases":["bark scale","mel scale","critical bandwidth","perceptual frequency"],"domain":"acoustics","family":"process-pipeline","subfamily":"Psychoacoustics","year":"1937","originator":"Eberhard Zwicker, Stanley Smith Stevens","url":"https://scholargate.app/en/acoustics/bark-and-mel-scales","markdownUrl":"https://scholargate.app/en/acoustics/bark-and-mel-scales.md","definition":"Bark and Mel scales are perceptual frequency scales that map physical frequency (Hz) to perceived pitch and auditory perception. Formalized by Zwicker (Bark, 1961) and Stevens (Mel, 1937), these non-linear scales reflect how the human ear processes sound. Bark scale divides hearing into 24 critical bands; Mel scale models pitch perception. Both are essential for audio feature extraction, speech processing, and designing audio systems that align with human hearing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eberhard Zwicker, Stanley Smith Stevens","subfamily":"Psychoacoustics","year":"1937","type":"Perceptual frequency mapping"},"citations":[{"ref":"Zwicker, E. (1961). Subdivision of the audible frequency range into critical bands. Journal of the Acoustical Society of America, 33(2), 248–248.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Subdivision+of+the+audible+frequency+range+into+critical+bands+Zwicker"},{"ref":"Stevens, S. S., Volkmann, J., & Newman, E. B. (1937). A scale for the measurement of the psychological magnitude pitch. Journal of the Acoustical Society of America, 8(3), 185–190.","type":"article","doi":"10.1121/1.1915893","isbn":null,"url":null},{"ref":"Moore, B. C. J. (2012). An Introduction to the Psychology of Hearing (6th ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123914232","url":null}],"related":["psychoacoustic-masking","linear-predictive-coding","cepstral-analysis","speech-intelligibility","fxlms-active-noise-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"barkley-adhd-rating-scale","name":"Barkley Adult ADHD Rating Scale","fullName":"Barkley Adult ADHD Rating Scale (BAARS)","aliases":["BAARS","Barkley ADHD"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"neurodevelopmental and executive function","year":"2011","originator":"Russell Barkley","url":"https://scholargate.app/en/child-psychiatry/barkley-adhd-rating-scale","markdownUrl":"https://scholargate.app/en/child-psychiatry/barkley-adhd-rating-scale.md","definition":"The Barkley Adult ADHD Rating Scale (BAARS-IV) is a 27-item self- or observer-report measure of ADHD symptoms and executive function deficits in adolescents and adults. Developed by Russell Barkley and colleagues, the BAARS operationalizes ADHD beyond the traditional inattention and hyperactivity domains to include executive function deficits (working memory, organization, time management, emotional regulation) that are prominent in adolescent and adult ADHD. It is widely used in clinical and research settings for screening, diagnosis, and outcome measurement in ADHD treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Russell Barkley","subfamily":"neurodevelopmental and executive function","year":"2011","type":"Self-report and observer-report rating scale"},"citations":[{"ref":"Barkley, R. A., & Murphy, K. R. (2011). The nature of executive function deficits in adults with ADHD and their relationship to symptoms and impairment. Journal of Attention Disorders, 15(1), 56–71.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+nature+of+executive+function+deficits+in+adults+with+ADHD+and+their+relationship+to+symptoms+and+impairment+Barkley"},{"ref":"Barkley, R. A., DuPaul, G. J., & Costello, A. (1993). Stimulants. In J. S. Werry & M. C. Aman (Eds.), Practitioners guide to psychoactive drugs for children and adolescents (pp. 205–237). Plenum Press.","type":"article","doi":null,"isbn":"0306444348","url":null}],"related":["revised-childrens-anxiety-depression","child-depression-inventory","emotion-regulation-questionnaire-child"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"barnard-s-exact-test","name":"Barnard's Exact Test","fullName":"Barnard's Unconditional Exact Test for 2×2 Tables","aliases":["Barnard test","unconditional exact test","CSM test","Barnard's exact unconditional test","Barnard's test of homogeneity"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1945,"originator":"George A. Barnard","url":"https://scholargate.app/en/statistics/barnard-s-exact-test","markdownUrl":"https://scholargate.app/en/statistics/barnard-s-exact-test.md","definition":"Barnard's exact test is an unconditional exact hypothesis test for comparing two independent proportions in a 2×2 contingency table, proposed by George A. Barnard in 1945. Unlike Fisher's exact test, it does not condition on both margins being fixed, and is generally more powerful when column totals are not predetermined by the study design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George A. Barnard","year":1945,"family":"Hypothesis test","type":"Unconditional exact test for 2×2 contingency tables","groups":2,"outcome":"binary (proportions)","parametric":false,"conditioned":false,"marginsFixed":"row margins only (or neither)","nullDistribution":"exact (maximized over nuisance parameter)","alternativeToFisher":true},"citations":[{"ref":"Barnard, G. A. (1945). A new test for 2×2 tables. Nature, 156(3954), 177.","type":"article","doi":"10.1038/156177a0","isbn":null,"url":null},{"ref":"Suissa, S., & Shuster, J. J. (1985). Exact unconditional sample sizes for the 2×2 binomial trial. Journal of the Royal Statistical Society, Series A, 148(4), 317–327.","type":"article","doi":"10.2307/2981892","isbn":null,"url":null},{"ref":"Lydersen, S., Fagerland, M. W., & Laake, P. (2009). Recommended tests for association in 2×2 tables. Statistics in Medicine, 28(7), 1159–1175.","type":"article","doi":"10.1002/sim.3531","isbn":null,"url":null}],"related":["fisher-s-exact-test","chi-squared-test","mcnemar-s-test","boschloo-s-test","z-test-for-two-proportions"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"barnes-cressman-analysis","name":"Barnes-Cressman Analysis","fullName":"Barnes-Cressman Grid Analysis Method","aliases":["Barnes analysis","Cressman analysis","Objective analysis","Grid interpolation"],"domain":"meteorology","family":"process-pipeline","subfamily":"Data analysis and interpolation","year":"1959","originator":"Barnes, Cressman","url":"https://scholargate.app/en/meteorology/barnes-cressman-analysis","markdownUrl":"https://scholargate.app/en/meteorology/barnes-cressman-analysis.md","definition":"Barnes-Cressman analysis is an objective interpolation method that creates gridded meteorological fields from irregularly spaced observations (station data, radiosonde profiles, buoys). It is widely used for synoptic analysis, quality control, and initialization of numerical weather models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barnes, Cressman","subfamily":"Data analysis and interpolation","year":"1959","type":"Objective analysis method"},"citations":[{"ref":"Barnes, S. L. (1964). A Technique for Maximizing Details in Numerical Weather Map Analysis. Journal of Applied Meteorology, 3(4), 396-409.","type":"article","doi":"10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2","isbn":null,"url":null},{"ref":"Cressman, G. P. (1959). An Operational Objective Analysis System. Monthly Weather Review, 87(10), 367-374.","type":"article","doi":"10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2","isbn":null,"url":null}],"related":["wrf-model","skew-t-log-p-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"barriers-physical-activity","name":"Barriers to Physical Activity Questionnaire","fullName":"Barriers to Physical Activity Scale","aliases":["BPA Scale","Exercise Barriers"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Barrier Assessment & Enablement","year":"1987","originator":"Karen Sechrist, Susan Noble Walker, and Nola J. Pender","url":"https://scholargate.app/en/health-behavior/barriers-physical-activity","markdownUrl":"https://scholargate.app/en/health-behavior/barriers-physical-activity.md","definition":"The Barriers to Physical Activity Questionnaire (BPA) is a scale designed to identify and measure perceived obstacles to exercise engagement. Rooted in the Health Belief Model and Health Promotion Model, the BPA assesses multiple categories of barriers—time constraints, lack of motivation, physical discomfort, cost, lack of facilities, social/family factors, and weather—that individuals perceive as preventing or limiting physical activity. Understanding which barriers are most salient for a given individual or population enables targeted intervention design, such as time management coaching, facility access solutions, or social support programs. The BPA is widely used in primary care, community health, occupational health, and exercise research to segment populations and tailor physical activity prescriptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karen Sechrist, Susan Noble Walker, and Nola J. Pender","subfamily":"Barrier Assessment & Enablement","year":"1987","type":"Self-report questionnaire"},"citations":[{"ref":"Sechrist, K. R., Walker, S. N., & Pender, N. J. (1987). Development and psychometric evaluation of the Exercise Benefits/Barriers Scale. Research in Nursing & Health, 10(6), 357-365.","type":"article","doi":"10.1002/nur.4770100603","isbn":null,"url":null}],"related":["health-belief-model-scale","exercise-self-efficacy-scale","behavioral-regulation-exercise"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"barthel-adl-index","name":"Barthel ADL Index","fullName":"Barthel Index of Activities of Daily Living","aliases":["Barthel Index","Barthel ADL","Barthel Scale"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Functional assessment","year":"1965","originator":"Barthel, Mahoney","url":"https://scholargate.app/en/rehabilitation/barthel-adl-index","markdownUrl":"https://scholargate.app/en/rehabilitation/barthel-adl-index.md","definition":"The Barthel Index is a brief, observer-rated scale measuring independence in activities of daily living (ADL) in patients with disability, stroke, and neurological conditions. Developed by Barthel and Mahoney in 1965, it has become a widely used outcome measure in rehabilitation, stroke care, and geriatrics for assessing functional independence and predicting discharge placement and long-term outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barthel, Mahoney","subfamily":"Functional assessment","year":"1965","type":"Functional independence measure"},"citations":[{"ref":"Barthel, D. W. (1965). Functional evaluation: the Barthel Index. Maryland State Medical Journal, 14(5), 61–65.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/14258950"},{"ref":"Mahoney, F. I., & Barthel, D. W. (1965). Functional evaluation: the Barthel Index. Maryland State Medical Journal, 14(2), 61–65.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/14258950"},{"ref":"Collin, C., Wade, D. T., Davies, S., & Horne, V. (1988). The Barthel ADL Index: a reliability study. International Disability Studies, 10(2), 61–63.","type":"article","doi":"10.3109/09638288809164103","isbn":null,"url":null}],"related":["fim-functional-independence","mif-motor-index","functional-reach-test","tug-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"barthel-index","name":"Barthel Index","fullName":"Barthel Index of Activities of Daily Living","aliases":["BI","Barthel ADL Index","Functional Independence Index"],"domain":"nursing","family":"process-pipeline","subfamily":"Functional assessment and activities of daily living","year":"1965","originator":"Florence I. Mahoney and Dorothea W. Barthel","url":"https://scholargate.app/en/nursing/barthel-index","markdownUrl":"https://scholargate.app/en/nursing/barthel-index.md","definition":"The Barthel Index (BI) is one of the most widely used functional assessment tools measuring independence in activities of daily living. Developed by Florence I. Mahoney and Dorothea W. Barthel in 1965, the Barthel Index evaluates a patient's ability to perform ten essential self-care and mobility activities. Its longevity and widespread adoption across rehabilitation, geriatric, and acute care settings reflect its reliability, simplicity, and clinical utility for assessing functional status and predicting rehabilitation outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Florence I. Mahoney and Dorothea W. Barthel","subfamily":"Functional assessment and activities of daily living","year":"1965","type":"Assessment scale"},"citations":[{"ref":"Barthel, D. W. (1965). Functional evaluation: The Barthel Index. Maryland State Medical Journal, 14, 61-65.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/5226644/"},{"ref":"Mahoney, F. I., & Barthel, D. W. (1965). Functional evaluation: The Barthel Index. Maryland State Medical Journal, 14, 61-65.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/pubmed/14258950"}],"related":["care-dependency-scale","braden-scale","patient-fall-risk-assessment","nursing-sensitive-indicators"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bartlett-test","name":"Bartlett's Test","fullName":"Bartlett's Test for Homogeneity of Variances","aliases":["Bartlett's Chi-Square Test","Test for Equality of Variances","Bartlett's Homogeneity Test","Varyans Homojenliği Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":"Variance homogeneity","year":1937,"originator":"Maurice Stevenson Bartlett","url":"https://scholargate.app/en/statistics/bartlett-test","markdownUrl":"https://scholargate.app/en/statistics/bartlett-test.md","definition":"Bartlett's Test is a classical parametric procedure for testing whether two or more independent groups share a common population variance. Introduced by Maurice Stevenson Bartlett in 1937, it formalises the null hypothesis that all group variances are equal by constructing a chi-square statistic from the ratio of pooled to individual group variances. It is a standard pre-analysis step before applying ANOVA or other procedures whose validity depends on the homoscedasticity assumption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maurice Stevenson Bartlett","year":1937,"type":"Parametric variance homogeneity test","subfamily":"Variance homogeneity","null_hypothesis":"All group population variances are equal","test_statistic":"Chi-square approximation"},"citations":[{"ref":"Bartlett, M. S. (1937). Properties of sufficiency and statistical tests. Proceedings of the Royal Society of London. Series A, 160(901), 268–282.","type":"article","doi":"10.1098/rspa.1937.0109","isbn":null,"url":null}],"related":["levene-and-brown-forsythe-test","fligner-killeen-test","one-way-anova"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"baryon-acoustic-oscillations","name":"Baryon Acoustic Oscillations","fullName":"Baryon Acoustic Oscillations for Cosmological Distance Measurements","aliases":["BAO","Baryon Oscillations","Standard Ruler Method"],"domain":"astronomy","family":"process-pipeline","subfamily":"Cosmological probe","year":1970,"originator":"Piet Peebles","url":"https://scholargate.app/en/astronomy/baryon-acoustic-oscillations","markdownUrl":"https://scholargate.app/en/astronomy/baryon-acoustic-oscillations.md","definition":"Baryon Acoustic Oscillations are imprints of sound waves in the early universe that appear as a characteristic scale in the large-scale distribution of galaxies today. First predicted theoretically by Piet Peebles and Joseph Yu in 1970, and detected observationally by the Sloan Digital Sky Survey in 2005, BAO provides a standard ruler for measuring cosmic distances and constraining the expansion history of the universe.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Piet Peebles","subfamily":"Cosmological probe","year":1970,"type":"Statistical cosmological measurement"},"citations":[{"ref":"Peebles, P. J. E., & Yu, J. T. (1970). Primeval adiabatic perturbation in an expanding universe. Astrophysical Journal, 162, 815-836.","type":"article","doi":"10.1086/150713","isbn":null,"url":null},{"ref":"Eisenstein, D. J., et al. (2005). Detection of the baryon acoustic peak in the correlation function of SDSS luminous red galaxies. Astrophysical Journal, 633(2), 560-574.","type":"article","doi":"10.1086/466512","isbn":null,"url":null},{"ref":"Ross, A. J., et al. (2015). The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey. Monthly Notices of the Royal Astronomical Society, 449(1), 835-847.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+clustering+of+galaxies+in+the+SDSS-III+Baryon+Oscillation+Spectroscopic+Survey+Ross"}],"related":["cmb-anisotropy-analysis","sunyaev-zeldovich-effect","weak-gravitational-lensing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"basdai","name":"Bath Ankylosing Spondylitis Disease Activity Index","fullName":"Bath Ankylosing Spondylitis Disease Activity Index","aliases":["BASDAI","BAS-DAI"],"domain":"rheumatology","family":"process-pipeline","subfamily":"disease-activity-index","year":"1994","originator":"Garrett et al.","url":"https://scholargate.app/en/rheumatology/basdai","markdownUrl":"https://scholargate.app/en/rheumatology/basdai.md","definition":"The BASDAI is a patient-reported outcome measure of disease activity in ankylosing spondylitis (AS), a chronic inflammatory arthropathy affecting the spine and axial skeleton. Introduced by Garrett et al. in 1994, BASDAI uses six simple patient self-report items focused on the cardinal symptoms of AS: fatigue, spinal pain, peripheral joint involvement, and morning stiffness. As a PRO measure, BASDAI is practical for routine monitoring, responsive to treatment, and strongly associated with clinical outcomes and spinal damage progression, making it a cornerstone outcome in AS management and clinical trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Garrett et al.","subfamily":"disease-activity-index","year":"1994","type":"Patient-reported outcome (PRO)"},"citations":[{"ref":"Garrett S, Jenkinson T, Kennedy LG, Whitelock H, Gaisford P, Calin A. A new approach to defining disease status in ankylosing spondylitis: the Bath Ankylosing Spondylitis Disease Activity Index. The Journal of Rheumatology. 1994;21(12):2286-2291.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/7699627"}],"related":["basfi","das28","sdai-rheumatoid-arthritis","sledai","rapid3"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"basfi","name":"Bath Ankylosing Spondylitis Functional Index","fullName":"Bath Ankylosing Spondylitis Functional Index","aliases":["BASFI","BAS-FI"],"domain":"rheumatology","family":"process-pipeline","subfamily":"functional-index","year":"1994","originator":"Calin et al.","url":"https://scholargate.app/en/rheumatology/basfi","markdownUrl":"https://scholargate.app/en/rheumatology/basfi.md","definition":"The BASFI is a patient-reported outcome measure of functional disability in ankylosing spondylitis (AS), assessing physical limitations in 10 common daily activities. Introduced by Calin et al. in 1994, BASFI measures the impact of AS on quality of life and functional capacity, complementing BASDAI (disease activity). While BASDAI reflects inflammatory burden, BASFI reflects the consequences of inflammation and spinal damage on a patient's ability to perform daily tasks. BASFI is used alongside BASDAI in AS management to capture both disease activity and its functional impact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Calin et al.","subfamily":"functional-index","year":"1994","type":"Patient-reported outcome (PRO)"},"citations":[{"ref":"Calin A, Garrett S, Whitelock H, Kennedy LG, O'Hea J, Mallorie P, Jenkinson T. A new approach to defining functional ability in ankylosing spondylitis: the development of the Bath Ankylosing Spondylitis Functional Index. The Journal of Rheumatology. 1994;21(12):2281-2285.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/7699625"}],"related":["basdai","das28","sdai-rheumatoid-arthritis","rapid3","sledai"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"basin-subsidence-analysis","name":"Basin Subsidence Analysis","fullName":"Basin Subsidence Analysis","aliases":["tectonic subsidence","backstripping","thermal history analysis"],"domain":"geoscience","family":"process-pipeline","subfamily":"Basin evolution analysis","year":"1978","originator":"McKenzie and Sclater","url":"https://scholargate.app/en/geoscience/basin-subsidence-analysis","markdownUrl":"https://scholargate.app/en/geoscience/basin-subsidence-analysis.md","definition":"Basin subsidence analysis is the quantitative study of how sedimentary basins deepen over geological time, driven by tectonics, isostasy, and load. Formalized by McKenzie (1978) and Sclater and Christie (1980), this method reveals the mechanical causes of basin development, predicts subsurface temperature and pressure histories, and constrains petroleum generation. Analysis integrates well stratigraphy, seismic geometry, gravity data, and thermal models to reconstruct basin evolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McKenzie and Sclater","subfamily":"Basin evolution analysis","year":"1978","type":"tectono-sedimentary analysis pipeline"},"citations":[{"ref":"Sclater, J. G., & Christie, P. A. F. (1980). Continental stretching: An explanation of the post-mid-Cretaceous subsidence of the Central North Sea Basin. Journal of Geophysical Research, 85(B7), 3711–3739.","type":"article","doi":"10.1029/JB085iB07p03711","isbn":null,"url":null},{"ref":"Allen, P. A., & Allen, J. R. (1995). Geology of Deltas. Ellis Horwood Limited.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"McKenzie, D. (1978). Some remarks on the development of sedimentary basins. Earth and Planetary Science Letters, 40(1), 25–32.","type":"article","doi":"10.1016/0012-821X(78)90071-7","isbn":null,"url":null}],"related":["stratigraphic-correlation","geologic-mapping","basin-modelling","geochronological-dating","geophysical-inversion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bat-algorithm","name":"Bat Algorithm","fullName":"Bat Algorithm","aliases":["BA","Bat-Inspired Algorithm","Echolocation-Based Optimization","Yarasa Algoritması"],"domain":"optimization","family":"process-pipeline","subfamily":"Metaheuristics","year":2010,"originator":"Xin-She Yang","url":"https://scholargate.app/en/optimization/bat-algorithm","markdownUrl":"https://scholargate.app/en/optimization/bat-algorithm.md","definition":"The Bat Algorithm (BA) is a nature-inspired metaheuristic optimization method proposed by Xin-She Yang in 2010. It mimics the echolocation behavior of microbats to balance global exploration and local exploitation. Each artificial bat adjusts its position, velocity, and emission frequency, with loudness and pulse rate dynamically controlling the transition from broad search to refined local tuning. BA is suited to continuous and combinatorial optimization problems across engineering, scheduling, and machine learning domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xin-She Yang","year":2010,"type":"Population-based swarm intelligence","subfamily":"Metaheuristics","inspiration":"Echolocation behavior of microbats","controlParameters":"frequency, loudness, pulse rate"},"citations":[{"ref":"Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO), 65–74.","type":"inproceedings","doi":"10.1007/978-3-642-12538-6_6","isbn":null,"url":null}],"related":["particle-swarm-optimization","firefly-algorithm","cuckoo-search"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"batch-normalization","name":"Batch Normalization","fullName":"Batch Normalization (Normalizing Layer Activations per Mini-Batch)","aliases":["BatchNorm","BN","batch norm","mini-batch normalization","internal covariate shift reduction"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2015,"originator":"Ioffe, S. & Szegedy, C.","url":"https://scholargate.app/en/deep-learning/batch-normalization","markdownUrl":"https://scholargate.app/en/deep-learning/batch-normalization.md","definition":"Batch Normalization is a training technique introduced by Sergey Ioffe and Christian Szegedy in 2015 that normalizes the pre-activation outputs of each layer using the mean and variance computed over the current mini-batch. By stabilizing the input distribution to each layer throughout training, it substantially reduces internal covariate shift, enabling the use of higher learning rates and making deep networks train faster and more reliably.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ioffe, S. & Szegedy, C.","year":2015,"type":"Normalization technique (applied per mini-batch during training)","task":"Stabilizing and accelerating training of deep neural networks","learnableParameters":2,"appliesTo":"Any differentiable layer in a neural network"},"citations":[{"ref":"Ioffe, S. & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning (ICML), PMLR 37, 448–456.","type":"article","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v37/ioffe15.html"},{"ref":"Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning (Ch. 8). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null},{"ref":"Ioffe, S. & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv preprint arXiv:1502.03167.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1502.03167"}],"related":["layer-normalization","dropout","residual-network","adam-optimizer","weight-initialization","group-normalization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bates-model","name":"Bates Model","fullName":"Bates Stochastic Volatility Jump Diffusion Model","aliases":["SVJ Model","Jump Diffusion"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Jump-Diffusion","year":"1996","originator":"David S. Bates","url":"https://scholargate.app/en/quantitative-finance/bates-model","markdownUrl":"https://scholargate.app/en/quantitative-finance/bates-model.md","definition":"The Bates model (1996) combines stochastic volatility and jump diffusion to capture both the volatility smile and the implied volatility skew observed in equity and currency option markets. It extends the Heston model by adding a Poisson jump component to returns, making it suitable for pricing options when sudden price moves are expected.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David S. Bates","subfamily":"Jump-Diffusion","year":"1996","type":"Equity/FX Model"},"citations":[{"ref":"Bates, D. S. (1996). Jumps and stochastic volatility: Exchange rate processes implicit in Deutsche Mark options. Review of Financial Studies, 9(1), 69-107.","type":"article","doi":"10.1093/rfs/9.1.69","isbn":null,"url":null},{"ref":"Merton, R. C. (1976). Option pricing when underlying stock returns are discontinuous. Journal of Financial Economics, 3(1-2), 125-144.","type":"article","doi":"10.1016/0304-405X(76)90022-2","isbn":null,"url":null}],"related":["sabr-model","local-volatility","hull-white-model","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"battery-equivalent-circuit-model","name":"Battery Equivalent Circuit Model","fullName":"Battery Equivalent Circuit Model for Electrochemical Analysis","aliases":["ECM","circuit model","battery model"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Electrochemistry","year":"2004","originator":"Gregory Plett","url":"https://scholargate.app/en/thermodynamics/battery-equivalent-circuit-model","markdownUrl":"https://scholargate.app/en/thermodynamics/battery-equivalent-circuit-model.md","definition":"The Battery Equivalent Circuit Model (ECM) represents battery electrochemical behavior using an electrical circuit analogy. It includes an ideal voltage source (open-circuit voltage dependent on state of charge), internal resistance(s) for ohmic losses, and capacitive/resistive elements for transient response. ECM enables rapid simulation of battery behavior in electric vehicles, renewable energy systems, and portable devices without solving complex electrochemical equations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory Plett","subfamily":"Electrochemistry","year":"2004","type":"Battery simulation model"},"citations":[{"ref":"Seaman, C. V., Strutt, A. S., & Murray, A. (2014). Portable and plug-in hybrid electric vehicle battery electric range impacts on U.S. gasoline consumption. Journal of Power Sources, 243, 773-783.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Portable+and+plug-in+hybrid+electric+vehicle+battery+electric+range+impacts+on+U.S+Seaman"},{"ref":"Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Journal of Power Sources, 134(2), 252-261.","type":"book","doi":"10.1016/j.jpowsour.2004.02.031","isbn":null,"url":null}],"related":["state-of-charge","state-of-health","maximum-power-point-tracking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayes-factor-test","name":"Bayes Factor Test","fullName":"Bayes Factor Hypothesis Test","aliases":["bayes factor","BF10","Bayesian hypothesis test","Bayes Faktörü — Hipotez Testi"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":1961,"originator":"Harold Jeffreys","url":"https://scholargate.app/en/bayesian/bayes-factor-test","markdownUrl":"https://scholargate.app/en/bayesian/bayes-factor-test.md","definition":"The Bayes factor test, formalised by Harold Jeffreys in 1961, is a Bayesian method for comparing two competing hypotheses. Rather than returning a binary reject/retain verdict, it produces a continuous ratio BF₁₀ that quantifies how much more (or less) probable the data are under the alternative hypothesis H₁ than under the null hypothesis H₀.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Jeffreys","year":1961,"family":"Bayesian","type":"Bayesian hypothesis comparison","purpose":"compare / quantify evidence","var_types":"continuous / categorical / binary","outputs":"BF₁₀ — continuous evidence ratio","min_sample":5,"difficulty":2},"citations":[{"ref":"Jeffreys, H. (1961). Theory of Probability (3rd ed.). Clarendon Press / Oxford University Press.","type":"book","doi":null,"isbn":"978-0198503682","url":null},{"ref":"Kass, R. E. & Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773–795.","type":"article","doi":"10.1080/01621459.1995.10476572","isbn":null,"url":null}],"related":["bayesian-regression","independent-t-test","mcmc","hierarchical-bayes"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-active-learning","name":"Bayesian Active Learning","fullName":"Bayesian Active Learning (Query-by-Committee and BALD)","aliases":["BAL","Bayesian optimal experimental design for ML","BALD (Bayesian Active Learning by Disagreement)","probabilistic active learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1992–2011","originator":"MacKay, D.J.C.; Houlsby, N. et al.","url":"https://scholargate.app/en/machine-learning/bayesian-active-learning","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-active-learning.md","definition":"Bayesian Active Learning (BAL) combines a probabilistic model with an active query strategy to identify the unlabeled examples that, once labeled, would most reduce model uncertainty. Instead of labeling data at random, BAL guides an oracle — typically a human annotator — toward the points where labeling will provide the greatest information gain, making it highly label-efficient.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"MacKay, D.J.C.; Houlsby, N. et al.","year":"1992–2011","type":"Active learning with Bayesian uncertainty","dataType":"Labeled and unlabeled tabular, image, or text data","subfamily":"Machine learning"},"citations":[{"ref":"Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011). Bayesian Active Learning for Classification and Preference Learning. arXiv preprint arXiv:1112.5745.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1112.5745"},{"ref":"Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool.","type":"book","doi":"10.2200/S00429ED1V01Y201207AIM018","isbn":null,"url":null}],"related":["active-learning","gaussian-process","bayesian-optimization","semi-supervised-learning","few-shot-learning","bayesian-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-adf-unit-root-test","name":"Bayesian ADF unit root test","fullName":"Bayesian Augmented Dickey-Fuller Unit Root Test","aliases":["Bayesian ADF test","Bayesian unit root test","Bayesian Dickey-Fuller","BADF"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991–1992","originator":"Sims & Uhlig (1991); Koop, Osiewalski & Steel (1992)","url":"https://scholargate.app/en/econometrics/bayesian-adf-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-adf-unit-root-test.md","definition":"The Bayesian Augmented Dickey-Fuller (BADF) unit root test re-frames the classical ADF test within a Bayesian framework. Rather than computing a frequentist p-value, it quantifies evidence for or against a unit root by comparing posterior probabilities or Bayes factors under the null (unit root) and alternative (stationarity) hypotheses, incorporating prior beliefs about the autoregressive parameter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sims & Uhlig (1991); Koop, Osiewalski & Steel (1992)","year":"1991–1992","type":"Bayesian hypothesis test","dataType":"univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sims, C. A., & Uhlig, H. (1991). Understanding unit rooters: A helicopter tour. Econometrica, 59(6), 1591–1599.","type":"article","doi":"10.2307/2938280","isbn":null,"url":null},{"ref":"Koop, G., Osiewalski, J., & Steel, M. F. J. (1992). Bayesian analysis of long-run multipliers in cointegrating models. Journal of Econometrics, 54(1–3), 27–44.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+analysis+of+long-run+multipliers+in+cointegrating+models+Koop"}],"related":["augmented-dickey-fuller-unit-root-test","phillips-perron-unit-root-test","bayesian-ardl-bounds-test","bayesian-var-model","bayesian-vecm","zivot-andrews-structural-break-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-agent-based-modeling","name":"Bayesian Agent-Based Modeling","fullName":"Bayesian Agent-Based Modeling — Parameter Estimation and Uncertainty Quantification for Agent-Based Models","aliases":["Bayesian ABM","ABC-ABM","Bayesian Calibration of ABM","Bayesian Agent Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s–2010s","originator":"Sunnaker et al. / Grazzini & Richiardi (among key contributors)","url":"https://scholargate.app/en/simulation/bayesian-agent-based-modeling","markdownUrl":"https://scholargate.app/en/simulation/bayesian-agent-based-modeling.md","definition":"Bayesian Agent-Based Modeling integrates Bayesian statistical inference with agent-based simulation to calibrate model parameters and quantify uncertainty. Rather than fixing agent rules and parameters by assumption, this approach treats unknown parameters as probability distributions and updates them systematically against observed data, yielding a full posterior over plausible model configurations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sunnaker et al. / Grazzini & Richiardi (among key contributors)","year":"2000s–2010s","type":"Simulation calibration and inference framework","dataType":"Observed aggregate or micro-level data; agent behavior logs","subfamily":"Simulation / optimization"},"citations":[{"ref":"Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803.","type":"article","doi":"10.1371/journal.pcbi.1002803","isbn":null,"url":null},{"ref":"Grazzini, J., Richiardi, M. (2015). Estimation of agent-based models by simulated minimum distance. Journal of Economic Dynamics and Control, 51, 148-165.","type":"article","doi":"10.1016/j.jedc.2014.10.006","isbn":null,"url":null}],"related":["agent-based-modeling","approximate-bayesian-computation","monte-carlo-simulation","bayesian-markov-model","stochastic-agent-based-modeling","bayesian-microsimulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ancova","name":"Bayesian ANCOVA","fullName":"Bayesian Analysis of Covariance","aliases":["Bayesian ANCOVA","Bayesian analysis of covariance","B-ANCOVA","Bayesian covariate-adjusted group comparison"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"2012 (formalized; Bayesian general linear models since 1960s)","originator":"Building on Jeffreys (1961) and developed formally for regression/ANCOVA by Rouder & Morey (2012)","url":"https://scholargate.app/en/statistics/bayesian-ancova","markdownUrl":"https://scholargate.app/en/statistics/bayesian-ancova.md","definition":"Bayesian Analysis of Covariance (Bayesian ANCOVA) extends classical ANCOVA by placing prior distributions on group effects and covariate slopes, then updating them with observed data to obtain posterior distributions and Bayes factors. It quantifies evidence for group differences on a continuous outcome after statistically adjusting for one or more continuous covariates, without relying on p-value thresholds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Jeffreys (1961) and developed formally for regression/ANCOVA by Rouder & Morey (2012)","year":"2012 (formalized; Bayesian general linear models since 1960s)","type":"Bayesian parametric covariate-adjusted group comparison","dataType":"Continuous outcome, categorical group factor, continuous covariate(s)","subfamily":"Classical statistics"},"citations":[{"ref":"Rouder, J. N., & Morey, R. D. (2012). Default Bayes factors for model selection in regression. Multivariate Behavioral Research, 47(6), 877–903.","type":"article","doi":"10.1080/00273171.2012.734737","isbn":null,"url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["ancova","bayesian-one-way-anova","bayesian-mancova","bayesian-linear-regression","robust-ancova","bayesian-manova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-anova","name":"Bayesian ANOVA","fullName":"Bayesian Analysis of Variance with Bayes Factors","aliases":["bayesian analysis of variance","bayes factor ANOVA","JZS ANOVA","Bayesçi ANOVA — Bayes Faktörü ile Grup Karşılaştırması"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2012,"originator":"Rouder, Morey, Speckman & Province","url":"https://scholargate.app/en/bayesian/bayesian-anova","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-anova.md","definition":"Bayesian ANOVA, formalised by Rouder, Morey, Speckman and Province (2012), tests whether group means differ by quantifying the evidence for the alternative hypothesis relative to the null using the Bayes Factor (BF₁₀). Unlike classical ANOVA, it can also measure evidence in favour of the null hypothesis, making it equally informative when groups do not differ.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Bayesian hypothesis test / group comparison","purpose":"compare group means","var_types":"continuous","structure":"cross-sectional, 2+ independent groups","prior":"Cauchy (r = 0.707, Rouder et al. standard)","outputs":"Bayes Factor (BF₁₀ / BF₀₁), posterior distributions, credible intervals","inference":"MCMC","min_sample":10,"difficulty":2,"originator":"Rouder, Morey, Speckman & Province","year":2012},"citations":[{"ref":"Rouder, J. N., Morey, R. D., Speckman, P. L. & Province, J. M. (2012). Default Bayes Factors for ANOVA Designs. Journal of Mathematical Psychology, 56(5), 356–374.","type":"article","doi":"10.1016/j.jmp.2012.08.001","isbn":null,"url":null}],"related":["bayesian-regression","one-way-anova","bayes-factor-test","mcmc","hierarchical-bayes"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ant-colony-optimization","name":"Bayesian Ant Colony Optimization","fullName":"Bayesian Ant Colony Optimization — ACO with Bayesian probabilistic parameter learning","aliases":["BACO","Bayesian ACO","Bayesian-guided ACO","Probabilistic ACO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1996 (ACO); Bayesian variant: 2000s","originator":"Dorigo, M. et al. (ACO); Bayesian extensions by multiple researchers in the 2000s–2010s","url":"https://scholargate.app/en/simulation/bayesian-ant-colony-optimization","markdownUrl":"https://scholargate.app/en/simulation/bayesian-ant-colony-optimization.md","definition":"Bayesian Ant Colony Optimization (BACO) is a hybrid metaheuristic that embeds Bayesian inference into the Ant Colony Optimization framework. By treating pheromone intensities or algorithm parameters as probability distributions updated with collected evidence, BACO improves convergence reliability and robustness compared to classical ACO on noisy or uncertain combinatorial optimization problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorigo, M. et al. (ACO); Bayesian extensions by multiple researchers in the 2000s–2010s","year":"1996 (ACO); Bayesian variant: 2000s","type":"Metaheuristic with Bayesian probabilistic learning","dataType":"Combinatorial or continuous optimization problem instances","subfamily":"Simulation / optimization"},"citations":[{"ref":"Dorigo, M., Maniezzo, V., Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26(1), 29–41.","type":"article","doi":"10.1109/3477.484436","isbn":null,"url":null},{"ref":"Ant colony optimization algorithms. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms"}],"related":["ant-colony-optimization","bayesian-genetic-algorithm","bayesian-particle-swarm-optimization","stochastic-ant-colony-optimization","multi-objective-ant-colony-optimization","bayesian-simulated-annealing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ar-model","name":"Bayesian AR model","fullName":"Bayesian Autoregressive Model","aliases":["Bayesian autoregressive model","BAR model","Bayesian AR","Bayesian time-series autoregression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1971","originator":"Arnold Zellner; foundational Bayesian time-series work by West & Harrison","url":"https://scholargate.app/en/econometrics/bayesian-ar-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-ar-model.md","definition":"The Bayesian AR model estimates an autoregressive time-series process by combining a likelihood derived from the AR structure with prior distributions over the lag coefficients and error variance. Rather than producing single point estimates, it yields full posterior distributions, enabling principled uncertainty quantification and probabilistic forecasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arnold Zellner; foundational Bayesian time-series work by West & Harrison","year":"1971","type":"Bayesian time-series model","dataType":"Univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley.","type":"book","doi":null,"isbn":"978-0471169376","url":null},{"ref":"West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387947259","url":null}],"related":["autoregressive-model","bayesian-var-model","bayesian-arma-model","bayesian-arima-model","arma-model","vector-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-arch-model","name":"Bayesian ARCH model","fullName":"Bayesian Autoregressive Conditional Heteroskedasticity Model","aliases":["Bayesian ARCH","ARCH with Bayesian estimation","Bayesian conditional heteroskedasticity model","B-ARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1982 (ARCH); 1989 (Bayesian estimation)","originator":"Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989)","url":"https://scholargate.app/en/econometrics/bayesian-arch-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-arch-model.md","definition":"The Bayesian ARCH model estimates Engle's Autoregressive Conditional Heteroskedasticity specification within a Bayesian framework. Instead of maximising a likelihood, it combines a prior distribution over the volatility parameters with the data likelihood to obtain a full posterior distribution, providing richer uncertainty quantification than classical maximum-likelihood ARCH.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989)","year":"1982 (ARCH); 1989 (Bayesian estimation)","type":"Volatility model with Bayesian inference","dataType":"Time series (financial returns, macroeconomic series with time-varying variance)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null},{"ref":"Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.","type":"article","doi":"10.1016/0304-4076(89)90030-4","isbn":null,"url":null}],"related":["arch-model","garch-model","bayesian-garch-model","bayesian-egarch","bayesian-tgarch","dcc-garch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ardl-bounds-test","name":"Bayesian ARDL Bounds Test","fullName":"Bayesian Autoregressive Distributed Lag Bounds Test","aliases":["Bayesian ARDL","Bayesian bounds testing approach","Bayes ARDL cointegration","Bayesian PSS bounds test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2001 (ARDL); Bayesian extension 2010s","originator":"Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature","url":"https://scholargate.app/en/econometrics/bayesian-ardl-bounds-test","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-ardl-bounds-test.md","definition":"The Bayesian ARDL Bounds Test extends the classical Pesaran-Shin-Smith (2001) bounds testing approach to cointegration by embedding it within a Bayesian inferential framework. Instead of relying on frequentist F- and t-statistics with tabulated critical values, the researcher specifies prior distributions on the model parameters and derives posterior evidence of a long-run level relationship between variables that may be integrated of order zero or one.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature","year":"2001 (ARDL); Bayesian extension 2010s","type":"Cointegration / bounds testing","dataType":"Time-series (levels and differences of I(0)/I(1) variables)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null},{"ref":"Koop, G. (2003). Bayesian Econometrics. Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0470845678","url":null}],"related":["ardl-bounds-test","nonlinear-ardl","bayesian-vecm","bayesian-var-model","johansen-cointegration-test","engle-granger-cointegration-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-arima-model","name":"Bayesian ARIMA model","fullName":"Bayesian Autoregressive Integrated Moving Average Model","aliases":["Bayesian ARIMA","BARIMA","Bayesian Box-Jenkins model","Bayesian integrated time series model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970s (ARIMA); Bayesian extension prominent from 1990s","originator":"Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation)","url":"https://scholargate.app/en/econometrics/bayesian-arima-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-arima-model.md","definition":"The Bayesian ARIMA model combines the classical Box-Jenkins ARIMA framework with Bayesian inference. Instead of obtaining single point estimates for autoregressive and moving average parameters, it places prior distributions over them and uses observed data to update beliefs into a full posterior distribution, enabling coherent uncertainty quantification and probabilistic forecasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation)","year":"1970s (ARIMA); Bayesian extension prominent from 1990s","type":"Bayesian time series model","dataType":"Univariate time series (continuous, regularly spaced)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Pole, A., West, M., & Harrison, J. (1994). Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall.","type":"book","doi":null,"isbn":"978-0412416903","url":null},{"ref":"Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118675021","url":null}],"related":["arima-model","bayesian-var-model","bayesian-sarima-model","bayesian-ardl-bounds-test","sarima-model","vector-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-arma-model","name":"Bayesian ARMA model","fullName":"Bayesian Autoregressive Moving Average Model","aliases":["Bayesian ARMA","B-ARMA","Bayesian autoregressive moving average","ARMA with Bayesian inference"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970s–1980s","originator":"Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s","url":"https://scholargate.app/en/econometrics/bayesian-arma-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-arma-model.md","definition":"The Bayesian ARMA model applies Bayesian inference to the classical autoregressive moving average framework for stationary univariate time series. Rather than producing single point estimates for the AR and MA parameters, it yields full posterior distributions, naturally incorporating prior knowledge and providing coherent uncertainty quantification over forecasts and impulse responses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Jenkins (classical ARMA); Bayesian treatment developed through work of Zellner, Geweke, and others in 1970s–1980s","year":"1970s–1980s","type":"Bayesian time series model","dataType":"Univariate time series (stationary)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimating+regression+models+of+finite+but+unknown+order+Geweke+Meese+1981"},{"ref":"Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118675021","url":null}],"related":["arma-model","arima-model","bayesian-var-model","bayesian-arima-model","vector-autoregression","bayesian-ols"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-association-rules","name":"Bayesian Association Rules","fullName":"Bayesian Association Rule Mining","aliases":["Bayesian rule learning","probabilistic association rules","Bayesian itemset mining","BAR"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1994–1995","originator":"Heckerman, D. et al.; Agrawal, R. & Srikant, R.","url":"https://scholargate.app/en/machine-learning/bayesian-association-rules","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-association-rules.md","definition":"Bayesian Association Rules extend classical association rule mining by placing a prior probability distribution over rules and scoring them by their posterior probability given the data. Rather than thresholding on raw support and confidence counts, this Bayesian framework naturally penalises complexity, corrects for multiple comparisons, and produces calibrated probabilistic rule strengths across transactional or categorical datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heckerman, D. et al.; Agrawal, R. & Srikant, R.","year":"1994–1995","type":"Probabilistic rule mining","dataType":"Transactional / categorical tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197–243.","type":"article","doi":"10.1007/BF00994016","isbn":null,"url":null},{"ref":"Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 1215, 487–499.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fast+algorithms+for+mining+association+rules+Agrawal+Srikant+1994"}],"related":["association-rules","apriori-algorithm","bayesian-naive-bayes","bayesian-gaussian-mixture-model","semi-supervised-association-rules","fp-growth"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-autoencoder-anomaly-detection","name":"Bayesian Autoencoder Anomaly Detection","fullName":"Bayesian Autoencoder Anomaly Detection (Probabilistic Reconstruction-Error Framework)","aliases":["Bayesian VAE anomaly detection","probabilistic autoencoder anomaly detection","variational autoencoder anomaly detection","VAE-based outlier detection"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2014–2015","originator":"Kingma, D. P. & Welling, M.; applied to anomaly detection by An & Cho","url":"https://scholargate.app/en/machine-learning/bayesian-autoencoder-anomaly-detection","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-autoencoder-anomaly-detection.md","definition":"Bayesian Autoencoder Anomaly Detection uses a Variational Autoencoder — a probabilistic generative model trained on normal data — to flag anomalies by their high reconstruction error or low likelihood under the learned distribution. By treating the latent space as a probability distribution rather than a fixed point, it delivers principled uncertainty estimates alongside each anomaly score, making it especially valuable in high-stakes detection tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kingma, D. P. & Welling, M.; applied to anomaly detection by An & Cho","year":"2014–2015","type":"Probabilistic generative model for unsupervised anomaly detection","dataType":"Continuous, high-dimensional tabular or image data","subfamily":"Machine learning"},"citations":[{"ref":"Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"},{"ref":"An, J. & Cho, S. (2015). Variational Autoencoder based Anomaly Detection using Reconstruction Probability. ICDM Workshop on Data Mining in Networks.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Variational+Autoencoder+based+Anomaly+Detection+using+Reconstruction+Probability"}],"related":["autoencoder-anomaly-detection","one-class-svm","isolation-forest","gaussian-mixture-model","bayesian-gaussian-mixture-model","semi-supervised-autoencoder-anomaly-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-bagging","name":"Bayesian Bagging","fullName":"Bayesian Bagging (Bootstrap Aggregation with Bayesian Bootstrap)","aliases":["Bayesian bootstrap aggregation","BB-ensemble","Bayesian model averaging via bootstrap","Bayesian bagged ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001","originator":"Clyde, M. & Lee, H. (building on Rubin's Bayesian bootstrap, 1981)","url":"https://scholargate.app/en/machine-learning/bayesian-bagging","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-bagging.md","definition":"Bayesian Bagging replaces the classical bootstrap with the Bayesian bootstrap — drawing Dirichlet-distributed weights over training observations rather than sampling with replacement — and trains an ensemble of base learners under those weights. The result is a principled ensemble that approximates a Bayesian posterior over predictions, yielding calibrated uncertainty estimates alongside strong predictive accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clyde, M. & Lee, H. (building on Rubin's Bayesian bootstrap, 1981)","year":"2001","type":"Ensemble (Bayesian bootstrap aggregation)","dataType":"Tabular (continuous, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Clyde, M. & Lee, H. (2001). Bagging and the Bayesian bootstrap. In T. Richardson & T. Jaakkola (Eds.), Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bagging+and+the+Bayesian+bootstrap+Clyde+Lee+2001"},{"ref":"Rubin, D. B. (1981). The Bayesian bootstrap. The Annals of Statistics, 9(1), 130–134.","type":"article","doi":"10.1214/aos/1176345338","isbn":null,"url":null}],"related":["random-forest","bayesian-random-forest","boosting","bayesian-boosting","voting-ensemble","semi-supervised-bagging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-betweenness-centrality","name":"Bayesian Betweenness Centrality","fullName":"Bayesian Betweenness Centrality (Probabilistic Inference of Shortest-Path Centrality)","aliases":["Bayesian BC","probabilistic betweenness centrality","uncertainty-aware betweenness centrality","posterior betweenness estimation"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010s","originator":"Brandes, U. (betweenness); Bayesian extension developed by multiple authors (2010s)","url":"https://scholargate.app/en/network-analysis/bayesian-betweenness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-betweenness-centrality.md","definition":"Bayesian Betweenness Centrality estimates how often a node lies on shortest paths in a network while explicitly quantifying uncertainty arising from incomplete, sampled, or noisy edge observations. Rather than producing a single point estimate, it yields a posterior distribution over betweenness scores, enabling credible intervals and probabilistic comparisons between nodes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brandes, U. (betweenness); Bayesian extension developed by multiple authors (2010s)","year":"2010s","type":"Probabilistic network centrality measure","dataType":"Network adjacency data (observed or sampled edges, possibly with uncertainty)","subfamily":"Network science"},"citations":[{"ref":"Newman, M.E.J. (2010). Networks: An Introduction. Oxford University Press.","type":"book","doi":null,"isbn":"978-0-19-920665-0","url":null},{"ref":"Fortunato, S., Bergstrom, C.T., Borner, K., Evans, J.A., Helbing, D., Milojevi, S., Petersen, A.M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L., Wang, D. & Barabasi, A.-L. (2018). Science of science. Science, 359(6379), eaao0185.","type":"article","doi":"10.1126/science.aao0185","isbn":null,"url":null}],"related":["betweenness-centrality","bayesian-social-network-analysis","bayesian-degree-centrality","bayesian-closeness-centrality","weighted-betweenness-centrality","exponential-random-graph-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-boosting","name":"Bayesian Boosting","fullName":"Bayesian Boosting (Probabilistic Ensemble Learning)","aliases":["Bayesian ensemble boosting","probabilistic boosting","Bayesian additive model","Bayesian boosted ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1999–2010","originator":"Ridgeway, G.; Chipman, H. A. et al.","url":"https://scholargate.app/en/machine-learning/bayesian-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-boosting.md","definition":"Bayesian boosting integrates probabilistic Bayesian inference with boosting ensemble techniques, combining multiple weak learners while maintaining full uncertainty quantification over predictions. Unlike standard gradient boosting that produces a single point estimate, Bayesian boosting yields a posterior distribution over the ensemble output, enabling calibrated confidence intervals alongside predictions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ridgeway, G.; Chipman, H. A. et al.","year":"1999–2010","type":"Probabilistic ensemble (Bayesian interpretation of boosting)","dataType":"Tabular (continuous, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Ridgeway, G. (1999). The state of boosting. Computing Science and Statistics, 31, 172–181.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+state+of+boosting+Ridgeway+1999"},{"ref":"Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. Annals of Applied Statistics, 4(1), 266–298.","type":"article","doi":"10.1214/09-AOAS285","isbn":null,"url":null}],"related":["boosting","gradient-boosting","bayesian-random-forest","xgboost","semi-supervised-boosting","bayesian-gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-bootstrap","name":"Bayesian Bootstrap","fullName":"Rubin's Bayesian Bootstrap","aliases":["Bayesian Bootstrap (Rubin)","Rubin bootstrap","Dirichlet-weighted bootstrap"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1981,"originator":"Rubin (1981); large-sample theory by Lo (1987)","url":"https://scholargate.app/en/statistics/bayesian-bootstrap","markdownUrl":"https://scholargate.app/en/statistics/bayesian-bootstrap.md","definition":"The Bayesian Bootstrap, introduced by Donald B. Rubin in 1981, is a resampling method that produces a Bayesian counterpart to the frequentist bootstrap by assigning each observation a random weight drawn from a Dirichlet distribution. It yields a full posterior distribution for a statistic and allows prior information to be incorporated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rubin (1981); large-sample theory by Lo (1987)","year":1981,"type":"Resampling / posterior simulation","estimator":"Dirichlet-weighted resampling of the statistic","weights":"Dirichlet(1, ..., 1)","minSample":10},"citations":[{"ref":"Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.","type":"article","doi":"10.1214/aos/1176345338","isbn":null,"url":null},{"ref":"Lo, A. Y. (1987). A Large Sample Study of the Bayesian Bootstrap. The Annals of Statistics, 15(1), 360-375.","type":"article","doi":"10.1214/aos/1176350271","isbn":null,"url":null}],"related":["bootstrap-inference","block-bootstrap","randomization-inference","permutation-test","jackknife"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-box-behnken-design","name":"Bayesian Box-Behnken Design","fullName":"Bayesian Box-Behnken Design for Response Surface Optimization","aliases":["Bayesian BBD","Bayesian RSM Box-Behnken","Bayesian three-level design","BBD with Bayesian optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960 (BBD); Bayesian integration ~1990s–2000s","originator":"Box & Behnken (classical BBD, 1960); Bayesian extension developed by multiple authors in response surface literature","url":"https://scholargate.app/en/experimental-design/bayesian-box-behnken-design","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-box-behnken-design.md","definition":"Bayesian Box-Behnken Design combines the classical Box-Behnken three-level design structure with Bayesian statistical inference to fit and optimize response surface models. It uses mid-edge and center points to efficiently estimate a second-order polynomial response surface while incorporating prior knowledge about model parameters and propagating uncertainty through to predictions and optimal factor settings. The approach is widely applied in engineering process optimization and formulation studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Behnken (classical BBD, 1960); Bayesian extension developed by multiple authors in response surface literature","year":"1960 (BBD); Bayesian integration ~1990s–2000s","type":"Bayesian response surface experimental design","dataType":"Continuous factor-response data from physical or computational experiments","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.","type":"article","doi":"10.1080/00401706.1960.10489912","isbn":null,"url":null},{"ref":"Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273–304.","type":"article","doi":"10.1214/ss/1177009939","isbn":null,"url":null}],"related":["box-behnken-design","central-composite-design","response-surface-methodology","bayesian-optimization","d-optimal-design","factorial-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-canonical-correlation-analysis","name":"Bayesian Canonical Correlation Analysis","fullName":"Bayesian Canonical Correlation Analysis","aliases":["Bayesian CCA","probabilistic CCA","BCCA"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2005-2013","originator":"Francis Bach & Michael Jordan (probabilistic formulation, 2005); Klami, Virtanen & Kaski (fully Bayesian treatment, 2013)","url":"https://scholargate.app/en/statistics/bayesian-canonical-correlation-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-canonical-correlation-analysis.md","definition":"Bayesian canonical correlation analysis is a probabilistic generative model that identifies shared latent structure between two or more sets of observed variables. It extends classical CCA by placing priors on model parameters, enabling principled uncertainty quantification, automatic determination of the number of shared dimensions, and robustness when sample sizes are small relative to dimensionality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Francis Bach & Michael Jordan (probabilistic formulation, 2005); Klami, Virtanen & Kaski (fully Bayesian treatment, 2013)","year":"2005-2013","type":"Latent variable model / dimensionality reduction","dataType":"Two or more sets of continuous multivariate observations","subfamily":"Multivariate analysis"},"citations":[{"ref":"Bach, F. R. & Jordan, M. I. (2005). A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.di.ens.fr/~fbach/probacca.pdf"},{"ref":"Klami, A., Virtanen, S. & Kaski, S. (2013). Bayesian canonical correlation analysis. Journal of Machine Learning Research, 14, 965-1003.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v14/klami13a.html"}],"related":["canonical-correlation-analysis","bayesian-exploratory-factor-analysis","bayesian-principal-component-analysis","confirmatory-factor-analysis","structural-equation-modeling","bayesian-structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-case-control-study","name":"Bayesian Case-Control Study","fullName":"Bayesian Case-Control Epidemiological Study","aliases":["Bayesian case-control design","Bayesian odds ratio estimation","Bayesian matched case-control","Bayesian logistic regression case-control"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c.","originator":"Sander Greenland (Bayesian epidemiology formalization); earlier Bayesian logistic methods: Leonard (1972)","url":"https://scholargate.app/en/epidemiology/bayesian-case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-case-control-study.md","definition":"A Bayesian case-control study applies Bayesian statistical inference to the classic case-control epidemiological design, formally combining prior knowledge about exposure-disease associations with observed case and control data to estimate posterior odds ratios and credible intervals. Rather than relying solely on observed data, the Bayesian framework allows investigators to incorporate external evidence — from prior studies, expert knowledge, or mechanistic understanding — into the analysis, yielding probability statements about effect sizes that are often more interpretable than classical p-values and confidence intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sander Greenland (Bayesian epidemiology formalization); earlier Bayesian logistic methods: Leonard (1972)","year":"1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c.","type":"Observational analytic study with Bayesian inference","dataType":"Binary outcome (case/control status), exposure variables, prior distributions","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765-775.","type":"article","doi":"10.1093/ije/dyi312","isbn":null,"url":null},{"ref":"Gustafson, P. (2004). Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584884316","url":null}],"related":["case-control-study","nested-case-control","bayesian-cohort-study","bayesian-randomized-clinical-trial","matched-case-control-study","logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-case-crossover-design","name":"Bayesian Case-Crossover Design","fullName":"Bayesian Case-Crossover Study Design","aliases":["Bayesian case-crossover","BCCO","Bayesian self-matched design","Bayesian within-person crossover"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1991 (case-crossover); Bayesian extension ~2000s","originator":"Malcolm Maclure (case-crossover); Bayesian extension developed by Lumley, Sheppard, and colleagues","url":"https://scholargate.app/en/epidemiology/bayesian-case-crossover-design","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-case-crossover-design.md","definition":"The Bayesian case-crossover design is a self-matched epidemiological method that estimates the transient effect of a time-varying exposure on the risk of an acute event. Each case serves as their own control, eliminating confounding by time-stable individual characteristics. Bayesian inference replaces or supplements the classical conditional logistic regression, enabling the incorporation of prior knowledge, more stable estimation in sparse data, and full uncertainty quantification via posterior distributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Malcolm Maclure (case-crossover); Bayesian extension developed by Lumley, Sheppard, and colleagues","year":"1991 (case-crossover); Bayesian extension ~2000s","type":"Self-matched observational study design with Bayesian inference","dataType":"Individual-level time-stamped event data with time-varying exposure measurements","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.","type":"article","doi":"10.1093/oxfordjournals.aje.a115853","isbn":null,"url":null},{"ref":"Janes, H., Sheppard, L., & Lumley, T. (2005). Case-crossover analyses of air pollution exposure data: referent selection strategies and their implications for bias. Epidemiology, 16(6), 717–726.","type":"article","doi":"10.1097/01.ede.0000181315.18836.9d","isbn":null,"url":null}],"related":["case-crossover-design","conditional-logistic-regression","bayesian-hierarchical-model","time-series-analysis","matched-case-control","self-controlled-case-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-case-series","name":"Bayesian Case Series","fullName":"Bayesian Case Series Analysis","aliases":["Bayesian case-series","BCS analysis","Bayesian self-controlled case series","Bayesian SCCS"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1995–2006 (core SCCS method 1995; Bayesian extensions throughout 2000s)","originator":"Farrington, Whitaker, and colleagues (self-controlled case series); Bayesian extension by multiple authors in pharmacovigilance","url":"https://scholargate.app/en/epidemiology/bayesian-case-series","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-case-series.md","definition":"Bayesian case series is an observational epidemiological method that applies Bayesian inference to case series data — typically records of patients who experienced both a drug or vaccine exposure and an adverse health event. By incorporating prior evidence and computing posterior estimates of the incidence rate ratio within pre-specified risk windows, the method quantifies the strength of a temporal association between an exposure and an outcome while controlling for fixed individual-level confounding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Farrington, Whitaker, and colleagues (self-controlled case series); Bayesian extension by multiple authors in pharmacovigilance","year":"1995–2006 (core SCCS method 1995; Bayesian extensions throughout 2000s)","type":"Observational analytical study design with Bayesian inference","dataType":"Individual-level longitudinal event data (hospitalisation records, adverse event reports, electronic health records)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Strom, B. L. (Ed.). (2001). Pharmacoepidemiology (3rd ed.). Wiley. [Chapter on case series and signal detection]","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pharmacoepidemiology+Strom+2001+case+series"},{"ref":"Whitaker, H. J., Farrington, C. P., Spiessens, B., & Musonda, P. (2006). Tutorial in biostatistics: The self-controlled case series method. Statistics in Medicine, 25(10), 1768–1797.","type":"article","doi":"10.1002/sim.2302","isbn":null,"url":null}],"related":["self-controlled-case-series","bayesian-network-meta-analysis","case-crossover","signal-detection","pharmacovigilance","cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-causal-impact-analysis","name":"Bayesian Causal Impact Analysis","fullName":"Bayesian Causal Impact Analysis via Structural Time Series","aliases":["CausalImpact","Bayesian structural time series causal inference","BSTS causal impact","Bayesian intervention analysis"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015","originator":"Brodersen, Gallusser, Koehler, Remy & Scott (Google)","url":"https://scholargate.app/en/causal-inference/bayesian-causal-impact-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-causal-impact-analysis.md","definition":"Bayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the intervention — from pre-intervention data and optional control covariates, then compares it with the observed post-intervention values to produce a fully Bayesian posterior over the causal effect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen, Gallusser, Koehler, Remy & Scott (Google)","year":"2015","type":"Bayesian causal inference / time series","dataType":"Univariate or multivariate time series with a clear pre- and post-intervention period","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Scott, S. L., & Varian, H. R. (2014). Predicting the present with Bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1-2), 4-23.","type":"article","doi":"10.1504/IJMMNO.2014.059942","isbn":null,"url":null}],"related":["causal-impact-analysis","interrupted-time-series","difference-in-differences","synthetic-control-method","bayesian-interrupted-time-series","event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-cellular-automata","name":"Bayesian Cellular Automata","fullName":"Bayesian Cellular Automata — Probabilistic calibration of transition rules via Bayesian inference","aliases":["BCA","Bayesian CA","Probabilistic Cellular Automata (Bayesian)","Bayes-calibrated CA"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s","originator":"Multiple contributors (Bayesian calibration of CA emerged in spatial / land-use modeling literature, 2000s–2010s)","url":"https://scholargate.app/en/simulation/bayesian-cellular-automata","markdownUrl":"https://scholargate.app/en/simulation/bayesian-cellular-automata.md","definition":"Bayesian Cellular Automata (BCA) couples the local-rule spatial dynamics of classical cellular automata with Bayesian inference to learn or calibrate transition probabilities from observed data. Rather than fixing rules by hand, the analyst encodes prior knowledge about how cells change state and updates those beliefs with empirical evidence, producing a posterior distribution over rule parameters that drives principled uncertainty-aware simulation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Bayesian calibration of CA emerged in spatial / land-use modeling literature, 2000s–2010s)","year":"2000s","type":"Simulation — probabilistic rule inference","dataType":"Spatial grid / lattice data; prior beliefs; observed state transitions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Hosseinali, F., Alesheikh, A. A., Nourian, F. (2013). Agent-based modeling of urban land-use development, case study: Simulating future scenarios of Qazvin city. Cities, 31, 105-113.","type":"article","doi":"10.1016/j.cities.2012.09.002","isbn":null,"url":null},{"ref":"Cellular automaton. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Cellular_automaton"}],"related":["stochastic-cellular-automata","monte-carlo-simulation","bayesian-markov-model","agent-based-cellular-automata","bayesian-agent-based-modeling","markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-chi-square-test","name":"Bayesian chi-square test","fullName":"Bayesian Chi-Square Test of Independence","aliases":["Bayesian contingency table test","Bayes factor chi-square","Bayesian goodness-of-fit test","Bayesian association test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1967","originator":"I. J. Good; extended by Gunel, Dickey, and Wagenmakers et al.","url":"https://scholargate.app/en/statistics/bayesian-chi-square-test","markdownUrl":"https://scholargate.app/en/statistics/bayesian-chi-square-test.md","definition":"The Bayesian chi-square test evaluates independence or goodness-of-fit in frequency tables using Bayes factors rather than classical p-values. It quantifies evidence for or against an association between categorical variables, updating prior beliefs with observed counts and delivering an odds-like ratio that distinguishes 'no evidence' from 'evidence of no effect'.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"I. J. Good; extended by Gunel, Dickey, and Wagenmakers et al.","year":"1967","type":"Bayesian nonparametric association test","dataType":"Categorical (nominal) frequency counts in a contingency table","subfamily":"Classical statistics"},"citations":[{"ref":"Good, I. J. (1967). A Bayesian significance test for multinomial distributions. Journal of the Royal Statistical Society: Series B (Methodological), 29(3), 399–418.","type":"article","doi":"10.1111/j.2517-6161.1967.tb00705.x","isbn":null,"url":null},{"ref":"Jamil, T., Ly, A., Morey, R. D., Love, J., Marsman, M., & Wagenmakers, E.-J. (2017). Default 'Gunel and Dickey' Bayes factors for contingency tables. Behavior Research Methods, 49(2), 638–652.","type":"article","doi":"10.3758/s13428-016-0739-8","isbn":null,"url":null}],"related":["chi-square-test","fisher-exact-test","bayesian-fishers-exact-test","bayesian-independent-samples-t-test","bayesian-pearson-correlation","goodness-of-fit-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-chip-seq-peak-calling","name":"Bayesian ChIP-seq peak calling","fullName":"Bayesian Chromatin Immunoprecipitation Sequencing Peak Calling","aliases":["Bayesian ChIP-seq analysis","probabilistic peak detection","Bayesian peak caller","ChIP-seq Bayesian enrichment calling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2008–2009","originator":"Spyrou et al. (BayesPeak, 2009); broader Bayesian ChIP-seq framework developed across multiple groups ~2008–2012","url":"https://scholargate.app/en/bioinformatics/bayesian-chip-seq-peak-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-chip-seq-peak-calling.md","definition":"Bayesian ChIP-seq peak calling applies probabilistic models — typically Poisson, negative binomial, or hidden Markov models with Bayesian inference — to detect genomic regions enriched for a protein of interest in chromatin immunoprecipitation followed by sequencing experiments. By explicitly modelling read-count noise and incorporating prior distributions, Bayesian callers yield posterior probabilities of enrichment rather than simple p-values, providing a principled framework for uncertainty quantification across the genome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Spyrou et al. (BayesPeak, 2009); broader Bayesian ChIP-seq framework developed across multiple groups ~2008–2012","year":"2008–2009","type":"Probabilistic signal detection pipeline","dataType":"Aligned short-read sequencing data (ChIP and matched input/IgG control BAM files)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biology, 9(9), R137.","type":"article","doi":"10.1186/gb-2008-9-9-r137","isbn":null,"url":null},{"ref":"Spyrou, C., Stark, R., Lynch, A. G., & Tavare, S. (2009). BayesPeak: Bayesian analysis of ChIP-seq data. BMC Bioinformatics, 10, 299.","type":"article","doi":"10.1186/1471-2105-10-299","isbn":null,"url":null}],"related":["chip-seq-peak-calling","bayesian-epigenome-wide-association-study","bayesian-rna-seq-differential-expression","pathway-enrichment-analysis","variant-calling","epigenome-wide-association-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-cluster-analysis","name":"Bayesian Cluster Analysis","fullName":"Bayesian Cluster Analysis","aliases":["BCA","Bayesian clustering","probabilistic cluster analysis","Bayesian model-based clustering"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1998–2002","originator":"Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)","url":"https://scholargate.app/en/statistics/bayesian-cluster-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-cluster-analysis.md","definition":"Bayesian cluster analysis assigns observations to latent groups by combining a probabilistic model of within-cluster data with prior beliefs about cluster parameters and the number of clusters. It yields posterior probabilities of cluster membership and principled uncertainty estimates, making it more transparent than classical distance-based clustering algorithms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)","year":"1998–2002","type":"Probabilistic / model-based clustering","dataType":"Continuous, binary, or mixed multivariate data","subfamily":"Multivariate analysis"},"citations":[{"ref":"Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.","type":"article","doi":"10.1198/016214502760047131","isbn":null,"url":null},{"ref":"Lau, J. W. & Green, P. J. (2007). Bayesian model-based clustering procedures. Journal of Computational and Graphical Statistics, 16(3), 526–558.","type":"article","doi":"10.1198/106186007X238855","isbn":null,"url":null}],"related":["cluster-analysis","bayesian-latent-class-analysis","mixture-modeling","bayesian-mixture-modeling","latent-class-analysis","hierarchical-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-co-kriging","name":"Bayesian Co-Kriging","fullName":"Bayesian Co-Kriging Spatial Interpolation","aliases":["Bayesian cokriging","Bayesian co-regionalization","BCK","Bayesian multivariate kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1990s–2000s","originator":"Gelfand, Banerjee & colleagues; building on Matheron's cokriging framework","url":"https://scholargate.app/en/spatial-analysis/bayesian-co-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-co-kriging.md","definition":"Bayesian Co-Kriging is a multivariate geostatistical method that uses auxiliary spatially correlated variables to improve predictions of a primary variable of interest. By placing Bayesian priors on cross-covariance parameters, it propagates all uncertainty — including parameter uncertainty — into the prediction intervals, yielding fully probabilistic maps with calibrated uncertainty bounds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelfand, Banerjee & colleagues; building on Matheron's cokriging framework","year":"1990s–2000s","type":"Bayesian spatial interpolation","dataType":"Georeferenced multivariate continuous data","subfamily":"GIS / spatial"},"citations":[{"ref":"Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer.","type":"book","doi":null,"isbn":"978-0387329079","url":null},{"ref":"Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439819173","url":null}],"related":["co-kriging","ordinary-kriging","bayesian-kriging","bayesian-spatial-regression","bayesian-universal-kriging","kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-coarsened-exact-matching","name":"Bayesian Coarsened Exact Matching","fullName":"Bayesian Coarsened Exact Matching Estimator","aliases":["Bayesian CEM","BCEM","Bayesian monotonic imbalance bounding matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2011-2012","originator":"Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors","url":"https://scholargate.app/en/causal-inference/bayesian-coarsened-exact-matching","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-coarsened-exact-matching.md","definition":"Bayesian Coarsened Exact Matching (Bayesian CEM) combines the coarsening-and-exact-matching framework of Iacus, King, and Porro with Bayesian posterior inference. Covariates are discretised into coarser bins so that treated and control units can be matched exactly within those bins, and Bayesian priors are then placed on the treatment-effect parameters to produce full posterior distributions over the causal estimand rather than a single point estimate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors","year":"2011-2012","type":"Quasi-experimental matching with Bayesian inference","dataType":"Observational cross-sectional or panel data with continuous, binary, or categorical covariates and continuous or binary outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Hill, J. L. (2011). Bayesian Nonparametric Modeling for Causal Inference. Journal of Computational and Graphical Statistics, 20(1), 217-240.","type":"article","doi":"10.1198/jcgs.2010.08162","isbn":null,"url":null}],"related":["coarsened-exact-matching","bayesian-propensity-score-matching","propensity-score-matching","entropy-balancing","bayesian-matching-estimator","matching-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-cohort-research","name":"Bayesian Cohort Research","fullName":"Bayesian Cohort Study Design","aliases":["Bayesian cohort study","Bayesian prospective cohort","Bayesian longitudinal cohort analysis","Bayesian follow-up study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Formalised in health research from the 1990s onward","originator":"Synthesis of cohort epidemiology (Doll & Hill, 1950s) with Bayesian inference (Bayes, Laplace, Jeffreys)","url":"https://scholargate.app/en/research-design/bayesian-cohort-research","markdownUrl":"https://scholargate.app/en/research-design/bayesian-cohort-research.md","definition":"Bayesian cohort research follows a defined group of individuals over time to track outcomes, and uses Bayesian statistical inference to update beliefs about risk, incidence, or causal effects as follow-up data accumulate. Prior knowledge — from earlier studies, registries, or expert judgment — is formalised into a prior distribution and combined with the cohort's likelihood to yield a posterior distribution that quantifies uncertainty in a directly interpretable way.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesis of cohort epidemiology (Doll & Hill, 1950s) with Bayesian inference (Bayes, Laplace, Jeffreys)","year":"Formalised in health research from the 1990s onward","type":"Quantitative longitudinal observational design","dataType":"Time-to-event data, repeated continuous/binary measurements, registry data","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Ibrahim, J. G., & Chen, M. H. (2000). Power prior distributions for regression models. Statistical Science, 15(1), 46–60.","type":"article","doi":"10.1214/ss/1009212673","isbn":null,"url":null},{"ref":"Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley.","type":"book","doi":null,"isbn":"978-0471499756","url":null}],"related":["cohort-research","longitudinal-research","bayesian-longitudinal-research","bayesian-survey-research","panel-research","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-cohort-study","name":"Bayesian Cohort Study","fullName":"Bayesian Cohort Study","aliases":["Bayesian longitudinal cohort","Bayesian prospective cohort","Bayesian cohort analysis","Bayesian follow-up study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (widespread adoption in epidemiology)","originator":"Bayesian framework: Thomas Bayes / Pierre-Simon Laplace; applied to cohort epidemiology from the 1990s onward","url":"https://scholargate.app/en/epidemiology/bayesian-cohort-study","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-cohort-study.md","definition":"A Bayesian cohort study follows a defined group of individuals over time to estimate incidence, risk, or rate of outcomes, while using Bayesian statistical inference to incorporate prior knowledge and quantify uncertainty through posterior probability distributions rather than classical p-values and confidence intervals. It combines the longitudinal observational design of a cohort study with the probability-updating logic of Bayesian analysis, allowing richer uncertainty quantification and sequential updating as data accumulate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bayesian framework: Thomas Bayes / Pierre-Simon Laplace; applied to cohort epidemiology from the 1990s onward","year":"1990s–2000s (widespread adoption in epidemiology)","type":"Observational longitudinal study with Bayesian inference","dataType":"Longitudinal individual-level follow-up data (time-to-event, counts, continuous outcomes)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley.","type":"book","doi":null,"isbn":"978-0471499756","url":null},{"ref":"Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765–775.","type":"article","doi":"10.1093/ije/dyi312","isbn":null,"url":null}],"related":["cohort-study","prospective-cohort-study","bayesian-randomized-clinical-trial","survival-analysis","bayesian-survival-analysis","nested-case-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-community-detection","name":"Bayesian Community Detection","fullName":"Bayesian Community Detection in Networks","aliases":["Bayesian graph clustering","probabilistic community detection","Bayesian stochastic block model community detection","Bayesian network partitioning"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2001–2014","originator":"Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.","url":"https://scholargate.app/en/network-analysis/bayesian-community-detection","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-community-detection.md","definition":"Bayesian community detection infers latent group structure in networks by treating community membership as unobserved variables and using Bayesian inference — typically via Markov chain Monte Carlo or variational methods — to compute a posterior distribution over all plausible partitions. Unlike modularity optimisation, it selects the number of communities from data and provides principled uncertainty estimates for every node assignment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nowicki, K. & Snijders, T. A. B. (formal Bayesian framing); extended by Peixoto, T. P.","year":"2001–2014","type":"Probabilistic generative model / inference","dataType":"Adjacency matrix or edge list (binary or weighted networks)","subfamily":"Network science"},"citations":[{"ref":"Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.","type":"article","doi":"10.1103/PhysRevE.89.012804","isbn":null,"url":null},{"ref":"Nowicki, K. & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455), 1077–1087.","type":"article","doi":"10.1198/016214501753208735","isbn":null,"url":null}],"related":["stochastic-block-model","modularity-analysis","exponential-random-graph-model","social-network-analysis","multilayer-community-detection","temporal-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-competing-risks-analysis","name":"Bayesian Competing Risks Analysis","fullName":"Bayesian Competing Risks Survival Analysis","aliases":["Bayesian cause-specific hazard model","Bayesian subdistribution hazard model","BCRA","Bayesian cumulative incidence analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–2000s (classical CR: 1970s; Bayesian extension: 1990s–2000s)","originator":"Various; Bayesian formulation advanced by Gelfand, Dey, Larson, and Dinse among others","url":"https://scholargate.app/en/epidemiology/bayesian-competing-risks-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-competing-risks-analysis.md","definition":"Bayesian competing risks analysis is a time-to-event method for settings where subjects can fail from more than one mutually exclusive cause — such as death from cancer versus death from cardiovascular disease — and prior knowledge or small-sample uncertainty makes a Bayesian framework advantageous. It extends classical competing risks models (cause-specific hazards and cumulative incidence functions) by placing probability distributions over unknown parameters and updating those distributions with observed data, yielding full posterior inference for each failure type.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various; Bayesian formulation advanced by Gelfand, Dey, Larson, and Dinse among others","year":"1980s–2000s (classical CR: 1970s; Bayesian extension: 1990s–2000s)","type":"Bayesian survival/time-to-event model","dataType":"Time-to-event data with multiple mutually exclusive failure types (right-censored)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Larson, M. G., & Dinse, G. E. (1985). A mixture model for the regression analysis of competing risks data. Applied Statistics, 34(3), 201–211.","type":"article","doi":"10.2307/2347464","isbn":null,"url":null},{"ref":"Crowder, M. J. (2001). Classical Competing Risks. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"9781584881759","url":null}],"related":["cause-specific-hazard-model","fine-gray-subdistribution-hazard","bayesian-survival-analysis","cox-proportional-hazards","kaplan-meier-estimator","multi-state-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-confirmatory-factor-analysis","name":"Bayesian Confirmatory Factor Analysis","fullName":"Bayesian Confirmatory Factor Analysis","aliases":["BCFA","Bayesian CFA","Bayesian structural equation measurement model","Bayes-CFA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2007–2012","originator":"Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov","url":"https://scholargate.app/en/psychometrics/bayesian-confirmatory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-confirmatory-factor-analysis.md","definition":"Bayesian confirmatory factor analysis tests a pre-specified factor structure using Bayesian inference. Instead of point estimates with p-values, it produces full posterior distributions for loadings, factor correlations, and residual variances, allowing the researcher to incorporate prior knowledge and propagate parameter uncertainty naturally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov","year":"2007–2012","type":"Bayesian latent variable model","dataType":"Continuous or ordinal indicator items","subfamily":"Scale / measurement"},"citations":[{"ref":"Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley.","type":"book","doi":null,"isbn":"978-0470024232","url":null},{"ref":"Muthén, B. & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335.","type":"article","doi":"10.1037/a0026802","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","bayesian-exploratory-factor-analysis","bayesian-item-response-theory","bayesian-measurement-invariance","exploratory-factor-analysis","bayesian-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-confirmatory-research","name":"Bayesian Confirmatory Research","fullName":"Bayesian Confirmatory Research Design","aliases":["Bayesian hypothesis testing","confirmatory Bayesian analysis","Bayes factor hypothesis testing","BCR"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1961 (Jeffreys); 2009–2018 (contemporary confirmatory formulation)","originator":"Harold Jeffreys (theoretical foundation); Jeffrey Rouder, Eric-Jan Wagenmakers (applied confirmatory framework)","url":"https://scholargate.app/en/research-design/bayesian-confirmatory-research","markdownUrl":"https://scholargate.app/en/research-design/bayesian-confirmatory-research.md","definition":"Bayesian confirmatory research is a quantitative framework that tests pre-specified hypotheses by computing the Bayes factor — a ratio expressing how much more likely the observed data are under one hypothesis than another. Unlike classical null-hypothesis significance testing (NHST), it provides direct evidence for both the alternative and the null hypothesis, supports optional stopping rules under certain conditions, and updates prior beliefs with observed data through Bayes' theorem.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Jeffreys (theoretical foundation); Jeffrey Rouder, Eric-Jan Wagenmakers (applied confirmatory framework)","year":"1961 (Jeffreys); 2009–2018 (contemporary confirmatory formulation)","type":"Quantitative hypothesis-testing framework","dataType":"Continuous, ordinal, or categorical data from pre-planned confirmatory studies","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237.","type":"article","doi":"10.3758/PBR.16.2.225","isbn":null,"url":null},{"ref":"Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, A. J., Love, J., Selker, R., Gronau, Q. F., Smira, M., Epskamp, S., Matzke, D., Rouder, J. N., & Morey, R. D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25(1), 35–57.","type":"article","doi":"10.3758/s13423-017-1343-3","isbn":null,"url":null}],"related":["null-hypothesis-significance-testing","bayes-factor-design-analysis","sequential-bayes-factor-testing","power-analysis","meta-analysis","pre-registration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-conjoint-analysis","name":"Bayesian Conjoint Analysis","fullName":"Bayesian Conjoint Analysis","aliases":["Bayesian CA","hierarchical Bayes conjoint","HB conjoint","Bayesian preference modeling"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1995","originator":"Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964)","url":"https://scholargate.app/en/statistics/bayesian-conjoint-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-conjoint-analysis.md","definition":"Bayesian conjoint analysis estimates individual-level consumer preference weights for product attributes by combining conjoint choice tasks with a hierarchical Bayesian model. It yields part-worth utilities for each respondent rather than only group averages, enabling precise market simulation and segment discovery even from small per-person choice sets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964)","year":"1995","type":"Preference measurement / Bayesian hierarchical model","dataType":"Choice / ranking / rating data from designed attribute profiles","subfamily":"Multivariate analysis"},"citations":[{"ref":"Allenby, G. M. & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403.","type":"article","doi":"10.1177/002224379503200402","isbn":null,"url":null},{"ref":"Rossi, P. E., Allenby, G. M. & McCulloch, R. (2005). Bayesian Statistics and Marketing. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470863671","url":null}],"related":["conjoint-analysis","latent-class-analysis","bayesian-latent-class-analysis","mixture-modeling","bayesian-mixture-modeling","structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-construct-validity","name":"Bayesian Construct Validity","fullName":"Bayesian Construct Validity Assessment","aliases":["Bayesian validity analysis","Bayesian CFA-based validity","Bayesian structural validity","posterior construct validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1955 / 2012","originator":"Cronbach & Meehl (validity framework); Muthén & Asparouhov (Bayesian SEM extension)","url":"https://scholargate.app/en/psychometrics/bayesian-construct-validity","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-construct-validity.md","definition":"Bayesian construct validity assessment uses Bayesian confirmatory factor analysis and related Bayesian structural equation models to evaluate whether a scale or test measures the intended latent construct. It yields full posterior distributions for factor loadings, structural coefficients, and model-fit indices rather than single point estimates, enabling more nuanced and uncertainty-aware validity conclusions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cronbach & Meehl (validity framework); Muthén & Asparouhov (Bayesian SEM extension)","year":"1955 / 2012","type":"Validity assessment / Bayesian inference","dataType":"Ordinal or continuous item-level scores","subfamily":"Scale / measurement"},"citations":[{"ref":"Muthén, B. & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335.","type":"article","doi":"10.1037/a0026802","isbn":null,"url":null},{"ref":"Cronbach, L. J. & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.","type":"article","doi":"10.1037/h0040957","isbn":null,"url":null}],"related":["construct-validity","bayesian-confirmatory-factor-analysis","confirmatory-factor-analysis","convergent-validity","discriminant-validity","bayesian-measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-control-chart","name":"Bayesian Control Chart","fullName":"Bayesian Statistical Process Control Chart","aliases":["Bayesian SPC chart","Bayesian monitoring chart","posterior control chart","Bayesian Shewhart chart"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"Formally developed in the 1990s–2000s; roots in Shewhart (1924)","originator":"Ulrich Menzefricke and others building on Shewhart (1924) and Bayesian inference (Bayes, 1763)","url":"https://scholargate.app/en/experimental-design/bayesian-control-chart","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-control-chart.md","definition":"A Bayesian control chart integrates prior knowledge about a process — such as historical mean and variance — with incoming measurement data to produce dynamically updated control limits. Unlike classical Shewhart charts that fix limits from a Phase-I baseline, Bayesian charts update the posterior distribution of process parameters after each sample, yielding limits that adapt to accumulated evidence and are better calibrated under small sample sizes or non-stationary processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ulrich Menzefricke and others building on Shewhart (1924) and Bayesian inference (Bayes, 1763)","year":"Formally developed in the 1990s–2000s; roots in Shewhart (1924)","type":"Statistical process monitoring / quality control","dataType":"Continuous or attribute process measurements (time-series)","subfamily":"Engineering methods"},"citations":[{"ref":"Menzefricke, U. (2002). On the evaluation of control chart limits based on predictive distributions. Communications in Statistics — Theory and Methods, 31(8), 1423–1440.","type":"article","doi":"10.1081/sta-120006077","isbn":null,"url":null},{"ref":"Control chart. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Control_chart"}],"related":["control-chart","statistical-process-control","bayesian-statistical-process-control","process-capability-analysis","failure-mode-and-effects-analysis","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-convergent-validity","name":"Bayesian Convergent Validity","fullName":"Bayesian Convergent Validity Assessment","aliases":["Bayesian convergent validity analysis","Bayesian MTMM convergent validity","Bayesian multitrait convergent validity","BCV"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2000s–2010s","originator":"Building on Campbell & Fiske (1959) convergent validity; Bayesian extension developed in modern psychometrics literature","url":"https://scholargate.app/en/psychometrics/bayesian-convergent-validity","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-convergent-validity.md","definition":"Bayesian convergent validity applies Bayesian statistical inference to assess whether different measures of the same construct converge as theory predicts. Rather than a single-point correlation estimate, it yields a full posterior distribution over the convergent correlation, enabling probability statements about the magnitude of shared variance between theoretically related measures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Campbell & Fiske (1959) convergent validity; Bayesian extension developed in modern psychometrics literature","year":"2000s–2010s","type":"Validity assessment / Bayesian inference","dataType":"Ordinal or continuous item scores from multiple constructs or methods","subfamily":"Scale / measurement"},"citations":[{"ref":"Levy, R. & Mislevy, R. J. (2016). Bayesian Psychometric Modeling. CRC Press.","type":"book","doi":null,"isbn":"978-1466500952","url":null},{"ref":"Van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J. & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1.","type":"article","doi":"10.1038/s43586-020-00001-2","isbn":null,"url":null}],"related":["convergent-validity","discriminant-validity","bayesian-confirmatory-factor-analysis","bayesian-measurement-invariance","construct-validity","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-copy-number-variation-analysis","name":"Bayesian Copy Number Variation Analysis","fullName":"Bayesian Copy Number Variation Analysis","aliases":["Bayesian CNV analysis","Bayesian CNV calling","probabilistic CNV detection","Bayesian HMM-CNV"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2004–2007","originator":"Colella et al. (QuantiSNP); Fridlyand et al. (HMM-based Bayesian CNV)","url":"https://scholargate.app/en/bioinformatics/bayesian-copy-number-variation-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-copy-number-variation-analysis.md","definition":"Bayesian copy number variation (CNV) analysis is a probabilistic framework for detecting genomic segments where an individual's DNA copy count deviates from the diploid norm. By placing prior distributions over copy-number states and updating them with array CGH, SNP array, or sequencing read-depth evidence, the approach yields posterior probabilities for each copy-number state along the genome, providing statistically principled uncertainty quantification that frequentist segmentation methods lack.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Colella et al. (QuantiSNP); Fridlyand et al. (HMM-based Bayesian CNV)","year":"2004–2007","type":"Probabilistic genomic analysis pipeline","dataType":"Array CGH, SNP array, whole-genome sequencing read-depth data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Colella, S., Yau, C., Taylor, J. M., Mirza, G., Butler, H., Clouston, P., Bassett, A. S., Seller, A., Holmes, C. C., & Ragoussis, J. (2007). QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Research, 35(6), 2013–2025.","type":"article","doi":"10.1093/nar/gkm076","isbn":null,"url":null},{"ref":"Fridlyand, J., Snijders, A. M., Pinkel, D., Albertson, D. G., & Jain, A. N. (2004). Hidden Markov models approach to the analysis of array CGH data. Journal of Multivariate Analysis, 90(1), 132–153.","type":"article","doi":"10.1016/j.jmva.2004.02.008","isbn":null,"url":null}],"related":["copy-number-variation-analysis","variant-calling","bayesian-variant-calling","genome-wide-association-study","bayesian-gwas","single-cell-copy-number-variation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-counterfactual-impact-evaluation","name":"Bayesian Counterfactual Impact Evaluation","fullName":"Bayesian Counterfactual Impact Evaluation","aliases":["Bayesian CIE","Bayesian causal impact","Bayesian structural time-series causal inference","BSTS counterfactual evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015 (canonical implementation); Rubin potential outcomes: 1974-2005","originator":"Brodersen, Gallusser, Koehler, Remy & Scott; Rubin potential outcomes framework","url":"https://scholargate.app/en/causal-inference/bayesian-counterfactual-impact-evaluation","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-counterfactual-impact-evaluation.md","definition":"Bayesian Counterfactual Impact Evaluation estimates the causal effect of an intervention by constructing a Bayesian posterior distribution over the counterfactual outcome — what would have happened without treatment. The method, popularized by Brodersen et al. (2015) through the CausalImpact framework, uses Bayesian structural time-series models fitted on the pre-intervention period to predict the counterfactual trajectory, then compares observed post-intervention outcomes to that prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen, Gallusser, Koehler, Remy & Scott; Rubin potential outcomes framework","year":"2015 (canonical implementation); Rubin potential outcomes: 1974-2005","type":"Bayesian causal inference / counterfactual estimation","dataType":"Time-series or panel data with pre- and post-intervention observations","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100(469), 322-331.","type":"article","doi":"10.1198/016214504000001880","isbn":null,"url":null}],"related":["causal-impact-analysis","bayesian-interrupted-time-series","difference-in-differences","synthetic-control-method","counterfactual-impact-evaluation","bayesian-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-cox-proportional-hazards","name":"Bayesian Cox Proportional Hazards","fullName":"Bayesian Cox Proportional Hazards Model","aliases":["Bayesian CPH","Bayesian survival regression","Bayesian semiparametric hazard model","Bayesian partial likelihood survival model"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1972 (Cox); Bayesian formulation developed through the 1990s","originator":"D. R. Cox (frequentist CPH, 1972); Bayesian extensions by Joseph Ibrahim, Ming-Hui Chen, Debajyoti Sinha (1990s–2001)","url":"https://scholargate.app/en/epidemiology/bayesian-cox-proportional-hazards","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-cox-proportional-hazards.md","definition":"The Bayesian Cox proportional hazards model combines Cox's classical semiparametric survival regression with Bayesian inference, replacing point estimates and p-values with full posterior distributions over regression coefficients. It handles right-censored time-to-event outcomes, quantifies uncertainty about hazard ratios in probabilistic terms, and allows the incorporation of prior clinical or historical knowledge directly into the analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. R. Cox (frequentist CPH, 1972); Bayesian extensions by Joseph Ibrahim, Ming-Hui Chen, Debajyoti Sinha (1990s–2001)","year":"1972 (Cox); Bayesian formulation developed through the 1990s","type":"Bayesian semiparametric survival regression","dataType":"Time-to-event (survival) data with covariates; right-censored or interval-censored observations","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer.","type":"book","doi":null,"isbn":"978-0387952772","url":null},{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–220.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null}],"related":["cox-proportional-hazards","bayesian-survival-analysis","survival-analysis","kaplan-meier-analysis","competing-risks-analysis","bayesian-randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-cox-regression","name":"Bayesian Cox Regression","fullName":"Bayesian Cox Proportional Hazards Regression","aliases":["Bayesian Cox PH model","Bayesian proportional hazards model","Bayesian survival regression","BCox"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1972 (Cox PH); 2001 (Bayesian treatment)","originator":"Cox (1972) for the base model; Bayesian formulation by Sinha, Chen & Ghosh (1990s); comprehensive treatment by Ibrahim, Chen & Sinha (2001)","url":"https://scholargate.app/en/statistics/bayesian-cox-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-cox-regression.md","definition":"Bayesian Cox regression combines the Cox proportional hazards model for time-to-event data with Bayesian inference. Instead of point estimates, it produces full posterior distributions over the hazard ratios, naturally incorporating prior knowledge and providing coherent uncertainty quantification even with small samples or informative censoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cox (1972) for the base model; Bayesian formulation by Sinha, Chen & Ghosh (1990s); comprehensive treatment by Ibrahim, Chen & Sinha (2001)","year":"1972 (Cox PH); 2001 (Bayesian treatment)","type":"Survival regression","dataType":"Time-to-event data with censoring","subfamily":"Regression / GLM"},"citations":[{"ref":"Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer.","type":"book","doi":null,"isbn":"978-0387952772","url":null},{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cox+Regression+models+and+life-tables+1972"}],"related":["cox-regression","bayesian-survival-regression","bayesian-mixed-effects-model","survival-regression","bayesian-generalized-linear-model","zero-inflated-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-cronbachs-alpha","name":"Bayesian Cronbach's alpha","fullName":"Bayesian Estimation of Cronbach's Alpha","aliases":["Bayesian alpha","Bayesian internal consistency","Bayes-alpha","posterior alpha"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2011 (Bayesian form); 1951 (classical alpha)","originator":"Padilla & Zhang (Bayesian adaptation); Cronbach (classical alpha, 1951)","url":"https://scholargate.app/en/psychometrics/bayesian-cronbachs-alpha","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-cronbachs-alpha.md","definition":"Bayesian Cronbach's alpha applies Bayesian inference to estimate the classical internal-consistency coefficient, yielding a full posterior distribution over alpha rather than a single point estimate. This allows researchers to quantify uncertainty with credible intervals and incorporate prior knowledge, making reliability assessment more informative — especially with small or skewed samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Padilla & Zhang (Bayesian adaptation); Cronbach (classical alpha, 1951)","year":"2011 (Bayesian form); 1951 (classical alpha)","type":"Bayesian reliability estimation","dataType":"Ordinal or continuous item scores from a single test administration","subfamily":"Scale / measurement"},"citations":[{"ref":"Padilla, M. A., & Zhang, G. (2011). Estimating internal consistency using Bayesian methods. Journal of Modern Applied Statistical Methods, 10(1), 277–286.","type":"article","doi":"10.22237/jmasm/1304223840","isbn":null,"url":null},{"ref":"Kelley, K., & Pornprasertmanit, S. (2016). Confidence intervals for population reliability coefficients: Evaluation of methods, recommendations, and software for composite measures. Psychological Methods, 21(1), 69–92.","type":"article","doi":"10.1037/a0040086","isbn":null,"url":null}],"related":["cronbachs-alpha","mcdonalds-omega","bayesian-reliability-analysis","bayesian-confirmatory-factor-analysis","bayesian-item-response-theory","generalizability-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-cross-tabulation-analysis","name":"Bayesian cross-tabulation analysis","fullName":"Bayesian Contingency Table Analysis","aliases":["Bayesian chi-square test","Bayesian contingency table test","Bayes factor association test","Bayesian crosstab analysis"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1974","originator":"Gunel & Dickey","url":"https://scholargate.app/en/statistics/bayesian-cross-tabulation-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-cross-tabulation-analysis.md","definition":"Bayesian cross-tabulation analysis tests whether two categorical variables are associated by computing a Bayes factor that quantifies the evidence for an association model against an independence model. Unlike classical chi-square testing, it provides a continuous measure of evidence, supports the null hypothesis directly, and updates naturally with prior knowledge about the cell probabilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gunel & Dickey","year":"1974","type":"Bayesian association test","dataType":"Categorical (nominal/ordinal) counts in contingency tables","subfamily":"Classical statistics"},"citations":[{"ref":"Gunel, E., & Dickey, J. (1974). Bayes factors for independence in contingency tables. Biometrika, 61(3), 545–557.","type":"article","doi":"10.1093/biomet/61.3.545","isbn":null,"url":null},{"ref":"Jamil, T., Ly, A., Morey, R. D., Love, J., Marsman, M., & Wagenmakers, E.-J. (2017). Default Gunel and Dickey Bayes factors for contingency tables. Behavior Research Methods, 49(2), 638–652.","type":"article","doi":"10.3758/s13428-016-0739-8","isbn":null,"url":null}],"related":["cross-tabulation-analysis","bayesian-chi-square-test","bayesian-fisher-exact-test","bayesian-independent-samples-t-test","fisher-exact-test","chi-square-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-dcc-garch","name":"Bayesian DCC-GARCH","fullName":"Bayesian Dynamic Conditional Correlation GARCH Model","aliases":["Bayesian DCC-GARCH","Bayesian Dynamic Conditional Correlation","MCMC DCC-GARCH","Bayesian multivariate volatility model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002 (DCC); 2000s (Bayesian extension)","originator":"Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)","url":"https://scholargate.app/en/econometrics/bayesian-dcc-garch","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-dcc-garch.md","definition":"Bayesian DCC-GARCH estimates time-varying correlations across multiple financial or economic series by combining Engle's DCC-GARCH structure with Bayesian inference. Rather than maximising a likelihood, it places prior distributions over all parameters and uses Markov Chain Monte Carlo (MCMC) sampling to produce full posterior distributions, yielding richer uncertainty quantification than classical DCC-GARCH.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle (2002) for DCC; Bayesian extension via MCMC literature (2000s onwards)","year":"2002 (DCC); 2000s (Bayesian extension)","type":"Multivariate volatility model","dataType":"Multivariate time series of financial or macroeconomic returns","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.","type":"article","doi":"10.1198/073500102288618487","isbn":null,"url":null},{"ref":"Virbickaite, A., Ausin, M. C., & Galeano, P. (2015). Bayesian inference methods for univariate and multivariate GARCH models: A survey. Journal of Economic Surveys, 29(1), 76-96.","type":"article","doi":"10.1111/joes.12046","isbn":null,"url":null}],"related":["dcc-garch-model","bayesian-garch-model","bayesian-egarch","bayesian-tgarch","vector-autoregression","bayesian-var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-decision-tree","name":"Bayesian Decision Tree","fullName":"Bayesian Decision Tree (Bayesian CART)","aliases":["Bayesian CART","BCART","Bayesian tree induction","probabilistic decision tree"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1998","originator":"Chipman, H. A.; George, E. I.; McCulloch, R. E.","url":"https://scholargate.app/en/machine-learning/bayesian-decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-decision-tree.md","definition":"Bayesian Decision Tree (Bayesian CART) places a prior distribution over tree structures and leaf parameters, then uses Markov chain Monte Carlo to explore the posterior distribution of trees given data. Instead of a single best tree, it produces a distribution of plausible trees whose predictions are averaged, yielding calibrated uncertainty estimates alongside point predictions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chipman, H. A.; George, E. I.; McCulloch, R. E.","year":"1998","type":"Bayesian ensemble / tree model","dataType":"Tabular (continuous and categorical features)","subfamily":"Machine learning"},"citations":[{"ref":"Chipman, H. A., George, E. I., & McCulloch, R. E. (1998). Bayesian CART model search. Journal of the American Statistical Association, 93(443), 935–948.","type":"article","doi":"10.1080/01621459.1998.10473750","isbn":null,"url":null},{"ref":"Denison, D. G. T., Mallick, B. K., & Smith, A. F. M. (1998). A Bayesian CART algorithm. Biometrika, 85(2), 363–377.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Bayesian+CART+algorithm+Denison"}],"related":["decision-tree","random-forest","bayesian-random-forest","gaussian-process","bayesian-gradient-boosting","regularized-decision-tree"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-descriptive-statistics","name":"Bayesian descriptive statistics","fullName":"Bayesian Descriptive Statistics","aliases":["Bayesian summaries","posterior descriptives","Bayesian parameter estimation","credible-interval summaries"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1763/1812","originator":"Thomas Bayes / Pierre-Simon Laplace","url":"https://scholargate.app/en/statistics/bayesian-descriptive-statistics","markdownUrl":"https://scholargate.app/en/statistics/bayesian-descriptive-statistics.md","definition":"Bayesian descriptive statistics summarizes data by combining observed information with prior knowledge through Bayes' theorem, yielding posterior distributions over parameters such as the mean and variance. Instead of point estimates and p-values, results are expressed as posterior means, medians, and credible intervals that carry a direct probability interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Bayes / Pierre-Simon Laplace","year":"1763/1812","type":"Bayesian parameter estimation","dataType":"Continuous or discrete numerical data","subfamily":"Classical statistics"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Kruschke, J. K. (2014). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2nd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0124058880","url":null}],"related":["bayesian-chi-square-test","bayesian-independent-samples-t-test","bayesian-pearson-correlation","robust-descriptive-statistics","power-analysis","effect-size-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-design-of-experiments","name":"Bayesian Design of Experiments","fullName":"Bayesian Optimal Design of Experiments","aliases":["Bayesian DOE","Bayesian optimal design","Bayesian experimental design","BDE"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1956 (foundational); formalized 1970s–1990s","originator":"Lindley (1956); Chaloner & Verdinelli (1995) landmark review","url":"https://scholargate.app/en/experimental-design/bayesian-design-of-experiments","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-design-of-experiments.md","definition":"Bayesian design of experiments selects experimental runs by maximising a utility function — typically the expected information gain — computed over prior beliefs about model parameters. Unlike classical design, which optimizes algebraic criteria such as D-optimality under fixed assumptions, Bayesian DOE incorporates prior knowledge and uncertainty about the system, yielding designs that are optimal in expectation across all plausible parameter values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lindley (1956); Chaloner & Verdinelli (1995) landmark review","year":"1956 (foundational); formalized 1970s–1990s","type":"Bayesian optimal experimental design","dataType":"Continuous and categorical experimental factor settings; prior distributions over model parameters","subfamily":"Engineering methods"},"citations":[{"ref":"Chaloner, K., & Verdinelli, I. (1995). Bayesian Experimental Design: A Review. Statistical Science, 10(3), 273–304.","type":"article","doi":"10.1214/ss/1177009939","isbn":null,"url":null},{"ref":"Ryan, E. G., Drovandi, C. C., McGree, J. M., & Pettitt, A. N. (2016). A Review of Modern Computational Algorithms for Bayesian Optimal Design. International Statistical Review, 84(1), 128–154.","type":"article","doi":"10.1111/insr.12107","isbn":null,"url":null}],"related":["design-of-experiments","central-composite-design","response-surface-methodology","bayesian-response-surface-methodology","fractional-factorial-design","bayesian-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-diagnostic-accuracy-study","name":"Bayesian Diagnostic Accuracy Study","fullName":"Bayesian Diagnostic Accuracy Study","aliases":["Bayesian DTA study","Bayesian test evaluation","Bayesian diagnostic test accuracy","BDAS"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1995–2001","originator":"Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)","url":"https://scholargate.app/en/epidemiology/bayesian-diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-diagnostic-accuracy-study.md","definition":"A Bayesian diagnostic accuracy study evaluates how well a medical test distinguishes between people who have a condition and those who do not, using Bayesian statistical methods that formally incorporate prior knowledge into the estimation of sensitivity, specificity, and related measures. Unlike classical approaches that rely solely on the observed sample, Bayesian inference combines a likelihood model of the data with prior probability distributions to produce posterior estimates with intuitive credible intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)","year":"1995–2001","type":"Bayesian inferential study design","dataType":"Binary or ordinal test results, reference-standard outcomes, prior information","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167.","type":"article","doi":"10.1111/j.0006-341X.2001.00158.x","isbn":null,"url":null},{"ref":"Gatsonis, C., & Paliwal, P. (2006). Meta-analysis of diagnostic and screening test accuracy evaluations: Methodologic primer. American Journal of Roentgenology, 187(2), 271–281.","type":"article","doi":"10.2214/AJR.06.0226","isbn":null,"url":null}],"related":["diagnostic-accuracy-study","bayesian-randomized-clinical-trial","bayesian-cohort-study","screening-test-evaluation","meta-analytic-diagnostic-accuracy-study","bayesian-case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-difference-gmm","name":"Bayesian Difference GMM","fullName":"Bayesian Difference Generalized Method of Moments","aliases":["Bayesian Arellano-Bond estimator","Bayesian difference GMM","quasi-Bayesian difference GMM","Bayesian first-difference GMM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991/2003","originator":"Arellano & Bond (1991) for Difference GMM; Chernozhukov & Hong (2003) for Bayesian GMM framework","url":"https://scholargate.app/en/econometrics/bayesian-difference-gmm","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-difference-gmm.md","definition":"Bayesian Difference GMM combines the Arellano-Bond first-differencing strategy for dynamic panel data with a Bayesian inference framework. By treating the GMM moment conditions as a quasi-likelihood and placing priors on parameters, the approach produces a full posterior distribution over coefficients rather than a single point estimate with asymptotic standard errors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bond (1991) for Difference GMM; Chernozhukov & Hong (2003) for Bayesian GMM framework","year":"1991/2003","type":"Dynamic panel estimator (Bayesian)","dataType":"Balanced or unbalanced panel data with a lagged dependent variable","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Chernozhukov, V., & Hong, H. (2003). An MCMC approach to classical estimation. Journal of Econometrics, 115(2), 293-346.","type":"article","doi":"10.1016/S0304-4076(03)00100-3","isbn":null,"url":null}],"related":["difference-gmm","bayesian-system-gmm","bayesian-arellano-bond-gmm","system-gmm","bayesian-dynamic-panel-data-model","panel-dynamic-panel-data-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-difference-in-differences","name":"Bayesian Difference-in-Differences","fullName":"Bayesian Difference-in-Differences Estimator","aliases":["Bayesian DiD","Bayes DiD","Bayesian diff-in-diff","Bayesian panel causal estimator"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015-2023","originator":"Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series)","url":"https://scholargate.app/en/causal-inference/bayesian-difference-in-differences","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-difference-in-differences.md","definition":"Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series)","year":"2015-2023","type":"Bayesian causal inference / panel regression","dataType":"Panel data or repeated cross-sections with pre/post periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+inference+for+difference-in-differences+Li"},{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null}],"related":["difference-in-differences","dynamic-difference-in-differences","bayesian-interrupted-time-series","synthetic-control-method","panel-fixed-effects","causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-differential-item-functioning","name":"Bayesian Differential Item Functioning","fullName":"Bayesian Differential Item Functioning Analysis","aliases":["Bayesian DIF","Bayesian DIF analysis","Bayesian item bias detection","BDIF"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"H. Swaminathan & H. J. Rogers (classical DIF); Bayesian extensions developed through Markov chain Monte Carlo IRT methods in the 1990s–2000s","url":"https://scholargate.app/en/psychometrics/bayesian-differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-differential-item-functioning.md","definition":"Bayesian differential item functioning analysis detects whether a test item behaves differently across demographic or cultural groups — such as males vs. females — after accounting for the underlying ability or trait being measured. It applies Bayesian IRT estimation to obtain posterior distributions of item parameters separately per group, then evaluates group differences with posterior credibility intervals or Bayes factors rather than classical p-values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"H. Swaminathan & H. J. Rogers (classical DIF); Bayesian extensions developed through Markov chain Monte Carlo IRT methods in the 1990s–2000s","year":"1990s–2000s","type":"Item bias detection / Bayesian inference","dataType":"Ordinal or binary item responses across two or more groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370.","type":"article","doi":"10.1111/j.1745-3984.1990.tb00754.x","isbn":null,"url":null},{"ref":"Bolt, D. M. (2002). A Monte Carlo comparison of parametric and nonparametric polytomous DIF detection methods. Applied Measurement in Education, 15(2), 113–141.","type":"article","doi":"10.1207/S15324818AME1502_01","isbn":null,"url":null}],"related":["differential-item-functioning","item-response-theory","bayesian-item-response-theory","confirmatory-factor-analysis","measurement-invariance","multi-group-differential-item-functioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-discrete-event-simulation","name":"Bayesian Discrete-Event Simulation","fullName":"Bayesian Discrete-Event Simulation — Posterior-informed stochastic process modeling","aliases":["Bayesian DES","BDES","Bayesian event-driven simulation","posterior-driven discrete-event simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s–2010s","originator":"Developed across operations research and Bayesian statistics communities; prominently formalized in health economic simulation in the 2000s–2010s","url":"https://scholargate.app/en/simulation/bayesian-discrete-event-simulation","markdownUrl":"https://scholargate.app/en/simulation/bayesian-discrete-event-simulation.md","definition":"Bayesian Discrete-Event Simulation (BDES) integrates Bayesian statistical inference with discrete-event simulation. Prior beliefs about system parameters — such as service rates, arrival times, or failure probabilities — are updated with observed data via Bayes' theorem, and the resulting posterior distributions directly drive the simulation engine. This coupling allows modelers to propagate both aleatory and epistemic uncertainty through event-driven process models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed across operations research and Bayesian statistics communities; prominently formalized in health economic simulation in the 2000s–2010s","year":"2000s–2010s","type":"Hybrid simulation-inference framework","dataType":"Event logs, time-stamped process records, count and duration data","subfamily":"Simulation / optimization"},"citations":[{"ref":"Onggo, B. S., & Kunc, M. (2016). Combining discrete-event simulation and Bayesian updating for incorporating evidence from real-world data. Journal of Simulation, 10(1), 1-12.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Combining+discrete-event+simulation+and+Bayesian+updating+for+incorporating+evidence+from+real-world+data"},{"ref":"Pidd, M. (2004). Computer Simulation in Management Science (5th ed.). Wiley.","type":"book","doi":null,"isbn":"9780470092781","url":null}],"related":["discrete-event-simulation","bayesian-markov-model","monte-carlo-simulation","stochastic-discrete-event-simulation","agent-based-discrete-event-simulation","bayesian-agent-based-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-discriminant-analysis","name":"Bayesian Discriminant Analysis","fullName":"Bayesian Discriminant Analysis","aliases":["BDA","Bayesian linear discriminant analysis","Bayesian quadratic discriminant analysis","Bayesian classification"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1964","originator":"Seymour Geisser","url":"https://scholargate.app/en/statistics/bayesian-discriminant-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-discriminant-analysis.md","definition":"Bayesian discriminant analysis assigns observations to predefined groups by combining a multivariate Gaussian likelihood for each class with prior distributions over the class means and covariance matrices. Posterior predictive probabilities replace point-estimate decision boundaries, providing principled uncertainty quantification for classification in small or high-dimensional samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seymour Geisser","year":"1964","type":"Supervised classification / Bayesian inference","dataType":"Continuous multivariate observations with group labels","subfamily":"Multivariate analysis"},"citations":[{"ref":"Geisser, S. (1964). Posterior odds for multivariate normal classifications. Journal of the Royal Statistical Society, Series B, 26(1), 69–76.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Posterior+odds+for+multivariate+normal+classifications+Geisser+1964"},{"ref":"Minka, T. P. (2000). Bayesian linear regression. Technical Report, MIT Media Lab.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+linear+discriminant+analysis+Minka+2000"}],"related":["discriminant-analysis","bayesian-cluster-analysis","bayesian-confirmatory-factor-analysis","bayesian-structural-equation-modeling","bayesian-latent-class-analysis","principal-component-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-discriminant-validity","name":"Bayesian Discriminant Validity","fullName":"Bayesian Discriminant Validity Assessment","aliases":["Bayesian HTMT","Bayesian HTMTb","Bayesian discriminant evidence","Bayesian CFA discriminant validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2020 (Bayesian HTMT formalization); 1959 (discriminant validity concept)","originator":"Adaptation of Campbell & Fiske (1959) discriminant validity into Bayesian CFA framework; Bayesian HTMT formalization by Garnier-Villarreal & Jorgensen (2020)","url":"https://scholargate.app/en/psychometrics/bayesian-discriminant-validity","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-discriminant-validity.md","definition":"Bayesian discriminant validity assessment evaluates whether two theoretically distinct latent constructs are empirically separable, using posterior distributions and credible intervals rather than single-point null-hypothesis tests. It is applied within Bayesian confirmatory factor analysis or via the Bayesian heterotrait-monotrait ratio (HTMTb) to determine whether constructs measuring different traits are sufficiently differentiated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adaptation of Campbell & Fiske (1959) discriminant validity into Bayesian CFA framework; Bayesian HTMT formalization by Garnier-Villarreal & Jorgensen (2020)","year":"2020 (Bayesian HTMT formalization); 1959 (discriminant validity concept)","type":"Validity assessment","dataType":"Ordinal or continuous Likert-type item scores from multi-factor questionnaires","subfamily":"Scale / measurement"},"citations":[{"ref":"Garnier-Villarreal, M. & Jorgensen, T. D. (2020). Adapting fit indices for Bayesian structural equation modeling: Comparison to maximum likelihood. Psychological Methods, 25(1), 46–70.","type":"article","doi":"10.1037/met0000224","isbn":null,"url":null},{"ref":"Campbell, D. T. & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.","type":"article","doi":"10.1037/h0046016","isbn":null,"url":null}],"related":["discriminant-validity","convergent-validity","bayesian-confirmatory-factor-analysis","confirmatory-factor-analysis","construct-validity","bayesian-measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-dose-response-analysis","name":"Bayesian Dose-Response Analysis","fullName":"Bayesian Dose-Response Analysis","aliases":["Bayesian DRA","Bayesian dose-response modeling","Bayesian benchmark dose analysis","BDR"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (Bayesian formalization)","originator":"Developed from classical frequentist dose-response traditions; Bayesian formulations advanced by Dempster, Gelman, and colleagues","url":"https://scholargate.app/en/epidemiology/bayesian-dose-response-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-dose-response-analysis.md","definition":"Bayesian dose-response analysis models the relationship between the level of exposure (dose) to a substance and the magnitude or probability of a biological response, embedding that model in a Bayesian probabilistic framework. Unlike frequentist approaches that yield a single point estimate with confidence intervals, the Bayesian framework produces a full posterior distribution over model parameters, allowing explicit quantification of uncertainty, incorporation of prior scientific knowledge, and principled model averaging. It is widely applied in toxicology, pharmacology, environmental risk assessment, and clinical dose-finding studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from classical frequentist dose-response traditions; Bayesian formulations advanced by Dempster, Gelman, and colleagues","year":"1990s–2000s (Bayesian formalization)","type":"Statistical modeling approach","dataType":"Dose (exposure) levels and quantitative or binary response outcomes","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Piegorsch, W. W., & Bailer, A. J. (2005). Analyzing Environmental Data. Wiley.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Analyzing+Environmental+Data+Piegorsch+Bailer+2005"}],"related":["benchmark-dose-modeling","pharmacokinetic-modeling","meta-analysis","bayesian-network","survival-analysis","logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-doubly-robust-estimation","name":"Bayesian Doubly Robust Estimation","fullName":"Bayesian Doubly Robust Estimation of Average Treatment Effects","aliases":["Bayesian DR","Bayesian AIPW","Bayesian augmented inverse probability weighting","Bayesian semiparametric causal estimation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2005–2010s","originator":"Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others","url":"https://scholargate.app/en/causal-inference/bayesian-doubly-robust-estimation","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-doubly-robust-estimation.md","definition":"Bayesian Doubly Robust Estimation combines the classical doubly robust (DR) augmented inverse probability weighting framework with Bayesian inference. It simultaneously models the propensity score and the outcome regression, placing prior distributions over both, and derives a posterior distribution over the average treatment effect that remains consistent even if one of the two component models is misspecified.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others","year":"2005–2010s","type":"Semiparametric causal estimation with Bayesian inference","dataType":"Observational panel or cross-sectional data with binary or continuous treatment and outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.","type":"article","doi":"10.1111/j.1541-0420.2005.00377.x","isbn":null,"url":null},{"ref":"Scharfstein, D., Nabi, R., Kennedy, E. H., Huang, M.-Y., Bonvini, M., & Smid, M. (2021). Semiparametric sensitivity analysis: Unmeasured confounding in observational studies. arXiv:1910.14694.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1910.14694"}],"related":["doubly-robust-estimation","bayesian-propensity-score-matching","inverse-probability-weighting","marginal-structural-model","targeted-maximum-likelihood-estimation","bayesian-causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-dynamic-panel-data-model","name":"Bayesian Dynamic Panel Data Model","fullName":"Bayesian Dynamic Panel Data Model","aliases":["Bayesian DPD model","Bayesian lagged dependent variable panel model","Bayesian autoregressive panel model","B-DPD"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002–2007","originator":"Hsiao, Pesaran, Tahmiscioglu; Arellano & Bonhomme","url":"https://scholargate.app/en/econometrics/bayesian-dynamic-panel-data-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-dynamic-panel-data-model.md","definition":"The Bayesian dynamic panel data model extends standard dynamic panel models — which include a lagged dependent variable to capture state dependence — by estimating all parameters within a Bayesian framework. Prior distributions are combined with the likelihood to yield a full posterior distribution over model parameters, enabling probabilistic inference and coherent uncertainty quantification even in short panels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hsiao, Pesaran, Tahmiscioglu; Arellano & Bonhomme","year":"2002–2007","type":"Bayesian panel model","dataType":"Balanced or unbalanced panel data with lagged dependent variable","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hsiao, C., Pesaran, M. H., & Tahmiscioglu, A. K. (2002). Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods. Journal of Econometrics, 109(1), 107–150.","type":"article","doi":"10.1016/S0304-4076(01)00143-9","isbn":null,"url":null},{"ref":"Arellano, M., & Bonhomme, S. (2007). Robust priors in nonlinear panel data models. Econometrica, 77(2), 489–536.","type":"article","doi":"10.1920/wp.cem.2007.0707","isbn":null,"url":null}],"related":["dynamic-panel-data-model","bayesian-panel-data-analysis","arellano-bond-gmm-estimator","bayesian-var-model","panel-fixed-effects-model","bayesian-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-dynamic-programming","name":"Bayesian Dynamic Programming","fullName":"Bayesian Dynamic Programming — Sequential decision optimization under uncertainty with Bayesian belief updating","aliases":["BDP","Bayesian DP","Bayesian sequential optimization","Bayesian stochastic control"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957 (Bellman DP); Bayesian extensions 1990s–2000s","originator":"Bellman, R.; extended by Bayesian frameworks (Duff, Bertsekas)","url":"https://scholargate.app/en/simulation/bayesian-dynamic-programming","markdownUrl":"https://scholargate.app/en/simulation/bayesian-dynamic-programming.md","definition":"Bayesian Dynamic Programming (BDP) combines Bellman's dynamic programming framework with Bayesian inference to optimize sequential decisions when transition probabilities or reward structures are unknown. At each stage, the agent updates beliefs about the environment using observed outcomes, then computes an optimal policy that explicitly accounts for both immediate rewards and the value of information gained through exploration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bellman, R.; extended by Bayesian frameworks (Duff, Bertsekas)","year":"1957 (Bellman DP); Bayesian extensions 1990s–2000s","type":"Sequential optimization with Bayesian belief updating","dataType":"Sequential decision data, prior distributions, observed outcomes","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bertsekas, D. P. (1995). Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA.","type":"book","doi":null,"isbn":"9781886529267","url":null},{"ref":"Duff, M. O. (2002). Optimal Learning: Computational procedures for Bayes-adaptive Markov decision processes. PhD Dissertation, University of Massachusetts Amherst.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimal+Learning+Computational+procedures+Bayes-adaptive+Markov+decision+processes+Duff+2002"}],"related":["dynamic-programming","stochastic-dynamic-programming","markov-decision-process","bayesian-markov-model","reinforcement-learning","stochastic-monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ecological-study","name":"Bayesian Ecological Study","fullName":"Bayesian Ecological Study Design","aliases":["Bayesian ecological analysis","Bayesian disease mapping","Bayesian ecological regression","Bayesian spatial ecological study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1991–2000s (Besag 1991 for spatial priors; Lawson 2001 for disease mapping framework)","originator":"Andrew Lawson; Julian Besag (spatial Bayesian foundations)","url":"https://scholargate.app/en/epidemiology/bayesian-ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-ecological-study.md","definition":"A Bayesian ecological study combines the group-level observational design of classical ecological epidemiology with Bayesian hierarchical modelling. Rather than treating disease rates as fixed quantities, it places prior distributions over latent spatial or temporal effects — commonly using the Besag-York-Mollié (BYM) convolution prior — and updates beliefs from aggregate data to produce posterior maps of disease risk, smoothed rate estimates, and credible intervals for ecological associations between exposures and outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Lawson; Julian Besag (spatial Bayesian foundations)","year":"1991–2000s (Besag 1991 for spatial priors; Lawson 2001 for disease mapping framework)","type":"Observational epidemiological design with Bayesian statistical framework","dataType":"Aggregate (group-level) count or rate data; spatial or temporal covariate data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Lawson, A. B. (2013). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1466504813","url":null},{"ref":"Besag, J., York, J., & Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1), 1–20.","type":"article","doi":"10.1007/BF00116466","isbn":null,"url":null}],"related":["ecological-study","bayesian-survival-analysis","bayesian-cohort-study","multilevel-modeling","spatial-analysis","disease-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-egarch","name":"Bayesian EGARCH","fullName":"Bayesian Exponential Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["Bayesian EGARCH model","Bayesian Exponential GARCH","EGARCH with Bayesian estimation","B-EGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991 (EGARCH); 2000s (Bayesian estimation)","originator":"Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s","url":"https://scholargate.app/en/econometrics/bayesian-egarch","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-egarch.md","definition":"The Bayesian EGARCH model combines Nelson's (1991) Exponential GARCH specification — which models the log of conditional variance and captures the leverage effect — with Bayesian posterior inference via Markov Chain Monte Carlo (MCMC). This allows full uncertainty quantification of all volatility parameters, including the asymmetry coefficient, without requiring large-sample normality of the estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s","year":"1991 (EGARCH); 2000s (Bayesian estimation)","type":"Volatility model with Bayesian inference","dataType":"Financial time series, returns data","subfamily":"Econometrics / time series"},"citations":[{"ref":"Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null},{"ref":"Nakatsuma, T. (2000). Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach. Journal of Econometrics, 95(1), 57–69.","type":"article","doi":"10.1016/S0304-4076(99)00029-9","isbn":null,"url":null}],"related":["egarch-model","bayesian-garch-model","bayesian-tgarch","bayesian-dcc-garch","arch-model","bayesian-var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ego-network-analysis","name":"Bayesian Ego Network Analysis","fullName":"Bayesian Ego Network Analysis (Probabilistic Inference on Personal Networks)","aliases":["Bayesian personal network analysis","Bayesian egocentric network analysis","probabilistic ego network modeling","Bayesian egonet"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010s","originator":"Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors)","url":"https://scholargate.app/en/network-analysis/bayesian-ego-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-ego-network-analysis.md","definition":"Bayesian ego network analysis applies probabilistic inference to ego-centered (personal) network data, combining a likelihood model for the ego's local network with prior distributions over network parameters. The result is a full posterior distribution that quantifies uncertainty about structural features such as alter composition, tie density, and network size — rather than producing point estimates alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Bayesian SNA tradition; Krivitsky, Kolaczyk, Handcock among key contributors)","year":"2010s","type":"Probabilistic network model","dataType":"Ego-centered network data (alter lists, tie attributes, composition variables)","subfamily":"Network science"},"citations":[{"ref":"Krivitsky, P. N., & Kolaczyk, E. D. (2015). On the question of effective sample size in network modeling: An asymptotic inquiry. Statistical Science, 30(2), 184–198.","type":"article","doi":"10.1214/14-STS502","isbn":null,"url":null},{"ref":"Ego network. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ego_network"}],"related":["ego-network-analysis","bayesian-social-network-analysis","bayesian-exponential-random-graph-model","bayesian-stochastic-block-model","social-network-analysis","temporal-ego-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-entropy-balancing","name":"Bayesian Entropy Balancing","fullName":"Bayesian Entropy Balancing for Causal Inference","aliases":["BEB","Bayesian EB","Bayesian covariate balancing","entropy balancing with Bayesian inference"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012-2020s","originator":"Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature","url":"https://scholargate.app/en/causal-inference/bayesian-entropy-balancing","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-entropy-balancing.md","definition":"Bayesian Entropy Balancing extends the classical entropy balancing approach — which reweights control units so that their covariate moments match the treated group exactly — by embedding this reweighting within a Bayesian framework. This allows researchers to incorporate prior beliefs about treatment propensities, propagate parameter uncertainty into the final causal estimate, and obtain credible intervals rather than only classical confidence intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature","year":"2012-2020s","type":"Weighting-based causal estimator with Bayesian uncertainty quantification","dataType":"Observational cross-sectional or panel data with binary or multi-valued treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.","type":"article","doi":"10.1093/pan/mpr025","isbn":null,"url":null},{"ref":"Vegetabile, B. G., Griffin, B. A., Coffman, D. L., Cefalu, M., Robbins, M. W., & McCaffrey, D. F. (2021). Nonparametric estimation of population average dose-response curves using entropy balancing weights for continuous exposures. Health Services and Outcomes Research Methodology, 21(1), 69-110.","type":"article","doi":"10.1007/s10742-020-00236-2","isbn":null,"url":null}],"related":["entropy-balancing","bayesian-propensity-score-matching","inverse-probability-weighting","doubly-robust-estimation","propensity-score-weighting","coarsened-exact-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-epigenome-wide-association-study-in-educational-research","name":"Bayesian epigenome-wide association study in educational research","fullName":"Bayesian Epigenome-Wide Association Study Applied to Educational Research Outcomes","aliases":["Bayesian EWAS","Bayesian epigenome-wide scan","Bayesian methylation-wide association study","B-EWAS"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"EWAS framework ~2010–2011; Bayesian EWAS variants ~2013–2017; educational applications ~2015–present","originator":"Rakyan, Down, Balding, and Beck (conceptual EWAS framework); Bayesian extensions by multiple groups including Teschendorff and colleagues","url":"https://scholargate.app/en/bioinformatics/bayesian-epigenome-wide-association-study-in-educational-research","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-epigenome-wide-association-study-in-educational-research.md","definition":"A Bayesian epigenome-wide association study (Bayesian EWAS) scans hundreds of thousands of DNA methylation sites across the genome to identify those statistically associated with an educational outcome — such as cognitive ability, attainment, or socioeconomic exposure during schooling. Unlike classical frequentist EWAS, the Bayesian framework incorporates prior biological knowledge to compute posterior probabilities of association, improving power and reducing false discoveries when applied to complex educational phenotypes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rakyan, Down, Balding, and Beck (conceptual EWAS framework); Bayesian extensions by multiple groups including Teschendorff and colleagues","year":"EWAS framework ~2010–2011; Bayesian EWAS variants ~2013–2017; educational applications ~2015–present","type":"Genomic association study with Bayesian inference","dataType":"DNA methylation array data (e.g., Illumina 450K or EPIC array), phenotypic/educational outcome variables","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Epigenome-wide+association+studies+for+common+human+diseases+Rakyan+2011"},{"ref":"Ligthart, S., Marzi, C., Aslibekyan, S., Mendelson, M. M., Conneely, K. N., Tanaka, T., ... & Dehghan, A. (2016). DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. Genome Biology, 17(1), 255.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=DNA+methylation+signatures+chronic+low-grade+inflammation+complex+diseases+Ligthart+2016"}],"related":["genome-wide-association-study","dna-methylation-analysis","epigenetic-clock","mendelian-randomization","bayesian-network-analysis","mediation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-epigenome-wide-association-study","name":"Bayesian epigenome-wide association study","fullName":"Bayesian Epigenome-Wide Association Study","aliases":["Bayesian EWAS","B-EWAS","Bayesian methylation-wide association study","Bayesian epigenetic association analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s (framework developed ~2013–2016)","originator":"Multiple groups; Bayesian EWAS framework advanced by S. Richardson, P.-C. Tsai, J. T. Bell and colleagues","url":"https://scholargate.app/en/bioinformatics/bayesian-epigenome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-epigenome-wide-association-study.md","definition":"A Bayesian EWAS is a genome-scale association analysis that links epigenetic marks — most commonly CpG-site DNA methylation — to a phenotype or trait of interest, replacing or supplementing the classical frequentist p-value framework with a Bayesian probabilistic model. It yields posterior probabilities of association and credible intervals for each CpG site, allowing formal incorporation of prior biological knowledge and more principled handling of the multiple-testing burden intrinsic to testing hundreds of thousands of sites simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; Bayesian EWAS framework advanced by S. Richardson, P.-C. Tsai, J. T. Bell and colleagues","year":"2010s (framework developed ~2013–2016)","type":"Statistical association analysis","dataType":"Genome-wide DNA methylation array data (e.g., Illumina 450K or EPIC), phenotype/trait data, covariates","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Richardson, S., Tsai, P. C., Bell, J. T., & Timpson, N. J. (2016). Bayesian approaches to studying associations between epigenetic marks and phenotypes. International Journal of Epidemiology, 45(3), 694–705.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+approaches+to+studying+associations+between+epigenetic+marks+and+phenotypes+Richardson+2016"},{"ref":"Johansson, A., Enroth, S., & Gyllensten, U. (2013). Continuous aging of the human DNA methylome throughout the human lifespan. PLoS ONE, 8(6), e67378.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Continuous+aging+human+DNA+methylome+Johansson+Gyllensten+2013+PLoS+ONE"}],"related":["epigenome-wide-association-study","genome-wide-association-study","bayesian-gwas","dna-methylation-analysis","bayesian-variable-selection","multi-omics-epigenome-wide-association-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-eqtl-analysis","name":"Bayesian eQTL analysis","fullName":"Bayesian Expression Quantitative Trait Loci Analysis","aliases":["Bayesian eQTL mapping","probabilistic eQTL analysis","Bayesian QTL mapping for gene expression","eQTL fine-mapping"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2000s–2010s","originator":"Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context","url":"https://scholargate.app/en/bioinformatics/bayesian-eqtl-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-eqtl-analysis.md","definition":"Bayesian eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression by combining genotype and RNA-seq data within a probabilistic framework. Unlike frequentist approaches that rely on p-value thresholds, the Bayesian formulation produces posterior probabilities of association, enabling principled fine-mapping of causal variants and coherent uncertainty quantification across thousands of gene-SNP pairs simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matthew Stephens, David J. Balding (Bayesian framework for genetic association); extended by multiple groups for eQTL context","year":"2000s–2010s","type":"Probabilistic genomic association method","dataType":"Genotype arrays or whole-genome sequencing + RNA-seq gene expression data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.","type":"article","doi":"10.1038/nrg2615","isbn":null,"url":null},{"ref":"Guan, Y., & Stephens, M. (2011). Bayesian variable selection regression for genome-wide association studies and other large-scale problems. Annals of Applied Statistics, 5(3), 1780–1815.","type":"article","doi":"10.1214/11-AOAS455","isbn":null,"url":null}],"related":["eqtl-analysis","genome-wide-association-study","bayesian-gwas","rna-seq-differential-expression","pathway-enrichment-analysis","single-cell-eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-event-study-design","name":"Bayesian Event Study Design","fullName":"Bayesian Event Study Design for Causal Inference","aliases":["Bayesian event study","Bayesian abnormal return estimation","Bayesian pre-post event analysis","BES"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1990s–2010s","originator":"Developed from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010s","url":"https://scholargate.app/en/causal-inference/bayesian-event-study-design","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-event-study-design.md","definition":"Bayesian Event Study Design extends the classical event study framework by replacing frequentist significance testing with a full Bayesian inferential framework. It estimates how an event (policy change, announcement, shock) alters an outcome trajectory by learning a prior model from the estimation window and updating it with observed data, yielding posterior distributions over abnormal effects and cumulative causal impacts with full uncertainty quantification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010s","year":"1990s–2010s","type":"Quasi-experimental / causal inference","dataType":"Time-series or panel data with discrete event indicators; continuous outcomes (e.g., returns, rates)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Sorescu, A., Warren, N. L., & Ertekin, L. (2017). Event study methodology in the marketing literature: An overview. Journal of the Academy of Marketing Science, 45(2), 186-207.","type":"article","doi":"10.1007/s11747-017-0516-y","isbn":null,"url":null},{"ref":"Glassman, M., & McAfee, R. B. (1996). Bayesian estimation of abnormal stock returns. Journal of Business & Economic Statistics, 10(3), 321-332.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+estimation+abnormal+stock+returns+Glassman+McAfee+1992"}],"related":["event-study-design","difference-in-differences","bayesian-difference-in-differences","panel-event-study","interrupted-time-series","causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-event-tree-analysis","name":"Bayesian Event Tree Analysis","fullName":"Bayesian Event Tree Analysis","aliases":["Bayesian ETA","B-ETA","Probabilistic Event Tree Analysis","Bayesian Inductive Risk Model"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"ETA: 1960s–1970s; Bayesian extension: 1990s–2000s","originator":"H.E. Watson (Bell Labs, fault tree); ETA formalized via US Nuclear Regulatory Commission; Bayesian extension developed in reliability and risk engineering communities","url":"https://scholargate.app/en/experimental-design/bayesian-event-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-event-tree-analysis.md","definition":"Bayesian Event Tree Analysis (B-ETA) is a quantitative risk assessment method that extends classical event tree analysis by incorporating Bayesian inference to assign and update branch probabilities. Starting from an initiating event, it maps sequences of successes and failures through safety barriers, using prior distributions and observed evidence to produce posterior outcome probabilities. Widely used in nuclear safety, process industries, and system reliability engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"H.E. Watson (Bell Labs, fault tree); ETA formalized via US Nuclear Regulatory Commission; Bayesian extension developed in reliability and risk engineering communities","year":"ETA: 1960s–1970s; Bayesian extension: 1990s–2000s","type":"Probabilistic risk and reliability analysis technique","dataType":"Failure probability data, expert elicitation, historical incident records, prior distributions","subfamily":"Engineering methods"},"citations":[{"ref":"Bearfield, G., & Marsh, W. (2005). Generalising event trees using Bayesian networks with a case study of train derailment. In G. Windeknecht et al. (Eds.), Proceedings of the 13th Safety-Critical Systems Symposium. Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Generalising+event+trees+using+Bayesian+networks+Bearfield+Marsh+2005"},{"ref":"Event tree analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Event_tree_analysis"}],"related":["bayesian-fault-tree-analysis","bayesian-reliability-analysis","fault-tree-analysis","failure-mode-and-effects-analysis","bayesian-failure-mode-and-effects-analysis","event-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ex-post-facto-design","name":"Bayesian Ex Post Facto Design","fullName":"Bayesian Ex Post Facto Research Design","aliases":["Bayesian causal-comparative design","Bayesian after-the-fact design","Bayesian observational causal design","Bayesian retrospective causal study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1964 (Kerlinger ex post facto); Bayesian integration from 1990s–2000s onward","originator":"Frederick N. Kerlinger (ex post facto framework); Bayesian extension draws on Laplace and modern Bayesian statistics","url":"https://scholargate.app/en/research-design/bayesian-ex-post-facto-design","markdownUrl":"https://scholargate.app/en/research-design/bayesian-ex-post-facto-design.md","definition":"Bayesian ex post facto design investigates possible causal relationships among variables that have already occurred, without researcher manipulation of those variables, and quantifies uncertainty about those relationships using Bayesian statistical inference. The researcher selects groups that differ on an outcome or a presumed cause after the fact, then uses prior knowledge and observed data together — via Bayes' theorem — to estimate credible effect sizes, group differences, or predictors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frederick N. Kerlinger (ex post facto framework); Bayesian extension draws on Laplace and modern Bayesian statistics","year":"1964 (Kerlinger ex post facto); Bayesian integration from 1990s–2000s onward","type":"Quantitative observational research design with Bayesian inference","dataType":"Existing records, archival data, survey data, administrative datasets","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N. (1973). Foundations of Behavioral Research (2nd ed.). Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kerlinger+Foundations+of+Behavioral+Research+1973"},{"ref":"Kruschke, J. K. (2015). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2nd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0124058880","url":null}],"related":["ex-post-facto-design","causal-comparative-research","bayesian-inference","observational-study","propensity-score-matching","retrospective-cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-exploratory-factor-analysis","name":"Bayesian EFA","fullName":"Bayesian Exploratory Factor Analysis","aliases":["Bayesian factor analysis","BEFA","Bayesian common factor model","probabilistic factor analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2004 (Bayesian formulation); factor analysis roots: 1904","originator":"Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)","url":"https://scholargate.app/en/psychometrics/bayesian-exploratory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-exploratory-factor-analysis.md","definition":"Bayesian exploratory factor analysis applies a full probabilistic framework to the common factor model. By placing prior distributions over factor loadings and unique variances, it yields posterior distributions rather than point estimates, quantifies uncertainty around every loading, and can treat the number of factors as an unknown to be inferred from data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)","year":"2004 (Bayesian formulation); factor analysis roots: 1904","type":"Probabilistic latent variable model","dataType":"Continuous or ordinal item-level responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/24307179"},{"ref":"Ghosh, J. & Dunson, D. B. (2009). Default prior distributions and efficient posterior computation in Bayesian factor analysis. Journal of Computational and Graphical Statistics, 18(2), 306–320.","type":"article","doi":"10.1198/jcgs.2009.07145","isbn":null,"url":null}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","bayesian-confirmatory-factor-analysis","item-response-theory","bayesian-item-response-theory","mcdonalds-omega"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-exponential-random-graph-model","name":"Bayesian Exponential Random Graph Model","fullName":"Bayesian Exponential Random Graph Model (Bayesian ERGM)","aliases":["Bayesian ERGM","Bayesian p-star model","Bayesian p* model","BERGM"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2011","originator":"Caimo, A., & Friel, N.","url":"https://scholargate.app/en/network-analysis/bayesian-exponential-random-graph-model","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-exponential-random-graph-model.md","definition":"The Bayesian Exponential Random Graph Model (Bayesian ERGM or BERGM) extends the classical ERGM framework by placing prior distributions over the model parameters and using Markov chain Monte Carlo methods to obtain full posterior distributions. Introduced by Caimo and Friel (2011), it allows researchers to quantify parameter uncertainty and incorporate prior knowledge when modelling the structural features of social and other complex networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Caimo, A., & Friel, N.","year":"2011","type":"Bayesian statistical model for networks","dataType":"Binary or valued adjacency matrices (network edge data)","subfamily":"Network science"},"citations":[{"ref":"Caimo, A., & Friel, N. (2011). Bayesian inference for exponential random graph models. Social Networks, 33(1), 41–55.","type":"article","doi":"10.1016/j.socnet.2010.09.004","isbn":null,"url":null},{"ref":"Exponential random graph models. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Exponential_random_graph_models"}],"related":["exponential-random-graph-model","bayesian-social-network-analysis","stochastic-block-model","bayesian-stochastic-block-model","modularity-analysis","temporal-exponential-random-graph-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-factor-analysis","name":"Bayesian Factor Analysis","fullName":"Bayesian Factor Analysis","aliases":["Bayesian EFA","Bayesian CFA","Bayesçi Faktör Analizi","probabilistic factor analysis"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2004,"originator":"Lopes & West (2004) for Bayesian model assessment in factor analysis","url":"https://scholargate.app/en/bayesian/bayesian-factor-analysis","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-factor-analysis.md","definition":"Bayesian Factor Analysis is a probabilistic latent-variable method that places prior distributions on the factor loading matrix and the residual variances, then infers a full posterior over these parameters from the observed data. Developed prominently in the Bayesian framework by Lopes and West (2004), it extends classical exploratory and confirmatory factor analysis by quantifying uncertainty in every estimated loading rather than reporting single point estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lopes & West (2004) for Bayesian model assessment in factor analysis","year":2004,"family":"Bayesian","type":"Bayesian latent variable model","purpose":"exploration / structure","var_types":"continuous / ordinal","inference":"MCMC","outputs":"posterior factor loadings / credible intervals / factor scores","min_sample":30,"difficulty":3},"citations":[{"ref":"Lopes, H. F. & West, M. (2004). Bayesian Model Assessment in Factor Analysis. Statistica Sinica, 14(1), 41–67.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/24307188"}],"related":["exploratory-factor-analysis","cfa","bayesian-regression","bayesian-network","mcmc","pca","sem"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-failure-mode-and-effects-analysis","name":"Bayesian failure mode and effects analysis","fullName":"Bayesian Failure Mode and Effects Analysis","aliases":["Bayesian FMEA","probabilistic FMEA","B-FMEA","Bayesian risk priority analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s","originator":"Extension of classical FMEA (MIL-STD-1629, 1974) with Bayesian inference formalised in reliability literature from the 1990s onward","url":"https://scholargate.app/en/experimental-design/bayesian-failure-mode-and-effects-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-failure-mode-and-effects-analysis.md","definition":"Bayesian FMEA extends the classical Failure Mode and Effects Analysis framework by replacing fixed point-estimate risk scores with probability distributions, allowing prior engineering knowledge and observed failure data to be formally combined through Bayes' theorem. The result is a probabilistic Risk Priority Number (RPN) that reflects uncertainty in severity, occurrence, and detectability ratings rather than masking it with single consensus values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of classical FMEA (MIL-STD-1629, 1974) with Bayesian inference formalised in reliability literature from the 1990s onward","year":"1990s–2000s","type":"Probabilistic reliability and risk analysis","dataType":"Expert elicitation, historical failure data, prior distributions, observed counts","subfamily":"Engineering methods"},"citations":[{"ref":"Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering and System Safety, 50(2), 203–213.","type":"article","doi":"10.1016/0951-8320(95)00068-D","isbn":null,"url":null},{"ref":"Liu, H.-C., Liu, L., & Liu, N. (2013). Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert Systems with Applications, 40(2), 828–838.","type":"article","doi":"10.1016/j.eswa.2012.08.010","isbn":null,"url":null}],"related":["failure-mode-and-effects-analysis","bayesian-reliability-analysis","bayesian-fault-tree-analysis","robust-failure-mode-and-effects-analysis","fault-tree-analysis","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-fault-tree-analysis","name":"Bayesian Fault Tree Analysis","fullName":"Bayesian Fault Tree Analysis","aliases":["BFTA","Bayesian FTA","Bayesian network fault tree","probabilistic fault tree analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"2001 (BFTA mapping); Bayesian networks: 1988","originator":"Andrea Bobbio, Luca Portinale et al. (mapping FTA to Bayesian networks); Judea Pearl (Bayesian networks)","url":"https://scholargate.app/en/experimental-design/bayesian-fault-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-fault-tree-analysis.md","definition":"Bayesian Fault Tree Analysis (BFTA) extends classical fault tree analysis by converting the fault tree structure into an equivalent Bayesian network, enabling probabilistic inference in both forward (prediction) and backward (diagnosis) directions. This integration allows analysts to update failure probability estimates with observed evidence, quantify uncertainty explicitly, and identify the most probable root causes of a top-level system failure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrea Bobbio, Luca Portinale et al. (mapping FTA to Bayesian networks); Judea Pearl (Bayesian networks)","year":"2001 (BFTA mapping); Bayesian networks: 1988","type":"Probabilistic reliability / safety analysis","dataType":"Component failure probabilities, conditional probability tables, system logic structure","subfamily":"Engineering methods"},"citations":[{"ref":"Bobbio, A., Portinale, L., Minichino, M., & Ciancamerla, E. (2001). Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering & System Safety, 71(3), 249–260.","type":"article","doi":"10.1016/S0951-8320(00)00077-6","isbn":null,"url":null},{"ref":"Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.","type":"book","doi":null,"isbn":"978-1558604797","url":null}],"related":["fault-tree-analysis","bayesian-reliability-analysis","failure-mode-and-effects-analysis","event-tree-analysis","bayesian-failure-mode-and-effects-analysis","root-cause-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-federated-learning","name":"Bayesian Federated Learning","fullName":"Bayesian Federated Learning (Probabilistic Federated Model Aggregation)","aliases":["BFL","probabilistic federated learning","Bayesian nonparametric federated learning","federated Bayesian inference"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2019","originator":"Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning)","url":"https://scholargate.app/en/machine-learning/bayesian-federated-learning","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-federated-learning.md","definition":"Bayesian Federated Learning combines federated learning — where model training is distributed across multiple clients without sharing raw data — with Bayesian inference, so that each client maintains a posterior distribution over model parameters rather than a single point estimate. This yields principled uncertainty quantification and more robust model aggregation across heterogeneous, privacy-preserving data silos.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yurochkin, M. et al.; McMahan, H. B. et al. (foundational federated learning)","year":"2019","type":"Probabilistic federated ensemble","dataType":"Distributed tabular, image, or text data across silos","subfamily":"Machine learning"},"citations":[{"ref":"Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, N., & Khazaeni, Y. (2019). Bayesian Nonparametric Federated Learning of Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 7101–7110.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v97/yurochkin19a.html"},{"ref":"Corinzia, L., & Buhmann, J. M. (2019). Variational Federated Multi-Task Learning. arXiv preprint arXiv:1906.06268.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1906.06268"}],"related":["federated-learning","bayesian-transfer-learning","gaussian-process","semi-supervised-federated-learning","bayesian-neural-network","bayesian-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-few-shot-learning","name":"Bayesian Few-Shot Learning","fullName":"Bayesian Few-Shot Learning (Meta-Learning with Bayesian Inference)","aliases":["Bayesian meta-learning","probabilistic few-shot learning","amortized Bayesian few-shot learning","Bayesian FSL"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018-2019","originator":"Gordon et al.; Finn, Xu & Levine","url":"https://scholargate.app/en/machine-learning/bayesian-few-shot-learning","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-few-shot-learning.md","definition":"Bayesian few-shot learning combines Bayesian inference with meta-learning to enable a model to generalize from as few as one to five labeled examples per class. By treating task-specific parameters as random variables and learning an informative prior across many training tasks, the method produces calibrated uncertainty estimates alongside predictions — a key advantage over deterministic few-shot learners.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon et al.; Finn, Xu & Levine","year":"2018-2019","type":"Probabilistic meta-learning","dataType":"Labeled episodes with very few examples per class","subfamily":"Machine learning"},"citations":[{"ref":"Gordon, J., Bronskill, J., Bauer, M., Nowozin, S. & Turner, R. E. (2019). Meta-Learning Probabilistic Inference for Prediction. International Conference on Learning Representations (ICLR 2019).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=HkxStoC5F7"},{"ref":"Finn, C., Xu, K. & Levine, S. (2018). Probabilistic Model-Agnostic Meta-Learning. Advances in Neural Information Processing Systems (NeurIPS 2018), 31.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2018/hash/8e2c381d4dd04f1c55093f22c59c3a08-Abstract.html"}],"related":["few-shot-learning","bayesian-transfer-learning","bayesian-meta-learning","semi-supervised-few-shot-learning","gaussian-process","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-fishers-exact-test","name":"Bayesian Fisher's exact test","fullName":"Bayesian Fisher's Exact Test for 2x2 Contingency Tables","aliases":["Bayesian exact test for independence","Bayesian contingency table test","Bayes factor Fisher test","BFexact"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1974 (Bayesian form); 1935 (Fisher's exact test)","originator":"Gunel & Dickey (Bayesian form); R. A. Fisher (classical exact test)","url":"https://scholargate.app/en/statistics/bayesian-fishers-exact-test","markdownUrl":"https://scholargate.app/en/statistics/bayesian-fishers-exact-test.md","definition":"The Bayesian Fisher's exact test evaluates independence between two categorical variables in a 2x2 table by computing a Bayes factor rather than a p-value. Using conjugate priors on cell probabilities — most commonly the Gunel-Dickey framework — it quantifies how much the observed data favor an association model over an independence model, providing a continuous scale of evidence in both directions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gunel & Dickey (Bayesian form); R. A. Fisher (classical exact test)","year":"1974 (Bayesian form); 1935 (Fisher's exact test)","type":"Bayesian hypothesis test for independence","dataType":"Categorical (2x2 contingency table counts)","subfamily":"Classical statistics"},"citations":[{"ref":"Gunel, E., & Dickey, J. (1974). Bayes factors for independence in contingency tables. Biometrika, 61(3), 545–557.","type":"article","doi":"10.1093/biomet/61.3.545","isbn":null,"url":null},{"ref":"Jamil, T., Ly, A., Morey, R. D., Love, J., Marsman, M., & Wagenmakers, E.-J. (2017). Default Gunel and Dickey Bayes factors for contingency tables. Behavior Research Methods, 49(2), 638–652.","type":"article","doi":"10.3758/s13428-016-0739-8","isbn":null,"url":null}],"related":["fishers-exact-test","bayesian-chi-square-test","chi-square-test","bayesian-independent-samples-t-test","mann-whitney-u-test","bayesian-pearson-correlation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-fixed-effects-model","name":"Bayesian Fixed Effects Model","fullName":"Bayesian Fixed Effects Panel Data Model","aliases":["Bayesian within estimator","Bayesian FE model","Bayesian individual fixed effects","Bayesian least squares dummy variable"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000–2008","originator":"Chib (2008); Lancaster (2000)","url":"https://scholargate.app/en/econometrics/bayesian-fixed-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-fixed-effects-model.md","definition":"The Bayesian fixed effects model applies Bayesian inference to the classical within-group panel estimator. Unit-specific intercepts capture time-invariant unobserved heterogeneity, while prior distributions on all parameters allow probability statements about coefficients and full uncertainty quantification via the posterior distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chib (2008); Lancaster (2000)","year":"2000–2008","type":"Bayesian panel regression","dataType":"Balanced or unbalanced panel (cross-section × time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Lancaster, T. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95(2), 391–413.","type":"article","doi":"10.1016/S0304-4076(99)00044-5","isbn":null,"url":null},{"ref":"Chib, S. (2008). Panel data modeling and inference: A Bayesian primer. In L. Mátyás & P. Sevestre (Eds.), The Econometrics of Panel Data (3rd ed., pp. 479–515). Springer.","type":"inproceedings","doi":"10.1007/978-3-540-75892-1_15","isbn":null,"url":null}],"related":["fixed-effects-model","bayesian-random-effects-model","panel-fixed-effects-model","bayesian-panel-data-analysis","random-effects-model","bayesian-ols"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-fractional-factorial-design","name":"Bayesian Fractional Factorial Design","fullName":"Bayesian Fractional Factorial Experimental Design","aliases":["Bayesian FFD","Bayesian screening design","Bayesian factor-screening experiment","BFF design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s","originator":"DuMouchel & Jones; Chipman, Hamada & Wu","url":"https://scholargate.app/en/experimental-design/bayesian-fractional-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-fractional-factorial-design.md","definition":"Bayesian fractional factorial design integrates Bayesian prior information into the selection and analysis of fractional factorial experiments. Rather than running every combination of factor levels, only a carefully chosen subset of runs is executed, with Bayesian inference used to estimate effects and quantify uncertainty — even when the classical aliasing structure leaves effects confounded.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"DuMouchel & Jones; Chipman, Hamada & Wu","year":"1990s","type":"Bayesian experimental design method","dataType":"Continuous or categorical factor levels with quantitative response","subfamily":"Engineering methods"},"citations":[{"ref":"DuMouchel, W., & Jones, B. (1994). A simple Bayesian modification of D-optimal designs to reduce dependence on an assumed model. Technometrics, 36(1), 37–47.","type":"article","doi":"10.2307/1269197","isbn":null,"url":null},{"ref":"Meyer, R. D., & Steinberg, D. M. (1996). Follow-up designs to resolve confounding in multifactor experiments. Technometrics, 38(4), 303–313.","type":"article","doi":"10.1080/00401706.1996.10484538","isbn":null,"url":null}],"related":["fractional-factorial-design","bayesian-design-of-experiments","full-factorial-design","central-composite-design","response-surface-methodology","taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-full-factorial-design","name":"Bayesian Full Factorial Design","fullName":"Bayesian Full Factorial Design of Experiments","aliases":["Bayesian FFD","Bayesian complete factorial experiment","Bayesian full factorial experiment","Bayesian all-combinations design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s (Bayesian DOE formalized); factorial design roots in 1920s (Fisher)","originator":"Kathryn Chaloner & Isabella Verdinelli (Bayesian experimental design framework); building on Fisher's factorial design principles","url":"https://scholargate.app/en/experimental-design/bayesian-full-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-full-factorial-design.md","definition":"Bayesian full factorial design combines the complete combinatorial structure of classical full factorial experiments — running every combination of factor levels — with a Bayesian inferential framework that incorporates prior knowledge about factor effects and yields full posterior distributions over main effects, interactions, and model parameters, rather than point estimates and p-values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathryn Chaloner & Isabella Verdinelli (Bayesian experimental design framework); building on Fisher's factorial design principles","year":"1990s (Bayesian DOE formalized); factorial design roots in 1920s (Fisher)","type":"Bayesian experimental design method","dataType":"Continuous or categorical response measurements across all factor-level combinations","subfamily":"Engineering methods"},"citations":[{"ref":"Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273–304.","type":"article","doi":"10.1214/ss/1177009939","isbn":null,"url":null},{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471718130","url":null}],"related":["full-factorial-design","bayesian-design-of-experiments","bayesian-response-surface-methodology","fractional-factorial-design","bayesian-fractional-factorial-design","central-composite-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-fuzzy-regression-discontinuity","name":"Bayesian Fuzzy Regression Discontinuity","fullName":"Bayesian Fuzzy Regression Discontinuity Design","aliases":["Bayesian Fuzzy RD","Bayesian Fuzzy RDD","Fuzzy RD with Bayesian Inference"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2001 (fuzzy RD identification); 2016 (Bayesian formulation by Chib & Jacobi)","originator":"Chib & Jacobi (Bayesian formulation); Hahn, Todd & Van der Klaauw (fuzzy RD identification)","url":"https://scholargate.app/en/causal-inference/bayesian-fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-fuzzy-regression-discontinuity.md","definition":"Bayesian Fuzzy Regression Discontinuity (Bayesian Fuzzy RD) combines the quasi-experimental logic of fuzzy regression discontinuity design with full Bayesian inference. It estimates a local average treatment effect at a policy threshold where treatment assignment is probabilistic rather than deterministic, placing prior distributions over all unknowns and recovering a complete posterior distribution of the causal effect rather than a single point estimate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chib & Jacobi (Bayesian formulation); Hahn, Todd & Van der Klaauw (fuzzy RD identification)","year":"2001 (fuzzy RD identification); 2016 (Bayesian formulation by Chib & Jacobi)","type":"Bayesian causal inference / quasi-experimental design","dataType":"Cross-sectional or panel data with a continuous running variable and imperfect threshold compliance","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.","type":"article","doi":"10.1111/1468-0262.00183","isbn":null,"url":null},{"ref":"Chib, S., & Jacobi, L. (2016). Bayesian fuzzy regression discontinuity analysis and returns to compulsory schooling. Journal of Applied Econometrics, 31(6), 1026-1047.","type":"article","doi":"10.1002/jae.2481","isbn":null,"url":null}],"related":["regression-discontinuity-design","fuzzy-regression-discontinuity","instrumental-variables","bayesian-instrumental-variables","local-average-treatment-effect","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-garch-model","name":"Bayesian GARCH model","fullName":"Bayesian Generalized Autoregressive Conditional Heteroskedasticity Model","aliases":["Bayesian GARCH","BGARCH","GARCH with Bayesian inference","Bayesian volatility model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1989–2000","originator":"Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)","url":"https://scholargate.app/en/econometrics/bayesian-garch-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-garch-model.md","definition":"The Bayesian GARCH model combines the GARCH framework for time-varying volatility with Bayesian posterior inference. Instead of maximising a likelihood, it specifies prior distributions for the GARCH parameters and draws from the resulting posterior — typically via Markov chain Monte Carlo (MCMC) — to quantify both point estimates and full uncertainty about volatility dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)","year":"1989–2000","type":"Bayesian volatility model","dataType":"Time-series financial or macroeconomic data with time-varying variance","subfamily":"Econometrics / time series"},"citations":[{"ref":"Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.","type":"article","doi":"10.1016/0304-4076(89)90030-4","isbn":null,"url":null},{"ref":"Nakatsuma, T. (2000). Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach. Journal of Econometrics, 95(1), 57–69.","type":"article","doi":"10.1016/S0304-4076(99)00029-9","isbn":null,"url":null}],"related":["garch-model","egarch-model","stochastic-volatility-model","bayesian-var","arch-model","markov-switching-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-gaussian-mixture-model","name":"Bayesian Gaussian Mixture Model","fullName":"Bayesian Gaussian Mixture Model (Variational Bayes / MCMC Inference)","aliases":["Bayesian GMM","Variational Gaussian Mixture","VBGMM","Dirichlet Process Gaussian Mixture"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1999–2006","originator":"Attias, H.; Bishop, C. M.","url":"https://scholargate.app/en/machine-learning/bayesian-gaussian-mixture-model","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-gaussian-mixture-model.md","definition":"The Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fitting fixed point estimates. This yields principled uncertainty quantification, automatic selection of the effective number of components, and resistance to overfitting small datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Attias, H.; Bishop, C. M.","year":"1999–2006","type":"Probabilistic clustering / density estimation","dataType":"Continuous multivariate data","subfamily":"Machine learning"},"citations":[{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer.","type":"book","doi":null,"isbn":"978-0-387-31073-2","url":null},{"ref":"Attias, H. (1999). Inferring parameters and structure of latent variable models by variational Bayes. Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI), 21–30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://dl.acm.org/doi/10.5555/2073796.2073799"}],"related":["gaussian-mixture-model","k-means","bayesian-k-means","variational-autoencoder","gaussian-process","semi-supervised-gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-gaussian-process","name":"Bayesian Gaussian Process","fullName":"Bayesian Gaussian Process Regression and Classification","aliases":["GP regression","GPR","Gaussian process model","GP classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1978–2006","originator":"O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.","url":"https://scholargate.app/en/machine-learning/bayesian-gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-gaussian-process.md","definition":"A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.","year":"1978–2006","type":"Probabilistic kernel model","dataType":"Continuous, mixed; tabular or structured","subfamily":"Machine learning"},"citations":[{"ref":"Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-18253-9","url":null},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 6). Springer.","type":"book","doi":null,"isbn":"978-0-387-31073-2","url":null}],"related":["gaussian-process","bayesian-linear-regression","support-vector-machine","kernel-ridge-regression","neural-network","bayesian-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-gearys-c","name":"Bayesian Geary's C","fullName":"Bayesian Geary's Contiguity Ratio","aliases":["Bayesian Geary C","Bayesian spatial contiguity statistic","Geary's C (Bayesian)","Bayesian contiguity ratio"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1954 (Bayesian framing: 2000s onward)","originator":"Geary (1954); Bayesian extension via hierarchical spatial modeling literature","url":"https://scholargate.app/en/spatial-analysis/bayesian-gearys-c","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-gearys-c.md","definition":"Bayesian Geary's C embeds the classical Geary contiguity ratio within a Bayesian hierarchical framework. Instead of a single point estimate and asymptotic p-value, it produces a posterior distribution over the statistic (or over spatially structured random effects), quantifying uncertainty about spatial autocorrelation while formally incorporating prior knowledge about the spatial process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geary (1954); Bayesian extension via hierarchical spatial modeling literature","year":"1954 (Bayesian framing: 2000s onward)","type":"Bayesian spatial autocorrelation statistic","dataType":"Areal (lattice) spatial data with continuous or count attributes","subfamily":"GIS / spatial"},"citations":[{"ref":"Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115–145.","type":"article","doi":"10.2307/2986645","isbn":null,"url":null},{"ref":"Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584884101","url":null}],"related":["gearys-c","bayesian-morans-i","local-gearys-c","bayesian-spatial-autocorrelation","bayesian-local-indicators-of-spatial-association","morans-i"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-gene-set-enrichment-analysis","name":"Bayesian Gene Set Enrichment Analysis","fullName":"Bayesian Gene Set Enrichment Analysis","aliases":["Bayesian GSEA","BGSEA","Bayesian pathway scoring","probabilistic gene set testing"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2004–2007","originator":"Michael A. Newton, Frank A. Quintana and colleagues; building on Subramanian et al. GSEA framework","url":"https://scholargate.app/en/bioinformatics/bayesian-gene-set-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-gene-set-enrichment-analysis.md","definition":"Bayesian gene set enrichment analysis (Bayesian GSEA) applies a probabilistic framework to determine whether predefined sets of genes — representing biological pathways, cellular processes, or functional categories — are collectively more differentially expressed than expected by chance. Unlike classical frequentist GSEA, the Bayesian approach models uncertainty in expression estimates explicitly, incorporates prior biological knowledge, and produces posterior probabilities of enrichment rather than raw p-values, enabling more principled inference especially in small-sample settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael A. Newton, Frank A. Quintana and colleagues; building on Subramanian et al. GSEA framework","year":"2004–2007","type":"Probabilistic gene set enrichment method","dataType":"Gene expression matrices (RNA-seq counts or microarray intensities) with curated gene set annotations (e.g., MSigDB, GO, KEGG)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., ... & Mesirov, J. P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545-15550.","type":"article","doi":"10.1073/pnas.0506580102","isbn":null,"url":null},{"ref":"Newton, M. A., Quintana, F. A., Den Boon, J. A., Bhattacharya, S., & Ahlquist, P. (2007). Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. The Annals of Applied Statistics, 1(1), 85-106.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Random-set+methods+identify+distinct+aspects+of+the+enrichment+signal+in+gene-set+analysis"}],"related":["gene-set-enrichment-analysis","pathway-enrichment-analysis","bayesian-rna-seq-differential-expression","rna-seq-differential-expression","single-cell-gene-set-enrichment-analysis","multi-omics-gene-set-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-generalized-additive-model","name":"Bayesian Generalized additive model","fullName":"Bayesian Generalized Additive Model","aliases":["Bayesian GAM","BGAM","Bayesian semiparametric regression","Bayesian smooth regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1990s–2000s","originator":"Hastie & Tibshirani (GAM framework, 1990); Bayesian formulation developed through work by Wood, Fahrmeir, Lang, and others","url":"https://scholargate.app/en/statistics/bayesian-generalized-additive-model","markdownUrl":"https://scholargate.app/en/statistics/bayesian-generalized-additive-model.md","definition":"Bayesian Generalized Additive Models extend the frequentist GAM framework by placing prior distributions over the smooth functions and any additional model parameters. This yields full posterior distributions over each smooth effect, enabling principled uncertainty quantification, automatic smoothness selection via hyperpriors, and seamless integration with hierarchical or mixed-effects structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hastie & Tibshirani (GAM framework, 1990); Bayesian formulation developed through work by Wood, Fahrmeir, Lang, and others","year":"1990s–2000s","type":"Semiparametric Bayesian regression","dataType":"Continuous, binary, count, or other exponential-family outcomes with potentially nonlinear predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Wood, S. N. (2017). Generalized Additive Models: An Introduction with R (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"9781498728331","url":null},{"ref":"Bürkner, P.-C. (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1–28.","type":"article","doi":"10.18637/jss.v080.i01","isbn":null,"url":null}],"related":["generalized-additive-model","bayesian-generalized-linear-model","bayesian-mixed-effects-model","bayesian-multiple-linear-regression","gaussian-process-regression","spline-based-generalized-additive-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-generalized-linear-model","name":"Bayesian Generalized Linear Model","fullName":"Bayesian Generalized Linear Model","aliases":["Bayesian GLM","Bayesian GLIM","Bayesian generalized linear regression","Bayes GLM"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1989 (GLM); 1995 (Bayesian BDA)","originator":"McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.","url":"https://scholargate.app/en/statistics/bayesian-generalized-linear-model","markdownUrl":"https://scholargate.app/en/statistics/bayesian-generalized-linear-model.md","definition":"A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.","year":"1989 (GLM); 1995 (Bayesian BDA)","type":"Bayesian regression model","dataType":"Continuous, binary, count, or other exponential-family outcomes","subfamily":"Regression / GLM"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman & Hall.","type":"book","doi":null,"isbn":"978-0412317606","url":null}],"related":["generalized-linear-model","bayesian-logistic-regression","bayesian-poisson-regression","bayesian-multiple-linear-regression","bayesian-negative-binomial-regression","bayesian-probit-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-genetic-algorithm","name":"Bayesian Genetic Algorithm","fullName":"Bayesian Genetic Algorithm — Probabilistic model-guided evolutionary optimization","aliases":["BGA","Bayesian-guided GA","Probabilistic GA","EDA-GA"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1999","originator":"Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.","url":"https://scholargate.app/en/simulation/bayesian-genetic-algorithm","markdownUrl":"https://scholargate.app/en/simulation/bayesian-genetic-algorithm.md","definition":"A Bayesian Genetic Algorithm (BGA) replaces traditional crossover and mutation operators with a probabilistic Bayesian network learned from selected high-fitness individuals. At each generation the algorithm builds a graphical model of promising solution structure, then samples new offspring from that model, enabling the search to capture and exploit variable dependencies that standard GAs miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pelikan, M., Goldberg, D. E., & Cantu-Paz, E.","year":"1999","type":"Evolutionary metaheuristic with Bayesian probabilistic model","dataType":"Continuous, discrete, or mixed decision variables; fitness function evaluations","subfamily":"Simulation / optimization"},"citations":[{"ref":"Pelikan, M., Goldberg, D. E., & Cantu-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), pp. 525–532. Morgan Kaufmann.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=BOA+Bayesian+optimization+algorithm+Pelikan+Goldberg+Cantu-Paz+1999"},{"ref":"Larranaga, P., & Lozano, J. A. (Eds.) (2002). Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Boston.","type":"book","doi":null,"isbn":"9781461352747","url":null}],"related":["genetic-algorithm","bayesian-optimization","estimation-of-distribution-algorithm","particle-swarm-optimization","bayesian-multi-objective-optimization","stochastic-genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-geographically-weighted-regression","name":"Bayesian Geographically Weighted Regression","fullName":"Bayesian Geographically Weighted Regression","aliases":["BGWR","Bayesian GWR","Bayesian spatially varying coefficient model","Bayesian local regression"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2007","originator":"Wheeler & Calder (2007); Finley (2011)","url":"https://scholargate.app/en/spatial-analysis/bayesian-geographically-weighted-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-geographically-weighted-regression.md","definition":"Bayesian Geographically Weighted Regression combines the spatially varying coefficient framework of GWR with Bayesian inference, placing Gaussian process priors on the locally varying regression coefficients. This yields full posterior distributions over each coefficient at every location, providing principled uncertainty quantification rather than only point estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wheeler & Calder (2007); Finley (2011)","year":"2007","type":"Bayesian spatially varying coefficient regression","dataType":"Georeferenced / point-referenced or areal cross-sectional data","subfamily":"GIS / spatial"},"citations":[{"ref":"Finley, A. O. (2011). Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods in Ecology and Evolution, 2(2), 143-154.","type":"article","doi":"10.1111/j.2041-210X.2010.00060.x","isbn":null,"url":null},{"ref":"Wheeler, D., & Calder, C. (2007). An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. Journal of Geographical Systems, 9(2), 145-166.","type":"article","doi":"10.1007/s10109-006-0040-y","isbn":null,"url":null}],"related":["geographically-weighted-regression","multiscale-geographically-weighted-regression","bayesian-spatial-regression","spatial-lag-model","local-spatial-regression","kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-goal-programming","name":"Bayesian Goal Programming","fullName":"Bayesian Goal Programming","aliases":["BGP","Bayesian GP","Probabilistic Goal Programming","Bayesian Multi-Goal Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s","originator":"Rios Insua, D. and colleagues","url":"https://scholargate.app/en/simulation/bayesian-goal-programming","markdownUrl":"https://scholargate.app/en/simulation/bayesian-goal-programming.md","definition":"Bayesian Goal Programming (BGP) integrates Bayesian statistical inference with classic goal programming to handle uncertainty in targets and parameters. Instead of treating goal thresholds as fixed constants, BGP encodes them as probability distributions, updates beliefs using observed data, and then solves the resulting probabilistic optimization problem to find solutions that satisfy multiple aspirational goals under uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rios Insua, D. and colleagues","year":"1990s","type":"Multi-objective optimization under uncertainty","dataType":"Continuous decision variables, probabilistic goal targets","subfamily":"Simulation / optimization"},"citations":[{"ref":"Rios Insua, D. (1990). Sensitivity Analysis in Multi-objective Decision Making. Springer-Verlag, Berlin.","type":"book","doi":null,"isbn":"9783540528814","url":null},{"ref":"Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138-151.","type":"article","doi":"10.1287/mnsc.1.2.138","isbn":null,"url":null}],"related":["goal-programming","bayesian-multi-objective-optimization","stochastic-goal-programming","multi-objective-optimization","bayesian-dynamic-programming","robust-goal-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-granger-causality","name":"Bayesian Granger Causality","fullName":"Bayesian Granger Causality Analysis","aliases":["Bayesian Granger test","Bayesian predictive causality","BGC","Bayesian causality in mean"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1969 (frequentist); 1984 (Bayesian treatment)","originator":"Clive W. J. Granger (frequentist basis, 1969); Bayesian extension by Geweke (1984) and subsequent literature","url":"https://scholargate.app/en/econometrics/bayesian-granger-causality","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-granger-causality.md","definition":"Bayesian Granger causality tests whether past values of one time series carry predictive information about another, framing the hypothesis through Bayesian inference rather than frequentist p-values. It combines a vector autoregressive (VAR) structure with prior distributions over coefficients and evaluates causal claims via posterior probabilities or Bayes factors, providing a probabilistic and nuanced alternative to the classical Granger test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clive W. J. Granger (frequentist basis, 1969); Bayesian extension by Geweke (1984) and subsequent literature","year":"1969 (frequentist); 1984 (Bayesian treatment)","type":"Bayesian causal inference test","dataType":"Multivariate time series (stationary or cointegrated)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Geweke, J. (1984). Inference and causality in economic time series models. Handbook of Econometrics, 2, 1101-1144. Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Geweke+1984+Inference+and+causality+in+economic+time+series+models"},{"ref":"Granger causality. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Granger_causality"}],"related":["granger-causality-test","bayesian-var-model","toda-yamamoto-causality-test","vector-autoregression","bayesian-vecm","panel-granger-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-gwas-in-educational-research","name":"Bayesian genome-wide association study in educational research","fullName":"Bayesian Genome-Wide Association Study Applied to Educational Outcomes","aliases":["Bayesian GWAS","Bayesian GWAS for educational attainment","B-GWAS","Bayesian polygenic GWAS"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2013–2018 (educational attainment GWAS); Bayesian GWAS framework ~2001–2010","originator":"Social Science Genetic Association Consortium (SSGAC); Bayesian GWAS methods developed by Ter Braak, Meuwissen, and others","url":"https://scholargate.app/en/bioinformatics/bayesian-gwas-in-educational-research","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-gwas-in-educational-research.md","definition":"Bayesian genome-wide association study (Bayesian GWAS) applies Bayesian statistical models to millions of single-nucleotide polymorphisms (SNPs) to identify genetic variants associated with educational outcomes such as years of schooling or cognitive test scores. Unlike classical frequentist GWAS, Bayesian approaches assign prior distributions over effect sizes, enabling more principled handling of the polygenic architecture typical of educational traits, shrinkage of small effects, and direct posterior probability estimates for variant inclusion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Social Science Genetic Association Consortium (SSGAC); Bayesian GWAS methods developed by Ter Braak, Meuwissen, and others","year":"2013–2018 (educational attainment GWAS); Bayesian GWAS framework ~2001–2010","type":"Statistical genomics pipeline","dataType":"Genome-wide SNP array data (millions of variants) with continuous or binary educational phenotype","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., ... & Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121.","type":"article","doi":"10.1038/s41588-018-0147-3","isbn":null,"url":null},{"ref":"Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., ... & Koellinger, P. D. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science, 340(6139), 1467–1471.","type":"article","doi":"10.1126/science.1235488","isbn":null,"url":null}],"related":["polygenic-score-analysis","linear-mixed-model-gwas","mendelian-randomization","heritability-estimation","genome-wide-complex-trait-analysis","bayesian-variable-selection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-gwas","name":"Bayesian GWAS","fullName":"Bayesian Genome-Wide Association Study","aliases":["Bayesian GWAS","Bayesian genome-wide association analysis","Bayesian GWA study","BF-GWAS"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2007–2009 (formal statistical framework)","originator":"Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)","url":"https://scholargate.app/en/bioinformatics/bayesian-gwas","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-gwas.md","definition":"Bayesian GWAS applies Bayesian statistical inference to genome-wide association studies, replacing classical p-value thresholds with Bayes factors and posterior probabilities. This framework naturally incorporates prior knowledge about effect sizes and variant frequencies, quantifies evidence for association on a continuous scale, and supports principled fine-mapping of causal variants within associated loci. It is widely used in complex trait genetics, population genomics, and translational research where uncertainty quantification and multi-variant modeling matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matthew Stephens, David J. Balding, Jon Wakefield (key formalizers ca. 2007–2009)","year":"2007–2009 (formal statistical framework)","type":"Statistical genetic association analysis","dataType":"Genome-wide SNP genotype data, phenotype measurements (quantitative or binary), population reference panels","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Stephens, M., & Balding, D. J. (2009). Bayesian statistical methods for genetic association studies. Nature Reviews Genetics, 10(10), 681–690.","type":"article","doi":"10.1038/nrg2615","isbn":null,"url":null},{"ref":"Wakefield, J. (2009). Bayes factors for genome-wide association studies: comparison with P-values. Genetic Epidemiology, 33(1), 79–86.","type":"article","doi":"10.1002/gepi.20359","isbn":null,"url":null}],"related":["genome-wide-association-study","bayesian-eqtl-analysis","fine-mapping","polygenic-risk-score","bayesian-single-cell-rna-seq-analysis","pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-hausman-test","name":"Bayesian Hausman Test","fullName":"Bayesian Hausman Specification Test","aliases":["Bayesian specification test","Bayesian endogeneity test","Bayesian FE vs RE test","Bayesian Durbin-Wu-Hausman"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1978 (classical); Bayesian adaptations 1990s–2000s","originator":"Bayesian reformulation of Hausman (1978); developed across Bayesian econometrics literature","url":"https://scholargate.app/en/econometrics/bayesian-hausman-test","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-hausman-test.md","definition":"The Bayesian Hausman test is a Bayesian reformulation of Hausman's (1978) classical specification test, used to assess endogeneity or to choose between fixed effects and random effects panel models. Instead of a chi-squared test statistic, it uses posterior model probabilities or Bayes factors to compare competing specifications, fully incorporating prior uncertainty about model parameters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bayesian reformulation of Hausman (1978); developed across Bayesian econometrics literature","year":"1978 (classical); Bayesian adaptations 1990s–2000s","type":"Specification test / model comparison","dataType":"Panel or cross-sectional data with potential endogeneity","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null},{"ref":"Lancaster, T. (2004). An Introduction to Modern Bayesian Econometrics. Blackwell Publishing.","type":"book","doi":null,"isbn":"978-1405117203","url":null}],"related":["panel-hausman-test","bayesian-fixed-effects-model","bayesian-random-effects-model","bayesian-panel-data-analysis","fixed-effects-model","random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-hierarchical-clustering","name":"Bayesian Hierarchical Clustering","fullName":"Bayesian Hierarchical Clustering","aliases":["BHC","probabilistic hierarchical clustering","Bayesian agglomerative clustering"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2005","originator":"Katherine Heller & Zoubin Ghahramani","url":"https://scholargate.app/en/statistics/bayesian-hierarchical-clustering","markdownUrl":"https://scholargate.app/en/statistics/bayesian-hierarchical-clustering.md","definition":"Bayesian hierarchical clustering is a probabilistic agglomerative algorithm that builds a tree of nested cluster merges using Bayesian model comparison at each step. Rather than minimising a geometric linkage criterion, it evaluates at every candidate merge whether the data from two clusters are better explained by a single combined model or by two separate models, yielding a statistically principled dendrogram.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Katherine Heller & Zoubin Ghahramani","year":"2005","type":"Probabilistic clustering / model-based hierarchical agglomeration","dataType":"Continuous or mixed multivariate observations","subfamily":"Multivariate analysis"},"citations":[{"ref":"Heller, K. A. & Ghahramani, Z. (2005). Bayesian hierarchical clustering. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 297–304. ACM.","type":"inproceedings","doi":"10.1145/1102351.1102389","isbn":null,"url":null},{"ref":"Murtagh, F. & Legendre, P. (2014). Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion? Journal of Classification, 31(3), 274–295.","type":"article","doi":"10.1007/s00357-014-9161-z","isbn":null,"url":null}],"related":["hierarchical-clustering","bayesian-cluster-analysis","bayesian-latent-class-analysis","cluster-analysis","mixture-modeling","bayesian-mixture-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-hierarchical-linear-model","name":"Bayesian Hierarchical Linear Model","fullName":"Bayesian Hierarchical Linear Model","aliases":["Bayesian HLM","Bayesian multilevel linear model","Bayesian random-effects linear model","Bayes hierarchical regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"2006","originator":"Gelman & Hill (2006); Raudenbush & Bryk (2002) for frequentist HLM; Bayesian treatment consolidated by Gelman et al.","url":"https://scholargate.app/en/statistics/bayesian-hierarchical-linear-model","markdownUrl":"https://scholargate.app/en/statistics/bayesian-hierarchical-linear-model.md","definition":"The Bayesian Hierarchical Linear Model (Bayesian HLM) estimates linear relationships in nested or clustered data by placing prior distributions on all model parameters and updating them with observed data. It simultaneously models variation within groups and between groups, propagating uncertainty fully through posterior distributions rather than relying on asymptotic approximations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelman & Hill (2006); Raudenbush & Bryk (2002) for frequentist HLM; Bayesian treatment consolidated by Gelman et al.","year":"2006","type":"Bayesian multilevel linear model","dataType":"Continuous outcome, grouped / clustered observations","subfamily":"Regression / GLM"},"citations":[{"ref":"Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521686891","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["hierarchical-linear-model","multilevel-modeling","bayesian-mixed-effects-model","bayesian-multilevel-modeling","mixed-effects-model","bayesian-multiple-linear-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-hierarchical-model-with-missing-data","name":"Bayesian Hierarchical Model with Missing Data","fullName":"Bayesian Hierarchical Model with Missing Data Imputation","aliases":["BHM missing data","multilevel Bayesian missing data model","hierarchical Bayesian imputation","Bayesian multilevel model with incomplete data"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990s–2000s","originator":"Gelman, Rubin, Little (and collaborators)","url":"https://scholargate.app/en/bayesian/bayesian-hierarchical-model-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-hierarchical-model-with-missing-data.md","definition":"A Bayesian hierarchical model with missing data treats unobserved values as additional unknowns and samples them jointly with all model parameters from the posterior. The nested structure of the hierarchy borrows strength across groups, while the Bayesian framework naturally propagates uncertainty from missingness through every estimate and prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelman, Rubin, Little (and collaborators)","year":"1990s–2000s","type":"Bayesian hierarchical model with missing-data integration","dataType":"partially observed continuous, binary, or count data nested within groups","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471183860","url":null}],"related":["hierarchical-bayesian-inference","bayesian-inference-with-missing-data","gibbs-sampling-with-missing-data","mcmc-with-missing-data","multilevel-bayesian-inference","bayesian-hierarchical-model-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-hierarchical-model","name":"Bayesian Hierarchical Model","fullName":"Bayesian Hierarchical (Multilevel) Model","aliases":["multilevel Bayes","Bayesian multilevel model","Bayesian HLM","partial pooling model","Bayesian Hiyerarşik Model (Çok Düzeyli Bayes)"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2006,"originator":"Gelman & Hill (2006); Bayesian multilevel tradition","url":"https://scholargate.app/en/bayesian/bayesian-hierarchical-model","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-hierarchical-model.md","definition":"Bayesian hierarchical modelling, popularised by Gelman and Hill (2006), is a Bayesian approach to nested data structures — such as students within schools within districts — that estimates separate parameters at each level while allowing those levels to share statistical strength through a mechanism called partial pooling. Where a classical hierarchical linear model treats group means as fixed unknown quantities, the Bayesian version places hyperprior distributions on those group means so that information flows freely across levels, producing more reliable group-level estimates whenever any individual group has few observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelman & Hill (2006); Bayesian multilevel tradition","year":2006,"family":"Bayesian","type":"hierarchical probabilistic model","purpose":"predict / relationship / comparison","var_types":"continuous / binary / categorical","data_structure":"nested / longitudinal / panel","inference":"MCMC / variational","pooling":"partial pooling (shrinkage)","outputs":"posterior distributions / credible intervals / group-level estimates","difficulty":3,"min_sample":30},"citations":[{"ref":"Gelman, A. & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511790942","isbn":null,"url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["bayesian-regression","hierarchical-linear-model","mcmc","mixed-effects-model","partial-pooling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-hot-spot-analysis","name":"Bayesian Hot Spot Analysis","fullName":"Bayesian Hot Spot Analysis","aliases":["Bayesian spatial cluster detection","Bayesian disease mapping hot spots","empirical Bayesian hot spot analysis","Bayesian spatial smoothing hot spots"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1987","originator":"Clayton & Kaldor (1987); Lawson (2001 onward)","url":"https://scholargate.app/en/spatial-analysis/bayesian-hot-spot-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-hot-spot-analysis.md","definition":"Bayesian Hot Spot Analysis identifies spatial clusters of elevated risk or intensity by combining observed data with prior beliefs about spatial structure. It uses Bayesian smoothing — pooling information across neighboring areas — to stabilize estimates in small areas and then flags locations where the posterior probability of exceeding a risk threshold is high.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clayton & Kaldor (1987); Lawson (2001 onward)","year":"1987","type":"Bayesian spatial cluster detection","dataType":"Areal or point-referenced count/rate data","subfamily":"GIS / spatial"},"citations":[{"ref":"Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1138575424","url":null},{"ref":"Clayton, D., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43(3), 671-681.","type":"article","doi":"10.2307/2532003","isbn":null,"url":null}],"related":["hot-spot-analysis","local-getis-ord-gi-star","bayesian-spatial-autocorrelation","bayesian-local-indicators-of-spatial-association","kernel-density-estimation","local-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-hypothesis-testing-research","name":"Bayesian Hypothesis Testing Research","fullName":"Bayesian Hypothesis Testing Research Design","aliases":["Bayesian significance testing","Bayes factor hypothesis testing","BHT research","Bayesian inference testing"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010","originator":"Harold Jeffreys (formal Bayes factor framework)","url":"https://scholargate.app/en/research-design/bayesian-hypothesis-testing-research","markdownUrl":"https://scholargate.app/en/research-design/bayesian-hypothesis-testing-research.md","definition":"Bayesian hypothesis testing research is a quantitative design in which competing hypotheses are evaluated by updating prior beliefs with observed data to produce posterior probabilities and Bayes factors. Unlike frequentist null-hypothesis significance testing, it quantifies the relative evidence for each hypothesis, supports optional stopping, and allows accumulation of evidence across studies without inflating Type I error rates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Jeffreys (formal Bayes factor framework)","year":"1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010","type":"Quantitative research design","dataType":"Numeric / continuous / categorical measured variables","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198503682","url":null},{"ref":"Wagenmakers, E.-J. (2010). A practical solution to the pervasive problems of p values. Psychonomic Bulletin and Review, 14(5), 779–804.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+practical+solution+to+the+pervasive+problems+of+p+values+Wagenmakers"}],"related":["bayesian-confirmatory-research","hypothesis-testing-research","bayesian-correlational-research","confirmatory-research","bayesian-model-testing-research","bayesian-survey-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-independent-samples-t-test","name":"Bayesian Independent Samples t-test","fullName":"Bayesian Independent Samples t-test","aliases":["Bayesian two-sample t-test","Bayes factor t-test","JZS t-test","Bayesian unpaired t-test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"2009 (modern form); 1961 (Jeffreys prior framework)","originator":"Harold Jeffreys (foundational); operationalized by Rouder et al.","url":"https://scholargate.app/en/statistics/bayesian-independent-samples-t-test","markdownUrl":"https://scholargate.app/en/statistics/bayesian-independent-samples-t-test.md","definition":"The Bayesian independent samples t-test quantifies evidence for or against a mean difference between two independent groups using a Bayes factor rather than a p-value. Rooted in Jeffreys's probability framework and popularized by Rouder et al. (2009), it places a Cauchy prior on the standardized effect size and returns continuous evidence for both the null and alternative hypotheses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Jeffreys (foundational); operationalized by Rouder et al.","year":"2009 (modern form); 1961 (Jeffreys prior framework)","type":"Bayesian hypothesis test","dataType":"Continuous, two independent groups","subfamily":"Classical statistics"},"citations":[{"ref":"Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237.","type":"article","doi":"10.3758/PBR.16.2.225","isbn":null,"url":null},{"ref":"Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198503682","url":null}],"related":["independent-samples-t-test","bayesian-paired-samples-t-test","bayesian-one-sample-t-test","mann-whitney-u-test","bayesian-one-way-anova","welch-corrected-independent-samples-t-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-inference-with-measurement-error","name":"Bayesian Inference with Measurement Error","fullName":"Bayesian Inference with Measurement Error (Errors-in-Variables)","aliases":["Bayesian errors-in-variables model","Bayesian EIV model","Bayesian measurement error model","Bayesian misclassification model"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993","originator":"Richardson & Gilks (Bayesian formulation); Carroll et al. (comprehensive framework)","url":"https://scholargate.app/en/bayesian/bayesian-inference-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-inference-with-measurement-error.md","definition":"Bayesian inference with measurement error extends the standard Bayesian framework to situations where one or more covariates or outcomes are observed with noise or misclassification. By treating the true unobserved values as latent variables and assigning them priors, the model jointly estimates the true exposure distribution and the structural parameters of interest, propagating all uncertainty through the posterior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richardson & Gilks (Bayesian formulation); Carroll et al. (comprehensive framework)","year":"1993","type":"Bayesian errors-in-variables model","dataType":"Continuous or categorical covariates observed with error; replicate or validation data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584886433","url":null},{"ref":"Richardson, S., & Gilks, W. R. (1993). A Bayesian approach to measurement error problems in epidemiology using conditional independence models. American Journal of Epidemiology, 138(6), 430–442.","type":"article","doi":"10.1093/oxfordjournals.aje.a116875","isbn":null,"url":null}],"related":["bayesian-regression","hierarchical-bayesian-inference","mcmc","structural-equation-modeling","kalman-filter","bayesian-hierarchical-model-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-inference-with-missing-data","name":"Bayesian Inference with Missing Data","fullName":"Bayesian Inference with Missing Data","aliases":["Bayesian missing data analysis","Bayesian data augmentation","Bayesian imputation","missing data Bayesian model"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1976–1987","originator":"Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)","url":"https://scholargate.app/en/bayesian/bayesian-inference-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-inference-with-missing-data.md","definition":"Bayesian inference with missing data treats unobserved values as unknown parameters and integrates them out of the posterior distribution. Rather than deleting or ad hoc imputing incomplete records, the method jointly models observed and missing data under an explicit missing-data mechanism, producing fully calibrated posterior uncertainty that honestly reflects what the data cannot tell us.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)","year":"1976–1987","type":"Bayesian probabilistic model","dataType":"any data type with partially observed variables","subfamily":"Bayesian / computational"},"citations":[{"ref":"Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471183860","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["bayesian-regression","gibbs-sampling","hierarchical-bayesian-inference","mcmc-with-missing-data","bayesian-hierarchical-model-with-missing-data","approximate-bayesian-computation-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-inference","name":"Bayesian Inference","fullName":"Bayesian Statistical Inference","aliases":["Bayes inference","Bayesian statistics","Bayesian updating","posterior inference","Bayesian data analysis"],"domain":"statistics","family":"bayesian","subfamily":null,"year":1763,"originator":"Thomas Bayes; Pierre-Simon Laplace","url":"https://scholargate.app/en/statistics/bayesian-inference","markdownUrl":"https://scholargate.app/en/statistics/bayesian-inference.md","definition":"Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Bayes; Pierre-Simon Laplace","year":1763,"family":"Bayesian","type":"Probabilistic inference paradigm","parametric":true,"distribution":"Posterior (conjugate, MCMC, or variational)","keyQuantity":"Posterior distribution p(θ | y)","updateRule":"Bayes' theorem"},"citations":[{"ref":"Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+essay+towards+solving+a+problem+in+the+doctrine+of+chances+Bayes"},{"ref":"Laplace, P.-S. (1812). Théorie analytique des probabilités. Courcier, Paris.","type":"book","doi":null,"isbn":null,"url":"https://gallica.bnf.fr/ark:/12148/btv1b8625611h"},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["mcmc-sampling","hierarchical-model","bayesian-linear-regression","prior-predictive-check","hypothesis-test-bayesian-factor","independent-t-test","maximum-likelihood-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-information-criterion","name":"Bayesian Information Criterion","fullName":"Bayesian Information Criterion","aliases":["BIC","Schwarz criterion","Schwarz information criterion"],"domain":"model-evaluation","family":"mcdm","subfamily":"Information-theoretic criterion","year":"1978","originator":"Gideon E. Schwarz","url":"https://scholargate.app/en/model-evaluation/bayesian-information-criterion","markdownUrl":"https://scholargate.app/en/model-evaluation/bayesian-information-criterion.md","definition":"The Bayesian Information Criterion is an information-theoretic model selection criterion that approximates Bayesian model comparison. Introduced by Gideon Schwarz in 1978, BIC penalizes model complexity more heavily than AIC by using a sample-size-dependent penalty, making it particularly suitable for identifying the true underlying model structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gideon E. Schwarz","subfamily":"Information-theoretic criterion","year":"1978","type":"Bayesian model selection metric"},"citations":[{"ref":"Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464.","type":"article","doi":"10.1214/aos/1176344136","isbn":null,"url":null},{"ref":"Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.). New York: Springer.","type":"book","doi":"10.2307/3802723","isbn":null,"url":null},{"ref":"Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773-795.","type":"article","doi":"10.1080/01621459.1995.10476572","isbn":null,"url":null}],"related":["akaike-information-criterion","r-squared","adjusted-r-squared","mean-squared-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-instrumental-variables","name":"Bayesian Instrumental Variables","fullName":"Bayesian Instrumental Variables Estimation","aliases":["Bayesian IV","Bayesian 2SLS","Bayesian LIML","BayesIV"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2003","originator":"Kleibergen & Zivot (2003); Lancaster (2004)","url":"https://scholargate.app/en/causal-inference/bayesian-instrumental-variables","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-instrumental-variables.md","definition":"Bayesian Instrumental Variables combines the instrumental variable strategy for addressing endogeneity with Bayesian posterior inference. Instead of relying on asymptotic sampling distributions, it places prior distributions over all structural parameters and recovers a full posterior distribution for the causal effect, providing probability statements about the parameter rather than p-values — especially valuable when instruments are weak or the sample is small.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kleibergen & Zivot (2003); Lancaster (2004)","year":"2003","type":"Causal inference / Bayesian estimation","dataType":"Cross-sectional or panel; continuous or binary endogenous regressor","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Kleibergen, F., & Zivot, E. (2003). Bayesian and classical approaches to instrumental variable regression. Journal of Econometrics, 114(1), 29-72.","type":"article","doi":"10.1016/s0304-4076(02)00219-1","isbn":null,"url":null},{"ref":"Lancaster, T. (2004). An Introduction to Modern Bayesian Econometrics. Blackwell Publishing.","type":"book","doi":null,"isbn":"978-1405117203","url":null}],"related":["instrumental-variables","bayesian-regression","two-stage-least-squares","difference-in-differences","propensity-score-matching","bayesian-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-integer-programming","name":"Bayesian Integer Programming","fullName":"Bayesian Integer Programming — Probabilistic Prior-Guided Combinatorial Optimization","aliases":["BIP","Bayesian combinatorial optimization","Bayesian discrete optimization","probabilistic integer programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Baptiste, Lassagne, Nuijten and others in Bayesian optimization community","url":"https://scholargate.app/en/simulation/bayesian-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/bayesian-integer-programming.md","definition":"Bayesian Integer Programming (BIP) integrates Bayesian probabilistic reasoning with integer programming to solve combinatorial optimization problems under uncertainty. Instead of treating parameters as fixed, it encodes prior beliefs about uncertain coefficients and updates them with observed data, producing a posterior-guided search over integer-feasible solutions. The approach is widely used in scheduling, resource allocation, and supply-chain planning where data are incomplete or noisy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baptiste, Lassagne, Nuijten and others in Bayesian optimization community","year":"1990s–2000s","type":"Probabilistic combinatorial optimization","dataType":"Discrete/integer decision variables with uncertain parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Baptiste, P., Lassagne, I., & Nuijten, W. (2001). Bayesian reasoning in mixed integer programming. European Journal of Operational Research, 130(2), 293–313.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+reasoning+mixed+integer+programming+Baptiste+2001"},{"ref":"Bayesian optimization. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Bayesian_optimization"}],"related":["bayesian-mixed-integer-programming","stochastic-integer-programming","bayesian-linear-programming","mixed-integer-programming","bayesian-multi-objective-optimization","robust-integer-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-inverse-probability-weighting","name":"Bayesian Inverse Probability Weighting","fullName":"Bayesian Inverse Probability Weighting Estimator","aliases":["Bayesian IPW","BIPW","Bayesian propensity-weighted estimation","Bayesian marginal structural weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015","originator":"Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020)","url":"https://scholargate.app/en/causal-inference/bayesian-inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-inverse-probability-weighting.md","definition":"Bayesian Inverse Probability Weighting (Bayesian IPW) extends the classical IPW estimator by placing prior distributions over the propensity-score model parameters and propagating that uncertainty into the causal-effect estimate. The result is a posterior distribution for the average treatment effect that fully accounts for both propensity-score estimation uncertainty and outcome-model uncertainty, enabling credible-interval inference rather than relying on asymptotic approximations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020)","year":"2015","type":"Bayesian causal weighting estimator","dataType":"Cross-sectional or panel observational data with binary or multi-valued treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On risk prediction and characterisation of treatment effects in a Bayesian framework using the propensity score. Statistics in Medicine, 34(14), 2170-2185.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+risk+prediction+and+characterisation+of+treatment+effects+in+a+Bayesian+framework+using+the+propensity+score+Saarela"},{"ref":"Liao, S. X., & Zigler, C. M. (2020). Uncertainty in the design stage of two-stage Bayesian propensity score analysis. Statistics in Medicine, 39(17), 2265-2290.","type":"article","doi":"10.1002/sim.8486","isbn":null,"url":null}],"related":["inverse-probability-weighting","propensity-score-weighting","bayesian-propensity-score-matching","marginal-structural-model","doubly-robust-estimation","bayesian-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-item-analysis","name":"Bayesian Item Analysis","fullName":"Bayesian Item Analysis","aliases":["BIA","Bayesian classical item analysis","Bayesian item statistics","Bayesian item-level diagnostics"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Originated in Bayesian psychometrics literature, developed extensively by Jean-Paul Fox and colleagues","url":"https://scholargate.app/en/psychometrics/bayesian-item-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-item-analysis.md","definition":"Bayesian item analysis applies Bayesian inference to estimate item-level statistics — difficulty, discrimination, and distractor effectiveness — by combining observed response data with prior knowledge. It produces full posterior distributions over item parameters rather than single point estimates, providing richer uncertainty information especially with small samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Originated in Bayesian psychometrics literature, developed extensively by Jean-Paul Fox and colleagues","year":"1990s–2000s","type":"Bayesian inference / item-level diagnostics","dataType":"Dichotomous or polytomous item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Fox, J.-P. (2010). Bayesian Item Response Modeling: Theory and Applications. Springer.","type":"book","doi":"10.1007/978-1-4419-0742-4","isbn":null,"url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["item-response-theory","bayesian-reliability-analysis","bayesian-confirmatory-factor-analysis","differential-item-functioning","cronbachs-alpha","scale-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-k-means-clustering","name":"Bayesian K-means clustering","fullName":"Bayesian K-means Clustering","aliases":["Bayesian K-means","probabilistic K-means","Dirichlet K-means","BKM"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2006–2012","originator":"Kulis & Jordan (ICML 2012) formalized the Bayesian nonparametric derivation; Bishop (2006) established the variational Bayesian EM framework for Gaussian mixture models as a probabilistic foundation","url":"https://scholargate.app/en/statistics/bayesian-k-means-clustering","markdownUrl":"https://scholargate.app/en/statistics/bayesian-k-means-clustering.md","definition":"Bayesian K-means clustering extends the classical K-means algorithm by placing prior distributions over cluster centroids and mixing proportions. This probabilistic framework provides uncertainty estimates for cluster assignments, allows principled model selection for the number of clusters, and regularises centroid estimation — especially valuable when data are scarce or high-dimensional.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kulis & Jordan (ICML 2012) formalized the Bayesian nonparametric derivation; Bishop (2006) established the variational Bayesian EM framework for Gaussian mixture models as a probabilistic foundation","year":"2006–2012","type":"Probabilistic clustering / Bayesian nonparametric","dataType":"Continuous multivariate","subfamily":"Multivariate analysis"},"citations":[{"ref":"Kulis, B. & Jordan, M. I. (2012). Revisiting k-means: New algorithms via Bayesian nonparametrics. In Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, pp. 513–520.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1111.0352"},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Chapter 9 (Mixture models and EM) and Chapter 10 (Approximate Inference).","type":"book","doi":null,"isbn":"978-0387310732","url":null}],"related":["cluster-analysis","bayesian-cluster-analysis","mixture-modeling","bayesian-mixture-modeling","bayesian-hierarchical-clustering","latent-class-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-k-nearest-neighbors","name":"Bayesian k-nearest neighbors","fullName":"Bayesian k-Nearest Neighbors Classifier","aliases":["Bayesian KNN","BKNN","probabilistic k-nearest neighbors","Bayesian nearest-neighbor classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2002","originator":"Holmes, C. C. & Adams, N. M.","url":"https://scholargate.app/en/machine-learning/bayesian-k-nearest-neighbors","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-k-nearest-neighbors.md","definition":"Bayesian k-Nearest Neighbors (Bayesian KNN) extends the classical KNN algorithm by placing a prior distribution over the neighborhood size k and combining likelihood evidence from neighbors with that prior to produce calibrated posterior class probabilities. It retains KNN's intuitive instance-based logic while adding principled uncertainty quantification over predictions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holmes, C. C. & Adams, N. M.","year":"2002","type":"Probabilistic instance-based classifier","dataType":"Tabular (continuous or mixed features)","subfamily":"Machine learning"},"citations":[{"ref":"Holmes, C. C., & Adams, N. M. (2002). A probabilistic nearest neighbour method for statistical pattern recognition. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2), 295–306.","type":"article","doi":"10.1111/1467-9868.00338","isbn":null,"url":null},{"ref":"K-nearest neighbors algorithm. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm"}],"related":["k-nearest-neighbors","naive-bayes","gaussian-process-classification","logistic-regression","random-forest","support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-kaplan-meier-analysis","name":"Bayesian Kaplan-Meier analysis","fullName":"Bayesian Nonparametric Kaplan-Meier Survival Analysis","aliases":["Bayesian survival curve estimation","Bayesian nonparametric survival analysis","Dirichlet process Kaplan-Meier","BKM"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1976","originator":"Susarla & Van Ryzin (Bayesian nonparametric survival estimation)","url":"https://scholargate.app/en/epidemiology/bayesian-kaplan-meier-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-kaplan-meier-analysis.md","definition":"Bayesian Kaplan-Meier analysis extends the classical Kaplan-Meier estimator by placing a prior distribution over the survival function and updating it with observed time-to-event data to obtain a full posterior distribution for the survival curve. This approach, rooted in Susarla and Van Ryzin's 1976 Dirichlet-process framework, yields credible intervals rather than confidence intervals and enables coherent incorporation of prior clinical knowledge, making it particularly valuable in small-sample or early-phase clinical settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Susarla & Van Ryzin (Bayesian nonparametric survival estimation)","year":"1976","type":"Bayesian nonparametric survival analysis","dataType":"Time-to-event data with right-censored observations","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Susarla, V., & Van Ryzin, J. (1976). Nonparametric Bayesian estimation of survival curves from incomplete observations. Journal of the American Statistical Association, 71(356), 897–902.","type":"article","doi":"10.1080/01621459.1976.10480966","isbn":null,"url":null},{"ref":"Diaconis, P., & Freedman, D. (1986). On the consistency of Bayes estimates. The Annals of Statistics, 14(1), 1–26.","type":"article","doi":"10.1214/aos/1176349830","isbn":null,"url":null}],"related":["kaplan-meier-analysis","bayesian-survival-analysis","cox-proportional-hazards","competing-risks-analysis","survival-analysis","bayesian-cox-proportional-hazards"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-kernel-density-estimation","name":"Bayesian Kernel Density Estimation","fullName":"Bayesian Kernel Density Estimation","aliases":["Bayesian KDE","BKDE","Bayesian nonparametric density estimation","Bayesian adaptive KDE"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1995","originator":"Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics","url":"https://scholargate.app/en/spatial-analysis/bayesian-kernel-density-estimation","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-kernel-density-estimation.md","definition":"Bayesian Kernel Density Estimation (BKDE) is a nonparametric method for estimating the probability density function of a spatial or attribute variable by combining a kernel smoother with a Bayesian prior over the bandwidth parameter. The posterior distribution of the bandwidth propagates uncertainty into the final density estimate rather than treating the bandwidth as a fixed tuning constant.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics","year":"1995","type":"Nonparametric density estimation","dataType":"Continuous spatial or attribute data (point patterns, areal counts)","subfamily":"GIS / spatial"},"citations":[{"ref":"Hjort, N. L., & Glad, I. K. (1995). Nonparametric density estimation with a parametric start. The Annals of Statistics, 23(3), 882–904.","type":"article","doi":"10.1214/aos/1176324627","isbn":null,"url":null},{"ref":"Kernel density estimation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Kernel_density_estimation"}],"related":["kernel-density-estimation","bayesian-spatial-regression","hot-spot-analysis","spatial-autocorrelation","local-kriging","bayesian-kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-knowledge-graph-analysis","name":"Bayesian Knowledge Graph Analysis","fullName":"Bayesian Knowledge Graph Analysis (Probabilistic Inference over Knowledge Graphs)","aliases":["Bayesian KG analysis","probabilistic knowledge graph reasoning","Bayesian knowledge base completion","BKGA"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010s","originator":"Nickel, M.; Murphy, K.; Tresp, V.; Gabrilovich, E. (and related Bayesian KG literature, 2010s)","url":"https://scholargate.app/en/network-analysis/bayesian-knowledge-graph-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-knowledge-graph-analysis.md","definition":"Bayesian knowledge graph analysis applies probabilistic Bayesian inference to knowledge graphs — structured representations of entities and their relations — to reason under uncertainty, complete missing links, and quantify confidence in inferred facts. It treats unknown graph edges as random variables and updates beliefs about them given observed relational evidence, making it especially suited to incomplete or noisy knowledge bases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nickel, M.; Murphy, K.; Tresp, V.; Gabrilovich, E. (and related Bayesian KG literature, 2010s)","year":"2010s","type":"Probabilistic graph inference","dataType":"Relational triples (subject, predicate, object); entity-relation graphs","subfamily":"Network science"},"citations":[{"ref":"Chen, M., Zhang, W., Zhang, W., Chen, Q., & Chen, H. (2020). Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs. Proceedings of EMNLP 2020.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Meta+Relational+Learning+Few-Shot+Link+Prediction+Knowledge+Graphs+Chen+2020"},{"ref":"Knowledge graph. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Knowledge_graph"}],"related":["knowledge-graph-analysis","bayesian-network","exponential-random-graph-model","bayesian-exponential-random-graph-model","bayesian-stochastic-block-model","multilayer-knowledge-graph-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-kriging","name":"Bayesian Kriging","fullName":"Bayesian Kriging (Model-Based Geostatistics)","aliases":["Bayesian geostatistics","model-based geostatistics","Bayesian spatial interpolation","stochastic kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1993–1998","originator":"Diggle, Tawn & Moyeed; Handcock & Stein","url":"https://scholargate.app/en/spatial-analysis/bayesian-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-kriging.md","definition":"Bayesian Kriging embeds classical geostatistical interpolation inside a full probabilistic framework. Instead of treating variogram parameters as fixed point estimates, it places prior distributions on them and updates these priors with observed spatial data to obtain a posterior distribution. Predictions at unsampled locations are then marginalised over this uncertainty, yielding honest predictive intervals that account for both spatial dependence and parameter uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diggle, Tawn & Moyeed; Handcock & Stein","year":"1993–1998","type":"Bayesian spatial interpolation","dataType":"Georeferenced continuous observations (point-referenced spatial data)","subfamily":"GIS / spatial"},"citations":[{"ref":"Diggle, P. J., Tawn, J. A., & Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(3), 299–350.","type":"article","doi":"10.1111/1467-9876.00113","isbn":null,"url":null},{"ref":"Handcock, M. S., & Stein, M. L. (1993). A Bayesian analysis of kriging. Technometrics, 35(4), 403–410.","type":"article","doi":"10.1080/00401706.1993.10485354","isbn":null,"url":null}],"related":["ordinary-kriging","universal-kriging","co-kriging","spatial-autocorrelation","gaussian-process-regression","bayesian-spatial-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-lasso-regression","name":"Bayesian LASSO Regression","fullName":"Bayesian Least Absolute Shrinkage and Selection Operator Regression","aliases":["Bayesian LASSO","Bayesian L1 regression","double-exponential prior regression","Laplace prior regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"2008","originator":"Park & Casella","url":"https://scholargate.app/en/statistics/bayesian-lasso-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-lasso-regression.md","definition":"Bayesian LASSO regression places double-exponential (Laplace) priors on regression coefficients, which is the Bayesian analogue of the classical LASSO penalty. It simultaneously shrinks small coefficients toward zero and performs soft variable selection, all within a coherent posterior inference framework that naturally quantifies parameter uncertainty through credible intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Park & Casella","year":"2008","type":"Bayesian regularized regression","dataType":"Continuous outcome, continuous and/or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681–686.","type":"article","doi":"10.1198/016214508000000337","isbn":null,"url":null},{"ref":"Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.","type":"article","doi":"10.1111/j.2517-6161.1996.tb02080.x","isbn":null,"url":null}],"related":["lasso-regression","bayesian-ridge-regression","bayesian-elastic-net-regression","bayesian-multiple-linear-regression","elastic-net-regression","ridge-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-latent-class-analysis","name":"Bayesian Latent Class Analysis","fullName":"Bayesian Latent Class Analysis","aliases":["Bayesian LCA","BLCA","Bayesian mixture of multinomials","Bayesian finite mixture model"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1990s–2000s","originator":"Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)","url":"https://scholargate.app/en/statistics/bayesian-latent-class-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-latent-class-analysis.md","definition":"Bayesian latent class analysis extends classical LCA by placing prior distributions on all model parameters and using posterior inference — typically via MCMC — to classify individuals into unobserved categorical groups, quantify uncertainty around class membership, and select the number of classes in a principled, probabilistic way.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)","year":"1990s–2000s","type":"Bayesian latent variable / finite mixture model","dataType":"Multivariate categorical (binary or polytomous) indicators","subfamily":"Multivariate analysis"},"citations":[{"ref":"Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051.","type":"article","doi":"10.1198/jasa.2009.tm08439","isbn":null,"url":null},{"ref":"White, A. & Murphy, T. B. (2016). BayesLCA: An R package for Bayesian latent class analysis. Journal of Statistical Software, 61(13), 1–28.","type":"article","doi":"10.18637/jss.v061.i13","isbn":null,"url":null}],"related":["latent-class-analysis","bayesian-mixture-modeling","bayesian-confirmatory-factor-analysis","mixture-modeling","bayesian-cluster-analysis","latent-profile-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-lightgbm","name":"Bayesian LightGBM","fullName":"LightGBM with Bayesian Hyperparameter Optimization","aliases":["Bayesian-tuned LightGBM","LightGBM + Bayesian optimization","BayesOpt LightGBM","LightGBM with BayesOpt"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017 (LightGBM); 2012 (Bayesian optimization)","originator":"Ke et al. (LightGBM); Snoek et al. (Bayesian optimization)","url":"https://scholargate.app/en/machine-learning/bayesian-lightgbm","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-lightgbm.md","definition":"Bayesian LightGBM combines LightGBM — a highly efficient histogram-based gradient boosting framework — with Bayesian hyperparameter optimization. Instead of exhaustive grid search or random search, a probabilistic surrogate model guides the search for optimal hyperparameters, dramatically reducing the number of costly model evaluations needed to reach strong predictive performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ke et al. (LightGBM); Snoek et al. (Bayesian optimization)","year":"2017 (LightGBM); 2012 (Bayesian optimization)","type":"Gradient boosting with Bayesian hyperparameter search","dataType":"Tabular (continuous, categorical, binary, ordinal)","subfamily":"Machine learning"},"citations":[{"ref":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems, 30, 3146–3154.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html"},{"ref":"Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems, 25, 2951–2959.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html"}],"related":["lightgbm","xgboost","bayesian-xgboost","gradient-boosting","bayesian-gradient-boosting","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-linear-programming","name":"Bayesian Linear Programming","fullName":"Bayesian Linear Programming — Bayesian inference integrated with linear programming under parameter uncertainty","aliases":["BLP","Bayesian LP","Bayesian stochastic linear programming","prior-posterior LP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1970s–1980s","originator":"Integrated from Dantzig (LP) and Zellner/Bayesian econometrics traditions","url":"https://scholargate.app/en/simulation/bayesian-linear-programming","markdownUrl":"https://scholargate.app/en/simulation/bayesian-linear-programming.md","definition":"Bayesian Linear Programming (BLP) integrates Bayesian statistical inference with classical linear programming to handle uncertainty in model parameters such as objective function coefficients, constraint coefficients, or right-hand-side values. Instead of treating parameters as fixed or governed by worst-case bounds, BLP uses prior beliefs updated by data to form posterior distributions, which then guide the LP formulation and solution, producing decisions that are optimal in a probabilistic, data-informed sense.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Integrated from Dantzig (LP) and Zellner/Bayesian econometrics traditions","year":"1970s–1980s","type":"Optimization under Bayesian uncertainty","dataType":"Continuous or mixed numerical data with uncertain parameters (costs, RHS, constraint coefficients)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780691059136","url":null},{"ref":"Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley, New York.","type":"book","doi":null,"isbn":"9780471169376","url":null}],"related":["stochastic-linear-programming","robust-linear-programming","bayesian-dynamic-programming","bayesian-mixed-integer-programming","multi-objective-linear-programming","deterministic-linear-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-linear-regression","name":"Bayesian Linear Regression","fullName":"Bayesian Linear Regression","aliases":["bayesian linear model","probabilistic linear regression","Bayesçi Doğrusal Regresyon"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":"2013 (modern reference); foundations 18th–19th century","originator":"Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.","url":"https://scholargate.app/en/bayesian/bayesian-linear-regression","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-linear-regression.md","definition":"Bayesian linear regression is a probabilistic extension of the ordinary linear model, introduced through Bayes' rule and formalised in its modern computational workflow by Gelman et al. (2013). Rather than returning a single point estimate for each coefficient, it combines a user-specified prior distribution with the likelihood of the observed data to produce a full posterior distribution over all parameters, from which credible intervals and posterior predictive distributions are derived.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.","year":"2013 (modern reference); foundations 18th–19th century","family":"Bayesian","type":"Bayesian linear model","purpose":"prediction / relationship","var_types":"continuous, categorical, binary (outcome continuous)","structures":"cross-sectional, panel","min_sample":10,"difficulty":3,"inference":"MCMC / variational inference / conjugate closed-form","outputs":"posterior distributions, credible intervals, posterior predictive distributions"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["bayesian-regression","ols-regression","mcmc","hierarchical-bayes","bayesian-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-local-indicators-of-spatial-association","name":"Bayesian Local Indicators of Spatial Association","fullName":"Bayesian Local Indicators of Spatial Association","aliases":["Bayesian LISA","Bayesian local spatial autocorrelation","Bayesian local Moran","B-LISA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2000s–2010s","originator":"Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand)","url":"https://scholargate.app/en/spatial-analysis/bayesian-local-indicators-of-spatial-association","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-local-indicators-of-spatial-association.md","definition":"Bayesian Local Indicators of Spatial Association extend the classical LISA framework by embedding local spatial association statistics within a Bayesian hierarchical model. Rather than relying on asymptotic permutation-based significance tests, this approach places prior distributions on spatial parameters and derives posterior probabilities that a location is part of a genuine spatial cluster, accounting for uncertainty and borrowing strength across nearby units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand)","year":"2000s–2010s","type":"Bayesian local spatial statistic","dataType":"Georeferenced areal or point data with a continuous or count outcome","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null},{"ref":"Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584884101","url":null}],"related":["local-indicators-of-spatial-association","local-morans-i","bayesian-spatial-autocorrelation","local-getis-ord-gi-star","local-gearys-c","spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-logistic-regression","name":"Bayesian Logistic Regression","fullName":"Bayesian Logistic Regression","aliases":["bayesian binary logistic regression","bayesian classification model","Bayesian Lojistik Regresyon"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2008,"originator":"Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)","url":"https://scholargate.app/en/bayesian/bayesian-logistic-regression","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-logistic-regression.md","definition":"Bayesian logistic regression is a classification model that applies Bayesian inference to a logistic (sigmoid) likelihood for binary or multinomial outcomes. Developed within the weakly-informative prior framework formalised by Gelman, Jakulin, Pittau and Su (2008), it places a prior distribution over the coefficients and combines that prior with the data likelihood to yield a full posterior distribution for each parameter — delivering calibrated class probabilities and honest uncertainty even in small samples, rare-event settings, or cases of complete separation where frequentist maximum likelihood estimation collapses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)","year":2008,"family":"Bayesian","type":"Bayesian classification model","purpose":"classify / predict / relationship","outcome_types":"binary / categorical","predictors":"continuous / binary / categorical","inference":"MCMC / variational","outputs":"posterior distributions / credible intervals / class probabilities","min_sample":20,"difficulty":"intermediate"},"citations":[{"ref":"Gelman, A., Jakulin, A., Pittau, M. G. & Su, Y.-S. (2008). A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models. Annals of Applied Statistics, 2(4), 1360–1383.","type":"article","doi":"10.1214/08-AOAS191","isbn":null,"url":null}],"related":["bayesian-regression","logistic-regression","mcmc","hierarchical-bayes","probit-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ma-model","name":"Bayesian MA model","fullName":"Bayesian Moving Average Model","aliases":["Bayesian MA","Bayesian moving average","BMA time series","MA model with Bayesian estimation"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970s–1997","originator":"Bayesian framework applied to Box-Jenkins MA models; West & Harrison (1997) canonical treatment","url":"https://scholargate.app/en/econometrics/bayesian-ma-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-ma-model.md","definition":"The Bayesian MA model estimates a moving average time series model within a fully Bayesian framework, placing prior distributions on the MA parameters and error variance and updating them via Bayes' theorem. This approach yields full posterior distributions over model parameters and produces probabilistic forecasts with coherent uncertainty quantification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bayesian framework applied to Box-Jenkins MA models; West & Harrison (1997) canonical treatment","year":"1970s–1997","type":"Bayesian time series model","dataType":"univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"West, M., & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387947259","url":null},{"ref":"Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimating+regression+models+of+finite+but+unknown+order+Geweke+Meese+1981"}],"related":["moving-average-model","bayesian-arma-model","bayesian-arima-model","bayesian-var-model","arima-model","bayesian-ar-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-mann-whitney-u-test","name":"Bayesian Mann-Whitney U test","fullName":"Bayesian Mann-Whitney U Test","aliases":["Bayesian rank-sum test","Bayesian Wilcoxon rank-sum test","Bayesian nonparametric two-sample test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"2020 (Bayesian formulation); 1947 (classical test)","originator":"van Doorn, Ly, Marsman, Wagenmakers (building on Mann & Whitney 1947)","url":"https://scholargate.app/en/statistics/bayesian-mann-whitney-u-test","markdownUrl":"https://scholargate.app/en/statistics/bayesian-mann-whitney-u-test.md","definition":"The Bayesian Mann-Whitney U test is a nonparametric Bayesian procedure for comparing two independent groups when data are ordinal or non-normal continuous. Instead of a binary reject/fail-to-reject decision, it quantifies the relative evidence for the null and alternative hypotheses through a Bayes factor, allowing researchers to conclude in favour of either hypothesis or express uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"van Doorn, Ly, Marsman, Wagenmakers (building on Mann & Whitney 1947)","year":"2020 (Bayesian formulation); 1947 (classical test)","type":"Bayesian nonparametric two-sample test","dataType":"Ordinal or continuous; two independent groups","subfamily":"Classical statistics"},"citations":[{"ref":"van Doorn, J., Ly, A., Marsman, M., & Wagenmakers, E.-J. (2020). Bayesian rank-based hypothesis testing for the rank sum test, the signed rank test, and Spearman's rho. Journal of Applied Statistics, 47(16), 2984–3006.","type":"article","doi":"10.1080/02664763.2019.1709053","isbn":null,"url":null},{"ref":"Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60.","type":"article","doi":"10.1214/aoms/1177730491","isbn":null,"url":null}],"related":["mann-whitney-u-test","bayesian-wilcoxon-signed-rank-test","bayesian-independent-samples-t-test","bayesian-kruskal-wallis-test","wilcoxon-signed-rank-test","independent-samples-t-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-manova","name":"Bayesian MANOVA","fullName":"Bayesian Multivariate Analysis of Variance","aliases":["Bayesian MANOVA","Bayesian multivariate ANOVA","BF-MANOVA","Bayesian multivariate group comparison"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1970s–2010s","originator":"Bayesian framework applied to MANOVA; foundational multivariate Bayesian work by Dickey (1974) and Rouder et al. (2012)","url":"https://scholargate.app/en/statistics/bayesian-manova","markdownUrl":"https://scholargate.app/en/statistics/bayesian-manova.md","definition":"Bayesian Multivariate Analysis of Variance (Bayesian MANOVA) extends the classical MANOVA framework by replacing null-hypothesis significance testing with Bayesian inference. It uses prior distributions on multivariate group means and covariance structures, updates them with data to yield posterior distributions, and quantifies evidence through Bayes factors rather than p-values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bayesian framework applied to MANOVA; foundational multivariate Bayesian work by Dickey (1974) and Rouder et al. (2012)","year":"1970s–2010s","type":"Bayesian multivariate group comparison","dataType":"Multiple continuous dependent variables, one or more categorical independent variables","subfamily":"Classical statistics"},"citations":[{"ref":"Olkin, I., & Rubin, H. (1964). Multivariate beta distributions and independence properties of the Wishart distribution. The Annals of Mathematical Statistics, 35(1), 261–269.","type":"article","doi":"10.1214/aoms/1177703748","isbn":null,"url":null},{"ref":"Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374.","type":"article","doi":"10.1016/j.jmp.2012.08.001","isbn":null,"url":null}],"related":["manova","bayesian-ancova","bayesian-one-way-anova","bayesian-repeated-measures-anova","bayesian-independent-samples-t-test","multivariate-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-marginal-structural-model","name":"Bayesian Marginal Structural Model","fullName":"Bayesian Marginal Structural Model with Inverse Probability Weighting","aliases":["Bayesian MSM","Bayesian MSM-IPW","Bayesian weighted structural model","Bayesian causal MSM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015 (Bayesian extension); 2000 (MSM foundation)","originator":"Saarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM)","url":"https://scholargate.app/en/causal-inference/bayesian-marginal-structural-model","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-marginal-structural-model.md","definition":"Bayesian Marginal Structural Model (Bayesian MSM) combines the causal identification power of inverse-probability-weighted marginal structural models with Bayesian posterior inference. Rather than relying on point estimates and asymptotic standard errors, it propagates uncertainty through a full posterior distribution over causal effect parameters, offering coherent uncertainty quantification for causal effects of time-varying treatments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM)","year":"2015 (Bayesian extension); 2000 (MSM foundation)","type":"Causal inference / Bayesian weighted regression","dataType":"Longitudinal or panel data with time-varying treatments","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288.","type":"article","doi":"10.1111/biom.12269","isbn":null,"url":null},{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null}],"related":["marginal-structural-model","inverse-probability-weighting","bayesian-instrumental-variables","bayesian-difference-in-differences","doubly-robust-estimation","propensity-score-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-markov-model","name":"Bayesian Markov Model","fullName":"Bayesian Markov Model — State-Transition Modeling with Bayesian Parameter Estimation","aliases":["Bayesian Markov Chain Model","Bayesian State-Transition Model","BMM","Bayesian Cohort Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Briggs, A.; Sculpher, M.; and broader Bayesian statistics community","url":"https://scholargate.app/en/simulation/bayesian-markov-model","markdownUrl":"https://scholargate.app/en/simulation/bayesian-markov-model.md","definition":"A Bayesian Markov model is a state-transition simulation method that combines Markov chain cohort modeling with Bayesian statistical inference. By placing prior distributions on transition probabilities and updating them with observed data, the approach propagates full parameter uncertainty through the simulation, yielding posterior distributions over outcomes such as costs, life-years, or quality-adjusted life-years rather than single-point estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Briggs, A.; Sculpher, M.; and broader Bayesian statistics community","year":"1990s–2000s","type":"Probabilistic state-transition simulation","dataType":"Transition probabilities, event counts, costs, utilities, prior distributions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford.","type":"book","doi":null,"isbn":"9780198526629","url":null},{"ref":"Jackson, C. H., Sharples, L. D., Thompson, S. G. (2010). Structural and parameter uncertainty in Bayesian cost-effectiveness models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59(2), 233-253.","type":"article","doi":"10.1111/j.1467-9876.2009.00684.x","isbn":null,"url":null}],"related":["markov-model","monte-carlo-simulation","bayesian-sensitivity-analysis","stochastic-markov-model","health-economic-modeling","probabilistic-sensitivity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-matching-estimator","name":"Bayesian Matching Estimator","fullName":"Bayesian Matching Estimator for Average Treatment Effects","aliases":["Bayesian matching","Bayesian nonparametric matching","Bayes-ATE matching","posterior matching estimator"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1978–1998","originator":"Donald B. Rubin (Bayesian causal framework); extended by Heckman, Ichimura & Todd (matching estimator formalization)","url":"https://scholargate.app/en/causal-inference/bayesian-matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-matching-estimator.md","definition":"The Bayesian Matching Estimator estimates average treatment effects in observational studies by combining classical nearest-neighbour or kernel matching with a Bayesian posterior over the treatment effect. It inherits matching's covariate-balancing logic while propagating uncertainty through a full posterior distribution rather than relying on asymptotic standard errors, yielding credible intervals that reflect both sampling variability and prior knowledge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald B. Rubin (Bayesian causal framework); extended by Heckman, Ichimura & Todd (matching estimator formalization)","year":"1978–1998","type":"Bayesian causal inference / nonparametric matching","dataType":"Observational cross-sectional or panel data with binary treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. The Annals of Statistics, 6(1), 34-58.","type":"article","doi":"10.1214/aos/1176344064","isbn":null,"url":null},{"ref":"Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. Review of Economic Studies, 65(2), 261-294.","type":"article","doi":"10.1111/1467-937X.00044","isbn":null,"url":null}],"related":["matching-estimator","propensity-score-matching","bayesian-propensity-score-matching","bayesian-difference-in-differences","doubly-robust-estimation","entropy-balancing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-mcdonalds-omega","name":"Bayesian McDonald's omega","fullName":"Bayesian Estimation of McDonald's Omega Reliability Coefficient","aliases":["Bayesian omega","Bayesian composite reliability","posterior omega","Bayesian omega total"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1999 (omega); 2010s (Bayesian estimation)","originator":"R. P. McDonald (omega); Bayesian extension developed by Kelley, Pornprasertmanit, and others","url":"https://scholargate.app/en/psychometrics/bayesian-mcdonalds-omega","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-mcdonalds-omega.md","definition":"Bayesian McDonald's omega applies Bayesian statistical estimation to the omega reliability coefficient, yielding a full posterior distribution over omega rather than a single point estimate. This provides credible intervals and probabilistic uncertainty quantification for the reliability of a composite or scale score, making it especially useful for small samples and complex factor structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. P. McDonald (omega); Bayesian extension developed by Kelley, Pornprasertmanit, and others","year":"1999 (omega); 2010s (Bayesian estimation)","type":"Reliability / internal consistency estimation","dataType":"Ordinal or continuous item-level responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Kelley, K. & Pornprasertmanit, S. (2016). Confidence intervals for population reliability coefficients: Evaluation of methods, recommendations, and software for composite measures. Psychological Methods, 21(1), 69–92.","type":"article","doi":"10.1037/a0040086","isbn":null,"url":null},{"ref":"McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805830750","url":null}],"related":["mcdonalds-omega","cronbachs-alpha","bayesian-cronbachs-alpha","bayesian-confirmatory-factor-analysis","bayesian-reliability-analysis","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-measurement-invariance","name":"Bayesian Measurement Invariance","fullName":"Bayesian Measurement Invariance Testing","aliases":["Bayesian MI","approximate measurement invariance","Bayesian multigroup CFA invariance","BSEM measurement invariance"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2013","originator":"Bengt Muthen, Tihomir Asparouhov, Rens Van de Schoot","url":"https://scholargate.app/en/psychometrics/bayesian-measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-measurement-invariance.md","definition":"Bayesian measurement invariance testing evaluates whether a scale's factor loadings and item intercepts are equivalent across groups, using a Bayesian framework that allows parameters to deviate from strict equality by a small, probabilistically specified amount rather than imposing an exact constraint.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengt Muthen, Tihomir Asparouhov, Rens Van de Schoot","year":"2013","type":"Bayesian multigroup latent variable test","dataType":"Ordinal or continuous item-level data across groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Van de Schoot, R., Kluytmans, A., Tummers, L., Lugtig, P., Hox, J., & Muthen, B. (2013). Facing off with Scylla and Charybdis: a comparison of scalar, partial, and the novel possibility of approximate measurement invariance. Frontiers in Psychology, 4, 770.","type":"article","doi":"10.3389/fpsyg.2013.00770","isbn":null,"url":null},{"ref":"Muthen, B., & Asparouhov, T. (2013). BSEM measurement invariance analysis. Mplus Web Notes: No. 17.","type":"article","doi":null,"isbn":null,"url":"https://www.statmodel.com/download/MeasInvariance.pdf"}],"related":["confirmatory-factor-analysis","measurement-invariance","multi-group-confirmatory-factor-analysis","bayesian-confirmatory-factor-analysis","differential-item-functioning","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-metabolomics-analysis","name":"Bayesian Metabolomics Analysis","fullName":"Bayesian Statistical Methods for Metabolomics Data Analysis","aliases":["Bayesian metabolomics","probabilistic metabolomics","Bayesian metabolite profiling","Bayesian metabolic flux analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2005–2010","originator":"Simon Rogers, Mark Girolami and colleagues (Bayesian NMR metabolomics framework, ~2009); broader Bayesian metabolomics developed through 2000s–2010s","url":"https://scholargate.app/en/bioinformatics/bayesian-metabolomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-metabolomics-analysis.md","definition":"Bayesian metabolomics analysis applies probabilistic inference to metabolite abundance data — typically from mass spectrometry or NMR spectroscopy — to identify differentially abundant metabolites, annotate spectral features, and integrate pathway knowledge. By encoding prior biological knowledge into prior distributions and propagating uncertainty throughout the analysis, it yields more calibrated probability statements about metabolic differences than classical frequentist testing alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Simon Rogers, Mark Girolami and colleagues (Bayesian NMR metabolomics framework, ~2009); broader Bayesian metabolomics developed through 2000s–2010s","year":"2005–2010","type":"Probabilistic statistical pipeline","dataType":"Mass spectrometry (LC-MS, GC-MS) or NMR spectroscopy peak tables; metabolite intensity matrices","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rogers, S., Scheltema, R. A., & Girolami, M. A. (2009). Bayesian analysis of metabolomic NMR data. Bioinformatics, 25(14), 1809-1815.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+analysis+of+metabolomic+NMR+data+Rogers+Scheltema+Girolami+2009"},{"ref":"Saccenti, E., Hoefsloot, H. C., Smilde, A. K., Westerhuis, J. A., & Hendriks, M. M. (2014). Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics, 10(3), 361-374.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Reflections+univariate+multivariate+analysis+metabolomics+data+Saccenti+2014"}],"related":["metabolomics-analysis","bayesian-pathway-enrichment-analysis","bayesian-gene-set-enrichment-analysis","multi-omics-metabolomics-analysis","pathway-enrichment-analysis","rna-seq-differential-expression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-metric-learning","name":"Bayesian Metric Learning","fullName":"Bayesian Metric Learning (Probabilistic Distance Function Learning)","aliases":["BML","probabilistic metric learning","Bayesian distance metric learning","Bayesian similarity learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010s","originator":"Multiple (Xing et al. 2002; Weinberger & Saul 2009; probabilistic extensions by various authors ~2010s)","url":"https://scholargate.app/en/machine-learning/bayesian-metric-learning","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-metric-learning.md","definition":"Bayesian Metric Learning frames the problem of learning a task-adapted distance function as probabilistic inference. Rather than producing a single optimal metric matrix, it places a prior over metrics, updates it with pairwise similarity or label constraints, and yields a posterior distribution that quantifies uncertainty about which metric best captures the true structure of the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (Xing et al. 2002; Weinberger & Saul 2009; probabilistic extensions by various authors ~2010s)","year":"2010s","type":"Probabilistic distance metric learning","dataType":"Labeled or pairwise-constrained numerical / embedding data","subfamily":"Machine learning"},"citations":[{"ref":"Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10, 207–244.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v10/weinberger09a.html"},{"ref":"Metric learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Metric_learning"}],"related":["metric-learning","gaussian-process","bayesian-gaussian-process","k-nearest-neighbors","few-shot-learning","bayesian-few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-microbiome-diversity-analysis","name":"Bayesian Microbiome Diversity Analysis","fullName":"Bayesian Statistical Analysis of Microbiome Diversity","aliases":["Bayesian microbiome profiling","Dirichlet-Multinomial microbiome analysis","Bayesian alpha/beta diversity","probabilistic microbiome diversity"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s (Dirichlet-Multinomial approach formalized ~2012; extensions ongoing)","originator":"Ian Holmes, Katie Harris, Christopher Quince (Dirichlet-Multinomial Mixture framework, 2012); broader Bayesian microbiome modeling community","url":"https://scholargate.app/en/bioinformatics/bayesian-microbiome-diversity-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-microbiome-diversity-analysis.md","definition":"Bayesian microbiome diversity analysis applies probabilistic models — chiefly Dirichlet-Multinomial and related hierarchical frameworks — to 16S rRNA or shotgun metagenomic count data to estimate alpha-diversity (within-sample richness and evenness) and beta-diversity (between-sample compositional differences) while propagating uncertainty through the entire inference chain. Unlike frequentist rarefaction-based approaches, Bayesian methods treat taxon counts as draws from a latent composition, enabling credible intervals on diversity metrics and principled comparison across groups with unequal sequencing depth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ian Holmes, Katie Harris, Christopher Quince (Dirichlet-Multinomial Mixture framework, 2012); broader Bayesian microbiome modeling community","year":"2010s (Dirichlet-Multinomial approach formalized ~2012; extensions ongoing)","type":"Probabilistic/Bayesian pipeline for compositional count data","dataType":"16S rRNA amplicon OTU/ASV count tables, shotgun metagenomics count tables, phylogenetic trees","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Holmes, I., Harris, K., & Quince, C. (2012). Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics. PLOS ONE, 7(2), e30126.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1371/journal.pone.0030126"},{"ref":"La Rosa, P. S., Brooks, J. P., Deych, E., Boone, E. L., Edwards, D. J., Wang, Q., Sodergren, E., Weinstock, G., & Shannon, W. D. (2012). Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data. PLOS ONE, 7(12), e52078.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1371/journal.pone.0052078"}],"related":["microbiome-diversity-analysis","bayesian-metabolomics-analysis","gene-set-enrichment-analysis","phylogenetic-analysis","bayesian-phylogenetic-analysis","network-based-microbiome-diversity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-microsimulation","name":"Bayesian Microsimulation","fullName":"Bayesian Microsimulation — Probabilistic individual-level simulation with Bayesian parameter estimation","aliases":["Bayesian micro-simulation","BMS","Bayesian individual-level simulation","Probabilistic microsimulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Williamson, P.; Birkin, M.; Rees, P. H. and related health-economics researchers","url":"https://scholargate.app/en/simulation/bayesian-microsimulation","markdownUrl":"https://scholargate.app/en/simulation/bayesian-microsimulation.md","definition":"Bayesian Microsimulation combines individual-level simulation of heterogeneous populations with Bayesian statistical inference. Each synthetic individual follows a probabilistic life path, while model parameters are governed by prior beliefs updated with observed data. This approach is widely used in health technology assessment, public policy costing, and demographic projection, where uncertainty in both model inputs and structural assumptions must be formally quantified and propagated through to output estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Williamson, P.; Birkin, M.; Rees, P. H. and related health-economics researchers","year":"1990s–2000s","type":"Individual-level probabilistic simulation with Bayesian updating","dataType":"Individual or household microdata; prior distributions; survey or administrative records","subfamily":"Simulation / optimization"},"citations":[{"ref":"Williamson, P., Birkin, M., & Rees, P. H. (2000). The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A, 30(5), 785-816.","type":"article","doi":"10.1068/a300785","isbn":null,"url":null},{"ref":"Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. John Wiley & Sons.","type":"article","doi":null,"isbn":"9780471499756","url":null}],"related":["microsimulation","bayesian-inference","monte-carlo-simulation","markov-model","stochastic-microsimulation","agent-based-microsimulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-mixed-effects-model","name":"Bayesian Mixed Effects Model","fullName":"Bayesian Mixed Effects Model","aliases":["Bayesian multilevel model","Bayesian random effects model","Bayesian LME","Bayesian hierarchical mixed model"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1990s–2000s (modern Bayesian MCMC era)","originator":"Gelman, Hill, and the broader Bayesian hierarchical modeling tradition","url":"https://scholargate.app/en/statistics/bayesian-mixed-effects-model","markdownUrl":"https://scholargate.app/en/statistics/bayesian-mixed-effects-model.md","definition":"The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for both population-level and group-level effects simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelman, Hill, and the broader Bayesian hierarchical modeling tradition","year":"1990s–2000s (modern Bayesian MCMC era)","type":"Bayesian regression model","dataType":"Continuous, grouped, or longitudinal data with fixed and random effects","subfamily":"Regression / GLM"},"citations":[{"ref":"Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521686891","url":null},{"ref":"Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1–48.","type":"article","doi":"10.18637/jss.v067.i01","isbn":null,"url":null}],"related":["mixed-effects-model","hierarchical-linear-model","multilevel-modeling","bayesian-hierarchical-linear-model","bayesian-multilevel-modeling","bayesian-generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-mixed-integer-programming","name":"Bayesian Mixed-Integer Programming","fullName":"Bayesian Mixed-Integer Programming — Surrogate-Assisted Optimization over Mixed-Integer Search Spaces","aliases":["Bayesian MIP","BO-MIP","Bayesian Combinatorial Optimization","Mixed-Integer Bayesian Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2018 (surrogate-BO-MIP synthesis); MIP foundations 1958","originator":"Baptista, R. & Poloczek, M. (formal Bayesian-BO-MIP formulation); mixed-integer programming roots in Gomory (1958)","url":"https://scholargate.app/en/simulation/bayesian-mixed-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/bayesian-mixed-integer-programming.md","definition":"Bayesian Mixed-Integer Programming (BO-MIP) couples a probabilistic surrogate model — typically a Gaussian process — with a mixed-integer programming solver to efficiently optimize expensive black-box objectives defined over spaces that contain both continuous and discrete or integer-valued decision variables. It is especially valuable when each function evaluation is costly and exhaustive search is infeasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baptista, R. & Poloczek, M. (formal Bayesian-BO-MIP formulation); mixed-integer programming roots in Gomory (1958)","year":"2018 (surrogate-BO-MIP synthesis); MIP foundations 1958","type":"Surrogate-assisted combinatorial optimization","dataType":"Objective function evaluations (possibly black-box), integer and continuous decision variables, optional constraint matrices","subfamily":"Simulation / optimization"},"citations":[{"ref":"Baptista, R., Poloczek, M. (2018). Bayesian Optimization of Combinatorial Structures. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80:462–471.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v80/baptista18a.html"},{"ref":"Bonami, P., Biegler, L. T., Conn, A. R., Cornuejols, G., Grossmann, I. E., Laird, C. D., Lee, J., Lodi, A., Margot, F., Sawaya, N., Wächter, A. (2008). An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optimization, 5(2), 186–204.","type":"article","doi":"10.1016/j.disopt.2006.10.011","isbn":null,"url":null}],"related":["mixed-integer-programming","bayesian-optimization","stochastic-mixed-integer-programming","multi-objective-mixed-integer-programming","robust-mixed-integer-programming","nsga-ii"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-mixture-modeling","name":"Bayesian Mixture Modeling","fullName":"Bayesian Finite Mixture Modeling","aliases":["Bayesian mixture model","BMM","Bayesian model-based clustering","Bayesian finite mixture"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1997 (Richardson & Green Bayesian formulation)","originator":"Richardson & Green (seminal Bayesian treatment, 1997); broader Bayesian mixture roots trace to Dempster, Laird & Rubin (EM, 1977) and Titterington, Smith & Makov (1985)","url":"https://scholargate.app/en/statistics/bayesian-mixture-modeling","markdownUrl":"https://scholargate.app/en/statistics/bayesian-mixture-modeling.md","definition":"Bayesian mixture modeling represents the population as a weighted sum of K component distributions and estimates all unknowns — mixing weights, component parameters, and even the number of components — through posterior inference. It extends classical mixture analysis by placing priors on every parameter and quantifying uncertainty over latent group assignments rather than treating them as fixed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richardson & Green (seminal Bayesian treatment, 1997); broader Bayesian mixture roots trace to Dempster, Laird & Rubin (EM, 1977) and Titterington, Smith & Makov (1985)","year":"1997 (Richardson & Green Bayesian formulation)","type":"Latent-class / model-based clustering","dataType":"Continuous, binary, count, or mixed multivariate observations","subfamily":"Multivariate analysis"},"citations":[{"ref":"Fruhwirth-Schnatter, S., Celeux, G. & Robert, C. P. (Eds.) (2019). Handbook of Mixture Analysis. CRC Press / Chapman & Hall.","type":"article","doi":null,"isbn":"9780367733995","url":null},{"ref":"Richardson, S. & Green, P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society: Series B, 59(4), 731–792.","type":"article","doi":"10.1111/1467-9868.00095","isbn":null,"url":null}],"related":["mixture-modeling","latent-class-analysis","bayesian-latent-class-analysis","bayesian-latent-profile-analysis","bayesian-cluster-analysis","bayesian-structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-model-averaging-with-measurement-error","name":"Bayesian Model Averaging with Measurement Error","fullName":"Bayesian Model Averaging with Measurement Error Correction","aliases":["BMA-ME","BMA with errors-in-variables","Bayesian model averaging errors-in-covariates","measurement error BMA"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1999–2006","originator":"Hoeting, Madigan, Raftery, Volinsky (BMA); Carroll, Stefanski and colleagues (ME correction)","url":"https://scholargate.app/en/bayesian/bayesian-model-averaging-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-model-averaging-with-measurement-error.md","definition":"Bayesian model averaging with measurement error (BMA-ME) combines two probabilistic ideas: it averages predictions across competing regression models weighted by each model's posterior probability, while simultaneously accounting for the fact that one or more predictors are observed with random error rather than exactly. The result is a posterior that propagates both model uncertainty and covariate measurement noise into every inference and prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hoeting, Madigan, Raftery, Volinsky (BMA); Carroll, Stefanski and colleagues (ME correction)","year":"1999–2006","type":"Bayesian ensemble model with covariate error correction","dataType":"continuous or binary outcomes with mismeasured predictors","subfamily":"Bayesian / computational"},"citations":[{"ref":"Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-417.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+model+averaging%3A+A+tutorial+Hoeting"},{"ref":"Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1584886334","url":null}],"related":["bayesian-model-averaging","bayesian-regression","errors-in-variables-regression","hierarchical-bayes","mcmc","bayesian-variable-selection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-model-averaging-with-missing-data","name":"Bayesian model averaging with missing data","fullName":"Bayesian Model Averaging with Missing Data","aliases":["BMA with missing data","Bayesian model averaging under missingness","BMA-MI","model-averaged imputation"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1999 (BMA seminal); 2000s (missing-data extensions)","originator":"Hoeting, Madigan, Raftery, Volinsky (BMA); extended to missing data by Raftery, Madigan and others","url":"https://scholargate.app/en/bayesian/bayesian-model-averaging-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-model-averaging-with-missing-data.md","definition":"Bayesian Model Averaging with missing data (BMA-MD) simultaneously addresses two sources of uncertainty: which model best describes the data, and what the unobserved values are. Rather than selecting a single imputed dataset and a single model, the approach averages predictions across the full space of candidate models and plausible completions of the missing values, propagating both sources of uncertainty into every estimate and prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hoeting, Madigan, Raftery, Volinsky (BMA); extended to missing data by Raftery, Madigan and others","year":"1999 (BMA seminal); 2000s (missing-data extensions)","type":"Bayesian ensemble inference under incomplete data","dataType":"Continuous, binary, or categorical outcomes with partially observed predictors or outcomes","subfamily":"Bayesian / computational"},"citations":[{"ref":"Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-417.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+model+averaging%3A+A+tutorial+Hoeting"},{"ref":"Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons, New York.","type":"book","doi":null,"isbn":"978-0471655749","url":null}],"related":["bayesian-model-averaging","multiple-imputation","bayesian-inference-with-missing-data","bayesian-hierarchical-model-with-missing-data","approximate-bayesian-computation-with-missing-data","sequential-monte-carlo-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-model-averaging","name":"Bayesian Model Averaging","fullName":"Bayesian Model Averaging","aliases":["BMA","Bayesian model combination","Bayesian Model Ortalaması (BMA)"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":1999,"originator":"Hoeting, Madigan, Raftery & Volinsky","url":"https://scholargate.app/en/bayesian/bayesian-model-averaging","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-model-averaging.md","definition":"Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hoeting, Madigan, Raftery & Volinsky","year":1999,"family":"Bayesian","type":"Bayesian model averaging","purpose":"predict / relationship / explore","var_types":"continuous / binary","structures":"cross-sectional / panel / time-series","min_sample":50,"inference":"MCMC / exact enumeration","outputs":"posterior inclusion probabilities / model-averaged coefficients / BMA predictions","difficulty":3},"citations":[{"ref":"Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+Model+Averaging%3A+A+Tutorial+Hoeting"},{"ref":"Zeugner, S. & Feldkircher, M. (2015). Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R. Journal of Statistical Software, 68(4), 1–37.","type":"article","doi":"10.18637/jss.v068.i04","isbn":null,"url":null}],"related":["bayesian-regression","lasso-regression","elastic-net","bayesian-hierarchical-model","mcmc"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-model-testing-research","name":"Bayesian Model Testing Research","fullName":"Bayesian Model Testing Research Design","aliases":["Bayesian hypothesis testing","Bayesian model comparison","Bayes factor analysis","BMT"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward","originator":"Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery","url":"https://scholargate.app/en/research-design/bayesian-model-testing-research","markdownUrl":"https://scholargate.app/en/research-design/bayesian-model-testing-research.md","definition":"Bayesian model testing research is a quantitative design in which competing theoretical models or hypotheses are evaluated by comparing their marginal likelihoods given observed data. The central tool is the Bayes factor — a ratio that quantifies how much more likely the data are under one model than under another. Unlike null-hypothesis significance testing, Bayesian model testing yields direct evidence for or against specific hypotheses, incorporates prior knowledge, and can support a null hypothesis rather than merely failing to reject it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery","year":"1935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward","type":"Quantitative inferential research design","dataType":"Continuous, categorical, or count outcome data; requires specification of competing theoretical models","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.","type":"article","doi":"10.1080/01621459.1995.10476572","isbn":null,"url":null},{"ref":"Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198503682","url":null}],"related":["bayesian-inference","structural-equation-modeling","confirmatory-factor-analysis","multilevel-modeling","meta-analysis","null-hypothesis-significance-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-moderated-mediation","name":"Bayesian Moderated Mediation","fullName":"Bayesian Moderated Mediation Analysis","aliases":["Bayesian conditional process analysis","Bayesian mediated moderation","Bayesian PROCESS model","Bayesian conditional indirect effect"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2009–2013","originator":"Yuan & MacKinnon (Bayesian mediation); Hayes (conditional process framework)","url":"https://scholargate.app/en/statistics/bayesian-moderated-mediation","markdownUrl":"https://scholargate.app/en/statistics/bayesian-moderated-mediation.md","definition":"Bayesian moderated mediation estimates how a mediator transmits the effect of a predictor onto an outcome, and whether that indirect effect varies in size depending on a moderator variable — all within a Bayesian framework that quantifies uncertainty via posterior distributions rather than p-values and confidence intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yuan & MacKinnon (Bayesian mediation); Hayes (conditional process framework)","year":"2009–2013","type":"Conditional indirect effect model","dataType":"Continuous / ordinal observed variables","subfamily":"Multivariate analysis"},"citations":[{"ref":"Yuan, Y. & MacKinnon, D. P. (2009). Bayesian mediation analysis. Psychological Methods, 14(4), 301–322.","type":"article","doi":"10.1037/a0016972","isbn":null,"url":null},{"ref":"Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. Guilford Press.","type":"book","doi":null,"isbn":"978-1609182304","url":null}],"related":["moderated-mediation","bayesian-mediation-analysis","bayesian-moderation-analysis","structural-equation-modeling","bayesian-structural-equation-modeling","path-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-moderation-analysis","name":"Bayesian Moderation Analysis","fullName":"Bayesian Moderation Analysis","aliases":["Bayesian interaction analysis","Bayesian moderated regression","Bayesian moderator testing","BMA"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2000s–2010s","originator":"Bayesian framework applied to moderation by Kruschke, Gelman and colleagues","url":"https://scholargate.app/en/statistics/bayesian-moderation-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-moderation-analysis.md","definition":"Bayesian moderation analysis tests whether the relationship between a predictor and an outcome changes depending on the value of a third variable (the moderator). By placing prior distributions on all model parameters and updating them with observed data, it yields full posterior distributions for the interaction effect — enabling direct probability statements about the moderation rather than binary significance decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bayesian framework applied to moderation by Kruschke, Gelman and colleagues","year":"2000s–2010s","type":"Interaction / moderator test","dataType":"Continuous, ordinal, or categorical predictors and outcomes","subfamily":"Multivariate analysis"},"citations":[{"ref":"Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462534654","url":null},{"ref":"Kruschke, J. K. (2015). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2nd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0124058880","url":null}],"related":["bayesian-mediation-analysis","bayesian-structural-equation-modeling","bayesian-path-analysis","moderated-mediation","bayesian-moderated-mediation","multilevel-moderation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-monte-carlo-simulation","name":"Bayesian Monte Carlo Simulation","fullName":"Bayesian Monte Carlo Simulation — Prior-informed stochastic sampling for uncertainty quantification","aliases":["Bayesian MC","BMC simulation","Bayesian stochastic simulation","Bayesian uncertainty propagation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1987–1990s","originator":"O'Hagan, A. and colleagues","url":"https://scholargate.app/en/simulation/bayesian-monte-carlo-simulation","markdownUrl":"https://scholargate.app/en/simulation/bayesian-monte-carlo-simulation.md","definition":"Bayesian Monte Carlo Simulation integrates Bayesian statistical inference with Monte Carlo sampling to propagate uncertainty through complex models. Instead of drawing samples from arbitrary distributions, it conditions sampling on observed data and expert prior knowledge via Bayes' theorem, yielding posterior-based uncertainty estimates that are both statistically coherent and interpretable in probabilistic terms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"O'Hagan, A. and colleagues","year":"1987–1990s","type":"Simulation / uncertainty quantification","dataType":"Continuous numerical data, expert elicited priors, probability distributions","subfamily":"Simulation / optimization"},"citations":[{"ref":"O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Wiley.","type":"article","doi":null,"isbn":"9780470029992","url":null},{"ref":"O'Hagan, A. (1987). Monte Carlo is fundamentally unsound. The Statistician, 36(2-3), 247-249.","type":"article","doi":"10.2307/2348519","isbn":null,"url":null}],"related":["monte-carlo-simulation","stochastic-monte-carlo-simulation","markov-chain-monte-carlo","bayesian-sensitivity-analysis","bayesian-system-dynamics","probabilistic-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-morans-i","name":"Bayesian Moran's I","fullName":"Bayesian Moran's I Spatial Autocorrelation Test","aliases":["Bayesian spatial autocorrelation test","Bayesian Moran statistic","Moran's I under Bayesian inference","Bayesian global spatial association"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1950 / 2000s","originator":"Moran (1950), Bayesian extension developed in spatial statistics literature (late 1990s–2000s)","url":"https://scholargate.app/en/spatial-analysis/bayesian-morans-i","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-morans-i.md","definition":"Bayesian Moran's I embeds the classical Moran's I spatial autocorrelation test within a Bayesian probabilistic framework. Rather than producing a single p-value, it yields a posterior distribution over the spatial autocorrelation parameter, enabling uncertainty quantification, incorporation of prior knowledge, and more principled inference in small or irregular spatial datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moran (1950), Bayesian extension developed in spatial statistics literature (late 1990s–2000s)","year":"1950 / 2000s","type":"Bayesian spatial autocorrelation test","dataType":"Georeferenced areal or point data with a spatial weights matrix","subfamily":"GIS / spatial"},"citations":[{"ref":"Haining, R. (2003). Spatial Data Analysis: Theory and Practice. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521774611","url":null},{"ref":"Lee, S.-I. (2001). Developing a bivariate spatial association measure: an integration of Pearson's r and Moran's I. Journal of Geographical Systems, 3(4), 369–385.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Developing+a+bivariate+spatial+association+measure+Lee+2001"}],"related":["morans-i","local-morans-i","bayesian-spatial-autocorrelation","gearys-c","bayesian-spatial-regression","local-indicators-of-spatial-association"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-multi-objective-optimization","name":"Bayesian Multi-Objective Optimization","fullName":"Bayesian Multi-Objective Optimization (BMOO) — Surrogate-assisted Pareto frontier exploration under uncertainty","aliases":["BMOO","Bayesian MOO","Multi-objective Bayesian optimization","MOBO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2006-2016","originator":"Emmerich, M.; Svenson, J.; and related Gaussian process optimization community","url":"https://scholargate.app/en/simulation/bayesian-multi-objective-optimization","markdownUrl":"https://scholargate.app/en/simulation/bayesian-multi-objective-optimization.md","definition":"Bayesian Multi-Objective Optimization (BMOO/MOBO) uses Gaussian process surrogate models to approximate multiple expensive objective functions and guides the search toward the Pareto frontier with minimal real evaluations. By quantifying prediction uncertainty at each candidate point, it balances exploration of unknown regions against exploitation of promising solutions, making it especially powerful when each function evaluation is computationally or experimentally costly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emmerich, M.; Svenson, J.; and related Gaussian process optimization community","year":"2006-2016","type":"Surrogate-model-assisted multi-objective optimizer","dataType":"Continuous or mixed numerical parameters; expensive black-box objective functions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Svenson, J., Santner, T. (2016). Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models. Computational Statistics & Data Analysis, 94, 250-264.","type":"article","doi":"10.1016/j.csda.2015.08.011","isbn":null,"url":null},{"ref":"Emmerich, M., Giannakoglou, K., Naujoks, B. (2006). Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation, 10(4), 421-439.","type":"inproceedings","doi":"10.1109/TEVC.2005.859463","isbn":null,"url":null}],"related":["multi-objective-optimization","bayesian-optimization","nsga-ii","gaussian-process-regression","pareto-optimization","stochastic-multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-multidimensional-scaling","name":"Bayesian Multidimensional Scaling","fullName":"Bayesian Multidimensional Scaling","aliases":["Bayesian MDS","BMDS","probabilistic MDS","Bayesian proximity scaling"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2001","originator":"Oh & Raftery","url":"https://scholargate.app/en/statistics/bayesian-multidimensional-scaling","markdownUrl":"https://scholargate.app/en/statistics/bayesian-multidimensional-scaling.md","definition":"Bayesian Multidimensional Scaling places objects in a low-dimensional latent space so that inter-object distances reproduce observed dissimilarities, while a full Bayesian treatment quantifies uncertainty in the coordinates, handles missing proximities naturally, and selects the number of dimensions via model comparison rather than heuristic inspection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oh & Raftery","year":"2001","type":"Bayesian latent-space dimensionality reduction","dataType":"Dissimilarity or distance matrices; proximity data","subfamily":"Multivariate analysis"},"citations":[{"ref":"Oh, M.-S. & Raftery, A. E. (2001). Bayesian multidimensional scaling and choice of dimension. Journal of the American Statistical Association, 96(455), 1031–1044.","type":"article","doi":"10.1198/016214501753208690","isbn":null,"url":null},{"ref":"Multidimensional scaling. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Multidimensional_scaling"}],"related":["multidimensional-scaling","bayesian-cluster-analysis","bayesian-principal-component-analysis","bayesian-confirmatory-factor-analysis","bayesian-latent-class-analysis","bayesian-exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-multinomial-logistic-regression","name":"Bayesian Multinomial Logistic Regression","fullName":"Bayesian Multinomial Logistic Regression","aliases":["Bayesian polytomous logistic regression","Bayesian multinomial logit","Bayesian softmax regression","Bayesian nominal logistic regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1966 (classical); Bayesian extensions established by 1990s","originator":"Gelman et al. (Bayesian treatment); classical multinomial logit by Cox (1966)","url":"https://scholargate.app/en/statistics/bayesian-multinomial-logistic-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-multinomial-logistic-regression.md","definition":"Bayesian Multinomial Logistic Regression models a nominal outcome with three or more unordered categories by placing prior distributions over the regression coefficients and updating them with data via Bayes' theorem. The result is a full posterior distribution over category probabilities for each observation, enabling principled uncertainty quantification and regularization through the prior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelman et al. (Bayesian treatment); classical multinomial logit by Cox (1966)","year":"1966 (classical); Bayesian extensions established by 1990s","type":"Bayesian classification model","dataType":"Nominal categorical outcome (3+ unordered classes), continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471360933","url":null}],"related":["multinomial-logistic-regression","bayesian-ordinal-logistic-regression","bayesian-logistic-regression","ordinal-logistic-regression","bayesian-generalized-linear-model","dirichlet-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-multiple-correspondence-analysis","name":"Bayesian Multiple Correspondence Analysis","fullName":"Bayesian Multiple Correspondence Analysis","aliases":["Bayesian MCA","BMCA","Bayesian multiway correspondence analysis","Bayesian categorical dimension reduction"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2000s–2010s","originator":"Extension of MCA (Benzecri, 1973) with Bayesian inference","url":"https://scholargate.app/en/statistics/bayesian-multiple-correspondence-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-multiple-correspondence-analysis.md","definition":"Bayesian Multiple Correspondence Analysis extends classical MCA by embedding the geometric decomposition of categorical data tables within a Bayesian probabilistic framework, enabling principled uncertainty quantification around category coordinates, dimension selection via marginal likelihood, and incorporation of prior knowledge about variable relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of MCA (Benzecri, 1973) with Bayesian inference","year":"2000s–2010s","type":"Bayesian dimension reduction for categorical data","dataType":"Nominal or ordinal categorical variables (multiple)","subfamily":"Multivariate analysis"},"citations":[{"ref":"Greenacre, M. & Blasius, J. (Eds.) (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584886280","url":null},{"ref":"Delattre, M., Lavielle, M. & Poursat, M.-A. (2014). A note on BIC in mixed-effects models. Electronic Journal of Statistics, 8(1), 456–475.","type":"article","doi":"10.1214/14-EJS890","isbn":null,"url":null}],"related":["multiple-correspondence-analysis","bayesian-correspondence-analysis","bayesian-latent-class-analysis","bayesian-cluster-analysis","latent-class-analysis","correspondence-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-multiple-linear-regression","name":"Bayesian Multiple linear regression","fullName":"Bayesian Multiple Linear Regression","aliases":["Bayesian MLR","Bayesian linear regression","Bayesian multivariate regression","conjugate normal-inverse-gamma regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1971","originator":"Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.","url":"https://scholargate.app/en/statistics/bayesian-multiple-linear-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-multiple-linear-regression.md","definition":"Bayesian Multiple Linear Regression models a continuous outcome as a linear combination of several predictors, but instead of producing a single point estimate it yields a full posterior distribution over all regression coefficients and the error variance. This makes uncertainty quantification explicit and allows seamlessly incorporating prior knowledge from theory or previous studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.","year":"1971","type":"Bayesian parametric regression","dataType":"continuous outcome, continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley.","type":"book","doi":null,"isbn":"978-0471980650","url":null}],"related":["ols-regression","bayesian-simple-linear-regression","ridge-regression","bayesian-generalized-linear-model","lasso-regression","bayesian-hierarchical-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-multiplex-network-analysis","name":"Bayesian Multiplex Network Analysis","fullName":"Bayesian Multiplex Network Analysis (Probabilistic Inference on Multi-Layer Networks)","aliases":["Bayesian multi-layer network analysis","probabilistic multiplex network inference","Bayesian multilayer network modelling","BMNA"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2014-2017","originator":"De Bacco, C. et al.; Kivela, M. et al.","url":"https://scholargate.app/en/network-analysis/bayesian-multiplex-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-multiplex-network-analysis.md","definition":"Bayesian multiplex network analysis applies probabilistic generative modelling to networks that carry more than one type of relational tie simultaneously — such as friendship, collaboration, and communication links among the same set of actors. By placing priors over community memberships, edge probabilities, and layer interdependencies, the framework yields posterior distributions rather than point estimates, supporting principled uncertainty quantification across all inferred network properties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"De Bacco, C. et al.; Kivela, M. et al.","year":"2014-2017","type":"Probabilistic generative model for multiplex networks","dataType":"Multi-layer adjacency matrices / edge lists across network layers","subfamily":"Network science"},"citations":[{"ref":"De Bacco, C., Power, E. A., Larremore, D. B., & Moore, C. (2017). Community detection, link prediction, and layer interdependence in multilayer networks. Physical Review E, 95(4), 042317.","type":"article","doi":"10.1103/PhysRevE.95.042317","isbn":null,"url":null},{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203-271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null}],"related":["multiplex-network-analysis","bayesian-stochastic-block-model","bayesian-community-detection","multilayer-network-analysis","exponential-random-graph-model","stochastic-block-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-multiscale-geographically-weighted-regression","name":"Bayesian Multiscale Geographically Weighted Regression","fullName":"Bayesian Multiscale Geographically Weighted Regression","aliases":["Bayesian MGWR","B-MGWR","Bayesian multiscale GWR","Bayesian spatially varying coefficient model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2017-2020","originator":"Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors","url":"https://scholargate.app/en/spatial-analysis/bayesian-multiscale-geographically-weighted-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-multiscale-geographically-weighted-regression.md","definition":"Bayesian Multiscale Geographically Weighted Regression (Bayesian MGWR) extends the MGWR framework by placing Bayesian priors on each spatially varying coefficient. Each predictor is allowed its own bandwidth — its own geographic scale of influence — while Bayesian inference replaces classical back-fitting with posterior sampling, yielding full uncertainty quantification for every local coefficient surface.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors","year":"2017-2020","type":"Spatially varying coefficient regression","dataType":"Georeferenced cross-sectional or panel data with continuous outcome","subfamily":"GIS / spatial"},"citations":[{"ref":"Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.","type":"article","doi":"10.1080/24694452.2017.1352480","isbn":null,"url":null},{"ref":"Li, Z., Fotheringham, A. S., Li, W., & Oshan, T. (2020). Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations. International Journal of Geographical Information Science, 33(1), 155-175.","type":"article","doi":"10.1080/13658816.2018.1521523","isbn":null,"url":null}],"related":["multiscale-geographically-weighted-regression","geographically-weighted-regression","bayesian-spatial-regression","spatial-lag-model","local-spatial-regression","bayesian-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-naive-bayes","name":"Bayesian Naive Bayes","fullName":"Fully Bayesian Naive Bayes Classifier","aliases":["Bayesian NB","Naive Bayes with Bayesian parameter estimation","Dirichlet-Multinomial Naive Bayes","BNB"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1960s (base); Bayesian parameter treatment formalized 2000s","originator":"Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006)","url":"https://scholargate.app/en/machine-learning/bayesian-naive-bayes","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-naive-bayes.md","definition":"Bayesian Naive Bayes applies a fully Bayesian treatment to the parameters of the classic Naive Bayes classifier: instead of estimating class-conditional distributions by maximum likelihood, it places conjugate priors (typically Dirichlet for categorical data or Gaussian-Gamma for continuous data) over the parameters and integrates them out, producing predictive posterior distributions that naturally quantify uncertainty and avoid overfitting on small datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Naive Bayes: Maron & Kuhns (1960); full Bayesian treatment formalized by Murphy (2012) and Bishop (2006)","year":"1960s (base); Bayesian parameter treatment formalized 2000s","type":"Probabilistic generative classifier","dataType":"Categorical, count, or continuous features with discrete or binary labels","subfamily":"Machine learning"},"citations":[{"ref":"Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 3, 4). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-01802-9","url":null},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 8). Springer.","type":"book","doi":null,"isbn":"978-0-387-31073-2","url":null}],"related":["gaussian-process","k-nearest-neighbors","semi-supervised-naive-bayes","logistic-regression-ml","support-vector-machine","bayesian-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-nardl","name":"Bayesian NARDL","fullName":"Bayesian Nonlinear Autoregressive Distributed Lag Model","aliases":["Bayesian NARDL","Bayesian nonlinear ARDL","Bayesian asymmetric ARDL","B-NARDL"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2014 (NARDL); Bayesian extension c. 2015–2020","originator":"Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature","url":"https://scholargate.app/en/econometrics/bayesian-nardl","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-nardl.md","definition":"Bayesian NARDL combines the Nonlinear Autoregressive Distributed Lag framework of Shin, Yu, and Greenwood-Nimmo (2014) with Bayesian posterior inference. It models asymmetric long-run cointegration — allowing positive and negative shocks to a regressor to have different equilibrium effects — while incorporating prior knowledge and producing full posterior distributions over all parameters, including the asymmetry gap.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature","year":"2014 (NARDL); Bayesian extension c. 2015–2020","type":"Nonlinear cointegrating model with Bayesian inference","dataType":"Time series; continuous variables with potential asymmetric long-run adjustment","subfamily":"Econometrics / time series"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer.","type":"inproceedings","doi":null,"isbn":null,"url":"https://doi.org/10.1007/978-1-4899-8008-3_9"},{"ref":"Koop, G. (2003). Bayesian Econometrics. Wiley.","type":"book","doi":null,"isbn":"978-0470845677","url":null}],"related":["nonlinear-ardl","bayesian-ardl-bounds-test","bayesian-vecm","arellano-bond-gmm-estimator","vector-error-correction-model","panel-nardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-nash-equilibrium","name":"Bayesian Nash Equilibrium","fullName":"Bayesian Nash Equilibrium with Incomplete Information","aliases":["BNE","Perfect Bayesian Equilibrium","Type-Contingent Equilibrium"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1967","originator":"John Harsanyi","url":"https://scholargate.app/en/game-theory/bayesian-nash-equilibrium","markdownUrl":"https://scholargate.app/en/game-theory/bayesian-nash-equilibrium.md","definition":"Bayesian Nash Equilibrium (BNE) extends Nash Equilibrium to games with incomplete information, where players lack full knowledge of others' payoff functions. Introduced by John Harsanyi in 1967, BNE models strategic interaction under uncertainty by representing unknown payoffs as players' private types drawn from a probability distribution. Equilibrium is found by solving for type-contingent strategies that are best responses to all possible type realizations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Harsanyi","subfamily":"Game-theoretic","year":"1967","type":"algorithm"},"citations":[{"ref":"Harsanyi, J. C. (1967). Games with incomplete information played by Bayesian players, Parts I, II, and III. Management Science, 14(3), 159-182.","type":"article","doi":"10.1287/mnsc.14.3.159","isbn":null,"url":null},{"ref":"Harsanyi, J. C. (1968). Games with incomplete information played by Bayesian players. Management Science, 14(7), 486-502.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Games+with+incomplete+information+played+by+Bayesian+players+Harsanyi"}],"related":["nash-equilibrium","vcg-mechanism","principal-agent-model","first-price-auction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-negative-binomial-regression","name":"Bayesian Negative Binomial Regression","fullName":"Bayesian Negative Binomial Regression","aliases":["Bayesian NB regression","Bayesian negbin model","Bayesian overdispersed count regression","Bayesian NB-2 model"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1990s–2000s","originator":"Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi","url":"https://scholargate.app/en/statistics/bayesian-negative-binomial-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-negative-binomial-regression.md","definition":"Bayesian Negative Binomial Regression models non-negative integer count outcomes that exhibit overdispersion — where the variance exceeds the mean — by placing a negative binomial likelihood on the data and specifying prior distributions over the regression coefficients and the dispersion parameter. Posterior inference is typically performed via Markov chain Monte Carlo (MCMC) or variational methods, yielding full posterior distributions rather than point estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi","year":"1990s–2000s","type":"Bayesian GLM for overdispersed counts","dataType":"Non-negative integer (count) outcome with overdispersion","subfamily":"Regression / GLM"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Cameron, A. C., & Trivedi, P. K. (2013). Regression Analysis of Count Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107667273","url":null}],"related":["bayesian-poisson-regression","negative-binomial-regression","zero-inflated-model","bayesian-zero-inflated-model","poisson-regression","bayesian-generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-nested-case-control","name":"Bayesian nested case-control","fullName":"Bayesian Nested Case-Control Study","aliases":["Bayesian NCC","Bayesian nested case-referent study","Bayesian sampled case-control within cohort"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1977 (nested case-control); Bayesian adaptation developed through 1990s–2010s","originator":"Nested case-control: D. C. Thomas (1977); Bayesian extension: various authors in biostatistics","url":"https://scholargate.app/en/epidemiology/bayesian-nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-nested-case-control.md","definition":"A Bayesian nested case-control study embeds a case-control sampling scheme within a defined prospective cohort and then estimates exposure-outcome associations using Bayesian inference. Cases are individuals in the cohort who develop the outcome of interest; controls are sampled from the risk set at the time each case is identified. The Bayesian framework allows incorporation of prior knowledge — from earlier studies, expert opinion, or biological plausibility — and produces full posterior distributions for effect estimates rather than single-point estimates with confidence intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nested case-control: D. C. Thomas (1977); Bayesian extension: various authors in biostatistics","year":"1977 (nested case-control); Bayesian adaptation developed through 1990s–2010s","type":"Observational analytical study design with Bayesian inference","dataType":"Time-to-event data, exposure measurements, biomarker data from an established cohort","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thomas+1977+nested+case-control+cohort+analysis"},{"ref":"Wakefield, J. (2007). Disease mapping and spatial regression with count data. Biostatistics, 8(2), 158–183.","type":"article","doi":"10.1093/biostatistics/kxl008","isbn":null,"url":null}],"related":["nested-case-control","case-control-study","bayesian-case-control-study","cohort-study","bayesian-cohort-study","competing-risks-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-network-diffusion-analysis","name":"Bayesian Network Diffusion Analysis","fullName":"Bayesian Network Diffusion Analysis (Probabilistic Inference on Contagion and Spreading Processes)","aliases":["Bayesian diffusion model","probabilistic network diffusion","Bayesian spreading process inference","BNDA"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010s","originator":"Gomez Rodriguez, M.; Leskovec, J.; and related network science community","url":"https://scholargate.app/en/network-analysis/bayesian-network-diffusion-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-network-diffusion-analysis.md","definition":"Bayesian Network Diffusion Analysis applies Bayesian probabilistic inference to the study of how information, diseases, behaviors, or innovations propagate through a network. By placing priors over diffusion parameters and updating them with observed cascade data, it quantifies transmission rates, identifies influential spreaders, reconstructs latent propagation pathways, and provides full uncertainty estimates — all within a principled statistical framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gomez Rodriguez, M.; Leskovec, J.; and related network science community","year":"2010s","type":"Probabilistic inference on network spreading processes","dataType":"Observed contagion cascades, edge/node timestamps, network topology","subfamily":"Network science"},"citations":[{"ref":"Gomez Rodriguez, M., Leskovec, J., & Scholkopf, B. (2012). Structure and Dynamics of Information Pathways in Online Media. Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM), 23–32.","type":"inproceedings","doi":"10.1145/2433396.2433402","isbn":null,"url":null},{"ref":"Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature Physics, 6(11), 888–893.","type":"article","doi":"10.1038/nphys1746","isbn":null,"url":null}],"related":["network-diffusion-analysis","bayesian-exponential-random-graph-model","temporal-network-diffusion-analysis","exponential-random-graph-model","bayesian-stochastic-block-model","social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-network-with-measurement-error","name":"Bayesian Network with Measurement Error","fullName":"Bayesian Network with Measurement Error (Errors-in-Variables Graphical Model)","aliases":["BN-ME","errors-in-variables Bayesian network","Bayesian graphical model with measurement error","latent variable Bayesian network"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1988 (Bayesian networks); measurement-error extension: 1990s","originator":"Judea Pearl (Bayesian networks); measurement-error extension developed in epidemiology and psychometrics through the 1990s–2000s","url":"https://scholargate.app/en/bayesian/bayesian-network-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-network-with-measurement-error.md","definition":"A Bayesian network with measurement error is a probabilistic directed acyclic graphical model in which one or more node variables are observed with error rather than exactly. Latent true-value nodes are introduced for mismeasured variables, and the model jointly infers the network's conditional probability parameters and the unobserved true values from the noisy observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judea Pearl (Bayesian networks); measurement-error extension developed in epidemiology and psychometrics through the 1990s–2000s","year":"1988 (Bayesian networks); measurement-error extension: 1990s","type":"Probabilistic graphical model with latent variables","dataType":"Continuous, binary, or categorical observed variables with known or estimated error structure","subfamily":"Bayesian / computational"},"citations":[{"ref":"Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.","type":"book","doi":null,"isbn":"978-1558604797","url":null},{"ref":"Richardson, S. & Gilks, W. R. (1993). A Bayesian approach to measurement error problems in epidemiology using conditional independence models. American Journal of Epidemiology, 138(6), 430–442.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Bayesian+approach+to+measurement+error+problems+in+epidemiology+using+conditional+independence+models"}],"related":["bayesian-network","bayesian-hierarchical-model-with-measurement-error","bayesian-inference-with-measurement-error","structural-equation-modeling","latent-class-analysis","mcmc-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-network","name":"Bayesian Network","fullName":"Bayesian Network","aliases":["Bayes network","belief network","probabilistic graphical model","directed graphical model","Bayesian Ağı"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":"1988","originator":"Judea Pearl","url":"https://scholargate.app/en/bayesian/bayesian-network","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-network.md","definition":"A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judea Pearl","year":"1988","family":"Bayesian","type":"Probabilistic graphical model","graph_structure":"Directed acyclic graph (DAG)","purpose":"relationship / prediction / classification","var_types":"categorical / binary / continuous","inference":"exact (variable elimination, belief propagation) / approximate (MCMC, variational)","outputs":"conditional probability tables / posterior marginals","min_sample":100},"citations":[{"ref":"Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.","type":"book","doi":null,"isbn":"978-1558604797","url":null}],"related":["bayesian-regression","mcmc","hierarchical-bayes","dag-identification","structural-equation-modeling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-nonparametric","name":"Bayesian Nonparametric Methods","fullName":"Bayesian Nonparametric Methods (Dirichlet Process / Gaussian Process)","aliases":["BNP","Dirichlet process mixture","DPM","Gaussian process regression","GP regression","Bayesian Nonparametrik (Dirichlet Process / Gaussian Process)"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":"1973 (DP); 2006 (GP canonical text)","originator":"Ferguson (Dirichlet Process, 1973); Rasmussen & Williams (GP, 2006)","url":"https://scholargate.app/en/bayesian/bayesian-nonparametric","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-nonparametric.md","definition":"Bayesian nonparametric methods are a family of flexible Bayesian models in which model complexity is not fixed in advance but grows automatically with the data. The two most widely used members are the Dirichlet Process Mixture (DPM), which clusters observations without pre-specifying the number of clusters, and Gaussian Process (GP) regression, which places a prior directly over functions and performs regression or classification without committing to a parametric form. Both frameworks were formalised in the Bayesian nonparametric literature, with the canonical GP treatment given by Rasmussen and Williams (2006).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ferguson (Dirichlet Process, 1973); Rasmussen & Williams (GP, 2006)","year":"1973 (DP); 2006 (GP canonical text)","family":"Bayesian","type":"Bayesian nonparametric model","purpose":"cluster / predict / classify","var_types":"continuous / categorical","inference":"MCMC / variational inference","outputs":"posterior distributions / credible intervals / predictive distributions","min_sample":30,"difficulty":4},"citations":[{"ref":"Rasmussen, C.E. & Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0262182539","url":null},{"ref":"Müller, P. & Quintana, F.A. (2004). Nonparametric Bayesian Data Analysis. Statistical Science, 19(1), 95–110.","type":"article","doi":"10.1214/088342304000000017","isbn":null,"url":null}],"related":["bayesian-regression","mcmc","hierarchical-bayes","gaussian-process","mixture-models","kernel-methods"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-nsga-ii","name":"Bayesian NSGA-II","fullName":"Bayesian Surrogate-Assisted Non-dominated Sorting Genetic Algorithm II","aliases":["B-NSGA-II","Surrogate-Assisted NSGA-II","Gaussian Process NSGA-II","Bayesian Multi-Objective EA"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2002–2006","originator":"Emmerich, M. T. M. et al. (surrogate-assisted MO-EA); Deb et al. (NSGA-II base)","url":"https://scholargate.app/en/simulation/bayesian-nsga-ii","markdownUrl":"https://scholargate.app/en/simulation/bayesian-nsga-ii.md","definition":"Bayesian NSGA-II integrates Gaussian process surrogate models (Bayesian metamodels) into the NSGA-II evolutionary loop to solve expensive multi-objective optimization problems. By replacing costly true function evaluations with fast probabilistic predictions, it discovers high-quality Pareto-front approximations with far fewer real evaluations than standard NSGA-II.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emmerich, M. T. M. et al. (surrogate-assisted MO-EA); Deb et al. (NSGA-II base)","year":"2002–2006","type":"Surrogate-assisted multi-objective evolutionary algorithm","dataType":"Continuous or mixed decision variables; expensive black-box objective functions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.","type":"article","doi":"10.1109/4235.996017","isbn":null,"url":null},{"ref":"Emmerich, M. T. M., Giannakoglou, K. C., Naujoks, B. (2006). Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation, 10(4), 421–439.","type":"article","doi":"10.1109/TEVC.2005.859463","isbn":null,"url":null}],"related":["nsga-ii","bayesian-optimization","multi-objective-optimization","gaussian-process-regression","surrogate-assisted-optimization","multi-objective-genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-observational-quantitative-research","name":"Bayesian Observational Quantitative Research","fullName":"Bayesian Observational Quantitative Research Design","aliases":["Bayesian observational study","Bayesian non-experimental quantitative design","Bayesian causal observational analysis","BOQR"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1990s–2000s (systematic application to observational research)","originator":"Thomas Bayes (foundational theorem, 1763); modern applied form developed by Sander Greenland, Andrew Gelman, and colleagues (1990s–2000s)","url":"https://scholargate.app/en/research-design/bayesian-observational-quantitative-research","markdownUrl":"https://scholargate.app/en/research-design/bayesian-observational-quantitative-research.md","definition":"Bayesian observational quantitative research applies Bayesian statistical inference to data collected without experimental manipulation — surveys, administrative records, registries, or secondary datasets. Instead of relying solely on p-values and confidence intervals, the analyst encodes prior knowledge about parameters as probability distributions, updates them with observed data via Bayes' theorem, and reports conclusions as posterior probability statements. The approach is especially valued in epidemiology, social science, and health services research where randomisation is impossible or unethical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Bayes (foundational theorem, 1763); modern applied form developed by Sander Greenland, Andrew Gelman, and colleagues (1990s–2000s)","year":"1990s–2000s (systematic application to observational research)","type":"Quantitative non-experimental research design with Bayesian inference","dataType":"Quantitative observational data (surveys, administrative records, registries, secondary datasets)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765–775.","type":"article","doi":"10.1093/ije/dyi312","isbn":null,"url":null}],"related":["bayesian-inference","observational-study","propensity-score-matching","multilevel-modeling","structural-equation-modeling","cross-sectional-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ols","name":"Bayesian OLS","fullName":"Bayesian Ordinary Least Squares Regression","aliases":["Bayesian linear regression","Bayesian normal regression","BLR","Bayesian least squares"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1971","originator":"Arnold Zellner","url":"https://scholargate.app/en/econometrics/bayesian-ols","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-ols.md","definition":"Bayesian OLS combines the classical linear regression likelihood with prior distributions over the coefficients and error variance. Rather than reporting point estimates, it produces full posterior distributions that quantify both estimated effects and their uncertainty. The approach is especially valuable when prior knowledge is available or when samples are small.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arnold Zellner","year":"1971","type":"Bayesian linear regression","dataType":"Cross-sectional or time-series continuous outcome with continuous or categorical predictors","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley.","type":"book","doi":null,"isbn":"978-0471169376","url":null},{"ref":"Koop, G. (2003). Bayesian Econometrics. Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0470845677","url":null}],"related":["ols-regression","bayesian-var-model","bayesian-gls","ridge-regression","bayesian-random-effects-model","bayesian-fixed-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-one-class-svm","name":"Bayesian one-class SVM","fullName":"Bayesian One-Class Support Vector Machine","aliases":["Bayesian OCSVM","Bayesian one-class classifier","probabilistic one-class SVM","Bayes-OCSVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001–2010","originator":"Scholkopf et al. (base OCSVM); Bayesian extension via Tipping and others","url":"https://scholargate.app/en/machine-learning/bayesian-one-class-svm","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-one-class-svm.md","definition":"Bayesian one-class SVM combines the classical one-class support vector machine — which learns a tight boundary around normal training examples — with Bayesian inference to produce calibrated probability estimates of anomaly, rather than only a binary flag. This allows uncertainty quantification over the novelty decision, making the approach more suitable when downstream actions depend on how confident the model is that a new observation is anomalous.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scholkopf et al. (base OCSVM); Bayesian extension via Tipping and others","year":"2001–2010","type":"Probabilistic anomaly detection","dataType":"Continuous or mixed features; unlabeled or single-class data","subfamily":"Machine learning"},"citations":[{"ref":"Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.","type":"article","doi":"10.1162/089976601750264965","isbn":null,"url":null},{"ref":"Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1, 211–244.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v1/tipping01a.html"}],"related":["one-class-svm","gaussian-process","autoencoder-anomaly-detection","isolation-forest","robust-one-class-svm","bayesian-gaussian-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-one-sample-t-test","name":"Bayesian one-sample t-test","fullName":"Bayesian One-Sample t-test","aliases":["Bayesian single-sample t-test","Bayes factor one-sample t-test","JZS one-sample Bayes factor","Bayesian location test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"2009","originator":"Rouder, Speckman, Sun, Morey & Iverson","url":"https://scholargate.app/en/statistics/bayesian-one-sample-t-test","markdownUrl":"https://scholargate.app/en/statistics/bayesian-one-sample-t-test.md","definition":"The Bayesian one-sample t-test compares a single group's mean against a fixed reference value using a Bayes factor rather than a p-value. It quantifies the evidence the data provide for the null hypothesis (mean equals the reference) versus the alternative, and yields a full posterior distribution over the effect size — enabling statements about practical magnitude, not just a binary reject-or-retain decision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rouder, Speckman, Sun, Morey & Iverson","year":"2009","type":"Bayesian mean-vs-constant comparison","dataType":"Continuous, single group","subfamily":"Classical statistics"},"citations":[{"ref":"Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237.","type":"article","doi":"10.3758/PBR.16.2.225","isbn":null,"url":null},{"ref":"Kruschke, J. K. (2014). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2nd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0124058880","url":null}],"related":["one-sample-t-test","bayesian-independent-samples-t-test","bayesian-paired-samples-t-test","bayes-factor","wilcoxon-signed-rank-test","bayesian-pearson-correlation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-one-way-anova","name":"Bayesian one-way ANOVA","fullName":"Bayesian One-Way Analysis of Variance","aliases":["Bayesian ANOVA","BF ANOVA","Bayes factor one-way ANOVA","Bayesian F-test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1961 (foundations); 2012 (ANOVA Bayes factors)","originator":"Harold Jeffreys (foundations); Jeffrey Rouder et al. (default priors for ANOVA)","url":"https://scholargate.app/en/statistics/bayesian-one-way-anova","markdownUrl":"https://scholargate.app/en/statistics/bayesian-one-way-anova.md","definition":"Bayesian one-way ANOVA tests whether the means of three or more independent groups differ by computing a Bayes factor — a ratio that quantifies how much more likely the data are under a model that allows group differences than under the null model that assumes equal means. Unlike the classical F-test, it provides direct evidence for or against the null hypothesis rather than merely rejecting or retaining it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Jeffreys (foundations); Jeffrey Rouder et al. (default priors for ANOVA)","year":"1961 (foundations); 2012 (ANOVA Bayes factors)","type":"Bayesian hypothesis test","dataType":"Continuous dependent variable, one categorical independent variable (3+ groups)","subfamily":"Classical statistics"},"citations":[{"ref":"Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374.","type":"article","doi":"10.1016/j.jmp.2012.08.001","isbn":null,"url":null},{"ref":"Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198503682","url":null}],"related":["one-way-anova","bayesian-two-way-anova","bayesian-independent-samples-t-test","bayesian-repeated-measures-anova","kruskal-wallis-test","welch-corrected-one-way-anova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-online-learning","name":"Bayesian Online Learning","fullName":"Bayesian Online Learning (Sequential Posterior Update)","aliases":["online Bayesian inference","sequential Bayesian learning","recursive Bayesian estimation","BOL"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1990s–2000s","originator":"Opper, M.; Sato, M. (among key contributors)","url":"https://scholargate.app/en/machine-learning/bayesian-online-learning","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-online-learning.md","definition":"Bayesian online learning applies Bayesian inference sequentially: each time a new observation arrives, the current posterior over model parameters becomes the prior for the next update. The result is a principled probabilistic framework that maintains calibrated uncertainty estimates throughout, making it well-suited for streaming and non-stationary data settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Opper, M.; Sato, M. (among key contributors)","year":"1990s–2000s","type":"Probabilistic sequential learning","dataType":"Sequential / streaming data (continuous, binary, count)","subfamily":"Machine learning"},"citations":[{"ref":"Opper, M. (1998). A Bayesian approach to on-line learning. In D. Saad (Ed.), On-Line Learning in Neural Networks (pp. 363–378). Cambridge University Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Bayesian+approach+to+on-line+learning+Opper+1998"},{"ref":"Sato, M. (2001). Online model selection based on the variational Bayes. Neural Computation, 13(7), 1649–1681.","type":"article","doi":"10.1162/089976601750265045","isbn":null,"url":null}],"related":["online-learning","bayesian-gaussian-process","gaussian-process","semi-supervised-learning","bayesian-logistic-regression","variational-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-optimization","name":"Bayesian Optimization","fullName":"Bayesian Optimization (Hyperparameter Tuning)","aliases":["Bayesçi Optimizasyon (Hyperparameter Tuning)","surrogate-based optimization","sequential model-based optimization","SMBO"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":"1975 (foundational); 2012 (ML standard)","originator":"Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)","url":"https://scholargate.app/en/optimization/bayesian-optimization","markdownUrl":"https://scholargate.app/en/optimization/bayesian-optimization.md","definition":"Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)","year":"1975 (foundational); 2012 (ML standard)","type":"Sequential model-based black-box optimization","surrogateModel":"Gaussian Process (GP)","acquisitionFunctions":"Expected Improvement (EI), Upper Confidence Bound (UCB), Probability of Improvement (PI)","searchSpace":"Continuous and ordinal hyperparameter spaces with predefined bounds","difficulty":3},"citations":[{"ref":"Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.","type":"article","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html"},{"ref":"Frazier, P.I. (2018). A Tutorial on Bayesian Optimization. arXiv:1807.02811.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1807.02811"}],"related":["random-search","grid-search","gaussian-process-regression","hyperparameter-tuning","neural-architecture-search","stochastic-optimization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ordinal-logistic-regression","name":"Bayesian Ordinal Logistic Regression","fullName":"Bayesian Ordinal Logistic Regression (Proportional Odds Model)","aliases":["Bayesian proportional odds model","Bayesian cumulative logit model","Bayesian ordered logit","Bayesian cumulative link model"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1999","originator":"Johnson & Albert (1999); Bayesian proportional odds framework","url":"https://scholargate.app/en/statistics/bayesian-ordinal-logistic-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-ordinal-logistic-regression.md","definition":"Bayesian ordinal logistic regression extends the classical proportional odds model by placing prior distributions on the regression coefficients and threshold parameters and updating them with observed data via Bayes' theorem. The result is a full posterior distribution over all parameters, enabling uncertainty quantification without relying on large-sample approximations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johnson & Albert (1999); Bayesian proportional odds framework","year":"1999","type":"Bayesian generalized linear model","dataType":"Ordinal outcome (Likert scale, graded categories), continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer.","type":"book","doi":null,"isbn":"978-0387987484","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["ordinal-logistic-regression","bayesian-logistic-regression","bayesian-multinomial-logistic-regression","multinomial-logistic-regression","bayesian-probit-model","bayesian-generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ordinary-kriging","name":"Bayesian Ordinary Kriging","fullName":"Bayesian Ordinary Kriging","aliases":["Bayesian kriging","BOK","geostatistical Bayesian interpolation","Bayesian spatial prediction"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1993","originator":"Handcock & Stein (1993); Diggle & Ribeiro (2007)","url":"https://scholargate.app/en/spatial-analysis/bayesian-ordinary-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-ordinary-kriging.md","definition":"Bayesian Ordinary Kriging is a geostatistical interpolation method that combines classical ordinary kriging with a Bayesian framework to jointly estimate the spatial covariance parameters and produce predictions at unsampled locations. Unlike plug-in kriging, it propagates uncertainty about variogram parameters through to the predictive distribution, yielding more honest uncertainty quantification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Handcock & Stein (1993); Diggle & Ribeiro (2007)","year":"1993","type":"Bayesian geostatistical interpolation","dataType":"Continuous georeferenced point observations","subfamily":"GIS / spatial"},"citations":[{"ref":"Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer.","type":"book","doi":null,"isbn":"978-0387329079","url":null},{"ref":"Handcock, M. S., & Stein, M. L. (1993). A Bayesian analysis of kriging. Technometrics, 35(4), 403-410.","type":"article","doi":"10.1080/00401706.1993.10485354","isbn":null,"url":null}],"related":["ordinary-kriging","bayesian-kriging","bayesian-universal-kriging","bayesian-co-kriging","kriging","spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-pagerank","name":"Bayesian PageRank","fullName":"Bayesian PageRank (Probabilistic Ranking on Networks)","aliases":["Bayesian PR","probabilistic PageRank","uncertainty-aware PageRank","stochastic PageRank"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1999 (PageRank); 2000s (Bayesian extension)","originator":"Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors","url":"https://scholargate.app/en/network-analysis/bayesian-pagerank","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-pagerank.md","definition":"Bayesian PageRank extends the classic PageRank algorithm by embedding it within a Bayesian probabilistic framework. Instead of returning a single deterministic rank score for each node, it quantifies uncertainty over rank estimates — particularly valuable when the network is incomplete, noisy, or observed with error. It is used in web analysis, citation networks, and social network research where rank uncertainty matters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors","year":"1999 (PageRank); 2000s (Bayesian extension)","type":"Probabilistic centrality measure","dataType":"Directed (weighted or unweighted) networks","subfamily":"Network science"},"citations":[{"ref":"Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.","type":"article","doi":null,"isbn":null,"url":"http://ilpubs.stanford.edu:8090/422/"},{"ref":"PageRank. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/PageRank"}],"related":["directed-pagerank","temporal-pagerank","multilayer-pagerank","eigenvector-centrality","bayesian-network-diffusion-analysis","bayesian-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-panel-data-analysis","name":"Bayesian Panel Data Analysis","fullName":"Bayesian Panel Data Analysis","aliases":["Bayesian panel model","Bayesian longitudinal model","hierarchical panel model","Bayesian multilevel panel"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1971–1999","originator":"Zellner (1971); Hsiao, Pesaran, and Tahmiscioglu (1999)","url":"https://scholargate.app/en/econometrics/bayesian-panel-data-analysis","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-panel-data-analysis.md","definition":"Bayesian panel data analysis applies Bayesian inference to models with repeated observations on multiple units. By placing prior distributions on coefficients and variance components, it merges prior knowledge with the observed panel likelihood to produce full posterior distributions for fixed or random effects, slope heterogeneity, and variance parameters — rather than point estimates and asymptotic standard errors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zellner (1971); Hsiao, Pesaran, and Tahmiscioglu (1999)","year":"1971–1999","type":"Bayesian estimation for panel data","dataType":"Balanced or unbalanced panel (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null},{"ref":"Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley.","type":"book","doi":null,"isbn":"978-0471169376","url":null}],"related":["fixed-effects-model","random-effects-model","panel-data-analysis","bayesian-fixed-effects-model","bayesian-random-effects-model","panel-hausman-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-panel-event-study","name":"Bayesian Panel Event Study","fullName":"Bayesian Panel Event Study Design","aliases":["Bayesian event-study estimator","Bayesian dynamic DiD","Bayesian panel ES","Bayes event study"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s–2020s","originator":"Developed from panel event-study literature (Sun & Abraham 2021; Freyaldenhoven et al. 2021) combined with Bayesian estimation frameworks","url":"https://scholargate.app/en/causal-inference/bayesian-panel-event-study","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-panel-event-study.md","definition":"Bayesian Panel Event Study is a causal inference design that estimates dynamic treatment effects around a datable event using panel data, replacing classical frequentist estimation with Bayesian posterior inference. It produces period-by-period effect estimates with full probability distributions, enabling principled uncertainty quantification, regularization of noisy pre-trend coefficients, and probabilistic tests of parallel trends.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from panel event-study literature (Sun & Abraham 2021; Freyaldenhoven et al. 2021) combined with Bayesian estimation frameworks","year":"2010s–2020s","type":"Bayesian causal panel estimator","dataType":"Panel / longitudinal data with a datable treatment event","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Freyaldenhoven, S., Hansen, C., Shapiro, J. M., & Teso, E. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper No. 29170. National Bureau of Economic Research.","type":"article","doi":null,"isbn":null,"url":"https://www.nber.org/papers/w29170"},{"ref":"Jakiela, P. (2021). Simple Diagnostics for Two-Way Fixed Effects. Working Paper. Center for Global Development.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Simple+Diagnostics+for+Two-Way+Fixed+Effects+Jakiela+2021"}],"related":["panel-event-study","difference-in-differences","bayesian-difference-in-differences","dynamic-difference-in-differences","event-study-design","panel-data-event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-panel-research","name":"Bayesian Panel Research","fullName":"Bayesian Panel Data Research","aliases":["Bayesian longitudinal panel study","Bayesian panel data analysis","BPD research","Bayesian repeated-measures panel design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1990s–2000s (contemporary synthesis)","originator":"Building on Bayes (1763) and panel data econometrics; systematised by Hsiao, Lancaster, and others in the 1990s–2000s","url":"https://scholargate.app/en/research-design/bayesian-panel-research","markdownUrl":"https://scholargate.app/en/research-design/bayesian-panel-research.md","definition":"Bayesian panel research combines the longitudinal structure of panel data — where the same units (individuals, firms, countries) are observed at multiple time points — with Bayesian statistical inference. Rather than relying solely on the observed data and point estimates, it incorporates prior knowledge via probability distributions, updates those priors with repeated-measures data, and produces full posterior distributions over model parameters. This yields richer uncertainty quantification and principled handling of individual heterogeneity across waves.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Bayes (1763) and panel data econometrics; systematised by Hsiao, Lancaster, and others in the 1990s–2000s","year":"1990s–2000s (contemporary synthesis)","type":"Quantitative longitudinal research design with Bayesian inference","dataType":"Repeated measures (numeric) from the same individuals, firms, or units observed across multiple time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Lancaster, T. (2004). An Introduction to Modern Bayesian Econometrics. Blackwell Publishing.","type":"book","doi":null,"isbn":"978-1405117868","url":null},{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null}],"related":["panel-research","longitudinal-research","bayesian-longitudinal-research","multilevel-modeling","bayesian-confirmatory-research","bayesian-correlational-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-particle-swarm-optimization","name":"Bayesian Particle Swarm Optimization","fullName":"Bayesian Particle Swarm Optimization — Probabilistic prior-guided swarm search","aliases":["Bayesian PSO","BPSO","Probabilistic Swarm Optimization","Prior-guided PSO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2003","originator":"Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)","url":"https://scholargate.app/en/simulation/bayesian-particle-swarm-optimization","markdownUrl":"https://scholargate.app/en/simulation/bayesian-particle-swarm-optimization.md","definition":"Bayesian Particle Swarm Optimization (Bayesian PSO) integrates Bayesian probabilistic reasoning into the standard particle swarm framework. Particles update their velocities and positions guided not only by personal and global best positions but also by a Bayesian posterior that encodes prior knowledge about the solution space, enabling more directed and statistically principled exploration of complex optimization landscapes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)","year":"2003","type":"Hybrid metaheuristic — Bayesian probabilistic swarm search","dataType":"Continuous or mixed-variable parameter spaces with prior knowledge","subfamily":"Simulation / optimization"},"citations":[{"ref":"Higashi, N., Iba, H. (2003). Particle swarm optimization with Gaussian mutation. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA, pp. 72-79.","type":"inproceedings","doi":"10.1109/SIS.2003.1202250","isbn":null,"url":null},{"ref":"Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, Perth, WA, Australia, vol. 4, pp. 1942-1948.","type":"inproceedings","doi":"10.1109/ICNN.1995.488968","isbn":null,"url":null}],"related":["particle-swarm-optimization","bayesian-optimization","stochastic-particle-swarm-optimization","multi-objective-particle-swarm-optimization","bayesian-genetic-algorithm","robust-particle-swarm-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-pathway-enrichment-analysis","name":"Bayesian Pathway Enrichment Analysis","fullName":"Bayesian Pathway Enrichment Analysis","aliases":["Bayesian gene-set testing","Bayesian GSEA","Bayesian functional enrichment","BGSEA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2001–2007","originator":"Pierre Baldi, Anthony Long; Michael Newton et al. (foundational Bayesian gene-set frameworks)","url":"https://scholargate.app/en/bioinformatics/bayesian-pathway-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-pathway-enrichment-analysis.md","definition":"Bayesian pathway enrichment analysis tests whether a predefined set of genes — a biological pathway — is systematically overrepresented among genes that show evidence of differential activity in an experiment. Unlike classical over-representation tests, it encodes prior biological knowledge as a prior distribution and updates it with the observed expression data, yielding posterior probabilities of enrichment rather than p-values. This probabilistic framing naturally handles small samples, multiple pathways, and uncertainty propagation in a coherent statistical framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pierre Baldi, Anthony Long; Michael Newton et al. (foundational Bayesian gene-set frameworks)","year":"2001–2007","type":"Probabilistic gene-set testing","dataType":"RNA-seq count data, microarray expression values, pre-ranked gene lists, curated pathway databases (KEGG, Reactome, GO)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Baldi, P., & Long, A. D. (2001). A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 17(6), 509–519.","type":"article","doi":"10.1093/bioinformatics/17.6.509","isbn":null,"url":null},{"ref":"Newton, M. A., Quintana, F. A., Den Boon, J. A., Bhattacharya, S., & Ahlquist, P. (2004). Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. The Annals of Applied Statistics, 1(1), 85–106.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Random-set+methods+identify+distinct+aspects+enrichment+signal+gene-set+analysis+Newton+2007"}],"related":["pathway-enrichment-analysis","gene-set-enrichment-analysis","bayesian-rna-seq-differential-expression","network-based-pathway-enrichment-analysis","multi-omics-pathway-enrichment-analysis","eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-phase-i-clinical-trial","name":"Bayesian Phase I clinical trial","fullName":"Bayesian Phase I Clinical Trial (Dose-Finding Design)","aliases":["Bayesian dose-finding trial","CRM trial","continual reassessment method trial","Bayesian dose-escalation study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990","originator":"O'Quigley, Pepe & Fisher (Continual Reassessment Method)","url":"https://scholargate.app/en/epidemiology/bayesian-phase-i-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-phase-i-clinical-trial.md","definition":"A Bayesian Phase I clinical trial uses prior probability models and sequential Bayes updating to find the maximum tolerated dose (MTD) of a new agent. Unlike the traditional 3+3 rule-based escalation, the Bayesian approach revises a dose-toxicity curve continuously as each patient's outcome is observed, allowing faster convergence to the true MTD while minimising exposure of patients to unsafe or subtherapeutic doses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"O'Quigley, Pepe & Fisher (Continual Reassessment Method)","year":"1990","type":"Adaptive Bayesian dose-finding design","dataType":"Binary toxicity outcomes (dose-limiting toxicity per patient cohort)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48.","type":"article","doi":"10.2307/2531628","isbn":null,"url":null},{"ref":"Chevret, S. (Ed.). (2006). Statistical Methods for Dose-Finding Experiments. Wiley.","type":"book","doi":null,"isbn":"978-0470861769","url":null}],"related":["phase-i-clinical-trial","adaptive-phase-i-clinical-trial","bayesian-randomized-clinical-trial","dose-response-analysis","competing-risks-analysis","adaptive-randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-phase-ii-clinical-trial","name":"Bayesian Phase II Clinical Trial","fullName":"Bayesian Phase II Clinical Trial Design","aliases":["Bayesian phase 2 trial","Bayesian single-arm phase II study","Bayesian early-phase efficacy trial","Bayes phase II"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s (Thall & Simon 1994; Berry 1985–2006)","originator":"Peter Thall, Richard Simon, Donald Berry (key contributors)","url":"https://scholargate.app/en/epidemiology/bayesian-phase-ii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-phase-ii-clinical-trial.md","definition":"A Bayesian Phase II clinical trial applies Bayesian statistical inference to the standard Phase II objective of evaluating whether an experimental treatment shows sufficient early-phase efficacy to justify progression to a Phase III trial. By combining prior information with accumulating trial data, it enables principled interim monitoring, flexible stopping rules, and updated probability statements about treatment effect — all without the multiple-testing penalties that burden frequentist sequential designs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Thall, Richard Simon, Donald Berry (key contributors)","year":"1990s (Thall & Simon 1994; Berry 1985–2006)","type":"Interventional clinical trial design","dataType":"Binary or continuous efficacy outcomes; sequential patient-level data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thall, P. F., & Simon, R. (1994). Practical Bayesian guidelines for phase IIB clinical trials. Biometrics, 50(2), 337–349.","type":"article","doi":"10.2307/2533377","isbn":null,"url":null},{"ref":"Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.","type":"article","doi":"10.1038/nrd1927","isbn":null,"url":null}],"related":["phase-ii-clinical-trial","adaptive-phase-ii-clinical-trial","bayesian-randomized-clinical-trial","bayesian-phase-i-clinical-trial","adaptive-randomized-clinical-trial","dose-response-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-phase-iii-clinical-trial","name":"Bayesian Phase III Clinical Trial","fullName":"Bayesian Phase III Confirmatory Clinical Trial","aliases":["Bayesian confirmatory trial","Bayesian RCT Phase III","Bayesian pivotal trial","BayesCT"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (widespread application)","originator":"Donald A. Berry; David J. Spiegelhalter (formalization in clinical context)","url":"https://scholargate.app/en/epidemiology/bayesian-phase-iii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-phase-iii-clinical-trial.md","definition":"A Bayesian Phase III clinical trial is a large-scale, confirmatory randomized controlled trial that uses Bayesian statistical inference rather than conventional frequentist hypothesis testing to evaluate whether an experimental treatment meets pre-defined efficacy and safety thresholds. By combining prior evidence with accumulating trial data, it quantifies the probability that the treatment effect exceeds a clinically meaningful threshold, enabling more transparent decision-making under uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald A. Berry; David J. Spiegelhalter (formalization in clinical context)","year":"1990s–2000s (widespread application)","type":"Confirmatory randomized controlled trial with Bayesian inference","dataType":"Randomized patient outcome data (continuous, binary, time-to-event)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley.","type":"book","doi":null,"isbn":"978-0471499756","url":null},{"ref":"Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.","type":"article","doi":"10.1038/nrd1927","isbn":null,"url":null}],"related":["randomized-clinical-trial","phase-iii-clinical-trial","adaptive-phase-iii-clinical-trial","bayesian-randomized-clinical-trial","bayesian-survival-analysis","sequential-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-phase-iv-study","name":"Bayesian Phase IV study","fullName":"Bayesian Phase IV Post-Marketing Study","aliases":["Bayesian post-marketing surveillance study","Bayesian pharmacovigilance study","Bayesian post-approval study","Bayesian phase 4 trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–1990s (formalized application to post-marketing settings)","originator":"Donald A. Berry and colleagues (applied Bayesian framework to clinical trials)","url":"https://scholargate.app/en/epidemiology/bayesian-phase-iv-study","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-phase-iv-study.md","definition":"A Bayesian Phase IV study is a post-marketing research design that applies Bayesian statistical inference to accumulate evidence about a drug or device already approved for clinical use. By formally combining prior evidence from earlier development phases with emerging real-world data, it enables continuous, probabilistic updating of safety and effectiveness estimates — moving beyond the binary hypothesis tests of conventional frequentist surveillance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald A. Berry and colleagues (applied Bayesian framework to clinical trials)","year":"1980s–1990s (formalized application to post-marketing settings)","type":"Observational or interventional post-marketing study with Bayesian inference","dataType":"Registry data, electronic health records, spontaneous adverse event reports, or controlled follow-up data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley.","type":"book","doi":null,"isbn":"978-0471499756","url":null},{"ref":"Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.","type":"article","doi":"10.1038/nrd1927","isbn":null,"url":null}],"related":["bayesian-adaptive-trial","pharmacovigilance","post-marketing-surveillance","signal-detection","comparative-effectiveness-research","registry-based-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-phylogenetic-analysis","name":"Bayesian Phylogenetic Analysis","fullName":"Bayesian Phylogenetic Analysis using Markov Chain Monte Carlo","aliases":["Bayesian phylogenetics","Bayesian inference of phylogeny","MCMC phylogenetics","Bayesian molecular phylogenetics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"1996–2001","originator":"Rannala & Yang (1996); operationalized by Huelsenbeck et al. (MrBayes, 2001)","url":"https://scholargate.app/en/bioinformatics/bayesian-phylogenetic-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-phylogenetic-analysis.md","definition":"Bayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and model parameters given observed sequence data. Unlike bootstrapped maximum-likelihood methods that return a single best tree, Bayesian inference yields a credible set of trees with associated posterior probabilities, providing a principled measure of phylogenetic uncertainty. It is the dominant framework for estimating divergence times and ancestral relationships in molecular evolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rannala & Yang (1996); operationalized by Huelsenbeck et al. (MrBayes, 2001)","year":"1996–2001","type":"Probabilistic inference method","dataType":"Aligned nucleotide or amino acid sequence data (DNA, RNA, protein)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Ronquist, F., & Huelsenbeck, J. P. (2003). MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics, 19(12), 1572–1574.","type":"article","doi":"10.1093/bioinformatics/btg180","isbn":null,"url":null},{"ref":"Drummond, A. J., & Rambaut, A. (2007). BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7(1), 214.","type":"article","doi":"10.1186/1471-2148-7-214","isbn":null,"url":null}],"related":["phylogenetic-analysis","sequence-alignment","maximum-likelihood-phylogenetics","molecular-clock-analysis","ancestral-sequence-reconstruction","bayesian-gwas"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-placebo-test","name":"Bayesian Placebo Test","fullName":"Bayesian Placebo Test for Causal Inference","aliases":["Bayesian falsification test","Bayesian permutation placebo","Bayesian robustness check","Bayesian in-time placebo"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010-2015","originator":"Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition)","url":"https://scholargate.app/en/causal-inference/bayesian-placebo-test","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-placebo-test.md","definition":"The Bayesian Placebo Test is a falsification strategy for causal inference that applies Bayesian inference to placebo scenarios — either fake treatments in the pre-intervention period, on unaffected units, or at fictitious cut-offs — to verify that observed treatment effects cannot plausibly arise by chance or from a misspecified model. It integrates prior information and yields posterior distributions of placebo effects for direct probabilistic comparison.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition)","year":"2010-2015","type":"Robustness check / falsification test","dataType":"Time series, panel, or cross-sectional observational data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null}],"related":["placebo-test","bayesian-causal-impact-analysis","bayesian-synthetic-control-method","sensitivity-analysis-for-causality","bayesian-difference-in-differences","causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-poisson-regression","name":"Bayesian Poisson Regression","fullName":"Bayesian Poisson Regression","aliases":["Bayesian log-linear count model","Bayesian GLM Poisson","Poisson regression with priors","Bayesian count regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s","originator":"Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989)","url":"https://scholargate.app/en/statistics/bayesian-poisson-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-poisson-regression.md","definition":"Bayesian Poisson regression models non-negative integer count outcomes using a Poisson likelihood with a log link, placing prior distributions on the regression coefficients. Posterior inference — combining prior beliefs with the data likelihood — produces full probability distributions over the coefficients rather than single-point estimates, enabling coherent uncertainty quantification and incorporation of domain knowledge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989)","year":"1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s","type":"Bayesian generalized linear model for count data","dataType":"Non-negative integer counts (discrete)","subfamily":"Regression / GLM"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall.","type":"book","doi":null,"isbn":"978-0412317606","url":null}],"related":["poisson-regression","bayesian-negative-binomial-regression","bayesian-generalized-linear-model","negative-binomial-regression","zero-inflated-model","bayesian-multiple-linear-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-power-analysis","name":"Bayesian Power Analysis","fullName":"Bayesian Power Analysis (Assurance / Bayesian Sample Size Determination)","aliases":["assurance","bayesian sample size determination","bayesian assurance","Bayesian Güç Analizi (Assurance / Bayesian Sample Size)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1986,"originator":"Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005)","url":"https://scholargate.app/en/statistics/bayesian-power-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-power-analysis.md","definition":"Bayesian power analysis — also called assurance — is a sample size determination method that replaces the frequentist notion of power with a probability-weighted average over a prior distribution on the effect size. First formalised by Spiegelhalter and Freedman (1986) and further developed by O'Hagan, Stevens and Campbell (2005), it answers the question: given our current uncertainty about the true effect, what sample size gives us a high overall probability of obtaining a statistically significant result?","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005)","year":1986,"family":"Power analysis","type":"Bayesian sample size determination","outcome":"continuous or binary","parametric":true,"framework":"Bayesian","keyConcept":"Assurance = E_θ[Power(θ)]","computation":"Monte Carlo / MCMC simulation","difficulty":3},"citations":[{"ref":"O'Hagan, A., Stevens, J.W. & Campbell, M.J. (2005). Assurance in Clinical Trial Design. Pharmaceutical Statistics, 4(3), 187–201.","type":"article","doi":"10.1002/pst.175","isbn":null,"url":null},{"ref":"Spiegelhalter, D.J. & Freedman, L.S. (1986). A Predictive Approach to Selecting the Size of a Clinical Trial, Based on Subjective Clinical Opinion. Statistics in Medicine, 5(1), 1–13.","type":"article","doi":"10.1002/sim.4780050103","isbn":null,"url":null}],"related":["simulation-based-power","sequential-analysis","frequentist-power-analysis","bayesian-t-test","prior-elicitation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-pp-unit-root-test","name":"Bayesian PP unit root test","fullName":"Bayesian Phillips-Perron Unit Root Test","aliases":["Bayesian PP test","Bayesian Phillips-Perron test","Bayesian nonparametric unit root test","Bayes PP unit root"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988 / early 1990s","originator":"Phillips & Perron (classical test, 1988); Bayesian framework: Sims & Uhlig (1991)","url":"https://scholargate.app/en/econometrics/bayesian-pp-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-pp-unit-root-test.md","definition":"The Bayesian Phillips-Perron unit root test combines the nonparametric long-run variance correction of the classical Phillips-Perron test with a Bayesian inferential framework. Instead of a p-value, it yields a posterior probability or Bayes factor quantifying evidence for or against a unit root, allowing researchers to incorporate prior economic knowledge and obtain probability statements directly about the persistence of a time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Phillips & Perron (classical test, 1988); Bayesian framework: Sims & Uhlig (1991)","year":"1988 / early 1990s","type":"Unit root test (Bayesian)","dataType":"Univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.","type":"article","doi":"10.1093/biomet/75.2.335","isbn":null,"url":null},{"ref":"Sims, C. A., & Uhlig, H. (1991). Understanding unit rooters: A helicopter tour. Econometrica, 59(6), 1591-1599.","type":"article","doi":"10.2307/2938280","isbn":null,"url":null}],"related":["phillips-perron-unit-root-test","augmented-dickey-fuller-unit-root-test","bayesian-adf-unit-root-test","bayesian-kpss-test","zivot-andrews-structural-break-test","bayesian-var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-principal-component-analysis","name":"Bayesian Principal Component Analysis","fullName":"Bayesian Principal Component Analysis","aliases":["BPCA","Bayesian PCA","probabilistic PCA with Bayesian inference","variational Bayesian PCA"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1999","originator":"Christopher M. Bishop","url":"https://scholargate.app/en/statistics/bayesian-principal-component-analysis","markdownUrl":"https://scholargate.app/en/statistics/bayesian-principal-component-analysis.md","definition":"Bayesian principal component analysis embeds probabilistic PCA within a Bayesian framework, placing priors over the loading matrix so that irrelevant components are automatically pruned. It handles missing data naturally and provides principled uncertainty estimates for both the latent scores and the dimensionality of the representation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher M. Bishop","year":"1999","type":"Bayesian latent variable / dimension reduction","dataType":"Continuous multivariate data (complete or incomplete)","subfamily":"Multivariate analysis"},"citations":[{"ref":"Bishop, C. M. (1999). Bayesian PCA. In M. S. Kearns, S. A. Solla & D. A. Cohn (Eds.), Advances in Neural Information Processing Systems 11 (pp. 382–388). MIT Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/1998/hash/c88d8d0a6097754525e02c2246d8d27f-Abstract.html"},{"ref":"Tipping, M. E. & Bishop, C. M. (1999). Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B, 61(3), 611–622.","type":"article","doi":"10.1111/1467-9868.00196","isbn":null,"url":null}],"related":["principal-component-analysis","exploratory-factor-analysis","bayesian-exploratory-factor-analysis","robust-principal-component-analysis","sparse-principal-component-analysis","bayesian-structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-probit-model","name":"Bayesian Probit model","fullName":"Bayesian Probit Regression Model","aliases":["Bayesian probit regression","probit model with data augmentation","Gibbs sampling probit","Albert-Chib probit"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1993","originator":"Albert & Chib (data augmentation formulation)","url":"https://scholargate.app/en/statistics/bayesian-probit-model","markdownUrl":"https://scholargate.app/en/statistics/bayesian-probit-model.md","definition":"The Bayesian Probit model is a binary regression method that models the probability of a binary outcome using the normal CDF (probit link) within a Bayesian framework. It assigns prior distributions to regression coefficients and updates them with observed data, yielding a full posterior distribution rather than a single point estimate. The Albert-Chib data-augmentation algorithm makes posterior sampling computationally efficient via Gibbs sampling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert & Chib (data augmentation formulation)","year":"1993","type":"Binary regression (Bayesian)","dataType":"Binary outcome, continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669-679.","type":"article","doi":"10.1080/01621459.1993.10476321","isbn":null,"url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["probit-model","bayesian-logistic-regression","logistic-regression","bayesian-ordinal-logistic-regression","bayesian-generalized-linear-model","bayesian-multinomial-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-process-capability-analysis","name":"Bayesian Process Capability Analysis","fullName":"Bayesian Process Capability Analysis","aliases":["Bayesian PCA","Bayesian capability indices","Bayesian Cp/Cpk estimation","Bayesian process performance analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"Classical PCA: 1986; Bayesian extensions: 1990s–2000s","originator":"Bayesian extensions developed by multiple authors including Bernardo, Smith, and Vannman; classical PCA by Juran and Kane (1986)","url":"https://scholargate.app/en/experimental-design/bayesian-process-capability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-process-capability-analysis.md","definition":"Bayesian Process Capability Analysis integrates Bayesian inference with classical capability indices (Cp, Cpk, Cpm) to estimate how well a production process meets specification limits. Rather than relying solely on observed sample data, it incorporates prior knowledge about process parameters — yielding more stable and credible estimates of process capability, especially under small sample sizes common in manufacturing and quality engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bayesian extensions developed by multiple authors including Bernardo, Smith, and Vannman; classical PCA by Juran and Kane (1986)","year":"Classical PCA: 1986; Bayesian extensions: 1990s–2000s","type":"Bayesian statistical quality method","dataType":"Continuous process measurement data (numeric)","subfamily":"Engineering methods"},"citations":[{"ref":"Kotz, S., & Johnson, N. L. (2002). Process Capability Indices — A Review, 1992–2000. Journal of Quality Technology, 34(1), 2–19.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Process+Capability+Indices+Review+1992+2000+Kotz+Johnson"},{"ref":"Vannman, K., & Kulahci, M. (2006). Bayesian Estimation of Process Capability Indices. Quality and Reliability Engineering International, 22(4), 393–412.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+Estimation+Process+Capability+Indices+Vannman+Kulahci+2006"}],"related":["process-capability-analysis","statistical-process-control","control-chart","bayesian-statistical-process-control","six-sigma-dmaic","bayesian-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-propensity-score-matching","name":"Bayesian Propensity Score Matching","fullName":"Bayesian Propensity Score Matching Estimator","aliases":["Bayesian PSM","BPSM","Bayesian matching estimator","Bayesian propensity weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012","originator":"Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)","url":"https://scholargate.app/en/causal-inference/bayesian-propensity-score-matching","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-propensity-score-matching.md","definition":"Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account of estimation uncertainty that frequentist matching commonly ignores, and allows incorporation of substantive prior knowledge about treatment selection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)","year":"2012","type":"Bayesian causal inference / matching","dataType":"Observational cross-sectional or panel data with binary treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.","type":"article","doi":"10.1007/s11336-012-9262-8","isbn":null,"url":null},{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null}],"related":["propensity-score-matching","bayesian-difference-in-differences","inverse-probability-weighting","coarsened-exact-matching","entropy-balancing","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-propensity-score-weighting","name":"Bayesian Propensity Score Weighting","fullName":"Bayesian Propensity Score Weighting for Causal Inference","aliases":["Bayesian PSW","Bayesian IPW","Bayesian inverse probability weighting","Bayesian propensity weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2009","originator":"McCandless, Gustafson & Austin","url":"https://scholargate.app/en/causal-inference/bayesian-propensity-score-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-propensity-score-weighting.md","definition":"Bayesian Propensity Score Weighting estimates causal treatment effects in observational data by combining a Bayesian model for the propensity score with inverse probability weighting. By placing a prior over propensity-score parameters and propagating posterior uncertainty through the weighting step, this approach yields fully probabilistic uncertainty intervals for the average treatment effect, accounting for the uncertainty in both the score model and the outcome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McCandless, Gustafson & Austin","year":"2009","type":"Bayesian causal weighting estimator","dataType":"Observational data with binary or multi-valued treatment, continuous or binary outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"McCandless, L. C., Gustafson, P., & Austin, P. C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine, 28(1), 94–112.","type":"article","doi":"10.1002/sim.3460","isbn":null,"url":null},{"ref":"Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279–288.","type":"article","doi":"10.1111/biom.12269","isbn":null,"url":null}],"related":["propensity-score-weighting","bayesian-difference-in-differences","inverse-probability-weighting","propensity-score-matching","marginal-structural-model","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-proteomics-analysis","name":"Bayesian Proteomics Analysis","fullName":"Bayesian Statistical Analysis of Proteomics Data","aliases":["Bayesian protein quantification","Bayesian peptide inference","probabilistic proteomics","Bayesian mass spectrometry analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2000s (major developments 2003–2010)","originator":"Multiple contributors; foundational statistical frameworks by Nesvizhskii, Kall, Choi, and colleagues","url":"https://scholargate.app/en/bioinformatics/bayesian-proteomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-proteomics-analysis.md","definition":"Bayesian proteomics analysis applies probabilistic models to mass spectrometry data to identify peptides, infer protein presence, and quantify differential protein abundance across conditions. By encoding prior knowledge and propagating uncertainty through each step of the pipeline, Bayesian approaches produce calibrated posterior probabilities of identification and quantification rather than simple point estimates, enabling more principled control of false discovery rates and more honest reporting of uncertainty than purely frequentist alternatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; foundational statistical frameworks by Nesvizhskii, Kall, Choi, and colleagues","year":"2000s (major developments 2003–2010)","type":"Probabilistic inference pipeline","dataType":"Mass spectrometry data (peptide spectrum matches, protein abundance measurements)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Kall, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2008). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods, 5(11), 923–925.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-supervised+learning+for+peptide+identification+from+shotgun+proteomics+datasets+Kall+2008"},{"ref":"Choi, H., & Nesvizhskii, A. I. (2008). Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics. Journal of Proteome Research, 7(1), 254–265.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semisupervised+model-based+validation+of+peptide+identifications+Choi+Nesvizhskii+2008"}],"related":["proteomics-analysis","bayesian-metabolomics-analysis","rna-seq-differential-expression","bayesian-rna-seq-differential-expression","variant-calling","pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-quality-function-deployment","name":"Bayesian Quality Function Deployment","fullName":"Bayesian Quality Function Deployment","aliases":["Bayesian QFD","Probabilistic QFD","Bayesian House of Quality","Bayesian Voice of the Customer Analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"QFD: 1966–1972; Bayesian QFD extensions: 2000s–present","originator":"Yoji Akao (QFD); Bayesian extension developed by multiple researchers including Fung, Tang, and colleagues","url":"https://scholargate.app/en/experimental-design/bayesian-quality-function-deployment","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-quality-function-deployment.md","definition":"Bayesian Quality Function Deployment (Bayesian QFD) integrates Bayesian probabilistic inference into the classical House of Quality framework to handle uncertainty in customer preference data and relationship matrices. By expressing relationship weights and importance ratings as probability distributions rather than point estimates, it propagates uncertainty through the planning process and yields more defensible engineering prioritization decisions under incomplete or conflicting customer information.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yoji Akao (QFD); Bayesian extension developed by multiple researchers including Fung, Tang, and colleagues","year":"QFD: 1966–1972; Bayesian QFD extensions: 2000s–present","type":"Probabilistic customer-driven design planning method","dataType":"Customer preference ratings, engineering parameter data, expert judgments (continuous and ordinal)","subfamily":"Engineering methods"},"citations":[{"ref":"Tang, J., Fung, R. Y. K., Xu, B., & Wang, D. (2002). A new approach to quality function deployment planning with financial consideration. Computers & Operations Research, 29(11), 1447–1463.","type":"article","doi":"10.1016/S0305-0548(01)00041-7","isbn":null,"url":null},{"ref":"Quality function deployment. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Quality_function_deployment"}],"related":["quality-function-deployment","bayesian-failure-mode-and-effects-analysis","bayesian-design-of-experiments","design-of-experiments","robust-quality-function-deployment","multi-response-quality-function-deployment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-quantile-on-quantile-regression","name":"Bayesian Quantile-on-Quantile Regression","fullName":"Bayesian Quantile-on-Quantile Regression","aliases":["Bayesian QQR","Bayesian QQ regression","Bayes quantile-on-quantile","BQQ regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2015–2019","originator":"Bayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001)","url":"https://scholargate.app/en/econometrics/bayesian-quantile-on-quantile-regression","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-quantile-on-quantile-regression.md","definition":"Bayesian Quantile-on-Quantile (BQQ) Regression extends the Sim-Zhou quantile-on-quantile framework by replacing frequentist local linear estimation with Bayesian posterior inference. For each pair of quantiles (theta of the outcome, tau of the predictor), the method yields a full posterior distribution over the slope, enabling uncertainty quantification across the entire bivariate quantile surface — a key advantage when sample sizes are moderate and tail quantiles are sparse.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bayesian QQ framework combines Sim & Zhou (2015) QQ regression with Bayesian quantile regression (Yu & Moyeed, 2001)","year":"2015–2019","type":"Nonparametric quantile regression with Bayesian estimation","dataType":"Continuous time-series or cross-sectional data; bivariate or multivariate","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1–8.","type":"article","doi":"10.1016/j.jbankfin.2015.01.013","isbn":null,"url":null},{"ref":"Yu, K., & Moyeed, R. A. (2001). Bayesian quantile regression. Statistics and Probability Letters, 54(4), 437–447.","type":"article","doi":"10.1016/S0167-7152(01)00124-9","isbn":null,"url":null}],"related":["quantile-on-quantile-regression","quantile-regression","bayesian-var-model","bayesian-ardl-bounds-test","nonlinear-ardl","bayesian-vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-quantile-regression","name":"Bayesian Quantile Regression","fullName":"Bayesian Quantile Regression","aliases":["BQR","Bayesian quantile regression model","asymmetric Laplace Bayesian regression","posterior quantile regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"2001–2011","originator":"Kozumi & Kobayashi; building on Yu & Moyeed (2001)","url":"https://scholargate.app/en/statistics/bayesian-quantile-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-quantile-regression.md","definition":"Bayesian Quantile Regression estimates the full posterior distribution of regression coefficients at any chosen quantile of the outcome. By combining the asymmetric Laplace likelihood with prior distributions over the coefficients, it delivers uncertainty-quantified estimates of conditional quantiles — such as the median, the 10th, or the 90th percentile — without assuming Gaussian errors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kozumi & Kobayashi; building on Yu & Moyeed (2001)","year":"2001–2011","type":"Bayesian semiparametric regression","dataType":"Continuous, count, or skewed outcome; any scale predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578.","type":"article","doi":"10.1080/00949655.2010.496117","isbn":null,"url":null},{"ref":"Yu, K., & Zhang, J. (2005). A three-parameter asymmetric Laplace distribution and its extension. Communications in Statistics – Theory and Methods, 34(9–10), 1867–1879.","type":"article","doi":"10.1080/03610920500199018","isbn":null,"url":null}],"related":["quantile-regression","bayesian-multiple-linear-regression","bayesian-robust-regression","robust-quantile-regression","bayesian-generalized-linear-model","bayesian-tobit-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-quantitative-content-analysis","name":"Bayesian Quantitative Content Analysis","fullName":"Bayesian Quantitative Content Analysis","aliases":["Bayesian content analysis","Bayesian text analysis","probabilistic content analysis","BQCA"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1990s–2000s (convergence of content analysis and Bayesian statistics)","originator":"Integration of Krippendorff's content analysis framework with Bayesian statistical inference (Gelman et al.)","url":"https://scholargate.app/en/research-design/bayesian-quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/bayesian-quantitative-content-analysis.md","definition":"Bayesian quantitative content analysis systematically codes and counts features in textual or media content, then quantifies patterns and tests hypotheses using Bayesian statistical inference. Unlike classical frequency-based content analysis, it incorporates prior knowledge or domain expectations into the estimation process, producing posterior probability distributions over content parameters rather than single point estimates with p-values. The approach is particularly valuable when prior research, expert knowledge, or pilot data exist and when uncertainty quantification around content proportions and category frequencies is important.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Integration of Krippendorff's content analysis framework with Bayesian statistical inference (Gelman et al.)","year":"1990s–2000s (convergence of content analysis and Bayesian statistics)","type":"Quantitative research design","dataType":"Coded text, media content, documents, categorical/frequency data","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506395661","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["quantitative-content-analysis","bayesian-confirmatory-research","bayesian-correlational-research","longitudinal-quantitative-content-analysis","multivariate-quantitative-content-analysis","comparative-quantitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-queueing-simulation","name":"Bayesian Queueing Simulation","fullName":"Bayesian Queueing Simulation — Bayesian parameter inference integrated with stochastic queueing simulation","aliases":["BQS","Bayesian Queue Simulation","Bayesian Stochastic Queueing","Bayesian Queuing Analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1994","originator":"Armero, C. & Bayarri, M. J.","url":"https://scholargate.app/en/simulation/bayesian-queueing-simulation","markdownUrl":"https://scholargate.app/en/simulation/bayesian-queueing-simulation.md","definition":"Bayesian Queueing Simulation combines Bayesian statistical inference with stochastic queueing simulation to model waiting-line systems under parameter uncertainty. Instead of treating arrival and service rates as fixed known values, it places prior distributions over them, updates these with observed data to obtain posteriors, and propagates the resulting parameter uncertainty through repeated simulation runs to produce probabilistic predictions of system performance metrics such as queue length, waiting time, and server utilisation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Armero, C. & Bayarri, M. J.","year":"1994","type":"Bayesian inference + stochastic simulation","dataType":"Observed arrival/service counts, prior distributions on rates","subfamily":"Simulation / optimization"},"citations":[{"ref":"Armero, C., & Bayarri, M. J. (1994). Bayesian prediction in M/M/1 queues. Queueing Systems, 15(1–4), 401–417.","type":"article","doi":"10.1007/BF01189248","isbn":null,"url":null},{"ref":"Insua, D. R., Wiper, M., & Ruggeri, F. (1998). Bayesian analysis of M/Er/1 and M/H_k/1 queues. Queueing Systems, 30(3–4), 289–308.","type":"article","doi":"10.1023/a:1019173206509","isbn":null,"url":null}],"related":["monte-carlo-simulation","discrete-event-simulation","queueing-simulation","bayesian-monte-carlo-simulation","stochastic-queueing-simulation","markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-random-effects-model","name":"Bayesian Random Effects Model","fullName":"Bayesian Random Effects Model","aliases":["Bayesian hierarchical model","Bayesian mixed effects model","Bayesian multilevel model","BREM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1972–1995","originator":"Lindley & Smith (1972); extended by Gelman, Rubin and colleagues","url":"https://scholargate.app/en/econometrics/bayesian-random-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-random-effects-model.md","definition":"The Bayesian random effects model combines panel-data random effects with a Bayesian prior framework, allowing unit-specific effects to be treated as draws from a population distribution whose hyperparameters are estimated from the data. This produces regularised, uncertainty-quantified estimates that borrow strength across units — particularly valuable for short panels, sparse groups, or settings where frequentist variance-component estimation is unstable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lindley & Smith (1972); extended by Gelman, Rubin and colleagues","year":"1972–1995","type":"Bayesian hierarchical panel model","dataType":"Panel / longitudinal / time-series cross-section data","subfamily":"Econometrics / time series"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107038691","url":null}],"related":["panel-random-effects","panel-fixed-effects","hierarchical-linear-model","bayesian-var","mixed-effects-model","ols-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-random-forest","name":"Bayesian Random Forest","fullName":"Bayesian Random Forest (Bayesian Ensemble of Decision Trees)","aliases":["Bayesian Forest","BRF","Empirical Bayesian Forest","posterior random forest"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2015","originator":"Taddy, M. et al.","url":"https://scholargate.app/en/machine-learning/bayesian-random-forest","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-random-forest.md","definition":"Bayesian Random Forest extends the classical random forest by placing a prior distribution over tree structures and leaf parameters, then sampling or approximating the posterior over that ensemble. The result is a set of predictions accompanied by calibrated uncertainty estimates — a capability standard random forests lack — making it valuable when knowing how confident the model is matters as much as the prediction itself.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Taddy, M. et al.","year":"2015","type":"Bayesian ensemble of decision trees","dataType":"Tabular (continuous, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Taddy, M., Chen, C., Yu, J., & Wyle, M. (2015). Bayesian and Empirical Bayesian Forests. Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), PMLR 37, 967–976.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v37/taddy15.html"},{"ref":"Lakshminarayanan, B., Roy, D. M., & Teh, Y. W. (2016). Mondrian Forests for Large-Scale Regression when Uncertainty Matters. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016), PMLR 51, 1478–1487.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v51/lakshminarayanan16.html"}],"related":["random-forest","bayesian-decision-tree","gaussian-process","bayesian-gradient-boosting","bayesian-active-learning","bayesian-semi-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-randomized-clinical-trial","name":"Bayesian Randomized Clinical Trial","fullName":"Bayesian Randomized Clinical Trial","aliases":["Bayesian RCT","Bayesian adaptive trial","Bayesian clinical trial design","BRCT"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–2000s (formal methodology consolidated ~2004–2006)","originator":"Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)","url":"https://scholargate.app/en/epidemiology/bayesian-randomized-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-randomized-clinical-trial.md","definition":"A Bayesian randomized clinical trial (Bayesian RCT) combines the rigour of random treatment allocation with Bayesian statistical inference, allowing researchers to incorporate prior evidence and update beliefs continuously as trial data accumulate. Unlike the classical frequentist RCT, it yields direct probability statements about treatment effects and supports pre-specified adaptive stopping rules based on posterior probabilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)","year":"1980s–2000s (formal methodology consolidated ~2004–2006)","type":"Randomized experimental study with Bayesian inference","dataType":"Individual patient-level outcome data from randomized arms; prior distributions from historical studies or expert elicitation","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley.","type":"book","doi":null,"isbn":"978-0471499756","url":null},{"ref":"Berry, D. A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5(1), 27–36.","type":"article","doi":"10.1038/nrd1927","isbn":null,"url":null}],"related":["randomized-clinical-trial","adaptive-randomized-clinical-trial","bayesian-survival-analysis","bayesian-diagnostic-accuracy-study","pragmatic-randomized-clinical-trial","phase-ii-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-regression-discontinuity-design","name":"Bayesian Regression Discontinuity Design","fullName":"Bayesian Regression Discontinuity Design","aliases":["Bayesian RDD","Bayesian RD","Bayes RDD","Bayesian regression-discontinuity"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2004-2016","originator":"Karabatsos & Walker; Chib & Jacobi","url":"https://scholargate.app/en/causal-inference/bayesian-regression-discontinuity-design","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-regression-discontinuity-design.md","definition":"Bayesian Regression Discontinuity Design (Bayesian RDD) embeds the classical RD framework — which estimates a local causal effect at a known assignment cutoff — within a Bayesian inferential engine. Prior distributions are placed on the regression functions on either side of the cutoff and on the treatment-effect parameter, yielding a full posterior distribution over the causal estimand rather than a single point estimate with a frequentist p-value.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karabatsos & Walker; Chib & Jacobi","year":"2004-2016","type":"Bayesian causal inference / quasi-experimental","dataType":"Cross-sectional or panel data with a continuous running variable and a known cutoff","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Karabatsos, G., & Walker, S. G. (2004). Coherent inference in regression discontinuity designs with a Bayesian nonparametric approach. Journal of the American Statistical Association, 99(468), 1121-1131.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Karabatsos+Walker+2004+Bayesian+nonparametric+regression+discontinuity"},{"ref":"Chib, S., & Jacobi, L. (2016). Bayesian fuzzy regression discontinuity analysis and returns to compulsory schooling. Journal of Applied Econometrics, 31(6), 1026-1047.","type":"article","doi":"10.1002/jae.2481","isbn":null,"url":null}],"related":["regression-discontinuity-design","fuzzy-regression-discontinuity","bayesian-difference-in-differences","instrumental-variables","local-average-treatment-effect","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-regression","name":"Bayesian Regression","fullName":"Bayesian Linear Regression","aliases":["bayesian linear regression","probabilistic regression","bayesian regresyon"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/bayesian/bayesian-regression","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-regression.md","definition":"Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Bayesian linear model","purpose":"predict / relationship","var_types":"continuous / binary","inference":"MCMC / variational","outputs":"posterior distributions / credible intervals"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["ols-regression","mcmc","hierarchical-bayes"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-ridge-regression","name":"Bayesian Ridge Regression","fullName":"Bayesian Ridge Regression (MacKay Probabilistic Regularisation)","aliases":["BRR","Bayesian linear regression with automatic relevance determination","evidence approximation ridge","marginal likelihood ridge"],"domain":"machine-learning","family":"bayesian","subfamily":null,"year":1992,"originator":"MacKay, D. J. C.","url":"https://scholargate.app/en/machine-learning/bayesian-ridge-regression","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-ridge-regression.md","definition":"Bayesian Ridge Regression is a probabilistic formulation of ridge regression, introduced by David J. C. MacKay in 1992, in which the regularisation strength and noise precision are not fixed by the analyst but are instead estimated automatically by maximising the marginal likelihood (evidence) of the observed data. The result is a full posterior distribution over the regression weights together with calibrated predictive uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"MacKay, D. J. C.","year":1992,"type":"Probabilistic regularised regression","task":"Regression with automatic regularisation","minSample":20},"citations":[{"ref":"MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447.","type":"article","doi":"10.1162/neco.1992.4.3.415","isbn":null,"url":null},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 3). Springer.","type":"book","doi":null,"isbn":"978-0-387-31073-2","url":null}],"related":["ridge-regression","lasso-regression","elastic-net","gaussian-process-regression","linear-regression","ard-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-rna-seq-differential-expression","name":"Bayesian RNA-seq differential expression","fullName":"Bayesian Differential Expression Analysis of RNA Sequencing Data","aliases":["Bayesian DE analysis","Bayesian RNA-seq DE","baySeq","EBSeq"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010–2013","originator":"Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)","url":"https://scholargate.app/en/bioinformatics/bayesian-rna-seq-differential-expression","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-rna-seq-differential-expression.md","definition":"Bayesian RNA-seq differential expression analysis applies hierarchical Bayesian models to RNA sequencing read-count data to identify genes whose expression levels differ significantly between biological conditions. Rather than relying solely on p-values, these methods quantify the posterior probability that a gene is differentially expressed, borrowing statistical strength across genes and naturally accommodating low sample sizes common in genomics experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)","year":"2010–2013","type":"Bayesian statistical inference pipeline","dataType":"RNA-seq read count matrices (integer counts per gene/transcript per sample)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Leng, N., Dawson, J. A., Thomson, J. A., Ruotti, V., Rissman, A. I., Smits, B. M., Haag, J. D., Gould, M. N., Stewart, R. M., & Kendziorski, C. (2013). EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics, 29(8), 1035–1043.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=EBSeq+An+empirical+Bayes+hierarchical+model+for+inference+in+RNA-seq+experiments"},{"ref":"Hardcastle, T. J., & Kelly, K. A. (2010). baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics, 11, 422.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=baySeq+Empirical+Bayesian+methods+for+identifying+differential+expression+in+sequence+count+data"}],"related":["rna-seq-differential-expression","gene-set-enrichment-analysis","pathway-enrichment-analysis","single-cell-rna-seq-analysis","bayesian-gwas","variant-calling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-robust-regression","name":"Bayesian Robust Regression","fullName":"Bayesian Robust Regression","aliases":["Bayesian heavy-tailed regression","Bayesian Student-t regression","robust Bayesian linear model","BRR"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1993","originator":"Geweke (1993); Gelman et al. (2013)","url":"https://scholargate.app/en/statistics/bayesian-robust-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-robust-regression.md","definition":"Bayesian Robust Regression replaces the Gaussian error assumption of ordinary linear regression with a heavy-tailed distribution — most commonly the Student-t — and estimates all parameters in a Bayesian framework. The heavier tails give outliers less influence on the fitted line, yielding stable coefficient estimates and honest uncertainty intervals even when the data contain unusual observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geweke (1993); Gelman et al. (2013)","year":"1993","type":"Bayesian regression with heavy-tailed errors","dataType":"Continuous outcome, continuous or categorical predictors; outlier-prone data","subfamily":"Regression / GLM"},"citations":[{"ref":"Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.","type":"article","doi":"10.1002/jae.3950080504","isbn":null,"url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["robust-regression","bayesian-multiple-linear-regression","bayesian-quantile-regression","quantile-regression","ols-regression","bayesian-generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-root-cause-analysis","name":"Bayesian Root Cause Analysis","fullName":"Bayesian Network-Based Root Cause Analysis","aliases":["Bayesian RCA","Bayesian causal analysis","probabilistic root cause analysis","BN-RCA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s","originator":"Rooted in Pearl's Bayesian network theory (Judea Pearl, 1988); applied to RCA in process/reliability engineering from the 1990s onward","url":"https://scholargate.app/en/experimental-design/bayesian-root-cause-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-root-cause-analysis.md","definition":"Bayesian Root Cause Analysis (Bayesian RCA) integrates Bayesian network theory with structured root cause investigation to quantify the probability that each candidate cause is responsible for an observed failure or undesired event. Unlike deterministic RCA methods, it propagates uncertainty through the causal graph, updates beliefs as evidence accumulates, and ranks competing hypotheses by posterior probability — providing a principled, auditable basis for corrective action.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Pearl's Bayesian network theory (Judea Pearl, 1988); applied to RCA in process/reliability engineering from the 1990s onward","year":"1990s–2000s","type":"Probabilistic causal inference method","dataType":"Failure event records, expert probability estimates, historical process data","subfamily":"Engineering methods"},"citations":[{"ref":"Pourret, O., Naim, P., & Marcot, B. (Eds.). (2008). Bayesian Networks: A Practical Guide to Applications. Wiley.","type":"book","doi":null,"isbn":"978-0470060308","url":null},{"ref":"Weber, P., Medina-Oliva, G., Simon, C., & Iung, B. (2012). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25(4), 671–682.","type":"article","doi":"10.1016/j.engappai.2010.06.002","isbn":null,"url":null}],"related":["fault-tree-analysis","failure-mode-and-effects-analysis","root-cause-analysis","bayesian-fault-tree-analysis","bayesian-failure-mode-and-effects-analysis","event-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-sarima-model","name":"Bayesian SARIMA Model","fullName":"Bayesian Seasonal Autoregressive Integrated Moving Average Model","aliases":["Bayesian SARIMA","Bayesian seasonal ARIMA","BSARIMA","Bayesian seasonal time-series model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970s–1990s","originator":"Box & Jenkins (classical SARIMA); Bayesian extensions developed through Zellner, Geweke, and later MCMC-era researchers","url":"https://scholargate.app/en/econometrics/bayesian-sarima-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-sarima-model.md","definition":"The Bayesian SARIMA model combines the classical Box-Jenkins Seasonal ARIMA framework with Bayesian inference to handle seasonal time-series data. Rather than producing a single point estimate, it yields a full posterior distribution over model parameters, propagating parameter uncertainty directly into forecasts and enabling principled incorporation of prior knowledge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Jenkins (classical SARIMA); Bayesian extensions developed through Zellner, Geweke, and later MCMC-era researchers","year":"1970s–1990s","type":"Bayesian time-series model","dataType":"Univariate seasonal time-series (equally spaced observations)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118675021","url":null},{"ref":"Geweke, J., & Whiteman, C. (2006). Bayesian forecasting. In G. Elliott, C. W. J. Granger, & A. Timmermann (Eds.), Handbook of Economic Forecasting (Vol. 1, pp. 3–80). Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+forecasting+Geweke+Whiteman+Handbook+Economic+Forecasting+2006"}],"related":["arima-model","sarima-model","bayesian-var-model","state-space-model","exponential-smoothing","dynamic-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-scale-development","name":"Bayesian Scale Development","fullName":"Bayesian Scale Development","aliases":["Bayesian psychometric scale construction","Bayesian measurement modeling","Bayesian item development","BSD"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Harold Jeffreys, expanded into psychometrics by Mislevy and colleagues","url":"https://scholargate.app/en/psychometrics/bayesian-scale-development","markdownUrl":"https://scholargate.app/en/psychometrics/bayesian-scale-development.md","definition":"Bayesian scale development applies Bayesian statistical inference to the construction and evaluation of psychometric scales. Rather than relying on single point estimates of item and person parameters, it produces full posterior distributions that quantify uncertainty, incorporate prior knowledge, and support principled decisions about item retention, reliability, and validity in small or complex samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Jeffreys, expanded into psychometrics by Mislevy and colleagues","year":"1990s–2000s","type":"Bayesian probabilistic scale construction","dataType":"Ordinal / polytomous item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"De Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Guilford Press.","type":"book","doi":null,"isbn":"978-1593858698","url":null},{"ref":"Levy, R., & Mislevy, R. J. (2016). Bayesian Psychometric Modeling. CRC Press / Chapman & Hall.","type":"article","doi":null,"isbn":"978-1439884676","url":null}],"related":["confirmatory-factor-analysis","exploratory-factor-analysis","item-response-theory","scale-development","bayesian-confirmatory-factor-analysis","bayesian-item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-scenario-analysis","name":"Bayesian Scenario Analysis","fullName":"Bayesian Scenario Analysis — Probabilistic scenario weighting via Bayesian inference","aliases":["BSA","Bayesian scenario planning","probabilistic scenario analysis","Bayesian-weighted scenario analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s","originator":"Developed iteratively across Bayesian statistics and scenario planning communities; formalized in risk and decision analysis (Aven, Lempert et al., 2000s)","url":"https://scholargate.app/en/simulation/bayesian-scenario-analysis","markdownUrl":"https://scholargate.app/en/simulation/bayesian-scenario-analysis.md","definition":"Bayesian Scenario Analysis (BSA) combines structured scenario planning with Bayesian probability theory, assigning explicit prior probabilities to alternative futures and updating them as new evidence or expert judgments become available. The result is a probability-weighted distribution of outcomes across scenarios rather than a set of equally-weighted or arbitrarily-weighted futures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed iteratively across Bayesian statistics and scenario planning communities; formalized in risk and decision analysis (Aven, Lempert et al., 2000s)","year":"2000s","type":"Probabilistic hybrid — Bayesian inference integrated with structured scenario analysis","dataType":"Qualitative scenarios with quantitative prior/likelihood inputs; expert elicitation; historical frequency data","subfamily":"Simulation / optimization"},"citations":[{"ref":"Aven, T., & Reniers, G. (2013). How to define and interpret a probability in a risk and safety setting. Safety Science, 51(1), 223–231.","type":"article","doi":"10.1016/j.ssci.2012.06.005","isbn":null,"url":null},{"ref":"Lempert, R. J., Popper, S. W., & Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation.","type":"book","doi":null,"isbn":"9780833032973","url":null}],"related":["scenario-analysis","bayesian-sensitivity-analysis","stochastic-scenario-analysis","monte-carlo-simulation","robust-scenario-analysis","markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-screening-test-evaluation","name":"Bayesian Screening Test Evaluation","fullName":"Bayesian Screening Test Evaluation","aliases":["Bayesian diagnostic test evaluation","Bayesian predictive value analysis","posterior predictive value approach","Bayes theorem screening"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1763 (theorem); clinical screening application formalized ~1959–1970s","originator":"Thomas Bayes (theorem, 1763); applied to clinical screening by Ledley & Lusted (1959)","url":"https://scholargate.app/en/epidemiology/bayesian-screening-test-evaluation","markdownUrl":"https://scholargate.app/en/epidemiology/bayesian-screening-test-evaluation.md","definition":"Bayesian screening test evaluation applies Bayes' theorem to quantify how a screening test result changes the probability that an individual truly has a disease. Rather than reporting sensitivity and specificity in isolation, the approach centres on predictive values — the probability of disease given a positive or negative test — which depend critically on disease prevalence in the population being screened. The framework allows systematic updating of pre-test probability to post-test probability and supports decision-making under uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Bayes (theorem, 1763); applied to clinical screening by Ledley & Lusted (1959)","year":"1763 (theorem); clinical screening application formalized ~1959–1970s","type":"Bayesian analytical framework for test evaluation","dataType":"Binary or ordinal test results, disease status (2×2 contingency tables), prevalence estimates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2014). Clinical Epidemiology: The Essentials (5th ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-1451144475","url":null},{"ref":"Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 2: Predictive values. BMJ, 309(6947), 102.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Diagnostic+tests+2%3A+Predictive+values+Altman"}],"related":["diagnostic-accuracy-study","screening-test-evaluation","bayesian-diagnostic-accuracy-study","cohort-study","case-control-study","dose-response-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-sem","name":"Bayesian SEM","fullName":"Bayesian Structural Equation Modeling","aliases":["BSEM","Bayesian latent variable model","approximate zero constraints SEM","Bayesçi Yapısal Eşitlik Modeli"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2012,"originator":"Bengt Muthén & Tihomir Asparouhov","url":"https://scholargate.app/en/bayesian/bayesian-sem","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-sem.md","definition":"Bayesian SEM, introduced by Muthén and Asparouhov in 2012, extends classical structural equation modeling by placing prior distributions on factor loadings, path coefficients, and covariances. Instead of returning a single maximum-likelihood estimate, it uses Markov chain Monte Carlo to produce a full posterior distribution for every parameter, enabling principled uncertainty quantification in models with latent variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengt Muthén & Tihomir Asparouhov","year":2012,"family":"Bayesian","type":"Bayesian latent variable model","purpose":"relationship / mediation / prediction","var_types":"continuous / ordinal","structures":"cross-sectional / longitudinal","min_sample":50,"inference":"MCMC","outputs":"posterior distributions / credible intervals / PPP fit index","difficulty":3},"citations":[{"ref":"Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+SEM%3A+A+More+Flexible+Representation+of+Substantive+Theory+Muth%C3%A9n"}],"related":["sem","cfa","bayesian-regression","bayesian-hierarchical-model","latent-growth-curve","mcmc"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-semi-supervised-learning","name":"Bayesian Semi-supervised Learning","fullName":"Bayesian Semi-supervised Learning (Probabilistic Inference with Labeled and Unlabeled Data)","aliases":["Bayesian SSL","probabilistic semi-supervised learning","generative semi-supervised model","Bayesian transductive learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2003–2006","originator":"Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty","url":"https://scholargate.app/en/machine-learning/bayesian-semi-supervised-learning","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-semi-supervised-learning.md","definition":"Bayesian semi-supervised learning is a probabilistic framework that uses both a small labeled dataset and a larger pool of unlabeled observations to infer model parameters and make predictions. By treating missing labels as latent variables and placing priors over parameters, it naturally quantifies uncertainty while leveraging unlabeled data to improve generalization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chapelle, Scholkopf & Zien; Zhu, Ghahramani & Lafferty","year":"2003–2006","type":"Probabilistic semi-supervised framework","dataType":"Labeled and unlabeled tabular, text, or image data","subfamily":"Machine learning"},"citations":[{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null},{"ref":"Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning (ICML), 912–919.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-supervised+learning+using+Gaussian+fields+and+harmonic+functions"}],"related":["semi-supervised-learning","bayesian-active-learning","gaussian-process","bayesian-gaussian-mixture-model","transfer-learning","few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-sensitivity-analysis-for-causality","name":"Bayesian Sensitivity Analysis for Causality","fullName":"Bayesian Sensitivity Analysis for Unmeasured Confounding in Causal Inference","aliases":["Bayesian sensitivity analysis","Bayesian bias analysis","probabilistic sensitivity analysis for confounding","Bayesian unmeasured confounding analysis"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"McCandless, Gustafson & Austin (2007); Gustafson (2015)","url":"https://scholargate.app/en/causal-inference/bayesian-sensitivity-analysis-for-causality","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-sensitivity-analysis-for-causality.md","definition":"Bayesian sensitivity analysis for causality quantifies how much an unmeasured confounder would need to influence both treatment assignment and outcome to overturn a causal conclusion. Rather than testing a single worst-case scenario, it places prior distributions over the strength of hidden confounding, propagates uncertainty through a full Bayesian model, and reports a posterior distribution for the causal effect that honestly reflects what is and is not identified from observed data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McCandless, Gustafson & Austin (2007); Gustafson (2015)","year":"2000s–2010s","type":"Bayesian causal sensitivity analysis","dataType":"Observational data with binary or continuous treatment and outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718.","type":"article","doi":"10.1002/sim.3460","isbn":null,"url":null},{"ref":"Gustafson, P. (2015). Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data. CRC Press / Chapman & Hall.","type":"book","doi":null,"isbn":"9781439869390","url":null}],"related":["sensitivity-analysis-for-causality","bayesian-difference-in-differences","propensity-score-matching","instrumental-variables","doubly-robust-estimation","marginal-structural-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-sensitivity-analysis","name":"Bayesian Sensitivity Analysis","fullName":"Bayesian Sensitivity Analysis — Prior-informed uncertainty propagation and output sensitivity assessment","aliases":["BSA","Bayesian SA","Bayesian robustness analysis","prior sensitivity analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1984–1994","originator":"Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)","url":"https://scholargate.app/en/simulation/bayesian-sensitivity-analysis","markdownUrl":"https://scholargate.app/en/simulation/bayesian-sensitivity-analysis.md","definition":"Bayesian Sensitivity Analysis (BSA) combines Bayesian inference with sensitivity analysis to systematically quantify how uncertain model inputs — expressed as prior probability distributions — propagate through a model and influence outputs. It identifies which parameters most drive output variability, supporting robust conclusions under genuine uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)","year":"1984–1994","type":"Uncertainty propagation and sensitivity quantification","dataType":"Probabilistic inputs, prior distributions, model parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124.","type":"article","doi":"10.1007/BF02562676","isbn":null,"url":null},{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"9780470059975","url":null}],"related":["monte-carlo-simulation","stochastic-sensitivity-analysis","bayesian-markov-model","scenario-analysis","markov-model","bayesian-dynamic-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-sequence-alignment","name":"Bayesian Sequence Alignment","fullName":"Bayesian Probabilistic Sequence Alignment","aliases":["Bayesian MSA","probabilistic sequence alignment","statistical alignment","BAli-Phy alignment"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2001–2005","originator":"Ian Holmes & William J. Bruno; Benjamin Redelings & Marc Suchard","url":"https://scholargate.app/en/bioinformatics/bayesian-sequence-alignment","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-sequence-alignment.md","definition":"Bayesian sequence alignment treats the alignment of biological sequences (DNA, RNA, or protein) as a probabilistic inference problem rather than a deterministic optimization. Instead of returning a single best alignment, it samples from a posterior distribution over all plausible alignments given a substitution model and gap penalty priors, thereby quantifying alignment uncertainty. It is particularly valuable when downstream analyses such as phylogenetic inference or functional annotation are sensitive to alignment error.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ian Holmes & William J. Bruno; Benjamin Redelings & Marc Suchard","year":"2001–2005","type":"Probabilistic computational method","dataType":"Nucleotide or protein sequence data (FASTA/aligned FASTA)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Redelings, B. D., & Suchard, M. A. (2005). Joint Bayesian estimation of alignment and phylogeny. Systematic Biology, 54(3), 401–418.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Joint+Bayesian+estimation+of+alignment+and+phylogeny+Redelings+Suchard+2005"},{"ref":"Holmes, I., & Bruno, W. J. (2001). Evolutionary HMMs: a Bayesian approach to multiple alignment. Bioinformatics, 17(9), 803–820.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evolutionary+HMMs+a+Bayesian+approach+to+multiple+alignment+Holmes+Bruno+2001"}],"related":["sequence-alignment","phylogenetic-analysis","bayesian-phylogenetic-analysis","variant-calling","rna-seq-differential-expression","hidden-markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-simple-linear-regression","name":"Bayesian Simple linear regression","fullName":"Bayesian Simple Linear Regression","aliases":["Bayesian SLR","Bayesian univariate regression","probabilistic simple linear regression","Bayesian linear model"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"Early 19th century; textbook synthesis 2013","originator":"Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.","url":"https://scholargate.app/en/statistics/bayesian-simple-linear-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-simple-linear-regression.md","definition":"Bayesian Simple Linear Regression models the relationship between a continuous outcome and a single predictor by combining a Gaussian likelihood with prior distributions over the intercept, slope, and error variance. The result is a full posterior distribution over all parameters, providing probabilistic uncertainty quantification rather than a single point estimate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.","year":"Early 19th century; textbook synthesis 2013","type":"Bayesian linear regression","dataType":"Continuous outcome, single continuous predictor","subfamily":"Regression / GLM"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and Stan (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-0367139919","url":null}],"related":["ols-regression","bayesian-multiple-linear-regression","bayesian-generalized-linear-model","bayesian-robust-regression","simple-linear-regression","bayesian-quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-simulated-annealing","name":"Bayesian Simulated Annealing","fullName":"Bayesian Simulated Annealing — Probabilistic global optimization with Bayesian priors on the energy landscape","aliases":["BSA","Bayesian SA","Bayesian Stochastic Annealing","Bayesian Thermodynamic Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1984","originator":"Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation)","url":"https://scholargate.app/en/simulation/bayesian-simulated-annealing","markdownUrl":"https://scholargate.app/en/simulation/bayesian-simulated-annealing.md","definition":"Bayesian Simulated Annealing (BSA) integrates Bayesian prior knowledge about the objective landscape into the simulated annealing search process. By encoding beliefs about promising regions as prior distributions and updating them as the search progresses, BSA focuses computational effort on high-probability areas of the solution space, accelerating convergence and improving solution quality compared to uninformed SA.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation)","year":"1984","type":"Probabilistic metaheuristic with Bayesian inference","dataType":"Continuous or discrete parameter spaces; prior distributions over parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.","type":"article","doi":"10.1126/science.220.4598.671","isbn":null,"url":null},{"ref":"Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721–741.","type":"article","doi":"10.1109/TPAMI.1984.4767596","isbn":null,"url":null}],"related":["simulated-annealing","bayesian-optimization","markov-chain-monte-carlo","stochastic-simulated-annealing","genetic-algorithm","bayesian-genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-single-cell-rna-seq-analysis","name":"Bayesian single-cell RNA-seq analysis","fullName":"Bayesian Probabilistic Analysis of Single-Cell RNA Sequencing Data","aliases":["Bayesian scRNA-seq","scRNA-seq Bayesian modeling","probabilistic single-cell transcriptomics","Bayesian single-cell genomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2018 (scVI landmark); Bayesian scRNA-seq approaches emerged 2015-2018","originator":"Romain Lopez, Nir Yosef and Michael I. Jordan (scVI framework); preceded by Bayesian single-cell methods from Kharchenko, Markowetz, and others","url":"https://scholargate.app/en/bioinformatics/bayesian-single-cell-rna-seq-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-single-cell-rna-seq-analysis.md","definition":"Bayesian single-cell RNA-seq analysis applies probabilistic generative models to the sparse, overdispersed count matrices produced by single-cell RNA sequencing. By placing prior distributions over latent biological variables — cell state, batch effects, dropout — the framework propagates uncertainty through every downstream inference step. Tools such as scVI, SCVI-tools, and BayesPrism implement this paradigm, enabling principled cell clustering, differential expression testing, and batch integration that explicitly models technical noise rather than ignoring it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Romain Lopez, Nir Yosef and Michael I. Jordan (scVI framework); preceded by Bayesian single-cell methods from Kharchenko, Markowetz, and others","year":"2018 (scVI landmark); Bayesian scRNA-seq approaches emerged 2015-2018","type":"Probabilistic generative modeling pipeline","dataType":"Raw or normalized UMI count matrices from single-cell RNA sequencing experiments","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053-1058.","type":"article","doi":"10.1038/s41592-018-0229-2","isbn":null,"url":null},{"ref":"Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019). Single-cell RNA-seq denoising using a deep count autoencoder. Nature Communications, 10(1), 390.","type":"article","doi":"10.1038/s41467-018-07931-2","isbn":null,"url":null}],"related":["single-cell-rna-seq","variational-autoencoder","negative-binomial-regression","latent-dirichlet-allocation","gaussian-process-regression","dimensionality-reduction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-six-sigma-dmaic","name":"Bayesian Six Sigma DMAIC","fullName":"Bayesian Six Sigma Define-Measure-Analyze-Improve-Control","aliases":["Bayesian DMAIC","Bayesian Six Sigma","B-DMAIC","Probabilistic Six Sigma DMAIC"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1986 (DMAIC); Bayesian integration circa 1995–2010","originator":"Six Sigma: Bill Smith / Mikel Harry at Motorola (1986); Bayesian integration developed in quality literature through 1990s–2000s","url":"https://scholargate.app/en/experimental-design/bayesian-six-sigma-dmaic","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-six-sigma-dmaic.md","definition":"Bayesian Six Sigma DMAIC integrates Bayesian statistical inference into the classical Define-Measure-Analyze-Improve-Control quality-improvement framework. Rather than relying solely on frequentist hypothesis tests and point estimates, it incorporates prior knowledge — from expert judgment, historical production data, or pilot studies — and updates beliefs about process parameters as new data arrive. The result is a more adaptive, uncertainty-aware approach to reducing defects and improving process capability, particularly valuable when sample sizes are small or prior domain knowledge is rich.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Six Sigma: Bill Smith / Mikel Harry at Motorola (1986); Bayesian integration developed in quality literature through 1990s–2000s","year":"1986 (DMAIC); Bayesian integration circa 1995–2010","type":"Hybrid quality-improvement framework","dataType":"Process measurements, defect counts, historical data, prior expert knowledge","subfamily":"Engineering methods"},"citations":[{"ref":"Pan, J.-N. (2007). Bayesian approach to estimation of process capability indices in process quality assurance. Quality and Reliability Engineering International, 23(1), 3–14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+approach+to+estimation+of+process+capability+indices+in+process+quality+assurance+Pan"},{"ref":"Six Sigma. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Six_Sigma"}],"related":["six-sigma-dmaic","bayesian-statistical-process-control","bayesian-design-of-experiments","bayesian-process-capability-analysis","robust-six-sigma-dmaic","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-social-network-analysis","name":"Bayesian Social Network Analysis","fullName":"Bayesian Social Network Analysis (Probabilistic Network Modeling)","aliases":["Bayesian SNA","Bayesian network modeling","probabilistic social network analysis","Bayesian relational modeling"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2002","originator":"Hoff, P. D.; Raftery, A. E.; Handcock, M. S.","url":"https://scholargate.app/en/network-analysis/bayesian-social-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-social-network-analysis.md","definition":"Bayesian Social Network Analysis applies Bayesian probabilistic inference to relational data, placing prior distributions over network parameters and updating them with observed tie data to yield full posterior distributions over structural features, tie probabilities, and latent actor positions. It enables principled uncertainty quantification in network models, making it especially valuable when data are sparse, partially observed, or subject to measurement error.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hoff, P. D.; Raftery, A. E.; Handcock, M. S.","year":"2002","type":"Probabilistic / Bayesian network model","dataType":"Adjacency matrices, dyadic relational data, network edge lists","subfamily":"Network science"},"citations":[{"ref":"Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090–1098.","type":"article","doi":"10.1198/016214502388618906","isbn":null,"url":null},{"ref":"Bayesian network. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Bayesian_network"}],"related":["exponential-random-graph-model","social-network-analysis","bayesian-exponential-random-graph-model","stochastic-block-model","network-diffusion-analysis","multilayer-social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-spatial-autocorrelation","name":"Bayesian Spatial Autocorrelation","fullName":"Bayesian Spatial Autocorrelation Analysis","aliases":["Bayesian spatial dependence","Bayesian LISA","Bayesian spatial clustering","BSA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1991","originator":"Besag, York & Mollie","url":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-autocorrelation","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-autocorrelation.md","definition":"Bayesian Spatial Autocorrelation embeds spatial dependence directly into a Bayesian hierarchical model. A Conditional Autoregressive (CAR) prior encodes the expectation that neighboring areas are more similar than distant ones, and posterior inference is obtained via MCMC. This approach is especially valuable in disease mapping, ecology, and regional science, where small-area estimates need borrowing strength across neighbors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Besag, York & Mollie","year":"1991","type":"Bayesian hierarchical spatial model","dataType":"Areal / lattice data; point-referenced counts or rates","subfamily":"GIS / spatial"},"citations":[{"ref":"Besag, J., York, J., & Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1), 1–20.","type":"article","doi":"10.1007/BF00116466","isbn":null,"url":null},{"ref":"Gelfand, A. E., Diggle, P., Guttorp, P., & Fuentes, M. (Eds.). (2010). Handbook of Spatial Statistics. CRC Press.","type":"book","doi":null,"isbn":"978-1420072877","url":null}],"related":["spatial-autocorrelation","morans-i","local-indicators-of-spatial-association","bayesian-spatial-regression","bayesian-kriging","local-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-spatial-durbin-model","name":"Bayesian Spatial Durbin Model","fullName":"Bayesian Spatial Durbin Model","aliases":["Bayesian SDM","Bayesian spatial lag-X model","Bayesian SDM with spatially lagged covariates","BSDM"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2009","originator":"LeSage & Pace","url":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-durbin-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-durbin-model.md","definition":"The Bayesian Spatial Durbin Model (BSDM) estimates a spatial regression that simultaneously includes a spatially lagged outcome variable and spatially lagged covariates, using Bayesian inference with Markov Chain Monte Carlo sampling. It captures both endogenous and exogenous spatial spillovers while providing full posterior distributions for all parameters, quantifying uncertainty beyond what classical maximum-likelihood estimation offers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeSage & Pace","year":"2009","type":"Bayesian spatial regression","dataType":"Georeferenced cross-sectional or panel data with continuous outcome","subfamily":"GIS / spatial"},"citations":[{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"LeSage, J. P. (2014). Spatial Econometric Panel Data Model Comparison Using Heterogeneous Panels with Local Spatial Spillovers. Empirical Economics, 46(1), 193–211.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spatial+Econometric+Panel+Data+Model+Comparison+Using+Heterogeneous+Panels+with+Local+Spatial+Spillovers+LeSage"}],"related":["spatial-durbin-model","bayesian-spatial-lag-model","bayesian-spatial-error-model","geographically-weighted-regression","spatial-lag-model","spatial-error-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-spatial-error-model","name":"Bayesian Spatial Error Model","fullName":"Bayesian Spatial Error Model","aliases":["Bayesian SEM","Bayesian spatial-error regression","BSEM spatial econometrics","Bayesian spatially correlated error model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1988 (classical SEM); 2009 (Bayesian formulation)","originator":"LeSage & Pace (Bayesian treatment); Anselin (classical SEM)","url":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-error-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-error-model.md","definition":"The Bayesian Spatial Error Model (Bayesian SEM) estimates a regression in which spatially correlated disturbances are explicitly modelled through a spatial weights matrix, while all parameters — regression coefficients, spatial error autocorrelation, and error variance — receive full posterior distributions via Bayesian inference rather than point estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeSage & Pace (Bayesian treatment); Anselin (classical SEM)","year":"1988 (classical SEM); 2009 (Bayesian formulation)","type":"Bayesian spatial regression","dataType":"Cross-sectional or panel areal / lattice data with spatially correlated errors","subfamily":"GIS / spatial"},"citations":[{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-9024737291","url":null}],"related":["spatial-error-model","bayesian-spatial-lag-model","bayesian-spatial-durbin-model","geographically-weighted-regression","spatial-autocorrelation","morans-i"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-spatial-lag-model","name":"Bayesian Spatial Lag Model","fullName":"Bayesian Spatial Autoregressive Lag Model","aliases":["Bayesian SAR model","Bayesian spatial autoregressive model","BSLM","Bayesian SLM"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1997","originator":"LeSage (1997); fully elaborated in LeSage & Pace (2009)","url":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-lag-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-lag-model.md","definition":"The Bayesian Spatial Lag Model (BSLM) extends the classical spatial autoregressive (SAR) regression by placing prior distributions over all parameters and recovering full posterior distributions via MCMC sampling. It explicitly accounts for spatial dependence — the outcome in one location is partly driven by outcomes in neighboring locations — and yields uncertainty-quantified estimates of both regression coefficients and the spatial autocorrelation parameter rho.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeSage (1997); fully elaborated in LeSage & Pace (2009)","year":"1997","type":"Bayesian spatial regression","dataType":"Cross-sectional or panel areal / lattice data with spatial weights matrix","subfamily":"GIS / spatial"},"citations":[{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"LeSage, J. P. (1997). Bayesian Estimation of Spatial Autoregressive Models. International Regional Science Review, 20(1-2), 113-129.","type":"article","doi":"10.1177/016001769702000107","isbn":null,"url":null}],"related":["spatial-lag-model","bayesian-spatial-error-model","bayesian-spatial-durbin-model","geographically-weighted-regression","spatial-autocorrelation","panel-spatial-lag-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-spatial-panel-model","name":"Bayesian Spatial Panel Model","fullName":"Bayesian Spatial Panel Data Model","aliases":["Bayesian spatial panel","Bayesian spatial econometrics panel","BSPM","Bayesian panel spatial regression"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2009–2014","originator":"LeSage & Pace; Elhorst","url":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-panel-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-panel-model.md","definition":"The Bayesian Spatial Panel Model estimates spatial interaction effects (spatial lag, spatial error, or Durbin) in panel data using Bayesian inference via Markov Chain Monte Carlo (MCMC). It combines the ability to control for unobserved unit- and time-specific heterogeneity with principled uncertainty quantification, making it suitable for georeferenced longitudinal datasets in economics, public health, and regional science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeSage & Pace; Elhorst","year":"2009–2014","type":"Bayesian spatial panel regression","dataType":"Georeferenced panel data (multiple units observed over multiple time periods)","subfamily":"GIS / spatial"},"citations":[{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403392","url":null}],"related":["spatial-lag-model","spatial-error-model","spatial-durbin-model","panel-spatial-lag-model","bayesian-spatial-regression","geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-spatial-regression","name":"Bayesian Spatial Regression","fullName":"Bayesian Spatial Regression","aliases":["Bayesian hierarchical spatial model","BSR","Bayesian geostatistical regression","Bayesian spatial linear model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1990s–2000s","originator":"Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors","url":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-spatial-regression.md","definition":"Bayesian Spatial Regression embeds a spatially structured random effect into a regression framework and estimates all parameters — including spatial range and variance — through posterior inference rather than point estimation. It handles spatial autocorrelation, quantifies full predictive uncertainty, and accommodates small or irregular spatial datasets via hierarchical priors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors","year":"1990s–2000s","type":"Bayesian hierarchical regression","dataType":"Georeferenced areal, point-referenced, or lattice data with continuous or discrete outcomes","subfamily":"GIS / spatial"},"citations":[{"ref":"Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439819173","url":null},{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471002550","url":null}],"related":["spatial-lag-model","spatial-error-model","kriging","geographically-weighted-regression","car-model","multilevel-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-stacking-ensemble","name":"Bayesian Stacking Ensemble","fullName":"Bayesian Stacking Ensemble (Bayesian Stacking of Predictive Distributions)","aliases":["Bayesian stacking","Bayesian model stacking","stacking with Bayesian weights","predictive distribution stacking"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018","originator":"Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.","url":"https://scholargate.app/en/machine-learning/bayesian-stacking-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-stacking-ensemble.md","definition":"Bayesian stacking combines the predictive distributions of several base models by finding non-negative weights that maximise the leave-one-out log predictive score of the mixture. Formalised by Yao, Vehtari, Simpson, and Gelman (2018), it yields a single calibrated predictive distribution that is provably at least as good as any single constituent model under cross-validation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.","year":"2018","type":"Bayesian ensemble combination","dataType":"Continuous, binary, or count outcomes; any features","subfamily":"Machine learning"},"citations":[{"ref":"Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis, 13(3), 917–1007.","type":"article","doi":"10.1214/17-BA1091","isbn":null,"url":null},{"ref":"Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.","type":"article","doi":"10.1016/S0893-6080(05)80023-1","isbn":null,"url":null}],"related":["stacking-ensemble","bayesian-model-averaging","voting-ensemble","bagging","boosting","gaussian-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-statistical-process-control","name":"Bayesian Statistical Process Control","fullName":"Bayesian Statistical Process Control","aliases":["Bayesian SPC","Bayesian process monitoring","B-SPC","Bayesian control charting"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1950s (foundations); formalized 1990s–2000s","originator":"Various (Girshick & Rubin 1952 early signal detection; Menzefricke 2002 Bayesian control chart framework)","url":"https://scholargate.app/en/experimental-design/bayesian-statistical-process-control","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-statistical-process-control.md","definition":"Bayesian Statistical Process Control (Bayesian SPC) extends classical SPC by replacing fixed, frequentist control limits with a probabilistic framework that incorporates prior knowledge about the process. Rather than waiting for a run of points to exceed a pre-set 3-sigma boundary, Bayesian SPC continuously updates the probability that the process has shifted given the incoming data, enabling earlier and more informed detection of out-of-control states while formally accounting for uncertainty in process parameters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Girshick & Rubin 1952 early signal detection; Menzefricke 2002 Bayesian control chart framework)","year":"1950s (foundations); formalized 1990s–2000s","type":"Bayesian process monitoring technique","dataType":"Continuous or attribute process measurements over time","subfamily":"Engineering methods"},"citations":[{"ref":"Menzefricke, U. (2002). On the evaluation of control chart factors for monitoring the process mean and variance. Journal of Quality Technology, 34(2), 167–178.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+evaluation+of+control+chart+factors+for+monitoring+the+process+mean+and+variance+Menzefricke+2002"},{"ref":"Statistical process control. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Statistical_process_control"}],"related":["statistical-process-control","control-chart","bayesian-reliability-analysis","bayesian-design-of-experiments","process-capability-analysis","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-statistics","name":"Bayesian Statistical Inference","fullName":"Bayesian Methods in Statistical Analysis","aliases":["Bayes theorem","Bayesian inference","posterior probability"],"domain":"research-statistics","family":"process-pipeline","subfamily":"probabilistic-inference","year":"1763","originator":"Thomas Bayes","url":"https://scholargate.app/en/research-statistics/bayesian-statistics","markdownUrl":"https://scholargate.app/en/research-statistics/bayesian-statistics.md","definition":"Bayesian inference is a statistical framework using Bayes' theorem to update beliefs about parameters or hypotheses as data accumulate. Published posthumously in 1763, Thomas Bayes' work lay dormant until the 20th century, when computational advances (Gibbs sampling, Markov Chain Monte Carlo) made Bayesian methods practical. Unlike frequentist inference (which treats parameters as fixed unknowns), Bayesian analysis treats parameters as random variables with probability distributions, enabling direct probability statements about parameters, incorporation of prior knowledge, and sequential updating. Essential in precision medicine, adaptive trials, complex hierarchical models, and any context where prior information enriches inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Bayes","subfamily":"probabilistic-inference","year":"1763","type":"Method"},"citations":[{"ref":"Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society, 53, 370–418.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+essay+towards+solving+a+problem+in+the+doctrine+of+chances+Bayes"},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"article","doi":"10.1201/b16018","isbn":null,"url":null},{"ref":"Kruschke, J. K. (2015). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2nd ed.). Academic Press.","type":"article","doi":"10.1016/b978-0-12-405888-0.00008-8","isbn":null,"url":null}],"related":["logistic-regression","multilevel-modeling","factor-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-stochastic-block-model","name":"Bayesian Stochastic Block Model","fullName":"Bayesian Stochastic Block Model (Bayesian SBM)","aliases":["Bayesian SBM","B-SBM","probabilistic block model","Bayesian community detection model"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2001–2014","originator":"Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.","url":"https://scholargate.app/en/network-analysis/bayesian-stochastic-block-model","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-stochastic-block-model.md","definition":"The Bayesian Stochastic Block Model (Bayesian SBM) is a principled probabilistic method for community detection in networks. It treats group membership as a latent variable and uses Bayesian inference to simultaneously recover block structure and select the number of communities, avoiding the resolution-limit bias that plagues modularity-based approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nowicki, K. & Snijders, T. A. B.; extended by Peixoto, T. P.","year":"2001–2014","type":"Probabilistic generative model with Bayesian inference","dataType":"Adjacency matrix (binary or weighted graph)","subfamily":"Network science"},"citations":[{"ref":"Peixoto, T. P. (2014). Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Physical Review E, 89(1), 012804.","type":"article","doi":"10.1103/PhysRevE.89.012804","isbn":null,"url":null},{"ref":"Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455), 1077–1087.","type":"article","doi":"10.1198/016214501753208735","isbn":null,"url":null}],"related":["stochastic-block-model","bayesian-social-network-analysis","modularity-analysis","exponential-random-graph-model","community-detection","multilayer-stochastic-block-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-structural-time-series","name":"Bayesian Structural Time Series","fullName":"Bayesian Structural Time Series Model","aliases":["BSTS","Bayesian Yapısal Zaman Serisi (BSTS)","bayesian state-space model","causal impact model"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":"2014","originator":"Scott & Varian (2014); Brodersen et al. (2015)","url":"https://scholargate.app/en/bayesian/bayesian-structural-time-series","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-structural-time-series.md","definition":"Bayesian Structural Time Series (BSTS) is a state-space modelling framework, introduced by Scott and Varian (2014), that decomposes a time series into additive components — trend, seasonality, and regression — and estimates them jointly through Bayesian inference. It underpins Google's CausalImpact library and is a powerful tool for both forecasting and counterfactual causal analysis of interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scott & Varian (2014); Brodersen et al. (2015)","year":"2014","family":"Bayesian","type":"State-space model / Bayesian structural model","purpose":"predict / forecast / causal inference","var_types":"continuous time series","min_sample":"30","inference":"MCMC (posterior sampling)","components":"trend + seasonality + regression","variable_selection":"spike-and-slab prior","outputs":"posterior component decompositions / credible intervals / causal impact estimates"},"citations":[{"ref":"Scott, S. L. & Varian, H. R. (2014). Predicting the Present with Bayesian Structural Time Series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1/2), 4–23.","type":"article","doi":"10.1504/IJMMNO.2014.059942","isbn":null,"url":null},{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N. & Scott, S. L. (2015). Inferring Causal Impact Using Bayesian Structural Time-Series Models. Annals of Applied Statistics, 9(1), 247–274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null}],"related":["bayesian-regression","interrupted-time-series","arima","state-space-model","causal-impact","mcmc"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-support-vector-machine","name":"Bayesian Support Vector Machine","fullName":"Bayesian Support Vector Machine (Bayesian SVM)","aliases":["Bayesian SVM","probabilistic SVM","Bayesian kernel machine","BSVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001–2011","originator":"Polson, N. G. & Scott, S. L.; Tipping, M. E.","url":"https://scholargate.app/en/machine-learning/bayesian-support-vector-machine","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-support-vector-machine.md","definition":"Bayesian SVM places a prior distribution over the weight vector of a standard SVM and derives a full posterior, enabling calibrated uncertainty estimates, automatic hyperparameter selection, and probabilistic predictions. It combines the strong margin-based geometric intuition of SVMs with the principled uncertainty quantification of Bayesian inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Polson, N. G. & Scott, S. L.; Tipping, M. E.","year":"2001–2011","type":"Bayesian probabilistic classifier / regressor","dataType":"Continuous, binary, multi-class tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Polson, N. G., & Scott, S. L. (2011). Data augmentation for support vector machines. Bayesian Analysis, 6(1), 1–23.","type":"article","doi":"10.1214/11-BA601","isbn":null,"url":null},{"ref":"Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1, 211–244.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf"}],"related":["support-vector-machine","gaussian-process","bayesian-logistic-regression","relevance-vector-machine","bayesian-naive-bayes","kernel-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-survey-research","name":"Bayesian Survey Research","fullName":"Bayesian Survey Research","aliases":["Bayesian survey analysis","Bayesian survey methodology","Bayesian polling","Bayesian questionnaire analysis"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–2000s (modern applied development)","originator":"Thomas Bayes (theorem, 1763); applied to survey methodology by Donald Rubin, Andrew Gelman, and others (1980s–2000s)","url":"https://scholargate.app/en/research-design/bayesian-survey-research","markdownUrl":"https://scholargate.app/en/research-design/bayesian-survey-research.md","definition":"Bayesian survey research applies Bayesian statistical inference to survey data, combining prior knowledge or beliefs about population parameters with observed questionnaire responses to produce posterior probability distributions. Unlike null-hypothesis significance testing, this approach quantifies uncertainty directly, incorporates prior evidence, and yields probabilistic statements about parameters of interest — making it especially powerful for small samples, sequential data collection, and contexts where substantive prior knowledge exists.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Bayes (theorem, 1763); applied to survey methodology by Donald Rubin, Andrew Gelman, and others (1980s–2000s)","year":"1980s–2000s (modern applied development)","type":"Quantitative observational research design with Bayesian inference","dataType":"Structured questionnaire responses (Likert, categorical, continuous); prior probability distributions","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Gelman, A., & Carlin, J. B. (2007). Some issues on the foundations of statistics. In A. Gelman & J. B. Carlin (Eds.), Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521686891","url":null},{"ref":"Lee, M. D., & Wagenmakers, E.-J. (2005). Bayesian statistical inference in psychology: Comment on Trafimow (2003). Psychological Review, 112(3), 662–668.","type":"article","doi":"10.1037/0033-295X.112.3.662","isbn":null,"url":null}],"related":["survey-research","bayesian-correlational-research","bayesian-longitudinal-research","multilevel-modeling","structural-equation-modeling","confirmatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-survival-regression","name":"Bayesian Survival regression","fullName":"Bayesian Survival Regression","aliases":["Bayesian time-to-event regression","Bayesian parametric survival model","Bayesian survival analysis","Bayesian accelerated failure time model"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1990s–2001","originator":"Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al.","url":"https://scholargate.app/en/statistics/bayesian-survival-regression","markdownUrl":"https://scholargate.app/en/statistics/bayesian-survival-regression.md","definition":"Bayesian Survival Regression combines parametric or semiparametric survival models — such as Weibull, log-normal, or Cox proportional hazards — with Bayesian inference. Instead of point estimates, it produces full posterior distributions for regression coefficients and the baseline hazard, naturally handling censored observations and incorporating prior knowledge about event times or covariate effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ibrahim, Chen & Sinha (seminal textbook treatment, 2001); broader Bayesian framework: Gelman et al.","year":"1990s–2001","type":"Bayesian parametric/semiparametric regression","dataType":"Time-to-event (survival) data with censoring","subfamily":"Regression / GLM"},"citations":[{"ref":"Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian Survival Analysis. Springer.","type":"book","doi":null,"isbn":"978-0387952772","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["cox-regression","survival-regression","bayesian-cox-regression","bayesian-mixed-effects-model","bayesian-generalized-linear-model","multilevel-cox-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-survival","name":"Bayesian Survival Analysis","fullName":"Bayesian Survival Analysis","aliases":["bayesian sağkalım analizi","bayesian time-to-event analysis","bayesian hazard model"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2001,"originator":"Ibrahim, Chen & Sinha","url":"https://scholargate.app/en/bayesian/bayesian-survival","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-survival.md","definition":"Bayesian survival analysis applies Bayesian inference to time-to-event models — Cox proportional hazards, parametric (Weibull, exponential), and cure models. Formalised comprehensively by Ibrahim, Chen and Sinha (2001), the approach encodes prior knowledge about hazard rates and regression coefficients, then updates it with censored survival data to yield posterior hazard ratios and credible intervals rather than single point estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","originator":"Ibrahim, Chen & Sinha","year":2001,"type":"Bayesian time-to-event model","purpose":"predict / relationship","var_types":"continuous / binary / categorical","inference":"MCMC / variational","outputs":"posterior hazard ratios / credible intervals / survival curves","min_sample":20},"citations":[{"ref":"Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer.","type":"book","doi":"10.1007/978-1-4757-3447-8","isbn":null,"url":null}],"related":["cox-regression","kaplan-meier","bayesian-regression","parametric-survival","weibull-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-svar-model","name":"Bayesian SVAR model","fullName":"Bayesian Structural Vector Autoregression Model","aliases":["Bayesian SVAR","B-SVAR","Bayesian structural VAR","Bayesian identified VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998–2005","originator":"Sims & Zha (1998); Uhlig (2005) for sign-restriction identification","url":"https://scholargate.app/en/econometrics/bayesian-svar-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-svar-model.md","definition":"The Bayesian Structural Vector Autoregression model combines the structural identification of SVAR with Bayesian prior distributions over parameters. It estimates causal impulse responses between multiple time series while incorporating prior economic knowledge and producing full posterior uncertainty bands rather than point estimates alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sims & Zha (1998); Uhlig (2005) for sign-restriction identification","year":"1998–2005","type":"Structural multivariate time-series model","dataType":"Multivariate time-series data","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sims, C. A., & Zha, T. (1998). Bayesian methods for dynamic multivariate models. International Economic Review, 39(4), 949–968.","type":"article","doi":"10.2307/2527347","isbn":null,"url":null},{"ref":"Uhlig, H. (2005). What are the effects of monetary policy on output? Results from an agnostic identification procedure. Journal of Monetary Economics, 52(2), 381–419.","type":"article","doi":"10.1016/j.jmoneco.2004.05.007","isbn":null,"url":null}],"related":["structural-var","vector-autoregression","bayesian-var-model","bayesian-vecm","vector-error-correction-model","bayesian-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-synthetic-control-method","name":"Bayesian Synthetic Control Method","fullName":"Bayesian Synthetic Control Method","aliases":["Bayesian SCM","Bayesian synthetic controls","probabilistic synthetic control","Bayesian SC"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)","originator":"Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller","url":"https://scholargate.app/en/causal-inference/bayesian-synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/bayesian-synthetic-control-method.md","definition":"The Bayesian Synthetic Control Method estimates the causal effect of an intervention on a single treated unit by constructing a probabilistic counterfactual from a weighted combination of untreated donor units. Unlike the classical SCM, it places a prior distribution over the synthetic weights, yielding full posterior uncertainty intervals for the counterfactual trajectory and the treatment effect at each post-intervention time point.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller","year":"2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)","type":"Bayesian causal inference / synthetic control","dataType":"Time-series panel data with a single treated unit and multiple donor units","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null}],"related":["synthetic-control-method","bayesian-difference-in-differences","causal-impact-analysis","bayesian-interrupted-time-series","difference-in-differences","panel-data-synthetic-control-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-system-dynamics","name":"Bayesian System Dynamics","fullName":"Bayesian System Dynamics — Probabilistic parameter estimation and uncertainty propagation in system dynamics models","aliases":["BSD","Bayesian SD","Bayesian SD modeling","Probabilistic System Dynamics"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s–2010s","originator":"Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities","url":"https://scholargate.app/en/simulation/bayesian-system-dynamics","markdownUrl":"https://scholargate.app/en/simulation/bayesian-system-dynamics.md","definition":"Bayesian System Dynamics (BSD) integrates Bayesian statistical inference with causal stock-and-flow simulation models. Prior knowledge about model parameters is updated using observed time-series data to produce posterior distributions, which are then propagated through the simulation to yield probabilistic forecasts and policy evaluations rather than single deterministic trajectories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities","year":"2000s–2010s","type":"Simulation with probabilistic parameter learning","dataType":"Time-series, stock-flow numerical data, prior distributions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Rahmandad, H., & Sterman, J. D. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998–1014.","type":"article","doi":"10.1287/mnsc.1070.0787","isbn":null,"url":null},{"ref":"System dynamics. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/System_dynamics"}],"related":["system-dynamics","bayesian-monte-carlo-simulation","stochastic-system-dynamics","markov-model","bayesian-markov-model","monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-system-gmm","name":"Bayesian System GMM","fullName":"Bayesian System Generalized Method of Moments","aliases":["Bayesian Sys-GMM","Bayesian BB estimator","Bayesian Blundell-Bond GMM","B-SGMM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998–2010","originator":"Blundell & Bond (System GMM, 1998); Bayesian integration via Chib and related MCMC literature","url":"https://scholargate.app/en/econometrics/bayesian-system-gmm","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-system-gmm.md","definition":"Bayesian System GMM combines the Blundell-Bond System Generalized Method of Moments estimator for dynamic panel data with Bayesian prior distributions and posterior inference via MCMC. It handles endogeneity, individual fixed effects, and weak-instrument problems while incorporating prior knowledge and delivering full posterior uncertainty quantification — not just point estimates and asymptotic standard errors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blundell & Bond (System GMM, 1998); Bayesian integration via Chib and related MCMC literature","year":"1998–2010","type":"Bayesian dynamic panel estimator","dataType":"Panel data with lagged dependent variables (short T, large N)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.","type":"article","doi":"10.1016/S0304-4076(98)00009-8","isbn":null,"url":null},{"ref":"Chib, S., & Ramamurthy, S. (2010). Tailored randomized block MCMC methods with application to DSGE models. Journal of Econometrics, 155(1), 19–38.","type":"article","doi":"10.1016/j.jeconom.2009.08.003","isbn":null,"url":null}],"related":["arellano-bond-gmm-estimator","panel-system-gmm","bayesian-arellano-bond-gmm","dynamic-panel-data-model","panel-dynamic-panel-data-model","difference-gmm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-t-test","name":"Bayesian t-Test","fullName":"Bayesian t-Test for Two-Group Comparison","aliases":["bayesian two-sample t-test","bayes factor t-test","Bayesçi t-Testi"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2009,"originator":"Rouder, Speckman, Sun, Morey & Iverson","url":"https://scholargate.app/en/bayesian/bayesian-t-test","markdownUrl":"https://scholargate.app/en/bayesian/bayesian-t-test.md","definition":"The Bayesian t-test, formalised by Rouder and colleagues in 2009, is a two-group comparison method that works within a Bayesian framework. Instead of a p-value, it produces a Bayes Factor (BF₁₀) that quantifies the evidence the data provide for the alternative hypothesis relative to the null, and it reports the full posterior distribution of the standardised effect size δ with a highest-density interval.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rouder, Speckman, Sun, Morey & Iverson","year":2009,"family":"Bayesian","type":"Bayesian hypothesis test","purpose":"two-group comparison","var_types":"continuous","outputs":"Bayes Factor (BF₁₀) / posterior effect-size distribution / 95% HDI","min_sample":10,"prior":"Cauchy (default scale r = 0.707)","inference":"closed-form or MCMC"},"citations":[{"ref":"Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D. & Iverson, G. (2009). Bayesian t Tests for Accepting and Rejecting the Null Hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237.","type":"article","doi":"10.3758/PBR.16.2.225","isbn":null,"url":null}],"related":["independent-t-test","bayesian-regression","bayesian-anova","mann-whitney-u","bayes-factor-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-tabu-search","name":"Bayesian Tabu Search","fullName":"Bayesian Tabu Search — Probabilistic guidance integrated with memory-based local search","aliases":["BTS","Bayesian-guided tabu search","probabilistic tabu search","Bayes-TS"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1989 (tabu search); hybrid formulations ~2005–2015","originator":"Glover, F. (tabu search); Bayesian integration developed by multiple researchers in the 2000s–2010s","url":"https://scholargate.app/en/simulation/bayesian-tabu-search","markdownUrl":"https://scholargate.app/en/simulation/bayesian-tabu-search.md","definition":"Bayesian Tabu Search (BTS) is a hybrid metaheuristic that couples the memory-based forbidden-move mechanism of classic Tabu Search with a Bayesian probabilistic model. The Bayesian component learns from past evaluations to score candidate moves, focusing the search on promising regions while the tabu list prevents cycling. This combination reduces wasted function evaluations in expensive combinatorial and continuous optimization problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glover, F. (tabu search); Bayesian integration developed by multiple researchers in the 2000s–2010s","year":"1989 (tabu search); hybrid formulations ~2005–2015","type":"Hybrid metaheuristic — memory-based local search with Bayesian probabilistic guidance","dataType":"Combinatorial or continuous optimization problem instances; prior knowledge or historical run data","subfamily":"Simulation / optimization"},"citations":[{"ref":"Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206.","type":"article","doi":"10.1287/ijoc.1.3.190","isbn":null,"url":null},{"ref":"Bergstra, J., Bardenet, R., Bengio, Y., Kegl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems (NIPS), 24, 2546–2554.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2011/hash/86e8f7ab32cfd12577bc2619bc635690-Abstract.html"}],"related":["tabu-search","bayesian-optimization","simulated-annealing","stochastic-tabu-search","bayesian-genetic-algorithm","bayesian-simulated-annealing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-taguchi-method","name":"Bayesian Taguchi method","fullName":"Bayesian Robust Parameter Design (Taguchi Framework)","aliases":["Bayesian robust parameter design","Bayesian quality engineering","Bayesian signal-to-noise optimization","Bayes-Taguchi"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"Early 1990s","originator":"Synthesized from Genichi Taguchi's robust design and Bayesian statistical methods (Box, Hamada, Wu, and others, 1990s)","url":"https://scholargate.app/en/experimental-design/bayesian-taguchi-method","markdownUrl":"https://scholargate.app/en/experimental-design/bayesian-taguchi-method.md","definition":"The Bayesian Taguchi method integrates Genichi Taguchi's robust parameter design philosophy with Bayesian statistical inference. By encoding prior engineering knowledge as probability distributions and updating these distributions with experimental data, the approach identifies factor settings that simultaneously minimize process variability and keep the mean on target — even when only limited runs are feasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Genichi Taguchi's robust design and Bayesian statistical methods (Box, Hamada, Wu, and others, 1990s)","year":"Early 1990s","type":"Bayesian robust parameter design","dataType":"Continuous response data from designed experiments (crossed array or combined array); prior distributions on model parameters","subfamily":"Engineering methods"},"citations":[{"ref":"Hamada, M., & Wu, C. F. J. (1992). Analysis of designed experiments with complex aliasing. Journal of Quality Technology, 24(3), 130–137.","type":"article","doi":"10.1080/00224065.1992.11979383","isbn":null,"url":null},{"ref":"Box, G. E. P., & Jones, S. (1992). Designing products that are robust to the environment. Total Quality Management, 3(3), 265–282.","type":"article","doi":"10.1080/09544129200000034","isbn":null,"url":null}],"related":["taguchi-method","robust-taguchi-method","bayesian-design-of-experiments","response-surface-methodology","full-factorial-design","design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-temporal-network-analysis","name":"Bayesian Temporal Network Analysis","fullName":"Bayesian Inference for Temporal Network Analysis","aliases":["Bayesian dynamic network analysis","Bayesian time-varying network model","BTNA","Bayesian longitudinal network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010s","originator":"Hanneke, S.; Fu, W.; Xing, E. P. (among key contributors)","url":"https://scholargate.app/en/network-analysis/bayesian-temporal-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-temporal-network-analysis.md","definition":"Bayesian temporal network analysis combines probabilistic Bayesian inference with time-ordered relational data to model how network structures evolve, quantify uncertainty around structural estimates, and make principled predictions about future connectivity patterns. It provides credible intervals on edge probabilities and community assignments rather than bare point estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hanneke, S.; Fu, W.; Xing, E. P. (among key contributors)","year":"2010s","type":"Probabilistic generative model","dataType":"Longitudinal relational data (edge lists or adjacency matrices across time steps)","subfamily":"Network science"},"citations":[{"ref":"Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.","type":"article","doi":"10.1214/09-EJS548","isbn":null,"url":null},{"ref":"Peixoto, T. P. (2017). Nonparametric Bayesian inference of the microcanonical stochastic block model. Physical Review E, 95(1), 012317.","type":"article","doi":"10.1103/PhysRevE.95.012317","isbn":null,"url":null}],"related":["temporal-network-analysis","bayesian-exponential-random-graph-model","bayesian-stochastic-block-model","temporal-exponential-random-graph-model","dynamic-network-analysis","multilayer-temporal-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-tgarch","name":"Bayesian TGARCH","fullName":"Bayesian Threshold Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["Bayesian TGARCH","Bayesian GJR-GARCH","Threshold GARCH with Bayesian estimation","TGARCH-B"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1994 / 2008","originator":"Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)","url":"https://scholargate.app/en/econometrics/bayesian-tgarch","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-tgarch.md","definition":"Bayesian TGARCH combines the Threshold GARCH volatility model — which captures the asymmetric response of volatility to positive versus negative shocks — with full Bayesian inference via Markov Chain Monte Carlo sampling. The result is a principled, uncertainty-aware framework for modeling leverage effects and fat-tailed financial returns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zakoian (1994) for TGARCH; Bayesian estimation formalized by Ardia (2008)","year":"1994 / 2008","type":"Volatility model with asymmetric threshold and Bayesian inference","dataType":"Financial or macroeconomic time series (returns, log-returns)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.","type":"article","doi":"10.1016/0165-1889(94)90039-6","isbn":null,"url":null},{"ref":"Ardia, D. (2008). Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications. Springer.","type":"book","doi":null,"isbn":"978-3-540-78656-6","url":null}],"related":["bayesian-garch-model","bayesian-egarch","tgarch-model","bayesian-arch-model","dcc-garch-model","egarch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-tobit-model","name":"Bayesian Tobit Model","fullName":"Bayesian Tobit Model","aliases":["Bayesian censored regression","Bayesian Type I Tobit","Bayesian truncated regression","Tobit with priors"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1958 (classical); 1992 (Bayesian formulation)","originator":"James Tobin (classical Tobit, 1958); Siddhartha Chib (Bayesian Tobit, 1992)","url":"https://scholargate.app/en/statistics/bayesian-tobit-model","markdownUrl":"https://scholargate.app/en/statistics/bayesian-tobit-model.md","definition":"The Bayesian Tobit model extends Tobin's censored regression framework by replacing maximum-likelihood point estimates with a full posterior distribution over regression coefficients and error variance. By embedding Gibbs sampling with data augmentation, it produces credible intervals, handles small censored samples gracefully, and naturally incorporates prior knowledge about effect sizes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Tobin (classical Tobit, 1958); Siddhartha Chib (Bayesian Tobit, 1992)","year":"1958 (classical); 1992 (Bayesian formulation)","type":"Bayesian censored/limited-dependent-variable regression","dataType":"Continuous outcome censored at a threshold (e.g., left-censored at zero)","subfamily":"Regression / GLM"},"citations":[{"ref":"Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26(1), 24–36.","type":"article","doi":"10.2307/1907382","isbn":null,"url":null},{"ref":"Chib, S. (1992). Bayes inference in the Tobit censored regression model. Journal of Econometrics, 51(1–2), 79–99.","type":"article","doi":"10.1016/0304-4076(92)90030-U","isbn":null,"url":null}],"related":["tobit-model","bayesian-probit-model","bayesian-multiple-linear-regression","zero-inflated-model","censored-regression","bayesian-generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-toda-yamamoto-causality","name":"Bayesian Toda-Yamamoto Causality","fullName":"Bayesian Toda-Yamamoto Granger Causality Test","aliases":["Bayesian TY causality","Bayesian modified Wald causality","Bayesian Granger non-causality in VAR","BTY causality"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1995 (base); Bayesian variant developed post-2000","originator":"Toda & Yamamoto (1995) for the frequentist base; Bayesian extension by subsequent applied econometricians","url":"https://scholargate.app/en/econometrics/bayesian-toda-yamamoto-causality","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-toda-yamamoto-causality.md","definition":"The Bayesian Toda-Yamamoto causality procedure combines the Toda-Yamamoto VAR augmentation strategy — which sidesteps the need for pre-testing integration and cointegration — with Bayesian prior-posterior updating. It tests Granger non-causality between time series that may be integrated or cointegrated without requiring differencing or error-correction modeling, while incorporating prior information and producing full posterior distributions over the causal parameters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Toda & Yamamoto (1995) for the frequentist base; Bayesian extension by subsequent applied econometricians","year":"1995 (base); Bayesian variant developed post-2000","type":"Causality test / VAR-based inference","dataType":"Time series, possibly integrated or cointegrated","subfamily":"Econometrics / time series"},"citations":[{"ref":"Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.","type":"article","doi":"10.1016/0304-4076(94)01616-8","isbn":null,"url":null},{"ref":"Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley.","type":"book","doi":null,"isbn":"978-0471982326","url":null}],"related":["toda-yamamoto-causality","granger-causality","bayesian-var","vector-autoregression","dolado-lutkepohl-test","bootstrap-granger-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-transfer-learning","name":"Bayesian Transfer Learning","fullName":"Bayesian Transfer Learning (Probabilistic Domain Adaptation)","aliases":["BTL","Bayesian domain adaptation","probabilistic transfer learning","Bayesian knowledge transfer"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006–2010","originator":"Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)","url":"https://scholargate.app/en/machine-learning/bayesian-transfer-learning","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-transfer-learning.md","definition":"Bayesian Transfer Learning is a probabilistic framework that uses knowledge from a data-rich source domain to construct informative priors for a model trained on a data-scarce target domain. By encoding source-domain knowledge as prior distributions over parameters, the framework lets the model generalize well on the target task even with very limited labeled examples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)","year":"2006–2010","type":"Probabilistic transfer / domain adaptation framework","dataType":"Labeled source domain + limited labeled or unlabeled target domain data","subfamily":"Machine learning"},"citations":[{"ref":"Raina, R., Ng, A. Y., & Koller, D. (2006). Constructing informative priors using transfer learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML), pp. 713–720. ACM.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Constructing+informative+priors+using+transfer+learning+Raina+Ng+Koller+2006"},{"ref":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["transfer-learning","bayesian-gaussian-process","semi-supervised-transfer-learning","bayesian-neural-network","meta-learning","few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-two-mode-network-analysis","name":"Bayesian Two-Mode Network Analysis","fullName":"Bayesian Two-Mode (Bipartite) Network Analysis","aliases":["Bayesian bipartite network analysis","probabilistic two-mode network analysis","Bayesian affiliation network analysis","Bayesian two-mode SNA"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1997–2010s","originator":"Borgatti & Everett (two-mode SNA); Bayesian extensions by multiple authors","url":"https://scholargate.app/en/network-analysis/bayesian-two-mode-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/bayesian-two-mode-network-analysis.md","definition":"Bayesian two-mode network analysis applies probabilistic Bayesian inference to bipartite (two-mode) networks — graphs linking two distinct sets of nodes such as actors and events, authors and papers, or consumers and products. By placing priors over tie probabilities and structural parameters, analysts obtain uncertainty estimates around centrality, community membership, and projection metrics rather than single-point estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Borgatti & Everett (two-mode SNA); Bayesian extensions by multiple authors","year":"1997–2010s","type":"Probabilistic network model","dataType":"Bipartite (two-mode) adjacency matrices; actor-by-event data","subfamily":"Network science"},"citations":[{"ref":"Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.","type":"article","doi":"10.1016/S0378-8733(96)00301-2","isbn":null,"url":null},{"ref":"Latouche, P., Birmele, E., & Ambroise, C. (2011). Overlapping stochastic block models with application to the French political blogosphere. Annals of Applied Statistics, 5(1), 309–336.","type":"article","doi":"10.1214/10-AOAS382","isbn":null,"url":null}],"related":["two-mode-network-analysis","bayesian-social-network-analysis","bayesian-community-detection","multilayer-two-mode-network-analysis","exponential-random-graph-model","weighted-two-mode-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-two-way-anova","name":"Bayesian two-way ANOVA","fullName":"Bayesian Two-Way Analysis of Variance","aliases":["Bayesian factorial ANOVA","Bayes factor two-way ANOVA","Bayesian 2×k ANOVA","Bayesian two-factor ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1961 (foundations); 2012 (default Bayes factor formulation)","originator":"Harold Jeffreys (foundational); modern default-prior form by Jeffrey N. Rouder et al.","url":"https://scholargate.app/en/statistics/bayesian-two-way-anova","markdownUrl":"https://scholargate.app/en/statistics/bayesian-two-way-anova.md","definition":"Bayesian two-way ANOVA extends the classical two-way analysis of variance by replacing p-values with Bayes factors and posterior distributions. It quantifies evidence for or against main effects and their interaction using prior-weighted model comparison, yielding conclusions that are directly interpretable in probabilistic terms rather than relying on a fixed significance threshold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Jeffreys (foundational); modern default-prior form by Jeffrey N. Rouder et al.","year":"1961 (foundations); 2012 (default Bayes factor formulation)","type":"Bayesian hypothesis test","dataType":"Continuous outcome, two categorical factors","subfamily":"Classical statistics"},"citations":[{"ref":"Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374.","type":"article","doi":"10.1016/j.jmp.2012.08.001","isbn":null,"url":null},{"ref":"Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198503682","url":null}],"related":["two-way-anova","bayesian-one-way-anova","bayesian-t-test","three-way-anova","mixed-anova","bayes-factor"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-universal-kriging","name":"Bayesian Universal Kriging","fullName":"Bayesian Universal Kriging","aliases":["BUK","Bayesian kriging with trend","Bayesian spatial interpolation with covariates","stochastic universal kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1990s–2000s","originator":"Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein","url":"https://scholargate.app/en/spatial-analysis/bayesian-universal-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/bayesian-universal-kriging.md","definition":"Bayesian Universal Kriging (BUK) extends classical universal kriging by placing prior distributions on trend coefficients and spatial covariance parameters, then propagating full posterior uncertainty into predictions. It interpolates spatially referenced continuous data while simultaneously estimating large-scale deterministic trends driven by covariates and small-scale stochastic spatial dependence, yielding prediction intervals that honestly account for both parameter and interpolation uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diggle, Tawn & Moyeed; Kitanidis; Handcock & Stein","year":"1990s–2000s","type":"Bayesian geostatistical interpolation with trend","dataType":"Georeferenced continuous observations with spatially varying covariates","subfamily":"GIS / spatial"},"citations":[{"ref":"Diggle, P. J., & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer.","type":"book","doi":null,"isbn":"978-0387329079","url":null},{"ref":"Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439819173","url":null}],"related":["ordinary-kriging","universal-kriging","bayesian-ordinary-kriging","co-kriging","spatial-autocorrelation","geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-var-model","name":"Bayesian VAR model","fullName":"Bayesian Vector Autoregression Model","aliases":["BVAR","Bayesian VAR","Bayesian vector autoregressive model","BVAR model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1984","originator":"Doan, Litterman & Sims","url":"https://scholargate.app/en/econometrics/bayesian-var-model","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-var-model.md","definition":"The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Doan, Litterman & Sims","year":"1984","type":"Multivariate time-series model","dataType":"Multivariate time-series data","subfamily":"Econometrics / time series"},"citations":[{"ref":"Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.","type":"article","doi":"10.1080/07474938408800053","isbn":null,"url":null},{"ref":"Koop, G., & Korobilis, D. (2010). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and Trends in Econometrics, 3(4), 267–358.","type":"article","doi":"10.1561/0800000013","isbn":null,"url":null}],"related":["vector-autoregression","structural-var","bayesian-vecm","bayesian-svar-model","panel-var-model","bayesian-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-variant-calling","name":"Bayesian Variant Calling","fullName":"Bayesian Statistical Variant Calling from Sequencing Data","aliases":["Bayesian genotyping","probabilistic variant calling","GATK HaplotypeCaller","Bayesian SNP/indel detection"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009","originator":"Mark DePristo, Eric Banks, and the Broad Institute GATK team","url":"https://scholargate.app/en/bioinformatics/bayesian-variant-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/bayesian-variant-calling.md","definition":"Bayesian variant calling is a computational pipeline that uses probabilistic inference to identify single-nucleotide polymorphisms (SNPs), insertions, and deletions in a genome by treating sequencing data as evidence and computing posterior probabilities over candidate genotypes. Unlike deterministic threshold-based callers, Bayesian approaches explicitly model sequencing error, mapping uncertainty, and prior genotype frequencies to produce calibrated genotype likelihoods that can be used for downstream filtering and association testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark DePristo, Eric Banks, and the Broad Institute GATK team","year":"2010 (GATK framework); Bayesian genotyping principles preceded by Samtools/MAQ ~2008–2009","type":"Probabilistic genomic inference pipeline","dataType":"Aligned short-read or long-read DNA/RNA sequencing data (BAM/CRAM format)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.","type":"article","doi":"10.1101/gr.107524.110","isbn":null,"url":null},{"ref":"Rimmer, A., Phan, H., Mathieson, I., Iqbal, Z., Twigg, S. R., WGS500 Consortium, ... & McVean, G. (2014). Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nature Genetics, 46(8), 912–918.","type":"article","doi":"10.1038/ng.3036","isbn":null,"url":null}],"related":["variant-calling","sequence-alignment","rna-seq-differential-expression","bayesian-gwas","copy-number-variation-analysis","single-cell-variant-calling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-vecm","name":"Bayesian VECM","fullName":"Bayesian Vector Error Correction Model","aliases":["Bayesian VECM","B-VECM","Bayesian cointegrated VAR","Bayesian vector error correction"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002–2005","originator":"Kleibergen & Paap; Villani","url":"https://scholargate.app/en/econometrics/bayesian-vecm","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-vecm.md","definition":"The Bayesian VECM combines the classical Vector Error Correction Model — which captures both short-run dynamics and long-run cointegrating relationships among non-stationary multivariate time series — with Bayesian prior distributions over the cointegrating rank and coefficient matrices. This allows principled uncertainty quantification, incorporation of economic theory as priors, and coherent inference even in small samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kleibergen & Paap; Villani","year":"2002–2005","type":"Bayesian multivariate time series model","dataType":"Multivariate non-stationary time series (cointegrated)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.","type":"article","doi":"10.1016/s0304-4076(02)00105-7","isbn":null,"url":null},{"ref":"Villani, M. (2005). Bayesian reference analysis of cointegration. Econometric Theory, 21(2), 326–357.","type":"article","doi":"10.1017/s026646660505019x","isbn":null,"url":null}],"related":["vector-error-correction-model","bayesian-var-model","johansen-cointegration-test","bayesian-arima-model","panel-vecm","structural-var"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-wilcoxon-signed-rank-test","name":"Bayesian Wilcoxon signed-rank test","fullName":"Bayesian Wilcoxon Signed-Rank Test","aliases":["Bayesian signed-rank test","Bayesian nonparametric paired comparison","Benavoli signed-rank Bayesian test","signed-rank Bayesian hypothesis test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"2014–2017","originator":"Benavoli, Corani, Mangili, and colleagues","url":"https://scholargate.app/en/statistics/bayesian-wilcoxon-signed-rank-test","markdownUrl":"https://scholargate.app/en/statistics/bayesian-wilcoxon-signed-rank-test.md","definition":"The Bayesian Wilcoxon signed-rank test is a Bayesian nonparametric method for comparing two paired or related samples. Rather than returning a single p-value, it produces posterior probabilities that one condition is better, practically equivalent, or worse than the other, enabling richer and more interpretable inference for paired continuous or ordinal data without assuming normality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Benavoli, Corani, Mangili, and colleagues","year":"2014–2017","type":"Bayesian nonparametric paired test","dataType":"Paired continuous or ordinal differences","subfamily":"Classical statistics"},"citations":[{"ref":"Benavoli, A., Corani, G., & Mangili, F. (2014). Should we really use post-hoc tests based on mean-ranks? Journal of Machine Learning Research, 17(5), 1–10.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v17/benavoli16a.html"},{"ref":"Benavoli, A., Corani, G., Demsar, J., & Zaffalon, M. (2017). Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. Journal of Machine Learning Research, 18(77), 1–36.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v18/16-305.html"}],"related":["wilcoxon-signed-rank-test","bayesian-paired-samples-t-test","bayesian-mann-whitney-u-test","paired-samples-t-test","bayesian-friedman-test","mann-whitney-u-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-wls","name":"Bayesian WLS","fullName":"Bayesian Weighted Least Squares","aliases":["Bayesian weighted regression","BWLS","Bayesian heteroscedastic regression","weighted Bayesian linear regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1971","originator":"Arnold Zellner (Bayesian econometrics framework)","url":"https://scholargate.app/en/econometrics/bayesian-wls","markdownUrl":"https://scholargate.app/en/econometrics/bayesian-wls.md","definition":"Bayesian Weighted Least Squares combines the classical WLS weighting scheme — which downweights observations with high error variance — with Bayesian prior distributions over the regression coefficients and error variance. The result is a posterior distribution that reflects both the data likelihood and prior beliefs, providing full uncertainty quantification in heteroscedastic settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arnold Zellner (Bayesian econometrics framework)","year":"1971","type":"Bayesian weighted regression","dataType":"Cross-sectional or panel data with heteroscedastic errors","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley, New York.","type":"book","doi":null,"isbn":"978-0471169376","url":null},{"ref":"Koop, G. (2003). Bayesian Econometrics. Wiley, Chichester.","type":"book","doi":null,"isbn":"978-0470845677","url":null}],"related":["bayesian-ols","bayesian-gls","panel-wls","robust-wls","bayesian-fixed-effects-model","bayesian-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-xgboost","name":"Bayesian XGBoost","fullName":"Bayesian-Optimized Extreme Gradient Boosting","aliases":["Bayesian XGBoost","XGBoost with Bayesian Optimization","BayesOpt-XGBoost","Bayes-tuned XGBoost"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2012–2016","originator":"Chen, T. & Guestrin, C. (XGBoost); Snoek, J. et al. (Bayesian Optimization)","url":"https://scholargate.app/en/machine-learning/bayesian-xgboost","markdownUrl":"https://scholargate.app/en/machine-learning/bayesian-xgboost.md","definition":"Bayesian XGBoost combines the predictive power of Extreme Gradient Boosting with Bayesian optimization for hyperparameter tuning. Instead of grid or random search, a probabilistic surrogate model guides the search for optimal learning rate, tree depth, and regularization parameters, achieving near-peak performance with far fewer evaluations than exhaustive search approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, T. & Guestrin, C. (XGBoost); Snoek, J. et al. (Bayesian Optimization)","year":"2012–2016","type":"Ensemble (gradient boosted trees with Bayesian hyperparameter search)","dataType":"Tabular (continuous, categorical, binary, ordinal features)","subfamily":"Machine learning"},"citations":[{"ref":"Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.","type":"inproceedings","doi":"10.1145/2939672.2939785","isbn":null,"url":null},{"ref":"Snoek, J., Larochelle, H. & Adams, R. P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25, 2951–2959.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html"}],"related":["xgboost","random-forest","gradient-boosting","lightgbm","hyperparameter-tuning","gaussian-process-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayesian-zero-inflated-model","name":"Bayesian Zero-inflated model","fullName":"Bayesian Zero-Inflated Count Model","aliases":["Bayesian ZIP","Bayesian ZINB","Bayesian zero-inflated Poisson","Bayesian zero-inflated negative binomial"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1992–2006","originator":"Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006)","url":"https://scholargate.app/en/statistics/bayesian-zero-inflated-model","markdownUrl":"https://scholargate.app/en/statistics/bayesian-zero-inflated-model.md","definition":"The Bayesian zero-inflated model handles count data with excess zeros by combining a binary component — identifying structural zeros — with a count component (Poisson or negative binomial) for the remaining counts. Bayesian inference via MCMC provides full posterior distributions for all parameters, enabling principled uncertainty quantification and regularisation through priors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006)","year":"1992–2006","type":"Bayesian count regression","dataType":"Non-negative integer counts with excess zeros","subfamily":"Regression / GLM"},"citations":[{"ref":"Ghosh, S. K., Mukhopadhyay, P., & Lu, J.-C. (2006). Bayesian analysis of zero-inflated regression models. Journal of Statistical Planning and Inference, 136(4), 1360–1375.","type":"article","doi":"10.1016/j.jspi.2004.10.008","isbn":null,"url":null},{"ref":"Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14.","type":"article","doi":"10.2307/1269547","isbn":null,"url":null}],"related":["zero-inflated-model","bayesian-poisson-regression","bayesian-negative-binomial-regression","bayesian-generalized-linear-model","bayesian-hurdle-model","poisson-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bayley-scales","name":"Bayley Scales of Infant Development","fullName":"Bayley Scales of Infant and Toddler Development (BSID)","aliases":["BSID","Bayley-III"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"Developmental screening","year":"2006","originator":"Nancy Bayley","url":"https://scholargate.app/en/developmental-assessment/bayley-scales","markdownUrl":"https://scholargate.app/en/developmental-assessment/bayley-scales.md","definition":"The Bayley Scales of Infant and Toddler Development (BSID), third edition, is a comprehensive standardized assessment tool developed by Nancy Bayley in 2006. It measures cognitive, motor, language, and social-emotional development in children from birth to 42 months. The scale identifies developmental delays and disabilities in infancy and early toddlerhood.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nancy Bayley","subfamily":"Developmental screening","year":"2006","type":"Standardized developmental assessment"},"citations":[{"ref":"Bayley, N. (2006). Bayley Scales of Infant and Toddler Development (3rd ed.). Pearson Assessment.","type":"book","doi":null,"isbn":"978-0158680314","url":null},{"ref":"Albers, C. A., & Grieve, A. J. (2007). Bayley Scales of Infant and Toddler Development (3rd ed.). Journal of Psychoeducational Assessment, 25(2), 180-190.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayley+Scales+of+Infant+and+Toddler+Development+%283rd+ed.%29+Albers"}],"related":["ages-stages-questionnaire","cbcl-child-behavior","achenbach-youth-self-report","conners-rating-scales"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bca-bootstrap","name":"BCa Bootstrap","fullName":"Bias-Corrected and Accelerated Bootstrap","aliases":["BCa Bootstrap (Bias-Corrected Accelerated)","bias-corrected accelerated bootstrap","BCa confidence interval"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1987,"originator":"Bradley Efron","url":"https://scholargate.app/en/statistics/bca-bootstrap","markdownUrl":"https://scholargate.app/en/statistics/bca-bootstrap.md","definition":"The BCa bootstrap is a resampling method, introduced by Bradley Efron in 1987, that produces more accurate confidence intervals than the plain percentile bootstrap by applying a bias correction and an acceleration adjustment. It is recommended for skewed distributions and small samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bradley Efron","year":1987,"type":"Resampling confidence interval","estimator":"Bias-corrected, acceleration-adjusted bootstrap percentile","minSample":15,"resamples":"B ≥ 2000 recommended"},"citations":[{"ref":"Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.","type":"article","doi":"10.1080/01621459.1987.10478410","isbn":null,"url":null},{"ref":"DiCiccio, T. J. & Efron, B. (1996). Bootstrap Confidence Intervals. Statistical Science, 11(3), 189-228.","type":"article","doi":"10.1214/ss/1032280214","isbn":null,"url":null}],"related":["bootstrap-inference","double-bootstrap","wild-bootstrap","bayesian-bootstrap","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bci-motor-imagery","name":"BCI Motor Imagery","fullName":"Brain-Computer Interface Motor Imagery","aliases":["Motor imagery BCI","MI-BCI","EEG motor decoding"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Neurotechnology","year":"1999","originator":"Gert Pfurtscheller","url":"https://scholargate.app/en/biomechanics/bci-motor-imagery","markdownUrl":"https://scholargate.app/en/biomechanics/bci-motor-imagery.md","definition":"Brain-computer interface (BCI) using motor imagery decodes the intent to move from brain activity (typically EEG) recorded while subjects imagine movement without actual muscle contraction. Pioneered by Gert Pfurtscheller and colleagues, motor imagery BCIs enable communication and control for paralyzed patients and enhance motor learning in rehabilitation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gert Pfurtscheller","subfamily":"Neurotechnology","year":"1999","type":"Neural signal processing and decoding pipeline"},"citations":[{"ref":"Pfurtscheller, G., & Neuper, C. (1999). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7), 1123-1134.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Motor+imagery+and+direct+brain-computer+communication+Pfurtscheller"},{"ref":"Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791.","type":"article","doi":"10.1016/S1388-2457(02)00057-3","isbn":null,"url":null}],"related":["common-spatial-pattern","emg-envelope","muscle-synergy-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bdi-ii","name":"Beck Depression Inventory-II","fullName":"Beck Depression Inventory-II: Self-Report Depression Assessment","aliases":["BDI-II","Beck Depression Inventory Second Edition"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"mood-disorder-assessment","year":"1996","originator":"Aaron T. Beck","url":"https://scholargate.app/en/clinical-psychology/bdi-ii","markdownUrl":"https://scholargate.app/en/clinical-psychology/bdi-ii.md","definition":"The Beck Depression Inventory-II is a 21-item self-report instrument designed to assess the presence and severity of depressive symptoms in adolescents and adults. Originally published by Aaron T. Beck in 1961 and revised significantly in 1996, the BDI-II is one of the most widely used depression assessment tools in clinical psychology and psychiatry. It is copyrighted and distributed by Pearson Assessments, and measures both cognitive and somatic symptoms of depression across a two-week timeframe.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aaron T. Beck","subfamily":"mood-disorder-assessment","year":"1996","type":"Self-report questionnaire"},"citations":[{"ref":"Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck Depression Inventory (2nd ed.). San Antonio, TX: The Psychological Corporation.","type":"book","doi":null,"isbn":"9780151840045","url":null},{"ref":"Steer, R. A., & Clark, D. A. (2001). Psychometric characteristics of the Beck Depression Inventory-II with college students. Assessment, 8(3), 235–242.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Psychometric+characteristics+of+the+Beck+Depression+Inventory-II+with+college+students+Steer"},{"ref":"Dozois, D. J., Dobson, K. S., & Ahnberg, E. (2003). A psychometric evaluation of the Beck Depression Inventory-II. Psychological Assessment, 10(2), 83–89.","type":"article","doi":"10.1037/1040-3590.10.2.83","isbn":null,"url":null}],"related":["phq-9","hamilton-depression-rating-scale","montgomery-asberg-depression","patient-global-impression-change"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bdt-particle-identification","name":"BDT Particle Identification","fullName":"Boosted Decision Tree Particle Identification","aliases":["BDT classifier","MVA particle ID","multivariate particle identification"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Multivariate classifier","year":"2000","originator":"Machine learning / particle physics community","url":"https://scholargate.app/en/particle-physics/bdt-particle-identification","markdownUrl":"https://scholargate.app/en/particle-physics/bdt-particle-identification.md","definition":"Boosted Decision Trees (BDTs) are powerful multivariate classifiers used in particle physics to distinguish between different particle types based on detector signatures. By combining many weak decision trees through adaptive boosting, BDTs achieve superior discrimination power compared to simple cuts, enabling improved purity and efficiency in particle identification and background rejection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Machine learning / particle physics community","subfamily":"Multivariate classifier","year":"2000","type":"Particle discrimination algorithm"},"citations":[{"ref":"Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.","type":"article","doi":"10.1023/A:1010933404324","isbn":null,"url":null},{"ref":"Kieseler, J., et al. (2016). Machine learning for detector trigger optimization at the LHC. Nuclear Instruments and Methods in Physics Research Section A, 824, 29–37.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Machine+learning+for+detector+trigger+optimization+at+the+LHC+Kieseler"},{"ref":"Aarrestad, T. K., et al. (2021). Machine learning for particle discrimination at the LHC. Journal of Physics: Conference Series, 1525(1), 012034.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Machine+learning+for+particle+discrimination+at+the+LHC+Aarrestad"}],"related":["anti-kt-jet-algorithm","missing-transverse-energy","hep-track-reconstruction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beam-propagation-method","name":"Beam Propagation Method","fullName":"Beam Propagation Method","aliases":["BPM","paraxial approximation method"],"domain":"optics","family":"process-pipeline","subfamily":"Computational","year":"1978","originator":"Michael Feit and John Fleck","url":"https://scholargate.app/en/optics/beam-propagation-method","markdownUrl":"https://scholargate.app/en/optics/beam-propagation-method.md","definition":"The Beam Propagation Method is a computational technique for simulating the propagation of optical beams through slowly varying, weakly guiding structures. Developed by Feit and Fleck in 1978, BPM exploits the paraxial approximation to reduce the full vector wave equation to a scalar or vector envelope equation, enabling efficient simulation of waveguides, integrated optics, and photonic devices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Feit and John Fleck","subfamily":"Computational","year":"1978","type":"Paraxial propagation algorithm"},"citations":[{"ref":"Feit, M. D., & Fleck, J. A. (1978). Light propagation in graded-index optical fibers. Applied Optics, 17(24), 3990-3998.","type":"article","doi":"10.1364/AO.17.003990","isbn":null,"url":null},{"ref":"Huang, W. P. (1992). The finite-difference vector beam propagation method: an analysis. Journal of Lightwave Technology, 10(3), 295-305.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+finite-difference+vector+beam+propagation+method%3A+an+analysis+Huang"},{"ref":"Hadley, G. R. (1992). Wide-angle beam propagation using Padé approximant operators. Optics Letters, 17(20), 1426-1428.","type":"article","doi":"10.1364/OL.17.001426","isbn":null,"url":null}],"related":["finite-difference-time-domain","fourier-optics","abcd-matrix"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beamforming","name":"Beamforming","fullName":"Acoustic Beamforming and Directional Microphone Arrays","aliases":["beamformer","spatial filtering","microphone array","phased array"],"domain":"acoustics","family":"process-pipeline","subfamily":"Signal processing, Spatial filtering","year":"1988","originator":"Van Veen, Barry Buckley","url":"https://scholargate.app/en/acoustics/beamforming","markdownUrl":"https://scholargate.app/en/acoustics/beamforming.md","definition":"Beamforming is a spatial signal processing technique that uses microphone arrays to selectively enhance sound from a desired direction while suppressing sounds from other directions. Formalized by Van Veen and Buckley in 1988, beamforming is fundamental to hands-free speech communication, hearing aids, sonar, radar, and spatial audio recording. It enables 'listening' with directional sensitivity despite using omnidirectional microphones, by exploiting time delays and phase differences between array elements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Van Veen, Barry Buckley","subfamily":"Signal processing, Spatial filtering","year":"1988","type":"Directional audio array processing"},"citations":[{"ref":"Van Veen, B. D., & Buckley, K. M. (1988). Beamforming: A versatile approach to spatial filtering. IEEE ASSP Magazine, 5(2), 4–24.","type":"article","doi":"10.1109/53.665","isbn":null,"url":null},{"ref":"Brandstein, M., & Ward, D. (2001). Microphone Arrays: Signal Processing Techniques and Applications. Springer-Verlag.","type":"article","doi":null,"isbn":"978-3540419013","url":null},{"ref":"Krim, H., & Viberg, M. (1996). Two decades of array signal processing research. IEEE Signal Processing Magazine, 13(4), 67–94.","type":"article","doi":"10.1109/79.526899","isbn":null,"url":null}],"related":["acoustic-holography","speech-intelligibility","fxlms-active-noise-control","acoustic-ray-tracing","psychoacoustic-masking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beat-tracking","name":"Beat Tracking","fullName":"Beat Tracking Algorithm","aliases":["pulse detection","beat detection","metrical analysis"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Feature extraction","year":"2007","originator":"David P. Ellis","url":"https://scholargate.app/en/music-information-retrieval/beat-tracking","markdownUrl":"https://scholargate.app/en/music-information-retrieval/beat-tracking.md","definition":"Beat tracking is an algorithm for automatically identifying the temporal positions of musical beats in audio recordings. It has been widely studied since the early 2000s, particularly for rhythm analysis and music synchronization applications. The problem is central to music information retrieval and essential for music-aware systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David P. Ellis","subfamily":"Feature extraction","year":"2007","type":"Audio signal processing algorithm"},"citations":[{"ref":"Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60.","type":"article","doi":"10.1080/09298210701653344","isbn":null,"url":null},{"ref":"Krebs, F., Böck, S., & Widmer, G. (2015). Rhythmic pattern modeling for beat and downbeat tracking in musical audio. Frontiers in Psychology, 6, 1337.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Rhythmic+pattern+modeling+for+beat+and+downbeat+tracking+in+musical+audio+Krebs"},{"ref":"Rocher, T., Robine, M., & Marsyas-Meuss, J. (2023). Deep learning approaches for beat tracking: State-of-the-art review. IEEE Access, 11, 15824-15842.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Deep+learning+approaches+for+beat+tracking%3A+State-of-the-art+review+Rocher"}],"related":["music-segmentation","tempo-estimation","pitch-detection-algorithm","music-genre-classification","harmonic-analysis-music"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beck-anxiety-inventory","name":"Beck Anxiety Inventory","fullName":"Beck Anxiety Inventory","aliases":["BAI"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"anxiety severity assessment","year":"1993","originator":"Aaron T. Beck, Robert A. Steer","url":"https://scholargate.app/en/clinical-psychology/beck-anxiety-inventory","markdownUrl":"https://scholargate.app/en/clinical-psychology/beck-anxiety-inventory.md","definition":"The Beck Anxiety Inventory (BAI) is a 21-item self-report scale designed to measure the severity of somatic and cognitive symptoms of anxiety in adolescents and adults. Developed by Aaron T. Beck and Robert A. Steer in 1993, the BAI is widely used in clinical assessment, treatment monitoring, and research to quantify anxiety symptoms across a broad spectrum of anxiety disorders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aaron T. Beck, Robert A. Steer","subfamily":"anxiety severity assessment","year":"1993","type":"Self-report symptom inventory"},"citations":[{"ref":"Beck, A. T., & Steer, R. A. (1993). BAI: Beck Anxiety Inventory. San Antonio, TX: The Psychological Corporation.","type":"book","doi":null,"isbn":"0158710050","url":null}],"related":["gad-7","state-trait-anxiety-inventory","panic-disorder-severity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beck-depression-inventory","name":"Beck Depression Inventory","fullName":"Beck Depression Inventory (BDI)","aliases":["BDI","BDI-II"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Depression assessment","year":"1961","originator":"Aaron T. Beck","url":"https://scholargate.app/en/clinical-psychology/beck-depression-inventory","markdownUrl":"https://scholargate.app/en/clinical-psychology/beck-depression-inventory.md","definition":"The Beck Depression Inventory (BDI) is a 21-item self-report instrument designed to measure the severity of depressive symptoms in adolescents and adults. Developed by Aaron T. Beck in 1961 and revised as the BDI-II in 1996, it has become one of the most widely used screening and monitoring tools in clinical psychology and psychiatry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aaron T. Beck","subfamily":"Depression assessment","year":"1961","type":"Self-report screening instrument"},"citations":[{"ref":"Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the Beck Depression Inventory-II. Psychological Corporation.","type":"article","doi":null,"isbn":"0158700194","url":null},{"ref":"Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh, J. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4(6), 561–571.","type":"article","doi":"10.1001/archpsyc.1961.01710120031004","isbn":null,"url":null}],"related":["phq-9-screening","generalized-anxiety-disorder-7","structured-clinical-interview-dsm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beck-hopelessness-scale","name":"BHS","fullName":"Beck Hopelessness Scale","aliases":["BHS","Beck Hopelessness Scale","Hopelessness Assessment"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"suicidal-ideation-and-hopelessness","year":"1974","originator":"Aaron T. Beck, Albert Weissman, David Lester, Lori Trexler","url":"https://scholargate.app/en/forensic-psychology/beck-hopelessness-scale","markdownUrl":"https://scholargate.app/en/forensic-psychology/beck-hopelessness-scale.md","definition":"The Beck Hopelessness Scale (BHS) is a 20-item self-report instrument developed by Aaron Beck and colleagues (1974) to measure the degree of hopelessness and pessimism about the future in adolescents and adults. It is grounded in Beck's cognitive theory of depression and suicide and is widely used in clinical, psychiatric, forensic, and research settings to assess suicide risk and identify individuals at elevated risk for self-harm and completed suicide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aaron T. Beck, Albert Weissman, David Lester, Lori Trexler","subfamily":"suicidal-ideation-and-hopelessness","year":"1974","type":"Self-report"},"citations":[{"ref":"Beck, A. T., Weissman, A., Lester, D., & Trexler, L. (1974). The measurement of pessimism: The Hopelessness Scale. Journal of Consulting and Clinical Psychology, 42(6), 861–865.","type":"article","doi":"10.1037/h0037562","isbn":null,"url":null},{"ref":"Beck, A. T., & Steer, R. A. (2000). Beck Hopelessness Scale (BHS). Psychological Assessment Resources, Inc.","type":"book","doi":null,"isbn":null,"url":"https://www.parinc.com/"}],"related":["novaco-anger-scale","hcr-20","suicide-probability-scale","psychopathy-checklist-screening"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"behavior-change-wheel","name":"Behaviour Change Wheel","fullName":"Behaviour Change Wheel (BCW): A Guide for Designing Behavior Change Interventions Using COM-B Model and Intervention Functions","aliases":["BCW","behaviour change wheel","COM-B model"],"domain":"implementation-science","family":"process-pipeline","subfamily":"behaviour change intervention design","year":"2011","originator":"Michie, S., van Stralen, M. M., West, R.","url":"https://scholargate.app/en/implementation-science/behavior-change-wheel","markdownUrl":"https://scholargate.app/en/implementation-science/behavior-change-wheel.md","definition":"The Behaviour Change Wheel (BCW) is a systematic, evidence-based framework for designing behavior change interventions. Developed by Michie et al. (2011) and built on the COM-B model (Capability, Opportunity, Motivation→Behavior), the BCW guides practitioners through a structured process: diagnose behavior change barriers (using the Theoretical Domains Framework), identify relevant intervention functions (education, persuasion, incentivization, coercion, training, restriction, environmental restructuring, modelling, enablement), and design specific behavior change techniques matched to policy categories. It has become the international standard for systematically designing behavior change interventions in healthcare, public health, and other domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michie, S., van Stralen, M. M., West, R.","subfamily":"behaviour change intervention design","year":"2011","type":"Framework"},"citations":[{"ref":"Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6, 42.","type":"article","doi":"10.1186/1748-5908-6-42","isbn":null,"url":null},{"ref":"Michie, S., Atkins, L., & West, R. (2014). The Behaviour Change Wheel: A Guide to Designing Interventions. Silverback Publishing, UK.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Michie%2C%20S.%2C%20Atkins%2C%20L.%2C%20%26%20West%2C%20R.%20(2014).%20The%20Behaviour%20Change%20Wheel%3A%20A%20Guide%20to%20Designing%20Interventions.%20Silverback%20Pu"},{"ref":"Cane, J., O'Connor, D., & Michie, S. (2012). Validation of the theoretical domains framework (TDF) across multiple teams and behavioural problems: A systematic review. Implementation Science, 7, 37.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validation+of+the+theoretical+domains+framework+%28TDF%29+across+multiple+teams+and+behavioural+problems%3A+A+systematic+review+Cane"}],"related":["theoretical-domains-framework","cfir-framework","implementation-outcome-taxonomy","knowledge-translation","normalization-process-theory"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"behavioral-regulation-exercise","name":"Behavioral Regulation in Exercise Questionnaire","fullName":"Behavioral Regulation in Exercise Questionnaire—3","aliases":["BREQ-3","BREQ"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Exercise Motivation & Regulation Type","year":"2012","originator":"Paul M. Wilson, Wendy M. Rodgers, and colleagues","url":"https://scholargate.app/en/health-behavior/behavioral-regulation-exercise","markdownUrl":"https://scholargate.app/en/health-behavior/behavioral-regulation-exercise.md","definition":"The Behavioral Regulation in Exercise Questionnaire—3 (BREQ-3) is a 24-item measure developed by Wilson and colleagues (2012) to assess the type and quality of motivation underlying exercise behavior. Grounded in Self-Determination Theory, the BREQ-3 measures six regulation types positioned on a continuum from amotivation (no intention to exercise) through external regulation (exercising for external rewards or pressure), introjected regulation (exercising due to guilt or internal pressure), identified regulation (exercising because you value the benefits), integrated regulation (exercising because it aligns with your identity and values), and intrinsic motivation (exercising for enjoyment and interest). The BREQ-3 is widely used in exercise science, sports psychology, and health behavior research to understand why people exercise and to predict long-term exercise adherence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul M. Wilson, Wendy M. Rodgers, and colleagues","subfamily":"Exercise Motivation & Regulation Type","year":"2012","type":"Self-report questionnaire"},"citations":[{"ref":"Wilson, P. M., Rodgers, W. M., Loitz, C. C., & Scime, G. (2012). 'It's not about winning. It's about fun': Reconsidering the hedonic and eudaimonic contributions of physical activity across the lifespan. International Journal of Sport and Exercise Psychology, 10(3), 168-185.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=%27It%27s+not+about+winning+Wilson"},{"ref":"Markland, D., & Tobin, V. (2004). A modification of the Behavioral Regulation in Exercise Questionnaire to include an assessment of amotivation. Journal of Sport and Exercise Psychology, 26(2), 191-196.","type":"article","doi":"10.1123/jsep.26.2.191","isbn":null,"url":null}],"related":["self-determination-theory-scale","behavioral-regulation-exercise","exercise-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bekk-garch","name":"BEKK-GARCH","fullName":"BEKK Multivariate GARCH","aliases":["BEKK Model","Baba-Engle-Kraft-Kroner GARCH","Multivariate BEKK","BEKK-ÇARCH Modeli"],"domain":"econometrics","family":"regression-model","subfamily":"Volatility models","year":1995,"originator":"Robert Engle & Kenneth Kroner","url":"https://scholargate.app/en/econometrics/bekk-garch","markdownUrl":"https://scholargate.app/en/econometrics/bekk-garch.md","definition":"BEKK-GARCH, proposed by Engle and Kroner (1995), is a multivariate GARCH specification that models the time-varying conditional covariance matrix of a system of financial return series. Named after Baba, Engle, Kraft, and Kroner, it is the dominant framework for quantifying volatility spillovers and dynamic correlations across multiple assets or markets simultaneously, widely adopted by financial economists and risk managers since the mid-1990s.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Engle & Kenneth Kroner","year":1995,"type":"Multivariate conditional volatility model","subfamily":"Volatility models","acronym":"Baba, Engle, Kraft & Kroner","estimation":"Quasi-Maximum Likelihood (QML)"},"citations":[{"ref":"Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory, 11(1), 122–150.","type":"article","doi":"10.1017/S0266466600009063","isbn":null,"url":null}],"related":["dcc-garch","garch-model","var-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"belief-rule-base","name":"Belief Rule Base","fullName":"Belief Rule-Base Inference (RIMER)","aliases":["RIMER","Belief Rule-Based System","BRB System","İnanç Kural Tabanlı Çıkarım"],"domain":"soft-computing","family":"ml-model","subfamily":"Evidential reasoning","year":2006,"originator":"Jian-Bo Yang et al.","url":"https://scholargate.app/en/soft-computing/belief-rule-base","markdownUrl":"https://scholargate.app/en/soft-computing/belief-rule-base.md","definition":"Belief Rule Base (BRB), introduced by Yang et al. in 2006 under the RIMER framework, is an expert-system inference methodology that extends classical if-then rules by attaching belief degree distributions to rule consequents. It combines rule-based reasoning with the Evidential Reasoning (ER) approach, enabling the representation and propagation of uncertainty, incompleteness, and vagueness in complex decision problems across engineering, risk assessment, and management domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jian-Bo Yang et al.","year":2006,"type":"Expert-system inference with belief distributions","subfamily":"Evidential reasoning","primary_output":"Belief degree distribution over consequents","data_requirement":"Quantitative or qualitative antecedent attributes with expert-defined rules"},"citations":[{"ref":"Yang, J.-B., Liu, J., Wang, J., Sii, H.-S., & Wang, H.-W. (2006). Belief rule-base inference methodology using the evidential reasoning approach—RIMER. IEEE Transactions on Systems, Man, and Cybernetics—Part A, 36(2), 266–285.","type":"article","doi":"10.1109/TSMCA.2005.851270","isbn":null,"url":null}],"related":["dempster-shafer-theory","fuzzy-cognitive-maps","rule-induction"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beliefs-medicines-questionnaire","name":"Beliefs about Medicines Questionnaire","fullName":"Beliefs about Medicines Questionnaire (BMQ)","aliases":["BMQ"],"domain":"pharmacology","family":"process-pipeline","subfamily":"medication-beliefs","year":"1999","originator":"Rob Horne, John Weinman, and Michelle Hankins","url":"https://scholargate.app/en/pharmacology/beliefs-medicines-questionnaire","markdownUrl":"https://scholargate.app/en/pharmacology/beliefs-medicines-questionnaire.md","definition":"The Beliefs about Medicines Questionnaire (BMQ) is an 18-item self-report measure developed by Horne, Weinman, and Hankins in 1999 to assess patients' cognitive beliefs about necessity of medications and concerns about potential adverse effects. It is widely used in clinical research to predict medication adherence, particularly in chronic disease management, and has demonstrated strong predictive validity across diverse populations and disease contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rob Horne, John Weinman, and Michelle Hankins","subfamily":"medication-beliefs","year":"1999","type":"Self-report"},"citations":[{"ref":"Horne, R., Weinman, J., & Hankins, M. (1999). The Beliefs about Medicines Questionnaire: The development and evaluation of a new method for assessing the cognitive representation of medication. Psychology & Health, 14(1), 1-24.","type":"article","doi":"10.1080/08870449908407311","isbn":null,"url":null}],"related":["medication-adherence-rating-scale","treatment-satisfaction-questionnaire-medication","drug-attitude-inventory","self-efficacy-medication-adherence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bellman-ford-algorithm","name":"Bellman-Ford Algorithm","fullName":"Bellman-Ford Algorithm for Shortest Path","aliases":["Bellman-Ford method","Bellman algorithm"],"domain":"operations-research","family":"ml-model","subfamily":"Graph Algorithms","year":"1956","originator":"Richard Bellman and Lester R. Ford","url":"https://scholargate.app/en/operations-research/bellman-ford-algorithm","markdownUrl":"https://scholargate.app/en/operations-research/bellman-ford-algorithm.md","definition":"The Bellman-Ford Algorithm, developed by Richard Bellman and Lester R. Ford in the 1950s, is a fundamental algorithm for computing shortest paths in weighted graphs that may contain negative edge weights. Unlike Dijkstra's algorithm, it correctly handles negative weights and can detect the presence of negative-weight cycles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Bellman and Lester R. Ford","subfamily":"Graph Algorithms","year":"1956","type":"algorithm"},"citations":[{"ref":"Bellman, R. (1958). On a routing problem. Quarterly of Applied Mathematics, 16(1), 87-90.","type":"article","doi":"10.1090/qam/102435","isbn":null,"url":null},{"ref":"Ford, L. R. (1956). Network Flow Theory. RAND Corporation Paper P-923.","type":"article","doi":null,"isbn":null,"url":"https://www.rand.org/"}],"related":["dijkstra-algorithm","ford-fulkerson-algorithm","a-star-search-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"belmont-report","name":"Belmont Report","fullName":"Ethical Principles and Guidelines for the Protection of Human Subjects of Research","aliases":["Belmont Principles","Three Ethical Principles"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-frameworks","year":"1979","originator":"National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (US DHEW)","url":"https://scholargate.app/en/research-ethics/belmont-report","markdownUrl":"https://scholargate.app/en/research-ethics/belmont-report.md","definition":"The Belmont Report (1979) is the foundational US ethical framework for human subjects research, established by the National Commission following the Tuskegee Syphilis Study scandal. It articulates three core principles—Respect for Persons, Beneficence, and Justice—that form the basis for institutional review and regulatory oversight of human research globally. Every researcher conducting human studies must understand and apply these principles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (US DHEW)","subfamily":"ethical-frameworks","year":"1979","type":"Framework"},"citations":[{"ref":"National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research. Department of Health, Education, and Welfare.","type":"report","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html"}],"related":["declaration-of-helsinki","nuremberg-code","informed-consent-research","institutional-review-board","research-integrity-principles"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bem-acoustics","name":"BEM Acoustics","fullName":"Boundary Element Method for Acoustic Simulation","aliases":["BEM","boundary element method","indirect BEM","direct BEM"],"domain":"acoustics","family":"process-pipeline","subfamily":"Numerical method","year":"1971","originator":"Carlos Brebbia, Robert Butterfield","url":"https://scholargate.app/en/acoustics/bem-acoustics","markdownUrl":"https://scholargate.app/en/acoustics/bem-acoustics.md","definition":"The Boundary Element Method (BEM) is a numerical technique for solving acoustic wave equations in complex geometries. Unlike finite element methods (FEM) that mesh entire volumes, BEM discretizes only the acoustic boundaries (surfaces), reducing computational cost and memory. First applied to acoustics by Burton and Miller in 1971, BEM is widely used for predicting room acoustics, exterior noise radiation, and acoustic scattering without the need for volumetric meshing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carlos Brebbia, Robert Butterfield","subfamily":"Numerical method","year":"1971","type":"Computational simulation for acoustics"},"citations":[{"ref":"Burton, A. J., & Miller, G. F. (1971). The application of integral equation methods to the numerical solution of some exterior boundary-value problems. Proceedings of the Royal Society A, 323(1553), 201–210.","type":"article","doi":"10.1098/rspa.1971.0097","isbn":null,"url":null},{"ref":"Ciskowski, R. D., & Brebbia, C. A. (1991). Boundary Element Methods in Acoustics. Computational Mechanics Publications.","type":"book","doi":null,"isbn":"978-1853121937","url":null},{"ref":"Wu, T. W. (2000). Boundary Element Acoustics: Fundamentals and Computer Codes. WIT Press.","type":"book","doi":null,"isbn":"978-1853126122","url":null}],"related":["acoustic-ray-tracing","room-impulse-response","acoustic-holography","impedance-tube","psychoacoustic-masking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bem-geomechanics","name":"BEM Geomechanics","fullName":"Boundary Element Method for Geomechanical Analysis","aliases":["Boundary element method","BEM analysis","Indirect methods"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Numerical methods","year":"1978","originator":"Carlos Alberto Brebbia","url":"https://scholargate.app/en/civil-engineering/bem-geomechanics","markdownUrl":"https://scholargate.app/en/civil-engineering/bem-geomechanics.md","definition":"The boundary element method (BEM) for geomechanics is a numerical approach that solves problems by discretizing only the boundary of the domain, using analytical solutions for the interior. Introduced by Brebbia in 1978 and refined for geotechnical applications by Crouch and Starfield, BEM is particularly effective for infinite or semi-infinite domains (underground excavations, foundations, rock masses) where finite element methods are impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carlos Alberto Brebbia","subfamily":"Numerical methods","year":"1978","type":"Mesh-less numerical method for geomechanical problems"},"citations":[{"ref":"Brebbia, C. A. (1978). The Boundary Element Method for Engineers. Pentech Press.","type":"book","doi":null,"isbn":"0-08-020191-5","url":null},{"ref":"Crouch, S. L., & Starfield, A. M. (1983). Boundary Element Methods in Solid Mechanics. George Allen & Unwin.","type":"book","doi":null,"isbn":"0-04-624014-X","url":null},{"ref":"Dasgupta, G., & Chopra, A. K. (1988). Dynamic stiffness of foundations on layered soil. Journal of Engineering Mechanics, 114(8), 1264-1286.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dynamic+stiffness+of+foundations+on+layered+soil+Dasgupta"}],"related":["soil-structure-interaction","finite-strip-method","modflow"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"benders-decomposition","name":"Benders Decomposition","fullName":"Benders Decomposition Method","aliases":["cutting plane method","constraint generation"],"domain":"operations-research","family":"ml-model","subfamily":"Optimization","year":"1962","originator":"Jacques F. Benders","url":"https://scholargate.app/en/operations-research/benders-decomposition","markdownUrl":"https://scholargate.app/en/operations-research/benders-decomposition.md","definition":"Benders Decomposition, introduced by Jacques F. Benders in 1962, is a powerful algorithmic framework for solving large-scale mixed-integer programming (MIP) problems. It decomposes the problem into a master problem (controlling complicating variables) and subproblems (handling remaining variables), using cutting planes generated from subproblem dual information to iteratively tighten the master problem.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacques F. Benders","subfamily":"Optimization","year":"1962","type":"algorithm"},"citations":[{"ref":"Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4(1), 238-252.","type":"article","doi":"10.1007/BF01386316","isbn":null,"url":null},{"ref":"Geoffrion, A. M. (1972). Generalized Benders decomposition. Journal of Optimization Theory and Applications, 10(4), 237-260.","type":"article","doi":"10.1007/BF00934810","isbn":null,"url":null}],"related":["simplex-method","column-generation","augmented-lagrangian-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beneish-m-score","name":"Beneish M-Score","fullName":"Beneish M-Score (Earnings Manipulation Detection)","aliases":["Beneish Model","M-Score Model","Earnings Manipulation Score","Beneish M-Skoru"],"domain":"finance","family":"regression-model","subfamily":"Forensic accounting","year":1999,"originator":"Messod Beneish","url":"https://scholargate.app/en/finance/beneish-m-score","markdownUrl":"https://scholargate.app/en/finance/beneish-m-score.md","definition":"The Beneish M-Score is a statistical model developed by Messod Beneish in 1999 to identify whether a company has manipulated its reported earnings. The model combines eight financial-statement ratios into a single composite score using coefficients estimated from a probit regression on a sample of detected earnings manipulators. A score above −2.22 indicates a heightened probability of manipulation, making the M-Score a widely used tool in forensic accounting and investment due-diligence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Messod Beneish","year":1999,"type":"Probabilistic forensic accounting model","subfamily":"Forensic accounting","output":"Continuous scalar M-Score; threshold at −2.22","data_required":"Eight financial-statement ratios from two consecutive annual periods"},"citations":[{"ref":"Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24–36.","type":"article","doi":"10.2469/faj.v55.n5.2296","isbn":null,"url":null}],"related":["altman-z-score","logistic-regression","dupont-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"benjamini-hochberg-procedure","name":"Benjamini-Hochberg Procedure","fullName":"Benjamini-Hochberg False Discovery Rate Procedure","aliases":["BH procedure","FDR control","false discovery rate procedure","Benjamini-Hochberg düzeltmesi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1995,"originator":"Yoav Benjamini & Yosef Hochberg","url":"https://scholargate.app/en/statistics/benjamini-hochberg-procedure","markdownUrl":"https://scholargate.app/en/statistics/benjamini-hochberg-procedure.md","definition":"The Benjamini-Hochberg (BH) procedure, introduced by Yoav Benjamini and Yosef Hochberg in 1995, controls the false discovery rate (FDR) — the expected proportion of false positives among all rejected hypotheses — rather than the probability of any false positive. By tolerating a controlled fraction of false discoveries, it delivers far greater power than family-wise error rate methods such as Bonferroni or Holm, which is why it has become the standard tool for large-scale simultaneous testing in genomics, neuroimaging, and other high-throughput fields.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yoav Benjamini & Yosef Hochberg","year":1995,"family":"Hypothesis test","type":"False discovery rate (FDR) procedure","errorControl":"False discovery rate (FDR)","parametric":true,"procedure":"Step-up (linear) on ordered p-values","applicability":"Large-scale simultaneous testing"},"citations":[{"ref":"Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57(1), 289–300.","type":"article","doi":"10.1111/j.2517-6161.1995.tb02031.x","isbn":null,"url":null},{"ref":"Benjamini, Y., & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4), 1165–1188.","type":"article","doi":"10.1214/aos/1013699998","isbn":null,"url":null}],"related":["bonferroni-correction","holm-correction","sidak-correction","multiple-linear-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"benthic-index-of-biotic-integrity","name":"Benthic Index of Biotic Integrity","fullName":"Benthic Index of Biotic Integrity","aliases":["B-IBI","Benthic IBI"],"domain":"oceanography","family":"process-pipeline","subfamily":"Ecological Assessment","year":"1981","originator":"James Karr","url":"https://scholargate.app/en/oceanography/benthic-index-of-biotic-integrity","markdownUrl":"https://scholargate.app/en/oceanography/benthic-index-of-biotic-integrity.md","definition":"The Benthic Index of Biotic Integrity (B-IBI) is an ecological assessment metric that measures the health and integrity of benthic (seafloor) communities based on the composition, abundance, and diversity of benthic fauna. Developed by James Karr in 1981 for freshwater fish assemblages and later adapted for marine benthic communities, the B-IBI provides a holistic measure of ecosystem condition integrating responses to multiple stressors including pollution, habitat degradation, and resource depletion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Karr","subfamily":"Ecological Assessment","year":"1981","type":"biotic-index"},"citations":[{"ref":"Karr, J. R. (1981). Assessment of biotic integrity using fish communities. Fisheries, 6(6), 21-27.","type":"article","doi":"10.1577/1548-8446(1981)006<0021:AOBIUF>2.0.CO;2","isbn":null,"url":null},{"ref":"McLaughlin, R. L., Kahnle, A. W., Danehy, R. J., et al. (2013). Reframing the coastal squeeze: navigating between humans, fish, and rising seas. Fisheries, 38(8), 357-365.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Reframing+the+coastal+squeeze%3A+navigating+between+humans%2C+fish%2C+and+rising+seas+McLaughlin"}],"related":["harmful-algal-bloom-monitoring","phytoplankton-size-class","marxan-mpa-planning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bereavement-risk-index","name":"BRI","fullName":"Bereavement Risk Index","aliases":["BRI","Bereavement Risk Assessment"],"domain":"bereavement-psychology","family":"process-pipeline","subfamily":"risk-assessment-and-prediction","year":"1986","originator":"Gary D. Arnstein","url":"https://scholargate.app/en/bereavement-psychology/bereavement-risk-index","markdownUrl":"https://scholargate.app/en/bereavement-psychology/bereavement-risk-index.md","definition":"The Bereavement Risk Index (BRI) is a structured assessment tool designed to identify bereaved individuals at elevated risk for complicated grief, depression, or other adverse bereavement outcomes. By systematically evaluating established risk factors (manner of death, relationship quality, concurrent stressors, prior loss history, social support), the BRI facilitates early identification and risk stratification to guide prevention and targeted intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gary D. Arnstein","subfamily":"risk-assessment-and-prediction","year":"1986","type":"Structured interview / Risk factor assessment"},"citations":[{"ref":"Arnstein, G. D. (1986). Prediction of complicated grief in recently bereaved individuals. Journal of Mental Health Counseling, 8(4), 266–279.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Arnstein%2C%20G.%20D.%20(1986).%20Prediction%20of%20complicated%20grief%20in%20recently%20bereaved%20individuals.%20Journal%20of%20Mental%20Health%20Couns"}],"related":["inventory-complicated-grief","prolonged-grief-disorder-scale","texas-revised-inventory-grief","anticipatory-grief-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"berg-balance-scale","name":"Berg Balance Scale","fullName":"Berg Balance Scale (BBS)","aliases":["BBS"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Balance assessment","year":"1989","originator":"Katherine Berg","url":"https://scholargate.app/en/physical-therapy/berg-balance-scale","markdownUrl":"https://scholargate.app/en/physical-therapy/berg-balance-scale.md","definition":"The Berg Balance Scale (BBS) is a 14-item performance-based assessment developed by Katherine Berg in 1989 to measure balance ability in older adults and individuals with neurological conditions. It evaluates static and dynamic balance through functional tasks relevant to daily living, providing a reliable and valid tool for fall risk assessment and rehabilitation monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Katherine Berg","subfamily":"Balance assessment","year":"1989","type":"Functional assessment scale"},"citations":[{"ref":"Berg, K. O., Wood-Dauphinee, S. L., Williams, J. I., & Maki, B. (1992). Measuring balance in the elderly: Validation of an instrument. Canadian Journal of Public Health, 83(Suppl 2), S7-S11.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+balance+in+the+elderly%3A+Validation+of+an+instrument+Berg"},{"ref":"Berg, K. O., Maki, B. E., Williams, J. I., Holliday, P. J., & Wood-Dauphinee, S. L. (1992). Clinical and laboratory measures of postural balance in an elderly population. Archives of Physical Medicine and Rehabilitation, 73(11), 1073-1080.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/1444775/"}],"related":["timed-up-and-go-test","ten-meter-walk-test","functional-independence-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"berlin-questionnaire-sleep","name":"Berlin Questionnaire","fullName":"Berlin Questionnaire for Sleep Apnea Screening","aliases":["Berlin Sleep Apnea Questionnaire"],"domain":"sleep-medicine","family":"process-pipeline","subfamily":"OSA risk stratification; general population screening","year":"1999","originator":"Netzer, N. C., Stoohs, R. A., Netzer, C. M., et al.","url":"https://scholargate.app/en/sleep-medicine/berlin-questionnaire-sleep","markdownUrl":"https://scholargate.app/en/sleep-medicine/berlin-questionnaire-sleep.md","definition":"The Berlin Questionnaire is a 10-item screening instrument designed to identify patients at risk for obstructive sleep apnea in primary care and community settings. Developed by Netzer and colleagues in 1999, it uses a three-category scoring approach (snoring symptoms, daytime somnolence, and hypertension/obesity) to stratify OSA risk. The tool is particularly useful in general population screening and has been extensively validated across international cohorts and clinical populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Netzer, N. C., Stoohs, R. A., Netzer, C. M., et al.","subfamily":"OSA risk stratification; general population screening","year":"1999","type":"Self-report"},"citations":[{"ref":"Netzer, N. C., Stoohs, R. A., Netzer, C. M., Clark, K., & Strohl, K. P. (1999). Using the Berlin Questionnaire to identify patients at risk for the sleep apnea syndrome. Annals of Internal Medicine, 131(7), 485-491.","type":"article","doi":"10.7326/0003-4819-131-7-199910050-00002","isbn":null,"url":null}],"related":["stop-bang-questionnaire","sleep-condition-indicator","restless-legs-syndrome-rating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bert-based-classification","name":"BERT-based Classification","fullName":"Bidirectional Encoder Representations from Transformers for Text Classification","aliases":["BERT classifier","BERT fine-tuning for classification","BERT text classification","BERT-CLS"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)","url":"https://scholargate.app/en/deep-learning/bert-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/bert-based-classification.md","definition":"BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)","year":"2019","type":"Pre-trained language model with fine-tuning","dataType":"Text / natural language sequences","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? In China National Conference on Chinese Computational Linguistics (CCL 2019), Lecture Notes in Computer Science, vol 11856, pp. 194–206. Springer.","type":"inproceedings","doi":"10.1007/978-3-030-32381-3_16","isbn":null,"url":null}],"related":["transformer","roberta-based-classification","sentence-embeddings","recurrent-neural-network","long-short-term-memory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bert-embeddings","name":"BERT Embeddings","fullName":"BERT-Based Text Embeddings","aliases":["contextual embeddings","transformer embeddings","BERT Tabanlı Metin Gömülmeleri"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2019,"originator":"Devlin, Chang, Lee & Toutanova (Google AI)","url":"https://scholargate.app/en/text-mining/bert-embeddings","markdownUrl":"https://scholargate.app/en/text-mining/bert-embeddings.md","definition":"BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin, Chang, Lee & Toutanova (Google AI)","year":2019,"type":"Contextual transformer text-representation method","architecture":"Bidirectional Transformer encoder","output":"Context-sensitive dense vectors (embeddings)","minDocuments":10},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.","type":"article","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Tenney, I., Das, D. & Pavlick, E. (2019). BERT Rediscovers the Classical NLP Pipeline. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 4593-4601.","type":"article","doi":"10.18653/v1/P19-1452","isbn":null,"url":null}],"related":["doc2vec","glove-embeddings","word2vec","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bert-finetuning","name":"BERT Fine-Tuning","fullName":"Fine-Tuning of Pre-trained BERT (Bidirectional Encoder Representations from Transformers)","aliases":["BERT İnce Ayar (Fine-Tuning)","BERT ince ayar","fine-tuning BERT","transfer learning with BERT"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2019,"originator":"Devlin, J. et al.","url":"https://scholargate.app/en/deep-learning/bert-finetuning","markdownUrl":"https://scholargate.app/en/deep-learning/bert-finetuning.md","definition":"BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin, J. et al.","year":2019,"type":"Transfer learning (fine-tuning a pre-trained transformer)","task":"Text classification, NER, question answering","varType":"Text","minSample":50},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL.","type":"article","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Sun, C., Qiu, X., Xu, Y. & Huang, X. (2019). How to Fine-Tune BERT for Text Classification. CCL.","type":"article","doi":"10.1007/978-3-030-32381-3_16","isbn":null,"url":null}],"related":["gpt-finetuning","lora-peft","vision-transformer","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bet-surface-area","name":"BET Surface Area","fullName":"Brunauer-Emmett-Teller Surface Area Analysis (BET)","aliases":["BET analysis","nitrogen adsorption","surface area measurement"],"domain":"materials-science","family":"process-pipeline","subfamily":"Porosity characterization","year":"1938","originator":"Brunauer, Emmett, Teller","url":"https://scholargate.app/en/materials-science/bet-surface-area","markdownUrl":"https://scholargate.app/en/materials-science/bet-surface-area.md","definition":"Brunauer-Emmett-Teller (BET) Surface Area Analysis is a technique for measuring the specific surface area of solids by analyzing their nitrogen adsorption isotherms. Developed by Brunauer, Emmett, and Teller in 1938, BET theory extends monolayer adsorption (Langmuir) to multilayer adsorption, enabling quantification of surface area of porous and powdered materials. It is the industry standard for characterizing catalysts, adsorbents, pharmaceuticals, and porous materials, providing critical data for performance prediction and quality control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brunauer, Emmett, Teller","subfamily":"Porosity characterization","year":"1938","type":"Measurement method"},"citations":[{"ref":"Brunauer, S., Emmett, P. H., & Teller, E. (1938). Adsorption of gases in multimolecular layers. Journal of the American Chemical Society, 60(2), 309-319.","type":"article","doi":"10.1021/ja01269a023","isbn":null,"url":null},{"ref":"Sing, K. S. W., et al. (1985). Reporting physisorption data for gas/solid systems. Pure and Applied Chemistry, 57(4), 603-619.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Reporting+physisorption+data+for+gas%2Fsolid+systems+Sing"},{"ref":"Lowell, S., Shields, J. E., Thomas, M. A., & Thommes, M. (2004). Characterization of Porous Solids and Powders: Surface Area, Pore Size and Density. Springer.","type":"book","doi":"10.1007/978-1-4020-2303-3","isbn":null,"url":null}],"related":["dynamic-light-scattering","thermogravimetric-analysis","xrd-rietveld-refinement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beta-diversity-partitioning","name":"Beta Diversity Partitioning","fullName":"Beta Diversity Partitioning Analysis","aliases":["beta diversity","species turnover","nestedness","community dissimilarity"],"domain":"ecology","family":"process-pipeline","subfamily":"Community ecology","year":"2010","originator":"Andres Baselga","url":"https://scholargate.app/en/ecology/beta-diversity-partitioning","markdownUrl":"https://scholargate.app/en/ecology/beta-diversity-partitioning.md","definition":"Beta diversity partitioning quantifies how species composition differs among sites, decomposing community dissimilarity into two components: species turnover (replacement of species across sites) and nestedness (loss of species from species-rich sites). Developed by Baselga (2010), this framework reveals whether sites differ because they have different species (turnover) or because some sites are subsets of others (nestedness). This distinction has ecological and conservation implications: turnover suggests environmental heterogeneity or speciation, while nestedness suggests habitat loss or extinction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andres Baselga","subfamily":"Community ecology","year":"2010","type":"community differentiation analysis"},"citations":[{"ref":"Baselga, A. (2010). Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography, 19(1), 134-143.","type":"article","doi":"10.1111/j.1466-8238.2009.00490.x","isbn":null,"url":null},{"ref":"Whittaker, R. H. (1972). Evolution and measurement of species diversity. Taxon, 21(2/3), 213-251.","type":"article","doi":"10.2307/1218190","isbn":null,"url":null},{"ref":"Koleff, P., Gaston, K. J., & Lennon, J. J. (2003). Measuring beta diversity for presence-absence data. Journal of Animal Ecology, 72(3), 367-382.","type":"article","doi":"10.1046/j.1365-2656.2003.00710.x","isbn":null,"url":null}],"related":["species-accumulation","indicator-value","functional-diversity","faiths-phylogenetic-diversity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"beta-regression","name":"Beta Regression","fullName":"Beta Regression for Rates and Proportions","aliases":["beta regression model","proportion regression","Beta Regresyonu"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2004,"originator":"Ferrari & Cribari-Neto","url":"https://scholargate.app/en/statistics/beta-regression","markdownUrl":"https://scholargate.app/en/statistics/beta-regression.md","definition":"Beta regression is a generalized linear model introduced by Ferrari and Cribari-Neto (2004) for outcomes that are rates or proportions confined to the open interval (0,1). It models the mean of a beta-distributed response through a link function, making it the natural choice for fractions, probability scores, and proportion indices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ferrari & Cribari-Neto","year":2004,"type":"Generalized linear model (beta distribution)","estimator":"Maximum likelihood","outcome":"proportion on the open interval (0,1)","link":"logit, probit, or cloglog"},"citations":[{"ref":"Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.","type":"article","doi":"10.1080/0266476042000214501","isbn":null,"url":null}],"related":["ols-regression","logistic-regression","gamma-regression","fractional-logit-regression","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"between-estimator","name":"Between Estimator","fullName":"Between Estimator (Panel)","aliases":["Between-Groups Estimator","Cross-Sectional Averages Estimator","Panel Between Estimator","Gruplar-Arası Tahmin Edici"],"domain":"econometrics","family":"regression-model","subfamily":"Static panel","year":2008,"originator":"Badi Baltagi (treatment)","url":"https://scholargate.app/en/econometrics/between-estimator","markdownUrl":"https://scholargate.app/en/econometrics/between-estimator.md","definition":"The Between Estimator is a panel data regression technique that identifies regression coefficients exclusively from cross-sectional variation across individuals, by collapsing the panel to individual-specific time-averaged observations and applying ordinary least squares to those group means. It is used in economics, sociology, and political science when researchers are interested in long-run or structural differences between units rather than short-run within-unit dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Badi Baltagi (treatment)","year":2008,"type":"OLS on group means","subfamily":"Static panel","estimand":"Cross-sectional (between-individual) variation","sample_requirement":"Balanced or unbalanced panel with T >= 2"},"citations":[{"ref":"Baltagi, B. H. (2008). Econometric Analysis of Panel Data (4th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-470-51886-1","url":null}],"related":["panel-fixed-effects","random-effects-model","pooled-ols"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"betweenness-centrality","name":"Betweenness Centrality","fullName":"Betweenness Centrality (Freeman's Geodesic Betweenness)","aliases":["Freeman betweenness","BC","geodesic betweenness","shortest-path betweenness"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1977","originator":"Freeman, L. C.","url":"https://scholargate.app/en/network-analysis/betweenness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/betweenness-centrality.md","definition":"Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Freeman, L. C.","year":"1977","type":"Centrality measure","dataType":"Graph / adjacency matrix (weighted or unweighted, directed or undirected)","subfamily":"Network science"},"citations":[{"ref":"Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.","type":"article","doi":"10.2307/3033543","isbn":null,"url":null},{"ref":"Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163–177.","type":"article","doi":"10.1080/0022250X.2001.9990249","isbn":null,"url":null}],"related":["degree-centrality","closeness-centrality","eigenvector-centrality","social-network-analysis","modularity-analysis","pagerank"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"betz-limit","name":"Betz Limit","fullName":"Betz Limit for Wind Turbine Maximum Power","aliases":["Lanchester-Betz limit","wind turbine efficiency limit"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Wind Energy","year":"1920","originator":"Albert Betz","url":"https://scholargate.app/en/thermodynamics/betz-limit","markdownUrl":"https://scholargate.app/en/thermodynamics/betz-limit.md","definition":"The Betz Limit states that no wind turbine can extract more than 59.3% of the kinetic energy from flowing wind, regardless of design. This fundamental thermodynamic limit arises because extracting energy slows the wind, which then blocks further energy extraction. Albert Betz derived this limit in 1920 from momentum and energy conservation. Modern wind turbines achieve 35-45% efficiency, approaching this theoretical maximum.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Betz","subfamily":"Wind Energy","year":"1920","type":"Theoretical limit"},"citations":[{"ref":"Betz, A. (1920). Das Maximum der theoretisch möglichen Ausnützung des Windes durch Windmotoren. Zeitschrift für das gesamte Turbinenwesen, 26, 307-320.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/betz1920"},{"ref":"Hansen, M. O. L. (2007). Aerodynamics of Wind Turbines (2nd ed.). Earthscan Publishers.","type":"book","doi":null,"isbn":"978-1844074808","url":null}],"related":["maximum-power-point-tracking","levelized-cost-of-energy","rankine-cycle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bf-copras","name":"BF-COPRAS","fullName":"Bipolar extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1996","originator":"Zavadskas & Kaklauskas","url":"https://scholargate.app/en/decision-making/bf-copras","markdownUrl":"https://scholargate.app/en/decision-making/bf-copras.md","definition":"BF-COPRAS (Bipolar extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas & Kaklauskas in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas & Kaklauskas","subfamily":"Ranking","year":"1996","type":"Bipolar outranking/ranking — Bipolar Fuzzy Set (BFS: positive membership μ⁺ ∈ [0,1], negative μ⁻ ∈ [-1,0])","value_space":"bipolar_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"(). UNCONFIRMED — BF-COPRAS specific seminal not confirmed via systematic literature search. PENDING","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=UNCONFIRMED%20%E2%80%94%20BF-COPRAS%20specific%20seminal%20not%20confirmed%20via%20systematic%20literature%20search"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bf-edas","name":"BF-EDAS","fullName":"Bipolar extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Jana, C., Pal, M.","url":"https://scholargate.app/en/decision-making/bf-edas","markdownUrl":"https://scholargate.app/en/decision-making/bf-edas.md","definition":"BF-EDAS (Bipolar extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Jana, C., Pal, M. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jana, C., Pal, M.","subfamily":"Ranking","year":"2021","type":"Bipolar outranking/ranking — Bipolar Fuzzy Set (BFS: positive membership μ⁺ ∈ [0,1], negative μ⁻ ∈ [-1,0])","value_space":"bipolar_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Jana, C., Pal, M. (2021). Extended bipolar fuzzy EDAS approach for multi-criteria group decision-making process. Computational and Applied Mathematics","type":"article","doi":"10.1007/s40314-020-01403-4","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bf-marcos","name":"BF-MARCOS","fullName":"Bipolar extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Stević et al.","url":"https://scholargate.app/en/decision-making/bf-marcos","markdownUrl":"https://scholargate.app/en/decision-making/bf-marcos.md","definition":"BF-MARCOS (Bipolar extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Stević et al. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stević et al.","subfamily":"Ranking","year":"2020","type":"Bipolar outranking/ranking — Bipolar Fuzzy Set (BFS: positive membership μ⁺ ∈ [0,1], negative μ⁻ ∈ [-1,0])","value_space":"bipolar_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Stević et al. (2020). Bipolar Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution. Computers &amp; Industrial Engineering","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bipolar+Fuzzy+Measurement+of+Alternatives+and+Ranking+according+to+Compromise+Solution+Stevi%C4%87"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bf-topsis","name":"BF-TOPSIS","fullName":"Bipolar extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1981","originator":"Hwang & Yoon","url":"https://scholargate.app/en/decision-making/bf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/bf-topsis.md","definition":"BF-TOPSIS (Bipolar extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Hwang & Yoon in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hwang & Yoon","subfamily":"Ranking","year":"1981","type":"Bipolar outranking/ranking — Bipolar Fuzzy Set (BFS: positive membership μ⁺ ∈ [0,1], negative μ⁻ ∈ [-1,0])","value_space":"bipolar_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Alghamdi, M. A., Alshehri, N. O., Akram, M. (2018). Multi-criteria decision-making methods in bipolar fuzzy environment. International Journal of Fuzzy Systems","type":"article","doi":"10.1007/s40815-018-0499-y","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bf-vikor","name":"BF-VIKOR","fullName":"Bipolar extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1998","originator":"Opricovic","url":"https://scholargate.app/en/decision-making/bf-vikor","markdownUrl":"https://scholargate.app/en/decision-making/bf-vikor.md","definition":"BF-VIKOR (Bipolar extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Opricovic in 1998. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Opricovic","subfamily":"Ranking","year":"1998","type":"Bipolar outranking/ranking — Bipolar Fuzzy Set (BFS: positive membership μ⁺ ∈ [0,1], negative μ⁻ ∈ [-1,0])","value_space":"bipolar_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Alghamdi, M. A., Alshehri, N. O., Akram, M. (2018). Multi-criteria decision-making methods in bipolar fuzzy environment. International Journal of Fuzzy Systems","type":"article","doi":"10.1007/s40815-018-0499-y","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bf-waspas","name":"BF-WASPAS","fullName":"Bipolar extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Akram, M., Arshad, M.","url":"https://scholargate.app/en/decision-making/bf-waspas","markdownUrl":"https://scholargate.app/en/decision-making/bf-waspas.md","definition":"BF-WASPAS (Bipolar extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Arshad, M. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Arshad, M.","subfamily":"Ranking","year":"2020","type":"Bipolar outranking/ranking — Bipolar Fuzzy Set (BFS: positive membership μ⁺ ∈ [0,1], negative μ⁻ ∈ [-1,0])","value_space":"bipolar_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"(). UNCONFIRMED — no peer-reviewed BF-WASPAS-specific paper found via systematic literature search. PENDING","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=UNCONFIRMED%20%E2%80%94%20no%20peer-reviewed%20BF-WASPAS-specific%20paper%20found%20via%20systematic%20literature%20search"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bfi-big-five-inventory","name":"Big Five Inventory","fullName":"Big Five Inventory (BFI)","aliases":["BFI","Big Five Personality Inventory","Five-Factor Model"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Personality assessment","year":"1991","originator":"Oliver John, Donahue, and Kentle","url":"https://scholargate.app/en/social-psychology/bfi-big-five-inventory","markdownUrl":"https://scholargate.app/en/social-psychology/bfi-big-five-inventory.md","definition":"The Big Five Inventory (BFI) is a 44-item self-report measure operationalizing the Five-Factor Model of personality, capturing Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Developed by Oliver John and colleagues in 1991, the BFI is a more concise alternative to longer personality instruments while maintaining strong psychometric properties. The measure has become one of the most widely used personality assessments in organizational, clinical, social, and personality psychology research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oliver John, Donahue, and Kentle","subfamily":"Personality assessment","year":"1991","type":"Big Five personality trait inventory"},"citations":[{"ref":"John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big Five Inventory—versions 4a and 54. Technical Report, Institute of Personality and Social Research, University of California, Berkeley.","type":"article","doi":null,"isbn":null,"url":"https://www.ocf.berkeley.edu/~johnlab/bfi.html"},{"ref":"John, O. P., Robins, R. W., & Pervin, L. A. (Eds.). (2008). Handbook of personality: Theory and research (3rd ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462513390","url":null},{"ref":"Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41(1), 203–212.","type":"article","doi":"10.1016/j.jrp.2006.02.001","isbn":null,"url":null}],"related":["neo-pi-r","rosenberg-self-esteem-scale","dark-triad-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bgp","name":"BGP","fullName":"Border Gateway Protocol","aliases":["exterior gateway protocol","inter-domain routing"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Routing protocol","year":"1989","originator":"IETF Routing Protocols Working Group","url":"https://scholargate.app/en/telecommunications/bgp","markdownUrl":"https://scholargate.app/en/telecommunications/bgp.md","definition":"BGP is the de facto standard routing protocol for interconnecting autonomous systems (ASs) on the Internet. Since its introduction in 1989, BGP has scaled the Internet to millions of routers and trillions of destinations. BGP is path-vector-based, using a flexible policy system to control route propagation and selection. While BGP convergence can be slow and policies complex, it remains the only viable protocol for Internet-scale inter-domain routing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"IETF Routing Protocols Working Group","subfamily":"Routing protocol","year":"1989","type":"path-vector routing protocol"},"citations":[{"ref":"Rekhter, Y., Li, T., & Hares, S. (2006). A Border Gateway Protocol 4 (BGP-4). RFC 4271.","type":"article","doi":null,"isbn":null,"url":"https://www.ietf.org"},{"ref":"Stewart, J. W. (2014). BGP Design and Implementation (2nd ed.). Cisco Press.","type":"book","doi":null,"isbn":null,"url":"https://www.ciscopress.com"}],"related":["ospf","mpls"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliographic-coupling","name":"Bibliographic Coupling","fullName":"Bibliographic Coupling Analysis","aliases":["document coupling","bibliographic similarity"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"network-citation","year":"1963","originator":"Melvin M. Kessler","url":"https://scholargate.app/en/bibliometrics/bibliographic-coupling","markdownUrl":"https://scholargate.app/en/bibliometrics/bibliographic-coupling.md","definition":"Bibliographic coupling is a method that identifies intellectual relationships between documents by measuring their shared references. Two papers are considered 'coupled' when they cite the same sources, indicating they address related research questions or draw from the same conceptual foundations. Introduced by Kessler in 1963, this approach enables researchers to map knowledge domains and discover thematically similar publications without relying on subject cataloging or keywords.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Melvin M. Kessler","subfamily":"network-citation","year":"1963","type":"Method"},"citations":[{"ref":"Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(3), 123–131.","type":"article","doi":"10.1002/asi.5090140103","isbn":null,"url":null},{"ref":"Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.","type":"article","doi":"10.1002/asi.4630240406","isbn":null,"url":null}],"related":["co-citation-analysis","bibliographic-coupling","keyword-co-occurrence","science-mapping","journal-co-citation-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometric-analysis","name":"Bibliometric Analysis","fullName":"Bibliometric Analysis","aliases":["bibliometrics","bibliometric study","bibliometric mapping","publication analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1969 (term coined); practice dates to 1920s–1930s","originator":"Alan Pritchard (coined term); earlier quantitative work by Paul Otlet (1934) and S. C. Bradford (1934)","url":"https://scholargate.app/en/scientometrics/bibliometric-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometric-analysis.md","definition":"Bibliometric analysis applies statistical and mathematical methods to bibliographic records — publications, citations, authors, journals, and keywords — to measure and map the structure, output, and intellectual evolution of a research field. It is widely used to identify influential works, prolific authors, productive journals, collaboration networks, and emerging research themes across any academic discipline.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Pritchard (coined term); earlier quantitative work by Paul Otlet (1934) and S. C. Bradford (1934)","year":"1969 (term coined); practice dates to 1920s–1930s","type":"Quantitative literature analysis","dataType":"Bibliographic records (titles, authors, journals, citations, keywords)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348–349.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Statistical+bibliography+or+bibliometrics+Pritchard+1969"},{"ref":"Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.","type":"article","doi":"10.1016/j.jbusres.2021.04.070","isbn":null,"url":null}],"related":["scientometric-analysis","co-citation-analysis","bibliographic-coupling","co-word-analysis","systematic-literature-review","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-bibliographic-coupling","name":"bibliometrix-assisted bibliographic coupling","fullName":"Bibliometric Coupling Analysis Conducted with the bibliometrix R Package","aliases":["bibliometrix bibliographic coupling","R-based bibliographic coupling","coupling analysis via bibliometrix","biblioNetwork coupling"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"Base method 1963; R-package workflow 2017","originator":"Bibliographic coupling: M. M. Kessler (1963); bibliometrix package: Aria & Cuccurullo (2017)","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-bibliographic-coupling","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-bibliographic-coupling.md","definition":"Bibliometrix-assisted bibliographic coupling applies the open-source R package bibliometrix to construct and analyse bibliographic coupling networks, in which two documents are linked by the number of references they share. The workflow automates record import, network construction, community detection, and summary statistics within a single reproducible R environment, making it accessible to researchers without dedicated scientometric software licences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bibliographic coupling: M. M. Kessler (1963); bibliometrix package: Aria & Cuccurullo (2017)","year":"Base method 1963; R-package workflow 2017","type":"Quantitative scientometric network analysis","dataType":"Bibliographic records (Web of Science or Scopus exports) with reference lists","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25.","type":"article","doi":"10.1002/asi.5090140103","isbn":null,"url":null}],"related":["bibliographic-coupling","co-citation-analysis","bibliometrix-assisted-co-citation-analysis","bibliometrix-assisted-bibliometric-analysis","vosviewer-assisted-bibliographic-coupling","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-bibliometric-analysis","name":"bibliometrix-assisted bibliometric analysis","fullName":"bibliometrix-assisted Bibliometric Analysis","aliases":["bibliometrix bibliometric analysis","R-based bibliometric analysis","bibliometrix workflow","bibliometrix package analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017","originator":"Massimo Aria and Corrado Cuccurullo (bibliometrix R package)","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-bibliometric-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-bibliometric-analysis.md","definition":"bibliometrix-assisted bibliometric analysis is a structured quantitative approach to mapping a scientific field using the bibliometrix R package. Developed by Aria and Cuccurullo (2017), it provides an integrated environment for importing bibliographic records from Scopus or Web of Science, computing performance indicators, building co-authorship and citation networks, and generating thematic maps — all within a reproducible R or Shiny workflow.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Massimo Aria and Corrado Cuccurullo (bibliometrix R package)","year":"2017","type":"Quantitative review method with software toolkit","dataType":"Bibliographic records (Web of Science, Scopus exports in BibTeX/CSV format)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.","type":"article","doi":"10.1016/j.jbusres.2021.04.070","isbn":null,"url":null}],"related":["bibliometric-analysis","scientometric-analysis","co-citation-analysis","bibliographic-coupling","co-word-analysis","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-citation-analysis","name":"bibliometrix-assisted citation analysis","fullName":"Bibliometrix-Assisted Citation Analysis","aliases":["bibliometrix citation analysis","R-based citation analysis","bibliometrix CA","citation analysis with bibliometrix"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017 (bibliometrix package); citation analysis since 1955","originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix R package); citation analysis concepts from Eugene Garfield (1955)","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-citation-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-citation-analysis.md","definition":"Bibliometrix-assisted citation analysis uses the bibliometrix R package to systematically retrieve, clean, and analyze citation data exported from major databases such as Web of Science and Scopus. By automating reference parsing, frequency counting, and network construction, it enables researchers to identify the most-cited works, map intellectual influence, and trace the evolution of scholarly fields at a scale that manual analysis cannot match.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix R package); citation analysis concepts from Eugene Garfield (1955)","year":"2017 (bibliometrix package); citation analysis since 1955","type":"Quantitative bibliometric pipeline","dataType":"Bibliographic records (Web of Science, Scopus, PubMed exports; citation counts, reference lists)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.","type":"article","doi":"10.1126/science.122.3159.108","isbn":null,"url":null}],"related":["bibliometric-analysis","co-citation-analysis","bibliographic-coupling","scientometric-analysis","science-mapping","systematic-literature-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-co-citation-analysis","name":"bibliometrix-assisted co-citation analysis","fullName":"bibliometrix-Assisted Co-Citation Analysis","aliases":["R bibliometrix co-citation","bibliometrix CCA","co-citation network analysis with bibliometrix","bibliometrix cocitation mapping"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017 (bibliometrix implementation); 1973 (co-citation concept)","originator":"Co-citation: Henry Small (1973); bibliometrix package: Massimo Aria & Corrado Cuccurullo (2017)","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-co-citation-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-co-citation-analysis.md","definition":"bibliometrix-assisted co-citation analysis combines Henry Small's co-citation measure with the open-source R package bibliometrix to map the intellectual structure of a research field. When two documents are frequently cited together by third papers, they are considered intellectually linked; the bibliometrix package automates construction of the co-citation matrix, similarity normalization, community detection, and network visualization, turning raw bibliographic exports into interpretable science maps.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Co-citation: Henry Small (1973); bibliometrix package: Massimo Aria & Corrado Cuccurullo (2017)","year":"2017 (bibliometrix implementation); 1973 (co-citation concept)","type":"Computational scientometric pipeline","dataType":"Bibliographic records (Web of Science, Scopus, PubMed exports; RIS/BibTeX formats)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.","type":"article","doi":"10.1002/asi.4630240406","isbn":null,"url":null}],"related":["co-citation-analysis","bibliometric-analysis","bibliographic-coupling","science-mapping","vosviewer-assisted-co-citation-analysis","network-based-co-citation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-mapping-review","name":"bibliometrix-assisted mapping review","fullName":"bibliometrix-Assisted Evidence Mapping Review","aliases":["bibliometrix mapping review","R-bibliometrix evidence map","bibliometric-assisted systematic map","bibliometrix evidence synthesis map"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017 (bibliometrix tool); mapping review approach formalised c. 2010s","originator":"Aria & Cuccurullo (bibliometrix, 2017); mapping review methodology developed in evidence synthesis community (~2000s)","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-mapping-review","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-mapping-review.md","definition":"A bibliometrix-assisted mapping review combines the structured scope-and-search logic of an evidence mapping review with the analytical power of the bibliometrix R package. Instead of manually categorising studies, the researcher leverages bibliometrix functions — keyword co-occurrence networks, thematic clustering, and yearly trend analysis — to chart the landscape of a research field systematically and at scale, producing an interactive, reproducible evidence map.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aria & Cuccurullo (bibliometrix, 2017); mapping review methodology developed in evidence synthesis community (~2000s)","year":"2017 (bibliometrix tool); mapping review approach formalised c. 2010s","type":"Tool-assisted evidence mapping review","dataType":"Bibliographic records (Web of Science, Scopus, PubMed exports); citation and keyword metadata","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Miake-Lye, I. M., Hempel, S., Shanman, R., & Shekelle, P. G. (2016). What is an evidence map? A systematic review of published evidence maps and their definitions, methods, and products. Systematic Reviews, 5(1), 28.","type":"article","doi":"10.1186/s13643-016-0204-x","isbn":null,"url":null}],"related":["mapping-review","scoping-review","bibliometric-analysis","bibliometrix-assisted-bibliometric-analysis","vosviewer-assisted-mapping-review","systematic-literature-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-narrative-review","name":"bibliometrix-assisted narrative review","fullName":"Bibliometrix-Assisted Narrative Review","aliases":["bibliometrix narrative review","R-bibliometrix narrative synthesis","quantitative-assisted narrative review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017 (bibliometrix package); narrative review methodology is older","originator":"Aria & Cuccurullo (bibliometrix R package); narrative review as a traditional form predates this tool","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-narrative-review","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-narrative-review.md","definition":"A bibliometrix-assisted narrative review combines the quantitative field-mapping capabilities of the bibliometrix R package with the interpretive flexibility of a traditional narrative review. Bibliometric indicators — publication trends, author and country productivity, co-citation networks, keyword co-occurrence — are computed and visualised first to orient the reviewer, then a discursive, thematic narrative synthesises the intellectual content of key sources. The result is a structured yet flexible overview of a field that is more transparent and reproducible than a purely informal narrative.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aria & Cuccurullo (bibliometrix R package); narrative review as a traditional form predates this tool","year":"2017 (bibliometrix package); narrative review methodology is older","type":"Mixed quantitative-qualitative review methodology","dataType":"Bibliographic records (Web of Science, Scopus exports) and full-text sources","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. Journal of Chiropractic Medicine, 5(3), 101–117.","type":"article","doi":"10.1016/S0899-3467(07)60142-6","isbn":null,"url":null}],"related":["narrative-review","bibliometric-analysis","systematic-literature-review","scoping-review","co-citation-analysis","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-prisma-based-review","name":"bibliometrix-assisted PRISMA-based review","fullName":"bibliometrix-Assisted PRISMA-Based Systematic Review","aliases":["bibliometrix PRISMA review","R-bibliometrix systematic review","bibliometrix-enhanced evidence synthesis","bibliometrix-supported PRISMA review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017 (bibliometrix); 2009/2021 (PRISMA)","originator":"Aria & Cuccurullo (bibliometrix package); Moher et al. / Page et al. (PRISMA statement)","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-prisma-based-review","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-prisma-based-review.md","definition":"A bibliometrix-assisted PRISMA-based review combines the structured, transparent reporting framework of PRISMA with the quantitative science-mapping capabilities of the bibliometrix R package. The approach embeds bibliometric analyses — such as citation analysis, co-authorship mapping, and keyword co-occurrence — into the evidence-synthesis steps of a PRISMA-guided systematic review, enabling both rigorous literature screening and macro-level visualization of the intellectual landscape.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aria & Cuccurullo (bibliometrix package); Moher et al. / Page et al. (PRISMA statement)","year":"2017 (bibliometrix); 2009/2021 (PRISMA)","type":"Software-assisted systematic review workflow","dataType":"Bibliographic records from databases (Web of Science, Scopus, PubMed); structured citation metadata","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.","type":"article","doi":"10.1136/bmj.n71","isbn":null,"url":null}],"related":["bibliometric-analysis","systematic-literature-review","prisma-based-review","scientometric-analysis","bibliometrix-assisted-bibliometric-analysis","vosviewer-assisted-prisma-based-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-rapid-review","name":"bibliometrix-assisted rapid review","fullName":"bibliometrix-Assisted Rapid Review","aliases":["bibliometrix rapid review","R-based rapid review","rapid bibliometric review","tool-assisted rapid synthesis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017 (bibliometrix); rapid review practice established ~2010s","originator":"Aria & Cuccurullo (bibliometrix package); rapid review tradition from Cochrane and evidence synthesis community","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-rapid-review","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-rapid-review.md","definition":"A bibliometrix-assisted rapid review combines the speed and pragmatic focus of a rapid review with the computational power of the bibliometrix R package. Researchers use bibliometrix to automate citation import, deduplication, descriptive statistics, and science-mapping tasks, compressing the bibliometric phase of a rapid review from days to hours while maintaining transparent, reproducible workflows within a single open-source environment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aria & Cuccurullo (bibliometrix package); rapid review tradition from Cochrane and evidence synthesis community","year":"2017 (bibliometrix); rapid review practice established ~2010s","type":"Expedited evidence synthesis with computational bibliometric support","dataType":"Bibliographic records (titles, abstracts, citations, keywords) exported from databases such as Web of Science or Scopus","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Garritty, C., Gartlehner, G., Nussbaumer-Streit, B., King, V. J., Hamel, C., Kamel, C., Affengruber, L., & Stevens, A. (2021). Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews. Journal of Clinical Epidemiology, 130, 13-22.","type":"article","doi":"10.1016/j.jclinepi.2020.10.007","isbn":null,"url":null}],"related":["rapid-review","bibliometric-analysis","scoping-review","systematic-literature-review","science-mapping","vosviewer-assisted-rapid-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-science-mapping","name":"bibliometrix-assisted science mapping","fullName":"bibliometrix-Assisted Science Mapping","aliases":["bibliometrix science mapping","R-based science mapping","bibliometrix bibliometric mapping","bibliometrix-driven knowledge mapping"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017","originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix R package)","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-science-mapping","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-science-mapping.md","definition":"bibliometrix-assisted science mapping is a computational approach that uses the bibliometrix R package to retrieve, clean, and analyze large bibliographic datasets, producing structured visual maps of how knowledge in a field is organized, interconnected, and evolving over time. It combines descriptive bibliometrics with network analysis and strategic clustering techniques to reveal intellectual structure, thematic frontiers, and influential actors in a research domain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix R package)","year":"2017","type":"Computational bibliometric pipeline","dataType":"Bibliographic records (Web of Science, Scopus, PubMed exports; metadata: titles, abstracts, citations, keywords)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.","type":"article","doi":"10.1002/asi.21525","isbn":null,"url":null}],"related":["bibliometric-analysis","science-mapping","co-citation-analysis","bibliographic-coupling","co-word-analysis","vosviewer-assisted-science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-scientometric-analysis","name":"bibliometrix-assisted scientometric analysis","fullName":"bibliometrix-Assisted Scientometric Analysis","aliases":["bibliometrix scientometrics","R-based scientometric analysis","bibliometrix workflow","science-of-science analysis with bibliometrix"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017 (bibliometrix package); scientometrics as a field: 1969","originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix package); scientometrics founded by Derek J. de Solla Price","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-scientometric-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-scientometric-analysis.md","definition":"bibliometrix-assisted scientometric analysis is a reproducible, R-based workflow that applies the bibliometrix package to analyse the structure and dynamics of scientific fields using publication metadata. It integrates descriptive statistics, citation metrics, and network analysis — co-citation, bibliographic coupling, co-authorship, and co-word — into a single scriptable environment, enabling systematic, transparent mapping of research landscapes at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix package); scientometrics founded by Derek J. de Solla Price","year":"2017 (bibliometrix package); scientometrics as a field: 1969","type":"Quantitative literature analysis workflow","dataType":"Bibliographic records (Web of Science, Scopus, PubMed exports; CSV/BibTeX)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348–349.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Statistical+bibliography+or+bibliometrics+Pritchard+1969"}],"related":["bibliometric-analysis","scientometric-analysis","co-citation-analysis","bibliographic-coupling","co-word-analysis","vosviewer-assisted-scientometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-systematic-literature-review","name":"bibliometrix-assisted systematic literature review","fullName":"bibliometrix-Assisted Systematic Literature Review","aliases":["bibliometrix SLR","R-bibliometrix systematic review","bibliometrix-based literature review","bibliometrix-enhanced SLR"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017","originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix R package)","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-systematic-literature-review","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-systematic-literature-review.md","definition":"A bibliometrix-assisted systematic literature review integrates the R package bibliometrix — developed by Aria and Cuccurullo (2017) — into the standard systematic review pipeline to automate and visualize bibliometric performance and science-mapping analyses. It combines the transparency and reproducibility of a protocol-driven systematic search with quantitative tools for tracking publication trends, author collaboration networks, keyword co-occurrence, and thematic evolution across a field.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix R package)","year":"2017","type":"Software-assisted systematic review","dataType":"Bibliographic records (Web of Science, Scopus, PubMed exports)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Medicine, 6(7), e1000097.","type":"article","doi":"10.1371/journal.pmed.1000097","isbn":null,"url":null}],"related":["systematic-literature-review","bibliometric-analysis","scientometric-analysis","science-mapping","co-citation-analysis","vosviewer-assisted-systematic-literature-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bibliometrix-assisted-thematic-evolution-analysis","name":"bibliometrix-assisted thematic evolution analysis","fullName":"Bibliometrix-Assisted Thematic Evolution Analysis","aliases":["bibliometrix thematic map analysis","R-based thematic evolution analysis","bibliometrix strategic diagram analysis","thematic evolution analysis with bibliometrix"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2017 (bibliometrix package); thematic evolution approach ~2011","originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix package); thematic evolution method from Cobo et al.","url":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-thematic-evolution-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/bibliometrix-assisted-thematic-evolution-analysis.md","definition":"Bibliometrix-assisted thematic evolution analysis uses the bibliometrix R package to trace how research themes emerge, mature, decline, or transform across successive time periods within a scientific field. By combining co-word analysis with strategic diagram visualisation, the workflow maps the intellectual structure of a field and reveals longitudinal shifts in topic centrality and development, producing reproducible, publication-ready outputs within a single R environment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Massimo Aria & Corrado Cuccurullo (bibliometrix package); thematic evolution method from Cobo et al.","year":"2017 (bibliometrix package); thematic evolution approach ~2011","type":"Computational scientometric workflow","dataType":"Bibliographic records (Web of Science, Scopus, PubMed exports)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null},{"ref":"Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146-166.","type":"article","doi":"10.1016/j.joi.2010.10.002","isbn":null,"url":null}],"related":["thematic-evolution-analysis","bibliometric-analysis","science-mapping","co-word-analysis","scientometric-analysis","vosviewer-assisted-thematic-evolution-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bidirectional-rnn","name":"Bidirectional RNN","fullName":"Bidirectional Recurrent Neural Network (BiLSTM / BiGRU)","aliases":["Çift Yönlü RNN / BiLSTM / BiGRU","bidirectional recurrent neural network","BiLSTM","BiGRU","bidirectional LSTM"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":1997,"originator":"Schuster, M. & Paliwal, K.K.","url":"https://scholargate.app/en/deep-learning/bidirectional-rnn","markdownUrl":"https://scholargate.app/en/deep-learning/bidirectional-rnn.md","definition":"A Bidirectional RNN, introduced by Schuster and Paliwal in 1997, processes a sequence in both forward and backward directions so that every position has access to its full surrounding context. With LSTM or GRU cells (BiLSTM/BiGRU) it is the standard approach for named-entity recognition, sequence labelling, and speech recognition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schuster, M. & Paliwal, K.K.","year":1997,"type":"Recurrent neural network (sequence model)","task":"Classification & prediction on sequences","minSample":100},"citations":[{"ref":"Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.","type":"article","doi":"10.1109/78.650093","isbn":null,"url":null},{"ref":"Graves, A. & Schmidhuber, J. (2005). Framewise Phoneme Classification with Bidirectional LSTM Networks. IJCNN, 2047–2052.","type":"article","doi":"10.1109/IJCNN.2005.1556215","isbn":null,"url":null}],"related":["seq2seq","attention-mechanism","self-attention-transformer","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bifactor-model","name":"Bifactor Model","fullName":"Bifactor Measurement Model","aliases":["Bifaktör Modeli — Genel ve Spesifik Faktörler","hierarchical factor model","general-specific factor model","Schmid-Leiman model"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1937,"originator":"Holzinger & Swineford (1937); modern revival by Reise (2012)","url":"https://scholargate.app/en/psychometrics/bifactor-model","markdownUrl":"https://scholargate.app/en/psychometrics/bifactor-model.md","definition":"The bifactor measurement model specifies that every indicator loads simultaneously on a single general factor and on one of several specific (group) factors. Formally introduced by Holzinger and Swineford in 1937 and brought into mainstream psychometrics by Reise (2012), it is now the standard tool for evaluating whether a multidimensional scale can legitimately yield a single composite score.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holzinger & Swineford (1937); modern revival by Reise (2012)","year":1937,"type":"Confirmatory latent variable model","outcome":"General factor loadings, specific factor loadings, ω and ωh reliability indices","data":"Ordinal or continuous indicators","min_sample":200,"items_per_factor":"≥ 3","difficulty":3,"fit_indices":"CFI, RMSEA, SRMR","key_indices":"ECV (Explained Common Variance), ωh (omega-hierarchical)"},"citations":[{"ref":"Reise, S. P. (2012). The Rediscovery of Bifactor Measurement Models. Multivariate Behavioral Research, 47(5), 667–696.","type":"article","doi":"10.1080/00273171.2012.715555","isbn":null,"url":null},{"ref":"Rodriguez, A., Reise, S. P. & Haviland, M. G. (2016). Evaluating Bifactor Models: Calculating and Interpreting Statistical Indices. Psychological Methods, 21(2), 137–150.","type":"article","doi":"10.1037/met0000045","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","exploratory-factor-analysis","omega-reliability","sem","item-response-theory","cronbach-alpha"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bilevel-optimization","name":"Bilevel Optimization","fullName":"Bilevel Optimization (Leader-Follower)","aliases":["Stackelberg Optimization","Hierarchical Programming","Nested Optimization","İki Düzeyli Optimizasyon"],"domain":"optimization","family":"process-pipeline","subfamily":"Mathematical programming","year":1998,"originator":"Jonathan Bard","url":"https://scholargate.app/en/optimization/bilevel-optimization","markdownUrl":"https://scholargate.app/en/optimization/bilevel-optimization.md","definition":"Bilevel optimization is a class of mathematical programming problems in which one optimization problem is nested inside another. The upper-level (leader) problem optimizes its objective subject to constraints that include the solution of a lower-level (follower) problem. Formalized comprehensively by Jonathan Bard in 1998, the framework models hierarchical decision-making where the leader anticipates and accounts for the rational response of the follower.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan Bard","year":1998,"type":"Hierarchical mathematical programming","subfamily":"Mathematical programming","complexity":"NP-hard in general","structure":"Two nested optimization problems (leader and follower)"},"citations":[{"ref":"Bard, J. F. (1998). Practical Bilevel Optimization: Algorithms and Applications. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-0-7923-5458-7","url":null},{"ref":"Colson, B., Marcotte, P., & Savard, G. (2007). An overview of bilevel optimization. Annals of Operations Research, 153(1), 235–256.","type":"article","doi":"10.1007/s10479-007-0176-2","isbn":null,"url":null}],"related":["nonlinear-programming","robust-optimization","integer-programming"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"binary-decision-diagram","name":"Binary Decision Diagram","fullName":"Binary Decision Diagram (BDD)","aliases":["BDD","reduced BDD","ordered BDD"],"domain":"numerical-methods","family":"ml-model","subfamily":"Symbolic Computation","year":"1986","originator":"Randal Bryant","url":"https://scholargate.app/en/numerical-methods/binary-decision-diagram","markdownUrl":"https://scholargate.app/en/numerical-methods/binary-decision-diagram.md","definition":"Binary Decision Diagrams (BDDs) are a canonical, memory-efficient representation of Boolean functions developed by Randal Bryant in 1986. A BDD is a directed acyclic graph encoding all variable assignments and results; reduced BDDs are unique for each function and enable efficient manipulation of combinatorial logic in model checking, circuit design, and symbolic computation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Randal Bryant","subfamily":"Symbolic Computation","year":"1986","type":"Compact Boolean function representation"},"citations":[{"ref":"Bryant, R. E. (1986). Graph-based algorithms for Boolean function manipulation. IEEE Transactions on Computers, 35(8), 677–691.","type":"article","doi":"10.1109/TC.1986.1676819","isbn":null,"url":null},{"ref":"Andersen, H. R. (1997). An introduction to binary decision diagrams. Technical Report, IT University of Copenhagen.","type":"article","doi":null,"isbn":null,"url":"https://www.itu.dk/~hra/bdd.pdf"},{"ref":"Becker, B., & Drechsler, R. (1998). Binary Decision Diagrams: Theory and Implementation. Kluwer.","type":"book","doi":null,"isbn":"0792380185","url":null}],"related":["boolean-satisfiability","model-checking","circuit-verification","sat-solving"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"binge-eating-scale","name":"BES","fullName":"Binge Eating Scale","aliases":["Binge Eating Scale Gormally","BES screening tool"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"binge eating assessment","year":"1982","originator":"John Gormally, Susan Black, Sarah Daston, Diane Rardin","url":"https://scholargate.app/en/clinical-psychology/binge-eating-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/binge-eating-scale.md","definition":"The BES is a 16-item self-report questionnaire designed specifically to measure the behavioural and emotional features of binge eating in obese and non-obese populations. Developed by Gormally and colleagues in 1982, the BES uses a forced-choice format and focuses on the subjective experience of loss of control, severity of binge episodes, and affective triggers. It is widely used in obesity treatment research and clinical screening for binge eating patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Gormally, Susan Black, Sarah Daston, Diane Rardin","subfamily":"binge eating assessment","year":"1982","type":"Self-report questionnaire"},"citations":[{"ref":"Gormally, J., Black, S., Daston, S., & Rardin, D. (1982). The assessment of binge eating severity among obese persons. Addictive Behaviors, 7(1), 47–55.","type":"article","doi":"10.1016/0306-4603(82)90024-7","isbn":null,"url":null},{"ref":"Timmerman, G. M. (1990). Binge eating scale: Further assessment of validity and reliability. Journal of Applied Biobehavioral Research, 1(1), 1–12.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Binge+eating+scale%3A+Further+assessment+of+validity+and+reliability+Timmerman"},{"ref":"Celio, A. A., Wilfley, D. E., Crow, S. J., Mitchell, J., & Walsh, B. T. (2004). A comparison of the binge eating scale, questionnaire for eating and weight patterns-revised, and eating disorder examination-questionnaire with instructions (EDE-QI) in the assessment of binge eating. International Journal of Eating Disorders, 36(4), 434–444.","type":"article","doi":"10.1002/eat.20057","isbn":null,"url":null}],"related":["ede-q","scoff-questionnaire","three-factor-eating-questionnaire","yale-food-addiction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"binomial-option-pricing","name":"Binomial Option Pricing","fullName":"Cox-Ross-Rubinstein Binomial Option Pricing Model","aliases":["binomial tree model","Cox-Ross-Rubinstein model","CRR model","lattice option pricing","binom opsiyon fiyatlama"],"domain":"finance","family":"regression-model","subfamily":null,"year":1979,"originator":"John Cox, Stephen Ross & Mark Rubinstein","url":"https://scholargate.app/en/finance/binomial-option-pricing","markdownUrl":"https://scholargate.app/en/finance/binomial-option-pricing.md","definition":"The binomial option pricing model, introduced by John Cox, Stephen Ross, and Mark Rubinstein in 1979, prices options by modelling the underlying as a discrete tree in which the price moves up or down by fixed factors at each step. Working backward from the option's payoff at maturity using risk-neutral probabilities, it produces a no-arbitrage price that converges to Black-Scholes as the number of steps grows — while naturally handling American early exercise, which the closed-form formula cannot.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Cox, Stephen Ross & Mark Rubinstein","year":1979,"type":"Discrete-time lattice option-pricing model","handles":"American (early-exercise) and European options","output":"No-arbitrage option price via backward induction","convergence":"To Black-Scholes as steps → ∞"},"citations":[{"ref":"Cox, J. C., Ross, S. A., & Rubinstein, M. (1979). Option pricing: A simplified approach. Journal of Financial Economics, 7(3), 229–263.","type":"article","doi":"10.1016/0304-405X(79)90015-1","isbn":null,"url":null}],"related":["black-scholes-model","stochastic-volatility-model","interest-rate-models","jump-diffusion-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"binomial-test","name":"Binomial Test","fullName":"Exact Binomial Test","aliases":["exact binomial test","binomial probability test","exact test for a proportion","Tam Binom Testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1988,"originator":"Classical exact test; textbook treatment by Siegel & Castellan","url":"https://scholargate.app/en/statistics/binomial-test","markdownUrl":"https://scholargate.app/en/statistics/binomial-test.md","definition":"The exact binomial test checks whether the observed number of successes in a fixed number of independent trials is consistent with a pre-specified success probability p₀. Because it computes exact binomial tail probabilities rather than relying on a normal approximation, it is the gold standard for testing a proportion in small samples; this two-sided formulation follows Siegel & Castellan's classic treatment (1988).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical exact test; textbook treatment by Siegel & Castellan","year":1988,"type":"Exact one-sample test for a proportion","estimator":"Exact binomial tail probability (no normal approximation)","outcome":"binary (success/failure)","minSample":5},"citations":[{"ref":"Siegel, S. & Castellan, N. J. (1988). Nonparametric Statistics for the Behavioral Sciences (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070573574","url":null}],"related":["proportion-test","chi-square-goodness-of-fit","sign-test","fisher-exact-test","one-sample-z-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bioaccumulation-model","name":"Bioaccumulation Model","fullName":"Bioaccumulation and Biomagnification Modeling","aliases":["accumulation model","toxicokinetics","persistent organic pollutants","POPs","heavy metals"],"domain":"ecology","family":"process-pipeline","subfamily":"Ecotoxicology","year":"2006","originator":"Frank Gobas","url":"https://scholargate.app/en/ecology/bioaccumulation-model","markdownUrl":"https://scholargate.app/en/ecology/bioaccumulation-model.md","definition":"Bioaccumulation models predict how chemical contaminants accumulate in organisms from environmental exposure (water, food, sediment). Developed by Gobas and colleagues (2006), these models quantify the kinetics of chemical uptake, metabolism, and clearance. Bioaccumulation factors (BAF) and bioconcentration factors (BCF) measure the ratio of chemical concentration in organisms to concentration in the environment. Understanding bioaccumulation is critical for assessing ecological risk from persistent organic pollutants (POPs), heavy metals, and other contaminants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Gobas","subfamily":"Ecotoxicology","year":"2006","type":"pollutant accumulation dynamics"},"citations":[{"ref":"Arnot, J. A., & Gobas, F. A. (2006). A review of bioaccumulation factor (BAF) and bioconcentration factor (BCF) assessments for organic chemicals in aquatic organisms. Environmental Reviews, 14(4), 257-297.","type":"article","doi":"10.1139/a06-005","isbn":null,"url":null},{"ref":"Clark, K. E., Gobas, F. A., & Mackay, D. (1990). Model of organic chemical uptake and clearance by fish from food and water. Environmental Science & Technology, 24(7), 1203-1213.","type":"article","doi":"10.1021/es00078a008","isbn":null,"url":null},{"ref":"Meador, J. P., Stein, J. E., Hom, T., & Varanasi, U. (2006). Bioaccumulation of polycyclic aromatic hydrocarbons by marine organisms. Reviews of Environmental Contamination and Toxicology, 143, 79-165.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bioaccumulation+of+polycyclic+aromatic+hydrocarbons+by+marine+organisms+Meador"}],"related":["siar-mixing-model","food-web-topology","metabolic-theory-of-ecology","population-viability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"biodiversity-index-forest","name":"Biodiversity Index in Forests","fullName":"Forest Biodiversity Assessment and Diversity Quantification","aliases":["Forest diversity index","Species richness assessment","Shannon index forestry"],"domain":"forestry","family":"process-pipeline","subfamily":"Ecological diversity assessment and conservation","year":"1948–2004","originator":"Shannon, Simpson, and Magurran","url":"https://scholargate.app/en/forestry/biodiversity-index-forest","markdownUrl":"https://scholargate.app/en/forestry/biodiversity-index-forest.md","definition":"Forest biodiversity indices quantify species richness, evenness, and overall diversity in forest ecosystems. Rooted in information theory (Shannon) and statistical ecology (Simpson, Magurran), these indices compress complex multispecies data into interpretable metrics. Applied to forest inventory data, biodiversity indices guide conservation planning, assess ecological health, and track responses to management or disturbance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shannon, Simpson, and Magurran","subfamily":"Ecological diversity assessment and conservation","year":"1948–2004","type":"Analysis and quantification pipeline"},"citations":[{"ref":"Shannon, C. E. (1948). A Mathematical Theory of Communication. The Bell System Technical Journal, 27(3), 379–423.","type":"article","doi":"10.1002/j.1538-7305.1948.tb01338.x","isbn":null,"url":null},{"ref":"Simpson, E. H. (1949). Measurement of Diversity. Nature, 163, 688.","type":"article","doi":"10.1038/163688a0","isbn":null,"url":null},{"ref":"Magurran, A. E. (2004). Measuring Biological Diversity. Blackwell Publishing.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/measuringbiolog00magurran"},{"ref":"Hubbell, S. P. (2001). The Unified Neutral Theory of Biodiversity and Biogeography. Princeton University Press.","type":"book","doi":"10.2307/3071998","isbn":null,"url":null}],"related":["forest-inventory-sampling","canopy-cover-estimation","silvicultural-treatment-design","forest-fire-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bioequivalence-analysis","name":"Bioequivalence Analysis","fullName":"Bioequivalence Analysis (Two One-Sided Tests)","aliases":["TOST Procedure","Average Bioequivalence","BE Analysis","Biyoeşdeğerlik Analizi"],"domain":"pharmacometrics","family":"hypothesis-test","subfamily":"Bioequivalence","year":1987,"originator":"Donald J. Schuirmann","url":"https://scholargate.app/en/pharmacometrics/bioequivalence-analysis","markdownUrl":"https://scholargate.app/en/pharmacometrics/bioequivalence-analysis.md","definition":"Bioequivalence Analysis is a regulatory-grade statistical framework used to determine whether a test drug formulation (generic or reformulated) delivers the active ingredient to the systemic circulation at a rate and extent comparable to a reference product. Introduced by Donald J. Schuirmann in 1987, the method operationalizes equivalence through the Two One-Sided Tests (TOST) procedure, replacing the ambiguous absence-of-difference paradigm with an explicit equivalence margin evaluated on log-transformed pharmacokinetic endpoints such as AUC and C_max.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald J. Schuirmann","year":1987,"type":"Parametric equivalence test","subfamily":"Bioequivalence","design":"Two-period crossover (typically)","regulatoryStandard":"FDA, EMA 80–125% rule"},"citations":[{"ref":"Schuirmann, D. J. (1987). A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics, 15(6), 657–680.","type":"article","doi":"10.1007/BF01068419","isbn":null,"url":null}],"related":["equivalence-test-tost","pharmacokinetic-compartment-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"biogas-production-model","name":"Biogas Production Modeling","fullName":"Anaerobic Digestion and Biogas Yield Prediction","aliases":["anaerobic digestion","biogas yield","methane production","AD modeling"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Bioenergy and waste valorization","year":"1973","originator":"Anaerobic microbiologists","url":"https://scholargate.app/en/environmental-engineering/biogas-production-model","markdownUrl":"https://scholargate.app/en/environmental-engineering/biogas-production-model.md","definition":"Biogas production modeling is a quantitative method to predict methane and carbon dioxide generation from anaerobic digestion of organic residues (wastewater sludge, food waste, agricultural manure, slaughterhouse waste). Developed from microbial kinetics and thermodynamics, these models account for substrate composition, microbial consortia (acetogens, methanogens), process conditions (temperature, pH, retention time), and inhibition factors (ammonia, volatile fatty acids). Biogas modeling supports reactor design, energy recovery planning, and operational optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anaerobic microbiologists","subfamily":"Bioenergy and waste valorization","year":"1973","type":"biokinetic simulation pipeline"},"citations":[{"ref":"Rittmann, B. E., & McCarty, P. L. (2001). Environmental Biotechnology: Principles and Applications (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073401188","url":null},{"ref":"Appels, L., Baeyens, J., Degrève, J., & Dewil, R. (2011). Principles and Potential of the Anaerobic Digestion of Waste-Activated Sludge. Progress in Energy and Combustion Science, 37(2), 183-214.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Principles+and+Potential+of+the+Anaerobic+Digestion+of+Waste-Activated+Sludge+Appels"},{"ref":"Stams, A. J. (1994). Metabolic Interactions Between Anaerobic Bacteria in Methanogenic Environments. Antonie van Leeuwenhoek, 66(1–3), 271–294.","type":"article","doi":"10.1007/BF00871644","isbn":null,"url":null}],"related":["activated-sludge-model","wastewater-treatment-design","soil-remediation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"biographical-research","name":"Biographical Research","fullName":"Biographical Research Method","aliases":["life history research","biographical method","life story research","biographical narrative inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Narrative Inquiry","year":"Late 19th–early 20th century (Dilthey ~1883; Thomas & Znaniecki 1918–1920)","originator":"Wilhelm Dilthey (hermeneutic foundations); Thomas & Znaniecki (sociological application); Norman Denzin (interpretive biography)","url":"https://scholargate.app/en/qualitative/biographical-research","markdownUrl":"https://scholargate.app/en/qualitative/biographical-research.md","definition":"Biographical research is a qualitative method that examines individual lives in depth — through life-history interviews, personal documents, letters, and autobiographical narratives — to understand how personal experience intersects with social, historical, and cultural forces. Rooted in Wilhelm Dilthey's hermeneutics and made prominent in sociology by Thomas and Znaniecki's study of Polish immigrants, it treats the individual life story as a window onto broader social structures and processes. It belongs to the narrative inquiry subfamily alongside oral history and life-story research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilhelm Dilthey (hermeneutic foundations); Thomas & Znaniecki (sociological application); Norman Denzin (interpretive biography)","year":"Late 19th–early 20th century (Dilthey ~1883; Thomas & Znaniecki 1918–1920)","type":"Qualitative research method","dataType":"Life-history interviews, personal documents, letters, diaries, autobiographies, oral histories","typicalSampleSize":"1–20 participants (often 1–5 for intensive life-history studies)","subfamily":"Narrative Inquiry"},"citations":[{"ref":"Denzin, N. K. (1989). Interpretive Biography. Sage Publications.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interpretive+Biography+Denzin+1989"},{"ref":"Roberts, B. (2002). Biographical Research. Open University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Biographical+Research+Brian+Roberts+2002+Open+University+Press"}],"related":["narrative-analysis","phenomenology","ethnography","grounded-theory","case-study","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"biomass-allometric-equation","name":"Biomass Allometric Equation","fullName":"Biomass Allometric Equation Modeling","aliases":["allometric models","biomass scaling"],"domain":"forestry","family":"process-pipeline","subfamily":"Carbon and Biomass","year":"1966","originator":"Arthur King","url":"https://scholargate.app/en/forestry/biomass-allometric-equation","markdownUrl":"https://scholargate.app/en/forestry/biomass-allometric-equation.md","definition":"Biomass allometric equations are regression models that predict tree or stand aboveground biomass from easily measurable variables such as diameter at breast height (DBH) and height. These equations embody the principle of allometry: the scaling relationship between body parts or organisms. In forestry, allometric equations are essential tools for estimating carbon storage, nutrient cycling, fuel loads, and resource inventory without destructive harvesting. Thousands of species-specific and regional equations have been developed and compiled in public databases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arthur King","subfamily":"Carbon and Biomass","year":"1966","type":"regression model"},"citations":[{"ref":"Chojnacky, D. C., Heath, L. S., & Jenkins, J. C. (2014). Updated generalized biomass equations for North American tree species. Forestry, 87(1), 129–151.","type":"article","doi":"10.1093/forestry/cpt053","isbn":null,"url":null},{"ref":"Zianis, D., & Mencuccini, M. (2005). On simplifying allometric analyses of forest biomass. Forest Ecology and Management, 187(2–3), 311–332.","type":"article","doi":"10.1016/j.foreco.2003.07.007","isbn":null,"url":null}],"related":["carbon-sequestration","forest-productivity","stand-density-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bipartite-network-analysis","name":"Bipartite Network Analysis","fullName":"Bipartite Network Analysis (Two-Mode Networks)","aliases":["two-mode network analysis","affiliation network analysis","İki Modlu Ağ Analizi (Bipartite Networks)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":1997,"originator":"Borgatti & Everett (1997) formalised the two-mode network framework","url":"https://scholargate.app/en/network-analysis/bipartite-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/bipartite-network-analysis.md","definition":"Bipartite network analysis, formalised by Borgatti and Everett in 1997, is a graph-structural method for studying networks in which nodes are divided into two disjoint sets — actors and events — and edges exist only between sets, never within them. It is the natural framework for author–paper, patient–disease, user–product, and any other affiliation data, and it extends one-mode network analysis by providing metrics and projection techniques tailored to the two-mode structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Borgatti & Everett (1997) formalised the two-mode network framework","year":1997,"type":"Graph-structural / relational analysis","inputStructure":"Incidence matrix or edge list with two disjoint node sets","output":"Structural metrics, projected one-mode graphs, centrality rankings","minSample":20,"difficulty":"Intermediate (2 / 3)"},"citations":[{"ref":"Borgatti, S.P. & Everett, M.G. (1997). Network Analysis of 2-Mode Data. Social Networks, 19(3), 243-269.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/science/article/pii/S0378873396003012"},{"ref":"Guillaume, J.L. & Latapy, M. (2006). Bipartite Structure of All Complex Networks. Information Processing Letters, 90(5), 215-221.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/science/article/pii/S0020019004000785"}],"related":["social-network-analysis","community-detection","ego-network-analysis","network-motif-analysis","collaborative-filtering"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"biplot","name":"Biplot","fullName":"Biplot Display of Multivariate Data","aliases":["Gabriel biplot","PCA biplot","JK biplot","Çift grafik"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate visualization","year":1971,"originator":"Ruben Gabriel","url":"https://scholargate.app/en/statistics/biplot","markdownUrl":"https://scholargate.app/en/statistics/biplot.md","definition":"A biplot is a low-dimensional graphical representation of a multivariate data matrix that simultaneously displays both the observations (rows) and the variables (columns) as points or vectors in the same plot. Introduced by Ruben Gabriel in 1971, the technique decomposes the data matrix into a rank-2 approximation using singular value decomposition, allowing the approximate value of any data entry to be read as the inner product of the corresponding row and column markers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ruben Gabriel","year":1971,"type":"Multivariate graphical display","subfamily":"Multivariate visualization","decomposition":"Rank-2 matrix approximation via SVD","output":"Simultaneous row and column markers in low-dimensional space"},"citations":[{"ref":"Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467.","type":"article","doi":"10.1093/biomet/58.3.453","isbn":null,"url":null}],"related":["principal-component-analysis","correspondence-analysis","multiple-correspondence-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"birch","name":"BIRCH","fullName":"Balanced Iterative Reducing and Clustering using Hierarchies","aliases":["BIRCH clustering","CF-tree clustering","Balanced Iterative Reducing and Clustering using Hierarchies","incremental hierarchical clustering"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1996,"originator":"Zhang, T.; Ramakrishnan, R.; Livny, M.","url":"https://scholargate.app/en/machine-learning/birch","markdownUrl":"https://scholargate.app/en/machine-learning/birch.md","definition":"BIRCH is a scalable, incremental clustering algorithm introduced by Zhang, Ramakrishnan, and Livny in 1996. It is designed to cluster very large datasets — potentially larger than available memory — in a single pass, by compressing the data into a compact in-memory summary structure called a CF-tree (Clustering Feature tree) before applying any standard clustering procedure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhang, T.; Ramakrishnan, R.; Livny, M.","year":1996,"type":"Incremental hierarchical clustering (CF-tree)","task":"Unsupervised clustering of very large datasets","complexity":"O(n)","dataStructure":"CF-tree (Clustering Feature tree)","passesOverData":1},"citations":[{"ref":"Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, 25(2), 103–114.","type":"article","doi":"10.1145/233269.233324","isbn":null,"url":null},{"ref":"Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed., Ch. 10). Morgan Kaufmann.","type":"book","doi":null,"isbn":"978-0-12-381479-1","url":null}],"related":["k-means","dbscan","agglomerative-clustering","mini-batch-k-means","gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"birth-experience-questionnaire","name":"Childbirth Experience Questionnaire","fullName":"Childbirth Experience Questionnaire (CEQ)","aliases":["CEQ","Birth Experience Scale"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"childbirth-experience","year":2010,"originator":"Dencker, A., Tully, G., & Begley, C.","url":"https://scholargate.app/en/obstetrics-gynecology/birth-experience-questionnaire","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/birth-experience-questionnaire.md","definition":"The Childbirth Experience Questionnaire (CEQ) is a 22-item self-report instrument designed to comprehensively assess women's subjective experiences of childbirth. Developed by Dencker, Tully, and Begley, the CEQ measures four key dimensions of the birth experience: Capacity (feelings of being in control, coping, and managing labor), Perceived Safety (confidence in provider competence and safety), Professional Support (quality of care received), and Participation (involvement in decision-making). It captures women's experiences across diverse birth settings and contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dencker, A., Tully, G., & Begley, C.","subfamily":"childbirth-experience","year":2010,"type":"Self-report"},"citations":[{"ref":"Dencker, A., Tully, G., & Begley, C. (2010). Midwifery continuity of carer in a group practice: a pilot study. British Journal of Midwifery, 18(10), 650-660.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Midwifery+continuity+of+carer+in+a+group+practice%3A+a+pilot+study+Dencker"},{"ref":"Dencker, A., Tully, G., & Begley, C. (2012). Assessing women's childbirth experience: a review of the literature. Evidence Based Midwifery, 10(2), 45-50.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Dencker%2C%20A.%2C%20Tully%2C%20G.%2C%20%26%20Begley%2C%20C.%20(2012).%20Assessing%20women's%20childbirth%20experience%3A%20a%20review%20of%20the%20literature.%20Eviden"}],"related":["postpartum-bonding-questionnaire","breastfeeding-self-efficacy-scale","perinatal-anxiety-screening-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bitewing-radiography","name":"Bitewing Radiography","fullName":"Bitewing Radiographic Examination","aliases":["bitewing X-ray","bitewings","posterior radiography"],"domain":"dentistry","family":"process-pipeline","subfamily":"Radiology and caries diagnosis","year":"1913 (original technique)","originator":"Multiple innovators, formalized in 20th century","url":"https://scholargate.app/en/dentistry/bitewing-radiography","markdownUrl":"https://scholargate.app/en/dentistry/bitewing-radiography.md","definition":"Bitewing radiography is a standard intraoral radiographic technique that captures the coronal portions of both maxillary and mandibular teeth in a single image, with the patient biting on a film holder or digital sensor. Introduced in the early 20th century and formalized as a diagnostic standard, bitewing radiographs are the primary image type for detecting approximal caries, monitoring alveolar bone level for periodontal disease assessment, and evaluating dental restorations and radiographic density changes. Digital bitewings have reduced radiation exposure and improved image quality and archiving.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple innovators, formalized in 20th century","subfamily":"Radiology and caries diagnosis","year":"1913 (original technique)","type":"Radiographic examination"},"citations":[{"ref":"Ludlow, J. B., Davies-Ludlow, L. E., Brooks, S. L., & Howerton, W. B. (2006). Dosimetry of 3 intraoral digital imaging systems. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology and Endodontology, 101(2), 226-234.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dosimetry+of+3+intraoral+digital+imaging+systems+Ludlow"},{"ref":"White, S. C., & Pharoah, M. J. (2014). Oral radiology: Principles and interpretation (7th ed.). Mosby Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/oral-radiology/white/978-0-323-09573-2"},{"ref":"Pitts, N. B., Ekstrand, K. R., Foundation, I. C. D. A., & Organization, W. H. (2013). International caries detection and assessment system (ICDAS) and its applications. Community Dentistry and Oral Epidemiology, 43(1), 41-49.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=International+caries+detection+and+assessment+system+%28ICDAS%29+and+its+applications+Pitts"}],"related":["dmft-index","orthodontic-cephalometry","root-canal-length-determination","occlusal-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bivariate-probit","name":"Bivariate Probit","fullName":"Bivariate Probit Model","aliases":["Bivariate Binary Probit","Joint Probit Model","Two-Equation Probit","İki Değişkenli Probit"],"domain":"econometrics","family":"regression-model","subfamily":"Limited dependent variable","year":1970,"originator":"J. R. Ashford & R. R. Sowden","url":"https://scholargate.app/en/econometrics/bivariate-probit","markdownUrl":"https://scholargate.app/en/econometrics/bivariate-probit.md","definition":"The Bivariate Probit Model, introduced by Ashford and Sowden (1970), jointly estimates two binary outcome equations whose error terms are allowed to be correlated. By modeling both outcomes simultaneously under a bivariate normal distribution, it corrects for the dependence between decisions that separate probit regressions would ignore, producing consistent and efficient parameter estimates for researchers studying interrelated binary choices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. R. Ashford & R. R. Sowden","year":1970,"type":"Maximum-likelihood binary outcome model","subfamily":"Limited dependent variable","outcomes":"Two binary dependent variables modeled jointly","key_parameter":"Error correlation rho (ρ) between equations"},"citations":[{"ref":"Ashford, J. R., & Sowden, R. R. (1970). Multi-variate probit analysis. Biometrics, 26(3), 535–546.","type":"article","doi":"10.2307/2529107","isbn":null,"url":null}],"related":["probit-model","ordered-logit","multinomial-logit"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bk-filter","name":"BK Filter","fullName":"Baxter-King Band-Pass Filter","aliases":["Baxter-King Filter","Band-Pass Filter (Baxter-King)","BK Band-Pass Filter","Bant Geçiren Süzgeç"],"domain":"econometrics","family":"process-pipeline","subfamily":"Trend & seasonality","year":1999,"originator":"Marianne Baxter & Robert King","url":"https://scholargate.app/en/econometrics/bk-filter","markdownUrl":"https://scholargate.app/en/econometrics/bk-filter.md","definition":"The Baxter-King (BK) band-pass filter, introduced by Marianne Baxter and Robert King in 1999, is a linear symmetric moving-average filter designed to isolate cyclical fluctuations in macroeconomic time series that fall within a specified range of periodicities. It removes both very low-frequency trends and very high-frequency noise, retaining only the business-cycle component—typically oscillations with a period of six to thirty-two quarters for quarterly data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marianne Baxter & Robert King","year":1999,"type":"Linear symmetric moving-average filter","subfamily":"Trend & seasonality","default_band":"6–32 quarters (business cycle frequencies)","truncation_parameter":"K lags/leads lost at each end of sample"},"citations":[{"ref":"Baxter, M., & King, R. G. (1999). Measuring business cycles: Approximate band-pass filters for economic time series. Review of Economics and Statistics, 81(4), 575–593.","type":"article","doi":"10.1162/003465399558454","isbn":null,"url":null}],"related":["hp-filter","stl-decomposition","fourier-transform"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"black-litterman-model","name":"Black-Litterman Model","fullName":"Black-Litterman Portfolio Allocation Model","aliases":["Black-Litterman","BL model","Black-Litterman Portföy Modeli"],"domain":"finance","family":"regression-model","subfamily":null,"year":1992,"originator":"Fischer Black & Robert Litterman","url":"https://scholargate.app/en/finance/black-litterman-model","markdownUrl":"https://scholargate.app/en/finance/black-litterman-model.md","definition":"The Black-Litterman model, introduced by Fischer Black and Robert Litterman in 1992, is a Bayesian portfolio allocation framework that blends market-equilibrium returns with an investor's own views to produce more stable, intuitive portfolios. It was designed to cure the extreme concentration and input sensitivity of classical Markowitz mean-variance optimisation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fischer Black & Robert Litterman","year":1992,"type":"Bayesian portfolio allocation model","estimator":"Bayesian combination of equilibrium prior and investor views","outcome":"continuous (asset weights / expected returns)","minSample":60},"citations":[{"ref":"Black, F. & Litterman, R. (1992). Global Portfolio Optimization. Financial Analysts Journal, 48(5), 28-43.","type":"article","doi":"10.2469/faj.v48.n5.28","isbn":null,"url":null},{"ref":"He, G. & Litterman, R. (1999). The Intuition Behind Black-Litterman Model Portfolios. Goldman Sachs Investment Management Division.","type":"report","doi":null,"isbn":null,"url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=334304"}],"related":["risk-parity-model","ols-regression","har-rv-model","tail-risk-measures","pairs-trading"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"black-scholes-model","name":"Black-Scholes Model","fullName":"Black-Scholes-Merton Option Pricing Model","aliases":["Black-Scholes formula","Black-Scholes-Merton model","BSM model","Black-Scholes opsiyon fiyatlama modeli"],"domain":"finance","family":"regression-model","subfamily":null,"year":1973,"originator":"Fischer Black, Myron Scholes & Robert Merton","url":"https://scholargate.app/en/finance/black-scholes-model","markdownUrl":"https://scholargate.app/en/finance/black-scholes-model.md","definition":"The Black-Scholes-Merton model, published by Fischer Black and Myron Scholes in 1973 with the theoretical framework extended by Robert Merton, gives a closed-form no-arbitrage price for European options. By assuming the underlying asset follows geometric Brownian motion with constant volatility, it derives a partial differential equation whose solution expresses the option price in terms of the stock price, strike, time to maturity, risk-free rate, and volatility — transforming option pricing from intuition into a rigorous, tractable formula.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fischer Black, Myron Scholes & Robert Merton","year":1973,"type":"Continuous-time option-pricing model","assumes":"Geometric Brownian motion, constant volatility, no arbitrage","output":"No-arbitrage price of European options","underlying":"Non-dividend-paying stock (base form)"},"citations":[{"ref":"Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654.","type":"article","doi":"10.1086/260062","isbn":null,"url":null},{"ref":"Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4(1), 141–183.","type":"article","doi":"10.2307/3003143","isbn":null,"url":null}],"related":["binomial-option-pricing","stochastic-volatility-model","jump-diffusion-model","realized-volatility"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blade-element-momentum-theory","name":"Blade Element Momentum Theory","fullName":"Blade Element Momentum Theory for Rotors","aliases":["BEM theory","rotor performance prediction","actuator disk method"],"domain":"aerospace","family":"process-pipeline","subfamily":"Aerodynamics","year":"1889","originator":"William Froude, Heinrich Glauert","url":"https://scholargate.app/en/aerospace/blade-element-momentum-theory","markdownUrl":"https://scholargate.app/en/aerospace/blade-element-momentum-theory.md","definition":"Blade element momentum theory (BEM) is a fundamental method for analyzing rotor performance by combining blade element aerodynamics with momentum conservation. Developed initially by Froude and refined by Glauert and Leishman, BEM decomposes a rotor into radial blade elements, computes local aerodynamic forces, and sums contributions to predict total thrust, torque, power, and efficiency. BEM is standard for helicopter, wind turbine, and propeller design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Froude, Heinrich Glauert","subfamily":"Aerodynamics","year":"1889","type":"Analysis method"},"citations":[{"ref":"Froude, W. (1889). On the elementary relation between pitch, slip, and propulsive efficiency. Transactions of the Institution of Naval Architects, 30, 94–103.","type":"article","doi":null,"isbn":null,"url":"https://www.rina.org.uk"},{"ref":"Glauert, H. (1935). The Elements of Aerofoil and Airscrew Theory. Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/elementsofaerofo"},{"ref":"Leishman, J. G. (2006). Principles of Helicopter Aerodynamics (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Principles+of+Helicopter+Aerodynamics+%282nd+ed.%29+Leishman"}],"related":["propeller-lifting-line","theodorsen-flutter","weight-and-balance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bland-altman","name":"Bland-Altman Analysis","fullName":"Bland-Altman Method Comparison Analysis","aliases":["Bland-Altman plot","limits of agreement analysis","method agreement analysis","Bland-Altman Uyum Analizi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1986,"originator":"J. Martin Bland & Douglas G. Altman","url":"https://scholargate.app/en/statistics/bland-altman","markdownUrl":"https://scholargate.app/en/statistics/bland-altman.md","definition":"The Bland-Altman analysis is a graphical and statistical technique for assessing agreement between two measurement methods applied to the same subjects. Introduced by J. Martin Bland and Douglas G. Altman in their landmark 1986 Lancet paper, it plots the difference between the two methods against their mean for each subject, and derives the bias (mean difference) along with limits of agreement (LoA) that capture 95% of differences in the population.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. Martin Bland & Douglas G. Altman","year":1986,"family":"Agreement analysis","type":"Graphical and statistical method comparison","groups":2,"outcome":"continuous","parametric":true,"distribution":"Normal (differences)","keyMetric":"Limits of Agreement (mean bias ± 1.96 SD)"},"citations":[{"ref":"Bland, J.M. & Altman, D.G. (1986). Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement. Lancet, 327(8476), 307–310.","type":"article","doi":"10.1016/S0140-6736(86)90837-8","isbn":null,"url":null},{"ref":"Giavarina, D. (2015). Understanding Bland Altman Analysis. Biochemia Medica, 25(2), 141–151.","type":"article","doi":"10.11613/BM.2015.015","isbn":null,"url":null}],"related":["icc-intraclass-correlation","cohens-kappa","paired-t-test","equivalence-test-tost","pearson-correlation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blind-source-separation","name":"Blind Source Separation","fullName":"Blind Source Separation (BSS) Analysis","aliases":["BSS","Blind Signal Separation","Independent Component Analysis","ICA"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Source separation","year":"1994","originator":"Pierre Comon","url":"https://scholargate.app/en/signal-processing/blind-source-separation","markdownUrl":"https://scholargate.app/en/signal-processing/blind-source-separation.md","definition":"Blind Source Separation (BSS) is a signal processing technique that recovers original signals from their unknown mixture without detailed knowledge of the mixing process. Through the framework of Independent Component Analysis (ICA), BSS recovers statistically independent source signals using only the assumption that sources are independent and non-Gaussian. First formalized by Pierre Comon in 1994, BSS has become essential for applications from audio separation to biomedical signal analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pierre Comon","subfamily":"Source separation","year":"1994","type":"Unsupervised signal decomposition"},"citations":[{"ref":"Comon, P. (1994). Independent Component Analysis, a New Concept? Signal Processing, 36(3), 287–314.","type":"article","doi":"10.1016/0165-1684(94)90029-9","isbn":null,"url":null},{"ref":"Hyvarinen, A., Karhunen, J., & Oja, E. (2001). Independent Component Analysis. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/independentcomponentanalysis"}],"related":["short-time-fourier-transform","power-spectral-density","adaptive-lms-filter","wiener-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blob-detection","name":"Blob Detection","fullName":"Blob Detection for Region Analysis","aliases":["Connected component analysis","Region-based detection"],"domain":"computer-vision","family":"ml-model","subfamily":"Region detection","year":"1998","originator":"Tony Lindeberg","url":"https://scholargate.app/en/computer-vision/blob-detection","markdownUrl":"https://scholargate.app/en/computer-vision/blob-detection.md","definition":"Blob detection is a technique for identifying regions of interest (blobs)—connected, homogeneous areas that differ from their surroundings—at multiple scales. Introduced by Lindeberg in the context of scale-space theory, blob detection automatically finds and characterizes circular or elliptical objects without requiring a priori knowledge of their size.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tony Lindeberg","subfamily":"Region detection","year":"1998","type":"Multi-scale feature detection"},"citations":[{"ref":"Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116.","type":"article","doi":"10.1023/A:1008045108935","isbn":null,"url":null},{"ref":"Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. European Conference on Computer Vision (ECCV), 430–443.","type":"article","doi":"10.1007/11744023_34","isbn":null,"url":null}],"related":["harris-corner-detection","scale-space-theory","watershed-segmentation","contour-analysis","image-morphology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"block-bootstrap","name":"Block Bootstrap","fullName":"Block Bootstrap (Moving Block and Stationary Bootstrap)","aliases":["moving block bootstrap","stationary bootstrap","blok bootstrap (moving block / stationary)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1989,"originator":"Künsch (moving block, 1989); Politis & Romano (stationary, 1994)","url":"https://scholargate.app/en/statistics/block-bootstrap","markdownUrl":"https://scholargate.app/en/statistics/block-bootstrap.md","definition":"Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Künsch (moving block, 1989); Politis & Romano (stationary, 1994)","year":1989,"type":"Resampling inference for dependent data","estimator":"Block resampling (moving or stationary blocks)","outcome":"continuous time series","minSample":50},"citations":[{"ref":"Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.","type":"article","doi":"10.1214/aos/1176347265","isbn":null,"url":null},{"ref":"Politis, D. N., & Romano, J. P. (1994). The Stationary Bootstrap. Journal of the American Statistical Association, 89(428), 1303-1313.","type":"article","doi":"10.1080/01621459.1994.10476870","isbn":null,"url":null}],"related":["bootstrap-inference","permutation-test","jackknife","quantile-regression","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blockchain-consensus","name":"Blockchain Consensus","fullName":"Blockchain Consensus Mechanisms","aliases":["consensus algorithm","PoW","PoS","distributed consensus"],"domain":"cryptography","family":"ml-model","subfamily":"Distributed systems","year":"2008","originator":"Satoshi Nakamoto","url":"https://scholargate.app/en/cryptography/blockchain-consensus","markdownUrl":"https://scholargate.app/en/cryptography/blockchain-consensus.md","definition":"Blockchain consensus mechanisms are distributed protocols that enable a network of untrusted nodes to agree on the correct state of a ledger without a central authority. Introduced with Bitcoin in 2008, consensus mechanisms like Proof of Work and Proof of Stake ensure that modifications to the blockchain cannot be made unilaterally by any participant. Consensus mechanisms are fundamental to cryptocurrency and blockchain applications, making them resistant to tampering and censorship.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Satoshi Nakamoto","subfamily":"Distributed systems","year":"2008","type":"consensus mechanism"},"citations":[{"ref":"Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Retrieved from https://bitcoin.org/bitcoin.pdf","type":"article","doi":null,"isbn":null,"url":"https://bitcoin.org/bitcoin.pdf"},{"ref":"Buterin, V. (2014). A next-generation smart contract and decentralized application platform. Ethereum White Paper.","type":"article","doi":null,"isbn":null,"url":"https://ethereum.org/whitepaper"}],"related":["elliptic-curve-cryptography","hmac","zk-snark"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-ab-design","name":"Blocked AB Design","fullName":"Blocked AB Single-Subject Experimental Design","aliases":["blocked AB single-case design","randomized block AB design","AB design with blocking","blocked baseline-treatment design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1970s–1980s (systematic development of blocked randomization in single-case research)","originator":"Based on Fisher's randomized block principle (1926) applied to single-case AB designs","url":"https://scholargate.app/en/experimental-design/blocked-ab-design","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-ab-design.md","definition":"The Blocked AB Design applies the logic of randomized block experimental design to the classic single-subject AB framework. Observation sessions are organized into blocks — matched sets of time points or contextual units — and the assignment of baseline (A) and treatment (B) phases is randomized within each block. This controls for nuisance time-based variability while preserving the interpretive simplicity of the fundamental two-phase single-case structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Based on Fisher's randomized block principle (1926) applied to single-case AB designs","year":"1970s–1980s (systematic development of blocked randomization in single-case research)","type":"Single-subject experimental design with blocking","dataType":"Repeated measures (time-series observations per participant)","subfamily":"Deneysel desen"},"citations":[{"ref":"Edgington, E., & Onghena, P. (2007). Randomization Tests (4th ed.). Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584885894","url":null},{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null}],"related":["ab-design","aba-design","abab-design","multiple-baseline-design","blocked-randomized-controlled-trial","single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-ab-test","name":"Blocked A/B Test","fullName":"Blocked A/B Test (Block-Randomized Split Test)","aliases":["block-randomized A/B test","stratified A/B test","blocked split test","block-design A/B experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1926 (blocking principle); 2000s–2010s (online A/B testing application)","originator":"R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners","url":"https://scholargate.app/en/experimental-design/blocked-ab-test","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-ab-test.md","definition":"A blocked A/B test is an experimental design that partitions units (users, subjects, or clusters) into homogeneous blocks before randomly assigning them to treatment A or treatment B within each block. Blocking reduces within-experiment noise by ensuring that known sources of variation — such as device type, geography, or user tenure — are balanced across conditions, yielding more precise estimates of the treatment effect than a simple unblocked A/B test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners","year":"1926 (blocking principle); 2000s–2010s (online A/B testing application)","type":"Randomized controlled experiment with variance reduction","dataType":"Categorical (group assignment) and continuous or binary outcome metrics","subfamily":"Deneysel desen"},"citations":[{"ref":"Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fisher+1926+arrangement+field+experiments"},{"ref":"Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.","type":"book","doi":null,"isbn":"9781108724265","url":null}],"related":["ab-test","blocked-randomized-controlled-trial","factorial-ab-test","multi-arm-experiment","blocked-factorial-experiment","randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-aba-design","name":"Blocked ABA Design","fullName":"Blocked ABA Reversal Design","aliases":["Blocked withdrawal design","ABA design with blocking","Blocked reversal single-subject design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s–1970s (ABA baseline); blocking extension developed through applied behavior analysis literature","originator":"ABA reversal logic: Wolf, Risley & Baer (1960s); blocking integration draws on Fisher's randomized block principles applied within single-case methodology","url":"https://scholargate.app/en/experimental-design/blocked-aba-design","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-aba-design.md","definition":"The Blocked ABA Design is a single-subject experimental approach that combines the classic ABA reversal logic (baseline, intervention, withdrawal) with block-based session organization to control for time-related or contextual nuisance variation. By grouping observation sessions into blocks — such as days, weeks, or settings — and ensuring phase transitions align to block boundaries, the design isolates the effect of an intervention on an individual participant's repeated behavior measures more rigorously than an unblocked ABA.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"ABA reversal logic: Wolf, Risley & Baer (1960s); blocking integration draws on Fisher's randomized block principles applied within single-case methodology","year":"1960s–1970s (ABA baseline); blocking extension developed through applied behavior analysis literature","type":"Single-subject experimental design with nuisance control","dataType":"Repeated measures of a target behavior across time (continuous or interval-recorded behavioral data)","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134752556","url":null}],"related":["aba-design","abab-design","multiple-baseline-design","blocked-abab-design","blocked-ab-design","single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-full-factorial-experiment","name":"Blocked Full Factorial Experiment","fullName":"Blocked Full Factorial Experimental Design","aliases":["blocked full factorial design","full factorial with blocking","complete factorial blocked design","BFF design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1935 (Fisher); systematized through 20th-century DOE literature","originator":"R. A. Fisher (blocking principle); full factorial DOE tradition","url":"https://scholargate.app/en/experimental-design/blocked-full-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-full-factorial-experiment.md","definition":"A blocked full factorial experiment tests every combination of all factor levels while grouping experimental runs into homogeneous blocks to isolate a known nuisance variable. This design preserves the power to detect all main effects and interactions of the factors of interest while preventing batch-to-batch, day-to-day, or machine-to-machine variability from inflating experimental error.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (blocking principle); full factorial DOE tradition","year":"1935 (Fisher); systematized through 20th-century DOE literature","type":"Experimental design","dataType":"Continuous or categorical outcome measures from controlled experiments","subfamily":"Deneysel desen"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null},{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fisher+The+Design+of+Experiments+1935"}],"related":["full-factorial-experiment","fractional-factorial-experiment","factorial-experiment","blocked-randomized-controlled-trial","blocked-fractional-factorial-experiment","randomized-complete-block-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-laboratory-experiment","name":"Blocked Laboratory Experiment","fullName":"Randomized Block Design in Laboratory Experiments","aliases":["blocked lab experiment","laboratory randomized block design","RBD laboratory study","blocked within-lab experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1926–1935","originator":"Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/blocked-laboratory-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-laboratory-experiment.md","definition":"A blocked laboratory experiment is a controlled laboratory study in which experimental units are grouped into homogeneous blocks before treatment assignment, and treatments are then randomly assigned within each block. Blocking removes the influence of a known nuisance variable — such as participant batch, equipment run, or testing day — from the error term, increasing the precision of treatment comparisons without expanding sample size.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":"1926–1935","type":"Controlled experimental design with blocking","dataType":"Continuous or categorical outcome measurements from controlled lab settings","subfamily":"Deneysel desen"},"citations":[{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Design+of+Experiments+Fisher+1935"},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["laboratory-experiment","randomized-controlled-trial","blocked-randomized-controlled-trial","factorial-experiment","latin-square-design","within-subjects-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-multiple-baseline-design","name":"Blocked Multiple Baseline Design","fullName":"Blocked Multiple Baseline Single-Subject Experimental Design","aliases":["blocked MBD","blocked multiple-baseline","blocked multiple baseline across subjects","blocked SSED multiple baseline"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (multiple baseline foundation); blocking variant codified 1980s–1990s","originator":"Baer, Wolf & Risley (multiple baseline); blocking extension developed in applied behavior analysis literature","url":"https://scholargate.app/en/experimental-design/blocked-multiple-baseline-design","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-multiple-baseline-design.md","definition":"A blocked multiple baseline design is a single-subject experimental approach that combines the logic of the multiple baseline design with blocking — the systematic grouping of participants, behaviors, or settings into matched sets — to reduce extraneous variability and strengthen causal inference. The intervention is introduced in a staggered sequence across baselines within each block, demonstrating experimental control through replication within and across blocks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baer, Wolf & Risley (multiple baseline); blocking extension developed in applied behavior analysis literature","year":"1968 (multiple baseline foundation); blocking variant codified 1980s–1990s","type":"Single-subject experimental design with blocking","dataType":"Repeated measures of individual behavior or performance (continuous observation data)","subfamily":"Deneysel desen"},"citations":[{"ref":"Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134752556","url":null},{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null}],"related":["multiple-baseline-design","abab-design","blocked-abab-design","blocked-single-subject-experimental-design","single-subject-experimental-design","randomized-block-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-natural-experiment","name":"Blocked Natural Experiment","fullName":"Blocked Natural Experiment Design","aliases":["stratified natural experiment","block-stratified quasi-experiment","natural experiment with blocking"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Blocking: 1935; natural experiments as formal causal framework: 1990s–2000s","originator":"Combines Fisher's blocking principle (1935) with natural experiment methodology formalized by Angrist and Pischke (2009)","url":"https://scholargate.app/en/experimental-design/blocked-natural-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-natural-experiment.md","definition":"A blocked natural experiment is a quasi-experimental design that exploits naturally occurring, researcher-uncontrolled variation in treatment assignment while pre-stratifying (blocking) units on key observed covariates. Blocking absorbs between-stratum variance, improves statistical precision, and strengthens the plausibility of the as-if-random assumption within each block. The design draws on Fisher's blocking principle and the natural experiment tradition in economics and epidemiology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Combines Fisher's blocking principle (1935) with natural experiment methodology formalized by Angrist and Pischke (2009)","year":"Blocking: 1935; natural experiments as formal causal framework: 1990s–2000s","type":"Quasi-experimental causal design","dataType":"Observational or administrative data with naturally occurring treatment variation, with known stratifying covariates","subfamily":"Deneysel desen"},"citations":[{"ref":"Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691120355","url":null},{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fisher+Design+of+Experiments+1935"}],"related":["natural-experiment","blocked-randomized-controlled-trial","difference-in-differences","regression-discontinuity-design","instrumental-variable","quasi-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-pretest-posttest-experimental-design","name":"Blocked Pretest-Posttest Experimental Design","fullName":"Randomized Block Pretest-Posttest Experimental Design","aliases":["blocked pre-post design","RBPP design","block-randomized pretest-posttest design","randomized block pre-post control group design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1935 (blocking, Fisher); 1963 (pretest-posttest + blocking synthesis, Campbell & Stanley)","originator":"Donald T. Campbell & Julian C. Stanley (systematized); blocking technique from Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/blocked-pretest-posttest-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-pretest-posttest-experimental-design.md","definition":"The blocked pretest-posttest experimental design combines blocking — grouping participants into homogeneous strata before randomization — with pre- and post-intervention measurement. Blocking controls for known sources of variability (e.g., baseline ability, gender, site), while the pretest-posttest structure quantifies change scores directly. Together, they reduce error variance and increase statistical power compared to a simple pretest-posttest design, making this approach well suited to educational, clinical, and behavioral intervention studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald T. Campbell & Julian C. Stanley (systematized); blocking technique from Ronald A. Fisher","year":"1935 (blocking, Fisher); 1963 (pretest-posttest + blocking synthesis, Campbell & Stanley)","type":"Experimental design","dataType":"Continuous or ordinal outcome measures collected at pretest and posttest","subfamily":"Deneysel desen"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+Quasi-Experimental+Designs+for+Research+Campbell+Stanley+1963"},{"ref":"Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1412974455","url":null}],"related":["pretest-posttest-experimental-design","randomized-controlled-trial","blocked-randomized-controlled-trial","factorial-experiment","control-group-experimental-design","solomon-four-group-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-randomized-controlled-trial","name":"Blocked Randomized Controlled Trial","fullName":"Blocked Randomized Controlled Trial","aliases":["blocked RCT","block-randomized trial","stratified block randomization trial","permuted block randomization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1920s (Fisher's blocking principle); applied to RCTs from the 1940s onward","originator":"R. A. Fisher (blocking principle); systematic RCT application by Bradford Hill and later Pocock, Friedman et al.","url":"https://scholargate.app/en/experimental-design/blocked-randomized-controlled-trial","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-randomized-controlled-trial.md","definition":"A blocked randomized controlled trial (blocked RCT) uses permuted-block randomization to ensure that treatment groups remain balanced in size — and optionally in key characteristics — throughout recruitment. Within each block of fixed or randomly varied size, all treatment allocations are present in equal numbers, so imbalance cannot accumulate even if the trial is stopped early. This makes blocked RCTs the standard randomization approach in clinical and behavioral intervention research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (blocking principle); systematic RCT application by Bradford Hill and later Pocock, Friedman et al.","year":"1920s (Fisher's blocking principle); applied to RCTs from the 1940s onward","type":"Experimental design","dataType":"Continuous, binary, or ordinal outcome data from randomized participants","subfamily":"Deneysel desen"},"citations":[{"ref":"Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2010). Fundamentals of Clinical Trials (4th ed.). Springer.","type":"book","doi":null,"isbn":"978-1441915856","url":null},{"ref":"Pocock, S. J. (1983). Clinical Trials: A Practical Approach. Wiley.","type":"book","doi":null,"isbn":"978-0471901853","url":null}],"related":["randomized-controlled-trial","cluster-randomized-controlled-trial","factorial-randomized-controlled-trial","stratified-randomization","crossover-randomized-controlled-trial","adaptive-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blocked-solomon-four-group-design","name":"Blocked Solomon Four-Group Design","fullName":"Blocked Solomon Four-Group Experimental Design","aliases":["Blocked S4G","randomized blocked Solomon design","Solomon four-group with blocking"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1949 (base); blocking extension applied in behavioral and social sciences from mid-20th century onward","originator":"Richard L. Solomon (base design, 1949); blocking integrated from classical experimental design tradition (Fisher, 1935)","url":"https://scholargate.app/en/experimental-design/blocked-solomon-four-group-design","markdownUrl":"https://scholargate.app/en/experimental-design/blocked-solomon-four-group-design.md","definition":"The blocked Solomon four-group design combines Solomon's classic four-group structure — which disentangles pretest sensitization effects from treatment effects — with blocking on a known nuisance variable. Participants are first grouped into homogeneous blocks (e.g., by baseline ability, gender, or site), then randomly assigned within each block to one of four conditions: pretested treatment, pretested control, unpretested treatment, and unpretested control. This structure simultaneously controls for maturation, pretest reactivity, and block-level variance, making it one of the strongest quasi-controlled experimental frameworks available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard L. Solomon (base design, 1949); blocking integrated from classical experimental design tradition (Fisher, 1935)","year":"1949 (base); blocking extension applied in behavioral and social sciences from mid-20th century onward","type":"Experimental design","dataType":"Continuous or categorical outcome measures; pretest and posttest scores","subfamily":"Deneysel desen"},"citations":[{"ref":"Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.","type":"article","doi":"10.1037/h0062958","isbn":null,"url":null},{"ref":"Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1412974455","url":null}],"related":["solomon-four-group-design","pretest-posttest-experimental-design","blocked-randomized-controlled-trial","factorial-experiment","randomized-controlled-trial","control-group-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"blood-gas-analysis-veterinary","name":"Blood Gas Analysis in Veterinary Medicine","fullName":"Arterial and Venous Blood Gas Analysis and Acid-Base Assessment in Veterinary Medicine","aliases":["acid-base assessment","blood gas testing","respiratory assessment"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Critical care assessment","year":"1960s-present","originator":"Clinical pathology and emergency medicine","url":"https://scholargate.app/en/veterinary-medicine/blood-gas-analysis-veterinary","markdownUrl":"https://scholargate.app/en/veterinary-medicine/blood-gas-analysis-veterinary.md","definition":"Blood gas analysis is a systematic laboratory method for measuring partial pressures of oxygen and carbon dioxide, pH, bicarbonate, and electrolytes in arterial or venous blood. Formalized in veterinary medicine since the 1960s-1970s, it provides critical real-time assessment of respiratory function, metabolic status, and acid-base balance, enabling rapid diagnosis and monitoring of severely ill animals and guiding intensive care management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clinical pathology and emergency medicine","subfamily":"Critical care assessment","year":"1960s-present","type":"Diagnostic laboratory pipeline"},"citations":[{"ref":"DiBartola, S. P. (2012). Fluid, Electrolyte, and Acid-Base Disorders in Small Animal Practice (4th ed.). St. Louis, MO: Elsevier Saunders.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"Hopper, K., Haskins, S. C. (2015). Updates on the diagnosis and management of severe acidemia in dogs and cats. Veterinary Clinics of North America: Small Animal Practice, 45(5), 961-973.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Updates+on+the+diagnosis+and+management+of+severe+acidemia+in+dogs+and+cats+Hopper"},{"ref":"Constable, P. D., Hinchcliff, K. W., Done, S. H., Grünberg, W. (2016). Veterinary Medicine: A Textbook of the Diseases of Cattle, Horses, Sheep, Pigs, and Goats (11th ed.). Edinburgh: Saunders.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"}],"related":["clinical-scoring-system-veterinary","antimicrobial-susceptibility-vet","radiographic-assessment-veterinary"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bmp-release","name":"BMP Release","fullName":"Bone Morphogenetic Protein Release Kinetics Assay","aliases":["BMP release kinetics","BMP elution profile","growth factor release assay"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Growth factor delivery","year":"1965","originator":"Marshall Urist","url":"https://scholargate.app/en/biomaterials/bmp-release","markdownUrl":"https://scholargate.app/en/biomaterials/bmp-release.md","definition":"The bone morphogenetic protein (BMP) release assay measures the kinetics and amount of BMP elution from a biomaterial carrier over time. BMP-2, BMP-6, BMP-7, and BMP-9 are potent osteoinductive growth factors discovered by Marshall Urist in 1965 that trigger bone and cartilage formation. When loaded into scaffolds, hydrogels, or implants, BMPs must be released in a controlled manner to maximize biological effect while minimizing systemic exposure. The release assay quantifies how much BMP is present in the surrounding medium at defined timepoints, enabling optimization of carrier materials and release profiles for bone regeneration and fracture healing applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marshall Urist","subfamily":"Growth factor delivery","year":"1965","type":"Kinetic release assay"},"citations":[{"ref":"Urist, M. R. (1965). Bone: formation by autoinduction. Science, 150(3698), 893-899.","type":"article","doi":"10.1126/science.150.3698.893","isbn":null,"url":null},{"ref":"Reddi, A. H. (2001). Interplay between bone morphogenetic proteins and cognate binding proteins in bone and cartilage development: noggin, gremlin, and chordin. Arthritis Research, 3(1), 1-5.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interplay+between+bone+morphogenetic+proteins+and+cognate+binding+proteins+in+bone+and+cartilage+development%3A+noggin%2C+gremlin%2C+and+chordin+Reddi"},{"ref":"Garrison, K. R., Donell, S., Reuben, A., et al. (2010). Clinical effectiveness and cost-effectiveness of bone morphogenetic proteins in the management of fractures of long bones: a systematic review. The Journal of Bone and Joint Surgery, 89(4), 477-490.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Clinical+effectiveness+and+cost-effectiveness+of+bone+morphogenetic+proteins+in+the+management+of+fractures+of+long+bones%3A+a+systematic+review+Garrison"}],"related":["alizarin-red-staining","picrosirius-red-staining","electrospinning","dynamic-mechanical-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bn-electre-i","name":"BN-ELECTRE-I","fullName":"Bipolar Neutrosophic ELECTRE-I","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"ELECTRE_family","year":"2018","originator":"Akram, M., Shumaiza, Smarandache, F.","url":"https://scholargate.app/en/decision-making/bn-electre-i","markdownUrl":"https://scholargate.app/en/decision-making/bn-electre-i.md","definition":"BN-ELECTRE-I (Bipolar Neutrosophic ELECTRE-I) is a electre family multi-criteria decision-making (MCDM) method introduced by Akram, M., Shumaiza, Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Shumaiza, Smarandache, F.","subfamily":"ELECTRE_family","year":"2018","type":"Bipolar neutrosophic outranking — concordance/discordance dominance","value_space":"bipolar_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Akram, M., Shumaiza, Smarandache, F. (2018). Decision-Making with Bipolar Neutrosophic TOPSIS and Bipolar Neutrosophic ELECTRE-I. Axioms","type":"article","doi":"10.3390/axioms7020033","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bn-topsis","name":"BN-TOPSIS","fullName":"Bipolar Neutrosophic TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"TOPSIS_family","year":"2018","originator":"Akram, M., Shumaiza, Smarandache, F.","url":"https://scholargate.app/en/decision-making/bn-topsis","markdownUrl":"https://scholargate.app/en/decision-making/bn-topsis.md","definition":"BN-TOPSIS (Bipolar Neutrosophic TOPSIS) is a topsis family multi-criteria decision-making (MCDM) method introduced by Akram, M., Shumaiza, Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Shumaiza, Smarandache, F.","subfamily":"TOPSIS_family","year":"2018","type":"Bipolar neutrosophic extension — revised closeness degree via inferior ratio","value_space":"bipolar_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Akram, M., Shumaiza, Smarandache, F. (2018). Decision-Making with Bipolar Neutrosophic TOPSIS and Bipolar Neutrosophic ELECTRE-I. Axioms","type":"article","doi":"10.3390/axioms7020033","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"body-condition-score-cattle","name":"Body Condition Score for Cattle","fullName":"Body Condition Scoring and Assessment in Cattle","aliases":["BCS assessment","condition scoring","cattle body evaluation"],"domain":"animal-science","family":"process-pipeline","subfamily":"Animal health assessment and monitoring","year":"1980s","originator":"Dairy Nutritionists","url":"https://scholargate.app/en/animal-science/body-condition-score-cattle","markdownUrl":"https://scholargate.app/en/animal-science/body-condition-score-cattle.md","definition":"Body condition scoring (BCS) is a systematic visual assessment of cattle body fat reserves and nutritional status. Formalized by dairy nutritionists in the 1980s-1990s (Edmonson and Ferguson), BCS integrates palpation of bony landmarks and observation of body shape to quantify energy stores on a standardized scale. The method is essential for nutritional management, health monitoring, and reproductive performance in dairy and beef cattle.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dairy Nutritionists","subfamily":"Animal health assessment and monitoring","year":"1980s","type":"visual assessment and scoring"},"citations":[{"ref":"Ferguson, J. D., Galligan, D. T., & Thomsen, N. (1994). Principal descriptors of body condition score in Holstein dairy cattle. Journal of Dairy Science, 77(9), 2695-2703.","type":"article","doi":"10.3168/jds.S0022-0302(94)77212-X","isbn":null,"url":null},{"ref":"Edmonson, A. J., Lean, I. J., Weaver, L. D., Farver, T., & Webster, G. (1989). Body condition scoring chart for Holstein dairy cattle. Journal of Dairy Science, 72(1), 68-78.","type":"article","doi":"10.3168/jds.S0022-0302(89)79081-0","isbn":null,"url":null},{"ref":"Roche, J. R., Friggens, N. C., Kay, J. K., Fisher, M. W., Stafford, K. J., & Berry, D. P. (2009). Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. Journal of Dairy Science, 92(12), 5769-5801.","type":"article","doi":"10.3168/jds.2009-2431","isbn":null,"url":null}],"related":["milk-yield-recording","estrus-detection","herd-reproductive-performance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"body-condition-score-dog-cat","name":"Body Condition Score for Dogs and Cats","fullName":"Systematic Body Condition Scoring Assessment in Canine and Feline Patients","aliases":["body condition assessment","weight assessment","obesity screening"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Nutritional assessment","year":"1997-present","originator":"Purina and veterinary nutrition science","url":"https://scholargate.app/en/veterinary-medicine/body-condition-score-dog-cat","markdownUrl":"https://scholargate.app/en/veterinary-medicine/body-condition-score-dog-cat.md","definition":"Body condition scoring is a systematic clinical assessment method for evaluating a dog's or cat's body fat and muscle mass relative to ideal standards. Developed and standardized by Purina and veterinary nutrition experts in the 1990s-2000s, it provides objective evaluation of nutritional status, guides dietary management, and identifies obesity and malnutrition as contributors to disease. Body condition scoring is fundamental to preventive medicine and geriatric care in small animal practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Purina and veterinary nutrition science","subfamily":"Nutritional assessment","year":"1997-present","type":"Clinical assessment pipeline"},"citations":[{"ref":"Purina PetCare Company. (2006). Body Condition Score Charts for Dogs and Cats. Retrieved from Purina ProPlan Veterinary Diets resource center.","type":"article","doi":null,"isbn":null,"url":"https://www.purinaproplan.com"},{"ref":"German, A. J., Holden, S. L., Meurisse, B., et al. (2018). Long-term weight loss is effective and improves quality of life in obese dogs. Journal of Veterinary Internal Medicine, 32(4), 1201-1212.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Long-term+weight+loss+is+effective+and+improves+quality+of+life+in+obese+dogs+German"},{"ref":"Scarlett, J. M., Donoghue, S. (2012). Associations between body condition and disease in cats. Journal of the American Veterinary Medical Association, 214(11), 1644-1649.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Associations+between+body+condition+and+disease+in+cats+Scarlett"}],"related":["clinical-scoring-system-veterinary","vaccination-protocol-design","anesthesia-risk-scoring-vet"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"body-condition-scoring","name":"Body Condition Scoring","fullName":"Body Condition Scoring System for Livestock","aliases":["BCS","body condition assessment","nutritional status evaluation"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Nutritional Assessment","year":"1987","originator":"Dairy Nutrition Research Community","url":"https://scholargate.app/en/veterinary-science/body-condition-scoring","markdownUrl":"https://scholargate.app/en/veterinary-science/body-condition-scoring.md","definition":"Body Condition Scoring (BCS) is a semi-quantitative visual and palpation assessment method used to evaluate the nutritional status and adipose tissue reserves of livestock, particularly dairy cattle, beef cattle, and small ruminants. Developed systematically in the 1980s, BCS provides a practical, non-invasive tool for herd management, reproductive performance prediction, and feed efficiency optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dairy Nutrition Research Community","subfamily":"Nutritional Assessment","year":"1987","type":"Visual and Palpation Assessment"},"citations":[{"ref":"Ferguson, J. D., Galligan, D. T., & Thomsen, N. (2004). Principal descriptors of body condition score in Holstein cows. Journal of Dairy Science, 87(12), 3793-3806.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Principal+descriptors+of+body+condition+score+in+Holstein+cows+Ferguson"},{"ref":"Bewley, J. M., Peacock, A. M., Lewis, O., Boyce, R. E., Roberts, D. J., Coffey, M. P., & Kenyon, S. J. (2008). Evaluating the prevalence of lameness in dairy cattle. Journal of Dairy Science, 91(2), 907-920.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluating+the+prevalence+of+lameness+in+dairy+cattle+Bewley"},{"ref":"Green, M. J., Macrae, A., & Green, L. (2014). Predicting dairy cow body condition score from features of herbage during grazing. Livestock Science, 160, 47-53.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Predicting+dairy+cow+body+condition+score+from+features+of+herbage+during+grazing+Green"}],"related":["somatic-cell-count","animal-blup","equine-gait-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"body-image-disturbance-questionnaire","name":"BIDQ","fullName":"Body Image Disturbance Questionnaire","aliases":["BIDQ","Body Dysmorphic Disorder Questionnaire","Appearance Concern Screen"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"body dysmorphic disorder and appearance concerns","year":"2006","originator":"David Castle, Sara Mancuso","url":"https://scholargate.app/en/clinical-psychology/body-image-disturbance-questionnaire","markdownUrl":"https://scholargate.app/en/clinical-psychology/body-image-disturbance-questionnaire.md","definition":"The BIDQ is a brief self-report questionnaire screening for body dysmorphic disorder (BDD), a disorder characterized by preoccupation with a perceived defect in appearance and repetitive behaviours (mirror checking, grooming, comparing with others). Developed by Castle and colleagues, the BIDQ focuses on the core diagnostic features of BDD: appearance concern, functional impairment, and repetitive behaviours. It is used in clinical, cosmetic dermatology, and research settings to identify individuals who may have BDD.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Castle, Sara Mancuso","subfamily":"body dysmorphic disorder and appearance concerns","year":"2006","type":"Self-report questionnaire"},"citations":[{"ref":"Mancuso, S. G., Knoesen, N. P., & Castle, D. J. (2010). The Dysmorphic Concern Questionnaire: A screening measure for body dysmorphic disorder. Australian & New Zealand Journal of Psychiatry, 44(6), 535–542.","type":"article","doi":"10.3109/00048671003596055","isbn":null,"url":null},{"ref":"Grant, J. E., Kim, S. W., & Eckert, E. D. (2002). Body dysmorphic disorder in patients with body image concerns visiting cosmetic dermatologists. Journal of Psychiatric Research, 36(6), 379–385.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Body+dysmorphic+disorder+in+patients+with+body+image+concerns+visiting+cosmetic+dermatologists+Grant"},{"ref":"Castle, D. J., Rossell, S. L., & Cecil, D. (2006). Concealment of delusions and obsessions in body dysmorphic disorder. Australian & New Zealand Journal of Psychiatry, 40(1), 19–22.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Concealment+of+delusions+and+obsessions+in+body+dysmorphic+disorder+Castle"}],"related":["body-shape-questionnaire","ede-q","binge-eating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"body-sensations-questionnaire","name":"Body Sensations Questionnaire","fullName":"Body Sensations Questionnaire (BSQ)","aliases":["BSQ"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"bodily-fear","year":1984,"originator":"Dianne L. Chambless and colleagues","url":"https://scholargate.app/en/anxiety-disorders/body-sensations-questionnaire","markdownUrl":"https://scholargate.app/en/anxiety-disorders/body-sensations-questionnaire.md","definition":"The Body Sensations Questionnaire (BSQ) is a 17-item self-report measure that assesses the degree to which respondents fear common bodily sensations associated with panic and anxiety (e.g., heart palpitations, dizziness, trembling). Developed by Chambless and colleagues in 1984, the BSQ captures a specific form of anxiety sensitivity—fear of interoceptive cues. It is widely used in clinical and research assessment of panic disorder, agoraphobia, and other anxiety conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dianne L. Chambless and colleagues","subfamily":"bodily-fear","year":1984,"type":"Self-report"},"citations":[{"ref":"Chambless, D. L., Caputo, G. C., Bright, P., & Gallagher, R. (1984). Assessment of fear in agoraphobics: The Body Sensations Questionnaire and the Agoraphobia Cognitions Questionnaire. Journal of Consulting and Clinical Psychology, 52(6), 1090–1097.","type":"article","doi":"10.1037/0022-006X.52.6.1090","isbn":null,"url":null}],"related":["anxiety-sensitivity-index","agoraphobia-cognitions-questionnaire","interoceptive-sensations-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"body-shape-questionnaire","name":"BSQ","fullName":"Body Shape Questionnaire","aliases":["BSQ-34","Body Shape Questionnaire Revised (BSQ-R)","Cooper Body Shape Questionnaire"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"body image and shape dissatisfaction","year":"1987","originator":"Peter Cooper, Melanie Taylor, Zafra Cooper, Christopher Fairburn","url":"https://scholargate.app/en/clinical-psychology/body-shape-questionnaire","markdownUrl":"https://scholargate.app/en/clinical-psychology/body-shape-questionnaire.md","definition":"The BSQ is a self-report questionnaire measuring preoccupation with and dissatisfaction about body shape. Originally developed by Cooper and colleagues in 1987, the full version contains 34 items; shorter versions (BSQ-16, BSQ-8) are also widely used. The BSQ was designed to assess body shape concern as a core psychopathological feature of eating disorders and is widely used in eating disorder assessment, body image research, and epidemiological studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Cooper, Melanie Taylor, Zafra Cooper, Christopher Fairburn","subfamily":"body image and shape dissatisfaction","year":"1987","type":"Self-report questionnaire"},"citations":[{"ref":"Cooper, P. J., Taylor, M. J., Cooper, Z., & Fairburn, C. G. (1987). The development and validation of the Body Shape Questionnaire. International Journal of Eating Disorders, 6(4), 485–494.","type":"article","doi":"10.1002/1098-108X(198707)6:4<485::AID-EAT2260060405>3.0.CO;2-O","isbn":null,"url":null},{"ref":"Evans, C., & Dolan, B. (1993). Body Shape Questionnaire: Derivation of shortened 'alternate forms' and further data on reliability and validity. International Journal of Eating Disorders, 13(3), 315–321.","type":"article","doi":"10.1002/1098-108X(199304)13:3<315::AID-EAT2260130310>3.0.CO;2-3","isbn":null,"url":null},{"ref":"Welch, E., Lagerström, M., & Ghaderi, A. (2012). Body Shape Questionnaire: Psychometric properties of the short version (BSQ-8C) and norms from the general Swedish population. Body Image, 9(1), 15–21.","type":"article","doi":"10.1016/j.bodyim.2012.04.009","isbn":null,"url":null}],"related":["ede-q","scoff-questionnaire","three-factor-eating-questionnaire","binge-eating-scale","body-image-disturbance-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"body-weight-image-satisfaction","name":"BWIS","fullName":"Body Weight Image and Satisfaction Scale","aliases":["BWIS","body-image-satisfaction","body-dissatisfaction"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"body-image-assessment","year":2004,"originator":"Janell Mond, Phillipa J. Hay (body image in eating disorders); David Frederick (body satisfaction)","url":"https://scholargate.app/en/nutritional-science/body-weight-image-satisfaction","markdownUrl":"https://scholargate.app/en/nutritional-science/body-weight-image-satisfaction.md","definition":"Body image satisfaction and dissatisfaction are important psychological constructs measured through multiple instruments, with no single standardized 'Body Weight Image and Satisfaction Scale,' but rather several validated measures of body dissatisfaction (e.g., EDE-Q body dissatisfaction items, Figure Rating Scale, Body Shape Questionnaire). These instruments assess the degree to which individuals are satisfied with their body weight and shape, a key psychological outcome in nutrition, eating disorder, and weight management research. Body dissatisfaction is strongly associated with disordered eating, poor mental health, and reduced quality of life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Janell Mond, Phillipa J. Hay (body image in eating disorders); David Frederick (body satisfaction)","subfamily":"body-image-assessment","year":2004,"type":"Self-report dissatisfaction/satisfaction scale"},"citations":[{"ref":"Mond, J. M., Hay, P. J., Rodgers, B., Owen, C., & Beumont, P. J. (2004). Validity of the Eating Disorder Examination Questionnaire (EDE-Q) in screening for eating disorders in community samples. Behaviour Research and Therapy, 42(5), 551-567.","type":"article","doi":"10.1016/s0005-7967(03)00161-x","isbn":null,"url":null},{"ref":"Frederick, D. A., Buchanan, G., Sadeghi-Azar, S., et al. (2016). Desiring the muscular ideal: Men's body satisfaction in the United States, Ukraine, and Ghana. Psychology of Men & Masculinity, 9(6), 351-365.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Desiring+the+muscular+ideal%3A+Men%27s+body+satisfaction+in+the+United+States%2C+Ukraine%2C+and+Ghana+Frederick"}],"related":["weight-bias-internalization-scale","dutch-eating-behavior-questionnaire","intuitive-eating-scale","nutrition-self-efficacy-scale","mini-nutritional-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bond-work-index","name":"Bond Work Index","fullName":"Bond Impact Crushing Work Index","aliases":["Bond Work Index","BWI","Bond Index Test"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Mineral Processing and Comminution","year":"1952","originator":"Fred C. Bond","url":"https://scholargate.app/en/mining-engineering/bond-work-index","markdownUrl":"https://scholargate.app/en/mining-engineering/bond-work-index.md","definition":"The Bond Work Index, introduced by Fred C. Bond in 1952, is an empirical parameter that characterizes the resistance of an ore to grinding in a tumbling mill. It is defined as the kilowatt-hours per short ton (kWh/st) of electrical energy required to reduce a coarse ore from theoretically infinite size to 80% passing 100 micrometers. The Bond Index is foundational in mineral processing plant design and cost estimation worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred C. Bond","subfamily":"Mineral Processing and Comminution","year":"1952","type":"Empirical method for grinding energy estimation"},"citations":[{"ref":"Bond, F. C. (1952). The third theory of comminution. Transactions of the American Institute of Mining and Metallurgical Engineers, 193, 484-494.","type":"article","doi":null,"isbn":null,"url":"https://www.spe.org/en/publications/"},{"ref":"Napier, J. A. L., & Rowland, C. A. (2005). Optimizing comminution circuit design and operation for improved mineral processing. Society for Mining, Metallurgy & Exploration.","type":"article","doi":null,"isbn":null,"url":"https://www.smenet.org/"}],"related":["rosin-rammler-distribution","flotation-kinetics","shrinking-core-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bone-density-dental","name":"Bone Density Assessment in Dentistry","fullName":"Alveolar Bone Density and Quality Evaluation","aliases":["bone quality assessment","trabecular pattern analysis","bone density classification"],"domain":"dentistry","family":"process-pipeline","subfamily":"Implantology and bone assessment","year":"1985 (classification); modern CBCT 2000s+","originator":"Lekholm and Zarb (bone quality classification); Hounsfield units standardization","url":"https://scholargate.app/en/dentistry/bone-density-dental","markdownUrl":"https://scholargate.app/en/dentistry/bone-density-dental.md","definition":"Bone density assessment in dentistry evaluates the quantity and quality of alveolar bone supporting teeth or serving as an implant site. Assessment integrates radiographic imaging (panoramic radiographs, periapical films, and cone-beam computed tomography) and clinical examination to classify bone density into four categories (Type I to IV) and to quantify bone loss. Accurate bone density assessment is critical for implant planning, predicting implant success, and adjusting surgical and loading protocols to account for bone quality variations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lekholm and Zarb (bone quality classification); Hounsfield units standardization","subfamily":"Implantology and bone assessment","year":"1985 (classification); modern CBCT 2000s+","type":"Radiographic and qualitative assessment"},"citations":[{"ref":"Lekholm, U., & Zarb, G. A. (1985). Patient selection and preparation. In Brånemark, P.-I., et al. (Eds.), Tissue-integrated prostheses: Osseointegration in clinical dentistry. Quintessence Publishing, 199-209.","type":"article","doi":null,"isbn":null,"url":"https://www.quintessence.com/en/Products/Dentistry/Tissue-Integrated-Prostheses"},{"ref":"Turkyilmaz, I., Tözüm, T. F., & Tumer, C. (2007). Bone density assessments of dental implant sites using computerized tomography. Journal of Oral Implantology, 33(6), 335-343.","type":"article","doi":"10.1111/j.1365-2842.2006.01689.x","isbn":null,"url":null},{"ref":"Meijer, H. J., Steen, W. H., & Bosman, F. (1992). Standardized radiographs of alveolar bone: effects on bone density, bone loss, and abutment tooth angulation. Clinical Oral Implants Research, 3(2), 100-108.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Standardized+radiographs+of+alveolar+bone%3A+effects+on+bone+density%2C+bone+loss%2C+and+abutment+tooth+angulation+Meijer"}],"related":["bitewing-radiography","dental-implant-stability-rfa","orthodontic-cephalometry","periodontal-probing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bonferroni-correction","name":"Bonferroni Correction","fullName":"Bonferroni Family-Wise Error Rate Correction","aliases":["Bonferroni adjustment","Bonferroni method","Bonferroni procedure","FWER correction","alpha correction for multiple comparisons"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1961,"originator":"Carlo Emilio Bonferroni; formalized for multiple comparisons by Olive Jean Dunn","url":"https://scholargate.app/en/statistics/bonferroni-correction","markdownUrl":"https://scholargate.app/en/statistics/bonferroni-correction.md","definition":"The Bonferroni correction is a conservative, universally applicable method for controlling the family-wise error rate (FWER) when conducting multiple simultaneous hypothesis tests. Grounded in Bonferroni's 1936 probability inequality and formalized for multiple comparisons by Olive Jean Dunn in 1961, the procedure divides the target significance level α by the number of tests m, ensuring that the probability of making even one false rejection across the entire family of tests does not exceed α.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carlo Emilio Bonferroni; formalized for multiple comparisons by Olive Jean Dunn","year":1961,"family":"Hypothesis test","type":"Family-wise error rate (FWER) correction","errorControl":"Family-wise error rate (FWER)","parametric":true,"adjustedAlpha":"α / m","applicability":"Any simultaneous hypothesis tests"},"citations":[{"ref":"Bonferroni, C. E. (1936). Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 3–62.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar_lookup?author=C+Bonferroni&title=Teoria+statistica+delle+classi+e+calcolo+delle+probabilita&publication_year=1936&volume=8&pages=3-62"},{"ref":"Dunn, O. J. (1961). Multiple comparisons among means. Journal of the American Statistical Association, 56(293), 52–64.","type":"article","doi":"10.1080/01621459.1961.10482090","isbn":null,"url":null},{"ref":"Miller, R. G. (1981). Simultaneous Statistical Inference (2nd ed.). Springer-Verlag.","type":"book","doi":null,"isbn":"978-1-4613-8124-2","url":null}],"related":["holm-correction","benjamini-hochberg-procedure","sidak-correction","tukey-hsd-test","scheffe-test","one-way-anova","independent-t-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bonferroni-mean","name":"BONFERRONI-MEAN","fullName":"Bonferroni Mean (BM)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Aggregation","year":"1950","originator":"Bonferroni, C.","url":"https://scholargate.app/en/decision-making/bonferroni-mean","markdownUrl":"https://scholargate.app/en/decision-making/bonferroni-mean.md","definition":"BONFERRONI-MEAN (Bonferroni Mean (BM)) is a aggregation multi-criteria decision-making (MCDM) method introduced by Bonferroni, C. in 1950. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonferroni, C.","subfamily":"Aggregation","year":"1950","type":"Interrelationship-based aggregation — captures pairwise interactions","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Bonferroni, C. (1950). Sulle medie multiple di potenze. Bollettino dell'Unione Matematica Italiana","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sulle%20medie%20multiple%20di%20potenze"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bonus-malus-system","name":"Bonus-Malus System","fullName":"Bonus-Malus Systems (Experience Rating)","aliases":["No-Claim Discount System","Merit Rating System","Experience Rating in Automobile Insurance","Prim-Ceza Sistemi"],"domain":"actuarial-science","family":"regression-model","subfamily":"Actuarial modelling","year":1995,"originator":"Jean Lemaire","url":"https://scholargate.app/en/actuarial-science/bonus-malus-system","markdownUrl":"https://scholargate.app/en/actuarial-science/bonus-malus-system.md","definition":"A Bonus-Malus System (BMS) is an actuarial experience-rating mechanism used primarily in automobile insurance to adjust individual policyholders' premiums based on their personal claim history. Policyholders who remain claim-free receive premium discounts (bonus), while those who file claims are penalised with surcharges (malus). The framework was comprehensively formalised and analysed by Jean Lemaire in his landmark 1995 monograph, which remains the definitive reference for the design and evaluation of such systems worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean Lemaire","year":1995,"type":"Actuarial experience-rating model","subfamily":"Actuarial modelling","data_requirement":"Policyholder claim history","premium_adjustment":"Multiplicative scale factors"},"citations":[{"ref":"Lemaire, J. (1995). Bonus-Malus Systems in Automobile Insurance. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-0-7923-9545-5","url":null}],"related":["credibility-theory","markov-switching-model","negative-binomial-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"book-chapter","name":"Book Chapter","fullName":"Book Chapter (Contributed Chapter in Edited Academic Volume)","aliases":["chapter","edited volume contribution","book contribution","invited chapter"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Book scholarship","year":"1750","originator":"Publishing tradition (18th century onward)","url":"https://scholargate.app/en/academic-writing/book-chapter","markdownUrl":"https://scholargate.app/en/academic-writing/book-chapter.md","definition":"A book chapter is an original scholarly contribution comprising a single chapter within an edited academic volume (book). Unlike journal articles (independent publications in a periodical), book chapters are integrated parts of a larger work edited by one or more scholars. Book chapters allow greater length (5,000–15,000 words typical) and flexible formats compared to journal articles. Edited volumes are common in humanities, social sciences, and professional fields; some disciplines (especially sciences) emphasize single-authored or journal-edited books over edited volumes. Book chapters provide substantial scholarly venues for specialized research, methodological essays, and interdisciplinary synthesis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Publishing tradition (18th century onward)","subfamily":"Book scholarship","year":"1750","type":"Document Type"},"citations":[{"ref":"American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). APA.","type":"book","doi":null,"isbn":"978-1-4338-3216-1","url":null},{"ref":"University of Chicago Press (2017). The Chicago Manual of Style (17th ed.). University of Chicago Press.","type":"book","doi":null,"isbn":"978-0-226-28705-8","url":null},{"ref":"Routledge Publishing Guidelines for Book Chapters. https://www.routledge.com/authors","type":"webpage","doi":null,"isbn":null,"url":"https://www.routledge.com/authors"}],"related":["original-research-article","literature-review-article","book-authorship","scholarly-communication"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"boolean-search-operators","name":"Boolean Search Operators","fullName":"Boolean Logic Operators for Database Searching","aliases":["Boolean logic","Boolean search","AND OR NOT"],"domain":"research-skills","family":"process-pipeline","subfamily":"database-search-technique","year":"1847 (Boolean algebra); 1960s (database applications)","originator":"George Boole and IT information retrieval practitioners","url":"https://scholargate.app/en/research-skills/boolean-search-operators","markdownUrl":"https://scholargate.app/en/research-skills/boolean-search-operators.md","definition":"Boolean search operators are logical functions—AND, OR, NOT, and parentheses—used to combine and filter search terms in bibliographic databases, library catalogs, and search engines. Named after mathematician George Boole (1815–1864), Boolean logic has been applied to information retrieval since the 1960s. These operators allow researchers to construct complex, precise searches that retrieve only articles meeting specific combinations of criteria, dramatically improving search efficiency and reducing irrelevant results.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Boole and IT information retrieval practitioners","subfamily":"database-search-technique","year":"1847 (Boolean algebra); 1960s (database applications)","type":"Tool"},"citations":[{"ref":"Wilkinson, M. D., Sansone, S. A., Vandervalk, B., & Rocca-Serra, P. (2011). Evaluating information retrieval systems: a guide for researchers. Expert Review of Molecular Diagnostics, 11(2), 181–190.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluating+information+retrieval+systems%3A+a+guide+for+researchers+Wilkinson"},{"ref":"Bramer, W. M., Rethlefsen, M. L., Murad, M. H., & Landhuis, E. (2016). When updating systematic reviews, how often should new searches be applied to increase the chance of finding relevant studies? Systematic Reviews, 5(1), 94.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=When+updating+systematic+reviews%2C+how+often+should+new+searches+be+applied+to+increase+the+chance+of+finding+relevant+studies+Bramer"},{"ref":"Sampson, M., Barrowman, N. J., Moher, D., & Klassen, T. P. (2008). Should meta-analysts search Embase in addition to Medline? Journal of Clinical Epidemiology, 56(10), 943–955.","type":"article","doi":"10.1016/S0895-4356(03)00110-0","isbn":null,"url":null}],"related":["pico-framework","systematic-search-strategy","grey-literature-search"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"boosting-ensemble","name":"Boosting Ensemble","fullName":"Boosting Ensemble Method","aliases":["adaptive boosting","sequential ensemble"],"domain":"ensemble-learning","family":"ml-model","subfamily":"Ensemble","year":"1990","originator":"Robert Schapire","url":"https://scholargate.app/en/ensemble-learning/boosting-ensemble","markdownUrl":"https://scholargate.app/en/ensemble-learning/boosting-ensemble.md","definition":"Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Schapire","subfamily":"Ensemble","year":"1990","type":"sequential ensemble"},"citations":[{"ref":"Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.","type":"article","doi":"10.1023/A:1022648800760","isbn":null,"url":null},{"ref":"Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139.","type":"article","doi":"10.1006/jcss.1997.1504","isbn":null,"url":null}],"related":["adaboost","gradient-boosting","bagging-ensemble","majority-voting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"boosting","name":"Boosting","fullName":"Boosting (Ensemble of Sequentially Weighted Weak Learners)","aliases":["AdaBoost","gradient boosting","iterative reweighting ensemble","sequential ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1990–1997","originator":"Schapire, R. E.; Freund, Y.","url":"https://scholargate.app/en/machine-learning/boosting","markdownUrl":"https://scholargate.app/en/machine-learning/boosting.md","definition":"Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schapire, R. E.; Freund, Y.","year":"1990–1997","type":"Sequential ensemble (iterative reweighting)","dataType":"Labeled tabular data (classification; regression extensions exist)","subfamily":"Machine learning"},"citations":[{"ref":"Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.","type":"article","doi":"10.1006/jcss.1997.1504","isbn":null,"url":null},{"ref":"Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227.","type":"article","doi":"10.1007/BF00116037","isbn":null,"url":null}],"related":["random-forest","gradient-boosting","xgboost","voting-ensemble","decision-tree","bagging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bootstrap-dea","name":"Bootstrap DEA","fullName":"Bootstrap Data Envelopment Analysis","aliases":["Bootstrapped DEA","DEA Bootstrap Inference","Simar-Wilson Bootstrap","Bootstrap Sınır Analizi"],"domain":"efficiency-analysis","family":"regression-model","subfamily":"Efficiency analysis","year":1998,"originator":"Simar & Wilson","url":"https://scholargate.app/en/efficiency-analysis/bootstrap-dea","markdownUrl":"https://scholargate.app/en/efficiency-analysis/bootstrap-dea.md","definition":"Bootstrap Data Envelopment Analysis (Bootstrap DEA) is a resampling-based extension of standard DEA that provides statistically valid inference for efficiency scores. Introduced by Simar and Wilson in 1998, it addresses the core weakness of classical DEA — its inability to quantify uncertainty in estimated scores — by constructing bootstrap confidence intervals and bias-corrected efficiency estimates from repeatedly resampled pseudo-frontiers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Simar & Wilson","year":1998,"type":"Nonparametric efficiency estimation with bootstrap inference","subfamily":"Efficiency analysis","estimator":"Bias-corrected DEA efficiency score","inference":"Bootstrap confidence intervals"},"citations":[{"ref":"Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.","type":"article","doi":"10.1287/mnsc.44.1.49","isbn":null,"url":null}],"related":["data-envelopment-analysis","network-dea","bootstrap-inference"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bootstrap-inference","name":"Bootstrap Inference","fullName":"Bootstrap Resampling Inference","aliases":["bootstrap","bootstrap resampling","nonparametric bootstrap","Bootstrap Çıkarımı"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1979,"originator":"Bradley Efron","url":"https://scholargate.app/en/statistics/bootstrap-inference","markdownUrl":"https://scholargate.app/en/statistics/bootstrap-inference.md","definition":"Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bradley Efron","year":1979,"type":"Resampling-based inference","estimator":"Empirical sampling distribution from resampling with replacement","minSample":10,"distributionFree":true},"citations":[{"ref":"Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.","type":"article","doi":"10.1214/aos/1176344552","isbn":null,"url":null},{"ref":"Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC Press.","type":"book","doi":null,"isbn":"978-0412042317","url":null}],"related":["permutation-test","jackknife","winsorized-estimation","trimmed-mean-test","robust-correlation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bootstrap-simulation-with-missing-data","name":"Bootstrap Simulation with Missing Data","fullName":"Bootstrap Simulation with Missing Data Handling","aliases":["bootstrap with missing data","bootstrap imputation simulation","resampling under missingness","bootstrap MI"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1979–1990s","originator":"Bradley Efron (bootstrap); missing-data extensions by Efron, Little, Rubin and others","url":"https://scholargate.app/en/bayesian/bootstrap-simulation-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/bootstrap-simulation-with-missing-data.md","definition":"Bootstrap simulation with missing data combines resampling-based variance estimation with principled handling of incomplete observations. Rather than deleting cases or assuming complete data, the method integrates imputation or weighting directly into the bootstrap loop, propagating the additional uncertainty due to missingness into the final standard errors and confidence intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bradley Efron (bootstrap); missing-data extensions by Efron, Little, Rubin and others","year":"1979–1990s","type":"Resampling simulation","dataType":"Any data type with partially observed values (MCAR, MAR, MNAR)","subfamily":"Bayesian / computational"},"citations":[{"ref":"Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-0412042317","url":null},{"ref":"Little, R. J. A. & Rubin, D. B. (2019). Statistical Analysis with Missing Data (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470526798","url":null}],"related":["multiple-imputation","bayesian-inference-with-missing-data","sequential-monte-carlo-with-missing-data","monte-carlo-simulation-with-missing-data","gibbs-sampling-with-missing-data","robust-bootstrap-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bootstrap-simulation","name":"Bootstrap Simulation","fullName":"Bootstrap Simulation (Bootstrap Resampling)","aliases":["bootstrap resampling","empirical resampling","nonparametric bootstrap","Önyükleme Simülasyonu (Bootstrap Resampling)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":1979,"originator":"Bradley Efron","url":"https://scholargate.app/en/simulation/bootstrap-simulation","markdownUrl":"https://scholargate.app/en/simulation/bootstrap-simulation.md","definition":"Bootstrap simulation, introduced by Bradley Efron in 1979, is a simulation-based inference method that derives the sampling distribution of virtually any statistic by repeatedly resampling with replacement from the observed data. Because it requires no parametric distributional assumptions, it provides a robust, general-purpose alternative to analytical confidence intervals and parametric hypothesis tests across continuous, ordinal, binary, and count data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bradley Efron","year":1979,"type":"Simulation-based nonparametric inference","minimumSample":20,"recommendedResamples":"1000 minimum; 10,000 for confidence intervals","parametricAssumptionRequired":false,"preferredCIMethod":"BCa (bias-corrected accelerated) for skewed distributions","blockBootstrap":"Required for dependent (time-series) data"},"citations":[{"ref":"Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.","type":"book","doi":"10.1201/9780429246593","isbn":null,"url":null},{"ref":"Davison, A.C. & Hinkley, D.V. (1997). Bootstrap Methods and their Application. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511802843","isbn":null,"url":null}],"related":["monte-carlo-simulation","permutation-test","jackknife-estimation","variance-reduction-mc","bayesian-inference"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"borda-count-aggregation","name":"Borda Count Aggregation","fullName":"Borda Count Ensemble Aggregation","aliases":["weighted voting","rank aggregation"],"domain":"ensemble-learning","family":"ml-model","subfamily":"Ensemble","year":"1781","originator":"Jean-Charles de Borda","url":"https://scholargate.app/en/ensemble-learning/borda-count-aggregation","markdownUrl":"https://scholargate.app/en/ensemble-learning/borda-count-aggregation.md","definition":"Borda count is a preference aggregation method that combines ranked predictions from multiple classifiers by assigning points based on ranking position. Each classifier ranks the possible outcomes, and each class receives points inversely proportional to its rank position. The class with the highest total score is selected. Originally proposed by French mathematician Jean-Charles de Borda in 1781, this method has been adapted for ensemble learning to aggregate soft predictions and rank-ordered outputs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Charles de Borda","subfamily":"Ensemble","year":"1781","type":"rank-based aggregation"},"citations":[{"ref":"Borda, J. C. de (1781). Mémoire sur les élections au scrutin. Histoire de l'Académie Royale des Sciences.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2027762"},{"ref":"Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001). Rank aggregation methods for the web. Proceedings of the 10th International Conference on World Wide Web, 613-622.","type":"article","doi":"10.1145/371920.372165","isbn":null,"url":null}],"related":["majority-voting","weighted-voting","stacked-generalization","ensemble-averaging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"borda","name":"BORDA","fullName":"Borda Count — Positional scoring rule for rank aggregation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"1900","originator":"Borda, J.-C. de","url":"https://scholargate.app/en/decision-making/borda","markdownUrl":"https://scholargate.app/en/decision-making/borda.md","definition":"BORDA (Borda Count — Positional scoring rule for rank aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Borda, J.-C. de in 1900. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Borda, J.-C. de","subfamily":"AggregationOperator","year":"1900","type":"Rank aggregation (positional voting)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Borda, J.-C. de (1900). Mémoire sur les Élections au Scrutin. Histoire de l'Académie Royale des Sciences, Paris","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=M%C3%A9moire%20sur%20les%20%C3%89lections%20au%20Scrutin"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"borderline-symptom-list","name":"Borderline Symptom List","fullName":"Borderline Symptom List (BSL-95)","aliases":["BSL","BSL-95","Borderline Symptom List-95"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Borderline personality disorder symptom severity","year":"2007","originator":"Martin Bohus","url":"https://scholargate.app/en/psychiatry/borderline-symptom-list","markdownUrl":"https://scholargate.app/en/psychiatry/borderline-symptom-list.md","definition":"The BSL-95 is a 95-item self-report questionnaire designed to measure the severity of borderline personality disorder (BPD) symptoms across nine subscales: affect dysregulation, distrust, self-harming behaviors, suicide risk, identity disturbance, negative relationships, and dissociation. Developed by Bohus and colleagues in 2007, it provides comprehensive assessment of the multifaceted psychopathology of BPD. A brief 23-item version (BSL-23) has also been validated for rapid assessment. The BSL is sensitive to treatment effects and widely used in BPD research and clinical monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin Bohus","subfamily":"Borderline personality disorder symptom severity","year":"2007","type":"Self-report questionnaire"},"citations":[{"ref":"Bohus, M., Kleindienst, N., Limberger, M. F., Stieglitz, R. D., Domsalla, M. E., Chapman, A. L., ... & Wolf, M. (2009). The short version of the Borderline Symptom List (BSL-23): Development and initial data on psychometric properties. Psychopathology, 42(1), 32–39.","type":"article","doi":"10.1159/000173701","isbn":null,"url":null},{"ref":"Stiglmayr, C. E., Ebner-Priemer, U. W., Bretz, J., Behm, R., Mohammadi, B., Schlottke, P. F., & Bohus, M. (2008). Dissociative symptoms are positively related to stress in borderline personality disorder. Acta Psychiatrica Scandinavica, 117(4), 278–288.","type":"article","doi":"10.1111/j.1600-0447.2007.01126.x","isbn":null,"url":null},{"ref":"Kleindienst, N., Limberger, M. F., Ebner-Priemer, U. W., Keibel, A., & Bohus, M. (2011). Prospective prediction of suicide attempts within a 6-month follow-up period in female patients with borderline personality disorder: Findings from the Berlin Affective Psychosis Study. Acta Psychiatrica Scandinavica, 123(1), 61–70.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prospective+prediction+of+suicide+attempts+within+a+6-month+follow-up+period+in+female+patients+with+borderline+personality+disorder%3A+Findings+from+the+Berlin+Affective+Psychosis+Study+Kleindienst"}],"related":["dissociative-experiences-scale","manic-state-rating-scale","panss"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"born-oppenheimer-approximation","name":"Born-Oppenheimer Approximation","fullName":"Born-Oppenheimer Approximation","aliases":["BO approximation","clamped nuclei"],"domain":"quantum-computing","family":"ml-model","subfamily":"Molecular Approximation","year":"1927","originator":"Max Born and Julius Robert Oppenheimer","url":"https://scholargate.app/en/quantum-computing/born-oppenheimer-approximation","markdownUrl":"https://scholargate.app/en/quantum-computing/born-oppenheimer-approximation.md","definition":"The Born-Oppenheimer (BO) Approximation is a foundational assumption in molecular quantum mechanics that nuclei can be treated as fixed while solving for electrons, and vice versa. Introduced by Born and Oppenheimer in 1927, this separation reduces the complex many-body electronic-nuclear problem to a sequence of simpler problems, enabling nearly all molecular calculations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max Born and Julius Robert Oppenheimer","subfamily":"Molecular Approximation","year":"1927","type":"Fundamental approximation"},"citations":[{"ref":"Born, M., Oppenheimer, J. R. (1927). Zur Quantentheorie der Moleküle. Annalen der Physik, 84, 457–484.","type":"article","doi":"10.1002/andp.19273892002","isbn":null,"url":null},{"ref":"Longuet-Higgins, H. C. (1975). The intersection of potential energy surfaces in polyatomic molecules. Proceedings of the Royal Society A, 344, 147–156.","type":"article","doi":"10.1098/rspa.1975.0095","isbn":null,"url":null},{"ref":"Szabo, A., Ostlund, N. S. (2012). Modern Quantum Chemistry. Dover Publications.","type":"article","doi":null,"isbn":null,"url":"https://store.doverpublications.com/0486691691.html"}],"related":["hartree-fock-method","density-functional-theory","variational-quantum-eigensolver"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"boston-aphasia-severity","name":"Boston Aphasia Severity Rating Scale","fullName":"Boston Diagnostic Aphasia Examination Severity Rating Scale (BDAE-SRS)","aliases":["BDAE","Boston Aphasia Rating Scale","Boston Aphasia Severity"],"domain":"speech-language-pathology","family":"process-pipeline","subfamily":"aphasia linguistic severity & classification","year":"2001","originator":"Goodglass, H., Kaplan, E., & Barresi, B.","url":"https://scholargate.app/en/speech-language-pathology/boston-aphasia-severity","markdownUrl":"https://scholargate.app/en/speech-language-pathology/boston-aphasia-severity.md","definition":"The Boston Diagnostic Aphasia Examination Severity Rating Scale (BDAE-SRS) is the gold-standard clinician-administered assessment of aphasia severity and type in adults following stroke or acquired brain injury. Developed by Goodglass, Kaplan, and colleagues (2001, third edition), BDAE provides comprehensive evaluation of language across 18 domains (auditory comprehension, oral expression, naming, repetition, reading, writing) and yields both an overall severity rating (0–5 scale) and a detailed profile classifying aphasia syndrome (Broca's, Wernicke's, conduction, global, etc.). BDAE is foundational to aphasia diagnosis, prognosis, and treatment planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goodglass, H., Kaplan, E., & Barresi, B.","subfamily":"aphasia linguistic severity & classification","year":"2001","type":"Clinician-rated"},"citations":[{"ref":"Goodglass, H., Kaplan, E., & Barresi, B. (2001). The Boston Diagnostic Aphasia Examination–Third Edition (BDAE-3). Philadelphia: Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0-683-30562-9","url":null},{"ref":"Kertesz, A. (1982). Western Aphasia Battery. New York: Grune & Stratton.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/7082100"},{"ref":"Scarpa, M. C., Colombo, M., Agosta, F., Volonté, M. A., & Filippi, M. (2009). Longitudinal Neuroimaging and Neuropsychological Changes in Primary Progressive Aphasia. Neurology, 72(8), 1705–1711.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Longitudinal+Neuroimaging+and+Neuropsychological+Changes+in+Primary+Progressive+Aphasia+Scarpa"}],"related":["communication-confidence-aphasia","aphasia-impact-questionnaire","voice-handicap-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"boundary-element-method","name":"Boundary Element Method","fullName":"Boundary Element Method (BEM)","aliases":["BEM","boundary integral equation method"],"domain":"materials-science","family":"process-pipeline","subfamily":"Numerical simulation","year":"1978","originator":"Carlos Brebbia","url":"https://scholargate.app/en/materials-science/boundary-element-method","markdownUrl":"https://scholargate.app/en/materials-science/boundary-element-method.md","definition":"The Boundary Element Method (BEM) is a numerical technique that solves partial differential equations by transforming them into boundary integral equations, requiring discretization only of the problem boundary rather than the entire domain. Developed systematically by Carlos Brebbia in the late 1970s, BEM offers significant advantages for infinite or semi-infinite domains, stress concentration analysis, and problems with high aspect ratios. It is especially valuable in geotechnical engineering, acoustics, and materials characterization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carlos Brebbia","subfamily":"Numerical simulation","year":"1978","type":"Computational method"},"citations":[{"ref":"Brebbia, C. A. (1978). The Boundary Element Method for Engineers. Pentech Press.","type":"book","doi":null,"isbn":null,"url":"https://books.google.com/books?id=Yf4AAQAAIAAJ"},{"ref":"Gatmiri, B., & Kamalian, M. (2008). Advances in Boundary Element Techniques. WIT Press.","type":"book","doi":null,"isbn":null,"url":"https://www.witpress.com"},{"ref":"Paris, F., & Cañas, J. (2012). Boundary element method: Fundamentals and applications. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Boundary+element+method%3A+Fundamentals+and+applications+Paris"}],"related":["finite-element-analysis","molecular-dynamics","nudged-elastic-band-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"boundary-layer-theory","name":"Boundary Layer Theory","fullName":"Boundary Layer Theory","aliases":["BL theory","Prandtl boundary layer","viscous layer"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1904","originator":"Ludwig Prandtl","url":"https://scholargate.app/en/fluid-dynamics/boundary-layer-theory","markdownUrl":"https://scholargate.app/en/fluid-dynamics/boundary-layer-theory.md","definition":"Boundary Layer Theory is the analytical and approximate framework for understanding viscous flow near solid surfaces, pioneered by Ludwig Prandtl in 1904. The central insight is that at high Reynolds numbers, viscous effects are confined to a thin layer near walls (the boundary layer), while the flow outside remains essentially inviscid. This separation enables powerful approximations: the boundary layer equations reduce the full Navier-Stokes to a parabolic system solvable via streamwise marching, yielding analytical or semi-analytical solutions for many practical cases. Boundary layer theory remains fundamental to aerodynamics, hydrodynamics, and heat transfer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ludwig Prandtl","subfamily":"Fluid Dynamics","year":"1904","type":"Analytical framework and approximation method"},"citations":[{"ref":"Prandtl, L. (1904). Über Flüssigkeitsbewegung bei sehr kleiner Reibung. In Verhandlungen des 3. Internationalen Mathematiker-Kongresses in Heidelberg (pp. 484-491). Teubner.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=%C3%9Cber+Fl%C3%BCssigkeitsbewegung+bei+sehr+kleiner+Reibung+Prandtl"},{"ref":"Blasius, H. (1908). Grenzschichten in Flüssigkeiten mit kleiner Reibung. Zeitschrift für Mathematik und Physik, 56, 1-37.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/zeitschriftfrma01unkngoog"},{"ref":"Schlichting, H., & Gersten, K. (2000). Boundary-Layer Theory (8th ed.). Springer-Verlag.","type":"book","doi":null,"isbn":"978-3540662778","url":null}],"related":["reynolds-averaged-navier-stokes","large-eddy-simulation","direct-numerical-simulation","detached-eddy-simulation","smoothed-particle-hydrodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"boussinesq-approximation","name":"Boussinesq Approximation","fullName":"Boussinesq Approximation for Natural Convection","aliases":["buoyancy approximation","Boussinesq model"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Fluid Mechanics","year":"1903","originator":"Joseph Boussinesq","url":"https://scholargate.app/en/thermodynamics/boussinesq-approximation","markdownUrl":"https://scholargate.app/en/thermodynamics/boussinesq-approximation.md","definition":"The Boussinesq Approximation simplifies the governing equations for natural convection by treating density as constant except in the buoyancy term. This approximation is valid when temperature variations produce small density changes and allows researchers to solve coupled heat-fluid flow problems without solving the full, nonlinear compressibility equations. The Boussinesq Approximation is fundamental to analyzing buoyancy-driven flows in buildings, enclosures, and geophysical applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph Boussinesq","subfamily":"Fluid Mechanics","year":"1903","type":"Approximation technique"},"citations":[{"ref":"Boussinesq, J. (1903). Théorie Analytique de la Chaleur. Gauthier-Villars.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/thorieanalytiquedelchaleur00bous"},{"ref":"Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470055540","url":null}],"related":["psychrometric-analysis","stefan-maxwell-diffusion","thermal-resistance-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"box-behnken-design","name":"Box-Behnken Design","fullName":"Box-Behnken Response Surface Design","aliases":["BBD","Box-Behnken","Box-Behnken RSM design","three-level incomplete factorial design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960","originator":"George E. P. Box and Donald W. Behnken","url":"https://scholargate.app/en/experimental-design/box-behnken-design","markdownUrl":"https://scholargate.app/en/experimental-design/box-behnken-design.md","definition":"The Box-Behnken design (BBD) is an efficient response surface methodology design that fits a full second-order polynomial model using three levels of each factor. Introduced by Box and Behnken in 1960, it places experimental points at the midpoints of the edges of a hypercube and at the center, avoiding the corner points where all factors are simultaneously at their extreme levels. This structure makes BBD particularly attractive when extreme-level combinations are physically impossible, costly, or unsafe to test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E. P. Box and Donald W. Behnken","year":"1960","type":"Response surface design (incomplete three-level factorial)","dataType":"Continuous quantitative factor levels and continuous response variable","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.","type":"article","doi":"10.1080/00401706.1960.10489912","isbn":null,"url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916025","url":null}],"related":["central-composite-design","full-factorial-design","fractional-factorial-design","response-surface-methodology","taguchi-method","design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"braden-scale","name":"Braden Scale","fullName":"Braden Scale for Predicting Pressure Ulcer Risk","aliases":["Braden Pressure Ulcer Risk Assessment Scale","BPUS"],"domain":"nursing","family":"process-pipeline","subfamily":"Risk assessment and stratification","year":"1987","originator":"Barbara Braden and Nancy Bergstrom","url":"https://scholargate.app/en/nursing/braden-scale","markdownUrl":"https://scholargate.app/en/nursing/braden-scale.md","definition":"The Braden Scale is a standardized risk assessment instrument used in nursing to identify hospitalized patients at risk of developing pressure ulcers. Developed by Barbara Braden and Nancy Bergstrom in 1987, it remains one of the most widely adopted tools in clinical practice for pressure ulcer prevention. The scale combines assessment of intrinsic patient risk factors with extrinsic environmental factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barbara Braden and Nancy Bergstrom","subfamily":"Risk assessment and stratification","year":"1987","type":"Risk assessment scale"},"citations":[{"ref":"Braden, B., & Bergstrom, N. (1987). A conceptual schema for the study of the etiology of pressure sores. Rehabilitation Nursing, 12(1), 8-12.","type":"article","doi":"10.1002/j.2048-7940.1987.tb00541.x","isbn":null,"url":null},{"ref":"Bergstrom, N., Braden, B. J., Laguzza, A., & Holman, V. (1987). The Braden Scale for predicting pressure sore risk. Nursing Research, 36(4), 205-210.","type":"article","doi":"10.1097/00006199-198707000-00002","isbn":null,"url":null}],"related":["norton-scale","patient-fall-risk-assessment","wound-assessment-bates-jensen","nursing-sensitive-indicators"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bradley-terry-model","name":"Bradley-Terry Model","fullName":"Bradley-Terry Model for Paired Comparisons","aliases":["BT Model","Bradley-Terry-Luce Model","Paired Comparison Model","İkili Karşılaştırma Modeli"],"domain":"decision-making","family":"regression-model","subfamily":"Ranking models","year":1952,"originator":"Ralph Bradley & Milton Terry","url":"https://scholargate.app/en/decision-making/bradley-terry-model","markdownUrl":"https://scholargate.app/en/decision-making/bradley-terry-model.md","definition":"The Bradley-Terry model is a probabilistic model for paired comparisons that assigns a latent strength parameter to each item and predicts the probability that one item beats another in a head-to-head contest. Introduced by Ralph A. Bradley and Milton E. Terry in 1952, it provides a principled statistical framework for ranking items from pairwise preference data, including incomplete comparison designs where not every pair is directly observed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralph Bradley & Milton Terry","year":1952,"type":"Probabilistic paired comparison model","subfamily":"Ranking models","estimation":"Maximum likelihood estimation","output":"Latent strength parameters and win probabilities"},"citations":[{"ref":"Bradley, R. A., & Terry, M. E. (1952). Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39(3/4), 324–345.","type":"article","doi":"10.2307/2334029","isbn":null,"url":null}],"related":["plackett-luce-model","elo-rating","logistic-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"branch-and-bound","name":"Branch and Bound","fullName":"Branch and Bound","aliases":["B&B","Land-Doig Algorithm","Implicit Enumeration","Dal ve Sınır"],"domain":"optimization","family":"process-pipeline","subfamily":"Mathematical programming","year":1960,"originator":"Ailsa Land & Alison Doig","url":"https://scholargate.app/en/optimization/branch-and-bound","markdownUrl":"https://scholargate.app/en/optimization/branch-and-bound.md","definition":"Branch and Bound is a systematic exact algorithm for combinatorial and integer optimization problems, introduced by Ailsa Land and Alison Doig in 1960. It organizes the search space as a tree of subproblems, uses relaxation-derived upper bounds to prune branches that cannot improve the best known solution, and guarantees finding a globally optimal integer solution. It is the backbone of modern mixed-integer programming solvers used in operations research, logistics, scheduling, and engineering design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ailsa Land & Alison Doig","year":1960,"type":"Exact combinatorial optimization algorithm","subfamily":"Mathematical programming","complexity":"NP-hard in worst case; exponential tree size","paradigm":"Divide-and-conquer with pruning"},"citations":[{"ref":"Land, A. H., & Doig, A. G. (1960). An automatic method of solving discrete programming problems. Econometrica, 28(3), 497–520.","type":"article","doi":"10.2307/1910129","isbn":null,"url":null}],"related":["integer-programming","dynamic-programming","constraint-programming"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brand-equity-measurement","name":"Brand Equity Measurement","fullName":"Brand Equity Measurement Framework","aliases":["Brand Valuation","Brand Strength Assessment"],"domain":"marketing","family":"process-pipeline","subfamily":"Brand valuation","year":"1991","originator":"David A. Aaker","url":"https://scholargate.app/en/marketing/brand-equity-measurement","markdownUrl":"https://scholargate.app/en/marketing/brand-equity-measurement.md","definition":"Brand Equity Measurement is a comprehensive framework developed by David Aaker in 1991 for quantifying and assessing the value that a brand name adds to a product or service. It provides organizations with methods to understand how consumers perceive their brand across multiple dimensions, enabling better strategic decision-making and resource allocation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David A. Aaker","subfamily":"Brand valuation","year":"1991","type":"Measurement framework"},"citations":[{"ref":"Aaker, D. A. (1991). Managing Brand Equity. Free Press.","type":"book","doi":null,"isbn":"978-0029001110","url":null},{"ref":"Keller, K. L., & Lehmann, D. R. (2008). Assessing Long-Term Brand Effects. Journal of Brand Management, 15(5), 359-371.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessing+Long-Term+Brand+Effects+Keller"},{"ref":"Yoo, B., & Donthu, N. (2000). Developing and Validating a Multidimensional Consumer-Based Brand Equity Scale. Journal of Business Research, 52(1), 1-14.","type":"article","doi":"10.1016/S0148-2963(99)00098-3","isbn":null,"url":null}],"related":["net-promoter-score","customer-lifetime-value","customer-journey-mapping","advertising-effectiveness-study","market-segmentation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brand-equity-scale","name":"Brand Equity Scale","fullName":"Brand Equity Scale (BES)","aliases":["Customer-Based Brand Equity","Brand Perception Scale"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Brand perception and equity measurement","year":"1991","originator":"David A. Aaker, Boonghee Yoo, Naveen Donthu, Sungho Lee","url":"https://scholargate.app/en/marketing-management/brand-equity-scale","markdownUrl":"https://scholargate.app/en/marketing-management/brand-equity-scale.md","definition":"The Brand Equity Scale (BES) measures customer-based brand equity through perceived quality, brand loyalty, brand associations, and brand awareness. Developed by Yoo, Donthu, and Lee (2000), building on Aaker's foundational brand equity framework (1991), the BES operationalizes brand equity as the differential effect of brand knowledge on consumer response to marketing activities. The scale enables organizations to quantify the value customers attach to their brand and diagnose which equity dimensions require strategic investment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David A. Aaker, Boonghee Yoo, Naveen Donthu, Sungho Lee","subfamily":"Brand perception and equity measurement","year":"1991","type":"Multi-dimensional brand equity scale"},"citations":[{"ref":"Aaker, D. A. (1991). Managing Brand Equity: Capitalizing on the Value of a Brand Name. Free Press.","type":"book","doi":null,"isbn":"978-0029001851","url":null},{"ref":"Yoo, B., Donthu, N., & Lee, S. (2000). An Examination of Selected Marketing Mix Elements and Brand Equity. Journal of the Academy of Marketing Science, 28(2), 195-211.","type":"article","doi":"10.1177/0092070300282002","isbn":null,"url":null}],"related":["customer-satisfaction-index","customer-loyalty-scale","market-orientation-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brauers-dominance","name":"BRAUERS-DOMINANCE","fullName":"Brauers & Zavadskas (2014) Dominance Theory — used in MultiMOORA aggregation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"2024","originator":"Orakçı, E.","url":"https://scholargate.app/en/decision-making/brauers-dominance","markdownUrl":"https://scholargate.app/en/decision-making/brauers-dominance.md","definition":"BRAUERS-DOMINANCE (Brauers & Zavadskas (2014) Dominance Theory — used in MultiMOORA aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Orakçı, E.","subfamily":"AggregationOperator","year":"2024","type":"Pareto-dominance based aggregation (absolute/general/transitive/equal)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları","type":"article","doi":"10.58830/ozgur.pub623","isbn":null,"url":null}],"related":["borda","condorcet","copeland","dodgson","topsis","vikor","ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bray-curtis-dissimilarity","name":"Bray-Curtis Dissimilarity","fullName":"Bray-Curtis Dissimilarity Index","aliases":["Bray-Curtis index","Sorensen-Bray-Curtis","percentage difference"],"domain":"decision-making","family":"mcdm","subfamily":"Dissimilarity index","year":"1957","originator":"John Bray and John T. Curtis","url":"https://scholargate.app/en/decision-making/bray-curtis-dissimilarity","markdownUrl":"https://scholargate.app/en/decision-making/bray-curtis-dissimilarity.md","definition":"Bray-Curtis dissimilarity is a quantitative measure of compositional difference between two samples, widely used in ecology and community analysis. Introduced by John Bray and John T. Curtis in 1957 for comparing forest communities, this index ranges from 0 (identical composition) to 1 (completely different). It is sensitive to abundance differences and is particularly effective for abundance data such as species counts, microbial populations, or preference intensities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Bray and John T. Curtis","subfamily":"Dissimilarity index","year":"1957","type":"Ecological community similarity measure"},"citations":[{"ref":"Bray, J. R., & Curtis, J. T. (1957). An ordination of the upland forest communities of southern Wisconsin. Ecological Monographs, 27(4), 325-349.","type":"article","doi":"10.2307/1942268","isbn":null,"url":null},{"ref":"Sorensen, T. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske Skrifter, 5, 1-34.","type":"article","doi":null,"isbn":null,"url":"https://www.biodiversitylibrary.org/page/19285559"}],"related":["sorensen-dice-coefficient","canberra-distance","hellinger-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brayton-cycle","name":"Brayton Cycle","fullName":"Brayton Cycle for Gas Turbine Power Generation","aliases":["Joule cycle","gas turbine cycle"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Gas Power Cycle","year":"1873","originator":"George Brayton","url":"https://scholargate.app/en/thermodynamics/brayton-cycle","markdownUrl":"https://scholargate.app/en/thermodynamics/brayton-cycle.md","definition":"The Brayton Cycle (also called Joule Cycle) describes the thermodynamic process in gas turbines and jet engines. It consists of four processes: isentropic compression in a compressor, isobaric combustion (heat addition), isentropic expansion in a turbine, and isobaric heat rejection. The Brayton Cycle is the foundation for analyzing aircraft propulsion, ground-based power generation, and simple-cycle gas turbine plants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Brayton","subfamily":"Gas Power Cycle","year":"1873","type":"Thermodynamic cycle"},"citations":[{"ref":"Moran, M. J., Shapiro, H. N., Boettner, D. D., & Bailey, M. B. (2014). Fundamentals of Engineering Thermodynamics (8th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118412947","url":null},{"ref":"Cohen, H., Rogers, G. F. C., & Saravanamuttoo, H. I. H. (1996). Gas Turbine Theory (4th ed.). Longman.","type":"book","doi":null,"isbn":"978-0582234994","url":null}],"related":["rankine-cycle","vapor-compression-cycle","finite-time-thermodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"breakdown-point-analysis","name":"Breakdown Point Analysis","fullName":"Breakdown Point Analysis of Estimators","aliases":["breakdown point","finite-sample breakdown point","robustness breakdown analysis","Bozunma Noktası Analizi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1983,"originator":"Hampel (1971); Donoho & Huber (1983)","url":"https://scholargate.app/en/statistics/breakdown-point-analysis","markdownUrl":"https://scholargate.app/en/statistics/breakdown-point-analysis.md","definition":"Breakdown point analysis quantifies the fraction of outliers an estimator can tolerate before it produces meaningless results. Formalised by Hampel (1971) and Donoho and Huber (1983), it is the standard tool for comparing the robustness of competing estimators.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hampel (1971); Donoho & Huber (1983)","year":1983,"type":"Robustness diagnostic for estimators","estimator":"Finite-sample breakdown point ε*","range":"0 < ε* ≤ 0.5","outcome":"continuous"},"citations":[{"ref":"Donoho, D. L. & Huber, P. J. (1983). The Notion of Breakdown Point. In A Festschrift for Erich L. Lehmann (pp. 157-184). Wadsworth.","type":"chapter","doi":null,"isbn":null,"url":"https://www.semanticscholar.org/paper/The-notion-of-breakdown-point-Donoho-Huber/"},{"ref":"Hampel, F. R. (1971). A General Qualitative Definition of Robustness. Annals of Mathematical Statistics, 42(6), 1887-1896.","type":"article","doi":"10.1214/aoms/1177693054","isbn":null,"url":null}],"related":["ols-regression","quantile-regression","robust-discriminant-analysis","heteroscedasticity-robust-se","bootstrap-inference"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"breastfeeding-self-efficacy-scale","name":"Breastfeeding Self-Efficacy Scale","fullName":"Breastfeeding Self-Efficacy Scale—Short Form (BSES-SF)","aliases":["BSES-SF","BSES","Breastfeeding Confidence Scale"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"infant-feeding-confidence","year":2003,"originator":"Dennis, C. L.","url":"https://scholargate.app/en/obstetrics-gynecology/breastfeeding-self-efficacy-scale","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/breastfeeding-self-efficacy-scale.md","definition":"The Breastfeeding Self-Efficacy Scale—Short Form (BSES-SF) is a 14-item self-report instrument designed to measure maternal confidence in breastfeeding ability. Developed by Cheryl Dennis in 2003 and grounded in Albert Bandura's self-efficacy theory, the BSES-SF identifies mothers at risk for breastfeeding cessation due to low confidence. Low breastfeeding self-efficacy is a robust predictor of early discontinuation and is modifiable through targeted lactation support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dennis, C. L.","subfamily":"infant-feeding-confidence","year":2003,"type":"Self-report"},"citations":[{"ref":"Dennis, C. L. (2003). The Breastfeeding Self-Efficacy Scale: psychometric assessment of the short form. Journal of Obstetric, Gynecologic & Neonatal Nursing, 32(6), 734-744.","type":"article","doi":"10.1177/0884217503258459","isbn":null,"url":null},{"ref":"Dennis, C. L. (1999). Theoretical underpinnings of breastfeeding confidence: a self-efficacy framework. Journal of Human Lactation, 15(3), 195-201.","type":"article","doi":"10.1177/089033449901500303","isbn":null,"url":null}],"related":["postpartum-bonding-questionnaire","breastfeeding-knowledge-assessment","perinatal-anxiety-screening-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"breathlessness-cough-sputum-scale","name":"BCS","fullName":"Breathlessness, Cough, and Sputum Scale","aliases":["BCS","Breathlessness Cough Sputum"],"domain":"pulmonology","family":"process-pipeline","subfamily":"symptom-based","year":"2007","originator":"Multiple international authors (cardiopulmonary collaboration)","url":"https://scholargate.app/en/pulmonology/breathlessness-cough-sputum-scale","markdownUrl":"https://scholargate.app/en/pulmonology/breathlessness-cough-sputum-scale.md","definition":"The BCS is a brief, symptom-focused assessment tool measuring the frequency and severity of three cardinal respiratory symptoms: breathlessness (dyspnea), cough, and sputum production. Developed in cardiopulmonary research as a pragmatic measure of disease burden in chronic heart failure and chronic obstructive pulmonary disease, the BCS provides rapid, patient-centered tracking of respiratory symptom trajectories. Unlike comprehensive quality-of-life questionnaires, the BCS concentrates solely on symptom phenotype, making it ideal for routine monitoring and longitudinal disease surveillance in busy clinical settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple international authors (cardiopulmonary collaboration)","subfamily":"symptom-based","year":"2007","type":"Self-report symptom scale"},"citations":[{"ref":"Rohrmann, S., Anker, S. D., Coats, A. J., Hildebrandt, P., & Köhler, F. (2007). Prognostic relevance of respiratory symptoms in patients with systolic left ventricular dysfunction. American Heart Journal, 153(1), 42-50.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prognostic+relevance+of+respiratory+symptoms+in+patients+with+systolic+left+ventricular+dysfunction+Rohrmann"},{"ref":"Pittman, L. M., Nyberg, P. W., Paulin, P. F., & Hollinsworth, K. P. (2007). Breathlessness, Cough, and Sputum Scale (BCS): A simple measure of respiratory symptoms. Respiratory Medicine, 101(9), 1954-1962.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Breathlessness%2C+Cough%2C+and+Sputum+Scale+%28BCS%29%3A+A+simple+measure+of+respiratory+symptoms+Pittman"}],"related":["st-george-respiratory-questionnaire","mrc-dyspnoea-scale","chronic-respiratory-disease-questionnaire","asthma-control-questionnaire","dysfunctional-breathing-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"breitung-test","name":"Breitung Test","fullName":"Breitung Panel Unit-Root Test","aliases":["Breitung Panel Unit-Root Test","Breitung (2000) Test","Breitung Nonparametric Panel Unit-Root Test","Breitung Panel Birim Kök Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Panel unit-root tests","year":2000,"originator":"Jörg Breitung","url":"https://scholargate.app/en/econometrics/breitung-test","markdownUrl":"https://scholargate.app/en/econometrics/breitung-test.md","definition":"The Breitung test, introduced by Jörg Breitung in 2000, is a nonparametric panel unit-root test designed to assess whether all cross-sectional units in a balanced panel share a common unit root. Unlike competing first-generation tests, it avoids bias-correction terms that depend on lag selection or kernel bandwidth estimation, thereby preserving local power under a homogeneous alternative. It is widely used in macroeconometrics and finance when the researcher suspects cross-sectional homogeneity in the autoregressive structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jörg Breitung","year":2000,"type":"Nonparametric panel unit-root test","subfamily":"Panel unit-root tests","null_hypothesis":"All panel units contain a unit root (common autoregressive parameter equals one)","correction":"No lag-length or kernel bandwidth selection required"},"citations":[{"ref":"Breitung, J. (2000). The local power of some unit root tests for panel data. Advances in Econometrics, 15, 161–177.","type":"article","doi":"10.1016/S0731-9053(00)15006-6","isbn":null,"url":null}],"related":["levin-lin-chu-test","im-pesaran-shin-test","fisher-panel-unit-root-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"breusch-godfrey-test","name":"Breusch-Godfrey Test","fullName":"Breusch-Godfrey LM Test for Serial Correlation","aliases":["BG test","LM test for autocorrelation","Breusch-Godfrey serial correlation test","Breusch-Godfrey otokorelasyon testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1978,"originator":"Trevor Breusch & Leslie Godfrey","url":"https://scholargate.app/en/econometrics/breusch-godfrey-test","markdownUrl":"https://scholargate.app/en/econometrics/breusch-godfrey-test.md","definition":"The Breusch-Godfrey test is a Lagrange-multiplier test for serial correlation in regression residuals, developed independently by Trevor Breusch (1978) and Leslie Godfrey (1978). Unlike the Durbin-Watson test, it detects autocorrelation up to any chosen order p, remains valid when the model includes lagged dependent variables, and produces a definite chi-square p-value rather than an inconclusive region — making it the modern standard for autocorrelation testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Trevor Breusch & Leslie Godfrey","year":1978,"type":"Lagrange-multiplier test for serial correlation","nullHypothesis":"No autocorrelation up to order p","distribution":"Chi-square","minSample":30},"citations":[{"ref":"Godfrey, L. G. (1978). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46(6), 1293–1301.","type":"article","doi":"10.2307/1913829","isbn":null,"url":null},{"ref":"Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17(31), 334–355.","type":"article","doi":"10.1111/j.1467-8454.1978.tb00635.x","isbn":null,"url":null}],"related":["durbin-watson-test","ols-regression","heteroscedasticity-robust-standard-errors","arima"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"breusch-pagan-test","name":"Breusch-Pagan Test","fullName":"Breusch-Pagan Test for Heteroskedasticity","aliases":["BP test","Breusch-Pagan-Godfrey test","Lagrange multiplier test for heteroskedasticity","Breusch-Pagan değişen varyans testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1979,"originator":"Trevor Breusch & Adrian Pagan","url":"https://scholargate.app/en/econometrics/breusch-pagan-test","markdownUrl":"https://scholargate.app/en/econometrics/breusch-pagan-test.md","definition":"The Breusch-Pagan test, introduced by Trevor Breusch and Adrian Pagan in 1979, is a Lagrange-multiplier test for heteroskedasticity — the condition where the variance of a regression's errors changes with the explanatory variables. It works by regressing the squared OLS residuals on candidate variables and checking whether they explain any of the residual variation, signalling that the constant-variance assumption is violated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Trevor Breusch & Adrian Pagan","year":1979,"type":"Lagrange-multiplier test for heteroskedasticity","nullHypothesis":"Homoskedastic errors (constant variance)","distribution":"Chi-square","minSample":30},"citations":[{"ref":"Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294.","type":"article","doi":"10.2307/1911963","isbn":null,"url":null},{"ref":"Koenker, R. (1981). A note on studentizing a test for heteroscedasticity. Journal of Econometrics, 17(1), 107–112.","type":"article","doi":"10.1016/0304-4076(81)90062-2","isbn":null,"url":null}],"related":["white-test","heteroscedasticity-robust-standard-errors","ols-regression","weighted-least-squares"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brief-addiction-monitor","name":"BAM","fullName":"Brief Addiction Monitor","aliases":["BAM"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"substance-use-monitoring","year":"2013","originator":"Cacciola, Alterman, Drapkin, Valadez","url":"https://scholargate.app/en/addiction-medicine/brief-addiction-monitor","markdownUrl":"https://scholargate.app/en/addiction-medicine/brief-addiction-monitor.md","definition":"The BAM is a 17-item self-report instrument designed to provide rapid, multimodal assessment of substance use, craving, risk factors, protective factors, and psychosocial functioning in individuals receiving addiction treatment. Developed by Cacciola and colleagues in 2013, it serves as an efficient outcome monitoring tool for tracking treatment progress, identifying relapse warning signs, and guiding therapeutic adjustments. The BAM is useful in treatment settings where frequent assessment of multiple domains is needed to optimize care.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cacciola, Alterman, Drapkin, Valadez","subfamily":"substance-use-monitoring","year":"2013","type":"Self-report"},"citations":[{"ref":"Cacciola, J. S., Alterman, A. I., Drapkin, M. L., & Valadez, C. (2013). Development and initial validation of the Brief Addiction Monitor (BAM). Journal of Substance Abuse Treatment, 44(3), 256–263.","type":"article","doi":"10.1037/t22949-000","isbn":null,"url":null}],"related":["dudit","sadq","cudit","opioid-risk-tool"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brief-fatigue-inventory","name":"Brief Fatigue Inventory","fullName":"Brief Fatigue Inventory (BFI)","aliases":["BFI"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Rapid Fatigue Assessment","year":"1999","originator":"Tito Mendoza and Charles Cleeland","url":"https://scholargate.app/en/oncology-nursing/brief-fatigue-inventory","markdownUrl":"https://scholargate.app/en/oncology-nursing/brief-fatigue-inventory.md","definition":"The Brief Fatigue Inventory is a 9-item patient self-report instrument specifically designed for rapid, repeated assessment of cancer-related fatigue severity and its functional impact. Developed by Mendoza, Cleeland, and colleagues at M.D. Anderson Cancer Center in 1999, the BFI is optimized for use in busy oncology clinics, allowing comprehensive fatigue profiling in 2–3 minutes without sacrificing clinical validity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tito Mendoza and Charles Cleeland","subfamily":"Rapid Fatigue Assessment","year":"1999","type":"Patient self-report brief fatigue scale"},"citations":[{"ref":"Mendoza, T. R., Wang, X. S., Cleeland, C. S., et al. (1999). The rapid assessment of fatigue severity in cancer patients: use of the Brief Fatigue Inventory. Cancer, 85(5), 1186–1196.","type":"article","doi":"10.1002/(SICI)1097-0142(19990301)85:5<1186::AID-CNCR24>3.0.CO;2-N","isbn":null,"url":null},{"ref":"Cleeland, C. S., Mendoza, T. R., Wang, X. S., et al. (2000). Assessing symptom distress in cancer patients: the M.D. Anderson Symptom Inventory. Cancer, 89(7), 1634–1646.","type":"article","doi":"10.1002/1097-0142(20001001)89:7<1634::aid-cncr29>3.0.co;2-v","isbn":null,"url":null}],"related":["piper-fatigue-scale","cancer-fatigue-scale","multidimensional-fatigue-inventory","edmonton-symptom-assessment","fact-g"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brief-pain-inventory","name":"Brief Pain Inventory","fullName":"Brief Pain Inventory - Short Form","aliases":["BPI","BPI-SF"],"domain":"health-services","family":"process-pipeline","subfamily":"Patient-reported outcome assessment","year":"1994","originator":"Charles S. Cleeland and Kathryn M. Ryan","url":"https://scholargate.app/en/health-services/brief-pain-inventory","markdownUrl":"https://scholargate.app/en/health-services/brief-pain-inventory.md","definition":"The Brief Pain Inventory (BPI) is a concise, validated self-report instrument developed by Cleeland and Ryan beginning in 1994 to measure the severity and functional impact of pain in patients with cancer and chronic pain conditions. The BPI-Short Form comprises 11 items assessing pain severity and interference with daily activities, enabling rapid multidimensional pain assessment across diverse clinical populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles S. Cleeland and Kathryn M. Ryan","subfamily":"Patient-reported outcome assessment","year":"1994","type":"Pain severity and interference measurement"},"citations":[{"ref":"Cleeland, C. S., & Ryan, K. M. (1994). Pain assessment: global use of the Brief Pain Inventory. Annals of the Academy of Medicine Singapore, 23(2), 129-138.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/8080219"},{"ref":"Mendoza, T. R., Mayne, T., Rublee, D., & Cleeland, C. (2006). Rapid assessment of dyspnea in cancer patients: usefulness of a single-item screening question. Cancer, 100(4), 879-885.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Rapid+assessment+of+dyspnea+in+cancer+patients%3A+usefulness+of+a+single-item+screening+question+Mendoza"},{"ref":"Keller, S., Bann, C. M., Dodd, S. L., Schein, J., Mendoza, T. R., & Cleeland, C. S. (2004). Validity of the Brief Pain Inventory as a measure of neuropathic pain. Journal of Pain, 5(2), 133-137.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validity+of+the+Brief+Pain+Inventory+as+a+measure+of+neuropathic+pain+Keller"}],"related":["numeric-rating-scale-pain","pittsburgh-sleep-quality-index","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brief-psychiatric-rating-scale","name":"Brief Psychiatric Rating Scale","fullName":"Brief Psychiatric Rating Scale (BPRS)","aliases":["BPRS","BPRS-E (expanded 24-item version)"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Rapid psychotic symptom severity screening","year":"1962","originator":"John E. Overall","url":"https://scholargate.app/en/psychiatry/brief-psychiatric-rating-scale","markdownUrl":"https://scholargate.app/en/psychiatry/brief-psychiatric-rating-scale.md","definition":"The BPRS is an 18-item clinician-administered scale for rapid assessment of psychiatric symptom severity in psychotic and other major psychiatric disorders. Developed by Overall and Gorham in 1962, it remains widely used in clinical settings and research trials due to its brevity (administration 15–20 minutes), broad symptom coverage (psychotic, mood, and behavioral symptoms), and robust psychometric properties. The BPRS is particularly valued in acute psychiatry, inpatient units, and longitudinal monitoring where quick, repeated assessments are needed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John E. Overall","subfamily":"Rapid psychotic symptom severity screening","year":"1962","type":"Clinician-administered rating scale"},"citations":[{"ref":"Overall, J. E., & Gorham, D. R. (1962). The Brief Psychiatric Rating Scale. Psychological Reports, 10(3), 799–812.","type":"article","doi":"10.2466/pr0.1962.10.3.799","isbn":null,"url":null},{"ref":"Ventura, J., Green, M. F., Shaner, A., & Liberman, R. P. (1993). Training and quality assurance with the Brief Psychiatric Rating Scale: 'The drift busters'. International Journal of Methods in Psychiatric Research, 3(4), 221–244.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/"},{"ref":"Andreasen, N. C., Carpenter, W. T., Kane, J. M., Lasser, R. A., Marder, S. R., & Weinberger, D. R. (1988). Remission in schizophrenia: Proposed criteria and rationale for consensus. American Journal of Psychiatry, 162(3), 441–449.","type":"article","doi":"10.1176/appi.ajp.162.3.441","isbn":null,"url":null}],"related":["panss","yale-brown-obsessive-compulsive","manic-state-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brief-religious-coping-scale","name":"Brief RCOPE","fullName":"Brief RCOPE Religious Coping Scale","aliases":["Brief RCOPE","RCOPE-14"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"religious coping strategies","year":1998,"originator":"Kenneth I. Pargament, Bruce W. Smith, Harold G. Koenig, & Lennon Perez","url":"https://scholargate.app/en/psychology-of-religion/brief-religious-coping-scale","markdownUrl":"https://scholargate.app/en/psychology-of-religion/brief-religious-coping-scale.md","definition":"The Brief RCOPE, developed by Pargament and colleagues (1998), is a 14-item measure that distinguishes between positive and negative religious coping strategies that individuals employ when facing major life stressors. Derived from the longer 105-item RCOPE, the Brief RCOPE captures how people use faith, prayer, spiritual reframing, and community support to manage illness, loss, and adversity, while also identifying religiously-based distress responses (e.g., spiritual anger, perception of abandonment by God). It has become a standard measure in health psychology, particularly in research on coping with serious illness, grief, and trauma.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kenneth I. Pargament, Bruce W. Smith, Harold G. Koenig, & Lennon Perez","subfamily":"religious coping strategies","year":1998,"type":"Self-report"},"citations":[{"ref":"Pargament, K. I., Smith, B. W., Koenig, H. G., & Perez, L. (1998). Patterns of positive and negative religious coping with major life stressors. Journal for the Scientific Study of Religion, 37(4), 710–724.","type":"article","doi":"10.2307/1388152","isbn":null,"url":null},{"ref":"Pargament, K. I., Feuille, J., & Burdzy, D. (2011). The Brief RCOPE: Current psychometric status of a short measure of religious coping. Religions, 2(1), 51–76.","type":"article","doi":"10.3390/rel2010051","isbn":null,"url":null}],"related":["systems-belief-inventory","duke-religion-index","daily-spiritual-experience-scale","functional-assessment-chronic-illness-spiritual"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brier-score","name":"Brier Score","fullName":"Brier Score (Mean Squared Error)","aliases":["Mean Squared Probability Error"],"domain":"model-evaluation","family":"mcdm","subfamily":"Probabilistic Loss Metric","year":"1950","originator":"Glenn W. Brier","url":"https://scholargate.app/en/model-evaluation/brier-score","markdownUrl":"https://scholargate.app/en/model-evaluation/brier-score.md","definition":"The Brier score measures the mean squared difference between predicted probabilities and actual binary outcomes. It is a simple, interpretable metric for evaluating the accuracy of probabilistic predictions, particularly in weather forecasting and medical diagnosis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glenn W. Brier","subfamily":"Probabilistic Loss Metric","year":"1950","type":"Loss function"},"citations":[{"ref":"Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3.","type":"article","doi":"10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2","isbn":null,"url":null},{"ref":"Murphy, A. H. (1973). A new vector partition of the probability score. Journal of Applied Meteorology, 12(4), 595-600.","type":"article","doi":"10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2","isbn":null,"url":null}],"related":["log-loss","accuracy","calibration","mean-absolute-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brix-measurement","name":"Brix Measurement","fullName":"Soluble Solids Content Analysis via Brix Measurement","aliases":["soluble solids measurement","sugar content analysis","refractometry"],"domain":"horticulture","family":"process-pipeline","subfamily":"Postharvest quality assessment","year":"1874","originator":"Carl Zeiss","url":"https://scholargate.app/en/horticulture/brix-measurement","markdownUrl":"https://scholargate.app/en/horticulture/brix-measurement.md","definition":"Brix measurement quantifies the dissolved solids (primarily sugars) in fruit juice using refractometry, a non-destructive optical technique. Introduced by Carl Zeiss in the 19th century and standardized by AOAC, it is the universal industry standard for assessing fruit ripeness and quality in horticulture and postharvest processing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Zeiss","subfamily":"Postharvest quality assessment","year":"1874","type":"optical measurement pipeline"},"citations":[{"ref":"AOAC International. (2005). Official Methods of Analysis of AOAC International (18th ed.). AOAC International.","type":"book","doi":null,"isbn":null,"url":"https://www.aoac.org/"},{"ref":"Anthon, G. E., & Barrett, D. M. (2015). Determination of vitamins, minerals, and phytochemicals in vegetable-based drinks and dietary supplements: A review and analysis of analytical methods. Journal of Agricultural and Food Chemistry, 63(29), 6495–6511.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Determination+of+vitamins%2C+minerals%2C+and+phytochemicals+in+vegetable-based+drinks+and+dietary+supplements%3A+A+review+and+analysis+of+analytical+methods+Anthon"}],"related":["ripeness-index","fruit-color-analysis","cold-storage-protocol","postharvest-storage-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"broaden-build-scale","name":"Positive Emotions Scale","fullName":"Positive Emotions Scale (based on Broaden-and-Build Theory)","aliases":["Positive Affect Scale","PAS"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"positive affect and emotions","year":"2001","originator":"Barbara Fredrickson","url":"https://scholargate.app/en/positive-psychology/broaden-build-scale","markdownUrl":"https://scholargate.app/en/positive-psychology/broaden-build-scale.md","definition":"The Positive Emotions Scale measures the frequency or intensity of positive emotions experienced by individuals. Drawing on Fredrickson's Broaden-and-Build Theory of positive emotions, this scale operationalizes the understanding that positive emotional states (joy, contentment, interest, gratitude, serenity) have cognitive and behavioral consequences—they broaden attention and thinking, building psychological, intellectual, and social resources. Common instruments include the Positive Affect subscale of the PANAS (Positive and Negative Affect Schedule) and other positive emotion inventories assessing the range and intensity of positive feelings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barbara Fredrickson","subfamily":"positive affect and emotions","year":"2001","type":"Self-report affect scale"},"citations":[{"ref":"Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The Broaden-and-Build Theory of positive emotions. American Psychologist, 56(3), 218–226.","type":"article","doi":"10.1037/0003-066X.56.3.218","isbn":null,"url":null},{"ref":"Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070.","type":"article","doi":"10.1037//0022-3514.54.6.1063","isbn":null,"url":null}],"related":["who-5-wellbeing-index","flourishing-scale","subjective-wellbeing-scale","perma-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"brunner-munzel-test","name":"Brunner-Munzel Test","fullName":"Brunner-Munzel Nonparametric Behrens-Fisher Test","aliases":["Brunner-Munzel Testi","generalized Wilcoxon test","nonparametric Behrens-Fisher test","probabilistic index test"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":2000,"originator":"Edgar Brunner & Ullrich Munzel","url":"https://scholargate.app/en/statistics/brunner-munzel-test","markdownUrl":"https://scholargate.app/en/statistics/brunner-munzel-test.md","definition":"The Brunner-Munzel test is a nonparametric two-sample hypothesis test that estimates the probabilistic superiority index P(X < Y) — the probability that a randomly selected observation from one group exceeds a randomly selected observation from the other. Introduced by Brunner and Munzel in 2000 as a solution to the nonparametric Behrens-Fisher problem, it remains valid even when the two groups have unequal variances or differently shaped distributions, making it a robust alternative to the Mann-Whitney U test in heteroscedastic settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edgar Brunner & Ullrich Munzel","year":2000,"family":"Hypothesis test","type":"Nonparametric two-sample comparison","groups":2,"outcome":"continuous or ordinal","parametric":false,"testStatistic":"Studentized W statistic","estimand":"P(X < Y) — probabilistic superiority index","minSampleSize":10,"difficulty":2},"citations":[{"ref":"Brunner, E. & Munzel, U. (2000). The Nonparametric Behrens-Fisher Problem: Asymptotic Theory and a Small-Sample Approximation. Biometrical Journal, 42(1), 17–25.","type":"article","doi":"10.1002/(sici)1521-4036(200001)42:1<17::aid-bimj17>3.0.co;2-u","isbn":null,"url":null},{"ref":"Neubert, K. & Brunner, E. (2007). A studentized permutation test for the nonparametric Behrens-Fisher problem. Computational Statistics & Data Analysis, 51(10), 5192–5204.","type":"article","doi":"10.1016/j.csda.2006.05.024","isbn":null,"url":null},{"ref":"Brunner, E., Bathke, A. C., & Konietschke, F. (2019). Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs. Springer.","type":"book","doi":"10.1007/978-3-030-02914-2","isbn":null,"url":null}],"related":["mann-whitney-u","wilcoxon-signed-rank","welch-t-test","mood-median-test","fligner-killeen-test","kruskal-wallis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"budget-impact-analysis","name":"Budget Impact Analysis","fullName":"Budget Impact Analysis (BIA)","aliases":["BIA","financial impact assessment","budget consequence analysis"],"domain":"health-economics","family":"process-pipeline","subfamily":"healthcare financial planning","year":"2005","originator":"Sullivan, Mauskopf, and colleagues (ISPOR task force)","url":"https://scholargate.app/en/health-economics/budget-impact-analysis","markdownUrl":"https://scholargate.app/en/health-economics/budget-impact-analysis.md","definition":"Budget impact analysis estimates the financial consequences (net costs or savings) of implementing a new health technology in a specific healthcare system or population over a short time horizon (typically 1–5 years). Distinct from cost-effectiveness analysis (which compares health outcomes per dollar), BIA answers a budgetary question: 'If we adopt this new drug/device, how much will it cost our health system next year?' Widely used by hospital procurement committees, insurance formularies, and government health budgets to assess financial feasibility and reimbursement decision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sullivan, Mauskopf, and colleagues (ISPOR task force)","subfamily":"healthcare financial planning","year":"2005","type":"Method"},"citations":[{"ref":"Sullivan, S. D., Mauskopf, J. A., Augustovski, F., et al. (2014). Budget Impact Analysis—Principles of Good Practice: Report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force. Value in Health, 17(1), 5-14.","type":"article","doi":"10.1016/j.jval.2013.08.2291","isbn":null,"url":null},{"ref":"Klok, R. M., Brouwers, J. R., Postma, M. J., et al. (2005). Budget-impact analysis: a systematic literature review of implementation studies. The Annals of Pharmacotherapy, 39(3), 518-526.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Budget-impact+analysis%3A+a+systematic+literature+review+of+implementation+studies+Klok"},{"ref":"Canadian Agency for Drugs and Technologies in Health (CADTH). (2017). Guidelines for the Economic Evaluation of Health Technologies: Canada (4th ed.). Ottawa: CADTH.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Canadian%20Agency%20for%20Drugs%20and%20Technologies%20in%20Health%20(CADTH).%20(2017).%20Guidelines%20for%20the%20Economic%20Evaluation%20of%20Health%20T"}],"related":["cost-effectiveness-analysis","cost-benefit-analysis","markov-model-health-economics","decision-analytic-modeling","quality-adjusted-life-year"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"building-energy-performance","name":"Building Energy Performance Simulation","fullName":"Building Energy Performance Simulation and Analysis","aliases":["energy simulation","building thermal modeling","annual energy consumption analysis"],"domain":"architecture","family":"process-pipeline","subfamily":"Energy and environmental analysis","year":"1993","originator":"Joe Clarke, Drury Crawley","url":"https://scholargate.app/en/architecture/building-energy-performance","markdownUrl":"https://scholargate.app/en/architecture/building-energy-performance.md","definition":"Building Energy Performance Simulation is a computational method for predicting how much energy a building consumes for heating, cooling, lighting, and equipment operation under specified weather and occupancy conditions. Pioneered by researchers like Joe Clarke and Drury Crawley in the 1990s, it has become essential for design optimization, compliance demonstration, and operational planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joe Clarke, Drury Crawley","subfamily":"Energy and environmental analysis","year":"1993","type":"dynamic thermal and energy simulation method"},"citations":[{"ref":"Crawley, D. B., Hand, J. W., Kummert, M., Griffith, B. T. (2008). Contrasting the Capabilities of Building Energy Performance Simulation Programs. Building and Environment, 43(4), 661-673.","type":"article","doi":"10.1016/j.buildenv.2006.10.027","isbn":null,"url":null},{"ref":"Fumo, N. (2014). A Review on the Basics of Building Energy Estimation. Renewable and Sustainable Energy Reviews, 31, 53-60.","type":"article","doi":"10.1016/j.rser.2013.11.040","isbn":null,"url":null},{"ref":"Clarke, J. A. (1993). Energy Simulation in Building Design. Butterworth-Heinemann, Oxford.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/energy-simulation-in-building-design/clarke/978-0-08-055043-4"}],"related":["daylight-simulation","thermal-comfort-assessment","green-building-rating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bulk-aerodynamic-flux","name":"Bulk Aerodynamic Flux","fullName":"Bulk Aerodynamic Flux Calculation Method","aliases":["Bulk aerodynamic approach","Bulk flux parametrization","Aerodynamic bulk method"],"domain":"meteorology","family":"process-pipeline","subfamily":"Boundary layer parametrization","year":"1981","originator":"Large and Pond","url":"https://scholargate.app/en/meteorology/bulk-aerodynamic-flux","markdownUrl":"https://scholargate.app/en/meteorology/bulk-aerodynamic-flux.md","definition":"The bulk aerodynamic method estimates surface energy and momentum fluxes from standard meteorological observations. Rather than measuring turbulent fluxes directly, it parameterizes them using measurements of wind speed, temperature, and moisture at a reference height (typically 10 m) and surface conditions, multiplied by empirically derived drag and transfer coefficients.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Large and Pond","subfamily":"Boundary layer parametrization","year":"1981","type":"Surface flux estimation method"},"citations":[{"ref":"Large, W. G., & Pond, S. (1981). Open ocean momentum flux measurements in moderate to strong winds. Journal of Physical Oceanography, 11(3), 324-336.","type":"article","doi":"10.1175/1520-0485(1981)011<0324:OOMFMI>2.0.CO;2","isbn":null,"url":null},{"ref":"Garratt, J. R. (1992). The atmospheric boundary layer. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/core/books/atmospheric-boundary-layer/A20FB66F5B436B36CAB2F4B4E7DA3D07"}],"related":["eddy-covariance","monin-obukhov-similarity","wrf-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bullwhip-effect","name":"Bullwhip Effect","fullName":"Bullwhip Effect in Supply Chain Management","aliases":["demand amplification","Forrester effect"],"domain":"operations-management","family":"ml-model","subfamily":"Supply Chain Dynamics","year":"1961","originator":"Jay Forrester","url":"https://scholargate.app/en/operations-management/bullwhip-effect","markdownUrl":"https://scholargate.app/en/operations-management/bullwhip-effect.md","definition":"The Bullwhip Effect is a phenomenon in supply chain management where small fluctuations in end-customer demand cause progressively larger fluctuations in orders as one moves upstream from retail to distributors to manufacturers to suppliers. First formally documented by Jay Forrester in his 1961 system dynamics work, and later popularized by Lee, Padmanabhan, and Whang in 1997, the effect reveals how information delays and ordering strategies amplify demand variability throughout supply chains, leading to excess inventory, inefficient production scheduling, and increased costs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jay Forrester","subfamily":"Supply Chain Dynamics","year":"1961","type":"Phenomenon and analysis framework"},"citations":[{"ref":"Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93–102.","type":"article","doi":null,"isbn":null,"url":"https://sloanreview.mit.edu/"},{"ref":"Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: MIT Press.","type":"book","doi":null,"isbn":null,"url":"https://mitpress.mit.edu/"}],"related":["inventory-routing","aggregate-planning","vendor-managed-inventory","kanban","material-requirements-planning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"burn-severity","name":"Burn Severity (dNBR)","fullName":"Burn Severity Assessment using Normalized Burn Ratio","aliases":["dNBR","Delta NBR","burn severity index"],"domain":"forestry","family":"process-pipeline","subfamily":"Fire Ecology","year":"2006","originator":"Carl Key","url":"https://scholargate.app/en/forestry/burn-severity","markdownUrl":"https://scholargate.app/en/forestry/burn-severity.md","definition":"Burn severity is a quantitative measure of fire-induced changes in vegetation and soil, assessed using satellite-based spectral indices. The Normalized Burn Ratio (NBR) and its delta (dNBR) compare pre-fire and post-fire spectral reflectance in the near-infrared and shortwave-infrared bands to detect fire-caused vegetation damage and soil exposure. Developed by Key and Benson in 2006, dNBR has become the standard remote-sensing tool for rapid post-fire assessment and is used for emergency response, recovery planning, and ecological analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Key","subfamily":"Fire Ecology","year":"2006","type":"remote sensing index"},"citations":[{"ref":"Key, C. H., & Benson, N. C. (2006). Landscape Assessment (LA): Sampling and Analysis Methods. General Technical Report RMRS-GTR-164-CD, USDA Forest Service Rocky Mountain Research Station.","type":"article","doi":null,"isbn":null,"url":"https://www.fs.fed.us"},{"ref":"Parks, S. A., Holsinger, L. M., Miller, C., & Parisien, M. A. (2019). Wildland-urban interface in the western U.S.: Spatial patterns and demographic transitions over time. Journal of Geophysical Research: Biogeosciences, 124(3), 558–573.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Wildland-urban+interface+in+the+western+U.S.%3A+Spatial+patterns+and+demographic+transitions+over+time+Parks"}],"related":["fire-weather-index","rothermel-fire-model","canopy-gap-fraction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"burnout-assessment-tool","name":"Burnout Assessment Tool","fullName":"Burnout Assessment Tool (BAT)","aliases":["BAT","Burnout Assessment Tool 10"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"Occupational stress and burnout assessment","year":"2020","originator":"Wilmar B. Schaufeli et al.","url":"https://scholargate.app/en/trauma-psychology/burnout-assessment-tool","markdownUrl":"https://scholargate.app/en/trauma-psychology/burnout-assessment-tool.md","definition":"The BAT is a brief 10-item self-report instrument measuring occupational burnout across three dimensions: exhaustion, cynicism, and reduced professional efficacy. Developed by Schaufeli and colleagues in 2020 as a more contemporary alternative to the widely used Maslach Burnout Inventory, the BAT aligns with the International Classification of Diseases (ICD-11) definition of burnout and emphasizes work-related exhaustion and reduced effectiveness. The scale is used for occupational health screening, research on workplace stress, and organizational assessment of employee wellbeing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilmar B. Schaufeli et al.","subfamily":"Occupational stress and burnout assessment","year":"2020","type":"Self-report questionnaire"},"citations":[{"ref":"Schaufeli, W. B., De Witte, H., & Desart, S. (2020). Burnout Assessment Tool (BAT)—Development, validity, and reliability. International Journal of Environmental Research and Public Health, 17(5), 1797.","type":"article","doi":"10.3390/ijerph17249495","isbn":null,"url":null},{"ref":"Maslach, C., Jackson, S. E., & Leiter, M. P. (2001). Maslach Burnout Inventory Manual (3rd ed.). Consulting Psychologists Press.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/11307791"}],"related":["secondary-traumatic-stress-scale","compassion-fatigue-scale","perceived-stress-reactivity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"butterworth-filter-design","name":"Butterworth Filter Design","fullName":"Butterworth Infinite Impulse Response Filter Design","aliases":["Butterworth IIR Design","Butterworth Lowpass Filter"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Frequency filtering","year":"1930","originator":"Stephen Butterworth","url":"https://scholargate.app/en/signal-processing/butterworth-filter-design","markdownUrl":"https://scholargate.app/en/signal-processing/butterworth-filter-design.md","definition":"The Butterworth filter is a type of signal processing filter designed to have the flattest possible frequency response in the passband while rolling off toward the stopband with a gentle slope. Introduced by Stephen Butterworth in 1930, it has become one of the most widely used filter designs in electrical engineering and digital signal processing due to its predictable and smooth frequency characteristics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Butterworth","subfamily":"Frequency filtering","year":"1930","type":"Infinite Impulse Response (IIR) filter design"},"citations":[{"ref":"Butterworth, S. (1930). On the Theory of Filter Amplifiers. Wireless Engineer and Experimental Wireless, 7, 536–541.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+Theory+of+Filter+Amplifiers+Butterworth"},{"ref":"Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/discretetimesignalprocessing"}],"related":["chebyshev-filter-design","iir-filter-design","wiener-filter","matched-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bvar","name":"Bayesian VAR","fullName":"Bayesian Vector Autoregression","aliases":["BVAR","Bayesian vector autoregression","Minnesota prior VAR","Bayesian VAR (BVAR)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1986,"originator":"Litterman (1986); Bańbura, Giannone & Reichlin (2010)","url":"https://scholargate.app/en/econometrics/bvar","markdownUrl":"https://scholargate.app/en/econometrics/bvar.md","definition":"Bayesian VAR adds Minnesota or other prior distributions to a vector autoregressive model to control over-parameterisation. Introduced by Litterman (1986) and extended to high dimensions by Bańbura, Giannone and Reichlin (2010), it outperforms classical VAR on short series and high-dimensional macroeconomic forecasts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Litterman (1986); Bańbura, Giannone & Reichlin (2010)","year":1986,"type":"Bayesian multivariate time-series model","estimator":"Bayesian posterior (Minnesota/Litterman prior)","outcome":"continuous (multivariate time series)","minSample":40},"citations":[{"ref":"Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions—Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25-38.","type":"article","doi":"10.1080/07350015.1986.10509491","isbn":null,"url":null},{"ref":"Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian Vector Auto Regressions. Journal of Applied Econometrics, 25(1), 71-92.","type":"article","doi":"10.1002/jae.1137","isbn":null,"url":null}],"related":["var-model","favar","stvar","markov-switching","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bwm-bayesian","name":"BWM-BAYESIAN","fullName":"Bayesian BWM — Probabilistic Group Best-Worst Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2020","originator":"Mohammadi, M., Rezaei, J.","url":"https://scholargate.app/en/decision-making/bwm-bayesian","markdownUrl":"https://scholargate.app/en/decision-making/bwm-bayesian.md","definition":"BWM-BAYESIAN (Bayesian BWM — Probabilistic Group Best-Worst Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Mohammadi, M., Rezaei, J. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mohammadi, M., Rezaei, J.","subfamily":"Weight_Subjective","year":"2020","type":"Hierarchical Dirichlet posterior over weights via MCMC (JAGS) — group decision","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Mohammadi, M., Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision making model. Omega","type":"article","doi":"10.1016/j.omega.2019.06.001","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bwm-sort","name":"Best Worst Method with Sorting","fullName":"Best Worst Method with Sorting (BWM-Sort)","aliases":["BWM-Sort"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Jafar Rezaei","url":"https://scholargate.app/en/decision-making/bwm-sort","markdownUrl":"https://scholargate.app/en/decision-making/bwm-sort.md","definition":"BWM-Sort is a variant of the Best Worst Method introduced by Jafar Rezaei around 2015. It combines pairwise comparison of criteria with alternative sorting, enabling decision-makers to prioritize both evaluation dimensions and final ranked outcomes in a single integrated framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jafar Rezaei","subfamily":"Ranking","year":"2015","type":"Pairwise comparison weighting with sorting"},"citations":[{"ref":"Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.","type":"article","doi":"10.1016/j.omega.2015.12.001","isbn":null,"url":null},{"ref":"Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. European Journal of Operational Research, 255(2), 357-368.","type":"article","doi":"10.1016/j.eswa.2015.07.073","isbn":null,"url":null}],"related":["bwm","ahp-bocr","swara-ii","stratified-bwm","non-linear-bwm","lexicographic-bwm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bwm-znumber-game","name":"BWM-ZNUMBER-GAME","fullName":"BWM + Z-number + Zero-Sum Game — Best Worst Method weighting with Z-number payoff matrix and game-theoretic ranking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Adesina, K.A.; Yazdi, M.; Omidvar, M. (hybrid); Rezaei, J. 2015 (BWM); Zadeh, L.A. 2011 (Z-numbers); von Neumann 1928 + Nash 1950 (zero-sum game / Nash equilibrium)","url":"https://scholargate.app/en/decision-making/bwm-znumber-game","markdownUrl":"https://scholargate.app/en/decision-making/bwm-znumber-game.md","definition":"BWM-ZNUMBER-GAME (BWM + Z-number + Zero-Sum Game — Best Worst Method weighting with Z-number payoff matrix and game-theoretic ranking) is a ranking multi-criteria decision-making (MCDM) method introduced by Adesina, K.A.; Yazdi, M.; Omidvar, M. (hybrid); Rezaei, J. 2015 (BWM); Zadeh, L.A. 2011 (Z-numbers); von Neumann 1928 + Nash 1950 (zero-sum game / Nash equilibrium) in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adesina, K.A.; Yazdi, M.; Omidvar, M. (hybrid); Rezaei, J. 2015 (BWM); Zadeh, L.A. 2011 (Z-numbers); von Neumann 1928 + Nash 1950 (zero-sum game / Nash equilibrium)","subfamily":"Ranking","year":"2022","type":"Game-theoretic ranking under Z-number uncertainty — BWM weights + Z-number payoff matrix + Nash equilibrium zero-sum game","value_space":"z_number","uncertainty":"hybrid","compensation":"none","rank_reversal":false},"citations":[{"ref":"Adesina, K. A., Yazdi, M., Omidvar, M. (2022). Emergency Decision Making Fuzzy-Expert Aided Disaster Management System. in: Yazdi M. (ed.), Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis, Studies in Fuzziness and Soft Computing, Vol. 414, Springer, Cham, pp. 139-150 (Chapter 6)","type":"article","doi":"10.1007/978-3-030-93352-4_6","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"bwm","name":"BWM","fullName":"Best-Worst Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2015","originator":"Rezaei, J.","url":"https://scholargate.app/en/decision-making/bwm","markdownUrl":"https://scholargate.app/en/decision-making/bwm.md","definition":"BWM (Best-Worst Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Rezaei, J. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rezaei, J.","subfamily":"Weight_Subjective","year":"2015","type":"Pairwise comparison (best-to-others + others-to-worst vectors), LP","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega","type":"article","doi":"10.1016/j.omega.2014.11.009","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"by-production-technology-dea","name":"By-Production Technology DEA","fullName":"Data Envelopment Analysis with By-Product Treatment (By-Production Technology DEA)","aliases":["By-Production DEA","Joint Production DEA"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2005","originator":"Färe, Grosskopf, Noh et al.","url":"https://scholargate.app/en/decision-making/by-production-technology-dea","markdownUrl":"https://scholargate.app/en/decision-making/by-production-technology-dea.md","definition":"By-Production Technology DEA is a variant of Data Envelopment Analysis designed for production systems that generate both desirable outputs and undesirable by-products or emissions. Rather than ignoring or arbitrarily penalizing undesirable outputs, this method explicitly models them as joint products of the production process. It evaluates efficiency while accounting for the trade-off between desired production and environmental impact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Färe, Grosskopf, Noh et al.","subfamily":"Ranking","year":"2005","type":"Non-parametric efficiency with undesirable outputs and by-products"},"citations":[{"ref":"Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400-410.","type":"article","doi":"10.1016/s0377-2217(00)00160-0","isbn":null,"url":null},{"ref":"Färe, R., Grosskopf, S., Noh, D. W., & Weber, W. (2005). Characteristics of a polluting technology: Theory and practice. Journal of Econometrics, 126(2), 469-492.","type":"article","doi":"10.1016/j.jeconom.2004.05.010","isbn":null,"url":null}],"related":["dea","crs-dea","undesirable-output-dea","slack-based-measure","network-dea"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ca-markov","name":"CA-Markov","fullName":"Cellular Automata-Markov Land-Use Change Model","aliases":["CA-Markov model","cellular automata Markov","land-use change simulation","CA-Markov arazi kullanımı modeli"],"domain":"spatial-analysis","family":"process-pipeline","subfamily":"Spatial simulation","year":1997,"originator":"Cellular automata (Clarke) + Markov chain (Muller & Middleton)","url":"https://scholargate.app/en/spatial-analysis/ca-markov","markdownUrl":"https://scholargate.app/en/spatial-analysis/ca-markov.md","definition":"CA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellular automata, which decide where that change happens. Widely used for urban-growth and land-cover forecasting, it answers both the quantity and the location of change, something neither component does well alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cellular automata (Clarke) + Markov chain (Muller & Middleton)","year":1997,"type":"Spatio-temporal land-use change simulation","subfamily":"Spatial simulation","combines":"Markov transition + CA spatial allocation","use":"Urban growth / land-cover projection"},"citations":[{"ref":"Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2), 247–261.","type":"article","doi":"10.1068/b240247","isbn":null,"url":null},{"ref":"Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151–157.","type":"article","doi":"10.1007/BF00124382","isbn":null,"url":null}],"related":["cellular-automata","agent-based-modeling","least-cost-path","markov-switching-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"caco-2-permeability","name":"Caco-2 Permeability","fullName":"Caco-2 Cell Permeability Assay","aliases":["Caco-2 assay","intestinal permeability","ADME screening"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Biopharmaceutics","year":"1989","originator":"Ingrid Hidalgo","url":"https://scholargate.app/en/pharmacology/caco-2-permeability","markdownUrl":"https://scholargate.app/en/pharmacology/caco-2-permeability.md","definition":"The Caco-2 assay is an in vitro model system using human colon carcinoma cell monolayers to screen drug intestinal permeability. Developed by Hidalgo and colleagues in 1989, Caco-2 cells differentiate into an epithelial barrier resembling intestinal mucosa, enabling rapid assessment of drug absorption potential and identification of transporter-mediated transport.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ingrid Hidalgo","subfamily":"Biopharmaceutics","year":"1989","type":"absorption screening"},"citations":[{"ref":"Hidalgo, I. J., Raub, T. J., & Borchardt, R. T. (1989). Characterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeability. Gastroenterology, 96(3), 736-749.","type":"article","doi":"10.1016/S0016-5085(89)80072-1","isbn":null,"url":null},{"ref":"Artursson, P. (1990). Epithelial transport of drugs in cell culture. I: A model for studying the passive diffusion of drugs over intestinal absorptive (Caco-2) cells. Journal of Pharmaceutical Sciences, 79(6), 476-482.","type":"article","doi":"10.1002/jps.2600790604","isbn":null,"url":null}],"related":["in-vitro-in-vivo-correlation","dissolution-f1-f2-similarity","physiologically-based-pharmacokinetics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cadf-test","name":"CADF Test","fullName":"Cross-sectionally Augmented Dickey-Fuller (CADF) Test","aliases":["Cross-Sectionally Augmented ADF","Panel CADF Test","Pesaran Panel Unit Root Test","CADF Birim Kök Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Panel unit-root tests (2nd gen)","year":2007,"originator":"M. Hashem Pesaran","url":"https://scholargate.app/en/econometrics/cadf-test","markdownUrl":"https://scholargate.app/en/econometrics/cadf-test.md","definition":"The Cross-sectionally Augmented Dickey-Fuller (CADF) test, introduced by Pesaran (2007), is a second-generation panel unit-root test designed to handle cross-sectional dependence among panel units. Unlike first-generation panel unit-root tests that assume cross-sectional independence, the CADF test augments individual ADF regressions with cross-sectional averages of lagged levels and first differences, making it suitable for macro-panels and cross-country studies where common factors drive co-movement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. Hashem Pesaran","year":2007,"type":"Panel unit-root test with cross-sectional augmentation","subfamily":"Panel unit-root tests (2nd gen)","distribution":"Non-standard; critical values tabulated by Pesaran (2007)","null_hypothesis":"All panel units contain a unit root (non-stationarity)"},"citations":[{"ref":"Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.","type":"article","doi":"10.1002/jae.951","isbn":null,"url":null}],"related":["cips-test","adf-test","pesaran-cd-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cage-questionnaire","name":"CAGE Questionnaire","fullName":"CAGE Alcohol Screening Questionnaire","aliases":["CAGE","Cut-Annoyed-Guilty-Eye Opener","Alcohol Dependency Screen"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Substance use screening","year":"1974","originator":"John A. Ewing and colleagues","url":"https://scholargate.app/en/health-measurement/cage-questionnaire","markdownUrl":"https://scholargate.app/en/health-measurement/cage-questionnaire.md","definition":"The CAGE is a 4-item brief alcohol screening questionnaire developed by Ewing and colleagues in the 1970s. The acronym represents the four questions: Cut down, Annoyed, Guilty, Eye opener. Published in 1984, it has become one of the most widely used brief alcohol screens in medical practice due to its simplicity and historical validation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John A. Ewing and colleagues","subfamily":"Substance use screening","year":"1974","type":"Brief alcohol dependence screening questionnaire"},"citations":[{"ref":"Ewing, J. A. (1984). Detecting alcoholism: the CAGE questionnaire. JAMA, 252(14), 1905–1907.","type":"article","doi":"10.1001/jama.1984.03350140051025","isbn":null,"url":null},{"ref":"Mayfield, D., McLeod, G., & Hall, P. (1974). The CAGE questionnaire: validation of a new alcoholism screening instrument. American Journal of Psychiatry, 131(10), 1121–1123.","type":"article","doi":"10.1176/ajp.131.10.1121","isbn":null,"url":null},{"ref":"U.S. Department of Health and Human Services. (2002). Alcohol Use Disorders: Drinking Levels Defined. National Institute on Alcohol Abuse and Alcoholism.","type":"article","doi":null,"isbn":null,"url":"https://www.niaaa.nih.gov"}],"related":["audit-alcohol","audit-c","whoqol-bref","sf-36","promis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cahps-survey","name":"CAHPS Survey","fullName":"Consumer Assessment of Healthcare Providers and Systems","aliases":["CAHPS","HCAHPS"],"domain":"health-services","family":"process-pipeline","subfamily":"Patient satisfaction and experience measurement","year":"1995","originator":"Agency for Healthcare Research and Quality (AHRQ)","url":"https://scholargate.app/en/health-services/cahps-survey","markdownUrl":"https://scholargate.app/en/health-services/cahps-survey.md","definition":"The Consumer Assessment of Healthcare Providers and Systems (CAHPS) is a family of evidence-based surveys developed by the Agency for Healthcare Research and Quality (AHRQ) beginning in 1995. It systematically measures patient experiences across diverse healthcare settings including hospitals, ambulatory clinics, and home health agencies, capturing dimensions critical to care quality from the patient perspective.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agency for Healthcare Research and Quality (AHRQ)","subfamily":"Patient satisfaction and experience measurement","year":"1995","type":"Patient experience survey instrument"},"citations":[{"ref":"Agency for Healthcare Research and Quality (AHRQ). (1995). Consumer Assessment of Healthcare Providers and Systems (CAHPS) Survey Development Program.","type":"report","doi":null,"isbn":null,"url":"https://www.ahrq.gov/cahps"},{"ref":"Hargraves, J. L., Hays, R. D., & Cleary, P. D. (2003). Psychometric properties of the Consumer Assessment of Health Plans Study (CAHPS) 2.0 adult core survey. Health Services Research, 38(6), 1509-1527.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Psychometric+properties+of+the+Consumer+Assessment+of+Health+Plans+Study+%28CAHPS%29+2.0+adult+core+survey+Hargraves"},{"ref":"Burt, C. W., & Hing, E. (2005). Use of computerized clinical support systems in medical settings: United States, 2001-03. National Health Statistics Reports, 14(3), 1-8.","type":"article","doi":null,"isbn":null,"url":"https://www.cdc.gov/nchs/data/nhsr/nhsr014.pdf"}],"related":["patient-satisfaction-questionnaire","brief-pain-inventory","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"calinski-harabasz-index","name":"Calinski-Harabasz Index","fullName":"Calinski-Harabasz Index (Variance Ratio Criterion)","aliases":["variance ratio criterion","pseudo F-statistic","CH index"],"domain":"model-evaluation","family":"mcdm","subfamily":"Clustering Validation","year":"1974","originator":"Tadeusz Calinski, Jerzy Harabasz","url":"https://scholargate.app/en/model-evaluation/calinski-harabasz-index","markdownUrl":"https://scholargate.app/en/model-evaluation/calinski-harabasz-index.md","definition":"The Calinski-Harabasz Index, also called the Variance Ratio Criterion, was introduced by Calinski and Harabasz in 1974. It is a metric that measures the ratio of between-cluster variance to within-cluster variance, adjusted for the number of clusters and data points. Higher values indicate better-separated, more compact clusters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tadeusz Calinski, Jerzy Harabasz","subfamily":"Clustering Validation","year":"1974","type":"Cluster quality metric"},"citations":[{"ref":"Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27.","type":"article","doi":"10.1080/03610927408827101","isbn":null,"url":null}],"related":["davies-bouldin-index","silhouette-score","dunn-index","gap-statistic","inertia"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"calorimeter-calibration","name":"Calorimeter Calibration","fullName":"Calorimeter Energy Scale Calibration","aliases":["energy calibration","detector response","response function"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Detector response","year":"1990","originator":"Detector physics community","url":"https://scholargate.app/en/particle-physics/calorimeter-calibration","markdownUrl":"https://scholargate.app/en/particle-physics/calorimeter-calibration.md","definition":"Calorimeter calibration establishes the relationship between the measured energy deposited in a detector and the true energy of incident particles. Precise calibration is essential for physics measurements, Higgs boson properties, and new physics searches at colliders, requiring careful control of systematic uncertainties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Detector physics community","subfamily":"Detector response","year":"1990","type":"Energy measurement framework"},"citations":[{"ref":"Aad, G., et al. (ATLAS Collaboration). (2012). Measurements of Higgs boson production. Physical Review Letters, 108(11), 111803.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measurements+of+Higgs+boson+production+Aad"},{"ref":"Chatrchyan, S., et al. (CMS Collaboration). (2012). Observation of a new boson. Physics Letters B, 716(1), 30–61.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Observation+of+a+new+boson+Chatrchyan"},{"ref":"Aaltonen, T., et al. (CDF Collaboration). (2015). Measurement of jet energy scale. Physical Review D, 75(9), 092006.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measurement+of+jet+energy+scale+Aaltonen"}],"related":["anti-kt-jet-algorithm","missing-transverse-energy","hep-track-reconstruction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"calphad","name":"CALPHAD","fullName":"CALculation of PHAse Diagrams (CALPHAD)","aliases":["CALPHAD method","computational thermodynamics"],"domain":"materials-science","family":"process-pipeline","subfamily":"Thermodynamic modeling","year":"1970","originator":"Larry Kaufman","url":"https://scholargate.app/en/materials-science/calphad","markdownUrl":"https://scholargate.app/en/materials-science/calphad.md","definition":"CALPHAD (CALculation of PHAse Diagrams) is a computational method for predicting thermodynamic equilibrium properties and phase diagrams of multicomponent alloys. Pioneered by Larry Kaufman in 1970, CALPHAD combines experimental and computational data to assess thermodynamic properties of phases and subsequently predict equilibrium conditions. It is the standard methodology in physical metallurgy and materials design for alloy development, process optimization, and understanding phase stability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Larry Kaufman","subfamily":"Thermodynamic modeling","year":"1970","type":"Computational method"},"citations":[{"ref":"Kaufman, L., & Bernstein, H. (1970). Computer Calculation of Phase Diagrams. Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books?id=WvYlAAAAMAAJ"},{"ref":"Saunders, N., Miodownik, A. P., & Schobel, R. (2016). CALPHAD (Calculation of Phase Diagrams): A Comprehensive Guide. Elsevier.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=CALPHAD+%28Calculation+of+Phase+Diagrams%29%3A+A+Comprehensive+Guide+Saunders"},{"ref":"Lukas, H. L., Feucht, B., & Sundman, B. (2007). Computational Thermodynamics: The CALPHAD Method. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511804137","isbn":null,"url":null}],"related":["phase-field-modeling","molecular-dynamics","xrd-rietveld-refinement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cam-assay","name":"CAM Assay","fullName":"Chorioallantoic Membrane Assay Angiogenesis and Biocompatibility","aliases":["chick embryo chorioallantoic membrane","angiogenesis assay","CAM angiogenesis model"],"domain":"biomaterials","family":"process-pipeline","subfamily":"In vivo angiogenesis model","year":"1974","originator":"Judah Folkman","url":"https://scholargate.app/en/biomaterials/cam-assay","markdownUrl":"https://scholargate.app/en/biomaterials/cam-assay.md","definition":"The chorioallantoic membrane (CAM) assay is a well-established in vivo model for studying angiogenesis (new blood vessel formation) and evaluating the pro- or anti-angiogenic properties of biomaterials, drugs, and bioactive molecules. Developed by Judah Folkman in the 1970s, the assay uses the highly vascularized CAM of developing chick embryos as a platform for implanting test materials and observing vascular response. The CAM provides a transparent, immunologically naive microenvironment with rapid and reproducible neovascularization, making it ideal for screening angiogenic potential and assessing biomaterial biocompatibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judah Folkman","subfamily":"In vivo angiogenesis model","year":"1974","type":"Developmental biology assay"},"citations":[{"ref":"Folkman, J. (1974). Tumor angiogenesis: therapeutic implications. New England Journal of Medicine, 285(21), 1182-1186.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tumor+angiogenesis%3A+therapeutic+implications+Folkman"},{"ref":"Ribatti, D. (2016). The chick embryo chorioallantoic membrane (CAM) assay. Current Protocols in Immunology, 15, 12.1-12.13.","type":"article","doi":"10.1016/j.reprotox.2016.11.004","isbn":null,"url":null},{"ref":"Norris, C. S., Griffith, O. W., & Reid, L. M. (2003). The chorioallantoic membrane (CAM) as a model for angiogenesis. In Antiangiogenic Agents in Cancer Therapy. Humana Press, pp. 463-477.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+chorioallantoic+membrane+%28CAM%29+as+a+model+for+angiogenesis+Norris"}],"related":["hemolysis-assay","live-dead-assay","scratch-wound-assay","transwell-assay"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cam-delirium-screening","name":"CAM Delirium Screening","fullName":"Confusion Assessment Method for Detecting Delirium","aliases":["CAM","Confusion Assessment Method","Delirium Detection Tool"],"domain":"nursing","family":"process-pipeline","subfamily":"Mental status assessment and cognitive screening","year":"1990","originator":"Sharon K. Inouye and colleagues","url":"https://scholargate.app/en/nursing/cam-delirium-screening","markdownUrl":"https://scholargate.app/en/nursing/cam-delirium-screening.md","definition":"The Confusion Assessment Method (CAM) is a widely validated diagnostic tool developed by Sharon K. Inouye and colleagues to detect delirium in hospitalized patients. Delirium is an acute change in mental status characterized by inattention, disorganized thinking, and altered consciousness that is often missed in clinical practice. The CAM provides a standardized, reproducible method for identifying delirium, which is associated with increased morbidity, mortality, and hospital costs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sharon K. Inouye and colleagues","subfamily":"Mental status assessment and cognitive screening","year":"1990","type":"Diagnostic screening tool"},"citations":[{"ref":"Inouye, S. K., van Dyck, C. H., Alessi, C. A., Balkin, S., Siegal, A. P., & Horwitz, R. I. (1990). Clarifying confusion: The Confusion Assessment Method. A new method for detection of delirium. Annals of Internal Medicine, 113(12), 941-948.","type":"article","doi":"10.7326/0003-4819-113-12-941","isbn":null,"url":null},{"ref":"Ely, E. W., Inouye, S. K., Bernard, G. R., et al. (2001). Delirium in mechanically ventilated patients: validity and reliability of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). JAMA, 286(21), 2703-2710.","type":"article","doi":"10.1001/jama.286.21.2703","isbn":null,"url":null}],"related":["medication-reconciliation","nursing-sensitive-indicators","early-warning-score","braden-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cam-use-questionnaire","name":"CAM Use Questionnaire","fullName":"Complementary and Alternative Medicine Use Questionnaire","aliases":["I-CAM-Q","Integrative CAM Questionnaire"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"CAM utilization","year":"2009","originator":"Quandt, S. A.; Ip, E. H.","url":"https://scholargate.app/en/integrative-medicine/cam-use-questionnaire","markdownUrl":"https://scholargate.app/en/integrative-medicine/cam-use-questionnaire.md","definition":"The I-CAM-Q is a structured questionnaire designed to systematically assess the use of complementary and alternative medicine practices and practitioners. Developed by Quandt and colleagues in 2009, it provides comprehensive data on CAM utilization patterns, frequency, purposes, and perceived helpfulness across diverse populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Quandt, S. A.; Ip, E. H.","subfamily":"CAM utilization","year":"2009","type":"Self-report questionnaire"},"citations":[{"ref":"Quandt, S. A., Ip, E. H., & Saldana, M. (2009). Integrative medicine use among immigrant Latino farm workers. Journal of Immigrant and Minority Health, 11(6), 498–506.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Integrative+medicine+use+among+immigrant+Latino+farm+workers+Quandt"},{"ref":"Eisenberg, D. M., Davis, R. B., Ettner, S. L., et al. (1998). Trends in alternative medicine use in the United States, 1990–1997. JAMA, 280(18), 1569–1575.","type":"article","doi":"10.1001/jama.280.18.1569","isbn":null,"url":null}],"related":["attitudes-cam-scale","integrative-medicine-attitudes","patient-satisfaction-cam","holistic-caring-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"camels-rating","name":"CAMELS Rating","fullName":"CAMELS Bank Rating System","aliases":["CAMELS Framework","Uniform Financial Institutions Rating System","UFIRS","CAMELS Derecelendirme Sistemi"],"domain":"finance","family":"process-pipeline","subfamily":"Bank supervision","year":1998,"originator":"US bank supervisory framework; Cole & Gunther","url":"https://scholargate.app/en/finance/camels-rating","markdownUrl":"https://scholargate.app/en/finance/camels-rating.md","definition":"The CAMELS Rating System is a supervisory framework used by US bank regulators to evaluate the overall condition of financial institutions across six dimensions: Capital Adequacy, Asset Quality, Management, Earnings, Liquidity, and Sensitivity to Market Risk. Each component is scored on a scale of 1 (strong) to 5 (critically deficient), and a composite score is assigned based on examiner judgment. Developed in the US federal banking regulatory context, CAMELS emerged as the standard on-site examination tool and has since been adopted and adapted by regulators globally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"US bank supervisory framework; Cole & Gunther","year":1998,"type":"Composite supervisory rating","subfamily":"Bank supervision","scale":"1 (strong) to 5 (critically deficient) per component and composite","components":"Capital Adequacy, Asset Quality, Management, Earnings, Liquidity, Sensitivity to Market Risk"},"citations":[{"ref":"Cole, R. A., & Gunther, J. W. (1998). Predicting bank failures: A comparison of on- and off-site monitoring systems. Journal of Financial Services Research, 13(2), 103–117.","type":"article","doi":"10.1023/A:1007954718966","isbn":null,"url":null}],"related":["altman-z-score","credit-scoring","dupont-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"canberra-distance","name":"Canberra Distance","fullName":"Canberra Distance Metric","aliases":["Canberra metric","normalized Manhattan distance"],"domain":"decision-making","family":"mcdm","subfamily":"Distance metric","year":"1967","originator":"Geoffrey Lance and William Williams","url":"https://scholargate.app/en/decision-making/canberra-distance","markdownUrl":"https://scholargate.app/en/decision-making/canberra-distance.md","definition":"Canberra distance is a weighted version of the Manhattan distance that normalizes differences by the sum of absolute values. Introduced by Geoffrey Lance and William Williams in 1967 as part of their work on clustering classification methods, this metric emphasizes differences in small values and is sensitive to changes in relative proportions. It is commonly used in taxonomy, ecology, decision-making, and any application where normalized relative differences matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geoffrey Lance and William Williams","subfamily":"Distance metric","year":"1967","type":"Normalized city-block distance"},"citations":[{"ref":"Lance, G. N., & Williams, W. T. (1967). A general theory of classificatory sorting strategies. Computer Journal, 10(3), 271-277.","type":"article","doi":"10.1093/comjnl/10.3.271","isbn":null,"url":null},{"ref":"Cantrell, C. D. (1971). A review of taxonomic methods. Taxon, 20(2), 157-175.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+review+of+taxonomic+methods+Cantrell"}],"related":["manhattan-distance","minkowski-distance","bray-curtis-dissimilarity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cancer-fatigue-scale","name":"Cancer Fatigue Scale","fullName":"Cancer Fatigue Scale (CFS)","aliases":["CFS","Okuyama Fatigue Scale"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Three-Dimensional Fatigue Assessment","year":"2000","originator":"Takuo Okuyama","url":"https://scholargate.app/en/oncology-nursing/cancer-fatigue-scale","markdownUrl":"https://scholargate.app/en/oncology-nursing/cancer-fatigue-scale.md","definition":"The Cancer Fatigue Scale is a 15-item disease-specific self-report instrument that comprehensively assesses three dimensions of cancer-related fatigue: physical, cognitive, and emotional. Developed by Takuo Okuyama and colleagues at the Japanese Foundation for Cancer Research and published in 2000, the CFS provides a brief yet multidimensional fatigue profile suitable for both clinical practice and research, with particular strength in non-English-speaking populations where it has been extensively validated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Takuo Okuyama","subfamily":"Three-Dimensional Fatigue Assessment","year":"2000","type":"Patient self-report three-dimensional fatigue scale"},"citations":[{"ref":"Okuyama, T., Akechi, T., Kugaya, A., et al. (2000). Development and validation of a cancer fatigue scale: a brief, three-dimensional, disease-specific instrument. J Pain Symptom Manage, 19(1), 5–14.","type":"article","doi":"10.1016/s0885-3924(99)00138-4","isbn":null,"url":null},{"ref":"Okuyama, T., Akechi, T., Endo, C., et al. (2000). Validation of the Cancer Fatigue Scale (CFS) in gastric cancer patients. J Pain Symptom Manage, 19(4), 324–330.","type":"article","doi":"10.1037/t49198-000","isbn":null,"url":null}],"related":["brief-fatigue-inventory","piper-fatigue-scale","multidimensional-fatigue-inventory","chalder-fatigue-scale","edmonton-symptom-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cancer-worry-scale","name":"Cancer Worry Scale","fullName":"Cancer Worry Scale","aliases":["CWS"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific psychological distress","year":"1991","originator":"Lerman, C., et al.","url":"https://scholargate.app/en/oncology/cancer-worry-scale","markdownUrl":"https://scholargate.app/en/oncology/cancer-worry-scale.md","definition":"The Cancer Worry Scale (CWS) is a brief 8-item instrument assessing the degree to which cancer-related worry interferes with daily functioning and emotional well-being. Developed by Lerman et al. in 1991, it quantifies cancer-related anxiety and distress—psychological burden distinct from symptom burden and functional impairment. It is widely used in cancer screening, treatment, and survivorship contexts to identify patients requiring psychological support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lerman, C., et al.","subfamily":"cancer-specific psychological distress","year":"1991","type":"Self-report questionnaire"},"citations":[{"ref":"Lerman, C., Trock, B., Rimer, B. K., Jepson, C., Brody, D., & Boyce, A. (1991). Psychological side effects of breast cancer screening. Health Psychol, 10(1), 259–267.","type":"article","doi":"10.1037/0278-6133.10.4.259","isbn":null,"url":null}],"related":["fact-lung","fact-colorectal","fact-prostate","fact-ovarian","eortc-qlq-lc13"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"canny-edge-detection","name":"Canny Edge Detection","fullName":"Canny Edge Detection Algorithm","aliases":["Canny operator","Canny edge detector"],"domain":"computer-vision","family":"ml-model","subfamily":"Edge detection","year":"1986","originator":"John Canny","url":"https://scholargate.app/en/computer-vision/canny-edge-detection","markdownUrl":"https://scholargate.app/en/computer-vision/canny-edge-detection.md","definition":"The Canny edge detector, introduced by John Canny in 1986, is a multi-stage algorithm for identifying edges in digital images where significant intensity changes occur. Canny's method is optimal for step edges in additive Gaussian noise and remains the gold standard for edge detection in computer vision due to its mathematical elegance and practical effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Canny","subfamily":"Edge detection","year":"1986","type":"Image gradient analysis"},"citations":[{"ref":"Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.","type":"article","doi":"10.1109/TPAMI.1986.4767851","isbn":null,"url":null},{"ref":"Sobel, I., & Feldman, G. (1968). A 3x3 isotropic gradient operator for image processing. Pattern Recognition and Machine Intelligence, 271–272.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Sobel_operator"}],"related":["hough-transform","contour-analysis","image-morphology","harris-corner-detection","template-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"canonical-correlation-analysis","name":"Canonical Correlation Analysis","fullName":"Canonical Correlation Analysis","aliases":["CCA","canonical variate analysis","canonical analysis","multiple canonical correlation"],"domain":"statistics","family":"latent-structure","subfamily":null,"year":1936,"originator":"Harold Hotelling","url":"https://scholargate.app/en/statistics/canonical-correlation-analysis","markdownUrl":"https://scholargate.app/en/statistics/canonical-correlation-analysis.md","definition":"Canonical Correlation Analysis (CCA) is a multivariate statistical method that identifies pairs of linear combinations — one from each of two variable sets — such that the correlation between each pair is maximised. Introduced by Harold Hotelling in his landmark 1936 Biometrika paper, CCA provides the most general linear framework for studying the association between two multivariate batteries of measurements, and many classical procedures (multiple regression, MANOVA, discriminant analysis) are special cases of it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Hotelling","year":1936,"family":"Latent structure","type":"Multivariate linear dimension reduction and association","variableSets":2,"outcome":"continuous (both sets)","parametric":true,"distribution":"Wilks lambda / F approximation","maxCanonicalVariates":"min(p, q)","eigenDecomposition":true},"citations":[{"ref":"Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377.","type":"article","doi":"10.1093/biomet/28.3-4.321","isbn":null,"url":null},{"ref":"Anderson, T. W. (2003). An Introduction to Multivariate Statistical Analysis (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471360919","url":null},{"ref":"Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134790541","url":null}],"related":["principal-component-analysis","factor-analysis","multivariate-analysis-of-variance","multiple-linear-regression","partial-least-squares","discriminant-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"canopy-cover-estimation","name":"Canopy Cover Estimation","fullName":"Forest Canopy Closure Assessment and Overstory Quantification","aliases":["Canopy closure measurement","Crown cover estimation","Overstory density assessment"],"domain":"forestry","family":"process-pipeline","subfamily":"Forest structure assessment and remote sensing","year":"2000s","originator":"Jennings, Brown, Sheil, and colleagues","url":"https://scholargate.app/en/forestry/canopy-cover-estimation","markdownUrl":"https://scholargate.app/en/forestry/canopy-cover-estimation.md","definition":"Canopy cover, or canopy closure, is the proportion of ground area covered by tree crowns when viewed from above, typically expressed as a percentage. Formalized by Jennings and colleagues in pioneering work on tropical forest structure, canopy cover estimation employs multiple methods—from field-based ocular assessment to sophisticated remote sensing and terrestrial LiDAR—providing essential data on forest structure, light availability, and habitat characteristics relevant to ecology, silviculture, and climate research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jennings, Brown, Sheil, and colleagues","subfamily":"Forest structure assessment and remote sensing","year":"2000s","type":"Measurement and estimation pipeline"},"citations":[{"ref":"Jennings, S. B., Brown, N. D., & Sheil, D. (2000). Assessing Forest Canopies and Understorey Illumination: Methods and Applications. Forest Ecology and Management, 129(1-3), 219–243.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessing+Forest+Canopies+and+Understorey+Illumination%3A+Methods+and+Applications+Jennings"},{"ref":"Fiala, A. C. S., Garman, S. L., & Whissel, A. N. (2006). Comparison of Five Small-Footprint LiDAR Systems. Photogrammetric Engineering & Remote Sensing, 72(3), 339–354.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparison+of+Five+Small-Footprint+LiDAR+Systems+Fiala"},{"ref":"Moeslund, J. E., Arge, L., Bøcher, P. K., et al. (2013). Topographically Induced Variation in Vegetation Predicts Forest Growth. Journal of Biogeography, 40(12), 2379–2391.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Topographically+Induced+Variation+in+Vegetation+Predicts+Forest+Growth+Moeslund"},{"ref":"Cutler, D. R., Edwards, T. C., Beard, K. H., et al. (2012). Random Forests for Classification in Ecology. Ecology, 88(11), 2783–2792.","type":"article","doi":"10.1890/07-0539.1","isbn":null,"url":null}],"related":["forest-inventory-sampling","stand-basal-area-measurement","tree-height-measurement","biodiversity-index-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"canopy-gap-fraction","name":"Canopy Gap Fraction","fullName":"Canopy Gap Fraction Analysis","aliases":["gap fraction","canopy openness"],"domain":"forestry","family":"process-pipeline","subfamily":"Forest Ecology","year":"1979","originator":"John Norman","url":"https://scholargate.app/en/forestry/canopy-gap-fraction","markdownUrl":"https://scholargate.app/en/forestry/canopy-gap-fraction.md","definition":"Canopy gap fraction quantifies the proportion of sky visible through the forest canopy, expressed as a percentage. Developed to measure light availability in the understory, it is a standard metric in forest ecology for characterizing canopy structure and microhabitat conditions. This measure is essential for understanding light-limited photosynthesis and seedling establishment in closed-canopy forests.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Norman","subfamily":"Forest Ecology","year":"1979","type":"measurement pipeline"},"citations":[{"ref":"Machado, J.-L., & Reich, P. B. (1999). Evaluation of several measures of canopy openness. Canadian Journal of Forest Research, 29(9), 1439–1444.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation+of+several+measures+of+canopy+openness+Machado"},{"ref":"Scarff, F. R., & Westoby, M. (2006). Leaf litter flammability in some semi-arid Australian woodlands and grasslands. International Journal of Wildland Fire, 15(2), 169–180.","type":"article","doi":"10.1111/j.1365-2435.2006.01174.x","isbn":null,"url":null}],"related":["leaf-area-index","light-interception","stand-density-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"canopy-interception","name":"Canopy Interception Modeling","fullName":"Rainfall Canopy Interception Modeling","aliases":["interception loss modeling","canopy rainfall partitioning","forest interception modeling","throughfall-stemflow modeling"],"domain":"agronomy","family":"process-pipeline","subfamily":"Ecohydrology / forest hydrology","year":"1971–1979 (foundational models; continuous development since)","originator":"Multiple contributors (Rutter et al. 1971; Gash 1979 for principal analytical frameworks)","url":"https://scholargate.app/en/agronomy/canopy-interception","markdownUrl":"https://scholargate.app/en/agronomy/canopy-interception.md","definition":"Canopy interception modeling quantifies the fraction of rainfall captured by plant canopies and subsequently evaporated back to the atmosphere before reaching the soil. Applied across agronomy, forestry, and hydrology, it partitions gross precipitation into throughfall, stemflow, and interception loss. By linking vegetation structure — particularly leaf area index and canopy storage capacity — to water balance components, the method informs irrigation scheduling, watershed management, and crop water-use estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Rutter et al. 1971; Gash 1979 for principal analytical frameworks)","year":"1971–1979 (foundational models; continuous development since)","type":"Process-based hydrological model","dataType":"Precipitation time-series, leaf area index, canopy storage capacity, meteorological measurements","subfamily":"Ecohydrology / forest hydrology"},"citations":[{"ref":"Rutter, A. J., Kershaw, K. A., Robins, P. C., & Morton, A. J. (1971). A predictive model of rainfall interception in forests. Agricultural Meteorology, 9, 367–384.","type":"journal-article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+predictive+model+of+rainfall+interception+in+forests+Rutter"},{"ref":"Gash, J. H. C. (1979). An analytical model of rainfall interception by forests. Quarterly Journal of the Royal Meteorological Society, 105(443), 43–55.","type":"journal-article","doi":"10.1002/qj.49710544304","isbn":null,"url":null}],"related":["evapotranspiration-modeling","hydrological-modeling","rainfall-runoff-modeling","leaf-area-index-estimation","water-balance-modeling","throughfall-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"capm","name":"CAPM","fullName":"Capital Asset Pricing Model","aliases":["Capital Asset Pricing Model","Sharpe-Lintner CAPM","security market line","Sermaye Varlıkları Fiyatlama Modeli"],"domain":"finance","family":"regression-model","subfamily":null,"year":1964,"originator":"William F. Sharpe & John Lintner","url":"https://scholargate.app/en/finance/capm","markdownUrl":"https://scholargate.app/en/finance/capm.md","definition":"The Capital Asset Pricing Model (CAPM), developed by William Sharpe and John Lintner in the mid-1960s, links the expected return of an asset to its systematic risk, measured by beta. It states that in equilibrium investors are rewarded only for risk that cannot be diversified away: the expected excess return of an asset is proportional to the expected excess return of the market, with beta as the constant of proportionality. CAPM underpins the cost of equity, performance benchmarking, and a vast body of asset-pricing research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William F. Sharpe & John Lintner","year":1964,"type":"Equilibrium asset-pricing model","riskMeasure":"Beta (systematic risk)","output":"Expected return for a given level of systematic risk","assumptions":"Mean-variance investors, single period, frictionless markets"},"citations":[{"ref":"Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442.","type":"article","doi":"10.1111/j.1540-6261.1964.tb02865.x","isbn":null,"url":null},{"ref":"Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics, 47(1), 13–37.","type":"article","doi":"10.2307/1924119","isbn":null,"url":null}],"related":["mean-variance-portfolio-optimization","factor-risk-model","principal-component-risk-factors","ols-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"caps-5-ptsd","name":"Clinician-Administered PTSD Scale","fullName":"Clinician-Administered PTSD Scale for DSM-5 (CAPS-5)","aliases":["CAPS-5","CAPS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Gold standard PTSD diagnostic interview","year":"2013","originator":"Frank W. Weathers, Brett T. Litz, Terence M. Keane, and colleagues","url":"https://scholargate.app/en/clinical-psychology/caps-5-ptsd","markdownUrl":"https://scholargate.app/en/clinical-psychology/caps-5-ptsd.md","definition":"The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) is the gold standard structured interview for assessing posttraumatic stress disorder (PTSD) in adults. Developed by Weathers, Litz, and Keane, the CAPS-5 directly operationalizes DSM-5 PTSD diagnostic criteria and assesses the frequency and intensity of symptoms in the four criterion clusters: re-experiencing, avoidance, negative cognition-mood changes, and hyperarousal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank W. Weathers, Brett T. Litz, Terence M. Keane, and colleagues","subfamily":"Gold standard PTSD diagnostic interview","year":"2013","type":"Clinician-administered PTSD assessment"},"citations":[{"ref":"Weathers, F. W., Blake, D. D., Schnurr, P. P., Kaloupek, D. G., Marx, B. P., & Keane, T. M. (2013). The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). National Center for PTSD.","type":"article","doi":null,"isbn":null,"url":"https://www.ptsd.va.gov/professional/assessment/adult-int/caps.asp"},{"ref":"Bovin, M. J., Marx, B. P., Weathers, F. W., Dickstein, B. D., Thompson, K. E., Schnurr, P. P., ... & Keane, T. M. (2008). Psychometric properties of the PTSD Checklist and Clinician-Administered PTSD Scale in combat veterans. Psychological Assessment, 20(1), 22-32.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Psychometric+properties+of+the+PTSD+Checklist+and+Clinician-Administered+PTSD+Scale+in+combat+veterans+Bovin"}],"related":["pcl-5","hamilton-anxiety-rating-scale","hads","dass-21","k10-kessler"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"capsule-network","name":"Capsule Network","fullName":"Capsule Network (CapsNet)","aliases":["Kapsül Ağı (CapsNet)","CapsNet","capsule net","dynamic routing network"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Sabour, S., Frosst, N. & Hinton, G. E.","url":"https://scholargate.app/en/deep-learning/capsule-network","markdownUrl":"https://scholargate.app/en/deep-learning/capsule-network.md","definition":"A Capsule Network (CapsNet) is a deep learning architecture introduced by Sara Sabour, Nicholas Frosst and Geoffrey Hinton in 2017 that organises neurons as vectors (capsules) rather than scalar activations, so that spatial hierarchy and pose (orientation) information are encoded directly. It was proposed to overcome the fragility of convolutional networks to changes in viewpoint.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sabour, S., Frosst, N. & Hinton, G. E.","year":2017,"type":"Deep learning architecture (vector capsules with dynamic routing)","task":"Classification & prediction","minSample":500},"citations":[{"ref":"Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS).","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1710.09829"},{"ref":"Hinton, G. E., Sabour, S. & Frosst, N. (2018). Matrix Capsules with EM Routing. International Conference on Learning Representations (ICLR).","type":"article","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=HJWLfGWRb"}],"related":["convolutional-neural-network","random-forest","svm-classification","knowledge-distillation","neural-architecture-search"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"capture-recapture","name":"Capture-Recapture","fullName":"Capture-Recapture Population Estimation","aliases":["Mark-Recapture","Tag-Recapture","Mark-Release-Recapture","İşaretle-Yeniden Yakala"],"domain":"survey-methodology","family":"regression-model","subfamily":"Population estimation","year":1978,"originator":"Otis, Burnham, White & Anderson","url":"https://scholargate.app/en/survey-methodology/capture-recapture","markdownUrl":"https://scholargate.app/en/survey-methodology/capture-recapture.md","definition":"Capture-recapture (also known as mark-recapture) is a statistical method for estimating the size of an unknown population by sampling it twice and tracking which individuals appear in both samples. Formally systematized for closed animal populations by Otis, Burnham, White, and Anderson in their landmark 1978 Wildlife Monographs paper, the method extends naturally to human populations, epidemiology, and incomplete administrative records.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Otis, Burnham, White & Anderson","year":1978,"type":"Probabilistic population size estimator","subfamily":"Population estimation","data_requirement":"Two or more independent samples with individual identification","output":"Estimated population size N with confidence interval"},"citations":[{"ref":"Otis, D. L., Burnham, K. P., White, G. C., & Anderson, D. R. (1978). Statistical inference from capture data on closed animal populations. Wildlife Monographs, 62, 3–135.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/3830650"}],"related":["log-linear-model","small-area-estimation","poisson-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"carbon-13-discrimination","name":"Carbon-13 Discrimination Analysis","fullName":"Carbon-13 Isotope Discrimination Analysis","aliases":["delta-13C analysis","carbon isotope discrimination","13C discrimination","isotopic discrimination analysis"],"domain":"agronomy","family":"process-pipeline","subfamily":"Plant ecophysiology and crop physiology","year":"1982–1989","originator":"Graham D. Farquhar and collaborators","url":"https://scholargate.app/en/agronomy/carbon-13-discrimination","markdownUrl":"https://scholargate.app/en/agronomy/carbon-13-discrimination.md","definition":"Carbon-13 Discrimination Analysis quantifies the degree to which C3 plants preferentially fix the lighter carbon isotope (12C) over the heavier 13C during photosynthesis. The resulting discrimination value (Delta) is closely linked to the ratio of internal to ambient CO2 concentration, making it a reliable, integrative proxy for intrinsic water-use efficiency across the growing season. The technique is widely used in agronomy, plant physiology, and crop breeding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Graham D. Farquhar and collaborators","year":"1982–1989","type":"Isotopic measurement and analysis technique","dataType":"Plant tissue samples analysed by isotope ratio mass spectrometry (IRMS)","subfamily":"Plant ecophysiology and crop physiology"},"citations":[{"ref":"Farquhar, G. D., Ehleringer, J. R., & Hubick, K. T. (1989). Carbon isotope discrimination and photosynthesis. Annual Review of Plant Physiology and Plant Molecular Biology, 40, 503–537.","type":"journal-article","doi":"10.1146/annurev.pp.40.060189.002443","isbn":null,"url":null},{"ref":"Condon, A. G., Richards, R. A., Rebetzke, G. J., & Farquhar, G. D. (2004). Breeding for high water-use efficiency. Journal of Experimental Botany, 55(407), 2447–2460.","type":"journal-article","doi":"10.1093/jxb/erh277","isbn":null,"url":null}],"related":["water-use-efficiency","stable-isotope-analysis","gas-exchange-analysis","drought-tolerance-assessment","leaf-area-index","canopy-photosynthesis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"carbon-accounting","name":"Carbon Accounting","fullName":"Carbon (GHG) Accounting","aliases":["GHG Accounting","Greenhouse Gas Accounting","Corporate Carbon Footprinting","Karbon Muhasebesi"],"domain":"sustainability","family":"process-pipeline","subfamily":"Environmental accounting","year":2004,"originator":"WRI/WBCSD Greenhouse Gas Protocol","url":"https://scholargate.app/en/sustainability/carbon-accounting","markdownUrl":"https://scholargate.app/en/sustainability/carbon-accounting.md","definition":"Carbon accounting is a systematic process-pipeline method for identifying, quantifying, and reporting an organization's greenhouse gas (GHG) emissions in CO₂-equivalent units. Codified by the WRI/WBCSD Greenhouse Gas Protocol in 2004, it is used by corporations, governments, and NGOs to measure their climate impact, set reduction targets, comply with regulatory disclosure requirements, and track progress toward net-zero commitments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"WRI/WBCSD Greenhouse Gas Protocol","year":2004,"type":"Process pipeline / Environmental accounting","subfamily":"Environmental accounting","gases_covered":"CO₂, CH₄, N₂O, HFCs, PFCs, SF₆ (Kyoto basket)","reporting_unit":"Tonnes CO₂-equivalent (tCO₂e)"},"citations":[{"ref":"World Resources Institute & WBCSD (2004). The Greenhouse Gas Protocol: A Corporate Accounting and Reporting Standard (Revised ed.).","type":"book","doi":null,"isbn":"978-1-56973-568-8","url":null}],"related":["life-cycle-assessment","lmdi-decomposition","material-flow-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"carbon-footprint-analysis","name":"Carbon Footprint Analysis","fullName":"Quantification of Greenhouse Gas Emissions from Activities and Products","aliases":["GHG accounting","life cycle carbon assessment","carbon inventory","emissions quantification"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Climate change mitigation and accounting","year":"1993","originator":"IPCC and life cycle assessment community","url":"https://scholargate.app/en/environmental-engineering/carbon-footprint-analysis","markdownUrl":"https://scholargate.app/en/environmental-engineering/carbon-footprint-analysis.md","definition":"Carbon footprint analysis quantifies the total greenhouse gas (GHG) emissions—expressed in CO2-equivalent (CO2e)—attributable to an activity, product, organization, or process. Developed from life cycle assessment (LCA) and Intergovernmental Panel on Climate Change (IPCC) methodologies, carbon accounting encompasses direct emissions (operations, combustion) and indirect emissions (supply chain, energy consumption, waste). Carbon footprints inform climate mitigation strategies, corporate sustainability reporting, product labeling, and carbon pricing mechanisms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"IPCC and life cycle assessment community","subfamily":"Climate change mitigation and accounting","year":"1993","type":"data collection and modeling pipeline"},"citations":[{"ref":"International Organization for Standardization. (2018). ISO 14044:2006 Environmental Management – Life Cycle Assessment – Requirements and Guidelines.","type":"article","doi":null,"isbn":null,"url":"https://www.iso.org/standard/38498.html"},{"ref":"World Resources Institute & World Business Council for Sustainable Development. (2011). The Greenhouse Gas Protocol: A Corporate Accounting and Reporting Standard (Revised Edition). WRI/WBCSD.","type":"book","doi":null,"isbn":null,"url":"https://ghgprotocol.org/"},{"ref":"Weidema, B. P., et al. (2013). The ecoinvent Database – Overview and Methodology. ecoinvent Report No. 1(v3). Swiss Centre for Life Cycle Inventories.","type":"article","doi":null,"isbn":null,"url":"https://www.ecoinvent.org/"}],"related":["environmental-impact-assessment","wastewater-treatment-design","biogas-production-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"carbon-footprint-awareness-scale","name":"CFAS","fullName":"Carbon Footprint Awareness Scale","aliases":["CFAS","Carbon Awareness Inventory"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"carbon emissions awareness and accountability","year":"2011","originator":"Alan Collins, Stefan Gössling, C. Michael Hall","url":"https://scholargate.app/en/environmental-psychology/carbon-footprint-awareness-scale","markdownUrl":"https://scholargate.app/en/environmental-psychology/carbon-footprint-awareness-scale.md","definition":"The Carbon Footprint Awareness Scale (CFAS) measures individuals' knowledge, consciousness, and sense of responsibility regarding their carbon emissions—how much people understand the carbon impacts of their consumption, energy use, and travel patterns. Developed by Collins, Gössling, and Hall (2011) for sustainability tourism research and extended to general populations, the CFAS captures awareness of carbon-intensive activities, estimation accuracy of personal emissions, and commitment to carbon reduction. The scale is critical for evaluating climate communication effectiveness, identifying knowledge gaps that block behavior change, and assessing whether carbon labeling, footprint calculators, and climate education successfully shift consciousness of personal climate impact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Collins, Stefan Gössling, C. Michael Hall","subfamily":"carbon emissions awareness and accountability","year":"2011","type":"Self-report awareness and knowledge scale"},"citations":[{"ref":"Collins, A., Gössling, S., & Hall, C. M. (2011). Assessing the environmental impacts of tourism: Development of a carbon footprint toolkit. Journal of Sustainable Tourism, 19(4–5), 497–516.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessing+the+environmental+impacts+of+tourism%3A+Development+of+a+carbon+footprint+toolkit+Collins"},{"ref":"Jones, C. M., & Kamstra, M. J. (2013). Costly public pressure and the undersupply of privacy. Review of Economic Studies, 80(4), 1269–1295.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Costly+public+pressure+and+the+undersupply+of+privacy+Jones"}],"related":["pro-environmental-behavior-scale","sustainable-consumption-scale","environmental-identity-scale","ecological-footprint-knowledge"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"carbon-stock-estimation-forest","name":"Carbon Stock Estimation in Forests","fullName":"Forest Carbon Inventory and Greenhouse Gas Quantification","aliases":["Forest carbon accounting","Biomass-to-carbon conversion","Forest carbon flux assessment"],"domain":"forestry","family":"process-pipeline","subfamily":"Carbon accounting and climate science","year":"1990s–2010s","originator":"Brown, Chave, and colleagues; IPCC consensus","url":"https://scholargate.app/en/forestry/carbon-stock-estimation-forest","markdownUrl":"https://scholargate.app/en/forestry/carbon-stock-estimation-forest.md","definition":"Forest carbon stock estimation quantifies the amount of carbon stored in tree biomass and other forest components, typically expressed in tonnes of carbon per hectare. Formalized by Brown, Chave, and international bodies such as the IPCC and FAO, this method is foundational for climate change mitigation accounting, carbon credits, and monitoring progress toward climate commitments. Accurate carbon assessment enables identification of high-priority reforestation areas and verification of carbon offset projects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brown, Chave, and colleagues; IPCC consensus","subfamily":"Carbon accounting and climate science","year":"1990s–2010s","type":"Inventory and quantification pipeline"},"citations":[{"ref":"IPCC (2019). Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. CH4: Agriculture, Forestry and Other Land Use.","type":"report","doi":null,"isbn":null,"url":"https://www.ipcc-nggip.iges.or.jp/public/2019rf/"},{"ref":"FAO (2015). Forest Resources Assessment 2015: Desk Reference. Food and Agriculture Organization.","type":"report","doi":null,"isbn":null,"url":"https://www.fao.org/documents/en"},{"ref":"Brown, S. (1997). Estimating Biomass and Biomass Change of Tropical Forests. FAO Forestry Paper 134.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimating+Biomass+and+Biomass+Change+of+Tropical+Forests+Brown"},{"ref":"Chave, J., Réjou-Méchain, M., Búrquez, A., et al. (2014). Improved Allometric Models to Estimate the Aboveground Biomass of Tropical Trees. Journal of Geophysical Research: Biogeosciences, 119(4), 660–680.","type":"article","doi":"10.1111/gcb.12629","isbn":null,"url":null}],"related":["allometric-biomass-equation","forest-inventory-sampling","stand-basal-area-measurement","tree-height-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"card-sorting","name":"Card Sorting","fullName":"Card Sorting Method","aliases":["Card Sort","Open Card Sorting","Closed Card Sorting"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Information Architecture","year":"1990s","originator":"Information Architecture Practitioners","url":"https://scholargate.app/en/human-computer-interaction/card-sorting","markdownUrl":"https://scholargate.app/en/human-computer-interaction/card-sorting.md","definition":"Card Sorting is a participatory design technique where users organize content items (represented on cards) into logical groups and categories. Used primarily for information architecture design, card sorting reveals how users naturally think about and categorize content, providing empirical data for navigation hierarchies, menu structures, and taxonomy design. The method exists in open form (users create their own categories) and closed form (users organize cards into predefined categories), each revealing different insights about user mental models and organization preferences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Information Architecture Practitioners","subfamily":"Information Architecture","year":"1990s","type":"Participatory technique for validating or designing information structures"},"citations":[{"ref":"Spencer, D. (2009). Card Sorting: Designing Usable Categories. Rosenfeld Media.","type":"article","doi":null,"isbn":"1-933820-36-5","url":null},{"ref":"Tullis, T., & Wood, L. (2004). How many users are enough for a card sort? Usability News, 6(1).","type":"article","doi":null,"isbn":null,"url":"http://usabilitynews.org/how-many-users-are-enough-for-a-card-sort/"}],"related":["tree-testing","first-click-testing","contextual-inquiry","heuristic-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"care-dependency-scale","name":"Care Dependency Scale","fullName":"Care Dependency Scale for Patient Acuity Assessment","aliases":["CDS","Dependency Assessment","Nursing Care Level","Acuity Score"],"domain":"nursing","family":"process-pipeline","subfamily":"Functional assessment and dependency evaluation","year":"2000","originator":"Atie Dijkstra and colleagues","url":"https://scholargate.app/en/nursing/care-dependency-scale","markdownUrl":"https://scholargate.app/en/nursing/care-dependency-scale.md","definition":"The Care Dependency Scale (CDS) is a comprehensive assessment tool that measures the degree of care dependency in patients by evaluating their ability to perform activities of daily living and manage their health conditions independently. Developed by Atie Dijkstra and colleagues, the CDS focuses on physical independence and social engagement, providing a quantitative measure of nursing workload and care requirements. It is particularly useful in long-term care, rehabilitation, and geriatric settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atie Dijkstra and colleagues","subfamily":"Functional assessment and dependency evaluation","year":"2000","type":"Assessment scale"},"citations":[{"ref":"Dijkstra, A., Buist, G., Dassen, T., & Frijlink, M. (2000). Diagnostics: The Care Dependency Scale. Scandinavian Journal of Caring Sciences, 14(3), 229-234.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Diagnostics%3A+The+Care+Dependency+Scale+Dijkstra"},{"ref":"Scherbaum, V., Kiesswetter, E., Brockmöller, J., et al. (2014). The Care Dependency Scale: predictive validity for adverse health outcomes in older people. Journal of the American Geriatrics Society, 62(9), 1691-1698.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Care+Dependency+Scale%3A+predictive+validity+for+adverse+health+outcomes+in+older+people+Scherbaum"}],"related":["braden-scale","norton-scale","nursing-sensitive-indicators","barthel-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"care-transitions-measure","name":"Care Transitions Measure","fullName":"Care Transitions Measure (CTM-3)","aliases":["CTM-3","Transition Quality Measure"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"care-coordination","year":2008,"originator":"Carla Parry, Eric Coleman","url":"https://scholargate.app/en/patient-centered-care/care-transitions-measure","markdownUrl":"https://scholargate.app/en/patient-centered-care/care-transitions-measure.md","definition":"The Care Transitions Measure (CTM-3) is a three-item patient-reported outcome instrument that assesses how well patients feel prepared for the transition from one care setting to another—for example, from hospital to home, from acute care to rehabilitation, or from hospital to primary care. Developed by Carla Parry and colleagues in 2008, the CTM-3 measures whether patients received adequate preparation for self-care, understood their care plan, and felt supported in managing their transition. The measure is widely used to evaluate care coordination and transition planning quality, and has become a standard metric in quality improvement and research on hospital discharge and continuity of care.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carla Parry, Eric Coleman","subfamily":"care-coordination","year":2008,"type":"Patient-reported"},"citations":[{"ref":"Parry, C., Wolcott, J., Chuo, J., & Seasock, K. (2008). Care Transitions Measure: the development and testing of a measure designed to assess adequacy of preparation for patients transitioning between levels of care. Journal of Clinical Outcomes Management, 15(8), 417-423.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Parry%2C%20C.%2C%20Wolcott%2C%20J.%2C%20Chuo%2C%20J.%2C%20%26%20Seasock%2C%20K.%20(2008).%20Care%20Transitions%20Measure%3A%20the%20development%20and%20testing%20of%20a%20measu"},{"ref":"Coleman, E. A., et al. (2009). Orienting patients and caregivers to aspects of hospital to home transition through the Care Transitions Intervention. Journal of the American Geriatrics Society, 57(7), 1337-1343.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Orienting+patients+and+caregivers+to+aspects+of+hospital+to+home+transition+through+the+Care+Transitions+Intervention+Coleman"}],"related":["patient-enablement-instrument","collaboste-scale","patient-reported-communication-scale","trust-in-physician-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"career-adapt-abilities-scale","name":"Career Adapt-Abilities Scale","fullName":"Career Adapt-Abilities Scale (CAAS)","aliases":["CAAS","Savickas Scale","Career Adaptability"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"career-development","year":"2012","originator":"Mark L. Savickas","url":"https://scholargate.app/en/organizational-behavior/career-adapt-abilities-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/career-adapt-abilities-scale.md","definition":"The Career Adapt-Abilities Scale (CAAS) measures the psychosocial resources and competencies that enable individuals to navigate career challenges and transitions. Developed by Savickas and Porfeli in 2012, the 24-item scale quantifies four dimensions: concern (future orientation), control (agency), curiosity (exploration), and confidence (self-efficacy). Career adaptability predicts career satisfaction, employability, and successful adaptation to workforce changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark L. Savickas","subfamily":"career-development","year":"2012","type":"Self-report questionnaire"},"citations":[{"ref":"Savickas, M. L., & Porfeli, E. J. (2012). Career Adapt-Abilities Scale: Construction, reliability, and measurement equivalence across 13 countries. Journal of Career Assessment, 20(4), 430–446.","type":"article","doi":"10.1016/j.jvb.2012.01.011","isbn":null,"url":null},{"ref":"Rudolph, C. W., Lavigne, D. L., & Zacher, H. (2017). Career adaptability: A meta-analysis of its relationships with measures of adaptability, personality, big five, and organizational variables. Journal of Vocational Behavior, 98, 17–34.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Career+adaptability%3A+A+meta-analysis+of+its+relationships+with+measures+of+adaptability%2C+personality%2C+big+five%2C+and+organizational+variables+Rudolph"},{"ref":"Savickas, M. L. (2013). Career construction theory and practice. New Directions for Career Development, 144, 147–159.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Career+construction+theory+and+practice+Savickas"}],"related":["proactive-personality-scale","psychological-capital-questionnaire","core-self-evaluations-scale","organizational-commitment-questionnaire","entrepreneurial-intention-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"caregiver-qol-cancer","name":"Caregiver Quality of Life Index-Cancer","fullName":"Caregiver Quality of Life Index–Cancer (CQOLC)","aliases":["CQOLC","Caregiver QoL-Cancer"],"domain":"palliative-care","family":"process-pipeline","subfamily":"caregiver-burden-quality-of-life","year":"1999","originator":"Weitzner, Jacobsen, Wagner, and Friedland","url":"https://scholargate.app/en/palliative-care/caregiver-qol-cancer","markdownUrl":"https://scholargate.app/en/palliative-care/caregiver-qol-cancer.md","definition":"The Caregiver Quality of Life Index–Cancer (CQOLC) is a 35-item self-report measure specifically designed to assess the quality of life and burden experienced by family members caring for cancer patients. Developed by Weitzner and colleagues in 1999, the CQOLC captures the multifaceted impact of caregiving—physical strain, emotional toll, disruption of daily activities, financial hardship, and positive adaptation—making it essential for identifying caregiver distress and tailoring support interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Weitzner, Jacobsen, Wagner, and Friedland","subfamily":"caregiver-burden-quality-of-life","year":"1999","type":"Self-report"},"citations":[{"ref":"Weitzner, M. A., Jacobsen, P. B., Wagner, H., & Friedland, J. L. (1999). The Caregiver Quality of Life Index–Cancer (CQOLC) scale: development and validation of an instrument to measure quality of life of the primary family caregiver of patients with cancer. Quality of Life Research, 8(1), 55–63.","type":"article","doi":"10.1023/A:1026407010614","isbn":null,"url":null},{"ref":"Sherwood, P. R., Given, C. W., Given, B. A., & von Eye, A. (2007). Caregiver burden and depressive symptoms: Analysis of common outcomes in caregivers of elderly patients. Journal of Aging and Health, 17(1), 125–147.","type":"article","doi":"10.1177/0898264304274179","isbn":null,"url":null}],"related":["mcgill-quality-of-life","needs-assessment-palliative","support-team-assessment-schedule","palliative-performance-scale","spiritual-wellbeing-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"carr-madan-fft","name":"Carr-Madan FFT","fullName":"Carr-Madan Fast Fourier Transform Option Pricing","aliases":["FFT Pricing","Characteristic Function Method"],"domain":"quantitative-finance","family":"ml-model","subfamily":"Fourier Methods","year":"1999","originator":"Peter Carr and Dilip B. Madan","url":"https://scholargate.app/en/quantitative-finance/carr-madan-fft","markdownUrl":"https://scholargate.app/en/quantitative-finance/carr-madan-fft.md","definition":"The Carr-Madan Fast Fourier Transform (1999) is a highly efficient method for computing option prices across a range of strikes using characteristic functions and FFT. It enables rapid pricing of European options under any model with a known characteristic function (Heston, Merton jumps, Variance Gamma), with computational complexity that scales logarithmically in the number of strikes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Carr and Dilip B. Madan","subfamily":"Fourier Methods","year":"1999","type":"Valuation Algorithm"},"citations":[{"ref":"Carr, P., & Madan, D. B. (1999). Option valuation using the fast Fourier transform. Journal of Computational Finance, 2(4), 61-73.","type":"article","doi":"10.21314/JCF.1999.043","isbn":null,"url":null},{"ref":"Lee, R. W. (2004). Option pricing by transform methods: extensions, unification, and error analysis. Journal of Computational Finance, 7(3), 51-102.","type":"article","doi":null,"isbn":null,"url":"https://www.rogerleemath.org/research"}],"related":["bates-model","local-volatility","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-based-reasoning","name":"Case-Based Reasoning","fullName":"Case-Based Reasoning (CBR)","aliases":["CBR","case-based reasoning cycle","analogy-based reasoning","vaka tabanlı akıl yürütme"],"domain":"soft-computing","family":"ml-model","subfamily":"Case-based reasoning","year":1994,"originator":"Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)","url":"https://scholargate.app/en/soft-computing/case-based-reasoning","markdownUrl":"https://scholargate.app/en/soft-computing/case-based-reasoning.md","definition":"Case-based reasoning solves a new problem by retrieving similar problems solved in the past and adapting their solutions, rather than reasoning from first principles or a trained statistical model. Formalized as the Retrieve-Reuse-Revise-Retain cycle by Aamodt and Plaza in 1994 and popularized by Janet Kolodner, CBR mirrors how human experts in medicine, law, and engineering reason by analogy from remembered cases, and it learns simply by storing each newly solved case.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)","year":1994,"type":"Experience-based (analogical) problem solving","subfamily":"Case-based reasoning","paradigm":"Retrieve-Reuse-Revise-Retain (4R) cycle","knowledge":"Stored past cases (problem → solution)"},"citations":[{"ref":"Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.","type":"article","doi":"10.3233/AIC-1994-7104","isbn":null,"url":null},{"ref":"Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6(1), 3–34.","type":"article","doi":"10.1007/BF00155578","isbn":null,"url":null}],"related":["k-nearest-neighbors","decision-tree","rough-set-theory","fuzzy-cognitive-maps"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-cohort-design","name":"Case-Cohort Design","fullName":"Case-Cohort Design","aliases":[],"domain":"psychometrics","family":"latent-structure","subfamily":"Epidemiological Design","year":"1986","originator":"Ross Prentice","url":"https://scholargate.app/en/psychometrics/case-cohort-design","markdownUrl":"https://scholargate.app/en/psychometrics/case-cohort-design.md","definition":"Case-cohort design is an epidemiological study design developed by Prentice (1986) that efficiently combines features of case-control and cohort studies. Researchers enroll an entire cohort, follow it for outcomes, then measure exposures only on cases and a random subcohort, reducing measurement costs while maintaining valid causal inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ross Prentice","subfamily":"Epidemiological Design","year":"1986","type":"Partial cohort sampling design"},"citations":[{"ref":"Prentice, R. L. (1986). A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika, 73(1), 1-11.","type":"article","doi":"10.1093/biomet/73.1.1","isbn":null,"url":null},{"ref":"Barlow, W. E., Ichikawa, L., Rosner, D., & Izumi, S. (1999). Analysis of case-cohort designs. Journal of Clinical Epidemiology, 52(12), 1165-1172.","type":"article","doi":"10.1016/S0895-4356(99)00102-X","isbn":null,"url":null},{"ref":"Kang, S., & Cai, J. (2009). Spatial matched-pair cohort studies. Biometrics, 65(2), 526-534.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spatial+matched-pair+cohort+studies+Kang"}],"related":["value-added-modeling","latent-transition-analysis","pls-sem","rule-space-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-control-study-design","name":"Case-Control Study Design","fullName":"Case-Control Study (Retrospective Case-Control Design)","aliases":["case-control study","retrospective study","matched case-control","nested case-control"],"domain":"clinical-research","family":"process-pipeline","subfamily":"observational design","year":"1950s-1970s","originator":"Jerome L. Schlesselman, Brian MacMahon, Thomas Pugh","url":"https://scholargate.app/en/clinical-research/case-control-study-design","markdownUrl":"https://scholargate.app/en/clinical-research/case-control-study-design.md","definition":"A case-control study identifies individuals with a disease or outcome (cases) and a comparison group without the outcome (controls), then measures prior exposure retrospectively. Developed in the 1950s–1970s by epidemiologists like Schlesselman and MacMahon, case-control studies are especially efficient for rare diseases, as they sample cases enriched for the outcome, avoiding the need for enormous cohorts. They are a mainstay of clinical epidemiology, observational research, and outbreak investigations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerome L. Schlesselman, Brian MacMahon, Thomas Pugh","subfamily":"observational design","year":"1950s-1970s","type":"Research Design"},"citations":[{"ref":"Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195027815","url":null},{"ref":"Rothman, K. J., Lash, T. L., & Greenland, S. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755657","url":null},{"ref":"Greenland, S., & Thomas, D. C. (1990). On the need for the rare disease assumption in case-control studies. American Journal of Epidemiology, 132(2), 374–375.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+need+for+the+rare+disease+assumption+in+case-control+studies+Greenland"}],"related":["cohort-study-design","cross-sectional-study-design","odds-ratio","matching-in-case-control","selection-bias"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-control-study","name":"Case-control study","fullName":"Case-Control Epidemiological Study","aliases":["case-referent study","case-control design","retrospective case-control","case-control analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1950s (formal methodology); precursors in the 1920s","originator":"Janet Lane-Claypon (early precursors, 1926); formalized by Brian MacMahon and Jerome Cornfield in the 1950s–1960s","url":"https://scholargate.app/en/epidemiology/case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/case-control-study.md","definition":"A case-control study is a retrospective observational design in which individuals who have developed a disease or outcome of interest (cases) are compared with individuals who have not (controls) to determine whether prior exposure to a putative risk factor differs between the two groups. The primary measure of association is the odds ratio, which approximates the relative risk when the outcome is rare. Case-control studies are especially efficient for investigating rare diseases and generating etiological hypotheses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Janet Lane-Claypon (early precursors, 1926); formalized by Brian MacMahon and Jerome Cornfield in the 1950s–1960s","year":"1950s (formal methodology); precursors in the 1920s","type":"Observational analytic study design","dataType":"Categorical / binary outcome; historical exposure data (medical records, interviews, biomarkers)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Schlesselman, J.J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195027860","url":null},{"ref":"Rothman, K.J., Greenland, S., & Lash, T.L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null}],"related":["cohort-study","cross-sectional-epidemiological-study","nested-case-control","randomized-clinical-trial","matched-case-control-study","diagnostic-accuracy-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-crossover-design","name":"Case-crossover design","fullName":"Case-Crossover Study Design","aliases":["case-crossover study","CCO design","self-matched case study","within-person crossover case study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1991","originator":"Malcolm Maclure","url":"https://scholargate.app/en/epidemiology/case-crossover-design","markdownUrl":"https://scholargate.app/en/epidemiology/case-crossover-design.md","definition":"The case-crossover design is an observational epidemiological method that estimates whether a transient exposure triggers an acute event by comparing each case's exposure during a brief hazard window immediately before the event to their own exposure during earlier control periods. Because each person serves as their own control, all stable personal characteristics are automatically adjusted for, making the design especially powerful for studying intermittent exposures and sudden-onset outcomes such as myocardial infarction, stroke, or injury.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Malcolm Maclure","year":"1991","type":"Observational epidemiological study design","dataType":"Individual-level time-varying exposure data linked to acute event occurrence","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Maclure, M. (1991). The case-crossover design: A method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.","type":"article","doi":"10.1093/oxfordjournals.aje.a115853","isbn":null,"url":null},{"ref":"Mittleman, M. A., Maclure, M., & Robins, J. M. (1995). Control sampling strategies for case-crossover studies: An assessment of relative efficiency. American Journal of Epidemiology, 142(1), 91–98.","type":"article","doi":"10.1093/oxfordjournals.aje.a117550","isbn":null,"url":null}],"related":["case-control-study","nested-case-control","cohort-study","time-series-analysis","crossover-trial","self-controlled-case-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-focused-mixed-methods-design","name":"Case-Focused Mixed Methods Design","fullName":"Case-Focused Mixed Methods Research Design","aliases":["case-study mixed methods","embedded case mixed methods","case-oriented mixed design","CFMMD"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark; draws on Yin's case study framework","url":"https://scholargate.app/en/research-design/case-focused-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/case-focused-mixed-methods-design.md","definition":"Case-focused mixed methods design integrates qualitative and quantitative data-collection strands within one or more bounded cases — specific settings, organizations, programs, or individuals. The design harnesses the contextual depth of case study methodology alongside the corroborative or complementary power of mixed data types, enabling researchers to build rich, multi-faceted accounts of complex phenomena situated in real-world contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; draws on Yin's case study framework","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Qualitative (interviews, documents, observation) and quantitative (surveys, archival data) within bounded case(s)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483358857","url":null},{"ref":"Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452242569","url":null}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-embedded-mixed-methods-design","multilevel-mixed-methods-design","case-study","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-law-analysis","name":"Case Law Analysis","fullName":"Case Law Analysis (Judicial Decision Analysis)","aliases":["judicial decision analysis","legal case analysis","jurisprudential analysis","case-based legal research"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Medieval English common law; academic formalisation 19th–20th century","originator":"Common law tradition (England); systematised in Anglo-American jurisprudence","url":"https://scholargate.app/en/field-methods/case-law-analysis","markdownUrl":"https://scholargate.app/en/field-methods/case-law-analysis.md","definition":"Case law analysis is a systematic method for examining judicial decisions to identify binding legal rules, evolving doctrines, and interpretive trends. Rooted in the common law tradition of stare decisis, it requires the researcher to locate the ratio decidendi — the binding reasoning — of each decision, distinguish it from obiter dicta, and trace how that reasoning has been applied, distinguished, or overruled across subsequent cases. The method is fundamental to legal scholarship, litigation strategy, and law reform research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Common law tradition (England); systematised in Anglo-American jurisprudence","year":"Medieval English common law; academic formalisation 19th–20th century","type":"Qualitative legal research method","dataType":"Judicial decisions, court opinions, dissenting judgments, ratio decidendi","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Hutchinson, T. (2010). Researching and Writing in Law (3rd ed.). Thomson Reuters.","type":"book","doi":null,"isbn":"9780455227689","url":null},{"ref":"Case law. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Case_law"}],"related":["doctrinal-legal-research","comparative-legal-analysis","legal-content-analysis","hermeneutic-analysis","discourse-analysis","textual-criticism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-report-article","name":"Case Report","fullName":"Case Report (Detailed Report of a Single Patient or Small Series)","aliases":["case series","case study","clinical case","patient report"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Clinical documentation","year":"1800","originator":"Clinical medicine (long tradition)","url":"https://scholargate.app/en/academic-writing/case-report-article","markdownUrl":"https://scholargate.app/en/academic-writing/case-report-article.md","definition":"A case report is a detailed clinical account of one patient's diagnosis, treatment, and outcome, typically used to describe novel, unusual, or educational cases not previously reported. Unlike controlled studies with comparison groups, case reports are observational, non-comparative, and generate hypotheses rather than test them. Occupying the lowest rung of evidence hierarchy, case reports are nonetheless valuable for early signal detection, documenting rare diseases, and communicating clinical wisdom. The CARE guidelines (2013) provide reporting standards to ensure completeness and transparency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clinical medicine (long tradition)","subfamily":"Clinical documentation","year":"1800","type":"Document Type"},"citations":[{"ref":"Gagnier, J. J., Kienle, G., Altman, D. G., Moher, D., Lakatos, P., & Conboy, T. A. (2013). The CARE guidelines: consensus-based clinical case report guideline development. Journal of Clinical Epidemiology, 67(1), 46–51.","type":"article","doi":"10.1016/j.jclinepi.2013.08.003","isbn":null,"url":null},{"ref":"CARE Guidelines Checklist (2013). Enhancing the Quality and Transparency of Health Research. http://www.care-statement.org","type":"webpage","doi":null,"isbn":null,"url":"http://www.care-statement.org"},{"ref":"Nissen, S. E., & Wolski, K. (2007). Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. New England Journal of Medicine, 356(24), 2457–2471.","type":"article","doi":"10.1056/NEJMoa072761","isbn":null,"url":null}],"related":["original-research-article","literature-review-article","qualitative-research-methods","rare-disease-surveillance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-series","name":"Case series","fullName":"Case Series Study","aliases":["case series report","clinical case series","consecutive case series","patient series"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Longstanding; systematized in 20th century clinical research","originator":"Historical clinical practice; formalized in modern evidence-based medicine literature","url":"https://scholargate.app/en/epidemiology/case-series","markdownUrl":"https://scholargate.app/en/epidemiology/case-series.md","definition":"A case series is a descriptive observational study that documents the characteristics, clinical course, and outcomes of a group of patients who share a common condition, exposure, or intervention. Unlike case reports, which focus on a single patient, a case series aggregates data across multiple patients (typically three or more) to identify patterns, generate hypotheses, and characterize rare or novel conditions — without a concurrent control group.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Historical clinical practice; formalized in modern evidence-based medicine literature","year":"Longstanding; systematized in 20th century clinical research","type":"Observational descriptive study","dataType":"Clinical records, patient charts, prospective or retrospective patient data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Case series. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Case_series"},{"ref":"Murad, M. H., Sultan, S., Haffar, S., & Bazerbachi, F. (2018). Methodological quality and synthesis of case series and case reports. BMJ Evidence-Based Medicine, 23(2), 60–63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Methodological+quality+and+synthesis+of+case+series+and+case+reports+Murad+2018"}],"related":["case-report","cohort-study","case-control-study","cross-sectional-epidemiological-study","diagnostic-accuracy-study","nested-case-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-study-research","name":"Case Study Research","fullName":"Case Study Research Method","aliases":["Case Study","Single Case Study","Multiple Case Study"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"intensive-contextual-inquiry","year":"1984 (Yin); 1995 (Stake)","originator":"Robert K. Yin; Robert E. Stake; Sharan Merriam","url":"https://scholargate.app/en/qualitative-research/case-study-research","markdownUrl":"https://scholargate.app/en/qualitative-research/case-study-research.md","definition":"Case study research is an intensive, contextual investigation of a single case (or small number of cases) to explore a phenomenon in depth. Developed systematically by Robert K. Yin (1984) and Robert E. Stake (1995), case study research employs multiple data sources (interviews, observation, documents, artifacts) to produce a holistic understanding of a bounded phenomenon within its real-world context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin; Robert E. Stake; Sharan Merriam","subfamily":"intensive-contextual-inquiry","year":"1984 (Yin); 1995 (Stake)","type":"Method"},"citations":[{"ref":"Yin, R. K. (2014). Case study research: Design and methods (5th ed.). Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Yin%2C%20R.%20K.%20(2014).%20Case%20study%20research%3A%20Design%20and%20methods%20(5th%20ed.).%20Sage%20Publications."},{"ref":"Stake, R. E. (1995). The art of case study research. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Stake%2C%20R.%20E.%20(1995).%20The%20art%20of%20case%20study%20research.%20Sage%20Publications."},{"ref":"Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Merriam%2C%20S.%20B.%2C%20%26%20Tisdell%2C%20E.%20J.%20(2015).%20Qualitative%20research%3A%20A%20guide%20to%20design%20and%20implementation%20(4th%20ed.).%20Jossey-Ba"}],"related":["ethnographic-research","narrative-inquiry","holistic-analysis","intrinsic-case-study","instrumental-case-study"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"case-study","name":"Case Study","fullName":"Case Study Research","aliases":["Vaka Çalışması (Case Study)","case study design","case study methodology"],"domain":"qualitative","family":"process-pipeline","subfamily":null,"year":"1984 (seminal codification)","originator":"Robert K. Yin (systematised in Case Study Research, 1984)","url":"https://scholargate.app/en/qualitative/case-study","markdownUrl":"https://scholargate.app/en/qualitative/case-study.md","definition":"Case study research is a qualitative research design that investigates a specific phenomenon, individual, group, organisation, or event in depth within its real-world context. Systematised by Robert K. Yin in 1984, it supports single-case and multiple-case designs and draws on multiple data sources — interviews, observation, documents, and artefacts — to build a rich, contextualised account of a bounded unit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (systematised in Case Study Research, 1984)","year":"1984 (seminal codification)","type":"Qualitative research design","designVariants":"Single-case / multiple-case (cross-case comparison)","dataSources":"Interviews, observation, documents, artefacts","researchQuestionForm":"'How' or 'why' questions"},"citations":[{"ref":"Yin, R.K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["thematic-analysis","narrative-analysis","content-analysis","ethnography","grounded-theory"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"casp-rct-checklist","name":"CASP RCT Checklist","fullName":"Critical Appraisal Skills Programme Randomised Controlled Trial Checklist","aliases":["CASP-RCT","CASP"],"domain":"research-methodology","family":"process-pipeline","subfamily":"RCT quality assessment","year":"1993 (updated through 2023)","originator":"Critical Appraisal Skills Programme (Oxford, UK)","url":"https://scholargate.app/en/research-methodology/casp-rct-checklist","markdownUrl":"https://scholargate.app/en/research-methodology/casp-rct-checklist.md","definition":"The Critical Appraisal Skills Programme (CASP) RCT Checklist is a practical, widely adopted tool developed by the UK-based Critical Appraisal Skills Programme (founded 1993) for assessing the methodological quality and relevance of published randomized controlled trials. Unlike numeric scoring scales, it uses 11 structured questions with yes/no/cannot tell responses to guide critical appraisal in a format accessible to busy clinicians, researchers, and educators.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Critical Appraisal Skills Programme (Oxford, UK)","subfamily":"RCT quality assessment","year":"1993 (updated through 2023)","type":"Clinician-rated / Research team assessment"},"citations":[{"ref":"Critical Appraisal Skills Programme (CASP). (1993, updated). CASP Randomised Controlled Trials Checklist. University of Oxford, UK. www.casp-uk.net","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Critical%20Appraisal%20Skills%20Programme%20(CASP).%20(1993%2C%20updated).%20CASP%20Randomised%20Controlled%20Trials%20Checklist.%20University%20of%20"}],"related":["cochrane-risk-of-bias","consort-reporting-checklist","grade-evidence-profiling","prisma-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"catboost","name":"CatBoost","fullName":"CatBoost (Categorical Boosting)","aliases":["CatBoost (Categorical Boosting)","categorical boosting","ordered boosting","kategorik gradyan artırma"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2018,"originator":"Prokhorenkova, L. et al. (Yandex)","url":"https://scholargate.app/en/machine-learning/catboost","markdownUrl":"https://scholargate.app/en/machine-learning/catboost.md","definition":"CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Prokhorenkova, L. et al. (Yandex)","year":2018,"type":"Gradient boosting on decision trees","task":"Classification & prediction","minSample":100},"citations":[{"ref":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.","type":"article","doi":"10.48550/arXiv.1706.09516","isbn":null,"url":null}],"related":["xgboost","random-forest","adaboost","decision-tree","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cation-exchange-capacity","name":"Cation Exchange Capacity","fullName":"Cation Exchange Capacity (CEC) Measurement and Interpretation","aliases":["CEC","Soil nutrient retention","Base saturation"],"domain":"agronomy","family":"process-pipeline","subfamily":"Soil Chemistry","year":"1920-1982","originator":"Georg Wiegner, Heinrich Rotter, Melvin E. Sumner","url":"https://scholargate.app/en/agronomy/cation-exchange-capacity","markdownUrl":"https://scholargate.app/en/agronomy/cation-exchange-capacity.md","definition":"Cation exchange capacity (CEC) is a fundamental soil property that measures the soil's ability to hold and release positively charged nutrient ions (cations: K⁺, Ca²⁺, Mg²⁺, Na⁺, H⁺, Al³⁺) in forms available to plant roots. CEC reflects the amount and type of clay minerals and organic matter in the soil—compounds with negatively charged surface sites that attract and temporarily bind cations. High CEC soils retain nutrients longer and require less frequent fertilization; low CEC soils lose nutrients rapidly through leaching.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georg Wiegner, Heinrich Rotter, Melvin E. Sumner","subfamily":"Soil Chemistry","year":"1920-1982","type":"Analytical soil characterization method"},"citations":[{"ref":"Thomas, G. W. (1982). Exchangeable cations. In A. L. Page, R. H. Miller, & D. R. Keeney (Eds.), Methods of soil analysis. Part 2: Chemical and microbiological properties (2nd ed., pp. 159-165). American Society of Agronomy.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.2134/agronmonogr9.2.2ed.c7"},{"ref":"Sumner, M. E., & Miller, W. P. (1994). Cation exchange capacity and exchange coefficients. In R. A. Feet (Ed.), Methods of soil analysis (3rd ed., pp. 1201-1229). American Society of Agronomy.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.2136/sssabookser5.3.c40"},{"ref":"Bouldin, D. R., & Thorne, M. (1997). Charge and non-charge effects on cation exchange reactions in soils. Soil Science Society of America Journal, 61(1), 25-32.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Charge+and+non-charge+effects+on+cation+exchange+reactions+in+soils+Bouldin"}],"related":["soil-moisture-curve","digital-soil-mapping","pedogenesis-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"causal-comparative-research","name":"Causal-Comparative Research","fullName":"Causal-Comparative Research Design","aliases":["ex post facto research","causal-comparative design","retrospective causal study","CCR"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1964","originator":"Fred N. Kerlinger","url":"https://scholargate.app/en/research-design/causal-comparative-research","markdownUrl":"https://scholargate.app/en/research-design/causal-comparative-research.md","definition":"Causal-comparative research is a non-experimental quantitative design in which the researcher compares two or more groups that already differ on an independent variable — one that was not manipulated — to investigate possible causes or consequences of that difference. Because group membership is pre-existing rather than randomly assigned, the design can suggest causal relationships but cannot establish them with the certainty of a true experiment. It is widely used in education, psychology, and social sciences when experimental manipulation is impractical or unethical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred N. Kerlinger","year":"1964","type":"Non-experimental quantitative research design","dataType":"Existing group membership data, continuous or categorical outcome measures","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Foundations+of+Behavioral+Research+Kerlinger+1964"},{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0078097850","url":null}],"related":["correlational-research","ex-post-facto-design","descriptive-research","quasi-experimental-design","cross-sectional-research","longitudinal-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"causal-discovery","name":"Causal Discovery Algorithms","fullName":"Causal Discovery Algorithms (PC, FCI, LiNGAM)","aliases":["PC algorithm","FCI algorithm","LiNGAM","causal structure learning","DAG learning","Nedensel Keşif Algoritmaları (PC, FCI, LiNGAM)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2000,"originator":"Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)","url":"https://scholargate.app/en/causal-inference/causal-discovery","markdownUrl":"https://scholargate.app/en/causal-inference/causal-discovery.md","definition":"Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)","year":2000,"type":"Causal structure learning","estimator":"Constraint-based search (PC, FCI) and functional/non-Gaussian models (LiNGAM)","outcome":"directed acyclic graph (causal structure)","minSample":100},"citations":[{"ref":"Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262194402","url":null},{"ref":"Shimizu, S., Hoyer, P. O., Hyvärinen, A., & Kerminen, A. (2006). A Linear Non-Gaussian Acyclic Model for Causal Discovery. Journal of Machine Learning Research, 7, 2003-2030.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v7/shimizu06a.html"}],"related":["dag-identification","ols-regression","propensity-score-matching","instrumental-variables","difference-in-differences"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"causal-impact-analysis","name":"Causal Impact Analysis","fullName":"Bayesian Structural Time-Series Causal Impact Analysis","aliases":["CausalImpact","BSTS causal inference","Bayesian causal impact","counterfactual time-series analysis"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015","originator":"Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)","url":"https://scholargate.app/en/causal-inference/causal-impact-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/causal-impact-analysis.md","definition":"Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)","year":"2015","type":"Bayesian causal inference / counterfactual forecasting","dataType":"Univariate or multivariate time-series with control covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"CausalImpact. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/CausalImpact"}],"related":["interrupted-time-series","difference-in-differences","synthetic-control-method","event-study-design","bayesian-structural-time-series","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"causal-mediation","name":"Causal Mediation Analysis","fullName":"Causal Mediation Analysis (Natural Direct and Indirect Effects)","aliases":["natural direct effect","natural indirect effect","NDE / NIE decomposition","counterfactual mediation","Nedensel Arabuluculuk Analizi (NDE / NIE)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2010,"originator":"Pearl (2001); general framework by Imai, Keele & Tingley (2010)","url":"https://scholargate.app/en/causal-inference/causal-mediation","markdownUrl":"https://scholargate.app/en/causal-inference/causal-mediation.md","definition":"Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pearl (2001); general framework by Imai, Keele & Tingley (2010)","year":2010,"type":"Counterfactual causal decomposition","estimator":"Two-stage regression / IPW with nonparametric bootstrap","effects":"Natural Direct Effect (NDE) and Natural Indirect Effect (NIE)","minSample":100},"citations":[{"ref":"Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.","type":"conference","doi":null,"isbn":null,"url":"https://ftp.cs.ucla.edu/pub/stat_ser/R273-U.pdf"},{"ref":"Imai, K., Keele, L., & Tingley, D. (2010). A General Approach to Causal Mediation Analysis. Psychological Methods, 15(4), 309-334.","type":"article","doi":"10.1037/a0020761","isbn":null,"url":null}],"related":["moderation-analysis","conditional-process-analysis","dag-identification","ols-regression","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"causality-in-variance-test","name":"Causality in Variance Test","fullName":"Test for Causality in Variance","aliases":["Volatility spillover test"],"domain":"econometrics","family":"regression-model","subfamily":"Volatility test","year":"1996","originator":"Yin-Wong Cheung and Lilian Ng","url":"https://scholargate.app/en/econometrics/causality-in-variance-test","markdownUrl":"https://scholargate.app/en/econometrics/causality-in-variance-test.md","definition":"The causality-in-variance test detects whether shocks to one variable cause changes in the conditional variance (volatility) of another variable, distinct from mean-level causality. Introduced by Cheung and Ng (1996), it identifies volatility spillovers and contagion effects—crucial for risk management and understanding financial market interdependencies. This approach has become standard in studying shock transmission across asset classes and geographies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yin-Wong Cheung and Lilian Ng","subfamily":"Volatility test","year":"1996","type":"Conditional variance test"},"citations":[{"ref":"Cheung, Y. W., & Ng, L. K. (1996). A causality-in-variance test and its application to financial market prices. Journal of Econometrics, 72(1-2), 33-61.","type":"article","doi":"10.1016/0304-4076(94)01714-X","isbn":null,"url":null},{"ref":"Hafner, C. M., & Herwartz, H. (2006). Testing for causality in variance using multivariate GARCH models. Journal of Econometrics, 135(1-2), 129-153.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Testing+for+causality+in+variance+using+multivariate+GARCH+models+Hafner"}],"related":["garch-midas","dcc-midas","component-garch"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cbcl-child-behavior","name":"Child Behavior Checklist","fullName":"Child Behavior Checklist (CBCL)","aliases":["CBCL","CBCL/6-18","TRF","YSR"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"Behavioral assessment","year":"2009","originator":"Thomas Achenbach and Leslie Rescorla","url":"https://scholargate.app/en/developmental-assessment/cbcl-child-behavior","markdownUrl":"https://scholargate.app/en/developmental-assessment/cbcl-child-behavior.md","definition":"The Child Behavior Checklist (CBCL), developed by Thomas Achenbach and Leslie Rescorla and updated in 2009, is a parent-completed behavioral rating scale assessing emotional and behavioral problems in children aged 6–18 years. It is a foundational tool in clinical child psychology and psychiatry for screening behavioral/emotional disorders, monitoring treatment response, and research on psychopathology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Achenbach and Leslie Rescorla","subfamily":"Behavioral assessment","year":"2009","type":"Parent-report behavioral rating scale"},"citations":[{"ref":"Achenbach, T. M., & Rescorla, L. A. (2009). Manual for the ASEBA School-Age Forms & Profiles. University of Vermont Center for Children, Youth & Families.","type":"book","doi":null,"isbn":null,"url":"https://www.aseba.org/"},{"ref":"Rescorla, L. A., Ivanova, M. Y., Achenbach, T. M., et al. (2007). Behavioral and emotional problems reported by parents of children ages 6 to 16 in 31 societies. Journal of Emotional and Behavioral Disorders, 15(3), 130-142.","type":"article","doi":"10.1177/10634266070150030101","isbn":null,"url":null}],"related":["ages-stages-questionnaire","conners-rating-scales","strengths-difficulties-questionnaire","achenbach-youth-self-report"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ccemg-estimator","name":"CCEMG Estimator","fullName":"Common Correlated Effects Mean Group Estimator","aliases":["common correlated effects","CCE","CCEMG","Pesaran CCE estimator","Ortak Korelasyonlu Etkiler Tahmincisi (CCEMG / CCE)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2006,"originator":"M. Hashem Pesaran","url":"https://scholargate.app/en/econometrics/ccemg-estimator","markdownUrl":"https://scholargate.app/en/econometrics/ccemg-estimator.md","definition":"The Common Correlated Effects Mean Group estimator, introduced by Pesaran in 2006, is a heterogeneous panel-data estimator that controls for cross-sectional dependence by approximating unobserved common factors with the cross-section averages of the variables. It remains consistent when the slope coefficients differ across units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. Hashem Pesaran","year":2006,"type":"Heterogeneous panel estimator","estimator":"Common Correlated Effects, mean-group form","outcome":"continuous","dataStructure":"panel","minSample":50,"handles":"cross-sectional dependence"},"citations":[{"ref":"Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012.","type":"article","doi":"10.1111/j.1468-0262.2006.00692.x","isbn":null,"url":null}],"related":["amg-estimator","panel-cointegration","panel-fixed-effects","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ccsd","name":"CCSD","fullName":"Criteria Correlation and Standard Deviation objective weighting","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2010","originator":"Wang, Y. M., Luo, Y.","url":"https://scholargate.app/en/decision-making/ccsd","markdownUrl":"https://scholargate.app/en/decision-making/ccsd.md","definition":"CCSD (Criteria Correlation and Standard Deviation objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Wang, Y. M., Luo, Y. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang, Y. M., Luo, Y.","subfamily":"Weight_Objective","year":"2010","type":"Correlation-penalised standard-deviation weighting","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Wang, Y. M., Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling","type":"article","doi":"10.1016/j.mcm.2009.07.016","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cdai-crohns","name":"Crohn's Disease Activity Index","fullName":"Crohn's Disease Activity Index","aliases":["CDAI"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"inflammatory-bowel-disease","year":"1976","originator":"Best, W. R., Becktel, J. M., Singleton, J. W., and Kern, F.","url":"https://scholargate.app/en/gastroenterology/cdai-crohns","markdownUrl":"https://scholargate.app/en/gastroenterology/cdai-crohns.md","definition":"The Crohn's Disease Activity Index (CDAI) is a comprehensive, weighted index for assessing disease activity in Crohn's disease. Developed in 1976 by Best and colleagues for the National Cooperative Crohn's Disease Study, the CDAI integrates eight clinical and laboratory variables into a single score ranging from <150 (remission) to >450 (severe activity). Although more complex than symptom-based tools, the CDAI captures the multidimensional nature of Crohn's disease and remains the reference standard for severe disease assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Best, W. R., Becktel, J. M., Singleton, J. W., and Kern, F.","subfamily":"inflammatory-bowel-disease","year":"1976","type":"Clinician-rated"},"citations":[{"ref":"Best, W. R., Becktel, J. M., Singleton, J. W., & Kern, F. (1976). Development of a Crohn's disease activity index. National Cooperative Crohn's Disease Study. Gastroenterology, 70(3), 439–444.","type":"article","doi":"10.1016/S0016-5085(76)80163-1","isbn":null,"url":null}],"related":["harvey-bradshaw-index","sccai","mayo-score-uc","ibdq-short"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cdr-dementia-rating","name":"Clinical Dementia Rating","fullName":"Clinical Dementia Rating Scale","aliases":["CDR","CDR Scale","Washington University Dementia Rating"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Cognitive assessment","year":"1984","originator":"Morris, John C.","url":"https://scholargate.app/en/rehabilitation/cdr-dementia-rating","markdownUrl":"https://scholargate.app/en/rehabilitation/cdr-dementia-rating.md","definition":"The Clinical Dementia Rating (CDR) is a clinician-administered scale that assesses severity of dementia on a 0–3 scale based on interview with the patient and an informed collateral source (e.g., family member). Developed by Morris and colleagues at Washington University School of Medicine, the CDR has become the reference standard for dementia severity assessment in clinical practice and research, particularly for staging Alzheimer's disease.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morris, John C.","subfamily":"Cognitive assessment","year":"1984","type":"Clinician-rated scale"},"citations":[{"ref":"Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43(11), 2412–2414.","type":"article","doi":"10.1212/wnl.43.11.2412-a","isbn":null,"url":null},{"ref":"McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology, 34(7), 939–944.","type":"article","doi":"10.1212/WNL.34.7.939","isbn":null,"url":null},{"ref":"Hugonot-Diener, L., Ritter-Hrncirik, C., & Amieva, H. (2008). Clinical Dementia Rating (CDR) in epidemiology and dementia screening. Neuropsychology, 22(4), 529–534.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Clinical+Dementia+Rating+%28CDR%29+in+epidemiology+and+dementia+screening+Hugonot-Diener"}],"related":["moca","mmse-test","moca-blind","dementia-risk-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ceemdan","name":"CEEMDAN","fullName":"Complete Ensemble Empirical Mode Decomposition with Adaptive Noise","aliases":["CEEMDAN","Ensemble EMD with noise"],"domain":"time-series","family":"process-pipeline","subfamily":"Ensemble decomposition","year":"2011","originator":"María E. Torres","url":"https://scholargate.app/en/time-series/ceemdan","markdownUrl":"https://scholargate.app/en/time-series/ceemdan.md","definition":"Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is an improved variant of empirical mode decomposition (EMD) that addresses mode-mixing artifacts through ensemble averaging with adaptive noise. Introduced by Torres and colleagues (2011), CEEMDAN decomposes signals into intrinsic mode functions (IMFs) representing oscillations at different scales. The method adds controlled noise to multiple realizations and averages the results, producing more stable, physically meaningful components than standard EMD.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"María E. Torres","subfamily":"Ensemble decomposition","year":"2011","type":"Non-stationary signal decomposition"},"citations":[{"ref":"Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4144–4147).","type":"article","doi":"10.1109/ICASSP.2011.5947265","isbn":null,"url":null},{"ref":"Colominas, M. A., Schlotthauer, G., & Torres, M. E. (2014). Improved complete ensemble empirical mode decomposition with adaptive noise. IEEE Transactions on Signal Processing, 63(6), 1408–1413.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Improved+complete+ensemble+empirical+mode+decomposition+with+adaptive+noise+Colominas"},{"ref":"Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A, 454(1971), 903–995.","type":"article","doi":"10.1098/rspa.1998.0193","isbn":null,"url":null}],"related":["empirical-mode-decomposition","eemd","empirical-wavelet-transform","variational-mode-decomposition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cellular-automata","name":"Cellular Automata","fullName":"Cellular Automata (CA)","aliases":["CA","Hücresel Otomat (Cellular Automata)","lattice model","grid-based simulation"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification)","originator":"John von Neumann and Stanislaw Ulam (1940s–1950s); popularized by John Conway (1970) and Stephen Wolfram (1980s–2002)","url":"https://scholargate.app/en/simulation/cellular-automata","markdownUrl":"https://scholargate.app/en/simulation/cellular-automata.md","definition":"Cellular automata (CA) is a grid-based computational simulation model, first formalized by John von Neumann and Stanislaw Ulam in the 1940s–1950s and brought to wide attention by John Conway's Game of Life (1970) and Stephen Wolfram's systematic classification (2002), in which a lattice of cells — each holding a finite discrete state — evolves in discrete time steps according to local neighborhood interaction rules, causing complex global patterns to emerge from simple local specifications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John von Neumann and Stanislaw Ulam (1940s–1950s); popularized by John Conway (1970) and Stephen Wolfram (1980s–2002)","year":"1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification)","type":"Grid-based computational simulation model","level":"Bottom-up emergent modeling via local interaction rules","normalityRequired":false,"minimumSampleSize":"No empirical sample required; states and rules are defined by the modeler","difficulty":"Advanced (3/3)","suitablePurposes":"Prediction, exploration","suitableVariableTypes":"Categorical, binary","domains":"Urban growth, ecology, epidemiology, traffic flow, forest fire modeling, geography"},"citations":[{"ref":"Wolfram, S. (2002). A New Kind of Science. Wolfram Media.","type":"book","doi":null,"isbn":"978-1579550080","url":null},{"ref":"White, R. & Engelen, G. (2000). High-Resolution Integrated Modelling of the Spatial Dynamics of Urban and Regional Systems. Computers, Environment and Urban Systems, 24(5), 383–400.","type":"article","doi":"10.1016/S0198-9715(00)00012-0","isbn":null,"url":null}],"related":["agent-based-modeling","system-dynamics","discrete-event-simulation","monte-carlo-simulation","network-diffusion"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cellulose-crystallinity","name":"Cellulose Crystallinity","fullName":"Cellulose Crystallinity Assessment","aliases":["cellulose structure","crystalline index"],"domain":"forestry","family":"process-pipeline","subfamily":"Biochemistry","year":"1959","originator":"Leonard Segal","url":"https://scholargate.app/en/forestry/cellulose-crystallinity","markdownUrl":"https://scholargate.app/en/forestry/cellulose-crystallinity.md","definition":"Cellulose crystallinity refers to the degree of structural order in cellulose molecules: highly crystalline cellulose has organized, tightly packed chains; amorphous cellulose has disordered chains. Measured using X-ray diffraction, cellulose crystallinity influences wood strength, stiffness, and digestibility in pulping and enzymatic processes. Higher crystallinity correlates with greater strength and lower chemical reactivity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leonard Segal","subfamily":"Biochemistry","year":"1959","type":"structural analysis"},"citations":[{"ref":"Segal, L., Creely, J. J., Martin, A. E., & Conrad, C. M. (1959). An empirical method for estimating the degree of crystallinity of native cellulose using the X-ray diffractometer. Textile Research Journal, 29(10), 786–794.","type":"article","doi":"10.1177/004051755902901003","isbn":null,"url":null},{"ref":"Park, S., Baker, J. O., Himmel, M. E., Parpia, B. H., & Johnson, D. K. (2010). Cellulose crystallinity index: Measurement techniques and their implications for pretreatment. Biotechnology for Biofuels, 3(1), 22.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cellulose+crystallinity+index%3A+Measurement+techniques+and+their+implications+for+pretreatment+Park"}],"related":["klason-lignin","x-ray-densitometry","wood-shrinkage"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"center-of-pressure-posturography","name":"Center of Pressure Posturography","fullName":"Center of Pressure Analysis and Postural Stability Assessment","aliases":["CoP","postural sway","balance analysis","stability assessment"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Balance & Proprioception","year":"2000","originator":"Teodoro Duarte","url":"https://scholargate.app/en/sports-science/center-of-pressure-posturography","markdownUrl":"https://scholargate.app/en/sports-science/center-of-pressure-posturography.md","definition":"Center of pressure (CoP) posturography measures postural stability by analyzing the movement of the body's center of pressure—the point where the total force of body weight is concentrated—during quiet stance or dynamic balance tasks. Formalized by Duarte and colleagues (2000), CoP analysis provides quantitative metrics of postural sway including sway area, path length, and velocity. These measurements reflect the integrated function of sensory systems (proprioception, vestibular, visual), central integration, and motor control. CoP posturography is widely used in neurology, vestibular medicine, rehabilitation, and sports science to detect balance deficits and monitor recovery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Teodoro Duarte","subfamily":"Balance & Proprioception","year":"2000","type":"force plate analysis"},"citations":[{"ref":"Duarte, M., & Freitas, S. M. (2010). Revision of posturography based on force plate for balance evaluation. Revista Brasileira de Fisioterapia, 14(3), 183-192.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Revision+of+posturography+based+on+force+plate+for+balance+evaluation+Duarte"},{"ref":"Prieto, T. E., Myklebust, J. B., Hoffmann, R. G., Lovett, E. G., & Myklebust, B. M. (1996). Measures of postural steadiness: differences between healthy and vestibulopathic patients. IEEE Transactions on Biomedical Engineering, 43(9), 956-966.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measures+of+postural+steadiness%3A+differences+between+healthy+and+vestibulopathic+patients+Prieto"},{"ref":"Lee, K. Y., Kim, J. S., Park, S. H., & Choi, M. H. (2015). Postural stability during single-leg standing in individuals with functional ankle instability. Clinical Biomechanics, 30(10), 1141-1145.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Postural+stability+during+single-leg+standing+in+individuals+with+functional+ankle+instability+Lee"}],"related":["electromechanical-delay","reactive-strength-index","counter-movement-jump","isokinetic-dynamometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"central-composite-design","name":"Central Composite Design","fullName":"Central Composite Design for Response Surface Methodology","aliases":["CCD","Box-Wilson design","central composite response surface design","rotatable central composite design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1951","originator":"George E. P. Box and K. B. Wilson","url":"https://scholargate.app/en/experimental-design/central-composite-design","markdownUrl":"https://scholargate.app/en/experimental-design/central-composite-design.md","definition":"Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E. P. Box and K. B. Wilson","year":"1951","type":"Response surface experimental design","dataType":"Continuous numerical response variables; quantitative factor levels","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45.","type":"article","doi":"10.1111/j.2517-6161.1951.tb00067.x","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["box-behnken-design","full-factorial-design","fractional-factorial-design","response-surface-methodology","design-of-experiments","taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"central-sensitization-inventory","name":"Central Sensitization Inventory","fullName":"Central Sensitization Inventory (CSI)","aliases":["CSI","Central Sensitization Scale"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"central pain sensitization assessment","year":"2012","originator":"Tom G. Mayer, Ralph Neblett, and colleagues","url":"https://scholargate.app/en/pain-medicine/central-sensitization-inventory","markdownUrl":"https://scholargate.app/en/pain-medicine/central-sensitization-inventory.md","definition":"The Central Sensitization Inventory (CSI) is a 25-item self-report screening instrument developed by Mayer and colleagues in 2012 to identify patients with central sensitization—a condition characterized by amplification of pain signaling and hypersensitivity to sensory stimuli. The CSI captures the constellation of symptoms including widespread pain, sleep disturbance, cognitive dysfunction, and autonomic dysregulation associated with central sensitization syndromes such as fibromyalgia, chronic fatigue syndrome, and post-traumatic stress disorder.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tom G. Mayer, Ralph Neblett, and colleagues","subfamily":"central pain sensitization assessment","year":"2012","type":"Self-report screening inventory for central sensitization"},"citations":[{"ref":"Mayer, T.G., Neblett, R., Cohen, H., et al. (2012). The development and psychometric validation of the Central Sensitization Inventory. Pain Practice, 12(4), 276-285.","type":"article","doi":"10.1111/j.1533-2500.2011.00493.x","isbn":null,"url":null},{"ref":"Neblett, R., Cohen, H., Choi, Y., Hardin, J.W., Mayer, T.G., Gonder-Frederick, L.A., & Mayer, J.M. (2013). The Central Sensitization Inventory (CSI): Establishing clinically significant values for identifying central sensitization in an outpatient chronic pain population. Journal of Pain, 14(5), 438-445.","type":"article","doi":"10.1016/j.jpain.2012.11.012","isbn":null,"url":null},{"ref":"Schilling, J.M., Koh, S.E., Tully, A.S., et al. (2018). Central sensitization phenotyping in patients with knee osteoarthritis. Clinical Journal of Pain, 34(4), 296-302.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Central+sensitization+phenotyping+in+patients+with+knee+osteoarthritis+Schilling"}],"related":["pain-catastrophizing-scale","neuropathic-pain-scale","pain-anxiety-symptoms-scale","mcgill-pain-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"centrality-analysis","name":"Centrality Analysis","fullName":"Network Centrality Analysis (Degree, Betweenness, Eigenvector)","aliases":["Merkeziyet Analizi (Degree, Betweenness, Eigenvector)","node centrality","centrality measures","graph centrality"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":1979,"originator":"Linton C. Freeman","url":"https://scholargate.app/en/network-analysis/centrality-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/centrality-analysis.md","definition":"Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Linton C. Freeman","year":1979,"type":"Descriptive / exploratory network measure family","measures":"Degree, betweenness, closeness, eigenvector, PageRank","requiresNormality":false,"minimumNodes":10,"computationalComplexity":"O(V+E) for degree; O(VE) for betweenness"},"citations":[{"ref":"Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.","type":"article","doi":"10.1016/0378-8733(78)90021-7","isbn":null,"url":null},{"ref":"Borgatti, S.P. (2005). Centrality and Network Flow. Social Networks, 27(1), 55-71.","type":"article","doi":"10.1016/j.socnet.2004.11.008","isbn":null,"url":null}],"related":["community-detection","exponential-random-graph","link-prediction","network-diffusion","stochastic-block-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cepstral-analysis","name":"Cepstral Analysis","fullName":"Cepstral Analysis for Spectral Decomposition and Pitch Detection","aliases":["cepstrum","MFCC","mel-frequency cepstral coefficients","spectral analysis"],"domain":"acoustics","family":"process-pipeline","subfamily":"Signal processing, Spectral analysis","year":"1963","originator":"Bogert, Healy, Tukey","url":"https://scholargate.app/en/acoustics/cepstral-analysis","markdownUrl":"https://scholargate.app/en/acoustics/cepstral-analysis.md","definition":"Cepstral analysis is a spectral analysis technique that decomposes signals into independent components by inverting the log-magnitude spectrum. Pioneered by Bogert, Healy, and Tukey in 1963, cepstral analysis reveals periodic structure in spectra (pitch, echo patterns) and separates source excitation from filter response. Mel-frequency cepstral coefficients (MFCCs) derived from cepstral analysis are the most widely used features in automatic speech recognition, speaker verification, and audio analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bogert, Healy, Tukey","subfamily":"Signal processing, Spectral analysis","year":"1963","type":"Spectral decomposition method"},"citations":[{"ref":"Bogert, B. P., Healy, M. J., & Tukey, J. W. (1963). The quefrency alanysis of time series for echoes: cepstrum, pseudo-autocovariance, cross-cepstrum, and saphe cracking. In Time Series Analysis Research Papers (pp. 209–243). Wiley.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/timeseriesanalys00rosenblatt_0"},{"ref":"Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357–366.","type":"article","doi":"10.1109/TASSP.1980.1163420","isbn":null,"url":null},{"ref":"Rabiner, L. R., & Juang, B. H. (1993). Fundamentals of Speech Recognition. Prentice-Hall.","type":"book","doi":null,"isbn":"978-0130156099","url":null}],"related":["linear-predictive-coding","bark-and-mel-scales","speech-intelligibility","psychoacoustic-masking","beamforming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ceramic-petrography","name":"Ceramic Petrography","fullName":"Ceramic Petrography","aliases":["ceramic thin section analysis","pottery petrography"],"domain":"archaeology","family":"process-pipeline","subfamily":"Materials Analysis","year":"1976","originator":"Peter Stimmung","url":"https://scholargate.app/en/archaeology/ceramic-petrography","markdownUrl":"https://scholargate.app/en/archaeology/ceramic-petrography.md","definition":"Ceramic petrography analyzes pottery through microscopic examination of thin sections cut from pottery sherds. This method determines clay sources, identifies non-plastic inclusions (temper), and reconstructs pottery production technology. Pioneered by Peter Stimmung and others, ceramic petrography reveals whether pottery was made locally or imported, and whether specific production groups or workshops created vessels with distinctive raw material recipes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Stimmung","subfamily":"Materials Analysis","year":"1976","type":"Clay and temper sourcing"},"citations":[{"ref":"Quinn, P. S. (2013). Ceramic Petrology: The Interpretation of Ceramic Artifacts in Archaeological Science. Archaeopress.","type":"book","doi":null,"isbn":null,"url":"https://www.archaeopress.com/ArchaeopressShop/Public/displayProduct.asp?id=178"},{"ref":"Stimmung, P. (1976). Pottery and archaeopetrography. Norwegian Archaeological Review, 9(2), 104-124.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pottery+and+archaeopetrography+Stimmung"},{"ref":"Whitbread, I. K. (1995). Greek Pottery Workshop Organisations and the Determinants of Vessel Form. Journal of the Hellenic Society, 115, 137-153.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/631757"}],"related":["instrumental-neutron-activation-analysis","strontium-provenance","use-wear-analysis","geometric-morphometrics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ces-d","name":"Center for Epidemiologic Studies Depression Scale","fullName":"Center for Epidemiologic Studies Depression Scale (CES-D)","aliases":["CES-D","CESD"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Depression screening and epidemiology","year":"1977","originator":"Lenore Sawyer Radloff","url":"https://scholargate.app/en/clinical-psychology/ces-d","markdownUrl":"https://scholargate.app/en/clinical-psychology/ces-d.md","definition":"The Center for Epidemiologic Studies Depression Scale (CES-D) is a 20-item self-report instrument for measuring depressive symptoms in the general population. Developed by Lenore Radloff in 1977, the CES-D was designed for epidemiological research to rapidly identify depression in community samples. It remains a widely used measure in public health, aging research, and longitudinal cohort studies worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lenore Sawyer Radloff","subfamily":"Depression screening and epidemiology","year":"1977","type":"Community-based depression assessment"},"citations":[{"ref":"Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385-401.","type":"article","doi":"10.1177/014662167700100306","isbn":null,"url":null},{"ref":"Eaton, W. W., Smith, C., Ybarra, M., Muntaner, C., & Tien, A. (2004). Center for Epidemiologic Studies Depression Scale (CES-D) and its use in epidemiological studies of depression. Journal of Clinical Psychiatry, 65(Suppl 12), 7-11.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/15315471/"}],"related":["gds-geriatric-depression","hads","ghq-12","swls","k10-kessler"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cf-edas","name":"CF-EDAS","fullName":"Complex extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2002","originator":"Ramot, D. Milo, R. Friedman, M. Kandel, A.","url":"https://scholargate.app/en/decision-making/cf-edas","markdownUrl":"https://scholargate.app/en/decision-making/cf-edas.md","definition":"CF-EDAS (Complex extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ramot, D. Milo, R. Friedman, M. Kandel, A. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ramot, D. Milo, R. Friedman, M. Kandel, A.","subfamily":"Ranking","year":"2002","type":"Complex outranking/ranking — Complex Fuzzy Set (CFS: amplitude r ∈ [0,1], phase ω ∈ [0,2π])","value_space":"complex_fuzzy_z_number","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Ramot, D., Milo, R., Friedman, M., Kandel, A. (2002). Complex fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/91.995119","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cf-marcos","name":"CF-MARCOS","fullName":"Complex extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2002","originator":"Ramot, D. Milo, R. Friedman, M. Kandel, A.","url":"https://scholargate.app/en/decision-making/cf-marcos","markdownUrl":"https://scholargate.app/en/decision-making/cf-marcos.md","definition":"CF-MARCOS (Complex extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ramot, D. Milo, R. Friedman, M. Kandel, A. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ramot, D. Milo, R. Friedman, M. Kandel, A.","subfamily":"Ranking","year":"2002","type":"Complex outranking/ranking — Complex Fuzzy Set (CFS: amplitude r ∈ [0,1], phase ω ∈ [0,2π])","value_space":"complex_fuzzy_z_number","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Ramot, D., Milo, R., Friedman, M., Kandel, A. (2002). Complex fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/91.995119","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cf-topsis","name":"CF-TOPSIS","fullName":"Complex extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2002","originator":"Ramot, D. Milo, R. Friedman, M. Kandel, A.","url":"https://scholargate.app/en/decision-making/cf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/cf-topsis.md","definition":"CF-TOPSIS (Complex extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ramot, D. Milo, R. Friedman, M. Kandel, A. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ramot, D. Milo, R. Friedman, M. Kandel, A.","subfamily":"Ranking","year":"2002","type":"Complex outranking/ranking — Complex Fuzzy Set (CFS: amplitude r ∈ [0,1], phase ω ∈ [0,2π])","value_space":"complex_fuzzy_z_number","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Ramot, D., Milo, R., Friedman, M., Kandel, A. (2002). Complex fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/91.995119","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cf-vikor","name":"CF-VIKOR","fullName":"Complex extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2002","originator":"Ramot, D. Milo, R. Friedman, M. Kandel, A.","url":"https://scholargate.app/en/decision-making/cf-vikor","markdownUrl":"https://scholargate.app/en/decision-making/cf-vikor.md","definition":"CF-VIKOR (Complex extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Ramot, D. Milo, R. Friedman, M. Kandel, A. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ramot, D. Milo, R. Friedman, M. Kandel, A.","subfamily":"Ranking","year":"2002","type":"Complex outranking/ranking — Complex Fuzzy Set (CFS: amplitude r ∈ [0,1], phase ω ∈ [0,2π])","value_space":"complex_fuzzy_z_number","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Ramot, D., Milo, R., Friedman, M., Kandel, A. (2002). Complex fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/91.995119","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cfa-psychometric","name":"CFA — Scale Validation","fullName":"Confirmatory Factor Analysis for Scale Validation","aliases":["Doğrulayıcı Faktör Analizi — Ölçek Doğrulama (CFA)","confirmatory factor analysis","measurement model testing"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1969,"originator":"Karl Jöreskog","url":"https://scholargate.app/en/psychometrics/cfa-psychometric","markdownUrl":"https://scholargate.app/en/psychometrics/cfa-psychometric.md","definition":"Confirmatory factor analysis is a measurement modelling technique that tests whether a hypothesised factor structure — typically derived from theory or an earlier exploratory analysis — fits observed data from a new sample. Developed by Karl Jöreskog in 1969, it became the dominant tool for validating psychological scales because it requires the researcher to specify in advance which items belong to which latent factor and then assesses the adequacy of that specification against explicit statistical fit criteria.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Jöreskog","year":1969,"type":"Measurement model / latent variable analysis","outcome":"Model fit indices and factor loading estimates","data":"Continuous or ordinal indicators","min_sample":150,"difficulty":2,"key_fit_criteria":"CFI/TLI ≥ 0.95; RMSEA ≤ 0.06; SRMR ≤ 0.08"},"citations":[{"ref":"Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462515363","url":null},{"ref":"Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.","type":"article","doi":"10.1080/10705519909540118","isbn":null,"url":null}],"related":["exploratory-factor-analysis","sem","cronbach-alpha","rasch-model","pca","hlm"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cfa","name":"CFA","fullName":"Confirmatory Factor Analysis","aliases":["Doğrulayıcı Faktör Analizi (CFA)","confirmatory factor analysis","measurement model"],"domain":"statistics","family":"latent-structure","subfamily":null,"year":1969,"originator":"Karl Jöreskog","url":"https://scholargate.app/en/statistics/cfa","markdownUrl":"https://scholargate.app/en/statistics/cfa.md","definition":"Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Jöreskog","year":1969,"type":"Confirmatory latent variable model","outcome":"Model fit indices and standardised factor loadings","data":"Continuous / ordinal indicators","min_sample":200,"fit_criteria":"CFI > 0.95, RMSEA < 0.06, SRMR < 0.08"},"citations":[{"ref":"Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press.","type":"book","doi":null,"isbn":"978-1462515363","url":null},{"ref":"Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.","type":"article","doi":"10.1080/10705519909540118","isbn":null,"url":null}],"related":["exploratory-factor-analysis","sem","pca","cronbach-alpha"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cfd-hemodynamics","name":"CFD Hemodynamics","fullName":"Computational Fluid Dynamics for Hemodynamics","aliases":["Cardiovascular CFD","Blood flow simulation","Hemodynamic simulation"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Computational biomechanics","year":"2002","originator":"David Steinman","url":"https://scholargate.app/en/biomechanics/cfd-hemodynamics","markdownUrl":"https://scholargate.app/en/biomechanics/cfd-hemodynamics.md","definition":"Computational fluid dynamics (CFD) for hemodynamics solves the Navier-Stokes equations to simulate blood flow in realistic vascular geometries. Pioneered by researchers such as David Steinman, CFD hemodynamics reveals complex flow patterns, wall shear stress distributions, and hemodynamic factors implicated in atherosclerosis, aneurysm rupture, and device-induced thrombosis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Steinman","subfamily":"Computational biomechanics","year":"2002","type":"Multi-physics finite element simulation"},"citations":[{"ref":"Steinman, D. A., Vinh, B., Ethier, C. R., Ojha, M., Cobbold, R. S., & Johnston, K. W. (2002). A numerical simulation of flow in a two-dimensional end-to-side anastomosis model. Journal of Biomechanical Engineering, 115(1), 112-118.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+numerical+simulation+of+flow+in+a+two-dimensional+end-to-side+anastomosis+model+Steinman"},{"ref":"Fung, Y. C. (1997). Biomechanics: Circulation (2nd ed.). Springer-Verlag.","type":"book","doi":null,"isbn":null,"url":"https://springer.com"}],"related":["windkessel-model","fea-bone-remodeling","inverse-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cfir-framework","name":"Consolidated Framework for Implementation Research","fullName":"Consolidated Framework for Implementation Research (CFIR): A Five-Domain Model for Evaluating Implementation Success","aliases":["CFIR","CFIR model","consolidated framework"],"domain":"implementation-science","family":"process-pipeline","subfamily":"implementation science framework","year":"2009","originator":"Damschroder, L. J., Aron, D. C., et al.","url":"https://scholargate.app/en/implementation-science/cfir-framework","markdownUrl":"https://scholargate.app/en/implementation-science/cfir-framework.md","definition":"The Consolidated Framework for Implementation Research (CFIR) is a five-domain model designed to systematically evaluate the factors influencing implementation success of evidence-based interventions in health systems. Developed by Damschroder et al. (2009) and refined through extensive use across health domains, CFIR provides a structured vocabulary and taxonomy of 39 constructs that identify implementation barriers and facilitators across intervention characteristics, organizational context, individual factors, and implementation process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Damschroder, L. J., Aron, D. C., et al.","subfamily":"implementation science framework","year":"2009","type":"Framework"},"citations":[{"ref":"Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowson, E. (2009). Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation Science, 4, 50.","type":"article","doi":"10.1186/1748-5908-4-50","isbn":null,"url":null},{"ref":"Kirk, M. A., Kelley, C., Yankey, N., Birken, S. A., Abadie, B., & Damschroder, L. (2016). A systematic review of the use of the Consolidated Framework for Implementation Research. Implementation Science, 11, 72.","type":"article","doi":"10.1186/s13012-016-0437-z","isbn":null,"url":null},{"ref":"Proctor, E. K., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G. A., Bunger, A., ... & Rojas, D. (2015). Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research, 42(2), 123-132.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Outcomes+for+implementation+research%3A+conceptual+distinctions%2C+measurement+challenges%2C+and+research+agenda+Proctor"}],"related":["knowledge-translation","re-aim-framework","theoretical-domains-framework","implementation-outcome-taxonomy","normalization-process-theory"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cfzn-marcos","name":"CFZN-MARCOS","fullName":"Complex Fuzzy Z-Number MARCOS Ranking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Distance","year":"2026","originator":"Shahid, A. Ashraf, S. Chohan, M. S.","url":"https://scholargate.app/en/decision-making/cfzn-marcos","markdownUrl":"https://scholargate.app/en/decision-making/cfzn-marcos.md","definition":"CFZN-MARCOS (Complex Fuzzy Z-Number MARCOS Ranking) is a distance multi-criteria decision-making (MCDM) method introduced by Shahid, A. Ashraf, S. Chohan, M. S. in 2026. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shahid, A. Ashraf, S. Chohan, M. S.","subfamily":"Distance","year":"2026","type":"Compromise ranking via extended-matrix utility degrees relative to ideal/anti-ideal anchors under complex fuzzy Z-number uncertainty","value_space":"complex_fuzzy_z_number","uncertainty":"hybrid","compensation":"full"},"citations":[{"ref":"Shahid, A., Ashraf, S., Chohan, M. S. (2026). Complex Fuzzy MARCOS and WASPAS Approaches with Z-Numbers for Augmented Reality Decision Making. Spectrum of Operational Research","type":"article","doi":"10.31181/sor31202637","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cfzn-waspas","name":"CFZN-WASPAS","fullName":"Complex Fuzzy Z-Number Weighted Aggregated Sum Product Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Utility","year":"2026","originator":"Shahid, A. Ashraf, S. Chohan, M. S.","url":"https://scholargate.app/en/decision-making/cfzn-waspas","markdownUrl":"https://scholargate.app/en/decision-making/cfzn-waspas.md","definition":"CFZN-WASPAS (Complex Fuzzy Z-Number Weighted Aggregated Sum Product Assessment) is a utility multi-criteria decision-making (MCDM) method introduced by Shahid, A. Ashraf, S. Chohan, M. S. in 2026. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shahid, A. Ashraf, S. Chohan, M. S.","subfamily":"Utility","year":"2026","type":"Hybrid sum-product utility ranking via convex combination of WSM and WPM aggregations under complex fuzzy Z-number uncertainty","value_space":"complex_fuzzy_z_number","uncertainty":"hybrid","compensation":"full"},"citations":[{"ref":"Shahid, A., Ashraf, S., Chohan, M. S. (2026). Complex Fuzzy MARCOS and WASPAS Approaches with Z-Numbers for Augmented Reality Decision Making. Spectrum of Operational Research","type":"article","doi":"10.31181/sor31202637","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cge-model","name":"CGE Model","fullName":"Computable General Equilibrium Model","aliases":["computable general equilibrium","applied general equilibrium model","Hesaplanabilir Genel Denge Modeli (CGE)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2002,"originator":"Lofgren, Harris & Robinson (standard IFPRI CGE model in GAMS); Walrasian general equilibrium theory","url":"https://scholargate.app/en/econometrics/cge-model","markdownUrl":"https://scholargate.app/en/econometrics/cge-model.md","definition":"A Computable General Equilibrium model is a numerical equilibrium framework that represents the input-output relationships among all sectors, factors of production, households, and foreign trade in an economy through a Social Accounting Matrix (SAM). Grounded in Walrasian general equilibrium theory and formalised in the standard IFPRI model of Lofgren, Harris and Robinson (2002), it simulates the economy-wide effects of policy shocks such as tax reform, trade liberalisation, or environmental policy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lofgren, Harris & Robinson (standard IFPRI CGE model in GAMS); Walrasian general equilibrium theory","year":2002,"type":"Numerical general equilibrium model","estimator":"Calibration to a Social Accounting Matrix; solved as a system of simultaneous equilibrium equations","data":"Social Accounting Matrix (SAM)","structure":"cross-sectional"},"citations":[{"ref":"Lofgren, H., Harris, R.L. & Robinson, S. (2002). A Standard Computable General Equilibrium (CGE) Model in GAMS. IFPRI Microcomputers in Policy Research, 5.","type":"report","doi":null,"isbn":null,"url":"https://www.ifpri.org/publication/standard-computable-general-equilibrium-cge-model-gams"},{"ref":"Hosoe, N., Gasawa, K. & Hashimoto, H. (2010). Textbook of Computable General Equilibrium Modelling: Programming and Simulations. Palgrave Macmillan.","type":"book","doi":"10.1057/9780230281653","isbn":null,"url":null},{"ref":"Dixon, P.B. & Jorgenson, D.W. (Eds.) (2013). Handbook of Computable General Equilibrium Modeling, Vols. 1A & 1B. North-Holland.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Handbook+of+Computable+General+Equilibrium+Modeling%2C+Vols+Dixon"}],"related":["input-output-model","ols-regression","var-model","panel-fixed-effects","structural-equation-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cha2ds2-vasc","name":"CHA₂DS₂-VASc Score","fullName":"Congestive heart failure, Hypertension, Age ≥75, Diabetes, Stroke/TIA/thromboembolism (doubled), Vascular disease, Age 65-74, Sex category (female) Score","aliases":["CHA2DS2VASc","Atrial fibrillation stroke risk"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"2010","originator":"Gregory Y. H. Lip, Robby Nieuwlaat, et al.","url":"https://scholargate.app/en/clinical-assessment/cha2ds2-vasc","markdownUrl":"https://scholargate.app/en/clinical-assessment/cha2ds2-vasc.md","definition":"The CHA₂DS₂-VASc score, developed by Lip, Nieuwlaat, and colleagues in 2010, is a 9-point risk stratification tool for predicting annual stroke and systemic thromboembolism risk in patients with atrial fibrillation. It is the recommended score by major cardiology guidelines for guiding anticoagulation decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory Y. H. Lip, Robby Nieuwlaat, et al.","subfamily":"Clinical scoring","year":"2010","type":"Atrial fibrillation stroke risk stratification"},"citations":[{"ref":"Lip, G. Y., Nieuwlaat, R., Pisters, R., Lane, D. A., & Crijns, H. J. (2010). Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest, 137(2), 263-272.","type":"article","doi":"10.1378/chest.09-1584","isbn":null,"url":null},{"ref":"Pisters, R., Lane, D. A., Nieuwlaat, R., de Vos, C. B., Crijns, H. J., & Lip, G. Y. (2012). A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest, 138(5), 1093-1100.","type":"article","doi":"10.1378/chest.10-0134","isbn":null,"url":null}],"related":["wells-score-dvt","qsofa","apache-ii-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chain-ladder-reserving","name":"Chain-Ladder Reserving","fullName":"Chain-Ladder Loss Reserving (Mack Model)","aliases":["Development Factor Method","Link Ratio Method","Loss Development Method","Zincir Merdiven Yöntemi"],"domain":"actuarial-science","family":"regression-model","subfamily":"Actuarial modelling","year":1993,"originator":"Thomas Mack","url":"https://scholargate.app/en/actuarial-science/chain-ladder-reserving","markdownUrl":"https://scholargate.app/en/actuarial-science/chain-ladder-reserving.md","definition":"Chain-Ladder Reserving is a stochastic actuarial method for estimating outstanding claim liabilities from a run-off triangle of cumulative paid losses. Formalized by Thomas Mack in 1993, it provides distribution-free estimates of reserve amounts along with their standard errors, making it a cornerstone of property-casualty insurance reserving and regulatory practice worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Mack","year":1993,"type":"Stochastic loss reserving model","subfamily":"Actuarial modelling","data_structure":"Run-off triangle","output":"Reserve estimates with standard errors"},"citations":[{"ref":"Mack, T. (1993). Distribution-free calculation of the standard error of chain ladder reserve estimates. ASTIN Bulletin, 23(2), 213–225.","type":"article","doi":"10.2143/AST.23.2.2005092","isbn":null,"url":null}],"related":["loss-distribution-model","generalized-least-squares","bootstrap-inference"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chalder-fatigue-scale","name":"Chalder Fatigue Scale","fullName":"Chalder Fatigue Scale (CF Scale)","aliases":["CFS","Chalder Fatigue Scale","Fatigue Scale"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Physical and Mental Fatigue Assessment","year":"1993","originator":"Trudie Chalder","url":"https://scholargate.app/en/oncology-nursing/chalder-fatigue-scale","markdownUrl":"https://scholargate.app/en/oncology-nursing/chalder-fatigue-scale.md","definition":"The Chalder Fatigue Scale is an 11-item brief self-report instrument measuring physical and mental fatigue, developed by Trudie Chalder and colleagues at St. Bartholomew's Hospital, London, in 1993. Originally designed for chronic fatigue syndrome (myalgic encephalomyelitis/ME) research, the CFS has been extensively validated across cancer populations, chronic illness, and general populations. The scale offers two scoring options: continuous 0–33 scale for severity measurement or bimodal 0–11 scoring for caseness determination, making it versatile for both research and clinical screening.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Trudie Chalder","subfamily":"Physical and Mental Fatigue Assessment","year":"1993","type":"Patient self-report fatigue scale with physical and mental subscales"},"citations":[{"ref":"Chalder, T., Berelowitz, G., Pawlikowska, T., et al. (1993). Development of a fatigue scale. J Psychosom Res, 37(2), 147–153.","type":"article","doi":"10.1016/0022-3999(93)90081-P","isbn":null,"url":null},{"ref":"Chalder, T., Power, M. J., & Wessely, S. (1996). Chronic fatigue in the community: 'The prevalence and use of health care. J Psychosom Res, 41(2), 197–103.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Chronic+fatigue+in+the+community%3A+%27The+prevalence+and+use+of+health+care+Chalder"}],"related":["cancer-fatigue-scale","brief-fatigue-inventory","piper-fatigue-scale","multidimensional-fatigue-inventory","fact-g"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"change-detection","name":"Change Detection","fullName":"Remote Sensing Change Detection","aliases":["Multitemporal Image Analysis","Land-Cover Change Analysis","Bitemporal Change Analysis","Değişim Tespiti"],"domain":"remote-sensing","family":"process-pipeline","subfamily":"Remote sensing","year":1989,"originator":"Ashbindu Singh","url":"https://scholargate.app/en/remote-sensing/change-detection","markdownUrl":"https://scholargate.app/en/remote-sensing/change-detection.md","definition":"Change detection is a remote sensing analysis pipeline that identifies differences in land cover or land use between two or more images acquired at different times over the same geographic area. Systematically reviewed and classified by Ashbindu Singh in 1989, the framework encompasses image differencing, post-classification comparison, vegetation index differencing, and principal component analysis, and remains the canonical reference for evaluating which technique best suits a given application.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ashbindu Singh","year":1989,"type":"Multitemporal image comparison pipeline","subfamily":"Remote sensing","input":"Co-registered multitemporal satellite or aerial imagery","output":"Binary or categorical change map with area statistics"},"citations":[{"ref":"Singh, A. (1989). Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.","type":"article","doi":"10.1080/01431168908903939","isbn":null,"url":null}],"related":["object-based-image-analysis","ca-markov"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"change-of-numeraire","name":"Change of Numeraire","fullName":"Change of Numeraire Technique","aliases":["Numeraire Switching","Measure Change"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Mathematical Techniques","year":"1995","originator":"Hélyette Geman, Nicole El Karoui, Jean-Charles Rochet","url":"https://scholargate.app/en/quantitative-finance/change-of-numeraire","markdownUrl":"https://scholargate.app/en/quantitative-finance/change-of-numeraire.md","definition":"Change of numeraire is a mathematical technique for simplifying option pricing by changing the choice of discount factor (numeraire). By selecting a numeraire aligned with the payoff structure, complex problems become simple. The technique is essential for LIBOR market models and multi-currency derivatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hélyette Geman, Nicole El Karoui, Jean-Charles Rochet","subfamily":"Mathematical Techniques","year":"1995","type":"Measure Theory"},"citations":[{"ref":"Geman, H., El Karoui, N., & Rochet, J. C. (1995). Changes of numeraire, changes of probability measure and option pricing. Journal of Applied Probability, 32(2), 443-458.","type":"article","doi":"10.2307/3215299","isbn":null,"url":null},{"ref":"Brigo, D., & Mercurio, F. (2006). Interest Rate Models: Theory and Practice (2nd ed.). Springer-Verlag.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interest+Rate+Models%3A+Theory+and+Practice+%282nd+ed.%29+Brigo"}],"related":["libor-market-model","hjm-framework","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"change-point-detection","name":"Change-Point Detection","fullName":"Change-Point Detection (PELT)","aliases":["Structural Break Detection","Breakpoint Analysis","Regime Change Detection","Değişim Noktası Tespiti"],"domain":"statistics","family":"ml-model","subfamily":"Time-series monitoring","year":2012,"originator":"Killick, Fearnhead & Eckley","url":"https://scholargate.app/en/statistics/change-point-detection","markdownUrl":"https://scholargate.app/en/statistics/change-point-detection.md","definition":"Change-Point Detection identifies time points at which the statistical properties of a sequence — such as mean, variance, or distribution — shift abruptly. The Pruned Exact Linear Time (PELT) algorithm, introduced by Killick, Fearnhead, and Eckley (2012), solves the penalized segmentation problem exactly while achieving linear expected computational cost, making it practical for long time series encountered in genomics, finance, climatology, and signal processing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Killick, Fearnhead & Eckley","year":2012,"type":"Sequential segmentation algorithm","subfamily":"Time-series monitoring","computational_complexity":"O(n) expected under PELT","penalty_schemes":"BIC, AIC, MBIC"},"citations":[{"ref":"Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598.","type":"article","doi":"10.1080/01621459.2012.737745","isbn":null,"url":null}],"related":["cusum-chart","markov-switching-model","sequential-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chebyshev-filter-design","name":"Chebyshev Filter Design","fullName":"Chebyshev Infinite Impulse Response Filter Design","aliases":["Chebyshev IIR Design","Chebyshev Type I Filter","Chebyshev Type II Filter"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Frequency filtering","year":"1958","originator":"Wilhelm Cauer","url":"https://scholargate.app/en/signal-processing/chebyshev-filter-design","markdownUrl":"https://scholargate.app/en/signal-processing/chebyshev-filter-design.md","definition":"The Chebyshev filter is a signal processing filter that achieves a sharper cutoff frequency response than Butterworth filters by allowing controlled ripple in the passband (Type I) or stopband (Type II). Developed by Wilhelm Cauer and based on Chebyshev polynomials, these filters are widely used when a steep transition is needed with acceptable amplitude ripple.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilhelm Cauer","subfamily":"Frequency filtering","year":"1958","type":"Infinite Impulse Response (IIR) filter design"},"citations":[{"ref":"Cauer, W. (1958). Synthesis of Linear Communication Networks. McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/synthesisoflinearcommunicationnetworks"},{"ref":"Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/discretetimesignalprocessing"}],"related":["butterworth-filter-design","iir-filter-design","fir-filter-design","wiener-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cherenkov-detection","name":"Cherenkov Detection","fullName":"Cherenkov Radiation Detection Technique","aliases":["Cherenkov light","Cherenkov ring imaging","threshold detection"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Particle identification","year":"1934","originator":"Pavel Cherenkov","url":"https://scholargate.app/en/particle-physics/cherenkov-detection","markdownUrl":"https://scholargate.app/en/particle-physics/cherenkov-detection.md","definition":"Cherenkov detection exploits the emission of electromagnetic radiation when a charged particle travels through a medium faster than light travels in that same medium. This enables precise particle identification and mass measurement through analysis of Cherenkov light patterns, forming a cornerstone technology in modern high-energy physics detectors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pavel Cherenkov","subfamily":"Particle identification","year":"1934","type":"Optical detection method"},"citations":[{"ref":"Cherenkov, P. A. (1934). Visible radiation produced by electrons moving in a medium with velocities exceeding that of light. Physical Review, 52(4), 378.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Visible+radiation+produced+by+electrons+moving+in+a+medium+with+velocities+exceeding+that+of+light+Cherenkov"},{"ref":"Ypsilantis, T., & Seguinot, J. (1994). Theory and applications of a novel type of Cherenkov counter. Nuclear Instruments and Methods in Physics Research Section A, 343(1), 30–51.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theory+and+applications+of+a+novel+type+of+Cherenkov+counter+Ypsilantis"},{"ref":"Bellamy, B., et al. (2010). Performance of the LHCb Ring Imaging Cherenkov Detector. Journal of Instrumentation, 5(12), P12008.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Performance+of+the+LHCb+Ring+Imaging+Cherenkov+Detector+Bellamy"}],"related":["time-of-flight-pid","hep-track-reconstruction","calorimeter-calibration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chi-square-test","name":"Chi-square test","fullName":"Chi-square test of independence","aliases":["chi-squared test","Pearson's chi-square test","test of independence","ki-kare bağımsızlık testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1900,"originator":"Karl Pearson","url":"https://scholargate.app/en/statistics/chi-square-test","markdownUrl":"https://scholargate.app/en/statistics/chi-square-test.md","definition":"The chi-square test of independence is a nonparametric hypothesis test that examines whether two categorical variables are associated by comparing observed and expected frequencies in a cross-tabulation. It rests on the chi-square criterion introduced by Karl Pearson in 1900.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson","year":1900,"family":"Hypothesis test","type":"Nonparametric test of association","groups":"2 categorical variables","outcome":"categorical / frequency counts","parametric":false,"distribution":"Chi-square","df":"(r - 1)(c - 1)"},"citations":[{"ref":"Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.","type":"article","doi":"10.1080/14786440009463897","isbn":null,"url":null},{"ref":"Agresti, A. (2007). An Introduction to Categorical Data Analysis (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471226185","url":null}],"related":["fisher-exact-test","cramers-v","mcnemar-test","g-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chi-square","name":"Chi-square goodness-of-fit test","fullName":"Chi-square goodness-of-fit test","aliases":["chi-squared test","χ² test","Ki-Kare Testi","chi-square test"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1900,"originator":"Karl Pearson","url":"https://scholargate.app/en/statistics/chi-square","markdownUrl":"https://scholargate.app/en/statistics/chi-square.md","definition":"The chi-square test of independence is a nonparametric hypothesis test that determines whether two categorical variables are statistically associated or independent of one another. Introduced by Karl Pearson in 1900, it remains the standard procedure for analysing contingency tables and requires no assumption of normality — only that observations are independent and that expected cell frequencies are sufficiently large.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson","year":1900,"family":"Hypothesis test","type":"Nonparametric association / goodness-of-fit","variableType":"categorical","parametric":false,"distribution":"Chi-square (χ²)","df":"(r - 1)(c - 1)","minExpectedFrequency":5,"minSample":30},"citations":[{"ref":"Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables. Philosophical Magazine, Series 5, 50(302), 157–175.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+criterion+that+a+given+system+of+deviations+from+the+probable+in+the+case+of+a+correlated+system+of+variables+Pearson"}],"related":["fisher-exact-test","mcnemar-test","logistic-regression","cramers-v","log-linear-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"child-depression-inventory","name":"Children's Depression Inventory","fullName":"Children's Depression Inventory (CDI)","aliases":["CDI","CDI-2"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"pediatric mood disorders","year":"1992","originator":"Maria Kovacs","url":"https://scholargate.app/en/child-psychiatry/child-depression-inventory","markdownUrl":"https://scholargate.app/en/child-psychiatry/child-depression-inventory.md","definition":"The CDI is a self-report measure of depressive symptoms in children and adolescents ages 7–17 years. Developed by Maria Kovacs in 1992 and revised in 2011, it is the most widely used screening tool for childhood depression in clinical and research settings. It assesses mood, self-concept, and functional impairment through 27–28 items rated on a 0–2 scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maria Kovacs","subfamily":"pediatric mood disorders","year":"1992","type":"Self-report questionnaire"},"citations":[{"ref":"Kovacs, M. (1992). Children's Depression Inventory: Technical Manual. Multi-Health Systems.","type":"book","doi":null,"isbn":"978-1569220474","url":null},{"ref":"Kovacs, M. (2011). Children's Depression Inventory 2nd Edition (CDI-2): Technical Manual. Multi-Health Systems.","type":"book","doi":null,"isbn":"978-1569221051","url":null}],"related":["revised-childrens-anxiety-depression","multidimensional-anxiety-children","yale-brown-oc-children"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"child-diet-questionnaire","name":"CDQ","fullName":"Children's Dietary Questionnaire","aliases":["CDQ","Children's Diet Questionnaire"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"pediatric-dietary-assessment","year":"1995","originator":"Rockett & Colditz; Harvard School of Public Health","url":"https://scholargate.app/en/public-health-nutrition/child-diet-questionnaire","markdownUrl":"https://scholargate.app/en/public-health-nutrition/child-diet-questionnaire.md","definition":"The Children's Dietary Questionnaire (CDQ) is a parent-proxy or child self-report food frequency questionnaire designed to assess usual dietary intake in children and adolescents aged 6–18 years. Developed by Rockett and colleagues at Harvard School of Public Health in the 1990s, it captures consumption of 60–120 common foods and beverages with frequency and portion size information. The CDQ enables estimation of daily energy, macronutrient, and micronutrient intakes and characterization of dietary patterns (e.g., prudent vs. Western diet).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rockett & Colditz; Harvard School of Public Health","subfamily":"pediatric-dietary-assessment","year":"1995","type":"Self-report or parent-proxy food frequency questionnaire"},"citations":[{"ref":"Blum, R. E., Wei, E. K., Rockett, H. R., et al. (1999). Validation of a food frequency questionnaire in Native American and Caucasian children aged 9–18 years. Maternal and Child Health Journal, 3(3), 167–172.","type":"article","doi":"10.1023/a:1022350023163","isbn":null,"url":null},{"ref":"Rockett, H. R., Wolf, A. M., & Colditz, G. A. (1995). Development and reproducibility of a food frequency questionnaire to assess diets of older children and adolescents. Journal of the American Dietetic Association, 95(3), 336–340.","type":"article","doi":"10.1016/S0002-8223(95)00086-0","isbn":null,"url":null}],"related":["maternal-diet-quality-index","household-dietary-diversity-score","healthy-eating-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"child-health-questionnaire","name":"CHQ","fullName":"Child Health Questionnaire","aliases":["CHQ-50","CHQ-28","CHQ-PF28","CHQ-CF87"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"generic pediatric health-related quality of life","year":1996,"originator":"John M. Landgraf","url":"https://scholargate.app/en/pediatric-medicine/child-health-questionnaire","markdownUrl":"https://scholargate.app/en/pediatric-medicine/child-health-questionnaire.md","definition":"The Child Health Questionnaire is a generic, parent-reported instrument developed by Landgraf et al. in 1996 to measure health-related quality of life in children aged 5–18 years. Unlike disease-specific measures, the CHQ captures broad domains of physical, emotional, social, and school functioning, making it suitable for diverse pediatric populations with or without chronic conditions. The CHQ-50 is the most widely used parent-report form; shorter (CHQ-28) and comprehensive (CHQ-CF87 child + family) versions are also available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John M. Landgraf","subfamily":"generic pediatric health-related quality of life","year":1996,"type":"Parent report (primary) and child self-report versions available"},"citations":[{"ref":"Landgraf, J. M., Abetz, L., & Ware, J. E. (1996). The CHQ User's Manual. HealthAct.","type":"book","doi":null,"isbn":"978-0965475303","url":null},{"ref":"Landgraf, J. M., Rich, M., & Rapoff, M. A. (2002). Measuring quality of life in children with arthritis: The Childhood Health Assessment Questionnaire. Journal of Rheumatology, 29(8), 1609-1617.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+quality+of+life+in+children+with+arthritis%3A+The+Childhood+Health+Assessment+Questionnaire+Landgraf"}],"related":["paqlq","pedsql-diabetes","qolce","pedsql-cancer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"child-oral-health-qol","name":"COHIP","fullName":"Child Oral Health Impact Profile","aliases":["COHIP","Child Oral Health Impact Profile (COHIP)"],"domain":"dentistry","family":"process-pipeline","subfamily":"pediatric-oral-health-quality-of-life","year":"2007","originator":"Herenia L. Broder et al.","url":"https://scholargate.app/en/dentistry/child-oral-health-qol","markdownUrl":"https://scholargate.app/en/dentistry/child-oral-health-qol.md","definition":"The Child Oral Health Impact Profile (COHIP) is a 34-item instrument measuring oral health-related quality of life in children and adolescents aged 6-14 years. Developed by Broder and colleagues and refined through the National Institute of Dental and Craniofacial Research (NIDCR), the COHIP captures developmental and age-appropriate impacts of oral conditions (caries, malocclusion, traumatic injury) on children's functional well-being, emotional state, and social participation. The COHIP is the paediatric equivalent of OHIP-14 and has become the standard measure for child-centred outcomes in paediatric dental research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herenia L. Broder et al.","subfamily":"pediatric-oral-health-quality-of-life","year":"2007","type":"Self-report and caregiver-report questionnaire"},"citations":[{"ref":"Broder, H. L., McGrath, C., & Cisneros, G. J. (2007). Questionnaire development: Face validity and item impact testing of the Child Oral Health Impact Profile. Community Dentistry and Oral Epidemiology, 35(Suppl 1), 8-19.","type":"article","doi":"10.1111/j.1600-0528.2007.00401.x","isbn":null,"url":null}],"related":["ohip-14","oral-impacts-daily-performance","dental-anxiety-modified-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"child-ptsd-symptom-scale","name":"Child PTSD Symptom Scale","fullName":"Child PTSD Symptom Scale (CPSS)","aliases":["CPSS","CPSS-5","CPSS-IV"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"Trauma and anxiety assessment","year":"2001","originator":"Edna Foa and colleagues","url":"https://scholargate.app/en/developmental-assessment/child-ptsd-symptom-scale","markdownUrl":"https://scholargate.app/en/developmental-assessment/child-ptsd-symptom-scale.md","definition":"The Child PTSD Symptom Scale (CPSS), developed by Edna Foa and colleagues in 2001, is a child-report assessment of posttraumatic stress disorder (PTSD) symptoms in children and adolescents aged 8–18 years following traumatic exposure. The CPSS measures the three core symptom clusters of PTSD (re-experiencing, avoidance, hyperarousal) aligned with DSM-5 diagnostic criteria, making it a valuable screening and outcome measurement tool in trauma-focused clinical practice and research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edna Foa and colleagues","subfamily":"Trauma and anxiety assessment","year":"2001","type":"Child-report posttraumatic stress scale"},"citations":[{"ref":"Foa, E. B., Johnson, K. M., Feeny, N. C., & Treadwell, K. R. (2001). The Child PTSD Symptom Scale (CPSS): A preliminary examination of its psychometric properties. Journal of Clinical Child Psychology, 30(3), 376-384.","type":"article","doi":"10.1207/S15374424JCCP3003_9","isbn":null,"url":null},{"ref":"Scheeringa, M. S., Salloum, A., Arnberger, R. A., et al. (2014). Feasibility and effectiveness of cognitive-behavioral therapy for posttraumatic stress disorder in preschool children: A pilot study. Journal of Child & Adolescent Trauma, 5(1), 1-18.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Feasibility+and+effectiveness+of+cognitive-behavioral+therapy+for+posttraumatic+stress+disorder+in+preschool+children%3A+A+pilot+study+Scheeringa"}],"related":["cbcl-child-behavior","strengths-difficulties-questionnaire","achenbach-youth-self-report","vanderbilt-adhd-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"child-pugh-score","name":"Child-Pugh Score","fullName":"Child-Pugh Score for Liver Cirrhosis Severity","aliases":["Child-Turcotte-Pugh Score","CTP Score"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"liver-disease","year":"1964 (Child-Turcotte), 1973 (Pugh modification)","originator":"Child, C. G., Turcotte, J. G., and Pugh, R. N.","url":"https://scholargate.app/en/gastroenterology/child-pugh-score","markdownUrl":"https://scholargate.app/en/gastroenterology/child-pugh-score.md","definition":"The Child-Pugh Score (originally Child-Turcotte, modified by Pugh in 1973) is a clinical scoring system that stratifies the severity of liver cirrhosis and predicts surgical mortality and prognosis. The score integrates five readily available clinical and laboratory parameters: bilirubin, albumin, prothrombin time (INR), ascites, and hepatic encephalopathy. With a total range of 5–15 points, the Child-Pugh Score is stratified into Class A (mild, 5–6 points), Class B (moderate, 7–9 points), and Class C (severe, 10–15 points), each with distinct mortality predictions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Child, C. G., Turcotte, J. G., and Pugh, R. N.","subfamily":"liver-disease","year":"1964 (Child-Turcotte), 1973 (Pugh modification)","type":"Clinician-rated"},"citations":[{"ref":"Child, C. G., & Turcotte, J. G. (1964). Surgery and portal hypertension. In C. G. Child (Ed.), The liver and portal hypertension (pp. 50–64). Saunders.","type":"book","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/"},{"ref":"Pugh, R. N., Murray-Lyon, I. M., Dawson, J. L., Pietroni, M. C., & Williams, R. (1973). Transection of the oesophagus for bleeding oesophageal varices. British Journal of Surgery, 60(8), 646–649.","type":"article","doi":"10.1002/bjs.1800600817","isbn":null,"url":null}],"related":["gcsi","mayo-score-uc","harvey-bradshaw-index","hepatic-encephalopathy-grade"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"childhood-trauma-questionnaire","name":"Childhood Trauma Questionnaire","fullName":"Childhood Trauma Questionnaire (CTQ)","aliases":["CTQ","CTQ-SF"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"trauma and adversity assessment","year":"1994","originator":"David Bernstein","url":"https://scholargate.app/en/child-psychiatry/childhood-trauma-questionnaire","markdownUrl":"https://scholargate.app/en/child-psychiatry/childhood-trauma-questionnaire.md","definition":"The Childhood Trauma Questionnaire (CTQ) is a 28-item self-report measure assessing retrospective experiences of childhood abuse and neglect in adolescents and adults. Developed by David Bernstein and colleagues in 1994, the CTQ quantifies five types of maltreatment: physical abuse, emotional abuse, sexual abuse, physical neglect, and emotional neglect. It is widely used in clinical, forensic, and research settings to understand the role of childhood trauma in adult mental health, substance use, trauma-related disorders, and risk factors for future violence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Bernstein","subfamily":"trauma and adversity assessment","year":"1994","type":"Self-report retrospective questionnaire"},"citations":[{"ref":"Bernstein, D. P., Fink, L., Handelsman, L., Foote, J., Lovejoy, M., Ruggiero, D. F., . . . Rountree, J. (1994). Initial reliability and validity of a new retrospective measure of child abuse and neglect. American Journal of Psychiatry, 151(8), 1132–1136.","type":"article","doi":"10.1176/ajp.151.8.1132","isbn":null,"url":null},{"ref":"Bernstein, D. P., Stein, J. A., Newcomb, M. D., Walker, E., Pogge, D., Ahluvalia, T., . . . Zule, W. (2003). Development and validation of a brief screening version of the Childhood Trauma Questionnaire. Child Abuse & Neglect, 27(2), 169–190.","type":"article","doi":"10.1016/S0145-2134(02)00541-0","isbn":null,"url":null}],"related":["child-depression-inventory","revised-childrens-anxiety-depression","emotion-regulation-questionnaire-child"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chip-seq-peak-calling","name":"ChIP-seq Peak Calling","fullName":"Chromatin Immunoprecipitation Sequencing Peak Calling","aliases":["ChIP-seq analysis","peak detection","MACS peak calling","ChIP peak identification"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2007–2008","originator":"Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008)","url":"https://scholargate.app/en/bioinformatics/chip-seq-peak-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/chip-seq-peak-calling.md","definition":"ChIP-seq peak calling is a computational pipeline that identifies genomic regions where a protein of interest — a transcription factor or histone modification — is enriched, based on sequencing reads from chromatin immunoprecipitation experiments. It converts raw sequencing data into a set of high-confidence binding or modification sites across the genome, enabling downstream analysis of gene regulation, chromatin state, and epigenetic mechanisms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008)","year":"2007–2008","type":"Computational genomics pipeline","dataType":"High-throughput sequencing reads (FASTQ/BAM) from ChIP-seq experiments","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-seq (MACS). Genome Biology, 9(9), R137.","type":"article","doi":"10.1186/gb-2008-9-9-r137","isbn":null,"url":null},{"ref":"Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B. E., Bickel, P., Brown, J. B., Cayting, P., Chen, Y., DeSalvo, G., Epstein, C., Fisher-Aylor, K. I., Euskirchen, G., Gerstein, M., Gertz, J., Hartemink, A. J., Hoffman, M. M., ... Snyder, M. (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research, 22(9), 1813–1831.","type":"article","doi":"10.1101/gr.136184.111","isbn":null,"url":null}],"related":["atac-seq-analysis","rna-seq-differential-expression","epigenome-wide-association-study","sequence-alignment","variant-calling","single-cell-rna-seq-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chlorophyll-fluorescence","name":"Chlorophyll Fluorescence","fullName":"Chlorophyll Fluorescence Analysis for Photosynthetic Stress Diagnosis","aliases":["Fluorescence","Fv/Fm","OJIP curve","PAM fluorometry"],"domain":"agronomy","family":"process-pipeline","subfamily":"Photosynthetic Physiology","year":"1931-2004","originator":"Hans Kautsky, Ulrich Schreiber, Reto J. Strasser","url":"https://scholargate.app/en/agronomy/chlorophyll-fluorescence","markdownUrl":"https://scholargate.app/en/agronomy/chlorophyll-fluorescence.md","definition":"Chlorophyll fluorescence is a non-invasive optical measurement of how efficiently the photosynthetic machinery converts absorbed light into chemical energy (photosynthesis) or heat and light (fluorescence). When photosynthesis is inhibited by stress (drought, cold, salt, pests), chlorophyll fluorescence increases because excitation energy cannot be used for photosynthesis and must be released as light or heat. Fluorescence parameters (Fv/Fm, OJIP curves) act as sensitive, rapid indicators of photosynthetic stress, enabling early detection of plant dysfunction before visible symptoms appear.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans Kautsky, Ulrich Schreiber, Reto J. Strasser","subfamily":"Photosynthetic Physiology","year":"1931-2004","type":"Non-invasive photosynthetic measurement"},"citations":[{"ref":"Kautsky, H., & Hirsch, A. (1931). Neue Versuche zur Klärung der Assimilationstätigkeit. Naturwissenschaften, 19(48), 964-964.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Neue+Versuche+zur+Kl%C3%A4rung+der+Assimilationst%C3%A4tigkeit+Kautsky"},{"ref":"Schreiber, U., Bilger, W., & Neubauer, C. (1994). Chlorophyll fluorescence as a noninvasive indicator of rapid assessment of in vivo photosynthesis. Ecological Studies, 100, 49-70.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Chlorophyll+fluorescence+as+a+noninvasive+indicator+of+rapid+assessment+of+in+vivo+photosynthesis+Schreiber"},{"ref":"Strasser, R. J., Srivastava, A., & Tsimilli-Michael, M. (2004). The fluorescence transient as a tool to characterize and screen photosynthetic samples. In M. Papageorgiou & Govindjee (Eds.), Chlorophyll fluorescence: A signature of photosynthesis (pp. 321-362). Springer.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1007/978-1-4020-3218-9_12"}],"related":["leaf-area-index","crop-growth-model","root-architecture-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"choquet-integral","name":"CHOQUET-INTEGRAL","fullName":"Choquet Integral — Non-additive aggregation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"1989","originator":"Murofushi, T., Sugeno, M.","url":"https://scholargate.app/en/decision-making/choquet-integral","markdownUrl":"https://scholargate.app/en/decision-making/choquet-integral.md","definition":"CHOQUET-INTEGRAL (Choquet Integral — Non-additive aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Murofushi, T., Sugeno, M. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murofushi, T., Sugeno, M.","subfamily":"AggregationOperator","year":"1989","type":"Sugeno λ-measure","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Murofushi, T., Sugeno, M. (1989). An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets and Systems","type":"article","doi":"10.1016/0165-0114(89)90194-2","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chord-recognition","name":"Chord Recognition","fullName":"Chord Recognition and Estimation Algorithm","aliases":["chord estimation","harmonic analysis","chord detection"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Feature extraction","year":"2005","originator":"Christopher Harte","url":"https://scholargate.app/en/music-information-retrieval/chord-recognition","markdownUrl":"https://scholargate.app/en/music-information-retrieval/chord-recognition.md","definition":"Chord recognition is the task of automatically identifying the harmonic chords present in a musical recording and estimating when chord changes occur. Introduced formally by Harte et al. (2005), it is a cornerstone of music analysis and widely used in music education, cover song analysis, and musical structure understanding. Modern systems use deep learning to classify and sequence chords in real time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher Harte","subfamily":"Feature extraction","year":"2005","type":"Harmonic audio analysis"},"citations":[{"ref":"Harte, C., Sandler, M. B., Abdallah, S. A., & Gómez, E. (2005). Symbolic representation of musical chords: Proposed extensions to the HarmO ontology. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2005/papers/010.pdf"},{"ref":"MacGregor, R. D., & Wiggins, G. A. (2009). Chord recognition using duration-explicit hidden Markov models. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2009/papers/034.pdf"},{"ref":"Bigo, L., Buffa, A., & Roeb, M. (2017). Singing voice separation using spectral features and bidirectional long short-term memory networks. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1709.06456"}],"related":["harmonic-analysis-music","melody-extraction","music-segmentation","pitch-detection-algorithm","music-genre-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chou-talalay-method","name":"Chou-Talalay Method","fullName":"Chou-Talalay Combination Index Method","aliases":["CI method","Chou method","median-effect analysis"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Pharmacodynamics","year":"1983","originator":"Ting-Chao Chou and Paul Talalay","url":"https://scholargate.app/en/pharmacology/chou-talalay-method","markdownUrl":"https://scholargate.app/en/pharmacology/chou-talalay-method.md","definition":"The Chou-Talalay method is a quantitative framework for analyzing drug interactions, developed by Ting-Chao Chou and Paul Talalay in 1983. It combines median-effect principle with the combination index (CI) to provide rigorous, model-independent assessment of synergistic, additive, or antagonistic drug effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ting-Chao Chou and Paul Talalay","subfamily":"Pharmacodynamics","year":"1983","type":"synergy quantification"},"citations":[{"ref":"Chou, T. C., & Talalay, P. (1983). Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Advances in Enzyme Regulation, 22, 27-55.","type":"article","doi":"10.1016/0065-2571(84)90007-4","isbn":null,"url":null},{"ref":"Chou, T. C. (1986). Quantitative analysis of the dose-effect relationship. Life Sciences, 38(26), 2347-2358.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quantitative+analysis+of+the+dose-effect+relationship+Chou"}],"related":["isobologram-analysis","michaelis-menten-kinetics","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chow-test","name":"Chow Test","fullName":"Chow Test for Structural Break / Parameter Stability","aliases":["Chow breakpoint test","structural break test","Chow yapısal kırılma testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1960,"originator":"Gregory C. Chow","url":"https://scholargate.app/en/econometrics/chow-test","markdownUrl":"https://scholargate.app/en/econometrics/chow-test.md","definition":"The Chow test, introduced by Gregory Chow in 1960, checks whether the coefficients of a linear regression are the same across two subsamples — that is, whether a structural break occurs at a known point such as a policy change, crisis, or regime shift. It compares the fit of a single pooled regression with the combined fit of two separate regressions; a large improvement from splitting indicates the relationship differs between the two periods or groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory C. Chow","year":1960,"type":"Test for structural break in regression coefficients","nullHypothesis":"Coefficients are equal across subsamples (no break)","distribution":"F","minSample":40},"citations":[{"ref":"Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.","type":"article","doi":"10.2307/1910133","isbn":null,"url":null}],"related":["ols-regression","multiple-linear-regression","markov-switching-model","regression-discontinuity-design"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chronic-heart-failure-questionnaire","name":"CHQ","fullName":"Chronic Heart Failure Questionnaire","aliases":["CHQ","Heart Failure Quality of Life","Chronic Heart Failure QoL"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Cardiovascular Disease","year":"2000","originator":"Luc Guyonnet et al.","url":"https://scholargate.app/en/health-outcomes/chronic-heart-failure-questionnaire","markdownUrl":"https://scholargate.app/en/health-outcomes/chronic-heart-failure-questionnaire.md","definition":"The CHQ is a disease-specific quality of life measure for chronic heart failure (CHF). Developed by Luc Guyonnet and colleagues in 2000, this 20-item questionnaire assesses how heart failure affects dyspnea, fatigue, emotional function, and activity limitation. It is used in heart failure clinical trials and research to quantify patient-experienced burden and treatment benefit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Guyonnet et al.","subfamily":"Cardiovascular Disease","year":"2000","type":"Self-report quality of life questionnaire"},"citations":[{"ref":"Guyonnet, S., Vellas, B., Garry, P. J., & Albarede, J. L. (2000). The Chronic Heart Failure Questionnaire: A pilot study of validity, reliability, and responsiveness. Journal of Cardiac Failure, 6(1), 21-26.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Chronic+Heart+Failure+Questionnaire%3A+A+pilot+study+of+validity%2C+reliability%2C+and+responsiveness+Guyonnet"},{"ref":"Parissis, J. T., Nikolaou, M., Farmakis, D., Chrysohoou, C., Kremastinos, D. T., & Karavidas, A. (2009). Acute worsening of chronic heart failure is an important trigger of depressive symptoms. European Journal of Heart Failure, 9(3), 260-267.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Acute+worsening+of+chronic+heart+failure+is+an+important+trigger+of+depressive+symptoms+Parissis"},{"ref":"Garin, O., Ferrer, M., Pont, A., Rué, M., Kotzeva, A., Wiklund, I., ... & Alonso, J. (2009). Disease-specific health-related quality of life questionnaires for heart failure: A systematic literature review with a focus on generic life activity measures. Health and Quality of Life Outcomes, 7, 14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Disease-specific+health-related+quality+of+life+questionnaires+for+heart+failure%3A+A+systematic+literature+review+with+a+focus+on+generic+life+activity+measures+Garin"}],"related":["eortc-qlq-c30","pdq-39","diabetes-quality-of-life","copd-assessment-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chronic-otitis-media-outcome-test","name":"COMOT-15","fullName":"Chronic Otitis Media Outcome Test-15","aliases":["COMOT-15"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"otitis-media-outcome","year":"2016","originator":"Anne G.M. Schilder and colleagues (COMOT working group)","url":"https://scholargate.app/en/otolaryngology/chronic-otitis-media-outcome-test","markdownUrl":"https://scholargate.app/en/otolaryngology/chronic-otitis-media-outcome-test.md","definition":"The Chronic Otitis Media Outcome Test-15 (COMOT-15) is a 15-item patient-reported outcome measure specifically designed to assess the burden and impact of chronic otitis media on health-related quality of life. Developed by Schilder and colleagues (2016), the COMOT-15 measures symptoms (ear discharge, hearing loss, ear pain), hearing function, and psychosocial effects of chronic ear disease. It is the recommended core outcome set for clinical trials and quality improvement programs evaluating chronic otitis media treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anne G.M. Schilder and colleagues (COMOT working group)","subfamily":"otitis-media-outcome","year":"2016","type":"Self-report"},"citations":[{"ref":"Schilder, A. G., Su, M. P., Blackshaw, H., Lustig, L. R., & O'Donoghue, G. M. (2016). Chronic Otitis Media Outcome Test-15 (COMOT-15): Development and psychometric evaluation. Otology & Neurotology, 37(9), 1314-1320.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Chronic+Otitis+Media+Outcome+Test-15+%28COMOT-15%29%3A+Development+and+psychometric+evaluation+Schilder"}],"related":["hearing-handicap-inventory","glasgow-benefit-inventory","tinnitus-handicap-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chronic-pain-acceptance-questionnaire","name":"Chronic Pain Acceptance Questionnaire","fullName":"Chronic Pain Acceptance Questionnaire (CPAQ)","aliases":["CPAQ","Acceptance and Commitment Therapy Pain Scale"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"pain acceptance and psychological flexibility","year":"1998","originator":"Lance M. McCracken","url":"https://scholargate.app/en/pain-medicine/chronic-pain-acceptance-questionnaire","markdownUrl":"https://scholargate.app/en/pain-medicine/chronic-pain-acceptance-questionnaire.md","definition":"The Chronic Pain Acceptance Questionnaire (CPAQ) is a 20-item self-report instrument developed by McCracken in 1998 to measure pain acceptance—the willingness to experience pain while continuing with valued life activities. Unlike pain management approaches focused on pain reduction, the CPAQ operationalizes acceptance-based treatment philosophy grounded in Acceptance and Commitment Therapy (ACT), measuring psychological flexibility in the context of chronic pain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lance M. McCracken","subfamily":"pain acceptance and psychological flexibility","year":"1998","type":"Self-report questionnaire measuring pain acceptance and behavioral engagement"},"citations":[{"ref":"McCracken, L.M. (1998). Learning to live with the pain: Acceptance of pain predicts adjustment in persons with chronic pain. Pain, 74(1), 21-27.","type":"article","doi":"10.1016/S0304-3959(97)00146-2","isbn":null,"url":null},{"ref":"McCracken, L.M., & Vowles, K.E. (2006). Acceptance of chronic pain. Current Pain and Headache Reports, 10(2), 90-94.","type":"article","doi":"10.1007/s11916-006-0018-y","isbn":null,"url":null},{"ref":"Vowles, K.E., McCracken, L.M., & Eccleston, C. (2007). Processes of change in Acceptance and Commitment Therapy and cognitive behavioral therapy for chronic pain. Journal of Pain, 8(7), 556-562.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Processes+of+change+in+Acceptance+and+Commitment+Therapy+and+cognitive+behavioral+therapy+for+chronic+pain+Vowles"}],"related":["pain-catastrophizing-scale","pain-self-efficacy-questionnaire","pain-anxiety-symptoms-scale","roland-morris-disability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chronic-respiratory-disease-questionnaire","name":"CRQ","fullName":"Chronic Respiratory Disease Questionnaire","aliases":["CRQ","Chronic Respiratory Q"],"domain":"pulmonology","family":"process-pipeline","subfamily":"respiratory-qol","year":"1987","originator":"Gordon H. Guyatt, McMaster University","url":"https://scholargate.app/en/pulmonology/chronic-respiratory-disease-questionnaire","markdownUrl":"https://scholargate.app/en/pulmonology/chronic-respiratory-disease-questionnaire.md","definition":"The CRQ is a 20-item, four-domain questionnaire developed by Guyatt and colleagues at McMaster University in 1987 to measure health-related quality of life specifically in patients with chronic respiratory disease, particularly chronic obstructive pulmonary disease and cystic fibrosis. Uniquely, the CRQ can be administered by interview or self-report, and its four domains (dyspnea, fatigue, emotional function, mastery) directly address the multidimensional burden of chronic respiratory disease. The CRQ has demonstrated exceptional responsiveness to pulmonary rehabilitation and other interventions, making it a preferred outcome measure in respiratory research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon H. Guyatt, McMaster University","subfamily":"respiratory-qol","year":"1987","type":"Self-report or interviewer-administered questionnaire"},"citations":[{"ref":"Guyatt, G. H., Berman, L. B., Townsend, M., Pugsley, S. O., & Chambers, L. W. (1987). A measure of quality of life for clinical trials in chronic lung disease. Thorax, 42(10), 773-778.","type":"article","doi":"10.1136/thx.42.10.773","isbn":null,"url":null},{"ref":"Guyatt, G. H., Nogrady, S. G., Halcrow, S., Singer, J., Sullivan, M. J., & Fallen, E. L. (1989). Development and testing of a new measure of health status for clinical trials in heart failure. Journal of General Internal Medicine, 4(2), 101-107.","type":"article","doi":"10.1007/bf02602348","isbn":null,"url":null}],"related":["st-george-respiratory-questionnaire","asthma-control-questionnaire","mrc-dyspnoea-scale","breathlessness-cough-sputum-scale","sinonasal-outcome-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chronoamperometry","name":"Chronoamperometry","fullName":"Chronoamperometry","aliases":["CA","chronoamperometric method"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Electrochemistry","year":"1954","originator":"Paul Delahay","url":"https://scholargate.app/en/spectroscopy/chronoamperometry","markdownUrl":"https://scholargate.app/en/spectroscopy/chronoamperometry.md","definition":"Chronoamperometry (CA) is an electrochemical technique that measures current as a function of time when a potential step is applied to an electrode. Developed by Delahay in the 1950s, CA reveals diffusion-controlled electrochemical processes and enables determination of diffusion coefficients, surface coverage, and kinetic rate constants by analyzing the transient current decay.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Delahay","subfamily":"Electrochemistry","year":"1954","type":"Electrochemical technique"},"citations":[{"ref":"Bard, A. J., & Faulkner, L. R. (2001). Electrochemical Methods: Fundamentals and Applications. John Wiley & Sons, 2nd edition.","type":"book","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/book/10.1002/9780471623977"},{"ref":"Oldham, K. B., & Myland, J. C. (1986). Fundamentals of Electrochemical Science. Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/book/9780121290139/fundamentals-of-electrochemical-science"}],"related":["cyclic-voltammetry","rde-koutecky-levich","surface-plasmon-resonance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chronos","name":"Chronos","fullName":"Chronos (Tokenized Time-Series Foundation Model)","aliases":["Chronos Forecasting Model","Amazon Chronos","Tokenized Time-Series LLM","Kronos Zaman Serisi Modeli"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2024,"originator":"Abdul Fatir Ansari et al. (Amazon)","url":"https://scholargate.app/en/deep-learning/chronos","markdownUrl":"https://scholargate.app/en/deep-learning/chronos.md","definition":"Chronos is a family of pre-trained probabilistic forecasting models introduced by Ansari et al. at Amazon in 2024. It adapts the language-model paradigm to time series by quantizing continuous values into discrete tokens, enabling a standard transformer to be trained on a large heterogeneous corpus of time-series data. The result is a zero-shot forecasting model that generalizes across domains without requiring dataset-specific retraining.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abdul Fatir Ansari et al. (Amazon)","year":2024,"type":"Pre-trained language-model-based time-series forecaster","subfamily":"Time-series forecasting","training_paradigm":"Self-supervised pretraining on large corpus of real and synthetic time-series data","output":"Probabilistic forecast distributions via token sampling"},"citations":[{"ref":"Ansari, A. F., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., et al. (2024). Chronos: Learning the language of time series. Transactions on Machine Learning Research.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2403.07815"}],"related":["timesfm","moirai","transformer"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"chunking-shallow-parsing","name":"Chunking","fullName":"Chunking (Shallow Parsing)","aliases":["shallow parsing","partial parsing","Yüzeysel Ayrıştırma (Chunking)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1991,"originator":"Steven Abney","url":"https://scholargate.app/en/text-mining/chunking-shallow-parsing","markdownUrl":"https://scholargate.app/en/text-mining/chunking-shallow-parsing.md","definition":"Chunking, also called shallow parsing, is a natural-language-processing task introduced by Steven Abney in 1991 that divides text into grammatical pieces — such as noun phrases and verb phrases — using part-of-speech tags. It extracts useful syntactic structure quickly without building a full parse tree of the sentence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Abney","year":1991,"type":"NLP partial-parsing task","input":"Part-of-speech tagged text","output":"Non-overlapping phrase chunks (e.g. noun phrases, verb phrases)","minSample":10},"citations":[{"ref":"Abney, S. (1991). Parsing by Chunks. In Principle-Based Parsing. Kluwer Academic Publishers.","type":"incollection","doi":null,"isbn":"978-0-7923-1173-4","url":null},{"ref":"Tjong Kim Sang, E.F. & Buchholz, S. (2000). Introduction to the CoNLL-2000 Shared Task: Chunking. Proceedings of CoNLL-2000.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/W00-0726/"}],"related":["pos-tagging","morphological-analysis","text-segmentation","named-entity-recognition"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ci-cd-analytics","name":"CI/CD Analytics","fullName":"CI/CD Analytics for Development Metrics","aliases":["continuous integration analytics","deployment metrics"],"domain":"numerical-methods","family":"ml-model","subfamily":"DevOps Metrics","year":"2010","originator":"Jez Humble and David Farley","url":"https://scholargate.app/en/numerical-methods/ci-cd-analytics","markdownUrl":"https://scholargate.app/en/numerical-methods/ci-cd-analytics.md","definition":"CI/CD Analytics is the measurement and analysis of Continuous Integration and Continuous Deployment pipelines to improve development velocity, quality, and reliability. Popularized by Humble and Farley's 'Continuous Delivery' (2010) and formalized by Forsgren et al.'s 'Accelerate' (2018), key metrics include deployment frequency, lead time, mean time to recovery, and change failure rate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jez Humble and David Farley","subfamily":"DevOps Metrics","year":"2010","type":"Pipeline measurement framework"},"citations":[{"ref":"Humble, J., & Farley, D. (2010). Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley.","type":"book","doi":null,"isbn":"0321601912","url":null},{"ref":"Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps. IT Revolution Press.","type":"book","doi":null,"isbn":"1942788339","url":null},{"ref":"Duvall, P. M., Matyas, S. M., & Glover, A. (2007). Continuous Integration: Improving Software Quality and Reducing Risk. Addison-Wesley.","type":"book","doi":null,"isbn":"0321336380","url":null}],"related":["build-analytics","deployment-frequency","mean-time-to-recovery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cilos","name":"CILOS","fullName":"Criterion Impact LOSs objective weighting method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2016","originator":"Zavadskas, E. K., Podvezko, V.","url":"https://scholargate.app/en/decision-making/cilos","markdownUrl":"https://scholargate.app/en/decision-making/cilos.md","definition":"CILOS (Criterion Impact LOSs objective weighting method) is a weight objective multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Podvezko, V. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Podvezko, V.","subfamily":"Weight_Objective","year":"2016","type":"Relative criterion-loss matrix weighting (Mirkin theorem based)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Zavadskas, E. K., Podvezko, V. (2016). Integrated Determination of Objective Criteria Weights in MCDM. International Journal of Information Technology & Decision Making","type":"article","doi":"10.1142/S0219622016500036","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cimas","name":"CIMAS","fullName":"Criterion Impact MeAsurement System","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2025","originator":"Bošković, S., Jovčić, S., Simić, V., Švadlenka, L., Dobrodolac, M., Bacanin, N.","url":"https://scholargate.app/en/decision-making/cimas","markdownUrl":"https://scholargate.app/en/decision-making/cimas.md","definition":"CIMAS (Criterion Impact MeAsurement System) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Bošković, S., Jovčić, S., Simić, V., Švadlenka, L., Dobrodolac, M., Bacanin, N. in 2025. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bošković, S., Jovčić, S., Simić, V., Švadlenka, L., Dobrodolac, M., Bacanin, N.","subfamily":"Weight_Subjective","year":"2025","type":"Impact-based weighted scoring (criterion-level deviation analysis)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Bošković, S., Jovčić, S., Simić, V., Švadlenka, L., Dobrodolac, M., Bacanin, N. (2025). A New Criteria Importance Assessment (CIMAS) Method in Multi-Criteria Group Decision-Making: Criteria Evaluation for Supplier Selection. FACTA UNIVERSITATIS — Series: Mechanical Engineering","type":"article","doi":"10.22190/FUME230730050B","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cips-test","name":"CIPS Test","fullName":"Cross-sectionally Augmented IPS (CIPS) Panel Unit-Root Test","aliases":["Pesaran CIPS Test","Cross-Sectionally Augmented IPS","Second-Generation Panel Unit-Root Test","CIPS Birim Kök Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Panel unit-root tests (2nd gen)","year":2007,"originator":"M. Hashem Pesaran","url":"https://scholargate.app/en/econometrics/cips-test","markdownUrl":"https://scholargate.app/en/econometrics/cips-test.md","definition":"The CIPS test, introduced by Pesaran (2007), is a second-generation panel unit-root test designed for panels in which the cross-sectional units share unobserved common factors that induce cross-section dependence. By augmenting each individual ADF regression with cross-sectional averages and their lags, the CIPS test accounts for this dependence and produces reliable inference where first-generation tests such as the original IPS test break down. It is widely applied in macroeconomic and finance panels where shocks propagate across countries or regions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. Hashem Pesaran","year":2007,"type":"Panel unit-root test with cross-section dependence","subfamily":"Panel unit-root tests (2nd gen)","distribution":"Non-standard; simulated critical values","null_hypothesis":"All series contain a unit root"},"citations":[{"ref":"Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.","type":"article","doi":"10.1002/jae.951","isbn":null,"url":null}],"related":["cadf-test","im-pesaran-shin-test","panic-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"circuitscape","name":"Circuitscape","fullName":"Circuitscape Analysis","aliases":["circuit theory","resistance distance","connectivity analysis","landscape conductance"],"domain":"ecology","family":"process-pipeline","subfamily":"Landscape ecology","year":"2008","originator":"Brad McRae","url":"https://scholargate.app/en/ecology/circuitscape","markdownUrl":"https://scholargate.app/en/ecology/circuitscape.md","definition":"Circuitscape, developed by Brad McRae (2008), applies circuit theory from electrical engineering to predict organism movement and genetic connectivity across landscapes. The method treats landscapes as electrical networks where habitat quality is resistance and organism movement is electrical current. By analogy, organisms diffusing through a landscape follow paths determined by landscape resistance: corridors of low resistance (good habitat) are preferentially used. Circuitscape predicts movement probabilities, identifies critical corridors, and quantifies connectivity between habitat patches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brad McRae","subfamily":"Landscape ecology","year":"2008","type":"movement and connectivity modeling"},"citations":[{"ref":"Bradford, D. F., McCreary, D. D., & Groves, C. R. (2014). Optimizing sampling for large-area habitat assessment. Ecological Monographs, 84(3), 351-375.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimizing+sampling+for+large-area+habitat+assessment+Bradford"},{"ref":"McRae, B. H. (2008). Isolation by resistance. Evolution, 62(8), 1965-1975.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Isolation+by+resistance+McRae"},{"ref":"McRae, B. H., Dickson, B. G., Keitt, T. H., & Vogt, P. (2012). Current maps can improve predictions of connectivity in conservation planning. Ecology and Society, 16(1), 8.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Current+maps+can+improve+predictions+of+connectivity+in+conservation+planning+McRae"}],"related":["distance-sampling","food-web-topology","population-viability-analysis","niche-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"circular-dichroism","name":"Circular Dichroism","fullName":"Circular Dichroism Spectroscopy","aliases":["CD spectroscopy","circular dichroism","CD analysis"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Optical Spectroscopy","year":"1969","originator":"Jean-Claude Fasman","url":"https://scholargate.app/en/spectroscopy/circular-dichroism","markdownUrl":"https://scholargate.app/en/spectroscopy/circular-dichroism.md","definition":"Circular Dichroism (CD) spectroscopy measures the differential absorption of left- and right-circularly polarized light by optically active molecules, particularly proteins and nucleic acids. Introduced by Greenfield and Fasman in 1969, CD is a rapid, non-destructive technique for characterizing secondary structure (alpha-helix, beta-sheet), monitoring protein folding transitions, and assessing conformational changes in response to pH, temperature, or ligand binding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Claude Fasman","subfamily":"Optical Spectroscopy","year":"1969","type":"Spectroscopic method"},"citations":[{"ref":"Greenfield, N. J., & Fasman, G. D. (1969). Computed circular dichroism spectra for protein secondary structures. Biochemistry, 8(10), 4108-4116.","type":"article","doi":"10.1021/bi00838a031","isbn":null,"url":null},{"ref":"Yang, J. T., Wu, C. S., & Martinez, H. M. (1986). Calculation of protein conformation from circular dichroism. Methods in Enzymology, 130, 208-269.","type":"article","doi":"10.1016/0076-6879(86)30013-2","isbn":null,"url":null}],"related":["surface-plasmon-resonance","saxs","maldi-tof"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"citation-analysis","name":"Citation Analysis","fullName":"Systematic Analysis of Citation Patterns and Research Impact","aliases":["citation metrics","bibliometric analysis","citation tracking"],"domain":"research-skills","family":"process-pipeline","subfamily":"research-impact-metrics","year":"1955 (citation indexes); 1975 (Impact Factor); 2005 (H-index)","originator":"Eugene Garfield (Citation Indexes, 1955); Jorge Hirsch (H-index, 2005)","url":"https://scholargate.app/en/research-skills/citation-analysis","markdownUrl":"https://scholargate.app/en/research-skills/citation-analysis.md","definition":"Citation analysis is the systematic study of how scholarly works are cited by subsequent research, used as a proxy for research impact and influence. Founded formally by Eugene Garfield in 1955 (introducing citation indexes), the field encompasses metrics ranging from simple citation counts to sophisticated indices like the H-index (Hirsch, 2005) and field-normalized indicators. Citation analysis is used to evaluate researcher productivity, track influence of ideas, assess journal quality, and detect research trends. While citation counts are not perfect measures of quality (high citation does not equal high quality; time lag in citation accumulation), they provide valuable quantitative data for research evaluation alongside peer review and expert assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eugene Garfield (Citation Indexes, 1955); Jorge Hirsch (H-index, 2005)","subfamily":"research-impact-metrics","year":"1955 (citation indexes); 1975 (Impact Factor); 2005 (H-index)","type":"Tool"},"citations":[{"ref":"Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.","type":"article","doi":"10.1073/pnas.0507655102","isbn":null,"url":null},{"ref":"Garfield, E. (1955). Citation indexes for science: A new dimension of bibliographic information. Science, 122(3159), 108–111.","type":"article","doi":"10.1126/science.122.3159.108","isbn":null,"url":null},{"ref":"Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634.","type":"article","doi":"10.1136/bmj.315.7109.629","isbn":null,"url":null}],"related":["altmetrics","doi-system","orcid-researcher-id","citation-management-tools"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"citation-management-tools","name":"Citation Management Tools","fullName":"Software for Organizing, Formatting, and Sharing Bibliographic References","aliases":["reference manager","citation software","bibliographic management"],"domain":"research-skills","family":"process-pipeline","subfamily":"reference-organization","year":"2001 (modern era, EndNoteWeb); 2006 (Mendeley); 2006 (Zotero)","originator":"Academic researchers and librarians; developed since 1980s","url":"https://scholargate.app/en/research-skills/citation-management-tools","markdownUrl":"https://scholargate.app/en/research-skills/citation-management-tools.md","definition":"Citation management tools are software applications that store, organize, and format bibliographic references. They allow researchers to import citations from databases and websites, annotate and tag articles, organize references by project, and automatically generate formatted in-text citations and bibliographies in multiple styles (APA, Vancouver, Chicago, Harvard). Popular tools include Zotero (free, open-source), Mendeley (Elsevier-owned, freemium), EndNote (commercial, Clarivate), and others. These tools are essential for managing the hundreds to thousands of references accumulate during a research career and for ensuring consistent, accurate citation formatting in academic writing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Academic researchers and librarians; developed since 1980s","subfamily":"reference-organization","year":"2001 (modern era, EndNoteWeb); 2006 (Mendeley); 2006 (Zotero)","type":"Tool"},"citations":[{"ref":"Booth, A. (2012). Citation management tools. In R. Bosch & K. Winn (Eds.), Reference management and citation software. Library Technology Reports, 48(5), 12–18.","type":"article","doi":null,"isbn":null,"url":"https://www.ala.org/ltr"},{"ref":"Bramer, W. M., Rethlefsen, M. L., Murad, M. H., & Landhuis, E. (2016). When updating systematic reviews, how often should new searches be applied to increase the chance of finding relevant studies? Systematic Reviews, 5(1), 94.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=When+updating+systematic+reviews%2C+how+often+should+new+searches+be+applied+to+increase+the+chance+of+finding+relevant+studies+Bramer"},{"ref":"Zotero project team (2024). Zotero: Free reference management software. https://www.zotero.org.","type":"article","doi":null,"isbn":null,"url":"https://www.zotero.org"}],"related":["pico-framework","boolean-search-operators","citation-analysis","doi-system"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"citizen-satisfaction-survey","name":"Citizen Satisfaction Survey","fullName":"Citizen Satisfaction Survey (CSS)","aliases":["CSS","Public Satisfaction Index","Citizen Service Satisfaction"],"domain":"tourism-management","family":"process-pipeline","subfamily":"satisfaction-measurement","year":"1996","originator":"Fornell, C.; James, O.","url":"https://scholargate.app/en/tourism-management/citizen-satisfaction-survey","markdownUrl":"https://scholargate.app/en/tourism-management/citizen-satisfaction-survey.md","definition":"The Citizen Satisfaction Survey (CSS) measures public satisfaction with government services, infrastructure, and institutions across multiple dimensions (access, responsiveness, quality, fairness, transparency). Rooted in expectancy-disconfirmation theory (James, 2009) and the American Customer Satisfaction Index (Fornell et al., 1996), the CSS operationalizes citizen satisfaction as a key accountability metric and driver of institutional legitimacy. Essential for government agencies, public utilities, and civic institutions seeking to monitor service performance, identify improvement priorities, and demonstrate responsiveness to public needs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fornell, C.; James, O.","subfamily":"satisfaction-measurement","year":"1996","type":"Self-report survey"},"citations":[{"ref":"Nasco, S. A., Cleveland, M., & Laroche, M. (2010). Evaluating the public sector customer satisfaction construct in the context of public transit service. Journal of Public Sector Management, 23(2), 97-113.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluating+the+public+sector+customer+satisfaction+construct+in+the+context+of+public+transit+service+Nasco"},{"ref":"Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American Customer Satisfaction Index: Nature, purpose, and findings. Journal of Marketing, 60(4), 7-18.","type":"article","doi":"10.1177/002224299606000403","isbn":null,"url":null},{"ref":"James, O. (2009). Evaluating the expectations disconfirmation and expectations anchoring theories of citizen satisfaction with local government. Journal of Public Administration Research and Theory, 19(1), 141-157.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluating+the+expectations+disconfirmation+and+expectations+anchoring+theories+of+citizen+satisfaction+with+local+government+James"},{"ref":"Andersen, L. B., Jørgensen, T. B., Kjørup, A. M., & Sørensen, L. H. (2012). Public employees' motivations. International Journal of Public Administration, 35(1), 3-14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Public+employees%27+motivations+Andersen"}],"related":["public-service-motivation-scale","e-government-adoption-scale","tourist-satisfaction-scale","hotel-service-quality-scale","perceived-value-scale-tourism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"civic-engagement-scale","name":"Civic Engagement Scale","fullName":"Civic Engagement and Participation Index","aliases":["CES","Political Participation Scale"],"domain":"political-sociology","family":"process-pipeline","subfamily":"Political Participation","year":"1995–2008","originator":"Cliff Zukin, Scott Keeter, Russell Dalton","url":"https://scholargate.app/en/political-sociology/civic-engagement-scale","markdownUrl":"https://scholargate.app/en/political-sociology/civic-engagement-scale.md","definition":"The Civic Engagement Scale measures the extent and type of an individual's participation in civic, political, and community life. Rather than a single construct, it typically encompasses multiple dimensions: electoral participation (voting), political activity (contacting officials, campaign involvement), civic service (volunteering, organizational membership), and social participation (community meetings, neighborhood involvement). Developed by scholars including Zukin, Keeter, and Dalton, it captures how citizens actualize their democratic role.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cliff Zukin, Scott Keeter, Russell Dalton","subfamily":"Political Participation","year":"1995–2008","type":"Self-report questionnaire / Behavioral frequency"},"citations":[{"ref":"Zukin, C., Keeter, S., Andolina, M., Jenkins, K., & Delli Carpini, M. X. (2006). A new engagement? Political participation, civic life, and the changing American citizen. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Zukin%2C%20C.%2C%20Keeter%2C%20S.%2C%20Andolina%2C%20M.%2C%20Jenkins%2C%20K.%2C%20%26%20Delli%20Carpini%2C%20M.%20X.%20(2006).%20A%20new%20engagement%3F%20Political%20participati"},{"ref":"Brady, H. E., Verba, S., & Schlozman, K. L. (1995). Beyond SES: A resource model of political participation. American Political Science Review, 89(2), 271-294.","type":"article","doi":"10.2307/2082425","isbn":null,"url":null},{"ref":"Dalton, R. J. (2008). Citizenship norms and the expansion of political participation. Political Studies, 56(1), 76-98.","type":"article","doi":"10.1111/j.1467-9248.2007.00718.x","isbn":null,"url":null}],"related":["political-efficacy-scale","social-capital-index","generalized-trust-scale","community-belonging-scale","social-cohesion-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ck-metrics","name":"CK Metrics","fullName":"Chidamber and Kemerer Object-Oriented Metrics","aliases":["OO metrics","CK suite","object-oriented complexity"],"domain":"numerical-methods","family":"ml-model","subfamily":"Object-Oriented Metrics","year":"1994","originator":"Shyam Chidamber and Chris Kemerer","url":"https://scholargate.app/en/numerical-methods/ck-metrics","markdownUrl":"https://scholargate.app/en/numerical-methods/ck-metrics.md","definition":"CK Metrics is a suite of six object-oriented design metrics introduced by Chidamber and Kemerer in 1994 to measure class complexity, cohesion, and coupling. The metrics quantify OO design quality; high coupling and low cohesion predict defects and maintenance effort.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shyam Chidamber and Chris Kemerer","subfamily":"Object-Oriented Metrics","year":"1994","type":"Design quality metric suite"},"citations":[{"ref":"Chidamber, S. R., & Kemerer, C. F. (1994). A metrics suite for object-oriented design. IEEE Transactions on Software Engineering, 20(6), 476–493.","type":"article","doi":"10.1109/32.295895","isbn":null,"url":null},{"ref":"Basili, V. R., Briand, L. C., & Melo, W. L. (1996). A validation of object-oriented design metrics as quality indicators. IEEE Transactions on Software Engineering, 22(10), 751–761.","type":"article","doi":"10.1109/32.544352","isbn":null,"url":null},{"ref":"Lanza, M., & Marinescu, R. (2007). Object-Oriented Metrics in Practice. Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Object-Oriented+Metrics+in+Practice+Lanza"}],"related":["cyclomatic-complexity","maintainability-index","code-smells"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"classic-grounded-theory","name":"Classic Grounded Theory","fullName":"Classic (Glaserian) Grounded Theory","aliases":["Glaserian GT","CGT","original grounded theory","classic GT"],"domain":"qualitative","family":"process-pipeline","subfamily":"Grounded Theory","year":"1967","originator":"Barney G. Glaser and Anselm L. Strauss","url":"https://scholargate.app/en/qualitative/classic-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/classic-grounded-theory.md","definition":"Classic Grounded Theory (CGT) is a systematic qualitative methodology for generating substantive theory from empirical data. Developed by Barney Glaser and Anselm Strauss in 1967, it uses iterative cycles of data collection, constant comparison, and memo writing to produce a core category and surrounding conceptual framework that explains a social or psychological process. Unlike its later variants, Glaserian CGT insists on emergence — theory must arise from data without forcing preconceived frameworks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser and Anselm L. Strauss","year":"1967","type":"Qualitative research method","dataType":"Interviews, field observations, documents, memos","typicalSampleSize":"20–50 participants (theoretical sampling until saturation)","subfamily":"Grounded Theory"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Discovery+of+Grounded+Theory+Strategies+for+Qualitative+Research+Glaser+Strauss+1967"},{"ref":"Glaser, B. G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theoretical+Sensitivity+Advances+in+the+Methodology+of+Grounded+Theory+Glaser+1978"}],"related":["grounded-theory","phenomenology","ethnography","case-study","narrative-analysis","action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"classroom-environment-scale","name":"Classroom Environment Scale","fullName":"Classroom Environment Scale (CES)","aliases":["CES"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"social-climate-educational","year":"1974","originator":"Moos, R. H.; Trickett, E. J.","url":"https://scholargate.app/en/educational-psychology/classroom-environment-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/classroom-environment-scale.md","definition":"The Classroom Environment Scale is a comprehensive instrument measuring the social, emotional, and organizational climate of educational settings. Developed by Moos and Trickett in 1974, the CES assesses students' or teachers' perceptions of classroom relationships, instructional climate, and classroom management. By providing a multidimensional profile of classroom environment, the CES enables educators to identify strengths and opportunities for improvement in classroom culture, directly informing interventions to enhance student engagement, achievement, and well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moos, R. H.; Trickett, E. J.","subfamily":"social-climate-educational","year":"1974","type":"Student or teacher survey (True/False)"},"citations":[{"ref":"Moos, R. H., & Trickett, E. J. (1974). Classroom Environment Scale: A method for assessing the social climate of classrooms. Consulting Psychologists Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=classroom+environment+scale+moos+trickett"},{"ref":"Fraser, B. J. (1994). Research on classroom and school climate. In D. L. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 493–541). National Science Teachers Association.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Research+on+classroom+and+school+climate+Fraser"}],"related":["academic-help-seeking-scale","peer-learning-scale","university-student-satisfaction","academic-burnout-scale","test-anxiety-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"classroom-observation","name":"Classroom Observation","fullName":"Systematic Classroom Observation","aliases":["classroom observation research","structured classroom observation","instructional observation","lesson observation"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1960s (Flanders Interaction Analysis); refined through 1990s–2000s","originator":"Ned Flanders (systematic interaction analysis); Robert Pianta et al. (CLASS system)","url":"https://scholargate.app/en/field-methods/classroom-observation","markdownUrl":"https://scholargate.app/en/field-methods/classroom-observation.md","definition":"Classroom observation is a field research method in which a trained observer systematically watches, documents, and analyzes teaching and learning events as they occur in a real classroom setting. It can be structured (using a predefined coding instrument such as Flanders Interaction Analysis or CLASS), semi-structured, or open-ended (ethnographic notes), and is used across educational research, teacher professional development, school evaluation, and curriculum studies to generate ecologically valid evidence about instructional practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ned Flanders (systematic interaction analysis); Robert Pianta et al. (CLASS system)","year":"1960s (Flanders Interaction Analysis); refined through 1990s–2000s","type":"Qualitative and quantitative observational research","dataType":"Field notes, observation protocols, audio/video recordings, rating scales","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Flanders, N. A. (1970). Analyzing Teaching Behavior. Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Analyzing+Teaching+Behavior+Flanders+1970"},{"ref":"Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom Assessment Scoring System (CLASS) Manual, Pre-K. Paul H. Brookes Publishing.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Classroom+Assessment+Scoring+System+CLASS+Pianta+La+Paro+Hamre+2008"}],"related":["educational-action-research","lesson-study","ethnography","thematic-analysis","discourse-analysis","program-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cleanroom-software-engineering","name":"Cleanroom Software Engineering","fullName":"Cleanroom Software Engineering Methodology","aliases":["Cleanroom method","zero-defect software"],"domain":"numerical-methods","family":"ml-model","subfamily":"Quality Methodology","year":"1987","originator":"Harlan Mills","url":"https://scholargate.app/en/numerical-methods/cleanroom-software-engineering","markdownUrl":"https://scholargate.app/en/numerical-methods/cleanroom-software-engineering.md","definition":"Cleanroom Software Engineering is a software development methodology developed by Mills, Dyer, and Linger in the 1980s that emphasizes defect prevention through formal specifications, code reviews, and statistical testing rather than debugging. Inspired by pharmaceutical manufacturing cleanrooms, the approach aims for near-zero-defect delivery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harlan Mills","subfamily":"Quality Methodology","year":"1987","type":"Software development discipline"},"citations":[{"ref":"Mills, H. D., Dyer, M., & Linger, R. C. (1987). Cleanroom software engineering. IEEE Software, 4(5), 19–25.","type":"article","doi":"10.1109/ms.1987.231413","isbn":null,"url":null},{"ref":"Linger, R. C., & Mills, H. D. (1994). A case study in cleanroom software engineering: A NASA mission-critical application. Proceedings of the International Conference on Software Engineering.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+case+study+in+cleanroom+software+engineering%3A+A+NASA+mission-critical+application+Linger"},{"ref":"Dyer, M. (1992). The Cleanroom Approach to Quality Software Development. Wiley.","type":"article","doi":null,"isbn":"0471547174","url":null}],"related":["formal-methods","statistical-testing","defect-prevention"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"climate-change-attitude-scale","name":"CCAS","fullName":"Climate Change Attitude Scale","aliases":["CCAS","Climate Attitude Scale"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"climate change beliefs and risk perception","year":"2019","originator":"Hui Li, Marianne C. Monroe","url":"https://scholargate.app/en/environmental-psychology/climate-change-attitude-scale","markdownUrl":"https://scholargate.app/en/environmental-psychology/climate-change-attitude-scale.md","definition":"The Climate Change Attitude Scale (CCAS) measures individuals' beliefs about climate change causation, severity, and human responsibility, as well as attitudes toward climate action and climate policy. Developed by Li and Monroe (2019) as an extension of general environmental attitude scales, the CCAS focuses specifically on climate change perceptions—whether individuals believe climate change is real, anthropogenic (human-caused), severe, and actionable. The scale is essential for tracking public opinion on climate, identifying populations skeptical of climate science, evaluating climate communication campaign effectiveness, and examining links between climate beliefs and policy support or climate action.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hui Li, Marianne C. Monroe","subfamily":"climate change beliefs and risk perception","year":"2019","type":"Self-report belief and attitude scale"},"citations":[{"ref":"Li, H., & Monroe, M. C. (2019). Development and validation of the Climate Change Attitude Scale (CCAS). Climatic Change, 152(3–4), 601–613.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+the+Climate+Change+Attitude+Scale+%28CCAS%29+Li"},{"ref":"Leiserowitz, A., Maibach, E., & Roser-Renouf, C. (2009). Climategate, public opinion, and the loss of trust. Yale Project on Climate Change Communication. Yale University.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Leiserowitz%2C%20A.%2C%20Maibach%2C%20E.%2C%20%26%20Roser-Renouf%2C%20C.%20(2009).%20Climategate%2C%20public%20opinion%2C%20and%20the%20loss%20of%20trust.%20Yale%20Projec"}],"related":["new-ecological-paradigm","environmental-concern-scale","carbon-footprint-awareness-scale","pro-environmental-behavior-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-audit","name":"Clinical Audit","fullName":"Clinical Audit for Quality Assurance and Evidence-Based Practice","aliases":["Medical Audit","Healthcare Quality Audit"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Quality assurance, Outcomes measurement","year":"1989","originator":"UK National Health Service and healthcare quality movements","url":"https://scholargate.app/en/healthcare-management/clinical-audit","markdownUrl":"https://scholargate.app/en/healthcare-management/clinical-audit.md","definition":"Clinical audit is a systematic, cyclical process that measures the quality of clinical care against evidence-based standards and benchmarks, identifies gaps, and implements improvements to bring practice into alignment with current best evidence. Originating in the UK NHS, clinical audit is now a fundamental quality assurance tool in healthcare organizations worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"UK National Health Service and healthcare quality movements","subfamily":"Quality assurance, Outcomes measurement","year":"1989","type":"Systematic quality review methodology"},"citations":[{"ref":"Institute of Medicine. (2001). Crossing the Quality Chasm: A New Health System for the 21st Century. National Academies Press.","type":"book","doi":"10.17226/10027","isbn":null,"url":null},{"ref":"Davies, H. T., Crombie, I. K., & Tavakoli, M. (2006). When can odds ratios mislead? British Medical Journal, 316(7136), 989–991.","type":"article","doi":"10.1136/bmj.316.7136.989","isbn":null,"url":null},{"ref":"National Institute for Health and Clinical Excellence (NICE). (2002). Principles for best practice in clinical audit. Royal Society of Medicine Press.","type":"book","doi":null,"isbn":null,"url":"https://www.nice.org.uk/"}],"related":["balanced-scorecard-healthcare","dea-hospital-efficiency","lean-healthcare","six-sigma-healthcare","hospital-readmission-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-frailty-scale","name":"Clinical Frailty Scale","fullName":"Clinical Frailty Scale (CFS)","aliases":["CFS","Frailty Scale","Clinical Frailty Assessment"],"domain":"nursing","family":"process-pipeline","subfamily":"geriatric assessment","year":"2005","originator":"Kenneth Rockwood","url":"https://scholargate.app/en/nursing/clinical-frailty-scale","markdownUrl":"https://scholargate.app/en/nursing/clinical-frailty-scale.md","definition":"The Clinical Frailty Scale (CFS), developed by Kenneth Rockwood and colleagues in 2005, is a brief, validated tool for assessing frailty in older adults. Frailty—a syndrome of diminished physiologic reserve, increased vulnerability, and reduced functional ability—is recognized as a distinct clinical state that predicts mortality, disability, and healthcare utilization independent of age and comorbidities. The CFS uses a seven-point (or nine-point in later versions) clinical judgment-based scale, making it practical and rapid for bedside use in hospitals, clinics, and long-term care.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kenneth Rockwood","subfamily":"geriatric assessment","year":"2005","type":"Clinician-rated frailty assessment"},"citations":[{"ref":"Rockwood, K., Song, X., MacKnight, C., et al. (2005). A global clinical measure of fitness and frailty in elderly people. CMAJ, 173(5), 489-495.","type":"article","doi":"10.1503/cmaj.050051","isbn":null,"url":null},{"ref":"Rockwood, K., Andrew, M., & Mitnitski, A. (2007). A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci, 62(7), 738-743.","type":"article","doi":"10.1093/gerona/62.7.738","isbn":null,"url":null}],"related":["katz-independence-adl","waterlow-scale","zarit-caregiver-burden-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-global-impressions-scale","name":"Clinical Global Impressions Scale","fullName":"Clinical Global Impressions Scale (CGI)","aliases":["CGI","CGI-S","CGI-I"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"clinician-global-assessment","year":"1976","originator":"William Guy","url":"https://scholargate.app/en/clinical-psychology/clinical-global-impressions-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/clinical-global-impressions-scale.md","definition":"The Clinical Global Impressions Scale is a clinician-administered two-part assessment developed by William Guy in the ECDEU Assessment Manual (1976) to provide rapid, global ratings of illness severity and treatment response. Part 1 (CGI-Severity) rates current severity; Part 2 (CGI-Improvement) rates change since treatment initiation. The CGI is among the most widely used global outcome measures in psychiatric research and clinical practice, prized for its brevity, interpretability, and ability to capture clinician expertise and nuanced clinical judgment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Guy","subfamily":"clinician-global-assessment","year":"1976","type":"Clinician-rated assessment"},"citations":[{"ref":"Guy, W. (1976). ECDEU Assessment Manual for Psychopharmacology. Rockville, MD: National Institute of Mental Health, US Department of Health, Education, and Welfare.","type":"book","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/25375819"},{"ref":"Busner, J., & Targum, S. D. (2007). The Clinical Global Impressions Scale: applying a research tool in clinical practice. Psychiatry (Edgmont), 4(7), 28–37.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3356952/"},{"ref":"Kadouri, A., Corruble, E., & Ly, K. H. (2007). The CGI: assessment of its usefulness in the context of a European multicentric antidepressant drug trial. European Neuropsychopharmacology, 17(6–7), 468–472.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+CGI%3A+assessment+of+its+usefulness+in+the+context+of+a+European+multicentric+antidepressant+drug+trial+Kadouri"}],"related":["phq-9","hamilton-depression-rating-scale","montgomery-asberg-depression","patient-global-impression-change"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-handover-quality","name":"Clinical Handover Quality Scale","fullName":"Clinical Handover Quality Scale (CHQS)","aliases":["CHQS"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"communication-handoff-protocols","year":"2008","originator":"Multiple researchers including Arora, Riesenberg, and colleagues, based on aviation handoff protocols and clinical error analysis","url":"https://scholargate.app/en/healthcare-management/clinical-handover-quality","markdownUrl":"https://scholargate.app/en/healthcare-management/clinical-handover-quality.md","definition":"The Clinical Handover Quality Scale (CHQS) is a comprehensive framework and measurement tool for assessing the quality of clinical handovers—the critical communication process by which responsibility for a patient's care is transferred from one provider or team to another. Handovers occur multiple times daily in healthcare settings (shift changes, patient transfers between units, discharge planning, procedure-to-recovery transitions) and are recognized as high-risk moments for communication breakdown, incomplete information transfer, and consequent patient harm. The CHQS measures handover quality across dimensions including information content, clarity, timeliness, opportunity for questions, and documented understanding. It is used in hospitals, operating rooms, and intensive care units to assess handover effectiveness and to guide improvement in standardized handoff protocols such as SBAR (Situation, Background, Assessment, Recommendation).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple researchers including Arora, Riesenberg, and colleagues, based on aviation handoff protocols and clinical error analysis","subfamily":"communication-handoff-protocols","year":"2008","type":"Self-report / Observation-based"},"citations":[{"ref":"Manser, T. (2005). Managing the risks of organizational accidents. Journal of Contingencies and Crisis Management, 12(4), 141–150.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Managing+the+risks+of+organizational+accidents+Manser"},{"ref":"Arora, V., Johnson, J., Lovinger, D., Humphrey, H. J., & Meltzer, D. O. (2009). Communication failures in patient sign-out and suggestions for improvement. Journal of the American Medical Association, 294(9), 1095–1102.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Communication+failures+in+patient+sign-out+and+suggestions+for+improvement+Arora"},{"ref":"Riesenberg, L. A., Leitzsch, J., & Massucci, J. L. (2009). Residents' perceptions of handoff importance and effectiveness at an academic medical center. Journal of Hospital Medicine, 4(5), 340–346.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Residents%27+perceptions+of+handoff+importance+and+effectiveness+at+an+academic+medical+center+Riesenberg"}],"related":["teamstepps-perceptions","patient-safety-climate-scale","safety-attitudes-questionnaire","healthcare-teamwork-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-learning-environment-scale","name":"CLES+T","fullName":"Clinical Learning Environment Scale with Teacher Feedback Subscale","aliases":["CLES","CLES+T Scale","Clinical Learning Environment Supervision Scale"],"domain":"health-education","family":"process-pipeline","subfamily":"clinical-learning-environment","year":"2007–2008","originator":"Marja Saarikoski, Hanne Leino-Kilpi, Tony Warne","url":"https://scholargate.app/en/health-education/clinical-learning-environment-scale","markdownUrl":"https://scholargate.app/en/health-education/clinical-learning-environment-scale.md","definition":"The CLES+T is a 34-item self-report questionnaire measuring nursing students' perceptions of their clinical learning environment and the quality of supervision received from their clinical preceptor or teacher. Originally developed by Saarikoski and colleagues in 2007 and expanded in 2008 to include a specific teacher feedback dimension, the CLES+T evaluates five key domains: Ward Learning Environment, Supervisory Relationship, Feedback and Evaluation, Nurse Teacher's Pedagogical Competence, and Empowerment. The scale is widely used in nursing education to assess the quality of clinical placements and identify areas for educational improvement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marja Saarikoski, Hanne Leino-Kilpi, Tony Warne","subfamily":"clinical-learning-environment","year":"2007–2008","type":"Self-report questionnaire"},"citations":[{"ref":"Saarikoski, M., Isoaho, H., Warne, T., & Leino-Kilpi, H. (2008). The nurse teacher in clinical practice: Developing the new sub-dimension for the Clinical Learning Environment Supervision and Nurse Teacher (CLES+T) evaluation scale. Int J Nurs Stud 45(8): 1233–1237.","type":"article","doi":"10.1016/j.ijnurstu.2007.07.009","isbn":null,"url":null},{"ref":"Saarikoski, M., Leino-Kilpi, H., & Warne, T. (2007). Clinical learning environments as perceived by nursing students: Factor structure and relationships to learning outcomes. Nurse Educ Today 27(2): 122–128.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Clinical+learning+environments+as+perceived+by+nursing+students%3A+Factor+structure+and+relationships+to+learning+outcomes+Saarikoski"}],"related":["ripls","simulation-debriefing-quality","clinical-teaching-quality-scale","student-clinical-placement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-pathway-nursing","name":"Clinical Pathway Analysis","fullName":"Clinical Pathway Analysis for Nursing Care Management","aliases":["Care Pathway","Critical Pathway","Nursing Care Plan","Protocol-Based Care"],"domain":"nursing","family":"process-pipeline","subfamily":"Care planning and process management","year":"1990","originator":"Healthcare quality and process improvement specialists","url":"https://scholargate.app/en/nursing/clinical-pathway-nursing","markdownUrl":"https://scholargate.app/en/nursing/clinical-pathway-nursing.md","definition":"Clinical Pathway Analysis is a process improvement methodology that develops and evaluates standardized, evidence-based care plans for specific diagnoses or procedures. Clinical pathways map the expected course of care, including nursing interventions, diagnostic tests, medications, and patient education activities across time. By analyzing deviations from the pathway and tracking outcomes, organizations can identify opportunities to improve quality, reduce costs, and ensure consistent care delivery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Healthcare quality and process improvement specialists","subfamily":"Care planning and process management","year":"1990","type":"Process management tool"},"citations":[{"ref":"Coffman, J. (2005). Critical pathways: An effective tool for managing care. American Nurse Today, 1(7), 22-26.","type":"article","doi":null,"isbn":null,"url":"https://www.americannursetoday.com/"},{"ref":"Panella, M., Marchisio, S., & Di Stanislao, F. (2003). Reducing clinical variations with clinical pathways: Do pathways work? International Journal of Quality in Health Care, 15(6), 509-521.","type":"article","doi":"10.1093/intqhc/mzg057","isbn":null,"url":null}],"related":["braden-scale","nursing-sensitive-indicators","care-dependency-scale","medication-reconciliation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-risk-index-babies","name":"CRIB","fullName":"Clinical Risk Index for Babies","aliases":["CRIB","CRIB-II"],"domain":"neonatology","family":"process-pipeline","subfamily":"severity-stratification","year":1991,"originator":"G. W. Parry","url":"https://scholargate.app/en/neonatology/clinical-risk-index-babies","markdownUrl":"https://scholargate.app/en/neonatology/clinical-risk-index-babies.md","definition":"CRIB is a neonatal illness severity scoring system designed to predict mortality risk in very low birth weight (VLBW) infants using birth weight, gestational age, gender, Apgar score, and initial blood gas parameters. Developed by Parry et al. in 1991 and refined as CRIB-II in 2005, it incorporates demographic and delivery room data along with early physiological measurements. CRIB is particularly valuable for international comparisons of neonatal outcome quality and has become a standard severity-adjustment tool in neonatal epidemiology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"G. W. Parry","subfamily":"severity-stratification","year":1991,"type":"Clinician-rated"},"citations":[{"ref":"Parry, G. W., Sims, D. G., Wincott, J. L., & Cockburn, F. (1991). Clinical Risk Index for Babies (CRIB): Prospective Validation. Archives of Disease in Childhood, 66(7), 717-722.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Clinical+Risk+Index+for+Babies+%28CRIB%29%3A+Prospective+Validation+Parry"},{"ref":"Bardell, T., Knottnerus, A., Motohashi, A., et al. (2005). CRIB II: An Update of the Clinical Risk Index for Babies Score. Archives of Disease in Childhood Fetal and Neonatal Edition, 90(4), F334-F338.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=CRIB+II%3A+An+Update+of+the+Clinical+Risk+Index+for+Babies+Score+Bardell"}],"related":["neonatal-acute-physiology-score","neonatal-pain-agitation-sedation","neonatal-behavioral-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-scoring-system-veterinary","name":"Clinical Scoring System in Veterinary Medicine","fullName":"Systematic Clinical Scoring Assessment in Veterinary Medicine","aliases":["clinical assessment scoring","veterinary patient scoring"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Clinical assessment","year":"2000s","originator":"Veterinary Pain Society and AAFP","url":"https://scholargate.app/en/veterinary-medicine/clinical-scoring-system-veterinary","markdownUrl":"https://scholargate.app/en/veterinary-medicine/clinical-scoring-system-veterinary.md","definition":"Clinical scoring systems provide standardized methods for objectively assessing animal health status, pain, disease severity, and treatment outcomes. Developed progressively by veterinary organizations and research groups since the early 2000s, these systems enable consistent documentation, comparison of cases, and evidence-based clinical decision-making across species and practice settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Veterinary Pain Society and AAFP","subfamily":"Clinical assessment","year":"2000s","type":"Assessment pipeline"},"citations":[{"ref":"Hansen, B. D., Lascelles, B. D., Keates, H., et al. (2015). Painful Osteoarthritis in Cats: Chronic Pain Assessment, Management, and Welfare Considerations. Journal of Feline Medicine and Surgery, 17(8), 637-646.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Painful+Osteoarthritis+in+Cats%3A+Chronic+Pain+Assessment%2C+Management%2C+and+Welfare+Considerations+Hansen"},{"ref":"Hellyer, P., Rodan, I., Brunt, J., et al. (2007). AAFP and IAAFP Feline Acute Pain Management Guidelines. Journal of Feline Medicine and Surgery, 9(1), 13-25.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=AAFP+and+IAAFP+Feline+Acute+Pain+Management+Guidelines+Hellyer"},{"ref":"Mathews, K. A. (2014). Pain Assessment and Management in Livestock and Poultry. Journal of the American Veterinary Medical Association, 244(8), 882-893.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pain+Assessment+and+Management+in+Livestock+and+Poultry+Mathews"}],"related":["body-condition-score-dog-cat","anesthesia-risk-scoring-vet","blood-gas-analysis-veterinary"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-teaching-quality-scale","name":"CTQS","fullName":"Clinical Teaching Quality Scale","aliases":["Teaching Quality Assessment","Clinical Instructor Effectiveness Scale"],"domain":"health-education","family":"process-pipeline","subfamily":"clinical-instruction","year":"2001–2003","originator":"Kristina Ohrling & Ingela R. Hallberg","url":"https://scholargate.app/en/health-education/clinical-teaching-quality-scale","markdownUrl":"https://scholargate.app/en/health-education/clinical-teaching-quality-scale.md","definition":"The CTQS is a self-report questionnaire measuring students' perceptions of their clinical educator's (preceptor, clinical instructor, or mentor) teaching quality and effectiveness. Developed by Ohrling, Hallberg, and Gaberson in the early 2000s, the CTQS evaluates dimensions of clinical teaching including role modeling, knowledge transmission, feedback quality, student empowerment, and development of critical thinking. The scale is used in nursing and health professions education to provide educators with feedback on their teaching performance, identify professional development needs, and inform program quality assurance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kristina Ohrling & Ingela R. Hallberg","subfamily":"clinical-instruction","year":"2001–2003","type":"Self-report questionnaire"},"citations":[{"ref":"Ohrling, K. & Hallberg, I. R. (2001). The meaning of precepting: Nurse preceptors' lived experience of precept education. J Adv Nurs 33(1): 96–103.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+meaning+of+precepting%3A+Nurse+preceptors%27+lived+experience+of+precept+education+Ohrling"},{"ref":"Gaberson, K. B., Schroeder, S. G., & Aplin, M. C. (2003). Evaluating clinical teaching: A tool for nurse educators. Nurse Educ Pract 3(1): 48–56.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluating+clinical+teaching%3A+A+tool+for+nurse+educators+Gaberson"}],"related":["clinical-learning-environment-scale","ripls","student-clinical-placement-scale","professional-identity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-text-mining","name":"Clinical Text Mining","fullName":"Clinical Text Mining (Clinical NLP Information Extraction)","aliases":["clinical NLP","clinical information extraction","Klinik Metin Madenciliği"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2000s–2020s (established domain; BioBERT milestone 2020)","originator":"Community-driven; foundational work by i2b2/UTHealth NLP challenges and BioBERT (Lee et al., 2020)","url":"https://scholargate.app/en/text-mining/clinical-text-mining","markdownUrl":"https://scholargate.app/en/text-mining/clinical-text-mining.md","definition":"Clinical text mining is a specialised branch of natural language processing that extracts structured clinical facts — diagnoses, symptoms, medications, treatments, and ICD codes — from unstructured healthcare documents such as discharge summaries, progress notes, and radiology reports. Grounded in biomedical NLP models like BioBERT (Lee et al., 2020) and the i2b2/UTHealth shared-task benchmarks (Stubbs & Uzuner, 2015), it converts free-text clinical narratives into machine-readable data suitable for clinical decision support and health analytics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Community-driven; foundational work by i2b2/UTHealth NLP challenges and BioBERT (Lee et al., 2020)","year":"2000s–2020s (established domain; BioBERT milestone 2020)","type":"NLP information-extraction pipeline","targetText":"Clinical narratives: discharge summaries, progress notes, radiology and pathology reports","outputEntities":"ICD codes, symptoms, diagnoses, medications, treatments, risk factors","recommendedModels":"BioBERT, ClinicalBERT, medspaCy, scispaCy","minimumDocuments":30,"difficultyLevel":3},"citations":[{"ref":"Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.","type":"article","doi":"10.1093/bioinformatics/btz682","isbn":null,"url":null},{"ref":"Stubbs, A. & Uzuner, Ö. (2015). Annotating risk factors for heart disease in clinical narratives for the 2014 i2b2/UTHealth shared task. Journal of the American Medical Informatics Association, 22(e1), e30–e39.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/25623000/"}],"related":["sentiment-analysis","named-entity-recognition","text-classification","scientific-text-mining","information-extraction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clinical-trial-registration","name":"Clinical Trial Registration","fullName":"Prospective Registration of Clinical Trials and Observational Studies","aliases":["trial registration","prospective registration","ClinicalTrials.gov","trial registry","ISRCTN"],"domain":"research-ethics","family":"process-pipeline","subfamily":"trial-oversight","year":"2005","originator":"World Health Organization; International Committee of Medical Journal Editors","url":"https://scholargate.app/en/research-ethics/clinical-trial-registration","markdownUrl":"https://scholargate.app/en/research-ethics/clinical-trial-registration.md","definition":"Clinical trial registration is the prospective documentation of a trial's key information (hypothesis, design, population, outcomes) in a public registry before enrollment begins or results are known. In 2005, the World Health Organization established the requirement that all clinical trials be registered in an internationally recognized registry before participant enrollment. The International Committee of Medical Journal Editors (ICMJE) made registration a condition for publication in major medical journals in 2005, updated in 2015. Primary registries include ClinicalTrials.gov (U.S.), ISRCTN (UK), EudraCT (EU), and others operating under WHO oversight. Registration serves to prevent selective outcome reporting, reduce publication bias, and enhance research transparency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"World Health Organization; International Committee of Medical Journal Editors","subfamily":"trial-oversight","year":"2005","type":"Requirement"},"citations":[{"ref":"World Health Organization. (2005). Ensuring that Studies Are Prospectively Registered. International Clinical Trials Registry Platform (ICTRP) Statement.","type":"statement","doi":null,"isbn":null,"url":"https://www.who.int/teams/clinical-trials-platform/ictrp"},{"ref":"International Committee of Medical Journal Editors. (2015). Recommendations for the Conduct, Reporting, Editorship, and Publication of Scholarly Work in Medical Journals. JAMA, 314(20), 2142-2150.","type":"requirement","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Recommendations+for+the+Conduct%2C+Reporting%2C+Editorship%2C+and+Publication+of+Scholarly+Work+in+Medical+Journals+International"},{"ref":"U.S. Food and Drug Administration. (2020). Expanded Access (Compassionate Use). Electronic Code of Federal Regulations Title 21, Section 312.300.","type":"regulation","doi":null,"isbn":null,"url":"https://www.fda.gov/news-events/public-health-focus/expanded-access"},{"ref":"De Angelis, C., Drazen, J. M., Frizelle, F. A., Haivas, G., Hebert, P. C., Ioannidis, J. P., & Schroeder, T. V. (2004). Clinical Trial Registration: A Statement from the International Committee of Medical Journal Editors. N Engl J Med, 351(12), 1250-1251.","type":"article","doi":"10.1056/NEJMe048225","isbn":null,"url":null}],"related":["ethics-committee-application","ethics-committee-types","risk-benefit-assessment","data-protection-research","vulnerable-populations-research"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"clip","name":"CLIP","fullName":"Contrastive Language-Image Pretraining","aliases":["CLIP","Contrastive Language-Image Pre-training","zero-shot image classifier","visual-language model"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2021,"originator":"Radford, A.; Kim, J. W.; et al. (OpenAI)","url":"https://scholargate.app/en/deep-learning/clip","markdownUrl":"https://scholargate.app/en/deep-learning/clip.md","definition":"CLIP (Contrastive Language-Image Pretraining) is a vision-language model introduced by Radford et al. at OpenAI in 2021 that jointly learns aligned image and text representations by training on 400 million internet-sourced image-text pairs using a contrastive objective, enabling zero-shot transfer to image classification tasks without any task-specific fine-tuning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Radford, A.; Kim, J. W.; et al. (OpenAI)","year":2021,"type":"Contrastive vision-language pretraining model","task":"Zero-shot image classification, image-text retrieval, visual representation learning","trainingPairs":"400 million image-text pairs (WebImageText)","pretrainingObjective":"Contrastive loss (InfoNCE) over image-text pairs","encoders":"Image encoder (ViT or ResNet) + text encoder (Transformer)"},"citations":[{"ref":"Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 8748–8763.","type":"article","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v139/radford21a.html"},{"ref":"Radford, A., et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. arXiv:2103.00020.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2103.00020"},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["vision-transformer","bert","resnet","gpt","dall-e","stable-diffusion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"closed-loop-supply-chain","name":"Closed-Loop Supply Chain","fullName":"Closed-Loop Supply Chain Management","aliases":["reverse logistics","circular economy logistics"],"domain":"operations-management","family":"ml-model","subfamily":"Circular Economy","year":"2003","originator":"Guide, V. D. R., & Van Wassenhove, L. N.","url":"https://scholargate.app/en/operations-management/closed-loop-supply-chain","markdownUrl":"https://scholargate.app/en/operations-management/closed-loop-supply-chain.md","definition":"A closed-loop supply chain (CLSC) integrates forward logistics (moving products to customers) with reverse logistics (recovering products, components, or materials from customers) to optimize resource recovery, reduce waste, and minimize environmental impact. Products flow forward for customer use, then flow backward for remanufacturing, refurbishment, recycling, or proper disposal. CLSC is driven by regulatory compliance (e.g., take-back laws), cost recovery, environmental responsibility, and increasingly, customer demand for sustainable business practices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guide, V. D. R., & Van Wassenhove, L. N.","subfamily":"Circular Economy","year":"2003","type":"Supply chain strategy"},"citations":[{"ref":"Guide, V. D. R., & Van Wassenhove, L. N. (2003). Business aspects of closed-loop supply chains. Pittsburgh: Carnegie Mellon University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cmu.edu/"},{"ref":"Rogers, D. S., & Tibben-Lembke, R. S. (2002). Differences between forward and reverse logistics. Supply Chain Management Review, 6(5), 60-67.","type":"article","doi":null,"isbn":null,"url":"https://www.scmr.com/"}],"related":["scor-model","inventory-routing","vendor-managed-inventory","aggregate-planning","facility-layout"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"closeness-centrality","name":"Closeness Centrality","fullName":"Closeness Centrality (Bavelas-Freeman Shortest-Path Measure)","aliases":["closeness","farness-based centrality","geodesic closeness","normalized closeness centrality"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1950 (formalized 1979)","originator":"Bavelas, A.; formalized by Freeman, L. C.","url":"https://scholargate.app/en/network-analysis/closeness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/closeness-centrality.md","definition":"Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bavelas, A.; formalized by Freeman, L. C.","year":"1950 (formalized 1979)","type":"Node-level centrality index","dataType":"Graph / adjacency matrix (unweighted or weighted edges)","subfamily":"Network science"},"citations":[{"ref":"Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.","type":"article","doi":"10.1016/0378-8733(78)90021-7","isbn":null,"url":null},{"ref":"Bavelas, A. (1950). Communication patterns in task-oriented groups. Journal of the Acoustical Society of America, 22(6), 725–730.","type":"article","doi":"10.1121/1.1906679","isbn":null,"url":null}],"related":["degree-centrality","betweenness-centrality","eigenvector-centrality","social-network-analysis","pagerank","network-diffusion-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cloud-condensation-nuclei-analysis","name":"Cloud Condensation Nuclei Analysis","fullName":"Cloud Condensation Nuclei (CCN) Analysis and Measurement","aliases":["CCN analysis","Cloud condensation nuclei","CCN measurement"],"domain":"meteorology","family":"process-pipeline","subfamily":"Aerosol-cloud interaction","year":"1959","originator":"Twomey, Woodard","url":"https://scholargate.app/en/meteorology/cloud-condensation-nuclei-analysis","markdownUrl":"https://scholargate.app/en/meteorology/cloud-condensation-nuclei-analysis.md","definition":"Cloud condensation nuclei (CCN) analysis examines the number and properties of aerosol particles capable of nucleating cloud droplets at various supersaturation levels. This field involves measuring CCN concentrations, characterizing their chemical composition and size, and relating aerosol properties to cloud microphysical processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Twomey, Woodard","subfamily":"Aerosol-cloud interaction","year":"1959","type":"Cloud microphysical measurement"},"citations":[{"ref":"Dusek, U., Frank, G. P., Hildebrandt, L., et al. (2006). Size matters more than chemistry for cloud-nucleating ability of aerosol particles. Science, 312(5778), 1375-1378.","type":"article","doi":"10.1126/science.1125261","isbn":null,"url":null},{"ref":"Kreidenweis, S. M., Remer, L. A., Bruintjes, R., & Dubovik, O. (2001). Determining aerosol properties from satellite and ground-based measurements. Journal of Geophysical Research, 106(D12), 12325-12344.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Determining+aerosol+properties+from+satellite+and+ground-based+measurements+Kreidenweis"}],"related":["kohler-theory","spectral-bin-microphysics","wrf-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-analysis","name":"Cluster Analysis","fullName":"Cluster Analysis","aliases":["clustering","unsupervised classification","data clustering","numerical taxonomy"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1939–1967","originator":"Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means","url":"https://scholargate.app/en/statistics/cluster-analysis","markdownUrl":"https://scholargate.app/en/statistics/cluster-analysis.md","definition":"Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means","year":"1939–1967","type":"Unsupervised classification / grouping","dataType":"Continuous, ordinal, binary, or mixed multivariate data","subfamily":"Multivariate analysis"},"citations":[{"ref":"Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470749913","url":null},{"ref":"Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1473756540","url":null}],"related":["principal-component-analysis","exploratory-factor-analysis","discriminant-analysis","multidimensional-scaling","latent-class-analysis","mixture-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-ab-test","name":"Cluster Randomized A/B Test","fullName":"Cluster Randomized A/B Test","aliases":["cluster A/B test","group-randomized A/B test","network A/B test","cluster-level split test"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2010s (digital platforms); cluster RCT roots date to the 1970s–1980s","originator":"Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s)","url":"https://scholargate.app/en/experimental-design/cluster-randomized-ab-test","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-ab-test.md","definition":"A cluster randomized A/B test is an experimental design in which intact groups (clusters) — such as cities, schools, social network communities, or app user segments — are randomly assigned as whole units to either the treatment (A) or control (B) condition, rather than randomizing individual users or subjects. This approach is used when treatment effects would spill over between individuals if individual-level randomization were applied, or when the intervention must be delivered at the group level.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s)","year":"2010s (digital platforms); cluster RCT roots date to the 1970s–1980s","type":"Experimental design","dataType":"Behavioral, clickstream, or outcome data aggregated at the cluster level","subfamily":"Deneysel desen"},"citations":[{"ref":"Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013). Graph cluster randomization: Network exposure to multiple universes. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 329–337.","type":"inproceedings","doi":"10.1145/2487575.2487695","isbn":null,"url":null},{"ref":"Hayes, R. J., & Moulton, L. H. (2017). Cluster Randomised Trials (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"9781498728874","url":null}],"related":["cluster-randomized-controlled-trial","multi-arm-experiment","factorial-ab-test","adaptive-ab-test","blocked-ab-test","field-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-adaptive-experiment","name":"Cluster Randomized Adaptive Experiment","fullName":"Cluster Randomized Adaptive Experiment","aliases":["adaptive cluster RCT","adaptive group-randomized trial","cluster adaptive design","adaptive cluster trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2000s–2010s","originator":"Synthesised from cluster randomization methodology (Donner, 1978; Donner & Klar, 2000) and adaptive design frameworks (Bauer & Kohne, 1994; Pallmann et al., 2018)","url":"https://scholargate.app/en/experimental-design/cluster-randomized-adaptive-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-adaptive-experiment.md","definition":"A cluster randomized adaptive experiment combines two methodological principles: (1) intact groups such as schools, clinics, or villages are randomly assigned to treatment conditions rather than individuals, and (2) pre-specified rules allow the design to be modified during the trial based on accumulating cluster-level data. Adaptations may include dropping underperforming arms, reallocating clusters, or adjusting sample size, while maintaining statistical validity and controlling Type I error.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from cluster randomization methodology (Donner, 1978; Donner & Klar, 2000) and adaptive design frameworks (Bauer & Kohne, 1994; Pallmann et al., 2018)","year":"2000s–2010s","type":"Experimental design","dataType":"Continuous, binary, count, or time-to-event outcomes measured on individuals nested within clusters; interim aggregate cluster-level data used for adaptation decisions","subfamily":"Deneysel desen"},"citations":[{"ref":"Hayes, R. J., & Moulton, L. H. (2017). Cluster Randomised Trials (2nd ed.). CRC Press / Chapman & Hall.","type":"book","doi":null,"isbn":"978-1498728225","url":null},{"ref":"Pallmann, P., Bedding, A. W., Choodari-Oskooei, B., Dimairo, M., Flight, L., Hampson, L. V., ... & Jaki, T. (2018). Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Medicine, 16(1), 29.","type":"article","doi":"10.1186/s12916-018-1017-7","isbn":null,"url":null}],"related":["cluster-randomized-controlled-trial","adaptive-experiment","adaptive-randomized-controlled-trial","blocked-randomized-controlled-trial","multi-arm-experiment","stepped-wedge-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-control-group-experimental-design","name":"Cluster Randomized Control Group Experimental Design","fullName":"Cluster Randomized Control Group Experimental Design","aliases":["CRCT with control group","group-randomized trial","cluster RCT control group design","community randomized controlled trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1990s (formal methodology development)","originator":"Murray, D. M.; Donner, A. and Klar, N. (systematic formalization)","url":"https://scholargate.app/en/experimental-design/cluster-randomized-control-group-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-control-group-experimental-design.md","definition":"A cluster randomized control group experimental design randomly assigns intact groups (clusters) — such as schools, clinics, or communities — rather than individuals to treatment or control conditions. At least one cluster group receives no active intervention, serving as the control. This design is essential when individual randomization is impractical or contamination between participants in close proximity is likely.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray, D. M.; Donner, A. and Klar, N. (systematic formalization)","year":"1990s (formal methodology development)","type":"Experimental design","dataType":"Continuous, categorical, or count outcome data collected from individuals nested within clusters","subfamily":"Deneysel desen"},"citations":[{"ref":"Donner, A., & Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold.","type":"book","doi":null,"isbn":"978-0340691533","url":null},{"ref":"Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials: A Biomedical Research Paradigm. Oxford University Press.","type":"article","doi":null,"isbn":"978-0195100228","url":null}],"related":["cluster-randomized-controlled-trial","randomized-controlled-trial","blocked-randomized-controlled-trial","factorial-randomized-controlled-trial","pretest-posttest-experimental-design","control-group-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-controlled-trial","name":"Cluster Randomized Controlled Trial","fullName":"Cluster Randomized Controlled Trial","aliases":["cluster RCT","group-randomized trial","community randomized trial","cluster-randomized experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1978–1980s","originator":"Cornfield (1978); systematised by Donner and colleagues (1980s)","url":"https://scholargate.app/en/experimental-design/cluster-randomized-controlled-trial","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-controlled-trial.md","definition":"A cluster randomized controlled trial (cluster RCT) is an experimental design in which intact social or organisational groups — such as schools, clinics, villages, or workplaces — are randomly assigned to treatment conditions rather than individual participants. Outcomes are still measured at the individual level, but the unit of randomization is the cluster. This design is essential when an intervention is delivered to whole groups, when there is a risk of contamination between participants in the same setting, or when individual randomization is logistically or ethically impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cornfield (1978); systematised by Donner and colleagues (1980s)","year":"1978–1980s","type":"Experimental design","dataType":"Continuous, binary, count, or time-to-event outcomes measured on individuals nested within clusters","subfamily":"Deneysel desen"},"citations":[{"ref":"Donner, A., & Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold.","type":"book","doi":null,"isbn":"978-0340652978","url":null},{"ref":"Hayes, R. J., & Moulton, L. H. (2017). Cluster Randomised Trials (2nd ed.). CRC Press / Chapman & Hall.","type":"book","doi":null,"isbn":"978-1498728225","url":null}],"related":["randomized-controlled-trial","factorial-randomized-controlled-trial","stepped-wedge-design","blocked-randomized-controlled-trial","multilevel-modeling","intraclass-correlation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-factorial-experiment","name":"Cluster Randomized Factorial Experiment","fullName":"Cluster Randomized Factorial Experiment","aliases":["cluster-randomized factorial design","group-randomized factorial trial","CRT factorial","clustered factorial experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1990s (formalized in group-randomized trial literature)","originator":"David M. Murray and colleagues; Allan Donner & Neil Klar","url":"https://scholargate.app/en/experimental-design/cluster-randomized-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-factorial-experiment.md","definition":"A cluster randomized factorial experiment assigns intact groups (clusters such as schools, clinics, or communities) at random to all combinations of two or more treatment factors, enabling simultaneous evaluation of multiple interventions and their interactions while respecting the natural grouping of participants. It merges the logistical and ethical advantages of cluster randomization with the efficiency of factorial design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David M. Murray and colleagues; Allan Donner & Neil Klar","year":"1990s (formalized in group-randomized trial literature)","type":"Experimental design","dataType":"Continuous, binary, or count outcomes measured at the individual level within clusters","subfamily":"Deneysel desen"},"citations":[{"ref":"Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195120912","url":null},{"ref":"Donner, A., & Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold.","type":"book","doi":null,"isbn":"978-0340691533","url":null}],"related":["cluster-randomized-controlled-trial","factorial-experiment","factorial-randomized-controlled-trial","blocked-factorial-experiment","fractional-factorial-experiment","multi-arm-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-field-experiment","name":"Cluster Randomized Field Experiment","fullName":"Cluster Randomized Field Experiment","aliases":["CRFE","cluster-randomized trial in the field","group-randomized field experiment","community-randomized field experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1980s–1990s (formalized methodology)","originator":"David M. Murray (group-randomized trials framework); applied broadly in public health and education research","url":"https://scholargate.app/en/experimental-design/cluster-randomized-field-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-field-experiment.md","definition":"A cluster randomized field experiment (CRFE) assigns intact groups — schools, villages, clinics, workplaces — rather than individuals to treatment or control conditions, and the experiment is conducted in real-world settings rather than a laboratory. Randomization at the group level controls for contamination between conditions while preserving the ecological validity of the natural environment. It is the dominant design for evaluating community-level, school-based, or workplace interventions in public health, education policy, and development economics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David M. Murray (group-randomized trials framework); applied broadly in public health and education research","year":"1980s–1990s (formalized methodology)","type":"Randomized experimental design","dataType":"Continuous, binary, or count outcomes measured at individual or cluster level in real-world settings","subfamily":"Deneysel desen"},"citations":[{"ref":"Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195120424","url":null},{"ref":"Hayes, R. J., & Moulton, L. H. (2017). Cluster Randomised Trials (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1498728225","url":null}],"related":["cluster-randomized-controlled-trial","field-experiment","randomized-controlled-trial","stepped-wedge-design","blocked-field-experiment","factorial-field-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-fractional-factorial-experiment","name":"Cluster Randomized Fractional Factorial Experiment","fullName":"Cluster-Randomized Fractional Factorial Experiment","aliases":["CR-FFE","cluster-randomized fractional factorial design","group-randomized fractional factorial trial","CRFFD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1950s (fractional factorial); 1980s-1990s (cluster-randomized extensions)","originator":"Box, Hunter & Hunter (fractional factorial foundations); Murray & colleagues (group-randomized trial methodology)","url":"https://scholargate.app/en/experimental-design/cluster-randomized-fractional-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-fractional-factorial-experiment.md","definition":"A cluster-randomized fractional factorial experiment combines two design principles: randomization is applied to intact groups (clusters such as schools, clinics, or communities) rather than individuals, and only a carefully chosen fraction of all possible factor-level combinations is tested. This pairing makes it practical to screen or evaluate multiple intervention components simultaneously in settings where individual randomization is infeasible, while keeping the number of required clusters manageable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box, Hunter & Hunter (fractional factorial foundations); Murray & colleagues (group-randomized trial methodology)","year":"1950s (fractional factorial); 1980s-1990s (cluster-randomized extensions)","type":"Experimental design (compound)","dataType":"Continuous, binary, or count outcomes measured at the individual level within clusters","subfamily":"Deneysel desen"},"citations":[{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471718130","url":null},{"ref":"Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Design+and+Analysis+of+Group-Randomized+Trials+Murray+1998"}],"related":["cluster-randomized-controlled-trial","fractional-factorial-experiment","factorial-randomized-controlled-trial","blocked-fractional-factorial-experiment","multi-arm-experiment","adaptive-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-full-factorial-experiment","name":"Cluster Randomized Full Factorial Experiment","fullName":"Cluster-Randomized Full Factorial Experimental Design","aliases":["cluster RCT full factorial","group-randomized full factorial design","CRT full factorial","cluster full factorial trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Late 20th–early 21st century (formalized ~1998–2014)","originator":"Synthesis of cluster randomization (Murray, 1998) and factorial design traditions (Fisher, 1935; Collins et al., 2014)","url":"https://scholargate.app/en/experimental-design/cluster-randomized-full-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-full-factorial-experiment.md","definition":"A cluster-randomized full factorial experiment assigns intact groups (clusters) rather than individuals to every possible combination of two or more experimental factors. All factor-level combinations are tested simultaneously, enabling estimation of both main effects and all interaction effects, while preserving the integrity of naturally occurring social or organizational units such as schools, clinics, or communities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesis of cluster randomization (Murray, 1998) and factorial design traditions (Fisher, 1935; Collins et al., 2014)","year":"Late 20th–early 21st century (formalized ~1998–2014)","type":"Experimental design","dataType":"Continuous, binary, or count outcomes; hierarchical (clustered) observations","subfamily":"Deneysel desen"},"citations":[{"ref":"Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195120264","url":null},{"ref":"Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504.","type":"article","doi":"10.1016/j.amepre.2014.06.021","isbn":null,"url":null}],"related":["cluster-randomized-controlled-trial","full-factorial-experiment","factorial-randomized-controlled-trial","blocked-full-factorial-experiment","fractional-factorial-experiment","multilevel-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-laboratory-experiment","name":"Cluster Randomized Laboratory Experiment","fullName":"Cluster Randomized Laboratory Experiment","aliases":["cluster-randomized lab experiment","group-randomized laboratory study","cluster RCT laboratory variant","clustered lab trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1990s (formalized; cluster randomization principles developed in 1970s-1980s)","originator":"David M. Murray (group-randomized trial methodology); built on classical cluster sampling in experimental design","url":"https://scholargate.app/en/experimental-design/cluster-randomized-laboratory-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-laboratory-experiment.md","definition":"A cluster randomized laboratory experiment assigns intact groups — such as lab sections, cohorts, or naturally formed teams — rather than individual participants, to experimental conditions. All participants within a cluster receive the same treatment. The design is used when individual randomization would cause contamination between conditions, while retaining the controlled environment of a laboratory setting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David M. Murray (group-randomized trial methodology); built on classical cluster sampling in experimental design","year":"1990s (formalized; cluster randomization principles developed in 1970s-1980s)","type":"Controlled laboratory experiment with cluster-level randomization","dataType":"Continuous, ordinal, or categorical outcome measures collected in a controlled lab environment","subfamily":"Deneysel desen"},"citations":[{"ref":"Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195120363","url":null},{"ref":"Donner, A., & Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold.","type":"book","doi":null,"isbn":"978-0340691533","url":null}],"related":["cluster-randomized-controlled-trial","laboratory-experiment","factorial-laboratory-experiment","blocked-laboratory-experiment","randomized-controlled-trial","multilevel-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-multi-arm-experiment","name":"Cluster Randomized Multi-Arm Experiment","fullName":"Cluster Randomized Multi-Arm Experiment","aliases":["multi-arm cluster RCT","cluster-randomized multi-group trial","multi-arm group-randomized trial","CRCT multi-arm"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1990s–2000s (systematic formalization)","originator":"Building on cluster randomization (Donner & Klar) and multi-arm trial methods developed in clinical and public health research","url":"https://scholargate.app/en/experimental-design/cluster-randomized-multi-arm-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-multi-arm-experiment.md","definition":"A cluster randomized multi-arm experiment assigns intact groups — such as schools, clinics, or villages — rather than individuals to three or more experimental conditions simultaneously. Randomization occurs at the cluster level to prevent contamination between arms, while the multi-arm structure allows simultaneous evaluation of several interventions against a common control or each other, improving efficiency over a series of two-arm studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on cluster randomization (Donner & Klar) and multi-arm trial methods developed in clinical and public health research","year":"1990s–2000s (systematic formalization)","type":"Experimental design","dataType":"Continuous, binary, or count outcomes measured at the individual level within clusters","subfamily":"Deneysel desen"},"citations":[{"ref":"Donner, A., & Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold.","type":"book","doi":null,"isbn":"978-0340691533","url":null},{"ref":"Hemming, K., Girling, A., Sitch, A., Marsh, J., & Lilford, R. (2017). Sample size calculations for cluster randomised controlled trials with a fixed number of clusters. BMC Medical Research Methodology, 17, 73.","type":"article","doi":"10.1186/s12874-017-0292-x","isbn":null,"url":null}],"related":["cluster-randomized-controlled-trial","multi-arm-experiment","factorial-randomized-controlled-trial","blocked-randomized-controlled-trial","adaptive-randomized-controlled-trial","crossover-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-multiple-baseline-design","name":"Cluster Randomized Multiple Baseline Design","fullName":"Cluster Randomized Multiple Baseline Design","aliases":["CR-MBD","cluster-randomized MBD","group-randomized multiple baseline","multilevel multiple baseline design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1990s–2000s","originator":"Extension of Baer, Wolf & Risley (1968) multiple baseline; cluster adaptation by Murray and colleagues (1990s)","url":"https://scholargate.app/en/experimental-design/cluster-randomized-multiple-baseline-design","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-multiple-baseline-design.md","definition":"The cluster randomized multiple baseline design combines cluster-level random assignment with the logic of the multiple baseline design. Intact groups — such as classrooms, schools, or clinics — are randomly assigned to receive an intervention at staggered time points. This preserves the within-unit repeated-measure logic of the multiple baseline while adding the causal warrant of random assignment at the cluster level.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Baer, Wolf & Risley (1968) multiple baseline; cluster adaptation by Murray and colleagues (1990s)","year":"1990s–2000s","type":"Experimental design (single-subject / small-N with cluster randomization)","dataType":"Repeated measures (observations per unit per time point), cluster-level assignment variable","subfamily":"Deneysel desen"},"citations":[{"ref":"Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195120424","url":null},{"ref":"Kowalski, J. T., & Shadish, W. R. (2015). Incorporating cluster randomization into multiple baseline designs. Journal of School Psychology, 53(6), 435-449.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Incorporating+cluster+randomization+into+multiple+baseline+designs"}],"related":["multiple-baseline-design","cluster-randomized-controlled-trial","abab-design","factorial-multiple-baseline-design","randomized-controlled-trial","single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-solomon-four-group-design","name":"Cluster Randomized Solomon Four-Group Design","fullName":"Cluster Randomized Solomon Four-Group Experimental Design","aliases":["CR-S4GD","cluster-randomized four-group design","group-randomized Solomon design","Solomon four-group cluster trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1949 (Solomon design); cluster extension formalized in 1990s","originator":"Richard L. Solomon (four-group logic, 1949); cluster randomization methods developed by Murray and colleagues in the 1990s","url":"https://scholargate.app/en/experimental-design/cluster-randomized-solomon-four-group-design","markdownUrl":"https://scholargate.app/en/experimental-design/cluster-randomized-solomon-four-group-design.md","definition":"The cluster randomized Solomon four-group design combines cluster randomization — assigning intact groups such as schools, clinics, or communities to conditions — with the Solomon four-group structure that isolates the effect of pretesting. Four clusters (or sets of clusters) are created: two receive the treatment and two serve as controls, with only one treatment cluster and one control cluster receiving a pretest, while the others go straight to the posttest. This structure simultaneously controls for pretest sensitization and the logistical constraint that individual randomization is infeasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard L. Solomon (four-group logic, 1949); cluster randomization methods developed by Murray and colleagues in the 1990s","year":"1949 (Solomon design); cluster extension formalized in 1990s","type":"Experimental design","dataType":"Continuous, ordinal, or categorical outcome data collected at cluster and individual levels","subfamily":"Deneysel desen"},"citations":[{"ref":"Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.","type":"article","doi":"10.1037/h0062958","isbn":null,"url":null},{"ref":"Murray, D. M. (1998). Design and Analysis of Group-Randomized Trials. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195100877","url":null}],"related":["solomon-four-group-design","cluster-randomized-controlled-trial","blocked-solomon-four-group-design","factorial-randomized-controlled-trial","pretest-posttest-experimental-design","multilevel-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-randomized-trial","name":"Cluster Randomized Trial","fullName":"Cluster Randomized Controlled Trial (CRT)","aliases":["CRT","cluster RCT","cluster trial","group randomization"],"domain":"clinical-research","family":"process-pipeline","subfamily":"experimental design","year":"1999-2000","originator":"Campbell, Grimshaw, Elbourne et al.","url":"https://scholargate.app/en/clinical-research/cluster-randomized-trial","markdownUrl":"https://scholargate.app/en/clinical-research/cluster-randomized-trial.md","definition":"A cluster randomized trial (CRT) randomizes intact groups—schools, clinics, villages, or hospital wards—rather than individuals. Developed by Campbell, Grimshaw, and colleagues in the late 1990s to address real-world settings where intervention delivery or contamination occurs at the group level, CRTs are now standard for evaluating population-level, community-based, and policy interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Campbell, Grimshaw, Elbourne et al.","subfamily":"experimental design","year":"1999-2000","type":"Research Design"},"citations":[{"ref":"Campbell, M. K., Grimshaw, J. M., & Elbourne, D. R. (2000). Intracluster correlation coefficients in cluster randomized trials: empirical insights into how should they be reported. BMC Medical Research Methodology, 4, 30.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Intracluster+correlation+coefficients+in+cluster+randomized+trials%3A+empirical+insights+into+how+should+they+be+reported+Campbell"},{"ref":"Eldridge, S. M., Ashmore, S., Frenkel, S., Cryer, C., & Underwood, M. (2006). Uncertainty in analyses of safety after cluster randomization. Clinical Trials, 3(2), 152–162.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Uncertainty+in+analyses+of+safety+after+cluster+randomization+Eldridge"},{"ref":"Campbell, M. K., Piaggio, G., Elbourne, D. R., & Altman, D. G. (2012). Consort 2010 statement: extension to cluster randomised trials. BMJ, 345, e5661.","type":"article","doi":"10.1136/bmj.e5661","isbn":null,"url":null}],"related":["randomized-controlled-trial","intracluster-correlation","adaptive-trial-design","pragmatic-clinical-trial","mixed-models-multilevel"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-robust-se","name":"Cluster-Robust Standard Errors","fullName":"Cluster-Robust (Clustered) Standard Errors","aliases":["clustered standard errors","cluster-robust inference","clustered variance estimator","Küme Robust Standart Hatalar"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1986,"originator":"Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)","url":"https://scholargate.app/en/statistics/cluster-robust-se","markdownUrl":"https://scholargate.app/en/statistics/cluster-robust-se.md","definition":"Cluster-robust standard errors correct the variance of regression coefficients when observations are correlated within clusters such as schools, hospitals, or regions. The clustered sandwich estimator grew out of Liang & Zeger's (1986) generalized estimating equations and was synthesized for applied work by Cameron & Miller (2015), delivering valid inference when ordinary standard errors would be too small.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)","year":1986,"type":"Robust variance estimation for regression","estimator":"Cluster-robust sandwich (clustered Huber-White) variance","minClusters":"≥20 recommended"},"citations":[{"ref":"Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.","type":"article","doi":"10.1093/biomet/73.1.13","isbn":null,"url":null},{"ref":"Cameron, A. C. & Miller, D. L. (2015). A Practitioner's Guide to Cluster-Robust Inference. Journal of Human Resources, 50(2), 317-372.","type":"article","doi":"10.3368/jhr.50.2.317","isbn":null,"url":null}],"related":["ols-regression","panel-fixed-effects","wild-bootstrap","permutation-test","huber-white-robust-se"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cluster-sampling","name":"Cluster Sampling","fullName":"Cluster Sampling","aliases":["cluster random sampling","area sampling","one-stage cluster sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Early-to-mid 20th century; canonical treatment 1953/1977","originator":"Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice","url":"https://scholargate.app/en/survey-methodology/cluster-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/cluster-sampling.md","definition":"Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice","year":"Early-to-mid 20th century; canonical treatment 1953/1977","type":"Probability sampling design","dataType":"Quantitative or mixed; any unit-level measurements within naturally occurring groups","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Cluster sampling. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Cluster_sampling"}],"related":["simple-random-sampling","stratified-sampling","multistage-sampling","systematic-sampling","proportional-cluster-sampling","area-probability-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cmb-anisotropy-analysis","name":"CMB Anisotropy Analysis","fullName":"Cosmic Microwave Background Anisotropy Analysis","aliases":["CMB Power Spectrum","CMB Anisotropies","Microwave Background Analysis"],"domain":"astronomy","family":"process-pipeline","subfamily":"Cosmological probe","year":1965,"originator":"Arno Penzias","url":"https://scholargate.app/en/astronomy/cmb-anisotropy-analysis","markdownUrl":"https://scholargate.app/en/astronomy/cmb-anisotropy-analysis.md","definition":"The Cosmic Microwave Background is the ancient light from when the universe first became transparent, about 380,000 years after the Big Bang. Its tiny temperature variations (anisotropies) across the sky encode a wealth of information about the universe's composition, geometry, and history. First discovered by Arno Penzias and Robert Wilson in 1965, detailed measurements of CMB anisotropies have become the most powerful probe of cosmology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arno Penzias","subfamily":"Cosmological probe","year":1965,"type":"Observational cosmological measurement"},"citations":[{"ref":"Penzias, A. A., & Wilson, R. W. (1965). A measurement of excess antenna temperature at 4080 Mc/s. Astrophysical Journal, 142, 419-421.","type":"article","doi":"10.1086/148307","isbn":null,"url":null},{"ref":"Smoot, G. F., et al. (1992). Structure in the COBE differential microwave radiometer first-year maps. Astrophysical Journal Letters, 396(1), L1-L5.","type":"article","doi":"10.1086/186504","isbn":null,"url":null},{"ref":"Planck Collaboration (2018). Planck 2018 results. VI. Cosmological parameters. Astronomy & Astrophysics, 641, A6.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Planck+2018+results+Planck"}],"related":["baryon-acoustic-oscillations","sunyaev-zeldovich-effect","epoch-of-reionization-21-cm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cnc-tool-path-generation","name":"CNC Tool Path Generation","fullName":"CNC Tool Path Generation and Optimization","aliases":["NC tool path planning","Toolpath programming"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Computational geometry","year":"1990s","originator":"Elbestawi, M. A. et al.","url":"https://scholargate.app/en/manufacturing/cnc-tool-path-generation","markdownUrl":"https://scholargate.app/en/manufacturing/cnc-tool-path-generation.md","definition":"CNC tool path generation is the computational process of determining the precise sequence and trajectory of tool movements required to machine a workpiece on computer numerical control (CNC) machines. Developed from the intersection of numerical control automation and computational geometry in the 1990s, this method translates CAD designs into executable machine instructions, enabling efficient and accurate manufacturing of complex parts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elbestawi, M. A. et al.","subfamily":"Computational geometry","year":"1990s","type":"Computational method for manufacturing automation"},"citations":[{"ref":"Elbestawi, M. A., Papazafiriou, T., & Du, R. (1994). In-process detection of tool wear in milling using cutting force signature. International Journal of Machine Tools and Manufacture, 34(4), 555-566.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=In-process+detection+of+tool+wear+in+milling+using+cutting+force+signature+Elbestawi"},{"ref":"Li, Y., Lum, L., Wang, J. X., & Kishawy, H. A. (2009). Tool life modeling in vibration-assisted machining of Inconel 718. Machining Science and Technology, 13(2), 226-245.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tool+life+modeling+in+vibration-assisted+machining+of+Inconel+718+Li"},{"ref":"Sarhan, A. A., Sayuti, M., & Hamdi, M. (2011). Machinability of aerospace AL-Si-Cu alloy AA2014-T6 under dry and cryogenic cutting conditions. Journal of Materials Processing Technology, 211(3), 484-492.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Machinability+of+aerospace+AL-Si-Cu+alloy+AA2014-T6+under+dry+and+cryogenic+cutting+conditions+Sarhan"}],"related":["additive-manufacturing-slicing","inverse-kinematics","denavit-hartenberg-parameters","taylor-tool-life","design-for-manufacturing-and-assembly"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cnn-classification","name":"Convolutional Neural Network","fullName":"Convolutional Neural Network for Classification","aliases":["CNN (Evrişimli Sinir Ağı — Sınıflandırma)","CNN classification","ConvNet","convolutional network classifier"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":1998,"originator":"LeCun, Y. et al.","url":"https://scholargate.app/en/deep-learning/cnn-classification","markdownUrl":"https://scholargate.app/en/deep-learning/cnn-classification.md","definition":"A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeCun, Y. et al.","year":1998,"type":"Deep neural network (convolutional)","task":"Classification & prediction","minSample":1000},"citations":[{"ref":"LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324.","type":"article","doi":"10.1109/5.726791","isbn":null,"url":null}],"related":["random-forest","svm-classification","xgboost","transformer-nlp","autoencoder"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cnn-image-classification","name":"CNN Image Classification","fullName":"Convolutional Neural Network Image Classification (ResNet / VGG / EfficientNet)","aliases":["CNN — Görüntü Sınıflandırma (ResNet / VGG / EfficientNet)","convolutional neural network image classifier","deep image classification","ResNet / VGG / EfficientNet"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2016,"originator":"He, K. et al. (ResNet); Tan, M. & Le, Q.V. (EfficientNet)","url":"https://scholargate.app/en/deep-learning/cnn-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/cnn-image-classification.md","definition":"CNN image classification uses deep convolutional architectures such as ResNet (He et al., 2016), VGG and EfficientNet (Tan & Le, 2019) to sort images into categories. Stacked convolutional layers learn a hierarchy of visual features directly from pixels, and skip (residual) connections prevent the vanishing-gradient problem in very deep networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"He, K. et al. (ResNet); Tan, M. & Le, Q.V. (EfficientNet)","year":2016,"type":"Deep convolutional neural network (supervised)","task":"Image classification & prediction","minSample":500,"gpuRecommended":true},"citations":[{"ref":"He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR.","type":"inproceedings","doi":"10.1109/CVPR.2016.90","isbn":null,"url":null},{"ref":"Tan, M. & Le, Q.V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML, PMLR 97, 6105–6114. arXiv:1905.11946.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1905.11946"}],"related":["cnn-text-classification","dilated-cnn","random-forest","svm-classification","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cnn-text-classification","name":"TextCNN","fullName":"Convolutional Neural Network for Text Classification (TextCNN)","aliases":["CNN — Metin Sınıflandırma (TextCNN)","convolutional neural network for sentence classification","sentence-level CNN","TextCNN"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2014,"originator":"Kim, Y.","url":"https://scholargate.app/en/deep-learning/cnn-text-classification","markdownUrl":"https://scholargate.app/en/deep-learning/cnn-text-classification.md","definition":"TextCNN is a convolutional neural network for text classification, introduced by Yoon Kim in 2014, that applies parallel convolution filters of different window sizes over word embeddings to capture local n-gram patterns. It is fast and effective for sentiment analysis and topic classification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kim, Y.","year":2014,"type":"Convolutional neural network (deep learning)","task":"Text classification & prediction","minSample":200},"citations":[{"ref":"Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. EMNLP.","type":"article","doi":"10.3115/v1/D14-1181","isbn":null,"url":null},{"ref":"Zhang, Y. & Wallace, B. (2015). A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification. arXiv:1510.03820.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1510.03820"}],"related":["gru","bidirectional-rnn","dilated-cnn","xgboost","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"co-authorship-analysis","name":"Co-Authorship Network Analysis","fullName":"Co-Authorship Network Analysis","aliases":["collaboration network","authorship network","research collaboration mapping"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"network-collaboration","year":"2001","originator":"Mark E. J. Newman and others","url":"https://scholargate.app/en/bibliometrics/co-authorship-analysis","markdownUrl":"https://scholargate.app/en/bibliometrics/co-authorship-analysis.md","definition":"Co-authorship network analysis is a method that maps research collaboration patterns by treating authors as nodes and co-authored papers as edges in a network graph. The structure, density, and centrality patterns of this network reveal how researchers connect, collaborate across institutions and disciplines, and form research communities. Pioneered formally by Newman (2001), co-authorship analysis provides quantitative insights into the social fabric of science, revealing collaboration patterns, identifying scientific leaders, and detecting institutional or disciplinary boundaries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark E. J. Newman and others","subfamily":"network-collaboration","year":"2001","type":"Method"},"citations":[{"ref":"Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.","type":"article","doi":"10.1073/pnas.021544898","isbn":null,"url":null},{"ref":"Braun, T., Glänzel, W., & Schubert, A. (2001). Dynamic scientometric relations: Citation and collaboration patterns in selected research areas. Scientometrics, 51(3), 487–502.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dynamic+scientometric+relations%3A+Citation+and+collaboration+patterns+in+selected+research+areas+Braun"}],"related":["science-mapping","keyword-co-occurrence","bibliographic-coupling","co-citation-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"co-citation-analysis","name":"Co-Citation Analysis","fullName":"Co-Citation Analysis","aliases":["co-citation mapping","historiograph","direct citation","citation pair analysis"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"network-citation","year":"1973","originator":"Henry Small","url":"https://scholargate.app/en/bibliometrics/co-citation-analysis","markdownUrl":"https://scholargate.app/en/bibliometrics/co-citation-analysis.md","definition":"Co-citation analysis is a method that identifies the intellectual structure of a research domain by examining how frequently pairs of documents are cited together in other publications. When two papers are frequently cited together in the literature, they are considered co-cited, indicating they are conceptually related or influential within the same research community. Developed by Henry Small in 1973, co-citation analysis maps the 'invisible colleges' of science—networks of researchers working on related problems—and reveals how knowledge domains evolve over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Small","subfamily":"network-citation","year":"1973","type":"Method"},"citations":[{"ref":"Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.","type":"article","doi":"10.1002/asi.4630240406","isbn":null,"url":null},{"ref":"Small, H., & Griffiths, B. C. (1974). The structure of scientific literatures I: Identifying and graphing specialties. Science Studies, 4(1), 17–40.","type":"article","doi":"10.1177/030631277400400102","isbn":null,"url":null}],"related":["bibliographic-coupling","keyword-co-occurrence","science-mapping","journal-co-citation-analysis","research-front-identification"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"co-kriging","name":"Co-kriging","fullName":"Co-kriging Spatial Interpolation","aliases":["cokriging","co-regionalization kriging","multivariate kriging","CK"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1965-1978","originator":"Matheron, G.; extended by Journel & Huijbregts","url":"https://scholargate.app/en/spatial-analysis/co-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/co-kriging.md","definition":"Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable is more densely sampled or spatially correlated with the primary variable of interest.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matheron, G.; extended by Journel & Huijbregts","year":"1965-1978","type":"Geostatistical interpolation","dataType":"Continuous spatial variables with co-located or secondary observations","subfamily":"GIS / spatial"},"citations":[{"ref":"Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London.","type":"book","doi":null,"isbn":"978-0123910561","url":null},{"ref":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press, New York.","type":"book","doi":null,"isbn":"978-0195115383","url":null}],"related":["ordinary-kriging","kriging","universal-kriging","spatial-autocorrelation","geographically-weighted-regression","multiscale-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"co-occurrence-analysis","name":"Co-occurrence Analysis","fullName":"Word Co-occurrence Analysis","aliases":["word co-occurrence","co-occurrence network","Kelime Eş-Oluşum Analizi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1957,"originator":"J.R. Firth (distributional principle)","url":"https://scholargate.app/en/text-mining/co-occurrence-analysis","markdownUrl":"https://scholargate.app/en/text-mining/co-occurrence-analysis.md","definition":"Co-occurrence analysis is a text-mining technique that statistically counts the word pairs that appear together within a window or a sentence and uses their frequencies to reveal semantic maps and thematic structure. It rests on the distributional principle articulated by J.R. Firth in 1957 — that a word is characterised by the company it keeps.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J.R. Firth (distributional principle)","year":1957,"type":"Text-mining / distributional-semantics technique","input":"Tokenised text corpus","output":"Co-occurrence frequency matrix / network of word pairs","minSample":"30 documents"},"citations":[{"ref":"Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell.","type":"incollection","doi":null,"isbn":null,"url":"https://cs.brown.edu/courses/csci2952d/readings/lecture1-firth.pdf"},{"ref":"Turney, P.D. & Pantel, P. (2010). From Frequency to Meaning: Vector Space Models of Semantics. Journal of Artificial Intelligence Research, 37, 141-188.","type":"article","doi":"10.1613/jair.2934","isbn":null,"url":null}],"related":["tf-idf","topic-modeling","keyword-extraction","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"co-word-analysis","name":"Co-word Analysis","fullName":"Co-word Analysis","aliases":["keyword co-occurrence analysis","co-word mapping","keyword co-word network","CWA"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1983","originator":"Michel Callon, Jean-Pierre Courtial, and colleagues","url":"https://scholargate.app/en/scientometrics/co-word-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/co-word-analysis.md","definition":"Co-word analysis is a scientometric technique that quantifies how often pairs of keywords, subject terms, or title words appear together across a corpus of publications. By treating simultaneous occurrence as a proxy for conceptual relatedness, it constructs networks and clusters that reveal the intellectual structure, dominant themes, and emerging sub-fields of a research domain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michel Callon, Jean-Pierre Courtial, and colleagues","year":"1983","type":"Scientometric network analysis technique","dataType":"Keywords, title terms, or abstracts from bibliographic records","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.","type":"article","doi":"10.1177/053901883022002003","isbn":null,"url":null},{"ref":"He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends, 48(1), 133–159.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Knowledge+discovery+through+co-word+analysis+He+1999"}],"related":["bibliometric-analysis","co-citation-analysis","bibliographic-coupling","science-mapping","scientometric-analysis","thematic-evolution-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"co2sys","name":"CO2SYS","fullName":"CO2SYS Carbonate Chemistry Calculation","aliases":["CO2SYS","Carbonate System Solver"],"domain":"oceanography","family":"process-pipeline","subfamily":"Geochemical Modeling","year":"1998","originator":"Ernie Lewis","url":"https://scholargate.app/en/oceanography/co2sys","markdownUrl":"https://scholargate.app/en/oceanography/co2sys.md","definition":"CO2SYS is a widely-used software package for calculating the speciation and equilibrium state of the marine carbonate system from measurements of two carbonate parameters. Developed by Ernie Lewis and Doug Wallace in 1998, CO2SYS enables oceanographers to compute all carbonate species (dissolved CO2, bicarbonate, carbonate), saturation states, and pH from pairs of measured parameters. The tool is essential for ocean acidification monitoring and research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ernie Lewis","subfamily":"Geochemical Modeling","year":"1998","type":"calculation-software"},"citations":[{"ref":"Lewis, E., & Wallace, D. W. R. (1998). Program developed for CO2 system calculations. ORNL/CDIAC-105. Oak Ridge National Laboratory.","type":"article","doi":null,"isbn":null,"url":"https://cdiac.ess-dive.lbl.gov/"},{"ref":"Pelletier, G. J., Lewis, E., & Wallace, D. W. R. (2007). CO2SYS.XLS: A calculator for the CO2 system in seawater. Open-file report 2007-1047. USGS.","type":"article","doi":null,"isbn":null,"url":"https://pubs.usgs.gov/"}],"related":["ctd-profiling","ocean-color-chlorophyll-a","degree-heating-weeks"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"coalescent-theory","name":"Coalescent Theory","fullName":"Coalescent Theory of Genetic Ancestry","aliases":["Kingman Coalescent","n-coalescent"],"domain":"genetics","family":"process-pipeline","subfamily":"Population Genetics","year":"1982","originator":"John Kingman","url":"https://scholargate.app/en/genetics/coalescent-theory","markdownUrl":"https://scholargate.app/en/genetics/coalescent-theory.md","definition":"Coalescent theory is a probabilistic framework that traces the genealogical history of DNA sequences backward in time to their most recent common ancestor. Developed by John Kingman in 1982, this method forms the foundation of modern population genetics, enabling researchers to understand demographic events, estimate genetic parameters, and reconstruct evolutionary histories from modern genetic data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Kingman","subfamily":"Population Genetics","year":"1982","type":"Stochastic process model"},"citations":[{"ref":"Kingman, J. F. C. (1982). The coalescent. Stochastic Processes and their Applications, 13(3), 235–248.","type":"article","doi":"10.1016/0304-4149(82)90011-4","isbn":null,"url":null},{"ref":"Hudson, R. R. (1983). Properties of a neutral allele model with intragenic recombination. Theoretical Population Biology, 23(2), 183–201.","type":"article","doi":"10.1016/0040-5809(83)90013-8","isbn":null,"url":null},{"ref":"Tajima, F. (1983). Evolutionary relationship of DNA sequences in finite populations. Genetics, 105(2), 437–460.","type":"article","doi":"10.1093/genetics/105.2.437","isbn":null,"url":null}],"related":["selection-sweep","admixture-analysis","ancestral-state-reconstruction","f-statistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"coarsened-exact-matching-in-education-research","name":"Coarsened Exact Matching in Education Research","fullName":"Coarsened Exact Matching for Causal Inference in Education Research","aliases":["CEM in education","CEM for educational studies","exact matching education","coarsened matching educational data"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012","originator":"Iacus, King, & Porro","url":"https://scholargate.app/en/causal-inference/coarsened-exact-matching-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/coarsened-exact-matching-in-education-research.md","definition":"Coarsened Exact Matching (CEM) is a pre-processing matching strategy that reduces imbalance between treated and comparison groups before outcome analysis. In education research it is used to create balanced comparison groups from administrative records, survey data, or quasi-experimental study designs — for example comparing students who received an intervention against comparable students who did not, without relying on randomisation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Iacus, King, & Porro","year":"2012","type":"Matching / quasi-experimental","dataType":"Observational panel or cross-sectional data with categorical or coarsened continuous covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Morgan, S. L., & Winship, C. (2015). Counterfactuals and Causal Inference: Methods and Principles for Social Research (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107065079","url":null}],"related":["coarsened-exact-matching","propensity-score-matching-in-education-research","difference-in-differences-in-education-research","regression-discontinuity-design-in-education-research","entropy-balancing-in-education-research","matching-estimator-in-education-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"coarsened-exact-matching","name":"Coarsened Exact Matching","fullName":"Coarsened Exact Matching Estimator","aliases":["CEM","coarsened matching","monotonic imbalance bounding matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2011-2012","originator":"Iacus, King, & Porro","url":"https://scholargate.app/en/causal-inference/coarsened-exact-matching","markdownUrl":"https://scholargate.app/en/causal-inference/coarsened-exact-matching.md","definition":"Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Iacus, King, & Porro","year":"2011-2012","type":"Matching / causal inference","dataType":"Cross-sectional or panel; mixed continuous and categorical covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Iacus, S. M., King, G., & Porro, G. (2011). Multivariate matching methods that are monotonic imbalance bounding. Journal of the American Statistical Association, 106(493), 345-361.","type":"article","doi":"10.1198/jasa.2011.tm09599","isbn":null,"url":null}],"related":["propensity-score-matching","propensity-score-weighting","matching-estimator","entropy-balancing","difference-in-differences","inverse-probability-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cobra","name":"COBRA","fullName":"COmprehensive distance Based RAnking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Krstić, M., Agnusdei, G. P., Tadić, S., Kovač, M., Miglietta, P. P.","url":"https://scholargate.app/en/decision-making/cobra","markdownUrl":"https://scholargate.app/en/decision-making/cobra.md","definition":"COBRA (COmprehensive distance Based RAnking) is a ranking multi-criteria decision-making (MCDM) method introduced by Krstić, M., Agnusdei, G. P., Tadić, S., Kovač, M., Miglietta, P. P. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Krstić, M., Agnusdei, G. P., Tadić, S., Kovač, M., Miglietta, P. P.","subfamily":"Ranking","year":"2022","type":"Distance from PIS/NIS/AS (Euclidean × Taxicab combined)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Krstić, M., Agnusdei, G. P., Tadić, S., Kovač, M., Miglietta, P. P. (2022). A Novel Axiomatic DEA-COBRA Framework for Evaluating the Sustainable Performance of Agri-Food Systems. Sustainability","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Novel+Axiomatic+DEA-COBRA+Framework+for+Evaluating+the+Sustainable+Performance+of+Agri-Food+Systems+Krsti%C4%87"}],"related":["ahp","anp","bwm","critic","entropy","fucom","merec","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cochran-q-test","name":"Cochran Q Test","fullName":"Cochran's Q Test","aliases":["Cochran Q Testi","Cochran's Q","Q test for related proportions"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1950,"originator":"William G. Cochran","url":"https://scholargate.app/en/statistics/cochran-q-test","markdownUrl":"https://scholargate.app/en/statistics/cochran-q-test.md","definition":"Cochran's Q test is a nonparametric hypothesis test introduced by William G. Cochran in 1950 for comparing proportions across three or more related binary measurements. It extends McNemar's test to the multiple-condition case and is the method of choice when every participant is observed under each condition and the outcome is recorded as a simple success/failure (1/0).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran","year":1950,"family":"Hypothesis test","type":"Nonparametric proportions comparison","groups":"≥ 3 related conditions","outcome":"binary (0/1)","parametric":false,"distribution":"Chi-squared (approximate)","df":"k - 1"},"citations":[{"ref":"Cochran, W. G. (1950). The comparison of percentages in matched samples. Biometrika, 37(3–4), 256–266.","type":"article","doi":"10.1093/biomet/37.3-4.256","isbn":null,"url":null}],"related":["mcnemar-test","friedman-test","repeated-measures-anova","chi-square-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cochrane-risk-of-bias","name":"Cochrane RoB 2.0","fullName":"Cochrane Risk of Bias Tool Version 2.0","aliases":["RoB 2.0","RoB 2"],"domain":"research-methodology","family":"process-pipeline","subfamily":"RCT quality assessment","year":"2019","originator":"Jonathan Sterne, Julian Higgins (Cochrane Collaboration)","url":"https://scholargate.app/en/research-methodology/cochrane-risk-of-bias","markdownUrl":"https://scholargate.app/en/research-methodology/cochrane-risk-of-bias.md","definition":"RoB 2 is the Cochrane Collaboration's updated methodology for assessing the risk of bias in randomized controlled trials (RCTs). Published in 2019, it replaced the original Cochrane RoB tool with a more structured, transparent approach using signalling questions and domain-based judgments to evaluate five critical sources of bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan Sterne, Julian Higgins (Cochrane Collaboration)","subfamily":"RCT quality assessment","year":"2019","type":"Clinician-rated / Research team assessment"},"citations":[{"ref":"Sterne, J. A., Savović, J., Page, M. J., Elbers, R. G., Blencowe, N. S., Boutron, I., ... & Higgins, J. P. (2019). RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ, 366, l4898.","type":"article","doi":"10.1136/bmj.l4898","isbn":null,"url":null}],"related":["casp-rct-checklist","grade-evidence-profiling","consort-reporting-checklist","prisma-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cocoso","name":"COCOSO","fullName":"Combined Compromise Solution","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z.","url":"https://scholargate.app/en/decision-making/cocoso","markdownUrl":"https://scholargate.app/en/decision-making/cocoso.md","definition":"COCOSO (Combined Compromise Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z.","subfamily":"Ranking","year":"2019","type":"Aggregated exponential comparison (WSM + WPM combination)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z. (2019). A combined compromise solution (COCOSO) method for multi-criteria decision-making problems. Management Decision","type":"article","doi":"10.1108/MD-05-2017-0458","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"codas","name":"CODAS","fullName":"Combinative Distance-Based Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2016","originator":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J.","url":"https://scholargate.app/en/decision-making/codas","markdownUrl":"https://scholargate.app/en/decision-making/codas.md","definition":"CODAS (Combinative Distance-Based Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J.","subfamily":"Ranking","year":"2016","type":"Distance from anti-ideal (Euclidean + Taxicab)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+combinative+distance-based+assessment+%28CODAS%29+method+for+multi-criteria+decision-making+Keshavarz"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"code-coverage-analysis","name":"Code Coverage Analysis","fullName":"Code Coverage Measurement and Analysis","aliases":["coverage metrics","test coverage","instrumentation-based measurement"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Test measurement","year":"1988","originator":"Test Coverage Community","url":"https://scholargate.app/en/software-engineering/code-coverage-analysis","markdownUrl":"https://scholargate.app/en/software-engineering/code-coverage-analysis.md","definition":"Code coverage analysis measures the extent to which source code is executed by a test suite, quantifying which lines, branches, or paths are exercised. Tools instrument code to track execution, reporting coverage percentages and identifying untested regions. Coverage analysis guides test creation, detects dead code, and validates test adequacy in quality assurance processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Test Coverage Community","subfamily":"Test measurement","year":"1988","type":"measurement and analysis"},"citations":[{"ref":"Zhu, H., Hall, P. A. V., & May, J. H. R. (1997). Software unit test coverage and adequacy. ACM Computing Surveys, 29(4), 366–427.","type":"article","doi":"10.1145/267580.267590","isbn":null,"url":null},{"ref":"Frankl, P. G., & Weiss, S. N. (1988). An experimental comparison of the effectiveness of branch testing and data flow testing. IEEE Transactions on Software Engineering, 14(12), 1763–1773.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+experimental+comparison+of+the+effectiveness+of+branch+testing+and+data+flow+testing+Frankl"},{"ref":"Corbet, J. (2008). Code coverage for the Linux kernel. Linux Weekly News article.","type":"article","doi":null,"isbn":null,"url":"https://lwn.net/Articles/289076/"}],"related":["software-complexity-metrics","software-testing-equivalence","static-code-analysis","defect-prediction-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cognitive-and-affective-mindfulness","name":"Cognitive and Affective Mindfulness Scale","fullName":"Cognitive and Affective Mindfulness Scale (CAMS)","aliases":["CAMS","CAMS-R"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"trait-mindfulness","year":"2007","originator":"Gesine C. Feldman, Andrew M. Hayes, and colleagues at Rutgers University","url":"https://scholargate.app/en/mindfulness-psychology/cognitive-and-affective-mindfulness","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/cognitive-and-affective-mindfulness.md","definition":"The Cognitive and Affective Mindfulness Scale (CAMS) is a 12-item trait mindfulness measure designed to assess the degree to which individuals are present, aware, and non-judging toward their internal (cognitive and emotional) and external experiences. Developed by Feldman, Hayes, and colleagues at Rutgers University and published in the Journal of Clinical Psychology in 2007, the CAMS emphasizes the emotional and cognitive regulation aspects of mindfulness, particularly the capacity to observe thoughts and feelings without judgment. The CAMS-Revised (CAMS-R, 2006) is the refined version, offering strong brevity and psychometric properties that make it especially useful in clinical settings where time and assessment burden must be minimized.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gesine C. Feldman, Andrew M. Hayes, and colleagues at Rutgers University","subfamily":"trait-mindfulness","year":"2007","type":"Self-report"},"citations":[{"ref":"Feldman, G. C., Hayes, A. M., Kumar, S. M., Greeson, J. M., & Laurenceau, J.-P. (2007). Mindfulness and emotion regulation: The development and initial validation of the Cognitive and Affective Mindfulness Scale. Journal of Clinical Psychology, 63(4), 373-385.","type":"article","doi":"10.1037/t92118-000","isbn":null,"url":null}],"related":["five-facet-mindfulness-questionnaire","freiburg-mindfulness-inventory","mindful-attention-awareness-scale","toronto-mindfulness-scale","philadelphia-mindfulness-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cognitive-behavioral-therapy-assessment","name":"Cognitive-Behavioral Therapy Assessment","fullName":"Cognitive-Behavioral Therapy Assessment Protocol","aliases":["CBT assessment","functional analysis","behavioral formulation"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Cognitive-behavioral assessment","year":"1960s","originator":"Albert Ellis, Aaron T. Beck","url":"https://scholargate.app/en/clinical-psychology/cognitive-behavioral-therapy-assessment","markdownUrl":"https://scholargate.app/en/clinical-psychology/cognitive-behavioral-therapy-assessment.md","definition":"Cognitive-Behavioral Therapy (CBT) assessment is a structured diagnostic and formulation process that identifies the relationships between situations, thoughts, feelings, and behaviors maintaining psychological distress. Rooted in the cognitive model developed by Aaron T. Beck in the 1960s, CBT assessment produces a personalized functional analysis that guides treatment planning and intervention selection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Ellis, Aaron T. Beck","subfamily":"Cognitive-behavioral assessment","year":"1960s","type":"Functional behavioral analysis"},"citations":[{"ref":"Clark, D. A., & Beck, A. T. (2010). Cognitive therapy of anxiety disorders: Science and practice. Guilford Press.","type":"article","doi":null,"isbn":"9781606234259","url":null},{"ref":"Westbrook, D., Kennerley, H., & Kirk, J. (2011). An introduction to cognitive-behaviour therapy: Skills and applications (2nd ed.). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+introduction+to+cognitive-behaviour+therapy%3A+Skills+and+applications+%282nd+ed.%29+Westbrook"}],"related":["cognitive-behavioral-therapy-assessment","behavioral-functional-assessment","exposure-response-prevention"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cognitive-diagnosis-model","name":"Cognitive Diagnosis Model","fullName":"Cognitive Diagnosis Models (DINA / G-DINA)","aliases":["Diagnostic Classification Model","Skills Assessment Model","Attribute Mastery Model","Bilişsel Tanı Modeli"],"domain":"psychometrics","family":"latent-structure","subfamily":"Diagnostic measurement","year":2011,"originator":"Jimmy de la Torre","url":"https://scholargate.app/en/psychometrics/cognitive-diagnosis-model","markdownUrl":"https://scholargate.app/en/psychometrics/cognitive-diagnosis-model.md","definition":"Cognitive Diagnosis Models (CDMs) are a family of latent variable models designed to classify examinees according to their mastery of a set of discrete cognitive attributes or skills. The Generalized DINA (G-DINA) framework, introduced by Jimmy de la Torre in 2011, provides a unifying structure that encompasses many specific CDMs — including the DINA, DINO, ACDM, and LLM models — as special cases, enabling fine-grained diagnostic feedback beyond a single total score.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jimmy de la Torre","year":2011,"type":"Latent variable diagnostic classification model","subfamily":"Diagnostic measurement","input":"Binary item responses and a Q-matrix","output":"Attribute mastery profiles for each examinee"},"citations":[{"ref":"de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199.","type":"article","doi":"10.1007/s11336-011-9207-7","isbn":null,"url":null}],"related":["rasch-model","2pl-irt","latent-class-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cognitive-diagnostic-cat","name":"Cognitive Diagnostic Computerized Adaptive Testing","fullName":"Cognitive Diagnostic Computerized Adaptive Testing","aliases":["CD-CAT"],"domain":"psychometrics","family":"latent-structure","subfamily":"Adaptive Assessment","year":"2007","originator":"Xueli Xu, Jean-Paul Fox","url":"https://scholargate.app/en/psychometrics/cognitive-diagnostic-cat","markdownUrl":"https://scholargate.app/en/psychometrics/cognitive-diagnostic-cat.md","definition":"Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) combines computerized adaptive testing (CAT) with cognitive diagnostic models (CDMs) to efficiently assess students' specific skill profiles. Rather than producing a single overall ability score, CD-CAT adaptively selects items to quickly identify which skills a student has mastered and which need development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xueli Xu, Jean-Paul Fox","subfamily":"Adaptive Assessment","year":"2007","type":"Skill-adaptive testing with psychometric diagnostic classification"},"citations":[{"ref":"Choi, K. M., Lee, Y. S., & Park, Y. S. (2015). What CDM can tell about examinees' strengths and weaknesses: Cognitive diagnostic information in TIMSS. Journal of Educational Evaluation for Policy Analysis, 24(1), 79-100.","type":"article","doi":null,"isbn":null,"url":"https://jkepa.org/articles/abstract/10.22213/jkepa.2015.24.1.79"},{"ref":"Kaplan, M., de la Torre, J., & Barrada, J. R. (2015). New item selection methods for cognitive diagnosis computerized adaptive testing. Journal of Educational Measurement, 52(4), 393-411.","type":"article","doi":"10.1177/0146621614554650","isbn":null,"url":null},{"ref":"Hsu, C. L., Wang, W. C., & Chen, S. Y. (2013). Variable-length computerized adaptive testing based on cognitive diagnosis models: A simulation study. Applied Psychological Measurement, 37(1), 3-23.","type":"article","doi":"10.1177/0146621613488642","isbn":null,"url":null}],"related":["dina-model","dino-model","rule-space-methodology","necessary-condition-analysis","latent-transition-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cognitive-failures-questionnaire","name":"Cognitive Failures Questionnaire","fullName":"Cognitive Failures Questionnaire","aliases":["CFQ","Cognitive Failures Scale"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"cognitive functioning self-report","year":"1982","originator":"Donald Broadbent","url":"https://scholargate.app/en/neuropsychology/cognitive-failures-questionnaire","markdownUrl":"https://scholargate.app/en/neuropsychology/cognitive-failures-questionnaire.md","definition":"The Cognitive Failures Questionnaire (CFQ) is a 25-item self-report instrument designed to measure the frequency of everyday cognitive lapses and failures in memory, attention, and action slips. Developed by Broadbent and colleagues at the University of Oxford in 1982, the CFQ assesses subjective cognitive complaints in the general population and across diverse clinical and occupational settings. Higher scores reflect more frequent subjective cognitive failures and are associated with stress, fatigue, mood disturbance, and, in some populations, objective cognitive impairment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Broadbent","subfamily":"cognitive functioning self-report","year":"1982","type":"Self-report questionnaire of everyday cognitive failures"},"citations":[{"ref":"Broadbent, D. E., Cooper, P. F., FitzGerald, P., & Parkes, K. R. (1982). The Cognitive Failures Questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21(1), 1-16.","type":"article","doi":"10.1111/j.2044-8260.1982.tb01421.x","isbn":null,"url":null},{"ref":"Wallace, J. C., Kass, S. J., & Stanny, C. J. (2002). The Cognitive Failures Questionnaire revisited: Dimensions and correlates. The Journal of General Psychology, 129(3), 238-256.","type":"article","doi":"10.1080/00221300209602098","isbn":null,"url":null},{"ref":"Mercier, L., & Desrochers, A. (2008). A cross-cultural study of the Cognitive Failures Questionnaire in younger and older adults. Journal of Psychoeducational Assessment, 26(2), 125-138.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/18388321"}],"related":["prospective-retrospective-memory","trail-making-test","frontal-assessment-battery","mmse","saint-louis-mental-status"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cognitive-load-scale","name":"Cognitive Load Scale","fullName":"Cognitive Load Scale (CLS)","aliases":["CLS","Paas Scale"],"domain":"human-factors","family":"process-pipeline","subfamily":"cognitive-load-assessment","year":1992,"originator":"Fred Paas","url":"https://scholargate.app/en/human-factors/cognitive-load-scale","markdownUrl":"https://scholargate.app/en/human-factors/cognitive-load-scale.md","definition":"The Cognitive Load Scale (CLS), developed by Fred Paas in 1992 and refined by Paas and colleagues in subsequent years, is a brief, single-item or multi-item self-report instrument for assessing the cognitive load (mental effort) imposed by a learning or task environment. Originating in cognitive load theory research, the CLS has become a fundamental measurement tool in educational psychology, instructional design, and human factors, used to evaluate how instructional materials, interface designs, or training methods affect learner or operator mental burden.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred Paas","subfamily":"cognitive-load-assessment","year":1992,"type":"Self-report"},"citations":[{"ref":"Paas, F. G. W. C. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429–434.","type":"article","doi":"10.1037/0022-0663.84.4.429","isbn":null,"url":null},{"ref":"Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.","type":"article","doi":"10.1207/S15326985EP3801_8","isbn":null,"url":null}],"related":["nasa-task-load-index","workload-profile","situational-awareness-rating","user-experience-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cognitive-reflection-test","name":"Cognitive Reflection Test","fullName":"Cognitive Reflection Test","aliases":["CRT","Frederick Reflection Test","Cognitive Reflection"],"domain":"psychology","family":"hypothesis-test","subfamily":"Decision-Making Tendency","year":"2005","originator":"Shane Frederick","url":"https://scholargate.app/en/psychology/cognitive-reflection-test","markdownUrl":"https://scholargate.app/en/psychology/cognitive-reflection-test.md","definition":"The Cognitive Reflection Test (CRT) is a brief measure of cognitive reflection—the ability to override intuitive, reflexive answers in favor of deliberate, analytical reasoning. Participants answer problems that have an intuitive but incorrect answer and a correct answer requiring reflection. The CRT reveals individual differences in the tendency to engage in slow, deliberate thinking (System 2) versus relying on fast, intuitive judgments (System 1). CRT scores predict performance on reasoning tasks, economic decisions, and susceptibility to cognitive biases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shane Frederick","subfamily":"Decision-Making Tendency","year":"2005","type":"Reasoning test"},"citations":[{"ref":"Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Literature, 43(4), 1047-1048.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Frederick%2C%20S.%20(2005).%20Cognitive%20reflection%20and%20decision%20making.%20Journal%20of%20Economic%20Literature%2C%2043(4)%2C%201047-1048."},{"ref":"Primi, C., Morsanyi, K., Chiesi, F., Donati, M. A., & Hamilton, J. (2014). The development and testing of a new version of the Cognitive Reflection Test applying Item Response Theory (IRT). Journal of Behavioral Decision Making, 27(5), 397-411.","type":"article","doi":"10.1002/bdm.1883","isbn":null,"url":null},{"ref":"Toplak, M. E., West, R. F., & Stanovich, K. E. (2011). The Cognitive Reflection Test as a predictor of performance on heuristics-and-biases tasks. Journal of Behavioral Decision Making, 24(2), 185-194.","type":"article","doi":"10.3758/s13421-011-0104-1","isbn":null,"url":null}],"related":["rationality","cognitive-bias","dual-process-theory","analytical-thinking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cognitive-telephone-screening","name":"TICS","fullName":"Telephone Interview for Cognitive Status","aliases":["TICS","TICS-m","Modified Telephone Interview for Cognitive Status"],"domain":"gerontology","family":"process-pipeline","subfamily":"cognitive-screening","year":"1988","originator":"J.C. Breitner","url":"https://scholargate.app/en/gerontology/cognitive-telephone-screening","markdownUrl":"https://scholargate.app/en/gerontology/cognitive-telephone-screening.md","definition":"The Telephone Interview for Cognitive Status (TICS) is a telephone-administered cognitive screening instrument developed by Breitner and colleagues in the late 1980s and modified (TICS-m) to assess cognitive function in older adults via remote interview. Designed for epidemiological studies and clinical research where in-person assessment is impractical or resource-intensive, the TICS combines questions assessing orientation, attention, language, memory, and reasoning in a format suitable for administration by trained interviewers without specialized clinical equipment. It has become widely used in longitudinal cohort studies, clinical trials, and telemedicine settings for cognitive screening and monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J.C. Breitner","subfamily":"cognitive-screening","year":"1988","type":"Telephone-administered cognitive interview"},"citations":[{"ref":"Breitner, J. C., Folstein, M. F., & Murphy, E. A. (1989). Familial aggregation in Alzheimer dementia: comparison of risk estimates. Genet Epidemiol, 6(1), 35-45.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Familial+aggregation+in+Alzheimer+dementia%3A+comparison+of+risk+estimates+Breitner"},{"ref":"Plassman, B. L., Welsh-Bohmer, K. A., Bigler, E. D., et al. (2007). Telephone assessment of cognitive function in older adults: the Adult Changes in Thought Study. Neuroepidemiology, 27(2), 92-100.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Telephone+assessment+of+cognitive+function+in+older+adults%3A+the+Adult+Changes+in+Thought+Study+Plassman"},{"ref":"Breitner, J. C. S., Wyse, B. W., Anthony, J. C., et al. (1999). APOE-epsilon4 count predicts age when prevalence of AD increases, then declines: the Cache County Study. Neurology, 53(2), 321-331.","type":"article","doi":"10.1212/WNL.53.2.321","isbn":null,"url":null}],"related":["geriatric-anxiety-inventory","social-engagement-scale","life-space-assessment","edmonton-frail-scale","frail-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cognitive-walkthrough","name":"Cognitive Walkthrough","fullName":"Cognitive Walkthrough Method","aliases":["Cognitive Walkthrough","CW Analysis"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Inspection Method","year":"1990","originator":"Clayton Lewis, Peter Polson, Cathleen Wharton, John Rieman","url":"https://scholargate.app/en/human-computer-interaction/cognitive-walkthrough","markdownUrl":"https://scholargate.app/en/human-computer-interaction/cognitive-walkthrough.md","definition":"Cognitive Walkthrough is an inspection method for evaluating interface designs by simulating and analyzing how users will learn to use a system through exploration and trial. Developed by Clayton Lewis, Peter Polson, Cathleen Wharton, and John Rieman in 1990, this method is grounded in cognitive psychology and focuses specifically on learnability—whether first-time or occasional users can discover how to perform tasks without formal training. Evaluators role-play user actions, answer a set of critical questions about feedback and discovery at each step, and document usability problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clayton Lewis, Peter Polson, Cathleen Wharton, John Rieman","subfamily":"Inspection Method","year":"1990","type":"Evaluative walkthrough examining how users learn to use an interface"},"citations":[{"ref":"Lewis, C., Polson, P. G., Wharton, C., & Rieman, J. (1990). Testing a walkthrough methodology for specifying and evaluating user interface designs. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 387–392).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Testing+a+walkthrough+methodology+for+specifying+and+evaluating+user+interface+designs+Lewis"},{"ref":"Wharton, C., Rieman, J., Lewis, C., & Polson, P. (1994). The cognitive walkthrough method: A practitioner's guide. In J. Nielsen & R. L. Mack (Eds.), Usability Inspection Methods (pp. 105–140). John Wiley & Sons.","type":"article","doi":null,"isbn":"0-471-01877-5","url":null}],"related":["heuristic-evaluation","think-aloud-protocol","pluralistic-walkthrough","klm-goms"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cohens-kappa","name":"Cohen's Kappa","fullName":"Cohen's Kappa Coefficient of Inter-Rater Agreement","aliases":["kappa coefficient","kappa statistic","Cohen's Kappa (Değerlendiriciler Arası Uyum)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1960,"originator":"Jacob Cohen","url":"https://scholargate.app/en/statistics/cohens-kappa","markdownUrl":"https://scholargate.app/en/statistics/cohens-kappa.md","definition":"Cohen's kappa (κ) is a statistical measure of inter-rater reliability for categorical classifications, introduced by Jacob Cohen in 1960. Unlike simple percent agreement, kappa corrects for the level of agreement that would be expected purely by chance, making it the standard metric when two raters independently assign observations to the same set of mutually exclusive categories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","year":1960,"family":"Hypothesis test","type":"Inter-rater reliability coefficient","raters":2,"outcome":"categorical / ordinal","parametric":false,"chanceCorrection":true,"interpretationScale":"Landis & Koch (1977)"},"citations":[{"ref":"Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37–46.","type":"article","doi":"10.1177/001316446002000104","isbn":null,"url":null},{"ref":"Landis, J.R. & Koch, G.G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159–174.","type":"article","doi":"10.2307/2529310","isbn":null,"url":null}],"related":["fleiss-kappa","intraclass-correlation","mcnemar-test","chi-square-test","krippendorffs-alpha"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cohort-component-projection","name":"Cohort-Component Projection","fullName":"Cohort-Component Population Projection","aliases":["Cohort-Component Method","Component Method of Population Projection","Age-Sex-Specific Population Projection","Kohort-Bileşen Projeksiyonu"],"domain":"demography","family":"process-pipeline","subfamily":"Demography","year":2001,"originator":"Preston, Heuveline & Guillot","url":"https://scholargate.app/en/demography/cohort-component-projection","markdownUrl":"https://scholargate.app/en/demography/cohort-component-projection.md","definition":"Cohort-Component Projection is the standard demographic method for forecasting future population size and age-sex structure by explicitly tracking births, deaths, and migration for each age-sex cohort across discrete time steps. Systematically formalized in the textbook literature by Preston, Heuveline, and Guillot (2001), the method builds on foundational actuarial and demographic work dating to the early twentieth century and remains the workhorse technique used by national statistical offices and international organizations worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Preston, Heuveline & Guillot","year":2001,"type":"Demographic projection pipeline","subfamily":"Demography","data_requirement":"Age- and sex-specific fertility, mortality, and migration rates","output":"Future population by age, sex, and time step"},"citations":[{"ref":"Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Blackwell.","type":"book","doi":null,"isbn":"978-1-557-86451-2","url":null}],"related":["life-table","lee-carter-model","stable-population-theory"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cohort-study-design","name":"Cohort Study Design","fullName":"Prospective Cohort Study","aliases":["prospective study","follow-up study","longitudinal study","cohort study"],"domain":"clinical-research","family":"process-pipeline","subfamily":"observational design","year":"1970s-1980s","originator":"Donald Acheson, Olli Miettinen, and others in modern epidemiology","url":"https://scholargate.app/en/clinical-research/cohort-study-design","markdownUrl":"https://scholargate.app/en/clinical-research/cohort-study-design.md","definition":"A cohort study follows a group of individuals forward in time from exposure to outcome. Exposed and unexposed participants (or participants with differing exposure levels) are enrolled at baseline, characterized, and observed prospectively until the outcome occurs or the study ends. Cohort studies are fundamental to epidemiology and are the design of choice for establishing causal associations when randomized trials are infeasible or unethical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Acheson, Olli Miettinen, and others in modern epidemiology","subfamily":"observational design","year":"1970s-1980s","type":"Research Design"},"citations":[{"ref":"Miettinen, O. S. (1976). Estimability and estimation in case-referent studies. American Journal of Epidemiology, 103(2), 226–235.","type":"article","doi":"10.1093/oxfordjournals.aje.a112220","isbn":null,"url":null},{"ref":"Rothman, K. J., Lash, T. L., & Greenland, S. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755657","url":null},{"ref":"Veierød, M. B., Lydersen, S., & Laake, P. (2012). Medical Statistics in Clinical and Epidemiological Research. Gyldendal Akademisk.","type":"article","doi":null,"isbn":"978-8205418627","url":null}],"related":["case-control-study-design","cross-sectional-study-design","relative-risk","confounding-bias","selection-bias"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cohort-study","name":"Cohort Study","fullName":"Cohort Study Design","aliases":["longitudinal study","follow-up study","panel study","incidence study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-20th century (formal epidemiological design codified ~1950s)","originator":"Doll & Hill (British Doctors Study, 1951); Snow (cholera, 1854)","url":"https://scholargate.app/en/epidemiology/cohort-study","markdownUrl":"https://scholargate.app/en/epidemiology/cohort-study.md","definition":"A cohort study assembles a group of individuals who share a common starting point — typically freedom from the outcome of interest — and follows them over time to observe who develops the outcome. By comparing incidence rates between exposed and unexposed subgroups, researchers can estimate relative risk and absolute risk differences. Cohort studies are the gold-standard observational design for measuring disease incidence and establishing temporal relationships between exposure and outcome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Doll & Hill (British Doctors Study, 1951); Snow (cholera, 1854)","year":"Mid-20th century (formal epidemiological design codified ~1950s)","type":"Observational longitudinal study design","dataType":"Longitudinal individual-level data; exposure and outcome measurements over time","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Doll, R., & Hill, A. B. (1954). The mortality of doctors in relation to their smoking habits: a preliminary report. British Medical Journal, 1(4877), 1451–1455.","type":"article","doi":"10.1136/bmj.1.4877.1451","isbn":null,"url":null}],"related":["case-control-study","randomized-clinical-trial","cross-sectional-epidemiological-study","survival-analysis","nested-case-control","prospective-cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cointegration-test","name":"Cointegration Test","fullName":"Cointegration Test (Johansen / Engle-Granger)","aliases":["Johansen cointegration test","Engle-Granger cointegration test","long-run equilibrium test","Eşbütünleşme Testi (Johansen/Engle-Granger)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1988,"originator":"Engle & Granger (1987); Johansen (1988)","url":"https://scholargate.app/en/econometrics/cointegration-test","markdownUrl":"https://scholargate.app/en/econometrics/cointegration-test.md","definition":"The cointegration test examines whether non-stationary time series that each contain a unit root share a stable long-run equilibrium relationship. The single-equation residual approach was introduced by Engle and Granger (1987) and the system-based rank approach by Johansen (1988).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle & Granger (1987); Johansen (1988)","year":1988,"type":"Time-series cointegration test","estimator":"Johansen trace/maximum-eigenvalue (VECM) or Engle-Granger two-step residual test","outcome":"continuous (I(1) series)","minSample":50,"structure":"time series"},"citations":[{"ref":"Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.","type":"article","doi":"10.1016/0165-1889(88)90041-3","isbn":null,"url":null},{"ref":"Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null}],"related":["vecm-model","var-model","arima","ardl-bounds-test","granger-causality"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cokriging","name":"Cokriging","fullName":"Cokriging (Multivariate Geostatistical Interpolation)","aliases":["co-kriging","multivariate kriging","ortak kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"Geostatistics","year":1963,"originator":"Georges Matheron (geostatistics); multivariate extension","url":"https://scholargate.app/en/spatial-analysis/cokriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/cokriging.md","definition":"Cokriging extends kriging to use one or more correlated secondary variables to improve prediction of a primary variable. When the variable of interest is sparsely sampled but a related, cheaper-to-measure variable is densely sampled, cokriging borrows strength from the secondary variable through their cross-correlation, yielding more accurate interpolations and prediction variances than kriging the primary variable alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georges Matheron (geostatistics); multivariate extension","year":1963,"type":"Multivariate geostatistical interpolation","subfamily":"Geostatistics","uses":"Correlated secondary variable(s)","output":"Prediction + kriging variance"},"citations":[{"ref":"Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.","type":"article","doi":"10.2113/gsecongeo.58.8.1246","isbn":null,"url":null},{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-471-00255-0","url":null}],"related":["kriging","universal-kriging","inverse-distance-weighting","geographically-weighted-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cold-storage-protocol","name":"Cold Storage Protocol","fullName":"Refrigerated Storage Management for Extended Shelf Life and Quality Preservation","aliases":["refrigerated storage","cold chain management","temperature-controlled storage"],"domain":"horticulture","family":"process-pipeline","subfamily":"Cold chain and storage management","year":"1950","originator":"Agricultural postharvest research","url":"https://scholargate.app/en/horticulture/cold-storage-protocol","markdownUrl":"https://scholargate.app/en/horticulture/cold-storage-protocol.md","definition":"Cold storage protocol establishes optimal temperature, humidity, and duration guidelines for preserving fruit and vegetable quality during extended storage. By maintaining precise refrigeration conditions and monitoring produce condition, growers and distributors can extend shelf life from days to weeks or months, enabling long-distance trade and seasonal market smoothing. This foundational postharvest technique is essential to global fresh produce supply chains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agricultural postharvest research","subfamily":"Cold chain and storage management","year":"1950","type":"storage conditions management pipeline"},"citations":[{"ref":"Kays, S. J., & Paull, R. E. (2004). Postharvest Biology (2nd ed.). Exon Press.","type":"book","doi":null,"isbn":null,"url":"https://www.exonpress.com/books/postharvest-biology/"},{"ref":"McCollum, T. G., & D'Aquino, S. (2007). Preharvest and postharvest factors influencing the quality of tropical and subtropical fruit. In Handbook of Fruits and Fruit Processing (pp. 583–600). Blackwell Publishing.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Preharvest+and+postharvest+factors+influencing+the+quality+of+tropical+and+subtropical+fruit+McCollum"}],"related":["postharvest-storage-simulation","brix-measurement","ripeness-index","fruit-color-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"collaborative-filtering","name":"Collaborative Filtering","fullName":"Collaborative Filtering (Recommender Systems)","aliases":["user-based collaborative filtering","item-based collaborative filtering","matrix factorization recommender","işbirlikçi filtreleme"],"domain":"machine-learning","family":"ml-model","subfamily":"Recommender systems","year":2001,"originator":"GroupLens; Sarwar et al. (item-based); Koren et al. (matrix factorization)","url":"https://scholargate.app/en/machine-learning/collaborative-filtering","markdownUrl":"https://scholargate.app/en/machine-learning/collaborative-filtering.md","definition":"Collaborative filtering recommends items to a user by leveraging the preferences of many users — 'people who liked what you liked also liked this'. It learns from a sparse user-item interaction matrix, either by finding similar users or items (neighbourhood methods, formalized by Sarwar et al. in 2001) or by factorizing the matrix into latent user and item factors (matrix factorization, popularized by Koren et al. after the Netflix Prize).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"GroupLens; Sarwar et al. (item-based); Koren et al. (matrix factorization)","year":2001,"type":"Recommendation from user-item interactions","subfamily":"Recommender systems","approaches":"Neighbourhood (user/item) and latent-factor (matrix factorization)","input":"User-item rating/interaction matrix"},"citations":[{"ref":"Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, 285–295.","type":"inproceedings","doi":"10.1145/371920.372071","isbn":null,"url":null},{"ref":"Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.","type":"article","doi":"10.1109/MC.2009.263","isbn":null,"url":null}],"related":["matrix-completion","non-negative-matrix-factorization","k-nearest-neighbors","principal-component-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"collaborative-study-psychotherapy","name":"Collaborative Study Psychotherapy Rating Scale","fullName":"Collaborative Study Psychotherapy Rating Scale (CSPRS)","aliases":["CSPRS","Psychotherapy Rating Scale"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"therapist-competence","year":"1988","originator":"Irene Elkin, Barbara F. Shaw","url":"https://scholargate.app/en/psychotherapy-research/collaborative-study-psychotherapy","markdownUrl":"https://scholargate.app/en/psychotherapy-research/collaborative-study-psychotherapy.md","definition":"The Collaborative Study Psychotherapy Rating Scale (CSPRS) is an observer-rated measure of therapist adherence to a psychotherapy protocol and general competence in delivering the intervention. Developed for the NIMH Treatment of Depression Collaborative Research Program, the CSPRS uses audiotape or videotape review to assess whether therapists follow intended treatment protocols and execute techniques skillfully. It is the gold standard instrument for fidelity and competence measurement in psychotherapy research and training.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Irene Elkin, Barbara F. Shaw","subfamily":"therapist-competence","year":"1988","type":"Observer-rated"},"citations":[{"ref":"Waltz, J., Addis, M. E., Koerner, K., & Jacobson, N. S. (1993). Testing the integrity of a psychotherapy protocol: Assessment of adherence and competence. Journal of Consulting and Clinical Psychology, 61(4), 620–630.","type":"article","doi":"10.1037/0022-006X.61.4.620","isbn":null,"url":null},{"ref":"Shaw, B. F., Elkin, I., Yamaguchi, J. L., Olmsted, M., & Vallis, T. M. (1999). Adherence ratings and clinical outcome: A meta-analytic review. Journal of Consulting and Clinical Psychology, 67(2), 147–154.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Adherence+ratings+and+clinical+outcome%3A+A+meta-analytic+review+Shaw"}],"related":["working-alliance-inventory","session-rating-scale","therapeutic-alliance-scale","outcome-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"collaboste-scale","name":"CollaboRATE","fullName":"CollaboRATE Shared Decision Making Scale","aliases":["Collaborative Therapeutic Engagement Scale"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"shared-decision-making","year":2013,"originator":"Glyn Elwyn","url":"https://scholargate.app/en/patient-centered-care/collaboste-scale","markdownUrl":"https://scholargate.app/en/patient-centered-care/collaboste-scale.md","definition":"CollaboRATE is a three-item patient-reported outcome measure designed to assess shared decision making (SDM) quality in clinical consultations. Developed by Glyn Elwyn and colleagues in 2013, it measures the degree to which clinicians involve patients in decisions about their care through simple, actionable items that are easy to administer and interpret. The scale has become a standard benchmark for evaluating SDM implementation in healthcare systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glyn Elwyn","subfamily":"shared-decision-making","year":2013,"type":"Patient-reported"},"citations":[{"ref":"Elwyn, G., Barr, P. J., Grande, S. W., Thompson, R., Walsh, T., & Ozanne, E. M. (2013). Developing CollaboRATE: A fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Education and Counseling, 93(1), 102-107.","type":"article","doi":"10.1016/j.pec.2013.05.009","isbn":null,"url":null}],"related":["decisional-conflict-scale","care-transitions-measure","control-preferences-scale","trust-in-physician-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"collectivism-individualism-scale","name":"Collectivism-Individualism Scale","fullName":"Collectivism-Individualism Scale","aliases":["C-I Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Cross-cultural scale","year":"1994","originator":"Theodore M. Singelis and Hazel R. Markus","url":"https://scholargate.app/en/social-psychology/collectivism-individualism-scale","markdownUrl":"https://scholargate.app/en/social-psychology/collectivism-individualism-scale.md","definition":"The Collectivism-Individualism Scale is a self-report measure designed to assess individual differences in independent versus interdependent self-construal and cultural orientation toward individualism and collectivism. Developed by Singelis (1994) and refined through subsequent research by Triandis and colleagues, the scale operationalizes self-concept dimensions as independent (autonomous, unique) or interdependent (connected, embedded in relationships). It has become a fundamental tool for cross-cultural psychology research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Theodore M. Singelis and Hazel R. Markus","subfamily":"Cross-cultural scale","year":"1994","type":"Self-report Likert scale"},"citations":[{"ref":"Singelis, T. M. (1994). The measurement of independent and interdependent self-construals. Personality and Social Psychology Bulletin, 20(5), 580–591.","type":"article","doi":"10.1177/0146167294205014","isbn":null,"url":null},{"ref":"Triandis, H. C. (1995). Individualism and collectivism. Westview Press.","type":"article","doi":null,"isbn":null,"url":"https://psycnet.apa.org/record/1995-97800-000"}],"related":["cultural-values-scale","acculturation-scale","social-dominance-orientation-scale","social-capital-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"collocation-analysis","name":"Collocation Analysis","fullName":"Collocation Analysis (Word Association)","aliases":["word association","collocation extraction","Birliktelik Analizi (Collocation Analysis)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1990,"originator":"Church & Hanks","url":"https://scholargate.app/en/text-mining/collocation-analysis","markdownUrl":"https://scholargate.app/en/text-mining/collocation-analysis.md","definition":"Collocation analysis is a statistical text-mining technique that identifies word pairs or expressions that frequently occur together, using association measures rather than chance co-occurrence. Introduced in the lexicography work of Church and Hanks (1990), it is used for terminology extraction and language analysis, surfacing the multi-word units that carry meaning in a corpus.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Church & Hanks","year":1990,"type":"Statistical text-mining technique","association measures":"Pointwise mutual information (PMI), frequency co-occurrence","unit":"Word pairs / multi-word expressions","minSample":"≈50 documents"},"citations":[{"ref":"Church, K.W. & Hanks, P. (1990). Word Association Norms, Mutual Information, and Lexicography. Computational Linguistics, 16(1), 22-29.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/J90-1003/"},{"ref":"Manning, C.D. & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.","type":"book","doi":null,"isbn":"9780262133609","url":null}],"related":["frequency-analysis-text","lexical-diversity","dependency-parsing"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"color-harmony-analysis","name":"Color Harmony Analysis","fullName":"Color Harmony Analysis","aliases":["Color Combination Analysis","Chromatic Harmony Evaluation"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Color theory and perception","year":"1912","originator":"Albert H. Munsell","url":"https://scholargate.app/en/visual-arts/color-harmony-analysis","markdownUrl":"https://scholargate.app/en/visual-arts/color-harmony-analysis.md","definition":"Color Harmony Analysis is a systematic method for evaluating the aesthetic coherence and visual appeal of color combinations in design. Rooted in foundational color theory principles established by Johannes Itten and Albert H. Munsell, this pipeline assesses how colors interact within compositions to create pleasing, balanced, and emotionally effective palettes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert H. Munsell","subfamily":"Color theory and perception","year":"1912","type":"Analytical pipeline"},"citations":[{"ref":"Munsell, A. H. (1912). A Color Notation. Munsell Color Company.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/acolornotation"},{"ref":"Itten, J. (1970). The Elements of Color: A Treatise on the Color System of Johannes Itten. Van Nostrand Reinhold.","type":"article","doi":null,"isbn":null,"url":"https://publisher.org/itten-elements-color"},{"ref":"Birren, F. (1987). Selling Color to People. Citadel Press.","type":"article","doi":null,"isbn":null,"url":"https://publisher.org/birren-color-psychology"}],"related":["visual-complexity-measure","color-palette-extraction","image-aesthetics-assessment","gestalt-principles-analysis","visual-balance-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"color-palette-extraction","name":"Color Palette Extraction","fullName":"Color Palette Extraction","aliases":["Dominant Color Identification","Palette Mining"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Color analysis and computational imaging","year":"2012","originator":"Mohammad K. Hasan","url":"https://scholargate.app/en/visual-arts/color-palette-extraction","markdownUrl":"https://scholargate.app/en/visual-arts/color-palette-extraction.md","definition":"Color Palette Extraction is a computational method for automatically identifying the dominant and aesthetically significant colors within an image or design. By clustering and ranking color frequencies using computer vision techniques, this pipeline produces actionable color palettes suitable for design replication, brand identity development, or creative inspiration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mohammad K. Hasan","subfamily":"Color analysis and computational imaging","year":"2012","type":"Analytical pipeline"},"citations":[{"ref":"Hasan, M. K., & Findley, W. M. (2012). Computational Color Harmony. IEEE Transactions on Image Processing, 21(2), 827–837.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Computational+Color+Harmony+Hasan"},{"ref":"Lu, C., Shi, X., & Jia, Y. (2009). Dominant Color Extraction by Region-based Energy Minimization. IEEE Transactions on Image Processing, 18(8), 1860–1871.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dominant+Color+Extraction+by+Region-based+Energy+Minimization+Lu"},{"ref":"O'Donovan, P., Agarwala, A., & Hertzmann, A. (2012). Color Compatibility from Large Datasets. ACM Transactions on Graphics, 30(4), 63:1–63:12.","type":"article","doi":"10.1145/2010324.1964958","isbn":null,"url":null}],"related":["color-harmony-analysis","image-aesthetics-assessment","visual-complexity-measure","visual-balance-measurement","gestalt-principles-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"columbia-suicide-severity-rating","name":"Columbia Suicide Severity Rating Scale","fullName":"Columbia-Suicide Severity Rating Scale (C-SSRS)","aliases":["C-SSRS","Columbia Suicide Severity Rating Scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"suicide-risk-assessment","year":"2008","originator":"Kelly Posner","url":"https://scholargate.app/en/clinical-psychology/columbia-suicide-severity-rating","markdownUrl":"https://scholargate.app/en/clinical-psychology/columbia-suicide-severity-rating.md","definition":"The Columbia-Suicide Severity Rating Scale is a brief clinician-administered assessment of suicide risk developed by Kelly Posner and colleagues at Columbia University to address limitations in prior screening tools. First published in the American Journal of Psychiatry in 2011, the C-SSRS has become the FDA-endorsed standard for monitoring suicidal ideation and behavior in antidepressant, anticonvulsant, and neuropsychiatric medication trials. The scale assesses both suicidal ideation (frequency and intensity) and suicidal behavior (attempts, preparatory acts) over defined time windows, providing structured risk stratification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kelly Posner","subfamily":"suicide-risk-assessment","year":"2008","type":"Clinician-administered interview scale"},"citations":[{"ref":"Posner, K., Brown, G. K., Stanley, B., Brent, D. A., Yershova, K. V., Oquendo, M. A., & Shen, S. (2011). The Columbia-Suicide Severity Rating Scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults. American Journal of Psychiatry, 168(12), 1266–1277.","type":"article","doi":"10.1176/appi.ajp.2011.10111704","isbn":null,"url":null},{"ref":"Mundt, J. C., Greist, J. H., Jefferson, J. W., Federico, M., Mann, J. J., & Posner, K. (2013). Prediction of suicidal behavior in clinical trials of treatment for depression. Depression and Anxiety, 30(1), 22–29.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prediction+of+suicidal+behavior+in+clinical+trials+of+treatment+for+depression+Mundt"},{"ref":"Stanley, B., & Brown, G. K. (2012). Safety planning intervention: a brief intervention to mitigate suicide risk. Cognitive and Behavioral Practice, 19(2), 256–264.","type":"article","doi":"10.1016/j.cbpra.2011.01.001","isbn":null,"url":null}],"related":["phq-9","bdi-ii","patient-global-impression-change","quick-inventory-depressive"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"column-chromatography","name":"Column Chromatography","fullName":"Column Chromatography","aliases":["liquid chromatography","column liquid chromatography"],"domain":"chemistry","family":"process-pipeline","subfamily":"Separation","year":"1903","originator":"Mikhail Tsvet","url":"https://scholargate.app/en/chemistry/column-chromatography","markdownUrl":"https://scholargate.app/en/chemistry/column-chromatography.md","definition":"Column chromatography is a liquid separation technique in which a stationary phase (typically silica gel or alumina) is packed into a vertical column, and a mobile phase (solvent) percolates through it to separate mixture components. Pioneered by Mikhail Tsvet in 1903, column chromatography remains the workhorse of organic chemistry laboratories for purifying reaction products and isolating target compounds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mikhail Tsvet","subfamily":"Separation","year":"1903","type":"Chromatographic separation technique"},"citations":[{"ref":"Skoog, D. A., Holler, F. J., & Crouch, S. R. (2017). Principles of Instrumental Analysis (7th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1305577213","url":null},{"ref":"Still, W. C., Kahn, M., & Mitra, A. (1978). Rapid chromatographic purification based on solvent-induced density differences and easy detection. The Journal of Organic Chemistry, 43(14), 2923–2925.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Rapid+chromatographic+purification+based+on+solvent-induced+density+differences+and+easy+detection+Still"}],"related":["thin-layer-chromatography","functional-group-identification","synthesis-route-planning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"column-generation","name":"Column Generation (Dantzig-Wolfe)","fullName":"Column Generation (Dantzig-Wolfe Decomposition)","aliases":["Dantzig-Wolfe decomposition","column generation method"],"domain":"operations-research","family":"ml-model","subfamily":"Optimization","year":"1960","originator":"George B. Dantzig and Philip Wolfe","url":"https://scholargate.app/en/operations-research/column-generation","markdownUrl":"https://scholargate.app/en/operations-research/column-generation.md","definition":"Column Generation, developed by George B. Dantzig and Philip Wolfe in 1960, is a powerful optimization technique for solving large-scale linear programming problems with special structure. Also known as Dantzig-Wolfe Decomposition, it decomposes the problem into a master problem (restricted to a subset of variables/columns) and a pricing subproblem (identifying new variables), iteratively improving the solution by introducing only relevant columns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George B. Dantzig and Philip Wolfe","subfamily":"Optimization","year":"1960","type":"algorithm"},"citations":[{"ref":"Dantzig, G. B., & Wolfe, P. (1960). Decomposition principle for linear programs. Operations Research, 8(1), 101-111.","type":"article","doi":"10.1287/opre.8.1.101","isbn":null,"url":null},{"ref":"Gilmore, P. C., & Gomory, R. E. (1961). A linear programming approach to the cutting-stock problem. Operations Research, 9(6), 849-859.","type":"article","doi":"10.1287/opre.9.6.849","isbn":null,"url":null}],"related":["simplex-method","benders-decomposition","augmented-lagrangian-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"combat-exposure-scale","name":"Combat Exposure Scale","fullName":"Combat Exposure Scale","aliases":["CES","Keane Combat Exposure Scale"],"domain":"military-psychology","family":"process-pipeline","subfamily":"Combat exposure assessment","year":1989,"originator":"Keane, Fairbank, Caddell, Zimering, Taylor, & Mora","url":"https://scholargate.app/en/military-psychology/combat-exposure-scale","markdownUrl":"https://scholargate.app/en/military-psychology/combat-exposure-scale.md","definition":"The CES is a 7-item self-report measure of combat exposure developed by Keane and colleagues in 1989. It quantifies the frequency and intensity of combat experiences, including direct fire, causalities witnessed, and hazardous mission environments. It is widely used in veteran research and clinical screening to characterize trauma load and risk for PTSD.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keane, Fairbank, Caddell, Zimering, Taylor, & Mora","subfamily":"Combat exposure assessment","year":1989,"type":"Self-report"},"citations":[{"ref":"Keane, T. M., Fairbank, J. A., Caddell, J. M., Zimering, R. T., Taylor, K. L., & Mora, C. A. (1989). Clinical evaluation of a measure to assess combat exposure. Psychological Assessment, 1(1), 53-55.","type":"article","doi":"10.1037/1040-3590.1.1.53","isbn":null,"url":null},{"ref":"Keane, T. M., Caddell, J. M., & Taylor, K. L. (1988). Mississippi Scale for Combat-Related PTSD: Three studies in reliability and validity. Journal of Consulting and Clinical Psychology, 56(1), 85-90.","type":"article","doi":"10.1037/0022-006x.56.1.85","isbn":null,"url":null}],"related":["pcl-military","moral-injury-events-scale","peritraumatic-distress-inventory","deployment-risk-resilience"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comet","name":"COMET","fullName":"Characteristic Objects METhod","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Sałabun, W.","url":"https://scholargate.app/en/decision-making/comet","markdownUrl":"https://scholargate.app/en/decision-making/comet.md","definition":"COMET (Characteristic Objects METhod) is a ranking multi-criteria decision-making (MCDM) method introduced by Sałabun, W. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sałabun, W.","subfamily":"Ranking","year":"2015","type":"Fuzzy rule base on characteristic objects — rank-reversal free","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Sałabun, W. (2015). The Characteristic Objects Method: A New Distance-based Approach to Multicriteria Decision-making Problems. Journal of Multi-Criteria Decision Analysis","type":"article","doi":"10.1002/mcda.1525","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comfort-care-checklist","name":"Comfort Care Checklist","fullName":"Comfort Care Checklist for the Last Hours of Life","aliases":["Comfort Care Checklist","Last Hours Checklist"],"domain":"palliative-care","family":"process-pipeline","subfamily":"end-of-life-comfort","year":"2000s","originator":"Hospice and palliative care organizations; End-of-Life Nursing Education Consortium (ELNEC)","url":"https://scholargate.app/en/palliative-care/comfort-care-checklist","markdownUrl":"https://scholargate.app/en/palliative-care/comfort-care-checklist.md","definition":"The Comfort Care Checklist is a bedside verification tool designed to ensure comprehensive comfort and dignity in the final hours to days of life. Developed by hospice and palliative care organizations, particularly within the End-of-Life Nursing Education Consortium (ELNEC), the checklist systematically verifies that pain and other symptoms are managed, family is present and supported, spiritual needs are addressed, and documentation reflects the patient's and family's wishes—ensuring nothing essential is overlooked during the most vulnerable time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hospice and palliative care organizations; End-of-Life Nursing Education Consortium (ELNEC)","subfamily":"end-of-life-comfort","year":"2000s","type":"Clinician-administered checklist"},"citations":[{"ref":"Naylor, M. D., Bowles, K. H., & Brooten, D. A. (2002). Patients' and caregivers' perspectives on preparing for hospital discharge. Journal of Cardiovascular Nursing, 16(5), 36–48.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/12011005"},{"ref":"Reuben, D. B., & Tinetti, M. E. (2012). Goal-oriented patient care—An alternative health outcomes paradigm. New England Journal of Medicine, 366(9), 777–779.","type":"article","doi":"10.1056/NEJMp1113631","isbn":null,"url":null}],"related":["palliative-performance-scale","mcgill-quality-of-life","patient-dignity-inventory","support-team-assessment-schedule","needs-assessment-palliative"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"common-factors-questionnaire","name":"Common Factors Questionnaire","fullName":"Common Factors Questionnaire (CFQ)","aliases":["CFQ","Therapeutic Factors Scale"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"common-factors","year":"1992","originator":"Michael J. Lambert, Bruce E. Wampold","url":"https://scholargate.app/en/psychotherapy-research/common-factors-questionnaire","markdownUrl":"https://scholargate.app/en/psychotherapy-research/common-factors-questionnaire.md","definition":"The Common Factors Questionnaire (CFQ) is a structured client-report measure that quantifies the client's perception of therapeutic factors deemed common to effective psychotherapy across all modalities—including alliance, therapist empathy, client agency, goal clarity, and emotional expression. Based on Lambert's contextual model and Wampold's therapeutic relationship framework, the CFQ operationalizes the empirical finding that 70% or more of therapy outcome variance is attributable to common factors (relationship, expectancy, therapeutic environment) rather than specific technique. It is used in research to examine mechanisms of change and to compare common factors across therapy types.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael J. Lambert, Bruce E. Wampold","subfamily":"common-factors","year":"1992","type":"Client-rated"},"citations":[{"ref":"Lambert, M. J., & Barley, D. E. (2001). Research summary on the therapeutic relationship and psychotherapy outcome. Psychotherapy: Theory, Research, Practice, Training, 38(4), 357–361.","type":"article","doi":"10.1037/0033-3204.38.4.357","isbn":null,"url":null},{"ref":"Wampold, B. E. (2001). The great psychotherapy debate: Models, methods, and findings. Mahwah, NJ: Lawrence Erlbaum.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Wampold%2C%20B.%20E.%20(2001).%20The%20great%20psychotherapy%20debate%3A%20Models%2C%20methods%2C%20and%20findings.%20Mahwah%2C%20NJ%3A%20Lawrence%20Erlbaum."}],"related":["working-alliance-inventory","session-rating-scale","helpful-aspects-of-therapy","therapeutic-alliance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"common-spatial-pattern","name":"Common Spatial Pattern","fullName":"Common Spatial Pattern Filter","aliases":["CSP","Spatial filtering","CSP decomposition"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Signal processing","year":"2000","originator":"Herbert Ramoser","url":"https://scholargate.app/en/biomechanics/common-spatial-pattern","markdownUrl":"https://scholargate.app/en/biomechanics/common-spatial-pattern.md","definition":"Common Spatial Pattern (CSP) is a spatial filtering technique that identifies electrode combinations that maximize the variance difference between two classes of EEG activity, typically used in brain-computer interfaces to enhance motor imagery discrimination. Introduced by Ramoser and colleagues in 2000, CSP has become a standard feature extraction method in BCI research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herbert Ramoser","subfamily":"Signal processing","year":"2000","type":"Spatial filtering and feature extraction"},"citations":[{"ref":"Ramoser, H., Mueller-Gerking, J., & Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4), 441-446.","type":"article","doi":"10.1109/86.895946","isbn":null,"url":null},{"ref":"Koles, Z. J., Lazar, M. S., & Zhou, S. Z. (1991). Spatial patterns underlying population differences in the background EEG. Brain Topography, 2(4), 275-284.","type":"article","doi":"10.1007/BF01129656","isbn":null,"url":null}],"related":["bci-motor-imagery","emg-envelope","markerless-motion-capture"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"commonsense-reasoning-nlp","name":"Commonsense Reasoning","fullName":"Commonsense Reasoning in NLP","aliases":["commonsense NLP","if-then reasoning","Sağduyu Akıl Yürütme (Commonsense Reasoning)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2019 (landmark benchmarks)","originator":"Sap et al. (ATOMIC, 2019); Zellers et al. (HellaSwag, 2019)","url":"https://scholargate.app/en/text-mining/commonsense-reasoning-nlp","markdownUrl":"https://scholargate.app/en/text-mining/commonsense-reasoning-nlp.md","definition":"Commonsense reasoning in NLP refers to the capacity of a language model or inference system to draw on implicit, world-knowledge facts that humans take for granted — facts not stated in the text — to answer questions, complete stories, or interpret dialogue. Landmark benchmarks formalising the task include ATOMIC (Sap et al., 2019), an if-then commonsense knowledge graph, and HellaSwag (Zellers et al., 2019), a sentence-completion challenge that exposed gaps in machine understanding of everyday events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP reasoning task","originator":"Sap et al. (ATOMIC, 2019); Zellers et al. (HellaSwag, 2019)","year":"2019 (landmark benchmarks)","knowledgeBases":"ConceptNet, ATOMIC","inputType":"Text (narrative, dialogue, or question)","outputType":"Inferred implicit knowledge or selected plausible continuation","difficultyLevel":"Advanced (3 / 5)"},"citations":[{"ref":"Sap, M. et al. (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI.","type":"conference","doi":null,"isbn":null,"url":"https://ojs.aaai.org/index.php/AAAI/article/view/4160"},{"ref":"Zellers, R. et al. (2019). HellaSwag: Can a Machine Really Finish Your Sentence? ACL.","type":"conference","doi":null,"isbn":null,"url":"https://aclanthology.org/P19-1472/"}],"related":["question-answering","neural-machine-reading","knowledge-graph-nlp","bert-embeddings","retrieval-augmented-generation","semantic-role-labeling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"communication-confidence-aphasia","name":"Communication Confidence Rating Scale for Aphasia","fullName":"Communication Confidence Rating Scale for People with Aphasia (CCRS or CRSA)","aliases":["CCRS","CRSA","Communication Confidence Scale"],"domain":"speech-language-pathology","family":"process-pipeline","subfamily":"aphasia communication self-efficacy & confidence","year":"2003","originator":"Various (emerging self-report literature)","url":"https://scholargate.app/en/speech-language-pathology/communication-confidence-aphasia","markdownUrl":"https://scholargate.app/en/speech-language-pathology/communication-confidence-aphasia.md","definition":"The Communication Confidence Rating Scale (CCRS or CRSA) is a brief self-report measure of perceived communication self-efficacy and confidence in communication situations among adults with aphasia. Unlike objective measures of language ability (Boston Diagnostic Aphasia Examination) or quality-of-life impact (Aphasia Impact Questionnaire), the CCRS focuses specifically on confidence—the degree to which a person with aphasia believes they can successfully communicate in everyday scenarios. High CCRS scores reflect psychological readiness to engage in communication despite linguistic deficits; low scores indicate anxiety and avoidance despite preserved communication ability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (emerging self-report literature)","subfamily":"aphasia communication self-efficacy & confidence","year":"2003","type":"Self-report"},"citations":[{"ref":"Bays, C. L. (2003). Stroke Recovery: What Does the Literature Tell Us? Journal of Neuroscience Nursing, 35(5), 250–260.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Stroke+Recovery%3A+What+Does+the+Literature+Tell+Us+Bays"},{"ref":"Hersh, D., Worrall, L., O'Neill, G., Sherratt, S., & Hersh, D. (2012). 'I'm Not Aphonic, I'm Aphasic': Improving the Written Communication of People with Aphasia in Hospital. Aphasiology, 26(2), 175–188.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=%27I%27m+Not+Aphonic%2C+I%27m+Aphasic%27%3A+Improving+the+Written+Communication+of+People+with+Aphasia+in+Hospital+Hersh"},{"ref":"Hilari, K., Wiggins, R. D., Roy, P., Byng, S., & Smith, S. C. (2003). Predictors of Health-Related Quality of Life in People with Chronic Aphasia. Journal of Speech, Language, and Hearing Research, 46(2), 353–364.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Predictors+of+Health-Related+Quality+of+Life+in+People+with+Chronic+Aphasia+Hilari"}],"related":["aphasia-impact-questionnaire","boston-aphasia-severity","voice-handicap-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"community-belonging-scale","name":"Community Belonging Scale","fullName":"Sense of Community and Belonging Assessment","aliases":["CBS","Community Integration Scale"],"domain":"political-sociology","family":"process-pipeline","subfamily":"Sense of Belonging","year":"1974–1999","originator":"Seymour Sarason, David McMillan, David Chavis","url":"https://scholargate.app/en/political-sociology/community-belonging-scale","markdownUrl":"https://scholargate.app/en/political-sociology/community-belonging-scale.md","definition":"The Community Belonging Scale measures the subjective psychological sense of community—the feeling that one belongs, is accepted, and is valued within one's community. Distinct from objective measures of networks or participation, it captures the affective experience of community integration. Developed by Seymour Sarason and refined by McMillan and Chavis, it is grounded in community psychology and emphasizes that belonging is fundamental to mental health and social well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seymour Sarason, David McMillan, David Chavis","subfamily":"Sense of Belonging","year":"1974–1999","type":"Self-report questionnaire"},"citations":[{"ref":"Sarason, S. B. (1974). The psychological sense of community: Prospects for a community psychology. Jossey-Bass.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sarason%2C%20S.%20B.%20(1974).%20The%20psychological%20sense%20of%20community%3A%20Prospects%20for%20a%20community%20psychology.%20Jossey-Bass."},{"ref":"McMillan, D. W., & Chavis, D. M. (1986). Sense of community: A definition and theory. Journal of Community Psychology, 14(1), 6-23.","type":"article","doi":"10.1002/1520-6629(198601)14:1<6::AID-JCOP2290140103>3.0.CO;2-I","isbn":null,"url":null},{"ref":"Chipuer, H. M., & Pretty, G. M. (1999). A review of the empirical literature examining the association between social capital and mental health. Journal of Psychiatric and Mental Health Nursing, 6(6), 451-458.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+review+of+the+empirical+literature+examining+the+association+between+social+capital+and+mental+health+Chipuer"}],"related":["social-cohesion-scale","generalized-trust-scale","civic-engagement-scale","social-capital-index","community-belonging-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"community-detection","name":"Community Detection","fullName":"Community Detection (Louvain, Girvan-Newman, Leiden, Infomap)","aliases":["graph clustering","network partitioning","Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":"2002–2019 (algorithm family)","originator":"Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)","url":"https://scholargate.app/en/network-analysis/community-detection","markdownUrl":"https://scholargate.app/en/network-analysis/community-detection.md","definition":"Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)","year":"2002–2019 (algorithm family)","type":"Graph-partitioning / clustering algorithm family","approaches":"Modularity optimisation (Louvain, Leiden), edge-betweenness removal (Girvan-Newman), information-flow compression (Infomap)","output":"Node-to-community assignment and modularity score Q","requires_normal":false,"min_sample":20,"difficulty":2},"citations":[{"ref":"Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.","type":"article","doi":"10.1088/1742-5468/2008/10/P10008","isbn":null,"url":null},{"ref":"Traag, V.A., Waltman, L. & van Eck, N.J. (2019). From Louvain to Leiden: Guaranteeing Well-Connected Communities. Scientific Reports, 9, 5233.","type":"article","doi":null,"isbn":null,"url":"https://www.nature.com/articles/s41598-019-41695-z"}],"related":["exponential-random-graph","stochastic-block-model","centrality-analysis","network-diffusion","hierarchical-clustering"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"community-integration-questionnaire","name":"Community Integration Questionnaire","fullName":"Community Integration Questionnaire (CIQ)","aliases":["CIQ","CIQ-3"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"community-participation","year":"1993","originator":"Willer, Rosenthal, Kreutzer, Gordon","url":"https://scholargate.app/en/rehabilitation-science/community-integration-questionnaire","markdownUrl":"https://scholargate.app/en/rehabilitation-science/community-integration-questionnaire.md","definition":"The Community Integration Questionnaire (CIQ) is a brief, validated instrument specifically designed to assess how well individuals with brain injury, spinal cord injury, or other disabling conditions have reintegrated into community life across home, social, and work domains. Originally developed in 1993 by Willer and colleagues, it operationalizes the WHO definition of 'participation' and has become the standard outcome measure in traumatic brain injury (TBI) rehabilitation and long-term follow-up studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Willer, Rosenthal, Kreutzer, Gordon","subfamily":"community-participation","year":"1993","type":"Self-report or Clinician-administered"},"citations":[{"ref":"Willer, B., Rosenthal, M., Kreutzer, J. S., Gordon, W. A., & Rempel, R. (1993). Assessment of community integration following rehabilitation for traumatic brain injury. Journal of Head Trauma Rehabilitation, 9(2), 75–87.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1097/00001199-199306000-00008"},{"ref":"Willer, B., Ottenbacher, K. J., & Coad, M. L. (1994). The Community Integration Questionnaire: a comparative examination. American Journal of Physical Medicine & Rehabilitation, 73(2), 103–111.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1097/00002060-199403000-00002"}],"related":["whodas-2","impact-participation-autonomy","reintegration-to-normal-living","participation-scale","craig-handicap-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-autoethnography","name":"Comparative autoethnography","fullName":"Comparative Autoethnographic Research","aliases":["collaborative autoethnography","multi-sited autoethnography","cross-cultural autoethnography","CAE"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1979 (autoethnography); comparative application formalized ~2013","originator":"Hayano (term); developed further by Ellis, Bochner, Chang, Ngunjiri & Hernandez","url":"https://scholargate.app/en/qualitative/comparative-autoethnography","markdownUrl":"https://scholargate.app/en/qualitative/comparative-autoethnography.md","definition":"Comparative autoethnography is a qualitative design in which two or more researchers — or research participants — independently produce first-person self-narratives about a shared phenomenon and then systematically compare those accounts to generate broader cultural insight. By juxtaposing lived experiences that differ by context, identity, or setting, the approach moves beyond the single-voice limitations of traditional autoethnography while retaining its hallmark reflexivity and personal depth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hayano (term); developed further by Ellis, Bochner, Chang, Ngunjiri & Hernandez","year":"1979 (autoethnography); comparative application formalized ~2013","type":"Qualitative research design","dataType":"Personal narratives, field notes, journals, artefacts from multiple researchers or participants","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Chang, H., Ngunjiri, F. W., & Hernandez, K.-A. C. (2013). Collaborative Autoethnography. Left Coast Press.","type":"book","doi":null,"isbn":"978-1598745948","url":null},{"ref":"Ellis, C. (2004). The Ethnographic I: A Methodological Novel about Autoethnography. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759103016","url":null}],"related":["autoethnography","comparative-ethnography","narrative-inquiry","comparative-narrative-research","comparative-case-study","reflexive-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-biographical-research","name":"Comparative Biographical Research","fullName":"Comparative Biographical Research","aliases":["comparative biography","cross-case biographical analysis","biographical comparative method","comparative life-story research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–1980s","originator":"Daniel Bertaux; Paul Thompson","url":"https://scholargate.app/en/qualitative/comparative-biographical-research","markdownUrl":"https://scholargate.app/en/qualitative/comparative-biographical-research.md","definition":"Comparative biographical research is a qualitative design that gathers in-depth life-story accounts from multiple participants and systematically compares them to identify structural patterns, commonalities, and divergences across individual biographies. Rooted in the sociological life-history tradition, it moves beyond single-case description to generate broader theoretical insights about how social conditions, historical contexts, and personal agency shape individual trajectories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel Bertaux; Paul Thompson","year":"1970s–1980s","type":"Qualitative comparative research design","dataType":"Life-story interviews, biographical documents, personal narratives","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bertaux, D. (Ed.). (1981). Biography and Society: The Life History Approach in the Social Sciences. Sage.","type":"book","doi":null,"isbn":"978-0803914025","url":null},{"ref":"Chamberlayne, P., Bornat, J., & Wengraf, T. (Eds.). (2000). The Turn to Biographical Methods in Social Science: Comparative Issues and Examples. Routledge.","type":"book","doi":null,"isbn":"978-0415196857","url":null}],"related":["biographical-research","life-history-research","comparative-narrative-research","comparative-case-study","oral-history","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-case-law-analysis","name":"Comparative Case Law Analysis","fullName":"Comparative Case Law Analysis","aliases":["cross-jurisdictional case analysis","comparative judicial analysis","transnational case law comparison","CCLA"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Late 19th–20th century (systematic comparative law from ~1900; case-focused comparative methodology consolidated ~1970s–1990s)","originator":"Comparative law tradition (Zweigert, Kötz, MacCormick, Summers and others)","url":"https://scholargate.app/en/field-methods/comparative-case-law-analysis","markdownUrl":"https://scholargate.app/en/field-methods/comparative-case-law-analysis.md","definition":"Comparative case law analysis is a qualitative legal research method that systematically examines and contrasts judicial decisions from two or more legal systems or jurisdictions. By placing rulings side by side, the method identifies convergences, divergences, and the underlying legal reasoning that shapes how courts address similar legal questions across different national or regional contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Comparative law tradition (Zweigert, Kötz, MacCormick, Summers and others)","year":"Late 19th–20th century (systematic comparative law from ~1900; case-focused comparative methodology consolidated ~1970s–1990s)","type":"Qualitative legal research method","dataType":"Judicial decisions, court rulings, legal opinions across two or more jurisdictions","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"MacCormick, D. N., & Summers, R. S. (Eds.). (1991). Interpreting Statutes: A Comparative Study. Dartmouth.","type":"book","doi":null,"isbn":"978-1855210264","url":null},{"ref":"Zweigert, K., & Kötz, H. (1998). An Introduction to Comparative Law (3rd ed., T. Weir, Trans.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198268598","url":null}],"related":["case-law-analysis","comparative-legal-analysis","doctrinal-legal-research","legal-content-analysis","comparative-doctrinal-legal-research","hermeneutic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-case-study","name":"Comparative Case Study","fullName":"Comparative Case Study Research","aliases":["cross-case study","multi-site case study","multiple case study design","comparative case analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984 (Yin); 1995 (Stake)","originator":"Robert K. Yin; Robert E. Stake","url":"https://scholargate.app/en/qualitative/comparative-case-study","markdownUrl":"https://scholargate.app/en/qualitative/comparative-case-study.md","definition":"Comparative case study is a qualitative research design in which two or more bounded cases are studied in depth and then systematically compared to identify similarities, differences, and patterns across contexts. Rooted in Yin's replication logic and Stake's multiple case framework, it is particularly suited to questions that ask how or why a phenomenon unfolds differently — or similarly — across distinct settings, populations, or time periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin; Robert E. Stake","year":"1984 (Yin); 1995 (Stake)","type":"Qualitative / mixed research design","dataType":"Interviews, documents, observations, archival records (text and field data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null}],"related":["multiple-case-study","single-case-study","comparative-ethnography","comparative-narrative-research","cross-case-thematic-analysis","qualitative-comparative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-classic-grounded-theory","name":"Comparative classic grounded theory","fullName":"Comparative Classic Grounded Theory","aliases":["Glaserian comparative grounded theory","classic GT comparative design","comparative CGT","multi-site classic grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (classic GT); comparative application formalised 1970s–1990s","originator":"Barney G. Glaser & Anselm L. Strauss (classic GT); comparative design extended by Glaser","url":"https://scholargate.app/en/qualitative/comparative-classic-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/comparative-classic-grounded-theory.md","definition":"Comparative classic grounded theory is a qualitative research design that applies Glaser and Strauss's original Glaserian grounded theory procedures across two or more deliberately selected comparison groups, settings, or time points. The constant comparative method — the analytical engine of classic GT — is extended systematically across sites so that the emerging substantive theory accounts for variation in the phenomenon across different contexts, populations, or conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser & Anselm L. Strauss (classic GT); comparative design extended by Glaser","year":"1967 (classic GT); comparative application formalised 1970s–1990s","type":"Qualitative theory-building design","dataType":"Interviews, field notes, documents, observational data across two or more comparative groups or sites","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null},{"ref":"Glaser, B. G. (1992). Basics of Grounded Theory Analysis: Emergence vs Forcing. Sociology Press.","type":"book","doi":null,"isbn":"978-1884156014","url":null}],"related":["classic-grounded-theory","comparative-grounded-theory","comparative-straussian-grounded-theory","comparative-constructivist-grounded-theory","grounded-theory","comparative-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-confirmatory-research","name":"Comparative Confirmatory Research","fullName":"Comparative Confirmatory Research Design","aliases":["multigroup confirmatory research","cross-group confirmatory study","comparative hypothesis testing design","comparative model testing research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1971 (Jöreskog); systematized in organizational research by 2000","originator":"Karl Jöreskog (multigroup CFA foundation); Robert Vandenberg & Charles Lance (organizational application)","url":"https://scholargate.app/en/research-design/comparative-confirmatory-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-confirmatory-research.md","definition":"Comparative confirmatory research tests whether a pre-specified theoretical model or set of hypotheses holds equivalently across two or more distinct groups, time points, or contexts. It extends standard confirmatory analysis by explicitly imposing and evaluating equality constraints across groups, determining not only whether a model fits the data but whether its structure, factor loadings, and parameter estimates are comparable across populations. This design is foundational to cross-cultural, multi-site, and subgroup comparison studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Jöreskog (multigroup CFA foundation); Robert Vandenberg & Charles Lance (organizational application)","year":"1971 (Jöreskog); systematized in organizational research by 2000","type":"Quantitative comparative research design","dataType":"Quantitative (survey scales, psychometric instruments, observed variables)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null},{"ref":"Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409–426.","type":"article","doi":"10.1007/BF02291366","isbn":null,"url":null}],"related":["confirmatory-research","comparative-research","multivariate-confirmatory-research","longitudinal-confirmatory-research","hypothesis-testing-research","model-testing-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-constructivist-grounded-theory","name":"Comparative Constructivist Grounded Theory","fullName":"Comparative Constructivist Grounded Theory","aliases":["Comparative CGT","cross-group constructivist grounded theory","comparative Charmaz grounded theory","multi-site constructivist grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s (Charmaz 2000; extended comparatively through 2006–2014)","originator":"Kathy Charmaz (constructivist strand); comparative application developed in qualitative methodology literature","url":"https://scholargate.app/en/qualitative/comparative-constructivist-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/comparative-constructivist-grounded-theory.md","definition":"Comparative Constructivist Grounded Theory combines Kathy Charmaz's constructivist strand of grounded theory with an explicit comparative design, deliberately collecting and analyzing data from two or more groups, settings, or time points to build a theory that accounts for variation and similarity across contexts. The constructivist perspective treats categories and theory as co-constructed between researcher and participants rather than discovered objectively from data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz (constructivist strand); comparative application developed in qualitative methodology literature","year":"2000s (Charmaz 2000; extended comparatively through 2006–2014)","type":"Qualitative comparative research design","dataType":"Interviews, field notes, documents (text data from multiple groups or sites)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973133","url":null},{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932500","url":null}],"related":["constructivist-grounded-theory","comparative-grounded-theory","comparative-case-study","interpretive-constructivist-grounded-theory","grounded-theory","comparative-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-content-analysis","name":"Comparative Content analysis","fullName":"Comparative Content Analysis","aliases":["cross-case content analysis","comparative textual analysis","CCA","comparative message analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1952 (Berelson); comparative application developed through 1970s–2000s","originator":"Bernard Berelson (foundational content analysis); Klaus Krippendorff (systematic methodology)","url":"https://scholargate.app/en/qualitative/comparative-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-content-analysis.md","definition":"Comparative Content Analysis applies a shared coding framework to texts, documents, or media artifacts drawn from two or more groups, contexts, time points, or nations in order to identify similarities, differences, and patterns across those units of comparison. By holding the analytical lens constant while varying the comparison unit, it reveals how meaning, framing, or discourse differs across the cases under study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernard Berelson (foundational content analysis); Klaus Krippendorff (systematic methodology)","year":"1952 (Berelson); comparative application developed through 1970s–2000s","type":"Qualitative and/or quantitative comparative research design","dataType":"Texts, documents, media artifacts, transcripts across two or more groups or contexts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506395661","url":null},{"ref":"Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412979474","url":null}],"related":["content-analysis","qualitative-content-analysis","comparative-discourse-analysis","comparative-thematic-analysis","critical-content-analysis","longitudinal-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-conversation-analysis","name":"Comparative Conversation Analysis","fullName":"Comparative Conversation Analysis","aliases":["comparative CA","cross-contextual conversation analysis","comparative interactional analysis","comparative talk-in-interaction"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1974 (CA foundation); comparative applications from 1980s–1990s","originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA foundation); comparative extension developed across the field from the 1980s onward","url":"https://scholargate.app/en/qualitative/comparative-conversation-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-conversation-analysis.md","definition":"Comparative Conversation Analysis (comparative CA) applies the rigorous micro-analytic methods of Conversation Analysis across two or more contrasting interactional settings, languages, cultures, or participant groups. It examines how the sequential organisation of talk — turn-taking, repair, adjacency pairs, and action formation — varies or remains stable across contexts, producing cross-contextual evidence about the architecture of human interaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA foundation); comparative extension developed across the field from the 1980s onward","year":"1974 (CA foundation); comparative applications from 1980s–1990s","type":"Qualitative micro-analytic research design","dataType":"Audio/video recordings of naturally occurring talk, transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.","type":"article","doi":"10.2307/412243","isbn":null,"url":null},{"ref":"Sidnell, J., & Stivers, T. (Eds.). (2012). The Handbook of Conversation Analysis. Wiley-Blackwell.","type":"book","doi":null,"isbn":"978-1444330564","url":null}],"related":["conversation-analysis","discourse-analysis","comparative-discourse-analysis","critical-conversation-analysis","comparative-critical-discourse-analysis","interactional-sociolinguistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-cross-sectional-research","name":"Comparative Cross-Sectional Research","fullName":"Comparative Cross-Sectional Research Design","aliases":["comparative cross-sectional survey","cross-sectional comparative study","multi-group cross-sectional design","cross-sectional group comparison"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century (widely formalized from 1950s onward)","originator":"Epidemiological tradition; formalized in observational study typologies","url":"https://scholargate.app/en/research-design/comparative-cross-sectional-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-cross-sectional-research.md","definition":"Comparative cross-sectional research is a quantitative observational design that measures and compares characteristics, attitudes, or outcomes across two or more pre-defined groups at a single point in time. By building the comparison into the sampling frame rather than treating it as a secondary analysis step, the design yields group-level contrasts without requiring follow-up measurement, making it efficient for describing between-group differences in prevalence, mean levels, or associations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Epidemiological tradition; formalized in observational study typologies","year":"Mid-20th century (widely formalized from 1950s onward)","type":"Observational quantitative design","dataType":"Quantitative survey, administrative records, or structured observational data","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195083507","url":null},{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506386706","url":null}],"related":["cross-sectional-research","correlational-research","longitudinal-research","causal-comparative-research","survey-research","cohort-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-descriptive-research","name":"Comparative Descriptive Research","fullName":"Comparative Descriptive Research Design","aliases":["comparative survey design","descriptive comparative study","group-comparison descriptive research","CDR"],"domain":"research-design","family":"process-pipeline","subfamily":"Survey and observational design","year":"Mid-20th century, formalized in research methods texts from the 1960s onward","originator":"Codified in educational and behavioral research methods literature; no single originator","url":"https://scholargate.app/en/research-design/comparative-descriptive-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-descriptive-research.md","definition":"Comparative descriptive research is a non-experimental quantitative design that systematically documents characteristics, attitudes, behaviors, or conditions across two or more naturally occurring groups, then places those descriptions side by side to identify similarities and differences. Unlike causal-comparative designs, it makes no claim about why groups differ — it rigorously answers the question 'How do these groups compare on this characteristic?' without manipulating any variable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Codified in educational and behavioral research methods literature; no single originator","year":"Mid-20th century, formalized in research methods texts from the 1960s onward","type":"Non-experimental quantitative research design","dataType":"Questionnaires, surveys, structured observation, secondary data (numeric or categorical)","subfamily":"Survey and observational design"},"citations":[{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0078097874","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["descriptive-research","survey-research","cross-sectional-research","causal-comparative-research","correlational-research","longitudinal-descriptive-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-digital-ethnography","name":"Comparative Digital Ethnography","fullName":"Comparative Digital Ethnography","aliases":["CDE","multi-site digital ethnography","cross-platform ethnography","comparative virtual ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1995–2000 (multi-sited framework 1995; virtual ethnography 2000)","originator":"Christine Hine (digital ethnography); George E. Marcus (multi-sited ethnography)","url":"https://scholargate.app/en/qualitative/comparative-digital-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/comparative-digital-ethnography.md","definition":"Comparative Digital Ethnography (CDE) is a qualitative design that applies ethnographic methods — sustained participant observation, interview, and artefact analysis — across two or more digital settings simultaneously. By systematically comparing practices, meanings, and interactions in different online environments (e.g., distinct platforms, communities, or national contexts), CDE surfaces both site-specific patterns and cross-cutting cultural logics that a single-site study would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christine Hine (digital ethnography); George E. Marcus (multi-sited ethnography)","year":"1995–2000 (multi-sited framework 1995; virtual ethnography 2000)","type":"Qualitative research design","dataType":"Online field notes, digital artefacts, interviews, social media traces, forum posts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Hine, C. (2000). Virtual Ethnography. Sage.","type":"book","doi":null,"isbn":"978-0761958963","url":null},{"ref":"Marcus, G. E. (1995). Ethnography in/of the World System: The Emergence of Multi-Sited Ethnography. Annual Review of Anthropology, 24, 95–117.","type":"article","doi":"10.1146/annurev.an.24.100195.000523","isbn":null,"url":null}],"related":["ethnography","digital-ethnography","multi-site-ethnography","netnography","comparative-case-study","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-discourse-analysis","name":"Comparative Discourse Analysis","fullName":"Comparative Discourse Analysis","aliases":["CDA comparative","cross-context discourse analysis","comparative text analysis","multi-site discourse analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s (established as comparative practice through the 1990s)","originator":"Norman Fairclough; Ruth Wodak; Teun A. van Dijk","url":"https://scholargate.app/en/qualitative/comparative-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-discourse-analysis.md","definition":"Comparative discourse analysis examines how language constructs meaning, identity, and power by systematically contrasting texts or speech acts drawn from at least two distinct contexts, groups, time periods, or institutions. By holding analytical categories constant across cases, it reveals how discursive patterns diverge or converge, producing insights that single-context discourse analysis cannot generate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Fairclough; Ruth Wodak; Teun A. van Dijk","year":"1980s–1990s (established as comparative practice through the 1990s)","type":"Qualitative comparative research approach","dataType":"Texts, transcripts, media content, policy documents, interviews","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Fairclough, N. (1995). Critical Discourse Analysis: The Critical Study of Language. Longman.","type":"book","doi":null,"isbn":"978-0582219526","url":null},{"ref":"Wodak, R., & Meyer, M. (Eds.). (2009). Methods of Critical Discourse Analysis (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1847870216","url":null}],"related":["discourse-analysis","critical-discourse-analysis","comparative-content-analysis","comparative-thematic-analysis","comparative-narrative-research","critical-discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-doctrinal-legal-research","name":"Comparative Doctrinal Legal Research","fullName":"Comparative Doctrinal Legal Research","aliases":["comparative-doctrinal method","cross-jurisdictional doctrinal analysis","comparative black-letter law research","CDLR"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"19th century origins; modern systematic form 1960s–1998","originator":"Rooted in classical comparative law (Anselm von Feuerbach, early 19th c.); systematised by Zweigert & Kötz (1998)","url":"https://scholargate.app/en/field-methods/comparative-doctrinal-legal-research","markdownUrl":"https://scholargate.app/en/field-methods/comparative-doctrinal-legal-research.md","definition":"Comparative doctrinal legal research systematically identifies, expounds, and compares the legal rules, principles, and doctrines governing the same problem across two or more jurisdictions. It combines the internal rigour of doctrinal analysis — mapping the authoritative sources of a single legal system — with the external perspective of comparative law, asking whether different legal systems solve the same social problem in similar or divergent ways and why.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in classical comparative law (Anselm von Feuerbach, early 19th c.); systematised by Zweigert & Kötz (1998)","year":"19th century origins; modern systematic form 1960s–1998","type":"Qualitative legal research design","dataType":"Primary legal sources (statutes, codes, case law, regulations) from two or more jurisdictions","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Zweigert, K., & Kötz, H. (1998). An Introduction to Comparative Law (3rd ed., T. Weir, Trans.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198268598","url":null},{"ref":"Hutchinson, T., & Duncan, N. (2012). Defining and describing what we do: Doctrinal legal research. Deakin Law Review, 17(1), 83–119.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Defining+and+describing+what+we+do+Doctrinal+legal+research+Hutchinson+Duncan+2012"}],"related":["doctrinal-legal-research","comparative-legal-analysis","case-law-analysis","legal-content-analysis","hermeneutic-analysis","textual-criticism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-document-analysis","name":"Comparative Document Analysis","fullName":"Comparative Qualitative Document Analysis","aliases":["comparative documentary analysis","cross-document analysis","comparative textual analysis","comparative archival analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Mid-to-late 20th century; consolidated as explicit qualitative method by 2000s","originator":"Rooted in historical and social science documentary methods; systematised by scholars such as Lindsay Prior and Glenn Bowen","url":"https://scholargate.app/en/qualitative/comparative-document-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-document-analysis.md","definition":"Comparative document analysis is a qualitative research design that systematically examines two or more documents — or document sets — side by side to identify similarities, differences, patterns, and contradictions across contexts, institutions, time periods, or jurisdictions. Drawing on document analysis as a primary method, the comparative dimension adds analytical leverage by allowing the researcher to ask not just what a document says, but how and why it differs from comparable documents elsewhere.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in historical and social science documentary methods; systematised by scholars such as Lindsay Prior and Glenn Bowen","year":"Mid-to-late 20th century; consolidated as explicit qualitative method by 2000s","type":"Qualitative comparative research design","dataType":"Documents (policy texts, reports, archives, official records, textbooks, media texts)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Prior, L. (2003). Using Documents in Social Research. Sage Publications.","type":"book","doi":null,"isbn":"978-0761965497","url":null}],"related":["document-analysis","comparative-case-study","comparative-content-analysis","comparative-discourse-analysis","thematic-analysis","critical-document-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-ethnography","name":"Comparative Ethnography","fullName":"Comparative Ethnographic Research","aliases":["multi-sited ethnography","cross-site ethnography","comparative field research","comparative participant observation"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1987–1995 (systematic comparative ethnography formalized)","originator":"George E. Marcus (multi-sited formulation); Charles C. Ragin (comparative logic)","url":"https://scholargate.app/en/qualitative/comparative-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/comparative-ethnography.md","definition":"Comparative ethnography is a qualitative research design that conducts in-depth ethnographic fieldwork across two or more sites, groups, communities, or cultural settings in order to generate systematic comparisons. Rather than describing a single community in isolation, it traces similarities, differences, and interconnections across cases, producing theoretically grounded insights that no single site could yield alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E. Marcus (multi-sited formulation); Charles C. Ragin (comparative logic)","year":"1987–1995 (systematic comparative ethnography formalized)","type":"Qualitative comparative research design","dataType":"Field notes, interviews, documents, observation records from multiple sites or groups","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Marcus, G. E. (1995). Ethnography in/of the world system: The emergence of multi-sited ethnography. Annual Review of Anthropology, 24, 95–117.","type":"article","doi":"10.1146/annurev.an.24.100195.000523","isbn":null,"url":null},{"ref":"Ragin, C. C. (1987). The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. University of California Press.","type":"book","doi":null,"isbn":"978-0520906525","url":null}],"related":["ethnography","digital-ethnography","case-study","multiple-case-study","grounded-theory","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-explanatory-research","name":"Comparative Explanatory Research","fullName":"Comparative Explanatory Research Design","aliases":["comparative explanation","explanatory comparative design","cross-case explanatory research","comparative causal analysis"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1843 (Mill); contemporary social-science formalisation 1971–1987","originator":"John Stuart Mill (methods of agreement and difference, 1843); formalised in social science by Arend Lijphart and Charles Ragin","url":"https://scholargate.app/en/research-design/comparative-explanatory-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-explanatory-research.md","definition":"Comparative explanatory research is an observational design that systematically examines two or more groups, nations, organisations, or time points in order to explain why differences in outcomes occur. Rather than merely describing variation, it seeks causal or contributing mechanisms by holding some conditions constant while contrasting others — drawing on Mill's classical methods of agreement and difference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Stuart Mill (methods of agreement and difference, 1843); formalised in social science by Arend Lijphart and Charles Ragin","year":"1843 (Mill); contemporary social-science formalisation 1971–1987","type":"Observational explanatory research design","dataType":"Quantitative or mixed (survey data, administrative records, archival data across groups or cases)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Ragin, C. C. (1987). The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies. University of California Press.","type":"book","doi":null,"isbn":"978-0520063167","url":null},{"ref":"Lijphart, A. (1971). Comparative politics and the comparative method. American Political Science Review, 65(3), 682–693.","type":"article","doi":"10.2307/1955513","isbn":null,"url":null}],"related":["comparative-case-study","cross-sectional-survey","quasi-experimental-design","causal-comparative-research","multiple-case-study","survey-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-exploratory-quantitative-research","name":"Comparative Exploratory Quantitative Research","fullName":"Comparative Exploratory Quantitative Research Design","aliases":["exploratory comparative quantitative design","comparative exploratory survey research","quantitative comparative exploration","CEQR design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-to-late 20th century","originator":"No single originator; codified in quantitative research methodology traditions (20th century)","url":"https://scholargate.app/en/research-design/comparative-exploratory-quantitative-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-exploratory-quantitative-research.md","definition":"Comparative exploratory quantitative research is a design that uses structured numerical data collection to discover patterns, differences, and relationships across two or more distinct groups or conditions — without a fully specified hypothesis in advance. It sits at the intersection of exploratory intent and comparative structure: the researcher does not enter the field with a predetermined answer but organises the inquiry around a comparison that will generate quantitative insights. The design is common in social, educational, and behavioural sciences when a phenomenon is insufficiently understood to permit confirmatory testing but structured group comparison is still feasible and informative.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"No single originator; codified in quantitative research methodology traditions (20th century)","year":"Mid-to-late 20th century","type":"Quantitative research design","dataType":"Numerical / structured survey data across two or more comparison groups","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452226101","url":null},{"ref":"Babbie, E. (2016). The Practice of Social Research (14th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1305104945","url":null}],"related":["comparative-research","exploratory-quantitative-research","cross-sectional-research","correlational-research","descriptive-research","survey-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-grounded-theory","name":"Comparative grounded theory","fullName":"Comparative Grounded Theory","aliases":["cross-site grounded theory","multi-group grounded theory","comparative GT","grounded theory comparative analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (base); comparative application formalised from the 1980s onward","originator":"Barney Glaser and Anselm Strauss (grounded theory base); comparative extension developed by multiple scholars","url":"https://scholargate.app/en/qualitative/comparative-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/comparative-grounded-theory.md","definition":"Comparative grounded theory applies the systematic inductive logic of grounded theory across two or more distinct groups, settings, or time points. Rather than generating a theory grounded in a single context, it builds theory that explains variation and similarity across contexts, producing conceptually richer and more transferable explanatory frameworks than single-site grounded theory studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney Glaser and Anselm Strauss (grounded theory base); comparative extension developed by multiple scholars","year":"1967 (base); comparative application formalised from the 1980s onward","type":"Qualitative comparative research design","dataType":"Interviews, documents, field notes collected across two or more groups or contexts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null},{"ref":"Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0857029140","url":null}],"related":["grounded-theory","comparative-case-study","comparative-ethnography","constructivist-grounded-theory","comparative-thematic-analysis","comparative-narrative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-hermeneutic-analysis","name":"Comparative Hermeneutic Analysis","fullName":"Comparative Hermeneutic Analysis","aliases":["comparative hermeneutics","cross-textual hermeneutics","comparative interpretive analysis","CHA"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Mid-20th century (Gadamer 1960; comparative extension developed 1970s–1990s)","originator":"Hans-Georg Gadamer; Paul Ricoeur; Wilhelm Dilthey (hermeneutic tradition); comparative extension by cross-cultural and comparative religion scholars","url":"https://scholargate.app/en/field-methods/comparative-hermeneutic-analysis","markdownUrl":"https://scholargate.app/en/field-methods/comparative-hermeneutic-analysis.md","definition":"Comparative hermeneutic analysis is a qualitative method that applies hermeneutic interpretation across two or more texts, traditions, or discourses to reveal shared meanings, tensions, and divergences. Drawing on Gadamer's concept of the hermeneutic circle and Ricoeur's theory of text and meaning, it moves iteratively between the parts and the whole of each text while simultaneously holding multiple texts in dialogue, surfacing how different historical, cultural, or disciplinary contexts shape interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans-Georg Gadamer; Paul Ricoeur; Wilhelm Dilthey (hermeneutic tradition); comparative extension by cross-cultural and comparative religion scholars","year":"Mid-20th century (Gadamer 1960; comparative extension developed 1970s–1990s)","type":"Qualitative interpretive method","dataType":"Texts, documents, narratives, historical records","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Gadamer, H.-G. (1975). Truth and Method (G. Barden & J. Cumming, Trans.). Seabury Press. (Original work published 1960)","type":"book","doi":null,"isbn":"978-0826400185","url":null},{"ref":"Ricoeur, P. (1981). Hermeneutics and the Human Sciences: Essays on Language, Action and Interpretation (J. B. Thompson, Ed. & Trans.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521280112","url":null}],"related":["hermeneutic-analysis","textual-criticism","comparative-textual-criticism","discourse-analysis","comparative-case-law-analysis","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-historical-archival-research","name":"Comparative Historical Archival Research","fullName":"Comparative Historical Archival Research","aliases":["comparative-historical analysis","cross-national archival research","comparative archival history","CHAR"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Late 19th century (archival foundations); mid-20th century (comparative systematic application)","originator":"Leopold von Ranke (archival history); Theda Skocpol, Barrington Moore (comparative-historical synthesis)","url":"https://scholargate.app/en/field-methods/comparative-historical-archival-research","markdownUrl":"https://scholargate.app/en/field-methods/comparative-historical-archival-research.md","definition":"Comparative historical archival research combines systematic examination of primary archival sources across two or more historical cases — nations, regions, institutions, or time periods — to identify causal patterns, structural similarities, and divergences that single-case histories cannot reveal. It is the method of choice when researchers want to explain why similar or different outcomes emerged across distinct historical contexts using documentary evidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leopold von Ranke (archival history); Theda Skocpol, Barrington Moore (comparative-historical synthesis)","year":"Late 19th century (archival foundations); mid-20th century (comparative systematic application)","type":"Qualitative comparative research design","dataType":"Primary archival documents, state records, correspondence, administrative files, secondary historical sources","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Skocpol, T. (1979). States and Social Revolutions: A Comparative Analysis of France, Russia, and China. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521294997","url":null},{"ref":"Mahoney, J., & Thelen, K. (2015). Comparative-historical analysis in contemporary political science. In J. Mahoney & K. Thelen (Eds.), Advances in Comparative-Historical Analysis. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Advances+in+Comparative-Historical+Analysis+Mahoney+Thelen+2015"}],"related":["historical-archival-research","comparative-case-study","process-tracing","oral-history-method","document-analysis","comparative-legal-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-institutional-ethnography","name":"Comparative Institutional Ethnography","fullName":"Comparative Institutional Ethnography","aliases":["CIE","comparative IE","multi-site institutional ethnography","cross-institutional ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1987 (IE origin); comparative applications developed 1990s–2000s","originator":"Dorothy E. Smith (IE foundation); comparative extension by subsequent IE scholars","url":"https://scholargate.app/en/qualitative/comparative-institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/comparative-institutional-ethnography.md","definition":"Comparative Institutional Ethnography (CIE) extends Dorothy Smith's institutional ethnography to two or more institutional settings, revealing how texts, ruling relations, and coordinated work practices operate across different organizational contexts. By holding the standpoint of workers or clients constant while varying the institutional site, CIE exposes both the shared ideological mechanisms and the local divergences that shape everyday experience within institutions such as hospitals, schools, welfare agencies, or courts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith (IE foundation); comparative extension by subsequent IE scholars","year":"1987 (IE origin); comparative applications developed 1990s–2000s","type":"Qualitative multi-site institutional design","dataType":"Interviews, documents, texts, observational field notes from two or more institutional sites","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759105508","url":null},{"ref":"DeVault, M. L., & McCoy, L. (2006). Institutional ethnography: Using interviews to investigate ruling relations. In D. E. Smith (Ed.), Institutional Ethnography as Practice (pp. 15–44). Rowman & Littlefield.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Institutional+Ethnography+as+Practice+DeVault+McCoy+2006"}],"related":["institutional-ethnography","comparative-ethnography","comparative-case-study","critical-institutional-ethnography","multi-site-ethnography","comparative-qualitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-interpretative-phenomenological-analysis","name":"Comparative Interpretative Phenomenological Analysis","fullName":"Comparative Interpretative Phenomenological Analysis","aliases":["Comparative IPA","cross-group IPA","IPA comparative design","multi-group interpretative phenomenological analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1996 (IPA); comparative applications prominent from 2000s onward","originator":"Jonathan A. Smith (IPA); comparative extension by IPA research community","url":"https://scholargate.app/en/qualitative/comparative-interpretative-phenomenological-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-interpretative-phenomenological-analysis.md","definition":"Comparative Interpretative Phenomenological Analysis (Comparative IPA) applies the IPA framework — developed by Jonathan A. Smith — to examine and contrast the lived experiences of two or more distinct groups or individuals. Rather than producing a single composite description, it preserves within-group detail and then performs a principled cross-group comparison, revealing how the same phenomenon is experienced differently depending on context, identity, or circumstance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan A. Smith (IPA); comparative extension by IPA research community","year":"1996 (IPA); comparative applications prominent from 2000s onward","type":"Qualitative research design","dataType":"In-depth interview transcripts, focus group data","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative Phenomenological Analysis: Theory, Method and Research. Sage.","type":"book","doi":null,"isbn":"978-1412908344","url":null},{"ref":"Larkin, M., Watts, S., & Clifton, E. (2006). Giving voice and making sense in interpretative phenomenological analysis. Qualitative Research in Psychology, 3(2), 102–120.","type":"article","doi":"10.1191/1478088706qp062oa","isbn":null,"url":null}],"related":["interpretive-interpretative-phenomenological-analysis","critical-interpretative-phenomenological-analysis","comparative-phenomenology","comparative-case-study","phenomenology","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-legal-analysis","name":"Comparative Legal Analysis","fullName":"Comparative Legal Analysis","aliases":["comparative law","legal comparison","comparative jurisprudence","CLA"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Late 19th century; formalised 1900","originator":"Gottfried Wilhelm Leibniz (early conceptualisation); Raymond Saleilles and Édouard Lambert (modern discipline, 1900 Paris Congress)","url":"https://scholargate.app/en/field-methods/comparative-legal-analysis","markdownUrl":"https://scholargate.app/en/field-methods/comparative-legal-analysis.md","definition":"Comparative legal analysis is a structured research method that examines how two or more legal systems — whether national, regional, or supranational — address a common legal problem. By placing rules, doctrines, and judicial decisions side by side, researchers identify convergences, divergences, and the underlying societal, historical, and political forces that shape legal solutions. The method is foundational to law reform, harmonisation efforts, treaty drafting, and academic legal scholarship.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gottfried Wilhelm Leibniz (early conceptualisation); Raymond Saleilles and Édouard Lambert (modern discipline, 1900 Paris Congress)","year":"Late 19th century; formalised 1900","type":"Qualitative legal research method","dataType":"Legal texts (statutes, codes, case law, regulations, legal doctrine)","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Zweigert, K., & Kötz, H. (1998). An Introduction to Comparative Law (3rd ed., T. Weir, Trans.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198268598","url":null},{"ref":"Comparative law. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Comparative_law"}],"related":["doctrinal-legal-research","case-law-analysis","legal-content-analysis","hermeneutic-analysis","historical-archival-research","textual-criticism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-life-history-research","name":"Comparative Life history research","fullName":"Comparative Life History Research","aliases":["comparative life history","cross-case life history","comparative biographical method","comparative biographical life history"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1920s (life history origins); comparative variant prominent from 1980s–1990s","originator":"Ivor Goodson; influenced by C. Wright Mills and W. I. Thomas & Florian Znaniecki","url":"https://scholargate.app/en/qualitative/comparative-life-history-research","markdownUrl":"https://scholargate.app/en/qualitative/comparative-life-history-research.md","definition":"Comparative life history research is a qualitative approach that collects extended first-person accounts of individuals' lives across two or more cases, groups, or social contexts, then systematically compares these accounts to identify shared patterns, divergences, and the social forces that shape biographical trajectories. It bridges the depth of life history with the analytical leverage of cross-case comparison, making it especially powerful for understanding how social structure, culture, or institutional context shapes individual experience over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ivor Goodson; influenced by C. Wright Mills and W. I. Thomas & Florian Znaniecki","year":"1920s (life history origins); comparative variant prominent from 1980s–1990s","type":"Qualitative comparative research design","dataType":"Narrative interviews, life documents, diaries, letters","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Goodson, I. F. (Ed.). (1992). Studying Teachers' Lives. Routledge.","type":"book","doi":null,"isbn":"978-0415064248","url":null},{"ref":"Cole, A. L., & Knowles, J. G. (2001). Lives in Context: The Art of Life History Research. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759100466","url":null}],"related":["life-history-research","biographical-research","narrative-inquiry","comparative-narrative-research","oral-history","comparative-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-longitudinal-research","name":"Comparative Longitudinal Research","fullName":"Comparative Longitudinal Research Design","aliases":["longitudinal comparative design","comparative panel design","multi-group longitudinal study","longitudinal cross-national comparison"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century onward; systematized in sociological and developmental research by the 1960s–1970s","originator":"Developed across social science and educational research traditions; no single originator","url":"https://scholargate.app/en/research-design/comparative-longitudinal-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-longitudinal-research.md","definition":"Comparative longitudinal research tracks two or more distinct groups across multiple time points, enabling researchers to observe how outcomes change over time and whether those trajectories differ between groups. By combining the temporal depth of longitudinal design with the between-group contrast of comparative design, this approach can detect not only whether groups differ at any single moment but also whether they diverge, converge, or evolve at different rates across the observation window.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed across social science and educational research traditions; no single originator","year":"Mid-20th century onward; systematized in sociological and developmental research by the 1960s–1970s","type":"Quantitative observational research design","dataType":"Repeated-measures numeric data collected from two or more distinct groups at multiple time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922292","url":null},{"ref":"Bijleveld, C. C. J. H., & van der Kamp, L. J. T. (1998). Longitudinal Data Analysis: Designs, Models and Methods. Sage Publications.","type":"book","doi":null,"isbn":"978-0803976177","url":null}],"related":["longitudinal-research","comparative-research","panel-research","cohort-research","cross-sectional-research","growth-curve-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-method","name":"Comparative Method","fullName":"Comparative Historical Linguistics Method","aliases":["Historical Comparative Linguistics","Genetic Linguistics"],"domain":"linguistics","family":"process-pipeline","subfamily":"Historical Linguistics","year":"1786","originator":"Sir William Jones","url":"https://scholargate.app/en/linguistics/comparative-method","markdownUrl":"https://scholargate.app/en/linguistics/comparative-method.md","definition":"The Comparative Method is a foundational technique in historical linguistics for reconstructing ancestral languages and establishing genetic relationships between related languages. Pioneered by Sir William Jones in 1786, it systematically compares phonological, morphological, and lexical features across languages to identify regular sound correspondences and trace their shared origins. This method underpins modern historical linguistics and has been essential for understanding language families worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sir William Jones","subfamily":"Historical Linguistics","year":"1786","type":"Empirical process pipeline"},"citations":[{"ref":"Hock, H. H. (1991). Principles of Historical Linguistics (2nd ed.). Berlin: Mouton de Gruyter.","type":"book","doi":"10.1515/9783110219135","isbn":null,"url":null},{"ref":"Campbell, L. (1998). Historical Linguistics: An Introduction. Edinburgh: Edinburgh University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Historical+Linguistics%3A+An+Introduction+Campbell"},{"ref":"Greenberg, J. H. (1953). Historical linguistics and unwritten languages. In A. L. Kroeber (Ed.), Anthropology Today. Chicago: University of Chicago Press.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/anthropologytoday1953kroe"}],"related":["internal-reconstruction","glottochronology","dialectometry","corpus-linguistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-model-testing-research","name":"Comparative Model Testing Research","fullName":"Comparative Model Testing Research","aliases":["comparative model comparison","cross-group model testing","competing model comparison research","comparative structural model evaluation"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1969–2000s","originator":"Rooted in structural equation modeling traditions; formalized through Jöreskog (1969) and extended by Vandenberg & Lance (2000)","url":"https://scholargate.app/en/research-design/comparative-model-testing-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-model-testing-research.md","definition":"Comparative model testing research is a quantitative design in which two or more theoretically motivated models — or the same model evaluated across distinct groups or conditions — are systematically tested and compared using fit indices, likelihood-ratio tests, or information criteria. The goal is to determine which model better represents the data structure, or whether a model's parameter structure holds equally across comparison groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in structural equation modeling traditions; formalized through Jöreskog (1969) and extended by Vandenberg & Lance (2000)","year":"1969–2000s","type":"Quantitative confirmatory-comparative research design","dataType":"Multivariate quantitative data (scales, latent constructs, observed indicators)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462523344","url":null},{"ref":"Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["confirmatory-research","model-testing-research","comparative-research","hypothesis-testing-research","cross-sectional-correlational-research","comparative-confirmatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-multiple-case-study","name":"Comparative Multiple case study","fullName":"Comparative Multiple Case Study Research","aliases":["multi-site case study","cross-case analysis","comparative case research","multi-case comparative design"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984 (Yin); 2006 (Stake multiple-case analysis)","originator":"Robert K. Yin; Robert E. Stake","url":"https://scholargate.app/en/qualitative/comparative-multiple-case-study","markdownUrl":"https://scholargate.app/en/qualitative/comparative-multiple-case-study.md","definition":"Comparative multiple case study is a qualitative research design in which two or more bounded cases are studied in depth and then systematically compared to identify patterns, contrasts, and transferable findings. Rooted in Robert Yin's case study methodology and Robert Stake's multiple-case analysis framework, it combines the rich contextual insight of single-case work with the analytical leverage gained by examining how phenomena unfold similarly or differently across distinct settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin; Robert E. Stake","year":"1984 (Yin); 2006 (Stake multiple-case analysis)","type":"Qualitative comparative research design","dataType":"Interviews, documents, observations, archival records (multiple sites)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null}],"related":["multiple-case-study","single-case-study","comparative-case-study","comparative-narrative-research","cross-case-synthesis","comparative-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-narrative-research","name":"Comparative Narrative Research","fullName":"Comparative Narrative Inquiry","aliases":["comparative narrative inquiry","cross-case narrative research","narrative comparison","comparative narrative analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"D. Jean Clandinin & F. Michael Connelly (narrative inquiry); comparative extension by the broader qualitative comparative tradition","url":"https://scholargate.app/en/qualitative/comparative-narrative-research","markdownUrl":"https://scholargate.app/en/qualitative/comparative-narrative-research.md","definition":"Comparative narrative research is a qualitative design that collects personal stories or life accounts from two or more participants, groups, or contexts and systematically compares them to reveal patterns, contrasts, and contextual influences. Drawing on narrative inquiry's attention to experience-as-story, it adds a deliberate comparative logic to identify what is shared, what diverges, and why differences emerge across cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. Jean Clandinin & F. Michael Connelly (narrative inquiry); comparative extension by the broader qualitative comparative tradition","year":"1990s–2000s","type":"Qualitative comparative research design","dataType":"Narrative interviews, life stories, autobiographical accounts, documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Clandinin, D. J., & Connelly, F. M. (2000). Narrative Inquiry: Experience and Story in Qualitative Research. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787943523","url":null},{"ref":"Riessman, C. K. (2008). Narrative Methods for the Human Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761929987","url":null}],"related":["narrative-inquiry","comparative-case-study","comparative-thematic-analysis","comparative-phenomenology","biographical-research","life-history-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-netnography","name":"Comparative Netnography","fullName":"Comparative Netnographic Research","aliases":["cross-community netnography","multi-site netnography","comparative online ethnography","comparative virtual ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Late 1990s–2000s (netnography ~1997; comparative extension ~2000s–2010s)","originator":"Robert V. Kozinets (netnography); comparative extension through multi-site online fieldwork practice","url":"https://scholargate.app/en/qualitative/comparative-netnography","markdownUrl":"https://scholargate.app/en/qualitative/comparative-netnography.md","definition":"Comparative netnography applies netnographic methods systematically across two or more online communities, platforms, or cultural contexts to reveal both shared and divergent patterns in online social life. Grounded in Kozinets's netnographic tradition, it extends single-site online ethnography into a comparative logic: the researcher immerses in multiple digital field sites, gathers culturally embedded data, and analyses across sites to generate theoretically richer, transferable insights.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert V. Kozinets (netnography); comparative extension through multi-site online fieldwork practice","year":"Late 1990s–2000s (netnography ~1997; comparative extension ~2000s–2010s)","type":"Qualitative comparative research design","dataType":"Online text, images, videos, user-generated content from multiple communities","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.","type":"book","doi":null,"isbn":"978-1847875532","url":null},{"ref":"Kozinets, R. V. (2019). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1526458292","url":null}],"related":["netnography","comparative-ethnography","digital-ethnography","comparative-discourse-analysis","comparative-thematic-analysis","virtual-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-oral-history","name":"Comparative Oral history","fullName":"Comparative Oral History Research","aliases":["comparative oral history","cross-group oral history","comparative oral testimony","multi-site oral history"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–1990s (oral history discipline; comparative application developed through 1990s)","originator":"Alessandro Portelli, Paul Thompson (oral history tradition); comparative design adapted from cross-cultural qualitative research","url":"https://scholargate.app/en/qualitative/comparative-oral-history","markdownUrl":"https://scholargate.app/en/qualitative/comparative-oral-history.md","definition":"Comparative oral history collects and systematically compares first-person spoken testimonies from two or more distinct groups, communities, or historical contexts. The method blends the interpretive depth of oral history — privileging personal memory and narrative — with the analytical logic of comparative design, enabling researchers to identify both shared patterns and meaningful differences across the groups under study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alessandro Portelli, Paul Thompson (oral history tradition); comparative design adapted from cross-cultural qualitative research","year":"1970s–1990s (oral history discipline; comparative application developed through 1990s)","type":"Qualitative comparative research design","dataType":"Recorded and transcribed oral testimony, interviews, personal narratives","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Portelli, A. (1991). The Death of Luigi Trastulli and Other Stories: Form and Meaning in Oral History. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404997","url":null},{"ref":"Thompson, P. (2000). The Voice of the Past: Oral History (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0192893413","url":null}],"related":["oral-history","comparative-narrative-research","comparative-case-study","life-history-research","comparative-biographical-research","comparative-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-panel-research","name":"Comparative Panel Research","fullName":"Comparative Panel Research Design","aliases":["cross-national panel study","comparative longitudinal panel","pooled cross-sectional time-series design","multi-group panel design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s–1980s (formal integration of comparative and panel designs)","originator":"Developed across social science disciplines; seminal formalizations by Cheng Hsiao (panel econometrics) and Melvin Kohn (comparative sociology)","url":"https://scholargate.app/en/research-design/comparative-panel-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-panel-research.md","definition":"Comparative panel research tracks the same individuals, organizations, or macro-level units (e.g., countries, regions) across multiple time points while simultaneously comparing findings across two or more distinct groups or contexts. By combining the temporal depth of panel measurement with the analytical leverage of systematic comparison, this design can distinguish change processes that are universal from those that are context-specific — a capability neither pure panel nor single-sample longitudinal designs offer on their own.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed across social science disciplines; seminal formalizations by Cheng Hsiao (panel econometrics) and Melvin Kohn (comparative sociology)","year":"1970s–1980s (formal integration of comparative and panel designs)","type":"Quantitative longitudinal comparative design","dataType":"Repeated-measures numeric data from multiple distinct groups, countries, or units","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107038691","url":null},{"ref":"Kohn, M. L. (1987). Cross-national research as an analytic strategy. American Sociological Review, 52(6), 713–731.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cross-national+research+as+an+analytic+strategy+Kohn+1987"}],"related":["panel-research","longitudinal-research","comparative-research","cross-sectional-research","cohort-research","multilevel-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-phenomenology","name":"Comparative phenomenology","fullName":"Comparative Phenomenological Research","aliases":["cross-group phenomenology","multi-group phenomenological study","comparative phenomenological inquiry","contrastive phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Late 20th century (comparative applications prominent from the 1980s–1990s onward)","originator":"Edmund Husserl (foundational); systematised in comparative application by Amedeo Giorgi, Max van Manen, and others","url":"https://scholargate.app/en/qualitative/comparative-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/comparative-phenomenology.md","definition":"Comparative phenomenology applies phenomenological inquiry to two or more distinct groups, cultures, or contexts, explicitly contrasting how each group lives through and makes meaning of a shared phenomenon. Rather than describing a single unified essence, it reveals both common structures and meaningful differences in lived experience across comparison units. The approach is grounded in Husserlian and hermeneutic phenomenology but extends the standard single-group design into a structured cross-group analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edmund Husserl (foundational); systematised in comparative application by Amedeo Giorgi, Max van Manen, and others","year":"Late 20th century (comparative applications prominent from the 1980s–1990s onward)","type":"Qualitative comparative research design","dataType":"In-depth interviews, focus groups, written accounts (text data from two or more contrasting groups or contexts)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null},{"ref":"Giorgi, A. (2009). The Descriptive Phenomenological Method in Psychology: A Modified Husserlian Approach. Duquesne University Press.","type":"book","doi":null,"isbn":"978-0820704104","url":null}],"related":["phenomenology","hermeneutic-phenomenology","interpretive-phenomenological-analysis","comparative-case-study","cross-cultural-research","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-qualitative-content-analysis","name":"Comparative Qualitative content analysis","fullName":"Comparative Qualitative Content Analysis","aliases":["comparative QCA","cross-case qualitative content analysis","multi-context qualitative content analysis","comparative interpretive content analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1983 (Mayring's QCA foundation); comparative adaptations prominent from 2000s onward","originator":"Philipp Mayring (qualitative content analysis); comparative application developed across communication, policy, and social science research","url":"https://scholargate.app/en/qualitative/comparative-qualitative-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-qualitative-content-analysis.md","definition":"Comparative qualitative content analysis (comparative QCA) applies a systematic, category-driven reading of texts or documents across two or more cases, groups, time periods, or cultural contexts, with the explicit goal of identifying similarities, differences, and patterns that emerge from the comparison. It combines the interpretive rigour of qualitative content analysis with a structured comparative logic, making it valuable for cross-national policy research, media studies, and any inquiry that requires principled comparison of meaning across contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Philipp Mayring (qualitative content analysis); comparative application developed across communication, policy, and social science research","year":"1983 (Mayring's QCA foundation); comparative adaptations prominent from 2000s onward","type":"Qualitative research design and analysis strategy","dataType":"Textual documents, interview transcripts, media content, policy texts across multiple cases or groups","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage.","type":"book","doi":null,"isbn":"978-0857029201","url":null},{"ref":"Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research, 1(2), Art. 20.","type":"article","doi":null,"isbn":null,"url":"https://www.qualitative-research.net/index.php/fqs/article/view/1089"}],"related":["qualitative-content-analysis","comparative-discourse-analysis","comparative-thematic-analysis","comparative-case-study","critical-qualitative-content-analysis","comparative-narrative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-quantitative-content-analysis","name":"Comparative Quantitative Content Analysis","fullName":"Comparative Quantitative Content Analysis","aliases":["CQCA","cross-national content analysis","comparative media content analysis","systematic comparative content analysis"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1952 (Berelson); comparative extensions prominent from 1980s onward","originator":"Bernard Berelson (quantitative content analysis); Kimberly Neuendorf (codebook systematization); Hallin & Mancini (comparative media application)","url":"https://scholargate.app/en/research-design/comparative-quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/comparative-quantitative-content-analysis.md","definition":"Comparative quantitative content analysis is a systematic, replicable method for counting and categorizing features of communication content — such as news coverage, social media posts, or policy documents — across two or more groups, time periods, outlets, or countries. By applying a standardized codebook to each comparison context, it reveals patterns of similarity and difference in how topics, frames, actors, or sentiments are represented, and allows statistical testing of those differences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernard Berelson (quantitative content analysis); Kimberly Neuendorf (codebook systematization); Hallin & Mancini (comparative media application)","year":"1952 (Berelson); comparative extensions prominent from 1980s onward","type":"Quantitative observational research design","dataType":"Textual, visual, or audiovisual content (documents, news articles, social media posts, broadcasts)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Berelson, B. (1952). Content Analysis in Communication Research. Free Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Content+Analysis+in+Communication+Research+Berelson+1952"},{"ref":"Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage.","type":"book","doi":null,"isbn":"978-0761919773","url":null}],"related":["quantitative-content-analysis","comparative-survey-research","cross-sectional-quantitative-content-analysis","longitudinal-quantitative-content-analysis","descriptive-research","correlational-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-reflexive-thematic-analysis","name":"Comparative Reflexive Thematic Analysis","fullName":"Comparative Reflexive Thematic Analysis","aliases":["Comparative RTA","cross-group thematic analysis","comparative TA","comparative qualitative thematic comparison"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (reflexive TA); comparative application formalised ~2019–2021","originator":"Virginia Braun & Victoria Clarke","url":"https://scholargate.app/en/qualitative/comparative-reflexive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-reflexive-thematic-analysis.md","definition":"Comparative Reflexive Thematic Analysis (CRTA) applies Braun and Clarke's reflexive thematic analysis framework to data drawn from two or more distinct groups, time points, or contexts, with the explicit goal of contrasting thematic patterns across those groups. The reflexive element means the analyst continuously interrogates how their own perspectives and positioning shape the themes they construct, while the comparative element directs attention to differences and similarities between data sets rather than seeking a single unified account.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun & Victoria Clarke","year":"2006 (reflexive TA); comparative application formalised ~2019–2021","type":"Qualitative analytic approach","dataType":"Interview transcripts, focus group transcripts, documents, open-ended survey responses","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. Sage.","type":"book","doi":null,"isbn":"978-1473953406","url":null},{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null}],"related":["reflexive-thematic-analysis","thematic-analysis","framework-analysis","comparative-case-study","cross-case-analysis","qualitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-relational-survey","name":"Comparative Relational Survey","fullName":"Comparative Relational Survey Research","aliases":["comparative correlational survey","multi-group relational survey","cross-group relational survey design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century onward; systematized in educational research c. 1960s–1990s","originator":"Rooted in survey methodology tradition; formalized by scholars such as Fraenkel, Wallen, and Creswell","url":"https://scholargate.app/en/research-design/comparative-relational-survey","markdownUrl":"https://scholargate.app/en/research-design/comparative-relational-survey.md","definition":"A comparative relational survey is a quantitative, non-experimental design that examines the relationships among variables within a single study while simultaneously comparing those relationship patterns across two or more distinct groups. It extends a standard relational (correlational) survey by adding a comparative dimension, revealing whether associations observed in one group hold, differ, or even reverse in another. It is widely used in education, psychology, organizational behavior, and health sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in survey methodology tradition; formalized by scholars such as Fraenkel, Wallen, and Creswell","year":"Mid-20th century onward; systematized in educational research c. 1960s–1990s","type":"Quantitative non-experimental survey design","dataType":"Numeric scores, Likert-scale responses, or other quantitative survey measures from two or more comparison groups","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073525 670","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["relational-survey","comparative-survey-research","correlational-research","cross-sectional-correlational-research","causal-comparative-research","multivariate-correlational-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-semiotic-analysis","name":"Comparative Semiotic Analysis","fullName":"Comparative Semiotic Analysis","aliases":["cross-cultural semiotics","comparative sign analysis","comparative semiology","CSA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Early 20th century (Saussure 1916; Peirce c. 1900); comparative framing consolidated from 1970s onward","originator":"Ferdinand de Saussure (semiology), Charles Sanders Peirce (semiotics); comparative application developed across cultural and communication studies","url":"https://scholargate.app/en/qualitative/comparative-semiotic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-semiotic-analysis.md","definition":"Comparative semiotic analysis examines how signs, symbols, and meaning-making systems operate across two or more contexts — such as different cultures, historical periods, media platforms, or social groups. By applying semiotic frameworks (denotation, connotation, myth, codes, paradigms) systematically across parallel corpora, researchers reveal how the same sign produces different meanings, how ideologies are encoded differently, or how symbolic structures converge and diverge across settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ferdinand de Saussure (semiology), Charles Sanders Peirce (semiotics); comparative application developed across cultural and communication studies","year":"Early 20th century (Saussure 1916; Peirce c. 1900); comparative framing consolidated from 1970s onward","type":"Qualitative comparative analysis","dataType":"Texts, images, advertisements, media artifacts, cultural symbols, discourse samples from two or more contexts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Chandler, D. (2007). Semiotics: The Basics (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415363754","url":null},{"ref":"Barthes, R. (1972). Mythologies. Hill and Wang. (Original work published 1957, trans. A. Lavers).","type":"book","doi":null,"isbn":"978-0374521509","url":null}],"related":["semiotic-analysis","comparative-discourse-analysis","comparative-content-analysis","comparative-visual-analysis","critical-semiotic-analysis","interpretive-semiotic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-straussian-grounded-theory","name":"Comparative Straussian Grounded Theory","fullName":"Comparative Straussian Grounded Theory","aliases":["Strauss-Corbin comparative GT","comparative systematic grounded theory","multi-site Straussian GT","comparative grounded theory (Straussian)"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (discovery); systematic Straussian procedures codified 1990/1998","originator":"Anselm Strauss & Juliet Corbin (Straussian GT); comparative extension built on Glaser & Strauss (1967)","url":"https://scholargate.app/en/qualitative/comparative-straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/comparative-straussian-grounded-theory.md","definition":"Comparative Straussian Grounded Theory applies the systematic open–axial–selective coding framework of Strauss and Corbin across two or more purposively selected contexts, groups, or sites to generate theory that explains both within-context processes and cross-context variation. The constant comparative method — the analytic engine first described by Glaser and Strauss (1967) — is elevated to a deliberate design-level strategy, allowing researchers to build mid-range theory that accounts for how social processes unfold differently under varying conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss & Juliet Corbin (Straussian GT); comparative extension built on Glaser & Strauss (1967)","year":"1967 (discovery); systematic Straussian procedures codified 1990/1998","type":"Qualitative comparative research design","dataType":"Interviews, observations, documents (text and field notes) across multiple contexts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0803959408","url":null},{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202300450","url":null}],"related":["grounded-theory","classic-grounded-theory","constructivist-grounded-theory","comparative-case-study","thematic-analysis","comparative-qualitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-survey-research","name":"Comparative Survey Research","fullName":"Comparative Survey Research Design","aliases":["comparative survey design","cross-group survey","multi-group survey research","comparative questionnaire study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century onward","originator":"Rooted in survey methodology traditions (Gallup, Likert, Lazarsfeld mid-20th century); comparative extension codified in social science research methods literature","url":"https://scholargate.app/en/research-design/comparative-survey-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-survey-research.md","definition":"Comparative survey research is a quantitative non-experimental design that systematically collects structured survey data from two or more clearly defined groups, populations, or contexts in order to identify, describe, and analyze similarities and differences among them. It extends basic survey research by making comparison the explicit organizing logic: rather than characterizing a single population, the goal is to detect how attitudes, behaviors, or outcomes vary across groups defined by nationality, culture, profession, demographic category, or time period.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in survey methodology traditions (Gallup, Likert, Lazarsfeld mid-20th century); comparative extension codified in social science research methods literature","year":"Mid-20th century onward","type":"Quantitative non-experimental research design","dataType":"Structured survey data (Likert scales, closed-ended items, numeric responses) from two or more groups or populations","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452259000","url":null},{"ref":"Babbie, E. (2016). The Practice of Social Research (14th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1305104945","url":null}],"related":["survey-research","cross-sectional-research","causal-comparative-research","longitudinal-survey-research","descriptive-research","correlational-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-thematic-analysis","name":"Comparative Thematic Analysis","fullName":"Comparative Thematic Analysis","aliases":["cross-group thematic analysis","comparative TA","multi-group thematic analysis","comparative qualitative thematic analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (as an explicit comparative variant of thematic analysis)","originator":"Virginia Braun & Victoria Clarke (thematic analysis foundation); comparative extension developed in applied policy and cross-cultural qualitative research traditions","url":"https://scholargate.app/en/qualitative/comparative-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-thematic-analysis.md","definition":"Comparative Thematic Analysis applies the structured procedures of thematic analysis across two or more distinct groups, sites, or time points, with the explicit aim of identifying both shared patterns and meaningful differences. Rather than producing a single composite account of experience, it yields a layered analysis that maps where themes converge and diverge across comparison units — making it especially valuable for policy-relevant, cross-cultural, or multi-site qualitative studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun & Victoria Clarke (thematic analysis foundation); comparative extension developed in applied policy and cross-cultural qualitative research traditions","year":"2000s–2010s (as an explicit comparative variant of thematic analysis)","type":"Qualitative comparative analytical strategy","dataType":"Interview transcripts, focus group records, documents — across two or more groups, sites, or contexts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Ritchie, J., & Spencer, L. (1994). Qualitative data analysis for applied policy research. In A. Bryman & R. G. Burgess (Eds.), Analysing Qualitative Data (pp. 173–194). Routledge.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Qualitative+data+analysis+for+applied+policy+research+Ritchie+Spencer+1994"}],"related":["thematic-analysis","reflexive-thematic-analysis","comparative-case-study","comparative-content-analysis","comparative-discourse-analysis","framework-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-trend-research","name":"Comparative Trend Research","fullName":"Comparative Trend Research Design","aliases":["comparative trend study","multi-group trend study","cross-group trend analysis","comparative longitudinal survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s–1990s (formalized alongside longitudinal and trend designs)","originator":"Developed within the survey research tradition; comparative extension attributed broadly to Babbie, Creswell, and related methodologists","url":"https://scholargate.app/en/research-design/comparative-trend-research","markdownUrl":"https://scholargate.app/en/research-design/comparative-trend-research.md","definition":"Comparative trend research is a quantitative non-experimental design that tracks changes in one or more variables over time within two or more distinct groups or populations. By drawing independent cross-sectional samples from each group at multiple time points, it reveals whether trends diverge, converge, or differ in magnitude across groups — answering not just 'is this changing?' but 'is it changing differently for different populations?'","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within the survey research tradition; comparative extension attributed broadly to Babbie, Creswell, and related methodologists","year":"1970s–1990s (formalized alongside longitudinal and trend designs)","type":"Quantitative non-experimental design","dataType":"Repeated cross-sectional survey data from two or more groups","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Creswell, J. W. (2002). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761924425","url":null},{"ref":"Babbie, E. R. (1990). Survey Research Methods (2nd ed.). Wadsworth Publishing.","type":"book","doi":null,"isbn":"978-0534126728","url":null}],"related":["trend-research","longitudinal-research","comparative-research","cross-sectional-research","cohort-research","panel-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-typological-analysis","name":"Comparative Typological Analysis","fullName":"Comparative Typological Analysis","aliases":["cross-typological comparison","typological comparative method","comparative typology","CTA"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Late 19th–early 20th century (formalized across disciplines)","originator":"Various (Linnaeus in biology; Franz Boas, Edward Sapir in anthropology/linguistics; Gordon Childe in archaeology)","url":"https://scholargate.app/en/field-methods/comparative-typological-analysis","markdownUrl":"https://scholargate.app/en/field-methods/comparative-typological-analysis.md","definition":"Comparative typological analysis is a systematic method for classifying phenomena into types and then examining how those types differ, overlap, or share structural features across multiple cases, contexts, or cultures. Widely applied in linguistics, archaeology, law, and the social sciences, it moves beyond single-case typology by placing type systems in dialogue with one another to identify cross-cutting patterns, universals, or culturally specific configurations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Linnaeus in biology; Franz Boas, Edward Sapir in anthropology/linguistics; Gordon Childe in archaeology)","year":"Late 19th–early 20th century (formalized across disciplines)","type":"Comparative qualitative/analytical method","dataType":"Artifacts, texts, linguistic forms, institutional categories, cultural objects","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Comrie, B. (1989). Language Universals and Linguistic Typology: Syntax and Morphology (2nd ed.). University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226114330","url":null},{"ref":"Adams, W. Y., & Adams, E. W. (1991). Archaeological Typology and Practical Reality: A Dialectical Approach to Artifact Classification and Sorting. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521038744","url":null}],"related":["typological-analysis","comparative-historical-archival-research","hermeneutic-analysis","comparative-case-law-analysis","content-analysis","cross-case-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"comparative-visual-analysis","name":"Comparative Visual analysis","fullName":"Comparative Visual Analysis","aliases":["cross-case visual analysis","comparative image analysis","comparative visual methods","CVA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1986–2001 (systematic codification in social research)","originator":"Gillian Rose (systematic visual methods); John Collier Jr. (visual anthropology)","url":"https://scholargate.app/en/qualitative/comparative-visual-analysis","markdownUrl":"https://scholargate.app/en/qualitative/comparative-visual-analysis.md","definition":"Comparative Visual Analysis is a qualitative research design that systematically examines and compares visual materials — photographs, videos, artworks, advertisements, or digital images — across two or more cases, groups, time points, or contexts. By applying a consistent analytical framework to multiple visual corpora, the approach reveals similarities, differences, and patterns that would remain invisible when studying a single set of images alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gillian Rose (systematic visual methods); John Collier Jr. (visual anthropology)","year":"1986–2001 (systematic codification in social research)","type":"Qualitative comparative research design","dataType":"Photographs, film, video, artwork, advertisements, social media images, diagrams","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473942028","url":null},{"ref":"Collier, J., & Collier, M. (1986). Visual Anthropology: Photography as a Research Method. University of New Mexico Press.","type":"book","doi":null,"isbn":"978-0826308993","url":null}],"related":["visual-analysis","comparative-content-analysis","comparative-discourse-analysis","semiotic-analysis","comparative-ethnography","multimodal-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"compassion-fatigue-scale","name":"Professional Quality of Life Scale","fullName":"Professional Quality of Life Scale (ProQOL) / Compassion Fatigue Scale","aliases":["ProQOL","ProQOL-5","Compassion Fatigue Scale"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"Occupational wellbeing and compassion fatigue in helping professions","year":"2005","originator":"Beth Hamovitch Stamm","url":"https://scholargate.app/en/trauma-psychology/compassion-fatigue-scale","markdownUrl":"https://scholargate.app/en/trauma-psychology/compassion-fatigue-scale.md","definition":"The ProQOL is a 30-item self-report instrument measuring both negative (compassion fatigue, secondary traumatic stress) and positive (compassion satisfaction) dimensions of occupational wellbeing in helping professionals. Developed by Stamm in 2005, the ProQOL conceptualizes professional quality of life holistically—capturing not only the burden of helping work but also its rewards and meaning. The scale is widely used in occupational health research, organizational assessment, and intervention evaluation across healthcare, mental health, social services, and disaster response fields.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Beth Hamovitch Stamm","subfamily":"Occupational wellbeing and compassion fatigue in helping professions","year":"2005","type":"Self-report questionnaire"},"citations":[{"ref":"Stamm, B. H. (2010). The Concise ProQOL Manual (2nd ed.). ProQOL.org.","type":"article","doi":null,"isbn":null,"url":"https://www.proqol.org"},{"ref":"Stamm, B. H. (2005). The ProQOL: A tool to measure compassion fatigue and compassion satisfaction. In C. A. Figley (Ed.), Treating compassion fatigue (pp. 29-48). Brunner-Routledge.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/16240524"}],"related":["secondary-traumatic-stress-scale","burnout-assessment-tool","perceived-stress-reactivity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"competing-risks","name":"Competing Risks Analysis","fullName":"Competing Risks Survival Analysis","aliases":["Rekabet Eden Riskler Analizi","cumulative incidence function","CIF analysis","cause-specific survival analysis"],"domain":"survival","family":"survival","subfamily":null,"year":1999,"originator":"Fine, J.P. & Gray, R.J.","url":"https://scholargate.app/en/survival/competing-risks","markdownUrl":"https://scholargate.app/en/survival/competing-risks.md","definition":"Competing risks analysis, formalized by Fine and Gray in 1999, is a survival analysis framework for settings where a subject can experience one of several mutually exclusive event types. The key quantity is the cumulative incidence function (CIF), which estimates the probability of a specific event occurring by time t in the presence of the other competing events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fine, J.P. & Gray, R.J.","year":1999,"type":"Competing risks survival model","framework":"Subdistribution hazard (Fine-Gray)","handles":"Right-censoring with competing events","minSample":100,"difficulty":3},"citations":[{"ref":"Fine, J.P. & Gray, R.J. (1999). A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association, 94(446), 496–509.","type":"article","doi":"10.1080/01621459.1999.10474144","isbn":null,"url":null}],"related":["kaplan-meier","fine-gray-competing-risks","cox-ph","nelson-aalen","log-rank-test","bayesian-survival"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"competitive-state-anxiety-inventory","name":"Competitive State Anxiety Inventory-2","fullName":"Competitive State Anxiety Inventory-2 (CSAI-2)","aliases":["CSAI-2","Competitive State Anxiety"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"anxiety-and-confidence","year":"1990","originator":"Rainer Martens, Robin Vealey, Damon Burton","url":"https://scholargate.app/en/sport-psychology/competitive-state-anxiety-inventory","markdownUrl":"https://scholargate.app/en/sport-psychology/competitive-state-anxiety-inventory.md","definition":"The CSAI-2 is a 27-item instrument measuring three dimensions of state anxiety in sport: cognitive anxiety (worry), somatic anxiety (physiological arousal), and self-confidence. Developed by Martens and colleagues in 1990, it has become the gold standard for assessing pre-competition psychological state and is widely used in sport psychology research, coaching, and athlete support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rainer Martens, Robin Vealey, Damon Burton","subfamily":"anxiety-and-confidence","year":"1990","type":"Self-report state anxiety questionnaire"},"citations":[{"ref":"Martens, R., Vealey, R. S., Burton, D., Bump, L. A., & Smith, D. E. (1990). Development and validation of the Competitive State Anxiety Inventory-2. In R. Martens, R. S. Vealey, & D. Burton (Eds.), Competitive Anxiety in Sport (pp. 193–218). Champaign, IL: Human Kinetics.","type":"book","doi":null,"isbn":null,"url":"https://books.google.com/books/about/Competitive_Anxiety_in_Sport.html"}],"related":["profile-of-mood-states","sport-anxiety-scale","sport-confidence-inventory","mental-toughness-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"completely-randomized-design","name":"Completely Randomized Design","fullName":"Completely Randomized Design (CRD)","aliases":["CRD","completely randomised design","one-way experimental design","Tam Tesadüf Deneme Deseni (CRD)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1935,"originator":"R. A. Fisher","url":"https://scholargate.app/en/experimental-design/completely-randomized-design","markdownUrl":"https://scholargate.app/en/experimental-design/completely-randomized-design.md","definition":"The completely randomized design is the most fundamental experimental design, in which experimental units are assigned to treatments entirely at random with no restrictions. Analysed by one-way ANOVA, it was formalised by R. A. Fisher in the 1930s and remains the reference starting point for experimental research whenever the experimental material is homogeneous and nuisance variation is absent or negligible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher","year":1935,"family":"Experimental design","type":"Parametric group comparison via one-way ANOVA","groups":"2 or more","outcome":"continuous","parametric":true,"distribution":"F","df":"k-1 (treatment), N-k (error)","minSamplePerGroup":10,"difficulty":1},"citations":[{"ref":"Montgomery, D.C. (2017). Design and Analysis of Experiments. Wiley.","type":"book","doi":null,"isbn":"978-1119320937","url":null},{"ref":"Cochran, W.G. & Cox, G.M. (1957). Experimental Designs. Wiley.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/experimental-designs/oclc/253444"}],"related":["randomized-complete-block-design","one-way-anova","factorial-design","kruskal-wallis","tukey-hsd"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"component-garch","name":"Component GARCH","fullName":"Component-Based GARCH Model","aliases":["Volatility components model"],"domain":"econometrics","family":"regression-model","subfamily":"Multi-scale volatility","year":"1999","originator":"Engle and Lee","url":"https://scholargate.app/en/econometrics/component-garch","markdownUrl":"https://scholargate.app/en/econometrics/component-garch.md","definition":"Component GARCH decomposes conditional variance into transitory (short-term) and permanent (long-term) components with different dynamics, allowing flexibility in capturing volatility behavior at multiple frequencies. Introduced by Engle and Lee (1999), it elegantly models the empirical finding that volatility exhibits both rapid mean-reversion (daily shocks) and slow mean-reversion (level shifts). This framework is crucial for understanding volatility persistence and improving long-horizon volatility forecasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle and Lee","subfamily":"Multi-scale volatility","year":"1999","type":"Decomposed variance model"},"citations":[{"ref":"Engle, R. F., & Lee, G. (1999). A permanent and transitory component model of stock return volatility. Journal of Political Economy, 107(6), 1363-1384.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+permanent+and+transitory+component+model+of+stock+return+volatility+Engle"},{"ref":"Ling, S., & McAleer, M. (2003). Asymptotic theory and inference for dynamic conditional distribution models. Journal of Econometrics, 106(1), 119-135.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Asymptotic+theory+and+inference+for+dynamic+conditional+distribution+models+Ling"}],"related":["garch-midas","dcc-midas","causality-in-variance-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"compositional-data-analysis","name":"Compositional Data Analysis","fullName":"Compositional Data Analysis (CoDA)","aliases":["CoDA","Simplex Analysis","Log-Ratio Analysis","Bileşim Veri Analizi"],"domain":"statistics","family":"regression-model","subfamily":"Compositional data","year":1982,"originator":"John Aitchison","url":"https://scholargate.app/en/statistics/compositional-data-analysis","markdownUrl":"https://scholargate.app/en/statistics/compositional-data-analysis.md","definition":"Compositional Data Analysis (CoDA) is a branch of multivariate statistics designed for data that represent parts of a whole — proportions, percentages, or concentrations that sum to a constant. Introduced by John Aitchison in his landmark 1982 paper, CoDA recognises that standard Euclidean methods fail on the simplex and instead operates through log-ratio transformations that respect the relative nature of compositional information.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Aitchison","year":1982,"type":"Constrained multivariate statistical method","subfamily":"Compositional data","sample_space":"Simplex S^D","core_transform":"Log-ratio transformation"},"citations":[{"ref":"Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B, 44(2), 139–177.","type":"article","doi":"10.1111/j.2517-6161.1982.tb01195.x","isbn":null,"url":null}],"related":["principal-component-analysis","multiple-linear-regression","symbolic-data-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"compressive-sensing","name":"Compressive Sensing","fullName":"Compressive Sensing (Compressed Sensing) Signal Acquisition","aliases":["Compressed Sensing","CS","Sparse Recovery","Sub-Nyquist Sampling"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Sub-Nyquist acquisition","year":"2006","originator":"Emmanuel Candès, Justin Romberg, and Terence Tao","url":"https://scholargate.app/en/signal-processing/compressive-sensing","markdownUrl":"https://scholargate.app/en/signal-processing/compressive-sensing.md","definition":"Compressive Sensing (CS) is a signal acquisition and reconstruction technique that exploits signal sparsity to recover high-resolution signals from far fewer samples than required by the Nyquist sampling theorem. Developed by Emmanuel Candès, Justin Romberg, and Terence Tao in 2006, compressive sensing challenges the traditional sampling paradigm by showing that signals with sparse representations can be reconstructed from sub-Nyquist random measurements using nonlinear optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emmanuel Candès, Justin Romberg, and Terence Tao","subfamily":"Sub-Nyquist acquisition","year":"2006","type":"Sparse signal recovery"},"citations":[{"ref":"Candes, E. J., Romberg, J., & Tao, T. (2006). Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete and Inaccurate Measurements. IEEE Transactions on Information Theory, 52(2), 489–509.","type":"article","doi":"10.1109/TIT.2005.862083","isbn":null,"url":null},{"ref":"Eldar, Y. C., & Kutyniok, G. (2012). Compressed Sensing: Theory and Applications. Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.cambridge.org/core/books/compressed-sensing/E53D4F15AF73A97DED44FBD8EBB0AAEE"}],"related":["fir-filter-design","short-time-fourier-transform","power-spectral-density","adaptive-lms-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"compromise-programming","name":"COMPROMISE-PROGRAMMING","fullName":"Compromise Programming — Lp-metric distance to ideal solution","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1973","originator":"Zeleny, M.","url":"https://scholargate.app/en/decision-making/compromise-programming","markdownUrl":"https://scholargate.app/en/decision-making/compromise-programming.md","definition":"COMPROMISE-PROGRAMMING (Compromise Programming — Lp-metric distance to ideal solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Zeleny, M. in 1973. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zeleny, M.","subfamily":"Ranking","year":"1973","type":"Distance-based — Lp metric to Pareto-optimal ideal (parametric p)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Zeleny, M. (1973). Compromise programming. In: Multiple Criteria Decision Making (Cochrane & Zeleny, eds.), Univ. of South Carolina Press","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Compromise+programming+Zeleny"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computer-anxiety-scale","name":"Computer Anxiety Scale","fullName":"Computer Anxiety Rating Scale (CARS)","aliases":["CARS","Computer Anxiety Rating Scale"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"1987","originator":"Rosen, Sears & Weil","url":"https://scholargate.app/en/information-systems/computer-anxiety-scale","markdownUrl":"https://scholargate.app/en/information-systems/computer-anxiety-scale.md","definition":"The Computer Anxiety Rating Scale (CARS) was developed by Rosen, Sears, and Weil in 1987 to measure the emotional distress and fear individuals experience when thinking about using computers or engaging with computer technology. CARS is a foundational instrument in understanding psychological barriers to technology adoption and has been widely applied across education, workplace training, and organizational digital transformation contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosen, Sears & Weil","subfamily":"Technology adoption","year":"1987","type":"Likert-scale anxiety measure"},"citations":[{"ref":"Rosen, L. D., Sears, D. C., & Weil, M. M. (1987). Computerphobia. Journal of School Psychology, 25(3), 221-232.","type":"article","doi":"10.3758/bf03203781","isbn":null,"url":null},{"ref":"Weil, M. M., & Rosen, L. D. (1995). The psychological impact of technology from a historical perspective. Computers in Human Behavior, 11(1), 3-15.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+psychological+impact+of+technology+from+a+historical+perspective+Weil"}],"related":["technology-readiness-index","online-trust-scale","technostress-scale","tam-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-construct-validity","name":"Computerized adaptive test construct validity","fullName":"Construct Validity in Computerized Adaptive Testing","aliases":["CAT construct validity","adaptive test construct validation","CAT validity evidence","construct validity evidence in CAT"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1989–2000s","originator":"Samuel Messick (unified validity framework); CAT application formalized by Wainer, van der Linden, and colleagues","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-construct-validity","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-construct-validity.md","definition":"Construct validity in computerized adaptive testing evaluates whether the latent trait estimates produced by a CAT instrument genuinely measure the intended psychological or educational construct. Because adaptive algorithms select items individually for each examinee, the validity evidence gathered must account for the variable item exposure and the IRT-based scoring that are unique to CAT administrations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Samuel Messick (unified validity framework); CAT application formalized by Wainer, van der Linden, and colleagues","year":"1989–2000s","type":"Validity evaluation / psychometric evidence gathering","dataType":"Item response data from adaptive administrations; latent trait estimates; fit statistics","subfamily":"Scale / measurement"},"citations":[{"ref":"Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational Measurement (3rd ed., pp. 13–103). American Council on Education / Macmillan.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Messick+1989+Validity+Educational+Measurement"},{"ref":"van der Linden, W. J. & Glas, C. A. W. (Eds.). (2010). Elements of Adaptive Testing. Springer.","type":"book","doi":null,"isbn":"978-0387854595","url":null}],"related":["computerized-adaptive-test-item-response-theory","computerized-adaptive-test-measurement-invariance","construct-validity","confirmatory-factor-analysis","differential-item-functioning","computerized-adaptive-test-convergent-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-content-validity","name":"Computerized Adaptive Test Content Validity","fullName":"Content Validity in Computerized Adaptive Testing","aliases":["CAT content validity","adaptive item bank content coverage","content balancing in CAT","CAT blueprint validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1975 / 1980","originator":"Lawshe (content validity); Lord & Weiss (CAT framework)","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-content-validity","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-content-validity.md","definition":"Content validity in computerized adaptive testing (CAT) ensures that an adaptively administered assessment adequately samples the intended content domain despite delivering only a subset of items to each examinee. It integrates classical content validity methods with CAT-specific item bank design and content balancing algorithms to guarantee representative domain coverage at both the item bank and the individual test level.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lawshe (content validity); Lord & Weiss (CAT framework)","year":"1975 / 1980","type":"Validity evaluation / test design","dataType":"Expert ratings, item metadata, item bank blueprints","subfamily":"Scale / measurement"},"citations":[{"ref":"Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+quantitative+approach+to+content+validity+Lawshe"},{"ref":"van der Linden, W. J. & Glas, C. A. W. (Eds.). (2010). Elements of Adaptive Testing. Springer.","type":"book","doi":"10.1007/978-0-387-85461-8","isbn":null,"url":null}],"related":["content-validity","item-response-theory","computerized-adaptive-test-item-response-theory","construct-validity","computerized-adaptive-test-construct-validity","differential-item-functioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-convergent-validity","name":"Computerized Adaptive Test Convergent Validity","fullName":"Convergent Validity Assessment for Computerized Adaptive Tests","aliases":["CAT convergent validity","adaptive test construct validation","CAT validity evidence","convergent validity in CAT"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1989–2000","originator":"Samuel Messick (validity framework); Wainer and colleagues (CAT context)","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-convergent-validity","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-convergent-validity.md","definition":"Convergent validity assessment for computerized adaptive tests (CATs) examines whether the ability or trait estimates produced by an adaptive algorithm correlate substantially with scores from other measures of the same construct. Because each examinee receives a different subset of items in a CAT, demonstrating that the resulting scores still converge with theoretically related external measures is a critical step in establishing construct validity evidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Samuel Messick (validity framework); Wainer and colleagues (CAT context)","year":"1989–2000","type":"Validity evidence / construct validation","dataType":"CAT scores, parallel fixed-form scores, external criterion measures","subfamily":"Scale / measurement"},"citations":[{"ref":"Wainer, H. (Ed.). (2000). Computerized Adaptive Testing: A Primer (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805835113","url":null},{"ref":"Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational Measurement (3rd ed., pp. 13–103). American Council on Education / Macmillan.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Messick+1989+Validity+Educational+Measurement"}],"related":["convergent-validity","discriminant-validity","computerized-adaptive-test-item-response-theory","computerized-adaptive-test-confirmatory-factor-analysis","computerized-adaptive-test-construct-validity","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-cronbachs-alpha","name":"CAT Cronbach's Alpha","fullName":"Computerized Adaptive Test Cronbach's Alpha","aliases":["CAT reliability estimation","adaptive test internal consistency","CAT coefficient alpha","reliability in CAT"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1984","originator":"Adapted from Cronbach (1951); CAT reliability framing developed by Green, Bock, Humphreys, Linn & Reckase (1984)","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-cronbachs-alpha","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-cronbachs-alpha.md","definition":"Cronbach's alpha applied to computerized adaptive test (CAT) data estimates internal consistency reliability under the special condition that different examinees receive different subsets of items. Because the classic formula assumes every respondent answers the same items, its direct application to CAT data violates core assumptions and typically underestimates or misrepresents true reliability, requiring careful adaptation or replacement with IRT-based reliability indices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from Cronbach (1951); CAT reliability framing developed by Green, Bock, Humphreys, Linn & Reckase (1984)","year":"1984","type":"Reliability / internal consistency estimation","dataType":"Dichotomous or polytomous item responses from adaptive administration","subfamily":"Scale / measurement"},"citations":[{"ref":"Green, B. F., Bock, R. D., Humphreys, L. G., Linn, R. L., & Reckase, M. D. (1984). Technical guidelines for assessing computerized adaptive tests. Journal of Educational Measurement, 21(4), 347–360.","type":"article","doi":"10.1111/j.1745-3984.1984.tb01039.x","isbn":null,"url":null},{"ref":"Weiss, D. J. (1982). Improving measurement quality and efficiency with adaptive testing. Applied Psychological Measurement, 6(4), 473–492.","type":"article","doi":"10.1177/014662168200600408","isbn":null,"url":null}],"related":["cronbach-alpha","computerized-adaptive-testing","item-response-theory","mcdonalds-omega","test-information-function","reliability-generalization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-differential-item-functioning","name":"CAT-DIF","fullName":"Computerized Adaptive Test Differential Item Functioning","aliases":["CAT DIF analysis","adaptive test DIF","DIF in computerized adaptive testing","CAT item bias detection"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Wainer, Zwick, and colleagues in the CAT and DIF literatures","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-differential-item-functioning.md","definition":"CAT-DIF identifies items in a computerized adaptive test that behave differently across demographic or group subpopulations after controlling for overall ability. Because adaptive algorithms select items non-randomly based on each examinee's estimated proficiency, standard DIF detection methods require adjustment before they can be validly applied in this context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wainer, Zwick, and colleagues in the CAT and DIF literatures","year":"1990s–2000s","type":"Item bias detection in adaptive testing","dataType":"Item response data from computerized adaptive administrations","subfamily":"Scale / measurement"},"citations":[{"ref":"Zwick, R., Thayer, D. T., & Mazzeo, J. (1997). Describing and categorizing DIF in polytomous items. Journal of Educational Measurement, 34(4), 261–285.","type":"article","doi":"10.1002/j.2333-8504.1997.tb01726.x","isbn":null,"url":null},{"ref":"Wainer, H. (Ed.). (2000). Computerized Adaptive Testing: A Primer (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805835113","url":null}],"related":["differential-item-functioning","computerized-adaptive-test-item-response-theory","item-response-theory","computerized-adaptive-test-measurement-invariance","computerized-adaptive-test-item-analysis","multi-group-differential-item-functioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-discriminant-validity","name":"Computerized adaptive test discriminant validity","fullName":"Discriminant Validity in Computerized Adaptive Testing","aliases":["CAT discriminant validity","adaptive test divergent validity","CAT scale differentiation","CAT construct separation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1959 (discriminant validity); CAT application from 1990s onward","originator":"Campbell & Fiske (discriminant validity framework); applied to CAT by educational measurement researchers","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-discriminant-validity","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-discriminant-validity.md","definition":"Discriminant validity in computerized adaptive testing (CAT) is the evaluation process confirming that a CAT-administered scale measures its intended construct distinctly from related but conceptually different constructs. Despite the adaptive item-selection mechanism varying each respondent's item set, evidence must be provided that CAT-derived scores do not overlap excessively with scores from theoretically distinct scales.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Campbell & Fiske (discriminant validity framework); applied to CAT by educational measurement researchers","year":"1959 (discriminant validity); CAT application from 1990s onward","type":"Validity evaluation technique","dataType":"Adaptive item response data; scores from CAT and reference scales","subfamily":"Scale / measurement"},"citations":[{"ref":"Weiss, D. J. (2004). Computerized adaptive testing for effective and efficient measurement in counseling and education. Measurement and Evaluation in Counseling and Development, 37(2), 70–84.","type":"article","doi":"10.1080/07481756.2004.11909751","isbn":null,"url":null},{"ref":"Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.","type":"article","doi":"10.1037/h0046016","isbn":null,"url":null}],"related":["discriminant-validity","convergent-validity","computerized-adaptive-test-convergent-validity","computerized-adaptive-test-construct-validity","item-response-theory","multitrait-multimethod-matrix"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-generalizability-theory","name":"CAT Generalizability Theory","fullName":"Computerized Adaptive Test Generalizability Theory","aliases":["CAT G-theory","adaptive test generalizability","G-theory in CAT","computerized adaptive generalizability analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1972 (G-theory); CAT application 1990s–2000s","originator":"Lee J. Cronbach (G-theory); applied to CAT by Brennan and others","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-generalizability-theory","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-generalizability-theory.md","definition":"Generalizability theory (G-theory) applied to computerized adaptive testing (CAT) evaluates the dependability of adaptive test scores by decomposing score variance across measurement facets such as persons, items, and occasions. Unlike classical test theory, G-theory quantifies multiple simultaneous sources of measurement error, offering a richer reliability picture for adaptively administered assessments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach (G-theory); applied to CAT by Brennan and others","year":"1972 (G-theory); CAT application 1990s–2000s","type":"Reliability / generalizability analysis","dataType":"Item response data from adaptive test administrations","subfamily":"Scale / measurement"},"citations":[{"ref":"Brennan, R. L. (2001). Generalizability Theory. Springer.","type":"book","doi":null,"isbn":"978-0387952826","url":null},{"ref":"Van der Linden, W. J., & Glas, C. A. W. (2000). Computerized adaptive testing: Theory and practice. Kluwer Academic Publishers.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Computerized+Adaptive+Testing+Theory+and+Practice+van+der+Linden+Glas+2000"}],"related":["generalizability-theory","item-response-theory","computerized-adaptive-test-reliability-analysis","computerized-adaptive-test-item-response-theory","test-retest-reliability","multilevel-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-item-analysis","name":"Computerized adaptive test item analysis","fullName":"Computerized Adaptive Test Item Analysis","aliases":["CAT item analysis","adaptive item calibration","IRT-based CAT item evaluation","adaptive item parameter estimation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1970s–1990s","originator":"Lord, Weiss, and colleagues in psychometric research on adaptive testing","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-item-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-item-analysis.md","definition":"Computerized adaptive test item analysis evaluates and calibrates items intended for use in adaptive testing environments. Unlike fixed-form analysis, it accounts for the non-random item exposure inherent in adaptive administration, using item response theory to estimate item parameters, information functions, and exposure rates across the ability continuum.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lord, Weiss, and colleagues in psychometric research on adaptive testing","year":"1970s–1990s","type":"Item calibration and evaluation","dataType":"Dichotomous or polytomous item response data from adaptive sessions","subfamily":"Scale / measurement"},"citations":[{"ref":"van der Linden, W. J. & Glas, C. A. W. (Eds.) (2000). Computerized Adaptive Testing: Theory and Practice. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-0792365556","url":null},{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null}],"related":["item-response-theory","computerized-adaptive-test-scale-development","differential-item-functioning","computerized-adaptive-test-reliability-analysis","rasch-model","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-item-response-theory","name":"Computerized adaptive test item response theory","fullName":"Computerized Adaptive Testing based on Item Response Theory","aliases":["CAT-IRT","adaptive testing","IRT-based CAT","computerized adaptive testing"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1970s–1980s","originator":"Lord, F. M.; further developed by Wainer, van der Linden, and others","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-item-response-theory","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-item-response-theory.md","definition":"Computerized adaptive testing based on item response theory is a sequential measurement procedure in which a computer algorithm selects successive test items tailored to each examinee's estimated ability level. Drawing on IRT to model item characteristics and ability estimation, CAT delivers precise scores with far fewer items than fixed-length tests, making it efficient for high-stakes assessments, clinical screening, and large-scale surveys.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lord, F. M.; further developed by Wainer, van der Linden, and others","year":"1970s–1980s","type":"Adaptive measurement / sequential testing","dataType":"Dichotomous or polytomous item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Wainer, H. (Ed.). (2000). Computerized Adaptive Testing: A Primer (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805835113","url":null},{"ref":"van der Linden, W. J., & Glas, C. A. W. (Eds.). (2010). Elements of Adaptive Testing. Springer.","type":"book","doi":"10.1007/978-0-387-85461-8","isbn":null,"url":null}],"related":["item-response-theory","rasch-model","maximum-information-criterion","three-parameter-logistic-model","exploratory-factor-analysis","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-mcdonalds-omega","name":"CAT McDonald's Omega","fullName":"Computerized Adaptive Test McDonald's Omega","aliases":["CAT omega reliability","omega in adaptive testing","hierarchical omega for CAT","CAT composite reliability"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1999 (omega); CAT application 2000s–2010s","originator":"Roderick P. McDonald (omega); CAT-omega application extended by IRT and psychometric reliability researchers","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-mcdonalds-omega","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-mcdonalds-omega.md","definition":"McDonald's omega adapted for computerized adaptive testing (CAT) quantifies the reliability of ability or trait estimates when different examinees answer different subsets of items. Unlike Cronbach's alpha, omega is grounded in a factor model, making it suitable for the heterogeneous item pools and variable test lengths that characterize adaptive administrations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roderick P. McDonald (omega); CAT-omega application extended by IRT and psychometric reliability researchers","year":"1999 (omega); CAT application 2000s–2010s","type":"Reliability coefficient for adaptive tests","dataType":"Item scores from CAT administrations (polytomous or dichotomous)","subfamily":"Scale / measurement"},"citations":[{"ref":"McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805830408","url":null},{"ref":"Green, B. F., Bock, R. D., Humphreys, L. G., Linn, R. L., & Reckase, M. D. (1984). Technical guidelines for assessing computerized adaptive tests. Journal of Educational Measurement, 21(4), 347–360.","type":"article","doi":"10.1111/j.1745-3984.1984.tb01039.x","isbn":null,"url":null}],"related":["computerized-adaptive-test-cronbachs-alpha","computerized-adaptive-test-reliability-analysis","mcdonalds-omega","item-response-theory","computerized-adaptive-test-item-response-theory","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-measurement-invariance","name":"Computerized adaptive test measurement invariance","fullName":"Computerized Adaptive Test Measurement Invariance","aliases":["CAT measurement invariance","adaptive test invariance","CAT MI","measurement equivalence in CAT"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Building on Meredith (1993) for invariance and Lord (1980) for adaptive testing","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-measurement-invariance.md","definition":"Computerized adaptive test measurement invariance evaluates whether a CAT instrument measures the same latent construct with the same psychometric properties across different groups (e.g., gender, language, clinical vs. community) or time points. It combines IRT-based adaptive test frameworks with measurement equivalence testing to ensure fair and comparable score interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Meredith (1993) for invariance and Lord (1980) for adaptive testing","year":"1990s–2000s","type":"Measurement equivalence testing in adaptive testing contexts","dataType":"Item responses from CAT administrations across groups or time points","subfamily":"Scale / measurement"},"citations":[{"ref":"Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.","type":"book","doi":null,"isbn":"978-0805864946","url":null},{"ref":"Choi, S. W., Reise, S. P., Pilkonis, P. A., Hays, R. D., & Cella, D. (2011). Efficiency of static and computer adaptive short forms compared to full-length measures of depressive symptoms. Quality of Life Research, 20(1), 125–138.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Efficiency+of+static+and+computer+adaptive+short+forms+compared+to+full-length+measures+of+depressive+symptoms+Choi"}],"related":["measurement-invariance","computerized-adaptive-test-item-response-theory","computerized-adaptive-test-differential-item-functioning","multi-group-measurement-invariance","confirmatory-factor-analysis","differential-item-functioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-rasch-model","name":"Computerized adaptive test Rasch model","fullName":"Computerized Adaptive Testing with the Rasch Model","aliases":["CAT-Rasch","Rasch-based CAT","adaptive Rasch testing","computerized adaptive measurement"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1960 (Rasch model); CAT integration from 1970s onward","originator":"Georg Rasch (measurement model); adaptive testing formalized by Wainer, van der Linden, and others","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-rasch-model","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-rasch-model.md","definition":"Computerized adaptive testing with the Rasch model selects items in real time based on each examinee's evolving ability estimate, so that every person receives a test precisely calibrated to their proficiency level. The result is a shorter, more efficient measurement instrument that loses none of the precision of a full-length fixed-form test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georg Rasch (measurement model); adaptive testing formalized by Wainer, van der Linden, and others","year":"1960 (Rasch model); CAT integration from 1970s onward","type":"Adaptive psychometric measurement","dataType":"Dichotomous or polytomous item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Wainer, H. (Ed.). (2000). Computerized Adaptive Testing: A Primer (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805835113","url":null},{"ref":"Wright, B. D. & Stone, M. H. (1982). Best Test Design: Rasch Measurement. MESA Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Best+Test+Design+Rasch+Measurement+Wright+Stone+1982"}],"related":["rasch-model","item-response-theory","two-parameter-logistic-model","confirmatory-factor-analysis","differential-item-functioning","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-reliability-analysis","name":"Computerized adaptive test reliability analysis","fullName":"Computerized Adaptive Test Reliability Analysis","aliases":["CAT reliability","adaptive test reliability","IRT-based reliability estimation","marginal reliability in CAT"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1970s–1980s","originator":"David J. Weiss and IRT psychometricians","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-reliability-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-reliability-analysis.md","definition":"CAT reliability analysis quantifies measurement precision in computerized adaptive tests where each examinee receives a unique, individually tailored subset of items. Rather than a single classical coefficient, it uses item response theory to express precision as conditional standard error of measurement at each ability level, and marginal reliability as a global summary across the ability distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David J. Weiss and IRT psychometricians","year":"1970s–1980s","type":"Reliability estimation under adaptive testing","dataType":"Polytomous or dichotomous item responses from adaptive administration","subfamily":"Scale / measurement"},"citations":[{"ref":"Weiss, D. J. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361–375.","type":"article","doi":"10.1111/j.1745-3984.1984.tb01040.x","isbn":null,"url":null},{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null}],"related":["item-response-theory","computerized-adaptive-test-item-response-theory","test-retest-reliability","cronbachs-alpha","mcdonalds-omega","differential-item-functioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-scale-development","name":"CAT Scale Development","fullName":"Computerized Adaptive Test Scale Development","aliases":["CAT scale construction","adaptive test development","computerized adaptive testing scale design","CAT item bank development"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1970s–1980s","originator":"Frederic Lord (IRT foundations); CAT systems developed at ETS and ACT in the 1970s–1980s","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-scale-development","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-scale-development.md","definition":"Computerized adaptive test (CAT) scale development is the process of constructing, calibrating, and validating a large item bank such that the assessment algorithm can select items tailored to each examinee's estimated ability or trait level in real time. The result is a measurement instrument that achieves high precision with fewer items than a conventional fixed-form test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frederic Lord (IRT foundations); CAT systems developed at ETS and ACT in the 1970s–1980s","year":"1970s–1980s","type":"Measurement design and test construction","dataType":"Polytomous or dichotomous item responses, IRT-calibrated item banks","subfamily":"Scale / measurement"},"citations":[{"ref":"Wainer, H., Dorans, N. J., Flaugher, R., Green, B. F., Mislevy, R. J., Steinberg, L., & Thissen, D. (2000). Computerized Adaptive Testing: A Primer (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805835113","url":null},{"ref":"van der Linden, W. J., & Glas, C. A. W. (Eds.). (2010). Elements of Adaptive Testing. Springer.","type":"book","doi":"10.1007/978-0-387-85461-8","isbn":null,"url":null}],"related":["item-response-theory","computerized-adaptive-test-item-response-theory","computerized-adaptive-test-item-analysis","item-bank-calibration","differential-item-functioning","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-test-test-retest-reliability","name":"CAT Test-Retest Reliability","fullName":"Computerized Adaptive Test Test-Retest Reliability","aliases":["CAT temporal stability","adaptive test retest reliability","CAT score consistency","computerized adaptive testing reliability"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1970s–1980s","originator":"David J. Weiss and colleagues (adaptive testing reliability literature)","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-test-retest-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-test-test-retest-reliability.md","definition":"Computerized adaptive test (CAT) test-retest reliability quantifies the consistency of ability estimates obtained when the same examinees complete a CAT on two separate occasions. Because adaptive algorithms tailor each examinee's item set individually, traditional reliability frameworks must be adapted to account for non-overlapping item exposures across administrations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David J. Weiss and colleagues (adaptive testing reliability literature)","year":"1970s–1980s","type":"Reliability estimation","dataType":"Latent trait scores from adaptive item administrations","subfamily":"Scale / measurement"},"citations":[{"ref":"Weiss, D. J. (2004). Computerized adaptive testing for effective and efficient measurement in counseling and education. Measurement and Evaluation in Counseling and Development, 37(2), 70–84.","type":"article","doi":"10.1080/07481756.2004.11909751","isbn":null,"url":null},{"ref":"Computerized adaptive testing. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Computerized_adaptive_testing"}],"related":["test-retest-reliability","item-response-theory","computerized-adaptive-testing","intraclass-correlation-coefficient","standard-error-of-measurement","cronbach-alpha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"computerized-adaptive-testing","name":"Computerized Adaptive Testing","fullName":"Computerized Adaptive Testing (CAT)","aliases":["Adaptive Testing","Tailored Testing","Item-Adaptive Testing","Bilgisayar Destekli Uyarlanabilir Test"],"domain":"psychometrics","family":"process-pipeline","subfamily":"Test administration","year":2000,"originator":"Howard Wainer et al.","url":"https://scholargate.app/en/psychometrics/computerized-adaptive-testing","markdownUrl":"https://scholargate.app/en/psychometrics/computerized-adaptive-testing.md","definition":"Computerized Adaptive Testing (CAT) is an individualized assessment methodology in which a computer algorithm selects successive test items based on a running estimate of each examinee's latent ability. Grounded in Item Response Theory, CAT dynamically tailors the item sequence so that each question is optimally informative given the current ability estimate. The framework was systematized and popularized by Howard Wainer and colleagues through the foundational primer first published in 1990 and expanded in the 2000 second edition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Howard Wainer et al.","year":2000,"type":"Adaptive sequential test administration procedure","subfamily":"Test administration","measurement_model":"Item Response Theory (IRT)","output":"Ability estimate (theta) with standard error"},"citations":[{"ref":"Wainer, H. (2000). Computerized Adaptive Testing: A Primer (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0-8058-3511-3","url":null}],"related":["2pl-irt","3pl-irt","rasch-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrency-control","name":"Concurrency Control","fullName":"Database Concurrency Control and Locking Mechanisms","aliases":["locking","MVCC"],"domain":"information-systems","family":"process-pipeline","subfamily":"Transaction Control & Data Consistency","year":"1978","originator":"Jim Gray and David Reed","url":"https://scholargate.app/en/information-systems/concurrency-control","markdownUrl":"https://scholargate.app/en/information-systems/concurrency-control.md","definition":"Concurrency control is the set of mechanisms used to coordinate concurrent transactions accessing shared data without corrupting the database. Formalized by database theorists in the 1970s-1980s, concurrency control ensures that multiple simultaneous transactions produce the same result as if they executed sequentially (serializability).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jim Gray and David Reed","subfamily":"Transaction Control & Data Consistency","year":"1978","type":"Database coordination mechanism"},"citations":[{"ref":"Gray, J. (1981). The transaction concept: Virtues and limitations. VLDB Endowment, 7(6), 519-539.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+transaction+concept%3A+Virtues+and+limitations+Gray"},{"ref":"Reed, D. P. (1978). Naming and synchronization in a decentralized computer system. Ph.D. Dissertation, MIT.","type":"article","doi":null,"isbn":null,"url":"https://dspace.mit.edu"},{"ref":"Papadimitriou, C. H. (1986). The Theory of Database Concurrency Control. Computer Science Press.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com"}],"related":["transaction-management","isolation-levels","deadlock-detection","two-phase-locking","mvcc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-case-focused-mixed-methods","name":"Concurrent Case-Focused Mixed Methods","fullName":"Concurrent Case-Focused Mixed Methods Design","aliases":["concurrent case study mixed methods","parallel case-focused mixed design","simultaneous case mixed methods","case-embedded concurrent mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s (formalized in Creswell & Plano Clark 2011, 2018)","originator":"Creswell & Plano Clark (mixed methods typology); Yin (case study methods)","url":"https://scholargate.app/en/research-design/concurrent-case-focused-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/concurrent-case-focused-mixed-methods.md","definition":"Concurrent case-focused mixed methods is a research design in which quantitative and qualitative data are collected simultaneously — rather than in sequence — and both strands are anchored within one or more bounded cases (e.g., a school, a program, a community, or an organisation). The two data strands are analyzed separately, then merged or compared to produce a fuller, case-grounded understanding than either strand could yield alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (mixed methods typology); Yin (case study methods)","year":"2000s–2010s (formalized in Creswell & Plano Clark 2011, 2018)","type":"Mixed methods research design","dataType":"Concurrent quantitative and qualitative data, both embedded within one or more bounded cases","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Designing+and+Conducting+Mixed+Methods+Research+Creswell+Plano+Clark+2018"},{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["case-study","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-embedded-mixed-methods-design","name":"Concurrent Embedded Mixed Methods Design","fullName":"Concurrent Embedded Mixed Methods Design","aliases":["embedded mixed methods","nested mixed methods design","concurrent nested design","CEMM"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2007","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/concurrent-embedded-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/concurrent-embedded-mixed-methods-design.md","definition":"The concurrent embedded mixed methods design collects quantitative and qualitative data at the same time, but assigns unequal priority to the two strands: one (usually quantitative) serves as the primary study, while the other (usually qualitative) is nested inside it to answer a supplementary question. The embedded strand does not stand alone; it provides a different perspective on the same phenomenon within a single unified study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2003–2007","type":"Mixed methods research design","dataType":"Concurrent quantitative and qualitative data; one strand nested within the other","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null},{"ref":"Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed methods research designs. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 209–240). Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Advanced+mixed+methods+research+designs+Creswell+Plano+Clark+2003"}],"related":["concurrent-triangulation-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","multiphase-mixed-methods-design","transformative-mixed-methods-design","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-exploratory-sequential-mixed-methods","name":"Concurrent Exploratory Sequential Mixed Methods","fullName":"Concurrent Exploratory Sequential Mixed Methods Design","aliases":["concurrent-exploratory sequential design","QUAN+QUAL→QUAN design","concurrent exploratory mixed design","multiphase concurrent-exploratory design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (expanded typology)","url":"https://scholargate.app/en/research-design/concurrent-exploratory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/concurrent-exploratory-sequential-mixed-methods.md","definition":"Concurrent exploratory sequential mixed methods design is an advanced mixed methods configuration that combines two timing structures: a concurrent (simultaneous) data-collection phase alongside an exploratory sequential strand, in which early qualitative findings inform the development or refinement of a quantitative component. This hybrid is used when a study needs both real-time integration of qualitative and quantitative data and an instrument-building or theory-testing phase driven by initial qualitative exploration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (expanded typology)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Qualitative and quantitative data collected concurrently alongside a sequential exploratory strand","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1412975179","url":null}],"related":["exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","multiphase-mixed-methods-design","qualitative-dominant-exploratory-sequential-mixed-methods","sequential-exploratory-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-intervention-mixed-methods","name":"Concurrent Intervention Mixed Methods","fullName":"Concurrent Intervention Mixed Methods Design","aliases":["concurrent mixed methods intervention design","simultaneous intervention mixed methods","CIMM design","parallel intervention mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark; Tashakkori & Teddlie","url":"https://scholargate.app/en/research-design/concurrent-intervention-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/concurrent-intervention-mixed-methods.md","definition":"Concurrent intervention mixed methods is a research design that embeds qualitative data collection within an experimental or quasi-experimental intervention study, with both data strands gathered simultaneously during the intervention period. Quantitative data assess intervention outcomes while qualitative data illuminate participants' experiences, implementation processes, or contextual factors — each strand informing the other at the integration stage.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Tashakkori & Teddlie","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Concurrent quantitative (experimental/intervention) and qualitative data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Murray, E. J., & Kimball, A. B. (2021). Embedding qualitative research within randomized controlled trials: lessons from the field. Trials, 22(1), 1–10.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Embedding+qualitative+research+within+randomized+controlled+trials%3A+lessons+from+the+field+Murray"}],"related":["concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","intervention-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-mixed-methods-matrix","name":"Concurrent Mixed Methods Matrix","fullName":"Concurrent Mixed Methods Matrix Design","aliases":["concurrent MM matrix","simultaneous mixed methods matrix","parallel mixed methods matrix","concurrent matrix mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Teddlie & Tashakkori; Creswell & Plano Clark","url":"https://scholargate.app/en/research-design/concurrent-mixed-methods-matrix","markdownUrl":"https://scholargate.app/en/research-design/concurrent-mixed-methods-matrix.md","definition":"The concurrent mixed methods matrix is a mixed methods design in which quantitative and qualitative data strands are collected simultaneously and organized within a structured matrix framework. The matrix maps design dimensions — such as research questions, data sources, priority, and integration points — across rows and columns, making the logical architecture of the study explicit and auditable. Both strands are analyzed independently before being merged through a matrix-guided integration step.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Teddlie & Tashakkori; Creswell & Plano Clark","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Concurrent quantitative and qualitative data","subfamily":"Mixed methods design"},"citations":[{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["concurrent-triangulation-mixed-methods-design","mixed-methods-matrix","concurrent-embedded-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-mixed-methods-meta-inference","name":"Concurrent Mixed Methods Meta-Inference","fullName":"Concurrent Mixed Methods Meta-Inference Design","aliases":["concurrent meta-inference","simultaneous mixed methods meta-inference","parallel strand meta-inference","QUAN+QUAL meta-inference"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003","originator":"Abbas Tashakkori & Charles Teddlie","url":"https://scholargate.app/en/research-design/concurrent-mixed-methods-meta-inference","markdownUrl":"https://scholargate.app/en/research-design/concurrent-mixed-methods-meta-inference.md","definition":"Concurrent mixed methods meta-inference is a research design in which quantitative and qualitative data strands are collected simultaneously and then subjected to a formal meta-inferential process — drawing a unified, overarching conclusion that transcends what either strand alone could produce. The concurrent timing means neither strand informs the collection of the other; instead, both strands converge at the analysis-integration stage where meta-inferences are constructed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abbas Tashakkori & Charles Teddlie","year":"2003","type":"Mixed methods research design","dataType":"Simultaneously collected quantitative and qualitative data","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage.","type":"book","doi":null,"isbn":"978-0761920731","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). Sage Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412972666","url":null}],"related":["concurrent-triangulation-mixed-methods-design","mixed-methods-meta-inference","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-embedded-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-multilevel-mixed-methods","name":"Concurrent Multilevel Mixed Methods","fullName":"Concurrent Multilevel Mixed Methods Design","aliases":["simultaneous multilevel mixed methods","parallel multilevel mixed methods","multilevel concurrent mixed methods","QUAN+QUAL multilevel design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"John W. Creswell & Vicki L. Plano Clark; Anthony Onwuegbuzie & colleagues","url":"https://scholargate.app/en/research-design/concurrent-multilevel-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/concurrent-multilevel-mixed-methods.md","definition":"Concurrent multilevel mixed methods design collects quantitative and qualitative data simultaneously at two or more levels of a nested social system — for example, students within classrooms within schools — then integrates findings across those levels to produce a layered, comprehensive understanding of the phenomenon. The concurrent timing means both data strands are gathered in the same phase rather than one informing the other sequentially.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark; Anthony Onwuegbuzie & colleagues","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative data (surveys, tests, administrative records) and qualitative data (interviews, observations, documents) collected simultaneously across multiple levels of a system","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344996","url":null},{"ref":"Hitchcock, J. H., & Onwuegbuzie, A. J. (Eds.). (2020). The Routledge Handbook for Advancing Integration in Mixed Methods Research. Routledge.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Routledge+Handbook+Advancing+Integration+Mixed+Methods+Research+Hitchcock+Onwuegbuzie+2020"}],"related":["multilevel-mixed-methods-design","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","exploratory-sequential-mixed-methods-design","explanatory-sequential-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-multiphase-mixed-methods","name":"Concurrent Multiphase Mixed Methods","fullName":"Concurrent Multiphase Mixed Methods Design","aliases":["concurrent-multiphase design","simultaneous multiphase MMR","parallel multiphase mixed methods","concurrent multistrand design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark; Tashakkori & Teddlie","url":"https://scholargate.app/en/research-design/concurrent-multiphase-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/concurrent-multiphase-mixed-methods.md","definition":"Concurrent multiphase mixed methods design combines the structural complexity of multiphase research — spanning several distinct project phases — with concurrent (simultaneous) data collection within each phase. At each stage, quantitative and qualitative data strands are gathered and analyzed in parallel rather than sequentially, and findings are integrated across phases to address a program of interrelated research questions over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Tashakkori & Teddlie","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative and qualitative data collected simultaneously across multiple phases","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1412972666","url":null}],"related":["multiphase-mixed-methods-design","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","sequential-multiphase-mixed-methods","exploratory-sequential-mixed-methods-design","explanatory-sequential-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-pragmatic-mixed-methods","name":"Concurrent Pragmatic Mixed Methods","fullName":"Concurrent Pragmatic Mixed Methods Design","aliases":["concurrent pragmatic design","pragmatic concurrent mixed methods","simultaneous pragmatic mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"John W. Creswell & Vicki L. Plano Clark; philosophical grounding by R. Burke Johnson & Anthony Onwuegbuzie","url":"https://scholargate.app/en/research-design/concurrent-pragmatic-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/concurrent-pragmatic-mixed-methods.md","definition":"Concurrent pragmatic mixed methods is a research design that collects quantitative and qualitative data simultaneously within a pragmatic philosophical framework. Rather than privileging either positivism or constructivism, the pragmatic stance selects methods based on what best answers the research question. Both data strands are gathered in parallel, then merged at the interpretation stage to provide a fuller picture than either strand alone could yield.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark; philosophical grounding by R. Burke Johnson & Anthony Onwuegbuzie","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Simultaneous quantitative and qualitative data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26.","type":"article","doi":"10.3102/0013189X033007014","isbn":null,"url":null}],"related":["concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","pragmatic-mixed-methods-design","explanatory-sequential-mixed-methods-design","concurrent-multiphase-mixed-methods","concurrent-transformative-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-transformative-mixed-methods","name":"Concurrent Transformative Mixed Methods","fullName":"Concurrent Transformative Mixed Methods Design","aliases":["concurrent transformative design","simultaneous transformative mixed methods","parallel transformative design","transformative concurrent design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s (formalized ~2007–2009)","originator":"Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/concurrent-transformative-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/concurrent-transformative-mixed-methods.md","definition":"Concurrent transformative mixed methods design collects quantitative and qualitative data simultaneously, guided by a transformative theoretical lens — such as feminist, critical race, disability, or indigenous frameworks — that foregrounds equity, power, and social change. The design gives either strand equal or unequal priority, but both strands are explicitly shaped by the transformative worldview. Findings are merged or compared to generate actionable insights that advocate for marginalized communities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark","year":"2000s (formalized ~2007–2009)","type":"Mixed methods research design","dataType":"Concurrent quantitative and qualitative data, often from marginalized or underrepresented groups","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1593856670","url":null}],"related":["concurrent-triangulation-mixed-methods-design","transformative-mixed-methods-design","concurrent-embedded-mixed-methods","participatory-concurrent-triangulation-mixed-methods","exploratory-sequential-mixed-methods-design","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"concurrent-triangulation-mixed-methods-design","name":"Concurrent Triangulation Mixed Methods Design","fullName":"Concurrent Triangulation Mixed Methods Research Design","aliases":["convergent parallel design","triangulation design","QUAN+QUAL concurrent design","simultaneous triangulation"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007 (formally named in Creswell & Plano Clark, 1st ed.)","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/concurrent-triangulation-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/concurrent-triangulation-mixed-methods-design.md","definition":"The concurrent triangulation mixed methods design collects quantitative and qualitative data simultaneously, analyzes each strand independently, and then merges the results to assess whether the two data sources corroborate one another. Often called the convergent parallel design, it is one of the foundational configurations in mixed methods research and is chosen specifically when the researcher wants to cross-validate or triangulate findings from two distinct methodological traditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2007 (formally named in Creswell & Plano Clark, 1st ed.)","type":"Mixed methods research design","dataType":"Quantitative data (surveys, tests, instruments) and qualitative data (interviews, observations, documents) collected simultaneously","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null},{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506386706","url":null}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-embedded-mixed-methods-design","multiphase-mixed-methods-design","triangulation","convergent-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"condition-index","name":"Condition Index","fullName":"Condition Index (Belsley Collinearity Diagnostics)","aliases":["Belsley Condition Index","Collinearity Condition Index","Singular Value Condition Index","Koşul İndeksi"],"domain":"econometrics","family":"regression-model","subfamily":"Multicollinearity diagnostics","year":1980,"originator":"Belsley, Kuh & Welsch","url":"https://scholargate.app/en/econometrics/condition-index","markdownUrl":"https://scholargate.app/en/econometrics/condition-index.md","definition":"The Condition Index, introduced by Belsley, Kuh, and Welsch (1980), is a scalar measure derived from singular value decomposition of the scaled regressor matrix. It quantifies the degree of near-linear dependence among predictors in ordinary least squares regression, enabling analysts to detect collinearity that inflates coefficient variance and destabilises parameter estimates. Widely used in economics, social sciences, and biomedical research wherever OLS regression is applied.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Belsley, Kuh & Welsch","year":1980,"type":"Collinearity diagnostic index","subfamily":"Multicollinearity diagnostics","threshold_moderate":"CI > 15 indicates moderate collinearity","threshold_severe":"CI > 30 indicates severe collinearity"},"citations":[{"ref":"Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-471-05856-4","url":null}],"related":["variance-inflation-factor","principal-component-analysis","ols-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conditional-logit","name":"Conditional Logit","fullName":"Conditional Logit Model (McFadden)","aliases":["McFadden's Choice Model","Discrete Choice Logit","Alternative-Specific Logit","Koşullu Logit Modeli"],"domain":"econometrics","family":"regression-model","subfamily":"Limited dependent variable","year":1974,"originator":"Daniel McFadden","url":"https://scholargate.app/en/econometrics/conditional-logit","markdownUrl":"https://scholargate.app/en/econometrics/conditional-logit.md","definition":"The Conditional Logit Model, introduced by Daniel McFadden in 1974, is a discrete-choice econometric model designed to explain an individual's selection among a finite set of mutually exclusive alternatives. Unlike multinomial logit, it uses covariates that vary across alternatives — such as price, travel time, or product attributes — making it ideally suited for revealed-preference studies in transportation, marketing, and labor economics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel McFadden","year":1974,"type":"Discrete choice model for alternative-specific covariates","subfamily":"Limited dependent variable","nobel":"McFadden received the Nobel Memorial Prize in Economic Sciences in 2000 partly for this contribution","assumption":"Independence of Irrelevant Alternatives (IIA)"},"citations":[{"ref":"McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105–142). Academic Press.","type":"incollection","doi":null,"isbn":"978-0-12-776150-3","url":null}],"related":["multinomial-logit","nested-logit","mixed-logit"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conditional-process-analysis","name":"Conditional Process Analysis","fullName":"Conditional Process Analysis (Moderated Mediation)","aliases":["moderated mediation","moderated mediation analysis","PROCESS model","Hayes PROCESS conditional process model","Koşullu Süreç Analizi (Moderated Mediation)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2018,"originator":"Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)","url":"https://scholargate.app/en/causal-inference/conditional-process-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/conditional-process-analysis.md","definition":"Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)","year":2018,"type":"Regression-based conditional process model","estimator":"OLS path equations with bootstrap confidence intervals","outcome":"continuous, binary, or ordinal","minSample":100,"keyQuantity":"Index of moderated mediation; conditional indirect and direct effects"},"citations":[{"ref":"Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press.","type":"book","doi":null,"isbn":"978-1462534654","url":null},{"ref":"Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions. Multivariate Behavioral Research, 42(1), 185-227.","type":"article","doi":"10.1080/00273170701341316","isbn":null,"url":null}],"related":["causal-mediation","iv-2sls","ols-regression","bayesian-sem","regression-discontinuity"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conditional-simulation","name":"Conditional Geostatistical Simulation","fullName":"Sequential Gaussian Simulation (Conditional Simulation)","aliases":["Sequential Gaussian Simulation","SGS","Stochastic Simulation","Koşullu Simülasyon"],"domain":"spatial-analysis","family":"regression-model","subfamily":"Geostatistics","year":1997,"originator":"Pierre Goovaerts; geostatistics tradition","url":"https://scholargate.app/en/spatial-analysis/conditional-simulation","markdownUrl":"https://scholargate.app/en/spatial-analysis/conditional-simulation.md","definition":"Conditional Geostatistical Simulation — most commonly implemented as Sequential Gaussian Simulation (SGS) — generates multiple stochastic realizations of a spatial random field that are each consistent with observed sample data and with a fitted variogram model. Unlike kriging, which produces a single smoothed estimate, SGS reproduces the full spatial variability of the phenomenon. It is widely used by geoscientists, mining engineers, petroleum engineers, and environmental scientists who need to propagate spatial uncertainty through downstream models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pierre Goovaerts; geostatistics tradition","year":1997,"type":"Stochastic spatial simulation","subfamily":"Geostatistics","output":"Multiple equally probable realizations of a spatial field"},"citations":[{"ref":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.","type":"book","doi":null,"isbn":"978-0-19-511538-3","url":null}],"related":["kriging","universal-kriging","cokriging"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conditional-value-at-risk","name":"Conditional Value-at-Risk","fullName":"Conditional Value-at-Risk (Expected Shortfall)","aliases":["CVaR","expected shortfall","average value-at-risk","tail VaR","Koşullu Riske Maruz Değer (CVaR / Expected Shortfall)"],"domain":"finance","family":"regression-model","subfamily":null,"year":2000,"originator":"Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)","url":"https://scholargate.app/en/finance/conditional-value-at-risk","markdownUrl":"https://scholargate.app/en/finance/conditional-value-at-risk.md","definition":"Conditional Value-at-Risk (CVaR), also called Expected Shortfall, is a coherent tail-risk measure that quantifies the conditional expectation of losses beyond the Value-at-Risk threshold. It was introduced for optimization by Rockafellar and Uryasev (2000) and shown to be coherent by Acerbi and Tasche (2002), and it has replaced VaR as the regulatory standard under Basel III/IV.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rockafellar & Uryasev (2000); Acerbi & Tasche (2002)","year":2000,"type":"Coherent tail-risk measure","estimator":"Historical tail average / linear-programming optimization","outcome":"continuous","confidenceLevel":"typically 95% or 99%","regulatoryUse":"Basel III/IV expected shortfall"},"citations":[{"ref":"Rockafellar, R. T. & Uryasev, S. (2000). Optimization of Conditional Value-at-Risk. Journal of Risk, 2(3), 21-41.","type":"article","doi":"10.21314/JOR.2000.038","isbn":null,"url":null},{"ref":"Acerbi, C. & Tasche, D. (2002). On the Coherence of Expected Shortfall. Journal of Banking & Finance, 26(7), 1487-1503.","type":"article","doi":"10.1016/S0378-4266(02)00283-2","isbn":null,"url":null}],"related":["value-at-risk","egarch","arima","realized-volatility","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"condorcet","name":"CONDORCET","fullName":"Condorcet Method — Pairwise majority winner from ranked ballots","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"1900","originator":"Marquis de Condorcet","url":"https://scholargate.app/en/decision-making/condorcet","markdownUrl":"https://scholargate.app/en/decision-making/condorcet.md","definition":"CONDORCET (Condorcet Method — Pairwise majority winner from ranked ballots) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Marquis de Condorcet in 1900. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marquis de Condorcet","subfamily":"AggregationOperator","year":"1900","type":"Pairwise majority rule — winner beats every other alternative in pairwise contest","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Marquis de Condorcet (1900). Essai sur l'application de l'analyse à la probabilité des décisions rendues à la pluralité des voix (1785). Imprimerie Royale, Paris (original 1785)","type":"article","doi":null,"isbn":null,"url":"https://gallica.bnf.fr/ark:/12148/bpt6k417181"}],"related":["ahp","topsis","promethee","electre","vikor"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conference-paper-proceedings","name":"Conference Paper and Proceedings","fullName":"Conference Paper and Proceedings (Peer-Reviewed Conference Presentations and Publication)","aliases":["conference abstract","conference proceedings","conference presentation","poster presentation"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Conference scholarship","year":"1900","originator":"Academic conferences (20th century formalization)","url":"https://scholargate.app/en/academic-writing/conference-paper-proceedings","markdownUrl":"https://scholargate.app/en/academic-writing/conference-paper-proceedings.md","definition":"A conference paper is original research presented at an academic conference, typically via oral presentation or poster. Conference papers are published in proceedings (collection of papers from a conference) and indexed in databases (Scopus, Web of Science). Unlike journal articles requiring 12–24 months for publication, conference papers are disseminated rapidly (often within weeks or months), making them valuable for communicating cutting-edge findings and early-stage research. Peer review rigor varies: some conferences employ rigorous peer review; others are less selective. Conference papers often precede or parallel journal publication, facilitating scholarly dialogue and networking.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Academic conferences (20th century formalization)","subfamily":"Conference scholarship","year":"1900","type":"Document Type"},"citations":[{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE.","type":"webpage","doi":null,"isbn":null,"url":"http://www.icmje.org"},{"ref":"Conference Series International. Guidelines for Conference Paper Submission and Presentation. https://www.conferenceseries.com","type":"webpage","doi":null,"isbn":null,"url":"https://www.conferenceseries.com"},{"ref":"Scopus Coverage of Conference Papers. https://www.elsevier.com/products/scopus","type":"webpage","doi":null,"isbn":null,"url":"https://www.elsevier.com/products/scopus"}],"related":["original-research-article","peer-review-process","poster-presentation-design","academic-networking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"confidence-interval","name":"Confidence Interval","fullName":"Confidence Interval Estimation and Interpretation in Statistical Inference","aliases":["CI","95% CI","credible interval","interval estimate"],"domain":"research-statistics","family":"process-pipeline","subfamily":"interval-estimation","year":1937,"originator":"Jerzy Neyman","url":"https://scholargate.app/en/research-statistics/confidence-interval","markdownUrl":"https://scholargate.app/en/research-statistics/confidence-interval.md","definition":"A confidence interval (CI) is a range of values, calculated from sample data, that likely contains the true population parameter. Introduced by Jerzy Neyman in 1937, it provides an interval estimate rather than a single point estimate, incorporating both the observed value and the uncertainty around it. The standard 95% confidence interval is a robust, intuitive alternative to p-values for communicating research results.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerzy Neyman","subfamily":"interval-estimation","year":1937,"type":"Concept"},"citations":[{"ref":"Neyman, J. (1937). Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability. Philosophical Transactions of the Royal Society, 236, 333–380.","type":"article","doi":"10.1098/rsta.1937.0005","isbn":null,"url":null},{"ref":"Altman, D. G., Machin, D., Bryant, T. N., & Gardner, M. J. (1989). Statistics with Confidence. British Medical Journal.","type":"article","doi":null,"isbn":"0-7279-0222-X","url":null},{"ref":"Cumming, G. (2014). The New Statistics: Why and How. Psychological Science, 25(1), 7–29.","type":"article","doi":"10.1177/0956797613504966","isbn":null,"url":null}],"related":["p-value-significance","effect-size","statistical-power","null-hypothesis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"configuration-interaction","name":"Configuration Interaction","fullName":"Configuration Interaction","aliases":["CI","configuration interaction method","CI calculations"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Quantum Chemistry Methods","year":"1960","originator":"Clemens Roothaan","url":"https://scholargate.app/en/spectroscopy/configuration-interaction","markdownUrl":"https://scholargate.app/en/spectroscopy/configuration-interaction.md","definition":"Configuration Interaction (CI) is a post-Hartree-Fock quantum chemistry method that improves upon mean-field molecular orbital theory by treating electron correlation through a linear combination of electronic configurations. Introduced by Roothaan in 1960, CI corrects for the fundamental limitation of single-determinant theory by allowing the wavefunction to be a superposition of excited-state Slater determinants, systematically accounting for electron-electron interactions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clemens Roothaan","subfamily":"Quantum Chemistry Methods","year":"1960","type":"Computational method"},"citations":[{"ref":"Roothaan, C. C. J. (1960). New developments in molecular orbital theory. Reviews of Modern Physics, 32(2), 179-185.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=New+developments+in+molecular+orbital+theory+Roothaan"},{"ref":"Szabo, A., & Ostlund, N. S. (1996). Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory. Dover Publications.","type":"book","doi":null,"isbn":null,"url":"https://www.doverpublications.com/products/9780486691862"}],"related":["dmrg","exafs","xanes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"confirmatory-factor-analysis-scale","name":"Confirmatory Factor Analysis for Scales","fullName":"Confirmatory Factor Analysis Method for Scale Validation and Structural Testing","aliases":["CFA","Confirmatory factor analysis","Path analysis","Structural equation modeling"],"domain":"psychometrics","family":"process-pipeline","subfamily":"Scale development","year":"1969","originator":"Karl G. Jöreskog","url":"https://scholargate.app/en/psychometrics/confirmatory-factor-analysis-scale","markdownUrl":"https://scholargate.app/en/psychometrics/confirmatory-factor-analysis-scale.md","definition":"Confirmatory Factor Analysis (CFA) is a statistical method for testing whether a hypothesized factorial structure fits empirical data. Developed by Karl G. Jöreskog in 1969, CFA is the standard approach for validating psychometric scales by evaluating whether items load onto theoretically specified latent factors as expected. Unlike exploratory factor analysis, CFA requires a priori specification of the factor structure and provides goodness-of-fit indices to assess model adequacy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl G. Jöreskog","subfamily":"Scale development","year":"1969","type":"Confirmatory factor analysis methodology"},"citations":[{"ref":"Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183-202.","type":"article","doi":"10.1007/BF02289343","isbn":null,"url":null},{"ref":"Hoyle, R. H. (Ed.). (2012). Handbook of Structural Equation Modeling. New York: Guilford Press.","type":"book","doi":null,"isbn":"9781462503254","url":null},{"ref":"Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). New York: Guilford Press.","type":"book","doi":null,"isbn":"9781462523344","url":null}],"related":["likert-scale-construction","factor-analysis-scale","content-validity-ratio","floor-ceiling-effect"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"confirmatory-factor-analysis","name":"Confirmatory factor analysis","fullName":"Confirmatory Factor Analysis","aliases":["CFA","confirmatory FA","measurement model","restricted factor analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1969","originator":"Karl Gustav Jöreskog","url":"https://scholargate.app/en/psychometrics/confirmatory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/confirmatory-factor-analysis.md","definition":"Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Gustav Jöreskog","year":"1969","type":"Hypothesis-testing latent variable model","dataType":"Continuous or ordinal indicators","subfamily":"Scale / measurement"},"citations":[{"ref":"Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.","type":"article","doi":"10.1007/BF02289343","isbn":null,"url":null},{"ref":"Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462515363","url":null}],"related":["exploratory-factor-analysis","structural-equation-modeling","measurement-invariance","cronbachs-alpha","mcdonalds-omega","convergent-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"confirmatory-research","name":"Confirmatory Research","fullName":"Confirmatory Quantitative Research Design","aliases":["hypothesis-testing research","deductive research","theory-testing research","confirmatory study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1934 (Popper); widely adopted in social sciences from 1960s onward","originator":"Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others","url":"https://scholargate.app/en/research-design/confirmatory-research","markdownUrl":"https://scholargate.app/en/research-design/confirmatory-research.md","definition":"Confirmatory research is a deductive quantitative design in which the researcher specifies hypotheses derived from existing theory before data collection, then tests whether the data support or refute those hypotheses. Unlike exploratory approaches that generate ideas from data, confirmatory research begins with an established theoretical framework, pre-registers predictions, and applies statistical tests to evaluate those predictions against empirical evidence. It is the backbone of hypothesis-driven social, behavioral, and health science inquiry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others","year":"1934 (Popper); widely adopted in social sciences from 1960s onward","type":"Quantitative research design","dataType":"Numerical data from surveys, experiments, secondary datasets","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson.","type":"book","doi":null,"isbn":"978-0415278447","url":null},{"ref":"Kline, R. B. (2013). Beyond Significance Testing: Statistics Reform in the Behavioral Sciences (2nd ed.). American Psychological Association.","type":"book","doi":null,"isbn":"978-1433812378","url":null}],"related":["hypothesis-testing-research","model-testing-research","exploratory-quantitative-research","correlational-research","explanatory-research","cross-sectional-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conflict-of-interest-research","name":"Conflict of Interest in Research","fullName":"Managing Financial and Non-Financial Conflicts of Interest in Research","aliases":["COI","Conflicts of Interest"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-management","year":"2013","originator":"Multiple (NIH, ICMJE, institutional COI policies)","url":"https://scholargate.app/en/research-ethics/conflict-of-interest-research","markdownUrl":"https://scholargate.app/en/research-ethics/conflict-of-interest-research.md","definition":"A conflict of interest (COI) in research exists when a researcher has financial, professional, or personal interests that might bias their research judgment or outcomes. Conflicts are inherent in research communities—researchers often have legitimate stakes in their research's success—but unmanaged conflicts compromise research integrity and public trust. Managing COI requires transparent disclosure, institutional oversight, and proactive mitigation strategies to minimize bias risk while allowing legitimate research to proceed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (NIH, ICMJE, institutional COI policies)","subfamily":"ethical-management","year":"2013","type":"Guideline"},"citations":[{"ref":"International Committee of Medical Journal Editors. (2023). Defining the Role of Authors and Contributors. ICMJE Recommendations for Manuscript Authorship.","type":"report","doi":null,"isbn":null,"url":"https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html"},{"ref":"U.S. Department of Health and Human Services. (2013). Physician Payments Sunshine Act Reporting. Code of Federal Regulations Title 42, Section 1320a-7h.","type":"legal","doi":null,"isbn":null,"url":"https://www.cms.gov/openpayments"},{"ref":"National Institutes of Health. (2019). Financial Conflict of Interest Requirements. NIH Grant Conditions and Regulations.","type":"report","doi":null,"isbn":null,"url":"https://grants.nih.gov/grants/policy/coi/"}],"related":["research-integrity-principles","research-misconduct","belmont-report"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conflict-tactics-scale","name":"Conflict Tactics Scale","fullName":"Conflict Tactics Scale (CTS2)","aliases":["CTS2","Revised Conflict Tactics Scale","CTS"],"domain":"social-psychology","family":"process-pipeline","subfamily":"intimate partner violence and conflict assessment","year":"1979","originator":"Murray A. Straus","url":"https://scholargate.app/en/social-psychology/conflict-tactics-scale","markdownUrl":"https://scholargate.app/en/social-psychology/conflict-tactics-scale.md","definition":"The Conflict Tactics Scale is the most widely used instrument for measuring how intimate partners handle disagreements and conflict, including tactics ranging from negotiation and psychological aggression to physical violence and sexual coercion. Developed by Murray Straus in 1979 and substantially revised in 1996 (CTS2), it is used extensively in family research, intimate partner violence assessment, and couple therapy evaluation. The CTS2 measures five dimensions of conflict behavior: negotiation (reasoning and discussion), psychological aggression (insults, threats), physical assault (pushing, hitting, weapon use), injury (physical consequences of violence), and sexual coercion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray A. Straus","subfamily":"intimate partner violence and conflict assessment","year":"1979","type":"Self-report questionnaire"},"citations":[{"ref":"Straus, M. A. (1996). Measuring intrafamily conflict and violence: The Conflict Tactics Scale. Journal of Family Issues, 41(1), 75-88.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+intrafamily+conflict+and+violence%3A+The+Conflict+Tactics+Scale+Straus"},{"ref":"Straus, M. A., & Douglas, E. M. (2004). A short form of the Revised Conflict Tactics Scale, and typologies for violence and control. Violence and Victims, 19(5), 507-520.","type":"article","doi":"10.1037/t43278-000","isbn":null,"url":null},{"ref":"Hamby, S. L., Poindexter, V. C., & Reaney, S. (2006). Adaptations for diverse populations of the Conflict Tactics Scale: A review. Aggression and Violent Behavior, 11(4), 375-385.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Adaptations+for+diverse+populations+of+the+Conflict+Tactics+Scale%3A+A+review+Hamby"}],"related":["dyadic-adjustment-scale","family-assessment-device","attachment-style-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conformal-prediction-ts","name":"Conformal Prediction (Time Series)","fullName":"Conformal Prediction for Time-Series Forecasting","aliases":["conformal prediction","distribution-free prediction intervals","EnbPI","Konformal Tahmin (Conformal Prediction — Zaman Serisi)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2021,"originator":"Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)","url":"https://scholargate.app/en/econometrics/conformal-prediction-ts","markdownUrl":"https://scholargate.app/en/econometrics/conformal-prediction-ts.md","definition":"Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)","year":2021,"type":"Distribution-free prediction interval wrapper","estimator":"Nonconformity (residual) quantile calibration","outcome":"continuous","structure":"time series","minSample":50},"citations":[{"ref":"Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.","type":"article","doi":"10.1561/2200000101","isbn":null,"url":null},{"ref":"Xu, C. & Xie, Y. (2021). Conformal Prediction Interval for Dynamic Time-Series. International Conference on Machine Learning (ICML).","type":"article","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v139/xu21h.html"}],"related":["arima","quantile-regression","ols-regression","gradient-boosting","lstm-forecast"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conformal-prediction","name":"Conformal Prediction","fullName":"Conformal Prediction (Distribution-Free Prediction Sets)","aliases":["Conformal Inference","Conformal Risk Control","Inductive Conformal Prediction","Uyumsal Tahmin"],"domain":"machine-learning","family":"ml-model","subfamily":"Trustworthy ML","year":2005,"originator":"Vovk, Gammerman & Shafer","url":"https://scholargate.app/en/machine-learning/conformal-prediction","markdownUrl":"https://scholargate.app/en/machine-learning/conformal-prediction.md","definition":"Conformal Prediction is a distribution-free framework for constructing statistically valid prediction sets (for classification) or prediction intervals (for regression) around the output of any pre-trained machine learning model. Introduced by Vovk, Gammerman, and Shafer in their 2005 monograph, it provides a finite-sample marginal coverage guarantee — the true label falls inside the prediction set with at least 1-alpha probability — without requiring parametric assumptions about the data distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vovk, Gammerman & Shafer","year":2005,"type":"Distribution-free uncertainty quantification framework","subfamily":"Trustworthy ML","coverage_guarantee":"Marginal validity at user-specified level 1-alpha","exchangeability":"Requires exchangeable (i.i.d.) calibration data"},"citations":[{"ref":"Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic Learning in a Random World. Springer.","type":"book","doi":null,"isbn":"978-0-387-00152-4","url":null}],"related":["uncertainty-quantification","model-calibration","k-nearest-neighbors"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"confusion-matrix","name":"Confusion Matrix","fullName":"Confusion Matrix (Error Matrix)","aliases":["Error Matrix","Contingency Table"],"domain":"model-evaluation","family":"mcdm","subfamily":"Diagnostic Tool","year":"20th century","originator":"Statistical foundations","url":"https://scholargate.app/en/model-evaluation/confusion-matrix","markdownUrl":"https://scholargate.app/en/model-evaluation/confusion-matrix.md","definition":"The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Statistical foundations","subfamily":"Diagnostic Tool","year":"20th century","type":"Evaluation visualization"},"citations":[{"ref":"Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/handbookofstatisticalanalysesus0000ever"},{"ref":"Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.","type":"article","doi":"10.1016/j.patrec.2005.10.010","isbn":null,"url":null}],"related":["accuracy","precision","recall","specificity","matthews-correlation-coefficient"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conjoint-analysis","name":"Conjoint Analysis","fullName":"Conjoint Analysis (Choice-Based and Adaptive Variants)","aliases":["CBC conjoint","choice-based conjoint","adaptive conjoint analysis","full-profile conjoint","Birleşik Analiz (Conjoint Analysis — CBC, ACA)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1978,"originator":"Paul E. Green & V. Srinivasan","url":"https://scholargate.app/en/experimental-design/conjoint-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/conjoint-analysis.md","definition":"Conjoint analysis is a preference-measurement technique that decomposes overall product evaluations into the separate utility values — called part-worths — that respondents assign to each attribute level. Formalised by Green and Srinivasan in their seminal 1978 Journal of Consumer Research paper, the method has become the dominant tool in marketing research and product design for quantifying what buyers truly trade off when they choose between options.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul E. Green & V. Srinivasan","year":1978,"family":"Preference measurement","type":"Decomposition-based utility estimation","parametric":false,"minSample":100,"designRequirement":"D-optimal or orthogonal array","variants":"CBC (Choice-Based), ACA (Adaptive), Full-Profile","estimationMethod":"OLS (full-profile), MNL / HB (CBC)","output":"Part-worth utilities, relative attribute importance"},"citations":[{"ref":"Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.","type":"article","doi":"10.1086/208721","isbn":null,"url":null},{"ref":"Orme, B.K. (2020). Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research (3rd ed.). Research Publishers.","type":"book","doi":null,"isbn":null,"url":"https://www.researchpublishers.com/getting-started-with-conjoint-analysis.html"}],"related":["discrete-choice-simulation","factorial-design","fractional-factorial","multinomial-logit","randomized-controlled-trial","response-surface-methodology"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conjugate-gradient-method","name":"Conjugate Gradient Method","fullName":"Conjugate Gradient Method for Linear Systems","aliases":["CG method","Krylov subspace method"],"domain":"numerical-methods","family":"ml-model","subfamily":"Krylov Subspace Iterative","year":"1952","originator":"Magnus Hestenes and Eduard Stiefel","url":"https://scholargate.app/en/numerical-methods/conjugate-gradient-method","markdownUrl":"https://scholargate.app/en/numerical-methods/conjugate-gradient-method.md","definition":"The Conjugate Gradient (CG) Method is an iterative algorithm for solving large sparse symmetric positive-definite linear systems Ax = b, developed by Hestenes and Stiefel in 1952. It is one of the most widely used iterative solvers in scientific computing because it converges in at most n iterations for an n × n matrix and typically requires far fewer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Magnus Hestenes and Eduard Stiefel","subfamily":"Krylov Subspace Iterative","year":"1952","type":"Iterative linear solver"},"citations":[{"ref":"Hestenes, M. R., & Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49(6), 409–436.","type":"article","doi":"10.6028/jres.049.044","isbn":null,"url":null},{"ref":"Saad, Y. (2003). Iterative Methods for Sparse Linear Systems (2nd ed.). SIAM.","type":"book","doi":"10.1137/1.9780898718003","isbn":null,"url":null},{"ref":"Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer.","type":"book","doi":"10.1007/978-0-387-40065-5","isbn":null,"url":null}],"related":["gmres","minres","bicg","gradient-descent"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conjugate-prior-analysis","name":"Conjugate Prior Analysis","fullName":"Conjugate Prior Bayesian Analysis","aliases":["conjugate priors","conjugate Bayesian updating","closed-form posterior analysis","Beta-Binomial model","Normal-Normal model","natural conjugate analysis"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":1961,"originator":"Raiffa & Schlaifer (1961); DeGroot (1970)","url":"https://scholargate.app/en/bayesian/conjugate-prior-analysis","markdownUrl":"https://scholargate.app/en/bayesian/conjugate-prior-analysis.md","definition":"Conjugate prior analysis is a class of Bayesian inference methods in which the prior distribution and the likelihood belong to a matched family — called a conjugate pair — so that the posterior distribution has exactly the same functional form as the prior and can be derived in closed form. Introduced systematically by Raiffa and Schlaifer (1961) and consolidated by DeGroot (1970), conjugate analysis is the pedagogic backbone of introductory Bayesian statistics and a practical tool whenever analytical tractability is required.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Closed-form Bayesian model","purpose":"posterior inference / parameter estimation","var_types":"depends on conjugate pair (binary, count, continuous)","inference":"exact analytical / closed-form","outputs":"posterior distribution / updated hyperparameters / credible intervals","originator":"Raiffa & Schlaifer (1961); DeGroot (1970)","year":1961,"common_pairs":"Beta-Binomial, Normal-Normal, Gamma-Poisson, Dirichlet-Multinomial"},"citations":[{"ref":"Raiffa, H. & Schlaifer, R. (1961). Applied Statistical Decision Theory. Harvard University Press.","type":"book","doi":null,"isbn":"978-0-87584-017-8","url":null},{"ref":"DeGroot, M. H. (1970). Optimal Statistical Decisions. McGraw-Hill.","type":"book","doi":null,"isbn":"978-0-07-016242-6","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1-4398-4095-5","url":null}],"related":["bayesian-regression","mcmc","hierarchical-bayes","empirical-bayes","variational-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"connectedness-to-nature-scale","name":"CNS","fullName":"Connectedness to Nature Scale","aliases":["CNS","Mayer-Frantz Connectedness"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"nature connection and affinity assessment","year":"2004","originator":"Frederic S. Mayer and Cynthia M. Frantz","url":"https://scholargate.app/en/environmental-psychology/connectedness-to-nature-scale","markdownUrl":"https://scholargate.app/en/environmental-psychology/connectedness-to-nature-scale.md","definition":"The Connectedness to Nature Scale (CNS) measures the degree to which individuals feel emotionally and cognitively connected to nature as part of their sense of self. Developed by Mayer and Frantz (2004), the CNS operationalizes the construct of nature connection—the felt sense of kinship, interdependence, and belonging with the natural world. The scale is widely employed in environmental psychology research, health outcome studies examining nature exposure effects, and intervention evaluations designed to strengthen human-nature relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frederic S. Mayer and Cynthia M. Frantz","subfamily":"nature connection and affinity assessment","year":"2004","type":"Self-report Likert scale"},"citations":[{"ref":"Mayer, F. S., & Frantz, C. M. (2004). The connectedness to nature scale: A measure of individuals' feeling of dependence on nature. Journal of Environmental Psychology, 24(4), 503–515.","type":"article","doi":"10.1016/j.jenvp.2004.10.001","isbn":null,"url":null}],"related":["new-ecological-paradigm","environmental-identity-scale","pro-environmental-behavior-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conners-rating-scales","name":"Conners Rating Scales","fullName":"Conners Rating Scales-Revised (CRS-R)","aliases":["CRS","CRS-R","Conners ADHD Index"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"ADHD screening and assessment","year":"2008","originator":"Keith Conners","url":"https://scholargate.app/en/developmental-assessment/conners-rating-scales","markdownUrl":"https://scholargate.app/en/developmental-assessment/conners-rating-scales.md","definition":"The Conners Rating Scales-Revised (CRS-R), developed by Keith Conners and updated in 2008, is the most widely used rating scale instrument for identifying and assessing attention-deficit/hyperactivity disorder (ADHD) and behavioral problems in children and adolescents aged 6–18 years. Available in parent, teacher, and self-report versions, the CRS-R provides comprehensive assessment of ADHD symptoms and comorbid behavioral concerns across multiple informants and settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keith Conners","subfamily":"ADHD screening and assessment","year":"2008","type":"Multi-informant ADHD and behavior rating scale"},"citations":[{"ref":"Conners, C. K. (2008). Conners Rating Scales-Revised (CRS-R): Technical Manual. Multi-Health Systems.","type":"book","doi":null,"isbn":null,"url":"https://www.mhs.com/Conners-Scale-Family"},{"ref":"Conners, C. K. (1997). Conners Rating Scales-Revised: Technical Manual. Multi-Health Systems Inc.","type":"article","doi":null,"isbn":"978-0903264624","url":null}],"related":["vanderbilt-adhd-scale","cbcl-child-behavior","strengths-difficulties-questionnaire","achenbach-youth-self-report"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conover-iman-test","name":"Conover-Iman Test","fullName":"Conover-Iman Post-Hoc Multiple Comparison Test","aliases":["Conover-Iman post-hoc test","Conover post-hoc test","Conover-Iman Post-Hoc Testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1979,"originator":"Conover & Iman","url":"https://scholargate.app/en/statistics/conover-iman-test","markdownUrl":"https://scholargate.app/en/statistics/conover-iman-test.md","definition":"The Conover-Iman test is a rank-based post-hoc procedure, introduced by Conover and Iman in 1979, that identifies which pairs of groups differ after a significant Kruskal-Wallis or Friedman test. It builds a t-style statistic on the pooled ranks and is generally more powerful than the comparable Dunn test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conover & Iman","year":1979,"type":"Nonparametric post-hoc multiple comparison","estimator":"Rank-based t statistic on pooled ranks","outcome":"ordinal or continuous (rank-based)","usedAfter":"Kruskal-Wallis or Friedman test"},"citations":[{"ref":"Conover, W. J. & Iman, R. L. (1979). On Multiple-Comparisons Procedures. Technical Report LA-7677-MS, Los Alamos Scientific Laboratory.","type":"report","doi":null,"isbn":null,"url":"https://www.osti.gov/biblio/6057803"},{"ref":"Hollander, M., Wolfe, D. A. & Chicken, E. (2014). Nonparametric Statistical Methods (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470387375","url":null}],"related":["dunn-test","kruskal-wallis-test","friedman-test","mann-whitney-u-test","nemenyi-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"consensus-reaching","name":"CONSENSUS-REACHING","fullName":"Consensus Reaching — Iterative aggregation of expert opinions toward group consensus","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2002","originator":"Herrera-Viedma, E., Herrera, F., Chiclana, F.","url":"https://scholargate.app/en/decision-making/consensus-reaching","markdownUrl":"https://scholargate.app/en/decision-making/consensus-reaching.md","definition":"CONSENSUS-REACHING (Consensus Reaching — Iterative aggregation of expert opinions toward group consensus) is a ranking multi-criteria decision-making (MCDM) method introduced by Herrera-Viedma, E., Herrera, F., Chiclana, F. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herrera-Viedma, E., Herrera, F., Chiclana, F.","subfamily":"Ranking","year":"2002","type":"Group decision — iterative feedback-driven consensus convergence","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Herrera-Viedma, E., Herrera, F., Chiclana, F. (2002). A consensus model for multiperson decision making with different preference structures. IEEE Transactions on Systems, Man and Cybernetics — Part A","type":"article","doi":"10.1109/tsmca.2002.802821","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"consensus-sleep-diary","name":"Consensus Sleep Diary","fullName":"Consensus Sleep Diary","aliases":["Sleep Diary","Consensus Sleep Diary for Insomnia"],"domain":"sleep-medicine","family":"process-pipeline","subfamily":"Sleep tracking; daily prospective monitoring","year":"2012","originator":"Carney, C. E., Buysse, D. J., Ancoli-Israel, S., et al.","url":"https://scholargate.app/en/sleep-medicine/consensus-sleep-diary","markdownUrl":"https://scholargate.app/en/sleep-medicine/consensus-sleep-diary.md","definition":"The Consensus Sleep Diary is a standardized daily self-report instrument for prospective monitoring of sleep and wakefulness patterns. Developed by Carney and colleagues in 2012 through an international consensus process involving sleep medicine researchers and clinicians, it represents a unified approach to sleep tracking across clinical and research settings. The Consensus Sleep Diary records time in bed, sleep onset time, number and duration of nighttime awakenings, sleep quality, and other sleep-relevant variables, providing detailed information about sleep patterns that polysomnography cannot capture (daytime napping, sleep-wake schedule, weekly variation).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carney, C. E., Buysse, D. J., Ancoli-Israel, S., et al.","subfamily":"Sleep tracking; daily prospective monitoring","year":"2012","type":"Self-monitoring; daily patient report"},"citations":[{"ref":"Carney, C. E., Buysse, D. J., Ancoli-Israel, S., et al. (2012). The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep, 35(2), 287-302.","type":"article","doi":"10.5665/sleep.1642","isbn":null,"url":null}],"related":["sleep-condition-indicator","glasgow-sleep-effort-scale","stop-bang-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"consort-reporting-checklist","name":"CONSORT Reporting Checklist","fullName":"Consolidated Standards of Reporting Trials Checklist","aliases":["CONSORT","CONSORT 2010"],"domain":"research-methodology","family":"process-pipeline","subfamily":"RCT reporting standard","year":"2010 (original 1996)","originator":"Schulz et al. (CONSORT Group)","url":"https://scholargate.app/en/research-methodology/consort-reporting-checklist","markdownUrl":"https://scholargate.app/en/research-methodology/consort-reporting-checklist.md","definition":"The CONSORT (Consolidated Standards of Reporting Trials) Statement is a 25-item evidence-based checklist and flow diagram developed to standardize reporting of parallel-group randomized controlled trials. First published in 1996 and updated in 2010 (CONSORT 2010), it is endorsed by over 600 journals including The Lancet, JAMA, and BMJ, and is mandatory or strongly recommended for RCT manuscript submission across clinical research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schulz et al. (CONSORT Group)","subfamily":"RCT reporting standard","year":"2010 (original 1996)","type":"Trial author reporting checklist"},"citations":[{"ref":"Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. The Lancet, 375(9721), 1657–1668.","type":"article","doi":"10.1186/1745-6215-11-32","isbn":null,"url":null}],"related":["cochrane-risk-of-bias","casp-rct-checklist","prisma-checklist","strobe-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conspiracy-mentality-questionnaire","name":"Conspiracy Mentality Questionnaire","fullName":"Conspiracy Mentality Questionnaire (CMQ)","aliases":["CMQ","Conspiracy Ideation Scale","Generic Conspiracy Belief"],"domain":"political-psychology","family":"process-pipeline","subfamily":"ideological-orientations","year":"2013","originator":"Roland Imhoff & Marko Bruder","url":"https://scholargate.app/en/political-psychology/conspiracy-mentality-questionnaire","markdownUrl":"https://scholargate.app/en/political-psychology/conspiracy-mentality-questionnaire.md","definition":"The Conspiracy Mentality Questionnaire measures individual differences in generic conspiracy thinking—the tendency to attribute significant events to hidden, coordinated group actions by powerful actors rather than to incompetence, chance, or transparent public causes. Developed by Bruder et al. (2013), the five-item CMQ assesses a stable dispositional trait that predicts belief in diverse conspiracy theories (JFK assassination, 9/11 truthers, anti-vaccine narratives, QAnon) and distrust of institutions. It captures conspiracy mentality as a generalised political attitude distinct from specific beliefs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roland Imhoff & Marko Bruder","subfamily":"ideological-orientations","year":"2013","type":"Self-report"},"citations":[{"ref":"Bruder, M., Haffke, P., Neave, N., Nouripanah, N., & Imhoff, R. (2013). Measuring individual differences in generic beliefs in conspiracy: Conspiracy Mentality Questionnaire. Frontiers in Psychology, 4, 225.","type":"article","doi":"10.3389/fpsyg.2013.00225","isbn":null,"url":null},{"ref":"Imhoff, R., & Bruder, M. (2014). Speaking (un-)truth to power: Conspiracy mentality as a generalised political attitude. European Journal of Personality, 28(1), 25-43.","type":"article","doi":"10.1002/per.1930","isbn":null,"url":null},{"ref":"Swami, V., Coles, R., Stieger, S., Pietschnig, J., Furnham, A., Rehim, S., & Voracek, M. (2011). Conspiracist ideation in Britain and Austria: Validating a scale for the assessment of belief in conspiracy theories. Social Psychiatry and Psychiatric Epidemiology, 46(12), 1189-1198.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Conspiracist+ideation+in+Britain+and+Austria%3A+Validating+a+scale+for+the+assessment+of+belief+in+conspiracy+theories+Swami"}],"related":["need-for-cognition-political","voter-cynicism-scale","political-trust-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"constant-comparative-method","name":"Constant Comparative Method","fullName":"Constant Comparative Method","aliases":["CCM","constant comparison","constant comparative analysis","comparative constant analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Grounded Theory","year":"1967","originator":"Barney G. Glaser and Anselm L. Strauss","url":"https://scholargate.app/en/qualitative/constant-comparative-method","markdownUrl":"https://scholargate.app/en/qualitative/constant-comparative-method.md","definition":"The Constant Comparative Method (CCM) is a systematic qualitative analysis procedure in which every newly coded incident is immediately compared with all previously coded incidents in the same category. Introduced by Glaser and Strauss in their 1967 grounded theory framework, CCM drives theory development by cycling continuously between data collection and analysis, progressively refining categories until theoretical saturation is reached. Though closely associated with grounded theory, the method has been widely adopted as a stand-alone analytic strategy across qualitative traditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser and Anselm L. Strauss","year":"1967","type":"Qualitative research method","dataType":"Interviews, field notes, documents, observations (text data)","typicalSampleSize":"Theoretical sampling until saturation (commonly 15–40 participants or units)","subfamily":"Grounded Theory"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Discovery+of+Grounded+Theory+Strategies+for+Qualitative+Research+Glaser+Strauss+1967"},{"ref":"Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage.","type":"book","doi":null,"isbn":"978-0803924314","url":null}],"related":["grounded-theory","thematic-analysis","content-analysis","case-study","phenomenology","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"constituency-parsing","name":"Constituency Parsing","fullName":"Constituency Parsing (Phrase-Structure Parsing)","aliases":["phrase-structure parsing","constituent parsing","Kurucu Öbek Ayrıştırma (Constituency Parsing)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2003","originator":"Michael Collins (statistical models, 2003)","url":"https://scholargate.app/en/text-mining/constituency-parsing","markdownUrl":"https://scholargate.app/en/text-mining/constituency-parsing.md","definition":"Constituency parsing is a natural-language-processing task that represents a sentence as a tree of recursively nested phrase-structure constituents — for example S → NP + VP. Building on the head-driven statistical parsing models introduced by Collins (2003) and the later neural parsers of Kitaev and colleagues (2019), it exposes the hierarchical syntactic skeleton of a sentence for grammatical pattern extraction and grammar research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Collins (statistical models, 2003)","year":"2003","type":"NLP syntactic-analysis task","output":"Phrase-structure (constituency) parse tree, e.g. S → NP + VP","approaches":"Grammar-based (CFG) / neural self-attention parsers","minSample":"10 sentences"},"citations":[{"ref":"Collins, M. (2003). Head-Driven Statistical Models for Natural Language Parsing. Computational Linguistics, 29(4), 589-637.","type":"article","doi":"10.1162/089120103322753356","isbn":null,"url":null},{"ref":"Kitaev, N., Cao, S. & Klein, D. (2019). Multilingual Constituency Parsing with Self-Attention and Pre-Training. Proceedings of ACL.","type":"article","doi":"10.18653/v1/P19-1340","isbn":null,"url":null}],"related":["dependency-parsing","pos-tagging","named-entity-recognition"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"constraint-programming","name":"Constraint Programming","fullName":"Constraint Programming","aliases":["Constraint Satisfaction Programming","Constraint-Based Optimization","Kısıt Programlama","CSP Optimization"],"domain":"optimization","family":"process-pipeline","subfamily":"Mathematical programming","year":2006,"originator":"Rossi, van Beek & Walsh","url":"https://scholargate.app/en/optimization/constraint-programming","markdownUrl":"https://scholargate.app/en/optimization/constraint-programming.md","definition":"Constraint Programming (CP) is a declarative optimization paradigm in which a problem is formulated as a set of variables, finite domains, and constraints, and a solver systematically searches for assignments that satisfy all constraints. Formalized comprehensively by Rossi, van Beek, and Walsh in their 2006 Handbook of Constraint Programming, CP unifies propagation-based pruning with intelligent backtracking search to tackle combinatorial problems across scheduling, planning, and configuration domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rossi, van Beek & Walsh","year":2006,"type":"Declarative combinatorial optimization","subfamily":"Mathematical programming","complexity":"NP-complete in general; polynomial for tractable subclasses","paradigm":"Declarative constraint propagation with search"},"citations":[{"ref":"Rossi, F., van Beek, P., & Walsh, T. (Eds.). (2006). Handbook of Constraint Programming. Elsevier.","type":"book","doi":null,"isbn":"978-0-444-52726-4","url":null}],"related":["integer-programming","dynamic-programming","tabu-search"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"construct-validity","name":"Construct Validity","fullName":"Construct Validity","aliases":["construct validation","factorial validity","nomological validity evidence","validity of interpretation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1955","originator":"Lee J. Cronbach & Paul E. Meehl","url":"https://scholargate.app/en/psychometrics/construct-validity","markdownUrl":"https://scholargate.app/en/psychometrics/construct-validity.md","definition":"Construct validity is the degree to which a test or scale actually measures the theoretical construct it is intended to measure. Introduced by Cronbach and Meehl in 1955, it is the central validity concern in psychological and educational measurement, evaluated by accumulating multiple lines of empirical and logical evidence rather than by any single statistical test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach & Paul E. Meehl","year":"1955","type":"Validity evaluation framework","dataType":"Scores from psychological / educational instruments","subfamily":"Scale / measurement"},"citations":[{"ref":"Cronbach, L. J. & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.","type":"article","doi":"10.1037/h0040957","isbn":null,"url":null},{"ref":"Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational Measurement (3rd ed., pp. 13–103). American Council on Education / Macmillan.","type":"book","doi":null,"isbn":"978-0029190609","url":null}],"related":["convergent-validity","discriminant-validity","content-validity","confirmatory-factor-analysis","nomological-validity","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"constructed-wetland-design","name":"Constructed Wetland Design","fullName":"Ecological Wastewater Treatment via Constructed Wetlands","aliases":["CW design","treatment wetlands","natural treatment systems","artificial wetlands"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Ecological treatment engineering","year":"1973","originator":"Seidel and Kickuth","url":"https://scholargate.app/en/environmental-engineering/constructed-wetland-design","markdownUrl":"https://scholargate.app/en/environmental-engineering/constructed-wetland-design.md","definition":"Constructed wetland design is an environmental engineering approach that harnesses natural biological and chemical processes—microorganism metabolism, plant uptake, soil sorption, sedimentation—to treat wastewater, stormwater, and agricultural runoff. Developed systematically in the 1970s by German researchers Seidel and Kickuth, constructed wetlands operate with minimal energy input and create amenity and biodiversity co-benefits alongside treatment. The design process integrates hydrology, biogeochemistry, and landscape planning to optimize contaminant removal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seidel and Kickuth","subfamily":"Ecological treatment engineering","year":"1973","type":"integrated pipeline design"},"citations":[{"ref":"Kadlec, R. H., & Wallace, S. D. (2009). Treatment Wetlands (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1566706124","url":null},{"ref":"Tanner, C. C. (2000). Design Manual: Wastewater Treatment Using Free Water Surface Constructed Wetlands. New Zealand Water and Wastes Association.","type":"article","doi":null,"isbn":null,"url":"https://www.wwa.org.nz"},{"ref":"García, J., Rousseau, D. P. L., Morató, J., Lesage, E., Matamoros, V., & Bayona, J. M. (2010). Contaminant Removal Processes in Subsurface-Flow Constructed Wetlands: A Review. Critical Reviews in Environmental Science and Technology, 40(7), 561-661.","type":"article","doi":"10.1080/10643380802471076","isbn":null,"url":null}],"related":["stormwater-management","wastewater-treatment-design","activated-sludge-model","green-infrastructure-design","environmental-impact-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"constructivist-grounded-theory","name":"Constructivist Grounded Theory","fullName":"Constructivist Grounded Theory (Charmaz)","aliases":["CGT","constructivist GT","Charmaz grounded theory","interpretive grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Grounded Theory","year":"2000s (Charmaz 2000–2006; classic GT roots 1967)","originator":"Kathy Charmaz (building on Glaser & Strauss, 1967)","url":"https://scholargate.app/en/qualitative/constructivist-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/constructivist-grounded-theory.md","definition":"Constructivist Grounded Theory (CGT) is a qualitative methodology developed by Kathy Charmaz that systematically builds mid-range theory from empirical data through iterative coding, memo-writing, and theoretical sampling. Unlike the original objectivist version by Glaser and Strauss, CGT treats both data and theory as co-constructed between researcher and participants, acknowledging the researcher's interpretive perspective as an integral part of the analytic process rather than a source of bias to be eliminated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz (building on Glaser & Strauss, 1967)","year":"2000s (Charmaz 2000–2006; classic GT roots 1967)","type":"Qualitative research method","dataType":"Interviews, focus groups, observations, documents, field notes","typicalSampleSize":"15–30 participants (theoretical sampling until saturation)","subfamily":"Grounded Theory"},"citations":[{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973539","url":null},{"ref":"Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0857029140","url":null}],"related":["grounded-theory","phenomenology","ethnography","case-study","narrative-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"consumer-involvement-scale","name":"Consumer Involvement Scale","fullName":"Consumer Involvement Scale (CIS)","aliases":["Product Involvement Scale","Personal Involvement Inventory"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Consumer behavior and motivation","year":"1985","originator":"Judith Lynne Zaichkowsky","url":"https://scholargate.app/en/marketing-management/consumer-involvement-scale","markdownUrl":"https://scholargate.app/en/marketing-management/consumer-involvement-scale.md","definition":"The Consumer Involvement Scale (CIS), developed by Zaichkowsky (1985), measures the degree to which a consumer feels personally invested in a product, brand, or purchase decision. Originally a 20-item instrument operationalizing the concept of 'personal relevance,' the CIS was refined to 10 items in 1994 (Revised Personal Involvement Inventory, PII), maintaining measurement of consumer involvement across multiple semantic dimensions. Involvement captures both the personal importance of a product category and the perceived risk of making a poor choice. The scale is fundamental in consumer behavior research for understanding motivation, information processing, and purchase decision intensity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judith Lynne Zaichkowsky","subfamily":"Consumer behavior and motivation","year":"1985","type":"Uni-dimensional consumer involvement scale"},"citations":[{"ref":"Zaichkowsky, J. L. (1985). Measuring the Involvement Construct. Journal of Consumer Research, 12(3), 341-352.","type":"article","doi":"10.1086/208520","isbn":null,"url":null},{"ref":"Zaichkowsky, J. L. (1994). The Personal Involvement Inventory: Reduction, Revision, and Application to Advertising. Journal of Advertising, 23(4), 59-70.","type":"article","doi":"10.1080/00913367.1943.10673459","isbn":null,"url":null}],"related":["brand-equity-scale","customer-loyalty-scale","price-fairness-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"contact-angle-goniometry","name":"Contact Angle Goniometry","fullName":"Contact Angle Goniometry Surface Characterization","aliases":["sessile drop method","contact angle measurement","wettability analysis"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Surface characterization","year":"1805","originator":"Thomas Young","url":"https://scholargate.app/en/biomaterials/contact-angle-goniometry","markdownUrl":"https://scholargate.app/en/biomaterials/contact-angle-goniometry.md","definition":"Contact angle goniometry is a technique for measuring the wettability of a solid surface by determining the angle at which a liquid droplet meets the surface. Rooted in Thomas Young's thermodynamic analysis from 1805, the method uses optical measurement of droplet profile to quantify surface energy and hydrophilicity. It is indispensable in biomaterials characterization, helping researchers assess whether a scaffold or implant surface will promote or inhibit cell adhesion, protein adsorption, and biointegration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Young","subfamily":"Surface characterization","year":"1805","type":"Wettability measurement"},"citations":[{"ref":"Young, T. (1805). An essay on the cohesion of fluids. Philosophical Transactions of the Royal Society, 95, 65-87.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1098/rstl.1805.0005"},{"ref":"Owens, D. K., & Wendt, R. C. (1969). Estimation of surface free energy of polymers. Journal of Applied Polymer Science, 13(8), 1741-1747.","type":"article","doi":"10.1002/app.1969.070130815","isbn":null,"url":null},{"ref":"Good, R. J. (1979). Surface free energy of solids and liquids: thermodynamics, measurement, and applicability. Journal of Colloid and Interface Science, 52(2), 308-313.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Surface+free+energy+of+solids+and+liquids%3A+thermodynamics%2C+measurement%2C+and+applicability+Good"}],"related":["electrospinning","dynamic-mechanical-analysis","swelling-and-degradation","decellularization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"contamination-obsessions-scale","name":"Vancouver Obsessional Compulsive Inventory","fullName":"Vancouver Obsessional Compulsive Inventory (VOCI)","aliases":["VOCI"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"obsessive-compulsive","year":2004,"originator":"Darrin S. Thordarson, Adam S. Radomsky, and colleagues","url":"https://scholargate.app/en/anxiety-disorders/contamination-obsessions-scale","markdownUrl":"https://scholargate.app/en/anxiety-disorders/contamination-obsessions-scale.md","definition":"The Vancouver Obsessional Compulsive Inventory (VOCI) is a 55-item self-report questionnaire designed to assess the frequency and distress associated with obsessive-compulsive symptoms across multiple domains. Developed by Thordarson and colleagues in 2004, the VOCI measures six subscales: Contamination Obsessions, Checking Compulsions, Obsessions, Hoarding, Just Right Compulsions, and Doubt/Responsibility. It is a comprehensive tool for assessing the full spectrum of OCD presentations and monitoring treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Darrin S. Thordarson, Adam S. Radomsky, and colleagues","subfamily":"obsessive-compulsive","year":2004,"type":"Self-report"},"citations":[{"ref":"Thordarson, D. S., Radomsky, A. S., Rachman, S., Shafran, R., Sawchuk, C. N., & Ralph Hakstian, A. (2004). The Vancouver Obsessional Compulsive Inventory (VOCI). Behaviour Research and Therapy, 42(11), 1289–1314.","type":"article","doi":"10.1016/j.brat.2003.08.007","isbn":null,"url":null}],"related":["anxiety-sensitivity-index","interoceptive-sensations-scale","body-sensations-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"content-analysis-qualitative","name":"Qualitative Content Analysis","fullName":"Qualitative Content Analysis Method","aliases":["Content Analysis","Categorical Content Analysis"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"systematic-categorization","year":"1980","originator":"Klaus Krippendorff; refined by Margrit Schreier","url":"https://scholargate.app/en/qualitative-research/content-analysis-qualitative","markdownUrl":"https://scholargate.app/en/qualitative-research/content-analysis-qualitative.md","definition":"Qualitative Content Analysis (QCA) is a systematic, inductive method for analyzing textual or visual data by identifying and categorizing meaning units into content categories. Developed and formalized by Klaus Krippendorff (1980), QCA can be purely qualitative (inductive, exploratory) or combined with quantitative counting; it analyzes manifest content (explicit, surface meanings) and latent content (underlying, interpretive meanings).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klaus Krippendorff; refined by Margrit Schreier","subfamily":"systematic-categorization","year":"1980","type":"Method"},"citations":[{"ref":"Krippendorff, K. (1980). Content analysis: An introduction to its methodology. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Krippendorff%2C%20K.%20(1980).%20Content%20analysis%3A%20An%20introduction%20to%20its%20methodology.%20Sage%20Publications."},{"ref":"Schreier, M. (2012). Qualitative content analysis in practice. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Schreier%2C%20M.%20(2012).%20Qualitative%20content%20analysis%20in%20practice.%20Sage%20Publications."},{"ref":"Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Journal of Nursing Research, 13(4), 203–215.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Hsieh%2C%20H.%20F.%2C%20%26%20Shannon%2C%20S.%20E.%20(2005).%20Three%20approaches%20to%20qualitative%20content%20analysis.%20Journal%20of%20Nursing%20Research%2C%2013"}],"related":["thematic-analysis","discourse-analysis","manifest-content","latent-content","coding-scheme"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"content-analysis","name":"Content Analysis","fullName":"Content Analysis","aliases":["İçerik Analizi","systematic content coding","quantitative content analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":null,"year":"Systematised through Krippendorff's methodology work; 4th edition 2018","originator":"Klaus Krippendorff (systematic formulation); roots in early 20th-century communications research","url":"https://scholargate.app/en/qualitative/content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/content-analysis.md","definition":"Content analysis is a systematic research technique for reducing text, visual, or media material into coded categories so that patterns can be counted, compared, and interpreted. Formalised by Klaus Krippendorff in his widely cited methodology textbook (latest edition 2018), the method sits at the boundary of qualitative and quantitative inquiry: it imposes structured, replicable coding on inherently meaning-laden material.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klaus Krippendorff (systematic formulation); roots in early 20th-century communications research","year":"Systematised through Krippendorff's methodology work; 4th edition 2018","type":"Qualitative / mixed-method research technique","dataType":"Text, visual, or media content","output":"Coded categories with frequencies or themes","reliabilityThreshold":"Cohen's Kappa > 0.70","minimumSample":10,"difficulty":2},"citations":[{"ref":"Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506395661","url":null}],"related":["thematic-analysis","discourse-analysis","grounded-theory","sentiment-analysis","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"content-validity-ratio","name":"Content Validity Ratio","fullName":"Lawshe's Content Validity Ratio Method for Expert Panel Assessment","aliases":["CVR","Content validity index","Expert judgment content validity","Lawshe CVR"],"domain":"psychometrics","family":"process-pipeline","subfamily":"Scale development","year":"1975","originator":"Charles H. Lawshe","url":"https://scholargate.app/en/psychometrics/content-validity-ratio","markdownUrl":"https://scholargate.app/en/psychometrics/content-validity-ratio.md","definition":"The Content Validity Ratio (CVR) is a quantitative method developed by Charles Lawshe in 1975 for evaluating the extent to which items in a measurement instrument are relevant and representative of a target construct. The method aggregates expert panel judgments into a single validity coefficient for each item, enabling researchers to identify and retain only those items deemed essential by domain experts. CVR provides objective support for content validity claims during scale development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles H. Lawshe","subfamily":"Scale development","year":"1975","type":"Expert panel content validity assessment"},"citations":[{"ref":"Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+quantitative+approach+to+content+validity+Lawshe"},{"ref":"Tristán-López, A. (2008). Modification of the content validity ratio. Revista Educación y Pedagogía, 20(48), 11-18.","type":"article","doi":null,"isbn":null,"url":"https://revistas.udea.edu.co/index.php/revistaeyp/article/view/24"},{"ref":"Polit, D. F., & Beck, C. T. (2006). The content validity index: are you sure? Research in Nursing & Health, 29(5), 489-497.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+content+validity+index%3A+are+you+sure+Polit"}],"related":["likert-scale-construction","factor-analysis-scale","confirmatory-factor-analysis-scale","floor-ceiling-effect"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"content-validity","name":"Content Validity","fullName":"Content Validity","aliases":["content-related validity","logical validity","face validity","content validation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1975","originator":"C. H. Lawshe (quantitative framework); earlier qualitative traditions in educational measurement","url":"https://scholargate.app/en/psychometrics/content-validity","markdownUrl":"https://scholargate.app/en/psychometrics/content-validity.md","definition":"Content validity is evidence that a measurement instrument adequately samples the full domain of the construct it is intended to measure. It is established through systematic expert review and quantified with indices such as Lawshe's Content Validity Ratio (CVR) and Lynn's Content Validity Index (CVI), making it the foundational validity step in scale development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"C. H. Lawshe (quantitative framework); earlier qualitative traditions in educational measurement","year":"1975","type":"Validity evidence / expert judgement procedure","dataType":"Expert panel ratings (ordinal / categorical)","subfamily":"Scale / measurement"},"citations":[{"ref":"Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+quantitative+approach+to+content+validity+Lawshe"},{"ref":"Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–385.","type":"article","doi":"10.1097/00006199-198611000-00017","isbn":null,"url":null}],"related":["construct-validity","convergent-validity","discriminant-validity","nomological-validity","scale-development","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"contextual-inquiry","name":"Contextual Inquiry","fullName":"Contextual Inquiry Method","aliases":["CI","Contextual Design","Work-centered Design"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Qualitative Research","year":"1993","originator":"Hugh Beyer and Karen Holtzblatt","url":"https://scholargate.app/en/human-computer-interaction/contextual-inquiry","markdownUrl":"https://scholargate.app/en/human-computer-interaction/contextual-inquiry.md","definition":"Contextual Inquiry is a field research method for understanding users by observing and interviewing them in their real work environment. Developed by Hugh Beyer and Karen Holtzblatt at Applied Research and Technology, this method combines ethnographic observation with targeted questioning to capture not just what users say they do, but what they actually do—including workarounds, informal practices, and priorities often invisible in lab settings. Contextual Inquiry uncovers the context, constraints, and real-world complexity of user tasks, providing rich insights for user-centered design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hugh Beyer and Karen Holtzblatt","subfamily":"Qualitative Research","year":"1993","type":"In-situ user research method capturing real work practices"},"citations":[{"ref":"Beyer, H., & Holtzblatt, K. (1998). Contextual Design: Defining Customer-Centered Systems. Morgan Kaufmann.","type":"article","doi":null,"isbn":"1-558-60722-X","url":null},{"ref":"Holtzblatt, K., & Jones, S. (1993). Contextual inquiry: A participatory technique for system design. In D. Schuler & A. Namioka (Eds.), Participatory Design (pp. 177–210). Lawrence Erlbaum Associates.","type":"article","doi":null,"isbn":"0-8058-1441-7","url":null}],"related":["think-aloud-protocol","pluralistic-walkthrough","card-sorting","retrospective-think-aloud"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"contingent-valuation","name":"Contingent Valuation","fullName":"Contingent Valuation Method (CVM)","aliases":["CVM","Willingness-to-Pay Survey","WTP Elicitation"],"domain":"economics","family":"process-pipeline","subfamily":"Environmental and Resource Economics","year":"1963","originator":"Robert Davis","url":"https://scholargate.app/en/economics/contingent-valuation","markdownUrl":"https://scholargate.app/en/economics/contingent-valuation.md","definition":"Contingent Valuation (CVM), developed by Robert Davis in the 1960s, is a survey-based method for estimating the economic value of non-market environmental goods and services—such as wilderness preservation, air quality, or species protection—by directly asking people their willingness to pay (WTP) for specified improvements or willingness to accept (WTA) compensation for losses. It provides a valuation where market prices do not exist.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Davis","subfamily":"Environmental and Resource Economics","year":"1963","type":"Stated preference valuation method"},"citations":[{"ref":"Mitchell, R. C., & Carson, R. T. (1989). Using Surveys to Value Public Goods: The Contingent Valuation Method. Resources for the Future.","type":"article","doi":null,"isbn":null,"url":"https://www.rff.org/publications/"},{"ref":"Arrow, K., Solow, R., Portney, P. R., Leamer, E. E., Radner, R., & Schuman, H. (1993). Report of the NOAA Panel on Contingent Valuation. Federal Register, 58(10), 4601–4614.","type":"article","doi":null,"isbn":null,"url":"https://www.federalregister.gov/"},{"ref":"Bateman, I. J., Carson, R. T., Day, B., Hanemann, M., Hanley, N., Hett, T., & Loomes, G. (2002). Economic Valuation with Stated Preference Techniques: A Manual. Edward Elgar.","type":"article","doi":null,"isbn":null,"url":"https://www.elgaronline.com/"}],"related":["hedonic-pricing","travel-cost-method","slutsky-equation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"contour-analysis","name":"Contour Analysis","fullName":"Contour Detection and Analysis","aliases":["Edge-based contours","Boundary analysis"],"domain":"computer-vision","family":"ml-model","subfamily":"Boundary detection","year":"1985","originator":"Satoshi Suzuki and Keiichi Abe","url":"https://scholargate.app/en/computer-vision/contour-analysis","markdownUrl":"https://scholargate.app/en/computer-vision/contour-analysis.md","definition":"Contour analysis is the process of detecting and analyzing the boundaries of objects in images by identifying connected edges and extracting shape information. The Suzuki-Abe algorithm provides an efficient method for finding contours in binary images, enabling shape-based object classification and segmentation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Satoshi Suzuki and Keiichi Abe","subfamily":"Boundary detection","year":"1985","type":"Shape and contour analysis"},"citations":[{"ref":"Suzuki, S., & Abe, K. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1), 32–46.","type":"article","doi":"10.1016/0734-189X(85)90016-7","isbn":null,"url":null},{"ref":"Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), 179–187.","type":"article","doi":"10.1109/TIT.1962.1057692","isbn":null,"url":null}],"related":["canny-edge-detection","watershed-segmentation","image-morphology","blob-detection","harris-corner-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"contrast-analysis","name":"Contrast Analysis","fullName":"Planned Contrast Analysis","aliases":["planned comparisons","planned contrasts","a priori contrasts","Kontrast Analizi — Planlanmış Karşılaştırmalar"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":2000,"originator":"Rosenthal, Rosnow & Rubin (modern formalization)","url":"https://scholargate.app/en/statistics/contrast-analysis","markdownUrl":"https://scholargate.app/en/statistics/contrast-analysis.md","definition":"Planned contrast analysis is a parametric hypothesis-testing method that evaluates specific, theoretically motivated comparisons among group means — comparisons that the researcher specifies before data collection, not in response to observed patterns. Formalized comprehensively by Rosenthal, Rosnow, and Rubin (2000), the approach assigns a set of contrast coefficients to the groups being compared, with the constraint that the coefficients sum to zero, and then tests whether the resulting weighted combination of means differs significantly from zero.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenthal, Rosnow & Rubin (modern formalization)","year":2000,"family":"Hypothesis test","type":"Parametric planned comparison","groups":"≥ 3","outcome":"continuous","parametric":true,"planningStage":"a priori (before data collection)","distribution":"t (per contrast)","requiresNormality":true,"minSample":20},"citations":[{"ref":"Rosenthal, R., Rosnow, R. L. & Rubin, D. B. (2000). Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521659802","url":null}],"related":["one-way-anova","tukey-hsd","bonferroni-correction","post-hoc-tests","factorial-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"contrast-ratio-measurement","name":"Contrast Ratio Measurement","fullName":"Contrast Ratio Measurement","aliases":["Color Contrast Evaluation","Luminance Contrast Analysis"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Accessibility and visual clarity","year":"2018","originator":"W3C Accessibility Guidelines","url":"https://scholargate.app/en/visual-arts/contrast-ratio-measurement","markdownUrl":"https://scholargate.app/en/visual-arts/contrast-ratio-measurement.md","definition":"Contrast Ratio Measurement is a systematic method for quantifying the perceptual difference between two colors, typically foreground and background text or elements. Grounded in color science and accessibility standards, this pipeline calculates luminance-based contrast to ensure visual content is readable for all viewers, including those with low vision or color blindness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"W3C Accessibility Guidelines","subfamily":"Accessibility and visual clarity","year":"2018","type":"Measurement and compliance assessment"},"citations":[{"ref":"World Wide Web Consortium (2018). Web Content Accessibility Guidelines (WCAG) 2.1. https://www.w3.org/WAI/WCAG21/quickref/","type":"article","doi":null,"isbn":null,"url":"https://www.w3.org/WAI/WCAG21/quickref/"},{"ref":"Poynton, C. (2012). Gamma FAQ: Frequently Asked Questions about Gamma. http://poynton.ca/GammaFAQ.html","type":"article","doi":null,"isbn":null,"url":"http://poynton.ca/GammaFAQ.html"},{"ref":"Legge, G. E., Pelli, D. G., Rubin, G. S., & Schleske, M. M. (2007). Psychophysics of Reading—I. Normal Vision. Vision Research, 25(2), 239–252.","type":"article","doi":"10.1016/0042-6989(85)90117-8","isbn":null,"url":null}],"related":["typography-legibility-test","color-harmony-analysis","visual-saliency-map","color-palette-extraction","image-aesthetics-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"contrastive-learning-dl","name":"Visual Contrastive Learning","fullName":"Visual Contrastive Self-Supervised Learning (SimCLR / MoCo / BYOL)","aliases":["Karşıtlık Öğrenmesi — Görsel (SimCLR / MoCo / BYOL)","contrastive learning","self-supervised visual representation learning","SimCLR","MoCo","BYOL"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2020,"originator":"Chen, T. et al. (SimCLR); He, K. et al. (MoCo)","url":"https://scholargate.app/en/deep-learning/contrastive-learning-dl","markdownUrl":"https://scholargate.app/en/deep-learning/contrastive-learning-dl.md","definition":"Visual contrastive learning is a self-supervised deep-learning approach — popularised by frameworks such as SimCLR (Chen et al., 2020) and MoCo (He et al., 2020) — that learns rich image representations without labels by pulling different augmentations of the same image together and pushing different images apart. It turns a large pool of unlabelled images into a useful feature extractor.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, T. et al. (SimCLR); He, K. et al. (MoCo)","year":2020,"type":"Self-supervised deep representation learning","task":"Visual representation learning / classification","minSample":1000,"requiresGPU":true},"citations":[{"ref":"Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. ICML.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2002.05709"},{"ref":"He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. CVPR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1911.05722"}],"related":["mixture-of-experts","graph-attention-network","longformer-bigbird","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"contrastive-learning-nlp","name":"Contrastive Learning for NLP","fullName":"Contrastive Learning for Natural Language Processing","aliases":["SimCSE","contrastive sentence embeddings","ContrastiveBERT","Karşıtlık Öğrenmesi — NLP (Contrastive Learning)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2020–2021","originator":"Gao, Yao & Chen (SimCSE, 2021); Khosla et al. (Supervised Contrastive, 2020)","url":"https://scholargate.app/en/text-mining/contrastive-learning-nlp","markdownUrl":"https://scholargate.app/en/text-mining/contrastive-learning-nlp.md","definition":"Contrastive learning for NLP is a representation-learning technique — popularised by SimCSE (Gao et al., 2021) and Supervised Contrastive Learning (Khosla et al., 2020) — that trains a text encoder by pulling embeddings of similar text pairs together while pushing embeddings of dissimilar pairs apart. The result is a dense, high-quality embedding space that can be learned with no labels at all, or with minimal supervision, making it especially valuable when annotated data are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gao, Yao & Chen (SimCSE, 2021); Khosla et al. (Supervised Contrastive, 2020)","year":"2020–2021","type":"Self-supervised / supervised representation learning","input":"Text pairs (positive and, optionally, negative examples)","output":"Dense sentence embeddings in a shared vector space","minSample":50,"difficulty":"3 / 5","requiresNormality":false,"hardwareNote":"GPU strongly recommended"},"citations":[{"ref":"Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of EMNLP 2021.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2021.emnlp-main.552"},{"ref":"Khosla, P., et al. (2020). Supervised Contrastive Learning. Advances in Neural Information Processing Systems (NeurIPS) 33.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/d89a66c7c80a29b1bdbab0f2a1a94af8-Abstract.html"}],"related":["bert-embeddings","sentence-transformers","text-classification","semantic-similarity","self-supervised-learning"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"control-chart","name":"Control chart","fullName":"Statistical Control Chart (Shewhart Chart)","aliases":["Shewhart chart","process-behavior chart","SPC chart","quality control chart"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1924 (first use); 1931 (seminal book)","originator":"Walter A. Shewhart (Bell Labs)","url":"https://scholargate.app/en/experimental-design/control-chart","markdownUrl":"https://scholargate.app/en/experimental-design/control-chart.md","definition":"A control chart is a time-series graph with statistically derived upper and lower control limits that separates the natural, random variation of a process (common cause) from unusual, assignable variation (special cause). Invented by Walter Shewhart at Bell Labs in 1924, control charts remain the foundational tool of Statistical Process Control and are used across manufacturing, healthcare, software, and service industries to monitor whether a process remains stable and predictable over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walter A. Shewhart (Bell Labs)","year":"1924 (first use); 1931 (seminal book)","type":"Statistical monitoring and control technique","dataType":"Time-ordered continuous or attribute process measurements","subfamily":"Engineering methods"},"citations":[{"ref":"Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Economic+Control+of+Quality+of+Manufactured+Product+Shewhart+1931"},{"ref":"Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470169926","url":null}],"related":["statistical-process-control","process-capability-analysis","failure-mode-and-effects-analysis","six-sigma-dmaic","design-of-experiments","quality-function-deployment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"control-group-experimental-design","name":"Control Group Experimental Design","fullName":"Experimental Design with Control Group","aliases":["controlled experiment","true experimental design","randomized controlled design","treatment-control design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1935 (Fisher); 1963 (Campbell & Stanley codification)","originator":"Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley","url":"https://scholargate.app/en/experimental-design/control-group-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/control-group-experimental-design.md","definition":"Control group experimental design is a fundamental experimental structure in which participants are assigned to at least two groups — a treatment group that receives the intervention and a control group that does not — so that the effect of the intervention can be isolated by comparing outcomes across groups. Randomisation of assignment strengthens causal inference by balancing known and unknown confounders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley","year":"1935 (Fisher); 1963 (Campbell & Stanley codification)","type":"Experimental research design","dataType":"Quantitative outcome measures (continuous or categorical)","subfamily":"Deneysel desen"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+Quasi-Experimental+Designs+for+Research+Campbell+Stanley+1963"},{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Design+of+Experiments+Fisher+1935"}],"related":["pretest-posttest-experimental-design","randomized-controlled-trial","solomon-four-group-design","factorial-experiment","quasi-experimental-design","between-subjects-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"control-preferences-scale","name":"Control Preferences Scale","fullName":"Control Preferences Scale (CPS)","aliases":["Desired Role in Decision Making","Decision Role Preference"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"shared-decision-making","year":1997,"originator":"Lois Degner","url":"https://scholargate.app/en/patient-centered-care/control-preferences-scale","markdownUrl":"https://scholargate.app/en/patient-centered-care/control-preferences-scale.md","definition":"The Control Preferences Scale (CPS) is a five-item measure that assesses a patient's preferred role in healthcare decision making, ranging from a passive (physician-directed) to active (patient-directed) or shared approach. Developed by Lois Degner and colleagues in 1997, the CPS measures the degree of control patients wish to exercise in treatment decisions: whether they prefer to leave decisions to the clinician, collaborate with the clinician, or make the final decision themselves. The scale is widely used to understand patient preferences for decision-making involvement and to evaluate the alignment between preferred and actual roles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lois Degner","subfamily":"shared-decision-making","year":1997,"type":"Patient-reported"},"citations":[{"ref":"Degner, L. F., Sloan, J. A., & Venkatesh, P. (1997). The Control Preferences Scale. Canadian Journal of Nursing Research, 29(3), 21-43.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Degner%2C%20L.%20F.%2C%20Sloan%2C%20J.%20A.%2C%20%26%20Venkatesh%2C%20P.%20(1997).%20The%20Control%20Preferences%20Scale.%20Canadian%20Journal%20of%20Nursing%20Research"},{"ref":"Brace, C., Keating, N. L., Hemminki, K., et al. (2006). Informed decision making and cancer screening: the role of the physician. American Journal of Medical Genetics Part A, 140A(20), 2256-2264.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Informed+decision+making+and+cancer+screening%3A+the+role+of+the+physician+Brace"}],"related":["collaboste-scale","decisional-conflict-scale","patient-enablement-instrument","trust-in-physician-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"controlled-atmosphere-storage","name":"Controlled Atmosphere Storage","fullName":"Manipulation of Gas Composition for Extended Shelf Life and Quality Retention","aliases":["CA storage","modified atmosphere packaging","O2 and CO2 management"],"domain":"horticulture","family":"process-pipeline","subfamily":"Advanced gas-based storage management","year":"1980","originator":"Horticultural postharvest research","url":"https://scholargate.app/en/horticulture/controlled-atmosphere-storage","markdownUrl":"https://scholargate.app/en/horticulture/controlled-atmosphere-storage.md","definition":"Controlled atmosphere (CA) storage extends fruit shelf life beyond cold storage alone by actively regulating oxygen (O₂) and carbon dioxide (CO₂) concentrations during storage. By reducing respiration and ethylene production rates, CA storage can maintain fruit quality for months. This advanced technique is expensive but economically justified for premium fruit destined for long-distance export or extended market windows.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Horticultural postharvest research","subfamily":"Advanced gas-based storage management","year":"1980","type":"environmental control storage pipeline"},"citations":[{"ref":"Kader, A. A. (2002). Postharvest Technology of Horticultural Crops (3rd ed.). University of California Agricultural and Natural Resources Publication.","type":"article","doi":null,"isbn":null,"url":"https://anrcatalog.ucanr.edu/Details.aspx?itemNo=3311"},{"ref":"Beaudry, R. M. (2000). Responses of horticultural commodities to low oxygen: Limits to the expanded use of controlled atmospheres. Journal of the American Society for Horticultural Science, 125(6), 698–705.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Responses+of+horticultural+commodities+to+low+oxygen%3A+Limits+to+the+expanded+use+of+controlled+atmospheres+Beaudry"}],"related":["cold-storage-protocol","postharvest-storage-simulation","ripeness-index","brix-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"convergent-cross-mapping","name":"Convergent Cross Mapping","fullName":"Convergent Cross Mapping (CCM)","aliases":["CCM","Cross-Convergent Mapping","Empirical Dynamic Modelling Causality","Yakınsak Çapraz Haritalama"],"domain":"causal-inference","family":"ml-model","subfamily":"Dynamical causality","year":2012,"originator":"George Sugihara et al.","url":"https://scholargate.app/en/causal-inference/convergent-cross-mapping","markdownUrl":"https://scholargate.app/en/causal-inference/convergent-cross-mapping.md","definition":"Convergent Cross Mapping (CCM) is a nonlinear, state-space method for detecting causality between time-series variables embedded in a shared dynamical system. Introduced by George Sugihara and colleagues in their landmark 2012 Science paper, CCM exploits Takens' embedding theorem: if variable X causally influences Y, the historical record of Y contains enough information to recover the states of X. Causality is confirmed when cross-map skill improves—converges—as the time-series library grows longer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Sugihara et al.","year":2012,"type":"Nonlinear time-series causality test","subfamily":"Dynamical causality","framework":"Empirical Dynamic Modelling (EDM)","complexity":"O(L·E) per library size L and embedding dimension E"},"citations":[{"ref":"Sugihara, G., et al. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500.","type":"article","doi":"10.1126/science.1227079","isbn":null,"url":null}],"related":["transfer-entropy","granger-causality","recurrence-quantification-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"convergent-validity","name":"Convergent Validity","fullName":"Convergent Validity","aliases":["convergent construct validity","convergence validity","AVE-based convergent validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1959","originator":"Donald T. Campbell & Donald W. Fiske","url":"https://scholargate.app/en/psychometrics/convergent-validity","markdownUrl":"https://scholargate.app/en/psychometrics/convergent-validity.md","definition":"Convergent validity is the degree to which multiple indicators that are theoretically expected to measure the same construct actually correlate with one another. It is one of the two complementary forms of construct validity identified by Campbell and Fiske (1959) and is now routinely assessed via factor loadings and the Average Variance Extracted (AVE) statistic in SEM-based scale validation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald T. Campbell & Donald W. Fiske","year":"1959","type":"Validity evidence / construct validation","dataType":"Ordinal or continuous item responses; factor loadings from CFA","subfamily":"Scale / measurement"},"citations":[{"ref":"Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.","type":"article","doi":"10.1037/h0046016","isbn":null,"url":null},{"ref":"Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.","type":"article","doi":"10.1177/002224378101800104","isbn":null,"url":null}],"related":["discriminant-validity","construct-validity","confirmatory-factor-analysis","cronbachs-alpha","mcdonalds-omega","nomological-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"conversation-analysis","name":"Conversation Analysis","fullName":"Conversation Analysis (CA)","aliases":["CA","talk-in-interaction","sequential analysis","interactional analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Discourse Analysis","year":"Late 1960s–1974 (foundational lectures 1964–1972; landmark article 1974)","originator":"Harvey Sacks, Emanuel Schegloff, and Gail Jefferson","url":"https://scholargate.app/en/qualitative/conversation-analysis","markdownUrl":"https://scholargate.app/en/qualitative/conversation-analysis.md","definition":"Conversation Analysis (CA) is a qualitative research method that examines the fine-grained sequential structure of naturally occurring talk and social interaction. Developed by sociologists Harvey Sacks, Emanuel Schegloff, and Gail Jefferson in the 1960s and 1970s, CA investigates how participants in a conversation accomplish social actions — such as invitations, refusals, or diagnoses — through the precise moment-by-moment organisation of their talk, including turn-taking, sequence structure, repair, and recipient design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey Sacks, Emanuel Schegloff, and Gail Jefferson","year":"Late 1960s–1974 (foundational lectures 1964–1972; landmark article 1974)","type":"Qualitative research method","dataType":"Audio or video recordings of naturally occurring talk and interaction","typicalSampleSize":"Varies widely; 10–100+ interaction episodes or 2–20 hours of recorded talk","subfamily":"Discourse Analysis"},"citations":[{"ref":"Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+simplest+systematics+for+the+organization+of+turn-taking+for+conversation"},{"ref":"Sidnell, J. (2010). Conversation Analysis: An Introduction. Wiley-Blackwell.","type":"book","doi":null,"isbn":"978-1405159098","url":null}],"related":["discourse-analysis","ethnography","narrative-analysis","phenomenology","thematic-analysis","content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"convex-optimization","name":"Convex Optimization","fullName":"Convex Optimization","aliases":["Convex Programming","Disciplined Convex Programming","Dışbükey Optimizasyon","Convex Mathematical Programming"],"domain":"optimization","family":"process-pipeline","subfamily":"Mathematical programming","year":2004,"originator":"Stephen Boyd & Lieven Vandenberghe","url":"https://scholargate.app/en/optimization/convex-optimization","markdownUrl":"https://scholargate.app/en/optimization/convex-optimization.md","definition":"Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Formalized and popularized by Stephen Boyd and Lieven Vandenberghe in their landmark 2004 textbook, the framework unifies a wide family of problems — including linear programming, quadratic programming, semidefinite programming, and second-order cone programming — under a single theoretical roof. Its defining property is that any locally optimal solution is also globally optimal, making it tractable and reliable for engineering, statistics, machine learning, and operations research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Boyd & Lieven Vandenberghe","year":2004,"type":"Mathematical optimization framework","subfamily":"Mathematical programming","solution_guarantee":"Global optimum (when problem is convex)","computational_complexity":"Polynomial-time solvable in general"},"citations":[{"ref":"Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-83378-3","url":null}],"related":["nonlinear-programming","linear-programming","robust-optimization"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cook-seiford","name":"COOK-SEIFORD","fullName":"Cook & Seiford (1978) — distance-based ranking aggregation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"2024","originator":"Orakçı, E.","url":"https://scholargate.app/en/decision-making/cook-seiford","markdownUrl":"https://scholargate.app/en/decision-making/cook-seiford.md","definition":"COOK-SEIFORD (Cook & Seiford (1978) — distance-based ranking aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Orakçı, E.","subfamily":"AggregationOperator","year":"2024","type":"Distance matrix + Hungarian assignment","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları","type":"article","doi":"10.58830/ozgur.pub623","isbn":null,"url":null}],"related":["borda","condorcet","copeland","dodgson","topsis","vikor","ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"coordination-compound-synthesis","name":"Coordination Compound Synthesis","fullName":"Coordination Compound Synthesis and Characterization","aliases":["complex synthesis","coordination complex","metal complex synthesis"],"domain":"chemistry","family":"process-pipeline","subfamily":"Synthesis","year":"1960s","originator":"Geoffrey Wilkinson & others","url":"https://scholargate.app/en/chemistry/coordination-compound-synthesis","markdownUrl":"https://scholargate.app/en/chemistry/coordination-compound-synthesis.md","definition":"Coordination compound synthesis is the methodology for preparing metal-ligand complexes, ranging from simple aqueous solutions of metal ions to sophisticated organometallic catalysts and biological metalloproteins. Developed systematically from the 1960s onward by pioneers like Geoffrey Wilkinson and others, coordination chemistry enables creation of compounds with tailored properties for catalysis, materials science, and medicine.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geoffrey Wilkinson & others","subfamily":"Synthesis","year":"1960s","type":"Synthetic methodology"},"citations":[{"ref":"Wilkinson, G., Gillard, R. D., & McCleverty, J. A. (1966). Comprehensive Coordination Chemistry (1st ed.). Pergamon Press.","type":"book","doi":null,"isbn":"978-0080161709","url":null},{"ref":"Constable, E. C. (2013). Metals and Ligand Reactivity. VCH.","type":"book","doi":null,"isbn":"978-3527328970","url":null}],"related":["crystal-field-theory","ligand-field-analysis","x-ray-crystallography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"copd-assessment-test","name":"CAT","fullName":"COPD Assessment Test","aliases":["CAT","COPD Assessment Test","COPD Assessment Tool"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Respiratory and Pulmonary Disease","year":"2009","originator":"Paul W. Jones et al.","url":"https://scholargate.app/en/health-outcomes/copd-assessment-test","markdownUrl":"https://scholargate.app/en/health-outcomes/copd-assessment-test.md","definition":"The COPD Assessment Test (CAT) is a simple, rapid, patient-centered measure of COPD symptom burden and functional impact. Developed by Paul Jones and colleagues in 2009, this 8-item questionnaire captures how COPD affects cough, sputum, chest tightness, breathing difficulty, activity limitation, confidence, sleep, and energy. It is used worldwide in clinical practice to guide disease management decisions and as a primary outcome measure in COPD clinical trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul W. Jones et al.","subfamily":"Respiratory and Pulmonary Disease","year":"2009","type":"Self-report symptom and impact questionnaire"},"citations":[{"ref":"Jones, P. W., Harding, G., Berry, P., Wiklund, I., Chen, W. H., & Kline Leidy, N. (2009). Development and first validation of the COPD Assessment Test. European Respiratory Journal, 34(3), 648-654.","type":"article","doi":"10.1183/09031936.00102509","isbn":null,"url":null},{"ref":"Dodd, J. W., Hogg, L., Nolan, J., Jeffries, C., Grant, E., Lord, V. M., ... & Hopkinson, N. S. (2012). The COPD assessment test (CAT): Response to pulmonary rehabilitation. A multicentre, prospective study. Thorax, 66(5), 425-429.","type":"article","doi":"10.1136/thx.2010.156372","isbn":null,"url":null},{"ref":"Tinkelman, D., White, B., & Murray, S. (2009). Tracking COPD patient outcomes with COPD Assessment Test (CAT): A validation study. Journal of COPD F, 6(1), 1-6.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/32160667"}],"related":["eortc-qlq-c30","asthma-control-test","chronic-heart-failure-questionnaire","diabetes-quality-of-life"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cope-guidelines","name":"COPE Guidelines for Publication Ethics","fullName":"Committee on Publication Ethics Guidelines and Resources","aliases":["COPE","Publication Ethics Flowcharts","Editorial Guidelines"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"publication-ethics","year":"1997","originator":"Committee on Publication Ethics (COPE)","url":"https://scholargate.app/en/publication-ethics/cope-guidelines","markdownUrl":"https://scholargate.app/en/publication-ethics/cope-guidelines.md","definition":"The Committee on Publication Ethics (COPE), founded in 1997, is an international organization of journal editors and publishers that promotes and advances research integrity and publication ethics. COPE provides practical guidance through flowcharts, position statements, and ethical guidelines addressing misconduct (fabrication, falsification, plagiarism), authorship disputes, conflicts of interest, and corrections. COPE's resources are freely available and adopted by thousands of journals globally. COPE is not a regulator; it advises editors and researchers on handling ethical issues and promotes a culture of integrity in publishing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Committee on Publication Ethics (COPE)","subfamily":"publication-ethics","year":"1997","type":"Standard"},"citations":[{"ref":"Committee on Publication Ethics (2023). COPE Guidelines. COPE Website.","type":"webpage","doi":null,"isbn":null,"url":"https://publicationethics.org/"},{"ref":"Committee on Publication Ethics (2022). COPE Handbook of Authorship, Authorship Disputes, and Conflicts of Interest. COPE.","type":"article","doi":null,"isbn":null,"url":"https://publicationethics.org/guidance/"},{"ref":"Oreskes, N. (2012). Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming. Bloomsbury Press.","type":"article","doi":null,"isbn":null,"url":"https://www.bloomsbury.com/us/merchants-of-doubt-9781596916105/"}],"related":["icmje-authorship-criteria","plagiarism-in-research","duplicate-publication","retraction-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"copeland","name":"COPELAND","fullName":"Copeland Method — Pairwise majority voting with net win-loss score","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"1951","originator":"Copeland, A. H.","url":"https://scholargate.app/en/decision-making/copeland","markdownUrl":"https://scholargate.app/en/decision-making/copeland.md","definition":"COPELAND (Copeland Method — Pairwise majority voting with net win-loss score) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Copeland, A. H. in 1951. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Copeland, A. H.","subfamily":"AggregationOperator","year":"1951","type":"Pairwise majority rule (Condorcet-based aggregation)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Copeland, A. H. (1951). A 'reasonable' social welfare function. Mimeograph, University of Michigan Seminar on Applications of Mathematics to Social Sciences","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A%20%27reasonable%27%20social%20welfare%20function"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"copenhagen-burnout-inventory","name":"Copenhagen Burnout Inventory","fullName":"Copenhagen Burnout Inventory (CBI)","aliases":["CBI"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Burnout assessment","year":2005,"originator":"Tage Søren Kristensen, Margrethe Borritz, Ebbe Villadsen, Karl B. Christensen","url":"https://scholargate.app/en/occupational-health/copenhagen-burnout-inventory","markdownUrl":"https://scholargate.app/en/occupational-health/copenhagen-burnout-inventory.md","definition":"The Copenhagen Burnout Inventory (CBI) is a multidimensional burnout assessment tool designed to measure exhaustion and disengagement in occupational settings. Developed by Kristensen and colleagues in 2005, the CBI distinguishes among personal, work-related, and client-related burnout, making it particularly valuable for healthcare, education, and social service professions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tage Søren Kristensen, Margrethe Borritz, Ebbe Villadsen, Karl B. Christensen","subfamily":"Burnout assessment","year":2005,"type":"Self-report questionnaire"},"citations":[{"ref":"Kristensen, T. S., Borritz, M., Villadsen, E., & Christensen, K. B. (2005). The Copenhagen Burnout Inventory: a new tool for the assessment of burnout. Work & Stress, 19(3), 192-207.","type":"article","doi":"10.1080/02678370500297720","isbn":null,"url":null}],"related":["oldenburg-burnout-inventory","effort-reward-imbalance-scale","areas-of-worklife-scale","recovery-experience-questionnaire","maslach-burnout-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"copm","name":"COPM","fullName":"Canadian Occupational Performance Measure","aliases":["COPM"],"domain":"occupational-therapy","family":"process-pipeline","subfamily":"client-centered occupational performance","year":"1990","originator":"Law, M., Baptiste, S., Carswell, A., McColl, M., Polatajko, H., & Pollock, N.","url":"https://scholargate.app/en/occupational-therapy/copm","markdownUrl":"https://scholargate.app/en/occupational-therapy/copm.md","definition":"The COPM is a client-centered, semi-structured assessment tool designed to measure change in occupational performance over time. Developed by Law and colleagues (1990) at McMaster University in Canada, it has become a cornerstone of occupational therapy practice, focusing on identifying and evaluating performance issues that matter most to each individual client.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Law, M., Baptiste, S., Carswell, A., McColl, M., Polatajko, H., & Pollock, N.","subfamily":"client-centered occupational performance","year":"1990","type":"Client self-report (semi-structured interview)"},"citations":[{"ref":"Law, M., Baptiste, S., Carswell, A., McColl, M. A., Polatajko, H., & Pollock, N. (1990). Canadian Occupational Performance Measure. CAOT Publications.","type":"book","doi":null,"isbn":null,"url":"https://www.thecopm.com"},{"ref":"Law, M., Steinwender, S., & Leclair, L. (1998). Occupation, health and well-being. Canadian Journal of Occupational Therapy, 65(2), 81-91.","type":"article","doi":"10.1177/000841749806500204","isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9675147"}],"related":["wolf-motor-function-test","upper-extremity-functional-scale","occupational-self-assessment","nine-hole-peg-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"copras","name":"COPRAS","fullName":"Complex Proportional Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1996","originator":"Zavadskas, E. K., Kaklauskas, A.","url":"https://scholargate.app/en/decision-making/copras","markdownUrl":"https://scholargate.app/en/decision-making/copras.md","definition":"COPRAS (Complex Proportional Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Kaklauskas, A. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Kaklauskas, A.","subfamily":"Ranking","year":"1996","type":"Proportional assessment (benefit/cost split)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zavadskas, E. K., Kaklauskas, A. (1996). Determination of an Efficient Contractor by Using the New Method of Multicriteria Assessment. International Symposium for The Organization and Management of Construction. Shaping Theory and Practice. Vol. 2: Managing the Construction Project and Managing Risk. CIB W 65","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Determination%20of%20an%20Efficient%20Contractor%20by%20Using%20the%20New%20Method%20of%20Multicriteria%20Assessment"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"copula-cdo-model","name":"Copula CDO Model","fullName":"Gaussian Copula CDO Pricing Model","aliases":["Copula Default Model","CDO Pricing"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Copula Models","year":"2000","originator":"David X. Li","url":"https://scholargate.app/en/quantitative-finance/copula-cdo-model","markdownUrl":"https://scholargate.app/en/quantitative-finance/copula-cdo-model.md","definition":"The copula CDO model (Li 2000) uses Gaussian copulas to price collateralized debt obligations (CDOs) by modeling joint default probabilities across a portfolio of bonds. The model became the industry standard for CDO pricing but was heavily criticized post-2008 for underestimating tail risk and correlation breakdowns during crises.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David X. Li","subfamily":"Copula Models","year":"2000","type":"Credit Portfolio Model"},"citations":[{"ref":"Li, D. X. (2000). On default correlation: A copula function approach. Journal of Fixed Income, 9(4), 43-54.","type":"article","doi":"10.3905/jfi.2000.319253","isbn":null,"url":null},{"ref":"Schonbucher, P. J. (2003). Credit Derivatives Pricing Models: Models, Pricing and Implementation. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Credit+Derivatives+Pricing+Models%3A+Models%2C+Pricing+and+Implementation+Schonbucher"}],"related":["merton-default-model","credit-valuation-adjustment","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"copula-models","name":"Copula Models","fullName":"Copula Models (Gaussian, t, Clayton, Gumbel, Frank)","aliases":["copulas","dependence copulas","vine copulas","Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1959,"originator":"Sklar (1959); dependence-concept treatment by Joe (1997)","url":"https://scholargate.app/en/finance/copula-models","markdownUrl":"https://scholargate.app/en/finance/copula-models.md","definition":"Copula models are a family of functions that describe the dependence structure between variables separately from their individual (marginal) distributions. The foundation is Sklar's theorem (1959), which shows that any multivariate distribution can be split into its marginals plus a copula; Joe (1997) developed the modern catalogue of dependence concepts. They are central to portfolio risk and credit modelling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sklar (1959); dependence-concept treatment by Joe (1997)","year":1959,"type":"Dependence model","estimator":"Inference Functions for Margins (IFM); maximum likelihood","outcome":"continuous (joint dependence)"},"citations":[{"ref":"Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 229-231.","type":"article","doi":null,"isbn":null,"url":"https://search.worldcat.org/title/2455974"},{"ref":"Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman & Hall.","type":"book","doi":null,"isbn":"978-0412073311","url":null}],"related":["pearson-correlation","johansen-cointegration","extreme-value-theory","value-at-risk","garch"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"copy-number-variation-analysis","name":"Copy Number Variation Analysis","fullName":"Copy Number Variation Analysis","aliases":["CNV analysis","copy number variant detection","CNV calling","somatic copy number alteration analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"1998–2006","originator":"Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)","url":"https://scholargate.app/en/bioinformatics/copy-number-variation-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/copy-number-variation-analysis.md","definition":"Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)","year":"1998–2006","type":"Genomic structural variant detection pipeline","dataType":"Whole-genome sequencing (WGS), SNP array, array CGH","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.","type":"article","doi":"10.1038/nature05329","isbn":null,"url":null},{"ref":"Olshen, A. B., Venkatraman, E. S., Lucito, R., & Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, 5(4), 557–572.","type":"article","doi":"10.1093/biostatistics/kxh008","isbn":null,"url":null}],"related":["variant-calling","genome-wide-association-study","rna-seq-differential-expression","sequence-alignment","epigenome-wide-association-study","single-cell-copy-number-variation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"core-self-evaluations-scale","name":"Core Self-Evaluations Scale","fullName":"Core Self-Evaluations Scale (CSES)","aliases":["CSES","Judge Scale","Core Self-Assessment"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"personality-disposition","year":"1997","originator":"Timothy A. Judge","url":"https://scholargate.app/en/organizational-behavior/core-self-evaluations-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/core-self-evaluations-scale.md","definition":"The Core Self-Evaluations Scale (CSES) measures fundamental assessments people make about their own worth, competence, and ability to meet life demands. Developed by Judge and colleagues starting in 1997, the 12-item scale captures a broad personality dimension encompassing self-esteem, self-efficacy, locus of control, and emotional stability. Core self-evaluations predict job satisfaction, life satisfaction, engagement, and performance across occupations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Timothy A. Judge","subfamily":"personality-disposition","year":"1997","type":"Self-report questionnaire"},"citations":[{"ref":"Judge, T. A., Locke, E. A., & Durham, C. C. (1997). The dispositional causes of job satisfaction: A core evaluations approach. Research in Organizational Behavior, 19, 151–188.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9089286"},{"ref":"Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J. (2005). Core self-evaluations and job and life satisfaction: The role of self-concordance and goal attainment. Journal of Applied Psychology, 90(2), 257–268.","type":"article","doi":"10.1037/0021-9010.90.2.257","isbn":null,"url":null},{"ref":"Judge, T. A., Bono, J. E., Erez, A., & Locke, E. A. (2005). Core self-evaluations and job and life satisfaction: A response to bottom line concerns. Journal of Organizational Behavior, 26(5), 491–511.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Core+self-evaluations+and+job+and+life+satisfaction%3A+A+response+to+bottom+line+concerns+Judge"}],"related":["psychological-capital-questionnaire","proactive-personality-scale","organizational-commitment-questionnaire","job-descriptive-index","leader-member-exchange-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"coreference-resolution","name":"Coreference Resolution","fullName":"Coreference Resolution","aliases":["coreference","anaphora resolution","Eşgönderim Çözümleme (Coreference Resolution)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1978,"originator":"Hobbs (1978); Lee et al. (2017, neural end-to-end)","url":"https://scholargate.app/en/text-mining/coreference-resolution","markdownUrl":"https://scholargate.app/en/text-mining/coreference-resolution.md","definition":"Coreference resolution is a natural-language-processing task that detects when different expressions in a text refer to the same entity — for example a name, a later pronoun, and a descriptive phrase all pointing at one person. Rooted in early linguistic work by Hobbs (1978) and advanced by the end-to-end neural model of Lee et al. (2017), it improves the quality of information extraction and text understanding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hobbs (1978); Lee et al. (2017, neural end-to-end)","year":1978,"type":"NLP information-extraction task","output":"Clusters of mentions that refer to the same entity","minSample":20},"citations":[{"ref":"Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D17-1018/"},{"ref":"Hobbs, J.R. (1978). Resolving Pronoun References. Lingua, 44(4), 311-338.","type":"article","doi":"10.1016/0024-3841(78)90006-2","isbn":null,"url":null}],"related":["named-entity-recognition","semantic-role-labeling","question-answering","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"corporate-governance-questionnaire","name":"Corporate Governance Questionnaire","fullName":"Corporate Governance Assessment and Maturity Questionnaire","aliases":["CG Assessment","Governance Maturity Scale"],"domain":"strategic-management","family":"process-pipeline","subfamily":"organizational-governance","year":"1976 (theory); 1992 (operational)","originator":"Jensen and Meckling (foundational); Cadbury Committee (operational framework)","url":"https://scholargate.app/en/strategic-management/corporate-governance-questionnaire","markdownUrl":"https://scholargate.app/en/strategic-management/corporate-governance-questionnaire.md","definition":"Corporate Governance encompasses the system of rules, practices, and processes by which a company is directed and controlled. Jensen and Meckling's (1976) agency theory formalized the principal-agent problem—how to ensure management (agents) acts in shareholders' (principals') interests despite information asymmetry and incentive misalignment. The Cadbury Report (1992) operationalized this into practical governance frameworks emphasizing board independence, audit committees, and transparency. This questionnaire assesses organizational governance maturity across multiple dimensions: board structure and independence, internal controls and risk management, audit and compliance, stakeholder engagement, and transparency. Strong governance reduces agency costs, improves decision quality, and protects against fraud and misconduct.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jensen and Meckling (foundational); Cadbury Committee (operational framework)","subfamily":"organizational-governance","year":"1976 (theory); 1992 (operational)","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360.","type":"article","doi":"10.1016/0304-405X(76)90026-X","isbn":null,"url":null},{"ref":"The Committee on the Financial Aspects of Corporate Governance (1992). Report of the Committee on the Financial Aspects of Corporate Governance (Cadbury Report). London: The Financial Reporting Council.","type":"report","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The%20Committee%20on%20the%20Financial%20Aspects%20of%20Corporate%20Governance%20(1992).%20Report%20of%20the%20Committee%20on%20the%20Financial%20Aspects%20"},{"ref":"Brown, L. D., & Caylor, M. L. (2009). Corporate governance and firm operating performance. Review of Quantitative Finance and Accounting, 32(2), 129–144.","type":"article","doi":"10.1007/s11156-007-0082-3","isbn":null,"url":null}],"related":["strategic-orientation-scale","organizational-resilience-scale","knowledge-management-scale","dynamic-capabilities-scale","balanced-scorecard-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"corporate-social-responsibility-scale","name":"CSR Scale","fullName":"Corporate Social Responsibility Scale (CSR Scale)","aliases":["Organizational Social Responsibility"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"2009","originator":"Multiple scholars; Turker formalized measurement","url":"https://scholargate.app/en/organizational-behavior/corporate-social-responsibility-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/corporate-social-responsibility-scale.md","definition":"The Corporate Social Responsibility (CSR) Scale is a 19-item instrument measuring organizational commitment to social and environmental responsibilities across multiple stakeholder dimensions. Formalized by Turker in 2009, the CSR Scale assesses employee perception of organizational CSR practices toward society, employees, customers, and the environment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple scholars; Turker formalized measurement","subfamily":"Organizational behavior","year":"2009","type":"Self-report scale"},"citations":[{"ref":"Carroll, A. B. (1979). A three-dimensional model of corporate performance. Academy of Management Review, 4(4), 497-505.","type":"article","doi":"10.5465/amr.1979.4498296","isbn":null,"url":null},{"ref":"Turker, D. (2009). Measuring corporate social responsibility: A scale development study. Journal of Business Ethics, 85(4), 411-427.","type":"article","doi":"10.1007/s10551-008-9780-6","isbn":null,"url":null}],"related":["organizational-culture-assessment","ethical-leadership-scale","organizational-citizenship-behavior","authentic-leadership-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"corpus-linguistics","name":"Corpus Linguistics","fullName":"Corpus Linguistics Analysis Method","aliases":["Corpus Analysis","Corpora Studies"],"domain":"linguistics","family":"process-pipeline","subfamily":"Empirical Linguistics","year":"1980","originator":"John Sinclair","url":"https://scholargate.app/en/linguistics/corpus-linguistics","markdownUrl":"https://scholargate.app/en/linguistics/corpus-linguistics.md","definition":"Corpus Linguistics is the study of language based on large, representative collections of texts (corpora) processed by computer. Pioneered by John Sinclair and others, the method uses statistical analysis, concordancing, and computational tools to examine patterns of actual language use. Corpus linguistics has transformed our understanding of English and other languages, revealing frequency patterns, collocation preferences, and register variation that were previously hidden. It serves theoretical linguistics, applied language teaching, and natural language processing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Sinclair","subfamily":"Empirical Linguistics","year":"1980","type":"Empirical process pipeline"},"citations":[{"ref":"Sinclair, J. M. (1991). Corpus, Concordance, Collocation. Oxford: Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Corpus%2C+Concordance%2C+Collocation+Sinclair"},{"ref":"McEnery, T., & Hardie, A. (2012). Corpus Linguistics: Method, Theory and Practice. Cambridge: Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511981395","isbn":null,"url":null},{"ref":"Biber, D., Conrad, S., & Reppen, R. (2006). Corpus Linguistics: Investigating Language Structure and Use. Cambridge: Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Corpus+Linguistics%3A+Investigating+Language+Structure+and+Use+Biber"}],"related":["dialectometry","computational-linguistics","sociolinguistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"correlation-vs-causation","name":"Correlation vs Causation","fullName":"Understanding the Distinction Between Correlation and Causation in Research","aliases":["correlation and causation","causal inference","spurious correlation","confounding"],"domain":"research-statistics","family":"process-pipeline","subfamily":"causal-reasoning","year":1965,"originator":"Multiple sources (Bradford Hill, Judea Pearl, Donald Rubin)","url":"https://scholargate.app/en/research-statistics/correlation-vs-causation","markdownUrl":"https://scholargate.app/en/research-statistics/correlation-vs-causation.md","definition":"Correlation measures the strength and direction of association between two variables; causation implies that changes in one variable directly produce changes in another. A strong correlation (e.g., r = 0.9) does not prove causation. Classic examples abound: shoe size and reading ability are correlated in children (confounded by age), but shoe size does not cause reading ability. Understanding when correlation implies causation requires evaluating study design, confounding variables, temporal precedence, and mechanism. Randomized experiments offer the strongest causal evidence; observational studies must carefully control for confounders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple sources (Bradford Hill, Judea Pearl, Donald Rubin)","subfamily":"causal-reasoning","year":1965,"type":"Concept"},"citations":[{"ref":"Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-89560-6","url":null},{"ref":"Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.","type":"article","doi":"10.1037/h0037350","isbn":null,"url":null},{"ref":"Hill, A. B. (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300.","type":"article","doi":"10.1177/003591576505800503","isbn":null,"url":null}],"related":["p-value-significance","null-hypothesis","effect-size","multiple-comparisons-problem"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"correspondence-analysis","name":"Correspondence Analysis","fullName":"Correspondence Analysis","aliases":["CA","Simple Correspondence Analysis","Reciprocal Averaging","Karşılıklı Uyum Analizi"],"domain":"statistics","family":"latent-structure","subfamily":"Dimensionality reduction","year":1984,"originator":"Jean-Paul Benzécri; Michael Greenacre","url":"https://scholargate.app/en/statistics/correspondence-analysis","markdownUrl":"https://scholargate.app/en/statistics/correspondence-analysis.md","definition":"Correspondence Analysis (CA) is an exploratory multivariate technique for visualizing the association structure of a two-way contingency table. Developed systematically by Jean-Paul Benzécri in France during the 1960s–1970s and brought to an English-language audience by Michael Greenacre in 1984, CA decomposes the chi-square statistic of a cross-tabulation to produce a low-dimensional joint display — called a biplot — in which rows and columns are represented as points whose proximities reflect their associations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Paul Benzécri; Michael Greenacre","year":1984,"type":"Exploratory multivariate technique for categorical data","subfamily":"Dimensionality reduction","input":"Two-way contingency table (cross-tabulation)","output":"Low-dimensional biplot of row and column profiles"},"citations":[{"ref":"Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press.","type":"book","doi":null,"isbn":"978-0-12-299050-2","url":null}],"related":["multiple-correspondence-analysis","principal-component-analysis","biplot"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cosine-distance","name":"Cosine Distance","fullName":"Cosine Distance Metric","aliases":["cosine similarity distance","angular distance"],"domain":"decision-making","family":"mcdm","subfamily":"Distance metric","year":"1975","originator":"Gerard Salton","url":"https://scholargate.app/en/decision-making/cosine-distance","markdownUrl":"https://scholargate.app/en/decision-making/cosine-distance.md","definition":"Cosine distance measures the angular distance between two non-zero vectors in a multi-dimensional space. Originally developed by Gerard Salton for information retrieval in 1975, it captures dissimilarity by computing one minus the cosine similarity, ranging from 0 (identical direction) to 1 (opposite direction). It is widely used in text analysis, document comparison, and decision-making contexts where direction matters more than magnitude.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gerard Salton","subfamily":"Distance metric","year":"1975","type":"Vector similarity measure"},"citations":[{"ref":"Spearman, C. (1904). The proof and measurement of association between two things. American Journal of Psychology, 15(1), 72-101.","type":"article","doi":"10.2307/1412159","isbn":null,"url":null},{"ref":"Salton, G., & McGill, M. J. (1975). Introduction to Modern Information Retrieval. McGraw-Hill.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/introductiontomo00salt"}],"related":["euclidean-distance","manhattan-distance","chebyshev-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cosmin-checklist","name":"COSMIN Checklist","fullName":"COnsensus-based Standards for the selection of health Measurement INstruments","aliases":["COSMIN"],"domain":"research-methodology","family":"process-pipeline","subfamily":"Patient-reported outcome measure (PROM) quality assessment","year":"2010","originator":"Mokkink et al. (COSMIN Group)","url":"https://scholargate.app/en/research-methodology/cosmin-checklist","markdownUrl":"https://scholargate.app/en/research-methodology/cosmin-checklist.md","definition":"COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) is a systematic framework and 10-item checklist developed by Mokkink et al. (2010) to evaluate the methodological quality of studies that assess the measurement properties of patient-reported outcome measures (PROMs), questionnaires, and clinical scales. COSMIN guides the development, validation, and selection of health measurement instruments across clinical research and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mokkink et al. (COSMIN Group)","subfamily":"Patient-reported outcome measure (PROM) quality assessment","year":"2010","type":"Measurement instrument evaluation"},"citations":[{"ref":"Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., ... & de Vet, H. C. (2010). The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Quality of Life Research, 19(4), 539–549.","type":"article","doi":"10.1007/s11136-010-9606-8","isbn":null,"url":null}],"related":["grade-evidence-profiling","casp-rct-checklist","cochrane-risk-of-bias","prisma-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cosmological-perturbation-theory","name":"Cosmological Perturbation Theory","fullName":"Cosmological Perturbation Theory and Structure Growth","aliases":["structure formation theory","linear perturbations","growth of density fluctuations"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Cosmology","year":"1902","originator":"James Jeans","url":"https://scholargate.app/en/applied-physics/cosmological-perturbation-theory","markdownUrl":"https://scholargate.app/en/applied-physics/cosmological-perturbation-theory.md","definition":"Cosmological perturbation theory describes how small density fluctuations in the early universe grow into galaxies, clusters, and large-scale structure under gravity. Originating from James Jeans's 1902 stability analysis and extended by Lifshitz, Bardeen, and others, this theory is the foundation of structure formation cosmology. It explains how quantum fluctuations in the early universe—amplified by inflation—seeded the growth of all cosmic structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Jeans","subfamily":"Cosmology","year":"1902","type":"Theoretical framework and computational method"},"citations":[{"ref":"Jeans, J. H. (1902). The stability of a spherical nebula. Philosophical Transactions of the Royal Society A, 199, 1-53.","type":"article","doi":"10.1098/rsta.1902.0012","isbn":null,"url":null},{"ref":"Lifshitz, E. M. (1946). On the gravitational stability of the expanding universe. Journal of Physics USSR, 10, 116.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Evgeny_Lifshitz"},{"ref":"Bardeen, J. M., Bond, J. R., Kaiser, N., & Szalay, A. S. (1986). The statistics of peaks of Gaussian random fields. The Astrophysical Journal, 304, 15-61.","type":"article","doi":"10.1086/164143","isbn":null,"url":null}],"related":["n-body-simulation","gravitational-wave-matched-filtering","light-curve-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cost-benefit-analysis","name":"Cost-Benefit Analysis","fullName":"Cost-Benefit Analysis (CBA)","aliases":["CBA","economic appraisal","benefit-cost ratio"],"domain":"health-economics","family":"process-pipeline","subfamily":"economic evaluation framework","year":"1970s","originator":"Boardman, Greenberg, and colleagues (welfare economics)","url":"https://scholargate.app/en/health-economics/cost-benefit-analysis","markdownUrl":"https://scholargate.app/en/health-economics/cost-benefit-analysis.md","definition":"Cost-benefit analysis compares the total monetary value of benefits produced by a program against its total monetary costs, reporting net present value (NPV) or benefit-cost ratio (BCR). Rooted in welfare economics and used extensively in public policy (transportation, environmental, education, health), CBA answers the question: 'Is this program worth doing from a societal perspective?' Unlike cost-effectiveness analysis, CBA monetizes both costs and benefits, enabling comparison across disparate program types.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Boardman, Greenberg, and colleagues (welfare economics)","subfamily":"economic evaluation framework","year":"1970s","type":"Method"},"citations":[{"ref":"Boardman, A. E., Greenberg, D. H., Vining, A. R., & Weimer, D. L. (2018). Cost-Benefit Analysis: Concepts and Practice (5th ed.). Cambridge: Cambridge University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Boardman%2C%20A.%20E.%2C%20Greenberg%2C%20D.%20H.%2C%20Vining%2C%20A.%20R.%2C%20%26%20Weimer%2C%20D.%20L.%20(2018).%20Cost-Benefit%20Analysis%3A%20Concepts%20and%20Practice%20("},{"ref":"Layard, R., & Glaister, S. (Eds.). (2003). Cost-Benefit Analysis (2nd ed.). Cambridge: Cambridge University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Layard%2C%20R.%2C%20%26%20Glaister%2C%20S.%20(Eds.).%20(2003).%20Cost-Benefit%20Analysis%20(2nd%20ed.).%20Cambridge%3A%20Cambridge%20University%20Press."},{"ref":"Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford: Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Drummond%2C%20M.%20F.%2C%20Sculpher%2C%20M.%20J.%2C%20Claxton%2C%20K.%2C%20Stoddart%2C%20G.%20L.%2C%20%26%20Torrance%2C%20G.%20W.%20(2015).%20Methods%20for%20the%20Economic%20Evalu"}],"related":["cost-effectiveness-analysis","quality-adjusted-life-year","markov-model-health-economics","decision-analytic-modeling","budget-impact-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cost-effectiveness-analysis-hta","name":"Cost-Effectiveness Analysis in HTA","fullName":"Cost-Effectiveness Analysis for Healthcare Technology Evaluation and Reimbursement","aliases":["CEA","Cost-Effectiveness Analysis Healthcare"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Health economics, Decision analysis","year":"1996","originator":"Diane Meade Drummond, Michael Gold","url":"https://scholargate.app/en/healthcare-management/cost-effectiveness-analysis-hta","markdownUrl":"https://scholargate.app/en/healthcare-management/cost-effectiveness-analysis-hta.md","definition":"Cost-Effectiveness Analysis (CEA) is an economic evaluation method that compares the cost and health benefits of alternative treatments to determine whether an intervention provides good value for money. Within Health Technology Assessment, CEA is the primary tool for recommending reimbursement and coverage decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diane Meade Drummond, Michael Gold","subfamily":"Health economics, Decision analysis","year":"1996","type":"Economic evaluation methodology"},"citations":[{"ref":"Gold, M. R., Siegel, J. E., Russell, L. B., & Weinstein, M. C. (Eds.). (1996). Cost-Effectiveness in Health and Medicine. Oxford University Press.","type":"book","doi":null,"isbn":"9780195108231","url":null},{"ref":"Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Methods+for+the+Economic+Evaluation+of+Health+Care+Programmes+%284th+ed.%29+Drummond"},{"ref":"Shiroiwa, T., Sung-Jae, I., Fukuda, T., & Sanon, M. (2016). International survey on QALYs and cost-effectiveness thresholds. Health Policy, 120(5), 504–514.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=International+survey+on+QALYs+and+cost-effectiveness+thresholds+Shiroiwa"}],"related":["health-technology-assessment","dea-hospital-efficiency","balanced-scorecard-healthcare","clinical-audit","staffing-ratio-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cost-effectiveness-analysis","name":"Cost-Effectiveness Analysis","fullName":"Cost-Effectiveness Analysis (CEA)","aliases":["CEA","ICER","Incremental Cost-Effectiveness Ratio"],"domain":"health-economics","family":"process-pipeline","subfamily":"economic evaluation framework","year":"1984","originator":"Drummond & Stoddart (Health Economics Research Group, McMaster University)","url":"https://scholargate.app/en/health-economics/cost-effectiveness-analysis","markdownUrl":"https://scholargate.app/en/health-economics/cost-effectiveness-analysis.md","definition":"Cost-effectiveness analysis compares the incremental cost per unit of health benefit gained by one intervention relative to a comparator (standard care or best alternative). Developed rigorously in the 1980s by Drummond, Stoddart, and colleagues, CEA is now the standard framework for technology appraisal globally. NICE, HAS, CADTH, and other health technology assessment bodies use CEA to decide which treatments warrant public funding and at what price.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Drummond & Stoddart (Health Economics Research Group, McMaster University)","subfamily":"economic evaluation framework","year":"1984","type":"Method"},"citations":[{"ref":"Gold, M. R., Siegel, J. E., Russell, L. B., & Weinstein, M. C. (Eds.). (1996). Cost-Effectiveness in Health and Medicine. New York: Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Gold%2C%20M.%20R.%2C%20Siegel%2C%20J.%20E.%2C%20Russell%2C%20L.%20B.%2C%20%26%20Weinstein%2C%20M.%20C.%20(Eds.).%20(1996).%20Cost-Effectiveness%20in%20Health%20and%20Medicine"},{"ref":"Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford: Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Drummond%2C%20M.%20F.%2C%20Sculpher%2C%20M.%20J.%2C%20Claxton%2C%20K.%2C%20Stoddart%2C%20G.%20L.%2C%20%26%20Torrance%2C%20G.%20W.%20(2015).%20Methods%20for%20the%20Economic%20Evalu"},{"ref":"National Institute for Health and Care Excellence (NICE). (2022). Guide to the Methods of Technology Appraisal. London: NICE.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=National%20Institute%20for%20Health%20and%20Care%20Excellence%20(NICE).%20(2022).%20Guide%20to%20the%20Methods%20of%20Technology%20Appraisal.%20London%3A%20"}],"related":["quality-adjusted-life-year","disability-adjusted-life-year","cost-benefit-analysis","markov-model-health-economics","decision-analytic-modeling"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cost-volume-profit-analysis","name":"Cost-Volume-Profit Analysis","fullName":"Cost-Volume-Profit (CVP) Analysis Framework for Managerial Decision Making","aliases":["Break-Even Analysis","CVP Analysis","Contribution Margin Analysis"],"domain":"accounting","family":"mcdm","subfamily":"Cost-Behavior Analysis and Profitability Planning","year":"1940s","originator":"Managerial accounting theorists and practitioners","url":"https://scholargate.app/en/accounting/cost-volume-profit-analysis","markdownUrl":"https://scholargate.app/en/accounting/cost-volume-profit-analysis.md","definition":"Cost-Volume-Profit (CVP) Analysis is a foundational managerial accounting method that examines the relationships among costs, sales volume, and profit. By analyzing how changes in production volume, selling price, and cost structure affect profitability, managers can make informed decisions about pricing, production, and strategic direction. CVP analysis provides insight into break-even points and the profit generated at various activity levels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Managerial accounting theorists and practitioners","subfamily":"Cost-Behavior Analysis and Profitability Planning","year":"1940s","type":"Managerial accounting and decision analysis framework"},"citations":[{"ref":"Garrison, R. H., Noreen, E. W., & Brewer, P. C. (2015). Managerial accounting (15th ed.). McGraw-Hill Education.","type":"article","doi":null,"isbn":null,"url":"https://www.mheducation.com/"},{"ref":"Horngren, C. T., Datar, S. M., & Rajan, M. V. (2015). Cost accounting: A managerial emphasis (15th ed.). Pearson Education.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com/"}],"related":["activity-based-costing","analytical-procedures-auditing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cosy","name":"COSY","fullName":"Correlation Spectroscopy","aliases":["COSY NMR","2D COSY","1H-1H COSY"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Multidimensional NMR","year":"1976","originator":"Wüthrich Kurt","url":"https://scholargate.app/en/spectroscopy/cosy","markdownUrl":"https://scholargate.app/en/spectroscopy/cosy.md","definition":"Correlation Spectroscopy (COSY) is a two-dimensional NMR technique that correlates proton chemical shifts through scalar coupling (J-coupling), revealing which protons are magnetically coupled and hence bonded through multiple bonds. Developed by Aue, Bartholdi, and Ernst in 1976, COSY became one of the most important tools in structural elucidation, enabling chemists to map out proton connectivity patterns and deduce molecular topology without isotopic labeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wüthrich Kurt","subfamily":"Multidimensional NMR","year":"1976","type":"Two-dimensional pulse sequence"},"citations":[{"ref":"Aue, W. P., Bartholdi, E., & Ernst, R. R. (1976). Two-dimensional spectroscopy. Application to nuclear magnetic resonance. The Journal of Chemical Physics, 64(5), 2229-2246.","type":"article","doi":"10.1063/1.432450","isbn":null,"url":null},{"ref":"Bax, A., & Davis, D. G. (1985). MLEV-17-based two-dimensional homonuclear magnetization transfer spectroscopy. Journal of Magnetic Resonance, 65(2), 355-360.","type":"article","doi":"10.1016/0022-2364(85)90018-6","isbn":null,"url":null}],"related":["noesy","hsqc","nmr-spin-echo","atr-ftir"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"coulometry","name":"Coulometry","fullName":"Coulometry","aliases":["coulometric titration","electrochemical coulometry","amperes titration"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Electrochemical Analysis","year":"1945","originator":"James Lingane","url":"https://scholargate.app/en/analytical-chemistry/coulometry","markdownUrl":"https://scholargate.app/en/analytical-chemistry/coulometry.md","definition":"Coulometry is an electrochemical analytical method that determines the concentration of an analyte by measuring the total electric charge (in coulombs) required to oxidize or reduce the analyte completely at an electrode. Developed by James J. Lingane in the 1940s, coulometry is highly accurate because it is based on fundamental constants (Faraday's law) and does not require external standards or calibration curves. This method is particularly valuable for trace analysis, water determination, and analysis of reactive species.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Lingane","subfamily":"Electrochemical Analysis","year":"1945","type":"electrochemical titration"},"citations":[{"ref":"Lingane, J. J. (1974). Electroanalytical Chemistry (2nd ed.). Interscience Publishers.","type":"book","doi":null,"isbn":"978-0486409023","url":null},{"ref":"Skoog, D. A., West, D. M., Holler, F. J., & Crouch, S. R. (2014). Fundamentals of Analytical Chemistry (9th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1133170960","url":null},{"ref":"Simonsen, K. B., Larsen, K. L., Frandsen, H., & Andersen, J. E. T. (2012). Coulometric titration for determination of peroxide compounds. Journal of Pharmaceutical and Biomedical Analysis, 71, 22–28.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Coulometric+titration+for+determination+of+peroxide+compounds+Simonsen"}],"related":["voltammetry","potentiometric-titration","ion-chromatography","uv-vis-spectrophotometry","atomic-absorption-spectroscopy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"counter-movement-jump","name":"Counter-Movement Jump","fullName":"Counter-Movement Jump and Lower Body Power Assessment","aliases":["CMJ","jump height","explosive power"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Power & Plyometrics","year":"1983","originator":"Paavo Komi","url":"https://scholargate.app/en/sports-science/counter-movement-jump","markdownUrl":"https://scholargate.app/en/sports-science/counter-movement-jump.md","definition":"The counter-movement jump (CMJ) is a simple, field-friendly test of lower-body explosive power in which the athlete stands on a force plate, descends into a shallow squat (counter-movement phase), and explosively extends to jump as high as possible. Pioneered by Bosco and Komi (1983), the CMJ captures the integrated function of strength, rate of force development, and elastic energy utilization. Jump height (measured via flight time from force plate or motion capture) and peak power are reported. The CMJ is among the most widely used tests in sports science, athlete monitoring, and research due to simplicity, objectivity, and relevance to explosive power in nearly all sports.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paavo Komi","subfamily":"Power & Plyometrics","year":"1983","type":"jumping test"},"citations":[{"ref":"Bosco, C., Luhtanen, P., & Komi, P. V. (1983). A simple method for measurement of mechanical power in jumping. European Journal of Applied Physiology, 50(2), 273-282.","type":"article","doi":"10.1007/BF00422166","isbn":null,"url":null},{"ref":"Linthorne, N. P. (2001). Analysis of standing vertical jumps using a force platform. American Journal of Physics, 69(11), 1198-1204.","type":"article","doi":"10.1119/1.1397460","isbn":null,"url":null},{"ref":"Cormie, P., McBride, J. M., & McCaulley, G. O. (2010). Validation of power measurement techniques in dynamic lower body resistance exercises. Journal of Applied Biomechanics, 23(2), 103-118.","type":"article","doi":"10.1123/jab.23.2.103","isbn":null,"url":null}],"related":["reactive-strength-index","force-velocity-profile","rate-of-force-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"counterfactual-explanations","name":"Counterfactual Explanations","fullName":"Counterfactual Explanations","aliases":["Algorithmic Recourse","Contrastive Explanations","What-If Explanations","Karşıolgusal Açıklamalar"],"domain":"machine-learning","family":"ml-model","subfamily":"Explainable AI","year":2017,"originator":"Sandra Wachter, Brent Mittelstadt & Chris Russell","url":"https://scholargate.app/en/machine-learning/counterfactual-explanations","markdownUrl":"https://scholargate.app/en/machine-learning/counterfactual-explanations.md","definition":"Counterfactual explanations, introduced by Wachter, Mittelstadt, and Russell in 2017, answer the question: 'What is the smallest change to the input that would have produced a different model output?' Rather than explaining why a model made a decision, they describe what would need to change for that decision to be reversed, making them particularly valuable for high-stakes applications such as credit scoring, medical diagnosis, and hiring decisions under frameworks like the EU GDPR.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sandra Wachter, Brent Mittelstadt & Chris Russell","year":2017,"type":"Post-hoc, model-agnostic explanation","subfamily":"Explainable AI","scope":"Individual prediction (local explanation)","output":"Minimal feature change to flip model decision"},"citations":[{"ref":"Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31, 841–887.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1711.00399"}],"related":["lime","shap","logistic-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"counterfactual-impact-evaluation-in-education-research","name":"Counterfactual Impact Evaluation in Education Research","fullName":"Counterfactual Impact Evaluation in Education Research","aliases":["CIE in education","counterfactual program evaluation","causal impact evaluation","education policy impact evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Blundell & Costa Dias; formalized for EU education policy by the European Commission Joint Research Centre","url":"https://scholargate.app/en/causal-inference/counterfactual-impact-evaluation-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/counterfactual-impact-evaluation-in-education-research.md","definition":"Counterfactual impact evaluation (CIE) is the systematic application of causal inference designs — such as difference-in-differences, regression discontinuity, matching, and instrumental variables — to measure the genuine effect of education programs, policies, or interventions by constructing a credible counterfactual: what would have happened to participants had they not been treated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blundell & Costa Dias; formalized for EU education policy by the European Commission Joint Research Centre","year":"2000s–2010s","type":"Quasi-experimental causal inference framework","dataType":"Panel data, repeated cross-sections, or administrative records on education participants","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Blundell, R., & Costa Dias, M. (2002). Alternative approaches to evaluation in empirical microeconomics. Portuguese Economic Journal, 1(2), 91-115.","type":"article","doi":"10.1007/s10258-002-0010-3","isbn":null,"url":null},{"ref":"Cerulli, G. (2015). Econometric Evaluation of Socio-Economic Programs: Theory and Applications. Springer.","type":"book","doi":null,"isbn":"978-3-662-46400-2","url":null}],"related":["difference-in-differences","propensity-score-matching","regression-discontinuity-design","instrumental-variables","synthetic-control-method","interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"counterfactual-impact-evaluation","name":"Counterfactual Impact Evaluation","fullName":"Counterfactual Impact Evaluation","aliases":["CIE","counterfactual evaluation","counterfactual policy evaluation","impact evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1970s–2000s","originator":"Heckman, Imbens, Rubin, and the program evaluation literature","url":"https://scholargate.app/en/causal-inference/counterfactual-impact-evaluation","markdownUrl":"https://scholargate.app/en/causal-inference/counterfactual-impact-evaluation.md","definition":"Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heckman, Imbens, Rubin, and the program evaluation literature","year":"1970s–2000s","type":"Causal inference / program evaluation","dataType":"Observational or quasi-experimental panel or cross-sectional data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.","type":"article","doi":"10.1016/S1573-4412(07)06070-9","isbn":null,"url":null},{"ref":"Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5-86.","type":"article","doi":"10.1257/jel.47.1.5","isbn":null,"url":null}],"related":["difference-in-differences","propensity-score-matching","instrumental-variables","regression-discontinuity-design","synthetic-control-method","causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"coupled-cluster-ccsd","name":"Coupled Cluster CCSD","fullName":"Coupled Cluster Singles and Doubles (CCSD(T))","aliases":["CCSD","CCSD(T)"],"domain":"quantum-computing","family":"ml-model","subfamily":"Post-Hartree-Fock Method","year":"1966","originator":"Jiri Cizek","url":"https://scholargate.app/en/quantum-computing/coupled-cluster-ccsd","markdownUrl":"https://scholargate.app/en/quantum-computing/coupled-cluster-ccsd.md","definition":"Coupled Cluster theory, particularly CCSD (Singles and Doubles) and CCSD(T) with perturbative triples, is one of the most accurate methods for molecular electronic structure. Developed by Jiri Cizek in 1966, CC theory treats the ground state wave function as an exponential of excitation operators applied to the Hartree-Fock reference, enabling systematic treatment of electron correlation with guaranteed size consistency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jiri Cizek","subfamily":"Post-Hartree-Fock Method","year":"1966","type":"Electronic correlation method"},"citations":[{"ref":"Cizek, J. (1966). On the correlation problem in atomic and molecular systems. Journal of Chemical Physics, 45, 4256–4266.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+correlation+problem+in+atomic+and+molecular+systems+Cizek"},{"ref":"Raghavachari, K., Trucks, G. W., Pople, J. A., Head-Gordon, M. (1989). A fifth-order perturbation comparison of electron correlation theories. Chemical Physics Letters, 157, 479–483.","type":"article","doi":"10.1016/S0009-2614(89)87395-6","isbn":null,"url":null},{"ref":"Szabo, A., Ostlund, N. S. (2012). Modern Quantum Chemistry. Dover Publications.","type":"article","doi":null,"isbn":null,"url":"https://store.doverpublications.com/0486691691.html"}],"related":["hartree-fock-method","moller-plesset-perturbation-theory","density-functional-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cournot-competition","name":"Cournot Competition","fullName":"Cournot Oligopoly Model","aliases":["Quantity Competition","Cournot Equilibrium","Cournot-Nash Equilibrium"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1838","originator":"Augustin-Louis Cournot","url":"https://scholargate.app/en/game-theory/cournot-competition","markdownUrl":"https://scholargate.app/en/game-theory/cournot-competition.md","definition":"Cournot Competition models oligopolistic markets where firms choose quantities simultaneously, not prices. Originally formulated by Augustin-Louis Cournot in 1838, the model assumes each firm's profit depends on the total market quantity produced. The resulting Cournot-Nash Equilibrium captures the strategic interaction where each firm maximizes profit given competitors' quantities, leading to prices between monopoly and perfect competition levels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Augustin-Louis Cournot","subfamily":"Game-theoretic","year":"1838","type":"algorithm"},"citations":[{"ref":"Cournot, A. A. (1838). Recherches sur les principes mathématiques de la théorie des richesses. L. Hachette.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/recherchesurles00cournot"},{"ref":"Vives, X. (1999). Oligopoly Pricing: Old Ideas and New Tools. MIT Press.","type":"book","doi":null,"isbn":null,"url":"https://mitpress.mit.edu/9780262220743/oligopoly-pricing/"}],"related":["stackelberg-competition","nash-equilibrium","bayesian-nash-equilibrium","evolutionary-game-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"course-experience-questionnaire","name":"Course Experience Questionnaire","fullName":"Course Experience Questionnaire (CEQ)","aliases":["CEQ","Learning Environment Questionnaire"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Course quality and learning environment","year":"1997","originator":"Kim Wilson, Lucia Lizzio, Paul Ramsden","url":"https://scholargate.app/en/educational-psychology/course-experience-questionnaire","markdownUrl":"https://scholargate.app/en/educational-psychology/course-experience-questionnaire.md","definition":"The Course Experience Questionnaire (CEQ) is an institutional assessment tool measuring students' perceptions of their learning environment and educational experience in a course. Developed by Wilson, Lizzio, and Ramsden (1997), it assesses dimensions including good teaching, clear goals, appropriate workload, appropriate assessment, appropriate feedback, and learning community. The CEQ provides universities with evaluative data to guide curriculum development and educational improvement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kim Wilson, Lucia Lizzio, Paul Ramsden","subfamily":"Course quality and learning environment","year":"1997","type":"Course experience assessment"},"citations":[{"ref":"Wilson, K. L., Lizzio, A., & Ramsden, P. (1997). The development, validation and application of the Course Experience Questionnaire. Studies in Higher Education, 22(1), 33-53.","type":"article","doi":"10.1080/03075079712331381121","isbn":null,"url":null},{"ref":"Pike, G. R. (2003). Membership in a learning community and college learning outcomes. New Directions for Institutional Research, 17, 5-24.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Membership+in+a+learning+community+and+college+learning+outcomes+Pike"}],"related":["study-process-questionnaire","teaching-effectiveness-scale","student-satisfaction-survey","student-engagement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"covid-19-anxiety-scale","name":"COVID-19 Anxiety Scale","fullName":"COVID-19 Anxiety Scale (CAS)","aliases":["CAS"],"domain":"public-health","family":"process-pipeline","subfamily":"pandemic-mental-health","year":"2020","originator":"Lipp et al.","url":"https://scholargate.app/en/public-health/covid-19-anxiety-scale","markdownUrl":"https://scholargate.app/en/public-health/covid-19-anxiety-scale.md","definition":"The COVID-19 Anxiety Scale (CAS) is a brief, self-administered instrument designed to assess anxiety symptoms specifically related to the COVID-19 pandemic. Developed by Lipp and colleagues in 2020, it captures worry about infection, social isolation, and pandemic-related uncertainties. The scale is widely used in epidemiological surveys and clinical research to identify individuals experiencing pandemic-related anxiety requiring intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lipp et al.","subfamily":"pandemic-mental-health","year":"2020","type":"Self-report"},"citations":[{"ref":"Lipp, A., Fazio, S., & Cohen, S. (2020). COVID-19 anxiety in the United States. International Journal of Mental Health and Addiction, 20(3), 1234–1248.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=COVID-19+anxiety+in+the+United+States+Lipp"}],"related":["fear-of-infection-scale","covid-19-mental-health-scale","pandemic-fatigue-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"covid-19-mental-health-scale","name":"COVID-19 Mental Health Impact Scale","fullName":"COVID-19 Mental Health Impact Scale (CMHIS)","aliases":["CMHIS"],"domain":"public-health","family":"process-pipeline","subfamily":"pandemic-mental-health-burden","year":"2020","originator":"Wang et al.","url":"https://scholargate.app/en/public-health/covid-19-mental-health-scale","markdownUrl":"https://scholargate.app/en/public-health/covid-19-mental-health-scale.md","definition":"The COVID-19 Mental Health Impact Scale (CMHIS) is a brief, multidimensional instrument assessing anxiety, depression, and stress symptoms triggered by the COVID-19 pandemic. Developed by Wang and colleagues in 2020 during the initial pandemic wave in China, it captures the spectrum of psychological distress across multiple symptom domains. The CMHIS has been widely adopted in pandemic surveillance and mental health research across 30+ countries, providing rapid assessment of population mental health burden.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang et al.","subfamily":"pandemic-mental-health-burden","year":"2020","type":"Self-report"},"citations":[{"ref":"Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) pandemic among the general population in China. International Journal of Environmental Research and Public Health, 17(5), 1729.","type":"article","doi":"10.3390/ijerph17051729","isbn":null,"url":null}],"related":["covid-19-anxiety-scale","fear-of-infection-scale","pandemic-fatigue-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cox-proportional-hazards","name":"Cox proportional hazards","fullName":"Cox Proportional Hazards Regression Model","aliases":["Cox regression","Cox PH model","proportional hazards model","CPH"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1972","originator":"Sir David Roxbee Cox","url":"https://scholargate.app/en/epidemiology/cox-proportional-hazards","markdownUrl":"https://scholargate.app/en/epidemiology/cox-proportional-hazards.md","definition":"The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sir David Roxbee Cox","year":"1972","type":"Semi-parametric regression model","dataType":"Time-to-event (survival) data with one or more covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null},{"ref":"Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439856789","url":null}],"related":["survival-analysis","kaplan-meier-analysis","competing-risks-analysis","cohort-study","logistic-regression","accelerated-failure-time"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cox-regression","name":"Cox Regression","fullName":"Cox Proportional Hazards Regression","aliases":["cox ph model","proportional hazards model","cox ph regression","Cox Orantılı Tehlikeler Regresyonu"],"domain":"survival","family":"survival","subfamily":null,"year":1972,"originator":"Cox, D. R.","url":"https://scholargate.app/en/survival/cox-regression","markdownUrl":"https://scholargate.app/en/survival/cox-regression.md","definition":"Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cox, D. R.","year":1972,"type":"Semi-parametric hazard regression model","handles":"Right-censoring, multiple covariates","key_output":"Hazard Ratio (HR) with 95% confidence interval","minimum_sample":"n ≥ 50; at least 10 events per predictor variable (EPV ≥ 10)"},"citations":[{"ref":"Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null},{"ref":"Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.","type":"book","doi":null,"isbn":"978-0387987842","url":null}],"related":["kaplan-meier","log-rank-test","fine-gray-model","nelson-aalen","weibull-aft"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cp-violation-measurement","name":"CP Violation Measurement","fullName":"Charge-Parity Violation Experimental Measurement","aliases":["CP asymmetry","matter-antimatter asymmetry","T-symmetry violation"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Symmetry violation","year":"1964","originator":"Fitch, Cronin, and collaborators","url":"https://scholargate.app/en/particle-physics/cp-violation-measurement","markdownUrl":"https://scholargate.app/en/particle-physics/cp-violation-measurement.md","definition":"Charge-Parity (CP) violation measurement is the experimental study of asymmetries between particle and antiparticle processes, a fundamental probe of physics beyond the Standard Model. By comparing decay rates and asymmetries in kaons, B mesons, and neutrinos, physicists constrain new sources of CP violation and address the cosmological matter-antimatter imbalance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fitch, Cronin, and collaborators","subfamily":"Symmetry violation","year":"1964","type":"Asymmetry measurement"},"citations":[{"ref":"Christenson, J. H., et al. (1964). Evidence for the 2π decay of the K₂⁰ meson. Physical Review Letters, 13(4), 138.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evidence+for+the+2%CF%80+decay+of+the+K%E2%82%82%E2%81%B0+meson+Christenson"},{"ref":"Aubert, B., et al. (BaBar Collaboration). (2001). Observation of CP violation in the B meson system. Physical Review Letters, 87(9), 091801.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Observation+of+CP+violation+in+the+B+meson+system+Aubert"},{"ref":"Kobayashi, M., & Maskawa, T. (1973). CP-violation in the renormalizable theory of weak interaction. Progress of Theoretical Physics, 49(2), 652–657.","type":"article","doi":"10.1143/PTP.49.652","isbn":null,"url":null}],"related":["neutrino-oscillation-analysis","matrix-element-method","effective-field-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cradis","name":"CRADIS","fullName":"Compromise Ranking of Alternatives from Distance to Ideal Solution","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Puška, A., Stević, Ž., Pamučar, D.","url":"https://scholargate.app/en/decision-making/cradis","markdownUrl":"https://scholargate.app/en/decision-making/cradis.md","definition":"CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Puška, A., Stević, Ž., Pamučar, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Puška, A., Stević, Ž., Pamučar, D.","subfamily":"Ranking","year":"2021","type":"Distance from ideal and anti-ideal (geometric mean compromise)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Puška, A., Stević, Ž., Pamučar, D. (2021). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability","type":"article","doi":"10.1007/s10668-021-01902-2","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"craig-handicap-assessment","name":"Craig Handicap Assessment and Reporting Technique","fullName":"Craig Handicap Assessment and Reporting Technique (CHART)","aliases":["CHART","CHART-SF"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"handicap-social-role","year":"1992","originator":"Whiteneck, Charlifue, Gerhart, Overholser, Richardson","url":"https://scholargate.app/en/rehabilitation-science/craig-handicap-assessment","markdownUrl":"https://scholargate.app/en/rehabilitation-science/craig-handicap-assessment.md","definition":"The Craig Handicap Assessment and Reporting Technique (CHART) is a comprehensive interview-based measure designed to quantify how much a disabling condition restricts participation in six key social roles: physical independence, mobility, occupation, social integration, economic self-sufficiency, and cognitive independence. Developed by Whiteneck and colleagues at the Craig Hospital (now national leader in spinal cord injury care), CHART has become the gold-standard outcome measure for long-term spinal cord injury and traumatic brain injury follow-up, extensively used in international outcomes research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Whiteneck, Charlifue, Gerhart, Overholser, Richardson","subfamily":"handicap-social-role","year":"1992","type":"Interview or Self-report"},"citations":[{"ref":"Whiteneck, G. G., Charlifue, S. W., Gerhart, K. A., Overholser, J. D., & Richardson, G. N. (1992). Quantifying handicap: a new measure of long-term rehabilitation outcomes. Archives of Physical Medicine and Rehabilitation, 73(6), 519–526.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/S0003-9993(92)90074-1"},{"ref":"Charlifue, S., Post, M. W., & Biering-Sørensen, F. (2012). International Spinal Cord Injury Community Survey: does the use of different outcome measures lead to different conclusions about quality of life after spinal cord injury? Spinal Cord, 50(6), 457–463.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1038/sc.2011.178"}],"related":["community-integration-questionnaire","impact-participation-autonomy","assessment-life-habits","participation-scale","whodas-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cramers-v","name":"Cramer's V","fullName":"Cramer's V (Effect Size for Chi-Square)","aliases":["cramers v","cramer v","phi coefficient (r×c)","Cramer's V (İlişki Kuvveti)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1946,"originator":"Harald Cramér","url":"https://scholargate.app/en/statistics/cramers-v","markdownUrl":"https://scholargate.app/en/statistics/cramers-v.md","definition":"Cramer's V is a nonparametric effect-size statistic that measures the strength of association between two categorical variables on a scale from 0 to 1. Introduced by the Swedish mathematician Harald Cramér in his 1946 work Mathematical Methods of Statistics, it generalises the phi coefficient to tables of any size, making it the standard companion statistic to the chi-square test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harald Cramér","year":1946,"family":"Effect size","type":"Nonparametric association measure","scale":"[0, 1]","outcome":"categorical","parametric":false,"baseTest":"Chi-square (χ²)","thresholds":"V < 0.10 weak; 0.10–0.30 moderate; ≥ 0.30 strong"},"citations":[{"ref":"Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691080420","url":null}],"related":["chi-square-test","fishers-exact-test","phi-coefficient","goodman-kruskal-lambda","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crank-nicolson-pricing","name":"Crank-Nicolson Pricing","fullName":"Crank-Nicolson Finite Difference Method","aliases":["CN Method","Implicit Finite Difference"],"domain":"quantitative-finance","family":"ml-model","subfamily":"Numerical Methods","year":"1947","originator":"John Crank and Phyllis Nicolson","url":"https://scholargate.app/en/quantitative-finance/crank-nicolson-pricing","markdownUrl":"https://scholargate.app/en/quantitative-finance/crank-nicolson-pricing.md","definition":"The Crank-Nicolson method is a widely-used implicit finite difference scheme for solving PDEs in option pricing. It provides second-order accuracy in both space and time, unconditional stability, and can efficiently price derivatives with early exercise features (American options) or complex boundary conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Crank and Phyllis Nicolson","subfamily":"Numerical Methods","year":"1947","type":"PDE Solver"},"citations":[{"ref":"Crank, J., & Nicolson, P. (1947). A practical method for numerical evaluation of solutions of partial differential equations of the heat-conduction type. Mathematical Proceedings of the Cambridge Philosophical Society, 43(1), 50-67.","type":"article","doi":"10.1017/S0305004100023197","isbn":null,"url":null},{"ref":"Fornberg, B. (1996). A Practical Guide to Pseudospectral Methods. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511626357","isbn":null,"url":null}],"related":["local-volatility","hull-white-model","sabr-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"craving-questionnaire","name":"QSU-Brief","fullName":"Questionnaire on Smoking Urges—Brief","aliases":["QSU-Brief","QSU"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"craving-assessment","year":"1996","originator":"Cox, Tiffany, Christen","url":"https://scholargate.app/en/addiction-medicine/craving-questionnaire","markdownUrl":"https://scholargate.app/en/addiction-medicine/craving-questionnaire.md","definition":"The QSU-Brief is a 10-item self-report instrument that rapidly assesses the intensity of craving for cigarettes and the intention to smoke. Developed by Cox, Tiffany, and Christen in 1996, it is a brief version of the longer Questionnaire on Smoking Urges (QSU) and is widely used in smoking cessation treatment and research settings to measure one of the strongest predictors of smoking relapse. The QSU-Brief is also applicable, with adaptation, to other addictive substances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cox, Tiffany, Christen","subfamily":"craving-assessment","year":"1996","type":"Self-report"},"citations":[{"ref":"Cox, L. S., Tiffany, S. T., & Christen, A. G. (1996). Evaluation of the brief Questionnaire of Smoking Urges (QSU-brief) in laboratory and clinical settings. Nicotine & Tobacco Research, 2(1), 7–16.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation+of+the+brief+Questionnaire+of+Smoking+Urges+%28QSU-brief%29+in+laboratory+and+clinical+settings+Cox"}],"related":["dudit","brief-addiction-monitor","alcohol-urge-questionnaire","treatment-motivation-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"credibility-theory","name":"Credibility Theory","fullName":"Actuarial Credibility Theory (Bühlmann)","aliases":["Bühlmann Credibility","Experience Rating","Linear Credibility Estimator","Güvenilirlik Teorisi"],"domain":"actuarial-science","family":"regression-model","subfamily":"Actuarial modelling","year":1967,"originator":"Hans Bühlmann","url":"https://scholargate.app/en/actuarial-science/credibility-theory","markdownUrl":"https://scholargate.app/en/actuarial-science/credibility-theory.md","definition":"Credibility Theory is an actuarial framework for estimating the pure premium of an individual risk by blending its own observed loss experience with the collective (portfolio) mean. Introduced by Hans Bühlmann in 1967, the method derives the optimal linear combination—the credibility-weighted premium—that minimises mean squared error. It extends classical experience rating to a rigorous statistical footing rooted in Bayesian and linear estimation principles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans Bühlmann","year":1967,"type":"Weighted linear blend of individual and collective experience","subfamily":"Actuarial modelling","estimator_class":"Best linear unbiased estimator (BLUE)","key_parameter":"Credibility factor k = a / (a + s²/n)"},"citations":[{"ref":"Bühlmann, H. (1967). Experience rating and credibility. ASTIN Bulletin, 4(3), 199–207.","type":"article","doi":"10.1017/S0515036100008989","isbn":null,"url":null}],"related":["bayesian-hierarchical-model","loss-distribution-model","bonus-malus-system"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"credit-risk-models","name":"Credit Risk Models","fullName":"Structural and Portfolio Credit Risk Models (Merton, KMV, CreditMetrics)","aliases":["Merton model","KMV model","CreditMetrics","structural credit risk model","default probability model","Kredi Risk Modelleri (Merton, KMV, CreditMetrics)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1974,"originator":"Robert C. Merton (structural model); J.P. Morgan / Gupton et al. (CreditMetrics)","url":"https://scholargate.app/en/finance/credit-risk-models","markdownUrl":"https://scholargate.app/en/finance/credit-risk-models.md","definition":"Credit risk models estimate the probability that a borrower defaults and the resulting distribution of credit losses. The structural approach was introduced by Robert C. Merton in 1974, treating a firm's equity as a call option on its assets, and was later extended into the KMV distance-to-default framework and the CreditMetrics rating-transition portfolio model published by J.P. Morgan in 1997.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert C. Merton (structural model); J.P. Morgan / Gupton et al. (CreditMetrics)","year":1974,"type":"Structural and portfolio credit risk model","estimator":"Default probability via distance-to-default; portfolio loss via rating-transition simulation","outcome":"Probability of default and credit loss distribution"},"citations":[{"ref":"Merton, R. C. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. The Journal of Finance, 29(2), 449-470.","type":"article","doi":"10.1111/j.1540-6261.1974.tb03058.x","isbn":null,"url":null},{"ref":"Gupton, G. M., Finger, C. C., & Bhatia, M. (1997). CreditMetrics Technical Document. J.P. Morgan, New York.","type":"report","doi":null,"isbn":null,"url":"https://www.msci.com/documents/10199/93396227-d449-4229-9143-24a94dab122f"}],"related":["interest-rate-models","backtesting-var","liquidity-risk-models","event-study-finance","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"credit-scoring","name":"Credit Scoring","fullName":"Credit Scoring (Scorecards, WoE/IV)","aliases":["Credit Scorecard","Application Scoring","Behavioural Scoring","Kredi Skorlama"],"domain":"finance","family":"regression-model","subfamily":"Credit risk","year":1997,"originator":"Hand & Henley; Thomas, Edelman & Crook","url":"https://scholargate.app/en/finance/credit-scoring","markdownUrl":"https://scholargate.app/en/finance/credit-scoring.md","definition":"Credit scoring is a statistical technique that estimates the probability that a borrower will default on a financial obligation. Using Weight of Evidence (WoE) binning, Information Value (IV) variable selection, and logistic regression, it converts raw applicant data into a single integer score. Formalized by Hand and Henley (1997) and elaborated by Thomas, Edelman, and Crook, the scorecard framework has become the regulatory standard for retail credit risk assessment in banking, lending, and insurance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hand & Henley; Thomas, Edelman & Crook","year":1997,"type":"Supervised binary classification model","subfamily":"Credit risk","output":"Probability of default + integer scorecard points","regulation":"Basel II/III compliant when documented"},"citations":[{"ref":"Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A, 160(3), 523–541.","type":"article","doi":"10.1111/j.1467-985X.1997.00078.x","isbn":null,"url":null}],"related":["logistic-regression","altman-z-score","xgboost"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"credit-valuation-adjustment","name":"Credit Valuation Adjustment","fullName":"Credit Valuation Adjustment (CVA)","aliases":["CVA","Counterparty Risk Adjustment"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Credit Risk","year":"2000s","originator":"Jon Gregory","url":"https://scholargate.app/en/quantitative-finance/credit-valuation-adjustment","markdownUrl":"https://scholargate.app/en/quantitative-finance/credit-valuation-adjustment.md","definition":"Credit Valuation Adjustment (CVA) is the market price of counterparty credit risk embedded in over-the-counter (OTC) derivatives. CVA measures the loss from counterparty default, accounting for both the probability of default and the exposure at that time. It has become a key component of derivative valuation and risk management since the 2008 financial crisis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jon Gregory","subfamily":"Credit Risk","year":"2000s","type":"Valuation Framework"},"citations":[{"ref":"Gregory, J. (2009). Counterparty Credit Risk: The New Challenge for Global Financial Markets. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Counterparty+Credit+Risk%3A+The+New+Challenge+for+Global+Financial+Markets+Gregory"},{"ref":"Pykhtin, M., & Zhu, S. (2007). A guide to modeling counterparty credit risk. GARP Risk Review, 1, 16-33.","type":"article","doi":null,"isbn":null,"url":"https://www.mathworks.com/content/dam/mathworks/mathworks-dot-com/images/responsive/supporting/solutions/finance/counterparty-risk.pdf"}],"related":["merton-default-model","debit-valuation-adjustment","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crime-linkage-analysis","name":"Crime Linkage Analysis","fullName":"Crime Linkage Analysis and Serial Crime Attribution","aliases":["case linkage","offender linking","serial crime attribution"],"domain":"forensics","family":"process-pipeline","subfamily":"Forensic analysis and pattern matching","year":"2002","originator":"Craig Bennell","url":"https://scholargate.app/en/forensics/crime-linkage-analysis","markdownUrl":"https://scholargate.app/en/forensics/crime-linkage-analysis.md","definition":"Crime linkage analysis is a forensic method that determines whether a series of crimes were committed by the same offender based on behavioral and modus operandi (MO) similarities. Developed systematically by Craig Bennell and colleagues in the early 2000s, crime linkage applies statistical and similarity-matching techniques to establish offender attribution. The method is essential in serial crime investigation, where establishing linkage enables consolidation of investigation resources, geographic profiling, and offender-focused surveillance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Craig Bennell","subfamily":"Forensic analysis and pattern matching","year":"2002","type":"Crime science and offender profiling method"},"citations":[{"ref":"Bennell, C., Canter, D. V., & Alison, L. J. (2002). Linking commercial burglaries by modus operandi: Tests using regression and ROC analysis. Science and Justice, 42(3), 153-164.","type":"article","doi":"10.1016/s1355-0306(02)71820-0","isbn":null,"url":null},{"ref":"Brants, L., de Ridder, H., & de Ridder, A. (2009). Offender linking in serial homicide. Forensic Science International, 171(2-3), 97-103.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Offender+linking+in+serial+homicide+Brants"},{"ref":"Tonkin, M., Santtila, P., Bull, R., & Bond, J. W. (2012). A systematic review of decision support tools for case linkage. Forensic Science International, 221(1-3), 1-13.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+systematic+review+of+decision+support+tools+for+case+linkage+Tonkin"}],"related":["geographic-profiling","network-analysis-of-case-law","risk-terrain-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crispr-screen-analysis","name":"CRISPR Screen Analysis","fullName":"CRISPR Screening Data Analysis and Hit Identification","aliases":["CRISPR pooled screen","genetic screen analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Functional genomics","year":"2013","originator":"Feng Zhang","url":"https://scholargate.app/en/bioinformatics/crispr-screen-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/crispr-screen-analysis.md","definition":"CRISPR screen analysis processes data from pooled genetic screens using CRISPR-Cas9 to identify genes required for cell growth, survival, or phenotype in specific conditions. Developed by Zhang, Sanjana, and others, this computational pipeline transforms sequencing readouts of guide RNA abundances into ranked lists of functional genes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Feng Zhang","subfamily":"Functional genomics","year":"2013","type":"High-throughput genetic screen pipeline"},"citations":[{"ref":"Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A., Mikkelsen, T. S., ... & Zhang, F. (2014). Genome-scale CRISPR-Cas9 knockout screening in human cells. Science, 343(6166), 84-87.","type":"article","doi":"10.1126/science.1247005","isbn":null,"url":null},{"ref":"Hart, T., Chandrashekhar, M., Aregger, M., Steinhart, Z., Brown, K. R., MacLeod, G., ... & Moffat, J. (2015). High-resolution CRISPR screens reveal fitness genes and pathways. Molecular Systems Biology, 11(8), 820.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=High-resolution+CRISPR+screens+reveal+fitness+genes+and+pathways+Hart"},{"ref":"King, J. B., Palmer, A. C., & Sorger, P. K. (2020). Application of a genetic algorithm designed for flexible objective optimization in pharmaceutical research and development. Cancer Research, 79(13 Supplement), 3435.","type":"article","doi":null,"isbn":null,"url":"https://aacrjournals.org"}],"related":["hmmer-profile-search","metagenomic-binning","de-novo-transcriptome-assembly"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"criteria-removal","name":"CRITERIA-REMOVAL","fullName":"Criteria Removal — Sensitivity analysis by sequential criterion exclusion","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2004","originator":"Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.","url":"https://scholargate.app/en/decision-making/criteria-removal","markdownUrl":"https://scholargate.app/en/decision-making/criteria-removal.md","definition":"CRITERIA-REMOVAL (Criteria Removal — Sensitivity analysis by sequential criterion exclusion) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.","subfamily":"Ranking","year":"2004","type":"Robustness diagnostic — criterion removal sensitivity","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester","type":"article","doi":"10.1002/0470870958","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critic-m","name":"CRITIC-M","fullName":"Criteria Importance Through Intercriteria Correlation - Modified (CRITIC-M)","aliases":["CRITIC-M","Modified CRITIC"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1995","originator":"Based on Diakoulaki et al.'s CRITIC; modified variants developed later","url":"https://scholargate.app/en/decision-making/critic-m","markdownUrl":"https://scholargate.app/en/decision-making/critic-m.md","definition":"CRITIC-M (Criteria Importance Through Intercriteria Correlation - Modified) is an objective weight derivation method that extends the classical CRITIC approach. It assigns weights to criteria based on two intrinsic properties of the decision matrix: variance (how much a criterion differentiates alternatives) and correlation (how much a criterion conflicts with or supplements others). Modified variants adjust the formulation to improve robustness or interpretability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Based on Diakoulaki et al.'s CRITIC; modified variants developed later","subfamily":"Ranking","year":"1995","type":"Objective weight derivation via correlation and variance"},"citations":[{"ref":"Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763-770.","type":"article","doi":"10.1016/0305-0548(94)00059-H","isbn":null,"url":null},{"ref":"Jahan, A., Mustapha, F., Sapuan, S. M., Ismail, M. Y., & Badruddin, I. A. (2012). A comprehensive VIKOR method for material selection. Materials & Design, 32(3), 1215-1221.","type":"article","doi":"10.1016/j.matdes.2010.10.015","isbn":null,"url":null}],"related":["critic","entropy-method","variance-based-weighting","merec-g","swara-ii"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critic","name":"CRITIC","fullName":"CRiteria Importance Through Intercriteria Correlation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"1995","originator":"Diakoulaki, D., Mavrotas, G., Papayannakis, L.","url":"https://scholargate.app/en/decision-making/critic","markdownUrl":"https://scholargate.app/en/decision-making/critic.md","definition":"CRITIC (CRiteria Importance Through Intercriteria Correlation) is a weight objective multi-criteria decision-making (MCDM) method introduced by Diakoulaki, D., Mavrotas, G., Papayannakis, L. in 1995. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diakoulaki, D., Mavrotas, G., Papayannakis, L.","subfamily":"Weight_Objective","year":"1995","type":"Statistical contrast intensity + correlation-based objective weighting","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Diakoulaki, D., Mavrotas, G., Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research","type":"article","doi":"10.1016/0305-0548(94)00059-H","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-autoethnography","name":"Critical Autoethnography","fullName":"Critical Autoethnographic Research","aliases":["CAE","critical auto-ethnography","critical self-ethnography","critical performative autoethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (crystallised as named approach ~2012)","originator":"Robin M. Boylorn, Mark P. Orbe (editors of foundational volume); D. Soyini Madison (critical ethnography lineage)","url":"https://scholargate.app/en/qualitative/critical-autoethnography","markdownUrl":"https://scholargate.app/en/qualitative/critical-autoethnography.md","definition":"Critical autoethnography combines the self-reflective personal narrative of autoethnography with the social-justice orientation of critical theory. The researcher uses their own lived experience as primary data to interrogate power structures, systemic inequalities, and cultural norms — treating the personal not merely as testimony but as a site for political and theoretical critique. It is widely used to center the voices of marginalized groups and challenge dominant social narratives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robin M. Boylorn, Mark P. Orbe (editors of foundational volume); D. Soyini Madison (critical ethnography lineage)","year":"2000s–2010s (crystallised as named approach ~2012)","type":"Qualitative research design","dataType":"Personal narrative, field notes, autoethnographic texts, performative vignettes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Madison, D. S. (2005). Critical Ethnography: Method, Ethics, and Performance. Sage.","type":"book","doi":null,"isbn":"978-0761929505","url":null},{"ref":"Boylorn, R. M., & Orbe, M. P. (Eds.). (2012). Critical Autoethnography: Intersecting Cultural Identities in Everyday Life. Left Coast Press.","type":"book","doi":null,"isbn":"978-1611320268","url":null}],"related":["autoethnography","critical-ethnography","interpretive-autoethnography","narrative-inquiry","critical-discourse-analysis","reflexive-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-case-law-analysis","name":"Critical Case Law Analysis","fullName":"Critical Case Law Analysis","aliases":["critical legal analysis","CLS case analysis","critical judicial analysis","critical legal reading"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Late 1970s–1980s (CLS conference 1977; Unger 1983)","originator":"Critical Legal Studies (CLS) movement; key figures include Duncan Kennedy, Roberto Unger, Mark Tushnet","url":"https://scholargate.app/en/field-methods/critical-case-law-analysis","markdownUrl":"https://scholargate.app/en/field-methods/critical-case-law-analysis.md","definition":"Critical case law analysis applies the theoretical tools of Critical Legal Studies (CLS) to the examination of judicial decisions. Rather than accepting legal reasoning at face value, this approach interrogates how courts construct legal arguments, whose interests those arguments serve, and how ideological commitments are concealed beneath the appearance of neutral doctrinal logic. It exposes the political and social dimensions embedded in judicial language and outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Critical Legal Studies (CLS) movement; key figures include Duncan Kennedy, Roberto Unger, Mark Tushnet","year":"Late 1970s–1980s (CLS conference 1977; Unger 1983)","type":"Qualitative legal research approach","dataType":"Court opinions, judicial decisions, legal texts","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Unger, R. M. (1983). The Critical Legal Studies Movement. Harvard Law Review, 96(3), 561–675.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Critical+Legal+Studies+Movement+Unger+1983"},{"ref":"Kennedy, D. (1976). Form and Substance in Private Law Adjudication. Harvard Law Review, 89(8), 1685–1778.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Form+and+Substance+in+Private+Law+Adjudication+Kennedy+1976"}],"related":["case-law-analysis","doctrinal-legal-research","comparative-legal-analysis","legal-content-analysis","hermeneutic-analysis","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-case-study","name":"Critical case study","fullName":"Critical Case Study Research","aliases":["critical case","strategic case study","critical-instance case study","paradigmatic case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–2006 (formalized)","originator":"Bent Flyvbjerg (formalized); Robert K. Yin (case study typology)","url":"https://scholargate.app/en/qualitative/critical-case-study","markdownUrl":"https://scholargate.app/en/qualitative/critical-case-study.md","definition":"A critical case study is a case study design in which the researcher deliberately selects a case that is strategically important for testing, confirming, challenging, or extending an existing proposition, theory, or policy claim. Rather than choosing a typical or representative case, the researcher argues that if the finding holds here — in this most-likely, least-likely, or paradigmatic instance — it can reasonably be expected to hold more broadly. This purposive logic transforms a single case into a powerful analytical tool.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bent Flyvbjerg (formalized); Robert K. Yin (case study typology)","year":"1980s–2006 (formalized)","type":"Qualitative research design","dataType":"Interviews, documents, observations, archival records","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219–245.","type":"article","doi":"10.1177/1077800405284363","isbn":null,"url":null},{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["case-study","interpretive-case-study","comparative-case-study","grounded-theory","critical-ethnography","critical-discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-constructivist-grounded-theory","name":"Critical constructivist grounded theory","fullName":"Critical Constructivist Grounded Theory","aliases":["Critical CGT","Critical constructivist GT","Emancipatory constructivist grounded theory","Critical Charmaz grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (post-Charmaz 2006)","originator":"Kathy Charmaz (constructivist GT base); synthesis with critical theory by various scholars (e.g., Belfrage, Hauf; Street; Wuest)","url":"https://scholargate.app/en/qualitative/critical-constructivist-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/critical-constructivist-grounded-theory.md","definition":"Critical constructivist grounded theory combines Kathy Charmaz's constructivist grounded theory with an explicit critical theoretical lens — typically feminist, critical race, or Freireian frameworks — to generate theory that not only explains a social process but also interrogates power relations, structural inequalities, and ideological forces that shape participants' experiences. The result is grounded theory with an emancipatory intent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz (constructivist GT base); synthesis with critical theory by various scholars (e.g., Belfrage, Hauf; Street; Wuest)","year":"2000s–2010s (post-Charmaz 2006)","type":"Qualitative research design","dataType":"Interviews, documents, field notes, participant observations","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0857029164","url":null},{"ref":"Belfrage, C., & Hauf, F. (2017). The Gentle Art of Retroduction: Critical Realism, Cultural Political Economy and Critical Grounded Theory. Organization Studies, 38(2), 251–271.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1177/0170840616663239"}],"related":["constructivist-grounded-theory","critical-grounded-theory","critical-ethnography","critical-thematic-analysis","interpretive-constructivist-grounded-theory","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-content-analysis","name":"Critical Content Analysis","fullName":"Critical Content Analysis","aliases":["CCA","critical textual analysis","ideological content analysis","critical qualitative content analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–2000s (consolidated in practice by the 1990s–2000s)","originator":"Building on Krippendorff (1980) and Altheide (1996); synthesised through critical theory traditions (Frankfurt School, feminist and race critical scholars)","url":"https://scholargate.app/en/qualitative/critical-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/critical-content-analysis.md","definition":"Critical content analysis is a qualitative approach that examines texts, media, and documents not merely for manifest meaning but for how they construct, reinforce, or contest relations of power, ideology, race, gender, and class. Grounded in critical theory traditions, it asks whose interests a text serves, what voices are silenced, and how language and representation naturalise dominant worldviews. It combines systematic analytic rigour with an explicitly emancipatory or transformative research stance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Krippendorff (1980) and Altheide (1996); synthesised through critical theory traditions (Frankfurt School, feminist and race critical scholars)","year":"1980s–2000s (consolidated in practice by the 1990s–2000s)","type":"Qualitative analytical approach","dataType":"Textual, visual, or multimodal documents and media","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Altheide, D. L. (1996). Qualitative Media Analysis. Sage.","type":"book","doi":null,"isbn":"978-0803970892","url":null},{"ref":"Rogers, R., Malancharuvil-Berkes, E., Mosley, M., Hui, D., & Joseph, G. O. (2005). Critical Discourse Analysis in Education: A Review of the Literature. Review of Educational Research, 75(3), 365–416.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Critical+Discourse+Analysis+in+Education+Rogers+2005"}],"related":["critical-discourse-analysis","interpretive-content-analysis","thematic-analysis","critical-thematic-analysis","semiotic-analysis","critical-qualitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-curriculum-analysis","name":"Critical Curriculum Analysis","fullName":"Critical Curriculum Analysis","aliases":["CCA","critical curriculum inquiry","critical curriculum critique","curriculum critique"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1970s–1980s","originator":"Michael W. Apple; Henry A. Giroux; Paulo Freire (critical pedagogy foundations)","url":"https://scholargate.app/en/field-methods/critical-curriculum-analysis","markdownUrl":"https://scholargate.app/en/field-methods/critical-curriculum-analysis.md","definition":"Critical curriculum analysis examines educational curricula — their content, organisation, and underlying assumptions — through a critical theory lens. Drawing on the work of Apple, Giroux, and Freire, it asks whose knowledge counts, whose interests the curriculum serves, and how schooling reproduces or challenges social inequalities. Rather than treating curriculum as neutral, it treats it as an ideologically saturated artifact shaped by relations of power, race, class, and gender.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael W. Apple; Henry A. Giroux; Paulo Freire (critical pedagogy foundations)","year":"1970s–1980s","type":"Critical qualitative research approach","dataType":"Curriculum documents, textbooks, policy texts, standards, syllabi","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Apple, M. W. (1979). Ideology and Curriculum. Routledge & Kegan Paul.","type":"book","doi":null,"isbn":"978-0415909242","url":null},{"ref":"Giroux, H. A. (1997). Pedagogy and the Politics of Hope: Theory, Culture, and Schooling. Westview Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Giroux+Pedagogy+and+the+Politics+of+Hope+1997"}],"related":["curriculum-analysis","critical-discourse-analysis","educational-action-research","hermeneutic-analysis","critical-textual-criticism","program-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-digital-ethnography","name":"Critical Digital Ethnography","fullName":"Critical Digital Ethnography","aliases":["CDE","critical online ethnography","critical virtual ethnography","digital critical ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Synthesised from critical ethnography (Thomas, 1993) and digital/virtual ethnography (Hine, 2000); scholars including Bhatt, de Roock, and Saldanha developed explicit critical-digital frameworks from the 2010s onward","url":"https://scholargate.app/en/qualitative/critical-digital-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/critical-digital-ethnography.md","definition":"Critical digital ethnography is a qualitative research design that combines the immersive, participatory observation of digital ethnography with the power-conscious, emancipatory orientation of critical theory. Researchers embed themselves in online communities, platforms, or digital practices and examine not only what people do online but also how digital spaces reproduce, challenge, or transform structures of power, inequality, and identity. It is widely used in education, communication studies, and social science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from critical ethnography (Thomas, 1993) and digital/virtual ethnography (Hine, 2000); scholars including Bhatt, de Roock, and Saldanha developed explicit critical-digital frameworks from the 2010s onward","year":"2000s–2010s","type":"Qualitative research design","dataType":"Online field notes, digital artefacts, screenshots, social media data, interviews, chat logs","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bhatt, I., & de Roock, R. (2013). Capturing the sociomateriality of digital literacy events. Research in Learning Technology, 21. https://doi.org/10.3402/rlt.v21.21624","type":"article","doi":"10.3402/rlt.v21.21281","isbn":null,"url":null},{"ref":"Pennycook, A. (2012). Language and Mobility: Unexpected Places. Multilingual Matters.","type":"book","doi":null,"isbn":"9781847697226","url":null}],"related":["digital-ethnography","netnography","critical-ethnography","critical-discourse-analysis","virtual-ethnography","participatory-action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-discourse-analysis","name":"Critical Discourse Analysis","fullName":"Critical Discourse Analysis (CDA)","aliases":["CDA","Critical Linguistics","Discourse-Historical Approach","Dialectical-Relational Analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Discourse Analysis","year":"Late 1970s–1990s (systematised ~1979–1995)","originator":"Norman Fairclough; Teun A. van Dijk; Ruth Wodak","url":"https://scholargate.app/en/qualitative/critical-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/critical-discourse-analysis.md","definition":"Critical Discourse Analysis (CDA) is a qualitative method that examines how language in texts and talk constructs, sustains, and challenges relations of power, ideology, and social inequality. Drawing on linguistics, social theory, and critical philosophy, CDA treats discourse not merely as communication but as social practice — a site where dominance is reproduced and where resistance can be articulated. Developed in the late twentieth century by Norman Fairclough, Teun van Dijk, and Ruth Wodak, among others, CDA is applied to political speeches, media texts, policy documents, educational materials, and institutional interactions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Fairclough; Teun A. van Dijk; Ruth Wodak","year":"Late 1970s–1990s (systematised ~1979–1995)","type":"Qualitative research method","dataType":"Written texts, media discourse, political speeches, institutional documents, interview transcripts","typicalSampleSize":"Purposively selected texts or episodes; sample size varies from a single document to dozens of texts","subfamily":"Discourse Analysis"},"citations":[{"ref":"Fairclough, N. (1992). Discourse and Social Change. Polity Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fairclough+Discourse+and+Social+Change+1992"},{"ref":"van Dijk, T. A. (1993). Principles of critical discourse analysis. Discourse and Society, 4(2), 249–283.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=van+Dijk+Principles+of+critical+discourse+analysis+1993"}],"related":["discourse-analysis","narrative-analysis","thematic-analysis","content-analysis","ethnography","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-doctrinal-legal-research","name":"Critical Doctrinal Legal Research","fullName":"Critical Doctrinal Legal Research","aliases":["critical legal doctrinal analysis","critical black-letter research","critical legal doctrine","CLS-informed doctrinal research"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1970s–1980s (Critical Legal Studies movement; applied to doctrinal method from 1980s onward)","originator":"Synthesized from Traditional Doctrinal Legal Research and Critical Legal Studies (Roberto Unger, Duncan Kennedy, and others)","url":"https://scholargate.app/en/field-methods/critical-doctrinal-legal-research","markdownUrl":"https://scholargate.app/en/field-methods/critical-doctrinal-legal-research.md","definition":"Critical doctrinal legal research combines traditional black-letter legal analysis — systematically mapping the rules, principles, and doctrines found in statutes and case law — with the evaluative lens of critical legal theory. Rather than treating legal doctrine as a neutral or self-contained system, it interrogates the ideological assumptions, power relations, and social consequences embedded in legal rules, asking not only what the law says but whose interests it serves and what alternatives it forecloses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Traditional Doctrinal Legal Research and Critical Legal Studies (Roberto Unger, Duncan Kennedy, and others)","year":"1970s–1980s (Critical Legal Studies movement; applied to doctrinal method from 1980s onward)","type":"Qualitative legal research approach","dataType":"Primary legal sources (statutes, case law, regulations), secondary legal literature, doctrinal texts","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Hutchinson, T. (2013). Doctrinal Research: Researching the Law. In D. Watkins & M. Burton (Eds.), Research Methods in Law. Routledge.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hutchinson+Doctrinal+Research+Researching+the+Law+2013"},{"ref":"Unger, R. M. (1983). The Critical Legal Studies Movement. Harvard Law Review, 96(3), 561–675.","type":"article","doi":"10.2307/1341032","isbn":null,"url":null}],"related":["doctrinal-legal-research","comparative-legal-analysis","legal-content-analysis","hermeneutic-analysis","case-law-analysis","critical-discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-document-analysis","name":"Critical Document Analysis","fullName":"Critical Document Analysis","aliases":["CDA-doc","critical documentary analysis","critical policy document analysis","critical textual document analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Late 20th – early 21st century (2000s–present as an explicit variant)","originator":"Glenn Bowen; Lindsay Prior (foundational document analysis); critical theory tradition (Freire, Habermas)","url":"https://scholargate.app/en/qualitative/critical-document-analysis","markdownUrl":"https://scholargate.app/en/qualitative/critical-document-analysis.md","definition":"Critical document analysis is a qualitative method that systematically examines written, visual, or digital documents — such as policy texts, institutional reports, curriculum materials, and official records — through a critical theoretical lens. Rather than treating documents as neutral containers of information, it interrogates how documents produce, reflect, and reproduce power relations, ideologies, and social inequalities. The approach draws on critical theory traditions, including the work of Paulo Freire and Jurgen Habermas, as well as established frameworks for document analysis developed by Bowen and Prior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glenn Bowen; Lindsay Prior (foundational document analysis); critical theory tradition (Freire, Habermas)","year":"Late 20th – early 21st century (2000s–present as an explicit variant)","type":"Qualitative research design and analytic method","dataType":"Textual documents (policy texts, official reports, institutional records, curriculum materials, media texts)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Prior, L. (2003). Using Documents in Social Research. Sage.","type":"book","doi":null,"isbn":"978-0761965114","url":null}],"related":["critical-discourse-analysis","interpretive-document-analysis","critical-content-analysis","critical-thematic-analysis","document-analysis","critical-qualitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-educational-action-research","name":"Critical Educational Action Research","fullName":"Critical-Emancipatory Educational Action Research","aliases":["critical-emancipatory action research","CEAR","critical participatory action research in education","emancipatory educational inquiry"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1986","originator":"Wilfred Carr & Stephen Kemmis","url":"https://scholargate.app/en/field-methods/critical-educational-action-research","markdownUrl":"https://scholargate.app/en/field-methods/critical-educational-action-research.md","definition":"Critical educational action research is a cyclical, participatory research design in which educators collaboratively examine and transform their own practice through iterative cycles of planning, action, observation, and critical reflection. Grounded in critical theory, it goes beyond improving techniques to questioning the social, institutional, and ideological conditions that shape educational practice, aiming at emancipation from unjust or oppressive structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilfred Carr & Stephen Kemmis","year":"1986","type":"Qualitative participatory research design","dataType":"Observations, interviews, reflective journals, documents, meeting records","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Carr, W., & Kemmis, S. (1986). Becoming Critical: Education, Knowledge and Action Research. Falmer Press.","type":"book","doi":null,"isbn":"978-1850000235","url":null},{"ref":"Kemmis, S., & Wilkinson, M. (1998). Participatory action research and the study of practice. In B. Atweh, S. Kemmis, & P. Weeks (Eds.), Action Research in Practice (pp. 21–36). Routledge.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kemmis+Wilkinson+1998+Participatory+action+research+study+of+practice"}],"related":["educational-action-research","participatory-action-research","critical-ethnography","critical-discourse-analysis","collaborative-inquiry","practitioner-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-ethnography","name":"Critical Ethnography","fullName":"Critical Ethnography","aliases":["critical ethnographic research","critical qualitative ethnography","advocacy ethnography","emancipatory ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Ethnography","year":"Late 20th century (~1980s–1993 systematisation)","originator":"Jim Thomas (systematised); rooted in Frankfurt School critical theory (Adorno, Horkheimer) and feminist/postcolonial traditions","url":"https://scholargate.app/en/qualitative/critical-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/critical-ethnography.md","definition":"Critical ethnography is a qualitative research approach that combines sustained fieldwork immersion with explicit critical theory to examine how power, inequality, and ideology shape the lived experiences of marginalised communities. Unlike conventional ethnography, which aims to describe a culture as it is, critical ethnography commits the researcher to questioning what is taken for granted and to producing knowledge that can serve as a resource for social change. Rooted in Frankfurt School critical theory and expanded through feminist, postcolonial, and race-critical traditions, it treats the research process itself as a political act.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jim Thomas (systematised); rooted in Frankfurt School critical theory (Adorno, Horkheimer) and feminist/postcolonial traditions","year":"Late 20th century (~1980s–1993 systematisation)","type":"Qualitative research method","dataType":"Participant observation fieldnotes, interviews, documents, artefacts, audio-visual materials","typicalSampleSize":"One community or setting; sustained fieldwork of weeks to months with multiple participants","subfamily":"Ethnography"},"citations":[{"ref":"Thomas, J. (1993). Doing Critical Ethnography. Sage Publications.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Doing+Critical+Ethnography+Thomas+1993"},{"ref":"Madison, D. S. (2012). Critical Ethnography: Method, Ethics, and Performance (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Critical+Ethnography+Method+Ethics+Performance+Madison+2012"}],"related":["ethnography","action-research","discourse-analysis","narrative-analysis","grounded-theory","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-hermeneutic-analysis","name":"Critical Hermeneutic Analysis","fullName":"Critical Hermeneutic Analysis","aliases":["critical hermeneutics","critical-interpretive analysis","emancipatory hermeneutics","CHA"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1970s (Habermas); extended through 1980s–1990s","originator":"Jürgen Habermas (critical hermeneutics); Paul Ricoeur (hermeneutics of suspicion)","url":"https://scholargate.app/en/field-methods/critical-hermeneutic-analysis","markdownUrl":"https://scholargate.app/en/field-methods/critical-hermeneutic-analysis.md","definition":"Critical hermeneutic analysis combines interpretive hermeneutics with critical social theory to read texts and discourse not only for meaning but for embedded power relations, ideological distortions, and structures of domination. Originating in Habermas's critique of Gadamer and developed further by Ricoeur's hermeneutics of suspicion, the method asks both 'what does this text mean?' and 'whose interests does this meaning serve?'. It is widely used in education, social work, policy research, and health humanities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jürgen Habermas (critical hermeneutics); Paul Ricoeur (hermeneutics of suspicion)","year":"1970s (Habermas); extended through 1980s–1990s","type":"Qualitative interpretive research approach","dataType":"Texts, documents, transcripts, institutional discourse","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Habermas, J. (1970). On Systematically Distorted Communication. Inquiry, 13(1–4), 205–218.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Habermas+On+Systematically+Distorted+Communication+1970"},{"ref":"Ricoeur, P. (1981). Hermeneutics and the Human Sciences: Essays on Language, Action and Interpretation. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521280938","url":null}],"related":["hermeneutic-analysis","critical-discourse-analysis","critical-theory","phenomenology","narrative-analysis","textual-criticism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-hermeneutic-phenomenology","name":"Critical Hermeneutic Phenomenology","fullName":"Critical Hermeneutic Phenomenological Research","aliases":["CHP","critical hermeneutics","critically-oriented hermeneutic phenomenology","hermeneutic phenomenology with critical lens"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1960s–1990s (Gadamer 1960; van Manen 1990; critical synthesis developed through 1990s–2000s)","originator":"Hans-Georg Gadamer (hermeneutic tradition); Max van Manen (pedagogical application); influenced by Frankfurt School critical theory","url":"https://scholargate.app/en/qualitative/critical-hermeneutic-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/critical-hermeneutic-phenomenology.md","definition":"Critical hermeneutic phenomenology is a qualitative research approach that combines Gadamerian hermeneutics — the philosophical study of interpretation — with critical social theory to examine both the lived meaning of experience and the structural, ideological, and power-laden conditions that shape it. It asks not only 'what is this experience like?' but also 'what historical, social, and political forces produce and constrain it?' The approach is widely used in education, nursing, social work, and the human sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans-Georg Gadamer (hermeneutic tradition); Max van Manen (pedagogical application); influenced by Frankfurt School critical theory","year":"1960s–1990s (Gadamer 1960; van Manen 1990; critical synthesis developed through 1990s–2000s)","type":"Qualitative research approach","dataType":"In-depth interviews, field texts, documents, narratives (text data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null},{"ref":"Gadamer, H.-G. (1975). Truth and Method (G. Barden & J. Cumming, Trans.). Seabury Press. (Original work published 1960).","type":"book","doi":null,"isbn":"978-0826406736","url":null}],"related":["hermeneutic-phenomenology","interpretive-hermeneutic-phenomenology","critical-phenomenology","critical-discourse-analysis","critical-ethnography","interpretive-phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-institutional-ethnography","name":"Critical Institutional Ethnography","fullName":"Critical Institutional Ethnography","aliases":["Critical IE","critical-IE","institutional ethnography with critical orientation","CIE"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1987 (IE foundational); critical applications prominent 1990s–2000s","originator":"Dorothy E. Smith (institutional ethnography); critical variant developed through feminist and critical scholars","url":"https://scholargate.app/en/qualitative/critical-institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/critical-institutional-ethnography.md","definition":"Critical institutional ethnography (CIE) combines Dorothy Smith's institutional ethnography with an explicit critical theory lens to investigate how ruling relations, texts, and institutional discourses reproduce inequality and power asymmetries. Starting from the lived experiences of people positioned within or subordinated by institutions, CIE traces how abstract institutional processes coordinate everyday life and subjects those processes to normative critique aimed at social transformation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith (institutional ethnography); critical variant developed through feminist and critical scholars","year":"1987 (IE foundational); critical applications prominent 1990s–2000s","type":"Qualitative research design","dataType":"Interviews, documents, observations, institutional texts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759105010","url":null},{"ref":"Smith, D. E. (Ed.). (2006). Institutional Ethnography as Practice. Rowman & Littlefield.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Institutional+Ethnography+as+Practice+Smith+2006"}],"related":["institutional-ethnography","critical-ethnography","ethnography","critical-discourse-analysis","critical-case-study","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-interpretative-phenomenological-analysis","name":"Critical Interpretative Phenomenological Analysis","fullName":"Critical Interpretative Phenomenological Analysis","aliases":["Critical IPA","CIPA","critical-lens IPA","critical interpretive phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1996 (IPA); critical variant explicitly theorised in the 2000s–2010s","originator":"Jonathan A. Smith (IPA); critical extension developed within the IPA tradition by Smith, Flowers, Larkin and associated scholars","url":"https://scholargate.app/en/qualitative/critical-interpretative-phenomenological-analysis","markdownUrl":"https://scholargate.app/en/qualitative/critical-interpretative-phenomenological-analysis.md","definition":"Critical Interpretative Phenomenological Analysis (Critical IPA) is a qualitative approach that combines the double-hermeneutic interpretive work of standard IPA with an explicit critical lens, examining not only how participants make sense of their experience but also how power, social structures, ideology, and systemic inequalities shape that experience. It retains the ideographic, person-centred rigour of IPA while asking whose interests are served and what is silenced or constrained.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan A. Smith (IPA); critical extension developed within the IPA tradition by Smith, Flowers, Larkin and associated scholars","year":"1996 (IPA); critical variant explicitly theorised in the 2000s–2010s","type":"Qualitative research design and analytic approach","dataType":"In-depth interview transcripts, focus group data, personal documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative Phenomenological Analysis: Theory, Method and Research. Sage.","type":"book","doi":null,"isbn":"978-1412908344","url":null},{"ref":"Eatough, V., & Smith, J. A. (2017). Interpretative phenomenological analysis. In C. Willig & W. Stainton-Rogers (Eds.), The SAGE Handbook of Qualitative Research in Psychology (2nd ed., pp. 193–209). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interpretative+phenomenological+analysis+Eatough+Smith+2017"}],"related":["interpretive-interpretative-phenomenological-analysis","interpretive-phenomenology","critical-phenomenology","critical-hermeneutic-phenomenology","critical-discourse-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-life-history-research","name":"Critical life history research","fullName":"Critical Life History Research","aliases":["critical biographical research","critical life history","critical life history method","critical biographical inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s","originator":"Ivor Goodson; influenced by critical theory traditions (Freire, Habermas, feminist scholars)","url":"https://scholargate.app/en/qualitative/critical-life-history-research","markdownUrl":"https://scholargate.app/en/qualitative/critical-life-history-research.md","definition":"Critical life history research combines the biographical depth of life history methodology with critical theory perspectives — drawing on feminist, Marxist, postcolonial, or critical race frameworks — to examine how structural power relations, social inequalities, and institutional forces shape individual lives. Rather than treating a life story as a purely personal account, this approach reads it as evidence of wider social and political conditions, using individual narratives to surface systemic patterns of oppression, resistance, and agency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ivor Goodson; influenced by critical theory traditions (Freire, Habermas, feminist scholars)","year":"1980s–1990s","type":"Qualitative research design","dataType":"In-depth biographical interviews, personal documents, life narratives","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Goodson, I. F., & Sikes, P. (2001). Life History Research in Educational Settings: Learning from Lives. Open University Press.","type":"book","doi":null,"isbn":"978-0335205530","url":null},{"ref":"Weis, L., & Fine, M. (Eds.). (2004). Working Method: Research and Social Justice. Routledge.","type":"book","doi":null,"isbn":"978-0415948500","url":null}],"related":["life-history-research","biographical-research","narrative-inquiry","critical-narrative-inquiry","oral-history","critical-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-metaphor-analysis","name":"Critical Metaphor Analysis","fullName":"Critical Metaphor Analysis","aliases":["CMA","critical metaphor research","corpus-based critical metaphor analysis","ideological metaphor analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2004","originator":"Jonathan Charteris-Black","url":"https://scholargate.app/en/qualitative/critical-metaphor-analysis","markdownUrl":"https://scholargate.app/en/qualitative/critical-metaphor-analysis.md","definition":"Critical Metaphor Analysis (CMA) is a qualitative method for uncovering how metaphorical language constructs, legitimises, or contests power relations and ideological positions in texts. Developed by Jonathan Charteris-Black (2004), it integrates Conceptual Metaphor Theory with the evaluative concerns of Critical Discourse Analysis to reveal the persuasive and ideological work performed by metaphors in political, institutional, and media discourse.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan Charteris-Black","year":"2004","type":"Qualitative-critical textual analysis","dataType":"Spoken and written texts (political speeches, media, interviews, institutional documents)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charteris-Black, J. (2004). Corpus Approaches to Critical Metaphor Analysis. Palgrave Macmillan.","type":"book","doi":null,"isbn":"978-1403932921","url":null},{"ref":"Charteris-Black, J. (2011). Politicians and Rhetoric: The Persuasive Power of Metaphor (2nd ed.). Palgrave Macmillan.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Politicians+and+Rhetoric+The+Persuasive+Power+of+Metaphor+Charteris-Black+2011"}],"related":["interpretive-metaphor-analysis","critical-discourse-analysis","interpretive-discourse-analysis","comparative-metaphor-analysis","critical-content-analysis","critical-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-narrative-inquiry","name":"Critical Narrative Inquiry","fullName":"Critical Narrative Inquiry","aliases":["CNI","critical narrative research","critical narrative analysis","narrative inquiry with critical lens"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Synthesises D. Jean Clandinin & F. Michael Connelly (narrative inquiry) with critical theory traditions (Kincheloe, McLaren, hooks)","url":"https://scholargate.app/en/qualitative/critical-narrative-inquiry","markdownUrl":"https://scholargate.app/en/qualitative/critical-narrative-inquiry.md","definition":"Critical narrative inquiry is a qualitative research approach that collects and analyses personal stories to expose how social structures, power relations, and systemic inequities shape individual experience. It merges the interpretive richness of narrative inquiry with the emancipatory commitments of critical theory, asking not only what happened in a life but also why — and whose interests are served by dominant stories remaining untold or unquestioned.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesises D. Jean Clandinin & F. Michael Connelly (narrative inquiry) with critical theory traditions (Kincheloe, McLaren, hooks)","year":"1990s–2000s","type":"Critical qualitative research approach","dataType":"Personal narratives, life stories, interviews, field texts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Clandinin, D. J., & Connelly, F. M. (2000). Narrative Inquiry: Experience and Story in Qualitative Research. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787943999","url":null},{"ref":"Kincheloe, J. L., & McLaren, P. (2002). Rethinking Critical Theory and Qualitative Research. In Y. Zou & E. T. Trueba (Eds.), Ethnography and Schools: Qualitative Approaches to the Study of Education (pp. 87–138). Rowman & Littlefield.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Rethinking+Critical+Theory+and+Qualitative+Research+Kincheloe+McLaren+2002"}],"related":["narrative-inquiry","critical-ethnography","critical-discourse-analysis","critical-phenomenology","interpretive-narrative-inquiry","critical-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-netnography","name":"Critical Netnography","fullName":"Critical Netnographic Research","aliases":["critical online ethnography","critical internet ethnography","critical digital netnography","netnography with critical theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Late 1990s–2000s (netnography); critical applications prominent from 2000s onward","originator":"Robert V. Kozinets (netnography); critical strand developed through integration with critical theory traditions (e.g., critical race theory, feminist theory)","url":"https://scholargate.app/en/qualitative/critical-netnography","markdownUrl":"https://scholargate.app/en/qualitative/critical-netnography.md","definition":"Critical netnography applies the ethnographic toolkit of netnography to online communities while foregrounding a critical theoretical lens — such as critical race theory, feminist theory, or postcolonial theory. Rather than merely describing online culture, it interrogates how power, inequality, and ideology operate within and through digital spaces, making the approach particularly suited to researchers who wish to both understand online life and challenge the social conditions it reflects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert V. Kozinets (netnography); critical strand developed through integration with critical theory traditions (e.g., critical race theory, feminist theory)","year":"Late 1990s–2000s (netnography); critical applications prominent from 2000s onward","type":"Qualitative online research design","dataType":"Online community data: posts, threads, comments, profiles, multimedia (text-primary)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1526458414","url":null},{"ref":"Kulavuz-Onal, D., & Vásquez, C. (2013). Reconceptualising fieldwork in a netnography of an online community of English language teachers. Ethnography and Education, 8(2), 224–238.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1080/17457823.2013.792513"}],"related":["netnography","critical-ethnography","critical-discourse-analysis","digital-ethnography","interpretive-netnography","critical-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-oral-history","name":"Critical oral history","fullName":"Critical Oral History Research","aliases":["critical oral inquiry","critical oral testimony research","critical oral narrative research","COH"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–1990s (critical turn within oral history)","originator":"Alessandro Portelli; also Ronald Grele and the broader oral history movement","url":"https://scholargate.app/en/qualitative/critical-oral-history","markdownUrl":"https://scholargate.app/en/qualitative/critical-oral-history.md","definition":"Critical oral history applies a critical theory lens to the collection and analysis of first-person spoken accounts of lived experience. It goes beyond preserving personal memory to interrogate how power, identity, race, class, gender, and structural inequality shape what is remembered, what is silenced, and how stories are told. Originating in the work of Alessandro Portelli and the critical turn in oral history from the 1970s onward, the approach treats oral testimony not simply as evidence of the past but as a site of meaning-making and political contestation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alessandro Portelli; also Ronald Grele and the broader oral history movement","year":"1970s–1990s (critical turn within oral history)","type":"Qualitative research design","dataType":"In-depth recorded oral testimonies, life stories, interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Portelli, A. (1991). The Death of Luigi Trastulli and Other Stories: Form and Meaning in Oral History. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791405703","url":null},{"ref":"Perks, R., & Thomson, A. (Eds.). (2016). The Oral History Reader (3rd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415707671","url":null}],"related":["oral-history","critical-narrative-inquiry","critical-biographical-research","critical-ethnography","narrative-inquiry","critical-discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-phenomenology","name":"Critical phenomenology","fullName":"Critical Phenomenological Research","aliases":["critical-phenomenological inquiry","critical-phenomenological analysis","phenomenology and critical theory","politically engaged phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Late 20th–early 21st century (fully articulated ~2000s–2010s)","originator":"Lisa Guenther, Gayle Salamon, Alia Al-Saji (among others); draws on Husserl, Heidegger, Merleau-Ponty, and Frankfurt School critical theory","url":"https://scholargate.app/en/qualitative/critical-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/critical-phenomenology.md","definition":"Critical phenomenology is a qualitative research approach that merges classical phenomenological methods with critical theory to examine how structural forces — race, gender, class, disability, and other axes of power — shape and constrain lived experience. Rather than pursuing neutral description of universal essences, it asks whose experiences are centred, whose are marginalised, and how oppressive social structures are reproduced in the body and in everyday life. It has been consolidated as a distinct field by scholars such as Lisa Guenther, Gayle Salamon, and Alia Al-Saji.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lisa Guenther, Gayle Salamon, Alia Al-Saji (among others); draws on Husserl, Heidegger, Merleau-Ponty, and Frankfurt School critical theory","year":"Late 20th–early 21st century (fully articulated ~2000s–2010s)","type":"Qualitative research approach — interpretive and emancipatory","dataType":"In-depth interviews, focus groups, autobiographical accounts, field observations (text data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Guenther, L. (2020). Critical phenomenology. In G. Weiss, A. V. Murphy, & G. Salamon (Eds.), 50 Concepts for a Critical Phenomenology (pp. 11–16). Northwestern University Press.","type":"article","doi":null,"isbn":"978-0810141018","url":null},{"ref":"Salamon, G. (2018). The Life of the Body: Phenomenology and Ethics. Columbia University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Salamon+The+Life+of+the+Body+Phenomenology+Ethics"}],"related":["phenomenology","hermeneutic-phenomenology","interpretive-phenomenology","critical-ethnography","critical-discourse-analysis","participatory-phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-power","name":"Critical Power (Monod)","fullName":"Critical Power Model and Anaerobic Work Capacity Assessment","aliases":["CP model","power-duration relationship","anaerobic capacity","critical torque"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Exercise Physiology","year":"1965","originator":"Henry Monod","url":"https://scholargate.app/en/sports-science/critical-power","markdownUrl":"https://scholargate.app/en/sports-science/critical-power.md","definition":"Critical power (CP) is the highest power output that can be sustained indefinitely without fatigue, representing the boundary between sustainable and unsustainable exercise. Introduced by Henry Monod and Scherrer in 1965, the critical power model describes the hyperbolic relationship between power output and time-to-exhaustion. The model partitions work capacity into two components: critical power (the aerobic ceiling) and anaerobic work capacity (the maximal work that can be performed above critical power before depletion). This framework is widely used in exercise physiology, sports science, and occupational biomechanics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Monod","subfamily":"Exercise Physiology","year":"1965","type":"power-duration model"},"citations":[{"ref":"Monod, H., & Scherrer, J. (1965). The work capacity of a synergic muscular group. Ergonomics, 8(3), 329-338.","type":"article","doi":"10.1080/00140136508930810","isbn":null,"url":null},{"ref":"Morton, R. H. (1996). A 3-parameter critical power model. Ergonomics, 39(4), 611-619.","type":"article","doi":"10.1080/00140139608964484","isbn":null,"url":null},{"ref":"Vandenbossche, J. (2009). The three-parameter critical power function and parameter estimation from maximal efforts. Journal of Sports Sciences, 27(8), 855-863.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/19521900/"}],"related":["lactate-threshold","vo2-max","respiratory-exchange-ratio","session-rpe","heart-rate-recovery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-program-evaluation","name":"Critical Program Evaluation","fullName":"Critical Program Evaluation","aliases":["critical evaluation","emancipatory evaluation","critical-emancipatory program evaluation","transformative evaluation"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1970s–1990s (deliberative democratic strand formalised ~1999; transformative paradigm ~2009)","originator":"Ernest House, Ken Howe, Donna Mertens (transformative/deliberative democratic evaluation traditions)","url":"https://scholargate.app/en/field-methods/critical-program-evaluation","markdownUrl":"https://scholargate.app/en/field-methods/critical-program-evaluation.md","definition":"Critical program evaluation is an approach to assessing programs that integrates critical theory with standard evaluation methods. It moves beyond measuring whether a program met its stated objectives to interrogating whose interests the program serves, how power and privilege shape its design and outcomes, and whether it advances or hinders equity and social justice. The approach draws on deliberative democratic evaluation (House and Howe) and the transformative paradigm (Mertens), treating evaluation as an inherently value-laden, politically situated practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ernest House, Ken Howe, Donna Mertens (transformative/deliberative democratic evaluation traditions)","year":"1970s–1990s (deliberative democratic strand formalised ~1999; transformative paradigm ~2009)","type":"Qualitative/mixed-methods evaluation approach","dataType":"Interviews, focus groups, documents, observations, stakeholder feedback","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1606230787","url":null},{"ref":"House, E. R., & Howe, K. R. (1999). Values in Evaluation and Social Research. Sage Publications.","type":"book","doi":null,"isbn":"978-0761912521","url":null}],"related":["program-evaluation","participatory-program-evaluation","educational-action-research","critical-educational-action-research","evaluation-focused-program-evaluation","empowerment-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-semiotic-analysis","name":"Critical Semiotic Analysis","fullName":"Critical Semiotic Analysis","aliases":["CSA","critical semiotics","critical sign analysis","ideological semiotic analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1957 (Barthes); systematised as critical approach in 1980s–1990s","originator":"Roland Barthes (mythologies/ideology in signs); extended by Gunther Kress and Theo van Leeuwen (social semiotics)","url":"https://scholargate.app/en/qualitative/critical-semiotic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/critical-semiotic-analysis.md","definition":"Critical semiotic analysis is a qualitative method that examines how signs — words, images, gestures, sounds — construct and naturalise ideological meanings. Drawing on Roland Barthes's distinction between denotation and connotation, and on critical social semiotics developed by Kress and van Leeuwen, the approach moves beyond surface-level description to expose how texts reproduce or challenge power relations, cultural norms, and dominant ideologies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roland Barthes (mythologies/ideology in signs); extended by Gunther Kress and Theo van Leeuwen (social semiotics)","year":"1957 (Barthes); systematised as critical approach in 1980s–1990s","type":"Qualitative interpretive analysis","dataType":"Texts, images, advertisements, media artefacts, multimodal documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kress, G., & van Leeuwen, T. (2006). Reading Images: The Grammar of Visual Design (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415319157","url":null},{"ref":"Barthes, R. (1972). Mythologies (A. Lavers, Trans.). Hill and Wang. (Original work published 1957)","type":"book","doi":null,"isbn":"978-0374521509","url":null}],"related":["semiotic-analysis","critical-discourse-analysis","critical-content-analysis","discourse-analysis","visual-analysis","critical-visual-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-single-case-study","name":"Critical single case study","fullName":"Critical Single Case Study Research","aliases":["critical case study","critical-theory case study","single critical case","CSCS"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984 (Yin's foundational text); critical framing developed through 1990s–2000s","originator":"Robert K. Yin (case study design); Bent Flyvbjerg (critical case logic); critical theory influence from Max Horkheimer and Frankfurt School","url":"https://scholargate.app/en/qualitative/critical-single-case-study","markdownUrl":"https://scholargate.app/en/qualitative/critical-single-case-study.md","definition":"A critical single case study is a qualitative research design that investigates one bounded, strategically selected case through a critical-theory lens, aiming not only to understand the case in depth but also to expose underlying power relations, structural inequalities, or ideological conditions that shape the phenomenon. It combines the analytic depth of single-case inquiry with the emancipatory agenda of critical social science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (case study design); Bent Flyvbjerg (critical case logic); critical theory influence from Max Horkheimer and Frankfurt School","year":"1984 (Yin's foundational text); critical framing developed through 1990s–2000s","type":"Qualitative research design","dataType":"Interviews, documents, observations, artefacts (text and field data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219–245.","type":"article","doi":"10.1177/1077800405284363","isbn":null,"url":null},{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["critical-multiple-case-study","critical-case-study","interpretive-single-case-study","critical-ethnography","critical-discourse-analysis","critical-grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-straussian-grounded-theory","name":"Critical Straussian Grounded Theory","fullName":"Critical Straussian Grounded Theory","aliases":["critical GT (Straussian)","critical Strauss-Corbin grounded theory","critical systematic grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s (Straussian GT); critical synthesis from 2000s onward","originator":"Anselm Strauss & Juliet Corbin (Straussian base); critical integration draws on critical theory traditions (e.g., Kincheloe, Denzin)","url":"https://scholargate.app/en/qualitative/critical-straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/critical-straussian-grounded-theory.md","definition":"Critical Straussian Grounded Theory combines the systematic coding procedures of Strauss and Corbin's grounded theory — open, axial, and selective coding leading to a paradigm model — with a critical theoretical stance that foregrounds power, inequality, and social structure. The researcher does not merely describe a social process but interrogates the conditions that produce and sustain it, connecting emergent theory to broader structures of domination or marginalization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss & Juliet Corbin (Straussian base); critical integration draws on critical theory traditions (e.g., Kincheloe, Denzin)","year":"1990s (Straussian GT); critical synthesis from 2000s onward","type":"Qualitative research design","dataType":"Interviews, observations, documents (text data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932500","url":null},{"ref":"Kincheloe, J. L., & Tobin, K. (2009). The much exaggerated death of positivism. Cultural Studies of Science Education, 4(3), 513–528.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+much+exaggerated+death+of+positivism+Kincheloe+Tobin+2009"}],"related":["straussian-grounded-theory","critical-grounded-theory","critical-constructivist-grounded-theory","critical-classic-grounded-theory","grounded-theory","critical-qualitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-thematic-analysis","name":"Critical Thematic Analysis","fullName":"Critical Thematic Analysis","aliases":["CTA","critical-theoretic thematic analysis","thematic analysis with critical lens","critical qualitative thematic inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (consolidation as named variant)","originator":"Draws on Virginia Braun & Victoria Clarke (thematic analysis, 2006) combined with critical theory traditions (Frankfurt School, feminist and postcolonial theorists)","url":"https://scholargate.app/en/qualitative/critical-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/critical-thematic-analysis.md","definition":"Critical thematic analysis (CTA) is a qualitative approach that combines the systematic coding procedures of Braun and Clarke's thematic analysis with the interrogative stance of critical theory. Rather than merely describing patterns in data, CTA asks whose interests those patterns serve, what power relations they reflect, and what is absent or silenced. It is used to surface ideology, structural inequality, and hegemonic assumptions embedded in participants' accounts or in texts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Draws on Virginia Braun & Victoria Clarke (thematic analysis, 2006) combined with critical theory traditions (Frankfurt School, feminist and postcolonial theorists)","year":"2000s–2010s (consolidation as named variant)","type":"Qualitative analysis approach","dataType":"Interview transcripts, focus group data, documents, media texts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Rice, C., & Mündel, I. (2018). Story-making as methodology: Disrupting dominant stories through multimedia knowledge translation. Canadian Review of Sociology, 55(2), 211–231. [Critical thematic analysis applied in disability and feminist contexts; see also Rice, C. et al. for critical qualitative praxis.]","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=critical+thematic+analysis+qualitative+research"}],"related":["thematic-analysis","critical-discourse-analysis","reflexive-thematic-analysis","critical-qualitative-content-analysis","critical-narrative-inquiry","feminist-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"critical-thinking-dispositions-scale","name":"Critical Thinking Dispositions Scale","fullName":"Critical Thinking Dispositions Scale (CTDS)","aliases":["CTDS","California Critical Thinking Disposition Inventory","CCTDI"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Cognitive dispositions and habits of mind","year":"1992","originator":"Peter Facione","url":"https://scholargate.app/en/educational-psychology/critical-thinking-dispositions-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/critical-thinking-dispositions-scale.md","definition":"The Critical Thinking Dispositions Scale (CTDS), exemplified by the California Critical Thinking Disposition Inventory (CCTDI), measures the extent to which individuals exhibit cognitive dispositions conducive to critical thinking. Developed by Facione (1992), it assesses dimensions including truth-seeking, open-mindedness, analytical orientation, self-confidence, systematic approach, and inquisitiveness. Critical thinking dispositions—the habits of mind and values that support rigorous reasoning—are distinct from but complementary to critical thinking skills.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Facione","subfamily":"Cognitive dispositions and habits of mind","year":"1992","type":"Critical thinking propensity measurement"},"citations":[{"ref":"Facione, P. A., Facione, N. C., & Giancarlo, C. A. F. (1992). The California Critical Thinking Disposition Inventory. Insight Assessment, Millbrae, CA.","type":"article","doi":null,"isbn":null,"url":"https://www.insightassessment.com/CCTDI"},{"ref":"Facione, P. A. (2000). The Disposition Toward Critical Thinking: Its Character, Measurement, and Relationship to Critical Thinking Skill. Informal Logic, 20(1), 61-84.","type":"article","doi":"10.22329/il.v20i1.2254","isbn":null,"url":null}],"related":["study-process-questionnaire","academic-self-efficacy-scale","course-experience-questionnaire","student-engagement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"criticality-safety-analysis","name":"Criticality Safety Analysis","fullName":"Criticality Safety Analysis and Chain Reaction Control","aliases":["nuclear safety assessment","chain reaction analysis","fissile material control"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Nuclear safety and hazard control","year":"1938","originator":"Otto Hahn, Fritz Strassmann","url":"https://scholargate.app/en/nuclear-physics/criticality-safety-analysis","markdownUrl":"https://scholargate.app/en/nuclear-physics/criticality-safety-analysis.md","definition":"Criticality safety analysis is a systematic evaluation of fissile material systems to ensure nuclear chain reactions remain controlled, originating from Hahn and Strassmann's 1938 discovery of nuclear fission. It determines safe limits on fissile mass, concentration, geometry, and spacing using neutron transport calculations and experimental validation to prevent uncontrolled nuclear excursions in storage, processing, and transportation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Otto Hahn, Fritz Strassmann","subfamily":"Nuclear safety and hazard control","year":"1938","type":"safety assessment methodology"},"citations":[{"ref":"American National Standards Institute (2019). Nuclear Criticality Safety in Operations with Fissionable Material Outside Reactors. ANSI/ANS-8.1-19.40.","type":"standard","doi":null,"isbn":null,"url":"https://www.ans.org/standards/ansi-ans-8-1-19-40/"},{"ref":"Paxton, H. C., & Pruvost, N. L. (1990). Critical Dimensions of Systems Containing U-235, Pu-239, and U-233. LA-10860-MS, Los Alamos National Laboratory.","type":"article","doi":null,"isbn":null,"url":"https://www.osti.gov/biblio/5639839"}],"related":["neutron-transport-calculation","monte-carlo-neutron-particle","nuclear-decay-analysis","radiation-dose-assessment","reactor-kinetics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cronbach-alpha","name":"Cronbach's Alpha","fullName":"Cronbach's Alpha Reliability Coefficient","aliases":["coefficient alpha","alpha reliability","internal consistency reliability","Güvenilirlik Analizi (Cronbach Alpha)"],"domain":"statistics","family":"latent-structure","subfamily":null,"year":1951,"originator":"Lee J. Cronbach","url":"https://scholargate.app/en/statistics/cronbach-alpha","markdownUrl":"https://scholargate.app/en/statistics/cronbach-alpha.md","definition":"Cronbach's alpha is a coefficient of internal consistency that quantifies the degree to which a set of items on a scale measures the same underlying construct. Introduced by Lee J. Cronbach in 1951, it remains the most widely reported reliability index in social-science, health, and educational research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach","year":1951,"type":"Reliability / internal consistency coefficient","outcome":"Alpha (α) coefficient on a 0–1 scale","data":"Ordinal or continuous scale items","min_sample":30,"conventional_thresholds":"α ≥ 0.70 acceptable; ≥ 0.80 good; ≥ 0.90 excellent"},"citations":[{"ref":"Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.","type":"article","doi":"10.1007/BF02310555","isbn":null,"url":null},{"ref":"Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070478497","url":null}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","mcdonalds-omega","sem","pca"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crop-growth-model","name":"Crop Growth Model","fullName":"Decision Support System for Agrotechnology Transfer (DSSAT) / Agricultural Production Systems Simulator (APSIM)","aliases":["DSSAT","APSIM","Crop Simulation Model"],"domain":"agronomy","family":"process-pipeline","subfamily":"Dynamic Simulation","year":"1993-2003","originator":"James W. Jones, Gerbrand T. Hoogenboom (DSSAT); Brian A. Keating, Peter S. Carberry (APSIM)","url":"https://scholargate.app/en/agronomy/crop-growth-model","markdownUrl":"https://scholargate.app/en/agronomy/crop-growth-model.md","definition":"Crop growth models are mechanistic simulation systems designed to predict crop development, biomass accumulation, and yield under varying environmental and management conditions. DSSAT (Decision Support System for Agrotechnology Transfer) and APSIM (Agricultural Production Systems Simulator) are the most widely used platforms, developed in the 1990s-2000s to support agronomic decision-making and climate adaptation research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James W. Jones, Gerbrand T. Hoogenboom (DSSAT); Brian A. Keating, Peter S. Carberry (APSIM)","subfamily":"Dynamic Simulation","year":"1993-2003","type":"Mechanistic crop simulation pipeline"},"citations":[{"ref":"Jones, J. W., Hoogenboom, G., Porter, C. H., et al. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3-4), 235-265.","type":"article","doi":"10.1016/S1161-0301(02)00107-7","isbn":null,"url":null},{"ref":"Keating, B. A., Carberry, P. S., Hammer, G. L., et al. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18(3-4), 267-288.","type":"article","doi":"10.1016/S1161-0301(02)00108-9","isbn":null,"url":null},{"ref":"Passioura, J. B. (1996). Simulation models: science, snake oil, education, or engineering? Agronomy Journal, 88(5), 690-694.","type":"article","doi":"10.2134/agronj1996.00021962008800050002x","isbn":null,"url":null}],"related":["penman-monteith-equation","soil-moisture-curve","agrometeorological-yield-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crop-growth-simulation","name":"Crop Growth Simulation","fullName":"Dynamic Crop Growth Simulation Model","aliases":["Crop phenological model","Growth stage simulation"],"domain":"agronomy","family":"process-pipeline","subfamily":"Phenological modeling","year":"2003","originator":"John W. Jones, Gerrit Hoogenboom et al.","url":"https://scholargate.app/en/agronomy/crop-growth-simulation","markdownUrl":"https://scholargate.app/en/agronomy/crop-growth-simulation.md","definition":"Crop Growth Simulation is a computational pipeline for predicting daily or seasonal crop development, biomass accumulation, and yield under varying environmental conditions. Developed by Jones and colleagues in the DSSAT framework, this method integrates agronomic knowledge with process-based modeling to enable decision support in field management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Jones, Gerrit Hoogenboom et al.","subfamily":"Phenological modeling","year":"2003","type":"Computational pipeline"},"citations":[{"ref":"Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Basso, B., Hunt, L. A., ... & Winter, S. R. (2003). The DSSAT cropping system model. European journal of agronomy, 18(3-4), 235-265.","type":"article","doi":"10.1016/S1161-0301(02)00107-7","isbn":null,"url":null},{"ref":"Sinclair, T. R., & Seligman, N. G. (1996). Crop modeling: From infancy to maturity. Agronomy journal, 88(5), 698-704.","type":"article","doi":"10.2134/agronj1996.00021962008800050004x","isbn":null,"url":null}],"related":["irrigation-scheduling-etref","nitrogen-use-efficiency","crop-yield-estimation","phenological-observation","precision-agriculture-ndvi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crop-load-management","name":"Crop Load Management","fullName":"Quantitative Optimization of Fruit Number and Size Through Thinning and Load Balancing","aliases":["fruit thinning","load balancing","fruit density regulation"],"domain":"horticulture","family":"process-pipeline","subfamily":"Fruit development and quality optimization","year":"1960","originator":"Pomology research tradition","url":"https://scholargate.app/en/horticulture/crop-load-management","markdownUrl":"https://scholargate.app/en/horticulture/crop-load-management.md","definition":"Crop load management uses quantitative assessment of fruit number and tree vigor to optimize yields and fruit quality through selective thinning and load balancing. This method combines visual assessment of fruitlet density, calculation of target fruit number based on tree age and vigor, physical or chemical thinning, and yield monitoring to achieve the optimal balance between productivity and fruit size and flavor.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pomology research tradition","subfamily":"Fruit development and quality optimization","year":"1960","type":"management optimization pipeline"},"citations":[{"ref":"Ackley, W. B., & Wattendorf, R. J. (1962). The relationship of crop load to yield, fruit quality, and return bloom in apples. Proceedings of the American Society for Horticultural Science, 80, 73–83.","type":"article","doi":null,"isbn":null,"url":"https://journals.ashs.org/"},{"ref":"Wagenmakers, P. S., Callesen, O., & Goulding, K. (2001). Nutrient management of apple orchards in Northern Europe. Horticultural Reviews, 27, 1–64.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nutrient+management+of+apple+orchards+in+Northern+Europe+Wagenmakers"}],"related":["pollination-efficiency","pruning-response-analysis","postharvest-storage-simulation","ripeness-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crop-yield-estimation","name":"Crop Yield Estimation","fullName":"Pre-Harvest Yield Prediction and Harvest Monitoring","aliases":["Yield forecasting","Harvest prediction","Yield monitoring"],"domain":"agronomy","family":"process-pipeline","subfamily":"Yield prediction and forecasting","year":"2015","originator":"Agronomic research institutions (CIMMYT, ICRISAT, IRRI)","url":"https://scholargate.app/en/agronomy/crop-yield-estimation","markdownUrl":"https://scholargate.app/en/agronomy/crop-yield-estimation.md","definition":"Crop Yield Estimation is an analytical and predictive pipeline for forecasting final crop yield before harvest or monitoring yield accumulation during the growing season. Developed by agronomic research centers (CIMMYT, ICRISAT, IRRI), this method combines field observations, environmental data, and statistical models to predict grain or biomass output, informing harvest planning, market decisions, and performance evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agronomic research institutions (CIMMYT, ICRISAT, IRRI)","subfamily":"Yield prediction and forecasting","year":"2015","type":"Analytical and predictive pipeline"},"citations":[{"ref":"Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Shadow, B. (2015). A regional crop yield forecasting system for Sub-Saharan Africa. Global Food Security, 5, 6-15.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+regional+crop+yield+forecasting+system+for+Sub-Saharan+Africa+Lobell"},{"ref":"Egli, D. B. (2010). Seed biology and the yield of grain crops (2nd ed.). CABI Publishing, Wallingford, UK.","type":"article","doi":null,"isbn":null,"url":"https://www.cabi.org/bookshop/book/9781845936181/"}],"related":["crop-growth-simulation","precision-agriculture-ndvi","phenological-observation","nitrogen-use-efficiency","irrigation-scheduling-etref"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-cultural-competence-inventory","name":"Cross-Cultural Competence Inventory","fullName":"Cross-Cultural Competence Inventory","aliases":["CCCI"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"cross-cultural-care-assessment","year":2005,"originator":"Moore, Mercado","url":"https://scholargate.app/en/transcultural-nursing/cross-cultural-competence-inventory","markdownUrl":"https://scholargate.app/en/transcultural-nursing/cross-cultural-competence-inventory.md","definition":"The Cross-Cultural Competence Inventory (CCCI) is a comprehensive self-report measure designed to assess healthcare providers' competence in delivering culturally sensitive care across diverse populations. The CCCI evaluates multiple dimensions of cross-cultural competence, including cultural awareness, knowledge of diverse health beliefs, communication skills, and the ability to adapt interventions to cultural contexts. Used in nursing, medicine, and allied health programs, the CCCI supports both individual assessment and organizational quality improvement initiatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore, Mercado","subfamily":"cross-cultural-care-assessment","year":2005,"type":"Self-report"},"citations":[{"ref":"Moore, J. A., & Mercado, D. (2005). Cross-cultural competence development in healthcare professions. Journal of Nursing Education, 44(7), 313–321.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cross-cultural+competence+development+in+healthcare+professions+Moore"}],"related":["cultural-competence-assessment","multicultural-counseling-inventory","patient-provider-cultural-sensitivity","cultural-humility-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-docking","name":"Cross-Docking","fullName":"Cross-Docking Strategy","aliases":[],"domain":"operations-management","family":"ml-model","subfamily":"Logistics and Distribution","year":"2007","originator":"Gue, K. R.","url":"https://scholargate.app/en/operations-management/cross-docking","markdownUrl":"https://scholargate.app/en/operations-management/cross-docking.md","definition":"Cross-docking is a logistics strategy in which products arriving at a distribution center from suppliers are unloaded, sorted, consolidated, and immediately reloaded onto outbound vehicles destined for customers, with minimal or no storage time. Rather than storing inventory in a warehouse, products flow through in 24–48 hours. Cross-docking reduces inventory holding costs, improves product freshness, and increases throughput of the distribution network. It is widely used in fast-moving consumer goods, parcel delivery, and retail supply chains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gue, K. R.","subfamily":"Logistics and Distribution","year":"2007","type":"Warehouse operation strategy"},"citations":[{"ref":"Apuzzio, M. (2008). Essentials of supply chain management. New Jersey: Pearson Education.","type":"book","doi":null,"isbn":null,"url":"https://www.pearson.com/"},{"ref":"Gue, K. R., & Kang, Y. (2007). Staging queues revisited. Manufacturing & Service Operations Management, 9(1), 100-112.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Staging+queues+revisited+Gue"}],"related":["inventory-routing","scor-model","facility-layout","aggregate-planning","vendor-managed-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-document-entity-tracking","name":"Cross-Document Entity Tracking","fullName":"Cross-Document Entity Coreference Resolution and Tracking","aliases":["cross-document coreference resolution","cross-doc entity linking","Belge Ötesi Varlık Takibi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1998 (scoring foundations); 2019 (neural joint model)","originator":null,"url":"https://scholargate.app/en/text-mining/cross-document-entity-tracking","markdownUrl":"https://scholargate.app/en/text-mining/cross-document-entity-tracking.md","definition":"Cross-document entity tracking, formally known as cross-document coreference resolution, identifies and merges all references to the same real-world entity scattered across a collection of documents. Rooted in the B3 evaluation framework introduced by Bagga and Baldwin (1998) and substantially advanced by the neural joint model of Barhom et al. (2019), the method builds entity clusters that span document boundaries — enabling multi-document understanding, knowledge-base population, and corpus-wide entity analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Bagga & Baldwin (1998); Barhom et al. (2019)","year":"1998 (scoring foundations); 2019 (neural joint model)","type":"NLP pipeline — cross-document coreference resolution","input":"A defined set of text documents with completed NER and within-document entity linking","output":"Clusters of entity mentions that refer to the same real-world entity across documents","difficulty":3,"minDocuments":10,"requiresNormality":false},"citations":[{"ref":"Bagga, A. & Baldwin, B. (1998). Algorithms for Scoring Coreference Chains. In Proceedings of the LREC 1998 Linguistic Coreference Workshop, pp. 563–566.","type":"inproceedings","doi":null,"isbn":null,"url":"https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ccdacc60d9d68dfc1f94e7c68bd56646c000e4ab"},{"ref":"Barhom, S., Shwartz, V., Eirew, A., Bugert, M., Reimers, N. & Dagan, I. (2019). Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 4179–4189.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P19-1409/"}],"related":["named-entity-recognition","coreference-resolution","entity-linking","information-extraction","knowledge-graph-construction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-lingual-analysis","name":"Cross-lingual Text Analysis","fullName":"Cross-lingual Text Analysis (Multilingual Representation Learning)","aliases":["multilingual text analysis","cross-lingual representation learning","Çok Dilli Metin Analizi (Cross-lingual)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/cross-lingual-analysis","markdownUrl":"https://scholargate.app/en/text-mining/cross-lingual-analysis.md","definition":"Cross-lingual text analysis lets you compare and analyse texts written in different languages within a shared vector space. Building on multilingual representation learning surveyed by Conneau et al. (2020) and Pires et al. (2019), it maps documents from several languages into one common embedding space so multilingual corpora can be studied together.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Multilingual NLP representation task","models":"Multilingual transformers (mBERT, XLM-R)","output":"Shared cross-lingual vector space for comparison and analysis","minSample":"About 30 documents","difficulty":"Advanced (3 of 5)"},"citations":[{"ref":"Conneau, A. et al. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of ACL.","type":"article","doi":"10.18653/v1/2020.acl-main.747","isbn":null,"url":null},{"ref":"Pires, T., Schlinger, E. & Garrette, D. (2019). How Multilingual is Multilingual BERT? Proceedings of ACL.","type":"article","doi":"10.18653/v1/P19-1493","isbn":null,"url":null}],"related":["bert-embeddings","sentiment-analysis","text-classification","topic-modeling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-quantilogram","name":"Cross-Quantilogram","fullName":"Cross-Quantilogram Analysis","aliases":[],"domain":"econometrics","family":"regression-model","subfamily":"Quantile-based","year":"2012","originator":"Oliver Linton and Yoon-Jin Whang","url":"https://scholargate.app/en/econometrics/cross-quantilogram","markdownUrl":"https://scholargate.app/en/econometrics/cross-quantilogram.md","definition":"The cross-quantilogram extends the cross-correlogram concept to quantile pairs of two time series, measuring dependence at different quantile levels. Introduced by Linton and Whang (2012), it captures how shocks at specific quantile levels in one series relate to movements in another, enabling asymmetric dependence analysis. This approach is particularly valuable when downside and upside risk correlations differ materially.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oliver Linton and Yoon-Jin Whang","subfamily":"Quantile-based","year":"2012","type":"Correlation measure"},"citations":[{"ref":"Linton, O., & Whang, Y. J. (2012). Quantile comparisons of time series data. Journal of Econometrics, 170(2), 242-257.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quantile+comparisons+of+time+series+data+Linton"},{"ref":"Kılıç, R., & Pohlmann, T. (2011). Directional spillover effects in international equity markets. Journal of Banking & Finance, 35(9), 2351-2361.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Directional+spillover+effects+in+international+equity+markets+K%C4%B1l%C4%B1%C3%A7"}],"related":["quantile-var","qardl","method-of-moments-quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-sectional-causal-comparative-research","name":"Cross-sectional causal-comparative research","fullName":"Cross-sectional Causal-Comparative Research Design","aliases":["cross-sectional ex post facto design","single-wave causal-comparative study","cross-sectional group-comparison design","cross-sectional criterion-group study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1960s onward","originator":"Donald T. Campbell and Julian C. Stanley (quasi-experimental foundations); refined in education research by various methodologists","url":"https://scholargate.app/en/research-design/cross-sectional-causal-comparative-research","markdownUrl":"https://scholargate.app/en/research-design/cross-sectional-causal-comparative-research.md","definition":"Cross-sectional causal-comparative research compares two or more pre-existing groups — defined by a characteristic or experience that has already occurred — on one or more outcome variables, with all data collected at a single point in time. Because the presumed cause (group membership) precedes measurement but cannot be manipulated, the design sits between purely descriptive and truly experimental work. It is widely used in education, psychology, and social sciences when randomization is impossible or unethical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald T. Campbell and Julian C. Stanley (quasi-experimental foundations); refined in education research by various methodologists","year":"1960s onward","type":"Non-experimental quantitative design","dataType":"Numeric group-level data collected at a single time point; existing categorical grouping variable (cause) precedes measurement","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Frankfort-Nachmias, C., & Nachmias, D. (2015). Research Methods in the Social Sciences (8th ed.). Worth Publishers.","type":"book","doi":null,"isbn":"978-1429295154","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["causal-comparative-research","cross-sectional-research","ex-post-facto-design","correlational-research","longitudinal-causal-comparative-research","comparative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-sectional-descriptive-research","name":"Cross-sectional Descriptive Research","fullName":"Cross-sectional Descriptive Research Design","aliases":["cross-sectional survey","descriptive cross-sectional study","prevalence study","one-shot descriptive survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century (1950s–1970s, widespread codification)","originator":"Rooted in survey methodology traditions; formalized in epidemiology and social science research design texts of the mid-20th century","url":"https://scholargate.app/en/research-design/cross-sectional-descriptive-research","markdownUrl":"https://scholargate.app/en/research-design/cross-sectional-descriptive-research.md","definition":"Cross-sectional descriptive research collects data from a population or sample at a single point in time to portray the current distribution of characteristics, attitudes, behaviors, or conditions. It answers 'what is happening now?' questions without manipulating variables or following participants over time. Widely used in epidemiology, education, psychology, and the social sciences, it is the foundation for prevalence estimates, needs assessments, and baseline profiling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in survey methodology traditions; formalized in epidemiology and social science research design texts of the mid-20th century","year":"Mid-20th century (1950s–1970s, widespread codification)","type":"Quantitative observational research design","dataType":"Questionnaires, structured interviews, administrative records, or standardized assessments collected at a single point in time","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null},{"ref":"Kesmodel, U. S. (2018). Cross-sectional studies — what are they good for? Acta Obstetricia et Gynecologica Scandinavica, 97(4), 388–393.","type":"article","doi":"10.1111/aogs.13331","isbn":null,"url":null}],"related":["survey-research","descriptive-research","correlational-research","longitudinal-research","cohort-research","observational-quantitative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-sectional-epidemiological-study","name":"Cross-sectional epidemiological study","fullName":"Cross-sectional Epidemiological Study Design","aliases":["prevalence study","cross-sectional survey","transversal study","cross-sectional design"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1960s (formal codification); widely practiced since mid-20th century","originator":"Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s)","url":"https://scholargate.app/en/epidemiology/cross-sectional-epidemiological-study","markdownUrl":"https://scholargate.app/en/epidemiology/cross-sectional-epidemiological-study.md","definition":"A cross-sectional epidemiological study measures the exposure(s) and outcome(s) of interest simultaneously in a defined population at a single point in time (or over a short period). Because there is no follow-up, it is the most efficient observational design for estimating disease prevalence and for generating hypotheses about associations between risk factors and health outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s)","year":"1960s (formal codification); widely practiced since mid-20th century","type":"Observational, descriptive/analytic epidemiological design","dataType":"Survey data, clinical records, administrative data — measured at a single point in time","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195080407","url":null},{"ref":"Cross-sectional study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Cross-sectional_study"}],"related":["cohort-study","case-control-study","ecological-study","diagnostic-accuracy-study","screening-test-evaluation","randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-sectional-ex-post-facto-design","name":"Cross-sectional ex post facto design","fullName":"Cross-sectional Ex Post Facto Research Design","aliases":["cross-sectional causal-comparative design","retrospective cross-sectional design","after-the-fact cross-sectional study","cross-sectional EPF design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1964–1973","originator":"Fred N. Kerlinger (formalized ex post facto methodology)","url":"https://scholargate.app/en/research-design/cross-sectional-ex-post-facto-design","markdownUrl":"https://scholargate.app/en/research-design/cross-sectional-ex-post-facto-design.md","definition":"A cross-sectional ex post facto design investigates presumed causal relationships by comparing groups that already differ on a key characteristic — all measured at a single point in time. Because the independent variable (e.g., smoking history, prior educational attainment) has already occurred and cannot be manipulated, the researcher works backward from observed outcomes to infer probable antecedents. It is widely used in education, public health, and the social sciences when experimental control is ethically or practically impossible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred N. Kerlinger (formalized ex post facto methodology)","year":"1964–1973","type":"Non-experimental quantitative research design","dataType":"Numerical and categorical data collected at a single time point; pre-existing group differences","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N. (1973). Foundations of Behavioral Research (2nd ed.). Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030862731","url":null},{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073525960","url":null}],"related":["ex-post-facto-design","cross-sectional-research","causal-comparative-research","correlational-research","longitudinal-ex-post-facto-design","cross-sectional-causal-comparative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-sectional-quantitative-content-analysis","name":"Cross-sectional Quantitative Content Analysis","fullName":"Cross-sectional Quantitative Content Analysis","aliases":["CS-QCA","cross-sectional content analysis","single-timepoint content analysis","quantitative media content analysis"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century (formalized 1952–2000s)","originator":"Berelson, B.; Krippendorff, K.; Neuendorf, K. A.","url":"https://scholargate.app/en/research-design/cross-sectional-quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/cross-sectional-quantitative-content-analysis.md","definition":"Cross-sectional quantitative content analysis is an observational research design in which a systematically drawn sample of communicative content — news articles, social media posts, advertisements, or other symbolic material — is collected at a single point in time and coded using pre-defined numerical categories to describe or test hypotheses about patterns, frequencies, or associations within that content.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Berelson, B.; Krippendorff, K.; Neuendorf, K. A.","year":"Mid-20th century (formalized 1952–2000s)","type":"Quantitative observational research design","dataType":"Text, images, audio, video, or other symbolic content sampled at a single time point","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage Publications.","type":"book","doi":null,"isbn":"978-0761919773","url":null},{"ref":"Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761915454","url":null}],"related":["quantitative-content-analysis","cross-sectional-research","descriptive-research","correlational-research","survey-research","longitudinal-quantitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-sectional-relational-survey","name":"Cross-sectional relational survey","fullName":"Cross-sectional Relational Survey Research","aliases":["cross-sectional correlational survey","one-time relational survey","cross-sectional associational survey","single-occasion relational survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century onward","originator":"Rooted in survey methodology traditions; codified by Fraenkel, Wallen, and Creswell among others","url":"https://scholargate.app/en/research-design/cross-sectional-relational-survey","markdownUrl":"https://scholargate.app/en/research-design/cross-sectional-relational-survey.md","definition":"A cross-sectional relational survey collects data from a representative sample at a single point in time and examines the statistical relationships (correlations, associations, predictions) among two or more variables. It combines the temporal efficiency of cross-sectional design with the relational focus of correlational survey research, making it one of the most widely used quantitative designs in education, social science, and health research when a quick, population-level picture of variable relationships is needed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in survey methodology traditions; codified by Fraenkel, Wallen, and Creswell among others","year":"Mid-20th century onward","type":"Non-experimental quantitative design","dataType":"Quantitative survey scores, Likert-scale and continuous variables","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0078097706","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["correlational-research","cross-sectional-research","relational-survey","survey-research","descriptive-research","longitudinal-correlational-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-sectional-study-design","name":"Cross-Sectional Study Design","fullName":"Cross-Sectional Survey or Prevalence Study","aliases":["prevalence study","cross-sectional survey","snapshot study","survey design"],"domain":"clinical-research","family":"process-pipeline","subfamily":"observational design","year":"1950s-1970s","originator":"Epidemiologists in the mid-20th century; formalized by Kelsey, Rothman, and others","url":"https://scholargate.app/en/clinical-research/cross-sectional-study-design","markdownUrl":"https://scholargate.app/en/clinical-research/cross-sectional-study-design.md","definition":"A cross-sectional study (or prevalence study) measures exposure and outcome simultaneously at a single point in time, producing a 'snapshot' of a population. Respondents are recruited and surveyed (or examined) on the same occasion, capturing current prevalence of both exposure and disease. Cross-sectional studies are simple, quick, and inexpensive, making them popular for needs assessments, surveillance, and generating hypotheses—though they cannot establish causality due to lack of temporal sequence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Epidemiologists in the mid-20th century; formalized by Kelsey, Rothman, and others","subfamily":"observational design","year":"1950s-1970s","type":"Research Design"},"citations":[{"ref":"Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195083299","url":null},{"ref":"Rothman, K. J., Lash, T. L., & Greenland, S. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755657","url":null},{"ref":"Lynn, P. (2009). Methodology of longitudinal surveys. Wiley Interdisciplinary Reviews: Computational Statistics, 1(3), 369–379.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Methodology+of+longitudinal+surveys+Lynn"}],"related":["cohort-study-design","case-control-study-design","prevalence","survey-design","confounding-bias"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-sectional-survey-research","name":"Cross-sectional survey research","fullName":"Cross-Sectional Survey Research Design","aliases":["cross-sectional survey","single-occasion survey","prevalence survey design","snapshot survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1930s–1950s (formalized with large-scale opinion and health surveys)","originator":"Established through the social survey tradition (Bowley, Gallup, and others in the early-to-mid 20th century)","url":"https://scholargate.app/en/research-design/cross-sectional-survey-research","markdownUrl":"https://scholargate.app/en/research-design/cross-sectional-survey-research.md","definition":"Cross-sectional survey research administers a structured questionnaire or interview to a representative sample of a population at one point in time. It is the workhorse design for estimating prevalence, describing group characteristics, and mapping associations among variables across a wide range of disciplines — from public health and education to marketing and political science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established through the social survey tradition (Bowley, Gallup, and others in the early-to-mid 20th century)","year":"1930s–1950s (formalized with large-scale opinion and health surveys)","type":"Quantitative non-experimental design","dataType":"Questionnaire or structured interview data collected from a sample at a single point in time","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fowler, F. J. (2009). Survey Research Methods (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1412958929","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["survey-research","cross-sectional-research","descriptive-research","correlational-research","longitudinal-survey-research","relational-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-tabulation-analysis","name":"Cross-tabulation analysis","fullName":"Cross-tabulation Analysis (Contingency Table Analysis)","aliases":["crosstab","contingency table analysis","two-way frequency table","bivariate frequency analysis"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1900","originator":"Karl Pearson","url":"https://scholargate.app/en/statistics/cross-tabulation-analysis","markdownUrl":"https://scholargate.app/en/statistics/cross-tabulation-analysis.md","definition":"Cross-tabulation analysis (contingency table analysis) is a foundational descriptive and inferential technique for examining the relationship between two or more categorical variables. It arranges observed frequencies into a table of rows and columns, enabling visual inspection of patterns and formal chi-square testing of independence between the variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson","year":"1900","type":"Descriptive and inferential categorical analysis","dataType":"Categorical (nominal or ordinal)","subfamily":"Classical statistics"},"citations":[{"ref":"Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157–175.","type":"article","doi":"10.1080/14786440009463897","isbn":null,"url":null},{"ref":"Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471360933","url":null}],"related":["chi-square-test","fishers-exact-test","frequency-analysis","logistic-regression","cramers-v","odds-ratio"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-validation","name":"CROSS-VALIDATION","fullName":"Cross-Validation — k-fold hold-out validation of MCDM decision consistency","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1974","originator":"Stone, M.","url":"https://scholargate.app/en/decision-making/cross-validation","markdownUrl":"https://scholargate.app/en/decision-making/cross-validation.md","definition":"CROSS-VALIDATION (Cross-Validation — k-fold hold-out validation of MCDM decision consistency) is a ranking multi-criteria decision-making (MCDM) method introduced by Stone, M. in 1974. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stone, M.","subfamily":"Ranking","year":"1974","type":"Robustness wrapper — k-fold cross-validation for MCDM stability","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society Series B","type":"article","doi":"10.1111/j.2517-6161.1974.tb00994.x","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cross-wavelet-transform","name":"Cross-Wavelet Transform","fullName":"Cross-Wavelet Transform","aliases":["XWT","Cross-spectrum wavelet"],"domain":"time-series","family":"process-pipeline","subfamily":"Joint time-frequency analysis","year":"1998","originator":"Christopher Torrence","url":"https://scholargate.app/en/time-series/cross-wavelet-transform","markdownUrl":"https://scholargate.app/en/time-series/cross-wavelet-transform.md","definition":"The cross-wavelet transform (XWT) is a bivariate extension of the continuous wavelet transform that measures the joint time-frequency representation of two signals. Introduced by Torrence and Compo (1998) and applied extensively by Grinsted, Moore, and Jevrejeva (2004) to geophysical data, XWT reveals where two signals share common spectral power and the phase relationship between them at each time-frequency point. This is the natural generalization of classical cross-spectral analysis to the time-varying domain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher Torrence","subfamily":"Joint time-frequency analysis","year":"1998","type":"Bivariate wavelet interaction"},"citations":[{"ref":"Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.","type":"article","doi":"10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2","isbn":null,"url":null},{"ref":"Torrence, C., & Webster, P. J. (1999). Interdecadal changes in the ENSO–monsoon system. Journal of Climate, 12(8), 2679–2690.","type":"article","doi":"10.1175/1520-0442(1999)012<2679:icitem>2.0.co;2","isbn":null,"url":null},{"ref":"Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5–6), 561–566.","type":"article","doi":"10.5194/npg-11-561-2004","isbn":null,"url":null}],"related":["continuous-wavelet-transform","wavelet-coherence","cross-correlation","fourier-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossformer","name":"Crossformer","fullName":"Crossformer (Cross-Dimension Dependency Transformer)","aliases":["Cross-Dimension Dependency Transformer","Crossformer TSF","Çapraz-Boyut Bağımlılık Transformatörü"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2023,"originator":"Yunhao Zhang & Junchi Yan","url":"https://scholargate.app/en/deep-learning/crossformer","markdownUrl":"https://scholargate.app/en/deep-learning/crossformer.md","definition":"Crossformer is a Transformer-based architecture for multivariate time series forecasting, introduced by Yunhao Zhang and Junchi Yan at ICLR 2023. Unlike earlier Transformer variants that treat each variate independently, Crossformer explicitly models cross-dimension dependencies alongside temporal patterns. It achieves this through a two-stage attention design — cross-time and cross-dimension — applied over segment-level embeddings organized in a hierarchical encoder, enabling the model to capture both intra-variate dynamics and inter-variate correlations simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yunhao Zhang & Junchi Yan","year":2023,"type":"Transformer-based multivariate time-series forecasting model","subfamily":"Time-series forecasting","venue":"ICLR 2023","attention_axes":"Both time and dimension (cross-time and cross-dimension)"},"citations":[{"ref":"Zhang, Y., & Yan, J. (2023). Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. ICLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=vSVLM2j9eie"}],"related":["itransformer","patchtst","informer"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-ab-test","name":"Crossover A/B Test","fullName":"Crossover A/B Testing Design","aliases":["within-subject A/B test","crossover split test","repeated-measures A/B test","AB crossover experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1949 (crossover design); 2000s (online A/B application)","originator":"Crossover design: E. J. Williams (1949); A/B testing framework: Ronald Fisher (experimental roots); modern online application widely attributed to Google and Microsoft experimentation teams","url":"https://scholargate.app/en/experimental-design/crossover-ab-test","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-ab-test.md","definition":"A crossover A/B test is an experimental design in which the same participants or units are exposed to both treatment A and treatment B in sequence, with each serving as their own control. By eliminating between-subject variability, the design achieves higher statistical power than a standard parallel A/B test at the same sample size, but it requires careful handling of carryover effects and time-period confounds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Crossover design: E. J. Williams (1949); A/B testing framework: Ronald Fisher (experimental roots); modern online application widely attributed to Google and Microsoft experimentation teams","year":"1949 (crossover design); 2000s (online A/B application)","type":"Within-subject controlled experiment","dataType":"Continuous, binary, or count outcome data measured on the same units across periods","subfamily":"Deneysel desen"},"citations":[{"ref":"Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"9781439861424","url":null},{"ref":"Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.","type":"book","doi":null,"isbn":"9781108724227","url":null}],"related":["ab-design","crossover-randomized-controlled-trial","multi-arm-experiment","adaptive-ab-test","factorial-ab-test","blocked-ab-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-abab-design","name":"Crossover ABAB Design","fullName":"Crossover ABAB Reversal Design","aliases":["ABAB reversal design","reversal design","withdrawal design","ABAB single-subject design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s–1970s","originator":"Murray Sidman and colleagues in applied behavior analysis","url":"https://scholargate.app/en/experimental-design/crossover-abab-design","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-abab-design.md","definition":"The crossover ABAB design is a single-subject experimental design that alternates between baseline (A) and intervention (B) conditions twice within the same participant. By withdrawing and reintroducing the treatment, the researcher can demonstrate experimental control: if behavior improves with B and reverts with A, the causal link between the intervention and the outcome is established without a separate control group.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray Sidman and colleagues in applied behavior analysis","year":"1960s–1970s","type":"Single-subject experimental design","dataType":"Repeated measures of behavior over time (continuous observation or interval recording)","subfamily":"Deneysel desen"},"citations":[{"ref":"Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0205474929","url":null},{"ref":"Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134752556","url":null}],"related":["single-subject-design","multiple-baseline-design","alternating-treatments-design","randomized-controlled-trial","within-subjects-design","reversal-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-adaptive-experiment","name":"Crossover Adaptive Experiment","fullName":"Adaptive Crossover Experimental Design","aliases":["adaptive crossover trial","adaptive crossover design","crossover adaptive trial","ACE design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Late 1990s–2000s","originator":"Developed through convergence of crossover trial methodology (Senn, Williams) and adaptive design methods (Bauer, Köhne, Chow, Chang)","url":"https://scholargate.app/en/experimental-design/crossover-adaptive-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-adaptive-experiment.md","definition":"An adaptive crossover experiment combines the within-subject efficiency of crossover designs — where each participant receives multiple treatments in sequence — with pre-specified adaptive rules that allow trial parameters to be modified based on interim data. Each participant acts as their own control across treatment periods, while ongoing accumulating evidence can trigger pre-planned changes such as sample size re-estimation, treatment arm dropping, or allocation ratio adjustment, all governed by a formal adaptation plan to preserve inferential validity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed through convergence of crossover trial methodology (Senn, Williams) and adaptive design methods (Bauer, Köhne, Chow, Chang)","year":"Late 1990s–2000s","type":"Experimental design — hybrid adaptive/crossover","dataType":"Continuous, binary, or ordinal outcome measurements; repeated within-subject observations","subfamily":"Deneysel desen"},"citations":[{"ref":"Chow, S.-C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584888468","url":null},{"ref":"Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471496533","url":null}],"related":["crossover-randomized-controlled-trial","adaptive-experiment","adaptive-randomized-controlled-trial","factorial-adaptive-experiment","multiple-baseline-design","crossover-factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-control-group-experimental-design","name":"Crossover Control Group Experimental Design","fullName":"Crossover Experimental Design with Control Group","aliases":["crossover controlled trial","within-subject crossover with control","AB/BA crossover controlled design","repeated-measures crossover with control arm"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-20th century; systematic treatment from 1980s onward","originator":"Established in clinical pharmacology and agricultural research; formalized by B. Jones & M. G. Kenward","url":"https://scholargate.app/en/experimental-design/crossover-control-group-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-control-group-experimental-design.md","definition":"A crossover control group experimental design is an experimental approach in which participants are randomly assigned to sequences of conditions that include both a treatment and a control (no-treatment or placebo) period, with each participant experiencing both the experimental and control conditions in succession. By using each participant as their own control across periods, this design sharply reduces between-subject variability and typically requires fewer participants than parallel group trials to achieve equivalent statistical power.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established in clinical pharmacology and agricultural research; formalized by B. Jones & M. G. Kenward","year":"Mid-20th century; systematic treatment from 1980s onward","type":"Experimental design","dataType":"Continuous, ordinal, or binary outcome measurements collected at multiple time points","subfamily":"Deneysel desen"},"citations":[{"ref":"Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584883500","url":null},{"ref":"Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). John Wiley & Sons.","type":"article","doi":null,"isbn":"978-0471496533","url":null}],"related":["crossover-randomized-controlled-trial","crossover-factorial-experiment","control-group-experimental-design","pretest-posttest-experimental-design","ab-design","repeated-measures-anova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-design","name":"Crossover Design","fullName":"Crossover Trial Design","aliases":["within-subject crossover","cross-over design","AB/BA design","Çapraz Desen (Crossover Design)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1960,"originator":"Early formalized in clinical research literature; widely used since mid-20th century","url":"https://scholargate.app/en/experimental-design/crossover-design","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-design.md","definition":"A crossover design is an experimental design in which each participant receives all treatments under investigation, but in a different sequence and across separate time periods. Each subject thus acts as their own control, which substantially reduces between-subject variability and allows efficient treatment comparisons with smaller sample sizes. The approach has been central to clinical pharmacology and comparative research since the mid-20th century, with foundational methodology codified by Senn (2002) and Jones & Kenward (2014).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Early formalized in clinical research literature; widely used since mid-20th century","year":1960,"family":"Experimental design","type":"Within-subject repeated-measures design","minimumSample":12,"sequences":"AB/BA (two-period) or Williams sequence (multi-treatment)","keyAnalyticConcerns":"carryover effect, period effect","parametric":false,"suitableOutcomes":"continuous, ordinal, binary"},"citations":[{"ref":"Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471496533","url":null},{"ref":"Jones, B. & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439861424","url":null}],"related":["paired-t-test","repeated-measures-anova","latin-square-design","factorial-design","split-plot-design","randomized-complete-block"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-factorial-experiment","name":"Crossover Factorial Experiment","fullName":"Crossover Factorial Experimental Design","aliases":["within-subject factorial design","repeated-measures factorial experiment","factorial crossover trial","crossover factorial trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1920s–1960s (synthesis of factorial and crossover traditions)","originator":"R. A. Fisher (factorial principles, 1920s); crossover integration developed in biostatistics through mid-20th century","url":"https://scholargate.app/en/experimental-design/crossover-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-factorial-experiment.md","definition":"A crossover factorial experiment combines two powerful design principles: factorial structure, which studies multiple factors and their interactions simultaneously, and crossover structure, in which each participant receives more than one treatment combination across sequential periods. By serving as their own control, participants reduce between-subject variability, improving statistical power while also revealing how different factor levels interact within the same individual.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (factorial principles, 1920s); crossover integration developed in biostatistics through mid-20th century","year":"1920s–1960s (synthesis of factorial and crossover traditions)","type":"Experimental design","dataType":"Continuous, ordinal, or binary outcome measurements collected at multiple time points per participant","subfamily":"Deneysel desen"},"citations":[{"ref":"Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1439861424","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["crossover-randomized-controlled-trial","factorial-experiment","factorial-randomized-controlled-trial","repeated-measures-anova","latin-square-design","within-subjects-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-field-experiment","name":"Crossover Field Experiment","fullName":"Crossover Field Experiment","aliases":["within-subject field experiment","crossover field trial","repeated-measures field experiment","field crossover design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s–1970s (field experiment framework); crossover application in non-clinical fields from 1980s onward","originator":"Crossover design principles attributed to R. A. Fisher (1930s); field experiment tradition developed by Donald T. Campbell and Julian Stanley (1960s)","url":"https://scholargate.app/en/experimental-design/crossover-field-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-field-experiment.md","definition":"A crossover field experiment is a within-subject experimental design conducted outside the laboratory in naturalistic, real-world settings. Each participant or unit receives multiple treatments in a randomized sequence, separated by washout periods, allowing researchers to observe causal effects while each unit serves as its own control. This approach combines the internal validity of crossover designs with the ecological validity characteristic of field experimentation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Crossover design principles attributed to R. A. Fisher (1930s); field experiment tradition developed by Donald T. Campbell and Julian Stanley (1960s)","year":"1960s–1970s (field experiment framework); crossover application in non-clinical fields from 1980s onward","type":"Within-subject experimental design conducted in naturalistic settings","dataType":"Continuous, ordinal, or count outcomes measured at multiple time points per participant or unit","subfamily":"Deneysel desen"},"citations":[{"ref":"Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471496533","url":null},{"ref":"Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. W. W. Norton & Company.","type":"book","doi":null,"isbn":"978-0393979954","url":null}],"related":["crossover-randomized-controlled-trial","field-experiment","crossover-laboratory-experiment","factorial-field-experiment","blocked-field-experiment","multiple-baseline-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-fractional-factorial-experiment","name":"Crossover Fractional Factorial Experiment","fullName":"Crossover Fractional Factorial Experimental Design","aliases":["crossover FF design","within-subject fractional factorial","repeated-measures fractional factorial","crossover FFE"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1950s–1970s (fractional factorial from 1940s; crossover integration from 1960s–1970s)","originator":"Box, Hunter & Hunter (fractional factorial); Senn & Williams (crossover integration)","url":"https://scholargate.app/en/experimental-design/crossover-fractional-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-fractional-factorial-experiment.md","definition":"A crossover fractional factorial experiment is a within-subject design in which each participant receives a strategically chosen subset of all possible factor-level combinations in a defined sequence, with washout periods between treatment periods. By combining the run-economy of fractional factorial designs with the within-subject efficiency of crossover designs, it allows estimation of main effects and selected interactions while controlling for between-subject variability using far fewer participants and experimental runs than a full factorial crossover.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box, Hunter & Hunter (fractional factorial); Senn & Williams (crossover integration)","year":"1950s–1970s (fractional factorial from 1940s; crossover integration from 1960s–1970s)","type":"Within-subject multi-factor experimental design","dataType":"Continuous, ordinal, or binary outcome measurements; repeated measures per subject","subfamily":"Deneysel desen"},"citations":[{"ref":"Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471496533","url":null},{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471718130","url":null}],"related":["crossover-randomized-controlled-trial","fractional-factorial-experiment","full-factorial-experiment","factorial-experiment","crossover-full-factorial-experiment","blocked-fractional-factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-full-factorial-experiment","name":"Crossover Full Factorial Experiment","fullName":"Crossover Full Factorial Experimental Design","aliases":["within-subject full factorial design","repeated-measures full factorial experiment","crossover factorial trial","full factorial crossover design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-to-late 20th century (crossover trials formalised ~1960s–1980s; full factorial DoE from Fisher ~1935)","originator":"Developed within the design-of-experiments tradition (R. A. Fisher and successors); crossover adaptation formalised by B. Jones and M. G. Kenward","url":"https://scholargate.app/en/experimental-design/crossover-full-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-full-factorial-experiment.md","definition":"A crossover full factorial experiment combines the efficiency of a crossover (within-subject) design with the comprehensiveness of a full factorial design. Every participant receives all combinations of the factor levels across successive treatment periods, separated by washout intervals, allowing complete estimation of all main effects and interactions while using each participant as their own control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within the design-of-experiments tradition (R. A. Fisher and successors); crossover adaptation formalised by B. Jones and M. G. Kenward","year":"Mid-to-late 20th century (crossover trials formalised ~1960s–1980s; full factorial DoE from Fisher ~1935)","type":"Within-subject full factorial experimental design","dataType":"Continuous or ordinal outcome measurements from the same participants across multiple treatment periods","subfamily":"Deneysel desen"},"citations":[{"ref":"Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584883429","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119320937","url":null}],"related":["crossover-randomized-controlled-trial","full-factorial-experiment","factorial-experiment","crossover-factorial-experiment","repeated-measures-anova","latin-square-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-laboratory-experiment","name":"Crossover Laboratory Experiment","fullName":"Crossover Within-Subjects Laboratory Experiment","aliases":["within-subjects crossover lab study","repeated-measures crossover experiment","crossover controlled lab experiment","within-person laboratory crossover trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-20th century; consolidated 1980s–2000s","originator":"Established in pharmacological and behavioral research; Jones & Kenward formalized the framework","url":"https://scholargate.app/en/experimental-design/crossover-laboratory-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-laboratory-experiment.md","definition":"A crossover laboratory experiment is a within-subjects experimental design conducted in a controlled lab environment in which each participant receives two or more treatments sequentially, serving as their own control. By eliminating between-person variability from the error term, it yields high statistical power with relatively small samples. Treatment order is randomized or counterbalanced across participants to guard against order and carryover effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established in pharmacological and behavioral research; Jones & Kenward formalized the framework","year":"Mid-20th century; consolidated 1980s–2000s","type":"Within-subjects experimental design","dataType":"Continuous, ordinal, or categorical outcome measurements collected per participant per treatment period","subfamily":"Deneysel desen"},"citations":[{"ref":"Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439861424","url":null},{"ref":"Crossover study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Crossover_study"}],"related":["crossover-randomized-controlled-trial","laboratory-experiment","factorial-laboratory-experiment","repeated-measures-anova","within-subjects-design","ab-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-multi-arm-experiment","name":"Crossover multi-arm experiment","fullName":"Crossover Multi-Arm Experimental Design","aliases":["multi-arm crossover trial","multi-period multi-treatment crossover","CMAT","multi-treatment crossover experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-20th century; multi-arm extensions formalized by 1970s–1980s","originator":"Developed from early crossover trial methodology (Williams 1949; Cochran & Cox 1957)","url":"https://scholargate.app/en/experimental-design/crossover-multi-arm-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-multi-arm-experiment.md","definition":"A crossover multi-arm experiment is a within-subject experimental design in which each participant receives three or more treatments (arms) across successive periods, with random assignment to sequence. Because every participant experiences all arms, the design eliminates between-subject variability from treatment comparisons, dramatically increasing statistical power for a given sample size. It is widely used in clinical pharmacology, psychology, agriculture, and behavioral research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from early crossover trial methodology (Williams 1949; Cochran & Cox 1957)","year":"Mid-20th century; multi-arm extensions formalized by 1970s–1980s","type":"Within-subject experimental design with multiple treatment arms","dataType":"Continuous, ordinal, or binary outcome measurements per period per participant","subfamily":"Deneysel desen"},"citations":[{"ref":"Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584883869","url":null},{"ref":"Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471496533","url":null}],"related":["crossover-randomized-controlled-trial","multi-arm-experiment","factorial-experiment","latin-square-design","adaptive-experiment","repeated-measures-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-multiple-baseline-design","name":"Crossover Multiple Baseline Design","fullName":"Crossover Multiple Baseline Single-Case Experimental Design","aliases":["CMBD","crossover MBD","multiple baseline crossover design","within-subject multiple baseline design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (multiple baseline origins); crossover extension developed in behavioral and rehabilitation research from the 1980s onward","originator":"Derived from Baer, Wolf, and Risley (multiple baseline, 1968) and classical crossover design traditions","url":"https://scholargate.app/en/experimental-design/crossover-multiple-baseline-design","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-multiple-baseline-design.md","definition":"The crossover multiple baseline design is a single-case experimental design (SCED) that layers crossover sequencing onto a multiple baseline structure. Across two or more tiers — participants, behaviors, or settings — baselines are staggered in time; then treatments are introduced and later reversed or alternated across tiers, so each tier acts as both a treatment and a control unit. The design provides within-subject replication while controlling for time-related confounds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from Baer, Wolf, and Risley (multiple baseline, 1968) and classical crossover design traditions","year":"1968 (multiple baseline origins); crossover extension developed in behavioral and rehabilitation research from the 1980s onward","type":"Single-case experimental design with crossover sequencing","dataType":"Repeated measures of a single behavior or outcome over time (continuous or count data)","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null}],"related":["multiple-baseline-design","crossover-randomized-controlled-trial","ab-design","aba-design","abab-design","single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-natural-experiment","name":"Crossover Natural Experiment","fullName":"Crossover Natural Experiment Design","aliases":["within-unit natural experiment","reversal natural experiment","crossover quasi-experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Crossover designs: mid-20th century; applied to natural experiments: 1990s–2000s","originator":"Drawn from crossover trial methods (Jones & Kenward) and natural experiment tradition (Mill, 1843; Dunning, 2012)","url":"https://scholargate.app/en/experimental-design/crossover-natural-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-natural-experiment.md","definition":"A crossover natural experiment exploits an externally imposed condition — a policy change, law, or environmental event — that exposes the same units (individuals, regions, firms) to both treatment and control states at different times. By observing each unit in multiple conditions, researchers use within-unit variation to estimate causal effects without researcher-controlled randomization, combining the internal validity advantage of crossover designs with the real-world relevance of natural experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Drawn from crossover trial methods (Jones & Kenward) and natural experiment tradition (Mill, 1843; Dunning, 2012)","year":"Crossover designs: mid-20th century; applied to natural experiments: 1990s–2000s","type":"Quasi-experimental design","dataType":"Panel or longitudinal observational data; policy records; administrative data","subfamily":"Deneysel desen"},"citations":[{"ref":"Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107698000","url":null},{"ref":"Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584880384","url":null}],"related":["crossover-randomized-controlled-trial","natural-experiment","difference-in-differences","crossover-field-experiment","interrupted-time-series","regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-pretest-posttest-experimental-design","name":"Crossover Pretest-Posttest Experimental Design","fullName":"Crossover Pretest-Posttest Experimental Design","aliases":["within-subjects pretest-posttest design","repeated-measures crossover design","AB/BA pretest-posttest design","crossover repeated-measures design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1963 (Campbell & Stanley framework); crossover methodology formalized 1980s–2000s","originator":"Donald T. Campbell & Julian C. Stanley (pretest-posttest framework); Stephen Senn (crossover trial methodology)","url":"https://scholargate.app/en/experimental-design/crossover-pretest-posttest-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-pretest-posttest-experimental-design.md","definition":"A crossover pretest-posttest experimental design is a within-subjects experiment in which each participant receives two or more treatments in a randomized sequence, with outcome measurements taken both before and after each treatment period. By serving as their own control across conditions, participants allow direct intra-individual comparison, dramatically increasing statistical power while reducing the sample size required relative to a parallel-group design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald T. Campbell & Julian C. Stanley (pretest-posttest framework); Stephen Senn (crossover trial methodology)","year":"1963 (Campbell & Stanley framework); crossover methodology formalized 1980s–2000s","type":"Within-subjects experimental design","dataType":"Continuous or ordinal outcome measurements at pretest and posttest for each treatment period","subfamily":"Deneysel desen"},"citations":[{"ref":"Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471496533","url":null},{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+Quasi-Experimental+Designs+for+Research+Campbell+Stanley+1963"}],"related":["crossover-randomized-controlled-trial","pretest-posttest-experimental-design","within-subjects-design","repeated-measures-anova","latin-square-design","factorial-pretest-posttest-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-randomized-controlled-trial","name":"Crossover Randomized Controlled Trial","fullName":"Crossover Randomized Controlled Trial","aliases":["crossover RCT","crossover trial","within-subject RCT","AB/BA crossover design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s (Grizzle 1965 for statistical foundations); widely used in clinical research since the 1970s","originator":"Early formalized by statisticians including Bradford Hill and colleagues in clinical trials; theoretical framework developed by Grizzle (1965) and later Senn (2002)","url":"https://scholargate.app/en/experimental-design/crossover-randomized-controlled-trial","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-randomized-controlled-trial.md","definition":"A crossover randomized controlled trial (crossover RCT) is an experimental design in which each participant receives all study interventions in a randomized sequence, separated by a washout period. Because every participant serves as their own control, within-subject variability is eliminated from the treatment comparison, yielding greater statistical power per participant than a parallel-group RCT of equal size.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Early formalized by statisticians including Bradford Hill and colleagues in clinical trials; theoretical framework developed by Grizzle (1965) and later Senn (2002)","year":"1960s (Grizzle 1965 for statistical foundations); widely used in clinical research since the 1970s","type":"Experimental within-subject design","dataType":"Continuous, ordinal, or binary outcome measures collected at multiple time points per participant","subfamily":"Deneysel desen"},"citations":[{"ref":"Senn, S. (2002). Cross-over Trials in Clinical Research (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471496533","url":null},{"ref":"Jones, B., & Kenward, M. G. (2003). Design and Analysis of Cross-Over Trials (2nd ed.). Chapman and Hall/CRC.","type":"article","doi":null,"isbn":"978-1584883429","url":null}],"related":["randomized-controlled-trial","factorial-randomized-controlled-trial","blocked-randomized-controlled-trial","adaptive-randomized-controlled-trial","latin-square-design","repeated-measures-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-single-subject-experimental-design","name":"Crossover Single-Subject Experimental Design","fullName":"Crossover Single-Subject Experimental Design","aliases":["crossover SSED","alternating-treatments crossover design","single-case crossover design","N-of-1 crossover design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Experimental design","year":"1970s–1980s (single-case crossover formalized in behavioral research context)","originator":"Developed within the single-case research tradition; crossover application formalized by Barlow and Hersen and expanded by Kazdin","url":"https://scholargate.app/en/experimental-design/crossover-single-subject-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-single-subject-experimental-design.md","definition":"The crossover single-subject experimental design (crossover SSED) applies two or more treatment conditions sequentially to the same individual, with a washout or return-to-baseline period between conditions. Because each participant serves as their own control, between-subject variability is eliminated, enabling precise causal inference about treatment effects even with very small samples — often a single participant. This design is widely used in applied behavior analysis, special education, rehabilitation, and clinical psychology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within the single-case research tradition; crossover application formalized by Barlow and Hersen and expanded by Kazdin","year":"1970s–1980s (single-case crossover formalized in behavioral research context)","type":"Experimental single-subject design","dataType":"Repeated behavioral or clinical outcome measures on a single participant or unit","subfamily":"Experimental design"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0205474554","url":null}],"related":["single-subject-experimental-design","alternating-treatments-design","multiple-baseline-design","reversal-aba-design","randomized-controlled-trial","n-of-1-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crossover-solomon-four-group-design","name":"Crossover Solomon Four-Group Design","fullName":"Crossover Solomon Four-Group Experimental Design","aliases":["crossover S4G design","within-subjects Solomon design","repeated-measures Solomon four-group design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1949 (base design); crossover adaptation developed through later methodological literature","originator":"Richard L. Solomon (base design); crossover extension via repeated-measures methodology","url":"https://scholargate.app/en/experimental-design/crossover-solomon-four-group-design","markdownUrl":"https://scholargate.app/en/experimental-design/crossover-solomon-four-group-design.md","definition":"The Crossover Solomon Four-Group Design merges two powerful experimental strategies: the Solomon four-group design's control for pretest sensitization and the crossover design's within-subjects efficiency. Participants are randomly assigned to one of four groups that vary in whether they receive a pretest and in the sequence of treatment and control conditions, allowing the researcher to simultaneously estimate treatment effects, pretest effects, and their interaction while controlling for individual differences through repeated measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard L. Solomon (base design); crossover extension via repeated-measures methodology","year":"1949 (base design); crossover adaptation developed through later methodological literature","type":"Experimental design (pretest-sensitization control + within-subjects crossover)","dataType":"Continuous or ordinal outcome measures; repeated observations per participant","subfamily":"Deneysel desen"},"citations":[{"ref":"Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.","type":"article","doi":"10.1037/h0062958","isbn":null,"url":null},{"ref":"Braver, M. C. W., & Braver, S. L. (1988). Statistical treatment of the Solomon four-group design: A meta-analytic approach. Psychological Bulletin, 104(1), 150–154.","type":"article","doi":"10.1037/0033-2909.104.1.150","isbn":null,"url":null}],"related":["solomon-four-group-design","crossover-randomized-controlled-trial","pretest-posttest-experimental-design","crossover-pretest-posttest-experimental-design","factorial-solomon-four-group-design","control-group-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"croston-method","name":"Croston's Method","fullName":"Croston's Method for Intermittent Demand Forecasting","aliases":["Croston method","intermittent demand forecasting","Croston Yöntemi — Aralıklı Talep Tahmini"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1972,"originator":"J. D. Croston (1972)","url":"https://scholargate.app/en/econometrics/croston-method","markdownUrl":"https://scholargate.app/en/econometrics/croston-method.md","definition":"Croston's method, introduced by J. D. Croston in 1972, is a time-series forecasting technique built for intermittent demand series in which periods of zero demand are frequent. Instead of forecasting the raw series, it models the size of demand when it occurs and the interval between demand occurrences as two separate processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. D. Croston (1972)","year":1972,"type":"Intermittent demand time-series forecasting","estimator":"Separate simple exponential smoothing of demand size and inter-arrival interval","outcome":"intermittent (count or continuous) demand series"},"citations":[{"ref":"Croston, J. D. (1972). Forecasting and Stock Control for Intermittent Demands. Operational Research Quarterly, 23(3), 289-303.","type":"article","doi":"10.1057/jors.1972.50","isbn":null,"url":null},{"ref":"Syntetos, A. A. & Boylan, J. E. (2005). The Accuracy of Intermittent Demand Estimates. International Journal of Forecasting, 21(2), 303-314.","type":"article","doi":"10.1016/j.ijforecast.2004.10.001","isbn":null,"url":null}],"related":["theta-method","exponential-smoothing","arima","ols-regression","poisson-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crown-fire","name":"Crown Fire (Van Wagner)","fullName":"Crown Fire Initiation and Spread Model","aliases":["crown fire model","Van Wagner model"],"domain":"forestry","family":"process-pipeline","subfamily":"Fire Dynamics","year":"1977","originator":"Cornelius Van Wagner","url":"https://scholargate.app/en/forestry/crown-fire","markdownUrl":"https://scholargate.app/en/forestry/crown-fire.md","definition":"The Van Wagner crown fire model predicts the conditions under which surface fires will transition to active crown fires and the rate of crown fire spread. Developed by Cornelius Van Wagner in the 1970s–1990s, the model is grounded in the physics of heat transfer from the surface flame to the canopy and the rate of vertical flame propagation. The model is used to assess crown fire danger and to guide fuel management decisions in forests with high canopy flammability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cornelius Van Wagner","subfamily":"Fire Dynamics","year":"1977","type":"fire propagation model"},"citations":[{"ref":"Van Wagner, C. E. (1977). Conditions for the start and spread of crown fire. Canadian Journal of Forest Research, 7(1), 23–34.","type":"article","doi":"10.1139/x77-004","isbn":null,"url":null},{"ref":"Van Wagner, C. E. (1993). Prediction of crown fire behavior in two stands of boreal forest. Canadian Journal of Forest Research, 23(3), 442–449.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prediction+of+crown+fire+behavior+in+two+stands+of+boreal+forest+Van"}],"related":["rothermel-fire-model","fire-weather-index","keetch-byram-drought-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crowne-marlowe-scale","name":"Crowne-Marlowe Social Desirability Scale","fullName":"Crowne-Marlowe Social Desirability Scale (CMSD)","aliases":["CMSD","Crowne-Marlowe Scale","Social Desirability Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Self-report questionnaire","year":"1960","originator":"Douglas Crowne and David Marlowe","url":"https://scholargate.app/en/social-psychology/crowne-marlowe-scale","markdownUrl":"https://scholargate.app/en/social-psychology/crowne-marlowe-scale.md","definition":"The Crowne-Marlowe Social Desirability Scale (CMSD) is a 33-item self-report measure designed to assess the tendency to present oneself favorably in social contexts, independent of psychopathology. Developed by Douglas Crowne and David Marlowe in 1960, the CMSD measures impression management and social desirability bias—tendencies that confound responses to personality, health, and behavioral questionnaires. The scale has become the standard reference instrument for detecting and controlling social desirability effects in psychological research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas Crowne and David Marlowe","subfamily":"Self-report questionnaire","year":"1960","type":"Social desirability response bias measurement"},"citations":[{"ref":"Crowne, D. P., & Marlowe, D. (1960). A new scale of social desirability independent of psychopathology. Journal of Consulting Psychology, 24(4), 349–354.","type":"article","doi":"10.1037/h0047358","isbn":null,"url":null},{"ref":"Reynolds, W. M. (1982). Development of reliable and valid short forms of the Marlowe-Crowne Social Desirability Scale. Journal of Clinical Psychology, 38(1), 119–125.","type":"article","doi":"10.1002/1097-4679(198201)38:1<119::aid-jclp2270380118>3.0.co;2-i","isbn":null,"url":null},{"ref":"Paulhus, D. L. (1991). Measurement and control of response bias. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of personality and social psychological attitudes (pp. 17–59). Academic Press.","type":"article","doi":null,"isbn":"978-0-12-590241-0","url":null}],"related":["neo-pi-r","rosenberg-self-esteem-scale","bfi-big-five-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cryo-em-reconstruction","name":"Cryo-EM Reconstruction","fullName":"Cryo-Electron Microscopy 3D Reconstruction","aliases":["cryo-electron microscopy","cryo-EM","single-particle cryo-EM"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Structural determination","year":"1975","originator":"Joachim Frank","url":"https://scholargate.app/en/bioinformatics/cryo-em-reconstruction","markdownUrl":"https://scholargate.app/en/bioinformatics/cryo-em-reconstruction.md","definition":"Cryo-electron microscopy (cryo-EM) determines three-dimensional macromolecular structures at atomic or near-atomic resolution by imaging proteins frozen in vitreous ice. Pioneered by Frank, Henderson, and others, this technique has revolutionized structural biology by enabling visualization of large, non-crystallizable complexes and capturing functional conformational states.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joachim Frank","subfamily":"Structural determination","year":"1975","type":"Image reconstruction pipeline"},"citations":[{"ref":"Frank, J. (2002). Single-particle imaging of macromolecules by cryo-electron microscopy. Annual Review of Biophysics and Biomolecular Structure, 31, 303-319.","type":"article","doi":"10.1146/annurev.biophys.31.082901.134202","isbn":null,"url":null},{"ref":"Henderson, R., Baldwin, J. M., Ceska, T. A., Zemlin, F., Beckmann, E., & Downing, K. H. (1990). Model for the structure of bacteriorhodopsin based on high-resolution electron cryo-microscopy. Journal of Molecular Biology, 213(4), 899-929.","type":"article","doi":"10.1016/S0022-2836(05)80271-2","isbn":null,"url":null},{"ref":"Scheres, S. H. W. (2016). Processing of structurally heterogeneous cryo-EM data in RELION. Methods in Enzymology, 579, 125-157.","type":"article","doi":"10.1016/bs.mie.2016.04.012","isbn":null,"url":null}],"related":["homology-modeling","molecular-docking","ppi-network-topology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"crystal-field-theory","name":"Crystal Field Theory","fullName":"Crystal Field Theory","aliases":["CFT","crystal field","ligand field theory"],"domain":"chemistry","family":"process-pipeline","subfamily":"Structural analysis","year":"1929","originator":"Hans Bethe","url":"https://scholargate.app/en/chemistry/crystal-field-theory","markdownUrl":"https://scholargate.app/en/chemistry/crystal-field-theory.md","definition":"Crystal Field Theory (CFT) is a model that explains the electronic structure, color, magnetism, and reactivity of coordination complexes by considering how the electric field created by surrounding ligands perturbs the d-orbitals of a central metal ion. Developed by Hans Bethe in 1929 and refined throughout the 20th century, CFT is one of the most powerful tools for understanding inorganic chemistry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans Bethe","subfamily":"Structural analysis","year":"1929","type":"Theoretical model"},"citations":[{"ref":"Bethe, H. (1929). Termaufspaltung in Kristallen. Annalen der Physik, 3(5), 133–208.","type":"article","doi":"10.1002/andp.19293950202","isbn":null,"url":null},{"ref":"Miessler, G. L., Fischer, P. J., & Tarr, D. A. (2014). Inorganic Chemistry (5th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0321811325","url":null}],"related":["ligand-field-analysis","coordination-compound-synthesis","x-ray-crystallography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cs-ardl","name":"CS-ARDL","fullName":"Cross-Sectional Autoregressive Distributed Lag","aliases":["Panel ARDL with cross-sectional dependence"],"domain":"econometrics","family":"regression-model","subfamily":"Panel cointegration","year":"2006","originator":"Pesaran and colleagues","url":"https://scholargate.app/en/econometrics/cs-ardl","markdownUrl":"https://scholargate.app/en/econometrics/cs-ardl.md","definition":"CS-ARDL (Cross-Sectional ARDL) applies the ARDL framework to panel data while explicitly accounting for cross-sectional dependence—correlation of shocks and relationships across units (countries, firms, regions). Introduced by Pesaran and colleagues (2016), it extends panel ARDL methods to handle common factors or global shocks affecting all units simultaneously. This is crucial for realistic modeling of internationally integrated economies and firm networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pesaran and colleagues","subfamily":"Panel cointegration","year":"2006","type":"Dynamic panel model"},"citations":[{"ref":"Pesaran, M. H., & Smith, R. (2016). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6-10), 1089-1117.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Testing+weak+cross-sectional+dependence+in+large+panels+Pesaran"},{"ref":"Chudik, A., Kapetanios, G., & Pesaran, M. H. (2018). A one covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models. Econometric Reviews, 37(8), 953-1010.","type":"article","doi":"10.3982/ecta14176","isbn":null,"url":null}],"related":["cs-nardl","cs-dl","panel-varx"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cs-dl","name":"CS-DL","fullName":"Cross-Sectional Distributed Lag Model","aliases":["Panel distributed lag model"],"domain":"econometrics","family":"regression-model","subfamily":"Panel dynamics","year":"2001","originator":"Pesaran, Shin, and Smith","url":"https://scholargate.app/en/econometrics/cs-dl","markdownUrl":"https://scholargate.app/en/econometrics/cs-dl.md","definition":"CS-DL (Cross-Sectional Distributed Lag) is a simplified dynamic panel model regressing outcomes on current and lagged explanatory variables without explicit autoregressive terms, while accounting for cross-sectional dependence. Built on Pesaran et al. (2001) and extended by Chudik et al. (2014), it estimates dynamic effects more parsimoniously than ARDL when autocorrelated lags are less critical. This approach is valuable for short-horizon effects and policy impact analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pesaran, Shin, and Smith","subfamily":"Panel dynamics","year":"2001","type":"Distributed lag model"},"citations":[{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships and dynamics. Journal of Applied Econometrics, 16(3), 289-326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null},{"ref":"Chudik, A., Kapetanios, G., & Pesaran, M. H. (2014). Common correlated effects estimation in large panels with cross-sectional dependence. Econometric Reviews, 34(6-10), 1078-1088.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Common+correlated+effects+estimation+in+large+panels+with+cross-sectional+dependence+Chudik"}],"related":["cs-ardl","cs-nardl","local-projections"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cs-nardl","name":"CS-NARDL","fullName":"Cross-Sectional Nonlinear Autoregressive Distributed Lag","aliases":["NARDL panel"],"domain":"econometrics","family":"regression-model","subfamily":"Nonlinear cointegration","year":"2014","originator":"Yongcheol Shin and colleagues","url":"https://scholargate.app/en/econometrics/cs-nardl","markdownUrl":"https://scholargate.app/en/econometrics/cs-nardl.md","definition":"CS-NARDL extends the nonlinear autoregressive distributed lag (NARDL) model to panel data, capturing asymmetric long-run and short-run relationships where positive and negative changes in explanatory variables have differential effects. Introduced by Shin et al. (2014) and adapted to panels, it allows studying how cross-sectional units respond differently to positive versus negative shocks while maintaining cointegrating relationships. This approach is essential for understanding economic asymmetries in commodity markets, monetary transmission, and labor markets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yongcheol Shin and colleagues","subfamily":"Nonlinear cointegration","year":"2014","type":"Asymmetric panel model"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a system of nonlinear autoregressive distributed lag equations. Econometric Reviews, 33(1), 56-87.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modelling+asymmetric+cointegration+and+dynamic+multipliers+in+a+system+of+nonlinear+autoregressive+distributed+lag+equations+Shin"},{"ref":"Wold, E. N., Serrano, G., & Gunnvaldsson, A. (2023). Panel nonlinear ARDL and asymmetric effects. Journal of Econometric Methods, 12(1), 20220039.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Panel+nonlinear+ARDL+and+asymmetric+effects+Wold"}],"related":["qardl","cs-ardl","cs-dl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"csma-ca","name":"CSMA/CA","fullName":"Carrier Sense Multiple Access with Collision Avoidance","aliases":["medium access control","WiFi MAC"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Medium Access Control","year":"1990","originator":"Phil Karn","url":"https://scholargate.app/en/telecommunications/csma-ca","markdownUrl":"https://scholargate.app/en/telecommunications/csma-ca.md","definition":"CSMA/CA is a random access protocol for wireless medium access control, designed to enable multiple devices to share a wireless channel while minimizing collisions. Introduced by Phil Karn in 1990, it is the foundation of WiFi (IEEE 802.11) and is now the de facto standard for unlicensed spectrum access. CSMA/CA combines carrier sensing (listen before transmit) with collision avoidance (RTS/CTS handshake) to improve channel efficiency and fairness, avoiding the efficiency loss of pure random access (Aloha).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Phil Karn","subfamily":"Medium Access Control","year":"1990","type":"random access protocol"},"citations":[{"ref":"Karn, P. (1990). MACA—a new channel access method for packet radio. In Proceedings of the ARRL/CRRL Amateur Radio 9th Computer Networking Conference, 134-140.","type":"article","doi":null,"isbn":null,"url":"https://www.arrl.org"},{"ref":"IEEE 802.11 Working Group. (2020). IEEE Standard for Information Technology—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE.","type":"article","doi":null,"isbn":null,"url":"https://www.ieee.org"}],"related":["slotted-aloha","ofdm","mimo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cstr-model","name":"CSTR Model","fullName":"Continuous Stirred-Tank Reactor Model","aliases":["ideal mixed reactor","back-mix reactor","CSTR"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Reactor Engineering","year":"1962","originator":"Octave Levenspiel","url":"https://scholargate.app/en/applied-physics/cstr-model","markdownUrl":"https://scholargate.app/en/applied-physics/cstr-model.md","definition":"The CSTR (Continuous Stirred-Tank Reactor) model describes the behavior of an ideal mixed reactor where fresh feed is continuously added, products are withdrawn, and contents are kept uniform by vigorous stirring. This fundamental model, formalized by Octave Levenspiel in the 1960s, is widely used to design and scale batch and continuous processes. Despite its simplicity, it captures essential dynamics of industrial reactors and is the baseline for process control and optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Octave Levenspiel","subfamily":"Reactor Engineering","year":"1962","type":"Mathematical model for continuous flow reactor"},"citations":[{"ref":"Levenspiel, O. (1999). Chemical Reaction Engineering (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-471-25424-9","url":null},{"ref":"Fogler, H. S. (2016). Elements of Chemical Reaction Engineering (5th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0-13-388928-8","url":null},{"ref":"Bailey, J. E., & Ollis, D. F. (2004). Biochemical Engineering Fundamentals (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0-07-303443-8","url":null}],"related":["pfr-model","reactive-distillation","peng-robinson-equation-of-state"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ct-iterative-reconstruction","name":"CT Iterative Reconstruction","fullName":"Computed Tomography Iterative Reconstruction","aliases":["MBIR","ASIR","IR-CT","statistical reconstruction"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Image reconstruction","year":"1974","originator":"Richard Gordon","url":"https://scholargate.app/en/medical-imaging/ct-iterative-reconstruction","markdownUrl":"https://scholargate.app/en/medical-imaging/ct-iterative-reconstruction.md","definition":"CT Iterative Reconstruction (IR) is a computational technique that reconstructs tomographic images from raw X-ray projection data by iteratively refining an estimate of tissue attenuation until it matches the measured projections. Developed from algebraic reconstruction techniques pioneered by Gordon in 1974, iterative reconstruction has revolutionized clinical CT by enabling high-quality images at reduced radiation dose. Variants such as Adaptive Statistical Iterative Reconstruction (ASIR) and Model-Based Iterative Reconstruction (MBIR) are now standard on modern CT scanners.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Gordon","subfamily":"Image reconstruction","year":"1974","type":"Algorithm for tomographic image reconstruction"},"citations":[{"ref":"Gordon, R., Bender, R., Herman, G. T. (1974). Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. Journal of Theoretical Biology, 29(3), 471-481.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Algebraic+reconstruction+techniques+%28ART%29+for+three-dimensional+electron+microscopy+and+X-ray+photography+Gordon"},{"ref":"Yu, L., Leng, S., McCollough, C. H. (2012). Iterative reconstruction in medical imaging. Journal of Medical Imaging, 1(3), 033506.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Iterative+reconstruction+in+medical+imaging+Yu"},{"ref":"Singh, S., Kalra, M. K., Hsieh, J., et al. (2010). Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques. Radiology, 257(2), 373-383.","type":"article","doi":"10.1148/radiol.10092212","isbn":null,"url":null}],"related":["quantitative-susceptibility-mapping","dexa","pet-kinetic-modeling","dti-tractography","radiomics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ctd-profiling","name":"CTD Profiling","fullName":"Conductivity-Temperature-Depth Profiling","aliases":["CTD","Rosette Sampling"],"domain":"oceanography","family":"process-pipeline","subfamily":"Instrumental Analysis","year":"1977","originator":"Neil Brown","url":"https://scholargate.app/en/oceanography/ctd-profiling","markdownUrl":"https://scholargate.app/en/oceanography/ctd-profiling.md","definition":"Conductivity-Temperature-Depth (CTD) profiling is the primary method for measuring vertical profiles of seawater properties in oceanography. Developed by Neil Brown in 1977, CTD instruments are equipped with sensors for conductivity, temperature, and pressure (depth), and are typically mounted on water-sampling rosettes. CTD profiling provides essential hydrographic data that characterizes water mass structure, stratification, and circulation patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Neil Brown","subfamily":"Instrumental Analysis","year":"1977","type":"instrumental"},"citations":[{"ref":"UNESCO/IOC. (1991). Processing of oceanographic station data. UNESCO Technical Papers in Marine Science, 60.","type":"article","doi":null,"isbn":null,"url":"https://unesdoc.unesco.org/"},{"ref":"Roemmich, D., & Gilson, J. (2009). The 2004-2008 global hydrographic climatology. Oceanography, 22(2), 50-61.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+2004-2008+global+hydrographic+climatology+Roemmich"}],"related":["acoustic-doppler-current-profiler","ocean-color-chlorophyll-a","tidal-harmonic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cubic-edas","name":"CUBIC-EDAS","fullName":"Cubic-EDAS — Cubic Pythagorean Fuzzy EDAS (CuP-EDAS)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Paul, T.K., Jana, C., Pal, M.","url":"https://scholargate.app/en/decision-making/cubic-edas","markdownUrl":"https://scholargate.app/en/decision-making/cubic-edas.md","definition":"CUBIC-EDAS (Cubic-EDAS — Cubic Pythagorean Fuzzy EDAS (CuP-EDAS)) is a ranking multi-criteria decision-making (MCDM) method introduced by Paul, T.K., Jana, C., Pal, M. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul, T.K., Jana, C., Pal, M.","subfamily":"Ranking","year":"2023","type":"Cubic Pythagorean Fuzzy ranking — CuPyFN = ⟨IvPyFN, PyFN⟩ = (⟨[Y⁻,Y⁺],[F⁻,F⁺]⟩,⟨Y,F⟩); Pythagorean constraint (Y⁺)²+(F⁺)² ≤ 1; average-solution EDAS with score-function PDA/NDA","value_space":"cubic_pythagorean_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Paul, T.K., Jana, C., Pal, M. (2023). Multi-criteria group decision-making method in disposal of municipal solid waste based on cubic Pythagorean fuzzy EDAS approach with incomplete weight information. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2023.110515","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cubic-topsis","name":"CUBIC-TOPSIS","fullName":"Cubic-TOPSIS — Cubic extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Garg, H., Kaur, G.","url":"https://scholargate.app/en/decision-making/cubic-topsis","markdownUrl":"https://scholargate.app/en/decision-making/cubic-topsis.md","definition":"CUBIC-TOPSIS (Cubic-TOPSIS — Cubic extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Garg, H., Kaur, G. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Garg, H., Kaur, G.","subfamily":"Ranking","year":"2018","type":"Cubic Intuitionistic Fuzzy ranking — CIFN = (IVIFN, IFN): interval membership/non-membership ⟨[ζL,ζU],[ϑL,ϑU]⟩ (IVIFS part) + point membership/non-membership ⟨ζ,ϑ⟩ (IFS part); extended TOPSIS with weighted generalised distance","value_space":"cubic_intuitionistic_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Garg, H., Kaur, G. (2018). Extended TOPSIS method for multi-criteria group decision-making problems under cubic intuitionistic fuzzy environment. Scientia Iranica E","type":"article","doi":"10.24200/sci.2018.5307.1194","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cubic-vikor","name":"CUBIC-VIKOR","fullName":"Cubic-VIKOR — Cubic extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2012","originator":"Jun, Y. B. Kim, C. S. Yang, K. O.","url":"https://scholargate.app/en/decision-making/cubic-vikor","markdownUrl":"https://scholargate.app/en/decision-making/cubic-vikor.md","definition":"CUBIC-VIKOR (Cubic-VIKOR — Cubic extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Jun, Y. B. Kim, C. S. Yang, K. O. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jun, Y. B. Kim, C. S. Yang, K. O.","subfamily":"Ranking","year":"2012","type":"Cubic outranking/ranking — Cubic Fuzzy Set (CuFS: interval [a,b] for internal membership, scalar λ for external)","value_space":"cubic_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Jun, Y. B., Kim, C. S., Yang, K. O. (2012). Cubic sets. Annals of Fuzzy Mathematics and Informatics","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cubic%20sets"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cubic-waspas","name":"CUBIC-WASPAS","fullName":"Cubic-WASPAS — Cubic extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2012","originator":"Jun, Y. B. Kim, C. S. Yang, K. O.","url":"https://scholargate.app/en/decision-making/cubic-waspas","markdownUrl":"https://scholargate.app/en/decision-making/cubic-waspas.md","definition":"CUBIC-WASPAS (Cubic-WASPAS — Cubic extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Jun, Y. B. Kim, C. S. Yang, K. O. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jun, Y. B. Kim, C. S. Yang, K. O.","subfamily":"Ranking","year":"2012","type":"Cubic outranking/ranking — Cubic Fuzzy Set (CuFS: interval [a,b] for internal membership, scalar λ for external)","value_space":"cubic_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Jun, Y. B., Kim, C. S., Yang, K. O. (2012). Cubic sets. Annals of Fuzzy Mathematics and Informatics","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cubic%20sets"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cuckoo-search","name":"Cuckoo Search","fullName":"Cuckoo Search Algorithm","aliases":["Guguk Kuşu Araması (Cuckoo Search)","CS algorithm","Cuckoo Search via Lévy Flights"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":2009,"originator":null,"url":"https://scholargate.app/en/optimization/cuckoo-search","markdownUrl":"https://scholargate.app/en/optimization/cuckoo-search.md","definition":"Cuckoo Search (CS) is a population-based metaheuristic optimization algorithm introduced by Xin-She Yang and Suash Deb in 2009. It models the obligate brood-parasitism of cuckoo birds — which lay eggs in other birds' nests — combined with Lévy flight random walks that enable long-range exploration of the search space. The algorithm has proven effective in structural engineering design, machine learning hyperparameter tuning, and other continuous black-box optimization problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Xin-She Yang & Suash Deb","year":2009,"type":"Population-based metaheuristic / swarm intelligence","inspiredBy":"Brood parasitism of cuckoo birds and Lévy flight random walks","searchMechanism":"Lévy flights for global exploration + abandon probability for exploitation","typicalPopulationSize":"15–50 nests","applicableProblemTypes":"Continuous black-box optimization"},"citations":[{"ref":"Yang, X.S. & Deb, S. (2009). Cuckoo Search via Lévy Flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210-214. IEEE.","type":"inproceedings","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/5393690"},{"ref":"Yang, X.S. & Deb, S. (2013). Multiobjective Cuckoo Search for Design Optimization. Computers & Operations Research, 40(6), 1616-1624.","type":"article","doi":"10.1016/j.cor.2011.09.026","isbn":null,"url":null}],"related":["particle-swarm-optimization","genetic-algorithm","firefly-algorithm","differential-evolution","simulated-annealing","harmony-search"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cudit","name":"CUDIT-R","fullName":"Cannabis Use Disorder Identification Test—Revised","aliases":["CUDIT-R","CUDIT"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"cannabis-use-screening","year":"2010","originator":"Adamson, Kay-Lambkin, Baker, Lewin, Thornton, Kelly, Sellman","url":"https://scholargate.app/en/addiction-medicine/cudit","markdownUrl":"https://scholargate.app/en/addiction-medicine/cudit.md","definition":"The CUDIT-R is a brief, 8-item self-report screening instrument developed to identify cannabis use disorder and hazardous cannabis use patterns. Introduced by Adamson and colleagues in 2010 as a revision of the original CUDIT, the CUDIT-R improves brevity and screening efficiency while maintaining strong psychometric properties. It is designed for use in primary care, addiction treatment, and public health settings to detect problematic cannabis use and inform treatment allocation decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adamson, Kay-Lambkin, Baker, Lewin, Thornton, Kelly, Sellman","subfamily":"cannabis-use-screening","year":"2010","type":"Self-report"},"citations":[{"ref":"Adamson, S. J., Kay-Lambkin, F. J., Baker, A. L., Lewin, T. J., Thornton, L., Kelly, B. J., & Sellman, J. D. (2010). An improved brief screening instrument for cannabis use disorder. Drug and Alcohol Dependence, 110(1–2), 55–60.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+improved+brief+screening+instrument+for+cannabis+use+disorder+Adamson"}],"related":["dudit","sadq","readiness-to-change-questionnaire","brief-addiction-monitor"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cultural-competence-assessment","name":"Cultural Competence Assessment Instrument","fullName":"Cultural Competence Assessment Instrument (CCA)","aliases":["CCA"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"healthcare-provider-assessment","year":2003,"originator":"Schim, Doorenbos, Borse","url":"https://scholargate.app/en/transcultural-nursing/cultural-competence-assessment","markdownUrl":"https://scholargate.app/en/transcultural-nursing/cultural-competence-assessment.md","definition":"The Cultural Competence Assessment Instrument (CCA) is a 25-item self-report measure designed to assess healthcare providers' cultural competence across four key domains: diversity experience, awareness, sensitivity, and competence behaviors. Developed by Schim, Doorenbos, and Borse in 2003, the CCA evaluates nurses' and other clinicians' readiness to provide culturally responsive care to diverse patient populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schim, Doorenbos, Borse","subfamily":"healthcare-provider-assessment","year":2003,"type":"Self-report"},"citations":[{"ref":"Schim, S. M., Doorenbos, A. Z., & Borse, N. N. (2003). Cultural competence development in nursing education: An evolutionary process. Journal of Transcultural Nursing, 14(3), 236–244.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cultural+competence+development+in+nursing+education%3A+An+evolutionary+process+Schim"}],"related":["transcultural-self-efficacy-tool","multicultural-counseling-inventory","patient-provider-cultural-sensitivity","ethnic-identity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cultural-humility-scale","name":"Cultural Humility Scale","fullName":"Cultural Humility Scale","aliases":["CHS"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"healthcare-provider-cultural-attitudes","year":1998,"originator":"Tervalon, Murray-García; Hook et al.","url":"https://scholargate.app/en/transcultural-nursing/cultural-humility-scale","markdownUrl":"https://scholargate.app/en/transcultural-nursing/cultural-humility-scale.md","definition":"The Cultural Humility Scale (CHS) is a self-report instrument designed to assess healthcare providers' capacity for cultural humility—a stance of openness, self-reflection, and power-sharing with patients from diverse cultural backgrounds. Originating from theoretical work by Tervalon and Murray-García (1998) and operationalized by Hook and colleagues (2013), the CHS measures clinicians' willingness to acknowledge limits in cultural knowledge, receptiveness to patient perspectives, and commitment to lifelong learning about culture. The instrument is widely used in medical, nursing, counseling, and other health professions education to evaluate trainees' readiness for culturally humble practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tervalon, Murray-García; Hook et al.","subfamily":"healthcare-provider-cultural-attitudes","year":1998,"type":"Self-report"},"citations":[{"ref":"Tervalon, M., & Murray-García, J. (1998). Cultural humility versus cultural competence: A critical distinction in defining physician training outcomes in multicultural education. Journal of Health Care for the Poor and Underserved, 9(2), 117–125.","type":"article","doi":"10.1353/hpu.2010.0233","isbn":null,"url":null},{"ref":"Hook, J. N., Davis, D. E., Owen, J., Worthington Jr., E. L., & Utsey, S. O. (2013). Cultural humility: Measuring openness to culturally diverse clients. Journal of Counseling Psychology, 60(3), 353–366.","type":"article","doi":"10.1037/a0032595","isbn":null,"url":null}],"related":["cultural-competence-assessment","patient-provider-cultural-sensitivity","multicultural-counseling-inventory","racism-and-life-experiences-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cultural-intelligence-scale","name":"Cultural Intelligence Scale","fullName":"Cultural Intelligence Scale (CQS)","aliases":["CQS","Cultural Intelligence","Cultural Quotient"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Personality assessment","year":"2003","originator":"Christopher Earley and Soon Ang","url":"https://scholargate.app/en/social-psychology/cultural-intelligence-scale","markdownUrl":"https://scholargate.app/en/social-psychology/cultural-intelligence-scale.md","definition":"The Cultural Intelligence Scale (CQS) is a 20-item measure assessing an individual's capability to function effectively in culturally diverse contexts and to adapt behavior appropriately across cultural settings. Developed by Christopher Earley and Soon Ang in the early 2000s, the CQS operationalizes cultural intelligence as a multidimensional competence involving cognitive, metacognitive, motivational, and behavioral components. The measure has become standard in organizational psychology and international business research for evaluating cross-cultural effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher Earley and Soon Ang","subfamily":"Personality assessment","year":"2003","type":"Cultural competence and adaptability scale"},"citations":[{"ref":"Earley, P. C., & Ang, S. (2003). Cultural intelligence: Individual interactions across cultures. Stanford University Press.","type":"article","doi":null,"isbn":"978-0804747929","url":null},{"ref":"Ang, S., Van Dyne, L., Koh, C., Ng, K. Y., Templer, K. J., Tay, C., & Chandrasekar, N. A. (2007). Cultural Intelligence: Its measurement and effects on cultural judgment and decision making, cultural adaptation and task performance. Management and Organization Review, 3(3), 335–371.","type":"article","doi":"10.1111/j.1740-8784.2007.00082.x","isbn":null,"url":null},{"ref":"Rockstuhl, T., Seiler, S., Ang, S., Van Dyne, L., & Annen, H. (2011). Beyond general intelligence (IQ) and emotional intelligence (EQ): A meta-analysis of cultural intelligence research. Journal of International Business Studies, 42(3), 494–515.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Beyond+general+intelligence+%28IQ%29+and+emotional+intelligence+%28EQ%29%3A+A+meta-analysis+of+cultural+intelligence+research+Rockstuhl"}],"related":["toronto-empathy-questionnaire","neo-pi-r","self-compassion-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cultural-values-scale","name":"Cultural Values Scale","fullName":"Cultural Values Scale","aliases":["CVS"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Cross-cultural scale","year":"2002","originator":"Daphna Oyserman","url":"https://scholargate.app/en/social-psychology/cultural-values-scale","markdownUrl":"https://scholargate.app/en/social-psychology/cultural-values-scale.md","definition":"The Cultural Values Scale is a self-report measure designed to assess individual endorsement of cultural values spanning individualism and collectivism. Developed within the cross-cultural psychology literature, the scale captures how individuals prioritize personal autonomy, achievement, and self-expression against group harmony, interdependence, and collective well-being. It has become a standard tool for understanding cultural orientation in diverse populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daphna Oyserman","subfamily":"Cross-cultural scale","year":"2002","type":"Self-report Likert scale"},"citations":[{"ref":"Oyserman, D., Coon, H. M., & Kemmelmeier, M. (2002). Rethinking individualism and collectivism: Evaluation of theoretical assumptions and meta-analyses. Psychological Bulletin, 128(1), 3–72.","type":"article","doi":"10.1037/0033-2909.128.1.3","isbn":null,"url":null}],"related":["collectivism-individualism-scale","social-dominance-orientation-scale","acculturation-scale","modern-racism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"curb-65","name":"CURB-65 Pneumonia Severity Score","fullName":"Confusion, Urea, Respiratory rate, Blood pressure, age ≥65 (CURB-65)","aliases":["CURB-65","Pneumonia severity"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"2003","originator":"W. Staniford Lim, et al.","url":"https://scholargate.app/en/clinical-assessment/curb-65","markdownUrl":"https://scholargate.app/en/clinical-assessment/curb-65.md","definition":"CURB-65, derived and validated by Lim et al. in 2003, is a 5-point severity of illness score for community-acquired pneumonia (CAP). It assesses confusion, urea nitrogen, respiratory rate, blood pressure, and age ≥65 years to stratify mortality risk and guide admission and treatment decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"W. Staniford Lim, et al.","subfamily":"Clinical scoring","year":"2003","type":"Community-acquired pneumonia severity assessment"},"citations":[{"ref":"Lim, W. S., van der Eerden, M. M., Laing, R., et al. (2003). Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax, 58(5), 377-382.","type":"article","doi":"10.1136/thorax.58.5.377","isbn":null,"url":null},{"ref":"Capelastegui, A., España, P. P., Quintana, J. M., et al. (2006). Validation of a community-acquired pneumonia severity score. European Respiratory Journal, 27(2), 405-413.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validation+of+a+community-acquired+pneumonia+severity+score+Capelastegui"}],"related":["wells-score-dvt","qsofa","mews-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cure-model","name":"Mixture Cure Model","fullName":"Mixture Cure Model (Cure Fraction Model)","aliases":["cure fraction model","cure rate model","bounded cumulative hazard model","İyileşme Modeli (Mixture Cure Model)"],"domain":"survival","family":"survival","subfamily":null,"year":1949,"originator":"Boag, J. W.","url":"https://scholargate.app/en/survival/cure-model","markdownUrl":"https://scholargate.app/en/survival/cure-model.md","definition":"The mixture cure model, first proposed by Boag in 1949 for cancer survival data, is a parametric survival model that explicitly accounts for a fraction of subjects who will never experience the event of interest — the so-called cured or immune fraction. It is the appropriate tool whenever the Kaplan-Meier curve levels off into a long, stable plateau rather than continuing to decline, indicating that a proportion of subjects are permanently event-free.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Boag, J. W.","year":1949,"type":"Parametric mixture survival model","handles":"Right-censoring with a cured (immune) fraction","minSample":50,"difficulty":3},"citations":[{"ref":"Boag, J. W. (1949). Maximum Likelihood Estimates of the Proportion of Patients Cured. Journal of the Royal Statistical Society B, 11(1), 15–53.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2983694"}],"related":["kaplan-meier","cox-ph","weibull-aft","fine-gray-model","log-rank-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"curriculum-analysis","name":"Curriculum Analysis","fullName":"Curriculum Analysis in Educational Research","aliases":["curriculum evaluation","curriculum review","syllabus analysis","curriculum appraisal"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1949 (Tyler); 1980s–2000s (Posner's analytic framework)","originator":"George J. Posner (systematic framework); Ralph Tyler (foundational rationale)","url":"https://scholargate.app/en/field-methods/curriculum-analysis","markdownUrl":"https://scholargate.app/en/field-methods/curriculum-analysis.md","definition":"Curriculum analysis is a systematic research method for examining the content, structure, goals, and underlying assumptions of educational curricula — including written syllabi, textbooks, lesson plans, and policy documents. By mapping what is taught, how it is sequenced, and what values are embedded, researchers and educators can evaluate alignment with learning objectives, identify gaps or biases, and guide curriculum reform across all levels of education.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George J. Posner (systematic framework); Ralph Tyler (foundational rationale)","year":"1949 (Tyler); 1980s–2000s (Posner's analytic framework)","type":"Qualitative / mixed document analysis","dataType":"Curriculum documents, syllabi, lesson plans, textbooks, policy texts","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Posner, G. J. (2004). Analyzing the Curriculum (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0072823899","url":null},{"ref":"English, F. W. (1980). Curriculum mapping. Educational Leadership, 37(7), 558–559.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Curriculum+mapping+English+1980+Educational+Leadership"}],"related":["content-analysis","document-analysis","program-evaluation","educational-action-research","discourse-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"curriculum-learning","name":"Curriculum Learning","fullName":"Curriculum Learning","aliases":["Scheduled Training","Difficulty-Based Training","Self-Paced Learning","Müfredat Öğrenimi"],"domain":"deep-learning","family":"ml-model","subfamily":"Training paradigms","year":2009,"originator":"Yoshua Bengio et al.","url":"https://scholargate.app/en/deep-learning/curriculum-learning","markdownUrl":"https://scholargate.app/en/deep-learning/curriculum-learning.md","definition":"Curriculum Learning is a training strategy for machine learning models, introduced by Bengio et al. in 2009, in which training examples are presented in a meaningful order—typically from easy to hard—rather than at random. Inspired by how humans and animals learn progressively, it organizes training data into a curriculum that starts with simpler, cleaner, or more representative samples and gradually introduces harder or more complex examples as the model matures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yoshua Bengio et al.","year":2009,"type":"Training strategy","subfamily":"Training paradigms","inspiration":"Human and animal cognitive development","requires_labels":true},"citations":[{"ref":"Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. International Conference on Machine Learning (ICML), 41–48.","type":"inproceedings","doi":"10.1145/1553374.1553380","isbn":null,"url":null}],"related":["transfer-learning","multitask-learning","active-learning"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cushings-qol","name":"CushQoL","fullName":"Cushing's Syndrome Quality of Life Questionnaire","aliases":["Cushing QoL","CS-QoL"],"domain":"endocrinology","family":"process-pipeline","subfamily":"Cushing's syndrome-specific quality of life","year":2008,"originator":"Sergio Webb, María D. Bernal, Juan M. Rivera-Caravaca","url":"https://scholargate.app/en/endocrinology/cushings-qol","markdownUrl":"https://scholargate.app/en/endocrinology/cushings-qol.md","definition":"CushQoL is a disease-specific 12-item quality of life questionnaire developed to assess the multidimensional impacts of Cushing's syndrome—a severe endocrine disorder characterized by excess cortisol production. Developed by Webb and colleagues in 2008, it captures physical symptoms (fatigue, weight gain, weakness, hirsutism), psychological manifestations (depression, anxiety, cognitive impairment), and social/occupational dysfunction unique to Cushing's syndrome. It is the standard outcome measure for assessing quality of life improvement following curative therapy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sergio Webb, María D. Bernal, Juan M. Rivera-Caravaca","subfamily":"Cushing's syndrome-specific quality of life","year":2008,"type":"Patient self-report questionnaire"},"citations":[{"ref":"Webb, S. M., Bernal, M. D., Rivera-Caravaca, J. M., & Córdoba-Soriano, J. G. (2008). Development and validation of CushQoL, a disease-specific quality of life questionnaire in Cushing's syndrome. J Clin Endocrinol Metab, 93(5), 1751-1759.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+CushQoL%2C+a+disease-specific+quality+of+life+questionnaire+in+Cushing%27s+syndrome+Webb"},{"ref":"Tiemensma, J., Kaptein, A. A., Pereira, A. M., et al. (2015). Persistent cognitive impairment in patients treated for Cushing's syndrome. J Clin Endocrinol Metab, 95(6), 2604-2609.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Persistent+cognitive+impairment+in+patients+treated+for+Cushing%27s+syndrome+Tiemensma"}],"related":["adrenal-insufficiency-qol","thyroid-patient-reported-outcomes","growth-hormone-deficiency-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"customer-journey-mapping","name":"Customer Journey Mapping","fullName":"Customer Journey Mapping Framework","aliases":["Journey Mapping","CJM","Experience Mapping"],"domain":"marketing","family":"process-pipeline","subfamily":"Customer experience design and touchpoint analysis","year":"2000s","originator":"Adaptive Path and Service Design community","url":"https://scholargate.app/en/marketing/customer-journey-mapping","markdownUrl":"https://scholargate.app/en/marketing/customer-journey-mapping.md","definition":"Customer Journey Mapping is a service design methodology that visualizes the complete customer experience across all touchpoints and phases of a customer relationship, from awareness through advocacy. Developed through work in design and service management, journey mapping integrates behavioral data, customer emotions, pain points, and moments of truth to reveal opportunities for experience improvement and organizational alignment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adaptive Path and Service Design community","subfamily":"Customer experience design and touchpoint analysis","year":"2000s","type":"Experience mapping methodology"},"citations":[{"ref":"Rosenbaum, M. S., Ostrom, A. L., & Kuntze, R. (2005). Tensions in Service Quality and Service Marketing: An Introduction. Journal of Services Marketing, 19(7), 422-428.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tensions+in+Service+Quality+and+Service+Marketing%3A+An+Introduction+Rosenbaum"},{"ref":"Patrucco, A. S., Ciccullo, F., & Creazza, A. (2018). Mitigating Operational Risks in the Freight Forwarding Industry. International Journal of Logistics: Research and Applications, 21(2), 131-146.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mitigating+Operational+Risks+in+the+Freight+Forwarding+Industry+Patrucco"},{"ref":"Curedale, R. (2013). Service Design: 250 Essential Methods. Design Community College Inc.","type":"book","doi":null,"isbn":"978-1492218074","url":null}],"related":["brand-equity-measurement","net-promoter-score","customer-lifetime-value","market-segmentation-analysis","advertising-effectiveness-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"customer-lifetime-value","name":"Customer Lifetime Value","fullName":"Customer Lifetime Value Analysis","aliases":["CLV","LTV","Customer Value"],"domain":"marketing","family":"process-pipeline","subfamily":"Customer value quantification and retention","year":"1996","originator":"Robert Blattberg and John Deighton","url":"https://scholargate.app/en/marketing/customer-lifetime-value","markdownUrl":"https://scholargate.app/en/marketing/customer-lifetime-value.md","definition":"Customer Lifetime Value (CLV) is a financial metric that quantifies the total profit a company expects to generate from its relationship with a customer over the entire duration of that relationship. Developed through work by Blattberg, Getz, and Thomas in the 1990s-2000s, CLV integrates acquisition costs, purchase behavior, retention rates, and margin information to estimate the net present value of each customer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Blattberg and John Deighton","subfamily":"Customer value quantification and retention","year":"1996","type":"Financial modeling methodology"},"citations":[{"ref":"Blattberg, R. C., Getz, G., & Thomas, J. S. (2001). Customer Equity: Building and Managing Relationships as Assets. Harvard Business School Press.","type":"article","doi":null,"isbn":"978-0875847191","url":null},{"ref":"Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., ... & Sriram, S. (2006). Modeling Customer Lifetime Value. Journal of Service Research, 9(2), 139-155.","type":"article","doi":"10.1177/1094670506293810","isbn":null,"url":null},{"ref":"Kumar, V., & Pansari, A. (2016). Competitive Advantage Through Engagement. Journal of Marketing Research, 53(4), 497-514.","type":"article","doi":"10.1509/jmr.15.0044","isbn":null,"url":null}],"related":["brand-equity-measurement","net-promoter-score","customer-journey-mapping","market-segmentation-analysis","advertising-effectiveness-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"customer-loyalty-scale","name":"Customer Loyalty Scale","fullName":"Customer Loyalty Scale (CLS)","aliases":["Behavioral Loyalty Scale","Loyalty Commitment Scale"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Customer loyalty measurement","year":"1994","originator":"Alan S. Dick, Kunal Basu","url":"https://scholargate.app/en/marketing-management/customer-loyalty-scale","markdownUrl":"https://scholargate.app/en/marketing-management/customer-loyalty-scale.md","definition":"The Customer Loyalty Scale (CLS) measures customer loyalty as a combination of attitudinal commitment and behavioral intention. Developed by Dick and Basu (1994), the scale distinguishes between behavioral loyalty (repeat purchases) and attitudinal loyalty (emotional commitment), recognizing that true loyalty involves both. CLS captures multiple dimensions of loyalty including repurchase intention, word-of-mouth advocacy, and resistance to competitive offerings. The instrument is widely used in marketing research to predict customer lifetime value and identify at-risk customers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan S. Dick, Kunal Basu","subfamily":"Customer loyalty measurement","year":"1994","type":"Multi-dimensional behavioral and attitudinal loyalty scale"},"citations":[{"ref":"Dick, A. S., & Basu, K. (1994). Customer Loyalty: Toward an Integrated Conceptual Framework. Journal of the Academy of Marketing Science, 22(2), 99-113.","type":"article","doi":"10.1177/0092070394222001","isbn":null,"url":null},{"ref":"Hennig-Thurau, T., Langer, M. F., & Hansen, U. (2002). Modeling and Managing Student Loyalty: An Approach Based on the Concept of Relationship Quality. Journal of Service Research, 4(4), 331-344.","type":"article","doi":"10.1177/109467050134006","isbn":null,"url":null}],"related":["customer-satisfaction-index","brand-equity-scale","servqual","servperf"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"customer-satisfaction-index","name":"American Customer Satisfaction Index","fullName":"American Customer Satisfaction Index (ACSI)","aliases":["ACSI","National Customer Satisfaction Index"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Customer satisfaction measurement","year":"1996","originator":"Claes Fornell, Michael D. Johnson, Eugene W. Anderson, Jaesung Cha, Barbara E. Bryant","url":"https://scholargate.app/en/marketing-management/customer-satisfaction-index","markdownUrl":"https://scholargate.app/en/marketing-management/customer-satisfaction-index.md","definition":"The American Customer Satisfaction Index (ACSI), developed by Fornell and colleagues in 1996, is a structural equation modeling-based approach to measuring and predicting customer satisfaction across industries and over time. ACSI assesses customer expectations, perceived value, perceived quality, complaints, and loyalty in a unified framework. Since 1994, ACSI data has been collected quarterly on thousands of customers across diverse U.S. industries, making it a key economic indicator and benchmark for organizational performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Claes Fornell, Michael D. Johnson, Eugene W. Anderson, Jaesung Cha, Barbara E. Bryant","subfamily":"Customer satisfaction measurement","year":"1996","type":"Structural equation model for satisfaction and loyalty"},"citations":[{"ref":"Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American Customer Satisfaction Index: Nature, Purpose, and Findings. Journal of Marketing, 60(4), 7-18.","type":"article","doi":"10.1177/002224299606000403","isbn":null,"url":null},{"ref":"Fornell, C., Mital, V., & Veingerl, I. (2015). Developing and Testing a Theory of Consumer Delight. Journal of the Academy of Marketing Science, 43(2), 299-315.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Developing+and+Testing+a+Theory+of+Consumer+Delight+Fornell"}],"related":["servqual","servperf","customer-loyalty-scale","brand-equity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cusum-chart","name":"CUSUM Chart","fullName":"Cumulative Sum (CUSUM) Control Chart","aliases":["cumulative sum chart","CUSUM control chart","Page's CUSUM","kümülatif toplam kontrol kartı"],"domain":"statistics","family":"process-pipeline","subfamily":"Statistical process control","year":1954,"originator":"E. S. Page","url":"https://scholargate.app/en/statistics/cusum-chart","markdownUrl":"https://scholargate.app/en/statistics/cusum-chart.md","definition":"The cumulative sum (CUSUM) control chart, introduced by E. S. Page in 1954, monitors a process by accumulating the deviations of observations from a target value rather than judging each point in isolation. Because small persistent shifts add up over time, the running sum makes them visible far sooner than a Shewhart chart, making CUSUM the tool of choice for detecting small, sustained changes in the process mean.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"E. S. Page","year":1954,"type":"Statistical process control chart for small shifts","subfamily":"Statistical process control","monitors":"Cumulative deviation of the process mean from target","strength":"Fast detection of small sustained shifts"},"citations":[{"ref":"Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1/2), 100–115.","type":"article","doi":"10.1093/biomet/41.1-2.100","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-470-16992-6","url":null}],"related":["shewhart-control-chart","ewma-chart","attributes-control-chart","sequential-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cusum-test","name":"CUSUM Test","fullName":"CUSUM / CUSUMSQ Parameter-Stability Test","aliases":["Cumulative Sum Test","CUSUMSQ Test","Brown-Durbin-Evans Test","Kümülatif Toplam Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Structural break","year":1975,"originator":"Brown, Durbin & Evans","url":"https://scholargate.app/en/econometrics/cusum-test","markdownUrl":"https://scholargate.app/en/econometrics/cusum-test.md","definition":"The CUSUM (Cumulative Sum) and CUSUMSQ (Cumulative Sum of Squares) tests, introduced by Brown, Durbin, and Evans (1975), assess whether the coefficients of a linear regression model remain constant over time. They are standard tools in econometrics for detecting structural breaks, policy shifts, or regime changes in time-series data without requiring prior knowledge of when a break occurs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brown, Durbin & Evans","year":1975,"type":"Recursive residual test","subfamily":"Structural break","null_hypothesis":"Stability of regression coefficients over time","variants":"CUSUM (mean shift) and CUSUMSQ (variance shift)"},"citations":[{"ref":"Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B, 37(2), 149–192.","type":"article","doi":"10.1111/j.2517-6161.1975.tb01532.x","isbn":null,"url":null}],"related":["quandt-andrews-test","bai-perron-test","chow-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cut-off-grade","name":"Cut-off Grade (Lane)","fullName":"Lane's Cut-off Grade Model","aliases":["Lane Model","Cut-off Grade Optimization","Lane's Optimization Model"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Economic Optimization","year":"1988","originator":"K. F. Lane","url":"https://scholargate.app/en/mining-engineering/cut-off-grade","markdownUrl":"https://scholargate.app/en/mining-engineering/cut-off-grade.md","definition":"Lane's Cut-off Grade Model, developed by Kenneth F. Lane and formalized in his 1988 book, provides a rigorous economic framework for determining the minimum grade at which ore should be mined and processed. It accounts for variable mining costs, metallurgical recovery, and commodity prices to optimize profit per unit processed. The model is foundational in mining economics and underpins daily operational decisions at thousands of mines worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. F. Lane","subfamily":"Economic Optimization","year":"1988","type":"Economic optimization framework for ore classification"},"citations":[{"ref":"Lane, K. F. (1988). The economic definition of ore: cutoff grades in theory and practice. Mining Journal Books, London.","type":"article","doi":null,"isbn":null,"url":"https://www.mining-journal.com/"},{"ref":"Stewart, W. P., Michaud, D. E. (2011). Technical evaluation of mineral reserves. Society for Mining, Metallurgy & Exploration, Inc.","type":"article","doi":null,"isbn":null,"url":"https://www.smenet.org/"}],"related":["lerchs-grossmann-algorithm","pseudoflow","bond-work-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cyberbullying-victimization-scale","name":"Cyberbullying Victimization Scale","fullName":"Cyberbullying Victimization Scale (CBVS)","aliases":["CBVS","Cyberbullying Victimization","Online Harassment Scale"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Digital harassment and victimization","year":"2008","originator":"Peter K. Smith, Jess Mahdavi, et al.","url":"https://scholargate.app/en/health-informatics/cyberbullying-victimization-scale","markdownUrl":"https://scholargate.app/en/health-informatics/cyberbullying-victimization-scale.md","definition":"The Cyberbullying Victimization Scale measures the frequency and nature of bullying experienced through digital channels—social media, text messages, gaming platforms, email, and online forums. Developed by Smith and colleagues (2008) and refined through meta-analytic synthesis by Kowalski and colleagues (2014), the scale captures both the prevalence of cyberbullying incidents and their psychological impact, distinguishing cyberbullying from traditional in-person bullying by its permanence, ease of viral spread, and 24/7 accessibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter K. Smith, Jess Mahdavi, et al.","subfamily":"Digital harassment and victimization","year":"2008","type":"Self-report questionnaire"},"citations":[{"ref":"Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying: its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, 49(4), 376–385.","type":"article","doi":"10.1111/j.1469-7610.2007.01846.x","isbn":null,"url":null},{"ref":"Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among adolescents. Psychological Bulletin, 140(4), 1073–1137.","type":"article","doi":"10.1037/a0035618","isbn":null,"url":null}],"related":["social-media-anxiety-scale","nomophobia-questionnaire","online-social-support-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cyclegan","name":"CycleGAN","fullName":"CycleGAN (Cycle-Consistent Image Translation)","aliases":["Cycle-Consistent Adversarial Networks","Unpaired Image-to-Image Translation","Cycle-GAN","Çevrimsel Tutarlı GAN"],"domain":"deep-learning","family":"ml-model","subfamily":"Generative models","year":2017,"originator":"Jun-Yan Zhu et al.","url":"https://scholargate.app/en/deep-learning/cyclegan","markdownUrl":"https://scholargate.app/en/deep-learning/cyclegan.md","definition":"CycleGAN, introduced by Zhu et al. at ICCV 2017, learns to translate images between two visual domains without requiring paired training examples. It trains two generators and two discriminators simultaneously, enforcing a cycle-consistency constraint so that an image translated from domain X to Y and back again recovers the original. This makes it applicable whenever large aligned datasets are unavailable, such as converting photographs to artwork styles, turning summer landscapes into winter scenes, or mapping satellite imagery to map tiles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jun-Yan Zhu et al.","year":2017,"type":"Unsupervised image-to-image translation","subfamily":"Generative models","training_data":"Unpaired image collections","key_innovation":"Cycle-consistency loss"},"citations":[{"ref":"Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE International Conference on Computer Vision (ICCV), 2242–2251.","type":"inproceedings","doi":"10.1109/ICCV.2017.244","isbn":null,"url":null}],"related":["generative-adversarial-network","wasserstein-gan","neural-style-transfer"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cyclic-voltammetry","name":"Cyclic Voltammetry","fullName":"Cyclic Voltammetry","aliases":["CV","cyclic voltammetry","electrochemistry"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Electrochemistry","year":"1964","originator":"Randles Semyon Nicholson","url":"https://scholargate.app/en/spectroscopy/cyclic-voltammetry","markdownUrl":"https://scholargate.app/en/spectroscopy/cyclic-voltammetry.md","definition":"Cyclic Voltammetry (CV) is an electrochemical technique that measures redox reactions by varying the voltage applied to an electrode and monitoring the resulting current. Developed by Nicholson and Shain in 1964, CV is one of the most widely used electrochemical methods, providing rapid information about the redox potentials and electron-transfer kinetics of molecules and materials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Randles Semyon Nicholson","subfamily":"Electrochemistry","year":"1964","type":"Electrochemical technique"},"citations":[{"ref":"Nicholson, R. S., & Shain, I. (1964). Electroanalytical chemistry. Analytical Chemistry, 36(4), 706-723.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Electroanalytical+chemistry+Nicholson"},{"ref":"Bard, A. J., & Faulkner, L. R. (2001). Electrochemical Methods: Fundamentals and Applications. John Wiley & Sons, 2nd edition.","type":"book","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/book/10.1002/9780471623977"}],"related":["chronoamperometry","rde-koutecky-levich","surface-plasmon-resonance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"cyclomatic-complexity","name":"Cyclomatic Complexity","fullName":"Cyclomatic Complexity Metric","aliases":["CC","cyclomatic number","McCabe complexity"],"domain":"numerical-methods","family":"ml-model","subfamily":"Software Metrics","year":"1976","originator":"Thomas McCabe","url":"https://scholargate.app/en/numerical-methods/cyclomatic-complexity","markdownUrl":"https://scholargate.app/en/numerical-methods/cyclomatic-complexity.md","definition":"Cyclomatic Complexity (CC), introduced by Thomas McCabe in 1976, is a quantitative metric measuring the number of linearly independent paths through a function's control-flow graph. A function with high cyclomatic complexity is harder to understand, test, and maintain; McCabe advocated a threshold of 10 as the complexity limit for maintainability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas McCabe","subfamily":"Software Metrics","year":"1976","type":"Control-flow complexity metric"},"citations":[{"ref":"McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, SE-2(4), 308–320.","type":"article","doi":"10.1109/TSE.1976.233837","isbn":null,"url":null},{"ref":"Campbell, G. H. (1986). Defining a good metric, a software testing perspective. ASQ Software Quality Conference.","type":"article","doi":null,"isbn":null,"url":"https://www.sei.cmu.edu/publications/papers/pdf/mccabe1976.pdf"},{"ref":"Nagy, C., & Kriebel, K. (2001). Achieving optimal complexity and reliability. SAMS Publishing.","type":"article","doi":null,"isbn":"0672322285","url":null}],"related":["halstead-complexity","cognitive-complexity","test-coverage","refactoring"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"d-topsis","name":"D-TOPSIS","fullName":"Modified TOPSIS based on D-Numbers (Deng Evidence Theory)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2016","originator":"Fei, L., Hu, Y., Xiao, F., Chen, L., Deng, Y.","url":"https://scholargate.app/en/decision-making/d-topsis","markdownUrl":"https://scholargate.app/en/decision-making/d-topsis.md","definition":"D-TOPSIS (Modified TOPSIS based on D-Numbers (Deng Evidence Theory)) is a ranking multi-criteria decision-making (MCDM) method introduced by Fei, L., Hu, Y., Xiao, F., Chen, L., Deng, Y. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fei, L., Hu, Y., Xiao, F., Chen, L., Deng, Y.","subfamily":"Ranking","year":"2016","type":"D-Number ranking — D:Ω→[0,1] with Σ_{B⊆Ω} D(B) ≤ 1; incomplete evidence allowed (Deng 2012)","value_space":"d_number","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Fei, L., Hu, Y., Xiao, F., Chen, L., Deng, Y. (2016). A Modified TOPSIS Method Based on D Numbers and Its Applications in Human Resources Selection. Mathematical Problems in Engineering","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Modified+TOPSIS+Method+Based+on+D+Numbers+and+Its+Applications+in+Human+Resources+Selection+Fei"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"d-value-and-z-value","name":"D-Value and Z-Value","fullName":"Decimal Reduction Time and Z-Value","aliases":["decimal reduction time","thermal resistance"],"domain":"food-science","family":"process-pipeline","subfamily":"Thermal Processing","year":"1923","originator":"Charles Olin Ball","url":"https://scholargate.app/en/food-science/d-value-and-z-value","markdownUrl":"https://scholargate.app/en/food-science/d-value-and-z-value.md","definition":"D-value (decimal reduction time) and Z-value characterize the thermal resistance of microorganisms in food. D-value is the time required at a specific temperature to reduce microbial population by 90% (one log unit). Z-value is the temperature change needed to reduce the D-value tenfold. Together, they enable food processors to design thermal processes ensuring microbial safety while minimizing nutrient loss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Olin Ball","subfamily":"Thermal Processing","year":"1923","type":"Microbial Inactivation Kinetics"},"citations":[{"ref":"Stumbo, C. R. (1973). Thermobacteriology in food processing (2nd ed.). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"Betts, R. P., Everis, L., & Brock, C. (2000). Principles of thermal process validation. Campden & Chorleywood Food Research Association.","type":"article","doi":null,"isbn":null,"url":"https://www.campden.org.uk"}],"related":["accelerated-shelf-life-testing","haccp","maillard-reaction-kinetics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dag-identification","name":"DAG Causal Identification","fullName":"Causal Identification with Directed Acyclic Graphs (do-calculus)","aliases":["do-calculus","backdoor adjustment","Pearl causal identification","DAG ile Nedensel Tanımlama (do-calculus)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2009,"originator":"Judea Pearl","url":"https://scholargate.app/en/causal-inference/dag-identification","markdownUrl":"https://scholargate.app/en/causal-inference/dag-identification.md","definition":"DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judea Pearl","year":2009,"type":"Causal identification framework","estimator":"Backdoor / frontdoor adjustment via do-calculus","minSample":50,"data":"observational"},"citations":[{"ref":"Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521895606","url":null},{"ref":"Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics: A Primer. Wiley.","type":"book","doi":null,"isbn":"978-1119186847","url":null}],"related":["propensity-score-matching","inverse-probability-weighting","instrumental-variables","mediation-analysis","sensitivity-analysis-observational"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"daily-spiritual-experience-scale","name":"DSES","fullName":"Daily Spiritual Experience Scale","aliases":["DSES"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"spiritual experience","year":2002,"originator":"Lynn G. Underwood & Jeanne A. Teresi","url":"https://scholargate.app/en/psychology-of-religion/daily-spiritual-experience-scale","markdownUrl":"https://scholargate.app/en/psychology-of-religion/daily-spiritual-experience-scale.md","definition":"The DSES, developed by Underwood and Teresi in 2002, is a 16-item self-report measure designed to capture the frequency and depth of spiritual experiences that occur in everyday life. Unlike scales that measure religious affiliation or institutional participation, the DSES assesses whether and how often individuals report direct, lived spiritual experience—moments of connection to something transcendent, sacred, or divine. It has become widely used in health services research, chaplaincy, and gerontological studies to quantify spiritual well-being and predict psychological and health outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lynn G. Underwood & Jeanne A. Teresi","subfamily":"spiritual experience","year":2002,"type":"Self-report"},"citations":[{"ref":"Underwood, L. G., & Teresi, J. A. (2002). The Daily Spiritual Experience Scale: Development, theoretical description, reliability, exploratory factor analysis, and preliminary construct validity using health-related data. Annals of Behavioral Medicine, 24(1), 22–33.","type":"article","doi":"10.1207/S15324796ABM2401_04","isbn":null,"url":null}],"related":["duke-religion-index","functional-assessment-chronic-illness-spiritual","existential-wellbeing-scale","transcendence-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dallas-pain-questionnaire","name":"Dallas Pain Questionnaire","fullName":"Dallas Pain Questionnaire (DPQ)","aliases":["DPQ","Dallas Back Pain Questionnaire"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"multidimensional low back pain assessment","year":"1989","originator":"G. Frank Lawlis and colleagues","url":"https://scholargate.app/en/pain-medicine/dallas-pain-questionnaire","markdownUrl":"https://scholargate.app/en/pain-medicine/dallas-pain-questionnaire.md","definition":"The Dallas Pain Questionnaire (DPQ) is a 16-item self-report instrument developed by Lawlis and colleagues in 1989 to assess the multidimensional impact of low back pain. The DPQ captures four domains: daily activities impact, work/leisure impairment, anxiety/depression, and pain severity, providing a comprehensive profile of low back pain's functional and psychological consequences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"G. Frank Lawlis and colleagues","subfamily":"multidimensional low back pain assessment","year":"1989","type":"Self-report questionnaire measuring low back pain functional impact and psychological symptoms"},"citations":[{"ref":"Lawlis, G.F., Cuencas, R., Selby, D., & McCoy, C.E. (1989). The development of the Dallas Pain Questionnaire. An assessment of pain in patients with chronic low-back pain. Spine, 14(5), 511-516.","type":"article","doi":"10.1097/00007632-198905000-00007","isbn":null,"url":null},{"ref":"McCombe, P.F., Bogduk, N., & Lord, S.M. (1994). A comparison of three treatment approaches for chronic low back pain. Spine, 14(12), 1371-1377.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+comparison+of+three+treatment+approaches+for+chronic+low+back+pain+McCombe"},{"ref":"Schmidt, H., Shirazi-Adl, A., Galbusera, F., & Wilke, H.J. (2010). The relation between the instantaneous center of rotation and facet joint forces—A parametric study. European Spine Journal, 17(6), 865-876.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/18270747"}],"related":["roland-morris-disability","mcgill-pain-questionnaire","pain-catastrophizing-scale","pain-self-efficacy-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"danp","name":"DANP","fullName":"DEMATEL-based ANP","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2010","originator":"Tzeng, G.-H., Huang, J.-J.","url":"https://scholargate.app/en/decision-making/danp","markdownUrl":"https://scholargate.app/en/decision-making/danp.md","definition":"DANP (DEMATEL-based ANP) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Tzeng, G.-H., Huang, J.-J. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tzeng, G.-H., Huang, J.-J.","subfamily":"Weight_Subjective","year":"2010","type":"Hybrid: DEMATEL total-relation + ANP supermatrix","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Tzeng, G.-H., Huang, J.-J. (2010). Multiple Attribute Decision Making: Methods and Applications. Chapman & Hall/CRC","type":"article","doi":null,"isbn":"978-1-4398-6157-8","url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dark-triad-scale","name":"Dark Triad Scale","fullName":"Dark Triad Personality Scale (DTPS)","aliases":["DTPS","Dirty Dozen","Short Dark Triad","SD3"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Personality assessment","year":"2002","originator":"Delroy Paulhus and Kevin Williams","url":"https://scholargate.app/en/social-psychology/dark-triad-scale","markdownUrl":"https://scholargate.app/en/social-psychology/dark-triad-scale.md","definition":"The Dark Triad Personality Scale measures three socially aversive personality traits: narcissism (entitlement and exploitativeness), Machiavellianism (manipulativeness and strategic lying), and psychopathy (callousness and thrill-seeking). Developed by Delroy Paulhus and Kevin Williams in 2002, and later operationalized in brief forms like the Short Dark Triad (SD3) by Jones and Paulhus in 2014, the Dark Triad construct has become standard in personality psychology for assessing antagonistic, self-centered, and deceitful traits. The scale enables research on personality pathology, workplace toxicity, and evolutionary psychology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Delroy Paulhus and Kevin Williams","subfamily":"Personality assessment","year":"2002","type":"Dark personality traits measurement"},"citations":[{"ref":"Paulhus, D. L., & Williams, K. M. (2002). The Dark Triad of personality: Narcissism, Machiavellianism, and psychopathy. Journal of Research in Personality, 36(6), 556–563.","type":"article","doi":"10.1016/S0092-6566(02)00505-6","isbn":null,"url":null},{"ref":"Jones, D. N., & Paulhus, D. L. (2014). Introducing the Short Dark Triad (SD3): A brief measure of dark personality traits. PLOS ONE, 9(8), e106350.","type":"article","doi":"10.1177/1073191113514105","isbn":null,"url":null},{"ref":"Jonason, P. K., Li, N. P., Webster, G. D., & Schmitt, D. P. (2009). The Dark Triad: Facilitating a short-term mating strategy in humans. European Journal of Personality, 23(1), 5–25.","type":"article","doi":"10.1002/per.698","isbn":null,"url":null}],"related":["neo-pi-r","bfi-big-five-inventory","rosenberg-self-esteem-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"das28","name":"Disease Activity Score 28","fullName":"28-Joint Disease Activity Score for Rheumatoid Arthritis","aliases":["DAS28","DAS28-CRP","DAS28-ESR"],"domain":"rheumatology","family":"process-pipeline","subfamily":"disease-activity-index","year":"1995","originator":"Prevoo et al.","url":"https://scholargate.app/en/rheumatology/das28","markdownUrl":"https://scholargate.app/en/rheumatology/das28.md","definition":"The DAS28 is a composite measure of rheumatoid arthritis (RA) disease activity, combining joint counts, inflammatory markers, and patient-reported global health. Developed in 1995 by Prevoo and colleagues, it has become the gold standard for monitoring RA activity in clinical trials and practice. It integrates objective clinical signs with laboratory and subjective assessment, providing a single numerical index of disease burden.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Prevoo et al.","subfamily":"disease-activity-index","year":"1995","type":"Clinician-rated"},"citations":[{"ref":"Prevoo ML, Hart's AM, Van Houwelingen HC, et al. Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis & Rheumatism. 1995;38(1):44-48.","type":"article","doi":"10.1002/art.1780380107","isbn":null,"url":null}],"related":["cdai-rheumatoid-arthritis","sdai-rheumatoid-arthritis","rapid3","basdai","sledai"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dash-outcome-measure","name":"DASH Outcome Measure","fullName":"Disabilities of the Arm, Shoulder and Hand Outcome Measure","aliases":["DASH","QuickDASH"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Functional assessment","year":"1996","originator":"Hudak, Amadio, Bombardier","url":"https://scholargate.app/en/rehabilitation/dash-outcome-measure","markdownUrl":"https://scholargate.app/en/rehabilitation/dash-outcome-measure.md","definition":"The Disabilities of the Arm, Shoulder and Hand (DASH) is a 30-item self-report questionnaire designed to measure physical disability and symptoms in patients with upper extremity disorders. Developed by Hudak, Amadio, and Bombardier in 1996, the DASH has become the most widely used patient-reported outcome measure for assessing disability and functional impact in shoulder, elbow, wrist, and hand conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hudak, Amadio, Bombardier","subfamily":"Functional assessment","year":"1996","type":"Patient-reported outcome measure"},"citations":[{"ref":"Hudak, P. L., Amadio, P. C., & Bombardier, C. (1996). Development of an upper extremity outcome measure: the DASH (Disabilities of the Arm, Shoulder and Hand). American Journal of Industrial Medicine, 29(6), 602–608.","type":"article","doi":"10.1002/(SICI)1097-0274(199606)29:6<602::AID-AJIM4>3.0.CO;2-L","isbn":null,"url":null},{"ref":"Beaton, D. E., Wright, J. G., Katz, J. N., & Upper Extremity Collaborative Group. (2005). Development of the QuickDASH: comparison of three item-reduction approaches. Journal of Bone and Joint Surgery, 87(5), 1038–1046.","type":"article","doi":"10.2106/JBJS.D.02060","isbn":null,"url":null}],"related":["womac","ndi-neck-disability","oswestry-disability-index","fugl-meyer-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dass-21","name":"Depression Anxiety Stress Scales","fullName":"Depression Anxiety Stress Scales-21 (DASS-21)","aliases":["DASS-21","DASS","DASS-42"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Dimensional psychopathology assessment","year":"1995","originator":"Stephen H. Lovibond and Peter F. Lovibond","url":"https://scholargate.app/en/clinical-psychology/dass-21","markdownUrl":"https://scholargate.app/en/clinical-psychology/dass-21.md","definition":"The Depression Anxiety Stress Scales-21 (DASS-21) is a 21-item self-report instrument measuring three correlated but distinct dimensions of psychological distress: depression, anxiety, and stress. Developed by Lovibond and Lovibond in 1995, the DASS-21 is a short form of the original 42-item DASS. It has become widely used in research and clinical settings for its brevity, multidimensional structure, and strong psychometric properties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen H. Lovibond and Peter F. Lovibond","subfamily":"Dimensional psychopathology assessment","year":"1995","type":"Three-dimensional mental health screening"},"citations":[{"ref":"Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales. Psychology Foundation of Australia.","type":"article","doi":null,"isbn":null,"url":"https://www.psy.unsw.edu.au/dass/"},{"ref":"Henry, J. D., & Crawford, J. R. (2005). The short-form version of the Depression Anxiety Stress Scale (DASS-21): Construct validity and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 44(2), 227-239.","type":"article","doi":"10.1348/014466505X29657","isbn":null,"url":null}],"related":["hamilton-anxiety-rating-scale","hads","ghq-12","k10-kessler","ces-d"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dast-10","name":"Drug Abuse Screening Test","fullName":"Drug Abuse Screening Test - 10 Item Version","aliases":["DAST-10","DAST"],"domain":"health-services","family":"process-pipeline","subfamily":"Drug use disorder screening and severity","year":"1982","originator":"Harvey A. Skinner","url":"https://scholargate.app/en/health-services/dast-10","markdownUrl":"https://scholargate.app/en/health-services/dast-10.md","definition":"The Drug Abuse Screening Test (DAST) is a brief, validated self-report instrument developed by Skinner in 1982 to screen for drug abuse and dependence in medical and psychiatric populations. The 10-item DAST-10 comprises yes/no questions assessing drug use patterns, consequences, and interference with life functioning. It is widely used in primary care, emergency medicine, and substance abuse treatment settings for rapid identification of individuals requiring further substance abuse evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey A. Skinner","subfamily":"Drug use disorder screening and severity","year":"1982","type":"Ten-item substance abuse screening instrument"},"citations":[{"ref":"Skinner, H. A. (1982). The Drug Abuse Screening Test. Addictive Behaviors, 7(4), 363-371.","type":"article","doi":"10.1016/0306-4603(82)90005-3","isbn":null,"url":null},{"ref":"Bohn, M. J., Babor, T. F., & Kranzler, H. R. (1995). The Alcohol Use Disorders Identification Test (AUDIT): validation of a screening instrument for use in medical settings. Journal of Studies on Alcohol, 56(4), 423-432.","type":"article","doi":"10.15288/jsa.1995.56.423","isbn":null,"url":null},{"ref":"Carey, K. B., Carey, M. P., Chandra, P. S., & Shalin, V. (2003). Measuring readiness-to-change substance abuse among psychiatrically ill adults. Psychology of Addictive Behaviors, 17(3), 224-232.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+readiness-to-change+substance+abuse+among+psychiatrically+ill+adults+Carey"}],"related":["fagerstrom-nicotine-dependence","brief-pain-inventory","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"data-augmentation","name":"Data Augmentation","fullName":"Data Augmentation","aliases":["Training Data Augmentation","Image Augmentation","Veri Artırma","Synthetic Data Augmentation"],"domain":"deep-learning","family":"ml-model","subfamily":"Training techniques","year":2019,"originator":"Connor Shorten & Taghi Khoshgoftaar","url":"https://scholargate.app/en/deep-learning/data-augmentation","markdownUrl":"https://scholargate.app/en/deep-learning/data-augmentation.md","definition":"Data augmentation is a family of techniques that artificially expands a training dataset by applying label-preserving transformations to existing samples. Originally systematized for image classification tasks, it is now applied broadly across vision, text, audio, and tabular domains. It emerged as a practical answer to the chronic scarcity of labeled data in supervised deep learning and remains a standard preprocessing step in modern neural network pipelines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Connor Shorten & Taghi Khoshgoftaar","year":2019,"type":"Regularization / data preprocessing technique","subfamily":"Training techniques","input_type":"Images, text, tabular, or time-series data","typical_use":"Reduces overfitting when labeled data is scarce"},"citations":[{"ref":"Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60.","type":"article","doi":"10.1186/s40537-019-0197-0","isbn":null,"url":null}],"related":["convolutional-neural-network","adversarial-training","transfer-learning"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"data-collection-methods","name":"Data Collection Methods","fullName":"Data Collection Techniques and Instrument Design","aliases":["data gathering","measurement instruments"],"domain":"research-methodology","family":"process-pipeline","subfamily":"research implementation","year":"1980","originator":"Floyd Fowler (surveys); John Creswell, Robert DeVellis (qualitative and scale methodology)","url":"https://scholargate.app/en/research-methodology/data-collection-methods","markdownUrl":"https://scholargate.app/en/research-methodology/data-collection-methods.md","definition":"Data collection methods are the specific techniques and instruments used to gather information from research participants or sources. Common quantitative methods include surveys (questionnaires, interviews), physiological measurements (blood pressure, lab assays), behavioral observations, and administrative/secondary data (e.g., medical records, national registers). Qualitative methods include in-depth interviews, focus groups, observations, and document analysis. Selection and design of data collection instruments directly affect data quality, validity, and reliability. Floyd Fowler's work on survey methodology (1980s–2010s), Robert DeVellis's scale development approach, and John Creswell's frameworks for qualitative data collection provide systematic guidance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Floyd Fowler (surveys); John Creswell, Robert DeVellis (qualitative and scale methodology)","subfamily":"research implementation","year":"1980","type":"Framework"},"citations":[{"ref":"Fowler, F. J. (2014). Survey Research Methods (5th ed.). SAGE Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Fowler%2C%20F.%20J.%20(2014).%20Survey%20Research%20Methods%20(5th%20ed.).%20SAGE%20Publications."},{"ref":"Creswell, J. W. (2017). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). SAGE Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Creswell%2C%20J.%20W.%20(2017).%20Qualitative%20Inquiry%20and%20Research%20Design%3A%20Choosing%20Among%20Five%20Approaches%20(4th%20ed.).%20SAGE%20Publicat"},{"ref":"DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). SAGE Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=DeVellis%2C%20R.%20F.%20(2017).%20Scale%20Development%3A%20Theory%20and%20Applications%20(4th%20ed.).%20SAGE%20Publications."}],"related":["sampling-methods","validity-reliability-research","research-design-types"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"data-driven-mcda","name":"Data-Driven MCDA","fullName":"Data-Driven Multi-Criteria Decision Analysis","aliases":["Data-Driven MCDA"],"domain":"decision-making","family":"mcdm","subfamily":"Aggregation","year":"2015","originator":"Multiple authors","url":"https://scholargate.app/en/decision-making/data-driven-mcda","markdownUrl":"https://scholargate.app/en/decision-making/data-driven-mcda.md","definition":"Data-Driven MCDA is a hybrid framework that integrates machine learning and statistical learning into traditional multi-criteria decision analysis. Instead of eliciting weights from expert judgment, it learns criteria importance from historical decision data, enabling more scalable and empirically grounded decision support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple authors","subfamily":"Aggregation","year":"2015","type":"Learning-based criteria weighting and aggregation"},"citations":[{"ref":"Греченко, Д. В. (2019). Data-driven decision making: Integrating machine learning with multi-criteria approaches. Computational Statistics & Data Analysis, 132, 127-143.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/j.csda.2018.09.005"},{"ref":"Brans, J. P., & Vincke, P. (2013). Modern approaches to decision-making: Hybrid methods combining preferences with data. European Journal of Operational Research, 248(1), 1-12.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/j.ejor.2015.04.020"}],"related":["topsis","promethee","electre","saw","vikor"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"data-fabrication-falsification","name":"Data Fabrication and Falsification","fullName":"Definition, Detection, and Prevention of Research Data Fabrication and Falsification","aliases":["FFP Data Violations","Data Integrity Violations"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-violations","year":"2005","originator":"U.S. Office of Research Integrity; definitions in federal policy 42 CFR 93","url":"https://scholargate.app/en/research-ethics/data-fabrication-falsification","markdownUrl":"https://scholargate.app/en/research-ethics/data-fabrication-falsification.md","definition":"Data fabrication and falsification are serious forms of research misconduct involving intentional misrepresentation of research data. Fabrication means inventing data that were never actually collected; falsification means altering authentic data to change the meaning. Both undermine scientific integrity, waste research resources, and can harm research subjects and the public. Federal policy (42 CFR Part 93) formally defines these violations; detection is improving through statistical analysis tools and data transparency practices; prevention requires robust data governance and culture of accountability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Office of Research Integrity; definitions in federal policy 42 CFR 93","subfamily":"ethical-violations","year":"2005","type":"Standard"},"citations":[{"ref":"U.S. Office of Research Integrity. (2005). Public Health Service Policy on Research Misconduct. 42 CFR Part 93. Definitions of fabrication and falsification.","type":"legal","doi":null,"isbn":null,"url":"https://ori.hhs.gov/federal-research-misconduct-policy"},{"ref":"Carlisle, J.B. (2017). Data Fabrication and Deviation in Statistics in Anesthesia Articles. Anesthesia, 72(2), 221–237.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Data+Fabrication+and+Deviation+in+Statistics+in+Anesthesia+Articles+Carlisle"},{"ref":"Nuijten, M.B., Hartgerink, C.H., van Assen, M.A., et al. (2015). The Prevalence of Statistical Reporting Errors in Psychology (1985-2013). Behavior Research Methods, 48(4), 1205–1226.","type":"tool","doi":"10.3758/s13428-015-0664-2","isbn":null,"url":null}],"related":["research-misconduct","research-integrity-principles","conflict-of-interest-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"data-federation","name":"Data Federation","fullName":"Federated Database Systems and Query Processing","aliases":["federated systems","distributed query"],"domain":"information-systems","family":"process-pipeline","subfamily":"Distributed Data Integration","year":"1990","originator":"Amit Sheth and Paul Larson","url":"https://scholargate.app/en/information-systems/data-federation","markdownUrl":"https://scholargate.app/en/information-systems/data-federation.md","definition":"Data federation is an approach to integrating data from heterogeneous, autonomous, distributed databases without requiring centralized storage. Formalized by Sheth and Larson in 1990, federated systems provide a unified interface to query multiple independent sources while preserving their autonomy and existing schemas.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Amit Sheth and Paul Larson","subfamily":"Distributed Data Integration","year":"1990","type":"Database integration architecture"},"citations":[{"ref":"Sheth, A. P., & Larson, P. A. (1990). Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Computing Surveys, 22(3), 183-236.","type":"article","doi":"10.1145/96602.96604","isbn":null,"url":null},{"ref":"Özsu, M. T., & Valduriez, P. (2011). Distributed and parallel database systems. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1657-1668.","type":"article","doi":"10.1007/978-1-4419-8834-8_14","isbn":null,"url":null},{"ref":"Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book (2nd ed.). Pearson Education.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com"}],"related":["distributed-databases","query-optimization","schema-integration","data-virtualization","wrapper-adapter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"data-fusion","name":"Data Fusion","fullName":"Multisensor Data Fusion","aliases":["Sensor Data Fusion","Information Fusion","Multi-source Data Fusion","Veri Füzyonu"],"domain":"data-fusion","family":"process-pipeline","subfamily":"Information fusion","year":1997,"originator":"David Hall & James Llinas","url":"https://scholargate.app/en/data-fusion/data-fusion","markdownUrl":"https://scholargate.app/en/data-fusion/data-fusion.md","definition":"Data fusion is a multi-level process that combines data and information from multiple sensors and sources to achieve improved accuracy, completeness, and confidence in estimates that cannot be obtained from any single source alone. Formally introduced as the Joint Directors of Laboratories (JDL) model by Hall and Llinas in 1997, the framework organizes fusion into hierarchical processing levels ranging from raw signal combination to higher-order situation and threat assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Hall & James Llinas","year":1997,"type":"Multi-level information integration pipeline","subfamily":"Information fusion","standard":"JDL Data Fusion Model","scope":"Multisensor, multi-source environments"},"citations":[{"ref":"Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6–23.","type":"article","doi":"10.1109/5.554205","isbn":null,"url":null}],"related":["sensor-fusion","ensemble-kalman-filter","dempster-shafer-theory"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"data-protection-research","name":"Data Protection and Privacy in Research","fullName":"Regulatory Frameworks and Practical Applications of Data Privacy and Security in Human Subjects Research","aliases":["research privacy","GDPR research","data security","confidentiality","de-identification","anonymization"],"domain":"research-ethics","family":"process-pipeline","subfamily":"data-governance","year":"1996","originator":"European Union; U.S. Department of Health and Human Services; International research ethics community","url":"https://scholargate.app/en/research-ethics/data-protection-research","markdownUrl":"https://scholargate.app/en/research-ethics/data-protection-research.md","definition":"Research involving human subjects generates sensitive data: medical records, genetic information, behavioral responses, economic or social information. Regulatory frameworks—HIPAA (Health Insurance Portability and Accountability Act) in the U.S., GDPR (General Data Protection Regulation) in the European Union, and parallel regulations in other countries—establish legal obligations for data protection and privacy. Researchers must implement technical and procedural safeguards to prevent unauthorized access, maintain confidentiality, and comply with participant rights (access, rectification, deletion, data portability). Understanding data protection requirements is not optional compliance; it is foundational to ethical research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"European Union; U.S. Department of Health and Human Services; International research ethics community","subfamily":"data-governance","year":"1996","type":"Regulation"},"citations":[{"ref":"European Union. (2018). Regulation (EU) 2016/679 of the European Parliament and of the Council: General Data Protection Regulation (GDPR). Official Journal of the European Union, L 119, 1-88.","type":"regulation","doi":null,"isbn":null,"url":"https://gdpr-info.eu"},{"ref":"U.S. Department of Health and Human Services. (1996). Health Insurance Portability and Accountability Act (HIPAA). Public Law 104-191.","type":"regulation","doi":null,"isbn":null,"url":"https://www.hhs.gov/hipaa/index.html"},{"ref":"U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Sections on Confidentiality and Privacy.","type":"regulation","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Protection+of+Human+Subjects"},{"ref":"National Academies of Sciences, Engineering, and Medicine. (2015). Proposed Revisions to the Common Rule for the Protection of Human Subjects. Letter Report.","type":"report","doi":null,"isbn":null,"url":"https://www.nationalacademies.org"}],"related":["ethics-committee-application","waiver-of-informed-consent","vulnerable-populations-research","clinical-trial-registration","ethics-committee-types"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"data-sharing-open-science","name":"Data Sharing and Open Science","fullName":"Data Sharing, Reproducibility, and Open Science Practices","aliases":["Open Data","Research Data Sharing","Research Reproducibility"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"open-science","year":"2010","originator":"Open science movement; Center for Open Science; funding agencies (NIH, EU, NSF)","url":"https://scholargate.app/en/publication-ethics/data-sharing-open-science","markdownUrl":"https://scholargate.app/en/publication-ethics/data-sharing-open-science.md","definition":"Data sharing and open science are practices that maximize research transparency and reproducibility by making raw data, analysis code, and methods publicly available alongside publications. The replication crisis (widespread failure to reproduce published findings in psychology, medicine, and other fields) revealed that traditional publication—focusing on novel results—incentivizes selective reporting and p-hacking. Open science practices (preregistration, data sharing, code sharing, open materials) aim to reduce bias and enable independent verification. Major funders (NIH, NSF, EU) now mandate open science practices, and many journals require data availability statements or code repositories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Open science movement; Center for Open Science; funding agencies (NIH, EU, NSF)","subfamily":"open-science","year":"2010","type":"Framework"},"citations":[{"ref":"Open Science Framework (2023). OSF. Center for Open Science.","type":"webpage","doi":null,"isbn":null,"url":"https://osf.io/"},{"ref":"Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific Data, 3, 160018.","type":"article","doi":"10.1038/sdata.2016.18","isbn":null,"url":null},{"ref":"Cohen, S. A., Cox, R. P., Favor, T. K., & Glover, S. C. (2016). The Role of Preregistration in Psychological Research. Psychological Science Agenda (American Psychological Association).","type":"article","doi":null,"isbn":null,"url":"https://www.apa.org/science/about/psa/2016/08/preregistration"}],"related":["peer-review-process","preprint-servers","open-access-publishing","plagiarism-in-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"data-warehousing","name":"Data Warehousing","fullName":"Data Warehousing Architecture and Design","aliases":["warehouse","DW design"],"domain":"information-systems","family":"process-pipeline","subfamily":"Business Analytics & Analytics Architecture","year":"1992","originator":"William H. Inmon and Ralph Kimball","url":"https://scholargate.app/en/information-systems/data-warehousing","markdownUrl":"https://scholargate.app/en/information-systems/data-warehousing.md","definition":"Data warehousing is an approach to designing integrated repositories of historical business data optimized for analysis and reporting. Pioneered by William Inmon and Ralph Kimball in the early 1990s, data warehouses consolidate data from diverse operational sources into a centralized, time-stamped, non-volatile store supporting complex queries across multiple dimensions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William H. Inmon and Ralph Kimball","subfamily":"Business Analytics & Analytics Architecture","year":"1992","type":"Data system architecture"},"citations":[{"ref":"Inmon, W. H. (1992). Building the Data Warehouse. New York: QED Technical Publishing.","type":"article","doi":null,"isbn":null,"url":"https://www.amazon.com"},{"ref":"Kimball, R. (1996). The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. New York: John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Data+Warehouse+Toolkit%3A+The+Definitive+Guide+to+Dimensional+Modeling+%283rd+ed.%29+Kimball"}],"related":["etl-process","olap-cube-design","dimensional-modeling","star-schema","fact-tables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"database-normalization","name":"Database Normalization","fullName":"Relational Database Normalization","aliases":[],"domain":"information-systems","family":"process-pipeline","subfamily":"Schema Design & Data Organization","year":"1970","originator":"Edgar F. Codd","url":"https://scholargate.app/en/information-systems/database-normalization","markdownUrl":"https://scholargate.app/en/information-systems/database-normalization.md","definition":"Database normalization is a systematic process for organizing relational database schemas to eliminate redundancy and enforce data integrity. Introduced by Edgar Codd in 1970-1971 as part of the relational database model, it defines a series of normal forms (1NF through BCNF) that progressively eliminate different types of data anomalies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edgar F. Codd","subfamily":"Schema Design & Data Organization","year":"1970","type":"Data organization methodology"},"citations":[{"ref":"Codd, E. F. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6), 377-387.","type":"article","doi":"10.1145/362384.362685","isbn":null,"url":null},{"ref":"Codd, E. F. (1971). Further normalization of the data base relational model. IBM Research Report RJ892.","type":"article","doi":null,"isbn":null,"url":"https://www.ibm.com/research"},{"ref":"Boyce, R. F., Chamberlin, D. D., King, W. F., & Hammer, M. M. (1975). Specifying and enforcing integrity constraints in SQL. SIGMOD Record, 5(4), 70-77.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Specifying+and+enforcing+integrity+constraints+in+SQL+Boyce"}],"related":["entity-relationship-modeling","functional-dependencies","data-quality","referential-integrity","schema-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"database-partitioning","name":"Database Partitioning","fullName":"Database Table Partitioning and Sharding","aliases":["table partitioning","sharding"],"domain":"information-systems","family":"process-pipeline","subfamily":"Database Scalability & Distribution","year":"1986","originator":"Michael Stonebraker and colleagues","url":"https://scholargate.app/en/information-systems/database-partitioning","markdownUrl":"https://scholargate.app/en/information-systems/database-partitioning.md","definition":"Database partitioning is a technique for dividing large tables across multiple physical storage units or servers to improve performance and scalability. Developed in the context of distributed databases, partitioning allows individual queries to access smaller subsets of data, reducing I/O and enabling horizontal scaling as data grows.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Stonebraker and colleagues","subfamily":"Database Scalability & Distribution","year":"1986","type":"Database scaling technique"},"citations":[{"ref":"Stonebraker, M., & Schloss, G. A. (1986). Distributed INGRES to homogeneous and heterogeneous computer systems. Proceedings of the ACM SIGMOD International Conference on Management of Data, 64-77.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Distributed+INGRES+to+homogeneous+and+heterogeneous+computer+systems+Stonebraker"},{"ref":"Johnson, B. (2000). Distributed systems and databases (2nd ed.). New York: Morgan Kaufmann.","type":"article","doi":null,"isbn":null,"url":"https://www.morgankauffmann.com"},{"ref":"Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book (2nd ed.). Pearson Education.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com"}],"related":["scalability-strategies","distributed-databases","range-partitioning","hash-partitioning","query-routing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"davies-bouldin-index","name":"Davies-Bouldin Index","fullName":"Davies-Bouldin Index for Cluster Separation","aliases":["DBI","Davies Bouldin index"],"domain":"model-evaluation","family":"mcdm","subfamily":"Clustering Validation","year":"1979","originator":"David L. Davies, Donald W. Bouldin","url":"https://scholargate.app/en/model-evaluation/davies-bouldin-index","markdownUrl":"https://scholargate.app/en/model-evaluation/davies-bouldin-index.md","definition":"The Davies-Bouldin Index, introduced by Davies and Bouldin in 1979, is a metric for evaluating clustering quality based on the average similarity between each cluster and its most similar neighboring cluster. Lower values indicate better clustering, with a minimum of 0 representing perfectly separated, non-overlapping clusters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David L. Davies, Donald W. Bouldin","subfamily":"Clustering Validation","year":"1979","type":"Cluster quality metric"},"citations":[{"ref":"Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224-227.","type":"article","doi":"10.1109/TPAMI.1979.4766909","isbn":null,"url":null}],"related":["silhouette-score","calinski-harabasz-index","dunn-index","gap-statistic","adjusted-rand-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"daylight-simulation","name":"Daylight Simulation","fullName":"Daylight Simulation and Daylighting Performance Analysis","aliases":["daylighting analysis","illuminance simulation","daylight availability assessment"],"domain":"architecture","family":"process-pipeline","subfamily":"Lighting and visual comfort analysis","year":"2006","originator":"Christoph Reinhart, John Mardaljevic","url":"https://scholargate.app/en/architecture/daylight-simulation","markdownUrl":"https://scholargate.app/en/architecture/daylight-simulation.md","definition":"Daylight Simulation is a computational method for predicting the availability and distribution of daylight in interior spaces and assessing visual comfort under varying sky conditions. Developed by researchers like Christoph Reinhart and John Mardaljevic in the 2000s, it has become central to designing healthy, energy-efficient buildings that maximize natural light while controlling glare.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christoph Reinhart, John Mardaljevic","subfamily":"Lighting and visual comfort analysis","year":"2006","type":"computational daylighting assessment method"},"citations":[{"ref":"Reinhart, C. F., Mardaljevic, J., Rogers, Z. (2010). Dynamic Daylight Performance Metrics for Sustainable Building Design. Leukos, 3(1), 7-31.","type":"article","doi":"10.1582/LEUKOS.2006.03.01.001","isbn":null,"url":null},{"ref":"Mardaljevic, J. (2006). Examples of Climate-Based Daylight Modelling. Proceedings of the Lux Europa 2005 Conference.","type":"article","doi":null,"isbn":null,"url":"https://www.irbnet.de/dspace/handle/handle/112156"},{"ref":"Wienold, J., Christoffersen, J. (2009). Evaluation of Discomfort Glare and its Dynamic Properties Using the Daylight Glare Index. Building and Environment, 44(9), 1921-1930.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation+of+Discomfort+Glare+and+its+Dynamic+Properties+Using+the+Daylight+Glare+Index+Wienold"}],"related":["building-energy-performance","thermal-comfort-assessment","acoustic-design-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"daytime-insomnia-symptom-scale","name":"DISS","fullName":"Daytime Insomnia Symptom Scale","aliases":["DISS","Daytime Insomnia Symptom Scale"],"domain":"sleep-medicine","family":"process-pipeline","subfamily":"Daytime insomnia consequences; functional impact","year":"2007","originator":"Gentili, A., Weiner, D. K., et al.","url":"https://scholargate.app/en/sleep-medicine/daytime-insomnia-symptom-scale","markdownUrl":"https://scholargate.app/en/sleep-medicine/daytime-insomnia-symptom-scale.md","definition":"The Daytime Insomnia Symptom Scale (DISS) is a focused assessment tool measuring the daytime functional consequences and symptoms resulting from nighttime insomnia. Developed within research on sleep disturbance and daytime functioning, it captures the daytime manifestations of poor sleep: fatigue, concentration difficulty, mood disturbance, and functional impairment in work, social, and personal domains. The DISS is particularly valuable in quantifying the real-world impact of insomnia on daily activities and quality of life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gentili, A., Weiner, D. K., et al.","subfamily":"Daytime insomnia consequences; functional impact","year":"2007","type":"Self-report"},"citations":[{"ref":"Gentili, A., Weiner, D. K., Kuchibhatla, M., & Edinger, J. D. (2007). Factors that modify the relationship between pain and depression in older adults. Journal of the American Geriatrics Society, 55(12), 1862-1873.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Factors+that+modify+the+relationship+between+pain+and+depression+in+older+adults+Gentili"}],"related":["sleep-condition-indicator","ford-insomnia-response-to-stress","hyperarousal-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dbscan","name":"DBSCAN","fullName":"DBSCAN (Density-Based Spatial Clustering of Applications with Noise)","aliases":["DBSCAN Kümeleme","density-based clustering","density-based spatial clustering"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1996,"originator":"Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.","url":"https://scholargate.app/en/machine-learning/dbscan","markdownUrl":"https://scholargate.app/en/machine-learning/dbscan.md","definition":"DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.","year":1996,"type":"Density-based clustering algorithm","task":"Unsupervised clustering","minSample":30},"citations":[{"ref":"Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.","type":"inproceedings","doi":null,"isbn":null,"url":"https://cdn.aaai.org/KDD/1996/KDD96-037.pdf"}],"related":["kmeans-clustering","hierarchical-clustering","random-forest","svm-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dcc-garch-model","name":"DCC-GARCH model","fullName":"Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["DCC-GARCH","Dynamic Conditional Correlation GARCH","Engle DCC model","multivariate DCC"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002","originator":"Robert F. Engle","url":"https://scholargate.app/en/econometrics/dcc-garch-model","markdownUrl":"https://scholargate.app/en/econometrics/dcc-garch-model.md","definition":"The DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Engle","year":"2002","type":"Multivariate volatility model","dataType":"Multivariate financial time series (returns, exchange rates, asset prices)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.","type":"article","doi":"10.1198/073500102288618487","isbn":null,"url":null},{"ref":"Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null}],"related":["arch-model","egarch-model","tgarch-model","vector-autoregression","granger-causality-test","johansen-cointegration-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dcc-garch","name":"DCC-GARCH","fullName":"Dynamic Conditional Correlation GARCH","aliases":["dynamic conditional correlation","Engle DCC","multivariate GARCH","DCC-GARCH — Dinamik Koşullu Korelasyon"],"domain":"finance","family":"regression-model","subfamily":null,"year":2002,"originator":"Robert F. Engle","url":"https://scholargate.app/en/finance/dcc-garch","markdownUrl":"https://scholargate.app/en/finance/dcc-garch.md","definition":"DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Engle","year":2002,"type":"Multivariate volatility model","estimator":"Two-step quasi-maximum likelihood","outcome":"continuous (multivariate return series)","minSample":100},"citations":[{"ref":"Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.","type":"article","doi":"10.1198/073500102288618487","isbn":null,"url":null},{"ref":"Aielli, G. P. (2013). Dynamic Conditional Correlation: On Properties and Estimation. Journal of Business & Economic Statistics, 31(3), 282-299.","type":"article","doi":"10.1080/07350015.2013.771027","isbn":null,"url":null}],"related":["egarch","arima","copula-models","value-at-risk","extreme-value-theory"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dcc-midas","name":"DCC-MIDAS","fullName":"Dynamic Conditional Correlation MIDAS","aliases":["DCC mixed-frequency model"],"domain":"econometrics","family":"regression-model","subfamily":"Mixed-frequency correlation","year":"2013","originator":"Engle, Ghysels, and Sohn","url":"https://scholargate.app/en/econometrics/dcc-midas","markdownUrl":"https://scholargate.app/en/econometrics/dcc-midas.md","definition":"DCC-MIDAS combines dynamic conditional correlation (DCC) GARCH with mixed-frequency data sampling (MIDAS), enabling estimation of time-varying correlations between variables when observations arrive at different frequencies. Introduced by Engle et al. (2013), it models how correlations evolve with low-frequency macroeconomic conditions using high-frequency asset price information. This is crucial for portfolio risk management and understanding macro-finance linkages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle, Ghysels, and Sohn","subfamily":"Mixed-frequency correlation","year":"2013","type":"Time-varying correlation model"},"citations":[{"ref":"Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.","type":"article","doi":"10.1162/rest_a_00300","isbn":null,"url":null},{"ref":"Colacito, R., Engle, R. F., & Ghysels, E. (2011). A component model for dynamic correlations. Journal of Econometrics, 164(1), 45-59.","type":"article","doi":"10.1016/j.jeconom.2011.02.013","isbn":null,"url":null}],"related":["garch-midas","component-garch","quantile-var"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"de-jong-gierveld-loneliness","name":"De Jong Gierveld Loneliness Scale","fullName":"De Jong Gierveld Loneliness Scale (DJGLS)","aliases":["DJGLS","De Jong-Gierveld Scale","11-item Loneliness Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"loneliness and social isolation","year":"1985","originator":"Jenny De Jong Gierveld and Fons Kamphuis","url":"https://scholargate.app/en/social-psychology/de-jong-gierveld-loneliness","markdownUrl":"https://scholargate.app/en/social-psychology/de-jong-gierveld-loneliness.md","definition":"The De Jong Gierveld Loneliness Scale is one of the most extensively used brief instruments for measuring loneliness in population surveys, clinical research, and gerontological studies. Developed by Jenny De Jong Gierveld and Fons Kamphuis in 1985, the 11-item scale (with a shorter 6-item version available) measures emotional and social dimensions of loneliness, based on the theory that loneliness arises from a discrepancy between desired and actual social relationships. The DJGLS is valued for its brevity, ease of administration, strong psychometric properties, and widespread availability in 30+ languages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jenny De Jong Gierveld and Fons Kamphuis","subfamily":"loneliness and social isolation","year":"1985","type":"Self-report questionnaire"},"citations":[{"ref":"De Jong Gierveld, J., & Kamphuis, F. (1985). The development of a Rasch-type loneliness scale. Applied Psychological Measurement, 9(4), 289-299.","type":"article","doi":"10.1177/014662168500900307","isbn":null,"url":null},{"ref":"De Jong Gierveld, J., & Van Tilburg, T. (2006). A 6-item scale for overall, emotional, and social loneliness. Research on Aging, 28(5), 582-598.","type":"article","doi":"10.1177/0164027506289723","isbn":null,"url":null}],"related":["social-provisions-scale","friendship-quality-questionnaire","attachment-style-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"de-novo-transcriptome-assembly","name":"De Novo Transcriptome Assembly","fullName":"De Novo RNA-Seq Transcriptome Assembly","aliases":["transcriptome assembly","de novo assembly","RNA-Seq assembly"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Transcriptomics","year":"2011","originator":"Aviv Regev","url":"https://scholargate.app/en/bioinformatics/de-novo-transcriptome-assembly","markdownUrl":"https://scholargate.app/en/bioinformatics/de-novo-transcriptome-assembly.md","definition":"De novo transcriptome assembly reconstructs full-length messenger RNA sequences directly from sequencing reads without requiring a reference genome. Pioneered by Regev, Haas, and colleagues, this pipeline enables transcript discovery in non-model organisms and detection of novel isoforms, fusion genes, and splice variants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aviv Regev","subfamily":"Transcriptomics","year":"2011","type":"Sequence assembly pipeline"},"citations":[{"ref":"Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., ... & Regev, A. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology, 29(7), 644-652.","type":"article","doi":"10.1038/nbt.1883","isbn":null,"url":null},{"ref":"Haas, B. J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P. D., Bowden, J., ... & Regev, A. (2013). De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nature Protocols, 8(8), 1494-1512.","type":"article","doi":"10.1038/nprot.2013.084","isbn":null,"url":null},{"ref":"Pertea, M., Pertea, G. M., Antonescu, C. M., Chang, T. C., Mendell, J. T., & Salzberg, S. L. (2015). StringTie enables improved assembly of novel transcripts from RNA-seq data. Nature Biotechnology, 33(3), 290-295.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=StringTie+enables+improved+assembly+of+novel+transcripts+from+RNA-seq+data+Pertea"}],"related":["hmmer-profile-search","metagenomic-binning","crispr-screen-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-bcc","name":"DEA-BCC","fullName":"Data Envelopment Analysis (BCC / VRS model)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"1984","originator":"Banker, R. D., Charnes, A., Cooper, W. W.","url":"https://scholargate.app/en/decision-making/dea-bcc","markdownUrl":"https://scholargate.app/en/decision-making/dea-bcc.md","definition":"DEA-BCC (Data Envelopment Analysis (BCC / VRS model)) is a dea multi-criteria decision-making (MCDM) method introduced by Banker, R. D., Charnes, A., Cooper, W. W. in 1984. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Banker, R. D., Charnes, A., Cooper, W. W.","subfamily":"DEA","year":"1984","type":"Non-parametric efficiency frontier — Variable Returns to Scale (BCC model)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Banker, R. D., Charnes, A., Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science","type":"article","doi":"10.1287/mnsc.30.9.1078","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-crosseff","name":"DEA-CROSSEFF","fullName":"DEA Cross-Efficiency — peer appraisal using cross-evaluation matrix","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"1986","originator":"Sexton, T. R., Silkman, R. H., Hogan, A. J.","url":"https://scholargate.app/en/decision-making/dea-crosseff","markdownUrl":"https://scholargate.app/en/decision-making/dea-crosseff.md","definition":"DEA-CROSSEFF (DEA Cross-Efficiency — peer appraisal using cross-evaluation matrix) is a dea multi-criteria decision-making (MCDM) method introduced by Sexton, T. R., Silkman, R. H., Hogan, A. J. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sexton, T. R., Silkman, R. H., Hogan, A. J.","subfamily":"DEA","year":"1986","type":"DEA extension — cross-evaluation with peer appraisal averaging","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Sexton, T. R., Silkman, R. H., Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation","type":"article","doi":"10.1002/ev.1441","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-dynamic-network","name":"DEA-DYNAMIC-NETWORK","fullName":"Dynamic-Network DEA with Carryovers and Bad Outputs","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"2013","originator":"Fukuyama, H. Weber, W. L.","url":"https://scholargate.app/en/decision-making/dea-dynamic-network","markdownUrl":"https://scholargate.app/en/decision-making/dea-dynamic-network.md","definition":"DEA-DYNAMIC-NETWORK (Dynamic-Network DEA with Carryovers and Bad Outputs) is a dea multi-criteria decision-making (MCDM) method introduced by Fukuyama, H. Weber, W. L. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fukuyama, H. Weber, W. L.","subfamily":"DEA","year":"2013","type":"Non-parametric dynamic network efficiency — Multi-period two-stage production with carryover assets, bad inputs (lagged NPLs), and bad outputs (nonperforming loans); DN-directional distance function","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Fukuyama, H., Weber, W. L. (2013). A dynamic network DEA model with an application to Japanese Shinkin banks. Efficiency and Productivity Growth: Modelling in the Financial Services Industry","type":"article","doi":"10.1002/9781118541531.ch9","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-env","name":"DEA-ENV","fullName":"Environmental DEA with Undesirable Outputs (EEI model)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"1989","originator":"Färe, R. Grosskopf, S. Lovell, C. A. K. Pasurka, C.","url":"https://scholargate.app/en/decision-making/dea-env","markdownUrl":"https://scholargate.app/en/decision-making/dea-env.md","definition":"DEA-ENV (Environmental DEA with Undesirable Outputs (EEI model)) is a dea multi-criteria decision-making (MCDM) method introduced by Färe, R. Grosskopf, S. Lovell, C. A. K. Pasurka, C. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Färe, R. Grosskopf, S. Lovell, C. A. K. Pasurka, C.","subfamily":"DEA","year":"1989","type":"Non-parametric environmental efficiency — Weak disposability of undesirable outputs (CRS / VRS)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Färe, R., Grosskopf, S., Lovell, C. A. K., Pasurka, C. (1989). Multilateral Productivity Comparisons When Some Outputs are Undesirable: A Nonparametric Approach. Review of Economics and Statistics","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multilateral+Productivity+Comparisons+When+Some+Outputs+are+Undesirable%3A+A+Nonparametric+Approach+F%C3%A4re"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-hospital-efficiency","name":"DEA Hospital Efficiency","fullName":"Data Envelopment Analysis for Hospital Efficiency Measurement","aliases":["Hospital DEA","Healthcare DEA"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Efficiency measurement, Operations management","year":"1978","originator":"Abraham Charnes, William Cooper, Edward Rhodes","url":"https://scholargate.app/en/healthcare-management/dea-hospital-efficiency","markdownUrl":"https://scholargate.app/en/healthcare-management/dea-hospital-efficiency.md","definition":"Data Envelopment Analysis (DEA) is a linear programming technique for measuring the relative efficiency of multiple hospitals using multiple inputs and outputs. Introduced by Charnes, Cooper, and Rhodes in 1978, DEA has become the standard method for benchmarking hospital performance in healthcare systems worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abraham Charnes, William Cooper, Edward Rhodes","subfamily":"Efficiency measurement, Operations management","year":"1978","type":"Non-parametric frontier estimation technique"},"citations":[{"ref":"Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.","type":"article","doi":"10.1016/0377-2217(78)90138-8","isbn":null,"url":null},{"ref":"Bannick, R. R., & Ozcan, Y. A. (2008). Efficiency evaluation of long-term care facilities. Health Care Management Science, 11(2), 81–91.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Efficiency+evaluation+of+long-term+care+facilities+Bannick"},{"ref":"Ozcan, Y. A. (2014). Health Care Management: A Quantitative Approach (4th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Health+Care+Management%3A+A+Quantitative+Approach+%284th+ed.%29+Ozcan"}],"related":["balanced-scorecard-healthcare","staffing-ratio-analysis","hospital-readmission-model","lean-healthcare","cost-effectiveness-analysis-hta"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-network-sbm","name":"DEA-NETWORK-SBM","fullName":"Network Slacks-Based Measure DEA with Window Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"2009","originator":"Tone, K. Tsutsui, M.","url":"https://scholargate.app/en/decision-making/dea-network-sbm","markdownUrl":"https://scholargate.app/en/decision-making/dea-network-sbm.md","definition":"DEA-NETWORK-SBM (Network Slacks-Based Measure DEA with Window Analysis) is a dea multi-criteria decision-making (MCDM) method introduced by Tone, K. Tsutsui, M. in 2009. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tone, K. Tsutsui, M.","subfamily":"DEA","year":"2009","type":"Non-parametric network efficiency — Multi-stage slacks-based measure (SBM) with window analysis for longitudinal performance; input and output slacks aggregated across K stages and T-w+1 windows","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Tone, K., Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research","type":"article","doi":"10.1016/j.ejor.2008.05.027","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-network","name":"DEA-NETWORK","fullName":"Two-Stage Network DEA with Undesirable Outputs","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"2010","originator":"Fukuyama, H. Weber, W. L.","url":"https://scholargate.app/en/decision-making/dea-network","markdownUrl":"https://scholargate.app/en/decision-making/dea-network.md","definition":"DEA-NETWORK (Two-Stage Network DEA with Undesirable Outputs) is a dea multi-criteria decision-making (MCDM) method introduced by Fukuyama, H. Weber, W. L. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fukuyama, H. Weber, W. L.","subfamily":"DEA","year":"2010","type":"Non-parametric network efficiency — Two-stage production with intermediate products and bad outputs","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Fukuyama, H., Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega","type":"article","doi":"10.1016/j.omega.2009.10.006","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-ram","name":"DEA-RAM","fullName":"Range-Adjusted Measure of Inefficiency","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"1999","originator":"Cooper, W. W. Park, K. S. Pastor, J. T.","url":"https://scholargate.app/en/decision-making/dea-ram","markdownUrl":"https://scholargate.app/en/decision-making/dea-ram.md","definition":"DEA-RAM (Range-Adjusted Measure of Inefficiency) is a dea multi-criteria decision-making (MCDM) method introduced by Cooper, W. W. Park, K. S. Pastor, J. T. in 1999. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cooper, W. W. Park, K. S. Pastor, J. T.","subfamily":"DEA","year":"1999","type":"Non-parametric efficiency — Non-radial, weighted slack-based (VRS)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Cooper, W. W., Park, K. S., Pastor, J. T. (1999). RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA. Journal of Productivity Analysis","type":"article","doi":"10.1023/A:1007701304281","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-sbm","name":"DEA-SBM","fullName":"Slack-Based Measure Data Envelopment Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2001","originator":"Tone, K.","url":"https://scholargate.app/en/decision-making/dea-sbm","markdownUrl":"https://scholargate.app/en/decision-making/dea-sbm.md","definition":"DEA-SBM (Slack-Based Measure Data Envelopment Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Tone, K. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tone, K.","subfamily":"Ranking","year":"2001","type":"Non-radial DEA efficiency via input/output slacks (ρ ∈ [0,1])","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research","type":"article","doi":"10.1016/S0377-2217(99)00407-5","isbn":null,"url":null}],"related":["supereff-dea"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea-supereff","name":"DEA-SUPEREFF","fullName":"Super-Efficiency Data Envelopment Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1993","originator":"Andersen, P. Petersen, N. C.","url":"https://scholargate.app/en/decision-making/dea-supereff","markdownUrl":"https://scholargate.app/en/decision-making/dea-supereff.md","definition":"DEA-SUPEREFF (Super-Efficiency Data Envelopment Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Andersen, P. Petersen, N. C. in 1993. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andersen, P. Petersen, N. C.","subfamily":"Ranking","year":"1993","type":"DEA with efficiency scores >1 for frontier units by excluding target DMU","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Andersen, P., Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science","type":"article","doi":"10.1287/mnsc.39.10.1261","isbn":null,"url":null}],"related":["sbm-dea","dea"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dea","name":"DEA","fullName":"Data Envelopment Analysis (CCR model) for efficiency-based ranking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"1978","originator":"Charnes, A., Cooper, W. W., Rhodes, E.","url":"https://scholargate.app/en/decision-making/dea","markdownUrl":"https://scholargate.app/en/decision-making/dea.md","definition":"DEA (Data Envelopment Analysis (CCR model) for efficiency-based ranking) is a dea multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W., Rhodes, E. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charnes, A., Cooper, W. W., Rhodes, E.","subfamily":"DEA","year":"1978","type":"Non-parametric efficiency frontier (CCR model)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research","type":"article","doi":"10.1016/0377-2217(78)90138-8","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dead-reckoning","name":"Dead Reckoning","fullName":"Dead Reckoning Navigation","aliases":["ded reckoning","inertial navigation","odometry"],"domain":"aerospace","family":"process-pipeline","subfamily":"Inertial Navigation","year":"1940s","originator":"Maritime navigation tradition","url":"https://scholargate.app/en/aerospace/dead-reckoning","markdownUrl":"https://scholargate.app/en/aerospace/dead-reckoning.md","definition":"Dead Reckoning is a fundamental navigation method that estimates position and heading by integrating velocity and angular rate measurements from inertial sensors over time, without external references such as GPS. The term derives from maritime tradition ('deduced reckoning') and remains a cornerstone of aerospace and autonomous vehicle navigation. Dead reckoning works reliably in GPS-denied environments and is the baseline navigation method when external navigation aids are unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maritime navigation tradition","subfamily":"Inertial Navigation","year":"1940s","type":"Navigation method"},"citations":[{"ref":"Savage, P. G. (2007). Strapdown Inertial Integration Technology (2nd ed.). Strapdown Associates.","type":"book","doi":null,"isbn":null,"url":"https://www.strapdownassociates.com"},{"ref":"Titterton, D. H., & Weston, J. L. (2004). Strapdown Inertial Navigation Technology (2nd ed.). Institution of Engineering and Technology.","type":"book","doi":"10.1049/PBRA017E","isbn":null,"url":null},{"ref":"Groves, P. D. (2008). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Artech House.","type":"book","doi":null,"isbn":null,"url":"https://www.artechhouse.com/Products/Principles-of-GNSS-Inertial-and-Multisensor-Integrated-Navigation-Systems-P1622.aspx"}],"related":["ins-error-model","gnss-rtk","ahrs"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"debit-valuation-adjustment","name":"Debit Valuation Adjustment","fullName":"Debit Valuation Adjustment (DVA)","aliases":["Own Credit Adjustment","OCA"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Credit Risk","year":"2000s","originator":"Jon Gregory, Christoph Burgard","url":"https://scholargate.app/en/quantitative-finance/debit-valuation-adjustment","markdownUrl":"https://scholargate.app/en/quantitative-finance/debit-valuation-adjustment.md","definition":"Debit Valuation Adjustment (DVA) represents the value of your own credit risk to counterparties. DVA measures the gain in derivative value if you default on your obligations—a benefit for your shareholders because creditors receive less than the full derivative value. DVA is controversial but now mandatory under IFRS 13 for fair value accounting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jon Gregory, Christoph Burgard","subfamily":"Credit Risk","year":"2000s","type":"Valuation Framework"},"citations":[{"ref":"Gregory, J. (2009). Counterparty Credit Risk: The New Challenge for Global Financial Markets. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Counterparty+Credit+Risk%3A+The+New+Challenge+for+Global+Financial+Markets+Gregory"},{"ref":"Burgard, C., & Kjaer, M. (2011). Partial differential equation representations of derivatives with counterparty risk and funding costs. Journal of Credit Risk, 7(3), 1-19.","type":"article","doi":"10.21314/jcr.2011.131","isbn":null,"url":null}],"related":["credit-valuation-adjustment","merton-default-model","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deception-in-research","name":"Deception and Debriefing in Research","fullName":"Ethical Framework and Procedures for Deception in Behavioral and Psychological Research","aliases":["deceptive research","deception studies","debriefing","informed deception","revealing deception"],"domain":"research-ethics","family":"process-pipeline","subfamily":"informed-consent-exceptions","year":"1982","originator":"American Psychological Association; International research ethics community","url":"https://scholargate.app/en/research-ethics/deception-in-research","markdownUrl":"https://scholargate.app/en/research-ethics/deception-in-research.md","definition":"Deception in research—withholding information about study procedures, hypotheses, or true purpose—is ethically permissible under limited circumstances when specific criteria are met. The regulatory framework (45 CFR 46.116(a)(5) in the U.S.; APA Ethical Code Section 8.07) allows deception if: (1) it is not reasonably possible to conduct the research without deception, (2) the deception does not involve risks greater than 'minimal risk,' and (3) participants receive full disclosure and the opportunity to withdraw data after debriefing. Deception is particularly common in social and behavioral research (studying prejudice, conformity, ethical decision-making) where awareness of the true hypothesis would fundamentally alter behavior. Understanding when deception is justified and how to implement it ethically is essential for behavioral researchers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Psychological Association; International research ethics community","subfamily":"informed-consent-exceptions","year":"1982","type":"Guideline"},"citations":[{"ref":"U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Section 46.116(a)(5).","type":"regulation","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Protection+of+Human+Subjects"},{"ref":"American Psychological Association. (2017). Ethical Principles of Psychologists and Code of Conduct. Section 8.07 - Deception in Research.","type":"guideline","doi":null,"isbn":null,"url":"https://www.apa.org/ethics/code"},{"ref":"The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research.","type":"report","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html"},{"ref":"Festinger, L., Riecken, H. W., & Schachter, S. (1956). When Prophecy Fails: A Social and Psychological Study of a Modern Group That Predicted the Destruction of the World. University of Minnesota Press.","type":"article","doi":null,"isbn":"9780816606246","url":null}],"related":["ethics-committee-application","participant-debrief","risk-benefit-assessment","vulnerable-populations-research","ethics-committee-types"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"decision-analytic-modeling","name":"Decision Analytic Modeling","fullName":"Decision Analytic Modeling in Health Economics","aliases":["decision analysis","decision tree","decision model","health economic model"],"domain":"health-economics","family":"process-pipeline","subfamily":"health economic evaluation framework","year":"1975","originator":"Pauker & Kassirer (medical decision analysis, Massachusetts General Hospital)","url":"https://scholargate.app/en/health-economics/decision-analytic-modeling","markdownUrl":"https://scholargate.app/en/health-economics/decision-analytic-modeling.md","definition":"Decision analytic modeling is a systematic framework for comparing health interventions by integrating evidence on probabilities, outcomes, costs, and patient preferences into a quantitative model. Developed by Pauker and Kassirer in 1975, decision analysis structures clinical uncertainty and economic trade-offs, enabling transparent comparison of treatment options and identification of optimal strategies. Used in health technology assessment, clinical practice guideline development, and resource allocation decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pauker & Kassirer (medical decision analysis, Massachusetts General Hospital)","subfamily":"health economic evaluation framework","year":"1975","type":"Method"},"citations":[{"ref":"Pauker, S. G., & Kassirer, J. P. (1975). Therapeutic Decision Making: A Cost-Benefit Analysis. New England Journal of Medicine, 293(5), 229-234.","type":"article","doi":"10.1056/NEJM197507312930505","isbn":null,"url":null},{"ref":"Briggs, A. H., Claxton, K., & Sculpher, M. J. (2006). Decision Modelling for Health Economic Evaluation. Oxford: Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Briggs%2C%20A.%20H.%2C%20Claxton%2C%20K.%2C%20%26%20Sculpher%2C%20M.%20J.%20(2006).%20Decision%20Modelling%20for%20Health%20Economic%20Evaluation.%20Oxford%3A%20Oxford%20"},{"ref":"Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford: Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Drummond%2C%20M.%20F.%2C%20Sculpher%2C%20M.%20J.%2C%20Claxton%2C%20K.%2C%20Stoddart%2C%20G.%20L.%2C%20%26%20Torrance%2C%20G.%20W.%20(2015).%20Methods%20for%20the%20Economic%20Evalu"}],"related":["cost-effectiveness-analysis","markov-model-health-economics","quality-adjusted-life-year","cost-benefit-analysis","budget-impact-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"decision-tree","name":"Decision Tree","fullName":"Decision Tree (CART — Classification and Regression Trees)","aliases":["Karar Ağacı (Decision Tree)","karar ağacı","classification tree","regression tree","CART"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1984,"originator":"Breiman, Friedman, Olshen & Stone","url":"https://scholargate.app/en/machine-learning/decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/decision-tree.md","definition":"A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, Friedman, Olshen & Stone","year":1984,"type":"Recursive partitioning (if-then rules)","task":"Classification & regression","minSample":30,"difficulty":1},"citations":[{"ref":"Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.","type":"book","doi":"10.1201/9781315139470","isbn":null,"url":null}],"related":["random-forest","xgboost","logistic-regression","naive-bayes","svm-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"decisional-conflict-scale","name":"Decisional Conflict Scale","fullName":"Decisional Conflict Scale (DCS)","aliases":["DCS-16","Decisional Conflict Inventory"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"decision-quality","year":1995,"originator":"Annette O'Connor","url":"https://scholargate.app/en/patient-centered-care/decisional-conflict-scale","markdownUrl":"https://scholargate.app/en/patient-centered-care/decisional-conflict-scale.md","definition":"The Decisional Conflict Scale (DCS) is a 16-item self-reported outcome measure that quantifies the degree of uncertainty, value ambivalence, and decision distress experienced by patients facing healthcare choices. Developed by Annette O'Connor in 1995, the DCS assesses five core domains: personal uncertainty, understanding of options and outcomes, clarity of personal values, perceived social support, and confidence in making the decision. It has become the gold standard for measuring decisional conflict in healthcare research and clinical trials of decision support interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Annette O'Connor","subfamily":"decision-quality","year":1995,"type":"Patient-reported"},"citations":[{"ref":"O'Connor, A. M. (1995). Validation of a decisional conflict scale. Medical Decision Making, 15(1), 25-30.","type":"article","doi":"10.1177/0272989X9501500105","isbn":null,"url":null},{"ref":"O'Connor, A. M. (2008). User Manual – Decisional Conflict Scale. University of Ottawa.","type":"article","doi":null,"isbn":null,"url":"https://decisionaid.ohri.ca/eval_dcs.html"}],"related":["sure-test-decision-quality","collaboste-scale","patient-enablement-instrument","control-preferences-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"declaration-of-helsinki","name":"Declaration of Helsinki","fullName":"World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects","aliases":["DoH","Helsinki Declaration"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-frameworks","year":"1964","originator":"World Medical Association (WMA)","url":"https://scholargate.app/en/research-ethics/declaration-of-helsinki","markdownUrl":"https://scholargate.app/en/research-ethics/declaration-of-helsinki.md","definition":"The Declaration of Helsinki (1964) is the foundational international ethical code for medical research involving human subjects, established by the World Medical Association. It extended earlier principles (Nuremberg Code 1947) to include therapeutic research and formalized the physician's ethical duty to prioritize subject welfare. Updated nine times through 2013, it remains the standard adopted by major medical journals, research ethics committees, and regulatory bodies worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"World Medical Association (WMA)","subfamily":"ethical-frameworks","year":"1964","type":"Framework"},"citations":[{"ref":"World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA, 310(20), 2191–2194.","type":"report","doi":null,"isbn":null,"url":"https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/"},{"ref":"World Medical Association. (1964). Declaration of Helsinki: Recommendations Guiding Physicians in Biomedical Research Involving Human Subjects. Helsinki: WMA.","type":"report","doi":null,"isbn":null,"url":"https://www.wma.net/what-we-do/history/declaration-of-helsinki/"}],"related":["belmont-report","nuremberg-code","informed-consent-research","institutional-review-board"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deep-belief-network","name":"Deep Belief Network","fullName":"Deep Belief Network (DBN)","aliases":["DBN","Deep Generative Network","Stacked RBM Network","Derin İnanç Ağı"],"domain":"deep-learning","family":"ml-model","subfamily":"Generative / pretraining","year":2006,"originator":"Geoffrey Hinton, Simon Osindero & Yee-Whye Teh","url":"https://scholargate.app/en/deep-learning/deep-belief-network","markdownUrl":"https://scholargate.app/en/deep-learning/deep-belief-network.md","definition":"A Deep Belief Network is a generative probabilistic model composed of multiple layers of stochastic, latent variables. Introduced by Hinton, Osindero, and Teh in 2006, DBNs were among the first deep architectures to be trained efficiently. Each pair of adjacent layers forms a Restricted Boltzmann Machine, and the network is trained greedily, one layer at a time, before optional supervised fine-tuning. DBNs revived interest in deep learning and demonstrated that hierarchical feature learning from raw data is tractable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geoffrey Hinton, Simon Osindero & Yee-Whye Teh","year":2006,"type":"Generative probabilistic model","subfamily":"Generative / pretraining","learningParadigm":"Unsupervised pretraining + supervised fine-tuning","buildingBlock":"Restricted Boltzmann Machine (RBM)"},"citations":[{"ref":"Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.","type":"article","doi":"10.1162/neco.2006.18.7.1527","isbn":null,"url":null}],"related":["restricted-boltzmann-machine","autoencoder","multilayer-perceptron"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deep-packet-inspection","name":"Deep Packet Inspection","fullName":"Deep Packet Inspection (DPI)","aliases":["DPI","complete packet inspection","packet filtering"],"domain":"cryptography","family":"ml-model","subfamily":"Network security analysis","year":"1990s","originator":"Unknown","url":"https://scholargate.app/en/cryptography/deep-packet-inspection","markdownUrl":"https://scholargate.app/en/cryptography/deep-packet-inspection.md","definition":"Deep Packet Inspection (DPI) is a network traffic analysis technique that examines the complete packet payload beyond header information to identify, classify, and potentially control data traffic. Developed in the 1990s for network monitoring and management, DPI analyzes packet contents to detect protocols, applications, and patterns, enabling security monitoring, quality of service management, and content filtering. DPI is widely used by Internet service providers, enterprises, and security organizations to monitor network traffic and enforce policies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Unknown","subfamily":"Network security analysis","year":"1990s","type":"packet inspection technique"},"citations":[{"ref":"Leconte, M., & Thomas, A. (2009). Deep Packet Inspection (DPI) technologies. In Proceedings of the Global Telecommunications Conference (GLOBECOM), 2009, pp. 1-6.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Deep+Packet+Inspection+%28DPI%29+technologies+Leconte"},{"ref":"Soro, F., & Visaggio, G. (2012). Deep packet inspection: evolution and challenges. In Proceedings of the 2012 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Deep+packet+inspection%3A+evolution+and+challenges+Soro"}],"related":["hmac","differential-cryptanalysis","taint-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deep-reinforcement-learning","name":"Deep Reinforcement Learning","fullName":"Deep Reinforcement Learning (DQN / PPO / A3C)","aliases":["Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C)","derin pekiştirmeli öğrenme","deep RL","DRL","DQN","PPO","A3C"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2015,"originator":"Mnih, V. et al. (DQN)","url":"https://scholargate.app/en/deep-learning/deep-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/deep-reinforcement-learning.md","definition":"Deep Reinforcement Learning combines neural networks with reinforcement learning so an agent learns by interacting with an environment, popularised by Mnih and colleagues' 2015 Nature work on human-level Atari control. Instead of learning from a fixed labelled dataset, the agent takes actions, observes rewards, and gradually shapes a policy that maximises long-run return.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mnih, V. et al. (DQN)","year":2015,"type":"Sequential decision-making (agent–environment interaction)","task":"Prediction & control (policy/value learning)","minSample":1000,"variants":"DQN, PPO, A3C"},"citations":[{"ref":"Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533.","type":"article","doi":"10.1038/nature14236","isbn":null,"url":null},{"ref":"Schulman, J. et al. (2017). Proximal Policy Optimization Algorithms. arXiv:1707.06347.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1707.06347"}],"related":["random-forest","xgboost","convolutional-neural-network","recurrent-neural-network","neural-architecture-search"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deep-remote-sensing","name":"Deep Remote Sensing","fullName":"Deep Learning for Remote Sensing Image Segmentation","aliases":["Deep Learning Remote Sensing","DL-based Remote Sensing Analysis","Neural Remote Sensing Segmentation","Derin Uzaktan Algılama"],"domain":"remote-sensing","family":"ml-model","subfamily":"Remote sensing","year":2017,"originator":"Zhu et al.","url":"https://scholargate.app/en/remote-sensing/deep-remote-sensing","markdownUrl":"https://scholargate.app/en/remote-sensing/deep-remote-sensing.md","definition":"Deep Learning for Remote Sensing Image Segmentation applies convolutional neural networks and encoder-decoder architectures to automatically classify and delineate objects in satellite or aerial imagery at the pixel level. Systematically reviewed by Zhu et al. (2017) in IEEE Geoscience and Remote Sensing Magazine, this paradigm unified previously fragmented approaches — scene classification, object detection, and semantic segmentation — under a single learned-feature framework capable of exploiting the spatial, spectral, and temporal richness of remote sensing data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhu et al.","year":2017,"type":"Supervised deep learning image analysis","subfamily":"Remote sensing","input":"Multi-spectral or hyperspectral satellite/aerial imagery","output":"Pixel-wise semantic segmentation maps"},"citations":[{"ref":"Zhu, X. X., et al. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8–36.","type":"article","doi":"10.1109/MGRS.2017.2762307","isbn":null,"url":null}],"related":["convolutional-neural-network","u-net","object-based-image-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deepar","name":"DeepAR","fullName":"DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks","aliases":["DeepAR — Olasılıksal RNN Tahmini","probabilistic autoregressive RNN forecasting","Amazon DeepAR"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2020,"originator":"Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)","url":"https://scholargate.app/en/deep-learning/deepar","markdownUrl":"https://scholargate.app/en/deep-learning/deepar.md","definition":"DeepAR is Amazon's industrial forecasting model, introduced by Salinas, Flunkert and Gasthaus (2017; published 2020), that uses an autoregressive recurrent neural network to estimate the parameters of a probability distribution at each step, producing a confidence interval rather than a single point forecast. It can model many related time series jointly within one model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Salinas, D., Flunkert, V. & Gasthaus, J. (Amazon)","year":2020,"type":"Autoregressive recurrent neural network (probabilistic forecasting)","task":"Time-series forecasting & prediction","minSample":100},"citations":[{"ref":"Salinas, D., Flunkert, V., Gasthaus, J. & Januschowski, T. (2020). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, 36(3), 1181–1191.","type":"article","doi":"10.1016/j.ijforecast.2019.07.001","isbn":null,"url":null},{"ref":"Salinas, D., Flunkert, V. & Gasthaus, J. (2017). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. arXiv:1704.04110.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1704.04110"}],"related":["nhits","patchtst","arima","random-forest","conformal-prediction-ts"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deephit","name":"DeepHit","fullName":"Deep Learning for Competing Risks","aliases":["Neural network competing risks","DL competing events"],"domain":"survival","family":"survival","subfamily":"Deep Learning","year":"2018","originator":"Changhee Lee","url":"https://scholargate.app/en/survival/deephit","markdownUrl":"https://scholargate.app/en/survival/deephit.md","definition":"DeepHit is a deep neural network framework for survival analysis with competing risks. Introduced by Lee et al. in 2018, it extends DeepSurv to handle settings where multiple, mutually exclusive events can occur, such as disease-specific mortality versus death from other causes. DeepHit solves the challenge of personalized risk prediction when subjects can experience different types of terminal events, a common scenario in medical and reliability applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Changhee Lee","subfamily":"Deep Learning","year":"2018","type":"Neural network competing risks model"},"citations":[{"ref":"Lee, C., Zame, W., Yoon, J., & van der Schaar, M. (2018). DeepHit: A deep learning approach for dynamic survival analysis with competing risks. AAAI Conference on Artificial Intelligence, 32(1), 2314–2321.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1801.07385"},{"ref":"Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.","type":"article","doi":"10.1080/01621459.1999.10474144","isbn":null,"url":null},{"ref":"Katzman, J. L., et al. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. Journal of Machine Learning Research, 40, 40–51.","type":"article","doi":"10.1186/s12874-018-0482-1","isbn":null,"url":null}],"related":["deepsurv","fine-gray-regression","cumulative-incidence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deepsurv","name":"DeepSurv","fullName":"Deep Learning for Survival Analysis","aliases":["Neural network survival","DL survival model"],"domain":"survival","family":"survival","subfamily":"Deep Learning","year":"2018","originator":"Jared Katzman","url":"https://scholargate.app/en/survival/deepsurv","markdownUrl":"https://scholargate.app/en/survival/deepsurv.md","definition":"DeepSurv is a deep neural network approach to survival analysis that learns personalized survival distributions directly from data. Introduced by Katzman et al. in 2018, it extends the Cox proportional hazards model using deep learning to capture complex, nonlinear relationships between covariates and survival outcomes. It solves the problem of modeling heterogeneous treatment effects and time-to-event predictions in high-dimensional settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jared Katzman","subfamily":"Deep Learning","year":"2018","type":"Neural network-based survival model"},"citations":[{"ref":"Faraggi, D., & Simon, R. (1995). A neural network model for survival data. Statistics in Medicine, 14(1), 73–82.","type":"article","doi":"10.1002/sim.4780140108","isbn":null,"url":null},{"ref":"Katzman, J. L., et al. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. Journal of Machine Learning Research, 40, 40–51.","type":"article","doi":"10.1186/s12874-018-0482-1","isbn":null,"url":null},{"ref":"Lee, C., Zame, W., Yoon, J., & van der Schaar, M. (2018). Deephit: A deep learning approach for dynamic survival analysis. AAAI Conference on Artificial Intelligence, 32(1).","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1801.07385"}],"related":["cox-regression","accelerated-failure-time","weibull-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"defect-prediction-model","name":"Defect Prediction Model","fullName":"Software Defect Prediction and Risk Classification","aliases":["fault prediction","bug prediction","defect classification"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Quality prediction","year":"2005","originator":"Thomas Ostrand, Elaine Weyuker, Robert Bell","url":"https://scholargate.app/en/software-engineering/defect-prediction-model","markdownUrl":"https://scholargate.app/en/software-engineering/defect-prediction-model.md","definition":"Defect prediction models forecast the likelihood of software faults in code modules using statistical or machine learning approaches. Pioneered by Ostrand, Weyuker, and Bell (2005), these models correlate code metrics (complexity, churn, coupling) with historical defect data to identify high-risk components. Organizations use predictions to allocate testing resources, guide code review, and prioritize refactoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Ostrand, Elaine Weyuker, Robert Bell","subfamily":"Quality prediction","year":"2005","type":"machine learning model"},"citations":[{"ref":"Ostrand, T. J., Weyuker, E. J., & Bell, R. M. (2005). Predicting the location and number of faults in large software systems. IEEE Transactions on Software Engineering, 31(4), 340–355.","type":"article","doi":"10.1109/tse.2005.49","isbn":null,"url":null},{"ref":"Nagappan, N., Ball, T., & Zeller, A. (2006). Mining metrics to predict component failures. In Proceedings of the 28th International Conference on Software Engineering (pp. 452–461).","type":"article","doi":"10.1145/1134285.1134349","isbn":null,"url":null},{"ref":"Menzies, T., Greenwald, J., & Russ, P. (2007). Problems with precision: A response to comments on 'Data mining static code attributes to learn defect predictors'. IEEE Transactions on Software Engineering, 33(9), 637–640.","type":"article","doi":"10.1109/tse.2007.70721","isbn":null,"url":null}],"related":["software-complexity-metrics","code-coverage-analysis","static-code-analysis","agile-velocity-tracking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"defuzz-alpha-cut","name":"DEFUZZ-ALPHA-CUT","fullName":"Alpha-Cut Defuzzification — Crisp interval or representative via α-level cut","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Defuzzification","year":"1965; 1985","originator":"Zadeh, L.A.","url":"https://scholargate.app/en/decision-making/defuzz-alpha-cut","markdownUrl":"https://scholargate.app/en/decision-making/defuzz-alpha-cut.md","definition":"DEFUZZ-ALPHA-CUT (Alpha-Cut Defuzzification — Crisp interval or representative via α-level cut) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Zadeh, L.A. in 1965; 1985. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zadeh, L.A.","subfamily":"Defuzzification","year":"1965; 1985","type":"Defuzzification operator — alpha-cut crisp approximation","value_space":"fuzzy_TFN","uncertainty":"fuzzy","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Zadeh, L.A. (1965). Fuzzy sets. Information and Control","type":"article","doi":"10.1016/S0019-9958(65)90241-X","isbn":null,"url":null}],"related":["topsis","vikor","waspas","fuzzy-ahp","fuzzy-topsis","fuzzy-vikor"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"defuzz-bisector","name":"DEFUZZ-BISECTOR","fullName":"Bisector of Area Defuzzification — Vertical line dividing fuzzy set area equally","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Defuzzification","year":"1999","originator":"van Leekwijck, W.; Kerre, E.E.","url":"https://scholargate.app/en/decision-making/defuzz-bisector","markdownUrl":"https://scholargate.app/en/decision-making/defuzz-bisector.md","definition":"DEFUZZ-BISECTOR (Bisector of Area Defuzzification — Vertical line dividing fuzzy set area equally) is a defuzzification multi-criteria decision-making (MCDM) method introduced by van Leekwijck, W.; Kerre, E.E. in 1999. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"van Leekwijck, W.; Kerre, E.E.","subfamily":"Defuzzification","year":"1999","type":"Defuzzification operator — bisector of area","value_space":"fuzzy_TFN","uncertainty":"fuzzy","compensation":"none","rank_reversal":false},"citations":[{"ref":"van Leekwijck, W., Kerre, E.E. (1999). Defuzzification: criteria and classification. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00337-0","isbn":null,"url":null}],"related":["topsis","vikor","fuzzy-ahp","fuzzy-topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"defuzz-centroid-gaussian","name":"DEFUZZ-CENTROID-GAUSSIAN","fullName":"Gaussian Centroid Defuzzification — Crisp representative of Gaussian fuzzy number","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Defuzzification","year":"1965","originator":"Zadeh, L.A.","url":"https://scholargate.app/en/decision-making/defuzz-centroid-gaussian","markdownUrl":"https://scholargate.app/en/decision-making/defuzz-centroid-gaussian.md","definition":"DEFUZZ-CENTROID-GAUSSIAN (Gaussian Centroid Defuzzification — Crisp representative of Gaussian fuzzy number) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Zadeh, L.A. in 1965. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zadeh, L.A.","subfamily":"Defuzzification","year":"1965","type":"Defuzzification operator — Gaussian membership function","value_space":"fuzzy_Gaussian","uncertainty":"fuzzy","compensation":"full","rank_reversal":false},"citations":[{"ref":"Zadeh, L.A. (1965). Fuzzy sets. Information and Control","type":"article","doi":"10.1016/S0019-9958(65)90241-X","isbn":null,"url":null}],"related":["topsis","vikor","waspas","fuzzy-ahp","fuzzy-topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"defuzz-centroid","name":"DEFUZZ-CENTROID","fullName":"Centroid Defuzzification — Centre of gravity crisp representative","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Defuzzification","year":"1975","originator":"Mamdani, E.H.","url":"https://scholargate.app/en/decision-making/defuzz-centroid","markdownUrl":"https://scholargate.app/en/decision-making/defuzz-centroid.md","definition":"DEFUZZ-CENTROID (Centroid Defuzzification — Centre of gravity crisp representative) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Mamdani, E.H. in 1975. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mamdani, E.H.","subfamily":"Defuzzification","year":"1975","type":"Defuzzification operator — centroid of area","value_space":"fuzzy_TFN","uncertainty":"fuzzy","compensation":"full","rank_reversal":false},"citations":[{"ref":"Mamdani, E.H., Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies","type":"article","doi":"10.1016/S0020-7373(75)80002-2","isbn":null,"url":null}],"related":["topsis","vikor","waspas","mabac","codas","fuzzy-ahp","fuzzy-topsis","fuzzy-vikor"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"defuzz-mom","name":"DEFUZZ-MOM","fullName":"Mean of Maxima (MOM) Defuzzification — Crisp value at the plateau of maximum membership","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Defuzzification","year":"1990","originator":"Lee, C.C.","url":"https://scholargate.app/en/decision-making/defuzz-mom","markdownUrl":"https://scholargate.app/en/decision-making/defuzz-mom.md","definition":"DEFUZZ-MOM (Mean of Maxima (MOM) Defuzzification — Crisp value at the plateau of maximum membership) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Lee, C.C. in 1990. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee, C.C.","subfamily":"Defuzzification","year":"1990","type":"Defuzzification operator — mean of maxima","value_space":"fuzzy_TFN","uncertainty":"fuzzy","compensation":"none","rank_reversal":false},"citations":[{"ref":"Lee, C.C. (1990). Fuzzy logic in control systems: fuzzy logic controller, Part I. IEEE Transactions on Systems, Man, and Cybernetics","type":"article","doi":"10.1109/21.52551","isbn":null,"url":null}],"related":["topsis","vikor","fuzzy-ahp","fuzzy-topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"defuzz-score-ifn","name":"DEFUZZ-SCORE-IFN","fullName":"Score Function Defuzzification — Crisp ranking score for IFN/PFN/q-ROFS families","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Defuzzification","year":"1994; extended 2014, 2017","originator":"Chen, S.M.; Tan, J.M.","url":"https://scholargate.app/en/decision-making/defuzz-score-ifn","markdownUrl":"https://scholargate.app/en/decision-making/defuzz-score-ifn.md","definition":"DEFUZZ-SCORE-IFN (Score Function Defuzzification — Crisp ranking score for IFN/PFN/q-ROFS families) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Chen, S.M.; Tan, J.M. in 1994; extended 2014, 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, S.M.; Tan, J.M.","subfamily":"Defuzzification","year":"1994; extended 2014, 2017","type":"Defuzzification operator — score and accuracy functions for membership-nonmembership pairs","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Chen, S.M., Tan, J.M. (1994). Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems","type":"article","doi":"10.1016/0165-0114(94)90084-1","isbn":null,"url":null}],"related":["topsis","vikor","waspas","mabac","codas","if-topsis","if-vikor","pf-topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"defuzz-type-reduction","name":"DEFUZZ-TYPE-REDUCTION","fullName":"Type Reduction Defuzzification — Karnik-Mendel algorithm for interval type-2 fuzzy sets","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Defuzzification","year":"2001","originator":"Karnik, N.N.; Mendel, J.M.","url":"https://scholargate.app/en/decision-making/defuzz-type-reduction","markdownUrl":"https://scholargate.app/en/decision-making/defuzz-type-reduction.md","definition":"DEFUZZ-TYPE-REDUCTION (Type Reduction Defuzzification — Karnik-Mendel algorithm for interval type-2 fuzzy sets) is a defuzzification multi-criteria decision-making (MCDM) method introduced by Karnik, N.N.; Mendel, J.M. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karnik, N.N.; Mendel, J.M.","subfamily":"Defuzzification","year":"2001","type":"Defuzzification operator — type-2 to type-1 reduction via Karnik-Mendel","value_space":"type2_fuzzy","uncertainty":"fuzzy","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Karnik, N.N., Mendel, J.M. (2001). Centroid of a type-2 fuzzy set. Information Sciences","type":"article","doi":"10.1016/S0020-0255(01)00069-X","isbn":null,"url":null}],"related":["topsis","vikor","waspas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"degradation-models","name":"Degradation Models","fullName":"Degradation Models (Accelerated Degradation)","aliases":["Accelerated Degradation Testing","Degradation Path Models","Performance Degradation Analysis","Bozunma Modelleri"],"domain":"reliability","family":"regression-model","subfamily":"Reliability & risk","year":1998,"originator":"Meeker, Escobar & Lu","url":"https://scholargate.app/en/reliability/degradation-models","markdownUrl":"https://scholargate.app/en/reliability/degradation-models.md","definition":"Degradation models estimate product lifetime by tracking measurable performance characteristics—such as crack length, light output, or insulation resistance—over time rather than waiting for outright failure. Introduced in rigorous form by Meeker, Escobar, and Lu (1998), these models fit a stochastic degradation path to repeated measurements and define failure as the first time the characteristic crosses a predetermined threshold, enabling reliable lifetime inference from accelerated test data with very few or no observed failures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Meeker, Escobar & Lu","year":1998,"type":"Stochastic degradation path model","subfamily":"Reliability & risk","data_requirement":"Repeated measurements over time","failure_criterion":"Threshold-crossing of a measurable characteristic"},"citations":[{"ref":"Meeker, W. Q., Escobar, L. A., & Lu, C. J. (1998). Accelerated degradation tests: modeling and analysis. Technometrics, 40(2), 89–99.","type":"article","doi":"10.1080/00401706.1998.10485191","isbn":null,"url":null}],"related":["reliability-analysis","weibull-regression","maintenance-optimization"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"degree-centrality","name":"Degree Centrality","fullName":"Degree Centrality (Freeman Node Connectivity Measure)","aliases":["node degree","degree score","DC","connectivity centrality"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1978","originator":"Freeman, L. C.","url":"https://scholargate.app/en/network-analysis/degree-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/degree-centrality.md","definition":"Degree centrality is the simplest and most intuitive measure of a node's importance in a network, defined as the number of direct ties a node has to other nodes. Normalized by dividing by the maximum possible ties, it allows comparison across networks of different sizes and is the starting point of almost every network analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Freeman, L. C.","year":"1978","type":"Node-level centrality measure","dataType":"Adjacency matrix or edge list (binary or weighted graph)","subfamily":"Network science"},"citations":[{"ref":"Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.","type":"article","doi":"10.1016/0378-8733(78)90021-7","isbn":null,"url":null},{"ref":"Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-38707-1","url":null}],"related":["betweenness-centrality","closeness-centrality","eigenvector-centrality","social-network-analysis","modularity-analysis","weighted-degree-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"degree-heating-weeks","name":"Degree Heating Weeks","fullName":"Degree Heating Weeks","aliases":["DHW","Thermal Stress Index"],"domain":"oceanography","family":"process-pipeline","subfamily":"Climate Impact Assessment","year":"2003","originator":"NOAA Coral Reef Watch","url":"https://scholargate.app/en/oceanography/degree-heating-weeks","markdownUrl":"https://scholargate.app/en/oceanography/degree-heating-weeks.md","definition":"Degree Heating Weeks (DHW) is a thermal stress metric that quantifies accumulated heat exposure above a coral bleaching threshold, computed from satellite sea surface temperature data. Developed by NOAA's Coral Reef Watch program in 2003, DHW provides a standardized index for predicting and monitoring coral bleaching stress globally. The metric combines intensity and duration of thermal anomalies to estimate cumulative physiological stress on coral colonies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"NOAA Coral Reef Watch","subfamily":"Climate Impact Assessment","year":"2003","type":"thermal-metric"},"citations":[{"ref":"Liu, G., Strong, A. E., & Skirving, W. (2003). Remote sensing of sea surface temperatures during 2002 Great Barrier Reef coral bleaching. EOS Transactions, 84(15), 137-141.","type":"article","doi":null,"isbn":null,"url":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2003EO150001"},{"ref":"Strong, A. E., Liu, G., Meyer, V., et al. (2016). Integrating thermal habitat monitoring and coral bleaching forecasting. Coral Reefs, 35(1), 1-7.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Integrating+thermal+habitat+monitoring+and+coral+bleaching+forecasting+Strong"}],"related":["ocean-color-chlorophyll-a","hydrothermal-plume-mapping","drifter-lagrangian-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"delirium-observation-screening","name":"Delirium Observation Screening Scale","fullName":"Delirium Observation Screening Scale (DOS)","aliases":["DOS","Delirium Screening Scale","Delirium Observation"],"domain":"nursing","family":"process-pipeline","subfamily":"psychiatric/neurological assessment","year":"2003","originator":"Mieke J. Schuurmans","url":"https://scholargate.app/en/nursing/delirium-observation-screening","markdownUrl":"https://scholargate.app/en/nursing/delirium-observation-screening.md","definition":"The Delirium Observation Screening Scale (DOS), developed by Mieke J. Schuurmans and colleagues in 2003, is a brief clinician-rated screening instrument designed to detect delirium in hospitalized older adults. Delirium—acute onset confusion, inattention, and disorganized thinking—is a common complication in hospitals and intensive care units that increases mortality, morbidity, and length of stay. The DOS captures the hallmark features of delirium through direct observation, making it practical for rapid, repeated screening in busy clinical settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mieke J. Schuurmans","subfamily":"psychiatric/neurological assessment","year":"2003","type":"Clinician-rated observation screening tool"},"citations":[{"ref":"Schuurmans, M. J., Shortridge-Baggett, L. M., & Duursma, S. A. (2003). The Delirium Observation Screening Scale: a screening instrument for delirium. Res Theory Nurs Pract, 17(1), 31-50.","type":"article","doi":"10.1891/rtnp.17.1.31.53169","isbn":null,"url":null},{"ref":"Schuurmans, M. J., Duursma, S. A., Shortridge-Baggett, L. M., Clevers, G. J., & van der Hoeven, J. G. (2003). Elderly patients with delirium in the hospital. Differences in patient characteristics. A comparative study. Int J Nurs Stud, 40(3), 255-263.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Elderly+patients+with+delirium+in+the+hospital+Schuurmans"}],"related":["clinical-frailty-scale","malnutrition-screening-tool","katz-independence-adl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"delphi-method","name":"Delphi Method","fullName":"Delphi Method (Structured Expert Consensus)","aliases":["Delphi Yöntemi","Delphi technique","expert consensus method"],"domain":"qualitative","family":"process-pipeline","subfamily":null,"year":1963,"originator":"Norman Dalkey & Olaf Helmer (RAND Corporation)","url":"https://scholargate.app/en/qualitative/delphi-method","markdownUrl":"https://scholargate.app/en/qualitative/delphi-method.md","definition":"The Delphi method is a structured, iterative survey technique developed by Norman Dalkey and Olaf Helmer at the RAND Corporation in 1963 for eliciting and converging expert opinion on complex topics where empirical data are unavailable or insufficient. It collects independent judgements from a geographically dispersed expert panel over multiple anonymous rounds, feeding aggregated results back to participants after each round so they can revise their views in light of the group's collective position.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Dalkey & Olaf Helmer (RAND Corporation)","year":1963,"type":"Structured iterative expert-elicitation process","minPanel":"10-15 experts","minRounds":2,"output":"Consensus ratings or priority rankings with inter-rater agreement metrics","dataTypes":"Ordinal / categorical (Likert-scale survey responses)"},"citations":[{"ref":"Dalkey, N. & Helmer, O. (1963). An Experimental Application of the Delphi Method to the Use of Experts. Management Science, 9(3), 458-467.","type":"article","doi":"10.1287/mnsc.9.3.458","isbn":null,"url":null}],"related":["focus-group","nominal-group-technique","content-analysis","action-research","mixed-methods"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"delphi-technique","name":"Delphi Technique","fullName":"Delphi Technique for Expert Consensus","aliases":["Delphi method","Delphi survey","expert consensus method","iterative expert panel"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1950s–1963","originator":"Norman Dalkey and Olaf Helmer (RAND Corporation)","url":"https://scholargate.app/en/survey-methodology/delphi-technique","markdownUrl":"https://scholargate.app/en/survey-methodology/delphi-technique.md","definition":"The Delphi technique is a structured, multi-round data collection method that harvests and refines expert opinion through iterative questionnaires and controlled feedback. Developed at RAND Corporation in the 1950s, it is designed to converge a dispersed expert panel toward a reliable consensus on complex, uncertain, or future-oriented questions — without the conformity pressures of face-to-face group discussion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Dalkey and Olaf Helmer (RAND Corporation)","year":"1950s–1963","type":"Iterative expert consensus technique","dataType":"Structured questionnaire responses from expert panels (mixed: ratings, rankings, open text)","subfamily":"Data collection"},"citations":[{"ref":"Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458–467.","type":"article","doi":"10.1287/mnsc.9.3.458","isbn":null,"url":null},{"ref":"Linstone, H. A., & Turoff, M. (Eds.). (1975). The Delphi Method: Techniques and Applications. Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Delphi+Method+Techniques+and+Applications+Linstone+Turoff+1975"}],"related":["survey","structured-interview","focus-group","nominal-group-technique","content-analysis","triangulated-delphi-technique"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"delphi","name":"DELPHI","fullName":"Delphi Method — iterative expert consensus for criterion importance elicitation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1963","originator":"Dalkey, N., Helmer, O.","url":"https://scholargate.app/en/decision-making/delphi","markdownUrl":"https://scholargate.app/en/decision-making/delphi.md","definition":"DELPHI (Delphi Method — iterative expert consensus for criterion importance elicitation) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Dalkey, N., Helmer, O. in 1963. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dalkey, N., Helmer, O.","subfamily":"Weight_Subjective","year":"1963","type":"Weight_Subjective (expert consensus, iterative Likert/ranking)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Dalkey, N., Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science","type":"article","doi":"10.1287/mnsc.9.3.458","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dematel","name":"DEMATEL","fullName":"Decision Making Trial and Evaluation Laboratory","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1972","originator":"Gabus, A., Fontela, E.","url":"https://scholargate.app/en/decision-making/dematel","markdownUrl":"https://scholargate.app/en/decision-making/dematel.md","definition":"DEMATEL (Decision Making Trial and Evaluation Laboratory) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Gabus, A., Fontela, E. in 1972. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gabus, A., Fontela, E.","subfamily":"Weight_Subjective","year":"1972","type":"Cause-effect influence network (total relation matrix) — produces prominence + relation for criteria weighting","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Gabus, A., Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Centre, Geneva, Switzerland","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=World%20problems%2C%20an%20invitation%20to%20further%20thought%20within%20the%20framework%20of%20DEMATEL"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dementia-rating-scale","name":"Mattis Dementia Rating Scale","fullName":"Mattis Dementia Rating Scale","aliases":["DRS","Mattis DRS","Dementia Rating Scale"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"comprehensive dementia assessment","year":"1988","originator":"Sandra Mattis","url":"https://scholargate.app/en/neuropsychology/dementia-rating-scale","markdownUrl":"https://scholargate.app/en/neuropsychology/dementia-rating-scale.md","definition":"The Mattis Dementia Rating Scale (DRS) is a comprehensive 36-item clinician-administered neuropsychological battery designed to assess and quantify cognitive decline in dementia. Developed by Sandra Mattis in 1988, the DRS measures five major cognitive domains—attention, initiation/perseveration, construction, conceptualization, and memory—and provides both a total score and subscale scores. The DRS is particularly valued in neurodegenerative disease research and clinical settings for its sensitivity to cognitive change over time and its utility in detecting cognitive impairment across the dementia spectrum.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sandra Mattis","subfamily":"comprehensive dementia assessment","year":"1988","type":"Clinician-administered comprehensive neuropsychological scale"},"citations":[{"ref":"Mattis, S. (1988). Dementia Rating Scale (DRS). Odessa, FL: Psychological Assessment Resources.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/3163202"},{"ref":"Monsch, A. U., Bondi, M. W., Butters, N., Salmon, D. P., Kluger, A., & Thal, L. J. (1992). Comparisons of verbal and nonverbal cognitive function in Alzheimer's disease. Neurology, 42(8), 1638-1644.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparisons+of+verbal+and+nonverbal+cognitive+function+in+Alzheimer%27s+disease+Monsch"},{"ref":"Vangel Jr, S. J., & Lichtenberg, P. A. (2008). Mattis Dementia Rating Scale: Clinical utility in older adults. Clinical Neuropsychologist, 22(1), 71-80.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mattis+Dementia+Rating+Scale%3A+Clinical+utility+in+older+adults+Vangel"}],"related":["mmse","adas-cog","addenbrookes-cognitive-examination","saint-louis-mental-status","trail-making-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"democratic-support-scale","name":"Democratic Support Scale","fullName":"Support for Democracy Scale (SFD)","aliases":["SFD","Democratic Legitimacy Scale","System Support Scale"],"domain":"political-psychology","family":"process-pipeline","subfamily":"institutional-attitudes","year":"1999","originator":"Russell Dalton & Pippa Norris","url":"https://scholargate.app/en/political-psychology/democratic-support-scale","markdownUrl":"https://scholargate.app/en/political-psychology/democratic-support-scale.md","definition":"The Democratic Support Scale measures citizen commitment to democracy as a regime type, including beliefs that democracy is the best system of government, willingness to defend democratic institutions, and rejection of non-democratic alternatives. Pioneered by Norris (1999) and Dalton (2004) in comparative research, the measure distinguishes regime support (belief in democracy's superiority) from performance support (satisfaction with current government). It addresses the paradox of 'critical citizens'—in advanced democracies, people often express dissatisfaction with current government performance while maintaining deep commitment to democratic principles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Russell Dalton & Pippa Norris","subfamily":"institutional-attitudes","year":"1999","type":"Self-report"},"citations":[{"ref":"Dalton, R. J. (2004). Democratic challenges, democratic choices: The erosion of political support in advanced industrial democracies. Oxford: Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Dalton%2C%20R.%20J.%20(2004).%20Democratic%20challenges%2C%20democratic%20choices%3A%20The%20erosion%20of%20political%20support%20in%20advanced%20industrial"},{"ref":"Norris, P. (1999). Critical citizens: Global support for democratic governance. Oxford: Oxford University Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Norris%2C%20P.%20(1999).%20Critical%20citizens%3A%20Global%20support%20for%20democratic%20governance.%20Oxford%3A%20Oxford%20University%20Press."},{"ref":"Gibson, J. L. (2004). Constraining the Klan: Civil society and the limits of democratic tolerance. In P. B. Baltes, N. J. Smelser, & P. B. Baltes (Eds.), International encyclopedia of the social & behavioral sciences. Oxford: Elsevier.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Gibson%2C%20J.%20L.%20(2004).%20Constraining%20the%20Klan%3A%20Civil%20society%20and%20the%20limits%20of%20democratic%20tolerance.%20In%20P.%20B.%20Baltes%2C%20N.%20J"}],"related":["political-trust-scale","voter-cynicism-scale","national-identity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"democratic-values-scale","name":"Democratic Values Scale","fullName":"Democratic Norms and Values Assessment Scale","aliases":["DVS","Democratic Attitudes Scale"],"domain":"political-sociology","family":"process-pipeline","subfamily":"Political Values","year":"1999–2015","originator":"Russell Dalton, Hans-Dieter Klingemann, Christian Welzel","url":"https://scholargate.app/en/political-sociology/democratic-values-scale","markdownUrl":"https://scholargate.app/en/political-sociology/democratic-values-scale.md","definition":"The Democratic Values Scale measures commitment to core principles of democratic governance including free speech, rule of law, fair elections, protection of minorities, and transparent institutions. Rather than measuring support for democracy as a system (which is nearly universal in principle), it captures depth of commitment to democratic norms, tolerance for dissent, and willingness to protect rights of political opponents. Developed by comparative political scientists including Dalton, Klingemann, and Welzel, it reveals psychological foundations of democratic stability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Russell Dalton, Hans-Dieter Klingemann, Christian Welzel","subfamily":"Political Values","year":"1999–2015","type":"Self-report questionnaire"},"citations":[{"ref":"Dalton, R. J. (2004). Democratic challenges, democratic choices: The erosion of political support in advanced industrial democracies. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dalton%2C%20R.%20J.%20(2004).%20Democratic%20challenges%2C%20democratic%20choices%3A%20The%20erosion%20of%20political%20support%20in%20advanced%20industrial"},{"ref":"Klingemann, H. D. (1999). Mapping political support in the 1990s: A global analysis. In P. Norris (Ed.), Critical citizens: Global support for democratic government (pp. 31-56). Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Klingemann%2C%20H.%20D.%20(1999).%20Mapping%20political%20support%20in%20the%201990s%3A%20A%20global%20analysis.%20In%20P.%20Norris%20(Ed.)%2C%20Critical%20citize"},{"ref":"Welzel, C., & Inglehart, R. (2015). Banyan model of egalitarianism and hierarchy: The cultural evolution of political values. Journal of Cross-Cultural Psychology, 46(1), 83-112.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Banyan+model+of+egalitarianism+and+hierarchy%3A+The+cultural+evolution+of+political+values+Welzel"}],"related":["political-efficacy-scale","institutional-trust-scale","civic-engagement-scale","generalized-trust-scale","xenophobia-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dempster-shafer-fusion","name":"Dempster-Shafer Fusion","fullName":"Dempster-Shafer Evidence Fusion","aliases":["belief function fusion","evidence combination"],"domain":"ensemble-learning","family":"ml-model","subfamily":"Probabilistic","year":"1968","originator":"Arthur Dempster","url":"https://scholargate.app/en/ensemble-learning/dempster-shafer-fusion","markdownUrl":"https://scholargate.app/en/ensemble-learning/dempster-shafer-fusion.md","definition":"Dempster-Shafer fusion is an ensemble method based on evidence theory (belief functions) that combines predictions from multiple sources by assigning basic probability masses to subsets of hypotheses. Rather than requiring a probability distribution over single outcomes, it allows uncertainty over sets of outcomes, providing a richer representation of confidence and doubt. Developed by Dempster (1968) and formalized by Shafer (1976), this method is particularly useful when sources are unreliable, conflicting, or provide partial evidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arthur Dempster","subfamily":"Probabilistic","year":"1968","type":"belief fusion"},"citations":[{"ref":"Dempster, A. P. (1968). A generalization of Bayesian inference. Journal of the Royal Statistical Society, 30(2), 205-247.","type":"article","doi":"10.1111/j.2517-6161.1968.tb00722.x","isbn":null,"url":null},{"ref":"Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.","type":"book","doi":null,"isbn":null,"url":"https://press.princeton.edu/books/paperback/9780691100425/a-mathematical-theory-of-evidence"}],"related":["bayesian-fusion","majority-voting","ensemble-averaging","weighted-voting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dempster-shafer-theory","name":"Dempster-Shafer Theory","fullName":"Dempster-Shafer Theory of Evidence (Belief Functions)","aliases":["evidence theory","belief functions","evidential reasoning","Dempster-Shafer kanıt teorisi"],"domain":"soft-computing","family":"ml-model","subfamily":"Evidence theory","year":1976,"originator":"Arthur P. Dempster & Glenn Shafer","url":"https://scholargate.app/en/soft-computing/dempster-shafer-theory","markdownUrl":"https://scholargate.app/en/soft-computing/dempster-shafer-theory.md","definition":"Dempster-Shafer theory is a mathematical framework for reasoning under uncertainty that generalizes Bayesian probability by representing ignorance explicitly. Instead of forcing a single probability on each hypothesis, it assigns belief mass to sets of hypotheses and derives a belief-plausibility interval, and it provides Dempster's rule for fusing evidence from multiple independent sources. Developed from Arthur Dempster's 1967 work and Glenn Shafer's 1976 monograph, it underpins evidential reasoning and sensor/decision fusion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arthur P. Dempster & Glenn Shafer","year":1976,"type":"Uncertainty calculus for combining evidence","subfamily":"Evidence theory","represents":"Belief and plausibility (ignorance explicitly)","generalizes":"Bayesian probability"},"citations":[{"ref":"Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.","type":"article","doi":"10.1214/aoms/1177698950","isbn":null,"url":null},{"ref":"Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.","type":"book","doi":null,"isbn":"978-0-691-08175-5","url":null}],"related":["bayesian-network","case-based-reasoning","naive-bayes","fuzzy-cognitive-maps"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"denavit-hartenberg-parameters","name":"Denavit-Hartenberg Parameters","fullName":"Denavit-Hartenberg Convention for Robot Kinematics","aliases":["DH parameters","DH convention","Robot kinematics convention"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Kinematic representation","year":"1955","originator":"Denavit, J. and Hartenberg, R. S.","url":"https://scholargate.app/en/manufacturing/denavit-hartenberg-parameters","markdownUrl":"https://scholargate.app/en/manufacturing/denavit-hartenberg-parameters.md","definition":"The Denavit-Hartenberg (DH) convention is a systematic mathematical method for assigning coordinate frames to the links of an articulated robot or mechanism, enabling compact representation and computation of forward and inverse kinematics. Introduced by Denavit and Hartenberg in 1955, this method uses only four parameters per joint to describe the spatial relationship between adjacent links, dramatically simplifying kinematic analysis and control of complex multi-jointed systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Denavit, J. and Hartenberg, R. S.","subfamily":"Kinematic representation","year":"1955","type":"Mathematical convention for describing articulated mechanisms"},"citations":[{"ref":"Denavit, J., & Hartenberg, R. S. (1955). A kinematic notation for lower-pair mechanisms based on matrices. Journal of Applied Mechanics, 22(2), 215-221.","type":"article","doi":null,"isbn":null,"url":"https://asmedigitalcollection.asme.org/appliedmechanics/article-abstract/22/2/215/403881"},{"ref":"Craig, J. J. (2005). Introduction to Robotics: Mechanics and Control (3rd ed.). Pearson Education.","type":"book","doi":null,"isbn":"0-13-123629-6","url":null},{"ref":"Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2006). Robot Modeling and Control. John Wiley & Sons.","type":"book","doi":null,"isbn":"0-471-64990-2","url":null}],"related":["inverse-kinematics","cnc-tool-path-generation","modal-analysis","design-for-manufacturing-and-assembly"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dendrochronology-method","name":"Dendrochronology Method","fullName":"Tree-Ring Analysis for Dating and Climate Reconstruction","aliases":["Tree-ring dating","Dendrochronological analysis","Ring-width chronology"],"domain":"forestry","family":"process-pipeline","subfamily":"Dendrochronology and paleoclimatology","year":"1901–1929","originator":"Andrew Ellicott Douglass","url":"https://scholargate.app/en/forestry/dendrochronology-method","markdownUrl":"https://scholargate.app/en/forestry/dendrochronology-method.md","definition":"Dendrochronology is the science of dating and analyzing tree rings to reconstruct past climatic conditions, chronologies, and tree growth patterns. Pioneered by Andrew Ellicott Douglass in the early twentieth century and formalized by Fritts and colleagues, dendrochronology enables precise dating of historical wood samples and generates millennial-length climate records, becoming indispensable for paleoclimatology, archaeology, and forest ecology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Ellicott Douglass","subfamily":"Dendrochronology and paleoclimatology","year":"1901–1929","type":"Historical and climatic inference pipeline"},"citations":[{"ref":"Fritts, H. C. (1976). Tree Rings and Climate. Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/treeringclimate"},{"ref":"Stokes, D. L., & Smiley, T. L. (1996). An Introduction to Tree-Ring Dating. University of Arizona Press.","type":"book","doi":null,"isbn":null,"url":"https://www.uapress.arizona.edu/9780816516049/"},{"ref":"Douglass, A. E. (1929). The Secret of the Southwest Solved by Talkless Rings. National Geographic Magazine, 56(6), 736–770.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/nationalgeographic"},{"ref":"Briffa, K. R. (2000). Annual Climate Variability in the Holocene: Interpreting the Message of Ancient Trees. The Quaternary Review, 9(2), 87–105.","type":"article","doi":"10.1016/s0277-3791(99)00056-6","isbn":null,"url":null}],"related":["forest-inventory-sampling","allometric-biomass-equation","carbon-stock-estimation-forest","stand-basal-area-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dendrochronology","name":"Dendrochronology","fullName":"Dendrochronology: Tree Ring Dating and Climate Reconstruction","aliases":["Tree-ring analysis","Chronology","Paleoclimatology"],"domain":"agronomy","family":"process-pipeline","subfamily":"Paleoclimatology","year":"1909","originator":"Andrew Ellicott Douglass","url":"https://scholargate.app/en/agronomy/dendrochronology","markdownUrl":"https://scholargate.app/en/agronomy/dendrochronology.md","definition":"Dendrochronology is the science of dating and interpreting wood and climate from tree rings. Each annual ring records the tree's growth response to weather during that year: wide rings indicate favorable conditions (adequate water, warmth, light); narrow rings indicate stress (drought, cold, shade). By crossmatching ring-width patterns across trees and backward in time using dead wood, researchers construct chronologies extending centuries to millennia, providing archives of regional precipitation, temperature, and hydroclimate independent of instrumental records.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Ellicott Douglass","subfamily":"Paleoclimatology","year":"1909","type":"Archival and climate reconstruction method"},"citations":[{"ref":"Douglass, A. E. (1909). Weather records in the growth of giant sequoias. Monthly Weather Review, 37(1), 713-714.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Weather+records+in+the+growth+of+giant+sequoias+Douglass"},{"ref":"Fritts, H. C. (1976). Tree rings and climate. Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/tree-rings-and-climate/fritts/978-0-12-268450-0"},{"ref":"Cook, E. R., & Krusic, P. J. (2015). The North American summer PDSI: Regional reconstructions and applications. Dendrochronologia, 26(3), 155-173.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+North+American+summer+PDSI%3A+Regional+reconstructions+and+applications+Cook"}],"related":["phytolith-analysis","palynology","pedogenesis-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"densenet","name":"DenseNet","fullName":"Densely Connected Convolutional Network (DenseNet)","aliases":["DenseNet","Dense Convolutional Network","densely connected CNN","DenseNet-121","DenseNet-169","DenseNet-201"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.","url":"https://scholargate.app/en/deep-learning/densenet","markdownUrl":"https://scholargate.app/en/deep-learning/densenet.md","definition":"DenseNet (Densely Connected Convolutional Network), introduced by Huang, Liu, van der Maaten, and Weinberger at CVPR 2017 (Best Paper Award), connects every layer to every subsequent layer within a dense block so that each layer receives the concatenated feature maps of all preceding layers — maximising feature reuse, strengthening gradient flow, and achieving competitive accuracy with substantially fewer parameters than comparable architectures such as ResNet.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.","year":2017,"type":"Dense convolutional neural network (feed-forward dense connectivity)","task":"Image classification, feature extraction, transfer learning","award":"CVPR 2017 Best Paper Award","keyHyperparameters":"growth rate k, number of dense blocks, compression factor theta, bottleneck layers"},"citations":[{"ref":"Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700–4708.","type":"article","doi":"10.1109/CVPR.2017.243","isbn":null,"url":"https://arxiv.org/abs/1608.06993"},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":"https://www.deeplearningbook.org"}],"related":["resnet","vgg","inception","efficientnet","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"density-functional-theory","name":"Density Functional Theory","fullName":"Density Functional Theory (DFT)","aliases":["DFT","Kohn-Sham equations"],"domain":"quantum-computing","family":"ml-model","subfamily":"Computational Chemistry","year":"1965","originator":"Walter Kohn","url":"https://scholargate.app/en/quantum-computing/density-functional-theory","markdownUrl":"https://scholargate.app/en/quantum-computing/density-functional-theory.md","definition":"Density Functional Theory (DFT) is a computational method for determining the properties of materials and molecules by modeling the ground state electron density. Developed by Walter Kohn and Lu Jeu Sham in the 1960s, DFT reduces the complexity of quantum chemistry from tracking individual electron coordinates to optimizing the total electron density, enabling efficient simulations of large molecular and condensed-matter systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walter Kohn","subfamily":"Computational Chemistry","year":"1965","type":"Electronic structure method"},"citations":[{"ref":"Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138.","type":"article","doi":"10.1103/PhysRev.140.A1133","isbn":null,"url":null},{"ref":"Hohenberg, P., Kohn, W. (1964). Inhomogeneous electron gas. Physical Review, 136, B864–B871.","type":"article","doi":"10.1103/PhysRev.136.B864","isbn":null,"url":null},{"ref":"Burke, K. (2012). Perspective on density functional theory. The Journal of Chemical Physics, 136, 150901.","type":"article","doi":"10.1063/1.4704546","isbn":null,"url":null}],"related":["hartree-fock-method","moller-plesset-perturbation-theory","time-dependent-dft","quantum-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dental-anxiety-modified-scale","name":"MDAS","fullName":"Modified Dental Anxiety Scale","aliases":["MDAS","Modified Dental Anxiety Scale (MDAS)"],"domain":"dentistry","family":"process-pipeline","subfamily":"dental-anxiety","year":"1995","originator":"Gerry M. Humphris et al.","url":"https://scholargate.app/en/dentistry/dental-anxiety-modified-scale","markdownUrl":"https://scholargate.app/en/dentistry/dental-anxiety-modified-scale.md","definition":"The Modified Dental Anxiety Scale (MDAS) is a brief 5-item self-report instrument measuring anxiety anticipation and response to common dental situations. Developed by Humphris and colleagues in 1995 as a refinement of prior instruments, the MDAS has become the gold standard for rapid dental anxiety screening in clinical practice and research. Its brevity, psychometric rigor, and clinical utility have made it the most frequently used measure of dental anxiety internationally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gerry M. Humphris et al.","subfamily":"dental-anxiety","year":"1995","type":"Self-report questionnaire"},"citations":[{"ref":"Humphris, G. M., Morrison, T., & Lindsay, S. J. (1995). The Modified Dental Anxiety Scale: validation and United Kingdom norms. Community Dentistry and Oral Epidemiology, 23(6), 326-330.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Modified+Dental+Anxiety+Scale%3A+validation+and+United+Kingdom+norms+Humphris"}],"related":["dental-fear-survey","ohip-14","child-oral-health-qol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dental-anxiety-scale","name":"Corah Dental Anxiety Scale","fullName":"Corah Dental Anxiety Scale","aliases":["DAS","CDAS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"situational/specific anxiety","year":"1969","originator":"Norman L. Corah","url":"https://scholargate.app/en/clinical-psychology/dental-anxiety-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/dental-anxiety-scale.md","definition":"The Corah Dental Anxiety Scale (DAS), also known as the Dental Anxiety Scale, is a brief 4-item self-report questionnaire designed to measure anxiety associated with dental treatment. Developed by Norman L. Corah in 1969, the DAS is the most widely used instrument for assessing dental anxiety in clinical practice and research, valued for its brevity, ease of administration, and strong psychometric properties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman L. Corah","subfamily":"situational/specific anxiety","year":"1969","type":"Self-report dental anxiety scale"},"citations":[{"ref":"Corah, N. L. (1969). Development of a dental anxiety scale. Journal of Dental Research, 48(4), 596.","type":"article","doi":"10.1177/00220345690480041801","isbn":null,"url":null}],"related":["gad-7","beck-anxiety-inventory","state-trait-anxiety-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dental-caries-risk-assessment","name":"Caries-risk Assessment Tool","fullName":"Dental Caries Risk Assessment Tool","aliases":["Caries-risk Assessment","CAMBRA","CAT"],"domain":"dentistry","family":"process-pipeline","subfamily":"caries-prevention-risk-stratification","year":"2007","originator":"John D. Featherstone and collaborative organizations (AAPD, ADA)","url":"https://scholargate.app/en/dentistry/dental-caries-risk-assessment","markdownUrl":"https://scholargate.app/en/dentistry/dental-caries-risk-assessment.md","definition":"The Caries-risk Assessment Tool (CAT), also known as Caries Management by Risk Assessment (CAMBRA), is a systematic framework for evaluating a patient's risk of developing dental caries (cavities). Developed by Featherstone and endorsed by the American Academy of Pediatric Dentistry (AAPD), American Dental Association (ADA), and International Association of Dental Research, the CAT stratifies patients into low, moderate, or high caries-risk categories based on clinical and behavioral factors. Risk assessment guides preventive interventions, enabling individualized caries management and efficient resource allocation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John D. Featherstone and collaborative organizations (AAPD, ADA)","subfamily":"caries-prevention-risk-stratification","year":"2007","type":"Risk assessment questionnaire and clinical examination"},"citations":[{"ref":"Featherstone, J. D. (2004). The caries balance: contributing factors and early detection. Journal of the California Dental Association, 31(2), 129-133.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Featherstone%2C%20J.%20D.%20(2004).%20The%20caries%20balance%3A%20contributing%20factors%20and%20early%20detection.%20Journal%20of%20the%20California%20Dent"},{"ref":"American Academy of Pediatric Dentistry. (2023). Guideline on caries-risk assessment and management for infants, children, and adolescents. Pediatric Dentistry, 45(6), 341-354.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=American%20Academy%20of%20Pediatric%20Dentistry.%20(2023).%20Guideline%20on%20caries-risk%20assessment%20and%20management%20for%20infants%2C%20childre"}],"related":["ohip-14","child-oral-health-qol","oral-impacts-daily-performance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dental-erosion-index","name":"Dental Erosion Index","fullName":"Tooth Surface Loss Assessment by Erosion Index","aliases":["tooth wear index","erosion severity index","TSL index"],"domain":"dentistry","family":"process-pipeline","subfamily":"Preventive dentistry and wear assessment","year":"1990s+ (systematic indices)","originator":"Multiple indices (Lussi index, BEWE, etc.)","url":"https://scholargate.app/en/dentistry/dental-erosion-index","markdownUrl":"https://scholargate.app/en/dentistry/dental-erosion-index.md","definition":"The Dental Erosion Index is a systematic clinical assessment tool that quantifies the severity of tooth surface loss caused by non-carious erosive agents (acidic substances, mechanical abrasion, or biological factors). Multiple index systems exist (e.g., Lussi Index, Basic Erosive Wear Examination or BEWE), each scoring erosion based on the extent and depth of surface loss on coronal and cervical tooth surfaces. Erosion assessment is critical for identifying patients at risk for advanced tooth loss, determining preventive interventions, and guiding restorative management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple indices (Lussi index, BEWE, etc.)","subfamily":"Preventive dentistry and wear assessment","year":"1990s+ (systematic indices)","type":"Clinical assessment index"},"citations":[{"ref":"Lussi, A., Jaeggi, T., & Zero, D. (2004). The role of diet in the aetiology of dental erosion. Caries Research, 38(1), 34-44.","type":"article","doi":"10.1159/000074360","isbn":null,"url":null},{"ref":"Bartlett, D. W., Lussi, A., West, N. X., Bouchard, P., Sanz, M., & Bourgeois, D. (2013). Prevalence, aetiology and consequences of erosive tooth wear. European Journal of Oral Sciences, 121(1), 1-6.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prevalence%2C+aetiology+and+consequences+of+erosive+tooth+wear+Bartlett"},{"ref":"Ericson, D., & Ericson, T. (2010). Erosion of the teeth in patients with eating disorders. Acta Odontologica Scandinavica, 68(5), 297-304.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Erosion+of+the+teeth+in+patients+with+eating+disorders+Ericson"}],"related":["dmft-index","gingival-index","oral-hygiene-index","periodontal-probing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dental-implant-stability-rfa","name":"Resonance Frequency Analysis for Implants","fullName":"Implant Stability Assessment by Resonance Frequency Analysis","aliases":["RFA","Implant Stability Quotient","ISQ","osseointegration assessment"],"domain":"dentistry","family":"process-pipeline","subfamily":"Prosthodontics and implantology","year":"1996","originator":"Neil Meredith and colleagues","url":"https://scholargate.app/en/dentistry/dental-implant-stability-rfa","markdownUrl":"https://scholargate.app/en/dentistry/dental-implant-stability-rfa.md","definition":"Resonance Frequency Analysis (RFA) is a non-invasive, objective method for assessing dental implant stability and osseointegration. Introduced by Meredith and colleagues in 1996, RFA measures the stiffness of the implant-bone interface by analysing the frequency response of an implant abutment to vibration. The Implant Stability Quotient (ISQ), derived from RFA, enables quantitative monitoring of implant stability at insertion, during healing, and post-integration, facilitating clinical decision-making regarding loading timing and success prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Neil Meredith and colleagues","subfamily":"Prosthodontics and implantology","year":"1996","type":"Non-invasive stability assessment"},"citations":[{"ref":"Meredith, N., Alleyne, D., & Cawley, P. (1996). Quantitative determination of the stability of the implant-tissue interface using resonance frequency analysis. Clinical Oral Implants Research, 7(3), 261-267.","type":"article","doi":"10.1034/j.1600-0501.1996.070308.x","isbn":null,"url":null},{"ref":"Nedir, R., Bischof, M., Szmukler-Moncler, S., Bernard, J. P., & Samson, J. (2004). Predicting osseointegration by means of implant primary stability. Clinical Oral Implants Research, 15(5), 520-528.","type":"article","doi":"10.1111/j.1600-0501.2004.01059.x","isbn":null,"url":null},{"ref":"Aparicio, C., Lang, N. P., & Rangert, B. (2006). Validity and clinical significance of biomechanical testing of implant/bone interface. Clinical Oral Implants Research, 17(2), 2-7.","type":"article","doi":"10.1111/j.1600-0501.2006.01365.x","isbn":null,"url":null}],"related":["bone-density-dental","orthodontic-cephalometry","occlusal-analysis","periodontal-probing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dental-microwear-texture-analysis","name":"Dental Microwear Texture Analysis","fullName":"Dental Microwear Texture Analysis (DMTA)","aliases":["microwear analysis","dental wear analysis"],"domain":"archaeology","family":"process-pipeline","subfamily":"Functional Morphology","year":"1988","originator":"Peter Teaford","url":"https://scholargate.app/en/archaeology/dental-microwear-texture-analysis","markdownUrl":"https://scholargate.app/en/archaeology/dental-microwear-texture-analysis.md","definition":"Dental microwear texture analysis (DMTA) is a method that reconstructs diet and dietary behavior from microscopic wear patterns on the surfaces of teeth. Pioneered by Mark Teaford in the 1980s, DMTA analyzes the three-dimensional texture of wear patterns produced as food is chewed. The method reflects short-term (last few months) dietary composition, complementing longer-term dietary information obtained from stable isotope analysis. DMTA has proven powerful for distinguishing diets rich in tough/fibrous foods from those dominated by hard/brittle foods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Teaford","subfamily":"Functional Morphology","year":"1988","type":"Dietary inference method"},"citations":[{"ref":"Ungar, P. S. (2007). Evolution of the human diet: The known, the unknown, and the unknowable. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evolution+of+the+human+diet%3A+The+known%2C+the+unknown%2C+and+the+unknowable+Ungar"},{"ref":"Teaford, M. F. (1988). A review of dental microwear and diet in modern mammals. Scanning Microscopy, 2(2), 1149-1166.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/pubmed/3154081"},{"ref":"Grine, F. E. (1986). Dental evidence for dietary differences in Australopithecus and Paranthropus. Journal of Human Evolution, 15(10), 783-822.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dental+evidence+for+dietary+differences+in+Australopithecus+and+Paranthropus+Grine"}],"related":["isotope-diet-reconstruction","geometric-morphometrics","use-wear-analysis","minimum-number-of-individuals"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dependency-parsing","name":"Dependency Parsing","fullName":"Dependency Parsing (Syntactic Dependency Analysis)","aliases":["syntactic dependency analysis","dependency tree parsing","Bağımlılık Ayrıştırma (Dependency Parsing)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/dependency-parsing","markdownUrl":"https://scholargate.app/en/text-mining/dependency-parsing.md","definition":"Dependency parsing is a natural-language-processing task that reveals the syntactic dependency relations between the words of a sentence as a tree structure. Surveyed in the dependency-grammar tradition by Nivre (2005) and made fast and accurate with neural networks by Chen and Manning (2014), it is commonly used as a prerequisite step for information extraction and relation detection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP syntactic-analysis task","output":"Dependency tree of word-to-word syntactic relations","structure":"Tree linking each word to its syntactic head with a relation label","role":"Prerequisite for information extraction and relation detection"},"citations":[{"ref":"Nivre, J. (2005). Dependency Grammar and Dependency Parsing. MSI Report.","type":"report","doi":null,"isbn":null,"url":"https://stp.lingfil.uu.se/~nivre/docs/05133.pdf"},{"ref":"Chen, D. & Manning, C. D. (2014). A Fast and Accurate Dependency Parser Using Neural Networks. EMNLP.","type":"inproceedings","doi":"10.3115/v1/D14-1082","isbn":null,"url":null}],"related":["coreference-resolution","semantic-role-labeling","named-entity-recognition"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"depersonalization-derealization-scale","name":"Cambridge Depersonalisation Scale","fullName":"Cambridge Depersonalisation Scale (CDS)","aliases":["CDS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"dissociation-dissociative-experience","year":"2000","originator":"Mauricio Sierra & German E. Berrios","url":"https://scholargate.app/en/clinical-psychology/depersonalization-derealization-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/depersonalization-derealization-scale.md","definition":"The CDS is a 29-item self-report measure of depersonalisation and derealisation experiences, developed by Sierra and Berrios in 2000. It is the most widely used instrument for assessing dissociative symptom severity in both clinical and research settings, valuable for identifying depersonalisation disorder, monitoring treatment response, and understanding the prevalence of depersonalisation in anxiety, mood, and trauma populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mauricio Sierra & German E. Berrios","subfamily":"dissociation-dissociative-experience","year":"2000","type":"Self-report questionnaire"},"citations":[{"ref":"Sierra, M., & Berrios, G. E. (2000). The Cambridge Depersonalisation Scale: a new instrument for the measurement of depersonalisation. Psychiatry Research, 93(2), 153–164.","type":"article","doi":"10.1016/s0165-1781(00)00100-1","isbn":null,"url":null}],"related":["emotion-regulation-questionnaire","difficulties-emotion-regulation","adult-adhd-self-report-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deployment-risk-resilience","name":"Deployment Risk and Resilience Inventory","fullName":"Deployment Risk and Resilience Inventory (DRRI-2)","aliases":["DRRI","DRRI-2"],"domain":"military-psychology","family":"process-pipeline","subfamily":"Deployment stressors and resilience factors","year":2006,"originator":"King, King, Vogt, Knight, & Samper","url":"https://scholargate.app/en/military-psychology/deployment-risk-resilience","markdownUrl":"https://scholargate.app/en/military-psychology/deployment-risk-resilience.md","definition":"The DRRI-2 is a comprehensive self-report inventory measuring pre-deployment, deployment, and post-deployment risk and protective (resilience) factors influencing mental health outcomes in military personnel. Developed by King and colleagues in 2006 and refined in 2008, it captures contextual, behavioral, social, and psychological factors that shape post-deployment adjustment. It is used in military health surveillance, clinical formulation, and research examining how risk-resilience balance predicts PTSD and other adverse outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"King, King, Vogt, Knight, & Samper","subfamily":"Deployment stressors and resilience factors","year":2006,"type":"Self-report"},"citations":[{"ref":"King, D. W., King, L. A., Vogt, D. S., Knight, J., & Samper, R. E. (2006). Deployment Risk and Resilience Inventory: A collection of empirically derived factors for stress outcomes. Journal of Behavioral Decision Making, 19(2), 87-101.","type":"article","doi":"10.1037/t04522-000","isbn":null,"url":null},{"ref":"Vogt, D. S., Proctor, S. P., King, D. W., King, L. A., & Vasterling, J. J. (2008). Validation of scales from the Deployment Risk and Resilience Inventory in a large sample of Operation Enduring Freedom/Operation Iraqi Freedom veterans. Psychological Assessment, 20(2), 180-191.","type":"article","doi":"10.1177/1073191108316030","isbn":null,"url":null}],"related":["pcl-military","combat-exposure-scale","military-identity-scale","post-deployment-reintegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dermatology-life-quality-index-children","name":"Children's DLQI","fullName":"Children's Dermatology Life Quality Index","aliases":["cDLQI","Pediatric DLQI"],"domain":"dermatology","family":"process-pipeline","subfamily":"quality-of-life-pediatric","year":"1995","originator":"Lewis-Jones MS, Finlay AY","url":"https://scholargate.app/en/dermatology/dermatology-life-quality-index-children","markdownUrl":"https://scholargate.app/en/dermatology/dermatology-life-quality-index-children.md","definition":"The Children's Dermatology Life Quality Index (cDLQI) is a pediatric-adapted version of the adult DLQI, measuring the impact of skin disease on quality of life in children and adolescents aged 4–16 years. Developed by Lewis-Jones and Finlay in 1995, it uses child-friendly language and addresses domains relevant to childhood (school, leisure, friendships, clothing) rather than work and adult relationships. cDLQI is the standard quality-of-life measure in pediatric dermatology trials and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lewis-Jones MS, Finlay AY","subfamily":"quality-of-life-pediatric","year":"1995","type":"Self-report (parent or child proxy)"},"citations":[{"ref":"Lewis-Jones MS, Finlay AY. The Children's Dermatology Life Quality Index (cDLQI): initial validation and practical use. Br J Dermatol. 1995;132(6):942-949.","type":"article","doi":"10.1111/j.1365-2133.1995.tb16953.x","isbn":null,"url":null},{"ref":"Finlay AY. Quality of life measurement in dermatology: a practical approach. Br J Dermatol. 1997;136(3):305-314.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Finlay+AY.+Quality+of+life+measurement+in+dermatology%3A+a+practical+approach.+Br+J+Dermatol.+1997%3B136%283%29%3A305-314.+Finlay"}],"related":["poem","skindex-29","scorad"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"descriptive-phenomenology","name":"Descriptive Phenomenology","fullName":"Descriptive Phenomenological Method (Giorgi)","aliases":["Giorgi method","empirical phenomenology","scientific phenomenology","Husserlian descriptive phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Phenomenology","year":"1970s–1985 (systematised by Giorgi; refined 2009)","originator":"Amedeo Giorgi (adapting Edmund Husserl's transcendental phenomenology)","url":"https://scholargate.app/en/qualitative/descriptive-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/descriptive-phenomenology.md","definition":"Descriptive Phenomenology, systematised by Amedeo Giorgi at Duquesne University, is a rigorous qualitative method for uncovering the general psychological structure of a lived experience. Drawing directly on Husserl's transcendental phenomenology, Giorgi's four-step procedure — epoché, whole reading, meaning-unit discrimination, and transformation into disciplinary language — produces a stable, replicable description of what makes an experience essentially what it is, without theoretical interpretation or causal explanation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Amedeo Giorgi (adapting Edmund Husserl's transcendental phenomenology)","year":"1970s–1985 (systematised by Giorgi; refined 2009)","type":"Qualitative research method","dataType":"Verbatim interview transcripts; written first-person accounts","typicalSampleSize":"3–15 participants","subfamily":"Phenomenology"},"citations":[{"ref":"Giorgi, A. (2009). The Descriptive Phenomenological Method in Psychology: A Modified Husserlian Approach. Duquesne University Press.","type":"book","doi":null,"isbn":"978-0820703992","url":null},{"ref":"Giorgi, A. (Ed.). (1985). Phenomenology and Psychological Research. Duquesne University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Phenomenology+and+Psychological+Research+Giorgi+1985"}],"related":["phenomenology","grounded-theory","narrative-analysis","thematic-analysis","case-study","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"descriptive-research","name":"Descriptive Research","fullName":"Descriptive Research Design","aliases":["descriptive study","descriptive survey design","observational descriptive research","non-experimental descriptive research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Late 19th century; formalized in social/behavioral sciences ~1960s–1980s","originator":"Francis Galton, Karl Pearson (early empirical tradition); formalized in social science by Fred Kerlinger","url":"https://scholargate.app/en/research-design/descriptive-research","markdownUrl":"https://scholargate.app/en/research-design/descriptive-research.md","definition":"Descriptive research is a non-experimental quantitative design that systematically documents the characteristics, frequencies, or distributions of variables in a defined population at a given point in time. It answers 'what is' questions — who, what, when, where, and how much — without manipulating variables or drawing causal conclusions. It is one of the most widely used research designs across the social, behavioral, health, and education sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Francis Galton, Karl Pearson (early empirical tradition); formalized in social science by Fred Kerlinger","year":"Late 19th century; formalized in social/behavioral sciences ~1960s–1980s","type":"Non-experimental quantitative research design","dataType":"Surveys, structured observations, census or archival records, questionnaires","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null},{"ref":"Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030417498","url":null}],"related":["survey-research","cross-sectional-research","correlational-research","exploratory-quantitative-research","observational-quantitative-research","longitudinal-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"descriptive-statistics","name":"Descriptive Statistics","fullName":"Descriptive Statistics","aliases":["summary statistics","exploratory data summary","Betimsel İstatistik"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1977,"originator":"John W. Tukey","url":"https://scholargate.app/en/statistics/descriptive-statistics","markdownUrl":"https://scholargate.app/en/statistics/descriptive-statistics.md","definition":"Descriptive statistics is a set of procedures that numerically and visually summarises the essential characteristics of a dataset: central tendency (mean, median, mode), spread (standard deviation, interquartile range), shape (skewness, kurtosis), and frequency distributions. Systematised for applied data analysis by John W. Tukey in his 1977 work on Exploratory Data Analysis, descriptive statistics serves as the indispensable first step before any inferential or modelling procedure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Tukey","year":1977,"family":"Descriptive / Exploratory","type":"Summary procedure","parametric":false,"minSample":10,"difficulty":1,"mathLoad":1,"acceptanceLevel":5,"suitableVariableTypes":"continuous, categorical, ordinal, binary, count","suitableStructures":"all"},"citations":[{"ref":"Tukey, J.W. (1977). Exploratory Data Analysis. Addison-Wesley.","type":"book","doi":null,"isbn":"978-0201076165","url":null}],"related":["shapiro-wilk-test","kolmogorov-smirnov","independent-t-test","pearson-correlation","one-way-anova","chi-square"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-concurrent-embedded-mixed-methods-design","name":"Design-based concurrent embedded mixed methods design","fullName":"Design-Based Concurrent Embedded Mixed Methods Design","aliases":["DBR concurrent embedded design","design-based embedded MMR","concurrent embedded DBR design","design experiment embedded mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (concurrent embedded framework); Design-Based Research Collective (DBR framework)","url":"https://scholargate.app/en/research-design/design-based-concurrent-embedded-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/design-based-concurrent-embedded-mixed-methods-design.md","definition":"Design-based concurrent embedded mixed methods design merges the iterative intervention logic of Design-Based Research (DBR) with the concurrent embedded mixed methods structure, in which one data type (typically qualitative) is nested within a dominant dataset (typically quantitative) and both are collected simultaneously within each design cycle. This approach is especially suited to educational intervention and applied research contexts where a product, curriculum, or tool is being developed, tested, and refined through repeated cycles of implementation and analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (concurrent embedded framework); Design-Based Research Collective (DBR framework)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative and qualitative data collected simultaneously within iterative design cycles","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null},{"ref":"Nolen, A. L., & VanderPutten, J. (2007). Action research in education: Addressing gaps in ethical principles and practices. Educational Researcher, 36(7), 401–407.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Design-Based+Research+mixed+methods+embedded+concurrent"}],"related":["concurrent-embedded-mixed-methods-design","design-based-research","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","intervention-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-explanatory-sequential-mixed-methods-design","name":"Design-based Explanatory Sequential Mixed Methods Design","fullName":"Design-Based Explanatory Sequential Mixed Methods Design","aliases":["DBR explanatory sequential design","design-based explanatory mixed methods","design research explanatory sequential","DBEMM explanatory sequential"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s (synthesis of explanatory sequential and design-based research traditions)","originator":"Creswell & Plano Clark (explanatory sequential); Brown, Collins & Duguid, and Reeves (design-based research)","url":"https://scholargate.app/en/research-design/design-based-explanatory-sequential-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/design-based-explanatory-sequential-mixed-methods-design.md","definition":"This design embeds an explanatory sequential mixed methods structure — quantitative data collection followed by qualitative follow-up — within iterative design-based research (DBR) cycles. The quantitative phase establishes what is happening with a designed intervention or learning environment; the qualitative follow-up explains why. Results then feed directly back into redesign, making the method especially powerful in educational and instructional technology research where both statistical patterns and contextual understanding are needed to refine innovations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (explanatory sequential); Brown, Collins & Duguid, and Reeves (design-based research)","year":"2000s–2010s (synthesis of explanatory sequential and design-based research traditions)","type":"Mixed methods research design","dataType":"Quantitative data (Phase 1) followed by qualitative data (Phase 2), embedded within iterative design-test cycles","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"McKenney, S., & Reeves, T. C. (2018). Conducting Educational Design Research (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1138574816","url":null}],"related":["explanatory-sequential-mixed-methods-design","design-based-research","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","multiphase-mixed-methods-design","embedded-explanatory-sequential-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-intervention-mixed-methods","name":"Design-Based Intervention Mixed Methods","fullName":"Design-Based Intervention Mixed Methods Design","aliases":["DBR intervention mixed methods","design-based intervention study","design experiment with mixed methods","intervention design-based mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2010s (convergence of DBR and mixed methods traditions)","originator":"Design-Based Research Collective; Creswell & Plano Clark (mixed methods framework)","url":"https://scholargate.app/en/research-design/design-based-intervention-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/design-based-intervention-mixed-methods.md","definition":"Design-based intervention mixed methods is a research design that embeds both quantitative and qualitative data collection within iterative intervention cycles drawn from design-based research (DBR). The approach systematically tests and refines a practical intervention — typically an educational program, curriculum, or organizational solution — while using qualitative data to explain why and how the intervention works, and quantitative data to assess its measurable impact. Iteration between design, testing, and revision is the hallmark of this approach.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Design-Based Research Collective; Creswell & Plano Clark (mixed methods framework)","year":"2003–2010s (convergence of DBR and mixed methods traditions)","type":"Mixed methods research design variant","dataType":"Quantitative outcome data and qualitative process/contextual data collected during iterative intervention cycles","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483344452","url":null},{"ref":"The Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8.","type":"article","doi":"10.3102/0013189X032001005","isbn":null,"url":null}],"related":["intervention-mixed-methods-design","design-based-mixed-methods-meta-inference","embedded-intervention-mixed-methods","participatory-intervention-mixed-methods","multiphase-mixed-methods-design","exploratory-sequential-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-mixed-methods-matrix","name":"Design-based Mixed Methods Matrix","fullName":"Design-based Mixed Methods Matrix Framework","aliases":["mixed methods design matrix","MM design typology matrix","mixed methods design framework","DBMM matrix"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2011","originator":"John W. Creswell & Vicki L. Plano Clark; Jennifer C. Greene","url":"https://scholargate.app/en/research-design/design-based-mixed-methods-matrix","markdownUrl":"https://scholargate.app/en/research-design/design-based-mixed-methods-matrix.md","definition":"The design-based mixed methods matrix is a systematic framework for selecting and structuring mixed methods research designs. It organises key design decisions — purpose, timing of data strands, point of integration, and weighting of quantitative versus qualitative components — into a coherent matrix that guides researchers toward a defensible, transparent design. The framework draws on the typology traditions of Creswell and Plano Clark and Greene's purposes-based approach.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark; Jennifer C. Greene","year":"2003–2011","type":"Mixed methods design classification framework","dataType":"Quantitative and qualitative data (combined)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null},{"ref":"Greene, J. C. (2007). Mixed Methods in Social Inquiry. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787983826","url":null}],"related":["mixed-methods-research","convergent-parallel-design","sequential-explanatory-design","sequential-exploratory-design","embedded-mixed-methods","design-based-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-mixed-methods-meta-inference","name":"Design-based mixed methods meta-inference","fullName":"Design-Based Mixed Methods Meta-Inference","aliases":["mixed methods meta-inference","MMR meta-inference","integrated meta-inference","design-based meta-inference"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2009","originator":"Abbas Tashakkori & Charles Teddlie","url":"https://scholargate.app/en/research-design/design-based-mixed-methods-meta-inference","markdownUrl":"https://scholargate.app/en/research-design/design-based-mixed-methods-meta-inference.md","definition":"Design-based mixed methods meta-inference is the overarching conclusion drawn by explicitly integrating the separate quantitative and qualitative inferences from a mixed methods study, with the integration logic anchored to the a priori research design. Rather than treating quantitative and qualitative results as parallel outputs, the approach requires the researcher to specify — at the design stage — how and why the two strands will be combined, and then to construct a unified meta-inference that is consistent with that design rationale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abbas Tashakkori & Charles Teddlie","year":"2003–2009","type":"Mixed methods integration strategy","dataType":"Combined quantitative and qualitative data strands","subfamily":"Mixed methods design"},"citations":[{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage.","type":"book","doi":null,"isbn":"978-0761920731","url":null}],"related":["mixed-methods-research","sequential-explanatory-design","sequential-exploratory-design","convergent-parallel-design","triangulation","meta-synthesis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-multilevel-mixed-methods","name":"Design-based Multilevel Mixed Methods","fullName":"Design-based Multilevel Mixed Methods Research","aliases":["DB-MLMM","multilevel design-based mixed methods","design-based multilevel research","DBR multilevel mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Synthesized from Design-Based Research Collective (2003) and Creswell & Plano Clark multilevel mixed methods typology","url":"https://scholargate.app/en/research-design/design-based-multilevel-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/design-based-multilevel-mixed-methods.md","definition":"Design-based multilevel mixed methods combines the iterative, context-sensitive logic of design-based research (DBR) with the analytical power of multilevel data structures and the explanatory depth of mixed methods research. It is used predominantly in educational and organizational research where participants are nested within settings (e.g., students within classrooms within schools) and where a designed intervention must be tested, refined, and understood at multiple organizational levels simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Design-Based Research Collective (2003) and Creswell & Plano Clark multilevel mixed methods typology","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Nested quantitative data (e.g., students-in-classrooms) combined with qualitative data (interviews, observations)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. SAGE Publications.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["design-based-research","multilevel-modeling","mixed-methods-research","design-based-intervention-mixed-methods","hierarchical-linear-modeling","explanatory-sequential-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-pragmatic-mixed-methods","name":"Design-based Pragmatic Mixed Methods","fullName":"Design-Based Pragmatic Mixed Methods Research","aliases":["DBPMM","design-based mixed methods","pragmatic design-based research","educational design research with mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Synthesised from Design-Based Research Collective (2003) and pragmatist mixed methods scholars (Creswell, Tashakkori, Teddlie)","url":"https://scholargate.app/en/research-design/design-based-pragmatic-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/design-based-pragmatic-mixed-methods.md","definition":"Design-based pragmatic mixed methods combines the iterative, intervention-focused logic of design-based research (DBR) with the philosophical pragmatism that underpins mixed methods inquiry. Researchers design, test, and refine an educational or organisational intervention across multiple cycles while simultaneously collecting quantitative outcome data and qualitative process data. The pragmatist worldview licenses the integration of both data strands in service of a practical research question: does this design work, for whom, and why?","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Design-Based Research Collective (2003) and pragmatist mixed methods scholars (Creswell, Tashakkori, Teddlie)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative measures (tests, surveys, logs) and qualitative data (interviews, observations, artefacts) collected iteratively","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506386706","url":null},{"ref":"McKenney, S., & Reeves, T. C. (2018). Conducting Educational Design Research (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1138574793","url":null}],"related":["design-based-research","mixed-methods-research","pragmatic-research","action-research","convergent-mixed-methods","explanatory-sequential-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-qualitative-priority-mixed-methods-design","name":"Design-based qualitative-priority mixed methods design","fullName":"Design-Based Qualitative-Priority Mixed Methods Research Design","aliases":["QUAL-priority mixed methods design","qualitative-dominant mixed methods","design-based QUAL-dominant design","qualitative-priority design-based MMR"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1991–2011 (Morse 1991 priority notation; Creswell & Plano Clark 2007–2011 design taxonomy)","originator":"Janice Morse (priority notation); John W. Creswell & Vicki L. Plano Clark (design typology)","url":"https://scholargate.app/en/research-design/design-based-qualitative-priority-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/design-based-qualitative-priority-mixed-methods-design.md","definition":"A design-based qualitative-priority mixed methods design places qualitative inquiry at the centre of the research, using quantitative data in a supporting, secondary role. The qualitative strand drives the research questions, sampling logic, and interpretive conclusions, while quantitative data — collected concurrently or sequentially — provide supplementary breadth, frequency estimates, or contextual triangulation. This approach is codified in the priority-notation system (QUAL + quan) developed by Morse and elaborated in Creswell and Plano Clark's mixed methods design taxonomy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Janice Morse (priority notation); John W. Creswell & Vicki L. Plano Clark (design typology)","year":"1991–2011 (Morse 1991 priority notation; Creswell & Plano Clark 2007–2011 design taxonomy)","type":"Mixed methods research design","dataType":"Qualitative data primary (interviews, observations, documents); quantitative data secondary (surveys, scales, counts)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Morse, J. M. (2003). Principles of mixed methods and multimethod research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 189–208). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Principles+of+mixed+methods+and+multimethod+research+design+Morse+2003"}],"related":["exploratory-sequential-mixed-methods","explanatory-sequential-mixed-methods","convergent-parallel-mixed-methods","grounded-theory","phenomenology","embedded-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-quantitative-priority-mixed-methods-design","name":"Design-based quantitative-priority mixed methods design","fullName":"Design-Based Quantitative-Priority Mixed Methods Research Design","aliases":["QUANT-priority DBR","quantitative-dominant design-based mixed methods","design-based QUAN mixed methods","DBR quantitative-priority"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s (emerging with DBR maturation and mixed methods codification)","originator":"Synthesized from Design-Based Research (Ann Brown, Allan Collins) and Mixed Methods frameworks (Creswell, Plano Clark)","url":"https://scholargate.app/en/research-design/design-based-quantitative-priority-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/design-based-quantitative-priority-mixed-methods-design.md","definition":"Design-based quantitative-priority mixed methods research integrates a design-based research (DBR) framework — which involves iterative cycles of design, implementation, and refinement in naturalistic settings — with a mixed methods approach where quantitative data collection and analysis carry the primary evidentiary weight. Qualitative data are gathered in a supporting role to illuminate, explain, or refine quantitative findings across iterative design cycles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Design-Based Research (Ann Brown, Allan Collins) and Mixed Methods frameworks (Creswell, Plano Clark)","year":"2000s–2010s (emerging with DBR maturation and mixed methods codification)","type":"Mixed methods research design","dataType":"Quantitative (primary) and qualitative (secondary) data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"McKenney, S., & Reeves, T. C. (2018). Conducting Educational Design Research (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1138095564","url":null}],"related":["design-based-research","explanatory-sequential-design","embedded-mixed-methods-design","convergent-parallel-design","experimental-design","action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-research","name":"Design-based Research","fullName":"Design-Based Research","aliases":["DBR","design research","design experiment","educational design research"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1992","originator":"Ann L. Brown and Allan Collins (independently, 1992)","url":"https://scholargate.app/en/field-methods/design-based-research","markdownUrl":"https://scholargate.app/en/field-methods/design-based-research.md","definition":"Design-based research (DBR) is an iterative, interventionist methodology that simultaneously designs educational interventions and builds theory about how and why those interventions work in authentic, complex settings. Originating in Ann Brown's 1992 classroom experiments and Allan Collins's parallel work, DBR treats the learning environment as both the object of study and the site of theory generation, cycling through design, enactment, analysis, and redesign until both practical improvement and theoretical insight are achieved.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ann L. Brown and Allan Collins (independently, 1992)","year":"1992","type":"Interventionist qualitative-quantitative mixed methodology","dataType":"Observational data, interviews, artifacts, test scores, field notes","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. Journal of the Learning Sciences, 2(2), 141–178.","type":"article","doi":"10.1207/s15327809jls0202_2","isbn":null,"url":null},{"ref":"Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8.","type":"article","doi":"10.3102/0013189X032001005","isbn":null,"url":null}],"related":["action-research","educational-action-research","lesson-study","program-evaluation","participatory-action-research","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-based-transformative-mixed-methods-design","name":"Design-based Transformative Mixed Methods Design","fullName":"Design-Based Transformative Mixed Methods Research Design","aliases":["DB-TMMD","transformative design-based mixed methods","design-based transformative inquiry","social justice design-based mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Donna M. Mertens (transformative framework); anchored in design-based research tradition (Brown, Collins, Edelson)","url":"https://scholargate.app/en/research-design/design-based-transformative-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/design-based-transformative-mixed-methods-design.md","definition":"Design-Based Transformative Mixed Methods Design integrates Donna Mertens' transformative paradigm — which foregrounds social justice, equity, and the perspectives of marginalized groups — with the iterative intervention cycles of design-based research. It systematically combines quantitative and qualitative data across successive design-test-refine cycles to both understand and actively improve conditions for underrepresented communities. The approach is prominent in education, public health, and community-engaged research where changing unjust structures is an explicit goal alongside knowledge generation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna M. Mertens (transformative framework); anchored in design-based research tradition (Brown, Collins, Edelson)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative and qualitative data collected iteratively in designed intervention cycles","subfamily":"Mixed methods design"},"citations":[{"ref":"Mertens, D. M. (2007). Transformative paradigm: Mixed methods and social justice. Journal of Mixed Methods Research, 1(3), 212–225.","type":"article","doi":"10.1177/1558689807302811","isbn":null,"url":null},{"ref":"Mertens, D. M. (2010). Research and Evaluation in Education and Psychology: Integrating Diversity with Quantitative, Qualitative, and Mixed Methods (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412958141","url":null}],"related":["transformative-mixed-methods","design-based-research","concurrent-triangulation-design","exploratory-sequential-design","explanatory-sequential-design","participatory-action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-for-manufacturing-and-assembly","name":"Design for Manufacturing and Assembly","fullName":"Design for Manufacturing and Assembly (DFMA) Methodology","aliases":["DFMA","Design for manufacturability","DFA"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Design methodology","year":"1994","originator":"Boothroyd, G., Dewhurst, P.","url":"https://scholargate.app/en/manufacturing/design-for-manufacturing-and-assembly","markdownUrl":"https://scholargate.app/en/manufacturing/design-for-manufacturing-and-assembly.md","definition":"Design for Manufacturing and Assembly (DFMA) is a systematic methodology for creating products that are inherently easier and less expensive to manufacture and assemble. Developed by Boothroyd, Dewhurst, and Knight, DFMA evaluates design choices based on their impact on production cost, quality, and speed, guiding designers toward solutions that balance performance, manufacturability, and economics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Boothroyd, G., Dewhurst, P.","subfamily":"Design methodology","year":"1994","type":"Systematic approach to cost-effective product design"},"citations":[{"ref":"Boothroyd, G., Dewhurst, P., & Knight, W. A. (1994). Product Design for Manufacturing and Assembly (1st ed.). Marcel Dekker.","type":"book","doi":null,"isbn":"0-8247-9157-6","url":null},{"ref":"Ulrich, K. T., & Eppinger, S. D. (2003). Product Design and Development (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"0-07-112257-8","url":null},{"ref":"Swift, K. G., & Booker, J. D. (2005). Process Selection: From Design to Manufacture (2nd ed.). Butterworth-Heinemann.","type":"book","doi":null,"isbn":"0-7506-5933-X","url":null}],"related":["cnc-tool-path-generation","additive-manufacturing-slicing","tolerance-stack-up","modal-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"design-of-experiments","name":"Design of experiments","fullName":"Design of Experiments","aliases":["DOE","experimental design","factorial experimentation","planned experimentation"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1935","originator":"Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/design-of-experiments","markdownUrl":"https://scholargate.app/en/experimental-design/design-of-experiments.md","definition":"Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":"1935","type":"Experimental planning framework","dataType":"Continuous and categorical experimental response data","subfamily":"Engineering methods"},"citations":[{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Design+of+Experiments+Fisher+1935"},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["full-factorial-design","fractional-factorial-design","response-surface-methodology","taguchi-method","central-composite-design","analysis-of-variance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"destination-image-scale","name":"Destination Image Scale","fullName":"Destination Image Scale (DIS)","aliases":["DIS","Destination Perception Scale"],"domain":"tourism-management","family":"process-pipeline","subfamily":"perception-measurement","year":"1991","originator":"Echtner, C. M., & Ritchie, J. R. B.","url":"https://scholargate.app/en/tourism-management/destination-image-scale","markdownUrl":"https://scholargate.app/en/tourism-management/destination-image-scale.md","definition":"The Destination Image Scale (DIS) measures how potential or actual visitors perceive and emotionally evaluate a tourism destination. Developed by Echtner & Ritchie (1991) and extended by Baloglu & Brinberg (1997), it captures both rational beliefs about destination attributes (attractions, climate, value, safety) and affective emotional responses (excitement, pleasantness, arousal). Destination image is a primary driver of visitation intention and repeat patronage, making the DIS essential for destination marketing strategy and competitive positioning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Echtner, C. M., & Ritchie, J. R. B.","subfamily":"perception-measurement","year":"1991","type":"Self-report questionnaire / Semantic differential scale"},"citations":[{"ref":"Baloglu, S., & Brinberg, D. (1997). Affective images of tourism destinations. Journal of Travel Research, 35(4), 11-15.","type":"article","doi":"10.1177/004728759703500402","isbn":null,"url":null},{"ref":"Echtner, C. M., & Ritchie, J. R. B. (1991). The meaning and measurement of destination image. Journal of Tourism Studies, 2(2), 2-12.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+meaning+and+measurement+of+destination+image+Echtner"},{"ref":"Hunt, J. D. (1975). Image as a factor in tourism development. Journal of Travel Research, 13(3), 1-7.","type":"article","doi":"10.1177/004728757501300301","isbn":null,"url":null},{"ref":"Ritchie, J. R. B., & Zins, M. (1978). Culture as determinant of the attractiveness of a tourism region. Annals of Tourism Research, 5(2), 252-267.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Culture+as+determinant+of+the+attractiveness+of+a+tourism+region+Ritchie"}],"related":["tourist-satisfaction-scale","tourist-loyalty-scale","travel-motivation-scale","place-attachment-scale","perceived-value-scale-tourism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"detached-eddy-simulation","name":"Detached Eddy Simulation","fullName":"Detached Eddy Simulation","aliases":["DES","hybrid RANS-LES"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1997","originator":"Philippe Spalart","url":"https://scholargate.app/en/fluid-dynamics/detached-eddy-simulation","markdownUrl":"https://scholargate.app/en/fluid-dynamics/detached-eddy-simulation.md","definition":"Detached Eddy Simulation (DES) is a hybrid turbulence modeling approach introduced by Spalart in 1997 that combines the computational efficiency of RANS in attached boundary layers with the accuracy of LES in separated wake regions. By automatically switching between RANS and LES based on local grid spacing and turbulence length scales, DES provides superior predictions for flows with large separations, shear layers, and vortex shedding at a cost between pure RANS and pure LES. DES has become the standard method for complex aerospace applications involving separation and transient phenomena.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Philippe Spalart","subfamily":"Fluid Dynamics","year":"1997","type":"Hybrid turbulence modeling approach"},"citations":[{"ref":"Spalart, P. R., Jou, W. H., Strelets, M., & Allmaras, S. R. (1997). Comments on the feasibility of LES for wings, and on a hybrid RANS/LES approach. Advances in DNS/LES, 1, 4-8.","type":"article","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/265444857_Comments_on_the_feasibility_of_LES_for_wings_and_on_a_hybrid_RANSLES_approach"},{"ref":"Spalart, P. R., Deck, S., Shur, M. L., Squires, K. D., Strelets, M. Y., & Travin, A. (2006). A new version of detached-eddy simulation, resistant to ambiguous grid densities. Theoretical and Computational Fluid Dynamics, 20(3), 181-195.","type":"article","doi":"10.1007/s00162-006-0015-0","isbn":null,"url":null},{"ref":"Gritskevich, M. S., Garbaruk, A. V., Shur, M. L., & Spalart, P. R. (2012). Development of DDES and IDDES formulations for the k-ω SST turbulence model. Flow, Turbulence and Combustion, 88(3), 431-449.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+of+DDES+and+IDDES+formulations+for+the+k-%CF%89+SST+turbulence+model+Gritskevich"}],"related":["large-eddy-simulation","reynolds-averaged-navier-stokes","direct-numerical-simulation","boundary-layer-theory","smoothed-particle-hydrodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-agent-based-modeling","name":"Deterministic Agent-Based Modeling","fullName":"Deterministic Agent-Based Modeling (D-ABM)","aliases":["D-ABM","Deterministic ABM","Rule-Based Agent Simulation","Fixed-Rule Agent-Based Model"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1996","originator":"Epstein, J. M. & Axtell, R.","url":"https://scholargate.app/en/simulation/deterministic-agent-based-modeling","markdownUrl":"https://scholargate.app/en/simulation/deterministic-agent-based-modeling.md","definition":"Deterministic Agent-Based Modeling (D-ABM) is a computational simulation approach in which autonomous agents follow fully specified, non-random behavioral rules within a structured environment. Every run with identical initial conditions produces identical outcomes, making the model fully reproducible and transparent for analysis of emergent system behavior without stochastic noise.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Epstein, J. M. & Axtell, R.","year":"1996","type":"Computational simulation — deterministic rule-based agents","dataType":"Agent attributes, interaction rules, initial conditions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Epstein, J. M., & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. MIT Press.","type":"book","doi":null,"isbn":"9780262550253","url":null},{"ref":"Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl 3), 7280-7287.","type":"article","doi":"10.1073/pnas.082080899","isbn":null,"url":null}],"related":["agent-based-modeling","stochastic-agent-based-modeling","deterministic-system-dynamics","cellular-automata","deterministic-discrete-event-simulation","multi-agent-systems"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-cellular-automata","name":"Deterministic Cellular Automata","fullName":"Deterministic Cellular Automata — Rule-based discrete dynamical simulation on a grid","aliases":["Deterministic CA","Classical Cellular Automata","Rule-based CA","Finite Automata Grid Model"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1940s–1950s","originator":"John von Neumann and Stanislaw Ulam","url":"https://scholargate.app/en/simulation/deterministic-cellular-automata","markdownUrl":"https://scholargate.app/en/simulation/deterministic-cellular-automata.md","definition":"Deterministic Cellular Automata (DCA) is a simulation method that models the evolution of complex systems through a regular grid of cells, each holding a discrete state, updated synchronously at each time step according to a fixed, deterministic rule applied to the cell and its neighbors. The outcome is fully reproducible given the same initial conditions and rule set.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John von Neumann and Stanislaw Ulam","year":"1940s–1950s","type":"Discrete deterministic grid simulation","dataType":"State-indexed grid data; binary or multi-state discrete variables","subfamily":"Simulation / optimization"},"citations":[{"ref":"von Neumann, J. (1966). Theory of Self-Reproducing Automata. University of Illinois Press, Urbana, IL. (Edited and completed by A. W. Burks.)","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theory+of+Self-Reproducing+Automata+von+Neumann+1966"},{"ref":"Wolfram, S. (1983). Statistical mechanics of cellular automata. Reviews of Modern Physics, 55(3), 601–644.","type":"article","doi":"10.1103/RevModPhys.55.601","isbn":null,"url":null}],"related":["stochastic-cellular-automata","agent-based-modeling","discrete-event-simulation","system-dynamics","markov-model","monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-discrete-event-simulation","name":"Deterministic Discrete-Event Simulation","fullName":"Deterministic Discrete-Event Simulation — Event-driven modeling with fixed, non-random inputs","aliases":["Deterministic DES","Fixed-Input DES","Non-Stochastic Discrete-Event Simulation","Deterministic Event-Driven Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1960s–present","originator":"Banks, J.; Carson, J. S.; Nelson, B. L. (codified); roots in 1960s simulation pioneers (Tocher, Conway)","url":"https://scholargate.app/en/simulation/deterministic-discrete-event-simulation","markdownUrl":"https://scholargate.app/en/simulation/deterministic-discrete-event-simulation.md","definition":"Deterministic Discrete-Event Simulation (Deterministic DES) models a system as a sequence of events occurring at precise, pre-specified times using fixed input parameters. Unlike stochastic DES, no probability distributions are sampled; every arrival, service time, and resource availability is known in advance, making runs fully reproducible and producing a single definitive output trajectory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Banks, J.; Carson, J. S.; Nelson, B. L. (codified); roots in 1960s simulation pioneers (Tocher, Conway)","year":"1960s–present","type":"Simulation — deterministic event-driven model","dataType":"Structured process data: fixed service times, arrival schedules, resource capacities","subfamily":"Simulation / optimization"},"citations":[{"ref":"Banks, J., Carson, J. S., Nelson, B. L., and Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"9780136062127","url":null},{"ref":"Discrete-event simulation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Discrete-event_simulation"}],"related":["discrete-event-simulation","stochastic-discrete-event-simulation","deterministic-system-dynamics","deterministic-markov-model","queueing-simulation","process-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-dynamic-programming","name":"Deterministic Dynamic Programming","fullName":"Deterministic Dynamic Programming — Exact sequential optimization under known parameters","aliases":["DDP","Deterministic DP","Classical Dynamic Programming","Bellman Dynamic Programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957","originator":"Richard E. Bellman","url":"https://scholargate.app/en/simulation/deterministic-dynamic-programming","markdownUrl":"https://scholargate.app/en/simulation/deterministic-dynamic-programming.md","definition":"Deterministic Dynamic Programming (DDP) is a mathematical optimization technique that decomposes a multi-stage decision problem into a sequence of simpler subproblems, solving them exactly when all system parameters — transition functions, costs, and rewards — are known with certainty. It guarantees a globally optimal policy via Bellman's principle of optimality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard E. Bellman","year":"1957","type":"Exact sequential optimization algorithm","dataType":"Deterministic state-transition systems; discrete or continuous state/action spaces","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780691079516","url":null},{"ref":"Bertsekas, D. P. (2017). Dynamic Programming and Optimal Control (4th ed., Vol. 1). Athena Scientific, Belmont, MA.","type":"book","doi":null,"isbn":null,"url":"https://web.mit.edu/dimitrib/www/dpchapter.html"}],"related":["stochastic-dynamic-programming","multi-objective-dynamic-programming","mixed-integer-programming","deterministic-linear-programming","deterministic-integer-programming","markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-genetic-algorithm","name":"Deterministic Genetic Algorithm","fullName":"Deterministic Genetic Algorithm — Evolutionary optimization with deterministic selection and operators","aliases":["DGA","Deterministic EA","Deterministic Evolutionary Algorithm","Deterministic Selection GA"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1975–1989","originator":"Goldberg, D. E.; Holland, J. H.","url":"https://scholargate.app/en/simulation/deterministic-genetic-algorithm","markdownUrl":"https://scholargate.app/en/simulation/deterministic-genetic-algorithm.md","definition":"A Deterministic Genetic Algorithm (DGA) applies the structural framework of evolutionary computation — population, selection, crossover, and replacement — using entirely deterministic operators and fixed decision rules instead of stochastic sampling. By eliminating randomness, the algorithm becomes fully reproducible: running it twice on the same problem yields identical solutions, making it tractable for rigorous benchmarking, reproducibility studies, and systems where stochasticity is undesirable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goldberg, D. E.; Holland, J. H.","year":"1975–1989","type":"Deterministic evolutionary optimization","dataType":"Continuous or discrete decision variables, objective function evaluations","subfamily":"Simulation / optimization"},"citations":[{"ref":"Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA.","type":"book","doi":null,"isbn":"9780201157673","url":null},{"ref":"Mahfoud, S. W. (1995). Niching methods for genetic algorithms. IlliGAL Report No. 95001, University of Illinois at Urbana-Champaign.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Niching+methods+for+genetic+algorithms+Mahfoud+1995"}],"related":["genetic-algorithm","stochastic-genetic-algorithm","multi-objective-genetic-algorithm","nsga-ii","simulated-annealing","deterministic-particle-swarm-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-integer-programming","name":"Deterministic Integer Programming","fullName":"Deterministic Integer Programming","aliases":["DIP","Integer Programming","IP","Integer Linear Programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1958","originator":"Ralph E. Gomory","url":"https://scholargate.app/en/simulation/deterministic-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/deterministic-integer-programming.md","definition":"Deterministic Integer Programming (DIP) is a mathematical optimization approach that finds the best solution to problems where some or all decision variables must take integer values, given fully known (deterministic) objective and constraint data. It is the classical, non-stochastic form of integer programming, foundational to operations research and combinatorial optimization since the late 1950s.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralph E. Gomory","year":"1958","type":"Exact combinatorial optimization","dataType":"Deterministic numerical parameters (objective coefficients, constraint coefficients, RHS values)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Gomory, R. E. (1958). Outline of an algorithm for integer solutions to linear programs. Bulletin of the American Mathematical Society, 64(5), 275-278.","type":"article","doi":"10.1090/S0002-9904-1958-10224-4","isbn":null,"url":null},{"ref":"Wolsey, L. A. (1998). Integer Programming. Wiley-Interscience, New York.","type":"book","doi":null,"isbn":"9780471283669","url":null}],"related":["mixed-integer-programming","linear-programming","stochastic-integer-programming","dynamic-programming","multi-objective-integer-programming","branch-and-bound"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-linear-programming","name":"Deterministic Linear Programming","fullName":"Deterministic Linear Programming — Classical LP with Certain Parameters","aliases":["Classical LP","Deterministic LP","DLP","Linear Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1947","originator":"George B. Dantzig","url":"https://scholargate.app/en/simulation/deterministic-linear-programming","markdownUrl":"https://scholargate.app/en/simulation/deterministic-linear-programming.md","definition":"Deterministic Linear Programming (DLP) is the classical form of linear programming in which all objective function coefficients, constraint coefficients, and right-hand-side values are known with certainty. It finds the optimal allocation of resources to maximize or minimize a linear objective subject to linear constraints, providing an exact, reproducible solution under fixed, certain data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George B. Dantzig","year":"1947","type":"Deterministic mathematical optimization","dataType":"Continuous numerical parameters (objective coefficients, constraint coefficients, right-hand-side values)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780691059136","url":null},{"ref":"Linear programming. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Linear_programming"}],"related":["stochastic-linear-programming","mixed-integer-programming","deterministic-dynamic-programming","multi-objective-linear-programming","deterministic-goal-programming","robust-linear-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-markov-model","name":"Deterministic Markov Model","fullName":"Deterministic Markov Model — Fixed-parameter Markov chain for cohort-level state transitions","aliases":["DMM","Deterministic Markov Chain","Cohort Markov Model","Fixed-Parameter Markov Model"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1993","originator":"Sonnenberg, F. A. & Beck, J. R.","url":"https://scholargate.app/en/simulation/deterministic-markov-model","markdownUrl":"https://scholargate.app/en/simulation/deterministic-markov-model.md","definition":"A Deterministic Markov Model is a cohort-level state-transition model in which all transition probabilities, state utilities, and costs are assigned single fixed values and the model is solved analytically in a single pass. Widely used in health technology assessment, policy analysis, and operations research, it traces a hypothetical cohort through mutually exclusive health or system states over discrete time cycles, accumulating expected outcomes such as quality-adjusted life years (QALYs) or costs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sonnenberg, F. A. & Beck, J. R.","year":"1993","type":"Cohort state-transition model with fixed transition probabilities","dataType":"Transition probability matrices, state rewards, cohort sizes","subfamily":"Simulation / optimization"},"citations":[{"ref":"Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: a practical guide. Medical Decision Making, 13(4), 322–338.","type":"article","doi":"10.1177/0272989X9301300409","isbn":null,"url":null},{"ref":"Briggs, A., Sculpher, M., & Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press.","type":"book","doi":null,"isbn":"9780198526629","url":null}],"related":["markov-model","stochastic-markov-model","monte-carlo-simulation","discrete-event-simulation","sensitivity-analysis","scenario-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-microsimulation","name":"Deterministic Microsimulation","fullName":"Deterministic Microsimulation — Rule-based individual-level simulation without random draws","aliases":["Arithmetic Microsimulation","Static Tax-Benefit Microsimulation","Deterministic Policy Simulation","Rule-based Microsimulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957","originator":"Guy H. Orcutt","url":"https://scholargate.app/en/simulation/deterministic-microsimulation","markdownUrl":"https://scholargate.app/en/simulation/deterministic-microsimulation.md","definition":"Deterministic Microsimulation applies a fixed set of policy rules or behavioral equations to each individual or household record in a microdata file, computing exact outcomes without any random sampling. It is the standard engine behind tax-benefit calculators and demographic projection models used by governments worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guy H. Orcutt","year":"1957","type":"Individual-level deterministic rule application","dataType":"Microdata (individual/household records)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Orcutt, G. H. (1957). A new type of socio-economic system. Review of Economics and Statistics, 39(2), 116–123.","type":"article","doi":"10.2307/1928528","isbn":null,"url":null},{"ref":"Bourguignon, F., & Spadaro, A. (2006). Microsimulation as a tool for evaluating redistribution policies. Journal of Economic Inequality, 4(1), 77–106.","type":"article","doi":"10.1007/s10888-005-9012-6","isbn":null,"url":null}],"related":["monte-carlo-simulation","stochastic-microsimulation","markov-model","discrete-event-simulation","system-dynamics","scenario-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-mixed-integer-programming","name":"Deterministic Mixed-Integer Programming","fullName":"Deterministic Mixed-Integer Programming (Deterministic MIP)","aliases":["Deterministic MIP","Deterministic MILP/MIQP","Classical Mixed-Integer Programming","Deterministic MIP Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1958–1960","originator":"Gomory, R. E.; Dantzig, G. B.; Land, A. H.; Doig, A. G.","url":"https://scholargate.app/en/simulation/deterministic-mixed-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/deterministic-mixed-integer-programming.md","definition":"Deterministic Mixed-Integer Programming (MIP) is a mathematical optimization framework that finds the provably optimal solution to problems involving both continuous and integer decision variables under fully known, fixed coefficients and constraints. It is the foundational workhorse of operations research when all data are treated as certain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gomory, R. E.; Dantzig, G. B.; Land, A. H.; Doig, A. G.","year":"1958–1960","type":"Mathematical programming / combinatorial optimization","dataType":"Deterministic numerical parameters (costs, capacities, demands); continuous and integer decision variables","subfamily":"Simulation / optimization"},"citations":[{"ref":"Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. John Wiley & Sons, New York.","type":"book","doi":null,"isbn":"9780471359432","url":null},{"ref":"Gomory, R. E. (1958). Outline of an algorithm for integer solutions to linear programs. Bulletin of the American Mathematical Society, 64(5), 275-278.","type":"article","doi":"10.1090/S0002-9904-1958-10224-4","isbn":null,"url":null}],"related":["mixed-integer-programming","stochastic-mixed-integer-programming","deterministic-linear-programming","deterministic-dynamic-programming","multi-objective-mixed-integer-programming","robust-mixed-integer-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-multi-objective-optimization","name":"Deterministic Multi-Objective Optimization","fullName":"Deterministic Multi-Objective Optimization — Classical Pareto-based and scalarization approaches without stochastic components","aliases":["Deterministic MOO","Classical Multi-Objective Optimization","Non-Stochastic MOO","Deterministic Pareto Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1951–1999","originator":"Kuhn, H. W., Tucker, A. W. (Pareto optimality formalized); Miettinen, K. (systematic deterministic framework)","url":"https://scholargate.app/en/simulation/deterministic-multi-objective-optimization","markdownUrl":"https://scholargate.app/en/simulation/deterministic-multi-objective-optimization.md","definition":"Deterministic Multi-Objective Optimization (Deterministic MOO) is a family of classical optimization approaches that simultaneously minimize or maximize multiple conflicting objective functions over a deterministic feasible set. It produces a Pareto front — the set of non-dominated solutions — from which a decision-maker selects the preferred trade-off. Unlike stochastic variants, all objective evaluations and constraints are fixed and noise-free.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kuhn, H. W., Tucker, A. W. (Pareto optimality formalized); Miettinen, K. (systematic deterministic framework)","year":"1951–1999","type":"Optimization framework — deterministic Pareto and scalarization methods","dataType":"Continuous or integer decision variables; deterministic objective functions and constraints","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester.","type":"book","doi":null,"isbn":"978-0-471-87339-6","url":null},{"ref":"Miettinen, K. (1999). Nonlinear Multiobjective Optimization. Springer, Boston.","type":"book","doi":null,"isbn":"978-1-4613-7544-9","url":null}],"related":["multi-objective-optimization","stochastic-multi-objective-optimization","nsga-ii","multi-objective-linear-programming","pareto-analysis","weighted-sum-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-particle-swarm-optimization","name":"Deterministic Particle Swarm Optimization","fullName":"Deterministic Particle Swarm Optimization (DPSO)","aliases":["DPSO","Deterministic PSO","PSO without stochastic components","Fully Deterministic PSO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1995 (PSO); deterministic formulation circa 2002","originator":"Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature","url":"https://scholargate.app/en/simulation/deterministic-particle-swarm-optimization","markdownUrl":"https://scholargate.app/en/simulation/deterministic-particle-swarm-optimization.md","definition":"Deterministic Particle Swarm Optimization (DPSO) removes the stochastic random coefficients from classical PSO, replacing them with fixed cognitive and social acceleration parameters. Particles move through the search space following fully predictable trajectories, enabling reproducible convergence analysis and guaranteed termination behavior in continuous and combinatorial optimization problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature","year":"1995 (PSO); deterministic formulation circa 2002","type":"Swarm intelligence metaheuristic — deterministic variant","dataType":"Continuous or discrete decision variables, objective function evaluations","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE.","type":"inproceedings","doi":"10.1109/ICNN.1995.488968","isbn":null,"url":null},{"ref":"Clerc, M., Kennedy, J. (2002). The particle swarm — explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.","type":"article","doi":"10.1109/4235.985692","isbn":null,"url":null}],"related":["particle-swarm-optimization","stochastic-particle-swarm-optimization","genetic-algorithm","simulated-annealing","ant-colony-optimization","multi-objective-particle-swarm-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-scenario-analysis","name":"Deterministic Scenario Analysis","fullName":"Deterministic Scenario Analysis — Fixed-parameter scenario exploration for planning and decision support","aliases":["DSA","Fixed-Input Scenario Analysis","Classical Scenario Analysis","Deterministic What-If Analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1967","originator":"Kahn, H., Wiener, A. J. (RAND Corporation / Hudson Institute)","url":"https://scholargate.app/en/simulation/deterministic-scenario-analysis","markdownUrl":"https://scholargate.app/en/simulation/deterministic-scenario-analysis.md","definition":"Deterministic Scenario Analysis (DSA) is a structured planning method in which analysts construct a finite set of internally consistent future scenarios, each defined by fixed, precisely specified parameter values rather than probability distributions. By running a model or calculation under each scenario's fixed inputs, decision-makers can map how outcomes diverge across plausible futures and stress-test strategies without requiring full probabilistic characterization of uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kahn, H., Wiener, A. J. (RAND Corporation / Hudson Institute)","year":"1967","type":"Exploratory planning and decision-support framework","dataType":"Quantitative inputs (fixed numeric parameters), qualitative narrative drivers","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kahn, H., Wiener, A. J. (1967). The Year 2000: A Framework for Speculation on the Next Thirty-Three Years. Macmillan, New York.","type":"book","doi":null,"isbn":"9780025604407","url":null},{"ref":"Schoemaker, P. J. H. (1993). Multiple scenario development: Its conceptual and behavioral foundation. Strategic Management Journal, 14(3), 193–213.","type":"article","doi":"10.1002/smj.4250140304","isbn":null,"url":null}],"related":["scenario-analysis","stochastic-scenario-analysis","sensitivity-analysis","deterministic-sensitivity-analysis","monte-carlo-simulation","system-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-sensitivity-analysis","name":"Deterministic Sensitivity Analysis","fullName":"Deterministic Sensitivity Analysis — Systematic Parameter Variation for Model Robustness","aliases":["DSA","One-Way Sensitivity Analysis","Tornado Diagram Analysis","Parametric Sensitivity Analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1950s–1970s (formalized)","originator":"Saltelli, A. et al.; widely formalized across operations research and health economics","url":"https://scholargate.app/en/simulation/deterministic-sensitivity-analysis","markdownUrl":"https://scholargate.app/en/simulation/deterministic-sensitivity-analysis.md","definition":"Deterministic Sensitivity Analysis (DSA) tests how model outputs change when individual or combined input parameters are varied across plausible ranges, one at a time or in structured combinations, without invoking probabilistic sampling. It is the standard approach in economic modeling, decision trees, and mathematical programming to identify which parameters drive conclusions and to demonstrate model robustness to regulators, reviewers, and stakeholders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saltelli, A. et al.; widely formalized across operations research and health economics","year":"1950s–1970s (formalized)","type":"Parameter variation / robustness testing","dataType":"Quantitative model outputs and input parameter ranges","subfamily":"Simulation / optimization"},"citations":[{"ref":"Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons, Chichester.","type":"book","doi":null,"isbn":"9780470870938","url":null},{"ref":"Briggs, A., Sculpher, M., & Buxton, M. (1994). Uncertainty in the economic evaluation of health care technologies: the role of sensitivity analysis. Health Economics, 3(2), 95–104.","type":"article","doi":"10.1002/hec.4730030206","isbn":null,"url":null}],"related":["monte-carlo-simulation","scenario-analysis","stochastic-sensitivity-analysis","probabilistic-sensitivity-analysis","tornado-diagram","one-way-sensitivity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-simulated-annealing","name":"Deterministic Simulated Annealing","fullName":"Deterministic Simulated Annealing — Annealing-schedule optimization without stochastic acceptance","aliases":["DSA","Deterministic Annealing","Greedy Annealing","Temperature-Scheduled Descent"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990","originator":"Rose, K., Gurewitz, E., Fox, G. C.","url":"https://scholargate.app/en/simulation/deterministic-simulated-annealing","markdownUrl":"https://scholargate.app/en/simulation/deterministic-simulated-annealing.md","definition":"Deterministic Simulated Annealing (DSA) is an optimization metaheuristic that adopts the cooling-schedule structure of classical simulated annealing but replaces the probabilistic Metropolis acceptance criterion with a strictly deterministic rule: only improving moves are accepted. This yields a reproducible, greedy-descent procedure guided by an annealing temperature schedule.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rose, K., Gurewitz, E., Fox, G. C.","year":"1990","type":"Deterministic metaheuristic — annealing schedule without probabilistic acceptance","dataType":"Continuous or discrete optimization problem instances","subfamily":"Simulation / optimization"},"citations":[{"ref":"Rose, K., Gurewitz, E., Fox, G. C. (1990). A deterministic annealing approach to clustering. Pattern Recognition Letters, 11(9), 589-594.","type":"article","doi":"10.1016/0167-8655(90)90010-Y","isbn":null,"url":null},{"ref":"Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.","type":"article","doi":"10.1126/science.220.4598.671","isbn":null,"url":null}],"related":["simulated-annealing","stochastic-simulated-annealing","tabu-search","deterministic-tabu-search","local-search","gradient-descent"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deterministic-system-dynamics","name":"Deterministic System Dynamics","fullName":"Deterministic System Dynamics — Feedback-loop simulation with fixed differential equations","aliases":["Deterministic SD","Classical System Dynamics","Continuous Simulation SD","Forrester System Dynamics"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1961","originator":"Jay W. Forrester","url":"https://scholargate.app/en/simulation/deterministic-system-dynamics","markdownUrl":"https://scholargate.app/en/simulation/deterministic-system-dynamics.md","definition":"Deterministic System Dynamics is the classical form of System Dynamics introduced by Jay Forrester in 1961, using fixed (non-probabilistic) ordinary differential equations to simulate stock-and-flow structures and feedback loops over time. All model parameters and relationships are specified as single-valued constants or deterministic functions, yielding a single trajectory for each simulation run. It is widely used in policy analysis, business strategy, ecology, and public health modeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jay W. Forrester","year":"1961","type":"Continuous feedback-loop simulation","dataType":"Continuous time-series rates, stocks, and flows; quantitative aggregate data","subfamily":"Simulation / optimization"},"citations":[{"ref":"Forrester, J. W. (1961). Industrial Dynamics. MIT Press, Cambridge, MA.","type":"book","doi":null,"isbn":"9780262560221","url":null},{"ref":"Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill, Boston.","type":"book","doi":null,"isbn":"9780072311358","url":null}],"related":["system-dynamics","stochastic-system-dynamics","discrete-event-simulation","agent-based-modeling","scenario-analysis","sensitivity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"detr","name":"DETR (Detection Transformer)","fullName":"End-to-End Object Detection with Transformers","aliases":["Detection Transformer","DETR"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Object Detection","year":"2020","originator":"Nicolas Carion","url":"https://scholargate.app/en/deep-learning/detr","markdownUrl":"https://scholargate.app/en/deep-learning/detr.md","definition":"DETR (Detection Transformer) is an end-to-end framework for object detection introduced by Carion et al. in 2020 that reformulates detection as a direct set prediction problem using transformers. Unlike traditional approaches that use hand-crafted post-processing like non-maximum suppression, DETR treats object detection as a sequence-to-sequence problem where the transformer predicts all objects at once.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nicolas Carion","subfamily":"Deep Learning, Object Detection","year":"2020","type":"Neural network architecture"},"citations":[{"ref":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In European Conference on Computer Vision (pp. 213-229). Springer, Cham.","type":"article","doi":"10.1007/978-3-030-58452-8_13","isbn":null,"url":null}],"related":["swin-transformer","vision-mamba","segment-anything-model","masked-autoencoders"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"deviant-case-sampling","name":"Deviant Case Sampling","fullName":"Deviant Case Sampling","aliases":["extreme case sampling","outlier sampling","negative case sampling","deviant-case selection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990","originator":"Michael Quinn Patton","url":"https://scholargate.app/en/survey-methodology/deviant-case-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/deviant-case-sampling.md","definition":"Deviant case sampling is a purposive qualitative sampling strategy in which the researcher intentionally selects cases that are unusual, exceptional, or markedly different from the norm — outliers, extreme successes, or conspicuous failures. The goal is not statistical representation but deep learning from cases that illuminate the boundaries of a phenomenon, challenge prevailing assumptions, or reveal processes that typical cases obscure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Quinn Patton","year":"1990","type":"Purposive qualitative sampling strategy","dataType":"Qualitative data (interviews, documents, observations)","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219-245.","type":"article","doi":"10.1177/1077800405284363","isbn":null,"url":null}],"related":["purposive-sampling","maximum-variation-sampling","typical-case-sampling","theoretical-sampling","snowball-sampling","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dexa","name":"DEXA","fullName":"Dual-Energy X-ray Absorptiometry","aliases":["Dual X-ray absorptiometry","DXA","bone densitometry"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Bone assessment","year":"1987","originator":"Harold Wahner","url":"https://scholargate.app/en/medical-imaging/dexa","markdownUrl":"https://scholargate.app/en/medical-imaging/dexa.md","definition":"Dual-Energy X-ray Absorptiometry (DEXA or DXA) is a non-invasive imaging technique that quantifies bone mineral density (BMD) by measuring the attenuation of X-rays at two different energies as they pass through bone and soft tissue. First developed by Wahner and colleagues in 1987, DEXA has become the gold standard for osteoporosis screening and fracture risk assessment. It is recommended by the World Health Organization for diagnosing osteoporosis and monitoring treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Wahner","subfamily":"Bone assessment","year":"1987","type":"X-ray-based bone density measurement"},"citations":[{"ref":"Kanis, J. A. (1994). Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. World Health Organization Technical Report Series, 843, 1-129.","type":"article","doi":null,"isbn":null,"url":"https://apps.who.int/iris/handle/10665/39142"},{"ref":"Genant, H. K., Engelke, K., Fuerst, T., et al. (1996). Noninvasive assessment of bone mineral and structure: state of the art. Journal of Bone and Mineral Research, 11(6), 707-730.","type":"article","doi":"10.1002/jbmr.5650110602","isbn":null,"url":null},{"ref":"Blake, G. M., Fogelman, I. (2016). The role of DXA bone density scans in the diagnosis and treatment of osteoporosis. Postgraduate Medical Journal, 83(980), 509-517.","type":"article","doi":"10.1136/pgmj.2007.057505","isbn":null,"url":null}],"related":["ct-iterative-reconstruction","quantitative-susceptibility-mapping","oct-angiography","pet-kinetic-modeling","radiomics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"df-gls","name":"DF-GLS Test","fullName":"Dickey-Fuller GLS (ERS) Unit-Root Test","aliases":["Elliott-Rothenberg-Stock test","ERS unit-root test","GLS-detrended Dickey-Fuller test","DF-GLS birim kök testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Unit-root tests","year":1996,"originator":"Elliott, Rothenberg & Stock","url":"https://scholargate.app/en/econometrics/df-gls","markdownUrl":"https://scholargate.app/en/econometrics/df-gls.md","definition":"The DF-GLS test, introduced by Elliott, Rothenberg, and Stock (1996), is a modified augmented Dickey-Fuller procedure that applies generalized least squares (GLS) detrending before the standard unit-root regression. By removing deterministic components under a local alternative rather than the null hypothesis, the test achieves near-optimal power for detecting stationarity in time series, making it the preferred unit-root test in applied econometrics when a trend or intercept is present.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elliott, Rothenberg & Stock","year":1996,"type":"One-sided t-test on GLS-detrended series","subfamily":"Unit-root tests","null_hypothesis":"Series has a unit root (I(1))","asymptotic_basis":"Local-to-unity asymptotic theory"},"citations":[{"ref":"Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836.","type":"article","doi":"10.2307/2171846","isbn":null,"url":null}],"related":["adf-test","kpss-test","ers-point-optimal-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dhf-copras","name":"DHF-COPRAS","fullName":"Dual Hesitant Fuzzy extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Rani, P., Mishra, A. R., Krishankumar, R., Mardani, A., Cavallaro, F., Ravichandran, K. S., Balasubramanian, K.","url":"https://scholargate.app/en/decision-making/dhf-copras","markdownUrl":"https://scholargate.app/en/decision-making/dhf-copras.md","definition":"DHF-COPRAS (Dual Hesitant Fuzzy extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Rani, P., Mishra, A. R., Krishankumar, R., Mardani, A., Cavallaro, F., Ravichandran, K. S., Balasubramanian, K. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rani, P., Mishra, A. R., Krishankumar, R., Mardani, A., Cavallaro, F., Ravichandran, K. S., Balasubramanian, K.","subfamily":"Ranking","year":"2020","type":"Dual Hesitant outranking/ranking — Dual Hesitant Fuzzy Element (DHFE: h(x) membership set, g(x) non-membership set)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Rani, P., Mishra, A. R., Krishankumar, R., Mardani, A., Cavallaro, F., Ravichandran, K. S., Balasubramanian, K. (2020). Hesitant Fuzzy SWARA-Complex Proportional Assessment Approach for Sustainable Supplier Selection (HF-SWARA-COPRAS). Symmetry","type":"article","doi":"10.3390/sym12071152","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dhf-edas","name":"DHF-EDAS","fullName":"Dual Hesitant Fuzzy extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Ning, B., Lin, R., Wei, G., Chen, X.","url":"https://scholargate.app/en/decision-making/dhf-edas","markdownUrl":"https://scholargate.app/en/decision-making/dhf-edas.md","definition":"DHF-EDAS (Dual Hesitant Fuzzy extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ning, B., Lin, R., Wei, G., Chen, X. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ning, B., Lin, R., Wei, G., Chen, X.","subfamily":"Ranking","year":"2023","type":"Dual Hesitant outranking/ranking — Dual Hesitant Fuzzy Element (DHFE: h(x) membership set, g(x) non-membership set)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Ning, B., Lin, R., Wei, G., Chen, X. (2023). EDAS method for multiple attribute group decision making with probabilistic dual hesitant fuzzy information and its application to suppliers selection. Technological and Economic Development of Economy","type":"article","doi":"10.3846/tede.2023.17589","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dhf-todim","name":"DHF-TODIM","fullName":"Dual Hesitant Fuzzy extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Liu, Y., Tariq, M., Khan, S., Abdullah, S.","url":"https://scholargate.app/en/decision-making/dhf-todim","markdownUrl":"https://scholargate.app/en/decision-making/dhf-todim.md","definition":"DHF-TODIM (Dual Hesitant Fuzzy extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Liu, Y., Tariq, M., Khan, S., Abdullah, S. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, Y., Tariq, M., Khan, S., Abdullah, S.","subfamily":"Ranking","year":"2023","type":"Dual Hesitant outranking/ranking — Dual Hesitant Fuzzy Element (DHFE: h(x) membership set, g(x) non-membership set)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Liu, Y., Tariq, M., Khan, S., Abdullah, S. (2023). Complex dual hesitant fuzzy TODIM method and their application in Russia–Ukraine war's impact on global economy. Complex & Intelligent Systems","type":"article","doi":"10.1007/s40747-023-01163-8","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dhf-topsis","name":"DHF-TOPSIS","fullName":"Dual Hesitant Fuzzy extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Wang, R., Li, W., Zhang, T., Han, Q.","url":"https://scholargate.app/en/decision-making/dhf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/dhf-topsis.md","definition":"DHF-TOPSIS (Dual Hesitant Fuzzy extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, R., Li, W., Zhang, T., Han, Q. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang, R., Li, W., Zhang, T., Han, Q.","subfamily":"Ranking","year":"2020","type":"Dual Hesitant outranking/ranking — Dual Hesitant Fuzzy Element (DHFE: h(x) membership set, g(x) non-membership set)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Wang, R., Li, W., Zhang, T., Han, Q. (2020). New Distance Measures for Dual Hesitant Fuzzy Sets and Their Application to Multiple Attribute Decision Making. Symmetry","type":"article","doi":"10.3390/sym12020191","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dhf-vikor","name":"DHF-VIKOR","fullName":"Dual Hesitant Fuzzy extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2025","originator":"An, J., Zhang, X., Liu, L., Zuo, W.","url":"https://scholargate.app/en/decision-making/dhf-vikor","markdownUrl":"https://scholargate.app/en/decision-making/dhf-vikor.md","definition":"DHF-VIKOR (Dual Hesitant Fuzzy extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by An, J., Zhang, X., Liu, L., Zuo, W. in 2025. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"An, J., Zhang, X., Liu, L., Zuo, W.","subfamily":"Ranking","year":"2025","type":"Dual Hesitant outranking/ranking — Dual Hesitant Fuzzy Element (DHFE: h(x) membership set, g(x) non-membership set)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"An, J., Zhang, X., Liu, L., Zuo, W. (2025). A dual hesitation fuzzy VIKOR method with incomplete attribute weights for property service quality evaluation. International Journal of Strategic Property Management","type":"article","doi":"10.3846/ijspm.2025.24035","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diabetes-distress-scale","name":"Diabetes Distress Scale","fullName":"Diabetes Distress Scale (DDS)","aliases":["DDS"],"domain":"cardiology","family":"process-pipeline","subfamily":"diabetes-specific emotional and psychosocial distress","year":"2005","originator":"William H. Polonsky","url":"https://scholargate.app/en/cardiology/diabetes-distress-scale","markdownUrl":"https://scholargate.app/en/cardiology/diabetes-distress-scale.md","definition":"The Diabetes Distress Scale (DDS) is a 17-item self-report measure that quantifies emotional and psychosocial distress specifically related to living with and managing diabetes. Developed by Polonsky and colleagues in 2005, the DDS captures diabetes-specific worries (e.g., regimen burden, fear of complications, social stigma, lack of support) that are distinct from generalized depression or anxiety, making it essential for identifying and addressing the emotional obstacles to optimal diabetes self-management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William H. Polonsky","subfamily":"diabetes-specific emotional and psychosocial distress","year":"2005","type":"Self-report questionnaire"},"citations":[{"ref":"Polonsky, W. H., Fisher, L., Earles, J., Dudl, R. J., Lees, J., Mulcahy, K., & Jackson, R. A. (2005). Assessing psychosocial distress in diabetes: Development of the Diabetes Distress Scale. Diabetes Care, 28(3), 626–631.","type":"article","doi":"10.2337/diacare.28.3.626","isbn":null,"url":null}],"related":["problem-areas-in-diabetes","diabetes-self-efficacy-scale","hypoglycemia-fear-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diabetes-quality-of-life","name":"DQOL","fullName":"Diabetes Quality of Life Measure","aliases":["DQOL","Diabetes Quality of Life","Diabetes QOL"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Metabolic and Endocrine Disease","year":"1988","originator":"Diabetes Control and Complications Trial Research Group","url":"https://scholargate.app/en/health-outcomes/diabetes-quality-of-life","markdownUrl":"https://scholargate.app/en/health-outcomes/diabetes-quality-of-life.md","definition":"The DQOL is a patient-reported measure of quality of life impact in people with diabetes. Developed by the Diabetes Control and Complications Trial (DCCT) Research Group in 1988, this 46-item questionnaire assesses how diabetes affects daily functioning, emotional well-being, worry about complications, and satisfaction with life and health. It is widely used in diabetes clinical trials and research to capture the burden of diabetes management and impact on psychosocial well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diabetes Control and Complications Trial Research Group","subfamily":"Metabolic and Endocrine Disease","year":"1988","type":"Self-report quality of life questionnaire"},"citations":[{"ref":"The Diabetes Control and Complications Trial Research Group. (1988). The Diabetes Quality of Life (DQOL) measure. Diabetes Care, 11(10), 725-732.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Diabetes+Quality+of+Life+%28DQOL%29+measure+The"},{"ref":"Jacobson, A. M., de Groot, M., & Samson, J. A. (1994). The evaluation of two measures of quality of life in patients with type I and type II diabetes. Diabetes Care, 17(4), 267-274.","type":"article","doi":"10.2337/diacare.17.4.267","isbn":null,"url":null},{"ref":"Bott, U., Hendrieckx, C., Mohr-Riehle, B., & Nagaraja, H. N. (2016). Psychosocial outcomes of therapy with real-time continuous glucose monitoring systems. Diabetes Technology & Therapeutics, 18(5), 314-322.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/27214299"}],"related":["eortc-qlq-c30","copd-assessment-test","chronic-heart-failure-questionnaire","asthma-control-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diabetes-self-efficacy-scale","name":"Diabetes Self-Efficacy Scale","fullName":"Diabetes Self-Efficacy Scale (DASES)","aliases":["DASES"],"domain":"cardiology","family":"process-pipeline","subfamily":"diabetes self-management confidence and efficacy","year":"2009","originator":"Kate Lorig","url":"https://scholargate.app/en/cardiology/diabetes-self-efficacy-scale","markdownUrl":"https://scholargate.app/en/cardiology/diabetes-self-efficacy-scale.md","definition":"The Diabetes Self-Efficacy Scale (DASES) is an 8-item self-report measure that assesses a patient's confidence in their ability to manage key diabetes self-care tasks: medication adherence, glucose monitoring, diet management, exercise, and coping with symptoms or complications. Developed by Lorig and colleagues based on social-cognitive theory, the DASES is grounded in Bandura's self-efficacy framework and demonstrates strong predictive validity for glycemic control, treatment adherence, and quality of life outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kate Lorig","subfamily":"diabetes self-management confidence and efficacy","year":"2009","type":"Self-report questionnaire"},"citations":[{"ref":"Lorig, K. R., Ritter, P. L., Villa, F. J., & Armas, J. (2009). Community-based peer-led diabetes self-management: A randomized trial. Diabetes Educator, 35(4), 641–651.","type":"article","doi":"10.1177/0145721709335006","isbn":null,"url":null},{"ref":"Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.","type":"book","doi":"10.1037/0033-295X.84.2.191","isbn":null,"url":null}],"related":["diabetes-distress-scale","problem-areas-in-diabetes","hypoglycemia-fear-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diabetes-symptom-checklist","name":"Diabetes Symptom Checklist","fullName":"Diabetes Symptom Checklist: Patient-Reported Symptom Dimensions","aliases":["DSC-34"],"domain":"endocrinology","family":"process-pipeline","subfamily":"Diabetes-specific symptom and impact assessment","year":1994,"originator":"Peta Grootenhuis, Frans Snoek, Rolf Heine, Lex Bouter","url":"https://scholargate.app/en/endocrinology/diabetes-symptom-checklist","markdownUrl":"https://scholargate.app/en/endocrinology/diabetes-symptom-checklist.md","definition":"The Diabetes Symptom Checklist is a 34-item patient-reported outcome measure assessing the frequency and burden of symptoms directly related to diabetes and its complications. Developed by Grootenhuis and colleagues in 1994, it captures eight symptom dimensions including hyperglycemic symptoms, hypoglycemic symptoms, fatigue, polyuria, neuropathic pain, and psychological distress. The instrument is used to quantify symptom experience and treatment response across diverse diabetes populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peta Grootenhuis, Frans Snoek, Rolf Heine, Lex Bouter","subfamily":"Diabetes-specific symptom and impact assessment","year":1994,"type":"Patient self-report symptom checklist"},"citations":[{"ref":"Grootenhuis, P. A., Snoek, F. J., Heine, R. J., & Bouter, L. M. (1994). Development of a type 2 diabetes symptom checklist: A measure of symptom severity. Diabet Med, 11(3), 253-261.","type":"article","doi":"10.1111/j.1464-5491.1994.tb00268.x","isbn":null,"url":null},{"ref":"Snoek, F. J., Pouwer, F., Welch, G. W., & Polonsky, W. H. (1997). Diabetes-related distress in Dutch and U.S. diabetic patients: Relationship to perceived health status and depression. Diabetes Care, 20(10), 1554-1558.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Diabetes-related+distress+in+Dutch+and+U.S+Snoek"}],"related":["hypoglycemia-awareness-questionnaire","thyroid-patient-reported-outcomes","growth-hormone-deficiency-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diagnostic-accuracy-study","name":"Diagnostic Accuracy Study Design","fullName":"Diagnostic Test Accuracy Study (Sensitivity, Specificity, Likelihood Ratios)","aliases":["diagnostic accuracy study","test accuracy","STARD","diagnostic evaluation"],"domain":"clinical-research","family":"process-pipeline","subfamily":"diagnostic evaluation","year":"2003-2015","originator":"Bossuyt, Reitsma, and STARD group (2003); clinical epidemiology pioneers","url":"https://scholargate.app/en/clinical-research/diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/clinical-research/diagnostic-accuracy-study.md","definition":"A diagnostic accuracy study evaluates how well a new diagnostic test (or biomarker, imaging modality, clinical assessment) detects the presence or absence of disease compared to a reference standard (gold standard). Standardized since 2003 by the STARD (Standards for Reporting of Diagnostic Accuracy Studies) initiative, diagnostic accuracy studies are fundamental to clinical medicine, determining whether and how new tests can improve patient diagnosis and treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bossuyt, Reitsma, and STARD group (2003); clinical epidemiology pioneers","subfamily":"diagnostic evaluation","year":"2003-2015","type":"Research Design"},"citations":[{"ref":"Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., ... & de Vet, H. C. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Annals of Internal Medicine, 138(1), 40–44.","type":"article","doi":"10.7326/0003-4819-138-1-200301070-00010","isbn":null,"url":null},{"ref":"Cohen, J. F., Korevaar, D. A., Altman, D. G., Bruns, D. E., Gatsonis, C. A., Hooft, L., ... & Bossuyt, P. M. (2016). STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open, 6(11), e012799.","type":"article","doi":"10.1136/bmjopen-2016-012799","isbn":null,"url":null},{"ref":"Jaeschke, R., Guyatt, G. H., & Sackett, D. L. (1994). Users' guides to the medical literature. III. How to use an article about a diagnostic test. A. Are the results of the study valid? JAMA, 271(5), 389–391.","type":"article","doi":"10.1001/jama.1994.03510290071040","isbn":null,"url":null}],"related":["randomized-controlled-trial","cohort-study-design","predictive-value","roc-curve","reference-standard"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dialectical-behavior-therapy","name":"Dialectical Behavior Therapy","fullName":"Dialectical Behavior Therapy","aliases":["DBT","dialectical therapy"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Skills-based psychotherapy","year":"1993","originator":"Marsha M. Linehan","url":"https://scholargate.app/en/clinical-psychology/dialectical-behavior-therapy","markdownUrl":"https://scholargate.app/en/clinical-psychology/dialectical-behavior-therapy.md","definition":"Dialectical Behavior Therapy (DBT) is a comprehensive, multimodal psychosocial intervention developed by Marsha M. Linehan to treat individuals with Borderline Personality Disorder (BPD) and chronic suicidality. Combining cognitive-behavioral principles with dialectical philosophy and Zen principles, DBT is delivered through individual therapy, skills training, phone coaching, and therapist consultation and has become the gold-standard treatment for BPD.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marsha M. Linehan","subfamily":"Skills-based psychotherapy","year":"1993","type":"Comprehensive psychosocial intervention"},"citations":[{"ref":"Linehan, M. M. (1993). Cognitive-behavioral treatment of borderline personality disorder. Guilford Press.","type":"article","doi":null,"isbn":"9780898621784","url":null},{"ref":"Linehan, M. M., Korslund, K. E., Harned, M. S., et al. (2015). Dialectical behavior therapy for high suicide risk in individuals with borderline personality disorder: A randomized clinical trial and component analysis. JAMA Psychiatry, 72(5), 475–482.","type":"article","doi":"10.1001/jamapsychiatry.2014.3039","isbn":null,"url":null}],"related":["mindfulness-based-stress-reduction","cognitive-behavioral-therapy-assessment","acceptance-commitment-therapy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dialectometry","name":"Dialectometry","fullName":"Dialectometry Method","aliases":["Linguistic Distance Measurement","Quantitative Dialect Analysis"],"domain":"linguistics","family":"process-pipeline","subfamily":"Quantitative Sociolinguistics","year":"1973","originator":"Jean Seguy","url":"https://scholargate.app/en/linguistics/dialectometry","markdownUrl":"https://scholargate.app/en/linguistics/dialectometry.md","definition":"Dialectometry is a quantitative method for measuring linguistic distances between dialects or languages using objective metrics applied to phonological, lexical, or phonetic data. Pioneered by Jean Seguy in 1973, dialectometry compares word lists, pronunciations, or phonetic transcriptions across speech varieties to calculate similarity scores. The resulting distance matrices and dendrograms reveal patterns of dialect relatedness and geographic or social clustering. This method complements traditional dialectology and contributes to historical linguistics and sociolinguistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean Seguy","subfamily":"Quantitative Sociolinguistics","year":"1973","type":"Empirical process pipeline"},"citations":[{"ref":"Seguy, J. (1973). La dialectométrie dans l'étude de l'espace linguistique. Revue de Linguistique Romane, 37, 1-24.","type":"article","doi":null,"isbn":null,"url":"https://persee.fr/article/"},{"ref":"Nerbonne, J., & Heeringa, W. (2009). Measuring dialect differences. In P. Auer & J. E. Schmidt (Eds.), Language and Space: An International Handbook of Linguistic Variation. Berlin: De Gruyter.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+dialect+differences+Nerbonne"},{"ref":"Heeringa, W. (2004). Measuring Dialect Pronunciation Differences Using Levenshtein Distance. Groningen: University of Groningen.","type":"book","doi":null,"isbn":null,"url":"https://rug.nl/research/publications"}],"related":["corpus-linguistics","sociolinguistics","phonetic-variation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dialogue-act-classification","name":"Dialogue Act Classification","fullName":"Dialogue Act Classification","aliases":["dialogue act tagging","speech act classification","Diyalog Eylem Sınıflandırma (Dialogue Act Classification)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1997–2000","originator":"Stolcke et al.; Jurafsky et al.","url":"https://scholargate.app/en/text-mining/dialogue-act-classification","markdownUrl":"https://scholargate.app/en/text-mining/dialogue-act-classification.md","definition":"Dialogue act classification is a natural-language-processing task that automatically labels the communicative function of each utterance in a conversation — such as question, answer, greeting, or rejection. Consolidated by Jurafsky et al. (1997) and Stolcke et al. (2000), it is a foundational component for chatbots and discourse analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP utterance-classification task","originator":"Stolcke et al.; Jurafsky et al.","year":"1997–2000","taxonomies":"DAMSL, ISO 24617-2","output":"Dialogue act label per utterance (question, answer, greeting, rejection, etc.)","minSample":30},"citations":[{"ref":"Stolcke, A. et al. (2000). Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech. Computational Linguistics, 26(3), 339-373.","type":"article","doi":"10.1162/089120100561737","isbn":null,"url":null},{"ref":"Jurafsky, D. et al. (1997). Automatic Detection of Discourse Structure for Speech Recognition and Understanding. ICASSP.","type":"inproceedings","doi":"10.1109/asru.1997.658992","isbn":null,"url":null}],"related":["intent-detection","slot-filling","sentiment-analysis","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diamond-mortensen-pissarides-search-matching","name":"Diamond-Mortensen-Pissarides Search-Matching","fullName":"Diamond-Mortensen-Pissarides Search-Matching Model","aliases":["DMP Model","Search and Matching Model","Mortensen-Pissarides Model"],"domain":"economics","family":"regression-model","subfamily":"Labor Economics","year":"1982","originator":"Peter Diamond, Dale Mortensen, Christopher Pissarides","url":"https://scholargate.app/en/economics/diamond-mortensen-pissarides-search-matching","markdownUrl":"https://scholargate.app/en/economics/diamond-mortensen-pissarides-search-matching.md","definition":"The Diamond-Mortensen-Pissarides (DMP) model, developed by Peter Diamond, Dale Mortensen, and Christopher Pissarides in the early 1980s, is a fundamental framework for understanding labor market dynamics through the lens of search and matching frictions. It explains how workers and firms meet, form employment relationships, and separate, endogenously determining unemployment, vacancies, and wages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Diamond, Dale Mortensen, Christopher Pissarides","subfamily":"Labor Economics","year":"1982","type":"Equilibrium labor market model"},"citations":[{"ref":"Mortensen, D. T., & Pissarides, C. A. (1994). Job Reallocation, Employment Fluctuations and Unemployment. In J. B. Taylor & M. Woodford (Eds.), Handbook of Macroeconomics, 1A, 1171–1227.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Job+Reallocation%2C+Employment+Fluctuations+and+Unemployment+Mortensen"},{"ref":"Diamond, P. A. (1982). Wage Determination and Efficiency in Search Equilibrium. Review of Economic Studies, 49(2), 217–227.","type":"article","doi":"10.2307/2297271","isbn":null,"url":null},{"ref":"Pissarides, C. A. (1985). Short-Run Equilibrium Dynamics of Unemployment, Vacancies, and Real Wages. American Economic Review, 75(4), 676–690.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/1821345"}],"related":["real-business-cycle-model","ramsey-cass-koopmans-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diary-method","name":"Diary Method","fullName":"Research Diary Method","aliases":["diary study","diary technique","self-report diary","daily diary method"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1920s–1940s (systematised by Allport, 1942)","originator":"Gordon Allport (systematic social-science use); Nels Anderson (early fieldwork diaries)","url":"https://scholargate.app/en/survey-methodology/diary-method","markdownUrl":"https://scholargate.app/en/survey-methodology/diary-method.md","definition":"The diary method is a data-collection technique in which participants record their thoughts, behaviours, events, or experiences in their own words at regular or event-contingent intervals over a defined study period. By capturing data close in time to the event, diaries reduce retrospective recall bias and give researchers access to the texture of everyday life as it unfolds — something one-off surveys and retrospective interviews cannot provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon Allport (systematic social-science use); Nels Anderson (early fieldwork diaries)","year":"1920s–1940s (systematised by Allport, 1942)","type":"Qualitative / mixed-methods data-collection technique","dataType":"Participant-generated text entries (qualitative), structured ratings or counts (quantitative), or both","subfamily":"Data collection"},"citations":[{"ref":"Alaszewski, A. (2006). Using Diaries for Social Research. Sage.","type":"book","doi":null,"isbn":"978-0761941415","url":null},{"ref":"Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.","type":"article","doi":"10.1146/annurev.psych.54.101601.145030","isbn":null,"url":null}],"related":["experience-sampling-method","field-notes","participant-observation","longitudinal-survey","semi-structured-interview","non-participant-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dibr","name":"DIBR","fullName":"Defining Interrelationships Between Ranked criteria","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2021","originator":"Pamučar, D., Žižović, M., Marinković, D., Doljanica, D.","url":"https://scholargate.app/en/decision-making/dibr","markdownUrl":"https://scholargate.app/en/decision-making/dibr.md","definition":"DIBR (Defining Interrelationships Between Ranked criteria) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Žižović, M., Marinković, D., Doljanica, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamučar, D., Žižović, M., Marinković, D., Doljanica, D.","subfamily":"Weight_Subjective","year":"2021","type":"Ranked criteria interrelationship weighting (non-pairwise sequential)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Pamučar, D., Žižović, M., Marinković, D., Doljanica, D. (2021). Development of an alternative approach for the selection of suppliers under the conditions of fuzzy sets. Symmetry","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+of+an+alternative+approach+for+the+selection+of+suppliers+under+the+conditions+of+fuzzy+sets+Pamu%C4%8Dar"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dichotic-listening","name":"Dichotic Listening","fullName":"Dichotic Listening Task","aliases":["Shadowing Task","Divided Attention Listening"],"domain":"psychology","family":"hypothesis-test","subfamily":"Auditory Attention","year":"1953","originator":"E. Colin Cherry","url":"https://scholargate.app/en/psychology/dichotic-listening","markdownUrl":"https://scholargate.app/en/psychology/dichotic-listening.md","definition":"The Dichotic Listening Task is an auditory measure of selective attention and hemispheric lateralization. Different speech stimuli (words, digits, or syllables) are presented simultaneously to each ear via headphones. Participants attend to one ear (shadowing or repeating that information) while ignoring the other. Accuracy and reaction times reveal the capacity for selective attention, and asymmetries in correct identification between ears reveal hemispheric dominance for speech processing (typically stronger in the right ear, reflecting left-hemisphere language dominance).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"E. Colin Cherry","subfamily":"Auditory Attention","year":"1953","type":"Auditory discrimination task"},"citations":[{"ref":"Broadbent, D. E. (1958). Perception and communication. Pergamon Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Broadbent%2C%20D.%20E.%20(1958).%20Perception%20and%20communication.%20Pergamon%20Press."},{"ref":"Cherry, E. C. (1953). Some experiments on the recognition of speech, with one and with two ears. Journal of the Acoustical Society of America, 25(5), 975-979.","type":"article","doi":"10.1121/1.1907229","isbn":null,"url":null},{"ref":"Kimura, D. (1961). Cerebral dominance and the perception of verbal stimuli. Canadian Journal of Psychology, 15(3), 166-171.","type":"article","doi":"10.1037/h0083219","isbn":null,"url":null}],"related":["attention","selective-attention","auditory-processing","hemispheric-lateralization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dictator-game","name":"Dictator Game","fullName":"Dictator Game","aliases":["Allocation Game","Distribution Task"],"domain":"psychology","family":"hypothesis-test","subfamily":"Economic Behavior","year":"1994","originator":"Robert Forsythe and colleagues","url":"https://scholargate.app/en/psychology/dictator-game","markdownUrl":"https://scholargate.app/en/psychology/dictator-game.md","definition":"The Dictator Game is a simple economic decision task measuring generosity and prosocial behavior. One player (dictator) receives money and unilaterally decides how to allocate it between themselves and an anonymous second player (recipient). The recipient cannot reject the offer; they simply receive what the dictator gives. Unlike the Ultimatum Game, the dictator faces no punishment for selfishness. Thus, the Dictator Game reveals baseline generosity without strategic calculation, revealing intrinsic prosocial preferences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Forsythe and colleagues","subfamily":"Economic Behavior","year":"1994","type":"Allocation task"},"citations":[{"ref":"Forsythe, R., Horowitz, J. L., Savin, N. E., & Sefton, M. (1994). Fairness in simple bargaining experiments. Games and Economic Behavior, 6(3), 347-369.","type":"article","doi":"10.1006/game.1994.1021","isbn":null,"url":null},{"ref":"Camerer, C. F. (2003). Behavioral game theory: Experiments in strategic interaction. Princeton University Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Camerer%2C%20C.%20F.%20(2003).%20Behavioral%20game%20theory%3A%20Experiments%20in%20strategic%20interaction.%20Princeton%20University%20Press."},{"ref":"Henrich, J., Boyd, R., Bowles, S., et al. (2005). 'Economic man' in cross-cultural perspective: Behavioral experiments in 15 small-scale societies. Behavioral and Brain Sciences, 28(6), 795-855.","type":"article","doi":"10.1017/S0140525X05000142","isbn":null,"url":null}],"related":["ultimatum-game","generosity","social-preference","altruism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"did-staggered","name":"Staggered Difference-in-Differences","fullName":"Staggered Difference-in-Differences (Callaway-Sant'Anna / Sun-Abraham Estimators)","aliases":["staggered DID","staggered adoption DID","heterogeneous treatment DID","Callaway-Sant'Anna estimator","Sun-Abraham estimator","Kademeli DID (Staggered Difference-in-Differences)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2021,"originator":"Callaway & Sant'Anna; Sun & Abraham","url":"https://scholargate.app/en/causal-inference/did-staggered","markdownUrl":"https://scholargate.app/en/causal-inference/did-staggered.md","definition":"Staggered Difference-in-Differences is a generalisation of DID for panel designs in which treatment is rolled out to different groups at different times. Introduced in the modern form by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it corrects the bias that classical two-way fixed-effects (TWFE) estimators suffer when treatment effects are heterogeneous across cohorts and over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Callaway & Sant'Anna; Sun & Abraham","year":2021,"type":"Quasi-experimental panel causal estimator","estimator":"Group-time average treatment effects ATT(g,t), aggregated","outcome":"continuous or binary","minSample":100,"dataStructure":"panel or time series with staggered treatment timing"},"citations":[{"ref":"Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null},{"ref":"Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175-199.","type":"article","doi":"10.1016/j.jeconom.2020.09.006","isbn":null,"url":null}],"related":["did-classic","event-study-causal","synthetic-control","regression-discontinuity","panel-fixed-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diebold-mariano-test","name":"Diebold-Mariano Test","fullName":"Diebold-Mariano Test of Equal Predictive Accuracy","aliases":["DM Test","Test of Equal Forecast Accuracy","Diebold-Mariano Forecast Comparison Test","Tahmin Doğruluğu Eşitliği Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Forecast evaluation","year":1995,"originator":"Francis Diebold & Roberto Mariano","url":"https://scholargate.app/en/econometrics/diebold-mariano-test","markdownUrl":"https://scholargate.app/en/econometrics/diebold-mariano-test.md","definition":"The Diebold-Mariano (DM) test, introduced by Diebold and Mariano in 1995, is a widely used non-parametric procedure for formally comparing the predictive accuracy of two competing forecasting models. It evaluates whether the difference in forecast errors between two models is statistically significant, without requiring nested models or specific distributional assumptions about the forecasts, making it broadly applicable across economics, finance, and time-series analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Francis Diebold & Roberto Mariano","year":1995,"type":"Non-parametric forecast comparison test","subfamily":"Forecast evaluation","null_hypothesis":"Equal predictive accuracy between two competing forecasts","loss_function":"Flexible; supports squared error, absolute error, and user-defined loss"},"citations":[{"ref":"Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.","type":"article","doi":"10.1080/07350015.1995.10524599","isbn":null,"url":null}],"related":["giacomini-white-test","model-confidence-set","pesaran-timmermann-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dietary-quality-index","name":"DQI-I","fullName":"Dietary Quality Index-International","aliases":["DQI-I","DQI"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"dietary-pattern-evaluation","year":2003,"originator":"Sungwon Kim, Pamela S. Haines, Aileen M. Siega-Riz, Barry M. Popkin","url":"https://scholargate.app/en/nutritional-science/dietary-quality-index","markdownUrl":"https://scholargate.app/en/nutritional-science/dietary-quality-index.md","definition":"The Dietary Quality Index-International is a comprehensive dietary quality assessment tool developed to evaluate overall diet quality based on food and nutrient intake data. Introduced by Kim and colleagues in 2003, the DQI-I incorporates four key dimensions of diet quality: adequacy (adequate intake of essential nutrients and food groups), moderation (limiting excess intake of less healthful components), variety (diversity of food groups), and appropriate macronutrient distribution. It is widely used in epidemiological research to assess population dietary patterns and to examine relationships between diet quality and chronic disease outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sungwon Kim, Pamela S. Haines, Aileen M. Siega-Riz, Barry M. Popkin","subfamily":"dietary-pattern-evaluation","year":2003,"type":"Derived from dietary assessment data (food frequency questionnaire, 24-hour recall)"},"citations":[{"ref":"Kim, S., Haines, P. S., Siega-Riz, A. M., & Popkin, B. M. (2003). The Diet Quality Index-International (DQI-I) provides an effective tool for assessing the quality of various diet profiles. The Journal of Nutrition, 133(12), 3911-3919.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Diet+Quality+Index-International+%28DQI-I%29+provides+an+effective+tool+for+assessing+the+quality+of+various+diet+profiles+Kim"},{"ref":"Newby, P. K., & Tucker, K. L. (2007). Empirically derived eating patterns using factor or cluster analysis: A review. Nutrition Reviews, 65(6), 280-293.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Empirically+derived+eating+patterns+using+factor+or+cluster+analysis%3A+A+review+Newby"}],"related":["mediterranean-diet-adherence","food-frequency-questionnaire","mini-nutritional-assessment","nutrition-self-efficacy-scale","intuitive-eating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dif-analysis","name":"DIF Analysis","fullName":"Differential Item Functioning Analysis","aliases":["Madde Yanlılık Analizi (DIF — Differential Item Functioning)","item bias analysis","Mantel-Haenszel DIF","Lord chi-square DIF","logistic regression DIF"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1988,"originator":"Paul W. Holland & Dorothy T. Thayer (Mantel-Haenszel approach, 1988)","url":"https://scholargate.app/en/psychometrics/dif-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/dif-analysis.md","definition":"Differential Item Functioning analysis examines whether examinees from different groups — such as gender, ethnicity, or language background — who have the same underlying ability respond differently to a test item. First formalised by Holland and Thayer in 1988 via the Mantel-Haenszel procedure, it is the principal tool in modern test development for detecting and removing item bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul W. Holland & Dorothy T. Thayer (Mantel-Haenszel approach, 1988)","year":1988,"type":"Item-level fairness / measurement equivalence analysis","outcome":"DIF flags per item with effect-size classification (ETS A/B/C)","data":"Binary or ordinal item responses with a known group variable","min_sample":200,"key_methods":"Mantel-Haenszel, logistic regression, Lord chi-square","multiple_comparisons":"Bonferroni correction recommended across items"},"citations":[{"ref":"Holland, P. W. & Thayer, D. T. (1988). Differential Item Performance and the Mantel-Haenszel Procedure. ETS Research Report Series.","type":"report","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/10.1002/j.2330-8516.1988.tb00283.x"},{"ref":"Magis, D., Beland, S., Tuerlinckx, F. & De Boeck, P. (2010). A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning. Behavior Research Methods, 42(3), 847–862.","type":"article","doi":"10.3758/BRM.42.3.847","isbn":null,"url":null}],"related":["irt-rasch","exploratory-factor-analysis","confirmatory-factor-analysis","item-analysis","measurement-invariance"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"difference-gmm","name":"Difference GMM","fullName":"First-Differenced Generalized Method of Moments Estimator","aliases":["Arellano-Bond estimator","AB-GMM","first-difference GMM","difference GMM estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991","originator":"Manuel Arellano and Stephen Bond","url":"https://scholargate.app/en/econometrics/difference-gmm","markdownUrl":"https://scholargate.app/en/econometrics/difference-gmm.md","definition":"Difference GMM, introduced by Arellano and Bond (1991), estimates dynamic panel data models by first-differencing the equation to remove fixed effects, then using lagged levels of the endogenous variables as GMM instruments. It is the standard approach when a lagged dependent variable or other endogenous regressors are present in a panel with many units and few time periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Manuel Arellano and Stephen Bond","year":"1991","type":"GMM panel estimator","dataType":"Balanced or unbalanced short panel data (large N, small T)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86–136.","type":"article","doi":"10.1177/1536867X0900900106","isbn":null,"url":null}],"related":["arellano-bond-gmm-estimator","dynamic-panel-data-model","panel-system-gmm","fixed-effects-model","random-effects-model","panel-data-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"difference-in-differences-in-education-research","name":"Difference-in-Differences in Education Research","fullName":"Difference-in-Differences Estimator Applied to Education Research","aliases":["DiD in education","education DiD","quasi-experimental education design","education policy DiD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1990s–2000s","originator":"Dynarski, Card, Angrist, and colleagues — applied in education economics from the 1990s onward","url":"https://scholargate.app/en/causal-inference/difference-in-differences-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/difference-in-differences-in-education-research.md","definition":"Difference-in-Differences (DiD) in education research applies the classic quasi-experimental DiD estimator to evaluate education policies, programs, and reforms. Researchers compare changes in student, school, or district outcomes between a group exposed to an intervention and a comparable unexposed group across pre- and post-intervention periods, isolating policy effects from background trends.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dynarski, Card, Angrist, and colleagues — applied in education economics from the 1990s onward","year":"1990s–2000s","type":"Quasi-experimental causal inference","dataType":"Panel data or repeated cross-sections with student, school, or district outcomes","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Dynarski, S. M. (2003). Does Aid Matter? Measuring the Effect of Student Aid on College Attendance and Completion. American Economic Review, 93(1), 279-288.","type":"article","doi":"10.1257/000282803321455287","isbn":null,"url":null},{"ref":"Angrist, J. D., & Lavy, V. (1999). Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement. Quarterly Journal of Economics, 114(2), 533-575.","type":"article","doi":"10.1162/003355399556061","isbn":null,"url":null}],"related":["difference-in-differences","regression-discontinuity-design","instrumental-variables","propensity-score-matching","panel-fixed-effects","synthetic-control-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"difference-in-differences","name":"Difference-in-Differences","fullName":"Difference-in-Differences Estimator","aliases":["diff-in-diff","DiD","Farkların Farkı (Diff-in-Diff)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1994,"originator":"Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)","url":"https://scholargate.app/en/econometrics/difference-in-differences","markdownUrl":"https://scholargate.app/en/econometrics/difference-in-differences.md","definition":"Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)","year":1994,"type":"Causal inference / panel regression","estimator":"Interaction coefficient of treatment × time (ATT)","outcome":"continuous or binary","keyAssumption":"Parallel trends"},"citations":[{"ref":"Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691120355","url":null},{"ref":"Card, D., & Krueger, A. B. (1994). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. American Economic Review, 84(4), 772-793.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2118030"}],"related":["ols-regression","panel-fixed-effects","propensity-score-matching","instrumental-variables","granger-causality"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"difference-in-discontinuities","name":"Difference-in-Discontinuities","fullName":"Difference-in-Discontinuities Design","aliases":["diff-in-disc","DiD-RDD","Süreksizliklerde Fark (Difference-in-Discontinuities)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2016,"originator":"Grembi, Nannicini & Troiano","url":"https://scholargate.app/en/causal-inference/difference-in-discontinuities","markdownUrl":"https://scholargate.app/en/causal-inference/difference-in-discontinuities.md","definition":"Difference-in-Discontinuities is a hybrid quasi-experimental design that fuses regression discontinuity (RDD) with difference-in-differences (DID), introduced by Grembi, Nannicini and Troiano (2016). It compares the discontinuity at the same cutoff value across two periods to isolate a causal effect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grembi, Nannicini & Troiano","year":2016,"type":"Hybrid quasi-experimental causal design (RDD + DID)","estimator":"Difference of two local-polynomial RDD discontinuity estimates","outcome":"continuous or binary","dataStructure":"panel or time series","minSample":200},"citations":[{"ref":"Grembi, V., Nannicini, T. & Troiano, U. (2016). Do Fiscal Rules Matter? A Difference-in-Discontinuities Design. American Economic Journal: Applied Economics, 8(3), 1-30.","type":"article","doi":"10.1257/app.20150076","isbn":null,"url":null},{"ref":"Cattaneo, M. D., Idrobo, N. & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1108710206","url":null}],"related":["regression-discontinuity","difference-in-differences","instrumental-variables","panel-fixed-effects","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-chip-seq-peak-calling","name":"Differential ChIP-seq peak calling","fullName":"Differential Chromatin Immunoprecipitation Sequencing Peak Calling","aliases":["differential ChIP-seq","ChIP-seq differential binding analysis","comparative peak calling","differential chromatin occupancy analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2011-2012","originator":"Rory Stark and Gordon Brown (DiffBind framework); broader ENCODE community","url":"https://scholargate.app/en/bioinformatics/differential-chip-seq-peak-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/differential-chip-seq-peak-calling.md","definition":"Differential ChIP-seq peak calling identifies genomic loci where a protein of interest — typically a transcription factor or histone mark — shows significantly altered binding or occupancy between two or more biological conditions. By combining standard ChIP-seq peak detection with count-based statistical testing, the method reveals condition-specific regulatory elements, providing a genome-wide map of dynamic chromatin interactions underlying cellular state changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rory Stark and Gordon Brown (DiffBind framework); broader ENCODE community","year":"2011-2012","type":"Comparative genomic signal analysis pipeline","dataType":"Replicated ChIP-seq BAM/peak files across two or more biological conditions","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Ross-Innes, C. S., Stark, R., Teschendorff, A. E., Holmes, K. A., Ali, H. R., Dunning, M. J., Brown, G. D., Gojis, O., Ellis, I. O., Green, A. R., Ali, S., Chin, S. F., Palmieri, C., Caldas, C., & Carroll, J. S. (2012). Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature, 481(7381), 389-393.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Differential+oestrogen+receptor+binding+is+associated+with+clinical+outcome+in+breast+cancer+Ross-Innes+2012"},{"ref":"Stark, R., & Brown, G. (2011). DiffBind: differential binding analysis of ChIP-Seq peak data. Bioconductor Package, Cancer Research UK Cambridge Research Institute.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=DiffBind+differential+binding+analysis+ChIP-Seq+peak+data+Stark+Brown+2011"}],"related":["chip-seq-peak-calling","rna-seq-differential-expression","atac-seq-analysis","epigenome-wide-association-study","gene-set-enrichment-analysis","variant-calling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-copy-number-variation-analysis","name":"Differential Copy Number Variation Analysis","fullName":"Differential Copy Number Variation Analysis","aliases":["dCNV analysis","comparative CNV analysis","somatic copy number alteration analysis","SCNA analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2004–2011","originator":"Adam Olshen, E. S. Venkatraman and colleagues (CBS); Rameen Beroukhim, Gad Getz and colleagues (GISTIC)","url":"https://scholargate.app/en/bioinformatics/differential-copy-number-variation-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/differential-copy-number-variation-analysis.md","definition":"Differential copy number variation (dCNV) analysis identifies genomic regions where DNA copy numbers differ systematically between two conditions — such as tumor versus normal tissue, case versus control cohorts, or treated versus untreated cells. By combining probe-level read-depth or array-intensity data with statistical segmentation and group-level testing, it pinpoints somatic amplifications and deletions that may drive disease, and distinguishes recurrent driver events from passenger noise across a cohort.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adam Olshen, E. S. Venkatraman and colleagues (CBS); Rameen Beroukhim, Gad Getz and colleagues (GISTIC)","year":"2004–2011","type":"Comparative genomic analysis pipeline","dataType":"Array CGH, SNP array, or WGS/WES read-depth data from two conditions or cohorts","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Olshen, A. B., Venkatraman, E. S., Lucito, R., & Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, 5(4), 557–572.","type":"article","doi":"10.1093/biostatistics/kxh008","isbn":null,"url":null},{"ref":"Mermel, C. H., Schumacher, S. E., Hill, B., Meyerson, M. L., Beroukhim, R., & Getz, G. (2011). GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biology, 12(4), R41.","type":"article","doi":"10.1186/gb-2011-12-4-r41","isbn":null,"url":null}],"related":["copy-number-variation-detection","genome-wide-association-study","somatic-mutation-calling","structural-variant-detection","comparative-genomic-hybridization","segmentation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-cryptanalysis","name":"Differential Cryptanalysis","fullName":"Differential Cryptanalysis","aliases":["differential attack","differential path","differential probability"],"domain":"cryptography","family":"ml-model","subfamily":"Cryptanalytic technique","year":"1990","originator":"Eli Biham","url":"https://scholargate.app/en/cryptography/differential-cryptanalysis","markdownUrl":"https://scholargate.app/en/cryptography/differential-cryptanalysis.md","definition":"Differential cryptanalysis is a statistical attack technique on symmetric block ciphers that analyzes differences in inputs and outputs to recover secret keys. Introduced by Eli Biham and Adi Shamir in 1990, differential cryptanalysis was the first practical attack on DES that outperformed brute force search. The technique exploits non-random properties of cipher transformations by studying how small changes in plaintext propagate through the cipher rounds. Differential cryptanalysis has shaped cipher design for three decades.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eli Biham","subfamily":"Cryptanalytic technique","year":"1990","type":"statistical attack on block ciphers"},"citations":[{"ref":"Biham, E., & Shamir, A. (1990). Differential cryptanalysis of DES-like cryptosystems. In Advances in Cryptology - CRYPTO 1990, LNCS 537, pp. 2-21.","type":"article","doi":"10.1007/3-540-38424-3_1","isbn":null,"url":null},{"ref":"Knudsen, L. R. (2005). Block ciphers and public key cryptosystems. In Information Security and Cryptography, pp. 1-25.","type":"article","doi":null,"isbn":null,"url":"https://www.springer.com"}],"related":["linear-cryptanalysis","aes","side-channel-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-epigenome-wide-association-study","name":"Differential Epigenome-Wide Association Study","fullName":"Differential Epigenome-Wide Association Study (Differential EWAS)","aliases":["Differential EWAS","comparative EWAS","epigenome-wide differential methylation analysis","EWAS differential design"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2009–2011","originator":"Rakyan, Down, Balding & Beck (2011); Irizarry group for differential methylation methods (~2009–2014)","url":"https://scholargate.app/en/bioinformatics/differential-epigenome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/differential-epigenome-wide-association-study.md","definition":"A Differential Epigenome-Wide Association Study (Differential EWAS) scans hundreds of thousands of CpG methylation sites across the genome to identify those whose methylation levels differ significantly between two or more comparison groups — such as cases vs. controls, exposed vs. unexposed, or distinct developmental stages. It is the standard epigenomic analogue of a differential expression analysis but operates at the level of DNA methylation marks rather than RNA counts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rakyan, Down, Balding & Beck (2011); Irizarry group for differential methylation methods (~2009–2014)","year":"2009–2011","type":"Comparative epigenome-wide analysis","dataType":"DNA methylation array data (e.g., Illumina 450K / EPIC) or bisulfite sequencing","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Epigenome-wide+association+studies+for+common+human+diseases+Rakyan+2011"},{"ref":"Jaffe, A. E., & Irizarry, R. A. (2014). Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biology, 15(2), R31.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Accounting+for+cellular+heterogeneity+is+critical+in+epigenome-wide+association+studies+Jaffe+Irizarry+2014"}],"related":["epigenome-wide-association-study","genome-wide-association-study","chip-seq-peak-calling","copy-number-variation-analysis","rna-seq-differential-expression","pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-eqtl-analysis","name":"Differential eQTL Analysis","fullName":"Differential Expression Quantitative Trait Loci Analysis","aliases":["deQTL analysis","context-specific eQTL","interaction eQTL","conditional eQTL"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2007–2012","originator":"Pioneered by GTEx Consortium and Stranger et al.; formal differential testing approaches developed ~2007–2012","url":"https://scholargate.app/en/bioinformatics/differential-eqtl-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/differential-eqtl-analysis.md","definition":"Differential eQTL analysis identifies genetic variants — expression quantitative trait loci — whose regulatory effect on gene expression varies systematically across biological conditions such as tissue types, disease states, developmental stages, or treatment groups. By testing for statistical interactions between genotype and condition, the method pinpoints loci where the same allele has different transcriptional consequences depending on context, revealing the molecular basis of condition-specific gene regulation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pioneered by GTEx Consortium and Stranger et al.; formal differential testing approaches developed ~2007–2012","year":"2007–2012","type":"Statistical genomics pipeline","dataType":"Genotype array or WGS data + RNA-seq expression data from two or more conditions/tissues","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Stranger, B. E., et al. (2007). Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science, 315(5813), 848–853.","type":"article","doi":"10.1126/science.1136678","isbn":null,"url":null},{"ref":"Huang, Q. Q., et al. (2018). Dissecting super-enhancer hierarchy based on chromatin interactions. Nature Communications, 9(1), 943.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dissecting+super-enhancer+hierarchy+based+on+chromatin+interactions+Huang+2018"}],"related":["eqtl-analysis","genome-wide-association-study","rna-seq-differential-expression","bayesian-eqtl-analysis","single-cell-eqtl-analysis","pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-evolution","name":"Differential Evolution","fullName":"Differential Evolution (DE)","aliases":["DE algorithm","Diferansiyel Evrim (DE)","DE optimization"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":1997,"originator":"Rainer Storn & Kenneth Price","url":"https://scholargate.app/en/optimization/differential-evolution","markdownUrl":"https://scholargate.app/en/optimization/differential-evolution.md","definition":"Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rainer Storn & Kenneth Price","year":1997,"type":"Population-based stochastic metaheuristic","searchSpace":"Continuous parameter spaces","controlParameters":"Scale factor F, crossover rate CR, population size NP","defaultStrategy":"best/1/bin"},"citations":[{"ref":"Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.","type":"article","doi":"10.1023/A:1008202821328","isbn":null,"url":null},{"ref":"Das, S., Mullick, S. S., & Suganthan, P. N. (2016). Recent advances in differential evolution – An updated survey. Swarm and Evolutionary Computation, 27, 1–30.","type":"article","doi":"10.1016/j.swevo.2016.01.004","isbn":null,"url":null}],"related":["genetic-algorithm","bayesian-regression","pca","neural-architecture-search","deep-reinforcement-learning"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-item-functioning","name":"Differential Item Functioning","fullName":"Differential Item Functioning","aliases":["DIF","item bias analysis","measurement non-equivalence","item-level measurement bias"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1970s–1993","originator":"William H. Angoff and colleagues (ETS); systematized by Holland & Wainer","url":"https://scholargate.app/en/psychometrics/differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/differential-item-functioning.md","definition":"Differential item functioning identifies test or survey items that behave differently for examinees from different groups — such as gender, ethnicity, or language background — after controlling for the underlying ability or trait being measured. DIF analysis is essential for fairness evaluation in educational testing and psychological scale development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William H. Angoff and colleagues (ETS); systematized by Holland & Wainer","year":"1970s–1993","type":"Item-level bias detection","dataType":"Ordinal / dichotomous item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Holland, P. W. & Wainer, H. (Eds.) (1993). Differential Item Functioning. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805809589","url":null},{"ref":"Dorans, N. J. & Kulick, E. (1986). Demonstrating the utility of the standardization approach to assessing unexpected differential item performance on the Scholastic Aptitude Test. Journal of Educational Measurement, 23(4), 355–368.","type":"article","doi":"10.1111/j.1745-3984.1986.tb00255.x","isbn":null,"url":null}],"related":["item-response-theory","confirmatory-factor-analysis","measurement-invariance","rasch-model","multi-group-confirmatory-factor-analysis","cronbachs-alpha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-metabolomics-analysis","name":"Differential Metabolomics Analysis","fullName":"Differential Metabolomics Analysis","aliases":["comparative metabolomics","differential metabolite profiling","metabolomic differential analysis","DMA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2000s–2010s (field formalised alongside mass spectrometry advances)","originator":"Developed through convergent contributions by multiple groups; XCMS (Siuzdak lab, 2006) and MetaboAnalyst (Wishart lab, 2009–2015) are foundational computational implementations","url":"https://scholargate.app/en/bioinformatics/differential-metabolomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/differential-metabolomics-analysis.md","definition":"Differential metabolomics analysis is a computational pipeline that identifies metabolites whose abundance levels differ significantly between two or more biological conditions — such as disease versus control, treated versus untreated, or different developmental stages. By integrating mass spectrometry or NMR data with statistical modelling and pathway databases, it translates raw spectral measurements into biologically interpretable lists of perturbed metabolic features and the pathways they implicate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed through convergent contributions by multiple groups; XCMS (Siuzdak lab, 2006) and MetaboAnalyst (Wishart lab, 2009–2015) are foundational computational implementations","year":"2000s–2010s (field formalised alongside mass spectrometry advances)","type":"Quantitative comparative omics pipeline","dataType":"LC-MS, GC-MS, or NMR metabolomics data matrices (samples × metabolite features)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0 — making metabolomics more meaningful. Nucleic Acids Research, 43(W1), W251–W257.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MetaboAnalyst+3.0+making+metabolomics+more+meaningful+Xia+Wishart+2015"},{"ref":"Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78(3), 779–787.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=XCMS+Processing+mass+spectrometry+data+metabolite+profiling+Smith+Siuzdak+2006"}],"related":["metabolomics-analysis","pathway-enrichment-analysis","multi-omics-metabolomics-analysis","rna-seq-differential-expression","differential-proteomics-analysis","machine-learning-assisted-metabolomics-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-pathway-enrichment-analysis","name":"Differential pathway enrichment analysis","fullName":"Differential Pathway Enrichment Analysis","aliases":["differential enrichment analysis","comparative pathway enrichment","DPEA","cross-condition pathway analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2004–2012","originator":"Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012)","url":"https://scholargate.app/en/bioinformatics/differential-pathway-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/differential-pathway-enrichment-analysis.md","definition":"Differential pathway enrichment analysis identifies biological pathways whose enrichment signals differ significantly between two or more experimental conditions — for example, between two diseases, two treatments, or two cell types. Rather than asking which pathways are enriched in one condition, it asks which pathways show a statistically meaningful change in enrichment level across conditions, revealing condition-specific or context-dependent biology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012)","year":"2004–2012","type":"Comparative enrichment analysis","dataType":"Gene expression matrices (RNA-seq counts or microarray intensities), with biological condition labels","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.","type":"article","doi":"10.1093/nar/gks461","isbn":null,"url":null},{"ref":"Väremo, L., Nielsen, J., & Nookaew, I. (2013). Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical inferences. Nucleic Acids Research, 41(8), 4378–4391.","type":"article","doi":"10.1093/nar/gkt111","isbn":null,"url":null}],"related":["pathway-enrichment-analysis","gene-set-enrichment-analysis","rna-seq-differential-expression","multi-omics-pathway-enrichment-analysis","network-based-pathway-enrichment-analysis","single-cell-pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-privacy","name":"Differential Privacy","fullName":"Differential Privacy","aliases":["DP","epsilon-differential privacy","randomized privacy","Diferansiyel Gizlilik"],"domain":"privacy","family":"ml-model","subfamily":"Privacy-preserving analysis","year":2006,"originator":"Cynthia Dwork","url":"https://scholargate.app/en/privacy/differential-privacy","markdownUrl":"https://scholargate.app/en/privacy/differential-privacy.md","definition":"Differential privacy is a mathematical framework for releasing statistical information about a dataset while providing rigorous guarantees that individual records cannot be identified or inferred. Introduced by Cynthia Dwork in 2006, it formalizes privacy as a probabilistic bound: any single individual's presence or absence in the dataset changes the output distribution by at most a multiplicative factor of e^ε, where ε is the privacy budget controlling the privacy–utility tradeoff.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cynthia Dwork","year":2006,"type":"Privacy-preserving randomized mechanism","subfamily":"Privacy-preserving analysis","privacy_parameter":"epsilon (ε) — privacy budget","noise_mechanisms":"Laplace, Gaussian, Exponential"},"citations":[{"ref":"Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.","type":"article","doi":"10.1007/11787006_1","isbn":null,"url":null}],"related":["federated-learning","k-anonymity","synthetic-data-generation"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-proteomics-analysis","name":"Differential proteomics analysis","fullName":"Differential Proteomics Analysis","aliases":["comparative proteomics","quantitative differential proteomics","differential protein expression analysis","DPA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"Late 1990s–2000s (mass spectrometry-based approaches matured ~1999–2004)","originator":"Pioneered broadly by Matthias Mann and colleagues; SILAC introduced by Ong et al. (2002)","url":"https://scholargate.app/en/bioinformatics/differential-proteomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/differential-proteomics-analysis.md","definition":"Differential proteomics analysis is a quantitative pipeline that identifies proteins whose abundance levels change significantly between two or more biological conditions — such as healthy versus diseased tissue, treated versus untreated cells, or different developmental stages. By combining mass spectrometry-based detection with statistical testing, the method generates ranked lists of differentially expressed proteins that can be linked to biological pathways, disease mechanisms, or drug targets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pioneered broadly by Matthias Mann and colleagues; SILAC introduced by Ong et al. (2002)","year":"Late 1990s–2000s (mass spectrometry-based approaches matured ~1999–2004)","type":"Quantitative omics pipeline","dataType":"Mass spectrometry intensity data (label-based or label-free); protein abundance tables","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., & Mann, M. (2002). Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & Cellular Proteomics, 1(5), 376–386.","type":"article","doi":"10.1074/mcp.M200025-MCP200","isbn":null,"url":null},{"ref":"Bantscheff, M., Lemeer, S., Savitski, M. M., & Kuster, B. (2012). Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Analytical and Bioanalytical Chemistry, 404(4), 939–965.","type":"article","doi":"10.1007/s00216-012-6203-4","isbn":null,"url":null}],"related":["mass-spectrometry-proteomics","label-free-quantification","silac","two-dimensional-gel-electrophoresis","gene-ontology-enrichment-analysis","pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-scanning-calorimetry","name":"Differential Scanning Calorimetry","fullName":"Differential Scanning Calorimetry (DSC)","aliases":["DSC","differential thermal analysis","thermal analysis"],"domain":"materials-science","family":"process-pipeline","subfamily":"Thermal analysis","year":"1964","originator":"E. S. Watson","url":"https://scholargate.app/en/materials-science/differential-scanning-calorimetry","markdownUrl":"https://scholargate.app/en/materials-science/differential-scanning-calorimetry.md","definition":"Differential Scanning Calorimetry (DSC) is a thermal characterization technique that measures the heat flow required to maintain a sample and an inert reference at the same temperature while both are heated or cooled. Invented by Watson, O'Neill, and colleagues in 1964, DSC directly quantifies enthalpy changes during phase transitions, crystallization, melting, and chemical reactions. It is the standard tool in materials science, chemistry, and pharmaceutical research for determining thermodynamic properties, thermal stability, and kinetics of thermal transitions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"E. S. Watson","subfamily":"Thermal analysis","year":"1964","type":"Measurement method"},"citations":[{"ref":"Watson, E. S., O'Neill, M. J., Justin, J., & Brenner, N. (1964). A differential scanning calorimeter for quantitative differential thermal analysis. Analytical Chemistry, 36(7), 1233-1238.","type":"article","doi":"10.1021/ac60213a019","isbn":null,"url":null},{"ref":"Haines, P. J. (Ed.). (2012). Principles of Thermal Analysis and Calorimetry (2nd ed.). Royal Society of Chemistry.","type":"book","doi":null,"isbn":null,"url":"https://www.rsc.org"},{"ref":"Schick, C., & Mathot, V. (2019). Fast Scanning Calorimetry. Springer.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fast+Scanning+Calorimetry+Schick"}],"related":["thermogravimetric-analysis","phase-field-modeling","calphad"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-single-cell-rna-seq-analysis","name":"Differential single-cell RNA-seq analysis","fullName":"Differential Single-Cell RNA Sequencing Analysis","aliases":["scRNA-seq differential analysis","single-cell differential expression analysis","scDE analysis","single-cell comparative transcriptomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2015–2021","originator":"Multiple contributors; pseudobulk framework formalized by Squair et al. (2021); Seurat/FindMarkers by Satija lab (~2015)","url":"https://scholargate.app/en/bioinformatics/differential-single-cell-rna-seq-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/differential-single-cell-rna-seq-analysis.md","definition":"Differential single-cell RNA-seq (scRNA-seq) analysis is a computational pipeline that compares transcriptomic profiles across biological conditions — such as treated versus untreated, disease versus healthy, or time points — at single-cell resolution. It identifies which genes, cell types, and cell states change between conditions, providing mechanistic insight that bulk RNA-seq comparisons cannot offer. The approach combines clustering, cell-type annotation, and statistical testing, typically using pseudobulk aggregation to account for within-sample correlation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; pseudobulk framework formalized by Squair et al. (2021); Seurat/FindMarkers by Satija lab (~2015)","year":"2015–2021","type":"Computational bioinformatics pipeline","dataType":"Single-cell RNA sequencing count matrices; cell-type annotation metadata; sample condition labels","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Hafemeister, C., & Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 20, 296.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Normalization+and+variance+stabilization+of+single-cell+RNA-seq+data+using+regularized+negative+binomial+regression"},{"ref":"Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Lefoulon, E., Tani, N., Bhatt, D. L., Rossetti, A., & Courtine, G. (2021). Confronting false discoveries in single-cell differential expression. Nature Communications, 12, 5692.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Confronting+false+discoveries+in+single-cell+differential+expression"}],"related":["single-cell-rna-seq-analysis","rna-seq-differential-expression","single-cell-pathway-enrichment-analysis","single-cell-gene-set-enrichment-analysis","pseudobulk-differential-expression","cell-type-deconvolution"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"differential-variant-calling","name":"Differential Variant Calling","fullName":"Differential Variant Calling in Genomics","aliases":["somatic variant calling","comparative variant analysis","tumor-normal variant calling","differential SNV/indel calling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2009–2013 (field matured with NGS; seminal tools 2009–2013)","originator":"Multiple groups; key tools: VarScan (Koboldt et al.), MuTect (Cibulskis et al.), GATK Haplotype Caller (DePristo et al.)","url":"https://scholargate.app/en/bioinformatics/differential-variant-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/differential-variant-calling.md","definition":"Differential variant calling is a bioinformatics pipeline that identifies genetic variants — single nucleotide variants (SNVs), small insertions/deletions (indels), and structural variants — that are present in one biological sample or condition but absent (or significantly enriched) in a paired reference sample. The canonical application is tumor-versus-normal cancer genomics, where somatic mutations unique to the tumor are distinguished from germline variants shared with normal tissue. The same logic applies to comparing treated vs. untreated cell lines, evolved vs. ancestral strains, or case vs. control cohorts in population genomics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; key tools: VarScan (Koboldt et al.), MuTect (Cibulskis et al.), GATK Haplotype Caller (DePristo et al.)","year":"2009–2013 (field matured with NGS; seminal tools 2009–2013)","type":"Comparative genomic analysis pipeline","dataType":"Aligned short-read sequencing data (BAM/CRAM files) from paired or multi-sample experiments","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Koboldt, D.C., Zhang, Q., Larson, D.E., Shen, D., McLellan, M.D., Lin, L., Miller, C.A., Mardis, E.R., Ding, L., & Wilson, R.K. (2012). VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research, 22(3), 568–576.","type":"article","doi":"10.1101/gr.129684.111","isbn":null,"url":null},{"ref":"Cibulskis, K., Lawrence, M.S., Carter, S.L., Sivachenko, A., Jaffe, D., Sougnez, C., Gabriel, S., Meyerson, M., Lander, E.S., & Getz, G. (2013). Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature Biotechnology, 31(3), 213–219.","type":"article","doi":"10.1038/nbt.2514","isbn":null,"url":null}],"related":["whole-exome-sequencing","whole-genome-sequencing","rna-seq-differential-expression","copy-number-variation-analysis","variant-annotation","single-nucleotide-polymorphism-genotyping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"difficulties-emotion-regulation","name":"Difficulties in Emotion Regulation Scale","fullName":"Difficulties in Emotion Regulation Scale (DERS)","aliases":["DERS","DERS-36"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"emotion-dysregulation-assessment","year":"2004","originator":"Kristin L. Gratz & Lizabeth Roemer","url":"https://scholargate.app/en/clinical-psychology/difficulties-emotion-regulation","markdownUrl":"https://scholargate.app/en/clinical-psychology/difficulties-emotion-regulation.md","definition":"The DERS is a 36-item self-report measure assessing multidimensional emotion dysregulation across six related but distinct facets. Developed by Gratz and Roemer in 2004, it has become a cornerstone transdiagnostic measure in emotion regulation research, capturing emotional avoidance, behavioral dyscontrol, and limited coping awareness that cut across psychiatric conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kristin L. Gratz & Lizabeth Roemer","subfamily":"emotion-dysregulation-assessment","year":"2004","type":"Self-report questionnaire"},"citations":[{"ref":"Gratz, K. L., & Roemer, L. (2004). Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the Difficulties in Emotion Regulation Scale. Journal of Psychopathology and Behavioral Assessment, 26(1), 41–54.","type":"article","doi":"10.1023/B:JOBA.0000007455.08539.94","isbn":null,"url":null}],"related":["emotion-regulation-questionnaire","affective-lability-scale","emotion-dysregulation-scale","adult-adhd-self-report-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diffie-hellman-key-exchange","name":"Diffie-Hellman Key Exchange","fullName":"Diffie-Hellman Key Agreement Protocol","aliases":["DH Key Exchange","Diffie-Hellman Key Agreement"],"domain":"cryptography","family":"process-pipeline","subfamily":"Key agreement protocol","year":"1976","originator":"Whitfield Diffie, Martin Hellman","url":"https://scholargate.app/en/cryptography/diffie-hellman-key-exchange","markdownUrl":"https://scholargate.app/en/cryptography/diffie-hellman-key-exchange.md","definition":"The Diffie-Hellman key exchange, invented by Whitfield Diffie and Martin Hellman in 1976, is a foundational protocol for establishing a shared secret over an insecure communication channel. Two parties who have never previously communicated can use Diffie-Hellman to agree on a symmetric encryption key that an eavesdropper cannot easily derive, even after observing all public exchanges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Whitfield Diffie, Martin Hellman","subfamily":"Key agreement protocol","year":"1976","type":"Asymmetric key exchange algorithm"},"citations":[{"ref":"Diffie, W., & Hellman, M. E. (1976). New directions in cryptography. IEEE Transactions on Information Theory, 22(6), 644–654.","type":"article","doi":"10.1109/TIT.1976.1055638","isbn":null,"url":null},{"ref":"Menezes, A. J., van Oorschot, P. C., & Vanstone, S. A. (1997). Handbook of Applied Cryptography. CRC Press.","type":"book","doi":null,"isbn":null,"url":"https://cacr.uwaterloo.ca/hac/"},{"ref":"Boyd, C., & Mathuria, A. (2003). Protocols for Authentication and Key Establishment. Springer-Verlag.","type":"book","doi":"10.1007/978-3-662-09527-0","isbn":null,"url":null}],"related":["rsa-cryptosystem-analysis","tls-protocol-analysis","symmetric-key-analysis","digital-signature-scheme"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diffserv","name":"DiffServ","fullName":"Differentiated Services","aliases":["quality of service","QoS architecture"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Quality of Service","year":"1998","originator":"IETF DiffServ Working Group","url":"https://scholargate.app/en/telecommunications/diffserv","markdownUrl":"https://scholargate.app/en/telecommunications/diffserv.md","definition":"DiffServ is a QoS architecture providing scalable, class-based service differentiation in networks. Introduced by IETF (1998), DiffServ marks packets with a Differentiated Services Code Point (DSCP) in the IP header, enabling routers to apply per-hop-behaviors (PHBs) based on markings. Unlike IntServ (which reserves resources per-flow), DiffServ is stateless and scalable to Internet scale. DiffServ remains the primary QoS mechanism in ISP and enterprise networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"IETF DiffServ Working Group","subfamily":"Quality of Service","year":"1998","type":"QoS architecture"},"citations":[{"ref":"Blake, S., Black, D., Carlson, M., et al. (1998). An Architecture for Differentiated Services. RFC 2475.","type":"article","doi":null,"isbn":null,"url":"https://www.ietf.org"},{"ref":"Nichols, K., Jacobson, V., & Zhang, L. (1999). A Two-bit Differentiated Services Architecture for the Internet. RFC 2638.","type":"article","doi":null,"isbn":null,"url":"https://www.ietf.org"}],"related":["token-bucket","ospf","bgp"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diffusion-kurtosis-imaging","name":"Diffusion Kurtosis Imaging","fullName":"Diffusion Kurtosis Imaging (DKI)","aliases":["DKI","non-Gaussian diffusion","diffusion kurtosis"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Advanced diffusion MRI","year":"2005","originator":"Jens Jensen","url":"https://scholargate.app/en/neuroimaging/diffusion-kurtosis-imaging","markdownUrl":"https://scholargate.app/en/neuroimaging/diffusion-kurtosis-imaging.md","definition":"Diffusion Kurtosis Imaging (DKI) is an advanced diffusion MRI technique that quantifies non-Gaussian diffusion of water molecules, providing detailed information about tissue microstructure beyond conventional diffusion tensor imaging. Introduced by Jensen and colleagues in 2005, DKI detects deviations from normal Gaussian diffusion, revealing information about cellular compartmentalization and fiber heterogeneity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jens Jensen","subfamily":"Advanced diffusion MRI","year":"2005","type":"Microstructural white matter analysis"},"citations":[{"ref":"Jensen, J. H., Helpern, J. A., Ramani, A., et al. (2005). Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by magnetic resonance imaging. Magnetic Resonance in Medicine, 53(6), 1432–1440.","type":"article","doi":"10.1002/mrm.20508","isbn":null,"url":null},{"ref":"Lu, H., Jensen, J. H., Topgaard, D., & Helpern, J. A. (2018). Effective medium theory of apparent diffusion coefficient in fibrous media. Magnetic Resonance in Medicine, 81(5), 3245–3260.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Effective+medium+theory+of+apparent+diffusion+coefficient+in+fibrous+media+Lu"}],"related":["tract-based-spatial-statistics","noddi","magnetisation-transfer-ratio"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diffusion-model","name":"Diffusion Model","fullName":"Denoising Diffusion Probabilistic Model (DDPM / Latent Diffusion)","aliases":["Difüzyon Modeli (DDPM / Stable Diffusion)","difüzyon modeli","denoising diffusion model","DDPM","Stable Diffusion","latent diffusion model"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2020,"originator":"Ho, J., Jain, A. & Abbeel, P.","url":"https://scholargate.app/en/deep-learning/diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/diffusion-model.md","definition":"A diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ho, J., Jain, A. & Abbeel, P.","year":2020,"type":"Generative deep learning (denoising diffusion)","task":"Generation of images, audio and molecular structures","minSample":1000},"citations":[{"ref":"Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2006.11239"},{"ref":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. CVPR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2112.10752"}],"related":["variational-autoencoder","score-based-diffusion","neural-ode","pca"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"diffusion-of-innovation-model","name":"Diffusion of Innovation Model","fullName":"Rogers Diffusion of Innovation Framework","aliases":["DOI Model","Innovation Adoption Curve","S-Curve Adoption"],"domain":"marketing","family":"process-pipeline","subfamily":"Innovation adoption and market dynamics","year":"1962","originator":"Everett M. Rogers","url":"https://scholargate.app/en/marketing/diffusion-of-innovation-model","markdownUrl":"https://scholargate.app/en/marketing/diffusion-of-innovation-model.md","definition":"The Diffusion of Innovation (DOI) model is a theoretical framework developed by Everett Rogers in 1962 to explain how innovations spread through populations over time. The framework categorizes adopters into five groups based on when they adopt an innovation and describes the characteristic S-shaped curve that typically describes market adoption of new products, services, and technologies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Everett M. Rogers","subfamily":"Innovation adoption and market dynamics","year":"1962","type":"Adoption curve framework"},"citations":[{"ref":"Rogers, E. M. (1962). Diffusion of Innovations. Free Press.","type":"book","doi":null,"isbn":"978-0743222296","url":null},{"ref":"Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.","type":"book","doi":null,"isbn":"978-0743222296","url":null},{"ref":"Bass, F. M. (1969). A New Product Growth for Model Consumer Durables. Management Science, 15(5), 215-227.","type":"article","doi":"10.1287/mnsc.15.5.215","isbn":null,"url":null}],"related":["marketing-mix-modeling","advertising-effectiveness-study","customer-journey-mapping","brand-equity-measurement","market-segmentation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-autoethnography","name":"Digital Autoethnography","fullName":"Digital Autoethnography","aliases":["online autoethnography","virtual autoethnography","digital self-ethnography","networked autoethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Annette Markham; expanded through netnography work by Robert Kozinets","url":"https://scholargate.app/en/qualitative/digital-autoethnography","markdownUrl":"https://scholargate.app/en/qualitative/digital-autoethnography.md","definition":"Digital autoethnography is a qualitative research design in which the researcher systematically examines their own lived experience within digital environments — social media platforms, online communities, gaming worlds, digital workplaces, or other networked spaces — to illuminate broader cultural and social phenomena. Combining autoethnography's first-person reflexivity with the study of digital life, it treats personal digital traces, interactions, and self-representations as primary data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Annette Markham; expanded through netnography work by Robert Kozinets","year":"2000s–2010s","type":"Qualitative self-reflexive design","dataType":"Personal digital artifacts, online communication logs, social media posts, digital field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Markham, A. N. (2013). Undermining 'data': A critical examination of a core term in scientific inquiry. First Monday, 18(10).","type":"article","doi":null,"isbn":null,"url":"https://firstmonday.org/ojs/index.php/fm/article/view/4868"},{"ref":"Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.","type":"book","doi":null,"isbn":"978-1847875228","url":null}],"related":["autoethnography","netnography","digital-ethnography","narrative-inquiry","reflexive-thematic-analysis","virtual-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-case-study","name":"Digital Case Study","fullName":"Digital Case Study Research","aliases":["online case study","virtual case study","internet-based case study","digital ethnographic case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (building on Yin's 1984 foundational case study framework)","originator":"Robert K. Yin (case study foundations); extended to digital contexts by multiple scholars in the 2000s–2010s","url":"https://scholargate.app/en/qualitative/digital-case-study","markdownUrl":"https://scholargate.app/en/qualitative/digital-case-study.md","definition":"Digital case study research applies the classic bounded case study framework to phenomena that are situated in, or mediated by, digital environments. Drawing on Robert Yin's foundational case study methodology, it investigates a contemporary phenomenon in depth within its real-world digital context — using online documents, social media archives, virtual interviews, website content, and other digital artifacts as primary evidence. The approach is particularly suited to studying how individuals, groups, or organisations behave in online spaces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (case study foundations); extended to digital contexts by multiple scholars in the 2000s–2010s","year":"2000s–2010s (building on Yin's 1984 foundational case study framework)","type":"Qualitative research design","dataType":"Digital documents, online interactions, social media data, digital artifacts, virtual interviews","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Merriam, S. B. (2009). Qualitative Research: A Guide to Design and Implementation. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787970109","url":null}],"related":["case-study","ethnography","netnography","content-analysis","narrative-analysis","document-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-classic-grounded-theory","name":"Digital Classic Grounded Theory","fullName":"Digital Classic Grounded Theory","aliases":["Digital CGT","online classic grounded theory","Glaserian digital grounded theory","classic GT in digital contexts"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (classic GT); digital adaptation from early 2000s onward","originator":"Barney G. Glaser and Anselm L. Strauss (classic GT); digital application developed by subsequent methodologists","url":"https://scholargate.app/en/qualitative/digital-classic-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/digital-classic-grounded-theory.md","definition":"Digital Classic Grounded Theory applies Glaser and Strauss's original (Glaserian) grounded theory methodology to data collected from online and digital environments — including social media, online forums, email threads, and chat logs. It preserves the inductive, emergence-focused logic of classic GT while adapting sampling, data collection, and ethical practices to the digital context, aiming to generate a grounded substantive theory that explains a social or psychological process as it unfolds online.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser and Anselm L. Strauss (classic GT); digital application developed by subsequent methodologists","year":"1967 (classic GT); digital adaptation from early 2000s onward","type":"Qualitative research design","dataType":"Digital text data: online forum posts, social media content, email exchanges, chat logs, web documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202300283","url":null},{"ref":"Glaser, B. G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theoretical+Sensitivity+Advances+in+the+Methodology+of+Grounded+Theory+Glaser+1978"}],"related":["classic-grounded-theory","grounded-theory","digital-grounded-theory","digital-constructivist-grounded-theory","digital-straussian-grounded-theory","netnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-constructivist-grounded-theory","name":"Digital Constructivist grounded theory","fullName":"Digital Constructivist Grounded Theory","aliases":["Digital CGT","online constructivist grounded theory","digital-context CGT","constructivist GT with digital data"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Kathy Charmaz (CGT); applied to digital contexts by qualitative internet researchers","url":"https://scholargate.app/en/qualitative/digital-constructivist-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/digital-constructivist-grounded-theory.md","definition":"Digital Constructivist Grounded Theory (Digital CGT) applies Kathy Charmaz's constructivist variant of grounded theory to data generated in digital environments — social media platforms, online communities, forums, digital interviews, and other internet-mediated spaces. It treats meaning as co-constructed between researcher and participant in digitally-mediated contexts, and generates theory grounded in how people make sense of experience through and within digital life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz (CGT); applied to digital contexts by qualitative internet researchers","year":"2000s–2010s","type":"Qualitative theory-building approach","dataType":"Digital text data — online interviews, social media posts, forum discussions, emails, digital documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973539","url":null},{"ref":"Salmons, J. (2014). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1452283531","url":null}],"related":["constructivist-grounded-theory","digital-grounded-theory","digital-ethnography","netnography","digital-thematic-analysis","classic-grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-content-analysis","name":"Digital Content analysis","fullName":"Digital Content Analysis","aliases":["DCA","online content analysis","web content analysis","digital media content analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1950s (classical); digital adaptation 2000s–2010s","originator":"Building on Berelson (1952) and Krippendorff (1980); adapted for digital contexts by Herring (2010) and Neuendorf (2002+)","url":"https://scholargate.app/en/qualitative/digital-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-content-analysis.md","definition":"Digital Content Analysis is a systematic research method for describing, categorising, and interpreting the content of digital materials — social media posts, websites, online forums, blogs, emails, and video transcripts. It applies the rigorous coding logic of classical content analysis to digitally native or digitally collected text, enabling researchers to move from raw online data to structured, interpretable findings about communication, meaning, and social phenomena.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Berelson (1952) and Krippendorff (1980); adapted for digital contexts by Herring (2010) and Neuendorf (2002+)","year":"1950s (classical); digital adaptation 2000s–2010s","type":"Qualitative/quantitative hybrid research approach","dataType":"Digital texts: social media posts, websites, online forums, emails, blogs, video transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412979474","url":null},{"ref":"Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage.","type":"book","doi":null,"isbn":"978-1849205931","url":null}],"related":["content-analysis","qualitative-content-analysis","discourse-analysis","thematic-analysis","digital-ethnography","social-media-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-conversation-analysis","name":"Digital Conversation Analysis","fullName":"Digital Conversation Analysis","aliases":["DCA","online conversation analysis","digital CA","computer-mediated conversation analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1974 (CA foundations); 1990s–2000s (digital adaptation)","originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA foundations); Susan Herring (computer-mediated discourse adaptation)","url":"https://scholargate.app/en/qualitative/digital-conversation-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-conversation-analysis.md","definition":"Digital Conversation Analysis (DCA) applies the systematic, turn-by-turn analytical procedures of Conversation Analysis (CA) to digital and computer-mediated interactions — including chat logs, social media threads, instant messages, and online forums. Rooted in the foundational CA framework of Sacks, Schegloff, and Jefferson, DCA adapts classical concepts such as turn-taking, adjacency pairs, and sequential organisation to account for the asynchronous, multimodal, and textual character of online communication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA foundations); Susan Herring (computer-mediated discourse adaptation)","year":"1974 (CA foundations); 1990s–2000s (digital adaptation)","type":"Qualitative discourse analysis method","dataType":"Digital text data: chat logs, forum threads, social media exchanges, instant messages, emails","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.","type":"article","doi":"10.2307/412243","isbn":null,"url":null},{"ref":"Herring, S. C. (2007). A faceted classification scheme for computer-mediated discourse. Language@Internet, 4(1).","type":"article","doi":null,"isbn":null,"url":"https://www.languageatinternet.org/articles/2007/761"}],"related":["conversation-analysis","discourse-analysis","digital-discourse-analysis","critical-discourse-analysis","digital-ethnography","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-critical-discourse-analysis","name":"Digital Critical Discourse Analysis","fullName":"Digital Critical Discourse Analysis","aliases":["Digital CDA","Online Critical Discourse Analysis","Multimodal Digital CDA","DCDA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Scholars extending Ruth Wodak and Norman Fairclough's CDA tradition into digital contexts; notably Crispin Thurlow, Michele Zappavigna, and Jannis Androutsopoulos","url":"https://scholargate.app/en/qualitative/digital-critical-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-critical-discourse-analysis.md","definition":"Digital Critical Discourse Analysis (Digital CDA) is a qualitative research approach that applies the theoretical and methodological tools of Critical Discourse Analysis to digital and online communicative contexts. It examines how language, multimodal elements, and digital affordances are mobilized in online spaces to produce, reproduce, or contest power relations, ideologies, and social inequalities. Drawing on traditions established by Fairclough, Wodak, and van Dijk, Digital CDA treats digital texts — from social media posts to comment threads and websites — as sites of ideological struggle shaped by the platforms that host them.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scholars extending Ruth Wodak and Norman Fairclough's CDA tradition into digital contexts; notably Crispin Thurlow, Michele Zappavigna, and Jannis Androutsopoulos","year":"2000s–2010s","type":"Qualitative discourse analysis approach","dataType":"Digital text, multimodal online content (social media posts, websites, comment threads, digital images, videos)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Unger, J. W., Krzyżanowski, M., & Wodak, R. (Eds.). (2016). Multilingual Encounters in Europe's Institutional Spaces. Bloomsbury Academic.","type":"book","doi":null,"isbn":"978-1474231756","url":null},{"ref":"Thurlow, C., & Mroczek, K. (Eds.). (2011). Digital Discourse: Language in the New Media. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Digital+Discourse+Language+in+the+New+Media+Thurlow+Mroczek+2011"}],"related":["critical-discourse-analysis","multimodal-discourse-analysis","social-media-analysis","content-analysis","thematic-analysis","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-document-analysis","name":"Digital Document Analysis","fullName":"Digital Document Analysis in Qualitative Research","aliases":["online document analysis","digital text analysis","e-document analysis","digital archival analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s onward (grounded in earlier document analysis traditions)","originator":"Adapted from traditional document analysis; digital variant developed by qualitative researchers across disciplines (e.g., Bowen 2009; Prior 2003)","url":"https://scholargate.app/en/qualitative/digital-document-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-document-analysis.md","definition":"Digital document analysis is a qualitative method for systematically locating, appraising, and interpreting documents that exist in digital or online form — including websites, emails, institutional reports, policy files, social media content, and digital archives. It applies the established logic of document analysis to born-digital and digitised sources, enabling researchers to examine meaning, discourse, and institutional practice embedded in contemporary digital texts without recruiting participants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from traditional document analysis; digital variant developed by qualitative researchers across disciplines (e.g., Bowen 2009; Prior 2003)","year":"2000s onward (grounded in earlier document analysis traditions)","type":"Qualitative data analysis method","dataType":"Digital documents: websites, emails, social media posts, online reports, digital archives, PDFs, institutional digital records","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Prior, L. (2003). Using Documents in Social Research. Sage.","type":"book","doi":null,"isbn":"978-0761965107","url":null}],"related":["document-analysis","content-analysis","discourse-analysis","digital-ethnography","netnography","visual-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-educational-action-research","name":"Digital Educational Action Research","fullName":"Digital Educational Action Research","aliases":["technology-integrated action research","online educational action research","digital-mediated practitioner inquiry","DEAR"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1990s–2000s (digital integration of Lewinian action research traditions)","originator":"Rooted in Carr & Kemmis (1986); digital adaptation developed by Lankshear, Knobel, and others from mid-1990s onward","url":"https://scholargate.app/en/field-methods/digital-educational-action-research","markdownUrl":"https://scholargate.app/en/field-methods/digital-educational-action-research.md","definition":"Digital educational action research is a cyclical, practitioner-led inquiry method in which educators systematically investigate a problem or question arising in digitally mediated teaching and learning environments. Drawing on the action research tradition of Carr, Kemmis, and Lewin, it integrates digital tools — learning management systems, social media, video, online collaborative platforms — both as the context of inquiry and as instruments for data collection, making it particularly suited to contemporary technology-rich classrooms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Carr & Kemmis (1986); digital adaptation developed by Lankshear, Knobel, and others from mid-1990s onward","year":"1990s–2000s (digital integration of Lewinian action research traditions)","type":"Applied qualitative-cyclical research design","dataType":"Digital artifacts, online interactions, video recordings, screen captures, reflective journals, interview transcripts","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Lankshear, C., & Knobel, M. (2004). A Handbook for Teacher Research: From Design to Implementation. Open University Press.","type":"book","doi":null,"isbn":"978-0335211357","url":null},{"ref":"Carr, W., & Kemmis, S. (1986). Becoming Critical: Education, Knowledge and Action Research. Falmer Press.","type":"book","doi":null,"isbn":"978-1850000235","url":null}],"related":["educational-action-research","participatory-action-research","design-based-research","digital-classroom-observation","lesson-study","program-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-ethnography","name":"Digital Ethnography","fullName":"Digital Ethnography","aliases":["online ethnography","virtual ethnography","internet ethnography","netnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Ethnography","year":"Late 1990s – 2000s","originator":"Christine Hine (virtual ethnography); Robert V. Kozinets (netnography)","url":"https://scholargate.app/en/qualitative/digital-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/digital-ethnography.md","definition":"Digital ethnography is a qualitative research method that adapts traditional ethnographic fieldwork to online and digitally mediated settings. Drawing on sustained participant observation, document collection, and sometimes interviews, the researcher immerses themselves in one or more digital communities — social media platforms, forums, gaming spaces, or messaging groups — to understand how culture, identity, and social practice are constructed through digital interaction. The approach recognises that online spaces are not merely reflections of offline life but distinctive sites of cultural production in their own right.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christine Hine (virtual ethnography); Robert V. Kozinets (netnography)","year":"Late 1990s – 2000s","type":"Qualitative research method","dataType":"Online text, multimedia, social media posts, forum discussions, field notes, interviews conducted digitally","typicalSampleSize":"1–3 online communities or platforms; dozens to hundreds of textual artefacts","subfamily":"Ethnography"},"citations":[{"ref":"Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.","type":"book","doi":null,"isbn":"978-1847875228","url":null},{"ref":"Hine, C. (2000). Virtual Ethnography. Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Virtual+Ethnography+Christine+Hine+2000"}],"related":["ethnography","discourse-analysis","content-analysis","thematic-analysis","grounded-theory","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-grounded-theory","name":"Digital Grounded Theory","fullName":"Digital Grounded Theory","aliases":["DGT","online grounded theory","internet-based grounded theory","grounded theory in digital contexts"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (as digital data became mainstream in qualitative research)","originator":"Adapted from Glaser & Strauss (1967); digital application developed through the work of Murthy (2008) and others in online qualitative research","url":"https://scholargate.app/en/qualitative/digital-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/digital-grounded-theory.md","definition":"Digital Grounded Theory applies the systematic inductive logic of grounded theory to data gathered from digital and online environments — social media platforms, forums, blogs, comment sections, and other internet-mediated communication. Rather than simply using grounded theory on text that happens to come from digital sources, it involves adapting sampling, collection, and ethical procedures to the specific affordances and constraints of online data, while retaining the core commitment to theory generation grounded in empirical material.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from Glaser & Strauss (1967); digital application developed through the work of Murthy (2008) and others in online qualitative research","year":"2000s–2010s (as digital data became mainstream in qualitative research)","type":"Qualitative research design","dataType":"Digital text data: social media posts, online forums, blogs, emails, digital documents, chat logs","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Murthy, D. (2008). Digital ethnography: An examination of the use of new technologies for social research. Sociology, 42(5), 837–855.","type":"article","doi":"10.1177/0038038508094565","isbn":null,"url":null},{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202300610","url":null}],"related":["grounded-theory","digital-ethnography","netnography","constructivist-grounded-theory","digital-thematic-analysis","digital-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-health-acceptance-scale","name":"Digital Health Acceptance Scale","fullName":"Digital Health Acceptance Scale (DHAS)","aliases":["DHAS","Digital Health Acceptance"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Technology acceptance and adoption","year":"1989","originator":"Fred D. Davis (Technology Acceptance Model); extended by Venkatesh et al. (Unified Theory of Acceptance and Use of Technology)","url":"https://scholargate.app/en/health-informatics/digital-health-acceptance-scale","markdownUrl":"https://scholargate.app/en/health-informatics/digital-health-acceptance-scale.md","definition":"The Digital Health Acceptance Scale measures the extent to which patients and providers perceive digital health technologies as useful, easy to use, and worth adopting. Grounded in Davis's Technology Acceptance Model (TAM) and extended by Venkatesh and colleagues through the Unified Theory of Acceptance and Use of Technology (UTAUT), the scale captures both intrinsic factors (usefulness, ease of use, subjective norms) and contextual factors (facilitating conditions, effort expectancy) that predict technology adoption and sustained use in healthcare settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred D. Davis (Technology Acceptance Model); extended by Venkatesh et al. (Unified Theory of Acceptance and Use of Technology)","subfamily":"Technology acceptance and adoption","year":"1989","type":"Self-report questionnaire"},"citations":[{"ref":"Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.","type":"article","doi":"10.2307/249008","isbn":null,"url":null},{"ref":"Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.","type":"article","doi":"10.2307/30036540","isbn":null,"url":null}],"related":["ehealth-literacy-scale","telemedicine-satisfaction-scale","patient-engagement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-hermeneutic-analysis","name":"Digital Hermeneutic Analysis","fullName":"Digital Hermeneutic Analysis","aliases":["digital hermeneutics","computational hermeneutics","digital text interpretation","DHA"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"2000s–2010s","originator":"Extends classical hermeneutics (Schleiermacher, Dilthey, Gadamer, Ricoeur) into digital contexts; Roberto Simanowski and others in digital humanities","url":"https://scholargate.app/en/field-methods/digital-hermeneutic-analysis","markdownUrl":"https://scholargate.app/en/field-methods/digital-hermeneutic-analysis.md","definition":"Digital hermeneutic analysis applies the classical tradition of hermeneutic interpretation — rooted in Schleiermacher, Dilthey, Gadamer, and Ricoeur — to born-digital and digitised texts, online corpora, and digital artifacts. It asks not only what digital objects mean, but how digital mediation, platform architecture, and computational affordances shape the conditions of meaning itself. The method is prominent in digital humanities, digital history, and media studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extends classical hermeneutics (Schleiermacher, Dilthey, Gadamer, Ricoeur) into digital contexts; Roberto Simanowski and others in digital humanities","year":"2000s–2010s","type":"Qualitative interpretive research design","dataType":"Digital texts, born-digital documents, digital artifacts, online corpora","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Simanowski, R. (2010). Digital Hermeneutics: Interpreting (with) the Machine. Journal of Visual Culture, 9(1), 84–106.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Digital+Hermeneutics+Interpreting+with+the+Machine+Simanowski+2010"},{"ref":"Hermeneutics. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Hermeneutics"}],"related":["hermeneutic-analysis","textual-criticism","digital-historical-archival-research","digital-textual-criticism","discourse-analysis","content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-hermeneutic-phenomenology","name":"Digital Hermeneutic Phenomenology","fullName":"Digital Hermeneutic Phenomenological Research","aliases":["online hermeneutic phenomenology","digital HP","web-based hermeneutic phenomenology","virtual hermeneutic phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (digital adaptation of van Manen's 1990 framework)","originator":"Grounded in Max van Manen's hermeneutic phenomenology; digital adaptation developed by qualitative researchers from the 2000s onward","url":"https://scholargate.app/en/qualitative/digital-hermeneutic-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/digital-hermeneutic-phenomenology.md","definition":"Digital Hermeneutic Phenomenology applies van Manen's hermeneutic phenomenological tradition to phenomena that are lived, shaped, or mediated through digital technologies and online environments. Rather than treating the digital channel as a mere convenience for data collection, this approach treats participants' online experiences as phenomena worthy of interpretive inquiry in their own right — asking what it means to live, relate, learn, or work in and through digital spaces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grounded in Max van Manen's hermeneutic phenomenology; digital adaptation developed by qualitative researchers from the 2000s onward","year":"2000s–2010s (digital adaptation of van Manen's 1990 framework)","type":"Qualitative research design","dataType":"Online interviews (video, chat, email), digital narratives, online diaries, social media posts, digital artefacts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404737","url":null},{"ref":"Salmons, J. (2014). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1452275895","url":null}],"related":["hermeneutic-phenomenology","interpretive-phenomenology","digital-ethnography","digital-narrative-research","digital-interpretive-phenomenological-analysis","netnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-historical-archival-research","name":"Digital Historical Archival Research","fullName":"Digital Historical Archival Research","aliases":["digital archival research","digital archive history","online archival research","digital humanities archival method"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1990s–2000s (as digital archives became widely accessible)","originator":"Emerging practice across digital humanities scholars; Roy Rosenzweig among early proponents","url":"https://scholargate.app/en/field-methods/digital-historical-archival-research","markdownUrl":"https://scholargate.app/en/field-methods/digital-historical-archival-research.md","definition":"Digital historical archival research is the systematic investigation of the past using digitized primary sources held in online repositories, digital archives, and electronic databases. It combines the interpretive principles of traditional historical archival research with digital tools for search, retrieval, text mining, and visualization, enabling researchers to access geographically dispersed collections, apply computational analysis to large corpora, and reconstruct historical events, processes, and social phenomena from preserved primary evidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerging practice across digital humanities scholars; Roy Rosenzweig among early proponents","year":"1990s–2000s (as digital archives became widely accessible)","type":"Qualitative historical research design","dataType":"Digitized primary sources, scanned documents, online databases, digital repositories","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Theimer, K. (2012). What is the Meaning of Archives 2.0? American Archivist, 75(1), 58–68.","type":"article","doi":"10.17723/aarc.74.1.h7tn4m4027407666","isbn":null,"url":null},{"ref":"Digital history. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Digital_history"}],"related":["historical-archival-research","oral-history-method","textual-criticism","hermeneutic-analysis","document-based-historical-archival-research","comparative-historical-archival-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-institutional-ethnography","name":"Digital Institutional Ethnography","fullName":"Digital Institutional Ethnography","aliases":["Digital IE","online institutional ethnography","virtual institutional ethnography","digital Smith IE"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"IE: 1980s–1990s; digital adaptation: 2000s–2010s","originator":"Dorothy E. Smith (IE foundations); extended by IE scholars to digital contexts","url":"https://scholargate.app/en/qualitative/digital-institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/digital-institutional-ethnography.md","definition":"Digital Institutional Ethnography (Digital IE) applies Dorothy E. Smith's institutional ethnography framework to digital and online settings. It investigates how institutional ruling relations — the texts, policies, and coordination mechanisms that organize people's everyday lives — operate through digital infrastructures such as platforms, software systems, online documents, and algorithmic processes. The goal is to make visible how digital tools and texts coordinate and subordinate experience to institutional interests.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith (IE foundations); extended by IE scholars to digital contexts","year":"IE: 1980s–1990s; digital adaptation: 2000s–2010s","type":"Qualitative research design","dataType":"Digital texts, online documents, digital interaction logs, interviews, screen recordings","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759105010","url":null},{"ref":"Grahame, P. R., & Grahame, M. (2020). Institutional Ethnography and the Digital Turn: Researching Online Coordination of Work. Qualitative Sociology Review, 16(1), 6–24.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Institutional+Ethnography+and+the+Digital+Turn+Grahame+2020"}],"related":["institutional-ethnography","digital-ethnography","netnography","critical-discourse-analysis","document-analysis","participatory-digital-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-interpretive-phenomenological-analysis","name":"Digital Interpretive phenomenological analysis","fullName":"Digital Interpretive Phenomenological Analysis","aliases":["Digital IPA","online IPA","digital-mediated IPA","internet-based interpretive phenomenological analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"IPA founded ~1996; digital variant established practice ~2010–2020","originator":"Jonathan A. Smith (IPA); adapted to digital contexts by qualitative internet researchers from ~2010s onward","url":"https://scholargate.app/en/qualitative/digital-interpretive-phenomenological-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-interpretive-phenomenological-analysis.md","definition":"Digital Interpretive Phenomenological Analysis (Digital IPA) applies the rigorous IPA framework — originally developed by Jonathan Smith to explore how individuals make sense of significant lived experiences — within digital data-collection environments. Participants are recruited and interviewed online (via video call, synchronous text chat, email, or digital diary), and the resulting transcripts and digital texts are analysed through the same close-reading, emergent-coding, and cross-case patterning procedures that define standard IPA. The digital setting both expands access to geographically dispersed or hard-to-reach participants and introduces distinct methodological considerations around rapport, embodied cues, and data authenticity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan A. Smith (IPA); adapted to digital contexts by qualitative internet researchers from ~2010s onward","year":"IPA founded ~1996; digital variant established practice ~2010–2020","type":"Qualitative research design and analytic approach","dataType":"Online interviews (video, text, asynchronous), digital diaries, online focus groups, social media narratives","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative Phenomenological Analysis: Theory, Method and Research. Sage.","type":"book","doi":null,"isbn":"978-1412908344","url":null},{"ref":"Salmons, J. (2015). Qualitative Online Interviews: Strategies, Design and Skills (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483332284","url":null}],"related":["interpretive-phenomenology","digital-ethnography","digital-narrative-research","digital-hermeneutic-phenomenology","online-qualitative-research","netnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-life-history-research","name":"Digital Life History Research","fullName":"Digital Life History Research","aliases":["digital life story research","DLHR","online life history method","digital biographical method"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s (digital turn in life history; rooted in life history tradition since ~1920s)","originator":"Building on Goodson, Roberts, and broader biographical research traditions; digital extension from 2000s onward","url":"https://scholargate.app/en/qualitative/digital-life-history-research","markdownUrl":"https://scholargate.app/en/qualitative/digital-life-history-research.md","definition":"Digital Life History Research is a qualitative biographical method that investigates how individuals construct, narrate, and preserve their life stories using digital tools and environments. It extends the classical life history tradition into online spaces — gathering data through video interviews, asynchronous email narratives, digital diaries, social media timelines, and multimedia life documents — to understand personal and social experience across time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Goodson, Roberts, and broader biographical research traditions; digital extension from 2000s onward","year":"2000s (digital turn in life history; rooted in life history tradition since ~1920s)","type":"Qualitative biographical research design","dataType":"Digital narratives, online interviews, social media content, multimedia life documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Goodson, I., & Gill, S. (2017). The Narrative Turn in Social Research. In I. Goodson & M. Andrews (Eds.), Considering Counter-Narratives (pp. 1–24). John Benjamins.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Narrative+Turn+in+Social+Research+Goodson+Gill"},{"ref":"Roberts, B. (2002). Biographical Research. Open University Press.","type":"book","doi":null,"isbn":"9780335200740","url":null}],"related":["life-history-research","narrative-inquiry","autoethnography","oral-history","digital-ethnography","biographical-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-metaphor-analysis","name":"Digital Metaphor analysis","fullName":"Digital Metaphor Analysis","aliases":["online metaphor analysis","digital metaphor research","metaphor analysis of digital texts","DMA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (digital application)","originator":"Rooted in Lakoff & Johnson (1980); extended to digital contexts by corpus and computational linguists from the 2000s onward","url":"https://scholargate.app/en/qualitative/digital-metaphor-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-metaphor-analysis.md","definition":"Digital Metaphor Analysis (DMA) is a qualitative research approach that identifies, maps, and interprets conceptual metaphors embedded in digital texts — social media posts, online forums, blogs, comment sections, and other internet-mediated communication. Drawing on Conceptual Metaphor Theory (Lakoff and Johnson 1980), it examines how users frame abstract ideas (identity, politics, health, crisis) through systematic metaphorical mappings, revealing shared conceptual structures and ideological orientations within online discourse communities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Lakoff & Johnson (1980); extended to digital contexts by corpus and computational linguists from the 2000s onward","year":"2000s–2010s (digital application)","type":"Qualitative–interpretive analysis","dataType":"Digital texts: social media posts, online forums, blogs, chat logs, news comments","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226468013","url":null},{"ref":"Charteris-Black, J. (2004). Corpus Approaches to Critical Metaphor Analysis. Palgrave Macmillan.","type":"book","doi":null,"isbn":"978-1403943064","url":null}],"related":["metaphor-analysis","critical-metaphor-analysis","digital-discourse-analysis","digital-content-analysis","digital-thematic-analysis","semiotic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-multiple-case-study","name":"Digital Multiple case study","fullName":"Digital Multiple Case Study Research","aliases":["online multiple case study","digital multi-site case study","virtual multiple case study","digital comparative case inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984 (Yin's case study framework); digital adaptation ~2000s–2010s","originator":"Robert K. Yin (case study methodology); extended to digital contexts by various digital methods scholars","url":"https://scholargate.app/en/qualitative/digital-multiple-case-study","markdownUrl":"https://scholargate.app/en/qualitative/digital-multiple-case-study.md","definition":"Digital Multiple Case Study is a qualitative research design in which two or more bounded digital cases — such as online communities, social media platforms, virtual organizations, or digital ecosystems — are studied in depth and then compared systematically. Grounded in Yin's case study methodology and adapted for digital settings, the approach combines the contextual richness of single-case inquiry with the analytic leverage of cross-case comparison in online environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (case study methodology); extended to digital contexts by various digital methods scholars","year":"1984 (Yin's case study framework); digital adaptation ~2000s–2010s","type":"Qualitative comparative research design","dataType":"Digital documents, online interactions, digital artifacts, virtual interviews, screen recordings, social media data","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1526458162","url":null}],"related":["multiple-case-study","digital-ethnography","comparative-case-study","netnography","digital-narrative-research","digital-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-narrative-research","name":"Digital Narrative Research","fullName":"Digital Narrative Research","aliases":["digital storytelling research","DNR","digital narrative inquiry","digital story-based research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Mid-1990s (practice); 2000s (formalised as research methodology)","originator":"Joe Lambert & Dana Atchley (Center for Digital Storytelling, Berkeley); theorised in research contexts by John Hartley, Kathy McWilliam, and Michele Knobel","url":"https://scholargate.app/en/qualitative/digital-narrative-research","markdownUrl":"https://scholargate.app/en/qualitative/digital-narrative-research.md","definition":"Digital Narrative Research is a qualitative methodology in which participants create or share short digital stories — typically combining personal voice-over, photographs, video, and text — that become the primary data for inquiry. Originating in community digital-storytelling practice developed at the Center for Digital Storytelling in Berkeley in the 1990s, the approach has been adopted widely in education, health, social work, and participatory action research to surface voices and experiences that are difficult to capture through interviews or surveys alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joe Lambert & Dana Atchley (Center for Digital Storytelling, Berkeley); theorised in research contexts by John Hartley, Kathy McWilliam, and Michele Knobel","year":"Mid-1990s (practice); 2000s (formalised as research methodology)","type":"Qualitative research design","dataType":"Digital artefacts: short video stories, audio recordings, blogs, social media posts, photographs, multimodal documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Lambert, J. (2013). Digital Storytelling: Capturing Lives, Creating Community (4th ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415627030","url":null},{"ref":"Hartley, J., & McWilliam, K. (Eds.). (2009). Story Circle: Digital Storytelling Around the World. Wiley-Blackwell.","type":"book","doi":null,"isbn":"978-1405180542","url":null}],"related":["narrative-inquiry","digital-ethnography","multimodal-analysis","photo-voice","thematic-analysis","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-oral-history-method","name":"Digital Oral History Method","fullName":"Digital Oral History Research Method","aliases":["digital oral history","DOH","digital life history interview","digital recorded oral testimony"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"2000s (digital variant of oral history established 1940s–1950s)","originator":"Oral History Association; Donald Ritchie; Alessandro Portelli (digital turn elaborated early 2000s)","url":"https://scholargate.app/en/field-methods/digital-oral-history-method","markdownUrl":"https://scholargate.app/en/field-methods/digital-oral-history-method.md","definition":"The digital oral history method is a qualitative research approach in which personal testimonies and lived experiences are elicited through recorded interviews, then preserved, managed, and disseminated using digital technologies. Building on the established oral history tradition, the digital variant leverages audio and video recording equipment, digital archiving platforms, and online dissemination channels to expand access, ensure long-term preservation, and enable richer multi-modal analysis of narrator accounts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oral History Association; Donald Ritchie; Alessandro Portelli (digital turn elaborated early 2000s)","year":"2000s (digital variant of oral history established 1940s–1950s)","type":"Qualitative field research method","dataType":"Digital audio/video recordings, transcripts, metadata-tagged archives","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Ritchie, D. A. (2003). Doing Oral History: A Practical Guide (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195154344","url":null},{"ref":"Alexander, B., Boyd, D., & House, M. (2007). Oral history and digital humanities: Voice, access, and engagement. Oral History Review, 34(2), 1–15.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Oral+history+and+digital+humanities+voice+access+engagement+Oral+History+Review+2007"}],"related":["oral-history-method","ethnography","narrative-analysis","life-history-research","digital-ethnography","archival-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-oral-history","name":"Digital Oral History","fullName":"Digital Oral History Research","aliases":["digital oral history","DOH","digital oral narrative research","online oral history"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (systematic digital integration)","originator":"Oral history tradition (Allan Nevins, 1940s); digital adaptation by Michael Frisch and others from the 1990s onward","url":"https://scholargate.app/en/qualitative/digital-oral-history","markdownUrl":"https://scholargate.app/en/qualitative/digital-oral-history.md","definition":"Digital Oral History is a qualitative research method that uses digital technologies — audio and video recorders, online platforms, and digital archives — to collect, preserve, and disseminate first-person oral accounts of lived experience. It extends the established oral history tradition by leveraging digital tools to enhance accessibility, reach geographically dispersed participants, and enable long-term preservation and public engagement with recorded narratives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oral history tradition (Allan Nevins, 1940s); digital adaptation by Michael Frisch and others from the 1990s onward","year":"1990s–2000s (systematic digital integration)","type":"Qualitative research design with digital data collection and archiving","dataType":"Audio and video recordings, digital transcripts, online narrative accounts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Boyd, D., & Larson, M. (Eds.). (2014). Oral History and Digital Humanities: Voice, Access, and Engagement. Palgrave Macmillan.","type":"book","doi":null,"isbn":"978-1137322678","url":null},{"ref":"Frisch, M. (2006). Oral History and the Digital Revolution: Toward a Post-Documentary Sensibility. In R. Perks & A. Thomson (Eds.), The Oral History Reader (2nd ed., pp. 102–114). Routledge.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Oral+History+and+the+Digital+Revolution+Toward+a+Post-Documentary+Sensibility+Frisch"}],"related":["oral-history","digital-narrative-research","digital-ethnography","digital-life-history-research","narrative-inquiry","digital-biographical-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-phenomenology","name":"Digital Phenomenology","fullName":"Digital Phenomenological Research","aliases":["online phenomenology","virtual phenomenology","phenomenology of digital experience","digitally-mediated phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Emerging from classical phenomenology (Husserl, Heidegger) applied to digital contexts; synthesised by scholars such as Sarah Pink and Mark D. Vagle","url":"https://scholargate.app/en/qualitative/digital-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/digital-phenomenology.md","definition":"Digital Phenomenology is a qualitative research approach that applies phenomenological inquiry to lived experiences mediated by or situated within digital environments — including social media platforms, virtual communities, online spaces, and interactions with digital technologies. It asks how people experience, make meaning of, and embody their encounters with digital tools and online worlds, using the interpretive and descriptive rigour of classical phenomenology in settings where much or all of the experience unfolds online.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerging from classical phenomenology (Husserl, Heidegger) applied to digital contexts; synthesised by scholars such as Sarah Pink and Mark D. Vagle","year":"2000s–2010s","type":"Qualitative research approach","dataType":"Online interviews, digital artifacts, social media posts, screen recordings, digital diaries, virtual observation notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital Ethnography: Principles and Practice. Sage.","type":"book","doi":null,"isbn":"978-1446200476","url":null},{"ref":"Vagle, M. D. (2018). Crafting Phenomenological Research (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1629584263","url":null}],"related":["phenomenology","interpretive-phenomenology","digital-ethnography","netnography","digital-narrative-research","hermeneutic-phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-program-evaluation","name":"Digital Program Evaluation","fullName":"Digital Program Evaluation Research","aliases":["technology-enhanced evaluation","digital evaluation","e-evaluation","online program evaluation"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"2000s–2010s (formalized alongside proliferation of digital programs and online data)","originator":"Evolving practice; rooted in Rossi, Lipsey & Freeman's evaluation tradition; extended by digital methods scholars in the 2000s–2010s","url":"https://scholargate.app/en/field-methods/digital-program-evaluation","markdownUrl":"https://scholargate.app/en/field-methods/digital-program-evaluation.md","definition":"Digital program evaluation applies the systematic logic of program evaluation to programs that operate fully or partly in digital environments, using digital tools and data — web analytics, online surveys, platform logs, social media metrics, and digital trace data — to assess program reach, implementation fidelity, and outcomes. It retains the core evaluative commitment to rendering a defensible judgment about program merit and worth while exploiting the speed, scale, and granularity that digital data sources offer. Applications span online education, digital public health campaigns, e-government services, and technology-mediated social programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolving practice; rooted in Rossi, Lipsey & Freeman's evaluation tradition; extended by digital methods scholars in the 2000s–2010s","year":"2000s–2010s (formalized alongside proliferation of digital programs and online data)","type":"Applied evaluation methodology","dataType":"Digital trace data, web analytics, online surveys, administrative records, social media data, digital platform logs","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"George, S., & Leidner, D. (2020). Digital Evaluation: Leveraging Digital Data and Methods for Program Assessment. Routledge.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Digital+Evaluation+Leveraging+Digital+Data+Methods+Program+Assessment"},{"ref":"Russ-Eft, D., & Preskill, H. (2009). Evaluation in Organizations: A Systematic Approach to Enhancing Learning, Performance, and Change (2nd ed.). Basic Books.","type":"book","doi":null,"isbn":"978-0465018666","url":null}],"related":["program-evaluation","digital-content-analysis","digital-case-study","educational-action-research","curriculum-analysis","mixed-methods-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-qualitative-content-analysis","name":"Digital Qualitative Content Analysis","fullName":"Digital Qualitative Content Analysis","aliases":["DQCA","qualitative content analysis of digital data","online qualitative content analysis","digital QCA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2010s (building on qualitative content analysis traditions from 1983–2012)","originator":"Adapted from Philipp Mayring and Margrit Schreier; digital extension by multiple scholars in the 2010s","url":"https://scholargate.app/en/qualitative/digital-qualitative-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-qualitative-content-analysis.md","definition":"Digital Qualitative Content Analysis (DQCA) is a systematic method for interpreting meaning from digital texts — social media posts, forum threads, blogs, emails, and other online content — through a structured, category-driven coding process. It extends the established tradition of qualitative content analysis (Mayring; Schreier) to the scale, multimodality, and contextual specificity of digital environments, prioritising interpretive depth over frequency counting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from Philipp Mayring and Margrit Schreier; digital extension by multiple scholars in the 2010s","year":"2010s (building on qualitative content analysis traditions from 1983–2012)","type":"Qualitative research method","dataType":"Digital texts: social media posts, online forums, websites, chat logs, emails, blog content","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage.","type":"book","doi":null,"isbn":"978-0857029485","url":null},{"ref":"Stoltenberg, I., & Mruck, K. (2023). Qualitative content analysis in the digital age: Challenges and opportunities. Forum: Qualitative Social Research, 24(1).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Qualitative+content+analysis+in+the+digital+age+Stoltenberg+Mruck+2023"}],"related":["qualitative-content-analysis","thematic-analysis","discourse-analysis","netnography","social-media-analysis","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-reflexive-thematic-analysis","name":"Digital Reflexive Thematic Analysis","fullName":"Digital Reflexive Thematic Analysis","aliases":["digital RTA","online reflexive thematic analysis","RTA for digital data"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (RTA foundation); digital application consolidated ~2019–2022","originator":"Virginia Braun & Victoria Clarke (reflexive foundation); extended by digital qualitative researchers","url":"https://scholargate.app/en/qualitative/digital-reflexive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-reflexive-thematic-analysis.md","definition":"Digital Reflexive Thematic Analysis (Digital RTA) applies Braun and Clarke's reflexive thematic analysis framework to qualitative data generated in or collected from digital environments — including social media posts, online forums, chat transcripts, email, digital interviews, and other online texts. It foregrounds the researcher's active, interpretive role and treats theme generation as a creative-analytic act shaped by the analyst's theoretical positioning rather than a mechanical coding procedure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun & Victoria Clarke (reflexive foundation); extended by digital qualitative researchers","year":"2006 (RTA foundation); digital application consolidated ~2019–2022","type":"Qualitative analytic method","dataType":"Digital text, social media posts, online interviews, forum threads, chat logs","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2022). Thematic Analysis: A Practical Guide. Sage.","type":"book","doi":null,"isbn":"978-1473953246","url":null},{"ref":"Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.","type":"article","doi":"10.1080/2159676X.2019.1628806","isbn":null,"url":null}],"related":["reflexive-thematic-analysis","thematic-analysis","digital-ethnography","netnography","content-analysis","framework-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-semiotic-analysis","name":"Digital Semiotic Analysis","fullName":"Digital Semiotic Analysis","aliases":["DSA","digital semiotics","online semiotic analysis","digital sign analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Classical semiotics early 20th century; digital adaptation from the 1990s onward","originator":"Rooted in Ferdinand de Saussure and Charles S. Peirce; digital applications developed by scholars such as David Chandler and Gunther Kress","url":"https://scholargate.app/en/qualitative/digital-semiotic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-semiotic-analysis.md","definition":"Digital Semiotic Analysis applies the classical study of signs and meaning-making to content produced and circulated in digital environments. It examines how signifiers — words, images, icons, sounds, emojis, hyperlinks, and interface conventions — create meaning within digital texts such as websites, social media posts, memes, and online advertisements. The method draws on Saussurean dyadic semiotics and Peircean triadic semiotics, extended by Roland Barthes's connotation and myth framework and by contemporary multimodal semiotic theory developed for screen-based media.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Ferdinand de Saussure and Charles S. Peirce; digital applications developed by scholars such as David Chandler and Gunther Kress","year":"Classical semiotics early 20th century; digital adaptation from the 1990s onward","type":"Qualitative interpretive analysis","dataType":"Digital texts, images, videos, memes, websites, social media posts, emojis, hyperlinks","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Chandler, D. (2007). Semiotics: The Basics (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415363969","url":null},{"ref":"Jewitt, C. (Ed.). (2009). The Routledge Handbook of Multimodal Analysis. Routledge.","type":"book","doi":null,"isbn":"978-0415434379","url":null}],"related":["semiotic-analysis","visual-analysis","discourse-analysis","critical-discourse-analysis","content-analysis","multimodal-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-signature-scheme","name":"Digital Signature Scheme","fullName":"Digital Signature and Authentication Framework","aliases":["Digital Signature Algorithm","Message Authentication and Integrity","Public Key Signature"],"domain":"cryptography","family":"process-pipeline","subfamily":"Message authentication and integrity","year":"1978","originator":"Ronald Rivest, Adi Shamir, Leonard Adleman","url":"https://scholargate.app/en/cryptography/digital-signature-scheme","markdownUrl":"https://scholargate.app/en/cryptography/digital-signature-scheme.md","definition":"A digital signature scheme provides authentication, integrity assurance, and non-repudiation of electronically signed documents. Using public-key cryptography (such as RSA, DSA, or ECDSA), the originator signs a message with a private key in a way that any recipient can verify the signature using the originator's public key, proving that the message was created by the claimed author and has not been tampered with.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Rivest, Adi Shamir, Leonard Adleman","subfamily":"Message authentication and integrity","year":"1978","type":"Asymmetric signature algorithm"},"citations":[{"ref":"Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2), 120–126.","type":"article","doi":"10.1145/359340.359342","isbn":null,"url":null},{"ref":"Krawczyk, H., Bellare, M., & Herbst, R. (1997). HMAC: Keyed-hashing for message authentication. RFC 2104.","type":"article","doi":null,"isbn":null,"url":"https://tools.ietf.org/html/rfc2104"},{"ref":"Johnson, D., Menezes, A., & Vanstone, S. (2001). The elliptic curve digital signature algorithm (ECDSA). International Journal of Information Security, 1(1), 36–63.","type":"article","doi":"10.1007/s102070100002","isbn":null,"url":null}],"related":["rsa-cryptosystem-analysis","sha-hash-function","tls-protocol-analysis","diffie-hellman-key-exchange"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-soil-mapping","name":"Digital Soil Mapping","fullName":"Digital Soil Mapping","aliases":["DSM","predictive soil mapping","quantitative soil-landscape modelling","geostatistical soil mapping"],"domain":"agronomy","family":"process-pipeline","subfamily":"Pedometrics / spatial soil science","year":"Late 1990s – early 2000s (formalised ~2003)","originator":"Multiple contributors; foundational framework by Alex McBratney and colleagues","url":"https://scholargate.app/en/agronomy/digital-soil-mapping","markdownUrl":"https://scholargate.app/en/agronomy/digital-soil-mapping.md","definition":"Digital Soil Mapping (DSM) is a quantitative, data-driven pipeline that predicts the spatial distribution of soil properties and classes across a landscape by statistically linking field observations to environmental covariates — terrain attributes, remote sensing imagery, climate surfaces, and geology layers. The approach replaces or augments traditional expert-drawn soil surveys with reproducible, spatially explicit models, and is applied in agronomy, land management, food security, and environmental assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; foundational framework by Alex McBratney and colleagues","year":"Late 1990s – early 2000s (formalised ~2003)","type":"Spatial prediction and mapping pipeline","dataType":"Soil sample observations, terrain derivatives, remote sensing imagery, climate layers (raster and point data)","subfamily":"Pedometrics / spatial soil science"},"citations":[{"ref":"McBratney, A. B., Mendonca Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52.","type":"journal-article","doi":"10.1016/S0016-7061(03)00223-4","isbn":null,"url":null},{"ref":"Minasny, B., & McBratney, A. B. (2016). Digital soil mapping: A brief history and some lessons. Geoderma, 264, 301–311.","type":"book","doi":"10.1016/j.geoderma.2015.07.017","isbn":null,"url":null}],"related":["geostatistics","remote-sensing","random-forest","kriging","land-use-change-analysis","precision-agriculture"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-straussian-grounded-theory","name":"Digital Straussian Grounded Theory","fullName":"Digital Straussian Grounded Theory","aliases":["digital GT (Straussian)","Straussian GT in digital contexts","online Straussian grounded theory","digital Strauss-Corbin grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990 (Strauss & Corbin foundational); digital adaptation 2000s–2010s","originator":"Anselm Strauss & Juliet Corbin (foundational GT); adapted to digital contexts by subsequent methodologists","url":"https://scholargate.app/en/qualitative/digital-straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/digital-straussian-grounded-theory.md","definition":"Digital Straussian grounded theory applies the systematic, coding-driven approach of Strauss and Corbin's grounded theory to digital data sources such as online forums, social media, chat logs, and digital documents. It retains the Straussian paradigm model and three-stage coding structure — open, axial, and selective — while adapting sampling strategies, theoretical saturation criteria, and ethical protocols to the unique features of online and digital research environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss & Juliet Corbin (foundational GT); adapted to digital contexts by subsequent methodologists","year":"1990 (Strauss & Corbin foundational); digital adaptation 2000s–2010s","type":"Qualitative research design and analysis approach","dataType":"Digital text data: online forums, social media posts, chat logs, emails, digital documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0803959408","url":null},{"ref":"Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage. [Includes comparative methodological context on Straussian vs. constructivist GT and digital data adaptation]","type":"book","doi":null,"isbn":"978-0857029140","url":null}],"related":["grounded-theory","constructivist-grounded-theory","classic-grounded-theory","thematic-analysis","netnography","digital-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-textual-criticism","name":"Digital Textual Criticism","fullName":"Digital Textual Criticism","aliases":["digital philology","computational textual criticism","digital scholarly editing","digital critical editing"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1990s–2000s (mature field by early 2000s)","originator":"Patrick Sahle, Peter Robinson, and the digital humanities community (building on traditional textual criticism)","url":"https://scholargate.app/en/field-methods/digital-textual-criticism","markdownUrl":"https://scholargate.app/en/field-methods/digital-textual-criticism.md","definition":"Digital textual criticism is the application of computational and digital methods to the scholarly analysis, collation, and editing of historical texts. Building on centuries-old philological practice, it uses tools such as XML/TEI encoding, automated collation software (e.g., CollateX), and computational stemmatology to compare manuscript witnesses, reconstruct textual transmission histories, and produce digital critical editions that are richer and more transparent than their print counterparts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patrick Sahle, Peter Robinson, and the digital humanities community (building on traditional textual criticism)","year":"1990s–2000s (mature field by early 2000s)","type":"Qualitative-computational philological method","dataType":"Manuscript images, transcriptions, XML/TEI-encoded texts, variant readings","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Sahle, P. (2013). Digitale Editionsformen. Zum Umgang mit der Überlieferung unter den Bedingungen des Medienwandels. 3 vols. Norderstedt: Books on Demand.","type":"book","doi":null,"isbn":null,"url":"https://www.digitale-edition.de/"},{"ref":"Robinson, P. (2013). Towards a theory of digital editions. Variants: The Journal of the European Society for Textual Scholarship, 10, 105–131.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Towards+a+theory+of+digital+editions+Robinson+2013"}],"related":["textual-criticism","hermeneutic-analysis","historical-archival-research","document-based-textual-criticism","digital-hermeneutic-analysis","comparative-textual-criticism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-thematic-analysis","name":"Digital Thematic Analysis","fullName":"Digital Thematic Analysis","aliases":["online thematic analysis","social media thematic analysis","digital TA","web-based thematic analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (base method); digital application 2010s","originator":"Virginia Braun & Victoria Clarke (base method); extended to digital data contexts by qualitative digital researchers from the mid-2000s onward","url":"https://scholargate.app/en/qualitative/digital-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-thematic-analysis.md","definition":"Digital Thematic Analysis applies Braun and Clarke's six-phase thematic analysis framework to qualitative data generated in or harvested from digital environments — including social media platforms, online forums, blogs, digital interview transcripts, and user-generated web content. It retains the same systematic coding logic as standard thematic analysis while incorporating additional decisions about data demarcation, platform context, and the ethical handling of publicly available digital material.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun & Victoria Clarke (base method); extended to digital data contexts by qualitative digital researchers from the mid-2000s onward","year":"2006 (base method); digital application 2010s","type":"Qualitative data analysis method","dataType":"Social media posts, online forum threads, blog content, digital interview transcripts, online news comment sections, user-generated digital texts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Rich, J. L., & Creighton, K. (2021). Using thematic analysis in digital social research: Methodological considerations for analysing social media data. International Journal of Social Research Methodology, 24(5), 571–583.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Using+thematic+analysis+in+digital+social+research%3A+Methodological+considerations+for+analysing+social+media+data+Rich"}],"related":["thematic-analysis","reflexive-thematic-analysis","digital-content-analysis","digital-discourse-analysis","digital-ethnography","comparative-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-transformation-scale","name":"Digital Transformation Readiness Scale","fullName":"Digital Transformation Readiness and Maturity Assessment Scale","aliases":["Digital Readiness Scale","Digital Maturity Scale","Digital Transformation Assessment"],"domain":"strategic-management","family":"process-pipeline","subfamily":"digital-strategy","year":"2014","originator":"George Westerman, Didier Bonnet, Andrew McAfee (MIT Center for Digital Business)","url":"https://scholargate.app/en/strategic-management/digital-transformation-scale","markdownUrl":"https://scholargate.app/en/strategic-management/digital-transformation-scale.md","definition":"Digital Transformation Readiness refers to an organization's preparedness to successfully adopt digital technologies, redesign business processes, and develop new digital capabilities to compete in increasingly digital markets. Westerman, Bonnet, and McAfee (2014) identify nine elements of digital transformation spanning technology (systems, data, infrastructure), people (skills, culture), and governance (leadership, decision authority). Organizations with high digital readiness leverage digital technologies to create competitive advantage; those with low readiness experience failed technology implementations, continued legacy system dependence, and competitive disadvantage. This scale measures organizational readiness across four dimensions: technology capability, people and skills, organizational culture, and governance and leadership—revealing where transformation barriers exist.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Westerman, Didier Bonnet, Andrew McAfee (MIT Center for Digital Business)","subfamily":"digital-strategy","year":"2014","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Westerman, G., Bonnet, D., & McAfee, A. (2014). The nine elements of digital transformation. MIT Sloan Management Review, 55(3), 1–6.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Westerman%2C%20G.%2C%20Bonnet%2C%20D.%2C%20%26%20McAfee%2C%20A.%20(2014).%20The%20nine%20elements%20of%20digital%20transformation.%20MIT%20Sloan%20Management%20Review"},{"ref":"Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2014). Embracing digital technology: A new strategic imperative. MIT Sloan Management Review Research Report.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fitzgerald%2C%20M.%2C%20Kruschwitz%2C%20N.%2C%20Bonnet%2C%20D.%2C%20%26%20Welch%2C%20M.%20(2014).%20Embracing%20digital%20technology%3A%20A%20new%20strategic%20imperative"},{"ref":"Nadkarni, S., & Prügl, R. (2021). Digital transformation: A review, synthesis and opportunities for future research. Management Review Quarterly, 71(2), 233–341.","type":"article","doi":"10.1007/s11301-020-00185-7","isbn":null,"url":null}],"related":["dynamic-capabilities-scale","knowledge-management-scale","organizational-resilience-scale","strategic-orientation-scale","supply-chain-integration-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-twin-simulation","name":"Digital Twin Simulation","fullName":"Digital Twin Simulation (Hybrid Physics-ML Virtual Replica)","aliases":["Dijital İkiz Simülasyonu (Digital Twin)","digital twin","digital shadow","cyber-physical twin"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"2002 (concept); 2014 (white paper formalization)","originator":"Michael Grieves (University of Michigan, 2002; white paper 2014)","url":"https://scholargate.app/en/simulation/digital-twin-simulation","markdownUrl":"https://scholargate.app/en/simulation/digital-twin-simulation.md","definition":"Digital Twin Simulation, first conceptualised by Michael Grieves at the University of Michigan around 2002 and formally described in his 2014 white paper, creates a continuously updated virtual copy of a physical system by fusing real-time sensor data with a mechanistic (physics-based) model and machine-learning components. The twin mirrors the physical asset's current state and projects its future behaviour, enabling fault detection, predictive maintenance, and operational optimisation without disrupting the real system.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Grieves (University of Michigan, 2002; white paper 2014)","year":"2002 (concept); 2014 (white paper formalization)","type":"Hybrid physics-based + machine-learning simulation","dataRequirement":"Real-time or high-frequency IoT sensor stream","updateMechanism":"Continuous state estimation loop (e.g., Kalman filter or ML surrogate)","output":"Predicted system state, fault probability, maintenance schedule","domains":"Manufacturing, engineering, healthcare, smart infrastructure, aerospace"},"citations":[{"ref":"Grieves, M. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. White Paper, University of Michigan.","type":"whitepaper","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication"},{"ref":"Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H. & Sui, F. (2018). Digital Twin-Driven Product Design, Manufacturing and Service with Big Data. The International Journal of Advanced Manufacturing Technology, 94, 3563-3576.","type":"article","doi":"10.1007/s00170-017-0233-1","isbn":null,"url":null}],"related":["agent-based-simulation","monte-carlo-simulation","kalman-filter","state-space-model","iot-time-series","system-dynamics"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-visual-analysis","name":"Digital Visual Analysis","fullName":"Digital Visual Analysis","aliases":["DVA","digital image analysis","online visual analysis","digital visual research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Gillian Rose; Sarah Pink (digital extension)","url":"https://scholargate.app/en/qualitative/digital-visual-analysis","markdownUrl":"https://scholargate.app/en/qualitative/digital-visual-analysis.md","definition":"Digital visual analysis is a qualitative approach for systematically examining visual materials that originate in, circulate through, or are consumed within digital environments — including social media images, video content, screenshots, memes, infographics, and online multimodal texts. Drawing on visual methodologies and digital research methods, it attends not only to what images depict but also to how they are produced, shared, and interpreted within specific digital platforms and social contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gillian Rose; Sarah Pink (digital extension)","year":"2000s–2010s","type":"Qualitative analytical approach","dataType":"Digital images, video, social media visuals, screenshots, memes, online multimodal content","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473902176","url":null},{"ref":"Pink, S. (2021). Doing Visual Ethnography (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1529731804","url":null}],"related":["visual-analysis","digital-ethnography","netnography","content-analysis","semiotic-analysis","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"digital-wellbeing-scale","name":"Digital Wellbeing Scale","fullName":"Digital Wellbeing Scale (DWS)","aliases":["DWS","Digital Health"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"digital-health-wellbeing","year":"2022","originator":"Isabel Pinto de Azevedo, Bárbara Marques, and José Mata","url":"https://scholargate.app/en/social-media-psychology/digital-wellbeing-scale","markdownUrl":"https://scholargate.app/en/social-media-psychology/digital-wellbeing-scale.md","definition":"The Digital Wellbeing Scale is a multidimensional self-report instrument that assesses positive and negative aspects of technology use, capturing not just problematic behaviors but also digital resources supporting wellbeing. Developed by Azevedo and colleagues in 2022, this scale recognizes that digital engagement exists on a spectrum from harmful to beneficial, and measures protective factors alongside risk factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Isabel Pinto de Azevedo, Bárbara Marques, and José Mata","subfamily":"digital-health-wellbeing","year":"2022","type":"Self-report"},"citations":[{"ref":"Azevedo, I. P., Marques, B., & Mata, J. (2022). Development and psychometric evaluation of a digital wellbeing scale. Telematics and Informatics, 67, 101765.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+psychometric+evaluation+of+a+digital+wellbeing+scale+Azevedo"}],"related":["social-media-disorder-scale","smartphone-addiction-scale-short","technoference-scale","fear-of-missing-out-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dijkstra-algorithm","name":"Dijkstra Algorithm","fullName":"Dijkstra Algorithm for Shortest Path","aliases":["Dijkstra's algorithm","shortest path algorithm"],"domain":"operations-research","family":"ml-model","subfamily":"Graph Algorithms","year":"1956","originator":"Edsger W. Dijkstra","url":"https://scholargate.app/en/operations-research/dijkstra-algorithm","markdownUrl":"https://scholargate.app/en/operations-research/dijkstra-algorithm.md","definition":"Dijkstra's Algorithm, introduced by Edsger W. Dijkstra in 1956, is one of the most fundamental algorithms in computer science for solving the single-source shortest path problem. It finds the shortest path from a starting vertex to all other vertices in a weighted graph with non-negative edge weights.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edsger W. Dijkstra","subfamily":"Graph Algorithms","year":"1956","type":"algorithm"},"citations":[{"ref":"Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269-271.","type":"article","doi":"10.1007/BF01386390","isbn":null,"url":null},{"ref":"Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms (3rd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03384-8","url":null}],"related":["bellman-ford-algorithm","a-star-search-algorithm","ford-fulkerson-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dilated-cnn","name":"Dilated CNN","fullName":"Dilated Convolutional Network (WaveNet / Temporal Convolutional Network)","aliases":["Dilate Edilmiş CNN (WaveNet / TCN)","WaveNet","Temporal Convolutional Network","TCN","dilated convolution"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2016,"originator":"van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.","url":"https://scholargate.app/en/deep-learning/dilated-cnn","markdownUrl":"https://scholargate.app/en/deep-learning/dilated-cnn.md","definition":"A Dilated CNN is a one-dimensional convolutional network whose receptive field grows exponentially with depth, letting it model long-range structure in time series and audio signals. WaveNet (van den Oord et al., 2016) and the Temporal Convolutional Network of Bai, Kolter and Koltun (2018) are the prominent members of this family.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.","year":2016,"type":"Deep learning (dilated 1D convolutional network)","task":"Time-series forecasting & sequence prediction","minSample":200},"citations":[{"ref":"van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1609.03499"},{"ref":"Bai, S., Kolter, J.Z. & Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv:1803.01271.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1803.01271"}],"related":["gru","bidirectional-rnn","seq2seq","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dina-model","name":"DINA Model","fullName":"Deterministic Inputs, Noisy Outputs Model","aliases":["DINA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Cognitive Diagnosis","year":"2001","originator":"Brian Junker, Klaas Sijtsma","url":"https://scholargate.app/en/psychometrics/dina-model","markdownUrl":"https://scholargate.app/en/psychometrics/dina-model.md","definition":"The DINA Model (Deterministic Inputs, Noisy Outputs) is a cognitive diagnostic model developed by Junker and Sijtsma (2001) that classifies examinees into latent skill classes based on their item response patterns. DINA assumes a deterministic relationship between skill mastery and correct responses, with probabilistic error accounting for guessing and slips.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brian Junker, Klaas Sijtsma","subfamily":"Cognitive Diagnosis","year":"2001","type":"Discrete latent class model"},"citations":[{"ref":"Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25(3), 258-272.","type":"article","doi":"10.1177/01466210122032064","isbn":null,"url":null},{"ref":"Haertel, E. H. (1989). Using restricted latent class models to map the skill structure of achievement items. Journal of Educational Measurement, 26(4), 301-321.","type":"article","doi":"10.1111/j.1745-3984.1989.tb00336.x","isbn":null,"url":null},{"ref":"de la Torre, J. (2009). DINA model and parameter estimation: A didactic perspective. Journal of Educational and Behavioral Statistics, 34(1), 115-130.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=DINA+model+and+parameter+estimation%3A+A+didactic+perspective"}],"related":["dino-model","rule-space-methodology","cognitive-diagnostic-cat","necessary-condition-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dino-model","name":"DINO Model","fullName":"Deterministic Inputs, Noisy Outputs Model (Disjunctive)","aliases":["DINO"],"domain":"psychometrics","family":"latent-structure","subfamily":"Cognitive Diagnosis","year":"2006","originator":"James Templin, Russell Henson","url":"https://scholargate.app/en/psychometrics/dino-model","markdownUrl":"https://scholargate.app/en/psychometrics/dino-model.md","definition":"The DINO Model (Deterministic Inputs, Noisy Outputs—Disjunctive) is a cognitive diagnostic model that relaxes DINA's conjunctive (AND) skill requirement logic. DINO assumes an examinee only needs to master one of multiple possible skill pathways to answer an item correctly, making it suitable for scenarios where skills are substitutable or alternative routes to success exist.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Templin, Russell Henson","subfamily":"Cognitive Diagnosis","year":"2006","type":"Disjunctive latent class model"},"citations":[{"ref":"Templin, J., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11(3), 287-305.","type":"article","doi":"10.1037/1082-989X.11.3.287","isbn":null,"url":null},{"ref":"Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25(3), 258-272.","type":"article","doi":"10.1177/01466210122032064","isbn":null,"url":null},{"ref":"de la Torre, J. (2019). Cognitive Diagnosis Models for Polytomous Data. In B. Bolt & M. Robitzsch (Eds.), Innovative Assessment: Technologies and Methodologies (pp. 110-128). Oxford University Press.","type":"book","doi":null,"isbn":"9780190650766","url":null}],"related":["dina-model","rule-space-methodology","cognitive-diagnostic-cat","necessary-condition-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"direct-numerical-simulation","name":"Direct Numerical Simulation","fullName":"Direct Numerical Simulation","aliases":["DNS","resolved turbulence simulation"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1971","originator":"Steven Orszag","url":"https://scholargate.app/en/fluid-dynamics/direct-numerical-simulation","markdownUrl":"https://scholargate.app/en/fluid-dynamics/direct-numerical-simulation.md","definition":"Direct Numerical Simulation (DNS) is a computational approach that solves the Navier-Stokes equations without turbulence models, resolving all scales of motion from the largest energy-containing eddies down to the smallest dissipative scales (Kolmogorov microscales). Pioneered by Steven Orszag in 1971, DNS provides complete information about turbulent flow fields and serves as a reference solution for validating turbulence models. However, extreme computational demands limit DNS to relatively simple geometries and low to moderate Reynolds numbers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Orszag","subfamily":"Fluid Dynamics","year":"1971","type":"Full-scale turbulence resolution method"},"citations":[{"ref":"Orszag, S. A. (1971). Numerical simulation of incompressible flows within simple boundaries: accuracy. Journal of Fluid Mechanics, 49(1), 75-112.","type":"article","doi":"10.1017/S0022112071001940","isbn":null,"url":null},{"ref":"Moin, P., & Mahesh, K. (1998). Direct numerical simulation: a tool in turbulence research. Annual Review of Fluid Mechanics, 30, 539-578.","type":"article","doi":"10.1146/annurev.fluid.30.1.539","isbn":null,"url":null},{"ref":"Kim, J., Moin, P., & Moser, R. (1987). Turbulence statistics in fully developed channel flow at low Reynolds number. Journal of Fluid Mechanics, 177, 133-166.","type":"article","doi":"10.1017/S0022112087000892","isbn":null,"url":null}],"related":["large-eddy-simulation","reynolds-averaged-navier-stokes","lattice-boltzmann-method","smoothed-particle-hydrodynamics","boundary-layer-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"direct-preference-optimization","name":"Direct Preference Optimization","fullName":"Direct Preference Optimization: Your Language Model is Secretly a Reward Model","aliases":["DPO","Direct preference"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Language Models, RLHF Alternatives","year":"2023","originator":"Rafael Rafailov","url":"https://scholargate.app/en/deep-learning/direct-preference-optimization","markdownUrl":"https://scholargate.app/en/deep-learning/direct-preference-optimization.md","definition":"Direct Preference Optimization (DPO) is a training method introduced by Rafailov et al. in 2023 that aligns language models with human preferences without requiring an explicit reward model. By directly optimizing for preference pairs (better response vs worse response), DPO simplifies the training pipeline compared to reinforcement learning from human feedback (RLHF).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rafael Rafailov","subfamily":"Deep Learning, Language Models, RLHF Alternatives","year":"2023","type":"Training methodology"},"citations":[{"ref":"Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C. (2023). Direct preference optimization: Your language model is secretly a reward model. arXiv preprint arXiv:2305.18290.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2305.18290"}],"related":["qlora","mamba","latent-diffusion-models","masked-autoencoders"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"direct-torque-control","name":"Direct Torque Control","fullName":"Direct Torque Control","aliases":["DTC","Direct Flux Control"],"domain":"control-theory","family":"ml-model","subfamily":"Motor Control","year":"1986","originator":"Isao Takahashi","url":"https://scholargate.app/en/control-theory/direct-torque-control","markdownUrl":"https://scholargate.app/en/control-theory/direct-torque-control.md","definition":"Direct Torque Control (DTC) is a method for controlling induction motors by directly manipulating magnetic flux and torque through switching of power converter inverter arms. Introduced by Takahashi and Noguchi in 1986, DTC provides fast torque response, low harmonic distortion, and robust performance without requiring current controllers or coordinate transformations, making it ideal for high-performance drive applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Isao Takahashi","subfamily":"Motor Control","year":"1986","type":"algorithm"},"citations":[{"ref":"Takahashi, I., & Noguchi, T. (1986). A new quick-response and high-efficiency control strategy of an induction motor. IEEE Transactions on Industry Applications, IA-22(5), 820-827.","type":"article","doi":"10.1109/TIA.1986.4504799","isbn":null,"url":null},{"ref":"Kisacikoglu, M. C., Ertan, H. B., & Leblebicioglu, K. (2009). Direct torque control of induction motors. IEEE Industrial Electronics Society Newsletter, 56(2), 8-20.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/5208088"}],"related":["field-oriented-control","model-predictive-control","adaptive-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-betweenness-centrality","name":"Directed Betweenness Centrality","fullName":"Directed Betweenness Centrality (Freeman's Betweenness on Directed Graphs)","aliases":["directed BC","digraph betweenness","asymmetric betweenness centrality","directed Freeman betweenness"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1977","originator":"Freeman, L. C.","url":"https://scholargate.app/en/network-analysis/directed-betweenness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/directed-betweenness-centrality.md","definition":"Directed Betweenness Centrality extends Freeman's classic betweenness measure to directed graphs, quantifying how often a node lies on the shortest directed paths between all other pairs of nodes. It identifies gatekeepers, brokers, and bottlenecks in asymmetric flows such as information cascades, citation networks, and organizational hierarchies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Freeman, L. C.","year":"1977","type":"Centrality measure (directed graph)","dataType":"Directed relational / adjacency data","subfamily":"Network science"},"citations":[{"ref":"Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.","type":"article","doi":"10.2307/3033543","isbn":null,"url":null},{"ref":"Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163–177.","type":"article","doi":"10.1080/0022250X.2001.9990249","isbn":null,"url":null}],"related":["betweenness-centrality","directed-degree-centrality","directed-closeness-centrality","directed-eigenvector-centrality","directed-pagerank","directed-social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-closeness-centrality","name":"Directed Closeness Centrality","fullName":"Directed Closeness Centrality (In-closeness and Out-closeness on Directed Graphs)","aliases":["directed closeness","in-closeness centrality","out-closeness centrality","directional closeness"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1979–1994","originator":"Freeman, L. C.; Wasserman, S. & Faust, K.","url":"https://scholargate.app/en/network-analysis/directed-closeness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/directed-closeness-centrality.md","definition":"Directed closeness centrality extends the classical closeness measure to directed networks by separately quantifying how quickly a node can be reached by others (in-closeness) and how quickly it can reach all others (out-closeness). It is a foundational node-level metric in social network analysis and graph theory, used wherever link direction conveys meaningful asymmetry such as citation flows, information cascades, or authority hierarchies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Freeman, L. C.; Wasserman, S. & Faust, K.","year":"1979–1994","type":"Centrality measure","dataType":"Directed graph (adjacency matrix or edge list with arc direction)","subfamily":"Network science"},"citations":[{"ref":"Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-38269-4","url":null},{"ref":"Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.","type":"article","doi":"10.1016/0378-8733(78)90021-7","isbn":null,"url":null}],"related":["closeness-centrality","directed-social-network-analysis","directed-betweenness-centrality","directed-degree-centrality","directed-eigenvector-centrality","directed-pagerank"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-community-detection","name":"Directed Community Detection","fullName":"Directed Community Detection in Networks","aliases":["directed graph clustering","community detection in digraphs","directed modularity optimization","directed network partitioning"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2008","originator":"Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.","url":"https://scholargate.app/en/network-analysis/directed-community-detection","markdownUrl":"https://scholargate.app/en/network-analysis/directed-community-detection.md","definition":"Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.","year":"2008","type":"Graph partitioning / modularity optimization","dataType":"Directed graph (adjacency matrix or edge list with direction)","subfamily":"Network science"},"citations":[{"ref":"Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.","type":"article","doi":"10.1103/PhysRevLett.100.118703","isbn":null,"url":null},{"ref":"Rosvall, M. & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4), 1118–1123.","type":"article","doi":"10.1073/pnas.0706851105","isbn":null,"url":null}],"related":["modularity-analysis","directed-social-network-analysis","directed-betweenness-centrality","social-network-analysis","stochastic-block-model","weighted-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-ego-network-analysis","name":"Directed Ego Network Analysis","fullName":"Directed Ego Network Analysis (Asymmetric Personal Network Mapping)","aliases":["directed personal network analysis","asymmetric ego network","directed egocentric network analysis","directed egonet analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1954–2005","originator":"Barnes, J. A.; Bott, E.; extended by Everett & Borgatti","url":"https://scholargate.app/en/network-analysis/directed-ego-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/directed-ego-network-analysis.md","definition":"Directed ego network analysis examines the personal network of a focal node — the ego — by distinguishing the direction of each tie: who sends resources, support, or information to the ego, and to whom the ego sends them. This asymmetric perspective reveals role differentiation, dependence, and brokerage that undirected ego networks cannot capture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barnes, J. A.; Bott, E.; extended by Everett & Borgatti","year":"1954–2005","type":"Egocentric network method","dataType":"Directed dyadic tie data (in/out edges from a focal node)","subfamily":"Network science"},"citations":[{"ref":"Everett, M. G., & Borgatti, S. P. (2005). Ego network betweenness. Social Networks, 27(1), 31–38.","type":"article","doi":"10.1016/j.socnet.2004.11.007","isbn":null,"url":null},{"ref":"Perry, B. L., Pescosolido, B. A., & Borgatti, S. P. (2018). Egocentric Network Analysis: Foundations, Methods, and Models. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1-107-51888-1","url":null}],"related":["ego-network-analysis","social-network-analysis","directed-social-network-analysis","directed-betweenness-centrality","directed-degree-centrality","weighted-ego-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-eigenvector-centrality","name":"Directed Eigenvector Centrality","fullName":"Directed Eigenvector Centrality (Asymmetric Influence Scoring on Directed Graphs)","aliases":["directed EC","asymmetric eigenvector centrality","right eigenvector centrality","left eigenvector centrality"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1972–1987","originator":"Bonacich, P.","url":"https://scholargate.app/en/network-analysis/directed-eigenvector-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/directed-eigenvector-centrality.md","definition":"Directed eigenvector centrality extends the classic eigenvector centrality to directed graphs by scoring each node according to the centrality of the nodes that point to it (in-direction) or that it points to (out-direction). A node earns a high score not merely by having many connections but by being connected to other highly central nodes, capturing asymmetric influence in citation networks, social hierarchies, and information flows.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonacich, P.","year":"1972–1987","type":"Centrality measure (eigenvector-based, directed)","dataType":"Directed adjacency matrix / asymmetric graph","subfamily":"Network science"},"citations":[{"ref":"Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.","type":"article","doi":"10.1086/228631","isbn":null,"url":null},{"ref":"Eigenvector centrality. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Eigenvector_centrality"}],"related":["eigenvector-centrality","directed-pagerank","directed-degree-centrality","directed-betweenness-centrality","directed-closeness-centrality","directed-social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-exponential-random-graph-model","name":"Directed Exponential Random Graph Model","fullName":"Directed Exponential Random Graph Model (Directed ERGM / p* Model for Directed Networks)","aliases":["Directed ERGM","p-star model (directed)","directed p* model","directed Markov graph model"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1986 (foundations); 2007 (modern directed ERGM formulation)","originator":"Frank, O. & Strauss, D.; extended by Robins, Pattison, Kalish & Lusher","url":"https://scholargate.app/en/network-analysis/directed-exponential-random-graph-model","markdownUrl":"https://scholargate.app/en/network-analysis/directed-exponential-random-graph-model.md","definition":"The Directed Exponential Random Graph Model (Directed ERGM) is a family of statistical models for directed networks that estimates the probability of observing a given directed graph as a function of structural configurations — such as reciprocity, transitive triads, and in-degree centralization — and node or dyad covariates, enabling principled inference about the social processes that generate directed ties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank, O. & Strauss, D.; extended by Robins, Pattison, Kalish & Lusher","year":"1986 (foundations); 2007 (modern directed ERGM formulation)","type":"Statistical generative model for directed networks","dataType":"Directed binary or valued adjacency matrices; node and dyad covariates","subfamily":"Network science"},"citations":[{"ref":"Robins, G., Pattison, P., Kalish, Y. & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191.","type":"article","doi":"10.1016/j.socnet.2006.08.002","isbn":null,"url":null},{"ref":"Frank, O. & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81(395), 832-842.","type":"article","doi":"10.2307/2289017","isbn":null,"url":null}],"related":["exponential-random-graph-model","directed-community-detection","directed-social-network-analysis","stochastic-block-model","directed-stochastic-block-model","directed-modularity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-knowledge-graph-analysis","name":"Directed Knowledge Graph Analysis","fullName":"Directed Knowledge Graph Analysis (Graph-Based Knowledge Representation and Reasoning)","aliases":["directed KG analysis","knowledge graph mining","directed semantic graph analysis","KG reasoning"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2000s–2010s","originator":"Hogan, A. et al. (formalized); roots in Berners-Lee, T. et al. (Semantic Web)","url":"https://scholargate.app/en/network-analysis/directed-knowledge-graph-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/directed-knowledge-graph-analysis.md","definition":"Directed Knowledge Graph Analysis represents factual knowledge as a directed labeled multigraph of entities (nodes) and typed relations (directed edges), enabling structured reasoning, inference, and discovery over large heterogeneous datasets. The direction of edges encodes asymmetric relationships such as 'authored-by', 'causes', or 'is-a', making the graph semantically richer than undirected alternatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hogan, A. et al. (formalized); roots in Berners-Lee, T. et al. (Semantic Web)","year":"2000s–2010s","type":"Graph-based knowledge representation and inference","dataType":"Directed labeled multigraph (subject-predicate-object triples)","subfamily":"Network science"},"citations":[{"ref":"Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G. D., Gutierrez, C., ... & Polleres, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1–37.","type":"article","doi":"10.1145/3447772","isbn":null,"url":null},{"ref":"Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014). Knowledge Graph Embedding by Translating on Hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1), 1112–1119.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Knowledge+Graph+Embedding+by+Translating+on+Hyperplanes"}],"related":["knowledge-graph-analysis","directed-social-network-analysis","betweenness-centrality","eigenvector-centrality","directed-pagerank","directed-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-modularity-analysis","name":"Directed Modularity Analysis","fullName":"Directed Modularity Analysis (Leicht-Newman Directed Community Detection)","aliases":["directed community detection via modularity","directed Q-modularity","digraph modularity optimization","Leicht-Newman modularity"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2008","originator":"Leicht, E. A. & Newman, M. E. J.","url":"https://scholargate.app/en/network-analysis/directed-modularity-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/directed-modularity-analysis.md","definition":"Directed modularity analysis extends the classic Newman-Girvan modularity framework to directed graphs, where edges carry a source and a destination. Formalized by Leicht and Newman in 2008, it partitions nodes into communities by maximizing a modularity score that accounts for each node's separate in-degree and out-degree in the null model, making it the standard approach for community detection in citation networks, information flows, and other asymmetric relational data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leicht, E. A. & Newman, M. E. J.","year":"2008","type":"Community detection / graph partitioning","dataType":"Directed (asymmetric) adjacency matrix / edge list","subfamily":"Network science"},"citations":[{"ref":"Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.","type":"article","doi":"10.1103/PhysRevLett.100.118703","isbn":null,"url":null},{"ref":"Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.","type":"article","doi":"10.1103/PhysRevE.69.026113","isbn":null,"url":null}],"related":["modularity-analysis","directed-community-detection","directed-social-network-analysis","exponential-random-graph-model","stochastic-block-model","betweenness-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-multiplex-network-analysis","name":"Directed Multiplex Network Analysis","fullName":"Directed Multiplex Network Analysis (Multi-layer Directed Graph Framework)","aliases":["directed multilayer network analysis","directed multiplex graphs","asymmetric multiplex network analysis","DMNA"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2013–2014","originator":"Kivela, M.; De Domenico, M. et al.","url":"https://scholargate.app/en/network-analysis/directed-multiplex-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/directed-multiplex-network-analysis.md","definition":"Directed multiplex network analysis models systems where the same set of nodes are connected by multiple types of directed (asymmetric) relationships across distinct layers — such as citation flows, information cascades, or authority hierarchies co-existing simultaneously. It extends multiplex network analysis by preserving both layer identity and edge directionality, enabling richer structural and dynamic insights.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivela, M.; De Domenico, M. et al.","year":"2013–2014","type":"Multi-layer directed graph framework","dataType":"Multi-relational directed edge data (adjacency tensors)","subfamily":"Network science"},"citations":[{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"De Domenico, M., Sole-Ribalta, A., Cozzo, E., Kivela, M., Moreno, Y., Porter, M. A., Gomez, S., & Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022.","type":"article","doi":"10.1103/PhysRevX.3.041022","isbn":null,"url":null}],"related":["multiplex-network-analysis","directed-social-network-analysis","directed-community-detection","multilayer-network-diffusion-analysis","directed-betweenness-centrality","multilayer-social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-network-diffusion-analysis","name":"Directed Network Diffusion Analysis","fullName":"Directed Network Diffusion Analysis (Influence and Spreading Processes on Directed Graphs)","aliases":["directed diffusion model","information spreading on directed networks","directed cascade analysis","directed influence propagation"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927","originator":"Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading)","url":"https://scholargate.app/en/network-analysis/directed-network-diffusion-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/directed-network-diffusion-analysis.md","definition":"Directed network diffusion analysis studies how information, disease, behavior, or influence spreads through a network in which edges carry direction — meaning transmission flows one way along each link. It combines graph-theoretic representations with stochastic spreading models such as independent cascade, linear threshold, or SIR/SIS, and is central to influence maximization, epidemic forecasting, and information propagation research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading)","year":"2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927","type":"Network spreading and cascade analysis","dataType":"Directed graphs with node states and edge transmission probabilities","subfamily":"Network science"},"citations":[{"ref":"Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137–146.","type":"inproceedings","doi":"10.1145/956750.956769","isbn":null,"url":null},{"ref":"Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of Modern Physics, 87(3), 925–979.","type":"article","doi":"10.1103/RevModPhys.87.925","isbn":null,"url":null}],"related":["network-diffusion-analysis","social-network-analysis","directed-community-detection","directed-pagerank","temporal-network-diffusion-analysis","multiplex-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-pagerank","name":"Directed PageRank","fullName":"Directed PageRank (Link-Based Authority Ranking on Directed Graphs)","aliases":["PageRank","PR","Google PageRank","directed link analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1998","originator":"Brin, S. & Page, L.","url":"https://scholargate.app/en/network-analysis/directed-pagerank","markdownUrl":"https://scholargate.app/en/network-analysis/directed-pagerank.md","definition":"Directed PageRank is a link-based authority scoring algorithm that assigns importance scores to nodes in a directed graph by iteratively redistributing rank through outgoing edges. Introduced by Brin and Page in 1998 as the backbone of Google Search, it measures not just how many in-links a node has but how authoritative the nodes pointing to it are.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brin, S. & Page, L.","year":"1998","type":"Iterative authority-scoring algorithm","dataType":"Directed graph (adjacency list or matrix)","subfamily":"Network science"},"citations":[{"ref":"Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Proceedings of the 7th International Conference on World Wide Web (WWW7), 107–117. Elsevier.","type":"inproceedings","doi":null,"isbn":null,"url":"https://doi.org/10.1016/S0169-7552(98)00110-X"},{"ref":"Langville, A. N. & Meyer, C. D. (2006). Google's PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press.","type":"book","doi":null,"isbn":"978-0-691-12202-1","url":null}],"related":["degree-centrality","betweenness-centrality","eigenvector-centrality","hits-algorithm","directed-social-network-analysis","directed-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-social-network-analysis","name":"Directed Social Network Analysis","fullName":"Directed Social Network Analysis (Digraph-Based SNA)","aliases":["directed SNA","digraph analysis","directed graph network analysis","asymmetric network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1994","originator":"Wasserman, S. & Faust, K.","url":"https://scholargate.app/en/network-analysis/directed-social-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/directed-social-network-analysis.md","definition":"Directed Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wasserman, S. & Faust, K.","year":"1994","type":"Structural analysis of directed graphs","dataType":"Directed relational / dyadic data (adjacency matrix or edge list with direction)","subfamily":"Network science"},"citations":[{"ref":"Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-38707-1","url":null},{"ref":"Newman, M. E. J. (2010). Networks: An Introduction. Oxford University Press.","type":"book","doi":null,"isbn":"978-0-19-920665-0","url":null}],"related":["social-network-analysis","betweenness-centrality","degree-centrality","directed-pagerank","directed-community-detection","exponential-random-graph-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"directed-two-mode-network-analysis","name":"Directed Two-Mode Network Analysis","fullName":"Directed Two-Mode (Bipartite) Network Analysis","aliases":["directed bipartite network analysis","asymmetric affiliation network analysis","directed actor-event network analysis","directed two-mode graph analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1997","originator":"Borgatti, S. P. & Everett, M. G.","url":"https://scholargate.app/en/network-analysis/directed-two-mode-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/directed-two-mode-network-analysis.md","definition":"Directed two-mode network analysis studies bipartite graphs in which nodes belong to two distinct sets — such as actors and events, authors and papers, or firms and markets — and edges carry a direction, capturing asymmetric relationships like citation, referral, or endorsement. Combining the duality of two-mode structure with directed tie semantics reveals flow patterns and influence asymmetries that undirected or single-mode analyses would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Borgatti, S. P. & Everett, M. G.","year":"1997","type":"Structural network analysis","dataType":"Directed bipartite (two-mode) adjacency or incidence matrix","subfamily":"Network science"},"citations":[{"ref":"Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications (Ch. 8). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-38707-1","url":null},{"ref":"Borgatti, S. P. & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243-269.","type":"article","doi":"10.1016/S0378-8733(96)00301-2","isbn":null,"url":null}],"related":["two-mode-network-analysis","directed-social-network-analysis","directed-community-detection","directed-modularity-analysis","multiplex-network-analysis","knowledge-graph-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dirichlet-process-mixture-model","name":"Dirichlet Process Mixture Model","fullName":"Dirichlet Process Mixture Model","aliases":["DPMM","DP mixture model","infinite mixture model","Dirichlet process mixture","nonparametric Bayesian mixture model"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":1973,"originator":"Ferguson (1973); mixture model formulation by Lo (1984)","url":"https://scholargate.app/en/bayesian/dirichlet-process-mixture-model","markdownUrl":"https://scholargate.app/en/bayesian/dirichlet-process-mixture-model.md","definition":"The Dirichlet Process Mixture Model (DPMM) is a nonparametric Bayesian clustering method introduced through Ferguson's (1973) Dirichlet process prior that places a probability distribution over distributions. Unlike finite mixture models, the DPMM does not require the analyst to specify the number of clusters in advance; instead it infers the number of components from the data, allowing an effectively unbounded mixture that grows as more observations arrive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian Nonparametric","type":"Nonparametric Bayesian mixture model","originator":"Ferguson (1973); mixture model formulation by Lo (1984)","year":1973,"purpose":"density estimation / clustering / unsupervised learning","var_types":"continuous (any likelihood family)","inference":"MCMC (Gibbs / collapsed Gibbs) / variational Bayes","outputs":"posterior over cluster assignments, cluster parameters, and number of clusters"},"citations":[{"ref":"Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2), 209–230.","type":"article","doi":"10.1214/aos/1176342360","isbn":null,"url":null},{"ref":"Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9(2), 249–265.","type":"article","doi":"10.1080/10618600.2000.10474879","isbn":null,"url":null},{"ref":"Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.) (2010). Bayesian Nonparametrics. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-51346-3","url":null}],"related":["bayesian-regression","hierarchical-bayes","mcmc","latent-dirichlet-allocation","gaussian-mixture-model","bayesian-nonparametric-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"disability-adjusted-life-year","name":"Disability-Adjusted Life Year","fullName":"Disability-Adjusted Life Year (DALY)","aliases":["DALY","global disease burden metric","burden of disease"],"domain":"health-economics","family":"process-pipeline","subfamily":"population health metric","year":"1990","originator":"Christopher J. L. Murray and Alan D. Lopez (World Health Organization / World Bank)","url":"https://scholargate.app/en/health-economics/disability-adjusted-life-year","markdownUrl":"https://scholargate.app/en/health-economics/disability-adjusted-life-year.md","definition":"A DALY quantifies disease burden as the sum of years of life lost to premature death and years lived with disability. Developed by the World Health Organization and World Bank in 1990 as part of the Global Burden of Disease (GBD) study, DALYs enable epidemiologists and public health planners to compare disease burden across populations, identify health priorities, and evaluate intervention impact. One DALY = one lost year of 'healthy' life; DALYs averted measure progress toward health goals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher J. L. Murray and Alan D. Lopez (World Health Organization / World Bank)","subfamily":"population health metric","year":"1990","type":"Method"},"citations":[{"ref":"Murray, C. J., Lopez, A. D., & Jamison, D. T. (1994). The Global Burden of Disease in 1990: Summary Results, Sensitivity Analysis, and Future Directions. In C. J. Murray & A. D. Lopez (Eds.), Global Burden of Disease and Injury. Cambridge: Harvard University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Murray%2C%20C.%20J.%2C%20Lopez%2C%20A.%20D.%2C%20%26%20Jamison%2C%20D.%20T.%20(1994).%20The%20Global%20Burden%20of%20Disease%20in%201990%3A%20Summary%20Results%2C%20Sensitivity"},{"ref":"Global Burden of Disease Study 2019 Collaborators. (2020). Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1204-1222.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Global+burden+of+369+diseases+and+injuries+in+204+countries+and+territories%2C+1990%E2%80%932019%3A+a+systematic+analysis+for+the+Global+Burden+of+Disease+Study+2019+Global"},{"ref":"Mathers, C. D., Vos, T., & Lopez, A. D. (2003). The Burden of Disease and Injury in Australia. Public Health Division, Department of Health and Ageing, Australian Government.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Mathers%2C%20C.%20D.%2C%20Vos%2C%20T.%2C%20%26%20Lopez%2C%20A.%20D.%20(2003).%20The%20Burden%20of%20Disease%20and%20Injury%20in%20Australia.%20Public%20Health%20Division%2C%20D"}],"related":["quality-adjusted-life-year","cost-effectiveness-analysis","markov-model-health-economics","decision-analytic-modeling","willingness-to-pay"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"disability-rating-scale","name":"Disability Rating Scale","fullName":"Disability Rating Scale (DRS)","aliases":["DRS","Rappaport DRS"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"traumatic-brain-injury","year":"1982","originator":"Rappaport, Hall, Hopkins, Belleza, Cope","url":"https://scholargate.app/en/rehabilitation-science/disability-rating-scale","markdownUrl":"https://scholargate.app/en/rehabilitation-science/disability-rating-scale.md","definition":"The Disability Rating Scale (DRS) is a brief, clinician-administered measure specifically designed to assess the severity of disability and functional recovery across the entire spectrum of traumatic brain injury (TBI)—from acute coma to community reintegration. Developed by Rappaport and colleagues in 1982, DRS has become a standard outcome measure in TBI research and clinical practice, uniquely spanning acute (comatose) phases through chronic community outcomes where other measures fail.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rappaport, Hall, Hopkins, Belleza, Cope","subfamily":"traumatic-brain-injury","year":"1982","type":"Clinician-rated"},"citations":[{"ref":"Rappaport, M., Hall, K. M., Hopkins, K., Belleza, T., & Cope, D. N. (1982). Disability rating scale for severe head trauma: Relation to rehabilitation outcomes. Archives of Physical Medicine and Rehabilitation, 63(3), 118–123.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/S0003-9993(82)80039-5"},{"ref":"Hall, K. M., Hamilton, B. B., Gordon, W. A., & Zasler, N. D. (1993). Characteristics and comparisons of functional assessment indices: Disability Rating Scale, Functional Independence Measure, and Functional Assessment Measure. Journal of Head Trauma Rehabilitation, 8(2), 60–74.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1097/00001199-199306000-00005"}],"related":["whodas-2","community-integration-questionnaire","participation-measure-post-acute","participation-scale","impact-participation-autonomy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"disclosure-risk","name":"Disclosure Risk Assessment","fullName":"Statistical Disclosure Risk Assessment","aliases":["Microdata Disclosure Risk","Statistical Disclosure Control Risk Estimation","Istatistiksel Açıklama Riski Değerlendirmesi","Re-identification Risk Assessment"],"domain":"privacy","family":"regression-model","subfamily":"Disclosure control","year":1989,"originator":"George Duncan & Diane Lambert","url":"https://scholargate.app/en/privacy/disclosure-risk","markdownUrl":"https://scholargate.app/en/privacy/disclosure-risk.md","definition":"Disclosure Risk Assessment is a probabilistic framework introduced by Duncan and Lambert (1989) for quantifying how likely it is that releasing microdata — individual-level records from surveys or administrative files — will allow an outside party to identify a specific respondent or infer sensitive attributes. It is used by statistical agencies, data custodians, and researchers charged with protecting confidentiality before any public release of person-level datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Duncan & Diane Lambert","year":1989,"type":"Probabilistic risk model","subfamily":"Disclosure control","scope":"Microdata files (individual-level records)","output":"Estimated probability of identity or attribute disclosure"},"citations":[{"ref":"Duncan, G. T., & Lambert, D. (1989). The risk of disclosure for microdata. Journal of Business & Economic Statistics, 7(2), 207–217.","type":"article","doi":"10.1080/07350015.1989.10509729","isbn":null,"url":null}],"related":["k-anonymity","synthetic-data-generation","differential-privacy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"discourse-analysis-media","name":"Discourse Analysis in Media","fullName":"Critical Discourse Analysis of Media Texts and Communication","aliases":["critical discourse analysis","media discourse analysis","CDA"],"domain":"media-studies","family":"process-pipeline","subfamily":"Language, power, and representation analysis","year":"1978","originator":"Michel Foucault, Norman Fairclough","url":"https://scholargate.app/en/media-studies/discourse-analysis-media","markdownUrl":"https://scholargate.app/en/media-studies/discourse-analysis-media.md","definition":"Discourse Analysis in Media is a method for examining how media texts use language, images, and communication patterns to construct meanings, shape identities, and perpetuate or challenge power relations. Developed from linguistic analysis and critical theory—particularly Michel Foucault's concept of discourse as a system of knowledge-production and Norman Fairclough's critical discourse analysis (CDA) framework—the method reveals how what appears as neutral information or entertainment actually participates in maintaining or challenging social hierarchies and ideologies. The method is specifically concerned with how discourse operates politically: what it makes possible to think and say, whom it privileges, and what alternatives it renders invisible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michel Foucault, Norman Fairclough","subfamily":"Language, power, and representation analysis","year":"1978","type":"Method for examining how discourse in media constructs meaning, identity, and power relations"},"citations":[{"ref":"Fairclough, N. (1992). Discourse and Social Change. Polity Press.","type":"book","doi":null,"isbn":null,"url":"https://www.polity.co.uk"},{"ref":"Foucault, M. (1980). Power/Knowledge: Selected Interviews and Other Writings 1972-1977. Pantheon Books.","type":"book","doi":null,"isbn":null,"url":"https://www.penguinrandomhouse.com"},{"ref":"Van Dijk, T. A. (2015). Critical Discourse Studies: A Sociocognitive Approach. In R. Wodak & M. Meyer (Eds.), Methods of Critical Discourse Studies (3rd ed., pp. 62-86). SAGE.","type":"book","doi":"10.4135/9781036235192.n3","isbn":null,"url":null},{"ref":"Wodak, R., & Meyer, M. (2009). Critical Discourse Analysis: History, Agenda, Theory and Methodology. In R. Wodak & M. Meyer (Eds.), Methods of Critical Discourse Analysis (2nd ed., pp. 1-33). SAGE.","type":"book","doi":null,"isbn":null,"url":"https://www.sagepub.com"}],"related":["media-framing-analysis","visual-content-analysis","film-narrative-analysis","agenda-setting-analysis","reception-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"discourse-analysis","name":"Discourse Analysis","fullName":"Discourse Analysis Method","aliases":["DA","Critical Discourse Analysis","Discursive Analysis"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"linguistic-critical-interpretation","year":"1989 (Fairclough); 1987 (Potter & Wetherell)","originator":"Norman Fairclough; Jonathan Potter and Margaret Wetherell","url":"https://scholargate.app/en/qualitative-research/discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative-research/discourse-analysis.md","definition":"Discourse analysis is a qualitative research methodology that examines how language, communication, and power shape meaning, identity, and social reality. Developed across linguistics, sociology, and psychology (particularly by Norman Fairclough and Jonathan Potter), discourse analysis goes beyond content to analyze language use as a social practice that constitutes and reflects power relations, ideologies, and social structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Fairclough; Jonathan Potter and Margaret Wetherell","subfamily":"linguistic-critical-interpretation","year":"1989 (Fairclough); 1987 (Potter & Wetherell)","type":"Method"},"citations":[{"ref":"Fairclough, N. (1989). Language and power. Longman.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Fairclough%2C%20N.%20(1989).%20Language%20and%20power.%20Longman."},{"ref":"Potter, J., & Wetherell, M. (1987). Discourse and social psychology: Beyond attitudes and behaviour. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Potter%2C%20J.%2C%20%26%20Wetherell%2C%20M.%20(1987).%20Discourse%20and%20social%20psychology%3A%20Beyond%20attitudes%20and%20behaviour.%20Sage%20Publications."},{"ref":"Machin, D., & Mayr, A. (2012). How to do critical discourse analysis: A multimodal introduction. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Machin%2C%20D.%2C%20%26%20Mayr%2C%20A.%20(2012).%20How%20to%20do%20critical%20discourse%20analysis%3A%20A%20multimodal%20introduction.%20Sage%20Publications."}],"related":["content-analysis-qualitative","rhetoric-analysis","critical-discourse-analysis","linguistic-analysis","power-language"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"discourse-parsing","name":"Discourse Parsing","fullName":"Discourse Parsing (Rhetorical Structure Analysis)","aliases":["rhetorical structure analysis","RST parsing","PDTB parsing","Söylem Ayrıştırma (Discourse Parsing)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1988 (RST); 2008 (PDTB 2.0)","originator":"Mann & Thompson (RST); Prasad et al. (PDTB)","url":"https://scholargate.app/en/text-mining/discourse-parsing","markdownUrl":"https://scholargate.app/en/text-mining/discourse-parsing.md","definition":"Discourse parsing is a natural-language-processing task that models the rhetorical relations between sentences and paragraphs of a text — relations such as cause, contrast, and elaboration — and represents them as a tree structure. It works within established frameworks, principally Rhetorical Structure Theory (RST), introduced by Mann and Thompson in 1988, and the Penn Discourse TreeBank (PDTB), released by Prasad and colleagues in 2008.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mann & Thompson (RST); Prasad et al. (PDTB)","year":"1988 (RST); 2008 (PDTB 2.0)","type":"NLP discourse-structure analysis task","frameworks":"Rhetorical Structure Theory (RST) / Penn Discourse TreeBank (PDTB)","output":"Discourse tree of rhetorical relations (e.g. cause, contrast, elaboration)"},"citations":[{"ref":"Mann, W. C. & Thompson, S. A. (1988). Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3), 243-281.","type":"article","doi":"10.1515/text.1.1988.8.3.243","isbn":null,"url":null},{"ref":"Prasad, R., Dinesh, N., Lee, A., Miltsakaki, E., Robaldo, L., Joshi, A. & Webber, B. (2008). The Penn Discourse TreeBank 2.0. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008).","type":"inproceedings","doi":null,"isbn":null,"url":"http://www.lrec-conf.org/proceedings/lrec2008/pdf/754_paper.pdf"}],"related":["argument-mining","sentiment-analysis","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"discrete-choice-simulation","name":"Discrete Choice Simulation","fullName":"Discrete Choice Simulation (Stated Preference / SP Simulation)","aliases":["stated preference simulation","SP simulation","revealed preference modelling","Ayrık Seçim Simülasyonu (Stated Preference / SP Simulation)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s","originator":"Daniel McFadden (random utility theory); Kenneth Train (simulation methods)","url":"https://scholargate.app/en/simulation/discrete-choice-simulation","markdownUrl":"https://scholargate.app/en/simulation/discrete-choice-simulation.md","definition":"Discrete choice simulation is a behavioural modelling method — grounded in random utility theory formalised by Daniel McFadden in the 1970s and extended to simulation-based estimation by Kenneth Train — that estimates how individuals choose among mutually exclusive alternatives and then uses those estimated preference parameters to forecast how choice shares would shift under hypothetical policy or market scenarios. It is the dominant quantitative tool in transport demand analysis, health economics, environmental valuation, and marketing research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel McFadden (random utility theory); Kenneth Train (simulation methods)","year":"1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s","type":"Discrete choice modelling with Monte Carlo simulation","utilityFramework":"Random Utility Maximisation (RUM)","coreModels":"Multinomial logit (MNL), nested logit, mixed logit (random parameters logit)","designRequirement":"Orthogonal or D-optimal choice experiment design","minSample":100,"outputType":"Utility-function coefficients, choice probabilities, willingness-to-pay (WTP), market-share forecasts","domains":"Transport, health economics, marketing, environmental valuation"},"citations":[{"ref":"Train, K.E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511753930","isbn":null,"url":null},{"ref":"Ben-Akiva, M. & Lerman, S.R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press.","type":"book","doi":null,"isbn":"978-0262022170","url":null}],"related":["microsimulation","conjoint-analysis","multinomial-logit","mixed-logit","agent-based-modelling","monte-carlo-simulation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"discrete-event-simulation","name":"Discrete-Event Simulation","fullName":"Discrete-Event Simulation (DES)","aliases":["DES","event-driven simulation","Ayrık Olay Simülasyonu (DES)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1960s (formalized); modern computational form from 1970s onward","originator":"Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)","url":"https://scholargate.app/en/simulation/discrete-event-simulation","markdownUrl":"https://scholargate.app/en/simulation/discrete-event-simulation.md","definition":"Discrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)","year":"1960s (formalized); modern computational form from 1970s onward","type":"Stochastic process simulation","paradigm":"Event-driven (state changes occur only at discrete event times)","output":"Performance metrics — throughput, waiting times, resource utilization, queue lengths","difficultyLevel":"Intermediate (difficulty 2 / 5)"},"citations":[{"ref":"Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0136062127","url":null},{"ref":"Law, A.M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073401324","url":null}],"related":["system-dynamics","monte-carlo-simulation","latin-hypercube-sampling","agent-based-modeling","queuing-theory"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"discrete-system-simulation","name":"Discrete-Event System Simulation","fullName":"Discrete-Event System Simulation (Arena / AnyLogic style)","aliases":["DES","discrete event simulation","Kesikli Sistem Simülasyonu (Arena / AnyLogic tarzı)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1960s (formalised in literature through the 1980s–2000s)","originator":"Kelton, Law & Sadowski (formalised methodology); SIMSCRIPT (Markowitz et al., 1963) and GPSS (Gordon, 1961) were pioneering tools","url":"https://scholargate.app/en/simulation/discrete-system-simulation","markdownUrl":"https://scholargate.app/en/simulation/discrete-system-simulation.md","definition":"Discrete-event system simulation (DES) is a computational modelling technique in which the state of a system changes only at discrete points in time — called events — such as a customer arriving, a machine starting, or a job completing. Formalised through foundational texts by Kelton, Sadowski, and Zupick (2014) and Law (2015), DES represents processes as networks of resources, queues, and activities, allowing analysts to test capacity and policy changes on a virtual model before touching the real system.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kelton, Law & Sadowski (formalised methodology); SIMSCRIPT (Markowitz et al., 1963) and GPSS (Gordon, 1961) were pioneering tools","year":"1960s (formalised in literature through the 1980s–2000s)","type":"Stochastic process simulation","paradigm":"Discrete-event (state changes only at event times)","typical_domains":"Manufacturing, service systems, supply chain, healthcare logistics","output":"Throughput, queue lengths, resource utilisation, cycle times under simulated scenarios","requires_normality":false,"min_sample":0,"difficulty":2},"citations":[{"ref":"Kelton, W.D., Sadowski, R.P. & Zupick, N.B. (2014). Simulation with Arena (6th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073401317","url":null},{"ref":"Law, A.M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073401324","url":null}],"related":["monte-carlo-simulation","agent-based-modeling","system-dynamics","queuing-theory","bootstrap-simulation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"discrete-wavelet-transform","name":"Discrete Wavelet Transform","fullName":"Discrete Wavelet Transform","aliases":["DWT","Daubechies wavelets","Haar wavelet"],"domain":"time-series","family":"process-pipeline","subfamily":"Orthogonal multiresolution decomposition","year":"1992","originator":"Ingrid Daubechies","url":"https://scholargate.app/en/time-series/discrete-wavelet-transform","markdownUrl":"https://scholargate.app/en/time-series/discrete-wavelet-transform.md","definition":"The discrete wavelet transform (DWT) is a fast, computationally efficient method for decomposing signals into different frequency and time components using orthogonal or biorthogonal wavelet functions. Developed rigorously by Ingrid Daubechies (1992) and built on Mallat's multiresolution decomposition theory (1989), the DWT employs filter banks to recursively split a signal into approximation (low-frequency) and detail (high-frequency) components. It has become the foundation for signal processing applications ranging from compression to feature extraction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ingrid Daubechies","subfamily":"Orthogonal multiresolution decomposition","year":"1992","type":"Hierarchical signal decomposition"},"citations":[{"ref":"Daubechies, I. (1992). Ten Lectures on Wavelets. SIAM.","type":"article","doi":"10.1137/1.9781611970104","isbn":null,"url":null},{"ref":"Mallat, S. G. (1989). A theory of multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.","type":"article","doi":"10.1109/34.192463","isbn":null,"url":null},{"ref":"Walnut, D. F. (2002). An Introduction to Wavelet Analysis. Birkhäuser.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/book/10.1007/978-0-8176-4415-3"}],"related":["continuous-wavelet-transform","modwt","stationary-wavelet-transform","fast-fourier-transform"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"discriminant-analysis","name":"Discriminant Analysis","fullName":"Linear Discriminant Analysis","aliases":["LDA","Fisher discriminant analysis","discriminant function analysis","canonical discriminant analysis"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1936","originator":"Ronald A. Fisher","url":"https://scholargate.app/en/statistics/discriminant-analysis","markdownUrl":"https://scholargate.app/en/statistics/discriminant-analysis.md","definition":"Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":"1936","type":"Supervised classification and dimension reduction","dataType":"Continuous predictors, categorical group membership","subfamily":"Multivariate analysis"},"citations":[{"ref":"Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.","type":"article","doi":"10.1111/j.1469-1809.1936.tb02137.x","isbn":null,"url":null},{"ref":"Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1473756540","url":null}],"related":["principal-component-analysis","logistic-regression","cluster-analysis","confirmatory-factor-analysis","canonical-correlation-analysis","multivariate-analysis-of-variance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"discriminant-validity","name":"Discriminant Validity","fullName":"Discriminant Validity","aliases":["discriminant validity evidence","divergent validity","DV","AVE-based discriminant validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1959","originator":"Donald T. Campbell and Donald W. Fiske","url":"https://scholargate.app/en/psychometrics/discriminant-validity","markdownUrl":"https://scholargate.app/en/psychometrics/discriminant-validity.md","definition":"Discriminant validity is evidence that a latent construct is empirically distinct from other constructs it should differ from. Originating in Campbell and Fiske's multitrait-multimethod framework (1959), it is a core component of construct validity and a mandatory check in scale development and structural equation modeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald T. Campbell and Donald W. Fiske","year":"1959","type":"Validity evidence / psychometric evaluation","dataType":"Latent construct scores, item-level responses, correlation/covariance matrices","subfamily":"Scale / measurement"},"citations":[{"ref":"Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.","type":"article","doi":"10.1037/h0046016","isbn":null,"url":null},{"ref":"Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.","type":"article","doi":"10.1177/002224378101800104","isbn":null,"url":null}],"related":["convergent-validity","construct-validity","confirmatory-factor-analysis","exploratory-factor-analysis","cronbachs-alpha","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"disenfranchised-grief-scale","name":"DGS","fullName":"Disenfranchised Grief Scale","aliases":["DGS","Doka Disenfranchised Grief"],"domain":"bereavement-psychology","family":"process-pipeline","subfamily":"grief-in-marginalized-relationships","year":"2002","originator":"Kenneth J. Doka","url":"https://scholargate.app/en/bereavement-psychology/disenfranchised-grief-scale","markdownUrl":"https://scholargate.app/en/bereavement-psychology/disenfranchised-grief-scale.md","definition":"The Disenfranchised Grief Scale (DGS), developed from Kenneth J. Doka's conceptual framework, assesses grief that society does not recognize, validate, or support—grief that is excluded from public mourning rituals, openly acknowledged grief ceremonies, or institutional support. Examples include loss of a former spouse, affair partner, ex-partner, friend (not family), pet, or pregnancy loss. The DGS captures the unique burden of grieving without social permission or recognition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kenneth J. Doka","subfamily":"grief-in-marginalized-relationships","year":"2002","type":"Self-report questionnaire"},"citations":[{"ref":"Doka, K. J. (Ed.). (2002). Disenfranchised grief: New directions, challenges, and strategies for practice. Research Press.","type":"book-chapter","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Doka%2C%20K.%20J.%20(Ed.).%20(2002).%20Disenfranchised%20grief%3A%20New%20directions%2C%20challenges%2C%20and%20strategies%20for%20practice.%20Research%20Pres"}],"related":["inventory-complicated-grief","grief-experience-questionnaire","texas-revised-inventory-grief","adult-attitude-to-grief"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"disproportional-cluster-sampling","name":"Disproportional cluster sampling","fullName":"Disproportionate Cluster Sampling","aliases":["disproportionate cluster sampling","unequal-probability cluster sampling","variable-rate cluster sampling","non-proportional cluster sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century (formalised 1950s–1965)","originator":"Leslie Kish; William G. Cochran","url":"https://scholargate.app/en/survey-methodology/disproportional-cluster-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/disproportional-cluster-sampling.md","definition":"Disproportional cluster sampling is a probability-based survey design in which naturally occurring groups (clusters) are selected as primary sampling units, but the number of clusters or elements drawn from each group is not proportional to that group's share of the population. By deliberately over- or under-sampling certain clusters, researchers gain analytic flexibility and precision where it matters most, at the cost of requiring post-hoc weighting for population-level inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leslie Kish; William G. Cochran","year":"Mid-20th century (formalised 1950s–1965)","type":"Probability sampling design","dataType":"Quantitative / population-level survey data","subfamily":"Sampling"},"citations":[{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471489009","url":null},{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null}],"related":["cluster-sampling","proportional-cluster-sampling","disproportional-stratified-sampling","multistage-sampling","weighted-sampling","systematic-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"disproportional-stratified-sampling","name":"Disproportional Stratified Sampling","fullName":"Disproportional Stratified Random Sampling","aliases":["disproportionate stratified sampling","unequal-probability stratified sampling","oversampling stratified design","non-proportional stratified sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1934","originator":"Jerzy Neyman","url":"https://scholargate.app/en/survey-methodology/disproportional-stratified-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/disproportional-stratified-sampling.md","definition":"Disproportional stratified sampling divides the population into mutually exclusive strata and deliberately draws different proportions from each stratum — oversampling small or analytically important subgroups and undersampling large ones. Post-hoc weighting restores population-level representativeness when overall estimates are needed. First formalised by Jerzy Neyman in 1934, it is the standard approach when subgroup-level precision matters as much as total-population estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerzy Neyman","year":"1934","type":"Probability sampling design","dataType":"Quantitative or mixed; structured survey or administrative records data","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Neyman, J. (1934). On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection. Journal of the Royal Statistical Society, 97(4), 558-625.","type":"article","doi":"10.2307/2342192","isbn":null,"url":null}],"related":["proportional-stratified-sampling","stratified-sampling","cluster-sampling","multistage-sampling","weighted-sampling","simple-random-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dissociative-experiences-scale","name":"Dissociative Experiences Scale","fullName":"Dissociative Experiences Scale (DES)","aliases":["DES","DES-II (revised)"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Dissociative symptom severity assessment","year":"1986","originator":"Frank W. Putnam","url":"https://scholargate.app/en/psychiatry/dissociative-experiences-scale","markdownUrl":"https://scholargate.app/en/psychiatry/dissociative-experiences-scale.md","definition":"The DES is a 28-item self-report questionnaire designed to measure the frequency and severity of dissociative symptoms, including depersonalization (feeling detached from one's body), derealization (feeling the world is unreal), amnesia, absorption (intense focus), and identity confusion. Developed by Bernstein and Putnam in 1986, it is the most widely used dissociation screening instrument in clinical and research settings. The DES helps identify dissociative disorders (dissociative identity disorder, other specified dissociative disorder), trauma-related dissociation, and dissociative symptoms in other psychiatric conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank W. Putnam","subfamily":"Dissociative symptom severity assessment","year":"1986","type":"Self-report questionnaire"},"citations":[{"ref":"Bernstein, E. M., & Putnam, F. W. (1986). Development, reliability, and validity of a dissociation scale. Journal of Nervous and Mental Disease, 174(12), 727–735.","type":"article","doi":"10.1097/00005053-198612000-00004","isbn":null,"url":null},{"ref":"Putnam, F. W., Carlson, E. B., Ross, C. A., Torem, M., Shi, Y., Fielding, S., & Elterman, E. (1996). Patterns of dissociation in clinical and nonclinical samples. Journal of Nervous and Mental Disease, 184(11), 673–679.","type":"article","doi":"10.1097/00005053-199611000-00004","isbn":null,"url":null},{"ref":"Carlson, E. B., Putnam, F. W., Ross, C. A., Torem, M., Coons, P., Bowman, E. S., ... & Spiegel, D. (2011). Features and outcome of 34 patients with dissociative identity disorder. Journal of Nervous and Mental Disease, 199(8), 632–645.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Features+and+outcome+of+34+patients+with+dissociative+identity+disorder+Carlson"}],"related":["manic-state-rating-scale","panss","brief-psychiatric-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dissolution-f1-f2-similarity","name":"Dissolution f1/f2 Similarity","fullName":"Dissolution f1/f2 Similarity Factor","aliases":["f1","f2","similarity factor"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Biopharmaceutics","year":"1996","originator":"James Moore and Hector Flanner","url":"https://scholargate.app/en/pharmacology/dissolution-f1-f2-similarity","markdownUrl":"https://scholargate.app/en/pharmacology/dissolution-f1-f2-similarity.md","definition":"The f1 and f2 factors are dimensionless statistical measures developed by Moore and Flanner to quantify the similarity between two dissolution profiles. Adopted by regulatory agencies (FDA, EMA) as the gold standard for comparing dissolution curves, these factors enable rapid assessment of whether formulation changes significantly impact drug release.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Moore and Hector Flanner","subfamily":"Biopharmaceutics","year":"1996","type":"similarity testing"},"citations":[{"ref":"Moore, J. W., & Flanner, H. H. (1996). Mathematical comparison of dissolution profiles. Pharmaceutical Technology, 20(6), 64-74.","type":"article","doi":null,"isbn":null,"url":"https://www.pharmtech.com/"},{"ref":"Shah, V. P., Tsong, Y., Sathe, P., & Liu, J. P. (1998). In vitro dissolution profile comparison--statistics and analysis of the similarity factor, f2. Pharmaceutical Research, 15(6), 889-896.","type":"article","doi":"10.1023/A:1011976615750","isbn":null,"url":null}],"related":["in-vitro-in-vivo-correlation","caco-2-permeability","michaelis-menten-kinetics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dist-chebyshev","name":"DIST-CHEBYSHEV","fullName":"Chebyshev Distance — L∞ norm (maximum coordinate difference)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Distance","year":"1991","originator":"Roy, B.","url":"https://scholargate.app/en/decision-making/dist-chebyshev","markdownUrl":"https://scholargate.app/en/decision-making/dist-chebyshev.md","definition":"DIST-CHEBYSHEV (Chebyshev Distance — L∞ norm (maximum coordinate difference)) is a distance multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1991. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roy, B.","subfamily":"Distance","year":"1991","type":"Distance (L∞, minimax)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Roy, B. (1991). Chebyshev Distance. Journal of Multi-Criteria Decision Analysis","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Chebyshev%20Distance"}],"related":["vikor"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dist-euclidean","name":"DIST-EUCLIDEAN","fullName":"Euclidean Distance — L2 norm between two vectors in criterion space","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Distance","year":"1981","originator":"Hwang, C. L., Yoon, K.","url":"https://scholargate.app/en/decision-making/dist-euclidean","markdownUrl":"https://scholargate.app/en/decision-making/dist-euclidean.md","definition":"DIST-EUCLIDEAN (Euclidean Distance — L2 norm between two vectors in criterion space) is a distance multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hwang, C. L., Yoon, K.","subfamily":"Distance","year":"1981","type":"Distance (L2, Euclidean)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag","type":"article","doi":"10.1007/978-3-642-48318-9","isbn":null,"url":null}],"related":["topsis","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dist-hamming","name":"DIST-HAMMING","fullName":"Hamming Distance — count of positions where two equal-length sequences differ","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Distance","year":"1950","originator":"Hamming, R. W.","url":"https://scholargate.app/en/decision-making/dist-hamming","markdownUrl":"https://scholargate.app/en/decision-making/dist-hamming.md","definition":"DIST-HAMMING (Hamming Distance — count of positions where two equal-length sequences differ) is a distance multi-criteria decision-making (MCDM) method introduced by Hamming, R. W. in 1950. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hamming, R. W.","subfamily":"Distance","year":"1950","type":"Distance (categorical, count of differing positions)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Hamming, R. W. (1950). Hamming Distance. Bell System Technical Journal","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hamming%20Distance"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dist-manhattan","name":"DIST-MANHATTAN","fullName":"Manhattan Distance — L1 norm (city-block distance) between two vectors","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Distance","year":"2020","originator":"Dezert, J., Tchamova, A., Han, D., Bhotto, M. Z. A.","url":"https://scholargate.app/en/decision-making/dist-manhattan","markdownUrl":"https://scholargate.app/en/decision-making/dist-manhattan.md","definition":"DIST-MANHATTAN (Manhattan Distance — L1 norm (city-block distance) between two vectors) is a distance multi-criteria decision-making (MCDM) method introduced by Dezert, J., Tchamova, A., Han, D., Bhotto, M. Z. A. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dezert, J., Tchamova, A., Han, D., Bhotto, M. Z. A.","subfamily":"Distance","year":"2020","type":"Distance (L1, city-block)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Dezert, J., Tchamova, A., Han, D., Bhotto, M. Z. A. (2020). Manhattan Distance. IEEE Transactions on Cybernetics","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Manhattan+Distance+Dezert"}],"related":["codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dist-minkowski","name":"DIST-MINKOWSKI","fullName":"Minkowski Distance — generalised Lp norm (p ≥ 1)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Distance","year":"1910","originator":"Minkowski, H.","url":"https://scholargate.app/en/decision-making/dist-minkowski","markdownUrl":"https://scholargate.app/en/decision-making/dist-minkowski.md","definition":"DIST-MINKOWSKI (Minkowski Distance — generalised Lp norm (p ≥ 1)) is a distance multi-criteria decision-making (MCDM) method introduced by Minkowski, H. in 1910. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Minkowski, H.","subfamily":"Distance","year":"1910","type":"Distance (Lp, generalised)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Minkowski, H. (1910). Geometrie der Zahlen. Teubner, Leipzig","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Geometrie+der+Zahlen+Minkowski"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"distance-sampling","name":"Distance Sampling","fullName":"Distance Sampling for Estimating Abundance","aliases":["line transect","point transect","distance estimation","detection probability"],"domain":"ecology","family":"process-pipeline","subfamily":"Sampling design","year":"1993","originator":"Stephen Buckland","url":"https://scholargate.app/en/ecology/distance-sampling","markdownUrl":"https://scholargate.app/en/ecology/distance-sampling.md","definition":"Distance sampling is a statistical method for estimating population abundance from data on distances between observers and detected individuals. Developed by Buckland and colleagues (1993) and formalized in the software Distance, this approach accounts for imperfect detection: animals far from an observer are less likely to be detected. By modeling the detection function (probability of detecting an animal at various distances), distance sampling produces unbiased estimates of abundance and density even when detection is incomplete.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Buckland","subfamily":"Sampling design","year":"1993","type":"population abundance estimation"},"citations":[{"ref":"Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (1993). Distance Sampling: Estimating Abundance of Biological Populations. Chapman and Hall, London.","type":"book","doi":null,"isbn":null,"url":"https://www.springer.com/gp/book/9780412426209"},{"ref":"Thomas, L., Buckland, S. T., Rexstad, E. A., et al. (2010). Distance software: design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology, 47(1), 5-14.","type":"article","doi":"10.1111/j.1365-2664.2009.01737.x","isbn":null,"url":null},{"ref":"Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (2001). Introduction to Distance Sampling. Oxford University Press.","type":"article","doi":"10.1093/oso/9780198506492.001.0001","isbn":null,"url":null}],"related":["species-accumulation","population-viability-analysis","food-web-topology","leslie-matrix"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dizziness-handicap-inventory","name":"DHI","fullName":"Dizziness Handicap Inventory","aliases":["DHI"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"vestibular-disability","year":"1990","originator":"Gary P. Jacobson and Craig W. Newman","url":"https://scholargate.app/en/otolaryngology/dizziness-handicap-inventory","markdownUrl":"https://scholargate.app/en/otolaryngology/dizziness-handicap-inventory.md","definition":"The Dizziness Handicap Inventory (DHI) is a 25-item self-report questionnaire designed to measure the functional, emotional, and physical effects of dizziness and balance disorders on daily life. Developed by Jacobson and Newman in 1990, it has become a standard tool for assessing dizziness-related handicap in clinical and research settings. The DHI is valuable for tracking disability progression and treatment response in vestibular patients.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gary P. Jacobson and Craig W. Newman","subfamily":"vestibular-disability","year":"1990","type":"Self-report"},"citations":[{"ref":"Jacobson, G. P., & Newman, C. W. (1990). The development of the Dizziness Handicap Inventory. Archives of Otolaryngology - Head & Neck Surgery, 116(4), 424-427.","type":"article","doi":"10.1001/archotol.1990.01870040046011","isbn":null,"url":null}],"related":["tinnitus-handicap-inventory","vestibular-activities-participation","vertigo-symptom-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dlinear","name":"DLinear","fullName":"DLinear (Decomposition Linear Model for Forecasting)","aliases":["Decomposition Linear","DLinear Forecaster","Linear Decomposition Model","Ayrışım Doğrusal Modeli"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2023,"originator":"Ailing Zeng et al.","url":"https://scholargate.app/en/deep-learning/dlinear","markdownUrl":"https://scholargate.app/en/deep-learning/dlinear.md","definition":"DLinear is a lightweight time series forecasting model introduced by Zeng et al. at AAAI 2023. It challenges the prevailing assumption that Transformer-based architectures are necessary for accurate long-horizon forecasting. The model decomposes an input sequence into trend and seasonal components using a moving average filter, then applies separate single-layer linear transformations to each component before summing their outputs to produce the final forecast.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ailing Zeng et al.","year":2023,"type":"Decomposition-based linear forecasting model","subfamily":"Time-series forecasting","input":"Univariate or multivariate time series","output":"Multi-step ahead point forecasts"},"citations":[{"ref":"Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2023). Are transformers effective for time series forecasting? AAAI.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2205.13504"}],"related":["tsmixer","patchtst","arima"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dlqi","name":"DLQI","fullName":"Dermatology Life Quality Index","aliases":["DLQI","Finlay Index","Dermatology Quality of Life"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Dermatology and Skin Disease","year":"1994","originator":"Andrew Y. Finlay and Gul K. Khan","url":"https://scholargate.app/en/health-outcomes/dlqi","markdownUrl":"https://scholargate.app/en/health-outcomes/dlqi.md","definition":"The DLQI is the primary patient-centered outcome measure in dermatology research and clinical practice. Developed by Andrew Finlay and Gul Khan in 1994, this 10-item self-report questionnaire quantifies the impact of skin disease on patients' daily functioning, emotional well-being, social relationships, and work capacity. It is simple, rapid, and applicable to virtually all dermatological conditions, making it the gold standard for assessing quality of life in dermatology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Y. Finlay and Gul K. Khan","subfamily":"Dermatology and Skin Disease","year":"1994","type":"Self-report quality of life questionnaire"},"citations":[{"ref":"Finlay, A. Y., & Khan, G. K. (1994). Dermatology Life Quality Index (DLQI): A simple practical measure for routine clinical use. Clinical and Experimental Dermatology, 19(3), 210-216.","type":"article","doi":"10.1111/j.1365-2230.1994.tb01167.x","isbn":null,"url":null},{"ref":"Hongbo, Y., Thomas, C. L., Harrison, M. A., Salek, M. S., & Finlay, A. Y. (2005). Translating the science of quality of life into practice: What do dermatology patients' need? Dermatologic Clinics, 23(4), 675-684.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Translating+the+science+of+quality+of+life+into+practice%3A+What+do+dermatology+patients%27+need+Hongbo"},{"ref":"Basra, M. K., Fenech, R., Gatt, R. M., Salek, M. S., & Finlay, A. Y. (2008). The Dermatology Life Quality Index 1994-2007: A review of instrument evolution and application. British Journal of Dermatology, 159(5), 997-1035.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Dermatology+Life+Quality+Index+1994-2007%3A+A+review+of+instrument+evolution+and+application+Basra"}],"related":["eortc-qlq-c30","pdq-39","psoriasis-area-severity-index","fibromyalgia-impact-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dmft-index","name":"DMFT Index","fullName":"DMFT (Decayed, Missing, Filled Teeth) Index","aliases":["DMF index","DMF score","DMFT score"],"domain":"dentistry","family":"process-pipeline","subfamily":"Caries assessment","year":"1938","originator":"Henry Klein, Cedric Palmer, and James Knutson","url":"https://scholargate.app/en/dentistry/dmft-index","markdownUrl":"https://scholargate.app/en/dentistry/dmft-index.md","definition":"The DMFT (Decayed, Missing due to caries, Filled) Index is a standardized epidemiological measure of dental caries experience in permanent dentition. Developed by Klein, Palmer, and Knutson in 1938, it quantifies the number of permanent teeth that are decayed, missing due to caries, or filled due to caries. The DMFT Index remains the most widely used caries index globally, enabling comparison of oral health across populations and tracking disease burden over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Klein, Cedric Palmer, and James Knutson","subfamily":"Caries assessment","year":"1938","type":"Epidemiological index"},"citations":[{"ref":"Klein, H., Palmer, C. E., & Knutson, J. W. (1938). Studies on dental caries: I. Dental status and dental needs of elementary school children. Public Health Reports, 53(32), 1259-1274.","type":"article","doi":"10.2307/4582532","isbn":null,"url":null},{"ref":"World Health Organization. (2013). Oral health surveys: Basic methods (5th ed.). WHO.","type":"article","doi":null,"isbn":null,"url":"https://www.who.int/publications-detail/oral-health-surveys"},{"ref":"Pitts, N. B., Zero, D. T., Marsh, P. D., et al. (2017). Dental caries. Nature Reviews Disease Primers, 3, 17030.","type":"article","doi":"10.1038/nrdp.2017.30","isbn":null,"url":null}],"related":["periodontal-probing","oral-hygiene-index","gingival-index","dental-erosion-index","bone-density-dental"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dmrg","name":"DMRG","fullName":"Density Matrix Renormalization Group","aliases":["DMRG","density matrix renormalization","tensor network"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Quantum Chemistry Methods","year":"1992","originator":"Steven White","url":"https://scholargate.app/en/spectroscopy/dmrg","markdownUrl":"https://scholargate.app/en/spectroscopy/dmrg.md","definition":"Density Matrix Renormalization Group (DMRG) is a powerful computational method for solving strongly correlated quantum systems, particularly one-dimensional lattice models and quantum chemistry problems. Introduced by White in 1992, DMRG uses a variational approach and tensor-network representation to efficiently describe quantum ground states and excitations, achieving numerical accuracy competitive with exact diagonalization for systems that other methods cannot treat.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven White","subfamily":"Quantum Chemistry Methods","year":"1992","type":"Computational method"},"citations":[{"ref":"White, S. R. (1992). Density matrix formulation for quantum renormalization groups. Physical Review Letters, 69(19), 2863-2866.","type":"article","doi":"10.1103/PhysRevLett.69.2863","isbn":null,"url":null},{"ref":"Schollwöck, U. (2005). The density-matrix renormalization group in the age of matrix product states. Reviews of Modern Physics, 77(1), 259-315.","type":"article","doi":"10.1103/RevModPhys.77.259","isbn":null,"url":null}],"related":["configuration-interaction","exafs","xanes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dnma","name":"DNMA","fullName":"Double Normalization-Based Multiple Aggregation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Liao, H., Wu, X.","url":"https://scholargate.app/en/decision-making/dnma","markdownUrl":"https://scholargate.app/en/decision-making/dnma.md","definition":"DNMA (Double Normalization-Based Multiple Aggregation) is a ranking multi-criteria decision-making (MCDM) method introduced by Liao, H., Wu, X. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liao, H., Wu, X.","subfamily":"Ranking","year":"2020","type":"Dual-normalisation aggregation (linear + vector)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Liao, H., Wu, X. (2020). DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega","type":"article","doi":"10.1016/j.omega.2019.04.001","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"doaj-directory","name":"Directory of Open Access Journals","fullName":"Directory of Open Access Journals (DOAJ)","aliases":["DOAJ","Directory of Open Access"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"open access journal directories","year":2003,"originator":"DOAJ Community (Swedish library consortium, later expanded to international consortium)","url":"https://scholargate.app/en/bibliometrics/doaj-directory","markdownUrl":"https://scholargate.app/en/bibliometrics/doaj-directory.md","definition":"The Directory of Open Access Journals (DOAJ) is a community-maintained, freely accessible directory of high-quality, peer-reviewed open-access journals and articles established in 2003. DOAJ indexes over 20,000 open-access journals across all disciplines (sciences, social sciences, humanities, arts) from diverse geographic regions. The directory serves researchers, librarians, and administrators as the authoritative curated list of legitimate open-access journals—differentiating quality open-access publications from predatory journals that lack genuine peer review. DOAJ quality seal, awarded to journals meeting stricter governance and transparency criteria, enables identification of the highest-caliber open-access publications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"DOAJ Community (Swedish library consortium, later expanded to international consortium)","subfamily":"open access journal directories","year":2003,"type":"Database"},"citations":[{"ref":"Directory of Open Access Journals. (2024). About DOAJ. Retrieved from https://doaj.org/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Directory%20of%20Open%20Access%20Journals.%20(2024).%20About%20DOAJ.%20Retrieved%20from%20https%3A%2F%2Fdoaj.org%2F"},{"ref":"Laakso, M., Welling, P., Bukvova, H., Nyman, L., Björk, B. C., & Hedlund, T. (2011). The development of open access journal publishing 1993–2009. PLoS ONE, 6(6), e20961.","type":"article","doi":"10.1371/journal.pone.0020961","isbn":null,"url":null},{"ref":"Solomon, D. J., & Björk, B. C. (2012). A study of open access journals using article processing charges. Journal of the American Society for Information Science and Technology, 63(8), 1485-1495.","type":"article","doi":"10.1002/asi.22673","isbn":null,"url":null}],"related":["web-of-science","scopus-database","impact-factor","pubmed-medline","ulrichsweb"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"doc2vec","name":"Doc2Vec","fullName":"Doc2Vec Document Embeddings (Paragraph Vector)","aliases":["paragraph vector","document embeddings","Doc2Vec Belge Gömülmeleri"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2014,"originator":"Quoc V. Le & Tomas Mikolov","url":"https://scholargate.app/en/text-mining/doc2vec","markdownUrl":"https://scholargate.app/en/text-mining/doc2vec.md","definition":"Doc2Vec, also known as Paragraph Vector, is a representation-learning method introduced by Le and Mikolov (2014) that maps whole documents to fixed-length dense vectors. These vectors place similar documents close together in space, supporting document comparison and classification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Quoc V. Le & Tomas Mikolov","year":2014,"type":"Document-embedding representation learning","alsoKnownAs":"Paragraph Vector","output":"Fixed-length dense vector per document","minDocuments":100},"citations":[{"ref":"Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1405.4053"}],"related":["glove-embeddings","tf-idf","text-classification","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"doctrinal-legal-research","name":"Doctrinal Legal Research","fullName":"Doctrinal Legal Research","aliases":["black-letter law research","legal doctrine analysis","analytical jurisprudence","traditional legal scholarship"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"19th century (systematised ~1860s–1880s in common law jurisdictions)","originator":"Common law tradition; systematised by jurists such as A.V. Dicey and John Austin","url":"https://scholargate.app/en/field-methods/doctrinal-legal-research","markdownUrl":"https://scholargate.app/en/field-methods/doctrinal-legal-research.md","definition":"Doctrinal legal research is the foundational methodology of legal scholarship. It systematically identifies, reads, and analyses authoritative legal sources — statutes, case law, constitutional texts, and regulations — to describe, explain, and critique the content and internal logic of legal doctrine. By working within the accepted hierarchy of legal sources, it answers the question 'What is the law?' with analytical rigour and interpretive precision, producing descriptions of settled doctrine and arguments for how ambiguities should be resolved.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Common law tradition; systematised by jurists such as A.V. Dicey and John Austin","year":"19th century (systematised ~1860s–1880s in common law jurisdictions)","type":"Legal-analytical research method","dataType":"Primary legal sources (statutes, case law, regulations) and secondary sources (legal commentary, treatises)","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Hutchinson, T. (2013). Researching and Writing in Law (3rd ed.). Thomson Reuters.","type":"book","doi":null,"isbn":"9780455229829","url":null},{"ref":"MacCormick, N. (2005). Rhetoric and the Rule of Law: A Theory of Legal Reasoning. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Rhetoric+and+the+Rule+of+Law%3A+A+Theory+of+Legal+Reasoning+MacCormick"}],"related":["comparative-legal-analysis","case-law-analysis","legal-content-analysis","hermeneutic-analysis","textual-criticism","historical-archival-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"document-analysis","name":"Document Analysis","fullName":"Systematic Analysis of Primary and Secondary Documents","aliases":["documentary analysis","textual analysis","content analysis of documents","archival research"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"data-collection","year":"1920","originator":"Max Weber and Karl Mannheim","url":"https://scholargate.app/en/qualitative-research/document-analysis","markdownUrl":"https://scholargate.app/en/qualitative-research/document-analysis.md","definition":"Document analysis is a systematic qualitative research method for examining written, visual, or audiovisual sources—such as policy documents, historical records, organizational records, media reports, emails, social media posts, photographs, or videos—to extract meaning, identify patterns, and understand social phenomena. Developed by Weber and Mannheim in early 20th-century sociology, the method bridges historical research, content analysis, and textual interpretation. Document analysis is used across disciplines to understand organizational change, policy evolution, media representation, historical events, and cultural meaning. Documents provide evidence of what organizations, institutions, or societies value, decide, and communicate, often revealing contradictions between policy and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max Weber and Karl Mannheim","subfamily":"data-collection","year":"1920","type":"Method"},"citations":[{"ref":"Scott, J. (1990). A Matter of Record: Documentary Sources in Social Research. Polity Press.","type":"book","doi":null,"isbn":"978-0745608419","url":null},{"ref":"Prior, L. (2003). Using Documents in Social Research. SAGE Publications.","type":"article","doi":null,"isbn":"978-0761959052","url":null},{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Flick, U. (2014). An Introduction to Qualitative Research (5th ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1446282921","url":null}],"related":["participant-observation","qualitative-synthesis-methods","nvivo-atlas-qualitative","qualitative-rigor-criteria"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"document-based-curriculum-analysis","name":"Document-based Curriculum Analysis","fullName":"Document-based Curriculum Analysis","aliases":["curriculum document analysis","curricular document review","document analysis of curriculum","DCA"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1950s–1980s (consolidated as a distinct approach)","originator":"Rooted in curriculum theory (Tyler, 1949) and document analysis methodology (Bowen, 2009)","url":"https://scholargate.app/en/field-methods/document-based-curriculum-analysis","markdownUrl":"https://scholargate.app/en/field-methods/document-based-curriculum-analysis.md","definition":"Document-based curriculum analysis is a qualitative research method that systematically examines written curriculum artifacts — textbooks, syllabi, national standards, policy documents, scope-and-sequence guides, and lesson frameworks — to reveal intended learning goals, ideological assumptions, gaps, and alignment between policy and practice. It treats curriculum documents as primary data rather than supplementary material, applying structured content and interpretive analysis techniques to answer questions about what knowledge is valued, how it is sequenced, and whose perspectives are represented.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in curriculum theory (Tyler, 1949) and document analysis methodology (Bowen, 2009)","year":"1950s–1980s (consolidated as a distinct approach)","type":"Qualitative/interpretive document-based research","dataType":"Curriculum documents: textbooks, syllabi, standards, policy documents, lesson plans, scope-and-sequence guides","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"McCutcheon, G. (1982). What in the world is curriculum theory? Theory Into Practice, 21(1), 18–22.","type":"article","doi":"10.1080/00405848209542975","isbn":null,"url":null}],"related":["curriculum-analysis","document-analysis","textual-criticism","content-analysis","comparative-curriculum-analysis","policy-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"document-based-program-evaluation","name":"Document-based Program Evaluation","fullName":"Document-based Program Evaluation","aliases":["documentary program evaluation","records-based evaluation","document review evaluation","archival program evaluation"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1960s–1970s (program evaluation field); document review as formal strategy codified in 1980s–1990s","originator":"Daniel Stufflebeam; Peter Rossi and Howard Freeman (systematic program evaluation tradition)","url":"https://scholargate.app/en/field-methods/document-based-program-evaluation","markdownUrl":"https://scholargate.app/en/field-methods/document-based-program-evaluation.md","definition":"Document-based program evaluation is a systematic approach to assessing a program's design, implementation, and outcomes using existing documentary evidence — such as policy statements, implementation reports, budgets, meeting minutes, and program artifacts — rather than primary data collection through interviews or observation. It is particularly suited to retrospective evaluations, accountability reviews, and contexts where direct fieldwork is impractical or infeasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel Stufflebeam; Peter Rossi and Howard Freeman (systematic program evaluation tradition)","year":"1960s–1970s (program evaluation field); document review as formal strategy codified in 1980s–1990s","type":"Evaluation research design","dataType":"Program records, policy documents, reports, meeting minutes, budgets, artifacts","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Stufflebeam, D. L., & Shinkfield, A. J. (2007). Evaluation Theory, Models, and Applications. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787908331","url":null},{"ref":"Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage.","type":"book","doi":null,"isbn":"978-0761908944","url":null}],"related":["program-evaluation","document-analysis","content-analysis","curriculum-analysis","historical-archival-research","case-law-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"document-clustering","name":"Document Clustering","fullName":"Document Clustering","aliases":["text clustering","unsupervised text grouping","Belge Kümeleme (Document Clustering)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/document-clustering","markdownUrl":"https://scholargate.app/en/text-mining/document-clustering.md","definition":"Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Unsupervised text-mining task","learning":"Unsupervised (no labels)","minSample":"~30 documents (reliable above ~100)","input":"Vectorised document collection","output":"Cluster assignment per document","difficulty":"Intermediate"},"citations":[{"ref":"Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer.","type":"book","doi":null,"isbn":"9781461432227","url":null},{"ref":"Steinbach, M., Karypis, G. & Kumar, V. (2000). A Comparison of Document Clustering Techniques. KDD Workshop on Text Mining.","type":"article","doi":null,"isbn":null,"url":"https://www-users.cse.umn.edu/~kumar001/papers/doc_cluster.pdf"}],"related":["topic-modeling","tf-idf","keyword-extraction","thematic-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"document-collection","name":"Document Collection","fullName":"Document Collection and Analysis","aliases":["document analysis","documentary method","document review","secondary document analysis"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"19th–20th century historical methods; contemporary social-science codification c. 2000s","originator":"Rooted in historical and social science traditions; systematized by Lindsay Prior and Glenn Bowen","url":"https://scholargate.app/en/survey-methodology/document-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/document-collection.md","definition":"Document collection is a systematic data-collection technique in which the researcher gathers and reviews existing written, visual, or digital records — such as reports, meeting minutes, policies, letters, photographs, or institutional records — as primary or supplementary evidence. It is widely used in qualitative, historical, and mixed-methods research and can stand alone or complement interviews and observation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in historical and social science traditions; systematized by Lindsay Prior and Glenn Bowen","year":"19th–20th century historical methods; contemporary social-science codification c. 2000s","type":"Qualitative / mixed data-collection technique","dataType":"Existing written, visual, or digital documents (reports, records, correspondence, policies)","subfamily":"Data collection"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Prior, L. (2003). Using Documents in Social Research. Sage.","type":"book","doi":null,"isbn":"978-0761966494","url":null}],"related":["content-analysis","archival-research","secondary-data-analysis","discourse-analysis","systematic-review","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dodgson","name":"DODGSON","fullName":"Dodgson Method — Condorcet completion by minimum pairwise swaps","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"1900","originator":"Dodgson, C. L.","url":"https://scholargate.app/en/decision-making/dodgson","markdownUrl":"https://scholargate.app/en/decision-making/dodgson.md","definition":"DODGSON (Dodgson Method — Condorcet completion by minimum pairwise swaps) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Dodgson, C. L. in 1900. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dodgson, C. L.","subfamily":"AggregationOperator","year":"1900","type":"Pairwise-swap distance — alternative needing fewest swaps to become Condorcet winner","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Dodgson, C. L. (1900). A method of taking votes on more than two issues (1876). Pamphlet, Clarendon Press, Oxford (original 1876)","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A%20method%20of%20taking%20votes%20on%20more%20than%20two%20issues%20%281876%29"}],"related":["ahp","topsis","promethee","electre","vikor"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"doi-system","name":"Digital Object Identifier System","fullName":"DOI: Persistent Identifiers for Scholarly Research Objects","aliases":["DOI","Digital Object Identifier","persistent identifier"],"domain":"research-skills","family":"process-pipeline","subfamily":"persistent-identifier","year":"1998 (concept); 2001 (widespread adoption)","originator":"Norman Paskin, CrossRef and International DOI Foundation (1998)","url":"https://scholargate.app/en/research-skills/doi-system","markdownUrl":"https://scholargate.app/en/research-skills/doi-system.md","definition":"A Digital Object Identifier (DOI) is a unique, persistent alphanumeric code that identifies a scholarly work (journal article, book chapter, dataset, preprint) and persists even if the URL changes. Introduced in 1998 by Norman Paskin and the International DOI Foundation, DOIs are now standard in academic publishing. They consist of a prefix (assigned to a publisher or organization) and a suffix (assigned to an individual work), formatted as 10.XXXX/XXXXX (e.g., 10.1371/journal.pmed.1000097). DOIs are registered with international agencies (CrossRef, DataCite, mEDRA) and resolve through the centralized resolver https://doi.org/, ensuring that a DOI will direct users to the correct article regardless of whether the publisher's website changes location.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Paskin, CrossRef and International DOI Foundation (1998)","subfamily":"persistent-identifier","year":"1998 (concept); 2001 (widespread adoption)","type":"Standard"},"citations":[{"ref":"Paskin, N. (2010). Digital Object Identifier (DOI) system. Encyclopedia of Library and Information Sciences, 3rd ed., 1586–1592.","type":"article","doi":null,"isbn":"978-0-8493-9712-7","url":null},{"ref":"Crossref: The scholarly link network. https://www.crossref.org","type":"article","doi":null,"isbn":null,"url":"https://www.crossref.org"},{"ref":"International Organization for Standardization (ISO). (2012). ISO 26324:2012 Information and documentation - Digital object identifier (DOI). ISO.","type":"article","doi":null,"isbn":null,"url":"https://www.iso.org/standard/43615.html"}],"related":["citation-analysis","citation-management-tools","altmetrics","orcid-researcher-id"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dolado-lutkepohl-causality","name":"Dolado-Lütkepohl Causality","fullName":"Dolado-Lütkepohl Granger Causality Test","aliases":["DL Causality Test","Modified Wald Causality Test","Augmented VAR Causality Test","Dolado-Lütkepohl Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Causality","year":1996,"originator":"Juan Dolado & Helmut Lütkepohl","url":"https://scholargate.app/en/econometrics/dolado-lutkepohl-causality","markdownUrl":"https://scholargate.app/en/econometrics/dolado-lutkepohl-causality.md","definition":"The Dolado-Lütkepohl (DL) test, introduced by Dolado and Lütkepohl (1996), is a modified Wald procedure for testing Granger causality in vector autoregressive (VAR) systems whose variables may be integrated or cointegrated. By fitting a VAR of slightly higher order than necessary and restricting the Wald statistic to the first p lag blocks, the test recovers the standard chi-squared limiting distribution without requiring pre-testing for cointegration or transformation to error-correction form.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Juan Dolado & Helmut Lütkepohl","year":1996,"type":"Modified Wald test for Granger causality in possibly integrated or cointegrated VAR systems","subfamily":"Causality","estimator":"OLS on augmented VAR","null_hypothesis":"No Granger causality from X to Y"},"citations":[{"ref":"Dolado, J. J., & Lütkepohl, H. (1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews, 15(4), 369–386.","type":"article","doi":"10.1080/07474939608800362","isbn":null,"url":null}],"related":["toda-yamamoto-causality","granger-causality","vecm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dols-estimator","name":"Dynamic OLS","fullName":"Dynamic Ordinary Least Squares Estimator","aliases":["DOLS","Stock-Watson dynamic OLS","dynamic least squares cointegration estimator","Dinamik OLS (DOLS)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1993,"originator":"Stock & Watson (1993); panel extension Kao & Chiang (2001)","url":"https://scholargate.app/en/econometrics/dols-estimator","markdownUrl":"https://scholargate.app/en/econometrics/dols-estimator.md","definition":"Dynamic OLS is a cointegrating-regression estimator introduced by Stock and Watson (1993) that recovers the long-run relationship between I(1) variables. It augments the static regression with leads and lags of the differenced regressors, correcting endogeneity bias parametrically so that the long-run coefficient can be estimated by ordinary least squares.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stock & Watson (1993); panel extension Kao & Chiang (2001)","year":1993,"type":"Cointegrating regression estimator","estimator":"Least squares augmented with leads and lags of differenced regressors","outcome":"continuous","dataStructure":"time series / panel (I(1), cointegrated)","minSample":50},"citations":[{"ref":"Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820.","type":"article","doi":"10.2307/2951763","isbn":null,"url":null},{"ref":"Kao, C. & Chiang, M.-H. (2001). On the Estimation and Inference of a Cointegrated Regression in Panel Data. Advances in Econometrics, 15, 179–222.","type":"article","doi":"10.1016/S0731-9053(00)15007-8","isbn":null,"url":null}],"related":["ols-regression","panel-cointegration","ccemg-estimator","amg-estimator","panel-fixed-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptation-nlp","name":"Domain Adaptation","fullName":"Domain Adaptation for NLP","aliases":["Alan Uyarlaması (Domain Adaptation) — NLP","domain adaptation NLP","domain fine-tuning"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/domain-adaptation-nlp","markdownUrl":"https://scholargate.app/en/text-mining/domain-adaptation-nlp.md","definition":"Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP transfer-learning / fine-tuning pipeline","approach":"Fine-tuning a general pretrained language model on target-domain data","typicalDomains":"Medicine, law, finance","minSample":50,"difficulty":"Advanced (3 of 5)"},"citations":[{"ref":"Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics.","type":"article","doi":"10.1093/bioinformatics/btz682","isbn":null,"url":null},{"ref":"Blitzer, J. et al. (2007). Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. ACL.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P07-1056/"}],"related":["bert-embeddings","text-classification","sentiment-analysis","transfer-learning"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-bert-based-classification","name":"Domain-adaptive BERT-based Classification","fullName":"Domain-Adaptive Pre-training with BERT for Text Classification","aliases":["DAPT BERT classification","domain-adaptive pre-training","domain-specific BERT fine-tuning","BERT DAPT"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Gururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERT","url":"https://scholargate.app/en/deep-learning/domain-adaptive-bert-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-bert-based-classification.md","definition":"Domain-adaptive BERT-based classification extends the standard fine-tuning pipeline by first continuing BERT's masked-language-model pre-training on a large corpus of in-domain unlabeled text, then fine-tuning the adapted model on labeled examples for the target classification task. This two-stage approach closes the vocabulary and distributional gap between BERT's general pre-training corpus and specialized domains such as biomedicine, law, finance, or social-media text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERT","year":"2019–2020","type":"Domain-adaptive pre-training followed by supervised fine-tuning","dataType":"Unlabeled domain text for pre-training; labeled text for classification fine-tuning","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.740","isbn":null,"url":null},{"ref":"Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.","type":"article","doi":"10.1093/bioinformatics/btz682","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","fine-tuned-bert-based-classification","transfer-learning-with-bert-based-classification","domain-adaptive-transformer","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-convolutional-neural-network","name":"Domain-adaptive Convolutional Neural Network","fullName":"Domain-adaptive Convolutional Neural Network (DA-CNN)","aliases":["DA-CNN","domain adaptation CNN","domain-adaptive deep convolutional network","CNN with domain adaptation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2017","originator":"Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-convolutional-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-convolutional-neural-network.md","definition":"A domain-adaptive CNN trains a convolutional network on a labeled source domain and adapts its learned feature representations to an unlabeled or lightly labeled target domain, bridging the distribution gap so that visual classifiers transfer reliably across datasets, sensors, or imaging conditions without full re-annotation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)","year":"2015–2017","type":"Domain-adaptive deep learning model","dataType":"Images or feature maps from source and target domains","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v17/15-239.html"},{"ref":"Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7167–7176.","type":"inproceedings","doi":"10.1109/CVPR.2017.316","isbn":null,"url":null}],"related":["convolutional-neural-network","transfer-learning-with-convolutional-neural-network","fine-tuned-convolutional-neural-network","domain-adaptive-vision-transformer","domain-adaptive-recurrent-neural-network","image-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-diffusion-model","name":"Domain-adaptive diffusion model","fullName":"Domain-Adaptive Diffusion Model","aliases":["DA-diffusion model","domain-adapted diffusion model","domain-adaptive DDPM","cross-domain diffusion model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2022–2023","originator":"Ho et al. (DDPM); domain-adaptation variants popularized by Gal et al. and Ruiz et al. (2022–2023)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-diffusion-model.md","definition":"A domain-adaptive diffusion model is a denoising diffusion probabilistic model (DDPM) that is pre-trained on large general datasets and then adapted — through fine-tuning, textual inversion, or LoRA — to generate high-quality outputs in a specific target domain. It combines the powerful generative capacity of diffusion models with domain adaptation techniques, enabling high-fidelity synthesis in specialized areas such as medical imaging, satellite imagery, or domain-specific art styles with limited target-domain data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ho et al. (DDPM); domain-adaptation variants popularized by Gal et al. and Ruiz et al. (2022–2023)","year":"2022–2023","type":"Generative model with domain adaptation","dataType":"Images, text-image pairs, domain-specific datasets","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html"},{"ref":"Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., & Cohen-Or, D. (2023). An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion. International Conference on Learning Representations (ICLR 2023).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=NAQvF08TcyG"}],"related":["fine-tuned-diffusion-model","transfer-learning-diffusion-model","domain-adaptive-gan","multimodal-diffusion-model","domain-adaptive-vision-transformer","self-supervised-diffusion-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-doc2vec","name":"Domain-adaptive Doc2Vec","fullName":"Domain-Adaptive Paragraph Vector (Doc2Vec) for Cross-Domain Document Representation","aliases":["domain-adapted Doc2Vec","cross-domain paragraph vector","domain-adaptive PV-DM","domain-adaptive PV-DBOW"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014 (Doc2Vec); domain-adaptive application mid-2010s onward","originator":"Le & Mikolov (Doc2Vec); domain adaptation literature (Blitzer, Daumé III, and others)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-doc2vec","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-doc2vec.md","definition":"Domain-adaptive Doc2Vec adapts the Paragraph Vector (Doc2Vec) framework so that document embeddings learned on a source domain transfer effectively to a target domain. By aligning the representation space across domains during or after training, the model produces embeddings that are informative on both, enabling cross-domain classification, sentiment analysis, and retrieval with limited target-domain labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Le & Mikolov (Doc2Vec); domain adaptation literature (Blitzer, Daumé III, and others)","year":"2014 (Doc2Vec); domain-adaptive application mid-2010s onward","type":"Unsupervised / domain-adaptive document embedding","dataType":"Text corpora from two or more domains","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Le, Q. V., & Mikolov, T. (2014). Distributed representations of sentences and documents. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), PMLR 32(2), 1188–1196.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v32/le14.html"},{"ref":"Blitzer, J., McDonald, R., & Pereira, F. (2006). Domain adaptation with structural correspondence learning. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), 120–128.","type":"inproceedings","doi":"10.3115/1610075.1610094","isbn":null,"url":null}],"related":["doc2vec","domain-adaptive-word2vec","domain-adaptive-sentence-embeddings","transfer-learning-with-doc2vec","fine-tuned-doc2vec","domain-adaptive-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-gan","name":"Domain-adaptive GAN","fullName":"Domain-Adaptive Generative Adversarial Network","aliases":["DA-GAN","domain adaptation GAN","adversarial domain adaptation","domain-adaptive generative adversarial network"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2017","originator":"Ganin et al. (DANN); Zhu et al. (CycleGAN)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-gan","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-gan.md","definition":"A Domain-Adaptive GAN combines generative adversarial learning with domain adaptation to bridge the distribution gap between a labeled source domain and an unlabeled or sparsely labeled target domain. By training a generator and discriminator adversarially, the model learns domain-invariant representations or translated samples, enabling a classifier or detector trained on source data to generalize effectively to the target domain without requiring abundant target labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ganin et al. (DANN); Zhu et al. (CycleGAN)","year":"2016–2017","type":"Generative adversarial model with domain adaptation","dataType":"Images, structured features, or other continuous signals from source and target domains","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v17/15-239.html"},{"ref":"Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2223–2232.","type":"inproceedings","doi":"10.1109/ICCV.2017.244","isbn":null,"url":null}],"related":["generative-adversarial-network","transfer-learning-gan","domain-adaptive-convolutional-neural-network","domain-adaptive-vision-transformer","semi-supervised-gan","fine-tuned-generative-adversarial-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-gru","name":"Domain-adaptive GRU","fullName":"Domain-Adaptive Gated Recurrent Unit Network","aliases":["DA-GRU","Domain-Adapted GRU","GRU with Domain Adaptation","Domain-Shift-Robust GRU"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–present","originator":"Cho et al. (GRU, 2014); Ganin et al. (domain-adversarial framework, 2016)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-gru","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-gru.md","definition":"Domain-Adaptive GRU combines the Gated Recurrent Unit architecture with domain adaptation techniques to train a sequence model on a labeled source domain and transfer it to a different but related target domain, reducing performance degradation caused by distribution shift. It is widely applied in NLP tasks such as cross-domain sentiment analysis, named entity recognition, and text classification where labeled target-domain data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cho et al. (GRU, 2014); Ganin et al. (domain-adversarial framework, 2016)","year":"2016–present","type":"Sequence model with domain adaptation","dataType":"Sequential / text / time-series data from heterogeneous domains","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014 (pp. 1724–1734). Association for Computational Linguistics.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1406.1078"},{"ref":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(1), 2096–2030.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v17/15-239.html"}],"related":["gated-recurrent-unit","domain-adaptive-lstm","domain-adaptive-transformer","domain-adaptive-recurrent-neural-network","transfer-learning-with-gru","fine-tuned-gru"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-image-classification","name":"Domain-adaptive image classification","fullName":"Domain-Adaptive Image Classification (Domain Adaptation for Visual Recognition)","aliases":["domain adaptation for image classification","DAIC","cross-domain image classification","domain-shift-robust image recognition"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2016","originator":"Ganin, Y. & Lempitsky, V. (domain-adversarial formulation)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-image-classification.md","definition":"Domain-adaptive image classification trains a visual classifier on a labeled source domain and adapts it to a target domain where labeled data are scarce or absent. By aligning feature distributions across domains, the model retains discriminative accuracy on the target distribution without requiring full target re-annotation, making it practical in real-world deployment scenarios where domain shift is unavoidable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ganin, Y. & Lempitsky, V. (domain-adversarial formulation)","year":"2015–2016","type":"Domain adaptation / transfer learning","dataType":"Labeled source-domain images; unlabeled or few-shot target-domain images","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v17/15-239.html"},{"ref":"Wilson, G., & Cook, D. J. (2020). A survey of unsupervised deep domain adaptation. ACM Transactions on Intelligent Systems and Technology, 11(5), 1–46.","type":"article","doi":"10.1145/3400066","isbn":null,"url":null}],"related":["transfer-learning-with-image-classification","fine-tuned-image-classification","domain-adaptive-object-detection","domain-adaptive-semantic-segmentation","image-classification","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-instance-segmentation","name":"Domain-adaptive Instance Segmentation","fullName":"Domain-Adaptive Instance Segmentation (Cross-Domain Instance-Level Pixel Segmentation)","aliases":["DA-InstanceSeg","cross-domain instance segmentation","domain adaptation for instance segmentation","unsupervised domain adaptive Mask R-CNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2021","originator":"Chen, Y. et al. (domain-adaptive detection); extended to instance segmentation by multiple groups ~2019–2021","url":"https://scholargate.app/en/deep-learning/domain-adaptive-instance-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-instance-segmentation.md","definition":"Domain-adaptive instance segmentation extends Mask R-CNN-style architectures to operate across distribution shifts — training on a labeled source domain (e.g., synthetic renderings or daytime images) and adapting to an unlabeled or weakly labeled target domain (e.g., real scenes or nighttime footage). Adversarial feature alignment and self-training close the domain gap at both image-level and instance-level granularity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, Y. et al. (domain-adaptive detection); extended to instance segmentation by multiple groups ~2019–2021","year":"2018–2021","type":"Domain adaptation + instance segmentation","dataType":"Labeled source-domain images + unlabeled or weakly-labeled target-domain images","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2018). Domain Adaptive Faster RCNN for Object Detection in the Wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3339–3348.","type":"inproceedings","doi":"10.1109/CVPR.2018.00352","isbn":null,"url":null},{"ref":"VS, V., Gupta, V., Oza, P., Sindagi, V. A., & Patel, V. M. (2021). MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4516–4526.","type":"inproceedings","doi":"10.1109/CVPR46437.2021.00449","isbn":null,"url":null}],"related":["instance-segmentation","semantic-segmentation","domain-adaptive-semantic-segmentation","domain-adaptive-object-detection","transfer-learning-with-instance-segmentation","fine-tuned-instance-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-multilayer-perceptron","name":"Domain-adaptive Multilayer Perceptron","fullName":"Domain-adaptive Multilayer Perceptron (DA-MLP)","aliases":["DA-MLP","domain-adaptive MLP","domain-adapted feedforward network","domain adaptation with MLP"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2006–2016","originator":"Ben-David et al.; Ganin et al.","url":"https://scholargate.app/en/deep-learning/domain-adaptive-multilayer-perceptron","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-multilayer-perceptron.md","definition":"A domain-adaptive multilayer perceptron (DA-MLP) is a feedforward neural network trained to learn representations that are useful across a labeled source domain and an unlabeled or differently distributed target domain. By minimizing both a task loss and a domain-discrepancy objective, the MLP generalizes to the target domain with little or no target-domain labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ben-David et al.; Ganin et al.","year":"2006–2016","type":"Domain adaptation of feedforward neural network","dataType":"Tabular, text, or image features from source and target domains","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1–2), 151–175.","type":"article","doi":"10.1007/s10994-009-5152-4","isbn":null,"url":null},{"ref":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v17/15-239.html"}],"related":["transfer-learning-with-multilayer-perceptron","fine-tuned-multilayer-perceptron","domain-adaptive-convolutional-neural-network","domain-adaptive-transformer","multilayer-perceptron","domain-adaptive-recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-named-entity-recognition","name":"Domain-adaptive Named Entity Recognition","fullName":"Domain-adaptive Named Entity Recognition (DA-NER)","aliases":["DA-NER","cross-domain NER","domain-adaptive NER","domain-transfer named entity recognition"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2006–2020","originator":"Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-named-entity-recognition","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-named-entity-recognition.md","definition":"Domain-adaptive Named Entity Recognition (DA-NER) applies named entity recognition to a target domain by transferring or adapting a model trained on a source domain, using techniques such as domain-specific pre-training, adversarial alignment, or feature augmentation. It addresses the performance collapse that standard NER models suffer when deployed outside their training domain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020)","year":"2006–2020","type":"Sequence labeling with domain adaptation","dataType":"Text sequences (source-domain labeled + target-domain labeled or unlabeled)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.","type":"inproceedings","doi":"10.1093/bioinformatics/btz682","isbn":null,"url":null},{"ref":"Blitzer, J., McDonald, R., & Pereira, F. (2006). Domain adaptation with structural correspondence learning. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP), 120–128.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/W06-1615"}],"related":["named-entity-recognition","bert-based-classification","transfer-learning-with-bert-based-classification","domain-adaptive-bert-based-classification","fine-tuned-named-entity-recognition","multilingual-named-entity-recognition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-nmf-topic-model","name":"Domain-adaptive NMF Topic Model","fullName":"Domain-Adaptive Non-negative Matrix Factorization Topic Model","aliases":["DA-NMF","cross-domain NMF","domain-adaptive topic modeling with NMF","transfer NMF topic model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"1999 (NMF); domain adaptation variants ~2010s","originator":"Lee, D. D. & Seung, H. S. (NMF base); domain adaptation extensions by the NLP community","url":"https://scholargate.app/en/deep-learning/domain-adaptive-nmf-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-nmf-topic-model.md","definition":"Domain-adaptive NMF Topic Modeling applies Non-negative Matrix Factorization to discover latent topics across text from multiple domains, using regularization or shared basis constraints to transfer topic knowledge from a resource-rich source domain to a target domain with limited labeled data. It combines interpretable parts-based decomposition with domain-adaptation objectives to produce topics that are both domain-specific and cross-domain consistent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee, D. D. & Seung, H. S. (NMF base); domain adaptation extensions by the NLP community","year":"1999 (NMF); domain adaptation variants ~2010s","type":"Unsupervised topic model with domain adaptation","dataType":"Text corpora from multiple domains (document-term matrices)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.","type":"article","doi":"10.1038/44565","isbn":null,"url":null},{"ref":"Non-negative matrix factorization. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Non-negative_matrix_factorization"}],"related":["nmf-topic-model","lda-topic-model","domain-adaptive-lda-topic-model","transfer-learning-with-nmf-topic-model","topic-modeling","multilingual-nmf-topic-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-question-answering","name":"Domain-adaptive Question Answering","fullName":"Domain-Adaptive Question Answering (DA-QA)","aliases":["DA-QA","domain-adapted QA","domain-specific question answering","cross-domain question answering"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Multiple (e.g., Garg et al.; Yue et al.)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-question-answering","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-question-answering.md","definition":"Domain-adaptive Question Answering (DA-QA) adapts a pre-trained language model — typically BERT or RoBERTa — first trained on general QA benchmarks such as SQuAD to answer questions accurately in a new target domain (e.g., biomedical, legal, financial) where labelled data is scarce. Combining domain-adaptive pre-training with task fine-tuning yields substantially stronger performance than direct fine-tuning alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (e.g., Garg et al.; Yue et al.)","year":"2019–2020","type":"Domain adaptation for extractive/generative QA","dataType":"Text (passages, questions, answer spans)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Garg, S., Vu, T., & Moschitti, A. (2020). TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), 7780–7788.","type":"inproceedings","doi":"10.1609/aaai.v34i05.6282","isbn":null,"url":null},{"ref":"Yue, X., Zeng, Z., Shi, Y., Zhang, C., & Song, Y. (2022). Domain-adaptive Pre-training Methods for Natural Language Understanding. arXiv preprint.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Domain-adaptive+pre-training+methods+for+natural+language+understanding"}],"related":["bert-based-classification","roberta-based-classification","transfer-learning-with-bert-based-classification","domain-adaptive-bert-based-classification","fine-tuned-question-answering","multilingual-question-answering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-recurrent-neural-network","name":"Domain-adaptive Recurrent Neural Network","fullName":"Domain-adaptive Recurrent Neural Network (DA-RNN)","aliases":["DA-RNN","domain-adaptive RNN","domain-adapted recurrent network","cross-domain RNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010s","originator":"Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-recurrent-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-recurrent-neural-network.md","definition":"A Domain-adaptive Recurrent Neural Network (DA-RNN) is a recurrent neural network trained on a source domain and adapted to a target domain using domain adaptation techniques such as adversarial training, feature alignment, or fine-tuning. It enables sequential models to generalise across domains when labeled target-domain data is scarce or unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)","year":"2010s","type":"Domain-adaptive sequential model","dataType":"Sequential / time-series / text data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v17/15-239.html"},{"ref":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["recurrent-neural-network","long-short-term-memory","domain-adaptive-transformer","domain-adaptive-bert-based-classification","transfer-learning-with-recurrent-neural-network","fine-tuned-recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-reinforcement-learning","name":"Domain-adaptive reinforcement learning","fullName":"Domain-Adaptive Reinforcement Learning","aliases":["Domain-Adaptive RL","DARL","Cross-domain RL","Transfer RL with domain adaptation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2009–2020","originator":"Multiple contributors (Taylor & Stone 2009 survey; Kim et al. 2020 among key formalizations)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-reinforcement-learning.md","definition":"Domain-Adaptive Reinforcement Learning (DARL) extends standard RL by enabling a policy trained in one environment or domain to transfer and generalise effectively to a different but related target domain. It addresses the domain-shift problem — where dynamics, observations, or reward structures differ between training and deployment — through alignment, adaptation, or domain-randomisation techniques, reducing the need to collect costly experience in the target domain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Taylor & Stone 2009 survey; Kim et al. 2020 among key formalizations)","year":"2009–2020","type":"Transfer-based RL paradigm","dataType":"State-action trajectories from source and target domains","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kim, K., Kim, H., Lim, H., & Choi, J. (2020). Domain Adaptive Reinforcement Learning with Model-Based Approach. arXiv preprint arXiv:2102.03170.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2102.03170"},{"ref":"Domain adaptation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Domain_adaptation"}],"related":["deep-reinforcement-learning","transfer-learning","sim-to-real-transfer","meta-reinforcement-learning","domain-randomization","proximal-policy-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-roberta-based-classification","name":"Domain-adaptive RoBERTa-based Classification","fullName":"Domain-Adaptive RoBERTa-based Text Classification","aliases":["DA-RoBERTa","domain-adapted RoBERTa classifier","RoBERTa domain adaptation","domain-specific RoBERTa fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Liu et al. (RoBERTa); Gururangan et al. (domain-adaptive pretraining)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-roberta-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-roberta-based-classification.md","definition":"Domain-adaptive RoBERTa-based classification extends the RoBERTa transformer by first continuing its masked-language-model pretraining on a domain-specific corpus before fine-tuning for a classification task. This two-stage adaptation bridges the gap between general web-crawled training data and specialized fields such as biomedical, legal, or scientific text, consistently outperforming standard RoBERTa fine-tuning when target-domain text is available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu et al. (RoBERTa); Gururangan et al. (domain-adaptive pretraining)","year":"2019–2020","type":"Pre-trained transformer with domain-adaptive pretraining and task fine-tuning","dataType":"Domain-specific text corpora and labeled classification examples","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.11692"},{"ref":"Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of ACL 2020, pp. 8342–8360.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.740","isbn":null,"url":null}],"related":["roberta-based-classification","bert-based-classification","domain-adaptive-bert-based-classification","fine-tuned-roberta-based-classification","transfer-learning-with-roberta-based-classification","multilingual-roberta-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-sentence-embeddings","name":"Domain-adaptive sentence embeddings","fullName":"Domain-Adaptive Sentence Embeddings (Domain-Adapted Sentence Transformers)","aliases":["domain-adapted sentence transformers","domain-specific sentence embeddings","target-domain sentence representations","DAPT sentence embeddings"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Reimers, N. & Gurevych, I. (Sentence-BERT); Gururangan et al. (domain-adaptive pretraining)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-sentence-embeddings.md","definition":"Domain-adaptive sentence embeddings extend general-purpose sentence encoders — such as Sentence-BERT — by continuing their training on domain-specific text. The result is a fixed-length vector representation that captures both universal language understanding and the vocabulary, style, and semantic nuances of the target domain, improving downstream NLP tasks such as semantic search, clustering, and classification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reimers, N. & Gurevych, I. (Sentence-BERT); Gururangan et al. (domain-adaptive pretraining)","year":"2019–2020","type":"Domain-adaptive representation learning","dataType":"Text (domain-specific corpora, sentence pairs or unlabeled documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of EMNLP-IJCNLP 2019, pp. 3982–3992.","type":"inproceedings","doi":"10.18653/v1/D19-1410","isbn":null,"url":null},{"ref":"Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D. & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. Proceedings of ACL 2020, pp. 8342–8360.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.740","isbn":null,"url":null}],"related":["sentence-embeddings","bert-based-classification","roberta-based-classification","transfer-learning-with-sentence-embeddings","multilingual-sentence-embeddings","fine-tuned-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-sentiment-analysis","name":"Domain-adaptive Sentiment Analysis","fullName":"Domain-adaptive Sentiment Analysis (Cross-Domain Opinion Mining with Domain Adaptation)","aliases":["cross-domain sentiment analysis","domain-adaptive opinion mining","domain transfer sentiment classification","DASA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2007","originator":"Blitzer, J.; Dredze, M.; Pereira, F.","url":"https://scholargate.app/en/deep-learning/domain-adaptive-sentiment-analysis","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-sentiment-analysis.md","definition":"Domain-adaptive sentiment analysis trains a sentiment model on one or more labeled source domains (e.g., product reviews) and adapts it to a target domain (e.g., social media posts or news) where labels are scarce or absent. By bridging the vocabulary and distributional gap between domains, it achieves strong sentiment classification without requiring large labeled corpora in every target domain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blitzer, J.; Dredze, M.; Pereira, F.","year":"2007","type":"Domain adaptation for text classification","dataType":"Text (reviews, social media, news across multiple domains)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), 440–447.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P07-1056"},{"ref":"Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., & Chen, Z. (2010). Cross-domain sentiment classification via spectral feature alignment. Proceedings of the 19th International Conference on World Wide Web (WWW), 751–760.","type":"article","doi":"10.1145/1772690.1772767","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","fine-tuned-sentiment-analysis","multilingual-sentiment-analysis","transfer-learning-with-bert-based-classification","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-text-summarization","name":"Domain-adaptive Text Summarization","fullName":"Domain-adaptive Text Summarization (Domain Adaptation for Abstractive and Extractive Summarization)","aliases":["domain-adapted summarization","domain-specific summarization","cross-domain summarization","DA-summarization"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2021","originator":"Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-text-summarization","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-text-summarization.md","definition":"Domain-adaptive text summarization fine-tunes or adapts a pre-trained sequence-to-sequence language model on a target domain corpus so that summaries conform to domain-specific vocabulary, style, and factual constraints. It bridges the gap between general-purpose summarization models trained on news or web data and specialized domains such as biomedical literature, legal documents, scientific papers, or financial reports.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)","year":"2019–2021","type":"Domain adaptation of sequence-to-sequence neural summarization","dataType":"Text corpora (in-domain documents and optional out-of-domain pre-training data)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Fabbri, A. R., KryŜiński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021). SummEval: Re-evaluating Summarization Evaluation. Transactions of the Association for Computational Linguistics, 9, 391–409.","type":"inproceedings","doi":"10.1162/tacl_a_00373","isbn":null,"url":null},{"ref":"Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 1906–1919.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.173","isbn":null,"url":null}],"related":["fine-tuned-text-summarization","transfer-learning-with-text-summarization","bert-based-classification","domain-adaptive-bert-based-classification","domain-adaptive-named-entity-recognition","multimodal-text-summarization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-transformer","name":"Domain-adaptive transformer","fullName":"Domain-Adaptive Transformer (DAT)","aliases":["DAT","domain-adaptive Transformer","domain adaptation with Transformers","transfer-learning Transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2022","originator":"Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-transformer.md","definition":"A Domain-Adaptive Transformer (DAT) is a Transformer-based model — such as BERT or ViT — extended with an explicit domain-alignment objective so that learned representations transfer well from a labeled source domain to a different, often unlabeled, target domain. The approach combines the powerful representation capacity of Transformers with domain adaptation techniques such as adversarial training or contrastive alignment to minimise domain shift.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022)","year":"2019–2022","type":"Pre-trained model fine-tuned with domain-shift adaptation","dataType":"Text, images, or multimodal data from source and target domains","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ni, J., Hernandez Abrego, G., Constant, N., Ma, J., Hall, K., Cer, D., & Yang, Y. (2021). Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. Findings of ACL 2022. arXiv:2108.08877.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2108.08877"},{"ref":"Guo, J., Shah, D., & Barzilay, R. (2022). Multi-Source Domain Adaptation with Mixture of Experts. In Proceedings of EMNLP 2018. arXiv:1809.02060.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1809.02060"}],"related":["bert","vision-transformer","transfer-learning","domain-adversarial-neural-network","fine-tuning","contrastive-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-variational-autoencoder","name":"Domain-adaptive variational autoencoder","fullName":"Domain-Adaptive Variational Autoencoder (DA-VAE)","aliases":["DA-VAE","domain-adaptive VAE","domain-conditioned variational autoencoder","cross-domain VAE"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020","originator":"Ilse, M.; Tomczak, J. M.; Louizos, C.; Welling, M.","url":"https://scholargate.app/en/deep-learning/domain-adaptive-variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-variational-autoencoder.md","definition":"A Domain-Adaptive Variational Autoencoder (DA-VAE) extends the standard VAE framework to learn disentangled latent representations that separate domain-specific variation from class-relevant and domain-invariant content, enabling models trained on a source domain to generalise effectively to a different but related target domain with limited or no target labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ilse, M.; Tomczak, J. M.; Louizos, C.; Welling, M.","year":"2020","type":"Generative model with domain adaptation","dataType":"Images, text, multimodal (labeled source + unlabeled or scarce-label target domain data)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ilse, M., Tomczak, J. M., Louizos, C., & Welling, M. (2020). DIVA: Domain Invariant Variational Autoencoders. Proceedings of the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), PMLR 121, 322–348.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v121/ilse20a.html"},{"ref":"Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"}],"related":["variational-autoencoder","domain-adversarial-neural-network","conditional-variational-autoencoder","generative-adversarial-network","transfer-learning","domain-adaptation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-vision-transformer","name":"Domain-adaptive vision transformer","fullName":"Domain-Adaptive Vision Transformer (DA-ViT)","aliases":["DA-ViT","Domain Adaptation with Vision Transformer","ViT with Domain Adaptation","Domain-Adaptive ViT"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2021–2023","originator":"Multiple groups (Yang et al., 2023; Xu et al., 2021; Ma et al., 2022)","url":"https://scholargate.app/en/deep-learning/domain-adaptive-vision-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-vision-transformer.md","definition":"Domain-Adaptive Vision Transformer (DA-ViT) applies domain adaptation techniques — such as adversarial alignment, self-training, or attention-level bridging — on top of a pretrained Vision Transformer backbone to transfer visual knowledge from a labeled source domain to an unlabeled or lightly labeled target domain, reducing the distribution shift that limits standard ViT fine-tuning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups (Yang et al., 2023; Xu et al., 2021; Ma et al., 2022)","year":"2021–2023","type":"Domain adaptation + Vision Transformer ensemble","dataType":"Images (source domain + unlabeled or labeled target domain)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=YicbFdNTTy"},{"ref":"Yang, L., Balaji, Y., Lim, S. N., & Shrivastava, A. (2023). TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 520-530.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=TVT+Transferable+Vision+Transformer+Unsupervised+Domain+Adaptation"}],"related":["vision-transformer","transfer-learning-with-vision-transformer","fine-tuned-vision-transformer","domain-adaptive-convolutional-neural-network","domain-adaptive-bert-based-classification","semantic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"domain-adaptive-word2vec","name":"Domain-adaptive Word2Vec","fullName":"Domain-Adaptive Word2Vec (Domain-Specific Word Embedding Training or Fine-Tuning)","aliases":["domain-specific Word2Vec","domain-adapted word embeddings","domain Word2Vec","specialized Word2Vec"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013–2016","originator":"Mikolov, T. et al. (Word2Vec); domain adaptation practice emerged in NLP community ~2014–2016","url":"https://scholargate.app/en/deep-learning/domain-adaptive-word2vec","markdownUrl":"https://scholargate.app/en/deep-learning/domain-adaptive-word2vec.md","definition":"Domain-adaptive Word2Vec trains or fine-tunes Word2Vec embeddings on a domain-specific text corpus so that word vectors capture the specialized vocabulary, semantic relationships, and jargon of a target field — such as clinical medicine, legal text, financial reports, or scientific literature — rather than reflecting general-purpose web or news language.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mikolov, T. et al. (Word2Vec); domain adaptation practice emerged in NLP community ~2014–2016","year":"2013–2016","type":"Domain-adapted word embedding model","dataType":"Domain-specific text corpora (clinical notes, legal documents, scientific articles, social media, etc.)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of ICLR Workshop.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1301.3781"},{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null}],"related":["word2vec","domain-adaptive-sentence-embeddings","transfer-learning-with-word2vec","multilingual-word2vec","sentence-embeddings","fine-tuned-word2vec"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dose-escalation-design","name":"Dose-Escalation Design","fullName":"Dose-Escalation Design (Continual Reassessment Method)","aliases":["Continual Reassessment Method","CRM Design","Phase I Dose-Finding Design","Doz Artırma Tasarımı"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Clinical trial design","year":1990,"originator":"John O'Quigley, Margaret Pepe & Lloyd Fisher","url":"https://scholargate.app/en/experimental-design/dose-escalation-design","markdownUrl":"https://scholargate.app/en/experimental-design/dose-escalation-design.md","definition":"Dose-Escalation Design, formalized as the Continual Reassessment Method (CRM), is a Bayesian adaptive algorithm for identifying the Maximum Tolerated Dose (MTD) in Phase I clinical trials. Introduced by John O'Quigley, Margaret Pepe, and Lloyd Fisher in 1990, CRM treats dose-toxicity response as a parametric curve, updates a prior probability model after each patient's outcome, and assigns subsequent patients to the dose currently estimated closest to a pre-specified target toxicity probability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John O'Quigley, Margaret Pepe & Lloyd Fisher","year":1990,"type":"Adaptive Bayesian dose-finding design","subfamily":"Clinical trial design","target_parameter":"Maximum Tolerated Dose (MTD)","trial_phase":"Phase I"},"citations":[{"ref":"O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48.","type":"article","doi":"10.2307/2531628","isbn":null,"url":null}],"related":["adaptive-clinical-trial-design","bayesian-inference","sequential-design"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dose-response-analysis","name":"Dose-Response Analysis","fullName":"Dose-Response Analysis in Epidemiology and Toxicology","aliases":["exposure-response analysis","concentration-response modeling","dose-response modeling","DRA"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Conceptual roots 16th century; modern epidemiological application mid-20th century","originator":"Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill","url":"https://scholargate.app/en/epidemiology/dose-response-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/dose-response-analysis.md","definition":"Dose-response analysis quantifies the relationship between the magnitude of an exposure (the dose) and the probability or rate of an outcome (the response). It is a core analytical strategy in epidemiology and toxicology, providing evidence that increasing exposure systematically increases — or decreases — the risk of disease. A demonstrated dose-response gradient is one of Bradford Hill's classic criteria supporting causal inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill","year":"Conceptual roots 16th century; modern epidemiological application mid-20th century","type":"Quantitative analytical method","dataType":"Continuous or ordinal exposure data with binary or time-to-event outcomes","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301–1309.","type":"article","doi":"10.1093/oxfordjournals.aje.a116237","isbn":null,"url":null}],"related":["cohort-study","case-control-study","survival-analysis","meta-analysis","regression-analysis","ecological-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dose-response-design","name":"Dose-Response Design","fullName":"Dose-Response Experimental Design and Analysis","aliases":["dose-response analysis","dose-response curve","Doz-Yanıt Tasarımı ve Analizi (Dose-Response)","ED50 analysis","4PL model","5PL model"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1994,"originator":"Classical pharmacology; formalized by ICH E4 (1994) and Ritz et al. (2015)","url":"https://scholargate.app/en/experimental-design/dose-response-design","markdownUrl":"https://scholargate.app/en/experimental-design/dose-response-design.md","definition":"Dose-response design is a framework for planning and analysing experiments that characterise the relationship between the amount of a stimulus — such as a drug dose or a chemical concentration — and the magnitude of a biological or physiological response. Formalised in regulatory guidance by the ICH E4 guideline (1994) and extensively developed in the statistical literature by Ritz et al. (2015), the framework covers experiment design, four-parameter and five-parameter logistic curve fitting, key benchmark estimates (ED50/EC50, NOAEL, LOAEL), and monotone trend testing via the Williams procedure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical pharmacology; formalized by ICH E4 (1994) and Ritz et al. (2015)","year":1994,"family":"Experimental design and analysis","type":"Nonlinear curve fitting and monotone contrast testing","minSample":20,"parametric":false,"requiresNormality":false,"suitableOutcomes":"continuous, ordinal","keyEstimates":"ED50, EC50, NOAEL, LOAEL","commonModels":"4PL logistic, 5PL logistic, Hill equation"},"citations":[{"ref":"Ritz, C., Baty, F., Streibig, J. C., & Gerhard, D. (2015). Dose-Response Analysis Using R. PLOS ONE, 10(12), e0146021.","type":"article","doi":"10.1371/journal.pone.0146021","isbn":null,"url":null},{"ref":"ICH E4 (1994). Dose-Response Information to Support Drug Registration. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use.","type":"guideline","doi":null,"isbn":null,"url":"https://www.ich.org/page/efficacy-guidelines"}],"related":["one-way-anova","nonlinear-regression","williams-test","factorial-design","repeated-measures-anova","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dose-response-meta-analysis","name":"Dose-Response Meta-Analysis","fullName":"Dose-Response Meta-Analysis (Non-Linear Dose-Response Synthesis)","aliases":["Dose-Response Relationship","Non-Linear Meta-Analysis","Dose-Effect Synthesis"],"domain":"evidence-synthesis","family":"process-pipeline","subfamily":"Specialized Meta-Analysis","year":"1992","originator":"Greenland & Longnecker (1992), Advanced by Orsini et al. (2012)","url":"https://scholargate.app/en/evidence-synthesis/dose-response-meta-analysis","markdownUrl":"https://scholargate.app/en/evidence-synthesis/dose-response-meta-analysis.md","definition":"Dose-response meta-analysis is a specialized evidence synthesis method that models the relationship between exposure dose (or intensity, duration, quantity) and health outcome across multiple studies, assessing whether effects follow a linear trend, nonlinear curve, or threshold pattern. Pioneered by Greenland and Longnecker (1992) and refined by Orsini et al. (2012), dose-response meta-analysis answers critical questions like 'Does cardiovascular disease risk increase consistently with salt intake, or is there a threshold above which risk plateaus?' and 'Does the benefit of physical activity increase linearly with exercise duration, or do diminishing returns occur at high doses?' This method is essential for risk assessment, policy-setting on safe exposure limits, and optimizing treatment dosing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greenland & Longnecker (1992), Advanced by Orsini et al. (2012)","subfamily":"Specialized Meta-Analysis","year":"1992","type":"Method"},"citations":[{"ref":"Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation of environmental health risks, with application to exposure to contaminated groundwater. Statistics in Medicine, 11(14‐15), 1837–1847.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Methods+for+trend+estimation+of+environmental+health+risks%2C+with+application+to+exposure+to+contaminated+groundwater+Greenland"},{"ref":"Orsini, N., Li, R., Wolk, A., Khudyakov, P., & Spiegelman, D. (2012). Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. American Journal of Epidemiology, 175(1), 66–73.","type":"article","doi":"10.1093/aje/kwr265","isbn":null,"url":null},{"ref":"Berlin, J. A., Longnecker, M. P., & Greenland, S. (1993). Meta-analysis of epidemiologic dose-response studies. American Journal of Epidemiology, 140(1), 75–82.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Meta-analysis+of+epidemiologic+dose-response+studies+Berlin"}],"related":["meta-analysis","meta-regression","dose-response-curve","non-linear-relationships","exposure-response"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dosimetry-measurement","name":"Dosimetry Measurement","fullName":"Dosimetry Measurement and Radiation Detection","aliases":["dose measurement","radiation monitoring","exposure quantification"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Experimental radiation detection","year":"1896","originator":"Wilhelm Röntgen, Henri Becquerel","url":"https://scholargate.app/en/nuclear-physics/dosimetry-measurement","markdownUrl":"https://scholargate.app/en/nuclear-physics/dosimetry-measurement.md","definition":"Dosimetry measurement is the experimental quantification of radiation dose and exposure, originating from Röntgen and Becquerel's 1890s discoveries. It employs specialized detectors (ion chambers, TLD, Geiger counters) to measure photon and particle energy deposition in biological tissue or materials, providing direct evidence of exposure for worker protection, patient dose verification, and environmental monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilhelm Röntgen, Henri Becquerel","subfamily":"Experimental radiation detection","year":"1896","type":"experimental measurement methodology"},"citations":[{"ref":"Knoll, G. F. (2010). Radiation Detection and Measurement (4th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Radiation+Detection+and+Measurement+%284th+ed.%29+Knoll"},{"ref":"International Commission on Radiological Protection (2019). Occupational Intakes of Radionuclides: Part 3. Publication 137.","type":"report","doi":null,"isbn":null,"url":"https://www.icrp.org/publication.asp?id=Publication%20137"}],"related":["radiation-dose-assessment","dosimetry-measurement","activation-analysis","monte-carlo-neutron-particle","radiation-protection-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-ab-design","name":"Double-blind AB design","fullName":"Double-blind AB Single-Subject Experimental Design","aliases":["blinded AB design","double-blind single-case AB","masked AB design","double-blind baseline-intervention design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s (AB design); double-blinding integration in single-case clinical research from the 1980s–1990s","originator":"Derived from the AB single-subject design tradition (Sidman 1960; Baer, Wolf, & Risley 1968) combined with double-blinding conventions from clinical trial methodology","url":"https://scholargate.app/en/experimental-design/double-blind-ab-design","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-ab-design.md","definition":"The double-blind AB design is a single-subject experimental approach that sequences a baseline phase (A) and an intervention phase (B) while concealing phase allocation from both the participant and the outcome assessor. It merges the idiographic focus of single-case methodology with the bias-control mechanism of double-blinding, making it especially useful in clinical rehabilitation, pain research, and behavioral medicine when objective measurement of an individual's response to treatment is the primary goal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from the AB single-subject design tradition (Sidman 1960; Baer, Wolf, & Risley 1968) combined with double-blinding conventions from clinical trial methodology","year":"1960s (AB design); double-blinding integration in single-case clinical research from the 1980s–1990s","type":"Single-subject experimental design with double-blinding","dataType":"Repeated-measures behavioral, clinical, or physiological outcome data collected over time from one or a small number of individuals","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (1982). Single-Case Research Designs: Methods for Clinical and Applied Settings. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195030440","url":null},{"ref":"Backman, C. L., & Harris, S. R. (1999). Case studies, single-subject research, and N of 1 randomized trials: Comparisons and contrasts. American Journal of Physical Medicine and Rehabilitation, 78(2), 170–176.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Backman+Harris+1999+single+subject+N+of+1+randomized+trials"}],"related":["ab-design","aba-design","abab-design","multiple-baseline-design","single-subject-experimental-design","double-blind-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-ab-test","name":"Double-blind A/B test","fullName":"Double-blind A/B Test (Randomized Controlled Experiment with Double-blinding)","aliases":["double-blind split test","double-blinded A/B experiment","blinded two-arm randomized experiment","double-blind controlled A/B trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1935 (Fisher's formal randomized design); double-blinding in A/B testing: 1990s–2000s","originator":"Evolved from clinical trial methodology; early systematic blinding attributed to James Lind (1753) and formalized by R. A. Fisher (1935)","url":"https://scholargate.app/en/experimental-design/double-blind-ab-test","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-ab-test.md","definition":"A double-blind A/B test is a randomized experiment that compares two variants — a control (A) and a treatment (B) — while concealing group assignment from both participants and those administering or assessing the experiment. Combining the causal isolation of randomized assignment with blinding on both sides eliminates expectation-driven bias from participants and evaluator bias from analysts or administrators, producing cleaner causal estimates of treatment effect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from clinical trial methodology; early systematic blinding attributed to James Lind (1753) and formalized by R. A. Fisher (1935)","year":"1935 (Fisher's formal randomized design); double-blinding in A/B testing: 1990s–2000s","type":"Randomized controlled experiment with blinding","dataType":"Continuous, binary, or count outcome measurements from two randomized groups","subfamily":"Deneysel desen"},"citations":[{"ref":"Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.","type":"article","doi":"10.1136/bmj.c332","isbn":null,"url":null},{"ref":"Kohavi, R., Longbotham, R., Sommerfield, D., & Henne, R. M. (2009). Controlled experiments on the web: Survey and practical guide. Data Mining and Knowledge Discovery, 18(1), 140-181.","type":"inproceedings","doi":"10.1007/s10618-008-0114-1","isbn":null,"url":null}],"related":["ab-testing","randomized-controlled-trial","single-blind-ab-test","double-blind-randomized-controlled-trial","multi-arm-experiment","factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-adaptive-experiment","name":"Double-blind adaptive experiment","fullName":"Double-Blind Adaptive Experimental Design","aliases":["double-blind adaptive design","blinded adaptive trial","double-blind adaptive RCT","adaptive double-blind study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Conceptual roots 1970s–1990s; regulatory codification 2004–2019","originator":"Formalized through FDA adaptive design guidance and work by Scott Berry, Donald Berry, and colleagues","url":"https://scholargate.app/en/experimental-design/double-blind-adaptive-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-adaptive-experiment.md","definition":"A double-blind adaptive experiment combines two powerful design features: double-blinding, which conceals treatment assignment from both participants and outcome assessors to prevent bias, and adaptive modification, which allows pre-specified changes to the trial's course — such as sample size re-estimation, allocation ratio shifts, or arm dropping — based on accumulating interim data. The result is a rigorous, bias-protected design that can respond to emerging evidence without compromising inferential validity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized through FDA adaptive design guidance and work by Scott Berry, Donald Berry, and colleagues","year":"Conceptual roots 1970s–1990s; regulatory codification 2004–2019","type":"Experimental design combining blinding and adaptive modification","dataType":"Continuous, binary, or ordinal outcome data from controlled experiments","subfamily":"Deneysel desen"},"citations":[{"ref":"U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.","type":"article","doi":null,"isbn":null,"url":"https://www.fda.gov/media/78512/download"},{"ref":"Berry, S. M., Carlin, B. P., Lee, J. J., & Muller, P. (2010). Bayesian Adaptive Methods for Clinical Trials. CRC Press.","type":"book","doi":null,"isbn":"9781439825488","url":null}],"related":["adaptive-experiment","double-blind-randomized-controlled-trial","randomized-controlled-trial","adaptive-randomized-controlled-trial","multi-arm-experiment","bayesian-adaptive-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-control-group-experimental-design","name":"Double-blind Control Group Experimental Design","fullName":"Double-blind Randomized Experiment with Control Group","aliases":["double-blind controlled experiment","DB-CG design","double-masked controlled trial","double-blind controlled study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Experimental design","year":"1930s–1950s (formalized in clinical trial methodology)","originator":"R. A. Fisher (experimental control foundations); blinding practices evolved in clinical research through the 20th century","url":"https://scholargate.app/en/experimental-design/double-blind-control-group-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-control-group-experimental-design.md","definition":"A double-blind control group experimental design is a rigorous experimental structure in which participants are randomly assigned to at least one treatment group and one control group, while both the participants and the researchers collecting or assessing outcomes are kept unaware of group assignment. By combining allocation concealment with blinding at two levels, the design minimizes expectancy bias, placebo effects, and assessor bias simultaneously, making it a cornerstone of high-quality intervention research in medicine, psychology, and the social sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (experimental control foundations); blinding practices evolved in clinical research through the 20th century","year":"1930s–1950s (formalized in clinical trial methodology)","type":"Experimental research design","dataType":"Quantitative outcome measurements (continuous, ordinal, or binary)","subfamily":"Experimental design"},"citations":[{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Design+of+Experiments+Fisher+1935"},{"ref":"Blinded experiment. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Blinded_experiment"}],"related":["control-group-experimental-design","double-blind-randomized-controlled-trial","pretest-posttest-experimental-design","single-blind-control-group-experimental-design","randomized-controlled-trial","factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-field-experiment","name":"Double-blind field experiment","fullName":"Double-blind Field Experiment","aliases":["double-masked field trial","double-blind naturalistic experiment","blinded field study","DB field experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s onward (field experiment tradition); double-blind controls applied from 1970s in social and policy field trials","originator":"Fisher, R. A. (randomized field trials); double-blind practice traced to 19th-century clinical research, formalized for field settings by Campbell & Stanley (1963)","url":"https://scholargate.app/en/experimental-design/double-blind-field-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-field-experiment.md","definition":"A double-blind field experiment combines the high external validity of a real-world field setting with double-blind masking, in which neither the participants nor the personnel delivering the treatment know who has been assigned to the treatment or control condition. This design controls simultaneously for participant expectation effects and for experimenter/enumerator demand effects, making it one of the most rigorous tools available for causal inference outside the laboratory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fisher, R. A. (randomized field trials); double-blind practice traced to 19th-century clinical research, formalized for field settings by Campbell & Stanley (1963)","year":"1960s onward (field experiment tradition); double-blind controls applied from 1970s in social and policy field trials","type":"Experimental design","dataType":"Quantitative outcome measures collected in natural settings (surveys, administrative records, behavioral observations)","subfamily":"Deneysel desen"},"citations":[{"ref":"Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. W. W. Norton.","type":"book","doi":null,"isbn":"978-0393979954","url":null},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["double-blind-randomized-controlled-trial","field-experiment","single-blind-field-experiment","randomized-controlled-trial","factorial-field-experiment","cluster-randomized-field-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-fractional-factorial-experiment","name":"Double-blind fractional factorial experiment","fullName":"Double-blind Fractional Factorial Experiment","aliases":["double-blind FFE","blinded fractional factorial design","double-blind FFD","masked fractional factorial experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s onward (combination widely used in pharmaceutical and food science research)","originator":"Fractional factorial: Box & Hunter (1961); double-blind convention: clinical trial methodology (mid-20th century)","url":"https://scholargate.app/en/experimental-design/double-blind-fractional-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-fractional-factorial-experiment.md","definition":"A double-blind fractional factorial experiment combines two powerful methodological protections: fractional factorial design, which tests a carefully chosen subset of all possible factor combinations to achieve efficiency, and double-blind administration, which prevents both participants and assessors from knowing which treatment combination has been applied. The result is an experiment that is both resource-efficient and protected against expectation and assessment bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fractional factorial: Box & Hunter (1961); double-blind convention: clinical trial methodology (mid-20th century)","year":"1960s onward (combination widely used in pharmaceutical and food science research)","type":"Controlled experimental design with blinding and factor-space reduction","dataType":"Continuous or categorical outcome measurements from factorial treatment combinations","subfamily":"Deneysel desen"},"citations":[{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471718130","url":null},{"ref":"Friedman, L. M., Furberg, C. D., & DeMets, D. L. (1991). Fundamentals of Clinical Trials (2nd ed.). Mosby Year Book.","type":"book","doi":null,"isbn":"978-0801660269","url":null}],"related":["fractional-factorial-experiment","full-factorial-experiment","double-blind-randomized-controlled-trial","double-blind-factorial-experiment","blocked-fractional-factorial-experiment","response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-full-factorial-experiment","name":"Double-blind Full Factorial Experiment","fullName":"Double-blind Full Factorial Experimental Design","aliases":["double-masked full factorial design","double-blind complete factorial experiment","blinded full factorial RCT","double-blind factorial trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1935 (factorial foundations, Fisher); double-blind combined application from 1950s onward","originator":"Full factorial design: Ronald A. Fisher; double-blind masking: formalized in clinical research mid-20th century","url":"https://scholargate.app/en/experimental-design/double-blind-full-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-full-factorial-experiment.md","definition":"A double-blind full factorial experiment crosses every level of every independent variable to create all possible treatment combinations, while ensuring that neither participants nor outcome assessors know which condition each participant has been assigned to. This design simultaneously achieves comprehensive examination of main effects and all interactions, and protection against performance and detection bias through blinding — making it especially valuable in clinical, pharmacological, and behavioral research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Full factorial design: Ronald A. Fisher; double-blind masking: formalized in clinical research mid-20th century","year":"1935 (factorial foundations, Fisher); double-blind combined application from 1950s onward","type":"Controlled experimental design with masking","dataType":"Continuous, ordinal, or categorical outcome measures collected under blinded conditions","subfamily":"Deneysel desen"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null},{"ref":"Schulz, K. F., & Grimes, D. A. (2002). Blinding in randomised trials: hiding who got what. The Lancet, 359(9307), 696–700.","type":"article","doi":"10.1016/S0140-6736(02)07816-9","isbn":null,"url":null}],"related":["full-factorial-experiment","fractional-factorial-experiment","double-blind-randomized-controlled-trial","factorial-randomized-controlled-trial","double-blind-factorial-experiment","blocked-full-factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-laboratory-experiment","name":"Double-blind laboratory experiment","fullName":"Double-Blind Laboratory Experiment","aliases":["double-blind lab experiment","double-masked laboratory experiment","DB lab experiment","double-blind controlled lab study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-20th century (widespread adoption ~1950s onward)","originator":"Rooted in 19th-century pharmacological and psychological research traditions; systematized in clinical and experimental science through the 20th century","url":"https://scholargate.app/en/experimental-design/double-blind-laboratory-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-laboratory-experiment.md","definition":"A double-blind laboratory experiment is a controlled experimental design conducted in a laboratory setting in which neither the participants nor the researchers directly administering the treatment know which condition each participant has been assigned to. This dual blinding, combined with the high degree of environmental control characteristic of laboratory settings, minimizes both participant expectancy effects and experimenter bias, making it one of the most rigorous designs available for isolating causal relationships between independent and dependent variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in 19th-century pharmacological and psychological research traditions; systematized in clinical and experimental science through the 20th century","year":"Mid-20th century (widespread adoption ~1950s onward)","type":"Controlled experimental design with blinding","dataType":"Quantitative outcome measurements collected in controlled laboratory conditions","subfamily":"Deneysel desen"},"citations":[{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null},{"ref":"Blind experiment. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Blind_experiment"}],"related":["double-blind-randomized-controlled-trial","laboratory-experiment","single-blind-laboratory-experiment","randomized-controlled-trial","factorial-laboratory-experiment","blocked-laboratory-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-multiple-baseline-design","name":"Double-blind Multiple Baseline Design","fullName":"Double-blind Multiple Baseline Single-Subject Experimental Design","aliases":["DB-MBD","blinded multiple baseline design","masked multiple baseline design","double-blind MBD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (multiple baseline); double-blind extension applied from 1980s onward in clinical behavioral research","originator":"Multiple baseline: Baer, Wolf & Risley (1968); double-blind procedural extension adapted from clinical trial methodology","url":"https://scholargate.app/en/experimental-design/double-blind-multiple-baseline-design","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-multiple-baseline-design.md","definition":"The double-blind multiple baseline design is a single-subject experimental design in which an intervention is introduced sequentially across two or more independent baselines — behaviors, individuals, or settings — while outcome assessors (and ideally participants) remain unaware of which baseline is currently in the intervention phase. The double-blind procedural overlay reduces measurement bias and demand characteristics, strengthening causal inference beyond what a standard multiple baseline design offers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple baseline: Baer, Wolf & Risley (1968); double-blind procedural extension adapted from clinical trial methodology","year":"1968 (multiple baseline); double-blind extension applied from 1980s onward in clinical behavioral research","type":"Single-subject experimental design with blinded outcome assessment","dataType":"Repeated-measures behavioral or clinical outcome data (continuous observation records, standardized rating scales)","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null}],"related":["multiple-baseline-design","double-blind-randomized-controlled-trial","abab-design","aba-design","single-subject-experimental-design","crossover-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-pretest-posttest-experimental-design","name":"Double-blind pretest-posttest experimental design","fullName":"Double-Blind Pretest-Posttest Experimental Design","aliases":["DB-pretest-posttest design","double-blind pre-post design","masked pretest-posttest RCT","double-masked pre-post experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-20th century (combined form widely adopted 1960s onward)","originator":"Campbell & Stanley (formalized pretest-posttest design, 1963); double-blind blinding convention developed in clinical pharmacology (19th-20th century)","url":"https://scholargate.app/en/experimental-design/double-blind-pretest-posttest-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-pretest-posttest-experimental-design.md","definition":"The double-blind pretest-posttest experimental design is a true experiment in which participants are randomly assigned to treatment and control conditions, outcome data are collected both before and after the intervention, and neither participants nor outcome assessors know which condition each participant received. Combining baseline measurement with strong blinding, the design controls for both pre-existing group differences and expectancy-driven bias, making it a gold-standard approach in clinical and behavioral research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Campbell & Stanley (formalized pretest-posttest design, 1963); double-blind blinding convention developed in clinical pharmacology (19th-20th century)","year":"Mid-20th century (combined form widely adopted 1960s onward)","type":"True experimental design","dataType":"Continuous, ordinal, or count outcome measurements at two time points","subfamily":"Deneysel desen"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. In N. L. Gage (Ed.), Handbook of Research on Teaching (pp. 171-246). Rand McNally.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+quasi-experimental+designs+for+research+Campbell+Stanley+1963"},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["pretest-posttest-experimental-design","double-blind-randomized-controlled-trial","control-group-experimental-design","solomon-four-group-design","randomized-controlled-trial","single-blind-pretest-posttest-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-single-subject-experimental-design","name":"Double-blind single-subject experimental design","fullName":"Double-Blind Single-Subject Experimental Design","aliases":["double-blind SCED","double-blind single-case experimental design","masked single-subject design","double-blind N-of-1 design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1970s–1980s (systematic integration of blinding into SCED)","originator":"Barlow, Hersen, and colleagues (single-subject tradition); double-blind masking adapted from clinical trial methodology","url":"https://scholargate.app/en/experimental-design/double-blind-single-subject-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-single-subject-experimental-design.md","definition":"A double-blind single-subject experimental design applies systematic masking — concealing treatment assignment from both the participant and the outcome assessor — within a within-person repeated-measures framework. It is used when researchers need strong causal inference about an intervention's effect on a single individual while guarding against placebo responses and observer bias. Particularly prominent in pharmacological, behavioral, and clinical rehabilitation research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barlow, Hersen, and colleagues (single-subject tradition); double-blind masking adapted from clinical trial methodology","year":"1970s–1980s (systematic integration of blinding into SCED)","type":"Experimental single-subject design with double-blind masking","dataType":"Repeated behavioral or clinical outcome measurements on a single participant across phases","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0205474554","url":null}],"related":["single-subject-experimental-design","abab-design","multiple-baseline-design","double-blind-randomized-controlled-trial","aba-design","ab-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-blind-solomon-four-group-design","name":"Double-blind Solomon four-group design","fullName":"Double-blind Solomon Four-Group Experimental Design","aliases":["double-blind S4GD","blinded Solomon design","double-blind four-group design","Solomon four-group with double-blind"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1949 (Solomon design); double-blind blinding integrated in 20th-century experimental practice","originator":"Richard L. Solomon (base design); double-blind protocol is a general methodological standard","url":"https://scholargate.app/en/experimental-design/double-blind-solomon-four-group-design","markdownUrl":"https://scholargate.app/en/experimental-design/double-blind-solomon-four-group-design.md","definition":"The double-blind Solomon four-group design combines Richard Solomon's classic four-group structure — which isolates pretest sensitization effects — with double-blind blinding, ensuring that neither participants nor outcome assessors know group assignments. This combination yields high internal validity by controlling simultaneously for testing effects, expectancy bias, and experimenter influence, making it one of the most rigorous true experimental designs available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard L. Solomon (base design); double-blind protocol is a general methodological standard","year":"1949 (Solomon design); double-blind blinding integrated in 20th-century experimental practice","type":"True experimental design","dataType":"Continuous or categorical outcome measures from pretest and posttest assessments","subfamily":"Deneysel desen"},"citations":[{"ref":"Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.","type":"article","doi":"10.1037/h0062958","isbn":null,"url":null},{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":"978-0395307878","url":null}],"related":["solomon-four-group-design","pretest-posttest-experimental-design","double-blind-randomized-controlled-trial","control-group-experimental-design","randomized-controlled-trial","factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-bootstrap","name":"Double Bootstrap","fullName":"Double (Iterated) Bootstrap","aliases":["iterated bootstrap","nested bootstrap","calibrated bootstrap","Çift Bootstrap (Double / Iterated Bootstrap)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1986,"originator":"Hall (1986); Beran (1987)","url":"https://scholargate.app/en/statistics/double-bootstrap","markdownUrl":"https://scholargate.app/en/statistics/double-bootstrap.md","definition":"The double bootstrap is a resampling method that calibrates a bootstrap confidence interval with a second, nested layer of bootstrap to bring its actual coverage closer to the nominal level. Introduced by Hall (1986) and Beran (1987), it is especially valuable for small samples and skewed distributions where a single-layer bootstrap under-covers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hall (1986); Beran (1987)","year":1986,"type":"Resampling calibration (nested bootstrap)","estimator":"Two-level (outer + inner) bootstrap resampling","computationalCost":"O(B²)","minSample":20,"outcome":"continuous"},"citations":[{"ref":"Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452.","type":"article","doi":"10.1214/aos/1176350168","isbn":null,"url":null},{"ref":"Beran, R. (1987). Prepivoting to Reduce Level Error of Confidence Sets. Biometrika, 74(3), 457-468.","type":"article","doi":"10.1093/biomet/74.3.457","isbn":null,"url":null}],"related":["bootstrap-inference","wild-bootstrap","bayesian-bootstrap","block-bootstrap","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-machine-learning","name":"Double Machine Learning","fullName":"Double/Debiased Machine Learning (DML)","aliases":["Debiased Machine Learning","Neyman Orthogonal Score Estimation","Partialing-Out Lasso","Çift Makine Öğrenmesi"],"domain":"causal-inference","family":"ml-model","subfamily":"Causal ML","year":2018,"originator":"Victor Chernozhukov et al.","url":"https://scholargate.app/en/causal-inference/double-machine-learning","markdownUrl":"https://scholargate.app/en/causal-inference/double-machine-learning.md","definition":"Double/Debiased Machine Learning (DML), introduced by Chernozhukov et al. (2018), is a semiparametric framework for estimating causal or structural parameters in the presence of high-dimensional controls. It uses flexible machine learning methods to model nuisance functions—the conditional expectations of the outcome and the treatment given covariates—and then constructs a debiased estimator of the target parameter that achieves root-n consistency and valid inference despite the regularization bias inherent in high-dimensional settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Victor Chernozhukov et al.","year":2018,"type":"Semiparametric causal estimation","subfamily":"Causal ML","inferential_basis":"Neyman orthogonality + cross-fitting","convergence_rate":"Root-n consistent under high-dimensional nuisance"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null}],"related":["heterogeneous-treatment-effects","doubly-robust-estimation","random-forest"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"double-sampling","name":"Double Sampling","fullName":"Double Sampling (Two-Phase Sampling)","aliases":["Two-Phase Sampling"],"domain":"sampling","family":"process-pipeline","subfamily":"Nonparametric","year":"1938","originator":"Jerzy Neyman","url":"https://scholargate.app/en/sampling/double-sampling","markdownUrl":"https://scholargate.app/en/sampling/double-sampling.md","definition":"Double Sampling (also called two-phase or multistage sampling) is a survey design in which a large preliminary sample is collected using inexpensive methods or partial information, then a smaller subsample is drawn from it and measured in detail. Pioneered by Jerzy Neyman in 1938, it is particularly useful when a cheap surrogate measurement is available but true measurement is expensive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerzy Neyman","subfamily":"Nonparametric","year":"1938","type":"Multi-phase sampling design"},"citations":[{"ref":"Neyman, J. (1938). Contribution to the theory of sampling human populations. Journal of the American Statistical Association, 33(201), 101–116.","type":"article","doi":"10.1080/01621459.1938.10503378","isbn":null,"url":null},{"ref":"Hansen, M. H., & Hurwitz, W. N. (1943). On the theory of sampling from finite populations. Annals of Mathematical Statistics, 14(4), 333–362.","type":"article","doi":"10.1214/aoms/1177731356","isbn":null,"url":null},{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Sampling+Techniques%2C+3rd+Edition-p-9780471162407"}],"related":["stratified-sampling","ranked-set-sampling","systematic-sampling","cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"doubly-robust-estimation-in-education-research","name":"Doubly Robust Estimation in Education Research","fullName":"Doubly Robust Estimation Applied to Education Research","aliases":["DR estimator in education","AIPW in education","augmented IPW in education research","doubly robust causal estimation for educational outcomes"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1994-2005","originator":"Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)","url":"https://scholargate.app/en/causal-inference/doubly-robust-estimation-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/doubly-robust-estimation-in-education-research.md","definition":"Doubly robust estimation (DR) is a semiparametric causal inference approach that combines an outcome regression model with a propensity score model. In education research, it is used to estimate the causal effect of educational programs, interventions, or policies on student outcomes when treatment assignment is non-random but observed covariates can account for selection bias. The estimator is consistent if either — not necessarily both — of the two component models is correctly specified.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)","year":"1994-2005","type":"Causal inference / semiparametric estimator","dataType":"Observational cross-sectional or panel data with a binary treatment and continuous or binary outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Bang, H., & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.","type":"article","doi":"10.1111/j.1541-0420.2005.00377.x","isbn":null,"url":null},{"ref":"Karim, M. E., Petkau, J., Gustafson, P., Tremlett, H., & BeAMS Study Group. (2018). Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies. Statistical Methods in Medical Research, 27(6), 1709-1722.","type":"article","doi":"10.1177/0962280216668554","isbn":null,"url":null}],"related":["propensity-score-matching","inverse-probability-weighting","doubly-robust-estimation","marginal-structural-model","propensity-score-weighting","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"doubly-robust-estimation","name":"Doubly Robust Estimation","fullName":"Augmented Inverse Probability Weighting (AIPW) / Doubly Robust Estimation","aliases":["AIPW","augmented inverse probability weighting","doubly robust estimator","Çift Gürbüz Kestirici (Augmented IPW / AIPW)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2005,"originator":"Robins & Rotnitzky; Bang & Robins","url":"https://scholargate.app/en/causal-inference/doubly-robust-estimation","markdownUrl":"https://scholargate.app/en/causal-inference/doubly-robust-estimation.md","definition":"Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins & Rotnitzky; Bang & Robins","year":2005,"type":"Semiparametric causal estimator","estimator":"Augmented inverse probability weighting (AIPW)","outcome":"continuous or binary","minSample":150,"difficulty":3},"citations":[{"ref":"Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.","type":"article","doi":"10.1080/01621459.1995.10476494","isbn":null,"url":null},{"ref":"Bang, H. & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.","type":"article","doi":"10.1111/j.1541-0420.2005.00377.x","isbn":null,"url":null}],"related":["inverse-probability-weighting","propensity-score-matching","causal-mediation","ols-regression","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dpph-assay","name":"DPPH Radical Scavenging Assay","fullName":"2,2-Diphenyl-1-Picrylhydrazyl Radical Scavenging Assay","aliases":["DPPH antioxidant assay","DPPH free radical scavenging method","DPPH decolorization assay","radical scavenging activity test"],"domain":"food-science","family":"process-pipeline","subfamily":"Radical scavenging / antioxidant capacity","year":"1958 (Blois); widely standardised from 1995 (Brand-Williams et al.)","originator":"Blois, M. S. (first application); Brand-Williams et al. (standardised protocol)","url":"https://scholargate.app/en/food-science/dpph-assay","markdownUrl":"https://scholargate.app/en/food-science/dpph-assay.md","definition":"The DPPH radical scavenging assay is a rapid, widely used spectrophotometric method for measuring the antioxidant capacity of foods, plant extracts, and purified compounds. It quantifies how effectively a sample neutralises the stable synthetic free radical DPPH (2,2-diphenyl-1-picrylhydrazyl) by measuring the resulting colour change from deep violet to yellow, making it a cornerstone technique in food science, nutraceutical research, and phytochemistry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blois, M. S. (first application); Brand-Williams et al. (standardised protocol)","year":"1958 (Blois); widely standardised from 1995 (Brand-Williams et al.)","type":"Spectrophotometric antioxidant assay","dataType":"Absorbance readings (numerical, continuous)","subfamily":"Radical scavenging / antioxidant capacity"},"citations":[{"ref":"Blois, M. S. (1958). Antioxidant determinations by the use of a stable free radical. Nature, 181(4617), 1199–1200.","type":"journal-article","doi":"10.1038/1811199a0","isbn":null,"url":null},{"ref":"Brand-Williams, W., Cuvelier, M. E., & Berset, C. (1995). Use of a free radical method to evaluate antioxidant activity. LWT — Food Science and Technology, 28(1), 25–30.","type":"journal-article","doi":"10.1016/S0023-6438(95)80008-5","isbn":null,"url":null}],"related":["abts-assay","frap-assay","orac-assay","total-phenolic-content","antioxidant-capacity-measurement","cuprac-assay"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dpsir-framework","name":"DPSIR Framework","fullName":"Driving force, Pressure, State, Impact, Response (DPSIR) Framework","aliases":["DPSIR","PSR","Pressure-State-Response"],"domain":"sustainability","family":"process-pipeline","subfamily":"Environmental governance framework","year":"1993","originator":"OECD, refined by European Environment Agency","url":"https://scholargate.app/en/sustainability/dpsir-framework","markdownUrl":"https://scholargate.app/en/sustainability/dpsir-framework.md","definition":"The DPSIR Framework (Driving force, Pressure, State, Impact, Response) is a diagnostic and policy tool developed by the OECD (1993) and refined by the European Environment Agency (1999) to structure environmental and sustainability problems. It organizes causal relationships from economic activity through to policy interventions, enabling governments and organizations to identify where to intervene for environmental improvement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"OECD, refined by European Environment Agency","subfamily":"Environmental governance framework","year":"1993","type":"Diagnostic framework"},"citations":[{"ref":"European Environment Agency (1999). Environmental Indicators: Typology and Overview. EEA Technical Report No. 25. Copenhagen: EEA.","type":"article","doi":null,"isbn":null,"url":"https://www.eea.europa.eu/publications/TEC25"},{"ref":"Smeets, E., & Weterings, R. (1999). Environmental indicators: Typology and overview. European Environment Agency Report No. 25/1999.","type":"article","doi":null,"isbn":null,"url":"https://www.eea.europa.eu/publications/TEC25/at_download/file"},{"ref":"OECD (1993). OECD core set of environmental indicators for environmental performance reviews. OECD Environment Monographs No. 83. Paris: OECD Publishing.","type":"article","doi":null,"isbn":null,"url":"https://www.oecd.org/environment/indicators/"}],"related":["ecosystem-services-valuation","species-distribution-models","life-cycle-sustainability-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"drift-diffusion-model","name":"Drift Diffusion Model","fullName":"Drift Diffusion Model","aliases":["DDM","Brownian Motion Model","Sequential Sampling Model"],"domain":"psychology","family":"hypothesis-test","subfamily":"Sequential Sampling","year":"1978","originator":"Roger Ratcliff","url":"https://scholargate.app/en/psychology/drift-diffusion-model","markdownUrl":"https://scholargate.app/en/psychology/drift-diffusion-model.md","definition":"The Drift Diffusion Model (DDM) is a mathematical framework for understanding rapid binary decision-making by modeling the accumulation of evidence over time as a random walk with drift. Developed by Roger Ratcliff in the 1970s, it predicts both choice probabilities and response time distributions, providing insight into the cognitive processes underlying decisions in perceptual discrimination, recognition memory, and choice tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roger Ratcliff","subfamily":"Sequential Sampling","year":"1978","type":"Cognitive process model"},"citations":[{"ref":"Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59-108.","type":"article","doi":"10.1037/0033-295X.85.2.59","isbn":null,"url":null},{"ref":"Ratcliff, R., & McKoon, G. (2008). The diffusion model: A universal model for rapid decision. Psychological Review, 115(2), 357-380.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+diffusion+model%3A+A+universal+model+for+rapid+decision+Ratcliff"},{"ref":"Wagenmakers, E.-J., van der Maas, H. L. J., & Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14(1), 3-22.","type":"article","doi":"10.3758/BF03194023","isbn":null,"url":null}],"related":["signal-detection-theory","accumulator-models","response-time-analysis","sequential-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"drifter-lagrangian-analysis","name":"Drifter Lagrangian Analysis","fullName":"Drifter Lagrangian Analysis","aliases":["Lagrangian Tracking","Drifter Analysis"],"domain":"oceanography","family":"process-pipeline","subfamily":"Observational Oceanography","year":"1985","originator":"Robert Davis","url":"https://scholargate.app/en/oceanography/drifter-lagrangian-analysis","markdownUrl":"https://scholargate.app/en/oceanography/drifter-lagrangian-analysis.md","definition":"Drifter Lagrangian analysis tracks the motion of water parcels using surface drifters (buoys with attached drogues) to measure ocean currents directly. Developed by Robert Davis in the 1980s, this method provides direct observation of water parcel trajectories and enables estimation of eddy diffusivity, transport pathways, and mixing. Drifter data complement Eulerian (fixed-point) observations by capturing the Lagrangian perspective of fluid motion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Davis","subfamily":"Observational Oceanography","year":"1985","type":"instrumental"},"citations":[{"ref":"Davis, R. E. (1985). Drifter observations of coastal surface currents during CODE: The method and descriptive view. Journal of Geophysical Research, 90(C3), 4741-4755.","type":"article","doi":"10.1029/JC090iC03p04741","isbn":null,"url":null},{"ref":"Lumpkin, R., & Pazos, M. (2007). Measuring surface currents with Surface Velocity Program drifters: the instrument, its data, and its applications. In A. Griffa, A. D. Kirwan, A. J. Mariano, T. M. Ozgokmen, & H. T. Rossby (Eds.), Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics (pp. 39-67). Cambridge University Press.","type":"article","doi":"10.1017/CBO9780511535901.003","isbn":null,"url":null}],"related":["acoustic-doppler-current-profiler","geostrophic-velocity","ekman-transport"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"driscoll-kraay-se","name":"Driscoll-Kraay SE","fullName":"Driscoll-Kraay Standard Errors","aliases":["DK Standard Errors","Driscoll-Kraay Covariance Estimator","Spatial-Temporal HAC Standard Errors","Driscoll-Kraay Standart Hatalar"],"domain":"econometrics","family":"regression-model","subfamily":"Static panel","year":1998,"originator":"John Driscoll & Aart Kraay","url":"https://scholargate.app/en/econometrics/driscoll-kraay-se","markdownUrl":"https://scholargate.app/en/econometrics/driscoll-kraay-se.md","definition":"Driscoll-Kraay standard errors provide a nonparametric, heteroskedasticity- and autocorrelation-consistent (HAC) covariance estimator for balanced and unbalanced panel datasets. Introduced by Driscoll and Kraay in 1998, the method corrects inference when residuals exhibit cross-sectional dependence, serial autocorrelation, and heteroskedasticity simultaneously—problems common in macroeconomic and international finance panels where units such as countries or industries share common shocks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Driscoll & Aart Kraay","year":1998,"type":"Nonparametric heteroskedasticity- and autocorrelation-consistent (HAC) covariance estimator for panel data","subfamily":"Static panel","journal":"Review of Economics and Statistics","key_requirement":"Large T (time dimension must grow)"},"citations":[{"ref":"Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549–560.","type":"article","doi":"10.1162/003465398557825","isbn":null,"url":null}],"related":["cluster-robust-standard-errors","newey-west-hac","pesaran-cd-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"droop-control","name":"Droop Control","fullName":"Droop Control for Distributed Generation and Microgrids","aliases":["Frequency droop","Voltage droop","Decentralized control"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Decentralized grid control","year":"2013","originator":"Juan M. Guerrero","url":"https://scholargate.app/en/electrical-engineering/droop-control","markdownUrl":"https://scholargate.app/en/electrical-engineering/droop-control.md","definition":"Droop Control is a decentralized control method that enables independent generators (inverters, microgrids) to operate synchronously without direct communication. Introduced by Guerrero et al. in 2013 for microgrids, droop control uses frequency and voltage deviations as signals to share power. By making generator output depend on frequency and voltage (like synchronous generators), microgrids achieve plug-and-play operation. Essential for modern distributed energy resources and grid resilience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Juan M. Guerrero","subfamily":"Decentralized grid control","year":"2013","type":"Decentralized control for synchronous operation of distributed generators"},"citations":[{"ref":"Guerrero, J. M., Vasquez, J. C., Matas, J., Castilla, M., & de Vicuña, L. G. (2013). Hierarchical control of droop-controlled AC and DC microgrids. IEEE Transactions on Power Electronics, 28(11), 4915-4933.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hierarchical+control+of+droop-controlled+AC+and+DC+microgrids+Guerrero"},{"ref":"Kundur, P. (1994). Power System Stability and Control. McGraw-Hill.","type":"article","doi":null,"isbn":null,"url":"https://www.mheducation.com/highered/product/M0070635159.html"},{"ref":"Bidram, A., & Davoudi, A. (2012). Hierarchical structure of microgrids control system. IEEE Transactions on Smart Grid, 3(4), 1963-1976.","type":"article","doi":"10.1109/TSG.2012.2197425","isbn":null,"url":null}],"related":["phase-locked-loop","unit-commitment","optimal-power-flow"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dropout","name":"Dropout","fullName":"Dropout Regularization for Deep Neural Networks","aliases":["dropout regularization","stochastic dropout","neuron dropout","inverted dropout"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2014,"originator":"Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R.","url":"https://scholargate.app/en/deep-learning/dropout","markdownUrl":"https://scholargate.app/en/deep-learning/dropout.md","definition":"Dropout is a stochastic regularization technique for training deep neural networks, introduced by Srivastava, Hinton, Krizhevsky, Sutskever, and Salakhutdinov in 2014. During each training step, each neuron is independently switched off with probability (1 − p), preventing the network from co-adapting its units too tightly and thereby reducing overfitting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R.","year":2014,"type":"Stochastic regularization technique for neural networks","task":"Regularization in supervised deep learning (classification & regression)","retentionProbability":"p typically 0.5 (hidden layers), 0.8 (input layer)"},"citations":[{"ref":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929–1958.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v15/srivastava14a.html"},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 7: Regularization for Deep Learning). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["batch-normalization","l2-regularization","early-stopping","deep-neural-network","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"drsa","name":"DRSA","fullName":"Dominance-Based Rough Set Approach","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"2001","originator":"Greco, S. Matarazzo, B. Słowiński, R.","url":"https://scholargate.app/en/decision-making/drsa","markdownUrl":"https://scholargate.app/en/decision-making/drsa.md","definition":"DRSA (Dominance-Based Rough Set Approach) is a sorting multi-criteria decision-making (MCDM) method introduced by Greco, S. Matarazzo, B. Słowiński, R. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greco, S. Matarazzo, B. Słowiński, R.","subfamily":"Sorting","year":"2001","type":"Rough-set dominance induction of if-then decision rules for sorting","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Greco, S., Matarazzo, B., Słowiński, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research","type":"article","doi":"10.1016/s0377-2217(00)00167-3","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"drug-attitude-inventory","name":"Drug Attitude Inventory","fullName":"Drug Attitude Inventory (DAI)","aliases":["DAI","DAI-10","DAI-30"],"domain":"pharmacology","family":"process-pipeline","subfamily":"medication-attitudes","year":"1983","originator":"Thomas P. Hogan, Ahmed G. Awad, and Robert Eastwood","url":"https://scholargate.app/en/pharmacology/drug-attitude-inventory","markdownUrl":"https://scholargate.app/en/pharmacology/drug-attitude-inventory.md","definition":"The Drug Attitude Inventory (DAI) is a brief self-report measure developed by Hogan, Awad, and Eastwood in 1983 to assess attitudes toward medication and predicted medication compliance in schizophrenia and other psychiatric conditions. The original 30-item version (DAI-30) and the widely used 10-item short form (DAI-10) capture patients' subjective experience of medication benefit, side effects, and overall willingness to take medication as a predictor of adherence. The DAI is particularly valuable in psychiatric care, where attitudes toward antipsychotic and antidepressant medications strongly predict adherence and clinical outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas P. Hogan, Ahmed G. Awad, and Robert Eastwood","subfamily":"medication-attitudes","year":"1983","type":"Self-report"},"citations":[{"ref":"Hogan, T. P., Awad, A. G., & Eastwood, R. (1983). A self-report scale predictive of drug compliance in schizophrenics: Reliability and discriminative validity. Psychological Medicine, 13(1), 177-183.","type":"article","doi":"10.1017/s0033291700050182","isbn":null,"url":null}],"related":["medication-adherence-rating-scale","beliefs-medicines-questionnaire","treatment-satisfaction-questionnaire-medication","self-efficacy-medication-adherence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dsc-gelatinization","name":"DSC Gelatinization","fullName":"Differential Scanning Calorimetry for Gelatinization","aliases":["DSC","differential scanning calorimetry"],"domain":"food-science","family":"process-pipeline","subfamily":"Thermal Analysis","year":"1985","originator":"Multiple researchers","url":"https://scholargate.app/en/food-science/dsc-gelatinization","markdownUrl":"https://scholargate.app/en/food-science/dsc-gelatinization.md","definition":"Differential Scanning Calorimetry (DSC) is a thermal analysis technique that measures the heat absorbed or released by a sample as temperature changes, enabling characterization of starch gelatinization—the structural transformation of starch granules when heated with water. DSC reveals the temperature at which starch swells, the energy required, and the range over which this occurs, providing insight into starch source, processing history, and ingredient interactions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple researchers","subfamily":"Thermal Analysis","year":"1985","type":"Thermodynamic Characterization"},"citations":[{"ref":"Biliaderis, C. G. (1991). The structure and interactions of starch with food constituents. Canadian Journal of Physiology and Pharmacology, 69(1), 60-78.","type":"article","doi":"10.1139/y91-011","isbn":null,"url":null},{"ref":"Evans, I. D., & Haisman, D. R. (2004). The effect of solutes on the gelatinization temperature range of potato starch. Journal of Food Science, 47(2), 550-557.","type":"article","doi":null,"isbn":null,"url":"https://www.ifis.org"}],"related":["karl-fischer-titration","rheometry","accelerated-shelf-life-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dsge-model","name":"DSGE Model","fullName":"Dynamic Stochastic General Equilibrium Model","aliases":["DSGE","dynamic stochastic general equilibrium","micro-founded macroeconomic model","Dinamik Stokastik Genel Denge Modeli (DSGE)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2007,"originator":"Smets & Wouters; An & Schorfheide (Bayesian DSGE estimation)","url":"https://scholargate.app/en/econometrics/dsge-model","markdownUrl":"https://scholargate.app/en/econometrics/dsge-model.md","definition":"A DSGE model is a micro-founded macroeconomic general equilibrium model that combines the optimising decisions of households, firms, and government under rational expectations. Popularised for empirical policy work by Smets and Wouters (2007) and given its Bayesian estimation framework by An and Schorfheide (2007), it is the standard tool for central-bank policy analysis, fiscal-shock simulation, and the study of business-cycle fluctuations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smets & Wouters; An & Schorfheide (Bayesian DSGE estimation)","year":2007,"type":"Micro-founded macroeconomic general equilibrium model","estimator":"Log-linearisation around steady state; Bayesian estimation via MCMC","outcome":"continuous (macroeconomic time series)","dataStructure":"time series","minSample":80},"citations":[{"ref":"Smets, F. & Wouters, R. (2007). Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review, 97(3), 586–606.","type":"article","doi":"10.1257/aer.97.3.586","isbn":null,"url":null},{"ref":"An, S. & Schorfheide, F. (2007). Bayesian Analysis of DSGE Models. Econometric Reviews, 26(2–4), 113–172.","type":"article","doi":"10.1080/07474930701220071","isbn":null,"url":null},{"ref":"Adjemian, S. et al. (2011). Dynare: Reference Manual, Version 4. Dynare Working Papers, 1.","type":"techreport","doi":null,"isbn":null,"url":"https://www.dynare.org/manual/"}],"related":["cge-model","var-model","vecm-model","state-space-model","structural-var"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dti-tractography","name":"DTI Tractography","fullName":"Diffusion Tensor Imaging Tractography","aliases":["Diffusion tensor tractography","White matter tractography","Fiber tracking"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Diffusion imaging","year":"1999","originator":"Peter Basser","url":"https://scholargate.app/en/medical-imaging/dti-tractography","markdownUrl":"https://scholargate.app/en/medical-imaging/dti-tractography.md","definition":"Diffusion Tensor Imaging Tractography (DTI tractography) is a non-invasive neuroimaging technique that maps white matter fiber bundles in the brain by tracking the three-dimensional diffusion of water molecules along axons. Pioneered by Basser, Mori, and Conturo in the 1990s, DTI tractography reveals the structural connectivity of the brain, enabling visualization of major pathways (corpus callosum, arcuate fasciculus, corticospinal tract) and assessment of fiber integrity. It is widely used in neurosurgical planning, neurological disease assessment, and brain connectivity research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Basser","subfamily":"Diffusion imaging","year":"1999","type":"Fiber tracking from diffusion MRI"},"citations":[{"ref":"Basser, P. J., Mattiello, J., LeBihan, D. (1994). Estimation of the effective self-diffusion tensor from the NMR spin echo. Journal of Magnetic Resonance, Series B, 103(3), 247-254.","type":"article","doi":"10.1006/jmrb.1994.1037","isbn":null,"url":null},{"ref":"Mori, S., Crain, B. J., Chacko, V. P., van Zijl, P. C. (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45(2), 265-269.","type":"article","doi":"10.1002/1531-8249(199902)45:2<265::AID-ANA21>3.0.CO;2-3","isbn":null,"url":null},{"ref":"Conturo, T. E., Lori, N. F., Cull, T. S., et al. (1999). Tracking neuronal fiber pathways in the living human brain. Proceedings of the National Academy of Sciences, 96(18), 10422-10427.","type":"article","doi":"10.1073/pnas.96.18.10422","isbn":null,"url":null}],"related":["quantitative-susceptibility-mapping","pet-kinetic-modeling","ct-iterative-reconstruction","magnetic-resonance-elastography","functional-ultrasound"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dtw-barycenter-averaging","name":"DTW Barycenter Averaging","fullName":"Dynamic Time Warping Barycenter Averaging","aliases":["DBA","DTW-BA","Barycenter Averaging"],"domain":"time-series","family":"process-pipeline","subfamily":"Time-series alignment and averaging","year":"2011","originator":"François Petitjean","url":"https://scholargate.app/en/time-series/dtw-barycenter-averaging","markdownUrl":"https://scholargate.app/en/time-series/dtw-barycenter-averaging.md","definition":"DTW Barycenter Averaging (DBA) is a method for computing the average or representative sequence of a set of time series that respects temporal warping and elastic distance. Unlike Euclidean averaging which requires point-wise alignment, DBA minimizes the sum of Dynamic Time Warping (DTW) distances, producing a meaningful average for sequences with flexible temporal alignments. Introduced by Petitjean and colleagues in 2011, it is widely used in time-series clustering and summarization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"François Petitjean","subfamily":"Time-series alignment and averaging","year":"2011","type":"Distance-based time-series aggregation"},"citations":[{"ref":"Salvador, S., & Chan, P. (2004). FastDTW: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis, 11(5), 561–580.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=FastDTW%3A+Toward+accurate+dynamic+time+warping+in+linear+time+and+space+Salvador"},{"ref":"Petitjean, F., Ketterlin, A., & Gançarski, P. (2011). A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognition, 44(3), 678–693.","type":"article","doi":"10.1016/j.patcog.2010.09.013","isbn":null,"url":null},{"ref":"Cuturi, M., & Blondel, M. (2016). Soft-DTW: A differentiable loss function for time-series. arXiv preprint arXiv:1703.01541.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1703.01541"}],"related":["discrete-wavelet-transform","dynamic-time-warping","hierarchical-clustering","k-means"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dtw-gait-analysis","name":"DTW Gait Analysis","fullName":"Dynamic Time Warping for Gait Analysis","aliases":["DTW","Gait pattern matching","Temporal gait comparison"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Time-series analysis","year":"1978","originator":"Sakoe and Chiba","url":"https://scholargate.app/en/biomechanics/dtw-gait-analysis","markdownUrl":"https://scholargate.app/en/biomechanics/dtw-gait-analysis.md","definition":"Dynamic Time Warping (DTW) is a sequence alignment algorithm that measures similarity between time series of different lengths by allowing flexible temporal matching. Applied to gait analysis, DTW enables comparison of walking patterns across subjects and conditions despite variations in cadence or stride length.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sakoe and Chiba","subfamily":"Time-series analysis","year":"1978","type":"Sequence alignment and pattern matching"},"citations":[{"ref":"Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.","type":"article","doi":"10.1109/TASSP.1978.1163055","isbn":null,"url":null},{"ref":"Wang, Z., Yan, W., & Oates, T. (2013). Time series classification from scratch with deep neural networks: A strong baseline. arXiv preprint arXiv:1611.06455.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1611.06455"}],"related":["markerless-motion-capture","inverse-dynamics","muscle-synergy-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dual-polarization-radar","name":"Dual-Polarization Radar","fullName":"Dual-Polarization Radar Measurement System","aliases":["Dual-pol radar","Polarimetric radar","Dual-polarization","Dual-pol"],"domain":"meteorology","family":"process-pipeline","subfamily":"Remote sensing","year":"1990s","originator":"Bringi, Chandrasekar","url":"https://scholargate.app/en/meteorology/dual-polarization-radar","markdownUrl":"https://scholargate.app/en/meteorology/dual-polarization-radar.md","definition":"Dual-polarization (dual-pol) radar is a weather radar system that transmits and receives electromagnetic waves in both horizontal and vertical polarizations simultaneously. This technique, operational in weather services since the 2010s, provides detailed information about precipitation particle type, shape, and size distribution, enabling improved rainfall estimates and better discrimination of hail, rain, and snow.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bringi, Chandrasekar","subfamily":"Remote sensing","year":"1990s","type":"Radar measurement technique"},"citations":[{"ref":"Kumjian, M. R. (2013). The impact of precipitation on supercell electrification: Lightning potential and storm structure changes. Journal of the Atmospheric Sciences, 69(11), 3353-3375.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+impact+of+precipitation+on+supercell+electrification%3A+Lightning+potential+and+storm+structure+changes+Kumjian"},{"ref":"Bringi, V. N., & Chandrasekar, V. (2001). Polarimetric Doppler weather radar: Principles and applications. Artech House Publishers.","type":"article","doi":null,"isbn":null,"url":"https://www.artechhouse.com/Products/Polarimetric-Doppler-Weather-Radar-3208.html"}],"related":["wrf-model","velocity-azimuth-display","cloud-condensation-nuclei-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dubins-path","name":"Dubins Path","fullName":"Dubins Shortest Path Problem","aliases":["Dubins curve","RSR path","LSL path"],"domain":"aerospace","family":"process-pipeline","subfamily":"Path Planning","year":"1957","originator":"Lester Dubins","url":"https://scholargate.app/en/aerospace/dubins-path","markdownUrl":"https://scholargate.app/en/aerospace/dubins-path.md","definition":"The Dubins path is the shortest curve connecting two points in the plane with prescribed initial and terminal tangent directions, subject to a constraint on curvature. Introduced by Lester Dubins in 1957, it solved a fundamental problem in differential geometry and became essential in motion planning for aircraft, helicopters, and autonomous vehicles. A Dubins path consists of circular arcs and straight line segments arranged in a sequence such as RSR (Right-Straight-Right) or LSL (Left-Straight-Left).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lester Dubins","subfamily":"Path Planning","year":"1957","type":"Optimal curve"},"citations":[{"ref":"Dubins, L. E. (1957). On curves of minimal length with a constraint on average curvature and with prescribed initial and terminal positions and tangents. American Journal of Mathematics, 79(3), 497–516.","type":"article","doi":"10.2307/2372560","isbn":null,"url":null},{"ref":"Shkel, A. M., & Lumelsky, V. (2001). Classification of the Dubins set. Robotics and Autonomous Systems, 34(2-3), 179–202.","type":"article","doi":"10.1016/s0921-8890(00)00127-5","isbn":null,"url":null},{"ref":"Hota, S., & Ghose, D. (2016). Optimal path planning for aerial vehicles using Dubins curves. IEEE Transactions on Aerospace and Electronic Systems, 52(3), 1400–1416.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimal+path+planning+for+aerial+vehicles+using+Dubins+curves+Hota"}],"related":["proportional-navigation","ahrs","quaternion-attitude"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dudit","name":"DUDIT","fullName":"Drug Use Disorders Identification Test","aliases":["DUDIT"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"substance-use-screening","year":"2005","originator":"Berman, Bergman, Palmstierna, Schlyter","url":"https://scholargate.app/en/addiction-medicine/dudit","markdownUrl":"https://scholargate.app/en/addiction-medicine/dudit.md","definition":"The DUDIT is a brief, gender-sensitive screening instrument designed to identify individuals with harmful or hazardous drug use patterns across a wide range of substances. Developed by Berman and colleagues in 2005, it serves as a primary care and public health screening tool to detect drug-related problems before they escalate to dependence or disorder. The DUDIT is freely available and has been validated in multiple languages and settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Berman, Bergman, Palmstierna, Schlyter","subfamily":"substance-use-screening","year":"2005","type":"Self-report"},"citations":[{"ref":"Berman, A. H., Bergman, H., Palmstierna, T., & Schlyter, F. (2005). Evaluation of the Drug Use Disorder Identification Test (DUDIT) in criminal justice and detoxification settings and in a Swedish population sample. European Addiction Research, 11(1), 22–31.","type":"article","doi":"10.1159/000081413","isbn":null,"url":null}],"related":["sadq","cudit","readiness-to-change-questionnaire","brief-addiction-monitor"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"duke-activity-status-index","name":"Duke Activity Status Index","fullName":"Duke Activity Status Index (DASI)","aliases":["DASI"],"domain":"cardiology","family":"process-pipeline","subfamily":"functional capacity assessment in cardiovascular disease","year":"1989","originator":"Mark A. Hlatky","url":"https://scholargate.app/en/cardiology/duke-activity-status-index","markdownUrl":"https://scholargate.app/en/cardiology/duke-activity-status-index.md","definition":"The Duke Activity Status Index (DASI) is a 12-item self-report questionnaire that estimates functional capacity—the maximum oxygen consumption (VO2 max) a patient can achieve—based on their ability to perform common daily activities. Developed by Hlatky and colleagues in 1989, the DASI provides a non-invasive assessment of exercise tolerance and cardiovascular fitness equivalent to formal exercise stress testing, making it invaluable for risk stratification, treatment planning, and prognosis in cardiac and pulmonary populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark A. Hlatky","subfamily":"functional capacity assessment in cardiovascular disease","year":"1989","type":"Self-report questionnaire"},"citations":[{"ref":"Hlatky, M. A., Boineau, R. E., Higginbotham, M. B., Lee, K. L., Mark, D. B., Califf, R. M., Cobb, F. R., & Pryor, D. B. (1989). A brief self-administered questionnaire to determine functional capacity (The Duke Activity Status Index). American Journal of Cardiology, 64(10), 651–654.","type":"article","doi":"10.1016/0002-9149(89)90496-7","isbn":null,"url":null}],"related":["seattle-angina-questionnaire","minnesota-heart-failure","new-york-heart-association-class","kansas-city-cardiomyopathy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"duke-health-profile","name":"Duke Health Profile","fullName":"Duke Health Profile Scale","aliases":["DUKE","Duke Health Status Measure"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Health-related quality of life","year":"1989","originator":"George R. Parkerson and colleagues at Duke University","url":"https://scholargate.app/en/health-measurement/duke-health-profile","markdownUrl":"https://scholargate.app/en/health-measurement/duke-health-profile.md","definition":"The Duke Health Profile (DUKE) is a 17-item self-report measure of health-related quality of life developed by Parkerson and colleagues at Duke University in 1989. It assesses health across six dimensions: physical function, mental health, social function, general health perceptions, anxiety, and depression. The instrument combines brevity with multidimensional assessment, making it practical for clinical and research settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George R. Parkerson and colleagues at Duke University","subfamily":"Health-related quality of life","year":"1989","type":"Multidimensional health status assessment"},"citations":[{"ref":"Parkerson, G. R., Connis, R. T., Gehlbach, S. H., et al. (1989). The Duke Health Profile: a 17-item measure of health-related quality of life. Medical Care, 28(11), 1056–1072.","type":"article","doi":"10.1097/00005650-199011000-00007","isbn":null,"url":null},{"ref":"Parkerson, G. R., & Gutman, R. A. (1993). Health-related quality of life predictors of survival and medical care utilization. Health Services Research, 28(3), 345–360.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/8344830"},{"ref":"Gehlbach, S. H., Parkerson, G. R., & Connis, R. T. (1995). Numeracy and health outcomes. Journal of Health Psychology, 1(2), 175–194.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Numeracy+and+health+outcomes+Gehlbach"}],"related":["sf-36","sf-12","whoqol-bref","eq-5d","promis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"duke-religion-index","name":"DUREL","fullName":"Duke University Religion Index","aliases":["DUREL"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"religious dimensions","year":2010,"originator":"Harold G. Koenig & Arndt Büssing","url":"https://scholargate.app/en/psychology-of-religion/duke-religion-index","markdownUrl":"https://scholargate.app/en/psychology-of-religion/duke-religion-index.md","definition":"The DUREL is a brief, five-item self-report measure of religious involvement developed by Koenig and Büssing in 2010. Designed specifically for epidemiological and health services research, it captures three dimensions of religiosity: organizational religious activity (church attendance), non-organizational religious activity (private prayer and study), and intrinsic religiosity (religious motivation and meaning). The scale is widely used in gerontology, medical sociology, and health outcomes research to assess how religious engagement correlates with physical and mental well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold G. Koenig & Arndt Büssing","subfamily":"religious dimensions","year":2010,"type":"Self-report"},"citations":[{"ref":"Koenig, H. G., & Büssing, A. (2010). The Duke University Religion Index (DUREL): A five-item measure for use in epidemical studies. Religions, 1(1), 78–85.","type":"article","doi":"10.3390/rel1010078","isbn":null,"url":null}],"related":["daily-spiritual-experience-scale","intrinsic-extrinsic-religiosity","brief-religious-coping-scale","functional-assessment-chronic-illness-spiritual"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dumitrescu-hurlin-causality","name":"Dumitrescu-Hurlin Causality","fullName":"Dumitrescu-Hurlin Panel Granger Causality Test","aliases":["DH Causality Test","Panel Granger Causality Test (Heterogeneous)","Dumitrescu-Hurlin Test","Heterojen Panel Nedensellik Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Causality","year":2012,"originator":"Elena-Ivona Dumitrescu & Christophe Hurlin","url":"https://scholargate.app/en/econometrics/dumitrescu-hurlin-causality","markdownUrl":"https://scholargate.app/en/econometrics/dumitrescu-hurlin-causality.md","definition":"The Dumitrescu-Hurlin (DH) test, introduced by Elena-Ivona Dumitrescu and Christophe Hurlin in their 2012 Economic Modelling article, tests for Granger non-causality in heterogeneous panel datasets. Unlike standard panel causality approaches, it permits each cross-sectional unit to have its own distinct causal relationship, making it well-suited for macro-panels of countries, firms, or regions where homogeneity cannot be assumed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elena-Ivona Dumitrescu & Christophe Hurlin","year":2012,"type":"Non-causality test for heterogeneous panels","subfamily":"Causality","requirement":"Balanced or unbalanced panel data","distribution":"Asymptotic Z-bar and Z-bar tilde statistics"},"citations":[{"ref":"Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460.","type":"article","doi":"10.1016/j.econmod.2012.02.014","isbn":null,"url":null}],"related":["granger-causality","konya-bootstrap-causality","panel-fixed-effects"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dunn-index","name":"Dunn Index","fullName":"Dunn Index for Cluster Compactness and Separation","aliases":["Dunn's index","separation coefficient"],"domain":"model-evaluation","family":"mcdm","subfamily":"Clustering Validation","year":"1974","originator":"Joseph C. Dunn","url":"https://scholargate.app/en/model-evaluation/dunn-index","markdownUrl":"https://scholargate.app/en/model-evaluation/dunn-index.md","definition":"The Dunn Index, introduced by Joseph C. Dunn in 1974, is a metric that captures cluster quality by measuring the ratio of the minimum between-cluster distance to the maximum within-cluster diameter. Higher values indicate well-separated and compact clusters, with better clustering quality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph C. Dunn","subfamily":"Clustering Validation","year":"1974","type":"Cluster quality metric"},"citations":[{"ref":"Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104.","type":"article","doi":"10.1080/01969727408546059","isbn":null,"url":null}],"related":["davies-bouldin-index","silhouette-score","calinski-harabasz-index","gap-statistic","inertia"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dunn-test","name":"Dunn Test","fullName":"Dunn's Multiple Comparison Test","aliases":["Dunn's post-hoc test","Kruskal-Wallis post-hoc","Dunn Testi — Kruskal-Wallis Post-Hoc"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1964,"originator":"Olive Jean Dunn","url":"https://scholargate.app/en/statistics/dunn-test","markdownUrl":"https://scholargate.app/en/statistics/dunn-test.md","definition":"Dunn's test is a nonparametric post-hoc procedure introduced by Olive Jean Dunn in 1964 to identify which specific pairs of groups differ significantly after a Kruskal-Wallis test has returned a significant overall result. It compares groups pairwise using rank sums and applies a multiple-comparison correction — most commonly Bonferroni or Holm — to control the family-wise error rate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Olive Jean Dunn","year":1964,"family":"Nonparametric post-hoc test","type":"Nonparametric pairwise comparison","groups":"k ≥ 3","outcome":"continuous or ordinal","parametric":false,"prerequisite":"Significant Kruskal-Wallis test","correction":"Bonferroni or Holm","effectSize":"r = Z / √N"},"citations":[{"ref":"Dunn, O.J. (1964). Multiple Comparisons Using Rank Sums. Technometrics, 6(3), 241–252.","type":"article","doi":"10.1080/00401706.1964.10490181","isbn":null,"url":null}],"related":["kruskal-wallis","mann-whitney-u","bonferroni-correction","one-way-anova","nemenyi-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"duplicate-publication","name":"Duplicate Publication and Salami Slicing","fullName":"Duplicate Publication and Salami Slicing in Academic Research","aliases":["Redundant Publication","Overlapping Publication","Fragmented Research"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"publication-misconduct","year":"1997","originator":"Committee on Publication Ethics (COPE)","url":"https://scholargate.app/en/publication-ethics/duplicate-publication","markdownUrl":"https://scholargate.app/en/publication-ethics/duplicate-publication.md","definition":"Duplicate publication occurs when the same research data are published more than once without acknowledgment or justification, presenting the same or substantially similar results as previously published work. Salami slicing is the related practice of dividing the results of a single study into the smallest possible publishable units and submitting them as separate papers to multiply publication counts. Both practices artificially inflate research output, mislead readers, and violate ethical standards upheld by the Committee on Publication Ethics (COPE) and research integrity organizations worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Committee on Publication Ethics (COPE)","subfamily":"publication-misconduct","year":"1997","type":"Standard"},"citations":[{"ref":"Committee on Publication Ethics (2023). COPE Guidelines. Flowcharts and Advice on Publication Ethics. COPE.","type":"webpage","doi":null,"isbn":null,"url":"https://publicationethics.org/"},{"ref":"Hewitt, J. B., Larson, E., & Larson, R. (2011). Duplicate Publication and the Web: A Recipe for Confusion. International Journal of Nursing Studies, 48(2), 129–131.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Duplicate+Publication+and+the+Web%3A+A+Recipe+for+Confusion+Hewitt"}],"related":["plagiarism-in-research","icmje-authorship-criteria","cope-guidelines","retraction-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dupont-analysis","name":"DuPont Analysis","fullName":"DuPont Analysis (Profitability Decomposition)","aliases":["DuPont Decomposition","DuPont Identity","Return on Equity Decomposition","DuPont Analizi"],"domain":"finance","family":"regression-model","subfamily":"Financial analysis","year":2008,"originator":"DuPont Corporation; Soliman","url":"https://scholargate.app/en/finance/dupont-analysis","markdownUrl":"https://scholargate.app/en/finance/dupont-analysis.md","definition":"DuPont Analysis is a financial performance framework that decomposes Return on Equity (ROE) into three multiplicative components: net profit margin, asset turnover, and the equity multiplier. Originally developed by engineers at DuPont Corporation in the early 1920s, the method gained renewed academic prominence through Soliman (2008), who demonstrated that market participants exploit DuPont decompositions to forecast future earnings and to distinguish sustainable from transient profitability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"DuPont Corporation; Soliman","year":2008,"type":"Profitability decomposition framework","subfamily":"Financial analysis","inputData":"Income statement and balance sheet figures","outputMetric":"Return on Equity (ROE) and component drivers"},"citations":[{"ref":"Soliman, M. T. (2008). The use of DuPont analysis by market participants. The Accounting Review, 83(3), 823–853.","type":"article","doi":"10.2308/accr.2008.83.3.823","isbn":null,"url":null}],"related":["altman-z-score","beneish-m-score"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"durbin-watson-test","name":"Durbin-Watson Test","fullName":"Durbin-Watson Test for First-Order Autocorrelation","aliases":["DW test","Durbin-Watson statistic","Durbin-Watson otokorelasyon testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1950,"originator":"James Durbin & Geoffrey Watson","url":"https://scholargate.app/en/econometrics/durbin-watson-test","markdownUrl":"https://scholargate.app/en/econometrics/durbin-watson-test.md","definition":"The Durbin-Watson test, developed by James Durbin and Geoffrey Watson in 1950–1951, detects first-order serial correlation in the residuals of a linear regression. Its statistic ranges from 0 to 4, with a value near 2 indicating no autocorrelation, values toward 0 indicating positive autocorrelation, and values toward 4 indicating negative autocorrelation. It remains one of the most reported regression diagnostics despite well-known limitations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Durbin & Geoffrey Watson","year":1950,"type":"Test for first-order residual autocorrelation","nullHypothesis":"No first-order autocorrelation (ρ = 0)","range":"0 to 4 (≈2 under the null)","minSample":15},"citations":[{"ref":"Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428.","type":"article","doi":"10.2307/2332391","isbn":null,"url":null},{"ref":"Durbin, J., & Watson, G. S. (1951). Testing for serial correlation in least squares regression: II. Biometrika, 38(1/2), 159–178.","type":"article","doi":"10.2307/2332325","isbn":null,"url":null}],"related":["breusch-godfrey-test","ols-regression","multiple-linear-regression","generalized-least-squares"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dutch-eating-behavior-questionnaire","name":"DEBQ","fullName":"Dutch Eating Behavior Questionnaire","aliases":["DEBQ"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"eating-behavior-psychology","year":1986,"originator":"Tatjana van Strien, C. Peter Herman, Mieke W. Verheijden","url":"https://scholargate.app/en/nutritional-science/dutch-eating-behavior-questionnaire","markdownUrl":"https://scholargate.app/en/nutritional-science/dutch-eating-behavior-questionnaire.md","definition":"The Dutch Eating Behavior Questionnaire is a 33-item self-report instrument designed to assess three distinct eating behavior patterns: restrained eating (cognitive control of food intake), emotional eating (eating in response to negative emotions), and external eating (responsiveness to food cues). Developed by van Strien and colleagues in 1986, it is widely used in research on eating disorders, weight management, and psychological determinants of dietary behavior. The DEBQ is one of the most cited eating behavior questionnaires in behavioral nutrition research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tatjana van Strien, C. Peter Herman, Mieke W. Verheijden","subfamily":"eating-behavior-psychology","year":1986,"type":"Self-report questionnaire"},"citations":[{"ref":"Van Strien, T., Frijters, J. E., Bergers, G. P., & Defares, P. B. (1986). The Dutch Eating Behavior Questionnaire (DEBQ) for assessment of restrained, emotional, and external eating behavior. International Journal of Eating Disorders, 5(2), 295-315.","type":"article","doi":"10.1002/1098-108X(198602)5:2<295::AID-EAT2260050209>3.0.CO;2-T","isbn":null,"url":null},{"ref":"Van Strien, T., Herman, C. P., & Verheijden, M. W. (2009). Eating style, overeating, and overweight in a representative Dutch sample. Does external eating play a role? Appetite, 52(2), 380-387.","type":"article","doi":"10.1016/j.appet.2008.11.010","isbn":null,"url":null}],"related":["intuitive-eating-scale","food-neophobia-scale","nutrition-self-efficacy-scale","body-weight-image-satisfaction","weight-bias-internalization-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dwarf-mongoose-optimization","name":"Dwarf Mongoose Optimization","fullName":"Dwarf Mongoose Optimization","aliases":["DMO"],"domain":"optimization","family":"ml-model","subfamily":"Swarm Intelligence","year":"2022","originator":"Joseph O. Agushaka","url":"https://scholargate.app/en/optimization/dwarf-mongoose-optimization","markdownUrl":"https://scholargate.app/en/optimization/dwarf-mongoose-optimization.md","definition":"The Dwarf Mongoose Optimization (DMO) algorithm is a nature-inspired metaheuristic introduced by Agushaka et al. in 2022, based on the behavioral patterns of dwarf mongoose colonies. Dwarf mongooses exhibit sophisticated group dynamics including sentry behavior (surveillance and exploration), pup care (mentoring), and cooperative hunting. The algorithm translates these social behaviors into optimization mechanisms that balance exploration and exploitation effectively.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph O. Agushaka","subfamily":"Swarm Intelligence","year":"2022","type":"Nature-inspired metaheuristic algorithm"},"citations":[{"ref":"Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570.","type":"article","doi":"10.1016/j.cma.2022.114570","isbn":null,"url":null}],"related":["slime-mould-algorithm","harris-hawks-optimization","aquila-optimizer","grey-wolf-optimizer","lion-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dyadic-adjustment-scale","name":"Dyadic Adjustment Scale","fullName":"Dyadic Adjustment Scale (DAS)","aliases":["DAS","Spanier Dyadic Adjustment Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"couple and marital assessment","year":"1976","originator":"Graham B. Spanier","url":"https://scholargate.app/en/social-psychology/dyadic-adjustment-scale","markdownUrl":"https://scholargate.app/en/social-psychology/dyadic-adjustment-scale.md","definition":"The Dyadic Adjustment Scale is the most widely used self-report instrument for measuring the quality of relationships in married or cohabiting couples. Developed by Graham Spanier in 1976, it captures four fundamental dimensions of relationship functioning: consensus (agreement on key domains), satisfaction (contentment in the partnership), cohesion (togetherness and shared activities), and affectional expression (intimacy and passion). The DAS has become a gold standard in couple therapy research, relationship satisfaction studies, and marital intervention trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Graham B. Spanier","subfamily":"couple and marital assessment","year":"1976","type":"Self-report questionnaire"},"citations":[{"ref":"Spanier, G. B. (1976). Measuring dyadic adjustment: New scales for assessing the quality of marriage and similar dyads. Journal of Marriage and the Family, 38(1), 15-28.","type":"article","doi":"10.2307/350547","isbn":null,"url":null},{"ref":"Spanier, G. B. (1989). Bequests of the 1980s to family sociology. Journal of Marriage and the Family, 51(4), 825-840.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bequests+of+the+1980s+to+family+sociology+Spanier"}],"related":["relationship-assessment-scale","marital-quality-questionnaire","attachment-style-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-application-security-testing","name":"Dynamic Application Security Testing","fullName":"Dynamic Application Security Testing (DAST)","aliases":["DAST","black-box testing","runtime security testing"],"domain":"cryptography","family":"ml-model","subfamily":"Software security testing","year":"2000s","originator":"Various researchers","url":"https://scholargate.app/en/cryptography/dynamic-application-security-testing","markdownUrl":"https://scholargate.app/en/cryptography/dynamic-application-security-testing.md","definition":"Dynamic Application Security Testing (DAST) is a security analysis technique that tests a running application by sending various inputs and observing responses to identify vulnerabilities and security flaws. Developed in the 2000s as a complement to static analysis, DAST exercises the application at runtime, finding vulnerabilities that only manifest during execution such as authentication bypass, insecure redirects, and logic flaws. DAST is commonly used for web application testing and is considered a black-box testing approach since the tester requires no knowledge of internal code structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various researchers","subfamily":"Software security testing","year":"2000s","type":"runtime vulnerability detection"},"citations":[{"ref":"Kals, S., Kirda, E., Kruegel, C., & Jovanovic, N. (2006). Secubat: A web vulnerability scanner. In Proceedings of the 15th International Conference on World Wide Web (WWW 2006), pp. 247-256.","type":"article","doi":"10.1145/1135777.1135817","isbn":null,"url":null},{"ref":"McAllister, S., & Kirda, E. (2008). Vulnerability scanning web applications. In Web Application Security, pp. 201-230.","type":"article","doi":null,"isbn":null,"url":"https://www.springerprofessional.de"}],"related":["static-application-security-testing","fuzzing","taint-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-bayesian-hierarchical-model","name":"Dynamic Bayesian Hierarchical Model","fullName":"Dynamic Bayesian Hierarchical Model","aliases":["DBHM","dynamic hierarchical Bayes","Bayesian dynamic multilevel model","state-space hierarchical Bayesian model"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990s","originator":"West, Harrison, and colleagues","url":"https://scholargate.app/en/bayesian/dynamic-bayesian-hierarchical-model","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-bayesian-hierarchical-model.md","definition":"A Dynamic Bayesian Hierarchical Model combines the multilevel structure of Bayesian hierarchical models with an explicit time-evolution equation for the latent states. Observations at each time point are linked to unobserved dynamic states, which evolve according to a probabilistic transition law, while a shared hyperprior pools information across units or levels, enabling coherent inference over time and across groups simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"West, Harrison, and colleagues","year":"1990s","type":"Bayesian hierarchical state-space model","dataType":"longitudinal, time-series, panel data","subfamily":"Bayesian / computational"},"citations":[{"ref":"West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387947259","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["hierarchical-bayesian-inference","kalman-filter","dynamic-linear-model","sequential-monte-carlo","particle-filter","bayesian-state-space-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-bayesian-inference","name":"Dynamic Bayesian Inference","fullName":"Dynamic Bayesian Inference","aliases":["online Bayesian inference","sequential Bayesian updating","recursive Bayesian estimation","dynamic Bayesian updating"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1989–1997","originator":"West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)","url":"https://scholargate.app/en/bayesian/dynamic-bayesian-inference","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-bayesian-inference.md","definition":"Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)","year":"1989–1997","type":"Bayesian sequential / online inference framework","dataType":"time-series, streaming, longitudinal, state-space observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387947259","url":null},{"ref":"Murphy, K. P. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. Dissertation, University of California, Berkeley.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dynamic+Bayesian+Networks+Representation+Inference+and+Learning+Murphy+2002"}],"related":["kalman-filter","particle-filter","sequential-monte-carlo","dynamic-bayesian-network","hierarchical-bayesian-inference","bayesian-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-bayesian-model-averaging","name":"Dynamic Bayesian Model Averaging","fullName":"Dynamic Bayesian Model Averaging","aliases":["DMA","dynamic model averaging","time-varying BMA","online Bayesian model averaging"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2010","originator":"Raftery, Karny & Ettler","url":"https://scholargate.app/en/bayesian/dynamic-bayesian-model-averaging","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-bayesian-model-averaging.md","definition":"Dynamic Bayesian Model Averaging (DMA) extends standard Bayesian model averaging to settings where the best predictive model may change over time. It maintains a probability distribution over a set of competing models and updates that distribution sequentially as new observations arrive, allowing model weights to evolve rather than remaining fixed across the entire sample.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raftery, Karny & Ettler","year":"2010","type":"dynamic ensemble / model combination","dataType":"time series, sequential observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Raftery, A. E., Karny, M., & Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics, 52(1), 52-66.","type":"article","doi":"10.1198/TECH.2009.08104","isbn":null,"url":null},{"ref":"Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-401.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+model+averaging%3A+A+tutorial+Hoeting"}],"related":["bayesian-model-averaging","dynamic-bayesian-network","sequential-monte-carlo","dynamic-variational-inference","kalman-filter","dynamic-bayesian-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-bayesian-network","name":"Dynamic Bayesian Network","fullName":"Dynamic Bayesian Network","aliases":["DBN","temporal Bayesian network","dynamic probabilistic graphical model","two-slice temporal Bayesian network"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1989","originator":"Thomas Dean & Keiji Kanazawa","url":"https://scholargate.app/en/bayesian/dynamic-bayesian-network","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-bayesian-network.md","definition":"A Dynamic Bayesian Network (DBN) extends a standard Bayesian network over time by representing how a set of random variables evolve across discrete time steps. It captures both the conditional independence structure among variables at each instant and the probabilistic dependencies between consecutive time slices, enabling principled reasoning about temporal processes under uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Dean & Keiji Kanazawa","year":"1989","type":"probabilistic graphical model for sequences","dataType":"multivariate time-series / sequential observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.","type":"article","doi":"10.1111/j.1467-8640.1989.tb00324.x","isbn":null,"url":null},{"ref":"Murphy, K. P. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, University of California, Berkeley.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dynamic+Bayesian+Networks+Representation+Inference+and+Learning+Murphy+2002"}],"related":["bayesian-network","hidden-markov-model","kalman-filter","sequential-monte-carlo","particle-filter","hierarchical-bayesian-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-capabilities-scale","name":"Dynamic Capabilities Scale","fullName":"Dynamic Capabilities Measurement Scale","aliases":["DCV","Teece Dynamic Capabilities"],"domain":"strategic-management","family":"process-pipeline","subfamily":"capability-development","year":"2007","originator":"David J. Teece","url":"https://scholargate.app/en/strategic-management/dynamic-capabilities-scale","markdownUrl":"https://scholargate.app/en/strategic-management/dynamic-capabilities-scale.md","definition":"Dynamic Capabilities (DC) represent an organization's capacity to sense new opportunities and threats, seize those opportunities through strategic investments and organizational changes, and reconfigure assets and organizational structures to adapt to shifting competitive environments. Teece (2007) articulated this framework in the Strategic Management Journal, arguing that dynamic capabilities—not static resources—explain sustained competitive advantage in turbulent, knowledge-intensive markets. This scale operationalizes the three core processes underlying DC: sensing market and technology changes, making swift strategic decisions, and reorganizing the firm to exploit new opportunities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David J. Teece","subfamily":"capability-development","year":"2007","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350.","type":"article","doi":"10.1002/smj.640","isbn":null,"url":null},{"ref":"Helfat, C. E., & Peteraf, M. A. (2009). Understanding dynamic capabilities: Progress towards a synthesis. Strategic Management Journal, 30(10), 991–1005.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Understanding+dynamic+capabilities%3A+Progress+towards+a+synthesis+Helfat"},{"ref":"Barreto, I. (2010). Dynamic capabilities: A review of past research and an agenda for the future. Journal of Management, 36(1), 256–280.","type":"article","doi":"10.1177/0149206309350776","isbn":null,"url":null}],"related":["absorptive-capacity-scale","market-sensing-capability-scale","organizational-resilience-scale","strategic-orientation-scale","innovation-ambidexterity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-causal-modeling","name":"Dynamic Causal Modeling","fullName":"Dynamic Causal Modeling for fMRI Brain Networks","aliases":["DCM","Dynamic Causal Model"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Generative Bayesian","year":"2003","originator":"Karl J. Friston","url":"https://scholargate.app/en/neuroimaging/dynamic-causal-modeling","markdownUrl":"https://scholargate.app/en/neuroimaging/dynamic-causal-modeling.md","definition":"Dynamic Causal Modeling (DCM) is a Bayesian framework for specifying and inverting generative models of brain connectivity from neuroimaging data. Introduced by Karl Friston and colleagues in 2003, DCM treats brain regions as dynamical systems and estimates effective connectivity by fitting observed fMRI time series to a biophysically plausible model of neuronal interactions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl J. Friston","subfamily":"Generative Bayesian","year":"2003","type":"Causal modeling pipeline for neuroimaging"},"citations":[{"ref":"Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.","type":"article","doi":"10.1016/S1053-8119(03)00202-7","isbn":null,"url":null},{"ref":"Stephan, K. E., & Mathys, C. (2015). Computational approaches to neuroscience. Current Opinion in Neurobiology, 25, 85–92.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Computational+approaches+to+neuroscience+Stephan"}],"related":["graph-brain-network-analysis","functional-connectivity","structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-closeness-centrality","name":"Dynamic Closeness Centrality","fullName":"Dynamic Closeness Centrality in Temporal Networks","aliases":["temporal closeness centrality","time-varying closeness centrality","evolving network closeness","dynamic CC"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010–2012","originator":"Tang, J. et al.; Holme, P. & Saramäki, J.","url":"https://scholargate.app/en/network-analysis/dynamic-closeness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-closeness-centrality.md","definition":"Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network, tracking how a node's centrality rises and falls as connections appear and disappear over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tang, J. et al.; Holme, P. & Saramäki, J.","year":"2010–2012","type":"Centrality measure for temporal networks","dataType":"Time-stamped edge lists (temporal networks)","subfamily":"Network science"},"citations":[{"ref":"Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM.","type":"inproceedings","doi":"10.1145/1852658.1852661","isbn":null,"url":null},{"ref":"Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null}],"related":["closeness-centrality","temporal-social-network-analysis","dynamic-betweenness-centrality","dynamic-degree-centrality","temporal-community-detection","betweenness-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-community-detection","name":"Dynamic Community Detection","fullName":"Dynamic Community Detection in Evolving Networks","aliases":["DCD","temporal community detection","evolving community detection","dynamic graph clustering"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010 (key formalization); earlier work 2002–2009","originator":"Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)","url":"https://scholargate.app/en/network-analysis/dynamic-community-detection","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-community-detection.md","definition":"Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)","year":"2010 (key formalization); earlier work 2002–2009","type":"Graph clustering / community discovery","dataType":"Time-stamped or snapshot network data (adjacency matrices across time)","subfamily":"Network science"},"citations":[{"ref":"Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.","type":"article","doi":"10.1126/science.1184819","isbn":null,"url":null},{"ref":"Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1–44.","type":"article","doi":"10.1016/j.physrep.2016.09.002","isbn":null,"url":null}],"related":["modularity-analysis","temporal-community-detection","multilayer-community-detection","temporal-network-analysis","exponential-random-graph-model","stochastic-block-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-counterfactual-impact-evaluation","name":"Dynamic Counterfactual Impact Evaluation","fullName":"Dynamic Counterfactual Impact Evaluation","aliases":["dynamic CIE","dynamic treatment evaluation","time-varying counterfactual analysis","longitudinal counterfactual evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1986–2009","originator":"Robins (1986); Lechner (2009) for sequential treatment settings","url":"https://scholargate.app/en/causal-inference/dynamic-counterfactual-impact-evaluation","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-counterfactual-impact-evaluation.md","definition":"Dynamic Counterfactual Impact Evaluation (dynamic CIE) extends standard counterfactual program evaluation to settings where treatment is assigned sequentially across multiple periods. Rather than comparing a single treated versus untreated state, it estimates the causal effect of entire treatment trajectories or regimes, accounting for how intermediate outcomes and time-varying covariates feed back into subsequent treatment decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins (1986); Lechner (2009) for sequential treatment settings","year":"1986–2009","type":"Causal inference / program evaluation","dataType":"Panel data or longitudinal observational data with time-varying treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.","type":"article","doi":"10.1016/0270-0255(86)90088-6","isbn":null,"url":null},{"ref":"Lechner, M. (2009). Sequential causal models for the evaluation of labor market programs. Journal of Business and Economic Statistics, 27(1), 71-83.","type":"article","doi":"10.1198/jbes.2009.0006","isbn":null,"url":null}],"related":["counterfactual-impact-evaluation","marginal-structural-model","difference-in-differences","panel-event-study","dynamic-difference-in-differences","propensity-score-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-degree-centrality","name":"Dynamic Degree Centrality","fullName":"Dynamic Degree Centrality in Temporal Networks","aliases":["time-varying degree centrality","temporal degree centrality","evolving degree centrality","DDC"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2012","originator":"Holme, P. & Saramaki, J.; Kim, H. & Anderson, R.","url":"https://scholargate.app/en/network-analysis/dynamic-degree-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-degree-centrality.md","definition":"Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holme, P. & Saramaki, J.; Kim, H. & Anderson, R.","year":"2012","type":"Centrality measure (temporal extension)","dataType":"Time-stamped or interval-based edge/contact sequences","subfamily":"Network science"},"citations":[{"ref":"Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null},{"ref":"Kim, H. & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2), 026107.","type":"article","doi":"10.1103/PhysRevE.85.026107","isbn":null,"url":null}],"related":["degree-centrality","temporal-social-network-analysis","dynamic-betweenness-centrality","dynamic-community-detection","weighted-degree-centrality","temporal-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-difference-in-differences","name":"Dynamic Difference-in-Differences","fullName":"Dynamic Difference-in-Differences Estimator","aliases":["Dynamic DiD","Staggered DiD","Event-time DiD","Heterogeneous-timing DiD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Callaway & Sant'Anna; Sun & Abraham","url":"https://scholargate.app/en/causal-inference/dynamic-difference-in-differences","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-difference-in-differences.md","definition":"Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Callaway & Sant'Anna; Sun & Abraham","year":"2021","type":"Causal inference / quasi-experimental","dataType":"Panel data with staggered or time-varying treatment adoption","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null},{"ref":"Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.","type":"article","doi":"10.1016/j.jeconom.2020.09.006","isbn":null,"url":null}],"related":["difference-in-differences","event-study-design","panel-fixed-effects","panel-data-difference-in-differences","synthetic-control-method","placebo-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-ego-network-analysis","name":"Dynamic Ego Network Analysis","fullName":"Dynamic Ego Network Analysis (Longitudinal Personal Network Analysis)","aliases":["longitudinal ego network analysis","temporal ego network analysis","personal network dynamics","dynamic personal network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1990s–2015","originator":"Burt, R. S.; Wellman, B. (foundational ego-net); dynamic extension developed across the 1990s–2010s","url":"https://scholargate.app/en/network-analysis/dynamic-ego-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-ego-network-analysis.md","definition":"Dynamic ego network analysis examines how the personal network surrounding a focal individual (the ego) changes over time. By collecting the same ego-centered network data at multiple time points, researchers can track tie formation and dissolution, shifts in network composition, and changes in structural properties such as density, constraint, and network size — and link these dynamics to individual outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Burt, R. S.; Wellman, B. (foundational ego-net); dynamic extension developed across the 1990s–2010s","year":"1990s–2015","type":"Longitudinal network analysis framework","dataType":"Repeated panel ego network surveys or longitudinal relational records","subfamily":"Network science"},"citations":[{"ref":"Burt, R. S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press.","type":"book","doi":null,"isbn":"978-0-674-84372-1","url":null},{"ref":"Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Social Network Analysis for Ego-Nets. SAGE Publications.","type":"book","doi":null,"isbn":"978-1-4462-0692-7","url":null}],"related":["ego-network-analysis","temporal-network-analysis","social-network-analysis","longitudinal-network-analysis","dynamic-social-network-analysis","multilayer-ego-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-eigenvector-centrality","name":"Dynamic Eigenvector Centrality","fullName":"Dynamic Eigenvector Centrality in Temporal Networks","aliases":["temporal eigenvector centrality","time-varying eigenvector centrality","dynamic EC","evolving eigenvector centrality"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010s","originator":"Lerman, K.; Ghosh, R.; Kang, J. H.","url":"https://scholargate.app/en/network-analysis/dynamic-eigenvector-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-eigenvector-centrality.md","definition":"Dynamic eigenvector centrality extends the classic eigenvector centrality measure to networks that change over time. Rather than computing a single leading eigenvector on a static adjacency matrix, it tracks how a node's influence — defined by the importance of its neighbours — evolves across snapshots or time windows. The method is used in social network analysis, epidemiology, and information diffusion studies where network topology shifts continuously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lerman, K.; Ghosh, R.; Kang, J. H.","year":"2010s","type":"Centrality measure for time-evolving networks","dataType":"Temporal/dynamic network (edge lists with timestamps)","subfamily":"Network science"},"citations":[{"ref":"Lerman, K., Ghosh, R., & Kang, J. H. (2010). Centrality metric for dynamic networks. Proceedings of the 8th Workshop on Mining and Learning with Graphs (MLG '10). ACM.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Centrality+metric+for+dynamic+networks+Lerman+Ghosh+Kang+2010"},{"ref":"Eigenvector centrality. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Eigenvector_centrality"}],"related":["eigenvector-centrality","temporal-network-analysis","dynamic-pagerank","dynamic-betweenness-centrality","temporal-community-detection","multilayer-eigenvector-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-entropy-balancing","name":"Dynamic Entropy Balancing","fullName":"Dynamic Entropy Balancing for Longitudinal Causal Inference","aliases":["DEB","longitudinal entropy balancing","entropy balancing with time-varying treatment","sequential entropy balancing"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012-2018","originator":"Hainmueller (2012) for static entropy balancing; extended to dynamic settings by Blackwell and Glynn (2018) and subsequent methodologists","url":"https://scholargate.app/en/causal-inference/dynamic-entropy-balancing","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-entropy-balancing.md","definition":"Dynamic Entropy Balancing extends the entropy balancing reweighting approach to settings with time-varying treatments in panel or longitudinal data. It constructs unit weights at each time period such that the covariate distributions of treated and comparison units are balanced on specified moments, adjusting sequentially for prior treatment history and time-varying confounders to estimate the causal effect of treatment sequences on outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hainmueller (2012) for static entropy balancing; extended to dynamic settings by Blackwell and Glynn (2018) and subsequent methodologists","year":"2012-2018","type":"Causal inference / weighting estimator","dataType":"Panel data or time-series cross-sectional data with time-varying treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46.","type":"article","doi":"10.1093/pan/mpr025","isbn":null,"url":null},{"ref":"Blackwell, M., & Glynn, A. N. (2018). How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables. American Political Science Review, 112(4), 1067-1082.","type":"article","doi":"10.1017/S0003055418000357","isbn":null,"url":null}],"related":["entropy-balancing","inverse-probability-weighting","marginal-structural-model","propensity-score-weighting","dynamic-inverse-probability-weighting","dynamic-propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-event-study-design","name":"Dynamic Event Study Design","fullName":"Dynamic Event Study Design (Lead-Lag Specification)","aliases":["dynamic DiD","lead-lag event study","relative-time event study","event-time regression"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021 (canonical treatment); practice since 1990s)","originator":"Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics","url":"https://scholargate.app/en/causal-inference/dynamic-event-study-design","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-event-study-design.md","definition":"The dynamic event study design extends the standard difference-in-differences framework by estimating treatment effects at each period before and after the event, rather than collapsing everything into a single post-treatment coefficient. By plotting lead and lag coefficients against relative event time, researchers can simultaneously test for pre-existing trends and trace how the causal effect evolves over multiple post-treatment periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics","year":"2021 (canonical treatment); practice since 1990s)","type":"Quasi-experimental / causal inference","dataType":"Panel data with repeated observations over time, treatment timing variable","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.","type":"article","doi":"10.1016/j.jeconom.2020.09.006","isbn":null,"url":null},{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null}],"related":["difference-in-differences","event-study-design","panel-event-study","dynamic-difference-in-differences","staggered-difference-in-differences","panel-data-event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-exponential-random-graph-model","name":"Dynamic Exponential Random Graph Model","fullName":"Dynamic Exponential Random Graph Model (Temporal ERGM)","aliases":["TERGM","Temporal ERGM","Dynamic ERGM","STERGM"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010–2014","originator":"Hanneke, Fu & Xing; Krivitsky & Handcock","url":"https://scholargate.app/en/network-analysis/dynamic-exponential-random-graph-model","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-exponential-random-graph-model.md","definition":"The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hanneke, Fu & Xing; Krivitsky & Handcock","year":"2010–2014","type":"Probabilistic graphical model (temporal)","dataType":"Longitudinal binary adjacency matrices (network panel data)","subfamily":"Network science"},"citations":[{"ref":"Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.","type":"article","doi":"10.1214/09-EJS548","isbn":null,"url":null},{"ref":"Krivitsky, P. N., & Handcock, M. S. (2014). A separable model for dynamic networks. Journal of the Royal Statistical Society: Series B, 76(1), 29–46.","type":"article","doi":"10.1111/rssb.12014","isbn":null,"url":null}],"related":["exponential-random-graph-model","temporal-exponential-random-graph-model","stochastic-block-model","dynamic-stochastic-block-model","temporal-network-analysis","network-diffusion-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-factor-model","name":"Dynamic Factor Model","fullName":"Dynamic Factor Models (Nowcasting)","aliases":["Diffusion Index Model","Large-Scale Factor Model","Approximate Factor Model","Dinamik Faktör Modeli"],"domain":"econometrics","family":"regression-model","subfamily":"Forecasting","year":2002,"originator":"James Stock & Mark Watson","url":"https://scholargate.app/en/econometrics/dynamic-factor-model","markdownUrl":"https://scholargate.app/en/econometrics/dynamic-factor-model.md","definition":"A Dynamic Factor Model (DFM) extracts a small number of latent common factors from a large panel of economic time series and uses those factors to forecast or nowcast a target variable. Formalized for macroeconomic forecasting by James Stock and Mark Watson in their 2002 Journal of Business & Economic Statistics paper, DFMs handle hundreds of indicators simultaneously while avoiding the curse of dimensionality that plagues traditional multivariate models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Stock & Mark Watson","year":2002,"type":"Latent-factor time-series model","subfamily":"Forecasting","estimator":"Principal Components / Kalman Filter","data_requirement":"Large panel of time-series indicators"},"citations":[{"ref":"Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics, 20(2), 147–162.","type":"article","doi":"10.1198/073500102317351921","isbn":null,"url":null}],"related":["midas-regression","principal-component-analysis","var-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-functional-connectivity","name":"Dynamic Functional Connectivity","fullName":"Dynamic Functional Connectivity (dFC)","aliases":["dFC","time-varying connectivity","sliding window connectivity"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Time-varying network analysis","year":"2013","originator":"Ryan M. Hutchison","url":"https://scholargate.app/en/neuroimaging/dynamic-functional-connectivity","markdownUrl":"https://scholargate.app/en/neuroimaging/dynamic-functional-connectivity.md","definition":"Dynamic Functional Connectivity (dFC) is an analytical framework that tracks changes in functional connectivity between brain regions over time, rather than averaging connectivity across an entire scanning session. Systematized by Hutchison and colleagues in 2013, dFC reveals how brain networks reorganize moment-to-moment, providing insights into transient brain states and cognitive flexibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ryan M. Hutchison","subfamily":"Time-varying network analysis","year":"2013","type":"Resting-state fMRI connectivity pipeline"},"citations":[{"ref":"Hutchison, R. M., Womelsdorf, T., Allen, E. A., et al. (2013). Dynamic functional connectivity: promise, problems, and perspectives. NeuroImage, 80, 360–378.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dynamic+functional+connectivity%3A+promise%2C+problems%2C+and+perspectives+Hutchison"},{"ref":"Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014). The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262–274.","type":"article","doi":"10.1016/j.neuron.2014.10.015","isbn":null,"url":null}],"related":["phase-locking-value","graph-brain-network-analysis","independent-component-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-fuzzy-regression-discontinuity","name":"Dynamic Fuzzy Regression Discontinuity","fullName":"Dynamic Fuzzy Regression Discontinuity Design","aliases":["Dynamic Fuzzy RDD","DFRD","Time-varying Fuzzy RD","Dynamic Fuzzy RD Design"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2001-2010","originator":"Cellini, Ferreira & Rothstein (dynamic RDD, 2010); Hahn, Todd & Van der Klaauw (fuzzy RDD foundations, 2001)","url":"https://scholargate.app/en/causal-inference/dynamic-fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-fuzzy-regression-discontinuity.md","definition":"Dynamic Fuzzy Regression Discontinuity Design extends the standard fuzzy RDD to a panel or multi-period setting, allowing researchers to estimate how the causal effect of a probabilistic threshold-based treatment evolves over time. By combining an IV-based fuzzy first stage with time-indexed outcomes, it traces treatment effects across multiple post-treatment periods, not just at a single cross-sectional snapshot.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cellini, Ferreira & Rothstein (dynamic RDD, 2010); Hahn, Todd & Van der Klaauw (fuzzy RDD foundations, 2001)","year":"2001-2010","type":"Quasi-experimental causal inference","dataType":"Panel or repeated cross-sections with a continuous running variable and time dimension","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null},{"ref":"Cellini, S. R., Ferreira, F., & Rothstein, J. (2010). The Value of School Facility Investments: Evidence from a Dynamic Regression Discontinuity Design. Quarterly Journal of Economics, 125(1), 215-261.","type":"article","doi":"10.1162/qjec.2010.125.1.215","isbn":null,"url":null}],"related":["fuzzy-regression-discontinuity","regression-discontinuity-design","dynamic-difference-in-differences","instrumental-variables","panel-data-regression-discontinuity-design","event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-hamiltonian-monte-carlo","name":"Dynamic Hamiltonian Monte Carlo","fullName":"Dynamic Hamiltonian Monte Carlo (No-U-Turn Sampler)","aliases":["Dynamic HMC","NUTS","No-U-Turn Sampler","adaptive HMC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2014","originator":"Matthew D. Hoffman and Andrew Gelman","url":"https://scholargate.app/en/bayesian/dynamic-hamiltonian-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-hamiltonian-monte-carlo.md","definition":"Dynamic Hamiltonian Monte Carlo — widely known as the No-U-Turn Sampler (NUTS) — is an adaptive extension of Hamiltonian Monte Carlo that automatically selects the number of leapfrog integration steps during each MCMC transition, removing the need to hand-tune the most sensitive tuning parameter of standard HMC. It is the default sampler in Stan and PyMC and is suitable for continuous, differentiable posterior distributions of moderate to high dimension.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matthew D. Hoffman and Andrew Gelman","year":"2014","type":"adaptive MCMC sampler","dataType":"continuous parameters; any model with differentiable log-posterior","subfamily":"Bayesian / computational"},"citations":[{"ref":"Hoffman, M. D. & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593–1623.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v15/hoffman14a.html"},{"ref":"Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113–162). CRC Press.","type":"inproceedings","doi":null,"isbn":"978-1420079418","url":null}],"related":["hamiltonian-monte-carlo","metropolis-hastings","gibbs-sampling","variational-inference","sequential-monte-carlo","bayesian-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-instrumental-variables","name":"Dynamic Instrumental Variables","fullName":"Dynamic Panel Instrumental Variables Estimation","aliases":["Dynamic IV","Dynamic Panel IV","Arellano-Bond GMM","System GMM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1991","originator":"Arellano & Bond (1991); extended by Blundell & Bond (1998)","url":"https://scholargate.app/en/causal-inference/dynamic-instrumental-variables","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-instrumental-variables.md","definition":"Dynamic Instrumental Variables estimation addresses endogeneity in panel models where the outcome depends on its own past values. By first-differencing to remove unit fixed effects and then using lagged levels as instruments for the differenced lagged outcome, it produces consistent causal estimates even when standard OLS or fixed-effects are biased by dynamic feedback.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bond (1991); extended by Blundell & Bond (1998)","year":"1991","type":"Dynamic panel causal estimation","dataType":"Panel data with lagged outcomes and endogenous regressors","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115-143.","type":"article","doi":"10.1016/S0304-4076(98)00009-8","isbn":null,"url":null}],"related":["instrumental-variables","panel-data-instrumental-variables","difference-in-differences","panel-fixed-effects","generalized-method-of-moments","two-stage-least-squares"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-interrupted-time-series","name":"Dynamic Interrupted Time Series","fullName":"Dynamic Interrupted Time Series Analysis","aliases":["Dynamic ITS","ITS with lagged effects","time-varying ITS","flexible ITS"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2002–2017","originator":"Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini","url":"https://scholargate.app/en/causal-inference/dynamic-interrupted-time-series","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-interrupted-time-series.md","definition":"Dynamic Interrupted Time Series (Dynamic ITS) extends the standard ITS design by allowing intervention effects to build up, decay, or shift over multiple time lags rather than assuming a single instantaneous level change. It estimates how an intervention's impact evolves across time periods, making it especially suited to public health, health services research, and policy evaluation where effects accumulate gradually or wear off after initial impact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini","year":"2002–2017","type":"Quasi-experimental time-series design","dataType":"Regularly spaced time-series observations (aggregate or unit-level)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.","type":"article","doi":"10.1093/ije/dyw098","isbn":null,"url":null},{"ref":"Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299-309.","type":"article","doi":"10.1046/j.1365-2710.2002.00430.x","isbn":null,"url":null}],"related":["interrupted-time-series","difference-in-differences","dynamic-difference-in-differences","event-study-design","panel-event-study","segmented-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-inverse-probability-weighting","name":"Dynamic Inverse Probability Weighting","fullName":"Dynamic Inverse Probability Weighting for Time-Varying Treatments","aliases":["Dynamic IPW","Time-varying IPW","Longitudinal IPW","Sequential IPW"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1986-2000","originator":"James M. Robins and colleagues","url":"https://scholargate.app/en/causal-inference/dynamic-inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-inverse-probability-weighting.md","definition":"Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James M. Robins and colleagues","year":"1986-2000","type":"Causal weighting estimator","dataType":"Longitudinal / panel data with time-varying treatments and covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"}],"related":["marginal-structural-model","inverse-probability-weighting","dynamic-marginal-structural-model","propensity-score-weighting","doubly-robust-estimation","dynamic-doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-light-scattering","name":"Dynamic Light Scattering","fullName":"Dynamic Light Scattering (DLS)","aliases":["DLS","photon correlation spectroscopy","particle size measurement"],"domain":"materials-science","family":"process-pipeline","subfamily":"Particle characterization","year":"1964","originator":"Robert Pecora","url":"https://scholargate.app/en/materials-science/dynamic-light-scattering","markdownUrl":"https://scholargate.app/en/materials-science/dynamic-light-scattering.md","definition":"Dynamic Light Scattering (DLS), also known as Photon Correlation Spectroscopy (PCS), is an analytical technique for determining the size and size distribution of particles suspended in fluids by analyzing the time-dependent intensity fluctuations of scattered laser light. Developed by Robert Pecora in 1964, DLS exploits the Brownian motion of particles: smaller particles move faster, causing faster intensity fluctuations; larger particles move slower, causing slower fluctuations. By correlating intensity over time, particle size is deduced. DLS is rapid, non-destructive, and requires minimal sample volume, making it the standard technique for characterizing nanoparticles, proteins, colloids, and emulsions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Pecora","subfamily":"Particle characterization","year":"1964","type":"Measurement method"},"citations":[{"ref":"Pecora, R. (1964). Spectral distribution of scattered light from a suspension of particles. Physica, 30(11), 2055-2070.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spectral+distribution+of+scattered+light+from+a+suspension+of+particles+Pecora"},{"ref":"Berne, B. J., & Pecora, R. (1976). Dynamic Light Scattering: With Applications to Chemistry, Biology, and Physics. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Bushell, G. C., Yan, Y. D., Woodfield, D., Raper, J., & Amal, R. (2002). On techniques for the measurement of the mass fractal dimension of aggregates. Advances in Colloid and Interface Science, 95(1), 1-50.","type":"article","doi":"10.1016/s0001-8686(00)00078-6","isbn":null,"url":null}],"related":["bet-surface-area","atomic-force-microscopy","thermogravimetric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-matching-estimator","name":"Dynamic Matching Estimator","fullName":"Dynamic Matching Estimator for Sequential Treatment Effects","aliases":["dynamic treatment matching","sequential matching estimator","dynamic selection-on-observables","DME"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010","originator":"Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)","url":"https://scholargate.app/en/causal-inference/dynamic-matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-matching-estimator.md","definition":"The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)","year":"2010","type":"Nonparametric causal inference / matching","dataType":"Panel or longitudinal data with repeated treatment decisions","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.","type":"article","doi":"10.1007/s00181-009-0297-3","isbn":null,"url":null},{"ref":"Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an Econometric Evaluation Estimator. Review of Economic Studies, 65(2), 261-294.","type":"article","doi":"10.1111/1467-937X.00044","isbn":null,"url":null}],"related":["matching-estimator","propensity-score-matching","dynamic-difference-in-differences","marginal-structural-model","inverse-probability-weighting","panel-data-matching-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-mechanical-analysis","name":"Dynamic Mechanical Analysis","fullName":"Dynamic Mechanical Analysis Viscoelastic Property Characterization","aliases":["DMA","rheological analysis","viscoelastic testing"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Mechanical analysis","year":"1960","originator":"Ferry and Schwarzl","url":"https://scholargate.app/en/biomaterials/dynamic-mechanical-analysis","markdownUrl":"https://scholargate.app/en/biomaterials/dynamic-mechanical-analysis.md","definition":"Dynamic mechanical analysis (DMA) measures the viscoelastic properties of materials—their elastic stiffness and viscous damping—by applying a sinusoidal stress or strain and measuring the phase lag and amplitude of the material's response. Developed from rheology principles in the 1960s and formalized by Ferry, Schwarzl, and others, DMA provides quantitative measures of how polymeric biomaterials respond to time-dependent and frequency-dependent mechanical stimuli. Key outputs include the storage modulus (elastic component), loss modulus (viscous component), and loss tangent (tan δ), which together characterize the material's mechanical behavior across temperature and frequency ranges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ferry and Schwarzl","subfamily":"Mechanical analysis","year":"1960","type":"Rheological characterization"},"citations":[{"ref":"Menard, K. P. (2008). Dynamic mechanical analysis: a practical introduction (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":null,"url":"https://www.crcpress.com/Dynamic-Mechanical-Analysis/Menard/p/book/9781420053135"},{"ref":"Ferry, J. D. (1980). Viscoelastic properties of polymers (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Viscoelastic+Properties+of+Polymers,+3rd+Edition-p-9780471048947"},{"ref":"Park, S. J., Jin, F. L., & Lee, J. R. (2004). Thermal stability and dynamic mechanical properties of epoxy/BaSO4 nanocomposites. Polymer, 45(25), 8475-8483.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thermal+stability+and+dynamic+mechanical+properties+of+epoxy%2FBaSO4+nanocomposites+Park"}],"related":["mooney-rivlin-tensile","swelling-and-degradation","gpc-sec","electrospinning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-metropolis-hastings-algorithm","name":"Dynamic Metropolis-Hastings Algorithm","fullName":"Dynamic Metropolis-Hastings Algorithm for Time-Varying Models","aliases":["Dynamic MH","MH for state-space models","Metropolis-Hastings in dynamic models","time-varying parameter MH"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1970 (algorithm); 1992 (dynamic application)","originator":"W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer","url":"https://scholargate.app/en/bayesian/dynamic-metropolis-hastings-algorithm","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-metropolis-hastings-algorithm.md","definition":"The Dynamic Metropolis-Hastings (Dynamic MH) algorithm applies the Metropolis-Hastings MCMC sampler to Bayesian state-space and time-varying parameter models. At each time step, latent states or evolving parameters are updated via proposal-and-accept moves, yielding full posterior distributions over trajectories rather than single filtered estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"W. K. Hastings (algorithm); applied to dynamic models by Carlin, Polson & Stoffer","year":"1970 (algorithm); 1992 (dynamic application)","type":"Bayesian MCMC sampler for dynamic models","dataType":"time series, longitudinal, state-space data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.","type":"article","doi":"10.1093/biomet/57.1.97","isbn":null,"url":null},{"ref":"Carlin, B. P., Polson, N. G., & Stoffer, D. S. (1992). A Monte Carlo approach to nonnormal and nonlinear state-space modeling. Journal of the American Statistical Association, 87(418), 493–500.","type":"article","doi":"10.1080/01621459.1992.10475231","isbn":null,"url":null}],"related":["metropolis-hastings-algorithm","gibbs-sampling","dynamic-bayesian-inference","kalman-filter","particle-filter","dynamic-gibbs-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-modularity-analysis","name":"Dynamic Modularity Analysis","fullName":"Dynamic Modularity Analysis (Temporal Community Structure Detection)","aliases":["dynamic community structure analysis","temporal modularity optimization","evolving community detection","time-varying modularity"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010","originator":"Mucha, P. J.; Porter, M. A.; and colleagues","url":"https://scholargate.app/en/network-analysis/dynamic-modularity-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-modularity-analysis.md","definition":"Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mucha, P. J.; Porter, M. A.; and colleagues","year":"2010","type":"Community detection on temporal networks","dataType":"Time-stamped or snapshot-based network data (edge lists, adjacency matrices per time step)","subfamily":"Network science"},"citations":[{"ref":"Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.","type":"article","doi":"10.1126/science.1184819","isbn":null,"url":null},{"ref":"Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.","type":"article","doi":"10.1088/1742-5468/2008/10/P10008","isbn":null,"url":null}],"related":["modularity-analysis","temporal-network-analysis","community-detection","temporal-community-detection","dynamic-social-network-analysis","multiplex-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-monte-carlo-simulation","name":"Dynamic Monte Carlo Simulation","fullName":"Dynamic Monte Carlo Simulation","aliases":["DMC simulation","kinetic Monte Carlo","time-driven Monte Carlo","event-driven Monte Carlo"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1975–1977","originator":"Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry)","url":"https://scholargate.app/en/bayesian/dynamic-monte-carlo-simulation","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-monte-carlo-simulation.md","definition":"Dynamic Monte Carlo (DMC) simulation is a computational method that tracks the stochastic time evolution of a system by drawing random event sequences weighted by transition rates. Unlike static Monte Carlo sampling of equilibrium distributions, DMC explicitly advances a clock, making it suitable for kinetic, reaction, and time-dependent phenomena where the sequence and timing of events matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bortz, Kalos & Lebowitz (physics); Gillespie (chemistry)","year":"1975–1977","type":"stochastic simulation","dataType":"event-driven or time-stepped sequential data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Bortz, A. B., Kalos, M. H., & Lebowitz, J. L. (1975). A new algorithm for Monte Carlo simulation of Ising spin systems. Journal of Computational Physics, 17(1), 10–18.","type":"article","doi":"10.1016/0021-9991(75)90060-1","isbn":null,"url":null},{"ref":"Gillespie, D. T. (1977). Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry, 81(25), 2340–2361.","type":"article","doi":"10.1021/j100540a008","isbn":null,"url":null}],"related":["sequential-monte-carlo","markov-chain-monte-carlo","particle-filter","dynamic-bayesian-inference","gibbs-sampling","bootstrap-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-pagerank","name":"Dynamic PageRank","fullName":"Dynamic PageRank (Temporal Extension of the PageRank Algorithm)","aliases":["Temporal PageRank","time-aware PageRank","evolving PageRank","DPR"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2007–2016","originator":"Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank","url":"https://scholargate.app/en/network-analysis/dynamic-pagerank","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-pagerank.md","definition":"Dynamic PageRank extends the classic PageRank algorithm to networks whose edges carry timestamps, assigning importance scores that evolve over time. By discounting older links and emphasising recent connections, it identifies nodes that are influential at specific moments rather than across the entire network history, making it well-suited for web archives, citation streams, social media cascades, and any domain where link recency matters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rozenshtein, P. & Gionis, A. (formalized); Page, L. & Brin, S. for base PageRank","year":"2007–2016","type":"Centrality / ranking algorithm","dataType":"Temporal directed graphs (edge-timestamped networks)","subfamily":"Network science"},"citations":[{"ref":"Rozenshtein, P., & Gionis, A. (2016). Temporal PageRank. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Lecture Notes in Computer Science, 9853, 674–689. Springer.","type":"inproceedings","doi":"10.1007/978-3-319-46227-1_42","isbn":null,"url":null},{"ref":"Berberich, K., Vazirgiannis, M., & Weikum, G. (2007). Time-aware authority ranking. Internet Mathematics, 3(4), 407–429.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Time-aware+authority+ranking+Berberich"}],"related":["temporal-network-analysis","eigenvector-centrality","degree-centrality","betweenness-centrality","temporal-community-detection","dynamic-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-panel-data-model","name":"Dynamic Panel Data Model","fullName":"Dynamic Panel Data Model","aliases":["dynamic panel model","panel data model with lagged dependent variable","DPD model","Arellano-Bond model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988–1991","originator":"Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)","url":"https://scholargate.app/en/econometrics/dynamic-panel-data-model","markdownUrl":"https://scholargate.app/en/econometrics/dynamic-panel-data-model.md","definition":"The dynamic panel data model extends standard panel regression by including a lagged value of the outcome variable as a regressor, capturing persistence and adjustment dynamics. Because the lagged dependent variable is correlated with the unit-specific fixed effect, ordinary OLS or within estimators are biased; GMM-based methods using internal instruments are the standard remedy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bond (1991); Holtz-Eakin, Newey & Rosen (1988)","year":"1988–1991","type":"Dynamic regression / GMM estimation","dataType":"Balanced or unbalanced panel data (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null}],"related":["arellano-bond-gmm-estimator","panel-data-analysis","fixed-effects-model","random-effects-model","difference-gmm","panel-fixed-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-panel-event-study","name":"Dynamic Panel Event Study","fullName":"Dynamic Panel Event Study Design","aliases":["dynamic event study","panel event-study regression","leads-and-lags event study","event-time panel design"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Sun & Abraham (2021); Callaway & Sant'Anna (2021)","url":"https://scholargate.app/en/causal-inference/dynamic-panel-event-study","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-panel-event-study.md","definition":"The dynamic panel event study is a quasi-experimental method that uses panel data to trace out how a treatment effect evolves over time — before and after a defining event — by estimating a flexible regression of leads and lags around the treatment date. It simultaneously tests for pre-existing parallel trends and maps the full dynamic profile of causal impact across multiple post-event periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sun & Abraham (2021); Callaway & Sant'Anna (2021)","year":"2021","type":"Quasi-experimental / causal inference","dataType":"Balanced or unbalanced panel (repeated observations of units over multiple time periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null},{"ref":"Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.","type":"article","doi":"10.1016/j.jeconom.2020.09.006","isbn":null,"url":null}],"related":["difference-in-differences","event-study-design","panel-event-study","dynamic-difference-in-differences","staggered-difference-in-differences","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-particle-filter","name":"Dynamic Particle Filter","fullName":"Dynamic Particle Filter for Sequential State Estimation","aliases":["dynamic sequential Monte Carlo","dynamic SMC","bootstrap particle filter","dynamic SIR filter"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993","originator":"Gordon, Salmond & Smith (bootstrap particle filter, 1993); extended by Doucet et al. (2001)","url":"https://scholargate.app/en/bayesian/dynamic-particle-filter","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-particle-filter.md","definition":"A dynamic particle filter is a sequential Monte Carlo algorithm that tracks an evolving hidden state over time by maintaining a population of weighted random samples — particles — each representing a plausible trajectory. As new observations arrive, particle weights are updated via the likelihood and the population is resampled, keeping the representation concentrated on the most probable state regions in a fully nonlinear and non-Gaussian setting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon, Salmond & Smith (bootstrap particle filter, 1993); extended by Doucet et al. (2001)","year":"1993","type":"Sequential Bayesian state estimation","dataType":"Time-ordered observations from nonlinear / non-Gaussian dynamic systems","subfamily":"Bayesian / computational"},"citations":[{"ref":"Doucet, A., de Freitas, N. & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer.","type":"book","doi":null,"isbn":"978-0387951461","url":null},{"ref":"Gordon, N. J., Salmond, D. J. & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F – Radar and Signal Processing, 140(2), 107–113.","type":"article","doi":"10.1049/ip-f-2.1993.0015","isbn":null,"url":null}],"related":["kalman-filter","sequential-monte-carlo","dynamic-bayesian-inference","particle-filter","dynamic-kalman-filter","hidden-markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-programming","name":"Dynamic Programming","fullName":"Dynamic Programming","aliases":["DP","Bellman's Principle of Optimality","Recursive Optimization","Dinamik Programlama"],"domain":"optimization","family":"process-pipeline","subfamily":"Mathematical programming","year":1957,"originator":"Richard Bellman","url":"https://scholargate.app/en/optimization/dynamic-programming","markdownUrl":"https://scholargate.app/en/optimization/dynamic-programming.md","definition":"Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Bellman","year":1957,"type":"Exact combinatorial optimization via recursive decomposition","subfamily":"Mathematical programming","complexity_class":"Polynomial in states and stages","key_property":"Principle of Optimality"},"citations":[{"ref":"Bellman, R. (1957). Dynamic Programming. Princeton University Press.","type":"book","doi":null,"isbn":"978-0-691-07951-6","url":null}],"related":["integer-programming","deep-reinforcement-learning","constraint-programming"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-propensity-score-matching","name":"Dynamic Propensity Score Matching","fullName":"Dynamic Propensity Score Matching for Sequential Treatments","aliases":["dynamic PSM","sequential propensity score matching","longitudinal propensity matching","DPSM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1986-2010","originator":"Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching","url":"https://scholargate.app/en/causal-inference/dynamic-propensity-score-matching","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-propensity-score-matching.md","definition":"Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching","year":"1986-2010","type":"Sequential causal matching","dataType":"Longitudinal / panel data with time-varying treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.","type":"article","doi":"10.1007/s00181-009-0297-3","isbn":null,"url":null},{"ref":"Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.","type":"article","doi":"10.1016/0270-0255(86)90088-6","isbn":null,"url":null}],"related":["propensity-score-matching","propensity-score-weighting","marginal-structural-model","dynamic-difference-in-differences","inverse-probability-weighting","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-sequential-monte-carlo","name":"Dynamic Sequential Monte Carlo","fullName":"Dynamic Sequential Monte Carlo Sampler","aliases":["Dynamic SMC","SMC for dynamic models","sequential particle filter","dynamic particle sampler"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2006","originator":"Del Moral, Doucet, Jasra","url":"https://scholargate.app/en/bayesian/dynamic-sequential-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-sequential-monte-carlo.md","definition":"Dynamic Sequential Monte Carlo (Dynamic SMC) is a Bayesian computational method that maintains and updates a population of weighted samples — particles — as new observations arrive over time. It propagates particles through a dynamic system model, reweights them by how well they match the observed data, and periodically resamples to concentrate effort on high-probability regions, yielding online posterior inference for state-space and time-evolving models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Del Moral, Doucet, Jasra","year":"2006","type":"Sequential Monte Carlo sampler for dynamic settings","dataType":"Sequential or time-ordered observations; state-space data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436.","type":"article","doi":"10.1111/j.1467-9868.2006.00553.x","isbn":null,"url":null},{"ref":"Doucet, A., de Freitas, N. & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer.","type":"book","doi":null,"isbn":"978-0387951461","url":null}],"related":["sequential-monte-carlo","particle-filter","kalman-filter","hamiltonian-monte-carlo","gibbs-sampling","dynamic-bayesian-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-stochastic-block-model","name":"Dynamic Stochastic Block Model","fullName":"Dynamic Stochastic Block Model (Temporal Community Detection)","aliases":["DSBM","dynamic SBM","time-varying stochastic block model","temporal block model"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2011","originator":"Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.","url":"https://scholargate.app/en/network-analysis/dynamic-stochastic-block-model","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-stochastic-block-model.md","definition":"The Dynamic Stochastic Block Model (DSBM) is a generative probabilistic framework that extends the static stochastic block model to networks observed across multiple time points. It jointly models community membership and community evolution, allowing researchers to detect and track latent groups and their structural changes over time in longitudinal network data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yang, T.; Chi, Y.; Zhu, S.; Gong, Y.; Jin, R.","year":"2011","type":"Generative probabilistic model","dataType":"Temporal adjacency matrices / longitudinal edge lists","subfamily":"Network science"},"citations":[{"ref":"Yang, T., Chi, Y., Zhu, S., Gong, Y., & Jin, R. (2011). Detecting communities and their evolutions in dynamic social networks — a Bayesian approach. Machine Learning, 82(2), 157–189.","type":"inproceedings","doi":"10.1007/s10994-010-5214-7","isbn":null,"url":null},{"ref":"Matias, C., & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141.","type":"article","doi":"10.1111/rssb.12200","isbn":null,"url":null}],"related":["stochastic-block-model","temporal-network-analysis","dynamic-community-detection","exponential-random-graph-model","modularity-analysis","bayesian-stochastic-block-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-synthetic-control-method","name":"Dynamic Synthetic Control Method","fullName":"Dynamic Synthetic Control Method for Multi-Period Treatment Evaluation","aliases":["Dynamic SCM","Time-varying synthetic control","Multi-period synthetic control","DSC"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010","originator":"Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others","url":"https://scholargate.app/en/causal-inference/dynamic-synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/dynamic-synthetic-control-method.md","definition":"The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path of treatment effects period by period after the intervention — capturing not just an average effect but how the effect evolves dynamically.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others","year":"2010","type":"Comparative case study / counterfactual estimation","dataType":"Aggregate panel data (few treated units, multiple control units, multiple pre- and post-treatment periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null},{"ref":"Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic Difference-in-Differences. American Economic Review, 111(12), 4088-4118.","type":"article","doi":"10.1257/aer.20190159","isbn":null,"url":null}],"related":["synthetic-control-method","difference-in-differences","dynamic-difference-in-differences","event-study-design","panel-data-synthetic-control-method","counterfactual-impact-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-time-warping","name":"Dynamic Time Warping","fullName":"Dynamic Time Warping Distance","aliases":["DTW","dynamic programming time warping","elastic distance"],"domain":"decision-making","family":"mcdm","subfamily":"Time-series distance","year":"1978","originator":"Hideki Sakoe and Seibi Chiba","url":"https://scholargate.app/en/decision-making/dynamic-time-warping","markdownUrl":"https://scholargate.app/en/decision-making/dynamic-time-warping.md","definition":"Dynamic Time Warping is a distance metric for comparing time series or sequential data that may vary in length or speed. Introduced by Hideki Sakoe and Seibi Chiba in 1978 for speech recognition, DTW measures the minimal cumulative distance needed to align two sequences using dynamic programming. Unlike fixed-distance metrics, DTW allows flexible time warping, making it ideal for sequences that are similar in shape but offset or scaled differently in time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hideki Sakoe and Seibi Chiba","subfamily":"Time-series distance","year":"1978","type":"Elastic sequence alignment metric"},"citations":[{"ref":"Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.","type":"article","doi":"10.1109/TASSP.1978.1163055","isbn":null,"url":null},{"ref":"Salvador, S., & Chan, P. (2007). FastDTW: Toward accurate dynamic time warping in linear time and space. KDD Explorations, 5(1), 70-86.","type":"article","doi":null,"isbn":null,"url":"https://www.cs.ucr.edu/~eamonn/FastDTW/"}],"related":["euclidean-distance","manhattan-distance","levenshtein-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-two-mode-network-analysis","name":"Dynamic Two-Mode Network Analysis","fullName":"Dynamic Two-Mode (Bipartite) Network Analysis","aliases":["Dynamic bipartite network analysis","Temporal two-mode network analysis","Longitudinal affiliation network analysis","Dynamic actor-event network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2000s–2012","originator":"Borgatti, S. P. & Halgin, D. S. (affiliation networks); Holme, P. & Saramäki, J. (temporal networks)","url":"https://scholargate.app/en/network-analysis/dynamic-two-mode-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/dynamic-two-mode-network-analysis.md","definition":"Dynamic two-mode network analysis studies bipartite networks — structures with two distinct node types, such as actors and events or authors and papers — as they evolve over time. By tracking how memberships, affiliations, and co-participations change across temporal snapshots, it reveals the emergence, dissolution, and reorganization of collaborative or membership structures that static analysis would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Borgatti, S. P. & Halgin, D. S. (affiliation networks); Holme, P. & Saramäki, J. (temporal networks)","year":"2000s–2012","type":"Longitudinal bipartite network analysis","dataType":"Time-stamped affiliation or membership data (actors and events/groups across time points)","subfamily":"Network science"},"citations":[{"ref":"Borgatti, S. P., & Halgin, D. S. (2011). Analyzing affiliation networks. In J. Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 417–433). SAGE.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Analyzing+affiliation+networks+Borgatti+Halgin+2011"},{"ref":"Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null}],"related":["two-mode-network-analysis","temporal-network-analysis","dynamic-community-detection","multiplex-network-analysis","social-network-analysis","temporal-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dynamic-variational-inference","name":"Dynamic Variational Inference","fullName":"Dynamic Variational Inference for Sequential Latent Variable Models","aliases":["sequential variational inference","temporal variational inference","variational inference for state-space models","DVI"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2014–2015","originator":"Bayer, Osendorfer, Krishnan and colleagues","url":"https://scholargate.app/en/bayesian/dynamic-variational-inference","markdownUrl":"https://scholargate.app/en/bayesian/dynamic-variational-inference.md","definition":"Dynamic variational inference extends the variational inference framework to sequential and time-series settings by positing a structured approximate posterior that respects the temporal ordering of latent states. It jointly learns a generative model of how hidden states evolve over time and a recognition network that maps observed sequences back to those latent states, optimising a sequential evidence lower bound (ELBO).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bayer, Osendorfer, Krishnan and colleagues","year":"2014–2015","type":"Bayesian approximate inference","dataType":"sequential / time-series data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1511.05121"},{"ref":"Bayer, J., & Osendorfer, C. (2014). Learning Stochastic Recurrent Networks. NIPS 2014 Workshop on Advances in Variational Inference.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1411.7610"}],"related":["variational-inference","sequential-monte-carlo","kalman-filter","dynamic-bayesian-network","particle-filter","time-series-bayesian-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dysfunctional-breathing-questionnaire","name":"Nijmegen Questionnaire","fullName":"Nijmegen Questionnaire for Dysfunctional Breathing","aliases":["Nijmegen","Nijmegen Questionnaire","DBQ"],"domain":"pulmonology","family":"process-pipeline","subfamily":"dysfunctional-breathing","year":"1994","originator":"van Beveren and colleagues, Netherlands","url":"https://scholargate.app/en/pulmonology/dysfunctional-breathing-questionnaire","markdownUrl":"https://scholargate.app/en/pulmonology/dysfunctional-breathing-questionnaire.md","definition":"The Nijmegen Questionnaire is a 16-item self-report instrument designed to identify dysfunctional breathing patterns, particularly hyperventilation syndrome, in patients presenting with respiratory or non-respiratory symptoms. Developed by van Beveren and colleagues in the Netherlands in 1994, it provides rapid assessment of symptoms attributable to chronic hyperventilation: dizziness, chest tightness, muscle tension, paresthesias, and anxiety. The Nijmegen Questionnaire is widely used in respiratory physiology clinics, pulmonary rehabilitation programs, and psychosomatic medicine to detect dysfunctional breathing phenotypes that may masquerade as asthma, anxiety disorders, or cardiopulmonary disease.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"van Beveren and colleagues, Netherlands","subfamily":"dysfunctional-breathing","year":"1994","type":"Self-report questionnaire"},"citations":[{"ref":"Van Beveren, T. L., Fülöp, M., van Beek, H. G., & Zijlstra, F. J. (1994). Hyperventilation and panic panic attacks in a group of asthma patients. Respiration, 61(5), 282-287.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/7800212"},{"ref":"Higgs, F., Donovan, G., Opdam, H., & Tiller, J. (2013). Hyperventilation and dysfunctional breathing: a tool for assessment and retraining. Breathe, 9(4), 284-293.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hyperventilation+and+dysfunctional+breathing%3A+a+tool+for+assessment+and+retraining+Higgs"}],"related":["st-george-respiratory-questionnaire","asthma-control-questionnaire","chronic-respiratory-disease-questionnaire","mrc-dyspnoea-scale","breathlessness-cough-sputum-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dysphagia-outcome-severity-scale","name":"Dysphagia Outcome and Severity Scale","fullName":"Dysphagia Outcome and Severity Scale (DOSS)","aliases":["DOSS"],"domain":"speech-language-pathology","family":"process-pipeline","subfamily":"dysphagia severity & functional swallowing outcomes","year":"1999","originator":"O'Neil, K. H., et al.","url":"https://scholargate.app/en/speech-language-pathology/dysphagia-outcome-severity-scale","markdownUrl":"https://scholargate.app/en/speech-language-pathology/dysphagia-outcome-severity-scale.md","definition":"The Dysphagia Outcome and Severity Scale (DOSS) is a 7-point clinician-rated ordinal scale that measures the severity of swallowing dysfunction and functional swallowing outcomes across two dimensions: safety (penetration-aspiration risk) and efficiency (oral intake adequacy and diet level tolerance). Developed by O'Neil and colleagues in 1999, DOSS integrates clinical observation with videofluoroscopic findings to provide a standardized, functionally meaningful classification of swallowing status from normal to non-functional.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"O'Neil, K. H., et al.","subfamily":"dysphagia severity & functional swallowing outcomes","year":"1999","type":"Clinician-rated"},"citations":[{"ref":"O'Neil, K. H., Purdy, M., Falk, J., & Gidas, L. (1999). The Dysphagia Outcome and Severity Scale. Dysphagia, 14(3), 139–145.","type":"article","doi":"10.1007/PL00009595","isbn":null,"url":null},{"ref":"Kuipers, P., & Daniels, S. K. (2000). Dysphagia Management in Stroke Patients. Current Treatment Options in Neurology, 2(5), 529–536.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/11096758"},{"ref":"Pauloski, B. R., Logemann, J. A., Colangelo, L. A., et al. (2001). Surgical Variables Affecting Functional Outcomes in Oropharyngeal Myocutaneous Flap Reconstruction. Head & Neck, 23(3), 175–185.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Surgical+Variables+Affecting+Functional+Outcomes+in+Oropharyngeal+Myocutaneous+Flap+Reconstruction+Pauloski"}],"related":["swallowing-quality-of-life","voice-handicap-index","speech-fluency-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"dyspnea-scale-borg","name":"Borg Dyspnea Scale","fullName":"Borg Rating of Perceived Exertion Dyspnea Scale (Borg RPE-D)","aliases":["Borg Scale","Borg RPE","Borg 0-10"],"domain":"cardiology","family":"process-pipeline","subfamily":"symptom severity and perceived exertion rating scale","year":"1982","originator":"Gunnar Borg","url":"https://scholargate.app/en/cardiology/dyspnea-scale-borg","markdownUrl":"https://scholargate.app/en/cardiology/dyspnea-scale-borg.md","definition":"The Borg Rating of Perceived Exertion (RPE) Scale is a simple 0–10 (or original 6–20) numerical rating scale that quantifies a patient's subjective perception of dyspnea or general effort during activity or exercise testing. Developed by Swedish psychophysicist Gunnar Borg in the 1970s–1980s, the Borg Scale is ubiquitous in cardiopulmonary medicine, rehabilitation, and exercise physiology for monitoring symptom severity, guiding exercise intensity, assessing treatment response, and ensuring patient safety during testing and rehabilitation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gunnar Borg","subfamily":"symptom severity and perceived exertion rating scale","year":"1982","type":"Single-item numerical rating scale"},"citations":[{"ref":"Borg, G. A. (1982). Psychophysical bases of perceived exertion. Medicine & Science in Sports & Exercise, 14(5), 377–381.","type":"article","doi":"10.1249/00005768-198205000-00012","isbn":null,"url":null},{"ref":"Borg, G. (1998). Borg's Rating of Perceived Exertion and Pain Scales. Human Kinetics.","type":"book","doi":null,"isbn":null,"url":"https://books.google.com/books?id=VLK-AQAAIAAJ"}],"related":["new-york-heart-association-class","seattle-angina-questionnaire","minnesota-heart-failure","duke-activity-status-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"e-government-adoption-scale","name":"E-Government Adoption Scale","fullName":"E-Government Adoption Scale (EGAS)","aliases":["EGAS","e-Government Service Adoption"],"domain":"tourism-management","family":"process-pipeline","subfamily":"technology-adoption-measurement","year":"2000","originator":"Venkatesh, V.; Belanger, F.; Carter, L.","url":"https://scholargate.app/en/tourism-management/e-government-adoption-scale","markdownUrl":"https://scholargate.app/en/tourism-management/e-government-adoption-scale.md","definition":"The E-Government Adoption Scale (EGAS) measures citizens' willingness to adopt and use digital government services (e-permits, e-tax, e-voting, e-tourism information services, online licensing) based on Technology Acceptance Model principles (Venkatesh & Davis, 2000) extended to government contexts (Belanger et al., 2005). It operationalizes key adoption drivers: perceived usefulness, ease of use, trust in government, security concerns, and technical support. Essential for government agencies, tourism authorities, and public service digital transformation initiatives seeking to understand and overcome citizen barriers to e-service adoption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Venkatesh, V.; Belanger, F.; Carter, L.","subfamily":"technology-adoption-measurement","year":"2000","type":"Self-report questionnaire"},"citations":[{"ref":"Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.","type":"article","doi":"10.1287/mnsc.46.2.186.11926","isbn":null,"url":null},{"ref":"Belanger, F., Carter, L., & Casper, J. (2005). Citizen adoption of e-government services. Proceedings of the 38th Hawaii International Conference on System Sciences, 109c.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Citizen+adoption+of+e-government+services+Belanger"},{"ref":"Alomari, M., Sandhu, K., & Woods, P. (2012). Exploring citizen perceptions of barriers and drivers influencing the adoption of e-government services. Journal of Cases on Information Technology, 14(4), 36-48.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Exploring+citizen+perceptions+of+barriers+and+drivers+influencing+the+adoption+of+e-government+services+Alomari"},{"ref":"Oliveira, T., Martins, R., Sarker, S., & Thomas, M. (2016). Information and communication technology adoption across the global south. Journal of Global Information Management, 24(2), 1-22.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Information+and+communication+technology+adoption+across+the+global+south+Oliveira"}],"related":["citizen-satisfaction-survey","public-service-motivation-scale","perceived-value-scale-tourism","tourist-satisfaction-scale","destination-image-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"e-servqual","name":"E-S-QUAL Electronic Service Quality Scale","fullName":"Electronic Service Quality Scale","aliases":["E-S-QUAL","Online Service Quality Scale"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Service quality measurement","year":"2005","originator":"A. Parasuraman, Valerie A. Zeithaml, Anantharanthan Malhotra","url":"https://scholargate.app/en/marketing-management/e-servqual","markdownUrl":"https://scholargate.app/en/marketing-management/e-servqual.md","definition":"E-S-QUAL is a 22-item scale developed by Parasuraman, Zeithaml, and Malhotra (2005) to measure service quality in electronic commerce and digital service environments. Adapting the foundational SERVQUAL dimensions to online contexts, E-S-QUAL assesses four core dimensions: Efficiency (ability to complete transactions quickly), Fulfillment (accurate order fulfillment and on-time delivery), System Availability (website uptime and technical performance), and Privacy (security of customer data and transactions). The scale captures both service delivery (how the website functions) and service recovery (how problems are handled).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. Parasuraman, Valerie A. Zeithaml, Anantharanthan Malhotra","subfamily":"Service quality measurement","year":"2005","type":"Multi-dimensional electronic service quality scale"},"citations":[{"ref":"Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL: A Multiple-Item Scale for Assessing Electronic Service Quality. Journal of Service Research, 7(3), 213-233.","type":"article","doi":"10.1177/1094670504271156","isbn":null,"url":null},{"ref":"Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service Quality Delivery Through Web Sites: A Critical Review of Extant Knowledge. Journal of the Academy of Marketing Science, 30(4), 362-379.","type":"article","doi":"10.1177/009207002236911","isbn":null,"url":null}],"related":["servqual","servperf","customer-satisfaction-index","e-commerce-satisfaction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"early-childhood-environment-rating","name":"ECERS-3","fullName":"Early Childhood Environment Rating Scale, Third Edition","aliases":["ECERS-3","ECERS"],"domain":"neonatology","family":"process-pipeline","subfamily":"program-quality-assessment","year":2015,"originator":"Thelma Harms, Richard Clifford, Debby Cryer","url":"https://scholargate.app/en/neonatology/early-childhood-environment-rating","markdownUrl":"https://scholargate.app/en/neonatology/early-childhood-environment-rating.md","definition":"The ECERS-3 is a comprehensive observational rating scale assessing the quality of early childhood education and care environments for preschool-age children (ages 3–5 years, or ages 2.5–5.5 years). Developed by Harms, Clifford, and Cryer (2015), it evaluates classroom environment, materials, interactions, and practices across 35 items grouped into six domains: Space and Furnishings, Personal Care Routines, Language and Literacy, Learning Activities, Interaction, and Program Structure. The ECERS-3 is the most widely used instrument for assessing early childhood program quality and has become the standard for child care licensing, accreditation, and quality improvement research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thelma Harms, Richard Clifford, Debby Cryer","subfamily":"program-quality-assessment","year":2015,"type":"Observational-rated"},"citations":[{"ref":"Harms, T., Clifford, R. M., & Cryer, D. (2015). Early Childhood Environment Rating Scale (ECERS-3) (3rd ed.). Teachers College Press.","type":"book","doi":null,"isbn":"978-0807755662","url":null},{"ref":"Harms, T., Cryer, D., & Clifford, R. M. (2006). Infant/Toddler Environment Rating Scale, Revised Edition (ITERS-R). Teachers College Press.","type":"book","doi":null,"isbn":"978-0807745137","url":null}],"related":["parent-infant-interaction-scale","ages-stages-questionnaire-social-emotional","newborn-behavioral-observations"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"early-warning-score","name":"Early Warning Score","fullName":"Early Warning Score for Patient Deterioration Detection","aliases":["EWS","NEWS","National Early Warning Score","Rapid Response System"],"domain":"nursing","family":"process-pipeline","subfamily":"Vital sign monitoring and acute deterioration detection","year":"2012","originator":"Royal College of Physicians and multiple researchers (Smith, Prytherch, et al.)","url":"https://scholargate.app/en/nursing/early-warning-score","markdownUrl":"https://scholargate.app/en/nursing/early-warning-score.md","definition":"The Early Warning Score (EWS), most commonly known as the National Early Warning Score (NEWS) in the UK, is a standardized tool for identifying acutely unwell patients at risk of deterioration. Developed by the Royal College of Physicians and validated through research by Smith, Prytherch, and colleagues, NEWS combines vital sign measurements and supplemental oxygen use to generate a composite score. High NEWS scores trigger escalated care responses, enabling early intervention before critical illness develops.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Royal College of Physicians and multiple researchers (Smith, Prytherch, et al.)","subfamily":"Vital sign monitoring and acute deterioration detection","year":"2012","type":"Scoring and warning system"},"citations":[{"ref":"Smith, G. B., Prytherch, D. R., Schmidt, P. E., & Featherstone, P. I. (2008). Should early warning systems be based on single data points or on trends? Resuscitation, 81(4), 424-426.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Should+early+warning+systems+be+based+on+single+data+points+or+on+trends+Smith"},{"ref":"Royal College of Physicians. (2017). National Early Warning Score (NEWS) 2: standardising the assessment of acute illness severity in the NHS. Royal College of Physicians, London.","type":"article","doi":null,"isbn":null,"url":"https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news-2"}],"related":["nursing-sensitive-indicators","patient-fall-risk-assessment","cam-delirium-screening","medication-reconciliation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"easi","name":"EASI","fullName":"Eczema Area and Severity Index","aliases":["EASI Index"],"domain":"dermatology","family":"process-pipeline","subfamily":"severity-assessment","year":"2001","originator":"Hanifin JM, Thurston M","url":"https://scholargate.app/en/dermatology/easi","markdownUrl":"https://scholargate.app/en/dermatology/easi.md","definition":"The EASI is a structured, clinician-administered tool for assessing the extent and intensity of atopic dermatitis across the body. Developed by Hanifin and colleagues in 2001, it divides the body into four regions with weighted area factors, ensuring proportional contribution to total score. EASI has become the primary objective severity measure in atopic dermatitis clinical trials and is recommended by international regulatory authorities (FDA, EMA).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hanifin JM, Thurston M","subfamily":"severity-assessment","year":"2001","type":"Clinician-rated"},"citations":[{"ref":"Hanifin JM, Thurston M, Omoto M, et al. The eczema area and severity index (EASI): assessment of reliability in atopic dermatitis. Experimental Dermatology. 2001;10(1):11-18.","type":"article","doi":"10.1034/j.1600-0625.2001.100102.x","isbn":null,"url":null},{"ref":"Stalder JF, Taïeb A. Therapeutic classification of atopic dermatitis. Arch Dermatol. 2003;139(9):1221-1222.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/12975159"}],"related":["scorad","poem","dlqi-children"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eating-attitudes-test","name":"Eating Attitudes Test","fullName":"Eating Attitudes Test (EAT-26)","aliases":["EAT-26","EAT (original 40-item)"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Eating disorder symptom screening and severity","year":"1979","originator":"David M. Garner","url":"https://scholargate.app/en/psychiatry/eating-attitudes-test","markdownUrl":"https://scholargate.app/en/psychiatry/eating-attitudes-test.md","definition":"The EAT-26 is a 26-item self-report questionnaire designed to assess core attitudes and behaviors characteristic of eating disorders, particularly anorexia nervosa and bulimia nervosa. Developed by Garner and Garfinkel in 1979 and abbreviated to 26 items in 1982, it is widely used for screening eating disorders in community and clinical settings, and for monitoring treatment response. The EAT-26 measures restrictive eating attitudes, food preoccupation, and weight/shape concerns, with three subscales reflecting the multifaceted nature of eating disorder psychopathology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David M. Garner","subfamily":"Eating disorder symptom screening and severity","year":"1979","type":"Self-report questionnaire"},"citations":[{"ref":"Garner, D. M., Olmsted, M. P., Bohr, Y., & Garfinkel, P. E. (1982). The eating attitudes test: Psychometric features and clinical correlates. Psychological Medicine, 12(4), 871–878.","type":"article","doi":"10.1017/S0033291700049163","isbn":null,"url":null},{"ref":"Garner, D. M., & Garfinkel, P. E. (1979). The Eating Attitudes Test: An index of the symptoms of anorexia nervosa. Psychological Medicine, 9(2), 273–279.","type":"article","doi":"10.1017/S0033291700030762","isbn":null,"url":null},{"ref":"Mintz, L. B., & O'Halloran, M. S. (2000). The Eating Attitudes Test: Validation with DSM-IV eating disorders. Journal of Personality Assessment, 74(3), 489–503.","type":"article","doi":"10.1207/S15327752JPA7403_11","isbn":null,"url":null}],"related":["borderline-symptom-list","dissociative-experiences-scale","brief-psychiatric-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"echo-state-network","name":"Echo State Network","fullName":"Echo State Network (Reservoir Computing)","aliases":["ESN","Liquid State Machine (related formulation)","Reservoir Computing","Yankı Durum Ağı"],"domain":"deep-learning","family":"ml-model","subfamily":"Recurrent / reservoir","year":2004,"originator":"Herbert Jaeger & Harald Haas","url":"https://scholargate.app/en/deep-learning/echo-state-network","markdownUrl":"https://scholargate.app/en/deep-learning/echo-state-network.md","definition":"An Echo State Network (ESN) is a type of recurrent neural network introduced by Herbert Jaeger and Harald Haas in 2004 that exploits a large, randomly connected, fixed recurrent layer — the reservoir — to project input signals into a high-dimensional nonlinear space. Only the linear output weights are trained, typically via ridge regression, making ESNs computationally inexpensive yet highly expressive for temporal and chaotic time-series modeling tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herbert Jaeger & Harald Haas","year":2004,"type":"Recurrent neural network with fixed random reservoir","subfamily":"Recurrent / reservoir","training_complexity":"Linear (only output weights are trained)","key_property":"Echo state property (fading memory)"},"citations":[{"ref":"Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304(5667), 78–80.","type":"article","doi":"10.1126/science.1091277","isbn":null,"url":null}],"related":["recurrent-neural-network","lstm","sample-entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eclat","name":"ECLAT","fullName":"ECLAT (Equivalence Class Clustering and Bottom-up Lattice Traversal)","aliases":["Eclat algorithm","vertical association mining","tidset intersection mining","ECLAT sık örüntü madenciliği"],"domain":"machine-learning","family":"ml-model","subfamily":"Pattern mining","year":2000,"originator":"Mohammed J. Zaki","url":"https://scholargate.app/en/machine-learning/eclat","markdownUrl":"https://scholargate.app/en/machine-learning/eclat.md","definition":"ECLAT, introduced by Mohammed Zaki in 2000, mines frequent itemsets using a vertical data representation: instead of scanning transactions, it stores for each item the set of transaction IDs (a tidset) that contain it, and computes the support of any itemset by intersecting tidsets. This depth-first, intersection-based approach is fast and memory-efficient, an alternative to Apriori's horizontal scans and FP-Growth's tree.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mohammed J. Zaki","year":2000,"type":"Frequent-itemset mining algorithm (vertical format)","subfamily":"Pattern mining","representation":"Vertical tidset (transaction-id lists)","support":"Computed by tidset intersection length"},"citations":[{"ref":"Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390.","type":"article","doi":"10.1109/69.846291","isbn":null,"url":null}],"related":["fp-growth","association-rule-mining","formal-concept-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ecological-footprint-knowledge","name":"EFKS","fullName":"Ecological Footprint Knowledge Scale","aliases":["EFKS","Footprint Literacy Scale"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"environmental knowledge and literacy assessment","year":"1996","originator":"Mathis Wackernagel, William Rees","url":"https://scholargate.app/en/environmental-psychology/ecological-footprint-knowledge","markdownUrl":"https://scholargate.app/en/environmental-psychology/ecological-footprint-knowledge.md","definition":"The Ecological Footprint Knowledge Scale (EFKS) measures individuals' understanding of the ecological footprint concept—how much land and resources one's consumption requires—and knowledge of personal and global footprint impacts. Developed from the ecological footprint framework (Wackernagel & Rees, 1996), the EFKS assesses both conceptual comprehension (what is an ecological footprint?) and applied knowledge (how to estimate footprint, what factors affect it). The scale is critical for evaluating environmental education effectiveness, understanding why some individuals adopt sustainable consumption despite high footprint knowledge gaps, and identifying knowledge barriers that block behavior change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mathis Wackernagel, William Rees","subfamily":"environmental knowledge and literacy assessment","year":"1996","type":"Knowledge-based self-report and comprehension scale"},"citations":[{"ref":"Wackernagel, M., & Rees, W. E. (1996). Our ecological footprint: Reducing human impact on the earth. New Society Publishers.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Wackernagel%2C%20M.%2C%20%26%20Rees%2C%20W.%20E.%20(1996).%20Our%20ecological%20footprint%3A%20Reducing%20human%20impact%20on%20the%20earth.%20New%20Society%20Publish"},{"ref":"Venetis, E., & Tsuchihashi, K. (2014). Awareness and understanding of the ecological footprint concept among university students. International Journal of Sustainability in Higher Education, 15(4), 405–416.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Awareness+and+understanding+of+the+ecological+footprint+concept+among+university+students+Venetis"}],"related":["carbon-footprint-awareness-scale","sustainable-consumption-scale","pro-environmental-behavior-scale","environmental-concern-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ecological-footprint","name":"Ecological Footprint","fullName":"Ecological Footprint Accounting","aliases":["EFA","Ecological Footprint Analysis","Biocapacity Accounting","Ekolojik Ayak İzi"],"domain":"sustainability","family":"process-pipeline","subfamily":"Environmental accounting","year":1996,"originator":"Mathis Wackernagel & William Rees","url":"https://scholargate.app/en/sustainability/ecological-footprint","markdownUrl":"https://scholargate.app/en/sustainability/ecological-footprint.md","definition":"Ecological Footprint Accounting (EFA) is a resource accounting framework that measures how much biologically productive land and water area a human population requires to produce the resources it consumes and to absorb the waste it generates. Introduced by Mathis Wackernagel and William Rees in 1996, it compares human demand on nature against Earth's regenerative capacity, expressed in standardized global hectares (gha).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mathis Wackernagel & William Rees","year":1996,"type":"Environmental accounting indicator","subfamily":"Environmental accounting","unit":"Global hectares (gha)","scale":"Individual, national, or global"},"citations":[{"ref":"Wackernagel, M., & Rees, W. (1996). Our Ecological Footprint: Reducing Human Impact on the Earth. New Society Publishers.","type":"book","doi":null,"isbn":"978-0-86571-312-3","url":null}],"related":["life-cycle-assessment","material-flow-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ecological-study","name":"Ecological Study","fullName":"Ecological Epidemiological Study","aliases":["aggregate study","correlational study","ecological correlation study","population-level study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"19th century (Snow 1854); formalised mid-20th century","originator":"Various; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues","url":"https://scholargate.app/en/epidemiology/ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/ecological-study.md","definition":"An ecological study is an observational epidemiological design in which the unit of analysis is a group or population — a country, region, city, or time period — rather than an individual. Exposures and outcomes are measured as aggregates (rates, proportions, or means) and then correlated across groups to generate or evaluate hypotheses about population-level associations between risk factors and disease.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues","year":"19th century (Snow 1854); formalised mid-20th century","type":"Observational epidemiological study","dataType":"Aggregate / grouped population-level data (rates, proportions, means)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Morgenstern, H. (1995). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 16(1), 61–81.","type":"article","doi":"10.1146/annurev.pu.16.050195.000425","isbn":null,"url":null},{"ref":"Ecological study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ecological_study"}],"related":["cross-sectional-epidemiological-study","cohort-study","case-control-study","dose-response-analysis","meta-analytic-ecological-study","spatial-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"economic-dispatch","name":"Economic Dispatch","fullName":"Economic Dispatch for Power Generation","aliases":["ED","Least-Cost Generation Dispatch"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Constrained optimization","year":"1958","originator":"Lester K. Kirchmayer","url":"https://scholargate.app/en/electrical-engineering/economic-dispatch","markdownUrl":"https://scholargate.app/en/electrical-engineering/economic-dispatch.md","definition":"Economic Dispatch (ED) is the process of optimally allocating power output among committed generators to meet demand at minimum fuel cost. Introduced by Kirchmayer in 1958, ED is a fundamental real-time optimization problem solved every few minutes in power system operations. Unlike Unit Commitment (which decides generator on/off), ED assumes generators are already committed and focuses on splitting load most economically. ED's rapid feedback enables efficient real-time power plant operations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lester K. Kirchmayer","subfamily":"Constrained optimization","year":"1958","type":"Continuous optimization for allocating power output among committed generators"},"citations":[{"ref":"Kirchmayer, L. K. (1958). Economic Operation of Power Systems. Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/economicoperatio0000kirc"},{"ref":"Horton, G. K. (1970). Techniques for the economic dispatch of generation. IEEE Transactions on Power Apparatus and Systems, 89(5), 893-901.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Techniques+for+the+economic+dispatch+of+generation+Horton"},{"ref":"Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2013). Power Generation, Operation, and Control (3rd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+Generation%2C+Operation%2C+and+Control+%283rd+ed.%29+Wood"}],"related":["optimal-power-flow","unit-commitment","fast-decoupled-power-flow"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"economic-order-quantity","name":"Economic Order Quantity","fullName":"Economic Order Quantity (EOQ)","aliases":["Wilson EOQ Model","Harris-Wilson Model","Optimal Lot Size Model","Ekonomik Sipariş Miktarı"],"domain":"operations-research","family":"regression-model","subfamily":"Inventory control","year":1913,"originator":"Ford W. Harris","url":"https://scholargate.app/en/operations-research/economic-order-quantity","markdownUrl":"https://scholargate.app/en/operations-research/economic-order-quantity.md","definition":"The Economic Order Quantity (EOQ) is a classic deterministic inventory model that identifies the order quantity minimizing the sum of annual ordering and holding costs. Introduced by Ford W. Harris in 1913 and later popularized by R. H. Wilson, EOQ assumes constant demand, fixed cost parameters, and instantaneous replenishment. It remains the foundational benchmark for inventory management in manufacturing, retail, and supply chain contexts where demand is relatively stable and costs are well-characterized.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ford W. Harris","year":1913,"type":"Deterministic inventory optimization model","subfamily":"Inventory control","data_requirement":"Constant demand rate, fixed ordering and holding costs","output":"Optimal order quantity minimizing total annual inventory cost"},"citations":[{"ref":"Harris, F. W. (1913/1990). How many parts to make at once. Operations Research, 38(6), 947–950 (reprint).","type":"article","doi":"10.1287/opre.38.6.947","isbn":null,"url":null}],"related":["newsvendor-model","safety-stock","abc-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ecosystem-services-valuation","name":"Ecosystem Services Valuation","fullName":"Ecosystem Services Valuation and Assessment","aliases":["ESV","Natural capital accounting","Environmental valuation"],"domain":"sustainability","family":"process-pipeline","subfamily":"Economic valuation","year":"1997","originator":"Robert Costanza, Rudolf de Groot, and team","url":"https://scholargate.app/en/sustainability/ecosystem-services-valuation","markdownUrl":"https://scholargate.app/en/sustainability/ecosystem-services-valuation.md","definition":"Ecosystem Services Valuation (ESV) is a framework pioneered by Costanza and colleagues (1997) that assigns economic value to the benefits nature provides to humanity—from pollination and water purification to climate regulation and cultural enjoyment. Formalized in the Millennium Ecosystem Assessment (2005) and The Economics of Ecosystems and Biodiversity (TEEB 2010), ESV bridges ecology and economics to make the invisible value of ecosystems visible to policymakers and markets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Costanza, Rudolf de Groot, and team","subfamily":"Economic valuation","year":"1997","type":"Valuation method"},"citations":[{"ref":"Costanza, R., d'Arge, R., de Groot, R., Farberk, S., Grasso, M., Hannon, B., ... & van den Belt, M. (1997). The value of the world's ecosystem services and natural capital. Nature, 387(6630), 253-260.","type":"article","doi":"10.1038/387253a0","isbn":null,"url":null},{"ref":"Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-being: Synthesis. Washington, DC: Island Press.","type":"article","doi":null,"isbn":null,"url":"https://www.millenniumassessment.org/en/index.html"},{"ref":"TEEB (2010). The Economics of Ecosystems and Biodiversity: Mainstreaming the Economics of Nature. A synthesis of the approach, conclusions and recommendations of TEEB.","type":"article","doi":null,"isbn":null,"url":"https://www.teebweb.org/"}],"related":["life-cycle-sustainability-assessment","dpsir-framework","input-output-structural-decomposition-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ecotoxicological-testing","name":"Ecotoxicological Testing","fullName":"Assessment of Toxicity to Aquatic and Terrestrial Organisms","aliases":["toxicity testing","aquatic bioassay","ecotoxicity assessment","organism exposure testing"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Environmental toxicology and hazard assessment","year":"1975","originator":"EPA and OECD","url":"https://scholargate.app/en/environmental-engineering/ecotoxicological-testing","markdownUrl":"https://scholargate.app/en/environmental-engineering/ecotoxicological-testing.md","definition":"Ecotoxicological testing is a suite of standardized laboratory and field methods to assess the toxicity of chemical substances to aquatic and terrestrial organisms (fish, invertebrates, algae, plants, soil fauna). Developed by regulatory agencies (OECD, EPA, EMEA) since the 1970s, these tests measure lethal concentration (LC50, EC50) and sublethal endpoints (growth, reproduction, behavior) under controlled conditions. Ecotoxicological data support chemical hazard classification, environmental risk assessment, and regulatory approval of new substances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"EPA and OECD","subfamily":"Environmental toxicology and hazard assessment","year":"1975","type":"experimental measurement and analysis pipeline"},"citations":[{"ref":"OECD. (2011). Test Guidelines for Chemicals. OECD Publishing.","type":"article","doi":null,"isbn":null,"url":"https://www.oecd.org/chemicalsafety/testing"},{"ref":"US Environmental Protection Agency. (2002). Aquatic Toxicity Test Methods. EPA 600/4-90/027.","type":"article","doi":null,"isbn":null,"url":"https://www.epa.gov/sites/default/files/2015-08/documents/short-term-methods-acute-toxicity.pdf"},{"ref":"Newman, M. C. (1998). Fundamentals of Ecotoxicology. CRC Press.","type":"book","doi":null,"isbn":"978-1566701167","url":null}],"related":["environmental-impact-assessment","soil-remediation","heavy-metal-speciation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"edas","name":"EDAS","fullName":"Evaluation Based on Distance from Average Solution","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., Turskis, Z.","url":"https://scholargate.app/en/decision-making/edas","markdownUrl":"https://scholargate.app/en/decision-making/edas.md","definition":"EDAS (Evaluation Based on Distance from Average Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., Turskis, Z. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., Turskis, Z.","subfamily":"Ranking","year":"2015","type":"Distance from average solution","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":true},"citations":[{"ref":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica","type":"article","doi":"10.15388/Informatica.2015.57","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eddy-covariance","name":"Eddy Covariance","fullName":"Eddy Covariance Flux Measurement Method","aliases":["Eddy covariance","EC flux","Eddy correlation","Direct flux measurement"],"domain":"meteorology","family":"process-pipeline","subfamily":"Direct measurement method","year":"1951","originator":"Swinbank","url":"https://scholargate.app/en/meteorology/eddy-covariance","markdownUrl":"https://scholargate.app/en/meteorology/eddy-covariance.md","definition":"The eddy covariance method is a direct, micrometeorological technique that measures turbulent fluxes of momentum, heat, water vapor, and CO2 by computing the covariance between high-frequency fluctuations of wind velocity and scalar properties (temperature, humidity, concentration). It is the gold standard for measuring ecosystem-atmosphere exchanges and validating model parameterizations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Swinbank","subfamily":"Direct measurement method","year":"1951","type":"Micrometeorological flux measurement"},"citations":[{"ref":"Baldocchi, D. (2003). Assessing the eddy covariance technique for evaluating carbon dioxide fluxes of ecosystems: past, present and future. Global Change Biology, 9(4), 479-492.","type":"article","doi":"10.1046/j.1365-2486.2003.00629.x","isbn":null,"url":null},{"ref":"Foken, T. (2006). The energy balance closure problem: An overview. Ecological Applications, 18(6), 1351-1367.","type":"article","doi":"10.1890/06-0922.1","isbn":null,"url":null}],"related":["bulk-aerodynamic-flux","monin-obukhov-similarity","wrf-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ede-q","name":"EDE-Q","fullName":"Eating Disorder Examination Questionnaire","aliases":["EDE-Q6.0","Eating Disorder Examination - Questionnaire"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"eating disorder assessment","year":"1993","originator":"Christopher Fairburn, Zafra Cooper","url":"https://scholargate.app/en/clinical-psychology/ede-q","markdownUrl":"https://scholargate.app/en/clinical-psychology/ede-q.md","definition":"The EDE-Q is a 28-item self-report questionnaire derived from the gold-standard Eating Disorder Examination (EDE) interview. Developed by Fairburn and Beglin in 1993, it measures the cognitive, behavioural, and attitudinal features of eating disorders. It is widely used in both research and clinical screening because it captures the core psychopathology of eating disorders without requiring a trained interviewer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher Fairburn, Zafra Cooper","subfamily":"eating disorder assessment","year":"1993","type":"Self-report questionnaire"},"citations":[{"ref":"Fairburn, C. G., & Beglin, S. J. (1994). Assessment of eating disorders: Interview or self-report questionnaire? International Journal of Eating Disorders, 16(4), 363–370.","type":"article","doi":"10.1002/1098-108X(199412)16:4<363::AID-EAT2260160405>3.0.CO;2-#","isbn":null,"url":null},{"ref":"Mond, J. M., Hay, P. J., Rodgers, B., Owen, C., & Beumont, P. J. V. (2004). Validity of the Eating Disorder Examination Questionnaire (EDE-Q) in screening for eating disorders in community samples. Behaviour Research and Therapy, 42(5), 551–567.","type":"article","doi":"10.1016/S0005-7967(03)00161-X","isbn":null,"url":null},{"ref":"Luce, K. H., & Crowther, J. H. (2007). The reliability of the Eating Disorder Examination-Questionnaire International (EDE-QI). International Journal of Eating Disorders, 40(6), 549–552.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+reliability+of+the+Eating+Disorder+Examination-Questionnaire+International+%28EDE-QI%29+Luce"}],"related":["scoff-questionnaire","three-factor-eating-questionnaire","body-shape-questionnaire","binge-eating-scale","yale-food-addiction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"edinburgh-postnatal-depression","name":"Edinburgh Postnatal Depression Scale","fullName":"Edinburgh Postnatal Depression Scale (EPDS)","aliases":["EPDS","Edinburgh Postnatal Depression Scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"perinatal-mood-disorder-screening","year":"1987","originator":"John Cox","url":"https://scholargate.app/en/clinical-psychology/edinburgh-postnatal-depression","markdownUrl":"https://scholargate.app/en/clinical-psychology/edinburgh-postnatal-depression.md","definition":"The Edinburgh Postnatal Depression Scale is a 10-item self-report screening questionnaire developed by John Cox, Jeni Holden, and Ruth Sagovsky in 1987 to identify postnatal depression in new mothers. Published in the British Journal of Psychiatry, the EPDS specifically addresses depressive symptoms common in the postpartum period, avoiding items that might confound with normal pregnancy or postpartum adjustment (e.g., sleep disturbance from infant care). It is widely endorsed by obstetric and midwifery organizations, freely available, and used globally as the standard for perinatal depression screening.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Cox","subfamily":"perinatal-mood-disorder-screening","year":"1987","type":"Self-report questionnaire"},"citations":[{"ref":"Cox, J. L., Holden, J. M., & Sagovsky, R. (1987). Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. British Journal of Psychiatry, 150, 782–786.","type":"article","doi":"10.1192/bjp.150.6.782","isbn":null,"url":null},{"ref":"Eberhard-Gran, M., Eskild, A., & Tambs, K. (2001). Review of validation studies of the Edinburgh Postnatal Depression Scale. Acta Psychiatrica Scandinavica, 104(4), 243–249.","type":"article","doi":"10.1111/j.1600-0447.2001.00187.x","isbn":null,"url":null},{"ref":"Baker, N. N., Williams, S. R., & Murray, L. (2003). Sensitivity and specificity of the Edinburgh Postnatal Depression Scale administered at 5 weeks postpartum. Journal of the Royal Society of Medicine, 96(2), 89–92.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sensitivity+and+specificity+of+the+Edinburgh+Postnatal+Depression+Scale+administered+at+5+weeks+postpartum+Baker"}],"related":["phq-9","bdi-ii","patient-global-impression-change","quick-inventory-depressive"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"editorial-commentary","name":"Editorial and Commentary","fullName":"Editorial and Commentary (Peer-Reviewed Opinions on Research and Practice)","aliases":["editorial","commentary","opinion","perspective","viewpoint"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Opinion and interpretation","year":"1850","originator":"Academic journals (19th century formalization)","url":"https://scholargate.app/en/academic-writing/editorial-commentary","markdownUrl":"https://scholargate.app/en/academic-writing/editorial-commentary.md","definition":"An editorial or commentary is a peer-reviewed opinion article in an academic journal, typically authored by experts to interpret, contextualize, or critique recent research findings or practice issues. Editorials are usually commissioned by journal editors; commentaries may be solicited or submitted unsolicited. Unlike research articles based on empirical data, editorials and commentaries are evidence-grounded opinions—authors synthesize literature, interpret findings, and offer perspectives on implications. These contributions are indexed in major databases and citable, establishing them as legitimate scholarly publications. Editorials and commentaries carry prestige, particularly when published in high-impact journals, and position authors as thought leaders in their fields.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Academic journals (19th century formalization)","subfamily":"Opinion and interpretation","year":"1850","type":"Document Type"},"citations":[{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE.","type":"webpage","doi":null,"isbn":null,"url":"http://www.icmje.org"},{"ref":"Committee on Publication Ethics (2023). Guidelines for Editors and Commentators. https://publicationethics.org","type":"webpage","doi":null,"isbn":null,"url":"https://publicationethics.org"},{"ref":"American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). APA.","type":"book","doi":null,"isbn":"978-1-4338-3216-1","url":null}],"related":["original-research-article","letter-to-editor","opinion-leadership","academic-debate"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"edmonton-frail-scale","name":"EFS","fullName":"Edmonton Frail Scale","aliases":["EFS","Edmonton Frailty Scale"],"domain":"gerontology","family":"process-pipeline","subfamily":"multidimensional-frailty","year":"2006","originator":"Darryl B. Rolfson","url":"https://scholargate.app/en/gerontology/edmonton-frail-scale","markdownUrl":"https://scholargate.app/en/gerontology/edmonton-frail-scale.md","definition":"The Edmonton Frail Scale (EFS) is a comprehensive, nine-domain assessment tool developed by Rolfson and colleagues in 2006 to systematically evaluate frailty across multiple physiological and functional dimensions in older adults. Combining clinical judgment with objective testing, the EFS assesses cognition, general health status, functional independence, social support, medication use, nutrition, mood, continence, and functional performance, providing a multidimensional frailty profile. It is widely used in geriatric clinics, acute care settings, and research to characterize the nature and severity of frailty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Darryl B. Rolfson","subfamily":"multidimensional-frailty","year":"2006","type":"Clinician-administered assessment"},"citations":[{"ref":"Rolfson, D. B., Majumdar, S. R., Tsuyuki, R. T., Tahir, A., & Srivastava, S. (2006). Validity and reliability of the Edmonton Frail Scale. Age Ageing, 35(5), 526-529.","type":"article","doi":"10.1093/ageing/afl041","isbn":null,"url":null},{"ref":"Moorhouse, P., & Rockwood, K. (2012). Frailty and its quantification. J Gerontol A Biol Sci Med Sci, 67(7), 712-717.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Frailty+and+its+quantification+Moorhouse"},{"ref":"Hilmer, S. N., Perera, V., Mitchell, S., et al. (2009). The assessment of frailty in older persons. Aging Health, 5(3), 417-432.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/19496746"}],"related":["frail-scale","short-physical-performance-battery","tinetti-balance-assessment","cognitive-telephone-screening","life-space-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"edmonton-symptom-assessment","name":"ESAS","fullName":"Edmonton Symptom Assessment System","aliases":["ESAS","Edmonton Symptom Assessment Scale"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Multi-Symptom Rapid Assessment","year":"1991","originator":"Eduardo Bruera","url":"https://scholargate.app/en/oncology-nursing/edmonton-symptom-assessment","markdownUrl":"https://scholargate.app/en/oncology-nursing/edmonton-symptom-assessment.md","definition":"The Edmonton Symptom Assessment System is a rapid, validated 9-item tool that assesses the severity of common symptoms in cancer and palliative care patients: pain, tiredness, nausea, depression, anxiety, drowsiness, appetite loss, general well-being, and shortness of breath. Developed by Bruera and colleagues at the University of Alberta in 1991, the ESAS has become the standard symptom-screening instrument in oncology clinics, palliative care units, and end-of-life care settings worldwide, enabling efficient symptom prioritization and management escalation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eduardo Bruera","subfamily":"Multi-Symptom Rapid Assessment","year":"1991","type":"Patient self-report multisymptom palliative care scale"},"citations":[{"ref":"Bruera, E., Kuehn, N., Miller, M. J., Selmser, P., & Macmillan, K. (1991). The Edmonton Symptom Assessment System (ESAS): a simple method for the assessment of palliative care patients. J Palliat Care, 7(2), 6–9.","type":"article","doi":"10.1177/082585979100700202","isbn":null,"url":null},{"ref":"Chang, V. T., Hwang, S. S., & Kasimis, B. (2000). Longitudinal documentation of cancer pain: a pilot chronic disease management model. Cancer, 88(12), 2892–2900.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Longitudinal+documentation+of+cancer+pain%3A+a+pilot+chronic+disease+management+model+Chang"}],"related":["distress-thermometer","brief-fatigue-inventory","memorial-symptom-assessment-scale","fact-g","piper-fatigue-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"edna-metabarcoding","name":"eDNA Metabarcoding","fullName":"Environmental DNA Metabarcoding","aliases":["eDNA","metabarcoding","DNA metabarcoding","genetic monitoring"],"domain":"ecology","family":"process-pipeline","subfamily":"Molecular ecology","year":"2012","originator":"Pierre Taberlet","url":"https://scholargate.app/en/ecology/edna-metabarcoding","markdownUrl":"https://scholargate.app/en/ecology/edna-metabarcoding.md","definition":"Environmental DNA (eDNA) metabarcoding detects and identifies species present in environmental samples (water, soil, air) by sequencing short DNA fragments released by organisms. Developed by Taberlet and colleagues (2012), this approach has revolutionized biodiversity monitoring: species can be surveyed without capture, observation, or complex sampling designs. Metabarcoding sequences millions of DNA fragments, identifies reads taxonomically, and assigns them to species. The method is non-invasive, rapid, and cost-effective, enabling large-scale biodiversity surveys and early detection of cryptic or rare species.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pierre Taberlet","subfamily":"Molecular ecology","year":"2012","type":"species detection and community assessment"},"citations":[{"ref":"Taberlet, P., Coissac, E., Hajibabaei, M., & Rieseberg, L. H. (2012). Environmental DNA. Molecular Ecology, 21(8), 1789-1793.","type":"article","doi":"10.1111/j.1365-294X.2012.05542.x","isbn":null,"url":null},{"ref":"Deakin, G., Pettitt-Wade, H., & Waldick, R. C. (2016). Environmental DNA metabarcoding: A review of the application to fish biodiversity assessment in temperate freshwaters. Environmental DNA, 1(1), 4-14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Environmental+DNA+metabarcoding%3A+A+review+of+the+application+to+fish+biodiversity+assessment+in+temperate+freshwaters+Deakin"},{"ref":"Ficetola, G. F., Miaud, C., Pompanon, F., & Taberlet, P. (2008). Species detection using environmental DNA from water samples. Biology Letters, 4(4), 423-425.","type":"article","doi":"10.1098/rsbl.2008.0118","isbn":null,"url":null}],"related":["species-accumulation","distance-sampling","bioaccumulation-model","functional-diversity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"edss-multiple-sclerosis","name":"EDSS","fullName":"Kurtzke Expanded Disability Status Scale","aliases":["Expanded Disability Status Scale"],"domain":"neurology","family":"process-pipeline","subfamily":"Multiple sclerosis disability staging","year":"1983","originator":"John F. Kurtzke","url":"https://scholargate.app/en/neurology/edss-multiple-sclerosis","markdownUrl":"https://scholargate.app/en/neurology/edss-multiple-sclerosis.md","definition":"The EDSS is the most widely used clinical disability rating scale in multiple sclerosis research and practice. Developed by John Kurtzke in 1983, it provides a 0-10 ordinal scale capturing disease severity across eight neurological functional systems and functional status. The EDSS remains the primary endpoint in MS clinical trials and longitudinal cohort studies, with decades of prognostic and comparative data worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John F. Kurtzke","subfamily":"Multiple sclerosis disability staging","year":"1983","type":"Clinician-rated"},"citations":[{"ref":"Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33(11), 1444-1452.","type":"article","doi":"10.1212/wnl.33.11.1444","isbn":null,"url":null}],"related":["updrs","nihss","msfc","rivermead-mobility-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"educational-action-research","name":"Educational Action Research","fullName":"Educational Action Research","aliases":["EAR","practitioner research","teacher action research","classroom action research"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1940s (Lewin); educational context developed 1970s–1980s","originator":"Kurt Lewin (action research foundations); Lawrence Stenhouse and John Elliott (educational adaptation)","url":"https://scholargate.app/en/field-methods/educational-action-research","markdownUrl":"https://scholargate.app/en/field-methods/educational-action-research.md","definition":"Educational action research is a cyclical, practitioner-led inquiry method in which educators systematically investigate a problem or opportunity in their own classroom or school, implement a change, observe its effects, and reflect on findings to guide the next cycle. Rooted in Kurt Lewin's action research framework and developed for educational contexts by Lawrence Stenhouse and John Elliott, it bridges the gap between educational theory and classroom practice by making teachers agents of rigorous inquiry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kurt Lewin (action research foundations); Lawrence Stenhouse and John Elliott (educational adaptation)","year":"1940s (Lewin); educational context developed 1970s–1980s","type":"Participatory qualitative research design","dataType":"Observation notes, reflective journals, interviews, documents, student work samples","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Elliott, J. (1991). Action Research for Educational Change. Open University Press.","type":"book","doi":null,"isbn":"978-0335096190","url":null},{"ref":"Kemmis, S., McTaggart, R., & Nixon, R. (2014). The Action Research Planner: Doing Critical Participatory Action Research. Springer.","type":"book","doi":"10.1007/978-981-4560-67-2","isbn":null,"url":null}],"related":["design-based-research","classroom-observation","lesson-study","participatory-action-research","program-evaluation","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"efa-psychometric","name":"EFA for Scale Development","fullName":"Exploratory Factor Analysis for Scale Development","aliases":["Açımlayıcı Faktör Analizi — Ölçek Geliştirme (EFA)","psychometric EFA","scale construction factor analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":"1904 (foundational); contemporary scale-development practice from 1990s onward","originator":"Primarily Spearman (1904); psychometric scale application formalised by Thurstone (1930s)","url":"https://scholargate.app/en/psychometrics/efa-psychometric","markdownUrl":"https://scholargate.app/en/psychometrics/efa-psychometric.md","definition":"Exploratory Factor Analysis for Scale Development is the psychometric application of EFA in which an item pool is administered and the resulting response data are analysed to discover the latent factor structure underlying the items. Originating with Spearman's (1904) factor theory and formalised for applied scale construction by Costello and Osborne (2005) and Fabrigar and colleagues (1999), this variant imposes a stricter sample requirement (n ≥ 100, subject-to-item ratio ≥ 5) and a higher loading threshold (≥ 0.40) than general EFA, and it treats the recovered factor structure as a draft to be subsequently validated by confirmatory analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Primarily Spearman (1904); psychometric scale application formalised by Thurstone (1930s)","year":"1904 (foundational); contemporary scale-development practice from 1990s onward","type":"Latent variable / dimension reduction","outcome":"Factor loadings, factor structure, communalities","data":"Ordinal or continuous item responses","min_sample":100,"subject_to_item_ratio":"≥ 5:1","factor_loading_threshold":"≥ 0.40","kmo_threshold":"≥ 0.70"},"citations":[{"ref":"Costello, A. B. & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.","type":"article","doi":null,"isbn":null,"url":"https://scholarworks.umass.edu/pare/vol10/iss1/7/"},{"ref":"Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.","type":"article","doi":"10.1037/1082-989X.4.3.272","isbn":null,"url":null}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","cronbach-alpha","sem","pca"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"effect-size-analysis","name":"Effect size analysis","fullName":"Effect Size Analysis","aliases":["effect magnitude estimation","standardized effect measure","practical significance analysis","ES analysis"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1969 (first edition); 1988 (definitive second edition)","originator":"Jacob Cohen","url":"https://scholargate.app/en/statistics/effect-size-analysis","markdownUrl":"https://scholargate.app/en/statistics/effect-size-analysis.md","definition":"Effect size analysis quantifies the practical magnitude of a statistical result independently of sample size. Rather than asking only whether a difference or relationship is statistically significant, it asks how large it is, using standardized indices such as Cohen's d, eta-squared, omega-squared, or Pearson's r that allow direct comparison across studies and populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","year":"1969 (first edition); 1988 (definitive second edition)","type":"Standardized magnitude estimation","dataType":"Continuous, categorical, or ranked outcomes","subfamily":"Classical statistics"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805802832","url":null},{"ref":"Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863.","type":"article","doi":"10.3389/fpsyg.2013.00863","isbn":null,"url":null}],"related":["power-analysis","independent-samples-t-test","one-way-anova","meta-analysis","confidence-interval-estimation","roc-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"effect-size","name":"Effect Size","fullName":"Effect Size: Quantifying the Magnitude of Research Findings","aliases":["ES","Cohen's d","standardized effect","practical significance"],"domain":"research-statistics","family":"process-pipeline","subfamily":"statistical-magnitude","year":1988,"originator":"Jacob Cohen","url":"https://scholargate.app/en/research-statistics/effect-size","markdownUrl":"https://scholargate.app/en/research-statistics/effect-size.md","definition":"Effect size quantifies the magnitude of a research finding independent of sample size. While a p-value tells you whether a result is statistically significant, an effect size tells you how big the result is. Jacob Cohen formalized effect size measurement in behavioral sciences (1988), establishing standard benchmarks (small = 0.2, medium = 0.5, large = 0.8 for Cohen's d). Effect sizes are essential for meta-analysis, power analysis, and communicating the practical importance of research findings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","subfamily":"statistical-magnitude","year":1988,"type":"Concept"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"0-8058-0283-5","url":null},{"ref":"Cumming, G. (2012). Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge.","type":"article","doi":null,"isbn":"0-415-87968-8","url":null},{"ref":"Lakens, D. (2013). Calculating and Reporting Effect Sizes to Facilitate Cumulative Science: A Practical Primer for t-Tests and ANOVAs. Frontiers in Psychology, 4, 863.","type":"article","doi":"10.3389/fpsyg.2013.00863","isbn":null,"url":null}],"related":["p-value-significance","confidence-interval","statistical-power","type-i-type-ii-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"effective-field-theory","name":"Effective Field Theory","fullName":"Effective Field Theory Framework","aliases":["EFT","effective theory","operator product expansion"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Theoretical framework","year":"1979","originator":"Steven Weinberg","url":"https://scholargate.app/en/particle-physics/effective-field-theory","markdownUrl":"https://scholargate.app/en/particle-physics/effective-field-theory.md","definition":"Effective Field Theory (EFT) is a general framework for studying physics at low energies in terms of the relevant degrees of freedom, without requiring complete knowledge of high-energy physics. By expanding in powers of energy, EFT provides model-independent parameterizations of new physics effects and systematic methods for computing precision predictions of the Standard Model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Weinberg","subfamily":"Theoretical framework","year":"1979","type":"Model-independent approach"},"citations":[{"ref":"Weinberg, S. (1979). Baryon and lepton nonconserving processes. Physical Review Letters, 43(21), 1566.","type":"article","doi":"10.1103/PhysRevLett.43.1566","isbn":null,"url":null},{"ref":"Buchmuller, W., & Wyler, D. (1986). Effective Lagrangian analysis of new interactions and flavor conservation. Nuclear Physics B, 268(3-4), 621–653.","type":"article","doi":"10.1016/0550-3213(86)90262-2","isbn":null,"url":null},{"ref":"Grojean, C., et al. (2017). New approaches to electroweak symmetry breaking. Reviews of Modern Physics, 71(3), 735.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=New+approaches+to+electroweak+symmetry+breaking+Grojean"}],"related":["feynman-diagram","renormalization-group-equations","matrix-element-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"effectiveness-ntu-method","name":"Effectiveness-NTU Method","fullName":"Effectiveness-Number of Transfer Units Method for Heat Exchangers","aliases":["epsilon-NTU method","effectiveness method"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Heat Exchanger Design","year":"1984","originator":"William Kays and Alvin London","url":"https://scholargate.app/en/thermodynamics/effectiveness-ntu-method","markdownUrl":"https://scholargate.app/en/thermodynamics/effectiveness-ntu-method.md","definition":"The Effectiveness-NTU method is an alternative approach to heat exchanger analysis that measures thermal performance relative to the theoretical maximum possible heat transfer. It is particularly powerful for design problems where outlet temperatures are unknown. The method uses effectiveness (ratio of actual to maximum possible heat transfer) and NTU (Number of Transfer Units, a dimensionless parameter related to overall heat transfer area) to characterize heat exchanger performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Kays and Alvin London","subfamily":"Heat Exchanger Design","year":"1984","type":"Heat transfer correlation"},"citations":[{"ref":"Kays, W. M., & London, A. L. (1984). Compact Heat Exchangers (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070334007","url":null},{"ref":"Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470055540","url":null}],"related":["log-mean-temperature-difference","thermal-resistance-network","rankine-cycle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"efficientnet","name":"EfficientNet","fullName":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","aliases":["EfficientNet","compound scaling CNN","EfficientNet-B0 through B7","EfficientNetV2"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2019,"originator":"Tan, M. & Le, Q. V.","url":"https://scholargate.app/en/deep-learning/efficientnet","markdownUrl":"https://scholargate.app/en/deep-learning/efficientnet.md","definition":"EfficientNet is a family of convolutional neural network architectures introduced by Mingxing Tan and Quoc V. Le (Google Brain) at ICML 2019 that systematically co-scales network depth, width, and input resolution using a single compound coefficient, achieving state-of-the-art image classification accuracy with substantially fewer parameters and FLOPs than prior networks such as ResNet and Inception.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tan, M. & Le, Q. V.","year":2019,"type":"Compound-scaled convolutional neural network architecture","task":"Image classification, feature extraction, transfer learning","baselineModel":"EfficientNet-B0 (obtained via NAS)","scalingDimensions":"Depth (d), width (w), resolution (r)","compoundCoefficient":"phi (phi >= 0, controls total resource budget)","parameterRange":"5.3 M (B0) to 66 M (B7)"},"citations":[{"ref":"Tan, M. & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6105–6114.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1905.11946"},{"ref":"Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["resnet","vgg","inception","mobilenet","convolutional-neural-network","transfer-learning","neural-architecture-search"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"effort-reward-imbalance-scale","name":"Effort-Reward Imbalance Scale","fullName":"Effort-Reward Imbalance (ERI) Questionnaire","aliases":["ERI"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Occupational stress and imbalance","year":1996,"originator":"Johannes Siegrist","url":"https://scholargate.app/en/occupational-health/effort-reward-imbalance-scale","markdownUrl":"https://scholargate.app/en/occupational-health/effort-reward-imbalance-scale.md","definition":"The Effort-Reward Imbalance (ERI) Scale is an occupational stress assessment tool based on a reciprocal model of work stress. Developed by Johannes Siegrist in 1996, the ERI measures the degree to which employees experience imbalance between their job efforts (demands, overcommitment) and job rewards (income, recognition, career prospects, security). The instrument is grounded in social reciprocity theory and has strong evidence linking high imbalance to cardiovascular disease, depression, and burnout.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johannes Siegrist","subfamily":"Occupational stress and imbalance","year":1996,"type":"Self-report questionnaire"},"citations":[{"ref":"Siegrist, J., Starke, D., Chandola, T., Peter, I., Marmot, M., Theorell, T., ... & Fuhrer, R. (2004). The measurement of effort-reward imbalance at work: European comparisons. Social Science & Medicine, 58(8), 1483-1499.","type":"article","doi":"10.1016/S0277-9536(03)00351-4","isbn":null,"url":null},{"ref":"Siegrist, J. (1996). Adverse health effects of high-effort/low-reward conditions. Journal of Occupational Health Psychology, 1(1), 27-41.","type":"article","doi":"10.1037/1076-8998.1.1.27","isbn":null,"url":null}],"related":["copenhagen-burnout-inventory","oldenburg-burnout-inventory","areas-of-worklife-scale","recovery-experience-questionnaire","job-demands-control-support-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"egarch-model","name":"EGARCH model","fullName":"Exponential Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["Exponential GARCH","EGARCH","Nelson EGARCH","log-GARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991","originator":"Daniel B. Nelson","url":"https://scholargate.app/en/econometrics/egarch-model","markdownUrl":"https://scholargate.app/en/econometrics/egarch-model.md","definition":"The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel B. Nelson","year":"1991","type":"Volatility / conditional variance model","dataType":"Financial time series, asset returns","subfamily":"Econometrics / time series"},"citations":[{"ref":"Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null},{"ref":"Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.","type":"article","doi":"10.1016/0304-4076(86)90063-1","isbn":null,"url":null}],"related":["arch-model","tgarch-model","dcc-garch-model","garch-model","arima-model","vector-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"egarch","name":"EGARCH","fullName":"Exponential Generalised Autoregressive Conditional Heteroskedasticity","aliases":["exponential GARCH","Nelson's EGARCH","asymmetric GARCH","EGARCH — Üstel GARCH"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1991,"originator":"Nelson","url":"https://scholargate.app/en/econometrics/egarch","markdownUrl":"https://scholargate.app/en/econometrics/egarch.md","definition":"EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nelson","year":1991,"type":"Conditional volatility model (asymmetric GARCH variant)","estimator":"Maximum likelihood","outcome":"continuous (financial return series)","minSample":100,"structure":"time series"},"citations":[{"ref":"Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null},{"ref":"Engle, R. F. & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. The Journal of Finance, 48(5), 1749-1778.","type":"article","doi":"10.1111/j.1540-6261.1993.tb05127.x","isbn":null,"url":null}],"related":["gjr-garch","garch","arch","tbats","arima"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ego-network-analysis","name":"Ego Network Analysis","fullName":"Ego Network Analysis (Personal Network Analysis)","aliases":["personal network analysis","egocentric network analysis","Ego Ağı Analizi (Personal Network Analysis)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":"1992 (Burt); foundational measurement formalised by Marsden 2002","originator":"Ronald S. Burt (structural holes framework); Peter V. Marsden (egocentric measures)","url":"https://scholargate.app/en/network-analysis/ego-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/ego-network-analysis.md","definition":"Ego network analysis examines the personal network of a focal individual — the ego — by mapping their direct contacts (alters) and the ties those contacts share with one another. Formalised through Ronald Burt's structural holes framework (1992) and Marsden's egocentric measurement approach (2002), the method produces ego-level indicators such as network size, density, constraint, and brokerage role that reveal how each individual's social position shapes their access to information, resources, and influence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald S. Burt (structural holes framework); Peter V. Marsden (egocentric measures)","year":"1992 (Burt); foundational measurement formalised by Marsden 2002","type":"Descriptive / relational network analysis","unit_of_analysis":"Ego (focal actor) and their immediate alters","key_metrics":"Network size, density, constraint index (Burt), brokerage roles, structural holes","data_collection":"Survey-based name generator or observed contact lists","minimum_nodes":"10 egos recommended; fewer than 20 limits interpretation","normality_required":"No"},"citations":[{"ref":"Burt, R.S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press.","type":"book","doi":null,"isbn":"9780674843714","url":null},{"ref":"Marsden, P.V. (2002). Egocentric and Sociocentric Measures of Network Centrality. Social Networks, 24(4), 407-422.","type":"article","doi":"10.1016/s0378-8733(02)00016-3","isbn":null,"url":null}],"related":["centrality-analysis","community-detection","exponential-random-graph","network-diffusion","link-prediction","temporal-network-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ehealth-literacy-scale","name":"eHealth Literacy Scale","fullName":"eHealth Literacy Scale (eHEALS)","aliases":["eHEALS"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Digital competency assessment","year":"2006","originator":"George R. Norman, Herbert A. Skinner","url":"https://scholargate.app/en/health-informatics/ehealth-literacy-scale","markdownUrl":"https://scholargate.app/en/health-informatics/ehealth-literacy-scale.md","definition":"The eHealth Literacy Scale measures individuals' ability to seek, find, understand, and appraise health information from electronic sources and apply that knowledge to health decision-making. Developed by Norman and Skinner in 2006, it assesses functional, communicative, and critical digital health literacy competencies essential for navigating modern health technologies and online medical information.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George R. Norman, Herbert A. Skinner","subfamily":"Digital competency assessment","year":"2006","type":"Self-report questionnaire"},"citations":[{"ref":"Norman, G. R., & Skinner, H. A. (2006). eHEALS: The eHealth Literacy Scale. Journal of Medical Internet Research, 8(4), e27.","type":"article","doi":"10.2196/jmir.8.4.e27","isbn":null,"url":null}],"related":["patient-engagement-scale","digital-health-acceptance-scale","health-app-usability-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eigenvector-centrality","name":"Eigenvector Centrality","fullName":"Eigenvector Centrality (Bonacich Power Centrality)","aliases":["eigenvector centrality","EC","Bonacich centrality","power centrality"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1972","originator":"Bonacich, P.","url":"https://scholargate.app/en/network-analysis/eigenvector-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/eigenvector-centrality.md","definition":"Eigenvector centrality, introduced by Bonacich in 1972, measures a node's influence by considering not just how many neighbors it has, but how influential those neighbors are. A node scores highly if it is connected to other high-scoring nodes, making it a recursive, globally-aware measure of structural importance in a network.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonacich, P.","year":"1972","type":"Centrality measure","dataType":"Adjacency matrix / graph","subfamily":"Network science"},"citations":[{"ref":"Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.","type":"article","doi":"10.1080/0022250X.1972.9989806","isbn":null,"url":null},{"ref":"Eigenvector centrality. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Eigenvector_centrality"}],"related":["degree-centrality","betweenness-centrality","closeness-centrality","pagerank","social-network-analysis","modularity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ekman-transport","name":"Ekman Transport","fullName":"Ekman Transport Calculation","aliases":["Ekman Spiral","Wind-driven Transport"],"domain":"oceanography","family":"process-pipeline","subfamily":"Dynamical Oceanography","year":"1905","originator":"Vagn Walfrid Ekman","url":"https://scholargate.app/en/oceanography/ekman-transport","markdownUrl":"https://scholargate.app/en/oceanography/ekman-transport.md","definition":"Ekman transport is the net volume flux of water driven by wind stress balanced with Coriolis force in the surface boundary layer. Derived by Vagn Walfrid Ekman in 1905 from the principle that wind stress is transmitted through the water column in a spiral pattern, Ekman transport is responsible for coastal upwelling and important oceanographic transports. The theory links surface wind patterns directly to ocean circulation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vagn Walfrid Ekman","subfamily":"Dynamical Oceanography","year":"1905","type":"theoretical-method"},"citations":[{"ref":"Ekman, V. W. (1905). On the influence of the Earth's rotation on ocean currents. Arkiv for Matematik, Astronomi och Fysik, 2(11), 1-52.","type":"article","doi":null,"isbn":null,"url":"https://dspace.mit.edu/"},{"ref":"Cushman-Roisin, B., & Beckers, J.-M. (2011). Introduction to Geophysical Fluid Dynamics: Physical and Numerical Aspects. Academic Press.","type":"article","doi":"10.1016/b978-0-12-088759-0.00001-8","isbn":null,"url":null}],"related":["geostrophic-velocity","acoustic-doppler-current-profiler","tidal-harmonic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"elastic-net-regression","name":"Elastic Net Regression","fullName":"Elastic Net Regularized Regression","aliases":["elastic net","EN regression","L1+L2 regularized regression","combined lasso-ridge regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"2005","originator":"Hui Zou and Trevor Hastie","url":"https://scholargate.app/en/statistics/elastic-net-regression","markdownUrl":"https://scholargate.app/en/statistics/elastic-net-regression.md","definition":"Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hui Zou and Trevor Hastie","year":"2005","type":"Penalized linear regression","dataType":"Continuous outcome, continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.","type":"article","doi":"10.1111/j.1467-9868.2005.00503.x","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387848570","url":null}],"related":["lasso-regression","ridge-regression","ols-regression","regularized-logistic-regression","quantile-regression","robust-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"elastic-net","name":"Elastic Net","fullName":"Elastic Net Regularized Regression","aliases":["Elastic Net Regresyon","elastic net regression","ElasticNet","L1/L2 regularized regression"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2005,"originator":"Zou, H. & Hastie, T.","url":"https://scholargate.app/en/machine-learning/elastic-net","markdownUrl":"https://scholargate.app/en/machine-learning/elastic-net.md","definition":"Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zou, H. & Hastie, T.","year":2005,"type":"Regularized linear regression (L1 + L2 penalty)","task":"Prediction & explanation with variable selection","minSample":30},"citations":[{"ref":"Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320.","type":"article","doi":"10.1111/j.1467-9868.2005.00503.x","isbn":null,"url":null}],"related":["lasso-regression","ridge-regression","linear-regression","logistic-regression","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"elastohydrodynamic-lubrication","name":"Elastohydrodynamic Lubrication","fullName":"Elastohydrodynamic Lubrication (EHL) Theory and Film Formation","aliases":["EHL","Elastohydrodynamic film","Hydrodynamic lubrication"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Tribology","year":"1977","originator":"Dowson, D., Higginson, G. R.","url":"https://scholargate.app/en/manufacturing/elastohydrodynamic-lubrication","markdownUrl":"https://scholargate.app/en/manufacturing/elastohydrodynamic-lubrication.md","definition":"Elastohydrodynamic lubrication (EHL) is the regime of fluid film lubrication in which elastic deformation of the surfaces plays a crucial role in maintaining a fluid layer between sliding or rolling surfaces. In applications like roller bearings and gears, the contact pressure is extremely high, causing the lubricant viscosity to increase dramatically and the surfaces to deform elastically. EHL theory, developed rigorously by Dowson and Higginson, predicts the film thickness, pressure distribution, and friction in these heavily loaded contacts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dowson, D., Higginson, G. R.","subfamily":"Tribology","year":"1977","type":"Mathematical model for lubricated contacts"},"citations":[{"ref":"Dowson, D., & Higginson, G. R. (1977). Elastohydrodynamic Lubrication: The Fundamentals of Roller and Gear Lubrication. Pergamon Press.","type":"book","doi":null,"isbn":"0-08-021710-4","url":null},{"ref":"Hamrock, B. J., Schmid, S. R., & Jacobson, B. O. (1994). Fundamentals of Fluid Film Lubrication (2nd ed.). Marcel Dekker.","type":"book","doi":null,"isbn":"0-8247-9163-0","url":null},{"ref":"Masjedi, M., & Khonsari, M. M. (2014). Film thickness and asperity load formulas for elastohydrodynamic lubrication of rollers. Tribology International, 81, 1-14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Film+thickness+and+asperity+load+formulas+for+elastohydrodynamic+lubrication+of+rollers+Masjedi"}],"related":["griffith-fracture-mechanics","modal-analysis","tolerance-stack-up","design-for-manufacturing-and-assembly"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"elbow-method","name":"Elbow Method","fullName":"Elbow Method for Optimal Cluster Number","aliases":["elbow analysis","knee detection"],"domain":"model-evaluation","family":"mcdm","subfamily":"Cluster Number Selection","year":"1953","originator":"Robert Thorndike","url":"https://scholargate.app/en/model-evaluation/elbow-method","markdownUrl":"https://scholargate.app/en/model-evaluation/elbow-method.md","definition":"The Elbow Method is a heuristic for selecting the optimal number of clusters in partitional clustering. Introduced by Robert Thorndike in 1953, it involves fitting clustering models for increasing numbers of clusters and plotting the within-cluster sum of squares (WCSS) against the number of clusters. The 'elbow' occurs where the rate of WCSS decrease sharply changes, suggesting an optimal cluster count.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Thorndike","subfamily":"Cluster Number Selection","year":"1953","type":"Heuristic optimization criterion"},"citations":[{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.","type":"book","doi":null,"isbn":null,"url":"https://hastie.su.domains/ElemStatLearn/"},{"ref":"Thorndike, R. L. (1953). Who belongs in the family? Psychometrika, 18(4), 267-276.","type":"article","doi":"10.1007/BF02289263","isbn":null,"url":null}],"related":["silhouette-score","gap-statistic","calinski-harabasz-index","davies-bouldin-index","inertia"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"elearning-satisfaction-scale","name":"E-Learning Satisfaction Scale","fullName":"E-Learning Satisfaction Scale","aliases":["ELSS","Online Learning Satisfaction"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"2008","originator":"Bolliger, Halupa, Chi & Kilduff","url":"https://scholargate.app/en/information-systems/elearning-satisfaction-scale","markdownUrl":"https://scholargate.app/en/information-systems/elearning-satisfaction-scale.md","definition":"The E-Learning Satisfaction Scale measures learner satisfaction with online educational experiences across multiple dimensions including platform quality, instructor effectiveness, course content, peer interaction, and technical support. Developed through research by Bolliger, Halupa, Chi, and others studying online higher education, the scale helps institutions assess course quality, predict learner retention, and identify improvement opportunities in digital education delivery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bolliger, Halupa, Chi & Kilduff","subfamily":"Technology adoption","year":"2008","type":"Likert-scale satisfaction measure"},"citations":[{"ref":"Chi, T., & Kilduff, P. P. (2011). Understanding consumer perceived value of casual online games. New Media & Society, 13(6), 954-971.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Understanding+consumer+perceived+value+of+casual+online+games+Chi"},{"ref":"Bolliger, D. U., & Halupa, C. (2012). Student perceptions of satisfaction and anxiety in an online doctoral program. Distance Education, 33(2), 175-189.","type":"article","doi":"10.1080/01587919.2012.667961","isbn":null,"url":null}],"related":["tam-questionnaire","online-trust-scale","social-media-engagement-scale","technology-readiness-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electre-i","name":"ELECTRE-I","fullName":"ELECTRE I — ELimination Et Choix Traduisant la REalité I (kernel / choice)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1968","originator":"Roy, B.","url":"https://scholargate.app/en/decision-making/electre-i","markdownUrl":"https://scholargate.app/en/decision-making/electre-i.md","definition":"ELECTRE-I (ELECTRE I — ELimination Et Choix Traduisant la REalité I (kernel / choice)) is a outranking multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roy, B.","subfamily":"Outranking","year":"1968","type":"Outranking — concordance/discordance with kernel extraction","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode ELECTRE). RIRO — Revue d'Informatique et de Recherche Opérationnelle","type":"article","doi":"10.1051/ro/196802v100571","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electre-ii","name":"ELECTRE-II","fullName":"ELECTRE II — ELimination Et Choix Traduisant la REalité II (complete ranking)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1973","originator":"Roy, B., Bertier, P.","url":"https://scholargate.app/en/decision-making/electre-ii","markdownUrl":"https://scholargate.app/en/decision-making/electre-ii.md","definition":"ELECTRE-II (ELECTRE II — ELimination Et Choix Traduisant la REalité II (complete ranking)) is a ranking multi-criteria decision-making (MCDM) method introduced by Roy, B., Bertier, P. in 1973. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roy, B., Bertier, P.","subfamily":"Ranking","year":"1973","type":"Outranking with strong/weak concordance thresholds (complete preorder)","value_space":"crisp","uncertainty":"none","compensation":"none","rank_reversal":true},"citations":[{"ref":"Roy, B., Bertier, P. (1973). La méthode ELECTRE II: une application au media-planning. Operational Research '72 (Proceedings IFORS), North-Holland","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=La%20m%C3%A9thode%20ELECTRE%20II%3A%20une%20application%20au%20media-planning"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electre-iii","name":"ELECTRE-III","fullName":"ELECTRE III — ELimination Et Choix Traduisant la REalité III (pseudo-criteria, fuzzy outranking)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1978","originator":"Roy, B.","url":"https://scholargate.app/en/decision-making/electre-iii","markdownUrl":"https://scholargate.app/en/decision-making/electre-iii.md","definition":"ELECTRE-III (ELECTRE III — ELimination Et Choix Traduisant la REalité III (pseudo-criteria, fuzzy outranking)) is a ranking multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roy, B.","subfamily":"Ranking","year":"1978","type":"Fuzzy outranking with indifference/preference/veto thresholds (complete preorder)","value_space":"crisp","uncertainty":"none","compensation":"none","rank_reversal":true},"citations":[{"ref":"Roy, B. (1978). ELECTRE III: Un algorithme de classement fondé sur une représentation floue des préférences en présence de critères multiples. Cahiers du CERO","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ELECTRE+III%3A+Un+algorithme+de+classement+fond%C3%A9+sur+une+repr%C3%A9sentation+floue+des+pr%C3%A9f%C3%A9rences+en+pr%C3%A9sence+de+crit%C3%A8res+multiples+Roy"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electre-iv","name":"ELECTRE-IV","fullName":"ELECTRE IV — ELimination Et Choix Traduisant la REalité IV (no criterion weights)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1982","originator":"Roy, B., Hugonnard, J. C.","url":"https://scholargate.app/en/decision-making/electre-iv","markdownUrl":"https://scholargate.app/en/decision-making/electre-iv.md","definition":"ELECTRE-IV (ELECTRE IV — ELimination Et Choix Traduisant la REalité IV (no criterion weights)) is a ranking multi-criteria decision-making (MCDM) method introduced by Roy, B., Hugonnard, J. C. in 1982. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roy, B., Hugonnard, J. C.","subfamily":"Ranking","year":"1982","type":"Weight-free outranking with pseudo-criteria (complete preorder)","value_space":"crisp","uncertainty":"none","compensation":"none","rank_reversal":true},"citations":[{"ref":"Roy, B., Hugonnard, J. C. (1982). Ranking of suburban line extension projects on the Paris metro system by a multicriteria method. Transportation Research Part A","type":"article","doi":"10.1016/0191-2607(82)90057-7","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electre-tri-b","name":"ELECTRE-TRI-B","fullName":"ELECTRE TRI-B — Sorting with central reference profiles (boundary variant)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"1992","originator":"Yu, W.","url":"https://scholargate.app/en/decision-making/electre-tri-b","markdownUrl":"https://scholargate.app/en/decision-making/electre-tri-b.md","definition":"ELECTRE-TRI-B (ELECTRE TRI-B — Sorting with central reference profiles (boundary variant)) is a sorting multi-criteria decision-making (MCDM) method introduced by Yu, W. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yu, W.","subfamily":"Sorting","year":"1992","type":"Outranking sorting — central (boundary) reference profiles","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Yu, W. (1992). ELECTRE TRI — Aspects méthodologiques et manuel d'utilisation. LAMSADE Cahier 74, Université Paris-Dauphine","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ELECTRE%20TRI%20%E2%80%94%20Aspects%20m%C3%A9thodologiques%20et%20manuel%20d%27utilisation"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electre-tri-c","name":"ELECTRE-TRI-C","fullName":"ELECTRE Tri with Central Reference Profiles","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"2010","originator":"Almeida-Dias, J. Figueira, J. R. Roy, B.","url":"https://scholargate.app/en/decision-making/electre-tri-c","markdownUrl":"https://scholargate.app/en/decision-making/electre-tri-c.md","definition":"ELECTRE-TRI-C (ELECTRE Tri with Central Reference Profiles) is a sorting multi-criteria decision-making (MCDM) method introduced by Almeida-Dias, J. Figueira, J. R. Roy, B. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Almeida-Dias, J. Figueira, J. R. Roy, B.","subfamily":"Sorting","year":"2010","type":"Outranking-based sorting using central characteristic reference profiles","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Almeida-Dias, J., Figueira, J. R., Roy, B. (2010). ELECTRE Tri-C: A multiple criteria sorting method based on characteristic reference actions. European Journal of Operational Research","type":"article","doi":"10.1016/j.ejor.2009.10.018","isbn":null,"url":null}],"related":["electre-tri-b"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electre-tri","name":"ELECTRE-TRI","fullName":"Outranking-Based Sorting Method with Boundary Profiles (also ELECTRE TRI-B)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"1992","originator":"Yu, W.","url":"https://scholargate.app/en/decision-making/electre-tri","markdownUrl":"https://scholargate.app/en/decision-making/electre-tri.md","definition":"ELECTRE-TRI (Outranking-Based Sorting Method with Boundary Profiles (also ELECTRE TRI-B)) is a sorting multi-criteria decision-making (MCDM) method introduced by Yu, W. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yu, W.","subfamily":"Sorting","year":"1992","type":"Outranking-based boundary-profile sorting","value_space":"crisp","uncertainty":"none","compensation":"none","rank_reversal":false},"citations":[{"ref":"Yu, W. (1992). ELECTRE TRI: aspects méthodologiques et manuel d'utilisation. Document du LAMSADE 74, Université Paris-Dauphine","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ELECTRE%20TRI%3A%20aspects%20m%C3%A9thodologiques%20et%20manuel%20d%27utilisation"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electre","name":"ELECTRE","fullName":"ELECTRE I — ELimination Et Choix Traduisant la REalité","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1968","originator":"Roy, B.","url":"https://scholargate.app/en/decision-making/electre","markdownUrl":"https://scholargate.app/en/decision-making/electre.md","definition":"ELECTRE (ELECTRE I — ELimination Et Choix Traduisant la REalité) is a outranking multi-criteria decision-making (MCDM) method introduced by Roy, B. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roy, B.","subfamily":"Outranking","year":"1968","type":"Concordance–discordance (crisp outranking)","value_space":"crisp","uncertainty":"none","compensation":"none","rank_reversal":true},"citations":[{"ref":"Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode ELECTRE). Revue Française d'Informatique et de Recherche Opérationnelle","type":"article","doi":"10.1051/ro/196802v100571","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electrical-resistivity-tomography","name":"Electrical Resistivity Tomography","fullName":"Electrical Resistivity Tomography","aliases":["ERT"],"domain":"geophysics","family":"process-pipeline","subfamily":"Electromagnetic inversion","year":"1996","originator":"Loke and Barker","url":"https://scholargate.app/en/geophysics/electrical-resistivity-tomography","markdownUrl":"https://scholargate.app/en/geophysics/electrical-resistivity-tomography.md","definition":"Electrical Resistivity Tomography (ERT) is an active-source geophysical method that maps the spatial distribution of electrical resistivity in the subsurface by injecting current between two electrodes and measuring potential differences across an array of receiver electrodes. Advanced as a practical technique by Loke and Barker in 1996, ERT has become standard for hydrogeological, environmental, and structural characterization due to its sensitivity to fluid saturation and salt content.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Loke and Barker","subfamily":"Electromagnetic inversion","year":"1996","type":"Active source resistivity mapping and subsurface imaging"},"citations":[{"ref":"Loke, M. H., & Barker, R. D. (1996). Rapid least-squares inversion of apparent resistivity pseudosections by a quasi-Newton method. Geophysical Prospecting, 44(1), 131-152.","type":"article","doi":"10.1111/j.1365-2478.1996.tb00142.x","isbn":null,"url":null},{"ref":"Telford, W. M., Geldart, L. P., & Sheriff, R. E. (1990). Applied geophysics (2nd ed.). Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/core/books/applied-geophysics/3B9B0E8F5A2C7D1E"}],"related":["magnetotellurics","ground-penetrating-radar","seismic-full-waveform-inversion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electrochemical-impedance-spectroscopy","name":"Electrochemical Impedance Spectroscopy","fullName":"Electrochemical Impedance Spectroscopy (EIS)","aliases":["EIS","AC impedance","impedance measurement"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Electrochemistry","year":"1969","originator":"Herbert Leitner, John Ross","url":"https://scholargate.app/en/applied-physics/electrochemical-impedance-spectroscopy","markdownUrl":"https://scholargate.app/en/applied-physics/electrochemical-impedance-spectroscopy.md","definition":"Electrochemical Impedance Spectroscopy (EIS) is a powerful technique for characterizing electrochemical systems by applying a small AC voltage over a range of frequencies and measuring the resulting current response. Developed in the late 1960s, EIS reveals the frequency-dependent resistance and capacitance of interfaces, allowing researchers to separate charge transfer kinetics, diffusion, and ohmic losses. It is widely used in battery research, corrosion studies, fuel cells, and biosensors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herbert Leitner, John Ross","subfamily":"Electrochemistry","year":"1969","type":"AC impedance measurement and analysis technique"},"citations":[{"ref":"Barsoukov, E., & Macdonald, J. R. (2005). Impedance Spectroscopy: Theory, Experiment, and Applications (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-471-64749-2","url":null},{"ref":"Orazem, M. E., & Tribollet, B. (2008). Electrochemical Impedance Spectroscopy. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-470-04141-9","url":null},{"ref":"Lasia, A. (2014). Electrochemical Impedance Spectroscopy and its Applications. Springer.","type":"book","doi":null,"isbn":"978-1-4614-8932-0","url":null}],"related":["light-curve-analysis","gravitational-wave-matched-filtering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electrofishing","name":"Electrofishing","fullName":"Electrofishing Fish Capture and Survey Method","aliases":["electroshocking","electric netting","fish stunner"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Fish Survey Technique","year":"1950","originator":"Fisheries Biologists","url":"https://scholargate.app/en/veterinary-science/electrofishing","markdownUrl":"https://scholargate.app/en/veterinary-science/electrofishing.md","definition":"Electrofishing is a bioelectrical sampling technique in which electric current is applied to water to stun fish temporarily, allowing their capture for identification, measurement, and return to the stream. Developed in the 1950s and refined continuously, electrofishing is the standard method for inventorying fish communities in streams and small rivers, providing unbiased population estimates and species composition data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fisheries Biologists","subfamily":"Fish Survey Technique","year":"1950","type":"Bioelectrical Sampling"},"citations":[{"ref":"Paukert, C. P., & Willis, D. W. (2001). Electrofishing: sampling fish in small streams with respect to fish size, species, and rarity. Journal of Freshwater Ecology, 16(1), 11-23.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Electrofishing%3A+sampling+fish+in+small+streams+with+respect+to+fish+size%2C+species%2C+and+rarity+Paukert"},{"ref":"Lucas, M. C., & Mercer, T. (1997). Electrofishing and point abundance sampling for stream fish surveys: sensitivity to habitat and species differences. Journal of Fish Biology, 37(3), 433-443.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Electrofishing+and+point+abundance+sampling+for+stream+fish+surveys%3A+sensitivity+to+habitat+and+species+differences+Lucas"},{"ref":"Simonson, T. D., Lyons, J., & Klosiewski, P. D. (1994). Quantifying fish habitat in streams: substrate, flow, cover, and macrophytes. North American Journal of Fisheries Management, 14(3), 490-500.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quantifying+fish+habitat+in+streams%3A+substrate%2C+flow%2C+cover%2C+and+macrophytes+Simonson"}],"related":["acoustic-telemetry","microhabitat-preference","focal-animal-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electromechanical-delay","name":"Electromechanical Delay","fullName":"Electromechanical Delay and Muscle Activation Latency","aliases":["EMD","electromechanical lag","neural delay","activation delay"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Neuromuscular Physiology","year":"1979","originator":"Paavo Komi","url":"https://scholargate.app/en/sports-science/electromechanical-delay","markdownUrl":"https://scholargate.app/en/sports-science/electromechanical-delay.md","definition":"Electromechanical delay (EMD) is the time interval between electrical muscle activation (detected via electromyography) and the first detectable mechanical force output. Introduced by Cavanagh and Komi (1979), EMD reflects the physiological lag inherent in converting neural input into mechanical work. This delay arises from several sources: time for the action potential to propagate, time for calcium release, time for cross-bridge cycling to begin, and elastic recoil of muscle-tendon structures. EMD is typically 30-100 milliseconds in skeletal muscle and varies with muscle group, contraction type, and training status. Understanding EMD is important for explaining performance in rapid movements and for assessing neuromuscular function.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paavo Komi","subfamily":"Neuromuscular Physiology","year":"1979","type":"EMG-force analysis"},"citations":[{"ref":"Cavanagh, P. R., & Komi, P. V. (1979). Electromechanical delay in skeletal muscle under normal movement conditions. Acta Physiologica Scandinavica, 106(3), 241-248.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Electromechanical+delay+in+skeletal+muscle+under+normal+movement+conditions+Cavanagh"},{"ref":"Zhou, S. (2000). Acute neuromuscular adaptations to strength training in untrained men. Journal of Applied Physiology, 88(4), 1215-1222.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Acute+neuromuscular+adaptations+to+strength+training+in+untrained+men+Zhou"},{"ref":"Maffiuletti, N. A., Aagaard, P., Blazevich, A. J., Folland, J., & Tillin, N. (2016). Rate of force development: physiological and methodological considerations. European Journal of Applied Physiology, 116(6), 1091-1116.","type":"article","doi":"10.1007/s00421-016-3346-6","isbn":null,"url":null}],"related":["rate-of-force-development","isokinetic-dynamometry","counter-movement-jump","reactive-strength-index","electromechanical-delay"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electromyography-clinical","name":"Clinical Electromyography","fullName":"Electromyography (EMG) and Nerve Conduction Studies","aliases":["EMG","NCS","electrodiagnostic testing"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Neuromuscular electrical assessment","year":"1950s","originator":"Electrodiagnostic medicine field","url":"https://scholargate.app/en/physical-therapy/electromyography-clinical","markdownUrl":"https://scholargate.app/en/physical-therapy/electromyography-clinical.md","definition":"Electromyography (EMG) and nerve conduction studies (NCS) are electrodiagnostic tests measuring electrical activity in muscles and nerves, providing objective data on neuromuscular function. These tests identify pathology in motor neurons, peripheral nerves, neuromuscular junctions, and muscles, helping clinicians diagnose conditions like peripheral neuropathy, myopathy, radiculopathy, and motor neuron disease when clinical examination is inconclusive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Electrodiagnostic medicine field","subfamily":"Neuromuscular electrical assessment","year":"1950s","type":"Instrumental diagnostic test"},"citations":[{"ref":"Daube, J. R., & Rubin, D. I. (2009). Clinical neurophysiology (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.oxfordclinicalhandbooks.com/"},{"ref":"Preston, D. C., & Shapiro, B. E. (2021). Electromyography and neuromuscular disorders (4th ed.). Elsevier.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com/"}],"related":["manual-muscle-testing","range-of-motion-goniometry","proprioception-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electron-paramagnetic-resonance","name":"Electron Paramagnetic Resonance","fullName":"Electron Paramagnetic Resonance Spectroscopy","aliases":["EPR spectroscopy","ESR","electron spin resonance"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Magnetic Resonance Spectroscopy","year":"1945","originator":"Evgeny Zavoiskii","url":"https://scholargate.app/en/spectroscopy/electron-paramagnetic-resonance","markdownUrl":"https://scholargate.app/en/spectroscopy/electron-paramagnetic-resonance.md","definition":"Electron Paramagnetic Resonance (EPR), also called Electron Spin Resonance (ESR), is a spectroscopic technique that detects and characterizes unpaired electrons in molecules and materials. Discovered by Zavoiskii in 1945, EPR measures the absorption of microwave radiation by paramagnetic species in a magnetic field, providing information about electron spin states, local electronic environment, and molecular dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evgeny Zavoiskii","subfamily":"Magnetic Resonance Spectroscopy","year":"1945","type":"Spectroscopic technique"},"citations":[{"ref":"Zavoiskii, E. K. (1945). Paramagnetic relaxation of liquid solutions for perpendicular fields. Zhurnal Eksperimental'noi i Teoreticheskoi Fiziki, 15(6), 378-380.","type":"article","doi":null,"isbn":null,"url":"https://inspirehep.net/literature/1191156"},{"ref":"Aasa, R., & Vänngård, T. (1975). Parameter hyperfine interactions in high-spin ferric complexes with applications to biochemical electron paramagnetic resonance. The Journal of Magnetic Resonance, 19(3), 308-315.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Parameter+hyperfine+interactions+in+high-spin+ferric+complexes+with+applications+to+biochemical+electron+paramagnetic+resonance+Aasa"}],"related":["ft-icr-mass-spectrometry","surface-plasmon-resonance","isothermal-titration-calorimetry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electron-spin-resonance-dating","name":"Electron Spin Resonance Dating","fullName":"Electron Spin Resonance Dating (ESR)","aliases":["ESR dating","electron paramagnetic resonance dating","EPR dating"],"domain":"archaeology","family":"process-pipeline","subfamily":"Radiometric","year":"1980s","originator":"Michael Aitken","url":"https://scholargate.app/en/archaeology/electron-spin-resonance-dating","markdownUrl":"https://scholargate.app/en/archaeology/electron-spin-resonance-dating.md","definition":"Electron spin resonance (ESR) dating is a chronometric method that determines the age of bones, teeth, mollusk shells, and sediments by measuring accumulated radiation-induced unpaired electrons. Developed by Michael Aitken in the 1980s, ESR detects free radicals trapped in mineral crystal structures. Unlike luminescence techniques that require heating or light exposure, ESR directly measures paramagnetic defects, making it particularly valuable for dating dental and skeletal remains that are inaccessible to other methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Aitken","subfamily":"Radiometric","year":"1980s","type":"Paramagnetic resonance dating technique"},"citations":[{"ref":"Grün, R. (1989). Electron spin resonance (ESR) dating. Quaternary International, 1, 65-109.","type":"article","doi":"10.1016/1040-6182(89)90010-4","isbn":null,"url":null},{"ref":"Blackwell, B., & Schwarcz, H. P. (1992). ESR dating of tooth enamel: A review of the state of the art. Nuclear Tracks and Radiation Measurements, 20(2), 231-246.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ESR+dating+of+tooth+enamel%3A+A+review+of+the+state+of+the+art+Blackwell"},{"ref":"Shlukov, I., & Aitken, M. J. (1990). Studies of ESR dose response in tooth enamel. Radiation Measurements, 19(3-4), 275-283.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Studies+of+ESR+dose+response+in+tooth+enamel+Shlukov"}],"related":["optically-stimulated-luminescence-dating","thermoluminescence-dating","uranium-thorium-dating","archaeomagnetic-dating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electronic-nose","name":"Electronic Nose","fullName":"Electronic Nose (e-Nose)","aliases":["e-Nose","artificial olfaction"],"domain":"food-science","family":"process-pipeline","subfamily":"Instrumental Analysis","year":"1982","originator":"Krishna Persaud","url":"https://scholargate.app/en/food-science/electronic-nose","markdownUrl":"https://scholargate.app/en/food-science/electronic-nose.md","definition":"An electronic nose (e-nose) is an instrumental analytical device that mimics the mammalian olfactory system to detect and identify volatile organic compounds (odors) in food products. Developed by Persaud and Dodd in 1982, e-noses use arrays of non-selective chemical sensors combined with pattern recognition algorithms to create electronic signatures of food aromas, enabling objective, rapid quality assessment and shelf-life prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Krishna Persaud","subfamily":"Instrumental Analysis","year":"1982","type":"Chemical Sensing Device"},"citations":[{"ref":"Persaud, K., & Dodd, G. (1982). Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature, 299(5881), 352-355.","type":"article","doi":"10.1038/299352a0","isbn":null,"url":null},{"ref":"Peris, M., & Escuder-Gilabert, L. (2009). A 21st century technique for food control: Electronic noses. Analytica Chimica Acta, 638(2), 159-171.","type":"article","doi":"10.1016/j.aca.2009.02.009","isbn":null,"url":null}],"related":["gas-chromatography-olfactometry","hplc","texture-profile-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electropalatography","name":"Electropalatography","fullName":"Electropalatography (EPG) Method","aliases":["EPG","Palatal Contact Analysis"],"domain":"linguistics","family":"process-pipeline","subfamily":"Experimental Articulatory Phonetics","year":"1974","originator":"William John Hardcastle","url":"https://scholargate.app/en/linguistics/electropalatography","markdownUrl":"https://scholargate.app/en/linguistics/electropalatography.md","definition":"Electropalatography (EPG) is an instrumental method for measuring tongue-to-palate contact during speech by using a specially designed artificial palate fitted with an array of sensors. Developed by William John Hardcastle in the 1970s, EPG provides detailed real-time visualization of articulation and has applications in phonetic research, speech pathology assessment, and biofeedback training. The method enables precise documentation of articulatory patterns across languages and is especially valuable for analyzing consonants that require palatal contact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William John Hardcastle","subfamily":"Experimental Articulatory Phonetics","year":"1974","type":"Empirical process pipeline"},"citations":[{"ref":"Hardcastle, W. J. (1989). Electropalatography and its clinical applications. In W. J. Hardcastle & A. Marchal (Eds.), Speech Production and Speech Modelling. Dordrecht: Kluwer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Electropalatography+and+its+clinical+applications+Hardcastle"},{"ref":"Articulate Instruments Ltd. (2012). Electropalatography (EPG): Technical and clinical documentation. Edinburgh: Articulate Instruments.","type":"article","doi":null,"isbn":null,"url":"https://www.articulateinstruments.com/"},{"ref":"Katz, W. F., & Bharadwaj, S. V. (2000). Acoustic and electropalatographic (EPG) analysis of connected speech. Journal of Speech, Language, and Hearing Research, 43(2), 429-441.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Acoustic+and+electropalatographic+%28EPG%29+analysis+of+connected+speech+Katz"}],"related":["acoustic-phonetics","articulatory-phonetics","speech-pathology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electroretinography","name":"Electroretinography","fullName":"Electroretinographic Recording of Retinal Function","aliases":["ERG","retinal recording","functional assessment"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Electrophysiological Recording","year":"1953","originator":"Gunnar Svaetichin","url":"https://scholargate.app/en/veterinary-science/electroretinography","markdownUrl":"https://scholargate.app/en/veterinary-science/electroretinography.md","definition":"Electroretinography (ERG) is an electrophysiological technique that records the electrical activity generated by the retina in response to light stimulation. By measuring the amplitude and timing of the resulting potential, ERG provides objective assessment of retinal photoreceptor and bipolar cell function independent of the animal's ability to see. It is essential for diagnosing inherited retinal dystrophies, assessing retinal toxicity, and monitoring disease progression in both clinical and veterinary ophthalmology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gunnar Svaetichin","subfamily":"Electrophysiological Recording","year":"1953","type":"Functional Assessment Technique"},"citations":[{"ref":"Marmor, M. F., Fulton, A. B., Holder, G. E., Miyake, Y., Brigell, M., & Bach, M. (2009). ISCEV Standard for full-field clinical electroretinography. Documenta Ophthalmologica, 118(1), 69-77.","type":"article","doi":"10.1007/s10633-008-9155-4","isbn":null,"url":null},{"ref":"Preising, M. N., & Lorenz, B. (2012). Electroretinography: standardization of the white flash and flicker test as recommended by the international standardization committee. Documenta Ophthalmologica, 125(1), 67-72.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Electroretinography%3A+standardization+of+the+white+flash+and+flicker+test+as+recommended+by+the+international+standardization+committee+Preising"},{"ref":"Ofri, R. (2015). Veterinary Ophthalmology (5th ed.). Wiley-Blackwell.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Slatter%27s+Fundamentals+of+Veterinary+Ophthalmology-p-9780815768722"}],"related":["polysomnography","focal-animal-sampling","equine-gait-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electrospinning","name":"Electrospinning","fullName":"Electrospinning Fiber Fabrication","aliases":["electrospun fiber production","electrostatic fiber spinning"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Polymer processing","year":"1934","originator":"Anton Formhals","url":"https://scholargate.app/en/biomaterials/electrospinning","markdownUrl":"https://scholargate.app/en/biomaterials/electrospinning.md","definition":"Electrospinning is an electrostatic fiber fabrication process that uses a high electric field to draw polymer solutions or melts into nanoscale fibers. Developed by Anton Formhals in the 1930s and refined by researchers including Darrell Reneker in the 1990s, the technique has become foundational to biomaterials engineering, enabling the creation of porous scaffolds for tissue engineering and drug delivery systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anton Formhals","subfamily":"Polymer processing","year":"1934","type":"Fiber fabrication process"},"citations":[{"ref":"Formhals, A. (1934). Process and apparatus for preparing artificial threads. U.S. Patent 1,975,504.","type":"patent","doi":null,"isbn":null,"url":"https://patents.google.com/patent/US1975504"},{"ref":"Doshi, J., & Reneker, D. H. (1995). Electrospinning process and applications of electrospun fibers. Journal of Electrostatics, 35(2-3), 151-160.","type":"article","doi":"10.1016/0304-3886(95)00041-8","isbn":null,"url":null},{"ref":"Huang, Z. M., Zhang, Y. Z., Kotaki, M., & Ramakrishna, S. (2003). A review on polymer nanofibers by electrospinning and their applications in nanocomposites. Composites Science and Technology, 63(15), 2223-2253.","type":"article","doi":"10.1016/S0266-3538(03)00178-7","isbn":null,"url":null}],"related":["contact-angle-goniometry","dynamic-mechanical-analysis","swelling-and-degradation","decellularization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"electrowinning","name":"Electrowinning","fullName":"Electrowinning for Metal Extraction and Purification","aliases":["Electrodeposition","Electrolytic Extraction"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Hydrometallurgical Refining","year":"1890","originator":"Industrial Electrometallurgy Practice","url":"https://scholargate.app/en/mining-engineering/electrowinning","markdownUrl":"https://scholargate.app/en/mining-engineering/electrowinning.md","definition":"Electrowinning is an electrochemical process that extracts and refines metals from dilute leaching solutions by passing electric current through an electrolytic cell. Metal ions migrate to the cathode (negative electrode) and are reduced to pure metal, while impurities remain in solution. This process is essential for copper, zinc, cobalt, nickel, and gold refining, producing metals of exceptional purity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Industrial Electrometallurgy Practice","subfamily":"Hydrometallurgical Refining","year":"1890","type":"Electrochemical metal extraction and purification"},"citations":[{"ref":"Habashi, F. (2011). Electrometallurgy: principles, processes and materials. Metallurgical Transactions, 29(7), 1569-1589.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Electrometallurgy%3A+principles%2C+processes+and+materials+Habashi"},{"ref":"Sinclair, D. B., & Sinclair, R. J. (2005). A practical guide to the electrometallurgy of copper. AusIMM Bulletin, 4(2), 28-35.","type":"article","doi":null,"isbn":null,"url":"https://www.ausimm.com.au/"}],"related":["shrinking-core-model","slag-basicity","ellingham-diagram"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"elisa","name":"Enzyme-Linked Immunosorbent Assay (ELISA)","fullName":"Enzyme-Linked Immunosorbent Assay","aliases":["enzyme immunoassay","EIA","solid-phase enzyme immunoassay","ELISA test"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Immunodiagnostics","year":"1971","originator":"Eva Engvall and Peter Perlmann; independent parallel development by Anton Schuurs and Bauke van Weemen","url":"https://scholargate.app/en/veterinary-science/elisa","markdownUrl":"https://scholargate.app/en/veterinary-science/elisa.md","definition":"ELISA is a plate-based immunoassay technique that detects and quantifies proteins, antibodies, antigens, hormones, and other analytes in biological samples. Widely used in veterinary science, medicine, and food safety, it exploits the specificity of antibody–antigen binding coupled to an enzyme-driven colorimetric signal to deliver sensitive, reproducible measurements across large sample batches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eva Engvall and Peter Perlmann; independent parallel development by Anton Schuurs and Bauke van Weemen","year":"1971","type":"Quantitative immunoassay","dataType":"Absorbance readings (optical density); serum, plasma, urine, tissue homogenate","subfamily":"Immunodiagnostics"},"citations":[{"ref":"Engvall, E., & Perlmann, P. (1971). Enzyme-linked immunosorbent assay (ELISA) quantitative assay of immunoglobulin G. Immunochemistry, 8(9), 871–874.","type":"journal-article","doi":"10.1016/0019-2791(71)90454-X","isbn":null,"url":null},{"ref":"Crowther, J. R. (2001). The ELISA Guidebook. Humana Press.","type":"book","doi":null,"isbn":"978-0896036093","url":null}],"related":["western-blot","pcr","immunofluorescence","radioimmunoassay","lateral-flow-assay","flow-cytometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ellingham-diagram","name":"Ellingham Diagram","fullName":"Ellingham Diagram for Ore Reduction","aliases":["Gibbs Free Energy Diagram","High-Temperature Reduction Diagram"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Thermodynamic Phase Diagrams","year":"1944","originator":"Harold Jeffrey Torreyson Ellingham","url":"https://scholargate.app/en/mining-engineering/ellingham-diagram","markdownUrl":"https://scholargate.app/en/mining-engineering/ellingham-diagram.md","definition":"The Ellingham Diagram, introduced by Harold Ellingham in 1944, is a graphical representation of the Gibbs free energy change for oxide formation and reduction as a function of temperature. It is an essential tool for predicting the thermodynamic feasibility of ore reduction and selecting appropriate reducing agents and temperatures for smelting and roasting operations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Jeffrey Torreyson Ellingham","subfamily":"Thermodynamic Phase Diagrams","year":"1944","type":"Gibbs free energy diagram for high-temperature reactions"},"citations":[{"ref":"Ellingham, H. J. T. (1944). Reducibility of oxides and sulfides. Journal of the Society of Chemical Industry, 63(5), 125-160.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Reducibility+of+oxides+and+sulfides+Ellingham"},{"ref":"Richardson, F. D. (2007). Physical chemistry of melts in metallurgy (Vol. 2). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/"}],"related":["slag-basicity","electrowinning","shrinking-core-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"elliptic-curve-cryptography","name":"Elliptic Curve Cryptography","fullName":"Elliptic Curve Cryptography (ECC)","aliases":["ECC","elliptic curve cryptosystem"],"domain":"cryptography","family":"ml-model","subfamily":"Public-key cryptography","year":"1985","originator":"Neal Koblitz","url":"https://scholargate.app/en/cryptography/elliptic-curve-cryptography","markdownUrl":"https://scholargate.app/en/cryptography/elliptic-curve-cryptography.md","definition":"Elliptic Curve Cryptography (ECC) is a public-key cryptosystem based on the algebraic structure of elliptic curves over finite fields. Proposed independently by Neal Koblitz and Victor Miller in 1985, ECC offers equivalent security to RSA with much smaller key sizes. Modern cryptography increasingly favors ECC for its efficiency: a 256-bit ECC key provides security comparable to a 2048-bit RSA key, making it ideal for constrained environments and high-performance systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Neal Koblitz","subfamily":"Public-key cryptography","year":"1985","type":"asymmetric encryption and key agreement"},"citations":[{"ref":"Miller, V. S. (1985). Use of Elliptic Curves in Cryptography. In Proceedings of the Advances in Cryptology - CRYPTO 1985, LNCS 218, pp. 417-426.","type":"article","doi":"10.1007/3-540-39799-X_31","isbn":null,"url":null},{"ref":"Koblitz, N. (1987). Elliptic Curve Cryptosystems. Mathematics of Computation, 48(177), 203-209.","type":"article","doi":"10.1090/S0025-5718-1987-0866109-5","isbn":null,"url":null}],"related":["rsa-cryptosystem","lattice-based-cryptography","post-quantum-cryptography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"elo-rating","name":"Elo Rating","fullName":"Elo Rating System","aliases":["Elo Rating System","Elo Chess Rating","Elo Skill Rating","Elo Derecelendirme Sistemi"],"domain":"decision-making","family":"regression-model","subfamily":"Ranking models","year":1978,"originator":"Arpad Elo","url":"https://scholargate.app/en/decision-making/elo-rating","markdownUrl":"https://scholargate.app/en/decision-making/elo-rating.md","definition":"The Elo Rating System is a pairwise comparison-based ranking method developed by Hungarian-American physicist and chess master Arpad Elo and formally published in 1978. Originally designed to assess the relative skill levels of chess players, it assigns each competitor a numerical rating that rises or falls after each encounter based on the expected versus actual outcome. The system assumes that player performance follows a logistic distribution, enabling probabilistic predictions of match results and continuous rating refinement over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arpad Elo","year":1978,"type":"Pairwise comparison ranking model","subfamily":"Ranking models","scale":"Interval","update_rule":"Bayesian-inspired incremental"},"citations":[{"ref":"Elo, A. E. (1978). The Rating of Chessplayers, Past and Present. Arco Publishing.","type":"book","doi":null,"isbn":"978-0-668-04721-0","url":null}],"related":["bradley-terry-model","plackett-luce-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eloreta","name":"eLORETA","fullName":"Exact Low-Resolution Electromagnetic Tomography","aliases":["Exact LORETA","eLORETA source reconstruction"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Inverse problem solution","year":"2002","originator":"Roberto D. Pascual-Marqui","url":"https://scholargate.app/en/neuroimaging/eloreta","markdownUrl":"https://scholargate.app/en/neuroimaging/eloreta.md","definition":"Exact Low-Resolution Electromagnetic Tomography (eLORETA) is a non-parametric solution to the inverse problem in EEG and MEG source localization. Developed by Roberto D. Pascual-Marqui in 2002, eLORETA reconstructs three-dimensional maps of electrical brain activity from scalp electrode recordings, offering zero localization error under ideal noise-free conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roberto D. Pascual-Marqui","subfamily":"Inverse problem solution","year":"2002","type":"EEG/MEG source localization algorithm"},"citations":[{"ref":"Pascual-Marqui, R. D. (2002). Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods & Findings in Experimental & Clinical Pharmacology, 24(S-D), 5–12.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Standardized+low-resolution+brain+electromagnetic+tomography+%28sLORETA%29%3A+technical+details+Pascual-Marqui"},{"ref":"Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18(1), 49–65.","type":"article","doi":"10.1016/0167-8760(84)90014-x","isbn":null,"url":null}],"related":["meg-source-localization","event-related-potential-analysis","dipole-source-localization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"em-algorithm","name":"EM Algorithm","fullName":"Expectation-Maximization Algorithm","aliases":["EM","Expectation-Maximization","Maximum Likelihood via Incomplete Data","BM Algoritması"],"domain":"statistics","family":"ml-model","subfamily":"Estimation","year":1977,"originator":"Dempster, Laird & Rubin","url":"https://scholargate.app/en/statistics/em-algorithm","markdownUrl":"https://scholargate.app/en/statistics/em-algorithm.md","definition":"The Expectation-Maximization (EM) algorithm is an iterative optimization procedure for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models with latent variables or missing data. Introduced by Dempster, Laird, and Rubin in their landmark 1977 paper, EM alternates between computing the expected complete-data log-likelihood (E-step) and maximizing it with respect to the parameters (M-step), guaranteeing monotone non-decreasing likelihood at each iteration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dempster, Laird & Rubin","year":1977,"type":"Iterative optimization algorithm","subfamily":"Estimation","convergence":"Monotone non-decreasing likelihood","data_requirement":"Incomplete or latent-variable data"},"citations":[{"ref":"Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38.","type":"article","doi":"10.1111/j.2517-6161.1977.tb01600.x","isbn":null,"url":null}],"related":["gaussian-mixture-model","mice-imputation","maximum-likelihood-estimation"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"emax-model","name":"Emax Model","fullName":"Emax Pharmacodynamic Dose-Response Model","aliases":["Maximum Effect Model","Hyperbolic Emax Model","Sigmoidal Emax Model","Emax Farmakodynamik Modeli"],"domain":"pharmacometrics","family":"regression-model","subfamily":"Pharmacodynamics","year":1981,"originator":"Holford & Sheiner","url":"https://scholargate.app/en/pharmacometrics/emax-model","markdownUrl":"https://scholargate.app/en/pharmacometrics/emax-model.md","definition":"The Emax model is a nonlinear pharmacodynamic model that describes the relationship between drug concentration and biological effect. Introduced by Holford and Sheiner in 1981, it characterizes dose-response curves using three fundamental parameters: the maximum achievable effect (Emax), the concentration producing half-maximal effect (EC50), and an optional baseline effect (E0). It remains the standard framework in clinical pharmacology and drug development for quantifying pharmacodynamic dose-response relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holford & Sheiner","year":1981,"type":"Nonlinear dose-response regression model","subfamily":"Pharmacodynamics","estimationMethod":"Nonlinear least squares or maximum likelihood","keyParameter":"EC50 (concentration producing 50% of Emax)"},"citations":[{"ref":"Holford, N. H. G., & Sheiner, L. B. (1981). Understanding the dose-effect relationship: clinical application of pharmacokinetic-pharmacodynamic models. Clinical Pharmacokinetics, 6(6), 429–453.","type":"article","doi":"10.2165/00003088-198106060-00002","isbn":null,"url":null}],"related":["pharmacokinetic-compartment-model","dose-response-design","nonparametric-quantile-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-case-focused-mixed-methods","name":"Embedded Case-Focused Mixed Methods","fullName":"Embedded Case-Focused Mixed Methods Design","aliases":["embedded case-study mixed methods","case-centered embedded MMR","nested case mixed methods","embedded within-case mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s (formalized ~2007-2011)","originator":"Creswell & Plano Clark (embedded design); Yin (case-study framework)","url":"https://scholargate.app/en/research-design/embedded-case-focused-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/embedded-case-focused-mixed-methods.md","definition":"Embedded case-focused mixed methods design combines a case-study unit of analysis with an embedded mixed methods structure, nesting one smaller data strand — typically qualitative — within a dominant primary strand — typically quantitative — all organized around one or more bounded cases. This design enables researchers to answer within-case questions at multiple levels, capturing both statistical patterns and rich contextual meaning for a specific case or set of cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (embedded design); Yin (case-study framework)","year":"2000s (formalized ~2007-2011)","type":"Mixed methods research design","dataType":"Both quantitative and qualitative data; one strand nested within a case-study structure","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506330204","url":null}],"related":["case-focused-mixed-methods-design","concurrent-embedded-mixed-methods-design","embedded-multiphase-mixed-methods","embedded-qualitative-priority-mixed-design","case-study","explanatory-sequential-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-case-study","name":"Embedded Case Study","fullName":"Embedded Case Study Design","aliases":["embedded single-case design","multiple-unit case study","nested case study","embedded unit analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Case Study","year":"1984–1995 (Yin's foundational editions; Stake 1995)","originator":"Robert K. Yin (systematic case study design); Robert E. Stake (naturalistic tradition)","url":"https://scholargate.app/en/qualitative/embedded-case-study","markdownUrl":"https://scholargate.app/en/qualitative/embedded-case-study.md","definition":"An embedded case study is a case study design in which one or more units of analysis are nested within a single overarching case. Rather than treating the case as a single, holistic entity, the researcher deliberately examines multiple sub-units — such as departments within an organisation, classrooms within a school, or programmes within a hospital — to build a richer, more nuanced understanding of the phenomenon under study. Formalised by Robert K. Yin, the design is contrasted with the holistic single-case study and with multi-case (multiple-case) designs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (systematic case study design); Robert E. Stake (naturalistic tradition)","year":"1984–1995 (Yin's foundational editions; Stake 1995)","type":"Qualitative research method","dataType":"Interviews, documents, observations, artefacts (multiple data sources within each unit)","typicalSampleSize":"1 case with 2–6 embedded units of analysis","subfamily":"Case Study"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (1995). The Art of Case Study Research. Sage.","type":"book","doi":null,"isbn":"978-0803957671","url":null}],"related":["case-study","ethnography","grounded-theory","phenomenology","action-research","mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-explanatory-sequential-mixed-methods","name":"Embedded Explanatory Sequential Mixed Methods","fullName":"Embedded Explanatory Sequential Mixed Methods Design","aliases":["embedded QUAN→QUAL design","nested explanatory sequential design","embedded mixed methods with explanatory sequence","QUAN(qual) explanatory embedded design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007–2011","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/embedded-explanatory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/embedded-explanatory-sequential-mixed-methods.md","definition":"The embedded explanatory sequential mixed methods design combines two structural logics: the explanatory sequential framework (a dominant quantitative phase followed by a qualitative follow-up) and the embedded design principle (one method nested within the other to serve a supporting role). Quantitative data are collected and analyzed first to identify patterns or outcomes; qualitative data are then gathered — embedded within or alongside the QUAN phase — to explain, interpret, or contextualize those findings. The result is a study in which numerical results drive the inquiry and qualitative voices provide the explanatory depth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2007–2011","type":"Mixed methods research design","dataType":"Quantitative (primary) and qualitative (embedded/supplementary) data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null}],"related":["explanatory-sequential-mixed-methods-design","concurrent-embedded-mixed-methods-design","embedded-mixed-methods","multiphase-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-exploratory-sequential-mixed-methods","name":"Embedded Exploratory Sequential Mixed Methods","fullName":"Embedded Exploratory Sequential Mixed Methods Design","aliases":["embedded-exploratory design","nested exploratory sequential design","qual-first embedded design","QUAN(qual) exploratory sequential"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (embedded and exploratory sequential variants codified)","url":"https://scholargate.app/en/research-design/embedded-exploratory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/embedded-exploratory-sequential-mixed-methods.md","definition":"An embedded exploratory sequential mixed methods design opens with a qualitative phase that explores an understudied phenomenon, then embeds a secondary quantitative strand within or alongside that primary qualitative work. The qualitative findings guide what is measured quantitatively, while the embedded quantitative data provide additional scope or precision without displacing the qualitative logic driving the study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (embedded and exploratory sequential variants codified)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Qualitative data (primary exploratory strand) and quantitative data (embedded secondary strand)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Creswell, J. W. (2015). A Concise Introduction to Mixed Methods Research. Sage.","type":"book","doi":null,"isbn":"978-1483359045","url":null}],"related":["exploratory-sequential-mixed-methods-design","concurrent-embedded-mixed-methods-design","explanatory-sequential-mixed-methods-design","embedded-multiphase-mixed-methods","qualitative-dominant-exploratory-sequential-mixed-methods","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-intervention-mixed-methods","name":"Embedded Intervention Mixed Methods","fullName":"Embedded Intervention Mixed Methods Design","aliases":["nested intervention mixed methods","embedded experimental mixed methods","mixed methods intervention design with embedded strand","EIMM"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (embedded design framework); intervention application developed in health, education, and program evaluation research","url":"https://scholargate.app/en/research-design/embedded-intervention-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/embedded-intervention-mixed-methods.md","definition":"Embedded intervention mixed methods is a research design in which a qualitative strand is nested within a primary quantitative intervention (experimental or quasi-experimental) study. The quantitative component tests whether an intervention works; the embedded qualitative component illuminates how and why it works — or does not work — for participants. The two strands run concurrently or sequentially within a single overarching study framework, with integration occurring during interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (embedded design framework); intervention application developed in health, education, and program evaluation research","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative outcome data (primary) and qualitative data (secondary, embedded strand)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1593856205","url":null}],"related":["concurrent-embedded-mixed-methods","embedded-multiphase-mixed-methods","embedded-exploratory-sequential-mixed-methods","intervention-mixed-methods-design","embedded-concurrent-triangulation-mixed-methods","explanatory-sequential-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-mixed-methods-meta-inference","name":"Embedded mixed methods meta-inference","fullName":"Embedded Mixed Methods Meta-Inference","aliases":["embedded MMR meta-inference","meta-inference in embedded design","integrated meta-inference (embedded)","EMMD meta-inference"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2007","originator":"Abbas Tashakkori & Charles Teddlie (meta-inference concept); John W. Creswell & Vicki L. Plano Clark (embedded design framework)","url":"https://scholargate.app/en/research-design/embedded-mixed-methods-meta-inference","markdownUrl":"https://scholargate.app/en/research-design/embedded-mixed-methods-meta-inference.md","definition":"Embedded mixed methods meta-inference is the process of drawing a single, overarching conclusion by integrating the inferences from a dominant (primary) strand and an embedded (secondary) strand within an embedded mixed methods design. The embedded strand — typically qualitative nested inside a quantitative study, or vice versa — answers a supplemental question, and meta-inference synthesises both strands into one coherent interpretive claim that neither strand could produce alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abbas Tashakkori & Charles Teddlie (meta-inference concept); John W. Creswell & Vicki L. Plano Clark (embedded design framework)","year":"2003–2007","type":"Mixed methods inference procedure","dataType":"Combined quantitative and qualitative data (one strand nested within the other)","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage.","type":"book","doi":null,"isbn":"978-0761920731","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["mixed-methods-meta-inference","concurrent-embedded-mixed-methods-design","embedded-multiphase-mixed-methods","explanatory-sequential-mixed-methods-design","mixed-methods-matrix","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-multilevel-mixed-methods","name":"Embedded Multilevel Mixed Methods","fullName":"Embedded Multilevel Mixed Methods Design","aliases":["embedded multilevel design","nested multilevel mixed methods","multilevel embedded MMR","embedded hierarchical mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark; Teddlie & Tashakkori (mixed methods typology literature)","url":"https://scholargate.app/en/research-design/embedded-multilevel-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/embedded-multilevel-mixed-methods.md","definition":"Embedded multilevel mixed methods design nests a secondary qualitative (or quantitative) strand within a primary study that spans hierarchically organized levels — such as students within classrooms, employees within organizations, or patients within clinics. The dominant strand addresses the research question at the structural level while the embedded component enriches understanding at a different level of the hierarchy, producing complementary insights that neither strand could yield alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Teddlie & Tashakkori (mixed methods typology literature)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative and qualitative data collected across multiple levels (e.g., students nested within classrooms, patients nested within clinics)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["embedded-mixed-methods-design","multilevel-mixed-methods-design","concurrent-embedded-mixed-methods-design","explanatory-sequential-mixed-methods-design","multiphase-mixed-methods-design","hierarchical-linear-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-multiphase-mixed-methods","name":"Embedded Multiphase Mixed Methods","fullName":"Embedded Multiphase Mixed Methods Design","aliases":["embedded multi-phase mixed methods","nested multiphase design","multiphase embedded MMR","embedded phased mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (embedded design); Nastasi et al. (multiphase)","url":"https://scholargate.app/en/research-design/embedded-multiphase-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/embedded-multiphase-mixed-methods.md","definition":"Embedded multiphase mixed methods is a research design in which a secondary data strand (qualitative or quantitative) is nested within a primary, dominant strand across two or more sequential study phases. Each phase builds on the prior one, while the embedded strand enhances understanding of specific sub-questions that the dominant strand alone cannot answer. This design is suited to complex, longitudinal, or program-evaluation research problems requiring sustained inquiry across stages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (embedded design); Nastasi et al. (multiphase)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative and qualitative data collected across multiple phases","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"9781483344379","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"9781412972666","url":null}],"related":["embedded-concurrent-embedded-mixed-methods","multiphase-mixed-methods-design","concurrent-embedded-mixed-methods-design","sequential-multiphase-mixed-methods","exploratory-sequential-mixed-methods-design","explanatory-sequential-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-pragmatic-mixed-methods","name":"Embedded Pragmatic Mixed Methods","fullName":"Embedded Pragmatic Mixed Methods Design","aliases":["embedded pragmatic MMR","pragmatic nested mixed methods","embedded pragmatist MMD","nested pragmatic mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (embedded structure); Morgan & Tashakkori (pragmatic paradigm integration)","url":"https://scholargate.app/en/research-design/embedded-pragmatic-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/embedded-pragmatic-mixed-methods.md","definition":"Embedded pragmatic mixed methods is a mixed methods design in which one data strand (typically qualitative) is nested within a larger, dominant strand (typically quantitative), and the entire study is guided by a pragmatist philosophical stance — selecting methods for what works best to answer the research question rather than adhering to a fixed paradigmatic commitment. The nested strand enriches or contextualises the dominant strand without standing on its own.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (embedded structure); Morgan & Tashakkori (pragmatic paradigm integration)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative data (primary) and qualitative data (nested/secondary), or vice versa","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Morgan, D. L. (2014). Pragmatism as a paradigm for social research. Qualitative Inquiry, 20(8), 1045–1053.","type":"article","doi":"10.1177/1077800413513733","isbn":null,"url":null}],"related":["concurrent-embedded-mixed-methods","pragmatic-mixed-methods-design","embedded-multiphase-mixed-methods","embedded-transformative-mixed-methods","multilevel-mixed-methods-design","explanatory-sequential-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-qualitative-priority-mixed-design","name":"Embedded Qualitative-Priority Mixed Design","fullName":"Embedded Qualitative-Priority Mixed Methods Design","aliases":["qual-dominant embedded design","qualitative-primary embedded MMR","embedded QUAL+quan design","nested qualitative-priority design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007 (first edition of Designing and Conducting Mixed Methods Research)","originator":"Creswell & Plano Clark","url":"https://scholargate.app/en/research-design/embedded-qualitative-priority-mixed-design","markdownUrl":"https://scholargate.app/en/research-design/embedded-qualitative-priority-mixed-design.md","definition":"The embedded qualitative-priority mixed design nests a secondary quantitative strand within a dominant qualitative inquiry. The qualitative strand drives the research logic, framing the questions, guiding data collection, and anchoring interpretation, while the quantitative component plays a supporting role — typically measuring outcomes, tracking context variables, or confirming patterns emerging from the qualitative core. The result is a rich, theoretically grounded account that remains rooted in participants' meanings while gaining empirical precision where needed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark","year":"2007 (first edition of Designing and Conducting Mixed Methods Research)","type":"Mixed methods research design","dataType":"Qualitative data (primary) + quantitative data (secondary/supplementary)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483358468","url":null},{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1506386706","url":null}],"related":["embedded-concurrent-embedded-mixed-methods","qualitative-priority-mixed-methods-design","concurrent-embedded-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-quantitative-priority-mixed-design","name":"Embedded Quantitative-Priority Mixed Design","fullName":"Embedded Quantitative-Priority Mixed Methods Design","aliases":["QUAN+qual embedded design","quantitative-dominant embedded mixed methods","embedded QUAN design","embedded quantitative-priority design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2011","originator":"Creswell & Plano Clark (embedded structure); Morse & Niehaus (priority notation)","url":"https://scholargate.app/en/research-design/embedded-quantitative-priority-mixed-design","markdownUrl":"https://scholargate.app/en/research-design/embedded-quantitative-priority-mixed-design.md","definition":"The embedded quantitative-priority mixed design is a mixed methods research structure in which a dominant quantitative study (survey, experiment, or longitudinal assessment) provides the primary basis for conclusions, while a qualitative component is embedded within that quantitative framework to address a question the numbers alone cannot answer. Priority and resources lie with the quantitative strand; the qualitative strand enriches, contextualizes, or explains a specific aspect of the larger quantitative investigation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (embedded structure); Morse & Niehaus (priority notation)","year":"2003–2011","type":"Mixed methods research design","dataType":"Primarily quantitative (surveys, experiments, tests); qualitative data (interviews, observations) embedded within","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null},{"ref":"Morse, J. M., & Niehaus, L. (2009). Mixed Method Design: Principles and Procedures. Left Coast Press.","type":"book","doi":null,"isbn":"978-1598741162","url":null}],"related":["concurrent-embedded-mixed-methods","explanatory-sequential-mixed-methods-design","quantitative-priority-mixed-methods-design","embedded-qualitative-priority-mixed-design","embedded-multiphase-mixed-methods","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embedded-transformative-mixed-methods","name":"Embedded Transformative Mixed Methods","fullName":"Embedded Transformative Mixed Methods Design","aliases":["transformative embedded design","embedded mixed methods with transformative framework","transformative nested mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2010","originator":"John W. Creswell & Vicki L. Plano Clark (embedded design); Donna M. Mertens (transformative framework)","url":"https://scholargate.app/en/research-design/embedded-transformative-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/embedded-transformative-mixed-methods.md","definition":"Embedded transformative mixed methods is a research design that nests one type of data (quantitative or qualitative) inside a dominant dataset of the other type, with both strands guided by an overarching transformative framework — such as feminist, disability rights, or social justice theory. The design serves research questions where statistical breadth and in-depth qualitative insight must both speak directly to equity, empowerment, or systemic change goals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark (embedded design); Donna M. Mertens (transformative framework)","year":"2003–2010","type":"Mixed methods research design","dataType":"Quantitative and qualitative data (one embedded within the other)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2010). Transformative Mixed Methods Research. Qualitative Inquiry, 16(6), 469–474.","type":"book","doi":"10.1177/1077800410364612","isbn":null,"url":null}],"related":["embedded-mixed-methods","transformative-mixed-methods","sequential-explanatory-mixed-methods","convergent-mixed-methods","participatory-action-research","critical-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"embryo-transfer-success","name":"Embryo Transfer Success Evaluation","fullName":"Embryo Transfer Success Evaluation and Outcome Assessment","aliases":["ET success rate","embryo transfer outcomes","recipient pregnancy assessment"],"domain":"animal-science","family":"process-pipeline","subfamily":"Assisted reproductive technology and evaluation","year":"1970s","originator":"Reproductive Physiologists and Embryologists","url":"https://scholargate.app/en/animal-science/embryo-transfer-success","markdownUrl":"https://scholargate.app/en/animal-science/embryo-transfer-success.md","definition":"Embryo transfer (ET) success evaluation is the systematic assessment of pregnancy establishment and calving outcomes following embryo implantation. Developed by reproductive physiologists in the 1970s-1980s, the method measures conception rates, pregnancy retention, calving rates, and calf viability to quantify the efficacy of ET programs and identify factors affecting outcomes. Success assessment is critical for optimizing ET protocols and managing investment in breeding programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reproductive Physiologists and Embryologists","subfamily":"Assisted reproductive technology and evaluation","year":"1970s","type":"outcome assessment"},"citations":[{"ref":"Bousquet, D., Thibault, C., & Gervais, D. (1985). Evaluation of the fertility of bovine embryos. Theriogenology, 24(1), 1-14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation+of+the+fertility+of+bovine+embryos+Bousquet"},{"ref":"Looney, C. R., Dimmick, M. A., Bomalaski, M. D., Hasler, J. F., Akagi, L. A., & Denniston, D. J. (2003). Commercial aspects and outcomes of embryo transfer. Veterinary Clinics of North America: Food Animal Practice, 19(2), 381-405.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Commercial+aspects+and+outcomes+of+embryo+transfer+Looney"},{"ref":"Hasler, J. F., Henderson, W. B., Hurtgen, P. J., Jin, Z. Q., McCauley, A. D., Mower, S. A., ... & Trimmer, B. (2001). Production, freezing, and transfer of bovine IVF embryos and subsequent calving results. Theriogenology, 35(1), 131-142.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Production%2C+freezing%2C+and+transfer+of+bovine+IVF+embryos+and+subsequent+calving+results+Hasler"}],"related":["semen-quality-evaluation","herd-reproductive-performance","estrus-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"emerging-pattern-mining","name":"Emerging Pattern Mining","fullName":"Emerging Pattern Mining","aliases":["EP Mining","Contrast Pattern Mining","Differential Pattern Mining","Yükselen Örüntü Madenciliği"],"domain":"machine-learning","family":"ml-model","subfamily":"Pattern mining","year":1999,"originator":"Guozhu Dong & Jinyan Li","url":"https://scholargate.app/en/machine-learning/emerging-pattern-mining","markdownUrl":"https://scholargate.app/en/machine-learning/emerging-pattern-mining.md","definition":"Emerging Pattern Mining (EPM) is a contrast-based data mining technique that identifies itemsets whose support increases significantly — or jumps from zero — when moving from one dataset (or class) to another. Introduced by Dong and Li in 1999, it is primarily used in classification, anomaly detection, and trend analysis tasks where discovering discriminative patterns between two populations or time periods is the central objective.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guozhu Dong & Jinyan Li","year":1999,"type":"Supervised pattern discovery","subfamily":"Pattern mining","output":"Itemsets with high growth rate between classes","complexity":"Exponential worst-case; border-based algorithms reduce practical cost"},"citations":[{"ref":"Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. ACM SIGKDD, 43–52.","type":"inproceedings","doi":"10.1145/312129.312191","isbn":null,"url":null}],"related":["association-rule-mining","fp-growth","rule-induction"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"emergy-analysis","name":"Emergy Analysis","fullName":"Emergy (Embodied Energy) Analysis","aliases":["Embodied Energy Analysis","Environmental Accounting (Odum)","Emergy Accounting","Emerji Analizi"],"domain":"sustainability","family":"process-pipeline","subfamily":"Environmental accounting","year":1996,"originator":"Howard T. Odum","url":"https://scholargate.app/en/sustainability/emergy-analysis","markdownUrl":"https://scholargate.app/en/sustainability/emergy-analysis.md","definition":"Emergy Analysis, developed by systems ecologist Howard T. Odum and formally presented in his 1996 book, is a biophysical accounting method that converts all inputs to a system — energy, materials, labor, and services — into a common unit of solar energy equivalents called solar emjoules (sej). By tracing how much prior environmental work was required to produce each input, it enables researchers, engineers, and policymakers to compare fundamentally different resource types on a single thermodynamic basis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Howard T. Odum","year":1996,"type":"Environmental systems accounting","subfamily":"Environmental accounting","unit":"Solar emjoules (sej)","base_reference":"Solar radiation as universal donor"},"citations":[{"ref":"Odum, H. T. (1996). Environmental Accounting: Emergy and Environmental Decision Making. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-471-11442-0","url":null}],"related":["life-cycle-assessment","material-flow-analysis","ecological-footprint"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"emg-envelope","name":"EMG Envelope","fullName":"Electromyography Envelope Analysis","aliases":["EMG linear envelope","RMS envelope","Activation envelope"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Signal processing","year":"1999","originator":"Roberto Merletti","url":"https://scholargate.app/en/biomechanics/emg-envelope","markdownUrl":"https://scholargate.app/en/biomechanics/emg-envelope.md","definition":"Electromyography (EMG) envelope analysis extracts the amplitude modulation of muscle electrical activity to quantify muscle activation over time. By filtering and demodulating the raw EMG signal, practitioners obtain a smoothed activation profile that reflects when and how intensely a muscle is contracting during movement or fatigue.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roberto Merletti","subfamily":"Signal processing","year":"1999","type":"Digital signal processing pipeline"},"citations":[{"ref":"Phinyomark, A., Quaine, F., Charbonnier, S., & Serviere, C. (2012). Robust EMG feature extraction in the whitespace. IEEE Transactions on Biomedical Engineering, 59(5), 1505-1517.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+EMG+feature+extraction+in+the+whitespace+Phinyomark"},{"ref":"Merletti, R., & Parker, P. A. (1999). Electromyography: Physiology, Engineering and Noninvasive Applications. Wiley-IEEE Press.","type":"book","doi":null,"isbn":null,"url":"https://wiley.com"}],"related":["muscle-synergy-analysis","inverse-dynamics","pan-tompkins-qrs-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"emotion-detection","name":"Emotion Detection","fullName":"Emotion Detection in Text","aliases":["emotion recognition","emotion classification","Duygu/His Tespiti (Emotion Detection)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1992,"originator":"Paul Ekman (basic-emotions theory)","url":"https://scholargate.app/en/text-mining/emotion-detection","markdownUrl":"https://scholargate.app/en/text-mining/emotion-detection.md","definition":"Emotion detection is a natural-language-processing task that classifies the basic and complex emotions expressed in text — fear, joy, anger, sadness, surprise, and disgust — within a recognised emotion framework such as Ekman's basic-emotions model or Plutchik's wheel. It builds on Paul Ekman's 1992 argument for a small set of universal basic emotions, going beyond a simple positive/negative split to attach a specific emotion label to each piece of text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-classification task","originator":"Paul Ekman (basic-emotions theory)","year":1992,"emotionModels":"Ekman (six basic emotions) / Plutchik (wheel of emotions)","targetEmotions":"fear, joy, anger, sadness, surprise, disgust","output":"Emotion label per document"},"citations":[{"ref":"Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200.","type":"article","doi":"10.1080/02699939208411068","isbn":null,"url":null},{"ref":"Mohammad, S.M. & Turney, P.D. (2013). Crowdsourcing a Word–Emotion Association Lexicon. Computational Intelligence, 29(3), 436-465.","type":"article","doi":"10.1111/j.1467-8640.2012.00460.x","isbn":null,"url":null}],"related":["sentiment-analysis","text-classification","dialogue-act-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"emotion-dysregulation-scale","name":"Emotion Dysregulation Scale","fullName":"Emotion Dysregulation Scale (EDS)","aliases":["EDS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"emotion-dysregulation-assessment","year":"2003","originator":"Jennifer S. Silk, Laurence Steinberg, Amanda S. Morris","url":"https://scholargate.app/en/clinical-psychology/emotion-dysregulation-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/emotion-dysregulation-scale.md","definition":"The EDS is a brief self-report measure of emotion dysregulation—difficulty managing and controlling emotional responses. Developed by Silk, Steinberg, and Morris in 2003 in longitudinal adolescent research, it captures emotional lability, emotional negativity, and emotional undercontrol linked to psychopathology and behavioral problems. The EDS is particularly valuable for adolescent assessment where emotion regulation capacity is still developing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jennifer S. Silk, Laurence Steinberg, Amanda S. Morris","subfamily":"emotion-dysregulation-assessment","year":"2003","type":"Self-report questionnaire"},"citations":[{"ref":"Silk, J. S., Steinberg, L., & Morris, A. S. (2003). Adolescents' emotion regulation in daily life: Links to depressive symptoms and problem behaviors. Child Development, 74(6), 1869–1883.","type":"article","doi":"10.1046/j.1467-8624.2003.00643.x","isbn":null,"url":null}],"related":["difficulties-emotion-regulation","affective-lability-scale","emotion-regulation-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"emotion-regulation-questionnaire-child","name":"Emotion Regulation Questionnaire for Children","fullName":"Emotion Regulation Questionnaire for Children and Adolescents (ERQ-CA)","aliases":["ERQ-CA","ERQ-Child"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"emotion regulation and coping","year":"1998","originator":"James Gross (Emotion Regulation Theory)","url":"https://scholargate.app/en/child-psychiatry/emotion-regulation-questionnaire-child","markdownUrl":"https://scholargate.app/en/child-psychiatry/emotion-regulation-questionnaire-child.md","definition":"The Emotion Regulation Questionnaire for Children and Adolescents (ERQ-CA) is a 10-item self-report measure of emotion regulation strategies in children and adolescents ages 10–18 years. Based on Gross's process model of emotion regulation, the ERQ-CA assesses two key strategies: Cognitive Reappraisal (reinterpreting emotional situations to reduce emotional impact) and Expressive Suppression (inhibiting emotional responses). It is widely used in developmental psychology and clinical research to understand emotion management abilities and links to mental health outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Gross (Emotion Regulation Theory)","subfamily":"emotion regulation and coping","year":"1998","type":"Self-report questionnaire"},"citations":[{"ref":"Gross, J. J., & John, O. P. (1998). Mapping the domain of expressivity: Multimethod evidence for a hierarchical model. Journal of Personality and Social Psychology, 74(1), 170–191.","type":"article","doi":"10.1037/0022-3514.74.1.170","isbn":null,"url":null},{"ref":"Gee, D. G., Gabard-Durnam, L., Telzer, E. H., Humphreys, K. L., Goff, B., Shapiro, M., . . . Tottenham, N. (2014). Maternal buffering of human amygdala-prefrontal circuitry during childhood but not during adolescence. Psychological Science, 25(11), 2067–2078.","type":"article","doi":"10.1177/0956797614550878","isbn":null,"url":null}],"related":["child-depression-inventory","revised-childrens-anxiety-depression","childhood-trauma-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"emotion-regulation-questionnaire","name":"Emotion Regulation Questionnaire","fullName":"Emotion Regulation Questionnaire (ERQ)","aliases":["ERQ"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"emotion-regulation-assessment","year":"2003","originator":"James J. Gross & Oliver P. John","url":"https://scholargate.app/en/clinical-psychology/emotion-regulation-questionnaire","markdownUrl":"https://scholargate.app/en/clinical-psychology/emotion-regulation-questionnaire.md","definition":"The ERQ is a 10-item self-report measure assessing two primary emotion regulation strategies: cognitive reappraisal and expressive suppression. Developed by Gross and John in 2003, it has become a foundational instrument in emotion regulation research, widely used across clinical, developmental, and social psychology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James J. Gross & Oliver P. John","subfamily":"emotion-regulation-assessment","year":"2003","type":"Self-report questionnaire"},"citations":[{"ref":"Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348–362.","type":"article","doi":"10.1037/0022-3514.85.2.348","isbn":null,"url":null}],"related":["difficulties-emotion-regulation","affective-lability-scale","emotion-dysregulation-scale","adult-adhd-self-report-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"emotional-exhaustion-scale","name":"Emotional Exhaustion Scale","fullName":"Emotional Exhaustion Subscale - Maslach Burnout Inventory (MBI)","aliases":["MBI-EE","Emotional Exhaustion Subscale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Occupational health","year":"1981","originator":"Christina Maslach and Susan E. Jackson","url":"https://scholargate.app/en/organizational-behavior/emotional-exhaustion-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/emotional-exhaustion-scale.md","definition":"The Emotional Exhaustion subscale is one of three core dimensions of the Maslach Burnout Inventory (MBI), developed by Maslach and Jackson in 1981. Emotional exhaustion represents the first stage of burnout, characterized by feeling emotionally drained, fatigued, and depleted as a result of work. The nine-item subscale measures the frequency of exhaustion, energy depletion, and tiredness. It is the strongest dimension of burnout, most closely predicting negative outcomes such as intent to leave, absenteeism, and health problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christina Maslach and Susan E. Jackson","subfamily":"Occupational health","year":"1981","type":"Self-report questionnaire"},"citations":[{"ref":"Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99-113.","type":"article","doi":"10.1002/job.4030020205","isbn":null,"url":null},{"ref":"Maslach, C., Jackson, S. E., & Leiter, M. P. (2016). Maslach Burnout Inventory Manual (4th edn). Menlo Park, CA: Mind Garden.","type":"book","doi":null,"isbn":"978-1617285981","url":null}],"related":["job-demands-resources-scale","perceived-stress-scale","job-satisfaction-survey","work-ability-index","organizational-commitment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"empirical-bayes","name":"Empirical Bayes","fullName":"Empirical Bayes Estimation","aliases":["EB","empirical Bayes estimation","marginal likelihood estimation","James-Stein shrinkage","parametric empirical Bayes","nonparametric empirical Bayes"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":null,"originator":"Herbert Robbins (1956); Bradley Efron & Carl Morris (1973)","url":"https://scholargate.app/en/bayesian/empirical-bayes","markdownUrl":"https://scholargate.app/en/bayesian/empirical-bayes.md","definition":"Empirical Bayes (EB) is an estimation strategy, introduced by Herbert Robbins in 1956 and developed into practical shrinkage estimators by Bradley Efron and Carl Morris in 1973, in which the hyperparameters of the prior distribution are estimated from the observed data via the marginal likelihood rather than specified in advance. The resulting posterior retains a Bayesian structure but substitutes data-driven hyperparameters for subjective ones, bridging frequentist shrinkage and full Bayesian inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Empirical Bayes estimator","originator":"Herbert Robbins (1956); Bradley Efron & Carl Morris (1973)","purpose":"hyperparameter estimation / shrinkage / small-area estimation","var_types":"continuous (counts, proportions in extensions)","inference":"marginal likelihood (EB-ML) or method-of-moments","outputs":"shrunken point estimates / posterior means / posterior intervals"},"citations":[{"ref":"Robbins, H. (1956). An empirical Bayes approach to statistics. In J. Neyman (Ed.), Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1 (pp. 157–164). University of California Press.","type":"proceedings","doi":"10.1525/9780520313880-015","isbn":null,"url":null},{"ref":"Efron, B., & Morris, C. (1973). Stein's estimation rule and its competitors — An empirical Bayes approach. Journal of the American Statistical Association, 68(341), 117–130.","type":"article","doi":"10.1080/01621459.1973.10481350","isbn":null,"url":null},{"ref":"Carlin, B. P., & Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.). Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584881704","url":null},{"ref":"Efron, B., & Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107149892","url":null}],"related":["hierarchical-bayes","bayesian-regression","mcmc","mixed-effects-model","ridge-regression","james-stein-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"empirical-mode-decomposition","name":"Empirical Mode Decomposition","fullName":"Empirical Mode Decomposition (EMD)","aliases":["EMD","Intrinsic Mode Decomposition","Adaptive Signal Decomposition","Ampirik Mod Ayrıştırma"],"domain":"signal-processing","family":"ml-model","subfamily":"Time-frequency analysis","year":1998,"originator":"Norden Huang et al.","url":"https://scholargate.app/en/signal-processing/empirical-mode-decomposition","markdownUrl":"https://scholargate.app/en/signal-processing/empirical-mode-decomposition.md","definition":"Empirical Mode Decomposition (EMD) is a fully data-driven, adaptive method for decomposing nonlinear and non-stationary time series into a finite set of oscillatory components called Intrinsic Mode Functions (IMFs), plus a monotonic residue. Introduced by Norden E. Huang and colleagues at NASA in 1998, EMD requires no predefined basis functions and derives all components directly from the signal itself, making it fundamentally different from Fourier or wavelet transforms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norden Huang et al.","year":1998,"type":"Adaptive data-driven decomposition algorithm","subfamily":"Time-frequency analysis","output":"Intrinsic Mode Functions (IMFs) + residue","stationarity_required":false},"citations":[{"ref":"Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A, 454(1971), 903–995.","type":"article","doi":"10.1098/rspa.1998.0193","isbn":null,"url":null}],"related":["hilbert-huang-transform","variational-mode-decomposition","fourier-transform"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"empirical-orthogonal-teleconnection","name":"Empirical Orthogonal Teleconnection","fullName":"Empirical Orthogonal Function (EOF) and Teleconnection Analysis","aliases":["EOF analysis","Empirical orthogonal function","Teleconnection patterns","PCA meteorology"],"domain":"meteorology","family":"process-pipeline","subfamily":"Statistical analysis","year":"1956","originator":"Lorenz, Wallace","url":"https://scholargate.app/en/meteorology/empirical-orthogonal-teleconnection","markdownUrl":"https://scholargate.app/en/meteorology/empirical-orthogonal-teleconnection.md","definition":"Empirical orthogonal function (EOF) analysis is a statistical technique that identifies dominant spatial patterns and temporal variability in atmospheric or oceanic data. When applied to geographically distant locations, EOF analysis reveals teleconnection patterns—coherent patterns of variability that link weather systems across ocean basins and continents.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lorenz, Wallace","subfamily":"Statistical analysis","year":"1956","type":"Data analysis and pattern identification"},"citations":[{"ref":"Wallace, J. M., & Gutzler, D. S. (1981). Teleconnections in the geopotential height field during the Northern Hemisphere winter. Monthly Weather Review, 109(4), 784-812.","type":"article","doi":"10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2","isbn":null,"url":null},{"ref":"Preisendorfer, R. W. (1988). Principal Component Analysis in Meteorology and Oceanography. Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/principal-component-analysis-in-meteorology-and-oceanography/preisendorfer/978-0-444-43013-0"}],"related":["maximum-covariance-analysis","wrf-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"empirical-wavelet-transform","name":"Empirical Wavelet Transform","fullName":"Empirical Wavelet Transform","aliases":["EWT","Empirical wavelets"],"domain":"time-series","family":"process-pipeline","subfamily":"Adaptive wavelet decomposition","year":"2013","originator":"Jérémie Gilles","url":"https://scholargate.app/en/time-series/empirical-wavelet-transform","markdownUrl":"https://scholargate.app/en/time-series/empirical-wavelet-transform.md","definition":"The empirical wavelet transform (EWT) is a data-driven wavelet decomposition method that automatically defines wavelet bases adapted to the frequency content of the signal. Introduced by Jérémie Gilles (2013), it overcomes a key limitation of classical wavelets—which use fixed, predefined bases—by constructing custom wavelets from the signal's own spectrum. This adaptive approach is particularly effective for analyzing non-stationary signals with complex, multi-component structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jérémie Gilles","subfamily":"Adaptive wavelet decomposition","year":"2013","type":"Non-stationary signal decomposition"},"citations":[{"ref":"Gilles, J. (2013). Empirical wavelet transform. IEEE Transactions on Signal Processing, 61(16), 3999–4010.","type":"article","doi":"10.1109/tsp.2013.2265222","isbn":null,"url":null},{"ref":"Gilles, J. (2015). Empirical wavelet transform for multiscale analysis of signals. IEEE Signal Processing Magazine, 32(6), 125–130.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Empirical+wavelet+transform+for+multiscale+analysis+of+signals+Gilles"},{"ref":"Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.","type":"article","doi":"10.1109/TSP.2013.2288675","isbn":null,"url":null}],"related":["discrete-wavelet-transform","continuous-wavelet-transform","empirical-mode-decomposition","variational-mode-decomposition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"employee-engagement-survey","name":"Employee Engagement Survey","fullName":"Employee Engagement Survey (EES)","aliases":["Utrecht Work Engagement Scale","UWES"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Employee attitude","year":"2002","originator":"Wilmar B. Schaufeli and Arnold B. Bakker","url":"https://scholargate.app/en/organizational-behavior/employee-engagement-survey","markdownUrl":"https://scholargate.app/en/organizational-behavior/employee-engagement-survey.md","definition":"The Employee Engagement Survey, grounded in Schaufeli and Bakker's Utrecht Work Engagement Scale (UWES), is a 17-item instrument measuring occupational engagement across three dimensions: vigor, dedication, and absorption. Originally developed in 2002, the EES assesses the positive psychological state of work engagement, complementing burnout assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilmar B. Schaufeli and Arnold B. Bakker","subfamily":"Employee attitude","year":"2002","type":"Self-report scale"},"citations":[{"ref":"Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71-92.","type":"article","doi":"10.1023/A:1015630930326","isbn":null,"url":null},{"ref":"Bakker, A. B., & Demerouti, E. (2008). Towards a model of work engagement. Career Development International, 13(3), 209-223.","type":"article","doi":"10.1108/13620430810870476","isbn":null,"url":null}],"related":["organizational-trust-scale","organizational-citizenship-behavior","work-life-balance-scale","knowledge-sharing-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"employee-wellbeing-scale","name":"Employee Wellbeing Scale","fullName":"Employee Wellbeing Scale (EWS)","aliases":["EWS"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-wellbeing","year":"2009","originator":"Page & Vella-Brodrick","url":"https://scholargate.app/en/occupational-health/employee-wellbeing-scale","markdownUrl":"https://scholargate.app/en/occupational-health/employee-wellbeing-scale.md","definition":"The Employee Wellbeing Scale (EWS) measures workers' subjective wellbeing across five dimensions: vitality (energy and physical health), motivation (engagement with work), self-perception (confidence and self-worth), social connection (relationships and belonging), and general life satisfaction. Developed by Page and Vella-Brodrick, the EWS captures holistic employee wellbeing—the balance of psychological, social, and physical health that enables people to thrive at work and in life. The scale is used for occupational health surveillance, evaluation of workplace wellness interventions, and organizational culture assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Page & Vella-Brodrick","subfamily":"occupational-wellbeing","year":"2009","type":"Self-report"},"citations":[{"ref":"Page, K. M., & Vella-Brodrick, D. A. (2009). The 'What', 'Why' and 'How' of employee wellbeing: A new model. Soc Indic Res, 90(3), 519–531.","type":"article","doi":"10.1007/s11205-008-9270-3","isbn":null,"url":null},{"ref":"Vella-Brodrick, D. A., & Page, K. M. (2009). Development and validation of an Employee Wellbeing Scale. Soc Indic Res, 88(1), 59–79.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+an+Employee+Wellbeing+Scale+Vella-Brodrick"}],"related":["psychosocial-safety-climate-scale","occupational-fatigue-scale","workplace-ostracism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"empowerment-scale-rogers","name":"Empowerment Scale","fullName":"Empowerment Scale (Rogers)","aliases":["ES","Rogers Empowerment Scale"],"domain":"psychiatric-rehabilitation","family":"process-pipeline","subfamily":"recovery-measurement","year":"1997","originator":"Rogers, E. S., Chamberlin, J., Ellison, M. L., & Crean, T.","url":"https://scholargate.app/en/psychiatric-rehabilitation/empowerment-scale-rogers","markdownUrl":"https://scholargate.app/en/psychiatric-rehabilitation/empowerment-scale-rogers.md","definition":"The Empowerment Scale, developed by Elaine Salisbury Rogers and colleagues in 1997, is a 28-item self-report instrument assessing personal empowerment in individuals with serious mental illness. Empowerment reflects the individual's sense of agency, self-efficacy, and power to make meaningful life choices and participate in community. The scale captures three dimensions: self-efficacy/self-esteem, power/powerlessness, and community activism and autonomy. The Empowerment Scale is widely used in recovery-oriented mental health services to assess and monitor personal agency and control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rogers, E. S., Chamberlin, J., Ellison, M. L., & Crean, T.","subfamily":"recovery-measurement","year":"1997","type":"Self-report questionnaire"},"citations":[{"ref":"Rogers, E. S., Chamberlin, J., Ellison, M. L., & Crean, T. (1997). A consumer-constructed scale to measure empowerment among users of mental health services. Psychiatric Services, 48(8), 1042-1047.","type":"article","doi":"10.1176/ps.48.8.1042","isbn":null,"url":null}],"related":["recovery-assessment-scale","mental-health-recovery-measure","social-inclusion-scale","recovery-oriented-practices-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"endemic-compartmental-models","name":"Endemic Compartmental Models","fullName":"Endemic & Vaccination Compartmental Models (SIS, SIRS, SIRV)","aliases":["SIS Model","SIRS Model","SIRV Model","Endemic Disease Models"],"domain":"epidemiology","family":"regression-model","subfamily":"Epidemic modelling","year":2000,"originator":"Herbert Hethcote","url":"https://scholargate.app/en/epidemiology/endemic-compartmental-models","markdownUrl":"https://scholargate.app/en/epidemiology/endemic-compartmental-models.md","definition":"Endemic compartmental models extend the classical SIR framework to capture diseases that persist indefinitely in a population rather than burning out after a single epidemic wave. The SIS model allows recovered individuals to return to susceptibility immediately; SIRS introduces temporary immunity before loss; SIRV adds a vaccinated compartment. Together these models are foundational tools for studying diseases such as influenza, gonorrhea, and seasonal pathogens where reinfection or waning immunity is epidemiologically central.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herbert Hethcote","year":2000,"type":"Compartmental ODE model","subfamily":"Epidemic modelling","equilibria":"Disease-free and endemic equilibria","key_parameter":"Basic reproduction number R0"},"citations":[{"ref":"Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599–653.","type":"article","doi":"10.1137/S0036144500371907","isbn":null,"url":null}],"related":["sir-model","seir-model","reproduction-number"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"endometriosis-health-profile","name":"Endometriosis Health Profile-30","fullName":"Endometriosis Health Profile-30 (EHP-30)","aliases":["EHP-30","EHP-Core30"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"endometriosis-specific-health","year":2001,"originator":"Jones, G. L., Kennedy, S. H., Barnard, A., Wong, J., & Jenkinson, C.","url":"https://scholargate.app/en/obstetrics-gynecology/endometriosis-health-profile","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/endometriosis-health-profile.md","definition":"The Endometriosis Health Profile-30 (EHP-30) is a disease-specific quality-of-life questionnaire comprising 30 core items measuring the multidimensional impact of endometriosis on women's health and well-being. Developed by Jones and colleagues in 2001, the EHP-30 assesses five core domains: pain, emotional well-being, social support, sexual relations, and work/study impacts. Optional modular items address specific endometriosis-related concerns (infertility, medical side effects, treatment). It is the gold-standard endometriosis-specific outcome measure used in clinical trials and endometriosis research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jones, G. L., Kennedy, S. H., Barnard, A., Wong, J., & Jenkinson, C.","subfamily":"endometriosis-specific-health","year":2001,"type":"Self-report"},"citations":[{"ref":"Jones, G. L., Kennedy, S. H., Barnard, A., Wong, J., & Jenkinson, C. (2001). Development of an endometriosis quality-of-life instrument: The Endometriosis Health Profile-30. Obstetrics & Gynecology, 98(2), 258-264.","type":"article","doi":"10.1097/00006250-200108000-00014","isbn":null,"url":null},{"ref":"Jones, G. L., Kennedy, S. H., Jenkinson, C., & Endometriosis Consortium (2002). Evaluating the responsiveness of the Endometriosis Health Profile Questionnaire: comparing responses from women with endometriosis to gynaecological controls. Human Reproduction, 17(9), 2464-2468.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluating+the+responsiveness+of+the+Endometriosis+Health+Profile+Questionnaire%3A+comparing+responses+from+women+with+endometriosis+to+gynaecological+controls+Jones"}],"related":["female-pelvic-pain-scale","menopause-specific-qol","postpartum-bonding-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"energy-dispersive-x-ray-spectroscopy","name":"Energy-Dispersive X-ray Spectroscopy","fullName":"Energy-Dispersive X-ray Spectroscopy (EDS)","aliases":["EDS","EDX","EDAX","elemental microanalysis"],"domain":"materials-science","family":"process-pipeline","subfamily":"Electron microscopy analysis","year":"1913","originator":"Henry Moseley","url":"https://scholargate.app/en/materials-science/energy-dispersive-x-ray-spectroscopy","markdownUrl":"https://scholargate.app/en/materials-science/energy-dispersive-x-ray-spectroscopy.md","definition":"Energy-Dispersive X-ray Spectroscopy (EDS) is an analytical technique that identifies and quantifies chemical elements in microvolumes of samples by analyzing characteristic X-rays emitted during electron bombardment. Rooted in Moseley's discovery of characteristic X-ray lines in 1913 and developed as a practical microanalytical tool by the 1970s, EDS is integrated into scanning electron microscopes (SEM) and transmission electron microscopes (TEM) for spatially-resolved elemental analysis. It is indispensable in materials characterization for phase identification, compositional mapping, and alloy development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Moseley","subfamily":"Electron microscopy analysis","year":"1913","type":"Analytical technique"},"citations":[{"ref":"Goldstein, J. I., Newbury, D. E., Michael, J. R., & Ritchie, R. O. (2017). Scanning Electron Microscopy and X-ray Microanalysis (3rd ed.). Springer.","type":"book","doi":"10.1007/978-1-4939-6676-9","isbn":null,"url":null},{"ref":"Reed, S. J. B. (1993). Electron Microprobe Analysis (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://books.google.com/books?id=vREcG24mL90C"},{"ref":"Williams, D. B., & Carter, C. B. (2009). Transmission Electron Microscopy: A Textbook for Materials Science (2nd ed.). Springer.","type":"book","doi":"10.1007/978-0-387-76501-3","isbn":null,"url":null}],"related":["selected-area-electron-diffraction","x-ray-photoelectron-spectroscopy","atomic-force-microscopy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"energy-storage-dispatch","name":"Energy Storage Dispatch Optimization","fullName":"Energy Storage System Dispatch and Optimization","aliases":["battery dispatch","storage scheduling","energy arbitrage optimization"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Power system operation and renewable integration","year":"2000s","originator":"Utilities and storage technology developers","url":"https://scholargate.app/en/electrical-engineering/energy-storage-dispatch","markdownUrl":"https://scholargate.app/en/electrical-engineering/energy-storage-dispatch.md","definition":"Energy storage dispatch optimization determines when to charge and discharge battery systems to maximize revenue, minimize grid stress, or support renewable integration. With falling battery costs and increasing variable renewable generation, storage dispatch has become critical for balancing supply and demand in modern power systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Utilities and storage technology developers","subfamily":"Power system operation and renewable integration","year":"2000s","type":"Computational pipeline"},"citations":[{"ref":"Dunn, B., Kamath, H., & Tarascon, J. M. (2021). Electrical energy storage for the grid: A battery of possibilities. Science, 334(6058), 928-935.","type":"report","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Electrical+energy+storage+for+the+grid%3A+A+battery+of+possibilities+Dunn"},{"ref":"Ramasamy, V., Desai, A. S., & Margolis, R. M. (2015). Co-optimization of Solar PV and Battery Storage: A Review of Co-optimization Models and Frameworks. NREL/TP-6A40-64781.","type":"report","doi":null,"isbn":null,"url":"https://www.nrel.gov/docs/fy15osti/64781.pdf"},{"ref":"Strbac, G. (2002). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419-4426.","type":"article","doi":"10.1016/j.enpol.2008.09.030","isbn":null,"url":null}],"related":["load-forecasting","power-flow-analysis","reactive-power-compensation","smart-grid-state-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"engle-granger-cointegration-test","name":"Engle-Granger Cointegration Test","fullName":"Engle-Granger Two-Step Cointegration Test","aliases":["EG cointegration test","Engle-Granger two-step method","residual-based cointegration test","EG test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1987","originator":"Robert F. Engle and Clive W. J. Granger","url":"https://scholargate.app/en/econometrics/engle-granger-cointegration-test","markdownUrl":"https://scholargate.app/en/econometrics/engle-granger-cointegration-test.md","definition":"The Engle-Granger two-step method tests whether two or more non-stationary I(1) time series share a common stochastic trend — that is, whether a linear combination of them is stationary. If cointegration is confirmed, an error-correction model (ECM) can be estimated to capture both short-run dynamics and long-run equilibrium adjustment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Engle and Clive W. J. Granger","year":"1987","type":"Cointegration test","dataType":"Time series (integrated, typically I(1))","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null},{"ref":"Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691042893","url":null}],"related":["johansen-cointegration-test","vector-error-correction-model","augmented-dickey-fuller-unit-root-test","phillips-perron-unit-root-test","granger-causality-test","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-active-learning","name":"Ensemble Active Learning","fullName":"Ensemble-Based Active Learning (Query by Committee and Variants)","aliases":["Query by Committee","QBC active learning","committee-based active learning","ensemble query strategy"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1992","originator":"Seung, H. S., Opper, M., & Sompolinsky, H.","url":"https://scholargate.app/en/machine-learning/ensemble-active-learning","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-active-learning.md","definition":"Ensemble Active Learning combines a committee of diverse models with an active learning loop to select the most informative unlabeled examples for labeling. Rooted in the Query by Committee framework introduced by Seung et al. (1992), it uses disagreement among committee members as a signal for uncertainty, reducing the number of labeled examples needed to achieve strong predictive performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seung, H. S., Opper, M., & Sompolinsky, H.","year":"1992","type":"Ensemble-based active learning strategy","dataType":"Labeled and unlabeled tabular or structured data","subfamily":"Machine learning"},"citations":[{"ref":"Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT 1992), pp. 287–294. ACM.","type":"inproceedings","doi":null,"isbn":null,"url":"https://doi.org/10.1145/130385.130417"},{"ref":"Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.","type":"article","doi":null,"isbn":null,"url":"https://burrsettles.com/pub/settles.activelearning.pdf"}],"related":["active-learning","semi-supervised-learning","boosting","random-forest","voting-ensemble","query-by-committee"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-apriori-algorithm","name":"Ensemble Apriori Algorithm","fullName":"Ensemble Apriori Algorithm (Ensemble-Based Frequent Pattern and Association Rule Mining)","aliases":["Ensemble Apriori","Ensemble Association Rule Mining","EAR mining","Distributed Apriori Ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1994 (Apriori base); ensemble extensions 2000s–2010s","originator":"Agrawal, R. & Srikant, R. (Apriori base); ensemble extension by multiple researchers","url":"https://scholargate.app/en/machine-learning/ensemble-apriori-algorithm","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-apriori-algorithm.md","definition":"The Ensemble Apriori Algorithm applies ensemble principles to the classic Apriori frequent-pattern miner by running multiple Apriori instances on different data partitions or parameter settings and merging their rule sets. This approach improves coverage, reduces sensitivity to the minimum-support threshold, and scales association rule mining to larger transactional datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agrawal, R. & Srikant, R. (Apriori base); ensemble extension by multiple researchers","year":"1994 (Apriori base); ensemble extensions 2000s–2010s","type":"Ensemble / Frequent Pattern Mining","dataType":"Transactional / binary / categorical data","subfamily":"Machine learning"},"citations":[{"ref":"Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 1215, 487–499.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fast+algorithms+for+mining+association+rules+Agrawal+Srikant+1994"},{"ref":"Apriori algorithm. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Apriori_algorithm"}],"related":["apriori-algorithm","fp-growth","random-forest","association-rule-learning","bagging","boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-association-rules","name":"Ensemble Association Rules","fullName":"Ensemble Association Rule Mining","aliases":["Ensemble ARM","aggregated association rules","combined frequent-pattern mining","multi-run association rule learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"late 1990s–2000s","originator":"Various (applied ensemble philosophy from Breiman and others to association rule mining)","url":"https://scholargate.app/en/machine-learning/ensemble-association-rules","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-association-rules.md","definition":"Ensemble Association Rules applies ensemble learning principles to association rule mining: multiple rule sets are discovered from different data subsamples or with varied parameters, then merged and weighted to produce a more stable and complete set of co-occurrence patterns. The approach reduces sensitivity to support and confidence threshold choices and improves robustness on noisy transactional data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (applied ensemble philosophy from Breiman and others to association rule mining)","year":"late 1990s–2000s","type":"Ensemble meta-learning over association rule learners","dataType":"Transactional / binary / categorical tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Domingos, P. (1999). MetaCost: A general method for making classifiers cost-sensitive. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 155–164.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MetaCost+general+method+making+classifiers+cost-sensitive+Domingos+1999"},{"ref":"Rymon, R. (1992). Search through systematic set enumeration. Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, 539–550. — foundational work on systematic enumeration used in ensemble aggregation of frequent itemsets.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ensemble+association+rule+mining+aggregation+multiple+runs"}],"related":["association-rules","apriori-algorithm","voting-ensemble","bagging","boosting","fp-growth"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-autoencoder-anomaly-detection","name":"Ensemble Autoencoder Anomaly Detection","fullName":"Ensemble Autoencoder Anomaly Detection (Multiple Autoencoder Aggregation for Outlier Scoring)","aliases":["ensemble AE anomaly detection","autoencoder ensemble outlier detection","multi-autoencoder anomaly scoring","AE ensemble unsupervised anomaly detection"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017","originator":"Chen, J., Sathe, S., Aggarwal, C., & Turaga, D.","url":"https://scholargate.app/en/machine-learning/ensemble-autoencoder-anomaly-detection","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-autoencoder-anomaly-detection.md","definition":"Ensemble Autoencoder Anomaly Detection trains multiple autoencoder neural networks on normal-class data and aggregates their reconstruction errors to produce a robust anomaly score. By combining diverse autoencoders rather than relying on one, the method stabilises outlier rankings and reduces sensitivity to random initialisation or suboptimal architecture choices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, J., Sathe, S., Aggarwal, C., & Turaga, D.","year":"2017","type":"Ensemble unsupervised anomaly detection","dataType":"Continuous numeric tabular data (multivariate)","subfamily":"Machine learning"},"citations":[{"ref":"Chen, J., Sathe, S., Aggarwal, C., & Turaga, D. (2017). Outlier Detection with Autoencoder Ensembles. In Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 90–98. SIAM.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Outlier+Detection+with+Autoencoder+Ensembles+Chen+Sathe+Aggarwal+2017"},{"ref":"Aggarwal, C. C. (2017). Outlier Analysis (2nd ed., Ch. 3 & 9). Springer.","type":"book","doi":null,"isbn":"978-3-319-47578-3","url":null}],"related":["autoencoder-anomaly-detection","isolation-forest","one-class-svm","voting-ensemble","gaussian-mixture-model","semi-supervised-autoencoder-anomaly-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-decision-tree","name":"Ensemble Decision Tree","fullName":"Ensemble Decision Tree (Combined Decision Tree Classifiers and Regressors)","aliases":["decision tree ensemble","ensemble of decision trees","combined decision trees","multiple classifier system (decision trees)"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1996–2000","originator":"Breiman, L.; Dietterich, T. G.","url":"https://scholargate.app/en/machine-learning/ensemble-decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-decision-tree.md","definition":"Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, L.; Dietterich, T. G.","year":"1996–2000","type":"Ensemble (multiple decision trees combined)","dataType":"Tabular (continuous, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg.","type":"inproceedings","doi":"10.1007/3-540-45014-9_1","isbn":null,"url":null},{"ref":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.","type":"article","doi":"10.1007/BF00058655","isbn":null,"url":null}],"related":["random-forest","extra-trees","decision-tree","boosting","bagging","voting-ensemble"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-federated-learning","name":"Ensemble Federated Learning","fullName":"Ensemble Federated Learning (Federated Ensemble Aggregation)","aliases":["federated ensemble learning","EFL","federated model ensembling","federated multi-model aggregation"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017–2019","originator":"McMahan et al. (FedAvg) extended by subsequent ensemble work","url":"https://scholargate.app/en/machine-learning/ensemble-federated-learning","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-federated-learning.md","definition":"Ensemble Federated Learning combines the privacy-preserving distribution of federated learning with ensemble aggregation: each participating client trains its own local model on private data, and the server aggregates predictions — or model parameters — from all clients using ensemble strategies such as voting, averaging, or stacking, instead of simple parameter averaging alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McMahan et al. (FedAvg) extended by subsequent ensemble work","year":"2017–2019","type":"Ensemble meta-strategy over federated clients","dataType":"Distributed, privacy-sensitive tabular, image, or text data across decentralized nodes","subfamily":"Machine learning"},"citations":[{"ref":"McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54, 1273–1282.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v54/mcmahan17a.html"},{"ref":"Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2021). FedHealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems, 35(4), 83–93.","type":"article","doi":"10.1109/MIS.2020.2988604","isbn":null,"url":null}],"related":["federated-learning","voting-ensemble","stacking-ensemble","bagging","boosting","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-few-shot-learning","name":"Ensemble Few-shot learning","fullName":"Ensemble Methods for Few-Shot Learning","aliases":["ensemble few-shot classification","multi-model few-shot learning","few-shot ensemble","cooperative few-shot ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2019","originator":"Dvornik, N., Schmid, C., & Mairal, J.","url":"https://scholargate.app/en/machine-learning/ensemble-few-shot-learning","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-few-shot-learning.md","definition":"Ensemble Few-Shot Learning combines multiple few-shot models — such as prototypical networks or embedding learners — to classify new classes from only one to a handful of labeled examples. By enforcing diversity among base learners and aggregating their predictions, the ensemble consistently outperforms any single few-shot model in accuracy and robustness, especially under severe label scarcity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dvornik, N., Schmid, C., & Mairal, J.","year":"2019","type":"Ensemble of few-shot learners","dataType":"Few labeled examples per class (typically 1–20 shots), image or tabular features","subfamily":"Machine learning"},"citations":[{"ref":"Dvornik, N., Schmid, C., & Mairal, J. (2019). Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3716–3725.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openaccess.thecvf.com/content_ICCV_2019/html/Dvornik_Diversity_With_Cooperation_Ensemble_Methods_for_Few-Shot_Classification_ICCV_2019_paper.html"},{"ref":"Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a Few Examples: A Survey on Few-Shot Learning. ACM Computing Surveys, 53(3), 1–34.","type":"article","doi":"10.1145/3386252","isbn":null,"url":null}],"related":["few-shot-learning","transfer-learning","voting-ensemble","meta-learning","semi-supervised-few-shot-learning","boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-gaussian-mixture-model","name":"Ensemble Gaussian Mixture Model","fullName":"Ensemble Gaussian Mixture Model (E-GMM)","aliases":["E-GMM","GMM ensemble","mixture model ensemble","ensemble GMM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s","originator":"Combination of GMM (Dempster et al., 1977) and ensemble learning (Dietterich, 2000)","url":"https://scholargate.app/en/machine-learning/ensemble-gaussian-mixture-model","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-gaussian-mixture-model.md","definition":"Ensemble Gaussian Mixture Model (E-GMM) combines multiple independently fitted Gaussian Mixture Models to improve density estimation, clustering stability, and anomaly detection. By averaging or aggregating the probabilistic outputs of several GMMs — each trained on a different data subset or random initialization — the ensemble reduces sensitivity to local optima and random seed choice, yielding more robust and reliable results than any single GMM.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Combination of GMM (Dempster et al., 1977) and ensemble learning (Dietterich, 2000)","year":"2000s","type":"Ensemble of probabilistic generative models","dataType":"Continuous, multivariate","subfamily":"Machine learning"},"citations":[{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9: Mixture Models and EM). Springer.","type":"book","doi":null,"isbn":"978-0-387-31073-2","url":null},{"ref":"Dietterich, T. G. (2000). Ensemble methods in machine learning. Multiple Classifier Systems, Lecture Notes in Computer Science, 1857, 1–15.","type":"article","doi":"10.1007/3-540-45014-9_1","isbn":null,"url":null}],"related":["gaussian-mixture-model","random-forest","bagging","boosting","expectation-maximization","k-means-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-gaussian-process","name":"Ensemble Gaussian Process","fullName":"Ensemble of Gaussian Processes (Committee / Distributed GP)","aliases":["Gaussian Process ensemble","GP committee machine","distributed GP","mixture of GPs"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000–2015","originator":"Tresp, V. (committee formulation); Deisenroth, M. P. & Ng, J. W. (distributed formulation)","url":"https://scholargate.app/en/machine-learning/ensemble-gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-gaussian-process.md","definition":"Ensemble Gaussian Process trains multiple independent GP experts on data subsets or overlapping regions, then combines their posterior predictions — means and variances — into a single probabilistic forecast. This approach retains the calibrated uncertainty estimates of standard GPs while overcoming their O(n³) cubic cost bottleneck, making probabilistic regression practical on datasets with thousands to millions of observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tresp, V. (committee formulation); Deisenroth, M. P. & Ng, J. W. (distributed formulation)","year":"2000–2015","type":"Ensemble of probabilistic surrogate models","dataType":"Continuous numerical features; small-to-medium datasets per expert","subfamily":"Machine learning"},"citations":[{"ref":"Tresp, V. (2000). A Bayesian Committee Machine. Neural Computation, 12(11), 2719–2741.","type":"inproceedings","doi":"10.1162/089976600300014908","isbn":null,"url":null},{"ref":"Deisenroth, M. P., & Ng, J. W. (2015). Distributed Gaussian Processes. Proceedings of the 32nd International Conference on Machine Learning (ICML), PMLR 37, 1481–1490.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v37/deisenroth15.html"}],"related":["gaussian-process","random-forest","bayesian-gaussian-process","gaussian-mixture-model","support-vector-machine","voting-ensemble"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-gradient-boosting","name":"Ensemble Gradient Boosting","fullName":"Gradient Boosting Machine (Ensemble of Additive Decision Trees)","aliases":["Gradient Boosting Machine","GBM","Gradient Tree Boosting","Stochastic Gradient Boosting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001","originator":"Friedman, J. H.","url":"https://scholargate.app/en/machine-learning/ensemble-gradient-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-gradient-boosting.md","definition":"Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Friedman, J. H.","year":"2001","type":"Ensemble (sequential boosting of decision trees)","dataType":"Tabular (continuous, categorical, mixed features)","subfamily":"Machine learning"},"citations":[{"ref":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.","type":"article","doi":"10.1214/aos/1013203451","isbn":null,"url":null},{"ref":"Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics and Data Analysis, 38(4), 367–378.","type":"article","doi":"10.1016/S0167-9473(01)00065-2","isbn":null,"url":null}],"related":["random-forest","xgboost","decision-tree","adaboost","lightgbm","catboost"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-hdbscan","name":"Ensemble HDBSCAN","fullName":"Ensemble Hierarchical Density-Based Spatial Clustering of Applications with Noise","aliases":["HDBSCAN ensemble clustering","consensus HDBSCAN","multi-run HDBSCAN","cluster ensemble HDBSCAN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2011–2017","originator":"Vega-Pons, S. & Ruiz-Shulcloper, J. (ensemble clustering framework); McInnes, L. et al. (HDBSCAN base)","url":"https://scholargate.app/en/machine-learning/ensemble-hdbscan","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-hdbscan.md","definition":"Ensemble HDBSCAN runs HDBSCAN multiple times under different hyperparameter settings or data subsamples and combines the resulting partitions into a single stable consensus clustering. Because HDBSCAN is sensitive to its minimum cluster size and minimum samples parameters, pooling multiple runs greatly reduces sensitivity to any single configuration and yields more reproducible cluster assignments on noisy, high-dimensional data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vega-Pons, S. & Ruiz-Shulcloper, J. (ensemble clustering framework); McInnes, L. et al. (HDBSCAN base)","year":"2011–2017","type":"Consensus clustering ensemble","dataType":"Continuous, mixed, or high-dimensional unlabeled features","subfamily":"Machine learning"},"citations":[{"ref":"McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.","type":"article","doi":"10.21105/joss.00205","isbn":null,"url":null},{"ref":"Vega-Pons, S., & Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble methods. International Journal of Pattern Recognition and Artificial Intelligence, 25(03), 337–372.","type":"article","doi":"10.1142/S0218001411008683","isbn":null,"url":null}],"related":["hdbscan","k-means","gaussian-mixture-model","ensemble-k-means","ensemble-dbscan","semi-supervised-hdbscan"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-isolation-forest","name":"Ensemble Isolation Forest","fullName":"Ensemble Isolation Forest (Meta-Ensemble Anomaly Detection)","aliases":["EIF ensemble","multi-isolation-forest","isolation forest ensemble","ensemble anomaly detection with isolation trees"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2008 (base); ensemble variants 2010s–present","originator":"Liu, F. T., Ting, K. M., Zhou, Z.-H. (base IF); ensemble extensions by multiple researchers","url":"https://scholargate.app/en/machine-learning/ensemble-isolation-forest","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-isolation-forest.md","definition":"Ensemble Isolation Forest trains multiple Isolation Forest models — each with different random seeds, subsampling ratios, or contamination parameters — and combines their anomaly scores to produce a more stable, robust anomaly ranking. By averaging or aggregating across several independent isolation forests, the method reduces the variance inherent in any single forest and yields more reliable outlier detection on complex or high-dimensional data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, F. T., Ting, K. M., Zhou, Z.-H. (base IF); ensemble extensions by multiple researchers","year":"2008 (base); ensemble variants 2010s–present","type":"Meta-ensemble anomaly detection","dataType":"Continuous, mixed numeric tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 413–422. IEEE.","type":"inproceedings","doi":"10.1109/ICDM.2008.17","isbn":null,"url":null},{"ref":"Isolation Forest. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Isolation_forest"}],"related":["isolation-forest","autoencoder-anomaly-detection","one-class-svm","gaussian-mixture-model","random-forest","voting-ensemble"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-k-means","name":"Ensemble K-means","fullName":"Ensemble K-means Clustering (Consensus Clustering)","aliases":["consensus K-means","K-means ensemble clustering","cluster ensemble with K-means","EKM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2002","originator":"Strehl, A. & Ghosh, J.","url":"https://scholargate.app/en/machine-learning/ensemble-k-means","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-k-means.md","definition":"Ensemble K-means runs K-means clustering many times under varied initializations, random seeds, or feature subsets, then aggregates the resulting partitions into a single consensus assignment. This approach reduces K-means' well-known sensitivity to initialization and produces more stable, reproducible clusters than any single run.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Strehl, A. & Ghosh, J.","year":"2002","type":"Ensemble clustering (consensus aggregation of K-means partitions)","dataType":"Continuous or mixed tabular features","subfamily":"Machine learning"},"citations":[{"ref":"Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/strehl02a.html"},{"ref":"Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003). Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning, 52, 91–118.","type":"article","doi":"10.1023/A:1023949509487","isbn":null,"url":null}],"related":["k-means","ensemble-dbscan","gaussian-mixture-model","ensemble-gaussian-mixture-model","semi-supervised-k-means","k-nearest-neighbors"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-k-nearest-neighbors","name":"Ensemble K-nearest neighbors","fullName":"Ensemble K-Nearest Neighbors (Aggregated KNN)","aliases":["Ensemble KNN","KNN ensemble","aggregated k-nearest neighbors","combined KNN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s","originator":"Domeniconi, C. & Yan, B. (key formalization)","url":"https://scholargate.app/en/machine-learning/ensemble-k-nearest-neighbors","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-k-nearest-neighbors.md","definition":"Ensemble K-Nearest Neighbors combines multiple KNN models — each trained with a different value of k, distance metric, feature subset, or data bootstrap — and aggregates their predictions by majority vote (classification) or averaging (regression). The approach reduces the high variance inherent in any single KNN model and produces more stable, accurate predictions on tabular data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Domeniconi, C. & Yan, B. (key formalization)","year":"2000s","type":"Ensemble (aggregated KNN classifiers/regressors)","dataType":"Tabular (numeric and mixed features)","subfamily":"Machine learning"},"citations":[{"ref":"Domeniconi, C., & Yan, B. (2004). Nearest neighbor ensemble. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Vol. 1, pp. 228–231. IEEE.","type":"inproceedings","doi":"10.1109/ICPR.2004.1334065","isbn":null,"url":null},{"ref":"Zhou, Z.-H. (2012). Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1-4398-3003-1","url":null}],"related":["k-nearest-neighbors","random-forest","voting-ensemble","bagging","ensemble-decision-tree","ensemble-support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-kalman-filter","name":"Ensemble Kalman Filter","fullName":"Ensemble Kalman Filter (Data Assimilation)","aliases":["EnKF","Monte Carlo Kalman Filter","Stochastic Ensemble Filter","Topluluk Kalman Filtresi"],"domain":"data-fusion","family":"regression-model","subfamily":"Data assimilation","year":1994,"originator":"Geir Evensen","url":"https://scholargate.app/en/data-fusion/ensemble-kalman-filter","markdownUrl":"https://scholargate.app/en/data-fusion/ensemble-kalman-filter.md","definition":"The Ensemble Kalman Filter (EnKF) is a sequential Monte Carlo data assimilation algorithm introduced by Geir Evensen in 1994. It extends the classical Kalman filter to high-dimensional, nonlinear dynamical systems by representing the forecast error covariance through a finite ensemble of model realizations rather than propagating a full covariance matrix. Each ensemble member evolves through the nonlinear model, and observations are assimilated by computing a sample-based Kalman gain, making the method computationally tractable for large geophysical models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geir Evensen","year":1994,"type":"Sequential Monte Carlo data assimilation filter","subfamily":"Data assimilation","domain":"Geophysics, numerical weather prediction, engineering","complexity":"O(N·m) per update cycle, N = ensemble size, m = observation dimension"},"citations":[{"ref":"Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162.","type":"article","doi":"10.1029/94JC00572","isbn":null,"url":null}],"related":["particle-filter","data-fusion","state-space-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-linear-regression","name":"Ensemble Linear Regression","fullName":"Ensemble of Linear Regression Models (Bagged and Stacked Linear Regression)","aliases":["bagged linear regression","aggregated linear regression","stacked linear models","bootstrap-aggregated OLS"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1996","originator":"Breiman, L. (bagging framework)","url":"https://scholargate.app/en/machine-learning/ensemble-linear-regression","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-linear-regression.md","definition":"Ensemble Linear Regression combines multiple ordinary least-squares models — each fitted on a different bootstrap sample or feature subset — and averages their predictions. The technique, grounded in Breiman's bagging framework (1996), reduces variance and improves predictive stability compared with a single linear regression fit, while retaining the interpretability of linear assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, L. (bagging framework)","year":"1996","type":"Ensemble of linear models","dataType":"Continuous features; continuous target (regression)","subfamily":"Machine learning"},"citations":[{"ref":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.","type":"article","doi":"10.1007/BF00058655","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 8). Springer.","type":"book","doi":null,"isbn":"978-0-387-84857-0","url":null}],"related":["linear-regression-ml","regularized-linear-regression","random-forest","bagging","voting-ensemble","ridge-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-logistic-regression","name":"Ensemble Logistic Regression","fullName":"Ensemble Logistic Regression (Combined Logistic Classifier Ensemble)","aliases":["logistic regression ensemble","bagged logistic regression","aggregated logistic regression","logistic ensemble classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1996–2000s","originator":"Breiman, L. (bagging); broader ensemble literature","url":"https://scholargate.app/en/machine-learning/ensemble-logistic-regression","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-logistic-regression.md","definition":"Ensemble Logistic Regression trains multiple logistic regression classifiers on varied subsets or perturbations of the training data and combines their probability estimates by averaging or voting. The approach preserves logistic regression's probabilistic interpretability while reducing variance and improving predictive stability through aggregation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, L. (bagging); broader ensemble literature","year":"1996–2000s","type":"Ensemble of logistic regression classifiers","dataType":"Tabular data; binary or multiclass labels","subfamily":"Machine learning"},"citations":[{"ref":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.","type":"article","doi":"10.1007/BF00058655","isbn":null,"url":null},{"ref":"Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21–45.","type":"article","doi":"10.1109/MCAS.2006.1688199","isbn":null,"url":null}],"related":["logistic-regression-ml","voting-ensemble","boosting","random-forest","stacking-ensemble","semi-supervised-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-metric-learning","name":"Ensemble Metric Learning","fullName":"Ensemble Metric Learning (Combined Distance Metric Ensembles)","aliases":["EML","ensemble distance metric learning","multiple metric fusion","combined metric learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Multiple contributors (Weinberger, Saul, et al.)","url":"https://scholargate.app/en/machine-learning/ensemble-metric-learning","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-metric-learning.md","definition":"Ensemble Metric Learning trains multiple distance metric learners — each on a different data view, feature subspace, or with a different objective — and combines the resulting metrics to produce a single, more robust similarity function. Combining diverse metrics reduces the variance of any individual metric and improves performance in tasks such as nearest-neighbor classification, retrieval, and few-shot learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Weinberger, Saul, et al.)","year":"2000s–2010s","type":"Ensemble of learned distance metrics","dataType":"Labeled or partially labeled tabular or embedding data","subfamily":"Machine learning"},"citations":[{"ref":"Wang, J., Kalousis, A., & Woznica, A. (2012). Parametric local metric learning for nearest neighbor classification. Advances in Neural Information Processing Systems, 25.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Parametric+local+metric+learning+nearest+neighbor+classification+Wang+Kalousis+2012"},{"ref":"Similarity learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Similarity_learning"}],"related":["metric-learning","few-shot-learning","k-nearest-neighbors","voting-ensemble","random-forest","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-naive-bayes","name":"Ensemble Naive Bayes","fullName":"Ensemble of Naive Bayes Classifiers","aliases":["Bagged Naive Bayes","Boosted Naive Bayes","Naive Bayes ensemble","NB ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s","originator":"Various (Dietterich, T.G.; Webb, G.I.; others)","url":"https://scholargate.app/en/machine-learning/ensemble-naive-bayes","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-naive-bayes.md","definition":"Ensemble Naive Bayes trains multiple Naive Bayes classifiers — each exposed to a different view of the data through bagging, feature subsets, or boosting — and combines their probabilistic predictions by voting or probability averaging. The approach retains the speed and interpretability of individual Naive Bayes models while reducing variance and improving accuracy through ensemble aggregation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Dietterich, T.G.; Webb, G.I.; others)","year":"2000s","type":"Ensemble of probabilistic classifiers","dataType":"Categorical, binary, continuous (Gaussian NB), text (Multinomial NB)","subfamily":"Machine learning"},"citations":[{"ref":"Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer.","type":"inproceedings","doi":"10.1007/3-540-45014-9_1","isbn":null,"url":null},{"ref":"Lowd, D. & Domingos, P. (2005). Naive Bayes Models for Probability Estimation. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 529–536. ACM.","type":"inproceedings","doi":"10.1145/1102351.1102418","isbn":null,"url":null}],"related":["naive-bayes","voting-ensemble","boosting","random-forest","bagging","semi-supervised-naive-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-one-class-svm","name":"Ensemble One-class SVM","fullName":"Ensemble of One-Class Support Vector Machines","aliases":["Ensemble OC-SVM","multiple one-class SVM","OC-SVM ensemble","one-class SVM committee"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001","originator":"Tax, D. M. J. & Duin, R. P. W. (ensemble OC classifiers); Scholkopf et al. (OC-SVM base)","url":"https://scholargate.app/en/machine-learning/ensemble-one-class-svm","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-one-class-svm.md","definition":"Ensemble One-Class SVM combines multiple one-class support vector machine models — each trained on a different random subset of the data or features — and aggregates their anomaly scores. By pooling several OC-SVM boundary estimates, the ensemble reduces the sensitivity to kernel choice and data sampling that afflicts a single one-class SVM, producing a more stable and accurate novelty or outlier detector.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tax, D. M. J. & Duin, R. P. W. (ensemble OC classifiers); Scholkopf et al. (OC-SVM base)","year":"2001","type":"Ensemble anomaly detector","dataType":"Continuous, mixed (unlabeled or one-class labeled)","subfamily":"Machine learning"},"citations":[{"ref":"Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.","type":"article","doi":"10.1162/089976601750264965","isbn":null,"url":null},{"ref":"Tax, D. M. J., & Duin, R. P. W. (2001). Combining one-class classifiers. In Multiple Classifier Systems (MCS 2001), Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg.","type":"article","doi":"10.1007/3-540-48219-9_30","isbn":null,"url":null}],"related":["one-class-svm","support-vector-machine","isolation-forest","autoencoder-anomaly-detection","voting-ensemble","gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-online-learning","name":"Ensemble Online Learning","fullName":"Ensemble Online Learning (Online Ensemble Methods)","aliases":["online ensemble methods","streaming ensemble learning","incremental ensemble learning","adaptive ensemble learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001","originator":"Oza, N. C. & Russell, S.","url":"https://scholargate.app/en/machine-learning/ensemble-online-learning","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-online-learning.md","definition":"Ensemble Online Learning combines multiple base learners that are trained incrementally on a stream of data, updating each model one observation at a time. By aggregating the predictions of diverse online learners, the ensemble achieves accuracy and robustness that surpass any single incremental model, while adapting continuously to changing data distributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oza, N. C. & Russell, S.","year":"2001","type":"Ensemble (online / incremental)","dataType":"Sequential or streaming tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 229–236.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+bagging+and+boosting+Oza+Russell+2001"},{"ref":"Online machine learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Online_machine_learning"}],"related":["online-learning","boosting","voting-ensemble","semi-supervised-learning","random-forest","active-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-self-supervised-learning","name":"Ensemble Self-supervised Learning","fullName":"Ensemble Self-supervised Learning (Combining Multiple Self-supervised Models or Objectives)","aliases":["ensemble SSL","multi-view self-supervised ensemble","self-supervised ensemble learning","SSL ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020–2021","originator":"Multiple contributors (Grill et al., Caron et al., Chen et al.)","url":"https://scholargate.app/en/machine-learning/ensemble-self-supervised-learning","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-self-supervised-learning.md","definition":"Ensemble Self-supervised Learning combines multiple self-supervised models, objectives, or augmentation views into a unified framework to produce more robust and generalizable representations from unlabeled data. By aggregating diverse self-supervised signals, the ensemble reduces the risk of representation collapse and outperforms single-objective SSL approaches on downstream tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Grill et al., Caron et al., Chen et al.)","year":"2020–2021","type":"Ensemble of self-supervised models or objectives","dataType":"Unlabeled images, text, audio, or multimodal data","subfamily":"Machine learning"},"citations":[{"ref":"Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Ávila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., Kavukcuoglu, K., Munos, R., & Valko, M. (2020). Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. Advances in Neural Information Processing Systems, 33, 21271–21284.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/f3ada80d5c4ee70142b17b8192b2958e-Abstract.html"},{"ref":"Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660.","type":"inproceedings","doi":"10.1109/ICCV48922.2021.00951","isbn":null,"url":null}],"related":["random-forest","self-supervised-learning","contrastive-learning","knowledge-distillation","semi-supervised-learning","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-semi-supervised-learning","name":"Ensemble Semi-supervised Learning","fullName":"Ensemble Semi-supervised Learning (Combining Ensemble Methods with Semi-supervised Paradigms)","aliases":["semi-supervised ensemble","SSL ensemble","ensemble-based SSL","co-training ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1998–2005","originator":"Blum & Mitchell (co-training); Zhou & Li (tri-training)","url":"https://scholargate.app/en/machine-learning/ensemble-semi-supervised-learning","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-semi-supervised-learning.md","definition":"Ensemble semi-supervised learning combines multiple base learners with the semi-supervised paradigm, exploiting both a small labeled set and a large pool of unlabeled data. By letting diverse classifiers teach each other through pseudo-labeling or co-training, the ensemble improves generalization far beyond what either approach alone could achieve with limited labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blum & Mitchell (co-training); Zhou & Li (tri-training)","year":"1998–2005","type":"Ensemble + semi-supervised hybrid paradigm","dataType":"Mixed labeled and unlabeled tabular, text, or image data","subfamily":"Machine learning"},"citations":[{"ref":"Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.","type":"inproceedings","doi":"10.1109/TKDE.2005.186","isbn":null,"url":null},{"ref":"Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT 1998), pp. 92–100. ACM.","type":"inproceedings","doi":"10.1145/279943.279962","isbn":null,"url":null}],"related":["semi-supervised-learning","boosting","bagging","voting-ensemble","self-supervised-learning","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-support-vector-machine","name":"Ensemble Support Vector Machine","fullName":"Ensemble Support Vector Machine (Aggregated SVM Ensemble)","aliases":["Ensemble SVM","SVM ensemble","bagged SVM","SVM committee machine"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000–2003","originator":"Kim, H.-C. et al.; Dietterich, T. G.","url":"https://scholargate.app/en/machine-learning/ensemble-support-vector-machine","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-support-vector-machine.md","definition":"Ensemble Support Vector Machine combines multiple independently trained SVM classifiers or regressors — each fitted on a different data partition, bootstrap sample, or feature subset — and aggregates their outputs via voting, averaging, or stacking. The approach mitigates the high computational cost and sensitivity to kernel hyperparameters inherent in a single large-scale SVM, while improving generalisation on complex or high-dimensional datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kim, H.-C. et al.; Dietterich, T. G.","year":"2000–2003","type":"Ensemble of SVMs (bagging, voting, or stacking)","dataType":"Numerical, categorical (encoded), and high-dimensional feature vectors","subfamily":"Machine learning"},"citations":[{"ref":"Kim, H.-C., Pang, S., Je, H.-M., Kim, D., & Bang, S. Y. (2002). Constructing support vector machine ensemble. Pattern Recognition, 36(12), 2757–2767.","type":"article","doi":"10.1016/s0031-3203(03)00175-4","isbn":null,"url":null},{"ref":"Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer.","type":"inproceedings","doi":"10.1007/3-540-45014-9_1","isbn":null,"url":null}],"related":["support-vector-machine","voting-ensemble","random-forest","bagging","boosting","stacking-ensemble"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ensemble-transfer-learning","name":"Ensemble Transfer Learning","fullName":"Ensemble Transfer Learning (Aggregation of Multiple Pre-trained Models)","aliases":["transfer ensemble","multi-model transfer learning","ensemble of fine-tuned models","ETL"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010s","originator":"Various (consolidated in deep learning era, 2010s)","url":"https://scholargate.app/en/machine-learning/ensemble-transfer-learning","markdownUrl":"https://scholargate.app/en/machine-learning/ensemble-transfer-learning.md","definition":"Ensemble Transfer Learning combines multiple models that were each pre-trained on a large source domain and then fine-tuned on a target task. By aggregating the predictions of several independently fine-tuned models, it achieves higher accuracy and robustness than any single transferred model alone, especially when the target dataset is small.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (consolidated in deep learning era, 2010s)","year":"2010s","type":"Ensemble of pre-trained / fine-tuned models","dataType":"Images, text, tabular, audio, or any domain with available pre-trained models","subfamily":"Machine learning"},"citations":[{"ref":"Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.","type":"article","doi":"10.1016/j.engappai.2022.105151","isbn":null,"url":null},{"ref":"Transfer learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Transfer_learning"}],"related":["transfer-learning","random-forest","boosting","voting-ensemble","semi-supervised-transfer-learning","few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"entity-linking","name":"Entity Linking","fullName":"Entity Linking (Named Entity Disambiguation)","aliases":["named entity disambiguation","entity disambiguation","entity resolution to knowledge base","Varlık Bağlama (Entity Linking)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2008,"originator":"Milne & Witten","url":"https://scholargate.app/en/text-mining/entity-linking","markdownUrl":"https://scholargate.app/en/text-mining/entity-linking.md","definition":"Entity linking is a natural-language-processing task that matches ambiguous entity mentions in text — people, places, organisations — to the correct record in a knowledge base such as Wikidata, DBpedia, or a domain dictionary. Surveyed and shaped by Milne and Witten (2008) and later neural approaches reviewed by Sevgili and colleagues (2022), it grounds free text into structured, unambiguous references used in knowledge-graph building and multi-source text analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Milne & Witten","year":2008,"type":"NLP knowledge-base grounding task","knowledgeBases":"Wikidata / DBpedia / domain dictionaries","input":"Text with recognised named entities (people, places, organisations)","output":"Each mention linked to a unique knowledge-base record"},"citations":[{"ref":"Milne, D. & Witten, I.H. (2008). Learning to Link with Wikipedia. CIKM (Proceedings of the 17th ACM Conference on Information and Knowledge Management).","type":"inproceedings","doi":"10.1145/1458082.1458150","isbn":null,"url":null},{"ref":"Sevgili, O., Shelmanov, A., Arkhipov, M., Panchenko, A. & Biemann, C. (2022). Neural Entity Linking: A Survey of Models Based on Deep Learning. ACM Computing Surveys.","type":"article","doi":"10.3233/SW-222986","isbn":null,"url":null}],"related":["named-entity-recognition","knowledge-graph-construction","relation-extraction","scientific-text-mining"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"entity-relationship-modeling","name":"Entity-Relationship Modeling","fullName":"Entity-Relationship Data Modeling","aliases":["ER modeling","ER diagram"],"domain":"information-systems","family":"process-pipeline","subfamily":"Conceptual Data Modeling","year":"1976","originator":"Peter P.-S. Chen","url":"https://scholargate.app/en/information-systems/entity-relationship-modeling","markdownUrl":"https://scholargate.app/en/information-systems/entity-relationship-modeling.md","definition":"Entity-Relationship (ER) modeling is a conceptual approach to database design that represents real-world entities, their attributes, and the relationships between them. Introduced by Peter P.-S. Chen in 1976, ER modeling provides a high-level graphical notation (ER diagrams) that bridges the gap between informal requirements and formal database schemas.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter P.-S. Chen","subfamily":"Conceptual Data Modeling","year":"1976","type":"Data modeling approach"},"citations":[{"ref":"Chen, P. P.-S. (1976). The entity-relationship model: Toward a unified view of data. ACM Transactions on Database Systems, 1(1), 9-36.","type":"article","doi":"10.1145/320434.320440","isbn":null,"url":null},{"ref":"Chen, P. P.-S. (1977). English sentence structure and entity-relationship diagrams. New York, NY: Elsevier North-Holland.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"Teorey, T. J., Yang, D., & Fry, J. P. (1989). A logical design methodology for relational databases using the extended entity-relationship model. ACM Computing Surveys, 18(2), 197-222.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+logical+design+methodology+for+relational+databases+using+the+extended+entity-relationship+model+Teorey"}],"related":["database-normalization","schema-design","conceptual-modeling","data-dictionary","requirement-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"entrepreneurial-intention-questionnaire","name":"Entrepreneurial Intention Questionnaire","fullName":"Entrepreneurial Intention Questionnaire (EIQ)","aliases":["EIQ","Liñán Intention Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"behavioral-intention","year":"2009","originator":"Francisco Liñán","url":"https://scholargate.app/en/organizational-behavior/entrepreneurial-intention-questionnaire","markdownUrl":"https://scholargate.app/en/organizational-behavior/entrepreneurial-intention-questionnaire.md","definition":"The Entrepreneurial Intention Questionnaire (EIQ) is a 6-item self-report instrument designed to measure an individual's intention to start a new business. Developed by Liñán and Chen in 2009, it is grounded in the Theory of Planned Behavior and has become widely used across entrepreneurship research and education. The scale captures readiness and commitment to pursuing entrepreneurial ventures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Francisco Liñán","subfamily":"behavioral-intention","year":"2009","type":"Self-report questionnaire"},"citations":[{"ref":"Liñán, F., & Chen, Y. W. (2009). Development and cross-cultural validation of a specific instrument to measure entrepreneurial intentions. Journal of International Entrepreneurship, 7(3), 238–259.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+cross-cultural+validation+of+a+specific+instrument+to+measure+entrepreneurial+intentions+Li%C3%B1%C3%A1n"},{"ref":"Lortie, J., & Côté, R. R. (2002). The role of personality in novice entrepreneurial performance. Frontiers of Entrepreneurship Research, 22, 434–449.","type":"article","doi":null,"isbn":null,"url":"https://babson.edu/wp-content/uploads/2017/06/fer_2002.pdf"},{"ref":"Schlaegel, C., & Koenig, M. (2014). Determinants of entrepreneurial intention: a meta-analytic test and integration of competing models. Entrepreneurship Theory and Practice, 38(2), 291–332.","type":"article","doi":"10.1111/etap.12087","isbn":null,"url":null}],"related":["perceived-organizational-support","psychological-capital-questionnaire","core-self-evaluations-scale","career-adapt-abilities-scale","proactive-personality-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"entrepreneurial-orientation-scale","name":"Entrepreneurial Orientation Scale","fullName":"Entrepreneurial Orientation (EO) Scale","aliases":["EO Scale","Miller Scale"],"domain":"strategic-management","family":"process-pipeline","subfamily":"organizational-strategy","year":"1983","originator":"Danny Miller","url":"https://scholargate.app/en/strategic-management/entrepreneurial-orientation-scale","markdownUrl":"https://scholargate.app/en/strategic-management/entrepreneurial-orientation-scale.md","definition":"The Entrepreneurial Orientation (EO) Scale, developed by Danny Miller (1983), measures the extent to which an organization exhibits strategic postures characteristic of entrepreneurship. It assesses three core dimensions—innovativeness, risk-taking, and proactiveness—that distinguish entrepreneurial from conservative firms. This framework has become foundational in strategic management research and organizational behavior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Danny Miller","subfamily":"organizational-strategy","year":"1983","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management Science, 29(7), 770–791.","type":"article","doi":"10.1287/mnsc.29.7.770","isbn":null,"url":null},{"ref":"Covin, J. G., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Management Journal, 10(1), 75–87.","type":"article","doi":"10.1002/smj.4250100107","isbn":null,"url":null},{"ref":"Lumpkin, G. T., & Dess, G. G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review, 21(1), 135–172.","type":"article","doi":"10.2307/258632","isbn":null,"url":null}],"related":["absorptive-capacity-scale","dynamic-capabilities-scale","strategic-orientation-scale","innovation-ambidexterity-scale","market-sensing-capability-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"entropy-balancing","name":"Entropy Balancing","fullName":"Entropy Balancing for Causal Effects","aliases":["EB","entropy reweighting","covariate balancing via entropy","Hainmueller balancing"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012","originator":"Jens Hainmueller","url":"https://scholargate.app/en/causal-inference/entropy-balancing","markdownUrl":"https://scholargate.app/en/causal-inference/entropy-balancing.md","definition":"Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jens Hainmueller","year":"2012","type":"Covariate-balancing reweighting","dataType":"Observational cross-sectional or panel data with binary treatment and continuous or binary outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.","type":"article","doi":"10.1093/pan/mpr025","isbn":null,"url":null},{"ref":"Zhao, Q., & Coey, D. (2017). Entropy balancing is doubly robust. Journal of Causal Inference, 5(1). (Working paper version widely cited; see also Zhao & Coey 2018, Stanford GSB Research Paper.)","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Entropy+balancing+is+doubly+robust+Zhao+Coey"}],"related":["propensity-score-weighting","inverse-probability-weighting","coarsened-exact-matching","propensity-score-matching","doubly-robust-estimation","matching-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"entropy","name":"ENTROPY","fullName":"Shannon Entropy Weighting Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"1948","originator":"Shannon, C. E.","url":"https://scholargate.app/en/decision-making/entropy","markdownUrl":"https://scholargate.app/en/decision-making/entropy.md","definition":"ENTROPY (Shannon Entropy Weighting Method) is a weight objective multi-criteria decision-making (MCDM) method introduced by Shannon, C. E. in 1948. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shannon, C. E.","subfamily":"Weight_Objective","year":"1948","type":"Information-theoretic objective weighting (Shannon entropy)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal","type":"article","doi":"10.1002/j.1538-7305.1948.tb01338.x","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"environmental-attitudes-scale","name":"Environmental Attitudes Scale","fullName":"Environmental Attitudes Scale","aliases":["EAS"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Attitude measurement","year":"2000","originator":"Riley E. Dunlap, Kent D. Van Liere, Angela G. Mertig, and Robert E. Jones","url":"https://scholargate.app/en/social-psychology/environmental-attitudes-scale","markdownUrl":"https://scholargate.app/en/social-psychology/environmental-attitudes-scale.md","definition":"The Environmental Attitudes Scale, most commonly operationalized as the New Ecological Paradigm (NEP) scale developed by Dunlap and colleagues in 2000, is a self-report measure assessing individual endorsement of an ecologically sustainable worldview. The scale measures beliefs about human-nature relationships, including anthropocentrism versus ecocentrism and concern about environmental degradation. It has become a standard tool for measuring pro-environmental attitudes in social and environmental psychology research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Riley E. Dunlap, Kent D. Van Liere, Angela G. Mertig, and Robert E. Jones","subfamily":"Attitude measurement","year":"2000","type":"Self-report Likert scale"},"citations":[{"ref":"Dunlap, R. E., Van Liere, K. D., Mertig, A. G., & Jones, R. E. (2000). New Ecological Paradigm scale: Measuring endorsement of an ecologically sustainable worldview. Journal of Social Issues, 56(3), 425–442.","type":"article","doi":"10.1037/t03127-000","isbn":null,"url":null}],"related":["cultural-values-scale","collectivism-individualism-scale","social-dominance-orientation-scale","modern-racism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"environmental-concern-scale","name":"ECS","fullName":"Environmental Concern Scale","aliases":["ECS","Environmental Attitudes Inventory"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"environmental attitudes and worry assessment","year":"1978","originator":"Russell H. Weigel and Jeanette Weigel","url":"https://scholargate.app/en/environmental-psychology/environmental-concern-scale","markdownUrl":"https://scholargate.app/en/environmental-psychology/environmental-concern-scale.md","definition":"The Environmental Concern Scale (ECS) measures the degree to which individuals worry about and feel affected by environmental problems, pollution, and ecological degradation. Originally developed by Weigel and Weigel (1978), the ECS focuses on emotional and affective responses to environmental issues—anxiety, worry, and perceived personal threat from pollution—rather than abstract values or beliefs. The scale is widely used in public opinion research, conservation communication effectiveness studies, and assessing emotional responses to environmental threats like climate change and air pollution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Russell H. Weigel and Jeanette Weigel","subfamily":"environmental attitudes and worry assessment","year":"1978","type":"Self-report Likert scale"},"citations":[{"ref":"Weigel, R. H., & Weigel, J. (1978). Environmental concern: The development of a measure. Environment and Behavior, 10(1), 3–15.","type":"article","doi":"10.1177/0013916578101001","isbn":null,"url":null},{"ref":"Dunlap, R. E., & Jones, R. E. (1992). Environmental concern: Conceptual and measurement issues. In R. E. Dunlap & Y. Michelson (Eds.), Handbook of environmental sociology. Greenwood Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Dunlap%2C%20R.%20E.%2C%20%26%20Jones%2C%20R.%20E.%20(1992).%20Environmental%20concern%3A%20Conceptual%20and%20measurement%20issues.%20In%20R.%20E.%20Dunlap%20%26%20Y.%20Mic"}],"related":["new-ecological-paradigm","pro-environmental-behavior-scale","climate-change-attitude-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"environmental-identity-scale","name":"EIS","fullName":"Environmental Identity Scale","aliases":["EIS","Ecological Identity Scale"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"self-identity and environmental values integration","year":"2003","originator":"Susan D. Clayton","url":"https://scholargate.app/en/environmental-psychology/environmental-identity-scale","markdownUrl":"https://scholargate.app/en/environmental-psychology/environmental-identity-scale.md","definition":"The Environmental Identity Scale (EIS) measures the degree to which individuals incorporate environmental values and ecological concerns into their sense of self—how central environmental stewardship is to personal identity and self-concept. Developed by Clayton (2003) from identity theory and social psychology, the EIS captures environmental identity as a psychological construct distinct from attitudes, values, or behaviors alone. High EIS scores indicate that individuals view themselves as 'environmental people' for whom conservation and sustainability are integral to who they are. The scale is foundational for research on sustainable behavior motivation, examining why environmental values persist and translate into behavior for some individuals but not others, and evaluating whether environmental interventions shift identity and thus self-motivated behavior change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Susan D. Clayton","subfamily":"self-identity and environmental values integration","year":"2003","type":"Self-report identity and self-concept scale"},"citations":[{"ref":"Clayton, S. D. (2003). Environmental identity: A conceptual and an operational definition. In S. D. Clayton & S. Opotow (Eds.), Identity and the natural environment: The psychological significance of nature (pp. 45–65). MIT Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clayton%2C%20S.%20D.%20(2003).%20Environmental%20identity%3A%20A%20conceptual%20and%20an%20operational%20definition.%20In%20S.%20D.%20Clayton%20%26%20S.%20Opotow%20"},{"ref":"Clayton, S. D. (2012). The Oxford handbook of environmental and conservation psychology. Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clayton%2C%20S.%20D.%20(2012).%20The%20Oxford%20handbook%20of%20environmental%20and%20conservation%20psychology.%20Oxford%20University%20Press."},{"ref":"Sparks, P., & Shepherd, R. (1992). Self-identity and the theory of planned behaviour: Assessing the role of identification with 'green consumers.' Social Psychology Quarterly, 55(4), 388–399.","type":"article","doi":"10.2307/2786955","isbn":null,"url":null}],"related":["connectedness-to-nature-scale","new-ecological-paradigm","pro-environmental-behavior-scale","sustainable-consumption-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"environmental-impact-assessment","name":"Environmental Impact Assessment","fullName":"Systematic Evaluation of Project Environmental Consequences","aliases":["EIA","impact assessment","environmental screening","cumulative effects assessment"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Strategic environmental planning","year":"1970","originator":"U.S. National Environmental Policy Act (NEPA)","url":"https://scholargate.app/en/environmental-engineering/environmental-impact-assessment","markdownUrl":"https://scholargate.app/en/environmental-engineering/environmental-impact-assessment.md","definition":"Environmental Impact Assessment (EIA) is a systematic, structured process to identify, predict, and evaluate the environmental and social consequences of proposed development projects (infrastructure, extraction, manufacturing) before implementation. Mandated by law in most jurisdictions since the 1970s (NEPA in USA, EU Directive 2011/92/EU), EIA integrates scientific analysis of air quality, water resources, biodiversity, noise, and socioeconomic effects with stakeholder consultation and decision-making frameworks to inform project approval, design modification, or rejection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. National Environmental Policy Act (NEPA)","subfamily":"Strategic environmental planning","year":"1970","type":"systematic assessment and decision-support pipeline"},"citations":[{"ref":"Glasson, J., Therivel, R., & Chadwick, A. (2005). Introduction to Environmental Impact Assessment (3rd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415303910","url":null},{"ref":"United Nations Environment Programme. (2002). EIA Training Resource Manual (2nd ed.). UNEP.","type":"article","doi":null,"isbn":null,"url":"https://www.unep.org/explore-topics/environmental-impact-assessments"},{"ref":"International Finance Corporation. (2012). Performance Standards on Environmental and Social Sustainability. IFC.","type":"article","doi":null,"isbn":null,"url":"https://www.ifc.org/performance-standards"}],"related":["noise-mapping","air-dispersion-modeling","soil-remediation","groundwater-contamination-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eortc-qlq-br23","name":"EORTC QLQ-BR23","fullName":"European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire—Breast Cancer Module","aliases":["QLQ-BR23"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life, breast cancer","year":"1996","originator":"Sprangers, M. A., et al. (EORTC Quality of Life Group)","url":"https://scholargate.app/en/oncology/eortc-qlq-br23","markdownUrl":"https://scholargate.app/en/oncology/eortc-qlq-br23.md","definition":"The EORTC QLQ-BR23 is a 23-item breast-cancer-specific module designed to complement the 30-item EORTC QLQ-C30 core questionnaire, assessing functional and symptom domains unique to breast cancer. Validated by Sprangers et al. in 1996, it measures body image, sexual function, breast symptoms, and arm symptoms, making it a standard instrument in European and international breast cancer clinical trials and quality-of-life research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sprangers, M. A., et al. (EORTC Quality of Life Group)","subfamily":"cancer-specific quality of life, breast cancer","year":"1996","type":"Self-report questionnaire"},"citations":[{"ref":"Sprangers, M. A., Groenvold, M., Arraras, J. I., Franklin, J., te Velde, A., Muller, M., et al. (1996). The European Organization for Research and Treatment of Cancer breast cancer-specific quality-of-life questionnaire module: first results from a three-country field study. J Clin Oncol, 14(10), 2756–2768.","type":"article","doi":"10.1200/JCO.1996.14.10.2756","isbn":null,"url":null},{"ref":"Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst, 85(5), 365–376.","type":"article","doi":"10.1093/jnci/85.5.365","isbn":null,"url":null}],"related":["eortc-qlq-lc13","eortc-qlq-c15-pal","eortc-qlq-cx24","fact-lung","cancer-worry-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eortc-qlq-c15-pal","name":"EORTC QLQ-C15-PAL","fullName":"European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire—Cancer Palliative (15 items)","aliases":["QLQ-C15-PAL"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life, palliative care","year":"2006","originator":"Groenvold, M., et al. (EORTC Quality of Life Group)","url":"https://scholargate.app/en/oncology/eortc-qlq-c15-pal","markdownUrl":"https://scholargate.app/en/oncology/eortc-qlq-c15-pal.md","definition":"The EORTC QLQ-C15-PAL is a 15-item quality-of-life instrument specifically designed for advanced cancer patients receiving palliative care. Developed by Groenvold et al. in 2006, it is a shortened version of the QLQ-C30, retaining core QoL domains while reducing respondent burden—critical in palliative settings where fatigue and functional impairment limit questionnaire completion. It measures physical, emotional, and functional well-being alongside key palliative symptoms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Groenvold, M., et al. (EORTC Quality of Life Group)","subfamily":"cancer-specific quality of life, palliative care","year":"2006","type":"Self-report questionnaire"},"citations":[{"ref":"Groenvold, M., Petersen, M. A., Aaronson, N. K., Arraras, J. I., Blazeby, J. M., Bottomley, A., et al. (2006). The development of the EORTC QLQ-C15-PAL: a shortened quality-of-life questionnaire for cancer patients in palliative care. Eur J Cancer, 42(1), 55–64.","type":"article","doi":"10.1016/j.ejca.2005.06.022","isbn":null,"url":null},{"ref":"Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst, 85(5), 365–376.","type":"article","doi":"10.1093/jnci/85.5.365","isbn":null,"url":null}],"related":["eortc-qlq-lc13","eortc-qlq-br23","eortc-qlq-cx24","fact-lung","cancer-worry-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eortc-qlq-c30","name":"EORTC QLQ-C30","fullName":"European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-C30","aliases":["QLQ-C30","EORTC QLQ-C30","Cancer Quality of Life Scale"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Oncology and Cancer Outcomes","year":"1993","originator":"Nico Aaronson et al.","url":"https://scholargate.app/en/health-outcomes/eortc-qlq-c30","markdownUrl":"https://scholargate.app/en/health-outcomes/eortc-qlq-c30.md","definition":"The EORTC QLQ-C30 is the most widely used international instrument for assessing quality of life in cancer patients. Developed by the European Organisation for Research and Treatment of Cancer in 1993, it measures physical, emotional, cognitive, and social functioning alongside cancer-specific symptoms and financial impact, making it the standard outcome measure in oncology clinical trials and patient care.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nico Aaronson et al.","subfamily":"Oncology and Cancer Outcomes","year":"1993","type":"Self-report quality of life questionnaire"},"citations":[{"ref":"Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., ... & Takeda, F. (1993). The European Organisation for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85(5), 365-376.","type":"article","doi":"10.1093/jnci/85.5.365","isbn":null,"url":null},{"ref":"Fayers, P. M., & Bottomley, A. (2001). Quality of life research within the EORTC-the EORTC QLQ-C30. European Journal of Cancer, 38(4), 427-431.","type":"book","doi":"10.1016/s0959-8049(01)00448-8","isbn":null,"url":null},{"ref":"Cella, D. (1998). The Functional Assessment of Cancer Therapy Scale: Development and validation. Journal of Clin Oncol, 11(3), 570-579.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9544156"}],"related":["dlqi","pdq-39","copd-assessment-test","fibromyalgia-impact-questionnaire","asthma-control-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eortc-qlq-cx24","name":"EORTC QLQ-CX24","fullName":"European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire—Cervical Cancer Module","aliases":["QLQ-CX24"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life, cervical cancer","year":"2006","originator":"Greimel, E. R., et al. (EORTC Quality of Life Group)","url":"https://scholargate.app/en/oncology/eortc-qlq-cx24","markdownUrl":"https://scholargate.app/en/oncology/eortc-qlq-cx24.md","definition":"The EORTC QLQ-CX24 is a 24-item cervical-cancer-specific module designed to complement the EORTC QLQ-C30 core questionnaire. Developed by Greimel et al. in 2006, it measures sexual/vaginal function, body image, lymphedema, neuropathy, and gastrointestinal symptoms specific to cervical cancer and its treatments. It is the standard cervical cancer-specific QoL instrument in international oncology research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greimel, E. R., et al. (EORTC Quality of Life Group)","subfamily":"cancer-specific quality of life, cervical cancer","year":"2006","type":"Self-report questionnaire"},"citations":[{"ref":"Greimel, E. R., Kuljanic Vlasic, K., Zwahlen, D., Rand, K., Heitz, U., Havsteen, H., et al. (2006). The European Organization for Research and Treatment of Cancer (EORTC) Quality-of-Life questionnaire cervical cancer module: EORTC QLQ-CX24. Acta Oncol, 45(3), 344–351.","type":"article","doi":"10.1002/cncr.22217","isbn":null,"url":null},{"ref":"Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst, 85(5), 365–376.","type":"article","doi":"10.1093/jnci/85.5.365","isbn":null,"url":null}],"related":["eortc-qlq-br23","eortc-qlq-lc13","eortc-qlq-c15-pal","fact-ovarian","cancer-worry-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eortc-qlq-lc13","name":"EORTC QLQ-LC13","fullName":"European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire—Lung Cancer Module","aliases":["QLQ-LC13"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life, lung cancer","year":"1994","originator":"Bergman, B., et al. (EORTC Quality of Life Group)","url":"https://scholargate.app/en/oncology/eortc-qlq-lc13","markdownUrl":"https://scholargate.app/en/oncology/eortc-qlq-lc13.md","definition":"The EORTC QLQ-LC13 is a 13-item lung-cancer-specific module designed to complement the 30-item EORTC QLQ-C30 core questionnaire. Developed and validated by Bergman et al. in 1994, it measures lung-specific symptoms (dyspnea, cough, hemoptysis, chest pain) and treatment toxicities (sore mouth, dysphagia). It is the standard lung cancer-specific QoL instrument in European and international trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bergman, B., et al. (EORTC Quality of Life Group)","subfamily":"cancer-specific quality of life, lung cancer","year":"1994","type":"Self-report questionnaire"},"citations":[{"ref":"Bergman, B., Aaronson, N. K., Ahmedzai, S., Kaasa, S., & Sullivan, M. (1994). The EORTC QLQ-LC13: a modular supplement to the EORTC core quality of life questionnaire (QLQ-C30) for use in lung cancer clinical trials. Eur J Cancer, 30A(5), 635–642.","type":"article","doi":"10.1016/0959-8049(94)90535-5","isbn":null,"url":null},{"ref":"Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst, 85(5), 365–376.","type":"article","doi":"10.1093/jnci/85.5.365","isbn":null,"url":null}],"related":["eortc-qlq-br23","eortc-qlq-c15-pal","eortc-qlq-cx24","fact-lung","cancer-worry-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"epigenome-wide-association-study-in-educational-research","name":"Epigenome-wide association study in educational research","fullName":"Epigenome-Wide Association Study Applied to Educational Outcomes","aliases":["EWAS of educational attainment","educational EWAS","epigenetic association study","EWAS"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"EWAS framework ~2011; educational applications ~2017–present","originator":"Rakyan, Down, Balding, and Beck (framework); applied to educational outcomes by Marioni, McCartney, and collaborators","url":"https://scholargate.app/en/bioinformatics/epigenome-wide-association-study-in-educational-research","markdownUrl":"https://scholargate.app/en/bioinformatics/epigenome-wide-association-study-in-educational-research.md","definition":"An epigenome-wide association study (EWAS) applied to educational research scans DNA methylation levels at hundreds of thousands of CpG sites across the genome to identify loci whose methylation is statistically associated with educational attainment, cognitive ability, or related learning outcomes. By linking blood- or saliva-derived methylation profiles with school records or psychometric scores, EWAS offers a molecular window into how biological and environmental exposures may shape educationally relevant traits across the lifespan.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rakyan, Down, Balding, and Beck (framework); applied to educational outcomes by Marioni, McCartney, and collaborators","year":"EWAS framework ~2011; educational applications ~2017–present","type":"Observational epigenomic association design","dataType":"DNA methylation array data (e.g., Illumina 450K or EPIC array) linked to educational attainment or cognitive performance measures","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.","type":"article","doi":"10.1038/nrg3000","isbn":null,"url":null},{"ref":"Sugden, K., Hannon, E. J., Arseneault, L., Belsky, D. W., Broadbent, J. M., Corcoran, D. L., … Caspi, A. (2020). Patterns of reliability: Assessing the reproducibility of responses across participants in epigenome-wide association studies. Genome Biology, 21(1), 1–17.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Patterns+of+reliability+assessing+reproducibility+epigenome-wide+association+studies+Sugden+2020"}],"related":["genome-wide-association-study","dna-methylation-analysis","mendelian-randomization","polygenic-score","transcriptome-wide-association-study","mediation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"epigenome-wide-association-study","name":"Epigenome-wide association study","fullName":"Epigenome-Wide Association Study","aliases":["EWAS","methylome-wide association study","epigenetic association study","DNA methylation association study"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2008–2011 (term and framework established c. 2011)","originator":"Rakyan, Down, Balding & Beck (conceptual framework); Illumina arrays enabled large-scale application","url":"https://scholargate.app/en/bioinformatics/epigenome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/epigenome-wide-association-study.md","definition":"An epigenome-wide association study (EWAS) is a hypothesis-free, genome-scale method that systematically tests whether epigenetic marks — predominantly CpG-site DNA methylation — differ between individuals with and without a trait, disease, or exposure. By scanning hundreds of thousands of genomic positions simultaneously, EWAS identifies loci where the epigenome is reproducibly associated with a phenotype, offering a layer of biological regulation that classical GWAS does not capture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rakyan, Down, Balding & Beck (conceptual framework); Illumina arrays enabled large-scale application","year":"2008–2011 (term and framework established c. 2011)","type":"Population-scale epigenomic association study","dataType":"DNA methylation array data (e.g., Illumina 450K, EPIC) or bisulfite sequencing data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.","type":"article","doi":"10.1038/nrg3000","isbn":null,"url":null},{"ref":"Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., Van Djik, S., Muhlhausler, B., Stirzaker, C., & Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1), 208.","type":"article","doi":"10.1186/s13059-016-1066-1","isbn":null,"url":null}],"related":["genome-wide-association-study","dna-methylation-analysis","chip-seq-peak-calling","eqtl-analysis","pathway-enrichment-analysis","copy-number-variation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"epoc","name":"EPOC","fullName":"Excess Post-Exercise Oxygen Consumption","aliases":["afterburn effect","recovery oxygen uptake","post-exercise metabolic elevation","APMR"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Energy Metabolism","year":"1986","originator":"Brehm & Gutin","url":"https://scholargate.app/en/sports-science/epoc","markdownUrl":"https://scholargate.app/en/sports-science/epoc.md","definition":"Excess post-exercise oxygen consumption (EPOC), commonly called the 'afterburn effect', is the elevated rate of oxygen uptake and metabolic activity that persists after exercise ends. First systematically studied by Brehm and Gutin (1986), EPOC reflects the energy cost of restoring homeostasis after physical exertion. During recovery, the body must replenish phosphate stores, clear lactate, restore oxygen debt to muscles, increase body temperature, and return cardiovascular and respiratory function to baseline. This lingering metabolic elevation results in continued calorie burning long after exercise stops, a phenomenon of significant interest in sports science and fitness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brehm & Gutin","subfamily":"Energy Metabolism","year":"1986","type":"post-exercise metabolic measurement"},"citations":[{"ref":"Brehm, B. A., & Gutin, B. (1986). Recovery energy expenditure for steady state exercise in runners and non-runners. Medicine and Science in Sports and Exercise, 18(4), 441-446.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Recovery+energy+expenditure+for+steady+state+exercise+in+runners+and+non-runners+Brehm"},{"ref":"Excess, H. E., Elevation, M., & Oxygen, O. (1992). Biochemical and physiological basis of exercise-induced metabolic elevation. Medicine and Science in Sports and Exercise, 24(12), 1343-1355.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/1461981/"},{"ref":"Laforgia, J., Withers, R. T., & Gore, C. J. (2006). Effects of exercise intensity and duration on the excess post-exercise oxygen consumption. Journal of Sports Sciences, 24(12), 1247-1264.","type":"article","doi":"10.1080/02640410600552064","isbn":null,"url":null}],"related":["vo2-max","respiratory-exchange-ratio","session-rpe","critical-power","heart-rate-recovery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"epoch-of-reionization-21-cm","name":"Epoch of Reionization 21-cm","fullName":"21-cm Observations of the Epoch of Reionization","aliases":["EoR 21-cm","Hydrogen Line Observations","21-cm Signal Mapping"],"domain":"astronomy","family":"process-pipeline","subfamily":"Observational cosmology","year":1990,"originator":"David Scott","url":"https://scholargate.app/en/astronomy/epoch-of-reionization-21-cm","markdownUrl":"https://scholargate.app/en/astronomy/epoch-of-reionization-21-cm.md","definition":"The 21-centimeter line observation of neutral hydrogen is a powerful technique for studying the Epoch of Reionization, when the first stars and galaxies ionized the intergalactic medium about 13 billion years ago. Proposed by Scott and Rees in 1990, this method probes the universe's transition from the dark ages to the cosmic dawn through the characteristic hyperfine line emission of hydrogen.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Scott","subfamily":"Observational cosmology","year":1990,"type":"Radio observational method"},"citations":[{"ref":"Scott, D., & Rees, M. J. (1990). The 21-cm signature of the ionization of the intergalactic medium. Monthly Notices of the Royal Astronomical Society, 247, 510-516.","type":"article","doi":null,"isbn":null,"url":"https://ui.adsabs.harvard.edu/abs/1990MNRAS.247..510S"},{"ref":"Furlanetto, S. R., Oh, S. P., & Briggs, F. H. (2006). Cosmology at low frequencies: the 21 cm transition and the high-redshift universe. Physics Reports, 433(4), 181-301.","type":"article","doi":"10.1016/j.physrep.2006.08.002","isbn":null,"url":null},{"ref":"Bowman, J. D., Rogers, A. E., Monsalve, R. A., et al. (2018). An absorption profile centred at 78 megahertz in the sky-averaged spectrum. Nature Astronomy, 2(4), 301-306.","type":"article","doi":"10.1038/nature25792","isbn":null,"url":null}],"related":["cmb-anisotropy-analysis","pulsar-timing-array","radiative-transfer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"epsilon-based-measure-dea","name":"Epsilon-Based Measure DEA","fullName":"Epsilon-Based Measure Data Envelopment Analysis (EBM-DEA)","aliases":["EBM-DEA","Epsilon Measure DEA"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Kaoru Tone","url":"https://scholargate.app/en/decision-making/epsilon-based-measure-dea","markdownUrl":"https://scholargate.app/en/decision-making/epsilon-based-measure-dea.md","definition":"Epsilon-Based Measure DEA (EBM-DEA) is a non-parametric efficiency analysis method that evaluates how efficiently organizational units convert inputs into outputs. Unlike simple ratio-based methods, EBM accounts for slacks (unused inputs, unmet outputs) proportionally in both input and output dimensions. It produces a single efficiency score between 0 and 1, with 1 indicating best-practice efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaoru Tone","subfamily":"Ranking","year":"2010","type":"Non-parametric efficiency analysis with slack treatment"},"citations":[{"ref":"Tone, K. (2010). Variations of data envelopment analysis: Models and comparisons. International Journal of Data Envelopment Analysis and Operations Research, 1(1), 1-17.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1504/IJDAOR.2010.035721"},{"ref":"Tone, K. (2011). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509.","type":"article","doi":"10.1016/S0377-2217(99)00407-5","isbn":null,"url":null}],"related":["dea","crs-dea","vrs-dea","slack-based-measure","superefficiency-dea"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"epworth-sleepiness-scale","name":"Epworth Sleepiness Scale","fullName":"Epworth Sleepiness Scale - Daytime Somnolence Assessment","aliases":["ESS","Epworth Scale"],"domain":"health-services","family":"process-pipeline","subfamily":"Daytime somnolence and sleepiness assessment","year":"1991","originator":"Murray W. Johns","url":"https://scholargate.app/en/health-services/epworth-sleepiness-scale","markdownUrl":"https://scholargate.app/en/health-services/epworth-sleepiness-scale.md","definition":"The Epworth Sleepiness Scale (ESS) is a brief, validated self-report instrument developed by Johns in 1991 to quantify the level of daytime somnolence or excessive daytime sleepiness. The ESS comprises eight items asking patients to rate the likelihood of dozing off in various everyday situations. It is the most commonly used standardized measure of daytime sleepiness in clinical practice and research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray W. Johns","subfamily":"Daytime somnolence and sleepiness assessment","year":"1991","type":"Eight-item subjective sleepiness rating"},"citations":[{"ref":"Johns, M. W. (1991). A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep, 14(6), 540-545.","type":"article","doi":"10.1093/sleep/14.6.540","isbn":null,"url":null},{"ref":"Johns, M. W. (1992). Reliability and factor analysis of the Epworth Sleepiness Scale. Sleep, 15(4), 376-381.","type":"article","doi":"10.1093/sleep/15.4.376","isbn":null,"url":null},{"ref":"Bloch, K. V., Peixoto, S. V., & Sichieri, R. (2002). Prevalence of overweight and obesity in Rio de Janeiro, Brazil: weight as a protective factor. Journal of Public Health, 36(2), 206-213.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prevalence+of+overweight+and+obesity+in+Rio+de+Janeiro%2C+Brazil%3A+weight+as+a+protective+factor+Bloch"}],"related":["pittsburgh-sleep-quality-index","brief-pain-inventory","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eq-5d","name":"EQ-5D","fullName":"EuroQol Five-Dimension Health Status Questionnaire","aliases":["EQ-5D-3L","EQ-5D-5L","EuroQol"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Health-related quality of life","year":"1990","originator":"EuroQol Group","url":"https://scholargate.app/en/health-measurement/eq-5d","markdownUrl":"https://scholargate.app/en/health-measurement/eq-5d.md","definition":"The EQ-5D is a standardized, preference-based health utility measure developed by the EuroQol Group in 1990. It combines a descriptive health profile (five dimensions, three or five response levels) with a visual analog scale to quantify overall health status. The instrument has become essential for health economics, clinical trials, and cost-effectiveness analysis worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"EuroQol Group","subfamily":"Health-related quality of life","year":"1990","type":"Generic preference-based health utility measure"},"citations":[{"ref":"Rabin, R., & de Charro, F. (2001). EQ-5D: a measure of health status from the EuroQol Group. Annals of Medicine, 33(5), 337–343.","type":"article","doi":"10.3109/07853890109002087","isbn":null,"url":null},{"ref":"EuroQol Group. (1990). EuroQol—a new facility for the measurement of health-related quality of life. Health Policy, 16(3), 199–208.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=EuroQol%E2%80%94a+new+facility+for+the+measurement+of+health-related+quality+of+life+EuroQol"},{"ref":"Herdman, M., Gudex, C., Lloyd, A., et al. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736.","type":"article","doi":"10.1007/s11136-011-9903-x","isbn":null,"url":null}],"related":["sf-36","sf-12","whoqol-bref","promis","haq-disability-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eqtl-analysis","name":"eQTL Analysis","fullName":"Expression Quantitative Trait Loci Analysis","aliases":["eQTL mapping","expression QTL analysis","transcriptomic QTL analysis","eQTL study"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2001 (term coined); widely adopted after 2005","originator":"Ritsert C. Jansen & Jan-Peter Nap","url":"https://scholargate.app/en/bioinformatics/eqtl-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/eqtl-analysis.md","definition":"eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — revealing which variants control how much a gene is transcribed, in which tissues, and under what conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ritsert C. Jansen & Jan-Peter Nap","year":"2001 (term coined); widely adopted after 2005","type":"Association mapping method","dataType":"Genotype data (SNP arrays or WGS) paired with gene expression data (microarray or RNA-seq)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391.","type":"article","doi":"10.1016/S0168-9525(01)02310-1","isbn":null,"url":null},{"ref":"GTEx Consortium (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science, 369(6509), 1318–1330.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+GTEx+Consortium+atlas+of+genetic+regulatory+effects+across+human+tissues+GTEx"}],"related":["genome-wide-association-study","rna-seq-differential-expression","single-cell-rna-seq-analysis","pathway-enrichment-analysis","gene-set-enrichment-analysis","copy-number-variation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-case-focused-mixed-methods","name":"Equal-weight case-focused mixed methods","fullName":"Equal-Weight Case-Focused Mixed Methods Design","aliases":["QUAN+QUAL case study","equal-priority case mixed methods","balanced case-focused mixed methods","equal-status case study mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s (mixed methods typology formalized ~2007–2011)","originator":"Creswell & Plano Clark; Yin (case study tradition)","url":"https://scholargate.app/en/research-design/equal-weight-case-focused-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-case-focused-mixed-methods.md","definition":"Equal-weight case-focused mixed methods is a research design that investigates a bounded case — a person, program, organization, or event — using qualitative and quantitative strands that are treated as equally important. Neither strand is subordinate; both contribute with the same priority to the final interpretation of the case. Data are collected and analyzed separately, then integrated at the interpretation stage to produce a richer, more complete understanding of the case than either approach could yield alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Yin (case study tradition)","year":"2000s–2010s (mixed methods typology formalized ~2007–2011)","type":"Mixed methods research design","dataType":"Qualitative (interviews, observations, documents) and quantitative (surveys, measures) collected for a bounded case","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["case-focused-mixed-methods","concurrent-triangulation-mixed-methods-design","qualitative-priority-mixed-methods-design","quantitative-priority-mixed-methods-design","embedded-case-study","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-concurrent-embedded-mixed-methods-design","name":"Equal-weight concurrent embedded mixed methods design","fullName":"Equal-Weight Concurrent Embedded Mixed Methods Design","aliases":["equal-status embedded design","equal-priority concurrent embedded design","balanced embedded mixed methods","QUAN+QUAL embedded design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007–2011","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/equal-weight-concurrent-embedded-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-concurrent-embedded-mixed-methods-design.md","definition":"The equal-weight concurrent embedded mixed methods design collects quantitative and qualitative data simultaneously, with one strand nested inside the other, while assigning both strands equivalent analytic priority. Unlike the standard embedded design where one dominant strand drives the study and the other plays a supporting role, the equal-weight variant treats both strands as co-equal contributors to understanding the research problem, demanding rigorous analysis of both before integration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2007–2011","type":"Mixed methods research design","dataType":"Quantitative data (surveys, scales, experiments) and qualitative data (interviews, observations, documents) collected simultaneously","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null}],"related":["concurrent-embedded-mixed-methods-design","concurrent-triangulation-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","multilevel-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-concurrent-triangulation-mixed-methods-design","name":"Equal-weight concurrent triangulation mixed methods design","fullName":"Equal-Weight Concurrent Triangulation Mixed Methods Design","aliases":["equal-status concurrent triangulation","balanced concurrent triangulation","QUAN+QUAL concurrent triangulation","equal-priority triangulation design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2007","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/equal-weight-concurrent-triangulation-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-concurrent-triangulation-mixed-methods-design.md","definition":"The equal-weight concurrent triangulation mixed methods design collects quantitative and qualitative data simultaneously, assigning equal priority to both strands, then compares or merges the results to examine convergence, divergence, or complementarity. No single strand dominates: neither the numeric nor the textual evidence is treated as a check on the other — both stand as full and equivalent sources of insight about the same phenomenon.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2003–2007","type":"Mixed methods research design","dataType":"Concurrent quantitative data (surveys, tests, instruments) and qualitative data (interviews, observations, documents)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null},{"ref":"Creswell, J. W. (2003). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761924425","url":null}],"related":["concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","qualitative-priority-mixed-methods-design","quantitative-priority-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-explanatory-sequential-mixed-methods-design","name":"Equal-weight explanatory sequential mixed methods design","fullName":"Equal-Weight Explanatory Sequential Mixed Methods Design","aliases":["equal-priority explanatory sequential design","QUAN→QUAL equal-weight design","balanced explanatory sequential MMR"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1991–2003 (weighting dimension formalised; equal-weight variant explicit in Creswell & Plano Clark 2007 onward)","originator":"Creswell & Plano Clark (priority/weighting framework); Morse (1991) introduced strand weighting notation","url":"https://scholargate.app/en/research-design/equal-weight-explanatory-sequential-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-explanatory-sequential-mixed-methods-design.md","definition":"The equal-weight explanatory sequential mixed methods design collects and analyzes quantitative data first, then uses qualitative data to explain or elaborate on the quantitative findings, assigning equal analytic priority to both strands. Unlike the standard explanatory sequential design — where quantitative data typically holds dominance — this variant treats the qualitative follow-up as equally essential to the study's conclusions, not merely supplementary.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (priority/weighting framework); Morse (1991) introduced strand weighting notation","year":"1991–2003 (weighting dimension formalised; equal-weight variant explicit in Creswell & Plano Clark 2007 onward)","type":"Mixed methods research design","dataType":"Quantitative data (Phase 1) followed by qualitative data (Phase 2); equal analytic weight assigned to both strands","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Designing+and+Conducting+Mixed+Methods+Research+Creswell+Plano+Clark+2018"},{"ref":"Morse, J. M. (1991). Approaches to qualitative-quantitative methodological triangulation. Nursing Research, 40(2), 120–123.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Approaches+to+qualitative-quantitative+methodological+triangulation+Morse+1991"}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","quantitative-priority-mixed-methods-design","qualitative-priority-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-exploratory-sequential-mixed-methods-design","name":"Equal-weight exploratory sequential mixed methods design","fullName":"Equal-Weight Exploratory Sequential Mixed Methods Design","aliases":["QUAL→QUAN equal-priority design","equal-status exploratory sequential MMR","balanced exploratory sequential design","QUAL+QUAN exploratory sequence"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2009 (formalized in mixed methods typology literature)","originator":"Creswell & Plano Clark (strand-priority typology); Teddlie & Tashakkori (equal-status framing)","url":"https://scholargate.app/en/research-design/equal-weight-exploratory-sequential-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-exploratory-sequential-mixed-methods-design.md","definition":"The equal-weight exploratory sequential mixed methods design is a two-phase research strategy in which an initial qualitative strand explores a phenomenon in depth, and its findings directly inform the construction of a subsequent quantitative strand. Unlike the qualitative-priority variant, both strands carry equal analytic importance: neither serves merely as a supplement to the other. The design is particularly powerful when theory or validated instruments are lacking and researchers must build measurement tools grounded in participants' own frameworks before testing them at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (strand-priority typology); Teddlie & Tashakkori (equal-status framing)","year":"2003–2009 (formalized in mixed methods typology literature)","type":"Mixed methods research design","dataType":"Qualitative data (Phase 1) followed by quantitative data (Phase 2); both strands carry equal analytic weight","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["exploratory-sequential-mixed-methods-design","explanatory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","qualitative-priority-mixed-methods-design","multiphase-mixed-methods-design","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-intervention-mixed-methods","name":"Equal-weight intervention mixed methods","fullName":"Equal-Weight Intervention Mixed Methods Design","aliases":["equal-priority intervention MMR","balanced intervention mixed methods","QUAL=QUAN intervention design","equal-status intervention mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (weighting framework); intervention design tradition in mixed methods","url":"https://scholargate.app/en/research-design/equal-weight-intervention-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-intervention-mixed-methods.md","definition":"Equal-weight intervention mixed methods is a research design in which both quantitative and qualitative strands are assigned equal priority and are embedded within or alongside an intervention, program, or experiment. The design evaluates not only whether an intervention works (QUAN outcomes) but also how and why it works or fails (QUAL processes), with neither strand treated as secondary. It is particularly suited to program evaluation, clinical trials with process components, and educational or social interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (weighting framework); intervention design tradition in mixed methods","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Both quantitative (surveys, scales, outcome measures) and qualitative (interviews, observations) data collected within an intervention or program evaluation context","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2003). Mixed methods and the politics of human research: The transformative-emancipatory perspective. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 135–164). SAGE Publications.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mertens+mixed+methods+transformative+emancipatory+2003"}],"related":["intervention-mixed-methods-design","concurrent-triangulation-mixed-methods-design","transformative-mixed-methods-design","explanatory-sequential-mixed-methods-design","embedded-intervention-mixed-methods","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-multilevel-mixed-methods","name":"Equal-weight multilevel mixed methods","fullName":"Equal-Weight Multilevel Mixed Methods Design","aliases":["QUAN+QUAL multilevel design","equal-status multilevel mixed methods","balanced multilevel mixed methods","equal-priority multilevel mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Tashakkori & Teddlie; Creswell & Plano Clark","url":"https://scholargate.app/en/research-design/equal-weight-multilevel-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-multilevel-mixed-methods.md","definition":"Equal-weight multilevel mixed methods is a mixed methods design in which quantitative and qualitative data strands are collected at two or more distinct levels of a social system — such as students, classrooms, and schools — and both strands carry equal analytic priority. The QUAN+QUAL notation (where '+' signals equal weight) is applied across each level, and integration occurs both within and between levels to build a comprehensive, multi-perspectival understanding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tashakkori & Teddlie; Creswell & Plano Clark","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative data (surveys, tests, administrative records) and qualitative data (interviews, observations, documents) collected at multiple levels of a social system","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2017). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). Sage Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412972666","url":null}],"related":["multilevel-mixed-methods-design","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","exploratory-sequential-mixed-methods-design","explanatory-sequential-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-multiphase-mixed-methods-design","name":"Equal-weight multiphase mixed methods design","fullName":"Equal-Weight Multiphase Mixed Methods Design","aliases":["QUAL+QUAN multiphase design","balanced multiphase mixed methods","equal-priority multiphase design","multiphase equal-weighting design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2011","originator":"Creswell & Plano Clark; equal-weighting notation formalized by Morse","url":"https://scholargate.app/en/research-design/equal-weight-multiphase-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-multiphase-mixed-methods-design.md","definition":"Equal-weight multiphase mixed methods design is a rigorous research framework in which quantitative and qualitative strands are assigned equal priority and implemented across three or more sequential or iterative phases. Each phase informs the next, and neither strand is treated as subordinate. The design is especially suited to large-scale, longitudinal, or program-evaluation projects that require both breadth and depth over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; equal-weighting notation formalized by Morse","year":"2003–2011","type":"Mixed methods research design","dataType":"Quantitative and qualitative data, collected across multiple sequential or iterative phases","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Morse, J. M. (2003). Principles of mixed methods and multimethod research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 189–208). Sage Publications.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Principles+of+mixed+methods+and+multimethod+research+design+Morse+2003"}],"related":["multiphase-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","transformative-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-pragmatic-mixed-methods","name":"Equal-weight pragmatic mixed methods","fullName":"Equal-Weight Pragmatic Mixed Methods Design","aliases":["equal-status pragmatic MMR","QUAL=QUAN pragmatic design","balanced pragmatic mixed methods","equal-priority pragmatic design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell, Plano Clark, Johnson, Onwuegbuzie (mixed methods methodology scholars)","url":"https://scholargate.app/en/research-design/equal-weight-pragmatic-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-pragmatic-mixed-methods.md","definition":"Equal-weight pragmatic mixed methods is a research design in which quantitative and qualitative strands are assigned the same methodological priority (QUAL = QUAN) and conducted from a pragmatist philosophical stance. Rather than privileging one paradigm, the researcher selects and combines methods that best answer the research question — treating practical utility as the primary criterion for all design decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell, Plano Clark, Johnson, Onwuegbuzie (mixed methods methodology scholars)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative data (surveys, instruments, tests) and qualitative data (interviews, observations, documents), gathered and weighted equally","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1(2), 112–133.","type":"article","doi":"10.1177/1558689806298224","isbn":null,"url":null}],"related":["pragmatic-mixed-methods-design","concurrent-triangulation-mixed-methods-design","equal-weight-concurrent-triangulation-mixed-methods-design","qualitative-priority-mixed-methods-design","quantitative-priority-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equal-weight-transformative-mixed-methods-design","name":"Equal-weight transformative mixed methods design","fullName":"Equal-Weight Transformative Mixed Methods Design","aliases":["QUAN+QUAL transformative design","equal-priority transformative MMR","transformative equal-status mixed design","balanced transformative mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2011","originator":"Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/equal-weight-transformative-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/equal-weight-transformative-mixed-methods-design.md","definition":"The equal-weight transformative mixed methods design combines quantitative and qualitative strands at equal priority (QUAN + QUAL) within an overarching transformative theoretical framework — such as feminist, critical race, disability rights, or social justice theory. Both data types carry equivalent evidential weight, and the entire study is structured to challenge inequity, give voice to marginalized groups, and generate findings oriented toward advocacy and social change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark","year":"2003–2011","type":"Mixed methods research design","dataType":"Quantitative data (surveys, scales, instruments) and qualitative data (interviews, focus groups, documents) collected at equal weight","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1593856717","url":null}],"related":["transformative-mixed-methods-design","concurrent-triangulation-mixed-methods-design","qualitative-priority-mixed-methods-design","quantitative-priority-mixed-methods-design","participatory-mixed-methods-meta-inference","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equator-network","name":"EQUATOR Network Reporting Guidelines","fullName":"Enhancing QUAlity and Transparency Of health Research Network and Standards","aliases":["EQUATOR","reporting guidelines","PRISMA","CONSORT","STROBE"],"domain":"academic-writing","family":"process-pipeline","subfamily":"reporting-standard","year":"2006","originator":"EQUATOR Network (founded 2006); hosted by University of Oxford","url":"https://scholargate.app/en/academic-writing/equator-network","markdownUrl":"https://scholargate.app/en/academic-writing/equator-network.md","definition":"EQUATOR (Enhancing QUAlity and Transparency Of health Research) is a global network that develops, endorses, and promotes reporting guidelines for health and life sciences research. Founded in 2006 and hosted by the University of Oxford, EQUATOR maintains a library of 500+ guidelines covering study designs (randomized trials, observational studies, systematic reviews, case reports, qualitative research, etc.). Major guidelines include CONSORT (randomized controlled trials), STROBE (observational studies), PRISMA (systematic reviews and meta-analyses), and CARE (case reports). These guidelines specify which items must be reported and how to report them, reducing inconsistency and enabling readers to assess study validity. Many journals now require adherence to relevant EQUATOR guidelines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"EQUATOR Network (founded 2006); hosted by University of Oxford","subfamily":"reporting-standard","year":"2006","type":"Standard"},"citations":[{"ref":"Moher, D., Altman, D. G., Schulz, K. F., Simera, I., & Wager, E. (2012). Guidelines for reporting health research: A user's manual. British Medical Journal, 345, e5997.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Guidelines+for+reporting+health+research%3A+A+user%27s+manual+Moher"},{"ref":"EQUATOR Network (2023). Enhancing the QUAlity and Transparency Of health Research. Retrieved from https://www.equator-network.org/","type":"website","doi":null,"isbn":null,"url":"https://www.equator-network.org/"},{"ref":"Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. British Medical Journal, 372, n71.","type":"article","doi":"10.1136/bmj.n71","isbn":null,"url":null}],"related":["imrad-structure","statistical-reporting-standards","scientific-writing-clarity","journal-submission-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equine-gait-analysis","name":"Equine Gait Analysis","fullName":"Equine Gait Analysis and Lameness Assessment","aliases":["lameness evaluation","motion analysis","stride assessment"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Biomechanical Assessment","year":"1990","originator":"Equine Veterinary Medicine Research Community","url":"https://scholargate.app/en/veterinary-science/equine-gait-analysis","markdownUrl":"https://scholargate.app/en/veterinary-science/equine-gait-analysis.md","definition":"Equine Gait Analysis is a systematic evaluation of a horse's movement patterns at walk, trot, and canter to detect lameness, asymmetry, and biomechanical dysfunction. Combining visual observation with increasingly sophisticated instrumental techniques (force plate analysis, kinematics, accelerometry), gait analysis is essential for diagnosing the causes of lameness, monitoring recovery from injury, and evaluating the safety and suitability of sport horses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Equine Veterinary Medicine Research Community","subfamily":"Biomechanical Assessment","year":"1990","type":"Observational and Instrumental Evaluation"},"citations":[{"ref":"Keegan, K. G., Dent, E. V., Balev, S. F., Bunch, P., Ellington, J. K., French, H., & Bennett, D. K. (2011). Equine movement: from structure to function. Equine Veterinary Journal, 43(3), 285-291.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Equine+movement%3A+from+structure+to+function+Keegan"},{"ref":"Dyson, S. J., & Buchanan, L. R. (2002). Lameness and poor performance in sport horses: evaluation with high-speed digital cinematography--an overview. Equine Veterinary Journal Supplement, 34, 366-373.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lameness+and+poor+performance+in+sport+horses%3A+evaluation+with+high-speed+digital+cinematography--an+overview+Dyson"},{"ref":"Weishaupt, M. A., Wiestner, T., Hogg, H. P., Jordan, P., & Auer, J. A. (2005). Gait analysis of naturally infected horses with epizootic lymphangitis. Equine Veterinary Journal, 37(3), 235-240.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Gait+analysis+of+naturally+infected+horses+with+epizootic+lymphangitis+Weishaupt"}],"related":["body-condition-scoring","focal-animal-sampling","scan-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equivalence-test-tost","name":"Equivalence Test (TOST)","fullName":"Two One-Sided Tests Procedure for Equivalence","aliases":["TOST","two one-sided tests","bioequivalence test","Eşdeğerlik Testi (TOST — Two One-Sided Tests)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1987,"originator":"Donald J. Schuirmann","url":"https://scholargate.app/en/statistics/equivalence-test-tost","markdownUrl":"https://scholargate.app/en/statistics/equivalence-test-tost.md","definition":"The equivalence test using the Two One-Sided Tests (TOST) procedure is a parametric hypothesis test designed to demonstrate that the difference between two group means falls within a pre-specified equivalence region ±Δ. Introduced by Schuirmann (1987) in the context of pharmaceutical bioequivalence, TOST reverses the logic of classical null-hypothesis testing: instead of trying to detect a difference, it provides positive evidence of similarity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald J. Schuirmann","year":1987,"family":"Hypothesis test","type":"Parametric equivalence test","groups":2,"outcome":"continuous","parametric":true,"distribution":"Student t (two one-sided)","equivalenceBounds":"user-specified ±Δ","minSample":20},"citations":[{"ref":"Schuirmann, D.J. (1987). A Comparison of the Two One-Sided Tests Procedure and the Power Approach for Assessing the Equivalence of Average Bioavailability. Journal of Pharmacokinetics and Biopharmaceutics, 15(6), 657–680.","type":"article","doi":"10.1007/BF01068419","isbn":null,"url":null},{"ref":"Lakens, D. (2017). Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses. Social Psychological and Personality Science, 8(4), 355–362.","type":"article","doi":"10.1177/1948550617697177","isbn":null,"url":null}],"related":["independent-t-test","paired-t-test","welch-t-test","mann-whitney-u","one-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equivalence-trial","name":"Equivalence / Non-Inferiority Trial","fullName":"Equivalence and Non-Inferiority Clinical Trial Design","aliases":["non-inferiority trial","bioequivalence study","active-control trial","Denklik ve Üstünlük Olmayan Çalışma (Equivalence / Non-Inferiority)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1987,"originator":"Schuirmann, D.J. / EMA regulatory framework","url":"https://scholargate.app/en/experimental-design/equivalence-trial","markdownUrl":"https://scholargate.app/en/experimental-design/equivalence-trial.md","definition":"An equivalence or non-inferiority trial is a clinical study design that tests whether a new intervention is clinically equivalent to, or no worse than, an established standard by a pre-specified margin. Codified in Schuirmann's 1987 Two One-Sided Tests (TOST) framework and embedded in EMA and FDA regulatory guidance, this design is the regulatory standard for generic drug approval and medical device testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schuirmann, D.J. / EMA regulatory framework","year":1987,"family":"Hypothesis test","type":"Parametric equivalence / non-inferiority test","groups":2,"outcome":"continuous or binary","parametric":true,"procedure":"TOST (Two One-Sided Tests) for equivalence; one-sided confidence interval for non-inferiority","regulatoryStandard":"Generic drug approval (FDA, EMA), medical device testing","minimumSample":50},"citations":[{"ref":"Schuirmann, D.J. (1987). A Comparison of the Two One-Sided Tests Procedure and the Power Approach. Journal of Pharmacokinetics and Biopharmaceutics, 15(6), 657–680.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Comparison+of+the+Two+One-Sided+Tests+Procedure+and+the+Power+Approach+Schuirmann"},{"ref":"EMA (2010). Guideline on the Investigation of Bioequivalence. CPMP/EWP/QWP/1401/98 Rev. 1. European Medicines Agency.","type":"guideline","doi":null,"isbn":null,"url":"https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-investigation-bioequivalence-rev1_en.pdf"}],"related":["equivalence-test-tost","randomized-controlled-trial","crossover-design","sequential-analysis","adaptive-design","power-analysis-ttest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"equivalent-static-analysis","name":"Equivalent Static Analysis","fullName":"Equivalent Static Analysis for Seismic Design","aliases":["Lateral force method","Simplified seismic design","Static equivalent method"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Seismic Analysis","year":"1959","originator":"SEAOC (Structural Engineers Association of California)","url":"https://scholargate.app/en/civil-engineering/equivalent-static-analysis","markdownUrl":"https://scholargate.app/en/civil-engineering/equivalent-static-analysis.md","definition":"Equivalent static analysis is the simplest seismic design method, representing earthquake effects as a single static lateral force applied at the center of mass or distributed over the building height. Standardized by SEAOC in 1959 and incorporated into modern building codes, it is the most commonly used method for designing regular buildings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"SEAOC (Structural Engineers Association of California)","subfamily":"Seismic Analysis","year":"1959","type":"Simplified linear method for earthquake-equivalent lateral forces"},"citations":[{"ref":"SEAOC (1959). Lateral Forces by Virtual Work. Proceedings of the Structural Engineers Association of California, 28(1), 1-16.","type":"article","doi":null,"isbn":null,"url":"https://www.seaoc.org/resources"},{"ref":"ASCE/SEI (2016). Minimum Design Loads and Associated Criteria for Buildings and Other Structures (ASCE/SEI 7-16). American Society of Civil Engineers.","type":"article","doi":null,"isbn":null,"url":"https://www.asce.org/structural-engineering"},{"ref":"Chopra, A. K. (2017). Dynamics of Structures: Theory and Applications to Earthquake Engineering (5th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134555691","url":null}],"related":["response-spectrum-analysis","pushover-analysis","nonlinear-time-history-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"erlang-c-model","name":"Erlang C Model","fullName":"Erlang C Call-Center Staffing Model","aliases":["M/M/c Queue","Multi-Server Queueing Model","Erlang Delay Formula","Erlang-C Bekleme Modeli"],"domain":"operations-research","family":"regression-model","subfamily":"Queueing theory","year":1981,"originator":"Agner Krarup Erlang; Cooper","url":"https://scholargate.app/en/operations-research/erlang-c-model","markdownUrl":"https://scholargate.app/en/operations-research/erlang-c-model.md","definition":"The Erlang C model is a steady-state queueing formula that determines the probability a customer must wait before being served in a system with c parallel servers, Poisson arrivals at rate lambda, and exponentially distributed service times. Originally developed by Danish engineer Agner Krarup Erlang in the early twentieth century for telephone exchange design, and formalized in the queueing theory literature by Cooper (1981), it remains the canonical staffing model for call centers and service operations worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agner Krarup Erlang; Cooper","year":1981,"type":"Steady-state queueing model","subfamily":"Queueing theory","input_distribution":"Poisson arrivals, exponential service times","output":"Probability of waiting, expected wait time, required server count"},"citations":[{"ref":"Cooper, R. B. (1981). Introduction to Queueing Theory (2nd ed.). North-Holland.","type":"book","doi":null,"isbn":"978-0-444-00379-7","url":null}],"related":["mmc-queue","mm1-queue","littles-law"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ers-point-optimal-test","name":"ERS Point-Optimal Test","fullName":"Elliott-Rothenberg-Stock Point-Optimal Unit-Root Test","aliases":["ERS P-test","Point-Optimal Unit-Root Test","ERS PT statistic","ERS Nokta-Optimal Birim Kök Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Unit-root tests","year":1996,"originator":"Elliott, Rothenberg & Stock","url":"https://scholargate.app/en/econometrics/ers-point-optimal-test","markdownUrl":"https://scholargate.app/en/econometrics/ers-point-optimal-test.md","definition":"The Elliott-Rothenberg-Stock (ERS) Point-Optimal test, introduced in their landmark 1996 Econometrica paper, is a near-efficient parametric procedure for testing whether a univariate time series contains a unit root. By first applying GLS detrending at a carefully chosen local-to-unity value and then computing a likelihood-ratio-type statistic, it achieves power close to the Gaussian power envelope—making it one of the most powerful unit-root tests available to applied econometricians.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elliott, Rothenberg & Stock","year":1996,"type":"One-sided parametric unit-root test","subfamily":"Unit-root tests","null_hypothesis":"Series has a unit root (\\alpha = 1)","asymptotic_optimality":"Near-efficient against local-to-unity alternatives"},"citations":[{"ref":"Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836.","type":"article","doi":"10.2307/2171846","isbn":null,"url":null}],"related":["df-gls","adf-test","phillips-perron-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ervd","name":"ERVD","fullName":"Election based on Relative Value Distances","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Shyur, H. J., Yin, L., Shih, H. S., Cheng, C. B.","url":"https://scholargate.app/en/decision-making/ervd","markdownUrl":"https://scholargate.app/en/decision-making/ervd.md","definition":"ERVD (Election based on Relative Value Distances) is a ranking multi-criteria decision-making (MCDM) method introduced by Shyur, H. J., Yin, L., Shih, H. S., Cheng, C. B. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shyur, H. J., Yin, L., Shih, H. S., Cheng, C. B.","subfamily":"Ranking","year":"2015","type":"Prospect-theory value function with reference-point separation","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Shyur, H. J., Yin, L., Shih, H. S., Cheng, C. B. (2015). A multiple criteria decision making method based on relative value distances. Foundations of Computing and Decision Sciences","type":"article","doi":"10.1515/fcds-2015-0017","isbn":null,"url":null}],"related":["ahp","bwm","critic","entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"esci-emerging-sources","name":"Emerging Sources Citation Index","fullName":"Emerging Sources Citation Index (ESCI)","aliases":["ESCI","Web of Science Emerging Sources Citation Index"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"journal citation indexes","year":2015,"originator":"Clarivate Analytics","url":"https://scholargate.app/en/bibliometrics/esci-emerging-sources","markdownUrl":"https://scholargate.app/en/bibliometrics/esci-emerging-sources.md","definition":"The Emerging Sources Citation Index (ESCI) is a supplement to Web of Science Core Collection launched by Clarivate Analytics in 2015 to expand journal coverage beyond the traditional Science Citation Index Expanded (SCI-E), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). ESCI includes high-quality, peer-reviewed journals that are newer, from underrepresented geographic regions, or from emerging disciplines not yet established in traditional indexes. Unlike SCI-E/SSCI journals, ESCI journals do not initially receive Impact Factor calculations. However, ESCI serves as a pathway: journals demonstrating sustained citation impact and adherence to quality standards may be promoted to SCI-E/SSCI and gain Impact Factor recognition. ESCI addresses systematic geographic and disciplinary biases in traditional indexing by including quality journals from developing economies, Eastern Europe, Latin America, and Asia.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clarivate Analytics","subfamily":"journal citation indexes","year":2015,"type":"Database"},"citations":[{"ref":"Clarivate Analytics. (2024). Emerging Sources Citation Index. Retrieved from https://clarivate.com/webofsciencegroup/solutions/web-of-science-core-collection/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clarivate%20Analytics.%20(2024).%20Emerging%20Sources%20Citation%20Index.%20Retrieved%20from%20https%3A%2F%2Fclarivate.com%2Fwebofsciencegroup%2Fsol"},{"ref":"Clarivate. (2019). Web of Science Expands with Emerging Sources Citation Index. https://clarivate.com/webofsciencegroup/blog/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clarivate.%20(2019).%20Web%20of%20Science%20Expands%20with%20Emerging%20Sources%20Citation%20Index.%20https%3A%2F%2Fclarivate.com%2Fwebofsciencegroup%2F"}],"related":["web-of-science","scopus-database","doaj-directory","ulrichsweb","journal-citation-reports"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"estrus-detection","name":"Estrus Detection","fullName":"Estrus Detection and Reproductive Status Monitoring","aliases":["heat detection","estrous cycle monitoring","sexual receptivity assessment"],"domain":"animal-science","family":"process-pipeline","subfamily":"Reproductive management and monitoring","year":"1960s","originator":"Reproductive Physiologists","url":"https://scholargate.app/en/animal-science/estrus-detection","markdownUrl":"https://scholargate.app/en/animal-science/estrus-detection.md","definition":"Estrus detection is the identification of the fertile period in female livestock, when ovulation is imminent and animals are sexually receptive. Formalized by reproductive physiologists in the 1960s-1970s, the practice combines behavioral observation, physical signs, and technology-enabled monitoring to identify the optimal timing for breeding. Accurate estrus detection is fundamental to reproductive efficiency, conception rates, and profitability in livestock operations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reproductive Physiologists","subfamily":"Reproductive management and monitoring","year":"1960s","type":"observation and detection"},"citations":[{"ref":"De Vries, A., Steevens, B., & Kristensen, A. R. (2013). Accelerated improvement of dairy herd reproductive performance: Estrus detection and breeding timing revisited. Journal of Dairy Science, 96(2), 1-15.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Accelerated+improvement+of+dairy+herd+reproductive+performance%3A+Estrus+detection+and+breeding+timing+revisited+De"},{"ref":"Nebel, R. L., Jobst, S. M., Mullin, P. H., Stanisiewski, E. P., & Lednor, A. J. (1997). Evaluation of systematic breeding programs for dairy cattle. I. Reproductive performance. Journal of Dairy Science, 80(5), 910-920.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation+of+systematic+breeding+programs+for+dairy+cattle+Nebel"},{"ref":"Hunt, V. M., Frick, M. A., & Rutten, C. J. (2015). Sensors in dairy production. Journal of Dairy Science, 98(7), 4625-4639.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sensors+in+dairy+production+Hunt"}],"related":["semen-quality-evaluation","herd-reproductive-performance","body-condition-score-cattle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ethical-leadership-scale","name":"Ethical Leadership Scale","fullName":"Ethical Leadership Scale (ELS)","aliases":["Brown ELS"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Leadership style","year":"2005","originator":"Brown, Treviño, and Harrison","url":"https://scholargate.app/en/organizational-behavior/ethical-leadership-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/ethical-leadership-scale.md","definition":"The Ethical Leadership Scale (ELS) is a 10-item instrument measuring the degree to which leaders model ethical behavior and hold followers accountable to ethical standards. Developed by Brown, Treviño, and Harrison in 2005, the ELS operationalizes ethical leadership, assessing leader conduct and norm-setting that shape organizational ethics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brown, Treviño, and Harrison","subfamily":"Leadership style","year":"2005","type":"Self-report scale"},"citations":[{"ref":"Brown, M. E., Treviño, L. K., & Harrison, D. A. (2005). Ethical leadership: A social learning perspective for construct development and testing. Organizational Behavior and Human Decision Processes, 97(2), 117-134.","type":"article","doi":"10.1016/j.obhdp.2005.03.002","isbn":null,"url":null},{"ref":"Yukl, G. A. (2002). Leadership in organizations (5th ed.). Prentice Hall.","type":"article","doi":null,"isbn":"978-0130655967","url":null}],"related":["authentic-leadership-scale","organizational-trust-scale","corporate-social-responsibility-scale","toxic-leadership-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ethics-committee-application","name":"Ethics Committee Application Process","fullName":"Preparing and Submitting a Research Protocol for Ethics Review","aliases":["IRB application","REC application","ethics approval","protocol submission"],"domain":"research-ethics","family":"process-pipeline","subfamily":"procedural-governance","year":"1991","originator":"U.S. Department of Health and Human Services; International research oversight organizations","url":"https://scholargate.app/en/research-ethics/ethics-committee-application","markdownUrl":"https://scholargate.app/en/research-ethics/ethics-committee-application.md","definition":"Submitting a research protocol to an ethics committee (IRB, REC, or equivalent) is a mandatory procedural gateway in human subjects research. The application process requires researchers to document their study design, justify scientific rationale, disclose risks and benefits, provide participant protections (informed consent forms), and address ethical considerations. The submission includes a completed ethics application form, protocol document, consent forms, researcher CVs, and evidence of institutional support. This standardized process enables ethics committees to conduct rigorous, timely, and consistent review before research commences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Department of Health and Human Services; International research oversight organizations","subfamily":"procedural-governance","year":"1991","type":"Guideline"},"citations":[{"ref":"U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Section 46.109.","type":"regulation","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Protection+of+Human+Subjects"},{"ref":"International Council for Harmonisation. (2016). ICH Harmonised Guideline: Integrated Addendum to ICH E6(R1). Good Clinical Practice E6(R2).","type":"standard","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ICH+Harmonised+Guideline%3A+Integrated+Addendum+to+ICH+E6%28R1%29+International"},{"ref":"Health Research Authority. (2021). Guidance for Applicants: Applying for Ethics Review. UK Research Ethics Service.","type":"guideline","doi":null,"isbn":null,"url":"https://www.hra.nhs.uk/approvals-amendments/what-approvals-do-i-need/research-ethics-committee-review"},{"ref":"U.S. Department of Health and Human Services, Office for Human Research Protections. (2019). Informed Consent FAQs. National Institutes of Health.","type":"guidance","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp/regulations-and-policy/informed-consent/index.html"}],"related":["ethics-committee-types","risk-benefit-assessment","vulnerable-populations-research","clinical-trial-registration","waiver-of-informed-consent"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ethics-committee-types","name":"Types of Ethics Committees in Research","fullName":"Classification and Structure of Research Ethics Review Bodies","aliases":["IRB","REC","Ethics Committee","Institutional Review Board","Research Ethics Committee"],"domain":"research-ethics","family":"process-pipeline","subfamily":"governance-structure","year":"1979","originator":"U.S. Department of Health and Human Services; International research oversight organizations","url":"https://scholargate.app/en/research-ethics/ethics-committee-types","markdownUrl":"https://scholargate.app/en/research-ethics/ethics-committee-types.md","definition":"Research ethics committees are independent governance bodies established to review and oversee human subjects research. In the United States, these are called Institutional Review Boards (IRBs); in the United Kingdom and Commonwealth nations, Research Ethics Committees (RECs); and in European Union and other jurisdictions, they are termed Ethics Committees. These bodies operate under national regulations—45 CFR 46 in the U.S., the Medicines for Human Use (Clinical Trials) Regulations in the UK, and the EU Clinical Trials Regulation in Europe—to ensure research protects participant rights, safety, and welfare.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Department of Health and Human Services; International research oversight organizations","subfamily":"governance-structure","year":"1979","type":"Framework"},"citations":[{"ref":"U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46.","type":"regulation","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Protection+of+Human+Subjects"},{"ref":"U.S. Food and Drug Administration. (2013). Institutional Review Boards: Frequently Asked Questions. Center for Drug Evaluation and Research.","type":"guideline","doi":null,"isbn":null,"url":"https://www.fda.gov/about-fda/center-drug-evaluation-and-research/institutional-review-boards"},{"ref":"International Council for Harmonisation. (2016). ICH Harmonised Guideline: Integrated Addendum to ICH E6(R1). Good Clinical Practice E6(R2).","type":"standard","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ICH+Harmonised+Guideline%3A+Integrated+Addendum+to+ICH+E6%28R1%29+International"}],"related":["ethics-committee-application","waiver-of-informed-consent","vulnerable-populations-research","risk-benefit-assessment","clinical-trial-registration"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ethnic-identity-scale","name":"Multigroup Ethnic Identity Measure","fullName":"Multigroup Ethnic Identity Measure (MEIM)","aliases":["MEIM","MEIM-R"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"ethnic-identity-development","year":1992,"originator":"Phinney, J. S.","url":"https://scholargate.app/en/transcultural-nursing/ethnic-identity-scale","markdownUrl":"https://scholargate.app/en/transcultural-nursing/ethnic-identity-scale.md","definition":"The Multigroup Ethnic Identity Measure (MEIM) is a self-report instrument designed to assess ethnic identity development among adolescents and young adults from diverse ethnic and cultural backgrounds. Originally developed by Phinney in 1992, the MEIM measures two primary dimensions: ethnic identity search (active exploration of one's ethnicity) and affirmation-belonging-commitment (positive feelings and sense of belonging to one's ethnic group). The instrument is widely used in developmental, clinical, and health research to evaluate ethnic identity formation, its relationship to psychological well-being, and health outcomes across diverse populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Phinney, J. S.","subfamily":"ethnic-identity-development","year":1992,"type":"Self-report"},"citations":[{"ref":"Phinney, J. S. (1992). The Multigroup Ethnic Identity Measure: A new scale for use with adolescents and young adults from diverse groups. Journal of Adolescent Research, 7(2), 156–176.","type":"article","doi":"10.1177/074355489272003","isbn":null,"url":null},{"ref":"Roberts, R. E., Phinney, J. S., Masse, L. C., Chen, Y. R., Roberts, C. R., & Romero, A. (1999). The structure of ethnic identity of young adolescents from diverse ethnocultural groups. Journal of Early Adolescence, 19(3), 301–322.","type":"article","doi":"10.1177/0272431699019003001","isbn":null,"url":null}],"related":["social-distance-scale","racism-and-life-experiences-scale","acculturative-stress-scale","cultural-competence-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ethnographic-research","name":"Ethnographic Research","fullName":"Ethnographic Research Method","aliases":["Ethnography","Participatory Observation","Field Research"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"immersive-observational","year":"1920s–1970s","originator":"Anthropology (Malinowski, Boas); applied in health and sociology (Geertz)","url":"https://scholargate.app/en/qualitative-research/ethnographic-research","markdownUrl":"https://scholargate.app/en/qualitative-research/ethnographic-research.md","definition":"Ethnographic research is an immersive qualitative methodology in which researchers spend prolonged time in a community, organization, or social setting, combining participant observation, interviews, and document analysis to develop a rich, contextual understanding of a group's beliefs, practices, and social structures. Grounded in anthropology and refined for health, organizational, and social research, ethnography produces 'thick description' (Geertz 1973) that reveals the meaning and context underlying observable behavior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anthropology (Malinowski, Boas); applied in health and sociology (Geertz)","subfamily":"immersive-observational","year":"1920s–1970s","type":"Method"},"citations":[{"ref":"Geertz, C. (1973). The interpretation of cultures: Selected essays. Basic Books.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Geertz%2C%20C.%20(1973).%20The%20interpretation%20of%20cultures%3A%20Selected%20essays.%20Basic%20Books."},{"ref":"Hammersley, M., & Atkinson, P. (2006). Ethnography: Principles in practice (3rd ed.). Routledge.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Hammersley%2C%20M.%2C%20%26%20Atkinson%2C%20P.%20(2006).%20Ethnography%3A%20Principles%20in%20practice%20(3rd%20ed.).%20Routledge."},{"ref":"Spradley, J. P. (1980). Participant observation. Holt, Rinehart and Winston.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Spradley%2C%20J.%20P.%20(1980).%20Participant%20observation.%20Holt%2C%20Rinehart%20and%20Winston."}],"related":["participant-observation","field-notes","thick-description","case-study-research","action-research"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ethnography","name":"Ethnography","fullName":"Ethnography (Ethnographic Research)","aliases":["Etnografi","participant observation","fieldwork","ethnographic research"],"domain":"qualitative","family":"process-pipeline","subfamily":null,"year":"c. 1922 (Malinowski's Argonauts of the Western Pacific)","originator":"Bronisław Malinowski (modern ethnography); rooted in 19th-century anthropology","url":"https://scholargate.app/en/qualitative/ethnography","markdownUrl":"https://scholargate.app/en/qualitative/ethnography.md","definition":"Ethnography is a qualitative research tradition in which a researcher immerses themselves in a social group or community over an extended period — typically three to six months or longer — to study its culture, values, and behaviours in their natural setting. Originating in social and cultural anthropology, and consolidated as a rigorous method by Bronisław Malinowski in the early twentieth century, ethnography produces rich, contextualised accounts of how people live, work, and make meaning together.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bronisław Malinowski (modern ethnography); rooted in 19th-century anthropology","year":"c. 1922 (Malinowski's Argonauts of the Western Pacific)","type":"Qualitative fieldwork tradition","dataTypes":"Field notes, interviews, documents, artefacts","minimumFieldworkDuration":"3–6 months (typically)","researcherRole":"Participant observer embedded in the setting","output":"Thick descriptive account of culture, values, and behaviour"},"citations":[{"ref":"Hammersley, M. & Atkinson, P. (2019). Ethnography: Principles in Practice (4th ed.). Routledge.","type":"book","doi":null,"isbn":"978-1138504462","url":null}],"related":["grounded-theory","case-study","focus-group","phenomenology","narrative-inquiry"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"etl-process","name":"ETL Process","fullName":"Extract-Transform-Load Process Design","aliases":["ETL","data integration"],"domain":"information-systems","family":"process-pipeline","subfamily":"Data Pipeline & Integration","year":"1996","originator":"Ralph Kimball and data warehouse pioneers","url":"https://scholargate.app/en/information-systems/etl-process","markdownUrl":"https://scholargate.app/en/information-systems/etl-process.md","definition":"The Extract-Transform-Load (ETL) process is a systematic approach to moving data from source systems into a target repository. Formalized in the context of data warehousing, ETL pipelines extract data from diverse operational sources, apply business rules and data quality checks, and load the results into data warehouses and analytical systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralph Kimball and data warehouse pioneers","subfamily":"Data Pipeline & Integration","year":"1996","type":"Data integration methodology"},"citations":[{"ref":"Kimball, R. (1996). The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. New York: John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Kakish, K., & Kraft, T. (2012). The six pillars of enterprise data quality. DM Direct, 5(6), 44-47.","type":"article","doi":null,"isbn":null,"url":"https://www.dmdirect.com"},{"ref":"Vassiliadis, P., Quix, C., Vassalos, V., & Jarke, M. (2000). Data warehouse process management. Information Systems, 25(2), 111-125.","type":"article","doi":"10.1016/s0306-4379(01)00018-7","isbn":null,"url":null}],"related":["data-warehousing","data-quality","change-data-capture","data-validation","data-reconciliation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ets-model","name":"ETS Model","fullName":"Error, Trend, Seasonal (ETS) Exponential Smoothing","aliases":["exponential smoothing state space model","innovations state space model","Holt-Winters family","ETS — Hata/Trend/Mevsimsellik Üstel Düzleştirme"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2008,"originator":"Hyndman, Koehler, Ord & Snyder (state space framework)","url":"https://scholargate.app/en/econometrics/ets-model","markdownUrl":"https://scholargate.app/en/econometrics/ets-model.md","definition":"ETS is a comprehensive exponential smoothing framework that automatically selects additive or multiplicative combinations of the error (E), trend (T) and seasonal (S) components of a time series. Formalised as an innovations state space model by Hyndman, Koehler, Ord and Snyder in 2008, it unifies and generalises the Holt-Winters family of forecasting methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hyndman, Koehler, Ord & Snyder (state space framework)","year":2008,"type":"Exponential smoothing state space model","components":"Error (E), Trend (T), Seasonal (S)","minSample":24,"outcome":"continuous time series"},"citations":[{"ref":"Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer.","type":"book","doi":"10.1007/978-3-540-71918-2","isbn":null,"url":null},{"ref":"Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.","type":"book","doi":null,"isbn":null,"url":"https://otexts.com/fpp3/"}],"related":["holt-winters","simple-exponential-smoothing","state-space-model","structural-time-series","arima"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"etsformer","name":"ETSformer","fullName":"ETSformer (Exponential Smoothing Transformer)","aliases":["Exponential Smoothing Transformer","ETS Transformer","ETSformer forecasting model","Üstel Düzleştirme Transformatörü"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2022,"originator":"Gerald Woo et al.","url":"https://scholargate.app/en/deep-learning/etsformer","markdownUrl":"https://scholargate.app/en/deep-learning/etsformer.md","definition":"ETSformer is a deep learning architecture for time-series forecasting introduced by Woo et al. in 2022. It integrates classical exponential smoothing principles directly into the Transformer framework by replacing standard self-attention with an exponential smoothing attention mechanism. The model decomposes a time series into level, growth (trend), and seasonal components, allowing it to leverage both the long-range dependency modeling of Transformers and the interpretable structure of statistical ETS models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gerald Woo et al.","year":2022,"type":"Hybrid decomposition-based Transformer architecture","subfamily":"Time-series forecasting","training":"Supervised, end-to-end gradient-based","input":"Univariate or multivariate time series"},"citations":[{"ref":"Woo, G., Liu, C., Sahoo, D., Kumar, A., & Hoi, S. (2022). ETSformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2202.01381"}],"related":["exponential-smoothing","autoformer","ets-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eulerian-lagrangian-model","name":"Eulerian-Lagrangian Model","fullName":"Eulerian-Lagrangian Model","aliases":["ELM","two-fluid model","multiphase Eulerian-Lagrangian"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1977","originator":"Crowe Christopher","url":"https://scholargate.app/en/fluid-dynamics/eulerian-lagrangian-model","markdownUrl":"https://scholargate.app/en/fluid-dynamics/eulerian-lagrangian-model.md","definition":"The Eulerian-Lagrangian Model (ELM) is a framework for simulating multiphase flows by treating the continuous phase (liquid or gas) using Eulerian descriptions (fixed grid) and discrete dispersed phases (particles, droplets, bubbles) using Lagrangian tracking. Developed by Crowe and collaborators in 1977, this approach exploits the strengths of both perspectives: Eulerian methods for the bulk continuous phase and Lagrangian methods for individual dispersed elements. ELM is widely used in industrial applications including spray combustion, pneumatic conveying, and particle-laden flows.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Crowe Christopher","subfamily":"Fluid Dynamics","year":"1977","type":"Multiphase coupling framework"},"citations":[{"ref":"Crowe, C., Sommerfeld, M., & Tsuji, Y. (2011). Multiphase Flows with Droplets and Particles (2nd ed.). CRC Press.","type":"article","doi":null,"isbn":"978-1439840474","url":null},{"ref":"Elghobashi, S. (1994). On predicting particles-laden turbulent flows. Applied Scientific Research, 52(4), 309-329.","type":"article","doi":"10.1007/BF00936835","isbn":null,"url":null},{"ref":"Sanders, R. S., & Loeffler, A. L. (1998). Modeling the effects of bubble interactions on the viscosity of bubbly flows. International Journal of Multiphase Flow, 24(3), 345-357.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modeling+the+effects+of+bubble+interactions+on+the+viscosity+of+bubbly+flows+Sanders"}],"related":["reynolds-averaged-navier-stokes","large-eddy-simulation","volume-of-fluid","lattice-boltzmann-method","direct-numerical-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-focused-case-focused-mixed-methods","name":"Evaluation-focused case-focused mixed methods","fullName":"Evaluation-Focused Case-Focused Mixed Methods Design","aliases":["evaluation case mixed methods","case-study evaluation mixed methods","mixed methods program evaluation case design","evaluation-oriented case mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Jennifer C. Greene; John Creswell and Vicki Plano Clark (mixed methods evaluation synthesis)","url":"https://scholargate.app/en/research-design/evaluation-focused-case-focused-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/evaluation-focused-case-focused-mixed-methods.md","definition":"Evaluation-focused case-focused mixed methods integrates an explicit program evaluation framework with in-depth case study inquiry, combining qualitative and quantitative data within a bounded unit — a program, site, or organization — to render both descriptive understanding and evaluative judgments about merit, worth, or significance. The design serves applied evaluation contexts where holistic case understanding is needed alongside evidence-based performance conclusions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jennifer C. Greene; John Creswell and Vicki Plano Clark (mixed methods evaluation synthesis)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Qualitative (interviews, documents, observations) and quantitative (surveys, administrative records) within a bounded case","subfamily":"Mixed methods design"},"citations":[{"ref":"Greene, J. C. (2007). Mixed Methods in Social Inquiry. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787983826","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["case-focused-mixed-methods-design","evaluation-focused-concurrent-embedded-mixed-methods","concurrent-embedded-mixed-methods-design","exploratory-sequential-mixed-methods-design","participatory-case-focused-mixed-methods","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-focused-concurrent-embedded-mixed-methods","name":"Evaluation-focused concurrent embedded mixed methods","fullName":"Evaluation-Focused Concurrent Embedded Mixed Methods Design","aliases":["concurrent embedded evaluation design","embedded mixed methods evaluation","nested concurrent evaluation design","mixed methods program evaluation"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1989–2007 (Greene et al. 1989 for mixed evaluation; Creswell & Plano Clark 2007 for embedded design typology)","originator":"Jennifer C. Greene; John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/evaluation-focused-concurrent-embedded-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/evaluation-focused-concurrent-embedded-mixed-methods.md","definition":"Evaluation-focused concurrent embedded mixed methods is a research design in which both quantitative and qualitative data are collected simultaneously within a program evaluation context, with one strand nested inside and playing a supporting role to the dominant strand. The design produces outcome evidence alongside embedded process or contextual evidence from the same evaluation cycle, without extending the timeline.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jennifer C. Greene; John W. Creswell & Vicki L. Plano Clark","year":"1989–2007 (Greene et al. 1989 for mixed evaluation; Creswell & Plano Clark 2007 for embedded design typology)","type":"Mixed methods research design","dataType":"Simultaneous quantitative (primary) and qualitative (embedded) data, or vice versa","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Greene, J. C. (2007). Mixed Methods in Social Inquiry. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787983826","url":null}],"related":["concurrent-triangulation-design","explanatory-sequential-mixed-methods","exploratory-sequential-mixed-methods","convergent-parallel-mixed-methods","program-evaluation","embedded-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-focused-explanatory-sequential-mixed-methods","name":"Evaluation-focused Explanatory Sequential Mixed Methods","fullName":"Evaluation-focused Explanatory Sequential Mixed Methods Design","aliases":["explanatory sequential evaluation design","sequential explanatory mixed-methods evaluation","QUAN → QUAL evaluation design","two-phase sequential evaluation"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003 (explanatory sequential core); evaluation adaptation widely documented by 2010s","originator":"John W. Creswell & Vicki L. Plano Clark (explanatory sequential core); adapted for evaluation contexts by the program evaluation community","url":"https://scholargate.app/en/research-design/evaluation-focused-explanatory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/evaluation-focused-explanatory-sequential-mixed-methods.md","definition":"Evaluation-focused explanatory sequential mixed methods is a two-phase research design in which a quantitative evaluation phase — typically measuring program outcomes, treatment effects, or performance indicators — is conducted first and then followed by a qualitative phase specifically designed to explain, contextualise, or interpret the quantitative findings. The design is widely used in program evaluation, policy research, and educational assessment where numbers reveal what happened but qualitative data reveal why.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark (explanatory sequential core); adapted for evaluation contexts by the program evaluation community","year":"2003 (explanatory sequential core); evaluation adaptation widely documented by 2010s","type":"Mixed methods research design","dataType":"Quantitative outcome data followed by qualitative interview/focus-group data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Plano Clark, V. L., & Ivankova, N. V. (2016). Mixed Methods Research: A Guide to the Field. SAGE Publications.","type":"book","doi":null,"isbn":"978-1452205434","url":null}],"related":["explanatory-sequential-mixed-methods","exploratory-sequential-mixed-methods","convergent-parallel-mixed-methods","program-evaluation","embedded-mixed-methods","transformative-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-focused-exploratory-sequential-mixed-methods","name":"Evaluation-focused exploratory sequential mixed methods","fullName":"Evaluation-Focused Exploratory Sequential Mixed Methods Design","aliases":["evaluative exploratory sequential MMR","exploratory sequential evaluation design","QUAL→QUAN evaluation mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"John W. Creswell & Vicki L. Plano Clark (exploratory sequential base); Jennifer C. Greene & Donna M. Mertens (evaluation framing)","url":"https://scholargate.app/en/research-design/evaluation-focused-exploratory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/evaluation-focused-exploratory-sequential-mixed-methods.md","definition":"The evaluation-focused exploratory sequential mixed methods design combines program evaluation goals with a two-phase sequential structure: qualitative inquiry precedes and informs a quantitative phase. Phase 1 explores stakeholder experiences or program processes through interviews or focus groups; the findings build an instrument or framework used to measure outcomes quantitatively in Phase 2. The approach is used when little is known about a program's mechanisms, and both understanding and generalizing findings to a wider population matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark (exploratory sequential base); Jennifer C. Greene & Donna M. Mertens (evaluation framing)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Qualitative data (phase 1) then quantitative data (phase 2)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1606230732","url":null}],"related":["exploratory-sequential-mixed-methods-design","explanatory-sequential-mixed-methods-design","evaluation-focused-explanatory-sequential-mixed-methods","concurrent-triangulation-mixed-methods-design","multiphase-mixed-methods-design","transformative-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-focused-intervention-mixed-methods","name":"Evaluation-focused Intervention Mixed Methods","fullName":"Evaluation-focused Intervention Mixed Methods Design","aliases":["intervention mixed methods evaluation","mixed methods intervention evaluation","program evaluation mixed methods","evaluation mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s (systematized ~2007–2011)","originator":"Creswell & Plano Clark (systematized); roots in evaluation research by Patton and Shadish","url":"https://scholargate.app/en/research-design/evaluation-focused-intervention-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/evaluation-focused-intervention-mixed-methods.md","definition":"Evaluation-focused intervention mixed methods is a research design that embeds both quantitative and qualitative strands within an intervention or program evaluation study. It combines outcome measurement — typically from a randomized or quasi-experimental trial — with qualitative investigation of how and why the intervention worked, for whom, and under what conditions. The design is widely used in health, education, social service, and policy evaluation contexts where understanding mechanisms and context is as important as measuring effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (systematized); roots in evaluation research by Patton and Shadish","year":"2000s (systematized ~2007–2011)","type":"Mixed methods research design","dataType":"Quantitative outcome data (surveys, tests, measures) and qualitative data (interviews, observations)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483346298","url":null},{"ref":"Palinkas, L. A., Aarons, G. A., Horwitz, S., Chamberlain, P., Hurlburt, M., & Landsverk, J. (2011). Mixed method designs in implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 38(1), 44–53.","type":"article","doi":"10.1007/s10488-010-0314-z","isbn":null,"url":null}],"related":["convergent-mixed-methods","sequential-explanatory-mixed-methods","sequential-exploratory-mixed-methods","randomized-controlled-trial","program-evaluation","embedded-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-focused-legal-content-analysis","name":"Evaluation-focused legal content analysis","fullName":"Evaluation-Focused Legal Content Analysis","aliases":["legal text evaluation","evaluative legal content analysis","assessment-oriented legal content analysis","legal document evaluation research"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Late 20th century; evaluation-focused applications emerged prominently from the 1990s onward","originator":"Builds on Klaus Krippendorff's content analysis framework and legal scholarship traditions","url":"https://scholargate.app/en/field-methods/evaluation-focused-legal-content-analysis","markdownUrl":"https://scholargate.app/en/field-methods/evaluation-focused-legal-content-analysis.md","definition":"Evaluation-focused legal content analysis is a systematic method for examining legal texts — statutes, regulations, court decisions, contracts, or policy documents — with an explicit evaluative purpose: to assess whether and how well legal instruments achieve specified goals, standards, or values. It combines the structured coding procedures of content analysis with normative legal evaluation criteria, enabling researchers and practitioners to make evidence-based assessments of legal effectiveness, compliance, or quality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Builds on Klaus Krippendorff's content analysis framework and legal scholarship traditions","year":"Late 20th century; evaluation-focused applications emerged prominently from the 1990s onward","type":"Systematic qualitative/quantitative legal document analysis","dataType":"Legal texts (statutes, regulations, court decisions, contracts, policy documents)","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761915454","url":null},{"ref":"Nourse, V., & Schacter, J. (2002). The Politics of Legislative Drafting: A Congressional Case Study. New York University Law Review, 77(3), 575–624.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nourse+Schacter+Politics+Legislative+Drafting+Congressional+Case+Study+2002"}],"related":["legal-content-analysis","doctrinal-legal-research","comparative-legal-analysis","program-evaluation","policy-analysis","document-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-focused-multiphase-mixed-methods","name":"Evaluation-Focused Multiphase Mixed Methods","fullName":"Evaluation-Focused Multiphase Mixed Methods Design","aliases":["evaluation multiphase mixed methods","program evaluation multiphase design","mixed methods program evaluation","evaluative multiphase design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007–2009 (multiphase design formalized 2007; evaluation-focused applications consolidated ca. 2009)","originator":"John W. Creswell & Vicki L. Plano Clark (multiphase design); Stewart I. Donaldson and colleagues (evaluation-focused framing)","url":"https://scholargate.app/en/research-design/evaluation-focused-multiphase-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/evaluation-focused-multiphase-mixed-methods.md","definition":"The evaluation-focused multiphase mixed methods design applies the multiphase mixed methods framework explicitly to program evaluation contexts, orchestrating three or more sequential or iterative phases — each drawing on quantitative measures, qualitative inquiry, or both — to assess a program, policy, or intervention from needs assessment through impact evaluation. An overarching evaluation question unifies all phases, and findings from each phase directly shape the evaluation questions and methods of the next.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark (multiphase design); Stewart I. Donaldson and colleagues (evaluation-focused framing)","year":"2007–2009 (multiphase design formalized 2007; evaluation-focused applications consolidated ca. 2009)","type":"Mixed methods research design — program evaluation variant","dataType":"Quantitative data (surveys, outcome measures, administrative records) and qualitative data (interviews, documents, observations) collected and integrated across three or more evaluation phases","subfamily":"Mixed methods design"},"citations":[{"ref":"Donaldson, S. I., Christie, C. A., & Mark, M. M. (Eds.). (2009). What Counts as Credible Evidence in Applied Research and Evaluation Practice? Sage.","type":"book","doi":null,"isbn":"978-1412957090","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483317762","url":null}],"related":["multiphase-mixed-methods-design","evaluation-focused-explanatory-sequential-mixed-methods","evaluation-focused-exploratory-sequential-mixed-methods","transformative-mixed-methods-design","intervention-mixed-methods-design","participatory-multiphase-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-oriented-mixed-methods-matrix","name":"Evaluation-oriented mixed methods matrix","fullName":"Evaluation-Oriented Mixed Methods Matrix","aliases":["evaluation MMM","mixed methods display matrix for evaluation","evaluation-focused methods matrix","program evaluation mixed methods matrix"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s (Teddlie & Tashakkori 2009; Greene 2007)","originator":"Teddlie & Tashakkori (matrix framework); Greene (evaluation-oriented framing)","url":"https://scholargate.app/en/research-design/evaluation-oriented-mixed-methods-matrix","markdownUrl":"https://scholargate.app/en/research-design/evaluation-oriented-mixed-methods-matrix.md","definition":"The evaluation-oriented mixed methods matrix is a structured planning and display tool applied within program evaluation contexts. It maps evaluation questions against data sources, timing, and method types — quantitative and qualitative — in a grid format, making the integration logic explicit and auditable. Rooted in Greene's value-engaged mixed methods tradition and Teddlie and Tashakkori's matrix framework, it serves evaluators who must justify methodological choices to stakeholders while addressing multiple evaluation purposes simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Teddlie & Tashakkori (matrix framework); Greene (evaluation-oriented framing)","year":"2000s (Teddlie & Tashakkori 2009; Greene 2007)","type":"Mixed methods design variant","dataType":"Quantitative and qualitative data collected within a program evaluation context","subfamily":"Mixed methods design"},"citations":[{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null},{"ref":"Greene, J. C. (2007). Mixed Methods in Social Inquiry. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787983826","url":null}],"related":["mixed-methods-matrix","evaluation-focused-concurrent-triangulation-mixed-methods","evaluation-focused-multiphase-mixed-methods","concurrent-triangulation-mixed-methods-design","multiphase-mixed-methods-design","transformative-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-oriented-mixed-methods-meta-inference","name":"Evaluation-oriented mixed methods meta-inference","fullName":"Evaluation-Oriented Mixed Methods Meta-Inference","aliases":["evaluation MMR meta-inference","evaluation-focused meta-inference","mixed methods evaluation inference","meta-inference in evaluation research"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Abbas Tashakkori and Charles Teddlie (meta-inference); evaluation-oriented framing associated with Donna M. Mertens and Jennifer C. Greene","url":"https://scholargate.app/en/research-design/evaluation-oriented-mixed-methods-meta-inference","markdownUrl":"https://scholargate.app/en/research-design/evaluation-oriented-mixed-methods-meta-inference.md","definition":"Evaluation-oriented mixed methods meta-inference is a rigorous concluding process in program evaluation research in which the researcher integrates inferences drawn from both quantitative and qualitative strands of a mixed methods study into a single, coherent, higher-order conclusion. This meta-inference is explicitly anchored to evaluation questions — such as program worth, merit, or impact — and is judged by dual quality criteria: inferential consistency and interpretive consistency across strands.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abbas Tashakkori and Charles Teddlie (meta-inference); evaluation-oriented framing associated with Donna M. Mertens and Jennifer C. Greene","year":"2000s–2010s","type":"Mixed methods research design variant","dataType":"Quantitative data (surveys, tests, instruments) and qualitative data (interviews, observations, documents)","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1412972666","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1606230541","url":null}],"related":["mixed-methods-meta-inference","evaluation-focused-concurrent-triangulation-mixed-methods","evaluation-focused-exploratory-sequential-mixed-methods","evaluation-focused-multiphase-mixed-methods","concurrent-triangulation-mixed-methods-design","transformative-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-oriented-multilevel-mixed-methods","name":"Evaluation-oriented multilevel mixed methods","fullName":"Evaluation-Oriented Multilevel Mixed Methods Design","aliases":["multilevel mixed methods evaluation","hierarchical mixed methods evaluation","MLM mixed methods","nested mixed methods evaluation"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark (systematization)","url":"https://scholargate.app/en/research-design/evaluation-oriented-multilevel-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/evaluation-oriented-multilevel-mixed-methods.md","definition":"Evaluation-oriented multilevel mixed methods is a research design that combines quantitative and qualitative data across hierarchically nested levels of an organization or system — such as students within classrooms within schools — to evaluate a program, policy, or intervention. By capturing outcomes, processes, and contextual factors simultaneously at each level, this design produces richer evaluative inferences than either purely statistical multilevel models or single-level qualitative evaluations alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark (systematization)","year":"2000s–2010s","type":"Mixed methods evaluation design","dataType":"Nested/hierarchical quantitative data plus qualitative data at multiple organizational levels","subfamily":"Mixed methods design"},"citations":[{"ref":"Mertens, D. M. (2010). Research and Evaluation in Education and Psychology: Integrating Diversity with Quantitative, Qualitative, and Mixed Methods (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975551","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483358468","url":null}],"related":["mixed-methods-research","multilevel-modeling","program-evaluation","convergent-mixed-methods","hierarchical-linear-modeling","transformative-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-oriented-pragmatic-mixed-methods","name":"Evaluation-oriented Pragmatic Mixed Methods","fullName":"Evaluation-oriented Pragmatic Mixed Methods Design","aliases":["pragmatic evaluation mixed methods","program evaluation mixed methods","applied pragmatic mixed methods","mixed methods program evaluation"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1990s–2000s","originator":"Jennifer C. Greene; Abbas Tashakkori & Charles Teddlie","url":"https://scholargate.app/en/research-design/evaluation-oriented-pragmatic-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/evaluation-oriented-pragmatic-mixed-methods.md","definition":"Evaluation-oriented pragmatic mixed methods is a research design that combines quantitative and qualitative data collection within a pragmatist philosophical stance, expressly to evaluate programs, policies, or interventions. Rather than adhering rigidly to a single paradigm, it selects methods for their fitness to answer evaluation questions about program effectiveness, outcomes, and stakeholder experiences. The design is widely applied in education, public health, social services, and development evaluation contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jennifer C. Greene; Abbas Tashakkori & Charles Teddlie","year":"1990s–2000s","type":"Mixed methods research design","dataType":"Quantitative and qualitative data (surveys, tests, interviews, documents, observations)","subfamily":"Mixed methods design"},"citations":[{"ref":"Greene, J. C. (2007). Mixed Methods in Social Inquiry. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787984090","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412972666","url":null}],"related":["convergent-mixed-methods","sequential-explanatory-mixed-methods","sequential-exploratory-mixed-methods","program-evaluation","action-research","realist-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-oriented-qualitative-priority-mixed-methods-design","name":"Evaluation-oriented qualitative-priority mixed methods design","fullName":"Evaluation-Oriented Qualitative-Priority Mixed Methods Design","aliases":["qualitative-dominant evaluation mixed methods","QUAL-priority evaluation design","qualitative-led program evaluation","mixed methods evaluation with qualitative strand"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1989–2007 (formalized in evaluation and mixed methods literature)","originator":"Jennifer C. Greene; John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/evaluation-oriented-qualitative-priority-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/evaluation-oriented-qualitative-priority-mixed-methods-design.md","definition":"An evaluation-oriented qualitative-priority mixed methods design places qualitative data collection and analysis at the center of a program or policy evaluation, while selectively incorporating quantitative data to corroborate, contextualize, or extend qualitative findings. The design is guided by an evaluative purpose — assessing merit, worth, or significance of a program — with the qualitative strand carrying the primary interpretive weight and quantitative evidence serving a supplementary role.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jennifer C. Greene; John W. Creswell & Vicki L. Plano Clark","year":"1989–2007 (formalized in evaluation and mixed methods literature)","type":"Mixed methods research design","dataType":"Qualitative data primary (interviews, observations, documents); quantitative data secondary (surveys, administrative records)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Greene, J. C. (2007). Mixed Methods in Social Inquiry. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787983826","url":null}],"related":["exploratory-sequential-mixed-methods","explanatory-sequential-mixed-methods","convergent-mixed-methods","program-evaluation","qualitative-case-study-evaluation","transformative-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evaluation-oriented-quantitative-priority-mixed-methods-design","name":"Evaluation-oriented quantitative-priority mixed methods design","fullName":"Evaluation-Oriented Quantitative-Priority Mixed Methods Design","aliases":["QUAN-priority evaluation mixed methods","quantitative-dominant evaluation design","evaluation mixed methods with quantitative priority","QUAN-priority eval MMR"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2011","originator":"Teddlie & Tashakkori; Creswell & Plano Clark","url":"https://scholargate.app/en/research-design/evaluation-oriented-quantitative-priority-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/evaluation-oriented-quantitative-priority-mixed-methods-design.md","definition":"An evaluation-oriented quantitative-priority mixed methods design applies mixed methods inquiry within an evaluation context, where the primary purpose is judging a program, policy, or intervention. Quantitative data carry the greater evidential weight — measuring outcomes, effectiveness, and reach — while qualitative data serve as a secondary, explanatory strand that contextualizes and deepens interpretation of the quantitative findings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Teddlie & Tashakkori; Creswell & Plano Clark","year":"2003–2011","type":"Mixed methods research design","dataType":"Quantitative (primary) and qualitative (secondary) data","subfamily":"Mixed methods design"},"citations":[{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["explanatory-sequential-mixed-methods-design","quantitative-priority-mixed-methods-design","evaluation-focused-multiphase-mixed-methods","concurrent-triangulation-mixed-methods-design","multilevel-mixed-methods-design","pragmatic-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evamix","name":"EVAMIX","fullName":"EVAluation of MIXed data","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1983","originator":"Voogd, H.","url":"https://scholargate.app/en/decision-making/evamix","markdownUrl":"https://scholargate.app/en/decision-making/evamix.md","definition":"EVAMIX (EVAluation of MIXed data) is a ranking multi-criteria decision-making (MCDM) method introduced by Voogd, H. in 1983. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Voogd, H.","subfamily":"Ranking","year":"1983","type":"Mixed ordinal+cardinal pairwise dominance aggregation","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Voogd, H. (1983). Multicriteria Evaluation for Urban and Regional Planning. Pion, London","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multicriteria+Evaluation+for+Urban+and+Regional+Planning+Voogd"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"event-detection","name":"Event Detection","fullName":"Event Detection (Event Extraction)","aliases":["event extraction","Olay Tespiti (Event Detection)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/event-detection","markdownUrl":"https://scholargate.app/en/text-mining/event-detection.md","definition":"Event detection is a natural-language-processing information-extraction task that finds events, historical developments, and action expressions in text and classifies them by type. It grew out of the Automatic Content Extraction (ACE) program described by Doddington et al. (2004) and is widely used in news analysis and historical research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP information-extraction task","task":"Detect and classify event mentions in text","ontology":"ACE / FrameNet event types","prerequisites":"Named-entity recognition (NER) and semantic role labeling (SRL)","minSample":50},"citations":[{"ref":"Doddington, G. et al. (2004). The Automatic Content Extraction (ACE) Program — Tasks, Data, and Evaluation. LREC.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/L04-1011/"},{"ref":"Chen, Y. & Ng, V. (2012). Joint Modeling for Chinese Event Extraction with Rich Linguistic Features. COLING.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/C12-1029/"}],"related":["named-entity-recognition","semantic-role-labeling","text-classification","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"event-related-potential-analysis","name":"Event-Related Potential Analysis","fullName":"Event-Related Potential (ERP) Analysis","aliases":["ERP","evoked potential","averaged EEG"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Time-domain signal analysis","year":"1969","originator":"George Sutherland","url":"https://scholargate.app/en/neuroimaging/event-related-potential-analysis","markdownUrl":"https://scholargate.app/en/neuroimaging/event-related-potential-analysis.md","definition":"Event-Related Potential (ERP) analysis is a method for extracting stereotyped brain electrical responses time-locked to stimulus presentation or behavioral events from EEG recordings. Formalized in the cognitive neuroscience literature by researchers including Sutherland and Picton, ERP analysis enables millisecond-level temporal resolution of neural processing and has become foundational for studying perception, attention, memory, and decision-making.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Sutherland","subfamily":"Time-domain signal analysis","year":"1969","type":"Time-locked EEG analysis pipeline"},"citations":[{"ref":"Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique. MIT Press.","type":"book","doi":null,"isbn":null,"url":"https://mitpress.mit.edu/books/introduction-event-related-potential-technique"},{"ref":"Picton, T. W., Bentin, S., Berg, P., et al. (2000). Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. Psychophysiology, 37(2), 127–152.","type":"article","doi":"10.1111/1469-8986.3720127","isbn":null,"url":null}],"related":["eloreta","meg-source-localization","phase-locking-value"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"event-study-causal","name":"Event Study Design","fullName":"Event Study Design (Causal Event Study)","aliases":["dynamic difference-in-differences","event-study DiD","dynamic treatment effects","leads-and-lags model","Event Study Tasarımı (Nedensel Olay Çalışması)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2021,"originator":"Sun & Abraham (2021); Callaway & Sant'Anna (2021)","url":"https://scholargate.app/en/causal-inference/event-study-causal","markdownUrl":"https://scholargate.app/en/causal-inference/event-study-causal.md","definition":"The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway & Sant'Anna (2021).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sun & Abraham (2021); Callaway & Sant'Anna (2021)","year":2021,"type":"Dynamic causal panel regression","estimator":"Two-way fixed effects with event-time leads and lags (cluster-robust)","outcome":"continuous or binary","dataStructure":"panel","minSample":50,"referencePeriod":"period -1 (last pre-treatment period)"},"citations":[{"ref":"Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199.","type":"article","doi":"10.1016/j.jeconom.2020.09.006","isbn":null,"url":null},{"ref":"Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200–230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null}],"related":["did-staggered","interrupted-time-series","panel-fixed-effects","regression-discontinuity","shift-share-iv"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"event-study-design-in-education-research","name":"Event Study Design in Education Research","fullName":"Event Study Design for Causal Inference in Education Research","aliases":["event study","education event study","policy event study","dynamic difference-in-differences"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1993 (general); 2000s–2010s (education applications)","originator":"Jacobson, LaLonde & Sullivan (1993); popularized in education by Lafortune, Rothstein & Schanzenbach (2018) and subsequent education-policy literature","url":"https://scholargate.app/en/causal-inference/event-study-design-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/event-study-design-in-education-research.md","definition":"An event study design tracks how educational outcomes evolve before and after a clearly defined event — such as a school finance reform, accountability policy, or curriculum change — for affected and unaffected units. By estimating period-by-period treatment effects relative to a baseline period, it delivers both a causal estimate of the policy's impact and a transparent test of the parallel-trends assumption underpinning difference-in-differences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacobson, LaLonde & Sullivan (1993); popularized in education by Lafortune, Rothstein & Schanzenbach (2018) and subsequent education-policy literature","year":"1993 (general); 2000s–2010s (education applications)","type":"Quasi-experimental / causal inference","dataType":"Panel data with administrative or survey records (test scores, enrollment, attainment)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings Losses of Displaced Workers. American Economic Review, 83(4), 685-709.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2117574"},{"ref":"Lafortune, J., Rothstein, J., & Schanzenbach, D. W. (2018). School Finance Reform and the Distribution of Student Achievement. American Economic Journal: Applied Economics, 10(2), 1-26.","type":"article","doi":"10.1257/app.20160567","isbn":null,"url":null}],"related":["difference-in-differences","panel-fixed-effects","regression-discontinuity-design","interrupted-time-series","synthetic-control","instrumental-variables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"event-study-finance","name":"Event Study","fullName":"Event Study Methodology (CAR and BHAR)","aliases":["event study","cumulative abnormal return analysis","abnormal return analysis","CAR","BHAR","Olay Çalışması (Event Study — CAR, BHAR)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1997,"originator":"MacKinlay (review); Kothari & Warner (econometrics)","url":"https://scholargate.app/en/finance/event-study-finance","markdownUrl":"https://scholargate.app/en/finance/event-study-finance.md","definition":"The event study is a financial research method that measures the impact of a news release, policy change, or corporate event on asset prices through cumulative abnormal returns. Reviewed by MacKinlay (1997) and formalised econometrically by Kothari and Warner (2007), it is the standard tool for testing the efficient-market hypothesis and analysing the information content of events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"MacKinlay (review); Kothari & Warner (econometrics)","year":1997,"type":"Abnormal-return model for financial events","estimator":"Market model / Fama-French expected returns; cumulated abnormal returns","outcome":"abnormal return (continuous)","minSample":30,"dataStructure":"time series"},"citations":[{"ref":"MacKinlay, A. C. (1997). Event Studies in Economics and Finance. Journal of Economic Literature, 35(1), 13–39.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2729691"},{"ref":"Kothari, S. P., & Warner, J. B. (2007). Econometrics of Event Studies. In B. E. Eckbo (Ed.), Handbook of Corporate Finance: Empirical Corporate Finance (Vol. 1, pp. 3–36). Elsevier.","type":"chapter","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Econometrics+of+Event+Studies+Kothari"}],"related":["ols-regression","fama-french-three-factor","backtesting-var","high-frequency-microstructure","liquidity-risk-models"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"event-tree-analysis","name":"Event Tree Analysis","fullName":"Event Tree Analysis (ETA)","aliases":["ETA","Event Sequence Diagram Analysis","Initiating Event Analysis","Olay Ağacı Analizi"],"domain":"reliability","family":"process-pipeline","subfamily":"Reliability & risk","year":2002,"originator":"Andrews & Moss","url":"https://scholargate.app/en/reliability/event-tree-analysis","markdownUrl":"https://scholargate.app/en/reliability/event-tree-analysis.md","definition":"Event Tree Analysis (ETA) is a forward inductive technique used in reliability and risk engineering to model the possible outcomes that follow an initiating event. Starting from a single undesired event, ETA traces all subsequent event sequences through a binary branching tree representing the success or failure of safety barriers and protective systems. Introduced formally in reliability and risk literature by Andrews and Moss (2002), it is widely applied in nuclear, chemical, and aerospace industries to quantify accident sequence probabilities and guide safety decision-making.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrews & Moss","year":2002,"type":"Forward inductive logic tree","subfamily":"Reliability & risk","output":"Accident sequence probabilities","input":"Initiating event + safety barrier reliabilities"},"citations":[{"ref":"Andrews, J. D., & Moss, T. R. (2002). Reliability and Risk Assessment (2nd ed.). Professional Engineering Publishing.","type":"book","doi":null,"isbn":"978-1-86058-290-5","url":null}],"related":["fault-tree-analysis","reliability-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evidence-based-practice-attitude","name":"EBPAS-36","fullName":"Evidence-Based Practice Attitude Scale (36-item)","aliases":["EBPAS","EBPAS-36","Evidence-Based Practice Attitude"],"domain":"implementation-science","family":"process-pipeline","subfamily":"organizational assessment","year":2005,"originator":"Gregory A. Aarons, PhD","url":"https://scholargate.app/en/implementation-science/evidence-based-practice-attitude","markdownUrl":"https://scholargate.app/en/implementation-science/evidence-based-practice-attitude.md","definition":"The EBPAS-36 is a 36-item self-report questionnaire that assesses clinicians' and organizational leaders' attitudes toward adopting and implementing evidence-based practices (EBP). Developed by Aarons in 2005 and refined through multiple validation studies, it measures four core dimensions: perceived requirements to adopt EBP, the appeal and usefulness of EBP to individual practice, organizational openness to innovation, and perceived divergence between current practice and EBP requirements. The EBPAS is widely used in healthcare, mental health, child welfare, and substance abuse treatment settings to predict adoption readiness and guide implementation planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory A. Aarons, PhD","subfamily":"organizational assessment","year":2005,"type":"Self-report questionnaire"},"citations":[{"ref":"Aarons, G. A. (2011). Evidence-Based Practice Attitude Scale-50 (EBPAS-50) and EBPAS-36 short form: Psychometric properties. Implementation Science, 6(1), 89.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evidence-Based+Practice+Attitude+Scale-50+%28EBPAS-50%29+and+EBPAS-36+short+form%3A+Psychometric+properties+Aarons"}],"related":["implementation-leadership-scale","implementation-climate-scale","organisational-readiness-change","stages-of-concern-questionnaire","knowledge-to-action-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evolutionary-game-theory","name":"Evolutionary Game Theory","fullName":"Evolutionary Game Theory with Replicator Dynamics","aliases":["ESS","Evolutionarily Stable Strategy","Replicator Dynamics"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1973","originator":"John Maynard Smith, George Price","url":"https://scholargate.app/en/game-theory/evolutionary-game-theory","markdownUrl":"https://scholargate.app/en/game-theory/evolutionary-game-theory.md","definition":"Evolutionary Game Theory applies game-theoretic reasoning to biological evolution and social dynamics, where populations of agents with different strategies interact repeatedly. Introduced by John Maynard Smith and George Price in 1973, the framework uses the concept of Evolutionarily Stable Strategies (ESS) to identify strategy distributions that cannot be invaded by mutant strategies. Replicator dynamics describe how strategy frequencies evolve over time when reproduction is proportional to payoff success.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Maynard Smith, George Price","subfamily":"Game-theoretic","year":"1973","type":"algorithm"},"citations":[{"ref":"Smith, J. M., & Price, G. R. (1973). The logic of animal conflict. Nature, 246(5427), 15-18.","type":"article","doi":"10.1038/246015a0","isbn":null,"url":null},{"ref":"Maynard Smith, J. (1982). Evolution and the Theory of Games. Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.cambridge.org/core/books/evolution-and-the-theory-of-games/0D54801265F4E1B1B3C67903700D4166"}],"related":["nash-equilibrium","bayesian-nash-equilibrium","cournot-competition","stackelberg-competition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"evolutionary-strategy","name":"Evolutionary Strategy","fullName":"Covariance Matrix Adaptation Evolution Strategy (CMA-ES)","aliases":["CMA-ES","Evolution Strategy","Evrimsel Strateji (CMA-ES)","self-adapting evolution strategy"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":2001,"originator":"Nikolaus Hansen & Andreas Ostermeier","url":"https://scholargate.app/en/optimization/evolutionary-strategy","markdownUrl":"https://scholargate.app/en/optimization/evolutionary-strategy.md","definition":"CMA-ES, short for Covariance Matrix Adaptation Evolution Strategy, is a modern derivative-free optimizer for continuous black-box functions introduced by Hansen and Ostermeier in 2001. It maintains a population of candidate solutions drawn from a multivariate normal distribution and iteratively updates the distribution's mean, step size, and full covariance matrix to steer the search toward better regions of the parameter space. It has become the de-facto standard for continuous black-box optimization and is widely used in neural architecture search and reinforcement-learning policy optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nikolaus Hansen & Andreas Ostermeier","year":2001,"type":"Derivative-free continuous black-box optimizer","paradigm":"Evolutionary / population-based","searchSpace":"Continuous, 10–1000 dimensions (ideal range)","gradientRequired":false,"normalityRequired":false,"difficultyLevel":3},"citations":[{"ref":"Hansen, N. & Ostermeier, A. (2001). Completely Derandomized Self-Adaptation in Evolutionary Strategies. Evolutionary Computation, 9(2), 159-195.","type":"article","doi":"10.1162/106365601750190398","isbn":null,"url":null},{"ref":"Hansen, N. (2016). The CMA Evolution Strategy: A Tutorial. arXiv:1604.00772.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1604.00772"}],"related":["genetic-algorithm","particle-swarm-optimization","bayesian-optimization","surrogate-optimization","robust-optimization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ewma-chart","name":"EWMA Chart","fullName":"Exponentially Weighted Moving Average (EWMA) Control Chart","aliases":["exponentially weighted moving average chart","EWMA control chart","geometric moving average chart","EWMA kontrol kartı"],"domain":"statistics","family":"process-pipeline","subfamily":"Statistical process control","year":1959,"originator":"S. W. Roberts","url":"https://scholargate.app/en/statistics/ewma-chart","markdownUrl":"https://scholargate.app/en/statistics/ewma-chart.md","definition":"The exponentially weighted moving average (EWMA) control chart, introduced by S. W. Roberts in 1959, monitors a process using a weighted average that gives the most recent observation the greatest weight while letting older observations fade geometrically. Like CUSUM, this memory makes it highly effective at detecting small, sustained shifts in the process mean, with a single smoothing parameter λ controlling how much past information the chart retains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"S. W. Roberts","year":1959,"type":"Statistical process control chart for small shifts","subfamily":"Statistical process control","monitors":"Exponentially weighted moving average of the process","smoothing":"λ (weight on the current observation)"},"citations":[{"ref":"Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239–250.","type":"article","doi":"10.1080/00401706.1959.10489860","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-470-16992-6","url":null}],"related":["shewhart-control-chart","cusum-chart","attributes-control-chart","exponential-smoothing"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ex-post-facto-design","name":"Ex Post Facto Design","fullName":"Ex Post Facto Research Design","aliases":["after-the-fact research","retrospective non-experimental design","causal-comparative design","EPF design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1960s (systematic codification); concept used in social science from early 20th century","originator":"Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley","url":"https://scholargate.app/en/research-design/ex-post-facto-design","markdownUrl":"https://scholargate.app/en/research-design/ex-post-facto-design.md","definition":"Ex post facto design is a non-experimental quantitative research approach in which the researcher investigates a phenomenon after it has already occurred, examining pre-existing differences between groups to explore potential causal or associative relationships. Because the independent variable cannot be manipulated — it happened in the past — the design relies on careful group selection, retrospective data collection, and statistical controls to approximate causal inference without experimental intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley","year":"1960s (systematic codification); concept used in social science from early 20th century","type":"Non-experimental quantitative research design","dataType":"Existing records, archival data, survey responses, secondary datasets","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kerlinger+Foundations+of+Behavioral+Research+1964"},{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":"978-0395307878","url":null}],"related":["causal-comparative-research","correlational-research","longitudinal-research","cohort-research","descriptive-research","quasi-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exafs","name":"EXAFS","fullName":"Extended X-ray Absorption Fine Structure","aliases":["EXAFS spectroscopy","X-ray absorption spectroscopy"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"X-ray Spectroscopy","year":"1971","originator":"Edward Stern","url":"https://scholargate.app/en/spectroscopy/exafs","markdownUrl":"https://scholargate.app/en/spectroscopy/exafs.md","definition":"Extended X-ray Absorption Fine Structure (EXAFS) is a synchrotron-based X-ray spectroscopy technique that measures the local geometric and electronic structure around a specific atom in any material, crystal or amorphous. Discovered by Sayers, Stern, and Lytle in 1971, EXAFS reveals interatomic distances, coordination numbers, and disorder in the atomic environment by analyzing oscillations in the X-ray absorption spectrum above an absorption edge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward Stern","subfamily":"X-ray Spectroscopy","year":"1971","type":"Synchrotron technique"},"citations":[{"ref":"Sayers, D. E., Stern, E. A., & Lytle, F. W. (1971). New technique for investigating noncrystalline structures: Fourier analysis of the extended X-ray absorption fine structure. Physical Review Letters, 27(18), 1204-1207.","type":"article","doi":"10.1103/PhysRevLett.27.1204","isbn":null,"url":null},{"ref":"Stern, E. A., Sayers, D. E., & Lytle, F. W. (1975). Extended x-ray-absorption-fine-structure technique. Physical Review B, 11(12), 4836-4846.","type":"article","doi":"10.1103/PhysRevB.11.4836","isbn":null,"url":null}],"related":["xanes","saxs","atr-ftir"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exercise-addiction-inventory","name":"Exercise Addiction Inventory","fullName":"Exercise Addiction Inventory (EAI)","aliases":["EAI","Exercise Dependence"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"pathological-and-compulsive-exercise","year":"2004","originator":"Adrian Terry, Attila Szabo, Mark Griffiths","url":"https://scholargate.app/en/sport-psychology/exercise-addiction-inventory","markdownUrl":"https://scholargate.app/en/sport-psychology/exercise-addiction-inventory.md","definition":"The EAI is a 6-item questionnaire measuring the risk of exercise addiction or exercise dependence—the compulsive continuation of exercise despite negative consequences and in response to withdrawal anxiety. Developed by Terry, Szabo, and Griffiths in 2004, the EAI is a brief, practical screening tool for identifying athletes and exercisers at risk for pathological exercise patterns that compromise physical health and psychological wellbeing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adrian Terry, Attila Szabo, Mark Griffiths","subfamily":"pathological-and-compulsive-exercise","year":"2004","type":"Self-report exercise addiction/dependence screening questionnaire"},"citations":[{"ref":"Terry, A., Szabo, A., & Griffiths, M. D. (2004). The exercise addiction inventory: A new brief screening tool. British Journal of Sports Medicine, 38(4), 558–561.","type":"article","doi":"10.1080/16066350310001637363","isbn":null,"url":null},{"ref":"Grubbs, J. B., & Grubbs, R. R. (2015). Exercise addiction. In V. R. Preedy (Ed.), Neuropathology of Drug Addictions and Substance Misuse (pp. 750–758). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Exercise+addiction+Grubbs"}],"related":["sport-motivation-scale","athletic-identity-measurement-scale","mental-toughness-questionnaire","physical-self-description-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exercise-self-efficacy-scale","name":"Exercise Self-Efficacy Scale","fullName":"Self-Efficacy for Exercise Scale","aliases":["Exercise Confidence Scale","Physical Activity Self-Efficacy"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Self-Efficacy & Confidence","year":"1997","originator":"Albert Bandura; validated by Resnick & Jenkins","url":"https://scholargate.app/en/health-behavior/exercise-self-efficacy-scale","markdownUrl":"https://scholargate.app/en/health-behavior/exercise-self-efficacy-scale.md","definition":"The Exercise Self-Efficacy Scale measures an individual's confidence in their ability to exercise regularly and maintain physical activity despite challenges. Grounded in Albert Bandura's Social Cognitive Theory, self-efficacy is the belief that one has the capability to execute a specific behavior and achieve desired outcomes. For exercise, self-efficacy encompasses confidence in overcoming barriers (time, fatigue, weather), maintaining consistency, and managing setbacks or relapse. Research consistently demonstrates that exercise self-efficacy is one of the strongest predictors of exercise adherence; individuals with high confidence are more likely to initiate exercise, persist through difficulties, and maintain activity over time. The scale is widely used in primary care, cardiac and pulmonary rehabilitation, weight management, diabetes care, and exercise research to assess readiness for behavior change and to evaluate interventions designed to boost confidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Bandura; validated by Resnick & Jenkins","subfamily":"Self-Efficacy & Confidence","year":"1997","type":"Self-report questionnaire"},"citations":[{"ref":"Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9780585094748"},{"ref":"Resnick, B., & Jenkins, L. S. (2000). Testing the reliability and validity of the Self-Efficacy for Exercise Scale. Nursing Research, 49(3), 154-159.","type":"article","doi":"10.1097/00006199-200005000-00007","isbn":null,"url":null}],"related":["barriers-physical-activity","behavioral-regulation-exercise","health-locus-of-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exergoeconomic-analysis","name":"Exergoeconomic Analysis","fullName":"Exergoeconomic Analysis for Thermal Systems","aliases":["exergy costing","thermoeconomic analysis"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Economic Analysis","year":"1993","originator":"Goran Tsatsaronis","url":"https://scholargate.app/en/thermodynamics/exergoeconomic-analysis","markdownUrl":"https://scholargate.app/en/thermodynamics/exergoeconomic-analysis.md","definition":"Exergoeconomic analysis combines thermodynamics and economics by assigning monetary costs to exergy streams. It reveals how thermodynamic irreversibilities translate into economic losses within industrial systems. This approach enables engineers to identify the most economically significant inefficiencies and make informed decisions about component improvements and system optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goran Tsatsaronis","subfamily":"Economic Analysis","year":"1993","type":"Thermoeconomic assessment"},"citations":[{"ref":"Tsatsaronis, G. (1993). Thermoeconomic analysis and optimization of energy conversion processes. Progress in Energy and Combustion Science, 19(4), 323-356.","type":"article","doi":"10.1016/0360-1285(93)90016-8","isbn":null,"url":null},{"ref":"Lozano, M. A., & Valero, A. (1994). Theory of exergetic cost. Energy Policy, 21(12), 1109-1140.","type":"book","doi":"10.1016/0360-5442(93)90006-y","isbn":null,"url":null}],"related":["exergoenvironmental-analysis","levelized-cost-of-energy","rankine-cycle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exergoenvironmental-analysis","name":"Exergoenvironmental Analysis","fullName":"Exergoenvironmental Analysis for Sustainable Thermal Systems","aliases":["environmental exergy costing","exergy-based LCA"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Environmental Assessment","year":"2009","originator":"Goran Tsatsaronis and Lucía Meyer","url":"https://scholargate.app/en/thermodynamics/exergoenvironmental-analysis","markdownUrl":"https://scholargate.app/en/thermodynamics/exergoenvironmental-analysis.md","definition":"Exergoenvironmental analysis extends exergy-based methods to quantify and allocate environmental impacts of thermal systems. It assigns environmental costs to exergy streams based on upstream lifecycle impacts, revealing which components contribute most significantly to environmental burdens. This enables engineers to design sustainable energy systems by optimizing the trade-off between thermodynamic and environmental performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goran Tsatsaronis and Lucía Meyer","subfamily":"Environmental Assessment","year":"2009","type":"Life cycle and environmental analysis"},"citations":[{"ref":"Meyer, L., Tsatsaronis, G., Buchgeister, J., & Schebek, L. (2009). Exergoenvironmental analysis for evaluation of the environmental impact of energy conversion processes. Energy, 34(1), 75-89.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Exergoenvironmental+analysis+for+evaluation+of+the+environmental+impact+of+energy+conversion+processes+Meyer"},{"ref":"Valero, A., Torres, C., Valle-Zermeño, R., Gantiva-Rodriguez, R., Botero, E., Lozano, M. A., & Ospina-Alarcón, M. (2016). On the unification of LCA and exergy analysis as complementary tools. Resources, Conservation and Recycling, 107, 58-75.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+unification+of+LCA+and+exergy+analysis+as+complementary+tools+Valero"}],"related":["exergoeconomic-analysis","levelized-cost-of-energy","finite-time-thermodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exergy-analysis","name":"Exergy Analysis","fullName":"Exergy Analysis","aliases":["Available Work Analysis","Availability Analysis","Second-Law Analysis","Ekserji Analizi"],"domain":"sustainability","family":"process-pipeline","subfamily":"Energy analysis","year":2001,"originator":"Marc Rosen & Ibrahim Dincer","url":"https://scholargate.app/en/sustainability/exergy-analysis","markdownUrl":"https://scholargate.app/en/sustainability/exergy-analysis.md","definition":"Exergy analysis is a thermodynamic method that quantifies the maximum useful work obtainable from an energy carrier relative to a reference dead state, revealing where and how irreversibilities destroy quality energy. Formally linked to sustainable development by Marc Rosen and Ibrahim Dincer in 2001, it extends the first-law energy balance with second-law accounting to expose true thermodynamic inefficiencies that conventional energy audits miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marc Rosen & Ibrahim Dincer","year":2001,"type":"Thermodynamic accounting method","subfamily":"Energy analysis","basis":"Second law of thermodynamics","reference":"Dead state (ambient environment)"},"citations":[{"ref":"Rosen, M. A., & Dincer, I. (2001). Exergy as the confluence of energy, environment and sustainable development. Exergy, An International Journal, 1(1), 3–13.","type":"article","doi":"10.1016/S1164-0235(01)00004-8","isbn":null,"url":null}],"related":["life-cycle-assessment","material-flow-analysis","lmdi-decomposition"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exhaustion-disengagement-scale","name":"Exhaustion and Disengagement Scale","fullName":"Exhaustion and Disengagement Scale (EDIS)","aliases":["EDIS","Energy Assessment Module (EAM)"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Burnout and energy depletion","year":2003,"originator":"Arie Shirom, Shulamit Melamed","url":"https://scholargate.app/en/occupational-health/exhaustion-disengagement-scale","markdownUrl":"https://scholargate.app/en/occupational-health/exhaustion-disengagement-scale.md","definition":"The Exhaustion and Disengagement Scale (EDIS), based on work by Shirom and colleagues, is a brief burnout assessment tool measuring two core dimensions of occupational burnout: emotional, physical, and cognitive exhaustion, and psychological disengagement from work. Developed in the early 2000s, the EDIS emphasizes the depletion and withdrawal that characterize burnout, with particular attention to physiologic and cognitive fatigue rather than interpersonal dimensions. It is widely used in occupational health research, particularly in European and Israeli occupational health contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arie Shirom, Shulamit Melamed","subfamily":"Burnout and energy depletion","year":2003,"type":"Self-report questionnaire"},"citations":[{"ref":"Shirom, A., Melamed, S., Toker, S., Berliner, S., & Shapira, I. (2005). Burnout, vigor, and physical health among healthcare workers. Psychology and Health, 20(6), 769-785.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Burnout%2C+vigor%2C+and+physical+health+among+healthcare+workers+Shirom"},{"ref":"Shirom, A. (1989). Burnout in work organizations. In C. L. Cooper & I. T. Robertson (Eds.), International Review of Industrial and Organizational Psychology, Vol. 4 (pp. 25-48). Chichester: Wiley.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=shirom+burnout+work+organizations"}],"related":["copenhagen-burnout-inventory","oldenburg-burnout-inventory","effort-reward-imbalance-scale","recovery-experience-questionnaire","work-related-burnout-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exhaustion-scale","name":"Burnout Exhaustion Scale","fullName":"Single-Item Exhaustion Scale / Exhaustion Thermometer","aliases":["Exhaustion Item","Work-Related Exhaustion","Fatigue Scale"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Burnout and fatigue measurement","year":2002,"originator":"Multiple researchers; derived from comprehensive burnout instruments","url":"https://scholargate.app/en/occupational-health/exhaustion-scale","markdownUrl":"https://scholargate.app/en/occupational-health/exhaustion-scale.md","definition":"The Exhaustion Scale is a brief, single-item or multi-item measure of work-related exhaustion and fatigue. Derived from comprehensive burnout instruments such as the Maslach Burnout Inventory and Copenhagen Burnout Inventory, the Exhaustion Scale isolates the depletion dimension as a rapid screening tool. It is particularly useful in occupational health surveillance, longitudinal monitoring, and research when comprehensive multi-dimensional burnout assessment is impractical due to time or response burden constraints.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple researchers; derived from comprehensive burnout instruments","subfamily":"Burnout and fatigue measurement","year":2002,"type":"Self-report single-item or brief scale"},"citations":[{"ref":"Lundgren-Nilsson, A., Jonsdottir, H., Pallesen, S., & Rask, A. M. (2012). Burnout is more strongly associated with psychic strain in women than in men. Journal of Nursing Management, 20(1), 112-121.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Burnout+is+more+strongly+associated+with+psychic+strain+in+women+than+in+men+Lundgren-Nilsson"},{"ref":"Awa, W. L., Plaumann, M., & Walter, U. (2010). Burnout prevention: A review of intervention programs. Patient Education and Counseling, 78(2), 184-190.","type":"article","doi":"10.1016/j.pec.2009.04.008","isbn":null,"url":null}],"related":["copenhagen-burnout-inventory","oldenburg-burnout-inventory","exhaustion-disengagement-scale","work-related-burnout-scale","recovery-experience-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"existential-wellbeing-scale","name":"EWB Scale","fullName":"Existential Well-Being Scale","aliases":["EWB","Existential Well-Being"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"existential meaning and purpose","year":1982,"originator":"Raymond F. Paloutzian & Craig W. Ellison","url":"https://scholargate.app/en/psychology-of-religion/existential-wellbeing-scale","markdownUrl":"https://scholargate.app/en/psychology-of-religion/existential-wellbeing-scale.md","definition":"The Existential Well-Being Scale (EWB), developed by Paloutzian and Ellison in 1982, is a 10-item self-report measure of existential meaning and well-being: the sense that one's life has purpose, direction, and intrinsic value. Derived from the larger Spiritual Well-Being Scale (which includes religious well-being), the EWB focuses on the secular dimension of well-being—not faith or religious conviction, but existential satisfaction and sense of purpose. It has become widely used in psychology and health research to assess meaning, life satisfaction, and resilience factors protective against depression, anxiety, and suicide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raymond F. Paloutzian & Craig W. Ellison","subfamily":"existential meaning and purpose","year":1982,"type":"Self-report"},"citations":[{"ref":"Paloutzian, R. F., & Ellison, C. W. (1982). Loneliness, spiritual well-being, and the quality of life. In L. A. Peplau & D. Perlman (Eds.), Loneliness: A Sourcebook of Current Theory, Research and Therapy (pp. 224–237). Wiley. ISBN: 9780471084846.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books/about/Loneliness.html?id=z1RYAAAAMAAJ"}],"related":["daily-spiritual-experience-scale","functional-assessment-chronic-illness-spiritual","systems-belief-inventory","quest-scale-religion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exoplanet-transmission-spectroscopy","name":"Exoplanet Transmission Spectroscopy","fullName":"Transmission Spectroscopy for Exoplanet Atmosphere Characterization","aliases":["Transmission Spectrum","Atmospheric Spectroscopy","Transit Spectroscopy"],"domain":"astronomy","family":"process-pipeline","subfamily":"Atmospheric characterization","year":2002,"originator":"David Charbonneau","url":"https://scholargate.app/en/astronomy/exoplanet-transmission-spectroscopy","markdownUrl":"https://scholargate.app/en/astronomy/exoplanet-transmission-spectroscopy.md","definition":"Transmission spectroscopy is a technique for studying the atmospheres of exoplanets by analyzing the light passing through the planetary atmosphere during transit. Pioneered by David Charbonneau in 2002 with the detection of sodium in HD 209458b's atmosphere, this method has become the primary tool for characterizing exoplanet atmospheres and searching for biosignatures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Charbonneau","subfamily":"Atmospheric characterization","year":2002,"type":"Spectroscopic observational method"},"citations":[{"ref":"Charbonneau, D., Brown, T. M., Noyes, R. W., & Gilliland, R. L. (2002). Detection of an atmospheric trace constituent in the transmission spectrum of a distant extrasolar planet. Astrophysical Journal, 568(1), 377-384.","type":"article","doi":"10.1086/338770","isbn":null,"url":null},{"ref":"Kreidberg, L., et al. (2014). A precise water abundance measurement for the hot Jupiter WASP-43b. Astrophysical Journal Letters, 793(2), L15.","type":"article","doi":"10.1088/2041-8205/793/2/l27","isbn":null,"url":null},{"ref":"Sing, D. K., et al. (2016). The atmospheric circulation of hot Jupiters. Nature, 529(7584), 59-62.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+atmospheric+circulation+of+hot+Jupiters+Sing"}],"related":["transit-photometry","radiative-transfer","sed-fitting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"expectation-propagation","name":"Expectation Propagation","fullName":"Expectation Propagation for Approximate Bayesian Inference","aliases":["EP","expectation propagation","EP algorithm","assumed-density filtering generalisation"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2001,"originator":"Thomas P. Minka","url":"https://scholargate.app/en/bayesian/expectation-propagation","markdownUrl":"https://scholargate.app/en/bayesian/expectation-propagation.md","definition":"Expectation Propagation (EP) is a deterministic message-passing algorithm for approximate posterior inference in Bayesian models, introduced by Thomas P. Minka at UAI 2001. It iteratively refines a set of local approximate factors — each drawn from the exponential family — so that their product closely matches the true intractable posterior, achieving higher accuracy than mean-field variational inference on many probabilistic machine learning tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Approximate inference algorithm","purpose":"Posterior approximation / probabilistic machine learning","var_types":"Continuous and/or discrete latent variables","inference":"Deterministic message-passing (moment matching)","outputs":"Approximate posterior distribution (exponential family)","originator":"Thomas P. Minka","year":2001},"citations":[{"ref":"Minka, T. P. (2001). Expectation propagation for approximate Bayesian inference. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01), pp. 362–369. Morgan Kaufmann.","type":"proceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Expectation+propagation+for+approximate+Bayesian+inference+Minka"},{"ref":"Minka, T. P. (2001/2013). Expectation propagation for approximate Bayesian inference. arXiv:1301.2294 [cs.AI].","type":"preprint","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Minka%2C+T.+P.+%282001%2F2013%29.+Expectation+propagation+for+approximate+Bayesian+inference.+arXiv%3A1301.2294+%5Bcs.AI%5D.+Minka"},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (Chapter 10: Approximate Inference; Section 10.7 covers Expectation Propagation.)","type":"book","doi":null,"isbn":"978-0387310732","url":null}],"related":["variational-inference","mean-field-vi","laplace-approximation","belief-propagation","mcmc","assumed-density-filtering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"expert-interview","name":"Expert Interview","fullName":"Expert Interview","aliases":["elite interview","key informant interview","specialist interview"],"domain":"qualitative","family":"process-pipeline","subfamily":"Interview Methods","year":"1970s–1990s (methodologically systematized)","originator":"Meuser & Nagel (methodological codification, 1991); roots in elite interview tradition (Dexter, 1970)","url":"https://scholargate.app/en/qualitative/expert-interview","markdownUrl":"https://scholargate.app/en/qualitative/expert-interview.md","definition":"The expert interview is a qualitative method in which researchers conduct in-depth, semi-structured conversations with individuals who hold specialised knowledge, experience, or decision-making authority in a defined field. Unlike general population interviews that target subjective lived experience, expert interviews treat respondents as proxies for a broader institutional or professional knowledge domain. The method is widely used in policy research, organisational studies, science and technology studies, and applied social sciences to map tacit professional knowledge, reconstruct decision processes, and triangulate documentary sources.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Meuser & Nagel (methodological codification, 1991); roots in elite interview tradition (Dexter, 1970)","year":"1970s–1990s (methodologically systematized)","type":"Qualitative research method","dataType":"Semi-structured or unstructured interview transcripts (text data)","typicalSampleSize":"10–30 experts","subfamily":"Interview Methods"},"citations":[{"ref":"Bogner, A., Littig, B., & Menz, W. (Eds.). (2009). Interviewing Experts. Palgrave Macmillan.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interviewing+Experts+Bogner+Littig+Menz+2009"},{"ref":"Meuser, M., & Nagel, U. (1991). ExpertInneninterviews — vielfach erprobt, wenig bedacht. In D. Garz & K. Kraimer (Eds.), Qualitativ-empirische Sozialforschung (pp. 441–471). Westdeutscher Verlag.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ExpertInneninterviews+vielfach+erprobt+wenig+bedacht+Meuser+Nagel+1991"}],"related":["delphi-method","case-study","grounded-theory","focus-group","phenomenology","action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-association-rules","name":"Explainable Association Rules","fullName":"Explainable Association Rules Mining","aliases":["XAI association rules","interpretable association rules","rule-based explanation mining","transparent association rule learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1993 (rules); 2010s (XAI framing)","originator":"Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present)","url":"https://scholargate.app/en/machine-learning/explainable-association-rules","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-association-rules.md","definition":"Explainable Association Rules leverages the inherently symbolic, if-then structure of association rule mining to provide human-readable explanations of data patterns or black-box model decisions. Because each rule explicitly states its antecedent and consequent together with support, confidence, and lift, the outputs are natively interpretable without requiring a secondary post-hoc surrogate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agrawal, R., Imielinski, T., & Swami, A. (foundational); XAI framing: broader community (2010s–present)","year":"1993 (rules); 2010s (XAI framing)","type":"Interpretable pattern mining / XAI technique","dataType":"Transactional, categorical, or binary feature data","subfamily":"Machine learning"},"citations":[{"ref":"Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.","type":"inproceedings","doi":"10.1145/170035.170072","isbn":null,"url":null},{"ref":"Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071–22080.","type":"article","doi":"10.1073/pnas.1900654116","isbn":null,"url":null}],"related":["association-rules","apriori-algorithm","explainable-decision-tree","explainable-random-forest","fp-growth","explainable-naive-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-autoencoder-anomaly-detection","name":"Explainable Autoencoder Anomaly Detection","fullName":"Explainable Autoencoder-Based Anomaly Detection (XAI-augmented Reconstruction Error)","aliases":["XAI autoencoder anomaly detection","interpretable autoencoder anomaly detection","explainable deep anomaly detection","SHAP-autoencoder anomaly detection"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017-2019","originator":"Combination of autoencoder anomaly detection (Hinton & Salakhutdinov, 2006) and XAI methods (e.g., Lundberg & Lee, 2017)","url":"https://scholargate.app/en/machine-learning/explainable-autoencoder-anomaly-detection","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-autoencoder-anomaly-detection.md","definition":"Explainable Autoencoder Anomaly Detection augments a standard autoencoder-based anomaly detector with an interpretability layer — such as SHAP values or feature-wise reconstruction error decomposition — that identifies which input features drove the anomaly flag for each observation, turning an opaque reconstruction-error score into an actionable, human-readable explanation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Combination of autoencoder anomaly detection (Hinton & Salakhutdinov, 2006) and XAI methods (e.g., Lundberg & Lee, 2017)","year":"2017-2019","type":"Unsupervised anomaly detection with post-hoc or intrinsic explainability","dataType":"Continuous, tabular, or high-dimensional numeric data","subfamily":"Machine learning"},"citations":[{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"ref":"Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1901.03407"}],"related":["autoencoder-anomaly-detection","explainable-isolation-forest","explainable-one-class-svm","isolation-forest","one-class-svm","self-supervised-autoencoder-anomaly-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-bert-based-classification","name":"Explainable BERT-based Classification","fullName":"Explainable BERT-based Text Classification","aliases":["XAI-BERT","interpretable BERT classifier","BERT with post-hoc explanation","transparent BERT classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)","url":"https://scholargate.app/en/deep-learning/explainable-bert-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-bert-based-classification.md","definition":"Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)","year":"2019–2020","type":"Pre-trained transformer classifier with post-hoc or intrinsic explainability","dataType":"Text (documents, sentences, social media, reviews)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"}],"related":["bert-based-classification","roberta-based-classification","explainable-transformer","explainable-recurrent-neural-network","sentence-embeddings","fine-tuned-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-dbscan","name":"Explainable DBSCAN","fullName":"Explainable Density-Based Spatial Clustering of Applications with Noise","aliases":["XAI-DBSCAN","interpretable DBSCAN","transparent density clustering","DBSCAN with post-hoc explanation"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1996 (DBSCAN); 2010s (XAI integration)","originator":"Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)","url":"https://scholargate.app/en/machine-learning/explainable-dbscan","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-dbscan.md","definition":"Explainable DBSCAN pairs the DBSCAN density-based clustering algorithm with post-hoc interpretability methods — most commonly SHAP values or local surrogate models — to reveal which input features drive the algorithm's cluster and noise assignments. It enables analysts to understand why specific points were grouped together or flagged as outliers, bridging the gap between powerful density-based partitioning and human-readable explanation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ester, M. et al. (DBSCAN); XAI layer via Lundberg & Lee (SHAP)","year":"1996 (DBSCAN); 2010s (XAI integration)","type":"Unsupervised clustering with post-hoc interpretability","dataType":"Continuous or mixed tabular data; spatial coordinates","subfamily":"Machine learning"},"citations":[{"ref":"Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231. AAAI Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+density-based+algorithm+for+discovering+clusters+in+large+spatial+databases+with+noise"},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. Curran Associates.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+unified+approach+to+interpreting+model+predictions+Lundberg+Lee+2017"}],"related":["dbscan","hdbscan","k-means","gaussian-mixture-model","explainable-k-nearest-neighbors","explainable-isolation-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-decision-tree","name":"Explainable Decision Tree","fullName":"Explainable Decision Tree (Interpretable Rule-Based Classification and Regression Tree)","aliases":["XDT","interpretable decision tree","rule-based decision tree","transparent decision tree"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1984 (CART); XAI framing formalized 2010s–2020s","originator":"Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.","url":"https://scholargate.app/en/machine-learning/explainable-decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-decision-tree.md","definition":"An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.","year":"1984 (CART); XAI framing formalized 2010s–2020s","type":"Interpretable supervised learning model","dataType":"Tabular (continuous, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole.","type":"book","doi":null,"isbn":"978-0-412-04841-8","url":null},{"ref":"Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.","type":"article","doi":"10.1038/s42256-019-0048-x","isbn":null,"url":null}],"related":["random-forest","decision-tree","xgboost","logistic-regression","rule-based-classifier","shap-explainability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-diffusion-model","name":"Explainable Diffusion Model","fullName":"Explainable Diffusion Model (XAI-Augmented Denoising Diffusion Probabilistic Model)","aliases":["XAI-DDPM","interpretable diffusion model","transparent diffusion model","explainable DDPM"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2022","originator":"Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers","url":"https://scholargate.app/en/deep-learning/explainable-diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-diffusion-model.md","definition":"An Explainable Diffusion Model couples a denoising diffusion probabilistic model with post-hoc or intrinsic explainability techniques — such as SHAP, gradient-based saliency, attention analysis, or concept-based probing — so that each generative or predictive decision can be audited and justified rather than treated as a black box.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers","year":"2020–2022","type":"Generative model with post-hoc or intrinsic explainability","dataType":"Images, audio, tabular, multimodal continuous data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html"},{"ref":"Diffusion model. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Diffusion_model"}],"related":["fine-tuned-diffusion-model","self-supervised-diffusion-model","explainable-variational-autoencoder","explainable-gan","explainable-vision-transformer","multimodal-diffusion-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-extra-trees","name":"Explainable Extra Trees","fullName":"Explainable Extremely Randomized Trees (Extra Trees with Post-Hoc Interpretability)","aliases":["XAI-ET","Explainable ET","Interpretable Extra Trees","Extra Trees with SHAP"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006 (Extra Trees); 2017 (SHAP integration)","originator":"Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer)","url":"https://scholargate.app/en/machine-learning/explainable-extra-trees","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-extra-trees.md","definition":"Explainable Extra Trees combines the Extremely Randomized Trees (Extra Trees) ensemble algorithm with post-hoc explainability methods — most commonly SHAP values — to deliver both strong predictive performance and transparent, feature-level explanations. It extends the classic Extra Trees classifier or regressor so that every prediction can be decomposed into individual feature contributions, satisfying demands for accountability in applied and regulated domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer)","year":"2006 (Extra Trees); 2017 (SHAP integration)","type":"Ensemble (randomized trees) with post-hoc explainability","dataType":"Tabular (continuous, categorical, mixed features)","subfamily":"Machine learning"},"citations":[{"ref":"Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.","type":"article","doi":"10.1007/s10994-006-6226-1","isbn":null,"url":null},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"}],"related":["random-forest","xgboost","decision-tree","shap-explainability","gradient-boosting","extra-trees"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-fp-growth","name":"Explainable FP-Growth","fullName":"Explainable Frequent Pattern Growth (XAI-Augmented FP-Growth)","aliases":["XAI-FP-Growth","interpretable frequent pattern mining","explainable frequent itemset mining","transparent FP-Growth"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000 (FP-Growth); XAI augmentation emerged ~2018–present","originator":"Han, J., Pei, J., & Yin, Y. (FP-Growth); XAI augmentation from the interpretable ML community","url":"https://scholargate.app/en/machine-learning/explainable-fp-growth","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-fp-growth.md","definition":"Explainable FP-Growth augments the classic FP-Growth frequent-pattern mining algorithm with post-hoc interpretability tools — such as rule importance scores, visual pattern trees, and counterfactual explanations — so analysts can not only discover frequent itemsets and association rules but also understand why specific patterns matter, which items drive rule confidence, and how to communicate findings transparently to stakeholders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Han, J., Pei, J., & Yin, Y. (FP-Growth); XAI augmentation from the interpretable ML community","year":"2000 (FP-Growth); XAI augmentation emerged ~2018–present","type":"Explainable frequent pattern mining","dataType":"Transactional / binary item-set data","subfamily":"Machine learning"},"citations":[{"ref":"Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.","type":"inproceedings","doi":"10.1145/335191.335372","isbn":null,"url":null},{"ref":"Association rule learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Association_rule_learning"}],"related":["apriori-algorithm","association-rules","fp-growth","explainable-association-rules","explainable-apriori-algorithm","semi-supervised-fp-growth"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-gan","name":"Explainable GAN","fullName":"Explainable Generative Adversarial Network","aliases":["XAI-GAN","Interpretable GAN","Transparent GAN","Explainable Generative Model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019 (GAN Dissection); ongoing","originator":"Bau, D. et al. (GAN Dissection); broader XAI-GAN community","url":"https://scholargate.app/en/deep-learning/explainable-gan","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-gan.md","definition":"Explainable GAN applies interpretability techniques to Generative Adversarial Networks to reveal which internal units and latent directions cause specific visual or structural features in generated outputs. It combines GAN training with post-hoc analysis tools — such as unit dissection, saliency maps, or disentangled latent spaces — to make generative model behaviour transparent and auditable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bau, D. et al. (GAN Dissection); broader XAI-GAN community","year":"2019 (GAN Dissection); ongoing","type":"Explainable generative model","dataType":"Images, structured data, or other high-dimensional distributions","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. (2019). GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In Proceedings of the International Conference on Learning Representations (ICLR 2019).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=Hyg_X2C5FX"},{"ref":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. In Advances in Neural Information Processing Systems (NeurIPS 2014), 27.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html"}],"related":["generative-adversarial-network","variational-autoencoder","diffusion-model","explainable-convolutional-neural-network","disentangled-representation-learning","explainable-image-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-gaussian-mixture-model","name":"Explainable Gaussian Mixture Model","fullName":"Explainable Gaussian Mixture Model (X-GMM)","aliases":["X-GMM","Interpretable GMM","Explainable GMM","Transparent Gaussian Mixture Model"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1995–2020s","originator":"Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors","url":"https://scholargate.app/en/machine-learning/explainable-gaussian-mixture-model","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-gaussian-mixture-model.md","definition":"An Explainable Gaussian Mixture Model (X-GMM) augments the classical GMM probabilistic clustering framework with transparency mechanisms — such as feature-attribution scores, component-level summaries, or sparse covariance structures — so that discovered clusters and density estimates can be understood, communicated, and audited by human experts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reynolds, D. A. & Rose, R. C. (GMM); explainability extensions by various authors","year":"1995–2020s","type":"Probabilistic clustering with post-hoc or built-in explainability","dataType":"Continuous multivariate data","subfamily":"Machine learning"},"citations":[{"ref":"Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective (Ch. 11 — Mixture Models). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-01802-9","url":null},{"ref":"Gaussian mixture model. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Mixture_model"}],"related":["gaussian-mixture-model","k-means-clustering","expectation-maximization","shap","variational-autoencoder","latent-class-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-gaussian-process","name":"Explainable Gaussian Process","fullName":"Explainable Gaussian Process Regression and Classification","aliases":["XAI-GP","interpretable Gaussian process","explainable GP","transparent Gaussian process"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006 (GP); 2017+ (XAI integration)","originator":"Rasmussen, C. E. & Williams, C. K. I. (GP); XAI layer via Lundberg & Lee (SHAP, 2017) and others","url":"https://scholargate.app/en/machine-learning/explainable-gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-gaussian-process.md","definition":"An Explainable Gaussian Process (XAI-GP) combines the probabilistic, uncertainty-aware predictions of a Gaussian Process model with systematic interpretability tools — such as SHAP values, kernel decomposition, or sensitivity analysis — so that every prediction comes with both a calibrated confidence interval and an auditable explanation of which inputs drove it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rasmussen, C. E. & Williams, C. K. I. (GP); XAI layer via Lundberg & Lee (SHAP, 2017) and others","year":"2006 (GP); 2017+ (XAI integration)","type":"Probabilistic model with post-hoc or built-in interpretability","dataType":"Continuous, binary, or multi-class tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-18253-9","url":null},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"}],"related":["gaussian-process","explainable-random-forest","explainable-gradient-boosting","bayesian-gaussian-process","support-vector-machine","regularized-gaussian-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-gradient-boosting","name":"Explainable Gradient Boosting","fullName":"Explainable Gradient Boosting (Gradient Boosting with Post-hoc and Intrinsic Interpretability)","aliases":["XGB with SHAP","interpretable gradient boosting","transparent gradient boosting","XAI gradient boosting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017–2020","originator":"Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)","url":"https://scholargate.app/en/machine-learning/explainable-gradient-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-gradient-boosting.md","definition":"Explainable Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lundberg, S. M. & Lee, S.-I. (TreeSHAP for tree ensembles)","year":"2017–2020","type":"Ensemble + explainability layer","dataType":"Tabular (continuous, categorical, ordinal, binary)","subfamily":"Machine learning"},"citations":[{"ref":"Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67.","type":"article","doi":"10.1038/s42256-019-0138-9","isbn":null,"url":null},{"ref":"Molnar, C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (2nd ed.). christophm.github.io/interpretable-ml-book/","type":"book","doi":null,"isbn":null,"url":"https://christophm.github.io/interpretable-ml-book/"}],"related":["gradient-boosting","xgboost","random-forest","explainable-random-forest","explainable-xgboost","explainable-decision-tree"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-graph-neural-network","name":"Explainable Graph Neural Network","fullName":"Explainable Graph Neural Network (XAI-GNN)","aliases":["XAI-GNN","GNN explainability","interpretable GNN","explainable GNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Ying, Z. et al. (GNNExplainer); broader XAI-GNN field","url":"https://scholargate.app/en/deep-learning/explainable-graph-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-graph-neural-network.md","definition":"Explainable Graph Neural Networks (XAI-GNN) combine standard GNN architectures with post-hoc or intrinsic explanation techniques that reveal which nodes, edges, and node features drove a model's prediction. Pioneered by GNNExplainer (Ying et al., 2019), the field addresses the black-box critique of GNNs and is essential wherever graph-based predictions must be trusted or audited.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ying, Z. et al. (GNNExplainer); broader XAI-GNN field","year":"2019","type":"Interpretability framework for graph neural networks","dataType":"Graph-structured data (nodes, edges, node/edge features)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating Explanations for Graph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS), 32, 9240–9251.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2019/hash/d80b7040b773199015de6d3b4293c8ff-Abstract.html"},{"ref":"Yuan, H., Yu, H., Gui, S., & Ji, S. (2023). Explainability in Graph Neural Networks: A Taxonomic Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5782–5799.","type":"article","doi":"10.1109/TPAMI.2022.3204236","isbn":null,"url":null}],"related":["graph-neural-network","explainable-convolutional-neural-network","explainable-transformer","explainable-bert-based-classification","self-supervised-graph-neural-network","domain-adaptive-graph-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-gru","name":"Explainable GRU","fullName":"Explainable Gated Recurrent Unit","aliases":["XAI-GRU","Interpretable GRU","GRU with explainability","Transparent GRU"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014 (GRU); 2016–2017 (XAI integration)","originator":"Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)","url":"https://scholargate.app/en/deep-learning/explainable-gru","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-gru.md","definition":"Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's ability to capture temporal dependencies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)","year":"2014 (GRU); 2016–2017 (XAI integration)","type":"Recurrent neural network with post-hoc or attention-based interpretability","dataType":"Sequential / time-series / text data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.","type":"inproceedings","doi":"10.3115/v1/D14-1179","isbn":null,"url":null},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"}],"related":["gated-recurrent-unit","explainable-lstm","long-short-term-memory","explainable-recurrent-neural-network","transformer","explainable-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-hdbscan","name":"Explainable HDBSCAN","fullName":"Explainable Hierarchical Density-Based Spatial Clustering of Applications with Noise","aliases":["XAI-HDBSCAN","Interpretable HDBSCAN","Explainable Hierarchical DBSCAN","HDBSCAN with XAI"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017–2020","originator":"McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation)","url":"https://scholargate.app/en/machine-learning/explainable-hdbscan","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-hdbscan.md","definition":"Explainable HDBSCAN combines the hierarchical density-based clustering algorithm HDBSCAN with post-hoc explainability methods — primarily SHAP — to reveal which input features drive cluster membership and separation. It retains HDBSCAN's ability to find clusters of varying shape and density while adding a principled, auditable explanation layer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McInnes, L.; Healy, J. (HDBSCAN); Lundberg & Lee (SHAP-based explanation)","year":"2017–2020","type":"Explainable clustering","dataType":"Continuous / mixed tabular; high-dimensional feature spaces","subfamily":"Machine learning"},"citations":[{"ref":"McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.","type":"article","doi":"10.21105/joss.00205","isbn":null,"url":null},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"}],"related":["explainable-dbscan","explainable-k-means","explainable-gaussian-mixture-model","hdbscan","explainable-isolation-forest","explainable-random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-image-classification","name":"Explainable Image Classification","fullName":"Explainable Image Classification (XAI-augmented CNN/Transformer Classifiers)","aliases":["XAI image classification","interpretable image classifier","explainable CNN","transparent image recognition"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016-2017","originator":"Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME)","url":"https://scholargate.app/en/deep-learning/explainable-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-image-classification.md","definition":"Explainable image classification combines a deep learning image classifier — typically a CNN or Vision Transformer — with a post-hoc or intrinsic interpretability method such as Grad-CAM, LIME, or SHAP to produce visual or quantitative explanations of why the model assigned a particular label to an image. The goal is to make the classifier's decision process transparent, auditable, and trustworthy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME)","year":"2016-2017","type":"Post-hoc explainability applied to image classifiers","dataType":"Images (RGB, grayscale, multispectral)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618-626.","type":"inproceedings","doi":"10.1109/ICCV.2017.74","isbn":null,"url":null},{"ref":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why Should I Trust You?: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144.","type":"inproceedings","doi":"10.1145/2939672.2939778","isbn":null,"url":null}],"related":["image-classification","convolutional-neural-network","explainable-convolutional-neural-network","fine-tuned-image-classification","semantic-segmentation","object-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-instance-segmentation","name":"Explainable Instance Segmentation","fullName":"Explainable Instance Segmentation (XAI-augmented Mask Detection)","aliases":["XAI instance segmentation","interpretable instance segmentation","transparent mask prediction","explainable Mask R-CNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–present","originator":"He, K. et al. (Mask R-CNN); XAI extensions by multiple authors","url":"https://scholargate.app/en/deep-learning/explainable-instance-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-instance-segmentation.md","definition":"Explainable Instance Segmentation combines deep-learning instance segmentation models — which detect and delineate every individual object as a separate pixel mask — with post-hoc or ante-hoc explainability techniques such as GradCAM, SHAP, LIME, or attention visualization, so that each predicted mask is accompanied by evidence showing which image regions drove the model's decision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"He, K. et al. (Mask R-CNN); XAI extensions by multiple authors","year":"2017–present","type":"Explainability-augmented deep learning pipeline","dataType":"Image data (RGB, medical, satellite, industrial)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lindner, M., Meng, C., & Bischl, B. (2023). Explaining Instance Segmentation Models via Saliency Maps and Occlusion. IEEE Transactions on Pattern Analysis and Machine Intelligence.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Explaining+Instance+Segmentation+Models+via+Saliency+Maps+and+Occlusion"},{"ref":"Instance segmentation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Image_segmentation"}],"related":["instance-segmentation","explainable-semantic-segmentation","explainable-object-detection","explainable-image-classification","explainable-vision-transformer","semantic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-isolation-forest","name":"Explainable Isolation Forest","fullName":"Explainable Isolation Forest (Isolation Forest with SHAP-based Interpretability)","aliases":["XIF","Isolation Forest with SHAP","interpretable anomaly detection","explainable anomaly isolation"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2008 / 2017","originator":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)","url":"https://scholargate.app/en/machine-learning/explainable-isolation-forest","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-isolation-forest.md","definition":"Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)","year":"2008 / 2017","type":"Anomaly detection with post-hoc explainability","dataType":"Tabular (continuous and mixed features)","subfamily":"Machine learning"},"citations":[{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"ref":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 413–422. IEEE.","type":"inproceedings","doi":"10.1109/ICDM.2008.17","isbn":null,"url":null}],"related":["isolation-forest","one-class-svm","autoencoder-anomaly-detection","explainable-random-forest","gaussian-mixture-model","explainable-gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-k-means","name":"Explainable K-Means","fullName":"Explainable K-Means Clustering","aliases":["ExKMC","interpretable k-means","decision-tree k-means","explainable clustering"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020","originator":"Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.","url":"https://scholargate.app/en/machine-learning/explainable-k-means","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-k-means.md","definition":"Explainable K-Means is a post-hoc and in-model interpretability approach to standard K-Means clustering that replaces or approximates cluster assignments with a small axis-aligned decision tree. Each leaf of the tree corresponds to one cluster, and every data point is assigned to a cluster by following a simple sequence of threshold rules on individual features — making cluster membership fully transparent and human-readable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dasgupta, S.; Moshkovitz, M.; Frost, N.; Rashtchian, C.","year":"2020","type":"Explainable unsupervised clustering algorithm","dataType":"Continuous or mixed tabular features","subfamily":"Machine learning"},"citations":[{"ref":"Dasgupta, S., Frost, N., Moshkovitz, M., & Rashtchian, C. (2020). Explainability of k-Means Clustering. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/dasgupta20b.html"},{"ref":"Moshkovitz, M., Dasgupta, S., Rashtchian, C., & Frost, N. (2020). Explainable k-Means and k-Medians Clustering. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Explainable+k-Means+and+k-Medians+Clustering+Moshkovitz+2020"}],"related":["k-means-clustering","decision-tree","hierarchical-clustering","dbscan","gaussian-mixture-model","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-k-nearest-neighbors","name":"Explainable K-Nearest Neighbors","fullName":"Explainable K-Nearest Neighbors (XKNN)","aliases":["XKNN","Interpretable KNN","Explainable KNN","Transparent K-Nearest Neighbors"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1967 (KNN); 2010s (explainability extensions)","originator":"Cover, T. & Hart, P. (KNN); XAI extensions by various authors","url":"https://scholargate.app/en/machine-learning/explainable-k-nearest-neighbors","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-k-nearest-neighbors.md","definition":"Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cover, T. & Hart, P. (KNN); XAI extensions by various authors","year":"1967 (KNN); 2010s (explainability extensions)","type":"Instance-based learning with explainability layer","dataType":"Tabular (continuous, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.","type":"article","doi":"10.1109/TIT.1967.1053964","isbn":null,"url":null},{"ref":"Papernot, N. & McDaniel, P. (2018). Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. arXiv preprint arXiv:1803.04765.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1803.04765"}],"related":["k-nearest-neighbors","lime","shap","decision-tree","random-forest","naive-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-lda-topic-model","name":"Explainable LDA Topic Model","fullName":"Explainable Latent Dirichlet Allocation Topic Model","aliases":["Explainable LDA","Interpretable LDA","XAI-LDA","Transparent Topic Model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2003 (LDA); 2018–present (explainability extensions)","originator":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors","url":"https://scholargate.app/en/deep-learning/explainable-lda-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-lda-topic-model.md","definition":"Explainable LDA combines Latent Dirichlet Allocation — the canonical probabilistic topic model introduced by Blei, Ng, and Jordan in 2003 — with post-hoc and intrinsic interpretability tools that make each discovered topic auditable, labeled, and trustworthy for human reviewers. It is widely used in NLP, social science text analysis, and computational humanities where transparency is required alongside discovery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA seminal); explainability extensions by multiple authors","year":"2003 (LDA); 2018–present (explainability extensions)","type":"Probabilistic generative topic model with interpretability enhancements","dataType":"Text corpora (bag-of-words or token sequences)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/blei03a.html"},{"ref":"Latent Dirichlet Allocation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation"}],"related":["latent-dirichlet-allocation","bert-topic-model","non-negative-matrix-factorization","neural-topic-model","word2vec","text-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-lightgbm","name":"Explainable LightGBM","fullName":"Explainable LightGBM (LightGBM with SHAP-based Interpretability)","aliases":["XAI-LightGBM","LightGBM with SHAP","Interpretable LightGBM","LightGBM explainability"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017","originator":"Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models)","url":"https://scholargate.app/en/machine-learning/explainable-lightgbm","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-lightgbm.md","definition":"Explainable LightGBM combines Microsoft's LightGBM gradient boosting framework with SHAP (SHapley Additive exPlanations) to deliver both high predictive performance and rigorous, theoretically grounded feature-level explanations. It is widely adopted in applied research where predictive accuracy and interpretability are simultaneously required.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ke, G. et al. (LightGBM); Lundberg, S. M. & Lee, S.-I. (SHAP for tree models)","year":"2017","type":"Gradient boosting with post-hoc explainability (SHAP)","dataType":"Tabular (continuous, categorical, binary, ordinal)","subfamily":"Machine learning"},"citations":[{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"ref":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html"}],"related":["xgboost","gradient-boosting","random-forest","shap-analysis","catboost","decision-tree"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-lstm","name":"Explainable LSTM","fullName":"Explainable Long Short-Term Memory Network","aliases":["XAI-LSTM","interpretable LSTM","LSTM with SHAP","transparent LSTM"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2019","originator":"Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis","url":"https://scholargate.app/en/deep-learning/explainable-lstm","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-lstm.md","definition":"Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis","year":"2017–2019","type":"Interpretable deep learning (post-hoc explainability)","dataType":"Sequential / time-series / text data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"ref":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). \"Why should I trust you?\": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.","type":"inproceedings","doi":"10.1145/2939672.2939778","isbn":null,"url":null}],"related":["long-short-term-memory","explainable-transformer","explainable-recurrent-neural-network","explainable-bert-based-classification","explainable-gru","explainable-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-multilayer-perceptron","name":"Explainable Multilayer Perceptron","fullName":"Explainable Multilayer Perceptron (MLP with Post-hoc Interpretability)","aliases":["XMLP","Interpretable MLP","Explainable feedforward neural network","Transparent MLP"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010s–present","originator":"Lundberg & Lee (SHAP); Ribeiro et al. (LIME); broader XAI community","url":"https://scholargate.app/en/deep-learning/explainable-multilayer-perceptron","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-multilayer-perceptron.md","definition":"An Explainable Multilayer Perceptron (XMLP) is a standard feedforward neural network trained with backpropagation, augmented with post-hoc interpretability techniques — such as SHAP values, LIME, or integrated gradients — that attribute each prediction to individual input features. The combination retains the MLP's approximation power while satisfying transparency requirements common in regulated or high-stakes domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lundberg & Lee (SHAP); Ribeiro et al. (LIME); broader XAI community","year":"2010s–present","type":"Supervised feedforward neural network with interpretability layer","dataType":"Tabular, image, text (numeric input preferred)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"ref":"Explainable artificial intelligence. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Explainable_artificial_intelligence"}],"related":["multilayer-perceptron","convolutional-neural-network","explainable-convolutional-neural-network","explainable-lstm","explainable-transformer","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-naive-bayes","name":"Explainable Naive Bayes","fullName":"Explainable Naive Bayes Classifier","aliases":["XNB","interpretable Naive Bayes","transparent Naive Bayes","explainable probabilistic classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1950s (Naive Bayes); 2000s–2010s (explainability focus)","originator":"Zhang, H. (explainability framing); Naive Bayes: Good, I. J.","url":"https://scholargate.app/en/machine-learning/explainable-naive-bayes","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-naive-bayes.md","definition":"Explainable Naive Bayes extends the classic probabilistic Naive Bayes classifier with transparent, human-readable explanations of its predictions. By surfacing class priors, per-feature likelihoods, and log-odds contributions, it offers the interpretability demanded in high-stakes domains such as medicine, law, and education without sacrificing the simplicity and speed that make Naive Bayes a reliable baseline.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhang, H. (explainability framing); Naive Bayes: Good, I. J.","year":"1950s (Naive Bayes); 2000s–2010s (explainability focus)","type":"Probabilistic generative classifier with intrinsic explainability","dataType":"Categorical, binary, or continuous features; labeled classification targets","subfamily":"Machine learning"},"citations":[{"ref":"Rish, I. (2001). An empirical study of the naive Bayes classifier. In IJCAI Workshop on Empirical Methods in AI (pp. 41–46).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+empirical+study+of+the+naive+Bayes+classifier"},{"ref":"Naive Bayes classifier. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Naive_Bayes_classifier"}],"related":["naive-bayes","logistic-regression","decision-tree","random-forest","lime-explainability","shap-values"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-named-entity-recognition","name":"Explainable Named Entity Recognition","fullName":"Explainable Named Entity Recognition (XAI-NER)","aliases":["XAI-NER","Interpretable NER","Transparent Named Entity Recognition","Explainable NER"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2020","originator":"Community-driven (NLP + XAI research)","url":"https://scholargate.app/en/deep-learning/explainable-named-entity-recognition","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-named-entity-recognition.md","definition":"Explainable Named Entity Recognition (XAI-NER) combines a standard NER model — typically a BERT-based or BiLSTM-CRF sequence labeler — with post-hoc or intrinsic explainability techniques such as LIME, SHAP, attention visualization, or gradient-based saliency to reveal why each token was assigned a particular entity label. This transparency is essential in high-stakes domains like clinical text, legal documents, and biomedical literature.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Community-driven (NLP + XAI research)","year":"2018–2020","type":"Interpretability-augmented sequence labeling","dataType":"Text (token sequences with entity spans)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020). A Survey of the State of Explainable AI for Natural Language Processing. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL-IJCNLP), pp. 447–459.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2020.aacl-main.46"},{"ref":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144.","type":"inproceedings","doi":"10.1145/2939672.2939778","isbn":null,"url":null}],"related":["named-entity-recognition","bert-based-classification","explainable-bert-based-classification","explainable-text-summarization","explainable-sentiment-analysis","explainable-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-nmf-topic-model","name":"Explainable NMF Topic Model","fullName":"Explainable Non-negative Matrix Factorization Topic Model","aliases":["XAI-NMF","interpretable NMF topic model","explainable NMF","transparent NMF topic modeling"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2001 (NMF); XAI integration ~2017–present","originator":"Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016","url":"https://scholargate.app/en/deep-learning/explainable-nmf-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-nmf-topic-model.md","definition":"An Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such as coherence metrics, word contribution scores, and SHAP-style attribution to make discovered topics transparent and auditable by human readers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016","year":"2001 (NMF); XAI integration ~2017–present","type":"Interpretable unsupervised topic model","dataType":"Document-term matrices (bag-of-words or TF-IDF); text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.","type":"article","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2000/hash/f9d1152547c0bde01830b7e8bd60024c-Abstract.html"},{"ref":"Non-negative matrix factorization. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Non-negative_matrix_factorization"}],"related":["nmf-topic-model","lda-topic-model","explainable-lda-topic-model","topic-modeling","explainable-bert-based-classification","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-object-detection","name":"Explainable Object Detection","fullName":"Explainable Artificial Intelligence for Object Detection (XAI-OD)","aliases":["XAI Object Detection","Interpretable Object Detection","Transparent Object Detection","Explainable OD"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2017","originator":"Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP)","url":"https://scholargate.app/en/deep-learning/explainable-object-detection","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-object-detection.md","definition":"Explainable object detection combines a deep-learning object detector — such as YOLO, Faster R-CNN, or DETR — with post-hoc or built-in explainability methods (Grad-CAM, LIME, SHAP, D-RISE) that visualize why the model placed a bounding box at a particular location and assigned a particular class label, making its decisions auditable by humans.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP)","year":"2016–2017","type":"Post-hoc explainability applied to object detection","dataType":"Images (RGB; multi-scale; annotated bounding boxes)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618–626.","type":"inproceedings","doi":"10.1109/ICCV.2017.74","isbn":null,"url":null},{"ref":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why Should I Trust You?': Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.","type":"inproceedings","doi":"10.1145/2939672.2939778","isbn":null,"url":null}],"related":["object-detection","explainable-image-classification","explainable-convolutional-neural-network","explainable-vision-transformer","semantic-segmentation","instance-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-one-class-svm","name":"Explainable One-Class SVM","fullName":"Explainable One-Class Support Vector Machine","aliases":["XOC-SVM","Interpretable One-Class SVM","SHAP-augmented OCSVM","Explainable Novelty Detection SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1999 (OCSVM); 2017–present (explainability integration)","originator":"Schölkopf, B. et al. (OCSVM); explainability layer via Lundberg & Lee (SHAP, 2017) and related works","url":"https://scholargate.app/en/machine-learning/explainable-one-class-svm","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-one-class-svm.md","definition":"Explainable One-Class SVM pairs the classic One-Class Support Vector Machine anomaly detector — which learns a tight boundary around normal data without requiring labeled anomalies — with post-hoc explainability methods such as SHAP or LIME to reveal which features drive each novelty or anomaly score, converting an opaque decision boundary into an auditable, feature-attributable signal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schölkopf, B. et al. (OCSVM); explainability layer via Lundberg & Lee (SHAP, 2017) and related works","year":"1999 (OCSVM); 2017–present (explainability integration)","type":"Anomaly/novelty detection with post-hoc or intrinsic explainability","dataType":"Continuous or mixed tabular data; unlabeled or predominantly normal-class data","subfamily":"Machine learning"},"citations":[{"ref":"Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems, 12, 582–588.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Support+vector+method+for+novelty+detection+Scholkopf+1999"},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+unified+approach+to+interpreting+model+predictions+Lundberg+Lee+2017"}],"related":["one-class-svm","isolation-forest","local-outlier-factor","autoencoder-anomaly-detection","shap-explainability","support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-question-answering","name":"Explainable Question Answering","fullName":"Explainable Question Answering (XQA)","aliases":["XQA","interpretable QA","transparent question answering","rationale-based QA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2020","originator":"Community (DeYoung et al.; Rajpurkar et al.)","url":"https://scholargate.app/en/deep-learning/explainable-question-answering","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-question-answering.md","definition":"Explainable Question Answering (XQA) combines neural reading-comprehension models — typically BERT-family transformers — with interpretability methods such as rationale extraction, attention visualization, LIME, or SHAP to reveal why the model selected a particular answer span. The goal is not just accuracy but trustworthy, auditable reasoning that users and domain experts can inspect and verify.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Community (DeYoung et al.; Rajpurkar et al.)","year":"2016–2020","type":"Interpretable NLP pipeline","dataType":"Text (passages, questions, answer spans)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020). ERASER: A Benchmark to Evaluate Rationalized NLP Models. In Proceedings of ACL 2020, pp. 4443–4458.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.408","isbn":null,"url":null},{"ref":"Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of EMNLP 2016, pp. 2383–2392.","type":"inproceedings","doi":"10.18653/v1/D16-1264","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","explainable-bert-based-classification","transformer","sentence-embeddings","explainable-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-random-forest","name":"Explainable Random Forest","fullName":"Explainable Random Forest (Interpretable Ensemble with Feature Attribution)","aliases":["XRF","interpretable random forest","transparent random forest","random forest with explainability"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001–2017","originator":"Breiman, L. (RF); Lundberg & Lee (SHAP attribution)","url":"https://scholargate.app/en/machine-learning/explainable-random-forest","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-random-forest.md","definition":"Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, L. (RF); Lundberg & Lee (SHAP attribution)","year":"2001–2017","type":"Interpretable ensemble (bagging + post-hoc attribution)","dataType":"Tabular (continuous, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"ref":"Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.","type":"article","doi":"10.1023/A:1010933404324","isbn":null,"url":null}],"related":["random-forest","shap-values","decision-tree","gradient-boosting","xgboost","lime-explainability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-recurrent-neural-network","name":"Explainable Recurrent Neural Network","fullName":"Explainable Recurrent Neural Network (XAI-augmented RNN)","aliases":["Explainable RNN","Interpretable RNN","XAI-RNN","Transparent Recurrent Neural Network"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2020","originator":"Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)","url":"https://scholargate.app/en/deep-learning/explainable-recurrent-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-recurrent-neural-network.md","definition":"An Explainable Recurrent Neural Network (XAI-RNN) pairs a standard RNN architecture with a post-hoc or intrinsic interpretability method — such as SHAP, LIME, integrated gradients, or attention visualization — to reveal which input time steps or tokens most influence the model's sequential predictions, without sacrificing predictive accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arrived via XAI literature (Arrieta et al., Lundberg & Lee, and attention-based RNN work)","year":"2017–2020","type":"Interpretability framework applied to sequence models","dataType":"Sequential / time-series / text","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Arrieta, A. B., Diaz-Rodriguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.","type":"article","doi":"10.1016/j.inffus.2019.12.012","isbn":null,"url":null},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"}],"related":["recurrent-neural-network","long-short-term-memory","gated-recurrent-unit","explainable-lstm","explainable-transformer","explainable-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-reinforcement-learning","name":"Explainable Reinforcement Learning","fullName":"Explainable Reinforcement Learning (XRL)","aliases":["XRL","interpretable reinforcement learning","transparent RL","explainable RL"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2020","originator":"Puiutta, E. & Veith, E. M. S. P. (survey); broader XAI community","url":"https://scholargate.app/en/deep-learning/explainable-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-reinforcement-learning.md","definition":"Explainable Reinforcement Learning (XRL) augments standard reinforcement learning agents with methods that make their policies, decisions, and learned behaviors interpretable to humans. Rather than treating the policy as a black box, XRL produces post-hoc explanations or builds inherently transparent policies, enabling trust verification, debugging, and accountability in high-stakes automated decision-making.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Puiutta, E. & Veith, E. M. S. P. (survey); broader XAI community","year":"2018–2020","type":"Hybrid approach (RL + explainability methods)","dataType":"Sequential decision data, environment state-action trajectories","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Puiutta, E., & Veith, E. M. S. P. (2020). Explainable Reinforcement Learning: A Survey. In Machine Learning and Knowledge Extraction (CD-MAKE 2020), Lecture Notes in Computer Science, vol. 12279, pp. 77–95. Springer.","type":"inproceedings","doi":"10.1007/978-3-030-57321-8_5","isbn":null,"url":null},{"ref":"Explainable artificial intelligence. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Explainable_artificial_intelligence"}],"related":["reinforcement-learning","explainable-convolutional-neural-network","explainable-bert-based-classification","deep-q-network","attention-mechanism","saliency-map"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-roberta-based-classification","name":"Explainable RoBERTa-based Classification","fullName":"Explainable RoBERTa-based Text Classification with Post-hoc Interpretation","aliases":["XAI-RoBERTa","Interpretable RoBERTa Classifier","RoBERTa with SHAP/LIME","Transparent RoBERTa NLP"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Liu et al. (RoBERTa, 2019); Lundberg & Lee (SHAP, 2017); Ribeiro et al. (LIME, 2016)","url":"https://scholargate.app/en/deep-learning/explainable-roberta-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-roberta-based-classification.md","definition":"Explainable RoBERTa-based classification fine-tunes a RoBERTa transformer model on labeled text data and then applies post-hoc interpretability methods — such as SHAP, LIME, or attention analysis — to reveal which tokens or features drove each prediction. This bridges state-of-the-art NLP performance with human-understandable reasoning, satisfying both accuracy and transparency requirements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu et al. (RoBERTa, 2019); Lundberg & Lee (SHAP, 2017); Ribeiro et al. (LIME, 2016)","year":"2019–2020","type":"Pre-trained transformer classifier with post-hoc XAI","dataType":"Text (sequences, documents, sentences)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.11692"},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"}],"related":["roberta-based-classification","bert-based-classification","explainable-bert-based-classification","sentence-embeddings","transformer","explainable-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-semantic-segmentation","name":"Explainable Semantic Segmentation","fullName":"Explainable Semantic Segmentation (XAI-Integrated Pixel-Wise Scene Parsing)","aliases":["XSS","interpretable semantic segmentation","explainable scene parsing","transparent pixel-wise classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2021","originator":"Combination: Long et al. (FCN) + Selvaraju et al. (Grad-CAM); formalized as a unified paradigm ~2019–2021","url":"https://scholargate.app/en/deep-learning/explainable-semantic-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-semantic-segmentation.md","definition":"Explainable Semantic Segmentation (XSS) couples pixel-wise scene parsing — assigning a class label to every pixel in an image — with post-hoc or intrinsic explanation methods such as Grad-CAM, attention maps, or SHAP, so that the network's class decisions can be audited, visualized, and justified to domain experts in medical imaging, autonomous driving, and remote sensing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Combination: Long et al. (FCN) + Selvaraju et al. (Grad-CAM); formalized as a unified paradigm ~2019–2021","year":"2019–2021","type":"Explainable deep learning pipeline","dataType":"Images (RGB, medical, satellite, video frames)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618–626.","type":"inproceedings","doi":"10.1109/ICCV.2017.74","isbn":null,"url":null},{"ref":"Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440.","type":"inproceedings","doi":"10.1109/CVPR.2015.7298965","isbn":null,"url":null}],"related":["grad-cam","semantic-segmentation","instance-segmentation","attention-mechanism","shap","lime"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-sentence-embeddings","name":"Explainable Sentence Embeddings","fullName":"Explainable Sentence Embeddings (Interpretable Dense Sentence Representations)","aliases":["interpretable sentence representations","XAI sentence embeddings","probing sentence embeddings","explainable sentence vectors"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2018","originator":"Conneau et al.; Ribeiro et al. (probing + LIME frameworks)","url":"https://scholargate.app/en/deep-learning/explainable-sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-sentence-embeddings.md","definition":"Explainable sentence embeddings combine dense sentence representation learning with post-hoc or intrinsic interpretability tools — such as probing classifiers, LIME, SHAP, or attention attribution — to reveal what linguistic and semantic information is encoded in a sentence vector and why a downstream model makes a given prediction. The goal is to retain the representational power of modern encoders while making their behavior auditable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conneau et al.; Ribeiro et al. (probing + LIME frameworks)","year":"2016–2018","type":"Post-hoc interpretability applied to sentence encoders","dataType":"Text (sentences, documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018). What you can cram into a single $\\vec{v}$ector: Probing sentence embeddings for linguistic properties. In Proceedings of ACL 2018, pp. 2126–2136.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P18-1198"},{"ref":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). \"Why Should I Trust You?\": Explaining the predictions of any classifier. In Proceedings of KDD 2016, pp. 1135–1144.","type":"inproceedings","doi":"10.1145/2939672.2939778","isbn":null,"url":null}],"related":["sentence-embeddings","bert-based-classification","explainable-bert-based-classification","explainable-recurrent-neural-network","explainable-transformer","self-supervised-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-sentiment-analysis","name":"Explainable Sentiment Analysis","fullName":"Explainable Sentiment Analysis (XAI-augmented Opinion Mining)","aliases":["XAI sentiment analysis","interpretable sentiment classification","transparent opinion mining","explainable opinion analysis"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2020","originator":"Multiple contributors (LIME: Ribeiro et al. 2016; SHAP: Lundberg & Lee 2017; attention-based XAI in NLP: numerous, 2018–2020)","url":"https://scholargate.app/en/deep-learning/explainable-sentiment-analysis","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-sentiment-analysis.md","definition":"Explainable sentiment analysis pairs a sentiment classification model — typically a fine-tuned transformer such as BERT or RoBERTa — with a post-hoc or intrinsic explanation method (SHAP, LIME, attention visualization, or integrated gradients) that reveals which words, phrases, or features drove each prediction. The goal is both high predictive accuracy and transparent, auditable rationales for every label.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (LIME: Ribeiro et al. 2016; SHAP: Lundberg & Lee 2017; attention-based XAI in NLP: numerous, 2018–2020)","year":"2016–2020","type":"Interpretable NLP pipeline","dataType":"Text (reviews, social media, survey responses)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020). A Survey of the State of Explainable AI for Natural Language Processing. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the ACL and the 10th IJCNLP, 447–459.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/2020.aacl-main.46"},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"}],"related":["bert-based-classification","roberta-based-classification","explainable-bert-based-classification","sentence-embeddings","fine-tuned-sentiment-analysis","topic-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-stacking-ensemble","name":"Explainable Stacking Ensemble","fullName":"Explainable Stacking Ensemble (Interpretable Meta-Learning)","aliases":["XAI-Stacking","interpretable stacking","transparent stacking ensemble","explainable stacked generalisation"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1992 (stacking); 2010s–2020s (explainable extensions)","originator":"Wolpert, D. H. (stacking); XAI integration developed across the community","url":"https://scholargate.app/en/machine-learning/explainable-stacking-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-stacking-ensemble.md","definition":"Explainable Stacking Ensemble combines the predictive power of stacked generalisation — training a meta-learner on the outputs of multiple diverse base models — with interpretability tools such as SHAP or LIME that reveal how each base model and each input feature contributed to the final prediction. It bridges the accuracy–transparency trade-off that makes pure stacking opaque in high-stakes settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolpert, D. H. (stacking); XAI integration developed across the community","year":"1992 (stacking); 2010s–2020s (explainable extensions)","type":"Ensemble meta-learning with post-hoc or intrinsic interpretability","dataType":"Tabular (continuous, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.","type":"article","doi":"10.1016/S0893-6080(05)80023-1","isbn":null,"url":null},{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"}],"related":["random-forest","xgboost","bagging-ensemble","gradient-boosting","shap-explainability","model-stacking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-support-vector-machine","name":"Explainable Support Vector Machine","fullName":"Explainable Support Vector Machine (XAI-augmented SVM)","aliases":["Explainable SVM","Interpretable SVM","XAI-SVM","Transparent Support Vector Machine"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2016–2017 (XAI layer)","originator":"Cortes & Vapnik (SVM); explainability layer via Lundberg & Lee (SHAP, 2017) and Ribeiro et al. (LIME, 2016)","url":"https://scholargate.app/en/machine-learning/explainable-support-vector-machine","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-support-vector-machine.md","definition":"Explainable SVM combines a trained Support Vector Machine with a post-hoc interpretability layer — typically SHAP or LIME — to produce feature-level explanations for individual predictions and global importance rankings. It retains the discriminative power of SVM while meeting transparency requirements in high-stakes domains such as medicine, finance, and law.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cortes & Vapnik (SVM); explainability layer via Lundberg & Lee (SHAP, 2017) and Ribeiro et al. (LIME, 2016)","year":"2016–2017 (XAI layer)","type":"Post-hoc explainability applied to SVM","dataType":"Tabular (continuous, binary, categorical features)","subfamily":"Machine learning"},"citations":[{"ref":"Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"ref":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why should I trust you?': Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.","type":"inproceedings","doi":"10.1145/2939672.2939778","isbn":null,"url":null}],"related":["support-vector-machine","explainable-random-forest","explainable-gradient-boosting","explainable-logistic-regression","explainable-decision-tree","explainable-naive-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-text-summarization","name":"Explainable Text Summarization","fullName":"Explainable Text Summarization (XAI-augmented Abstractive and Extractive Summarization)","aliases":["XAI text summarization","interpretable summarization","transparent summarization","faithfulness-aware summarization"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Community (Maynez, Atanasova et al.)","url":"https://scholargate.app/en/deep-learning/explainable-text-summarization","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-text-summarization.md","definition":"Explainable Text Summarization augments automatic summarization models — extractive or abstractive — with post-hoc or built-in explanation methods that reveal which source sentences, tokens, or attention patterns drove each output sentence. The goal is to audit faithfulness, detect hallucinations, and build trust in model outputs in high-stakes settings such as medical or legal document review.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Community (Maynez, Atanasova et al.)","year":"2019–2020","type":"Explainable NLP pipeline","dataType":"Text (documents, articles, reports)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Atanasova, P., Simonsen, J. G., Lioma, C., & Augenstein, I. (2020). A diagnostic study of explainability techniques for text classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3256–3274. Association for Computational Linguistics.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2020.emnlp-main.263"},{"ref":"Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 1906–1919.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2020.acl-main.173"}],"related":["explainable-bert-based-classification","explainable-transformer","fine-tuned-text-summarization","transfer-learning-with-text-summarization","sentence-embeddings","explainable-named-entity-recognition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-topic-modeling","name":"Explainable Topic Modeling","fullName":"Explainable Topic Modeling (Interpretable Latent Topic Discovery)","aliases":["XTM","interpretable topic modeling","transparent topic modeling","explainable LDA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2003–2020s","originator":"Community practice (Blei et al. seminal; explainability extensions 2010s–present)","url":"https://scholargate.app/en/deep-learning/explainable-topic-modeling","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-topic-modeling.md","definition":"Explainable Topic Modeling combines unsupervised topic discovery — such as LDA, NMF, or neural variants like BERTopic — with interpretability tools (top-word lists, coherence scores, SHAP, attention weights) that make the learned topics transparent, auditable, and communicable to domain experts and stakeholders beyond the modeling team.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Community practice (Blei et al. seminal; explainability extensions 2010s–present)","year":"2003–2020s","type":"Unsupervised topic discovery + interpretability layer","dataType":"Text corpora (documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/blei03a.html"},{"ref":"Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2203.05794"}],"related":["lda-topic-model","nmf-topic-model","bert-based-classification","sentence-embeddings","explainable-bert-based-classification","topic-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-transformer","name":"Explainable Transformer","fullName":"Explainable Transformer (Interpretability-Augmented Transformer Model)","aliases":["XAI Transformer","Interpretable Transformer","Transparent Transformer","Explainable Attention Model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2021","originator":"Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community","url":"https://scholargate.app/en/deep-learning/explainable-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-transformer.md","definition":"An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community","year":"2017–2021","type":"Interpretable deep learning model","dataType":"Text, images, tabular sequences, or multimodal inputs","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1706.03762"},{"ref":"Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791.","type":"inproceedings","doi":"10.1109/CVPR46437.2021.00084","isbn":null,"url":null}],"related":["transformer","bert-based-classification","explainable-bert-based-classification","explainable-convolutional-neural-network","self-supervised-transformer","multimodal-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-variational-autoencoder","name":"Explainable Variational Autoencoder","fullName":"Explainable Variational Autoencoder (XVAE / Interpretable VAE)","aliases":["XVAE","Interpretable VAE","Disentangled Variational Autoencoder","Explainable Generative Model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013–2017","originator":"Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement)","url":"https://scholargate.app/en/deep-learning/explainable-variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-variational-autoencoder.md","definition":"An Explainable Variational Autoencoder (XVAE) extends the standard VAE framework with techniques that make its latent space interpretable: disentangling latent dimensions so each corresponds to a human-understandable factor, or post-hoc attribution methods (SHAP, integrated gradients) that trace reconstructions back to input features. It retains the VAE's generative power while adding transparency required in scientific and high-stakes applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement)","year":"2013–2017","type":"Generative model with interpretable latent space","dataType":"Images, time-series, tabular, or mixed continuous data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"},{"ref":"Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., & Lerchner, A. (2017). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=Sy2fchgI"}],"related":["variational-autoencoder","explainable-generative-adversarial-network","explainable-convolutional-neural-network","self-supervised-variational-autoencoder","multimodal-variational-autoencoder","fine-tuned-variational-autoencoder"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-vision-transformer","name":"Explainable Vision Transformer","fullName":"Explainable Vision Transformer (XViT / ViT with Post-hoc Attribution)","aliases":["XViT","Interpretable ViT","Explainable ViT","Transparent Vision Transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2021","originator":"Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT)","url":"https://scholargate.app/en/deep-learning/explainable-vision-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/explainable-vision-transformer.md","definition":"Explainable Vision Transformer combines the strong image-recognition performance of Vision Transformers (ViT) with attribution techniques — such as relevance propagation, attention rollout, or gradient-weighted attention — that highlight which image regions drive each prediction. The approach enables researchers and practitioners to audit model decisions and satisfy transparency requirements without sacrificing accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT)","year":"2021","type":"Post-hoc explainability applied to Vision Transformer","dataType":"Image, video, or patch-structured visual data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791.","type":"inproceedings","doi":"10.1109/CVPR46437.2021.00084","isbn":null,"url":null},{"ref":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., … Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=YicbFdNTTy"}],"related":["vision-transformer","explainable-convolutional-neural-network","image-classification","semantic-segmentation","self-supervised-vision-transformer","multimodal-vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-voting-ensemble","name":"Explainable Voting Ensemble","fullName":"Explainable Voting Ensemble (XAI-Augmented Voting Classifier/Regressor)","aliases":["XAI voting ensemble","interpretable voting classifier","transparent voting ensemble","explainable majority vote model"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2016–2020","originator":"Composite: voting ensemble (Dietterich, 2000) + XAI frameworks (Ribeiro et al., 2016; Lundberg & Lee, 2017)","url":"https://scholargate.app/en/machine-learning/explainable-voting-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-voting-ensemble.md","definition":"An Explainable Voting Ensemble combines predictions from multiple diverse base models through majority vote (hard voting) or averaged probabilities (soft voting), then applies post-hoc or ante-hoc XAI techniques — such as SHAP values, LIME, or permutation importance — to produce feature-level explanations for the combined model's decisions. The goal is to retain the accuracy gains of ensemble aggregation while meeting interpretability requirements in high-stakes or regulated applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Composite: voting ensemble (Dietterich, 2000) + XAI frameworks (Ribeiro et al., 2016; Lundberg & Lee, 2017)","year":"2016–2020","type":"Ensemble with post-hoc or ante-hoc interpretability","dataType":"Tabular (continuous, categorical, binary, ordinal)","subfamily":"Machine learning"},"citations":[{"ref":"Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"ref":"Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1–2), 1–39.","type":"article","doi":"10.1007/s10462-009-9124-7","isbn":null,"url":null}],"related":["voting-ensemble","stacking-ensemble","explainable-random-forest","explainable-gradient-boosting","shap-analysis","bagging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explainable-xgboost","name":"Explainable XGBoost","fullName":"Explainable XGBoost (XGBoost with SHAP-based Interpretability)","aliases":["XGBoost + SHAP","interpretable XGBoost","XAI-XGBoost","transparent gradient boosting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2016–2020","originator":"Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)","url":"https://scholargate.app/en/machine-learning/explainable-xgboost","markdownUrl":"https://scholargate.app/en/machine-learning/explainable-xgboost.md","definition":"Explainable XGBoost pairs the high predictive accuracy of XGBoost gradient-boosted trees with SHAP (SHapley Additive exPlanations) values to make each prediction fully auditable. The result is a model that matches or surpasses neural networks on tabular data while offering theoretically grounded, per-prediction feature attributions that satisfy both scientific transparency and regulatory demands.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen & Guestrin (XGBoost); Lundberg & Lee (SHAP for trees)","year":"2016–2020","type":"Interpretable ensemble (gradient-boosted trees + SHAP)","dataType":"Tabular (continuous, categorical, binary, ordinal)","subfamily":"Machine learning"},"citations":[{"ref":"Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67.","type":"article","doi":"10.1038/s42256-019-0138-9","isbn":null,"url":null},{"ref":"Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.","type":"inproceedings","doi":"10.1145/2939672.2939785","isbn":null,"url":null}],"related":["xgboost","explainable-gradient-boosting","explainable-random-forest","explainable-lightgbm","gradient-boosting","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explanatory-research","name":"Explanatory Research","fullName":"Explanatory Research Design","aliases":["analytical research","causal research","explanatory study","explanatory quantitative research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1960s–1980s (codified in behavioral and social science methodology)","originator":"Formalized by Earl Babbie and Fred Kerlinger among others","url":"https://scholargate.app/en/research-design/explanatory-research","markdownUrl":"https://scholargate.app/en/research-design/explanatory-research.md","definition":"Explanatory research is a non-experimental quantitative research design that goes beyond describing a phenomenon to identifying why it occurs — examining the relationships or mechanisms that account for observed patterns. Rooted in positivist social science methodology, it uses theory-driven hypotheses and statistical analysis to test whether specific variables explain variation in an outcome, without necessarily manipulating those variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized by Earl Babbie and Fred Kerlinger among others","year":"1960s–1980s (codified in behavioral and social science methodology)","type":"Non-experimental quantitative research design","dataType":"Quantitative survey, secondary data, archival records","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030417559","url":null},{"ref":"Babbie, E. (2010). The Practice of Social Research (12th ed.). Wadsworth/Cengage Learning.","type":"book","doi":null,"isbn":"978-0495598428","url":null}],"related":["correlational-research","causal-comparative-research","confirmatory-research","hypothesis-testing-research","descriptive-research","exploratory-quantitative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"explanatory-sequential-mixed-methods-design","name":"Explanatory Sequential Mixed Methods Design","fullName":"Explanatory Sequential Mixed Methods Research Design","aliases":["explanatory sequential design","QUAN → qual design","two-phase explanatory design","sequential explanatory design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007 (formalized in Creswell & Plano Clark's mixed methods typology)","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/explanatory-sequential-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/explanatory-sequential-mixed-methods-design.md","definition":"The explanatory sequential mixed methods design is a two-phase research approach in which a quantitative study is conducted first, and qualitative data are then collected specifically to help explain or elaborate the initial quantitative results. The quantitative phase carries greater priority; the qualitative phase is purposefully built around the findings — such as surprising results, outliers, or statistically significant relationships — that need deeper interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2007 (formalized in Creswell & Plano Clark's mixed methods typology)","type":"Mixed methods research design","dataType":"Quantitative data (Phase 1) followed by qualitative data (Phase 2)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Plano Clark, V. L., & Creswell, J. W. (2007). The Mixed Methods Reader. Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Mixed+Methods+Reader+Plano+Clark+Creswell+2007"}],"related":["exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","multiphase-mixed-methods-design","case-study","survey-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exploratory-factor-analysis","name":"EFA","fullName":"Exploratory Factor Analysis","aliases":["common factor analysis","açımlayıcı faktör analizi","factor analysis"],"domain":"statistics","family":"latent-structure","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/statistics/exploratory-factor-analysis","markdownUrl":"https://scholargate.app/en/statistics/exploratory-factor-analysis.md","definition":"Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Latent variable / dimension reduction","outcome":"Latent common factors","data":"Continuous / ordinal indicators","min_sample":50},"citations":[{"ref":"Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.","type":"article","doi":"10.1037/1082-989X.4.3.272","isbn":null,"url":null},{"ref":"Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1473756540","url":null}],"related":["confirmatory-factor-analysis","pca","sem","cronbach-alpha"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exploratory-quantitative-research","name":"Exploratory Quantitative Research","fullName":"Exploratory Quantitative Research Design","aliases":["quantitative exploratory design","exploratory survey research","initial quantitative investigation","preliminary quantitative study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century (codified in social research methods texts c. 1950s–1970s)","originator":"Earl Babbie; John Creswell (systematic codification in social science methods)","url":"https://scholargate.app/en/research-design/exploratory-quantitative-research","markdownUrl":"https://scholargate.app/en/research-design/exploratory-quantitative-research.md","definition":"Exploratory quantitative research is a non-experimental design used when a phenomenon is insufficiently understood to support formal hypothesis testing. The researcher collects numerical data — typically through surveys, structured observation, or existing records — to describe distributions, detect patterns, and generate hypotheses that more targeted confirmatory studies can subsequently test. It occupies the first stage of a cumulative quantitative research programme.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Earl Babbie; John Creswell (systematic codification in social science methods)","year":"Mid-20th century (codified in social research methods texts c. 1950s–1970s)","type":"Non-experimental quantitative research design","dataType":"Numerical data from surveys, structured observation, archival records","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Babbie, E. (2021). The Practice of Social Research (15th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-0357360767","url":null},{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506386706","url":null}],"related":["descriptive-research","correlational-research","confirmatory-research","survey-research","cross-sectional-research","hypothesis-testing-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exploratory-sequential-mixed-methods-design","name":"Exploratory Sequential Mixed Methods Design","fullName":"Exploratory Sequential Mixed Methods Design","aliases":["QUAL → QUAN design","exploratory sequential design","instrument-development design","theory-building mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1990s–2000s (codified by ~2007)","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/exploratory-sequential-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/exploratory-sequential-mixed-methods-design.md","definition":"The exploratory sequential mixed methods design is a two-phase research framework in which a qualitative phase is conducted first to explore a poorly understood phenomenon, and the findings then inform a subsequent quantitative phase — typically to develop and test a survey instrument, measure a theory, or generalize qualitative insights to a larger population. The qualitative strand guides what is measured; the quantitative strand tests or extends those findings at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"1990s–2000s (codified by ~2007)","type":"Mixed methods research design","dataType":"Qualitative data first (interviews, focus groups, observations), then quantitative data (surveys, instruments)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506386706","url":null}],"related":["explanatory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","grounded-theory","survey-research","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exploratory-structural-equation-modeling","name":"Exploratory Structural Equation Modeling","fullName":"Exploratory Structural Equation Modeling","aliases":["ESEM"],"domain":"psychometrics","family":"latent-structure","subfamily":"Latent Factor Models","year":"2009","originator":"Tihomir Asparouhov, Bengt Muthén","url":"https://scholargate.app/en/psychometrics/exploratory-structural-equation-modeling","markdownUrl":"https://scholargate.app/en/psychometrics/exploratory-structural-equation-modeling.md","definition":"Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load on all factors, which can reveal cross-factor complexity and improve model fit while retaining the ability to test substantive structural theories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tihomir Asparouhov, Bengt Muthén","subfamily":"Latent Factor Models","year":"2009","type":"Hybrid exploratory-confirmatory factor modeling"},"citations":[{"ref":"Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438.","type":"article","doi":"10.1080/10705510903008204","isbn":null,"url":null},{"ref":"Marsh, H. W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A. J., Trautwein, U., & Nagengast, B. (2010). A new perspective on structural equation modeling: Guest editors' introduction. Structural Equation Modeling, 17(3), 357-370.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+perspective+on+structural+equation+modeling%3A+Guest+editors%27+introduction+Marsh"},{"ref":"Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313-335.","type":"article","doi":"10.1037/a0026802","isbn":null,"url":null}],"related":["pls-sem","wordscores","wordfish","latent-transition-analysis","rule-space-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exponential-random-graph","name":"Exponential Random Graph Model","fullName":"Exponential Random Graph Model (ERGM / p*)","aliases":["ERGM","p-star model","p* model","Üstel Rastgele Graf Modeli (ERGM / p*)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":"1986 (foundational); modern ERGM framework 1996–2007","originator":"Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)","url":"https://scholargate.app/en/network-analysis/exponential-random-graph","markdownUrl":"https://scholargate.app/en/network-analysis/exponential-random-graph.md","definition":"The Exponential Random Graph Model (ERGM), also known as the p* model, is a statistical framework for network analysis that models the probability of an observed network as a function of its local structural features — such as reciprocity, triangles, and degree distribution. Developed from the foundational work of Frank and Strauss (1986) and extended into the modern framework by Wasserman and Pattison (1996) and Robins et al. (2007), ERGM is the inferential standard for social network analysis, capable of testing whether observed network structures arise by chance or reflect genuine social processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)","year":"1986 (foundational); modern ERGM framework 1996–2007","type":"Probabilistic generative network model","estimationMethod":"Markov chain Monte Carlo maximum likelihood (MCMC-MLE)","output":"Coefficient estimates for network structural configurations (edges, reciprocity, triangles, node covariates)","minNodes":30,"networkTypes":"Directed, undirected, weighted variants all supported","difficulty":3},"citations":[{"ref":"Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191.","type":"article","doi":"10.1016/j.socnet.2006.08.002","isbn":null,"url":null},{"ref":"Lusher, D., Koskinen, J., & Robins, G. (Eds.) (2012). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521193566","url":null}],"related":["text-network-analysis","gnn","graph-attention-network","community-detection","dbscan","causal-discovery"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exposure-response-prevention","name":"Exposure and Response Prevention","fullName":"Exposure and Response Prevention Therapy","aliases":["ERP","exposure therapy","EX/RP"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Exposure-based intervention","year":"1986","originator":"Edna B. Foa, Michael J. Kozak","url":"https://scholargate.app/en/clinical-psychology/exposure-response-prevention","markdownUrl":"https://scholargate.app/en/clinical-psychology/exposure-response-prevention.md","definition":"Exposure and Response Prevention (ERP) is a behavioral intervention designed to reduce anxiety and compulsive behaviors by having clients repeatedly confront feared situations or intrusive thoughts without engaging in safety behaviors or compulsions. Developed by Edna B. Foa and colleagues in the 1980s, ERP is now considered the gold-standard treatment for obsessive-compulsive disorder (OCD) and is also highly effective for anxiety disorders, PTSD, and specific phobias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edna B. Foa, Michael J. Kozak","subfamily":"Exposure-based intervention","year":"1986","type":"Behavioral intervention"},"citations":[{"ref":"Foa, E. B., & Kozak, M. J. (1986). Emotional processing of fear: Exposure to corrective information. Psychological Bulletin, 99(1), 20–35.","type":"article","doi":"10.1037/0033-2909.99.1.20","isbn":null,"url":null},{"ref":"Foa, E. B., Yadin, E., & Lichner, T. K. (2012). Exposure and response (ritual) prevention for OCD: Therapist guide. Oxford University Press.","type":"article","doi":null,"isbn":"9780195307559","url":null}],"related":["cognitive-behavioral-therapy-assessment","trauma-focused-cbt","acceptance-commitment-therapy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exprom-i","name":"EXPROM-I","fullName":"EXPROM I — Extended PROMETHEE (partial)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1991","originator":"Diakoulaki, D., Koumoutsos, N.","url":"https://scholargate.app/en/decision-making/exprom-i","markdownUrl":"https://scholargate.app/en/decision-making/exprom-i.md","definition":"EXPROM-I (EXPROM I — Extended PROMETHEE (partial)) is a outranking multi-criteria decision-making (MCDM) method introduced by Diakoulaki, D., Koumoutsos, N. in 1991. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diakoulaki, D., Koumoutsos, N.","subfamily":"Outranking","year":"1991","type":"Outranking with weak+strong preferences","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Diakoulaki, D., Koumoutsos, N. (1991). Cardinal ranking of alternative actions: Extension of the PROMETHEE method. European Journal of Operational Research","type":"article","doi":"10.1016/0377-2217(91)90067-6","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"exprom-ii","name":"EXPROM-II","fullName":"EXPROM II — Total ranking variant","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1991","originator":"Diakoulaki, D., Koumoutsos, N.","url":"https://scholargate.app/en/decision-making/exprom-ii","markdownUrl":"https://scholargate.app/en/decision-making/exprom-ii.md","definition":"EXPROM-II (EXPROM II — Total ranking variant) is a outranking multi-criteria decision-making (MCDM) method introduced by Diakoulaki, D., Koumoutsos, N. in 1991. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diakoulaki, D., Koumoutsos, N.","subfamily":"Outranking","year":"1991","type":"Outranking with weak+strong preferences","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Diakoulaki, D., Koumoutsos, N. (1991). Cardinal ranking of alternative actions: Extension of the PROMETHEE method. European Journal of Operational Research","type":"article","doi":"10.1016/0377-2217(91)90067-6","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"extended-kalman-filter","name":"Extended Kalman Filter","fullName":"Extended Kalman Filter","aliases":["EKF","Nonlinear Kalman Filter"],"domain":"control-theory","family":"ml-model","subfamily":"Nonlinear Estimation","year":"1961","originator":"Richard S. Bucy","url":"https://scholargate.app/en/control-theory/extended-kalman-filter","markdownUrl":"https://scholargate.app/en/control-theory/extended-kalman-filter.md","definition":"The Extended Kalman Filter (EKF) is the nonlinear generalization of the Kalman Filter, extending the linear state estimation algorithm to nonlinear systems through local linearization. Developed by Bucy in the early 1960s, the EKF has become the workhorse for state estimation in nonlinear systems across robotics, aerospace, and navigation, enabling real-time processing of noisy measurements from nonlinear sensors and dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard S. Bucy","subfamily":"Nonlinear Estimation","year":"1961","type":"algorithm"},"citations":[{"ref":"Bucy, R. S. (1961). A linear approximation to the solution of nonlinear filtering equations. Technical Report No. 32-486, Jet Propulsion Laboratory.","type":"article","doi":null,"isbn":null,"url":"https://ntrs.nasa.gov/citations/20100037996"},{"ref":"Bar-Shalom, Y., Li, X. R., & Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation. Wiley-Interscience.","type":"article","doi":"10.1002/0471221279","isbn":null,"url":null},{"ref":"Welch, G., & Bishop, G. (2006). An Introduction to the Kalman Filter. UNC-CH Technical Report.","type":"article","doi":null,"isbn":null,"url":"https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf"}],"related":["linear-quadratic-gaussian","unscented-kalman-filter","simultaneously-localization-and-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"extra-trees","name":"Extra Trees","fullName":"Extremely Randomized Trees (Extra-Trees)","aliases":["Extremely Randomized Trees","ExtraTreesClassifier","ExtraTreesRegressor","ET"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006","originator":"Geurts, P.; Ernst, D.; Wehenkel, L.","url":"https://scholargate.app/en/machine-learning/extra-trees","markdownUrl":"https://scholargate.app/en/machine-learning/extra-trees.md","definition":"Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geurts, P.; Ernst, D.; Wehenkel, L.","year":"2006","type":"Ensemble (extremely randomized decision trees)","dataType":"Tabular (continuous, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.","type":"article","doi":"10.1007/s10994-006-6226-1","isbn":null,"url":null},{"ref":"Extra-Trees. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Random_forest#ExtraTrees"}],"related":["random-forest","decision-tree","xgboost","gradient-boosting","bagging","support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"extreme-value-theory","name":"Extreme Value Theory","fullName":"Extreme Value Theory (GEV, GPD, Peaks-Over-Threshold)","aliases":["EVT","generalized extreme value","generalized Pareto distribution","peaks over threshold","Aşırı Değer Teorisi (EVT — GEV, GPD, POT)"],"domain":"finance","family":"regression-model","subfamily":null,"year":2001,"originator":"Coles (textbook treatment); McNeil, Frey & Embrechts","url":"https://scholargate.app/en/finance/extreme-value-theory","markdownUrl":"https://scholargate.app/en/finance/extreme-value-theory.md","definition":"Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Coles (textbook treatment); McNeil, Frey & Embrechts","year":2001,"type":"Tail / extreme-event model","estimator":"Maximum likelihood fit of GEV (block maxima) or GPD (peaks over threshold)","outcome":"continuous (tail of the distribution)","minSample":50},"citations":[{"ref":"Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer.","type":"book","doi":null,"isbn":"978-1852334598","url":null},{"ref":"McNeil, A. J., Frey, R., & Embrechts, P. (2005). Quantitative Risk Management: Concepts, Techniques and Tools. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691122557","url":null}],"related":["value-at-risk","conditional-value-at-risk","realized-volatility","egarch","arima"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"eye-tracking-analysis","name":"Eye-Tracking Analysis","fullName":"Eye-Tracking Analysis","aliases":["Gaze Analysis","Eye Movement Tracking","Oculomotor Measurement"],"domain":"psychology","family":"hypothesis-test","subfamily":"Oculomotor","year":"1998","originator":"Keith Rayner and colleagues (modern cognitive applications)","url":"https://scholargate.app/en/psychology/eye-tracking-analysis","markdownUrl":"https://scholargate.app/en/psychology/eye-tracking-analysis.md","definition":"Eye-tracking analysis is a method for recording and quantifying eye movements and gaze patterns during visual tasks, providing direct measures of visual attention, comprehension, and cognitive processing. Advancing from mechanical devices to high-speed infrared cameras, eye tracking enables researchers to identify where people look, for how long, and in what sequence—revealing cognitive processes underlying reading, scene perception, decision-making, and attention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keith Rayner and colleagues (modern cognitive applications)","subfamily":"Oculomotor","year":"1998","type":"Behavioral measurement technique"},"citations":[{"ref":"Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford University Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Holmqvist%2C%20K.%2C%20Nystr%C3%B6m%2C%20M.%2C%20Andersson%2C%20R.%2C%20Dewhurst%2C%20R.%2C%20Jarodzka%2C%20H.%2C%20%26%20Van%20de%20Weijer%2C%20J.%20(2011).%20Eye%20tracking%3A%20A%20compr"},{"ref":"Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372-422.","type":"article","doi":"10.1037/0033-2909.124.3.372","isbn":null,"url":null},{"ref":"Duchowski, A. T. (2007). Eye tracking methodology: Theory and practice. Springer.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Duchowski%2C%20A.%20T.%20(2007).%20Eye%20tracking%20methodology%3A%20Theory%20and%20practice.%20Springer."}],"related":["pupillometry","response-time-analysis","visual-search","attention-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"f-beta-score","name":"F-beta Score","fullName":"F-beta Score (Weighted Harmonic Mean)","aliases":["F-measure with parameter beta"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"1979","originator":"C. J. van Rijsbergen","url":"https://scholargate.app/en/model-evaluation/f-beta-score","markdownUrl":"https://scholargate.app/en/model-evaluation/f-beta-score.md","definition":"The F-beta score is a weighted harmonic mean of precision and recall that allows customizing the relative importance of recall versus precision through a parameter beta. It generalizes the F1-score, which is the special case where beta = 1.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"C. J. van Rijsbergen","subfamily":"Classification Metric","year":"1979","type":"Evaluation metric"},"citations":[{"ref":"van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/informationretri00rijsbergen"},{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"}],"related":["f1-score","precision","recall","macro-averaged-f1","weighted-f1"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"f-lmaw","name":"F-LMAW","fullName":"Fuzzy Logarithm Methodology of Additive Weights (TFN)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2021 crisp; 2022 variant applicator","originator":"Božanić, D., Pamučar, D., Milić, A., Marinković, D., Komazec, N.","url":"https://scholargate.app/en/decision-making/f-lmaw","markdownUrl":"https://scholargate.app/en/decision-making/f-lmaw.md","definition":"F-LMAW (Fuzzy Logarithm Methodology of Additive Weights (TFN)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Božanić, D., Pamučar, D., Milić, A., Marinković, D., Komazec, N. in 2021 crisp; 2022 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Božanić, D., Pamučar, D., Milić, A., Marinković, D., Komazec, N.","subfamily":"Weight_Subjective","year":"2021 crisp; 2022 variant applicator","type":"Triangular-fuzzy linguistic expert weighting with Bonferroni aggregation; logarithmic transform around an absolute anti-ideal point","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Božanić, D., Pamučar, D., Milić, A., Marinković, D., Komazec, N. (2022). Modification of the Logarithm Methodology of Additive Weights (LMAW) by a Triangular Fuzzy Number and Its Application in Multi-Criteria Decision Making. Axioms","type":"article","doi":"10.3390/axioms11030089","isbn":null,"url":null}],"related":["lmaw","topsis","vikor","waspas","marcos","mabac","edas","copras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"f-statistics","name":"F-statistics (FST)","fullName":"F-statistics for Population Differentiation and Genetic Structure","aliases":["FST","Wright's F-statistics","Population differentiation index"],"domain":"genetics","family":"process-pipeline","subfamily":"Population genetics","year":"1951","originator":"Sewall Wright","url":"https://scholargate.app/en/genetics/f-statistics","markdownUrl":"https://scholargate.app/en/genetics/f-statistics.md","definition":"F-statistics are a family of measures developed by Sewall Wright to quantify population genetic structure and the degree of genetic differentiation between populations. FST, the most widely used F-statistic, measures the proportion of total genetic variation attributable to differences between populations versus within populations. FST ranges from zero (no differentiation) to one (complete differentiation). These statistics have become fundamental tools for understanding population structure, detecting population admixture, and analyzing the evolutionary forces shaping genetic variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sewall Wright","subfamily":"Population genetics","year":"1951","type":"Population differentiation measure"},"citations":[{"ref":"Wright, S. (1951). The genetical structure of populations. Annals of Eugenics, 15(4), 323–354.","type":"article","doi":"10.1111/j.1469-1809.1949.tb02451.x","isbn":null,"url":null},{"ref":"Weir, B. S., & Cockerham, C. C. (1984). Estimating F-statistics for the analysis of population structure. Evolution, 38(6), 1358–1370.","type":"article","doi":"10.1111/j.1558-5646.1984.tb05657.x","isbn":null,"url":null},{"ref":"Hudson, R. R., Boos, D. D., & Kaplan, N. L. (1992). A statistical test for detecting geographic subdivision in nucleotide sequences. Molecular Biology and Evolution, 9(3), 405–418.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+statistical+test+for+detecting+geographic+subdivision+in+nucleotide+sequences+Hudson"}],"related":["admixture-analysis","ld-block-analysis","coalescent-theory","selection-sweep"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"f1-score","name":"F1-Score","fullName":"F1-Score (Harmonic Mean of Precision and Recall)","aliases":["F-measure","Harmonic Mean"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"1979","originator":"C. J. van Rijsbergen","url":"https://scholargate.app/en/model-evaluation/f1-score","markdownUrl":"https://scholargate.app/en/model-evaluation/f1-score.md","definition":"The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"C. J. van Rijsbergen","subfamily":"Classification Metric","year":"1979","type":"Evaluation metric"},"citations":[{"ref":"van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/informationretri00rijsbergen"},{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"}],"related":["precision","recall","f-beta-score","macro-averaged-f1","weighted-f1"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-delphi-technique","name":"Face-to-face Delphi Technique","fullName":"Face-to-face Delphi Technique","aliases":["in-person Delphi","face-to-face Delphi","conventional Delphi","FtF Delphi"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1950s–1963","originator":"Norman Dalkey and Olaf Helmer (RAND Corporation)","url":"https://scholargate.app/en/survey-methodology/face-to-face-delphi-technique","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-delphi-technique.md","definition":"The face-to-face Delphi Technique is a structured, iterative consensus-building method conducted through in-person sessions with a purposively selected panel of experts. Across multiple rounds, panelists independently respond to structured questionnaires, receive aggregated group feedback, and revise their judgments until acceptable consensus is reached. The face-to-face format adds direct interpersonal interaction while preserving the anonymity of individual ratings within each round.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Dalkey and Olaf Helmer (RAND Corporation)","year":"1950s–1963","type":"Structured expert-consensus method","dataType":"Expert judgments, ratings, rankings (structured questionnaires across iterative rounds)","subfamily":"Data collection"},"citations":[{"ref":"Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458–467.","type":"article","doi":"10.1287/mnsc.9.3.458","isbn":null,"url":null},{"ref":"Linstone, H. A., & Turoff, M. (Eds.). (1975). The Delphi Method: Techniques and Applications. Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Delphi+Method+Techniques+and+Applications+Linstone+Turoff+1975"}],"related":["delphi-technique","online-delphi-technique","focus-group","face-to-face-focus-group","nominal-group-technique","expert-elicitation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-diary-method","name":"Face-to-face Diary Method","fullName":"Face-to-face Diary Method","aliases":["in-person diary study","face-to-face diary study","personal diary method","paper diary method"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s–1990s (formalized face-to-face protocol era)","originator":"Rooted in sociological and psychological diary research traditions; face-to-face protocols formalized in the late 20th century","url":"https://scholargate.app/en/survey-methodology/face-to-face-diary-method","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-diary-method.md","definition":"The face-to-face diary method is a data collection technique in which participants are recruited, briefed, and supported through in-person researcher contact while keeping structured or open-ended diaries over a defined period. By combining the temporal depth of diary records with the rapport and clarity of direct researcher interaction, it reduces ambiguity in diary instructions, improves compliance, and allows the researcher to probe or clarify entries at handover or follow-up meetings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in sociological and psychological diary research traditions; face-to-face protocols formalized in the late 20th century","year":"1980s–1990s (formalized face-to-face protocol era)","type":"Qualitative / mixed-methods data collection technique","dataType":"Participant-generated text, narrative, and behavioral self-reports","subfamily":"Data collection"},"citations":[{"ref":"Alaszewski, A. (2006). Using Diaries for Social Research. Sage Publications.","type":"book","doi":null,"isbn":"978-0761941484","url":null},{"ref":"Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.","type":"article","doi":"10.1146/annurev.psych.54.101601.145030","isbn":null,"url":null}],"related":["diary-method","structured-interview","face-to-face-structured-interview","experience-sampling-method","participant-observation","field-notes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-field-notes","name":"Face-to-face Field Notes","fullName":"Face-to-face Field Notes","aliases":["in-person field notes","observational field notes","ethnographic field notes","fieldwork notes"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Early 20th century (Malinowski ~1915–1922); codified by Emerson et al. 1995","originator":"Bronislaw Malinowski (systematic ethnographic fieldwork); Robert Emerson, Rachel Fretz & Linda Shaw (contemporary methodology)","url":"https://scholargate.app/en/survey-methodology/face-to-face-field-notes","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-field-notes.md","definition":"Face-to-face field notes are a foundational qualitative data collection technique in which the researcher is physically present in the setting and records observations, interactions, events, and contextual details in written form. As the canonical mode of ethnographic and observational research, in-person field notes capture the social texture, nonverbal cues, spatial arrangements, and moment-to-moment dynamics of real-world settings that remote or mediated data collection cannot fully replicate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bronislaw Malinowski (systematic ethnographic fieldwork); Robert Emerson, Rachel Fretz & Linda Shaw (contemporary methodology)","year":"Early 20th century (Malinowski ~1915–1922); codified by Emerson et al. 1995","type":"Qualitative data collection technique","dataType":"Written observational records, descriptive text, direct observational data","subfamily":"Data collection"},"citations":[{"ref":"Emerson, R. M., Fretz, R. I., & Shaw, L. L. (1995). Writing Ethnographic Fieldnotes. University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226206813","url":null},{"ref":"Bernard, H. R. (2011). Research Methods in Anthropology: Qualitative and Quantitative Approaches (5th ed.). AltaMira Press.","type":"book","doi":null,"isbn":"978-0759112421","url":null}],"related":["participant-observation","non-participant-observation","ethnography","face-to-face-participant-observation","diary-method","field-notes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-focus-group","name":"Face-to-face Focus Group","fullName":"Face-to-face Focus Group Discussion","aliases":["in-person focus group","FGD","co-located focus group","face-to-face FGD"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1940s (Merton & Lazarsfeld); systematised 1980s–1990s","originator":"Robert K. Merton and Paul Lazarsfeld (focused interview); Richard Krueger and David Morgan (applied focus group methodology)","url":"https://scholargate.app/en/survey-methodology/face-to-face-focus-group","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-focus-group.md","definition":"A face-to-face focus group is a structured, moderated group discussion conducted in a shared physical space, typically with 6–10 participants who are selected because they share a relevant characteristic. The moderator follows a semi-structured topic guide to elicit opinions, perceptions, and experiences. Unlike surveys, focus groups capture social interaction — agreement, disagreement, and the group dynamics through which attitudes are formed and expressed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Merton and Paul Lazarsfeld (focused interview); Richard Krueger and David Morgan (applied focus group methodology)","year":"1940s (Merton & Lazarsfeld); systematised 1980s–1990s","type":"Qualitative group data-collection technique","dataType":"Verbal discussion data from small groups (audio/video recorded transcripts)","subfamily":"Data collection"},"citations":[{"ref":"Krueger, R. A., & Casey, M. A. (2015). Focus Groups: A Practical Guide for Applied Research (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483365244","url":null},{"ref":"Morgan, D. L. (1997). Focus Groups as Qualitative Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761903437","url":null}],"related":["online-focus-group","structured-interview","face-to-face-semi-structured-interview","face-to-face-in-depth-interview","participant-observation","survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-participant-observation","name":"Face-to-face Participant Observation","fullName":"Face-to-face Participant Observation","aliases":["in-person participant observation","direct participant observation","fieldwork participant observation","co-present observation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Early 20th century (Chicago School ~1920s; Spradley formalisation 1980)","originator":"Chicago School sociologists (Robert Park, Ernest Burgess); systematised by Raymond Gold (1958) and James Spradley (1980)","url":"https://scholargate.app/en/survey-methodology/face-to-face-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-participant-observation.md","definition":"Face-to-face participant observation is a qualitative data collection technique in which the researcher physically enters a setting and engages with participants in real time to document social behaviour, interactions, and meaning-making as they naturally occur. Unlike online or remote variants, the researcher is bodily present, enabling direct sensory access to context, non-verbal cues, and the full texture of everyday life in the setting under study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chicago School sociologists (Robert Park, Ernest Burgess); systematised by Raymond Gold (1958) and James Spradley (1980)","year":"Early 20th century (Chicago School ~1920s; Spradley formalisation 1980)","type":"Qualitative data collection technique","dataType":"Field notes, observational records, informal conversations, artefacts","subfamily":"Data collection"},"citations":[{"ref":"Spradley, J. P. (1980). Participant Observation. Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030445019","url":null},{"ref":"Gold, R. L. (1958). Roles in sociological field observations. Social Forces, 36(3), 217–223.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.2307/2573808"}],"related":["ethnography","non-participant-observation","face-to-face-in-depth-interview","field-notes","case-study","action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-research-diary","name":"Face-to-face Research Diary","fullName":"Face-to-face Research Diary","aliases":["in-person research journal","fieldwork reflexive diary","face-to-face researcher journal","in-person reflexive log"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1981–1989 (systematic articulation in qualitative fieldwork literature)","originator":"Robert G. Burgess (systematic research diary in fieldwork); Mary Louise Holly (professional journal writing)","url":"https://scholargate.app/en/survey-methodology/face-to-face-research-diary","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-research-diary.md","definition":"A face-to-face research diary is a systematic reflexive log maintained by the researcher during in-person fieldwork. Unlike participant diaries, this is the researcher's own running record of observations, analytic thoughts, methodological decisions, and emotional responses captured during or immediately after direct, embodied encounters with participants or field settings. It serves simultaneously as a data source, an audit trail, and a reflexivity instrument within qualitative research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert G. Burgess (systematic research diary in fieldwork); Mary Louise Holly (professional journal writing)","year":"1981–1989 (systematic articulation in qualitative fieldwork literature)","type":"Qualitative data collection and reflexivity tool","dataType":"Researcher-generated narrative text, field reflections, analytic memos","subfamily":"Data collection"},"citations":[{"ref":"Holly, M. L. (1989). Writing to Grow: Keeping a Personal-Professional Journal. Heinemann.","type":"book","doi":null,"isbn":"978-0435084592","url":null},{"ref":"Burgess, R. G. (1981). Keeping a research diary. Cambridge Journal of Education, 11(1), 75–83.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1080/0305764810110109"}],"related":["research-diary","field-notes","face-to-face-diary-method","participant-observation","reflexive-thematic-analysis","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-semi-structured-interview","name":"Face-to-face Semi-structured Interview","fullName":"Face-to-face Semi-structured Interview","aliases":["in-person semi-structured interview","semi-structured personal interview","guided face-to-face interview","FFSSI"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1940s onward; widely codified in the 1980s–1990s","originator":"Rooted in sociological interview traditions; systematised by researchers including Robert Merton and Paul Lazarsfeld (focused interview, 1940s) and later elaborated by Steinar Kvale","url":"https://scholargate.app/en/survey-methodology/face-to-face-semi-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-semi-structured-interview.md","definition":"A face-to-face semi-structured interview is a qualitative data collection technique in which a researcher meets a participant in person and follows a prepared topic guide of open-ended questions while retaining the flexibility to probe, reorder, and explore emerging themes. It combines the consistency of a predetermined agenda with the depth and responsiveness of an open dialogue, making it one of the most widely used methods in qualitative and mixed-methods research across the social, health, and educational sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in sociological interview traditions; systematised by researchers including Robert Merton and Paul Lazarsfeld (focused interview, 1940s) and later elaborated by Steinar Kvale","year":"1940s onward; widely codified in the 1980s–1990s","type":"Qualitative data collection technique","dataType":"Verbal responses, interview transcripts","subfamily":"Data collection"},"citations":[{"ref":"Bryman, A. (2016). Social Research Methods (5th ed.). Oxford University Press.","type":"book","doi":null,"isbn":"9780198722519","url":null},{"ref":"Kvale, S., & Brinkmann, S. (2009). InterViews: Learning the Craft of Qualitative Research Interviewing (2nd ed.). Sage.","type":"book","doi":null,"isbn":"9780761925422","url":null}],"related":["structured-interview","in-depth-interview","focus-group","online-semi-structured-interview","telephone-assisted-semi-structured-interview","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-sensor-data-collection","name":"Face-to-face Sensor Data Collection","fullName":"Face-to-face Sensor Data Collection","aliases":["in-person sensor data collection","proximate biosensor data collection","face-to-face ambulatory assessment","on-site sensor recording"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (growth with wearable/biosensor technology)","originator":"Emerging from ambulatory assessment and wearable computing research communities","url":"https://scholargate.app/en/survey-methodology/face-to-face-sensor-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-sensor-data-collection.md","definition":"Face-to-face sensor data collection involves attaching or deploying sensors — physiological, motion, environmental, or proximity-based — on or around participants during in-person research sessions. The co-present setting allows direct researcher oversight of equipment, real-time signal monitoring, and immediate troubleshooting, yielding high-fidelity continuous or event-triggered data streams that capture objective behavioral and physiological indicators as they unfold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerging from ambulatory assessment and wearable computing research communities","year":"1990s–2000s (growth with wearable/biosensor technology)","type":"Quantitative / mixed-methods data collection technique","dataType":"Physiological, behavioral, and environmental sensor signals collected during in-person interaction","subfamily":"Data collection"},"citations":[{"ref":"Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176.","type":"article","doi":"10.1146/annurev-clinpsy-050212-185510","isbn":null,"url":null},{"ref":"Sensor. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Sensor"}],"related":["sensor-data-collection","mobile-sensor-data-collection","mobile-experience-sampling","face-to-face-participant-observation","ecological-momentary-assessment","physiological-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-structured-interview","name":"Face-to-face structured interview","fullName":"Face-to-face Structured Interview","aliases":["FTFSI","personal interview","in-person structured interview","face-to-face survey interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Mid-20th century (formalized 1950s–1960s)","originator":"Cannell, Kahn, and survey methodology tradition","url":"https://scholargate.app/en/survey-methodology/face-to-face-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-structured-interview.md","definition":"A face-to-face structured interview is a data collection method in which a trained interviewer meets each respondent in person and asks a fixed set of questions in a predetermined order, recording responses verbatim or using a closed-response format. It combines the response-rate advantages of personal contact with the standardization of a fixed instrument, making it a cornerstone of large-scale social, health, and policy surveys.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cannell, Kahn, and survey methodology tradition","year":"Mid-20th century (formalized 1950s–1960s)","type":"Quantitative / mixed-methods data collection","dataType":"Verbal responses to fixed questions (categorical, ordinal, scalar)","subfamily":"Data collection"},"citations":[{"ref":"Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470465462","url":null},{"ref":"Fowler, F. J. (2009). Survey Research Methods (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1412958417","url":null}],"related":["structured-interview","semi-structured-interview","online-structured-interview","telephone-assisted-structured-interview","survey","face-to-face-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"face-to-face-survey","name":"Face-to-face Survey","fullName":"Face-to-face Survey (Interviewer-Administered Questionnaire)","aliases":["personal interview survey","in-person survey","PAPI survey","door-to-door survey"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1930s–1940s (systematic survey era)","originator":"Established practice formalised in survey methodology (Gallup, Likert, and others from the 1930s–1940s)","url":"https://scholargate.app/en/survey-methodology/face-to-face-survey","markdownUrl":"https://scholargate.app/en/survey-methodology/face-to-face-survey.md","definition":"A face-to-face survey is a structured data collection method in which a trained interviewer meets respondents in person and administers a standardised questionnaire. The interviewer reads questions aloud, clarifies wording when permitted by protocol, and records answers — either on paper (PAPI) or a laptop/tablet (CAPI). This mode consistently achieves higher response rates and better data quality for complex or sensitive questionnaires than self-administered alternatives, and is the reference standard in large-scale population surveys.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established practice formalised in survey methodology (Gallup, Likert, and others from the 1930s–1940s)","year":"1930s–1940s (systematic survey era)","type":"Quantitative / mixed-mode data collection","dataType":"Structured questionnaire responses (numerical ratings, categorical choices, open-ended text)","subfamily":"Data collection"},"citations":[{"ref":"Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452259000","url":null},{"ref":"Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470465462","url":null}],"related":["survey","structured-interview","online-survey","telephone-assisted-survey","mobile-survey","computer-assisted-personal-interviewing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"facility-layout","name":"Facility Layout (SLP)","fullName":"Systematic Layout Planning","aliases":["SLP","plant layout"],"domain":"operations-management","family":"ml-model","subfamily":"Operations Design","year":"1973","originator":"Muther, R.","url":"https://scholargate.app/en/operations-management/facility-layout","markdownUrl":"https://scholargate.app/en/operations-management/facility-layout.md","definition":"Systematic Layout Planning (SLP) is a structured methodology developed by Richard Muther in the 1960s–1970s for designing optimal plant and facility layouts. The approach systematizes the consideration of material flow, personnel movement, equipment relationships, and space constraints to minimize material handling costs, improve safety, and enhance flexibility. SLP has become the foundational framework for facility design in manufacturing, warehousing, and service environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Muther, R.","subfamily":"Operations Design","year":"1973","type":"Layout design methodology"},"citations":[{"ref":"Muther, R. (1973). Systematic layout planning (2nd ed.). Boston: Cahners Books.","type":"book","doi":null,"isbn":null,"url":"https://www.engineersedge.com/"},{"ref":"Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. (2010). Facilities planning (4th ed.). Hoboken, NJ: John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/"}],"related":["assembly-line-balancing","job-shop-scheduling","scor-model","kanban","cross-docking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"facit-palliative","name":"FACIT-Palliative Subscale","fullName":"Functional Assessment of Chronic Illness Therapy–Palliative subscale","aliases":["FACIT-Pal","FACIT-Palliative","FACIT-Spiritual subscale"],"domain":"palliative-care","family":"process-pipeline","subfamily":"quality-of-life-spiritual","year":"2002","originator":"Peterman, Fitchett, Brady, and colleagues (funded by National Cancer Institute)","url":"https://scholargate.app/en/palliative-care/facit-palliative","markdownUrl":"https://scholargate.app/en/palliative-care/facit-palliative.md","definition":"The FACIT-Palliative (FACIT-Pal) is a 12-item self-report subscale of the Functional Assessment of Chronic Illness Therapy (FACIT) family, specifically designed to measure spiritual well-being and existential meaning in patients with advanced cancer and life-limiting illness. Developed by Peterman and colleagues in 2002 and funded by the National Cancer Institute, the FACIT-Pal is embedded within larger FACIT instruments and has become a standard spiritual quality-of-life measure in oncology trials, hospice research, and palliative care programs internationally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peterman, Fitchett, Brady, and colleagues (funded by National Cancer Institute)","subfamily":"quality-of-life-spiritual","year":"2002","type":"Self-report"},"citations":[{"ref":"Peterman, A. H., Fitchett, G., Brady, M. J., Hernandez, L., & Cella, D. (2002). Measuring spiritual well-being in people with cancer: The Functional Assessment of Chronic Illness Therapy–Spiritual Well-Being scale. Annals of Behavioral Medicine, 24(1), 49–58.","type":"article","doi":"10.1207/S15324796ABM2401_06","isbn":null,"url":null},{"ref":"Cella, D., Peterman, A., Passik, S., Jacobsen, P., & Breitbart, W. (2009). Progress toward guidelines for the management of fatigue. Oncology, 12(11A), 369–377.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9625554"}],"related":["spiritual-wellbeing-scale","mcgill-quality-of-life","patient-dignity-inventory","palliative-performance-scale","comfort-care-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fact-anemia","name":"FACT-Anemia","fullName":"Functional Assessment of Cancer Therapy—Anemia","aliases":["FACT-An"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life, treatment side effects","year":"1997","originator":"Yellen, S. B., Cella, D. F., et al. (built on Cella, D. F. FACT-G framework)","url":"https://scholargate.app/en/oncology/fact-anemia","markdownUrl":"https://scholargate.app/en/oncology/fact-anemia.md","definition":"The FACT-Anemia (FACT-An) is a quality-of-life measure combining the 27-item FACT-G core with a disease-specific subscale focusing on fatigue and anemia-related symptoms common in cancer patients receiving chemotherapy or dealing with cancer-induced anemia. Developed by Yellen et al. in 1997, it quantifies the impact of chemotherapy-induced anemia on functional and emotional well-being, supporting clinical trials and anemia treatment research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yellen, S. B., Cella, D. F., et al. (built on Cella, D. F. FACT-G framework)","subfamily":"cancer-specific quality of life, treatment side effects","year":"1997","type":"Self-report questionnaire"},"citations":[{"ref":"Yellen, S. B., Cella, D. F., Webster, K., Blendowski, C., & Kaplan, E. (1997). Measuring fatigue and other anemia-related symptoms with the Functional Assessment of Cancer Therapy (FACT) measurement system. J Pain Symptom Manage, 13(2), 63–74.","type":"article","doi":"10.1016/S0885-3924(96)00274-6","isbn":null,"url":null},{"ref":"Cella, D. F., Tulsky, D. S., Gray, G., Sarafian, B., Linn, E., Bonomi, A., et al. (1993). The Functional Assessment of Cancer Therapy scale: development and validation of the general measure. J Clin Oncol, 11(3), 570–579.","type":"article","doi":"10.1200/JCO.1993.11.3.570","isbn":null,"url":null}],"related":["fact-lung","fact-colorectal","fact-prostate","cancer-worry-scale","eortc-qlq-c15-pal"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fact-b-breast-cancer","name":"FACT-B","fullName":"Functional Assessment of Cancer Therapy-Breast","aliases":["FACT-B","FACT-Breast"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Breast Cancer-Specific Quality of Life","year":"1997","originator":"Marilyn Brady and David Cella","url":"https://scholargate.app/en/oncology-nursing/fact-b-breast-cancer","markdownUrl":"https://scholargate.app/en/oncology-nursing/fact-b-breast-cancer.md","definition":"The FACT-B is a comprehensive 36-item disease-specific quality-of-life instrument that integrates the generic FACT-G (27 items covering physical, social, emotional, and functional well-being) with a 9-item breast-cancer-specific subscale addressing body image, sexual function, arm symptoms, and treatment side effects. Developed by Brady et al. in 1997, the FACT-B is the gold-standard QoL measure for breast cancer research and clinical practice, used in hundreds of clinical trials and enabling comparison across breast cancer populations and treatment modalities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marilyn Brady and David Cella","subfamily":"Breast Cancer-Specific Quality of Life","year":"1997","type":"Patient self-report disease-specific QoL instrument"},"citations":[{"ref":"Brady, M. J., Cella, D. F., Mo, F., et al. (1997). Reliability and validity of the Functional Assessment of Cancer Therapy-Breast quality-of-life instrument. J Clin Oncol, 15(3), 974–986.","type":"article","doi":"10.1200/JCO.1997.15.3.974","isbn":null,"url":null},{"ref":"Cella, D. F., Tulsky, D. S., Gray, G., et al. (1993). The Functional Assessment of Cancer Therapy scale: development and validation of a general measure. J Clin Oncol, 11(3), 570–579.","type":"article","doi":"10.1200/JCO.1993.11.3.570","isbn":null,"url":null}],"related":["fact-g","distress-thermometer","edmonton-symptom-assessment","memorial-symptom-assessment-scale","functional-living-index-cancer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fact-colorectal","name":"FACT-Colorectal","fullName":"Functional Assessment of Cancer Therapy—Colorectal","aliases":["FACT-C"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life","year":"1999","originator":"Ward, W. L., et al. (built on Cella, D. F. FACT-G framework)","url":"https://scholargate.app/en/oncology/fact-colorectal","markdownUrl":"https://scholargate.app/en/oncology/fact-colorectal.md","definition":"The FACT-Colorectal (FACT-C) is a disease-specific quality-of-life instrument designed for patients with colorectal cancer. It combines the 27-item FACT-G core (general cancer) with a 9-item colorectal-specific subscale addressing bowel function, sexual function, and cancer-related digestive concerns. Validated by Ward et al. in 1999, it is a standard endpoint in colorectal cancer clinical trials and clinical practice monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ward, W. L., et al. (built on Cella, D. F. FACT-G framework)","subfamily":"cancer-specific quality of life","year":"1999","type":"Self-report questionnaire"},"citations":[{"ref":"Ward, W. L., Hahn, E. A., Mo, F., Hernandez, L., Tulsky, D. S., & Cella, D. (1999). Reliability and validity of the Functional Assessment of Cancer Therapy-Colorectal (FACT-C) quality of life instrument. Qual Life Res, 8(3), 181–195.","type":"article","doi":"10.1023/a:1008821826499","isbn":null,"url":null},{"ref":"Cella, D. F., Tulsky, D. S., Gray, G., Sarafian, B., Linn, E., Bonomi, A., et al. (1993). The Functional Assessment of Cancer Therapy scale: development and validation of the general measure. J Clin Oncol, 11(3), 570–579.","type":"article","doi":"10.1200/JCO.1993.11.3.570","isbn":null,"url":null}],"related":["fact-lung","fact-prostate","fact-ovarian","eortc-qlq-c15-pal","cancer-worry-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fact-g","name":"FACT-G","fullName":"Functional Assessment of Cancer Therapy-General","aliases":["FACT-General"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Quality of Life in Cancer","year":"1993","originator":"David Cella","url":"https://scholargate.app/en/oncology-nursing/fact-g","markdownUrl":"https://scholargate.app/en/oncology-nursing/fact-g.md","definition":"The FACT-G is a 27-item self-report questionnaire measuring health-related quality of life in cancer patients across four key domains: physical, social/family, emotional, and functional well-being. Developed by Cella et al. in 1993, it has become one of the most widely used generic QoL instruments in oncology research and clinical practice, translated into 40+ languages and validated across diverse cancer populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Cella","subfamily":"Quality of Life in Cancer","year":"1993","type":"Patient self-report questionnaire"},"citations":[{"ref":"Cella, D. F., Tulsky, D. S., Gray, G., et al. (1993). The Functional Assessment of Cancer Therapy scale: development and validation of a general measure. J Clin Oncol, 11(3), 570–579.","type":"article","doi":"10.1200/JCO.1993.11.3.570","isbn":null,"url":null},{"ref":"Brady, M. J., Cella, D. F., Mo, F., et al. (1997). Reliability and validity of the Functional Assessment of Cancer Therapy-Breast quality-of-life instrument. J Clin Oncol, 15(3), 974–986.","type":"article","doi":"10.1200/JCO.1997.15.3.974","isbn":null,"url":null}],"related":["fact-b-breast-cancer","edmonton-symptom-assessment","distress-thermometer","piper-fatigue-scale","brief-fatigue-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fact-lung","name":"FACT-Lung","fullName":"Functional Assessment of Cancer Therapy—Lung","aliases":["FACT-L"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life","year":"1995","originator":"David F. Cella, Ph.D.","url":"https://scholargate.app/en/oncology/fact-lung","markdownUrl":"https://scholargate.app/en/oncology/fact-lung.md","definition":"The FACT-Lung (FACT-L) is a lung-cancer-specific quality-of-life measure that combines a 27-item general cancer assessment with a 7-item lung cancer subscale. Developed by Cella et al. in 1995, it quantifies physical, emotional, social, and functional well-being specifically relevant to lung cancer patients. It is widely used in clinical trials and practice to assess treatment impact and symptom burden.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David F. Cella, Ph.D.","subfamily":"cancer-specific quality of life","year":"1995","type":"Self-report questionnaire"},"citations":[{"ref":"Cella, D. F., Bonomi, A. E., Lloyd, S. R., Tulsky, D. S., Kaplan, E., & Bonomi, P. (1995). Validation of the Functional Assessment of Cancer Therapy-Lung (FACT-L) quality of life instrument for patients with lung cancer. J Clin Oncol, 13(1), 142–153.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validation+of+the+Functional+Assessment+of+Cancer+Therapy-Lung+%28FACT-L%29+quality+of+life+instrument+for+patients+with+lung+cancer+Cella"},{"ref":"Cella, D. F., Tulsky, D. S., Gray, G., Sarafian, B., Linn, E., Bonomi, A., et al. (1993). The Functional Assessment of Cancer Therapy scale: development and validation of the general measure. J Clin Oncol, 11(3), 570–579.","type":"article","doi":"10.1200/JCO.1993.11.3.570","isbn":null,"url":null}],"related":["fact-colorectal","fact-prostate","fact-ovarian","eortc-qlq-lc13","cancer-worry-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fact-ovarian","name":"FACT-Ovarian","fullName":"Functional Assessment of Cancer Therapy—Ovarian","aliases":["FACT-O"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life","year":"2001","originator":"Basen-Engquist, K., et al. (built on Cella, D. F. FACT-G framework)","url":"https://scholargate.app/en/oncology/fact-ovarian","markdownUrl":"https://scholargate.app/en/oncology/fact-ovarian.md","definition":"The FACT-Ovarian (FACT-O) is a disease-specific quality-of-life measure for women with ovarian cancer, integrating the 27-item FACT-G core with a 12-item ovarian-specific subscale addressing cancer-related symptoms, sexual function, abdominal distension, and treatment side effects. Validated by Basen-Engquist et al. in 2001, it is a standard endpoint in ovarian cancer clinical trials and supportive care research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Basen-Engquist, K., et al. (built on Cella, D. F. FACT-G framework)","subfamily":"cancer-specific quality of life","year":"2001","type":"Self-report questionnaire"},"citations":[{"ref":"Basen-Engquist, K., Bodurka, D. C., Lu, H., Sill, M. W., Thaker, P. H., Deavers, M. T., et al. (2001). Reliability and validity of the Functional Assessment of Cancer Therapy-Ovarian (FACT-O) quality of life instrument. Gynecol Oncol, 89(3), 478–488.","type":"article","doi":"10.1200/jco.2001.19.6.1809","isbn":null,"url":null},{"ref":"Cella, D. F., Tulsky, D. S., Gray, G., Sarafian, B., Linn, E., Bonomi, A., et al. (1993). The Functional Assessment of Cancer Therapy scale: development and validation of the general measure. J Clin Oncol, 11(3), 570–579.","type":"article","doi":"10.1200/JCO.1993.11.3.570","isbn":null,"url":null}],"related":["fact-lung","fact-colorectal","fact-prostate","eortc-qlq-br23","cancer-worry-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fact-prostate","name":"FACT-Prostate","fullName":"Functional Assessment of Cancer Therapy—Prostate","aliases":["FACT-P"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life","year":"1997","originator":"Esper, P., et al. (built on Cella, D. F. FACT-G framework)","url":"https://scholargate.app/en/oncology/fact-prostate","markdownUrl":"https://scholargate.app/en/oncology/fact-prostate.md","definition":"The FACT-Prostate (FACT-P) is a disease-specific quality-of-life instrument for men with prostate cancer, combining the 27-item FACT-G core with a 12-item prostate-specific subscale addressing urinary, sexual, and bowel function concerns. Developed and validated by Esper et al. in 1997, it is a standard outcome measure in prostate cancer clinical trials and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Esper, P., et al. (built on Cella, D. F. FACT-G framework)","subfamily":"cancer-specific quality of life","year":"1997","type":"Self-report questionnaire"},"citations":[{"ref":"Esper, P., Mo, F., Chodak, G., Sinner, M., Cella, D., & Pienta, K. J. (1997). Measuring quality of life in men with prostate cancer using the Functional Assessment of Cancer Therapy-Prostate instrument. Urology, 50(6), 920–928.","type":"article","doi":"10.1016/s0090-4295(97)00459-7","isbn":null,"url":null},{"ref":"Cella, D. F., Tulsky, D. S., Gray, G., Sarafian, B., Linn, E., Bonomi, A., et al. (1993). The Functional Assessment of Cancer Therapy scale: development and validation of the general measure. J Clin Oncol, 11(3), 570–579.","type":"article","doi":"10.1200/JCO.1993.11.3.570","isbn":null,"url":null}],"related":["fact-lung","fact-colorectal","fact-ovarian","ucla-prostate-cancer-index","cancer-worry-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factor-analysis-scale","name":"Factor Analysis for Scale Development","fullName":"Exploratory Factor Analysis Method for Psychometric Scale Construction and Validation","aliases":["Exploratory factor analysis","EFA for scale development","Factorial structure analysis"],"domain":"psychometrics","family":"process-pipeline","subfamily":"Scale development","year":"1947","originator":"Louis Thurstone","url":"https://scholargate.app/en/psychometrics/factor-analysis-scale","markdownUrl":"https://scholargate.app/en/psychometrics/factor-analysis-scale.md","definition":"Exploratory factor analysis (EFA) is a statistical method for discovering the underlying dimensional structure of a set of items or variables. Pioneered by Louis Thurstone in the mid-20th century, EFA is widely used to develop and validate psychometric scales by identifying groups of items that correlate together, thereby revealing latent dimensions of the construct being measured. The method reduces item sets to a smaller number of interpretable factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Louis Thurstone","subfamily":"Scale development","year":"1947","type":"Exploratory factor analysis methodology"},"citations":[{"ref":"Thurstone, L. L. (1947). Multiple-Factor Analysis: A Development and Expansion of the Vectors of Mind (2nd ed.). Chicago: University of Chicago Press.","type":"book","doi":null,"isbn":"9780226797557","url":null},{"ref":"Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299.","type":"article","doi":"10.1037/1082-989X.4.3.272","isbn":null,"url":null},{"ref":"DeVellis, R. F. (2016). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage Publications.","type":"book","doi":null,"isbn":"9781506330174","url":null}],"related":["likert-scale-construction","guttman-scale","content-validity-ratio","confirmatory-factor-analysis-scale","floor-ceiling-effect"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factor-analysis","name":"Factor Analysis","fullName":"Exploratory and Confirmatory Factor Analysis","aliases":["EFA","CFA","latent variable modeling"],"domain":"research-statistics","family":"process-pipeline","subfamily":"dimension-reduction","year":"1931","originator":"Louis Leon Thurstone","url":"https://scholargate.app/en/research-statistics/factor-analysis","markdownUrl":"https://scholargate.app/en/research-statistics/factor-analysis.md","definition":"Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Louis Leon Thurstone","subfamily":"dimension-reduction","year":"1931","type":"Method"},"citations":[{"ref":"Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.","type":"article","doi":"10.2307/2304512","isbn":null,"url":null},{"ref":"Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.","type":"article","doi":"10.1007/BF02289343","isbn":null,"url":null},{"ref":"Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151.","type":"article","doi":"10.1177/001316446002000116","isbn":null,"url":null}],"related":["multiple-regression-analysis","structural-equation-modeling","nonparametric-tests"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factor-risk-model","name":"Factor Risk Model","fullName":"Multi-Factor Risk Model (Fama-French, Arbitrage Pricing Theory)","aliases":["Fama-French model","Fama-French three-factor model","Fama-French five-factor model","arbitrage pricing theory","APT","multi-factor model","Faktör Risk Modeli (Fama-French, APT)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1993,"originator":"Fama & French (factor model); Ross (Arbitrage Pricing Theory)","url":"https://scholargate.app/en/finance/factor-risk-model","markdownUrl":"https://scholargate.app/en/finance/factor-risk-model.md","definition":"A factor risk model is a multi-factor framework that links asset returns to systematic risk factors such as the market, value, size, and momentum. The Fama-French three- and five-factor models (1993) and Ross's Arbitrage Pricing Theory (1976) decompose portfolio risk and detect alpha.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fama & French (factor model); Ross (Arbitrage Pricing Theory)","year":1993,"type":"Multi-factor linear regression model","estimator":"Time-series OLS regression of excess returns on risk factors","outcome":"continuous (asset excess returns)","minSample":60},"citations":[{"ref":"Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), 3-56.","type":"article","doi":"10.1016/0304-405X(93)90023-5","isbn":null,"url":null},{"ref":"Ross, S. A. (1976). The Arbitrage Theory of Capital Asset Pricing. Journal of Economic Theory, 13(3), 341-360.","type":"article","doi":"10.1016/0022-0531(76)90046-6","isbn":null,"url":null}],"related":["principal-component-risk","portfolio-optimization-mean-variance","ols-regression","credit-risk-models","stochastic-volatility-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-ab-test","name":"Factorial A/B Test","fullName":"Factorial A/B Test (Multi-Factor Online Experiment)","aliases":["factorial split test","multi-factor A/B test","factorial online experiment","factorial controlled experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s","originator":"Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s","url":"https://scholargate.app/en/experimental-design/factorial-ab-test","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-ab-test.md","definition":"A factorial A/B test is a controlled online experiment that simultaneously manipulates two or more independent factors, each at two or more levels, exposing different user groups to every combination of factor levels. Rooted in Fisher's factorial design and operationalised at scale by tech companies, it enables researchers to estimate both the independent main effect of each factor and the interaction effects between factors — all from a single experimental run.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s","year":"Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s","type":"Controlled online/field experiment","dataType":"Binary or continuous outcome metrics (clicks, conversions, revenue, engagement); user-level or session-level observations","subfamily":"Deneysel desen"},"citations":[{"ref":"Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1108724265","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["factorial-experiment","ab-design","full-factorial-experiment","fractional-factorial-experiment","multi-arm-experiment","adaptive-ab-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-aba-design","name":"Factorial ABA Design","fullName":"Factorial ABA Reversal Design","aliases":["Factorial reversal design","Multi-factor ABA design","Factorial withdrawal design","SCED factorial ABA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (ABA base); factorial extensions developed through 1980s–2000s","originator":"Derived from ABA reversal design (Baer, Wolf & Risley, 1968) extended with factorial manipulation principles","url":"https://scholargate.app/en/experimental-design/factorial-aba-design","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-aba-design.md","definition":"The Factorial ABA design embeds a factorial treatment structure within the ABA reversal framework. Rather than testing a single treatment against baseline, the researcher systematically varies two or more independent variables (factors) across treatment phases, using the ABA withdrawal logic to establish experimental control. This makes it possible to examine main effects and interactions among treatment components within a single-case or small-N experimental context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from ABA reversal design (Baer, Wolf & Risley, 1968) extended with factorial manipulation principles","year":"1968 (ABA base); factorial extensions developed through 1980s–2000s","type":"Single-case experimental design with factorial treatment structure","dataType":"Repeated-measures behavioral or continuous outcome data collected on individual participants","subfamily":"Deneysel desen"},"citations":[{"ref":"Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association.","type":"book","doi":null,"isbn":"978-1433807909","url":null},{"ref":"Kennedy, C. H. (2005). Single-Case Designs for Educational Research. Allyn & Bacon.","type":"book","doi":null,"isbn":"978-0205332014","url":null}],"related":["aba-design","abab-design","factorial-experiment","multiple-baseline-design","single-subject-experimental-design","factorial-single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-abab-design","name":"Factorial ABAB Design","fullName":"Factorial ABAB Reversal Design","aliases":["factorial reversal design","factorial withdrawal design","multi-factor ABAB design","factorial single-subject reversal"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s–1970s (integration of factorial and single-subject reversal traditions)","originator":"Derived from Sidman (1960) reversal logic and Fisher & Yates factorial principles; systematized in applied behavior analysis","url":"https://scholargate.app/en/experimental-design/factorial-abab-design","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-abab-design.md","definition":"The factorial ABAB design embeds a factorial structure within the classical ABAB reversal framework, enabling a single participant or a small set of participants to experience multiple factor combinations across alternating baseline (A) and treatment (B) phases. By systematically withdrawing and reinstating treatment conditions that vary across two or more factors, the design allows examination of both main effects and interactions at the individual level, providing strong experimental control through within-subject replication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from Sidman (1960) reversal logic and Fisher & Yates factorial principles; systematized in applied behavior analysis","year":"1960s–1970s (integration of factorial and single-subject reversal traditions)","type":"Single-subject experimental design","dataType":"Repeated behavioral measures over time (continuous observation data)","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134752556","url":null}],"related":["abab-design","aba-design","factorial-experiment","multiple-baseline-design","single-subject-experimental-design","alternating-treatments-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-control-group-experimental-design","name":"Factorial Control Group Experimental Design","fullName":"Factorial Experiment with Control Group Design","aliases":["factorial controlled experiment","factorial design with control","factorial RCT with control arm","multi-factor controlled experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1926–1935","originator":"Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/factorial-control-group-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-control-group-experimental-design.md","definition":"A factorial control group experimental design crosses two or more independent variables (factors) in a fully factorial structure while including at least one condition that serves as a no-treatment or standard-treatment control. This allows researchers to simultaneously estimate the main effect of each factor, their interactions, and the size of those effects relative to a meaningful baseline, maximising both causal precision and experimental efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":"1926–1935","type":"Experimental design","dataType":"Continuous, ordinal, or categorical outcome measures","subfamily":"Deneysel desen"},"citations":[{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fisher+1935+The+Design+of+Experiments"},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["factorial-experiment","full-factorial-experiment","randomized-controlled-trial","control-group-experimental-design","fractional-factorial-experiment","factorial-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-design","name":"Full Factorial Design","fullName":"Full Factorial Experimental Design (2^k)","aliases":["factorial experiment","2^k factorial","full factorial","Faktöriyel Deneme Deseni (Full Factorial, 2^k)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1926,"originator":"R. A. Fisher","url":"https://scholargate.app/en/experimental-design/factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-design.md","definition":"A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher","year":1926,"family":"Experimental design","type":"Parametric factorial experiment","minSample":16,"outcome":"continuous","parametric":true,"factors":"2 or more (each at 2+ levels)","runCount":"2^k for k two-level factors"},"citations":[{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471718130","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["fractional-factorial","response-surface-methodology","one-way-anova","two-way-anova","randomized-complete-block-design","taguchi-methods"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-experiment","name":"Factorial Experiment","fullName":"Factorial Experimental Design","aliases":["factorial design","factorial ANOVA design","multi-factor experiment","crossed-factor design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1926–1935","originator":"Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-experiment.md","definition":"A factorial experiment is an experimental design in which two or more independent variables (factors) are manipulated simultaneously, and every combination of their levels is tested. Introduced by Ronald Fisher in the 1920s–1930s, it is the standard approach whenever a researcher needs to detect not only the main effect of each factor but also whether the effect of one factor depends on the level of another — the interaction effect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":"1926–1935","type":"Quantitative experimental design","dataType":"Continuous or categorical outcome measures; manipulated categorical or continuous factors","subfamily":"Deneysel desen"},"citations":[{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fisher+The+Design+of+Experiments+1935"},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119320937","url":null}],"related":["full-factorial-experiment","fractional-factorial-experiment","randomized-controlled-trial","analysis-of-variance","response-surface-methodology","latin-square-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-field-experiment","name":"Factorial Field Experiment","fullName":"Factorial Field Experiment","aliases":["factorial design in the field","field factorial design","multi-factor field trial","factorial field trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1920s–1935 (Fisher's foundational work); widely applied through 20th century","originator":"Ronald A. Fisher (factorial principle); extended to field settings in agricultural and social sciences","url":"https://scholargate.app/en/experimental-design/factorial-field-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-field-experiment.md","definition":"A factorial field experiment applies factorial experimental design — simultaneously manipulating two or more independent factors across all combinations of their levels — in a real-world field setting rather than a controlled laboratory. It allows researchers to estimate both main effects and interaction effects of multiple factors on an outcome under ecologically valid conditions, making findings directly relevant to practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher (factorial principle); extended to field settings in agricultural and social sciences","year":"1920s–1935 (Fisher's foundational work); widely applied through 20th century","type":"Experimental design","dataType":"Continuous and categorical outcome measures collected in real-world field settings","subfamily":"Deneysel desen"},"citations":[{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Design+of+Experiments+Fisher+1935"},{"ref":"Cochran, W. G., & Cox, G. M. (1957). Experimental Designs (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471162971","url":null}],"related":["factorial-experiment","full-factorial-experiment","fractional-factorial-experiment","field-experiment","randomized-controlled-trial","blocked-factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-laboratory-experiment","name":"Factorial Laboratory Experiment","fullName":"Factorial Laboratory Experiment","aliases":["factorial lab experiment","laboratory factorial design","factorial controlled experiment","multi-factor lab study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1926 (Fisher's factorial principle); laboratory application systematized mid-20th century","originator":"Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/factorial-laboratory-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-laboratory-experiment.md","definition":"A factorial laboratory experiment is a controlled experimental design in which two or more independent variables (factors) are simultaneously manipulated, each at two or more levels, within a laboratory setting. This design allows researchers to estimate both the individual main effect of each factor and the interaction effects between factors — making it one of the most efficient and informative designs in behavioral, psychological, and natural science research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":"1926 (Fisher's factorial principle); laboratory application systematized mid-20th century","type":"Experimental research design","dataType":"Quantitative (continuous or categorical outcome measures collected under controlled conditions)","subfamily":"Deneysel desen"},"citations":[{"ref":"Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1412974455","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["randomized-controlled-trial","between-subjects-design","within-subjects-design","mixed-factorial-design","analysis-of-variance","full-factorial-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-multi-arm-experiment","name":"Factorial Multi-Arm Experiment","fullName":"Factorial Multi-Arm Experimental Design","aliases":["multi-arm factorial trial","factorial multi-arm trial","multi-arm factorial experiment","MAFT"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1926 (factorial basis); multi-arm factorial trials formalized 1980s–1990s","originator":"R. A. Fisher (factorial foundations); multi-arm extension established in clinical trial methodology","url":"https://scholargate.app/en/experimental-design/factorial-multi-arm-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-multi-arm-experiment.md","definition":"A factorial multi-arm experiment simultaneously tests multiple factors (each at two or more levels) by assigning participants to distinct arms that represent unique combinations of those factors. This design efficiently estimates the independent main effects of each factor and their interactions, all within a single study — making it far more informative than running separate one-factor experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (factorial foundations); multi-arm extension established in clinical trial methodology","year":"1926 (factorial basis); multi-arm factorial trials formalized 1980s–1990s","type":"Experimental design","dataType":"Continuous, binary, or ordinal outcome measures across experimental units","subfamily":"Deneysel desen"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null},{"ref":"Juszczak, E., Altman, D. G., Hopewell, S., & Schulz, K. (2019). Reporting of multi-arm parallel-group randomized trials: Extension of the CONSORT 2010 Statement. JAMA, 321(16), 1610–1620.","type":"article","doi":"10.1001/jama.2019.3087","isbn":null,"url":null}],"related":["factorial-experiment","multi-arm-experiment","factorial-randomized-controlled-trial","full-factorial-experiment","fractional-factorial-experiment","adaptive-multi-arm-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-natural-experiment","name":"Factorial Natural Experiment","fullName":"Factorial Natural Experiment Design","aliases":["factorial quasi-experiment","multi-factor natural experiment","factorial exogenous variation design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1920s (factorial origins, Fisher); natural experiment formalization 1990s–2000s; factorial natural experiment usage widespread 2000s–present","originator":"Extension of natural experiment tradition (Dunning, Angrist & Pischke) combined with factorial design logic (Fisher)","url":"https://scholargate.app/en/experimental-design/factorial-natural-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-natural-experiment.md","definition":"A factorial natural experiment exploits naturally occurring exogenous variation across two or more factors simultaneously, allowing researchers to estimate main effects and interactions without random assignment. Natural events, policy changes, or institutional rules create treatment conditions that approximate a factorial structure, enabling causal inference in observational settings where controlled experimentation is infeasible or unethical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of natural experiment tradition (Dunning, Angrist & Pischke) combined with factorial design logic (Fisher)","year":"1920s (factorial origins, Fisher); natural experiment formalization 1990s–2000s; factorial natural experiment usage widespread 2000s–present","type":"Quasi-experimental research design","dataType":"Observational data with multiple exogenously varying treatment dimensions (continuous or categorical)","subfamily":"Deneysel desen"},"citations":[{"ref":"Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107698000","url":null},{"ref":"Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691120355","url":null}],"related":["natural-experiment","factorial-experiment","factorial-randomized-controlled-trial","difference-in-differences","regression-discontinuity-design","instrumental-variable"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-pretest-posttest-experimental-design","name":"Factorial Pretest-Posttest Experimental Design","fullName":"Factorial Pretest-Posttest Experimental Design","aliases":["factorial pre-post design","factorial repeated-measures pretest-posttest design","multi-factor pretest-posttest design","FPPD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1963 (canonical formalization)","originator":"Codified by Donald T. Campbell and Julian C. Stanley","url":"https://scholargate.app/en/experimental-design/factorial-pretest-posttest-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-pretest-posttest-experimental-design.md","definition":"A factorial pretest-posttest experimental design combines the simultaneous manipulation of two or more independent variables (factors) with measurement of the dependent variable both before and after treatment. This structure allows researchers to assess the main effect of each factor, all possible interaction effects between factors, and the magnitude of change from pretest to posttest — all within a single, fully randomised experiment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Codified by Donald T. Campbell and Julian C. Stanley","year":"1963 (canonical formalization)","type":"True experimental design","dataType":"Continuous or ordinal outcome measures (pretest and posttest scores); categorical independent variables (factors)","subfamily":"Deneysel desen"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+Quasi-Experimental+Designs+for+Research+Campbell+Stanley+1963"},{"ref":"Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1412974455","url":null}],"related":["factorial-experiment","pretest-posttest-experimental-design","full-factorial-experiment","split-plot-design","repeated-measures-anova","solomon-four-group-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-randomized-controlled-trial","name":"Factorial Randomized Controlled Trial","fullName":"Factorial Randomized Controlled Trial","aliases":["Factorial RCT","factorial trial","multi-factor RCT","factorial experiment with randomization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)","originator":"R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)","url":"https://scholargate.app/en/experimental-design/factorial-randomized-controlled-trial","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-randomized-controlled-trial.md","definition":"A factorial randomized controlled trial (factorial RCT) is an experimental design in which participants are randomly assigned to every possible combination of two or more independent factors (treatments or intervention components) simultaneously. This allows researchers to estimate the main effect of each factor and their interactions within a single, efficient trial, rather than running separate experiments for each factor.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (factorial design foundations); adapted into clinical trials via MOST framework (Collins et al., 2014)","year":"1926 (Fisher factorial foundations); 2000s–2010s (clinical factorial RCT formalization)","type":"Experimental trial design","dataType":"Continuous, binary, or ordinal outcome data from randomized participants","subfamily":"Deneysel desen"},"citations":[{"ref":"Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014). Factorial experiments: Efficient tools for evaluation of intervention components. American Journal of Preventive Medicine, 47(4), 498–504.","type":"article","doi":"10.1016/j.amepre.2014.06.021","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["factorial-experiment","randomized-controlled-trial","fractional-factorial-experiment","full-factorial-experiment","adaptive-randomized-controlled-trial","crossover-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"factorial-single-subject-experimental-design","name":"Factorial Single-Subject Experimental Design","fullName":"Factorial Single-Subject Experimental Design","aliases":["factorial SCED","factorial single-case design","factorial N-of-1 design","factorial within-subject experimental design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1970s–1980s","originator":"Applied behavior analysis tradition; systematized in Barlow & Hersen (1984) and Kazdin (1982)","url":"https://scholargate.app/en/experimental-design/factorial-single-subject-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/factorial-single-subject-experimental-design.md","definition":"A factorial single-subject experimental design applies the logic of factorial experiments — manipulating two or more independent variables simultaneously to study main effects and interactions — within a single-subject (N=1 or small N) repeated-measures framework. Instead of comparing groups, the same individual serves as their own control across systematically varied conditions, enabling fine-grained analysis of how multiple treatment components combine to influence behavior or clinical outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Applied behavior analysis tradition; systematized in Barlow & Hersen (1984) and Kazdin (1982)","year":"1970s–1980s","type":"Experimental single-subject design with multiple independent variables","dataType":"Repeated behavioral or clinical measurements on one or few individuals","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0205474554","url":null}],"related":["single-subject-experimental-design","factorial-experiment","multiple-baseline-design","abab-design","crossover-single-subject-experimental-design","factorial-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fagan-inspection","name":"Fagan Inspection","fullName":"Fagan Inspection Process for Software Quality","aliases":["Fagan method","code inspection","formal review"],"domain":"numerical-methods","family":"ml-model","subfamily":"Quality Review","year":"1976","originator":"Michael Fagan","url":"https://scholargate.app/en/numerical-methods/fagan-inspection","markdownUrl":"https://scholargate.app/en/numerical-methods/fagan-inspection.md","definition":"Fagan Inspection is a formal, structured code review process developed by Michael Fagan at IBM in 1976 that systematically identifies defects before testing. Using defined roles and checklists, Fagan inspections are far more effective at catching bugs than ad-hoc reviews; studies show 70–90% defect detection rate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Fagan","subfamily":"Quality Review","year":"1976","type":"Formal inspection process"},"citations":[{"ref":"Fagan, M. E. (1976). Design and code inspections to reduce errors in program development. IBM Systems Journal, 15(3), 182–211.","type":"article","doi":"10.1147/sj.153.0182","isbn":null,"url":null},{"ref":"Fagan, M. E. (1986). Advances in software inspections. IEEE Transactions on Software Engineering, SE-12(7), 744–751.","type":"article","doi":"10.1109/tse.1986.6312976","isbn":null,"url":null},{"ref":"Gilb, T., & Graham, D. (1993). Software Inspection. Addison-Wesley.","type":"book","doi":null,"isbn":"0201631814","url":null}],"related":["code-review","peer-review","defect-prevention"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fagerstrom-nicotine-dependence","name":"Fagerström Test for Nicotine Dependence","fullName":"Fagerström Test for Nicotine Dependence - Smoking Severity Assessment","aliases":["FTND","Fagerstrom Test"],"domain":"health-services","family":"process-pipeline","subfamily":"Nicotine dependence and tobacco use severity","year":"1991","originator":"Karl O. Fagerstrom","url":"https://scholargate.app/en/health-services/fagerstrom-nicotine-dependence","markdownUrl":"https://scholargate.app/en/health-services/fagerstrom-nicotine-dependence.md","definition":"The Fagerström Test for Nicotine Dependence (FTND) is a brief, validated self-report instrument originally developed by Fagerstrom in 1978 and revised by Heatherton and colleagues in 1991 to quantify the severity of nicotine dependence in cigarette smokers. The FTND comprises six items assessing morning cigarette use, daily cigarette consumption, and the subjective difficulty of abstaining from smoking. It is the most widely used measure of nicotine dependence in smoking cessation research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl O. Fagerstrom","subfamily":"Nicotine dependence and tobacco use severity","year":"1991","type":"Six-item nicotine dependence screening"},"citations":[{"ref":"Heatherton, T. F., Kozlowski, L. T., Frecker, R. C., & Fagerstrom, K. O. (1991). The Fagerström Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. British Journal of Addiction, 86(9), 1119-1127.","type":"article","doi":"10.1111/j.1360-0443.1991.tb01879.x","isbn":null,"url":null},{"ref":"Fagerstrom, K. O. (1978). Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addictive Behaviors, 3(3-4), 235-241.","type":"article","doi":"10.1016/0306-4603(78)90024-2","isbn":null,"url":null},{"ref":"Kozlowski, L. T., & Pill, C. K. (2007). Epidemiology, public health, and supplies of nicotine medications to treat tobacco dependence. Preventive Medicine, 45(2-3), 239-245.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Epidemiology%2C+public+health%2C+and+supplies+of+nicotine+medications+to+treat+tobacco+dependence+Kozlowski"}],"related":["dast-10","brief-pain-inventory","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"failure-mode-and-effects-analysis","name":"Failure Mode and Effects Analysis","fullName":"Failure Mode and Effects Analysis (FMEA)","aliases":["FMEA","Failure Modes and Effects Analysis","FMECA","Failure Mode Effects and Criticality Analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1949 (military); widespread industrial adoption 1970s–1980s","originator":"U.S. Military / NASA (formalized by MIL-P-1629, 1949)","url":"https://scholargate.app/en/experimental-design/failure-mode-and-effects-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/failure-mode-and-effects-analysis.md","definition":"Failure Mode and Effects Analysis (FMEA) is a structured, proactive risk management technique used to identify potential failure modes in a system, process, or product design, evaluate their consequences, and prioritize corrective actions before failures occur. Originally developed for the U.S. military in 1949 and later adopted by NASA, automotive, and manufacturing industries, FMEA is now a cornerstone quality-engineering tool embedded in standards such as AIAG-VDA and ISO 9001-aligned processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Military / NASA (formalized by MIL-P-1629, 1949)","year":"1949 (military); widespread industrial adoption 1970s–1980s","type":"Proactive risk analysis technique","dataType":"Process/design knowledge, expert judgment, historical failure data","subfamily":"Engineering methods"},"citations":[{"ref":"Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873895989","url":null},{"ref":"Failure mode and effects analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Failure_mode_and_effects_analysis"}],"related":["fault-tree-analysis","root-cause-analysis","statistical-process-control","control-chart","six-sigma-dmaic","reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fairness-aware-ml","name":"Fairness-Aware ML","fullName":"Fairness-Aware Machine Learning","aliases":["Algorithmic Fairness","Fair Classification","Bias-Mitigating ML","Adil Makine Öğrenmesi"],"domain":"machine-learning","family":"ml-model","subfamily":"Trustworthy ML","year":2016,"originator":"Moritz Hardt, Eric Price & Nati Srebro","url":"https://scholargate.app/en/machine-learning/fairness-aware-ml","markdownUrl":"https://scholargate.app/en/machine-learning/fairness-aware-ml.md","definition":"Fairness-Aware Machine Learning is a family of techniques that train, constrain, or post-process predictive models so that their error rates or outcomes are equitable across protected demographic groups such as race, gender, or age. The foundational framework of equalized odds and equality of opportunity was formalized by Moritz Hardt, Eric Price, and Nati Srebro in their landmark 2016 NeurIPS paper, establishing rigorous statistical criteria for non-discriminatory classifiers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moritz Hardt, Eric Price & Nati Srebro","year":2016,"type":"Constrained supervised learning framework","subfamily":"Trustworthy ML","objective":"Equalize error rates across protected groups","key_criterion":"Equalized Odds / Equality of Opportunity"},"citations":[{"ref":"Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2016/hash/9d2682367c3935defcb1f9e247a97c0d-Abstract.html"}],"related":["model-calibration","logistic-regression","shap"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"faiths-phylogenetic-diversity","name":"Faith's Phylogenetic Diversity","fullName":"Faith's Phylogenetic Diversity (PD)","aliases":["phylogenetic diversity","PD","evolutionary distinctiveness","branch length"],"domain":"ecology","family":"process-pipeline","subfamily":"Phylogenetics","year":"1992","originator":"David Faith","url":"https://scholargate.app/en/ecology/faiths-phylogenetic-diversity","markdownUrl":"https://scholargate.app/en/ecology/faiths-phylogenetic-diversity.md","definition":"Faith's Phylogenetic Diversity (PD), introduced by David Faith (1992), measures the evolutionary diversity within a community by summing the branch lengths of a phylogenetic tree connecting all species. Unlike species richness, which counts species equally regardless of evolutionary relationships, PD weights species by their evolutionary distinctiveness: a community with evolutionarily distant species has higher PD than one dominated by recently diverged species. PD is widely used in conservation to prioritize protection of species and habitats that preserve evolutionary history.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Faith","subfamily":"Phylogenetics","year":"1992","type":"evolutionary diversity quantification"},"citations":[{"ref":"Faith, D. P. (1992). Conservation evaluation and phylogenetic diversity. Biological Conservation, 61(1), 1-10.","type":"article","doi":"10.1016/0006-3207(92)91201-3","isbn":null,"url":null},{"ref":"Steel, M., & Mooers, A. O. (2010). The expected value of shed phylogenetic diversity. PLoS Biology, 8(9), e1000475.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+expected+value+of+shed+phylogenetic+diversity+Steel"},{"ref":"Redding, D. W., & Mooers, A. O. (2006). Incorporating evolutionary measures into conservation prioritization. Conservation Biology, 20(6), 1670-1678.","type":"article","doi":"10.1111/j.1523-1739.2006.00555.x","isbn":null,"url":null}],"related":["species-accumulation","functional-diversity","beta-diversity-partitioning","population-viability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fake-news-detection","name":"Fake News Detection","fullName":"Fake News Detection (Misinformation Classification)","aliases":["misinformation detection","false news classification","automated fact checking","Yanlış/Sahte Haber Tespiti"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/fake-news-detection","markdownUrl":"https://scholargate.app/en/text-mining/fake-news-detection.md","definition":"Fake news detection is a natural-language-processing classification task that assesses the credibility of news text and labels content as fake or genuine. Building on the social-media framing of Shu et al. (2017) and the automated-fact-checking framing of Thorne and Vlachos (2018), it turns unstructured news articles into a supervised credibility decision learned from labelled examples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-classification task","approaches":"TF-IDF feature classifier / BERT-based model","output":"Credibility label (fake / real)","minSample":100,"supervision":"Requires a labelled training set"},"citations":[{"ref":"Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD.","type":"article","doi":null,"isbn":null,"url":"https://dl.acm.org/doi/10.1145/3137597.3137600"},{"ref":"Thorne, J. & Vlachos, A. (2018). Automated Fact Checking. COLING.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/C18-1283/"}],"related":["sentiment-analysis","tf-idf","bert-embeddings","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"falls-efficacy-scale","name":"Falls Efficacy Scale International","fullName":"Falls Efficacy Scale International (FES-I)","aliases":["FES-I","International Falls Efficacy Scale","Falls Self-Efficacy"],"domain":"nursing","family":"process-pipeline","subfamily":"balance/fall prevention assessment","year":"2005","originator":"Lucy Yardley","url":"https://scholargate.app/en/nursing/falls-efficacy-scale","markdownUrl":"https://scholargate.app/en/nursing/falls-efficacy-scale.md","definition":"The Falls Efficacy Scale-International (FES-I), developed by Lucy Yardley and colleagues in 2005, is a validated tool measuring fear of falling and confidence in balance in older adults and others at risk of falls. The 16-item scale assesses how confident a person feels performing daily activities without falling (self-efficacy for fall avoidance). Fear of falling is not anxiety disorder but a rational concern that, if excessive, can lead to activity restriction, deconditioning, and further fall risk. The FES-I is used internationally in clinical practice and research to identify patients at risk for this vicious cycle and guide fall prevention interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lucy Yardley","subfamily":"balance/fall prevention assessment","year":"2005","type":"Patient self-report questionnaire"},"citations":[{"ref":"Yardley, L., Beyer, N., Eklund, K., et al. (2005). Development and initial validation of the Falls Efficacy Scale-International (FES-I). Age Ageing, 34(6), 614-619.","type":"article","doi":"10.1093/ageing/afi196","isbn":null,"url":null},{"ref":"Tinetti, M. E., Mendes de Leon, C. F., Doucette, J. T., & Baker, D. I. (1994). Fear of falling and fall-related efficacy in relationship to functioning among community-living elders. J Gerontol, 49(3), M140-M147.","type":"article","doi":"10.1093/geronj/49.3.M140","isbn":null,"url":null}],"related":["clinical-frailty-scale","katz-independence-adl","waterlow-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fama-macbeth-regression","name":"Fama-MacBeth Regression","fullName":"Fama-MacBeth Two-Step Regression","aliases":["Two-step cross-sectional regression"],"domain":"econometrics","family":"regression-model","subfamily":"Panel regression","year":"1973","originator":"Eugene Fama and James MacBeth","url":"https://scholargate.app/en/econometrics/fama-macbeth-regression","markdownUrl":"https://scholargate.app/en/econometrics/fama-macbeth-regression.md","definition":"The Fama-MacBeth procedure is a two-step regression methodology for analyzing cross-sectional relationships while controlling for time-series structure. Introduced by Fama and MacBeth (1973), it first estimates time-series parameters for each cross-sectional unit, then regresses outcomes on those parameters across the cross-section, averaging results over time. This approach elegantly separates within-unit dynamics from cross-sectional heterogeneity and provides standard errors robust to panel structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eugene Fama and James MacBeth","subfamily":"Panel regression","year":"1973","type":"Cross-sectional regression"},"citations":[{"ref":"Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607-636.","type":"article","doi":"10.1086/260061","isbn":null,"url":null},{"ref":"Shanken, J. (1992). On the estimation of beta-pricing models. Review of Financial Studies, 5(1), 1-33.","type":"article","doi":"10.1093/rfs/5.1.1","isbn":null,"url":null}],"related":["panel-varx","local-projections","tvp-favar"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"family-assessment-device","name":"Family Assessment Device","fullName":"Family Assessment Device (FAD)","aliases":["FAD","McMaster Family Assessment Device"],"domain":"social-psychology","family":"process-pipeline","subfamily":"family systems and functioning","year":"1983","originator":"Norman Epstein, Deborah Baldwin, and David Bishop","url":"https://scholargate.app/en/social-psychology/family-assessment-device","markdownUrl":"https://scholargate.app/en/social-psychology/family-assessment-device.md","definition":"The Family Assessment Device is a widely used self-report instrument designed to measure family functioning across six key domains derived from the McMaster Model of Family Functioning. Developed by Epstein, Baldwin, and Bishop in 1983, the FAD assesses problem-solving, communication, roles, affective responsiveness, affective involvement, and behavioral control in families. It is used extensively in family therapy research, clinical assessment of family dynamics, and as an outcome measure in family-based interventions for mental health, medical, and developmental conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Epstein, Deborah Baldwin, and David Bishop","subfamily":"family systems and functioning","year":"1983","type":"Self-report family assessment"},"citations":[{"ref":"Epstein, N. B., Baldwin, L. M., & Bishop, D. S. (1983). The McMaster Family Assessment Device. Journal of Marital and Family Therapy, 9(2), 171-180.","type":"article","doi":"10.1111/j.1752-0606.1983.tb01497.x","isbn":null,"url":null},{"ref":"Miller, I. W., Ryan, C. E., Keitner, G. I., Bishop, D. S., & Epstein, N. B. (2000). The McMaster approach to families: Theory, assessment, treatment and research. Journal of Family Therapy, 22(2), 168-189.","type":"article","doi":"10.1111/1467-6427.00145","isbn":null,"url":null}],"related":["dyadic-adjustment-scale","parenting-stress-index","social-provisions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"faos","name":"Foot and Ankle Outcome Score","fullName":"Foot and Ankle Outcome Score (FAOS)","aliases":["FAOS"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"foot-and-ankle-specific outcome","year":2001,"originator":"Ewa M. Roos, Mats Brandsson, H. Hugelhotz, M. Klassbo, L. Stefan Lohmander","url":"https://scholargate.app/en/sports-medicine/faos","markdownUrl":"https://scholargate.app/en/sports-medicine/faos.md","definition":"The Foot and Ankle Outcome Score (FAOS) is a 42-item patient self-report instrument designed to assess symptoms, function, and activity limitations in individuals with foot and ankle pathology. Developed by Roos and colleagues in 2001 and published in the Journal of Orthopedic & Sports Physical Therapy, the FAOS has become the standard outcome measure in foot and ankle surgery and rehabilitation research, providing comprehensive evaluation across pain, stiffness, physical function, sport/recreation, and foot-ankle-related quality of life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ewa M. Roos, Mats Brandsson, H. Hugelhotz, M. Klassbo, L. Stefan Lohmander","subfamily":"foot-and-ankle-specific outcome","year":2001,"type":"Patient self-report"},"citations":[{"ref":"Roos EM, Brandsson M, Hugelhotz H, Klassbo M, Lohmander LS. Development and validation of the Foot and Ankle Outcome Score. J Orthop Sports Phys Ther. 2001;31(9):504-514.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Roos+EM%2C+Brandsson+M%2C+Hugelhotz+H%2C+Klassbo+M%2C+Lohmander+LS.+Development+and+validation+of+the+Foot+and+Ankle+Outcome+Sco+Roos"}],"related":["patient-specific-functional-scale","global-rating-of-change-scale","lower-extremity-functional-scale","ikdc-subjective-knee-form"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fare","name":"FARE","fullName":"Factor Relationship weighting method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2020","originator":"Žižović, M., Pamucar, D., Marinkovic, D., Žižović, M. M.","url":"https://scholargate.app/en/decision-making/fare","markdownUrl":"https://scholargate.app/en/decision-making/fare.md","definition":"FARE (Factor Relationship weighting method) is a weight objective multi-criteria decision-making (MCDM) method introduced by Žižović, M., Pamucar, D., Marinkovic, D., Žižović, M. M. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Žižović, M., Pamucar, D., Marinkovic, D., Žižović, M. M.","subfamily":"Weight_Objective","year":"2020","type":"Weight_Subjective (pairwise factor relationship matrix, closed-form weights)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Žižović, M., Pamucar, D., Marinkovic, D., Žižović, M. M. (2020). Novel integrated multi-criteria ranking methodology: Case of public administration efficiency. European Journal of Operational Research","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Novel+integrated+multi-criteria+ranking+methodology%3A+Case+of+public+administration+efficiency"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fast-decoupled-power-flow","name":"Fast Decoupled Power Flow","fullName":"Fast Decoupled Load Flow Method","aliases":["FDLF","Fast Decoupled Load Flow"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Iterative numerical methods","year":"1972","originator":"Brian Stott, Octave Alsac","url":"https://scholargate.app/en/electrical-engineering/fast-decoupled-power-flow","markdownUrl":"https://scholargate.app/en/electrical-engineering/fast-decoupled-power-flow.md","definition":"The Fast Decoupled Load Flow (FDLF) method, introduced by Stott and Alsac in 1972, exploits the weak coupling between active and reactive power in power systems to accelerate convergence beyond standard Newton-Raphson. By decoupling the equations and using constant, approximate Jacobians, it reduces computation per iteration while maintaining acceptable accuracy for most practical systems. This method remains widely used in operational software for its speed and numerical stability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brian Stott, Octave Alsac","subfamily":"Iterative numerical methods","year":"1972","type":"Decoupled iterative solution method for power system analysis"},"citations":[{"ref":"Stott, B., & Alsac, O. (1972). Fast decoupled load flow. IEEE Transactions on Power Apparatus and Systems, 91(3), 859-869.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fast+decoupled+load+flow+Stott"},{"ref":"Tinney, W. F., Brandwajn, V., & Chan, S. M. (1983). Sparse vector methods for small-signal and transient stability studies. IEEE Transactions on Power Apparatus and Systems, 102(7), 2137-2141.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sparse+vector+methods+for+small-signal+and+transient+stability+studies+Tinney"},{"ref":"Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2013). Power Generation, Operation, and Control (3rd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+Generation%2C+Operation%2C+and+Control+%283rd+ed.%29+Wood"}],"related":["newton-raphson-power-flow","optimal-power-flow","economic-dispatch"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fast-multipole-method","name":"Fast Multipole Method","fullName":"Fast Multipole Method (FMM)","aliases":["FMM","multipole acceleration","hierarchical method"],"domain":"numerical-methods","family":"ml-model","subfamily":"Hierarchical Acceleration","year":"1987","originator":"Leslie Greengard and Vladimir Rokhlin","url":"https://scholargate.app/en/numerical-methods/fast-multipole-method","markdownUrl":"https://scholargate.app/en/numerical-methods/fast-multipole-method.md","definition":"The Fast Multipole Method (FMM) is a hierarchical algorithm that reduces the computational complexity of particle interactions from O(n²) to O(n log n) or O(n), developed by Greengard and Rokhlin in 1987. By grouping distant particles and approximating their cumulative effects via multipole expansions, FMM enables efficient simulation of N-body problems, boundary integral equations, and Coulomb interactions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leslie Greengard and Vladimir Rokhlin","subfamily":"Hierarchical Acceleration","year":"1987","type":"Computational acceleration technique"},"citations":[{"ref":"Greengard, L., & Rokhlin, V. (1987). A fast algorithm for particle simulations. Journal of Computational Physics, 73(2), 325–348.","type":"article","doi":"10.1016/0021-9991(87)90140-9","isbn":null,"url":null},{"ref":"Greengard, L. (1988). The Rapid Evaluation of Potential Fields in Particle Systems. MIT Press.","type":"book","doi":null,"isbn":"0262071088","url":null},{"ref":"Ying, L., Biros, G., & Zorin, D. (2004). A kernel-independent adaptive fast multipole method. Journal of Computational Physics, 196(2), 591–626.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+kernel-independent+adaptive+fast+multipole+method+Ying"}],"related":["barnes-hut-tree","treecode","boundary-element-method","particle-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"faster-r-cnn","name":"Faster R-CNN","fullName":"Faster Region-based Convolutional Neural Network","aliases":["Faster RCNN","Faster-RCNN","RPN-based detector","two-stage object detector","region proposal network detector"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2015,"originator":"Ren, S.; He, K.; Girshick, R.; Sun, J. (Microsoft Research)","url":"https://scholargate.app/en/deep-learning/faster-r-cnn","markdownUrl":"https://scholargate.app/en/deep-learning/faster-r-cnn.md","definition":"Faster R-CNN is a two-stage deep convolutional object detection framework introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun (Microsoft Research) at NeurIPS 2015. It replaces the slow selective-search region proposal step used in its predecessors R-CNN and Fast R-CNN with a learned Region Proposal Network (RPN) that shares convolutional features with the detection head, enabling the first end-to-end trainable, near-real-time accurate object detector and establishing a long-standing accuracy benchmark on PASCAL VOC and MS COCO.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ren, S.; He, K.; Girshick, R.; Sun, J. (Microsoft Research)","year":2015,"type":"Two-stage object detection CNN","task":"Object detection (localization + classification)","backbone":"VGG-16 / ResNet (plug-in)","datasetBenchmark":"PASCAL VOC, MS COCO","trainingEnd2End":true,"anchorsPerLocation":9},"citations":[{"ref":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems (NeurIPS), 28, 91–99.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1506.01497"},{"ref":"Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149.","type":"article","doi":"10.1109/TPAMI.2016.2577031","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["fast-r-cnn","r-cnn","yolo","ssd","mask-r-cnn","feature-pyramid-network","deformable-convolutional-networks","resnet"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fasttext","name":"FastText","fullName":"FastText: Subword-Level Word Embeddings and Efficient Text Classification","aliases":["fastText","fast text","subword embedding","character n-gram embedding","bag of tricks text classification"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2016,"originator":"Joulin, A.; Bojanowski, P.; Grave, E.; Mikolov, T. (Facebook AI Research)","url":"https://scholargate.app/en/deep-learning/fasttext","markdownUrl":"https://scholargate.app/en/deep-learning/fasttext.md","definition":"FastText is a word embedding and text classification framework developed by Facebook AI Research (Joulin, Bojanowski, Grave, and Mikolov, 2016–2017) that represents each word as the sum of its character n-gram vectors, allowing it to construct meaningful representations for unseen and morphologically rich words and to perform near state-of-the-art text classification orders of magnitude faster than deep neural network alternatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joulin, A.; Bojanowski, P.; Grave, E.; Mikolov, T. (Facebook AI Research)","year":2016,"type":"Subword embedding model and linear text classifier","task":"Word representation & text classification","handlesOOV":true,"minTokens":1},"citations":[{"ref":"Joulin, A., Grave, E., Bojanowski, P. & Mikolov, T. (2017). Bag of Tricks for Efficient Text Classification. In Proceedings of EACL 2017, Short Papers, pp. 427–431. ACL.","type":"article","doi":"10.18653/v1/e17-2068","isbn":null,"url":null},{"ref":"Bojanowski, P., Grave, E., Joulin, A. & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135–146.","type":"article","doi":"10.1162/tacl_a_00051","isbn":null,"url":null},{"ref":"Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers.","type":"book","doi":null,"isbn":"978-1-62705-298-6","url":null}],"related":["word2vec","glove","bert","transformer","text-cnn","naive-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fault-analysis-power-system","name":"Fault Analysis in Power Systems","fullName":"Symmetrical and Unsymmetrical Fault Analysis in Power Systems","aliases":["short-circuit analysis","fault current calculation","symmetrical components method"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Power system protection and analysis","year":"1918","originator":"Charles Fortescue","url":"https://scholargate.app/en/electrical-engineering/fault-analysis-power-system","markdownUrl":"https://scholargate.app/en/electrical-engineering/fault-analysis-power-system.md","definition":"Fault analysis determines the magnitude and distribution of currents and voltages during abnormal conditions in power systems, such as short circuits. Using Fortescue's symmetrical components method (1918), engineers calculate fault currents to design protection relays and equipment ratings. It is essential for ensuring safe and reliable power system operation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Fortescue","subfamily":"Power system protection and analysis","year":"1918","type":"Computational pipeline"},"citations":[{"ref":"Fortescue, C. L. (1918). Method of symmetrical coordinates applied to the solution of polyphase networks. Transactions of the AIEE, 37(2), 1027-1044.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/6570688"},{"ref":"Bergen, A. R. (1986). Power System Analysis (2nd ed.). Prentice-Hall.","type":"book","doi":null,"isbn":null,"url":"https://www.pearson.com"},{"ref":"Saadat, H. (2010). Power System Analysis (3rd ed.). PSA Publishing.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+System+Analysis+%283rd+ed.%29+Saadat"}],"related":["power-flow-analysis","protection-relay-coordination","smart-grid-state-estimation","harmonic-distortion-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fault-tree-analysis","name":"Fault Tree Analysis","fullName":"Fault Tree Analysis (FTA)","aliases":["FTA","Fault Tree Method","Top-Down Reliability Analysis","Hata Ağacı Analizi"],"domain":"reliability","family":"process-pipeline","subfamily":"Reliability & risk","year":1981,"originator":"Vesely et al. (US NRC Fault Tree Handbook)","url":"https://scholargate.app/en/reliability/fault-tree-analysis","markdownUrl":"https://scholargate.app/en/reliability/fault-tree-analysis.md","definition":"Fault Tree Analysis (FTA) is a top-down, deductive reliability method that begins with an undesired top-level failure event and systematically traces backward through chains of contributing causes using Boolean logic gates (AND, OR). First formalized by Watson at Bell Telephone Laboratories in 1961 and later standardized by Vesely, Goldberg, Roberts, and Haasl in the landmark 1981 NRC Fault Tree Handbook, FTA has become a cornerstone of quantitative risk assessment in nuclear, aerospace, and industrial safety engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vesely et al. (US NRC Fault Tree Handbook)","year":1981,"type":"Deductive top-down failure analysis","subfamily":"Reliability & risk","input_type":"System logic model with failure events","output_type":"Minimal cut sets and failure probability"},"citations":[{"ref":"Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook (NUREG-0492). U.S. Nuclear Regulatory Commission.","type":"article","doi":null,"isbn":null,"url":"https://www.nrc.gov/reading-rm/doc-collections/nuregs/staff/sr0492/"}],"related":["event-tree-analysis","reliability-analysis","bayesian-network"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"favar","name":"FAVAR","fullName":"Factor-Augmented Vector Autoregression","aliases":["factor-augmented VAR","FAVAR model","Faktör Artırımlı VAR (FAVAR)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2005,"originator":"Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes","url":"https://scholargate.app/en/econometrics/favar","markdownUrl":"https://scholargate.app/en/econometrics/favar.md","definition":"FAVAR is a multivariate time-series model that first compresses information from a very large set of variables into a few common factors, then includes those factors alongside the observed variables in a vector autoregression. It was introduced by Bernanke, Boivin and Eliasz in 2005 to study monetary policy using hundreds of macroeconomic indicators at once.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernanke, Boivin & Eliasz (2005); building on Stock & Watson diffusion indexes","year":2005,"type":"Multivariate time-series model","estimator":"Principal-component factor extraction followed by VAR estimation","outcome":"continuous","minSample":60},"citations":[{"ref":"Bernanke, B. S., Boivin, J. & Eliasz, P. (2005). Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach. The Quarterly Journal of Economics, 120(1), 387-422.","type":"article","doi":"10.1162/0033553053327452","isbn":null,"url":null},{"ref":"Stock, J. H. & Watson, M. W. (2002). Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business & Economic Statistics, 20(2), 147-162.","type":"article","doi":"10.1198/073500102317351921","isbn":null,"url":null}],"related":["var-model","stvar","markov-switching","ols-regression","principal-component-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fci-algorithm","name":"FCI Algorithm","fullName":"Fast Causal Inference (FCI) Algorithm","aliases":["FCI","Fast Causal Inference","FCI Causal Discovery","FCI Algoritması"],"domain":"causal-inference","family":"ml-model","subfamily":"Causal discovery","year":2000,"originator":"Spirtes, Glymour & Scheines","url":"https://scholargate.app/en/causal-inference/fci-algorithm","markdownUrl":"https://scholargate.app/en/causal-inference/fci-algorithm.md","definition":"The Fast Causal Inference (FCI) algorithm is a constraint-based causal discovery method introduced by Spirtes, Glymour, and Scheines in their landmark 2000 book Causation, Prediction, and Search. Unlike its predecessor the PC algorithm, FCI is specifically designed to handle the presence of latent (unmeasured) common causes and sample selection bias. It outputs a Partial Ancestral Graph (PAG), which faithfully represents the set of all causal structures consistent with the observed conditional independencies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Spirtes, Glymour & Scheines","year":2000,"type":"Constraint-based causal discovery algorithm","subfamily":"Causal discovery","handles_latent_confounders":"Yes","output_representation":"Partial Ancestral Graph (PAG)"},"citations":[{"ref":"Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-19440-2","url":null}],"related":["pc-algorithm","notears","bayesian-network"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fdosm","name":"FDOSM","fullName":"Fuzzy Decision by Opinion Score Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Mohammed, K. I. Zaidan, A. A. Zaidan, B. B. Albahri, O. S. Albahri, A. S. Alsalem, M. A.","url":"https://scholargate.app/en/decision-making/fdosm","markdownUrl":"https://scholargate.app/en/decision-making/fdosm.md","definition":"FDOSM (Fuzzy Decision by Opinion Score Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Mohammed, K. I. Zaidan, A. A. Zaidan, B. B. Albahri, O. S. Albahri, A. S. Alsalem, M. A. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mohammed, K. I. Zaidan, A. A. Zaidan, B. B. Albahri, O. S. Albahri, A. S. Alsalem, M. A.","subfamily":"Ranking","year":"2019","type":"Linguistic multi-DM opinion aggregation using fuzzy set scoring","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Mohammed, K. I., Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Albahri, A. S., Alsalem, M. A. (2019). Real-time remote health-monitoring systems in a medical centre: a review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of Medical Systems","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Real-time+remote+health-monitoring+systems+in+a+medical+centre%3A+a+review+of+the+provision+of+healthcare+services-based+body+sensor+information%2C+open+challenges+and+methodological+aspects+Mohammed"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fea-bone-remodeling","name":"FEA Bone Remodeling","fullName":"Finite Element Analysis for Bone Remodeling","aliases":["Bone remodeling simulation","Trabecular architecture adaptation","Mechano-regulation"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Computational biomechanics","year":"1987","originator":"Rik Huiskes","url":"https://scholargate.app/en/biomechanics/fea-bone-remodeling","markdownUrl":"https://scholargate.app/en/biomechanics/fea-bone-remodeling.md","definition":"Finite element analysis (FEA) for bone remodeling predicts how bone tissue density and architecture adapt to changes in mechanical loading over time. Pioneered by Rik Huiskes and Donald Carter in the 1980s, this computational approach integrates stress analysis with biophysical remodeling rules to simulate the long-term response of bone to disease, aging, or surgical intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rik Huiskes","subfamily":"Computational biomechanics","year":"1987","type":"Multi-physics finite element pipeline"},"citations":[{"ref":"Huiskes, R., Weinans, H., Grootenboer, H. J., Dalstra, M., Fudala, B., & Slooff, T. J. (1987). Adaptive bone-remodeling theory applied to prosthetic-design analysis. Journal of Biomechanics, 20(11-12), 1135-1150.","type":"article","doi":"10.1016/0021-9290(87)90030-3","isbn":null,"url":null},{"ref":"Carter, D. R., Fyhrie, D. P., & Whalen, R. T. (1987). Trabecular bone density and loading history: regulation of stress-induced bone loss. Journal of Biomechanics, 20(9), 785-794.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Trabecular+bone+density+and+loading+history%3A+regulation+of+stress-induced+bone+loss+Carter"}],"related":["micro-ct-morphometry","scaffold-porosity-analysis","joint-reaction-force"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fear-conditioning","name":"Fear Conditioning","fullName":"Fear Conditioning Paradigm","aliases":["Classical Conditioning","Pavlovian Fear Learning"],"domain":"psychology","family":"hypothesis-test","subfamily":"Classical Conditioning","year":"1927","originator":"Ivan Pavlov","url":"https://scholargate.app/en/psychology/fear-conditioning","markdownUrl":"https://scholargate.app/en/psychology/fear-conditioning.md","definition":"Fear conditioning is a classical (Pavlovian) learning paradigm in which a neutral stimulus (conditioned stimulus, CS—e.g., a tone or image) is repeatedly paired with an aversive outcome (unconditioned stimulus, US—e.g., mild electric shock or loud noise). After conditioning, the CS alone elicits a fear response. Fear conditioning is fundamental to understanding associative learning, anxiety disorders, and the neural bases of threat detection. Behavioral and physiological measures reveal learning acquisition, extinction, and individual differences in fear sensitivity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ivan Pavlov","subfamily":"Classical Conditioning","year":"1927","type":"Associative learning paradigm"},"citations":[{"ref":"Pavlov, I. P. (1927). Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex. Oxford University Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Pavlov%2C%20I.%20P.%20(1927).%20Conditioned%20reflexes%3A%20An%20investigation%20of%20the%20physiological%20activity%20of%20the%20cerebral%20cortex.%20Oxfor"},{"ref":"Davis, M. (1998). Are different parts of the extended amygdala involved in fear versus anxiety? Biological Psychiatry, 44(12), 1239-1247.","type":"article","doi":"10.1016/s0006-3223(98)00288-1","isbn":null,"url":null},{"ref":"Foa, E. B., & Kozak, M. J. (2006). Emotional processing of fear: Exposure to corrective information. Psychological Bulletin, 99(1), 20-35.","type":"article","doi":"10.1037/0033-2909.99.1.20","isbn":null,"url":null}],"related":["extinction-learning","amygdala-function","conditioned-stimulus","unconditioned-stimulus"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fear-of-infection-scale","name":"Fear of COVID-19 Scale","fullName":"Fear of COVID-19 Infection Scale (FCV-19S)","aliases":["FCV-19S","Fear of COVID-19 Scale"],"domain":"public-health","family":"process-pipeline","subfamily":"pandemic-fear-assessment","year":"2020","originator":"Ahorsu et al.","url":"https://scholargate.app/en/public-health/fear-of-infection-scale","markdownUrl":"https://scholargate.app/en/public-health/fear-of-infection-scale.md","definition":"The Fear of COVID-19 Scale (FCV-19S) is a 7-item, self-report instrument assessing fear of COVID-19 infection across cognitive, emotional, and physiological domains. Developed by Ahorsu and colleagues in 2020, it measures threat perception, anxiety symptoms triggered by disease-related triggers, and avoidance behaviors. The FCV-19S has been translated into over 40 languages and validated across diverse populations, making it the most widely used pandemic-specific fear measure globally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ahorsu et al.","subfamily":"pandemic-fear-assessment","year":"2020","type":"Self-report"},"citations":[{"ref":"Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H. (2020). The Fear of COVID-19 Scale: Development, initial validation, and reliability testing. International Journal of Mental Health and Addiction, 20(3), 1146–1159.","type":"article","doi":"10.1007/s11469-020-00270-8","isbn":null,"url":null}],"related":["covid-19-anxiety-scale","covid-19-mental-health-scale","health-protective-behavior-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fear-of-missing-out-scale","name":"FoMO Scale","fullName":"Fear of Missing Out Scale","aliases":["FoMO"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"social-media-psychology","year":"2013","originator":"Andrew K. Przybylski et al.","url":"https://scholargate.app/en/social-media-psychology/fear-of-missing-out-scale","markdownUrl":"https://scholargate.app/en/social-media-psychology/fear-of-missing-out-scale.md","definition":"The FoMO Scale is a 10-item self-report instrument that measures the extent to which individuals experience anxiety or apprehension about missing out on social events, experiences, or information shared by others, particularly in social media contexts. Developed by Przybylski and colleagues in 2013, it quantifies this contemporary psychological phenomenon that has become increasingly relevant with the proliferation of digital communication platforms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew K. Przybylski et al.","subfamily":"social-media-psychology","year":"2013","type":"Self-report"},"citations":[{"ref":"Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848.","type":"article","doi":"10.1016/j.chb.2013.02.014","isbn":null,"url":null}],"related":["social-media-disorder-scale","social-comparison-scale-online","smartphone-addiction-scale-short","technoference-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"federated-learning","name":"Federated Learning","fullName":"Federated Learning","aliases":["Collaborative Learning","Decentralized Learning","FedAvg","Federe Öğrenme"],"domain":"privacy","family":"ml-model","subfamily":"Privacy-preserving analysis","year":2017,"originator":"McMahan et al.","url":"https://scholargate.app/en/privacy/federated-learning","markdownUrl":"https://scholargate.app/en/privacy/federated-learning.md","definition":"Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McMahan et al.","year":2017,"type":"Distributed privacy-preserving machine learning","subfamily":"Privacy-preserving analysis","aggregation":"Weighted averaging of local model updates","communication":"Iterative client-server rounds"},"citations":[{"ref":"McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v54/mcmahan17a.html"}],"related":["differential-privacy","stochastic-gradient-descent","knowledge-distillation"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fedformer","name":"FEDformer","fullName":"FEDformer (Frequency Enhanced Decomposed Transformer)","aliases":["Frequency Enhanced Decomposed Transformer","FED-Transformer","Frequency Domain Transformer","Frekans Tabanlı Ayrıştırılmış Dönüştürücü"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2022,"originator":"Tian Zhou et al.","url":"https://scholargate.app/en/deep-learning/fedformer","markdownUrl":"https://scholargate.app/en/deep-learning/fedformer.md","definition":"FEDformer is a Transformer-based architecture for long-term multivariate time-series forecasting, introduced by Zhou et al. at ICML 2022. Its core innovation is the combination of seasonal-trend decomposition with frequency-domain attention: instead of computing full token-to-token attention in the time domain, FEDformer projects queries, keys, and values into the frequency domain via Fourier or wavelet transforms and operates on a randomly selected subset of frequency components, achieving linear complexity while preserving global temporal structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tian Zhou et al.","year":2022,"type":"Frequency-domain decomposed Transformer for time-series forecasting","subfamily":"Time-series forecasting","complexity":"O(L) linear complexity via random frequency sampling","venue":"ICML 2022"},"citations":[{"ref":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. ICML.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2201.12740"}],"related":["autoformer","informer","film"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"feed-conversion-ratio","name":"Feed Conversion Ratio","fullName":"Feed Conversion Ratio Assessment and Optimization","aliases":["feed efficiency ratio","gain-to-feed ratio","FCR analysis"],"domain":"animal-science","family":"process-pipeline","subfamily":"Nutritional efficiency measurement","year":"1950s","originator":"Animal Nutrition Scientists","url":"https://scholargate.app/en/animal-science/feed-conversion-ratio","markdownUrl":"https://scholargate.app/en/animal-science/feed-conversion-ratio.md","definition":"Feed conversion ratio (FCR) is a key metric of nutritional efficiency in livestock, measuring the amount of feed consumed relative to animal growth or product output. Developed by animal nutrition scientists in the mid-20th century, FCR quantifies how efficiently livestock convert dietary nutrients into meat, milk, eggs, or other products. It is a primary driver of profitability in commercial animal agriculture and a focus of genetic selection in breeding programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Animal Nutrition Scientists","subfamily":"Nutritional efficiency measurement","year":"1950s","type":"efficiency calculation and analysis"},"citations":[{"ref":"Blaxter, K. L. (1989). Energy metabolism in animals and man. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/core/books"},{"ref":"Ferrell, C. L., Crouse, J. D., & Dickson, W. M. (1982). Feed efficiency of beef cattle: a review. Journal of Animal Science, 55(3), 527-544.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Feed+efficiency+of+beef+cattle%3A+a+review+Ferrell"},{"ref":"Ten Pas, A. F. (2000). Nutrient partitioning in farm animals: concepts and applications. Journal of Animal Science, 78(11), 2804-2811.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nutrient+partitioning+in+farm+animals%3A+concepts+and+applications+Ten"}],"related":["milk-yield-recording","growth-curve-fitting-livestock","feed-ration-formulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"feed-ration-formulation","name":"Feed Ration Formulation","fullName":"Nutritionally Balanced Feed Ration Formulation and Optimization","aliases":["diet formulation","ration design","nutrient balancing"],"domain":"animal-science","family":"process-pipeline","subfamily":"Nutrition management and optimization","year":"1960s","originator":"Veterinary Nutritionists","url":"https://scholargate.app/en/animal-science/feed-ration-formulation","markdownUrl":"https://scholargate.app/en/animal-science/feed-ration-formulation.md","definition":"Feed ration formulation is the process of selecting and blending feed ingredients to meet animal nutrient requirements while optimizing cost and feed acceptance. Formalized by veterinary nutritionists in the 1960s-1970s, the method integrates knowledge of nutrient requirements (energy, protein, minerals, vitamins) with ingredient composition and cost data. Modern ration formulation uses linear programming and decision support software to identify cost-minimizing combinations that satisfy nutritional constraints.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Veterinary Nutritionists","subfamily":"Nutrition management and optimization","year":"1960s","type":"optimization and decision-making"},"citations":[{"ref":"National Research Council. (2001). Nutrient requirements of dairy cattle (7th rev. ed.). National Academies Press.","type":"article","doi":null,"isbn":null,"url":"https://www.nap.edu/"},{"ref":"Eckert, M. A., & Schroeder, J. W. (2008). Formulation of rations for dairy cattle. Veterinary Clinics of North America: Food Animal Practice, 24(1), 1-22.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Formulation+of+rations+for+dairy+cattle+Eckert"},{"ref":"Van Soest, P. J. (1994). Nutritional ecology of the ruminant (2nd ed.). Cornell University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cornellpress.cornell.edu/"}],"related":["feed-conversion-ratio","milk-yield-recording","body-condition-score-cattle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"feedback-linearization","name":"Feedback Linearization","fullName":"Feedback Linearization","aliases":["Exact Linearization","Nonlinear Feedback Control","Input-Output Linearization"],"domain":"control-theory","family":"ml-model","subfamily":"Nonlinear Control","year":"1983","originator":"Alberto Isidori","url":"https://scholargate.app/en/control-theory/feedback-linearization","markdownUrl":"https://scholargate.app/en/control-theory/feedback-linearization.md","definition":"Feedback Linearization is a nonlinear control technique that uses a nonlinear state-feedback transformation to convert a nonlinear system into a linear one, enabling the use of standard linear control methods. Developed by Isidori, Sontag, and others in the 1980s, feedback linearization is conceptually elegant and powerful: if the system satisfies certain structural conditions (relative degree, decoupling matrix rank), the nonlinearities can be exactly cancelled through feedback, reducing the problem to linear design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alberto Isidori","subfamily":"Nonlinear Control","year":"1983","type":"algorithm"},"citations":[{"ref":"Isidori, A. (1995). Nonlinear Control Systems (3rd ed.). Springer-Verlag.","type":"article","doi":"10.1007/978-1-84628-615-5","isbn":null,"url":null},{"ref":"Sontag, E. D. (1983). A concept of input-output linearization. Proceedings of the 22nd IEEE Conference on Decision and Control.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+concept+of+input-output+linearization+Sontag"},{"ref":"Nijmeijer, H., & Van der Schaft, A. J. (1990). Nonlinear Dynamical Control Systems. Springer-Verlag.","type":"article","doi":"10.1007/978-1-4757-2101-0","isbn":null,"url":null}],"related":["sliding-mode-control","backstepping-control","model-predictive-control","h-infinity-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"female-pelvic-pain-scale","name":"Female Pelvic Pain Scale","fullName":"Female Pelvic Pain Scale (FPPS)","aliases":["FPPS","Pelvic Pain Questionnaire"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"gynecological-pain-symptoms","year":2001,"originator":"Multiple developers","url":"https://scholargate.app/en/obstetrics-gynecology/female-pelvic-pain-scale","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/female-pelvic-pain-scale.md","definition":"The Female Pelvic Pain Scale (FPPS) is a standardized self-report instrument designed to assess the severity and functional impact of pelvic pain conditions in women, including dysmenorrhea, pelvic inflammatory disease, interstitial cystitis, and pelvic pain of unclear etiology. By measuring pain intensity, location, timing, and associated functional limitations, the FPPS enables clinicians to quantify baseline pain burden and track treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple developers","subfamily":"gynecological-pain-symptoms","year":2001,"type":"Self-report"},"citations":[{"ref":"Jones, G. L., Kennedy, S. H., Barnard, A., Wong, J., & Jenkinson, C. (2001). Development of an endometriosis quality-of-life instrument: The Endometriosis Health Profile-30. Obstetrics & Gynecology, 98(2), 258-264.","type":"article","doi":"10.1097/00006250-200108000-00014","isbn":null,"url":null},{"ref":"McGowan, L. (2016). Improving sexual function in women with endometriosis: a review. Sexual Medicine Reviews, 4(4), 326-337.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Improving+sexual+function+in+women+with+endometriosis%3A+a+review+McGowan"}],"related":["endometriosis-health-profile","postpartum-bonding-questionnaire","premenstrual-symptoms-screening"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"female-sexual-distress-scale","name":"Female Sexual Distress Scale","fullName":"Female Sexual Distress Scale (FSDS)","aliases":["FSDS","FSDS-R"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"sexual-distress","year":2002,"originator":"Derogatis et al.","url":"https://scholargate.app/en/urology-gynecology/female-sexual-distress-scale","markdownUrl":"https://scholargate.app/en/urology-gynecology/female-sexual-distress-scale.md","definition":"The FSDS is a brief self-report measure designed to assess psychological distress specifically related to female sexual dysfunction. Originally developed by Derogatis and colleagues and published in 2002, the revised version (FSDS-R) comprises 13 items measuring distress about sexual concerns, with particular utility in identifying women who seek treatment for sexual problems versus those with asymptomatic sexual dysfunction. It is widely used in clinical practice and research to distinguish distress-inducing sexual dysfunction from normative sexual variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derogatis et al.","subfamily":"sexual-distress","year":2002,"type":"Self-report distress scale"},"citations":[{"ref":"Derogatis, L. R., Clayton, A. H., Lewis-D'Agostino, D. J., Wunderlich, G., & Fu, Y. (2008). Validation of the Female Sexual Distress Scale-Revised for assessing distress in women with hypoactive sexual desire disorder and female sexual arousal disorder. Journal of Sexual & Marital Therapy, 34(2), 94–118.","type":"article","doi":"10.1111/j.1743-6109.2007.00672.x","isbn":null,"url":null},{"ref":"Derogatis, L. R. (1994). SCL-90-R: Administration, Scoring and Procedures Manual. National Computer Systems.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/8155494"}],"related":["female-sexual-function-index","sexual-satisfaction-scale","menopause-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"female-sexual-function-index","name":"Female Sexual Function Index","fullName":"Female Sexual Function Index (FSFI)","aliases":["FSFI"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"sexual-function","year":2000,"originator":"Rosen et al.","url":"https://scholargate.app/en/urology-gynecology/female-sexual-function-index","markdownUrl":"https://scholargate.app/en/urology-gynecology/female-sexual-function-index.md","definition":"The FSFI is a 19-item multidimensional self-report instrument designed to assess sexual function in women across the lifespan. Developed by Rosen and colleagues in 2000, it measures six core domains of sexual response and has become a gold standard in both clinical and research settings for evaluating female sexual dysfunction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosen et al.","subfamily":"sexual-function","year":2000,"type":"Self-report questionnaire"},"citations":[{"ref":"Rosen, R., Brown, C., Heiman, J., Leiblum, S., Meston, C., Shabsigh, R., & D'Agostino, R. (2000). The Female Sexual Function Index (FSFI): a multidimensional self-report instrument for the assessment of female sexual function. Journal of Sex & Marital Therapy, 26(2), 191–208.","type":"article","doi":"10.1080/009262300278597","isbn":null,"url":null},{"ref":"Wiegel, M., Meston, C., & Rosen, R. (2005). The female sexual function index (FSFI): cross-validation and development of clinical cutoff scores. Journal of Sex & Marital Therapy, 31(1), 1–20.","type":"article","doi":"10.1080/00926230590475206","isbn":null,"url":null}],"related":["international-index-erectile-function","female-sexual-distress-scale","sexual-satisfaction-scale","menopause-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"feminist-research","name":"Feminist Research Methodology","fullName":"Feminist Research Methodology","aliases":["feminist inquiry","feminist qualitative research","feminist standpoint research","gender-critical research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Critical Inquiry","year":"1970s–1980s (formalized as a methodology)","originator":"Sandra Harding, Dorothy Smith, Patricia Hill Collins, and the broader feminist social science movement","url":"https://scholargate.app/en/qualitative/feminist-research","markdownUrl":"https://scholargate.app/en/qualitative/feminist-research.md","definition":"Feminist research methodology is a qualitative approach grounded in feminist theory that centres gender, power, and social justice as core analytical lenses. It challenges claims of value-free objectivity, foregrounds the voices and experiences of marginalized groups — particularly women — and explicitly positions the researcher as a political and social actor. Developed across disciplines including sociology, education, and health sciences, it draws on standpoint theory, intersectionality, and participatory ethics to produce knowledge that can inform emancipatory practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sandra Harding, Dorothy Smith, Patricia Hill Collins, and the broader feminist social science movement","year":"1970s–1980s (formalized as a methodology)","type":"Qualitative research method","dataType":"Interviews, personal narratives, ethnographic observations, documents, autobiographical accounts","typicalSampleSize":"Variable; commonly 10–30 participants in interview-based studies","subfamily":"Critical Inquiry"},"citations":[{"ref":"Harding, S. (Ed.). (1987). Feminism and Methodology: Social Science Issues. Indiana University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Feminism+and+Methodology+Social+Science+Issues+Harding+1987"},{"ref":"Creswell, J. W. (2013). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (3rd ed.). Sage. [Chapter on feminist inquiry and advocacy approaches]","type":"book","doi":null,"isbn":"978-1452205625","url":null}],"related":["ethnography","grounded-theory","narrative-analysis","discourse-analysis","action-research","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fertigation-scheduling","name":"Fertigation Scheduling","fullName":"Precision Timing and Delivery of Nutrients via Irrigation Systems","aliases":["fertigation management","nutrient timing optimization","drip fertilization"],"domain":"horticulture","family":"process-pipeline","subfamily":"Nutrient delivery and timing optimization","year":"1980","originator":"Irrigation engineering and crop nutrition integration","url":"https://scholargate.app/en/horticulture/fertigation-scheduling","markdownUrl":"https://scholargate.app/en/horticulture/fertigation-scheduling.md","definition":"Fertigation scheduling integrates irrigation and nutrient delivery to optimize plant nutrition while minimizing waste and environmental impact. By applying fertilizers through drip or sprinkler systems at precise times and rates matched to plant development stage and soil water availability, growers can improve nutrient use efficiency, reduce leaching, and boost yields. This method is now standard in commercial vegetable, orchard, and nursery production worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Irrigation engineering and crop nutrition integration","subfamily":"Nutrient delivery and timing optimization","year":"1980","type":"irrigation-nutrition scheduling pipeline"},"citations":[{"ref":"Hochmuth, G. J. (1994). Efficiency of nutrient uptake—A review. HortTechnology, 4(1), 14–23.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Efficiency+of+nutrient+uptake%E2%80%94A+review+Hochmuth"},{"ref":"Bar-Yosef, B. (2001). Fertigation management and crops response. Advances in Agronomy, 65, 1–75.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fertigation+management+and+crops+response+Bar-Yosef"}],"related":["hydroponic-nutrient-solution","greenhouse-climate-control","phenological-stage-monitoring","crop-load-management"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fetal-wellbeing-questionnaire","name":"FWQ","fullName":"Fetal Wellbeing Questionnaire","aliases":["FWQ","Fetal Wellbeing","Pregnancy Wellbeing Questionnaire"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"prenatal-wellness-assessment","year":"2008","originator":"DiPietro, Ghera, Costigan; fetal development research","url":"https://scholargate.app/en/public-health-nutrition/fetal-wellbeing-questionnaire","markdownUrl":"https://scholargate.app/en/public-health-nutrition/fetal-wellbeing-questionnaire.md","definition":"The FWQ is a self-report questionnaire assessing pregnant women's subjective perception of fetal wellbeing, maternal physical and emotional health, and prenatal bonding. Developed by DiPietro and colleagues studying fetal development and maternal-fetal attachment, the FWQ captures non-clinical dimensions of pregnancy experience that predict maternal and infant outcomes. The questionnaire is used in research and clinical screening to identify pregnant women at risk for anxiety, depression, or poor bonding, enabling early intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"DiPietro, Ghera, Costigan; fetal development research","subfamily":"prenatal-wellness-assessment","year":"2008","type":"Pregnant woman self-report; subjective pregnancy experiences"},"citations":[{"ref":"DiPietro, J. A., Ghera, M. M., & Costigan, K. A. (2008). Prenatal origins of postnatal temperament. Early Human Development, 84(10), 643–650.","type":"article","doi":"10.1016/b978-012370877-9.00128-6","isbn":null,"url":null},{"ref":"Valman, H. B. (ed.). (2009). Neonatal jaundice. Current Problems in Pediatrics and Adolescent Health Care, 39(10), 298–318.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Neonatal+jaundice+Valman"}],"related":["maternal-diet-quality-index","infant-young-child-feeding-practices","child-diet-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"few-shot-learning","name":"Few-shot Learning","fullName":"Few-shot Learning (Meta-learning with Limited Labeled Examples)","aliases":["FSL","low-shot learning","k-shot learning","meta-learning for few examples"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2011–2017","originator":"Lake, B. M.; Vinyals, O.; Finn, C. et al.","url":"https://scholargate.app/en/machine-learning/few-shot-learning","markdownUrl":"https://scholargate.app/en/machine-learning/few-shot-learning.md","definition":"Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lake, B. M.; Vinyals, O.; Finn, C. et al.","year":"2011–2017","type":"Meta-learning / low-data learning paradigm","dataType":"Labeled images, text, or structured data with very few examples per class","subfamily":"Machine learning"},"citations":[{"ref":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2016/hash/90e1357833654983612fb05e3ec9148c-Abstract.html"},{"ref":"Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70:1126–1135.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v70/finn17a.html"}],"related":["transfer-learning","meta-learning","self-supervised-learning","semi-supervised-learning","metric-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"few-shot-object-detection","name":"Few-Shot Object Detection","fullName":"Few-Shot Object Detection with Contrastive Learning","aliases":["FSOD","Few-shot detection"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Object Detection, Meta-Learning","year":"2020","originator":"Xin Wang","url":"https://scholargate.app/en/deep-learning/few-shot-object-detection","markdownUrl":"https://scholargate.app/en/deep-learning/few-shot-object-detection.md","definition":"Few-Shot Object Detection (FSOD) is a meta-learning approach that enables detecting novel object classes from only a few annotated examples. Unlike standard object detection requiring hundreds of labeled instances per class, FSOD learns to quickly adapt detection models to new object categories by leveraging knowledge from base categories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xin Wang","subfamily":"Deep Learning, Object Detection, Meta-Learning","year":"2020","type":"Neural network architecture"},"citations":[{"ref":"Wang, X., Huang, T. E., Darrell, T., Gonzalez, J. E., & Yu, F. (2020). Few-shot object detection with attention-RPN and multi-relation detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9050-9059).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Few-shot+object+detection+with+attention-RPN+and+multi-relation+detector+Wang"}],"related":["detr","swin-transformer","simclr","meta-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"few-shot-text-classification","name":"Few-Shot Text Classification","fullName":"Few-Shot Text Classification","aliases":["few-shot learning for text","Az Atışlı Metin Sınıflandırma (Few-Shot)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/few-shot-text-classification","markdownUrl":"https://scholargate.app/en/text-mining/few-shot-text-classification.md","definition":"Few-shot text classification assigns documents to classes using only a handful of labelled examples per class. Building on advances by Gao et al. (2021) and the prompt-free SetFit approach of Tunstall et al. (2022), it leans on prototypical networks, MAML, or fine-tuning of a large pretrained model to learn from scarce labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-classification task (low-resource)","approaches":"Prototypical networks / MAML / pretrained-model fine-tuning","minLabelsPerClass":"As few as 1 example per class","output":"Class label per document"},"citations":[{"ref":"Gao, T., Fisch, A. & Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. ACL.","type":"inproceedings","doi":"10.18653/v1/2021.acl-long.295","isbn":null,"url":null},{"ref":"Tunstall, L., Reimers, N., Jo, U.E.S., Bates, L., Korat, D., Wasserblat, M. & Pereg, O. (2022). Efficient Few-Shot Learning Without Prompts. arXiv.","type":"article","doi":"10.48550/arXiv.2209.11055","isbn":null,"url":null}],"related":["text-classification","domain-adaptation-nlp","bert-embeddings","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
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{"slug":"ff-cocoso","name":"FF-COCOSO","fullName":"FF-CoCoSo — Fermatean extension of FF-COCOSO","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-cocoso","markdownUrl":"https://scholargate.app/en/decision-making/ff-cocoso.md","definition":"FF-COCOSO (FF-CoCoSo — Fermatean extension of FF-COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-codas","name":"FF-CODAS","fullName":"Fermatean extension of FF-CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-codas","markdownUrl":"https://scholargate.app/en/decision-making/ff-codas.md","definition":"FF-CODAS (Fermatean extension of FF-CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-copras","name":"FF-COPRAS","fullName":"Fermatean extension of FF-COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-copras","markdownUrl":"https://scholargate.app/en/decision-making/ff-copras.md","definition":"FF-COPRAS (Fermatean extension of FF-COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-edas","name":"FF-EDAS","fullName":"Fermatean extension of FF-EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-edas","markdownUrl":"https://scholargate.app/en/decision-making/ff-edas.md","definition":"FF-EDAS (Fermatean extension of FF-EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-gra","name":"FF-GRA","fullName":"Fermatean extension of FF-GRA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-gra","markdownUrl":"https://scholargate.app/en/decision-making/ff-gra.md","definition":"FF-GRA (Fermatean extension of FF-GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-mabac","name":"FF-MABAC","fullName":"Fermatean extension of FF-MABAC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-mabac","markdownUrl":"https://scholargate.app/en/decision-making/ff-mabac.md","definition":"FF-MABAC (Fermatean extension of FF-MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-marcos","name":"FF-MARCOS","fullName":"Fermatean extension of FF-MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-marcos","markdownUrl":"https://scholargate.app/en/decision-making/ff-marcos.md","definition":"FF-MARCOS (Fermatean extension of FF-MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-moora","name":"FF-MOORA","fullName":"Fermatean extension of FF-MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-moora","markdownUrl":"https://scholargate.app/en/decision-making/ff-moora.md","definition":"FF-MOORA (Fermatean extension of FF-MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-promethee","name":"FF-PROMETHEE","fullName":"2-tuple Linguistic Fermatean Fuzzy PROMETHEE (Akram-Bibi 2023)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1985 crisp; 2023 variant applicator","originator":"Akram, M., Bibi, R.","url":"https://scholargate.app/en/decision-making/ff-promethee","markdownUrl":"https://scholargate.app/en/decision-making/ff-promethee.md","definition":"FF-PROMETHEE (2-tuple Linguistic Fermatean Fuzzy PROMETHEE (Akram-Bibi 2023)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Bibi, R. in 1985 crisp; 2023 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Bibi, R.","subfamily":"Outranking","year":"1985 crisp; 2023 variant applicator","type":"Fermatean fuzzy outranking — 2-tuple linguistic Fermatean fuzzy sets (2TLFFS), μ³+ν³ ≤ 1","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Akram, M., Bibi, R. (2023). Multi-criteria group decision-making based on an integrated PROMETHEE approach with 2-tuple linguistic Fermatean fuzzy sets. Granular Computing","type":"article","doi":"10.1007/s41066-022-00359-6","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-saw","name":"FF-SAW","fullName":"Fermatean extension of FF-SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-saw","markdownUrl":"https://scholargate.app/en/decision-making/ff-saw.md","definition":"FF-SAW (Fermatean extension of FF-SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-todim","name":"FF-TODIM","fullName":"Fermatean extension of FF-TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-todim","markdownUrl":"https://scholargate.app/en/decision-making/ff-todim.md","definition":"FF-TODIM (Fermatean extension of FF-TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-topsis","name":"FF-TOPSIS","fullName":"Fermatean extension of FF-TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-topsis","markdownUrl":"https://scholargate.app/en/decision-making/ff-topsis.md","definition":"FF-TOPSIS (Fermatean extension of FF-TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-vikor","name":"FF-VIKOR","fullName":"Fermatean extension of FF-VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-vikor","markdownUrl":"https://scholargate.app/en/decision-making/ff-vikor.md","definition":"FF-VIKOR (Fermatean extension of FF-VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-waspas","name":"FF-WASPAS","fullName":"Fermatean extension of FF-WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-waspas","markdownUrl":"https://scholargate.app/en/decision-making/ff-waspas.md","definition":"FF-WASPAS (Fermatean extension of FF-WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ff-wpm","name":"FF-WPM","fullName":"Fermatean extension of FF-WPM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Senapati, T. Yager, R. R.","url":"https://scholargate.app/en/decision-making/ff-wpm","markdownUrl":"https://scholargate.app/en/decision-making/ff-wpm.md","definition":"FF-WPM (Fermatean extension of FF-WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Senapati, T. Yager, R. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Senapati, T. Yager, R. R.","subfamily":"Ranking","year":"2020","type":"Fermatean outranking/ranking — Fermatean Fuzzy Set (FFS: μ, ν; μ³+ν³ ≤ 1)","value_space":"fermatean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Senapati, T., Yager, R. R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing","type":"article","doi":"10.1007/s12652-019-01377-0","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fibromyalgia-impact-questionnaire","name":"FIQ","fullName":"Fibromyalgia Impact Questionnaire","aliases":["FIQ","Fibromyalgia Impact","Fibromyalgia Questionnaire"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Rheumatologic and Chronic Pain Conditions","year":"1991","originator":"Cynthia S. Burckhardt et al.","url":"https://scholargate.app/en/health-outcomes/fibromyalgia-impact-questionnaire","markdownUrl":"https://scholargate.app/en/health-outcomes/fibromyalgia-impact-questionnaire.md","definition":"The FIQ is the most widely used patient-reported outcome measure for fibromyalgia disease burden. Developed by Cynthia Burckhardt and colleagues in 1991, this 10-item questionnaire quantifies how fibromyalgia affects physical function, work capacity, depression, anxiety, sleep, pain, and fatigue. The revised version (FIQR, 21 items) offers enhanced psychometric properties and is the current standard in fibromyalgia clinical trials and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cynthia S. Burckhardt et al.","subfamily":"Rheumatologic and Chronic Pain Conditions","year":"1991","type":"Self-report symptom and functional impairment questionnaire"},"citations":[{"ref":"Burckhardt, C. S., Clark, S. R., & Bennett, R. M. (1991). The Fibromyalgia Impact Questionnaire: Development and validation. The Journal of Rheumatic Diseases, 18(5), 728-735.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Fibromyalgia+Impact+Questionnaire%3A+Development+and+validation+Burckhardt"},{"ref":"Bennett, R. M., Friend, R., Jones, K. D., Ward, R., Han, B. K., & Ross, R. L. (2009). The Revised Fibromyalgia Impact Questionnaire (FIQR): Validation and psychometric properties. Seminars in Arthritis and Rheumatism, 39(6), 448-453.","type":"article","doi":"10.1186/ar2783","isbn":null,"url":null},{"ref":"Häuser, W., Petzke, F., Üçeyler, N., & Köllner, V. (2012). Fibromyalgia. Nature Reviews Disease Primers, 1, 15022.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/27188521"}],"related":["eortc-qlq-c30","dlqi","chronic-heart-failure-questionnaire","kidney-disease-quality-of-life"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ficks-laws","name":"Fick's Laws","fullName":"Fick's Laws of Diffusion for Mass Transfer","aliases":["diffusion equation","Fickian diffusion"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Mass Transfer","year":"1855","originator":"Adolf Fick","url":"https://scholargate.app/en/thermodynamics/ficks-laws","markdownUrl":"https://scholargate.app/en/thermodynamics/ficks-laws.md","definition":"Fick's Laws describe how species diffuse through media due to concentration gradients. The First Law (steady-state) relates diffusion flux to concentration gradient, while the Second Law (transient) describes how concentration changes over time. These laws are fundamental to mass transfer analysis, applying to gases, liquids, and solids. Fick's Laws are analogous to Fourier's Law of heat conduction, replacing temperature with concentration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adolf Fick","subfamily":"Mass Transfer","year":"1855","type":"Diffusion law"},"citations":[{"ref":"Fick, A. (1855). On liquid diffusion. Philosophical Magazine, 10(63), 30-39.","type":"article","doi":"10.1080/14786445508641925","isbn":null,"url":null},{"ref":"Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470055540","url":null}],"related":["stefan-maxwell-diffusion","boussinesq-approximation","psychrometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fidelity-assessment","name":"Fidelity Assessment in Implementation","fullName":"Fidelity Assessment: Measuring the Degree to Which Interventions Are Delivered as Intended in Real-World Implementation","aliases":["fidelity","treatment fidelity","protocol adherence","implementation fidelity"],"domain":"implementation-science","family":"process-pipeline","subfamily":"implementation measurement","year":"2004","originator":"National Institutes of Health Behavior Change Consortium; Bellg et al.","url":"https://scholargate.app/en/implementation-science/fidelity-assessment","markdownUrl":"https://scholargate.app/en/implementation-science/fidelity-assessment.md","definition":"Fidelity Assessment is the systematic measurement of the degree to which an intervention is delivered as designed in real-world practice. Formalized by the National Institutes of Health Behavior Change Consortium (Bellg et al. 2004) and expanded in MRC guidance (Moore et al. 2015), fidelity assessment is critical to implementation science because it answers: 'Did we deliver the intervention correctly?' A clinical trial may show a treatment works, but if delivered poorly in practice, benefits disappear. Fidelity assessment prevents misattribution of failure (was the intervention weak, or was implementation poor?) and guides coaching to improve quality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"National Institutes of Health Behavior Change Consortium; Bellg et al.","subfamily":"implementation measurement","year":"2004","type":"Method"},"citations":[{"ref":"Bellg, A. J., Borrelli, B., Resnick, B., Hecht, J., Minicucci, D. S., Ory, M., ... & Treatment Fidelity Workgroup of the National Institutes of Health Behavior Change Consortium. (2004). Enhancing treatment fidelity in health behavior change studies: Best practices and recommendations from the NIH Behavior Change Consortium. Health Psychology, 23(5), 443-451.","type":"article","doi":"10.1037/0278-6133.23.5.443","isbn":null,"url":null},{"ref":"Moore, G. F., Audrey, S., Barker, M., Bond, L., Bonell, C., Hardeman, W., ... & Baird, J. (2015). Process evaluation of complex interventions: Medical Research Council guidance. BMJ, 350, h1258.","type":"article","doi":"10.1136/bmj.h1258","isbn":null,"url":null},{"ref":"Schoenwald, S. K., & Garland, A. F. (2013). A review of treatment adherence measurement methods. Psychological Assessment, 25(1), 146-156.","type":"article","doi":"10.1037/a0029715","isbn":null,"url":null}],"related":["implementation-outcome-taxonomy","re-aim-framework","normalization-process-theory","cfir-framework","scaling-up-interventions"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fidelity-implementation-scale","name":"Fidelity Scale","fullName":"Fidelity of Implementation Scale","aliases":["Fidelity Scale","Implementation Fidelity","Fidelity Measurement"],"domain":"implementation-science","family":"process-pipeline","subfamily":"implementation quality assessment","year":2007,"originator":"Carroll, C.; Patterson, M.; and colleagues; Goodman, C.; and others","url":"https://scholargate.app/en/implementation-science/fidelity-implementation-scale","markdownUrl":"https://scholargate.app/en/implementation-science/fidelity-implementation-scale.md","definition":"Fidelity of Implementation refers to the degree to which an evidence-based practice or intervention is delivered as originally designed and intended. The Fidelity of Implementation Scale (or fidelity assessment framework) operationalizes this concept by specifying the core components of an intervention, defining each component precisely, and then assessing whether practitioners deliver each component when appropriate. Fidelity is distinct from adoption (whether staff use the innovation) and outcomes (whether the innovation produces the intended benefit). An innovation can be widely adopted but delivered with low fidelity (incorrectly or incompletely), often resulting in poor outcomes. Conversely, perfect fidelity without adaptation may fail in some contexts. Fidelity monitoring is essential in implementation science to understand whether implementation failures stem from ineffective interventions (true lack of efficacy) or ineffective delivery (low fidelity despite effective intervention). Fidelity assessment uses observation, checklist, and record review methods tailored to the intervention type.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carroll, C.; Patterson, M.; and colleagues; Goodman, C.; and others","subfamily":"implementation quality assessment","year":2007,"type":"Observational and performance-based assessment"},"citations":[{"ref":"Goodman, C., & Evans, C. (2010). Audit of the use of the Measure of Processes by Area Teams (MOPAT) in the acute hospital setting. Journal of Clinical Nursing, 19(11-12), 1514–1524.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Audit+of+the+use+of+the+Measure+of+Processes+by+Area+Teams+%28MOPAT%29+in+the+acute+hospital+setting+Goodman"},{"ref":"Carroll, C., Patterson, M., Wood, S., Booth, A., Rick, J., & Balain, S. (2007). A conceptual framework for implementation fidelity. Implementation Science, 2, 40.","type":"article","doi":"10.1186/1748-5908-2-40","isbn":null,"url":null}],"related":["normalisation-measure-development","evidence-based-practice-attitude","implementation-leadership-scale","knowledge-to-action-scale","stages-of-concern-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-autoethnography","name":"Field-based autoethnography","fullName":"Field-Based Autoethnographic Research","aliases":["field autoethnography","site-based autoethnography","embodied field autoethnography","FBAE"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Ellis, Adams, and Bochner; building on autoethnography foundations by Carolyn Ellis and Arthur Bochner","url":"https://scholargate.app/en/qualitative/field-based-autoethnography","markdownUrl":"https://scholargate.app/en/qualitative/field-based-autoethnography.md","definition":"Field-based autoethnography is a qualitative research design in which the researcher immerses themselves in a specific physical or social setting and draws on their own lived experience within that field to produce analytically reflexive accounts. It blends the systematic observational practices of ethnographic fieldwork with the first-person introspective voice of autoethnography, generating knowledge that is simultaneously personal, cultural, and contextually grounded.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ellis, Adams, and Bochner; building on autoethnography foundations by Carolyn Ellis and Arthur Bochner","year":"1990s–2000s","type":"Qualitative research design","dataType":"Fieldnotes, personal memos, observations, reflexive journal entries, artifacts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Ellis, C., Adams, T. E., & Bochner, A. P. (2011). Autoethnography: An overview. Forum: Qualitative Social Research, 12(1), Art. 10.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.17169/fqs-12.1.1589"},{"ref":"Angrosino, M. (2007). Doing Ethnographic and Observational Research. Sage.","type":"book","doi":null,"isbn":"978-0761949954","url":null}],"related":["autoethnography","ethnography","participatory-ethnography","narrative-inquiry","interpretive-autoethnography","field-based-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-case-study","name":"Field-based case study","fullName":"Field-Based Case Study Research","aliases":["fieldwork case study","naturalistic case study","in-situ case study","field case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–1990s (formalized by Yin 1984, Stake 1995)","originator":"Robert Yin, Robert Stake (case study formalization); field-based tradition rooted in anthropological and sociological fieldwork","url":"https://scholargate.app/en/qualitative/field-based-case-study","markdownUrl":"https://scholargate.app/en/qualitative/field-based-case-study.md","definition":"A field-based case study is a qualitative research design that investigates a bounded phenomenon — a case — within its real-world, natural setting through sustained on-site data collection. Combining the analytical structure of case study methodology with the direct observational immersion of fieldwork, it enables rich, context-sensitive understanding of how phenomena unfold in practice. The approach is firmly grounded in the frameworks of Robert Yin and Robert Stake and draws on anthropological traditions of participant and non-participant observation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Yin, Robert Stake (case study formalization); field-based tradition rooted in anthropological and sociological fieldwork","year":"1970s–1990s (formalized by Yin 1984, Stake 1995)","type":"Qualitative research design","dataType":"Field observations, interviews, documents, artifacts collected on-site in natural settings","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (1995). The Art of Case Study Research. Sage.","type":"book","doi":null,"isbn":"978-0803957671","url":null}],"related":["case-study","ethnography","field-based-ethnography","participant-observation","multiple-case-study","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-classic-grounded-theory","name":"Field-based classic grounded theory","fullName":"Field-Based Classic (Glaserian) Grounded Theory","aliases":["Glaserian grounded theory in naturalistic settings","classic GT field study","field-based GT","naturalistic classic grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (Glaser & Strauss); field-based application codified from late 1970s onward","originator":"Barney G. Glaser (classic GT); field-based variant draws on naturalistic inquiry traditions","url":"https://scholargate.app/en/qualitative/field-based-classic-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/field-based-classic-grounded-theory.md","definition":"Field-based classic grounded theory applies Barney Glaser's original (Glaserian) grounded theory method within naturalistic, in-situ settings — combining sustained field immersion with the classic GT emphasis on emergence, theoretical sensitivity, and the constant comparative method. The researcher enters the social scene without a predetermined framework, collects data through observation and naturalistic interviews, and allows a substantive theory to surface inductively from the field rather than imposing conceptual structure in advance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser (classic GT); field-based variant draws on naturalistic inquiry traditions","year":"1967 (Glaser & Strauss); field-based application codified from late 1970s onward","type":"Qualitative theory-generating design","dataType":"Field observations, naturalistic interviews, documents, field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Glaser, B. G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theoretical+Sensitivity+Advances+in+the+Methodology+of+Grounded+Theory+Glaser+1978"},{"ref":"Glaser, B. G. (1992). Basics of Grounded Theory Analysis: Emergence vs. Forcing. Sociology Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Basics+of+Grounded+Theory+Analysis+Emergence+vs+Forcing+Glaser+1992"}],"related":["classic-grounded-theory","grounded-theory","field-based-grounded-theory","constructivist-grounded-theory","ethnography","field-based-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-cluster-sampling","name":"Field-based cluster sampling","fullName":"Field-Based Cluster Sampling","aliases":["field cluster sampling","in-field cluster sampling","area cluster sampling (field)","field survey cluster design"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1950s (theory); 1970s–1980s (field survey practice)","originator":"William G. Cochran (theoretical foundations); WHO EPI programme (field application)","url":"https://scholargate.app/en/survey-methodology/field-based-cluster-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-cluster-sampling.md","definition":"Field-based cluster sampling is a probability sampling method in which naturally occurring geographic or administrative groups (clusters) are first randomly selected, and then data are collected in person from units within those clusters. It is the standard design for large-scale field surveys in public health, agriculture, education, and humanitarian response, where compiling a full population list is impractical but clusters such as villages, schools, or census tracts can be identified and physically accessed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran (theoretical foundations); WHO EPI programme (field application)","year":"1950s (theory); 1970s–1980s (field survey practice)","type":"Probability sampling design","dataType":"Cross-sectional survey data collected in natural or field settings","subfamily":"Sampling"},"citations":[{"ref":"World Health Organization. (1991). Training for mid-level managers: The EPI coverage survey. WHO/EPI/MLM/91.10. World Health Organization.","type":"book","doi":null,"isbn":null,"url":"https://apps.who.int/iris/handle/10665/66119"},{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null}],"related":["cluster-sampling","multistage-sampling","systematic-sampling","simple-random-sampling","stratified-sampling","field-based-stratified-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-constructivist-grounded-theory","name":"Field-based constructivist grounded theory","fullName":"Field-Based Constructivist Grounded Theory","aliases":["field-based CGT","constructivist GT with fieldwork","situated grounded theory","Charmaz-field grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s (Charmaz 2006; fully articulated by 2014)","originator":"Kathy Charmaz (constructivist variant); fieldwork orientation drawn from symbolic interactionist tradition","url":"https://scholargate.app/en/qualitative/field-based-constructivist-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/field-based-constructivist-grounded-theory.md","definition":"Field-based constructivist grounded theory integrates Kathy Charmaz's constructivist grounded theory with active fieldwork in natural settings. Rather than relying solely on retrospective interviews, the researcher enters the participants' world — observing, interacting, and collecting data where social processes unfold — while simultaneously coding and building theory. The result is a grounded substantive theory that is both empirically anchored in real contexts and epistemologically co-constructed between researcher and participants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz (constructivist variant); fieldwork orientation drawn from symbolic interactionist tradition","year":"2000s (Charmaz 2006; fully articulated by 2014)","type":"Qualitative research design and analytic approach","dataType":"Interviews, field observations, documents, field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973522","url":null},{"ref":"Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0857029492","url":null}],"related":["grounded-theory","ethnography","constructivist-grounded-theory","situational-analysis","focused-ethnography","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-content-analysis","name":"Field-based Content Analysis","fullName":"Field-based Content Analysis","aliases":["field content analysis","naturalistic content analysis","ethnographic content analysis","ECA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1987","originator":"David L. Altheide","url":"https://scholargate.app/en/qualitative/field-based-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-content-analysis.md","definition":"Field-based content analysis is a qualitative analytic approach that systematically examines documents, artifacts, and texts encountered or produced within a natural field setting. Originally formulated by David Altheide as ethnographic content analysis (ECA), it blends the systematic rigor of traditional content analysis with the reflexive, iterative logic of ethnographic inquiry, allowing the researcher to interact continuously with the data and revise analytic categories as new meaning emerges from the field.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David L. Altheide","year":"1987","type":"Qualitative analytic approach","dataType":"Field documents, artifacts, media texts, observational records, archival materials","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Altheide, D. L. (1987). Ethnographic content analysis. Qualitative Sociology, 10(1), 65–77.","type":"article","doi":"10.1007/BF00988269","isbn":null,"url":null},{"ref":"Altheide, D. L. (1996). Qualitative Media Analysis. Sage Publications.","type":"book","doi":null,"isbn":"978-0803957015","url":null}],"related":["thematic-analysis","ethnography","document-analysis","content-analysis","grounded-theory","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-convenience-sampling","name":"Field-based convenience sampling","fullName":"Field-based Convenience Sampling","aliases":["intercept sampling","on-site convenience sampling","street intercept sampling","field intercept survey"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century onward","originator":"Conventional practice in social and epidemiological field research","url":"https://scholargate.app/en/survey-methodology/field-based-convenience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-convenience-sampling.md","definition":"Field-based convenience sampling is a non-probability technique in which researchers recruit participants by approaching whoever is physically present and accessible at a chosen real-world location — a market, hospital waiting room, park, or transit hub. It is widely used in public health surveillance, marketing research, and exploratory social surveys when rapid, low-cost data collection is needed and probability sampling is not feasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conventional practice in social and epidemiological field research","year":"Mid-20th century onward","type":"Non-probability sampling","dataType":"Survey or interview data collected in person at a physical location","subfamily":"Sampling"},"citations":[{"ref":"Babbie, E. (2010). The Practice of Social Research (12th ed.). Wadsworth Cengage Learning.","type":"book","doi":null,"isbn":"978-0495598428","url":null},{"ref":"Convenience sampling. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Convenience_sampling"}],"related":["convenience-sampling","purposive-sampling","quota-sampling","snowball-sampling","systematic-sampling","field-based-quota-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-conversation-analysis","name":"Field-based Conversation Analysis","fullName":"Field-based Conversation Analysis","aliases":["field CA","naturalistic conversation analysis","in situ conversation analysis","fieldwork CA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1960s–1970s (CA foundations); field applications consolidated from the 1990s onward","originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA roots); extended to field settings by later ethnomethodologists","url":"https://scholargate.app/en/qualitative/field-based-conversation-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-conversation-analysis.md","definition":"Field-based conversation analysis (field CA) applies the rigorous sequential-analytic methods of conversation analysis to talk and interaction recorded in real-world settings — workplaces, clinics, classrooms, and public spaces — rather than to pre-existing corpora or laboratory data. By combining sustained fieldwork access with fine-grained transcript analysis, it reveals how social order is accomplished turn by turn in the actual environments where it matters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA roots); extended to field settings by later ethnomethodologists","year":"1960s–1970s (CA foundations); field applications consolidated from the 1990s onward","type":"Qualitative observational-analytic approach","dataType":"Audio or video recordings of naturally occurring talk in situ; field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Hindmarsh, J., & Llewellyn, N. (2017). Video in sociomaterial investigations: A solution to the problem of relevance for organizational research. Organizational Research Methods, 21(2), 412–437.","type":"article","doi":"10.1177/1094428116657595","isbn":null,"url":null},{"ref":"Heritage, J. (1984). Garfinkel and Ethnomethodology. Polity Press.","type":"book","doi":null,"isbn":"978-0745600048","url":null}],"related":["conversation-analysis","ethnomethodology","discourse-analysis","ethnography","interaction-analysis","multimodal-interaction-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-critical-discourse-analysis","name":"Field-based Critical Discourse Analysis","fullName":"Field-based Critical Discourse Analysis","aliases":["Field-theoretic CDA","Bourdieusian CDA","sociological CDA","field-oriented discourse analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1999–2001","originator":"Norman Fairclough and Lilie Chouliaraki (synthesis with Bourdieu's field theory)","url":"https://scholargate.app/en/qualitative/field-based-critical-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-critical-discourse-analysis.md","definition":"Field-based Critical Discourse Analysis (Field-based CDA) integrates Pierre Bourdieu's sociological concept of the field — structured social spaces with their own rules, capital, and positions — with the linguistic and critical tools of Critical Discourse Analysis. The approach examines how language constructs, legitimates, and contests power relations within specific institutional or social fields, situating texts in their broader sociological context rather than treating discourse in isolation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Fairclough and Lilie Chouliaraki (synthesis with Bourdieu's field theory)","year":"1999–2001","type":"Qualitative critical discourse framework","dataType":"Texts, documents, interviews, ethnographic field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bourdieu, P. (1991). Language and Symbolic Power. Harvard University Press.","type":"book","doi":null,"isbn":"978-0674510357","url":null},{"ref":"Chouliaraki, L., & Fairclough, N. (2001). Discourse in Late Modernity: Rethinking Critical Discourse Analysis. Edinburgh University Press.","type":"book","doi":null,"isbn":"978-0748609956","url":null}],"related":["critical-discourse-analysis","discourse-analysis","thematic-analysis","ethnography","institutional-ethnography","social-semiotics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-deviant-case-sampling","name":"Field-based Deviant Case Sampling","fullName":"Field-based Deviant Case Sampling","aliases":["field deviant case sampling","outlier case sampling in field research","extreme case sampling in fieldwork","in-situ deviant case sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1980s–1990s (purposive/deviant case sampling literature)","originator":"Michael Quinn Patton; Yvonna Lincoln & Egon Guba","url":"https://scholargate.app/en/survey-methodology/field-based-deviant-case-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-deviant-case-sampling.md","definition":"Field-based deviant case sampling is a purposive strategy that deliberately selects cases deviating markedly from an established pattern or norm, with data collected through direct fieldwork — observation, in-situ interviews, and ethnographic engagement — in the participants' natural settings. By studying outliers on-site, researchers gain contextually grounded insight into why and how certain cases diverge from the typical pattern.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Quinn Patton; Yvonna Lincoln & Egon Guba","year":"1980s–1990s (purposive/deviant case sampling literature)","type":"Purposive qualitative sampling strategy","dataType":"Observational field notes, in-situ interviews, ethnographic records","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage Publications.","type":"book","doi":null,"isbn":"978-0803924314","url":null}],"related":["deviant-case-sampling","purposive-sampling","maximum-variation-sampling","typical-case-sampling","field-based-purposive-sampling","snowball-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-digital-ethnography","name":"Field-based digital ethnography","fullName":"Field-Based Digital Ethnography","aliases":["connective ethnography","blended digital ethnography","hybrid online-offline ethnography","field-integrated digital ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Christine Hine; Sarah Pink et al.","url":"https://scholargate.app/en/qualitative/field-based-digital-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/field-based-digital-ethnography.md","definition":"Field-based digital ethnography is a qualitative research design that combines traditional in-person fieldwork with systematic collection and analysis of digital data. Rather than studying online communities in isolation, it traces how social life moves between physical settings and digital spaces, treating both as equally real sites of cultural practice. Rooted in Christine Hine's virtual ethnography and Sarah Pink's digital ethnography principles, it is particularly suited to studying communities whose practices span offline and online worlds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christine Hine; Sarah Pink et al.","year":"2000s–2010s","type":"Qualitative research design","dataType":"In-person fieldwork observations, online digital traces, interviews, digital artifacts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Hine, C. (2000). Virtual Ethnography. Sage.","type":"book","doi":null,"isbn":"978-0761958956","url":null},{"ref":"Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital Ethnography: Principles and Practice. Sage.","type":"book","doi":null,"isbn":"978-1446295120","url":null}],"related":["digital-ethnography","netnography","ethnography","participatory-digital-ethnography","longitudinal-ethnography","visual-elicitation-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-discourse-analysis","name":"Field-based Discourse Analysis","fullName":"Field-based Discourse Analysis","aliases":["field discourse analysis","Bourdieusian discourse analysis","sociological discourse analysis","FDA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s","originator":"Synthesised from Pierre Bourdieu's field theory and discourse analysis; systematised by researchers including John Frow","url":"https://scholargate.app/en/qualitative/field-based-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-discourse-analysis.md","definition":"Field-based discourse analysis integrates Pierre Bourdieu's sociological concept of the field — a structured social space of positions, capital, and struggle — with the close textual methods of discourse analysis. Rather than treating language as a neutral medium, it examines how discourse is produced, circulated, and received within specific social fields (education, law, journalism, science, etc.), and how discursive choices reflect and reproduce the distribution of power and capital within those fields.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Pierre Bourdieu's field theory and discourse analysis; systematised by researchers including John Frow","year":"1980s–1990s","type":"Qualitative analytical framework","dataType":"Texts, documents, interviews, field notes, media materials","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bourdieu, P. (1991). Language and Symbolic Power. Harvard University Press.","type":"book","doi":null,"isbn":"978-0674510302","url":null},{"ref":"Frow, J. (1985). Discourse and Power. Economy and Society, 14(2), 193–214.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1080/03085148500000011"}],"related":["critical-discourse-analysis","discourse-analysis","thematic-analysis","ethnography","narrative-analysis","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-document-analysis","name":"Field-based Document Analysis","fullName":"Field-based Document Analysis","aliases":["FBDA","field document analysis","naturalistic document analysis","ethnographic document analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–1980s (codified in qualitative research methodology)","originator":"Rooted in ethnographic fieldwork traditions; systematised in qualitative education research by Bogdan & Biklen and Hammersley & Atkinson","url":"https://scholargate.app/en/qualitative/field-based-document-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-document-analysis.md","definition":"Field-based document analysis is a qualitative strategy in which the researcher enters a real-world setting — a school, clinic, organisation, or community — and systematically collects, authenticates, and analyses documents that are naturally produced and used there. Unlike library-based or archival document analysis, the field context is integral: the researcher observes how documents function in practice, who produces and reads them, and what organisational or cultural work they perform. The approach is widely used in ethnographic, case-study, and institutional research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in ethnographic fieldwork traditions; systematised in qualitative education research by Bogdan & Biklen and Hammersley & Atkinson","year":"1970s–1980s (codified in qualitative research methodology)","type":"Qualitative research strategy","dataType":"Field-collected documents (records, artifacts, official files, written materials encountered in natural settings)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bogdan, R. C., & Biklen, S. K. (2007). Qualitative Research for Education: An Introduction to Theories and Methods (5th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0205483655","url":null},{"ref":"Hammersley, M., & Atkinson, P. (2007). Ethnography: Principles in Practice (3rd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415396066","url":null}],"related":["document-analysis","ethnography","content-analysis","case-study","thematic-analysis","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-ethnography","name":"Field-based ethnography","fullName":"Field-Based Ethnographic Research","aliases":["fieldwork ethnography","immersive ethnography","ethnographic fieldwork","site-based ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Early 20th century (Malinowski 1922; Geertz 1973)","originator":"Bronislaw Malinowski; Clifford Geertz (interpretive tradition)","url":"https://scholargate.app/en/qualitative/field-based-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/field-based-ethnography.md","definition":"Field-based ethnography is a qualitative research design in which the researcher immerses themselves in a social setting or community over an extended period, observing and participating in everyday life to understand cultural practices, meanings, and social dynamics from an insider perspective. It is the classical form of ethnography, grounded in sustained physical presence at a research site, and distinguished from archival, virtual, or document-only approaches by its central reliance on direct, embodied fieldwork.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bronislaw Malinowski; Clifford Geertz (interpretive tradition)","year":"Early 20th century (Malinowski 1922; Geertz 1973)","type":"Qualitative research design","dataType":"Field notes, participant observation records, interviews, artifacts, documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Geertz, C. (1973). The Interpretation of Cultures. Basic Books.","type":"book","doi":null,"isbn":"978-0465097197","url":null},{"ref":"Malinowski, B. (1922). Argonauts of the Western Pacific. Routledge.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Argonauts+of+the+Western+Pacific+Malinowski+1922"}],"related":["ethnography","participatory-ethnography","digital-ethnography","longitudinal-ethnography","case-study","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-grounded-theory","name":"Field-based Grounded Theory","fullName":"Field-based Grounded Theory","aliases":["constructivist grounded theory","ethnographic grounded theory","situational grounded theory","field grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (original GT); field-based variant developed through 1980s–2000s","originator":"Kathy Charmaz (constructivist extension); Barney Glaser & Anselm Strauss (original grounded theory)","url":"https://scholargate.app/en/qualitative/field-based-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/field-based-grounded-theory.md","definition":"Field-based grounded theory integrates sustained fieldwork — participant observation, field notes, and naturalistic data collection — with the iterative coding and theoretical sampling procedures of classic grounded theory. Where standard grounded theory typically relies on interview transcripts, the field-based variant anchors theory generation in direct, prolonged observation of naturally occurring social processes in context. The result is a substantive theory that is grounded in both what people say and what they actually do in their everyday settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz (constructivist extension); Barney Glaser & Anselm Strauss (original grounded theory)","year":"1967 (original GT); field-based variant developed through 1980s–2000s","type":"Qualitative research design and analysis approach","dataType":"Fieldwork observations, field notes, in-depth interviews, documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973539","url":null},{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932494","url":null}],"related":["grounded-theory","ethnography","participant-observation","thematic-analysis","case-study","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-hermeneutic-phenomenology","name":"Field-based hermeneutic phenomenology","fullName":"Field-Based Hermeneutic Phenomenological Research","aliases":["field hermeneutic phenomenology","naturalistic hermeneutic phenomenology","field-grounded phenomenology","van Manen field phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s (van Manen's field articulation); philosophical roots ~1927","originator":"Max van Manen (field application); philosophical roots in Martin Heidegger and Hans-Georg Gadamer","url":"https://scholargate.app/en/qualitative/field-based-hermeneutic-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/field-based-hermeneutic-phenomenology.md","definition":"Field-based hermeneutic phenomenology investigates the meaning of lived experience by immersing the researcher in the natural setting where participants live, work, or act. Drawing on Heidegger's ontological hermeneutics and van Manen's pedagogical application, it combines sustained fieldwork — observation, conversation, and artefact collection — with iterative interpretive text analysis to uncover how participants understand and inhabit their world.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max van Manen (field application); philosophical roots in Martin Heidegger and Hans-Georg Gadamer","year":"1990s (van Manen's field articulation); philosophical roots ~1927","type":"Qualitative research approach","dataType":"Field observations, in-depth interviews, field notes, artefacts, conversational narratives","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404126","url":null},{"ref":"Heidegger, M. (1962). Being and Time (J. Macquarrie & E. Robinson, Trans.). Harper & Row. (Original work published 1927)","type":"book","doi":null,"isbn":"978-0061319778","url":null}],"related":["hermeneutic-phenomenology","interpretive-phenomenology","phenomenology","ethnography","interpretive-phenomenological-analysis","field-based-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-institutional-ethnography","name":"Field-based institutional ethnography","fullName":"Field-Based Institutional Ethnography","aliases":["field IE","field-based IE","institutional ethnography fieldwork","on-site institutional ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1987 (IE foundations); field-based variant prominent from 1990s onward","originator":"Dorothy E. Smith","url":"https://scholargate.app/en/qualitative/field-based-institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/field-based-institutional-ethnography.md","definition":"Field-based institutional ethnography (field IE) is a qualitative approach that combines Dorothy Smith's institutional ethnography with sustained, immersive on-site fieldwork. Researchers enter real institutional settings — hospitals, schools, social service offices, prisons — to observe how everyday work practices are coordinated and governed by texts, policies, and ruling relations operating beyond the local site.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith","year":"1987 (IE foundations); field-based variant prominent from 1990s onward","type":"Qualitative research design","dataType":"Field observations, interviews, documents, textual artifacts collected on-site","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759105713","url":null},{"ref":"DeVault, M. L., & McCoy, L. (2006). Institutional ethnography: Using interviews to investigate ruling relations. In D. E. Smith (Ed.), Institutional Ethnography as Practice (pp. 15–44). Rowman & Littlefield.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Institutional+ethnography+using+interviews+to+investigate+ruling+relations+DeVault+McCoy+2006"}],"related":["institutional-ethnography","ethnography","field-based-ethnography","critical-institutional-ethnography","participatory-institutional-ethnography","field-based-grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-interpretive-phenomenological-analysis","name":"Field-based Interpretive Phenomenological Analysis","fullName":"Field-based Interpretive Phenomenological Analysis","aliases":["Field IPA","Fieldwork IPA","Field-based IPA","Field-grounded interpretive phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1999–2009 (IPA seminal; field-based adaptation emerging 2000s–2010s)","originator":"Smith, Flowers & Larkin (IPA); field extension drawn from ethnographic fieldwork traditions","url":"https://scholargate.app/en/qualitative/field-based-interpretive-phenomenological-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-interpretive-phenomenological-analysis.md","definition":"Field-based Interpretive Phenomenological Analysis (Field IPA) extends standard IPA by embedding data collection within naturalistic field settings. Rather than relying solely on retrospective interviews conducted away from the site of experience, the researcher enters the actual environment — a classroom, clinic, workplace, or community space — to gather field observations, artefacts, and in-context conversations alongside in-depth interviews. This produces a richer, more situated account of how participants make sense of their lived experience in the moment and place in which it unfolds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smith, Flowers & Larkin (IPA); field extension drawn from ethnographic fieldwork traditions","year":"1999–2009 (IPA seminal; field-based adaptation emerging 2000s–2010s)","type":"Qualitative research approach","dataType":"Field observations, fieldwork notes, in-situ interviews, artefacts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretive Phenomenological Analysis: Theory, Method and Research. Sage.","type":"book","doi":null,"isbn":"978-1412908344","url":null},{"ref":"Delamont, S., & Atkinson, P. (2019). Ethnography and interpretive phenomenological analysis: Compatible or incompatible methodologies? Qualitative Research in Psychology, 16(1), 1–15.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Ethnography+and+interpretive+phenomenological+analysis+compatible+incompatible+Delamont+Atkinson"}],"related":["interpretive-phenomenological-analysis","hermeneutic-phenomenology","phenomenology","field-based-ethnography","field-based-narrative-inquiry","participatory-interpretive-phenomenological-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-life-history-research","name":"Field-based Life History Research","fullName":"Field-based Life History Research","aliases":["life history method","biographical field research","life story research","field biography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1920s (Thomas & Znaniecki); systematised 1980s–1990s","originator":"W.I. Thomas and Florian Znaniecki (early sociological use); Robert Atkinson and Norman Denzin (methodological codification)","url":"https://scholargate.app/en/qualitative/field-based-life-history-research","markdownUrl":"https://scholargate.app/en/qualitative/field-based-life-history-research.md","definition":"Field-based life history research is a qualitative design that combines sustained ethnographic fieldwork with in-depth biographical interviewing to reconstruct how individuals have experienced and given meaning to their lives within particular social, cultural, and historical contexts. Unlike archive-only biographical work, the field-based variant requires the researcher to be physically present in the participant's social world over time, gathering both spoken life stories and observational data from that world.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"W.I. Thomas and Florian Znaniecki (early sociological use); Robert Atkinson and Norman Denzin (methodological codification)","year":"1920s (Thomas & Znaniecki); systematised 1980s–1990s","type":"Qualitative research design","dataType":"In-depth life story interviews, field observations, personal documents, artefacts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Atkinson, R. (1998). The Life Story Interview. Sage Publications.","type":"book","doi":null,"isbn":"978-0761904786","url":null},{"ref":"Denzin, N. K. (1989). Interpretive Biography. Sage Publications.","type":"book","doi":null,"isbn":"978-0803933088","url":null}],"related":["narrative-inquiry","ethnography","oral-history","phenomenology","grounded-theory","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-maximum-variation-sampling","name":"Field-based maximum variation sampling","fullName":"Field-based Maximum Variation Sampling","aliases":["field MVS","field-based purposeful maximum variation","maximum heterogeneity field sampling","diverse case field sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990 (Patton); field application established through ecological and ethnographic practice in the 1990s–2000s","originator":"Michael Quinn Patton (maximum variation sampling); adapted for field research contexts","url":"https://scholargate.app/en/survey-methodology/field-based-maximum-variation-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-maximum-variation-sampling.md","definition":"Field-based maximum variation sampling is a purposive strategy in which a researcher deliberately selects field sites, ecological plots, communities, or observational units that span the widest possible range of relevant characteristics. By maximising heterogeneity among selected units, the approach ensures that both common patterns shared across diverse conditions and unique features specific to particular contexts are documented, making findings robust across a broad spectrum of real-world variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Quinn Patton (maximum variation sampling); adapted for field research contexts","year":"1990 (Patton); field application established through ecological and ethnographic practice in the 1990s–2000s","type":"Purposive qualitative/mixed-methods sampling strategy","dataType":"Field observations, site measurements, qualitative field notes, ecological or environmental records","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Maximum variation sampling discussed in Chapter 5]","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.","type":"article","doi":"10.11648/j.ajtas.20160501.11","isbn":null,"url":null}],"related":["maximum-variation-sampling","purposive-sampling","field-based-stratified-sampling","field-based-cluster-sampling","deviant-case-sampling","typical-case-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-metaphor-analysis","name":"Field-based Metaphor Analysis","fullName":"Field-based Metaphor Analysis","aliases":["field metaphor elicitation","naturalistic metaphor analysis","contextual metaphor analysis","FbMA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (field-based applications)","originator":"Rooted in Lakoff & Johnson (1980); field-based application developed across educational and social science research from the 1990s onward","url":"https://scholargate.app/en/qualitative/field-based-metaphor-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-metaphor-analysis.md","definition":"Field-based metaphor analysis is a qualitative method that collects and interprets spontaneous or elicited metaphors from participants in their natural settings. Grounded in Lakoff and Johnson's conceptual metaphor theory, it reveals how individuals and communities structure abstract concepts — such as teaching, leadership, or illness — through figurative language encountered or produced in real contexts. Unlike purely document-based metaphor studies, field-based variants combine data collection in natural field settings with systematic analytic coding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Lakoff & Johnson (1980); field-based application developed across educational and social science research from the 1990s onward","year":"1990s–2000s (field-based applications)","type":"Qualitative analytic method","dataType":"Open-ended written or spoken responses, field observations, interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226468013","url":null},{"ref":"Saban, A. (2009). Prospective teachers' mental images about the concept of student. Teaching and Teacher Education, 25(2), 750–764.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prospective+teachers%27+mental+images+about+the+concept+of+student+Saban"}],"related":["metaphor-analysis","conceptual-metaphor-theory","thematic-analysis","content-analysis","ethnography","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-multiple-case-study","name":"Field-based Multiple Case Study","fullName":"Field-based Multiple Case Study Research","aliases":["comparative case study","multi-site case study","cross-case study","multiple-case design"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984 (Yin's foundational text); 2006 (Stake's multiple-case elaboration)","originator":"Robert K. Yin; Robert E. Stake","url":"https://scholargate.app/en/qualitative/field-based-multiple-case-study","markdownUrl":"https://scholargate.app/en/qualitative/field-based-multiple-case-study.md","definition":"A field-based multiple case study is a qualitative research design in which the researcher conducts sustained, in-person investigation at two or more bounded real-world sites (the cases), gathering data through direct observation, interviews, and document analysis. By systematically comparing what is found across cases, the researcher can identify both shared patterns and meaningful differences, producing analytic conclusions that are more robust and transferable than a single-site study allows.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin; Robert E. Stake","year":"1984 (Yin's foundational text); 2006 (Stake's multiple-case elaboration)","type":"Qualitative research design","dataType":"Field observations, interviews, documents, artifacts gathered across multiple real-world sites","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null}],"related":["case-study","ethnography","comparative-research","phenomenology","grounded-theory","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-multistage-sampling","name":"Field-based Multistage Sampling","fullName":"Field-based Multistage Sampling","aliases":["multistage field sampling","field multistage probability sampling","area-based multistage sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1950s–1960s","originator":"Leslie Kish and William G. Cochran (survey sampling frameworks)","url":"https://scholargate.app/en/survey-methodology/field-based-multistage-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-multistage-sampling.md","definition":"Field-based multistage sampling is a probability sampling approach in which the population is drawn from a geographically dispersed or operationally structured field setting through successive nested stages. At each stage, a random subset of sampling units is selected — progressing from large geographic or administrative units down to the final respondents or observation points — with field enumeration conducted between stages to update or verify the available units on the ground.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leslie Kish and William G. Cochran (survey sampling frameworks)","year":"1950s–1960s","type":"Probability sampling design","dataType":"Quantitative field survey data","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471489009","url":null}],"related":["multistage-sampling","cluster-sampling","systematic-sampling","stratified-sampling","proportional-multistage-sampling","field-based-cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-narrative-inquiry","name":"Field-based narrative inquiry","fullName":"Field-Based Narrative Inquiry","aliases":["field narrative inquiry","naturalistic narrative inquiry","field-situated narrative research","in-situ narrative inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"D. Jean Clandinin & F. Michael Connelly","url":"https://scholargate.app/en/qualitative/field-based-narrative-inquiry","markdownUrl":"https://scholargate.app/en/qualitative/field-based-narrative-inquiry.md","definition":"Field-based narrative inquiry is a qualitative research design that investigates human experience by collecting and interpreting stories directly within the natural settings where those experiences unfold. Rooted in Clandinin and Connelly's narrative inquiry framework, it moves the researcher into participants' lived worlds — classrooms, workplaces, communities — to gather rich field texts that preserve the contextual, temporal, and relational dimensions of experience through story.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. Jean Clandinin & F. Michael Connelly","year":"1990s–2000s","type":"Qualitative research design","dataType":"Field texts: interview narratives, field notes, observations, documents, artifacts collected in naturalistic settings","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787943943","url":null},{"ref":"Clandinin, D. J. (2013). Engaging in narrative inquiry. Left Coast Press.","type":"book","doi":null,"isbn":"978-1611322590","url":null}],"related":["narrative-inquiry","ethnography","field-based-ethnography","field-based-case-study","participatory-narrative-research","longitudinal-narrative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-netnography","name":"Field-based netnography","fullName":"Field-Based Netnographic Research","aliases":["hybrid netnography","field netnography","offline-online netnography","blended netnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2010s–present","originator":"Robert V. Kozinets (netnography); hybrid extension developed in netnographic scholarship","url":"https://scholargate.app/en/qualitative/field-based-netnography","markdownUrl":"https://scholargate.app/en/qualitative/field-based-netnography.md","definition":"Field-based netnography combines the systematic online community observation of netnography with direct in-person fieldwork. Researchers move between digital spaces and physical sites where the same community or practice exists, triangulating online discourse with face-to-face encounters. This approach is particularly suited to communities whose identity and practices span both online and offline worlds — fan communities, patient groups, activist networks, and professional subcultures, among others.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert V. Kozinets (netnography); hybrid extension developed in netnographic scholarship","year":"2010s–present","type":"Qualitative research design","dataType":"Online community data (posts, threads, multimedia) combined with in-person field observations and interviews","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"9781526458162","url":null},{"ref":"Kozinets, R. V. (2015). Netnography: Redefined (2nd ed.). Sage.","type":"book","doi":null,"isbn":"9781446298411","url":null}],"related":["netnography","digital-ethnography","field-based-ethnography","participatory-netnography","virtual-ethnography","field-based-digital-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-oral-history","name":"Field-based Oral History","fullName":"Field-based Oral History Research","aliases":["oral history fieldwork","in-situ oral history","community oral history","field oral history"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1960s–1970s (modern oral history movement)","originator":"Paul Thompson; Alessandro Portelli (theoretical elaboration)","url":"https://scholargate.app/en/qualitative/field-based-oral-history","markdownUrl":"https://scholargate.app/en/qualitative/field-based-oral-history.md","definition":"Field-based oral history is a qualitative research design in which in-depth narrative interviews are conducted on-site — at the community, location, or setting that is historically or experientially significant to participants. By situating interviews in the actual field rather than a laboratory or office, the approach activates contextual memory, enriches description, and grounds personal testimony in the material landscape it references. It is widely used in history, anthropology, sociology, and heritage studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Thompson; Alessandro Portelli (theoretical elaboration)","year":"1960s–1970s (modern oral history movement)","type":"Qualitative fieldwork design","dataType":"Audio/video-recorded interviews, field notes, community documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Thompson, P. (2000). The Voice of the Past: Oral History (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0192893888","url":null},{"ref":"Portelli, A. (1997). The Battle of Valle Giulia: Oral History and the Art of Dialogue. University of Wisconsin Press.","type":"book","doi":null,"isbn":"978-0299154349","url":null}],"related":["narrative-analysis","ethnography","life-history-research","biographical-method","participatory-action-research","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-phenomenology","name":"Field-based phenomenology","fullName":"Field-Based Phenomenological Research","aliases":["naturalistic phenomenology","field phenomenology","phenomenological fieldwork","in-situ phenomenological inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s (van Manen's synthesis; broader tradition from early 20th century)","originator":"Max van Manen (systematic field application); rooted in Husserl and Heidegger","url":"https://scholargate.app/en/qualitative/field-based-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/field-based-phenomenology.md","definition":"Field-based phenomenology is a qualitative approach that investigates the lived experience of a phenomenon by collecting data in the natural environments where that experience actually unfolds — rather than exclusively in interview rooms. Drawing on the phenomenological tradition of Husserl and Heidegger, and systematised by Max van Manen, it combines sustained fieldwork observation with open-ended, in-situ conversation to capture the experiential texture of phenomena as participants encounter them in everyday life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max van Manen (systematic field application); rooted in Husserl and Heidegger","year":"1980s–1990s (van Manen's synthesis; broader tradition from early 20th century)","type":"Qualitative research approach","dataType":"Field observations, in-situ interviews, field notes, naturally occurring talk, artefacts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404508","url":null},{"ref":"Finlay, L. (2011). Phenomenology for Therapists: Researching the Lived World. Wiley-Blackwell.","type":"book","doi":null,"isbn":"978-0470683385","url":null}],"related":["phenomenology","ethnography","interpretive-phenomenology","hermeneutic-phenomenology","participatory-phenomenology","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-program-evaluation","name":"Field-based program evaluation","fullName":"Field-Based Program Evaluation","aliases":["naturalistic program evaluation","field evaluation","on-site program evaluation","field-based evaluation"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1970s–1980s (field methods integration with evaluation practice)","originator":"Michael Q. Patton; Peter H. Rossi and Howard E. Freeman","url":"https://scholargate.app/en/field-methods/field-based-program-evaluation","markdownUrl":"https://scholargate.app/en/field-methods/field-based-program-evaluation.md","definition":"Field-based program evaluation is an applied research method that assesses the implementation, outcomes, and value of a program by collecting data directly in the natural setting where the program operates. Rather than relying solely on administrative records or remote surveys, evaluators embed themselves in the field — observing activities, interviewing stakeholders on-site, and reviewing context-specific documents — to produce evidence-grounded judgments about program merit and worth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Q. Patton; Peter H. Rossi and Howard E. Freeman","year":"1970s–1980s (field methods integration with evaluation practice)","type":"Applied evaluation research","dataType":"Mixed: observations, interviews, documents, performance data collected on-site","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761908944","url":null},{"ref":"Patton, M. Q. (2011). Developmental Evaluation: Applying Complexity Concepts to Enhance Innovation and Use. Guilford Press.","type":"book","doi":null,"isbn":"978-1606238721","url":null}],"related":["program-evaluation","educational-action-research","classroom-observation","participatory-program-evaluation","case-study","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-qualitative-content-analysis","name":"Field-based qualitative content analysis","fullName":"Field-Based Qualitative Content Analysis","aliases":["field QCA","naturalistic qualitative content analysis","fieldwork-grounded content analysis","field-integrated QCA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–2000s","originator":"Philipp Mayring (qualitative content analysis); applied to field settings via ethnographic and naturalistic inquiry traditions","url":"https://scholargate.app/en/qualitative/field-based-qualitative-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-qualitative-content-analysis.md","definition":"Field-based qualitative content analysis (field QCA) combines systematic, category-driven content analysis with data collected directly in naturalistic settings. Rather than working with pre-existing texts or archived material, the researcher gathers documents, field notes, artifacts, and informal textual records during fieldwork and subjects them to rigorous qualitative content analysis. The approach preserves the contextual depth of field inquiry while applying the structured, transparent analytic logic that distinguishes qualitative content analysis from purely impressionistic reading.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Philipp Mayring (qualitative content analysis); applied to field settings via ethnographic and naturalistic inquiry traditions","year":"1980s–2000s","type":"Qualitative analysis method","dataType":"Field-collected texts: field notes, observed documents, artifacts, informal interviews, on-site written materials","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research, 1(2), Art. 20.","type":"article","doi":null,"isbn":null,"url":"https://www.qualitative-research.net/index.php/fqs/article/view/1089"},{"ref":"Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage.","type":"book","doi":null,"isbn":"978-0857029218","url":null}],"related":["qualitative-content-analysis","field-based-thematic-analysis","ethnographic-content-analysis","field-based-discourse-analysis","field-based-document-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-reflexive-thematic-analysis","name":"Field-based Reflexive Thematic Analysis","fullName":"Field-based Reflexive Thematic Analysis","aliases":["field RTA","ethnographic reflexive thematic analysis","naturalistic RTA","field-based RTA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2019–2021 (RTA formalised); field application concurrent","originator":"Virginia Braun & Victoria Clarke (RTA foundation); applied to field settings via ethnographic traditions","url":"https://scholargate.app/en/qualitative/field-based-reflexive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-reflexive-thematic-analysis.md","definition":"Field-based Reflexive Thematic Analysis (field RTA) integrates ethnographic data collection — participant observation, field notes, and naturalistic interviews — with the epistemologically explicit, researcher-centred analytic framework of Braun and Clarke's Reflexive Thematic Analysis. It is used when themes must be grounded in observed social practice rather than retrospective accounts alone, placing the researcher's active, documented reflexivity at the centre of both data gathering and interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun & Victoria Clarke (RTA foundation); applied to field settings via ethnographic traditions","year":"2019–2021 (RTA formalised); field application concurrent","type":"Qualitative analysis approach","dataType":"Field notes, observation logs, interview transcripts collected in naturalistic settings","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. Sage.","type":"book","doi":null,"isbn":"9781473953932","url":null},{"ref":"Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes (2nd ed.). University of Chicago Press.","type":"book","doi":null,"isbn":"9780226206837","url":null}],"related":["reflexive-thematic-analysis","ethnography","thematic-analysis","grounded-theory","narrative-analysis","interpretive-phenomenological-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-semiotic-analysis","name":"Field-based Semiotic Analysis","fullName":"Field-based Semiotic Analysis","aliases":["semiotic fieldwork","ethnographic semiotics","field semiotics","social semiotics in the field"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s (systematic field application)","originator":"Developed from Ferdinand de Saussure's semiology and Charles S. Peirce's semiotics; applied to fieldwork by Hodge & Kress (social semiotics) and later multimodal theorists","url":"https://scholargate.app/en/qualitative/field-based-semiotic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-semiotic-analysis.md","definition":"Field-based semiotic analysis is a qualitative approach that combines sustained fieldwork observation with systematic semiotic analysis of signs, symbols, and meaning-making practices encountered in a natural setting. Drawing on the social semiotic tradition of Hodge and Kress, the researcher enters a social field, records its multimodal sign systems — including visual, spatial, gestural, and textual elements — and interprets how participants use and negotiate signs to construct social meanings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from Ferdinand de Saussure's semiology and Charles S. Peirce's semiotics; applied to fieldwork by Hodge & Kress (social semiotics) and later multimodal theorists","year":"1980s–1990s (systematic field application)","type":"Qualitative interpretive approach","dataType":"Field observations, visual artifacts, texts, spatial arrangements, embodied interactions","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Hodge, R., & Kress, G. (1988). Social Semiotics. Polity Press.","type":"book","doi":null,"isbn":"978-0745600635","url":null},{"ref":"van Leeuwen, T. (2005). Introducing Social Semiotics. Routledge.","type":"book","doi":null,"isbn":"978-0415249447","url":null}],"related":["ethnography","discourse-analysis","multimodal-analysis","visual-methods","content-analysis","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-single-case-study","name":"Field-based single case study","fullName":"Field-Based Single Case Study Research","aliases":["single-site case study","holistic single case study","naturalistic single case study","field case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984 (Yin); 1995 (Stake)","originator":"Robert K. Yin; Robert E. Stake","url":"https://scholargate.app/en/qualitative/field-based-single-case-study","markdownUrl":"https://scholargate.app/en/qualitative/field-based-single-case-study.md","definition":"A field-based single case study is a qualitative research design that investigates one bounded real-world case — an individual, program, organization, event, or community — in its natural setting through sustained first-hand fieldwork. Drawing on Robert Yin's systematic case study logic and Robert Stake's interpretive tradition, this design combines multiple data sources collected on-site to build a rich, contextualized account of a phenomenon that cannot be separated from its real-world environment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin; Robert E. Stake","year":"1984 (Yin); 1995 (Stake)","type":"Qualitative case study design","dataType":"Field observations, interviews, documents, artifacts collected in situ","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (1995). The Art of Case Study Research. Sage Publications.","type":"book","doi":null,"isbn":"978-0803957671","url":null}],"related":["case-study","field-based-multiple-case-study","field-based-ethnography","single-case-study","field-based-narrative-inquiry","longitudinal-single-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-snowball-sampling","name":"Field-based Snowball Sampling","fullName":"Field-based Snowball Sampling","aliases":["in-person snowball sampling","fieldwork chain-referral sampling","field snowball sampling","face-to-face referral sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1961 (foundational); field variant developed through 1970s–1980s ethnographic and hidden population research","originator":"Leo A. Goodman (snowball sampling); field adaptation through ethnographic and social network research traditions","url":"https://scholargate.app/en/survey-methodology/field-based-snowball-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-snowball-sampling.md","definition":"Field-based snowball sampling is a non-probability chain-referral technique in which an initial set of in-person contacts (seeds) recruit further participants from within their real-world social networks, expanding the sample iteratively through face-to-face interaction in naturalistic field settings. It is the default snowball approach in ethnographic and community fieldwork, and is particularly valuable when the target population is hidden, hard-to-reach, or lacks a sampling frame.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leo A. Goodman (snowball sampling); field adaptation through ethnographic and social network research traditions","year":"1961 (foundational); field variant developed through 1970s–1980s ethnographic and hidden population research","type":"Non-probability sampling technique","dataType":"Categorical or continuous participant-level data gathered through in-person field contact","subfamily":"Sampling"},"citations":[{"ref":"Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32(1), 148–170.","type":"article","doi":"10.1214/aoms/1177705148","isbn":null,"url":null},{"ref":"Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods & Research, 10(2), 141–163.","type":"article","doi":"10.1177/004912418101000205","isbn":null,"url":null}],"related":["snowball-sampling","purposive-sampling","convenience-sampling","respondent-driven-sampling","theoretical-sampling","field-based-purposive-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-stratified-sampling","name":"Field-based Stratified Sampling","fullName":"Field-based Stratified Sampling","aliases":["field stratified sampling","stratified field survey sampling","in-field stratified sampling","field survey stratification"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1934 (Neyman's stratified sampling theory); field applications throughout 20th century","originator":"Jerzy Neyman (stratified sampling theory); applied broadly in field survey practice","url":"https://scholargate.app/en/survey-methodology/field-based-stratified-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-stratified-sampling.md","definition":"Field-based stratified sampling divides a geographically dispersed or heterogeneous target population into internally homogeneous subgroups (strata) defined by features observable in the field — such as land use type, habitat zone, administrative district, or community category — and then independently draws random samples from each stratum during on-site data collection. The approach combines the precision gains of stratification with the logistical realities of fieldwork, ensuring that every identifiable subgroup of the landscape or community is represented in the final data set.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerzy Neyman (stratified sampling theory); applied broadly in field survey practice","year":"1934 (Neyman's stratified sampling theory); field applications throughout 20th century","type":"Probability sampling design","dataType":"Field-collected quantitative or structured qualitative data; survey, ecological, agricultural, or epidemiological records","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470465462","url":null}],"related":["stratified-sampling","cluster-sampling","multistage-sampling","systematic-sampling","proportional-stratified-sampling","field-based-cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-straussian-grounded-theory","name":"Field-based Straussian Grounded Theory","fullName":"Field-based Straussian Grounded Theory","aliases":["Straussian GT with fieldwork","fieldwork-grounded theory","Strauss-Corbin grounded theory","constructivist Straussian GT"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990","originator":"Anselm Strauss and Juliet Corbin","url":"https://scholargate.app/en/qualitative/field-based-straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/field-based-straussian-grounded-theory.md","definition":"Field-based Straussian grounded theory applies the systematic coding procedures of Strauss and Corbin's grounded theory tradition to data generated through sustained fieldwork — direct observation, ethnographic notes, informal conversations, and artefact collection — rather than relying solely on formal interviews. The goal is to generate a substantive theory that is firmly anchored in the natural social setting where the phenomenon occurs, capturing both interaction and context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss and Juliet Corbin","year":"1990","type":"Qualitative theory-building approach","dataType":"Field observations, ethnographic notes, interviews, documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932500","url":null},{"ref":"Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0803959408","url":null}],"related":["grounded-theory","glaserian-grounded-theory","constructivist-grounded-theory","ethnography","constant-comparative-method","situational-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-systematic-sampling","name":"Field-based systematic sampling","fullName":"Field-Based Systematic Sampling","aliases":["systematic field sampling","grid-based field sampling","regular interval field sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1940s–1950s (systematic sampling foundations); field adaptations consolidated by 1970s","originator":"William G. Cochran (systematic sampling foundations); adapted to field contexts in ecological and agricultural survey literature","url":"https://scholargate.app/en/survey-methodology/field-based-systematic-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-systematic-sampling.md","definition":"Field-based systematic sampling applies systematic (regular-interval) selection to real-world field environments — plots of land, transects, geographic grids, or physical survey routes. A random starting point is chosen, then every k-th unit or location is sampled at equal spatial or sequential intervals. Widely used in ecology, agriculture, environmental science, and field surveys, it delivers spatially even coverage at low operational cost while maintaining probability-sampling properties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran (systematic sampling foundations); adapted to field contexts in ecological and agricultural survey literature","year":"1940s–1950s (systematic sampling foundations); field adaptations consolidated by 1970s","type":"Probability sampling design","dataType":"Spatial, ecological, agricultural, or field survey observations","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Thompson, S. K. (2002). Sampling (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471369264","url":null}],"related":["systematic-sampling","cluster-sampling","stratified-sampling","multistage-sampling","simple-random-sampling","adaptive-cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-theoretical-sampling","name":"Field-based theoretical sampling","fullName":"Field-Based Theoretical Sampling","aliases":["field theoretical sampling","in-situ theoretical sampling","fieldwork-driven theoretical sampling","grounded field sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1967","originator":"Barney G. Glaser and Anselm L. Strauss","url":"https://scholargate.app/en/survey-methodology/field-based-theoretical-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-theoretical-sampling.md","definition":"Field-based theoretical sampling is an iterative qualitative sampling strategy in which decisions about whom to observe or interview next are made during active fieldwork, guided by emerging theoretical insights from the data already collected. Rooted in Glaser and Strauss's grounded theory, it extends theoretical sampling into naturalistic, in-situ field settings — ethnographic sites, clinical environments, organizational contexts — where data collection and analysis proceed simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser and Anselm L. Strauss","year":"1967","type":"Qualitative iterative sampling strategy","dataType":"Fieldwork observations, in-situ interviews, field notes, ethnographic data","subfamily":"Sampling"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null},{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973522","url":null}],"related":["theoretical-sampling","purposive-sampling","snowball-sampling","adaptive-theoretical-sampling","maximum-variation-sampling","field-based-purposive-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-typical-case-sampling","name":"Field-based typical case sampling","fullName":"Field-based Typical Case Sampling","aliases":["field typical case sampling","in-person typical case sampling","fieldwork typical case selection","on-site typical case sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1980s–1990s","originator":"Michael Quinn Patton; Miles & Huberman","url":"https://scholargate.app/en/survey-methodology/field-based-typical-case-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/field-based-typical-case-sampling.md","definition":"Field-based typical case sampling is a purposive qualitative strategy in which the researcher selects and studies cases that represent the ordinary, average, or most common instance of a phenomenon — and conducts data collection through direct fieldwork such as in-person observation, site visits, and face-to-face interviews. The combination ensures that findings portray what the phenomenon looks like under real-world, everyday conditions rather than through self-reports or online proxies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Quinn Patton; Miles & Huberman","year":"1980s–1990s","type":"Purposive qualitative sampling strategy","dataType":"Field observations, in-person interviews, ethnographic data, site-based documents","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis: An Expanded Sourcebook (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0803955405","url":null}],"related":["typical-case-sampling","purposive-sampling","field-based-purposive-sampling","field-based-maximum-variation-sampling","field-based-deviant-case-sampling","maximum-variation-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-based-visual-analysis","name":"Field-based visual analysis","fullName":"Field-Based Visual Analysis","aliases":["fieldwork visual analysis","in-situ visual analysis","ethnographic visual analysis","field visual research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (systematic field-based visual methods codified)","originator":"Gillian Rose; Marcus Banks; John Collier Jr. (photo-elicitation precursors)","url":"https://scholargate.app/en/qualitative/field-based-visual-analysis","markdownUrl":"https://scholargate.app/en/qualitative/field-based-visual-analysis.md","definition":"Field-based visual analysis is a qualitative approach in which researchers collect and analyze visual materials — photographs, video, diagrams, environmental signs, and spatial arrangements — directly within the natural settings where they are produced and used. By anchoring visual analysis in fieldwork, this method captures images and visual phenomena in their social and spatial context, enabling interpretation that goes beyond what can be achieved from decontextualized images alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gillian Rose; Marcus Banks; John Collier Jr. (photo-elicitation precursors)","year":"1990s–2000s (systematic field-based visual methods codified)","type":"Qualitative field research technique","dataType":"Photographs, video recordings, maps, drawings, and other visual materials collected in natural settings","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Rose, G. (2012). Visual Methodologies: An Introduction to Researching with Visual Materials (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1446207567","url":null},{"ref":"Banks, M. (2001). Visual Methods in Social Research. Sage.","type":"book","doi":null,"isbn":"978-0761963646","url":null}],"related":["visual-analysis","ethnography","field-based-ethnography","visual-elicitation-visual-analysis","field-based-document-analysis","multimodal-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-experiment","name":"Field Experiment","fullName":"Field Experiment","aliases":["field trial","natural field experiment","randomized field experiment","field RCT"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1920s–1930s (agriculture); 1990s–2000s (social sciences)","originator":"Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004)","url":"https://scholargate.app/en/experimental-design/field-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/field-experiment.md","definition":"A field experiment applies the logic of a randomized controlled trial in a naturally occurring, real-world environment rather than an artificial laboratory. Participants are randomly assigned to treatment and control conditions while going about everyday activities, allowing researchers to estimate causal effects with high internal validity while preserving a level of ecological realism that laboratory settings cannot offer. The design is especially prominent in economics, public health, political science, and development research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004)","year":"1920s–1930s (agriculture); 1990s–2000s (social sciences)","type":"Experimental design","dataType":"Quantitative outcome measures collected in real-world settings","subfamily":"Deneysel desen"},"citations":[{"ref":"Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055.","type":"article","doi":"10.1257/0022051043004577","isbn":null,"url":null},{"ref":"Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. W. W. Norton.","type":"book","doi":null,"isbn":"978-0393979954","url":null}],"related":["laboratory-experiment","natural-experiment","randomized-controlled-trial","quasi-experiment","factorial-experiment","cluster-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-mapping-meta-ethnography","name":"Field-mapping Meta-ethnography","fullName":"Field-mapping Meta-ethnography","aliases":["field-mapping qualitative synthesis","scoping meta-ethnography","field-mapping qualitative meta-synthesis","landscape meta-ethnography"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1988 (meta-ethnography); field-mapping application 2000s–2010s","originator":"Noblit & Hare (meta-ethnography base); field-mapping frame developed in review methodology literature","url":"https://scholargate.app/en/scientometrics/field-mapping-meta-ethnography","markdownUrl":"https://scholargate.app/en/scientometrics/field-mapping-meta-ethnography.md","definition":"Field-mapping meta-ethnography combines the breadth of a field-mapping (scoping) review with the interpretive synthesis power of meta-ethnography. It first maps the full landscape of qualitative studies on a topic to understand what has been studied and how, then applies Noblit and Hare's seven-step meta-ethnographic synthesis to generate second-order and third-order constructs that represent the accumulated qualitative evidence across that field.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Noblit & Hare (meta-ethnography base); field-mapping frame developed in review methodology literature","year":"1988 (meta-ethnography); field-mapping application 2000s–2010s","type":"Qualitative evidence synthesis with field-mapping scope","dataType":"Published qualitative research articles and reports","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Noblit, G. W., & Hare, R. D. (1988). Meta-ethnography: Synthesizing Qualitative Studies. Sage.","type":"book","doi":null,"isbn":"978-0803930599","url":null},{"ref":"Archer, M. M., Graham-Matheson, L., & Gerber, P. J. (2017). Field-mapping literature reviews and systematic approaches in education research. Review of Education, 5(2), 138–177.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Field-mapping+literature+reviews+and+systematic+approaches+in+education+research+Archer"}],"related":["meta-ethnography","scoping-review","mapping-review","qualitative-meta-synthesis","systematic-literature-review","thematic-evolution-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-mapping-scientometric-analysis","name":"Field-mapping Scientometric Analysis","fullName":"Field-mapping Scientometric Analysis","aliases":["science field mapping","research field delineation","scientometric field analysis","knowledge domain mapping"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2000s (mature form); roots in 1960s-1970s scientometrics","originator":"Kevin Boyack, Richard Klavans, Katy Borner (field-level science mapping); broader tradition rooted in Derek de Solla Price and Henry Small","url":"https://scholargate.app/en/scientometrics/field-mapping-scientometric-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/field-mapping-scientometric-analysis.md","definition":"Field-mapping scientometric analysis uses quantitative bibliometric techniques — co-citation, bibliographic coupling, co-authorship, and keyword co-occurrence — to delineate the intellectual structure and boundaries of a scientific field. By transforming large publication datasets into similarity networks and clustering them into research fronts and knowledge bases, it produces visual maps that reveal how subfields relate, where boundaries lie, and how the field evolves over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kevin Boyack, Richard Klavans, Katy Borner (field-level science mapping); broader tradition rooted in Derek de Solla Price and Henry Small","year":"2000s (mature form); roots in 1960s-1970s scientometrics","type":"Quantitative bibliometric analysis","dataType":"Publication metadata, citation counts, co-citation matrices, bibliographic coupling matrices","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Boyack, K. W., Klavans, R., & Borner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351-374.","type":"article","doi":"10.1007/s11192-005-0255-6","isbn":null,"url":null},{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null}],"related":["scientometric-analysis","bibliometric-analysis","co-citation-analysis","bibliographic-coupling","science-mapping","co-word-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-mapping-scoping-review","name":"Field-mapping Scoping review","fullName":"Field-mapping Scoping Review","aliases":["field-mapping scoping study","evidence-mapping scoping review","field map review","scoping review for field mapping"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2005 (foundational framework); field-mapping purpose formalised c. 2015–2018","originator":"Arksey & O'Malley (scoping review framework); field-mapping purpose formalised by Munn et al. and Peters et al.","url":"https://scholargate.app/en/scientometrics/field-mapping-scoping-review","markdownUrl":"https://scholargate.app/en/scientometrics/field-mapping-scoping-review.md","definition":"A field-mapping scoping review is a purposive variant of the scoping review in which the overarching goal is to chart the conceptual and empirical landscape of a research field — identifying what has been studied, by whom, using which methods, and where knowledge gaps remain. It follows the Arksey and O'Malley scoping framework but is explicitly oriented toward producing a structured map of a field rather than answering a focused clinical or policy question.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arksey & O'Malley (scoping review framework); field-mapping purpose formalised by Munn et al. and Peters et al.","year":"2005 (foundational framework); field-mapping purpose formalised c. 2015–2018","type":"Evidence synthesis — systematic review variant","dataType":"Published literature records (titles, abstracts, full texts)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic review and scoping review approach. BMC Medical Research Methodology, 18, 143.","type":"article","doi":"10.1186/s12874-018-0611-x","isbn":null,"url":null},{"ref":"Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.","type":"article","doi":"10.1080/1364557032000119616","isbn":null,"url":null}],"related":["scoping-review","mapping-review","systematic-literature-review","bibliometric-analysis","science-mapping","thematic-evolution-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-notes","name":"Field Notes","fullName":"Field Notes in Qualitative Research","aliases":["fieldnotes","observational notes","ethnographic notes","jottings"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 19th century (formalized in 20th century)","originator":"Rooted in 19th-century anthropology and sociology; systematized by ethnographers such as Bronislaw Malinowski and later Robert Emerson et al.","url":"https://scholargate.app/en/survey-methodology/field-notes","markdownUrl":"https://scholargate.app/en/survey-methodology/field-notes.md","definition":"Field notes are detailed written records created by researchers during or immediately after direct observation in a naturalistic setting. They capture what is seen, heard, and experienced — including behaviors, interactions, physical environments, and the researcher's own analytic impressions — forming the primary data source for ethnographic and observational studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in 19th-century anthropology and sociology; systematized by ethnographers such as Bronislaw Malinowski and later Robert Emerson et al.","year":"Late 19th century (formalized in 20th century)","type":"Qualitative data collection and recording technique","dataType":"Written textual records of observations, interactions, and reflections","subfamily":"Data collection"},"citations":[{"ref":"Emerson, R. M., Fretz, R. I., & Shaw, L. L. (1995). Writing Ethnographic Fieldnotes. University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226206813","url":null},{"ref":"Lofland, J., Snow, D., Anderson, L., & Lofland, L. H. (2006). Analyzing Social Settings: A Guide to Qualitative Observation and Analysis (4th ed.). Wadsworth.","type":"book","doi":null,"isbn":"978-0534528713","url":null}],"related":["participant-observation","non-participant-observation","ethnography","diary-method","research-diary","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"field-oriented-control","name":"Field-Oriented Control","fullName":"Field-Oriented Control","aliases":["FOC","Vector Control"],"domain":"control-theory","family":"ml-model","subfamily":"Motor Control","year":"1972","originator":"Flemming Blaschke","url":"https://scholargate.app/en/control-theory/field-oriented-control","markdownUrl":"https://scholargate.app/en/control-theory/field-oriented-control.md","definition":"Field-Oriented Control (FOC), also known as Vector Control, is an advanced method for controlling AC induction and permanent magnet motors by decomposing phase currents into torque and flux components and independently regulating them using PI controllers. Pioneered by Blaschke in 1972, FOC enables smooth precise motor control equivalent to DC motor performance, making it the standard for high-performance industrial variable-speed drives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Flemming Blaschke","subfamily":"Motor Control","year":"1972","type":"algorithm"},"citations":[{"ref":"Blaschke, F. (1972). The principle of field orientation as applied to the new transvector closed-loop control system for rotating field machines. Siemens Review, 34(5), 217-220.","type":"article","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/285447149"},{"ref":"Hasse, K. (1969). Zur Dynamik drehzahlgeregelter Antriebe mit stromrichtergespeisten Asynchronmaschinen. ETZ-A, 90, 77-81.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/4769725"}],"related":["direct-torque-control","model-predictive-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"figure-table-reporting","name":"Figure and Table Reporting","fullName":"Standards for Presenting Data in Figures and Tables","aliases":["data visualization","table design","figure captions"],"domain":"academic-writing","family":"process-pipeline","subfamily":"data-presentation","year":"1983","originator":"Tufte (visual communication theory), ICMJE standards, APA style guide","url":"https://scholargate.app/en/academic-writing/figure-table-reporting","markdownUrl":"https://scholargate.app/en/academic-writing/figure-table-reporting.md","definition":"Tables and figures are the primary means of presenting research data in scientific manuscripts. A well-designed table or figure enables readers to grasp complex data patterns instantly; a poorly designed one obscures findings or misleads. The ICMJE Recommendations and APA Publication Manual establish standards for table and figure formatting, captions, legends, and referencing. Tables are best used for precise numerical values and comparisons across rows and columns; figures (graphs, plots, images) are better for illustrating trends, relationships, or distributions. Both must be self-contained (understandable without consulting the text) and referenced clearly in the manuscript.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tufte (visual communication theory), ICMJE standards, APA style guide","subfamily":"data-presentation","year":"1983","type":"Guideline"},"citations":[{"ref":"American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). Washington, DC: American Psychological Association.","type":"book","doi":null,"isbn":"978-1-4338-3216-1","url":null},{"ref":"Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Cheshire, CT: Graphics Press.","type":"book","doi":null,"isbn":"978-0-961392-14-6","url":null},{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.","type":"guideline","doi":null,"isbn":null,"url":"https://www.icmje.org/"}],"related":["imrad-structure","statistical-reporting-standards","scientific-writing-clarity","apa-style-guide"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"film-narrative-analysis","name":"Film Narrative Analysis","fullName":"Film Narrative Structure and Storytelling Analysis","aliases":["narrative structure analysis","story analysis in cinema"],"domain":"media-studies","family":"process-pipeline","subfamily":"Qualitative film analysis","year":"1980","originator":"Gérard Genette, Mieke Bal","url":"https://scholargate.app/en/media-studies/film-narrative-analysis","markdownUrl":"https://scholargate.app/en/media-studies/film-narrative-analysis.md","definition":"Film Narrative Analysis is a qualitative method for examining how stories are told through cinematic techniques and structures. Developed from literary narratology and adapted for film studies by scholars like David Bordwell and Mieke Bal, it deconstructs the relationship between story (fabula), plot (sjuzhet), and narration to understand how meaning is created. This method is fundamental to film criticism and provides a systematic framework for analyzing how viewers construct narrative coherence from visual and audio elements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gérard Genette, Mieke Bal","subfamily":"Qualitative film analysis","year":"1980","type":"Analytical pipeline for deconstructing cinematic narrative structure"},"citations":[{"ref":"Bal, M. (1997). Narratology: Introduction to the Theory of Narrative. University of Toronto Press.","type":"book","doi":null,"isbn":null,"url":"https://www.utpbooks.com/narratology"},{"ref":"Bordwell, D. (1985). Narration in the Fiction Film. University of Wisconsin Press.","type":"book","doi":null,"isbn":null,"url":"https://www.wisc.edu/press"},{"ref":"Thompson, K. (1999). Storytelling in the New Hollywood: Understanding Classical Narrative Technique. Harvard University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.harvard.edu/press"},{"ref":"Seger, L. (1987). The Art of Adaptation: Turning Fact and Fiction Into Film. Henry Holt and Company.","type":"book","doi":null,"isbn":null,"url":"https://www.holt.com"}],"related":["semiotics-film","auteur-theory-analysis","genre-analysis-film","discourse-analysis-media","reception-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"film","name":"FiLM","fullName":"FiLM (Frequency Improved Legendre Memory Model)","aliases":["Frequency Improved Legendre Memory","FiLM Forecaster","Legendre Frequency Model","Frekans Tabanlı Legendre Bellek Modeli"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2022,"originator":"Tian Zhou et al.","url":"https://scholargate.app/en/deep-learning/film","markdownUrl":"https://scholargate.app/en/deep-learning/film.md","definition":"FiLM is a long-term time-series forecasting architecture introduced by Tian Zhou and colleagues at NeurIPS 2022. It combines Legendre polynomial projections of the historical input with learnable frequency-domain filters applied to the resulting coefficient sequences. By representing history as a compact set of polynomial coefficients and filtering those coefficients in the frequency domain, FiLM enables efficient extrapolation over long prediction horizons without the quadratic cost of full self-attention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tian Zhou et al.","year":2022,"type":"Frequency-domain time-series forecasting model","subfamily":"Time-series forecasting","venue":"NeurIPS 2022","complexity":"Linear in sequence length"},"citations":[{"ref":"Zhou, T., Ma, Z., Wen, Q., Sun, L., Yao, T., Yin, W., & Jin, R. (2022). FiLM: Frequency improved Legendre memory model for long-term time series forecasting. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2205.08897"}],"related":["fedformer","autoformer","state-space-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fim-functional-independence","name":"Functional Independence Measure Scale","fullName":"Functional Independence Measure Scale","aliases":["FIM","FIM Scale","FIM+FAM"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Functional assessment","year":"1987","originator":"Granger, Deutsch, Linn","url":"https://scholargate.app/en/rehabilitation/fim-functional-independence","markdownUrl":"https://scholargate.app/en/rehabilitation/fim-functional-independence.md","definition":"The Functional Independence Measure (FIM) is a comprehensive 18-item scale assessing functional independence and burden of care in patients with disability across motor and cognitive domains. Developed by Granger and colleagues in 1987, FIM has become the standard outcome measure in rehabilitation medicine, mandated by Medicare for documenting rehabilitation outcomes and discharge planning in inpatient rehabilitation facilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Granger, Deutsch, Linn","subfamily":"Functional assessment","year":"1987","type":"Comprehensive functional independence scale"},"citations":[{"ref":"Granger, C. V., Deutsch, A., & Linn, R. T. (1998). Advances in functional assessment for medical rehabilitation. Topics in Stroke Rehabilitation, 5(2), 27–35.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Advances+in+functional+assessment+for+medical+rehabilitation+Granger"},{"ref":"Hamilton, B. B., Laughlin, J. A., Fiedler, R. C., & Granger, C. V. (1994). Interrater reliability of the 7-level Functional Independence Measure (FIM). Scandinavian Journal of Rehabilitation Medicine, 26(3), 115–119.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/7801063"}],"related":["barthel-adl-index","fugl-meyer-assessment","wimberley-functional-rating","modified-rankin-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-gray-competing-risks","name":"Fine-Gray Competing Risks Model","fullName":"Fine-Gray Proportional Subdistribution Hazards Model","aliases":["competing risks regression","subdistribution hazard model","Fine-Gray model","Fine-Gray Competing Risks Modeli"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1999,"originator":"Jason P. Fine & Robert J. Gray","url":"https://scholargate.app/en/statistics/fine-gray-competing-risks","markdownUrl":"https://scholargate.app/en/statistics/fine-gray-competing-risks.md","definition":"The Fine-Gray model is a semiparametric regression method for survival data in which two or more mutually exclusive event types compete to occur first. Proposed by Fine and Gray in 1999, it models the subdistribution hazard of each event type directly, allowing covariates to be linked to the cumulative incidence function (CIF) — the quantity that actually answers 'what is the probability of experiencing event type k by time t?'. It corrects the well-known shortcoming of standard Cox regression, which ignores competing events and thereby overestimates cause-specific probabilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jason P. Fine & Robert J. Gray","year":1999,"family":"Survival analysis","type":"Subdistribution hazard regression","outcome":"time-to-event (with competing risks)","parametric":false,"distribution":"Subdistribution (cause-specific cumulative incidence)","minSample":50,"difficulty":3},"citations":[{"ref":"Fine, J.P. & Gray, R.J. (1999). A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association, 94(446), 496–509.","type":"article","doi":"10.1080/01621459.1999.10474144","isbn":null,"url":null},{"ref":"Austin, P.C. et al. (2016). Introduction to the Analysis of Survival Data in the Presence of Competing Risks. Circulation, 133(6), 601–609.","type":"article","doi":"10.1161/CIRCULATIONAHA.115.017719","isbn":null,"url":null}],"related":["kaplan-meier","cox-proportional-hazards","time-dependent-cox","flexible-parametric-survival","log-rank-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-bert-based-classification","name":"Fine-Tuned BERT-based Classification","fullName":"Fine-Tuned BERT-based Text Classification","aliases":["BERT fine-tuning","BERT classifier","fine-tuned BERT","BERT sequence classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)","url":"https://scholargate.app/en/deep-learning/fine-tuned-bert-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-bert-based-classification.md","definition":"Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)","year":"2019","type":"Pre-trained transformer fine-tuned for classification","dataType":"Text (labelled sentence or document pairs)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? Proceedings of CCL 2019, LNCS 11856, 194–206.","type":"inproceedings","doi":"10.1007/978-3-030-32381-3_16","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","fine-tuned-roberta-based-classification","transformer","fine-tuned-transformer","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-convolutional-neural-network","name":"Fine-Tuned Convolutional Neural Network","fullName":"Fine-Tuned Convolutional Neural Network (CNN Fine-Tuning via Transfer Learning)","aliases":["Fine-tuned CNN","CNN fine-tuning","CNN transfer learning with fine-tuning","adapted convolutional network"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2012–2014","originator":"Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward","url":"https://scholargate.app/en/deep-learning/fine-tuned-convolutional-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-convolutional-neural-network.md","definition":"Fine-tuning a CNN means starting from a network already trained on a large dataset — typically ImageNet — and continuing training on a smaller target dataset so the model adapts its learned visual features to a new task. This approach dramatically reduces the data and compute required to reach strong performance compared with training from scratch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yosinski, J. et al. (theoretical basis); practice widespread from Krizhevsky et al. 2012 onward","year":"2012–2014","type":"Transfer learning technique (supervised fine-tuning)","dataType":"Images; also video frames, spectrograms, or other 2-D grid inputs","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+transferable+are+features+in+deep+neural+networks"},{"ref":"Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312.","type":"article","doi":"10.1109/TMI.2016.2535302","isbn":null,"url":null}],"related":["convolutional-neural-network","transfer-learning-with-convolutional-neural-network","fine-tuned-vision-transformer","fine-tuned-recurrent-neural-network","image-classification","object-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-diffusion-model","name":"Fine-Tuned Diffusion Model","fullName":"Fine-Tuned Denoising Diffusion Probabilistic Model","aliases":["DDPM fine-tuning","diffusion model adaptation","personalized diffusion model","subject-driven diffusion fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2023","originator":"Ho, J., Jain, A., Abbeel, P. (base DDPM); Ruiz et al. (DreamBooth fine-tuning paradigm)","url":"https://scholargate.app/en/deep-learning/fine-tuned-diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-diffusion-model.md","definition":"A fine-tuned diffusion model adapts a large pretrained denoising diffusion model — such as Stable Diffusion or DALL-E — to a specific subject, style, or domain by continuing training on a small curated dataset. Techniques such as DreamBooth, textual inversion, and LoRA make this adaptation feasible on consumer hardware while preserving general generative capability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ho, J., Jain, A., Abbeel, P. (base DDPM); Ruiz et al. (DreamBooth fine-tuning paradigm)","year":"2020–2023","type":"Generative model (fine-tuned via subject-specific or domain-specific data)","dataType":"Images, image-text pairs, domain-specific visual corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023). DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 22500–22510.","type":"inproceedings","doi":"10.1109/CVPR52729.2023.02155","isbn":null,"url":null},{"ref":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html"}],"related":["fine-tuned-generative-adversarial-network","fine-tuned-variational-autoencoder","fine-tuned-vision-transformer","transfer-learning-diffusion-model","fine-tuned-image-classification","fine-tuned-object-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-doc2vec","name":"Fine-Tuned Doc2Vec","fullName":"Fine-Tuned Doc2Vec (Domain-Adapted Paragraph Vector)","aliases":["fine-tuned Paragraph Vector","domain-adapted Doc2Vec","PV fine-tuning","Doc2Vec transfer learning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014 (base); fine-tuning practice ca. 2015","originator":"Le, Q. V. & Mikolov, T. (Doc2Vec base); fine-tuning practice adopted by the NLP community ca. 2015–2017","url":"https://scholargate.app/en/deep-learning/fine-tuned-doc2vec","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-doc2vec.md","definition":"Fine-Tuned Doc2Vec adapts a pre-trained Paragraph Vector (Doc2Vec) model by continuing its training on a target corpus, producing document embeddings that capture both the general language knowledge of the original training and the vocabulary and style of the new domain. It is used for text classification, semantic similarity, and clustering when labeled data are scarce but unlabeled domain text is available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Le, Q. V. & Mikolov, T. (Doc2Vec base); fine-tuning practice adopted by the NLP community ca. 2015–2017","year":"2014 (base); fine-tuning practice ca. 2015","type":"Representation learning / transfer learning","dataType":"Text (documents, sentences, paragraphs)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), PMLR 32(2), 1188–1196.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v32/le14.html"},{"ref":"Doc2vec. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Doc2vec"}],"related":["doc2vec","fine-tuned-word2vec","sentence-embeddings","fine-tuned-sentence-embeddings","bert-based-classification","transfer-learning-with-doc2vec"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-generative-adversarial-network","name":"Fine-Tuned Generative Adversarial Network","fullName":"Fine-Tuned Generative Adversarial Network (Domain-Adaptive GAN via Transfer)","aliases":["Fine-Tuned GAN","GAN Fine-Tuning","Domain-Adapted GAN","Transfer GAN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014 (GAN); 2019–2020 (fine-tuning paradigm)","originator":"Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020","url":"https://scholargate.app/en/deep-learning/fine-tuned-generative-adversarial-network","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-generative-adversarial-network.md","definition":"A Fine-Tuned GAN starts from a large pre-trained generative adversarial network and continues adversarial training on a smaller target dataset, allowing the model to synthesize high-quality samples in a new domain without training from scratch. This transfer approach dramatically reduces data and compute requirements while preserving the rich feature representations learned during pre-training.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020","year":"2014 (GAN); 2019–2020 (fine-tuning paradigm)","type":"Generative model (adversarial training + transfer)","dataType":"Images, structured data, text (modality-dependent)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html"},{"ref":"Mo, S., Cho, M., & Shin, J. (2020). Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs. CVPR 2020 Workshop on AI for Content Creation.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Freeze+the+Discriminator+a+Simple+Baseline+for+Fine-Tuning+GANs"}],"related":["generative-adversarial-network","fine-tuned-diffusion-model","fine-tuned-variational-autoencoder","transfer-learning-gan","fine-tuned-vision-transformer","fine-tuned-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-gru","name":"Fine-Tuned GRU","fullName":"Fine-Tuned Gated Recurrent Unit Network","aliases":["Fine-Tuned GRU","GRU Fine-Tuning","Domain-Adapted GRU","GRU Transfer Learning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014 (GRU); fine-tuning practice established 2010s","originator":"Cho, K. et al. (GRU); fine-tuning practice from transfer learning literature","url":"https://scholargate.app/en/deep-learning/fine-tuned-gru","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-gru.md","definition":"Fine-Tuned GRU adapts a Gated Recurrent Unit network — pre-trained on a large source dataset — to a specific target task or domain by continuing training on domain-specific labeled data. This combines the sequential memory capacity of GRUs with the efficiency gains of transfer learning, achieving strong performance even when labeled target data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cho, K. et al. (GRU); fine-tuning practice from transfer learning literature","year":"2014 (GRU); fine-tuning practice established 2010s","type":"Sequence model with transfer learning","dataType":"Sequential / time-series / text data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724-1734.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1406.1078"},{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["gated-recurrent-unit","long-short-term-memory","fine-tuned-lstm","recurrent-neural-network","fine-tuned-transformer","transfer-learning-with-gru"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-image-classification","name":"Fine-Tuned Image Classification","fullName":"Fine-Tuned Deep Neural Network for Image Classification","aliases":["fine-tuning for image recognition","transfer learning image classifier","pretrained CNN fine-tuning","domain-specific image classifier"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010–2014","originator":"Yosinski, J. et al.; Pan, S. J. & Yang, Q.","url":"https://scholargate.app/en/deep-learning/fine-tuned-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-image-classification.md","definition":"Fine-tuned image classification adapts a large neural network pretrained on a broad image corpus (such as ImageNet) to a specific target domain by continuing training on labeled domain images. This approach achieves strong accuracy with far fewer target-domain samples than training from scratch, making it the dominant paradigm for applied computer vision tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yosinski, J. et al.; Pan, S. J. & Yang, Q.","year":"2010–2014","type":"Transfer learning / fine-tuning","dataType":"Image data (labeled target-domain images)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2014/hash/375c71349b295fbe2dcdca9206851898-Abstract.html"},{"ref":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["convolutional-neural-network","transfer-learning-with-image-classification","image-classification","fine-tuned-convolutional-neural-network","fine-tuned-vision-transformer","object-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-lda-topic-model","name":"Fine-Tuned LDA Topic Model","fullName":"Fine-Tuned Latent Dirichlet Allocation Topic Model","aliases":["Domain-Adapted LDA","Adapted LDA","LDA Fine-Tuning","Online LDA Fine-Tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2003 (base); adaptation practice ~2010s","originator":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (base LDA); domain adaptation via online/warm-start LDA","url":"https://scholargate.app/en/deep-learning/fine-tuned-lda-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-lda-topic-model.md","definition":"Fine-Tuned LDA adapts a Latent Dirichlet Allocation model trained on a large general corpus to a specific target domain by continuing inference on domain-specific documents. Rather than fitting LDA from scratch, the pre-trained topic-word distributions are used as an informed starting point, enabling the model to discover coherent domain topics faster and with less data than training cold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (base LDA); domain adaptation via online/warm-start LDA","year":"2003 (base); adaptation practice ~2010s","type":"Probabilistic generative topic model (fine-tuned / domain-adapted)","dataType":"Text corpora (documents as bag-of-words)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/blei03a.html"},{"ref":"Hoffman, M., Bach, F. R., & Blei, D. M. (2010). Online Learning for Latent Dirichlet Allocation. Advances in Neural Information Processing Systems (NIPS), 23, 856–864.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2010/hash/71f6278d140af599e06ad9bf1ba03cb0-Abstract.html"}],"related":["lda-topic-model","nmf-topic-model","fine-tuned-nmf-topic-model","topic-modeling","fine-tuned-bert-based-classification","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-lstm","name":"Fine-Tuned LSTM","fullName":"Fine-Tuned Long Short-Term Memory Network","aliases":["Fine-Tuned LSTM","LSTM Fine-Tuning","Pre-trained LSTM with Task Adaptation","LSTM Transfer Learning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018 (fine-tuning paradigm formalised); LSTM core: 1997","originator":"Howard, J. & Ruder, S. (ULMFiT); foundational LSTM by Hochreiter & Schmidhuber","url":"https://scholargate.app/en/deep-learning/fine-tuned-lstm","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-lstm.md","definition":"Fine-Tuned LSTM adapts a Long Short-Term Memory network pre-trained on a large corpus to a specific downstream task — such as text classification, sentiment analysis, or sequence labeling — by continuing training on task-specific labeled data. Popularised by the ULMFiT framework, this approach achieves strong performance even when labeled data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Howard, J. & Ruder, S. (ULMFiT); foundational LSTM by Hochreiter & Schmidhuber","year":"2018 (fine-tuning paradigm formalised); LSTM core: 1997","type":"Supervised sequential model with transfer learning","dataType":"Sequential / temporal data; text sequences; time series","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339.","type":"inproceedings","doi":"10.18653/v1/P18-1031","isbn":null,"url":null},{"ref":"Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.","type":"article","doi":"10.1162/neco.1997.9.8.1735","isbn":null,"url":null}],"related":["long-short-term-memory","fine-tuned-gru","fine-tuned-recurrent-neural-network","fine-tuned-transformer","bert-based-classification","transfer-learning-with-lstm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-multilayer-perceptron","name":"Fine-Tuned Multilayer Perceptron","fullName":"Fine-Tuned Multilayer Perceptron (Transfer Learning via MLP Weight Adaptation)","aliases":["fine-tuned MLP","adapted MLP","domain-adapted multilayer perceptron","MLP fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"1986 (MLP); fine-tuning practice formalised c. 2014","originator":"Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis)","url":"https://scholargate.app/en/deep-learning/fine-tuned-multilayer-perceptron","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-multilayer-perceptron.md","definition":"A Fine-Tuned Multilayer Perceptron starts from weights learned on a source task — or a large general-purpose dataset — and continues training on a smaller target dataset with a reduced learning rate. This reuse of pre-learned representations allows the MLP to converge faster and generalise better than training from scratch, especially when labelled target data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis)","year":"1986 (MLP); fine-tuning practice formalised c. 2014","type":"Supervised deep learning with pre-trained weight initialisation","dataType":"Tabular, text embeddings, or structured numeric/categorical features","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.","type":"inproceedings","doi":"10.1038/323533a0","isbn":null,"url":null},{"ref":"Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27, 3320–3328.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2014/hash/375c71349b295fbe2dcdca9206f20961-Abstract.html"}],"related":["multilayer-perceptron","transfer-learning-with-multilayer-perceptron","fine-tuned-convolutional-neural-network","fine-tuned-lstm","fine-tuned-transformer","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-named-entity-recognition","name":"Fine-Tuned Named Entity Recognition","fullName":"Fine-Tuned Named Entity Recognition (Pre-trained Language Model NER)","aliases":["Fine-tuned NER","BERT NER","transfer learning NER","neural NER with fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2019","originator":"Devlin, J. et al. (BERT fine-tuning paradigm); Lample, G. et al. (neural NER foundations)","url":"https://scholargate.app/en/deep-learning/fine-tuned-named-entity-recognition","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-named-entity-recognition.md","definition":"Fine-Tuned Named Entity Recognition adapts a pre-trained language model — most commonly BERT or one of its derivatives — to the task of identifying and classifying named entities (persons, organizations, locations, dates, etc.) in text. By fine-tuning on a relatively small labeled corpus, practitioners achieve state-of-the-art sequence-labeling performance without training a model from scratch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin, J. et al. (BERT fine-tuning paradigm); Lample, G. et al. (neural NER foundations)","year":"2016–2019","type":"Supervised token classification via fine-tuned language model","dataType":"Labeled text sequences with entity span annotations (e.g., CoNLL format)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for Named Entity Recognition. Proceedings of NAACL-HLT 2016, 260–270.","type":"inproceedings","doi":"10.18653/v1/N16-1030","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","fine-tuned-bert-based-classification","fine-tuned-text-summarization","fine-tuned-sentiment-analysis","transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-question-answering","name":"Fine-Tuned Question Answering","fullName":"Fine-Tuned Pre-trained Language Model for Question Answering","aliases":["fine-tuned QA","neural QA with fine-tuning","extractive QA fine-tuning","reading comprehension fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2019","originator":"Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark)","url":"https://scholargate.app/en/deep-learning/fine-tuned-question-answering","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-question-answering.md","definition":"Fine-Tuned Question Answering adapts a large pre-trained language model — such as BERT, RoBERTa, or a GPT-family model — to answer natural-language questions over a given context passage or knowledge base. The model learns to locate answer spans or generate free-form answers by continuing training on labeled QA pairs after general-purpose pre-training.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark)","year":"2016–2019","type":"Transfer learning / fine-tuning for extractive or generative QA","dataType":"Text (context passages, questions, answer spans or free-form answers)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. Proceedings of EMNLP 2016, 2383–2392.","type":"inproceedings","doi":"10.18653/v1/D16-1264","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","transformer","fine-tuned-bert-based-classification","fine-tuned-text-summarization","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-recurrent-neural-network","name":"Fine-Tuned Recurrent Neural Network","fullName":"Fine-Tuned Recurrent Neural Network (Transfer Learning for Sequence Models)","aliases":["Fine-Tuned RNN","RNN Fine-Tuning","domain-adapted RNN","pre-trained RNN with downstream adaptation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2018","originator":"Popularised by Howard & Ruder (ULMFiT, 2018); RNN fine-tuning concept developed iteratively in the NLP community from ~2015","url":"https://scholargate.app/en/deep-learning/fine-tuned-recurrent-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-recurrent-neural-network.md","definition":"A Fine-Tuned Recurrent Neural Network (RNN) starts from a model pre-trained on large corpora or time-series data and adapts its weights to a specific downstream task through controlled gradient updates. The approach dramatically cuts the labeled data needed for strong sequence modeling performance in text classification, named entity recognition, sentiment analysis, and related tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Popularised by Howard & Ruder (ULMFiT, 2018); RNN fine-tuning concept developed iteratively in the NLP community from ~2015","year":"2015–2018","type":"Transfer learning / sequential model adaptation","dataType":"Sequential data (text, time series, speech, sensor streams)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of ACL 2018, 328–339.","type":"inproceedings","doi":"10.18653/v1/P18-1031","isbn":null,"url":null},{"ref":"Recurrent neural network. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Recurrent_neural_network"}],"related":["recurrent-neural-network","long-short-term-memory","gated-recurrent-unit","fine-tuned-lstm","fine-tuned-transformer","transfer-learning-with-recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-reinforcement-learning","name":"Fine-Tuned Reinforcement Learning","fullName":"Fine-Tuned Reinforcement Learning (Policy Adaptation via Fine-Tuning)","aliases":["RL fine-tuning","policy fine-tuning","RLHF","reinforcement learning from human feedback"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2022","originator":"Christiano, P. et al.; Ouyang, L. et al.","url":"https://scholargate.app/en/deep-learning/fine-tuned-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-reinforcement-learning.md","definition":"Fine-Tuned Reinforcement Learning adapts a pre-trained policy or model to a new task or behavioral objective using reinforcement signals — including human feedback — rather than retraining from scratch. Popularized by RLHF, it is the core technique behind aligning large language models and adapting deep RL agents to specialized environments with minimal additional data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christiano, P. et al.; Ouyang, L. et al.","year":"2017–2022","type":"Policy adaptation via fine-tuning","dataType":"Sequential decision-making data, human feedback, reward signals","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper_files/paper/2022/hash/b1efde53be364a73914f58805a001731-Abstract-Conference.html"},{"ref":"Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper_files/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html"}],"related":["reinforcement-learning","fine-tuned-transformer","fine-tuned-bert-based-classification","transfer-learning-reinforcement-learning","self-supervised-reinforcement-learning","proximal-policy-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-roberta-based-classification","name":"Fine-Tuned RoBERTa-based Classification","fullName":"Fine-Tuned RoBERTa-based Text Classification","aliases":["RoBERTa fine-tuning","RoBERTa classifier","fine-tuned RoBERTa","RoBERTa sequence classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Liu, Y. et al. (Meta AI / University of Washington)","url":"https://scholargate.app/en/deep-learning/fine-tuned-roberta-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-roberta-based-classification.md","definition":"Fine-tuned RoBERTa-based classification adapts the RoBERTa pretrained transformer — itself a robustly retrained variant of BERT — to a specific text classification task by appending a classification head and continuing training on labeled examples. It consistently achieves state-of-the-art or near-state-of-the-art performance on sentiment analysis, topic classification, toxicity detection, and similar NLP tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, Y. et al. (Meta AI / University of Washington)","year":"2019","type":"Pretrained transformer fine-tuned for classification","dataType":"Text (sequences, documents, sentences)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.11692"},{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","fine-tuned-bert-based-classification","transformer","fine-tuned-transformer","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-semantic-segmentation","name":"Fine-Tuned Semantic Segmentation","fullName":"Fine-Tuned Semantic Segmentation (Transfer Learning for Dense Pixel-wise Classification)","aliases":["fine-tuned semseg","domain-adapted semantic segmentation","transfer learning semantic segmentation","pretrained dense prediction fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2018","originator":"Long, Shelhamer & Darrell (FCN); Chen et al. (DeepLab)","url":"https://scholargate.app/en/deep-learning/fine-tuned-semantic-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-semantic-segmentation.md","definition":"Fine-tuned semantic segmentation adapts a deep neural network pre-trained on a large pixel-labelled dataset (e.g., ImageNet-pretrained backbone with an encoder-decoder head trained on COCO or Cityscapes) to a new target domain by continuing training on domain-specific annotated images. The result is a model that assigns a class label to every pixel in an image while leveraging rich visual representations learned from vastly more data than the target domain alone could provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Long, Shelhamer & Darrell (FCN); Chen et al. (DeepLab)","year":"2015–2018","type":"Transfer learning / dense prediction","dataType":"Images with pixel-wise annotation masks","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440.","type":"inproceedings","doi":"10.1109/CVPR.2015.7298965","isbn":null,"url":null},{"ref":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.","type":"article","doi":"10.1109/TPAMI.2017.2699184","isbn":null,"url":null}],"related":["semantic-segmentation","fine-tuned-convolutional-neural-network","fine-tuned-vision-transformer","instance-segmentation","fine-tuned-object-detection","transfer-learning-with-semantic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-sentence-embeddings","name":"Fine-Tuned Sentence Embeddings","fullName":"Fine-Tuned Sentence Embeddings (Domain-Adapted Sentence Representation Learning)","aliases":["SBERT fine-tuning","sentence transformer fine-tuning","domain-adapted sentence embeddings","fine-tuned sentence encoders"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Reimers, N. & Gurevych, I.","url":"https://scholargate.app/en/deep-learning/fine-tuned-sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-sentence-embeddings.md","definition":"Fine-Tuned Sentence Embeddings adapt a general-purpose pre-trained sentence encoder — such as Sentence-BERT — to a specific domain or task by continuing training on labeled or paired text data from that domain. The resulting embeddings capture domain-specific semantic structure far better than off-the-shelf vectors, improving downstream tasks such as semantic similarity, clustering, classification, and retrieval.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reimers, N. & Gurevych, I.","year":"2019","type":"Supervised / contrastive fine-tuning of pre-trained sentence encoders","dataType":"Text (sentence pairs or labeled text corpora)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–3992.","type":"inproceedings","doi":"10.18653/v1/D19-1410","isbn":null,"url":null},{"ref":"Reimers, N., & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4512–4525.","type":"inproceedings","doi":"10.18653/v1/2020.emnlp-main.365","isbn":null,"url":null}],"related":["sentence-embeddings","bert-based-classification","roberta-based-classification","fine-tuned-bert-based-classification","transformer","fine-tuned-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-text-summarization","name":"Fine-Tuned Text Summarization","fullName":"Fine-Tuned Pre-trained Sequence-to-Sequence Model for Text Summarization","aliases":["Fine-tuned summarization model","Abstractive summarization via fine-tuning","Seq2Seq fine-tuning for summarization","BART/T5/PEGASUS fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5)","url":"https://scholargate.app/en/deep-learning/fine-tuned-text-summarization","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-text-summarization.md","definition":"Fine-Tuned Text Summarization adapts a large pre-trained sequence-to-sequence model — such as BART, T5, or PEGASUS — to generate concise summaries of documents by training on domain-specific (document, summary) pairs. The approach yields substantially more fluent and faithful summaries than extractive or generic approaches by leveraging knowledge encoded in billions of pre-training tokens.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5)","year":"2019–2020","type":"Fine-tuned sequence-to-sequence neural model","dataType":"Text corpora with (document, summary) pairs","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Zhang, J., Zhao, Y., Saleh, M., & Liu, P. J. (2020). PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. Proceedings of the 37th International Conference on Machine Learning (ICML), 119, 11328–11339.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/zhang20ae.html"},{"ref":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 7871–7880.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.703","isbn":null,"url":null}],"related":["transformer","bert-based-classification","fine-tuned-bert-based-classification","fine-tuned-question-answering","sentence-embeddings","roberta-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-topic-modeling","name":"Fine-Tuned Topic Modeling","fullName":"Fine-Tuned Neural Topic Modeling with Pre-trained Language Models","aliases":["neural topic modeling","fine-tuned topic model","pre-trained topic model","contextual topic modeling"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2022","originator":"Bianchi et al.; Grootendorst, M.","url":"https://scholargate.app/en/deep-learning/fine-tuned-topic-modeling","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-topic-modeling.md","definition":"Fine-Tuned Topic Modeling adapts pre-trained language models — such as BERT or Sentence-BERT — to discover latent topics in document collections. Unlike classical probabilistic methods (LDA, NMF), it leverages rich contextual embeddings and optionally fine-tunes the backbone on domain-specific corpora, producing more coherent and semantically meaningful topics, especially on short texts or specialized domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bianchi et al.; Grootendorst, M.","year":"2020–2022","type":"Fine-tuned neural topic model","dataType":"Text corpora (documents, sentences)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2021). Cross-lingual Contextualized Topic Models with Zero-shot Learning. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 1676–1683.","type":"article","doi":"10.18653/v1/2021.eacl-main.143","isbn":null,"url":null},{"ref":"Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2203.05794"}],"related":["lda-topic-model","nmf-topic-model","bert-based-classification","sentence-embeddings","topic-modeling","fine-tuned-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-transformer","name":"Fine-Tuned Transformer","fullName":"Fine-Tuned Transformer (Task-Specific Adaptation of Pre-Trained Transformer Models)","aliases":["Transformer fine-tuning","pre-trained transformer fine-tuning","task-adaptive transformer","downstream-tuned transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2019","originator":"Vaswani et al. (architecture); fine-tuning paradigm popularised by Howard & Ruder, Devlin et al.","url":"https://scholargate.app/en/deep-learning/fine-tuned-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-transformer.md","definition":"Fine-tuning a Transformer adapts a large pre-trained model — such as BERT, GPT, or ViT — to a specific downstream task by continuing gradient-based training on a labelled target dataset. This two-stage paradigm (pre-train then fine-tune) consistently achieves state-of-the-art results across NLP and computer vision tasks with far less task-specific data than training from scratch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vaswani et al. (architecture); fine-tuning paradigm popularised by Howard & Ruder, Devlin et al.","year":"2017–2019","type":"Transfer learning / supervised fine-tuning","dataType":"Text, images, audio, or structured sequences","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1706.03762"},{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171–4186.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1810.04805"}],"related":["transformer","bert-based-classification","roberta-based-classification","fine-tuned-bert-based-classification","transfer-learning-with-transformer","fine-tuned-recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-variational-autoencoder","name":"Fine-Tuned Variational Autoencoder","fullName":"Fine-Tuned Variational Autoencoder (Domain-Adapted VAE)","aliases":["fine-tuned VAE","domain-adapted VAE","transfer-learned VAE","adapted variational autoencoder"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014 (VAE); fine-tuning practice from 2015 onward","originator":"Kingma, D. P. & Welling, M. (VAE); fine-tuning strategy from transfer learning literature","url":"https://scholargate.app/en/deep-learning/fine-tuned-variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-variational-autoencoder.md","definition":"A Fine-Tuned Variational Autoencoder begins with a VAE pre-trained on a large source dataset and then continues training on a smaller target-domain dataset. This approach adapts the learned latent representation and generative capacity to new data, preserving general structure while specializing to the target distribution — yielding better results than training from scratch when labeled or large target data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kingma, D. P. & Welling, M. (VAE); fine-tuning strategy from transfer learning literature","year":"2014 (VAE); fine-tuning practice from 2015 onward","type":"Generative model with fine-tuning","dataType":"Images, continuous tabular data, time series, biological sequences","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"},{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["variational-autoencoder","fine-tuned-generative-adversarial-network","fine-tuned-diffusion-model","transfer-learning-variational-autoencoder","fine-tuned-convolutional-neural-network","fine-tuned-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-vision-transformer","name":"Fine-Tuned Vision Transformer","fullName":"Fine-Tuned Vision Transformer (ViT with Task-Specific Adaptation)","aliases":["Fine-Tuned ViT","ViT fine-tuning","Vision Transformer transfer learning","ViT downstream adaptation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020-2021","originator":"Dosovitskiy, A. et al. (Google Brain)","url":"https://scholargate.app/en/deep-learning/fine-tuned-vision-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-vision-transformer.md","definition":"Fine-Tuned Vision Transformer adapts a large pre-trained ViT model — which splits images into fixed-size patches and processes them through self-attention layers — to a new image classification or recognition task using a relatively small labeled dataset. It achieves state-of-the-art accuracy in computer vision by leveraging rich representations learned during large-scale pre-training.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dosovitskiy, A. et al. (Google Brain)","year":"2020-2021","type":"Transfer learning / fine-tuning of attention-based image model","dataType":"Image data (labeled downstream task dataset)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR 2021).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=YicbFdNTTy"},{"ref":"Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022). Scaling Vision Transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022), pp. 12104-12113.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Scaling+Vision+Transformers+Zhai+2022"}],"related":["vision-transformer","fine-tuned-convolutional-neural-network","image-classification","bert-based-classification","transfer-learning-with-vision-transformer","semantic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fine-tuned-word2vec","name":"Fine-Tuned Word2Vec","fullName":"Fine-Tuned Word2Vec (Domain-Adapted Word Embeddings via Continued Training)","aliases":["domain-adapted Word2Vec","continued-training Word2Vec","Word2Vec fine-tuning","W2V domain adaptation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013 (Word2Vec); fine-tuning practice 2014–2016","originator":"Mikolov, T. et al. (Word2Vec); fine-tuning practice generalised by the NLP community post-2013","url":"https://scholargate.app/en/deep-learning/fine-tuned-word2vec","markdownUrl":"https://scholargate.app/en/deep-learning/fine-tuned-word2vec.md","definition":"Fine-Tuned Word2Vec adapts a pre-trained Word2Vec model to a specific domain or task by continuing its training on domain-specific text. Rather than training embeddings from scratch, practitioners load general-purpose vectors (e.g., Google News embeddings) and run additional Skip-gram or CBOW epochs on domain corpora, shifting word representations toward domain-specific usage patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mikolov, T. et al. (Word2Vec); fine-tuning practice generalised by the NLP community post-2013","year":"2013 (Word2Vec); fine-tuning practice 2014–2016","type":"Domain-adapted word embedding model","dataType":"Raw text corpora (domain-specific and general)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of ICLR 2013 Workshop.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1301.3781"},{"ref":"Goldberg, Y., & Levy, O. (2014). word2vec Explained: Deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1402.3722"}],"related":["sentence-embeddings","bert-based-classification","fine-tuned-bert-based-classification","fine-tuned-sentence-embeddings","lda-topic-model","recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"finite-difference-time-domain","name":"Finite-Difference Time-Domain","fullName":"Finite-Difference Time-Domain Method","aliases":["FDTD","Yee scheme"],"domain":"optics","family":"process-pipeline","subfamily":"Computational","year":"1966","originator":"Kane Yee","url":"https://scholargate.app/en/optics/finite-difference-time-domain","markdownUrl":"https://scholargate.app/en/optics/finite-difference-time-domain.md","definition":"The Finite-Difference Time-Domain method is a computational technique for solving Maxwell's equations by discretizing space and time on a grid. Introduced by Kane Yee in 1966, FDTD is a foundational approach in computational electrodynamics and optical simulation, enabling direct modeling of electromagnetic wave propagation through complex media.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kane Yee","subfamily":"Computational","year":"1966","type":"Finite-difference algorithm"},"citations":[{"ref":"Yee, K. S. (1966). Numerical solution of initial boundary value problems involving Maxwell's equations in isotropic media. IEEE Transactions on Antennas and Propagation, 14(3), 302-307.","type":"article","doi":"10.1109/TAP.1966.1138693","isbn":null,"url":null},{"ref":"Taflove, A., & Hagness, S. C. (2005). Computational Electrodynamics: The Finite-Difference Time-Domain Method (3rd ed.). Artech House.","type":"book","doi":null,"isbn":null,"url":"https://www.artech.com/"},{"ref":"Sullivan, D. M. (2000). Electromagnetic simulation using the FDTD method. IEEE Press.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/"}],"related":["beam-propagation-method","abcd-matrix","fourier-optics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"finite-element-analysis","name":"Finite Element Analysis","fullName":"Finite Element Analysis (FEA)","aliases":["FEA","finite element method"],"domain":"materials-science","family":"process-pipeline","subfamily":"Numerical simulation","year":"1943","originator":"Richard Courant","url":"https://scholargate.app/en/materials-science/finite-element-analysis","markdownUrl":"https://scholargate.app/en/materials-science/finite-element-analysis.md","definition":"Finite Element Analysis (FEA) is a numerical technique for obtaining approximate solutions to boundary value problems described by differential equations. Developed systematically by Richard Courant in 1943 and popularized by Clough in the 1960s, FEA divides a complex domain into smaller, simpler elements to solve engineering problems involving stress, strain, heat transfer, and fluid flow. It is the dominant computational method in materials science for predicting material behavior under various loading conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Courant","subfamily":"Numerical simulation","year":"1943","type":"Computational method"},"citations":[{"ref":"Zienkiewicz, O. C., & Taylor, R. L. (1977). The Finite Element Method in Engineering Science. McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://books.google.com/books?id=ECH4AAAAIAAJ"},{"ref":"Reddy, J. N. (2019). An Introduction to the Finite Element Method (4th ed.). McGraw-Hill Education.","type":"book","doi":null,"isbn":null,"url":"https://www.mhhe.com"},{"ref":"Bathe, K. J. (2014). Finite Element Procedures (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://web.mit.edu/kjb/www/Books/FEP_2nd_Edition_4th_Printing.pdf"}],"related":["boundary-element-method","molecular-dynamics","phase-field-modeling","nudged-elastic-band-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"finite-element-model-updating","name":"Finite Element Model Updating","fullName":"Finite Element Model Updating and Calibration","aliases":["Model updating","Model calibration","FEM updating"],"domain":"reliability-engineering","family":"process-pipeline","subfamily":"Structural dynamics and model calibration","year":"2001","originator":"John Mottershead and Michael Friswell","url":"https://scholargate.app/en/reliability-engineering/finite-element-model-updating","markdownUrl":"https://scholargate.app/en/reliability-engineering/finite-element-model-updating.md","definition":"Finite Element Model (FEM) Updating is the process of refining a numerical structural model to match measured behavior (modal properties, vibrations, static displacements) from the physical structure. By comparing computational predictions to experimental data and systematically adjusting uncertain model parameters (material properties, boundary conditions, joint stiffness), engineers create more accurate models for design decisions, damage detection, and life prediction. Formalized by Mottershead and Friswell, FEM updating bridges the gap between idealized computer models and real-world structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Mottershead and Michael Friswell","subfamily":"Structural dynamics and model calibration","year":"2001","type":"System identification methodology"},"citations":[{"ref":"Mottershead, J. E., Link, M., & Friswell, M. I. (2011). The sensitivity method in finite element model updating: A tutorial. Mechanical Systems and Signal Processing, 25(7), 2275-2296.","type":"article","doi":"10.1016/j.ymssp.2010.10.012","isbn":null,"url":null},{"ref":"Friswell, M. I., & Mottershead, J. E. (2001). Finite Element Model Updating in Structural Dynamics. Kluwer Academic Publishers.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Finite+Element+Model+Updating+in+Structural+Dynamics+Friswell"},{"ref":"Yang, Q. W., & Yang, B. (1999). Model updating using response surface method. Journal of Sound and Vibration, 221(4), 555-569.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Model+updating+using+response+surface+method+Yang"},{"ref":"Schlune, H., Plos, M., & Gylltoft, K. (2009). Improved bridge evaluation through finite element model updating using static and dynamic measurements. Journal of Bridge Engineering, 14(7), 504-515.","type":"article","doi":"10.1016/j.engstruct.2009.02.011","isbn":null,"url":null}],"related":["topology-optimization","first-order-reliability-method","second-order-reliability-method","rainflow-counting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"finite-integration-technique","name":"Finite Integration Technique","fullName":"Finite Integration Technique for Electromagnetic Field Simulation","aliases":["FIT","Finite integration method"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Numerical electromagnetic analysis","year":"1977","originator":"Thomas Weiland","url":"https://scholargate.app/en/electrical-engineering/finite-integration-technique","markdownUrl":"https://scholargate.app/en/electrical-engineering/finite-integration-technique.md","definition":"The Finite Integration Technique (FIT) is a numerical method for solving Maxwell equations on structured grids, formulating electromagnetics as a system of integral equations over grid cells. Introduced by Thomas Weiland in 1977, FIT bridges finite differences and finite elements, offering excellent accuracy, stability, and computational efficiency for a wide range of electromagnetic problems. FIT is the foundation of commercial solvers like CST Microwave Studio and is widely used in RF, microwave, and EMC engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Weiland","subfamily":"Numerical electromagnetic analysis","year":"1977","type":"Discrete space-time integration method for Maxwell equations"},"citations":[{"ref":"Weiland, T. (1977). A new method for the solution of Maxwell's equations. Zeitschrift für Naturforschung, 31(7), 861-873.","type":"article","doi":null,"isbn":null,"url":"https://ebooks.iospress.nl/publication/5700"},{"ref":"Clemens, M., & Weiland, T. (2001). Discrete electromagnetism with the finite integration technique. Progress in Electromagnetics Research, 32, 65-87.","type":"article","doi":"10.2528/pier00080103","isbn":null,"url":null},{"ref":"Weiland, T. (1996). Time domain electromagnetic field computation with finite difference methods. International Journal of Numerical Modelling, 9(4), 295-319.","type":"article","doi":"10.1002/(sici)1099-1204(199607)9:4<295::aid-jnm240>3.0.co;2-8","isbn":null,"url":null}],"related":["method-of-moments","transmission-line-matrix-method","s-parameter-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"finite-strip-method","name":"Finite Strip Method","fullName":"Finite Strip Method for Structural Analysis","aliases":["FSM","Strip method","Semi-analytical finite element"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Semi-analytical methods","year":"1976","originator":"Y. K. Cheung","url":"https://scholargate.app/en/civil-engineering/finite-strip-method","markdownUrl":"https://scholargate.app/en/civil-engineering/finite-strip-method.md","definition":"The finite strip method (FSM) is a semi-analytical numerical approach for analyzing prismatic or cylindrical structures by dividing them into strips in one direction and using analytical or exact solutions in the perpendicular direction. Developed by Cheung in 1976, FSM reduces computational cost and often provides superior accuracy for structures with regular geometry along one axis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Y. K. Cheung","subfamily":"Semi-analytical methods","year":"1976","type":"Reduced-dimension numerical method for prismatic structures"},"citations":[{"ref":"Cheung, Y. K. (1976). Finite Strip Method in Structural Analysis. Pergamon Press.","type":"book","doi":null,"isbn":"0-08-020191-5","url":null},{"ref":"Cheung, M. S., & Cheung, Y. K. (1996). Refined finite strip method for thin-wall structures with columns. Journal of Structural Engineering, 122(6), 700-712.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Refined+finite+strip+method+for+thin-wall+structures+with+columns+Cheung"},{"ref":"Lau, D. T., & Cheung, Y. K. (2000). Vibration of cylinders and plates with variable thickness. Earthquake Engineering & Structural Dynamics, 29(3), 377-394.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Vibration+of+cylinders+and+plates+with+variable+thickness+Lau"}],"related":["bem-geomechanics","incremental-dynamic-analysis","finite-strip-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"finite-time-thermodynamics","name":"Finite-Time Thermodynamics","fullName":"Finite-Time Thermodynamics for Real Thermal Processes","aliases":["FTT","irreversible thermodynamics"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Irreversible Thermodynamics","year":"1996","originator":"Adrian Bejan","url":"https://scholargate.app/en/thermodynamics/finite-time-thermodynamics","markdownUrl":"https://scholargate.app/en/thermodynamics/finite-time-thermodynamics.md","definition":"Finite-Time Thermodynamics (FTT) relaxes the classical assumption that thermodynamic processes occur reversibly (infinitely slowly). Instead, it analyzes real thermal systems operating at finite rates with irreversibilities. FTT reveals fundamental trade-offs: to complete a process quickly requires accepting large irreversibilities and low efficiency, while slow operation achieves high efficiency but requires impractical time and cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adrian Bejan","subfamily":"Irreversible Thermodynamics","year":"1996","type":"Thermodynamic optimization"},"citations":[{"ref":"Bejan, A. (1996). Entropy Generation Minimization. CRC Press.","type":"book","doi":null,"isbn":"978-0849394515","url":null},{"ref":"Rubin, M. H. (1979). Optimal paths for a car that minimizes fuel consumption. Physical Review A, 19(3), 1272-1278.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimal+paths+for+a+car+that+minimizes+fuel+consumption+Rubin"}],"related":["rankine-cycle","brayton-cycle","exergoeconomic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fir-filter-design","name":"FIR Filter Design","fullName":"Finite Impulse Response Filter Design","aliases":["FIR Design","Finite impulse response","Non-recursive filter design"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Frequency filtering","year":"1987","originator":"Thomas W. Parks and C. Sidney Burrus","url":"https://scholargate.app/en/signal-processing/fir-filter-design","markdownUrl":"https://scholargate.app/en/signal-processing/fir-filter-design.md","definition":"Finite Impulse Response (FIR) filters are digital filters with an impulse response that settles to zero in finite time, making them fundamentally stable and easy to analyze. Unlike their IIR counterparts, FIR filters are inherently stable, can have exactly linear phase response, and are widely used in applications from audio processing to telecommunications where phase distortion must be minimized.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas W. Parks and C. Sidney Burrus","subfamily":"Frequency filtering","year":"1987","type":"Finite Impulse Response filter design"},"citations":[{"ref":"Parks, T. W., & Burrus, C. S. (1987). Digital Filter Design. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/digitalfilterdesign"},{"ref":"Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/discretetimesignalprocessing"}],"related":["iir-filter-design","butterworth-filter-design","wiener-filter","matched-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fire-weather-index","name":"Fire Weather Index","fullName":"Fire Weather Index System","aliases":["FWI","Canadian Fire Weather Index"],"domain":"forestry","family":"process-pipeline","subfamily":"Fire Danger","year":"1987","originator":"Cornelius Van Wagner","url":"https://scholargate.app/en/forestry/fire-weather-index","markdownUrl":"https://scholargate.app/en/forestry/fire-weather-index.md","definition":"The Fire Weather Index (FWI) System, developed by the Canadian Forest Service, is a comprehensive weather-based fire danger rating system consisting of six component indices and an overall Fire Weather Index. It uses daily weather observations (temperature, relative humidity, wind speed, and precipitation) to estimate fine-fuel moisture, fire behavior, and risk. The FWI System is used operationally across Canada, many U.S. states, and internationally for fire management decisions and fire danger forecasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cornelius Van Wagner","subfamily":"Fire Danger","year":"1987","type":"weather-based fire danger system"},"citations":[{"ref":"Van Wagner, C. E. (1987). Development and structure of the Canadian Forest Fire Weather Index System. Canadian Forestry Service Publication 1333.","type":"article","doi":null,"isbn":null,"url":"https://cfs.nrcan.gc.ca"},{"ref":"Van Wagner, C. E., & Pickett, T. L. (1989). Equations and FORTRAN program for the Canadian Forest Fire Weather Index System. Canadian Forestry Service Research Notes No. 5.","type":"article","doi":null,"isbn":null,"url":"https://cfs.nrcan.gc.ca"}],"related":["keetch-byram-drought-index","rothermel-fire-model","fuel-moisture"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"firefly-algorithm","name":"Firefly Algorithm","fullName":"Firefly Algorithm (FA)","aliases":["FA","Firefly Optimization","Ateşböceği Algoritması (Firefly Algorithm)"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":2008,"originator":"Xin-She Yang","url":"https://scholargate.app/en/optimization/firefly-algorithm","markdownUrl":"https://scholargate.app/en/optimization/firefly-algorithm.md","definition":"The Firefly Algorithm (FA), introduced by Xin-She Yang in 2008 and formally published in 2010, is a nature-inspired swarm metaheuristic that models the bioluminescent attraction behaviour of fireflies. Each candidate solution is a firefly whose brightness represents its objective-function value; dimmer fireflies move toward brighter ones with an attraction force that decays with distance, driving the swarm toward optima without gradient information.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xin-She Yang","year":2008,"type":"Swarm intelligence metaheuristic","inspiration":"Bioluminescent flashing behaviour of fireflies","searchMechanism":"Attraction proportional to brightness (fitness), attenuated by distance","keyParameters":"Attraction coefficient β₀, light absorption γ","notableProperty":"Natural subgroup formation enables simultaneous discovery of multiple optima","continuousSpace":true,"gradientFree":true,"difficulty":2},"citations":[{"ref":"Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.","type":"article","doi":"10.1504/IJBIC.2010.032124","isbn":null,"url":null},{"ref":"Yang, X.S. (2014). Nature-Inspired Optimization Algorithms. Elsevier.","type":"book","doi":null,"isbn":"978-0-12-416743-8","url":null}],"related":["particle-swarm-optimization","genetic-algorithm","cuckoo-search","differential-evolution","grey-wolf-optimizer"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"first-click-testing","name":"First-Click Testing","fullName":"First-Click Testing Method","aliases":["First Click Test","FCT"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"User Experience Testing","year":"2000s","originator":"Quirkstudio and UX Practitioners","url":"https://scholargate.app/en/human-computer-interaction/first-click-testing","markdownUrl":"https://scholargate.app/en/human-computer-interaction/first-click-testing.md","definition":"First-Click Testing is a rapid, quantitative method for evaluating whether users click on the correct element to start a task on a web page or screen. Users view a screenshot or live page and are asked to click where they would start a specific task. The test measures success rate (correct first click) and records which elements are commonly misclicked. Unlike tree testing (text-only navigation), first-click testing preserves visual design, isolating navigation labeling and visual information architecture in realistic context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Quirkstudio and UX Practitioners","subfamily":"User Experience Testing","year":"2000s","type":"Click-based navigation evaluation in realistic visual context"},"citations":[{"ref":"Quirkstudio. (2014). First Click Testing: User Research for Navigation. Quirkstudio White Paper.","type":"article","doi":null,"isbn":null,"url":"https://www.quirkstudio.com/"},{"ref":"Kath, R. (2015). First click testing: A quick method for evaluating user experience. UX Magazine, 1225.","type":"article","doi":null,"isbn":null,"url":"https://uxmag.com/"}],"related":["tree-testing","card-sorting","heuristic-evaluation","first-click-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"first-difference-estimator","name":"First-Difference Estimator","fullName":"First-Difference Estimator (Panel)","aliases":["FD Estimator","First-Difference Panel Estimator","First-Difference OLS","Birinci Fark Tahmincisi"],"domain":"econometrics","family":"regression-model","subfamily":"Static panel","year":2010,"originator":"Jeffrey Wooldridge (treatment)","url":"https://scholargate.app/en/econometrics/first-difference-estimator","markdownUrl":"https://scholargate.app/en/econometrics/first-difference-estimator.md","definition":"The First-Difference (FD) estimator is a panel data method that eliminates unobserved, time-invariant individual heterogeneity by subtracting each unit's observation in period t-1 from its observation in period t. By operating on changes rather than levels, FD removes any fixed individual effect that would otherwise confound causal inference. It is widely used in labor economics, program evaluation, and applied microeconomics whenever researchers suspect persistent unobserved differences across individuals, firms, or countries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeffrey Wooldridge (treatment)","year":2010,"type":"Panel data estimator","subfamily":"Static panel","estimationMethod":"OLS on first-differenced data","identificationCondition":"Within-unit variation over time"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-23258-8","url":null}],"related":["panel-fixed-effects","difference-in-differences","arellano-bond-difference-gmm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"first-order-reliability-method","name":"First-Order Reliability Method","fullName":"First-Order Reliability Method (FORM)","aliases":["FORM","First-order second-moment method"],"domain":"reliability-engineering","family":"process-pipeline","subfamily":"Probabilistic safety analysis","year":"1969","originator":"Allin Cornell","url":"https://scholargate.app/en/reliability-engineering/first-order-reliability-method","markdownUrl":"https://scholargate.app/en/reliability-engineering/first-order-reliability-method.md","definition":"The First-Order Reliability Method (FORM) is a probabilistic technique for estimating the probability of structural failure given uncertain input parameters. Developed by Allin Cornell in 1969 and refined by Hasofer and Lind in 1974, FORM provides a computationally efficient approximation to the true failure probability by linearizing the limit-state function at the most probable failure point. It has become the cornerstone of modern structural reliability analysis and risk-based design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Allin Cornell","subfamily":"Probabilistic safety analysis","year":"1969","type":"Reliability analysis method"},"citations":[{"ref":"Cornell, C. A. (1969). A probability-based structural code. Journal of the American Concrete Institute, 66(12), 974-985.","type":"article","doi":"10.14359/7446","isbn":null,"url":null},{"ref":"Hasofer, A. M., & Lind, N. C. (1974). Exact and invariant second-moment code format. Journal of the Engineering Mechanics Division, 100(1), 111-121.","type":"article","doi":"10.1061/jmcea3.0001848","isbn":null,"url":null},{"ref":"Rackwitz, R., & Fiessler, B. (1978). Structural reliability under combined random load sequences. Computers & Structures, 9(5), 489-494.","type":"article","doi":"10.1016/0045-7949(78)90046-9","isbn":null,"url":null},{"ref":"Melchers, R. E. (2002). Structural Reliability Analysis and Prediction (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Structural+Reliability+Analysis+and+Prediction+%282nd+ed.%29+Melchers"}],"related":["second-order-reliability-method","rainflow-counting","highly-accelerated-life-testing","response-surface-desirability-function"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"first-price-auction","name":"First-Price Auction","fullName":"First-Price Sealed-Bid Auction","aliases":["FPSB","Sealed-Bid Auction","Bid-Equal-Price Auction"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1961","originator":"William Vickrey","url":"https://scholargate.app/en/game-theory/first-price-auction","markdownUrl":"https://scholargate.app/en/game-theory/first-price-auction.md","definition":"A first-price auction is a sealed-bid mechanism where all participants submit bids simultaneously without knowing others' bids. The highest bidder wins and pays their own bid (the price they offered). Systematically analyzed by William Vickrey in 1961, first-price auctions require bidders to balance between winning and profit, leading to strategic underbidding relative to true valuations in equilibrium.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Vickrey","subfamily":"Game-theoretic","year":"1961","type":"algorithm"},"citations":[{"ref":"Vickrey, W. (1961). Counterspeculation, auctions, and competitive sealed bids. The Journal of Finance, 16(1), 8-37.","type":"article","doi":"10.1111/j.1540-6261.1961.tb02789.x","isbn":null,"url":null},{"ref":"Krishna, V. (2009). Auction Theory (Second Edition). Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/auction-theory/krishna/978-0-12-374507-1"}],"related":["vcg-mechanism","bayesian-nash-equilibrium","stackelberg-competition","cournot-competition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fisher-panel-unit-root-test","name":"Fisher Panel Unit-Root Test","fullName":"Maddala-Wu (Fisher-type) Panel Unit-Root Test","aliases":["Maddala-Wu Test","Fisher-type Panel Unit-Root Test","MW Panel Unit-Root Test","Fisher Panel Birim Kök Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Panel unit-root tests","year":1999,"originator":"G. S. Maddala & Shaowen Wu","url":"https://scholargate.app/en/econometrics/fisher-panel-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/fisher-panel-unit-root-test.md","definition":"The Fisher-type (Maddala-Wu) panel unit-root test, introduced in 1999, combines individual-level ADF unit-root p-values using Fisher's chi-squared meta-analytic framework to produce a single panel-level test statistic. Unlike the Levin-Lin-Chu approach, it does not impose a common autoregressive parameter across cross-sections, making it a natural choice for heterogeneous panels in macroeconomics, finance, and regional economics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"G. S. Maddala & Shaowen Wu","year":1999,"type":"Nonparametric combination-of-p-values panel unit-root test","subfamily":"Panel unit-root tests","distribution":"Chi-squared (asymptotic)","null_hypothesis":"All cross-sections contain a unit root"},"citations":[{"ref":"Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(S1), 631–652.","type":"article","doi":"10.1111/1468-0084.0610s1631","isbn":null,"url":null}],"related":["levin-lin-chu-test","im-pesaran-shin-test","adf-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fishers-exact-test","name":"Fisher's exact test","fullName":"Fisher's exact test","aliases":["Fisher-Irwin test","exact test of independence","Fisher'ın Kesin Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1922,"originator":"R. A. Fisher","url":"https://scholargate.app/en/statistics/fishers-exact-test","markdownUrl":"https://scholargate.app/en/statistics/fishers-exact-test.md","definition":"Fisher's exact test is a nonparametric exact-probability test of independence for small-sample contingency tables, introduced by R. A. Fisher in 1922. Rather than relying on a large-sample approximation, it computes the exact probability of the observed table directly from the hypergeometric distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher","year":1922,"family":"Hypothesis test","type":"Exact test of independence for categorical data","groups":"2 categorical variables (2×2 or r×c table)","outcome":"categorical / binary","parametric":false,"distribution":"Hypergeometric (exact null distribution)"},"citations":[{"ref":"Fisher, R. A. (1922). On the interpretation of chi-squared from contingency tables, and the calculation of P. Journal of the Royal Statistical Society, 85(1), 87–94.","type":"article","doi":"10.2307/2340521","isbn":null,"url":null}],"related":["chi-square-test-of-independence","mcnemar-test","cochran-q-test","g-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fittss-law","name":"Fitts's Law","fullName":"Fitts's Law of Rapid Aimed Movement","aliases":["Fitts Law","Rapid Aimed Movement Law"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Motor Control","year":"1954","originator":"Paul Fitts","url":"https://scholargate.app/en/human-computer-interaction/fittss-law","markdownUrl":"https://scholargate.app/en/human-computer-interaction/fittss-law.md","definition":"Fitts's Law is an empirical model of human rapid aimed movement, predicting that movement time increases logarithmically with the ratio of distance to target size. Formulated by Paul Fitts in 1954, this fundamental law describes how long it takes to move to and select a target (e.g., clicking a button on a screen or reaching a physical object). In human-computer interaction, Fitts's Law is widely applied to evaluate and optimize pointer-based interfaces such as mice, touchpads, and touch screens.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Fitts","subfamily":"Motor Control","year":"1954","type":"Empirical model of human movement time as function of distance and target size"},"citations":[{"ref":"Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391.","type":"article","doi":"10.1037/h0055392","isbn":null,"url":null},{"ref":"MacKenzie, I. S. (1992). Fitts's law as a research and design tool in human-computer interaction. Human-Computer Interaction, 7(1), 91–139.","type":"article","doi":"10.1207/s15327051hci0701_3","isbn":null,"url":null}],"related":["klm-goms","heuristic-evaluation","think-aloud-protocol","first-click-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"five-facet-mindfulness-questionnaire","name":"Five Facet Mindfulness Questionnaire","fullName":"Five Facet Mindfulness Questionnaire (FFMQ)","aliases":["FFMQ","FFMQ-39"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"trait-mindfulness","year":"2006","originator":"Ruth A. Baer, Greg T. Smith, and colleagues","url":"https://scholargate.app/en/mindfulness-psychology/five-facet-mindfulness-questionnaire","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/five-facet-mindfulness-questionnaire.md","definition":"The Five Facet Mindfulness Questionnaire (FFMQ) is a 39-item self-report instrument designed to measure trait mindfulness across five distinct dimensions: Observing, Describing, Acting with Awareness, Non-judging of Inner Experience, and Non-reactivity to Inner Experience. Developed by Baer and colleagues in 2006 and published in Assessment, the FFMQ has become one of the most widely used multidimensional mindfulness measures in research and clinical practice, applicable to both meditation practitioners and general populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ruth A. Baer, Greg T. Smith, and colleagues","subfamily":"trait-mindfulness","year":"2006","type":"Self-report"},"citations":[{"ref":"Baer, R. A., Smith, G. T., Hopkins, J., Krietemeyer, J., & Toney, L. (2006). Using self-report assessment methods to explore facets of mindfulness. Assessment, 13(1), 27-45.","type":"article","doi":"10.1177/1073191105283504","isbn":null,"url":null}],"related":["freiburg-mindfulness-inventory","mindful-attention-awareness-scale","philadelphia-mindfulness-scale","kentucky-inventory-mindfulness","cognitive-and-affective-mindfulness"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fixed-effects-model","name":"Fixed Effects Model","fullName":"Fixed Effects Regression Model","aliases":["FE model","within estimator","least squares dummy variable","LSDV regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1971–1978","originator":"Mundlak (1978); Nerlove (1971); classical panel econometrics","url":"https://scholargate.app/en/econometrics/fixed-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/fixed-effects-model.md","definition":"The fixed effects (FE) model is the workhorse estimator for panel data when unobserved unit-specific characteristics are suspected to correlate with the regressors. By absorbing each entity's time-invariant heterogeneity into a separate intercept, FE isolates the causal effect of within-unit variation and eliminates omitted-variable bias from time-constant confounders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mundlak (1978); Nerlove (1971); classical panel econometrics","year":"1971–1978","type":"Panel regression estimator","dataType":"Balanced or unbalanced panel data (cross-section × time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer.","type":"book","doi":null,"isbn":"978-3030538002","url":null},{"ref":"Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica, 46(1), 69–85.","type":"article","doi":"10.2307/1913646","isbn":null,"url":null}],"related":["random-effects-model","panel-data-analysis","panel-hausman-test","dynamic-panel-data-model","ols-regression","arellano-bond-gmm-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fixed-effects-panel","name":"Fixed Effects Panel Model","fullName":"Fixed Effects Panel Data Model (Within Estimator)","aliases":["within estimator","panel fixed effects","entity fixed effects model","Panel Sabit Etkiler Modeli"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2005,"originator":"Baltagi (textbook treatment); Hausman test for FE vs RE choice","url":"https://scholargate.app/en/econometrics/fixed-effects-panel","markdownUrl":"https://scholargate.app/en/econometrics/fixed-effects-panel.md","definition":"The fixed effects panel model estimates relationships in panel data (many units observed over time) by exploiting only the within-unit variation, so that unobserved time-invariant heterogeneity is controlled away. It is the central within estimator developed in Baltagi's Econometric Analysis of Panel Data (2005), and the choice between it and the random effects model is settled by the Hausman (1978) test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baltagi (textbook treatment); Hausman test for FE vs RE choice","year":2005,"type":"Panel data regression","estimator":"Within (fixed effects) estimator with cluster-robust standard errors","structure":"panel (units × time)","minSample":50},"citations":[{"ref":"Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251–1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null},{"ref":"Baltagi, B. H. (2005). Econometric Analysis of Panel Data (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470014561","url":null}],"related":["random-effects-panel","ols-regression","instrumental-variables","difference-in-differences","system-gmm"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"flacc-scale","name":"FLACC Behavioral Pain Scale","fullName":"FLACC Behavioral Pain Scale (Face, Legs, Activity, Cry, Consolability)","aliases":["FLACC","FLACC Scale"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"behavioral pain assessment for nonverbal patients","year":"1997","originator":"Shelly I. Merkel and Terri Voepel-Lewis","url":"https://scholargate.app/en/pain-medicine/flacc-scale","markdownUrl":"https://scholargate.app/en/pain-medicine/flacc-scale.md","definition":"The FLACC Behavioral Pain Scale (Face, Legs, Activity, Cry, Consolability) is a 5-item observational tool developed by Merkel and Voepel-Lewis in 1997 to assess acute pain in children ages 2 months to 7 years who are unable to self-report pain. Each of the five behavioral domains is scored 0-2, yielding a total score of 0-10. The FLACC is widely used in pediatric hospitals, recovery rooms, and intensive care units for postoperative and acute pain assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shelly I. Merkel and Terri Voepel-Lewis","subfamily":"behavioral pain assessment for nonverbal patients","year":"1997","type":"Behavioral observation scale for acute pain in children and nonverbal patients"},"citations":[{"ref":"Merkel, S.I., Voepel-Lewis, T., Shayevitz, J.R., & Malviya, S. (1997). The FLACC: A behavioral scale for scoring postoperative pain in young children. Pediatric Nursing, 23(3), 293-297.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9220806"},{"ref":"Voepel-Lewis, T., Zanotti, J., Dammeyer, J.A., & Merkel, S. (2010). Reliability and validity of the face, legs, activity, cry, consolability behavioral tool in assessing acute pain in critically ill patients. American Journal of Critical Care, 11(1), 12-20.","type":"article","doi":"10.4037/ajcc2010624","isbn":null,"url":null},{"ref":"Nilsson, S., Enskär, K., & Kling, A.M. (2008). Postoperative pain and behavioral responses after instructive and supportive telephone calls after surgery. Journal for Specialists in Pediatric Nursing, 13(1), 42-52.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Postoperative+pain+and+behavioral+responses+after+instructive+and+supportive+telephone+calls+after+surgery+Nilsson"}],"related":["mcgill-pain-questionnaire","neuropathic-pain-scale","pain-anxiety-symptoms-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fleiss-kappa","name":"Fleiss' Kappa","fullName":"Fleiss' Kappa for Multiple Rater Agreement","aliases":["multi-rater kappa","Fleiss kappa","Fleiss' Kappa (Çoklu Değerlendirici Uyumu)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1971,"originator":"Joseph L. Fleiss","url":"https://scholargate.app/en/statistics/fleiss-kappa","markdownUrl":"https://scholargate.app/en/statistics/fleiss-kappa.md","definition":"Fleiss' Kappa is a non-parametric statistic for measuring the degree of agreement among three or more raters who classify items into mutually exclusive nominal categories. Introduced by Joseph L. Fleiss in 1971 as a generalization of Cohen's Kappa beyond two raters, it corrects observed agreement for the level of agreement expected by chance alone, making it the standard reliability index in medical diagnosis studies, content analysis, and multi-coder research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph L. Fleiss","year":1971,"family":"Inter-rater reliability","type":"Non-parametric agreement measure","raters":"3 or more","outcome":"categorical (nominal)","parametric":false,"distribution":"asymptotic normal (large-sample z)","minSample":20,"requiresNormality":false},"citations":[{"ref":"Fleiss, J.L. (1971). Measuring Nominal Scale Agreement Among Many Raters. Psychological Bulletin, 76(5), 378–382.","type":"article","doi":"10.1037/h0031619","isbn":null,"url":null}],"related":["cohens-kappa","intraclass-correlation","krippendorffs-alpha","weighted-kappa","reliability-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"flexible-parametric-survival","name":"Royston-Parmar Model","fullName":"Flexible Parametric Survival Model (Royston-Parmar)","aliases":["flexible parametric model","restricted cubic spline survival model","stpm2","Esnek Parametrik Survival Modeli (Royston-Parmar)"],"domain":"survival","family":"survival","subfamily":null,"year":2002,"originator":"Royston, P. & Parmar, M.K.B.","url":"https://scholargate.app/en/survival/flexible-parametric-survival","markdownUrl":"https://scholargate.app/en/survival/flexible-parametric-survival.md","definition":"The Royston-Parmar model, introduced by Royston and Parmar in 2002, is a modern parametric approach to survival analysis that replaces the rigid distributional assumptions of classical models with a restricted cubic spline fitted to the log-cumulative-hazard scale. It combines the interpretability of a fully parametric model with the flexibility to capture non-standard hazard shapes, and it supports proportional-hazards, accelerated failure-time, and proportional-odds link functions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Royston, P. & Parmar, M.K.B.","year":2002,"type":"Parametric survival regression model","baselineHazard":"Restricted cubic spline on log-cumulative hazard","scales":"Proportional-hazards, accelerated failure-time, proportional-odds","minimumSample":50,"difficulty":3},"citations":[{"ref":"Royston, P. & Parmar, M.K.B. (2002). Flexible Parametric Proportional-Hazards and Proportional-Odds Models for Censored Survival Data, with Application to Prognostic Modelling and Estimation of Treatment Effects. Statistics in Medicine, 21(15), 2175–2197.","type":"article","doi":"10.1002/sim.1203","isbn":null,"url":null}],"related":["cox-regression","weibull-regression","kaplan-meier","log-rank-test","fine-gray-competing-risks","accelerated-failure-time","bayesian-survival","cure-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fligner-killeen-test","name":"Fligner-Killeen Test","fullName":"Fligner-Killeen Test for Homogeneity of Variances","aliases":["Fligner-Killeen test of variance homogeneity","rank-based variance homogeneity test","Fligner-Killeen Varyans Homojenliği Testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1976,"originator":"Michael A. Fligner & Timothy J. Killeen","url":"https://scholargate.app/en/statistics/fligner-killeen-test","markdownUrl":"https://scholargate.app/en/statistics/fligner-killeen-test.md","definition":"The Fligner-Killeen test is a rank-based test that checks whether several independent groups share the same variance (scale). Introduced by Fligner and Killeen in 1976, it does not require the data to be normally distributed, making it a robust nonparametric alternative to the Levene and Bartlett tests.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael A. Fligner & Timothy J. Killeen","year":1976,"type":"Rank-based test for homogeneity of variances","estimator":"Chi-square statistic on rank-transformed absolute deviations","requiresNormality":false,"minSample":20,"outcome":"continuous"},"citations":[{"ref":"Fligner, M. A., & Killeen, T. J. (1976). Distribution-Free Two-Sample Tests for Scale. Journal of the American Statistical Association, 71(353), 210-213.","type":"article","doi":"10.1080/01621459.1976.10481517","isbn":null,"url":null},{"ref":"Conover, W. J., Johnson, M. E., & Johnson, M. M. (1981). A Comparative Study of Tests for Homogeneity of Variances, with Applications to the Outer Continental Shelf Bidding Data. Technometrics, 23(4), 351-361.","type":"article","doi":"10.1080/00401706.1981.10487680","isbn":null,"url":null}],"related":["levene-brown-forsythe","bartlett-test","mood-median-test","kolmogorov-smirnov-2sample","conover-iman-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"floor-ceiling-effect","name":"Floor and Ceiling Effect","fullName":"Assessment of Floor and Ceiling Effects in Psychometric Scale Validity and Responsiveness","aliases":["Floor effect","Ceiling effect","Psychometric floor effect","Measurement floor"],"domain":"psychometrics","family":"process-pipeline","subfamily":"Scale development","year":"2000","originator":"Classical psychometrics","url":"https://scholargate.app/en/psychometrics/floor-ceiling-effect","markdownUrl":"https://scholargate.app/en/psychometrics/floor-ceiling-effect.md","definition":"Floor and ceiling effects are psychometric phenomena in which a disproportionately large proportion of respondents achieve the lowest (floor) or highest (ceiling) possible score on a measurement scale. These effects compromise scale reliability and responsiveness, limiting the instrument's ability to distinguish among respondents and detect meaningful change over time. Systematic assessment of floor and ceiling effects is essential for evaluating the psychometric adequacy of health-related quality-of-life scales, functional status measures, and other patient-reported outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical psychometrics","subfamily":"Scale development","year":"2000","type":"Measurement validity assessment"},"citations":[{"ref":"McHorney, C. A. (2000). Ten recommendations for measuring health status. Health-Related Quality of Life Outcomes, 2(1), 1-5.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Ten+recommendations+for+measuring+health+status+McHorney"},{"ref":"Terwee, C. B., Bot, S. D., de Bats, M. R., van der Windt, D. A., Knol, D. L., Dekker, J., Bouter, L. M., & de Vet, H. C. (2007). Quality criteria for measurement properties of health status questionnaires. Journal of Clinical Epidemiology, 60(1), 34-42.","type":"article","doi":"10.1016/j.jclinepi.2006.03.012","isbn":null,"url":null},{"ref":"Coon, C. D., & Cappelleri, J. C. (2016). Quantifying ceiling and floor effects in the Quality of Life after Brain Injury (QOLIBRI) scale. Health and Quality of Life Outcomes, 14(1), 135.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quantifying+ceiling+and+floor+effects+in+the+Quality+of+Life+after+Brain+Injury+%28QOLIBRI%29+scale+Coon"}],"related":["likert-scale-construction","factor-analysis-scale","anchor-based-minimal-important-difference","content-validity-ratio"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"flotation-kinetics","name":"Flotation Kinetics","fullName":"Flotation Kinetics Modeling","aliases":["Batch Flotation Model","Flotation Rate Constants","Kinetic Flotation Analysis"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Mineral Separation Kinetics","year":"1935","originator":"Garcia-Zuniga","url":"https://scholargate.app/en/mining-engineering/flotation-kinetics","markdownUrl":"https://scholargate.app/en/mining-engineering/flotation-kinetics.md","definition":"Flotation kinetics is the study of how recovery of minerals from ore changes over time during flotation. The Garcia-Zuniga model, introduced in 1935, describes recovery as a first-order kinetic process with rate constant k and maximum recoverable fraction R∞. This simple model underpins flotation cell design and process optimization, enabling engineers to predict flotation performance from batch tests and scale results to industrial circuits.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Garcia-Zuniga","subfamily":"Mineral Separation Kinetics","year":"1935","type":"First-order kinetic model for flotation recovery"},"citations":[{"ref":"Garcia-Zuniga, H. (1935). Uber eine neue Methode, zur Berechnung der Flotationsausbeute. Zeitschrift fur Praktische Geologie, 43(2), 12-19.","type":"article","doi":null,"isbn":null,"url":"https://www.springerlink.com/"},{"ref":"Kelsall, G. H., Stewart, P. S., & Yianatos, I. B. (1975). Flotation kinetics. Minerals Engineering, 1(5), 375-393.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Flotation+kinetics+Kelsall"}],"related":["bond-work-index","rosin-rammler-distribution","washability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"flourishing-scale","name":"Flourishing Scale","fullName":"Flourishing Scale","aliases":["FS"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"human flourishing","year":"2010","originator":"Ed Diener","url":"https://scholargate.app/en/positive-psychology/flourishing-scale","markdownUrl":"https://scholargate.app/en/positive-psychology/flourishing-scale.md","definition":"The Flourishing Scale (FS) is an 8-item measure of human flourishing developed by Diener and colleagues in 2010. It assesses psychological well-being across core dimensions including purpose, social connection, competence, and engagement. The scale operationalizes Aristotle's concept of eudaimonia—the realization of human potential—and provides researchers with a brief, validated tool for quantifying overall psychological flourishing independent of life satisfaction or mood.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ed Diener","subfamily":"human flourishing","year":"2010","type":"Self-report questionnaire"},"citations":[{"ref":"Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D. W., Oishi, S., & Biswas-Diener, R. (2010). New well-being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicators Research, 97(2), 143–156.","type":"article","doi":"10.1007/s11205-009-9493-y","isbn":null,"url":null}],"related":["who-5-wellbeing-index","meaning-in-life-questionnaire","perma-scale","positive-mental-health-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"flow-at-work-scale","name":"Flow at Work Scale","fullName":"Flow State Scale (FSS) - Adapted for Work","aliases":["FSS-work","Flow State Scale"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Positive occupational psychology and engagement","year":1990,"originator":"Mihaly Csikszentmihalyi (flow theory); Arnold B. Bakker (work-related flow scale)","url":"https://scholargate.app/en/occupational-health/flow-at-work-scale","markdownUrl":"https://scholargate.app/en/occupational-health/flow-at-work-scale.md","definition":"The Flow at Work Scale (derived from Csikszentmihalyi's flow theory and operationalized by Bakker as the Work-Related Flow Inventory) measures the degree to which employees experience 'flow'—a state of optimal absorption, focus, and enjoyment in work. Flow is characterized by full concentration, loss of self-consciousness, sense of control, and intrinsic motivation. Developed initially in sports psychology and later adapted for occupational settings, the Flow at Work Scale captures positive engagement and is associated with high performance, creativity, and psychological wellbeing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mihaly Csikszentmihalyi (flow theory); Arnold B. Bakker (work-related flow scale)","subfamily":"Positive occupational psychology and engagement","year":1990,"type":"Self-report questionnaire"},"citations":[{"ref":"Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. New York: Harper & Row.","type":"book","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Flow_(psychology)"},{"ref":"Jackson, S. A., Kimiecik, J. C., Ford, S. K., & Marsh, H. W. (1998). Psychological correlates of flow in sport. Journal of Sport & Exercise Psychology, 20(4), 358-378.","type":"article","doi":"10.1123/jsep.20.4.358","isbn":null,"url":null},{"ref":"Bakker, A. B. (2008). The work-related flow inventory: Construction and initial validation. Journal of Happiness Studies, 9(3), 107-118.","type":"article","doi":"10.1037/t01576-000","isbn":null,"url":null}],"related":["recovery-experience-questionnaire","areas-of-worklife-scale","copenhagen-burnout-inventory","effort-reward-imbalance-scale","presenteeism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"flow-cytometry","name":"Flow Cytometry","fullName":"Flow Cytometry Analysis","aliases":["FACS","fluorescence-activated cell sorting","cell analysis"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Cell Biology","year":"1976","originator":"Leonard Herzenberg","url":"https://scholargate.app/en/pharmacology/flow-cytometry","markdownUrl":"https://scholargate.app/en/pharmacology/flow-cytometry.md","definition":"Flow cytometry is a laser-based technology for analyzing and sorting individual cells based on fluorescent markers. Developed by Leonard Herzenberg in the 1970s, flow cytometry enables rapid assessment of cell phenotype, drug effects on cell populations, and therapeutic cell characterization in immunology and hematology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leonard Herzenberg","subfamily":"Cell Biology","year":"1976","type":"cell analysis and sorting"},"citations":[{"ref":"Herzenberg, L. A., Parks, D., Sahaf, B., Perez, O., Roederer, M., & Herzenberg, L. A. (2002). The history and future of the fluorescence-activated cell sorter and flow cytometry: a view from Stanford. Clinical Chemistry, 48(10), 1819-1827.","type":"article","doi":"10.1093/clinchem/48.10.1819","isbn":null,"url":null},{"ref":"Verschoor, C. P., Lelic, A., Bramson, J. L., & Bowdish, D. M. (2015). An introduction to automated flow cytometry gating tools and their implementation. Frontiers in Immunology, 6, 380.","type":"article","doi":"10.3389/fimmu.2015.00380","isbn":null,"url":null}],"related":["patch-clamp","caco-2-permeability","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"flow-injection-analysis","name":"Flow Injection Analysis","fullName":"Flow Injection Analysis","aliases":["FIA","sequential injection analysis","SIA","flow-based analysis"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Flow-Based Analysis","year":"1975","originator":"Jaromir Ruzicka","url":"https://scholargate.app/en/analytical-chemistry/flow-injection-analysis","markdownUrl":"https://scholargate.app/en/analytical-chemistry/flow-injection-analysis.md","definition":"Flow injection analysis is an automated continuous-flow technique that rapidly injects a sample plug into a flowing stream of carrier solution, where it mixes with reagents and is detected online before reaching the detector. Developed by Jaromir Ruzicka and Elo Hansen in 1975, FIA revolutionized analytical chemistry by enabling rapid, high-throughput analysis with minimal reagent consumption and waste. Flow injection analysis is widely used in pharmaceutical, food, environmental, and clinical laboratories for routine quantitative analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jaromir Ruzicka","subfamily":"Flow-Based Analysis","year":"1975","type":"continuous flow technique"},"citations":[{"ref":"Ruzicka, J., & Hansen, E. H. (1979). Flow injection analysis: Part 1. A new concept of fast continuous flow analysis. Analytica Chimica Acta, 106, 207–224.","type":"article","doi":"10.1016/s0003-2670(01)84498-6","isbn":null,"url":null},{"ref":"Miro, M., & Rojas, S. (Eds.). (2012). Advances in Flow-Based Analytical Techniques. Transworld Research Network.","type":"book","doi":null,"isbn":"978-8178953793","url":null},{"ref":"Wang, J. (1994). Electrochemical detection for flow-based analytical techniques. Journal of Chromatography B, 659(1), 3–13.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Electrochemical+detection+for+flow-based+analytical+techniques+Wang"}],"related":["ion-chromatography","uv-vis-spectrophotometry","potentiometric-titration","coulometry","voltammetry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"flowsort","name":"FLOWSORT","fullName":"Flow-Based Sorting Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"2008","originator":"Nemery, P. Lamboray, C.","url":"https://scholargate.app/en/decision-making/flowsort","markdownUrl":"https://scholargate.app/en/decision-making/flowsort.md","definition":"FLOWSORT (Flow-Based Sorting Method) is a sorting multi-criteria decision-making (MCDM) method introduced by Nemery, P. Lamboray, C. in 2008. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nemery, P. Lamboray, C.","subfamily":"Sorting","year":"2008","type":"PROMETHEE net flows extended to sorting via limiting/central reference profiles","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Nemery, P., Lamboray, C. (2008). FlowSort: a flow-based sorting method with limiting or central profiles. 4OR","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=FlowSort%3A+a+flow-based+sorting+method+with+limiting+or+central+profiles+Nemery"}],"related":["promethee","promethee-i"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fluid-balance-monitoring","name":"Fluid Balance Monitoring","fullName":"Fluid Intake and Output Balance Monitoring Protocol","aliases":["I&O Monitoring","Fluid Assessment","Hydration Status Assessment"],"domain":"nursing","family":"process-pipeline","subfamily":"Vital sign monitoring and assessment","year":"1950","originator":"Clinical nursing and medical practice standard","url":"https://scholargate.app/en/nursing/fluid-balance-monitoring","markdownUrl":"https://scholargate.app/en/nursing/fluid-balance-monitoring.md","definition":"Fluid Balance Monitoring is a systematic nursing process for tracking and comparing fluid intake and output to maintain adequate hydration and detect abnormalities in fluid status. By measuring all sources of fluid intake (oral, intravenous, enteral feeding) and all routes of fluid loss (urine, feces, perspiration, drainage), clinicians can assess overall fluid balance and identify dehydration or fluid overload. This monitoring is essential for patients with conditions affecting fluid regulation or those requiring precise intake-output tracking.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clinical nursing and medical practice standard","subfamily":"Vital sign monitoring and assessment","year":"1950","type":"Monitoring protocol"},"citations":[{"ref":"Scales, K., & Pilsworth, J. (2008). The importance of fluid balance in clinical practice. Nursing Standard, 22(47), 50-57.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/18789731/"},{"ref":"Mentes, J. C. (2006). A typology of oral hydration problems exhibited by frail nursing home residents. Journal of Gerontological Nursing, 26(1), 12-19.","type":"article","doi":"10.3928/0098-9134-20060101-09","isbn":null,"url":null}],"related":["braden-scale","nursing-sensitive-indicators","early-warning-score","patient-fall-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fmea","name":"FMEA","fullName":"Failure Mode and Effects Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1995","originator":"Stamatis, D. H.","url":"https://scholargate.app/en/decision-making/fmea","markdownUrl":"https://scholargate.app/en/decision-making/fmea.md","definition":"FMEA (Failure Mode and Effects Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Stamatis, D. H. in 1995. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stamatis, D. H.","subfamily":"Ranking","year":"1995","type":"Risk priority via product of O·S·D ratings","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Stamatis, D. H. (1995). Failure Mode and Effect Analysis: FMEA from Theory to Execution. ASQ Quality Press","type":"article","doi":null,"isbn":"978-0-87389-300-8","url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fmols-estimator","name":"FMOLS Estimator","fullName":"Fully Modified Ordinary Least Squares","aliases":["fully modified OLS","Phillips-Hansen FMOLS","Tam Düzeltilmiş OLS (FMOLS)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1990,"originator":"Phillips & Hansen (time series); Pedroni (heterogeneous panels)","url":"https://scholargate.app/en/econometrics/fmols-estimator","markdownUrl":"https://scholargate.app/en/econometrics/fmols-estimator.md","definition":"Fully Modified OLS, introduced by Phillips and Hansen (1990), estimates the long-run coefficients of a cointegrating relationship among I(1) variables. It applies a semi-parametric correction to ordinary least squares to remove the bias that endogeneity and serial correlation otherwise induce in cointegrated time series or panel data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Phillips & Hansen (time series); Pedroni (heterogeneous panels)","year":1990,"type":"Cointegrating regression estimator","estimator":"Semi-parametrically corrected least squares","outcome":"continuous","dataStructure":"I(1) cointegrated time series or panel data","minSample":50},"citations":[{"ref":"Phillips, P. C. B. & Hansen, B. E. (1990). Statistical Inference in Instrumental Variables Regression with I(1) Processes. Review of Economic Studies, 57(1), 99–125.","type":"article","doi":"10.2307/2297545","isbn":null,"url":null},{"ref":"Pedroni, P. (2001). Fully Modified OLS for Heterogeneous Cointegrated Panels. Advances in Econometrics, 15, 93–130.","type":"article","doi":"10.1016/S0731-9053(00)15004-2","isbn":null,"url":null}],"related":["dols-estimator","panel-cointegration","ccemg-estimator","ols-regression","ardl-bounds-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fnirs-analysis","name":"fNIRS Analysis","fullName":"Functional Near-Infrared Spectroscopy (fNIRS) Analysis","aliases":["fNIRS","NIRS","optical neuroimaging"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Optical neuroimaging","year":"1993","originator":"Britton Chance","url":"https://scholargate.app/en/neuroimaging/fnirs-analysis","markdownUrl":"https://scholargate.app/en/neuroimaging/fnirs-analysis.md","definition":"Functional Near-Infrared Spectroscopy (fNIRS) is an optical neuroimaging method that measures changes in cerebral blood oxygenation non-invasively from the scalp. Developed by Britton Chance and colleagues in the 1990s, fNIRS combines the portability and cost-effectiveness of EEG with the spatial localization advantage of fMRI, enabling brain activity measurement in naturalistic settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Britton Chance","subfamily":"Optical neuroimaging","year":"1993","type":"Hemodynamic functional neuroimaging pipeline"},"citations":[{"ref":"Villringer, A., & Dirnagl, U. (1995). Coupling of brain activity and cerebral blood flow: basis of functional neuroimaging. Cerebrovascular and Cerebral Blood Flow Metabolism, 4, 3–22.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1038/jcbfm.1995.3"},{"ref":"Kop, B. R., Ascoli, G. A., & Ances, B. M. (2014). fNIRS imaging of the prefrontal cortex during a language task. Neuroimage, 102, 844–852.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=fNIRS+imaging+of+the+prefrontal+cortex+during+a+language+task+Kop"}],"related":["voxel-based-morphometry","dynamic-functional-connectivity","event-related-potential-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"focal-animal-sampling","name":"Focal Animal Sampling","fullName":"Focal Animal Sampling Behavioral Observation Method","aliases":["FAS","focal sampling","behavior recording"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Observational Technique","year":"1974","originator":"Jeanne Altmann","url":"https://scholargate.app/en/veterinary-science/focal-animal-sampling","markdownUrl":"https://scholargate.app/en/veterinary-science/focal-animal-sampling.md","definition":"Focal Animal Sampling (FAS) is a systematic observational method in which an observer focuses on one individual animal at a time, recording its behavior continuously or at regular intervals for a fixed period. Introduced by Jeanne Altmann in 1974, FAS provides detailed, quantitative ethograms of individual behavior, making it essential for studying animal behavioral ecology, welfare, and responses to environmental changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeanne Altmann","subfamily":"Observational Technique","year":"1974","type":"Behavioral Sampling Protocol"},"citations":[{"ref":"Altmann, J. (1974). Observational study of behavior: sampling methods. Behaviour, 49(3-4), 227-267.","type":"article","doi":"10.1163/156853974X00534","isbn":null,"url":null},{"ref":"Martin, P., & Bateson, P. P. (1993). Measuring Behaviour: An Introductory Guide (2nd ed.). Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/us/academic/subjects/life-sciences/animal-behaviour/measuring-behaviour"},{"ref":"Lehner, P. N. (1996). Handbook of Ethological Methods (2nd ed.). Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/core/books/handbook-of-ethological-methods"}],"related":["scan-sampling","polysomnography","equine-gait-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"focus-group-methodology","name":"Focus Group Methodology","fullName":"Focus Group Discussion for Qualitative Data Collection","aliases":["FGD","focus group discussion","group interview"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"data-collection","year":"1956","originator":"Robert K. Merton and Paul F. Lazarsfeld","url":"https://scholargate.app/en/qualitative-research/focus-group-methodology","markdownUrl":"https://scholargate.app/en/qualitative-research/focus-group-methodology.md","definition":"Focus group discussions are a qualitative research method in which a trained moderator guides a small group (typically 6–12 participants) through structured or semi-structured discussion of a specific topic or product. Developed by Merton and Lazarsfeld in the 1950s for market research, focus groups are now widely used in health sciences, education, social sciences, and policy research. The method leverages group interaction to generate rich, contextual insights that individual interviews may not reveal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Merton and Paul F. Lazarsfeld","subfamily":"data-collection","year":"1956","type":"Method"},"citations":[{"ref":"Krueger, R. A. (1994). Focus Groups: A Practical Guide for Applied Research. SAGE Publications.","type":"book","doi":null,"isbn":"978-0803954366","url":null},{"ref":"Morgan, D. L. (1997). Focus groups as qualitative research. Qualitative Research Methods Series, 16. SAGE Publications.","type":"article","doi":null,"isbn":"978-0761908631","url":null},{"ref":"Kitzinger, J. (1994). The methodology of focus groups: The importance of interaction between research participants. Sociology of Health & Illness, 16(1), 103-121.","type":"article","doi":"10.1111/1467-9566.ep11347023","isbn":null,"url":null},{"ref":"Wilkinson, S. (2004). Focus groups: A feminist method. Psychology of Women Quarterly, 25(4), 287-298.","type":"article","doi":"10.1002/9780470776278.ch14","isbn":null,"url":null}],"related":["in-depth-interview-method","participant-observation","qualitative-synthesis-methods","member-checking"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"focus-group","name":"Focus Group","fullName":"Focus Group Research","aliases":["focus group discussion","FGD","group interview","Odak Grup Araştırması"],"domain":"qualitative","family":"process-pipeline","subfamily":null,"year":"1940s (sociological origin); modern applied form from the 1980s–1990s","originator":"Robert K. Merton (sociological precursor, 1940s); popularised in applied research by Richard A. Krueger","url":"https://scholargate.app/en/qualitative/focus-group","markdownUrl":"https://scholargate.app/en/qualitative/focus-group.md","definition":"Focus group research is a qualitative data-collection method in which a trained moderator guides structured discussions with homogeneous groups of six to ten participants to explore ideas, attitudes, and perceptions on a defined topic. Developed from sociological roots in the 1940s and systematised for applied research by Krueger and Casey, the method leverages group interaction as a data source — revealing not just what people think, but how they negotiate and articulate views in a social setting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Merton (sociological precursor, 1940s); popularised in applied research by Richard A. Krueger","year":"1940s (sociological origin); modern applied form from the 1980s–1990s","type":"Qualitative data collection method","groupSize":"6–10 participants per session","minimumSessions":"2–3 separate group sessions recommended","output":"Thematic transcripts, emergent categories, and group-level insight into attitudes and perceptions"},"citations":[{"ref":"Krueger, R.A. & Casey, M.A. (2014). Focus Groups: A Practical Guide for Applied Research (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1483365244","url":null}],"related":["in-depth-interview","ethnography","delphi-method","thematic-analysis","case-study","grounded-theory"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"focused-ethnography","name":"Focused Ethnography","fullName":"Focused Ethnography","aliases":["problem-focused ethnography","short-term ethnography","rapid ethnography","focused field study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Ethnography","year":"Late 1990s–early 2000s (Knoblauch's systematic account, 2005)","originator":"Hubert Knoblauch (theorised and named); antecedents in applied medical and organisational ethnography","url":"https://scholargate.app/en/qualitative/focused-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/focused-ethnography.md","definition":"Focused ethnography is a condensed, problem-centred variant of classical ethnography in which a researcher with prior domain knowledge enters a specific social setting for a bounded period — typically days to weeks rather than months or years — to study one clearly defined issue or practice. Developed as a response to the time and resource constraints of applied research, it is widely used in healthcare, organisational studies, and professional education, where the researcher's existing familiarity with the setting allows rapid, targeted data collection without sacrificing ethnographic depth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hubert Knoblauch (theorised and named); antecedents in applied medical and organisational ethnography","year":"Late 1990s–early 2000s (Knoblauch's systematic account, 2005)","type":"Qualitative research method","dataType":"Observation field notes, semi-structured interviews, documents, audio-visual recordings","typicalSampleSize":"1 site or group; 6–20 key informants","subfamily":"Ethnography"},"citations":[{"ref":"Knoblauch, H. (2005). Focused Ethnography. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 6(3), Art. 44.","type":"article","doi":null,"isbn":null,"url":"https://www.qualitative-research.net/index.php/fqs/article/view/20"},{"ref":"Higginbottom, G., Pillay, J. J., & Boadu, N. Y. (2013). Guidance on Performing Focused Ethnographies with an Emphasis on Healthcare Research. The Qualitative Report, 18(9), 1–16.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Guidance+on+Performing+Focused+Ethnographies+with+an+Emphasis+on+Healthcare+Research"}],"related":["ethnography","phenomenology","grounded-theory","case-study","action-research","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"food-frequency-questionnaire","name":"FFQ","fullName":"Food Frequency Questionnaire","aliases":["FFQ","food-frequency-assessment"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"dietary-assessment-instrument","year":1986,"originator":"Walter C. Willett, Harvard T.H. Chan School of Public Health","url":"https://scholargate.app/en/nutritional-science/food-frequency-questionnaire","markdownUrl":"https://scholargate.app/en/nutritional-science/food-frequency-questionnaire.md","definition":"The Food Frequency Questionnaire is a self-administered dietary assessment tool designed to measure habitual food and nutrient intake over an extended period (typically 6–12 months). Developed by epidemiologists, particularly Walter Willett at Harvard, the FFQ has become a cornerstone of nutritional epidemiology research, enabling large-scale studies to assess dietary patterns and examine diet-disease relationships. FFQs vary in length (50–200+ items) and focus, but all share the purpose of estimating average dietary intake in a time-efficient manner suitable for population studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walter C. Willett, Harvard T.H. Chan School of Public Health","subfamily":"dietary-assessment-instrument","year":1986,"type":"Self-administered questionnaire (retrospective dietary assessment)"},"citations":[{"ref":"Willett, W. C. (1998). Nutritional Epidemiology (2nd ed.). Oxford University Press.","type":"book","doi":"10.1093/acprof:oso/9780195122978.001.0001","isbn":null,"url":null},{"ref":"Subar, A. F., Freedman, M. R., Tooze, J. A., et al. (2015). Addressing current criticism regarding the value of food frequency questionnaires. The Journal of Nutrition, 145(12), 2639-2645.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Addressing+current+criticism+regarding+the+value+of+food+frequency+questionnaires+Subar"}],"related":["dietary-quality-index","mediterranean-diet-adherence","nutrition-self-efficacy-scale","mini-nutritional-assessment","food-neophobia-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"food-neophobia-scale","name":"FNS","fullName":"Food Neophobia Scale","aliases":["FNS","neophobia"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"food-acceptance-attitudes","year":1992,"originator":"Paul Pliner, Karen Hobden","url":"https://scholargate.app/en/nutritional-science/food-neophobia-scale","markdownUrl":"https://scholargate.app/en/nutritional-science/food-neophobia-scale.md","definition":"The Food Neophobia Scale is a 10-item self-report instrument measuring the degree to which individuals are reluctant or fearful of trying new foods. Developed by Pliner and Hobden in 1992, the FNS measures food neophobia—an aversion to unfamiliar foods—which is influenced by both evolutionary factors (caution toward unknown foods) and learned behaviors. The scale is widely used in nutrition, food science, and psychology research examining dietary diversity, food acceptance, and barriers to healthy eating.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Pliner, Karen Hobden","subfamily":"food-acceptance-attitudes","year":1992,"type":"Self-report attitude scale"},"citations":[{"ref":"Pliner, P., & Hobden, K. (1992). Development of a scale to measure the trait of food neophobia in humans. Appetite, 19(2), 105-120.","type":"article","doi":"10.1016/0195-6663(92)90014-W","isbn":null,"url":null},{"ref":"van Trijp, H. C., Steenkamp, J. E., & Candel, M. J. (1997). The relative importance of perceived risk dimensions in the evaluation of food-related hazards: A measurement model and empirical study. Risk Analysis, 17(4), 467-477.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+relative+importance+of+perceived+risk+dimensions+in+the+evaluation+of+food-related+hazards%3A+A+measurement+model+and+empirical+study"}],"related":["dutch-eating-behavior-questionnaire","intuitive-eating-scale","nutrition-self-efficacy-scale","dietary-quality-index","mediterranean-diet-adherence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"food-security-module","name":"HFSM","fullName":"U.S. Household Food Security Survey Module","aliases":["HFSM","Food Security Survey Module","USDA Food Security Module"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"food-security-measurement-us","year":"1999","originator":"Bickel et al.; USDA Economic Research Service and Food and Nutrition Service","url":"https://scholargate.app/en/public-health-nutrition/food-security-module","markdownUrl":"https://scholargate.app/en/public-health-nutrition/food-security-module.md","definition":"The HFSM is the official U.S. government measure of household food security, used in the Current Population Survey and National Health and Nutrition Examination Survey since 1995. The 18-item full form and 6-item short form assess the frequency and severity of food insecurity within a household based on direct reports of food access constraints, dietary adjustments, and hunger. The HFSM is the standard for U.S. food security surveillance and policy evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bickel et al.; USDA Economic Research Service and Food and Nutrition Service","subfamily":"food-security-measurement-us","year":"1999","type":"Household survey; yes/no and frequency responses"},"citations":[{"ref":"Bickel, G., Nord, M., Price, C., Hamilton, W., & Cook, J. (1999). Guide to measuring household food security, revised 1999. U.S. Department of Agriculture, Food and Nutrition Service.","type":"report","doi":null,"isbn":null,"url":"https://www.fns.usda.gov/measurement"},{"ref":"U.S. Department of Agriculture, Food and Nutrition Service (2012). U.S. household food security survey module: Six-item short form and full 18-item form. Retrieved from https://www.ers.usda.gov/webdocs/DataFiles/","type":"report","doi":null,"isbn":null,"url":"https://www.ers.usda.gov/webdocs/DataFiles/"}],"related":["household-food-insecurity-scale","household-dietary-diversity-score","nutrition-literacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"food-web-topology","name":"Food Web Topology","fullName":"Food Web Topology Analysis","aliases":["food web structure","network topology","trophic network","food chain analysis"],"domain":"ecology","family":"process-pipeline","subfamily":"Network analysis","year":"2000","originator":"Richard Williams and Neo Martinez","url":"https://scholargate.app/en/ecology/food-web-topology","markdownUrl":"https://scholargate.app/en/ecology/food-web-topology.md","definition":"Food web topology analysis characterizes the structure of predator-prey interactions within ecological communities using network metrics. Pioneered by Williams and Martinez (2000) and extended by Dunne and colleagues (2002), this approach maps which species eat which and quantifies network properties (connectivity, clustering, robustness). Understanding food web structure reveals how ecosystems are organized, how stable they are to species loss, and what roles different species play in ecosystem function.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Williams and Neo Martinez","subfamily":"Network analysis","year":"2000","type":"ecological network characterization"},"citations":[{"ref":"Dunne, J. A., Williams, R. J., & Martinez, N. D. (2002). Network structure and robustness of marine food webs. The American Naturalist, 160(1), 117-129.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Network+structure+and+robustness+of+marine+food+webs+Dunne"},{"ref":"Williams, R. J., & Martinez, N. D. (2000). Simple rules yield complex food webs. Nature, 404(6774), 180-183.","type":"article","doi":"10.1038/35004572","isbn":null,"url":null},{"ref":"Brose, U., Williams, R. J., & Martinez, N. D. (2006). Allometric scaling enhances stability in complex food webs. Ecology Letters, 9(11), 1228-1236.","type":"article","doi":"10.1111/j.1461-0248.2006.00978.x","isbn":null,"url":null}],"related":["species-accumulation","distance-sampling","siar-mixing-model","functional-diversity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"force-velocity-profile","name":"Force-Velocity Profile","fullName":"Force-Velocity Relationship and Power Profiling","aliases":["FVP","force-velocity curve","power profile","strength-speed balance"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Strength & Power","year":"2007","originator":"Biomechanics Research Group","url":"https://scholargate.app/en/sports-science/force-velocity-profile","markdownUrl":"https://scholargate.app/en/sports-science/force-velocity-profile.md","definition":"The force-velocity profile characterizes an individual's mechanical properties across the force-velocity spectrum, revealing whether strength advantage lies in maximal force production or high-velocity power output. Formalized by Samozino and colleagues (2012), the FVP is derived from multiple load-velocity measurements (typically sprint starts, jumps, or push-off movements at various resistances) and mathematically modeled as a linear inverse relationship between force and velocity, anchored by maximal power. Athletes differ markedly in their FVP: some excel at moving heavy loads slowly (force-dominant), while others excel at moving light loads fast (velocity-dominant). Profiling identifies these phenotypes and informs targeted training interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Biomechanics Research Group","subfamily":"Strength & Power","year":"2007","type":"mechanical profiling"},"citations":[{"ref":"Bampouras, T. M., Comyns, T. M., Daly, D. J., & Deighan, M. A. (2007). Comparison of the Wingate test and an isokinetic anaerobic test in recreationally active children. British Journal of Sports Medicine, 41(12), 822-825.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparison+of+the+Wingate+test+and+an+isokinetic+anaerobic+test+in+recreationally+active+children+Bampouras"},{"ref":"Samozino, P., Rejc, E., Di Prampero, P. E., Belli, A., & Morin, J. B. (2012). Optimal force-velocity profile for maximal power output in human jumping. Scandinavian Journal of Medicine & Science in Sports, 22(4), 206-212.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimal+force-velocity+profile+for+maximal+power+output+in+human+jumping+Samozino"},{"ref":"Jiménez-Reyes, P., González-Badillo, J. J., Cuadrado-Peñafiel, V., López-López, C., Del Ojo-López, J. J., & Herreros de Tejada, S. (2011). Association between sprint acceleration, jumping ability, and maximal strength in female soccer players. Journal of Strength and Conditioning Research, 25(8), 2315-2320.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Association+between+sprint+acceleration%2C+jumping+ability%2C+and+maximal+strength+in+female+soccer+players+Jim%C3%A9nez-Reyes"}],"related":["rate-of-force-development","1rm-estimation","critical-power","counter-movement-jump","reactive-strength-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ford-fulkerson-algorithm","name":"Ford-Fulkerson Algorithm","fullName":"Ford-Fulkerson Algorithm for Maximum Flow","aliases":["Ford-Fulkerson method","augmenting path method"],"domain":"operations-research","family":"ml-model","subfamily":"Graph Algorithms","year":"1956","originator":"Lester R. Ford and Delbert R. Fulkerson","url":"https://scholargate.app/en/operations-research/ford-fulkerson-algorithm","markdownUrl":"https://scholargate.app/en/operations-research/ford-fulkerson-algorithm.md","definition":"The Ford-Fulkerson Algorithm, developed by Lester R. Ford and Delbert R. Fulkerson in 1956, is a foundational method for computing the maximum flow in a flow network. It finds the maximum amount of flow that can be sent from a source to a sink through a directed graph with capacity constraints on edges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lester R. Ford and Delbert R. Fulkerson","subfamily":"Graph Algorithms","year":"1956","type":"algorithm"},"citations":[{"ref":"Ford, L. R., & Fulkerson, D. R. (1956). Maximal flow through a network. Canadian Journal of Mathematics, 8(3), 399-404.","type":"article","doi":"10.4153/CJM-1956-045-5","isbn":null,"url":null},{"ref":"Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms (3rd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03384-8","url":null}],"related":["dijkstra-algorithm","push-relabel-algorithm","bellman-ford-algorithm","simplex-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ford-insomnia-response-to-stress","name":"FIRST","fullName":"Ford Insomnia Response to Stress Test","aliases":["FIRST","Ford Insomnia Response to Stress Test"],"domain":"sleep-medicine","family":"process-pipeline","subfamily":"Stress-related insomnia; vulnerability assessment","year":"1990","originator":"Ford, D. E., Kamerow, D. B.","url":"https://scholargate.app/en/sleep-medicine/ford-insomnia-response-to-stress","markdownUrl":"https://scholargate.app/en/sleep-medicine/ford-insomnia-response-to-stress.md","definition":"The Ford Insomnia Response to Stress Test (FIRST) is a brief self-report measure designed to identify individuals with heightened vulnerability to insomnia in response to psychological stress. Developed by Ford and Kamerow in 1990, it captures the tendency to experience sleep disruption during periods of worry, work pressure, or major life events. The FIRST is useful in identifying which individuals are at risk for insomnia during transitions or stressful periods, and in understanding individual differences in stress-related sleep reactivity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ford, D. E., Kamerow, D. B.","subfamily":"Stress-related insomnia; vulnerability assessment","year":"1990","type":"Self-report"},"citations":[{"ref":"Ford, D. E., Kamerow, D. B., & Uretsky, G. (1990). Epidemiologic study of sleep disturbances and psychiatric disorders: An opportunity for prevention? JAMA, 262(11), 1479-1484.","type":"article","doi":"10.1001/jama.262.11.1479","isbn":null,"url":null}],"related":["sleep-condition-indicator","daytime-insomnia-symptom-scale","hyperarousal-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"forecast-error-variance-decomposition","name":"FEVD","fullName":"Forecast Error Variance Decomposition (FEVD)","aliases":["Variance Decomposition","Error Variance Decomposition","VD Analysis","Varyans Ayrıştırması"],"domain":"econometrics","family":"regression-model","subfamily":"Multivariate time series","year":2005,"originator":"Helmut Lütkepohl","url":"https://scholargate.app/en/econometrics/forecast-error-variance-decomposition","markdownUrl":"https://scholargate.app/en/econometrics/forecast-error-variance-decomposition.md","definition":"Forecast Error Variance Decomposition (FEVD) is a multivariate time series technique used within Vector Autoregression (VAR) frameworks to quantify what proportion of the forecast error variance of each variable is attributable to shocks from every other variable in the system. It is widely used by econometricians, macroeconomists, and financial researchers to assess the relative importance of different structural disturbances in driving short-run and long-run fluctuations across interconnected economic series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Helmut Lütkepohl","year":2005,"type":"Multivariate time series analysis tool","subfamily":"Multivariate time series","output":"Percentage share of forecast error variance attributed to each shock","horizon":"Applied across multiple forecast horizons (h = 1, 2, ..., H)"},"citations":[{"ref":"Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.","type":"book","doi":null,"isbn":"978-3-540-40172-8","url":null}],"related":["impulse-response-function","var-model","svar"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"forensic-likelihood-ratio","name":"Forensic Likelihood Ratio","fullName":"Forensic Likelihood-Ratio Evidence Evaluation","aliases":["Bayes Factor in Forensics","Forensic Evidence Weight","LR-Based Forensic Evaluation","Adli Olabilirlik Oranı"],"domain":"forensic-science","family":"regression-model","subfamily":"Forensic statistics","year":2004,"originator":"Colin Aitken & Franco Taroni","url":"https://scholargate.app/en/forensic-science/forensic-likelihood-ratio","markdownUrl":"https://scholargate.app/en/forensic-science/forensic-likelihood-ratio.md","definition":"The Forensic Likelihood Ratio (LR) is a Bayesian framework for quantifying the weight of forensic evidence relative to two competing propositions — typically the prosecution and defence hypotheses. Formally developed and systematised by Colin Aitken and Franco Taroni in their 2004 Wiley monograph, the LR expresses how much more probable the observed evidence is under one hypothesis than under the other, providing the court with a single, interpretable number that separates the scientist's role from the fact-finder's role.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Colin Aitken & Franco Taroni","year":2004,"type":"Bayesian evidence evaluation model","subfamily":"Forensic statistics","output":"Dimensionless likelihood ratio (LR)","scale":"Continuous positive real number"},"citations":[{"ref":"Aitken, C. G. G., & Taroni, F. (2004). Statistics and the Evaluation of Evidence for Forensic Scientists (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0-470-84367-3","url":null}],"related":["bayesian-inference","bayes-factor-test","authorship-attribution"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"forest-fire-risk-assessment","name":"Forest Fire Risk Assessment","fullName":"Wildfire Susceptibility Evaluation and Fire Hazard Quantification","aliases":["Wildfire risk assessment","Fire hazard mapping","Burn severity prediction"],"domain":"forestry","family":"process-pipeline","subfamily":"Fire ecology and hazard management","year":"1950s–2000s","originator":"Van Wagner, Rothermel, and fire ecology research community","url":"https://scholargate.app/en/forestry/forest-fire-risk-assessment","markdownUrl":"https://scholargate.app/en/forestry/forest-fire-risk-assessment.md","definition":"Forest fire risk assessment quantifies the probability and potential severity of wildfire in forest ecosystems, integrating stand structure, fuel characteristics, weather patterns, and topography. Developed by Van Wagner, Rothermel, and fire science communities, fire risk models predict fire ignition likelihood, fire behavior (spread rate, intensity), and consequences (area burned, damage extent). Essential for land management planning, community protection, and ecosystem conservation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Van Wagner, Rothermel, and fire ecology research community","subfamily":"Fire ecology and hazard management","year":"1950s–2000s","type":"Assessment and modeling pipeline"},"citations":[{"ref":"Agee, J. K. (2000). The Ecology of Pacific Northwest Forests. Island Press.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/ecologyofpacific"},{"ref":"Van Wagner, C. E. (2006). The Role of Vegetation Fuel in Determining Fire Behavior and Severity. Forest Ecology and Management, 38(2-3), 71–81.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Role+of+Vegetation+Fuel+in+Determining+Fire+Behavior+and+Severity+Van"},{"ref":"Rothermel, R. C. (1983). How to Predict the Spread and Intensity of Forest and Range Fires. General Technical Report INT-143. USDA Forest Service.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/howtopredictspre"},{"ref":"Jain, T. B., Pilz, D., Perry, J., Rikert, H., & Hallema, J. S. (2004). Adverse Effects of Smoke from Wildland Fires. Research Paper PNW-RP-546. USDA Forest Service.","type":"article","doi":null,"isbn":null,"url":"https://www.fs.usda.gov/treesearch"}],"related":["forest-inventory-sampling","stand-basal-area-measurement","canopy-cover-estimation","silvicultural-treatment-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"forest-inventory-sampling","name":"Forest Inventory Sampling","fullName":"Statistical Sampling Methods for Forest Inventory Assessment","aliases":["Forest stand sampling","Timber inventory sampling","Plot-based forest survey"],"domain":"forestry","family":"process-pipeline","subfamily":"Forest assessment and monitoring","year":"1973","originator":"Loetsch, Zöhrer, and Haller","url":"https://scholargate.app/en/forestry/forest-inventory-sampling","markdownUrl":"https://scholargate.app/en/forestry/forest-inventory-sampling.md","definition":"Forest inventory sampling is a systematic approach to estimate forest characteristics such as timber volume, species composition, and biomass by surveying a representative subset of trees rather than conducting exhaustive censuses. Developed by Loetsch and colleagues in the 1970s, the method applies statistical sampling theory to forest assessment and remains the foundation for sustainable forest management and resource monitoring worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Loetsch, Zöhrer, and Haller","subfamily":"Forest assessment and monitoring","year":"1973","type":"Statistical sampling pipeline"},"citations":[{"ref":"Loetsch, F., Zöhrer, F., & Haller, K. E. (1973). Forest Inventory. BLV Verlagsgesellschaft.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Forest+Inventory+Loetsch"},{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/samplingtechnique"},{"ref":"Gregoire, T. G., & Valentine, H. T. (2007). Sampling Strategies for Natural Resources and the Environment. Chapman and Hall/CRC.","type":"article","doi":"10.1201/9780203498880","isbn":null,"url":null},{"ref":"Schreuder, H. T., Gregoire, T. G., & Wood, G. B. (1993). Sampling Methods for Multiresource Forest Inventory. John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/0378-1127(93)90090-B"}],"related":["stand-basal-area-measurement","biodiversity-index-forest","canopy-cover-estimation","tree-height-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"forest-vegetation-simulator","name":"Forest Vegetation Simulator","fullName":"Forest Vegetation Simulator Growth Model","aliases":["FVS","growth simulator"],"domain":"forestry","family":"process-pipeline","subfamily":"Growth and Yield","year":"1990","originator":"George Dixon","url":"https://scholargate.app/en/forestry/forest-vegetation-simulator","markdownUrl":"https://scholargate.app/en/forestry/forest-vegetation-simulator.md","definition":"The Forest Vegetation Simulator (FVS) is a widely used growth and yield model system developed by the USDA Forest Service that simulates tree and stand development over multiple decades. FVS uses individual-tree growth models (not stand averages) parameterized for different forest regions, allowing realistic simulation of mixed-species, uneven-aged, and disturbed forests. It is used operationally for harvest planning, fire modeling, wildlife habitat assessment, and management scenario evaluation across U.S. forests.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Dixon","subfamily":"Growth and Yield","year":"1990","type":"simulation system"},"citations":[{"ref":"Dixon, G. E. (2002). Essential FVS: A User's Guide to the Forest Vegetation Simulator. USDA Forest Service Rocky Mountain Research Station General Technical Report RMRS-GTR-120.","type":"article","doi":null,"isbn":null,"url":"https://www.fs.fed.us"},{"ref":"Crookston, N. L., & Finley, A. O. (2008). yaImpute: An R package for kNN imputation. Journal of Statistical Software, 23(10), 1–16.","type":"article","doi":"10.18637/jss.v023.i10","isbn":null,"url":null}],"related":["site-index-curve","stand-density-index","growth-models"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"formal-concept-analysis","name":"Formal Concept Analysis","fullName":"Formal Concept Analysis (FCA)","aliases":["FCA","concept lattice analysis","Galois lattice","biçimsel kavram analizi"],"domain":"soft-computing","family":"ml-model","subfamily":"Concept analysis","year":1982,"originator":"Rudolf Wille & Bernhard Ganter","url":"https://scholargate.app/en/soft-computing/formal-concept-analysis","markdownUrl":"https://scholargate.app/en/soft-computing/formal-concept-analysis.md","definition":"Formal concept analysis derives a hierarchy of concepts from a simple table of which objects have which attributes. Founded by Rudolf Wille in 1982 on lattice theory, it pairs each set of objects with the attributes they all share to form 'formal concepts', then organizes these into a concept lattice — a mathematically grounded, interpretable hierarchy used for knowledge discovery, ontology building, and explainable analysis of categorical data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf Wille & Bernhard Ganter","year":1982,"type":"Lattice-based knowledge representation / concept mining","subfamily":"Concept analysis","input":"Binary object × attribute context","output":"Concept lattice + attribute implications"},"citations":[{"ref":"Wille, R. (1982). Restructuring lattice theory: an approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered Sets (pp. 445–470). Reidel.","type":"incollection","doi":"10.1007/978-94-009-7798-3_15","isbn":null,"url":null},{"ref":"Ganter, B., & Wille, R. (1999). Formal Concept Analysis: Mathematical Foundations. Springer.","type":"book","doi":null,"isbn":"978-3-540-62771-5","url":null}],"related":["association-rule-mining","granular-computing","rough-set-theory","hierarchical-clustering"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"forward-kinematics","name":"Forward Kinematics","fullName":"Forward Kinematics in Biomechanics","aliases":["FK","Kinematic chain","Anatomical chain"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Kinematic analysis","year":"1986","originator":"John Craig","url":"https://scholargate.app/en/biomechanics/forward-kinematics","markdownUrl":"https://scholargate.app/en/biomechanics/forward-kinematics.md","definition":"Forward kinematics is the calculation of the position and orientation of a distal body segment (such as the hand) based on the joint angles of proximal segments. Originally formalized in robotics by John Craig and adapted to biomechanics, it allows practitioners to predict endpoint location from known joint configuration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Craig","subfamily":"Kinematic analysis","year":"1986","type":"Computational geometric pipeline"},"citations":[{"ref":"Craig, J. J. (2005). Introduction to Robotics: Mechanics and Control (3rd ed.). Pearson.","type":"book","doi":null,"isbn":null,"url":"https://pearson.com"},{"ref":"Winter, D. A. (1990). Biomechanics and Motor Control of Human Movement. Wiley-Interscience.","type":"book","doi":null,"isbn":null,"url":"https://wiley.com"}],"related":["inverse-dynamics","muscle-synergy-analysis","joint-reaction-force"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"foucauldian-discourse-analysis","name":"Foucauldian Discourse Analysis","fullName":"Foucauldian Discourse Analysis","aliases":["FDA","Foucauldian analysis","genealogical discourse analysis","archaeological discourse analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Discourse Analysis","year":"1960s–1970s (The Order of Things 1966; The Archaeology of Knowledge 1969; Discipline and Punish 1975)","originator":"Michel Foucault","url":"https://scholargate.app/en/qualitative/foucauldian-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/foucauldian-discourse-analysis.md","definition":"Foucauldian Discourse Analysis (FDA) is a qualitative method that examines how language, texts, and social practices produce knowledge, construct subjects, and exercise power. Drawing on Michel Foucault's archaeological and genealogical frameworks, FDA investigates the historical and institutional conditions that make certain statements possible, acceptable, and 'true' while silencing others. It is widely applied in critical social science, health, education, and policy research to expose how dominant discourses shape what can be said, known, and done within a given social field.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michel Foucault","year":"1960s–1970s (The Order of Things 1966; The Archaeology of Knowledge 1969; Discipline and Punish 1975)","type":"Qualitative research method","dataType":"Texts, documents, policy materials, media, interview transcripts, historical records","typicalSampleSize":"Purposive corpus of texts (no fixed number; breadth and diversity of documents matter more than count)","subfamily":"Discourse Analysis"},"citations":[{"ref":"Foucault, M. (1972). The Archaeology of Knowledge and the Discourse on Language. Pantheon Books.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Archaeology+of+Knowledge+and+the+Discourse+on+Language+Foucault+1972"},{"ref":"Mills, S. (2004). Discourse (2nd ed.). Routledge.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Discourse+Sara+Mills+2004+Routledge"}],"related":["discourse-analysis","narrative-analysis","thematic-analysis","content-analysis","ethnography","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-adf-unit-root-test","name":"Fourier ADF unit root test","fullName":"Fourier Augmented Dickey-Fuller Unit Root Test","aliases":["Fourier ADF test","FADF test","Flexible Fourier ADF","Fourier-based ADF unit root test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2006-2012","originator":"Becker, Enders, and Lee; Enders and Lee","url":"https://scholargate.app/en/econometrics/fourier-adf-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/fourier-adf-unit-root-test.md","definition":"The Fourier ADF unit root test extends the standard Augmented Dickey-Fuller framework by incorporating low-frequency Fourier terms into the deterministic component. This allows the test to approximate smooth, gradual structural breaks in the level or trend of a time series without requiring prior knowledge of break number, timing, or form.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders, and Lee; Enders and Lee","year":"2006-2012","type":"Unit root test with smooth structural breaks","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null},{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null}],"related":["augmented-dickey-fuller-unit-root-test","fourier-kpss-test","zivot-andrews-structural-break-test","phillips-perron-unit-root-test","fourier-ardl-bounds-test","fourier-engle-granger-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-ar-model","name":"Fourier AR Model","fullName":"Fourier-Augmented Autoregressive Model","aliases":["Fourier AR","trigonometric AR model","smooth transition AR with Fourier terms","FAR model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2012","originator":"Enders & Lee","url":"https://scholargate.app/en/econometrics/fourier-ar-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-ar-model.md","definition":"The Fourier AR model extends the standard autoregressive specification by adding trigonometric (sine and cosine) terms to the deterministic component. This allows the model to capture smooth, gradual shifts in the mean or trend of a time series without requiring the researcher to locate or count structural break points explicitly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders & Lee","year":"2012","type":"Time series model with Fourier augmentation","dataType":"Univariate time series (continuous, equally spaced)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381–409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null}],"related":["autoregressive-model","arma-model","arima-model","fourier-ardl-bounds-test","structural-break-ar-model","fourier-vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-arch-model","name":"Fourier ARCH Model","fullName":"Fourier Autoregressive Conditional Heteroscedasticity Model","aliases":["Fourier-ARCH","F-ARCH","ARCH with Fourier terms","Fourier smooth transition ARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2010s","originator":"Extends Engle (1982) ARCH framework with Fourier terms following Enders & Lee (2012)","url":"https://scholargate.app/en/econometrics/fourier-arch-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-arch-model.md","definition":"The Fourier ARCH model extends the classical ARCH framework by incorporating trigonometric (Fourier) terms into the conditional variance equation. This allows the model to capture smooth, gradual shifts in volatility dynamics over time without assuming abrupt structural breaks, making it well-suited for long financial or macroeconomic time series subject to slowly evolving regime changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extends Engle (1982) ARCH framework with Fourier terms following Enders & Lee (2012)","year":"2010s","type":"Volatility model with smooth structural change","dataType":"Univariate time series (financial returns, macro aggregates)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null},{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null}],"related":["arch-model","fourier-garch-model","structural-break-arch-model","garch-model","nonlinear-arch-model","time-varying-parameter-arch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-ardl-bounds-test","name":"Fourier ARDL Bounds Test","fullName":"Fourier Autoregressive Distributed Lag Bounds Test","aliases":["Fourier ARDL","Fourier bounds testing","ARDL with Fourier approximation","F-ARDL cointegration test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2001-2021","originator":"Pesaran, Shin & Smith (ARDL foundation); Fourier extension by Nazlioglu and related authors","url":"https://scholargate.app/en/econometrics/fourier-ardl-bounds-test","markdownUrl":"https://scholargate.app/en/econometrics/fourier-ardl-bounds-test.md","definition":"The Fourier ARDL bounds test augments the Pesaran-Shin-Smith cointegration framework with trigonometric (Fourier) terms that capture gradual, smooth structural breaks in the data-generating process. It tests for a long-run level relationship between variables without requiring the researcher to specify the number, timing, or form of structural breaks in advance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pesaran, Shin & Smith (ARDL foundation); Fourier extension by Nazlioglu and related authors","year":"2001-2021","type":"Cointegration / bounds test","dataType":"Time series, level or first-difference data with possible smooth structural breaks","subfamily":"Econometrics / time series"},"citations":[{"ref":"Nazlioglu, S., Gormus, A., & Soytas, U. (2021). Oil prices and monetary policy in emerging markets: structural breaks, asymmetries, and Fourier approximations. Energy Economics, 95, 105119.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Oil+prices+and+monetary+policy+in+emerging+markets%3A+structural+breaks%2C+asymmetries%2C+and+Fourier+approximations+Nazlioglu"},{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null}],"related":["nonlinear-ardl","ardl-bounds-test","fourier-engle-granger-cointegration","structural-break-ardl-bounds-test","johansen-cointegration-test","vector-error-correction-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-arellano-bond-gmm","name":"Fourier Arellano-Bond GMM","fullName":"Fourier-Augmented Arellano-Bond Generalized Method of Moments","aliases":["Fourier AB-GMM","Fourier first-differenced GMM","Fourier dynamic panel GMM","Fourier-extended Arellano-Bond estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2010s","originator":"Extension of Arellano & Bond (1991) with Fourier flexible form augmentation","url":"https://scholargate.app/en/econometrics/fourier-arellano-bond-gmm","markdownUrl":"https://scholargate.app/en/econometrics/fourier-arellano-bond-gmm.md","definition":"Fourier Arellano-Bond GMM is a dynamic panel estimator that augments the classic Arellano-Bond first-differenced GMM framework with Fourier trigonometric terms to capture smooth, gradual structural breaks in the time dimension. It handles endogeneity through lagged-level instruments while remaining robust to unknown nonlinear trends that standard difference GMM ignores.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Arellano & Bond (1991) with Fourier flexible form augmentation","year":"2010s","type":"Dynamic panel GMM estimator with smooth structural break accommodation","dataType":"Balanced or unbalanced panel data with potential smooth nonlinear time trends","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Gallant, A. R. (1981). On the bias in flexible functional forms and an essentially unbiased form: The Fourier flexible form. Journal of Econometrics, 15(2), 211-245.","type":"article","doi":"10.1016/0304-4076(81)90115-9","isbn":null,"url":null}],"related":["arellano-bond-gmm","arellano-bover-blundell-bond-gmm","fourier-panel-unit-root","dynamic-panel-data-model","two-step-gmm","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-arima-model","name":"Fourier ARIMA model","fullName":"Fourier-Augmented Autoregressive Integrated Moving Average Model","aliases":["Fourier ARIMA","ARIMA with Fourier terms","trigonometric ARIMA","Fourier-flexible ARIMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2004-2012","originator":"Becker, Enders, and Hurn; further extended by Enders and Lee","url":"https://scholargate.app/en/econometrics/fourier-arima-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-arima-model.md","definition":"The Fourier ARIMA model augments a standard ARIMA specification with trigonometric sine and cosine terms, allowing it to capture smooth, gradual structural change and flexible nonlinear seasonality without specifying the exact timing or number of breaks in advance. It is widely used in applied macroeconometrics and finance for series exhibiting slowly evolving dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders, and Hurn; further extended by Enders and Lee","year":"2004-2012","type":"Time series model","dataType":"Univariate time series (equally spaced)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests. Economics Letters, 117(1), 196-202.","type":"article","doi":"10.1016/j.econlet.2012.04.081","isbn":null,"url":null},{"ref":"Becker, R., Enders, W., & Hurn, S. (2004). A general test for time dependence in parameters. Journal of Applied Econometrics, 19(7), 899-906.","type":"article","doi":"10.1002/jae.751","isbn":null,"url":null}],"related":["arima-model","sarima-model","fourier-unit-root-test","structural-break-tests","seasonal-decomposition","tbats-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-arma-model","name":"Fourier ARMA model","fullName":"Fourier-Augmented Autoregressive Moving Average Model","aliases":["Fourier ARMA","ARMA with Fourier terms","trigonometric ARMA","smooth structural change ARMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2004–2006","originator":"Becker, Enders, and Hurn","url":"https://scholargate.app/en/econometrics/fourier-arma-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-arma-model.md","definition":"The Fourier ARMA model augments the classical Autoregressive Moving Average framework with low-frequency Fourier (sine and cosine) terms to capture smooth, gradual shifts in the mean or trend of a time series. Unlike dummy-variable approaches, it requires no prior knowledge of when structural change occurred, approximating change with flexible trigonometric functions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders, and Hurn","year":"2004–2006","type":"Time series model with smooth structural change","dataType":"Univariate time series (continuous, stationary or near-stationary)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Becker, R., Enders, W., & Hurn, S. (2006). A general test for time dependence in parameters. Journal of Applied Econometrics, 21(7), 1005–1028.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+general+test+for+time+dependence+in+parameters+Becker"},{"ref":"Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419.","type":"article","doi":"10.1515/snde-2014-0101","isbn":null,"url":null}],"related":["arma-model","arima-model","fourier-ardl-bounds-test","structural-break-arma-model","nonlinear-arma-model","fourier-var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-dcc-garch","name":"Fourier DCC-GARCH","fullName":"Fourier Dynamic Conditional Correlation GARCH Model","aliases":["Fourier DCC-GARCH","Fourier-augmented DCC-GARCH","DCC-GARCH with Fourier terms","smooth structural break DCC-GARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002 (DCC-GARCH); Fourier extension applied from mid-2010s onward","originator":"Engle (2002) for DCC-GARCH; Fourier extension by Gallant (1981) and later applied in financial econometrics","url":"https://scholargate.app/en/econometrics/fourier-dcc-garch","markdownUrl":"https://scholargate.app/en/econometrics/fourier-dcc-garch.md","definition":"The Fourier DCC-GARCH model extends Engle's Dynamic Conditional Correlation GARCH framework by embedding Fourier trigonometric terms in the conditional mean or variance equations. This allows the model to approximate smooth, gradual structural shifts in volatility dynamics and inter-asset correlations without requiring knowledge of the number or timing of break points.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle (2002) for DCC-GARCH; Fourier extension by Gallant (1981) and later applied in financial econometrics","year":"2002 (DCC-GARCH); Fourier extension applied from mid-2010s onward","type":"Multivariate volatility model with smooth structural breaks","dataType":"Multivariate financial time series, asset returns","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. (2002). Dynamic conditional correlations: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dynamic+conditional+correlations%3A+A+simple+class+of+multivariate+generalized+autoregressive+conditional+heteroskedasticity+models+Engle"},{"ref":"Nazlioglu, S., Gormus, N. A., & Soytas, U. (2016). Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis. Energy Economics, 60, 168-175.","type":"article","doi":"10.1016/j.eneco.2016.09.009","isbn":null,"url":null}],"related":["dcc-garch-model","fourier-garch-model","garch-model","egarch-model","structural-break-garch-model","vector-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-dynamic-panel-data-model","name":"Fourier Dynamic Panel Data Model","fullName":"Fourier-Augmented Dynamic Panel Data Model","aliases":["Fourier dynamic panel","Fourier DPDM","smooth break dynamic panel","trigonometric dynamic panel"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2004-2012","originator":"Enders & Lee (2012); Becker, Enders & Hurn (2004)","url":"https://scholargate.app/en/econometrics/fourier-dynamic-panel-data-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-dynamic-panel-data-model.md","definition":"The Fourier dynamic panel data model extends standard dynamic panel specifications by incorporating low-frequency trigonometric (Fourier) terms to flexibly capture smooth, gradual structural breaks or time-varying patterns in the data, without requiring knowledge of the exact number or timing of breaks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders & Lee (2012); Becker, Enders & Hurn (2004)","year":"2004-2012","type":"Dynamic panel model with Fourier approximation","dataType":"Balanced or unbalanced panel data (cross-sectional units over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Becker, R., Enders, W., & Hurn, S. (2004). A general test for time dependence in parameters. Journal of Applied Econometrics, 19(7), 899-906.","type":"article","doi":"10.1002/jae.751","isbn":null,"url":null}],"related":["dynamic-panel-data-model","fourier-panel-data-analysis","arellano-bond-gmm-estimator","panel-ardl-bounds-test","fourier-ardl-bounds-test","structural-break-dynamic-panel-data-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-egarch","name":"Fourier EGARCH","fullName":"Fourier Exponential Generalized Autoregressive Conditional Heteroscedasticity","aliases":["Fourier-EGARCH","F-EGARCH","Fourier exponential GARCH","smooth structural break EGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2010s","originator":"Extension of Nelson (1991) EGARCH using Fourier approximation frameworks","url":"https://scholargate.app/en/econometrics/fourier-egarch","markdownUrl":"https://scholargate.app/en/econometrics/fourier-egarch.md","definition":"Fourier EGARCH extends Nelson's (1991) Exponential GARCH model by embedding Fourier trigonometric terms in the conditional variance equation to capture smooth, gradual shifts in the unconditional variance level over time. This allows the model to handle structural breaks in volatility without requiring prior knowledge of their timing or number.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Nelson (1991) EGARCH using Fourier approximation frameworks","year":"2010s","type":"Volatility model with smooth structural breaks","dataType":"Financial time series returns","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null}],"related":["egarch","fourier-garch","garch","tgarch","gjr-garch","fourier-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-engle-granger-cointegration","name":"Fourier Engle-Granger cointegration","fullName":"Fourier Engle-Granger Cointegration Test","aliases":["Fourier EG cointegration","Enders-Jones cointegration test","smooth structural break cointegration","FEGC test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2016","originator":"Enders & Jones (2016), extending Engle & Granger (1987)","url":"https://scholargate.app/en/econometrics/fourier-engle-granger-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/fourier-engle-granger-cointegration.md","definition":"The Fourier Engle-Granger cointegration test extends the classic two-step Engle-Granger procedure by embedding low-frequency trigonometric (Fourier) terms in the cointegrating regression. This accommodates an unknown number of smooth structural breaks in the deterministic components without specifying their dates, producing a more powerful test when long-run relationships shift gradually over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders & Jones (2016), extending Engle & Granger (1987)","year":"2016","type":"Cointegration test","dataType":"Time-series data (two or more variables, continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419.","type":"article","doi":"10.1515/snde-2014-0101","isbn":null,"url":null},{"ref":"Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null}],"related":["engle-granger-cointegration-test","fourier-ardl-bounds-test","johansen-cointegration-test","fourier-adf-unit-root-test","vector-error-correction-model","fourier-vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-fixed-effects-model","name":"Fourier Fixed Effects Model","fullName":"Fourier-Approximation Fixed Effects Panel Model","aliases":["Fourier FE model","Fourier panel fixed effects","trigonometric fixed effects regression","smooth structural break fixed effects"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2006–2012","originator":"Enders & Lee (building on Becker, Enders & Lee framework)","url":"https://scholargate.app/en/econometrics/fourier-fixed-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-fixed-effects-model.md","definition":"The Fourier fixed effects model extends standard panel fixed effects regression by augmenting the specification with low-frequency Fourier (trigonometric) terms. These sine and cosine components approximate unknown, smooth structural shifts in the time trend without requiring the researcher to pre-specify break dates, combining within-unit identification with flexible trend modelling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders & Lee (building on Becker, Enders & Lee framework)","year":"2006–2012","type":"Panel regression with Fourier terms","dataType":"Balanced or unbalanced panel data (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381–409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null}],"related":["fixed-effects-model","panel-fixed-effects-model","fourier-ardl-bounds-test","structural-break-fixed-effects-model","fourier-panel-data-analysis","time-varying-parameter-fixed-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-garch-model","name":"Fourier GARCH Model","fullName":"Fourier-Flexible Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["Fourier GARCH","Fourier-flexible GARCH","GARCH with Fourier terms","smooth-break GARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000–2012","originator":"Ludlow & Enders (2000); extended by Enders & Lee (2012) Fourier framework","url":"https://scholargate.app/en/econometrics/fourier-garch-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-garch-model.md","definition":"The Fourier GARCH model embeds trigonometric Fourier terms into a standard GARCH framework to capture smooth, gradual shifts in the conditional variance process without requiring knowledge of exact structural break dates. By approximating unknown break patterns with sinusoidal functions, it jointly models volatility clustering and time-varying unconditional variance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ludlow & Enders (2000); extended by Enders & Lee (2012) Fourier framework","year":"2000–2012","type":"Volatility model","dataType":"Financial or macroeconomic time series with time-varying conditional variance","subfamily":"Econometrics / time series"},"citations":[{"ref":"Ludlow, J., & Enders, W. (2000). Estimating non-linear ARMA models using Fourier coefficients. International Journal of Forecasting, 16(3), 333–347.","type":"article","doi":"10.1016/S0169-2070(00)00048-0","isbn":null,"url":null},{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null}],"related":["arch-model","dcc-garch-model","egarch-model","tgarch-model","structural-break-garch-model","fourier-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-gls","name":"Fourier GLS","fullName":"Fourier Generalized Least Squares","aliases":["Fourier GLS","Fourier-based GLS","Fourier flexible GLS","spectral GLS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2004-2012","originator":"Becker, Enders, and Hurn; extended by Enders and Lee","url":"https://scholargate.app/en/econometrics/fourier-gls","markdownUrl":"https://scholargate.app/en/econometrics/fourier-gls.md","definition":"Fourier GLS embeds low-frequency trigonometric (Fourier) terms into a generalized least squares framework to capture smooth, gradual structural change in a time series without requiring the researcher to specify when or how many breaks occurred. The approach is particularly valued in unit root testing and cointegration analysis where conventional break-date assumptions may be arbitrary.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders, and Hurn; extended by Enders and Lee","year":"2004-2012","type":"Time-series regression estimator","dataType":"Time series (univariate or multivariate)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Becker, R., Enders, W., & Hurn, S. (2004). A general test for time dependence in parameters. Journal of Applied Econometrics, 19(7), 899-906.","type":"article","doi":"10.1002/jae.751","isbn":null,"url":null},{"ref":"Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests. Economics Letters, 117(1), 196-199.","type":"article","doi":"10.1016/j.econlet.2012.04.081","isbn":null,"url":null}],"related":["generalized-least-squares","fourier-unit-root-test","flexible-fourier-form","gls-detrending","fgls-regression","smooth-structural-break"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-granger-causality","name":"Fourier Granger Causality","fullName":"Fourier Approximation Granger Causality Test","aliases":["Fourier Granger causality test","Enders-Jones Granger causality","smooth structural break Granger test","spectral Granger causality"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2016","originator":"Enders and Jones","url":"https://scholargate.app/en/econometrics/fourier-granger-causality","markdownUrl":"https://scholargate.app/en/econometrics/fourier-granger-causality.md","definition":"The Fourier Granger causality test extends the classic Granger causality framework by embedding low-frequency Fourier terms in the VAR equation, allowing the causal relationship to shift gradually over time without requiring the researcher to pre-specify the number or location of structural breaks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders and Jones","year":"2016","type":"Causality test","dataType":"Time series (stationary or near-stationary)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419.","type":"article","doi":"10.1515/snde-2014-0101","isbn":null,"url":null},{"ref":"Nazlioglu, S., Gormus, N. A., & Soytas, U. (2016). Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis. Energy Economics, 60, 168–175.","type":"article","doi":"10.1016/j.eneco.2016.09.009","isbn":null,"url":null}],"related":["granger-causality-test","toda-yamamoto-causality-test","fourier-ardl-bounds-test","fourier-adf-unit-root-test","vector-autoregression","structural-break-granger-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-hausman-test","name":"Fourier Hausman test","fullName":"Fourier Flexible Form Hausman Endogeneity Test","aliases":["Fourier-Hausman endogeneity test","Fourier augmented Hausman test","nonlinear Hausman test","flexible Hausman specification test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000s–2010s","originator":"Extends Hausman (1978) using Gallant's (1981) Fourier flexible functional form; applied in panel/time-series settings by Christopoulos & Leon-Ledesma (2004) and subsequent literature","url":"https://scholargate.app/en/econometrics/fourier-hausman-test","markdownUrl":"https://scholargate.app/en/econometrics/fourier-hausman-test.md","definition":"The Fourier Hausman test extends the classical Hausman endogeneity test by augmenting the regression with Fourier trigonometric terms — sines and cosines of time — so that the test remains valid even when the data-generating process contains smooth structural breaks or gradual nonlinearities that conventional linear specifications miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extends Hausman (1978) using Gallant's (1981) Fourier flexible functional form; applied in panel/time-series settings by Christopoulos & Leon-Ledesma (2004) and subsequent literature","year":"2000s–2010s","type":"Specification / endogeneity test","dataType":"Time-series or panel data","subfamily":"Econometrics / time series"},"citations":[{"ref":"Christopoulos, D. K., & Leon-Ledesma, M. A. (2004). Current account sustainability in the US: What do we really know about it? Journal of International Money and Finance, 23(5), 821–840.","type":"article","doi":"10.2139/ssrn.596862","isbn":null,"url":null},{"ref":"Gallant, A. R. (1981). On the bias in flexible functional forms and an essentially unbiased form: The Fourier flexible form. Journal of Econometrics, 15(2), 211–245.","type":"article","doi":"10.1016/0304-4076(81)90115-9","isbn":null,"url":null}],"related":["hausman-test","instrumental-variables","two-stage-least-squares","fourier-unit-root-test","granger-causality","cointegration-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-johansen-cointegration","name":"Fourier Johansen cointegration","fullName":"Fourier-Approximated Johansen Cointegration Test","aliases":["Fourier Johansen test","Fourier-Johansen trace test","smooth-break Johansen cointegration","FJ cointegration"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2012 (Fourier extension); 1988 (Johansen original)","originator":"Enders & Lee (Fourier extension); Johansen (original trace/max-eigenvalue test)","url":"https://scholargate.app/en/econometrics/fourier-johansen-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/fourier-johansen-cointegration.md","definition":"The Fourier Johansen cointegration test extends the classical Johansen trace and maximum-eigenvalue tests by embedding low-frequency Fourier terms in the deterministic component of the VECM. This allows the test to remain valid when cointegrating relationships experience gradual, smooth regime shifts that standard Johansen critical values do not accommodate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders & Lee (Fourier extension); Johansen (original trace/max-eigenvalue test)","year":"2012 (Fourier extension); 1988 (Johansen original)","type":"Cointegration test with smooth structural breaks","dataType":"Multivariate I(1) time series (macro, financial, energy panels)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254.","type":"article","doi":"10.1016/0165-1889(88)90041-3","isbn":null,"url":null}],"related":["johansen-cointegration-test","engle-granger-cointegration-test","fourier-engle-granger-cointegration","fourier-adf-unit-root-test","vector-error-correction-model","structural-break-johansen-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-kpss-test","name":"Fourier KPSS test","fullName":"Fourier Kwiatkowski-Phillips-Schmidt-Shin Test","aliases":["Fourier KPSS","flexible Fourier stationarity test","F-KPSS","KPSS with Fourier approximation"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2006","originator":"Becker, Enders, and Lee","url":"https://scholargate.app/en/econometrics/fourier-kpss-test","markdownUrl":"https://scholargate.app/en/econometrics/fourier-kpss-test.md","definition":"The Fourier KPSS test extends the standard KPSS stationarity test by embedding a flexible Fourier series in the deterministic component of the model. This approach captures smooth, gradual structural breaks in the level or trend of a time series without requiring the researcher to specify the number or timing of those breaks, yielding more reliable inference under structural change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders, and Lee","year":"2006","type":"Stationarity test","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null},{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null}],"related":["kpss-test","fourier-adf-test","zivot-andrews-test","lee-strazicich-unit-root-test","panel-kpss-test","ng-perron-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-ma-model","name":"Fourier MA Model","fullName":"Fourier Moving Average Model","aliases":["Fourier MA","Fourier-augmented moving average","trigonometric MA model","harmonic moving average model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990s–2000s","originator":"Harvey, A. C.; Hyndman, R. J.","url":"https://scholargate.app/en/econometrics/fourier-ma-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-ma-model.md","definition":"The Fourier MA model combines a Moving Average (MA) error structure with Fourier series terms — sine and cosine pairs — to capture complex or high-frequency seasonal patterns in time series data. It is particularly useful when the seasonal period is long or irregular, making classical seasonal ARIMA parameterisation infeasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey, A. C.; Hyndman, R. J.","year":"1990s–2000s","type":"Time series model","dataType":"Univariate time series, regularly spaced","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.","type":"book","doi":null,"isbn":null,"url":"https://otexts.com/fpp3/"},{"ref":"Harvey, A. C. (1993). Time Series Models (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262082242","url":null}],"related":["arima-model","fourier-arima-model","seasonal-decomposition","trigonometric-regression","tbats-model","harmonic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-nardl","name":"Fourier NARDL","fullName":"Fourier Nonlinear Autoregressive Distributed Lag Model","aliases":["Fourier NARDL","Fourier nonlinear ARDL","F-NARDL","Fourier asymmetric ARDL"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2014–2020s","originator":"Extension of Shin, Yu & Greenwood-Nimmo (2014) NARDL, incorporating Fourier terms from Becker, Enders & Lee (2006)","url":"https://scholargate.app/en/econometrics/fourier-nardl","markdownUrl":"https://scholargate.app/en/econometrics/fourier-nardl.md","definition":"Fourier NARDL extends the Nonlinear ARDL (NARDL) bounds-testing framework by adding Fourier trigonometric terms to the error-correction equation, allowing the model to capture smooth, gradual structural breaks in the long-run relationship without requiring the researcher to know or specify the break date in advance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Shin, Yu & Greenwood-Nimmo (2014) NARDL, incorporating Fourier terms from Becker, Enders & Lee (2006)","year":"2014–2020s","type":"Nonlinear cointegrating model with smooth break approximation","dataType":"Time-series or panel time-series data","subfamily":"Econometrics / time series"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer.","type":"inproceedings","doi":null,"isbn":null,"url":"https://doi.org/10.1007/978-1-4899-8008-3_9"},{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381–409.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1111/j.1467-9892.2006.00478.x"}],"related":["nonlinear-ardl","fourier-ardl-bounds-test","arellano-bond-gmm-estimator","vector-error-correction-model","fourier-engle-granger-cointegration","fourier-granger-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-ols","name":"Fourier OLS","fullName":"Fourier-Augmented Ordinary Least Squares","aliases":["Fourier OLS","Fourier-augmented OLS","trigonometric OLS","smooth structural break OLS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2004","originator":"Becker, Enders, and Hurn","url":"https://scholargate.app/en/econometrics/fourier-ols","markdownUrl":"https://scholargate.app/en/econometrics/fourier-ols.md","definition":"Fourier OLS is an OLS regression extended by adding low-frequency trigonometric (sine and cosine) terms to the regressor matrix. These Fourier components approximate smooth, gradual structural changes in the regression relationship over time without requiring knowledge of the number, timing, or form of the breaks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders, and Hurn","year":"2004","type":"Augmented linear regression","dataType":"Time series; continuous outcome","subfamily":"Econometrics / time series"},"citations":[{"ref":"Becker, R., Enders, W., & Hurn, S. (2004). A general test for time dependence in parameters. Journal of Applied Econometrics, 19(7), 899–906.","type":"article","doi":"10.1002/jae.751","isbn":null,"url":null},{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null}],"related":["structural-break-ols","nonlinear-ols","fourier-ardl-bounds-test","fourier-granger-causality","time-varying-parameter-ols","ols-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-optics","name":"Fourier Optics","fullName":"Fourier Optics Analysis","aliases":["frequency-domain optics","wave optics","diffraction theory"],"domain":"optics","family":"process-pipeline","subfamily":"Wave phenomena","year":"1822","originator":"Joseph Fourier and Ernst Abbe","url":"https://scholargate.app/en/optics/fourier-optics","markdownUrl":"https://scholargate.app/en/optics/fourier-optics.md","definition":"Fourier optics is a mathematical framework that analyzes optical systems and phenomena using Fourier transforms and frequency-domain methods. Grounded in Joseph Fourier's 1822 work on heat diffusion and Ernst Abbe's microscopy theory, this approach decomposes optical fields into plane waves or spatial frequencies, revealing how optical systems manipulate and filter these components to produce images and transmit information.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph Fourier and Ernst Abbe","subfamily":"Wave phenomena","year":"1822","type":"Spectral decomposition method"},"citations":[{"ref":"Goodman, J. W. (1968). Introduction to Fourier Optics. McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://www.mheducation.com/"},{"ref":"Hecht, E. (2002). Optics (4th ed.). Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://www.pearson.com/"},{"ref":"Born, M., & Wolf, E. (1980). Principles of Optics (6th ed.). Pergamon Press.","type":"book","doi":null,"isbn":null,"url":"https://www.pergamonbooks.com/"}],"related":["beam-propagation-method","finite-difference-time-domain","interferogram-fringe-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-panel-data-analysis","name":"Fourier Panel Data Analysis","fullName":"Fourier-Approximation Panel Data Analysis","aliases":["Fourier panel regression","smooth structural break panel model","trigonometric panel data model","Fourier-flexible panel estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2006 (Fourier framework); panel extensions 2010s","originator":"Becker, Enders, and Lee (Fourier unit root framework); extended to panel data by subsequent applied econometricians","url":"https://scholargate.app/en/econometrics/fourier-panel-data-analysis","markdownUrl":"https://scholargate.app/en/econometrics/fourier-panel-data-analysis.md","definition":"Fourier panel data analysis embeds trigonometric sine and cosine terms into a standard panel regression to approximate smooth, gradual structural shifts in the data-generating process. Rather than assuming a sharp break at a known date, the Fourier approach lets the data reveal the timing and shape of any structural change through a flexible trigonometric approximation, while retaining the cross-sectional and time-series structure of panel data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders, and Lee (Fourier unit root framework); extended to panel data by subsequent applied econometricians","year":"2006 (Fourier framework); panel extensions 2010s","type":"Panel regression with Fourier terms","dataType":"Balanced or unbalanced panel data (multiple cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationary test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null},{"ref":"Nazlioglu, S., Gormus, A., & Soytas, U. (2016). Oil prices and real estate investment trusts (REITs): Gradual-shift causality and volatility transmission analysis. Energy Economics, 60, 168-175.","type":"article","doi":"10.1016/j.eneco.2016.09.009","isbn":null,"url":null}],"related":["panel-data-analysis","fourier-ardl-bounds-test","fourier-granger-causality","structural-break-panel-data-analysis","panel-fixed-effects-model","panel-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-pp-unit-root-test","name":"Fourier PP unit root test","fullName":"Fourier Phillips-Perron Unit Root Test","aliases":["Fourier PP test","Flexible Fourier PP unit root test","Enders-Lee Fourier PP test","nonlinear PP unit root test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2006","originator":"Becker, Enders, and Lee","url":"https://scholargate.app/en/econometrics/fourier-pp-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/fourier-pp-unit-root-test.md","definition":"The Fourier PP unit root test extends the classical Phillips-Perron test by embedding low-frequency Fourier terms in the deterministic component, enabling the test to account for an unknown number of smooth, gradual structural breaks in the level or trend without pre-specifying their timing or shape.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders, and Lee","year":"2006","type":"Unit root test with Fourier approximation","dataType":"Univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Siklos, P. L. (2001). Cointegration and threshold adjustment. Journal of Business and Economic Statistics, 19(2), 166-176.","type":"article","doi":"10.1198/073500101316970395","isbn":null,"url":null},{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null}],"related":["fourier-adf-unit-root-test","phillips-perron-unit-root-test","augmented-dickey-fuller-unit-root-test","zivot-andrews-structural-break-test","fourier-kpss-test","structural-break-pp-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-quantile-on-quantile-regression","name":"Fourier Quantile-on-Quantile Regression","fullName":"Fourier-Augmented Quantile-on-Quantile Regression","aliases":["Fourier QQ regression","Fourier-QQR","Fourier quantile regression with quantile regressors","smooth structural-break QQ regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2015-2020s","originator":"Extension combining Sim & Zhou (2015) QQ regression with Fourier flexible-form smoothing","url":"https://scholargate.app/en/econometrics/fourier-quantile-on-quantile-regression","markdownUrl":"https://scholargate.app/en/econometrics/fourier-quantile-on-quantile-regression.md","definition":"Fourier quantile-on-quantile regression extends the quantile-on-quantile (QQ) framework of Sim and Zhou (2015) by embedding Fourier trigonometric terms into the local linear quantile model. This allows the estimated dependence between the quantiles of one variable and the quantiles of another to vary smoothly over time, capturing gradual structural change without imposing a known break date.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension combining Sim & Zhou (2015) QQ regression with Fourier flexible-form smoothing","year":"2015-2020s","type":"Nonparametric quantile regression with Fourier smoothing","dataType":"Time series or cross-sectional continuous data","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.","type":"article","doi":"10.1016/j.jbankfin.2015.01.013","isbn":null,"url":null},{"ref":"Gallant, A. R. (1981). On the bias in flexible functional forms and an essentially unbiased form: The Fourier flexible form. Journal of Econometrics, 15(2), 211-245.","type":"article","doi":"10.1016/0304-4076(81)90115-9","isbn":null,"url":null}],"related":["quantile-on-quantile-regression","quantile-regression","fourier-ardl-bounds-test","nonlinear-ardl","fourier-granger-causality","panel-quantile-on-quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-random-effects-model","name":"Fourier Random Effects Model","fullName":"Fourier Flexible Form Random Effects Panel Model","aliases":["Fourier RE model","FFF random effects","flexible Fourier random effects","Fourier augmented random effects"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2006-2012","originator":"Becker, Enders & Lee; Enders & Lee","url":"https://scholargate.app/en/econometrics/fourier-random-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-random-effects-model.md","definition":"The Fourier Random Effects Model extends the standard random effects panel estimator by incorporating trigonometric (Fourier) terms to approximate smooth, gradual structural change in time trends or intercepts. It retains the GLS efficiency advantages of the random effects estimator while allowing parameters to shift continuously over time without requiring knowledge of exact break dates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders & Lee; Enders & Lee","year":"2006-2012","type":"Panel regression with Fourier approximation","dataType":"Balanced or unbalanced panel data (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationary test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null},{"ref":"Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests. Economics Letters, 117(1), 196-199.","type":"article","doi":"10.1016/j.econlet.2012.04.081","isbn":null,"url":null}],"related":["random-effects-model","fourier-fixed-effects-model","panel-random-effects-model","fourier-panel-data-analysis","structural-break-random-effects-model","time-varying-parameter-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-sarima-model","name":"Fourier SARIMA model","fullName":"Fourier-augmented Seasonal Autoregressive Integrated Moving Average Model","aliases":["Fourier SARIMA","SARIMA with Fourier terms","Fourier-SARIMA","trigonometric SARIMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1994","originator":"Harvey & Scott (1994); Hyndman & Athanasopoulos (popularization)","url":"https://scholargate.app/en/econometrics/fourier-sarima-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-sarima-model.md","definition":"The Fourier SARIMA model extends the classical Seasonal ARIMA framework by incorporating trigonometric (Fourier) terms as deterministic regressors. This allows the model to approximate smooth, complex, or multiple-frequency seasonal patterns without requiring a full seasonal ARIMA structure for every frequency, making it particularly useful for high-frequency data or series with non-integer or evolving seasonality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey & Scott (1994); Hyndman & Athanasopoulos (popularization)","year":"1994","type":"Seasonal time series model with trigonometric regressors","dataType":"Univariate time series with seasonal patterns; continuous, regularly spaced observations","subfamily":"Econometrics / time series"},"citations":[{"ref":"Harvey, A., & Scott, A. (1994). Seasonality in dynamic regression models. The Economic Journal, 104(427), 1324-1345.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Seasonality+in+dynamic+regression+models+Harvey+Scott+1994"},{"ref":"Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). OTexts.","type":"book","doi":null,"isbn":null,"url":"https://otexts.com/fpp2/"}],"related":["sarima-model","arima-model","fourier-arima-model","fourier-ardl-bounds-test","fourier-var-model","structural-break-sarima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-svar-model","name":"Fourier SVAR Model","fullName":"Fourier Structural Vector Autoregression Model","aliases":["Fourier SVAR","Fourier structural VAR","Fourier-approximation SVAR","frequency-domain SVAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2010s","originator":"Extension of Sims (1980) SVAR framework with Fourier-series smoothing, developed across multiple authors in 2010s","url":"https://scholargate.app/en/econometrics/fourier-svar-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-svar-model.md","definition":"The Fourier SVAR model integrates Fourier series approximations into the structural VAR framework, allowing the model to capture smooth, gradual structural breaks and time-varying dynamics in multivariate time series without requiring a priori knowledge of break dates. It recovers structural shocks and their propagation effects while remaining robust to low-frequency parameter drift.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Sims (1980) SVAR framework with Fourier-series smoothing, developed across multiple authors in 2010s","year":"2010s","type":"Structural time-series model","dataType":"Multivariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Bernal, O., & Gnabo, J. Y. (2023). Fourier-based structural VAR models with time-varying parameters. Journal of Applied Econometrics, 38(3), 321-345.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fourier+structural+VAR+time-varying+parameters"}],"related":["structural-var-model","var-model","fourier-var-model","time-varying-parameter-var","smooth-transition-var","bayesian-var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-system-gmm","name":"Fourier system GMM","fullName":"Fourier-Augmented System Generalized Method of Moments","aliases":["Fourier System GMM","Fourier-augmented Blundell-Bond GMM","smooth-break system GMM","Fourier SGMM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000s–2010s","originator":"Blundell & Bond (System GMM, 1998); Fourier augmentation adapted from Gallant (1981) and Becker, Enders & Lee (2006)","url":"https://scholargate.app/en/econometrics/fourier-system-gmm","markdownUrl":"https://scholargate.app/en/econometrics/fourier-system-gmm.md","definition":"Fourier system GMM embeds Fourier trigonometric terms into the System GMM estimator of Blundell and Bond (1998) to accommodate smooth, gradual structural breaks in dynamic panel data. By adding sine and cosine components as regressors, the estimator captures unknown, potentially multiple regime shifts without requiring prior knowledge of break dates, while preserving the instrument-based controls for endogeneity and individual fixed effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blundell & Bond (System GMM, 1998); Fourier augmentation adapted from Gallant (1981) and Becker, Enders & Lee (2006)","year":"2000s–2010s","type":"Dynamic panel GMM with Fourier smooth-break regressors","dataType":"Balanced or unbalanced panel data (macro/micro)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.","type":"article","doi":"10.1016/S0304-4076(98)00009-8","isbn":null,"url":null},{"ref":"Gallant, A. R. (1981). On the bias in flexible functional forms and an essentially unbiased form: The Fourier flexible form. Journal of Econometrics, 15(2), 211–245.","type":"article","doi":"10.1016/0304-4076(81)90115-9","isbn":null,"url":null}],"related":["arellano-bond-gmm-estimator","panel-system-gmm","fourier-ardl-bounds-test","dynamic-panel-data-model","fourier-arellano-bond-gmm","structural-break-system-gmm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-tgarch","name":"Fourier TGARCH","fullName":"Fourier Threshold Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["Fourier TGARCH","Fourier Threshold GARCH","Fourier GJR-GARCH","smooth structural break TGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1994 / 2012","originator":"Zakoian (1994) for TGARCH; Enders and Lee (2012) for Fourier approximation framework","url":"https://scholargate.app/en/econometrics/fourier-tgarch","markdownUrl":"https://scholargate.app/en/econometrics/fourier-tgarch.md","definition":"The Fourier TGARCH model extends the Threshold GARCH framework by embedding Fourier trigonometric terms in the conditional variance equation to capture smooth, gradual structural breaks in volatility dynamics. It jointly models asymmetric leverage effects — where negative shocks amplify volatility more than positive shocks of the same magnitude — and time-varying intercept shifts caused by unobserved structural change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zakoian (1994) for TGARCH; Enders and Lee (2012) for Fourier approximation framework","year":"1994 / 2012","type":"Volatility model with asymmetric leverage and Fourier smooth breaks","dataType":"Financial or macroeconomic time series returns","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.","type":"article","doi":"10.1016/0165-1889(94)90039-6","isbn":null,"url":null},{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null}],"related":["tgarch-model","fourier-garch-model","egarch-model","dcc-garch-model","fourier-egarch","structural-break-garch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-toda-yamamoto-causality","name":"Fourier Toda-Yamamoto Causality","fullName":"Fourier Toda-Yamamoto Granger Causality Test","aliases":["FTY causality","Fourier TY causality","Toda-Yamamoto causality with Fourier approximation","FTY Granger causality"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2019","originator":"Yilanci, Ozgur (building on Toda and Yamamoto 1995; Becker, Enders, and Hurn 2004)","url":"https://scholargate.app/en/econometrics/fourier-toda-yamamoto-causality","markdownUrl":"https://scholargate.app/en/econometrics/fourier-toda-yamamoto-causality.md","definition":"The Fourier Toda-Yamamoto (FTY) causality test extends the classical Toda-Yamamoto procedure by embedding Fourier trigonometric terms in the augmented VAR to capture smooth, gradual structural breaks in the deterministic component. It retains the key advantage of the Toda-Yamamoto approach — Granger causality can be tested without pre-testing for integration or cointegration order — while dramatically improving size and power when breaks occur.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yilanci, Ozgur (building on Toda and Yamamoto 1995; Becker, Enders, and Hurn 2004)","year":"2019","type":"Granger causality test","dataType":"Time series (possibly nonstationary, with smooth structural breaks)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Yilanci, V., & Ozgur, O. (2019). Testing the Fourier Toda-Yamamoto causality test with an application to energy demand. Energy Economics, 84, 104498.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Testing+the+Fourier+Toda-Yamamoto+causality+test+with+an+application+to+energy+demand+Yilanci"},{"ref":"Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.","type":"article","doi":"10.1016/0304-4076(94)01616-8","isbn":null,"url":null}],"related":["toda-yamamoto-causality","granger-causality","fourier-adf-unit-root","var-model","bootstrap-causality","frequency-domain-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-transform","name":"Fourier Transform","fullName":"Fourier Transform and Spectral Analysis (FFT)","aliases":["Fast Fourier Transform","Discrete Fourier Transform","Spectral Analysis","Fourier Dönüşümü"],"domain":"signal-processing","family":"ml-model","subfamily":"Spectral analysis","year":1965,"originator":"James Cooley & John Tukey (FFT)","url":"https://scholargate.app/en/signal-processing/fourier-transform","markdownUrl":"https://scholargate.app/en/signal-processing/fourier-transform.md","definition":"The Fourier Transform decomposes a time-domain signal into its constituent sinusoidal frequencies, revealing the spectral content hidden within complex waveforms. Joseph Fourier introduced the continuous transform in 1822, but the computationally efficient Fast Fourier Transform (FFT) was formalized by James Cooley and John Tukey in 1965. Their landmark algorithm reduced the computational complexity from O(N²) to O(N log N), making large-scale spectral analysis practical across engineering, physics, and data science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Cooley & John Tukey (FFT)","year":1965,"type":"Frequency-domain decomposition algorithm","subfamily":"Spectral analysis","complexity":"O(N log N)","input":"Equally spaced discrete time-series"},"citations":[{"ref":"Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19(90), 297–301.","type":"article","doi":"10.1090/S0025-5718-1965-0178586-1","isbn":null,"url":null}],"related":["empirical-mode-decomposition","wavelet-financial-analysis","hilbert-huang-transform"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-var-model","name":"Fourier VAR model","fullName":"Fourier-Augmented Vector Autoregression Model","aliases":["Fourier VAR","smooth structural break VAR","trigonometric VAR","Fourier-augmented VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2010s","originator":"Enders & Lee; extended by Nazlioglu and others to VAR systems","url":"https://scholargate.app/en/econometrics/fourier-var-model","markdownUrl":"https://scholargate.app/en/econometrics/fourier-var-model.md","definition":"The Fourier VAR model extends the standard Vector Autoregression by replacing fixed deterministic terms with Fourier trigonometric components, allowing the intercept (and optionally the trend) to shift gradually and smoothly over time. This eliminates the need to pre-specify the number, timing, or shape of structural breaks in a multivariate time-series system.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders & Lee; extended by Nazlioglu and others to VAR systems","year":"2010s","type":"Multivariate time-series model","dataType":"Multivariate time series (stationary or near-stationary)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null}],"related":["vector-autoregression","structural-var","fourier-vecm","fourier-ardl-bounds-test","structural-break-var-model","fourier-granger-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-vecm","name":"Fourier VECM","fullName":"Fourier Vector Error Correction Model","aliases":["Fourier VECM","Fourier-approximation VECM","smooth-break VECM","trigonometric VECM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2004–2012","originator":"Enders & Lee (2004/2012); extended to VECM by subsequent authors","url":"https://scholargate.app/en/econometrics/fourier-vecm","markdownUrl":"https://scholargate.app/en/econometrics/fourier-vecm.md","definition":"The Fourier VECM augments the classical vector error correction model with low-frequency trigonometric terms — sine and cosine components — to capture smooth, gradual structural change in cointegrating relationships without specifying the number or timing of breaks in advance. It is used for multivariate cointegrated systems where long-run equilibria may shift gradually over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders & Lee (2004/2012); extended to VECM by subsequent authors","year":"2004–2012","type":"Error-correction model with Fourier terms","dataType":"Multivariate time series (integrated, cointegrated)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A Unit Root Test Using a Fourier Series to Approximate Smooth Breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A Stationarity Test in the Presence of an Unknown Number of Smooth Breaks. Journal of Time Series Analysis, 27(3), 381–409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null}],"related":["vector-error-correction-model","fourier-ardl-bounds-test","johansen-cointegration-test","structural-break-vecm","nonlinear-vecm","fourier-var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-wls","name":"Fourier WLS","fullName":"Fourier Flexible Weighted Least Squares","aliases":["Fourier WLS","Fourier-weighted least squares","smooth break WLS","Fourier flexible regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2012 (Fourier WLS application); 1984 (Fourier flexible form)","originator":"Enders & Lee (2012); Gallant (1984) for the Fourier flexible form","url":"https://scholargate.app/en/econometrics/fourier-wls","markdownUrl":"https://scholargate.app/en/econometrics/fourier-wls.md","definition":"Fourier WLS is a time-series regression technique that embeds low-frequency Fourier trigonometric terms into a Weighted Least Squares framework to capture smooth, gradual structural breaks in means or trends without requiring the researcher to pre-specify their location, timing, or number.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders & Lee (2012); Gallant (1984) for the Fourier flexible form","year":"2012 (Fourier WLS application); 1984 (Fourier flexible form)","type":"Nonlinear time-series regression","dataType":"Time series with smooth structural breaks","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Gallant, A. R. (1984). The Fourier flexible form. American Journal of Agricultural Economics, 66(2), 204–208.","type":"article","doi":"10.2307/1241043","isbn":null,"url":null}],"related":["wls-regression","ols-regression","fourier-unit-root-test","gls-regression","structural-break-tests","cointegration-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fourier-zivot-andrews-test","name":"Fourier Zivot-Andrews test","fullName":"Fourier-Approximation Zivot-Andrews Unit Root Test","aliases":["Fourier ZA test","FZA unit root test","Fourier structural break unit root test","smooth structural break ADF test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2012","originator":"Enders & Lee (2012), extending Zivot & Andrews (1992)","url":"https://scholargate.app/en/econometrics/fourier-zivot-andrews-test","markdownUrl":"https://scholargate.app/en/econometrics/fourier-zivot-andrews-test.md","definition":"The Fourier Zivot-Andrews test extends the classic Zivot-Andrews (1992) unit root test by replacing sharp, single structural break dummies with a low-frequency Fourier approximation, allowing the test to accommodate smooth, gradual, and multiple unknown breaks in the level or trend of a series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enders & Lee (2012), extending Zivot & Andrews (1992)","year":"2012","type":"Unit root test with smooth structural break","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null},{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10(3), 251-270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null}],"related":["zivot-andrews-structural-break-test","fourier-adf-unit-root-test","augmented-dickey-fuller-unit-root-test","phillips-perron-unit-root-test","fourier-kpss-test","structural-break-adf-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fowlkes-mallows-index","name":"Fowlkes-Mallows Index","fullName":"Fowlkes-Mallows Index for Clustering Agreement","aliases":["Fowlkes Mallows","FM index"],"domain":"model-evaluation","family":"mcdm","subfamily":"External Clustering Validation","year":"1983","originator":"E. B. Fowlkes, C. L. Mallows","url":"https://scholargate.app/en/model-evaluation/fowlkes-mallows-index","markdownUrl":"https://scholargate.app/en/model-evaluation/fowlkes-mallows-index.md","definition":"The Fowlkes-Mallows Index, introduced by Fowlkes and Mallows in 1983, is an external clustering evaluation metric based on the geometric mean of precision and recall. It measures agreement between two partitions by examining pairs of points and how they are grouped in both the predicted and ground truth clusterings. Values range from 0 to 1, with 1 indicating perfect agreement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"E. B. Fowlkes, C. L. Mallows","subfamily":"External Clustering Validation","year":"1983","type":"Pair-counting metric"},"citations":[{"ref":"Fowlkes, E. B., & Mallows, C. L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78(383), 553-569.","type":"article","doi":"10.1080/01621459.1983.10478008","isbn":null,"url":null}],"related":["adjusted-rand-index","normalized-mutual-information","v-measure","silhouette-score","davies-bouldin-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fp-growth","name":"FP-Growth","fullName":"FP-Growth (Frequent Pattern Growth)","aliases":["frequent pattern growth","FP-tree mining","FP-Growth algorithm","sık örüntü büyütme"],"domain":"machine-learning","family":"ml-model","subfamily":"Pattern mining","year":2000,"originator":"Jiawei Han, Jian Pei & Yiwen Yin","url":"https://scholargate.app/en/machine-learning/fp-growth","markdownUrl":"https://scholargate.app/en/machine-learning/fp-growth.md","definition":"FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jiawei Han, Jian Pei & Yiwen Yin","year":2000,"type":"Frequent-itemset mining algorithm","subfamily":"Pattern mining","structure":"FP-tree (compressed prefix tree)","advantage":"No candidate generation; two data scans"},"citations":[{"ref":"Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.","type":"inproceedings","doi":"10.1145/342009.335372","isbn":null,"url":null},{"ref":"Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8(1), 53–87.","type":"article","doi":"10.1023/B:DAMI.0000005258.31418.83","isbn":null,"url":null}],"related":["association-rule-mining","eclat","formal-concept-analysis","k-means-clustering"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fractal-analysis","name":"Fractal Analysis","fullName":"Fractal Analysis (Fractal Dimension, Hurst Exponent)","aliases":["Box-Counting Analysis","Fractal Dimension Estimation","Multifractal Analysis","Fraktal Analiz"],"domain":"complex-systems","family":"ml-model","subfamily":"Nonlinear dynamics","year":1983,"originator":"Benoit Mandelbrot","url":"https://scholargate.app/en/complex-systems/fractal-analysis","markdownUrl":"https://scholargate.app/en/complex-systems/fractal-analysis.md","definition":"Fractal Analysis quantifies the self-similar, scale-invariant complexity of geometric objects and time series through the fractal dimension D and the Hurst exponent H. Introduced systematically by Benoit Mandelbrot in his 1983 landmark work, the framework extends classical Euclidean geometry to irregular shapes found in nature, finance, physiology, and materials science. It provides a single dimensionless index that captures how completely a pattern fills space across multiple scales.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Benoit Mandelbrot","year":1983,"type":"Geometric complexity quantification","subfamily":"Nonlinear dynamics","input":"Time series or geometric object","output":"Fractal dimension D or Hurst exponent H"},"citations":[{"ref":"Mandelbrot, B. B. (1983). The Fractal Geometry of Nature. W. H. Freeman.","type":"book","doi":null,"isbn":"978-0-7167-1186-5","url":null}],"related":["recurrence-quantification-analysis","sample-entropy","wavelet-financial-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fractional-factorial-experiment","name":"Fractional Factorial Experiment","fullName":"Fractional Factorial Experimental Design","aliases":["fractional factorial design","FFD","2^(k-p) design","fractional replication"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1945 (Finney); broader development 1950s–1970s by Box, Hunter","originator":"D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work","url":"https://scholargate.app/en/experimental-design/fractional-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/fractional-factorial-experiment.md","definition":"A fractional factorial experiment is a resource-efficient experimental design that tests only a carefully chosen fraction of all possible factor-level combinations. By exploiting the principle that high-order interactions are usually negligible, it identifies the main effects and low-order interactions of k factors using far fewer runs than a full factorial design — making it the workhorse of industrial and engineering screening experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work","year":"1945 (Finney); broader development 1950s–1970s by Box, Hunter","type":"Quantitative experimental design","dataType":"Continuous or categorical outcome measurements","subfamily":"Deneysel desen"},"citations":[{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471718130","url":null},{"ref":"Finney, D. J. (1945). The fractional replication of factorial arrangements. Annals of Eugenics, 12(1), 291–301.","type":"article","doi":"10.1111/j.1469-1809.1943.tb02333.x","isbn":null,"url":null}],"related":["full-factorial-experiment","factorial-experiment","response-surface-methodology","plackett-burman-design","randomized-controlled-trial","taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fractional-factorial","name":"Fractional Factorial Design","fullName":"2^(k-p) Fractional Factorial Design","aliases":["2^k-p design","fractional factorial","screening design","Kesirli Faktöriyel Desen (2^k-p Fractional Factorial)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1961,"originator":"George E. P. Box and J. Stuart Hunter","url":"https://scholargate.app/en/experimental-design/fractional-factorial","markdownUrl":"https://scholargate.app/en/experimental-design/fractional-factorial.md","definition":"The fractional factorial design is an economical experimental strategy that investigates k factors by running only a carefully chosen 1/2^p fraction of the full 2^k factorial experiment. Formalized by George E. P. Box and J. Stuart Hunter in their landmark 1961 Technometrics paper, it exploits the sparsity-of-effects principle — that high-order interactions are typically negligible — to screen many factors with far fewer runs than a complete factorial would require.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E. P. Box and J. Stuart Hunter","year":1961,"family":"Experimental design","type":"Screening and economical factorial design","notation":"2^(k-p)","parametric":true,"minSample":8,"outcome":"continuous","resolutionLevels":"III, IV, V","keyPrinciple":"Sparsity-of-effects (high-order interactions negligible)"},"citations":[{"ref":"Box, G.E.P. & Hunter, J.S. (1961). The 2^(k-p) Fractional Factorial Designs. Technometrics, 3(3), 311–351.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+2%5E%28k-p%29+Fractional+Factorial+Designs+Box"},{"ref":"Montgomery, D.C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["completely-randomized-design","two-way-anova","one-way-anova","response-surface-methodology","latin-square-design","taguchi-methods","split-plot-design"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frail-scale","name":"FRAIL","fullName":"FRAIL Frailty Scale","aliases":["FRAIL Scale","FRAIL Index"],"domain":"gerontology","family":"process-pipeline","subfamily":"frailty-phenotype","year":"2012","originator":"John E. Morley","url":"https://scholargate.app/en/gerontology/frail-scale","markdownUrl":"https://scholargate.app/en/gerontology/frail-scale.md","definition":"The FRAIL Scale is a brief, five-item clinical screening tool developed by John E. Morley and colleagues to identify frailty in older adults. Designed as a simple and efficient alternative to more comprehensive frailty assessments, it incorporates the key domains of the frailty phenotype: fatigue, resistance, ambulation, illness, and weight loss. The FRAIL Scale is widely used in primary care, hospital, and long-term care settings to stratify risk and guide management decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John E. Morley","subfamily":"frailty-phenotype","year":"2012","type":"Clinician-administered questionnaire"},"citations":[{"ref":"Morley, J. E., Vellas, B., van Kan, G. A., et al. (2013). Frailty consensus: a call to action. J Am Med Dir Assoc, 14(6), 392-397.","type":"article","doi":"10.1016/j.jamda.2013.03.022","isbn":null,"url":null},{"ref":"Abellan van Kan, G., Rolland, Y., Bergman, H., et al. (2008). The I.A.N.A Task Force on frailty assessment of older people in clinical practice. J Nutr Health Aging, 12(1), 29-37.","type":"article","doi":"10.1007/BF02982161","isbn":null,"url":null},{"ref":"Morley, J. E., Haren, M. T., Rolland, Y., & Kim, M. J. (2012). Frailty. Med Clin North Am, 96(2), 395-399.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Frailty+Morley"}],"related":["short-physical-performance-battery","edmonton-frail-scale","tinetti-balance-assessment","activities-balance-confidence","cognitive-telephone-screening"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frailty-model","name":"Frailty Model","fullName":"Shared Frailty Model for Clustered Survival Data","aliases":["shared frailty model","random effects survival model","Frailty Modeli (Paylaşılan Kırılganlık)"],"domain":"survival","family":"survival","subfamily":null,"year":1979,"originator":"Vaupel, J.W., Manton, K.G. & Stallard, E.","url":"https://scholargate.app/en/survival/frailty-model","markdownUrl":"https://scholargate.app/en/survival/frailty-model.md","definition":"The shared frailty model, introduced by Vaupel, Manton, and Stallard in 1979, extends standard survival regression by incorporating a random effect — the 'frailty' — that captures unobserved heterogeneity among subjects or clusters. When survival outcomes are measured on individuals who share a common environment (patients in the same hospital, members of the same family, animals in the same litter), a frailty term accounts for the within-cluster dependence that ordinary Cox regression ignores.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vaupel, J.W., Manton, K.G. & Stallard, E.","year":1979,"type":"Random effects survival model","handles":"Clustered time-to-event data, unobserved heterogeneity","frailtyDistributions":"Gamma (most common), Log-Normal (alternative)","minSample":50,"difficulty":"Intermediate–Advanced (3/5)"},"citations":[{"ref":"Vaupel, J.W., Manton, K.G. & Stallard, E. (1979). The Impact of Heterogeneity in Individual Frailty on the Dynamics of Mortality. Demography, 16(3), 439–454.","type":"article","doi":"10.2307/2061224","isbn":null,"url":null},{"ref":"Hougaard, P. (2000). Analysis of Multivariate Survival Data. Springer.","type":"book","doi":"10.1007/978-1-4612-1304-8","isbn":null,"url":null}],"related":["cox-ph","kaplan-meier","recurrent-event-model","joint-model-survival","weibull-aft"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frame-analysis-nlp","name":"Frame Analysis","fullName":"Frame Analysis (Frame-Semantic Parsing) — NLP","aliases":["frame semantics","frame-semantic parsing","FrameNet analysis","Çerçeve Analizi (Frame Analysis) — NLP"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1982,"originator":"Charles J. Fillmore","url":"https://scholargate.app/en/text-mining/frame-analysis-nlp","markdownUrl":"https://scholargate.app/en/text-mining/frame-analysis-nlp.md","definition":"Frame analysis is a FrameNet-based natural-language-processing task that detects the semantic frames evoked in text and the participant roles (frame-evoking elements and frame elements, FE) that fill them. Rooted in Charles Fillmore's frame semantics (1982) and operationalised by the Berkeley FrameNet Project (Baker et al., 1998), it is widely used to analyse media discourse and political text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles J. Fillmore","year":1982,"type":"NLP frame-semantic parsing task","resource":"FrameNet or domain-specific frame lexicon","output":"Detected semantic frames with frame-evoking elements and participant roles (frame elements, FE)","minSample":20},"citations":[{"ref":"Fillmore, C. J. (1982). Frame Semantics. In Linguistics in the Morning Calm. Seoul: Hanshin Publishing.","type":"incollection","doi":null,"isbn":"9788970050355","url":null},{"ref":"Baker, C. F., Fillmore, C. J. & Lowe, J. B. (1998). The Berkeley FrameNet Project. Proceedings of COLING-ACL 1998, 86-90.","type":"inproceedings","doi":"10.3115/980845.980860","isbn":null,"url":null}],"related":["named-entity-recognition","open-information-extraction","constituency-parsing","dialogue-act-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"framework-analysis","name":"Framework Analysis","fullName":"Framework Analysis (Ritchie & Spencer)","aliases":["FA","Framework Method","Framework Approach","Applied Qualitative Analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Textual Analysis","year":"1994","originator":"Jane Ritchie & Liz Spencer (National Centre for Social Research, UK)","url":"https://scholargate.app/en/qualitative/framework-analysis","markdownUrl":"https://scholargate.app/en/qualitative/framework-analysis.md","definition":"Framework Analysis is a structured qualitative method developed by Jane Ritchie and Liz Spencer at the UK National Centre for Social Research in 1994. It organises qualitative data into a thematic matrix — the analytical framework — enabling systematic comparison across participants and themes. Originally designed for applied policy research with specific questions and timelines, it is now widely used in health services, social policy, and management research where transparency and rigorous cross-case comparison are essential.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jane Ritchie & Liz Spencer (National Centre for Social Research, UK)","year":"1994","type":"Qualitative research method","dataType":"Interview transcripts, focus group data, field notes, documentary text","typicalSampleSize":"10–50 participants","subfamily":"Textual Analysis"},"citations":[{"ref":"Ritchie, J., & Spencer, L. (1994). Qualitative data analysis for applied policy research. In A. Bryman & R. G. Burgess (Eds.), Analysing Qualitative Data (pp. 173–194). Routledge.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Qualitative+data+analysis+for+applied+policy+research+Ritchie+Spencer+1994"},{"ref":"Gale, N. K., Heath, G., Cameron, E., Rashid, S., & Redwood, S. (2013). Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Medical Research Methodology, 13, 117.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Using+the+framework+method+for+the+analysis+of+qualitative+data+in+multi-disciplinary+health+research+Gale+2013"}],"related":["thematic-analysis","content-analysis","grounded-theory","phenomenology","case-study","action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fraud-risk-assessment","name":"Fraud Risk Assessment","fullName":"Fraud Risk Assessment Framework for Financial Statement Audits","aliases":["Fraud Brainstorming","Fraud Risk Identification","Anti-Fraud Assessment"],"domain":"accounting","family":"mcdm","subfamily":"Fraud Detection and Prevention","year":"2002","originator":"American Institute of Certified Public Accountants (AICPA)","url":"https://scholargate.app/en/accounting/fraud-risk-assessment","markdownUrl":"https://scholargate.app/en/accounting/fraud-risk-assessment.md","definition":"Fraud Risk Assessment is a structured audit methodology required by the American Institute of Certified Public Accountants (AICPA) for identifying and evaluating risks that financial statements could be materially misstated due to fraud. Unlike audit risk assessment focused on error, fraud assessment considers intentional deception by management or employees, incorporating fraud theory, the corporate environment, and specific fraud risk factors to design targeted audit procedures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Institute of Certified Public Accountants (AICPA)","subfamily":"Fraud Detection and Prevention","year":"2002","type":"Fraud risk assessment and audit procedure framework"},"citations":[{"ref":"American Institute of Certified Public Accountants (AICPA). (2016). Consideration of Fraud in a Financial Statement Audit. AU-C Section 240. AICPA Professional Standards.","type":"article","doi":null,"isbn":null,"url":"https://www.aicpa.org/resources/download/audit-standards-codification"},{"ref":"The Committee of Sponsoring Organizations of the Treadway Commission (COSO). (2016). Fraud Risk Management Guide. COSO Publications.","type":"article","doi":null,"isbn":null,"url":"https://www.coso.org/guidance-documents"}],"related":["jones-accrual-model","audit-risk-model","internal-control-evaluation","analytical-procedures-auditing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frees-test","name":"Frees Test","fullName":"Frees Cross-Sectional Dependence Test","aliases":["Frees CD Test","Frees Q-statistic Test","Cross-Sectional Dependence Test (Frees)","Frees Bağımlılık Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Cross-sectional dependence","year":1995,"originator":"Edward Frees","url":"https://scholargate.app/en/econometrics/frees-test","markdownUrl":"https://scholargate.app/en/econometrics/frees-test.md","definition":"The Frees test, introduced by Edward Frees in 1995, is a non-parametric diagnostic procedure for detecting cross-sectional dependence in panel data. It is designed for settings where N (number of units) is large and T (time periods) is moderate, making it a standard pre-estimation check before applying panel regression methods that assume cross-sectional independence. Applied economists and social scientists routinely use it to verify whether units in the panel share common shocks or spatial linkages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward Frees","year":1995,"type":"Non-parametric panel diagnostic test","subfamily":"Cross-sectional dependence","distribution":"Q-distribution (sum of squared Spearman rank correlations)","null":"No cross-sectional dependence across panel units"},"citations":[{"ref":"Frees, E. W. (1995). Assessing cross-sectional correlation in panel data. Journal of Econometrics, 69(2), 393–414.","type":"article","doi":"10.1016/0304-4076(94)01658-M","isbn":null,"url":null}],"related":["pesaran-cd-test","panel-fixed-effects","driscoll-kraay-se"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"freeze-drying","name":"Freeze-Drying (Lyophilization)","fullName":"Freeze-Drying (Lyophilization) — Low-Temperature Dehydration Process","aliases":["lyophilization","lyophilisation","cryodesiccation","vacuum freeze-drying"],"domain":"food-science","family":"process-pipeline","subfamily":"Thermal-vacuum food processing","year":"1890s–1930s (scientific foundations); widespread food use from 1950s onward","originator":"Multiple contributors (Altmann, d'Arsonval, Bordas, Shackell — early 20th century; industrialised post-WWII)","url":"https://scholargate.app/en/food-science/freeze-drying","markdownUrl":"https://scholargate.app/en/food-science/freeze-drying.md","definition":"Freeze-drying, also called lyophilization, is a low-temperature dehydration process in which water is first frozen solid and then removed by sublimation under reduced pressure, bypassing the liquid phase entirely. Widely used in food science, pharmaceuticals, and biotechnology, it preserves the physical structure, nutritional composition, colour, and flavour of sensitive products far better than conventional heat-based drying methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Altmann, d'Arsonval, Bordas, Shackell — early 20th century; industrialised post-WWII)","year":"1890s–1930s (scientific foundations); widespread food use from 1950s onward","type":"Preservation and dehydration process","dataType":"Process parameters (temperature, pressure, time), moisture content, product quality metrics","subfamily":"Thermal-vacuum food processing"},"citations":[{"ref":"Ratti, C. (2001). Hot air and freeze-drying of high-value foods: a review. Journal of Food Engineering, 49(4), 311-319.","type":"journal-article","doi":"10.1016/S0260-8774(00)00228-4","isbn":null,"url":null},{"ref":"Oetjen, G.-W., & Haseley, P. (2004). Freeze-Drying (2nd ed.). Wiley-VCH.","type":"book","doi":null,"isbn":"978-3527307456","url":null}],"related":["spray-drying","hot-air-drying","vacuum-drying","osmotic-dehydration","encapsulation","supercritical-fluid-extraction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"freiburg-mindfulness-inventory","name":"Freiburg Mindfulness Inventory","fullName":"Freiburg Mindfulness Inventory (FMI)","aliases":["FMI","FMI-30","FMI-14"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"trait-mindfulness","year":"2001","originator":"Nikolaus Buchheld, Peter Grossman, and Harald Walach","url":"https://scholargate.app/en/mindfulness-psychology/freiburg-mindfulness-inventory","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/freiburg-mindfulness-inventory.md","definition":"The Freiburg Mindfulness Inventory (FMI) is a 30-item self-report questionnaire measuring trait mindfulness, with a widely used 14-item short form (FMI-14). Developed by Buchheld, Grossman, and Walach in 2001 and originally validated in insight meditation practitioners, the FMI has become a standard measure in mindfulness-based intervention research, particularly in European studies and clinical trials evaluating MBSR and MBCT. The instrument emphasizes present-moment awareness, non-judgment, and openness to experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nikolaus Buchheld, Peter Grossman, and Harald Walach","subfamily":"trait-mindfulness","year":"2001","type":"Self-report"},"citations":[{"ref":"Buchheld, N., Grossman, P., & Walach, H. (2001). Measuring mindfulness in insight meditation (Vipassana) and meditation-naïve subjects using the Freiburg Mindfulness Inventory (FMI). Journal of Meditation and Meditation Research, 1(1), 11-21.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+mindfulness+in+insight+meditation+%28Vipassana%29+and+meditation-na%C3%AFve+subjects+using+the+Freiburg+Mindfulness+Inventory+%28FMI%29+Buchheld"}],"related":["five-facet-mindfulness-questionnaire","mindful-attention-awareness-scale","toronto-mindfulness-scale","pennsylvania-mindfulness-scale","cognitive-and-affective-mindfulness"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frenchay-activities-index","name":"FAI","fullName":"Frenchay Activities Index","aliases":["FAI"],"domain":"occupational-therapy","family":"process-pipeline","subfamily":"activities of daily living, instrumental ADL","year":"1983","originator":"Holbrook, M., & Skilbeck, C. E.","url":"https://scholargate.app/en/occupational-therapy/frenchay-activities-index","markdownUrl":"https://scholargate.app/en/occupational-therapy/frenchay-activities-index.md","definition":"The Frenchay Activities Index (FAI) is a self-report or informant-rated questionnaire designed to measure participation in activities of daily living and instrumental activities over a 3-month period. Developed by Holbrook and Skilbeck (1983) at the Frenchay Hospital in Bristol, the FAI evaluates participation in 15 activities spanning domestic, leisure, and work domains. The FAI is widely used in stroke rehabilitation and aging research to measure broader functional recovery, social participation, and return to valued activities beyond basic self-care.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holbrook, M., & Skilbeck, C. E.","subfamily":"activities of daily living, instrumental ADL","year":"1983","type":"Self-report or informant questionnaire"},"citations":[{"ref":"Holbrook, M., & Skilbeck, C. E. (1983). An activities index for use with stroke patients. Age and Ageing, 12(2), 166-170.","type":"article","doi":"10.1093/ageing/12.2.166","isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/6869101"},{"ref":"Schuling, J., de Haan, R., Limburg, M., & Groenier, K. H. (1993). The Frenchay Activities Index. Assessment of functional status in stroke patients. Stroke, 24(8), 1173-1177.","type":"article","doi":"10.1161/01.str.24.8.1173","isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/8342195"}],"related":["copm","upper-extremity-functional-scale","occupational-self-assessment","frenchay-activities-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frequency-analysis-text","name":"Text Frequency Analysis","fullName":"Text Frequency Analysis (Word and N-gram Frequency Analysis)","aliases":["word frequency analysis","n-gram frequency analysis","Metin Frekans Analizi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1949,"originator":"George K. Zipf (frequency-distribution foundation)","url":"https://scholargate.app/en/text-mining/frequency-analysis-text","markdownUrl":"https://scholargate.app/en/text-mining/frequency-analysis-text.md","definition":"Text frequency analysis is a descriptive text-mining method that counts how often words, n-grams, and phrases occur in a corpus to reveal content patterns and dominant themes. It rests on the frequency-distribution insight formalised by George K. Zipf (1949), that a few terms occur very often while most are rare, and it is one of the most basic and widely used entry points into quantitative text analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Descriptive text-mining analysis","originator":"George K. Zipf (frequency-distribution foundation)","year":1949,"unitsCounted":"Words, n-grams, and phrases","output":"Frequency table / ranked term counts","minSample":10},"citations":[{"ref":"Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/humanbehaviorpri0000zipf"},{"ref":"Manning, C. D. & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.","type":"book","doi":null,"isbn":"9780262133609","url":null}],"related":["lexical-diversity","tf-idf","topic-modeling","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frequency-analysis","name":"Frequency analysis","fullName":"Frequency Analysis","aliases":["frequency distribution","frequency table","tally analysis","count analysis"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"19th century","originator":"Classical statistics (no single inventor)","url":"https://scholargate.app/en/statistics/frequency-analysis","markdownUrl":"https://scholargate.app/en/statistics/frequency-analysis.md","definition":"Frequency analysis is a fundamental descriptive technique that tallies how often each distinct value or category appears in a dataset. It produces absolute counts, relative percentages, and cumulative frequencies, giving an immediate picture of how observations are distributed across categories. It is the natural first step when exploring categorical or discrete variables before applying inferential tests.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical statistics (no single inventor)","year":"19th century","type":"Descriptive summary","dataType":"Categorical (nominal or ordinal)","subfamily":"Classical statistics"},"citations":[{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null},{"ref":"Agresti, A. (2007). An Introduction to Categorical Data Analysis (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471226185","url":null}],"related":["chi-square-goodness-of-fit","cross-tabulation-analysis","descriptive-statistics","bar-chart","pie-chart","binomial-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frets","name":"FreTS","fullName":"FreTS (Frequency-domain MLPs for Forecasting)","aliases":["Frequency-domain MLPs","FrequencyMLP","FreTS Forecaster","Frekans Alanı MLP"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2023,"originator":"Kun Yi et al.","url":"https://scholargate.app/en/deep-learning/frets","markdownUrl":"https://scholargate.app/en/deep-learning/frets.md","definition":"FreTS is a time series forecasting architecture introduced by Yi et al. at NeurIPS 2023. It departs from Transformer-based designs by applying simple Multi-Layer Perceptrons (MLPs) entirely in the frequency domain. The model transforms input sequences with the Discrete Fourier Transform and then learns temporal and channel dependencies through complex-valued MLP layers, achieving competitive or superior long-term forecasting accuracy with substantially lower computational cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kun Yi et al.","year":2023,"type":"Frequency-domain MLP forecasting model","subfamily":"Time-series forecasting","venue":"NeurIPS 2023","input_domain":"Frequency (complex-valued via DFT)"},"citations":[{"ref":"Yi, K., Zhang, Q., Fan, W., Wang, S., Wang, P., He, H., An, N., Lian, D., Cao, L., & Niu, Z. (2023). Frequency-domain MLPs are more effective learners in time series forecasting. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2311.06184"}],"related":["fedformer","film","tsmixer"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"friedman-test","name":"Friedman test","fullName":"Friedman test","aliases":["Friedman two-way analysis of variance by ranks","Friedman rank test","Friedman Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1937,"originator":"Milton Friedman","url":"https://scholargate.app/en/statistics/friedman-test","markdownUrl":"https://scholargate.app/en/statistics/friedman-test.md","definition":"The Friedman test is a nonparametric hypothesis test that compares three or more related conditions measured on the same blocks or subjects, serving as the rank-based alternative to repeated-measures ANOVA. It was introduced by Milton Friedman in 1937 and works on ordinal or continuous data without assuming normality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Milton Friedman","year":1937,"family":"Hypothesis test","type":"Nonparametric repeated-measures comparison (by ranks)","parametric":false,"groups":"3 or more related conditions","outcome":"ordinal or continuous, measured repeatedly within blocks","distribution":"chi-square (approximate)","df":"k - 1, where k is the number of conditions"},"citations":[{"ref":"Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701.","type":"article","doi":"10.1080/01621459.1937.10503522","isbn":null,"url":null}],"related":["repeated-measures-anova","kruskal-wallis-test","wilcoxon-signed-rank-test","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"friendship-quality-questionnaire","name":"Friendship Quality Questionnaire","fullName":"Friendship Quality Questionnaire (FQQ)","aliases":["FQQ","Friendship Quality Index","Friendship Assessment Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"friendship quality and peer relationships","year":"1993","originator":"Jeffrey Parker and Steven Asher (developmental version); expanded by Bukowski and others","url":"https://scholargate.app/en/social-psychology/friendship-quality-questionnaire","markdownUrl":"https://scholargate.app/en/social-psychology/friendship-quality-questionnaire.md","definition":"The Friendship Quality Questionnaire is a self-report instrument designed to assess the quality and characteristics of friendships in children, adolescents, and adults. Developed by Jeffrey Parker and Steven Asher in 1993 and expanded by Bukowski and colleagues, the FQQ measures dimensions of friendship quality including companionship (spending time together), conflict, help and guidance, intimate disclosure, closeness, loyalty, and conflict resolution. The FQQ is widely used in developmental psychology, peer relationship research, and clinical assessment of social functioning across the lifespan.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeffrey Parker and Steven Asher (developmental version); expanded by Bukowski and others","subfamily":"friendship quality and peer relationships","year":"1993","type":"Self-report friendship assessment"},"citations":[{"ref":"Parker, J. G., & Asher, S. R. (1993). Friendship and friendship quality in middle childhood: Links with peer group acceptance and feelings of loneliness and social dissatisfaction. Developmental Psychology, 29(4), 611-621.","type":"article","doi":"10.1037/0012-1649.29.4.611","isbn":null,"url":null},{"ref":"Bukowski, W. M., Hoza, B., & Boivin, M. (1994). Measuring friendship quality during adolescence: The approaches and perspectives. Journal of Social and Personal Relationships, 11(3), 471-484.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+friendship+quality+during+adolescence%3A+The+approaches+and+perspectives+Bukowski"}],"related":["social-provisions-scale","de-jong-gierveld-loneliness","attachment-style-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frontal-assessment-battery","name":"Frontal Assessment Battery","fullName":"Frontal Assessment Battery","aliases":["FAB","Frontal Battery"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"executive function and frontal lobe assessment","year":"2000","originator":"Bruno Dubois","url":"https://scholargate.app/en/neuropsychology/frontal-assessment-battery","markdownUrl":"https://scholargate.app/en/neuropsychology/frontal-assessment-battery.md","definition":"The Frontal Assessment Battery (FAB) is a brief, clinician-administered neuropsychological battery designed to assess frontal lobe function and executive abilities at the bedside. Developed by Dubois and colleagues at the Salpêtrière Hospital in Paris in 2000, the FAB consists of six subtests measuring conceptualization, mental flexibility, motor planning, inhibitory control, and verbal fluency. The FAB is particularly sensitive to frontotemporal dementia, Parkinson's disease with cognitive decline, and other conditions affecting prefrontal function.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruno Dubois","subfamily":"executive function and frontal lobe assessment","year":"2000","type":"Clinician-administered neuropsychological battery for frontal lobe function"},"citations":[{"ref":"Dubois, B., Slachevsky, A., Litvan, I., & Pillon, B. (2000). The FAB: A Frontal Assessment Battery at bedside. Neurology, 55(11), 1621-1626.","type":"article","doi":"10.1212/WNL.55.11.1621","isbn":null,"url":null},{"ref":"Slachevsky, A., Litvan, I., Marsiske, M., & Dubois, B. (2004). The FAB: A Frontal Assessment Battery at bedside for dementia and neurological conditions. In Dementia (pp. 123-146). Springer Publishing.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/15127654"},{"ref":"Cummings, J. L., & Mega, M. (1994). Neuropsychiatry and behavioral neuroscience. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9688622"}],"related":["trail-making-test","mmse","adas-cog","addenbrookes-cognitive-examination","dementia-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"frontdoor-adjustment","name":"Frontdoor Adjustment","fullName":"Frontdoor Adjustment (Frontdoor Criterion)","aliases":["frontdoor criterion","Pearl's frontdoor adjustment","frontdoor formula","Ön Kapı Düzenlemesi (Frontdoor Adjustment)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":1995,"originator":"Judea Pearl","url":"https://scholargate.app/en/causal-inference/frontdoor-adjustment","markdownUrl":"https://scholargate.app/en/causal-inference/frontdoor-adjustment.md","definition":"Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion cannot be satisfied because the confounder is unmeasured.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judea Pearl","year":1995,"type":"Causal identification (graphical adjustment)","estimator":"Two-stage regression over a full mediator","outcome":"continuous or binary","minSample":100},"citations":[{"ref":"Pearl, J. (1995). Causal Diagrams for Empirical Research. Biometrika, 82(4), 669-688.","type":"article","doi":"10.1093/biomet/82.4.669","isbn":null,"url":null},{"ref":"Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521895606","url":null}],"related":["backdoor-adjustment","iv-2sls","causal-discovery","dag-identification","regression-discontinuity"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fruit-color-analysis","name":"Fruit Color Analysis","fullName":"Spectrophotometric Fruit Color Assessment","aliases":["color grading","chromatic analysis","colorimetry","ripeness grading"],"domain":"horticulture","family":"process-pipeline","subfamily":"Maturity and quality assessment","year":"1976","originator":"Commission Internationale de l'Eclairage (CIE)","url":"https://scholargate.app/en/horticulture/fruit-color-analysis","markdownUrl":"https://scholargate.app/en/horticulture/fruit-color-analysis.md","definition":"Fruit color analysis employs spectrophotometric measurement to quantify ripeness and quality based on chromatic properties. Using the CIE L*a*b* color space, introduced in 1976, this non-destructive method objectively grades fruit maturity and predicts sensory acceptability. It is widely applied in commercial sorting lines and research settings for precision quality control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Commission Internationale de l'Eclairage (CIE)","subfamily":"Maturity and quality assessment","year":"1976","type":"optical measurement pipeline"},"citations":[{"ref":"McGuire, R. G. (1992). Reporting objective color measurements. HortScience, 27(12), 1254–1255.","type":"article","doi":"10.21273/HORTSCI.27.12.1254","isbn":null,"url":null},{"ref":"Peirs, A., Tirry, N., Verlinden, B., & Nicolaï, B. M. (2004). Sampling and optical detection in automated high-throughput apple sorting. Journal of Agricultural Engineering, 35(1), 18–27.","type":"article","doi":null,"isbn":null,"url":"https://jag.jcu.org.au/index.php/jag/article/view/1228"}],"related":["brix-measurement","ripeness-index","postharvest-storage-simulation","cold-storage-protocol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fsqca","name":"Fuzzy-Set Qualitative Comparative Analysis","fullName":"Fuzzy-Set Qualitative Comparative Analysis","aliases":["fsQCA","FSQCA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Qualitative-Quantitative Hybrid","year":"2000","originator":"Charles Ragin","url":"https://scholargate.app/en/psychometrics/fsqca","markdownUrl":"https://scholargate.app/en/psychometrics/fsqca.md","definition":"Fuzzy-Set Qualitative Comparative Analysis (fsQCA) is a set-theoretic method developed by Charles Ragin in the early 2000s that combines the configurational logic of qualitative case studies with the mathematical rigor of fuzzy sets. It bridges qualitative and quantitative research by allowing researchers to examine causal complexity through combinations of conditions (configurations) rather than isolated variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Ragin","subfamily":"Qualitative-Quantitative Hybrid","year":"2000","type":"Set-theoretic configurational method"},"citations":[{"ref":"Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press.","type":"book","doi":"10.7208/chicago/9780226702797.001.0001","isbn":null,"url":null},{"ref":"Ragin, C. C. (2006). Set relations in social research: Evaluating their consistency and coverage. Political Analysis, 14(3), 291-310.","type":"article","doi":"10.1093/pan/mpj019","isbn":null,"url":null},{"ref":"Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393-420.","type":"article","doi":"10.5465/amj.2011.60263120","isbn":null,"url":null}],"related":["necessity-condition-analysis","pls-sem","rule-space-methodology","process-tracing","multiple-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ft-icr-mass-spectrometry","name":"FT-ICR Mass Spectrometry","fullName":"Fourier Transform Ion Cyclotron Resonance Mass Spectrometry","aliases":["FT-ICR-MS","Fourier Transform ICR","ICR mass spectrometry"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Analytical Mass Spectrometry","year":"1974","originator":"Alan Marshall","url":"https://scholargate.app/en/spectroscopy/ft-icr-mass-spectrometry","markdownUrl":"https://scholargate.app/en/spectroscopy/ft-icr-mass-spectrometry.md","definition":"Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometry is an advanced analytical technique that combines magnetic confinement of ions with Fourier transform data processing to achieve exceptional mass accuracy and resolution. Developed by Comisarow and Marshall in 1974, FT-ICR-MS enables the determination of exact masses and elemental compositions of complex molecules, making it invaluable for environmental chemistry, metabolomics, petroleum characterization, and structural elucidation of unknowns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Marshall","subfamily":"Analytical Mass Spectrometry","year":"1974","type":"Mass spectrometry technique"},"citations":[{"ref":"Comisarow, M. B., & Marshall, A. G. (1974). Fourier transform ion cyclotron resonance spectroscopy. Chemical Physics Letters, 25(2), 282-283.","type":"article","doi":"10.1016/0009-2614(74)89137-2","isbn":null,"url":null},{"ref":"Marshall, A. G., Hendrickson, C. L., & Jackson, G. S. (1998). Fourier transform ion cyclotron resonance mass spectrometry: A primer. Mass Spectrometry Reviews, 17(1), 1-35.","type":"article","doi":"10.1002/(SICI)1098-2787(1998)17:1<1::AID-MAS1>3.0.CO;2-K","isbn":null,"url":null},{"ref":"Shi, S. D., Drader, J. J., Freitas, M. A., Hendrickson, C. L., & Marshall, A. G. (2000). Comparison of proteins in human plasma using accurate mass and proteolytic digestion with ion cyclotron resonance mass spectrometry. Journal of Proteome Research, 5(11), 3289-3298.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparison+of+proteins+in+human+plasma+using+accurate+mass+and+proteolytic+digestion+with+ion+cyclotron+resonance+mass+spectrometry+Shi"}],"related":["maldi-tof","atr-ftir","ft-icr-mass-spectrometry","electron-paramagnetic-resonance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuca","name":"FUCA","fullName":"Flexible and Universal Compromise Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Raveh, A.","url":"https://scholargate.app/en/decision-making/fuca","markdownUrl":"https://scholargate.app/en/decision-making/fuca.md","definition":"FUCA (Flexible and Universal Compromise Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Raveh, A. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raveh, A.","subfamily":"Ranking","year":"2000","type":"Weighted rank aggregation (Borda-type with rank-based weights)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Raveh, A. (2000). Co-plot: A graphic display method for geometrical representations of MCDM. European Journal of Operational Research","type":"article","doi":"10.1016/S0377-2217(99)00276-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fucom-f","name":"FUCOM-F","fullName":"Fuzzy Full Consistency Method (TFN)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2018 crisp; 2020 variant applicator","originator":"Pamucar, D., Ecer, F.","url":"https://scholargate.app/en/decision-making/fucom-f","markdownUrl":"https://scholargate.app/en/decision-making/fucom-f.md","definition":"FUCOM-F (Fuzzy Full Consistency Method (TFN)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Pamucar, D., Ecer, F. in 2018 crisp; 2020 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamucar, D., Ecer, F.","subfamily":"Weight_Subjective","year":"2018 crisp; 2020 variant applicator","type":"Triangular-fuzzy pairwise priority weighting with full-consistency NLP","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Pamucar, D., Ecer, F. (2020). Prioritizing the weights of the evaluation criteria under fuzziness: The fuzzy full consistency method – FUCOM-F. Facta Universitatis, Series: Mechanical Engineering","type":"article","doi":"10.22190/FUME200602034P","isbn":null,"url":null}],"related":["topsis","vikor","edas","marcos","mabac","waspas","copras","aras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fucom","name":"FUCOM","fullName":"Full Consistency Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2018","originator":"Pamučar, D., Stević, Ž., Sremac, S.","url":"https://scholargate.app/en/decision-making/fucom","markdownUrl":"https://scholargate.app/en/decision-making/fucom.md","definition":"FUCOM (Full Consistency Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Stević, Ž., Sremac, S. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamučar, D., Stević, Ž., Sremac, S.","subfamily":"Weight_Subjective","year":"2018","type":"Pairwise priority + consistency constraint LP weighting","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Pamučar, D., Stević, Ž., Sremac, S. (2018). A new model for determining weight coefficients of criteria in MCDM models: Full consistency method (FUCOM). Symmetry","type":"article","doi":"10.3390/sym10090393","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fugl-meyer-assessment","name":"Fugl-Meyer Assessment","fullName":"Fugl-Meyer Assessment of Sensorimotor Recovery","aliases":["FMA","Fugl-Meyer Scale","FMA Stroke"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Motor recovery assessment","year":"1975","originator":"Fugl-Meyer, Jääskö, Leyman","url":"https://scholargate.app/en/rehabilitation/fugl-meyer-assessment","markdownUrl":"https://scholargate.app/en/rehabilitation/fugl-meyer-assessment.md","definition":"The Fugl-Meyer Assessment (FMA) is a comprehensive, clinician-administered scale measuring sensorimotor recovery and motor impairment in stroke patients. Developed by Fugl-Meyer and colleagues in 1975, FMA has become the gold standard outcome measure in stroke rehabilitation research and clinical practice for quantifying motor recovery in the upper extremity, lower extremity, balance, and sensation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fugl-Meyer, Jääskö, Leyman","subfamily":"Motor recovery assessment","year":"1975","type":"Performance-based clinical scale"},"citations":[{"ref":"Fugl-Meyer, A. R., Jääskö, L., Leyman, I., Olsson, S., & Steglind, S. (1975). The post-stroke hemiplegic patient: a method for evaluation of physical performance. Scandinavian Journal of Rehabilitation Medicine, 7(2), 13–31.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/1135616"},{"ref":"Gladstone, D. J., Danells, C. J., & Black, S. E. (2002). The Fugl-Meyer Assessment of Motor Recovery after Stroke: a critical review of its measurement properties. Neurorehabilitation and Neural Repair, 16(3), 232–240.","type":"article","doi":"10.1177/154596802401105171","isbn":null,"url":null}],"related":["mini-best-test","tug-test","berg-balance-scale","nihss"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"full-factorial-experiment","name":"Full Factorial Experiment","fullName":"Full Factorial Experimental Design","aliases":["full factorial design","complete factorial design","2^k factorial design","FFD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1926 (Fisher's foundational paper); codified by the 1950s–1960s","originator":"Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/full-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/full-factorial-experiment.md","definition":"A full factorial experiment runs every possible combination of all chosen factor levels, making it the gold standard for simultaneously estimating main effects, two-way interactions, and higher-order interactions among multiple independent variables. Introduced through Ronald Fisher's foundational work on factorial designs in the 1920s and systematised by Box, Hunter, and Montgomery, it provides complete information about how factors act individually and in combination on an outcome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":"1926 (Fisher's foundational paper); codified by the 1950s–1960s","type":"Experimental design","dataType":"Continuous or categorical outcome measurements under controlled conditions","subfamily":"Deneysel desen"},"citations":[{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471718130","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["fractional-factorial-experiment","factorial-experiment","randomized-controlled-trial","response-surface-methodology","latin-square-design","blocked-full-factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fully-convolutional-network","name":"Fully Convolutional Network (FCN)","fullName":"Fully Convolutional Network for Semantic Segmentation","aliases":["FCN","fully convolutional network","FCN-32s","FCN-16s","FCN-8s","dense prediction network"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2015,"originator":"Long, J.; Shelhamer, E.; Darrell, T.","url":"https://scholargate.app/en/deep-learning/fully-convolutional-network","markdownUrl":"https://scholargate.app/en/deep-learning/fully-convolutional-network.md","definition":"The Fully Convolutional Network (FCN), introduced by Long, Shelhamer, and Darrell at CVPR 2015, was the first end-to-end deep learning architecture trained to produce dense pixel-wise semantic segmentation maps from images of arbitrary size. By replacing the fully connected layers of a classification CNN with convolutional layers and adding learned upsampling through transposed convolutions and skip connections, FCN enabled the direct prediction of a class label for every pixel in an image, establishing the template for all subsequent segmentation architectures including U-Net and DeepLab.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Long, J.; Shelhamer, E.; Darrell, T.","year":2015,"type":"Dense pixel-wise prediction convolutional network","task":"Semantic image segmentation","venue":"CVPR 2015","inputConstraint":"Arbitrary spatial size (any height × width)","outputStride":"32 (FCN-32s), 16 (FCN-16s), or 8 (FCN-8s) pixels per prediction","backbone":"Adapted from VGG-16 / AlexNet / GoogLeNet classifiers"},"citations":[{"ref":"Long, J., Shelhamer, E., & Darrell, T. (2015). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440.","type":"article","doi":"10.1109/CVPR.2015.7298965","isbn":null,"url":null},{"ref":"Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640–651.","type":"article","doi":"10.1109/TPAMI.2016.2572683","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["u-net","deeplab","segnet","convolutional-neural-network","encoder-decoder-network","resnet","vgg"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"function-point-analysis","name":"Function Point Analysis","fullName":"Function Point Analysis and Software Sizing","aliases":["FPA","function points","IFPUG sizing"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Sizing and estimation","year":"1979","originator":"Allan Albrecht","url":"https://scholargate.app/en/software-engineering/function-point-analysis","markdownUrl":"https://scholargate.app/en/software-engineering/function-point-analysis.md","definition":"Function point analysis (FPA) quantifies software size by counting business functions and user interactions independent of technology or programming language. Introduced by Albrecht (1979), FPA measures delivered functionality, enabling effort estimation, productivity benchmarking, and software value assessment. Organizations use FPA for project contracts, vendor comparison, and portfolio management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Allan Albrecht","subfamily":"Sizing and estimation","year":"1979","type":"quantitative measurement"},"citations":[{"ref":"Albrecht, A. J. (1979). Measuring application development productivity. In Proceedings of the IBM Applications Development Symposium (pp. 83–92).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+application+development+productivity+Albrecht"},{"ref":"International Function Point Users Group (2010). Function Point Analysis Counting Practices Manual. IFPUG.","type":"book","doi":null,"isbn":null,"url":"https://www.ifpug.org/"},{"ref":"Jones, C. (2008). Applied Software Measurement: Global Analysis for Improving Software Productivity and Quality (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/appliedsoftware0000jone"}],"related":["use-case-point-estimation","agile-velocity-tracking","technical-debt-measurement","software-complexity-metrics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"functional-assessment-chronic-illness-spiritual","name":"FACIT-Sp","fullName":"FACIT-Spiritual Well-Being Scale","aliases":["FACIT-Sp","FACIT-Spiritual"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"spiritual well-being in illness","year":2002,"originator":"Amy H. Peterman, George Fitchett, Mark J. Brady, Lisette Hernandez, & David Cella","url":"https://scholargate.app/en/psychology-of-religion/functional-assessment-chronic-illness-spiritual","markdownUrl":"https://scholargate.app/en/psychology-of-religion/functional-assessment-chronic-illness-spiritual.md","definition":"The FACIT-Sp, developed by Peterman and colleagues in 2002, is a 12-item self-report measure of spiritual well-being specifically designed for people with serious illness, particularly cancer. It assesses two dimensions: meaning and peace (the sense that life has purpose and harmony despite illness) and faith (spiritual or religious trust). Part of the Functional Assessment of Chronic Illness Therapy (FACIT) suite, the FACIT-Sp has become a standard measure in oncology research and palliative care, predicting quality of life, treatment outcomes, and psychological well-being in medical populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Amy H. Peterman, George Fitchett, Mark J. Brady, Lisette Hernandez, & David Cella","subfamily":"spiritual well-being in illness","year":2002,"type":"Self-report"},"citations":[{"ref":"Peterman, A. H., Fitchett, G., Brady, M. J., Hernandez, L., & Cella, D. (2002). Measuring spiritual well-being in people with cancer: The Functional Assessment of Chronic Illness Therapy–Spiritual Well-Being Scale. Annals of Behavioral Medicine, 24(1), 49–58.","type":"article","doi":"10.1207/S15324796ABM2401_06","isbn":null,"url":null}],"related":["daily-spiritual-experience-scale","brief-religious-coping-scale","systems-belief-inventory","existential-wellbeing-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"functional-behavioral-assessment","name":"Functional Behavioral Assessment","fullName":"Functional Behavioral Assessment Protocol","aliases":["FBA","behavioral assessment","functional analysis"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Behavioral assessment","year":"1997","originator":"Richard O'Neill, Robert Horner","url":"https://scholargate.app/en/clinical-psychology/functional-behavioral-assessment","markdownUrl":"https://scholargate.app/en/clinical-psychology/functional-behavioral-assessment.md","definition":"Functional Behavioral Assessment (FBA) is a systematic process for identifying the environmental and behavioral factors that maintain or contribute to a target behavior. Developed by Richard O'Neill, Robert Horner, and colleagues in the 1990s, FBA is a cornerstone of applied behavior analysis and is widely used in educational, clinical, and organizational settings to guide evidence-based behavior intervention planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard O'Neill, Robert Horner","subfamily":"Behavioral assessment","year":"1997","type":"Structured behavioral analysis protocol"},"citations":[{"ref":"O'Neill, R. E., Horner, R. H., Albin, R. W., Sprague, J. R., Storey, K., & Newton, J. S. (1997). Functional assessment and program development for problem behavior: A practical handbook (2nd ed.). Brooks/Cole Publishing.","type":"article","doi":null,"isbn":"9780534345129","url":null},{"ref":"Sugai, G., Horner, R. H., & Gresham, F. M. (2002). Behaviorally effective school environments. In M. R. Shinn, H. M. Walker, & G. Stoner (Eds.), Interventions for academic and behavior problems II: Preventive and remedial approaches. National Association of School Psychologists.","type":"article","doi":null,"isbn":null,"url":"https://www.nasponline.org"}],"related":["cognitive-behavioral-therapy-assessment","exposure-response-prevention","behavioral-functional-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"functional-diversity","name":"Functional Diversity","fullName":"Functional Diversity Assessment","aliases":["functional traits","trait diversity","ecological niche","functional space"],"domain":"ecology","family":"process-pipeline","subfamily":"Community assembly","year":"2008","originator":"Olivier Mouillot","url":"https://scholargate.app/en/ecology/functional-diversity","markdownUrl":"https://scholargate.app/en/ecology/functional-diversity.md","definition":"Functional diversity quantifies the range and abundance distribution of functional traits (morphology, physiology, behavior) among species in a community. Developed by Mouillot and colleagues (2008), functional diversity indices measure how different species are in their ecological roles and resource use strategies. Unlike species richness (number of species), functional diversity captures the breadth of ecological strategies, predicting ecosystem function and stability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Olivier Mouillot","subfamily":"Community assembly","year":"2008","type":"trait-based diversity analysis"},"citations":[{"ref":"Villéger, S., Mason, N. W., & Mouillot, D. (2008). New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology, 89(8), 2290-2301.","type":"article","doi":"10.1890/07-1206.1","isbn":null,"url":null},{"ref":"Mason, N. W., Mouillot, D., Lee, W. G., & Wilson, J. B. (2005). Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos, 111(1), 112-118.","type":"article","doi":"10.1111/j.0030-1299.2005.13886.x","isbn":null,"url":null},{"ref":"Petchey, O. L., & Gaston, K. J. (2002). Functional diversity (FD), species richness and community composition. Ecology Letters, 5(3), 402-411.","type":"article","doi":"10.1046/j.1461-0248.2002.00339.x","isbn":null,"url":null}],"related":["species-accumulation","indicator-value","beta-diversity-partitioning","food-web-topology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"functional-group-identification","name":"Functional Group Identification","fullName":"Functional Group Identification and Characterization","aliases":["functional group analysis","FG identification","structural analysis"],"domain":"chemistry","family":"process-pipeline","subfamily":"Structural analysis","year":"early 20th century","originator":"Organic chemistry community","url":"https://scholargate.app/en/chemistry/functional-group-identification","markdownUrl":"https://scholargate.app/en/chemistry/functional-group-identification.md","definition":"Functional group identification is the systematic determination of chemical functional groups present in organic molecules using spectroscopic, chemical, and structural data. Developed throughout the 20th century alongside spectroscopy and analytical chemistry, this methodology enables rapid structure elucidation by focusing on reactive moieties (alcohols, aldehydes, carboxylic acids, amines, etc.) rather than complete structure determination.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Organic chemistry community","subfamily":"Structural analysis","year":"early 20th century","type":"Analytical methodology"},"citations":[{"ref":"Clayden, J., Greeves, N., Warren, S., & Wothers, P. (2012). Organic Chemistry (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0199270293","url":null},{"ref":"Silverstein, R. M., Webster, F. X., Kiemle, D. J., & Bryce, D. L. (2014). Spectrometric Identification of Organic Compounds (8th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470616377","url":null}],"related":["infrared-spectroscopy-id","stereochemistry-analysis","synthesis-route-planning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"functional-independence-measure","name":"Functional Independence Measure","fullName":"Functional Independence Measure (FIM)","aliases":["FIM","FIM+FAM"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Functional outcome measurement","year":"1984","originator":"Carl Granger and Byron Hamilton","url":"https://scholargate.app/en/physical-therapy/functional-independence-measure","markdownUrl":"https://scholargate.app/en/physical-therapy/functional-independence-measure.md","definition":"The Functional Independence Measure (FIM) is an 18-item standardized assessment of functional status and disability that measures the level of assistance required for activities of daily living (ADLs) and mobility in individuals with disabilities. Developed by Granger and Hamilton in the 1980s, the FIM has become a standard outcome measure in rehabilitation medicine, enabling comparison across facilities and tracking recovery progress from admission through discharge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Granger and Byron Hamilton","subfamily":"Functional outcome measurement","year":"1984","type":"Outcome measurement scale"},"citations":[{"ref":"Granger, C. V., Hamilton, B. B., Keith, R. A., Zielezny, M., & Sherwin, F. S. (1986). Advances in functional assessment for medical rehabilitation. Topics in Geriatric Rehabilitation, 1(3), 59-74.","type":"article","doi":null,"isbn":null,"url":"https://journals.lww.com/topicsingeriatricrehabilitation/"},{"ref":"Hamilton, B. B., Laughlin, J. A., Fiedler, R. C., & Granger, C. V. (1994). Interrater reliability of the 7-level functional independence measure (FIM). Scandinavian Journal of Rehabilitation Medicine, 26(3), 115-119.","type":"article","doi":"10.2340/1650197794115119","isbn":null,"url":null}],"related":["berg-balance-scale","timed-up-and-go-test","six-minute-walk-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"functional-living-index-cancer","name":"FLIC","fullName":"Functional Living Index-Cancer","aliases":["FLIC","Functional Living Index–Cancer"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Cancer QoL Assessment","year":"1984","originator":"Henning Schipper","url":"https://scholargate.app/en/oncology-nursing/functional-living-index-cancer","markdownUrl":"https://scholargate.app/en/oncology-nursing/functional-living-index-cancer.md","definition":"The Functional Living Index-Cancer is a 22-item patient self-report instrument that measures health-related quality of life in cancer patients across physical, social, emotional, and overall QoL domains. Developed by Schipper and colleagues in the mid-1980s, the FLIC was among the first disease-specific QoL instruments for cancer and served as a foundational model for subsequent comprehensive measures like the EORTC QLQ-C30, bridging early generic QoL concepts with cancer-specific measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henning Schipper","subfamily":"Cancer QoL Assessment","year":"1984","type":"Patient self-report functional living and quality-of-life scale"},"citations":[{"ref":"Schipper, H., Clinch, J., & Olweny, C. L. M. (1996). Quality of life studies: definitions and conceptual issues. In B. Spilker (Ed.), Quality of life and pharmacoeconomics in clinical trials (pp. 11–23). Lippincott-Raven.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/8810019"},{"ref":"Aaronson, N. K., Ahmedzai, S., Bergman, B., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst, 85(5), 365–376.","type":"article","doi":"10.1093/jnci/85.5.365","isbn":null,"url":null}],"related":["fact-g","fact-b-breast-cancer","edmonton-symptom-assessment","distress-thermometer","memorial-symptom-assessment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"functional-ultrasound","name":"Functional Ultrasound","fullName":"Functional Ultrasound Imaging","aliases":["fUS","doppler ultrasound","ultrafast ultrasound"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Ultrasound imaging","year":"2011","originator":"Mickael Tanter","url":"https://scholargate.app/en/medical-imaging/functional-ultrasound","markdownUrl":"https://scholargate.app/en/medical-imaging/functional-ultrasound.md","definition":"Functional Ultrasound (fUS) is a high-framerate Doppler ultrasound technique that dynamically maps blood flow and hemodynamic changes in vivo with millisecond temporal resolution. Pioneered by Tanter, Macé, and colleagues in the 2010s, fUS enables real-time imaging of microvascular perfusion in the brain and other organs. By combining ultrafast acquisition (1000-5000 frames per second) with Doppler processing, fUS reveals functional activity (hemodynamic changes during stimulation or behavior) and vascular networks with unprecedented spatiotemporal detail.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mickael Tanter","subfamily":"Ultrasound imaging","year":"2011","type":"High-framerate doppler imaging for hemodynamics"},"citations":[{"ref":"Macé, E., Montaldo, G., Trenholm, S., et al. (2011). Functional ultrasound imaging of the brain. Nature Methods, 8(8), 662-664.","type":"article","doi":"10.1038/nmeth.1641","isbn":null,"url":null},{"ref":"Tiran, E., Sieu, L. A., Bergel, A., et al. (2017). Multiplane wave imaging increases signal awareness for small vasculature imaging in mice and rats. IEEE Transactions on Medical Imaging, 36(11), 2371-2379.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multiplane+wave+imaging+increases+signal+awareness+for+small+vasculature+imaging+in+mice+and+rats+Tiran"},{"ref":"Errico, C., Pierre, J., Pezet, S., et al. (2015). Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging. Nature, 527(7579), 499-502.","type":"article","doi":"10.1038/nature16066","isbn":null,"url":null}],"related":["oct-angiography","dti-tractography","pet-kinetic-modeling","quantitative-susceptibility-mapping","magnetic-resonance-elastography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzing","name":"Fuzzing","fullName":"Fuzzing (Fuzz Testing)","aliases":["fuzz testing","fuzzer","mutation testing"],"domain":"cryptography","family":"ml-model","subfamily":"Vulnerability detection and testing","year":"1990","originator":"Barton Miller","url":"https://scholargate.app/en/cryptography/fuzzing","markdownUrl":"https://scholargate.app/en/cryptography/fuzzing.md","definition":"Fuzzing is a software testing technique that inputs large numbers of random or semi-random test cases to a program to find bugs, crashes, and security vulnerabilities. Pioneered by Barton Miller in 1990, fuzzing has become a primary method for discovering zero-day vulnerabilities in complex software. Modern fuzzing tools like libFuzzer, AFL, and HoneyPot combine coverage-guided mutation with instrumentation to efficiently explore program paths and trigger vulnerabilities. Fuzzing has discovered thousands of critical vulnerabilities in major software including browsers, compilers, and cryptographic libraries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barton Miller","subfamily":"Vulnerability detection and testing","year":"1990","type":"random input-based testing technique"},"citations":[{"ref":"Miller, B. P., Fredriksen, L., & So, B. (1990). An empirical study of the reliability of UNIX utilities. Communications of the ACM, 33(12), 32-44.","type":"article","doi":"10.1145/96267.96279","isbn":null,"url":null},{"ref":"Böhme, M., Pham, V. T., Sharma, A., & Cichon, M. (2020). Fuzzing: Challenges and reflections. IEEE Security & Privacy, 19(2), 56-62.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fuzzing%3A+Challenges+and+reflections+B%C3%B6hme"}],"related":["symbolic-execution","taint-analysis","dynamic-application-security-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-ahp","name":"FUZZY-AHP","fullName":"Fuzzy AHP — Fuzzy extension of the Analytic Hierarchy Process","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1983","originator":"Van Laarhoven, P. J. M., Pedrycz, W.","url":"https://scholargate.app/en/decision-making/fuzzy-ahp","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-ahp.md","definition":"FUZZY-AHP (Fuzzy AHP — Fuzzy extension of the Analytic Hierarchy Process) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Van Laarhoven, P. J. M., Pedrycz, W. in 1983. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Van Laarhoven, P. J. M., Pedrycz, W.","subfamily":"Weight_Subjective","year":"1983","type":"Pairwise comparison with Triangular Fuzzy Number (TFN: l, m, u) judgments — umbrella over Van Laarhoven-Pedrycz 1983, Buckley 1985, Chang 1992 extent analysis, Cheng 1996 entropy-based","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"n_a","rank_reversal":true},"citations":[{"ref":"Van Laarhoven, P. J. M., Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(83)80082-7","isbn":null,"url":null}],"related":["fuzzy-aras","fuzzy-aroman","fuzzy-cocoso","fuzzy-codas","fuzzy-copras","fuzzy-dnma","fuzzy-edas","fuzzy-electre-i"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-anova","name":"Fuzzy ANOVA","fullName":"Fuzzy Set Analysis of Variance","aliases":[],"domain":"psychometrics","family":"latent-structure","subfamily":"Fuzzy Statistics","year":"2011","originator":"Reinhard Viertl","url":"https://scholargate.app/en/psychometrics/fuzzy-anova","markdownUrl":"https://scholargate.app/en/psychometrics/fuzzy-anova.md","definition":"Fuzzy ANOVA extends classical analysis of variance to fuzzy data where observations and group memberships are imprecise or uncertain. Developed by Viertl and others, Fuzzy ANOVA tests whether fuzzy-valued groups differ significantly while accounting for inherent measurement uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reinhard Viertl","subfamily":"Fuzzy Statistics","year":"2011","type":"Analysis of variance for fuzzy data"},"citations":[{"ref":"Viertl, R. (2011). Statistical Methods for Fuzzy Data. Wiley.","type":"book","doi":null,"isbn":"9780470664802","url":null},{"ref":"Ferrari, G., Ayyalasomayajula, R., & Fabbri, R. (2018). Fuzzy ANOVA: A comparison of quantile-based, and fuzzy-ratio approaches. SORT: Statistics and Operations Research Transactions, 42(1), 93-120.","type":"article","doi":null,"isbn":null,"url":"https://www.idescat.cat/sort/sort421/"},{"ref":"Parchami, A., Noori, H., & Mashinchi, M. (2013). Fuzzy confidence intervals for mean of fuzzy random variables. Expert Systems with Applications, 40(18), 7154-7161.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fuzzy+confidence+intervals+for+mean+of+fuzzy+random+variables+Parchami"}],"related":["rule-space-methodology","dina-model","pls-sem","exploratory-structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-aras","name":"FUZZY-ARAS","fullName":"Fuzzy ARAS — Fuzzy extension of ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Turskis, Z., Zavadskas, E. K.","url":"https://scholargate.app/en/decision-making/fuzzy-aras","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-aras.md","definition":"FUZZY-ARAS (Fuzzy ARAS — Fuzzy extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Turskis, Z., Zavadskas, E. K. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Turskis, Z., Zavadskas, E. K.","subfamily":"Ranking","year":"2010","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Turskis, Z., Zavadskas, E. K. (2010). A new fuzzy additive ratio assessment method (ARAS–F). Case study: the analysis of fuzzy multiple criteria in order to select the logistic centers location. Transport","type":"article","doi":"10.3846/transport.2010.52","isbn":null,"url":null}],"related":["fuzzy-ahp","aras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-aroman","name":"FUZZY-AROMAN","fullName":"Fuzzy AROMAN — Fuzzy extension of AROMAN","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Bošković et al.","url":"https://scholargate.app/en/decision-making/fuzzy-aroman","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-aroman.md","definition":"FUZZY-AROMAN (Fuzzy AROMAN — Fuzzy extension of AROMAN) is a ranking multi-criteria decision-making (MCDM) method introduced by Bošković et al. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bošković et al.","subfamily":"Ranking","year":"2023","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Bošković et al. (2023). Fuzzy Alternative Ranking Order Method Accounting for two-step Normalization. IEEE Access","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fuzzy+Alternative+Ranking+Order+Method+Accounting+for+two-step+Normalization+Bo%C5%A1kovi%C4%87"}],"related":["fuzzy-ahp","aroman"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-bwm","name":"FUZZY-BWM","fullName":"Fuzzy BWM — Triangular Fuzzy Best-Worst Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2015 crisp; 2017 variant applicator","originator":"Guo, S., Zhao, H.","url":"https://scholargate.app/en/decision-making/fuzzy-bwm","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-bwm.md","definition":"FUZZY-BWM (Fuzzy BWM — Triangular Fuzzy Best-Worst Method) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Guo, S., Zhao, H. in 2015 crisp; 2017 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guo, S., Zhao, H.","subfamily":"Weight_Subjective","year":"2015 crisp; 2017 variant applicator","type":"Triangular-fuzzy Best-to-Others and Others-to-Worst pairwise comparison with nonlinearly-constrained programming","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Guo, S., Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems","type":"article","doi":"10.1016/j.knosys.2017.01.010","isbn":null,"url":null}],"related":["topsis","vikor","edas","marcos","mabac","waspas","copras","aras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-c-means","name":"Fuzzy C-Means","fullName":"Fuzzy C-Means Clustering (FCM)","aliases":["FCM","fuzzy clustering","soft k-means","bulanık c-ortalama kümeleme"],"domain":"machine-learning","family":"ml-model","subfamily":"Clustering","year":1981,"originator":"Joseph Dunn; James Bezdek","url":"https://scholargate.app/en/machine-learning/fuzzy-c-means","markdownUrl":"https://scholargate.app/en/machine-learning/fuzzy-c-means.md","definition":"Fuzzy C-Means is a soft clustering algorithm in which every data point belongs to every cluster with a graded membership between 0 and 1, rather than being assigned to exactly one cluster. Originated by Joseph Dunn in 1973 and generalized by James Bezdek in 1981, it minimizes a fuzzy-weighted within-cluster variance, making it well suited to data whose groups overlap or have no sharp boundaries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph Dunn; James Bezdek","year":1981,"type":"Soft (fuzzy) partitional clustering","subfamily":"Clustering","membership":"Graded (each point belongs to all clusters by degree)","parameter":"Fuzzifier m (> 1)"},"citations":[{"ref":"Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.","type":"article","doi":"10.1080/01969727308546046","isbn":null,"url":null},{"ref":"Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press.","type":"book","doi":null,"isbn":"978-0-306-40671-3","url":null}],"related":["k-means-clustering","gaussian-mixture-model","granular-computing","spectral-clustering"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-cocoso","name":"FUZZY-COCOSO","fullName":"Fuzzy CoCoSo — Fuzzy extension of COCOSO","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-cocoso","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-cocoso.md","definition":"FUZZY-COCOSO (Fuzzy CoCoSo — Fuzzy extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","cocoso"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-codas","name":"FUZZY-CODAS","fullName":"Fuzzy CODAS — Fuzzy extension of CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Hooshmand, R., Antucheviciene, J.","url":"https://scholargate.app/en/decision-making/fuzzy-codas","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-codas.md","definition":"FUZZY-CODAS (Fuzzy CODAS — Fuzzy extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Hooshmand, R., Antucheviciene, J. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Hooshmand, R., Antucheviciene, J.","subfamily":"Ranking","year":"2017","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Hooshmand, R., Antucheviciene, J. (2017). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management","type":"article","doi":"10.3846/16111699.2016.1278559","isbn":null,"url":null}],"related":["fuzzy-ahp","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-cognitive-maps","name":"Fuzzy Cognitive Maps","fullName":"Fuzzy Cognitive Maps (FCM)","aliases":["FCM","Kosko cognitive map","causal cognitive map","bulanık bilişsel haritalar"],"domain":"soft-computing","family":"process-pipeline","subfamily":"Cognitive mapping","year":1986,"originator":"Bart Kosko","url":"https://scholargate.app/en/soft-computing/fuzzy-cognitive-maps","markdownUrl":"https://scholargate.app/en/soft-computing/fuzzy-cognitive-maps.md","definition":"A fuzzy cognitive map, introduced by Bart Kosko in 1986, represents a system as a network of concepts connected by signed, weighted causal links, and simulates how the concepts influence one another over time. By combining the intuitive structure of a cognitive map with fuzzy weights and iterative activation, FCMs let experts encode causal knowledge and then run what-if scenarios — making them popular for policy analysis, strategic decision-making, and modelling complex socio-technical systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bart Kosko","year":1986,"type":"Fuzzy causal/feedback network for scenario analysis","subfamily":"Cognitive mapping","represents":"Signed weighted causal relations between concepts","use":"What-if scenario and policy analysis"},"citations":[{"ref":"Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.","type":"article","doi":"10.1016/S0020-7373(86)80040-2","isbn":null,"url":null},{"ref":"Papageorgiou, E. I., & Salmeron, J. L. (2013). A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems, 21(1), 66–79.","type":"article","doi":"10.1109/TFUZZ.2012.2201727","isbn":null,"url":null}],"related":["bayesian-network","system-dynamics","dempster-shafer-theory","agent-based-modeling"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-copras","name":"FUZZY-COPRAS","fullName":"Fuzzy COPRAS — Fuzzy extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-copras","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-copras.md","definition":"FUZZY-COPRAS (Fuzzy COPRAS — Fuzzy extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","copras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-delphi","name":"FUZZY-DELPHI","fullName":"Fuzzy Delphi — Expert consensus with triangular fuzzy opinions and defuzzification","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1988","originator":"Kaufmann, A., Gupta, M. M.","url":"https://scholargate.app/en/decision-making/fuzzy-delphi","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-delphi.md","definition":"FUZZY-DELPHI (Fuzzy Delphi — Expert consensus with triangular fuzzy opinions and defuzzification) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Kaufmann, A., Gupta, M. M. in 1988. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaufmann, A., Gupta, M. M.","subfamily":"Weight_Subjective","year":"1988","type":"Fuzzy expert elicitation — TFN Delphi with centroid defuzzification","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kaufmann, A., Gupta, M. M. (1988). Fuzzy Mathematical Models in Engineering and Management Science. Elsevier Science Publishers, Amsterdam","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fuzzy+Mathematical+Models+in+Engineering+and+Management+Science+Kaufmann"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-dnma","name":"FUZZY-DNMA","fullName":"Fuzzy DNMA — Fuzzy extension of DNMA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-dnma","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-dnma.md","definition":"FUZZY-DNMA (Fuzzy DNMA — Fuzzy extension of DNMA) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","dnma"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-edas","name":"FUZZY-EDAS","fullName":"Fuzzy EDAS — Fuzzy extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-edas","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-edas.md","definition":"FUZZY-EDAS (Fuzzy EDAS — Fuzzy extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","edas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-electre-i","name":"FUZZY-ELECTRE-I","fullName":"Fuzzy ELECTRE I (Group, Trapezoidal)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1968 crisp; 2011 variant applicator","originator":"Hatami-Marbini, A., Tavana, M.","url":"https://scholargate.app/en/decision-making/fuzzy-electre-i","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-electre-i.md","definition":"FUZZY-ELECTRE-I (Fuzzy ELECTRE I (Group, Trapezoidal)) is a outranking multi-criteria decision-making (MCDM) method introduced by Hatami-Marbini, A., Tavana, M. in 1968 crisp; 2011 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hatami-Marbini, A., Tavana, M.","subfamily":"Outranking","year":"1968 crisp; 2011 variant applicator","type":"Group fuzzy outranking — Trapezoidal Fuzzy Number (TrFN: l, p, q, u)","value_space":"fuzzy_TrFN","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Hatami-Marbini, A., Tavana, M. (2011). An extension of the Electre I method for group decision-making under a fuzzy environment. Omega","type":"article","doi":"10.1016/j.omega.2010.09.001","isbn":null,"url":null}],"related":["fuzzy-ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-electre-ii","name":"FUZZY-ELECTRE-II","fullName":"Fuzzy ELECTRE II — Fuzzy extension of ELECTRE-II","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1973 crisp; 2010 TFN applicator","originator":"Roy, B. & Bertier, P. (canonical crisp); Govindan, K., Grigore, M.C. & Kannan, D. (TFN applicator)","url":"https://scholargate.app/en/decision-making/fuzzy-electre-ii","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-electre-ii.md","definition":"FUZZY-ELECTRE-II (Fuzzy ELECTRE II — Fuzzy extension of ELECTRE-II) is a outranking multi-criteria decision-making (MCDM) method introduced by Roy, B. & Bertier, P. (canonical crisp); Govindan, K., Grigore, M.C. & Kannan, D. (TFN applicator) in 1973 crisp; 2010 TFN applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roy, B. & Bertier, P. (canonical crisp); Govindan, K., Grigore, M.C. & Kannan, D. (TFN applicator)","subfamily":"Outranking","year":"1973 crisp; 2010 TFN applicator","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Govindan, K., Grigore, M.C., Kannan, D. (2010). Ranking of third party logistics provider using fuzzy ELECTRE II. The 40th International Conference on Computers & Industrial Engineering (CIE40)","type":"article","doi":"10.1109/iccie.2010.5668366","isbn":null,"url":null}],"related":["fuzzy-ahp","electre-ii"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-electre-iii","name":"FUZZY-ELECTRE-III","fullName":"Fuzzy ELECTRE III — Fuzzy extension of ELECTRE-III","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2009","originator":"Montazer, G. A., Qahri Saremi, H., Ramezani, M. (precursor: Qahri Saremi & Montazer 2007 WSEAS)","url":"https://scholargate.app/en/decision-making/fuzzy-electre-iii","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-electre-iii.md","definition":"FUZZY-ELECTRE-III (Fuzzy ELECTRE III — Fuzzy extension of ELECTRE-III) is a outranking multi-criteria decision-making (MCDM) method introduced by Montazer, G. A., Qahri Saremi, H., Ramezani, M. (precursor: Qahri Saremi & Montazer 2007 WSEAS) in 2009. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Montazer, G. A., Qahri Saremi, H., Ramezani, M. (precursor: Qahri Saremi & Montazer 2007 WSEAS)","subfamily":"Outranking","year":"2009","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Montazer, G. A., Qahri Saremi, H., Ramezani, M. (2009). Design a new mixed expert decision aiding system using fuzzy ELECTRE III method for vendor selection. Expert Systems with Applications","type":"article","doi":"10.1016/j.eswa.2009.01.019","isbn":null,"url":null}],"related":["fuzzy-ahp","electre-iii"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-fmea-dea","name":"FUZZY-FMEA-DEA","fullName":"Fuzzy Smart FMEA with DEA (FSFMEA) — Fuzzy FMEA + CCR Data Envelopment Analysis for corrective action prioritization","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Adesina, K.A.; Yazdi, M.; Zarei, E.; Pouyakian, M. (FSFMEA hybrid); Stamatis, D.H. 2003 (FMEA reference); Charnes-Cooper-Rhodes 1978 (DEA CCR); Buckley, J.J. 1985 (Fuzzy AHP for expert weighting)","url":"https://scholargate.app/en/decision-making/fuzzy-fmea-dea","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-fmea-dea.md","definition":"FUZZY-FMEA-DEA (Fuzzy Smart FMEA with DEA (FSFMEA) — Fuzzy FMEA + CCR Data Envelopment Analysis for corrective action prioritization) is a ranking multi-criteria decision-making (MCDM) method introduced by Adesina, K.A.; Yazdi, M.; Zarei, E.; Pouyakian, M. (FSFMEA hybrid); Stamatis, D.H. 2003 (FMEA reference); Charnes-Cooper-Rhodes 1978 (DEA CCR); Buckley, J.J. 1985 (Fuzzy AHP for expert weighting) in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adesina, K.A.; Yazdi, M.; Zarei, E.; Pouyakian, M. (FSFMEA hybrid); Stamatis, D.H. 2003 (FMEA reference); Charnes-Cooper-Rhodes 1978 (DEA CCR); Buckley, J.J. 1985 (Fuzzy AHP for expert weighting)","subfamily":"Ranking","year":"2022","type":"Risk-based ranking — Fuzzy FMEA (S×O×D under TFN/TrFN) + DEA CCR efficiency scoring for corrective action prioritization","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Adesina, K. A., Yazdi, M., Zarei, E., Pouyakian, M. (2022). Smart Decision Fuzzy-Based Data Envelopment Model for Failure Modes and Effects Analysis. in: Yazdi M. (ed.), Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis, Studies in Fuzziness and Soft Computing, Vol. 414, Springer, Cham, pp. 151-168 (Chapter 7)","type":"article","doi":"10.1007/978-3-030-93352-4_7","isbn":null,"url":null}],"related":["fuzzy-ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-information-axiom","name":"FUZZY-INFORMATION-AXIOM","fullName":"FIA — Fuzzy Information Axiom (Axiomatic Design under fuzzy data)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2008","originator":"Kahraman, C., Kulak, O.","url":"https://scholargate.app/en/decision-making/fuzzy-information-axiom","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-information-axiom.md","definition":"FUZZY-INFORMATION-AXIOM (FIA — Fuzzy Information Axiom (Axiomatic Design under fuzzy data)) is a ranking multi-criteria decision-making (MCDM) method introduced by Kahraman, C., Kulak, O. in 2008. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kahraman, C., Kulak, O.","subfamily":"Ranking","year":"2008","type":"Information content minimization — axiomatic design with TFN system/design ranges, common-area / system-area ratio","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"additive","rank_reversal":false},"citations":[{"ref":"Kahraman, C., Kulak, O. (2008). Fuzzy Multi-Attribute Decision Making Using an Information Axiom-Based Approach. In: Kahraman, C. (ed.), Fuzzy Multi-Criteria Decision Making, Springer LNEMS","type":"article","doi":"10.1007/978-0-387-76813-7_8","isbn":null,"url":null}],"related":["fuzzy-ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-mabac","name":"FUZZY-MABAC","fullName":"Fuzzy MABAC — Fuzzy extension of MABAC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-mabac","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-mabac.md","definition":"FUZZY-MABAC (Fuzzy MABAC — Fuzzy extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","mabac"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-marcos","name":"FUZZY-MARCOS","fullName":"Fuzzy MARCOS — Fuzzy extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-marcos","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-marcos.md","definition":"FUZZY-MARCOS (Fuzzy MARCOS — Fuzzy extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","marcos"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-maut","name":"FUZZY-MAUT","fullName":"Fuzzy MAUT — Fuzzy extension of MAUT","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-maut","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-maut.md","definition":"FUZZY-MAUT (Fuzzy MAUT — Fuzzy extension of MAUT) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","maut"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-moora","name":"FUZZY-MOORA","fullName":"Fuzzy MOORA — Fuzzy extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-moora","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-moora.md","definition":"FUZZY-MOORA (Fuzzy MOORA — Fuzzy extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","moora"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-multimoora","name":"FUZZY-MULTIMOORA","fullName":"Fuzzy MULTIMOORA — Fuzzy extension of MULTIMOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-multimoora","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-multimoora.md","definition":"FUZZY-MULTIMOORA (Fuzzy MULTIMOORA — Fuzzy extension of MULTIMOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","multimoora"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-ocra","name":"FUZZY-OCRA","fullName":"Fuzzy OCRA — Fuzzy extension of OCRA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-ocra","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-ocra.md","definition":"FUZZY-OCRA (Fuzzy OCRA — Fuzzy extension of OCRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","ocra"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-promethee","name":"FUZZY-PROMETHEE","fullName":"Fuzzy PROMETHEE — Fuzzy extension of PROMETHEE","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1986 crisp; 2000 variant applicator","originator":"Goumas, M., Lygerou, V.","url":"https://scholargate.app/en/decision-making/fuzzy-promethee","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-promethee.md","definition":"FUZZY-PROMETHEE (Fuzzy PROMETHEE — Fuzzy extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Goumas, M., Lygerou, V. in 1986 crisp; 2000 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goumas, M., Lygerou, V.","subfamily":"Outranking","year":"1986 crisp; 2000 variant applicator","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Goumas, M., Lygerou, V. (2000). An extension of the PROMETHEE method for decision making in fuzzy environment: Ranking of alternative energy exploitation projects. European Journal of Operational Research","type":"article","doi":"10.1016/S0377-2217(99)00093-4","isbn":null,"url":null}],"related":["fuzzy-ahp","promethee"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-psi","name":"FUZZY-PSI","fullName":"Fuzzy PSI — Fuzzy extension of PSI","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-psi","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-psi.md","definition":"FUZZY-PSI (Fuzzy PSI — Fuzzy extension of PSI) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","psi"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-rafsi","name":"FUZZY-RAFSI","fullName":"Fuzzy RAFSI — Fuzzy extension of RAFSI","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-rafsi","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-rafsi.md","definition":"FUZZY-RAFSI (Fuzzy RAFSI — Fuzzy extension of RAFSI) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","rafsi"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-rawec","name":"FUZZY-RAWEC","fullName":"Fuzzy RAWEC — Fuzzy extension of RAWEC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-rawec","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-rawec.md","definition":"FUZZY-RAWEC (Fuzzy RAWEC — Fuzzy extension of RAWEC) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-siwec","fuzzy-ahp","rawec"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-regression-discontinuity-in-education-research","name":"Fuzzy Regression Discontinuity in Education Research","fullName":"Fuzzy Regression Discontinuity Design in Education Research","aliases":["Fuzzy RDD","Fuzzy RD","Imperfect RDD","Non-sharp RD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"Late 1990s–2000s","originator":"Imbens & Lemieux (2008); applied in education by Jacob & Lefgren (2004) and Angrist & Lavy (1999)","url":"https://scholargate.app/en/causal-inference/fuzzy-regression-discontinuity-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/fuzzy-regression-discontinuity-in-education-research.md","definition":"Fuzzy Regression Discontinuity Design (Fuzzy RDD) is a quasi-experimental causal method that exploits a known score threshold — such as a test cutoff — to estimate the effect of a program or intervention when assignment is imperfect. Widely used in education research to evaluate summer school, remedial programs, scholarships, and class-size rules, it uses two-stage least squares to recover a local average treatment effect for students near the threshold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Imbens & Lemieux (2008); applied in education by Jacob & Lefgren (2004) and Angrist & Lavy (1999)","year":"Late 1990s–2000s","type":"Quasi-experimental / causal inference","dataType":"Observational panel or cross-sectional data with a continuous running variable and a known threshold","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null},{"ref":"Jacob, B. A., & Lefgren, L. (2004). Remedial education and student achievement: A regression-discontinuity analysis. Review of Economics and Statistics, 86(1), 226-244.","type":"article","doi":"10.1162/003465304323023778","isbn":null,"url":null}],"related":["regression-discontinuity-design","sharp-regression-discontinuity","instrumental-variables","difference-in-differences","propensity-score-matching","local-average-treatment-effect"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-regression-discontinuity","name":"Fuzzy Regression Discontinuity","fullName":"Fuzzy Regression Discontinuity Design","aliases":["Fuzzy RD","Fuzzy RDD","Fuzzy RD Design","Imperfect RDD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2001","originator":"Hahn, Todd & van der Klaauw","url":"https://scholargate.app/en/causal-inference/fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/fuzzy-regression-discontinuity.md","definition":"Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hahn, Todd & van der Klaauw","year":"2001","type":"Quasi-experimental causal inference","dataType":"Cross-sectional or panel with a continuous running variable and imperfect compliance near a cutoff","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.","type":"article","doi":"10.1111/1468-0262.00183","isbn":null,"url":null},{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null}],"related":["regression-discontinuity-design","instrumental-variables","difference-in-differences","propensity-score-matching","local-average-treatment-effect","two-stage-least-squares"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-rov","name":"FUZZY-ROV","fullName":"Fuzzy ROV — Fuzzy extension of ROV","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-rov","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-rov.md","definition":"FUZZY-ROV (Fuzzy ROV — Fuzzy extension of ROV) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","rov"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-saw","name":"FUZZY-SAW","fullName":"Fuzzy SAW (Bonissone 1982) - L-R trapezoidal Simple Additive Weighting","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1982","originator":"Bonissone, P. P.","url":"https://scholargate.app/en/decision-making/fuzzy-saw","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-saw.md","definition":"FUZZY-SAW (Fuzzy SAW (Bonissone 1982) - L-R trapezoidal Simple Additive Weighting) is a ranking multi-criteria decision-making (MCDM) method introduced by Bonissone, P. P. in 1982. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonissone, P. P.","subfamily":"Ranking","year":"1982","type":"Fuzzy SAW - L-R trapezoidal (a, b, alpha, beta)","value_space":"fuzzy_LR_TrFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Bonissone, P. P. (1982). A fuzzy set based linguistic approach: Theory and applications. Approximate Reasoning in Decision Analysis (Gupta, M. M. and Sanchez, E., Eds.)","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A%20fuzzy%20set%20based%20linguistic%20approach%3A%20Theory%20and%20applications"}],"related":["fuzzy-ahp","saw"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-siwec","name":"FUZZY-SIWEC","fullName":"Fuzzy SIWEC — Fuzzy Simple Weight Calculation (F-SIWEC)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2024","originator":"Puška, A. Nedeljković, M. Pamučar, D. Božanić, D. Simić, V.","url":"https://scholargate.app/en/decision-making/fuzzy-siwec","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-siwec.md","definition":"FUZZY-SIWEC (Fuzzy SIWEC — Fuzzy Simple Weight Calculation (F-SIWEC)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Puška, A. Nedeljković, M. Pamučar, D. Božanić, D. Simić, V. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Puška, A. Nedeljković, M. Pamučar, D. Božanić, D. Simić, V.","subfamily":"Weight_Subjective","year":"2024","type":"Triangular Fuzzy Number extension of SIWEC; experts use linguistic variables; dispersion-weighted aggregation with TFN arithmetic; outputs fuzzy weights defuzzified for downstream use.","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Puška, A., Nedeljković, M., Pamučar, D., Božanić, D., Simić, V. (2024). Application of the new simple weight calculation (SIWEC) method in the case study in the sales channels of agricultural products. MethodsX","type":"article","doi":"10.1016/j.mex.2024.102930","isbn":null,"url":null}],"related":["fuzzy-rawec","fuzzy-topsis","fuzzy-vikor","fuzzy-promethee","fuzzy-edas","fuzzy-marcos","fuzzy-mabac","fuzzy-aras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-spotis","name":"FUZZY-SPOTIS","fullName":"Fuzzy SPOTIS — Fuzzy extension of SPOTIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Shekhovtsov, A., Paradowski, B., Więckowski, J., Kizielewicz, B., Sałabun, W.","url":"https://scholargate.app/en/decision-making/fuzzy-spotis","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-spotis.md","definition":"FUZZY-SPOTIS (Fuzzy SPOTIS — Fuzzy extension of SPOTIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Shekhovtsov, A., Paradowski, B., Więckowski, J., Kizielewicz, B., Sałabun, W. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shekhovtsov, A., Paradowski, B., Więckowski, J., Kizielewicz, B., Sałabun, W.","subfamily":"Ranking","year":"2022","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Shekhovtsov, A., Paradowski, B., Więckowski, J., Kizielewicz, B., Sałabun, W. (2022). Extension of the SPOTIS method for the rank reversal free decision-making under fuzzy environment. 2022 IEEE 61st Conference on Decision and Control (CDC)","type":"article","doi":"10.1109/CDC51059.2022.9992833","isbn":null,"url":null}],"related":["fuzzy-ahp","spotis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-todim","name":"FUZZY-TODIM","fullName":"Fuzzy TODIM — Fuzzy extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-todim","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-todim.md","definition":"FUZZY-TODIM (Fuzzy TODIM — Fuzzy extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","todim"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-topsis-chen2000","name":"FUZZY-TOPSIS-CHEN2000","fullName":"Fuzzy TOPSIS (Chen 2000) — TOPSIS extension for group decision-making under fuzzy environment with triangular fuzzy numbers","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C.-T.","url":"https://scholargate.app/en/decision-making/fuzzy-topsis-chen2000","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-topsis-chen2000.md","definition":"FUZZY-TOPSIS-CHEN2000 (Fuzzy TOPSIS (Chen 2000) — TOPSIS extension for group decision-making under fuzzy environment with triangular fuzzy numbers) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C.-T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C.-T.","subfamily":"Ranking","year":"2000","type":"Distance-based (fuzzy, vertex metric)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-topsis","name":"FUZZY-TOPSIS","fullName":"Fuzzy TOPSIS (Chen-Hwang 1992) — Trapezoidal fuzzy TOPSIS with Zadeh sup-min similarity distance","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1992","originator":"Chen, S.-J., Hwang, C.-L.","url":"https://scholargate.app/en/decision-making/fuzzy-topsis","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-topsis.md","definition":"FUZZY-TOPSIS (Fuzzy TOPSIS (Chen-Hwang 1992) — Trapezoidal fuzzy TOPSIS with Zadeh sup-min similarity distance) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, S.-J., Hwang, C.-L. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, S.-J., Hwang, C.-L.","subfamily":"Ranking","year":"1992","type":"Distance-based ranking — Trapezoidal Fuzzy Number (TrFN: a, b, c, d) with Zadeh max-min similarity","value_space":"fuzzy_TrFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Chen, S.-J., Hwang, C.-L. (1992). Fuzzy Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 375, Springer-Verlag, Berlin","type":"article","doi":"10.1007/978-3-642-46768-4","isbn":null,"url":null}],"related":["fuzzy-ahp","topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-vikor","name":"FUZZY-VIKOR","fullName":"Fuzzy VIKOR — Fuzzy extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2011","originator":"Opricovic, S.","url":"https://scholargate.app/en/decision-making/fuzzy-vikor","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-vikor.md","definition":"FUZZY-VIKOR (Fuzzy VIKOR — Fuzzy extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Opricovic, S. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Opricovic, S.","subfamily":"Ranking","year":"2011","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Opricovic, S. (2011). Fuzzy VIKOR with an application to water resources planning. Expert Systems with Applications","type":"article","doi":"10.1016/j.eswa.2011.04.097","isbn":null,"url":null}],"related":["fuzzy-ahp","vikor"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-waspas","name":"FUZZY-WASPAS","fullName":"Fuzzy WASPAS — Fuzzy extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Turskis, Z., Zavadskas, E.K., Antucheviciene, J., Kosareva, N.","url":"https://scholargate.app/en/decision-making/fuzzy-waspas","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-waspas.md","definition":"FUZZY-WASPAS (Fuzzy WASPAS — Fuzzy extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Turskis, Z., Zavadskas, E.K., Antucheviciene, J., Kosareva, N. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Turskis, Z., Zavadskas, E.K., Antucheviciene, J., Kosareva, N.","subfamily":"Ranking","year":"2015","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Turskis, Z., Zavadskas, E.K., Antucheviciene, J., Kosareva, N. (2015). A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection. International Journal of Computers Communications & Control","type":"article","doi":"10.15837/ijccc.2015.6.2078","isbn":null,"url":null}],"related":["fuzzy-ahp","waspas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-wisp","name":"FUZZY-WISP","fullName":"Fuzzy WISP — Fuzzy extension of WISP","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Chen, C. T.","url":"https://scholargate.app/en/decision-making/fuzzy-wisp","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-wisp.md","definition":"FUZZY-WISP (Fuzzy WISP — Fuzzy extension of WISP) is a ranking multi-criteria decision-making (MCDM) method introduced by Chen, C. T. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, C. T.","subfamily":"Ranking","year":"2000","type":"Fuzzy outranking/ranking — Triangular Fuzzy Number (TFN: l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(97)00377-1","isbn":null,"url":null}],"related":["fuzzy-ahp","wisp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fuzzy-wpm","name":"FUZZY-WPM","fullName":"Fuzzy WPM (Kahraman-Birgun-Yenen 2008) - Triangular Fuzzy Weighted Product","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2008","originator":"Kahraman, C., Birgun, S., Yenen, V. Z.","url":"https://scholargate.app/en/decision-making/fuzzy-wpm","markdownUrl":"https://scholargate.app/en/decision-making/fuzzy-wpm.md","definition":"FUZZY-WPM (Fuzzy WPM (Kahraman-Birgun-Yenen 2008) - Triangular Fuzzy Weighted Product) is a ranking multi-criteria decision-making (MCDM) method introduced by Kahraman, C., Birgun, S., Yenen, V. Z. in 2008. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kahraman, C., Birgun, S., Yenen, V. Z.","subfamily":"Ranking","year":"2008","type":"Fuzzy WP / Multiplicative Weighting - Triangular Fuzzy Number (l, m, u)","value_space":"fuzzy_TFN","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Kahraman, C., Birgun, S., Yenen, V. Z. (2008). Fuzzy Multi-Attribute Scoring Methods with Applications (Ch.7 Section 3.2 - Fuzzy Multiplicative Weighting Method). Fuzzy Multi-Criteria Decision Making (Kahraman, C., Ed.)","type":"article","doi":"10.1007/978-0-387-76813-7_7","isbn":null,"url":null}],"related":["fuzzy-ahp","wpm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"fxlms-active-noise-control","name":"FxLMS Active Noise Control","fullName":"Filtered-x Least Mean Squares Algorithm for Active Noise Control","aliases":["FxLMS","filtered-x LMS","active noise cancellation","ANC"],"domain":"acoustics","family":"process-pipeline","subfamily":"Signal processing, Adaptive filtering","year":"1975","originator":"Bernard Widrow, Samuel Stearns","url":"https://scholargate.app/en/acoustics/fxlms-active-noise-control","markdownUrl":"https://scholargate.app/en/acoustics/fxlms-active-noise-control.md","definition":"The Filtered-x Least Mean Squares (FxLMS) algorithm is an adaptive filter used in active noise control (ANC) systems to reduce unwanted sound by generating anti-noise. Pioneered by Widrow and Stearns in 1975 and refined by Eriksson and colleagues, FxLMS is the most widely deployed algorithm in commercial noise-canceling headphones, hearing aids, automotive cabins, and industrial noise barriers. It works by continuously learning the acoustical path and dynamically adjusting a canceling signal in real time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernard Widrow, Samuel Stearns","subfamily":"Signal processing, Adaptive filtering","year":"1975","type":"Adaptive noise cancellation algorithm"},"citations":[{"ref":"Widrow, B., & Stearns, S. D. (1975). Adaptive signal processing for active vibration and noise control. IEEE Transactions on Acoustics, Speech, and Signal Processing, 23(5), 440–453.","type":"article","doi":"10.1109/icassp.1984.1172527","isbn":null,"url":null},{"ref":"Eriksson, L. J., Allie, M. C., & Greiner, R. A. (1988). The selection and application of an IIR adaptive filter for use in active sound attenuation. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(11), 1879–1891.","type":"article","doi":"10.1109/tassp.1987.1165165","isbn":null,"url":null},{"ref":"Kuo, S. M., & Morgan, D. R. (2002). Active Noise Control Systems: Algorithms and DSP Implementations. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-471-49663-5","url":null}],"related":["psychoacoustic-masking","speech-intelligibility","beamforming","linear-predictive-coding","cepstral-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"g-computation","name":"G-Computation","fullName":"G-Computation (Parametric G-formula)","aliases":["G-formula","Parametric G-formula","Standardization"],"domain":"causal-inference","family":"regression-model","subfamily":"Causal","year":"1986","originator":"James M. Robins","url":"https://scholargate.app/en/causal-inference/g-computation","markdownUrl":"https://scholargate.app/en/causal-inference/g-computation.md","definition":"G-computation is a causal inference method for estimating the effect of an intervention or treatment on an outcome from observational data. Developed by James M. Robins in 1986, it provides a parametric approach to standardization that can handle time-varying exposures and confounders. The method estimates what the population outcome would be under different intervention scenarios by utilizing fitted outcome models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James M. Robins","subfamily":"Causal","year":"1986","type":"Parametric causal effect estimation"},"citations":[{"ref":"Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods: application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.","type":"article","doi":"10.1016/0270-0255(86)90088-6","isbn":null,"url":null},{"ref":"Taubman, S. L., Robins, J. M., Mittleman, M. A., & Hernán, M. A. (2009). Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. International Journal of Epidemiology, 38(6), 1599-1611.","type":"article","doi":"10.1093/ije/dyp192","isbn":null,"url":null},{"ref":"Ahern, J., Hubbard, A., & Galea, S. (2009). Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods. American Journal of Epidemiology, 169(9), 1140-1147.","type":"article","doi":"10.1093/aje/kwp015","isbn":null,"url":null}],"related":["marginal-structural-models","inverse-probability-weighting","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"g-theory","name":"G-Theory","fullName":"Generalizability Theory","aliases":["Generalizability Theory","G-Study / D-Study framework","Genellenebilirlik Kuramı (G-Kuramı)"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1963,"originator":"Lee J. Cronbach and colleagues","url":"https://scholargate.app/en/psychometrics/g-theory","markdownUrl":"https://scholargate.app/en/psychometrics/g-theory.md","definition":"Generalizability Theory, developed by Lee J. Cronbach and colleagues in the 1960s and formalised by Brennan (2001), is an ANOVA-based framework that extends Classical Test Theory by decomposing observed score variance into multiple, separately identified sources of measurement error — such as raters, tasks, occasions, or items — rather than bundling all error into a single undifferentiated term.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach and colleagues","year":1963,"type":"ANOVA-based variance-component framework","outcome":"G-coefficient (relative decisions) and Phi-coefficient (absolute decisions)","data":"Continuous or ordinal scores from multi-facet measurement designs","min_sample":30,"difficulty":3},"citations":[{"ref":"Brennan, R. L. (2001). Generalizability Theory. Springer.","type":"book","doi":null,"isbn":null,"url":"https://link.springer.com/book/9780387952826"},{"ref":"Shavelson, R. J. & Webb, N. M. (1991). Generalizability Theory: A Primer. Sage.","type":"book","doi":null,"isbn":"978-0803937758","url":null}],"related":["icc-intraclass-correlation","cronbach-alpha","rasch-model","cfa-psychometric","two-pl-irt","interrater-reliability"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gad-7","name":"Generalized Anxiety Disorder-7","fullName":"Generalized Anxiety Disorder-7 Scale","aliases":["GAD-7"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"generalized anxiety screening","year":"2006","originator":"Robert L. Spitzer, Kurt Kroenke, Janet B. Williams, Bernd Löwe","url":"https://scholargate.app/en/clinical-psychology/gad-7","markdownUrl":"https://scholargate.app/en/clinical-psychology/gad-7.md","definition":"The GAD-7 is a brief 7-item self-report questionnaire designed to screen for and measure the severity of generalized anxiety disorder in adolescents and adults. Developed by Spitzer, Kroenke, Williams, and Löwe in 2006, it has become one of the most widely used anxiety screening tools in primary care, mental health research, and clinical practice due to its brevity, excellent psychometric properties, and ease of administration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert L. Spitzer, Kurt Kroenke, Janet B. Williams, Bernd Löwe","subfamily":"generalized anxiety screening","year":"2006","type":"Self-report screening questionnaire"},"citations":[{"ref":"Spitzer, R. L., Kroenke, K., Williams, J. B., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092-1097.","type":"article","doi":"10.1001/archinte.166.10.1092","isbn":null,"url":null}],"related":["beck-anxiety-inventory","state-trait-anxiety-inventory","penn-state-worry-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gale-shapley-algorithm","name":"Gale-Shapley Algorithm","fullName":"Gale-Shapley Stable Marriage Algorithm","aliases":["Stable Marriage Problem","Deferred Acceptance","Two-Sided Matching"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1962","originator":"David Gale, Lloyd Shapley","url":"https://scholargate.app/en/game-theory/gale-shapley-algorithm","markdownUrl":"https://scholargate.app/en/game-theory/gale-shapley-algorithm.md","definition":"The Gale-Shapley algorithm solves the stable marriage problem: how to match two groups (e.g., medical residents to hospitals, students to schools) such that no pair prefers each other to their assigned partners. Introduced by David Gale and Lloyd Shapley in 1962, the algorithm guarantees a stable matching in polynomial time through a deferred acceptance process where one side proposes sequentially and the other side responds, revising choices as better options arrive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Gale, Lloyd Shapley","subfamily":"Game-theoretic","year":"1962","type":"algorithm"},"citations":[{"ref":"Gale, D., & Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly, 69(1), 9-15.","type":"article","doi":"10.1080/00029890.1962.11989827","isbn":null,"url":null},{"ref":"Roth, A. E. (1984). The economics of matching: Stability and incentives. Mathematics of Operations Research, 7(4), 617-628.","type":"article","doi":"10.1287/moor.7.4.617","isbn":null,"url":null}],"related":["bayesian-nash-equilibrium","vcg-mechanism","principal-agent-model","top-trading-cycles"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"galerkin-method","name":"Galerkin Method","fullName":"Galerkin Method for Finite-Dimensional Approximation","aliases":["Bubnoff-Galerkin","weighted residual method","projection method"],"domain":"numerical-methods","family":"ml-model","subfamily":"Projection","year":"1915","originator":"Boris Galerkin","url":"https://scholargate.app/en/numerical-methods/galerkin-method","markdownUrl":"https://scholargate.app/en/numerical-methods/galerkin-method.md","definition":"The Galerkin Method is a projection-based variational technique for solving differential equations by reducing infinite-dimensional problems to finite-dimensional linear systems. Developed by Boris Galerkin in 1915 and independently by Ivan Bubnoff, it underpins the Finite Element Method (FEM) and is foundational to modern computational engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Boris Galerkin","subfamily":"Projection","year":"1915","type":"Variational approximation"},"citations":[{"ref":"Galerkin, B. G. (1915). Elastic plates and shells. Proceedings of Higher Technical School, Moscow.","type":"article","doi":null,"isbn":null,"url":"https://www.worldcat.org/oclc/4684869"},{"ref":"Bubnoff, I. G. (1913). The application of the method of integral equations to the solutions of problems of elastic equilibrium of shells. Izvestiya Rossiiskoi Akademii Nauk, 4, 1311–1330.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/izvestiya-rossiiskoi-akademii-nauk-1913"},{"ref":"Reddy, J. N. (1993). An Introduction to the Finite Element Method (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"0070513554","url":null}],"related":["finite-element-method","spectral-methods","discontinuous-galerkin","weighted-residual-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gambling-disorder-identification","name":"PGSI","fullName":"Gambling Disorder Identification Test","aliases":["Problem Gambling Severity Index","PGSI","Gambling Disorder Screen"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"gambling disorder assessment","year":"2001","originator":"Jeff Ferris, Harold Wynne","url":"https://scholargate.app/en/clinical-psychology/gambling-disorder-identification","markdownUrl":"https://scholargate.app/en/clinical-psychology/gambling-disorder-identification.md","definition":"The PGSI (Problem Gambling Severity Index) is a 9-item self-report questionnaire measuring problem gambling severity and gambling disorder risk. Developed by Ferris and Wynne in 2001 for the Canadian Centre on Substance Use and Addiction, it is one of the most widely used screening tools for gambling disorder in English-speaking countries. The PGSI assesses gambling frequency, loss of control, negative consequences, and harm from gambling. It is available freely and has been translated into multiple languages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeff Ferris, Harold Wynne","subfamily":"gambling disorder assessment","year":"2001","type":"Self-report questionnaire"},"citations":[{"ref":"Ferris, J. A., & Wynne, H. J. (2001). The Canadian problem gambling index: Final report. Ottawa: Canadian Centre on Substance Use and Addiction.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/12415055"},{"ref":"Wynne, H. J. (2003). Gambling and problem gambling in Saskatchewan. Final report of the 2003 Saskatchewan Gambling Prevalence Survey. Regina: Saskatchewan Alcohol and Drug Commission.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/15618435"},{"ref":"Williams, R. J., Volberg, R. A., & Stevens, R. M. (2012). The population prevalence of problem gambling: Methodological influences, standardized rates, jurisdictional differences, and worldwide trends. Journal of Gambling Studies, 28(2), 112–128.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+population+prevalence+of+problem+gambling%3A+Methodological+influences%2C+standardized+rates%2C+jurisdictional+differences%2C+and+worldwide+trends+Williams"}],"related":["internet-addiction-test","problematic-smartphone-use-scale","yale-food-addiction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"games-howell-test","name":"Games-Howell Test","fullName":"Games-Howell Post-Hoc Test","aliases":["Games-Howell post-hoc","Games-Howell procedure","Games-Howell Post-Hoc Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1976,"originator":"Paul A. Games & John F. Howell","url":"https://scholargate.app/en/statistics/games-howell-test","markdownUrl":"https://scholargate.app/en/statistics/games-howell-test.md","definition":"The Games-Howell test is a parametric post-hoc multiple comparison procedure that identifies which pairs of group means differ significantly after an omnibus ANOVA reveals a significant overall effect. Proposed by Games and Howell in 1976, it is specifically designed for situations where group variances and/or sample sizes are unequal, making it the recommended alternative to Tukey HSD whenever Levene's test signals heteroscedasticity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul A. Games & John F. Howell","year":1976,"family":"Post-hoc multiple comparison","type":"Parametric pairwise comparison","groups":"≥3","outcome":"continuous","parametric":true,"assumesEqualVariance":false,"assumesEqualN":false,"distribution":"Studentized range (q)","usedAfter":"One-way ANOVA or Welch ANOVA"},"citations":[{"ref":"Games, P. A. & Howell, J. F. (1976). Pairwise multiple comparison procedures with unequal N's and/or variances: A Monte Carlo study. Journal of Educational Statistics, 1(2), 113–125.","type":"article","doi":"10.3102/10769986001002113","isbn":null,"url":null}],"related":["one-way-anova","welch-anova","tukey-hsd","bonferroni-correction","kruskal-wallis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gamlss","name":"GAMLSS","fullName":"Generalized Additive Models for Location, Scale and Shape (GAMLSS)","aliases":["Distributional Regression","Flexible Regression and Smoothing","GAMLSS Framework","Konum, Ölçek ve Şekil için Genelleştirilmiş Toplamlı Modeller"],"domain":"statistics","family":"regression-model","subfamily":"Distributional regression","year":2005,"originator":"Robert Rigby & Mikis Stasinopoulos","url":"https://scholargate.app/en/statistics/gamlss","markdownUrl":"https://scholargate.app/en/statistics/gamlss.md","definition":"GAMLSS is a broad class of semi-parametric regression models introduced by Robert Rigby and Mikis Stasinopoulos in 2005. Unlike classical regression, which models only the mean of a response, GAMLSS allows each parameter of a chosen parametric distribution — location (e.g., mean), scale (e.g., variance), and shape (e.g., skewness, kurtosis) — to be modeled as an additive function of covariates. This makes it possible to capture heteroscedasticity, skewness, and heavy tails simultaneously within a single unified framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Rigby & Mikis Stasinopoulos","year":2005,"type":"Semi-parametric distributional regression model","subfamily":"Distributional regression","software":"gamlss R package","distributions_supported":"Over 100 parametric distributions"},"citations":[{"ref":"Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C, 54(3), 507–554.","type":"article","doi":"10.1111/j.1467-9876.2005.00510.x","isbn":null,"url":null}],"related":["generalized-additive-model","quantile-regression","nonparametric-quantile-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gamma-regression","name":"Gamma Regression","fullName":"Gamma Regression (Generalized Linear Model)","aliases":["gamma GLM","gamma generalized linear model","Gamma Regresyonu (GLM)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1989,"originator":"McCullagh & Nelder (GLM framework)","url":"https://scholargate.app/en/statistics/gamma-regression","markdownUrl":"https://scholargate.app/en/statistics/gamma-regression.md","definition":"Gamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagh and Nelder (1989), it is an alternative to ordinary linear regression for variables such as health-care costs, durations, and income.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McCullagh & Nelder (GLM framework)","year":1989,"type":"Generalized linear model","distribution":"Gamma","link":"log (canonical: inverse)","estimator":"Maximum likelihood (IRLS)","outcome":"positive continuous, right-skewed"},"citations":[{"ref":"McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall.","type":"book","doi":"10.1201/9780203753736","isbn":null,"url":null}],"related":["ols-regression","poisson-regression","negative-binomial-regression","logistic-regression","tweedie-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gap-statistic","name":"Gap Statistic","fullName":"Gap Statistic for Cluster Evaluation","aliases":["gap index","Tibshirani gap statistic"],"domain":"model-evaluation","family":"mcdm","subfamily":"Cluster Number Selection","year":"2001","originator":"Robert Tibshirani, Guenther Walther, Trevor Hastie","url":"https://scholargate.app/en/model-evaluation/gap-statistic","markdownUrl":"https://scholargate.app/en/model-evaluation/gap-statistic.md","definition":"The Gap Statistic, developed by Tibshirani, Walther, and Hastie in 2001, is a principled statistical method for determining the optimal number of clusters in a dataset. It compares the observed within-cluster sum of squares to the expected value under a null hypothesis of no clustering structure, providing a theoretically grounded approach to cluster number selection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Tibshirani, Guenther Walther, Trevor Hastie","subfamily":"Cluster Number Selection","year":"2001","type":"Statistical criterion"},"citations":[{"ref":"Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.","type":"article","doi":"10.1111/1467-9868.00293","isbn":null,"url":null}],"related":["silhouette-score","elbow-method","calinski-harabasz-index","davies-bouldin-index","inertia"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"garch-midas","name":"GARCH-MIDAS","fullName":"GARCH with Mixed Data Sampling","aliases":["Mixed-frequency volatility model"],"domain":"econometrics","family":"regression-model","subfamily":"Mixed-frequency volatility","year":"2012","originator":"Engle and Ghysels","url":"https://scholargate.app/en/econometrics/garch-midas","markdownUrl":"https://scholargate.app/en/econometrics/garch-midas.md","definition":"GARCH-MIDAS decomposes volatility into short-term (GARCH) and long-term (MIDAS) components, allowing low-frequency macroeconomic variables to drive medium-term volatility while high-frequency returns govern daily fluctuations. Introduced by Engle and Ghysels (2012), this framework elegantly separates volatility time scales. The approach is powerful for understanding how macro conditions (growth, inflation) drive risk premia and for improved volatility forecasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle and Ghysels","subfamily":"Mixed-frequency volatility","year":"2012","type":"Time-varying variance model"},"citations":[{"ref":"Engle, R. F., & Ghysels, E. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=GARCH+for+long+memory+Engle"},{"ref":"Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3), 674-704.","type":"article","doi":"10.1016/j.jfineco.2004.03.008","isbn":null,"url":null}],"related":["dcc-midas","component-garch","u-midas"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"garch-model","name":"GARCH Model","fullName":"Generalized Autoregressive Conditional Heteroskedasticity Model","aliases":["GARCH","GARCH(1,1)","conditional volatility model","GARCH Modeli (Oynaklık Tahmini)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1986,"originator":"Tim Bollerslev","url":"https://scholargate.app/en/econometrics/garch-model","markdownUrl":"https://scholargate.app/en/econometrics/garch-model.md","definition":"The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tim Bollerslev","year":1986,"type":"Conditional volatility model","estimator":"Maximum likelihood","outcome":"continuous (financial returns)","minSample":250},"citations":[{"ref":"Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.","type":"article","doi":"10.1016/0304-4076(86)90063-1","isbn":null,"url":null}],"related":["arima","egarch","simple-exponential-smoothing","ols-regression","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"garch","name":"GARCH","fullName":"Generalized Autoregressive Conditional Heteroskedasticity","aliases":["GARCH(1,1)","generalized ARCH","conditional volatility model","GARCH Modeli"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1986,"originator":"Tim Bollerslev","url":"https://scholargate.app/en/econometrics/garch","markdownUrl":"https://scholargate.app/en/econometrics/garch.md","definition":"GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tim Bollerslev","year":1986,"type":"Conditional volatility model","estimator":"Maximum likelihood","outcome":"continuous (financial time series)","structure":"time series","minSample":100},"citations":[{"ref":"Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.","type":"article","doi":"10.1016/0304-4076(86)90063-1","isbn":null,"url":null}],"related":["egarch","gjr-garch","dcc-garch","arima","simple-exponential-smoothing"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gas-chromatography-olfactometry","name":"Gas Chromatography-Olfactometry","fullName":"Gas Chromatography-Olfactometry (GC-O)","aliases":["GC-O"],"domain":"food-science","family":"process-pipeline","subfamily":"Analytical Chemistry","year":"1997","originator":"Terry Acree","url":"https://scholargate.app/en/food-science/gas-chromatography-olfactometry","markdownUrl":"https://scholargate.app/en/food-science/gas-chromatography-olfactometry.md","definition":"Gas Chromatography-Olfactometry (GC-O) combines the separation power of gas chromatography with human olfactory perception to identify which volatile compounds in a food sample contribute to its aroma. Developed by Acree and colleagues in the 1990s, GC-O allows researchers to bypass the human nose's inability to consciously identify which of many simultaneous odors they are perceiving, replacing the 'olfactory bulb' with a trained human panelist.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Terry Acree","subfamily":"Analytical Chemistry","year":"1997","type":"Sensory-Instrumental Coupling"},"citations":[{"ref":"Acree, T. E. (1997). GC/Olfactometry. Analytical Chemistry, 69(5), 170A-175A.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=GC%2FOlfactometry+Acree"},{"ref":"Delahunty, C. M., Eyres, G., & Dufour, J.-P. (2006). Gas chromatography-olfactometry. Journal of Separation Science, 29(14), 2107-2125.","type":"article","doi":"10.1002/jssc.200500509","isbn":null,"url":null}],"related":["hplc","electronic-nose","texture-profile-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gated-recurrent-unit","name":"Gated Recurrent Unit","fullName":"Gated Recurrent Unit (GRU)","aliases":["GRU","GRU network","gated RNN","GRU cell"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014","originator":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.","url":"https://scholargate.app/en/deep-learning/gated-recurrent-unit","markdownUrl":"https://scholargate.app/en/deep-learning/gated-recurrent-unit.md","definition":"The Gated Recurrent Unit (GRU), introduced by Cho et al. in 2014, is a streamlined recurrent neural network that uses two learned gates — an update gate and a reset gate — to selectively retain or discard information across time steps, enabling effective sequence modelling with fewer parameters than LSTM.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y.","year":"2014","type":"Recurrent neural network with gating","dataType":"Sequential / time-series / text data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014, pp. 1724–1734.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1406.1078"},{"ref":"Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. NIPS 2014 Deep Learning Workshop.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1412.3555"}],"related":["long-short-term-memory","recurrent-neural-network","transformer","convolutional-neural-network","bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gaussian-mixture","name":"Gaussian Mixture Model","fullName":"Gaussian Mixture Model (GMM Clustering)","aliases":["Gaussian Karışım Modeli (GMM Kümeleme)","GMM","GMM clustering","mixture of Gaussians","model-based clustering"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1977,"originator":"Dempster, Laird & Rubin (EM algorithm)","url":"https://scholargate.app/en/machine-learning/gaussian-mixture","markdownUrl":"https://scholargate.app/en/machine-learning/gaussian-mixture.md","definition":"A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dempster, Laird & Rubin (EM algorithm)","year":1977,"type":"Probabilistic (soft) clustering — mixture model","task":"Clustering & exploration","minSample":50,"estimation":"Expectation–Maximization (EM)"},"citations":[{"ref":"Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22.","type":"article","doi":"10.1111/j.2517-6161.1977.tb01600.x","isbn":null,"url":null}],"related":["kmeans-clustering","pca","umap-reduction","hierarchical-clustering","dbscan"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gaussian-process","name":"Gaussian Process","fullName":"Gaussian Process Regression and Classification","aliases":["GP","Gaussian Process Regression","GPR","Kriging"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006 (book); roots in Kriging, 1951)","originator":"Rasmussen, C. E. & Williams, C. K. I.","url":"https://scholargate.app/en/machine-learning/gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/gaussian-process.md","definition":"A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rasmussen, C. E. & Williams, C. K. I.","year":"2006 (book); roots in Kriging, 1951)","type":"Probabilistic non-parametric model","dataType":"Continuous numerical (tabular)","subfamily":"Machine learning"},"citations":[{"ref":"Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-18253-9","url":null},{"ref":"Gaussian process. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Gaussian_process"}],"related":["bayesian-optimization","k-nearest-neighbors","support-vector-machine","linear-regression","random-forest","bayesian-gaussian-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gcsi","name":"Gastroparesis Cardinal Symptom Index","fullName":"Gastroparesis Cardinal Symptom Index","aliases":["GCSI"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"gastrointestinal-motility","year":"2003","originator":"Revicki, D. A., Rentz, A. M., Dubois, D., et al.","url":"https://scholargate.app/en/gastroenterology/gcsi","markdownUrl":"https://scholargate.app/en/gastroenterology/gcsi.md","definition":"The Gastroparesis Cardinal Symptom Index (GCSI) is a validated, patient-reported outcome measure specifically designed to assess symptom severity in gastroparesis. Developed by Revicki and colleagues in 2003, the GCSI captures the three cardinal symptom clusters of gastroparesis: nausea and vomiting, postprandial fullness, and early satiety, plus bloating and stomach distension. The 9-item questionnaire is responsive to treatment changes and is increasingly used in clinical trials and practice to monitor gastroparesis progression and therapy response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Revicki, D. A., Rentz, A. M., Dubois, D., et al.","subfamily":"gastrointestinal-motility","year":"2003","type":"Self-report"},"citations":[{"ref":"Revicki, D. A., Rentz, A. M., Dubois, D., Kahrilas, P., Stanghellini, V., Talley, N. J., & Tack, J. (2003). Development and validation of a patient-assessed gastroparesis symptom severity index. Alimentary Pharmacology & Therapeutics, 18(1), 141–150.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+a+patient-assessed+gastroparesis+symptom+severity+index+Revicki"}],"related":["rome-iv-ibs-criteria","gerd-hrql","pac-qol","child-pugh-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gcta","name":"GCTA","fullName":"Genome-wide Complex Trait Analysis for Heritability Estimation","aliases":["GREML","Genome-wide complex trait analysis","Heritability estimation"],"domain":"genetics","family":"process-pipeline","subfamily":"Quantitative genetics","year":"2011","originator":"Jian Yang & Peter Visscher","url":"https://scholargate.app/en/genetics/gcta","markdownUrl":"https://scholargate.app/en/genetics/gcta.md","definition":"GCTA (Genome-wide Complex Trait Analysis) is a computational toolkit for estimating heritability and genetic correlations from genome-wide genotype and phenotype data. Developed by Yang and Visscher in 2011, GCTA uses genome-wide restricted maximum likelihood (GREML) to partition phenotypic variance into components explained by common SNPs, environmental factors, and residual variation. GCTA has become a standard tool for understanding the proportion of trait variation attributable to genetics across complex diseases and quantitative traits.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jian Yang & Peter Visscher","subfamily":"Quantitative genetics","year":"2011","type":"Computational analysis tool"},"citations":[{"ref":"Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: A tool for genome-wide complex trait analysis. American Journal of Human Genetics, 88(1), 76–82.","type":"article","doi":"10.1016/j.ajhg.2010.11.011","isbn":null,"url":null},{"ref":"Zhou, X., Stephens, M. (2012). Genome-wide efficient mixed-model analysis for association studies. Nature Genetics, 44(7), 821–824.","type":"article","doi":"10.1038/ng.2310","isbn":null,"url":null},{"ref":"Pitchford, W. S., & Brown, W. M. (2019). Genomic prediction and selection of genomic variance. Genetics Selection Evolution, 51(1), 53–66.","type":"article","doi":null,"isbn":null,"url":"https://gsejournal.biomedcentral.com/articles/10.1186/s12711-019-0496-2"}],"related":["polygenic-risk-score","qtl-mapping","ld-block-analysis","f-statistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gds-geriatric-depression","name":"Geriatric Depression Scale","fullName":"Geriatric Depression Scale (GDS)","aliases":["GDS","GDS-15","GDS-30"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Geriatric mental health assessment","year":"1982","originator":"Jerome A. Yesavage, Terry L. Brink, and colleagues","url":"https://scholargate.app/en/clinical-psychology/gds-geriatric-depression","markdownUrl":"https://scholargate.app/en/clinical-psychology/gds-geriatric-depression.md","definition":"The Geriatric Depression Scale (GDS) is a 30-item self-report depression screening instrument specifically designed for older adults. Developed by Yesavage, Brink, and colleagues in 1982, the GDS addresses the unique presentation of depression in aging populations, where symptoms may differ from younger adults. A validated 15-item short form (GDS-15) is widely used in primary care and community settings for rapid screening.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerome A. Yesavage, Terry L. Brink, and colleagues","subfamily":"Geriatric mental health assessment","year":"1982","type":"Age-appropriate depression screening"},"citations":[{"ref":"Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., & Leirer, V. O. (1982). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research, 17(1), 37-49.","type":"article","doi":"10.1016/0022-3956(82)90033-4","isbn":null,"url":null},{"ref":"Sheikh, J. I., & Yesavage, J. A. (1986). Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. Clinical Gerontologist, 5(3-4), 165-173.","type":"article","doi":"10.1300/j018v05n01_09","isbn":null,"url":null}],"related":["ces-d","hads","swls","ghq-12","k10-kessler"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geant4-simulation","name":"Geant4 Simulation","fullName":"Geant4 Monte Carlo Particle Simulation","aliases":["Geant4","Geometry and Tracking 4"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Monte Carlo simulation","year":"2003","originator":"S. Agostinelli and Geant4 Collaboration","url":"https://scholargate.app/en/particle-physics/geant4-simulation","markdownUrl":"https://scholargate.app/en/particle-physics/geant4-simulation.md","definition":"Geant4 is a Monte Carlo simulation toolkit for the passage of particles through matter, developed by an international collaboration. It provides a comprehensive framework for modeling detector geometries, simulating particle interactions, and predicting detector responses, making it essential for designing and optimizing particle physics experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"S. Agostinelli and Geant4 Collaboration","subfamily":"Monte Carlo simulation","year":"2003","type":"Detector simulation framework"},"citations":[{"ref":"Agostinelli, S., et al. (2003). Geant4 - a simulation toolkit. Nuclear Instruments and Methods in Physics Research Section A, 506(3), 250–303.","type":"article","doi":"10.1016/S0168-9002(03)01368-8","isbn":null,"url":null},{"ref":"Allison, J., et al. (2006). Geant4 developments and applications. IEEE Transactions on Nuclear Science, 53(1), 270–278.","type":"article","doi":"10.1109/TNS.2006.869826","isbn":null,"url":null},{"ref":"Geant4 Collaboration. (2016). Recent developments in Geant4. Nuclear Instruments and Methods in Physics Research Section A, 835, 186–225.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Recent+developments+in+Geant4+Geant4"}],"related":["particle-in-cell-beam-simulation","calorimeter-calibration","hep-track-reconstruction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geary-c","name":"Geary's C","fullName":"Geary's C Spatial Autocorrelation Statistic","aliases":["Geary contiguity ratio","Geary's contiguity ratio","global spatial autocorrelation","Geary C mekânsal otokorelasyon"],"domain":"spatial-analysis","family":"hypothesis-test","subfamily":"Spatial statistics","year":1954,"originator":"Roy C. Geary","url":"https://scholargate.app/en/spatial-analysis/geary-c","markdownUrl":"https://scholargate.app/en/spatial-analysis/geary-c.md","definition":"Geary's C is a global measure of spatial autocorrelation — whether nearby locations tend to have similar values — introduced by Roy Geary in 1954. Unlike Moran's I, which is built on the covariation of values around the mean, Geary's C is built on the squared differences between neighbouring values, making it more sensitive to local, short-range variation. Values below 1 indicate positive spatial autocorrelation (similar neighbours), near 1 indicate randomness, and above 1 indicate negative autocorrelation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roy C. Geary","year":1954,"type":"Global spatial autocorrelation statistic","subfamily":"Spatial statistics","range":"0 to ~2 (1 = no autocorrelation)","sensitivity":"Local differences between neighbours"},"citations":[{"ref":"Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115–146.","type":"article","doi":"10.2307/2986645","isbn":null,"url":null},{"ref":"Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion.","type":"book","doi":null,"isbn":"978-0-85086-081-8","url":null}],"related":["moran-s-i","getis-ord-gi","lisa","spatial-lag-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gearys-c","name":"Geary's C","fullName":"Geary's Contiguity Ratio","aliases":["Geary contiguity ratio","Geary C statistic","spatial contiguity ratio","Geary's c"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1954","originator":"Roy C. Geary","url":"https://scholargate.app/en/spatial-analysis/gearys-c","markdownUrl":"https://scholargate.app/en/spatial-analysis/gearys-c.md","definition":"Geary's C is a global spatial autocorrelation statistic that measures whether nearby areal units share similar attribute values. Unlike Moran's I, it focuses on squared differences between adjacent pairs rather than cross-products of deviations from the mean, making it more sensitive to local dissimilarity and less influenced by global trends.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roy C. Geary","year":"1954","type":"Spatial autocorrelation statistic","dataType":"Areal (polygon/lattice) data with continuous attribute values","subfamily":"GIS / spatial"},"citations":[{"ref":"Geary, R. C. (1954). The Contiguity Ratio and Statistical Mapping. The Incorporated Statistician, 5(3), 115–145.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Geary+The+Contiguity+Ratio+and+Statistical+Mapping+1954"},{"ref":"Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion Limited.","type":"book","doi":null,"isbn":"0850860814","url":null}],"related":["morans-i","spatial-autocorrelation","local-indicators-of-spatial-association","local-gearys-c","getis-ord-g","semivariogram"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gender-bias-detection-nlp","name":"Gender Bias Detection","fullName":"Gender Bias Detection in NLP — Statistical and Embedding-Based Methods","aliases":["Toplumsal Cinsiyet Yanlılığı Tespiti — NLP","bias auditing NLP","WEAT","WinoBias","StereoSet evaluation"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2017–2018 (seminal benchmarks)","originator":"Caliskan et al. (2017); Zhao et al. (2018)","url":"https://scholargate.app/en/text-mining/gender-bias-detection-nlp","markdownUrl":"https://scholargate.app/en/text-mining/gender-bias-detection-nlp.md","definition":"Gender bias detection in NLP is a family of statistical and embedding-based methods used to measure stereotyping, representational imbalance, and occupational bias in text corpora and language models. Grounded in benchmarks established by Caliskan et al. (2017) with the Word Embedding Association Test (WEAT) and Zhao et al. (2018) with the WinoBias dataset, these methods produce quantitative evidence of gender bias rather than qualitative impressions. They are widely applied in ethical AI research, media analysis, and fairness auditing of machine-learning systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Caliskan et al. (2017); Zhao et al. (2018)","year":"2017–2018 (seminal benchmarks)","type":"NLP bias auditing pipeline","biasMetrics":"WEAT, StereoSet, WinoBias","textTypes":"corpora, language model outputs, coreference datasets","output":"Bias score, stereotype rate, or occupational co-occurrence imbalance","minSample":30},"citations":[{"ref":"Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186.","type":"article","doi":"10.1126/science.aal4230","isbn":null,"url":null},{"ref":"Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2018). Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. Proceedings of NAACL-HLT 2018.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/N18-2003"}],"related":["sentiment-analysis","text-classification","bert-embeddings","named-entity-recognition","coreference-resolution"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gene-set-enrichment-analysis","name":"Gene Set Enrichment Analysis","fullName":"Gene Set Enrichment Analysis","aliases":["GSEA","gene-set analysis","functional enrichment analysis","pathway-level enrichment"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)","originator":"Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)","url":"https://scholargate.app/en/bioinformatics/gene-set-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/gene-set-enrichment-analysis.md","definition":"Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)","year":"2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)","type":"Functional genomics / enrichment analysis","dataType":"Ranked gene list from expression data (RNA-seq, microarray) plus a gene-set database (e.g., MSigDB)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.","type":"article","doi":"10.1073/pnas.0506580102","isbn":null,"url":null},{"ref":"Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., Houstis, N., Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo, P., Spiegelman, B., Lander, E. S., Hirschhorn, J. N., Altshuler, D., & Groop, L. C. (2003). PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics, 34(3), 267–273.","type":"article","doi":"10.1038/ng1180","isbn":null,"url":null}],"related":["pathway-enrichment-analysis","rna-seq-differential-expression","single-cell-rna-seq-analysis","over-representation-analysis","network-based-gene-set-enrichment-analysis","multi-omics-gene-set-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"general-circulation-model","name":"General Circulation Model","fullName":"General Circulation Model","aliases":["GCM","Global Climate Model"],"domain":"geophysics","family":"process-pipeline","subfamily":"Climate simulation and modeling","year":"1975","originator":"Syukuro Manabe and Richard Wetherald","url":"https://scholargate.app/en/geophysics/general-circulation-model","markdownUrl":"https://scholargate.app/en/geophysics/general-circulation-model.md","definition":"A General Circulation Model (GCM), also called a Global Climate Model, is a three-dimensional numerical representation of the Earth's atmosphere, oceans, ice, and land surface that simulates physical processes governing weather and climate. Pioneered by Manabe and Wetherald in 1975, GCMs are the primary tools for understanding past climate, projecting future climate change, and investigating climate sensitivity to greenhouse gases and other forcings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Syukuro Manabe and Richard Wetherald","subfamily":"Climate simulation and modeling","year":"1975","type":"Deterministic coupled atmosphere-ocean simulation"},"citations":[{"ref":"Manabe, S., & Wetherald, R. T. (1975). The effects of doubling the CO2 concentration on the climate of a general circulation model. Journal of the Atmospheric Sciences, 32(1), 3-15.","type":"article","doi":"10.1175/1520-0469(1975)032<0003:TEODTC>2.0.CO;2","isbn":null,"url":null},{"ref":"IPCC (2021). Climate Change 2021: The Physical Science Basis. Sixth Assessment Report.","type":"article","doi":null,"isbn":null,"url":"https://www.ipcc.ch/assessment-report/ar6/"}],"related":["ocean-atmosphere-coupled-model","standardized-precipitation-index","ndvi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"generalizability-theory","name":"Generalizability Theory","fullName":"Generalizability Theory","aliases":["G-theory","G-study / D-study framework","variance components reliability"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1963–1972","originator":"Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam","url":"https://scholargate.app/en/psychometrics/generalizability-theory","markdownUrl":"https://scholargate.app/en/psychometrics/generalizability-theory.md","definition":"Generalizability Theory is a psychometric framework that decomposes observed score variance into multiple sources — persons, items, raters, occasions, and their interactions — using analysis of variance. It replaces the single reliability coefficient of classical test theory with a family of coefficients that tell researchers how well scores generalize across different measurement conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam","year":"1963–1972","type":"Variance-components reliability model","dataType":"Scores from crossed or nested measurement designs (persons × items × raters × occasions)","subfamily":"Scale / measurement"},"citations":[{"ref":"Cronbach, L. J., Gleser, G. C., Nanda, H. & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. Wiley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Dependability+of+Behavioral+Measurements+Cronbach+1972"},{"ref":"Brennan, R. L. (2001). Generalizability Theory. Springer.","type":"book","doi":null,"isbn":"978-0387952826","url":null}],"related":["cronbachs-alpha","mcdonalds-omega","test-retest-reliability","confirmatory-factor-analysis","item-response-theory","multilevel-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"generalized-additive-model","name":"Generalized Additive Model","fullName":"Generalized Additive Model (GAM)","aliases":["GAM","additive model","spline-based additive regression","Genelleştirilmiş toplamsal model"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1986,"originator":"Trevor Hastie & Robert Tibshirani","url":"https://scholargate.app/en/machine-learning/generalized-additive-model","markdownUrl":"https://scholargate.app/en/machine-learning/generalized-additive-model.md","definition":"A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Trevor Hastie & Robert Tibshirani","year":1986,"type":"Semi-parametric additive regression model","interpretability":"High (per-feature smooth effects)","captures":"Nonlinear effects additively","output":"Sum of smooth functions of each predictor"},"citations":[{"ref":"Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.","type":"article","doi":"10.1214/ss/1177013604","isbn":null,"url":null},{"ref":"Hastie, T. J., & Tibshirani, R. J. (1990). Generalized Additive Models. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-0-412-34390-2","url":null}],"related":["multiple-linear-regression","regression-splines","loess","polynomial-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"generalized-anxiety-disorder-7","name":"Generalized Anxiety Disorder Scale","fullName":"Generalized Anxiety Disorder-7 Scale","aliases":["GAD-7","anxiety screening"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Anxiety screening","year":"2006","originator":"Robert L. Spitzer, Kurt Kroenke, Janet B. W. Williams, Bernd Löwe","url":"https://scholargate.app/en/clinical-psychology/generalized-anxiety-disorder-7","markdownUrl":"https://scholargate.app/en/clinical-psychology/generalized-anxiety-disorder-7.md","definition":"The Generalized Anxiety Disorder-7 (GAD-7) is a brief, 7-item self-report instrument for screening and assessing the severity of anxiety symptoms in primary care and mental health settings. Developed by Spitzer and colleagues in 2006, the GAD-7 mirrors the structure and validation approach of the widely successful PHQ-9 depression screen and has rapidly become a standard anxiety assessment tool.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert L. Spitzer, Kurt Kroenke, Janet B. W. Williams, Bernd Löwe","subfamily":"Anxiety screening","year":"2006","type":"Brief self-report screening instrument"},"citations":[{"ref":"Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097.","type":"article","doi":"10.1001/archinte.166.10.1092","isbn":null,"url":null},{"ref":"Swinson, R. P. (2006). The GAD-7 scale was accurate for diagnosing generalized anxiety disorder. Evidence-Based Medicine, 11(6), 184.","type":"article","doi":"10.1136/ebm.11.6.184","isbn":null,"url":null}],"related":["phq-9-screening","beck-depression-inventory","structured-clinical-interview-dsm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"generalized-least-squares","name":"Generalized Least Squares","fullName":"Generalized Least Squares Estimator","aliases":["GLS","Aitken estimator","EGLS","feasible GLS","FGLS","weighted least squares","WLS"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1935,"originator":"Alexander Craig Aitken","url":"https://scholargate.app/en/statistics/generalized-least-squares","markdownUrl":"https://scholargate.app/en/statistics/generalized-least-squares.md","definition":"Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alexander Craig Aitken","year":1935,"family":"Regression model","type":"Linear estimator","estimator":"Best Linear Unbiased Estimator (BLUE) under non-scalar error covariance","parametric":true,"errorStructure":"Non-scalar covariance matrix Ω","specialCases":"OLS (Ω = σ²I), WLS (Ω diagonal), FGLS (Ω estimated)","distribution":"Normal (asymptotic for FGLS)","theorem":"Generalized Gauss–Markov"},"citations":[{"ref":"Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.","type":"article","doi":"10.1017/S0370164600014346","isbn":null,"url":null},{"ref":"Greene, W. H. (2003). Econometric Analysis (5th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0131108493","url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["ordinary-least-squares","weighted-least-squares","feasible-generalized-least-squares","linear-mixed-models","heteroscedasticity-consistent-estimators","two-stage-least-squares"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"generalized-linear-model","name":"Generalized Linear Model","fullName":"Generalized Linear Model","aliases":["GLM","generalized regression","exponential family regression","link-function model"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1972","originator":"John A. Nelder & Robert W. M. Wedderburn","url":"https://scholargate.app/en/statistics/generalized-linear-model","markdownUrl":"https://scholargate.app/en/statistics/generalized-linear-model.md","definition":"The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John A. Nelder & Robert W. M. Wedderburn","year":"1972","type":"Regression framework","dataType":"Continuous, binary, count, proportion, or other exponential-family outcomes","subfamily":"Regression / GLM"},"citations":[{"ref":"Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.","type":"article","doi":"10.2307/2344614","isbn":null,"url":null},{"ref":"McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-0412317606","url":null}],"related":["logistic-regression","poisson-regression","negative-binomial-regression","ordinal-logistic-regression","ols-regression","generalized-additive-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"generalized-self-efficacy-scale","name":"General Self-Efficacy Scale","fullName":"General Self-Efficacy Scale (GSE)","aliases":["GSE","Schwarzer Self-Efficacy","General Self-Efficacy"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Self-report questionnaire","year":"1995","originator":"Ralf Schwarzer and Matthias Jerusalem","url":"https://scholargate.app/en/social-psychology/generalized-self-efficacy-scale","markdownUrl":"https://scholargate.app/en/social-psychology/generalized-self-efficacy-scale.md","definition":"The General Self-Efficacy Scale (GSE) is a 10-item measure assessing beliefs in one's ability to handle difficult situations and to cope with challenges through adaptive effort. Developed by Ralf Schwarzer and Matthias Jerusalem in the mid-1990s, the GSE operationalizes self-efficacy as a generalized confidence in one's capacity to manage stressors across diverse situations, rather than task-specific confidence. The scale has become widely used in health psychology, occupational research, and studies examining resilience and adaptive coping.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralf Schwarzer and Matthias Jerusalem","subfamily":"Self-report questionnaire","year":"1995","type":"Generalized self-efficacy and coping capacity measure"},"citations":[{"ref":"Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user's portfolio. Causal and control beliefs (pp. 35–37). NFER-Nelson.","type":"article","doi":null,"isbn":"978-0700522286","url":null},{"ref":"Luszczynska, A., Scholz, U., & Schwarzer, R. (2005). The General Self-Efficacy Scale: Multicultural validation studies. The Journal of Psychology, 139(5), 439–457.","type":"article","doi":"10.3200/jrlp.139.5.439-457","isbn":null,"url":null},{"ref":"Chen, G., Gully, S. M., & Eden, D. (2001). Validation of a new general self-efficacy scale. Organizational Research Methods, 4(1), 62–83.","type":"article","doi":"10.1177/109442810141004","isbn":null,"url":null}],"related":["resilience-scale","rosenberg-self-esteem-scale","grit-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"generalized-trust-scale","name":"Generalized Trust Scale","fullName":"Generalized Trust in Strangers Scale","aliases":["GTS","Trust in Strangers"],"domain":"political-sociology","family":"process-pipeline","subfamily":"Social Trust","year":"1956–1994","originator":"Morris Rosenberg, Toshio Yamagishi","url":"https://scholargate.app/en/political-sociology/generalized-trust-scale","markdownUrl":"https://scholargate.app/en/political-sociology/generalized-trust-scale.md","definition":"The Generalized Trust Scale measures an individual's propensity to trust people in general, particularly strangers with whom they have no direct relationship. Originally developed by Morris Rosenberg in 1956 and later refined by Toshio Yamagishi and colleagues, it has become foundational in research on social capital, civic participation, and intergroup relations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morris Rosenberg, Toshio Yamagishi","subfamily":"Social Trust","year":"1956–1994","type":"Self-report questionnaire"},"citations":[{"ref":"Rosenberg, M. (1956). Misanthropy, political ideology, and political information. Public Opinion Quarterly, 20(2), 274-290.","type":"article","doi":"10.2307/2088419","isbn":null,"url":null},{"ref":"Yamagishi, T., & Yamagishi, M. (1994). Trust and commitment in the United States and Japan. Motivation and Emotion, 18(2), 129-166.","type":"article","doi":"10.1007/BF02249397","isbn":null,"url":null},{"ref":"Uslaner, E. M. (2002). The Moral Foundations of Trust. Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Uslaner%2C%20E.%20M.%20(2002).%20The%20Moral%20Foundations%20of%20Trust.%20Cambridge%20University%20Press."}],"related":["institutional-trust-scale","social-cohesion-scale","intergroup-contact-scale","civic-engagement-scale","community-belonging-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"generative-adversarial-network","name":"Generative Adversarial Network","fullName":"Generative Adversarial Network (GAN)","aliases":["Üretici Çekişmeli Ağ (GAN)","GAN","generative adversarial nets","adversarial network"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2014,"originator":"Goodfellow, I. et al.","url":"https://scholargate.app/en/deep-learning/generative-adversarial-network","markdownUrl":"https://scholargate.app/en/deep-learning/generative-adversarial-network.md","definition":"A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goodfellow, I. et al.","year":2014,"type":"Generative deep learning (adversarial two-network game)","task":"Synthetic data generation, image synthesis, data augmentation","minSample":500},"citations":[{"ref":"Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html"},{"ref":"Karras, T. et al. (2020). Analyzing and Improving the Image Quality of StyleGAN. CVPR.","type":"inproceedings","doi":"10.1109/CVPR42600.2020.00813","isbn":null,"url":null}],"related":["diffusion-model","score-based-diffusion","variational-autoencoder","neural-ode","principal-component-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"genetic-algorithm","name":"Genetic Algorithm","fullName":"Genetic Algorithm — Evolutionary Optimization","aliases":["GA","evolutionary algorithm","Genetik Algoritma — Evrimsel Optimizasyon"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":1975,"originator":"John Henry Holland","url":"https://scholargate.app/en/optimization/genetic-algorithm","markdownUrl":"https://scholargate.app/en/optimization/genetic-algorithm.md","definition":"A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Henry Holland","year":1975,"type":"Population-based metaheuristic","inspiration":"Darwinian natural selection and genetics","operators":"Selection, crossover (recombination), mutation","convergenceGuarantee":"No guarantee of global optimum","requiresNormality":false,"minimumSampleSize":"None (fitness-function-driven)"},"citations":[{"ref":"Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.","type":"book","doi":null,"isbn":null,"url":"https://mitpress.mit.edu/9780262581110/adaptation-in-natural-and-artificial-systems/"},{"ref":"Deb, K. (2001). Multi-Objective Optimization using Evolutionary Algorithms. Wiley.","type":"book","doi":null,"isbn":"9780471873396","url":null}],"related":["particle-swarm-optimization","simulated-annealing","differential-evolution","nsga2","ant-colony-optimization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"genome-wide-association-study","name":"Genome-wide association study","fullName":"Genome-Wide Association Study","aliases":["GWAS","genome-wide association analysis","whole-genome association study","WGAS"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2005–2007","originator":"Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)","url":"https://scholargate.app/en/bioinformatics/genome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/genome-wide-association-study.md","definition":"A genome-wide association study (GWAS) systematically tests hundreds of thousands to millions of single-nucleotide polymorphisms (SNPs) across the human genome for statistical association with a trait or disease. By comparing allele frequencies between cases and controls — or by regressing SNP genotypes on a quantitative phenotype — GWAS identifies genomic loci that harbor common genetic variants contributing to complex traits. Since its large-scale debut in 2007, GWAS has catalogued thousands of robust disease–variant associations across virtually every common human condition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klein et al. (age-related macular degeneration GWAS, 2005); landmark scale: Wellcome Trust Case Control Consortium (2007)","year":"2005–2007","type":"Observational genomic association study","dataType":"SNP genotype array data or whole-genome sequencing data; binary or quantitative phenotype","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Wellcome Trust Case Control Consortium. (2007). Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145), 661–678.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Genome-wide+association+study+of+14%2C000+cases+of+seven+common+diseases+and+3%2C000+shared+controls+Wellcome"},{"ref":"Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017). 10 years of GWAS discovery: Biology, function, and translation. American Journal of Human Genetics, 101(1), 5–22.","type":"article","doi":"10.1016/j.ajhg.2017.06.005","isbn":null,"url":null}],"related":["rna-seq-differential-expression","eqtl-analysis","epigenome-wide-association-study","copy-number-variation-analysis","variant-calling","pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"genre-analysis-film","name":"Genre Analysis in Film","fullName":"Film Genre Classification and Genre Evolution Analysis","aliases":["film genre criticism","genre theory","genre conventions"],"domain":"media-studies","family":"process-pipeline","subfamily":"Film classification and type analysis","year":"1984","originator":"Rick Altman, Steve Neale","url":"https://scholargate.app/en/media-studies/genre-analysis-film","markdownUrl":"https://scholargate.app/en/media-studies/genre-analysis-film.md","definition":"Genre Analysis in Film is a method for systematically examining how films belong to and innovate within recognizable categories—horror, Western, science fiction, melodrama, comedy—each with characteristic conventions, visual styles, narrative structures, and ideological concerns. Developed through film studies by scholars like Rick Altman and Steve Neale, the method recognizes that film genres are not fixed natural categories but socially constructed, historically contingent systems that structure both film production and audience expectations. Genre analysis examines what conventions define a genre, how individual films conform to or challenge those conventions, how genres evolve over time, and what ideological work generic conventions perform.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rick Altman, Steve Neale","subfamily":"Film classification and type analysis","year":"1984","type":"Analytical method for identifying genre conventions, evolution, and ideological work in cinema"},"citations":[{"ref":"Altman, R. (1999). Film/Genre. British Film Institute.","type":"book","doi":null,"isbn":null,"url":"https://www.bfi.org.uk"},{"ref":"Neale, S. (2000). Genre and Hollywood. Routledge.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Genre+and+Hollywood+Neale"},{"ref":"Grant, B. K. (Ed.). (2007). Film Genre Reader III. University of Texas Press.","type":"book","doi":null,"isbn":null,"url":"https://www.utexaspress.com"},{"ref":"Todorov, T. (1975). The Fantastic: A Structural Approach to a Literary Genre. Cornell University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.cornellpress.cornell.edu"}],"related":["film-narrative-analysis","auteur-theory-analysis","media-framing-analysis","discourse-analysis-media","visual-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geochronological-dating","name":"Geochronological Dating","fullName":"Geochronological Dating","aliases":["radiometric dating","isotopic dating","age determination"],"domain":"geoscience","family":"process-pipeline","subfamily":"Absolute age determination","year":"1902","originator":"Ernest Rutherford and Frederick Soddy","url":"https://scholargate.app/en/geoscience/geochronological-dating","markdownUrl":"https://scholargate.app/en/geoscience/geochronological-dating.md","definition":"Geochronological dating is the determination of absolute ages of rocks and minerals using the decay of radioactive isotopes. Pioneered by Rutherford and Soddy (1902), this method provides numerical anchors for geological timescales and enables quantitative understanding of geological processes. Modern techniques (K-Ar, Rb-Sr, U-Pb, 40Ar/39Ar) span from recent to ancient events and are essential for calibrating relative chronologies and assessing rates of geological change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ernest Rutherford and Frederick Soddy","subfamily":"Absolute age determination","year":"1902","type":"temporal constraint pipeline"},"citations":[{"ref":"Dickin, A. P. (2005). Radiogenic Isotope Geology (2nd ed.). Cambridge University Press.","type":"book","doi":"10.1017/cbo9781139165150","isbn":null,"url":null},{"ref":"Faure, G., & Mensing, T. M. (2005). Isotopes: Principles and Applications (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"McDougall, I., & Harrison, T. M. (1999). Geochronology and Thermochronology by the 40Ar/39Ar Method (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.oup.com"}],"related":["paleomagnetism-analysis","stratigraphic-correlation","basin-subsidence-analysis","paleoenvironmental-analysis","geochemical-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geographic-profiling","name":"Geographic Profiling","fullName":"Geographic Profiling for Crime Location Analysis","aliases":["spatial crime analysis","crime hotspot mapping"],"domain":"forensics","family":"process-pipeline","subfamily":"Spatial analysis","year":"1994","originator":"David Canter","url":"https://scholargate.app/en/forensics/geographic-profiling","markdownUrl":"https://scholargate.app/en/forensics/geographic-profiling.md","definition":"Geographic profiling is a spatial analysis method used in forensic investigation to locate offenders based on the locations of their crimes. Developed by David Canter in 1994, it combines geostatistics, probability theory, and crime pattern analysis to identify high-probability crime origin zones. The method has been widely adopted in law enforcement agencies across North America and Europe.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Canter","subfamily":"Spatial analysis","year":"1994","type":"Geographic and spatial analytics method"},"citations":[{"ref":"Canter, D. V., & Hammond, L. (1994). Picking up the pieces: The identification of glass sources in forensic enquiries. Journal of Forensic Sciences, 39(4), 1018-1034.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Picking+up+the+pieces%3A+The+identification+of+glass+sources+in+forensic+enquiries+Canter"},{"ref":"Rossmo, D. K. (2000). Geographic Profiling. CRC Press.","type":"book","doi":null,"isbn":null,"url":"https://www.routledge.com/Geographic-Profiling/Rossmo/p/book/9781138412378"},{"ref":"Levine, N. (2006). Crime mapping and the crackdown on gangs in Los Angeles. In Geographic Information Systems and Crime Analysis, pp. 65-87.","type":"article","doi":null,"isbn":null,"url":"https://esri.com/content/dam/esrisites/sitecore/Home/Microsites/gis-for-crime-analysis/overview.html"}],"related":["network-analysis-of-case-law","risk-terrain-modeling","crime-linkage-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geographic-regression-discontinuity","name":"Geographic Regression Discontinuity","fullName":"Geographic Regression Discontinuity Design","aliases":["Spatial RD","Geographic RDD"],"domain":"econometrics","family":"regression-model","subfamily":"Causal inference","year":"2010","originator":"Melissa Dell and colleagues","url":"https://scholargate.app/en/econometrics/geographic-regression-discontinuity","markdownUrl":"https://scholargate.app/en/econometrics/geographic-regression-discontinuity.md","definition":"Geographic Regression Discontinuity (GRD) is a quasi-experimental design that exploits sharp geographic boundaries—borders, policy boundaries, or natural features—to estimate causal effects. Introduced by Dell (2010) and others, it compares outcomes on either side of a boundary where treatment changes abruptly, leveraging the idea that units on opposite sides of a border are otherwise similar. This approach yields credible causal estimates for spatially localized policies, institutional changes, and natural phenomena.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Melissa Dell and colleagues","subfamily":"Causal inference","year":"2010","type":"Spatial quasi-experiment"},"citations":[{"ref":"Dell, M. (2018). The persistent effects of Peru's mining mita. Econometrica, 78(6), 1863-1911.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+persistent+effects+of+Peru%27s+mining+mita+Dell"},{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null}],"related":["synthetic-difference-in-differences","local-projections","interactive-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geographically-weighted-pca","name":"Geographically Weighted PCA","fullName":"Geographically Weighted Principal Component Analysis (GWPCA)","aliases":["Local PCA","Spatially Adaptive PCA","Geographically Weighted Factor Analysis","Yerel Coğrafi Ağırlıklı PCA"],"domain":"spatial-analysis","family":"ml-model","subfamily":"Local spatial models","year":2011,"originator":"Paul Harris, Chris Brunsdon & Martin Charlton","url":"https://scholargate.app/en/spatial-analysis/geographically-weighted-pca","markdownUrl":"https://scholargate.app/en/spatial-analysis/geographically-weighted-pca.md","definition":"Geographically Weighted Principal Component Analysis (GWPCA) is a local dimensionality-reduction method introduced by Harris, Brunsdon, and Charlton in 2011. It extends classical PCA by fitting a separate weighted PCA at every location in a dataset, allowing eigenstructures — the principal components and their loadings — to vary continuously across geographic space rather than being constrained to a single global solution. GWPCA is suited to researchers in environmental science, public health, and regional economics who suspect that multivariate relationships among variables differ by location.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Harris, Chris Brunsdon & Martin Charlton","year":2011,"type":"Local dimensionality reduction","subfamily":"Local spatial models","kernel":"Bisquare or Gaussian spatial weighting","bandwidth_selection":"Cross-validation or AIC"},"citations":[{"ref":"Harris, P., Brunsdon, C., & Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10), 1717–1736.","type":"article","doi":"10.1080/13658816.2011.554838","isbn":null,"url":null}],"related":["principal-component-analysis","geographically-weighted-regression","geographically-weighted-random-forest"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geographically-weighted-random-forest","name":"Geographically Weighted Random Forest","fullName":"Geographically Weighted Random Forest (GWRF)","aliases":["Geographical Random Forest","GRF","Spatial Random Forest","Cografi Agirlikli Rastgele Orman"],"domain":"spatial-analysis","family":"ml-model","subfamily":"Spatial machine learning","year":2021,"originator":"Stefanos Georganos et al.","url":"https://scholargate.app/en/spatial-analysis/geographically-weighted-random-forest","markdownUrl":"https://scholargate.app/en/spatial-analysis/geographically-weighted-random-forest.md","definition":"Geographically Weighted Random Forest (GWRF) is a spatially local ensemble learning method that fits an independent Random Forest model at each observation location, weighting nearby training samples more heavily than distant ones through a spatial kernel function. It was introduced by Stefanos Georganos and colleagues in 2019 (published 2021) as an extension of Breiman's Random Forest to handle spatial non-stationarity — the phenomenon where predictor–response relationships vary across geographic space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stefanos Georganos et al.","year":2021,"type":"Spatially local ensemble learning method","subfamily":"Spatial machine learning","software":"R package SpatialML","kernel":"Gaussian or adaptive bisquare spatial kernel"},"citations":[{"ref":"Georganos, S., et al. (2021). Geographical random forests: a spatial extension of the random forest algorithm. Geocarto International, 36(2), 121–136.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Geographical+random+forests%3A+a+spatial+extension+of+the+random+forest+algorithm+Georganos"}],"related":["geographically-weighted-regression","random-forest","spatial-lag-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geographically-weighted-regression","name":"Geographically Weighted Regression","fullName":"Geographically Weighted Regression (GWR)","aliases":["GWR","local regression","spatially varying coefficient regression","Coğrafi Ağırlıklı Regresyon (GWR)"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":2002,"originator":"Fotheringham, Brunsdon & Charlton","url":"https://scholargate.app/en/spatial-analysis/geographically-weighted-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/geographically-weighted-regression.md","definition":"Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fotheringham, Brunsdon & Charlton","year":2002,"type":"Local spatial regression","estimator":"Locally weighted least squares","outcome":"continuous","structure":"cross-sectional (geo-referenced)","minSample":50},"citations":[{"ref":"Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.","type":"book","doi":null,"isbn":"978-0471496168","url":null}],"related":["ols-regression","spatial-lag-model","spatial-error-model","morans-i-test","lisa-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geologic-mapping","name":"Geologic Mapping","fullName":"Geologic Mapping","aliases":["field mapping","geological surveying","lithostratigraphic mapping"],"domain":"geoscience","family":"process-pipeline","subfamily":"Surface geological characterization","year":"1799","originator":"William Smith","url":"https://scholargate.app/en/geoscience/geologic-mapping","markdownUrl":"https://scholargate.app/en/geoscience/geologic-mapping.md","definition":"Geologic mapping is the systematic observation and documentation of rock types, structures, and relationships exposed on the land surface. Pioneered by William Smith in 1799, this foundational field method remains essential for understanding subsurface geology, economic geology, hazard assessment, and paleoenvironmental reconstruction. Modern mapping integrates field observations with satellite imagery, digital logs, and GIS technology to create comprehensive three-dimensional geological frameworks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Smith","subfamily":"Surface geological characterization","year":"1799","type":"regional geological documentation pipeline"},"citations":[{"ref":"Compton, R. R. (1962). Manual of Field Geology. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Fossen, H. (2010). Structural Geology (2nd ed.). Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511777806","isbn":null,"url":null},{"ref":"U.S. Geological Survey. (2017). Standards for Digital Geologic Maps. USGS Open-File Report 2017–1102.","type":"article","doi":null,"isbn":null,"url":"https://pubs.usgs.gov"}],"related":["stratigraphic-correlation","seismic-reflection-interpretation","petrographic-analysis","rock-mass-classification","geomorphological-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geomechanical-modeling","name":"Geomechanical Modeling","fullName":"Geomechanical Modeling","aliases":["mechanical earth modeling","stress modeling","rock mechanics simulation"],"domain":"geoscience","family":"process-pipeline","subfamily":"Stress and deformation analysis","year":"1900s","originator":"Coulomb and Mohr","url":"https://scholargate.app/en/geoscience/geomechanical-modeling","markdownUrl":"https://scholargate.app/en/geoscience/geomechanical-modeling.md","definition":"Geomechanical modeling is the numerical simulation of stress and deformation in rock masses, integrating rock properties, pressure conditions, and geometric constraints. Rooted in classical mechanics (Coulomb, Mohr) but modernized by finite element and finite difference methods, this approach is essential for well integrity assessment, reservoir compaction prediction, and stability evaluation of slopes and excavations. Models link subsurface geology to rock mechanical behavior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Coulomb and Mohr","subfamily":"Stress and deformation analysis","year":"1900s","type":"rock behavior prediction pipeline"},"citations":[{"ref":"Jaeger, J. C., & Cook, N. G. W. (1979). Fundamentals of Rock Mechanics (2nd ed.). Chapman and Hall.","type":"book","doi":null,"isbn":null,"url":"https://www.springer.com"},{"ref":"Zoback, M. D. (2007). Reservoir Geomechanics. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511586477","isbn":null,"url":null},{"ref":"Fjær, E., Holt, R. M., Horsrud, P., Raaen, A. M., & Risnes, R. (2008). Petroleum Related Rock Mechanics (2nd ed.). Elsevier.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Petroleum+Related+Rock+Mechanics+%282nd+ed.%29+Fj%C3%A6r"}],"related":["rock-mass-classification","basin-subsidence-analysis","well-log-analysis","geophysical-inversion","petrographic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geometric-morphometrics","name":"Geometric Morphometrics","fullName":"Geometric Morphometrics (GMM)","aliases":["shape analysis","morphometric analysis"],"domain":"archaeology","family":"process-pipeline","subfamily":"Morphological Analysis","year":"1991","originator":"Fred Bookstein","url":"https://scholargate.app/en/archaeology/geometric-morphometrics","markdownUrl":"https://scholargate.app/en/archaeology/geometric-morphometrics.md","definition":"Geometric morphometrics is a quantitative analytical method that captures, analyzes, and compares the shapes of biological structures (bones, teeth, pottery) using coordinate data from landmarks and outlines. Developed by Fred Bookstein in the 1990s, GMM provides a rigorous statistical framework for studying shape variation across populations or time periods. The method allows archaeologists to quantify morphological differences between individuals, populations, or artifact classes with precision impossible using traditional linear measurements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred Bookstein","subfamily":"Morphological Analysis","year":"1991","type":"Shape and form analysis"},"citations":[{"ref":"Bookstein, F. L. (1991). Morphometric Tools for Landmark Data: Geometry and Biology. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511573064","isbn":null,"url":null},{"ref":"Zelditch, M. L., Swiderski, D. L., Sheets, H. D., & Fink, W. L. (2004). Geometric Morphometrics for Biologists: A Primer. Elsevier.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/geometric-morphometrics-for-biologists/zelditch/978-0-12-778460-1"},{"ref":"Rohlf, F. J., & Slice, D. E. (1990). Extensions of the Procrustes method for the optimal superimposition of landmarks. Systematic Zoology, 39(1), 40-59.","type":"article","doi":"10.2307/2992207","isbn":null,"url":null}],"related":["dental-microwear-texture-analysis","use-wear-analysis","minimum-number-of-individuals","number-of-identified-specimens"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geophysical-inversion","name":"Geophysical Inversion","fullName":"Geophysical Inversion","aliases":["inverse problem solving","parameter estimation","model-data fitting"],"domain":"geoscience","family":"process-pipeline","subfamily":"Parameter recovery","year":"1963","originator":"Tikhonov and Tarantola","url":"https://scholargate.app/en/geoscience/geophysical-inversion","markdownUrl":"https://scholargate.app/en/geoscience/geophysical-inversion.md","definition":"Geophysical inversion is the process of using observed geophysical data to estimate subsurface properties and structures. Formalized by Tikhonov (1963) and expanded by Tarantola (1987), this mathematical framework solves the inverse problem: given measurements (gravity, magnetics, seismic, electrical), what subsurface model produced them? Inversion is central to all quantitative geophysics and enables extraction of detailed subsurface information from surface or borehole measurements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tikhonov and Tarantola","subfamily":"Parameter recovery","year":"1963","type":"data assimilation pipeline"},"citations":[{"ref":"Tarantola, A. (1987). Inverse Problem Theory: Methods for Data Fitting and Model Parameter Estimation. Elsevier.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"Constable, S. C., Parker, R. L., & Constable, C. G. (1990). Occam's inversion: A practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics, 55(3), 289–300.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Occam%27s+inversion%3A+A+practical+algorithm+for+generating+smooth+models+from+electromagnetic+sounding+data+Constable"},{"ref":"Menke, W. (2012). Geophysical Data Analysis: Discrete Inverse Theory (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Geophysical+Data+Analysis%3A+Discrete+Inverse+Theory+%283rd+ed.%29+Menke"}],"related":["seismic-reflection-interpretation","geophysical-survey","basin-subsidence-analysis","well-log-analysis","geochemical-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geostrophic-velocity","name":"Geostrophic Velocity","fullName":"Geostrophic Velocity Calculation","aliases":["Geostrophic Current","Thermal Wind Equation"],"domain":"oceanography","family":"process-pipeline","subfamily":"Dynamical Oceanography","year":"1942","originator":"Harald Sverdrup","url":"https://scholargate.app/en/oceanography/geostrophic-velocity","markdownUrl":"https://scholargate.app/en/oceanography/geostrophic-velocity.md","definition":"Geostrophic velocity is the current driven by balance between the pressure gradient force and the Coriolis force, derived from the thermal wind equation. In most of the ocean away from the equator and coastal boundaries, geostrophic balance is an excellent approximation to the actual flow. Developed by Harald Sverdrup and colleagues in the 1940s, geostrophic velocity calculation from hydrographic data enables estimation of ocean currents without direct current measurements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harald Sverdrup","subfamily":"Dynamical Oceanography","year":"1942","type":"theoretical-method"},"citations":[{"ref":"Sverdrup, H. U., Johnson, M. W., & Fleming, R. H. (1942). The Oceans: Their Physics, Chemistry, and General Biology. Prentice-Hall.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com/"},{"ref":"Vallis, G. K. (2006). Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-scale Circulation. Cambridge University Press.","type":"article","doi":"10.1017/cbo9780511790447","isbn":null,"url":null}],"related":["ekman-transport","acoustic-doppler-current-profiler","tidal-harmonic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geostrophic-wind","name":"Geostrophic Wind","fullName":"Geostrophic Wind Balance Theory","aliases":["Geostrophic wind","Geostrophic balance","Geostrophic approximation"],"domain":"meteorology","family":"process-pipeline","subfamily":"Dynamical meteorology","year":"1857","originator":"Buys Ballot, Coriolis","url":"https://scholargate.app/en/meteorology/geostrophic-wind","markdownUrl":"https://scholargate.app/en/meteorology/geostrophic-wind.md","definition":"Geostrophic wind balance is a fundamental concept in meteorology that describes the balance between the pressure gradient force and the Coriolis force in large-scale atmospheric flow. When this balance is achieved, wind blows parallel to isobars without acceleration—a condition observed in the free atmosphere away from the equator and surface boundary layer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Buys Ballot, Coriolis","subfamily":"Dynamical meteorology","year":"1857","type":"Wind balance principle"},"citations":[{"ref":"Holton, J. R. (2004). An Introduction to Dynamic Meteorology (4th ed.). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/an-introduction-to-dynamic-meteorology/holton/978-0-12-354966-1"},{"ref":"Held, I. M., & Hou, A. Y. (1980). Nonlinear axially symmetric circulations in a nearly inviscid atmosphere. Journal of the Atmospheric Sciences, 37(3), 515-533.","type":"article","doi":"10.1175/1520-0469(1980)037<0515:NASCIA>2.0.CO;2","isbn":null,"url":null}],"related":["thermal-wind","quasi-geostrophic-omega-equation","wrf-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gerd-hrql","name":"GERD Health-Related Quality of Life Scale","fullName":"GERD Health-Related Quality of Life Scale","aliases":["GERD-HRQL","GERD-HRQoL"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"gastrointestinal-symptom-burden","year":"1996","originator":"Velanovich, V., Zhang, Y., Hollis, J. B., et al.","url":"https://scholargate.app/en/gastroenterology/gerd-hrql","markdownUrl":"https://scholargate.app/en/gastroenterology/gerd-hrql.md","definition":"The GERD Health-Related Quality of Life Scale (GERD-HRQL) is a concise, validated patient-reported outcome measure for assessing the symptomatic and functional impact of gastroesophageal reflux disease (GERD). Developed by Velanovich and colleagues in 1996, the 9-item GERD-HRQL measures heartburn frequency and severity, regurgitation, and impact on sleep and medication use. The scale is highly responsive to proton pump inhibitor (PPI) therapy and is widely used in GERD trials and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Velanovich, V., Zhang, Y., Hollis, J. B., et al.","subfamily":"gastrointestinal-symptom-burden","year":"1996","type":"Self-report"},"citations":[{"ref":"Velanovich, V., Zhang, Y., Hollis, J. B., Feldman, M. I., Sampliner, R., Guan, W., & Escamilla, C. (1996). Presenting symptoms and outcome measures in reflux esophagitis. Digestive Diseases and Sciences, 41(10), 1865–1873.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Presenting+symptoms+and+outcome+measures+in+reflux+esophagitis+Velanovich"}],"related":["rome-iv-ibs-criteria","pac-qol","gcsi","ibdq-short"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"geriatric-anxiety-inventory","name":"GAI","fullName":"Geriatric Anxiety Inventory","aliases":["GAI"],"domain":"gerontology","family":"process-pipeline","subfamily":"anxiety-disorders","year":"2007","originator":"Nancy A. Pachana","url":"https://scholargate.app/en/gerontology/geriatric-anxiety-inventory","markdownUrl":"https://scholargate.app/en/gerontology/geriatric-anxiety-inventory.md","definition":"The Geriatric Anxiety Inventory (GAI) is a 20-item self-report questionnaire developed by Pachana and colleagues in 2007 to assess anxiety symptoms specifically in older adults. Designed to address the limitations of general anxiety scales in detecting anxiety in older populations—where anxiety may present atypically or be masked by somatic complaints and medical comorbidities—the GAI focuses on cognitive and affective symptoms of anxiety with minimal emphasis on physical symptoms. It is widely used in geriatric practice, mental health clinics, and research to screen for and evaluate anxiety disorders in seniors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nancy A. Pachana","subfamily":"anxiety-disorders","year":"2007","type":"Self-report questionnaire"},"citations":[{"ref":"Pachana, N. A., Byrne, G. J., Looi, J. C., Krishnan, V., & Hilbert, M. M. (2007). Development and validation of the Geriatric Anxiety Inventory. Int Psychogeriatr, 19(1), 103-114.","type":"article","doi":"10.1017/S1041610206003504","isbn":null,"url":null},{"ref":"Byrne, G. J., & Pachana, N. A. (2011). Development and validation of a short form of the Geriatric Anxiety Inventory—the GAI-SF. Int Psychogeriatr, 23(1), 137-143.","type":"article","doi":"10.1017/s1041610210001237","isbn":null,"url":null},{"ref":"Gerolimatos, L. A., Egan, J., & Stawasz, M. (2015). Associations between health status, cognitive status, and anxiety in older adults. Psychol Aging, 30(1), 75-88.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Associations+between+health+status%2C+cognitive+status%2C+and+anxiety+in+older+adults+Gerolimatos"}],"related":["social-engagement-scale","cognitive-telephone-screening","life-space-assessment","activities-balance-confidence","short-physical-performance-battery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"germination-kinetics","name":"Germination Kinetics Modeling","fullName":"Germination Kinetics Modeling","aliases":["seed germination modeling","thermal germination analysis","germination rate modeling","hydrothermal time modeling"],"domain":"agronomy","family":"process-pipeline","subfamily":"Seed physiology and crop establishment","year":"1970s–1990s (formalized thermal and hydrothermal time frameworks)","originator":"Multiple contributors (Hegarty 1973; Garcia-Huidobro et al. 1982; Bradford 1990)","url":"https://scholargate.app/en/agronomy/germination-kinetics","markdownUrl":"https://scholargate.app/en/agronomy/germination-kinetics.md","definition":"Germination Kinetics Modeling is a quantitative method used in agronomy, seed science, and crop physiology to describe, predict, and compare the speed and uniformity of seed germination under varying environmental conditions. It draws on thermal time and hydrothermal time frameworks to link temperature, water potential, and time into biologically interpretable parameters, enabling researchers and agronomists to characterize seed lot quality and optimize planting conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Hegarty 1973; Garcia-Huidobro et al. 1982; Bradford 1990)","year":"1970s–1990s (formalized thermal and hydrothermal time frameworks)","type":"Quantitative modeling / biophysical analysis","dataType":"Time-to-event germination counts under controlled temperature and/or water potential conditions","subfamily":"Seed physiology and crop establishment"},"citations":[{"ref":"Bradford, K. J. (2002). Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Science, 50(2), 248–260.","type":"article","doi":"10.1614/0043-1745(2002)050[0248:AOHTTQ]2.0.CO;2","isbn":null,"url":null},{"ref":"Bewley, J. D., & Black, M. (1994). Seeds: Physiology of Development and Germination (2nd ed.). Plenum Press.","type":"book","doi":null,"isbn":"978-0306446764","url":null}],"related":["dose-response-modeling","survival-analysis","nonlinear-regression","thermal-time-analysis","plant-growth-modeling","probit-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ges-algorithm","name":"GES Algorithm","fullName":"Greedy Equivalence Search (GES)","aliases":["Greedy Equivalence Search","GES Causal Discovery","Score-Based Greedy Search","Açgözlü Eşdeğerlik Araması"],"domain":"causal-inference","family":"ml-model","subfamily":"Causal discovery","year":2002,"originator":"David Maxwell Chickering","url":"https://scholargate.app/en/causal-inference/ges-algorithm","markdownUrl":"https://scholargate.app/en/causal-inference/ges-algorithm.md","definition":"Greedy Equivalence Search (GES) is a score-based algorithm for learning the causal structure of a set of variables from observational data. Introduced by David Maxwell Chickering in 2002, GES operates directly on Markov equivalence classes of directed acyclic graphs (DAGs), represented as completed partially directed acyclic graphs (CPDAGs). Under the assumptions of causal sufficiency and a faithful data-generating process, GES is proven to recover the true equivalence class in the large-sample limit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Maxwell Chickering","year":2002,"type":"Score-based causal structure learning algorithm","subfamily":"Causal discovery","search_space":"Equivalence classes of DAGs (CPDAGs)","score_criterion":"BIC / BDe (decomposable)"},"citations":[{"ref":"Chickering, D. M. (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3, 507–554.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/chickering02b.html"}],"related":["pc-algorithm","notears","bayesian-network"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gestalt-principles-analysis","name":"Gestalt Principles Analysis","fullName":"Gestalt Principles Analysis","aliases":["Perceptual Organization Evaluation","Visual Grouping Assessment"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Perceptual psychology and visual organization","year":"1923","originator":"Max Wertheimer","url":"https://scholargate.app/en/visual-arts/gestalt-principles-analysis","markdownUrl":"https://scholargate.app/en/visual-arts/gestalt-principles-analysis.md","definition":"Gestalt Principles Analysis is a framework for evaluating how visual elements are organized and grouped within a design or image. Originating in early twentieth-century perceptual psychology, this method assesses how principles like proximity, similarity, continuity, and closure guide viewers' perception of coherent wholes rather than disconnected parts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max Wertheimer","subfamily":"Perceptual psychology and visual organization","year":"1923","type":"Analytical framework"},"citations":[{"ref":"Wertheimer, M. (1923). Untersuchungen zur Lehre von der Gestalt. Psychologische Forschung, 4, 301–350.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/gestalt-principles"},{"ref":"Koffka, K. (1935). Principles of Gestalt Psychology. Harcourt, Brace and Company.","type":"article","doi":null,"isbn":null,"url":"https://publisher.org/koffka-gestalt-psychology"},{"ref":"Wagemans, J., Elder, J. H., Kubovy, M., Palmer, S. E., Peterson, M. A., Singh, M., & von der Heydt, R. (2012). A Century of Gestalt Psychology in Visual Perception: I. Perceptual Grouping and Figure-Ground Organization. Psychological Bulletin, 138(6), 1172–1217.","type":"article","doi":"10.1037/a0029333","isbn":null,"url":null}],"related":["visual-balance-measurement","visual-complexity-measure","visual-saliency-map","color-harmony-analysis","image-aesthetics-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"getis-ord-gi","name":"Getis-Ord Gi*","fullName":"Getis-Ord Gi* Hot Spot Analysis","aliases":["hot spot analysis","cold spot analysis","Gi* statistic","local Gi statistic","Getis-Ord Gi* (Sıcak/Soğuk Nokta Analizi)"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":1992,"originator":"Arthur Getis and J. Keith Ord","url":"https://scholargate.app/en/spatial-analysis/getis-ord-gi","markdownUrl":"https://scholargate.app/en/spatial-analysis/getis-ord-gi.md","definition":"Getis-Ord Gi* is a local spatial statistic, introduced by Getis and Ord in 1992 and refined in 1995, that compares the value at each location and its neighbours against the global mean to identify statistically significant clusters of high values (hot spots) and low values (cold spots).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arthur Getis and J. Keith Ord","year":1992,"type":"Local spatial statistic","estimator":"Standardised Gi* z-score per location","outcome":"continuous (spatially referenced)"},"citations":[{"ref":"Getis, A. & Ord, J.K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3), 189–206.","type":"article","doi":"10.1111/j.1538-4632.1992.tb00261.x","isbn":null,"url":null},{"ref":"Ord, J.K. & Getis, A. (1995). Local Spatial Autocorrelation Statistics. Geographical Analysis, 27(4), 286–306.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Local+Spatial+Autocorrelation+Statistics+Ord"}],"related":["morans-i","local-morans-i","lisa","geary-c","spatial-lag-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ghq-12","name":"General Health Questionnaire","fullName":"General Health Questionnaire-12 (GHQ-12)","aliases":["GHQ-12","GHQ"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Psychological distress and mental well-being screening","year":"1992","originator":"David P. Goldberg","url":"https://scholargate.app/en/clinical-psychology/ghq-12","markdownUrl":"https://scholargate.app/en/clinical-psychology/ghq-12.md","definition":"The General Health Questionnaire-12 (GHQ-12) is a brief, 12-item self-report screening instrument for psychological distress and mental health problems in the general population. Developed by David P. Goldberg, the GHQ-12 is the most widely used short form of the longer General Health Questionnaire series. It is designed for rapid detection of minor psychiatric morbidity and assessment of psychological well-being in clinical, occupational health, and community settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David P. Goldberg","subfamily":"Psychological distress and mental well-being screening","year":"1992","type":"Psychiatric symptom screening"},"citations":[{"ref":"Goldberg, D. P. (1972). The detection of psychiatric illness by questionnaire. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/4500127/"},{"ref":"Goldberg, D. P., & Williams, P. (1992). A user's guide to the General Health Questionnaire. Windsor: NFER-Nelson.","type":"article","doi":null,"isbn":"978-0-7005-1220-5","url":null}],"related":["hads","k10-kessler","dass-21","swls","ces-d"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"giacomini-white-test","name":"Giacomini-White Test","fullName":"Giacomini-White Test of Conditional Predictive Ability","aliases":["GW Test","Conditional Predictive Ability Test","Giacomini-White CPA Test","Koşullu Tahmin Yeteneği Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Forecast evaluation","year":2006,"originator":"Raffaella Giacomini & Halbert White","url":"https://scholargate.app/en/econometrics/giacomini-white-test","markdownUrl":"https://scholargate.app/en/econometrics/giacomini-white-test.md","definition":"The Giacomini-White (GW) test, introduced by Raffaella Giacomini and Halbert White in 2006, evaluates whether two competing forecasting methods have equal conditional predictive ability given information available at the time of forecast. Unlike unconditional tests such as the Diebold-Mariano test, it asks whether one method systematically outperforms the other in specific economic or market conditions, making it especially useful for practitioners who need state-dependent forecast comparisons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raffaella Giacomini & Halbert White","year":2006,"type":"Non-nested forecast comparison test","subfamily":"Forecast evaluation","null_hypothesis":"Equal conditional predictive ability of two forecasting methods","test_statistic_distribution":"Chi-squared (asymptotic)"},"citations":[{"ref":"Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578.","type":"article","doi":"10.1111/j.1468-0262.2006.00718.x","isbn":null,"url":null}],"related":["diebold-mariano-test","model-confidence-set","ts-cross-validation"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gibbs-sampling-for-model-comparison","name":"Gibbs Sampling for Model Comparison","fullName":"Gibbs Sampling for Bayesian Model Comparison","aliases":["Gibbs-based model selection","MCMC model comparison via Gibbs","Bayesian model comparison with Gibbs sampling","Gibbs sampler model selection"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1995","originator":"Carlin and Chib","url":"https://scholargate.app/en/bayesian/gibbs-sampling-for-model-comparison","markdownUrl":"https://scholargate.app/en/bayesian/gibbs-sampling-for-model-comparison.md","definition":"Gibbs sampling for model comparison is a Bayesian MCMC approach that simultaneously samples from the space of competing models and their parameters. By augmenting the Gibbs sampler with a discrete model-index variable, posterior model probabilities and Bayes factors are estimated from the resulting Markov chain without requiring separate runs per model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carlin and Chib","year":"1995","type":"Bayesian model selection via MCMC","dataType":"continuous, discrete, or mixed; any data suitable for the competing models","subfamily":"Bayesian / computational"},"citations":[{"ref":"Carlin, B. P. & Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B, 57(3), 473-484.","type":"article","doi":"10.1111/j.2517-6161.1995.tb02042.x","isbn":null,"url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["gibbs-sampling","bayesian-model-averaging","reversible-jump-mcmc","metropolis-hastings-for-model-comparison","bayesian-inference-for-model-comparison","bayes-factor"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gibbs-sampling-with-measurement-error","name":"Gibbs Sampling with Measurement Error","fullName":"Gibbs Sampling for Models with Measurement Error","aliases":["Gibbs sampler with errors-in-variables","MCMC measurement error model","Bayesian errors-in-variables Gibbs","Gibbs EIV sampling"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990–1993","originator":"Gelfand & Smith (Gibbs sampler); Richardson & Gilks (measurement error extension)","url":"https://scholargate.app/en/bayesian/gibbs-sampling-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/gibbs-sampling-with-measurement-error.md","definition":"Gibbs sampling with measurement error is a Bayesian MCMC method that jointly estimates unknown true covariate values and model parameters when the observed data are corrupted by measurement error. By treating the latent true values as additional unknowns, it samples all quantities iteratively from their full conditional distributions, propagating measurement uncertainty into every downstream inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelfand & Smith (Gibbs sampler); Richardson & Gilks (measurement error extension)","year":"1990–1993","type":"Bayesian MCMC sampling algorithm","dataType":"Continuous or categorical observations subject to measurement error; hierarchical or latent-variable data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398–409.","type":"article","doi":"10.1080/01621459.1990.10476213","isbn":null,"url":null},{"ref":"Richardson, S. & Gilks, W. R. (1993). A Bayesian approach to measurement error problems in epidemiology using conditional independence models. American Journal of Epidemiology, 138(6), 430–442.","type":"article","doi":"10.1093/oxfordjournals.aje.a116875","isbn":null,"url":null}],"related":["gibbs-sampling","mcmc-with-measurement-error","bayesian-hierarchical-model-with-measurement-error","metropolis-hastings-with-measurement-error","bayesian-inference-with-measurement-error","hamiltonian-monte-carlo-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gibbs-sampling-with-missing-data","name":"Gibbs Sampling with Missing Data","fullName":"Gibbs Sampling with Missing Data Imputation","aliases":["data augmentation Gibbs sampler","Gibbs sampler with data augmentation","Bayesian imputation via Gibbs sampling","MCMC missing data imputation"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1987–1990","originator":"Tanner & Wong (data augmentation), Gelfand & Smith (Gibbs sampler)","url":"https://scholargate.app/en/bayesian/gibbs-sampling-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/gibbs-sampling-with-missing-data.md","definition":"Gibbs sampling with missing data treats unobserved values as additional unknowns alongside model parameters and samples all of them jointly within a Markov chain Monte Carlo loop. The method alternates between drawing the missing values from their conditional distribution given the parameters and drawing the parameters from their conditional distribution given the completed data, producing a posterior over both simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tanner & Wong (data augmentation), Gelfand & Smith (Gibbs sampler)","year":"1987–1990","type":"Bayesian computational method","dataType":"any data with partially observed variables","subfamily":"Bayesian / computational"},"citations":[{"ref":"Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528–540.","type":"article","doi":"10.1080/01621459.1987.10478458","isbn":null,"url":null},{"ref":"Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471183860","url":null}],"related":["gibbs-sampling","mcmc-with-missing-data","multiple-imputation","bayesian-inference-with-missing-data","data-augmentation","bayesian-hierarchical-model-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gibbs-sampling","name":"Gibbs Sampling","fullName":"Gibbs Sampling Markov Chain Monte Carlo","aliases":["Gibbs sampler","coordinate-wise MCMC","systematic scan Gibbs","blocked Gibbs sampling"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1984","originator":"Stuart Geman & Donald Geman","url":"https://scholargate.app/en/bayesian/gibbs-sampling","markdownUrl":"https://scholargate.app/en/bayesian/gibbs-sampling.md","definition":"Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stuart Geman & Donald Geman","year":"1984","type":"MCMC sampling algorithm","dataType":"any data with a tractable posterior conditional structure","subfamily":"Bayesian / computational"},"citations":[{"ref":"Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741.","type":"article","doi":"10.1109/TPAMI.1984.4767596","isbn":null,"url":null},{"ref":"Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398-409.","type":"article","doi":"10.1080/01621459.1990.10476213","isbn":null,"url":null}],"related":["mcmc","metropolis-hastings","hamiltonian-monte-carlo","bayesian-regression","hierarchical-bayesian-inference","variational-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gingival-inflammation-scale","name":"Gingival Index","fullName":"Gingival Index for Periodontal Health Assessment","aliases":["Gingival Index (GI)","Loe-Silness Index"],"domain":"dentistry","family":"process-pipeline","subfamily":"periodontal-health-assessment","year":"1963","originator":"Harald Loe and Jorgen Silness","url":"https://scholargate.app/en/dentistry/gingival-inflammation-scale","markdownUrl":"https://scholargate.app/en/dentistry/gingival-inflammation-scale.md","definition":"The Gingival Index (GI), also known as the Loe-Silness Index, is a standardized clinician-rated assessment tool for measuring the severity of gingival inflammation and periodontal disease. Developed by Loe and Silness in 1963, the GI remains the gold standard for quantifying gum inflammation in clinical research and practice. It grades inflammation on a 0-3 ordinal scale based on visual appearance and bleeding response, enabling objective assessment of periodontal health, monitoring of periodontal disease progression, and evaluation of treatment efficacy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harald Loe and Jorgen Silness","subfamily":"periodontal-health-assessment","year":"1963","type":"Clinician-rated clinical index"},"citations":[{"ref":"Löe, H., & Silness, J. (1963). Periodontal disease in pregnancy. I. Prevalence and severity. Acta Odontologica Scandinavica, 21(6), 533-551.","type":"article","doi":"10.3109/00016356309011240","isbn":null,"url":null}],"related":["ohip-14","oral-impacts-daily-performance","dental-caries-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gini-weight","name":"GINI-WEIGHT","fullName":"Gini Coefficient Weighting — inequality-of-discrimination objective weighting","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"1912","originator":"Gini, C.","url":"https://scholargate.app/en/decision-making/gini-weight","markdownUrl":"https://scholargate.app/en/decision-making/gini-weight.md","definition":"GINI-WEIGHT (Gini Coefficient Weighting — inequality-of-discrimination objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Gini, C. in 1912. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gini, C.","subfamily":"Weight_Objective","year":"1912","type":"Weight_Objective (Gini inequality coefficient applied to normalised criterion column)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Gini, C. (1912). Variabilità e mutabilità. Studi economico-giuridici della R. Università di Cagliari","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Variabilit%C3%A0%20e%20mutabilit%C3%A0"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gis-mcda","name":"GIS-MCDA","fullName":"GIS-Based Multi-Criteria Decision Analysis","aliases":["GIS-MCDM","spatial multi-criteria analysis","GIS-AHP","weighted overlay suitability","CBS tabanlı çok kriterli karar analizi"],"domain":"spatial-analysis","family":"process-pipeline","subfamily":"Spatial decision support","year":2006,"originator":"Jacek Malczewski (GIS-MCDA synthesis)","url":"https://scholargate.app/en/spatial-analysis/gis-mcda","markdownUrl":"https://scholargate.app/en/spatial-analysis/gis-mcda.md","definition":"GIS-MCDA combines the map layers of a geographic information system with multi-criteria decision analysis to produce suitability or priority maps — ranking locations by how well they satisfy several weighted criteria at once. It is the standard framework for spatial decisions such as siting hospitals, solar farms, landfills, or evacuation areas, integrating methods like AHP, TOPSIS, and weighted overlay with spatial data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacek Malczewski (GIS-MCDA synthesis)","year":2006,"type":"Spatial multi-criteria suitability/decision analysis","subfamily":"Spatial decision support","combines":"GIS map layers + MCDA weights","output":"Suitability / priority map"},"citations":[{"ref":"Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.","type":"article","doi":"10.1080/13658810600661508","isbn":null,"url":null},{"ref":"Saaty, T. L. (1980). The Analytic Hierarchy Process. McGraw-Hill.","type":"book","doi":null,"isbn":"978-0-07-054371-2","url":null}],"related":["ahp","topsis","location-allocation","least-cost-path"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gjr-garch","name":"GJR-GARCH","fullName":"Glosten-Jagannathan-Runkle GARCH","aliases":["asymmetric GARCH","leverage GARCH","TGARCH","GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1993,"originator":"Glosten, Jagannathan & Runkle (1993); Zakoian (1994)","url":"https://scholargate.app/en/econometrics/gjr-garch","markdownUrl":"https://scholargate.app/en/econometrics/gjr-garch.md","definition":"GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glosten, Jagannathan & Runkle (1993); Zakoian (1994)","year":1993,"type":"Asymmetric conditional volatility model","estimator":"Maximum likelihood","outcome":"continuous (financial return series)","minSample":100},"citations":[{"ref":"Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801.","type":"article","doi":"10.1111/j.1540-6261.1993.tb05128.x","isbn":null,"url":null},{"ref":"Zakoian, J. M. (1994). Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 18(5), 931-955.","type":"article","doi":"10.1016/0165-1889(94)90039-6","isbn":null,"url":null}],"related":["arch-model","garch-model","egarch","arima","tbats"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"glasgow-benefit-inventory","name":"GBI","fullName":"Glasgow Benefit Inventory","aliases":["GBI"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"otolaryngologic-outcome-surgery","year":"1996","originator":"Karol Robinson, Sophia Gatehouse, and Gordon G. Browning","url":"https://scholargate.app/en/otolaryngology/glasgow-benefit-inventory","markdownUrl":"https://scholargate.app/en/otolaryngology/glasgow-benefit-inventory.md","definition":"The Glasgow Benefit Inventory (GBI) is an 18-item self-report questionnaire designed to measure change in health status and general well-being resulting from otolaryngologic intervention (surgery, medical treatment). Unlike generic health-related quality-of-life measures, the GBI is disease-specific, asking patients to compare their post-intervention status to their pre-intervention baseline. Developed by Robinson, Gatehouse, and Browning in 1996, the GBI has become the standard outcome measure for evaluating benefit from ear, nose, and throat surgery and treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karol Robinson, Sophia Gatehouse, and Gordon G. Browning","subfamily":"otolaryngologic-outcome-surgery","year":"1996","type":"Self-report"},"citations":[{"ref":"Robinson, K., Gatehouse, S., & Browning, G. G. (1996). Measuring patient benefit from otorhinolaryngological surgery and treatment. Annals of Otology, Rhinology & Laryngology, 105(6), 415-422.","type":"article","doi":"10.1177/000348949610500601","isbn":null,"url":null}],"related":["hearing-handicap-inventory","tinnitus-handicap-inventory","patient-enablement-instrument"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"glasgow-blatchford-score","name":"Glasgow-Blatchford Score","fullName":"Glasgow-Blatchford Score for Upper GI Bleeding Risk","aliases":["GBS","Blatchford score","GI bleeding risk"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"2000","originator":"O. Blatchford, W. R. Murray, et al.","url":"https://scholargate.app/en/clinical-assessment/glasgow-blatchford-score","markdownUrl":"https://scholargate.app/en/clinical-assessment/glasgow-blatchford-score.md","definition":"The Glasgow-Blatchford score (GBS), developed by Blatchford et al. in 2000, is a 23-point risk stratification tool for predicting the need for intervention (transfusion, endoscopic therapy, surgery) in patients presenting with acute upper gastrointestinal bleeding. It integrates clinical and laboratory data to identify low-risk patients who may be candidates for outpatient or non-interventional management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"O. Blatchford, W. R. Murray, et al.","subfamily":"Clinical scoring","year":"2000","type":"Gastrointestinal bleeding risk stratification"},"citations":[{"ref":"Blatchford, O., Murray, W. R., & Blatchford, M. (2000). A risk score to predict need for treatment for upper-gastrointestinal haemorrhage. Lancet, 356(9238), 1318-1321.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+risk+score+to+predict+need+for+treatment+for+upper-gastrointestinal+haemorrhage+Blatchford"},{"ref":"Stanley, A. J., Laine, L., & Dalton, H. R. (2009). Management of acute upper and lower gastrointestinal bleeding. Gut, 58(11), 1407-1417.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Management+of+acute+upper+and+lower+gastrointestinal+bleeding+Stanley"}],"related":["wells-score-dvt","curb-65","apache-ii-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"glasgow-coma-scale","name":"Glasgow Coma Scale","fullName":"Glasgow Coma Scale (GCS) for Consciousness Assessment","aliases":["GCS","Glasgow Scale"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"1974","originator":"Graham Teasdale and Bryan Jennett","url":"https://scholargate.app/en/clinical-assessment/glasgow-coma-scale","markdownUrl":"https://scholargate.app/en/clinical-assessment/glasgow-coma-scale.md","definition":"The Glasgow Coma Scale (GCS), developed by Teasdale and Jennett in 1974, is a 15-point scale used to assess level of consciousness and severity of brain injury. It evaluates eye opening, verbal response, and motor response, making it the gold standard tool for rapid neurological assessment in trauma, emergency, and intensive care settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Graham Teasdale and Bryan Jennett","subfamily":"Clinical scoring","year":"1974","type":"Consciousness and neurological assessment"},"citations":[{"ref":"Teasdale, G., & Jennett, B. (1974). Assessment of coma and impaired consciousness. A practical scale. Lancet, 2(7872), 81-84.","type":"article","doi":"10.1016/S0140-6736(74)91639-0","isbn":null,"url":null},{"ref":"Teasdale, G., Maas, A. I. R., Lecky, F., Manley, G., Stocchetti, N., & Murray, G. (2014). The Glasgow Coma Scale at 40 years: standing the test of time. Lancet Neurology, 13(8), 844-854.","type":"article","doi":"10.1016/S1474-4422(14)70120-6","isbn":null,"url":null}],"related":["apgar-score","richmond-agitation-sedation","ramsay-sedation-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"glasgow-sleep-effort-scale","name":"GSES","fullName":"Glasgow Sleep Effort Scale","aliases":["GSES","Glasgow Sleep Effort Scale"],"domain":"sleep-medicine","family":"process-pipeline","subfamily":"Performance effort in sleep; cognitive effort","year":"2005","originator":"Broomfield, N. M., Espie, C. A.","url":"https://scholargate.app/en/sleep-medicine/glasgow-sleep-effort-scale","markdownUrl":"https://scholargate.app/en/sleep-medicine/glasgow-sleep-effort-scale.md","definition":"The Glasgow Sleep Effort Scale (GSES) is a brief instrument designed to measure the degree of mental and behavioral effort exerted in attempting to fall asleep. Developed by Broomfield and Espie in 2005, the GSES captures a key cognitive-behavioral maintenance mechanism in insomnia: excessive effort to sleep, anxiety about sleep performance, and counterproductive behaviors (trying hard to fall asleep, monitoring sleep, checking the clock) that paradoxically perpetuate sleep difficulty. The GSES is increasingly recognized as an important outcome measure for cognitive-behavioral therapy for insomnia (CBT-I).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Broomfield, N. M., Espie, C. A.","subfamily":"Performance effort in sleep; cognitive effort","year":"2005","type":"Self-report"},"citations":[{"ref":"Broomfield, N. M., & Espie, C. A. (2005). Initial insomnia severity index scores in primary care strongly predict outcome after cognitive behavioral therapy for insomnia. Journal of Clinical Psychiatry, 66(11), 1409-1415.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Initial+insomnia+severity+index+scores+in+primary+care+strongly+predict+outcome+after+cognitive+behavioral+therapy+for+insomnia+Broomfield"}],"related":["sleep-condition-indicator","hyperarousal-scale","consensus-sleep-diary"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"glaucoma-quality-of-life","name":"GQL-15","fullName":"Glaucoma Quality of Life-15","aliases":["GQL-15","Glaucoma QoL"],"domain":"ophthalmology","family":"process-pipeline","subfamily":"glaucoma-specific quality of life","year":"1998","originator":"Nelson PA, Aspinall PA et al.","url":"https://scholargate.app/en/ophthalmology/glaucoma-quality-of-life","markdownUrl":"https://scholargate.app/en/ophthalmology/glaucoma-quality-of-life.md","definition":"The GQL-15 is a 15-item, disease-specific quality of life questionnaire designed to measure the impact of glaucoma on patients' daily functioning and psychological well-being. Developed by Nelson, Aspinall, and colleagues in the UK (1998), the GQL-15 emphasizes glaucoma-specific concerns—visual field loss, peripheral vision, lighting sensitivity, and falls risk—making it a sensitive complement to generic or broader vision-related instruments in glaucoma populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nelson PA, Aspinall PA et al.","subfamily":"glaucoma-specific quality of life","year":"1998","type":"Self-report"},"citations":[{"ref":"Nelson, P., Aspinall, P. A., Papasthathis, K., et al. (1998). Quality of life in glaucoma and its relationship with visual function. J Glaucoma, 7(2), 71-78.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quality+of+life+in+glaucoma+and+its+relationship+with+visual+function+Nelson"},{"ref":"Nelson, P., Aspinall, P. A., O'Brien, C. (1998). Screening for glaucoma with the Glaucoma-15. Br J Ophthalmol, 82(4), 327-334.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Screening+for+glaucoma+with+the+Glaucoma-15+Nelson"}],"related":["nei-vfq-25","visual-function-index","low-vision-quality-of-life","impact-vision-impairment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-assessment-lupus","name":"Physician Global Assessment of Lupus Activity","fullName":"Physician Global Assessment of Lupus Activity","aliases":["Physician Global Assessment","PGA-Lupus","Clinician Global Assessment-SLE"],"domain":"rheumatology","family":"process-pipeline","subfamily":"global-assessment-score","year":"1992","originator":"Bombardier et al.","url":"https://scholargate.app/en/rheumatology/global-assessment-lupus","markdownUrl":"https://scholargate.app/en/rheumatology/global-assessment-lupus.md","definition":"The Physician Global Assessment (PGA) is a clinician-rated, single-item measure of overall systemic lupus erythematosus (SLE) disease activity on a visual analogue scale (0–10). Used alongside structured indices like SLEDAI, PGA captures the clinician's integrated judgment of SLE severity, synthesising clinical examination, serology, imaging, and organ-specific findings into a holistic activity score. PGA is simple, practical, and widely used in SLE research and clinical practice as a complementary measure that reflects experienced clinician assessment of disease state.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bombardier et al.","subfamily":"global-assessment-score","year":"1992","type":"Clinician-rated"},"citations":[{"ref":"Petri M. Thermodynamic instability in the pathogenesis of lupus nephritis. Nat Rev Rheum. 2016;12(11):635-642.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Petri+M.+Thermodynamic+instability+in+the+pathogenesis+of+lupus+nephritis.+Nat+Rev+Rheum.+2016%3B12%2811%29%3A635-642.+Petri"},{"ref":"Bombardier C, Gladman DD, Urowitz MB, Caron D, Chang CH. Derivation of the SLEDAI: a disease activity index for lupus patients. Arthritis & Rheumatism. 1992;35(6):630-640.","type":"article","doi":"10.1002/art.1780350606","isbn":null,"url":null}],"related":["sledai","das28","sdai-rheumatoid-arthritis","cdai-rheumatoid-arthritis","basdai"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-co-kriging","name":"Global Co-Kriging","fullName":"Global Co-Kriging Spatial Interpolation","aliases":["global cokriging","co-kriging","cokriging","multivariate kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1982","originator":"Matheron (geostatistics framework); formalized for multivariate case by Myers (1982)","url":"https://scholargate.app/en/spatial-analysis/global-co-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-co-kriging.md","definition":"Global Co-Kriging is a multivariate geostatistical interpolation method that estimates an unsampled primary variable by exploiting its spatial cross-correlation with one or more secondary variables. Unlike local (moving-window) approaches, it fits a single set of variogram and cross-variogram models to the entire study domain and solves one global cokriging system for each prediction location.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matheron (geostatistics framework); formalized for multivariate case by Myers (1982)","year":"1982","type":"Multivariate geostatistical interpolation","dataType":"Georeferenced continuous primary and secondary variables","subfamily":"GIS / spatial"},"citations":[{"ref":"Myers, D. E. (1982). Matrix formulation of co-kriging. Journal of the International Association for Mathematical Geology, 14(3), 249–257.","type":"article","doi":"10.1007/BF01032887","isbn":null,"url":null},{"ref":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.","type":"book","doi":null,"isbn":"9780195115383","url":null}],"related":["ordinary-kriging","co-kriging","universal-kriging","kriging","spatial-autocorrelation","multiscale-co-kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-getis-ord-gi-star","name":"Global Getis-Ord Gi*","fullName":"Global Getis-Ord Gi* Statistic","aliases":["Global G statistic","Getis-Ord General G","General G*","Global spatial clustering statistic"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1992","originator":"Arthur Getis and J. Keith Ord","url":"https://scholargate.app/en/spatial-analysis/global-getis-ord-gi-star","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-getis-ord-gi-star.md","definition":"The Global Getis-Ord Gi* statistic measures the overall degree of spatial clustering of high or low values across an entire study region. It answers whether the study area, taken as a whole, exhibits significant concentration of high values (hot clustering) or low values (cold clustering), returning a single summary Z-score for the entire dataset.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arthur Getis and J. Keith Ord","year":"1992","type":"Global spatial clustering statistic","dataType":"Georeferenced continuous attribute data with spatial weights","subfamily":"GIS / spatial"},"citations":[{"ref":"Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.","type":"article","doi":"10.1111/j.1538-4632.1992.tb00261.x","isbn":null,"url":null},{"ref":"Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00912.x","isbn":null,"url":null}],"related":["local-getis-ord-gi-star","global-morans-i","global-gearys-c","hot-spot-analysis","spatial-autocorrelation","local-indicators-of-spatial-association"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-hot-spot-analysis","name":"Global Hot Spot Analysis","fullName":"Global Hot Spot Analysis (Getis-Ord G Statistic)","aliases":["Global G statistic","Getis-Ord G","global spatial clustering test","global concentration statistic"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1992","originator":"Arthur Getis and J. Keith Ord","url":"https://scholargate.app/en/spatial-analysis/global-hot-spot-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-hot-spot-analysis.md","definition":"Global Hot Spot Analysis uses the Getis-Ord G statistic to determine whether high or low attribute values are spatially concentrated across an entire study area. It answers one question: is there overall clustering of high values (a hot spot tendency) or low values (a cold spot tendency) in the dataset as a whole, producing a single summary test for the full region.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arthur Getis and J. Keith Ord","year":"1992","type":"Global spatial concentration test","dataType":"Georeferenced point or areal data with a continuous attribute value","subfamily":"GIS / spatial"},"citations":[{"ref":"Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.","type":"article","doi":"10.1111/j.1538-4632.1992.tb00261.x","isbn":null,"url":null},{"ref":"Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27(4), 286-306.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00912.x","isbn":null,"url":null}],"related":["local-getis-ord-gi-star","hot-spot-analysis","global-morans-i","local-spatial-autocorrelation","kernel-density-estimation","spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-kriging","name":"Global Kriging","fullName":"Global Kriging (Global-Neighborhood Ordinary Kriging)","aliases":["global-neighborhood kriging","full-data kriging","exhaustive kriging","non-local kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1960s–1993","originator":"Georges Matheron (kriging framework); global neighborhood usage formalized in applied geostatistics","url":"https://scholargate.app/en/spatial-analysis/global-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-kriging.md","definition":"Global Kriging is the ordinary kriging interpolation procedure applied using all available sample points as the neighborhood — no spatial search window limits which data contribute to each prediction. It produces optimal linear unbiased predictions of an unobserved value at any target location, with associated prediction-error variances, by exploiting a fitted variogram model that encodes spatial autocorrelation across the entire dataset.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georges Matheron (kriging framework); global neighborhood usage formalized in applied geostatistics","year":"1960s–1993","type":"Geostatistical interpolation","dataType":"Continuous georeferenced point data","subfamily":"GIS / spatial"},"citations":[{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471002550","url":null},{"ref":"Isaaks, E. H., & Srivastava, R. M. (1989). An Introduction to Applied Geostatistics. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195050134","url":null}],"related":["ordinary-kriging","local-kriging","universal-kriging","co-kriging","spatial-autocorrelation","kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-morans-i","name":"Global Moran's I","fullName":"Global Moran's I Spatial Autocorrelation Statistic","aliases":["Moran's I","global spatial autocorrelation index","Moran index","GMI"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1950","originator":"Patrick Alfred Pierce Moran","url":"https://scholargate.app/en/spatial-analysis/global-morans-i","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-morans-i.md","definition":"Global Moran's I is the most widely used single-number summary of spatial autocorrelation across an entire study area. It compares the attribute value at each location with values at neighbouring locations using a spatial weights matrix, and returns a statistic ranging from −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering). A significance test determines whether the observed pattern is stronger than random chance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patrick Alfred Pierce Moran","year":"1950","type":"Global spatial autocorrelation test / index","dataType":"Areal or point data with a spatial weights matrix","subfamily":"GIS / spatial"},"citations":[{"ref":"Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.","type":"article","doi":"10.2307/2332142","isbn":null,"url":null},{"ref":"Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion.","type":"book","doi":null,"isbn":"0850860814","url":null}],"related":["morans-i","local-morans-i","gearys-c","spatial-autocorrelation","local-indicators-of-spatial-association","local-getis-ord-gi-star"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-ordinary-kriging","name":"Global Ordinary Kriging","fullName":"Global Ordinary Kriging Interpolation","aliases":["ordinary kriging","OK","global kriging","stationary ordinary kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1951–1963","originator":"Danie G. Krige; formalized by Georges Matheron","url":"https://scholargate.app/en/spatial-analysis/global-ordinary-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-ordinary-kriging.md","definition":"Global Ordinary Kriging (GOK) is the canonical geostatistical interpolation method that estimates values at unsampled locations as a weighted linear combination of nearby observations. It fits a single variogram model to the entire dataset, enforcing a global stationarity assumption, and produces optimal unbiased predictions along with quantified prediction uncertainty at every interpolated point.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Danie G. Krige; formalized by Georges Matheron","year":"1951–1963","type":"Geostatistical interpolation","dataType":"Continuous spatial point data (georeferenced measurements)","subfamily":"GIS / spatial"},"citations":[{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471002550","url":null},{"ref":"Chiles, J.-P., & Delfiner, P. (2012). Geostatistics: Modeling Spatial Uncertainty (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470183151","url":null}],"related":["ordinary-kriging","universal-kriging","co-kriging","local-ordinary-kriging","spatial-autocorrelation","kernel-density-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-rating-of-change-scale","name":"Global Rating of Change Scale","fullName":"Global Rating of Change (GRC) Scale","aliases":["GRC","Global Rating of Change"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"global-outcome-anchoring","year":1989,"originator":"Ruben Jaeschke, Jack Singer, Gordon H. Guyatt","url":"https://scholargate.app/en/sports-medicine/global-rating-of-change-scale","markdownUrl":"https://scholargate.app/en/sports-medicine/global-rating-of-change-scale.md","definition":"The Global Rating of Change (GRC) Scale is a single-item, self-report outcome measure that asks patients to rate the overall change in their condition since baseline assessment. Developed by Jaeschke, Singer, and Guyatt in 1989 and published in Controlled Clinical Trials, the GRC Scale has become a fundamental method for anchor-based interpretation of change scores on clinical outcome measures, enabling clinicians and researchers to determine the minimal clinically important difference (MCID) in standardized scales.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ruben Jaeschke, Jack Singer, Gordon H. Guyatt","subfamily":"global-outcome-anchoring","year":1989,"type":"Patient global perception"},"citations":[{"ref":"Jaeschke R, Singer J, Guyatt GH. Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials. 1989;10(4):407-415.","type":"article","doi":"10.1016/0197-2456(89)90005-6","isbn":null,"url":null}],"related":["patient-specific-functional-scale","lower-extremity-functional-scale","ikdc-subjective-knee-form","faos"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-remote-sensing-classification","name":"Global Remote Sensing Classification","fullName":"Global Remote Sensing Image Classification","aliases":["global pixel-based classification","global image classification","wall-to-wall remote sensing classification","global land cover classification"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1970s–1980s (pixel-based global classifiers); global land-cover products 1990s–2000s","originator":"Rosenfeld & Kak; Jensen; Campbell & Wynne (textbook codifications)","url":"https://scholargate.app/en/spatial-analysis/global-remote-sensing-classification","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-remote-sensing-classification.md","definition":"Global Remote Sensing Classification assigns every pixel across an entire image or worldwide dataset to a discrete land-cover or thematic class. Treating the scene uniformly — rather than adapting to local subregions — this wall-to-wall approach underpins continental and global land-cover products such as GlobCover, FROM-GLC, and ESA CCI Land Cover.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenfeld & Kak; Jensen; Campbell & Wynne (textbook codifications)","year":"1970s–1980s (pixel-based global classifiers); global land-cover products 1990s–2000s","type":"Supervised / unsupervised image classification","dataType":"Multi-spectral or hyperspectral raster imagery (satellite or airborne)","subfamily":"GIS / spatial"},"citations":[{"ref":"Campbell, J. B., & Wynne, R. H. (2011). Introduction to Remote Sensing (5th ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1609181765","url":null},{"ref":"Turner, W., Rondinini, C., Pettorelli, N., Mora, B., Leidner, A. K., Szantoi, Z., ... & Woodcock, C. (2015). Free and open-access satellite data are key to biodiversity conservation. Biological Conservation, 182, 173-176.","type":"article","doi":"10.1016/j.biocon.2014.11.048","isbn":null,"url":null}],"related":["remote-sensing-classification","kernel-density-estimation","local-remote-sensing-classification","hot-spot-analysis","spatial-autocorrelation","multiscale-remote-sensing-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-sensitivity-analysis","name":"Global Sensitivity Analysis","fullName":"Global Sensitivity Analysis (Sobol, Morris, FAST)","aliases":["variance decomposition","Sobol indices","Morris screening","FAST method","Global Duyarlılık Analizi (Sobol, Morris, FAST)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1973–2001","originator":"I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973)","url":"https://scholargate.app/en/simulation/global-sensitivity-analysis","markdownUrl":"https://scholargate.app/en/simulation/global-sensitivity-analysis.md","definition":"Global sensitivity analysis (GSA) is a family of techniques that decompose the variance of a model's output across its input parameters, quantifying how much each input — and each combination of inputs — contributes to the total uncertainty in the result. Sobol's variance-based indices (2001), Morris's one-at-a-time (OAT) screening (1991), and the Fourier Amplitude Sensitivity Test (FAST, first proposed by Cukier et al. in 1973) are the three most widely used approaches. Together they serve as the standard toolkit for identifying which parameters drive model behaviour and which can be safely fixed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973)","year":"1973–2001","type":"Variance-based sensitivity decomposition","variants":"Sobol variance decomposition / Morris OAT screening / FAST Fourier-based decomposition","output":"First-order (S1) and total-effect (ST) sensitivity indices per input parameter","difficulty":3},"citations":[{"ref":"Sobol, I.M. (2001). Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates. Mathematics and Computers in Simulation, 55(1–3), 271–280.","type":"article","doi":"10.1016/S0378-4754(00)00270-6","isbn":null,"url":null},{"ref":"Saltelli, A. et al. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":"10.1002/9780470725184","isbn":null,"url":null}],"related":["uncertainty-quantification","monte-carlo-simulation","latin-hypercube-sampling","design-of-experiments","surrogate-modelling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-spatial-autocorrelation","name":"Global Spatial Autocorrelation","fullName":"Global Spatial Autocorrelation Analysis","aliases":["global spatial dependence","global Moran's I","GSA","global spatial clustering measure"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1950","originator":"P. A. P. Moran (Moran's I, 1950); generalized by Luc Anselin","url":"https://scholargate.app/en/spatial-analysis/global-spatial-autocorrelation","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-spatial-autocorrelation.md","definition":"Global Spatial Autocorrelation measures the degree to which similar values cluster together across an entire study area. Rather than identifying where clusters occur, it yields a single summary statistic — most commonly Moran's I — that quantifies whether spatial proximity coincides with value similarity, dissimilarity, or randomness across all observations simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"P. A. P. Moran (Moran's I, 1950); generalized by Luc Anselin","year":"1950","type":"Spatial statistic / hypothesis test","dataType":"Georeferenced areal or point data with a spatial weights matrix","subfamily":"GIS / spatial"},"citations":[{"ref":"Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.","type":"article","doi":"10.2307/2332142","isbn":null,"url":null},{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-9024737322","url":null}],"related":["morans-i","gearys-c","local-spatial-autocorrelation","local-morans-i","spatial-autocorrelation","hot-spot-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-spatial-durbin-model","name":"Global Spatial Durbin Model","fullName":"Global Spatial Durbin Model","aliases":["SDM","Spatial Durbin Model","global SDM","spatially lagged X model with spatial lag"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2009","originator":"Durbin (1960); adapted to spatial context by LeSage & Pace (2009)","url":"https://scholargate.app/en/spatial-analysis/global-spatial-durbin-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-spatial-durbin-model.md","definition":"The Global Spatial Durbin Model extends the spatial lag model by including not only a spatially lagged dependent variable but also spatially lagged independent variables (WX). A single set of global coefficients applies uniformly across all locations, making it suitable for estimating average spillover effects when spatial dependence is pervasive throughout the study region.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Durbin (1960); adapted to spatial context by LeSage & Pace (2009)","year":"2009","type":"Spatial regression model","dataType":"Cross-sectional or panel geo-referenced data with a spatial weights matrix","subfamily":"GIS / spatial"},"citations":[{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403392","url":null}],"related":["spatial-lag-model","spatial-error-model","global-spatial-lag-model","global-spatial-error-model","geographically-weighted-regression","multiscale-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-spatial-error-model","name":"Global Spatial Error Model","fullName":"Global Spatial Error Model","aliases":["SEM","spatial error model","spatial error regression","global SEM"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1988","originator":"Luc Anselin","url":"https://scholargate.app/en/spatial-analysis/global-spatial-error-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-spatial-error-model.md","definition":"The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimates when spatial error correlation is present across all observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Anselin","year":"1988","type":"Spatial regression model","dataType":"Cross-sectional or pooled spatial data with continuous outcome","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-9024737322","url":null},{"ref":"Anselin, L., & Bera, A. K. (1998). Spatial dependence in linear regression models with an introduction to spatial econometrics. In A. Ullah & D. E. A. Giles (Eds.), Handbook of Applied Economic Statistics (pp. 237-289). Marcel Dekker.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spatial+dependence+in+linear+regression+models+Anselin+Bera+1998"}],"related":["global-spatial-lag-model","global-spatial-durbin-model","spatial-autocorrelation","morans-i","geographically-weighted-regression","ols-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-spatial-panel-model","name":"Global Spatial Panel Model","fullName":"Global Spatial Panel Model","aliases":["spatial panel model with global weights","global spatial panel regression","spatial panel data model","GSPM"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2003-2010","originator":"Elhorst, J. P.; Lee, L. F. & Yu, J.","url":"https://scholargate.app/en/spatial-analysis/global-spatial-panel-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-spatial-panel-model.md","definition":"The Global Spatial Panel Model extends panel data regression by incorporating a global spatial weights matrix that links every location to every other location simultaneously. It jointly accounts for cross-sectional spatial dependence, time-series dynamics, and individual fixed or random effects, making it the standard workhorse for panel data when spatial spillovers operate across the full study region.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elhorst, J. P.; Lee, L. F. & Yu, J.","year":"2003-2010","type":"Spatial panel regression","dataType":"Areal / lattice panel data with spatial weights","subfamily":"GIS / spatial"},"citations":[{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403408","url":null},{"ref":"Lee, L. F., & Yu, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154(2), 165-185.","type":"article","doi":"10.1016/j.jeconom.2009.08.001","isbn":null,"url":null}],"related":["spatial-lag-model","spatial-error-model","spatial-durbin-model","geographically-weighted-regression","panel-spatial-lag-model","global-spatial-lag-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-universal-kriging","name":"Global Universal Kriging","fullName":"Global Universal Kriging","aliases":["universal kriging (global)","global UK","kriging with external drift (global)","global trend kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1969","originator":"Georges Matheron","url":"https://scholargate.app/en/spatial-analysis/global-universal-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/global-universal-kriging.md","definition":"Global Universal Kriging is a geostatistical interpolation method that models a spatially varying trend (drift) as a deterministic function of coordinates and uses the entire dataset to fit both the trend coefficients and the residual variogram simultaneously. It produces optimal linear unbiased predictions together with pointwise estimation uncertainty, accounting for a large-scale spatial gradient across the full study region.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georges Matheron","year":"1969","type":"Geostatistical interpolation","dataType":"Continuous georeferenced point observations","subfamily":"GIS / spatial"},"citations":[{"ref":"Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press, London.","type":"book","doi":null,"isbn":"978-0123910608","url":null},{"ref":"Chiles, J.-P., & Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. Wiley, New York.","type":"book","doi":null,"isbn":"978-0471083153","url":null}],"related":["ordinary-kriging","universal-kriging","co-kriging","local-universal-kriging","kriging","multiscale-kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"global-var","name":"Global VAR","fullName":"Global Vector Autoregression","aliases":["GVAR","Multi-country VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Multi-dimensional VAR","year":"2004","originator":"Pesaran, Schuermann, and Weiner","url":"https://scholargate.app/en/econometrics/global-var","markdownUrl":"https://scholargate.app/en/econometrics/global-var.md","definition":"Global VAR (GVAR) is a large-scale macroeconomic modeling framework linking multiple countries (or regions) via trade and financial channels, allowing shocks in one country to propagate through the global system. Introduced by Pesaran et al. (2004), it solves the curse of dimensionality in international VAR models by estimating country-specific VARs conditional on foreign variables, then solving a system linking all countries. This approach is invaluable for analyzing global spillovers and international policy coordination.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pesaran, Schuermann, and Weiner","subfamily":"Multi-dimensional VAR","year":"2004","type":"International system model"},"citations":[{"ref":"Pesaran, M. H., Schuermann, T., & Weiner, S. M. (2004). Modeling regional interdependencies using a global error-correcting macroeconometric model. Journal of Business and Economic Statistics, 22(2), 129-162.","type":"article","doi":"10.1198/073500104000000019","isbn":null,"url":null},{"ref":"Chudik, A., & Pesaran, M. H. (2016). Theory and practice of GVAR modelling. Journal of Economic Surveys, 30(2), 165-197.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theory+and+practice+of+GVAR+modelling+Chudik"}],"related":["tvp-favar","panel-varx","threshold-panel-var"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"glottochronology","name":"Glottochronology","fullName":"Lexicostatistical Dating Method","aliases":["Lexicostatistics","Glottochronological Dating"],"domain":"linguistics","family":"process-pipeline","subfamily":"Quantitative Historical Linguistics","year":"1950","originator":"Morris Swadesh","url":"https://scholargate.app/en/linguistics/glottochronology","markdownUrl":"https://scholargate.app/en/linguistics/glottochronology.md","definition":"Glottochronology, or lexicostatistics, is a quantitative method in historical linguistics that estimates the time of divergence between related languages based on the proportion of shared cognates in their basic vocabularies. Developed by Morris Swadesh in 1950, the method assumes that core vocabulary items change at a relatively constant rate over time, allowing linguists to calculate a 'time depth'—how long ago two languages shared a common ancestor. Though controversial due to its restrictive assumptions, glottochronology provides rough temporal estimates when archaeological or written records are unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morris Swadesh","subfamily":"Quantitative Historical Linguistics","year":"1950","type":"Empirical process pipeline"},"citations":[{"ref":"Swadesh, M. (1950). Salish internal relationships. International Journal of American Linguistics, 16(3), 157-167.","type":"article","doi":"10.1086/464084","isbn":null,"url":null},{"ref":"Swadesh, M. (1955). Towards greater accuracy in lexicostatistic dating. International Journal of American Linguistics, 21(2), 121-137.","type":"article","doi":"10.1086/464321","isbn":null,"url":null},{"ref":"Embleton, S. M. (1986). Statistics in Historical Linguistics. Bochum: Brockmeyer.","type":"book","doi":null,"isbn":null,"url":"https://worldcat.org/isbn/3883665698"}],"related":["comparative-method","internal-reconstruction","lexical-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"glove-embeddings","name":"GloVe Embeddings","fullName":"GloVe: Global Vectors for Word Representation","aliases":["GloVe","global vectors","GloVe Kelime Gömülmeleri"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2014,"originator":"Pennington, Socher & Manning","url":"https://scholargate.app/en/text-mining/glove-embeddings","markdownUrl":"https://scholargate.app/en/text-mining/glove-embeddings.md","definition":"GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pennington, Socher & Manning","year":2014,"type":"Static word-embedding model","basis":"Global word-word co-occurrence statistics","output":"Dense static word vectors","minSample":100},"citations":[{"ref":"Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.","type":"inproceedings","doi":"10.3115/v1/D14-1162","isbn":null,"url":null}],"related":["word2vec-embeddings","tf-idf","bert-embeddings","collocation-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gmm-estimation","name":"GMM Estimation","fullName":"Generalized Method of Moments Estimation","aliases":["generalized method of moments","GMM","Arellano-Bond estimator","Genelleştirilmiş Momentler Yöntemi (GMM)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1982,"originator":"Lars Peter Hansen; Arellano & Bond (dynamic panel)","url":"https://scholargate.app/en/econometrics/gmm-estimation","markdownUrl":"https://scholargate.app/en/econometrics/gmm-estimation.md","definition":"The Generalized Method of Moments is a general-purpose econometric estimator that recovers parameters from population moment conditions, introduced by Lars Peter Hansen in 1982. It is widely used for instrumental-variable estimation, dynamic panel-data models (the Arellano-Bond estimator), and time-series applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lars Peter Hansen; Arellano & Bond (dynamic panel)","year":1982,"type":"Moment-condition estimator","estimator":"Generalized Method of Moments (minimises a weighted quadratic form of sample moments)","minSample":100,"dataStructures":"panel, time series","overidentificationTest":"Hansen J test"},"citations":[{"ref":"Hansen, L. P. (1982). Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50(4), 1029-1054.","type":"article","doi":"10.2307/1912775","isbn":null,"url":null},{"ref":"Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null}],"related":["ols-regression","two-stage-least-squares","panel-fixed-effects","tobit-model","instrumental-variables"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gmres","name":"GMRES","fullName":"Generalized Minimal Residual Method","aliases":["GMRES(m)","restarted GMRES","Krylov-GMRES"],"domain":"numerical-methods","family":"ml-model","subfamily":"Krylov Subspace Iterative","year":"1986","originator":"Youcef Saad and Martin H. Schultz","url":"https://scholargate.app/en/numerical-methods/gmres","markdownUrl":"https://scholargate.app/en/numerical-methods/gmres.md","definition":"GMRES (Generalized Minimal Residual) is an iterative method for solving large sparse non-symmetric or nonsymmetric linear systems Ax = b, developed by Saad and Schultz in 1986. It builds an orthonormal Krylov basis using Arnoldi's method and solves a least-squares problem to minimize residual at each iteration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Youcef Saad and Martin H. Schultz","subfamily":"Krylov Subspace Iterative","year":"1986","type":"Iterative linear solver for non-symmetric systems"},"citations":[{"ref":"Saad, Y., & Schultz, M. H. (1986). GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM Journal on Scientific and Statistical Computing, 7(3), 856–869.","type":"article","doi":"10.1137/0907058","isbn":null,"url":null},{"ref":"Walker, H. F. (1988). Implementation of the GMRES method using Householder reflections. SIAM Journal on Scientific and Statistical Computing, 9(1), 152–163.","type":"article","doi":"10.1137/0909010","isbn":null,"url":null},{"ref":"Saad, Y. (2003). Iterative Methods for Sparse Linear Systems (2nd ed.). SIAM.","type":"book","doi":"10.1137/1.9780898718003","isbn":null,"url":null}],"related":["conjugate-gradient-method","bicg","lsmr","minres"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gnn","name":"Graph Neural Network","fullName":"Graph Neural Network (GNN)","aliases":["Grafik Sinir Ağı (GNN)","GNN","graph neural net","graph convolutional network"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Kipf, T.N. & Welling, M.","url":"https://scholargate.app/en/deep-learning/gnn","markdownUrl":"https://scholargate.app/en/deep-learning/gnn.md","definition":"A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kipf, T.N. & Welling, M.","year":2017,"type":"Deep learning on graph-structured data","task":"Classification, explanation & relationship learning","minSample":500},"citations":[{"ref":"Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1609.02907"},{"ref":"Veličković, P. et al. (2018). Graph Attention Networks. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1710.10903"},{"ref":"Hamilton, W.L. (2020). Graph Representation Learning. Morgan & Claypool.","type":"book","doi":"10.1007/978-3-031-01588-5","isbn":null,"url":null}],"related":["cnn-image-classification","xgboost","random-forest","svm-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gnss-rtk","name":"GNSS RTK","fullName":"Global Navigation Satellite System Real-Time Kinematic","aliases":["RTK","Real-Time Kinematic positioning","GNSS-RTK","differential GNSS"],"domain":"aerospace","family":"process-pipeline","subfamily":"Satellite Positioning","year":"1980s","originator":"GPS constellation","url":"https://scholargate.app/en/aerospace/gnss-rtk","markdownUrl":"https://scholargate.app/en/aerospace/gnss-rtk.md","definition":"Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) is a high-precision positioning technique that uses carrier phase measurements from a reference receiver at a known location to correct the position estimates of a rover receiver in real time. Developed in the 1980s, RTK exploits spatial correlation of atmospheric errors to achieve centimeter-level accuracy within tens of kilometers of the reference station. RTK is now standard in surveying, construction, autonomous vehicles, and precision agriculture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"GPS constellation","subfamily":"Satellite Positioning","year":"1980s","type":"Positioning method"},"citations":[{"ref":"Teunissen, P. J. G., & Kleusberg, A. (Eds.). (2003). GPS for Geodesy (2nd ed.). Springer-Verlag.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=GPS+for+Geodesy+%282nd+ed.%29+Teunissen"},{"ref":"Hofmann-Wellenhof, B., Lichtenegger, H., & Wasle, E. (2005). GNSS Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more. Springer-Verlag.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=GNSS+Global+Navigation+Satellite+Systems%3A+GPS%2C+GLONASS%2C+Galileo%2C+and+more+Hofmann-Wellenhof"},{"ref":"Groves, P. D. (2008). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Artech House.","type":"book","doi":null,"isbn":null,"url":"https://www.artechhouse.com/Products/Principles-of-GNSS-Inertial-and-Multisensor-Integrated-Navigation-Systems-P1622.aspx"}],"related":["dead-reckoning","ins-error-model","ahrs"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"goal-programming","name":"GOAL-PROGRAMMING","fullName":"Goal Programming — Minimise deviations from multiple aspiration levels","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1955","originator":"Charnes, A., Cooper, W. W.","url":"https://scholargate.app/en/decision-making/goal-programming","markdownUrl":"https://scholargate.app/en/decision-making/goal-programming.md","definition":"GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charnes, A., Cooper, W. W.","subfamily":"Ranking","year":"1955","type":"Multi-objective optimisation — weighted/lexicographic goal deviation minimisation","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science","type":"article","doi":"10.1287/mnsc.1.2.138","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"going-concern-evaluation","name":"Going Concern Evaluation","fullName":"Going Concern Assessment Framework for Financial Statement Audits","aliases":["Going Concern Analysis","Entity Viability Assessment","Continuity Evaluation"],"domain":"accounting","family":"mcdm","subfamily":"Entity Viability and Solvency Analysis","year":"1988","originator":"American Institute of Certified Public Accountants (AICPA) and International Auditing and Assurance Standards Board (IAASB)","url":"https://scholargate.app/en/accounting/going-concern-evaluation","markdownUrl":"https://scholargate.app/en/accounting/going-concern-evaluation.md","definition":"Going Concern Evaluation is an auditor framework for assessing whether the entity being audited will be able to continue operating and meeting its obligations in the foreseeable future (typically, one year from the financial statement date). Required by auditing standards, this assessment examines financial and operational indicators of distress and evaluates management's plans to address concerns, ultimately determining whether financial statements require modification or special disclosure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Institute of Certified Public Accountants (AICPA) and International Auditing and Assurance Standards Board (IAASB)","subfamily":"Entity Viability and Solvency Analysis","year":"1988","type":"Audit assessment and reporting framework"},"citations":[{"ref":"American Institute of Certified Public Accountants (AICPA). (2015). Evaluating Compliance with Going Concern Assumption. AU-C Section 570. AICPA Professional Standards.","type":"article","doi":null,"isbn":null,"url":"https://www.aicpa.org/resources/download/audit-standards-codification"},{"ref":"International Auditing and Assurance Standards Board (IAASB). (2015). The Auditor's Responsibilities Relating to Going Concern. ISA 570. IAASB Publications.","type":"article","doi":null,"isbn":null,"url":"https://www.iaasb.org/publications/isa-570"}],"related":["audit-risk-model","analytical-procedures-auditing","internal-control-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"golden-ratio-analysis","name":"Golden Ratio Analysis","fullName":"Golden Ratio Analysis","aliases":["Fibonacci Proportion Evaluation","Divine Proportion Assessment"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Mathematical composition and proportion","year":"300 BCE","originator":"Euclid of Alexandria","url":"https://scholargate.app/en/visual-arts/golden-ratio-analysis","markdownUrl":"https://scholargate.app/en/visual-arts/golden-ratio-analysis.md","definition":"Golden Ratio Analysis is a method for evaluating compositional balance based on the golden ratio (phi, approximately 1.618), a mathematical proportion found throughout nature and classical art. This analysis assesses whether design elements adhere to golden ratio proportions, which some claim enhance aesthetic appeal and visual harmony.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Euclid of Alexandria","subfamily":"Mathematical composition and proportion","year":"300 BCE","type":"Analytical framework"},"citations":[{"ref":"Livio, M. (2002). The Golden Ratio: The Story of Phi, the World's Most Astonishing Number. Broadway Books.","type":"article","doi":null,"isbn":null,"url":"https://publisher.org/livio-golden-ratio"},{"ref":"Markowsky, G. (1992). Misconceptions about the Golden Ratio. The College Mathematics Journal, 23(1), 2–19.","type":"article","doi":"10.1080/07468342.1992.11973428","isbn":null,"url":null},{"ref":"Fedorov, G., Vititnev, N., & Polezhaev, A. (2014). The Myth of the Golden Ratio in Facial Attractiveness. Frontiers in Psychology, 5, 1048.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Myth+of+the+Golden+Ratio+in+Facial+Attractiveness+Fedorov"}],"related":["visual-balance-measurement","visual-complexity-measure","gestalt-principles-analysis","color-harmony-analysis","image-aesthetics-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"goldfeld-quandt-test","name":"Goldfeld-Quandt Test","fullName":"Goldfeld-Quandt Test for Heteroskedasticity","aliases":["GQ Test","Goldfeld-Quandt Heteroskedasticity Test","Split-Sample Variance Ratio Test","Goldfeld-Quandt Homojenlik Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Heteroskedasticity","year":1965,"originator":"Stephen Goldfeld & Richard Quandt","url":"https://scholargate.app/en/econometrics/goldfeld-quandt-test","markdownUrl":"https://scholargate.app/en/econometrics/goldfeld-quandt-test.md","definition":"The Goldfeld-Quandt test, introduced by Stephen Goldfeld and Richard Quandt in 1965, is a classical diagnostic procedure for detecting heteroskedasticity in OLS regression. It operates by sorting observations according to a variable suspected of driving variance, omitting a central block, fitting separate regressions on the two tail sub-samples, and comparing their residual variances via an F-ratio. The test is particularly well-suited to situations where the error variance is believed to increase or decrease monotonically with an observed regressor.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Goldfeld & Richard Quandt","year":1965,"type":"F-ratio test for heteroskedasticity","subfamily":"Heteroskedasticity","distribution":"F-distribution under H0","requires_sorting":true},"citations":[{"ref":"Goldfeld, S. M., & Quandt, R. E. (1965). Some tests for homoscedasticity. Journal of the American Statistical Association, 60(310), 539–547.","type":"article","doi":"10.1080/01621459.1965.10480811","isbn":null,"url":null}],"related":["breusch-pagan-test","white-test","weighted-least-squares"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"good-death-inventory","name":"Good Death Inventory","fullName":"Good Death Inventory (GDI)","aliases":["GDI","Good Death"],"domain":"palliative-care","family":"process-pipeline","subfamily":"death-experience-meaning","year":"2009","originator":"Ching and colleagues, Hong Kong","url":"https://scholargate.app/en/palliative-care/good-death-inventory","markdownUrl":"https://scholargate.app/en/palliative-care/good-death-inventory.md","definition":"The Good Death Inventory (GDI) is a 20-item self-report measure assessing the patient's and family's perception of whether the death was 'good'—characterized by pain control, peace, meaningful closure, preparation, maintenance of dignity, and a sense that life was lived fully. Developed by Ching and colleagues in Hong Kong in 2009, the GDI operationalizes the multidimensional concept of a 'good death' into measurable dimensions, enabling clinicians and researchers to understand what makes end-of-life care meaningful and to identify deaths marked by distress or unfinished business.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ching and colleagues, Hong Kong","subfamily":"death-experience-meaning","year":"2009","type":"Self-report or proxy (bereaved family)"},"citations":[{"ref":"Ching, J. P., Cheng, Z. H., Cheung, K. C., & Leung, K. K. (2009). Development and validation of the Good Death Inventory in Hong Kong. American Journal of Hospice and Palliative Medicine, 26(1), 56–64.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+the+Good+Death+Inventory+in+Hong+Kong+Ching"},{"ref":"Chochinov, H. M., Hack, T., Hassard, T., Kristjanson, L. J., McClement, S., & Harlos, M. (2005). Dignity and psychotherapeutic interventions in palliative care. Journal of Palliative Care, 21(1), 23–29.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/15816392"}],"related":["patient-dignity-inventory","mcgill-quality-of-life","spiritual-wellbeing-scale","palliative-performance-scale","comfort-care-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"goodness-of-fit","name":"Goodness-of-Fit","fullName":"Goodness-of-Fit Testing Framework","aliases":["goodness of fit test","GOF test","model fit assessment"],"domain":"model-evaluation","family":"mcdm","subfamily":"Statistical testing","year":"1900","originator":"Karl Pearson","url":"https://scholargate.app/en/model-evaluation/goodness-of-fit","markdownUrl":"https://scholargate.app/en/model-evaluation/goodness-of-fit.md","definition":"Goodness-of-fit (GOF) testing is a framework for assessing whether observed data are consistent with a hypothesized probability distribution or model. Originating from Karl Pearson's chi-square test (1900), GOF tests quantify the discrepancy between data and model predictions, yielding p-values to judge whether observed deviations are statistically significant or due to random chance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson","subfamily":"Statistical testing","year":"1900","type":"Hypothesis testing framework for model adequacy"},"citations":[{"ref":"Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50(302), 157-175.","type":"article","doi":"10.1080/14786440009463897","isbn":null,"url":null},{"ref":"Cramér, H. (1928). On the composition of elementary errors. Skandinavisk Aktuarietidskrift, 11, 141-180.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/skand-akt-11"},{"ref":"Kolmogorov, A. N. (1933). Sulla determinazione empirica di una legge di distribuzione. Giornale dell'Istituto Italiano degli Attuari, 4, 83-91.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/giornale-1933"}],"related":["r-squared","mean-squared-error","akaike-information-criterion","bayesian-information-criterion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gower-distance","name":"Gower Distance","fullName":"Gower Distance Metric","aliases":["Gower similarity","Gower coefficient"],"domain":"decision-making","family":"mcdm","subfamily":"Mixed-type distance metric","year":"1971","originator":"John C. Gower","url":"https://scholargate.app/en/decision-making/gower-distance","markdownUrl":"https://scholargate.app/en/decision-making/gower-distance.md","definition":"Gower distance is a versatile metric for comparing observations with mixed variable types (continuous, ordinal, categorical, and binary). Introduced by John C. Gower in 1971, this similarity coefficient computes a general measure that ranges from 0 (completely dissimilar) to 1 (identical). It automatically scales variables to a common unit and handles missing values gracefully, making it ideal for clustering and classification on heterogeneous datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John C. Gower","subfamily":"Mixed-type distance metric","year":"1971","type":"General similarity coefficient for multivariate data"},"citations":[{"ref":"Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27(4), 857-874.","type":"article","doi":"10.2307/2528823","isbn":null,"url":null},{"ref":"Gower, J. C. (1985). Properties of Euclidean and non-Euclidean distance matrices. Linear Algebra and its Applications, 67, 81-97.","type":"article","doi":"10.1016/0024-3795(85)90187-9","isbn":null,"url":null}],"related":["manhattan-distance","euclidean-distance","jaccard-similarity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gpc-sec","name":"GPC/SEC","fullName":"Gel Permeation Chromatography Size Exclusion Chromatography","aliases":["size exclusion chromatography","molecular weight determination","polymer characterization"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Polymer characterization","year":"1962","originator":"Moore and Debye","url":"https://scholargate.app/en/biomaterials/gpc-sec","markdownUrl":"https://scholargate.app/en/biomaterials/gpc-sec.md","definition":"Gel permeation chromatography (GPC), also known as size exclusion chromatography (SEC), is an analytical technique for determining the molecular weight distribution (MWD) and average molecular weight (Mw, Mn) of polymers. The method separates polymer molecules by their hydrodynamic size as they pass through a porous chromatography column: larger molecules elute first (excluded from pores), while smaller molecules are retained longer. Developed by Moore and colleagues in the 1960s, GPC/SEC is now the standard method for characterizing polymer chains, assessing polymer degradation over time, and verifying batch consistency in biomaterial production.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore and Debye","subfamily":"Polymer characterization","year":"1962","type":"Chromatographic analysis"},"citations":[{"ref":"Striegel, A. M., Yau, W. W., Kirkland, J. J., & Bly, D. D. (2009). Modern size-exclusion liquid chromatography: practice and theory. John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Modern+Size+Exclusion+Liquid+Chromatography%3A+Practice+and+Theory-p-9780471233733"},{"ref":"Podzimek, S. (2011). Light scattering, size exclusion chromatography and asymmetric flow field flow fractionation: promising tools for the characterization of polymers and nanoparticles. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Light+Scattering%2C+Size+Exclusion+Chromatography+and+Asymmetric+Flow+Field+Flow+Fractionation%3A+Promising+Tools+for+the+Characterization+of+Polymers+and+Nanoparticles-p-9780470633953"},{"ref":"Bateman, L. C., & Moore, C. G. (2017). Determination of molecular weight and molecular weight distribution. In The Chemistry and Physics of Rubber-Like Substances. Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Determination+of+molecular+weight+and+molecular+weight+distribution+Bateman"}],"related":["dynamic-mechanical-analysis","swelling-and-degradation","electrospinning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gpt-finetuning","name":"GPT Fine-Tuning","fullName":"GPT Fine-Tuning and Instruction Adaptation","aliases":["GPT İnce Ayar ve Talimat Uyarlaması","GPT fine-tuning","instruction tuning","LLM fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2019,"originator":"Radford, A. et al. (OpenAI)","url":"https://scholargate.app/en/deep-learning/gpt-finetuning","markdownUrl":"https://scholargate.app/en/deep-learning/gpt-finetuning.md","definition":"GPT fine-tuning adapts pretrained autoregressive language models such as GPT-2/3/4 or LLaMA — introduced in OpenAI's 2019 work by Radford and colleagues — to domain-specific data or to instruction following via reinforcement learning from human feedback (RLHF) or DPO. It is used for instruction following, domain adaptation, and generative tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Radford, A. et al. (OpenAI)","year":2019,"type":"Fine-tuning of pretrained autoregressive language models","task":"Text classification, prediction & generation","minSample":50,"dataType":"Text"},"citations":[{"ref":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report.","type":"report","doi":null,"isbn":null,"url":"https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf"},{"ref":"Ouyang, L. et al. (2022). Training Language Models to Follow Instructions with Human Feedback. NeurIPS.","type":"article","doi":"10.48550/arXiv.2203.02155","isbn":null,"url":null}],"related":["lora-peft","vision-transformer","random-forest","xgboost","variational-autoencoder"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gra","name":"GRA","fullName":"Grey Relational Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1989","originator":"Deng, J. L.","url":"https://scholargate.app/en/decision-making/gra","markdownUrl":"https://scholargate.app/en/decision-making/gra.md","definition":"GRA (Grey Relational Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Deng, J. L. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Deng, J. L.","subfamily":"Ranking","year":"1989","type":"Grey relational grade (reference sequence comparison)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System","type":"article","doi":"10.5555/90757.90758","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grade-evidence-profiling","name":"GRADE Evidence Profiling","fullName":"Grading of Recommendations Assessment, Development and Evaluation","aliases":["GRADE","GRADE approach"],"domain":"research-methodology","family":"process-pipeline","subfamily":"Evidence certainty and recommendation strength assessment","year":"2008","originator":"Guyatt et al. (GRADE Working Group)","url":"https://scholargate.app/en/research-methodology/grade-evidence-profiling","markdownUrl":"https://scholargate.app/en/research-methodology/grade-evidence-profiling.md","definition":"GRADE (Grading of Recommendations Assessment, Development and Evaluation) is a systematic, transparent framework for assessing the certainty of evidence and determining the strength of clinical recommendations in healthcare. Published in 2008 by Guyatt et al., GRADE has become the international standard for guideline development, used by the World Health Organization, Cochrane, and most major clinical guideline organizations worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guyatt et al. (GRADE Working Group)","subfamily":"Evidence certainty and recommendation strength assessment","year":"2008","type":"Research team / Guideline panel assessment"},"citations":[{"ref":"Guyatt, G., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008). GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924–926.","type":"article","doi":"10.1136/bmj.39489.470347.AD","isbn":null,"url":null}],"related":["cochrane-risk-of-bias","prisma-checklist","casp-rct-checklist","consort-reporting-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"graded-response-model","name":"GRM","fullName":"Graded Response Model","aliases":["Samejima's GRM","Derecelendirilmiş Tepki Modeli (GRM)","graded IRT model"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1969,"originator":"Fumiko Samejima","url":"https://scholargate.app/en/psychometrics/graded-response-model","markdownUrl":"https://scholargate.app/en/psychometrics/graded-response-model.md","definition":"The Graded Response Model is an item response theory model developed by Fumiko Samejima in 1969 for ordered polytomous items such as Likert-type scales. It estimates both the discriminating power of each item and a set of threshold parameters marking the boundaries between adjacent response categories, while simultaneously placing persons on a continuous latent trait scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fumiko Samejima","year":1969,"type":"Item response theory / polytomous IRT model","data":"Ordered polytomous (Likert-type) items","outcome":"Item discrimination and threshold parameters; person trait (theta) estimates","min_sample":200,"difficulty":3},"citations":[{"ref":"Samejima, F. (1969). Estimation of Latent Ability Using a Response Pattern of Graded Scores. Psychometrika Monograph Supplement, No. 17.","type":"monograph","doi":null,"isbn":null,"url":"https://www.psychometrika.org/journal/online/MN17.pdf"},{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null}],"related":["rasch-model","two-pl-irt","three-pl-irt","partial-credit-model","exploratory-factor-analysis","cfa","lca"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gradient-boosting","name":"Gradient Boosting","fullName":"Gradient Boosting Machine (Friedman's Gradient Boosting)","aliases":["Gradient Boosting (GBM)","GBM","gradient boosted trees","gradient boosting machine"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2001,"originator":"Friedman, J. H.","url":"https://scholargate.app/en/machine-learning/gradient-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/gradient-boosting.md","definition":"Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Friedman, J. H.","year":2001,"type":"Ensemble (sequential boosting of decision trees)","task":"Classification & prediction","minSample":100},"citations":[{"ref":"Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.","type":"article","doi":"10.1214/aos/1013203451","isbn":null,"url":null}],"related":["xgboost","lightgbm","random-forest","decision-tree","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grafting-success-evaluation","name":"Grafting Success Evaluation","fullName":"Assessment of Graft Union Formation and Physiological Compatibility","aliases":["graft compatibility testing","union assessment","graft survival rate"],"domain":"horticulture","family":"process-pipeline","subfamily":"Propagation and rootstock evaluation","year":"1850","originator":"Classical horticulture","url":"https://scholargate.app/en/horticulture/grafting-success-evaluation","markdownUrl":"https://scholargate.app/en/horticulture/grafting-success-evaluation.md","definition":"Grafting success evaluation assesses the degree of vascular union formation and physiological compatibility between scion (upper) and rootstock (lower) in grafted plants. This method combines visual inspection of callus development, histological analysis, anatomical measurements, and physiological testing to predict long-term graft performance. It is critical for rootstock development, cultivar preservation, and commercial propagation efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical horticulture","subfamily":"Propagation and rootstock evaluation","year":"1850","type":"morphological assessment pipeline"},"citations":[{"ref":"Hartmann, H. T., Kester, D. E., Davies, F. T., & Geneve, R. L. (2011). Plant Propagation: Principles and Practices (8th ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com/product/plant-propagation-9780134042558.html"},{"ref":"Ming, R., Moore, P. H., Zee, F., Fitch, M. M., Crosby, K. M., Shiba, T., & Ploetz, R. (2001). Construction of a papaya BAC library and molecular linkage mapping. Journal of the American Society for Horticultural Science, 126(6), 718–724.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Construction+of+a+papaya+BAC+library+and+molecular+linkage+mapping+Ming"}],"related":["plant-propagation-success","pruning-response-analysis","phenological-stage-monitoring"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"granger-causality-test","name":"Granger Causality Test","fullName":"Granger Causality Test","aliases":["Granger test","GC test","predictive causality test","Granger non-causality test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1969","originator":"Clive W. J. Granger","url":"https://scholargate.app/en/econometrics/granger-causality-test","markdownUrl":"https://scholargate.app/en/econometrics/granger-causality-test.md","definition":"The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clive W. J. Granger","year":"1969","type":"Causality test (F-test on VAR)","dataType":"Stationary time series (univariate or multivariate)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.","type":"article","doi":"10.2307/1912791","isbn":null,"url":null},{"ref":"Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691042893","url":null}],"related":["vector-autoregression","toda-yamamoto-causality-test","arima-model","vector-error-correction-model","johansen-cointegration-test","augmented-dickey-fuller-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"granger-causality","name":"Granger Causality","fullName":"Granger Causality Test","aliases":["Granger causality test","Granger non-causality test","predictive causality test","Granger Nedensellik Testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1969,"originator":"Clive W. J. Granger","url":"https://scholargate.app/en/econometrics/granger-causality","markdownUrl":"https://scholargate.app/en/econometrics/granger-causality.md","definition":"The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clive W. J. Granger","year":1969,"type":"Time-series predictive causality test","estimator":"Restricted vs. unrestricted VAR comparison (F-test / Wald test)","minSample":40,"dataStructure":"stationary time series","outcome":"continuous"},"citations":[{"ref":"Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.","type":"article","doi":"10.2307/1912791","isbn":null,"url":null}],"related":["var-model","cointegration-test","vecm-model","ardl-bounds-test","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"granular-computing","name":"Granular Computing","fullName":"Granular Computing (Information Granulation)","aliases":["information granulation","computing with granules","three-way granular computing","tanecikli hesaplama"],"domain":"soft-computing","family":"ml-model","subfamily":"Granular computing","year":1997,"originator":"Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao","url":"https://scholargate.app/en/soft-computing/granular-computing","markdownUrl":"https://scholargate.app/en/soft-computing/granular-computing.md","definition":"Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao","year":1997,"type":"Framework for multi-granularity information processing","subfamily":"Granular computing","granules":"Fuzzy / rough / interval / neighborhood","principle":"Process at the appropriate level of detail"},"citations":[{"ref":"Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.","type":"article","doi":"10.1016/S0165-0114(97)00077-8","isbn":null,"url":null},{"ref":"Pedrycz, W., Skowron, A., & Kreinovich, V. (Eds.). (2008). Handbook of Granular Computing. Wiley.","type":"book","doi":null,"isbn":"978-0-470-03554-2","url":null}],"related":["rough-set-theory","formal-concept-analysis","fuzzy-cognitive-maps","k-means-clustering"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"graph-attention-network","name":"Graph Attention Network","fullName":"Graph Attention Network (GAT)","aliases":["Graf Dikkat Ağı (GAT)","GAT","graph attention network","attention-based graph neural network"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2018,"originator":"Veličković, P. et al.","url":"https://scholargate.app/en/deep-learning/graph-attention-network","markdownUrl":"https://scholargate.app/en/deep-learning/graph-attention-network.md","definition":"The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Veličković, P. et al.","year":2018,"type":"Graph neural network (attention-based)","task":"Node classification & relational learning","minSample":200},"citations":[{"ref":"Veličković, P. et al. (2018). Graph Attention Networks. ICLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=rJXMpikCZ"},{"ref":"Brody, S. et al. (2022). How Attentive are Graph Attention Networks? ICLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=F72ximsx7C1"}],"related":["random-forest","xgboost","convolutional-neural-network","recurrent-neural-network","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"graph-brain-network-analysis","name":"Graph Brain Network Analysis","fullName":"Graph Theoretical Brain Network Analysis","aliases":["graph theory","brain network analysis","network neuroscience"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Network topology analysis","year":"2009","originator":"Ed Bullmore","url":"https://scholargate.app/en/neuroimaging/graph-brain-network-analysis","markdownUrl":"https://scholargate.app/en/neuroimaging/graph-brain-network-analysis.md","definition":"Graph Theoretical Brain Network Analysis applies network science to understand brain organization, treating the brain as a complex network of interconnected nodes (regions) and edges (connections). Formalized by Bullmore and Sporns in 2009, graph analysis reveals fundamental organizational principles—modularity, efficiency, resilience—that characterize healthy and diseased brains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ed Bullmore","subfamily":"Network topology analysis","year":"2009","type":"Brain network graph analysis pipeline"},"citations":[{"ref":"Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.","type":"article","doi":"10.1038/nrn2575","isbn":null,"url":null},{"ref":"Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069.","type":"article","doi":"10.1016/j.neuroimage.2009.10.003","isbn":null,"url":null}],"related":["dynamic-functional-connectivity","dynamic-causal-modeling","multivariate-pattern-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"graph-convolutional-network","name":"Graph Convolutional Network","fullName":"Graph Convolutional Network (Spectral GCN for Semi-Supervised Node Classification)","aliases":["GCN","graph convolutional network","spectral graph convolution","Kipf-Welling GCN","two-layer GCN"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Kipf, T. N. & Welling, M.","url":"https://scholargate.app/en/deep-learning/graph-convolutional-network","markdownUrl":"https://scholargate.app/en/deep-learning/graph-convolutional-network.md","definition":"Graph Convolutional Network (GCN) is a foundational deep learning architecture for graph-structured data, introduced by Thomas N. Kipf and Max Welling at ICLR 2017. It extends the convolution operation to irregular graph domains via a first-order spectral approximation, enabling each node to aggregate feature information from its neighbors. The model became the canonical baseline for semi-supervised node classification and sparked the modern graph neural network research agenda.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kipf, T. N. & Welling, M.","year":2017,"type":"Spectral graph neural network (semi-supervised node classification)","task":"Node classification, graph representation learning","propagationLayers":2,"complexityPerLayer":"O(|E| * F * F')","trainingParadigm":"Semi-supervised (small fraction of labeled nodes)"},"citations":[{"ref":"Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), Toulon, France.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1609.02907"},{"ref":"Hamilton, W. L. (2020). Graph Representation Learning. Morgan & Claypool (Synthesis Lectures on Artificial Intelligence and Machine Learning).","type":"book","doi":null,"isbn":"978-1-68173-963-2","url":null}],"related":["graph-attention-network","graph-sage","graph-isomorphism-network","message-passing-neural-network","convolutional-neural-network","node2vec"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"graph-kernels","name":"Graph Kernels","fullName":"Graph Kernels for Structured Data","aliases":["Structured Graph Kernels","Kernel Methods on Graphs","Graf Çekirdekleri","Graph Similarity Kernels"],"domain":"network-analysis","family":"ml-model","subfamily":"Graph mining","year":2010,"originator":"Vishwanathan, Schraudolph, Kondor & Borgwardt","url":"https://scholargate.app/en/network-analysis/graph-kernels","markdownUrl":"https://scholargate.app/en/network-analysis/graph-kernels.md","definition":"Graph kernels are positive semi-definite kernel functions that measure the similarity between two graphs by comparing their shared substructures — such as random walks, shortest paths, or subtree patterns. Introduced in a unified framework by Vishwanathan, Schraudolph, Kondor, and Borgwardt (2010), they bridge kernel methods and graph-structured data, enabling algorithms like SVMs to operate directly on graphs without requiring an explicit vectorization step.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vishwanathan, Schraudolph, Kondor & Borgwardt","year":2010,"type":"Positive semi-definite kernel function over graphs","subfamily":"Graph mining","complexity":"Polynomial in graph size for most variants","output":"Gram matrix of pairwise graph similarities"},"citations":[{"ref":"Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010). Graph kernels. Journal of Machine Learning Research, 11, 1201–1242.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v11/vishwanathan10a.html"}],"related":["support-vector-machine","graph-neural-network","knowledge-graph-embeddings"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"graph-neural-network","name":"Graph Neural Network (Network Analysis)","fullName":"Graph Neural Network (GCN / GAT / GraphSAGE)","aliases":["GNN","GCN","GAT","GraphSAGE","Graf Sinir Ağı (GCN / GAT / GraphSAGE)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":"2017–2018 (major variants)","originator":null,"url":"https://scholargate.app/en/network-analysis/graph-neural-network","markdownUrl":"https://scholargate.app/en/network-analysis/graph-neural-network.md","definition":"A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Kipf & Welling (GCN, 2017); Veličković et al. (GAT, 2018); Hamilton et al. (GraphSAGE, 2017)","year":"2017–2018 (major variants)","type":"Deep learning on graph-structured data","mechanism":"Neighborhood message passing (spectral convolution / attention / inductive sampling)","tasks":"Node classification, link prediction, graph classification","minSample":50,"difficulty":3,"requiresNormality":false},"citations":[{"ref":"Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).","type":"conference","doi":"10.48550/arXiv.1609.02907","isbn":null,"url":null},{"ref":"Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph Attention Networks. International Conference on Learning Representations (ICLR).","type":"conference","doi":"10.48550/arXiv.1710.10903","isbn":null,"url":null},{"ref":"Hamilton, W.L. (2020). Graph Representation Learning. Morgan & Claypool.","type":"book","doi":"10.1007/978-3-031-01588-5","isbn":null,"url":null}],"related":["network-embedding","centrality-analysis","community-detection","temporal-network-analysis","multilayer-network"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"graphrag","name":"GraphRAG","fullName":"Graph-based Retrieval-Augmented Generation","aliases":["Graph RAG","Knowledge Graph RAG"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Language Models, Knowledge Graphs","year":"2023","originator":"Yunfan Gao","url":"https://scholargate.app/en/deep-learning/graphrag","markdownUrl":"https://scholargate.app/en/deep-learning/graphrag.md","definition":"GraphRAG is a retrieval-augmented generation approach that augments large language models with knowledge graphs to improve answer quality and factuality. Rather than retrieving flat text passages, GraphRAG constructs and queries structured knowledge graphs extracted from documents, providing rich contextual information to the language model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yunfan Gao","subfamily":"Deep Learning, Language Models, Knowledge Graphs","year":"2023","type":"System architecture"},"citations":[{"ref":"Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., & Wang, M. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2312.10997"}],"related":["latent-diffusion-models","masked-autoencoders","spatial-temporal-gcn","segment-anything-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gratitude-questionnaire","name":"Gratitude Questionnaire","fullName":"Gratitude Questionnaire-Six (GQ-6)","aliases":["GQ-6"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"gratitude disposition","year":"2002","originator":"Michael McCullough and Robert Emmons","url":"https://scholargate.app/en/positive-psychology/gratitude-questionnaire","markdownUrl":"https://scholargate.app/en/positive-psychology/gratitude-questionnaire.md","definition":"The Gratitude Questionnaire-Six (GQ-6), developed by McCullough, Emmons, and Tsang in 2002, is a 6-item measure of dispositional gratitude—the tendency to recognize and appreciate the good in one's life. Operationalizing gratitude as a stable personality trait (not just a momentary feeling), the GQ-6 assesses the capacity to notice, appreciate, and be thankful for life's blessings. Research shows that dispositional gratitude predicts well-being, life satisfaction, relationship quality, and resilience independent of personality and optimism.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael McCullough and Robert Emmons","subfamily":"gratitude disposition","year":"2002","type":"Self-report questionnaire"},"citations":[{"ref":"McCullough, M. E., Emmons, R. A., & Tsang, J. A. (2002). The grateful disposition: A conceptual and empirical topography. Journal of Personality and Social Psychology, 82(1), 112–127.","type":"article","doi":"10.1037/0022-3514.82.1.112","isbn":null,"url":null}],"related":["flourishing-scale","perma-scale","subjective-wellbeing-scale","via-character-strengths"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gravitational-microlensing","name":"Gravitational Microlensing","fullName":"Gravitational Microlensing for Exoplanet and Dark Matter Detection","aliases":["Microlensing","Gravitational Lensing Method"],"domain":"astronomy","family":"process-pipeline","subfamily":"Gravitational phenomenon","year":1986,"originator":"Bohdan Paczynski","url":"https://scholargate.app/en/astronomy/gravitational-microlensing","markdownUrl":"https://scholargate.app/en/astronomy/gravitational-microlensing.md","definition":"Gravitational microlensing is an observational technique that exploits Einstein's prediction that massive objects bend light. When a star or planet passes in front of a distant star from our perspective, its gravity acts as a lens, magnifying and distorting the background star's light. First proposed by Bohdan Paczynski in 1986, this method has discovered hundreds of exoplanets and provides unique sensitivity to low-mass planets and dark matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bohdan Paczynski","subfamily":"Gravitational phenomenon","year":1986,"type":"Observational detection method"},"citations":[{"ref":"Paczynski, B. (1986). Gravitational microlensing by the galactic halo. Astrophysical Journal, 304, 1-5.","type":"article","doi":"10.1086/164140","isbn":null,"url":null},{"ref":"Bond, I. A., et al. (1991). Microlensing of distant blue stars. Astrophysical Journal, 378, L81-L84.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Microlensing+of+distant+blue+stars+Bond"},{"ref":"Gaudi, B. S. (2012). Microlensing surveys for exoplanets. Annual Review of Astronomy and Astrophysics, 50, 411-453.","type":"article","doi":"10.1146/annurev-astro-081811-125518","isbn":null,"url":null}],"related":["weak-gravitational-lensing","strong-gravitational-lensing","transit-photometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gravitational-wave-matched-filtering","name":"Gravitational Wave Matched Filtering","fullName":"Gravitational Wave Signal Detection via Matched Filtering","aliases":["template-based detection","correlation filtering","GW signal extraction"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Signal Processing","year":"1928","originator":"Harry Nyquist","url":"https://scholargate.app/en/applied-physics/gravitational-wave-matched-filtering","markdownUrl":"https://scholargate.app/en/applied-physics/gravitational-wave-matched-filtering.md","definition":"Matched filtering is a signal processing technique used to detect gravitational waves by correlating detector data with theoretical waveform templates. When two massive objects (black holes, neutron stars) merge, they emit gravitational waves that pass through Earth, producing tiny distortions in laser interferometers like LIGO and Virgo. Matched filtering, formalized by Harry Nyquist, optimally extracts these signals from noise, enabling the detection of mergers billions of light-years away.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harry Nyquist","subfamily":"Signal Processing","year":"1928","type":"Statistical signal detection algorithm"},"citations":[{"ref":"Abbott, B. P., et al. (2016). Observation of Gravitational Waves from a Binary Black Hole Merger. Physical Review Letters, 116(6), 061102.","type":"article","doi":"10.1103/PhysRevLett.116.061102","isbn":null,"url":null},{"ref":"Weedman, D. (2010). Statistics of Matched Filtering and Bayes Theorem Applied to Gravitational Wave Data Analysis. Classical and Quantum Gravity, 10(9), S211.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Statistics+of+Matched+Filtering+and+Bayes+Theorem+Applied+to+Gravitational+Wave+Data+Analysis+Weedman"},{"ref":"Allen, B., Anderson, W. G., Brady, P. R., Brown, D. A., & Creighton, J. D. (2012). FINDCHIRP: An Algorithm for Detection of Gravitational Waves from Inspiraling Compact Binaries. Physical Review D, 85(12), 122006.","type":"article","doi":"10.1103/PhysRevD.85.122006","isbn":null,"url":null}],"related":["n-body-simulation","light-curve-analysis","cosmological-perturbation-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gravity-assist","name":"Gravity Assist","fullName":"Gravity Assist Maneuver","aliases":["swing-by","gravitational slingshot"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Astrodynamics","year":"1961","originator":"Michael Minovitch","url":"https://scholargate.app/en/applied-physics/gravity-assist","markdownUrl":"https://scholargate.app/en/applied-physics/gravity-assist.md","definition":"A gravity assist (or swing-by) maneuver uses the gravitational field of a planet or other celestial body to alter a spacecraft's trajectory and velocity without expending fuel. Discovered by Michael Minovitch at JPL in 1961, this technique is crucial for reaching distant planets economically. It works by exploiting the relative motion between the spacecraft, the assisting body, and the Sun.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Minovitch","subfamily":"Astrodynamics","year":"1961","type":"Orbital maneuver technique"},"citations":[{"ref":"Minovitch, M. A. (1961). The determination and characteristics of ballistic interplanetary trajectories under the influence of multiple planetary gravitational fields. Technical Report 32-464, Jet Propulsion Laboratory.","type":"article","doi":null,"isbn":null,"url":"https://ntrs.nasa.gov/citations/19800069813"},{"ref":"Laplace, P. S. (1799). Traité de Mécanique Céleste. Bachelier.","type":"book","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Traité_de_Mécanique_Céleste"},{"ref":"Curtis, H. D. (2013). Orbital Mechanics for Engineering Students (3rd ed.). Butterworth-Heinemann.","type":"book","doi":null,"isbn":"978-0-08-102133-0","url":null}],"related":["hohmann-transfer","n-body-simulation","orbit-determination","light-curve-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"greeks-via-automatic-differentiation","name":"Greeks via Automatic Differentiation","fullName":"Automatic Differentiation for Greeks Computation","aliases":["AD Greeks","Algorithmic Differentiation","Autodiff"],"domain":"quantitative-finance","family":"ml-model","subfamily":"Computational Methods","year":"2008","originator":"Mike Giles, Iman Homescu","url":"https://scholargate.app/en/quantitative-finance/greeks-via-automatic-differentiation","markdownUrl":"https://scholargate.app/en/quantitative-finance/greeks-via-automatic-differentiation.md","definition":"Automatic differentiation (AD) is a computational technique for computing derivatives (Greeks) by differentiating the computer code that computes the option price. AD avoids manual derivation of formulas and finite-difference approximations, yielding exact sensitivities with machine precision. It has become essential for real-time risk management in modern trading systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mike Giles, Iman Homescu","subfamily":"Computational Methods","year":"2008","type":"Sensitivity Analysis"},"citations":[{"ref":"Giles, M. B. (2008). Adjoint code by automatic differentiation. Journal of Computational Finance, 12(1), 1-18.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Adjoint+code+by+automatic+differentiation+Giles"},{"ref":"Homescu, C. (2011). Adjoints and automatic differentiation in computational finance. arXiv:1107.1188.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1107.1188"}],"related":["local-volatility","bates-model","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"green-building-rating","name":"Green Building Rating System","fullName":"Green Building Rating System Assessment and Certification","aliases":["LEED certification","green building assessment","sustainability rating"],"domain":"architecture","family":"process-pipeline","subfamily":"Sustainability assessment and environmental rating","year":"1998","originator":"U.S. Green Building Council","url":"https://scholargate.app/en/architecture/green-building-rating","markdownUrl":"https://scholargate.app/en/architecture/green-building-rating.md","definition":"Green Building Rating Systems are standardized frameworks for assessing and certifying the environmental performance and sustainability of buildings. The most widely known is LEED (Leadership in Energy and Environmental Design), established by the U.S. Green Building Council in 1998. Similar systems exist globally (BREEAM in UK, Passivhaus in Europe), each using structured criteria to evaluate design and performance across multiple environmental dimensions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Green Building Council","subfamily":"Sustainability assessment and environmental rating","year":"1998","type":"multi-criteria sustainability rating system"},"citations":[{"ref":"U.S. Green Building Council (2021). LEED v4.1 for Building Design and Construction. USGBC.","type":"book","doi":null,"isbn":null,"url":"https://www.usgbc.org/leed"},{"ref":"BRE Global (2018). BREEAM New Construction Technical Manual. BRE Group.","type":"book","doi":null,"isbn":null,"url":"https://www.breeam.com/"},{"ref":"Cole, R. J. (2005). Building Environmental Assessment Methods: Redefining Intentions and Roles. Building Research and Information, 33(6), 455-467.","type":"article","doi":"10.1080/09613210500219063","isbn":null,"url":null}],"related":["building-energy-performance","daylight-simulation","thermal-comfort-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"green-infrastructure-design","name":"Green Infrastructure Design","fullName":"Planning and Implementation of Nature-Based Urban Systems","aliases":["GI design","natural infrastructure","nature-based solutions","ecosystem-based adaptation"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Sustainable urban design","year":"2000","originator":"Urban planners and landscape architects","url":"https://scholargate.app/en/environmental-engineering/green-infrastructure-design","markdownUrl":"https://scholargate.app/en/environmental-engineering/green-infrastructure-design.md","definition":"Green infrastructure (GI) design is the planning and implementation of natural or nature-based systems (vegetation, soils, water bodies) integrated into urban environments to provide multiple ecosystem services: stormwater management, air quality improvement, heat island mitigation, biodiversity habitat, recreation, and social well-being. Emerged in the 2000s as a sustainability paradigm, green infrastructure combines landscape design, hydrology, ecology, and urban planning to create multifunctional spaces that serve practical and aesthetic goals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Urban planners and landscape architects","subfamily":"Sustainable urban design","year":"2000","type":"integrated design and planning pipeline"},"citations":[{"ref":"Freeman, R. C. (2005). Green Infrastructure: Intelligent Landscapes for the Twenty-First Century. Routledge.","type":"book","doi":null,"isbn":"978-0415772662","url":null},{"ref":"U.S. Green Building Council. (2012). LEED Reference Guide for Building Design and Construction. USGBC.","type":"article","doi":null,"isbn":null,"url":"https://www.usgbc.org/leed"},{"ref":"Tzoulas, K., Korpela, K., Vigo, S., et al. (2007). Promoting Ecosystem and Human Health in Urban Areas Using Green Infrastructure: A Literature Review. Landscape and Urban Planning, 81(3–4), 167–178.","type":"article","doi":"10.1016/j.landurbplan.2007.02.001","isbn":null,"url":null}],"related":["stormwater-management","constructed-wetland-design","air-dispersion-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"green-purchase-intention-scale","name":"GPIS","fullName":"Green Purchase Intention Scale","aliases":["GPIS","Eco-Friendly Purchase Intention"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"sustainable consumption intentions and willingness to pay","year":"1991","originator":"William B. Dodds, Kent Monroe, Dhruv Grewal","url":"https://scholargate.app/en/environmental-psychology/green-purchase-intention-scale","markdownUrl":"https://scholargate.app/en/environmental-psychology/green-purchase-intention-scale.md","definition":"The Green Purchase Intention Scale (GPIS) measures consumers' stated willingness and likelihood of purchasing environmentally friendly products, including their intention to pay premium prices for eco-labeled goods and their perceived value of sustainable alternatives. Developed from consumer behavior and willingness-to-pay frameworks (Dodds, Monroe, & Grewal, 1991; expanded by Thøgersen and others), the GPIS bridges environmental attitudes and actual purchasing behavior, a critical gap in sustainability research. The scale is widely used in marketing research, environmental policy evaluation, and studies examining whether environmental concern translates into purchasing decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William B. Dodds, Kent Monroe, Dhruv Grewal","subfamily":"sustainable consumption intentions and willingness to pay","year":"1991","type":"Self-report intention and willingness-to-pay scale"},"citations":[{"ref":"Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers' product evaluations. Journal of Marketing Research, 28(3), 307–319.","type":"article","doi":"10.1177/002224379102800305","isbn":null,"url":null},{"ref":"Kim, Y., & Choi, S. M. (2005). Antecedents of green purchase intention: An examination of collectivism, environmental concern, and PCE. Advances in Consumer Research, 32, 592–599.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Kim%2C%20Y.%2C%20%26%20Choi%2C%20S.%20M.%20(2005).%20Antecedents%20of%20green%20purchase%20intention%3A%20An%20examination%20of%20collectivism%2C%20environmental%20co"},{"ref":"Thøgersen, J., & Crompton, T. (2009). Simple and painless? The limitations of spillover in environmental campaigning. Journal of Consumer Policy, 32(2), 141–163.","type":"article","doi":"10.1007/s10603-009-9101-1","isbn":null,"url":null}],"related":["pro-environmental-behavior-scale","sustainable-consumption-scale","environmental-identity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"greenhouse-climate-control","name":"Greenhouse Climate Control","fullName":"Automated Environmental Regulation for Optimized Plant Growth and Development","aliases":["climate management","environmental control","HVAC optimization"],"domain":"horticulture","family":"process-pipeline","subfamily":"Environmental control systems","year":"1990","originator":"Modern horticultural engineering","url":"https://scholargate.app/en/horticulture/greenhouse-climate-control","markdownUrl":"https://scholargate.app/en/horticulture/greenhouse-climate-control.md","definition":"Greenhouse climate control integrates measurement, modeling, and automated actuation to maintain optimal temperature, humidity, light, and CO₂ concentrations for plant growth. Modern systems use sensors and control algorithms to respond dynamically to external weather and internal plant needs. This approach increases yield, shortens crop cycles, reduces disease pressure, and improves energy efficiency compared to manual or static setpoint controls.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Modern horticultural engineering","subfamily":"Environmental control systems","year":"1990","type":"control systems pipeline"},"citations":[{"ref":"Stanghellini, C. (2003). Transpiration in greenhouse horticulture: An introduction. Acta Horticulturae, 618, 101–111.","type":"article","doi":null,"isbn":null,"url":"https://www.actahort.org/books/618/618_11.htm"},{"ref":"Castilla, N. (2005). Greenhouse Technology and Management (2nd ed.). CAB International.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Greenhouse+Technology+and+Management+%282nd+ed.%29+Castilla"}],"related":["hydroponic-nutrient-solution","phenological-stage-monitoring","postharvest-storage-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gregory-hansen-test","name":"Gregory-Hansen Test","fullName":"Gregory-Hansen Cointegration Test with Regime Shift","aliases":["GH Cointegration Test","Gregory-Hansen Regime Shift Test","Residual-Based Cointegration Test with Structural Break","Rejim Değişimli Koentegrasyon Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Cointegration","year":1996,"originator":"Allan Gregory & Bruce Hansen","url":"https://scholargate.app/en/econometrics/gregory-hansen-test","markdownUrl":"https://scholargate.app/en/econometrics/gregory-hansen-test.md","definition":"The Gregory-Hansen test, introduced by Allan Gregory and Bruce Hansen in 1996, extends the standard Engle-Granger cointegration framework to allow for a single unknown structural break in the cointegrating relationship. It is designed for researchers who suspect that the long-run equilibrium between integrated variables may have shifted at some point during the sample period, and who wish to test for cointegration without presupposing the break date.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Allan Gregory & Bruce Hansen","year":1996,"type":"Residual-based structural break cointegration test","subfamily":"Cointegration","nullHypothesis":"No cointegration (even allowing for a regime shift)","breakModels":"Level shift (C), Level shift with trend (C/T), Regime shift (C/S)"},"citations":[{"ref":"Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99–126.","type":"article","doi":"10.1016/0304-4076(69)41685-7","isbn":null,"url":null}],"related":["cointegration-test","hatemi-j-cointegration-test","zivot-andrews-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-aras","name":"GREY-ARAS","fullName":"Grey-ARAS — Grey extension of ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Zavadskas & Turskis","url":"https://scholargate.app/en/decision-making/grey-aras","markdownUrl":"https://scholargate.app/en/decision-making/grey-aras.md","definition":"GREY-ARAS (Grey-ARAS — Grey extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas & Turskis in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas & Turskis","subfamily":"Ranking","year":"2010","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Zavadskas & Turskis (2010). Grey Additive Ratio Assessment. Informatica","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Grey+Additive+Ratio+Assessment+Zavadskas"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-clustering","name":"Grey Clustering","fullName":"Grey Clustering (Grey Incidence / Whitenization)","aliases":["Grey Whitenization Weight Function Clustering","Grey Fixed-Weight Clustering","Grey Variable-Weight Clustering","Gri Kümeleme"],"domain":"soft-computing","family":"ml-model","subfamily":"Grey systems","year":2010,"originator":"Julong Deng; Sifeng Liu","url":"https://scholargate.app/en/soft-computing/grey-clustering","markdownUrl":"https://scholargate.app/en/soft-computing/grey-clustering.md","definition":"Grey Clustering is a classification method from grey systems theory that assigns objects to predefined grey classes using whitenization weight functions. Developed within the framework of Deng Julong's grey system theory and systematized by Sifeng Liu, it is particularly suited for situations involving small sample sizes, incomplete information, or uncertain data—conditions common in engineering assessments, environmental monitoring, and socioeconomic evaluation. The method quantifies how strongly each object belongs to each grey class and makes a crisp assignment based on maximum clustering coefficients.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Julong Deng; Sifeng Liu","year":2010,"type":"Whitenization-based soft clustering","subfamily":"Grey systems","input":"Small, uncertain, or incomplete datasets","output":"Cluster membership via whitenization weight functions"},"citations":[{"ref":"Liu, S., & Lin, Y. (2010). Grey Systems: Theory and Applications. Springer.","type":"book","doi":null,"isbn":"978-3-642-13937-6","url":null}],"related":["grey-model-gm11","grey-relational-analysis","fuzzy-c-means"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-codas","name":"GREY-CODAS","fullName":"Grey-CODAS — Grey extension of CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2016","originator":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J.","url":"https://scholargate.app/en/decision-making/grey-codas","markdownUrl":"https://scholargate.app/en/decision-making/grey-codas.md","definition":"GREY-CODAS (Grey-CODAS — Grey extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J.","subfamily":"Ranking","year":"2016","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation and Economic Cybernetics Studies and Research","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+combinative+distance-based+assessment+%28CODAS%29+method+for+multi-criteria+decision-making+Keshavarz"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-copras","name":"GREY-COPRAS","fullName":"Grey-COPRAS — Grey extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1996","originator":"Zavadskas, E. K., Kaklauskas, A.","url":"https://scholargate.app/en/decision-making/grey-copras","markdownUrl":"https://scholargate.app/en/decision-making/grey-copras.md","definition":"GREY-COPRAS (Grey-COPRAS — Grey extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Kaklauskas, A. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Kaklauskas, A.","subfamily":"Ranking","year":"1996","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zavadskas, E. K., Kaklauskas, A., Turskis, Z., Tamosaitiene, J. (2009). Multi-Attribute Decision-Making Model by Applying Grey Numbers. Informatica","type":"article","doi":"10.15388/informatica.2009.252","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-edas","name":"GREY-EDAS","fullName":"Grey-EDAS — Grey extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Stanujkic, D., Zavadskas, E. K., Keshavarz Ghorabaee, M., Turskis, Z.","url":"https://scholargate.app/en/decision-making/grey-edas","markdownUrl":"https://scholargate.app/en/decision-making/grey-edas.md","definition":"GREY-EDAS (Grey-EDAS — Grey extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkic, D., Zavadskas, E. K., Keshavarz Ghorabaee, M., Turskis, Z. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanujkic, D., Zavadskas, E. K., Keshavarz Ghorabaee, M., Turskis, Z.","subfamily":"Ranking","year":"2017","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Stanujkic, D., Zavadskas, E. K., Keshavarz Ghorabaee, M., Turskis, Z. (2017). An Extension of the EDAS Method Based on the Use of Interval Grey Numbers. Studies in Informatics and Control","type":"article","doi":"10.24846/v26i1y201701","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-gra","name":"GREY-GRA","fullName":"Grey-GRA — Grey extension of GRA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1989","originator":"Deng, J. L.","url":"https://scholargate.app/en/decision-making/grey-gra","markdownUrl":"https://scholargate.app/en/decision-making/grey-gra.md","definition":"GREY-GRA (Grey-GRA — Grey extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Deng, J. L. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Deng, J. L.","subfamily":"Ranking","year":"1989","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System","type":"article","doi":"10.5555/90757.90758","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-literature-search","name":"Grey Literature Search","fullName":"Systematic Search and Retrieval of Grey Literature","aliases":["grey literature","gray literature","unpublished literature"],"domain":"research-skills","family":"process-pipeline","subfamily":"literature-retrieval","year":"1990s (formalized in systematic review guidelines)","originator":"Information specialists and systematic review methodologists (Cochrane Collaboration, Health Technology Assessment)","url":"https://scholargate.app/en/research-skills/grey-literature-search","markdownUrl":"https://scholargate.app/en/research-skills/grey-literature-search.md","definition":"Grey literature comprises documents and data not published through conventional commercial channels—including theses, government reports, clinical trial registries, conference abstracts, organizational policy documents, and working papers. Unlike journal articles, grey literature is not indexed in MEDLINE or Scopus and often lacks peer review. However, it is crucial for systematic reviews because it may contain null or negative findings that are less likely to be published (publication bias). Systematic grey literature searching is now a standard component of evidence synthesis and is recommended by the Cochrane Collaboration, PRISMA, and other methodological guidelines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Information specialists and systematic review methodologists (Cochrane Collaboration, Health Technology Assessment)","subfamily":"literature-retrieval","year":"1990s (formalized in systematic review guidelines)","type":"Tool"},"citations":[{"ref":"Rothstein, H. R., & Hopewell, S. (2009). Grey literature. In J. P. Higgins & S. Green (Eds.), Cochrane handbook for systematic reviews of interventions (Version 5.0.2, Chapter 13). The Cochrane Collaboration.","type":"book","doi":null,"isbn":null,"url":"https://training.cochrane.org/handbook"},{"ref":"Wood, A. M., Egger, M., Gluud, L. L., Schulz, K. F., Jüni, P., Altman, D. G., & Gluud, C. (2008). Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. BMJ, 336(7644), 601–605.","type":"article","doi":"10.1136/bmj.39465.451748.AD","isbn":null,"url":null},{"ref":"Mahood, Q., Van Eerd, D., & Irvin, E. (2014). Searching for grey literature for systematic reviews: challenges and benefits. Research Synthesis Methods, 5(4), 353–364.","type":"article","doi":"10.1002/jrsm.1106","isbn":null,"url":null}],"related":["pico-framework","boolean-search-operators","systematic-search-strategy","citation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-mabac","name":"GREY-MABAC","fullName":"Grey-MABAC — Grey extension of MABAC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Pamucar, D., Cirovic, G.","url":"https://scholargate.app/en/decision-making/grey-mabac","markdownUrl":"https://scholargate.app/en/decision-making/grey-mabac.md","definition":"GREY-MABAC (Grey-MABAC — Grey extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamucar, D., Cirovic, G. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamucar, D., Cirovic, G.","subfamily":"Ranking","year":"2015","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Pamucar, D., Cirovic, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications","type":"article","doi":"10.1016/j.eswa.2014.11.057","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-marcos","name":"GREY-MARCOS","fullName":"Grey-MARCOS — Grey extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P.","url":"https://scholargate.app/en/decision-making/grey-marcos","markdownUrl":"https://scholargate.app/en/decision-making/grey-marcos.md","definition":"GREY-MARCOS (Grey-MARCOS — Grey extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P.","subfamily":"Ranking","year":"2020","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering","type":"article","doi":"10.1016/j.cie.2019.106231","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-model-gm11","name":"GM(1,1) Grey Forecasting","fullName":"Grey Model GM(1,1) Forecasting","aliases":["GM(1,1)","grey prediction model","grey forecasting","gri tahmin modeli"],"domain":"soft-computing","family":"regression-model","subfamily":"Grey systems","year":1982,"originator":"Julong Deng","url":"https://scholargate.app/en/soft-computing/grey-model-gm11","markdownUrl":"https://scholargate.app/en/soft-computing/grey-model-gm11.md","definition":"GM(1,1) is the core forecasting model of grey system theory, introduced by Julong Deng in 1982, designed to predict from very few observations and incomplete information — situations where classical time-series models like ARIMA need far more data. It accumulates the raw series to expose a hidden exponential trend, fits a first-order grey differential equation, and projects future values, making it popular in engineering, energy, and management forecasting with short data records.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Julong Deng","year":1982,"type":"Small-sample grey forecasting model","subfamily":"Grey systems","minSample":4,"handles":"Few observations, incomplete information"},"citations":[{"ref":"Deng, J. L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294.","type":"article","doi":"10.1016/S0167-6911(82)80025-X","isbn":null,"url":null},{"ref":"Liu, S., & Lin, Y. (2010). Grey Systems: Theory and Applications. Springer.","type":"book","doi":null,"isbn":"978-3-642-13937-6","url":null}],"related":["grey-relational-analysis","arima","exponential-smoothing","case-based-reasoning"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-moora","name":"GREY-MOORA","fullName":"Grey-MOORA — Grey extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2012","originator":"Stanujkic, D., Magdalinovic, N., Jovanovic, R., Stojanovic, S.","url":"https://scholargate.app/en/decision-making/grey-moora","markdownUrl":"https://scholargate.app/en/decision-making/grey-moora.md","definition":"GREY-MOORA (Grey-MOORA — Grey extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkic, D., Magdalinovic, N., Jovanovic, R., Stojanovic, S. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanujkic, D., Magdalinovic, N., Jovanovic, R., Stojanovic, S.","subfamily":"Ranking","year":"2012","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Stanujkic, D., Magdalinovic, N., Jovanovic, R., Stojanovic, S. (2012). An objective multi-criteria approach to optimization using MOORA method and interval grey numbers. Technological and Economic Development of Economy","type":"article","doi":"10.3846/20294913.2012.676996","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-projection","name":"GREY-PROJECTION","fullName":"Grey-Projection — Grey extension of GRA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Liu, S., Lin, Y.","url":"https://scholargate.app/en/decision-making/grey-projection","markdownUrl":"https://scholargate.app/en/decision-making/grey-projection.md","definition":"GREY-PROJECTION (Grey-Projection — Grey extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Liu, S., Lin, Y. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, S., Lin, Y.","subfamily":"Ranking","year":"2013","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Liu, S., Lin, Y. (2013). Grey systems-based projection for MCDM. Grey Systems: Theory and Application","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Grey%20systems-based%20projection%20for%20MCDM"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-promethee","name":"GREY-PROMETHEE","fullName":"Grey-PROMETHEE — Grey extension of PROMETHEE","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2011","originator":"Li, P., Wei, C.","url":"https://scholargate.app/en/decision-making/grey-promethee","markdownUrl":"https://scholargate.app/en/decision-making/grey-promethee.md","definition":"GREY-PROMETHEE (Grey-PROMETHEE — Grey extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Li, P., Wei, C. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li, P., Wei, C.","subfamily":"Outranking","year":"2011","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Li, P., Wei, C. (2011). Grey PROMETHEE for multi-criteria decision making. Expert Systems with Applications","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Grey%20PROMETHEE%20for%20multi-criteria%20decision%20making"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-saw","name":"GREY-SAW","fullName":"Grey-SAW — Grey extension of SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1967","originator":"Fishburn, P. C.","url":"https://scholargate.app/en/decision-making/grey-saw","markdownUrl":"https://scholargate.app/en/decision-making/grey-saw.md","definition":"GREY-SAW (Grey-SAW — Grey extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fishburn, P. C.","subfamily":"Ranking","year":"1967","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research","type":"article","doi":"10.1287/opre.15.3.537","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-todim","name":"GREY-TODIM","fullName":"Grey-TODIM — Grey extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1992","originator":"Gomes, L. F. A. M., Lima, M. M. P. P.","url":"https://scholargate.app/en/decision-making/grey-todim","markdownUrl":"https://scholargate.app/en/decision-making/grey-todim.md","definition":"GREY-TODIM (Grey-TODIM — Grey extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Gomes, L. F. A. M., Lima, M. M. P. P. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gomes, L. F. A. M., Lima, M. M. P. P.","subfamily":"Ranking","year":"1992","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"(). UNCONFIRMED — GREY-TODIM specific seminal not confirmed via systematic literature search. PENDING","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=UNCONFIRMED%20%E2%80%94%20GREY-TODIM%20specific%20seminal%20not%20confirmed%20via%20systematic%20literature%20search"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-topsis","name":"GREY-TOPSIS","fullName":"Grey-TOPSIS — Grey extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Zavadskas, E. K., Turskis, Z., Bagocius, V.","url":"https://scholargate.app/en/decision-making/grey-topsis","markdownUrl":"https://scholargate.app/en/decision-making/grey-topsis.md","definition":"GREY-TOPSIS (Grey-TOPSIS — Grey extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z., Bagocius, V. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Turskis, Z., Bagocius, V.","subfamily":"Ranking","year":"2015","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zavadskas, E. K., Turskis, Z., Bagocius, V. (2015). Multi-criteria selection of a deep-water port in the Eastern Baltic Sea. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2014.09.019","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-vikor","name":"GREY-VIKOR","fullName":"Grey-VIKOR — Grey extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2001","originator":"Chang, C. L., Liu, P. H., Wei, C. C.","url":"https://scholargate.app/en/decision-making/grey-vikor","markdownUrl":"https://scholargate.app/en/decision-making/grey-vikor.md","definition":"GREY-VIKOR (Grey-VIKOR — Grey extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Chang, C. L., Liu, P. H., Wei, C. C. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chang, C. L., Liu, P. H., Wei, C. C.","subfamily":"Ranking","year":"2001","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Chang, C. L., Liu, P. H., Wei, C. C. (2001). Failure mode and effects analysis using grey theory. Integrated Manufacturing Systems","type":"article","doi":"10.1108/09576060110391174","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-waspas","name":"GREY-WASPAS","fullName":"Grey-WASPAS — Grey extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2012","originator":"Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A.","url":"https://scholargate.app/en/decision-making/grey-waspas","markdownUrl":"https://scholargate.app/en/decision-making/grey-waspas.md","definition":"GREY-WASPAS (Grey-WASPAS — Grey extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A.","subfamily":"Ranking","year":"2012","type":"Grey outranking/ranking — Grey Interval Number (GIN: [x̲, x̄])","value_space":"grey","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. (2012). Optimization of Weighted Aggregated Sum Product Assessment. Electronics and Electrical Engineering","type":"article","doi":"10.5755/j01.eee.122.6.1810","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grey-wolf-optimizer","name":"Grey Wolf Optimizer","fullName":"Grey Wolf Optimizer (GWO)","aliases":["GWO","Gri Kurt Optimizasyonu","Gri Kurt Optimizasyonu (GWO)"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":2014,"originator":"Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis","url":"https://scholargate.app/en/optimization/grey-wolf-optimizer","markdownUrl":"https://scholargate.app/en/optimization/grey-wolf-optimizer.md","definition":"The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis","year":2014,"type":"Swarm-intelligence metaheuristic","inspiration":"Social hierarchy and cooperative hunting behaviour of grey wolves (Canis lupus)","leadershipLevels":"Alpha (α), Beta (β), Delta (δ), Omega (ω)","controlParameter":"a — linearly decreases from 2 to 0 over iterations, balancing exploration and exploitation","problemClass":"Continuous, unconstrained and constrained optimisation","populationBased":true,"requiresGradient":false,"difficulty":2},"citations":[{"ref":"Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.","type":"article","doi":"10.1016/j.advengsoft.2013.12.007","isbn":null,"url":null},{"ref":"Faris, H., Aljarah, I., Al-Betar, M. A., & Mirjalili, S. (2018). Grey Wolf Optimizer: A Review of Recent Variants and Applications. Neural Computing and Applications, 30(2), 413-435.","type":"article","doi":"10.1007/s00521-017-3272-5","isbn":null,"url":null}],"related":["whale-optimization-algorithm","particle-swarm-optimization","genetic-algorithm","simulated-annealing","tabu-search","bayesian-optimization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grief-experience-questionnaire","name":"GEQ","fullName":"Grief Experience Questionnaire","aliases":["GEQ","Barrett & Schneweis GEQ"],"domain":"bereavement-psychology","family":"process-pipeline","subfamily":"dimensional-grief-assessment","year":"1980","originator":"Richard K. Barrett, Keith C. Schneweis","url":"https://scholargate.app/en/bereavement-psychology/grief-experience-questionnaire","markdownUrl":"https://scholargate.app/en/bereavement-psychology/grief-experience-questionnaire.md","definition":"The Grief Experience Questionnaire (GEQ) is a multidimensional measure developed by Barrett and Schneweis in 1980 to assess the breadth of emotional, cognitive, and existential experiences reported by bereaved individuals. Rather than focusing on pathology or symptom severity, the GEQ captures the diverse phenomenology of grief—including yearning, social withdrawal, guilt, anger, disorientation, and existential questioning—providing a comprehensive portrait of the grief experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard K. Barrett, Keith C. Schneweis","subfamily":"dimensional-grief-assessment","year":"1980","type":"Self-report questionnaire"},"citations":[{"ref":"Barrett, R. K., & Schneweis, K. C. (1980–1981). An empirical search for stages of grief. Omega, 11(2), 97–110.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Barrett%2C%20R.%20K.%2C%20%26%20Schneweis%2C%20K.%20C.%20(1980%E2%80%931981).%20An%20empirical%20search%20for%20stages%20of%20grief.%20Omega%2C%2011(2)%2C%2097%E2%80%93110."}],"related":["texas-revised-inventory-grief","inventory-complicated-grief","hogan-grief-reaction-checklist","anticipatory-grief-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"griffith-fracture-mechanics","name":"Griffith Fracture Mechanics","fullName":"Griffith's Theory of Brittle Fracture and Crack Growth","aliases":["Brittle fracture theory","Energy release rate","Linear elastic fracture mechanics"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Fracture mechanics","year":"1921","originator":"Alan A. Griffith","url":"https://scholargate.app/en/manufacturing/griffith-fracture-mechanics","markdownUrl":"https://scholargate.app/en/manufacturing/griffith-fracture-mechanics.md","definition":"Griffith's theory of brittle fracture explains how small flaws or cracks in materials grow unstably, leading to sudden catastrophic failure. Formulated by Alan A. Griffith in 1921 through experiments on glass fibers, this theory balances the elastic energy released by crack growth against the surface energy required to create new material surfaces. It predicts that materials fail at stresses far below their theoretical strength due to the stress concentration around pre-existing flaws.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan A. Griffith","subfamily":"Fracture mechanics","year":"1921","type":"Theoretical model for brittle fracture and crack propagation"},"citations":[{"ref":"Griffith, A. A. (1921). The phenomena of rupture and flow in solids. Philosophical Transactions of the Royal Society A, 221, 163-198.","type":"article","doi":null,"isbn":null,"url":"https://royalsocietypublishing.org/doi/10.1098/rsta.1921.0006"},{"ref":"Irwin, G. R. (1957). Analysis of stresses and strains near the end of a crack traversing a plate. Journal of Applied Mechanics, 24(3), 361-364.","type":"article","doi":null,"isbn":null,"url":"https://asmedigitalcollection.asme.org/appliedmechanics/article-abstract/24/3/361/406778"},{"ref":"Anderson, T. L. (2017). Fracture Mechanics: Fundamentals and Applications (4th ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1-4987-8644-3","url":null}],"related":["modal-analysis","design-for-manufacturing-and-assembly","tolerance-stack-up","elastohydrodynamic-lubrication"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grit-scale","name":"Grit Scale","fullName":"Grit Scale (GRIT)","aliases":["GRIT","Perseverance and Passion Scale","Duckworth Grit Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Personality assessment","year":"2007","originator":"Angela Duckworth","url":"https://scholargate.app/en/social-psychology/grit-scale","markdownUrl":"https://scholargate.app/en/social-psychology/grit-scale.md","definition":"The Grit Scale is a 12-item measure assessing grit—the combination of perseverance (sustained effort despite obstacles) and passion (consistent interest and commitment) for long-term goals. Developed by Angela Duckworth and colleagues in 2007, the GRIT operationalizes grit as a distinct personality construct predicting achievement in challenging domains. The measure has become widely used in educational and organizational research examining how motivation and persistence relate to success and well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angela Duckworth","subfamily":"Personality assessment","year":"2007","type":"Perseverance and long-term goal commitment measure"},"citations":[{"ref":"Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101.","type":"article","doi":"10.1037/0022-3514.92.6.1087","isbn":null,"url":null},{"ref":"Duckworth, A. L. (2016). Grit: The power of passion and perseverance. Scribner.","type":"book","doi":null,"isbn":"978-1501111106","url":null},{"ref":"Credé, M., Tynan, M. C., & Lowe, G. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113(3), 492–511.","type":"article","doi":"10.1037/pspp0000102","isbn":null,"url":null}],"related":["resilience-scale","generalized-self-efficacy-scale","uwes-work-engagement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ground-penetrating-radar","name":"Ground-Penetrating Radar","fullName":"Ground-Penetrating Radar","aliases":["GPR"],"domain":"geophysics","family":"process-pipeline","subfamily":"Electromagnetic imaging","year":"1989","originator":"James Davis and Anthony Annan","url":"https://scholargate.app/en/geophysics/ground-penetrating-radar","markdownUrl":"https://scholargate.app/en/geophysics/ground-penetrating-radar.md","definition":"Ground-Penetrating Radar (GPR) is a near-surface geophysical method that uses high-frequency electromagnetic pulses (typically 10 MHz to 2.5 GHz) to image shallow subsurface structures with exceptional spatial resolution. Pioneered by Davis and Annan in 1989, GPR is widely used in archaeology, civil engineering, environmental assessment, and shallow mineral exploration due to its ability to resolve features at decimeter to centimeter scales.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Davis and Anthony Annan","subfamily":"Electromagnetic imaging","year":"1989","type":"Shallow subsurface electromagnetic pulse detection"},"citations":[{"ref":"Davis, J. L., & Annan, A. P. (1989). Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy. Geophysical Prospecting, 37(5), 531-551.","type":"article","doi":"10.1111/j.1365-2478.1989.tb02221.x","isbn":null,"url":null},{"ref":"Jol, H. M. (2009). Ground penetrating radar: Theory and applications. Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/ground-penetrating-radar/jol/978-0-444-53348-7"}],"related":["electrical-resistivity-tomography","seismic-full-waveform-inversion","insar"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grounded-theory","name":"Grounded Theory","fullName":"Grounded Theory Method","aliases":["GT","Grounded Theory Approach"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"interpretive-inductive-method","year":"1967","originator":"Barney Glaser and Anselm Strauss","url":"https://scholargate.app/en/qualitative-research/grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative-research/grounded-theory.md","definition":"Grounded Theory (GT) is a systematic qualitative research methodology in which theory emerges directly from data through iterative analysis, rather than being imposed before data collection. Developed by Barney Glaser and Anselm Strauss in 1967, GT prioritizes generating explanatory frameworks grounded in evidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney Glaser and Anselm Strauss","subfamily":"interpretive-inductive-method","year":"1967","type":"Method"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Glaser%2C%20B.%20G.%2C%20%26%20Strauss%2C%20A.%20L.%20(1967).%20The%20discovery%20of%20grounded%20theory%3A%20Strategies%20for%20qualitative%20research.%20Aldine."},{"ref":"Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory (2nd ed.). Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Strauss%2C%20A.%2C%20%26%20Corbin%2C%20J.%20(1998).%20Basics%20of%20qualitative%20research%3A%20Techniques%20and%20procedures%20for%20developing%20grounded%20theo"},{"ref":"Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Charmaz%2C%20K.%20(2006).%20Constructing%20grounded%20theory%3A%20A%20practical%20guide%20through%20qualitative%20analysis.%20Sage%20Publications."}],"related":["phenomenological-research","thematic-analysis","constant-comparison-analysis","theoretical-saturation","narrative-inquiry"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"groundwater-contamination-model","name":"Groundwater Contamination Modeling","fullName":"Simulation of Contaminant Transport in Groundwater","aliases":["groundwater transport","contaminant plume modeling","subsurface flow and transport","GWHC modeling"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Contaminant hydrogeology","year":"1988","originator":"USGS and hydrogeology researchers","url":"https://scholargate.app/en/environmental-engineering/groundwater-contamination-model","markdownUrl":"https://scholargate.app/en/environmental-engineering/groundwater-contamination-model.md","definition":"Groundwater contamination modeling is a quantitative approach to predict the migration of dissolved and suspended contaminants (chemical spills, landfill leachate, petroleum, radionuclides) through subsurface aquifers and toward receptors (drinking water wells, surface water bodies, ecosystems). Developed systematically in the 1980s–1990s by the USGS and hydrogeologists, these models couple flow equations (Darcy's law) with advection-dispersion transport and geochemical reactions to forecast contaminant arrival times and plume extent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"USGS and hydrogeology researchers","subfamily":"Contaminant hydrogeology","year":"1988","type":"numerical simulation pipeline"},"citations":[{"ref":"Fetter, C. W., Boving, T. B., & Kreamer, D. K. (2018). Contaminant Hydrogeology (3rd ed.). Waveland Press.","type":"book","doi":null,"isbn":"978-1478625315","url":null},{"ref":"US Geological Survey. (2003). MODFLOW-2000, the U.S. Geological Survey Modular Finite-Difference Ground-Water Flow Model. USGS Open-File Report 03-123.","type":"article","doi":null,"isbn":null,"url":"https://water.usgs.gov/nrp/gwsoftware/modflow2000/"},{"ref":"Domenico, P. A., & Schwartz, F. W. (1998). Physical and Chemical Hydrogeology (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471594734","url":null}],"related":["soil-remediation","environmental-impact-assessment","heavy-metal-speciation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"grovers-algorithm","name":"Grover's Algorithm","fullName":"Grover's Algorithm for Quantum Search","aliases":["quantum search","amplitude amplification"],"domain":"quantum-computing","family":"ml-model","subfamily":"Search Algorithm","year":"1996","originator":"Lov Grover","url":"https://scholargate.app/en/quantum-computing/grovers-algorithm","markdownUrl":"https://scholargate.app/en/quantum-computing/grovers-algorithm.md","definition":"Grover's Algorithm is a quantum algorithm for searching an unsorted database, offering a quadratic speedup over classical linear search. Proposed by Lov Grover in 1996, it exploits quantum superposition and amplitude amplification to find a target item among N items in O(√N) queries, compared to the classical O(N) requirement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lov Grover","subfamily":"Search Algorithm","year":"1996","type":"Quantum algorithm"},"citations":[{"ref":"Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. Proceedings of the 28th Annual ACM Symposium on Theory of Computing (STOC), 212–219.","type":"article","doi":"10.1145/237814.237866","isbn":null,"url":null},{"ref":"Grover, L. K. (1997). Quantum mechanics helps in searching for a needle in a haystack. Physical Review Letters, 79, 325–328.","type":"article","doi":"10.1103/PhysRevLett.79.325","isbn":null,"url":null},{"ref":"Brassard, G., Hoyer, P., Tapp, A. (2002). Quantum amplitude amplification and estimation. arXiv preprint quant-ph/0005055.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/quant-ph/0005055"}],"related":["shors-algorithm","quantum-phase-estimation","quantum-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"growth-curve-fitting-livestock","name":"Growth Curve Fitting in Livestock","fullName":"Growth Curve Fitting and Trajectory Analysis in Livestock","aliases":["growth model fitting","trajectory analysis","growth kinetics modeling"],"domain":"animal-science","family":"process-pipeline","subfamily":"Growth quantification and modeling","year":"1970s","originator":"Animal Biologists and Agricultural Statisticians","url":"https://scholargate.app/en/animal-science/growth-curve-fitting-livestock","markdownUrl":"https://scholargate.app/en/animal-science/growth-curve-fitting-livestock.md","definition":"Growth curve fitting is the mathematical modeling of animal body weight and size changes over time. Developed by animal biologists and statisticians in the 1970s-1980s (Fitzhugh), the method applies nonlinear regression to weight data, extracting parameters that characterize growth rate, time to maturity, and asymptotic mature weight. Curve fitting supports comparisons of genetics, nutrition, and management effects on growth efficiency and enables prediction of market weight and slaughter timing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Animal Biologists and Agricultural Statisticians","subfamily":"Growth quantification and modeling","year":"1970s","type":"statistical modeling"},"citations":[{"ref":"Menchaca, M. A., & Chase, C. C. (2002). Body measurements and condition scores for beef cattle. Veterinary Clinics of North America: Food Animal Practice, 19(3), 387-405.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Body+measurements+and+condition+scores+for+beef+cattle+Menchaca"},{"ref":"Brown, J. L., Cummins, L., Herring, W., Waldner, T., & Roesler, R. (2003). Comparative evaluation of growth models for modeling beef cattle growth. Journal of Animal Science, 81(7), 1813-1820.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparative+evaluation+of+growth+models+for+modeling+beef+cattle+growth+Brown"},{"ref":"Fitzhugh, H. A. (1976). Analysis of growth curves and strategies for altering their shape. Journal of Animal Science, 42(4), 1036-1051.","type":"article","doi":"10.2527/jas1976.4241036x","isbn":null,"url":null}],"related":["feed-conversion-ratio","meat-quality-assessment","body-condition-score-cattle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"growth-hormone-deficiency-scale","name":"AGHDA","fullName":"Adult Growth Hormone Deficiency Assessment Scale","aliases":["AGHDA-25"],"domain":"endocrinology","family":"process-pipeline","subfamily":"Pituitary hormone deficiency quality of life","year":2000,"originator":"Anthony Hunt, Garry Werther, Peter Wrightson","url":"https://scholargate.app/en/endocrinology/growth-hormone-deficiency-scale","markdownUrl":"https://scholargate.app/en/endocrinology/growth-hormone-deficiency-scale.md","definition":"The AGHDA is a 25-item disease-specific quality of life questionnaire designed to assess the burden of adult growth hormone (GH) deficiency. Developed by Hunt, Werther, and colleagues in 2000, it evaluates symptoms and functional impairments directly related to GH deficiency, including fatigue, reduced muscle strength, weight gain, and psychological difficulties. The instrument is widely used in endocrinology practice and clinical trials to quantify the impact of GH replacement therapy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anthony Hunt, Garry Werther, Peter Wrightson","subfamily":"Pituitary hormone deficiency quality of life","year":2000,"type":"Patient self-report questionnaire"},"citations":[{"ref":"Hunt, A. E., Werther, G. A., & Wrightson, P. (2000). The utility of AGHDA in identifying GH-deficient adults. Clin Endocrinol (Oxf), 52(3), 341-346.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+utility+of+AGHDA+in+identifying+GH-deficient+adults+Hunt"},{"ref":"Abs, R., Feldt-Rasmussen, U., Mattsson, A. F., et al. (1999). Assessment of GH status in adults with childhood-onset GH deficiency. Clin Endocrinol (Oxf), 50(4), 457-463.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessment+of+GH+status+in+adults+with+childhood-onset+GH+deficiency+Abs"}],"related":["thyroid-patient-reported-outcomes","diabetes-symptom-checklist","adrenal-insufficiency-qol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"growth-mixture-model","name":"GMM","fullName":"Growth Mixture Model","aliases":["Büyüme Karışım Modeli (Growth Mixture Model — GMM)","GMM","latent class growth analysis extension","mixture latent growth curve model"],"domain":"statistics","family":"latent-structure","subfamily":null,"year":1999,"originator":"Bengt O. Muthén & Kerby Shedden","url":"https://scholargate.app/en/statistics/growth-mixture-model","markdownUrl":"https://scholargate.app/en/statistics/growth-mixture-model.md","definition":"The Growth Mixture Model, introduced by Muthén and Shedden in 1999, is a longitudinal latent variable method that identifies distinct subpopulations — latent trajectory classes — each following its own growth curve over time. It extends the standard Latent Growth Curve (LGC) model by allowing the sample to be composed of an unknown mixture of classes with different intercepts, slopes, and variance structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengt O. Muthén & Kerby Shedden","year":1999,"type":"Latent class / longitudinal growth model","outcome":"Latent trajectory classes with class-specific growth parameters","data":"Continuous repeated measures (longitudinal / panel)","min_sample":200,"min_timepoints":3,"estimation":"EM algorithm (Expectation–Maximization)","class_selection":"BIC, AIC, entropy, Lo–Mendell–Rubin LRT"},"citations":[{"ref":"Muthén, B. O. & Shedden, K. (1999). Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm. Biometrics, 55(2), 463–469.","type":"article","doi":"10.1111/j.0006-341x.1999.00463.x","isbn":null,"url":null}],"related":["lca","hlm","sem","exploratory-factor-analysis","multiple-imputation","latent-class-growth-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gru","name":"GRU","fullName":"Gated Recurrent Unit","aliases":["Kapılı Tekrarlayan Birim (GRU)","gated recurrent unit","gated recurrent network"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2014,"originator":"Cho, K. et al.","url":"https://scholargate.app/en/deep-learning/gru","markdownUrl":"https://scholargate.app/en/deep-learning/gru.md","definition":"The Gated Recurrent Unit (GRU) is a gated recurrent neural network cell introduced by Cho and colleagues in 2014 that captures long-range dependencies in sequential data using update and reset gates, achieving performance comparable to LSTM with fewer parameters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cho, K. et al.","year":2014,"type":"Gated recurrent neural network unit","task":"Prediction, forecasting & classification on sequences","minSample":100},"citations":[{"ref":"Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/D14-1179/"},{"ref":"Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. NIPS 2014 Deep Learning Workshop. arXiv:1412.3555","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1412.3555"}],"related":["bidirectional-rnn","seq2seq","attention-mechanism","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"guttman-scale","name":"Guttman Scale","fullName":"Louis Guttman's Cumulative Unidimensional Scaling Method","aliases":["Cumulative scale","Scalogram analysis","Guttman scaling","Unidimensional cumulative scale"],"domain":"psychometrics","family":"process-pipeline","subfamily":"Scale development","year":"1944","originator":"Louis Guttman","url":"https://scholargate.app/en/psychometrics/guttman-scale","markdownUrl":"https://scholargate.app/en/psychometrics/guttman-scale.md","definition":"Guttman scaling is a methodology for constructing unidimensional scales with a cumulative property, developed by Louis Guttman in 1944. The method assumes that items form a perfect or near-perfect hierarchy: if a respondent endorses a harder item, they must endorse all easier items below it. This creates a reproducible scale structure useful for measuring constructs with ordinal properties such as difficulty, intensity, or severity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Louis Guttman","subfamily":"Scale development","year":"1944","type":"Cumulative unidimensional scaling methodology"},"citations":[{"ref":"Guttman, L. (1944). A basis for scaling qualitative data. American Sociological Review, 9(2), 139-150.","type":"article","doi":"10.2307/2086306","isbn":null,"url":null},{"ref":"Guttman, L. (1950). The basis for scalogram analysis. In S. A. Stouffer et al. (Eds.), Measurement and Prediction. Princeton, NJ: Princeton University Press.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books?id=hLYrAAAAYAAJ"},{"ref":"Menzel, H. (1953). A new coefficient for scalogram analysis. Public Opinion Quarterly, 17(2), 268-280.","type":"article","doi":"10.1086/266460","isbn":null,"url":null}],"related":["likert-scale-construction","factor-analysis-scale","content-validity-ratio","floor-ceiling-effect"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"gwas-in-educational-research","name":"Genome-wide association study in educational research","fullName":"Genome-Wide Association Study Applied to Educational Outcomes","aliases":["GWAS in education","educational GWAS","GWAS for cognitive traits","genomic study of educational attainment"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2013 (first large-scale educational attainment GWAS); refined through major studies in 2016, 2018, 2022","originator":"Social Science Genetic Association Consortium (SSGAC); Rietveld et al. 2013 pioneered educational attainment GWAS","url":"https://scholargate.app/en/bioinformatics/gwas-in-educational-research","markdownUrl":"https://scholargate.app/en/bioinformatics/gwas-in-educational-research.md","definition":"A genome-wide association study (GWAS) applied to educational research scans millions of single-nucleotide polymorphisms (SNPs) across the human genome to identify genetic variants statistically associated with educational outcomes such as years of schooling, degree attainment, or cognitive test scores. Large consortia — most prominently the Social Science Genetic Association Consortium — have conducted landmark studies in hundreds of thousands to millions of individuals, establishing GWAS as the principal genomic tool for understanding the heritable architecture of educational phenotypes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Social Science Genetic Association Consortium (SSGAC); Rietveld et al. 2013 pioneered educational attainment GWAS","year":"2013 (first large-scale educational attainment GWAS); refined through major studies in 2016, 2018, 2022","type":"Quantitative genomic association analysis","dataType":"Genome-wide SNP array data (millions of variants) linked to educational outcome phenotypes (years of schooling, degree attainment, cognitive test scores)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Okbay, A., Turley, P., Georgios, K., et al. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437–449.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Polygenic+prediction+of+educational+attainment+within+and+between+families+from+genome-wide+association+analyses+in+3+million+individuals"},{"ref":"Lee, J. J., Wedow, R., Okbay, A., et al. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8), 1112–1121.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Gene+discovery+and+polygenic+prediction+from+a+genome-wide+association+study+of+educational+attainment+in+1.1+million+individuals"}],"related":["polygenic-score-analysis","linkage-disequilibrium","mendelian-randomization","principal-component-analysis","meta-analysis","heritability-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"h-index","name":"H-Index","fullName":"H-Index (Hirsch Index) Metric","aliases":["Hirsch index","h factor","h-number"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"researcher impact metrics","year":2005,"originator":"Jorge Hirsch, University of California San Diego","url":"https://scholargate.app/en/bibliometrics/h-index","markdownUrl":"https://scholargate.app/en/bibliometrics/h-index.md","definition":"The h-index, or Hirsch index, is a quantitative metric proposed by physicist Jorge Hirsch in 2005 to measure researcher productivity and citation impact simultaneously. A researcher has an h-index of h if they have published at least h papers, each cited at least h times. For example, an h-index of 20 means the researcher has 20 papers each cited at least 20 times. The h-index is widely used in research evaluation, hiring, and promotion decisions, though experts debate its limitations. It provides a single number balancing quantity of publications against quality of citations, offering an intuitive summary of research career impact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jorge Hirsch, University of California San Diego","subfamily":"researcher impact metrics","year":2005,"type":"Metric"},"citations":[{"ref":"Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences USA, 102(46), 16569-16572.","type":"article","doi":"10.1073/pnas.0507655102","isbn":null,"url":null},{"ref":"Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131-152.","type":"article","doi":"10.1007/s11192-006-0144-7","isbn":null,"url":null},{"ref":"Bornmann, L., Mutz, R., & Daniel, H. D. (2010). The h index for Journal Impact Factor: A new approach for assessing journal impact. Journal of Informetrics, 4(3), 358-365.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+h+index+for+Journal+Impact+Factor%3A+A+new+approach+for+assessing+journal+impact+Bornmann"}],"related":["web-of-science","scopus-database","impact-factor","journal-citation-reports","pubmed-medline"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"h-infinity-control","name":"H-infinity Control","fullName":"H-infinity Control","aliases":["H∞ Control","Robust Control","Minimax Control"],"domain":"control-theory","family":"ml-model","subfamily":"Robust Control","year":"1981","originator":"George Zames","url":"https://scholargate.app/en/control-theory/h-infinity-control","markdownUrl":"https://scholargate.app/en/control-theory/h-infinity-control.md","definition":"H-infinity (H∞) control is a robust control method that minimizes the worst-case gain from disturbances to controlled outputs, formulated as a minimax optimization problem. Pioneered by Zames in the early 1980s, H∞ control provides a principled way to design feedback controllers that tolerate model uncertainty, unmodeled dynamics, and disturbances while maintaining stability and performance, making it essential for applications requiring guaranteed robustness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Zames","subfamily":"Robust Control","year":"1981","type":"algorithm"},"citations":[{"ref":"Zames, G. (1981). Feedback and optimal sensitivity: Model reference transformations, multiplicative seminorms, and approximate inverses. IEEE Transactions on Automatic Control, 26(2), 301-320.","type":"article","doi":"10.1109/TAC.1981.1102603","isbn":null,"url":null},{"ref":"Francis, B. A. (1987). A Course in H∞ Control Theory. Lecture Notes in Control and Information Sciences, Springer-Verlag.","type":"article","doi":"10.1007/BFb0007371","isbn":null,"url":null},{"ref":"Zhou, K., Doyle, J. C., & Glover, K. (1996). Robust and Optimal Control. Prentice Hall.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books/about/Robust_and_Optimal_Control.html"}],"related":["linear-quadratic-regulator","model-predictive-control","adaptive-control","feedback-linearization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"haccp","name":"HACCP","fullName":"Hazard Analysis and Critical Control Points","aliases":[],"domain":"food-science","family":"process-pipeline","subfamily":"Quality Assurance","year":"1988","originator":"Frank Bryan","url":"https://scholargate.app/en/food-science/haccp","markdownUrl":"https://scholargate.app/en/food-science/haccp.md","definition":"HACCP (Hazard Analysis and Critical Control Points) is a systematic preventive approach to food safety developed in the late 1980s by Bryan and colleagues. It identifies potential biological, chemical, and physical hazards in food production processes and establishes critical control points to prevent contamination. HACCP is now globally recognized as the gold standard for food safety management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Bryan","subfamily":"Quality Assurance","year":"1988","type":"Risk Management Framework"},"citations":[{"ref":"Bryan, F. L. (1992). Hazard Analysis Critical Control Point Evaluations: A Guide to Identifying Hazards and Assessing Risks Associated with Food Preparation and Storage. Journal of Food Protection, 55(1), 51-59.","type":"article","doi":null,"isbn":null,"url":"https://www.foodprotection.org"},{"ref":"Codex Alimentarius Commission (1997). HACCP System and Guidelines for Its Application.","type":"article","doi":null,"isbn":null,"url":"http://www.fao.org/codex"}],"related":["quantitative-descriptive-analysis","accelerated-shelf-life-testing","d-value-and-z-value"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hads","name":"Hospital Anxiety and Depression Scale","fullName":"Hospital Anxiety and Depression Scale (HADS)","aliases":["HADS","HADS-A","HADS-D"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Hospital and medical setting assessment","year":"1983","originator":"Andrew S. Zigmond and Richard P. Snaith","url":"https://scholargate.app/en/clinical-psychology/hads","markdownUrl":"https://scholargate.app/en/clinical-psychology/hads.md","definition":"The Hospital Anxiety and Depression Scale (HADS) is a 14-item self-report instrument measuring anxiety and depression symptoms in medically ill populations. Developed by Zigmond and Snaith in 1983, the HADS was specifically designed for hospital and general medical settings where somatic symptoms of medical illness may confound assessment. It remains the standard anxiety-depression measure in medical, oncology, and cardiac populations worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew S. Zigmond and Richard P. Snaith","subfamily":"Hospital and medical setting assessment","year":"1983","type":"Anxiety and depression screening in medical populations"},"citations":[{"ref":"Zigmond, A. S., & Snaith, R. P. (1983). The Hospital Anxiety and Depression Scale. Acta Psychiatrica Scandinavica, 67(6), 361-370.","type":"article","doi":"10.1111/j.1600-0447.1983.tb09716.x","isbn":null,"url":null},{"ref":"Bjelland, I., Dahl, A. A., Haug, T. T., & Neckelmann, D. (2002). The validity of the Hospital Anxiety and Depression Scale: An updated literature review. Journal of Psychosomatic Research, 52(2), 69-77.","type":"article","doi":"10.1016/S0022-3999(01)00296-3","isbn":null,"url":null}],"related":["hamilton-anxiety-rating-scale","ghq-12","das-21","ces-d","k10-kessler"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hair-loss-impact-questionnaire","name":"ALPPQ","fullName":"Alopecia Areata Patient Priority Outcomes Questionnaire","aliases":["Hair Loss Impact Questionnaire","Alopecia Areata QoL"],"domain":"dermatology","family":"process-pipeline","subfamily":"disease-specific-quality-of-life","year":"2014","originator":"Gupta AK, Strober BE et al.","url":"https://scholargate.app/en/dermatology/hair-loss-impact-questionnaire","markdownUrl":"https://scholargate.app/en/dermatology/hair-loss-impact-questionnaire.md","definition":"The Hair Loss Impact Questionnaire (Alopecia Areata Patient Priority Outcomes Questionnaire, ALPPQ) is a disease-specific, patient-administered quality-of-life measure assessing the psychosocial and functional burden of alopecia areata, a chronic autoimmune disorder causing patchy hair loss. Alopecia areata affects appearance, self-esteem, and social functioning disproportionately, often causing depression and anxiety. The ALPPQ captures these impacts, ensuring that treatment efficacy encompasses meaningful quality-of-life outcomes. It is increasingly used in clinical trials and observational studies of alopecia areata therapeutics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gupta AK, Strober BE et al.","subfamily":"disease-specific-quality-of-life","year":"2014","type":"Self-report"},"citations":[{"ref":"Gupta AK, Talukder M. Alopecia areata: autoimmune basis of hair loss and available treatment options. Can J Dermatol. 2014;12(5):289-304.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/25089137"},{"ref":"Strober BE, Mengesha YM, Clay FJ, et al. Impact of alopecia areata severity on quality of life measured by the Skindex-29. J Am Acad Dermatol. 2005;52(3):S45.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Strober+BE%2C+Mengesha+YM%2C+Clay+FJ%2C+et+al.+Impact+of+alopecia+areata+severity+on+quality+of+life+measured+by+the+Skindex-2+Strober"}],"related":["poem","skindex-29","dermatology-life-quality-index-children","acne-qol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hallucination-detection","name":"Hallucination Detection","fullName":"Hallucination Detection (Factual Consistency)","aliases":["factual consistency checking","faithfulness evaluation","LLM output verification","Hallüsinasyon Tespiti (Factual Consistency)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2020 (faithfulness framing); 2023 (SelfCheckGPT)","originator":"Established as a formal task by Maynez et al. (2020); SelfCheckGPT zero-resource variant by Manakul et al. (2023)","url":"https://scholargate.app/en/text-mining/hallucination-detection","markdownUrl":"https://scholargate.app/en/text-mining/hallucination-detection.md","definition":"Hallucination detection is a natural-language-processing pipeline that measures whether the output of a language model is consistent with a reference source document or with verifiable facts. Formalised as a faithfulness evaluation task by Maynez et al. (2020) and extended to a zero-resource black-box setting by Manakul et al. (2023) with SelfCheckGPT, the approach is used to flag unreliable LLM outputs in high-stakes domains such as medicine, law, and journalism.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established as a formal task by Maynez et al. (2020); SelfCheckGPT zero-resource variant by Manakul et al. (2023)","year":"2020 (faithfulness framing); 2023 (SelfCheckGPT)","type":"NLP evaluation / quality-assurance pipeline","approaches":"NLI-based / QA-based / sampling-based (SelfCheckGPT)","output":"Consistency / faithfulness score or binary hallucination label per claim or sentence","minSample":10,"difficulty":3},"citations":[{"ref":"Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 1906-1919.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2020.acl-main.173"},{"ref":"Manakul, P., Liusie, A., & Gales, M.J.F. (2023). SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 9004-9017.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2023.emnlp-main.557"}],"related":["sentiment-analysis","text-classification","bert-embeddings","named-entity-recognition","question-answering"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"halo-occupation-distribution","name":"Halo Occupation Distribution","fullName":"Halo Occupation Distribution Modeling for Galaxy Clustering","aliases":["HOD","Halo Model","Galaxy-Halo Connection"],"domain":"astronomy","family":"process-pipeline","subfamily":"Galaxy-halo modeling","year":2000,"originator":"Jia Peacock","url":"https://scholargate.app/en/astronomy/halo-occupation-distribution","markdownUrl":"https://scholargate.app/en/astronomy/halo-occupation-distribution.md","definition":"Halo Occupation Distribution (HOD) modeling is a framework for relating observed galaxy clustering to the distribution of galaxies within dark matter halos. Developed by Jia Peacock and others around 2000, HOD provides a flexible, physically motivated approach to interpreting galaxy surveys and understanding how galaxies populate dark matter halos across cosmic time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jia Peacock","subfamily":"Galaxy-halo modeling","year":2000,"type":"Statistical modeling method"},"citations":[{"ref":"Peacock, J. A., & Smith, R. E. (2000). Halo occupation numbers and galaxy bias. Monthly Notices of the Royal Astronomical Society, 318(4), 1144-1156.","type":"article","doi":"10.1046/j.1365-8711.2000.03779.x","isbn":null,"url":null},{"ref":"Berlind, A. A., & Weinberg, D. H. (2002). The statistics of galaxy clustering: are models with constant bias sufficient? Astrophysical Journal, 575(2), 587-605.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+statistics+of+galaxy+clustering%3A+are+models+with+constant+bias+sufficient+Berlind"},{"ref":"Zheng, Z., et al. (2007). Measuring galaxy clustering: imprints of galaxy formation on large scales. Astrophysical Journal, 667(2), 760-779.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+galaxy+clustering%3A+imprints+of+galaxy+formation+on+large+scales+Zheng"}],"related":["baryon-acoustic-oscillations","weak-gravitational-lensing","rotation-curve-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"halstead-complexity","name":"Halstead Complexity","fullName":"Halstead Complexity Metrics","aliases":["Halstead metrics","program length","volume metric"],"domain":"numerical-methods","family":"ml-model","subfamily":"Software Metrics","year":"1977","originator":"Maurice Halstead","url":"https://scholargate.app/en/numerical-methods/halstead-complexity","markdownUrl":"https://scholargate.app/en/numerical-methods/halstead-complexity.md","definition":"Halstead Complexity Metrics are a set of static code analysis measures developed by Maurice Halstead in 1977 that quantify software quality using operator and operand counts. Metrics like program volume, difficulty, and effort estimate code complexity, maintainability, and defect likelihood from source code structure alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maurice Halstead","subfamily":"Software Metrics","year":"1977","type":"Static code analysis metric"},"citations":[{"ref":"Halstead, M. H. (1977). Elements of Software Science. Elsevier.","type":"book","doi":null,"isbn":"0444002057","url":null},{"ref":"Kitchenham, B. A., Pickard, L. M., & Linkman, S. J. (1995). An empirical study of source code defects. IEEE Transactions on Software Engineering, 21(2), 147–156.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+empirical+study+of+source+code+defects+Kitchenham"},{"ref":"Harrison, W. (2007). Using metrics to evaluate software system maintainability. IEEE Software, 24(4), 44–50.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Using+metrics+to+evaluate+software+system+maintainability+Harrison"}],"related":["cyclomatic-complexity","maintainability-index","code-coverage","defect-prediction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hamilton-anxiety-rating-scale","name":"Hamilton Anxiety Rating Scale","fullName":"Hamilton Anxiety Rating Scale (HAM-A)","aliases":["HAM-A","HARS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Clinician-rated scale","year":"1959","originator":"Max Hamilton","url":"https://scholargate.app/en/clinical-psychology/hamilton-anxiety-rating-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/hamilton-anxiety-rating-scale.md","definition":"The Hamilton Anxiety Rating Scale (HAM-A) is a clinician-administered assessment tool for quantifying the severity of anxiety symptoms in adults. Developed by Max Hamilton in 1959, it remains one of the most widely used instruments for evaluating anxiety in clinical and research settings. The scale measures both psychological and somatic manifestations of anxiety across 14 items.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max Hamilton","subfamily":"Clinician-rated scale","year":"1959","type":"Clinician-administered anxiety assessment"},"citations":[{"ref":"Hamilton, M. (1959). The assessment of anxiety states by rating. British Journal of Medical Psychology, 32(1), 50-55.","type":"article","doi":"10.1111/j.2044-8341.1959.tb00467.x","isbn":null,"url":null},{"ref":"Maier, W., Buller, R., Philipp, M., & Heuser, I. (1988). The Hamilton Anxiety Rating Scale: reliability, validity and sensitivity to change in anxiety and depressive disorders. Journal of Affective Disorders, 14(1), 61-68.","type":"article","doi":"10.1016/0165-0327(88)90072-9","isbn":null,"url":null}],"related":["panas","hads","zung-anxiety-scale","dass-21","gds-geriatric-depression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hamilton-depression-rating-scale","name":"Hamilton Depression Rating Scale","fullName":"Hamilton Depression Rating Scale (17-item HDRS or HAM-D)","aliases":["HAM-D","HDRS","Hamilton Rating Scale for Depression"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"mood-disorder-assessment-clinician-rated","year":"1960","originator":"Max Hamilton","url":"https://scholargate.app/en/clinical-psychology/hamilton-depression-rating-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/hamilton-depression-rating-scale.md","definition":"The Hamilton Depression Rating Scale, published by Max Hamilton in 1960, is a clinician-administered interview assessment of depressive symptom severity. The most common version contains 17 items (HAM-D-17), though 21-item and 24-item versions exist. It is considered the gold standard outcome measure in antidepressant drug trials and remains the most cited depression rating scale in the psychiatric literature. Unlike self-report measures, HAM-D requires clinician judgment and observation, making it particularly valuable in research settings where standardized measurement by trained raters is essential.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max Hamilton","subfamily":"mood-disorder-assessment-clinician-rated","year":"1960","type":"Clinician-rated interview scale"},"citations":[{"ref":"Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery & Psychiatry, 23(1), 56–62.","type":"article","doi":"10.1136/jnnp.23.1.56","isbn":null,"url":null},{"ref":"Bagby, R. M., Ryder, A. G., Schuller, D. R., & Marshall, M. B. (1997). The Hamilton Depression Rating Scale: has the gold standard become a lead weight? American Journal of Psychiatry, 161(12), 2163–2177.","type":"article","doi":"10.1176/appi.ajp.161.12.2163","isbn":null,"url":null},{"ref":"Williams, J. B. (1988). A structured interview guide for the Hamilton Depression Rating Scale. Archives of General Psychiatry, 45(8), 742–747.","type":"article","doi":"10.1001/archpsyc.1988.01800320058007","isbn":null,"url":null}],"related":["phq-9","bdi-ii","montgomery-asberg-depression","quick-inventory-depressive","clinical-global-impressions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hamilton-jacobi-bellman-equation","name":"Hamilton-Jacobi-Bellman Equation","fullName":"Hamilton-Jacobi-Bellman Equation","aliases":["HJB Equation","Bellman Equation","Dynamic Programming"],"domain":"control-theory","family":"ml-model","subfamily":"Optimal Control","year":"1957","originator":"Richard Bellman","url":"https://scholargate.app/en/control-theory/hamilton-jacobi-bellman-equation","markdownUrl":"https://scholargate.app/en/control-theory/hamilton-jacobi-bellman-equation.md","definition":"The Hamilton-Jacobi-Bellman (HJB) equation is a partial differential equation characterizing the optimal cost-to-go function in dynamic programming. Developed by Bellman in 1957, HJB provides both necessary and sufficient conditions for optimality, enabling elegant theoretical analysis and numerical solutions for optimal control problems. HJB is fundamental to reinforcement learning, approximate dynamic programming, and real-time control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Bellman","subfamily":"Optimal Control","year":"1957","type":"algorithm"},"citations":[{"ref":"Bellman, R. (1957). Dynamic Programming. Princeton University Press.","type":"article","doi":null,"isbn":null,"url":"https://press.princeton.edu/books/paperback/9780691146683/dynamic-programming"},{"ref":"Kirk, D. E. (2004). Optimal Control Theory: An Introduction (2nd ed.). Dover Publications.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimal+Control+Theory%3A+An+Introduction+%282nd+ed.%29+Kirk"}],"related":["pontryagin-maximum-principle","linear-quadratic-regulator","model-predictive-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hamiltonian-monte-carlo-with-measurement-error","name":"Hamiltonian Monte Carlo with Measurement Error","fullName":"Hamiltonian Monte Carlo for Bayesian Measurement Error Models","aliases":["HMC measurement error model","Bayesian errors-in-variables with HMC","HMC latent variable measurement error","Hamiltonian MCMC with covariate error"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2006-2011","originator":"Neal (2011) for HMC; Carroll et al. (2006) for measurement error framework","url":"https://scholargate.app/en/bayesian/hamiltonian-monte-carlo-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/hamiltonian-monte-carlo-with-measurement-error.md","definition":"Hamiltonian Monte Carlo (HMC) with measurement error is a Bayesian computational strategy for fitting models where one or more covariates are observed with noise. HMC samples jointly from the posterior over model parameters and the unobserved true covariate values, using gradient-based proposals that explore the high-dimensional posterior efficiently and avoid the slow random-walk behaviour of standard Metropolis sampling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Neal (2011) for HMC; Carroll et al. (2006) for measurement error framework","year":"2006-2011","type":"Bayesian sampling algorithm for latent-variable models","dataType":"continuous or categorical outcomes with error-prone continuous covariates","subfamily":"Bayesian / computational"},"citations":[{"ref":"Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584886334","url":null},{"ref":"Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones, & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113-162). CRC Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MCMC+using+Hamiltonian+dynamics+Neal+2011"}],"related":["hamiltonian-monte-carlo","mcmc-with-measurement-error","bayesian-inference-with-measurement-error","gibbs-sampling-with-measurement-error","kalman-filter-with-measurement-error","variational-inference-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hamiltonian-monte-carlo-with-missing-data","name":"Hamiltonian Monte Carlo with Missing Data","fullName":"Hamiltonian Monte Carlo with Missing Data Imputation","aliases":["HMC with missing data","HMC data augmentation","Bayesian HMC imputation","HMC with data augmentation"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1996–2011","originator":"Radford M. Neal (HMC, 1996/2011); missing-data treatment via Bayesian data augmentation (Tanner & Wong, 1987)","url":"https://scholargate.app/en/bayesian/hamiltonian-monte-carlo-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/hamiltonian-monte-carlo-with-missing-data.md","definition":"Hamiltonian Monte Carlo with missing data extends the gradient-based HMC sampler to handle incomplete observations by treating missing values as additional unknown parameters. The posterior over model parameters and missing values is sampled jointly in one efficient pass, exploiting gradient information to explore the high-dimensional joint space with far fewer rejected proposals than random-walk MCMC.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Radford M. Neal (HMC, 1996/2011); missing-data treatment via Bayesian data augmentation (Tanner & Wong, 1987)","year":"1996–2011","type":"Bayesian computational sampler","dataType":"continuous, mixed, or incomplete multivariate data with MAR or MCAR missingness","subfamily":"Bayesian / computational"},"citations":[{"ref":"Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113-162). CRC Press.","type":"inproceedings","doi":null,"isbn":"978-1420079418","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. Chapter 18: Missing-data imputation.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["hamiltonian-monte-carlo","mcmc-with-missing-data","gibbs-sampling-with-missing-data","bayesian-inference-with-missing-data","multiple-imputation","variational-inference-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hamiltonian-monte-carlo","name":"Hamiltonian Monte Carlo","fullName":"Hamiltonian Monte Carlo Sampling","aliases":["HMC","Hybrid Monte Carlo","NUTS","No-U-Turn Sampler","gradient-based MCMC"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":"1987","originator":null,"url":"https://scholargate.app/en/bayesian/hamiltonian-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/hamiltonian-monte-carlo.md","definition":"Hamiltonian Monte Carlo (HMC) is a gradient-based Markov chain Monte Carlo algorithm that uses the geometry of the log-posterior surface to make large, informed jumps through parameter space instead of the small random steps of classical MCMC. Originally introduced for lattice field theory by Duane, Kennedy, Pendleton, and Roweth (1987) under the name Hybrid Monte Carlo, and brought into mainstream statistics by Radford Neal's authoritative 2011 chapter, HMC is today the default sampler in Stan and PyMC and is widely regarded as the state-of-the-art engine for Bayesian posterior inference in high-dimensional models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian / MCMC","type":"Gradient-based Markov chain Monte Carlo sampler","originators":"Duane, Kennedy, Pendleton & Roweth (1987); popularised for statistics by Neal (2011)","year":"1987","purpose":"Draw samples from high-dimensional posterior distributions efficiently","inference":"MCMC with gradient-guided proposals","outputs":"Posterior samples, posterior means, credible intervals, convergence diagnostics","software":"Stan (default engine), PyMC, NumPyro, TensorFlow Probability"},"citations":[{"ref":"Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987). Hybrid Monte Carlo. Physics Letters B, 195(2), 216–222.","type":"article","doi":"10.1016/0370-2693(87)91197-X","isbn":null,"url":null},{"ref":"Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. L. Jones, & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 116–162). Chapman and Hall/CRC.","type":"chapter","doi":null,"isbn":"978-1420079418","url":"https://arxiv.org/abs/1206.1901"},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["bayesian-regression","mcmc","metropolis-hastings","variational-inference","hierarchical-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hamming-loss","name":"Hamming Loss","fullName":"Hamming Loss (Multi-label Classification)","aliases":["Hamming Distance","Subset Accuracy Loss"],"domain":"model-evaluation","family":"mcdm","subfamily":"Multi-label Metric","year":"2000s","originator":"Information theory and multi-label learning","url":"https://scholargate.app/en/model-evaluation/hamming-loss","markdownUrl":"https://scholargate.app/en/model-evaluation/hamming-loss.md","definition":"Hamming loss measures the fraction of labels that are incorrectly predicted in multi-label classification. It counts the number of label mistakes divided by the total number of labels, providing a simple metric for multi-label problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Information theory and multi-label learning","subfamily":"Multi-label Metric","year":"2000s","type":"Loss function"},"citations":[{"ref":"Schapire, R. E., & Singer, Y. (2000). BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2-3), 135-168.","type":"article","doi":"10.1023/A:1007649029923","isbn":null,"url":null},{"ref":"Tsoumakas, G., & Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1-13.","type":"article","doi":"10.4018/jdwm.2007070101","isbn":null,"url":null}],"related":["jaccard-index","subset-accuracy","hamming-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"haq-disability-index","name":"HAQ Disability Index","fullName":"Health Assessment Questionnaire Disability Index","aliases":["HAQ-DI","Health Assessment Questionnaire","Disability Index"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Functional assessment","year":"1980","originator":"James Fries and colleagues at Stanford University","url":"https://scholargate.app/en/health-measurement/haq-disability-index","markdownUrl":"https://scholargate.app/en/health-measurement/haq-disability-index.md","definition":"The Health Assessment Questionnaire Disability Index (HAQ-DI) is a 20-item self-report measure of functional disability developed by Fries and colleagues at Stanford University in 1980. Originally designed for rheumatoid arthritis, the HAQ-DI has become the gold-standard functional assessment instrument across diverse rheumatic diseases and chronic conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Fries and colleagues at Stanford University","subfamily":"Functional assessment","year":"1980","type":"Functional disability measurement for arthritis and chronic disease"},"citations":[{"ref":"Bruce, B., & Fries, J. F. (1989). The Stanford Health Assessment Questionnaire: a review of its history, issues, progress, and documentation. Journal of Rheumatology, 16(8), 1055–1064.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/2569649"},{"ref":"Fries, J. F., Spitz, P., Kraines, R. G., & Holman, H. R. (1980). Measurement of patient outcome in arthritis. Arthritis & Rheumatism, 23(2), 137–145.","type":"article","doi":"10.1002/art.1780230202","isbn":null,"url":null},{"ref":"Pincus, T., Swearingen, C., & Wolfe, F. (2011). Toward a multidimensional Health Assessment Questionnaire (MDHAQ): assessment of advanced activities of daily living and psychological well-being. Seminars in Arthritis and Rheumatism, 30(2), 10–17.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Toward+a+multidimensional+Health+Assessment+Questionnaire+%28MDHAQ%29%3A+assessment+of+advanced+activities+of+daily+living+and+psychological+well-being+Pincus"}],"related":["sf-36","eq-5d","whoqol-bref","mos-social-support-survey","duke-health-profile"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"har-rv-model","name":"HAR-RV Model","fullName":"Heterogeneous Autoregressive Model of Realized Volatility","aliases":["HAR-RV","heterogeneous autoregressive realized volatility","Corsi HAR model","HAR-RV Modeli (Heterogeneous Autoregressive Realized Volatility)"],"domain":"finance","family":"regression-model","subfamily":null,"year":2009,"originator":"Fulvio Corsi","url":"https://scholargate.app/en/finance/har-rv-model","markdownUrl":"https://scholargate.app/en/finance/har-rv-model.md","definition":"The HAR-RV model, introduced by Fulvio Corsi in 2009, forecasts realized volatility by decomposing it into daily, weekly, and monthly components. It is a simple linear regression that mirrors how market participants with different investment horizons react to volatility, and it naturally captures the long-memory behaviour of volatility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fulvio Corsi","year":2009,"type":"Linear time-series regression for volatility","estimator":"Ordinary least squares","outcome":"continuous (realized volatility)","minSample":250,"dataStructure":"time series"},"citations":[{"ref":"Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174–196.","type":"article","doi":"10.1093/jjfinec/nbp001","isbn":null,"url":null}],"related":["ols-regression","garch-model","regime-switching-finance","tail-risk-measures","wavelet-finance"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hardy-cross-method","name":"Hardy Cross Method","fullName":"Hardy Cross Method for Pipe Network Analysis","aliases":["Cross method","Moment distribution method","Iterative balancing"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Network analysis","year":"1936","originator":"Hardy Cross","url":"https://scholargate.app/en/civil-engineering/hardy-cross-method","markdownUrl":"https://scholargate.app/en/civil-engineering/hardy-cross-method.md","definition":"The Hardy Cross method is an iterative technique for solving steady-state flow distribution in pipe networks, originally developed for water distribution systems. Introduced by Hardy Cross in 1936, this method balances flow continuity and pressure head constraints through successive iterations, making it ideal for hand calculations and gaining physical insight into network behavior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hardy Cross","subfamily":"Network analysis","year":"1936","type":"Iterative method for pipe network flow distribution"},"citations":[{"ref":"Cross, H. (1936). Analysis of flow in networks of conduits or conductors. University of Illinois Bulletin, 34(17), 3-29.","type":"article","doi":null,"isbn":null,"url":"https://engineering.illinois.edu"},{"ref":"Duffy, A., Malone, D., & O'Neill, J. (1987). The Hardy Cross Method for Water Distribution Networks. Water Research Centre.","type":"book","doi":null,"isbn":"0-906957-66-4","url":null},{"ref":"Jeppson, R. W. (1976). Analysis of Flow in Pipe Networks. Ann Arbor Science Publishers.","type":"book","doi":null,"isbn":"0-250-40157-7","url":null}],"related":["modflow","terzaghi-consolidation","traffic-flow"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"harmful-algal-bloom-monitoring","name":"Harmful Algal Bloom Monitoring","fullName":"Harmful Algal Bloom Monitoring","aliases":["HAB Monitoring","Red Tide Detection"],"domain":"oceanography","family":"process-pipeline","subfamily":"Ecological Monitoring","year":"1995","originator":"Oceanographic Community","url":"https://scholargate.app/en/oceanography/harmful-algal-bloom-monitoring","markdownUrl":"https://scholargate.app/en/oceanography/harmful-algal-bloom-monitoring.md","definition":"Harmful algal bloom (HAB) monitoring is an integrated approach combining satellite remote sensing, in situ observations, and predictive modeling to detect, track, and forecast toxic algal outbreaks in marine and freshwater systems. HAB monitoring has become essential for public health protection, as certain algal species produce potent toxins that accumulate in shellfish and pose severe health risks to consumers and marine life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oceanographic Community","subfamily":"Ecological Monitoring","year":"1995","type":"integrated-system"},"citations":[{"ref":"Davidson, K., Miller, P., Wilding, T. A., & Shutler, J. (2016). Harmful algal bloom risk assessment in the context of climate change. Harmful Algae, 53, 34-41.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Harmful+algal+bloom+risk+assessment+in+the+context+of+climate+change+Davidson"},{"ref":"Glibert, P. M., Allen, J. I., Bouwman, A. F., et al. (2010). Modeling of harmful algal blooms. Journal of Marine Systems, 83(3-4), 261-271.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modeling+of+harmful+algal+blooms+Glibert"}],"related":["ocean-color-chlorophyll-a","ctd-profiling","phytoplankton-size-class"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"harmonic-analysis-music","name":"Harmonic Analysis in Music","fullName":"Harmonic Analysis and Harmonic Function Identification Algorithm","aliases":["functional harmony analysis","harmonic progression detection","tonal function estimation"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Tonal analysis","year":"2002","originator":"Bryan Pardo","url":"https://scholargate.app/en/music-information-retrieval/harmonic-analysis-music","markdownUrl":"https://scholargate.app/en/music-information-retrieval/harmonic-analysis-music.md","definition":"Harmonic analysis is the computational study of chord progressions, harmonic function, and tonal relationships in music. Formalized for audio by Pardo and Birmingham (2002), it goes beyond simple chord identification to interpret harmonic role and structure. Harmonic analysis is essential for music theory education, compositional understanding, and music generation systems. It requires understanding both the chords themselves and their functional relationships within a tonal context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bryan Pardo","subfamily":"Tonal analysis","year":"2002","type":"Harmonic function and progression analysis"},"citations":[{"ref":"Pardo, B., & Birmingham, W. P. (2002). Algorithms for chordal analysis. Computer Music Journal, 26(4), 27-49.","type":"article","doi":"10.1162/014892602760137167","isbn":null,"url":null},{"ref":"Pauwels, G., Salamon, J., Gómez, E., & Ryckebusch, P. (2015). Automatic chord estimation from audio using a Deep Neural Network trained on music theory. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2015/papers/039.pdf"},{"ref":"Chen, T. P., Tan, A. H., & Zhu, S. (2012). Speech emotion recognition using robust temporal dynamics-based feature extraction methods. In Proceedings of the International Conference on Neural Information Processing.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1210.2129"}],"related":["chord-recognition","key-detection-music","pitch-detection-algorithm","melody-extraction","music-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"harmonic-distortion-analysis","name":"Harmonic Distortion Analysis","fullName":"Harmonic Analysis and Total Harmonic Distortion Measurement","aliases":["harmonic content analysis","THD analysis","Fourier harmonic decomposition"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Power quality analysis","year":"1822","originator":"Jean-Baptiste Joseph Fourier","url":"https://scholargate.app/en/electrical-engineering/harmonic-distortion-analysis","markdownUrl":"https://scholargate.app/en/electrical-engineering/harmonic-distortion-analysis.md","definition":"Harmonic distortion analysis quantifies the deviation of voltage or current waveforms from sinusoidal shape due to nonlinear loads. Using Fourier decomposition, engineers separate the waveform into its fundamental frequency and harmonic components (integer multiples of 50 or 60 Hz). Harmonic analysis is critical for assessing power quality and designing filters in modern power systems with high penetration of nonlinear devices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Baptiste Joseph Fourier","subfamily":"Power quality analysis","year":"1822","type":"Computational pipeline"},"citations":[{"ref":"IEEE Std 519-1992: IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems.","type":"standard","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/1104363"},{"ref":"Arrillaga, J., Watson, N. R., & Chen, S. (2003). Power System Quality Assessment. Wiley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+System+Quality+Assessment+Arrillaga"},{"ref":"Dugan, R. C., McGranaghan, M. F., Santoso, S., & Beaty, H. W. (2012). Electrical Power Systems Quality (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://www.mheducation.com"}],"related":["power-quality-assessment","power-flow-analysis","load-forecasting","smart-grid-state-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"harmony-search","name":"Harmony Search","fullName":"Harmony Search Algorithm","aliases":["HS algorithm","Harmoni Araması (Harmony Search)","music-inspired optimization"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":2001,"originator":"Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan","url":"https://scholargate.app/en/optimization/harmony-search","markdownUrl":"https://scholargate.app/en/optimization/harmony-search.md","definition":"Harmony Search (HS) is a population-based metaheuristic optimization algorithm introduced by Geem, Kim, and Loganathan in 2001. It mimics the improvisation process of jazz musicians seeking a perfect state of harmony, using three operators — memory consideration, pitch adjustment, and random selection — to generate candidate solutions. The algorithm applies to both continuous and discrete variables and has found wide use in engineering design, water distribution network optimization, and combinatorial problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan","year":2001,"type":"Metaheuristic population-based optimization","inspiration":"Jazz improvisation process","key_parameters":"HMS (harmony memory size), HMCR (harmony memory consideration rate), PAR (pitch adjustment rate)","variable_types":"Continuous and discrete","gradients_required":false},"citations":[{"ref":"Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60–68.","type":"article","doi":"10.1177/003754970107600201","isbn":null,"url":null},{"ref":"Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An Improved Harmony Search Algorithm for Solving Optimization Problems. Applied Mathematics and Computation, 188(2), 1567–1579.","type":"article","doi":"10.1016/j.amc.2006.11.033","isbn":null,"url":null}],"related":["genetic-algorithm","particle-swarm-optimization","simulated-annealing","differential-evolution","ant-colony-optimization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"harris-corner-detection","name":"Harris Corner Detection","fullName":"Harris and Stephens Corner Detection","aliases":["Harris Corner Detector","Harris-Stephens Detector","Plessey Operator"],"domain":"computer-vision","family":"ml-model","subfamily":"Corner detection","year":"1988","originator":"Chris Harris and Mike Stephens","url":"https://scholargate.app/en/computer-vision/harris-corner-detection","markdownUrl":"https://scholargate.app/en/computer-vision/harris-corner-detection.md","definition":"The Harris corner detector, introduced by Chris Harris and Mike Stephens in 1988, is a foundational method for identifying corners and interest points in digital images. Harris corners are points where two edges meet at a significant angle, making them stable and repeatable features for image analysis, matching, and 3D reconstruction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chris Harris and Mike Stephens","subfamily":"Corner detection","year":"1988","type":"Interest point detector"},"citations":[{"ref":"Harris, C., & Stephens, M. (1988). A combined corner and edge detector. Alvey Vision Conference, 147–152.","type":"article","doi":null,"isbn":null,"url":"https://pubs.aip.org/AIP/proceedings/Alvey-Vision-Conference-1988"},{"ref":"Förstner, W., & Gülch, E. (1987). A fast operator for detection and precise localization of distinct points, corners and centres of circular features. ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, 281–305.","type":"article","doi":null,"isbn":null,"url":"https://www.isprs.org/"}],"related":["sift-feature-detection","orb-feature-descriptor","blob-detection","scale-space-theory","image-morphology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"harris-hawks-optimization","name":"Harris Hawks Optimization","fullName":"Harris Hawks Optimization","aliases":["HHO"],"domain":"optimization","family":"ml-model","subfamily":"Swarm Intelligence","year":"2019","originator":"Ali Asghar Heidari","url":"https://scholargate.app/en/optimization/harris-hawks-optimization","markdownUrl":"https://scholargate.app/en/optimization/harris-hawks-optimization.md","definition":"Harris Hawks Optimization (HHO) is a metaheuristic algorithm introduced by Heidari et al. in 2019, inspired by the hunting strategies of Harris's hawks. The algorithm models the cooperative hunting behavior and escape strategies of these raptors to solve complex optimization problems. HHO balances exploration through perching and exploitation through dynamic pursuit, making it effective for multimodal and high-dimensional optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ali Asghar Heidari","subfamily":"Swarm Intelligence","year":"2019","type":"Nature-inspired metaheuristic algorithm"},"citations":[{"ref":"Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.","type":"article","doi":"10.1016/j.future.2019.02.028","isbn":null,"url":null}],"related":["slime-mould-algorithm","aquila-optimizer","particle-swarm-optimization","grey-wolf-optimizer","whale-optimization-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hartree-fock-method","name":"Hartree-Fock Method","fullName":"Hartree-Fock Method (HF)","aliases":["HF","self-consistent field"],"domain":"quantum-computing","family":"ml-model","subfamily":"Self-Consistent Field","year":"1928","originator":"Douglas Hartree and Vladimir Fock","url":"https://scholargate.app/en/quantum-computing/hartree-fock-method","markdownUrl":"https://scholargate.app/en/quantum-computing/hartree-fock-method.md","definition":"The Hartree-Fock (HF) method is a foundational self-consistent field approach for solving the many-electron Schrödinger equation. Developed independently by Douglas Hartree and Vladimir Fock in the late 1920s, it approximates the ground state by assuming electrons move in an average field generated by all other electrons, enabling tractable quantum chemistry calculations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas Hartree and Vladimir Fock","subfamily":"Self-Consistent Field","year":"1928","type":"Electronic structure method"},"citations":[{"ref":"Fock, V. (1930). Näherungsmethode zur Lösung des quantenmechanischen Mehrkörperproblems. Zeitschrift für Physik, 61, 126–148.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=N%C3%A4herungsmethode+zur+L%C3%B6sung+des+quantenmechanischen+Mehrk%C3%B6rperproblems+Fock"},{"ref":"Hartree, D. R. (1928). The wave mechanics of an atom with a non-coulomb central field. Mathematical Proceedings of the Cambridge Philosophical Society, 24, 89–110.","type":"article","doi":"10.1017/S0305004100011919","isbn":null,"url":null},{"ref":"Szabo, A., Ostlund, N. S. (2012). Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory. Dover Publications.","type":"article","doi":null,"isbn":null,"url":"https://store.doverpublications.com/0486691691.html"}],"related":["density-functional-theory","moller-plesset-perturbation-theory","coupled-cluster-ccsd","quantum-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"harvey-bradshaw-index","name":"Harvey-Bradshaw Index","fullName":"Harvey-Bradshaw Index for Crohn's Disease Activity","aliases":["HBI"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"inflammatory-bowel-disease","year":"1980","originator":"R. F. Harvey and J. M. Bradshaw","url":"https://scholargate.app/en/gastroenterology/harvey-bradshaw-index","markdownUrl":"https://scholargate.app/en/gastroenterology/harvey-bradshaw-index.md","definition":"The Harvey-Bradshaw Index (HBI) is a simple, clinician-administered tool for assessing disease activity in Crohn's disease. Developed in 1980, it measures five clinical parameters including abdominal pain, stool frequency, and extraintestinal manifestations. The HBI is widely used in clinical practice and research for monitoring disease progression and treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. F. Harvey and J. M. Bradshaw","subfamily":"inflammatory-bowel-disease","year":"1980","type":"Clinician-rated"},"citations":[{"ref":"Harvey, R. F., & Bradshaw, J. M. (1980). A simple index of Crohn's-disease activity. Lancet, 315(8167), 514.","type":"article","doi":"10.1016/s0140-6736(80)92767-1","isbn":null,"url":null}],"related":["cdai-crohns","mayo-score-uc","sccai","ibdq-short","child-pugh-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hate-speech-detection","name":"Hate Speech Detection","fullName":"Automated Hate Speech Detection","aliases":["offensive language detection","toxic content detection","Nefret Söylemi Tespiti"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/hate-speech-detection","markdownUrl":"https://scholargate.app/en/text-mining/hate-speech-detection.md","definition":"Hate speech detection is a natural-language-processing task that automatically identifies hateful, offensive, or harmful text on social media and online platforms. The task was sharpened by Davidson and colleagues (2017), who showed why separating genuine hate speech from merely offensive language is a hard, distinct classification problem rather than a single toxicity score.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-classification task","approaches":"Feature-based machine learning / fine-tuned transformer models","output":"Class label (hate speech / offensive / neither)","minSample":"About 100 labelled documents","difficulty":"Intermediate"},"citations":[{"ref":"Davidson, T., Warmsley, D., Macy, M. & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. ICWSM, 11(1), 512-515.","type":"article","doi":"10.1609/icwsm.v11i1.14955","isbn":null,"url":null},{"ref":"Fortuna, P. & Nunes, S. (2018). A Survey on Automatic Detection of Hate Speech in Text. ACM Computing Surveys, 51(4), 1-30.","type":"article","doi":"10.1145/3232676","isbn":null,"url":null}],"related":["sentiment-analysis","fake-news-detection","text-classification","bert-embeddings"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hatemi-j-asymmetric-causality","name":"Hatemi-J Asymmetric Causality","fullName":"Hatemi-J Asymmetric Causality Test","aliases":["Hatemi-J Asymmetric Causality Test","Asymmetric Causality Test","Positive and Negative Causality Test","Asimetrik Nedensellik Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Causality","year":2012,"originator":"Abdulnasser Hatemi-J","url":"https://scholargate.app/en/econometrics/hatemi-j-asymmetric-causality","markdownUrl":"https://scholargate.app/en/econometrics/hatemi-j-asymmetric-causality.md","definition":"The Hatemi-J asymmetric causality test, introduced by Abdulnasser Hatemi-J in 2012, extends the Granger causality framework to allow causal relationships between the positive and negative components of integrated time series to differ. By decomposing each series into cumulative positive and negative partial sums and embedding the Toda-Yamamoto approach within a VAR, the test enables researchers to distinguish whether positive shocks, negative shocks, or both drive causation between economic variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abdulnasser Hatemi-J","year":2012,"type":"Nonlinear Granger causality test","subfamily":"Causality","inferenceMethod":"Bootstrap critical values","softwareNote":"GAUSS code provided by Hatemi-J"},"citations":[{"ref":"Hatemi-J, A. (2012). Asymmetric causality tests with an application. Empirical Economics, 43(1), 447–456.","type":"article","doi":"10.1007/s00181-011-0484-x","isbn":null,"url":null}],"related":["granger-causality","toda-yamamoto-causality","hatemi-j-cointegration-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hatemi-j-cointegration-test","name":"Hatemi-J Cointegration Test","fullName":"Hatemi-J Cointegration Test with Two Regime Shifts","aliases":["Hatemi-J Test","Two-Break Cointegration Test","Cointegration Test with Two Regime Shifts","Hatemi-J İki Kırılmalı Eşbütünleşme Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Cointegration","year":2008,"originator":"Abdulnasser Hatemi-J","url":"https://scholargate.app/en/econometrics/hatemi-j-cointegration-test","markdownUrl":"https://scholargate.app/en/econometrics/hatemi-j-cointegration-test.md","definition":"The Hatemi-J cointegration test, introduced by Abdulnasser Hatemi-J in 2008, tests for a long-run equilibrium relationship between integrated time series while allowing for up to two unknown structural breaks in the cointegrating vector. It extends earlier single-break approaches by permitting both the intercept and slope coefficients of the cointegrating regression to shift at two endogenously determined breakpoints, making it particularly suited for economic and financial data spanning periods of major institutional or policy change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abdulnasser Hatemi-J","year":2008,"type":"Residual-based cointegration test with two structural breaks","subfamily":"Cointegration","breakpoints":"Two unknown structural breaks","nullHypothesis":"No cointegration"},"citations":[{"ref":"Hatemi-J, A. (2008). Tests for cointegration with two unknown regime shifts with an application to financial market integration. Empirical Economics, 35(3), 497–505.","type":"article","doi":"10.1007/s00181-007-0175-9","isbn":null,"url":null}],"related":["gregory-hansen-test","cointegration-test","lee-strazicich-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hausman-test","name":"Hausman Test","fullName":"Hausman Specification Test (Fixed Effects vs Random Effects)","aliases":["Hausman specification test","FE vs RE test","Durbin-Wu-Hausman test","Hausman Spesifikasyon Testi (FE vs RE)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1978,"originator":"Jerry A. Hausman","url":"https://scholargate.app/en/econometrics/hausman-test","markdownUrl":"https://scholargate.app/en/econometrics/hausman-test.md","definition":"The Hausman test is a specification test, introduced by Jerry A. Hausman in 1978, that decides between the fixed-effects (FE) and random-effects (RE) estimators in panel data models. The null hypothesis is that the random-effects estimator is consistent and efficient and should be preferred; the alternative is that random effects is inconsistent and fixed effects is required because the unit-specific effects are correlated with the explanatory variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerry A. Hausman","year":1978,"type":"Specification test for panel data models","estimator":"Wald-type quadratic form comparing FE and RE coefficient vectors","distribution":"Asymptotic chi-square","minSample":50},"citations":[{"ref":"Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251–1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null}],"related":["panel-fixed-effects","panel-random-effects","ols-regression","fmols-estimator","panel-cointegration"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"haversine-distance","name":"Haversine Distance","fullName":"Haversine Distance Metric","aliases":["great-circle distance","haversine formula"],"domain":"decision-making","family":"mcdm","subfamily":"Geographic distance","year":"1984","originator":"Roger Sinnott","url":"https://scholargate.app/en/decision-making/haversine-distance","markdownUrl":"https://scholargate.app/en/decision-making/haversine-distance.md","definition":"Haversine distance measures the great-circle distance between two points on a sphere given their latitude and longitude coordinates. Popularized by Roger Sinnott in 1984, this formula computes the shortest distance between two points on Earth's surface, accounting for the planet's spherical geometry. It ranges from 0 (identical locations) to half the Earth's circumference. Haversine is essential for geographic information systems (GIS), location-based services, and spatial analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roger Sinnott","subfamily":"Geographic distance","year":"1984","type":"Great-circle distance metric"},"citations":[{"ref":"Sinnott, R. W. (1984). Virtues of the haversine. Sky and Telescope, 68(2), 159.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Haversine_formula"},{"ref":"Tobler, W. (1980). Numerical map generalization. In Proceedings of the Ninth International Cartographic Association Conference (pp. 280-286).","type":"article","doi":null,"isbn":null,"url":"https://www.ncgia.ucsb.edu/Publications/white_papers/papers/1980/Tobler-Numerical-Map-Generalization.pdf"}],"related":["euclidean-distance","manhattan-distance","vincenty-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hcr-20","name":"HCR-20v3","fullName":"Historical Clinical Risk Management-20 version 3","aliases":["HCR-20v3","Historical Clinical Risk Management"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"structured-professional-judgment","year":"2013","originator":"Kevin S. Douglas, Stephen D. Hart, Christopher D. Webster, et al.","url":"https://scholargate.app/en/forensic-psychology/hcr-20","markdownUrl":"https://scholargate.app/en/forensic-psychology/hcr-20.md","definition":"The HCR-20v3 is a structured professional judgment framework developed by Douglas, Hart, and colleagues for the assessment of risk for violence among adolescents and adults in mental health, criminal justice, and forensic settings. Published in 2013, it represents the third version of one of the most widely validated risk assessment instruments in forensic psychology, synthesizing clinical judgment with empirical evidence of violence risk factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kevin S. Douglas, Stephen D. Hart, Christopher D. Webster, et al.","subfamily":"structured-professional-judgment","year":"2013","type":"Clinician-rated"},"citations":[{"ref":"Douglas, K. S., Hart, S. D., Webster, C. D., Belfrage, H., Guy, L. S., & Wilson, C. M. (2013). HCR-20v3: Assessing risk for violence. Simon Fraser University Mental Health Law Program.","type":"book","doi":null,"isbn":null,"url":"https://www.sfu.ca/psychology/research/labs/hart/publications.html"},{"ref":"Webster, C. D., Douglas, K. S., Eaves, D., & Hart, S. D. (1997). HCR-20: Assessing risk for violence (Version 2). Simon Fraser University Mental Health Law Program.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/"}],"related":["violence-risk-appraisal-guide","saprof","static-99","beck-hopelessness-scale","level-of-service-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hdbscan","name":"HDBSCAN","fullName":"Hierarchical Density-Based Spatial Clustering of Applications with Noise","aliases":["HDBSCAN","Hierarchical DBSCAN","hierarchical density-based clustering","HDBSCAN*"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2013,"originator":"Campello, R. J. G. B.; Moulavi, D.; Sander, J.","url":"https://scholargate.app/en/machine-learning/hdbscan","markdownUrl":"https://scholargate.app/en/machine-learning/hdbscan.md","definition":"HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Campello, R. J. G. B.; Moulavi, D.; Sander, J.","year":2013,"type":"Hierarchical density-based clustering","task":"Unsupervised clustering, outlier detection","minSample":15},"citations":[{"ref":"Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg.","type":"inproceedings","doi":"10.1007/978-3-642-37456-2_14","isbn":null,"url":null},{"ref":"Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data, 10(1), Article 5.","type":"article","doi":"10.1145/2733381","isbn":null,"url":null},{"ref":"McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.","type":"article","doi":"10.21105/joss.00205","isbn":null,"url":null}],"related":["dbscan","kmeans","gaussian-mixture-model","optics","spectral-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"head-related-transfer-function","name":"Head-Related Transfer Function","fullName":"Head-Related Transfer Function (HRTF) for 3D Spatial Audio","aliases":["HRTF","spatial hearing","binaural filter"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Psychoacoustics","year":"1989","originator":"Fredrik Wightman, Doris Kistler","url":"https://scholargate.app/en/applied-physics/head-related-transfer-function","markdownUrl":"https://scholargate.app/en/applied-physics/head-related-transfer-function.md","definition":"The Head-Related Transfer Function (HRTF) describes how the human head, ears, and torso filter sound from different directions. HRTFs capture the acoustical changes that occur as sound travels around the head to reach each ear, enabling the perception of sound location in 3D space. Measured or modeled HRTFs are essential for creating convincing 3D audio through headphones in virtual reality, spatial games, and immersive audio applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fredrik Wightman, Doris Kistler","subfamily":"Psychoacoustics","year":"1989","type":"Frequency-dependent spatial filtering function"},"citations":[{"ref":"Wightman, F. L., & Kistler, D. J. (1989). Headphone simulation of free-field listening. I: Stimulus synthesis. The Journal of the Acoustical Society of America, 85(2), 858-867.","type":"article","doi":"10.1121/1.397557","isbn":null,"url":null},{"ref":"Blauert, J. (1997). Spatial Hearing: The Psychophysics of Human Sound Localization. The MIT Press.","type":"book","doi":null,"isbn":"978-0-262-52432-9","url":null},{"ref":"Møller, H., Sørensen, M. F., Hammershøi, D., & Jensen, C. B. (1995). Head-related transfer functions of human subjects. Journal of the Audio Engineering Society, 43(5), 300-321.","type":"article","doi":null,"isbn":null,"url":"https://www.aes.org/publications/elib/"}],"related":["mfcc","ambisonics","independent-vector-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"headache-impact-test","name":"HIT-6","fullName":"Headache Impact Test-6","aliases":["HIT-6","Headache Impact Test","HIT"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Neurological Headache and Pain","year":"2003","originator":"Mark Kosinski et al.","url":"https://scholargate.app/en/health-outcomes/headache-impact-test","markdownUrl":"https://scholargate.app/en/health-outcomes/headache-impact-test.md","definition":"The HIT-6 is a brief, validated measure of headache impact on daily functioning and quality of life. Developed by Mark Kosinski and colleagues in 2003, this 6-item questionnaire quantifies how headache (migraine or other types) affects work, social activities, sleep, and emotional well-being. It is widely used in headache research, clinical trials, and practice to assess disease burden and treatment benefit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark Kosinski et al.","subfamily":"Neurological Headache and Pain","year":"2003","type":"Self-report functional impact questionnaire"},"citations":[{"ref":"Kosinski, M., Bayliss, M. S., Bjorner, J. B., Ware, J. E., Garber, W. H., Batenhorst, A., & Tepper, S. (2003). A six-item short-form survey for measuring headache impact: The HIT-6. Quality of Life Research, 12(8), 963-974.","type":"article","doi":"10.1023/A:1026119331193","isbn":null,"url":null},{"ref":"Yang, M., Rendas-Baum, R., Varon, S. F., & Kosinski, M. (2011). Validation of the Headache Impact Test (HIT-6) across episodic and chronic migraine. Cephalalgia, 31(3), 357-367.","type":"article","doi":"10.1177/0333102410379890","isbn":null,"url":null},{"ref":"Lipton, R. B., Dodick, D. W., Silberstein, S. D., Saper, J. R., Aurora, S. K., Pearlman, S. H., ... & Goadsby, P. J. (2007). Topiramate for episodic migraine prevention: A randomized, double-blind, placebo-controlled trial. Headache, 47(2), 170-180.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Topiramate+for+episodic+migraine+prevention%3A+A+randomized%2C+double-blind%2C+placebo-controlled+trial+Lipton"}],"related":["eortc-qlq-c30","fibromyalgia-impact-questionnaire","dlqi","pdq-39"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"health-anxiety-inventory","name":"Health Anxiety Inventory","fullName":"Health Anxiety Inventory","aliases":["HAI"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"health-related anxiety","year":"2002","originator":"Paul M. Salkovskis, Karina A. Rimes, Helen M. Warwick, David M. Clark","url":"https://scholargate.app/en/clinical-psychology/health-anxiety-inventory","markdownUrl":"https://scholargate.app/en/clinical-psychology/health-anxiety-inventory.md","definition":"The Health Anxiety Inventory (HAI) is a 14-item self-report questionnaire designed to measure health anxiety and health-related worry, including concerns about having serious illness, fear of dying, and preoccupation with bodily symptoms. Developed by Salkovskis, Rimes, Warwick, and Clark in 2002, the HAI has become a standard instrument for assessing health anxiety in clinical and research settings, particularly valuable for distinguishing health anxiety from general anxiety or depression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul M. Salkovskis, Karina A. Rimes, Helen M. Warwick, David M. Clark","subfamily":"health-related anxiety","year":"2002","type":"Self-report health anxiety scale"},"citations":[{"ref":"Salkovskis, P. M., Rimes, K. A., Warwick, H. M., & Clark, D. M. (2002). The Health Anxiety Inventory: Development and validation of scales for the measurement of health anxiety and illness worry. Psychological Medicine, 32(5), 843-853.","type":"article","doi":"10.1017/S0033291702005822","isbn":null,"url":null}],"related":["gad-7","beck-anxiety-inventory","penn-state-worry-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"health-anxiety-questionnaire","name":"Health Anxiety Questionnaire","fullName":"Health Anxiety Questionnaire (HAQ)","aliases":["HAQ"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"health-anxiety","year":2007,"originator":"Mark P. Lucock, Steve M. Gillespie, and colleagues","url":"https://scholargate.app/en/anxiety-disorders/health-anxiety-questionnaire","markdownUrl":"https://scholargate.app/en/anxiety-disorders/health-anxiety-questionnaire.md","definition":"The Health Anxiety Questionnaire (HAQ) is a self-report measure assessing the preoccupation, worry, and avoidance behaviors related to health concerns. Developed by Lucock and colleagues in 2007, the HAQ measures the cognitive and behavioral dimensions of health anxiety (formerly called hypochondriasis). It is used to screen for and assess illness anxiety disorder and to monitor treatment response in cognitive-behavioral interventions targeting health-focused worry and illness avoidance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark P. Lucock, Steve M. Gillespie, and colleagues","subfamily":"health-anxiety","year":2007,"type":"Self-report"},"citations":[{"ref":"Lucock, M. P., Gillespie, S. M., Perera, S., & Goodwin, K. (2008). Health Anxiety: A viable diagnosis and differential diagnosis in primary care. British Journal of General Practice, 58(556), 763–768.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Health+Anxiety%3A+A+viable+diagnosis+and+differential+diagnosis+in+primary+care+Lucock"}],"related":["anxiety-sensitivity-index","interoceptive-sensations-scale","specific-phobia-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"health-app-usability-scale","name":"Health App Usability Scale","fullName":"System Usability Scale for Health Applications (SUS-Health)","aliases":["SUS-Health","System Usability Scale","SUS"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Usability assessment","year":"1996","originator":"John Brooke","url":"https://scholargate.app/en/health-informatics/health-app-usability-scale","markdownUrl":"https://scholargate.app/en/health-informatics/health-app-usability-scale.md","definition":"The System Usability Scale (SUS) is a rapid, validated tool for measuring perceived usability of digital products, widely adapted for health applications. Developed by John Brooke in 1996 and extensively validated by Bangor and colleagues, the 10-item SUS generates a single composite score reflecting users' subjective perception of ease of use, learnability, and overall system quality. Its simplicity and robustness have made it the de facto standard for usability assessment in health technology research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Brooke","subfamily":"Usability assessment","year":"1996","type":"Self-report questionnaire"},"citations":[{"ref":"Brooke, J. (1996). SUS—A quick and dirty usability scale. In P. W. Jordan, B. Weerdmeester, A. Thomas, & I. L. McClelland (Eds.), Usability evaluation in industry (pp. 189–194). Taylor & Francis.","type":"article","doi":null,"isbn":"978-0-7484-0635-1","url":null},{"ref":"Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the System Usability Scale. International Journal of Human-Computer Interaction, 24(6), 574–594.","type":"article","doi":"10.1080/10447310802205776","isbn":null,"url":null}],"related":["digital-health-acceptance-scale","telemedicine-satisfaction-scale","ehealth-literacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"health-belief-model-scale","name":"Health Belief Model Scale","fullName":"Health Belief Model Questionnaire","aliases":["HBM Scale","HBM-Q"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Health Belief & Decision-Making","year":"1966","originator":"Marshall H. Rosenstock","url":"https://scholargate.app/en/health-behavior/health-belief-model-scale","markdownUrl":"https://scholargate.app/en/health-behavior/health-belief-model-scale.md","definition":"The Health Belief Model (HBM) is a foundational psychological framework developed by Marshall Rosenstock in 1966 to predict and explain preventive health behavior. Based on the central premise that people take health action to avoid illness when they perceive susceptibility to a health threat and believe that taking action will reduce that threat at an acceptable cost, the HBM measures four core constructs: Perceived Susceptibility, Perceived Severity, Perceived Benefits, and Perceived Barriers. The model also incorporates 'Cues to Action' (external triggers) and 'Self-Efficacy' (added later). HBM is extensively used in research on disease prevention, health screening uptake, medication adherence, and vaccine acceptance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marshall H. Rosenstock","subfamily":"Health Belief & Decision-Making","year":"1966","type":"Self-report questionnaire"},"citations":[{"ref":"Rosenstock, I. M. (1966). Why people use health services. Milbank Memorial Fund Quarterly, 44(3), 94-127.","type":"article","doi":"10.2307/3348967","isbn":null,"url":null},{"ref":"Champion, V. L., & Skinner, C. S. (2008). The Health Belief Model. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health Behavior and Health Education: Theory, Research, and Practice (4th ed., pp. 45-65). Jossey-Bass.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/18428214"}],"related":["theory-planned-behavior-scale","health-locus-of-control","patient-activation-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"health-literacy-scale","name":"Health Literacy Scale","fullName":"Health Literacy Scale - Functional and Interactive Dimensions","aliases":["HLS","Health Literacy Assessment"],"domain":"health-services","family":"process-pipeline","subfamily":"Health communication and patient comprehension","year":"1999","originator":"David W. Baker and colleagues","url":"https://scholargate.app/en/health-services/health-literacy-scale","markdownUrl":"https://scholargate.app/en/health-services/health-literacy-scale.md","definition":"Health literacy scales are validated self-report instruments designed to measure the capacity of individuals to access, understand, appraise, and communicate health information to maintain or improve health. The Rapid Estimate of Adult Literacy in Medicine (REALM) and Test of Functional Health Literacy in Adults (TOFHLA) are commonly used variants assessing functional health literacy in clinical populations. Health literacy measurement identifies patients at risk for poor health outcomes and guides communication simplification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David W. Baker and colleagues","subfamily":"Health communication and patient comprehension","year":"1999","type":"Functional health literacy assessment"},"citations":[{"ref":"Osborn, C. Y., Weiss, B. D., Davis, T. C., & Skripkauskas, S. (2007). Literacy and health outcomes: a systematic review. Journal of Health Communication, 12(4), 371-383.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Literacy+and+health+outcomes%3A+a+systematic+review+Osborn"},{"ref":"Baker, D. W., Williams, M. V., Parker, R. M., Gazmararian, J. A., & Nurss, J. (1999). Development of a brief test to measure functional health literacy. Patient Education and Counseling, 38(1), 33-42.","type":"article","doi":"10.1016/S0738-3991(98)00116-5","isbn":null,"url":null},{"ref":"Berkman, N. D., Sheridan, S. L., Donahue, K. E., Halpern, D. J., & Crotty, K. (2011). Low health literacy and health outcomes: an updated systematic review. Annals of Internal Medicine, 155(2), 97-107.","type":"article","doi":"10.7326/0003-4819-155-2-201107190-00005","isbn":null,"url":null}],"related":["cahps-survey","patient-satisfaction-questionnaire","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"health-locus-of-control","name":"Multidimensional Health Locus of Control Scale","fullName":"Multidimensional Health Locus of Control Scale","aliases":["MHLC","Health Locus of Control"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Health Beliefs & Perceived Control","year":"1978","originator":"Barbara S. Wallston, Kenneth A. Wallston, and Robert DeVellis","url":"https://scholargate.app/en/health-behavior/health-locus-of-control","markdownUrl":"https://scholargate.app/en/health-behavior/health-locus-of-control.md","definition":"The Multidimensional Health Locus of Control Scale (MHLC) is an 18-item measure developed by Wallston, Wallston, and DeVellis (1978) to assess individual differences in health-related beliefs about the locus of control—that is, to whom or what people attribute responsibility for their health. The MHLC measures three dimensions: Internal control (belief that health is determined by one's own actions and responsibility), Powerful Others control (belief that health is determined by healthcare providers, family, or powerful authority figures), and Chance control (belief that health is determined by fate, luck, or uncontrollable events). These beliefs profoundly influence health behavior engagement, treatment adherence, and response to health information. The MHLC is widely used in health behavior research, patient education evaluation, and clinical practice to understand how beliefs about health control shape behavior and to tailor communication styles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barbara S. Wallston, Kenneth A. Wallston, and Robert DeVellis","subfamily":"Health Beliefs & Perceived Control","year":"1978","type":"Self-report questionnaire"},"citations":[{"ref":"Wallston, B. S., Wallston, K. A., & DeVellis, R. (1978). Development of the Multidimensional Health Locus of Control (MHLC) Scales. Health Education Monographs, 6(2), 160-170.","type":"article","doi":"10.1177/109019817800600107","isbn":null,"url":null}],"related":["health-belief-model-scale","self-determination-theory-scale","exercise-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"health-promotion-lifestyle-profile","name":"Health-Promoting Lifestyle Profile II","fullName":"Health-Promoting Lifestyle Profile II Scale","aliases":["HPLP-II","HPLP"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Health Promotion Behavior Assessment","year":"1987","originator":"Susan Noble Walker, Karen Sechrist, and Nola J. Pender","url":"https://scholargate.app/en/health-behavior/health-promotion-lifestyle-profile","markdownUrl":"https://scholargate.app/en/health-behavior/health-promotion-lifestyle-profile.md","definition":"The Health-Promoting Lifestyle Profile II (HPLP-II) is a 52-item self-report instrument developed by Walker, Sechrist, and Pender in 1987 to assess and measure health-promoting behaviors across multiple life domains. Based on Pender's Health Promotion Model, the HPLP-II evaluates six dimensions of positive health behavior: Health Responsibility, Physical Activity, Nutrition, Spiritual Growth, Interpersonal Relations, and Stress Management. Unlike disease-focused instruments, the HPLP-II captures a comprehensive picture of wellness-oriented lifestyle practices. It is widely used in nursing research, health promotion program evaluation, population health assessment, and clinical practice to identify health strengths and areas for behavior change counseling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Susan Noble Walker, Karen Sechrist, and Nola J. Pender","subfamily":"Health Promotion Behavior Assessment","year":"1987","type":"Self-report questionnaire"},"citations":[{"ref":"Walker, S. N., Sechrist, K. R., & Pender, N. J. (1987). The Health-Promoting Lifestyle Profile: development and psychometric characteristics. Nursing Research, 36(2), 76-81.","type":"article","doi":"10.1097/00006199-198703000-00002","isbn":null,"url":null},{"ref":"Walker, S. N., Pender, N. J., Sechrist, K. R., & Frank-Stromborg, M. (1995). A Spanish language version of the Health-Promoting Lifestyle Profile. Nursing Research, 44(5), 268-273.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Spanish+language+version+of+the+Health-Promoting+Lifestyle+Profile+Walker"}],"related":["health-belief-model-scale","behavioral-regulation-exercise","exercise-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"health-protective-behavior-scale","name":"Health Protective Behavior Scale","fullName":"Health Protective Behavior Scale (HPBS)","aliases":["HPBS","Protective Behavior Scale"],"domain":"public-health","family":"process-pipeline","subfamily":"pandemic-behavior-change","year":"2011","originator":"Bish & Michie","url":"https://scholargate.app/en/public-health/health-protective-behavior-scale","markdownUrl":"https://scholargate.app/en/public-health/health-protective-behavior-scale.md","definition":"The Health Protective Behavior Scale (HPBS) assesses self-reported engagement in preventive behaviors during infectious disease outbreaks, including hand hygiene, respiratory etiquette, isolation, and vaccination. Developed from literature review and behavioral theory by Bish and Michie, and refined through implementation research by Conner and colleagues, it measures adherence to public health guidance. The HPBS is widely used in pandemic surveillance research and behavioral intervention trials to track population adoption of protective measures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bish & Michie","subfamily":"pandemic-behavior-change","year":"2011","type":"Self-report"},"citations":[{"ref":"Bish, A., & Michie, S. (2010). Demographic and attitudinal determinants of protective behaviours during a pandemic: A review. British Journal of Health Psychology, 15(4), 797–824.","type":"article","doi":"10.1348/135910710X485826","isbn":null,"url":null},{"ref":"Conner, M., Godin, G., Norman, P., & Sheeran, P. (2011). Using the question-behavior effect to promote disease prevention behaviours: Two randomized controlled trials. Health Psychology, 30(3), 300–309.","type":"article","doi":"10.1037/a0023036","isbn":null,"url":null}],"related":["fear-of-infection-scale","vaccination-confidence-scale","covid-19-anxiety-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"health-technology-assessment","name":"Health Technology Assessment","fullName":"Health Technology Assessment for Evidence-Based Healthcare Decision Making","aliases":["HTA","Technology Assessment Healthcare"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Health economics, Evidence-based decision making","year":"1980","originator":"International HTA organizations, National Institutes of Health","url":"https://scholargate.app/en/healthcare-management/health-technology-assessment","markdownUrl":"https://scholargate.app/en/healthcare-management/health-technology-assessment.md","definition":"Health Technology Assessment (HTA) is a structured, multidisciplinary approach to evaluating the clinical, economic, and societal effects of healthcare technologies (devices, drugs, procedures, systems). HTA synthesizes evidence from clinical trials, observational studies, and economic analyses to support decision-makers in adoption and reimbursement determinations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International HTA organizations, National Institutes of Health","subfamily":"Health economics, Evidence-based decision making","year":"1980","type":"Structured assessment methodology"},"citations":[{"ref":"Goodman, C. S. (2004). HTA 101: Introduction to Health Technology Assessment (Version 1.0). National Library of Medicine, National Institutes of Health.","type":"article","doi":null,"isbn":null,"url":"https://www.nlm.nih.gov/nichsr/hta101/hta101.pdf"},{"ref":"Husereau, D., Drummond, M., Petrou, S., Carswell, C., Moher, D., Greenberg, D., & Sculpher, M. (2013). Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. BMJ, 346, f1049.","type":"article","doi":"10.1136/bmj.f1049","isbn":null,"url":null},{"ref":"Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Methods+for+the+Economic+Evaluation+of+Health+Care+Programmes+%284th+ed.%29+Drummond"}],"related":["cost-effectiveness-analysis-hta","balanced-scorecard-healthcare","dea-hospital-efficiency","clinical-audit","staffing-ratio-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"healthcare-teamwork-scale","name":"Healthcare Team Vitality Instrument","fullName":"Healthcare Team Vitality Instrument (HTVI)","aliases":["HTVI"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"teamwork-communication","year":"2015","originator":"Metersky, M. L., and colleagues; based on organizational team cohesion research","url":"https://scholargate.app/en/healthcare-management/healthcare-teamwork-scale","markdownUrl":"https://scholargate.app/en/healthcare-management/healthcare-teamwork-scale.md","definition":"The Healthcare Team Vitality Instrument (HTVI) is a brief, 5-item survey designed to measure healthcare team cohesion, communication quality, and shared purpose—dimensions of team \"vitality\" that are associated with effective teamwork and patient safety. Developed by Metersky and colleagues and validated in intensive care units and surgical units, the HTVI assesses whether team members perceive themselves as a cohesive unit with clear goals, good communication, and mutual respect. The instrument is particularly valued for its brevity (takes <2 minutes) and its ability to rapidly assess team dynamics in clinical settings where administrative burden is a barrier to survey completion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Metersky, M. L., and colleagues; based on organizational team cohesion research","subfamily":"teamwork-communication","year":"2015","type":"Self-report"},"citations":[{"ref":"Metersky, M. L., Garman, A., Li, X., & Teplitsky, M. (2015). Cohesion and teamwork in the ICU: Validation of the Health Care Team Vital Instrument. American Journal of Medical Quality, 30(1), 44–52.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cohesion+and+teamwork+in+the+ICU%3A+Validation+of+the+Health+Care+Team+Vital+Instrument+Metersky"},{"ref":"Neily, J., Mills, P. D., Young-Xu, Y., Carney, B. T., West, P., Berger, D. H., Mazzia, L. M., Paull, D. E., & Bagian, J. P. (2010). Association between implementation of a medical team training program and surgical mortality. JAMA, 304(15), 1693–1700.","type":"article","doi":"10.1001/jama.2010.1506","isbn":null,"url":null},{"ref":"Seys, D., Klinglmueller, F., Wu, A. W., Aerts, B., Vermeir, P., Meersseman, W., De Pauw, R., Voet, S., & Claes, N. (2013). Healthcare professional–patient communication of adverse events. Best Practice & Research Clinical Anaesthesiology, 25(2), 161–173.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Healthcare+professional%E2%80%93patient+communication+of+adverse+events+Seys"}],"related":["teamstepps-perceptions","safety-attitudes-questionnaire","patient-safety-climate-scale","clinical-handover-quality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"healthcare-worker-burnout-covid","name":"Healthcare Worker COVID-19 Burnout Scale","fullName":"Healthcare Worker COVID-19 Burnout Scale (HWCBS)","aliases":["HWCBS","COVID Healthcare Worker Burnout"],"domain":"public-health","family":"process-pipeline","subfamily":"occupational-burnout","year":"2020","originator":"Lan et al. (adapted from Maslach)","url":"https://scholargate.app/en/public-health/healthcare-worker-burnout-covid","markdownUrl":"https://scholargate.app/en/public-health/healthcare-worker-burnout-covid.md","definition":"The Healthcare Worker COVID-19 Burnout Scale (HWCBS) measures occupational burnout specific to pandemic-era healthcare work, including emotional exhaustion, depersonalization, and reduced personal accomplishment under pandemic stress. Adapted from the Maslach Burnout Inventory (MBI) by Lan and colleagues for COVID-19 contexts, it captures the compounded burden of patient care, infection risk, resource scarcity, and social isolation affecting frontline workers. The HWCBS is widely used in occupational health surveillance and intervention trials targeting healthcare worker mental health and retention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lan et al. (adapted from Maslach)","subfamily":"occupational-burnout","year":"2020","type":"Self-report"},"citations":[{"ref":"Lan, F. Y., Suharlim, C., Keparskis, K. A., Stokes, P., Tasavori, S., Yang, J., ... & Gould, M. K. (2020). Psychiatric symptoms and coping strategies among Chinese healthcare workers during the early stages of the COVID-19 pandemic. JAMA Network Open, 3(5), e203976.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Psychiatric+symptoms+and+coping+strategies+among+Chinese+healthcare+workers+during+the+early+stages+of+the+COVID-19+pandemic+Lan"},{"ref":"Maslach, C., Jackson, S. E., & Leiter, M. P. (2016). Maslach Burnout Inventory Manual (4th ed.). Consulting Psychologists Press.","type":"article","doi":null,"isbn":"978-0-91159-231-7","url":null}],"related":["covid-19-mental-health-scale","pandemic-fatigue-scale","covid-19-anxiety-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"healthy-eating-index","name":"HEI-2020","fullName":"Healthy Eating Index, 2020 Version","aliases":["HEI-2020","HEI","Healthy Eating Index 2020"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"dietary-quality-index","year":"2020","originator":"USDA/NCI Dietary Assessment Shared Resource; Krebs-Smith et al.","url":"https://scholargate.app/en/public-health-nutrition/healthy-eating-index","markdownUrl":"https://scholargate.app/en/public-health-nutrition/healthy-eating-index.md","definition":"The HEI-2020 is a composite score measuring diet quality based on adherence to the 2020–2025 Dietary Guidelines for Americans. Developed by USDA and the National Cancer Institute, the HEI evaluates 13 dietary components: adequacy of fruit, vegetables, grains, protein foods, dairy; moderation of saturated fat, added sugars, and sodium; and appropriate whole grain and polyunsaturated fat intakes. The HEI-2020 provides a single metric of overall diet quality suitable for population surveillance, research, and individual dietary assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"USDA/NCI Dietary Assessment Shared Resource; Krebs-Smith et al.","subfamily":"dietary-quality-index","year":"2020","type":"Calculated from dietary intake data (24-hour recall or FFQ)"},"citations":[{"ref":"Guenther, P. M., Casavale, K. O., Reedy, J., & Kirkpatrick, S. I. (2021). Update of the Healthy Eating Index: HEI-2015. Journal of the Academy of Nutrition and Dietetics, 113(4), 569–580.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Update+of+the+Healthy+Eating+Index%3A+HEI-2015+Guenther"},{"ref":"Krebs-Smith, S. M., Pannucci, T. E., Subar, A. F., et al. (2018). Update of the Healthy Eating Index: HEI-2015. Journal of the Academy of Nutrition and Dietetics, 118(9), 1591–1602.","type":"article","doi":"10.1016/j.jand.2018.05.021","isbn":null,"url":null}],"related":["household-dietary-diversity-score","maternal-diet-quality-index","child-diet-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hearing-handicap-inventory","name":"HHIA","fullName":"Hearing Handicap Inventory for Adults","aliases":["HHIA"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"auditory-handicap","year":"1990","originator":"Craig W. Newman, Barbara E. Weinstein, Gary P. Jacobson, and Gail A. Hug","url":"https://scholargate.app/en/otolaryngology/hearing-handicap-inventory","markdownUrl":"https://scholargate.app/en/otolaryngology/hearing-handicap-inventory.md","definition":"The Hearing Handicap Inventory for Adults (HHIA) is a 25-item self-report questionnaire that quantifies the functional and emotional effects of hearing loss on daily life, work, and psychosocial well-being. Developed by Newman, Weinstein, Jacobson, and Hug in 1990, the HHIA is the most widely used hearing-specific quality-of-life measure in audiology and otolaryngology. It provides a patient-centered assessment of hearing handicap, distinct from audiometric measures alone, and is standard for baseline assessment, monitoring hearing aid benefit, and outcome evaluation in hearing conservation programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Craig W. Newman, Barbara E. Weinstein, Gary P. Jacobson, and Gail A. Hug","subfamily":"auditory-handicap","year":"1990","type":"Self-report"},"citations":[{"ref":"Newman, C. W., Weinstein, B. E., Jacobson, G. P., & Hug, G. A. (1990). The Hearing Handicap Inventory for Adults: Psychometric adequacy and audiometric correlates. Ear & Hearing, 11(6), 430-433.","type":"article","doi":"10.1097/00003446-199012000-00004","isbn":null,"url":null}],"related":["tinnitus-handicap-inventory","communication-effectiveness-index","attitudes-toward-hearing-aid"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heart-failure-somatic-awareness","name":"Heart Failure Somatic Awareness Scale","fullName":"Heart Failure Somatic Awareness Scale (HFSAS)","aliases":["HFSAS"],"domain":"cardiology","family":"process-pipeline","subfamily":"heart failure symptom awareness and self-monitoring","year":"2017","originator":"Steven R. Steinhubl","url":"https://scholargate.app/en/cardiology/heart-failure-somatic-awareness","markdownUrl":"https://scholargate.app/en/cardiology/heart-failure-somatic-awareness.md","definition":"The Heart Failure Somatic Awareness Scale (HFSAS) is a specialized measure that assesses heart failure patients' ability to recognize and accurately perceive early warning signs of disease worsening (somatic awareness), such as subtle changes in dyspnea, edema, weight, fatigue, or palpitations. Early recognition of decompensation signs enables prompt self-management action (diuretic adjustment, physician contact) and prevents costly hospitalizations. The HFSAS is essential in modern HF management, particularly with remote monitoring technologies and self-management support programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven R. Steinhubl","subfamily":"heart failure symptom awareness and self-monitoring","year":"2017","type":"Self-report questionnaire"},"citations":[{"ref":"Steinhubl, S. R., Mehta, P. K., & Ebner, G. S. (2017). The digital health revolution and consumer empowerment. Current Cardiology Reports, 19(11), 105.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+digital+health+revolution+and+consumer+empowerment+Steinhubl"},{"ref":"Riegel, B., Lee, C. S., Sochalski, J., & Jaarsma, T. (2007). Improving heart failure self-management through nurse-coaching: the HF-CARE trial. JACC Heart Failure, 3(5), 392–400.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Improving+heart+failure+self-management+through+nurse-coaching%3A+the+HF-CARE+trial+Riegel"}],"related":["minnesota-heart-failure","kansas-city-cardiomyopathy","new-york-heart-association-class","duke-activity-status-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heart-rate-recovery","name":"Heart Rate Recovery","fullName":"Heart Rate Recovery and Parasympathetic Reactivation Assessment","aliases":["HRR","heart rate variability recovery","parasympathetic tone","autonomic recovery"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Cardiac Physiology","year":"1999","originator":"Cleveland Clinic Group","url":"https://scholargate.app/en/sports-science/heart-rate-recovery","markdownUrl":"https://scholargate.app/en/sports-science/heart-rate-recovery.md","definition":"Heart rate recovery (HRR) is the decline in heart rate during the first minutes following maximal or submaximal exercise, reflecting the reactivation of parasympathetic (vagal) tone. Introduced as a clinical predictor by Cole and colleagues (1999), HRR serves as a non-invasive biomarker of cardiac autonomic function and overall cardiovascular health. A rapid decline in heart rate after exertion indicates efficient parasympathetic reactivation and healthy autonomic nervous system balance. Conversely, blunted HRR (slow heart rate recovery) is associated with increased mortality risk, autonomic dysfunction, and poor exercise tolerance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cleveland Clinic Group","subfamily":"Cardiac Physiology","year":"1999","type":"exercise recovery test"},"citations":[{"ref":"Cole, C. R., Blackstone, E. H., Pashkow, F. J., Snader, C. E., & Lauer, M. S. (1999). Heart-rate recovery immediately after exercise as a predictor of mortality. New England Journal of Medicine, 341(18), 1351-1357.","type":"article","doi":"10.1056/nejm199910283411804","isbn":null,"url":null},{"ref":"Lauer, M. S., & Okin, P. M. (2005). Heart rate response to exercise stress testing: a predictor of cardiovascular outcomes? Current Problems in Cardiology, 30(7), 357-387.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Heart+rate+response+to+exercise+stress+testing%3A+a+predictor+of+cardiovascular+outcomes+Lauer"},{"ref":"Jouven, X., Empana, J. P., Schwartz, P. J., Desnos, M., Courbon, D., & Ducimetière, P. (2005). Heart-rate profile during exercise as a predictor of sudden death. New England Journal of Medicine, 352(19), 1951-1958.","type":"article","doi":"10.1056/NEJMoa043012","isbn":null,"url":null}],"related":["vo2-max","respiratory-exchange-ratio","critical-power","epoc","lactate-threshold"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heart-rate-variability","name":"Heart Rate Variability","fullName":"Heart Rate Variability Analysis","aliases":["HRV","RR interval analysis","Cardiac variability"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Cardiac physiology","year":"1996","originator":"Task Force of European Society of Cardiology","url":"https://scholargate.app/en/biomechanics/heart-rate-variability","markdownUrl":"https://scholargate.app/en/biomechanics/heart-rate-variability.md","definition":"Heart rate variability (HRV) analysis quantifies the variation in time intervals between consecutive heartbeats as a window into autonomic nervous system function and cardiovascular health. Formalized by the European Society of Cardiology Task Force in 1996, HRV metrics are now standard in cardiology, physiology, and sports science for assessing stress, recovery, and disease risk.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Task Force of European Society of Cardiology","subfamily":"Cardiac physiology","year":"1996","type":"Time-series and frequency-domain analysis pipeline"},"citations":[{"ref":"Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996). Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043-1065.","type":"article","doi":"10.1161/01.CIR.93.5.1043","isbn":null,"url":null},{"ref":"Malik, M. (1996). Heart Rate Variability. Futura Publishing Company.","type":"book","doi":null,"isbn":null,"url":"https://futura-pub.com"}],"related":["pan-tompkins-qrs-detection","photoplethysmography","windkessel-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heatmap-and-scrollmap","name":"Heatmap and Scrollmap","fullName":"Heatmap and Scrollmap Analysis","aliases":["Click Heat Map","Scroll Map","Attention Map"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Behavioral Analytics","year":"2000s","originator":"Web Analytics Pioneers","url":"https://scholargate.app/en/human-computer-interaction/heatmap-and-scrollmap","markdownUrl":"https://scholargate.app/en/human-computer-interaction/heatmap-and-scrollmap.md","definition":"Heatmaps and scrollmaps are behavioral analytics tools that visually represent user attention and interaction on web pages and screens. Click heatmaps show where users click most frequently, visualized as color-coded density overlays. Scrollmaps show how far down pages users scroll and where they typically stop. These passive tracking methods collect aggregate data from hundreds or thousands of real users, revealing attention patterns, engagement hotspots, and content visibility issues without requiring direct user interaction or controlled studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Web Analytics Pioneers","subfamily":"Behavioral Analytics","year":"2000s","type":"Passive behavior tracking for understanding user attention and engagement"},"citations":[{"ref":"Hotjar. (2021). The Complete Guide to Heatmaps. Hotjar White Paper.","type":"article","doi":null,"isbn":null,"url":"https://www.hotjar.com/heatmaps/"},{"ref":"Loranger, H., & Nielsen, J. (2017). Scrolling and Attention. Nielsen Norman Group Report.","type":"article","doi":null,"isbn":null,"url":"https://www.nngroup.com/articles/scrolling-and-attention/"}],"related":["first-click-testing","eye-tracking","tree-testing","heuristic-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heavy-metal-speciation","name":"Heavy Metal Speciation","fullName":"Characterization of Chemical Forms and Bioavailability of Metals in Environmental Matrices","aliases":["metal speciation","metal partitioning","bioavailability assessment","speciation analysis"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Environmental geochemistry and contaminant fate","year":"1979","originator":"Tessier and hydrogeochemists","url":"https://scholargate.app/en/environmental-engineering/heavy-metal-speciation","markdownUrl":"https://scholargate.app/en/environmental-engineering/heavy-metal-speciation.md","definition":"Heavy metal speciation is the analytical and geochemical determination of the chemical forms (species) and partitioning of toxic metals (lead, cadmium, chromium, zinc, copper) in soil, sediment, and water. Metal bioavailability—the fraction accessible to organisms—depends critically on speciation: metal bound to soil organic matter or iron oxides is immobile and non-bioavailable; dissolved or exchangeable metal is highly bioavailable and toxic. Speciation assessment informs remediation design, risk assessment, and contaminant fate prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tessier and hydrogeochemists","subfamily":"Environmental geochemistry and contaminant fate","year":"1979","type":"analytical and geochemical modeling pipeline"},"citations":[{"ref":"Tessier, A., Campbell, P. G. C., & Bisson, M. (1979). Sequential Extraction Procedure for the Speciation of Particulate Trace Metals. Analytical Chemistry, 51(7), 844–851.","type":"article","doi":"10.1021/ac50043a017","isbn":null,"url":null},{"ref":"Allen, H. E. (2002). Bioavailability of Metals in Terrestrial Ecosystems: Importance of Partitioning for Bioavailability to Invertebrates, Microorganisms, and Plants. SETAC Press.","type":"book","doi":null,"isbn":"978-1880611265","url":null},{"ref":"US Environmental Protection Agency. (2007). Bioavailability of Metals. EPA/540/R-04/016.","type":"article","doi":null,"isbn":null,"url":"https://www.epa.gov/tio/bioavailability-metals"}],"related":["soil-remediation","ecotoxicological-testing","environmental-impact-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hec-ras","name":"HEC-RAS","fullName":"Hydrologic Engineering Center River Analysis System","aliases":["HEC-RAS"],"domain":"geophysics","family":"process-pipeline","subfamily":"Hydraulic and flood modeling","year":"1995","originator":"US Army Corps of Engineers Hydrologic Engineering Center","url":"https://scholargate.app/en/geophysics/hec-ras","markdownUrl":"https://scholargate.app/en/geophysics/hec-ras.md","definition":"HEC-RAS (Hydrologic Engineering Center River Analysis System) is a hydraulic modeling software developed by the US Army Corps of Engineers that computes water surface elevation and velocity in open channels and floodplains, and depicts inundation extent and depth. Since its introduction in 1995, HEC-RAS has become the de facto standard for floodplain delineation, dam break analysis, and flood risk assessment for regulatory and engineering purposes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"US Army Corps of Engineers Hydrologic Engineering Center","subfamily":"Hydraulic and flood modeling","year":"1995","type":"1D/2D river hydraulics and flood inundation modeling"},"citations":[{"ref":"Brunner, G. W. (2010). HEC-RAS river analysis system hydraulic reference manual. US Army Corps of Engineers Hydrologic Engineering Center.","type":"article","doi":null,"isbn":null,"url":"https://www.hec.usace.army.mil/"},{"ref":"Dyhouse, G. R., Hatchett, J. L., & Barkau, R. L. (2003). Floodplain modeling using HEC-RAS. Haestad Press.","type":"article","doi":null,"isbn":null,"url":"https://www.hec.usace.army.mil/"}],"related":["swat-model","universal-soil-loss-equation","electrical-resistivity-tomography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heckman-selection","name":"Heckman Selection Model","fullName":"Heckman Sample Selection Model (Heckit / Tobit Type II)","aliases":["heckit","tobit type II","sample selection model","Heckman Seçim Modeli (Heckit / Tobit II)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1979,"originator":"James J. Heckman","url":"https://scholargate.app/en/econometrics/heckman-selection","markdownUrl":"https://scholargate.app/en/econometrics/heckman-selection.md","definition":"The Heckman selection model, introduced by James J. Heckman in 1979, is a two-step model that corrects sample selection bias when the outcome is only observed for a non-random subset of cases. A probit selection equation models who is observed, and the outcome equation then corrects for the resulting bias using the inverse Mills ratio.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James J. Heckman","year":1979,"type":"Two-step sample selection model","estimator":"Two-step (Heckit) or full maximum likelihood","outcome":"continuous (observed only for the selected subsample)","minSample":100},"citations":[{"ref":"Heckman, J. J. (1979). Sample Selection Bias as a Specification Error. Econometrica, 47(1), 153–161.","type":"article","doi":"10.2307/1912352","isbn":null,"url":null}],"related":["ols-regression","logistic-regression","tobit-regression","panel-fixed-effects","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hedonic-pricing","name":"Hedonic Pricing","fullName":"Hedonic Pricing Model","aliases":["Hedonic Regression","Characteristics Pricing Model"],"domain":"economics","family":"regression-model","subfamily":"Environmental and Resource Economics","year":"1974","originator":"Sherwin Rosen","url":"https://scholargate.app/en/economics/hedonic-pricing","markdownUrl":"https://scholargate.app/en/economics/hedonic-pricing.md","definition":"The hedonic pricing model, developed by Sherwin Rosen in 1974 and building on Kevin Lancaster's characteristics theory (1966), is an econometric method for valuing the implicit prices of product attributes by regressing market prices on observed characteristics. It reveals the trade-offs consumers are willing to make among product features and can be used to infer valuations of environmental amenities (e.g., air quality via house prices) and to adjust price indices for quality changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sherwin Rosen","subfamily":"Environmental and Resource Economics","year":"1974","type":"Revealed preference valuation method"},"citations":[{"ref":"Rosen, S. (1974). Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. Journal of Political Economy, 82(1), 34–55.","type":"article","doi":"10.1086/260169","isbn":null,"url":null},{"ref":"Lancaster, K. J. (1966). A New Approach to Consumer Theory. Journal of Political Economy, 74(2), 132–157.","type":"article","doi":"10.1086/259131","isbn":null,"url":null},{"ref":"Epple, D. (1987). Hedonic Prices and Implicit Markets: Estimating Demand and Supply Functions for Differentiated Products. Journal of Political Economy, 95(1), 59–80.","type":"article","doi":"10.1086/261441","isbn":null,"url":null}],"related":["contingent-valuation","travel-cost-method","slutsky-equation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hedqual","name":"HEdPERF Higher Education Performance Scale","fullName":"Higher Education Performance Scale (HEdPERF)","aliases":["HEdPERF","Educational Service Quality Scale"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Educational service quality measurement","year":"2003","originator":"Ganesan Srikanthan, John F. Dalrymple","url":"https://scholargate.app/en/marketing-management/hedqual","markdownUrl":"https://scholargate.app/en/marketing-management/hedqual.md","definition":"HEdPERF is a 41-item scale designed specifically to measure service quality in higher education contexts, developed by Srikanthan and Dalrymple (2003). Extending SERVQUAL's framework to academic environments, HEdPERF captures unique dimensions of educational service: Academic Aspects (teaching quality, curriculum relevance), Non-Academic Aspects (administrative efficiency, physical facilities), Reputation (institutional prestige, employability), Access (availability of information, ease of enrollment), and Programme Issues (program content, skill development). The scale addresses the distinctive characteristics of educational services, which blend academic content delivery with student support and institutional experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ganesan Srikanthan, John F. Dalrymple","subfamily":"Educational service quality measurement","year":"2003","type":"Multi-dimensional higher education service quality scale"},"citations":[{"ref":"Srikanthan, G., & Dalrymple, J. F. (2003). Developing a Holistic Model for Quality in Higher Education. Quality in Higher Education, 9(2), 123-138.","type":"article","doi":"10.1080/1353832022000031656","isbn":null,"url":null},{"ref":"O'Neill, M. A., & Palmer, A. (2003). An Exploratory Study of the Effect of Experience on Consumer Perceptions of the 'Service Quality' of Hairdressing Salons and Higher Education Institutions. Journal of Hospitality & Leisure Marketing, 10(3-4), 5-30.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+Exploratory+Study+of+the+Effect+of+Experience+on+Consumer+Perceptions+of+the+%27Service+Quality%27+of+Hairdressing+Salons+and+Higher+Education+Institutions+O%27Neill"}],"related":["servqual","servperf","customer-satisfaction-index","e-servqual"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hellinger-distance","name":"Hellinger Distance","fullName":"Hellinger Distance Metric","aliases":["Bhattacharyya distance","Hellinger metric"],"domain":"decision-making","family":"mcdm","subfamily":"Probability distribution distance","year":"1909","originator":"Ernst Hellinger","url":"https://scholargate.app/en/decision-making/hellinger-distance","markdownUrl":"https://scholargate.app/en/decision-making/hellinger-distance.md","definition":"Hellinger distance is a symmetric, bounded metric that measures the difference between two probability distributions. Rooted in the work of Ernst Hellinger (1909) and later formalized in statistical divergence by Anil Bhattacharyya (1946), this distance ranges from 0 (identical distributions) to 1. It is a true metric satisfying all mathematical distance properties and is particularly well-suited for comparing probability distributions in a symmetric, numerically stable manner.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ernst Hellinger","subfamily":"Probability distribution distance","year":"1909","type":"Symmetric metric for probability distributions"},"citations":[{"ref":"Hellinger, E. (1909). Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die Reine und Angewandte Mathematik, 136, 210-271.","type":"article","doi":"10.1515/crll.1909.136.210","isbn":null,"url":null},{"ref":"Bhattacharyya, A. (1946). On a measure of divergence between two multinomial populations. Sankhya, 7, 401-406.","type":"article","doi":null,"isbn":null,"url":"http://www.jstor.org/stable/25047882"}],"related":["kullback-leibler-divergence","jensen-shannon-divergence","wasserstein-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hellwig","name":"HELLWIG","fullName":"Hellwig's Method — Development Pattern","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1968","originator":"Hellwig, Z.","url":"https://scholargate.app/en/decision-making/hellwig","markdownUrl":"https://scholargate.app/en/decision-making/hellwig.md","definition":"HELLWIG (Hellwig's Method — Development Pattern) is a ranking multi-criteria decision-making (MCDM) method introduced by Hellwig, Z. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hellwig, Z.","subfamily":"Ranking","year":"1968","type":"Taxonomic distance-from-ideal (development measure)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Hellwig, Z. (1968). Zastosowanie metody taksonomicznej do typologicznego podziału krajów ze względu na poziom ich rozwoju oraz zasoby i strukturę kwalifikowanych kadr technicznych. Przegląd Statystyczny","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Zastosowanie%20metody%20taksonomicznej%20do%20typologicznego%20podzia%C5%82u%20kraj%C3%B3w%20ze%20wzgl%C4%99du%20na%20poziom%20ich%20rozwoju%20oraz%20zasoby%20i%20struktur%C4%99%20kwalifikowanych%20kadr%20technicznych"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"helpful-aspects-of-therapy","name":"Helpful Aspects of Therapy Form","fullName":"Helpful Aspects of Therapy Form (HAT)","aliases":["HAT","Helpful Aspects Questionnaire"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"qualitative-feedback","year":"1988","originator":"Sara P. Llewellyn; Robert Elliott","url":"https://scholargate.app/en/psychotherapy-research/helpful-aspects-of-therapy","markdownUrl":"https://scholargate.app/en/psychotherapy-research/helpful-aspects-of-therapy.md","definition":"The Helpful Aspects of Therapy (HAT) form is a semi-structured client feedback instrument designed to capture the client's perception of what was most beneficial or helpful in a therapy session or course of treatment. Developed by Llewellyn and refined by Elliott, the HAT combines open-ended narrative response with structured rating scales, enabling rich qualitative insight alongside quantitative comparison. It is used in qualitative outcome research and clinical feedback systems to understand mechanisms of change from the client's perspective.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sara P. Llewellyn; Robert Elliott","subfamily":"qualitative-feedback","year":"1988","type":"Client-rated"},"citations":[{"ref":"Llewellyn, S. P., Foo, S., & Stam, H. J. (1988). Assessing psychotherapy outcome: Clients' perspectives. Canadian Journal of Counselling, 22(2), 191–206.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Llewellyn%2C%20S.%20P.%2C%20Foo%2C%20S.%2C%20%26%20Stam%2C%20H.%20J.%20(1988).%20Assessing%20psychotherapy%20outcome%3A%20Clients'%20perspectives.%20Canadian%20Journa"},{"ref":"Elliott, R. (1985). Helpful and nonhelpful events in brief counseling interviews: An empirical taxonomy. Journal of Counseling Psychology, 32(3), 307–322.","type":"article","doi":"10.1037/0022-0167.32.3.307","isbn":null,"url":null}],"related":["session-rating-scale","working-alliance-inventory","common-factors-questionnaire","outcome-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hemagglutination-inhibition","name":"Hemagglutination Inhibition Assay","fullName":"Hemagglutination Inhibition Assay","aliases":["HI assay","HAI test","haemagglutination inhibition test","HI test"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Serology / virology diagnostics","year":"1942","originator":"George K. Hirst","url":"https://scholargate.app/en/veterinary-science/hemagglutination-inhibition","markdownUrl":"https://scholargate.app/en/veterinary-science/hemagglutination-inhibition.md","definition":"The Hemagglutination Inhibition (HI) Assay is a classical serological test used to detect and quantify antibodies against hemagglutinating viruses — most notably influenza and Newcastle disease virus — in animal and human serum. Widely employed in veterinary diagnostics, vaccine efficacy evaluation, and epidemiological surveillance, it relies on the principle that specific antibodies in serum will block a known quantity of virus from agglutinating red blood cells.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George K. Hirst","year":"1942","type":"Serological diagnostic assay","dataType":"Serum antibody titers (numeric, ordinal dilution series)","subfamily":"Serology / virology diagnostics"},"citations":[{"ref":"Hirst, G. K. (1942). The quantitative determination of influenza virus and antibodies by means of red cell agglutination. Journal of Experimental Medicine, 75(1), 49–64.","type":"journal-article","doi":"10.1084/jem.75.1.49","isbn":null,"url":null},{"ref":"World Organisation for Animal Health (WOAH/OIE). (2021). Manual of Diagnostic Tests and Vaccines for Terrestrial Animals, Chapter 3.3.4 (Avian Influenza). WOAH.","type":"manual","doi":null,"isbn":null,"url":"https://www.woah.org/en/what-we-do/standards/codes-and-manuals/terrestrial-manual-online-access/"}],"related":["hemagglutination-assay","virus-neutralization-test","elisa","serum-neutralization","agar-gel-immunodiffusion","microneutralization-assay"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hemolysis-assay","name":"Hemolysis Assay","fullName":"Hemolysis Assay Red Blood Cell Toxicity Evaluation","aliases":["RBC lysis assay","hemolytic compatibility test","hemolytic potential test"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Blood compatibility testing","year":"1950","originator":"Clinical hematology traditions","url":"https://scholargate.app/en/biomaterials/hemolysis-assay","markdownUrl":"https://scholargate.app/en/biomaterials/hemolysis-assay.md","definition":"The hemolysis assay is a standard method for evaluating the blood compatibility of biomaterials by quantifying the extent to which a material or substance damages red blood cells (RBCs) and causes hemoglobin release. Codified in standards including ASTM F756 and ISO 10993-4, the hemolysis assay is essential for regulatory approval of blood-contacting devices such as stents, catheters, artificial heart valves, and hemodialysis membranes. The assay provides a simple, quantitative measure of hemolytic potential that correlates with clinical safety.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clinical hematology traditions","subfamily":"Blood compatibility testing","year":"1950","type":"Hemolytic compatibility assay"},"citations":[{"ref":"ASTM F756-17 (2017). Standard Practice for Assessment of Hemolytic Properties of Materials. ASTM International.","type":"standard","doi":null,"isbn":null,"url":"https://www.astm.org/Standards/F756.htm"},{"ref":"ISO 10993-4:2017. Biological Evaluation of Medical Devices. Part 4: Selection of tests for interactions with blood.","type":"standard","doi":null,"isbn":null,"url":"https://www.iso.org/standard/68936.html"},{"ref":"Tomos, M. S., Morrison, S., & Williams, D. F. (2012). Comparison of hemolytic methods for assessing the blood compatibility of biomaterials. Journal of Biomedical Materials Research, 105(7), 1926-1933.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparison+of+hemolytic+methods+for+assessing+the+blood+compatibility+of+biomaterials+Tomos"}],"related":["mtt-mts-assay","live-dead-assay","cam-assay","electrospinning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hep-track-reconstruction","name":"HEP Track Reconstruction","fullName":"High-Energy Physics Track Reconstruction","aliases":["tracking","charged particle reconstruction","trajectory fitting"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Reconstruction algorithm","year":"1987","originator":"Charged particle physics community","url":"https://scholargate.app/en/particle-physics/hep-track-reconstruction","markdownUrl":"https://scholargate.app/en/particle-physics/hep-track-reconstruction.md","definition":"Track reconstruction is the process of identifying and measuring the trajectories of charged particles through a detector, providing momentum and impact parameter information essential for particle identification, vertex reconstruction, and physics analysis in high-energy physics experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charged particle physics community","subfamily":"Reconstruction algorithm","year":"1987","type":"Pattern recognition method"},"citations":[{"ref":"Fruhwirth, R. (1987). Application of Kalman filtering to track and vertex fitting. Nuclear Instruments and Methods in Physics Research Section A, 262(2-3), 444–450.","type":"article","doi":"10.1016/0168-9002(87)90887-4","isbn":null,"url":null},{"ref":"Mankel, R. (2006). Pattern recognition and reconstruction. Nuclear Instruments and Methods in Physics Research Section A, 559(1), 88–91.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pattern+recognition+and+reconstruction+Mankel"},{"ref":"Aad, G., et al. (ATLAS Collaboration). (2010). The ATLAS inner detector commissioning. European Physical Journal C, 70(3), 787–821.","type":"article","doi":"10.1140/epjc/s10052-010-1366-7","isbn":null,"url":null}],"related":["calorimeter-calibration","anti-kt-jet-algorithm","bdt-particle-identification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hepatic-encephalopathy-grade","name":"West Haven Criteria for Hepatic Encephalopathy","fullName":"West Haven Criteria for Hepatic Encephalopathy Grading","aliases":["Hepatic Encephalopathy Grading","HE Grade","West Haven Grade"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"liver-disease-complication","year":"1966 (original), 1978 (formalized)","originator":"West Haven Group (Parsons, Williams, Sherlock, Trey, Davidson)","url":"https://scholargate.app/en/gastroenterology/hepatic-encephalopathy-grade","markdownUrl":"https://scholargate.app/en/gastroenterology/hepatic-encephalopathy-grade.md","definition":"The West Haven Criteria are the standard for grading hepatic encephalopathy (HE) severity, ranging from subclinical (Grade 0) to deep coma (Grade 4). Developed by Trey and Davidson in the 1960s and refined by the West Haven group, these criteria integrate mental status changes (confusion, asterixis, disorientation) and consciousness level to stage HE. The West Haven grade is a strong predictor of short-term prognosis in cirrhosis and guides urgency of intervention (lactulose, rifaxomicin, mannitol, intubation).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"West Haven Group (Parsons, Williams, Sherlock, Trey, Davidson)","subfamily":"liver-disease-complication","year":"1966 (original), 1978 (formalized)","type":"Clinician-rated"},"citations":[{"ref":"Parsons, P. L., Williams, R., & Sherlock, S. (1978). The role of plasma amino acids in hepatic encephalopathy and the effect of branched-chain amino acid infusion. Gut, 19(10), 969–978.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+role+of+plasma+amino+acids+in+hepatic+encephalopathy+and+the+effect+of+branched-chain+amino+acid+infusion+Parsons"},{"ref":"Trey, C., & Davidson, C. S. (1966). The management of fulminant hepatic failure. Progress in Liver Diseases, 2, 282–298.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/"}],"related":["child-pugh-score","gcsi","mayo-score-uc","harvey-bradshaw-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"herd-reproductive-performance","name":"Herd Reproductive Performance","fullName":"Herd Reproductive Performance Assessment and Management","aliases":["fertility monitoring","reproductive efficiency evaluation","breeding performance analysis"],"domain":"animal-science","family":"process-pipeline","subfamily":"Reproductive performance evaluation","year":"1990s","originator":"Dairy Veterinarians and Herd Health Specialists","url":"https://scholargate.app/en/animal-science/herd-reproductive-performance","markdownUrl":"https://scholargate.app/en/animal-science/herd-reproductive-performance.md","definition":"Herd reproductive performance assessment integrates multiple metrics to evaluate the efficiency of breeding programs and overall population fertility. Formalized in the 1990s-2000s by dairy veterinarians and herd health specialists, the method combines individual animal records (conception rates, calving intervals) with population-level indicators (age structure, open days, pregnancy rate) to identify reproductive constraints. Assessment is fundamental to dairy profitability and sustainability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dairy Veterinarians and Herd Health Specialists","subfamily":"Reproductive performance evaluation","year":"1990s","type":"data analysis and performance assessment"},"citations":[{"ref":"Macmillan, K. L. (2002). Fertility and production in grazing dairy cattle. Veterinary Record, 150(9), 267-273.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fertility+and+production+in+grazing+dairy+cattle+Macmillan"},{"ref":"Buckley, F., O'Sullivan, K., Mee, J. F., Evans, R. D., & Berry, D. P. (2003). Relationships among milk yield, body condition, cow welfare, and reproductive performance in spring-calving Irish dairy cows. Journal of Dairy Science, 86(7), 2308-2319.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Relationships+among+milk+yield%2C+body+condition%2C+cow+welfare%2C+and+reproductive+performance+in+spring-calving+Irish+dairy+cows+Buckley"},{"ref":"Darwash, A. O., Lamming, G. E., & Woolliams, J. A. (2000). The fertility of dairy cattle in the United Kingdom as affected by calving season and service period fertility on subsequent performance. Journal of Dairy Science, 79(3), 453-463.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+fertility+of+dairy+cattle+in+the+United+Kingdom+as+affected+by+calving+season+and+service+period+fertility+on+subsequent+performance+Darwash"}],"related":["milk-yield-recording","estrus-detection","body-condition-score-cattle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hermeneutic-analysis","name":"Hermeneutic Analysis","fullName":"Hermeneutic Analysis","aliases":["hermeneutics","hermeneutical interpretation","interpretive hermeneutics","philosophical hermeneutics"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"19th–20th century (Schleiermacher ~1819; Dilthey ~1883; Gadamer 1960; Ricoeur 1969)","originator":"Friedrich Schleiermacher; Wilhelm Dilthey; Hans-Georg Gadamer; Paul Ricoeur","url":"https://scholargate.app/en/field-methods/hermeneutic-analysis","markdownUrl":"https://scholargate.app/en/field-methods/hermeneutic-analysis.md","definition":"Hermeneutic analysis is a qualitative interpretive method for uncovering the meaning of texts, documents, spoken discourse, or human actions. Rooted in 19th-century biblical and legal scholarship and systematised by Schleiermacher, Dilthey, Gadamer, and Ricoeur, it operates through the hermeneutic circle: the meaning of a part is understood through the whole, and the meaning of the whole is revised as parts are interpreted. The goal is not to measure or code, but to achieve a deepening, dialogic understanding of the object of interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Friedrich Schleiermacher; Wilhelm Dilthey; Hans-Georg Gadamer; Paul Ricoeur","year":"19th–20th century (Schleiermacher ~1819; Dilthey ~1883; Gadamer 1960; Ricoeur 1969)","type":"Qualitative interpretive method","dataType":"Texts, documents, interviews, historical records","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Gadamer, H.-G. (1975). Truth and Method (G. Barden & J. Cumming, Trans.). Seabury Press. (Original work published 1960 as Wahrheit und Methode).","type":"book","doi":null,"isbn":"978-0826400185","url":null},{"ref":"Ricoeur, P. (1981). Hermeneutics and the Human Sciences: Essays on Language, Action and Interpretation (J. B. Thompson, Ed. & Trans.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521280167","url":null}],"related":["phenomenology","textual-criticism","discourse-analysis","narrative-analysis","critical-discourse-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hermeneutic-phenomenology-in-education-research","name":"Hermeneutic phenomenology in education research","fullName":"Hermeneutic Phenomenological Research in Education","aliases":["van Manen phenomenology","pedagogical hermeneutics","lived-experience inquiry in education","human science pedagogy"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990","originator":"Max van Manen","url":"https://scholargate.app/en/qualitative/hermeneutic-phenomenology-in-education-research","markdownUrl":"https://scholargate.app/en/qualitative/hermeneutic-phenomenology-in-education-research.md","definition":"Hermeneutic phenomenology in education research is a qualitative approach — developed principally by Max van Manen — that investigates the lived, meaning-laden dimensions of educational experience. Drawing on Heidegger's interpretive philosophy and Gadamer's hermeneutics, it asks what it is like, from the inside, to be a teacher, a learner, or a student navigating a formative moment, and renders that understanding through carefully crafted, evocative writing rather than through codes or statistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max van Manen","year":"1990","type":"Qualitative interpretive research approach","dataType":"In-depth interviews, field texts, anecdotes, journal entries, observations","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null},{"ref":"van Manen, M. (2016). Phenomenology of Practice: Meaning-Giving Methods in Phenomenological Research and Writing. Routledge.","type":"book","doi":null,"isbn":"978-1629581040","url":null}],"related":["phenomenology","interpretive-phenomenological-analysis","hermeneutics","narrative-inquiry","case-study","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hermeneutic-phenomenology","name":"Hermeneutic Phenomenology","fullName":"Hermeneutic (Heideggerian) Phenomenology","aliases":["Heideggerian phenomenology","interpretive phenomenology","hermeneutic inquiry","van Manen phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Phenomenology","year":"Philosophical roots 1927 (Heidegger); systematic research method from 1980s–1990s","originator":"Martin Heidegger (philosophical foundation); Max van Manen (methodological application)","url":"https://scholargate.app/en/qualitative/hermeneutic-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/hermeneutic-phenomenology.md","definition":"Hermeneutic phenomenology is a qualitative research approach that investigates the interpreted meaning of lived experience from within the existential conditions that shape it. Rooted in Heidegger's ontology and developed as an empirical method by Max van Manen, it does not seek to bracket or suspend the researcher's understanding but instead treats that understanding as the very medium through which the meaning of experience can be disclosed. The approach is widely used in education, nursing, and social sciences to explore how people dwell in, and make sense of, their world.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin Heidegger (philosophical foundation); Max van Manen (methodological application)","year":"Philosophical roots 1927 (Heidegger); systematic research method from 1980s–1990s","type":"Qualitative research method","dataType":"In-depth interviews, field observations, written narratives, diaries, artefacts","typicalSampleSize":"6–20 participants","subfamily":"Phenomenology"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null},{"ref":"Heidegger, M. (1962). Being and Time (J. Macquarrie & E. Robinson, Trans.). Harper & Row. (Original work published 1927).","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Heidegger+Being+and+Time+1962"}],"related":["phenomenology","grounded-theory","narrative-analysis","ethnography","discourse-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heronian-mean","name":"HERONIAN-MEAN","fullName":"Heronian Mean (HM)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Aggregation","year":"2012","originator":"Yu, D.","url":"https://scholargate.app/en/decision-making/heronian-mean","markdownUrl":"https://scholargate.app/en/decision-making/heronian-mean.md","definition":"HERONIAN-MEAN (Heronian Mean (HM)) is a aggregation multi-criteria decision-making (MCDM) method introduced by Yu, D. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yu, D.","subfamily":"Aggregation","year":"2012","type":"Interrelationship-based aggregation — geometric pairwise interactions","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Yu, D. (2012). Intuitionistic fuzzy geometric Heronian mean aggregation operators. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2012.09.021","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-causal-impact-analysis","name":"Heterogeneous treatment effect Causal impact analysis","fullName":"Heterogeneous Treatment Effect Causal Impact Analysis","aliases":["HTE-CausalImpact","CATE causal impact","heterogeneous causal impact","subgroup causal impact analysis"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015-2016","originator":"Brodersen et al. (causal impact framework, 2015); Athey & Imbens (HTE estimation, 2016)","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-causal-impact-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-causal-impact-analysis.md","definition":"Heterogeneous treatment effect causal impact analysis extends the Bayesian structural time-series causal impact framework to estimate not just the average effect of an intervention but how that effect varies across subgroups or individual units. By combining counterfactual prediction with conditional average treatment effect (CATE) estimation, it reveals which groups benefit most or least from an intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen et al. (causal impact framework, 2015); Athey & Imbens (HTE estimation, 2016)","year":"2015-2016","type":"Causal inference / heterogeneous effects estimation","dataType":"Time series, panel, or observational data with subgroup identifiers","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.","type":"article","doi":"10.1073/pnas.1510489113","isbn":null,"url":null}],"related":["causal-impact-analysis","heterogeneous-treatment-effect-difference-in-differences","synthetic-control-method","interrupted-time-series","propensity-score-matching","dynamic-causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-coarsened-exact-matching","name":"Heterogeneous Treatment Effect Coarsened Exact Matching","fullName":"Heterogeneous Treatment Effect Estimation via Coarsened Exact Matching","aliases":["HTE-CEM","CEM with CATE estimation","subgroup CEM","coarsened exact matching with effect heterogeneity"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012-2013","originator":"Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-coarsened-exact-matching","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-coarsened-exact-matching.md","definition":"Heterogeneous treatment effect coarsened exact matching (HTE-CEM) extends the coarsened exact matching framework to estimate how treatment effects vary across subgroups or individual characteristics. After CEM creates balanced strata by coarsening continuous covariates into bins and exactly matching units within each bin, conditional average treatment effects (CATEs) are computed within or across these strata, revealing where treatment works, for whom, and by how much.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues","year":"2012-2013","type":"Matching-based causal inference with subgroup CATE estimation","dataType":"Cross-sectional or panel; mixed continuous and categorical covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Imai, K., & Ratkovic, M. (2013). Estimating treatment effect heterogeneity in randomized program evaluation. Annals of Applied Statistics, 7(1), 443-470.","type":"article","doi":"10.1214/12-AOAS593","isbn":null,"url":null}],"related":["coarsened-exact-matching","propensity-score-matching","heterogeneous-treatment-effect-propensity-score-matching","entropy-balancing","causal-forest","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-counterfactual-impact-evaluation","name":"Heterogeneous treatment effect Counterfactual impact evaluation","fullName":"Heterogeneous Treatment Effect Counterfactual Impact Evaluation","aliases":["HTE-CIE","heterogeneous CIE","CATE-based counterfactual evaluation","subgroup counterfactual impact evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s","originator":"Cerulli (2010) for CIE framework; Athey & Wager (2019) for causal forest-based CATE within CIE","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-counterfactual-impact-evaluation","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-counterfactual-impact-evaluation.md","definition":"Heterogeneous Treatment Effect Counterfactual Impact Evaluation (HTE-CIE) extends standard counterfactual impact evaluation by estimating how the causal effect of a policy or intervention varies across subgroups defined by pre-treatment characteristics. Rather than reporting a single average treatment effect, it maps the Conditional Average Treatment Effect (CATE) across the covariate space, revealing who benefits most or least from an intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cerulli (2010) for CIE framework; Athey & Wager (2019) for causal forest-based CATE within CIE","year":"2010s","type":"Quasi-experimental causal inference with subgroup heterogeneity","dataType":"Cross-sectional or panel data with treatment indicator and moderating covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Cerulli, G. (2010). Modelling and measuring the effect of public subsidies on business R&D: A critical review of the econometric literature. Economic Record, 86(274), 421-449.","type":"article","doi":"10.1111/j.1475-4932.2009.00615.x","isbn":null,"url":null},{"ref":"Athey, S., & Wager, S. (2019). Estimating treatment effects with causal forests: An application. Observational Studies, 5(2), 37-51.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimating+treatment+effects+with+causal+forests+Athey+Wager+2019"}],"related":["counterfactual-impact-evaluation","causal-forest","heterogeneous-treatment-effect-difference-in-differences","propensity-score-matching","regression-discontinuity-design","marginal-structural-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-difference-in-differences","name":"Heterogeneous Treatment Effect Difference-in-Differences","fullName":"Heterogeneous Treatment Effect Difference-in-Differences Estimator","aliases":["HTE-DiD","heterogeneous DiD","CATT estimator","group-time ATT"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Callaway & Sant'Anna; Sun & Abraham","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-difference-in-differences","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-difference-in-differences.md","definition":"HTE-DiD extends the classic Difference-in-Differences estimator to settings where treatment effects vary across units, time periods, or treatment cohorts. Developed formally by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it avoids the biases that arise when a conventional two-way fixed-effects regression is used with staggered adoption or effect heterogeneity, by estimating cohort-and-time-specific average treatment effects that can then be aggregated flexibly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Callaway & Sant'Anna; Sun & Abraham","year":"2021","type":"Causal inference / panel regression","dataType":"Panel or repeated cross-sections with staggered or simultaneous treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null},{"ref":"Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.","type":"article","doi":"10.1016/j.jeconom.2020.09.006","isbn":null,"url":null}],"related":["difference-in-differences","dynamic-difference-in-differences","panel-data-difference-in-differences","staggered-difference-in-differences","event-study-design","synthetic-control-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-doubly-robust-estimation","name":"Heterogeneous treatment effect Doubly robust estimation","fullName":"Doubly Robust Estimation of Heterogeneous Treatment Effects","aliases":["DR-HTE","augmented IPW for HTE","doubly robust CATE estimation","semiparametric HTE estimation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2018-2023","originator":"Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018)","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-doubly-robust-estimation","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-doubly-robust-estimation.md","definition":"Doubly robust estimation of heterogeneous treatment effects (HTE) estimates how the causal effect of a treatment varies across subgroups or individual covariate values. By combining an outcome model and a propensity score model, it retains consistency if either model is correctly specified, and supports flexible machine learning nuisance estimators through cross-fitting to produce valid conditional average treatment effect (CATE) estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018)","year":"2018-2023","type":"Semiparametric causal inference","dataType":"Observational cross-sectional or panel data with continuous or binary treatment and outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Kennedy, E. H. (2023). Towards optimal doubly robust estimation of heterogeneous causal effects. Electronic Journal of Statistics, 17(2), 3008-3049.","type":"article","doi":"10.1214/23-EJS2157","isbn":null,"url":null},{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null}],"related":["doubly-robust-estimation","propensity-score-weighting","inverse-probability-weighting","causal-forest","marginal-structural-model","machine-learning-augmented-doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-entropy-balancing","name":"Heterogeneous Treatment Effect Entropy Balancing","fullName":"Heterogeneous Treatment Effect Estimation with Entropy Balancing","aliases":["HTE entropy balancing","CATE with entropy balancing","heterogeneous effects EB","subgroup entropy balancing"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012-2016","originator":"Hainmueller (2012) for entropy balancing; Athey & Imbens (2016) for heterogeneous effect estimation","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-entropy-balancing","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-entropy-balancing.md","definition":"Heterogeneous Treatment Effect Entropy Balancing combines entropy balancing — a preprocessing step that reweights control units to match the treatment group on covariate moments — with methods that estimate how the treatment effect varies across subgroups or individuals. It produces covariate-balanced weights without parametric propensity models, then uses those weights to estimate conditional average treatment effects (CATEs) across moderating variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hainmueller (2012) for entropy balancing; Athey & Imbens (2016) for heterogeneous effect estimation","year":"2012-2016","type":"Causal inference / heterogeneous effect estimation","dataType":"Observational cross-sectional or panel data with a binary treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.","type":"article","doi":"10.1093/pan/mpr025","isbn":null,"url":null},{"ref":"Athey, S., & Imbens, G. W. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.","type":"article","doi":"10.1073/pnas.1510489113","isbn":null,"url":null}],"related":["entropy-balancing","propensity-score-weighting","heterogeneous-treatment-effect-propensity-score-matching","causal-forest","inverse-probability-weighting","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-event-study-design","name":"Heterogeneous Treatment Effect Event Study Design","fullName":"Heterogeneous Treatment Effect Event Study Design","aliases":["HTE event study","heterogeneous effects event study","group-time ATT event study","dynamic HTE design"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Sun & Abraham (2021); Callaway & Sant'Anna (2021)","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-event-study-design","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-event-study-design.md","definition":"Heterogeneous Treatment Effect Event Study Design is a causal-inference framework that uses event study regression to estimate how treatment effects vary across groups, cohorts, or time relative to a treatment event. Unlike classical two-way fixed-effects event studies — which assume a homogeneous effect — this approach explicitly models and recovers group-time average treatment effects (ATTs), addressing the contamination bias that arises when effects differ across treated units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sun & Abraham (2021); Callaway & Sant'Anna (2021)","year":"2021","type":"Quasi-experimental causal inference","dataType":"Panel data with staggered or simultaneous treatment timing","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.","type":"article","doi":"10.1016/j.jeconom.2020.09.006","isbn":null,"url":null},{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null}],"related":["event-study-design","difference-in-differences","dynamic-difference-in-differences","panel-event-study","panel-data-event-study-design","regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-fuzzy-regression-discontinuity","name":"Heterogeneous Treatment Effect Fuzzy Regression Discontinuity","fullName":"Heterogeneous Treatment Effect Estimation in Fuzzy Regression Discontinuity Design","aliases":["HTE-Fuzzy RDD","heterogeneous LATE at threshold","subgroup fuzzy RD","fuzzy RD with effect heterogeneity"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2001","originator":"Hahn, Todd & Van der Klaauw (2001); extensions by Calonico, Cattaneo & Titiunik (2014)","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-fuzzy-regression-discontinuity.md","definition":"Heterogeneous Treatment Effect Fuzzy RDD extends the standard fuzzy regression discontinuity design — where treatment probability, not treatment status itself, jumps at a threshold — by examining whether the Local Average Treatment Effect (LATE) estimated at the threshold differs systematically across subgroups defined by covariates such as gender, socioeconomic status, or prior ability. It combines the instrumental-variable logic of fuzzy RDD with structured heterogeneity analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hahn, Todd & Van der Klaauw (2001); extensions by Calonico, Cattaneo & Titiunik (2014)","year":"2001","type":"Quasi-experimental causal inference / heterogeneity analysis","dataType":"Observational panel or cross-sectional with a running variable and threshold","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Econometrica, 69(1), 201-209.","type":"article","doi":"10.1111/1468-0262.00183","isbn":null,"url":null},{"ref":"Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica, 82(6), 2295-2326.","type":"article","doi":"10.3982/ECTA11757","isbn":null,"url":null}],"related":["fuzzy-regression-discontinuity","regression-discontinuity-design","heterogeneous-treatment-effect-regression-discontinuity-design","instrumental-variables","local-average-treatment-effect","quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-instrumental-variables","name":"Heterogeneous treatment effect Instrumental variables","fullName":"Instrumental Variables Estimation with Heterogeneous Treatment Effects","aliases":["HTE-IV","LATE estimator","IV with effect heterogeneity","local average treatment effect IV"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1994","originator":"Imbens & Angrist","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-instrumental-variables","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-instrumental-variables.md","definition":"Heterogeneous treatment effect IV applies instrumental variables estimation while explicitly acknowledging and modelling that the treatment effect differs across units. Rather than recovering a single average effect, it focuses on the Local Average Treatment Effect (LATE) — the causal effect for compliers, the subpopulation whose treatment status is actually shifted by the instrument — and extends analysis to variation in that effect across observed subgroups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Imbens & Angrist","year":"1994","type":"Causal inference / IV with effect heterogeneity","dataType":"Cross-sectional or panel; continuous or binary outcome; binary or multi-valued instrument","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.","type":"article","doi":"10.2307/2951620","isbn":null,"url":null},{"ref":"Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691120355","url":null}],"related":["instrumental-variables","two-stage-least-squares","heterogeneous-treatment-effect-propensity-score-matching","local-average-treatment-effect","regression-discontinuity-design","marginal-treatment-effect"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-interrupted-time-series","name":"Heterogeneous Treatment Effect Interrupted Time Series","fullName":"Heterogeneous Treatment Effect Interrupted Time Series Analysis","aliases":["HTE-ITS","Subgroup ITS","Effect-modifier ITS","Segmented ITS with interaction"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Extensions of Shadish, Cook & Campbell (2002) ITS framework; HTE formulation developed by Lopez Bernal and colleagues","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-interrupted-time-series","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-interrupted-time-series.md","definition":"Heterogeneous Treatment Effect Interrupted Time Series extends the standard ITS design to detect whether an intervention's effect on a time series differs systematically across subgroups or in response to unit-level moderators. Where ordinary ITS yields a single level-change and slope-change estimate, HTE-ITS adds interaction terms for a moderating variable, revealing who benefits more or less from the intervention and by how much.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extensions of Shadish, Cook & Campbell (2002) ITS framework; HTE formulation developed by Lopez Bernal and colleagues","year":"2000s–2010s","type":"Quasi-experimental segmented regression with subgroup moderation","dataType":"Longitudinal / time-series, continuous or count outcomes with a defined intervention point","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.","type":"article","doi":"10.1093/ije/dyw098","isbn":null,"url":null},{"ref":"Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350, h2750.","type":"article","doi":"10.1136/bmj.h2750","isbn":null,"url":null}],"related":["interrupted-time-series","difference-in-differences","heterogeneous-treatment-effect-difference-in-differences","segmented-regression","panel-data-interrupted-time-series","event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-inverse-probability-weighting","name":"Heterogeneous Treatment Effect Inverse Probability Weighting","fullName":"Heterogeneous Treatment Effect Estimation via Inverse Probability Weighting","aliases":["HTE-IPW","CATE-IPW","heterogeneous IPW","conditional effect IPW"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2003–2015","originator":"Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-inverse-probability-weighting.md","definition":"HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli","year":"2003–2015","type":"Causal inference / weighted regression","dataType":"Observational panel or cross-sectional data with a binary or multi-valued treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.","type":"article","doi":"10.1111/1468-0262.00442","isbn":null,"url":null},{"ref":"Abrevaya, J., Hsu, Y.-C., & Lieli, R. P. (2015). Estimating conditional average treatment effects. Journal of Business and Economic Statistics, 33(4), 485-505.","type":"article","doi":"10.1080/07350015.2014.975555","isbn":null,"url":null}],"related":["propensity-score-weighting","inverse-probability-weighting","heterogeneous-treatment-effect-propensity-score-matching","doubly-robust-estimation","causal-forest","marginal-structural-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-marginal-structural-model","name":"Heterogeneous Treatment Effect Marginal Structural Model","fullName":"Heterogeneous Treatment Effect Marginal Structural Model","aliases":["HTE-MSM","heterogeneous MSM","subgroup MSM","effect-modified marginal structural model"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000–2010s","originator":"Robins, Hernan & Brumback (foundational MSM framework, 2000); heterogeneous-effect extensions developed throughout 2000s–2010s","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-marginal-structural-model","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-marginal-structural-model.md","definition":"The Heterogeneous Treatment Effect Marginal Structural Model extends the classic MSM framework of Robins, Hernan, and Brumback to estimate how treatment effects vary across subgroups or individual-level moderators. By weighting observations with inverse probability of treatment weights (IPTW) and interacting the treatment with effect modifiers in the weighted outcome model, the approach produces subgroup-specific or continuous causal effect estimates from observational data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Hernan & Brumback (foundational MSM framework, 2000); heterogeneous-effect extensions developed throughout 2000s–2010s","year":"2000–2010s","type":"Causal inference / weighted regression with effect modification","dataType":"Observational longitudinal or cross-sectional data with time-varying or static treatments and potential confounders","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"}],"related":["marginal-structural-model","inverse-probability-weighting","doubly-robust-estimation","propensity-score-weighting","causal-forests","heterogeneous-treatment-effect-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-matching-estimator","name":"Heterogeneous Treatment Effect Matching Estimator","fullName":"Heterogeneous Treatment Effect Matching Estimator","aliases":["HTE matching","subgroup matching estimator","conditional matching estimator","CATE matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1997-2006","originator":"Heckman, Ichimura & Todd; Abadie & Imbens","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-matching-estimator.md","definition":"The Heterogeneous Treatment Effect (HTE) Matching Estimator extends standard matching to recover how treatment impacts differ across subgroups or covariate values. Rather than reporting a single average treatment effect, it pairs treated and control units on observed characteristics and then estimates the conditional average treatment effect (CATE) as a function of those characteristics — revealing who benefits most, least, or not at all.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heckman, Ichimura & Todd; Abadie & Imbens","year":"1997-2006","type":"Causal inference / nonparametric matching","dataType":"Observational cross-sectional or panel; continuous or binary outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme. Review of Economic Studies, 64(4), 605-654.","type":"article","doi":"10.2307/2971733","isbn":null,"url":null},{"ref":"Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.","type":"article","doi":"10.1111/j.1468-0262.2006.00655.x","isbn":null,"url":null}],"related":["propensity-score-matching","coarsened-exact-matching","matching-estimator","heterogeneous-treatment-effect-difference-in-differences","doubly-robust-estimation","entropy-balancing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-panel-event-study","name":"Heterogeneous treatment effect Panel event study","fullName":"Heterogeneous Treatment Effect Panel Event Study","aliases":["HTE panel event study","heterogeneous effects event study","staggered panel event study","CATT event study"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Sun & Abraham; Callaway & Sant'Anna","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-panel-event-study","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-panel-event-study.md","definition":"A heterogeneous treatment effect panel event study estimates how treatment impacts vary across units and over time in a panel setting, allowing each cohort of treated units to have its own dynamic response. Seminal contributions by Sun and Abraham (2021) and Callaway and Sant'Anna (2021) showed that standard two-way fixed-effects event studies mask sign-reversing treatment heterogeneity across cohorts, motivating cohort-specific estimation followed by flexible aggregation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sun & Abraham; Callaway & Sant'Anna","year":"2021","type":"Causal inference / quasi-experimental","dataType":"Panel data with multiple pre- and post-treatment periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.","type":"article","doi":"10.1016/j.jeconom.2020.09.006","isbn":null,"url":null},{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null}],"related":["event-study-design","panel-event-study","dynamic-difference-in-differences","difference-in-differences","panel-data-event-study-design","heterogeneous-treatment-effect-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-placebo-test","name":"Heterogeneous treatment effect Placebo test","fullName":"Placebo Test for Heterogeneous Treatment Effects","aliases":["HTE placebo test","heterogeneous-effect placebo check","subgroup placebo test","CATE placebo validation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Rosenbaum (placebo test concept); Athey & Imbens (HTE estimation framework)","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-placebo-test","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-placebo-test.md","definition":"A placebo test for heterogeneous treatment effects is a falsification strategy used to validate whether estimated variation in treatment effects across subgroups or covariate values is genuine rather than an artifact of model specification, overfitting, or coincidental patterns. By applying the same estimation procedure to pseudo-treatments, fake outcomes, or subgroups that logically should not differ, researchers check that observed heterogeneity reflects real causal variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenbaum (placebo test concept); Athey & Imbens (HTE estimation framework)","year":"2000s–2010s","type":"Validation / falsification test","dataType":"Panel, cross-sectional, or experimental data with subgroup or covariate information","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521885881","url":null},{"ref":"Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.","type":"article","doi":"10.1073/pnas.1510489113","isbn":null,"url":null}],"related":["placebo-test","heterogeneous-treatment-effect-difference-in-differences","difference-in-differences","regression-discontinuity-design","causal-forest","sensitivity-analysis-for-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-propensity-score-matching","name":"Heterogeneous Treatment Effect Propensity Score Matching","fullName":"Heterogeneous Treatment Effect Estimation via Propensity Score Matching","aliases":["HTE-PSM","CATE via PSM","subgroup treatment effect matching","conditional average treatment effect matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1983–2016","originator":"Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016)","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-propensity-score-matching","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-propensity-score-matching.md","definition":"Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016)","year":"1983–2016","type":"Causal inference / matching with effect heterogeneity","dataType":"Observational cross-sectional or panel data with binary treatment and covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Athey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.","type":"article","doi":"10.1073/pnas.1510489113","isbn":null,"url":null},{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70(1), 41-55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null}],"related":["propensity-score-matching","causal-forest","difference-in-differences","matching-estimator","doubly-robust-estimation","heterogeneous-treatment-effect-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-regression-discontinuity-design","name":"Heterogeneous Treatment Effect Regression Discontinuity Design","fullName":"Heterogeneous Treatment Effect Regression Discontinuity Design","aliases":["HTE-RDD","heterogeneous RDD","subgroup RDD","effect heterogeneity RD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015","originator":"Dong & Lewbel (2015); Chiang, Hsu & Sasaki (2019)","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-regression-discontinuity-design","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-regression-discontinuity-design.md","definition":"Heterogeneous Treatment Effect RDD extends the classic regression discontinuity framework to detect and estimate how the causal effect of crossing an assignment cutoff varies across subgroups or along covariates. Rather than reporting a single local average treatment effect at the threshold, HTE-RDD maps how treatment impact differs by individual characteristics, enabling richer policy conclusions about who benefits most or least from a threshold-based intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dong & Lewbel (2015); Chiang, Hsu & Sasaki (2019)","year":"2015","type":"Quasi-experimental causal inference with effect heterogeneity","dataType":"Cross-sectional or panel data with a continuous running variable and a cutoff","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Dong, Y., & Lewbel, A. (2015). Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models. Review of Economics and Statistics, 97(5), 1081-1092.","type":"article","doi":"10.1162/REST_a_00510","isbn":null,"url":null},{"ref":"Chiang, H. D., Hsu, Y.-C., & Sasaki, Y. (2019). Causal Inference by Quantile Regression Kink Designs. Journal of Econometrics, 210(2), 405-433.","type":"article","doi":"10.1016/j.jeconom.2019.02.005","isbn":null,"url":null}],"related":["regression-discontinuity-design","fuzzy-regression-discontinuity","heterogeneous-treatment-effect-difference-in-differences","quantile-regression","local-average-treatment-effect","conditional-average-treatment-effect"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-sensitivity-analysis-for-causality","name":"Heterogeneous Treatment Effect Sensitivity Analysis for Causality","fullName":"Sensitivity Analysis for Causality under Heterogeneous Treatment Effects","aliases":["HTE sensitivity analysis","heterogeneous-effects sensitivity analysis","sensitivity analysis with effect heterogeneity","HTE robustness analysis"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Rosenbaum (sensitivity analysis framework); extended to heterogeneous effects by Crump, Imbens, and others","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-sensitivity-analysis-for-causality","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-sensitivity-analysis-for-causality.md","definition":"Heterogeneous Treatment Effect Sensitivity Analysis examines how robust subgroup-specific causal estimates are to unobserved confounding. Rather than testing a single average treatment effect, it asks whether the estimated variation in treatment effects across units or subgroups could be explained away by hidden bias, and at what level of hidden bias the causal conclusions for each subgroup would break down.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenbaum (sensitivity analysis framework); extended to heterogeneous effects by Crump, Imbens, and others","year":"2000s–2010s","type":"Robustness / sensitivity check","dataType":"Observational data with treatment and outcome variables; panel or cross-section","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387989679","url":null},{"ref":"Crump, R. K., Hotz, V. J., Imbens, G. W., & Mitnik, O. A. (2008). Nonparametric tests for treatment effect heterogeneity. Review of Economics and Statistics, 90(3), 389-405.","type":"article","doi":"10.1162/rest.90.3.389","isbn":null,"url":null}],"related":["sensitivity-analysis-for-causality","heterogeneous-treatment-effect-difference-in-differences","propensity-score-matching","difference-in-differences","regression-discontinuity-design","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effect-synthetic-control-method","name":"Heterogeneous Treatment Effect Synthetic Control Method","fullName":"Heterogeneous Treatment Effect Synthetic Control Method","aliases":["HTE-SCM","heterogeneous SCM","heterogeneous synthetic control","SCM with HTE"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010-2021","originator":"Abadie, Diamond & Hainmueller (SCM foundation); Ben-Michael, Feller & Rothstein (augmented/HTE extensions)","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-synthetic-control-method.md","definition":"The Heterogeneous Treatment Effect Synthetic Control Method (HTE-SCM) extends the classical synthetic control framework by allowing the causal effect of an intervention to vary across time periods, subgroups, or outcome dimensions rather than collapsing it to a single average estimate. It combines the counterfactual donor-pool matching logic of Abadie et al. (2010) with modern heterogeneous-effects machinery to recover time-varying or subgroup-specific treatment paths.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie, Diamond & Hainmueller (SCM foundation); Ben-Michael, Feller & Rothstein (augmented/HTE extensions)","year":"2010-2021","type":"Quasi-experimental causal inference","dataType":"Aggregate time-series panel (donor pool + treated unit)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null},{"ref":"Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The Augmented Synthetic Control Method. Journal of the American Statistical Association, 116(536), 1789-1803.","type":"article","doi":"10.1080/01621459.2021.1929245","isbn":null,"url":null}],"related":["synthetic-control-method","difference-in-differences","heterogeneous-treatment-effect-difference-in-differences","panel-data-synthetic-control-method","matching-estimator","causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heterogeneous-treatment-effects","name":"Heterogeneous Treatment Effects","fullName":"Heterogeneous Treatment Effects (CATE / Meta-Learners)","aliases":["conditional average treatment effect","CATE","meta-learners","causal forest","X-Learner","T-Learner","S-Learner","R-Learner","Heterojen Tedavi Etkileri (CATE / Meta-Learners)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2018,"originator":"Wager & Athey (causal forest); Künzel et al. (meta-learners)","url":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effects","markdownUrl":"https://scholargate.app/en/causal-inference/heterogeneous-treatment-effects.md","definition":"Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-Learner, X-Learner and R-Learner alongside the causal forest of Wager and Athey (2018) and Künzel et al. (2019).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wager & Athey (causal forest); Künzel et al. (meta-learners)","year":2018,"type":"Causal machine-learning framework","estimator":"Meta-learners (T/S/X/R) and causal forest with cross-fitting","outcome":"continuous, binary, or categorical","minSample":200},"citations":[{"ref":"Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.","type":"article","doi":"10.1080/01621459.2017.1319839","isbn":null,"url":null},{"ref":"Künzel, S. R., Sekhon, J. S., Bickel, P. J. & Yu, B. (2019). Metalearners for Estimating Heterogeneous Treatment Effects using Machine Learning. Proceedings of the National Academy of Sciences (PNAS).","type":"article","doi":"10.1073/pnas.1804597116","isbn":null,"url":null}],"related":["propensity-score-matching","iv-2sls","causal-discovery","frontdoor-adjustment","regression-discontinuity"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heteroscedasticity-robust-se","name":"Heteroscedasticity-Robust Standard Errors","fullName":"Heteroscedasticity-Consistent (HC) Standard Errors","aliases":["robust standard errors","White standard errors","Huber-Eicker-White standard errors","sandwich standard errors","HC0","HC1","HC2","HC3","HC4","Heterokedastisiteye Robust Standart Hatalar (HC)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1980,"originator":"Eicker; Huber; White (1980); MacKinnon & White (1985)","url":"https://scholargate.app/en/statistics/heteroscedasticity-robust-se","markdownUrl":"https://scholargate.app/en/statistics/heteroscedasticity-robust-se.md","definition":"Heteroscedasticity-robust standard errors are a correction to the covariance matrix of an OLS regression that yields valid inference when the error variance is not constant. Introduced by Halbert White in 1980 and refined into the finite-sample variants HC1-HC4 by MacKinnon and White in 1985, they leave the coefficient estimates unchanged but rebuild the standard errors so that t and F tests remain trustworthy under heteroscedasticity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eicker; Huber; White (1980); MacKinnon & White (1985)","year":1980,"type":"Robust covariance estimator for linear regression","estimator":"Sandwich (Huber-Eicker-White) covariance matrix, HC0-HC4","outcome":"continuous"},"citations":[{"ref":"White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.","type":"article","doi":"10.2307/1912934","isbn":null,"url":null},{"ref":"MacKinnon, J. G. & White, H. (1985). Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties. Journal of Econometrics, 29(3), 305-325.","type":"article","doi":"10.1016/0304-4076(85)90158-7","isbn":null,"url":null}],"related":["ols-regression","cluster-robust-se","wild-bootstrap","quantile-regression","weighted-least-squares"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"heuristic-evaluation","name":"Heuristic Evaluation","fullName":"Heuristic Evaluation of User Interfaces","aliases":["HE","Expert Evaluation","Nielsen's Heuristics"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Inspection Method","year":"1990","originator":"Jakob Nielsen and Rolf Molich","url":"https://scholargate.app/en/human-computer-interaction/heuristic-evaluation","markdownUrl":"https://scholargate.app/en/human-computer-interaction/heuristic-evaluation.md","definition":"Heuristic Evaluation is a usability inspection method in which small teams of expert evaluators examine an interface and judge its compliance with established usability principles (heuristics). Developed by Jakob Nielsen and Rolf Molich in 1990, this method is rapid and low-cost, identifying 60–90% of usability problems with as few as 3–5 evaluators. Nielsen's Ten Usability Heuristics—visibility of system status, match between system and real world, user control and freedom, consistency and standards, error prevention and recovery, recognition over recall, flexibility and efficiency, aesthetic and minimalist design, error recovery, and documentation—form the basis of most evaluations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jakob Nielsen and Rolf Molich","subfamily":"Inspection Method","year":"1990","type":"Expert-based inspection using established design principles"},"citations":[{"ref":"Nielsen, J. (1994). Heuristic evaluation of user interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 249–256).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Heuristic+evaluation+of+user+interfaces+Nielsen"},{"ref":"Nielsen, J., & Molich, R. (1990). Heuristic evaluation of user interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 249–256).","type":"article","doi":"10.1145/97243.97281","isbn":null,"url":null}],"related":["cognitive-walkthrough","pluralistic-walkthrough","think-aloud-protocol","system-usability-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-ahp","name":"HF-AHP","fullName":"Hesitant Fuzzy AHP (AHP-Hesitant Group Decision Making via HMPM)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2014","originator":"Zhu, B., Xu, Z.","url":"https://scholargate.app/en/decision-making/hf-ahp","markdownUrl":"https://scholargate.app/en/decision-making/hf-ahp.md","definition":"HF-AHP (Hesitant Fuzzy AHP (AHP-Hesitant Group Decision Making via HMPM)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Zhu, B., Xu, Z. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhu, B., Xu, Z.","subfamily":"Weight_Subjective","year":"2014","type":"Hesitant multiplicative pairwise comparison (HMPR) — linear-programming prioritisation (HMPM)","value_space":"hesitant","uncertainty":"epistemic","compensation":"n_a","rank_reversal":true},"citations":[{"ref":"Zhu, B., Xu, Z. (2014). Analytic hierarchy process-hesitant group decision making. European Journal of Operational Research","type":"article","doi":"10.1016/j.ejor.2014.06.019","isbn":null,"url":null}],"related":["ahpsort","aras","cobra","cocoso","codas","copras","edas","gra"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-aras","name":"HF-ARAS","fullName":"Hesitant Fuzzy Additive Ratio Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Mishra, A. R., Rani, P., Krishankumar, R., Ravichandran, K. S., Kar, S.","url":"https://scholargate.app/en/decision-making/hf-aras","markdownUrl":"https://scholargate.app/en/decision-making/hf-aras.md","definition":"HF-ARAS (Hesitant Fuzzy Additive Ratio Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Mishra, A. R., Rani, P., Krishankumar, R., Ravichandran, K. S., Kar, S. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mishra, A. R., Rani, P., Krishankumar, R., Ravichandran, K. S., Kar, S.","subfamily":"Ranking","year":"2021","type":"Hesitant fuzzy utility-degree ranker (P_i / P_0 against ideal alternative row)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Mishra, A. R., Rani, P., Krishankumar, R., Ravichandran, K. S., Kar, S. (2021). A multi-criteria framework for evaluating the sustainable drug selection for COVID-19 patients using hesitant fuzzy information and ARAS method. Applied Soft Computing Journal","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+multi-criteria+framework+for+evaluating+the+sustainable+drug+selection+for+COVID-19+patients+using+hesitant+fuzzy+information+and+ARAS+method+Mishra"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-codas","name":"HF-CODAS","fullName":"Hesitant extension of HF-CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Torra, V.","url":"https://scholargate.app/en/decision-making/hf-codas","markdownUrl":"https://scholargate.app/en/decision-making/hf-codas.md","definition":"HF-CODAS (Hesitant extension of HF-CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Torra, V. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Torra, V.","subfamily":"Ranking","year":"2010","type":"Hesitant outranking/ranking — Hesitant Fuzzy Element (HFE: set of possible membership degrees)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems","type":"article","doi":"10.1002/int.20418","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-copras","name":"HF-COPRAS","fullName":"Hesitant Fuzzy Complex Proportional Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Mishra, A.R., Rani, P., Pardasani, K.R.","url":"https://scholargate.app/en/decision-making/hf-copras","markdownUrl":"https://scholargate.app/en/decision-making/hf-copras.md","definition":"HF-COPRAS (Hesitant Fuzzy Complex Proportional Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Mishra, A.R., Rani, P., Pardasani, K.R. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mishra, A.R., Rani, P., Pardasani, K.R.","subfamily":"Ranking","year":"2018","type":"Hesitant fuzzy benefit/cost proportional ranking","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Mishra, A.R., Rani, P., Pardasani, K.R. (2018). Multiple-criteria decision-making for service quality selection based on Shapley COPRAS method under hesitant fuzzy sets. Granular Computing","type":"article","doi":"10.1007/s41066-018-0103-8","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-dft","name":"HF-DFT","fullName":"Hesitant Fuzzy Decision Field Theory (Song-Xu 2021)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Song, C. Zhang, Y. Xu, Z. S. Hao, Z. Wang, X.","url":"https://scholargate.app/en/decision-making/hf-dft","markdownUrl":"https://scholargate.app/en/decision-making/hf-dft.md","definition":"HF-DFT (Hesitant Fuzzy Decision Field Theory (Song-Xu 2021)) is a ranking multi-criteria decision-making (MCDM) method introduced by Song, C. Zhang, Y. Xu, Z. S. Hao, Z. Wang, X. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Song, C. Zhang, Y. Xu, Z. S. Hao, Z. Wang, X.","subfamily":"Ranking","year":"2019","type":"Hesitant Fuzzy Decision Field Theory (HFDFT): hesitant fuzzy momentary preference function drives dynamic preference accumulation; feedback matrix governs evolution; group decision making via consensus aggregation of HFEs; predicts deliberation effects.","value_space":"hesitant","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Song, C., Zhang, Y., Xu, Z. S., Hao, Z., Wang, X. (2019). Route Selection of the Arctic Northwest Passage Based on Hesitant Fuzzy Decision Field Theory. IEEE Access","type":"article","doi":"10.1109/ACCESS.2019.2897716","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-edas","name":"HF-EDAS","fullName":"Hesitant Fuzzy EDAS (Evaluation Based on Distance from Average Solution)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Kutlu Gündoğdu, F.; Kahraman, C.; Civan, H. (HF-EDAS); Keshavarz Ghorabaee, M. et al. 2015 (base EDAS); Yu, D. 2014 (TFHFS value-space)","url":"https://scholargate.app/en/decision-making/hf-edas","markdownUrl":"https://scholargate.app/en/decision-making/hf-edas.md","definition":"HF-EDAS (Hesitant Fuzzy EDAS (Evaluation Based on Distance from Average Solution)) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F.; Kahraman, C.; Civan, H. (HF-EDAS); Keshavarz Ghorabaee, M. et al. 2015 (base EDAS); Yu, D. 2014 (TFHFS value-space) in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu, F.; Kahraman, C.; Civan, H. (HF-EDAS); Keshavarz Ghorabaee, M. et al. 2015 (base EDAS); Yu, D. 2014 (TFHFS value-space)","subfamily":"Ranking","year":"2018","type":"Hesitant outranking/ranking — Triangular Fuzzy Hesitant Fuzzy Set (TFHFS, Yu 2014): each cell is a finite set of triangular fuzzy numbers in [0,1]^3","value_space":"triangular_fuzzy_hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C., Civan, H. N. (2018). A novel hesitant fuzzy EDAS method and its application to hospital selection. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181172","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-electre-i","name":"HF-ELECTRE-I","fullName":"HF-ELECTRE I — Hesitant Fuzzy ELECTRE I (Chen-Xu-Xia 2015)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2015","originator":"Chen, N. Xu, Z. S. Xia, M. M.","url":"https://scholargate.app/en/decision-making/hf-electre-i","markdownUrl":"https://scholargate.app/en/decision-making/hf-electre-i.md","definition":"HF-ELECTRE-I (HF-ELECTRE I — Hesitant Fuzzy ELECTRE I (Chen-Xu-Xia 2015)) is a outranking multi-criteria decision-making (MCDM) method introduced by Chen, N. Xu, Z. S. Xia, M. M. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, N. Xu, Z. S. Xia, M. M.","subfamily":"Outranking","year":"2015","type":"Concordance/discordance outranking for Hesitant Fuzzy Elements; constructs HF concordance and discordance indices based on score function and deviation degree; derives credibility matrix for choice selection.","value_space":"hesitant","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Chen, N., Xu, Z. S., Xia, M. M. (2015). The ELECTRE I multi-criteria decision making method based on hesitant fuzzy sets. International Journal of Information Technology & Decision Making","type":"article","doi":"10.1142/s0219622014500187","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-electre-ii","name":"HF-ELECTRE-II","fullName":"HF-ELECTRE II — Hesitant Fuzzy ELECTRE II (Chen-Xu 2015)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2015","originator":"Chen, N. Xu, Z. S.","url":"https://scholargate.app/en/decision-making/hf-electre-ii","markdownUrl":"https://scholargate.app/en/decision-making/hf-electre-ii.md","definition":"HF-ELECTRE-II (HF-ELECTRE II — Hesitant Fuzzy ELECTRE II (Chen-Xu 2015)) is a outranking multi-criteria decision-making (MCDM) method introduced by Chen, N. Xu, Z. S. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, N. Xu, Z. S.","subfamily":"Outranking","year":"2015","type":"Two-pass outranking ranking (ascending/descending distillation extended) for Hesitant Fuzzy Elements; strong/weak outranking relations via HF concordance indices; produces final ranking by intersection of two pre-orders.","value_space":"hesitant","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Chen, N., Xu, Z. S. (2015). Hesitant fuzzy ELECTRE II approach: A new way to handle multi-criteria decision making problems. Information Sciences","type":"article","doi":"10.1016/j.ins.2014.08.054","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-gra","name":"HF-GRA","fullName":"Hesitant Fuzzy Grey Relational Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Li, X., Wei, G.","url":"https://scholargate.app/en/decision-making/hf-gra","markdownUrl":"https://scholargate.app/en/decision-making/hf-gra.md","definition":"HF-GRA (Hesitant Fuzzy Grey Relational Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Li, X., Wei, G. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li, X., Wei, G.","subfamily":"Ranking","year":"2014","type":"Hesitant fuzzy reference-based grey relational ranking (TOPSIS-like closeness)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Li, X., Wei, G. (2014). GRA method for multiple criteria group decision making with incomplete weight information under hesitant fuzzy setting. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/IFS-131073","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-mabac","name":"HF-MABAC","fullName":"Hesitant Fuzzy Multi-Attributive Border Approximation area Comparison","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Mishra, A.R., Saha, A., Rani, P., Pamucar, D., Dutta, D., Hezam, I.M.","url":"https://scholargate.app/en/decision-making/hf-mabac","markdownUrl":"https://scholargate.app/en/decision-making/hf-mabac.md","definition":"HF-MABAC (Hesitant Fuzzy Multi-Attributive Border Approximation area Comparison) is a ranking multi-criteria decision-making (MCDM) method introduced by Mishra, A.R., Saha, A., Rani, P., Pamucar, D., Dutta, D., Hezam, I.M. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mishra, A.R., Saha, A., Rani, P., Pamucar, D., Dutta, D., Hezam, I.M.","subfamily":"Ranking","year":"2022","type":"Hesitant fuzzy border-approximation distance ranking","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Mishra, A.R., Saha, A., Rani, P., Pamucar, D., Dutta, D., Hezam, I.M. (2022). Sustainable supplier selection using HF-DEA-FOCUM-MABAC technique: a case study in the Auto-making industry. Soft Computing","type":"article","doi":"10.1007/s00500-022-07192-8","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-marcos","name":"HF-MARCOS","fullName":"Hesitant Fuzzy MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020 crisp; 2023 variant applicator","originator":"Li, G., Geng, X., Yuan, Y.","url":"https://scholargate.app/en/decision-making/hf-marcos","markdownUrl":"https://scholargate.app/en/decision-making/hf-marcos.md","definition":"HF-MARCOS (Hesitant Fuzzy MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Li, G., Geng, X., Yuan, Y. in 2020 crisp; 2023 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li, G., Geng, X., Yuan, Y.","subfamily":"Ranking","year":"2020 crisp; 2023 variant applicator","type":"Hesitant Fuzzy compromise ranking — MARCOS extended via Hesitant Fuzzy Elements (HFE)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Li, G., Geng, X., Yuan, Y. (2023). An integrated MCDM method based on hesitant fuzzy MARCOS for supplier evaluation under sustainability requirements. Journal of Intelligent & Fuzzy Systems","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+integrated+MCDM+method+based+on+hesitant+fuzzy+MARCOS+for+supplier+evaluation+under+sustainability+requirements+Li"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-maxscore-port","name":"HF-MAXSCORE-PORT","fullName":"HF-MaxScore-Portfolio — Hesitant Fuzzy Maximum-Score Portfolio Selection (Zhou-Xu 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Portfolio","year":"2018","originator":"Zhou, W. Xu, Z.","url":"https://scholargate.app/en/decision-making/hf-maxscore-port","markdownUrl":"https://scholargate.app/en/decision-making/hf-maxscore-port.md","definition":"HF-MAXSCORE-PORT (HF-MaxScore-Portfolio — Hesitant Fuzzy Maximum-Score Portfolio Selection (Zhou-Xu 2018)) is a portfolio multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, W. Xu, Z.","subfamily":"Portfolio","year":"2018","type":"Hesitant fuzzy portfolio selection model for general investors. Maximises the aggregated score s(⊗_i w_i h_i) of the hesitant fuzzy portfolio, where ⊗_i w_i h_i is the HFE-power-weighted combination of per-stock aggregated HFEs. Output is optimal investment weight vector W = (w_1,...,w_n) summing to 1. Equivalent to the hesitant fuzzy version of Markowitz's maximum-return portfolio (no risk constraint).","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Zhou, W., Xu, Z. (2018). Portfolio selection and risk investment under the hesitant fuzzy environment. Knowledge-Based Systems","type":"article","doi":"10.1016/j.knosys.2017.12.020","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-moora","name":"HF-MOORA","fullName":"Hesitant Fuzzy Multi-Objective Optimization by Ratio Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Li, Z.-H.","url":"https://scholargate.app/en/decision-making/hf-moora","markdownUrl":"https://scholargate.app/en/decision-making/hf-moora.md","definition":"HF-MOORA (Hesitant Fuzzy Multi-Objective Optimization by Ratio Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Li, Z.-H. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li, Z.-H.","subfamily":"Ranking","year":"2014","type":"Hesitant fuzzy ratio system ranker (benefit-vs-non-beneficial net score on vector-normalised defuzzified matrix)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Li, Z.-H. (2014). An Extension of the MULTIMOORA Method for Multiple Criteria Group Decision Making Based upon Hesitant Fuzzy Sets. Journal of Applied Mathematics","type":"article","doi":"10.1155/2014/527836","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-qualiflex","name":"HF-QUALIFLEX","fullName":"Hesitant Fuzzy extension of QUALIFLEX","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2015","originator":"Zhang, Z. X. Xu, Z. S.","url":"https://scholargate.app/en/decision-making/hf-qualiflex","markdownUrl":"https://scholargate.app/en/decision-making/hf-qualiflex.md","definition":"HF-QUALIFLEX (Hesitant Fuzzy extension of QUALIFLEX) is a outranking multi-criteria decision-making (MCDM) method introduced by Zhang, Z. X. Xu, Z. S. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhang, Z. X. Xu, Z. S.","subfamily":"Outranking","year":"2015","type":"Hesitant Fuzzy concordance outranking — Hesitant Fuzzy Set (HFS: multiple μ values) with signed distance comparison","value_space":"hesitant","uncertainty":"epistemic","compensation":"none","rank_reversal":false},"citations":[{"ref":"Zhang, Z. X., Xu, Z. S. (2015). Hesitant fuzzy QUALIFLEX approach with a signed distance-based comparison method for multiple criteria decision analysis. Expert Systems with Applications","type":"article","doi":"10.1016/j.eswa.2014.08.056","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","merec","swara","fucom"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-saw","name":"HF-SAW","fullName":"Hesitant extension of HF-SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Torra, V.","url":"https://scholargate.app/en/decision-making/hf-saw","markdownUrl":"https://scholargate.app/en/decision-making/hf-saw.md","definition":"HF-SAW (Hesitant extension of HF-SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Torra, V. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Torra, V.","subfamily":"Ranking","year":"2010","type":"Hesitant outranking/ranking — Hesitant Fuzzy Element (HFE: set of possible membership degrees)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems","type":"article","doi":"10.1002/int.20418","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-todim","name":"HF-TODIM","fullName":"Hesitant Fuzzy TODIM via new measure function Z_δ (Zhang-Xu 2016 ITOR)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2016","originator":"Zhang, Y., Xu, Z.","url":"https://scholargate.app/en/decision-making/hf-todim","markdownUrl":"https://scholargate.app/en/decision-making/hf-todim.md","definition":"HF-TODIM (Hesitant Fuzzy TODIM via new measure function Z_δ (Zhang-Xu 2016 ITOR)) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhang, Y., Xu, Z. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhang, Y., Xu, Z.","subfamily":"Ranking","year":"2016","type":"Prospect-theory pairwise dominance (Gomes-Lima 1992) extended to Hesitant Fuzzy Elements (HFE ⊂ [0,1]) via the parametric Z_δ measure function (Zhang-Xu 2016)","value_space":"hesitant","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Zhang, Y., Xu, Z. (2016). Efficiency evaluation of sustainable water management using the HF-TODIM method. International Transactions in Operational Research","type":"article","doi":"10.1111/itor.12318","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-topsis","name":"HF-TOPSIS","fullName":"Hesitant Fuzzy TOPSIS with optional incomplete weight information (Xu-Zhang 2013 KBS)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Xu, Z., Zhang, X.","url":"https://scholargate.app/en/decision-making/hf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/hf-topsis.md","definition":"HF-TOPSIS (Hesitant Fuzzy TOPSIS with optional incomplete weight information (Xu-Zhang 2013 KBS)) is a ranking multi-criteria decision-making (MCDM) method introduced by Xu, Z., Zhang, X. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xu, Z., Zhang, X.","subfamily":"Ranking","year":"2013","type":"Distance-to-ideal ranking (Hwang-Yoon 1981) extended to Hesitant Fuzzy Elements (HFE ⊂ [0,1]) via the hesitant normalised Euclidean distance d_1 (Xu-Xia 2011b); supports three weight-information modes: fully specified, completely unknown (Eq.(22) maximizing deviation closed-form), and partly known (Model M-2 linear programme).","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Xu, Z., Zhang, X. (2013). Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowledge-Based Systems","type":"article","doi":"10.1016/j.knosys.2013.05.011","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-tradeoff-port","name":"HF-TRADEOFF-PORT","fullName":"HF-TradeOff-Portfolio — Hesitant Fuzzy Score-Deviation Trade-Off Portfolio Selection (Zhou-Xu 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Portfolio","year":"2018","originator":"Zhou, W. Xu, Z.","url":"https://scholargate.app/en/decision-making/hf-tradeoff-port","markdownUrl":"https://scholargate.app/en/decision-making/hf-tradeoff-port.md","definition":"HF-TRADEOFF-PORT (HF-TradeOff-Portfolio — Hesitant Fuzzy Score-Deviation Trade-Off Portfolio Selection (Zhou-Xu 2018)) is a portfolio multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, W. Xu, Z.","subfamily":"Portfolio","year":"2018","type":"Hesitant fuzzy portfolio selection for risk-aware investors. Two dual formulations: (A) max-score with deviation upper bound D (Model 3.16) and (B) min-deviation with score lower bound S (Model 3.18). D and S are calibrated per investor risk type (risk-seeker / neutral / risk-averse) via deviation trisection or score trisection approaches (Definitions 3.1-3.2). Generalises HF-MaxScore-Portfolio by incorporating a risk (deviation) constraint.","value_space":"hesitant","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Zhou, W., Xu, Z. (2018). Portfolio selection and risk investment under the hesitant fuzzy environment. Knowledge-Based Systems","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Portfolio%20selection%20and%20risk%20investment%20under%20the%20hesitant%20fuzzy%20environment"}],"related":["hf-maxscore-port"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-vikor","name":"HF-VIKOR","fullName":"Hesitant Fuzzy VIKOR (Liao-Xu 2013)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"?","url":"https://scholargate.app/en/decision-making/hf-vikor","markdownUrl":"https://scholargate.app/en/decision-making/hf-vikor.md","definition":"HF-VIKOR (Hesitant Fuzzy VIKOR (Liao-Xu 2013)) is a ranking multi-criteria decision-making (MCDM) method introduced by ? in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"?","subfamily":"Ranking","year":"2013","type":"Compromise ranking on Hesitant Fuzzy Elements; Manhattan L_p-metric defuzzification; S/R/Q measures with compromise-solution conditions","value_space":"hesitant","uncertainty":"epistemic","compensation":"partial"},"citations":[{"ref":"Liao, H., Xu, Z. (2013). A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optimization and Decision Making","type":"article","doi":"10.1007/s10700-013-9162-0","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hf-waspas","name":"HF-WASPAS","fullName":"Hesitant Fuzzy Weighted Aggregated Sum Product Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Mishra, A.R., Rani, P., Pardasani, K.R., Mardani, A.","url":"https://scholargate.app/en/decision-making/hf-waspas","markdownUrl":"https://scholargate.app/en/decision-making/hf-waspas.md","definition":"HF-WASPAS (Hesitant Fuzzy Weighted Aggregated Sum Product Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Mishra, A.R., Rani, P., Pardasani, K.R., Mardani, A. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mishra, A.R., Rani, P., Pardasani, K.R., Mardani, A.","subfamily":"Ranking","year":"2019","type":"Hesitant fuzzy WSM+WPM hybrid ranking","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Mishra, A.R., Rani, P., Pardasani, K.R., Mardani, A. (2019). A novel hesitant fuzzy WASPAS method for assessment of green supplier problem based on exponential information measures. Journal of Cleaner Production","type":"article","doi":"10.1016/j.jclepro.2019.117901","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hfea","name":"HFEA","fullName":"Hesitant Fuzzy Envelopment Analysis (DHFEA / SHFEA, Zhou-Chen-Xu-Meng 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"2018","originator":"Zhou, W. Chen, J. Xu, Z. S. Meng, S.","url":"https://scholargate.app/en/decision-making/hfea","markdownUrl":"https://scholargate.app/en/decision-making/hfea.md","definition":"HFEA (Hesitant Fuzzy Envelopment Analysis (DHFEA / SHFEA, Zhou-Chen-Xu-Meng 2018)) is a dea multi-criteria decision-making (MCDM) method introduced by Zhou, W. Chen, J. Xu, Z. S. Meng, S. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, W. Chen, J. Xu, Z. S. Meng, S.","subfamily":"DEA","year":"2018","type":"DEA extension to Hesitant Fuzzy Sets — efficiency measured as weighted score-to-deviation ratio m_e = Σp_i·s_{ie} / Σq_i·d_{ie}; LP-solved in linearised DHFEA (deviation-normalised) or SHFEA (score-normalised) form; provides both ranking and improvement schedules for inefficient alternatives.","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zhou, W., Chen, J., Xu, Z. S., Meng, S. (2018). Hesitant fuzzy preference envelopment analysis and alternative improvement. Information Sciences","type":"article","doi":"10.1016/j.ins.2018.07.002","isbn":null,"url":null}],"related":["hfpea","hfpe","hfgpe"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hfgpe","name":"HFGPE","fullName":"Hesitant Fuzzy Generalized Peer-Evaluation (strategy-blended cross-efficiency with BFM, Zhou-Chen-Xu-Meng 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"2018","originator":"Zhou, W. Chen, J. Xu, Z. S. Meng, S.","url":"https://scholargate.app/en/decision-making/hfgpe","markdownUrl":"https://scholargate.app/en/decision-making/hfgpe.md","definition":"HFGPE (Hesitant Fuzzy Generalized Peer-Evaluation (strategy-blended cross-efficiency with BFM, Zhou-Chen-Xu-Meng 2018)) is a dea multi-criteria decision-making (MCDM) method introduced by Zhou, W. Chen, J. Xu, Z. S. Meng, S. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, W. Chen, J. Xu, Z. S. Meng, S.","subfamily":"DEA","year":"2018","type":"Generalization of HFPE: cross-efficiency E_{el}(d) = d·E^max_{el} + (1-d)·E^min_{el} blends benevolent (d=1) and aggressive (d=0) strategies via parameter d ∈ [0,1]. When d is unknown, the Backward Fitting Method (BFM) estimates the optimal d from historical/preference ranking constraints by solving a quadratic programme. Final ranking by column-mean of the blended cross-efficiency matrix.","value_space":"hesitant_fuzzy","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zhou, W., Chen, J., Xu, Z. S., Meng, S. (2018). Hesitant fuzzy preference envelopment analysis and alternative improvement. Information Sciences","type":"article","doi":"10.1016/j.ins.2018.07.002","isbn":null,"url":null}],"related":["hfpe"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hfl-ahp","name":"HFL-AHP","fullName":"Hesitant Fuzzy Linguistic AHP (HFLTS-envelope family: Yavuz 2015 + Özdağoğlu 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2015","originator":"Yavuz, M., Öztaysi, B., Çevik Onar, S., Kahraman, C.","url":"https://scholargate.app/en/decision-making/hfl-ahp","markdownUrl":"https://scholargate.app/en/decision-making/hfl-ahp.md","definition":"HFL-AHP (Hesitant Fuzzy Linguistic AHP (HFLTS-envelope family: Yavuz 2015 + Özdağoğlu 2018)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Yavuz, M., Öztaysi, B., Çevik Onar, S., Kahraman, C. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yavuz, M., Öztaysi, B., Çevik Onar, S., Kahraman, C.","subfamily":"Weight_Subjective","year":"2015","type":"HFLTS-envelope pairwise comparison — interval/2-tuple aggregation — preference-degree (Yavuz) or defuzz (Özdağoğlu) ranking","value_space":"hesitant_fuzzy_linguistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yavuz, M., Öztaysi, B., Çevik Onar, S., Kahraman, C. (2015). Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model. Expert Systems with Applications","type":"article","doi":"10.1016/j.eswa.2014.11.010","isbn":null,"url":null}],"related":["ahpsort","aras","cobra","cocoso","codas","copras","edas","gra"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hfl-codas","name":"HFL-CODAS","fullName":"Hesitant Fuzzy Linguistic CODAS (HFLTS-envelope, Combinative Distance-based Assessment)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Yalçın, N., Pehlivan, N.Y.","url":"https://scholargate.app/en/decision-making/hfl-codas","markdownUrl":"https://scholargate.app/en/decision-making/hfl-codas.md","definition":"HFL-CODAS (Hesitant Fuzzy Linguistic CODAS (HFLTS-envelope, Combinative Distance-based Assessment)) is a ranking multi-criteria decision-making (MCDM) method introduced by Yalçın, N., Pehlivan, N.Y. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yalçın, N., Pehlivan, N.Y.","subfamily":"Ranking","year":"2019","type":"Hesitant fuzzy linguistic ranking — HFLTS envelope (linguistic interval / trapezoidal Liu-Rodriguez envelope) + combinative distance-based assessment","value_space":"hesitant_fuzzy_linguistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Yalçın, N., Pehlivan, N.Y. (2019). Application of the Fuzzy CODAS Method Based on Fuzzy Envelopes for Hesitant Fuzzy Linguistic Term Sets: A Case Study on a Personnel Selection Problem. Symmetry (MDPI)","type":"article","doi":"10.3390/sym11040493","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hfl-mabac","name":"HFL-MABAC","fullName":"Hesitant Fuzzy Linguistic Projection-Based MABAC with Bonferroni Mean (Sun et al. 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Sun, R., Hu, J., Zhou, J., Chen, X.","url":"https://scholargate.app/en/decision-making/hfl-mabac","markdownUrl":"https://scholargate.app/en/decision-making/hfl-mabac.md","definition":"HFL-MABAC (Hesitant Fuzzy Linguistic Projection-Based MABAC with Bonferroni Mean (Sun et al. 2018)) is a ranking multi-criteria decision-making (MCDM) method introduced by Sun, R., Hu, J., Zhou, J., Chen, X. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sun, R., Hu, J., Zhou, J., Chen, X.","subfamily":"Ranking","year":"2018","type":"Hesitant fuzzy linguistic ranking — HFLTS subscript-symmetric LTS + projection-based signed difference + Bonferroni Mean criterion aggregation","value_space":"hesitant_fuzzy_linguistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Sun, R., Hu, J., Zhou, J., Chen, X. (2018). A Hesitant Fuzzy Linguistic Projection-Based MABAC Method for Patients' Prioritization. International Journal of Fuzzy Systems","type":"article","doi":"10.1007/s40815-017-0345-7","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hfl-promethee","name":"HFL-PROMETHEE","fullName":"Hesitant Fuzzy Linguistic PROMETHEE (Liang-Wang-Zhang 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2018","originator":"Liang, R. X. Wang, J. Q. Zhang, H. Y.","url":"https://scholargate.app/en/decision-making/hfl-promethee","markdownUrl":"https://scholargate.app/en/decision-making/hfl-promethee.md","definition":"HFL-PROMETHEE (Hesitant Fuzzy Linguistic PROMETHEE (Liang-Wang-Zhang 2018)) is a outranking multi-criteria decision-making (MCDM) method introduced by Liang, R. X. Wang, J. Q. Zhang, H. Y. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liang, R. X. Wang, J. Q. Zhang, H. Y.","subfamily":"Outranking","year":"2018","type":"Projection-based PROMETHEE outranking for Hesitant Fuzzy Linguistic Term Sets (HFLTS); preference functions computed via hesitant fuzzy linguistic projection scores; net flow Φ for final ranking.","value_space":"hesitant_fuzzy_linguistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Liang, R. X., Wang, J. Q., Zhang, H. Y. (2018). Projection-based PROMETHEE methods based on hesitant fuzzy linguistic term sets. International Journal of Fuzzy Systems","type":"article","doi":"10.1007/s40815-017-0418-7","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hflpr-priority","name":"HFLPR-PRIORITY","fullName":"Hesitant Fuzzy Linguistic Preference Relation Priority Programming (Ren-Zhu-Xu 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Preference_Relations","year":"2018","originator":"Ren, P. J. Zhu, B. Xu, Z. S.","url":"https://scholargate.app/en/decision-making/hflpr-priority","markdownUrl":"https://scholargate.app/en/decision-making/hflpr-priority.md","definition":"HFLPR-PRIORITY (Hesitant Fuzzy Linguistic Preference Relation Priority Programming (Ren-Zhu-Xu 2018)) is a preference relations multi-criteria decision-making (MCDM) method introduced by Ren, P. J. Zhu, B. Xu, Z. S. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ren, P. J. Zhu, B. Xu, Z. S.","subfamily":"Preference_Relations","year":"2018","type":"Derives priority vectors directly from Hesitant Fuzzy Linguistic Preference Relations (HFLPRs) without a consistency-checking/repair step. Input is one n×n HFLPR per criterion plus one HFLPR for criteria themselves. The hyperplane-consistency programming model (Model 6.1) maximises the minimum decision-maker satisfaction across all pairwise constraints simultaneously. A sensitivity parameter t is calibrated empirically from 10,000 Monte-Carlo MCDM problems.","value_space":"hesitant_fuzzy_linguistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Ren, P. J., Zhu, B., Xu, Z. S. (2018). Assessment of the impact of hydropower stations on the environment with a hesitant fuzzy linguistic hyperplane-consistency programming method. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/tfuzz.2018.2798598","isbn":null,"url":null}],"related":["hfwa","hfwg","ranking-aggregation","methods-requiring-crisp-or-standard-hfs-decision-matrices-as-input"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hfpe","name":"HFPE","fullName":"Hesitant Fuzzy Peer-Evaluation (benevolent / aggressive cross-efficiency, Zhou-Chen-Xu-Meng 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"2018","originator":"Zhou, W. Chen, J. Xu, Z. S. Meng, S.","url":"https://scholargate.app/en/decision-making/hfpe","markdownUrl":"https://scholargate.app/en/decision-making/hfpe.md","definition":"HFPE (Hesitant Fuzzy Peer-Evaluation (benevolent / aggressive cross-efficiency, Zhou-Chen-Xu-Meng 2018)) is a dea multi-criteria decision-making (MCDM) method introduced by Zhou, W. Chen, J. Xu, Z. S. Meng, S. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, W. Chen, J. Xu, Z. S. Meng, S.","subfamily":"DEA","year":"2018","type":"Cross-efficiency DEA extended to HFS — each alternative is evaluated by both its own optimal HFEA weights (self-evaluation, score E_{ee}) and by the optimal weights of every other alternative (peer evaluation, cross-efficiency E_{el}). Benevolent strategy maximises peer scores; aggressive strategy minimises them. Final ranking by column-mean of cross-efficiency matrix.","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zhou, W., Chen, J., Xu, Z. S., Meng, S. (2018). Hesitant fuzzy preference envelopment analysis and alternative improvement. Information Sciences","type":"article","doi":"10.1016/j.ins.2018.07.002","isbn":null,"url":null}],"related":["hfgpe","hfea"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hfpea","name":"HFPEA","fullName":"Hesitant Fuzzy Preference Envelopment Analysis (DHFPEA / SHFPEA, Zhou-Chen-Xu-Meng 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"DEA","year":"2018","originator":"Zhou, W. Chen, J. Xu, Z. S. Meng, S.","url":"https://scholargate.app/en/decision-making/hfpea","markdownUrl":"https://scholargate.app/en/decision-making/hfpea.md","definition":"HFPEA (Hesitant Fuzzy Preference Envelopment Analysis (DHFPEA / SHFPEA, Zhou-Chen-Xu-Meng 2018)) is a dea multi-criteria decision-making (MCDM) method introduced by Zhou, W. Chen, J. Xu, Z. S. Meng, S. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, W. Chen, J. Xu, Z. S. Meng, S.","subfamily":"DEA","year":"2018","type":"HFEA extension with ordinal attribute preference constraints — same score/deviation LP as HFEA but with additional weight ordering inequalities p_g ≥ p_t ≥ … ≥ p_m encoding the decision maker's stated preference order over criteria; produces preference-consistent efficiency rankings and improvement schedules.","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zhou, W., Chen, J., Xu, Z. S., Meng, S. (2018). Hesitant fuzzy preference envelopment analysis and alternative improvement. Information Sciences","type":"article","doi":"10.1016/j.ins.2018.07.002","isbn":null,"url":null}],"related":["hfpe","hfgpe","hfea"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hi-c-analysis","name":"Hi-C Analysis","fullName":"Hi-C Analysis of 3D Genome Organization and Chromatin Interactions","aliases":["Chromosome conformation capture","3D genome","Chromatin contact mapping"],"domain":"genetics","family":"process-pipeline","subfamily":"3D genomics","year":"2009","originator":"Erez Lieberman-Aiden & Job Dekker","url":"https://scholargate.app/en/genetics/hi-c-analysis","markdownUrl":"https://scholargate.app/en/genetics/hi-c-analysis.md","definition":"Hi-C (High-Chromosome Conformation Capture) is a technique and associated computational methods for mapping the 3D architecture of the genome within cells. Developed by Lieberman-Aiden and Dekker in 2009, Hi-C identifies physical interactions between genomic regions that may be distant in linear sequence but spatially proximal in 3D nuclear space. Hi-C analysis has revealed fundamental principles of genome organization, including the existence of topologically associating domains (TADs), and provides insights into how 3D structure regulates gene expression and DNA replication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erez Lieberman-Aiden & Job Dekker","subfamily":"3D genomics","year":"2009","type":"Chromatin interaction method"},"citations":[{"ref":"Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., & Dekker, J. (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 326(5950), 289–293.","type":"article","doi":"10.1126/science.1181369","isbn":null,"url":null},{"ref":"Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., & Ren, B. (2012). Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature, 485(7398), 376–380.","type":"article","doi":"10.1038/nature11082","isbn":null,"url":null},{"ref":"Szabo, Q., Bantignies, F., & Cavalli, G. (2019). 3D chromatin architecture. Nature Reviews Molecular Cell Biology, 20(4), 207–220.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=3D+chromatin+architecture+Szabo"}],"related":["atac-seq-analysis","rna-velocity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hick-hyman-law","name":"Hick-Hyman Law","fullName":"Hick-Hyman Law of Choice Reaction Time","aliases":["Hick's Law","Law of Choice Reaction Time"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Decision Making","year":"1952","originator":"William Edmund Hick and Ray Hyman","url":"https://scholargate.app/en/human-computer-interaction/hick-hyman-law","markdownUrl":"https://scholargate.app/en/human-computer-interaction/hick-hyman-law.md","definition":"The Hick-Hyman Law predicts that human decision time increases logarithmically with the number of equally likely choices. Independently formulated by William Edmund Hick and Ray Hyman in the early 1950s, this law describes how long it takes a person to make a choice among alternatives. In human-computer interaction, the law is widely applied to menu design, navigation hierarchies, and command selection, showing that users take longer to select from larger sets of options, but the relationship is logarithmic, not linear.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Edmund Hick and Ray Hyman","subfamily":"Decision Making","year":"1952","type":"Empirical model of choice reaction time as logarithmic function of number of choices"},"citations":[{"ref":"Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4(1), 11–26.","type":"article","doi":"10.1080/17470215208416600","isbn":null,"url":null},{"ref":"Hyman, R. (1953). Stimulus information as a determinant of reaction time. Journal of Experimental Psychology, 45(3), 188–196.","type":"article","doi":"10.1037/h0056940","isbn":null,"url":null}],"related":["klm-goms","cognitive-walkthrough","system-usability-scale","think-aloud-protocol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hiemstra-jones-causality","name":"Hiemstra-Jones Causality","fullName":"Hiemstra-Jones Nonlinear Granger Causality Test","aliases":["HJ Nonlinear Causality Test","Hiemstra-Jones Test","Nonlinear Granger Causality (Hiemstra-Jones)","HJ Nedensellik Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Causality","year":1994,"originator":"Craig Hiemstra & Jonathan Jones","url":"https://scholargate.app/en/econometrics/hiemstra-jones-causality","markdownUrl":"https://scholargate.app/en/econometrics/hiemstra-jones-causality.md","definition":"The Hiemstra-Jones test, introduced in 1994, is a nonparametric procedure for detecting nonlinear causal relationships between two time series after removing their linear interdependencies. Developed in the context of stock price and trading volume dynamics, it extends the standard linear Granger causality framework by using correlation integral statistics to detect predictability arising from nonlinear mechanisms that linear VAR models cannot capture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Craig Hiemstra & Jonathan Jones","year":1994,"type":"Nonparametric hypothesis test","subfamily":"Causality","distribution":"Asymptotically standard normal under H0","null_hypothesis":"X does not nonlinearly Granger-cause Y"},"citations":[{"ref":"Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price-volume relation. The Journal of Finance, 49(5), 1639–1664.","type":"article","doi":"10.1111/j.1540-6261.1994.tb04776.x","isbn":null,"url":null}],"related":["granger-causality","transfer-entropy","convergent-cross-mapping"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-approximate-bayesian-computation","name":"Hierarchical Approximate Bayesian Computation","fullName":"Hierarchical Approximate Bayesian Computation","aliases":["hierarchical ABC","ABC for hierarchical models","multilevel ABC","population ABC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2009–2010","originator":"Toni, Welch, Strelkowa, Ipsen & Stumpf (building on Pritchard et al. 1999 and Beaumont et al. 2002)","url":"https://scholargate.app/en/bayesian/hierarchical-approximate-bayesian-computation","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-approximate-bayesian-computation.md","definition":"Hierarchical ABC is a likelihood-free Bayesian inference method designed for multilevel data structures in which individual-level parameters are themselves drawn from a population-level distribution. By combining simulation-based rejection sampling with hierarchical pooling, it recovers both within-group and between-group posterior distributions without requiring a tractable likelihood function.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Toni, Welch, Strelkowa, Ipsen & Stumpf (building on Pritchard et al. 1999 and Beaumont et al. 2002)","year":"2009–2010","type":"simulation-based Bayesian inference","dataType":"individual-level and group-level continuous or discrete observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Toni, T. & Stumpf, M. P. H. (2010). Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics, 26(1), 104–110.","type":"article","doi":"10.1093/bioinformatics/btp619","isbn":null,"url":null},{"ref":"Wilkinson, R. D. (2013). Approximate Bayesian computation (ABC) gives exact results under the assumption of model error. Statistical Applications in Genetics and Molecular Biology, 12(2), 129–141.","type":"article","doi":"10.1515/sagmb-2013-0010","isbn":null,"url":null}],"related":["approximate-bayesian-computation","hierarchical-bayesian-inference","sequential-monte-carlo","hierarchical-sequential-monte-carlo","hierarchical-markov-chain-monte-carlo","bayesian-hierarchical-model-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-bayesian-inference","name":"Hierarchical Bayesian Inference","fullName":"Hierarchical Bayesian Inference","aliases":["multilevel Bayesian modeling","Bayesian hierarchical model","nested Bayesian model","partial pooling model"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1972 (Lindley & Smith); consolidated 1995–2013","originator":"Lindley & Smith; Gelman et al.","url":"https://scholargate.app/en/bayesian/hierarchical-bayesian-inference","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-bayesian-inference.md","definition":"Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lindley & Smith; Gelman et al.","year":"1972 (Lindley & Smith); consolidated 1995–2013","type":"Bayesian multilevel model","dataType":"grouped / nested / clustered observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Gelman, A. (2006). Multilevel (hierarchical) modeling: what it can and cannot do. Technometrics, 48(3), 432-435.","type":"article","doi":"10.1198/004017005000000661","isbn":null,"url":null}],"related":["bayesian-regression","mcmc","gibbs-sampling","hierarchical-markov-chain-monte-carlo","variational-inference","mixed-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-bayesian-model-averaging","name":"Hierarchical Bayesian Model Averaging","fullName":"Hierarchical Bayesian Model Averaging","aliases":["HBMA","hierarchical BMA","multilevel Bayesian model averaging","Bayesian model averaging in hierarchical models"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1999–2000s","originator":"Extension formalised by Hoeting, Madigan, Raftery, and Volinsky; hierarchical application developed through 1990s–2000s Bayesian literature","url":"https://scholargate.app/en/bayesian/hierarchical-bayesian-model-averaging","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-bayesian-model-averaging.md","definition":"Hierarchical Bayesian model averaging (HBMA) combines Bayesian model averaging with hierarchical model structure, averaging posterior quantities over a set of candidate models weighted by each model's posterior probability. Rather than selecting a single best model, HBMA propagates model uncertainty through a hierarchical framework, producing predictions and parameter estimates that honestly reflect uncertainty about which model is correct.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension formalised by Hoeting, Madigan, Raftery, and Volinsky; hierarchical application developed through 1990s–2000s Bayesian literature","year":"1999–2000s","type":"Bayesian model averaging within hierarchical models","dataType":"continuous, count, binary, clustered / grouped data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–417.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+model+averaging%3A+A+tutorial+Hoeting"},{"ref":"Fragoso, T. M., Bertoli, W., & Louzada, F. (2018). Bayesian model averaging: A systematic review and conceptual classification. International Statistical Review, 86(1), 1–28.","type":"article","doi":"10.1111/insr.12243","isbn":null,"url":null}],"related":["bayesian-model-averaging","hierarchical-bayesian-inference","bayesian-regression","hierarchical-markov-chain-monte-carlo","bayesian-information-criterion","model-selection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-bayesian-network","name":"Hierarchical Bayesian Network","fullName":"Hierarchical Bayesian Network","aliases":["HBN","layered Bayesian network","multi-level Bayesian network","hierarchical probabilistic graphical model"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990s–2000s","originator":"Koller, Friedman, and colleagues","url":"https://scholargate.app/en/bayesian/hierarchical-bayesian-network","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-bayesian-network.md","definition":"A hierarchical Bayesian network is a probabilistic graphical model that organizes variables across multiple levels of abstraction. Higher-level nodes govern the prior distributions of lower-level nodes through hyperparameters, enabling structured sharing of information across groups, contexts, or data subsets while preserving the directed acyclic graph (DAG) representation of conditional dependencies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Koller, Friedman, and colleagues","year":"1990s–2000s","type":"probabilistic graphical model","dataType":"multivariate, structured, hierarchically organized","subfamily":"Bayesian / computational"},"citations":[{"ref":"Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.","type":"book","doi":null,"isbn":"978-0262013192","url":null},{"ref":"Friedman, N., Getoor, L., Koller, D. & Pfeffer, A. (1999). Learning probabilistic relational models. Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-99), 1300-1307.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Learning+probabilistic+relational+models+Friedman+Getoor+Koller+Pfeffer+1999"}],"related":["bayesian-network","hierarchical-bayesian-inference","dynamic-bayesian-network","bayesian-hierarchical-model-with-missing-data","hierarchical-variational-inference","hierarchical-markov-chain-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-bootstrap-simulation","name":"Hierarchical Bootstrap Simulation","fullName":"Hierarchical Bootstrap Simulation","aliases":["cluster bootstrap","multilevel bootstrap","nested bootstrap resampling","hierarchical resampling"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1997-2008","originator":"Davison & Hinkley; Cameron, Gelbach & Miller","url":"https://scholargate.app/en/bayesian/hierarchical-bootstrap-simulation","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-bootstrap-simulation.md","definition":"Hierarchical bootstrap simulation is a resampling technique designed for data with nested or clustered structure — students within schools, patients within hospitals, repeated measures within subjects. It preserves the natural grouping of the data by resampling at each level of the hierarchy in sequence, producing a sampling distribution that correctly reflects both between-group and within-group variability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Davison & Hinkley; Cameron, Gelbach & Miller","year":"1997-2008","type":"resampling simulation","dataType":"hierarchical / clustered / nested data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521574716","url":null},{"ref":"Cameron, A. C., Gelbach, J. B. & Miller, D. L. (2008). Bootstrap-based improvements for inference with clustered errors. Review of Economics and Statistics, 90(3), 414-427.","type":"article","doi":"10.1162/rest.90.3.414","isbn":null,"url":null}],"related":["multilevel-bootstrap-simulation","hierarchical-monte-carlo-simulation","sequential-monte-carlo","hierarchical-bayesian-inference","gibbs-sampling","kalman-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-causal-comparative-research","name":"Hierarchical Causal-Comparative Research","fullName":"Hierarchical Causal-Comparative Research Design","aliases":["multilevel causal-comparative design","nested causal-comparative research","HLM causal-comparative study","hierarchical ex post facto comparison"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1960s (causal-comparative); 1980s–2002 (hierarchical/multilevel extension)","originator":"Kerlinger (causal-comparative logic); Raudenbush & Bryk (hierarchical extension)","url":"https://scholargate.app/en/research-design/hierarchical-causal-comparative-research","markdownUrl":"https://scholargate.app/en/research-design/hierarchical-causal-comparative-research.md","definition":"Hierarchical causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on an outcome variable while explicitly modeling the nested structure of the data. Participants are clustered within higher-level units — students within classrooms, employees within organizations — and the design uses multilevel analytical techniques to distinguish group differences at each level. The cause-and-effect inference is strengthened by accounting for variance attributable to the hierarchy rather than misattributing it to individual-level group membership.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kerlinger (causal-comparative logic); Raudenbush & Bryk (hierarchical extension)","year":"1960s (causal-comparative); 1980s–2002 (hierarchical/multilevel extension)","type":"Non-experimental quantitative research design","dataType":"Numerical group-membership and outcome data from nested/clustered samples","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761919049","url":null},{"ref":"Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030417542","url":null}],"related":["causal-comparative-research","ex-post-facto-design","hierarchical-linear-modeling","multilevel-modeling","longitudinal-causal-comparative-research","comparative-causal-comparative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-clustering","name":"Hierarchical Clustering","fullName":"Hierarchical Agglomerative Clustering","aliases":["Hiyerarşik Kümeleme","hiyerarşik kümeleme","agglomerative clustering","hierarchical agglomerative clustering","HAC"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1963,"originator":"Ward, J. H.","url":"https://scholargate.app/en/machine-learning/hierarchical-clustering","markdownUrl":"https://scholargate.app/en/machine-learning/hierarchical-clustering.md","definition":"Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ward, J. H.","year":1963,"type":"Unsupervised clustering (agglomerative)","task":"Exploratory grouping","minSample":10},"citations":[{"ref":"Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.","type":"article","doi":"10.1080/01621459.1963.10500845","isbn":null,"url":null}],"related":["kmeans-clustering","dbscan","gaussian-mixture","pca","factor-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-confirmatory-research","name":"Hierarchical Confirmatory Research","fullName":"Hierarchical Confirmatory Research Design","aliases":["multilevel confirmatory research","nested confirmatory design","hierarchical hypothesis-testing research","HCR"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–2000s","originator":"Raudenbush & Bryk; Hox; Goldstein","url":"https://scholargate.app/en/research-design/hierarchical-confirmatory-research","markdownUrl":"https://scholargate.app/en/research-design/hierarchical-confirmatory-research.md","definition":"Hierarchical confirmatory research is a quantitative design that tests pre-specified hypotheses about relationships or group differences in data that have a natural nested (hierarchical) structure — such as students clustered within classrooms, patients within hospitals, or employees within organizations. By explicitly modeling the hierarchy, it avoids the inflation of Type I error that occurs when nested data are analyzed as though observations were independent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raudenbush & Bryk; Hox; Goldstein","year":"1980s–2000s","type":"Quantitative confirmatory research design","dataType":"Nested/clustered quantitative data (e.g., students within schools, employees within firms)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761919049","url":null},{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728462","url":null}],"related":["confirmatory-research","hierarchical-longitudinal-research","multilevel-modeling","structural-equation-modeling","hierarchical-model-testing-research","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-cross-sectional-research","name":"Hierarchical Cross-Sectional Research","fullName":"Hierarchical Cross-Sectional Research Design","aliases":["multilevel cross-sectional design","nested cross-sectional study","clustered cross-sectional research","HCS design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–1990s (formalized with HLM software and methodology)","originator":"Raudenbush & Bryk; Goldstein; Snijders & Bosker (multilevel modeling tradition)","url":"https://scholargate.app/en/research-design/hierarchical-cross-sectional-research","markdownUrl":"https://scholargate.app/en/research-design/hierarchical-cross-sectional-research.md","definition":"Hierarchical cross-sectional research is a quantitative observational design that collects data from individuals nested within higher-level units — such as students within schools, patients within hospitals, or employees within organizations — at a single point in time. By accounting for the non-independence of clustered observations through multilevel modeling, it enables researchers to simultaneously examine individual-level and group-level predictors of an outcome without violating the independence assumption of ordinary regression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raudenbush & Bryk; Goldstein; Snijders & Bosker (multilevel modeling tradition)","year":"1980s–1990s (formalized with HLM software and methodology)","type":"Quantitative observational design","dataType":"Cross-sectional survey or administrative data with nested units (individuals within groups)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1849202015","url":null},{"ref":"Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761919049","url":null}],"related":["multilevel-modeling","cross-sectional-survey","cluster-sampling","hierarchical-linear-modeling","stratified-random-sampling","nested-anova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-descriptive-research","name":"Hierarchical Descriptive Research","fullName":"Hierarchical Descriptive Research Design","aliases":["multilevel descriptive design","nested descriptive study","hierarchical survey design","stratified descriptive research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–1990s (multilevel descriptive formalization)","originator":"Formalized within survey and educational research traditions; associated with Hox, Raudenbush, Bryk, and Creswell","url":"https://scholargate.app/en/research-design/hierarchical-descriptive-research","markdownUrl":"https://scholargate.app/en/research-design/hierarchical-descriptive-research.md","definition":"Hierarchical descriptive research is an observational design that documents the current state of a phenomenon across two or more nested levels — for example, students within classrooms within schools, or employees within teams within organizations. Rather than testing hypotheses or explaining causation, it describes distributions, frequencies, and relationships at each level, making explicit the structured, layered nature of the population being studied.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized within survey and educational research traditions; associated with Hox, Raudenbush, Bryk, and Creswell","year":"1980s–1990s (multilevel descriptive formalization)","type":"Quantitative observational/descriptive design","dataType":"Structured survey data, archival records, observational counts from nested units","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728455","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["cross-sectional-survey","multilevel-modeling","stratified-sampling","descriptive-survey-design","cluster-sampling","comparative-descriptive-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-exploratory-quantitative-research","name":"Hierarchical Exploratory Quantitative Research","fullName":"Hierarchical Exploratory Quantitative Research Design","aliases":["stratified exploratory survey design","hierarchical survey research","multilevel exploratory quantitative design","hierarchical descriptive-quantitative design"],"domain":"research-design","family":"process-pipeline","subfamily":"Survey and observational design","year":"mid-20th century onward","originator":"Developed from survey research traditions (Kish, 1965; Babbie, 1990s)","url":"https://scholargate.app/en/research-design/hierarchical-exploratory-quantitative-research","markdownUrl":"https://scholargate.app/en/research-design/hierarchical-exploratory-quantitative-research.md","definition":"Hierarchical exploratory quantitative research is a survey and observational design that structures both sampling and analysis across nested population levels — such as students within classrooms within schools — to explore patterns, distributions, and relationships in numerical data without a pre-specified directional hypothesis. It is oriented toward discovery and description rather than confirmation, making it appropriate early in a research programme when the phenomenon is not yet well-mapped.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from survey research traditions (Kish, 1965; Babbie, 1990s)","year":"mid-20th century onward","type":"Quantitative observational and survey design","dataType":"Structured questionnaire / survey data collected across hierarchically organized population strata","subfamily":"Survey and observational design"},"citations":[{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452226101","url":null},{"ref":"Babbie, E. (2016). The Practice of Social Research (14th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1305104945","url":null}],"related":["stratified-random-sampling","hierarchical-linear-modeling","cluster-sampling","exploratory-factor-analysis","cross-sectional-survey","descriptive-quantitative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-hamiltonian-monte-carlo","name":"Hierarchical Hamiltonian Monte Carlo","fullName":"Hamiltonian Monte Carlo for Hierarchical Models","aliases":["Hierarchical HMC","HMC for hierarchical models","HMC with reparameterization","NUTS for hierarchical Bayesian models"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2015","originator":"Betancourt & Girolami","url":"https://scholargate.app/en/bayesian/hierarchical-hamiltonian-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-hamiltonian-monte-carlo.md","definition":"Hierarchical Hamiltonian Monte Carlo (Hierarchical HMC) applies Hamiltonian Monte Carlo sampling to Bayesian hierarchical models, addressing the severe geometric challenges those models pose. By combining non-centered parameterizations with HMC's gradient-driven proposals, it achieves efficient posterior exploration of the multi-level funnel-shaped geometries that standard MCMC methods struggle with.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Betancourt & Girolami","year":"2015","type":"Bayesian sampling algorithm","dataType":"continuous, hierarchically structured","subfamily":"Bayesian / computational"},"citations":[{"ref":"Betancourt, M. & Girolami, M. (2015). Hamiltonian Monte Carlo for hierarchical models. In S. K. Upadhyay, U. Singh, D. K. Dey & A. Loganathan (Eds.), Current Trends in Bayesian Methodology with Applications (pp. 79-101). CRC Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hamiltonian+Monte+Carlo+for+hierarchical+models+Betancourt+Girolami+2015"},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["hamiltonian-monte-carlo","hierarchical-bayesian-inference","hierarchical-markov-chain-monte-carlo","mcmc","bayesian-regression","nuts-sampler"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-kalman-filter","name":"Hierarchical Kalman Filter","fullName":"Hierarchical Kalman Filter","aliases":["multi-scale Kalman filter","multilevel Kalman filter","hierarchical state-space filter","HKF"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1994","originator":"Chou, Willsky & Benveniste","url":"https://scholargate.app/en/bayesian/hierarchical-kalman-filter","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-kalman-filter.md","definition":"The Hierarchical Kalman Filter (HKF) extends the classic Kalman filter to systems with multiple levels or scales of state representation. It applies Kalman recursions at each level of a hierarchy — from coarse to fine resolution or from global to local subsystems — and passes information across levels via upward and downward sweeps, producing optimal linear state estimates throughout a structured state-space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chou, Willsky & Benveniste","year":"1994","type":"recursive Bayesian state estimator","dataType":"multivariate time-series / multi-resolution signal data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Chou, K. C., Willsky, A. S., & Benveniste, A. (1994). Multiscale recursive estimation, data fusion, and regularization. IEEE Transactions on Automatic Control, 39(3), 464–478.","type":"article","doi":"10.1109/9.280746","isbn":null,"url":null},{"ref":"Sarkka, S. (2013). Bayesian Filtering and Smoothing. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107619289","url":null}],"related":["kalman-filter","hierarchical-bayesian-inference","particle-filter","sequential-monte-carlo","hierarchical-sequential-monte-carlo","dynamic-kalman-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-linear-model","name":"Hierarchical Linear Model","fullName":"Hierarchical Linear Model","aliases":["HLM","multilevel linear model","nested data model","random coefficient model"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1992","originator":"Bryk & Raudenbush","url":"https://scholargate.app/en/statistics/hierarchical-linear-model","markdownUrl":"https://scholargate.app/en/statistics/hierarchical-linear-model.md","definition":"The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bryk & Raudenbush","year":"1992","type":"Multilevel linear regression","dataType":"Continuous outcome; nested / clustered observations","subfamily":"Regression / GLM"},"citations":[{"ref":"Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919049","url":null},{"ref":"Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1849202015","url":null}],"related":["multilevel-modeling","mixed-effects-model","ols-regression","panel-multiple-linear-regression","random-effects-model","generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-markov-chain-monte-carlo","name":"Hierarchical Markov Chain Monte Carlo","fullName":"Markov Chain Monte Carlo for Hierarchical Bayesian Models","aliases":["hierarchical MCMC","MCMC for multilevel models","Bayesian hierarchical MCMC","multilevel MCMC sampling"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990","originator":"Gelfand & Smith (1990), building on Geman & Geman (1984)","url":"https://scholargate.app/en/bayesian/hierarchical-markov-chain-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-markov-chain-monte-carlo.md","definition":"Hierarchical Markov chain Monte Carlo applies MCMC sampling to hierarchical Bayesian models, jointly drawing from the posterior over both observation-level parameters and the hyperparameters that govern them. This allows principled uncertainty propagation across all levels of a multilevel structure, from individuals to groups to population, using algorithms such as Gibbs sampling, Metropolis-Hastings, or Hamiltonian Monte Carlo.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelfand & Smith (1990), building on Geman & Geman (1984)","year":"1990","type":"Bayesian computational sampler","dataType":"any data amenable to hierarchical Bayesian modelling","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Robert, C. P. & Casella, G. (2004). Monte Carlo Statistical Methods (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387212395","url":null}],"related":["hierarchical-bayesian-inference","gibbs-sampling","metropolis-hastings-algorithm","hamiltonian-monte-carlo","bayesian-regression","variational-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-model-testing-research","name":"Hierarchical Model Testing Research","fullName":"Hierarchical Model Testing Research","aliases":["multilevel model testing","hierarchical SEM","nested model testing","HLM model testing"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994)","originator":"Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen","url":"https://scholargate.app/en/research-design/hierarchical-model-testing-research","markdownUrl":"https://scholargate.app/en/research-design/hierarchical-model-testing-research.md","definition":"Hierarchical model testing research is a quantitative design that evaluates theoretically derived models using data with a nested or clustered structure — for example, students within classrooms, employees within organisations, or patients within hospitals. It applies hierarchical linear models (HLM) or multilevel structural equation models (ML-SEM) to test whether a proposed set of relationships holds after properly accounting for the non-independence introduced by grouping.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Raudenbush and Anthony Bryk (HLM); extended to multilevel SEM by Bengt Muthen","year":"1980s–1990s (Raudenbush & Bryk 1986; Muthen 1994)","type":"Quantitative confirmatory research design","dataType":"Nested/clustered quantitative data (continuous, ordinal, or count outcomes)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761919049","url":null},{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728462","url":null}],"related":["multilevel-modeling","structural-equation-modeling","confirmatory-research","hierarchical-regression","longitudinal-model-testing-research","model-testing-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-particle-filter","name":"Hierarchical Particle Filter","fullName":"Hierarchical Particle Filter","aliases":["nested particle filter","multilevel particle filter","hierarchical SMC","HPF"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2000s–2010s","originator":"Briers, Doucet, and colleagues","url":"https://scholargate.app/en/bayesian/hierarchical-particle-filter","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-particle-filter.md","definition":"A hierarchical particle filter extends Sequential Monte Carlo to state-space models with multiple levels of latent variables. Particles are propagated at each level of the hierarchy, allowing the method to track both fine-grained state dynamics and slower-varying hyperparameters simultaneously, yielding calibrated posterior distributions across all levels of the model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Briers, Doucet, and colleagues","year":"2000s–2010s","type":"Sequential Monte Carlo / hierarchical state-space inference","dataType":"Sequential / time-series data with hierarchical structure","subfamily":"Bayesian / computational"},"citations":[{"ref":"Briers, M., Doucet, A. & Maskell, S. (2010). Smoothing algorithms for state-space models. Annals of the Institute of Statistical Mathematics, 62(1), 61-89.","type":"article","doi":"10.1007/s10463-009-0236-2","isbn":null,"url":null},{"ref":"Chopin, N., Jacob, P. E. & Papaspiliopoulos, O. (2013). SMC2: an efficient algorithm for sequential analysis of state-space models. Journal of the Royal Statistical Society: Series B, 75(3), 397-426.","type":"article","doi":"10.1111/j.1467-9868.2012.01046.x","isbn":null,"url":null}],"related":["particle-filter","sequential-monte-carlo","hierarchical-bayesian-inference","kalman-filter","hierarchical-markov-chain-monte-carlo","hierarchical-sequential-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-quantitative-content-analysis","name":"Hierarchical Quantitative Content Analysis","fullName":"Hierarchical Quantitative Content Analysis","aliases":["hierarchical coding content analysis","nested category content analysis","tree-structured content analysis","HQCA"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–1990s (formalized in Krippendorff 1980; elaborated through subsequent editions)","originator":"Klaus Krippendorff (hierarchical category systems formalized in content analysis methodology)","url":"https://scholargate.app/en/research-design/hierarchical-quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/hierarchical-quantitative-content-analysis.md","definition":"Hierarchical quantitative content analysis is a systematic method for coding and counting text or media content using nested, tree-structured category schemes. Rather than a flat list of mutually exclusive codes, categories are organized into parent-child levels — broad themes subdivide into specific sub-themes — enabling researchers to aggregate or disaggregate frequencies at any level of the hierarchy and to produce richly structured numerical summaries of large corpora.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klaus Krippendorff (hierarchical category systems formalized in content analysis methodology)","year":"1980s–1990s (formalized in Krippendorff 1980; elaborated through subsequent editions)","type":"Quantitative research design","dataType":"Text, media content, documents (coded into nested category systems)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506395678","url":null},{"ref":"Neuendorf, K. A. (2016). The Content Analysis Guidebook (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412979474","url":null}],"related":["quantitative-content-analysis","hierarchical-cluster-analysis","thematic-analysis","coding-reliability-analysis","text-mining","systematic-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-relational-survey","name":"Hierarchical Relational Survey","fullName":"Hierarchical Relational Survey Research","aliases":["nested relational survey","multilevel relational survey","HLM-based relational survey","hierarchical correlational survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–2002 (modern HLM-based survey tradition)","originator":"Raudenbush & Bryk (multilevel framework); Hox (multilevel survey analysis)","url":"https://scholargate.app/en/research-design/hierarchical-relational-survey","markdownUrl":"https://scholargate.app/en/research-design/hierarchical-relational-survey.md","definition":"A hierarchical relational survey combines the correlational goals of relational survey research with a multilevel data structure in which respondents are nested within higher-level units such as classrooms, schools, hospitals, or organizations. The design acknowledges that observations within the same group are not independent, and uses hierarchical linear modeling (HLM) or equivalent multilevel techniques to examine relationships among variables both within and between levels simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raudenbush & Bryk (multilevel framework); Hox (multilevel survey analysis)","year":"1980s–2002 (modern HLM-based survey tradition)","type":"Quantitative survey design with multilevel relational analysis","dataType":"Survey questionnaire data with nested/clustered structure (e.g., students within schools, employees within organizations)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761919049","url":null},{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728462","url":null}],"related":["hierarchical-linear-modeling","multilevel-modeling","correlational-research","relational-survey","longitudinal-survey-research","hierarchical-cross-sectional-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-survey-research","name":"Hierarchical Survey Research","fullName":"Hierarchical Survey Research (Multilevel Survey Design)","aliases":["multilevel survey research","nested survey design","multilevel survey design","HLM-based survey research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1986–1992 (formalization of multilevel methods for nested survey data)","originator":"Developed through contributions of Aitkin, Longford, Goldstein, Bryk, and Raudenbush in the 1980s–1990s","url":"https://scholargate.app/en/research-design/hierarchical-survey-research","markdownUrl":"https://scholargate.app/en/research-design/hierarchical-survey-research.md","definition":"Hierarchical survey research is a quantitative design that collects survey data from respondents who are naturally nested within higher-level units — such as students within classrooms, employees within organizations, or patients within hospitals — and uses multilevel (hierarchical linear) modeling to analyze variation at each level simultaneously. It is the standard approach whenever survey data have a clustered structure that would violate the independence assumption of ordinary regression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed through contributions of Aitkin, Longford, Goldstein, Bryk, and Raudenbush in the 1980s–1990s","year":"1986–1992 (formalization of multilevel methods for nested survey data)","type":"Quantitative survey design with multilevel analysis","dataType":"Structured survey questionnaire data from nested/clustered samples","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1849202015","url":null},{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728462","url":null}],"related":["survey-research","multilevel-modeling","longitudinal-survey-research","cluster-sampling","comparative-survey-research","panel-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hierarchical-variational-inference","name":"Hierarchical Variational Inference","fullName":"Hierarchical Variational Inference","aliases":["HVI","hierarchical variational models","hierarchical VI","hierarchical approximate inference"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2016","originator":"Ranganath, Altosaar, Tran & Blei","url":"https://scholargate.app/en/bayesian/hierarchical-variational-inference","markdownUrl":"https://scholargate.app/en/bayesian/hierarchical-variational-inference.md","definition":"Hierarchical variational inference (HVI) extends standard variational inference by placing a richer, hierarchical structure on the variational family itself. Instead of using a simple mean-field approximation, HVI introduces auxiliary latent variables that capture dependencies among the main latent variables, yielding tighter evidence lower bounds and more accurate posterior approximations for complex Bayesian models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ranganath, Altosaar, Tran & Blei","year":"2016","type":"Bayesian approximate inference","dataType":"continuous / count / mixed","subfamily":"Bayesian / computational"},"citations":[{"ref":"Ranganath, R., Altosaar, J., Tran, D. & Blei, D. M. (2016). Hierarchical Variational Models. Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), PMLR 48, 324-333.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v48/ranganath16.html"},{"ref":"Jordan, M. I., Ghahramani, Z., Jaakkola, T. S. & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183-233.","type":"article","doi":"10.1023/A:1007665907178","isbn":null,"url":null}],"related":["variational-inference","hierarchical-bayesian-inference","hierarchical-markov-chain-monte-carlo","bayesian-regression","mcmc","hierarchical-gibbs-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"high-frequency-microstructure","name":"Market Microstructure Analysis","fullName":"High-Frequency Data and Market Microstructure Analysis","aliases":["market microstructure","high-frequency financial econometrics","tick data analysis","Yüksek Frekanslı Veri ve Piyasa Mikro Yapısı"],"domain":"finance","family":"regression-model","subfamily":null,"year":2007,"originator":"Hasbrouck (2007); Aït-Sahalia & Jacod (2014)","url":"https://scholargate.app/en/finance/high-frequency-microstructure","markdownUrl":"https://scholargate.app/en/finance/high-frequency-microstructure.md","definition":"Market microstructure analysis studies how prices form from tick-level trade and quote data, examining order-book dynamics, the bid-ask spread, and price discovery. The modern econometric framework was set out by Hasbrouck (2007) and extended for high-frequency data by Aït-Sahalia and Jacod (2014).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hasbrouck (2007); Aït-Sahalia & Jacod (2014)","year":2007,"type":"Market microstructure / high-frequency econometrics","estimator":"Realized variance with subsampling; effective-spread decomposition","data":"Tick-level intraday trade and quote data","minSample":500,"structure":"Time series (intraday)"},"citations":[{"ref":"Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195301649","url":null},{"ref":"Aït-Sahalia, Y. & Jacod, J. (2014). High-Frequency Financial Econometrics. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691161433","url":null}],"related":["liquidity-risk-models","jump-diffusion-model","har-rv-model","backtesting-var","interest-rate-models"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"highly-accelerated-life-testing","name":"Highly Accelerated Life Testing","fullName":"Highly Accelerated Life Testing (HALT)","aliases":["HALT","Accelerated stress testing","HASS"],"domain":"reliability-engineering","family":"process-pipeline","subfamily":"Accelerated life testing","year":"1990s","originator":"William Leis and others","url":"https://scholargate.app/en/reliability-engineering/highly-accelerated-life-testing","markdownUrl":"https://scholargate.app/en/reliability-engineering/highly-accelerated-life-testing.md","definition":"Highly Accelerated Life Testing (HALT) is a methodology for rapidly identifying design weaknesses and determining the margin between normal operating conditions and product failure. By applying extreme but non-destructive stress profiles (thermal, vibration, etc.), HALT accelerates the failure clock to reveal latent defects in weeks rather than years. Developed intensively from the 1980s onward and refined by practitioners in electronics and mechanical systems, HALT has become essential in accelerated product development and reliability validation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Leis and others","subfamily":"Accelerated life testing","year":"1990s","type":"Product reliability testing methodology"},"citations":[{"ref":"Leis, B. N., & Stephens, D. R. (2011). Reliability methodologies for structural integrity assessment. Journal of Pressure Vessel Technology, 133(5), 051204.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Reliability+methodologies+for+structural+integrity+assessment+Leis"},{"ref":"Nelson, W. B. (1990). Accelerated Testing: Statistical Models, Test Plans, and Data Analyses. Wiley.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Accelerated+Testing-p-9780471071587"},{"ref":"Hobbs, G. K. (1997). Physical Modeling of Electronic Products for Reliability and Shelf Life. IEEE Transactions on Components, Packaging, and Manufacturing Technology, 20(2), 82-95.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Physical+Modeling+of+Electronic+Products+for+Reliability+and+Shelf+Life+Hobbs"},{"ref":"Alfirevic, D., Callerame, F., & Roberts, G. (2011). A comprehensive overview of HALT and HASS. Proceedings of the EPTC 2011.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=HALT+HASS+comprehensive+overview"}],"related":["rainflow-counting","first-order-reliability-method","prognostics-and-remaining-useful-life","response-surface-desirability-function"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hilbert-huang-transform","name":"Hilbert-Huang Transform","fullName":"Hilbert-Huang Transform","aliases":["HHT","EMD-Hilbert Spectral Analysis","Hilbert Spektral Analizi","Adaptive Time-Frequency Decomposition"],"domain":"signal-processing","family":"ml-model","subfamily":"Time-frequency analysis","year":1998,"originator":"Norden Huang et al.","url":"https://scholargate.app/en/signal-processing/hilbert-huang-transform","markdownUrl":"https://scholargate.app/en/signal-processing/hilbert-huang-transform.md","definition":"The Hilbert-Huang Transform (HHT) is an adaptive, data-driven method for analyzing non-linear and non-stationary time series, introduced by Norden E. Huang and colleagues in 1998. It combines Empirical Mode Decomposition (EMD), which decomposes a signal into intrinsic mode functions (IMFs), with the Hilbert spectral analysis to produce instantaneous frequency and amplitude representations without assuming signal stationarity or linearity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norden Huang et al.","year":1998,"type":"Adaptive time-frequency analysis method","subfamily":"Time-frequency analysis","basis":"Empirical, data-driven","signal_assumption":"Non-linear and non-stationary"},"citations":[{"ref":"Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A, 454(1971), 903–995.","type":"article","doi":"10.1098/rspa.1998.0193","isbn":null,"url":null}],"related":["empirical-mode-decomposition","fourier-transform"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hill-bone-compliance-scale","name":"Hill-Bone Compliance Scale","fullName":"Hill-Bone Compliance Scale (HBCS)","aliases":["HBCS"],"domain":"pharmacology","family":"process-pipeline","subfamily":"medication-adherence","year":"1999","originator":"Marjorie T. Kim, Mozella N. Hill, Lisa R. Bone, and Debra M. Levine","url":"https://scholargate.app/en/pharmacology/hill-bone-compliance-scale","markdownUrl":"https://scholargate.app/en/pharmacology/hill-bone-compliance-scale.md","definition":"The Hill-Bone Compliance Scale (HBCS) is a brief, disease-specific self-report measure designed to assess medication and lifestyle adherence in hypertension management. Developed by Kim, Hill, Bone, and Levine at Johns Hopkins University in 1999, the HBCS measures three dimensions of hypertension adherence: medication-taking, dietary sodium restriction, and appointment keeping. Unlike generic adherence measures, the HBCS captures the multifaceted nature of hypertension self-management, recognizing that many hypertensive patients struggle equally with medication adherence and behavioral changes (diet, exercise, weight management, stress management). The scale has demonstrated strong reliability and validity in diverse hypertensive populations and remains widely used in hypertension research and clinical management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marjorie T. Kim, Mozella N. Hill, Lisa R. Bone, and Debra M. Levine","subfamily":"medication-adherence","year":"1999","type":"Self-report"},"citations":[{"ref":"Kim, M. T., Hill, M. N., Bone, L. R., & Levine, D. M. (1999). Development and Testing of the Hill-Bone Compliance Scale. Journal of Cardiovascular Nursing, 4(1), 54-59. (Also: Hill, M. N., Bone, L. R., & Kim, M. T. (1996). Perspective on compliance research in hypertension. Journal of Clinical Hypertension, 8(1), 12-17.)","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+Testing+of+the+Hill-Bone+Compliance+Scale+Kim"}],"related":["medication-adherence-rating-scale","beliefs-medicines-questionnaire","tablet-questionnaire","treatment-satisfaction-questionnaire-medication"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hip-outcome-score","name":"Hip Outcome Score","fullName":"Hip Outcome Score (HOS)","aliases":["HOS"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"hip-specific outcome","year":2006,"originator":"Phillip M. Philippon, Ryan L. Martin, Bryan T. Kelly","url":"https://scholargate.app/en/sports-medicine/hip-outcome-score","markdownUrl":"https://scholargate.app/en/sports-medicine/hip-outcome-score.md","definition":"The Hip Outcome Score (HOS) is a 29-item patient self-report instrument designed to measure symptoms, functional limitations, and activity restrictions in individuals with hip disorders. Originally developed and published by Philippon, Kelly, and Martin in 2006 in Arthroscopy, the HOS has become the standard outcome measure in hip arthroscopy, hip preservation surgery, and hip rehabilitation research, providing comprehensive assessment of pain, function in daily activities, and return-to-sport capability in hip-specific populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Phillip M. Philippon, Ryan L. Martin, Bryan T. Kelly","subfamily":"hip-specific outcome","year":2006,"type":"Patient self-report"},"citations":[{"ref":"Martin RL, Kelly BT, Philippon MJ. Evidence of validity for the Hip Outcome Score. Arthroscopy. 2006;22(12):1304-1311.","type":"article","doi":"10.1016/j.arthro.2006.07.027","isbn":null,"url":null}],"related":["patient-specific-functional-scale","lower-extremity-functional-scale","faos","global-rating-of-change-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"histogram-equalization","name":"Histogram Equalization","fullName":"Histogram Equalization for Image Contrast Enhancement","aliases":["Histogram stretching","Contrast enhancement"],"domain":"computer-vision","family":"ml-model","subfamily":"Image enhancement","year":"1970s","originator":"Signal processing community","url":"https://scholargate.app/en/computer-vision/histogram-equalization","markdownUrl":"https://scholargate.app/en/computer-vision/histogram-equalization.md","definition":"Histogram equalization is an image preprocessing technique that redistributes pixel intensities to improve contrast and visibility of details. By spreading the histogram of pixel values evenly across the available range, histogram equalization enhances images with poor contrast, making features more visually distinct and easier to process algorithmically.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Signal processing community","subfamily":"Image enhancement","year":"1970s","type":"Contrast enhancement and preprocessing"},"citations":[{"ref":"Gonzalez, R. C., & Woods, R. E. (1992). Digital Image Processing. Addison-Wesley, 2nd edition, Chapter 3.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Digital+Image+Processing+-+Rafael+C.+Gonzalez-p-9781260566667"},{"ref":"Pizer, S. M., Amburn, E. P., Austin, J. D., et al. (1987). Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 39(3), 355–368.","type":"article","doi":"10.1016/S0734-189X(87)80186-X","isbn":null,"url":null}],"related":["image-morphology","contour-analysis","canny-edge-detection","blob-detection","template-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"historical-archival-research","name":"Historical Archival Research","fullName":"Historical Archival Research Method","aliases":["archival research","historical document analysis","archival history","primary source research"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"19th century (formalized ~1820s–1880s)","originator":"Historians and archivists; systematised through the professionalization of historical scholarship in the 19th century","url":"https://scholargate.app/en/field-methods/historical-archival-research","markdownUrl":"https://scholargate.app/en/field-methods/historical-archival-research.md","definition":"Historical archival research is a systematic method of investigating the past through the critical examination of primary source documents preserved in archives, libraries, and institutional collections. Researchers locate, access, authenticate, and interpret original records — such as government documents, correspondence, diaries, maps, and institutional files — to reconstruct events, trace processes, and build evidence-based historical arguments. It is foundational to historiography and widely applied across humanities and social science disciplines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Historians and archivists; systematised through the professionalization of historical scholarship in the 19th century","year":"19th century (formalized ~1820s–1880s)","type":"Qualitative primary-source research","dataType":"Primary documents, manuscripts, government records, correspondence, photographs, maps, institutional files","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Hill, M. R. (1993). Archival Strategies and Techniques. Sage Publications.","type":"book","doi":null,"isbn":"978-0803951853","url":null},{"ref":"Archival research. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Archival_research"}],"related":["oral-history-method","doctrinal-legal-research","textual-criticism","hermeneutic-analysis","content-analysis","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hjm-framework","name":"HJM Framework","fullName":"Heath-Jarrow-Morton Framework","aliases":["Forward Rate Model","No-Arbitrage Drift Condition"],"domain":"quantitative-finance","family":"regression-model","subfamily":"No-Arbitrage Framework","year":"1992","originator":"David Heath, Robert Jarrow, and Andrew Morton","url":"https://scholargate.app/en/quantitative-finance/hjm-framework","markdownUrl":"https://scholargate.app/en/quantitative-finance/hjm-framework.md","definition":"The Heath-Jarrow-Morton (HJM) framework (1992) is a general no-arbitrage approach to modeling the entire term structure of forward rates. Unlike short-rate models, HJM works directly with forward rates f(t,T) and specifies their volatility; the drift is then determined by arbitrage constraints. This flexibility enables multi-factor modeling and accurate calibration to swaption matrices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Heath, Robert Jarrow, and Andrew Morton","subfamily":"No-Arbitrage Framework","year":"1992","type":"Interest Rate Framework"},"citations":[{"ref":"Heath, D., Jarrow, R. A., & Morton, A. (1992). Bond pricing and the term structure of interest rates: A new methodology for contingent claims valuation. Econometrica, 60(1), 77-105.","type":"article","doi":"10.2307/2951677","isbn":null,"url":null},{"ref":"Brigo, D., & Mercurio, F. (2006). Interest Rate Models: Theory and Practice (2nd ed.). Springer-Verlag.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interest+Rate+Models%3A+Theory+and+Practice+%282nd+ed.%29+Brigo"}],"related":["hull-white-model","libor-market-model","risk-neutral-valuation","change-of-numeraire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hka-test","name":"HKA Test","fullName":"Hudson-Kreitman-Aguade Test for Detecting Selection","aliases":["HKA test","Polymorphism divergence test"],"domain":"genetics","family":"process-pipeline","subfamily":"Polymorphism testing","year":"1987","originator":"Richard Hudson, Martin Kreitman & Montserrat Aguade","url":"https://scholargate.app/en/genetics/hka-test","markdownUrl":"https://scholargate.app/en/genetics/hka-test.md","definition":"The Hudson-Kreitman-Aguade (HKA) test is a statistical method that tests for neutral evolution by comparing levels of within-population polymorphism and between-population divergence at multiple loci. Developed by Hudson, Kreitman, and Aguade in 1987, this test uses the principle that neutral loci should show expected relationships between polymorphism and divergence. Loci deviating from these relationships are candidates for selection. The HKA test is particularly useful for detecting selection in genome-wide surveys because it uses relative comparisons across loci rather than requiring external calibration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Hudson, Martin Kreitman & Montserrat Aguade","subfamily":"Polymorphism testing","year":"1987","type":"Statistical test"},"citations":[{"ref":"Hudson, R. R., Kreitman, M., & Aguadé, M. (1987). A test of neutral molecular evolution based on nucleotide data. Genetics, 116(1), 153–159.","type":"article","doi":"10.1093/genetics/116.1.153","isbn":null,"url":null},{"ref":"Wakeley, J., Nielsen, R., Liu-Cordova, S. N., & Ardlie, K. (2012). The discovery of single-nucleotide polymorphisms and inferences about human demographic history. American Journal of Human Genetics, 69(6), 1332–1347.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+discovery+of+single-nucleotide+polymorphisms+and+inferences+about+human+demographic+history+Wakeley"},{"ref":"Biswas, S., & Akey, J. M. (2006). Genome-wide scan for selection on derived alleles. Evolutionary Biology, 36(1), 64–79.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/16633673/"}],"related":["mcdonald-kreitman-test","selection-sweep","f-statistics","coalescent-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hlm","name":"Hierarchical Linear Modeling","fullName":"Hierarchical Linear Modeling (HLM / Multilevel Modeling)","aliases":["HLM","MLM","multilevel modeling","multilevel analysis","mixed-effects modeling","Hiyerarşik Doğrusal Modelleme (HLM / MLM)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1986,"originator":"Raudenbush & Bryk (popularized); Goldstein (parallel development)","url":"https://scholargate.app/en/statistics/hlm","markdownUrl":"https://scholargate.app/en/statistics/hlm.md","definition":"Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raudenbush & Bryk (popularized); Goldstein (parallel development)","year":1986,"family":"Multilevel / Mixed-effects model","type":"Parametric nested-data regression","levels":"≥ 2","outcome":"continuous (base form)","parametric":true,"randomEffects":true,"iccThreshold":"> 0.05 justifies HLM","minGroupSize":"≥ 5 units per group recommended"},"citations":[{"ref":"Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761919049","url":null},{"ref":"Hox, J.J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":"10.4324/9780203852279","isbn":null,"url":null}],"related":["linear-regression","mixed-effects-model","repeated-measures-anova","one-way-anova","sem"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hmac","name":"HMAC","fullName":"Hash-Based Message Authentication Code","aliases":["HMAC","keyed hash function"],"domain":"cryptography","family":"ml-model","subfamily":"Message authentication code","year":"1997","originator":"Hugo Krawczyk","url":"https://scholargate.app/en/cryptography/hmac","markdownUrl":"https://scholargate.app/en/cryptography/hmac.md","definition":"HMAC (Hash-Based Message Authentication Code) is a cryptographic algorithm for authenticating messages using a secret key and a hash function. Standardized in RFC 2104 (1997), HMAC can be combined with any cryptographic hash function (SHA-256, SHA-3, etc.) to create a message authentication code (MAC). HMAC provides both data integrity and authentication, detecting both accidental corruption and deliberate tampering, and is widely used in web security (TLS/SSL), API authentication, and network protocols.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hugo Krawczyk","subfamily":"Message authentication code","year":"1997","type":"cryptographic authentication mechanism"},"citations":[{"ref":"Krawczyk, H., Bellare, M., & Crechanko, R. (1997). HMAC: Keyed-Hashing for Message Authentication. RFC 2104.","type":"article","doi":null,"isbn":null,"url":"https://tools.ietf.org/html/rfc2104"},{"ref":"Bellare, M., Canetti, R., & Krawczyk, H. (1996). Keying hash functions for message authentication. In Advances in Cryptology - CRYPTO 1996, LNCS 1109, pp. 1-15.","type":"article","doi":"10.1007/3-540-68697-5_1","isbn":null,"url":null}],"related":["aes","rsa-cryptosystem","differential-cryptanalysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hmmer-profile-search","name":"HMMER Profile Search","fullName":"Hidden Markov Model Profile Search for Sequence Homology","aliases":["profile-hidden Markov model","HMM profile search","HMMER"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Sequence homology search","year":"1994","originator":"Sean Eddy","url":"https://scholargate.app/en/bioinformatics/hmmer-profile-search","markdownUrl":"https://scholargate.app/en/bioinformatics/hmmer-profile-search.md","definition":"HMMER profile search identifies distant protein sequence homologs using probabilistic models of protein families, known as profile Hidden Markov Models (HMMs). Developed by Eddy and colleagues, this method captures sequence variation patterns within protein families and detects homologs with far greater sensitivity than position-weight matrices or pairwise alignment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sean Eddy","subfamily":"Sequence homology search","year":"1994","type":"Probabilistic sequence search pipeline"},"citations":[{"ref":"Krogh, A., Brown, M., Mian, I. S., Sjölander, K., & Haussler, D. (1994). Hidden Markov models in computational biology: applications to protein modeling. Journal of Molecular Biology, 235(5), 1501-1531.","type":"article","doi":"10.1006/jmbi.1994.1104","isbn":null,"url":null},{"ref":"Eddy, S. R. (1998). Profile hidden Markov models. Bioinformatics, 14(9), 755-763.","type":"article","doi":"10.1093/bioinformatics/14.9.755","isbn":null,"url":null},{"ref":"Finn, R. D., Clements, J., & Eddy, S. R. (2011). HMMER web server: interactive sequence similarity searching. Nucleic Acids Research, 39(Web Server issue), W29-W37.","type":"article","doi":"10.1093/nar/gkr367","isbn":null,"url":null}],"related":["molecular-docking","cryo-em-reconstruction","metagenomic-binning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hodgkin-huxley-model","name":"Hodgkin-Huxley Model","fullName":"Hodgkin-Huxley Model of Neuronal Excitability","aliases":["Hodgkin-Huxley equations","Action potential model","Ionic channel dynamics"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Computational neuroscience","year":"1952","originator":"Alan Hodgkin","url":"https://scholargate.app/en/biomechanics/hodgkin-huxley-model","markdownUrl":"https://scholargate.app/en/biomechanics/hodgkin-huxley-model.md","definition":"The Hodgkin-Huxley model is a mathematical description of how action potentials in neurons are generated by the flow of sodium and potassium ions across the cell membrane. Developed by Alan Hodgkin and Andrew Huxley in 1952, it is a foundational model in neuroscience and earned them the Nobel Prize, establishing quantitative biophysics as a discipline.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Hodgkin","subfamily":"Computational neuroscience","year":"1952","type":"Differential equation model of neuronal dynamics"},"citations":[{"ref":"Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500-544.","type":"article","doi":"10.1113/jphysiol.1952.sp004764","isbn":null,"url":null},{"ref":"Koch, C. (2004). Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://oxford.org"}],"related":["integrate-and-fire-model","bci-motor-imagery","muscle-synergy-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hoek-brown-criterion","name":"Hoek-Brown Criterion","fullName":"Hoek-Brown Failure Criterion for Rock Masses","aliases":["Generalized Hoek-Brown Criterion","HB Criterion"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Rock Failure Criterion","year":"1980","originator":"Evert Hoek and E. T. Brown","url":"https://scholargate.app/en/mining-engineering/hoek-brown-criterion","markdownUrl":"https://scholargate.app/en/mining-engineering/hoek-brown-criterion.md","definition":"The Hoek-Brown Criterion, developed by Evert Hoek and E. T. Brown starting in 1980, is an empirical failure criterion that predicts the shear strength of rock masses as a function of confining pressure. It accounts for rock quality (via the Geological Strength Index, GSI) and thus bridges laboratory rock mechanics and field behavior. The criterion is widely used in mining for slope stability, pillar design, and stress analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evert Hoek and E. T. Brown","subfamily":"Rock Failure Criterion","year":"1980","type":"Empirical criterion for rock mass strength prediction"},"citations":[{"ref":"Hoek, E., & Brown, E. T. (2002). The Hoek-Brown failure criterion and GSI: 2018 update. Journal of Rock Mechanics and Geotechnical Engineering, 10(2), 445-463.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Hoek-Brown+failure+criterion+and+GSI%3A+2018+update+Hoek"},{"ref":"Carter, T. G., Marinos, V., & Marinos, P. (2018). Guidelines for the classification of rock masses in Turkey. Bulletin of Engineering Geology and the Environment, 77(4), 1639-1681.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Guidelines+for+the+classification+of+rock+masses+in+Turkey+Carter"}],"related":["rock-mass-rating","q-system","stope-layout"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hogan-grief-reaction-checklist","name":"HGRC","fullName":"Hogan Grief Reaction Checklist","aliases":["HGRC","Hogan GRC","Grief Reaction Checklist"],"domain":"bereavement-psychology","family":"process-pipeline","subfamily":"comprehensive-grief-outcome-measurement","year":"2001","originator":"Nancy S. Hogan","url":"https://scholargate.app/en/bereavement-psychology/hogan-grief-reaction-checklist","markdownUrl":"https://scholargate.app/en/bereavement-psychology/hogan-grief-reaction-checklist.md","definition":"The Hogan Grief Reaction Checklist (HGRC) is a 61-item comprehensive measure developed by Nancy S. Hogan and colleagues in 2001 to assess the full spectrum of grief reactions—encompassing not only grief distress and symptoms but also post-loss growth and resilience. Unique among grief instruments, the HGRC explicitly measures positive outcomes of bereavement (personal growth, meaning, strengthened relationships), reflecting contemporary understanding that grief can coexist with adaptive change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nancy S. Hogan","subfamily":"comprehensive-grief-outcome-measurement","year":"2001","type":"Self-report questionnaire"},"citations":[{"ref":"Hogan, N. S., Greenfield, D. B., & Schmidt, L. A. (2001). Development and validation of the Hogan Grief Reaction Checklist. Death Studies, 25(1), 1–32.","type":"article","doi":"10.1080/074811801750058609","isbn":null,"url":null}],"related":["inventory-complicated-grief","texas-revised-inventory-grief","grief-experience-questionnaire","anticipatory-grief-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hohmann-transfer","name":"Hohmann Transfer","fullName":"Hohmann Transfer Orbit","aliases":["Hohmann-Vallado transfer","two-impulse maneuver"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Astrodynamics","year":"1925","originator":"Walter Hohmann","url":"https://scholargate.app/en/applied-physics/hohmann-transfer","markdownUrl":"https://scholargate.app/en/applied-physics/hohmann-transfer.md","definition":"The Hohmann transfer is a maneuver that transfers a spacecraft between two circular orbits using two impulsive burns (velocity changes). Introduced by German engineer Walter Hohmann in 1925, it is the most fuel-efficient method for coplanar orbital transfers when the transfer time is not severely constrained. The transfer orbit is an ellipse tangent to both the initial and final orbits.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walter Hohmann","subfamily":"Astrodynamics","year":"1925","type":"Trajectory optimization algorithm"},"citations":[{"ref":"Hohmann, W. (1925). Die Erreichbarkeit der Himmelskörper. R. Oldenbourg.","type":"book","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Hohmann_transfer_orbit"},{"ref":"Curtis, H. D. (2013). Orbital Mechanics for Engineering Students (3rd ed.). Butterworth-Heinemann.","type":"book","doi":null,"isbn":"978-0-08-102133-0","url":null},{"ref":"Vallado, D. A., Crawford, P., Hujsak, R., & Kelso, T. S. (2006). Revisiting Spacetrack Report #3: Orbit Determination using Modern Computers. In AIAA/AAS Astrodynamics Specialist Conference.","type":"book","doi":"10.2514/6.2006-6753","isbn":null,"url":null}],"related":["orbit-determination","n-body-simulation","gravity-assist","light-curve-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"holistic-caring-inventory","name":"Holistic Caring Inventory","fullName":"Holistic Caring Inventory","aliases":["HCI"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"Holistic nursing care competence","year":"1998","originator":"Dossey, B. M.; Keegan, L.; Guzetta, C. E.","url":"https://scholargate.app/en/integrative-medicine/holistic-caring-inventory","markdownUrl":"https://scholargate.app/en/integrative-medicine/holistic-caring-inventory.md","definition":"The Holistic Caring Inventory (HCI) is a clinical assessment tool measuring nurses' and healthcare providers' capacity to deliver holistic, person-centered care that integrates physical, emotional, spiritual, and social dimensions. Developed in the context of Watson's theory of human caring, it operationalizes the philosophical principles of holistic nursing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dossey, B. M.; Keegan, L.; Guzetta, C. E.","subfamily":"Holistic nursing care competence","year":"1998","type":"Self-report and observer-rated scale"},"citations":[{"ref":"Dossey, B. M., Keegan, L., & Guzetta, C. E. (2005). Holistic nursing: A handbook for practice (4th ed.). Jones & Bartlett Publishers.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Holistic+nursing%3A+A+handbook+for+practice+%284th+ed.%29+Dossey"},{"ref":"Watson, J. (1999). Postmodern nursing and beyond. Edinburgh: Churchill Livingstone.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Watson%2C%20J.%20(1999).%20Postmodern%20nursing%20and%20beyond.%20Edinburgh%3A%20Churchill%20Livingstone."}],"related":["spiritual-care-competence-scale","attitudes-cam-scale","integrative-medicine-attitudes","therapeutic-touch-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"holm-correction","name":"Holm Correction","fullName":"Holm Step-Down Family-Wise Error Rate Correction","aliases":["Holm-Bonferroni method","Holm step-down procedure","Holm's sequentially rejective procedure","Holm düzeltmesi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1979,"originator":"Sture Holm","url":"https://scholargate.app/en/statistics/holm-correction","markdownUrl":"https://scholargate.app/en/statistics/holm-correction.md","definition":"The Holm correction, introduced by Sture Holm in 1979, is a step-down multiple-comparison procedure that controls the family-wise error rate (FWER) at level α while rejecting at least as many hypotheses as the classical Bonferroni correction. It orders the observed p-values from smallest to largest and compares each against a threshold that starts strict and relaxes as testing proceeds, making it uniformly more powerful than Bonferroni at the same level of error control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sture Holm","year":1979,"family":"Hypothesis test","type":"Family-wise error rate (FWER) correction","errorControl":"Family-wise error rate (FWER)","parametric":true,"procedure":"Step-down (sequentially rejective)","applicability":"Any simultaneous hypothesis tests"},"citations":[{"ref":"Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/4615733"},{"ref":"Bonferroni, C. E. (1936). Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 3–62.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar_lookup?title=Teoria+statistica+delle+classi+e+calcolo+delle+probabilita&author=C+Bonferroni&publication_year=1936"}],"related":["bonferroni-correction","benjamini-hochberg-procedure","sidak-correction","tukey-hsd-test","one-way-anova"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"holt-winters","name":"Holt-Winters","fullName":"Holt-Winters Triple Exponential Smoothing","aliases":["triple exponential smoothing","Winters' method","Holt-Winters seasonal method","Holt-Winters Üçlü Üstel Düzleştirme"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1960,"originator":"Charles C. Holt and Peter R. Winters","url":"https://scholargate.app/en/econometrics/holt-winters","markdownUrl":"https://scholargate.app/en/econometrics/holt-winters.md","definition":"Holt-Winters triple exponential smoothing is a forecasting model that extends Holt's double smoothing by adding a seasonal component, introduced by Peter Winters in 1960 building on Charles Holt's work. It tracks three evolving quantities — level, trend, and season — and combines them to forecast a continuous time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles C. Holt and Peter R. Winters","year":1960,"type":"Exponential smoothing forecasting model","estimator":"Recursive level, trend, and seasonal updating","outcome":"continuous time series","variants":"additive and multiplicative seasonality","minSample":24},"citations":[{"ref":"Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342.","type":"article","doi":"10.1287/mnsc.6.3.324","isbn":null,"url":null},{"ref":"Holt, C. C. (2004). Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages. International Journal of Forecasting, 20(1), 5-10.","type":"article","doi":"10.1016/j.ijforecast.2003.09.015","isbn":null,"url":null}],"related":["simple-exponential-smoothing","state-space-model","structural-time-series","arima","seasonal-arima"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"holtrop-mennen-method","name":"Holtrop-Mennen Method","fullName":"Holtrop-Mennen Ship Resistance and Propulsion Method","aliases":["Holtrop method","Mennen method","ship resistance prediction"],"domain":"aerospace","family":"process-pipeline","subfamily":"Naval Hydrodynamics","year":"1982","originator":"Jelte Holtrop, Gert Mennen","url":"https://scholargate.app/en/aerospace/holtrop-mennen-method","markdownUrl":"https://scholargate.app/en/aerospace/holtrop-mennen-method.md","definition":"The Holtrop-Mennen Method is an empirical regression-based technique for predicting total ship resistance from geometric parameters and operating conditions. Developed by Jelte Holtrop and Gert Mennen in 1982, the method decomposes total resistance into friction, pressure, wave-making, and form drag components, each estimated from ship dimensions, hull shape, and speed. Widely adopted in maritime engineering, the Holtrop-Mennen method remains the industry standard for preliminary ship design and propulsion power estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jelte Holtrop, Gert Mennen","subfamily":"Naval Hydrodynamics","year":"1982","type":"Prediction method"},"citations":[{"ref":"Holtrop, J., & Mennen, G. G. J. (1984). An approximate power prediction method for fast monohull ships. International Shipbuilding Progress, 29(335), 166–170.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+approximate+power+prediction+method+for+fast+monohull+ships+Holtrop"},{"ref":"Holtrop, J. (2001). Ship propulsion and trim. Maritime Engineering Proceedings, 153(1), 35–42.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Ship+propulsion+and+trim+Holtrop"},{"ref":"Birk, L. (2019). The influence of nonlinear viscous pressure drag on ship resistance. Ocean Engineering, 171, 62–74.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+influence+of+nonlinear+viscous+pressure+drag+on+ship+resistance+Birk"}],"related":["seakeeping-strip-theory","propeller-lifting-line","blade-element-momentum-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"home-food-environment-questionnaire","name":"HFEQ","fullName":"Home Food Environment Questionnaire","aliases":["HFEQ","Home Food Environment","Food Environment Assessment"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"food-environment-assessment","year":"2014","originator":"Boles et al.; Fulkerson et al.; Environmental Research on Child Health","url":"https://scholargate.app/en/public-health-nutrition/home-food-environment-questionnaire","markdownUrl":"https://scholargate.app/en/public-health-nutrition/home-food-environment-questionnaire.md","definition":"The HFEQ is a parent-report questionnaire measuring the household food environment—the availability of healthy and unhealthy foods, parent feeding practices, and family mealtime characteristics. Developed by Boles, Fulkerson, and colleagues, the HFEQ captures multiple dimensions of the home environment that influence children's dietary intake and weight status. The home food environment is a key modifiable target for childhood obesity prevention interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Boles et al.; Fulkerson et al.; Environmental Research on Child Health","subfamily":"food-environment-assessment","year":"2014","type":"Parent-report questionnaire; household food availability and feeding practices"},"citations":[{"ref":"Boles, R. E., Scharf, C., Fiese, B. H., et al. (2013). Differences in the home food environment and eating behaviors by child weight status: A qualitative study. Journal of the Academy of Nutrition and Dietetics, 113(7), 926–933.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Differences+in+the+home+food+environment+and+eating+behaviors+by+child+weight+status%3A+A+qualitative+study+Boles"},{"ref":"Fulkerson, J. A., Larson, N., Horowitz, M., & Neumark-Sztainer, D. (2014). A qualitative study of factors affecting family food choices and the home food environment of lower-income families with preschool children. Appetite, 76, 76–85.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+qualitative+study+of+factors+affecting+family+food+choices+and+the+home+food+environment+of+lower-income+families+with+preschool+children+Fulkerson"}],"related":["household-dietary-diversity-score","child-diet-questionnaire","household-food-insecurity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"homology-modeling","name":"Homology Modeling","fullName":"Homology-based Protein Structure Prediction","aliases":["comparative modeling","template-based modeling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Structural bioinformatics","year":"1993","originator":"Andrej Sali","url":"https://scholargate.app/en/bioinformatics/homology-modeling","markdownUrl":"https://scholargate.app/en/bioinformatics/homology-modeling.md","definition":"Homology modeling, also called comparative modeling, predicts the three-dimensional structure of a protein using an experimentally-solved structure of a homologous protein as a template. Introduced by Sali and Blundell in 1993, this method exploits the principle that homologous proteins share similar spatial structures despite differing in amino acid sequence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrej Sali","subfamily":"Structural bioinformatics","year":"1993","type":"Comparative structure prediction pipeline"},"citations":[{"ref":"Sali, A. & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234(3), 779-815.","type":"article","doi":"10.1006/jmbi.1993.1626","isbn":null,"url":null},{"ref":"Arnold, K., Bordoli, L., Kopp, J., & Schwede, T. (2006). The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics, 22(2), 195-201.","type":"article","doi":"10.1093/bioinformatics/bti770","isbn":null,"url":null},{"ref":"Fiser, A., Do, R. K., & Sali, A. (2000). ModellerX and SOAP protein structure modelling. Trends in Biochemical Sciences, 25(12), 589-592.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ModellerX+and+SOAP+protein+structure+modelling+Fiser"}],"related":["molecular-docking","pharmacophore-modeling","cryo-em-reconstruction","ppi-network-topology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"homomorphic-encryption","name":"Homomorphic Encryption","fullName":"Fully Homomorphic Encryption","aliases":["FHE","Fully Homomorphic Encryption","Leveled Homomorphic Encryption","Homomorfik Şifreleme"],"domain":"privacy","family":"ml-model","subfamily":"Privacy-preserving computation","year":2009,"originator":"Craig Gentry","url":"https://scholargate.app/en/privacy/homomorphic-encryption","markdownUrl":"https://scholargate.app/en/privacy/homomorphic-encryption.md","definition":"Homomorphic Encryption (HE) is a cryptographic framework that allows arbitrary computations to be performed directly on encrypted data without requiring decryption. First realized as a fully general construction by Craig Gentry in 2009 using ideal lattices, it enables a server to process sensitive data and return an encrypted result that, when decrypted by the data owner, equals the result of performing the same computation on the plaintext. It is foundational to privacy-preserving machine learning, secure cloud computing, and confidential analytics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Craig Gentry","year":2009,"type":"Lattice-based cryptographic scheme","subfamily":"Privacy-preserving computation","hardness_assumption":"Learning With Errors (LWE) / ideal lattices","operation_classes":"Addition and multiplication over ciphertexts"},"citations":[{"ref":"Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. ACM Symposium on Theory of Computing (STOC), 169–178.","type":"inproceedings","doi":"10.1145/1536414.1536440","isbn":null,"url":null}],"related":["secure-multiparty-computation","differential-privacy","federated-learning"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"honey-badger-algorithm","name":"Honey Badger Algorithm","fullName":"Honey Badger Algorithm","aliases":["HBA"],"domain":"optimization","family":"ml-model","subfamily":"Swarm Intelligence","year":"2023","originator":"Fatma A. Hashim","url":"https://scholargate.app/en/optimization/honey-badger-algorithm","markdownUrl":"https://scholargate.app/en/optimization/honey-badger-algorithm.md","definition":"The Honey Badger Algorithm (HBA) is a nature-inspired metaheuristic optimization algorithm presented by Hashim et al. in 2023, modeled on the hunting behavior and intelligent strategies of honey badgers (Mellivora capensis). Honey badgers are known for their remarkable problem-solving abilities, fearlessness, and persistent pursuit of prey and food sources despite significant obstacles. HBA captures these behavioral traits to create an effective optimization framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fatma A. Hashim","subfamily":"Swarm Intelligence","year":"2023","type":"Nature-inspired metaheuristic algorithm"},"citations":[{"ref":"Hashim, F. A., Hussain, K., & Houssein, E. H. (2023). Honey badger algorithm: A new meta-heuristic optimization algorithm. Neural Computing and Applications, 35(17), 12265-12287.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Honey+badger+algorithm%3A+A+new+meta-heuristic+optimization+algorithm+Hashim"}],"related":["harris-hawks-optimization","slime-mould-algorithm","aquila-optimizer","grey-wolf-optimizer","particle-swarm-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hoos","name":"Hip Disability and Osteoarthritis Outcome Score","fullName":"Hip Disability and Osteoarthritis Outcome Score","aliases":["HOOS","HOOS Scale"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Functional assessment","year":"2003","originator":"Nilsdotter, Lohmander, Klassbo, Roos","url":"https://scholargate.app/en/rehabilitation/hoos","markdownUrl":"https://scholargate.app/en/rehabilitation/hoos.md","definition":"The Hip Disability and Osteoarthritis Outcome Score (HOOS) is a patient-reported outcome measure developed to assess pain, symptoms, function, and quality of life in patients with hip osteoarthritis and hip disability. Developed by Nilsdotter and colleagues in 2003, HOOS parallels the structure of KOOS (Knee Injury and Osteoarthritis Outcome Score) but is specifically tailored for the hip joint, making it the reference standard for hip osteoarthritis outcomes in clinical trials and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nilsdotter, Lohmander, Klassbo, Roos","subfamily":"Functional assessment","year":"2003","type":"Patient-reported outcome measure"},"citations":[{"ref":"Nilsdotter, A. K., Lohmander, L. S., Klassbo, M., & Roos, E. M. (2003). Hip Disability and Osteoarthritis Outcome Score (HOOS): development and validation against generic outcome measures in hip osteoarthritis. Arthritis & Rheumatism, 48(11), 3336–3345.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hip+Disability+and+Osteoarthritis+Outcome+Score+%28HOOS%29%3A+development+and+validation+against+generic+outcome+measures+in+hip+osteoarthritis+Nilsdotter"},{"ref":"Nilsdotter, A. K., Ackermann, P. W., & Roos, E. M. (2007). Scoring instructions and guide for the hip disability and osteoarthritis outcome score (HOOS) v2.0 and HOOS junior. Stockholm, Sweden: Karolinska Institute.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/14872954"}],"related":["womac","koos","hoos-child","mocha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hope-scale","name":"Adult Dispositional Hope Scale","fullName":"Adult Dispositional Hope Scale (Hope Scale)","aliases":["Hope Scale","Adult Hope Scale"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"agency and pathways thinking","year":"1991","originator":"C. Rick Snyder","url":"https://scholargate.app/en/positive-psychology/hope-scale","markdownUrl":"https://scholargate.app/en/positive-psychology/hope-scale.md","definition":"The Adult Dispositional Hope Scale, developed by C. Rick Snyder in 1991, is a 12-item measure assessing hope as a cognitive motivational system composed of two independent dimensions: Agency (the motivation and determination to pursue goals) and Pathways (the ability to generate routes to achieve those goals). Grounded in hope theory, the scale operationalizes hope not as wishful thinking but as an actionable psychological state combining goal-directed determination with flexible problem-solving.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"C. Rick Snyder","subfamily":"agency and pathways thinking","year":"1991","type":"Self-report questionnaire"},"citations":[{"ref":"Snyder, C. R., Harris, C., Anderson, J. R., Holleran, S. A., Irving, L. M., Sigmon, S. T., ... & Harney, P. (1991). The will and the ways: Development and validation of an individual-differences measure of hope. Journal of Personality and Social Psychology, 60(4), 570–585.","type":"article","doi":"10.1037/0022-3514.60.4.570","isbn":null,"url":null}],"related":["flourishing-scale","optimism-life-orientation-test","meaning-in-life-questionnaire","positive-mental-health-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hospital-bed-occupancy-model","name":"Hospital Bed Occupancy Model","fullName":"Stochastic Hospital Bed Occupancy Forecasting Model","aliases":["Bed Occupancy Forecasting","Hospital Census Prediction"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Capacity planning, Forecasting","year":"2000","originator":"Healthcare operations researchers","url":"https://scholargate.app/en/healthcare-management/hospital-bed-occupancy-model","markdownUrl":"https://scholargate.app/en/healthcare-management/hospital-bed-occupancy-model.md","definition":"Hospital bed occupancy models forecast the number of occupied beds at future times by analyzing admission patterns, length of stay distributions, and discharge dynamics. These models support tactical decisions about staffing, supply chain management, and strategic decisions about capacity expansion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Healthcare operations researchers","subfamily":"Capacity planning, Forecasting","year":"2000","type":"Stochastic simulation and time-series forecasting"},"citations":[{"ref":"Tikk, D., Kóczy, L. T., & Gedeon, T. D. (2003). A survey on fuzzy relational equations and their applications in web intelligence. In W. Pedrycz (Ed.), Handbook of Granular Computing (pp. 521–542). John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+survey+on+fuzzy+relational+equations+and+their+applications+in+web+intelligence+Tikk"},{"ref":"McCarthy, M. L., Zeger, S. L., Ding, R., Levin, S. R., Desmond, J. S., Lee, J., & Aronsky, D. (2008). The challenge of predicting demand for emergency department services. Academic Emergency Medicine, 15(4), 337–346.","type":"article","doi":"10.1111/j.1553-2712.2008.00083.x","isbn":null,"url":null},{"ref":"Helm, J. E., AhmadBeygi, S., & Van Oyen, M. P. (2011). Design and analysis of hospital admission control for optimizing quality of care. Operations Research, 59(5), 1153–1166.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Design+and+analysis+of+hospital+admission+control+for+optimizing+quality+of+care+Helm"}],"related":["queuing-theory-healthcare","patient-flow-simulation","hospital-readmission-model","staffing-ratio-analysis","lean-healthcare"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hospital-consumer-assessment","name":"HCAHPS Hospital Consumer Assessment Survey","fullName":"Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS)","aliases":["HCAHPS","H-CAHPS"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"patient-experience-satisfaction","year":"2006","originator":"Centers for Medicare & Medicaid Services (CMS) and Agency for Healthcare Research and Quality (AHRQ)","url":"https://scholargate.app/en/healthcare-management/hospital-consumer-assessment","markdownUrl":"https://scholargate.app/en/healthcare-management/hospital-consumer-assessment.md","definition":"The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) is a 27-item, CMS-mandated patient experience survey administered to a random sample of hospital inpatients after discharge. Launched in 2006 by the Centers for Medicare & Medicaid Services and the Agency for Healthcare Research and Quality, HCAHPS measures patient perceptions of hospital care across 10 key composites: communication with nurses, communication with physicians, responsiveness to patient needs, pain management, communication about medications, discharge information, cleanliness and quietness of the hospital environment, and overall rating of the hospital. HCAHPS is publicly reported on the CMS Hospital Compare website, incorporated into hospital payment incentive programs, and is one of the most widely recognized measures of hospital quality from the patient perspective.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Centers for Medicare & Medicaid Services (CMS) and Agency for Healthcare Research and Quality (AHRQ)","subfamily":"patient-experience-satisfaction","year":"2006","type":"Self-report (patient/family-reported)"},"citations":[{"ref":"Centers for Medicare & Medicaid Services and Agency for Healthcare Research and Quality. (2006). Hospital Consumer Assessment of Healthcare Providers and Systems. U.S. Department of Health and Human Services.","type":"report","doi":null,"isbn":null,"url":"https://www.hcahpsonline.org"},{"ref":"Hargraves, J. L., Hays, R. D., & Cleary, P. D. (2003). Psychometric properties of the Agency for Healthcare Research and Quality's Healthcare Experiences with Care questionnaire. Health Services Research, 38(6), 1457–1479.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Psychometric+properties+of+the+Agency+for+Healthcare+Research+and+Quality%27s+Healthcare+Experiences+with+Care+questionnaire+Hargraves"},{"ref":"Jaipaul, C. K., & Rosenthal, G. E. (2005). Are differences in medical complications, length of stay, and mortality rate useful markers of hospital quality? Journal of Hospital Medicine, 1(2), 107–118.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Are+differences+in+medical+complications%2C+length+of+stay%2C+and+mortality+rate+useful+markers+of+hospital+quality+Jaipaul"}],"related":["hospital-survey-patient-safety","patient-reported-experience-measure","safety-attitudes-questionnaire","nurse-work-environment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hospital-readmission-model","name":"Hospital Readmission Prediction Model","fullName":"Predictive Modeling for Hospital Readmission Risk and Prevention","aliases":["Readmission Risk Prediction","Hospital Readmission Forecasting"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Predictive modeling, Patient risk stratification","year":"1998","originator":"Healthcare data analytics and outcomes research","url":"https://scholargate.app/en/healthcare-management/hospital-readmission-model","markdownUrl":"https://scholargate.app/en/healthcare-management/hospital-readmission-model.md","definition":"Hospital readmission prediction models use statistical and machine learning techniques to identify patients at high risk of returning to the hospital shortly after discharge. These models guide targeted discharge planning and follow-up to improve outcomes and reduce costs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Healthcare data analytics and outcomes research","subfamily":"Predictive modeling, Patient risk stratification","year":"1998","type":"Logistic regression and machine learning methodology"},"citations":[{"ref":"Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in the Medicare fee-for-service program. New England Journal of Medicine, 360(14), 1418–1428.","type":"article","doi":"10.1056/NEJMsa0803563","isbn":null,"url":null},{"ref":"Krumholz, H. M., Normand, S. L. T., & Wang, Y. (2014). Trends in hospitalizations and outcomes for acute myocardial infarction, 2006 to 2011. Circulation, 132(4), 362–366.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Trends+in+hospitalizations+and+outcomes+for+acute+myocardial+infarction%2C+2006+to+2011+Krumholz"},{"ref":"Philbin, E. F., & DiSalvo, T. G. (1998). Prediction of hospital readmissions for heart failure: development of a simple risk score based on administrative data. Journal of the American College of Cardiology, 33(6), 1560–1566.","type":"article","doi":"10.1016/s0735-1097(99)00059-5","isbn":null,"url":null}],"related":["lean-healthcare","patient-flow-simulation","dea-hospital-efficiency","staffing-ratio-analysis","hospital-bed-occupancy-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hospital-survey-patient-safety","name":"Hospital Survey on Patient Safety Culture","fullName":"Hospital Survey on Patient Safety Culture (HSOPS)","aliases":["HSOPS"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"organizational-safety-culture","year":"2004","originator":"Agency for Healthcare Research and Quality (AHRQ) in collaboration with researchers at Westat, Inc.","url":"https://scholargate.app/en/healthcare-management/hospital-survey-patient-safety","markdownUrl":"https://scholargate.app/en/healthcare-management/hospital-survey-patient-safety.md","definition":"The Hospital Survey on Patient Safety Culture (HSOPS) is a 42-item standardized instrument developed by the Agency for Healthcare Research and Quality (AHRQ) to measure patient safety culture in hospital settings. First released in 2004 and revised in 2018, the HSOPS assesses 12 composite dimensions of safety culture across organizational, unit, and individual levels. It is one of the most frequently used and publicly reported safety culture measures, with data from over 1,000 hospitals contributing to AHRQ's national benchmarking database.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agency for Healthcare Research and Quality (AHRQ) in collaboration with researchers at Westat, Inc.","subfamily":"organizational-safety-culture","year":"2004","type":"Self-report"},"citations":[{"ref":"Sorra, J. S., & Dyer, N. (2010). Multilevel analysis of the Agency for Healthcare Research and Quality Hospital Survey on Patient Safety Culture. BMJ Quality & Safety, 19(5), 413–417.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multilevel+analysis+of+the+Agency+for+Healthcare+Research+and+Quality+Hospital+Survey+on+Patient+Safety+Culture+Sorra"},{"ref":"Westat, Inc. (2008). Hospital Survey on Patient Safety Culture 2008: Summary of Results for a Large Convenience Sample. Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services.","type":"report","doi":null,"isbn":null,"url":"https://www.ahrq.gov/sites/default/files/wysiwyg/hospital-survey-patient-safety-culture-2008-summary.pdf"},{"ref":"Nieva, V. F., & Sorra, J. (2003). Safety culture assessment: a tool for improving patient safety in healthcare organizations. Quality & Safety in Health Care, 12(2), ii17–ii23.","type":"article","doi":"10.1136/qhc.12.suppl_2.ii17","isbn":null,"url":null}],"related":["safety-attitudes-questionnaire","patient-safety-climate-scale","teamstepps-perceptions","nurse-work-environment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hot-spot-analysis","name":"Hot Spot Analysis","fullName":"Hot Spot Analysis (Getis-Ord Gi*)","aliases":["Getis-Ord Gi* statistic","spatial hot spot detection","cluster and outlier analysis","HSA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1992","originator":"Arthur Getis and J. Keith Ord","url":"https://scholargate.app/en/spatial-analysis/hot-spot-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/hot-spot-analysis.md","definition":"Hot Spot Analysis uses the Getis-Ord Gi* local spatial statistic to identify geographic locations where high or low attribute values cluster together to a degree that is statistically significant. Each feature is evaluated in relation to its neighbours, producing a z-score that flags genuine spatial hot spots and cold spots against a background of random variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arthur Getis and J. Keith Ord","year":"1992","type":"Local spatial statistic","dataType":"Georeferenced point or polygon data with a continuous attribute","subfamily":"GIS / spatial"},"citations":[{"ref":"Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.","type":"article","doi":"10.1111/j.1538-4632.1992.tb00261.x","isbn":null,"url":null},{"ref":"Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00912.x","isbn":null,"url":null}],"related":["local-indicators-of-spatial-association","spatial-autocorrelation","kernel-density-estimation","local-morans-i","morans-i","geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hotel-service-quality-scale","name":"Hotel Service Quality Scale","fullName":"Hotel Service Quality Scale (HSQS)","aliases":["HSQS","Lodging Quality Index","LQI"],"domain":"tourism-management","family":"process-pipeline","subfamily":"service-quality-measurement","year":"2003","originator":"Getty, J. M., & Getty, R. L.","url":"https://scholargate.app/en/tourism-management/hotel-service-quality-scale","markdownUrl":"https://scholargate.app/en/tourism-management/hotel-service-quality-scale.md","definition":"The Hotel Service Quality Scale (HSQS), including the Lodging Quality Index (LQI) developed by Getty & Getty (2003), measures guest perceptions of hotel service quality across multiple dimensions (room comfort, staff responsiveness, facilities, value). Using expectancy-disconfirmation theory, it captures not only perceived quality but the gap between expectations and reality, enabling precise diagnosis of service strengths and improvement priorities. Essential for hospitality managers seeking competitive positioning through service excellence and for franchisees maintaining brand standards.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Getty, J. M., & Getty, R. L.","subfamily":"service-quality-measurement","year":"2003","type":"Self-report questionnaire / Expectancy-disconfirmation scale"},"citations":[{"ref":"Getty, J. M., & Getty, R. L. (2003). Lodging quality index (LQI): Assessing Expectations and Perceptions of Lodging Quality. Cornell Hotel and Restaurant Administration Quarterly, 44(2), 33-46.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lodging+quality+index+%28LQI%29%3A+Assessing+Expectations+and+Perceptions+of+Lodging+Quality+Getty"},{"ref":"Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SERVQUAL%3A+A+multiple-item+scale+for+measuring+consumer+perceptions+of+service+quality+Parasuraman"},{"ref":"Rauch, D. A., Collins, M. D., Nale, R. D., & Barr, P. B. (2015). Measuring Service Quality in Mid-Scale Hotels. International Journal of Contemporary Hospitality Management, 27(1), 119-106.","type":"article","doi":"10.1108/ijchm-06-2013-0254","isbn":null,"url":null},{"ref":"Lockyer, T. (2005). The perceived importance of price as one hotel selection factor. International Journal of Hospitality Management, 24(4), 617-628.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+perceived+importance+of+price+as+one+hotel+selection+factor+Lockyer"}],"related":["tourist-satisfaction-scale","perceived-value-scale-tourism","destination-image-scale","tourist-loyalty-scale","place-attachment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hotelling-t2","name":"Hotelling's T² Test","fullName":"Hotelling's Two-Sample T-Squared Test","aliases":["Hotelling T² Testi — Çok Değişkenli t-Testi","multivariate t-test","Hotelling T-squared"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1931,"originator":"Harold Hotelling","url":"https://scholargate.app/en/statistics/hotelling-t2","markdownUrl":"https://scholargate.app/en/statistics/hotelling-t2.md","definition":"Hotelling's T² test is a multivariate parametric hypothesis test that simultaneously compares the mean vectors of two independent groups across multiple continuous outcome variables. It was introduced by Harold Hotelling in 1931 as the direct multivariate generalization of Student's t-test, replacing the scalar mean difference with a vector difference scaled by the pooled variance-covariance matrix.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Hotelling","year":1931,"family":"Hypothesis test","type":"Multivariate parametric mean comparison","groups":2,"outcome":"continuous (multivariate)","parametric":true,"distribution":"F (via T² to F transformation)","minSample":30},"citations":[{"ref":"Hotelling, H. (1931). The Generalization of Student's Ratio. Annals of Mathematical Statistics, 2(3), 360–378.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2957502"}],"related":["manova","independent-t-test","welch-t-test","multivariate-regression","mancova","one-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hough-transform","name":"Hough Transform","fullName":"Hough Transform for Line and Shape Detection","aliases":["Hough Line Detection","Generalized Hough Transform"],"domain":"computer-vision","family":"ml-model","subfamily":"Shape detection","year":"1962","originator":"Paul Hough","url":"https://scholargate.app/en/computer-vision/hough-transform","markdownUrl":"https://scholargate.app/en/computer-vision/hough-transform.md","definition":"The Hough Transform is a technique for detecting lines, circles, and other geometric shapes in digital images. Originally patented by Paul Hough in 1962 and popularized in computer vision by Duda and Hart in 1972, the Hough Transform converts edge points in image space to curves in a parameter space (accumulator space), where collinear or co-circular points cluster and become easily identifiable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Hough","subfamily":"Shape detection","year":"1962","type":"Feature extraction and pattern recognition"},"citations":[{"ref":"Hough, P. V. C. (1962). Method and means for recognizing complex patterns. U.S. Patent 3,069,654.","type":"article","doi":null,"isbn":null,"url":"https://patents.google.com/patent/US3069654A/"},{"ref":"Duda, R. O., & Hart, P. E. (1972). Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 15(1), 11–15.","type":"article","doi":"10.1145/361237.361242","isbn":null,"url":null}],"related":["canny-edge-detection","contour-analysis","template-matching","image-morphology","harris-corner-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"household-dietary-diversity-score","name":"HDDS","fullName":"Household Dietary Diversity Score","aliases":["HDDS","Dietary Diversity Score"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"dietary-diversity-assessment","year":"2011","originator":"Kennedy, Ballard, Dop; FAO","url":"https://scholargate.app/en/public-health-nutrition/household-dietary-diversity-score","markdownUrl":"https://scholargate.app/en/public-health-nutrition/household-dietary-diversity-score.md","definition":"The HDDS is a simple, 12-item food group checklist that captures the diversity of the household diet in the preceding 24 hours. Developed by the FAO in 2011 as a proxy indicator of dietary quality and nutrient adequacy, the HDDS enables rapid assessment of the nutritional vulnerability of households in resource-limited settings. A household with access to foods from many groups is more likely to consume adequate calories, protein, and micronutrients.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kennedy, Ballard, Dop; FAO","subfamily":"dietary-diversity-assessment","year":"2011","type":"Household 24-hour recall"},"citations":[{"ref":"Kennedy, G., Ballard, T., & Dop, M. C. (2011). Guidelines for measuring household and individual dietary diversity. Food and Agriculture Organization of the United Nations.","type":"article","doi":null,"isbn":null,"url":"https://www.fao.org/3/i1983e/i1983e.pdf"}],"related":["household-food-insecurity-scale","maternal-diet-quality-index","healthy-eating-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"household-food-insecurity-scale","name":"HFIAS","fullName":"Household Food Insecurity Access Scale","aliases":["HFIAS","Food Insecurity Access Scale"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"food-insecurity-measurement","year":"2007","originator":"Coates, Swindale, Bilinsky; FANTA Project","url":"https://scholargate.app/en/public-health-nutrition/household-food-insecurity-scale","markdownUrl":"https://scholargate.app/en/public-health-nutrition/household-food-insecurity-scale.md","definition":"The HFIAS is a 9-item survey designed to measure the frequency and severity of food insecurity at the household level in resource-limited settings. Developed by the FANTA Project in 2007, it assesses four domains of food access: anxiety, dietary diversity, food consumption frequency, and household member deprivation. It is widely used in low- and middle-income countries for program evaluation and surveillance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Coates, Swindale, Bilinsky; FANTA Project","subfamily":"food-insecurity-measurement","year":"2007","type":"Household survey"},"citations":[{"ref":"Coates, J., Swindale, A., & Bilinsky, P. (2007). Household Food Insecurity Access Scale (HFIAS) for measurement of food access: indicator guide. Food and Nutrition Technical Assistance Project, Academy for Educational Development.","type":"article","doi":"10.1037/e576842013-001","isbn":null,"url":null}],"related":["household-dietary-diversity-score","food-security-module","maternal-diet-quality-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hp-filter","name":"HP Filter","fullName":"Hodrick-Prescott Filter","aliases":["Hodrick-Prescott Filter","HP Decomposition","Trend-Cycle Filter","HP Filtresi"],"domain":"econometrics","family":"process-pipeline","subfamily":"Trend & seasonality","year":1997,"originator":"Robert Hodrick & Edward Prescott","url":"https://scholargate.app/en/econometrics/hp-filter","markdownUrl":"https://scholargate.app/en/econometrics/hp-filter.md","definition":"The Hodrick-Prescott (HP) filter is a penalized least-squares technique used in macroeconomics and empirical finance to decompose a time series into a smooth long-run trend component and a short-run cyclical component. Introduced by Hodrick and Prescott (1997) using postwar U.S. business cycle data, it has become one of the most widely applied filters in business cycle analysis, monetary policy research, and applied econometrics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Hodrick & Edward Prescott","year":1997,"type":"Penalized least-squares smoother","subfamily":"Trend & seasonality","smoothing_parameter":"λ (lambda); conventional values: 100 for annual, 1600 for quarterly, 14400 for monthly data"},"citations":[{"ref":"Hodrick, R. J., & Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 1–16.","type":"article","doi":"10.2307/2953682","isbn":null,"url":null}],"related":["bk-filter","stl-decomposition","state-space-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hplc","name":"HPLC","fullName":"High-Performance Liquid Chromatography","aliases":["HPLC","high-pressure liquid chromatography"],"domain":"food-science","family":"process-pipeline","subfamily":"Analytical Chemistry","year":"1970","originator":"Csaba Horváth","url":"https://scholargate.app/en/food-science/hplc","markdownUrl":"https://scholargate.app/en/food-science/hplc.md","definition":"High-Performance Liquid Chromatography (HPLC) is an analytical technique that separates, identifies, and quantifies components in a complex food sample by passing the sample through a pressurized column packed with a stationary phase. Developed by Horváth in the early 1970s, HPLC enables rapid, sensitive measurement of nutrients, contaminants, additives, and bioactive compounds in food products with high precision and accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Csaba Horváth","subfamily":"Analytical Chemistry","year":"1970","type":"Separation and Quantification Technique"},"citations":[{"ref":"Snyder, L. R., Kirkland, J. J., & Dolan, J. W. (2010). Introduction to modern liquid chromatography (3rd ed.). Wiley.","type":"article","doi":"10.1002/9780470508183","isbn":null,"url":null},{"ref":"Giddings, J. C. (1965). Dynamics of chromatography. Journal of Chromatography A, 3, 520.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"}],"related":["gas-chromatography-olfactometry","electronic-nose","karl-fischer-titration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hpsg","name":"HPSG","fullName":"Head-Driven Phrase Structure Grammar","aliases":["HPSG Grammar","Constraint-Based Syntax"],"domain":"linguistics","family":"process-pipeline","subfamily":"Constraint-Based Syntax","year":"1987","originator":"Carl Pollard and Ivan Sag","url":"https://scholargate.app/en/linguistics/hpsg","markdownUrl":"https://scholargate.app/en/linguistics/hpsg.md","definition":"Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based grammatical framework developed by Carl Pollard and Ivan Sag in 1987. HPSG represents linguistic information (phonological, syntactic, semantic) in typed feature structures and derives well-formed expressions through constraints on these structures. Unlike movement-based theories, HPSG models word order and long-distance dependencies through feature sharing and principles of grammar. It has been extensively applied to modeling diverse language phenomena and remains influential in computational linguistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Pollard and Ivan Sag","subfamily":"Constraint-Based Syntax","year":"1987","type":"Empirical process pipeline"},"citations":[{"ref":"Pollard, C., & Sag, I. A. (1994). Head-Driven Phrase Structure Grammar. Chicago: University of Chicago Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Head-Driven+Phrase+Structure+Grammar+Pollard"},{"ref":"Sag, I. A., Wasow, T., & Bender, E. M. (2003). Syntactic Theory: A Formal Introduction (2nd ed.). Stanford, CA: CSLI Publications.","type":"article","doi":null,"isbn":null,"url":"https://www-csli.stanford.edu/publications"},{"ref":"Borsley, R. D. (2011). A Grammar of Welsh. Berlin: De Gruyter.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Grammar+of+Welsh+Borsley"}],"related":["optimality-theory","lexical-functional-grammar","minimalist-program"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hraf-cross-cultural-analysis","name":"HRAF Cross-Cultural Analysis","fullName":"Human Relations Area Files (HRAF) Cross-Cultural Analysis","aliases":["cross-cultural comparison","comparative ethnography"],"domain":"archaeology","family":"process-pipeline","subfamily":"Comparative Anthropology","year":"1967","originator":"George Murdock","url":"https://scholargate.app/en/archaeology/hraf-cross-cultural-analysis","markdownUrl":"https://scholargate.app/en/archaeology/hraf-cross-cultural-analysis.md","definition":"HRAF (Human Relations Area Files) cross-cultural analysis compares ethnographic data from diverse societies to identify patterns and test hypotheses about human social organization and cultural practices. Developed by George Murdock and colleagues, the method uses a standardized database of ethnographic information coded for comparative analysis. HRAF provides a framework for systematic cross-cultural comparison, helping archaeologists interpret prehistoric patterns through ethnographic analogy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Murdock","subfamily":"Comparative Anthropology","year":"1967","type":"Ethnographic comparison"},"citations":[{"ref":"Murdock, G. P. (1967). Ethnographic Atlas. University of Pittsburgh Press.","type":"book","doi":null,"isbn":null,"url":"https://www.pitt.edu/~pittcms/media/document/5644/Ethnographic_Atlas.pdf"},{"ref":"Murdock, G. P., & White, D. R. (1980). Standard cross-cultural sample. Ethnology, 9(4), 329-369.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Standard+cross-cultural+sample+Murdock"}],"related":["minimum-number-of-individuals","number-of-identified-specimens"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hsqc","name":"HSQC","fullName":"Heteronuclear Single-Quantum Coherence","aliases":["HSQC NMR","1H-13C HSQC","heteronuclear correlation"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Heteronuclear 2D NMR","year":"1980","originator":"Anil Kumar","url":"https://scholargate.app/en/spectroscopy/hsqc","markdownUrl":"https://scholargate.app/en/spectroscopy/hsqc.md","definition":"Heteronuclear Single-Quantum Coherence (HSQC) is a 2D NMR technique that correlates proton and carbon-13 (or other heteronuclei) chemical shifts through one-bond coupling constants (1JHX). Developed in the early 1980s, HSQC rapidly became the workhorse of structural chemistry because it directly maps which carbons bear which protons, providing a comprehensive view of carbon skeleton connectivity and substitution patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anil Kumar","subfamily":"Heteronuclear 2D NMR","year":"1980","type":"Heteronuclear correlation sequence"},"citations":[{"ref":"Bodenhausen, G., & Ruben, D. J. (1981). Natural abundance nitrogen-15 NMR by enhanced heteronuclear spectroscopy. Chemical Physics Letters, 69(2), 185-189.","type":"article","doi":"10.1016/0009-2614(80)80041-8","isbn":null,"url":null},{"ref":"Patt, S. L., & Shoolery, J. N. (1992). Attached proton test (APT). Journal of Magnetic Resonance, 46(3), 535-539.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Attached+proton+test+%28APT%29+Patt"},{"ref":"Bax, A., Griffey, R. H., & Hawkins, B. L. (1983). Correlation of proton and nitrogen-15 chemical shifts by heteronuclear multiquantum NMR. Journal of the American Chemical Society, 105(24), 7188-7190.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Correlation+of+proton+and+nitrogen-15+chemical+shifts+by+heteronuclear+multiquantum+NMR+Bax"}],"related":["hsqc","cosy","noesy","ft-icr-mass-spectrometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"huber-regression","name":"Huber Regression","fullName":"Huber Robust Regression (M-estimation)","aliases":["Huber M-estimator","Huber loss regression","robust regression","Huber Regresyonu"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1964,"originator":"Peter J. Huber","url":"https://scholargate.app/en/statistics/huber-regression","markdownUrl":"https://scholargate.app/en/statistics/huber-regression.md","definition":"Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Huber","year":1964,"type":"Robust linear regression (M-estimation)","estimator":"Huber M-estimator (quadratic loss for small residuals, absolute loss for large residuals)","outcome":"continuous","minSample":30,"breakdownPoint":"robust up to about 25% outliers"},"citations":[{"ref":"Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.","type":"article","doi":"10.1214/aoms/1177703732","isbn":null,"url":null},{"ref":"Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust Statistics: The Approach Based on Influence Functions. Wiley.","type":"book","doi":null,"isbn":"978-0471735779","url":null}],"related":["ols-regression","m-estimator","mm-estimator","least-trimmed-squares","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"huff-model","name":"Huff Model","fullName":"Huff Retail Gravity Model","aliases":["Huff Gravity Model","Probabilistic Retail Gravity Model","Huff Trade Area Model","Huff Çekim Modeli"],"domain":"spatial-analysis","family":"regression-model","subfamily":"Spatial interaction","year":1964,"originator":"David Huff","url":"https://scholargate.app/en/spatial-analysis/huff-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/huff-model.md","definition":"Proposed by David Huff in 1964, the Huff Model is a probabilistic spatial interaction model that estimates the likelihood that consumers located in a given geographic zone will choose to shop at a particular retail outlet. It extends deterministic gravity models by assigning each consumer zone a probability of patronage across all competing stores, weighting store attractiveness (typically measured by floor area) against the friction of travel time or distance. The model is widely used in retail site selection, trade area delineation, and market share forecasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Huff","year":1964,"type":"Probabilistic spatial interaction model","subfamily":"Spatial interaction","input":"Store size, travel time/distance, population","output":"Probability of patronage per store per zone"},"citations":[{"ref":"Huff, D. L. (1964). Defining and estimating a trading area. Journal of Marketing, 28(3), 34–38.","type":"article","doi":"10.1177/002224296402800307","isbn":null,"url":null}],"related":["spatial-interaction-model","location-allocation","radiation-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hull-white-model","name":"Hull-White Model","fullName":"Hull-White One-Factor Interest Rate Model","aliases":["Extended Vasicek","Generalized Vasicek"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Mean Reversion","year":"1990","originator":"John C. Hull and Alan White","url":"https://scholargate.app/en/quantitative-finance/hull-white-model","markdownUrl":"https://scholargate.app/en/quantitative-finance/hull-white-model.md","definition":"The Hull-White model (1990) is a one-factor short-rate model with time-dependent mean reversion and volatility, designed to fit the initial yield curve exactly. It generalizes the Vasicek model to allow better calibration to observed bond and derivative prices, and is widely used for pricing interest rate exotics and managing interest rate risk.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John C. Hull and Alan White","subfamily":"Mean Reversion","year":"1990","type":"Interest Rate Model"},"citations":[{"ref":"Hull, J., & White, A. (1990). Pricing interest-rate-derivative securities. Review of Financial Studies, 3(4), 573-592.","type":"article","doi":"10.1093/rfs/3.4.573","isbn":null,"url":null},{"ref":"Brigo, D., & Mercurio, F. (2006). Interest Rate Models: Theory and Practice (2nd ed.). Springer-Verlag.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interest+Rate+Models%3A+Theory+and+Practice+%282nd+ed.%29+Brigo"}],"related":["sabr-model","hjm-framework","libor-market-model","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"human-error-assessment","name":"Human Error Assessment and Reduction Technique","fullName":"Human Error Assessment and Reduction Technique (HEART)","aliases":["HEART"],"domain":"human-factors","family":"process-pipeline","subfamily":"error-assessment","year":1988,"originator":"Jeremy C. Williams","url":"https://scholargate.app/en/human-factors/human-error-assessment","markdownUrl":"https://scholargate.app/en/human-factors/human-error-assessment.md","definition":"The Human Error Assessment and Reduction Technique (HEART), developed by Jeremy Williams in 1988 for the nuclear industry, is a structured method for assessing the probability of human error in safety-critical tasks and identifying error reduction strategies. Unlike scales that measure subjective experience (workload, situational awareness), HEART is an analytical tool combining expert judgment, task analysis, and empirical error rates to quantify task-specific error probability and guide human factors interventions in high-stakes operations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeremy C. Williams","subfamily":"error-assessment","year":1988,"type":"Expert-rated / Observational"},"citations":[{"ref":"Williams, J. C. (1988). A data-based method for assessing and reducing human error to improve operational performance. In IEEE Fourth Conference on Human Factors and Power Plants (pp. 436-450). IEEE.","type":"article","doi":"10.1109/hfpp.1988.27540","isbn":null,"url":null}],"related":["operator-performance-scale","situational-awareness-rating","workload-profile","nasa-task-load-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hunt-hess-scale","name":"Hunt and Hess Scale","fullName":"Hunt and Hess Grading Scale for Subarachnoid Hemorrhage","aliases":["Hunt-Hess Grade"],"domain":"neurology","family":"process-pipeline","subfamily":"Subarachnoid hemorrhage severity and prognosis","year":"1968","originator":"William E. Hunt and Robert M. Hess","url":"https://scholargate.app/en/neurology/hunt-hess-scale","markdownUrl":"https://scholargate.app/en/neurology/hunt-hess-scale.md","definition":"The Hunt and Hess Scale is the most widely used clinical grading system for assessing severity and prognosis in subarachnoid hemorrhage (SAH) caused by ruptured intracranial aneurysm. Developed by neurosurgeons William Hunt and Robert Hess in 1968, the five-point ordinal scale measures level of consciousness and presence of focal neurological deficits. Hunt-Hess grade at admission is the single strongest predictor of 30-day mortality and functional outcome and guides urgency of neurosurgical intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William E. Hunt and Robert M. Hess","subfamily":"Subarachnoid hemorrhage severity and prognosis","year":"1968","type":"Clinician-rated"},"citations":[{"ref":"Hunt, W. E., Hess, R. M. (1968). Surgical risk as related to time of intervention in the repair of intracranial aneurysms. Journal of Neurosurgery, 28(1), 14-20.","type":"article","doi":"10.3171/jns.1968.28.1.0014","isbn":null,"url":null}],"related":["world-federation-neurosurgeons","nihss","updrs","edss-multiple-sclerosis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"huntington-qol","name":"HD-QoL","fullName":"Huntington's Disease Health-Related Quality of Life Scale","aliases":["Huntington Disease QoL","HD-QoL Scale"],"domain":"neurology","family":"process-pipeline","subfamily":"disease-specific quality of life","year":"2001","originator":"Helder et al., University of Leiden","url":"https://scholargate.app/en/neurology/huntington-qol","markdownUrl":"https://scholargate.app/en/neurology/huntington-qol.md","definition":"The HD-QoL is a disease-specific quality-of-life instrument designed to measure the multidimensional impact of Huntington's disease on patients' physical, emotional, social, and cognitive functioning. Developed by Helder and colleagues in 2001, it uniquely addresses the progressive motor, cognitive, and psychiatric manifestations characteristic of HD. The scale recognizes that HD burden extends beyond neurological deficits to profound impacts on identity, family relationships, and existential well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Helder et al., University of Leiden","subfamily":"disease-specific quality of life","year":"2001","type":"Self-report questionnaire"},"citations":[{"ref":"Helder, D. I., Kaptein, A. A., van Kempen, G. M., Weinman, J., van Houwelingen, H. C., & Roos, R. A. (2001). Living with Huntington's disease: Illness perceptions, coping mechanisms, and patients' well-being. Journal of Psychosomatic Research, 50(1), 1-7.","type":"article","doi":"10.1348/135910702320645417","isbn":null,"url":null}],"related":["msqol-54","qolie-89","parkinson-non-motor-scale","stroke-specific-qol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hurdle-model","name":"Hurdle Model","fullName":"Hurdle Model for Count Data","aliases":["hurdle count model","two-part count model","zero-truncated count model","Engel Modeli (Hurdle Model)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1986,"originator":"Mullahy","url":"https://scholargate.app/en/statistics/hurdle-model","markdownUrl":"https://scholargate.app/en/statistics/hurdle-model.md","definition":"The hurdle model is a two-part count-data model introduced by Mullahy (1986). A first stage models the binary choice of crossing a hurdle (a zero versus a non-zero count), and a second stage models the strictly positive counts with a zero-truncated distribution such as a zero-truncated Poisson or negative binomial.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mullahy","year":1986,"type":"Two-part count model","estimator":"Maximum likelihood (binary stage + zero-truncated count stage)","outcome":"count","minSample":50},"citations":[{"ref":"Mullahy, J. (1986). Specification and Testing of Some Modified Count Data Models. Journal of Econometrics, 33(3), 341–365.","type":"article","doi":"10.1016/0304-4076(86)90002-3","isbn":null,"url":null}],"related":["zero-inflated-poisson","negative-binomial-regression","poisson-regression","logistic-regression","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-box-behnken-design","name":"Hybrid Box-Behnken Design","fullName":"Hybrid Box-Behnken Response Surface Design","aliases":["Hybrid BBD","augmented Box-Behnken design","modified Box-Behnken design","extended BBD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960 (standard BBD); hybrid variants developed from 1970s onward","originator":"Box & Behnken (1960), extended by various authors for hybrid configurations","url":"https://scholargate.app/en/experimental-design/hybrid-box-behnken-design","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-box-behnken-design.md","definition":"The Hybrid Box-Behnken Design (Hybrid BBD) is a three-level response surface design that extends the classical Box-Behnken Design by incorporating additional design points — such as axial, face-centered, or space-filling runs — to improve estimation efficiency, handle larger factor sets, or achieve better predictive coverage. It retains BBD's avoidance of extreme corner runs while gaining the flexibility needed for complex engineering optimization problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Behnken (1960), extended by various authors for hybrid configurations","year":"1960 (standard BBD); hybrid variants developed from 1970s onward","type":"Response surface experimental design","dataType":"Continuous quantitative factor levels; measured response values","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.","type":"article","doi":"10.1080/00401706.1960.10489912","isbn":null,"url":null},{"ref":"Ferreira, S. L. C., Bruns, R. E., Ferreira, H. S., Matos, G. D., David, J. M., Brandão, G. C., ... & dos Santos, W. N. L. (2007). Box-Behnken design: An alternative for the optimization of analytical methods. Analytica Chimica Acta, 597(2), 179–186.","type":"article","doi":"10.1016/j.aca.2007.07.011","isbn":null,"url":null}],"related":["box-behnken-design","central-composite-design","response-surface-methodology","d-optimal-design","face-centered-central-composite-design","factorial-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-central-composite-design","name":"Hybrid Central Composite Design","fullName":"Hybrid Central Composite Design for Response Surface Methodology","aliases":["Hybrid CCD","HCCD","modified central composite design","hybrid RSM design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1976","originator":"K. G. Roquemore","url":"https://scholargate.app/en/experimental-design/hybrid-central-composite-design","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-central-composite-design.md","definition":"Hybrid Central Composite Design (Hybrid CCD) is a class of response surface designs introduced by Roquemore (1976) that combines the structural properties of classical central composite designs with modified or reduced point configurations to achieve rotatability or near-rotatability with fewer experimental runs than a standard CCD, making it especially practical when the number of factors is three to six and experimental resources are limited.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. G. Roquemore","year":"1976","type":"Response surface experimental design","dataType":"Continuous factor settings with quantitative response measurements","subfamily":"Engineering methods"},"citations":[{"ref":"Roquemore, K. G. (1976). Hybrid designs for quadratic response surfaces. Technometrics, 18(4), 419–423.","type":"article","doi":"10.1080/00401706.1976.10489473","isbn":null,"url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470174463","url":null}],"related":["central-composite-design","box-behnken-design","response-surface-methodology","full-factorial-design","d-optimal-design","face-centered-central-composite-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-control-chart","name":"Hybrid Control Chart","fullName":"Hybrid Statistical Process Control Chart","aliases":["combined control chart","hybrid SPC chart","composite control chart","integrated control chart"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1982 (CUSUM-Shewhart hybrid); broader hybrid frameworks 1990s–2000s","originator":"Developed incrementally; CUSUM-Shewhart hybrid attributed to Lucas & Crosier (1982) and prior work by Page (1954)","url":"https://scholargate.app/en/experimental-design/hybrid-control-chart","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-control-chart.md","definition":"A hybrid control chart integrates two or more classical charting schemes — most commonly a Shewhart chart with a CUSUM or EWMA chart — into a single monitoring procedure. By combining the strengths of each component, hybrid charts can detect both large, sudden shifts and small, sustained drifts in a process more effectively than any single chart alone. They are used in manufacturing quality control, healthcare monitoring, and any continuous process where rapid and sensitive detection of out-of-control conditions is critical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed incrementally; CUSUM-Shewhart hybrid attributed to Lucas & Crosier (1982) and prior work by Page (1954)","year":"1982 (CUSUM-Shewhart hybrid); broader hybrid frameworks 1990s–2000s","type":"Statistical process monitoring procedure","dataType":"Continuous or attribute process measurement data collected over time","subfamily":"Engineering methods"},"citations":[{"ref":"Lucas, J. M., & Crosier, R. B. (1982). Fast initial response for CUSUM quality-control schemes: Give your CUSUM a head start. Technometrics, 24(3), 199–205.","type":"article","doi":"10.1080/00401706.1982.10487759","isbn":null,"url":null},{"ref":"Control chart. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Control_chart"}],"related":["control-chart","statistical-process-control","design-of-experiments","failure-mode-and-effects-analysis","process-capability-analysis","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-design-of-experiments","name":"Hybrid design of experiments","fullName":"Hybrid Design of Experiments","aliases":["hybrid DOE","combined experimental design","mixed experimental design","hybrid experimental strategy"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1989–2000s","originator":"Multiple contributors; notably Sacks, Welch, Mitchell & Wynn (computer experiments); broader hybrid concept developed across 1980s–2000s","url":"https://scholargate.app/en/experimental-design/hybrid-design-of-experiments","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-design-of-experiments.md","definition":"Hybrid design of experiments (hybrid DOE) combines two or more experimental design strategies within a single study to exploit the complementary strengths of each. Common combinations include factorial or fractional-factorial arrays paired with computer simulation runs, space-filling Latin hypercube designs merged with response surface augmentations, or Taguchi orthogonal arrays integrated with response surface methodology. The approach is widely used when a single design type cannot efficiently cover all phases of an engineering investigation — from screening through to optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; notably Sacks, Welch, Mitchell & Wynn (computer experiments); broader hybrid concept developed across 1980s–2000s","year":"1989–2000s","type":"Combined experimental design strategy","dataType":"Continuous, categorical, and/or simulation-based output data","subfamily":"Engineering methods"},"citations":[{"ref":"Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer.","type":"book","doi":null,"isbn":"978-1441929921","url":null},{"ref":"Loeppky, J. L., Sacks, J., & Welch, W. J. (2009). Choosing the sample size of a computer experiment: A practical guide. Technometrics, 51(4), 366–376.","type":"article","doi":"10.1198/TECH.2009.08040","isbn":null,"url":null}],"related":["design-of-experiments","response-surface-methodology","taguchi-method","central-composite-design","fractional-factorial-design","simulation-assisted-design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-event-tree-analysis","name":"Hybrid Event Tree Analysis","fullName":"Hybrid Event Tree Analysis","aliases":["Hybrid ETA","Integrated Event Tree Analysis","Combined Event Tree Analysis","Fuzzy-Bayesian Event Tree Analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (as extensions to classical ETA developed from the 1960s)","originator":"Multiple contributors; hybrid extensions emerged from the reliability and safety engineering community","url":"https://scholargate.app/en/experimental-design/hybrid-event-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-event-tree-analysis.md","definition":"Hybrid Event Tree Analysis (Hybrid ETA) extends classical Event Tree Analysis by integrating complementary methods — such as Bayesian networks, fuzzy set theory, or Monte Carlo simulation — to overcome ETA's limitations in handling uncertainty, dependency between events, and sparse data. It is applied in safety-critical industries to model accident sequences and quantify outcome probabilities with greater fidelity than standalone ETA.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; hybrid extensions emerged from the reliability and safety engineering community","year":"1990s–2000s (as extensions to classical ETA developed from the 1960s)","type":"Probabilistic risk and safety assessment technique","dataType":"Event probabilities, conditional branch probabilities, expert judgements, simulation outputs","subfamily":"Engineering methods"},"citations":[{"ref":"Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521773201","url":null},{"ref":"Khakzad, N., Khan, F., & Amyotte, P. (2011). Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches. Reliability Engineering and System Safety, 96(8), 925–932.","type":"article","doi":"10.1016/j.ress.2011.03.012","isbn":null,"url":null}],"related":["event-tree-analysis","fault-tree-analysis","bayesian-event-tree-analysis","failure-mode-and-effects-analysis","hybrid-fault-tree-analysis","reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-failure-mode-and-effects-analysis","name":"Hybrid Failure Mode and Effects Analysis","fullName":"Hybrid Failure Mode and Effects Analysis","aliases":["Hybrid FMEA","Fuzzy FMEA","Integrated FMEA","Enhanced FMEA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1995 onward (classical FMEA: 1949)","originator":"Hybrid variants pioneered by J. B. Bowles & C. E. Pelaez (fuzzy FMEA, 1995); subsequent integrations with AHP, TOPSIS, and grey theory by multiple researchers","url":"https://scholargate.app/en/experimental-design/hybrid-failure-mode-and-effects-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-failure-mode-and-effects-analysis.md","definition":"Hybrid Failure Mode and Effects Analysis (Hybrid FMEA) extends classical FMEA by integrating it with multi-criteria decision methods — such as fuzzy logic, AHP, TOPSIS, or grey theory — to overcome the well-documented limitations of the traditional Risk Priority Number. The hybrid approach enables more nuanced, weighted, and uncertainty-aware prioritization of failure risks in engineering systems, manufacturing processes, and complex sociotechnical environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hybrid variants pioneered by J. B. Bowles & C. E. Pelaez (fuzzy FMEA, 1995); subsequent integrations with AHP, TOPSIS, and grey theory by multiple researchers","year":"1995 onward (classical FMEA: 1949)","type":"Reliability and risk analysis technique","dataType":"Expert judgments, failure records, linguistic ratings, quantitative probability/severity data","subfamily":"Engineering methods"},"citations":[{"ref":"Liu, H.-C., Liu, L., & Liu, N. (2013). Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert Systems with Applications, 40(2), 828–838.","type":"article","doi":"10.1016/j.eswa.2012.08.010","isbn":null,"url":null},{"ref":"Bowles, J. B., & Pelaez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering & System Safety, 50(2), 203–213.","type":"article","doi":"10.1016/0951-8320(95)00068-D","isbn":null,"url":null}],"related":["failure-mode-and-effects-analysis","fault-tree-analysis","analytic-hierarchy-process","fuzzy-set-theory","risk-priority-number","failure-mode-effects-criticality-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-fault-tree-analysis","name":"Hybrid Fault Tree Analysis","fullName":"Hybrid Fault Tree Analysis","aliases":["Hybrid FTA","Fuzzy-Bayesian FTA","Extended Fault Tree Analysis","Integrated FTA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1983–2001 (multiple extensions)","originator":"Tanaka et al. (fuzzy extension, 1983); Bobbio et al. (Bayesian integration, 2001)","url":"https://scholargate.app/en/experimental-design/hybrid-fault-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-fault-tree-analysis.md","definition":"Hybrid Fault Tree Analysis (Hybrid FTA) extends classical Fault Tree Analysis by integrating complementary modelling paradigms — most commonly fuzzy set theory, Bayesian networks, or event-tree logic — to overcome the strict data requirements and static assumptions of traditional FTA. The hybrid approach allows analysts to handle uncertainty in failure probability estimates, capture dynamic dependencies between components, and update risk assessments as new evidence becomes available, making it especially valuable in complex engineering systems where complete statistical failure data are rarely available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tanaka et al. (fuzzy extension, 1983); Bobbio et al. (Bayesian integration, 2001)","year":"1983–2001 (multiple extensions)","type":"Quantitative safety and reliability analysis method","dataType":"Boolean logic, failure probability data, expert elicitation, fuzzy membership functions","subfamily":"Engineering methods"},"citations":[{"ref":"Tanaka, H., Fan, L. T., Lai, F. S., & Toguchi, K. (1983). Fault-tree analysis by fuzzy probability. IEEE Transactions on Reliability, 32(5), 453–457.","type":"article","doi":"10.1109/TR.1983.5221727","isbn":null,"url":null},{"ref":"Bobbio, A., Portinale, L., Minichino, M., & Ciancamerla, E. (2001). Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering & System Safety, 71(3), 249–260.","type":"article","doi":"10.1016/S0951-8320(00)00077-6","isbn":null,"url":null}],"related":["fault-tree-analysis","bayesian-network","fuzzy-set-theory","event-tree-analysis","failure-mode-and-effects-analysis","reliability-block-diagram"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-fractional-factorial-design","name":"Hybrid Fractional Factorial Design","fullName":"Hybrid Fractional Factorial Design","aliases":["HFFD","hybrid FFD","combined fractional factorial design","mixed fractional factorial design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1970s–1990s (formalized as a distinct design class)","originator":"Developed across the DOE community; foundational contributions by Box, Hunter & Hunter and Wu & Hamada","url":"https://scholargate.app/en/experimental-design/hybrid-fractional-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-fractional-factorial-design.md","definition":"A hybrid fractional factorial design (HFFD) merges two or more fractional factorial sub-designs — often involving factors at different numbers of levels or with different aliasing structures — into a single coordinated experiment. The goal is to achieve estimation capabilities (main effects, targeted two-factor interactions) that no single standard fractional design can provide within the same run count, making it especially valuable in engineering development and industrial process optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed across the DOE community; foundational contributions by Box, Hunter & Hunter and Wu & Hamada","year":"1970s–1990s (formalized as a distinct design class)","type":"Experimental design","dataType":"Continuous or categorical factor settings with quantitative response measurements","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null},{"ref":"Wu, C. F. J., & Hamada, M. S. (2000). Experiments: Planning, Analysis, and Parameter Design Optimization. Wiley.","type":"book","doi":null,"isbn":"978-0471255116","url":null}],"related":["fractional-factorial-design","full-factorial-design","response-surface-methodology","taguchi-methods","central-composite-design","plackett-burman-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-full-factorial-design","name":"Hybrid Full Factorial Design","fullName":"Hybrid Full Factorial Experimental Design","aliases":["hybrid factorial design","mixed full factorial design","combined factorial design","HFFD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s–2000s (building on Fisher's 1935 factorial framework)","originator":"Derived from classical factorial design theory (Fisher, 1935); hybrid extensions developed across engineering literature from the 1980s onward","url":"https://scholargate.app/en/experimental-design/hybrid-full-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-full-factorial-design.md","definition":"Hybrid full factorial design is an experimental strategy that applies a full factorial structure to a selected subset of factors — those believed to have the strongest interactions — while treating remaining factors with a reduced or fractional scheme. This hybrid approach balances the complete interaction information of a full factorial with the run-count efficiency of fractional designs, making it practical for studies with many factors where a pure full factorial would be prohibitively expensive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from classical factorial design theory (Fisher, 1935); hybrid extensions developed across engineering literature from the 1980s onward","year":"1980s–2000s (building on Fisher's 1935 factorial framework)","type":"Experimental design strategy","dataType":"Continuous or categorical factor levels; numeric response variables","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null},{"ref":"Antony, J. (2014). Design of Experiments for Engineers and Scientists (2nd ed.). Elsevier.","type":"book","doi":null,"isbn":"978-0080994178","url":null}],"related":["full-factorial-design","fractional-factorial-design","response-surface-methodology","central-composite-design","taguchi-method","latin-hypercube-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-process-capability-analysis","name":"Hybrid process capability analysis","fullName":"Hybrid Process Capability Analysis","aliases":["hybrid PCA","integrated process capability analysis","combined capability index analysis","multi-method process capability assessment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s","originator":"Various; systematised through extensions of Kane (1986) and Pearn, Kotz & Johnson (1992)","url":"https://scholargate.app/en/experimental-design/hybrid-process-capability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-process-capability-analysis.md","definition":"Hybrid process capability analysis combines two or more capability assessment techniques — for example, classical indices (Cp, Cpk) with fuzzy logic, bootstrap inference, or Bayesian estimation — to overcome the limitations of any single approach. By integrating complementary methods, it delivers more robust capability statements for non-normal, asymmetric, or short-run processes where standard indices alone would mislead quality decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various; systematised through extensions of Kane (1986) and Pearn, Kotz & Johnson (1992)","year":"1990s–2000s","type":"Quantitative process quality assessment","dataType":"Continuous measurement data from manufacturing or service processes","subfamily":"Engineering methods"},"citations":[{"ref":"Pearn, W. L., Kotz, S., & Johnson, N. L. (1992). Distributional and inferential properties of process capability indices. Journal of Quality Technology, 24(4), 216–231.","type":"article","doi":"10.1080/00224065.1992.11979403","isbn":null,"url":null},{"ref":"Chen, K. S., & Pearn, W. L. (2003). Capability indices for processes with asymmetric tolerances. Journal of the Chinese Institute of Engineers, 26(2), 197–208.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Capability+indices+for+processes+with+asymmetric+tolerances+Chen"}],"related":["process-capability-analysis","statistical-process-control","control-chart","six-sigma-dmaic","robust-process-capability-analysis","multi-response-process-capability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-quality-function-deployment","name":"Hybrid Quality Function Deployment","fullName":"Hybrid Quality Function Deployment","aliases":["Hybrid QFD","Integrated QFD","QFD hybrid approach","Extended Quality Function Deployment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1966 (QFD foundation); hybrid variants from mid-1990s onward","originator":"Yoji Akao (QFD foundation); hybrid extensions by various authors integrating fuzzy sets, AHP, TOPSIS, and optimization","url":"https://scholargate.app/en/experimental-design/hybrid-quality-function-deployment","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-quality-function-deployment.md","definition":"Hybrid Quality Function Deployment (Hybrid QFD) extends the classic House of Quality framework by embedding additional analytical techniques — such as fuzzy set theory, Analytic Hierarchy Process, TOPSIS, or optimization algorithms — directly into the QFD pipeline. This integration addresses known weaknesses of standard QFD, such as imprecision in customer ratings and subjectivity in relationship matrices, while preserving the method's core strength: systematically translating the voice of the customer into actionable engineering specifications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yoji Akao (QFD foundation); hybrid extensions by various authors integrating fuzzy sets, AHP, TOPSIS, and optimization","year":"1966 (QFD foundation); hybrid variants from mid-1990s onward","type":"Integrated engineering design and decision method","dataType":"Customer requirement ratings, engineering specifications, pairwise comparisons, quantitative performance metrics","subfamily":"Engineering methods"},"citations":[{"ref":"Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press.","type":"book","doi":null,"isbn":"978-0915299416","url":null},{"ref":"Chan, L.-K., & Wu, M.-L. (2002). Quality function deployment: A literature review. European Journal of Operational Research, 143(3), 463–497.","type":"article","doi":"10.1016/S0377-2217(02)00178-9","isbn":null,"url":null}],"related":["quality-function-deployment","failure-mode-and-effects-analysis","taguchi-method","robust-quality-function-deployment","design-of-experiments","multi-response-quality-function-deployment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-reliability-analysis","name":"Hybrid Reliability Analysis","fullName":"Hybrid Probabilistic and Non-Probabilistic Reliability Analysis","aliases":["HRA","hybrid uncertainty reliability","combined reliability analysis","probabilistic-possibilistic reliability analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (consolidated formulation ~2000–2006)","originator":"Xiaoping Du, Achintya Haldar, and others; synthesized across structural and mechanical engineering communities","url":"https://scholargate.app/en/experimental-design/hybrid-reliability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-reliability-analysis.md","definition":"Hybrid Reliability Analysis (HRA) quantifies the probability that an engineering system will perform its intended function when uncertain inputs are of two fundamentally different kinds: aleatory uncertainties (natural randomness, modelled with probability distributions) and epistemic uncertainties (lack of knowledge, modelled with intervals or fuzzy sets). By treating both uncertainty types simultaneously rather than collapsing them into a single probabilistic framework, HRA produces more truthful reliability estimates in design, structural, and systems engineering problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xiaoping Du, Achintya Haldar, and others; synthesized across structural and mechanical engineering communities","year":"1990s–2000s (consolidated formulation ~2000–2006)","type":"Quantitative reliability / uncertainty analysis method","dataType":"Mixed uncertain inputs: probability distributions (aleatory) and intervals or fuzzy sets (epistemic)","subfamily":"Engineering methods"},"citations":[{"ref":"Du, X., Sudjianto, A., & Huang, B. (2006). Reliability-Based Design With the Mixture of Random and Interval Variables. Journal of Mechanical Design, 127(6), 1068–1076.","type":"article","doi":"10.1115/1.1992510","isbn":null,"url":null},{"ref":"Moore, R. E. (1966). Interval Analysis. Prentice-Hall.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Moore+Interval+Analysis+1966+Prentice-Hall"}],"related":["monte-carlo-simulation","first-order-reliability-method","second-order-reliability-method","fuzzy-set-theory","bayesian-reliability-analysis","sensitivity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-response-surface-methodology","name":"Hybrid Response Surface Methodology","fullName":"Hybrid Response Surface Methodology","aliases":["Hybrid RSM","RSM-hybrid optimization","combined RSM","meta-model hybrid optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (systematic hybrid applications)","originator":"Box & Wilson (RSM foundation, 1951); hybrid extensions by various authors from the 1990s onward","url":"https://scholargate.app/en/experimental-design/hybrid-response-surface-methodology","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-response-surface-methodology.md","definition":"Hybrid Response Surface Methodology (Hybrid RSM) couples classical response surface designs — which fit low-order polynomial approximations of a system response — with a secondary optimizer such as a genetic algorithm, particle swarm, or artificial neural network. The combination overcomes RSM's limitation of assuming smooth, near-quadratic response landscapes by letting the surrogate model be explored globally, making it widely used in engineering process optimization, product design, and simulation-based studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Wilson (RSM foundation, 1951); hybrid extensions by various authors from the 1990s onward","year":"1990s–2000s (systematic hybrid applications)","type":"Optimization methodology","dataType":"Numerical experimental data, simulation outputs, physical measurements","subfamily":"Engineering methods"},"citations":[{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916032","url":null},{"ref":"Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley.","type":"book","doi":null,"isbn":"978-0471873396","url":null}],"related":["response-surface-methodology","central-composite-design","box-behnken-design","genetic-algorithm","artificial-neural-network","design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-six-sigma-dmaic","name":"Hybrid Six Sigma DMAIC","fullName":"Hybrid Six Sigma Define-Measure-Analyze-Improve-Control","aliases":["Lean Six Sigma DMAIC","Hybrid DMAIC","Integrated Six Sigma DMAIC","DMAIC Hybrid Framework"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s (Six Sigma); Hybrid/Lean integration widely adopted ~2000–2002","originator":"Hybrid formalized through Lean Six Sigma integration; foundational DMAIC rooted in Motorola's Six Sigma program (Bill Smith, Mikel Harry)","url":"https://scholargate.app/en/experimental-design/hybrid-six-sigma-dmaic","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-six-sigma-dmaic.md","definition":"Hybrid Six Sigma DMAIC combines the rigorous five-phase DMAIC cycle (Define, Measure, Analyze, Improve, Control) with complementary methodologies — most commonly Lean principles, Agile practices, or Design Thinking — to address quality defects and process inefficiencies simultaneously. By integrating speed-focused tools from Lean with the statistical discipline of Six Sigma, hybrid approaches close the gap that pure Six Sigma frameworks sometimes leave when waste elimination and cycle-time reduction are equally critical goals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hybrid formalized through Lean Six Sigma integration; foundational DMAIC rooted in Motorola's Six Sigma program (Bill Smith, Mikel Harry)","year":"1980s (Six Sigma); Hybrid/Lean integration widely adopted ~2000–2002","type":"Process improvement and quality management framework","dataType":"Process performance data, measurement system data, designed experiments, control charts","subfamily":"Engineering methods"},"citations":[{"ref":"George, M. L. (2002). Lean Six Sigma: Combining Six Sigma Quality with Lean Speed. McGraw-Hill.","type":"book","doi":null,"isbn":"978-0071385213","url":null},{"ref":"Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring Business Excellence, 6(4), 20–27.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1108/13683040210451679"}],"related":["lean-manufacturing","dmadv","statistical-process-control","design-of-experiments","total-quality-management","failure-mode-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-statistical-process-control","name":"Hybrid Statistical Process Control","fullName":"Hybrid Statistical Process Control","aliases":["Hybrid SPC","combined SPC","integrated SPC","hybrid process monitoring"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s","originator":"Evolved from classical SPC (Shewhart, 1920s); hybrid extensions developed broadly from the 1990s onward by researchers including Montgomery, Woodall, and various neural-network SPC authors","url":"https://scholargate.app/en/experimental-design/hybrid-statistical-process-control","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-statistical-process-control.md","definition":"Hybrid Statistical Process Control integrates classical control-chart methods (Shewhart, CUSUM, EWMA) with complementary techniques — such as neural networks, fuzzy logic, economic design, or multivariate statistics — to monitor and control manufacturing or service processes more effectively than any single approach alone. The hybrid architecture addresses known weaknesses of conventional SPC, including slow detection of small shifts, pattern-recognition limitations, and inability to handle non-normal or autocorrelated data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from classical SPC (Shewhart, 1920s); hybrid extensions developed broadly from the 1990s onward by researchers including Montgomery, Woodall, and various neural-network SPC authors","year":"1990s–2000s","type":"Process monitoring and control methodology","dataType":"Continuous or attribute process measurement data collected over time","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0-470-16992-6","url":null},{"ref":"Guh, R.-S., & Hsieh, Y.-C. (2008). A Neural Network-Based Model for Abnormal Pattern Recognition of Control Charts. Computers and Industrial Engineering, 35(1–2), 35–38.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Neural+Network+Abnormal+Pattern+Recognition+Control+Charts+Guh+Hsieh"}],"related":["statistical-process-control","control-charts","ewma-control-chart","cusum-chart","multivariate-spc","fuzzy-control-chart"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hybrid-taguchi-method","name":"Hybrid Taguchi Method","fullName":"Hybrid Taguchi Optimization Method","aliases":["Taguchi hybrid optimization","integrated Taguchi method","Taguchi-based hybrid design","combined Taguchi approach"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (building on Taguchi's work of the 1950s–1980s)","originator":"Genichi Taguchi (foundational Taguchi method); hybrid extensions developed by various engineering researchers from the 1990s onward","url":"https://scholargate.app/en/experimental-design/hybrid-taguchi-method","markdownUrl":"https://scholargate.app/en/experimental-design/hybrid-taguchi-method.md","definition":"The Hybrid Taguchi Method combines Taguchi's orthogonal array experimental design and signal-to-noise ratio analysis with a secondary optimization or analysis technique — such as grey relational analysis, response surface methodology, artificial neural networks, or fuzzy logic — to handle multiple response variables or complex nonlinear relationships that classical Taguchi alone cannot resolve efficiently. It is widely used in manufacturing, materials engineering, and process optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Genichi Taguchi (foundational Taguchi method); hybrid extensions developed by various engineering researchers from the 1990s onward","year":"1990s–2000s (building on Taguchi's work of the 1950s–1980s)","type":"Hybrid experimental optimization method","dataType":"Quantitative experimental data (continuous process/quality responses)","subfamily":"Engineering methods"},"citations":[{"ref":"Taguchi, G. (1987). System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs. UNIPUB/Kraus International Publications.","type":"book","doi":null,"isbn":"978-0527916213","url":null},{"ref":"Lin, C. L. (2004). Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics. Materials and Manufacturing Processes, 19(2), 209–220.","type":"article","doi":"10.1081/AMP-120029852","isbn":null,"url":null}],"related":["taguchi-method","grey-relational-analysis","response-surface-methodology","design-of-experiments","robust-parameter-design","signal-to-noise-ratio"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hydrogel-rheology","name":"Hydrogel Rheology","fullName":"Hydrogel Rheological Characterization","aliases":["Viscoelastic analysis","Storage modulus","Gel characterization"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Material mechanics","year":"1994","originator":"Christopher Macosko","url":"https://scholargate.app/en/biomechanics/hydrogel-rheology","markdownUrl":"https://scholargate.app/en/biomechanics/hydrogel-rheology.md","definition":"Hydrogel rheology characterizes the mechanical viscoelastic properties of hydrogels used in tissue engineering, drug delivery, and biomedical devices. By measuring storage modulus (elastic component), loss modulus (viscous component), and their frequency dependence, practitioners assess gel stiffness, degradation, and suitability for specific applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher Macosko","subfamily":"Material mechanics","year":"1994","type":"Mechanical material characterization"},"citations":[{"ref":"Almquist, B. D., & Lu, T. W. (2002). A simple stochastic parameter estimation technique for complex models. IEEE Transactions on Biomedical Engineering, 49(10), 1188-1193.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org"},{"ref":"Macosko, C. W. (1994). Rheology: Principles, Measurements, and Applications. Wiley-VCH.","type":"book","doi":null,"isbn":null,"url":"https://wiley.com"}],"related":["scaffold-porosity-analysis","fea-bone-remodeling","muscle-synergy-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hydrogeological-survey","name":"Hydrogeological Survey","fullName":"Hydrogeological Survey","aliases":["groundwater assessment","hydrogeologic characterization","aquifer mapping"],"domain":"geoscience","family":"process-pipeline","subfamily":"Aquifer characterization","year":"1856","originator":"Darcy and Theis","url":"https://scholargate.app/en/geoscience/hydrogeological-survey","markdownUrl":"https://scholargate.app/en/geoscience/hydrogeological-survey.md","definition":"Hydrogeological survey is the systematic characterization of groundwater systems, including aquifer geometry, water quality, flow paths, and recharge-discharge dynamics. Rooted in Darcy's law (1856) and quantified by Theis (1935), this method is essential for water resource management, contaminant remediation, and hazard assessment. Modern surveys integrate geology, geophysics, geochemistry, and numerical modeling to understand complex subsurface flow systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Darcy and Theis","subfamily":"Aquifer characterization","year":"1856","type":"groundwater systems analysis pipeline"},"citations":[{"ref":"Fetter, C. W. (2018). Applied Hydrogeology (5th ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://www.pearson.com"},{"ref":"Todd, D. K., & Mays, L. W. (2005). Groundwater Hydrology (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Groundwater+Hydrology+%283rd+ed.%29+Todd"},{"ref":"U.S. Geological Survey. (1998). Groundwater and Surface Water: A Single Resource. USGS Circular 1139.","type":"article","doi":null,"isbn":null,"url":"https://pubs.usgs.gov"}],"related":["well-log-analysis","geologic-mapping","stratigraphic-correlation","geomorphological-analysis","geochemical-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hydroponic-nutrient-solution","name":"Hydroponic Nutrient Solution Management","fullName":"Composition, Monitoring, and Optimization of Nutrient Solutions in Soilless Production Systems","aliases":["nutrient solution formulation","hydroponic monitoring","EC/pH management"],"domain":"horticulture","family":"process-pipeline","subfamily":"Nutrient management and soilless production","year":"1970","originator":"Hydroponics research tradition","url":"https://scholargate.app/en/horticulture/hydroponic-nutrient-solution","markdownUrl":"https://scholargate.app/en/horticulture/hydroponic-nutrient-solution.md","definition":"Hydroponic nutrient solution management involves formulating, monitoring, and adjusting the chemical composition of water-based growing media to deliver optimal nutrition without soil. This method combines analytical chemistry (nutrient analysis, pH, electrical conductivity) with plant physiology to diagnose deficiencies and optimize yield and quality. It is essential for commercial hydroponics, vertical farms, and propagation systems where precision nutrition directly impacts profitability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hydroponics research tradition","subfamily":"Nutrient management and soilless production","year":"1970","type":"analytical measurement pipeline"},"citations":[{"ref":"Resh, H. M. (2012). Hydroponic Food Production (7th ed.). CRC Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hydroponic+Food+Production+%287th+ed.%29+Resh"},{"ref":"Jones, J. B. (2005). Hydroponics: A Practical Guide for the Soilless Grower (2nd ed.). CRC Press.","type":"article","doi":null,"isbn":null,"url":"https://www.routledge.com/Hydroponics-A-Practical-Guide-for-the-Soilless-Grower/Jones/p/book/9780367571290"}],"related":["greenhouse-climate-control","phenological-stage-monitoring","postharvest-storage-simulation","plant-propagation-success"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hydrothermal-plume-mapping","name":"Hydrothermal Plume Mapping","fullName":"Hydrothermal Plume Mapping","aliases":["Vent Plume Detection","Hydrothermal Vent Survey"],"domain":"oceanography","family":"process-pipeline","subfamily":"Deep-sea Geochemistry","year":"1987","originator":"Ed Baker","url":"https://scholargate.app/en/oceanography/hydrothermal-plume-mapping","markdownUrl":"https://scholargate.app/en/oceanography/hydrothermal-plume-mapping.md","definition":"Hydrothermal plume mapping is an integrated method for detecting, characterizing, and tracking buoyant plumes of hot, mineral-rich water discharged from submarine hydrothermal vents on the seafloor. Developed by Ed Baker and colleagues in the 1980s, hydrothermal plume mapping combines temperature, conductivity, optical, and chemical sensors to identify vent signatures and map their dispersal in the water column. The method enables discovery of new vents and assessment of chemical cycling in deep-sea ecosystems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ed Baker","subfamily":"Deep-sea Geochemistry","year":"1987","type":"integrated-system"},"citations":[{"ref":"Baker, E. T., Massoth, G. J., Feely, R. A., et al. (1987). Hydrothermal event plumes from the Juan de Fuca Ridge. Eos, Transactions American Geophysical Union, 68(44), 1574.","type":"article","doi":null,"isbn":null,"url":"https://agupubs.onlinelibrary.wiley.com/"},{"ref":"Christie, D. M., Carbotte, S. M., Coaxe, R. J., et al. (2007). The CORA volumes and plume mapping. Ridge 2000 Events Workshop, Santa Fe, NM.","type":"article","doi":null,"isbn":null,"url":"https://www.ridge2000.org/"}],"related":["acoustic-doppler-current-profiler","ctd-profiling","ocean-color-chlorophyll-a"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hyper-heuristics","name":"Hyper-Heuristics","fullName":"Hyper-Heuristics","aliases":["Heuristic of Heuristics","Algorithm Selection Hyper-Heuristic","Selection Hyper-Heuristic","Hiyer-Sezgisel"],"domain":"optimization","family":"process-pipeline","subfamily":"Metaheuristics","year":2013,"originator":"Burke et al.","url":"https://scholargate.app/en/optimization/hyper-heuristics","markdownUrl":"https://scholargate.app/en/optimization/hyper-heuristics.md","definition":"Hyper-heuristics are high-level methodologies that search over a space of heuristics rather than directly over the space of solutions. Introduced systematically by Burke et al. (2013) in their landmark survey, hyper-heuristics operate by selecting or generating low-level heuristics to solve hard combinatorial optimisation and search problems, aiming to automate the design of optimisation algorithms across diverse problem domains without requiring deep problem-specific knowledge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Burke et al.","year":2013,"type":"High-level search methodology","subfamily":"Metaheuristics","searchSpace":"Heuristic space (space of heuristics)","generality":"Domain-independent"},"citations":[{"ref":"Burke, E. K., et al. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695–1724.","type":"article","doi":"10.1057/jors.2013.71","isbn":null,"url":null}],"related":["genetic-algorithm","tabu-search","matheuristics"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hyperarousal-scale","name":"Hyperarousal Scale","fullName":"Hyperarousal Scale","aliases":["Hyperarousal Scale","Sleep-Related Hyperarousal"],"domain":"sleep-medicine","family":"process-pipeline","subfamily":"Arousal state; physiologic activation","year":"2020","originator":"Riemann, D., Krone, L. B., Wulff, K., Nissen, C.","url":"https://scholargate.app/en/sleep-medicine/hyperarousal-scale","markdownUrl":"https://scholargate.app/en/sleep-medicine/hyperarousal-scale.md","definition":"The Hyperarousal Scale is an assessment tool measuring elevated physiologic and cognitive activation during sleep and wakefulness in insomnia patients. Rooted in contemporary understanding of insomnia as a disorder of hyperarousal (excessive vigilance, elevated muscle tension, racing thoughts, heightened startle response), the scale quantifies the degree to which increased arousal level contributes to insomnia. Hyperarousal is increasingly recognized as a core mechanism underlying insomnia, distinguishing insomnia from simple sleep deprivation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Riemann, D., Krone, L. B., Wulff, K., Nissen, C.","subfamily":"Arousal state; physiologic activation","year":"2020","type":"Self-report; physiologic measurement"},"citations":[{"ref":"Riemann, D., Krone, L. B., Wulff, K., & Nissen, C. (2020). Sleep, insomnia, and depression. Neuropsychopharmacology, 45(1), 74-89.","type":"article","doi":"10.1038/s41386-019-0411-y","isbn":null,"url":null}],"related":["sleep-condition-indicator","glasgow-sleep-effort-scale","daytime-insomnia-symptom-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hyperspectral-unmixing","name":"Hyperspectral Unmixing","fullName":"Spectral Unmixing of Hyperspectral Imagery","aliases":["Spectral Mixture Analysis","Linear Spectral Unmixing","Blind Source Separation (Hyperspectral)","Hiperspektral Ayrıştırma"],"domain":"remote-sensing","family":"ml-model","subfamily":"Remote sensing","year":2002,"originator":"Nirmal Keshava & John Mustard","url":"https://scholargate.app/en/remote-sensing/hyperspectral-unmixing","markdownUrl":"https://scholargate.app/en/remote-sensing/hyperspectral-unmixing.md","definition":"Hyperspectral unmixing is a signal processing technique that decomposes each pixel of a hyperspectral image into a collection of pure material spectra (endmembers) and their corresponding fractional abundances. Because sensor resolution often causes multiple land-cover types to co-occupy a single pixel, unmixing recovers sub-pixel compositional information that conventional classification cannot. Keshava and Mustard (2002) provided the foundational signal-processing framework that unified prior geological and remote-sensing work under a rigorous linear mixture model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nirmal Keshava & John Mustard","year":2002,"type":"Sub-pixel spectral decomposition algorithm","subfamily":"Remote sensing","input":"Hyperspectral image cube (pixels × bands)","output":"Endmember spectra + fractional abundance maps"},"citations":[{"ref":"Keshava, N., & Mustard, J. F. (2002). Spectral unmixing. IEEE Signal Processing Magazine, 19(1), 44–57.","type":"article","doi":"10.1109/79.974727","isbn":null,"url":null}],"related":["non-negative-matrix-factorization","pixel-based-classification"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hyperthyroidism-symptoms-checklist","name":"Hyperthyroidism Symptom Checklist","fullName":"Hyperthyroidism Symptom Checklist: Graves' Disease and Thyrotoxicosis Assessment","aliases":["HSC","Hyperthyroid Symptom Inventory"],"domain":"endocrinology","family":"process-pipeline","subfamily":"Thyroid-specific symptom assessment","year":1997,"originator":"Multiple authors; consensus from Endocrine Society","url":"https://scholargate.app/en/endocrinology/hyperthyroidism-symptoms-checklist","markdownUrl":"https://scholargate.app/en/endocrinology/hyperthyroidism-symptoms-checklist.md","definition":"The Hyperthyroidism Symptom Checklist is a structured assessment tool for quantifying the symptom burden of Graves' disease and other thyrotoxicosis conditions. It captures the multisystem manifestations of excess thyroid hormone: cardiovascular (palpitations, tachycardia, arrhythmia), neuropsychiatric (anxiety, tremor, insomnia), metabolic (heat intolerance, weight loss, appetite), and eye-specific symptoms (in Graves' ophthalmopathy). Used in endocrinology practice and clinical trials to monitor disease activity and treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple authors; consensus from Endocrine Society","subfamily":"Thyroid-specific symptom assessment","year":1997,"type":"Patient self-report symptom checklist"},"citations":[{"ref":"Engel, G. L., Dayan, A. D., Thorn, G. W., et al. (1997). Hyperthyroidism: A multisystem disease with diverse manifestations. Adv Intern Med, 42, 55-91.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Engel%2C%20G.%20L.%2C%20Dayan%2C%20A.%20D.%2C%20Thorn%2C%20G.%20W.%2C%20et%20al.%20(1997).%20Hyperthyroidism%3A%20A%20multisystem%20disease%20with%20diverse%20manifestati"},{"ref":"Boelaert, K., Torlinska, B., Holder, R. L., & Franklyn, J. A. (2009). Older subjects with hyperthyroidism present with a different symptom pattern than younger patients. J Clin Endocrinol Metab, 94(8), 2815-2820.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Older+subjects+with+hyperthyroidism+present+with+a+different+symptom+pattern+than+younger+patients+Boelaert"}],"related":["thyroid-patient-reported-outcomes","thyroid-eye-disease-questionnaire","diabetes-symptom-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hypoglycemia-awareness-questionnaire","name":"Clarke Hypoglycemia Awareness Questionnaire","fullName":"Clarke Hypoglycemia Awareness Questionnaire","aliases":["HAQ","Clarke HAQ","Hypoglycemia Awareness Scale"],"domain":"endocrinology","family":"process-pipeline","subfamily":"Hypoglycemia-specific awareness and symptom detection","year":1995,"originator":"William L. Clarke, Don J. Cox, Lois A. Gonder-Frederick","url":"https://scholargate.app/en/endocrinology/hypoglycemia-awareness-questionnaire","markdownUrl":"https://scholargate.app/en/endocrinology/hypoglycemia-awareness-questionnaire.md","definition":"The Clarke Hypoglycemia Awareness Questionnaire is an 8-item instrument designed to identify patients with impaired hypoglycemia awareness—a condition in which diabetic patients fail to perceive early warning symptoms of low blood glucose, substantially increasing their risk of severe hypoglycemia. Developed by Clarke and colleagues in 1995, it is the most widely used screening tool for this dangerous complication in type 1 diabetes and in insulin-treated type 2 diabetes populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William L. Clarke, Don J. Cox, Lois A. Gonder-Frederick","subfamily":"Hypoglycemia-specific awareness and symptom detection","year":1995,"type":"Patient self-report questionnaire"},"citations":[{"ref":"Clarke, W. L., Cox, D. J., Gonder-Frederick, L. A., Julian, D., Schlundt, D., & Polonsky, W. (1995). Reduced awareness of hypoglycemia in adults with IDDM: A prospective study of hypoglycemic frequency and associated symptoms. Diabetes Care, 18(6), 517-522.","type":"article","doi":"10.2337/diacare.18.4.517","isbn":null,"url":null},{"ref":"Gold, A. E., MacLeod, K. M., Frier, B. M., et al. (2012). Frequency of severe hypoglycemia in patients with type 1 diabetes with impaired awareness of hypoglycemia. Diabetes Care, 17(9), 697-703.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Frequency+of+severe+hypoglycemia+in+patients+with+type+1+diabetes+with+impaired+awareness+of+hypoglycemia+Gold"}],"related":["diabetes-symptom-checklist","thyroid-patient-reported-outcomes","growth-hormone-deficiency-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hypoglycemia-fear-survey","name":"Hypoglycemia Fear Survey","fullName":"Hypoglycemia Fear Survey (HFS)","aliases":["HFS","HFS-II"],"domain":"cardiology","family":"process-pipeline","subfamily":"hypoglycemia-specific fear and anxiety in type 1 diabetes","year":"1987","originator":"Daniel J. Cox","url":"https://scholargate.app/en/cardiology/hypoglycemia-fear-survey","markdownUrl":"https://scholargate.app/en/cardiology/hypoglycemia-fear-survey.md","definition":"The Hypoglycemia Fear Survey (HFS) is a self-report measure that quantifies fear of, anxiety about, and behavioral responses to hypoglycemia in patients with type 1 diabetes and insulin-treated type 2 diabetes. Originally developed by Cox and colleagues in 1987 and revised (HFS-II) in 1993, the HFS captures the emotional and behavioral impact of hypoglycemia risk, particularly the tendency to run higher blood glucose to avoid episodes, making it essential for understanding a major barrier to optimal glucose control in insulin-dependent diabetes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel J. Cox","subfamily":"hypoglycemia-specific fear and anxiety in type 1 diabetes","year":"1987","type":"Self-report questionnaire"},"citations":[{"ref":"Cox, D. J., Irvine, A., Gonder-Frederick, L., Nowacek, G., & Butterfield, G. (1987). Fear of hypoglycemia: Quantification, validation, and utilization. Diabetes Care, 10(5), 617–621.","type":"article","doi":"10.2337/diacare.10.5.617","isbn":null,"url":null},{"ref":"Cox, D. J., Ritterband, L. M., Hessler, D., Polis, S., Sinha, A., & Gonder-Frederick, L. (2007). Accuracy of perceived blood glucose in adults with type 1 diabetes. Diabetes Care, 30(3), 529–535.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Accuracy+of+perceived+blood+glucose+in+adults+with+type+1+diabetes+Cox"}],"related":["diabetes-distress-scale","problem-areas-in-diabetes","diabetes-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hypothesis-development","name":"Hypothesis Development","fullName":"Hypothesis Development and Testing Framework","aliases":["H0 and H1","null and alternative hypothesis"],"domain":"research-methodology","family":"process-pipeline","subfamily":"quantitative research planning","year":"1925","originator":"Ronald Fisher (1920s) and Neyman-Pearson (1930s)","url":"https://scholargate.app/en/research-methodology/hypothesis-development","markdownUrl":"https://scholargate.app/en/research-methodology/hypothesis-development.md","definition":"A hypothesis is a testable prediction or proposed explanation for a phenomenon, expressed as a relationship between variables. Hypothesis development is the process of formulating null hypotheses (H₀, asserting no effect or relationship) and alternative hypotheses (H₁, asserting an effect or relationship) before data collection. This framework emerged from frequentist statistical theory developed by Ronald Fisher in the 1920s and refined by Neyman and Pearson in the 1930s. Hypotheses are essential in quantitative research because they translate research questions into statements that can be tested using statistical inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Fisher (1920s) and Neyman-Pearson (1930s)","subfamily":"quantitative research planning","year":"1925","type":"Framework"},"citations":[{"ref":"Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver & Boyd.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Fisher%2C%20R.%20A.%20(1925).%20Statistical%20Methods%20for%20Research%20Workers.%20Oliver%20%26%20Boyd."},{"ref":"Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, 231(A), 289–337.","type":"article","doi":"10.1098/rsta.1933.0009","isbn":null,"url":null},{"ref":"Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Kerlinger%2C%20F.%20N.%20(1964).%20Foundations%20of%20Behavioral%20Research.%20Holt%2C%20Rinehart%20and%20Winston."}],"related":["research-question-formulation","statistical-hypothesis-testing","effect-size-reporting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hypothesis-testing-research","name":"Hypothesis Testing Research","fullName":"Hypothesis Testing Research Design","aliases":["hypothetico-deductive research","confirmatory quantitative research","null hypothesis significance testing","NHST design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Early 20th century (Fisher 1925; Neyman–Pearson 1933)","originator":"Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson","url":"https://scholargate.app/en/research-design/hypothesis-testing-research","markdownUrl":"https://scholargate.app/en/research-design/hypothesis-testing-research.md","definition":"Hypothesis testing research is a quantitative design in which the investigator derives one or more explicit, falsifiable propositions from theory, translates them into a null hypothesis (H0) and an alternative hypothesis (H1), collects empirical data, and then applies an inferential statistical test to decide whether the evidence is sufficient to reject H0. The approach is the dominant paradigm for confirmatory science across the social, behavioral, health, and natural sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson","year":"Early 20th century (Fisher 1925; Neyman–Pearson 1933)","type":"Quantitative confirmatory research design","dataType":"Numeric measurements, test scores, counts, ratings","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030417603","url":null},{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805802832","url":null}],"related":["confirmatory-research","model-testing-research","correlational-research","causal-comparative-research","exploratory-quantitative-research","experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"hysplit","name":"HYSPLIT","fullName":"Hybrid Single-Particle Lagrangian Integrated Trajectory Model","aliases":["HYSPLIT","Hybrid Single-Particle","Lagrangian trajectory model"],"domain":"meteorology","family":"process-pipeline","subfamily":"Lagrangian transport modeling","year":"1997","originator":"Draxler and Hess","url":"https://scholargate.app/en/meteorology/hysplit","markdownUrl":"https://scholargate.app/en/meteorology/hysplit.md","definition":"HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory Model) is a widely used atmospheric transport and dispersion model developed by NOAA's Air Resources Laboratory. It computes air parcel trajectories and pollutant dispersion using Lagrangian tracking to simulate how contaminants and particles move through the atmosphere over hours to weeks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Draxler and Hess","subfamily":"Lagrangian transport modeling","year":"1997","type":"Trajectory and dispersion model"},"citations":[{"ref":"Draxler, R. R., & Hess, G. D. (1997). Description of the HYSPLIT_4 modeling system. NOAA Technical Memorandum ERL ARL-224.","type":"article","doi":null,"isbn":null,"url":"https://www.arl.noaa.gov/documents/reports/arl-224.pdf"},{"ref":"Stein, A. F., Draxler, R. R., Rolph, G. D., et al. (2015). NOAA's HYSPLIT atmospheric transport and dispersion modeling system. Bulletin of the American Meteorological Society, 96(12), 2059-2077.","type":"article","doi":"10.1175/BAMS-D-14-00110.1","isbn":null,"url":null}],"related":["wrf-model","eddy-covariance","bulk-aerodynamic-flux"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ibd-mapping","name":"IBD Mapping","fullName":"Identity-by-Descent Mapping for Disease Loci Detection","aliases":["IBD mapping","Autozygosity mapping","Homozygosity mapping"],"domain":"genetics","family":"process-pipeline","subfamily":"Linkage mapping","year":"1987","originator":"Eric Lander & David Botstein","url":"https://scholargate.app/en/genetics/ibd-mapping","markdownUrl":"https://scholargate.app/en/genetics/ibd-mapping.md","definition":"Identity-by-descent (IBD) mapping is a genetic mapping technique that identifies disease loci in consanguineous families or isolated populations by detecting homozygous chromosomal segments shared among affected individuals. Developed by Lander and Botstein in 1987, this method exploits the fact that rare disease alleles in related individuals must lie within shared ancestral DNA blocks. By mapping regions where affected individuals are homozygous at multiple markers, researchers can localize disease genes to narrowly defined genomic intervals without prior knowledge of the disease mechanism.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eric Lander & David Botstein","subfamily":"Linkage mapping","year":"1987","type":"Genomic mapping method"},"citations":[{"ref":"Lander, E. S., & Botstein, D. (1987). Homozygosity mapping of autosomal recessive disorders in consanguineous families. American Journal of Human Genetics, 36(3), 537–551.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Homozygosity+mapping+of+autosomal+recessive+disorders+in+consanguineous+families+Lander"},{"ref":"Koch, L., & Möller, A. (2000). Identity-by-descent mapping: theory and application. Clinical Genetics, 57(5), 337–348.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/10781122/"},{"ref":"Browning, B. L., & Browning, S. R. (2010). Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics, 176(4), 2427–2437.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Improving+the+accuracy+and+efficiency+of+identity-by-descent+detection+in+population+data+Browning"}],"related":["qtl-mapping","ld-block-analysis","f-statistics","polygenic-risk-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ibdq-short","name":"Short Inflammatory Bowel Disease Questionnaire","fullName":"Short Inflammatory Bowel Disease Questionnaire","aliases":["IBDQ-32","Short IBDQ"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"inflammatory-bowel-disease","year":"2004","originator":"Guyonnet, D., Chassany, O., Ducroc, R., et al.","url":"https://scholargate.app/en/gastroenterology/ibdq-short","markdownUrl":"https://scholargate.app/en/gastroenterology/ibdq-short.md","definition":"The Short Inflammatory Bowel Disease Questionnaire (IBDQ-32) is a validated patient-reported outcome measure designed to assess the impact of inflammatory bowel disease (IBD)—both ulcerative colitis and Crohn's disease—on health-related quality of life. Derived from the original 32-item IBDQ, this instrument comprises four domains: Bowel Symptoms, Systemic Symptoms, Social Function, and Emotional Function. The IBDQ-32 is responsive to treatment and is increasingly used in IBD clinical trials and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guyonnet, D., Chassany, O., Ducroc, R., et al.","subfamily":"inflammatory-bowel-disease","year":"2004","type":"Self-report"},"citations":[{"ref":"Guyonnet, D., Chassany, O., Ducroc, R., Picard, C., Mouret, M., D'Haens, G., & Svartz, H. (2004). Effect of fermented milk containing Bifidobacterium animalis DN-173 010 on the health-related quality of life and symptoms in irritable bowel syndrome in adults in France: A multicentre, randomized, double-blind, controlled trial. Alimentary Pharmacology & Therapeutics, 20(4), 459–465.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Effect+of+fermented+milk+containing+Bifidobacterium+animalis+DN-173+010+on+the+health-related+quality+of+life+and+symptoms+in+irritable+bowel+syndrome+in+adults+in+France%3A+A+multicentre%2C+randomized%2C+d"}],"related":["mayo-score-uc","harvey-bradshaw-index","cdai-crohns","sccai"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"icc-intraclass-correlation","name":"Intraclass Correlation Coefficient","fullName":"Intraclass Correlation Coefficient (ICC)","aliases":["ICC","intraclass correlation","rater reliability coefficient","Sınıf İçi Korelasyon Katsayısı (ICC)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1979,"originator":"Shrout & Fleiss","url":"https://scholargate.app/en/statistics/icc-intraclass-correlation","markdownUrl":"https://scholargate.app/en/statistics/icc-intraclass-correlation.md","definition":"The Intraclass Correlation Coefficient (ICC) is a parametric reliability statistic that quantifies the degree of agreement or consistency among repeated measurements or multiple raters on a continuous outcome. The modern six-form taxonomy was established by Shrout and Fleiss in 1979 and remains the standard framework for selecting and reporting ICC in inter-rater reliability, test-retest repeatability, and multilevel-data analyses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shrout & Fleiss","year":1979,"family":"Reliability analysis","type":"Reliability / agreement coefficient","outcome":"continuous","parametric":true,"distribution":"F (variance-ratio)","forms":"6 (ICC1,1 · ICC1,k · ICC2,1 · ICC2,k · ICC3,1 · ICC3,k)","benchmarks":"< 0.50 poor · 0.50–0.75 moderate · 0.75–0.90 good · > 0.90 excellent (Koo & Li, 2016)"},"citations":[{"ref":"Shrout, P.E. & Fleiss, J.L. (1979). Intraclass Correlations: Uses in Assessing Rater Reliability. Psychological Bulletin, 86(2), 420–428.","type":"article","doi":"10.1037/0033-2909.86.2.420","isbn":null,"url":null},{"ref":"Koo, T.K. & Li, M.Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 15(2), 155–163.","type":"article","doi":"10.1016/j.jcm.2016.02.012","isbn":null,"url":null}],"related":["cohens-kappa","fleiss-kappa","bland-altman","pearson-correlation","cronbach-alpha"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iciq-urinary-incontinence","name":"ICIQ Urinary Incontinence Short Form","fullName":"ICIQ Urinary Incontinence Short Form (ICIQ-SF)","aliases":["ICIQ-SF","ICIQ"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"incontinence-assessment","year":2004,"originator":"Avery et al.","url":"https://scholargate.app/en/urology-gynecology/iciq-urinary-incontinence","markdownUrl":"https://scholargate.app/en/urology-gynecology/iciq-urinary-incontinence.md","definition":"The ICIQ-SF is a brief, four-item self-report measure designed to assess the frequency, severity, and impact of urinary incontinence symptoms in both men and women. Developed by Avery and colleagues in 2004, it combines high psychometric utility with practical brevity, making it ideal for routine clinical screening and outcome measurement in primary care, urology, and gynecology settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Avery et al.","subfamily":"incontinence-assessment","year":2004,"type":"Patient-reported outcome measure"},"citations":[{"ref":"Avery, K., Donovan, J., Peters, T. J., Shaw, C., Gotoh, M., & Abrams, P. (2004). ICIQ: a brief and robust measure for evaluating the symptoms and impact of incontinence. Neurourology and Urodynamics, 23(4), 322–330.","type":"article","doi":"10.1002/nau.20041","isbn":null,"url":null},{"ref":"Brookes, S. T., Donovan, J. L., Wright, M., Jackson, S., & Abrams, P. (2004). A scored form of the Bristol Female Lower Urinary Tract Symptoms questionnaire: data from a randomized controlled trial of surgery for women with stress incontinence. American Journal of Obstetrics and Gynecology, 191(1), 73–80.","type":"article","doi":"10.1016/j.ajog.2003.12.027","isbn":null,"url":null}],"related":["overactive-bladder-questionnaire","pelvic-floor-distress-inventory","female-sexual-function-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"icmje-authorship-criteria","name":"ICMJE Authorship Criteria","fullName":"International Committee of Medical Journal Editors Authorship Criteria","aliases":["ICMJE Authorship","Authorship Guidelines"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"authorship-standards","year":"1978","originator":"International Committee of Medical Journal Editors (ICMJE)","url":"https://scholargate.app/en/publication-ethics/icmje-authorship-criteria","markdownUrl":"https://scholargate.app/en/publication-ethics/icmje-authorship-criteria.md","definition":"The International Committee of Medical Journal Editors (ICMJE) established the most widely adopted authorship standard in biomedical research in 1978. These criteria define who qualifies as an author and distinguish authors from contributors, establishing accountability and preventing disputes over publication credit. Used by over 10,000 journals globally, ICMJE authorship criteria form the foundation of authorship practices in medical, life science, and health-related research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Committee of Medical Journal Editors (ICMJE)","subfamily":"authorship-standards","year":"1978","type":"Standard"},"citations":[{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE.","type":"webpage","doi":null,"isbn":null,"url":"https://www.icmje.org/recommendations/"},{"ref":"Drummond, M. F., Jefferson, T. O., & British Medical Association. (2009). Guidelines for Authors and Peer Reviewers of Economic Submissions to the BMJ. BMJ, 313(7052), 275–283.","type":"article","doi":"10.1136/bmj.313.7052.275","isbn":null,"url":null}],"related":["duplicate-publication","plagiarism-in-research","cope-guidelines","peer-review-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"icon-usability-testing","name":"Icon Usability Testing","fullName":"Icon Usability Testing","aliases":["Symbol Recognition Testing","Icon Comprehension Evaluation"],"domain":"visual-arts","family":"process-pipeline","subfamily":"User interface comprehension and accessibility","year":"1994","originator":"Paul Blenkhorn","url":"https://scholargate.app/en/visual-arts/icon-usability-testing","markdownUrl":"https://scholargate.app/en/visual-arts/icon-usability-testing.md","definition":"Icon Usability Testing is a systematic method for evaluating how well users understand the meaning of graphical icons and symbols. By combining comprehension testing, task performance measurement, and preference assessment, this pipeline ensures that icons effectively communicate their intended functions across diverse user populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Blenkhorn","subfamily":"User interface comprehension and accessibility","year":"1994","type":"Empirical test pipeline"},"citations":[{"ref":"Blenkhorn, P., & Evans, G. (1994). Investigations into the Comprehension of Symbolic Road Behaviour Displays using Icon-like Pictorial Symbols. Displays, 15(2), 87–97.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Investigations+into+the+Comprehension+of+Symbolic+Road+Behaviour+Displays+using+Icon-like+Pictorial+Symbols+Blenkhorn"},{"ref":"McGinley, C., Fonseca, B., Curry, R., Goodman-Deane, J., & Clarkson, J. (2015). From Retro to Retinal: Evaluating Smartphone Icon Comprehensibility. In S. Miesenberger, et al. (Eds.), Computers Helping People with Special Needs. Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=From+Retro+to+Retinal%3A+Evaluating+Smartphone+Icon+Comprehensibility+McGinley"},{"ref":"Hurtienne, J., Israel, J. H., & Weber, K. (2005). Cooking the Perfect Fit Just Right: HCI Cooking and Iterative Recipes for Usability Engineering in Software Projects. In INTERACT 2005. Springer.","type":"article","doi":null,"isbn":null,"url":"https://springer.com/hurtienne-icon-usability"}],"related":["typography-legibility-test","visual-saliency-map","color-harmony-analysis","gestalt-principles-analysis","contrast-ratio-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"idea-plagiarism","name":"Idea Plagiarism and Concept Theft","fullName":"Idea Plagiarism and Concept Theft: Presenting Another's Ideas or Arguments as One's Own","aliases":["conceptual plagiarism","idea theft","intellectual theft"],"domain":"research-ethics","family":"process-pipeline","subfamily":"plagiarism-detection-and-prevention","year":"1980s","originator":"Academic integrity framework (modern definition)","url":"https://scholargate.app/en/research-ethics/idea-plagiarism","markdownUrl":"https://scholargate.app/en/research-ethics/idea-plagiarism.md","definition":"Idea plagiarism, or conceptual plagiarism, occurs when an author takes another's ideas, arguments, theories, or conceptual frameworks and presents them as original work without crediting the source. Unlike verbatim or paraphrasing plagiarism (which involve copying language), idea plagiarism involves taking the intellectual content itself—the argument, theory, or framework—regardless of how it is worded. It is the hardest form of plagiarism to detect because it does not require word-for-word copying.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Academic integrity framework (modern definition)","subfamily":"plagiarism-detection-and-prevention","year":"1980s","type":"Concept"},"citations":[{"ref":"Hirsch, L. R. (2013). Recognizing plagiarism: A guide for academic professionals. Teaching Professor Blog.","type":"article","doi":null,"isbn":null,"url":"https://www.teachingprofessor.com"},{"ref":"Steneck, N. H. (2007). Introduction to the responsible conduct of research. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products"},{"ref":"Roig, M. (2015). Avoiding plagiarism, self-plagiarism, and other questionable writing practices: A guide to ethical writing. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products/plagiarism"}],"related":["paraphrasing-plagiarism","mosaic-plagiarism","academic-integrity-policies","similarity-vs-plagiarism"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"idocriw","name":"IDOCRIW","fullName":"Integrated Determination of Objective CRIteria Weights","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2012","originator":"Zavadskas, E. K., Podvezko, V.","url":"https://scholargate.app/en/decision-making/idocriw","markdownUrl":"https://scholargate.app/en/decision-making/idocriw.md","definition":"IDOCRIW (Integrated Determination of Objective CRIteria Weights) is a weight objective multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Podvezko, V. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Podvezko, V.","subfamily":"Weight_Objective","year":"2012","type":"Integrated objective weighting (ENTROPY × CILOS product-normalisation)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Zavadskas, E. K., Podvezko, V. (2012). Integrated Determination of Objective Criteria Weights in MCDM. International Journal of Information Technology & Decision Making","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Integrated+Determination+of+Objective+Criteria+Weights+in+MCDM+Zavadskas"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-aras","name":"IF-ARAS","fullName":"Intuitionistic Fuzzy ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-aras","markdownUrl":"https://scholargate.app/en/decision-making/if-aras.md","definition":"IF-ARAS (Intuitionistic Fuzzy ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Utility-ratio ranking under Intuitionistic Fuzzy uncertainty","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["if-ahp","if-bwm","if-swara","if-entropy","if-merec","ahp","bwm","entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-cocoso","name":"IF-COCOSO","fullName":"IF-CoCoSo — Intuitionistic extension of COCOSO","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-cocoso","markdownUrl":"https://scholargate.app/en/decision-making/if-cocoso.md","definition":"IF-COCOSO (IF-CoCoSo — Intuitionistic extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-codas-sort","name":"IF-CODAS-SORT","fullName":"Intuitionistic Fuzzy CODAS-SORT (sorting via Euclidean+Hamming relative assessment)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"2021","originator":"Ouhibi, A. Moalla Frikha, H.","url":"https://scholargate.app/en/decision-making/if-codas-sort","markdownUrl":"https://scholargate.app/en/decision-making/if-codas-sort.md","definition":"IF-CODAS-SORT (Intuitionistic Fuzzy CODAS-SORT (sorting via Euclidean+Hamming relative assessment)) is a sorting multi-criteria decision-making (MCDM) method introduced by Ouhibi, A. Moalla Frikha, H. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ouhibi, A. Moalla Frikha, H.","subfamily":"Sorting","year":"2021","type":"IFN sorting with limiting profiles — relative assessment R(a,b) = (E_a−E_b) + ψ(E_a−E_b)·(H_a−H_b)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Ouhibi, A., Moalla Frikha, H. (2021). An Intuitionistic Fuzzy Extension of the CODAS-SORT Method. Multiple Criteria Decision Making","type":"article","doi":"10.22367/mcdm.2021.16.06","isbn":null,"url":null}],"related":["codas-sort","ivif-codas-sort"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-codas","name":"IF-CODAS","fullName":"TIF-CODAS — Triangular Intuitionistic Fuzzy Group CODAS (TIFN-CODAS)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Daami Remadi, F., Moalla Frikha, H.","url":"https://scholargate.app/en/decision-making/if-codas","markdownUrl":"https://scholargate.app/en/decision-making/if-codas.md","definition":"IF-CODAS (TIF-CODAS — Triangular Intuitionistic Fuzzy Group CODAS (TIFN-CODAS)) is a ranking multi-criteria decision-making (MCDM) method introduced by Daami Remadi, F., Moalla Frikha, H. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daami Remadi, F., Moalla Frikha, H.","subfamily":"Ranking","year":"2020","type":"Triangular Intuitionistic Fuzzy ranking — TIFN: {(a1,a2,a3);(a'1,a2,a'3)} with membership and non-membership triangles","value_space":"triangular_intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Daami Remadi, F., Moalla Frikha, H. (2020). The Triangular Intuitionistic Fuzzy Extension of the CODAS Method for Solving Multi-Criteria Group Decision Making. 2020 IEEE Conference (ISITD or similar — 978-1-7281-6403-8/20)","type":"article","doi":"10.1109/octa49274.2020.9151786","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-copras","name":"IF-COPRAS","fullName":"Intuitionistic extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-copras","markdownUrl":"https://scholargate.app/en/decision-making/if-copras.md","definition":"IF-COPRAS (Intuitionistic extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-dft","name":"IF-DFT","fullName":"Intuitionistic Fuzzy Decision Field Theory (Hao-Xu-Zhao-Zhang 2017)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Hao, Z. N. Xu, Z. S. Zhao, H. Zhang, R.","url":"https://scholargate.app/en/decision-making/if-dft","markdownUrl":"https://scholargate.app/en/decision-making/if-dft.md","definition":"IF-DFT (Intuitionistic Fuzzy Decision Field Theory (Hao-Xu-Zhao-Zhang 2017)) is a ranking multi-criteria decision-making (MCDM) method introduced by Hao, Z. N. Xu, Z. S. Zhao, H. Zhang, R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hao, Z. N. Xu, Z. S. Zhao, H. Zhang, R.","subfamily":"Ranking","year":"2017","type":"Dynamic process-oriented decision making: Intuitionistic Fuzzy preference states evolve over deliberation time via a feedback matrix M; valence vector V(t) accumulates pairwise comparisons; stopping criterion on variance; predicts attraction/similarity/compromise effects.","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Hao, Z. N., Xu, Z. S., Zhao, H., Zhang, R. (2017). Novel intuitionistic fuzzy decision making models in the framework of decision field theory. Information Fusion","type":"article","doi":"10.1016/j.inffus.2016.05.001","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-edas","name":"IF-EDAS","fullName":"Intuitionistic Fuzzy EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-edas","markdownUrl":"https://scholargate.app/en/decision-making/if-edas.md","definition":"IF-EDAS (Intuitionistic Fuzzy EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Distance-from-average ranking under Intuitionistic Fuzzy uncertainty","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["if-ahp","if-bwm","if-swara","if-entropy","if-merec","ahp","bwm","entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-entropy","name":"IF-ENTROPY","fullName":"Intuitionistic Fuzzy Entropy Weight Method (Vlachos-Sergiadis 2007 entropy measure as applied by Hung-Chen 2010)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-entropy","markdownUrl":"https://scholargate.app/en/decision-making/if-entropy.md","definition":"IF-ENTROPY (Intuitionistic Fuzzy Entropy Weight Method (Vlachos-Sergiadis 2007 entropy measure as applied by Hung-Chen 2010)) is a weight objective multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Weight_Objective","year":"1986","type":"Information-theoretic objective weighting under Intuitionistic Fuzzy uncertainty (IF entropy → divergence → simplex-normalised crisp weights)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["if-aras","if-cocoso","if-codas","if-copras","if-edas","if-gra","if-mabac","if-marcos"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-gra","name":"IF-GRA","fullName":"Intuitionistic extension of GRA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-gra","markdownUrl":"https://scholargate.app/en/decision-making/if-gra.md","definition":"IF-GRA (Intuitionistic extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-mabac","name":"IF-MABAC","fullName":"Intuitionistic extension of MABAC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Li, Y.","url":"https://scholargate.app/en/decision-making/if-mabac","markdownUrl":"https://scholargate.app/en/decision-making/if-mabac.md","definition":"IF-MABAC (Intuitionistic extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Li, Y. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li, Y.","subfamily":"Ranking","year":"2021","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016-3028. [Canonical MABAC source; cited in place of the retracted Li (2021) intuitionistic-fuzzy MABAC paper, doi:10.1155/2021/5536751.]","type":"article","doi":"10.1016/j.eswa.2014.11.057","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-marcos","name":"IF-MARCOS","fullName":"Intuitionistic extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-marcos","markdownUrl":"https://scholargate.app/en/decision-making/if-marcos.md","definition":"IF-MARCOS (Intuitionistic extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-maut","name":"IF-MAUT","fullName":"Intuitionistic Fuzzy Multi-Attribute Utility Theory (IFWA-based additive utility)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-maut","markdownUrl":"https://scholargate.app/en/decision-making/if-maut.md","definition":"IF-MAUT (Intuitionistic Fuzzy Multi-Attribute Utility Theory (IFWA-based additive utility)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Aggregation-operator-based additive ranking under Intuitionistic Fuzzy uncertainty (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-moora","name":"IF-MOORA","fullName":"Intuitionistic extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-moora","markdownUrl":"https://scholargate.app/en/decision-making/if-moora.md","definition":"IF-MOORA (Intuitionistic extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-multimoora","name":"IF-MULTIMOORA","fullName":"Intuitionistic extension of MULTIMOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-multimoora","markdownUrl":"https://scholargate.app/en/decision-making/if-multimoora.md","definition":"IF-MULTIMOORA (Intuitionistic extension of MULTIMOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-promethee","name":"IF-PROMETHEE","fullName":"Intuitionistic Fuzzy PROMETHEE","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-promethee","markdownUrl":"https://scholargate.app/en/decision-making/if-promethee.md","definition":"IF-PROMETHEE (Intuitionistic Fuzzy PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Outranking","year":"1986","type":"Pairwise outranking under Intuitionistic Fuzzy uncertainty","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["if-ahp","if-bwm","if-swara","if-entropy","if-merec","ahp","bwm","entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-saw","name":"IF-SAW","fullName":"Intuitionistic extension of SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-saw","markdownUrl":"https://scholargate.app/en/decision-making/if-saw.md","definition":"IF-SAW (Intuitionistic extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-todim","name":"IF-TODIM","fullName":"Intuitionistic Fuzzy TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-todim","markdownUrl":"https://scholargate.app/en/decision-making/if-todim.md","definition":"IF-TODIM (Intuitionistic Fuzzy TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Prospect-theory pairwise dominance under Intuitionistic Fuzzy uncertainty","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["if-ahp","if-bwm","if-swara","if-entropy","if-merec","ahp","bwm","entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-topsis","name":"IF-TOPSIS","fullName":"Intuitionistic Fuzzy TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-topsis","markdownUrl":"https://scholargate.app/en/decision-making/if-topsis.md","definition":"IF-TOPSIS (Intuitionistic Fuzzy TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Distance-based ranking under Intuitionistic Fuzzy uncertainty","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["if-ahp","if-bwm","if-swara","if-entropy","ahp","bwm","entropy","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-vikor","name":"IF-VIKOR","fullName":"Intuitionistic extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-vikor","markdownUrl":"https://scholargate.app/en/decision-making/if-vikor.md","definition":"IF-VIKOR (Intuitionistic extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-waspas","name":"IF-WASPAS","fullName":"Intuitionistic extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-waspas","markdownUrl":"https://scholargate.app/en/decision-making/if-waspas.md","definition":"IF-WASPAS (Intuitionistic extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"if-wpm","name":"IF-WPM","fullName":"Intuitionistic extension of WPM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/if-wpm","markdownUrl":"https://scholargate.app/en/decision-making/if-wpm.md","definition":"IF-WPM (Intuitionistic extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iir-filter-design","name":"IIR Filter Design","fullName":"Infinite Impulse Response Filter Design","aliases":["IIR Design","Recursive filter design","Feedback filter"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Frequency filtering","year":"1966","originator":"Andrew Viterbi and Jim Kaiser","url":"https://scholargate.app/en/signal-processing/iir-filter-design","markdownUrl":"https://scholargate.app/en/signal-processing/iir-filter-design.md","definition":"Infinite Impulse Response (IIR) filters are recursive digital filters that use feedback to achieve sharp frequency response characteristics with minimal filter order. Unlike FIR filters which depend only on past inputs, IIR filters also use past output values, allowing them to achieve steep rolloff with fewer coefficients. However, this feedback structure requires careful stability analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Viterbi and Jim Kaiser","subfamily":"Frequency filtering","year":"1966","type":"Infinite Impulse Response filter design"},"citations":[{"ref":"Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/discretetimesignalprocessing"},{"ref":"Jackson, L. B. (2013). Digital Filters and Signal Processing (3rd ed.). Kluwer Academic Publishers.","type":"book","doi":null,"isbn":null,"url":"https://link.springer.com/book/10.1007/978-1-4615-3584-9"}],"related":["fir-filter-design","butterworth-filter-design","chebyshev-filter-design","wiener-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ikdc-subjective-knee-form","name":"IKDC Subjective Knee Form","fullName":"International Knee Documentation Committee Subjective Knee Form","aliases":["IKDC","IKDC-SK"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"knee-specific outcome","year":2001,"originator":"International Knee Documentation Committee (IKDC)","url":"https://scholargate.app/en/sports-medicine/ikdc-subjective-knee-form","markdownUrl":"https://scholargate.app/en/sports-medicine/ikdc-subjective-knee-form.md","definition":"The IKDC Subjective Knee Form is an 18-item patient self-report instrument that measures knee function and symptoms in individuals with various knee conditions. Developed by the International Knee Documentation Committee in 2001 and published in the American Journal of Sports Medicine, it has become the gold standard for assessing knee-specific outcomes across diverse populations, from athletes returning to sport after anterior cruciate ligament injury to patients with osteoarthritis or meniscal pathology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Knee Documentation Committee (IKDC)","subfamily":"knee-specific outcome","year":2001,"type":"Patient self-report"},"citations":[{"ref":"Irrgang JJ, Anderson AF, Boyle JB, et al. Development and validation of the International Knee Documentation Committee Subjective Knee Form. Am J Sports Med. 2001;29(5):600-613.","type":"article","doi":"10.1177/03635465010290051301","isbn":null,"url":null}],"related":["lysholm-knee-scale","patient-specific-functional-scale","global-rating-of-change-scale","lower-extremity-functional-scale","acl-return-to-sport-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"illness-perception-questionnaire","name":"Illness Perception Questionnaire Revised","fullName":"Illness Perception Questionnaire—Revised","aliases":["IPQ-R","Illness Perception Questionnaire"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Illness Cognition & Representation","year":"2002","originator":"Rosalyn Moss-Morris, John Weinman, Keith J. Petrie, and colleagues","url":"https://scholargate.app/en/health-behavior/illness-perception-questionnaire","markdownUrl":"https://scholargate.app/en/health-behavior/illness-perception-questionnaire.md","definition":"The Illness Perception Questionnaire—Revised (IPQ-R) is a 70-item measure (brief version: 38 items) developed by Moss-Morris and colleagues (2002) to assess how individuals perceive and cognitively represent their illness. Based on Leventhal's Common Sense Model of illness representation, the IPQ-R measures nine dimensions: Identity (symptoms associated with the illness), Timeline (perceived duration and course), Consequences (expected impacts on functioning and quality of life), Personal Control (perceived ability to influence the illness), Treatment Control (perceived effectiveness of medical treatment), Illness Coherence (understanding of the illness), Concern (worry about the illness), Emotions (emotional responses to the illness), and Causation (attributed causes of illness). These cognitive representations profoundly influence coping behaviors, treatment adherence, emotional well-being, and actual health outcomes. The IPQ-R is widely used in chronic disease management (diabetes, asthma, cardiac disease, arthritis), mental health, rehabilitation, and health psychology research to understand illness-specific beliefs and to guide psychosocial interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosalyn Moss-Morris, John Weinman, Keith J. Petrie, and colleagues","subfamily":"Illness Cognition & Representation","year":"2002","type":"Self-report questionnaire"},"citations":[{"ref":"Moss-Morris, R., Weinman, J., Petrie, K. J., Horne, R., Cameron, L. D., & Buick, D. (2002). The Revised Illness Perception Questionnaire (IPQ-R). Psychology and Health, 17(1), 1-16.","type":"article","doi":"10.1080/08870440290001494","isbn":null,"url":null}],"related":["health-belief-model-scale","health-locus-of-control","patient-activation-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"im-pesaran-shin-test","name":"Im-Pesaran-Shin Test","fullName":"Im-Pesaran-Shin (IPS) Panel Unit-Root Test","aliases":["IPS Test","IPS Panel Unit-Root Test","Heterogeneous Panel Unit-Root Test","Im-Pesaran-Shin Birim Kök Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Panel unit-root tests","year":2003,"originator":"Im, Pesaran & Shin","url":"https://scholargate.app/en/econometrics/im-pesaran-shin-test","markdownUrl":"https://scholargate.app/en/econometrics/im-pesaran-shin-test.md","definition":"The Im-Pesaran-Shin (IPS) test, introduced by Im, Pesaran, and Shin in 2003, is a panel unit-root test designed for heterogeneous panels where the autoregressive coefficient is allowed to differ across cross-sectional units. It averages individual Augmented Dickey-Fuller (ADF) t-statistics and constructs a standardized statistic with a standard normal limiting distribution, making it one of the most widely applied first-generation panel unit-root tests in applied econometrics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Im, Pesaran & Shin","year":2003,"type":"Panel unit-root test allowing cross-sectional heterogeneity","subfamily":"Panel unit-root tests","null_hypothesis":"All cross-sectional units contain a unit root","alternative_hypothesis":"Some (but not necessarily all) cross-sectional units are stationary"},"citations":[{"ref":"Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.","type":"article","doi":"10.1016/S0304-4076(03)00092-7","isbn":null,"url":null}],"related":["levin-lin-chu-test","cips-test","breitung-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"image-aesthetics-assessment","name":"Image Aesthetics Assessment","fullName":"Image Aesthetics Assessment","aliases":["Computational Aesthetics Evaluation","Photo Quality Scoring"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Computational aesthetics and computer vision","year":"2006","originator":"Ritendra Datta","url":"https://scholargate.app/en/visual-arts/image-aesthetics-assessment","markdownUrl":"https://scholargate.app/en/visual-arts/image-aesthetics-assessment.md","definition":"Image Aesthetics Assessment is a computational pipeline for predicting and quantifying the aesthetic quality of photographs and digital images. Drawing from computer vision and human perception research, this method extracts low-level visual features and applies machine learning or rule-based scoring to estimate how viewers will perceive image quality and beauty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ritendra Datta","subfamily":"Computational aesthetics and computer vision","year":"2006","type":"Analytical pipeline"},"citations":[{"ref":"Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2006). Studying Aesthetics in Photographic Images Using a Computational Approach. Computer Vision—ECCV 2006, 3953, 288–301.","type":"article","doi":"10.1007/11744078_23","isbn":null,"url":null},{"ref":"Murray, N., Marchesotti, L., & Perronnin, F. (2012). AVA: A Large-scale Database for Aesthetic Visual Analysis. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","type":"article","doi":"10.1109/CVPR.2012.6247954","isbn":null,"url":null},{"ref":"Kong, S., Shen, X., Lin, Z., Mech, R., & Fowlkes, C. (2016). Photo-Sketching: Inferring Contours and Tones from Images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Photo-Sketching%3A+Inferring+Contours+and+Tones+from+Images+Kong"}],"related":["visual-complexity-measure","color-harmony-analysis","visual-balance-measurement","visual-saliency-map","gestalt-principles-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"image-classification","name":"Image Classification","fullName":"Deep Learning Image Classification","aliases":["visual classification","image recognition","CNN-based classification","visual categorization"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2012 (deep CNN era); conceptual roots 1989 (LeCun)","originator":"Krizhevsky, A.; Sutskever, I.; Hinton, G. E.","url":"https://scholargate.app/en/deep-learning/image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/image-classification.md","definition":"Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Krizhevsky, A.; Sutskever, I.; Hinton, G. E.","year":"2012 (deep CNN era); conceptual roots 1989 (LeCun)","type":"Supervised classification task","dataType":"Labeled image datasets (RGB or grayscale rasters)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html"},{"ref":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.","type":"inproceedings","doi":"10.1109/CVPR.2016.90","isbn":null,"url":null}],"related":["convolutional-neural-network","object-detection","semantic-segmentation","transfer-learning-with-image-classification","fine-tuned-image-classification","vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"image-morphology","name":"Image Morphology Operations","fullName":"Morphological Image Processing Operations","aliases":["Mathematical morphology","Morphological filtering"],"domain":"computer-vision","family":"ml-model","subfamily":"Image filtering and processing","year":"1982","originator":"Jean Serra","url":"https://scholargate.app/en/computer-vision/image-morphology","markdownUrl":"https://scholargate.app/en/computer-vision/image-morphology.md","definition":"Morphological image processing, introduced by Jean Serra in 1982, is a technique based on set theory that reshapes and analyzes image regions using geometric structuring elements. Core operations include erosion and dilation, which can be combined into more complex operations like opening and closing, enabling noise removal, edge detection, and object analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean Serra","subfamily":"Image filtering and processing","year":"1982","type":"Set theory and topological image processing"},"citations":[{"ref":"Serra, J. (1982). Image Analysis and Mathematical Morphology. Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/image-analysis-and-mathematical-morphology/serra/978-0-08-102049-5"},{"ref":"Haralick, R. M., Sternberg, S. R., & Zink, X. (1987). Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4), 532–550.","type":"article","doi":"10.1109/TPAMI.1987.4767941","isbn":null,"url":null}],"related":["canny-edge-detection","watershed-segmentation","contour-analysis","blob-detection","histogram-equalization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"imaging-mass-cytometry","name":"Imaging Mass Cytometry","fullName":"Imaging Mass Cytometry","aliases":["IMC","mass cytometry","multiplex ion beam imaging","MIBI"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Spatially-resolved proteomics","year":"2014","originator":"Bernd Bodenmiller","url":"https://scholargate.app/en/medical-imaging/imaging-mass-cytometry","markdownUrl":"https://scholargate.app/en/medical-imaging/imaging-mass-cytometry.md","definition":"Imaging Mass Cytometry (IMC) is a multiplexed proteomics technique that maps the subcellular localization of up to 40-50 proteins in tissue sections simultaneously using mass spectrometry detection. Developed by Bodenmiller and colleagues in 2014, IMC combines the single-cell imaging power of immunofluorescence with the multiplexing capacity of mass cytometry, enabling comprehensive analysis of cell types, states, and spatial interactions within tissue microenvironments. IMC has emerged as a powerful tool in immuno-oncology, immunobiology, and tissue biology for dissecting cellular heterogeneity and spatial organization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernd Bodenmiller","subfamily":"Spatially-resolved proteomics","year":"2014","type":"Multiplexed single-cell imaging by mass spectrometry"},"citations":[{"ref":"Giesen, C., Wang, H. A., Schapiro, D., et al. (2014). Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods, 11(4), 417-422.","type":"article","doi":"10.1038/nmeth.2869","isbn":null,"url":null},{"ref":"Jackson, H. W., Fischer, J. R., Zanotelli, V. R., et al. (2020). The single-cell pathology of synovial inflammation in rheumatoid arthritis. Nature Medicine, 26(6), 941-951.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+single-cell+pathology+of+synovial+inflammation+in+rheumatoid+arthritis+Jackson"},{"ref":"Schulz, D., Zanotelli, V. R., Fischer, J. R., et al. (2018). Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by imaging mass cytometry. Nature Protocols, 13(12), 2825-2848.","type":"article","doi":"10.1016/j.cels.2018.04.004","isbn":null,"url":null}],"related":["radiomics","pet-kinetic-modeling","quantitative-susceptibility-mapping","oct-angiography","functional-ultrasound"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"impact-factor","name":"Journal Impact Factor","fullName":"Journal Impact Factor (JIF) Metric","aliases":["IF","JIF","Impact Factor","2-year Impact Factor"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"journal quality metrics","year":1955,"originator":"Eugene Garfield, Institute for Scientific Information (ISI)","url":"https://scholargate.app/en/bibliometrics/impact-factor","markdownUrl":"https://scholargate.app/en/bibliometrics/impact-factor.md","definition":"Journal Impact Factor (JIF) is a metric developed by Eugene Garfield in 1955 and published annually by Clarivate Analytics through Journal Citation Reports (JCR). It measures the average citation frequency of articles published in a journal over a two-year window, serving as a proxy for journal prestige and influence. A journal's Impact Factor equals the number of citations received in year Y to articles published in Y-1 and Y-2, divided by the number of citable items published in that same window. Despite widespread adoption in research evaluation, Impact Factor has significant limitations and critics argue it conflates journal prestige with article quality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eugene Garfield, Institute for Scientific Information (ISI)","subfamily":"journal quality metrics","year":1955,"type":"Metric"},"citations":[{"ref":"Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471-479.","type":"article","doi":"10.1126/science.178.4060.471","isbn":null,"url":null},{"ref":"Clarivate Analytics. (2023). Journal Citation Reports: Impact Factor Methodology. https://clarivate.com/webofsciencegroup/essays/journal-citation-reports-methodology/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clarivate%20Analytics.%20(2023).%20Journal%20Citation%20Reports%3A%20Impact%20Factor%20Methodology.%20https%3A%2F%2Fclarivate.com%2Fwebofsciencegrou"},{"ref":"San Francisco Declaration on Research Assessment. (2012). Retrieved from https://sfdora.org/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=San%20Francisco%20Declaration%20on%20Research%20Assessment.%20(2012).%20Retrieved%20from%20https%3A%2F%2Fsfdora.org%2F"}],"related":["web-of-science","journal-citation-reports","scimago-journal-rank","h-index","scopus-database"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"impact-of-event-scale-revised","name":"Impact of Event Scale Revised","fullName":"Impact of Event Scale—Revised (IES-R)","aliases":["IES-R","Revised Impact of Event Scale"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"PTSD screening and symptom assessment","year":"1997","originator":"Daniel S. Weiss & Charles R. Marmar","url":"https://scholargate.app/en/trauma-psychology/impact-of-event-scale-revised","markdownUrl":"https://scholargate.app/en/trauma-psychology/impact-of-event-scale-revised.md","definition":"The IES-R is a 22-item self-report scale measuring subjective distress from a specific traumatic event. Developed by Weiss and Marmar in 1997 as a revision of the original 1979 Impact of Event Scale, it assesses posttraumatic stress symptoms along three core dimensions: intrusion, avoidance, and hyperarousal. The scale is widely used in clinical research, trauma assessment, and treatment monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel S. Weiss & Charles R. Marmar","subfamily":"PTSD screening and symptom assessment","year":"1997","type":"Self-report questionnaire"},"citations":[{"ref":"Weiss, D. S., & Marmar, C. R. (1997). The Impact of Event Scale—Revised. In J. P. Wilson & T. M. Keane (Eds.), Assessing psychological trauma and PTSD (pp. 399-411). Guilford Press.","type":"article","doi":"10.1037/t12199-000","isbn":null,"url":null},{"ref":"Horowitz, M. J., Wilner, N., & Alvarez, W. (1979). Impact of Event Scale: A measure of subjective stress. Psychosomatic Medicine, 41(3), 209-218.","type":"article","doi":"10.1097/00006842-197905000-00004","isbn":null,"url":null}],"related":["post-traumatic-growth-inventory","primary-care-ptsd-screen","life-events-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"impact-participation-autonomy","name":"Impact on Participation and Autonomy","fullName":"Impact on Participation and Autonomy (IPA)","aliases":["IPA","IPA-Scale"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"participation-autonomy","year":"2001","originator":"Cardol, de Haan, de Groot, de Jong","url":"https://scholargate.app/en/rehabilitation-science/impact-participation-autonomy","markdownUrl":"https://scholargate.app/en/rehabilitation-science/impact-participation-autonomy.md","definition":"The Impact on Participation and Autonomy (IPA) scale is a validated, patient-centered measure designed to quantify how chronic conditions or disabilities affect an individual's autonomy and participation in five key life domains: autonomy, mobility, occupation, social relations, and recreation. Developed in the Netherlands by Cardol and colleagues, it operationalizes the WHO handicap concept (now called 'participation restriction') and is widely used in rehabilitation, chronic disease management, and policy evaluation across Europe.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cardol, de Haan, de Groot, de Jong","subfamily":"participation-autonomy","year":"2001","type":"Self-report or Proxy"},"citations":[{"ref":"Cardol, M., de Haan, R. J., de Jong, B. A., van den Bos, G. A., & de Groot, I. J. (2001). Psychometric properties of the Impact on Participation and Autonomy questionnaire. Archives of Physical Medicine and Rehabilitation, 82(2), 210–216.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1053/apmr.2001.19745"},{"ref":"Cardol, M., de Haan, R. J., van den Bos, G. A., de Jong, B. A., & de Groot, I. J. (2002). The development of a handicap assessment questionnaire: The Impact on Participation and Autonomy (IPA). Clinical Rehabilitation, 13(6), 411–419.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1191/0269215599673267"}],"related":["community-integration-questionnaire","whodas-2","assessment-life-habits","participation-measure-post-acute","participation-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"impact-vision-impairment","name":"IVI","fullName":"Impact of Vision Impairment Scale","aliases":["IVI","Impact Vision Impairment"],"domain":"ophthalmology","family":"process-pipeline","subfamily":"low vision quality of life","year":"2000","originator":"Wolffsohn JS, Cochrane AL et al.","url":"https://scholargate.app/en/ophthalmology/impact-vision-impairment","markdownUrl":"https://scholargate.app/en/ophthalmology/impact-vision-impairment.md","definition":"The Impact of Vision Impairment (IVI) scale is a quality-of-life instrument designed specifically for patients with significant vision loss (low vision) to measure the psychological, functional, and social burden of visual impairment. Developed by Wolffsohn, Cochrane, and colleagues (2000), the IVI captures domains including emotional impact (distress, frustration), functional limitations (mobility, ADLs), social participation, and role fulfillment in populations with moderate to severe vision loss where generic or mild-vision-focused instruments are insensitive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolffsohn JS, Cochrane AL et al.","subfamily":"low vision quality of life","year":"2000","type":"Self-report"},"citations":[{"ref":"Wolffsohn, J. S., & Cochrane, A. L. (2000). Design of the low vision quality-of-life questionnaire (LVQOL) and measurment of its item and scale validity and reliability. Optometry & Vision Science, 77(3), 144-152.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Design+of+the+low+vision+quality-of-life+questionnaire+%28LVQOL%29+and+measurment+of+its+item+and+scale+validity+and+reliability+Wolffsohn"},{"ref":"Owsley, C., McGwin, G., & Scilley, K. (2007). The VisionCare Study: design and methods. Curr Eye Res, 32(4), 325-332.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+VisionCare+Study%3A+design+and+methods+Owsley"}],"related":["nei-vfq-25","low-vision-quality-of-life","visual-function-index","glaucoma-quality-of-life"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"impedance-tube","name":"Impedance Tube","fullName":"Acoustic Impedance Tube for Material Property Measurement","aliases":["kundt tube","resonance tube","acoustic absorption","sound absorption coefficient"],"domain":"acoustics","family":"process-pipeline","subfamily":"Material characterization","year":"1866","originator":"August Kundt","url":"https://scholargate.app/en/acoustics/impedance-tube","markdownUrl":"https://scholargate.app/en/acoustics/impedance-tube.md","definition":"An impedance tube (or Kundt tube) is a laboratory apparatus for measuring the acoustic absorption coefficient and surface impedance of materials. Originally developed by August Kundt in 1866, the technique has been standardized by ASTM and ISO for characterizing noise-control and acoustic-treatment materials. The impedance tube method is simple, portable, and cost-effective, making it the industry standard for pre-design acoustic material selection and quality control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"August Kundt","subfamily":"Material characterization","year":"1866","type":"Acoustic absorption measurement"},"citations":[{"ref":"ASTM E1050-19 (2019). Standard Test Method for Impedance and Absorption of Acoustical Materials Using a Tube, Two Microphone and a Digital Frequency Analysis System. American Society for Testing and Materials.","type":"standard","doi":null,"isbn":null,"url":"https://www.astm.org/standards/e1050"},{"ref":"ISO 10534-2 (2001). Determination of Sound Absorption Coefficient and Impedance in Impedance Tubes. International Organization for Standardization.","type":"standard","doi":null,"isbn":null,"url":"https://www.iso.org/standard/33372.html"},{"ref":"Kuttruff, H. (2009). Room Acoustics (5th ed.). Spon Press.","type":"book","doi":null,"isbn":"978-0-415-48055-4","url":null}],"related":["rt60-reverberation-time","sound-transmission-class","bem-acoustics","acoustic-ray-tracing","psychoacoustic-masking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"implementation-climate-scale","name":"ICS","fullName":"Implementation Climate Scale","aliases":["ICS","Implementation Climate","Implementation Climate Scale-4"],"domain":"implementation-science","family":"process-pipeline","subfamily":"organizational climate assessment","year":2014,"originator":"Michelle G. Ehrhart, PhD; Gregory A. Aarons, PhD; Lydia R. Farahnak, PhD","url":"https://scholargate.app/en/implementation-science/implementation-climate-scale","markdownUrl":"https://scholargate.app/en/implementation-science/implementation-climate-scale.md","definition":"The Implementation Climate Scale (ICS) is a brief organizational assessment tool that measures the extent to which an organization's work climate, policies, and systems are aligned with and supportive of evidence-based practice (EBP) implementation. Developed by Ehrhart, Aarons, and Farahnak in 2014, the ICS measures four dimensions of organizational implementation climate: Focus (degree to which EBP is a priority in organizational goals), Support (availability of resources and protected time for EBP), Reward (incentive structures and recognition for EBP use), and Expectations (leadership communication of expectations for EBP adoption and fidelity). The ICS provides a concise snapshot of whether organizational systems and policies actively facilitate or hinder EBP uptake, and has demonstrated strong predictive validity for implementation fidelity and sustainability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michelle G. Ehrhart, PhD; Gregory A. Aarons, PhD; Lydia R. Farahnak, PhD","subfamily":"organizational climate assessment","year":2014,"type":"Self-report organizational survey"},"citations":[{"ref":"Ehrhart, M. G., Aarons, G. A., & Farahnak, L. R. (2014). Assessing the organizational context for EBP implementation: The Development and Validity Testing of the Implementation Climate Scale (ICS). Implementation Science, 9, 157.","type":"article","doi":"10.1186/s13012-014-0157-1","isbn":null,"url":null}],"related":["evidence-based-practice-attitude","implementation-leadership-scale","organisational-readiness-change","perceived-organizational-readiness","implementation-climate-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"implementation-leadership-scale","name":"ILS","fullName":"Implementation Leadership Scale","aliases":["ILS","Implementation Leadership","ILS-12"],"domain":"implementation-science","family":"process-pipeline","subfamily":"leadership assessment","year":2014,"originator":"Gregory A. Aarons, PhD; Michelle G. Ehrhart, PhD; Lydia R. Farahnak, PhD","url":"https://scholargate.app/en/implementation-science/implementation-leadership-scale","markdownUrl":"https://scholargate.app/en/implementation-science/implementation-leadership-scale.md","definition":"The Implementation Leadership Scale (ILS) is a 12-item self-report measure that assesses unit-level leadership behaviors critical to successful implementation of evidence-based practices and innovations. Developed by Aarons, Ehrhart, and Farahnak in 2014, the ILS measures four dimensions of implementation leadership: proactive leadership, knowledgeable leadership, supportive leadership, and perseverant leadership. This brief, validated instrument is designed to capture frontline leaders' (managers, supervisors, unit heads) implementation-specific behaviors as perceived by clinical staff, and is widely used in healthcare implementation research to evaluate leadership effectiveness and predict implementation success.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory A. Aarons, PhD; Michelle G. Ehrhart, PhD; Lydia R. Farahnak, PhD","subfamily":"leadership assessment","year":2014,"type":"Self-report questionnaire"},"citations":[{"ref":"Aarons, G. A., Ehrhart, M. G., Farahnak, L. R., & Sklar, M. (2014). Aligning leadership across systems and organizations to develop integrated care. Journal of Behavioral Health Services & Research, 41(2), 159–178.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Aligning+leadership+across+systems+and+organizations+to+develop+integrated+care+Aarons"},{"ref":"Aarons, G. A., Ehrhart, M. G., & Farahnak, L. R. (2014). The Implementation Leadership Scale (ILS): Development of a brief measure of unit level implementation leadership. Implementation Science, 9, 106.","type":"article","doi":"10.1186/1748-5908-9-45","isbn":null,"url":null}],"related":["evidence-based-practice-attitude","implementation-climate-scale","organisational-readiness-change","knowledge-to-action-scale","perceived-organizational-readiness"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"implementation-outcome-taxonomy","name":"Implementation Outcome Taxonomy","fullName":"Implementation Outcomes: A Taxonomy of Eight Measurable Dimensions for Assessing Implementation Fidelity and Success","aliases":["implementation outcomes","Proctor framework","implementation success measures"],"domain":"implementation-science","family":"process-pipeline","subfamily":"implementation outcomes measurement","year":"2011","originator":"Proctor, E. K., Silmere, H., Raghavan, R., et al.","url":"https://scholargate.app/en/implementation-science/implementation-outcome-taxonomy","markdownUrl":"https://scholargate.app/en/implementation-science/implementation-outcome-taxonomy.md","definition":"The Implementation Outcome Taxonomy is a framework defining eight measurable dimensions for assessing implementation success: Acceptability, Adoption, Appropriateness, Feasibility, Fidelity, Implementation Cost, Penetration, and Sustainability. Developed by Proctor et al. (2011), it provides a standardized vocabulary and measurement approach to distinguish implementation process outcomes (how well was the intervention delivered?) from clinical outcomes (did patients get better?). This taxonomy is foundational to implementation science because it acknowledges that an evidence-based intervention can be effective (clinical outcome) but poorly implemented (implementation outcome), or feasible to deliver but not adopted by organizations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Proctor, E. K., Silmere, H., Raghavan, R., et al.","subfamily":"implementation outcomes measurement","year":"2011","type":"Taxonomy"},"citations":[{"ref":"Proctor, E. K., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G. A., Bunger, A., ... & Rojas, D. (2011). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research, 38(2), 65-76.","type":"article","doi":"10.1007/s10488-010-0319-7","isbn":null,"url":null},{"ref":"Proctor, E. K., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G. A., Bunger, A., ... & Rojas, D. (2015). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health, 42(2), 123-132.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Outcomes+for+implementation+research%3A+Conceptual+distinctions%2C+measurement+challenges%2C+and+research+agenda+Proctor"},{"ref":"Raghavan, R., Bright, C. L., & Shadoin, A. L. (2008). Toward a policy ecology of implementation of evidence-based practices in public mental health settings. Administration and Policy in Mental Health and Mental Health Services Research, 35(3), 142-154.","type":"article","doi":"10.1186/1748-5908-3-26","isbn":null,"url":null}],"related":["re-aim-framework","cfir-framework","fidelity-assessment","knowledge-translation","normalization-process-theory"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"implicit-association-test","name":"Implicit Association Test","fullName":"Implicit Association Test","aliases":["IAT","Implicit Attitude Test"],"domain":"psychology","family":"hypothesis-test","subfamily":"Social Cognition","year":"1998","originator":"Anthony Greenwald, Debbie McGhee, and Jordan Schwartz","url":"https://scholargate.app/en/psychology/implicit-association-test","markdownUrl":"https://scholargate.app/en/psychology/implicit-association-test.md","definition":"The Implicit Association Test (IAT) is a computerized measure designed to detect automatic associations between concepts in memory, such as implicit attitudes toward social groups or implicit self-concepts. Introduced by Greenwald, McGhee, and Schwartz in 1998, it infers the strength and valence of associations from the ease and speed with which people categorize stimuli when pairing concepts, revealing unconscious biases and attitudes that may not appear in explicit self-report measures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anthony Greenwald, Debbie McGhee, and Jordan Schwartz","subfamily":"Social Cognition","year":"1998","type":"Computerized reaction-time measure"},"citations":[{"ref":"Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74(6), 1464-1480.","type":"article","doi":"10.1037/0022-3514.74.6.1464","isbn":null,"url":null},{"ref":"Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. In J. A. Bargh (Ed.), Social psychology and the unconscious: The automaticity of higher mental processes (pp. 265-292). Psychology Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Implicit+Association+Test+at+age+7%3A+A+methodological+and+conceptual+review+Nosek"},{"ref":"Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197-216.","type":"article","doi":"10.1037/0022-3514.85.2.197","isbn":null,"url":null}],"related":["signal-detection-theory","response-time-analysis","stroop-task","affective-priming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"implicit-sentiment-analysis","name":"Implicit Sentiment Analysis","fullName":"Implicit Sentiment Analysis (Context-Dependent Opinion Detection)","aliases":["Örtük Duygu Analizi (Implicit Sentiment)","implicit opinion mining","indirect sentiment detection"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2016 (aspect-level formulation); LLM-based reasoning formulation c. 2023","originator":"Rooted in aspect-level and deep-memory sentiment research; Tang et al. (2016) and Zhao et al. (2023) are key references","url":"https://scholargate.app/en/text-mining/implicit-sentiment-analysis","markdownUrl":"https://scholargate.app/en/text-mining/implicit-sentiment-analysis.md","definition":"Implicit sentiment analysis detects indirect, context-dependent sentiment in text where no explicit opinion word is present — such as irony, metaphor, or understated criticism. Unlike standard sentiment analysis, which relies on surface-level polarity signals, this method interprets meaning from surrounding context, pragmatic cues, and world knowledge. It is typically addressed using large language models or fine-tuned transformers, drawing on work by Tang et al. (2016) on deep-memory aspect-level classification and Zhao et al. (2023) on LLM-based sentiment reasoning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in aspect-level and deep-memory sentiment research; Tang et al. (2016) and Zhao et al. (2023) are key references","year":"2016 (aspect-level formulation); LLM-based reasoning formulation c. 2023","type":"NLP text-classification task","inputRequirement":"Text corpus — minimum 50 documents recommended","modelPreference":"Large language model or fine-tuned transformer with context-sensitive decoding","difficultyLevel":"3 / 5"},"citations":[{"ref":"Zhao, W. et al. (2023). Is ChatGPT a Good Sentiment Reasoner? A Preliminary Study. arXiv preprint.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2304.09582"},{"ref":"Tang, D. et al. (2016). Aspect Level Sentiment Classification with Deep Memory Network. Proceedings of EMNLP 2016.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/D16-1021"}],"related":["sentiment-analysis","aspect-based-sentiment-analysis","irony-detection","negation-detection","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"importance-sampling","name":"Importance Sampling","fullName":"Importance Sampling (Variance Reduction Monte Carlo)","aliases":["IS","weighted Monte Carlo","Önem Örneklemesi"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":1951,"originator":"Herman Kahn & Theodore Harris (RAND Corporation, 1951)","url":"https://scholargate.app/en/simulation/importance-sampling","markdownUrl":"https://scholargate.app/en/simulation/importance-sampling.md","definition":"Importance sampling is a Monte Carlo variance-reduction technique that shifts the sampling distribution toward the region of interest — typically a rare or extreme event — so that informative samples are drawn far more often than under the original distribution. Developed at the RAND Corporation by Herman Kahn and Theodore Harris around 1951, it makes tail-probability estimation (such as Value-at-Risk or system-failure probability) tractable where standard Monte Carlo would require an astronomically large number of runs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herman Kahn & Theodore Harris (RAND Corporation, 1951)","year":1951,"type":"Monte Carlo variance-reduction technique","difficulty":3,"targetEvents":"Rare / tail events (probabilities < 0.01)","keyQuantity":"Likelihood-ratio weight w = p(x) / q(x)","output":"Unbiased probability estimate with reduced variance"},"citations":[{"ref":"Rubinstein, R.Y. & Kroese, D.P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley.","type":"book","doi":"10.1002/9781118631980","isbn":null,"url":null},{"ref":"Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer.","type":"book","doi":"10.1007/978-0-387-21617-1","isbn":null,"url":null}],"related":["monte-carlo-simulation","stratified-sampling","latin-hypercube-sampling","value-at-risk","extreme-value-theory","bootstrap-resampling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"imprecise-probability","name":"Imprecise Probability","fullName":"Imprecise Probability (Lower-Upper Probabilities)","aliases":["Lower-Upper Probability","Robust Bayesian Analysis","Credal Set Theory","Belirsiz Olasılık"],"domain":"soft-computing","family":"bayesian","subfamily":"Uncertainty theory","year":1991,"originator":"Peter Walley","url":"https://scholargate.app/en/soft-computing/imprecise-probability","markdownUrl":"https://scholargate.app/en/soft-computing/imprecise-probability.md","definition":"Imprecise probability is a generalization of standard probability theory that represents epistemic uncertainty through sets of probability measures, called credal sets, rather than a single precise distribution. Introduced systematically by Peter Walley in his 1991 monograph, the framework characterizes beliefs via lower and upper probabilities (or previsions), bracketing the range of plausible probability assignments when available information is insufficient to determine a unique measure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Walley","year":1991,"type":"Set-valued probability model","subfamily":"Uncertainty theory","representation":"Credal set (convex set of probability measures)","key_concept":"Lower and upper previsions (expectations)"},"citations":[{"ref":"Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman & Hall.","type":"book","doi":null,"isbn":"978-0-412-28660-5","url":null}],"related":["dempster-shafer-theory","possibility-theory","bayesian-inference"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"impulse-response-function","name":"Impulse Response Function","fullName":"Impulse Response Function (IRF)","aliases":["IRF","Dynamic Multiplier","Shock Response Function","Etki Tepki Fonksiyonu"],"domain":"econometrics","family":"regression-model","subfamily":"Multivariate time series","year":2005,"originator":"Helmut Lütkepohl","url":"https://scholargate.app/en/econometrics/impulse-response-function","markdownUrl":"https://scholargate.app/en/econometrics/impulse-response-function.md","definition":"The Impulse Response Function (IRF) traces the dynamic response of each variable in a Vector Autoregression (VAR) system to a one-unit shock in one of its error terms over a user-specified forecast horizon. It is the primary tool for structural analysis following VAR estimation and is widely used in macroeconomics, monetary economics, and finance to quantify how shocks propagate through interconnected time series systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Helmut Lütkepohl","year":2005,"type":"Post-estimation diagnostic","subfamily":"Multivariate time series","output":"Time-path of responses to a unit shock","horizon":"User-defined finite forecast horizon"},"citations":[{"ref":"Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.","type":"book","doi":null,"isbn":"978-3-540-40172-8","url":null}],"related":["var-model","svar","forecast-error-variance-decomposition"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"imrad-structure","name":"IMRaD Structure","fullName":"Introduction Methods Results and Discussion Structure","aliases":["IMRaD","IMRAD","scientific manuscript structure"],"domain":"academic-writing","family":"process-pipeline","subfamily":"document-structure","year":"1970","originator":"International scientific publishing community (adopted widely by 1970s)","url":"https://scholargate.app/en/academic-writing/imrad-structure","markdownUrl":"https://scholargate.app/en/academic-writing/imrad-structure.md","definition":"IMRaD is the standard organizational framework for scientific manuscripts in biomedical and natural sciences research. It separates reporting into four sequential sections—Introduction (why the research was conducted), Methods (how it was done), Results (what was found), and Discussion (what the findings mean)—enabling readers to understand, evaluate, and reproduce the work. Adopted as best practice by the International Committee of Medical Journal Editors (ICMJE) since the 1970s, IMRaD structure is now mandated or strongly recommended by most peer-reviewed journals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International scientific publishing community (adopted widely by 1970s)","subfamily":"document-structure","year":"1970","type":"Guideline"},"citations":[{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.","type":"guideline","doi":null,"isbn":null,"url":"https://www.icmje.org/"},{"ref":"Sollaci, L. B., & Pereira, M. G. (2004). The Introduction, Methods, Results, and Discussion (IMRAD) structure: a fifty-year survey. Journal of the Medical Library Association, 92(3), 364–371.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Introduction%2C+Methods%2C+Results%2C+and+Discussion+%28IMRAD%29+structure%3A+a+fifty-year+survey+Sollaci"}],"related":["abstract-writing","scientific-writing-clarity","statistical-reporting-standards","equator-network","apa-style-guide"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"in-depth-interview-method","name":"In-Depth Interview Method","fullName":"Semi-Structured and Unstructured In-Depth Interviewing","aliases":["IDI","qualitative interview","one-on-one interview","in-depth interviewing"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"data-collection","year":"1954","originator":"Carl Rogers and Herbert H. Hyman","url":"https://scholargate.app/en/qualitative-research/in-depth-interview-method","markdownUrl":"https://scholargate.app/en/qualitative-research/in-depth-interview-method.md","definition":"In-depth interviews are a qualitative research method in which a trained interviewer conducts one-on-one conversations with individual participants using open-ended questions to explore their experiences, perspectives, and understandings of a phenomenon. Developed in the 1950s by Rogers and Hyman, the method varies along a spectrum from structured (standardized question sets) to semi-structured (guided topic areas with flexibility) to unstructured (emergent, conversational). In-depth interviews are widely used in sociology, psychology, health sciences, anthropology, and organizational research to capture rich, detailed narratives and personal meaning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Rogers and Herbert H. Hyman","subfamily":"data-collection","year":"1954","type":"Method"},"citations":[{"ref":"Kvale, S. (1996). InterViews: An Introduction to Qualitative Research Interviewing. SAGE Publications.","type":"book","doi":null,"isbn":"978-0761908631","url":null},{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-0761919676","url":null},{"ref":"Bernard, H. R. (2006). Research Methods in Anthropology: Qualitative and Quantitative Approaches (4th ed.). Rowman & Littlefield Publishers.","type":"book","doi":null,"isbn":"978-0742539136","url":null},{"ref":"Rubin, H. J., & Rubin, I. S. (2005). Qualitative Interviewing: The Art of Hearing Data (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-0761928479","url":null}],"related":["focus-group-methodology","participant-observation","reflexivity-in-research","member-checking","qualitative-synthesis-methods"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"in-depth-interview","name":"In-Depth Interview","fullName":"In-Depth Qualitative Interview","aliases":["IDI","semi-structured interview","unstructured interview","qualitative interview"],"domain":"qualitative","family":"process-pipeline","subfamily":"Interview Methods","year":"Mid-20th century (formalised in qualitative social research from the 1950s onward)","originator":"Rooted in sociological interviewing traditions; systematised by researchers including Steinar Kvale and Herbert J. Rubin","url":"https://scholargate.app/en/qualitative/in-depth-interview","markdownUrl":"https://scholargate.app/en/qualitative/in-depth-interview.md","definition":"The in-depth interview is a one-to-one qualitative data-collection method in which a researcher engages a participant in an extended, open-ended conversation to elicit rich, detailed accounts of experiences, perceptions, beliefs, or meanings. Unlike structured surveys, the interview guide serves as a flexible road map rather than a fixed script, allowing the researcher to probe unexpected directions as they emerge. The approach is foundational to qualitative inquiry and is used directly as a primary method or as the data-collection arm of phenomenology, grounded theory, narrative analysis, and other frameworks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in sociological interviewing traditions; systematised by researchers including Steinar Kvale and Herbert J. Rubin","year":"Mid-20th century (formalised in qualitative social research from the 1950s onward)","type":"Qualitative research method","dataType":"Spoken narrative data collected via one-to-one conversation (audio-recorded and transcribed)","typicalSampleSize":"10–30 participants (until theoretical saturation)","subfamily":"Interview Methods"},"citations":[{"ref":"Kvale, S. (1996). InterViews: An Introduction to Qualitative Research Interviewing. Sage.","type":"book","doi":null,"isbn":"978-0803958203","url":null},{"ref":"Rubin, H. J., & Rubin, I. S. (2005). Qualitative Interviewing: The Art of Hearing Data (2nd ed.). Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Qualitative+Interviewing+The+Art+of+Hearing+Data+Rubin+2005"}],"related":["phenomenology","grounded-theory","ethnography","narrative-analysis","thematic-analysis","focus-group"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"in-vitro-in-vivo-correlation","name":"In Vitro-In Vivo Correlation","fullName":"In Vitro-In Vivo Correlation (IVIVC)","aliases":["IVIVC"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Biopharmaceutics","year":"1995","originator":"Gordon Amidon","url":"https://scholargate.app/en/pharmacology/in-vitro-in-vivo-correlation","markdownUrl":"https://scholargate.app/en/pharmacology/in-vitro-in-vivo-correlation.md","definition":"IVIVC is a mathematical relationship between in vitro and in vivo properties of a drug, developed to predict oral bioavailability from dissolution data. Introduced by Amidon and colleagues in the 1995 Biopharmaceutics Classification System, it bridges laboratory measurements and clinical outcomes to streamline drug development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon Amidon","subfamily":"Biopharmaceutics","year":"1995","type":"bioavailability prediction"},"citations":[{"ref":"Amidon, G. L., Lennernäs, H., Shah, V. P., & Crison, J. R. (1995). A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharmaceutical Research, 12(3), 413-420.","type":"article","doi":"10.1023/A:1016212804288","isbn":null,"url":null},{"ref":"Shah, V. P., Amidon, G. L., & Levy, G. (1996). Level A, B, and C strategic approaches for biopharmaceutical classification-based dissolution specifications and in vitro-in vivo correlations. Pharmaceutical Research, 13(12), 1799-1801.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Level+A%2C+B%2C+and+C+strategic+approaches+for+biopharmaceutical+classification-based+dissolution+specifications+and+in+vitro-in+vivo+correlations+Shah"}],"related":["dissolution-f1-f2-similarity","michaelis-menten-kinetics","physiologically-based-pharmacokinetics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"in-vivo-coding","name":"In Vivo Coding","fullName":"In Vivo Coding","aliases":["verbatim coding","literal coding","first-cycle in vivo coding","indigenous coding"],"domain":"qualitative","family":"process-pipeline","subfamily":"Qualitative Coding","year":"1967 (grounded theory origins); widely codified as a distinct method from the 1990s onward","originator":"Barney G. Glaser and Anselm L. Strauss (grounded theory tradition); systematised and named by Johnny Saldaña","url":"https://scholargate.app/en/qualitative/in-vivo-coding","markdownUrl":"https://scholargate.app/en/qualitative/in-vivo-coding.md","definition":"In vivo coding is a qualitative first-cycle coding strategy in which the researcher uses the participants' own words or short phrases verbatim as code labels, rather than imposing researcher-generated or theoretical language. The technique preserves the voice, meaning, and conceptual priorities of participants, making it especially valuable in grounded theory, phenomenology, and any study where honouring the emic (insider) perspective is central to analytic integrity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser and Anselm L. Strauss (grounded theory tradition); systematised and named by Johnny Saldaña","year":"1967 (grounded theory origins); widely codified as a distinct method from the 1990s onward","type":"Qualitative research method","dataType":"Interview transcripts, focus group transcripts, field notes, documents, diary entries","typicalSampleSize":"Varies; typically 10–30 participants or equivalent text units","subfamily":"Qualitative Coding"},"citations":[{"ref":"Saldaña, J. (2021). The Coding Manual for Qualitative Researchers (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1529731743","url":null},{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973539","url":null}],"related":["grounded-theory","thematic-analysis","phenomenology","narrative-analysis","content-analysis","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inception-network","name":"Inception Network","fullName":"Inception / GoogLeNet","aliases":["GoogLeNet","Inception v1","Deep Convolutional Neural Network (Google)","Başlangıç Ağı"],"domain":"deep-learning","family":"ml-model","subfamily":"CNN architectures","year":2015,"originator":"Christian Szegedy et al. (Google)","url":"https://scholargate.app/en/deep-learning/inception-network","markdownUrl":"https://scholargate.app/en/deep-learning/inception-network.md","definition":"The Inception Network, introduced by Szegedy et al. at Google in 2015 and submitted to CVPR under the name GoogLeNet, is a 22-layer deep convolutional neural network designed for large-scale image recognition. Its defining contribution is the Inception module, which applies convolutions of multiple kernel sizes in parallel and concatenates their outputs, enabling the network to capture spatial features at different scales simultaneously without a proportional increase in computational cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christian Szegedy et al. (Google)","year":2015,"type":"Deep CNN with parallel multi-scale convolutions","subfamily":"CNN architectures","depth":"22 layers (with learned weights)","competition":"Won ILSVRC 2014 image classification"},"citations":[{"ref":"Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9.","type":"inproceedings","doi":"10.1109/CVPR.2015.7298594","isbn":null,"url":null}],"related":["convolutional-neural-network","resnet","vggnet"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"incremental-dynamic-analysis","name":"Incremental Dynamic Analysis","fullName":"Incremental Dynamic Analysis for Seismic Risk Assessment","aliases":["IDA","Intensity-based analysis","Fragility curve development"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Seismic Risk Assessment","year":"2002","originator":"Dimitrios Vamvatsikos and C. Allin Cornell","url":"https://scholargate.app/en/civil-engineering/incremental-dynamic-analysis","markdownUrl":"https://scholargate.app/en/civil-engineering/incremental-dynamic-analysis.md","definition":"Incremental dynamic analysis (IDA) is a method that runs time-history analyses on a structure with a single ground motion record, progressively increasing the intensity until the structure reaches a specified performance level or collapses. Introduced by Vamvatsikos and Cornell in 2002, this approach efficiently generates fragility curves relating earthquake intensity to structural damage and collapse probability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dimitrios Vamvatsikos and C. Allin Cornell","subfamily":"Seismic Risk Assessment","year":"2002","type":"Intensity-based dynamic analysis for fragility assessment"},"citations":[{"ref":"Vamvatsikos, D., & Cornell, C. A. (2002). Incremental dynamic analysis of seismic performance of structures. Earthquake Engineering & Structural Dynamics, 31(3), 491-514.","type":"article","doi":"10.1002/eqe.141","isbn":null,"url":null},{"ref":"Cornell, C. A., Jalayer, F., Hamburger, R. O., & Foutch, D. A. (2002). Probabilistic basis for 2000 SAC federal emergency management agency steel moment frame guidelines. Journal of Structural Engineering, 128(4), 526-533.","type":"article","doi":"10.1061/(ASCE)0733-9445(2002)128:4(526)","isbn":null,"url":null},{"ref":"Luco, N., & Cornell, C. A. (2007). Structure-specific scalar intensity measures for near-source and broadband ground motions. Journal of Engineering Mechanics, 133(4), 445-456.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Structure-specific+scalar+intensity+measures+for+near-source+and+broadband+ground+motions+Luco"}],"related":["nonlinear-time-history-analysis","pushover-analysis","probabilistic-seismic-hazard-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"independent-component-analysis","name":"Independent Component Analysis","fullName":"Independent Component Analysis (ICA)","aliases":["ICA","blind source separation","BSS","FastICA","independent components"],"domain":"machine-learning","family":"latent-structure","subfamily":null,"year":1994,"originator":"Comon, P.","url":"https://scholargate.app/en/machine-learning/independent-component-analysis","markdownUrl":"https://scholargate.app/en/machine-learning/independent-component-analysis.md","definition":"Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive, statistically independent subcomponents. Formalized by Pierre Comon in 1994, ICA became the foundational framework for blind source separation and is widely applied in neuroimaging (fMRI, EEG), speech processing, and biomedical signal analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Comon, P.","year":1994,"type":"Blind source separation / latent-structure decomposition","task":"Unsupervised signal separation and feature extraction","minSample":100},"citations":[{"ref":"Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314.","type":"article","doi":"10.1016/0165-1684(94)90029-9","isbn":null,"url":null},{"ref":"Hyvärinen, A., Karhunen, J., & Oja, E. (2001). Independent Component Analysis. Wiley.","type":"book","doi":null,"isbn":"978-0-471-40540-5","url":null}],"related":["principal-component-analysis","factor-analysis","non-negative-matrix-factorization","autoencoders","singular-value-decomposition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"independent-samples-t-test","name":"Independent samples t-test","fullName":"Independent Samples t-test","aliases":["two-sample t-test","unpaired t-test","Student t-test","independent groups t-test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1908","originator":"Student (W. S. Gosset)","url":"https://scholargate.app/en/statistics/independent-samples-t-test","markdownUrl":"https://scholargate.app/en/statistics/independent-samples-t-test.md","definition":"The independent samples t-test is a parametric hypothesis test that determines whether the means of two independent, unrelated groups differ significantly on a continuous outcome variable. Derived from Gosset's 1908 t-distribution, it is one of the most widely used inferential tests in social, behavioral, biomedical, and experimental sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Student (W. S. Gosset)","year":"1908","type":"Parametric mean comparison","dataType":"Continuous (interval or ratio)","subfamily":"Classical statistics"},"citations":[{"ref":"Student (W. S. Gosset) (1908). The probable error of a mean. Biometrika, 6(1), 1–25.","type":"article","doi":"10.1093/biomet/6.1.1","isbn":null,"url":null},{"ref":"Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1526419521","url":null}],"related":["paired-samples-t-test","one-sample-t-test","mann-whitney-u-test","one-way-anova","welch-corrected-independent-samples-t-test","independent-t-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"independent-t-test","name":"Independent t-test","fullName":"Independent Samples t-test","aliases":["student t-test","two-sample t-test","unpaired t-test","bağımsız örneklem t-testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1908,"originator":"Student (W. S. Gosset)","url":"https://scholargate.app/en/statistics/independent-t-test","markdownUrl":"https://scholargate.app/en/statistics/independent-t-test.md","definition":"The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Student (W. S. Gosset)","year":1908,"family":"Hypothesis test","type":"Parametric mean comparison","groups":2,"outcome":"continuous","parametric":true,"distribution":"Student t","df":"n_1 + n_2 - 2"},"citations":[{"ref":"Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.","type":"article","doi":"10.1093/biomet/6.1.1","isbn":null,"url":null},{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null}],"related":["paired-t-test","welch-t-test","mann-whitney-u","one-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"independent-vector-analysis","name":"Independent Vector Analysis","fullName":"Independent Vector Analysis for Multivariate Blind Source Separation","aliases":["IVA","multivariate ICA","vector blind source separation"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Blind Source Separation","year":"2007","originator":"Tae-Won Lee, Mark Lewicki, Terrence Sejnowski","url":"https://scholargate.app/en/applied-physics/independent-vector-analysis","markdownUrl":"https://scholargate.app/en/applied-physics/independent-vector-analysis.md","definition":"Independent Vector Analysis (IVA) is a multivariate extension of Independent Component Analysis that jointly separates multiple datasets while maintaining dependencies within each dataset. Developed by Lee, Lewicki, and Sejnowski in the 2000s, IVA is used for blind source separation in multi-channel audio, brain imaging, and signal processing. It exploits both the independence between sources and correlations within frequency bands or time-frequency structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tae-Won Lee, Mark Lewicki, Terrence Sejnowski","subfamily":"Blind Source Separation","year":"2007","type":"Multivariate matrix decomposition algorithm"},"citations":[{"ref":"Lee, T. W., Lewicki, M. S., & Sejnowski, T. J. (2007). Independent Component Analysis for Source Localization in Biomedical Signals. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp. 97-100.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Independent+Component+Analysis+for+Source+Localization+in+Biomedical+Signals+Lee"},{"ref":"Kim, T., Attias, H. T., Lee, S. Y., & Lee, T. W. (2006). Blind source separation exploiting higher-order frequency dependencies. IEEE Transactions on Audio, Speech, and Language Processing, 15(1), 70-79.","type":"article","doi":"10.1109/tasl.2006.872618","isbn":null,"url":null},{"ref":"Comon, P., Jutten, C., & Herault, J. (2010). Blind Separation of Sources, Part II: Problems Statement. IEEE Transactions on Signal Processing, 59(11), 4711-4721.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Blind+Separation+of+Sources%2C+Part+II%3A+Problems+Statement+Comon"}],"related":["mfcc","ambisonics","head-related-transfer-function"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"indexing-strategy","name":"Indexing Strategy","fullName":"Database Indexing Strategy and Design","aliases":["index design","indexing"],"domain":"information-systems","family":"process-pipeline","subfamily":"Query Processing & Performance","year":"1972","originator":"Rudolf Bayer and Edward M. McCreight","url":"https://scholargate.app/en/information-systems/indexing-strategy","markdownUrl":"https://scholargate.app/en/information-systems/indexing-strategy.md","definition":"Indexing strategy is the practice of systematically designing database indexes to accelerate query performance. Developed following Bayer and McCreight's foundational B-tree work in 1972, effective indexing requires analyzing query patterns, choosing appropriate index structures, and maintaining index health as data evolves.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf Bayer and Edward M. McCreight","subfamily":"Query Processing & Performance","year":"1972","type":"Database optimization technique"},"citations":[{"ref":"Bayer, R., & McCreight, E. (1972). Organization and maintenance of large ordered indices. Acta Informatica, 1(3), 173-189.","type":"article","doi":"10.1007/BF00288683","isbn":null,"url":null},{"ref":"Seltzer, M., & Bostic, K. (1994). An implementation of a log-structured file system for UNIX. Winter USENIX Conference, 307-326.","type":"article","doi":null,"isbn":null,"url":"https://www.usenix.org"},{"ref":"Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book (2nd ed.). Pearson Education.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com"}],"related":["query-optimization","b-tree-indexes","hash-indexes","bitmap-indexes","query-execution-plans"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"indicator-value","name":"Indicator Value","fullName":"Indicator Value Analysis (IndVal)","aliases":["IndVal","indicator species","fidelity","specificity","association analysis"],"domain":"ecology","family":"process-pipeline","subfamily":"Community ecology","year":"1997","originator":"Marc Dufrene and Pierre Legendre","url":"https://scholargate.app/en/ecology/indicator-value","markdownUrl":"https://scholargate.app/en/ecology/indicator-value.md","definition":"Indicator Value (IndVal) analysis, developed by Dufrene and Legendre (1997), identifies species that reliably indicate the presence of particular environmental conditions, habitat types, or community groups. The method quantifies the association between species and habitat, producing an indicator value that combines specificity (exclusive preference for certain habitats) and fidelity (consistent presence when the habitat occurs). IndVal is widely used in conservation to identify species of management concern, in habitat typing to discover indicator species, and in restoration ecology to assess whether recovered communities match reference conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marc Dufrene and Pierre Legendre","subfamily":"Community ecology","year":"1997","type":"species-habitat association analysis"},"citations":[{"ref":"Dufrene, M., & Legendre, P. (1997). Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecological Monographs, 67(3), 345-366.","type":"article","doi":"10.2307/2963459","isbn":null,"url":null},{"ref":"Bakus, G. J. (2007). Quantitative Ecology and the Brown Algae. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://global.oup.com/academic/product/quantitative-ecology-and-the-brown-algae-9780195169935"},{"ref":"Caceres, M. D., & Legendre, P. (2010). Stability analysis of species-by-trait matrices. Methods in Ecology and Evolution, 1(3), 217-226.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Stability+analysis+of+species-by-trait+matrices+Caceres"}],"related":["species-accumulation","functional-diversity","food-web-topology","beta-diversity-partitioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"individual-patient-data-meta-analysis","name":"Individual Patient Data Meta-Analysis","fullName":"Individual Patient Data Meta-Analysis (IPD-MA)","aliases":["IPD Meta-Analysis","Participant-Level Data Synthesis","One-Stage Meta-Analysis"],"domain":"evidence-synthesis","family":"process-pipeline","subfamily":"Advanced Meta-Analysis","year":"1990s","originator":"Cochrane Collaboration, Pioneered by Stewart & Clarke","url":"https://scholargate.app/en/evidence-synthesis/individual-patient-data-meta-analysis","markdownUrl":"https://scholargate.app/en/evidence-synthesis/individual-patient-data-meta-analysis.md","definition":"Individual patient data meta-analysis (IPD-MA) is a systematic synthesis method where researchers obtain and analyze raw data at the patient level from multiple randomized controlled trials, rather than relying on published summary statistics (aggregate data). Pioneered by the Cochrane Collaboration and formalized by Stewart, Clarke, and Riley, IPD-MA is considered the gold standard for evidence synthesis because it enables consistent outcome definition across trials, robust subgroup analysis, and detection of treatment-covariate interactions. Though time-intensive and resource-demanding, IPD-MA provides the most reliable estimates of intervention effects and is preferred for critical clinical decisions, particularly for identifying which patients benefit most from treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cochrane Collaboration, Pioneered by Stewart & Clarke","subfamily":"Advanced Meta-Analysis","year":"1990s","type":"Method"},"citations":[{"ref":"Stewart, L. A., Clarke, M. J., & Cochrane IPD Meta-analysis Methods Group. (2015). Practical methodology of meta-analyses (including IPD) of randomised trials reporting time to event data. Cochrane Database of Systematic Reviews, 2015(10), MR000027.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Practical+methodology+of+meta-analyses+%28including+IPD%29+of+randomised+trials+reporting+time+to+event+data+Stewart"},{"ref":"Riley, R. D., Lambert, P. C., & Abo-Zaid, G. (2010). Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ, 340, c221.","type":"article","doi":"10.1136/bmj.c221","isbn":null,"url":null},{"ref":"Higgins, P. T., & Green, S. (Eds.). (2011). Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1.0). The Cochrane Collaboration.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cochrane+Handbook+for+Systematic+Reviews+of+Interventions+%28Version+5.1.0%29+Higgins"}],"related":["pairwise-meta-analysis","network-meta-analysis","systematic-review","subgroup-analysis","evidence-synthesis-framework"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inductively-coupled-plasma","name":"Inductively Coupled Plasma Spectrometry","fullName":"Inductively Coupled Plasma Spectrometry","aliases":["ICP-OES","ICP-AES","ICP-MS","plasma emission spectroscopy"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Atomic Spectroscopy","year":"1964","originator":"Stanley Greenfield","url":"https://scholargate.app/en/analytical-chemistry/inductively-coupled-plasma","markdownUrl":"https://scholargate.app/en/analytical-chemistry/inductively-coupled-plasma.md","definition":"Inductively coupled plasma spectrometry is a powerful multi-element analytical technique that ionizes a sample in a high-temperature plasma and measures the emitted light (ICP-OES) or ion masses (ICP-MS) to determine elemental concentrations. Developed in the 1960s by Stanley Greenfield, ICP techniques have become the standard for trace element analysis across environmental, geological, biological, and industrial fields. The method combines exceptional sensitivity, wide dynamic range, and the ability to analyze dozens of elements simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanley Greenfield","subfamily":"Atomic Spectroscopy","year":"1964","type":"multi-element analysis technique"},"citations":[{"ref":"Greenfield, S., Jones, I. L., & Berry, C. T. (1968). High-pressure plasma jet source for use in atomic spectroscopy. Analyst, 93(1108), 694–697.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=High-pressure+plasma+jet+source+for+use+in+atomic+spectroscopy+Greenfield"},{"ref":"Montaser, A. (Ed.). (2008). Inductively Coupled Plasma Mass Spectrometry (2nd ed.). Wiley-VCH.","type":"book","doi":null,"isbn":"978-3527606955","url":null},{"ref":"Houk, R. S. (1986). Mass spectrometry of inductively coupled plasma. Analytical Chemistry, 58(1), 97A–105A.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mass+spectrometry+of+inductively+coupled+plasma+Houk"}],"related":["atomic-absorption-spectroscopy","uv-vis-spectrophotometry","potentiometric-titration","ion-chromatography","flow-injection-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"industrial-applications-full-factorial-design","name":"Industrial applications full factorial design","fullName":"Full Factorial Design for Industrial Applications","aliases":["industrial FFD","full factorial experiment","complete factorial design","2^k factorial design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1926 (foundational); industrially systematized by Box, Hunter & Hunter ~1950s–1978","originator":"Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/industrial-applications-full-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/industrial-applications-full-factorial-design.md","definition":"Full factorial design (FFD) applied in industrial settings is a structured experimental methodology in which every combination of factor levels is tested, enabling engineers to quantify main effects and all interaction effects among process or product variables. Widely used in manufacturing, chemical processing, materials science, and quality engineering, it provides a complete picture of how input factors jointly influence a response variable such as yield, strength, or defect rate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":"1926 (foundational); industrially systematized by Box, Hunter & Hunter ~1950s–1978","type":"Experimental design / factorial experiment","dataType":"Continuous or categorical process/product measurements (yield, strength, defect rate, etc.)","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null},{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471718130","url":null}],"related":["fractional-factorial-design","full-factorial-design","central-composite-design","taguchi-method","response-surface-methodology","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"industrial-applications-response-surface-methodology","name":"Industrial Applications Response Surface Methodology","fullName":"Response Surface Methodology for Industrial Process Optimization","aliases":["Industrial RSM","RSM for manufacturing","process optimization RSM","industrial response surface analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1951 (origin); widespread industrial adoption from 1980s onward","originator":"George E. P. Box & K. B. Wilson; industrialized by Douglas Montgomery and colleagues","url":"https://scholargate.app/en/experimental-design/industrial-applications-response-surface-methodology","markdownUrl":"https://scholargate.app/en/experimental-design/industrial-applications-response-surface-methodology.md","definition":"Industrial Applications Response Surface Methodology (RSM) applies the classical Box-Wilson response surface framework to manufacturing and process engineering problems. It builds an empirical polynomial model linking controllable process inputs — such as temperature, pressure, feed rate, or catalyst concentration — to one or more quality responses, then mathematically locates the input settings that optimize those responses. It is the de-facto standard statistical tool for process characterization and optimization in chemical, mechanical, food, materials, and pharmaceutical manufacturing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E. P. Box & K. B. Wilson; industrialized by Douglas Montgomery and colleagues","year":"1951 (origin); widespread industrial adoption from 1980s onward","type":"Empirical optimization technique","dataType":"Continuous numerical process measurements (quality characteristics, yield, strength, efficiency)","subfamily":"Engineering methods"},"citations":[{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916018","url":null},{"ref":"Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45.","type":"article","doi":"10.1111/j.2517-6161.1951.tb00067.x","isbn":null,"url":null}],"related":["response-surface-methodology","central-composite-design","box-behnken-design","design-of-experiments","taguchi-method","optimization-assisted-response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inertia","name":"Inertia (Within-Cluster Sum of Squares)","fullName":"Inertia: Sum of Squared Distances to Cluster Centroids","aliases":["WCSS","within-cluster sum of squares","cluster cohesion"],"domain":"model-evaluation","family":"mcdm","subfamily":"Cluster Cohesion Measure","year":"1967","originator":"Stuart Lloyd, James MacQueen","url":"https://scholargate.app/en/model-evaluation/inertia","markdownUrl":"https://scholargate.app/en/model-evaluation/inertia.md","definition":"Inertia, also called Within-Cluster Sum of Squares (WCSS), is a measure of cluster cohesion that quantifies how tightly points are grouped around their cluster centroids. Lower values indicate more compact, cohesive clusters. Inertia is the primary objective function for k-means clustering and has been a fundamental metric since the method's introduction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stuart Lloyd, James MacQueen","subfamily":"Cluster Cohesion Measure","year":"1967","type":"Clustering quality metric"},"citations":[{"ref":"Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.","type":"article","doi":"10.1109/TIT.1982.1056489","isbn":null,"url":null},{"ref":"MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281-297).","type":"article","doi":null,"isbn":null,"url":"https://projecteuclid.org/proceedings/berkeley-symposium-on-mathematical-statistics-and-probability"}],"related":["elbow-method","silhouette-score","davies-bouldin-index","calinski-harabasz-index","dunn-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"infant-toddler-qol-questionnaire","name":"ITQOL","fullName":"Infant and Toddler Quality of Life Questionnaire","aliases":["ITQOL","Infant-Toddler QoL"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"generic infant and toddler health-related quality of life","year":1997,"originator":"John M. Landgraf","url":"https://scholargate.app/en/pediatric-medicine/infant-toddler-qol-questionnaire","markdownUrl":"https://scholargate.app/en/pediatric-medicine/infant-toddler-qol-questionnaire.md","definition":"The ITQOL is a generic parent-report instrument developed by Landgraf et al. in 1997 to measure health-related quality of life in infants and toddlers aged 2 months to 5 years. Addressing the developmental uniqueness of the very young, the ITQOL captures health-related functioning across domains relevant to early childhood: physical growth and development, respiratory functioning, sleep patterns, emotional security, parental time and emotional burden, and family social functioning. A 97-item full form and 47-item abbreviated form are available, enabling comprehensive or brief assessment depending on clinical context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John M. Landgraf","subfamily":"generic infant and toddler health-related quality of life","year":1997,"type":"Parent report of infant/toddler health and functioning"},"citations":[{"ref":"Landgraf, J. M., Abetz, L., & Ware, J. E. (2002). The infant and toddler quality of life questionnaire: User's manual and interpretation guide. Health Act.","type":"article","doi":null,"isbn":"978-1881667360","url":null},{"ref":"Landgraf, J. M., Rich, M., & Rapoff, M. A. (1997). Development of the Infant and Toddler Quality of Life Questionnaire (ITQOL): Measurement model validation and reliability testing. Pediatrics, 100(4), 624-632.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+of+the+Infant+and+Toddler+Quality+of+Life+Questionnaire+%28ITQOL%29%3A+Measurement+model+validation+and+reliability+testing+Landgraf"}],"related":["paqlq","pedsql-diabetes","child-health-questionnaire","qolce"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"infant-young-child-feeding-practices","name":"IYCF Practices","fullName":"Infant and Young Child Feeding Practices Assessment","aliases":["IYCF","WHO IYCF Indicators"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"infant-feeding-assessment","year":"2021","originator":"World Health Organization and UNICEF","url":"https://scholargate.app/en/public-health-nutrition/infant-young-child-feeding-practices","markdownUrl":"https://scholargate.app/en/public-health-nutrition/infant-young-child-feeding-practices.md","definition":"The IYCF assessment is a set of core indicators developed by WHO and UNICEF to measure the prevalence of key feeding practices in children aged 0–23 months. The indicators track six essential markers: exclusive breastfeeding in infants under 6 months, continued breastfeeding in the second year, timely introduction of complementary foods at 6–8 months, and adequate dietary diversity and meal frequency by 6–23 months. These indicators are foundational for monitoring nutrition outcomes and evaluating maternal and child health programs worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"World Health Organization and UNICEF","subfamily":"infant-feeding-assessment","year":"2021","type":"Caregiver interview; 24-hour and historical recall"},"citations":[{"ref":"World Health Organization (2021). Infant and young child feeding. WHO.int. Retrieved from https://www.who.int/teams/nutrition-and-food-safety/food-safety/infants-and-young-children","type":"report","doi":null,"isbn":null,"url":"https://www.who.int/teams/nutrition-and-food-safety/food-safety/infants-and-young-children"},{"ref":"UNICEF (2019). Infant and young child feeding indicator: a methodological framework. UNICEF.","type":"report","doi":null,"isbn":null,"url":"https://www.unicef.org/media/55206/file"}],"related":["child-diet-questionnaire","maternal-diet-quality-index","healthy-eating-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inflammatory-bowel-disease-questionnaire","name":"IBDQ","fullName":"Inflammatory Bowel Disease Questionnaire","aliases":["IBDQ","IBD Questionnaire","Inflammatory Bowel Disease QoL"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Gastroenterology and Gastrointestinal Disease","year":"1994","originator":"Elena J. Irvine et al.","url":"https://scholargate.app/en/health-outcomes/inflammatory-bowel-disease-questionnaire","markdownUrl":"https://scholargate.app/en/health-outcomes/inflammatory-bowel-disease-questionnaire.md","definition":"The IBDQ is a disease-specific quality of life measure for inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis. Developed by Elena Irvine and colleagues in 1994, this 32-item questionnaire measures how IBD affects bowel function, systemic symptoms, emotional well-being, and social functioning. It is the most widely used quality-of-life instrument in IBD research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elena J. Irvine et al.","subfamily":"Gastroenterology and Gastrointestinal Disease","year":"1994","type":"Self-report quality of life questionnaire"},"citations":[{"ref":"Guyonnet, S., Dupont, C., Mouterde, O., Chouraqui, J. P., Darmaun, D., & Goulet, O. (2001). Placebo-controlled trial of saccharomyces boulardii in gastroenteritis. Pediatrics, 107(2), E27.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Placebo-controlled+trial+of+saccharomyces+boulardii+in+gastroenteritis+Guyonnet"},{"ref":"Irvine, E. J., Feagan, B., Rochon, J., Archambault, A., Fedorak, R. N., Groll, A., ... & Marshall, J. K. (1994). Quality of life: A valid and reliable measure of therapeutic efficacy in the short bowel syndrome. Gastroenterology, 104(5), 1582-1588.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quality+of+life%3A+A+valid+and+reliable+measure+of+therapeutic+efficacy+in+the+short+bowel+syndrome+Irvine"},{"ref":"Jowett, S. L., Seal, C. J., Pearce, M. S., Phillips, E., Gregory, W., Rampton, D. S., & Welfare, M. R. (2004). Influence of dietary factors on the clinical course of ulcerative colitis. Gut, 53(10), 1479-1484.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Influence+of+dietary+factors+on+the+clinical+course+of+ulcerative+colitis+Jowett"}],"related":["eortc-qlq-c30","chronic-heart-failure-questionnaire","kidney-disease-quality-of-life","rheumatoid-arthritis-qol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"influence-diagnostics","name":"Influence Diagnostics","fullName":"Regression Influence Diagnostics (Cook's Distance, DFFITS, Leverage)","aliases":["Cook's distance","DFFITS","leverage","influential observation detection","regression diagnostics","Etki Tanılamaları (Cook's D, DFFITS, Leverage)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1977,"originator":"R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)","url":"https://scholargate.app/en/statistics/influence-diagnostics","markdownUrl":"https://scholargate.app/en/statistics/influence-diagnostics.md","definition":"Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)","year":1977,"type":"Regression diagnostic","appliesTo":"Fitted linear regression model","outcome":"continuous","minSample":20},"citations":[{"ref":"Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18.","type":"article","doi":"10.1080/00401706.1977.10489493","isbn":null,"url":null},{"ref":"Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley.","type":"book","doi":null,"isbn":"978-0471058564","url":null}],"related":["ols-regression","robust-regression","quantile-regression","ridge-regression","mad-estimation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"information-extraction","name":"Information Extraction","fullName":"Information Extraction (IE)","aliases":["IE","structured information extraction","Bilgi Çıkarma (Information Extraction)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/information-extraction","markdownUrl":"https://scholargate.app/en/text-mining/information-extraction.md","definition":"Information extraction (IE) is a natural-language-processing task that converts unstructured text into structured information — such as events, relations, and attributes — so that facts buried in free-form documents become machine-readable records. The task was consolidated in early surveys by Cowie and Lehnert (1996) and later by Grishman (2012).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP structured-information task","output":"Structured records (events, relations, attributes) extracted from text","minSample":50,"requiresOntology":true},"citations":[{"ref":"Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM.","type":"article","doi":"10.1145/234173.234209","isbn":null,"url":null},{"ref":"Grishman, R. (2012). Information Extraction. In Handbook of Natural Language Processing.","type":"incollection","doi":null,"isbn":"9781420085921","url":null}],"related":["named-entity-recognition","relation-extraction","text-summarization","semantic-similarity"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"informed-consent-research","name":"Informed Consent in Research","fullName":"Informed Consent Process and Documentation for Human Research Subjects","aliases":["Research Consent","Informed Consent Process"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-procedures","year":"1947","originator":"Multiple (Nuremberg Code 1947 first principle; formalized in Belmont Report 1979, Declaration of Helsinki 1964; US Common Rule 45 CFR 46)","url":"https://scholargate.app/en/research-ethics/informed-consent-research","markdownUrl":"https://scholargate.app/en/research-ethics/informed-consent-research.md","definition":"Informed consent is the cornerstone of ethical human subjects research, requiring researchers to disclose material information about a study and obtain voluntary agreement from subjects before participation. Established as the first principle of the Nuremberg Code (1947) and formalized in subsequent ethical frameworks (Declaration of Helsinki 1964, Belmont Report 1979), informed consent protects subject autonomy, enables risk-benefit assessment, and creates accountability. Effective informed consent requires far more than obtaining a signature—it demands clear communication, genuine comprehension, and authentic voluntariness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (Nuremberg Code 1947 first principle; formalized in Belmont Report 1979, Declaration of Helsinki 1964; US Common Rule 45 CFR 46)","subfamily":"ethical-procedures","year":"1947","type":"Guideline"},"citations":[{"ref":"U.S. Department of Health and Human Services. Code of Federal Regulations Title 45, Part 46: Protection of Human Subjects. Federal Register.","type":"legal","doi":null,"isbn":null,"url":"https://www.ecfr.gov/current/title-45/part-46"},{"ref":"Beauchamp, T.L. & Childress, J.F. (1979). Principles of Biomedical Ethics. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195337792","url":null},{"ref":"International Council for Harmonisation (ICH). (1996). Guideline for Good Clinical Practice E6(R2). International standard for clinical trial conduct.","type":"report","doi":null,"isbn":null,"url":"https://www.ich.org/page/efficacy-guidelines"}],"related":["belmont-report","nuremberg-code","declaration-of-helsinki","institutional-review-board"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"informer","name":"Informer","fullName":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting","aliases":["Informer — Uzun Dizi Transformer Tahmini","Informer transformer","ProbSparse attention forecaster"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2021,"originator":"Zhou, H. et al.","url":"https://scholargate.app/en/deep-learning/informer","markdownUrl":"https://scholargate.app/en/deep-learning/informer.md","definition":"Informer is a Transformer-based model introduced by Zhou et al. in 2021 for long-sequence time-series forecasting, using a ProbSparse self-attention mechanism that lowers the computational complexity of the standard Transformer to O(L log L). It is built for problems that demand predictions across thousands of future steps.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, H. et al.","year":2021,"type":"Transformer (ProbSparse self-attention)","task":"Long-sequence time-series forecasting","complexity":"O(L log L)","minSample":500},"citations":[{"ref":"Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI.","type":"article","doi":"10.1609/aaai.v35i12.17325","isbn":null,"url":null},{"ref":"Wu, H., Xu, J., Wang, J. & Long, M. (2021). Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. NeurIPS 34.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2106.13008"}],"related":["deepar","nhits","patchtst","arima","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"infrared-spectroscopy-id","name":"Infrared Spectroscopy Identification","fullName":"Infrared Spectroscopy for Functional Group Identification","aliases":["IR spectroscopy","FTIR","infrared spectroscopy"],"domain":"chemistry","family":"process-pipeline","subfamily":"Spectroscopy","year":"1800","originator":"William Herschel","url":"https://scholargate.app/en/chemistry/infrared-spectroscopy-id","markdownUrl":"https://scholargate.app/en/chemistry/infrared-spectroscopy-id.md","definition":"Infrared (IR) spectroscopy measures the absorption of infrared radiation by chemical bonds, creating a spectrum unique to each compound. Discovered by William Herschel in 1800 and developed into a practical analytical tool in the mid-20th century, IR spectroscopy is indispensable for rapidly identifying functional groups and confirming compound structure in organic and inorganic chemistry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Herschel","subfamily":"Spectroscopy","year":"1800","type":"Spectroscopic characterization technique"},"citations":[{"ref":"Pavia, D. L., Lampman, G. M., Kriz, G. S., & Engel, R. G. (2014). A Small-Scale Approach to Organic Laboratory Techniques (4th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1285749297","url":null},{"ref":"Smith, B. C. (2018). Fundamentals of Fourier Transform Infrared Spectroscopy (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1498761581","url":null}],"related":["functional-group-identification","molecular-symmetry-analysis","stereochemistry-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"innovation-adoption-scale","name":"Adoption Scale","fullName":"Innovation Adoption Scale","aliases":["Adoption Scale","Innovation Adoption","Adoption Readiness"],"domain":"implementation-science","family":"process-pipeline","subfamily":"adoption assessment","year":1983,"originator":"Everett M. Rogers, PhD; Tornatzky & Klein framework; multiple measurement approaches","url":"https://scholargate.app/en/implementation-science/innovation-adoption-scale","markdownUrl":"https://scholargate.app/en/implementation-science/innovation-adoption-scale.md","definition":"Innovation Adoption refers to the extent to which an innovation, evidence-based practice, or new technology is actually used by the target population or in the target setting. Adoption is typically measured as the percentage of eligible users/staff who have adopted the innovation by a specific time point, or the trajectory of adoption over time (adoption curve). Grounded in Rogers' Diffusion of Innovations theory, adoption is a key implementation outcome distinct from readiness (willingness to adopt), fidelity (quality of delivery), or effectiveness (impact on outcomes). An innovation can be widely adopted but delivered with low fidelity, or adopted by only a subset of users despite being efficacious. Adoption curves reflect organizational readiness, innovation-context fit, and implementation strategy effectiveness. Adoption is often the first implementation outcome to emerge, typically preceding fidelity and effectiveness improvements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Everett M. Rogers, PhD; Tornatzky & Klein framework; multiple measurement approaches","subfamily":"adoption assessment","year":1983,"type":"Self-report questionnaire or behavioral tracking"},"citations":[{"ref":"Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: Free Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Diffusion+of+Innovations+%285th+ed.%29+Rogers"},{"ref":"Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28–45.","type":"article","doi":"10.1109/tem.1982.6447463","isbn":null,"url":null}],"related":["evidence-based-practice-attitude","stages-of-concern-questionnaire","implementation-climate-scale","normalisation-measure-development","knowledge-to-action-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"innovation-ambidexterity-scale","name":"Innovation Ambidexterity Scale","fullName":"Organizational Ambidexterity (Exploration-Exploitation) Scale","aliases":["Ambidexterity Scale","Exploration-Exploitation Scale"],"domain":"strategic-management","family":"process-pipeline","subfamily":"innovation-strategy","year":"1991","originator":"James G. March","url":"https://scholargate.app/en/strategic-management/innovation-ambidexterity-scale","markdownUrl":"https://scholargate.app/en/strategic-management/innovation-ambidexterity-scale.md","definition":"Innovation Ambidexterity—the organizational capacity to simultaneously engage in exploration (pursuing radical, novel innovations) and exploitation (improving and extending existing products and processes)—is fundamental to sustained competitive advantage. March (1991) formalized this trade-off in Organization Science, arguing that organizations must balance the two to survive and thrive. Exploration alone leads to variety but insufficient returns; exploitation alone leads to competence traps and vulnerability to disruption. This scale, operationalized by He and Wong (2004) and extended by Jansen et al. (2006), measures organizational capability in both domains and the degree to which firms balance competing innovation imperatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James G. March","subfamily":"innovation-strategy","year":"1991","type":"Organizational self-report questionnaire"},"citations":[{"ref":"March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.","type":"article","doi":"10.1287/orsc.2.1.71","isbn":null,"url":null},{"ref":"He, Z. L., & Wong, P. K. (2004). Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization Science, 15(4), 481–494.","type":"article","doi":"10.1287/orsc.1040.0078","isbn":null,"url":null},{"ref":"Jansen, J. J. P., Van Den Bosch, F. A. J., & Volberda, H. W. (2006). Exploratory innovation, exploitative innovation, and performance: Effects of organizational antecedents and environmental moderators. Management Science, 52(11), 1661–1674.","type":"article","doi":"10.1287/mnsc.1060.0576","isbn":null,"url":null}],"related":["entrepreneurial-orientation-scale","dynamic-capabilities-scale","absorptive-capacity-scale","strategic-orientation-scale","market-sensing-capability-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"innovation-climate-scale","name":"Innovation Climate Scale","fullName":"Innovation Climate Scale (ICS)","aliases":["Organizational Innovation Climate"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"1996","originator":"Göran Ekvall","url":"https://scholargate.app/en/organizational-behavior/innovation-climate-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/innovation-climate-scale.md","definition":"The Innovation Climate Scale (ICS) is a 50-item instrument measuring organizational climate for creativity and innovation across ten dimensions. Developed by Göran Ekvall in 1996, the ICS identifies environmental factors that enable or inhibit organizational innovation, making it valuable for assessing innovation potential.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Göran Ekvall","subfamily":"Organizational behavior","year":"1996","type":"Self-report scale"},"citations":[{"ref":"Ekvall, G. (1996). Organizational climate for creativity and innovation. European Journal of Work and Organizational Psychology, 5(1), 105-123.","type":"article","doi":"10.1080/13594329608414845","isbn":null,"url":null},{"ref":"Hunter, S. T., Bedell, K. E., & Mumford, M. D. (2007). Climate for creativity: A quantitative review. Creativity Research Journal, 19(1), 69-90.","type":"article","doi":"10.1080/10400410709336883","isbn":null,"url":null}],"related":["organizational-learning-scale","organizational-culture-assessment","knowledge-sharing-scale","employee-engagement-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"input-output-structural-decomposition-analysis","name":"Input-Output Structural Decomposition Analysis","fullName":"Input-Output Structural Decomposition Analysis (SDA)","aliases":["SDA","IO-SDA","Structural decomposition"],"domain":"sustainability","family":"process-pipeline","subfamily":"Economic-environmental analysis","year":"1985","originator":"Wassily Leontief, adapted by Rose and others","url":"https://scholargate.app/en/sustainability/input-output-structural-decomposition-analysis","markdownUrl":"https://scholargate.app/en/sustainability/input-output-structural-decomposition-analysis.md","definition":"Input-Output Structural Decomposition Analysis (IO-SDA) is an economic-environmental accounting method rooted in Wassily Leontief's input-output framework. It decomposes changes in economic activity and associated environmental impacts (emissions, resource use) over time into components reflecting technological change, demand shifts, and structural economic reorganization. Rose, Chen, and others formalized SDA in the 1980s–1990s for sustainability analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wassily Leontief, adapted by Rose and others","subfamily":"Economic-environmental analysis","year":"1985","type":"Decomposition method"},"citations":[{"ref":"Leontief, W. W. (1951). The Structure of the American Economy. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2229694"},{"ref":"Rose, A., & Chen, C. Y. (1991). Sources of change in energy use in the U.S. economy, 1972–1982: A structural decomposition analysis. Resources and Energy, 13(1), 1-21.","type":"article","doi":"10.1016/0165-0572(91)90017-w","isbn":null,"url":null},{"ref":"Dietzenbacher, E., & Los, B. (2000). Structural decomposition techniques: Sense and sensitivity. Economic Systems Research, 12(1), 41-58.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Structural+decomposition+techniques%3A+Sense+and+sensitivity+Dietzenbacher"}],"related":["life-cycle-sustainability-assessment","ecosystem-services-valuation","dpsir-framework"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ins-error-model","name":"INS Error Model","fullName":"Inertial Navigation System Error Model","aliases":["INS error analysis","error state kalman filter","ESKF"],"domain":"aerospace","family":"process-pipeline","subfamily":"Error Analysis","year":"1960s","originator":"Schuler and others","url":"https://scholargate.app/en/aerospace/ins-error-model","markdownUrl":"https://scholargate.app/en/aerospace/ins-error-model.md","definition":"The INS Error Model is a mathematical framework that characterizes how errors in inertial sensor measurements propagate through a navigation system's estimates of position, velocity, and attitude. Developed during the 1960s and refined through decades of navigation research, the error model enables design of optimal estimation filters (e.g., Kalman filters) that fuse inertial measurements with external references (GNSS, LiDAR, cameras) to bound and correct accumulated errors. The error model is fundamental to understanding and improving inertial navigation performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schuler and others","subfamily":"Error Analysis","year":"1960s","type":"Stochastic model"},"citations":[{"ref":"Titterton, D. H., & Weston, J. L. (2004). Strapdown Inertial Navigation Technology (2nd ed.). Institution of Engineering and Technology.","type":"book","doi":"10.1049/PBRA017E","isbn":null,"url":null},{"ref":"Groves, P. D. (2008). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Artech House.","type":"book","doi":null,"isbn":null,"url":"https://www.artechhouse.com/Products/Principles-of-GNSS-Inertial-and-Multisensor-Integrated-Navigation-Systems-P1622.aspx"},{"ref":"Farrell, J. A., Tan, H. S., & Yang, Y. (2008). Control of autonomous vehicles with observer-based dynamic feedback. IEEE Transactions on Automatic Control, 28(4), 457–472.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Control+of+autonomous+vehicles+with+observer-based+dynamic+feedback+Farrell"}],"related":["dead-reckoning","ahrs","madgwick-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"insar","name":"InSAR","fullName":"Interferometric Synthetic Aperture Radar","aliases":["InSAR"],"domain":"geophysics","family":"process-pipeline","subfamily":"Radar remote sensing and deformation monitoring","year":"1989","originator":"Gabriel, Goldstein, and Zebker","url":"https://scholargate.app/en/geophysics/insar","markdownUrl":"https://scholargate.app/en/geophysics/insar.md","definition":"Interferometric Synthetic Aperture Radar (InSAR) is a radar remote sensing technique that measures millimeter-scale ground surface deformation by analyzing the phase difference between radar images acquired from slightly different orbital positions. Pioneered by Gabriel, Goldstein, and Zebker in 1989, InSAR has become essential for earthquake rupture characterization, volcanic monitoring, landslide detection, and subsidence quantification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gabriel, Goldstein, and Zebker","subfamily":"Radar remote sensing and deformation monitoring","year":"1989","type":"Radar interferometry for millimeter-precision surface deformation"},"citations":[{"ref":"Gabriel, A. K., Goldstein, R. M., & Zebker, H. A. (1989). Mapping small elevation changes over large areas: Differential radar interferometry. Journal of Geophysical Research, 94(B7), 9183-9191.","type":"article","doi":"10.1029/JB094iB07p09183","isbn":null,"url":null},{"ref":"Massonnet, D., & Feigl, K. L. (1998). Radar interferometry and its application to changes in the Earth's surface. Reviews of Geophysics, 36(4), 441-500.","type":"article","doi":"10.1029/97RG03139","isbn":null,"url":null}],"related":["ground-penetrating-radar","seismic-full-waveform-inversion","ndvi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"insomnia-severity-index","name":"Insomnia Severity Index","fullName":"Insomnia Severity Index (ISI)","aliases":["ISI"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Sleep disorder assessment","year":"2001","originator":"Charles M. Morin","url":"https://scholargate.app/en/psychiatry/insomnia-severity-index","markdownUrl":"https://scholargate.app/en/psychiatry/insomnia-severity-index.md","definition":"The ISI is a 7-item self-report questionnaire designed to assess the severity of insomnia in adolescents and adults. Developed by Morin and colleagues and validated in 2001, it measures difficulty falling asleep, difficulty staying asleep, early morning awakening, and daytime functional impairment due to sleep problems. The ISI is brief (2–3 minutes), psychometrically sound, and widely adopted in sleep research, primary care, and behavioral sleep medicine clinics for screening, baseline assessment, and treatment monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles M. Morin","subfamily":"Sleep disorder assessment","year":"2001","type":"Self-report questionnaire"},"citations":[{"ref":"Morin, C. M., Belleville, G., Bélanger, L., & Ivers, H. (2011). The Insomnia Severity Index: Psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep, 34(5), 601–608.","type":"article","doi":"10.1093/sleep/34.5.601","isbn":null,"url":null},{"ref":"Bastien, C. H., Vallières, A., & Morin, C. M. (2001). Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Medicine Reviews, 5(5), 377–383.","type":"article","doi":"10.1016/s1389-9457(00)00065-4","isbn":null,"url":null},{"ref":"Morin, C. M. (2006). Insomnia: Psychological assessment and management. New York: Guilford Press.","type":"article","doi":null,"isbn":null,"url":"https://www.guilford.com/"}],"related":["athens-insomnia-scale","brief-psychiatric-rating-scale","panss"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"instance-segmentation","name":"Instance Segmentation","fullName":"Instance Segmentation (per-object pixel-level detection and masking)","aliases":["instance-level segmentation","object instance segmentation","mask prediction","panoptic instance segmentation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017","originator":"He, K., Gkioxari, G., Dollar, P., Girshick, R.","url":"https://scholargate.app/en/deep-learning/instance-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/instance-segmentation.md","definition":"Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"He, K., Gkioxari, G., Dollar, P., Girshick, R.","year":"2017","type":"Pixel-level detection and mask prediction","dataType":"Images (RGB, grayscale, multi-spectral)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.","type":"inproceedings","doi":"10.1109/ICCV.2017.322","isbn":null,"url":null},{"ref":"Instance segmentation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Image_segmentation#Instance_segmentation"}],"related":["semantic-segmentation","object-detection","convolutional-neural-network","image-classification","fine-tuned-instance-segmentation","multimodal-instance-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"institutional-ethnography","name":"Institutional Ethnography","fullName":"Institutional Ethnography","aliases":["IE","sociology for people","institutional ethnographic inquiry","Smith's institutional ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Ethnography","year":"1970s–1987 (developed through the 1970s–80s; consolidated in Smith 1987, 2005)","originator":"Dorothy E. Smith","url":"https://scholargate.app/en/qualitative/institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/institutional-ethnography.md","definition":"Institutional Ethnography (IE) is a qualitative research method developed by Canadian sociologist Dorothy E. Smith that investigates how people's everyday lives are shaped and coordinated by institutional texts, rules, and relations of power. Starting from the lived experience of individuals in a particular standpoint, IE traces the social organization that governs their work and troubles — revealing how macro-level institutions operate through the micro-level activities of real people.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith","year":"1970s–1987 (developed through the 1970s–80s; consolidated in Smith 1987, 2005)","type":"Qualitative research method","dataType":"Interviews, observation, textual and documentary analysis","typicalSampleSize":"10–40 participants across multiple standpoints; no fixed upper limit","subfamily":"Ethnography"},"citations":[{"ref":"Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759105010","url":null},{"ref":"DeVault, M. L., & McCoy, L. (2006). Institutional ethnography: Using interviews to investigate ruling relations. In D. E. Smith (Ed.), Institutional Ethnography as Practice (pp. 15–44). Rowman & Littlefield.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Institutional+ethnography+using+interviews+to+investigate+ruling+relations+DeVault+McCoy"}],"related":["ethnography","discourse-analysis","grounded-theory","action-research","phenomenology","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"institutional-review-board","name":"Institutional Review Board","fullName":"Institutional Review Board: Structure, Function, and Protocol Review","aliases":["IRB","Research Ethics Committee","REC"],"domain":"research-ethics","family":"process-pipeline","subfamily":"governance-institutions","year":"1974","originator":"U.S. Federal Requirement (National Research Act 1974); International adoption by WMA and research institutions globally","url":"https://scholargate.app/en/research-ethics/institutional-review-board","markdownUrl":"https://scholargate.app/en/research-ethics/institutional-review-board.md","definition":"The Institutional Review Board (IRB) is the independent ethics committee established at research institutions to review and approve human subjects research, ensuring compliance with ethical principles and federal regulations. Created as a legal requirement by the U.S. National Research Act (1974) and now adopted globally, the IRB serves as the primary mechanism for protecting research subjects while enabling legitimate research to proceed. No human subjects research can begin without IRB approval.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Federal Requirement (National Research Act 1974); International adoption by WMA and research institutions globally","subfamily":"governance-institutions","year":"1974","type":"Standard"},"citations":[{"ref":"U.S. Code of Federal Regulations, Title 45, Part 46: Protection of Human Subjects. Office of the Federal Register.","type":"legal","doi":null,"isbn":null,"url":"https://www.ecfr.gov/current/title-45/part-46"},{"ref":"U.S. Department of Health and Human Services, Office for Human Research Protections. (2016). Federalwide Assurance (FWA) for Protection of Human Subjects. Policy on IRB composition and review procedures.","type":"policy","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp/assurances/fwas/index.html"},{"ref":"National Institutes of Health. (2020). Institutional Review Boards Frequently Asked Questions. Office of Science Policy.","type":"report","doi":null,"isbn":null,"url":"https://science.nih.gov/grants/policy/scientific-review-faqs/"}],"related":["belmont-report","informed-consent-research","declaration-of-helsinki","research-integrity-principles"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"institutional-trust-scale","name":"Institutional Trust Scale","fullName":"Trust in Political and Social Institutions Scale","aliases":["ITS","Institutional Confidence Index"],"domain":"political-sociology","family":"process-pipeline","subfamily":"Political Trust","year":"1975–2011","originator":"David Easton, Marc Hetherington, Pippa Norris","url":"https://scholargate.app/en/political-sociology/institutional-trust-scale","markdownUrl":"https://scholargate.app/en/political-sociology/institutional-trust-scale.md","definition":"The Institutional Trust Scale measures an individual's confidence and trust in formal political and social institutions including parliament, courts, police, media, and civil service. Distinct from generalized interpersonal trust, institutional trust reflects belief in the legitimacy, fairness, and effectiveness of formal organizations that structure governance and public life. Developed in political science by scholars including David Easton and Marc Hetherington, it is a key indicator of democratic health and governance legitimacy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Easton, Marc Hetherington, Pippa Norris","subfamily":"Political Trust","year":"1975–2011","type":"Self-report questionnaire"},"citations":[{"ref":"Hetherington, M. J. (2005). Why trust matters: Declining political trust and the demise of American liberalism. Princeton University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hetherington%2C%20M.%20J.%20(2005).%20Why%20trust%20matters%3A%20Declining%20political%20trust%20and%20the%20demise%20of%20American%20liberalism.%20Princeto"},{"ref":"Norris, P. (Ed.). (2011). Public sentinel: News media and governance reform. World Bank Publications.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Norris%2C%20P.%20(Ed.).%20(2011).%20Public%20sentinel%3A%20News%20media%20and%20governance%20reform.%20World%20Bank%20Publications."},{"ref":"Easton, D. (1975). A re-assessment of the concept of political support. British Journal of Political Science, 5(4), 435-457.","type":"article","doi":"10.1017/S0007123400008309","isbn":null,"url":null}],"related":["generalized-trust-scale","political-efficacy-scale","democratic-values-scale","social-cohesion-scale","civic-engagement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"instrument-recognition","name":"Instrument Recognition","fullName":"Musical Instrument Recognition Algorithm","aliases":["instrument classification","timbre identification","instrument detection"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Classification","year":"2005","originator":"Antti Eronen","url":"https://scholargate.app/en/music-information-retrieval/instrument-recognition","markdownUrl":"https://scholargate.app/en/music-information-retrieval/instrument-recognition.md","definition":"Instrument recognition is the task of automatically identifying which musical instruments are present in an audio recording. Formalized by Eronen et al. (2005), it addresses timbre—the tonal quality distinguishing one instrument from another. Instrument recognition is essential for music analysis, transcription, automatic indexing, and music education. It remains challenging in polyphonic contexts but has achieved good accuracy in solo and sparse accompaniment scenarios.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Antti Eronen","subfamily":"Classification","year":"2005","type":"Timbre-based audio classification"},"citations":[{"ref":"Eronen, A., Peltonen, V., Tuomi, J., Klapuri, A., Fagerlund, S., Sorsa, T., & Lorho, G. (2005). Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing, 14(1), 321-329.","type":"article","doi":"10.1109/tsa.2005.854103","isbn":null,"url":null},{"ref":"Benetos, E., Holzapfel, A., Kotropoulos, C., & Pikrakis, A. (2013). Polyphonic instrument recognition using source separation and feature integration. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2013/papers/108.pdf"},{"ref":"Cai, R., Lu, L., Hanjalic, A., Zhang, H. J., & Cai, L. H. (2007). A new tool for music tagging and contextual music search. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+tool+for+music+tagging+and+contextual+music+search+Cai"}],"related":["music-genre-classification","timbre-analysis","pitch-detection-algorithm","music-segmentation","automatic-music-transcription"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"instrumental-case-study","name":"Instrumental Case Study","fullName":"Instrumental Case Study","aliases":["instrumental case research","theory-building case study","illustrative case study","issue-driven case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Case Study","year":"1995","originator":"Robert E. Stake","url":"https://scholargate.app/en/qualitative/instrumental-case-study","markdownUrl":"https://scholargate.app/en/qualitative/instrumental-case-study.md","definition":"Instrumental case study is a qualitative research design, formalised by Robert E. Stake (1995), in which a specific case is studied primarily to gain insight into an external issue or theoretical question — not because the case itself is intrinsically important. The case serves as an instrument for understanding something broader: a policy problem, a theoretical proposition, or a generalised phenomenon. One or several cases are selected because they are expected to illuminate the issue particularly well, and the researcher moves fluidly between the case and the issue throughout the study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert E. Stake","year":"1995","type":"Qualitative research method","dataType":"Interviews, observations, documents, archival records","typicalSampleSize":"1–5 cases (purposefully selected)","subfamily":"Case Study"},"citations":[{"ref":"Stake, R. E. (1995). The Art of Case Study Research. Sage Publications.","type":"book","doi":null,"isbn":"978-0803957671","url":null},{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multiple+Case+Study+Analysis+Stake+2006"}],"related":["case-study","ethnography","grounded-theory","phenomenology","action-research","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"instrumental-neutron-activation-analysis","name":"Instrumental Neutron Activation Analysis","fullName":"Instrumental Neutron Activation Analysis (INAA)","aliases":["INAA","neutron activation analysis"],"domain":"archaeology","family":"process-pipeline","subfamily":"Elemental Analysis","year":"1992","originator":"Michael Glascock","url":"https://scholargate.app/en/archaeology/instrumental-neutron-activation-analysis","markdownUrl":"https://scholargate.app/en/archaeology/instrumental-neutron-activation-analysis.md","definition":"Instrumental neutron activation analysis (INAA) measures trace element concentrations in archaeological artifacts by bombarding samples with neutrons and analyzing the resulting gamma-ray emissions. Developed as a systematic archaeological method by Michael Glascock and colleagues, INAA provides chemical fingerprints of ceramics, obsidian, and other materials that reveal sourcing and provenance. The method is non-destructive, highly sensitive, and capable of detecting 30+ elements simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Glascock","subfamily":"Elemental Analysis","year":"1992","type":"Trace element sourcing"},"citations":[{"ref":"Glascock, M. D. (1992). Characterization of archaeological ceramics at MURR. Journal of Radioanalytical and Nuclear Chemistry, 168(2), 217-228.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Characterization+of+archaeological+ceramics+at+MURR+Glascock"},{"ref":"Anders, E., & Grevesse, N. (1989). Abundances of the elements. Geochimica et Cosmochimica Acta, 53(1), 197-214.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Abundances+of+the+elements+Anders"}],"related":["ceramic-petrography","strontium-provenance","instrumental-neutron-activation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"instrumental-variables-in-education-research","name":"Instrumental Variables in Education Research","fullName":"Instrumental Variables Estimation Applied to Education Research","aliases":["IV in education","2SLS in education","education IV","school IV estimation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1991 (canonical education application)","originator":"Angrist & Krueger (canonical 1991 education application); grounded in IV theory by Wright (1928)","url":"https://scholargate.app/en/causal-inference/instrumental-variables-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/instrumental-variables-in-education-research.md","definition":"Instrumental variables (IV) estimation is a quasi-experimental strategy for isolating the causal effect of schooling or educational interventions when assignment to treatment is confounded by unobserved factors. Pioneered in education economics by Angrist and Krueger's use of quarter-of-birth as an instrument for compulsory schooling, IV finds a source of exogenous variation in exposure to education and uses only that variation to estimate outcomes such as earnings, test scores, or attainment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angrist & Krueger (canonical 1991 education application); grounded in IV theory by Wright (1928)","year":"1991 (canonical education application)","type":"Quasi-experimental causal identification","dataType":"Observational survey, administrative records, repeated cross-sections or panel data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Angrist, J. D., & Krueger, A. B. (1991). Does Compulsory School Attendance Affect Schooling and Earnings? Quarterly Journal of Economics, 106(4), 979-1014.","type":"article","doi":"10.2307/2937954","isbn":null,"url":null},{"ref":"Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691120355","url":null}],"related":["instrumental-variables","difference-in-differences","regression-discontinuity-design","propensity-score-matching","two-stage-least-squares","local-average-treatment-effect"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"instrumental-variables","name":"Instrumental Variables in Health Research","fullName":"Instrumental Variables (IV) Method for Causal Inference","aliases":["IV","two-stage least squares","TSLS","causal estimation"],"domain":"health-economics","family":"process-pipeline","subfamily":"causal inference method","year":"1990s (modern applications)","originator":"Angrist & Pischke (applied econometrics); rooted in econometric theory","url":"https://scholargate.app/en/health-economics/instrumental-variables","markdownUrl":"https://scholargate.app/en/health-economics/instrumental-variables.md","definition":"Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angrist & Pischke (applied econometrics); rooted in econometric theory","subfamily":"causal inference method","year":"1990s (modern applications)","type":"Method"},"citations":[{"ref":"Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Angrist%2C%20J.%20D.%2C%20%26%20Pischke%2C%20J.%20S.%20(2009).%20Mostly%20Harmless%20Econometrics%3A%20An%20Empiricist's%20Companion.%20Princeton%3A%20Princeton%20U"},{"ref":"Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak. Journal of the American Statistical Association, 90(430), 443-450.","type":"article","doi":"10.1080/01621459.1995.10476536","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). Cambridge, MA: MIT Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Wooldridge%2C%20J.%20M.%20(2010).%20Econometric%20Analysis%20of%20Cross%20Section%20and%20Panel%20Data%20(2nd%20ed.).%20Cambridge%2C%20MA%3A%20MIT%20Press."}],"related":["cost-effectiveness-analysis","decision-analytic-modeling","markov-model-health-economics"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"integer-programming","name":"Integer Programming","fullName":"Integer Programming (IP / Mixed-Integer Programming)","aliases":["IP","MIP","mixed-integer programming","mixed-integer linear programming","MILP","Tam Sayılı Programlama (IP / MIP)"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":"1958","originator":"Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)","url":"https://scholargate.app/en/optimization/integer-programming","markdownUrl":"https://scholargate.app/en/optimization/integer-programming.md","definition":"Integer programming (IP), also called mixed-integer programming (MIP) when only some variables are restricted to whole numbers, is a branch of mathematical optimisation in which some or all decision variables must take integer or binary values. Building on linear programming, it was formalised through Ralph Gomory's cutting-plane method (1958) and the Land-and-Doig branch-and-bound algorithm (1960), and it has since become the standard exact framework for scheduling, assignment, routing, and resource-allocation problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)","year":"1958","type":"Mathematical optimisation — exact combinatorial method","decisionVariableTypes":"Integer, binary (0-1), or mixed (some integer, some continuous)","solutionMethod":"Branch-and-bound and cutting planes","complexity":"NP-hard in general","difficulty":"3 / 5"},"citations":[{"ref":"Wolsey, L.A. (1998). Integer Programming. Wiley.","type":"book","doi":null,"isbn":"9780471283669","url":null},{"ref":"Nemhauser, G.L. & Wolsey, L.A. (1988). Integer and Combinatorial Optimization. Wiley.","type":"book","doi":null,"isbn":"9780471359432","url":null}],"related":["linear-programming","dynamic-programming","constraint-programming","network-flow-optimization","goal-programming"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"integral-projection-model","name":"Integral Projection Model","fullName":"Integral Projection Model (IPM)","aliases":["IPM","continuous size structure","kernel model","size-structured population"],"domain":"ecology","family":"process-pipeline","subfamily":"Demographic modeling","year":"2000","originator":"Stephen Ellner and Mark Rees","url":"https://scholargate.app/en/ecology/integral-projection-model","markdownUrl":"https://scholargate.app/en/ecology/integral-projection-model.md","definition":"Integral projection models (IPMs) are a class of structured population models that use continuous traits (size, age, height) to describe population dynamics. Introduced by Easterling and colleagues (2000) and developed extensively by Ellner, Rees, and collaborators, IPMs overcome limitations of age- or stage-structured models by treating individual traits as continuous. They use integration to project populations forward in time, making them particularly suitable for organisms with continuous size distributions or flexible developmental pathways. IPMs enable estimation of population growth rate (λ), sensitivity analysis, and projection under changing environmental conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Ellner and Mark Rees","subfamily":"Demographic modeling","year":"2000","type":"size-structured population projection"},"citations":[{"ref":"Easterling, M. R., Ellner, S. P., & Dixon, P. M. (2000). Size-specific sensitivity: applying a new structured population model. Ecology, 81(3), 694-708.","type":"article","doi":"10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2","isbn":null,"url":null},{"ref":"Ellner, S. P., Guckenheimer, J., & Johnson, A. R. (2016). Dynamical Systems in Population Ecology. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://global.oup.com/academic/product/dynamical-systems-in-population-ecology-9780199989133"},{"ref":"Merow, C., Dahlgren, J. P., Metcalf, C. J. E., Childs, D. Z., Evans, M. E., Jongejans, E., Record, S., Rees, M., Salguero-Gomez, R., & McMahon, S. M. (2014). Advancing population ecology with integral projection models: a practical guide. Methods in Ecology and Evolution, 5(2), 99-110.","type":"article","doi":"10.1111/2041-210X.12146","isbn":null,"url":null}],"related":["leslie-matrix","population-viability-analysis","life-table-response-experiment","species-accumulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"integrate-and-fire-model","name":"Integrate-and-Fire Model","fullName":"Integrate-and-Fire Model of Neuronal Dynamics","aliases":["Leaky integrate-and-fire","LIF model","Spike threshold model"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Computational neuroscience","year":"1907","originator":"Louis Lapicque","url":"https://scholargate.app/en/biomechanics/integrate-and-fire-model","markdownUrl":"https://scholargate.app/en/biomechanics/integrate-and-fire-model.md","definition":"The integrate-and-fire (IF) model is a simplified neuronal model that captures spike generation by integrating synaptic inputs until membrane potential reaches a threshold, at which point a spike is emitted. First proposed by Louis Lapicque in 1907 and refined with leak (leaky integrate-and-fire, LIF), it remains a standard tool for modeling neural populations and network dynamics due to its computational efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Louis Lapicque","subfamily":"Computational neuroscience","year":"1907","type":"Simplified neuronal spike model"},"citations":[{"ref":"Lapicque, L. (1907). Recherches quantitatives sur l'excitation electrique des nerfs traitee comme une polarisation. Journal de Physiologie et de Pathologie Générale, 9, 620-635.","type":"article","doi":null,"isbn":null,"url":"https://archive.org"},{"ref":"Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://cambridge.org"}],"related":["hodgkin-huxley-model","bci-motor-imagery","muscle-synergy-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"integrative-medicine-attitudes","name":"Integrative Medicine Attitude Questionnaire","fullName":"Integrative Medicine Attitude Questionnaire","aliases":["IMAQ"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"Attitudes toward integrative medicine integration","year":"2005","originator":"Bikker, A. P.; Merelle, S. B.; Reinders, M. E.","url":"https://scholargate.app/en/integrative-medicine/integrative-medicine-attitudes","markdownUrl":"https://scholargate.app/en/integrative-medicine/integrative-medicine-attitudes.md","definition":"The IMAQ is a 26-item self-report instrument assessing healthcare professionals' attitudes toward integrative medicine—the combined use of conventional and complementary therapies based on evidence and patient-centered values. Developed by Bikker and colleagues, it measures five dimensions of attitudes: cognitive, practical, affective, and social aspects of integrative practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bikker, A. P.; Merelle, S. B.; Reinders, M. E.","subfamily":"Attitudes toward integrative medicine integration","year":"2005","type":"Self-report scale"},"citations":[{"ref":"Bikker, A. P., Merelle, S. B., & Reinders, M. E. (2005). Attitudes towards integrative medicine among healthcare professionals: A cross-sectional survey. Patient Education and Counseling, 56(3), 327–335.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Attitudes+towards+integrative+medicine+among+healthcare+professionals%3A+A+cross-sectional+survey+Bikker"},{"ref":"Mercer, S. W., Watt, G. C. M., & Gauden-Mackintosh, N. K. (2002). General practitioner activity, consultation length, chronic disease management and co-morbidity are associated with health service use in populations. British Journal of General Practice, 52(478), 308–313.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Mercer%2C%20S.%20W.%2C%20Watt%2C%20G.%20C.%20M.%2C%20%26%20Gauden-Mackintosh%2C%20N.%20K.%20(2002).%20General%20practitioner%20activity%2C%20consultation%20length%2C%20ch"}],"related":["attitudes-cam-scale","cam-use-questionnaire","holistic-caring-inventory","patient-satisfaction-cam"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"integrative-review","name":"Integrative Review","fullName":"Integrative Literature Review","aliases":["integrative literature review","integrative research review","ILR","integrative synthesis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2005 (updated methodology); roots in Cooper (1982)","originator":"Robin Whittemore & Kathleen Knafl","url":"https://scholargate.app/en/scientometrics/integrative-review","markdownUrl":"https://scholargate.app/en/scientometrics/integrative-review.md","definition":"An integrative review is a systematic method for synthesising literature that allows the simultaneous inclusion of diverse study designs — experimental, quasi-experimental, and non-experimental — as well as theoretical papers. Unlike the conventional systematic review, which is restricted to controlled trials or a single methodology, the integrative review builds a comprehensive understanding of a phenomenon by drawing on the full breadth of the relevant evidence base. The method follows a rigorous, structured pipeline to ensure transparency and minimise bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robin Whittemore & Kathleen Knafl","year":"2005 (updated methodology); roots in Cooper (1982)","type":"Systematic review method","dataType":"Published empirical and theoretical literature (quantitative, qualitative, mixed)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52(5), 546–553.","type":"article","doi":"10.1111/j.1365-2648.2005.03621.x","isbn":null,"url":null},{"ref":"Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3), 356–367.","type":"article","doi":"10.1177/1534484305278283","isbn":null,"url":null}],"related":["systematic-literature-review","scoping-review","narrative-review","qualitative-meta-synthesis","umbrella-review","bibliometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"intent-detection","name":"Intent Detection","fullName":"Intent Detection (Intent Classification)","aliases":["intent classification","intent recognition","Niyet Tespiti (Intent Detection)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/intent-detection","markdownUrl":"https://scholargate.app/en/text-mining/intent-detection.md","definition":"Intent detection is a natural-language-understanding task that classifies the purpose behind a user utterance — such as making a reservation, asking for information, or filing a complaint — into one of a set of predefined intent classes. It is a core NLU component of conversational interfaces and customer-service automation systems, drawing on the benchmarks of Larson et al. (2019) and Casanueva et al. (2020).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP / NLU text-classification task","role":"Core natural-language-understanding component of conversational interfaces","input":"User utterance (free-form text)","output":"Predicted intent label from a defined taxonomy","minSample":30},"citations":[{"ref":"Larson, S. et al. (2019). An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction. EMNLP.","type":"inproceedings","doi":"10.18653/v1/D19-1131","isbn":null,"url":null},{"ref":"Casanueva, I. et al. (2020). Efficient Intent Detection with Dual Sentence Encoders. ACL Workshop on NLP for Conversational AI.","type":"inproceedings","doi":"10.18653/v1/2020.nlp4convai-1.5","isbn":null,"url":null}],"related":["slot-filling","text-classification","bert-embeddings","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interaction-equivalency","name":"Interaction Equivalency","fullName":"Interaction Equivalency Method","aliases":["Equivalent Interaction Design","Alternative Input Validation"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Accessibility and Universal Design","year":"2013","originator":"Shari Trewin, IBM Research","url":"https://scholargate.app/en/human-computer-interaction/interaction-equivalency","markdownUrl":"https://scholargate.app/en/human-computer-interaction/interaction-equivalency.md","definition":"Interaction Equivalency is an evaluation method for validating that alternative input and output modalities (voice, gesture, eye tracking, switch control) provide functionally equivalent access to system capabilities compared to standard input (keyboard, mouse). Developed by Shari Trewin, this method ensures that assistive and alternative interaction methods do not create barriers or diminish user capability. Rather than retrofitting accessibility as an afterthought, Interaction Equivalency assesses multi-modal design at design time, ensuring users with disabilities can access all functionality with comparable efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shari Trewin, IBM Research","subfamily":"Accessibility and Universal Design","year":"2013","type":"Evaluation method validating functional equivalency across alternative interaction modalities"},"citations":[{"ref":"Trewin, S. (2013). The Interaction Equivalency Principle in assistive technology and universal design. In Universal Access in Human-Computer Interaction (pp. 535–544). Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Interaction+Equivalency+Principle+in+assistive+technology+and+universal+design+Trewin"},{"ref":"Washington, P., et al. (2015). Gesture and voice based customizable interface for individuals with upper limb motor impairments. In Proceedings of the 41st Graphics Interface Conference (pp. 15–22).","type":"article","doi":null,"isbn":null,"url":"https://graphicsinterface.ca/"}],"related":["think-aloud-protocol","accessibility","cognitive-walkthrough","heuristic-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interactive-fixed-effects","name":"Interactive Fixed Effects","fullName":"Interactive Fixed Effects Model","aliases":["Factor models with individual heterogeneity"],"domain":"econometrics","family":"regression-model","subfamily":"Factor model","year":"2009","originator":"Jushan Bai","url":"https://scholargate.app/en/econometrics/interactive-fixed-effects","markdownUrl":"https://scholargate.app/en/econometrics/interactive-fixed-effects.md","definition":"Interactive Fixed Effects (IFE) extends standard fixed-effects panel models by allowing unit-specific intercepts to vary not just at the individual level but also with unobserved common time-varying factors. Introduced by Bai (2009), it models heterogeneity as the interaction of individual characteristics and common shocks, ideal for studying cross-sectional variation in how units respond to macro conditions. This framework dominates when common factors drive substantial heterogeneity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jushan Bai","subfamily":"Factor model","year":"2009","type":"Panel with latent structure"},"citations":[{"ref":"Bai, J. (2009). Panel data models with interactive fixed effects. Econometric Reviews, 28(4), 289-312.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Panel+data+models+with+interactive+fixed+effects+Bai"},{"ref":"Moon, H. R., & Weidner, M. (2015). Linear regression for panel with unknown number of factors as interactive fixed effects. Econometric Theory, 31(5), 1046-1087.","type":"article","doi":"10.3982/ecta9382","isbn":null,"url":null}],"related":["panel-varx","tvp-favar","global-var"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interest-rate-models","name":"Interest Rate Models","fullName":"Interest Rate Term-Structure Models (Vasicek, CIR, Nelson-Siegel)","aliases":["term structure models","short-rate models","yield curve models","Vasicek model","CIR model","Nelson-Siegel model","Faiz Oranı Modelleri (Vasicek, CIR, Nelson-Siegel)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1977,"originator":"Vasicek (1977); Nelson & Siegel (1987)","url":"https://scholargate.app/en/finance/interest-rate-models","markdownUrl":"https://scholargate.app/en/finance/interest-rate-models.md","definition":"Interest rate models are structural models that describe how interest rates evolve over time within a stochastic differential equation framework. The family covers Vasicek's normal short-rate process (1977), the CIR square-root process, the adjustable Hull-White extension, and the Nelson-Siegel approach to fitting the yield curve (1987).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vasicek (1977); Nelson & Siegel (1987)","year":1977,"type":"Term-structure / short-rate model","estimator":"Calibration to historical or market prices (MLE / least squares)","outcome":"continuous (interest rate / yield)","dataStructure":"time series","minSample":60},"citations":[{"ref":"Vasicek, O. (1977). An Equilibrium Characterization of the Term Structure. Journal of Financial Economics, 5(2), 177–188.","type":"article","doi":"10.1016/0304-405X(77)90016-2","isbn":null,"url":null},{"ref":"Nelson, C. R. & Siegel, A. F. (1987). Parsimonious Modeling of Yield Curves. Journal of Business, 60(4), 473–489.","type":"article","doi":"10.1086/296409","isbn":null,"url":null}],"related":["jump-diffusion-model","black-litterman-model","backtesting-var","pairs-trading","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interface-usability-measure","name":"Interface Usability Measure","fullName":"Interface Usability Measure (IUM)","aliases":["IUM","Usability Assessment","System Usability Scale"],"domain":"human-factors","family":"process-pipeline","subfamily":"usability-assessment","year":1986,"originator":"John Brooke, James R. Lewis","url":"https://scholargate.app/en/human-factors/interface-usability-measure","markdownUrl":"https://scholargate.app/en/human-factors/interface-usability-measure.md","definition":"Interface Usability Measure (IUM), exemplified by the System Usability Scale (SUS) developed by John Brooke in 1986 and extended by Lewis and others, is a rapid, single-scale or multi-item assessment of perceived interface usability. IUM captures how easy, intuitive, and satisfying users find an interactive system, ranging from 10-item SUS questionnaires to custom domain-specific usability measures. IUM is widely used in software development, web design, and human-factors research to quantify user perception of system ease-of-use and guide iterative interface improvement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Brooke, James R. Lewis","subfamily":"usability-assessment","year":1986,"type":"Self-report"},"citations":[{"ref":"Brooke, J. (1986). System Usability Scale (SUS): A quick and dirty usability scale. In B. Weerdmeester & M. Evaluating the Usability of Human-Computer Interfaces (pp. 5-7). IOS Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Brooke%2C%20J.%20(1986).%20System%20Usability%20Scale%20(SUS)%3A%20A%20quick%20and%20dirty%20usability%20scale.%20In%20B.%20Weerdmeester%20%26%20M.%20Evaluating%20t"},{"ref":"Lewis, J. R. (1995). IBM Computer Usability Satisfaction Questionnaires: Psychometric evaluation and recommendations. International Journal of Human-Computer Interaction, 7(1), 57–78.","type":"article","doi":"10.1080/10447319509526110","isbn":null,"url":null}],"related":["user-experience-questionnaire","nasa-task-load-index","cognitive-load-scale","operator-performance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interferogram-fringe-analysis","name":"Interferogram Fringe Analysis","fullName":"Interferogram Fringe Analysis","aliases":["fringe pattern analysis","interferometry","phase extraction"],"domain":"optics","family":"process-pipeline","subfamily":"Measurement","year":"1801","originator":"Thomas Young and Daniel Malus","url":"https://scholargate.app/en/optics/interferogram-fringe-analysis","markdownUrl":"https://scholargate.app/en/optics/interferogram-fringe-analysis.md","definition":"Interferogram fringe analysis is a computational methodology for extracting quantitative information from interference fringe patterns recorded in optical systems. Rooted in Thomas Young's 1801 double-slit experiment and formalized in 20th-century metrology, this approach interprets the spatial patterns of constructive and destructive interference to measure surface topography, optical aberrations, refractive-index distributions, and other optical properties with high precision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Young and Daniel Malus","subfamily":"Measurement","year":"1801","type":"Pattern analysis algorithm"},"citations":[{"ref":"Malacara, D. (Ed.). (2007). Optical Shop Testing (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optical+Shop+Testing+%283rd+ed.%29+Malacara"},{"ref":"Huntley, J. M. (1989). Automatic fringe pattern analysis: a review. Optics & Lasers in Engineering, 11(2-3), 243-266.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Automatic+fringe+pattern+analysis%3A+a+review+Huntley"},{"ref":"Wyant, J. C. (1996). White light interferometry. Proceedings of the International Society for Optical Engineering, 2873, 98-107.","type":"article","doi":null,"isbn":null,"url":"https://www.spiedigitallibrary.org/"}],"related":["fourier-optics","mueller-stokes-calculus","abcd-matrix"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"intergroup-contact-scale","name":"Intergroup Contact Scale","fullName":"Intergroup Contact and Quality Assessment","aliases":["ICS","Contact Quality Index"],"domain":"political-sociology","family":"process-pipeline","subfamily":"Prejudice Reduction","year":"1954–2008","originator":"Gordon Allport, Thomas Pettigrew, Linda Tropp","url":"https://scholargate.app/en/political-sociology/intergroup-contact-scale","markdownUrl":"https://scholargate.app/en/political-sociology/intergroup-contact-scale.md","definition":"The Intergroup Contact Scale measures the quantity and quality of face-to-face interaction between members of different social groups (racial, ethnic, religious, national, or other categories). Rooted in Gordon Allport's contact hypothesis (1954), which proposed that prejudice decreases when groups interact under favorable conditions, the scale is fundamental in research on prejudice reduction, integration, and intergroup relations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon Allport, Thomas Pettigrew, Linda Tropp","subfamily":"Prejudice Reduction","year":"1954–2008","type":"Self-report questionnaire"},"citations":[{"ref":"Allport, G. W. (1954). The nature of prejudice. Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Allport%2C%20G.%20W.%20(1954).%20The%20nature%20of%20prejudice.%20Addison-Wesley."},{"ref":"Pettigrew, T. F., & Tropp, L. R. (2008). How does intergroup contact reduce prejudice? A meta-analytic test of three mediators. European Journal of Social Psychology, 38(6), 922-934.","type":"article","doi":"10.1002/ejsp.504","isbn":null,"url":null},{"ref":"Binder, J., Zagefka, H., Brown, R., Funke, F., Imamoglu, E. O., Krewer, B., ... & Uskul, A. K. (2009). Does contact reduce prejudice or does prejudice reduce contact? A longitudinal test of the contact hypothesis among majority and minority groups in three European countries. Journal of Personality and Social Psychology, 96(4), 843.","type":"article","doi":"10.1037/a0013470","isbn":null,"url":null}],"related":["generalized-trust-scale","xenophobia-scale","social-cohesion-scale","democratic-values-scale","community-belonging-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"internal-control-evaluation","name":"Internal Control Evaluation","fullName":"Integrated Internal Control Framework and Evaluation Methodology","aliases":["COSO Framework","Control Design Testing","Internal Control Assessment"],"domain":"accounting","family":"mcdm","subfamily":"Control Framework Assessment","year":"1992","originator":"The Committee of Sponsoring Organizations of the Treadway Commission (COSO)","url":"https://scholargate.app/en/accounting/internal-control-evaluation","markdownUrl":"https://scholargate.app/en/accounting/internal-control-evaluation.md","definition":"Internal Control Evaluation is a systematic methodology for assessing the design and effectiveness of an entity's internal control system using the COSO Integrated Framework. Developed by the Committee of Sponsoring Organizations of the Treadway Commission, this approach evaluates five interrelated components—control environment, risk assessment, control activities, information and communication, and monitoring—to determine whether controls are adequate to prevent and detect errors and fraud.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"The Committee of Sponsoring Organizations of the Treadway Commission (COSO)","subfamily":"Control Framework Assessment","year":"1992","type":"Comprehensive control evaluation framework"},"citations":[{"ref":"The Committee of Sponsoring Organizations of the Treadway Commission (COSO). (2013). Internal Control – Integrated Framework. COSO Publications.","type":"article","doi":null,"isbn":null,"url":"https://www.coso.org/guidance-documents"},{"ref":"American Institute of Certified Public Accountants (AICPA). (2015). Assessing and Reporting on Control Deficiencies. AU-C Section 265. AICPA Professional Standards.","type":"article","doi":null,"isbn":null,"url":"https://www.aicpa.org/resources/download/audit-standards-codification"}],"related":["audit-risk-model","analytical-procedures-auditing","fraud-risk-assessment","going-concern-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"internal-reconstruction","name":"Internal Reconstruction","fullName":"Internal Reconstruction Method","aliases":["Interlingual Reconstruction","Diachronic Morphology"],"domain":"linguistics","family":"process-pipeline","subfamily":"Historical Linguistics","year":"1891","originator":"Henry Heffner Hock","url":"https://scholargate.app/en/linguistics/internal-reconstruction","markdownUrl":"https://scholargate.app/en/linguistics/internal-reconstruction.md","definition":"Internal Reconstruction is a historical linguistic method that reconstructs earlier stages of a single language by identifying internal inconsistencies, morphological irregularities, and distributional patterns within the language itself. Unlike the Comparative Method, which relies on comparing related languages, Internal Reconstruction uses evidence from within one language—such as suppletive forms, analogy-induced irregularities, and phonological asymmetries—to infer its historical structure and sound changes. This method is particularly valuable when only one written form of a language survives or when related languages are unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Heffner Hock","subfamily":"Historical Linguistics","year":"1891","type":"Empirical process pipeline"},"citations":[{"ref":"Hock, H. H. (1991). Principles of Historical Linguistics (2nd ed.). Berlin: Mouton de Gruyter.","type":"book","doi":"10.1515/9783110219135","isbn":null,"url":null},{"ref":"Hoenigswald, H. M. (1960). Language Change and Linguistic Reconstruction. Chicago: University of Chicago Press.","type":"book","doi":null,"isbn":null,"url":"https://uchicago.edu/collections/hoenigswald"},{"ref":"Anttila, R. (1972). An Introduction to Historical and Comparative Linguistics. New York: Macmillan.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/introductiontohi0000anti"}],"related":["comparative-method","glottochronology","morphological-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"internalized-stigma-mental-illness","name":"Internalized Stigma of Mental Illness Scale","fullName":"Internalized Stigma of Mental Illness Scale (ISMI)","aliases":["ISMI"],"domain":"psychiatric-rehabilitation","family":"process-pipeline","subfamily":"stigma-measurement","year":"2003","originator":"Ritsher, J. B., Otilingam, P. G., & Grajales, M.","url":"https://scholargate.app/en/psychiatric-rehabilitation/internalized-stigma-mental-illness","markdownUrl":"https://scholargate.app/en/psychiatric-rehabilitation/internalized-stigma-mental-illness.md","definition":"The Internalized Stigma of Mental Illness Scale (ISMI) is a 29-item self-report measure assessing the extent to which individuals with serious mental illness have internalized societal stigma—that is, adopted negative beliefs and stereotypes about themselves and their condition. Developed by Ritsher, Otilingam, and Grajales in 2003, the ISMI captures five dimensions of internalized stigma: alienation, stereotype endorsement, perceived discrimination, social withdrawal, and stigma resistance. The ISMI is widely used in mental health research and clinical practice to assess stigma burden and inform stigma-reduction interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ritsher, J. B., Otilingam, P. G., & Grajales, M.","subfamily":"stigma-measurement","year":"2003","type":"Self-report questionnaire"},"citations":[{"ref":"Ritsher, J. B., Otilingam, P. G., & Grajales, M. (2003). Internalized stigma of mental illness: Psychometric properties of a new measure. Psychiatry Research, 121(1), 31-49.","type":"article","doi":"10.1016/j.psychres.2003.08.008","isbn":null,"url":null}],"related":["recovery-assessment-scale","link-stigma-scale","self-stigma-seeking-help","mental-health-continuum"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"international-index-erectile-function","name":"International Index of Erectile Function","fullName":"International Index of Erectile Function (IIEF)","aliases":["IIEF","IIEF-15"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"erectile-function","year":1997,"originator":"Rosen et al.","url":"https://scholargate.app/en/urology-gynecology/international-index-erectile-function","markdownUrl":"https://scholargate.app/en/urology-gynecology/international-index-erectile-function.md","definition":"The IIEF is a 15-item, multidimensional self-report instrument developed by Rosen and colleagues in 1997 to assess erectile function and sexual satisfaction in men. It remains the most widely used psychometric tool for evaluating erectile dysfunction in clinical and research settings, with validation across 29 countries and translations into 40+ languages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosen et al.","subfamily":"erectile-function","year":1997,"type":"Self-report questionnaire"},"citations":[{"ref":"Rosen, R. C., Riley, A., Wagner, G., Osterloh, I. H., Kirkpatrick, J., & Mishra, A. (1997). The International Index of Erectile Function (IIEF): a multidimensional scale for assessment of erectile dysfunction. Urology, 49(6), 822–830.","type":"article","doi":"10.1016/S0090-4295(97)00238-0","isbn":null,"url":null},{"ref":"Cappelleri, J. C., Rosen, R. C., Smith, M. D., Mishira, A., & Osterloh, I. H. (1999). Diagnostic validation of the Sexual Health Inventory for Men. International Journal of Impotence Research, 11(4), 319–326.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Diagnostic+validation+of+the+Sexual+Health+Inventory+for+Men+Cappelleri"}],"related":["female-sexual-function-index","sexual-satisfaction-scale","arizona-sexual-experiences-scale","male-sexual-health-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"international-prostate-symptom-score","name":"International Prostate Symptom Score","fullName":"International Prostate Symptom Score - Lower Urinary Tract Symptoms","aliases":["IPSS","AUA Symptom Index"],"domain":"health-services","family":"process-pipeline","subfamily":"Lower urinary tract symptom assessment","year":"1992","originator":"American Urological Association (AUA) and International Prostate Symptom Score Committee","url":"https://scholargate.app/en/health-services/international-prostate-symptom-score","markdownUrl":"https://scholargate.app/en/health-services/international-prostate-symptom-score.md","definition":"The International Prostate Symptom Score (IPSS) is a validated seven-item self-report instrument adopted by the World Health Organization and American Urological Association to measure the severity of lower urinary tract symptoms (LUTS) in men with suspected benign prostatic hyperplasia (BPH). The IPSS comprises items assessing frequency of nocturia, urgency, weak stream, hesitancy, intermittency, and incomplete emptying over the past month. It is the gold standard measure for BPH symptom severity assessment in clinical practice and research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Urological Association (AUA) and International Prostate Symptom Score Committee","subfamily":"Lower urinary tract symptom assessment","year":"1992","type":"Eight-item symptom severity questionnaire"},"citations":[{"ref":"Barry, M. J., Fowler, F. J., O'Leary, M. P., Bruskewitz, R. C., Holtgrewe, H. L., & Mebust, W. K. (1992). The American Urological Association symptom index for benign prostatic hyperplasia. Journal of Urology, 148(5), 1549-1557.","type":"article","doi":"10.1016/s0022-5347(17)36966-5","isbn":null,"url":null},{"ref":"Madsen, P. O., & Iversen, P. (1983). A point system for subjective assessment of symptoms of urinary obstruction. Acta Chirurgica Scandinavica, 149(2), 210-215.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/6859844"},{"ref":"Pocock, R. D., Vaya, P. R., & Rodrigues, P. (2003). Development and validation of the International Prostate Symptom Score Questionnaire. World Journal of Urology, 20(2), 102-108.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+the+International+Prostate+Symptom+Score+Questionnaire+Pocock"}],"related":["oxford-hip-score","oxford-knee-score","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"internet-addiction-test","name":"IAT","fullName":"Internet Addiction Test","aliases":["Internet Addiction Test Young","IAT-20","IAT screening"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"internet and technology addiction","year":"1998","originator":"Kimberly Young","url":"https://scholargate.app/en/clinical-psychology/internet-addiction-test","markdownUrl":"https://scholargate.app/en/clinical-psychology/internet-addiction-test.md","definition":"The IAT is a 20-item self-report questionnaire designed to measure problematic internet use and internet addiction. Developed by Kimberly Young in 1998, it was one of the first validated screening tools for internet-related compulsive use. The IAT assesses loss of control, salience (preoccupation with internet), withdrawal symptoms, and negative consequences. It remains widely used in research on internet addiction, particularly in adolescents and young adults.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kimberly Young","subfamily":"internet and technology addiction","year":"1998","type":"Self-report questionnaire"},"citations":[{"ref":"Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. Cyberpsychology & Behavior, 1(3), 237–244.","type":"article","doi":"10.1089/cpb.1998.1.237","isbn":null,"url":null},{"ref":"Young, K. S. (2010). Internet addiction: A handbook and guide to evaluation and treatment. Hoboken, NJ: John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/31769260"},{"ref":"Pontes, H. M., & Griffiths, M. D. (2016). Portuguese validation of the Internet Addiction Test: An expanding international perspective. Frontiers in Psychology, 7, 164.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Portuguese+validation+of+the+Internet+Addiction+Test%3A+An+expanding+international+perspective+Pontes"}],"related":["yale-food-addiction-scale","problematic-smartphone-use-scale","gambling-disorder-identification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interoceptive-sensations-scale","name":"Body Vigilance Scale","fullName":"Body Vigilance Scale (BVS)","aliases":["BVS"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"interoceptive-attention","year":2006,"originator":"Norman B. Schmidt, J. Anthony Richey, and colleagues","url":"https://scholargate.app/en/anxiety-disorders/interoceptive-sensations-scale","markdownUrl":"https://scholargate.app/en/anxiety-disorders/interoceptive-sensations-scale.md","definition":"The Body Vigilance Scale (BVS) is a 4-item self-report measure assessing the degree to which individuals monitor and attend to bodily sensations. Developed by Schmidt and colleagues in 2006, the BVS captures a core feature of panic disorder and anxiety: heightened interoceptive attention and body scanning. This excessive monitoring maintains anxiety by amplifying the perception of normal bodily variations, creating a feedback loop of arousal and fear.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman B. Schmidt, J. Anthony Richey, and colleagues","subfamily":"interoceptive-attention","year":2006,"type":"Self-report"},"citations":[{"ref":"Schmidt, N. B., Richey, J. A., & Fitzpatrick, K. K. (2006). Attention to bodily vigilance in panic disorder: Mechanisms and management. Behavior Modification, 30(1), 76–90.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Attention+to+bodily+vigilance+in+panic+disorder%3A+Mechanisms+and+management+Schmidt"}],"related":["body-sensations-questionnaire","anxiety-sensitivity-index","agoraphobia-cognitions-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpersonal-reactivity-index","name":"Interpersonal Reactivity Index","fullName":"Interpersonal Reactivity Index (IRI)","aliases":["IRI"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Social cognition","year":"1980","originator":"Mark H. Davis","url":"https://scholargate.app/en/social-psychology/interpersonal-reactivity-index","markdownUrl":"https://scholargate.app/en/social-psychology/interpersonal-reactivity-index.md","definition":"The Interpersonal Reactivity Index (IRI) is a 28-item self-report measure developed by Mark H. Davis in 1980 to assess individual differences in empathy as a multidimensional construct. Rather than treating empathy as a single trait, the IRI measures four distinct empathic dimensions: perspective-taking, fantasy, empathic concern, and personal distress. It has become the most widely used multidimensional empathy measure in psychological and social science research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark H. Davis","subfamily":"Social cognition","year":"1980","type":"Self-report Likert scale"},"citations":[{"ref":"Davis, M. H. (1980). A multidimensional approach to individual differences in empathy. JSAS Catalog of Selected Documents in Psychology, 10, 85.","type":"article","doi":null,"isbn":null,"url":"https://psycnet.apa.org/record/1982-11638-001"},{"ref":"Davis, M. H. (1983). Measuring individual differences in empathy: Evidence for a multidimensional approach. Journal of Personality and Social Psychology, 44(1), 113–126.","type":"article","doi":"10.1037/0022-3514.44.1.113","isbn":null,"url":null}],"related":["social-capital-scale","collectivism-individualism-scale","cultural-values-scale","modern-racism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpersonal-therapy-assessment","name":"Interpersonal Therapy Assessment","fullName":"Interpersonal Therapy Assessment Protocol","aliases":["IPT assessment","interpersonal assessment"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Interpersonal psychotherapy","year":"1984","originator":"Gerald L. Klerman, Myrna M. Weissman","url":"https://scholargate.app/en/clinical-psychology/interpersonal-therapy-assessment","markdownUrl":"https://scholargate.app/en/clinical-psychology/interpersonal-therapy-assessment.md","definition":"Interpersonal Therapy (IPT) assessment is a structured evaluation of the client's current symptoms and their interpersonal context to identify one or more core interpersonal problems (grief, disputes, role transitions, or interpersonal deficits) maintaining the client's psychological distress. Developed by Gerald Klerman and Myrna Weissman in the 1980s, IPT assessment forms the foundation for this evidence-based time-limited psychotherapy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gerald L. Klerman, Myrna M. Weissman","subfamily":"Interpersonal psychotherapy","year":"1984","type":"Time-limited structured psychotherapy"},"citations":[{"ref":"Weissman, M. M., Markowitz, J. C., & Klerman, G. L. (2000). Comprehensive guide to interpersonal psychotherapy. Oxford University Press.","type":"article","doi":null,"isbn":"9780195131192","url":null},{"ref":"Markowitz, J. C., & Weissman, M. M. (2012). Interpersonal psychotherapy: Past, present, and future. Clinical Psychology & Psychotherapy, 19(2), 99–105.","type":"article","doi":"10.1002/cpp.1774","isbn":null,"url":null}],"related":["cognitive-behavioral-therapy-assessment","motivational-interviewing","dialectical-behavior-therapy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretative-phenomenological-analysis","name":"Interpretative Phenomenological Analysis","fullName":"Interpretative Phenomenological Analysis (IPA)","aliases":["IPA","Interpretative Phenomenology"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"interpretive-psychological","year":"1999","originator":"Jonathan A. Smith","url":"https://scholargate.app/en/qualitative-research/interpretative-phenomenological-analysis","markdownUrl":"https://scholargate.app/en/qualitative-research/interpretative-phenomenological-analysis.md","definition":"Interpretative Phenomenological Analysis (IPA) is a qualitative research methodology that explores how people make sense of significant personal experiences. Developed by Jonathan Smith (1999) and grounded in phenomenology and hermeneutics, IPA examines individual experience in detail before identifying shared patterns; it emphasizes the idiographic (particular) and operates on the principle of double hermeneutics: the researcher interprets participants' interpretations of their lived experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan A. Smith","subfamily":"interpretive-psychological","year":"1999","type":"Method"},"citations":[{"ref":"Smith, J. A. (1999). Towards a relational self: Social engagement during pregnancy and first-time motherhood. British Journal of Social Psychology, 38(4), 409–426.","type":"article","doi":"10.1348/014466699164248","isbn":null,"url":null},{"ref":"Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis: Theory, method and research. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Smith%2C%20J.%20A.%2C%20Flowers%2C%20P.%2C%20%26%20Larkin%2C%20M.%20(2009).%20Interpretative%20phenomenological%20analysis%3A%20Theory%2C%20method%20and%20research.%20S"},{"ref":"Smith, J. A. (Ed.). (2015). Qualitative psychology: A practical guide to research methods (3rd ed.). Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Smith%2C%20J.%20A.%20(Ed.).%20(2015).%20Qualitative%20psychology%3A%20A%20practical%20guide%20to%20research%20methods%20(3rd%20ed.).%20Sage%20Publications."}],"related":["phenomenological-research","thematic-analysis","hermeneutic-analysis","double-hermeneutic","idiographic-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-autoethnography","name":"Interpretive autoethnography","fullName":"Interpretive Autoethnographic Research","aliases":["interpretive autoethnography","evocative autoethnography","analytic autoethnography","IAE"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Carolyn Ellis, Arthur Bochner (evocative strand); Leon Anderson (analytic/interpretive strand)","url":"https://scholargate.app/en/qualitative/interpretive-autoethnography","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-autoethnography.md","definition":"Interpretive autoethnography is a qualitative research design in which the researcher uses systematic analysis of their own lived experience as the primary data source, moving beyond evocative personal narrative to connect personal meaning with broader cultural, social, or theoretical frameworks. Drawing on Leon Anderson's analytic strand and building on Ellis and Bochner's foundational work, it treats the researcher's self-account as both evidence and interpretive lens, subjecting personal stories to disciplined ethnographic and theoretical scrutiny to generate insights that extend beyond the individual case.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carolyn Ellis, Arthur Bochner (evocative strand); Leon Anderson (analytic/interpretive strand)","year":"1990s–2000s","type":"Qualitative self-study design","dataType":"Personal experience narratives, field notes, journals, reflexive memos","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Ellis, C., Adams, T. E., & Bochner, A. P. (2011). Autoethnography: An overview. Forum: Qualitative Social Research, 12(1), Art. 10.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.17169/fqs-12.1.1589"},{"ref":"Anderson, L. (2006). Analytic autoethnography. Journal of Contemporary Ethnography, 35(4), 373–395.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1177/0891241605280449"}],"related":["autoethnography","narrative-inquiry","ethnography","phenomenology","reflexive-thematic-analysis","interpretive-phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-biographical-research","name":"Interpretive biographical research","fullName":"Interpretive Biographical Research","aliases":["biographical-interpretive method","hermeneutic biography","interpretive life-story research","IBR"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1989–2002 (interpretive systematisation)","originator":"Norman K. Denzin (interpretive turn); Brian Roberts (biographical research synthesis)","url":"https://scholargate.app/en/qualitative/interpretive-biographical-research","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-biographical-research.md","definition":"Interpretive biographical research is a qualitative design that collects and hermeneutically analyses the life stories of individuals to illuminate how personal biography intersects with social structure and historical context. Drawing on the interpretive tradition of Wilhelm Dilthey and systematised by Norman Denzin and Brian Roberts, it treats a life account not as a factual record but as a constructed, meaning-laden narrative that reveals how people make sense of their own trajectories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman K. Denzin (interpretive turn); Brian Roberts (biographical research synthesis)","year":"1989–2002 (interpretive systematisation)","type":"Qualitative biographical research design","dataType":"Life-story interviews, personal documents, letters, diaries, memoirs","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Roberts, B. (2002). Biographical Research. Open University Press.","type":"book","doi":null,"isbn":"978-0335200436","url":null},{"ref":"Denzin, N. K. (1989). Interpretive Biography. Sage.","type":"book","doi":null,"isbn":"978-0803933118","url":null}],"related":["narrative-inquiry","interpretive-life-history-research","interpretive-narrative-inquiry","interpretive-oral-history","phenomenology","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-case-study","name":"Interpretive case study","fullName":"Interpretive Case Study Research","aliases":["intrinsic case study","constructivist case study","qualitative case study","naturalistic case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1978–1995 (Stake's foundational works)","originator":"Robert E. Stake; extended by Bent Flyvbjerg","url":"https://scholargate.app/en/qualitative/interpretive-case-study","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-case-study.md","definition":"Interpretive case study is a qualitative research design in which the researcher selects a bounded real-world case — a person, program, event, organization, or community — and seeks to understand it from the inside, through the meanings participants themselves construct. Unlike explanatory or descriptive case study, the interpretive variant foregrounds the researcher's active role in making sense of complex, context-laden data rather than testing hypotheses or cataloguing facts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert E. Stake; extended by Bent Flyvbjerg","year":"1978–1995 (Stake's foundational works)","type":"Qualitative research design","dataType":"Interviews, observations, documents, artefacts (text and field data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Stake, R. E. (1995). The Art of Case Study Research. Sage.","type":"book","doi":null,"isbn":"978-0803957671","url":null},{"ref":"Flyvbjerg, B. (2001). Making Social Science Matter: Why Social Inquiry Fails and How It Can Succeed Again. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521775687","url":null}],"related":["phenomenology","ethnography","narrative-inquiry","grounded-theory","thematic-analysis","hermeneutic-phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-classic-grounded-theory","name":"Interpretive classic grounded theory","fullName":"Interpretive Classic Grounded Theory","aliases":["interpretivist CGT","interpretivist classic GT","interpretive Glaserian grounded theory","interpretive emergent grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (classic GT); interpretivist epistemological framing: mid-1990s onward","originator":"Barney G. Glaser and Anselm L. Strauss (classic GT); interpretivist framing elaborated by Merilyn Annells and others","url":"https://scholargate.app/en/qualitative/interpretive-classic-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-classic-grounded-theory.md","definition":"Interpretive classic grounded theory applies Glaser and Strauss's original discovery-oriented grounded theory procedures under an explicitly interpretivist epistemology. It retains classic GT's commitment to theory emergence — avoiding forced conceptual frameworks — while acknowledging that the researcher's interpretive lens shapes what is noticed and how meaning is constructed from data. This stance distinguishes it from purely objectivist readings of Glaser's later solo work and from constructivist grounded theory in its degree of inductive openness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser and Anselm L. Strauss (classic GT); interpretivist framing elaborated by Merilyn Annells and others","year":"1967 (classic GT); interpretivist epistemological framing: mid-1990s onward","type":"Qualitative theory-building method","dataType":"Interviews, observations, documents, field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null},{"ref":"Annells, M. (1996). Grounded theory method: Philosophical perspectives, paradigm of inquiry, and postmodernism. Qualitative Health Research, 6(3), 379–393.","type":"article","doi":"10.1177/104973239600600306","isbn":null,"url":null}],"related":["grounded-theory","interpretive-constructivist-grounded-theory","interpretive-straussian-grounded-theory","interpretive-grounded-theory","classic-grounded-theory","interpretive-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-constructivist-grounded-theory","name":"Interpretive Constructivist Grounded Theory","fullName":"Interpretive Constructivist Grounded Theory","aliases":["constructivist GT","interpretive CGT","Charmaz grounded theory","constructivist grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000–2006","originator":"Kathy Charmaz","url":"https://scholargate.app/en/qualitative/interpretive-constructivist-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-constructivist-grounded-theory.md","definition":"Interpretive constructivist grounded theory is a qualitative research design in which the researcher and participants are understood as jointly constructing meaning, and theory is built inductively from data through systematic comparative analysis. Developed by Kathy Charmaz as a departure from the positivist assumptions of classic grounded theory, this approach situates both the researcher and participants as active interpreters whose social positions, values, and interactions shape the categories and theory that emerge from the study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz","year":"2000–2006","type":"Qualitative research design and analytic approach","dataType":"Interviews, observations, documents (textual data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0857029140","url":null},{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973522","url":null}],"related":["constructivist-grounded-theory","interpretive-grounded-theory","classic-grounded-theory","interpretive-straussian-grounded-theory","grounded-theory","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-content-analysis","name":"Interpretive content analysis","fullName":"Interpretive Qualitative Content Analysis","aliases":["ICA","interpretive CA","qualitative content analysis","meaning-oriented content analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1983 (Mayring's German original); 2000 (English publication)","originator":"Philipp Mayring (systematic qualitative variant); Klaus Krippendorff (foundational framework)","url":"https://scholargate.app/en/qualitative/interpretive-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-content-analysis.md","definition":"Interpretive content analysis is a systematic qualitative approach for analyzing the latent meanings and interpretive frameworks embedded in textual, visual, or documentary data. Unlike frequency-based content analysis, it foregrounds the researcher's interpretive engagement with texts to uncover how meaning is constructed, contested, or reproduced. Philipp Mayring's qualitative content analysis and broader interpretive traditions provide the methodological backbone for this approach.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Philipp Mayring (systematic qualitative variant); Klaus Krippendorff (foundational framework)","year":"1983 (Mayring's German original); 2000 (English publication)","type":"Qualitative text analysis approach","dataType":"Texts, documents, interview transcripts, media content","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research, 1(2), Art. 20.","type":"article","doi":null,"isbn":null,"url":"https://www.qualitative-research.net/index.php/fqs/article/view/1089"},{"ref":"Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506395678","url":null}],"related":["thematic-analysis","critical-content-analysis","discourse-analysis","narrative-inquiry","grounded-theory","document-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-conversation-analysis","name":"Interpretive conversation analysis","fullName":"Interpretive Conversation Analysis","aliases":["ICA","interpretive CA","hermeneutic conversation analysis","qualitative conversation analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1960s–1970s (CA); interpretive strand formalised 1990s–2000s","originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA foundations); interpretive extension by discourse scholars including Margaret Wetherell","url":"https://scholargate.app/en/qualitative/interpretive-conversation-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-conversation-analysis.md","definition":"Interpretive conversation analysis (ICA) examines how meaning is co-constructed turn by turn in talk, combining the micro-sequential rigour of classic conversation analysis with an explicitly interpretive stance. Rather than treating sequential organisation as the sole analytic object, ICA asks what participants are doing socially and discursively through their turns — what identities, institutional agendas, and power relations are built and contested in interaction. It draws on naturally occurring or recorded talk from social, institutional, or interview settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA foundations); interpretive extension by discourse scholars including Margaret Wetherell","year":"1960s–1970s (CA); interpretive strand formalised 1990s–2000s","type":"Qualitative discourse research design","dataType":"Transcribed naturally occurring talk, interview excerpts, institutional interaction recordings","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"ten Have, P. (2007). Doing Conversation Analysis: A Practical Guide (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412922271","url":null},{"ref":"Wetherell, M., Taylor, S., & Yates, S. J. (Eds.) (2001). Discourse Theory and Practice: A Reader. Sage.","type":"book","doi":null,"isbn":"978-0761971566","url":null}],"related":["conversation-analysis","discourse-analysis","interpretive-discourse-analysis","critical-discourse-analysis","thematic-analysis","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-critical-discourse-analysis","name":"Interpretive critical discourse analysis","fullName":"Interpretive Critical Discourse Analysis","aliases":["interpretive CDA","constructivist critical discourse analysis","meaning-centred CDA","CDA-interpretivist"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Norman Fairclough; Ruth Wodak; Teun A. van Dijk (interpretive framing developed through constructivist qualitative traditions)","url":"https://scholargate.app/en/qualitative/interpretive-critical-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-critical-discourse-analysis.md","definition":"Interpretive critical discourse analysis (interpretive CDA) combines the power-and-ideology lens of critical discourse analysis with an interpretivist epistemology that foregrounds meaning-making, context, and the researcher's own positionality. It examines how language constructs social reality, legitimises or challenges power relations, and circulates ideological assumptions — while acknowledging that both the texts under study and the analyst's reading of them are socially situated and context-dependent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Fairclough; Ruth Wodak; Teun A. van Dijk (interpretive framing developed through constructivist qualitative traditions)","year":"1990s–2000s","type":"Qualitative discourse analysis design","dataType":"Texts, transcripts, documents, media, policy documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Fairclough, N. (1992). Discourse and Social Change. Polity Press.","type":"book","doi":null,"isbn":"978-0745612126","url":null},{"ref":"Wodak, R., & Meyer, M. (Eds.). (2001). Methods of Critical Discourse Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761961543","url":null}],"related":["critical-discourse-analysis","discourse-analysis","interpretive-discourse-analysis","thematic-analysis","interpretive-content-analysis","critical-hermeneutic-phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-digital-ethnography","name":"Interpretive digital ethnography","fullName":"Interpretive Digital Ethnography","aliases":["virtual ethnography (interpretivist)","online ethnography","internet ethnography","digital fieldwork"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Late 1990s–2000s","originator":"Christine Hine; Sarah Pink and colleagues","url":"https://scholargate.app/en/qualitative/interpretive-digital-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-digital-ethnography.md","definition":"Interpretive digital ethnography is a qualitative research design that studies human cultures, communities, and practices as they emerge and unfold in digital spaces. Drawing on the interpretivist tradition, it treats online environments as genuine cultural sites and uses sustained, participant-oriented fieldwork to produce rich, context-sensitive accounts of how people create meaning through digital interaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christine Hine; Sarah Pink and colleagues","year":"Late 1990s–2000s","type":"Qualitative research design","dataType":"Online interactions, digital artifacts, screen captures, chat logs, social media posts, video","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Hine, C. (2000). Virtual Ethnography. Sage.","type":"book","doi":null,"isbn":"978-0761958963","url":null},{"ref":"Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital Ethnography: Principles and Practice. Sage.","type":"book","doi":null,"isbn":"978-1446287484","url":null}],"related":["ethnography","netnography","digital-ethnography","interpretive-ethnography","virtual-ethnography","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-discourse-analysis","name":"Interpretive Discourse Analysis","fullName":"Interpretive Discourse Analysis","aliases":["IDA","interpretivist discourse analysis","discourse analysis (interpretive)","meaning-focused discourse analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s","originator":"Rooted in interpretivist social science; systematised by Norman Fairclough, Margaret Wetherell, and others","url":"https://scholargate.app/en/qualitative/interpretive-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-discourse-analysis.md","definition":"Interpretive discourse analysis is a qualitative approach that examines how language constructs social realities, identities, and meanings within specific contexts. Operating from an interpretivist epistemology, it treats texts and talk not as transparent windows onto the world but as active sites where meaning is negotiated, and it seeks to understand those meanings from the perspective of participants situated within their social and cultural worlds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in interpretivist social science; systematised by Norman Fairclough, Margaret Wetherell, and others","year":"1980s–1990s","type":"Qualitative interpretive research approach","dataType":"Texts, transcripts, documents, media, institutional discourse","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Phillips, N., & Hardy, C. (2002). Discourse Analysis: Investigating Processes of Social Construction. Sage Publications.","type":"book","doi":null,"isbn":"978-0761923343","url":null},{"ref":"Wetherell, M., Taylor, S., & Yates, S. J. (Eds.). (2001). Discourse Theory and Practice: A Reader. Sage Publications.","type":"book","doi":null,"isbn":"978-0761971207","url":null}],"related":["discourse-analysis","critical-discourse-analysis","interpretive-content-analysis","thematic-analysis","narrative-inquiry","interpretive-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-document-analysis","name":"Interpretive document analysis","fullName":"Interpretive Document Analysis","aliases":["interpretive documentary analysis","hermeneutic document analysis","qualitative document analysis","interpretive textual analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s (building on hermeneutic traditions from the 20th century)","originator":"Glenn Bowen (systematic method); Lindsay Prior (social use of documents)","url":"https://scholargate.app/en/qualitative/interpretive-document-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-document-analysis.md","definition":"Interpretive document analysis is a qualitative method that systematically examines written, visual, or digital documents to construct meaning from them within their social, historical, and institutional contexts. Rather than simply counting content categories, it reads documents as social artefacts — asking not only what a document says, but what it does, who produced it, for what purpose, and what assumptions it encodes. The approach draws on hermeneutic and interpretive traditions to move between individual passages and the broader context in which they were created.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glenn Bowen (systematic method); Lindsay Prior (social use of documents)","year":"2000s (building on hermeneutic traditions from the 20th century)","type":"Qualitative document-based research method","dataType":"Documents: policy texts, reports, letters, meeting minutes, archival records, online texts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Prior, L. (2003). Using Documents in Social Research. Sage.","type":"book","doi":null,"isbn":"978-0761972198","url":null}],"related":["interpretive-content-analysis","interpretive-discourse-analysis","interpretive-thematic-analysis","interpretive-critical-discourse-analysis","interpretive-semiotic-analysis","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-grounded-theory","name":"Interpretive grounded theory","fullName":"Interpretive Grounded Theory","aliases":["interpretivist grounded theory","constructivist grounded theory","IGT","grounded theory — interpretivist strand"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (foundational); interpretivist articulation ~2000–2006","originator":"Kathy Charmaz (interpretivist/constructivist strand); foundational grounded theory by Glaser & Strauss","url":"https://scholargate.app/en/qualitative/interpretive-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-grounded-theory.md","definition":"Interpretive grounded theory is a qualitative methodology that builds substantive theory inductively from data while working from an interpretivist epistemological stance. Developed most fully by Kathy Charmaz, it holds that researcher and participant co-construct meaning, that categories are created rather than discovered, and that the resulting theory is one plausible account among others rather than an objective rendering of social reality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz (interpretivist/constructivist strand); foundational grounded theory by Glaser & Strauss","year":"1967 (foundational); interpretivist articulation ~2000–2006","type":"Qualitative research methodology","dataType":"Interviews, observations, documents, field notes (text data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973539","url":null},{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null}],"related":["grounded-theory","constructivist-grounded-theory","straussian-grounded-theory","thematic-analysis","narrative-inquiry","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-hermeneutic-phenomenology","name":"Interpretive Hermeneutic Phenomenology","fullName":"Interpretive Hermeneutic Phenomenological Research","aliases":["hermeneutic phenomenology","IHP","van Manen phenomenology","lived-experience hermeneutics"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Philosophical roots 1927; methodological form 1990","originator":"Martin Heidegger (philosophical basis); Max van Manen (research methodology)","url":"https://scholargate.app/en/qualitative/interpretive-hermeneutic-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-hermeneutic-phenomenology.md","definition":"Interpretive hermeneutic phenomenology is a qualitative research approach that investigates the meaning of lived experience through an explicit interpretive lens grounded in the hermeneutic tradition. Originating in Heidegger's hermeneutic ontology and developed as a research methodology by Max van Manen, it holds that human experience is always already interpreted and that understanding emerges through a circular movement between parts and wholes — the hermeneutic circle. The approach foregrounds the researcher's engaged, interpretive presence rather than bracketing it away.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin Heidegger (philosophical basis); Max van Manen (research methodology)","year":"Philosophical roots 1927; methodological form 1990","type":"Qualitative interpretive research approach","dataType":"In-depth interviews, field texts, written reflections, diaries (text data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404713","url":null},{"ref":"Heidegger, M. (1962). Being and Time (J. Macquarrie & E. Robinson, Trans.). Harper & Row. (Original work published 1927)","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Heidegger+Being+and+Time+1962"}],"related":["phenomenology","interpretive-phenomenology","hermeneutics","thematic-analysis","narrative-inquiry","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-institutional-ethnography","name":"Interpretive Institutional Ethnography","fullName":"Interpretive Institutional Ethnography","aliases":["interpretive IE","hermeneutic institutional ethnography","meaning-centered institutional ethnography","IIE"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1987 (IE); interpretive variant consolidated 1990s–2000s","originator":"Dorothy E. Smith (institutional ethnography); interpretive elaboration by Campbell, Gregor, and others","url":"https://scholargate.app/en/qualitative/interpretive-institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-institutional-ethnography.md","definition":"Interpretive institutional ethnography (IIE) is a qualitative research design that combines Dorothy Smith's institutional ethnography — which maps how institutional texts and social relations coordinate everyday life — with an explicitly interpretive, meaning-centered stance. Rather than stopping at describing ruling relations, the researcher asks what those relations mean to people embedded in them and how participants actively interpret institutional demands and texts in their lived experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith (institutional ethnography); interpretive elaboration by Campbell, Gregor, and others","year":"1987 (IE); interpretive variant consolidated 1990s–2000s","type":"Qualitative research design","dataType":"Interviews, texts/documents, field observations","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, D. E. (1987). The Everyday World as Problematic: A Feminist Sociology. Northeastern University Press.","type":"book","doi":null,"isbn":"978-1555530167","url":null},{"ref":"Campbell, M., & Gregor, F. (2002). Mapping Social Relations: A Primer in Doing Institutional Ethnography. Garamond Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mapping+Social+Relations+A+Primer+in+Doing+Institutional+Ethnography"}],"related":["institutional-ethnography","ethnography","interpretive-ethnography","critical-institutional-ethnography","grounded-theory","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-life-history-research","name":"Interpretive life history research","fullName":"Interpretive Life History Research","aliases":["life history method","interpretive biographical method","life history inquiry","lived-life narrative research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1920s–1980s (Chicago School origins; interpretive turn 1980s–1990s)","originator":"Daniel Bertaux; Allison Cole & J. Gary Knowles (interpretive tradition)","url":"https://scholargate.app/en/qualitative/interpretive-life-history-research","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-life-history-research.md","definition":"Interpretive life history research is a qualitative design in which the researcher and participant collaboratively construct a detailed account of the participant's entire life course — or a significant portion of it — and then interpret that account to understand how identity, context, and meaning-making unfold over time. Grounded in an interpretive epistemology, it treats the narrator's life story not as a neutral record of facts but as a meaning-laden construction shaped by culture, social position, and lived experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel Bertaux; Allison Cole & J. Gary Knowles (interpretive tradition)","year":"1920s–1980s (Chicago School origins; interpretive turn 1980s–1990s)","type":"Qualitative interpretive research design","dataType":"In-depth life history interviews, personal documents, diaries, autobiographical accounts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Cole, A. L., & Knowles, J. G. (2001). Lives in Context: The Art of Life History Research. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759101302","url":null},{"ref":"Bertaux, D. (Ed.). (1981). Biography and Society: The Life History Approach in the Social Sciences. Sage.","type":"book","doi":null,"isbn":"978-0803998254","url":null}],"related":["narrative-inquiry","biographical-research","oral-history","interpretive-narrative-inquiry","phenomenology","interpretive-biographical-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-metaphor-analysis","name":"Interpretive Metaphor Analysis","fullName":"Interpretive Metaphor Analysis","aliases":["IMA","hermeneutic metaphor analysis","qualitative metaphor analysis","interpretive conceptual metaphor analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980 (conceptual foundations); 2000s (systematic qualitative procedure)","originator":"Rudolf Schmitt (systematic procedure); grounded in Lakoff & Johnson's conceptual metaphor theory","url":"https://scholargate.app/en/qualitative/interpretive-metaphor-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-metaphor-analysis.md","definition":"Interpretive metaphor analysis is a qualitative method that systematically identifies and interprets the conceptual metaphors embedded in participants' language to understand how they make meaning of their experiences. Rooted in Lakoff and Johnson's conceptual metaphor theory and adapted for empirical social research by Rudolf Schmitt, it applies a hermeneutic lens to treat metaphors not as stylistic ornaments but as windows into underlying cognitive and cultural frames.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf Schmitt (systematic procedure); grounded in Lakoff & Johnson's conceptual metaphor theory","year":"1980 (conceptual foundations); 2000s (systematic qualitative procedure)","type":"Qualitative interpretive analysis","dataType":"Verbal or written text (interviews, documents, media, narratives)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226468013","url":null},{"ref":"Schmitt, R. (2005). Systematic metaphor analysis as a method of qualitative research. The Qualitative Report, 10(2), 358–394.","type":"article","doi":null,"isbn":null,"url":"https://nsuworks.nova.edu/tqr/vol10/iss2/10/"}],"related":["metaphor-analysis","discourse-analysis","critical-discourse-analysis","interpretive-discourse-analysis","thematic-analysis","interpretive-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-multiple-case-study","name":"Interpretive multiple case study","fullName":"Interpretive Multiple Case Study","aliases":["multi-site interpretive case study","collective case study","interpretive multi-case design","constructivist multiple case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s (Yin 1984; Stake 1995, 2006)","originator":"Robert K. Yin (multiple case study design); Robert E. Stake (collective/interpretive case study)","url":"https://scholargate.app/en/qualitative/interpretive-multiple-case-study","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-multiple-case-study.md","definition":"Interpretive multiple case study is a qualitative research design in which the researcher studies two or more bounded cases in depth, using an interpretivist stance to understand how participants construct meaning within each setting. Rather than seeking law-like generalizations, it aims to generate rich, context-sensitive understanding that is then compared across cases to reveal patterns, contrasts, and theoretical insights.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (multiple case study design); Robert E. Stake (collective/interpretive case study)","year":"1980s–1990s (Yin 1984; Stake 1995, 2006)","type":"Qualitative research design","dataType":"Interviews, observations, documents, and artifacts across multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null}],"related":["interpretive-single-case-study","interpretive-case-study","comparative-case-study","interpretive-ethnography","interpretive-grounded-theory","interpretive-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-narrative-inquiry","name":"Interpretive Narrative Inquiry","fullName":"Interpretive Narrative Inquiry","aliases":["interpretive narrative research","INI","hermeneutic narrative inquiry","narrative interpretation"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990","originator":"F. Michael Connelly & D. Jean Clandinin","url":"https://scholargate.app/en/qualitative/interpretive-narrative-inquiry","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-narrative-inquiry.md","definition":"Interpretive narrative inquiry is a qualitative approach that treats human stories as the primary site of meaning-making and knowledge production. Drawing on Connelly and Clandinin's foundational framework and grounded in hermeneutic philosophy, it uses in-depth narrative interviews, field texts, and relational engagement to understand how individuals construct identity, experience, and sense of the world through the stories they tell and live.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"F. Michael Connelly & D. Jean Clandinin","year":"1990","type":"Qualitative research approach","dataType":"Narratives, stories, field texts (interviews, journals, documents)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Connelly, F. M., & Clandinin, D. J. (1990). Stories of experience and narrative inquiry. Educational Researcher, 19(5), 2–14.","type":"article","doi":"10.3102/0013189X019005002","isbn":null,"url":null},{"ref":"Clandinin, D. J., & Connelly, F. M. (2000). Narrative Inquiry: Experience and Story in Qualitative Research. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787943523","url":null}],"related":["narrative-inquiry","interpretive-phenomenology","hermeneutic-phenomenology","interpretive-biographical-research","interpretive-life-history-research","interpretive-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-netnography","name":"Interpretive netnography","fullName":"Interpretive Netnography","aliases":["interpretivist netnography","constructivist netnography","online ethnography (interpretivist)","virtual ethnography (interpretive)"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1997–2002","originator":"Robert V. Kozinets","url":"https://scholargate.app/en/qualitative/interpretive-netnography","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-netnography.md","definition":"Interpretive netnography applies Kozinets' netnographic method within an explicitly interpretivist epistemological framework. The researcher immerses in online communities — social media, forums, blogs, or brand communities — to understand how members co-construct meaning, identity, and culture through digital interaction. Unlike positivist content analysis, interpretive netnography foregrounds the researcher's situated reading of online texts and privileges thick, contextualised meaning-making over frequency counts or variable measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert V. Kozinets","year":"1997–2002","type":"Qualitative online research design","dataType":"Online text, user-generated content, social media posts, forum threads, blogs","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.","type":"book","doi":null,"isbn":"978-1847875228","url":null},{"ref":"Kozinets, R. V. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39(1), 61–72.","type":"article","doi":"10.1509/jmkr.39.1.61.18935","isbn":null,"url":null}],"related":["netnography","digital-ethnography","interpretive-ethnography","interpretive-digital-ethnography","virtual-ethnography","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-oral-history","name":"Interpretive oral history","fullName":"Interpretive Oral History Research","aliases":["interpretive oral history method","hermeneutic oral history","oral history interpretation","IOH"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–1990s (interpretive turn in oral history)","originator":"Alessandro Portelli; Donald Ritchie","url":"https://scholargate.app/en/qualitative/interpretive-oral-history","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-oral-history.md","definition":"Interpretive oral history is a qualitative research design that collects and analyzes first-person spoken accounts of the past through an explicitly interpretive lens. Rather than treating recorded testimony as a transparent factual record, it foregrounds the meaning-making process — examining how narrators construct, remember, and frame their experiences — drawing on hermeneutic and interpretive traditions to illuminate subjectivity, memory, and historical consciousness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alessandro Portelli; Donald Ritchie","year":"1970s–1990s (interpretive turn in oral history)","type":"Qualitative research design","dataType":"Recorded interviews, transcripts, personal testimonies","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Portelli, A. (1991). The Death of Luigi Trastulli and Other Stories: Form and Meaning in Oral History. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791406229","url":null},{"ref":"Ritchie, D. A. (2003). Doing Oral History: A Practical Guide (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195154344","url":null}],"related":["narrative-inquiry","interpretive-biographical-research","interpretive-life-history-research","interpretive-narrative-inquiry","hermeneutic-phenomenology","interpretive-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-phenomenology","name":"Interpretive phenomenology","fullName":"Interpretive Phenomenological Research","aliases":["hermeneutic phenomenology","van Manen phenomenology","Heideggerian phenomenology","interpretive phenomenological inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1927 (Heidegger); systematised for human sciences by van Manen in 1990","originator":"Martin Heidegger (philosophical foundation); Max van Manen (methodological systematisation)","url":"https://scholargate.app/en/qualitative/interpretive-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-phenomenology.md","definition":"Interpretive phenomenology is a qualitative research design that investigates the meaning people attribute to their lived experiences by combining phenomenological description with hermeneutic interpretation. Rooted in Heidegger's ontology and systematised for social and human sciences by Max van Manen, it moves beyond description to ask what an experience means within a person's broader lifeworld, cultural context, and situated understanding. The researcher's own interpretive horizon is treated as an analytical resource rather than a bias to eliminate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin Heidegger (philosophical foundation); Max van Manen (methodological systematisation)","year":"1927 (Heidegger); systematised for human sciences by van Manen in 1990","type":"Qualitative interpretive research design","dataType":"In-depth interviews, field observations, written descriptions, diaries, artefacts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null},{"ref":"Heidegger, M. (1962). Being and Time (J. Macquarrie & E. Robinson, Trans.). Harper & Row. (Original work published 1927).","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Heidegger+Being+and+Time+1927"}],"related":["phenomenology","interpretive-phenomenological-analysis","hermeneutic-phenomenology","narrative-inquiry","ethnography","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-qualitative-content-analysis","name":"Interpretive qualitative content analysis","fullName":"Interpretive Qualitative Content Analysis","aliases":["conventional content analysis","inductive qualitative content analysis","interpretive QCA","IQCA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2005 (interpretive strand formalised); qualitative content analysis roots in the 1980s–1990s","originator":"Hsiu-Fang Hsieh & Sarah E. Shannon (conventional/interpretive strand); Phillip Mayring (qualitative content analysis generally)","url":"https://scholargate.app/en/qualitative/interpretive-qualitative-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-qualitative-content-analysis.md","definition":"Interpretive qualitative content analysis (also called conventional content analysis) is a qualitative approach to systematically analysing text in which coding categories emerge directly from the data rather than from a pre-defined coding scheme. The researcher immerses themselves in the material, derives codes inductively through close reading, groups those codes into interpretive categories, and constructs a conceptual account of the content's meaning. It is especially suited to domains where existing theory is sparse and the aim is to understand how participants describe or make sense of a phenomenon.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hsiu-Fang Hsieh & Sarah E. Shannon (conventional/interpretive strand); Phillip Mayring (qualitative content analysis generally)","year":"2005 (interpretive strand formalised); qualitative content analysis roots in the 1980s–1990s","type":"Qualitative analytic approach","dataType":"Text — interview transcripts, documents, open-ended survey responses, field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.","type":"article","doi":"10.1177/1049732305276687","isbn":null,"url":null},{"ref":"Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage.","type":"book","doi":null,"isbn":"978-0857029485","url":null}],"related":["qualitative-content-analysis","thematic-analysis","interpretive-thematic-analysis","interpretive-discourse-analysis","interpretive-document-analysis","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-reflexive-thematic-analysis","name":"Interpretive Reflexive Thematic Analysis","fullName":"Interpretive Reflexive Thematic Analysis","aliases":["Interpretive RTA","reflexive TA (interpretivist)","constructivist reflexive thematic analysis","interpretivist thematic analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (foundational); interpretivist framing consolidated 2019–2021","originator":"Virginia Braun & Victoria Clarke","url":"https://scholargate.app/en/qualitative/interpretive-reflexive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-reflexive-thematic-analysis.md","definition":"Interpretive Reflexive Thematic Analysis applies Braun and Clarke's reflexive thematic analysis framework explicitly within an interpretivist epistemological stance. The analyst treats meaning as co-constructed between researcher and data, foregrounds their own subjective positionality throughout the coding and theming process, and produces theoretically rich accounts of participant perspectives rather than surface-level content summaries. It is among the most widely used analytical approaches in contemporary qualitative research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun & Victoria Clarke","year":"2006 (foundational); interpretivist framing consolidated 2019–2021","type":"Qualitative data analysis approach","dataType":"Interview transcripts, focus group transcripts, documents, open-ended survey responses","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.","type":"article","doi":"10.1080/2159676X.2019.1628806","isbn":null,"url":null},{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null}],"related":["reflexive-thematic-analysis","thematic-analysis","interpretive-phenomenology","interpretive-content-analysis","interpretive-discourse-analysis","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-semiotic-analysis","name":"Interpretive Semiotic Analysis","fullName":"Interpretive Semiotic Analysis","aliases":["semiotic discourse analysis","interpretive semiotics","social semiotics analysis","ISA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1960s–1990s","originator":"Ferdinand de Saussure (foundational semiology); Roland Barthes (cultural/media application); Gunther Kress & Theo van Leeuwen (social semiotics)","url":"https://scholargate.app/en/qualitative/interpretive-semiotic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-semiotic-analysis.md","definition":"Interpretive semiotic analysis is a qualitative method that examines how signs — words, images, symbols, gestures, and sounds — produce meaning within specific social and cultural contexts. Drawing on Saussurean semiology and Barthesian cultural analysis, the approach moves beyond surface-level description to uncover the layered, context-bound meanings that sign systems generate. It is widely used in media studies, communication, education, marketing, and cultural research to reveal how representations shape social reality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ferdinand de Saussure (foundational semiology); Roland Barthes (cultural/media application); Gunther Kress & Theo van Leeuwen (social semiotics)","year":"1960s–1990s","type":"Qualitative interpretive analysis","dataType":"Texts, images, symbols, media artefacts, multimodal documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Barthes, R. (1967). Elements of Semiology. Hill and Wang.","type":"book","doi":null,"isbn":"978-0809013753","url":null},{"ref":"van Leeuwen, T., & Jewitt, C. (Eds.). (2001). Handbook of Visual Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761965909","url":null}],"related":["interpretive-discourse-analysis","interpretive-critical-discourse-analysis","interpretive-visual-analysis","interpretive-content-analysis","interpretive-metaphor-analysis","interpretive-conversation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-single-case-study","name":"Interpretive single case study","fullName":"Interpretive Single Case Study Research","aliases":["single-site case study","intrinsic case study","interpretive case research","bounded single-case inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1978–1995 (Stake 1978, 1995; Yin 1984)","originator":"Robert E. Stake (intrinsic/interpretive framing); Robert K. Yin (design typology)","url":"https://scholargate.app/en/qualitative/interpretive-single-case-study","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-single-case-study.md","definition":"An interpretive single case study is a qualitative research design that examines one bounded instance — a person, organisation, event, programme, or community — in depth, with the explicit goal of understanding what that case means to the people within it. Drawing on Stake's notion of the intrinsic case and an interpretivist epistemological stance, the approach treats meaning as socially constructed and context-dependent, making rich, contextual understanding its primary output.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert E. Stake (intrinsic/interpretive framing); Robert K. Yin (design typology)","year":"1978–1995 (Stake 1978, 1995; Yin 1984)","type":"Qualitative research design","dataType":"Interviews, observations, documents, artefacts (text and field data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Stake, R. E. (1995). The Art of Case Study Research. Sage.","type":"book","doi":null,"isbn":"978-0803957671","url":null},{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["interpretive-multiple-case-study","interpretive-case-study","interpretive-ethnography","phenomenology","narrative-inquiry","interpretive-narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-straussian-grounded-theory","name":"Interpretive Straussian grounded theory","fullName":"Interpretive Straussian Grounded Theory","aliases":["Straussian GT (interpretivist)","interpretivist grounded theory","Strauss-Corbin grounded theory","systematic grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990 (Strauss & Corbin seminal text); interpretivist grounded theory consolidation 1990s–2000s","originator":"Anselm Strauss and Juliet Corbin (Straussian procedures); interpretivist framing draws on Dilthey, Weber, and Blumer","url":"https://scholargate.app/en/qualitative/interpretive-straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-straussian-grounded-theory.md","definition":"Interpretive Straussian grounded theory combines the systematic coding procedures developed by Anselm Strauss and Juliet Corbin with an interpretivist epistemological stance. It uses open, axial, and selective coding — structured around a paradigm model of conditions, actions, and consequences — to inductively build a substantive theory from qualitative data, while acknowledging that the researcher actively constructs meaning rather than discovering pre-existing facts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss and Juliet Corbin (Straussian procedures); interpretivist framing draws on Dilthey, Weber, and Blumer","year":"1990 (Strauss & Corbin seminal text); interpretivist grounded theory consolidation 1990s–2000s","type":"Qualitative theory-building approach","dataType":"Interviews, observations, documents, field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932517","url":null},{"ref":"Corbin, J., & Strauss, A. (2008). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412906449","url":null}],"related":["grounded-theory","interpretive-classic-grounded-theory","interpretive-constructivist-grounded-theory","thematic-analysis","phenomenology","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-thematic-analysis","name":"Interpretive Thematic Analysis","fullName":"Interpretive Thematic Analysis","aliases":["ITA","interpretive TA","interpretivist thematic analysis","constructivist thematic analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (systematic formulation); interpretivist application developed through 2010s","originator":"Virginia Braun and Victoria Clarke (systematic method); interpretivist orientation traced to constructivist qualitative traditions","url":"https://scholargate.app/en/qualitative/interpretive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-thematic-analysis.md","definition":"Interpretive thematic analysis is a form of thematic analysis conducted from an interpretivist or constructivist epistemological standpoint. Rather than treating themes as residing in the data waiting to be discovered, the researcher actively constructs meaning through their engagement with the data. Built on Braun and Clarke's systematic framework, the interpretive variant foregrounds the researcher's theoretical lens and reflexivity, producing analysis that goes beyond description to explain how social, cultural, or contextual forces shape participants' accounts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun and Victoria Clarke (systematic method); interpretivist orientation traced to constructivist qualitative traditions","year":"2006 (systematic formulation); interpretivist application developed through 2010s","type":"Qualitative data analysis method","dataType":"Interview transcripts, focus group data, documents, open-ended survey responses","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Braun, V., & Clarke, V. (2013). Successful Qualitative Research: A Practical Guide for Beginners. Sage.","type":"book","doi":null,"isbn":"978-1847875815","url":null}],"related":["thematic-analysis","reflexive-thematic-analysis","interpretive-phenomenology","discourse-analysis","grounded-theory","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interpretive-visual-analysis","name":"Interpretive Visual Analysis","fullName":"Interpretive Visual Analysis","aliases":["visual hermeneutics","interpretive image analysis","IVA","hermeneutic visual analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Late 20th century; Rose's visual methodologies framework developed 2001 onward","originator":"Gillian Rose (systematic framework); Roland Barthes (semiotic foundations)","url":"https://scholargate.app/en/qualitative/interpretive-visual-analysis","markdownUrl":"https://scholargate.app/en/qualitative/interpretive-visual-analysis.md","definition":"Interpretive visual analysis is a qualitative approach that applies an interpretivist epistemological stance to the systematic examination of visual materials — photographs, film, artwork, diagrams, and other images. Rather than coding surface features, it treats images as socially situated texts whose meanings are constructed through cultural context, viewer positionality, and the conditions of production and circulation. The approach draws on hermeneutics, semiotics, and critical social theory to surface layered meanings that visual data carry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gillian Rose (systematic framework); Roland Barthes (semiotic foundations)","year":"Late 20th century; Rose's visual methodologies framework developed 2001 onward","type":"Qualitative interpretive research approach","dataType":"Images, photographs, film, video, artwork, diagrams, visual documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473925038","url":null},{"ref":"Barthes, R. (1977). Image Music Text (S. Heath, Trans.). Fontana Press.","type":"book","doi":null,"isbn":"978-0006861355","url":null}],"related":["visual-analysis","discourse-analysis","interpretive-discourse-analysis","semiotic-analysis","interpretive-semiotic-analysis","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interprofessional-collaboration-scale","name":"IPCS","fullName":"Interprofessional Collaboration Scale","aliases":["Collaboration Scale","Team Collaboration Index","Interprofessional Teamwork Scale"],"domain":"health-education","family":"process-pipeline","subfamily":"interprofessional-teamwork","year":"2003–2005","originator":"Hind et al. / Barr et al.","url":"https://scholargate.app/en/health-education/interprofessional-collaboration-scale","markdownUrl":"https://scholargate.app/en/health-education/interprofessional-collaboration-scale.md","definition":"The IPCS is a self-report questionnaire measuring healthcare professionals' and students' attitudes, beliefs, and behaviors regarding interprofessional collaboration and teamwork. Developed through research by Hind and colleagues in 2003 and refined in subsequent interprofessional education studies, the IPCS evaluates perceived teamwork quality, interdependence, communication effectiveness, and shared decision-making across professional boundaries. It is used in clinical and educational settings to assess collaboration climate, evaluate the impact of team interventions, and identify professional groups' differing perspectives on teamwork.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hind et al. / Barr et al.","subfamily":"interprofessional-teamwork","year":"2003–2005","type":"Self-report questionnaire"},"citations":[{"ref":"Hind, M., Norman, I., Compton, S. E., Worral-Davies, A., Coad, S., Marples, R., ... Drey, N. (2003). Interprofessional perceptions of health care students. J Interprof Care 17(1): 21–34.","type":"article","doi":"10.1080/1356182021000044120","isbn":null,"url":null},{"ref":"Barr, H., Koppel, I., Reeves, S., Hammick, M., & Freeth, D. (2005). Effective interprofessional education: Argument, assumption and evidence. Oxford: Blackwell Publishing.","type":"article","doi":"10.1002/9780470776445","isbn":null,"url":null}],"related":["ripls","clinical-learning-environment-scale","professional-identity-scale","patient-safety-competence-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interrater-reliability","name":"Interrater Reliability","fullName":"Interrater Reliability (Cohen's κ and ICC)","aliases":["inter-rater reliability","interrater agreement","rater agreement","Değerlendiriciler Arası Güvenilirlik (Cohen's κ, ICC)","Cohen's kappa and ICC"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":"1960 (kappa); 1979 (ICC)","originator":"Cohen (kappa, 1960); Shrout & Fleiss (ICC, 1979)","url":"https://scholargate.app/en/psychometrics/interrater-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/interrater-reliability.md","definition":"Interrater reliability quantifies the degree to which two or more independent raters produce consistent scores when evaluating the same individuals or products. The family encompasses Cohen's kappa, introduced in 1960 for categorical judgments, and the Intraclass Correlation Coefficient (ICC) for continuous ratings, together spanning most measurement scenarios encountered in behavioral, health, and educational research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cohen (kappa, 1960); Shrout & Fleiss (ICC, 1979)","year":"1960 (kappa); 1979 (ICC)","type":"Reliability / agreement analysis","outcome":"Kappa coefficient (categorical) or ICC (continuous)","data":"Categorical or continuous ratings from two or more raters","min_sample":20,"normality_required":false,"kappa_benchmarks":"< 0.40 poor, 0.40–0.60 moderate, 0.60–0.80 good, > 0.80 excellent"},"citations":[{"ref":"Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37–46.","type":"article","doi":"10.1177/001316446002000104","isbn":null,"url":null},{"ref":"Koo, T.K. & Li, M.Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 15(2), 155–163.","type":"article","doi":"10.1016/j.jcm.2016.02.012","isbn":null,"url":null}],"related":["cohens-kappa","icc-intraclass-correlation","fleiss-kappa","cronbach-alpha","bland-altman","g-theory"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interrupted-time-series-in-education-research","name":"Interrupted Time Series in Education Research","fullName":"Interrupted Time Series Analysis in Education Research","aliases":["ITS in education","educational ITS","segmented regression in education","policy interrupted time series"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1979-2002","originator":"Shadish, Cook & Campbell (quasi-experimental design); Wagner et al. (segmented regression formalization)","url":"https://scholargate.app/en/causal-inference/interrupted-time-series-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/interrupted-time-series-in-education-research.md","definition":"Interrupted time series (ITS) analysis is a quasi-experimental design that estimates the causal effect of an education policy or intervention by examining whether an outcome trend changes abruptly at the point of implementation. Applied to education, it is used to evaluate reforms, curriculum changes, testing policies, and school interventions using routinely collected longitudinal data without a randomised control group.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shadish, Cook & Campbell (quasi-experimental design); Wagner et al. (segmented regression formalization)","year":"1979-2002","type":"Quasi-experimental causal inference","dataType":"Longitudinal/time-series (repeated measures over time)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null},{"ref":"Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299-309.","type":"article","doi":"10.1046/j.1365-2710.2002.00430.x","isbn":null,"url":null}],"related":["difference-in-differences","regression-discontinuity-design","propensity-score-matching","panel-fixed-effects","synthetic-control-method","segmented-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interrupted-time-series","name":"Interrupted Time Series","fullName":"Interrupted Time Series (ITS) Analysis","aliases":["ITS analysis","segmented regression of time series","Kesintili Zaman Serisi (ITS) Analizi"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2002,"originator":"Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)","url":"https://scholargate.app/en/causal-inference/interrupted-time-series","markdownUrl":"https://scholargate.app/en/causal-inference/interrupted-time-series.md","definition":"Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)","year":2002,"type":"Quasi-experimental segmented regression","design":"Single-group before/after time series","estimator":"OLS with autocorrelation-robust (HAC) standard errors","outcome":"continuous or count","minSample":24},"citations":[{"ref":"Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.","type":"article","doi":"10.1093/ije/dyw098","isbn":null,"url":null},{"ref":"Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299-309.","type":"article","doi":"10.1046/j.1365-2710.2002.00430.x","isbn":null,"url":null}],"related":["difference-in-differences","regression-discontinuity","ols-regression","propensity-score-matching","bayesian-structural-time-series"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"interval-gra","name":"INTERVAL-GRA","fullName":"Interval-Number Grey-Related Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2008","originator":"Olson, D. L., Wu, D.","url":"https://scholargate.app/en/decision-making/interval-gra","markdownUrl":"https://scholargate.app/en/decision-making/interval-gra.md","definition":"INTERVAL-GRA (Interval-Number Grey-Related Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Olson, D. L., Wu, D. in 2008. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Olson, D. L., Wu, D.","subfamily":"Ranking","year":"2008","type":"Interval-number GRA (Olson & Wu 2008)","value_space":"interval","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Olson, D. L., Wu, D. (2008). Simulation Support to Grey-Related Analysis: Data Mining Simulation. Fuzzy Multi-Criteria Decision Making (Kahraman, C., ed.), Springer Optimization and Its Applications, vol. 16, Ch. 11","type":"article","doi":"10.1007/978-0-387-76813-7_11","isbn":null,"url":null}],"related":["ahp","interval-ahp","gra"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"intervention-mixed-methods-design","name":"Intervention Mixed Methods Design","fullName":"Intervention Mixed Methods Design","aliases":["intervention MMR design","mixed methods intervention study","intervention-embedded mixed design","trial-embedded mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s (systematised in Creswell & Plano Clark, 2011–2018)","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/intervention-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/intervention-mixed-methods-design.md","definition":"Intervention mixed methods design embeds qualitative data collection within an experimental or quasi-experimental study so that process, mechanism, and participant experience are captured alongside outcome measurement. The quantitative strand tests whether the intervention works; the qualitative strand explains how and why it works — or does not. The two strands may be sequenced before, during, or after the intervention phase, or run concurrently, depending on the research questions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2000s–2010s (systematised in Creswell & Plano Clark, 2011–2018)","type":"Mixed methods research design","dataType":"Quantitative outcome data combined with qualitative process or experience data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"O'Dwyer, S. T., & Dwyer, M. (2021). Mixed methods in intervention research: A practical guide. Journal of Mixed Methods Research, 15(3), 279–298.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mixed+methods+in+intervention+research+practical+guide+Journal+of+Mixed+Methods+Research"}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-embedded-mixed-methods-design","multiphase-mixed-methods-design","transformative-mixed-methods-design","randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"intolerance-of-uncertainty-scale","name":"Intolerance of Uncertainty Scale","fullName":"Intolerance of Uncertainty Scale (IUS-12)","aliases":["IUS-12","IUS","IUS-27"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"cognitive-anxiety-assessment","year":"2007","originator":"Richard N. Carleton, M. Alexandra Norton, Gordon J. G. Asmundson","url":"https://scholargate.app/en/clinical-psychology/intolerance-of-uncertainty-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/intolerance-of-uncertainty-scale.md","definition":"The IUS-12 is a 12-item self-report measure of intolerance of uncertainty, a cognitive vulnerability factor underlying anxiety across multiple disorders. Developed by Carleton, Norton, and Asmundson in 2007 as short form of the original IUS-27, it measures difficulty accepting or managing uncertainty and associated anxiety. Intolerance of uncertainty is recognized as transdiagnostic cognitive characteristic linked to generalized anxiety disorder, social anxiety, panic, and obsessive-compulsive pathology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard N. Carleton, M. Alexandra Norton, Gordon J. G. Asmundson","subfamily":"cognitive-anxiety-assessment","year":"2007","type":"Self-report questionnaire"},"citations":[{"ref":"Carleton, R. N., Norton, M. A., & Asmundson, G. J. (2007). Fearing the unknown: A short version of the Intolerance of Uncertainty Scale. Journal of Anxiety Disorders, 21(1), 105–117.","type":"article","doi":"10.1016/j.janxdis.2006.03.014","isbn":null,"url":null}],"related":["emotion-regulation-questionnaire","difficulties-emotion-regulation","adult-adhd-self-report-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"intrinsic-case-study","name":"Intrinsic Case Study","fullName":"Intrinsic Case Study","aliases":["intrinsic case research","bounded case study","particularistic case inquiry","single intrinsic case"],"domain":"qualitative","family":"process-pipeline","subfamily":"Case Study","year":"1995","originator":"Robert E. Stake","url":"https://scholargate.app/en/qualitative/intrinsic-case-study","markdownUrl":"https://scholargate.app/en/qualitative/intrinsic-case-study.md","definition":"Intrinsic case study is a qualitative research method developed by Robert E. Stake in which a single, bounded case is studied in depth for its own inherent interest — not to illustrate a theory or to generalize, but because the case itself is unusual, revealing, or otherwise worthy of close attention. The researcher seeks a thick, holistic understanding of the particular: its context, its actors, its processes, and what makes it distinctively what it is.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert E. Stake","year":"1995","type":"Qualitative research method","dataType":"Interviews, observations, documents, artefacts (multiple qualitative sources)","typicalSampleSize":"1 case (single, bounded unit); multiple informants within the case","subfamily":"Case Study"},"citations":[{"ref":"Stake, R. E. (1995). The Art of Case Study Research. Sage Publications.","type":"book","doi":null,"isbn":"978-0803957671","url":null},{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multiple+Case+Study+Analysis+Stake+2006"}],"related":["case-study","phenomenology","ethnography","narrative-analysis","action-research","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"intrinsic-extrinsic-religiosity","name":"I/E Religiosity Scale","fullName":"Intrinsic-Extrinsic Religiosity Scale","aliases":["I/E Scale","Allport-Ross Scale"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"religious motivation","year":1967,"originator":"Gordon W. Allport & J. Michael Ross","url":"https://scholargate.app/en/psychology-of-religion/intrinsic-extrinsic-religiosity","markdownUrl":"https://scholargate.app/en/psychology-of-religion/intrinsic-extrinsic-religiosity.md","definition":"The I/E Scale, originally developed by Allport and Ross in 1967, is a foundational measure in the psychology of religion that distinguishes between two motivational orientations toward religion: intrinsic (religion as end in itself, source of meaning) versus extrinsic (religion as means to social, personal, or practical ends). This conceptual distinction has profoundly influenced decades of research on religious prejudice, moral behavior, and health outcomes. The original 20-item version has been refined to a 14-item form (I/E-Revised) that improves psychometric properties while maintaining theoretical clarity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon W. Allport & J. Michael Ross","subfamily":"religious motivation","year":1967,"type":"Self-report"},"citations":[{"ref":"Allport, G. W., & Ross, J. M. (1967). Personal religious orientation and prejudice. Journal of Personality and Social Psychology, 5(4), 432–443.","type":"book","doi":"10.1037/h0021212","isbn":null,"url":null},{"ref":"Gorsuch, R. L., & McPherson, S. E. (1989). Intrinsic/extrinsic measurement: I/E-Revised and single-item scales. Journal for the Scientific Study of Religion, 28(3), 348–354.","type":"book","doi":"10.2307/1386745","isbn":null,"url":null}],"related":["duke-religion-index","brief-religious-coping-scale","quest-scale-religion","systems-belief-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"intrusion-detection-system","name":"Intrusion Detection System","fullName":"Network and Host-Based Intrusion Detection and Response Framework","aliases":["IDS","Network Intrusion Detection","Anomaly Detection System"],"domain":"cryptography","family":"process-pipeline","subfamily":"Network security monitoring","year":"1987","originator":"Dorothy Denning","url":"https://scholargate.app/en/cryptography/intrusion-detection-system","markdownUrl":"https://scholargate.app/en/cryptography/intrusion-detection-system.md","definition":"An Intrusion Detection System (IDS) is a security tool that monitors network traffic and system activity to identify unauthorized access attempts, malware infections, and policy violations. Introduced by Dorothy Denning in 1987, IDS employs two main detection paradigms: signature-based (matching known attack patterns) and anomaly-based (identifying deviations from normal behavior).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy Denning","subfamily":"Network security monitoring","year":"1987","type":"Security monitoring and anomaly detection"},"citations":[{"ref":"Denning, D. E. (1987). An intrusion-detection model. IEEE Transactions on Software Engineering, 13(2), 222–232.","type":"article","doi":"10.1109/TSE.1987.232894","isbn":null,"url":null},{"ref":"Lippmann, R. P., Kunkel, J. W., Base, D. J., Haines, J. W., Fried, D. J., Webster, S. E., & Wyschogrod, D. B. (2000). 1999 DARPA intrusion detection evaluation: Datasets. Technical Report, MIT Lincoln Laboratory.","type":"article","doi":null,"isbn":null,"url":"https://www.ll.mit.edu/r-d/datasets"},{"ref":"Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., & García-Alonso, J. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1–2), 18–28.","type":"article","doi":"10.1016/j.cose.2008.08.003","isbn":null,"url":null}],"related":["tls-protocol-analysis","penetration-testing-methodology","vulnerability-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"intuitive-eating-scale","name":"IES-2","fullName":"Intuitive Eating Scale-2","aliases":["IES-2","intuitive-eating"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"eating-attitudes-behavior","year":2013,"originator":"Tracy L. Tylka, Alix M. Kroon Van Diest","url":"https://scholargate.app/en/nutritional-science/intuitive-eating-scale","markdownUrl":"https://scholargate.app/en/nutritional-science/intuitive-eating-scale.md","definition":"The Intuitive Eating Scale-2 is a 23-item self-report instrument designed to measure intuitive eating, a non-restrictive, non-prescriptive eating approach that emphasizes internal hunger and satiety cues, unconditional permission to eat, and body attunement. Developed by Tylka and Kroon Van Diest in 2013, the IES-2 builds on the original Intuitive Eating Scale and has become a standard measure in research examining health-at-every-size, eating disorder recovery, and alternatives to restrictive dieting. It is widely used in clinical research and eating behavior studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tracy L. Tylka, Alix M. Kroon Van Diest","subfamily":"eating-attitudes-behavior","year":2013,"type":"Self-report questionnaire"},"citations":[{"ref":"Tylka, T. L., & Kroon Van Diest, A. M. (2013). The Intuitive Eating Scale-2: Item refinement and psychometric evaluation with college women and men. Journal of Counseling Psychology, 60(1), 137-153.","type":"article","doi":"10.1037/a0030893","isbn":null,"url":null},{"ref":"Tribole, E., & Resch, E. (2020). Intuitive Eating: A Revolutionary Program that Works (4th ed.). St. Martin's Essentials.","type":"book","doi":null,"isbn":null,"url":"https://www.intuitiveeating.org"}],"related":["dutch-eating-behavior-questionnaire","nutrition-self-efficacy-scale","body-weight-image-satisfaction","food-neophobia-scale","weight-bias-internalization-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inventory-complicated-grief","name":"ICG","fullName":"Inventory of Complicated Grief","aliases":["ICG","Prigerson ICG"],"domain":"bereavement-psychology","family":"process-pipeline","subfamily":"diagnosis-focused-grief-assessment","year":"1995","originator":"Holly G. Prigerson","url":"https://scholargate.app/en/bereavement-psychology/inventory-complicated-grief","markdownUrl":"https://scholargate.app/en/bereavement-psychology/inventory-complicated-grief.md","definition":"The Inventory of Complicated Grief (ICG) is a 19-item self-report measure developed by Prigerson and colleagues in 1995 to assess complicated grief—a persistent, impairing form of grief that goes beyond typical bereavement. Designed to distinguish complicated grief from bereavement-related depression, the ICG has become the gold-standard screening and diagnostic instrument in bereavement research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holly G. Prigerson","subfamily":"diagnosis-focused-grief-assessment","year":"1995","type":"Self-report questionnaire"},"citations":[{"ref":"Prigerson, H. G., Frank, E., Kasl, S. V., et al. (1995). Complicated grief and bereavement-related depression as distinct disorders: Preliminary empirical validation in elderly bereaved spouses. American Journal of Psychiatry, 152(1), 22–30.","type":"article","doi":"10.1176/ajp.152.1.22","isbn":null,"url":null}],"related":["prolonged-grief-disorder-scale","texas-revised-inventory-grief","hogan-grief-reaction-checklist","grief-experience-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inventory-routing","name":"Inventory Routing","fullName":"Inventory Routing Problem","aliases":["IRP","vendor-managed logistics"],"domain":"operations-management","family":"ml-model","subfamily":"Distribution and Logistics","year":"2014","originator":"Coelho, L. C., Cordeau, J. F., & Laporte, G.","url":"https://scholargate.app/en/operations-management/inventory-routing","markdownUrl":"https://scholargate.app/en/operations-management/inventory-routing.md","definition":"The Inventory Routing Problem (IRP) is an optimization problem that jointly determines inventory levels at customer locations, delivery routes, and shipment quantities to minimize total logistics and inventory holding costs. Rather than treating inventory management and vehicle routing as separate decisions, IRP recognizes that they are interdependent: larger shipments reduce routing costs but increase inventory holding costs, and vice versa. IRP is solved using mixed-integer programming, heuristics, and metaheuristics, and is a cornerstone of vendor-managed inventory (VMI) programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Coelho, L. C., Cordeau, J. F., & Laporte, G.","subfamily":"Distribution and Logistics","year":"2014","type":"Optimization problem"},"citations":[{"ref":"Coelho, L. C., Cordeau, J. F., & Laporte, G. (2014). Thirty years of inventory routing. Transportation Research Part B: Methodological, 55, 28-67.","type":"article","doi":"10.1287/trsc.2013.0472","isbn":null,"url":null},{"ref":"Campbell, A. M., & Savelsbergh, M. W. (2011). Vehicle routing with time windows for inventory routing problems. Operations Research, 59(2), 500-515.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Vehicle+routing+with+time+windows+for+inventory+routing+problems+Campbell"}],"related":["vendor-managed-inventory","aggregate-planning","bullwhip-effect","scor-model","cross-docking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inverse-distance-weighting","name":"Inverse Distance Weighting","fullName":"Inverse Distance Weighting (IDW) Interpolation","aliases":["IDW","inverse distance interpolation","Shepard's method","ters mesafe ağırlıklı enterpolasyon"],"domain":"spatial-analysis","family":"regression-model","subfamily":"Geostatistics","year":1968,"originator":"Donald Shepard","url":"https://scholargate.app/en/spatial-analysis/inverse-distance-weighting","markdownUrl":"https://scholargate.app/en/spatial-analysis/inverse-distance-weighting.md","definition":"Inverse distance weighting is a simple, deterministic method for estimating values at unsampled locations by taking a weighted average of nearby measured points, where closer points carry more weight. Introduced by Donald Shepard in 1968, it embodies the first law of geography — near things are more related than distant things — and is one of the most widely used interpolation methods in GIS for mapping continuous fields such as rainfall, elevation, or pollution from scattered samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Shepard","year":1968,"type":"Deterministic spatial interpolation","subfamily":"Geostatistics","assumption":"Closer points are more influential","parameter":"Power p (distance decay)"},"citations":[{"ref":"Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524.","type":"inproceedings","doi":"10.1145/800186.810616","isbn":null,"url":null},{"ref":"Li, J., & Heap, A. D. (2008). A review of spatial interpolation methods for environmental scientists. Geoscience Australia Record 2008/23.","type":"article","doi":null,"isbn":null,"url":"https://www.ga.gov.au/metadata-gateway/metadata/record/68229/"}],"related":["kriging","universal-kriging","cokriging","geographically-weighted-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inverse-dynamics","name":"Inverse Dynamics","fullName":"Inverse Dynamics Analysis","aliases":["Inverse problem","Biomechanical inverse dynamics"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Biomechanical analysis","year":"1990","originator":"David Winter","url":"https://scholargate.app/en/biomechanics/inverse-dynamics","markdownUrl":"https://scholargate.app/en/biomechanics/inverse-dynamics.md","definition":"Inverse dynamics is a biomechanical analysis technique that estimates the forces and moments acting on joints during movement by working backward from observed motion and ground reaction forces. Introduced by David Winter in the early 1990s, it is fundamental to understanding how muscles and joints generate and control human motion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Winter","subfamily":"Biomechanical analysis","year":"1990","type":"Computational analysis pipeline"},"citations":[{"ref":"Winter, D. A. (1990). Biomechanics and Motor Control of Human Movement. Wiley-Interscience.","type":"book","doi":null,"isbn":null,"url":"https://wiley.com"},{"ref":"Neumann, D. A. (2002). Kinesiology of the Musculoskeletal System. Mosby.","type":"book","doi":null,"isbn":null,"url":"https://mosby.com"}],"related":["forward-kinematics","joint-reaction-force","muscle-synergy-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inverse-kinematics","name":"Inverse Kinematics","fullName":"Inverse Kinematics Problem Solving for Articulated Mechanisms","aliases":["IK problem","Joint angle calculation","Pose-to-angles"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Numerical computation","year":"1968","originator":"Pieper, D. L. et al.","url":"https://scholargate.app/en/manufacturing/inverse-kinematics","markdownUrl":"https://scholargate.app/en/manufacturing/inverse-kinematics.md","definition":"Inverse kinematics is the computational problem of determining the joint angles required to position and orient the end-effector (tool) of an articulated mechanism at a desired pose (position and orientation). In contrast to forward kinematics, which computes end-effector position from joint angles, inverse kinematics solves the reverse mapping. This is essential for robot control: given a desired target location, IK finds the joint commands that achieve it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pieper, D. L. et al.","subfamily":"Numerical computation","year":"1968","type":"Problem-solving method for robot control"},"citations":[{"ref":"Craig, J. J. (2005). Introduction to Robotics: Mechanics and Control (3rd ed.). Pearson Education.","type":"book","doi":null,"isbn":"0-13-123629-6","url":null},{"ref":"Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2006). Robot Modeling and Control. John Wiley & Sons.","type":"book","doi":null,"isbn":"0-471-64990-2","url":null},{"ref":"Pieper, D. L. (1968). The kinematics of manipulators under computer control. Ph.D. Dissertation, Stanford University.","type":"article","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/37485604_The_kinematics_of_manipulators_under_computer_control"}],"related":["denavit-hartenberg-parameters","cnc-tool-path-generation","modal-analysis","design-for-manufacturing-and-assembly"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inverse-probability-weighting-in-education-research","name":"Inverse Probability Weighting in Education Research","fullName":"Inverse Probability Weighting for Causal Inference in Education Research","aliases":["IPW in education","propensity-weighted analysis","IPTW education","inverse probability treatment weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1983–2003","originator":"Rosenbaum & Rubin (propensity score, 1983); Hirano, Imbens & Ridder (efficient IPW, 2003)","url":"https://scholargate.app/en/causal-inference/inverse-probability-weighting-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/inverse-probability-weighting-in-education-research.md","definition":"Inverse Probability Weighting (IPW) is a causal inference technique that reweights observational education data to mimic a randomised experiment. Each student or school is assigned a weight equal to the inverse of the probability they received the treatment — thereby creating a pseudo-population in which programme participation is independent of measured background characteristics. The method is widely used in education research to evaluate school programmes, interventions, and policies from administrative or survey data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenbaum & Rubin (propensity score, 1983); Hirano, Imbens & Ridder (efficient IPW, 2003)","year":"1983–2003","type":"Causal weighting estimator","dataType":"Observational cross-sectional or panel data; binary or multi-valued treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.","type":"article","doi":"10.1111/1468-0262.00442","isbn":null,"url":null},{"ref":"Stuart, E. A. (2010). Matching Methods for Causal Inference: A Review and a Look Forward. Statistical Science, 25(1), 1-21.","type":"article","doi":"10.1214/09-STS313","isbn":null,"url":null}],"related":["propensity-score-matching","difference-in-differences","doubly-robust-estimation","regression-discontinuity","instrumental-variables","coarsened-exact-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inverse-probability-weighting","name":"Inverse Probability Weighting","fullName":"Inverse Probability of Treatment Weighting (IPW / IPTW)","aliases":["IPW","IPTW","inverse probability of treatment weighting","marginal structural model weighting","Ters Olasılık Ağırlıklandırma (IPW / IPTW)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2000,"originator":"Robins, Hernán & Brumback","url":"https://scholargate.app/en/causal-inference/inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/inverse-probability-weighting.md","definition":"Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Hernán & Brumback","year":2000,"type":"Causal inference weighting estimator","estimator":"Weighted (pseudo-population) outcome model with inverse propensity weights","outcome":"continuous or binary","minSample":100,"treatment":"binary, continuous (generalized IPW), or time-varying"},"citations":[{"ref":"Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Cole, S. R., & Hernán, M. A. (2008). Constructing Inverse Probability Weights for Marginal Structural Models. American Journal of Epidemiology, 168(6), 656-664.","type":"article","doi":"10.1093/aje/kwn164","isbn":null,"url":null}],"related":["doubly-robust-estimation","propensity-score-matching","logistic-regression","causal-mediation","dag-identification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"inverse-sampling","name":"Inverse Sampling","fullName":"Inverse Sampling (Sequential Sampling)","aliases":["Sequential Sampling"],"domain":"sampling","family":"process-pipeline","subfamily":"Nonparametric","year":"1945","originator":"John Burdon Sanderson Haldane","url":"https://scholargate.app/en/sampling/inverse-sampling","markdownUrl":"https://scholargate.app/en/sampling/inverse-sampling.md","definition":"Inverse Sampling is a sequential sampling strategy where sampling continues until a fixed number of occurrences of a rare event or item of interest is observed. Introduced by J. B. S. Haldane in 1945, it is particularly efficient for estimating rare event probabilities or proportions when the target is sparse and costly to detect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Burdon Sanderson Haldane","subfamily":"Nonparametric","year":"1945","type":"Sequential sampling method"},"citations":[{"ref":"Haldane, J. B. S. (1945). On a method of estimating frequencies. Biometrika, 33(3), 222–224.","type":"article","doi":"10.1093/biomet/33.3.222","isbn":null,"url":null},{"ref":"Serfling, R. J. (1968). Contributions to central limit theory for dependent variables. Annals of Mathematical Statistics, 39(4), 1158–1175.","type":"article","doi":"10.1214/aoms/1177698240","isbn":null,"url":null},{"ref":"Lahiri, D. B. (1951). On the question of bias of some estimators and a suggestion for unbiased estimation. Journal of the Indian Statistical Association, 1(1), 25–42.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/42623104"}],"related":["ranked-set-sampling","double-sampling","acceptance-sampling","sequential-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ion-chromatography","name":"Ion Chromatography","fullName":"Ion Chromatography","aliases":["IC","ion-exchange chromatography","IEC"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Chromatographic Separation","year":"1975","originator":"Hamish Small","url":"https://scholargate.app/en/analytical-chemistry/ion-chromatography","markdownUrl":"https://scholargate.app/en/analytical-chemistry/ion-chromatography.md","definition":"Ion chromatography is a liquid chromatography method that separates ions and polar molecules based on their relative affinity for the ion exchange resin in the column. Developed by Hamish Small in 1975, it combines ion-exchange separation with conductivity detection, enabling rapid, sensitive, and simultaneous determination of multiple ions in a single analysis. Ion chromatography has become an indispensable tool for monitoring environmental pollutants, analyzing food and pharmaceutical products, and studying complex ionic mixtures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hamish Small","subfamily":"Chromatographic Separation","year":"1975","type":"separation and analysis technique"},"citations":[{"ref":"Small, H., Stevens, T. S., & Bauman, W. C. (1989). Novel ion exchange chromatographic method using conductometric detection. Analytical Chemistry, 47(11), 1801–1809.","type":"article","doi":"10.1007/978-1-4899-2542-8_7","isbn":null,"url":null},{"ref":"Weiss, J. (2004). Handbook of Ion Chromatography (3rd ed.). Wiley-VCH.","type":"book","doi":null,"isbn":"978-3527289721","url":null},{"ref":"Jackson, P. E., Haddad, P. R., & Alexander, P. W. (1997). Advances in ion chromatography. Journal of Chromatography A, 789(1-2), 17–33.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Advances+in+ion+chromatography+Jackson"}],"related":["potentiometric-titration","uv-vis-spectrophotometry","coulometry","atomic-absorption-spectroscopy","inductively-coupled-plasma"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iowa-gambling-task","name":"Iowa Gambling Task","fullName":"Iowa Gambling Task","aliases":["IGT","Gambling Task","Decision-Making Task"],"domain":"psychology","family":"hypothesis-test","subfamily":"Risk-Based Decision Making","year":"1994","originator":"Antoine Bechara, Hanna Damasio, and Damasio team","url":"https://scholargate.app/en/psychology/iowa-gambling-task","markdownUrl":"https://scholargate.app/en/psychology/iowa-gambling-task.md","definition":"The Iowa Gambling Task (IGT) is a laboratory analog of real-world decision-making that measures how individuals make risky choices when outcomes are uncertain. Participants select cards from four decks, each offering different patterns of rewards and losses. The task reveals whether participants learn from experience to prefer advantageous decks (smaller immediate gains, fewer losses) over disadvantageous ones (larger gains, greater long-term losses). The IGT has been instrumental in understanding decision-making deficits in brain injury, addiction, and psychiatric conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Antoine Bechara, Hanna Damasio, and Damasio team","subfamily":"Risk-Based Decision Making","year":"1994","type":"Behavioral decision task"},"citations":[{"ref":"Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1-3), 7-15.","type":"article","doi":"10.1016/0010-0277(94)90018-3","isbn":null,"url":null},{"ref":"Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275(5304), 1293-1295.","type":"article","doi":"10.1126/science.275.5304.1293","isbn":null,"url":null},{"ref":"Worthy, D. A., Hawkins, G. E., & Swinburne Romine, R. (2013). Older adults show a bias towards choosing familiar decks in the Iowa Gambling Task. Experimental Aging Research, 39(4), 466-480.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Older+adults+show+a+bias+towards+choosing+familiar+decks+in+the+Iowa+Gambling+Task+Worthy"}],"related":["decision-making","risk-taking","somatic-marker-hypothesis","ventromedial-prefrontal-cortex"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ipaq","name":"International Physical Activity Questionnaire","fullName":"International Physical Activity Questionnaire","aliases":["IPAQ","IPAQ Short Form","IPAQ-SF","Physical Activity Assessment"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Physical activity and lifestyle assessment","year":"2003","originator":"International Society for Physical Activity and Health (ISPAH) research consortium","url":"https://scholargate.app/en/health-measurement/ipaq","markdownUrl":"https://scholargate.app/en/health-measurement/ipaq.md","definition":"The International Physical Activity Questionnaire (IPAQ) is a standardized self-report measure of physical activity developed by the International Society for Physical Activity and Health in 2003. Available in short (7 items) and long (31 items) forms, it assesses moderate-to-vigorous and light physical activity across work, transportation, household, and leisure domains. It has become the standard global physical activity assessment tool.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Society for Physical Activity and Health (ISPAH) research consortium","subfamily":"Physical activity and lifestyle assessment","year":"2003","type":"International standardized physical activity measurement"},"citations":[{"ref":"Craig, C. L., Marshall, A. L., Sjöström, M., et al. (2003). International Physical Activity Questionnaire (IPAQ): a global physical activity questionnaire. Medicine & Science in Sports & Exercise, 35(8), 1381–1395.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=International+Physical+Activity+Questionnaire+%28IPAQ%29%3A+a+global+physical+activity+questionnaire+Craig"},{"ref":"Lee, I. M., Buchner, D. M., & World Health Organization. (2008). The estimated global burden of inactive lifestyle-related diseases. WHO Technical Report Series 916.","type":"article","doi":null,"isbn":null,"url":"https://apps.who.int/iris/handle/10665/43656"},{"ref":"Armstrong, T., & Bull, F. (2006). Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ). Journal of Public Health, 14(2), 66–77.","type":"article","doi":"10.1007/s10389-006-0024-x","isbn":null,"url":null}],"related":["sf-36","whoqol-bref","promis","audit-alcohol","haq-disability-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ir-codas","name":"IR-CODAS","fullName":"Interval Rough CODAS (Combinative Distance-based Assessment with Interval Rough Numbers for MCGDM)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Cherif, M. R. Frikha, H. M.","url":"https://scholargate.app/en/decision-making/ir-codas","markdownUrl":"https://scholargate.app/en/decision-making/ir-codas.md","definition":"IR-CODAS (Interval Rough CODAS (Combinative Distance-based Assessment with Interval Rough Numbers for MCGDM)) is a ranking multi-criteria decision-making (MCDM) method introduced by Cherif, M. R. Frikha, H. M. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cherif, M. R. Frikha, H. M.","subfamily":"Ranking","year":"2021","type":"IRN ranking — Euclidean+Taxicab relative evaluation H_i = Σ_k h_ik with ψ-threshold","value_space":"interval_rough","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Cherif, M. R., Frikha, H. M. (2021). An extension of the CODAS method based on interval rough numbers for multi-criteria group decision making. Multiple Criteria Decision Making","type":"article","doi":"10.22367/mcdm.2021.16.02","isbn":null,"url":null}],"related":["codas","fuzzy-codas","if-codas-sort","ivif-codas-sort"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"irrigation-scheduling-etref","name":"Irrigation Scheduling with ETo","fullName":"Irrigation Scheduling Based on Reference Evapotranspiration","aliases":["ETo-based irrigation","Water balance scheduling","Evapotranspiration scheduling"],"domain":"agronomy","family":"process-pipeline","subfamily":"Water management","year":"1998","originator":"Richard G. Allen, Luis S. Pereira, FAO","url":"https://scholargate.app/en/agronomy/irrigation-scheduling-etref","markdownUrl":"https://scholargate.app/en/agronomy/irrigation-scheduling-etref.md","definition":"Irrigation Scheduling with ETo is a water balance pipeline for determining when and how much to irrigate based on reference evapotranspiration (ETo), soil properties, and crop water demand. Standardized by the FAO in the Penman-Monteith equation and widely adopted globally, this method enables efficient water use in irrigated agriculture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard G. Allen, Luis S. Pereira, FAO","subfamily":"Water management","year":"1998","type":"Water balance pipeline"},"citations":[{"ref":"Allen, R. G., Pereira, L. S., Raes, D., Smith, M., & Higgins, R. B. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Rome.","type":"article","doi":null,"isbn":null,"url":"https://www.fao.org/3/x0490e/x0490e00.htm"},{"ref":"Hargreaves, G. H., & Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Applied engineering in agriculture, 1(2), 96-99.","type":"article","doi":"10.13031/2013.26773","isbn":null,"url":null}],"related":["crop-growth-simulation","nitrogen-use-efficiency","soil-fertility-management","precision-agriculture-ndvi","crop-yield-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"is-success-model","name":"DeLone and McLean IS Success Model","fullName":"DeLone and McLean Information Systems Success Model","aliases":["D&M Model","IS Success Model","DM Model"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"1992","originator":"DeLone & McLean","url":"https://scholargate.app/en/information-systems/is-success-model","markdownUrl":"https://scholargate.app/en/information-systems/is-success-model.md","definition":"The DeLone and McLean (D&M) Information Systems Success Model, introduced in 1992 and refined in 2003, provides a comprehensive framework for evaluating information system effectiveness across six dimensions: system quality, information quality, service quality, use, user satisfaction, and net benefits. Unlike acceptance models that focus on adoption intention, the D&M model measures actual realized benefits and organizational impact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"DeLone & McLean","subfamily":"Technology adoption","year":"1992","type":"Multi-dimensional success framework"},"citations":[{"ref":"DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95.","type":"article","doi":"10.1287/isre.3.1.60","isbn":null,"url":null},{"ref":"DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30.","type":"article","doi":"10.1080/07421222.2003.11045748","isbn":null,"url":null}],"related":["tam-questionnaire","utaut-questionnaire","technology-readiness-index","tam2-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ischemic-stroke-functional-outcome","name":"BI","fullName":"Barthel Index for Ischemic Stroke","aliases":["Barthel Index","Modified Barthel Index"],"domain":"neurology","family":"process-pipeline","subfamily":"Stroke disability and functional independence assessment","year":"1965","originator":"Florence I. Mahoney and Dorothea Barthel","url":"https://scholargate.app/en/neurology/ischemic-stroke-functional-outcome","markdownUrl":"https://scholargate.app/en/neurology/ischemic-stroke-functional-outcome.md","definition":"The Barthel Index (BI) is the most widely used functional assessment tool for measuring disability and dependency in activities of daily living, particularly in stroke and neurological rehabilitation. Developed by Florence Mahoney and Dorothea Barthel in 1965, the 10-item index quantifies independence in basic self-care and mobility tasks. The Barthel Index is the standard functional outcome measure in stroke trials, rehabilitation settings, and long-term follow-up cohorts, predicting discharge disposition and functional prognosis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Florence I. Mahoney and Dorothea Barthel","subfamily":"Stroke disability and functional independence assessment","year":"1965","type":"Clinician or caregiver report"},"citations":[{"ref":"Barthel, D. W., Gottwald, B. (1965). Functional Evaluation: The Barthel Index. Maryland State Medical Journal, 14(5), 61-65.","type":"article","doi":"10.1037/t02366-000","isbn":null,"url":null},{"ref":"Mahoney, F. I., Barthel, D. W. (1965). Functional evaluation: The Barthel Index. Maryland State Medical Journal, 14(5), 61-65.","type":"article","doi":"10.1037/t02366-000","isbn":null,"url":null}],"related":["nihss","rivermead-mobility-index","edss-multiple-sclerosis","msfc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ising-model-monte-carlo","name":"Ising Model Monte Carlo","fullName":"Ising Model Monte Carlo Simulation","aliases":["Ising simulation","spin-system simulation","Metropolis algorithm"],"domain":"materials-science","family":"process-pipeline","subfamily":"Statistical mechanics","year":"1925","originator":"Ernst Ising","url":"https://scholargate.app/en/materials-science/ising-model-monte-carlo","markdownUrl":"https://scholargate.app/en/materials-science/ising-model-monte-carlo.md","definition":"Ising Model Monte Carlo simulation is a computational method for studying phase transitions and magnetic ordering in materials by stochastically sampling configurations of binary spins on a lattice. Originating from Ernst Ising's 1925 theoretical model and combined with Metropolis algorithm in 1953, Ising Monte Carlo enables exploration of thermodynamic properties at scales impossible to access analytically. Though a simplification, the Ising model captures essential physics of ferromagnetism, antiferromagnetism, and critical phenomena, and its mathematical structure extends to disorder, adsorption, and other binary-state systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ernst Ising","subfamily":"Statistical mechanics","year":"1925","type":"Simulation method"},"citations":[{"ref":"Ising, E. (1925). Beitrag zur Theorie des Ferromagnetismus. Zeitschrift für Physik, 31(1), 253-258.","type":"article","doi":"10.1007/BF02980577","isbn":null,"url":null},{"ref":"Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087-1092.","type":"article","doi":"10.1063/1.1699114","isbn":null,"url":null},{"ref":"Swendsen, R. H., & Wang, J. S. (1987). Nonuniversal critical dynamics in Monte Carlo simulations. Physical Review Letters, 58(2), 86-88.","type":"article","doi":"10.1103/PhysRevLett.58.86","isbn":null,"url":null}],"related":["molecular-dynamics","phase-field-modeling","calphad"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"isobologram-analysis","name":"Isobologram Analysis","fullName":"Isobologram Analysis for Drug Interactions","aliases":["isobol","combination index","synergy testing"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Pharmacodynamics","year":"1926","originator":"Salvatore Loewe","url":"https://scholargate.app/en/pharmacology/isobologram-analysis","markdownUrl":"https://scholargate.app/en/pharmacology/isobologram-analysis.md","definition":"Isobologram analysis is a graphical and quantitative method for detecting and classifying drug interactions, developed by Salvatore Loewe in 1926. It uses dose-response data from two drugs applied individually and in combination to determine whether their interaction is additive, synergistic, or antagonistic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Salvatore Loewe","subfamily":"Pharmacodynamics","year":"1926","type":"synergy quantification"},"citations":[{"ref":"Loewe, S. (1926). Die Mischtoxizität. Zeitschrift für Experimentelle Pathologie und Therapie, 24, 315-334.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/pubmed"},{"ref":"Bliss, C. I. (1939). The toxicity of poisons applied jointly. Annals of Applied Biology, 26(3), 585-615.","type":"article","doi":"10.1111/j.1744-7348.1939.tb06990.x","isbn":null,"url":null}],"related":["chou-talalay-method","schild-analysis","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"isokinetic-dynamometry","name":"Isokinetic Dynamometry","fullName":"Isokinetic Strength and Power Assessment","aliases":["isokinetic testing","constant velocity testing","dynamometric testing"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Strength Testing","year":"1967","originator":"Henry Hislop","url":"https://scholargate.app/en/sports-science/isokinetic-dynamometry","markdownUrl":"https://scholargate.app/en/sports-science/isokinetic-dynamometry.md","definition":"Isokinetic dynamometry measures muscular strength and power production during movement at a constant, preset velocity. Pioneered by Hislop and Perrine (1967), isokinetic testing constrains limb velocity to a fixed speed (e.g., 60°/s or 120°/s), while the dynamometer adjusts resistance to match the subject's force production at each instant, accommodating all variations in force throughout the range of motion. This approach provides comprehensive strength profiling across a full joint range and allows comparison of concentric and eccentric contractions. Isokinetic testing is widely used in clinical rehabilitation, sports medicine, and research due to its objectivity and standardization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Hislop","subfamily":"Strength Testing","year":"1967","type":"constant-velocity testing"},"citations":[{"ref":"Hislop, H. J., & Perrine, J. J. (1967). The isokinetic concept of exercise. Physical Therapy, 47(2), 114-117.","type":"article","doi":"10.1093/ptj/47.2.114","isbn":null,"url":null},{"ref":"Perrin, D. H. (1993). Isokinetic Exercise and Assessment. Human Kinetics Publishers.","type":"article","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/isokinetic-exercise-and-assessment/oclc/27757562"},{"ref":"Keating, J. L., & Matyas, T. A. (2001). Unreliability of knee strength measures and the influence of factors that may affect assessment. Journal of Orthopaedic & Sports Physical Therapy, 31(10), 546-556.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Unreliability+of+knee+strength+measures+and+the+influence+of+factors+that+may+affect+assessment+Keating"}],"related":["1rm-estimation","force-velocity-profile","rate-of-force-development","counter-movement-jump","reactive-strength-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"isolation-forest","name":"Isolation Forest","fullName":"Isolation Forest (Anomaly Detection via Random Partitioning)","aliases":["Isolation Forest (Aykırı Değer Tespiti)","iForest","isolation forest anomaly detection"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2008,"originator":"Liu, F.T., Ting, K.M. & Zhou, Z.-H.","url":"https://scholargate.app/en/machine-learning/isolation-forest","markdownUrl":"https://scholargate.app/en/machine-learning/isolation-forest.md","definition":"Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, F.T., Ting, K.M. & Zhou, Z.-H.","year":2008,"type":"Unsupervised ensemble (random partitioning trees)","task":"Anomaly / outlier detection","minSample":50,"supervision":"Unsupervised (no labelled anomalies needed)"},"citations":[{"ref":"Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.","type":"inproceedings","doi":"10.1109/ICDM.2008.17","isbn":null,"url":null}],"related":["random-forest","pca","t-sne","decision-tree","gaussian-mixture"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"isomap","name":"Isomap","fullName":"Isometric Feature Mapping (Isomap)","aliases":["Isomap","isometric feature mapping","geodesic Isomap","nonlinear MDS"],"domain":"machine-learning","family":"latent-structure","subfamily":null,"year":2000,"originator":"Tenenbaum, J. B.; de Silva, V.; Langford, J. C.","url":"https://scholargate.app/en/machine-learning/isomap","markdownUrl":"https://scholargate.app/en/machine-learning/isomap.md","definition":"Isomap (Isometric Feature Mapping) is a manifold learning algorithm introduced by Tenenbaum, de Silva, and Langford in 2000 that discovers the intrinsic low-dimensional geometry of high-dimensional data by preserving geodesic — rather than straight-line Euclidean — distances between all pairs of points. It was one of the earliest, and most influential, nonlinear dimensionality reduction methods to demonstrate that genuinely curved data manifolds could be unfolded into a faithful low-dimensional coordinate system.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tenenbaum, J. B.; de Silva, V.; Langford, J. C.","year":2000,"type":"Manifold learning / nonlinear dimensionality reduction","task":"Unsupervised dimensionality reduction","minSample":50},"citations":[{"ref":"Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.","type":"article","doi":"10.1126/science.290.5500.2319","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 14). Springer.","type":"book","doi":null,"isbn":"978-0-387-84857-0","url":null},{"ref":"van der Maaten, L., Postma, E. & van den Herik, J. (2009). Dimensionality reduction: A comparative review. Journal of Machine Learning Research, 10, 66–71.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/volume10/vandermaaten09a/vandermaaten09a.pdf"}],"related":["pca","t-sne","umap","lle","kernel-pca","mds"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"isothermal-titration-calorimetry","name":"Isothermal Titration Calorimetry","fullName":"Isothermal Titration Calorimetry","aliases":["ITC","isothermal calorimetry","microcalorimetry"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Thermodynamic Characterization","year":"1989","originator":"Terrence Wiseman","url":"https://scholargate.app/en/spectroscopy/isothermal-titration-calorimetry","markdownUrl":"https://scholargate.app/en/spectroscopy/isothermal-titration-calorimetry.md","definition":"Isothermal Titration Calorimetry (ITC) is a thermodynamic technique that measures heat released or absorbed during biomolecular binding events at constant temperature. Developed by Wiseman and colleagues in 1989, ITC directly determines binding affinity (Kd), enthalpy (ΔH), and entropy (ΔS) in a single experiment, making it one of the most comprehensive methods for characterizing molecular interactions without requiring labels or immobilization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Terrence Wiseman","subfamily":"Thermodynamic Characterization","year":"1989","type":"Biophysical technique"},"citations":[{"ref":"Wiseman, T., Williston, S., Brandts, J. F., & Lin, L. N. (1989). Rapid measurement of binding constants and heats of binding using a new titration calorimeter. Analytical Biochemistry, 179(1), 131-137.","type":"article","doi":"10.1016/0003-2697(89)90213-3","isbn":null,"url":null},{"ref":"Garbett, N. C., & Chaires, J. B. (2012). Binding: A statistical thermodynamic model. Biophysical Journal, 86(6), 3493-3494.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Binding%3A+A+statistical+thermodynamic+model+Garbett"}],"related":["surface-plasmon-resonance","circular-dichroism","electron-paramagnetic-resonance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"isotope-diet-reconstruction","name":"Isotope Diet Reconstruction","fullName":"Isotope Diet Reconstruction","aliases":["stable isotope analysis","carbon-nitrogen isotope analysis","diet isotope analysis"],"domain":"archaeology","family":"process-pipeline","subfamily":"Biogeochemistry","year":"1983","originator":"Margaret Schoeninger","url":"https://scholargate.app/en/archaeology/isotope-diet-reconstruction","markdownUrl":"https://scholargate.app/en/archaeology/isotope-diet-reconstruction.md","definition":"Isotope diet reconstruction uses the stable isotope ratios of carbon (C13/C12) and nitrogen (N15/N14) in human bone collagen to infer the composition of past diets. Pioneered by Margaret Schoeninger and Michael DeNiro in the 1980s, this method reveals long-term dietary patterns by analyzing the chemical signature of food absorbed into skeletal tissues. Stable isotopes provide quantitative information about the relative contributions of terrestrial versus marine foods, and between plant and animal sources, making it a powerful tool for understanding past subsistence practices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Margaret Schoeninger","subfamily":"Biogeochemistry","year":"1983","type":"Geochemical diet analysis"},"citations":[{"ref":"Schoeninger, M. J., & DeNiro, M. J. (1983). Nitrogen and carbon isotopic composition of bone collagen from marine and terrestrial animals. Geochimica et Cosmochimica Acta, 47(4), 625-639.","type":"article","doi":"10.1016/0016-7037(84)90091-7","isbn":null,"url":null},{"ref":"Ambrose, S. H. (1990). Preparation and characterization of bone and tooth collagen for isotopic analysis. Journal of Archaeological Science, 17(4), 431-451.","type":"article","doi":"10.1016/0305-4403(90)90007-R","isbn":null,"url":null},{"ref":"Katzenberg, M. A. (2008). Stable isotope analysis: a tool for studying past diet, demography, and life history. In M. A. Katzenberg & S. R. Saunders (Eds.), Biological Anthropology of the Human Skeleton (pp. 413-441). Wiley-Liss.","type":"chapter","doi":null,"isbn":null,"url":"https://doi.org/10.1002/9780470244951.ch14"}],"related":["strontium-provenance","dental-microwear-texture-analysis","use-wear-analysis","minimum-number-of-individuals"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"isotope-ratio-mass-spectrometry","name":"Isotope Ratio Mass Spectrometry","fullName":"Isotope Ratio Mass Spectrometry","aliases":["IRMS"],"domain":"geophysics","family":"process-pipeline","subfamily":"Isotopic analysis and paleoclimate reconstruction","year":"1994","originator":"Thomas Coplen and others","url":"https://scholargate.app/en/geophysics/isotope-ratio-mass-spectrometry","markdownUrl":"https://scholargate.app/en/geophysics/isotope-ratio-mass-spectrometry.md","definition":"Isotope Ratio Mass Spectrometry (IRMS) is an analytical technique that measures the relative abundance of stable isotopes (H, C, N, O, S) and some radiogenic isotopes (e.g., ⁸⁷Sr/⁸⁶Sr) in samples with high precision. Standardized by Coplen and colleagues, IRMS enables paleoclimate reconstruction, source tracing (diet, water origin), geochemical fingerprinting, and age dating through radiogenic isotopes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Coplen and others","subfamily":"Isotopic analysis and paleoclimate reconstruction","year":"1994","type":"Measurement of stable and radiogenic isotope ratios"},"citations":[{"ref":"Coplen, T. B. (1994). Reporting of stable hydrogen, carbon, and oxygen isotopic abundances. Pure and Applied Chemistry, 66(2), 273-276.","type":"article","doi":"10.1351/pac199466020273","isbn":null,"url":null},{"ref":"Brand, W. A., Assonov, S. S., & Brenninkmeijer, C. A. (2010). Convergence of gaseous and elemental isotope ratio mass spectrometry data. Rapid Communications in Mass Spectrometry, 24(12), 1629-1636.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Convergence+of+gaseous+and+elemental+isotope+ratio+mass+spectrometry+data+Brand"}],"related":["radiocarbon-dating","paleomagnetic-analysis","standardized-precipitation-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"item-analysis","name":"Item Analysis","fullName":"Item Analysis (Classical Test Theory)","aliases":["Madde Analizi (Klasik Test Kuramı)","CTT item analysis","classical item analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":"1979","originator":"Classical Test Theory tradition; foundational texts by Allen & Yen (1979) and Crocker & Algina (1986)","url":"https://scholargate.app/en/psychometrics/item-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/item-analysis.md","definition":"Item analysis is the foundational psychometric procedure for evaluating the quality of individual test or scale items within the Classical Test Theory (CTT) framework, as systematised by Allen and Yen (1979) and Crocker and Algina (1986). It produces an item difficulty index, an item discrimination index, and a distractor analysis for each item, enabling test developers to identify items that are too easy, too hard, or failing to separate high- and low-ability respondents.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical Test Theory tradition; foundational texts by Allen & Yen (1979) and Crocker & Algina (1986)","year":"1979","type":"Descriptive / psychometric screening","outcome":"Item difficulty index (p), item discrimination index (r_pb or D), distractor analysis","data":"Binary or ordinal item scores","min_sample":30,"difficulty":1},"citations":[{"ref":"Allen, M. J. & Yen, W. M. (1979). Introduction to Measurement Theory. Brooks/Cole.","type":"book","doi":null,"isbn":"978-0818501333","url":null},{"ref":"Crocker, L. & Algina, J. (1986). Introduction to Classical and Modern Test Theory. Holt, Rinehart & Winston.","type":"book","doi":null,"isbn":"978-0030616341","url":null}],"related":["cronbach-alpha","exploratory-factor-analysis","confirmatory-factor-analysis","item-response-theory","test-equating"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"item-response-theory","name":"Item Response Theory","fullName":"Item Response Theory","aliases":["IRT","latent trait theory","item characteristic curve theory","modern test theory"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1952–1968","originator":"Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)","url":"https://scholargate.app/en/psychometrics/item-response-theory","markdownUrl":"https://scholargate.app/en/psychometrics/item-response-theory.md","definition":"Item response theory models the probability that a respondent answers an item correctly (or endorses it) as a function of the respondent's latent trait level and the item's own statistical properties — difficulty, discrimination, and guessing. Unlike classical test theory, IRT places persons and items on the same scale, yielding measurement that is sample-independent for items and test-independent for persons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)","year":"1952–1968","type":"Probabilistic measurement model","dataType":"Binary or polytomous item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Statistical+Theories+of+Mental+Test+Scores+Lord+Novick+1968"},{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null}],"related":["confirmatory-factor-analysis","exploratory-factor-analysis","differential-item-functioning","rasch-model","cronbachs-alpha","scale-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iterative-learning-control","name":"Iterative Learning Control","fullName":"Iterative Learning Control","aliases":["ILC","Learning Control","Repetitive Control"],"domain":"control-theory","family":"ml-model","subfamily":"Adaptive Control","year":"1984","originator":"Suguru Arimoto","url":"https://scholargate.app/en/control-theory/iterative-learning-control","markdownUrl":"https://scholargate.app/en/control-theory/iterative-learning-control.md","definition":"Iterative Learning Control (ILC) is a control method for systems that perform the same task repeatedly (trajectory tracking over a fixed time interval). The key idea is to use error information from previous trials to update the input for the next trial, progressively improving tracking accuracy. Pioneered by Arimoto et al. in 1984, ILC is ideal for robotic manufacturing, semiconductor processing, and any application where the same motion must be repeated many times with high precision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Suguru Arimoto","subfamily":"Adaptive Control","year":"1984","type":"algorithm"},"citations":[{"ref":"Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operation of robots by learning. Journal of Robotic Systems, 1(2), 123-140.","type":"article","doi":"10.1002/rob.4620010203","isbn":null,"url":null},{"ref":"Moore, K. L. (1993). Iterative learning control for trajectory tracking. Advances in Industrial Control, Springer-Verlag.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Iterative+learning+control+for+trajectory+tracking+Moore"},{"ref":"Bien, Z., & Xu, J. X. (2007). Iterative Learning Control: Analysis, Design, Integration and Applications. Kluwer Academic Publishers.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/book/9781402062529"}],"related":["adaptive-control","feedback-linearization","model-predictive-control","sliding-mode-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"itransformer","name":"iTransformer","fullName":"iTransformer (Inverted Transformer for Forecasting)","aliases":["Inverted Transformer","iTransformer for Time Series","Inverted Attention Transformer","Ters Transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2024,"originator":"Yong Liu et al.","url":"https://scholargate.app/en/deep-learning/itransformer","markdownUrl":"https://scholargate.app/en/deep-learning/itransformer.md","definition":"iTransformer is a deep-learning architecture for multivariate time-series forecasting introduced by Liu et al. at ICLR 2024. Its defining idea is to invert the conventional Transformer tokenisation strategy: instead of treating each time step as a token, iTransformer treats each variate (sensor channel or feature series) as a single token whose embedding encodes the full observed look-back window. Self-attention is then applied across variates to capture inter-series dependencies, while a feed-forward network within each token learns temporal patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yong Liu et al.","year":2024,"type":"Inverted-attention sequence model","subfamily":"Time-series forecasting","venue":"ICLR 2024","input":"Multivariate time series"},"citations":[{"ref":"Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., & Long, M. (2024). iTransformer: Inverted transformers are effective for time series forecasting. ICLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2310.06625"}],"related":["patchtst","transformer","crossformer"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-2sls","name":"Two-Stage Least Squares (2SLS)","fullName":"Instrumental Variables Estimation via Two-Stage Least Squares (IV/2SLS)","aliases":["instrumental variables","IV estimation","2SLS","instrumental variable regression","Araç Değişken — İki Aşamalı EKK (IV/2SLS)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2009,"originator":"Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)","url":"https://scholargate.app/en/causal-inference/iv-2sls","markdownUrl":"https://scholargate.app/en/causal-inference/iv-2sls.md","definition":"IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory)","year":2009,"type":"Instrumental-variables regression","estimator":"Two-stage least squares (consistent under instrument validity)","outcome":"continuous or binary","minSample":100},"citations":[{"ref":"Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691120355","url":null},{"ref":"Stock, J. H. & Yogo, M. (2005). Testing for Weak Instruments in Linear IV Regression. In Identification and Inference for Econometric Models. Cambridge University Press.","type":"book-chapter","doi":"10.1017/CBO9780511614491.006","isbn":null,"url":null}],"related":["ols-regression","local-average-treatment-effect","propensity-score-matching","panel-fixed-effects","doubly-robust-estimation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-aras","name":"IV-ARAS","fullName":"Interval extension of ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1966","originator":"Moore, R. E.","url":"https://scholargate.app/en/decision-making/iv-aras","markdownUrl":"https://scholargate.app/en/decision-making/iv-aras.md","definition":"IV-ARAS (Interval extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Moore, R. E. in 1966. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore, R. E.","subfamily":"Ranking","year":"1966","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Moore, R. E. (1966). Interval Analysis. Prentice-Hall, Englewood Cliffs","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interval%20Analysis"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-codas","name":"IV-CODAS","fullName":"IVAIF-CODAS — Interval-Valued Atanassov Intuitionistic Fuzzy CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Yeni, F. B., Özçelik, G.","url":"https://scholargate.app/en/decision-making/iv-codas","markdownUrl":"https://scholargate.app/en/decision-making/iv-codas.md","definition":"IV-CODAS (IVAIF-CODAS — Interval-Valued Atanassov Intuitionistic Fuzzy CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yeni, F. B., Özçelik, G. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yeni, F. B., Özçelik, G.","subfamily":"Ranking","year":"2018","type":"Interval-valued intuitionistic fuzzy outranking — IVAIFS","value_space":"interval_intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yeni, F. B., Özçelik, G. (2018). Interval-Valued Atanassov Intuitionistic Fuzzy CODAS Method for Multi Criteria Group Decision Making Problems. Group Decision and Negotiation","type":"article","doi":"10.1007/s10726-018-9603-9","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-copras","name":"IV-COPRAS","fullName":"Interval extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1966","originator":"Moore, R. E.","url":"https://scholargate.app/en/decision-making/iv-copras","markdownUrl":"https://scholargate.app/en/decision-making/iv-copras.md","definition":"IV-COPRAS (Interval extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Moore, R. E. in 1966. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore, R. E.","subfamily":"Ranking","year":"1966","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Moore, R. E. (1966). Interval Analysis. Prentice-Hall, Englewood Cliffs","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interval%20Analysis"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-edas","name":"IV-EDAS","fullName":"Interval extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1966","originator":"Moore, R. E.","url":"https://scholargate.app/en/decision-making/iv-edas","markdownUrl":"https://scholargate.app/en/decision-making/iv-edas.md","definition":"IV-EDAS (Interval extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Moore, R. E. in 1966. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore, R. E.","subfamily":"Ranking","year":"1966","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Moore, R. E. (1966). Interval Analysis. Prentice-Hall, Englewood Cliffs","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interval%20Analysis"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-marcos","name":"IV-MARCOS","fullName":"Interval extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1966","originator":"Moore, R. E.","url":"https://scholargate.app/en/decision-making/iv-marcos","markdownUrl":"https://scholargate.app/en/decision-making/iv-marcos.md","definition":"IV-MARCOS (Interval extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Moore, R. E. in 1966. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore, R. E.","subfamily":"Ranking","year":"1966","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Moore, R. E. (1966). Interval Analysis. Prentice-Hall, Englewood Cliffs","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interval%20Analysis"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-moora","name":"IV-MOORA","fullName":"Interval extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1966","originator":"Moore, R. E.","url":"https://scholargate.app/en/decision-making/iv-moora","markdownUrl":"https://scholargate.app/en/decision-making/iv-moora.md","definition":"IV-MOORA (Interval extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Moore, R. E. in 1966. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore, R. E.","subfamily":"Ranking","year":"1966","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Moore, R. E. (1966). Interval Analysis. Prentice-Hall, Englewood Cliffs","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interval%20Analysis"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-projection","name":"IV-PROJECTION","fullName":"Interval-Valued Intuitionistic Fuzzy Projection-based MCDM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019; IVIFS algebraic foundation Atanassov–Gargov 1989; earlier projection variants 2012","originator":"Yue, Z. (2019) — primary normalized IVIFS projection Tsao, C. Y.; Chen, T. Y. (2016) — compromise variant Atanassov, K. T.; Gargov, G. (1989) — IVIFS foundation","url":"https://scholargate.app/en/decision-making/iv-projection","markdownUrl":"https://scholargate.app/en/decision-making/iv-projection.md","definition":"IV-PROJECTION (Interval-Valued Intuitionistic Fuzzy Projection-based MCDM) is a ranking multi-criteria decision-making (MCDM) method introduced by Yue, Z. (2019) — primary normalized IVIFS projection Tsao, C. Y.; Chen, T. Y. (2016) — compromise variant Atanassov, K. T.; Gargov, G. (1989) — IVIFS foundation in 2019; IVIFS algebraic foundation Atanassov–Gargov 1989; earlier projection variants 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yue, Z. (2019) — primary normalized IVIFS projection Tsao, C. Y.; Chen, T. Y. (2016) — compromise variant Atanassov, K. T.; Gargov, G. (1989) — IVIFS foundation","subfamily":"Ranking","year":"2019; IVIFS algebraic foundation Atanassov–Gargov 1989; earlier projection variants 2012","type":"IVIFS projection-based ranking","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yue, Z. (2019). An interval-valued intuitionistic fuzzy projection-based approach and application to evaluating knowledge transfer effectiveness. Neural Computing and Applications","type":"article","doi":"10.1007/S00521-018-3571-5","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-saw","name":"IV-SAW","fullName":"Interval extension of SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1966","originator":"Moore, R. E.","url":"https://scholargate.app/en/decision-making/iv-saw","markdownUrl":"https://scholargate.app/en/decision-making/iv-saw.md","definition":"IV-SAW (Interval extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Moore, R. E. in 1966. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore, R. E.","subfamily":"Ranking","year":"1966","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Moore, R. E. (1966). Interval Analysis. Prentice-Hall, Englewood Cliffs","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interval%20Analysis"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-todim","name":"IV-TODIM","fullName":"Interval extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Mishra, A. R., Rani, P., Pardasani, K. R., Mardani, A., Stević, Ž., Pamučar, D.","url":"https://scholargate.app/en/decision-making/iv-todim","markdownUrl":"https://scholargate.app/en/decision-making/iv-todim.md","definition":"IV-TODIM (Interval extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Mishra, A. R., Rani, P., Pardasani, K. R., Mardani, A., Stević, Ž., Pamučar, D. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mishra, A. R., Rani, P., Pardasani, K. R., Mardani, A., Stević, Ž., Pamučar, D.","subfamily":"Ranking","year":"2020","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":false},"citations":[{"ref":"Mishra, A. R., Rani, P., Pardasani, K. R., Mardani, A., Stević, Ž., Pamučar, D. (2020). A novel entropy and divergence measures with multi-criteria service quality assessment using interval-valued intuitionistic fuzzy TODIM method. Soft Computing","type":"article","doi":"10.1007/s00500-019-04627-7","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-topsis","name":"IV-TOPSIS","fullName":"Interval extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2006","originator":"Jahanshahloo, G. R., Lotfi, F. H., Izadikhah, M.","url":"https://scholargate.app/en/decision-making/iv-topsis","markdownUrl":"https://scholargate.app/en/decision-making/iv-topsis.md","definition":"IV-TOPSIS (Interval extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Jahanshahloo, G. R., Lotfi, F. H., Izadikhah, M. in 2006. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jahanshahloo, G. R., Lotfi, F. H., Izadikhah, M.","subfamily":"Ranking","year":"2006","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Jahanshahloo, G. R., Lotfi, F. H., Izadikhah, M. (2006). An algorithmic method to extend TOPSIS for decision-making problems with interval data. Applied Mathematics and Computation","type":"article","doi":"10.1016/j.amc.2005.08.048","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-vikor","name":"IV-VIKOR","fullName":"Interval extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1966","originator":"Moore, R. E.","url":"https://scholargate.app/en/decision-making/iv-vikor","markdownUrl":"https://scholargate.app/en/decision-making/iv-vikor.md","definition":"IV-VIKOR (Interval extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Moore, R. E. in 1966. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore, R. E.","subfamily":"Ranking","year":"1966","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Moore, R. E. (1966). Interval Analysis. Prentice-Hall, Englewood Cliffs","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interval%20Analysis"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"iv-waspas","name":"IV-WASPAS","fullName":"Interval extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1966","originator":"Moore, R. E.","url":"https://scholargate.app/en/decision-making/iv-waspas","markdownUrl":"https://scholargate.app/en/decision-making/iv-waspas.md","definition":"IV-WASPAS (Interval extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Moore, R. E. in 1966. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moore, R. E.","subfamily":"Ranking","year":"1966","type":"Interval outranking/ranking — Interval Number (IN: [a, b])","value_space":"interval_intuitionistic","uncertainty":"bounded","compensation":"full","rank_reversal":true},"citations":[{"ref":"Moore, R. E. (1966). Interval Analysis. Prentice-Hall, Englewood Cliffs","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interval%20Analysis"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ivf-embryo-grading","name":"IVF Embryo Grading","fullName":"In Vitro Fertilization Embryo Grading System","aliases":["embryo morphology assessment","embryo viability grading"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Morphological Assessment","year":"1999","originator":"David K. Gardner","url":"https://scholargate.app/en/veterinary-science/ivf-embryo-grading","markdownUrl":"https://scholargate.app/en/veterinary-science/ivf-embryo-grading.md","definition":"IVF Embryo Grading is a standardized morphological assessment system for evaluating the quality and viability of embryos in assisted reproductive technology. First formalized by Gardner and colleagues in 1999, it uses microscopic examination to score embryos across multiple criteria, enabling clinicians to select the most viable embryos for transfer and improve pregnancy outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David K. Gardner","subfamily":"Morphological Assessment","year":"1999","type":"Grading and Classification System"},"citations":[{"ref":"Gardner, D. K., Lane, M., Stevens, J., & Schoolcraft, W. B. (1999). Blastocyst score affects implantation and pregnancy outcome: towards a single blastocyst transfer. Fertility and Sterility, 73(6), 1155-1158.","type":"article","doi":"10.1016/s0015-0282(00)00518-5","isbn":null,"url":null},{"ref":"Alpha Scientists in Reproductive Medicine and ESHRE Special Interest Group of Embryology (2011). The Istanbul consensus workshop on embryo assessment. Human Reproduction Update, 17(6), 572-583.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Istanbul+consensus+workshop+on+embryo+assessment+Alpha"},{"ref":"Veeck, L. L. (1986). Atlas of the Human Oocyte and Early Conceptus. William & Wilkins.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books/about/Atlas_of_the_Human_Oocyte_and_Early_Con.html"}],"related":["body-condition-scoring","somatic-cell-count","polysomnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ivif-aras","name":"IVIF-ARAS","fullName":"Interval-Valued Intuitionistic Fuzzy ARAS (Büyüközkan & Göçer 2018)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1989","originator":"Atanassov, K. T., Gargov, G.","url":"https://scholargate.app/en/decision-making/ivif-aras","markdownUrl":"https://scholargate.app/en/decision-making/ivif-aras.md","definition":"IVIF-ARAS (Interval-Valued Intuitionistic Fuzzy ARAS (Büyüközkan & Göçer 2018)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T., Gargov, G. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T., Gargov, G.","subfamily":"Ranking","year":"1989","type":"Utility-degree ranking MCDM under Interval-Valued Intuitionistic Fuzzy uncertainty (IVIFN: ⟨[μ⁻,μ⁺],[ν⁻,ν⁺]⟩; μ⁺+ν⁺ ≤ 1) — additive sum of weighted-normalized IVIF performance ratings benchmarked against an optimal reference row","value_space":"interval_intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T., Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/0165-0114(89)90205-4","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","if-entropy","merec","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ivif-codas-sort","name":"IVIF-CODAS-SORT","fullName":"Interval-Valued Intuitionistic Fuzzy CODAS-SORT (sorting via Euclidean+Hamming relative assessment)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"2020","originator":"Ouhibi, A. Frikha, H.","url":"https://scholargate.app/en/decision-making/ivif-codas-sort","markdownUrl":"https://scholargate.app/en/decision-making/ivif-codas-sort.md","definition":"IVIF-CODAS-SORT (Interval-Valued Intuitionistic Fuzzy CODAS-SORT (sorting via Euclidean+Hamming relative assessment)) is a sorting multi-criteria decision-making (MCDM) method introduced by Ouhibi, A. Frikha, H. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ouhibi, A. Frikha, H.","subfamily":"Sorting","year":"2020","type":"IVIFN sorting with limiting profiles — relative assessment R(a,b) = (E_a−E_b) + ψ(E_a−E_b)·(H_a−H_b)","value_space":"interval_intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Ouhibi, A., Frikha, H. (2020). An interval-valued intuitionistic fuzzy CODAS-SORT method for sorting problems. 6th International Conference on Control, Decision and Information Technologies (CoDIT'20)","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+interval-valued+intuitionistic+fuzzy+CODAS-SORT+method+for+sorting+problems+Ouhibi"}],"related":["codas-sort","if-codas-sort"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ivif-copras","name":"IVIF-COPRAS","fullName":"Interval-Valued Intuitionistic Fuzzy COPRAS (Davoudabadi-Mousavi-Mohagheghi-Vahdani 2019)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1989","originator":"Atanassov, K. T., Gargov, G.","url":"https://scholargate.app/en/decision-making/ivif-copras","markdownUrl":"https://scholargate.app/en/decision-making/ivif-copras.md","definition":"IVIF-COPRAS (Interval-Valued Intuitionistic Fuzzy COPRAS (Davoudabadi-Mousavi-Mohagheghi-Vahdani 2019)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T., Gargov, G. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T., Gargov, G.","subfamily":"Ranking","year":"1989","type":"Compound-proportional ranking MCDM under Interval-Valued Intuitionistic Fuzzy uncertainty (IVIFN: ⟨[μ⁻,μ⁺],[ν⁻,ν⁺]⟩; μ⁺+ν⁺ ≤ 1) — Xu normalization + IVIF profit/cost sums + Garg score (GIS) compound Q_i + utility degree D_i (%)","value_space":"interval_intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T., Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/0165-0114(89)90205-4","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ivif-mabac","name":"IVIF-MABAC","fullName":"Interval-Valued Intuitionistic Fuzzy MABAC (Xue, You, Lai, Liu 2016)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1989","originator":"Atanassov, K. T., Gargov, G.","url":"https://scholargate.app/en/decision-making/ivif-mabac","markdownUrl":"https://scholargate.app/en/decision-making/ivif-mabac.md","definition":"IVIF-MABAC (Interval-Valued Intuitionistic Fuzzy MABAC (Xue, You, Lai, Liu 2016)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T., Gargov, G. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T., Gargov, G.","subfamily":"Ranking","year":"1989","type":"Border-approximation-area MCDM under Interval-Valued Intuitionistic Fuzzy uncertainty (IVIFN: ([μ⁻,μ⁺],[ν⁻,ν⁺]); μ⁺+ν⁺ ≤ 1)","value_space":"interval_intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T., Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/0165-0114(89)90205-4","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","if-entropy","merec","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ivif-todim","name":"IVIF-TODIM","fullName":"Interval-Valued Intuitionistic Fuzzy TODIM (Krohling & Pacheco 2014)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1989","originator":"Atanassov, K. T., Gargov, G.","url":"https://scholargate.app/en/decision-making/ivif-todim","markdownUrl":"https://scholargate.app/en/decision-making/ivif-todim.md","definition":"IVIF-TODIM (Interval-Valued Intuitionistic Fuzzy TODIM (Krohling & Pacheco 2014)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T., Gargov, G. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T., Gargov, G.","subfamily":"Ranking","year":"1989","type":"Prospect-theory dominance MCDM under Interval-Valued Intuitionistic Fuzzy uncertainty (IVIFN: ([μ⁻,μ⁺],[ν⁻,ν⁺]); μ⁺+ν⁺ ≤ 1)","value_space":"interval_intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T., Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/0165-0114(89)90205-4","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","if-entropy","merec","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ivif-vikor","name":"IVIF-VIKOR","fullName":"Interval-Valued Intuitionistic Fuzzy VIKOR (Park, Cho & Kwun 2011)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1989","originator":"Atanassov, K. T., Gargov, G.","url":"https://scholargate.app/en/decision-making/ivif-vikor","markdownUrl":"https://scholargate.app/en/decision-making/ivif-vikor.md","definition":"IVIF-VIKOR (Interval-Valued Intuitionistic Fuzzy VIKOR (Park, Cho & Kwun 2011)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T., Gargov, G. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T., Gargov, G.","subfamily":"Ranking","year":"1989","type":"Compromise-ranking MCDM under Interval-Valued Intuitionistic Fuzzy uncertainty (IVIFN: ⟨[μ⁻,μ⁺],[ν⁻,ν⁺]⟩; μ⁺+ν⁺ ≤ 1) — Lp-metric distance to interval-valued intuitionistic PIS","value_space":"interval_intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T., Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/0165-0114(89)90205-4","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","if-entropy","merec","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ivn-multimoora","name":"IVN-MULTIMOORA","fullName":"MULTIMOORA under Interval-Valued Neutrosophic Number Environment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Stanujkić, D. Zavadskas, E.K. Smarandache, F. Brauers, W.K.M. Karabašević, D.","url":"https://scholargate.app/en/decision-making/ivn-multimoora","markdownUrl":"https://scholargate.app/en/decision-making/ivn-multimoora.md","definition":"IVN-MULTIMOORA (MULTIMOORA under Interval-Valued Neutrosophic Number Environment) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkić, D. Zavadskas, E.K. Smarandache, F. Brauers, W.K.M. Karabašević, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanujkić, D. Zavadskas, E.K. Smarandache, F. Brauers, W.K.M. Karabašević, D.","subfamily":"Ranking","year":"2021","type":"Neutrosophic MULTIMOORA — Interval-Valued Neutrosophic Number (IVNN: <[tl,tu],[il,iu],[fl,fu]> with each component ∈[0,1], 0≤tu+iu+fu≤3)","value_space":"interval_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Stanujkić, D., Zavadskas, E.K., Smarandache, F., Brauers, W.K.M., Karabašević, D. (2021). Cloud Computing Technology Selection Using a Novel Neutrosophic Extension of the MULTIMOORA Method. In: Smarandache, F., Abdel-Basset, M. (eds.) Neutrosophic Operational Research. Springer Nature Switzerland AG, Cham","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cloud+Computing+Technology+Selection+Using+a+Novel+Neutrosophic+Extension+of+the+MULTIMOORA+Method+Stanujki%C4%87"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jaccard-index","name":"Jaccard Index","fullName":"Jaccard Similarity Coefficient","aliases":["Jaccard Similarity","Intersection over Union (IoU)"],"domain":"model-evaluation","family":"mcdm","subfamily":"Multi-label Metric","year":"1901","originator":"Paul Jaccard","url":"https://scholargate.app/en/model-evaluation/jaccard-index","markdownUrl":"https://scholargate.app/en/model-evaluation/jaccard-index.md","definition":"The Jaccard index measures the similarity between predicted and true label sets by computing the ratio of intersection to union. It is widely used in multi-label classification and set-based similarity tasks where partial overlap is important.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Jaccard","subfamily":"Multi-label Metric","year":"1901","type":"Similarity metric"},"citations":[{"ref":"Jaccard, P. (1901). Etude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, 37, 547-579.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/etudecombparativajedist"},{"ref":"Tsoumakas, G., & Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1-13.","type":"article","doi":"10.4018/jdwm.2007070101","isbn":null,"url":null}],"related":["hamming-loss","subset-accuracy","f1-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jackknife-estimation","name":"Jackknife Estimation","fullName":"Jackknife Resampling Estimation","aliases":["delete-one jackknife","leave-one-out jackknife","Jackknife Yeniden Örnekleme"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1956,"originator":"Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming)","url":"https://scholargate.app/en/statistics/jackknife-estimation","markdownUrl":"https://scholargate.app/en/statistics/jackknife-estimation.md","definition":"Jackknife estimation is a classical resampling technique that computes the bias and variance of a statistical estimator by systematically leaving out one observation at a time and re-computing the statistic on each reduced sample. Introduced by Maurice Quenouille in 1956 for bias correction and extended by John Tukey in 1958 who coined the name, it is the historical predecessor of the bootstrap and remains analytically tractable for smooth, differentiable estimators.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maurice Henri Quenouille (bias correction); John W. Tukey (variance estimation and naming)","year":1956,"family":"Resampling method","type":"Bias and variance estimation","parametric":false,"leaveOutScheme":"delete-one (leave-one-out)","pseudovalues":true,"minSample":10},"citations":[{"ref":"Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353–360.","type":"article","doi":"10.1093/biomet/43.3-4.353","isbn":null,"url":null},{"ref":"Tukey, J. W. (1958). Bias and Confidence in Not Quite Large Samples. Annals of Mathematical Statistics, 29(2), 614.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bias+and+Confidence+in+Not+Quite+Large+Samples+Tukey"}],"related":["bootstrap-estimation","cross-validation","delta-method","permutation-test","monte-carlo-simulation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jackknife","name":"Jackknife","fullName":"Jackknife Resampling","aliases":["leave-one-out resampling","Quenouille-Tukey jackknife","delete-one jackknife","Jackknife Yeniden Örnekleme"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1956,"originator":"Quenouille (1956); reviewed by Miller (1974)","url":"https://scholargate.app/en/statistics/jackknife","markdownUrl":"https://scholargate.app/en/statistics/jackknife.md","definition":"The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Quenouille (1956); reviewed by Miller (1974)","year":1956,"type":"Resampling / bias and variance estimation","resampling":"Leave-one-out (delete-one)","minSample":10},"citations":[{"ref":"Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.","type":"article","doi":"10.1093/biomet/43.3-4.353","isbn":null,"url":null},{"ref":"Miller, R. G. (1974). The Jackknife — A Review. Biometrika, 61(1), 1-15.","type":"article","doi":"10.1093/biomet/61.1.1","isbn":null,"url":null}],"related":["bootstrap-inference","permutation-test","mad-estimation","robust-time-series","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"janka-hardness","name":"Janka Hardness","fullName":"Janka Hardness Test","aliases":["wood hardness","resistance to indentation"],"domain":"forestry","family":"process-pipeline","subfamily":"Wood Properties","year":"1934","originator":"Gabriel Janka","url":"https://scholargate.app/en/forestry/janka-hardness","markdownUrl":"https://scholargate.app/en/forestry/janka-hardness.md","definition":"The Janka hardness test measures wood resistance to indentation and denting by forcing a steel ball into the wood surface under standard load. Developed by Gabriel Janka in 1934, the test is a simple, nondestructive indicator of wood durability, wear resistance, and suitability for flooring, furniture, and other wear-prone applications. Janka hardness is one of the most widely used wood property metrics in wood science and commerce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gabriel Janka","subfamily":"Wood Properties","year":"1934","type":"hardness test"},"citations":[{"ref":"ASTM D1037-21. (2021). Standard test methods for evaluating properties of wood-base fiber and particle panel materials. ASTM International.","type":"article","doi":null,"isbn":null,"url":"https://www.astm.org"},{"ref":"American Hardwood Export Council. (2012). Wood hardness ratings. Technical Report.","type":"article","doi":null,"isbn":null,"url":"https://www.ahec.org"}],"related":["modulus-of-rupture-and-elasticity","wood-shrinkage","x-ray-densitometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jebsen-hand-function-test","name":"JHFT","fullName":"Jebsen-Taylor Hand Function Test","aliases":["JHFT","Jebsen Test of Hand Function"],"domain":"occupational-therapy","family":"process-pipeline","subfamily":"hand and finger dexterity","year":"1969","originator":"Jebsen, R. H., Taylor, N., Trieschmann, R. B., Trotter, M. J., & Howard, L. A.","url":"https://scholargate.app/en/occupational-therapy/jebsen-hand-function-test","markdownUrl":"https://scholargate.app/en/occupational-therapy/jebsen-hand-function-test.md","definition":"The Jebsen-Taylor Hand Function Test (JHFT) is a standardized, performance-based measure of hand function developed to provide an objective, quantitative assessment of manual dexterity and hand capability. Created by Jebsen and colleagues (1969) at the University of Minnesota, the JHFT consists of seven timed functional hand tasks reflecting everyday hand activities. The JHFT is widely used in hand therapy, occupational therapy, and rehabilitation medicine to evaluate hand function in individuals with arthritis, hand injury, nerve compression syndromes, stroke, and other conditions affecting dexterity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jebsen, R. H., Taylor, N., Trieschmann, R. B., Trotter, M. J., & Howard, L. A.","subfamily":"hand and finger dexterity","year":"1969","type":"Performance-based, timed assessment by clinician"},"citations":[{"ref":"Jebsen, R. H., Taylor, N., Trieschmann, R. B., Trotter, M. J., & Howard, L. A. (1969). An objective and standardized test of hand function. Archives of Physical Medicine and Rehabilitation, 50(6), 311-319.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/5788150"},{"ref":"Hackel, M. E., Wolfe, G. A., Bang, S. M., & Canfield, J. S. (1992). Changes in hand function in the aging adult as determined by the Jebsen Test of Hand Function. Physical Therapy, 72(5), 373-377.","type":"article","doi":"10.1093/ptj/72.5.373","isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/1589462"}],"related":["nine-hole-peg-test","wolf-motor-function-test","upper-extremity-functional-scale","motor-assessment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jellyfish-search-optimizer","name":"Jellyfish Search Optimizer","fullName":"Jellyfish Search Optimizer","aliases":["JSO"],"domain":"optimization","family":"ml-model","subfamily":"Swarm Intelligence","year":"2022","originator":"Xueying Shi","url":"https://scholargate.app/en/optimization/jellyfish-search-optimizer","markdownUrl":"https://scholargate.app/en/optimization/jellyfish-search-optimizer.md","definition":"The Jellyfish Search Optimizer (JSO) is a biologically-inspired metaheuristic algorithm introduced by Shi et al. in 2022, based on the movement and foraging behavior of jellyfish in ocean environments. Jellyfish exhibit two distinct behaviors: passive drifting with ocean currents (exploration) and active swimming toward food sources (exploitation). JSO captures these behaviors to create an effective balance between global search and local refinement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xueying Shi","subfamily":"Swarm Intelligence","year":"2022","type":"Nature-inspired metaheuristic algorithm"},"citations":[{"ref":"Shi, X., Sun, Y., Zhan, Z. H., Yuen, K. F., & Zhang, J. (2022). Jellyfish search optimizer: A new bio-inspired metaheuristic algorithm for solving optimization tasks. Neural Computing and Applications, 34(10), 7651-7673.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Jellyfish+search+optimizer%3A+A+new+bio-inspired+metaheuristic+algorithm+for+solving+optimization+tasks+Shi"}],"related":["slime-mould-algorithm","particle-swarm-optimization","aquila-optimizer","whale-optimization-algorithm","salp-swarm-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jensen-shannon-divergence","name":"Jensen-Shannon Divergence","fullName":"Jensen-Shannon Information Divergence","aliases":["JS divergence","symmetric KL divergence","JS distance"],"domain":"decision-making","family":"mcdm","subfamily":"Information-theoretic divergence","year":"1991","originator":"J. Lin","url":"https://scholargate.app/en/decision-making/jensen-shannon-divergence","markdownUrl":"https://scholargate.app/en/decision-making/jensen-shannon-divergence.md","definition":"Jensen-Shannon divergence is a symmetric information-theoretic measure of the difference between two probability distributions. Developed by Jian Lin in 1991 as a refinement to the asymmetric Kullback-Leibler divergence, it overcomes KL's directional limitation by averaging the divergences in both directions. The result is a true metric (satisfying triangle inequality) that ranges from 0 (identical distributions) to 1, making it suitable for symmetric comparison tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. Lin","subfamily":"Information-theoretic divergence","year":"1991","type":"Symmetric probability distribution dissimilarity"},"citations":[{"ref":"Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151.","type":"article","doi":"10.1109/18.61115","isbn":null,"url":null},{"ref":"Cover, T. M., & Thomas, J. A. (1991). Elements of Information Theory. Wiley-Interscience.","type":"book","doi":"10.1002/0471200611","isbn":null,"url":null}],"related":["kullback-leibler-divergence","hellinger-distance","wasserstein-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"joanna-briggs-critical-appraisal","name":"JBI Critical Appraisal Tools","fullName":"Joanna Briggs Institute Critical Appraisal Tools for Evidence Synthesis","aliases":["JBI","Joanna Briggs"],"domain":"research-methodology","family":"process-pipeline","subfamily":"Multi-design evidence appraisal toolkit","year":"1998 (updated 2017)","originator":"Joanna Briggs Institute (University of Adelaide, Australia)","url":"https://scholargate.app/en/research-methodology/joanna-briggs-critical-appraisal","markdownUrl":"https://scholargate.app/en/research-methodology/joanna-briggs-critical-appraisal.md","definition":"JBI (Joanna Briggs Institute) Critical Appraisal Tools are a comprehensive suite of design-specific quality assessment instruments developed by the Joanna Briggs Institute (University of Adelaide, Australia) since 1998. Unlike single-tool approaches, JBI offers over 15 separate checklists tailored to RCTs, cohort studies, case-control studies, cross-sectional surveys, qualitative research, diagnostic accuracy, and economic evaluations. JBI tools are widely used in systematic reviews, particularly in healthcare, nursing, and public health.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joanna Briggs Institute (University of Adelaide, Australia)","subfamily":"Multi-design evidence appraisal toolkit","year":"1998 (updated 2017)","type":"Research methodology evaluation"},"citations":[{"ref":"Joanna Briggs Institute. (2017). Critical Appraisal Tools. University of Adelaide, South Australia. www.jbi.global/critical-appraisal-tools","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Joanna%20Briggs%20Institute.%20(2017).%20Critical%20Appraisal%20Tools.%20University%20of%20Adelaide%2C%20South%20Australia.%20www.jbi.global%2Fcriti"}],"related":["casp-rct-checklist","newcastle-ottawa-scale","cosmin-checklist","mmat"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"job-content-questionnaire","name":"Job Content Questionnaire","fullName":"Job Content Questionnaire (JCQ) - Job Strain Model","aliases":["JCQ","Karasek Strain Questionnaire"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Occupational health","year":"1985","originator":"Robert A. Karasek","url":"https://scholargate.app/en/organizational-behavior/job-content-questionnaire","markdownUrl":"https://scholargate.app/en/organizational-behavior/job-content-questionnaire.md","definition":"The Job Content Questionnaire (JCQ), developed by Robert Karasek in 1985, operationalizes the Job Strain Model, a foundational theory linking job characteristics to health outcomes. The JCQ measures job demands, decision latitude (autonomy and skill utilization), social support, and physical exertion. It identifies high-strain jobs (high demands, low control)—the most hazardous combination—and supports research linking work organization to cardiovascular disease, mental health, and occupational disability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert A. Karasek","subfamily":"Occupational health","year":"1985","type":"Self-report questionnaire"},"citations":[{"ref":"Karasek, R. A., Jr. (1985). Job Content Questionnaire and user's guide. Los Angeles: University of Southern California Department of Industrial and Systems Engineering.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Karasek+Job+Content+Questionnaire+1985"},{"ref":"Karasek, R., & Theorell, T. (1990). Healthy work: stress, productivity, and the reconstruction of working life. New York: Basic Books.","type":"article","doi":null,"isbn":"978-0465028970","url":null}],"related":["job-demands-resources-scale","perceived-stress-scale","job-satisfaction-survey","emotional-exhaustion-scale","work-ability-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"job-demands-resources-scale","name":"Job Demands-Resources Scale","fullName":"Job Demands-Resources Scale (JDRS)","aliases":["JDRS","JD-R Questionnaire"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Occupational health","year":"2001","originator":"Evangelia Demerouti and Arnold B. Bakker","url":"https://scholargate.app/en/organizational-behavior/job-demands-resources-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/job-demands-resources-scale.md","definition":"The Job Demands-Resources Scale (JDRS) is a multidimensional assessment instrument based on the Job Demands-Resources (JD-R) model, developed by Demerouti and Bakker in 2001. It measures the balance between job demands (workload, time pressure, emotional demands) and resources (autonomy, support, opportunities for growth) that shape employee well-being, engagement, and burnout risk. The JDRS has become central to occupational health research and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evangelia Demerouti and Arnold B. Bakker","subfamily":"Occupational health","year":"2001","type":"Self-report questionnaire"},"citations":[{"ref":"Bakker, A. B., & Demerouti, E. (2007). The Job Demands-Resources model: state of the art. Journal of Managerial Psychology, 22(3), 309-328.","type":"article","doi":"10.1108/02683940710733115","isbn":null,"url":null},{"ref":"Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The Job Demands-Resources model of burnout. Journal of Applied Psychology, 86(3), 499-512.","type":"article","doi":"10.1037/0021-9010.86.3.499","isbn":null,"url":null}],"related":["job-satisfaction-survey","emotional-exhaustion-scale","work-ability-index","perceived-stress-scale","organizational-commitment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"job-descriptive-index","name":"Job Descriptive Index","fullName":"Job Descriptive Index (JDI)","aliases":["JDI","Smith Kendall Hulin Scale","Job Satisfaction Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"job-satisfaction","year":"1969","originator":"Patricia Cain Smith","url":"https://scholargate.app/en/organizational-behavior/job-descriptive-index","markdownUrl":"https://scholargate.app/en/organizational-behavior/job-descriptive-index.md","definition":"The Job Descriptive Index (JDI) is a comprehensive self-report measure of job satisfaction across five distinct dimensions: work, supervision, coworkers, pay, and promotions. Developed by Smith, Kendall, and Hulin in 1969, it has become one of the most widely used and empirically validated job satisfaction instruments in organizational research. The JDI is prized for its multidimensional structure and strong psychometric properties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patricia Cain Smith","subfamily":"job-satisfaction","year":"1969","type":"Self-report questionnaire"},"citations":[{"ref":"Smith, P. C., Kendall, L. M., & Hulin, C. L. (1969). The measurement of satisfaction in work and retirement: A strategy for the study of attitudes. Rand McNally.","type":"book","doi":null,"isbn":"978-0528614110","url":null},{"ref":"Balzer, W. K., Kihm, J. A., Smith, P. C., Irwin, J. L., Bachiochi, P. D., Robie, C., ... & Parra, L. F. (1997). Users' manual for the Job Descriptive Index (JDI) and the Job in General (JIG) Scales. Bowling Green State University.","type":"article","doi":null,"isbn":null,"url":"https://www.bgsu.edu/arts-and-sciences/psychology/community-research-center/jdi.html"},{"ref":"Brodsky, A., Donovan, L. A., & Farh, J. L. (2011). A review of the Job Descriptive Index: A review of literature 2009-2011. In Handbook of employee satisfaction. Springer.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/21686079"}],"related":["perceived-organizational-support","leader-member-exchange-scale","organizational-commitment-questionnaire","psychological-capital-questionnaire","occupational-stress-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"job-satisfaction-survey","name":"Job Satisfaction Survey","fullName":"Job Satisfaction Survey (JSS)","aliases":["JSS"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Occupational health","year":"1985","originator":"Paul E. Spector","url":"https://scholargate.app/en/organizational-behavior/job-satisfaction-survey","markdownUrl":"https://scholargate.app/en/organizational-behavior/job-satisfaction-survey.md","definition":"The Job Satisfaction Survey (JSS) is a 36-item, multidimensional self-report questionnaire developed by Paul Spector in 1985. It assesses nine facets of job satisfaction including pay, promotion, supervision, work itself, fringe benefits, coworkers, communication, working conditions, and management. The JSS has become one of the most widely used job satisfaction instruments in organizational research and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul E. Spector","subfamily":"Occupational health","year":"1985","type":"Self-report questionnaire"},"citations":[{"ref":"Spector, P. E. (1985). Measurement of human service staff satisfaction: development of the Job Satisfaction Survey. American Journal of Community Psychology, 13(6), 693-713.","type":"article","doi":"10.1007/BF00929796","isbn":null,"url":null},{"ref":"Spector, P. E. (1997). Job satisfaction: Application, assessment, causes, and consequences. Thousand Oaks, CA: SAGE Publications.","type":"book","doi":null,"isbn":"978-0803973305","url":null}],"related":["minnesota-satisfaction-questionnaire","organizational-commitment-scale","job-demands-resources-scale","emotional-exhaustion-scale","psychological-safety-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"job-shop-scheduling","name":"Job Shop Scheduling","fullName":"Job Shop Scheduling","aliases":["job scheduling","machine scheduling"],"domain":"operations-management","family":"ml-model","subfamily":"Optimization","year":"2016","originator":"Pinedo, M. L.","url":"https://scholargate.app/en/operations-management/job-shop-scheduling","markdownUrl":"https://scholargate.app/en/operations-management/job-shop-scheduling.md","definition":"Job shop scheduling is the problem of assigning a set of jobs (tasks) to a set of machines (resources) over time, subject to precedence and capacity constraints, with the goal of optimizing performance metrics such as makespan (total completion time), lateness, or cost. The job shop problem is a classic combinatorial optimization problem in operations research, addressed through heuristics (greedy dispatching rules, simulated annealing, genetic algorithms) and exact algorithms (branch-and-bound, constraint programming). It is fundamental to manufacturing, project management, and computational scheduling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pinedo, M. L.","subfamily":"Optimization","year":"2016","type":"Combinatorial scheduling problem"},"citations":[{"ref":"Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Cham: Springer.","type":"book","doi":"10.1007/978-3-319-26580-3","isbn":null,"url":null},{"ref":"Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278-285.","type":"article","doi":"10.1016/0377-2217(93)90182-M","isbn":null,"url":null}],"related":["aggregate-planning","material-requirements-planning","assembly-line-balancing","facility-layout","scor-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"johansen-cointegration","name":"Johansen Cointegration Test","fullName":"Johansen Cointegration Test and Vector Error Correction Model (VECM)","aliases":["Johansen test","VECM","vector error correction model","multivariate cointegration","Johansen Eşbütünleşme Testi ve VECM"],"domain":"finance","family":"regression-model","subfamily":null,"year":1991,"originator":"Søren Johansen","url":"https://scholargate.app/en/finance/johansen-cointegration","markdownUrl":"https://scholargate.app/en/finance/johansen-cointegration.md","definition":"The Johansen procedure is a multivariate cointegration framework, introduced by Søren Johansen in 1991, that tests for long-run equilibrium relationships among several I(1) time series. It determines how many cointegrating vectors link the series and then builds a Vector Error Correction Model (VECM) to describe the short-run dynamics around that equilibrium.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Søren Johansen","year":1991,"type":"Multivariate cointegration / vector error correction model","estimator":"Maximum likelihood (reduced-rank regression)","tests":"Trace and maximum eigenvalue statistics","minSample":50,"dataRequirement":"Multiple I(1) (first-order integrated) time series"},"citations":[{"ref":"Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551-1580.","type":"article","doi":"10.2307/2938278","isbn":null,"url":null},{"ref":"Johansen, S. (1995). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198774501","url":null}],"related":["ardl-bounds-test","var-model","arima","engle-granger-cointegration","augmented-dickey-fuller"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"joint-model-survival","name":"Joint Model for Longitudinal and Survival Data","fullName":"Joint Model for Longitudinal and Time-to-Event Data","aliases":["joint model","shared random effects model","longitudinal-survival joint model","Joint Model (Boylamsal + Sağkalım Birleşik Model)"],"domain":"survival","family":"survival","subfamily":null,"year":2004,"originator":"Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.","url":"https://scholargate.app/en/survival/joint-model-survival","markdownUrl":"https://scholargate.app/en/survival/joint-model-survival.md","definition":"The joint model for longitudinal and time-to-event data, formalised by Tsiatis and Davidian in 2004 and extended comprehensively by Rizopoulos in 2012, simultaneously estimates a mixed-effects model for repeatedly measured biomarkers and a survival model for the time to an event, linking the two processes through shared random effects. It resolves two major problems that simpler approaches cannot handle: informative dropout from longitudinal studies and the endogeneity of time-varying biomarkers used as covariates in a Cox model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.","year":2004,"type":"Semiparametric regression model","subprocesses":"Longitudinal (mixed-effects) + Survival (Cox or parametric)","linkage":"Shared random effects","estimation":"EM algorithm or MCMC","minimumSample":100,"difficulty":4},"citations":[{"ref":"Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.","type":"book","doi":"10.1201/b12208","isbn":null,"url":null},{"ref":"Tsiatis, A.A. & Davidian, M. (2004). Joint Modeling of Longitudinal and Time-to-Event Data: An Overview. Statistica Sinica, 14(3), 809–834.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/24307215"}],"related":["cox-ph","kaplan-meier","time-dependent-cox","frailty-model","landmark-analysis","mixed-effects-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"joint-reaction-force","name":"Joint Reaction Force","fullName":"Joint Reaction Force Estimation","aliases":["Joint contact force","Tibiofemoral force","Joint loading"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Biomechanical loading","year":"2001","originator":"Georg Bergmann","url":"https://scholargate.app/en/biomechanics/joint-reaction-force","markdownUrl":"https://scholargate.app/en/biomechanics/joint-reaction-force.md","definition":"Joint reaction force (JRF) estimation calculates the contact forces transmitted across joints during movement using inverse dynamics combined with anatomical modeling. First validated in vivo by Bergmann and colleagues using instrumented hip implants, JRF estimation is essential for understanding joint degeneration, designing orthopedic implants, and assessing injury risk.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georg Bergmann","subfamily":"Biomechanical loading","year":"2001","type":"Force analysis and joint loading"},"citations":[{"ref":"Bergmann, G., Deuretzbacher, G., Heller, M., Graichen, F., Rohlmann, A., Strauss, J., & Duda, G. N. (2001). Hip forces and gait patterns from routine activities. Journal of Biomechanics, 34(7), 859-871.","type":"article","doi":"10.1016/S0021-9290(01)00040-9","isbn":null,"url":null},{"ref":"Winter, D. A. (1990). Biomechanics and Motor Control of Human Movement. Wiley-Interscience.","type":"book","doi":null,"isbn":null,"url":"https://wiley.com"}],"related":["inverse-dynamics","forward-kinematics","cfd-hemodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jonckheere-terpstra","name":"Jonckheere-Terpstra Test","fullName":"Jonckheere-Terpstra Test for Ordered Alternatives","aliases":["Jonckheere-Terpstra Testi","JT test","ordered k-sample test","trend test for ordered groups"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1952,"originator":"A. R. Jonckheere and T. J. Terpstra","url":"https://scholargate.app/en/statistics/jonckheere-terpstra","markdownUrl":"https://scholargate.app/en/statistics/jonckheere-terpstra.md","definition":"The Jonckheere-Terpstra test is a nonparametric hypothesis test that detects a monotone trend across k ordered groups — testing whether the outcome rises (or falls) systematically as the group order increases. Developed independently by T. J. Terpstra (1952) and A. R. Jonckheere (1954), it is the directional, ordered-alternative counterpart to the Kruskal-Wallis test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. R. Jonckheere and T. J. Terpstra","year":1952,"family":"Hypothesis test","type":"Nonparametric trend test","groups":"k ≥ 2 (ordered)","outcome":"continuous or ordinal","parametric":false,"nullDistribution":"Normal approximation (large samples)","minSample":15},"citations":[{"ref":"Jonckheere, A. R. (1954). A distribution-free k-sample test against ordered alternatives. Biometrika, 41(1-2), 133–145.","type":"article","doi":"10.1093/biomet/41.1-2.133","isbn":null,"url":null},{"ref":"Terpstra, T. J. (1952). The asymptotic normality and consistency of Kendall's test against trend, when ties are present in one ranking. Indagationes Mathematicae, 14, 327–333.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/journal/indagationes-mathematicae"},{"ref":"Hollander, M., Wolfe, D. A., & Chicken, E. (2014). Nonparametric Statistical Methods (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470387375","url":null}],"related":["kruskal-wallis","mann-whitney-u","spearman-correlation","friedman-test","one-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jones-accrual-model","name":"Jones Accrual Model","fullName":"Jones Accrual Model for Detecting Earnings Management","aliases":["Modified Jones Model"],"domain":"accounting","family":"mcdm","subfamily":"Earnings Quality Assessment","year":"1991","originator":"Jennifer J. Jones","url":"https://scholargate.app/en/accounting/jones-accrual-model","markdownUrl":"https://scholargate.app/en/accounting/jones-accrual-model.md","definition":"The Jones Accrual Model, developed by Jennifer J. Jones in 1991, is a statistical method for detecting earnings management in financial statements by isolating abnormal accruals. It distinguishes between normal business accruals and potentially manipulated accruals, helping auditors and analysts identify potential financial statement fraud.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jennifer J. Jones","subfamily":"Earnings Quality Assessment","year":"1991","type":"Financial statement analysis technique"},"citations":[{"ref":"Jones, J. J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2), 193-228.","type":"article","doi":"10.2307/2491047","isbn":null,"url":null},{"ref":"Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. The Accounting Review, 70(2), 193-225.","type":"article","doi":"10.2308/tar-9505096112","isbn":null,"url":null}],"related":["audit-risk-model","analytical-procedures-auditing","internal-control-evaluation","fraud-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jones-calculus","name":"Jones Calculus","fullName":"Jones Calculus for Polarized Light","aliases":["Jones vector method","Jones matrix","polarization calculus"],"domain":"optics","family":"process-pipeline","subfamily":"Polarization","year":"1941","originator":"Robert Clark Jones","url":"https://scholargate.app/en/optics/jones-calculus","markdownUrl":"https://scholargate.app/en/optics/jones-calculus.md","definition":"Jones calculus is a mathematical formalism for analyzing the propagation and manipulation of polarized light using vectors and matrices. Developed by Robert Clark Jones in 1941, it represents the electric field of a coherent optical beam as a two-component complex vector (Jones vector) and optical elements as matrices (Jones matrices), enabling elegant tracking of polarization through optical systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Clark Jones","subfamily":"Polarization","year":"1941","type":"Vector-matrix formalism"},"citations":[{"ref":"Jones, R. C. (1941). A new calculus for the treatment of optical systems: I. Description and discussion of the calculus. Journal of the Optical Society of America, 31(7), 488-493.","type":"article","doi":"10.1364/JOSA.31.000488","isbn":null,"url":null},{"ref":"Born, M., & Wolf, E. (1980). Principles of Optics (6th ed.). Pergamon Press.","type":"book","doi":null,"isbn":null,"url":"https://www.pergamonbooks.com/"},{"ref":"Goldstein, D. H. (2003). Polarized Light (2nd ed.). Marcel Dekker.","type":"book","doi":null,"isbn":null,"url":"https://www.dekker.com/"}],"related":["mueller-stokes-calculus","fourier-optics","abcd-matrix"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"journal-citation-reports","name":"Journal Citation Reports","fullName":"Journal Citation Reports (JCR) Platform","aliases":["JCR","Clarivate Journal Citation Reports"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"journal performance analytics platforms","year":1975,"originator":"Institute for Scientific Information (ISI), now Clarivate Analytics","url":"https://scholargate.app/en/bibliometrics/journal-citation-reports","markdownUrl":"https://scholargate.app/en/bibliometrics/journal-citation-reports.md","definition":"Journal Citation Reports (JCR) is an annual publication by Clarivate Analytics providing comprehensive citation metrics and performance analytics for journals indexed in Web of Science Core Collection. Launched in 1975, JCR publishes Impact Factor, the most widely recognized journal quality metric, alongside supplementary metrics (5-year IF, Journal Citation Indicator, Immediacy Index, Cited Half-Life, and citation distribution analysis). JCR is the authoritative source for journal ranking, benchmarking, and impact assessment in research evaluation systems globally. Access requires institutional subscription, though some institutions provide free access to affiliated researchers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Institute for Scientific Information (ISI), now Clarivate Analytics","subfamily":"journal performance analytics platforms","year":1975,"type":"Tool"},"citations":[{"ref":"Clarivate Analytics. (2024). Journal Citation Reports. Retrieved from https://clarivate.com/webofsciencegroup/solutions/journal-citation-reports/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clarivate%20Analytics.%20(2024).%20Journal%20Citation%20Reports.%20Retrieved%20from%20https%3A%2F%2Fclarivate.com%2Fwebofsciencegroup%2Fsolutions%2F"},{"ref":"Clarivate Analytics. (2023). JCR Methodology Overview. https://clarivate.com/webofsciencegroup/essays/journal-citation-reports-methodology/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clarivate%20Analytics.%20(2023).%20JCR%20Methodology%20Overview.%20https%3A%2F%2Fclarivate.com%2Fwebofsciencegroup%2Fessays%2Fjournal-citation-r"},{"ref":"Garfield, E. (1979). Citation indexing: Its theory and applications in science, technology, and humanities. Philadelphia: ISI Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Garfield%2C%20E.%20(1979).%20Citation%20indexing%3A%20Its%20theory%20and%20applications%20in%20science%2C%20technology%2C%20and%20humanities.%20Philadelphia"}],"related":["web-of-science","impact-factor","scimago-journal-rank","scopus-database","h-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"journal-co-citation-analysis","name":"Journal Co-Citation Analysis","fullName":"Journal Co-Citation Analysis","aliases":["journal citation mapping","journal network analysis","cited source co-citation"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"network-citation","year":"1981","originator":"Henry Small, Henry White, and others","url":"https://scholargate.app/en/bibliometrics/journal-co-citation-analysis","markdownUrl":"https://scholargate.app/en/bibliometrics/journal-co-citation-analysis.md","definition":"Journal co-citation analysis is a bibliometric method that maps the intellectual structure of a research field by analyzing how frequently pairs of journals are cited together in the same papers. Two journals are co-cited when papers cite both journals, indicating that the journals are perceived as intellectually related by the citing authors. This extension of paper-level co-citation analysis to the journal level reveals the topological structure of journal relationships, disciplinary boundaries, and the role of different journals within research communities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Small, Henry White, and others","subfamily":"network-citation","year":"1981","type":"Method"},"citations":[{"ref":"White, H. D., & Griffith, B. C. (1981). Author co-citation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163–171.","type":"article","doi":"10.1002/asi.4630320302","isbn":null,"url":null},{"ref":"McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science, 41(6), 433–443.","type":"article","doi":"10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q","isbn":null,"url":null}],"related":["co-citation-analysis","science-mapping","bibliographic-coupling","keyword-co-occurrence"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"journal-submission-process","name":"Journal Submission Process","fullName":"Steps in Submitting a Manuscript to a Peer-Reviewed Journal","aliases":["manuscript submission","journal submission","peer review process"],"domain":"academic-writing","family":"process-pipeline","subfamily":"publication-process","year":"1950","originator":"Journal editors and publishing community; standards documented by ICMJE and COPE","url":"https://scholargate.app/en/academic-writing/journal-submission-process","markdownUrl":"https://scholargate.app/en/academic-writing/journal-submission-process.md","definition":"Submitting a manuscript to a peer-reviewed journal is a multi-stage process: preparation, submission, editorial triage, peer review, revision, and publication. Understanding each stage helps authors avoid common pitfalls and set realistic expectations. Most journals use online submission systems (ScholarOne, Editorial Manager, OJS) that guide authors through the process. From submission to first editorial decision typically takes 30–90 days; acceptance to publication can take another 30–180 days depending on the journal's backlog and production timeline. Journals vary in acceptance rates (Nature ~5%, specialized journals 30–50%) and review times. Knowing the journal's policies and timelines before submitting prevents frustration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Journal editors and publishing community; standards documented by ICMJE and COPE","subfamily":"publication-process","year":"1950","type":"Guideline"},"citations":[{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.","type":"guideline","doi":null,"isbn":null,"url":"https://www.icmje.org/"},{"ref":"Committee on Publication Ethics (2019). COPE Handbook on Publication Ethics. Retrieved from https://publicationethics.org/","type":"guideline","doi":null,"isbn":null,"url":"https://publicationethics.org/"},{"ref":"Wager, E., & Wieland, B. (2011). Responsibilities of journal editors. Lancet, 337(9834), 1807–1809.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Responsibilities+of+journal+editors+Wager"}],"related":["imrad-structure","apa-style-guide","vancouver-style","revision-response-to-reviewers"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"jump-diffusion-model","name":"Jump-Diffusion Model","fullName":"Merton Jump-Diffusion Model","aliases":["Merton jump-diffusion","jump-diffusion process","Atlama Difüzyon Modeli (Merton Jump-Diffusion)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1976,"originator":"Robert C. Merton","url":"https://scholargate.app/en/finance/jump-diffusion-model","markdownUrl":"https://scholargate.app/en/finance/jump-diffusion-model.md","definition":"The Merton Jump-Diffusion model, introduced by Robert C. Merton in 1976, extends Geometric Brownian Motion by adding sudden price jumps generated by a Poisson process. It captures the volatility smile and the fat-tailed return behaviour that standard Black-Scholes cannot explain, and is widely used in option pricing and risk management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert C. Merton","year":1976,"type":"Continuous-time asset price model (diffusion plus Poisson jumps)","estimator":"Maximum likelihood or option-implied calibration","outcome":"continuous (asset price / return path)"},"citations":[{"ref":"Merton, R. C. (1976). Option Pricing When Underlying Stock Returns Are Discontinuous. Journal of Financial Economics, 3(1–2), 125–144.","type":"article","doi":"10.1016/0304-405X(76)90022-2","isbn":null,"url":null}],"related":["har-rv-model","pairs-trading","black-litterman-model","garch-model","geometric-brownian-motion"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"just-about-right-scaling","name":"Just-About-Right Scaling","fullName":"Just-About-Right Scaling (JAR)","aliases":["JAR"],"domain":"food-science","family":"process-pipeline","subfamily":"Sensory Evaluation","year":"1995","originator":"Henry Lawless","url":"https://scholargate.app/en/food-science/just-about-right-scaling","markdownUrl":"https://scholargate.app/en/food-science/just-about-right-scaling.md","definition":"Just-About-Right (JAR) Scaling is a consumer-based sensory evaluation method that asks respondents to rate sensory attributes not on intensity alone, but on whether they perceive the attribute as too weak, just right, or too strong for the product. Developed by Lawless in the mid-1990s, JAR scaling bridges the gap between descriptive sensory analysis and consumer preference, directly linking attribute levels to consumer satisfaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Lawless","subfamily":"Sensory Evaluation","year":"1995","type":"Consumer Preference Scaling"},"citations":[{"ref":"Lawless, H. T. (1995). Evaluation of world wide web sites with sensory evaluation methods. Food Technology, 49(12), 90-92.","type":"article","doi":null,"isbn":null,"url":"https://www.ift.org"},{"ref":"Meilgaard, M. C., Carr, B. T., & Civille, G. V. (2006). Sensory evaluation techniques (4th ed.). CRC Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sensory+evaluation+techniques+%284th+ed.%29+Meilgaard"}],"related":["quantitative-descriptive-analysis","texture-profile-analysis","temporal-dominance-of-sensations"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"juvenile-arthritis-parent-assessment","name":"JAFAR","fullName":"Juvenile Arthritis Functional Assessment Report","aliases":["JAFAR","Juvenile Arthritis Functional Assessment Scale"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"rheumatologic disease pediatric functional assessment","year":1989,"originator":"David J. Lovell","url":"https://scholargate.app/en/pediatric-medicine/juvenile-arthritis-parent-assessment","markdownUrl":"https://scholargate.app/en/pediatric-medicine/juvenile-arthritis-parent-assessment.md","definition":"The JAFAR is a parent-report instrument developed by Lovell et al. in 1989 to assess functional ability in children and adolescents with juvenile idiopathic arthritis (JIA). Measuring across multiple domains including lower extremity function, upper extremity function, and activities of daily living, the JAFAR quantifies the extent to which arthritis and its treatment affect the child's mobility, self-care, and participation in age-appropriate activities. It remains a standard functional outcome measure in pediatric rheumatology research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David J. Lovell","subfamily":"rheumatologic disease pediatric functional assessment","year":1989,"type":"Parent report of child functional status"},"citations":[{"ref":"Lovell, D. J., Howe, S., Shear, E., Hartner, S., McGirr, G., Schulte, M., & Jaffe, R. (1989). Development of a disability measurement tool for juvenile rheumatoid arthritis. Arthritis & Rheumatism, 32(11), 1390-1395.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+of+a+disability+measurement+tool+for+juvenile+rheumatoid+arthritis+Lovell"},{"ref":"Lovell, D. J., Giannini, E. H., & Reiff, A. (1994). Etanercept in children with polyarticular juvenile rheumatoid arthritis. New England Journal of Medicine, 342(11), 763-769.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Etanercept+in+children+with+polyarticular+juvenile+rheumatoid+arthritis+Lovell"}],"related":["paqlq","pedsql-diabetes","child-health-questionnaire","qolce"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"k-anonymity","name":"k-Anonymity","fullName":"k-Anonymity Data Anonymization","aliases":["k-Anonymization","k-Anonymous Microdata","Quasi-Identifier Suppression Model","k-Anonimlik"],"domain":"privacy","family":"ml-model","subfamily":"Privacy-preserving analysis","year":2002,"originator":"Latanya Sweeney","url":"https://scholargate.app/en/privacy/k-anonymity","markdownUrl":"https://scholargate.app/en/privacy/k-anonymity.md","definition":"k-Anonymity is a formal privacy model introduced by Latanya Sweeney in 2002 to protect individuals when personal data is released for research or public use. It requires that every record in a published dataset be indistinguishable from at least k−1 other records with respect to a designated set of quasi-identifying attributes — such as age, gender, and ZIP code — preventing re-identification by linking released data to external sources.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Latanya Sweeney","year":2002,"type":"Privacy-preserving data transformation","subfamily":"Privacy-preserving analysis","input":"Microdata table with quasi-identifiers","output":"Anonymized table where each record is indistinguishable from at least k-1 others"},"citations":[{"ref":"Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.","type":"article","doi":"10.1142/S0218488502001648","isbn":null,"url":null}],"related":["differential-privacy","synthetic-data-generation"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"k-core-decomposition","name":"k-Core Decomposition","fullName":"k-Core Decomposition of Networks","aliases":["Core Decomposition","Coreness Decomposition","Shell Decomposition","Çekirdek Ayrıştırma"],"domain":"network-analysis","family":"process-pipeline","subfamily":"Network structure","year":1983,"originator":"Stephen B. Seidman","url":"https://scholargate.app/en/network-analysis/k-core-decomposition","markdownUrl":"https://scholargate.app/en/network-analysis/k-core-decomposition.md","definition":"k-Core Decomposition is a graph-theoretic method that partitions the vertices of a network into a nested sequence of subgraphs called k-cores. A k-core is the maximal subgraph in which every vertex has at least k neighbors within that subgraph. Introduced by Stephen B. Seidman in 1983, the method assigns each vertex a coreness number that captures its structural centrality relative to the local connectivity of the graph.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen B. Seidman","year":1983,"type":"Graph pruning and hierarchical decomposition","subfamily":"Network structure","complexity":"O(m) for sparse graphs","output":"Coreness number per vertex"},"citations":[{"ref":"Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287.","type":"article","doi":"10.1016/0378-8733(83)90028-X","isbn":null,"url":null}],"related":["centrality-analysis","community-detection","pagerank"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"k-means-clustering","name":"K-Means Clustering","fullName":"K-Means Clustering (Lloyd–MacQueen Algorithm)","aliases":["K-Ortalamalar Kümeleme","k-ortalamalar kümeleme","k-means","centroid clustering"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1967,"originator":"MacQueen, J.","url":"https://scholargate.app/en/machine-learning/k-means-clustering","markdownUrl":"https://scholargate.app/en/machine-learning/k-means-clustering.md","definition":"K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"MacQueen, J.","year":1967,"type":"Partitional clustering (centroid-based)","task":"Unsupervised clustering / segmentation","minSample":50,"requiresNormality":false},"citations":[{"ref":"MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.","type":"article","doi":null,"isbn":null,"url":"https://projecteuclid.org/euclid.bsmsp/1200512992"}],"related":["hierarchical-clustering","lda-classification","principal-component-analysis","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"k-means","name":"K-means","fullName":"K-means Clustering Algorithm","aliases":["k-means clustering","Lloyd's algorithm","k-means partitioning","hard k-means"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1967 (formalized 1982)","originator":"MacQueen, J. B.; Lloyd, S. P.","url":"https://scholargate.app/en/machine-learning/k-means","markdownUrl":"https://scholargate.app/en/machine-learning/k-means.md","definition":"K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"MacQueen, J. B.; Lloyd, S. P.","year":"1967 (formalized 1982)","type":"Partitional clustering","dataType":"Continuous (numeric) features","subfamily":"Machine learning"},"citations":[{"ref":"Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.","type":"article","doi":"10.1109/TIT.1982.1056489","isbn":null,"url":null},{"ref":"MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Some+methods+for+classification+and+analysis+of+multivariate+observations+MacQueen+1967"}],"related":["gaussian-mixture-model","dbscan","hierarchical-clustering","k-nearest-neighbors","pca","t-sne"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"k10-kessler","name":"Kessler Psychological Distress Scale","fullName":"Kessler Psychological Distress Scale-10 (K10)","aliases":["K10","K6","Kessler Scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Population screening and epidemiology","year":"2002","originator":"Ronald C. Kessler and colleagues","url":"https://scholargate.app/en/clinical-psychology/k10-kessler","markdownUrl":"https://scholargate.app/en/clinical-psychology/k10-kessler.md","definition":"The Kessler Psychological Distress Scale-10 (K10) is a 10-item self-report measure of non-specific psychological distress and mental health problems. Developed by Kessler and colleagues in 2002, the K10 was designed as an ultra-brief screening instrument for population surveys and epidemiological research. A shorter 6-item version (K6) is also widely used for rapid case identification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald C. Kessler and colleagues","subfamily":"Population screening and epidemiology","year":"2002","type":"Non-specific psychological distress screening"},"citations":[{"ref":"Kessler, R. C., Andrews, G., Colpe, L. Y., Hiripi, E., Mroczek, D. K., Normand, S. L., ... & Zaslavsky, A. M. (2002). Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine, 32(6), 959-976.","type":"article","doi":"10.1017/S0033291702006074","isbn":null,"url":null},{"ref":"Furukawa, T. A., Kessler, R. C., Slade, T., & Andrews, G. (2003). The performance of the K6 and K10 screening scales for psychological distress in the Australian National Survey of Mental Health and Well-Being. Psychological Medicine, 33(2), 357-362.","type":"article","doi":"10.1017/S0033291702006700","isbn":null,"url":null}],"related":["ghq-12","hads","dass-21","ces-d","swls"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kalman-filter-finance","name":"Kalman Filter (Finance)","fullName":"Kalman Filter — Financial State-Space Model","aliases":["state-space model","dynamic linear model","recursive Bayesian filter","Kalman Filtresi — Finansal Durum Uzayı Modeli"],"domain":"finance","family":"regression-model","subfamily":null,"year":1989,"originator":"Harvey (structural time series treatment); Kim & Nelson (state-space with regime switching)","url":"https://scholargate.app/en/finance/kalman-filter-finance","markdownUrl":"https://scholargate.app/en/finance/kalman-filter-finance.md","definition":"The Kalman filter is a recursive algorithm that estimates financial models with time-varying parameters, hidden factors, and noisy observations inside a dynamic state-space framework. The structural time series treatment was set out by Harvey (1989), with state-space and regime-switching extensions developed by Kim and Nelson (1999); it is widely applied to pairs trading, time-varying beta estimation, and yield-curve modelling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey (structural time series treatment); Kim & Nelson (state-space with regime switching)","year":1989,"type":"Linear state-space model","estimator":"Recursive predict-update filter; maximum likelihood / EM for parameters","structure":"time series","minSample":50},"citations":[{"ref":"Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521405737","url":null},{"ref":"Kim, C. J. & Nelson, C. R. (1999). State-Space Models with Regime Switching. MIT Press.","type":"book","doi":null,"isbn":"978-0262112383","url":null}],"related":["arima","factor-risk-model","stochastic-volatility-model","principal-component-risk","long-memory-models"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kalman-filter-signal","name":"Kalman Filter for Signal Tracking","fullName":"Kalman Filter for Signal Estimation and Tracking","aliases":["Kalman Filtering","Recursive State Estimation","Optimal Filtering"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Optimal state estimation","year":"1960","originator":"Rudolf E. Kalman","url":"https://scholargate.app/en/signal-processing/kalman-filter-signal","markdownUrl":"https://scholargate.app/en/signal-processing/kalman-filter-signal.md","definition":"The Kalman filter is a recursive algorithm that optimally estimates the state of a linear dynamic system from noisy measurements, minimizing mean-square error. Introduced by Rudolf Kalman in 1960, it revolutionized control theory, navigation, and signal processing by enabling real-time optimal estimation for time-varying systems. The Kalman filter became indispensable for spacecraft tracking, GPS navigation, and countless modern applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf E. Kalman","subfamily":"Optimal state estimation","year":"1960","type":"Recursive optimal filter"},"citations":[{"ref":"Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35–45.","type":"article","doi":"10.1115/1.3662552","isbn":null,"url":null},{"ref":"Grewal, M. S., & Andrews, A. P. (2015). Kalman Filtering: Theory and Practice with MATLAB (4th ed.). Wiley-IEEE Press.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Kalman+Filtering%3A+Theory+and+Practice+with+MATLAB%2C+4th+Edition-p-9781118984987"}],"related":["wiener-filter","adaptive-lms-filter","matched-filter","fir-filter-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kalman-filter-with-measurement-error","name":"Kalman Filter with Measurement Error","fullName":"Kalman Filter with Explicit Measurement Error Modeling","aliases":["Kalman filter measurement noise","state-space model with observation error","KF with measurement uncertainty","linear quadratic estimation with observation noise"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1960","originator":"Rudolf E. Kalman","url":"https://scholargate.app/en/bayesian/kalman-filter-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/kalman-filter-with-measurement-error.md","definition":"The Kalman filter with measurement error is a recursive Bayesian state-space algorithm that estimates the true hidden state of a dynamic system from noisy observations. It explicitly separates process noise (system dynamics uncertainty) from measurement noise (observation uncertainty), propagating both sources of error through a two-step predict-update cycle to yield optimal filtered state estimates and their associated uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf E. Kalman","year":"1960","type":"Recursive Bayesian state-space estimator","dataType":"Sequential / time-series observations with additive Gaussian noise","subfamily":"Bayesian / computational"},"citations":[{"ref":"Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45.","type":"article","doi":"10.1115/1.3662552","isbn":null,"url":null},{"ref":"Durbin, J. & Koopman, S. J. (2012). Time Series Analysis by State Space Methods (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0199641178","url":null}],"related":["kalman-filter","particle-filter","sequential-monte-carlo","kalman-filter-with-missing-data","dynamic-bayesian-inference","bayesian-hierarchical-model-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kalman-filter-with-missing-data","name":"Kalman Filter with Missing Data","fullName":"Kalman Filter for State-Space Models with Missing Observations","aliases":["Kalman smoother with missing data","state-space model with missing observations","EM Kalman filter","KF-EM"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1982","originator":"Rudolf E. Kálmán (Kalman filter, 1960); missing-data extension formalised by Shumway & Stoffer (1982) via EM","url":"https://scholargate.app/en/bayesian/kalman-filter-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/kalman-filter-with-missing-data.md","definition":"The Kalman filter with missing data extends the classical Kalman filter to handle time series in which some observations are absent. When an observation is missing at time t the update step is skipped and the state estimate is carried forward from the prediction step alone. Combined with the Expectation-Maximisation (EM) algorithm, the approach also estimates unknown model parameters from incomplete data, making it a practical tool for real-world irregularly observed series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf E. Kálmán (Kalman filter, 1960); missing-data extension formalised by Shumway & Stoffer (1982) via EM","year":"1982","type":"Sequential Bayesian filter / state-space smoother","dataType":"Multivariate time series with intermittent missing observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Shumway, R. H. & Stoffer, D. S. (2000). Time Series Analysis and Its Applications. Springer.","type":"book","doi":null,"isbn":"978-0387989501","url":null},{"ref":"Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521405737","url":null}],"related":["kalman-filter","particle-filter-with-missing-data","sequential-monte-carlo","bayesian-inference-with-missing-data","state-space-model","em-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kalman-filter","name":"Kalman Filter","fullName":"Kalman Filter (Linear-Gaussian State-Space Filter)","aliases":["linear quadratic estimator","LQE","Kalman-Bucy filter","optimal recursive filter"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1960","originator":"Rudolf E. Kalman","url":"https://scholargate.app/en/bayesian/kalman-filter","markdownUrl":"https://scholargate.app/en/bayesian/kalman-filter.md","definition":"The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf E. Kalman","year":"1960","type":"recursive Bayesian filter","dataType":"sequential / time-series, continuous observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.","type":"article","doi":"10.1115/1.3662552","isbn":null,"url":null},{"ref":"Welch, G. & Bishop, G. (2006). An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill, Technical Report TR 95-041.","type":"article","doi":null,"isbn":null,"url":"https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf"}],"related":["sequential-monte-carlo","particle-filter","bayesian-regression","hidden-markov-model","extended-kalman-filter","dynamic-bayesian-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kanban","name":"Kanban","fullName":"Kanban System","aliases":["visual management","pull system"],"domain":"operations-management","family":"ml-model","subfamily":"Lean Manufacturing","year":"1950","originator":"Taiichi Ohno","url":"https://scholargate.app/en/operations-management/kanban","markdownUrl":"https://scholargate.app/en/operations-management/kanban.md","definition":"Kanban is a pull-based production control system developed by Taiichi Ohno at Toyota in the 1950s that uses visual signals (traditionally cards or bins) to trigger production and movement of materials based on actual demand rather than forecasts. The Japanese word 'kanban' means 'visual card' or 'sign,' and the system operates on the principle that work should flow in response to downstream requirements. Kanban is a foundational element of the Toyota Production System and lean manufacturing, enabling just-in-time production, reduced inventory, and improved flow efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Taiichi Ohno","subfamily":"Lean Manufacturing","year":"1950","type":"Production control system"},"citations":[{"ref":"Ohno, T. (1988). Toyota production system: Beyond large-scale production. Cambridge, MA: Productivity Press.","type":"book","doi":null,"isbn":null,"url":"https://www.productivitypress.com/"},{"ref":"Rother, M., & Shook, J. (2003). Learning to see: Value stream mapping to add value and eliminate muda. Cambridge, MA: Lean Enterprise Institute.","type":"book","doi":null,"isbn":null,"url":"https://www.lean.org/"}],"related":["material-requirements-planning","aggregate-planning","vendor-managed-inventory","smed","scor-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kano-model","name":"Kano Model","fullName":"Kano Model of Customer Satisfaction","aliases":["Kano Analysis","Attractive-Performance-Basic Model"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Customer Satisfaction Theory","year":"1984","originator":"Noriaki Kano","url":"https://scholargate.app/en/human-computer-interaction/kano-model","markdownUrl":"https://scholargate.app/en/human-computer-interaction/kano-model.md","definition":"The Kano Model is a framework for categorizing product or service features based on their impact on customer satisfaction. Developed by Noriaki Kano, this model distinguishes three types of features: basic (must-have) features that satisfy minimally but cause significant dissatisfaction if absent; performance features that increase satisfaction proportionally with their level; and attractive (delightful) features that exceed expectations and generate disproportionate satisfaction. By classifying features using the Kano Model, product teams prioritize development efforts, balance risk and innovation, and design experiences that delight rather than merely satisfy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Noriaki Kano","subfamily":"Customer Satisfaction Theory","year":"1984","type":"Two-dimensional model categorizing product/service features by satisfaction impact"},"citations":[{"ref":"Kano, N., Seraku, N., Takahashi, F., & Tsjui, S. (1984). Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control, 14(2), 147–156.","type":"article","doi":null,"isbn":null,"url":"https://www.jstage.jst.go.jp/article/jsqc1971/14/2/14_2_147/_article"},{"ref":"Cohen, L. (2007). Quality function deployment and six sigma. Pearson Education.","type":"article","doi":null,"isbn":"0-13-513338-2","url":null}],"related":["system-usability-scale","nasa-tlx","attrakdiff-ueq","user-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kansas-city-cardiomyopathy","name":"Kansas City Cardiomyopathy Questionnaire","fullName":"Kansas City Cardiomyopathy Questionnaire (KCCQ)","aliases":["KCCQ"],"domain":"cardiology","family":"process-pipeline","subfamily":"cardiomyopathy and heart failure quality of life","year":"2000","originator":"John A. Spertus","url":"https://scholargate.app/en/cardiology/kansas-city-cardiomyopathy","markdownUrl":"https://scholargate.app/en/cardiology/kansas-city-cardiomyopathy.md","definition":"The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a 23-item, multidimensional self-report measure that evaluates heart failure-related symptoms, functional limitations, and quality of life in patients with cardiomyopathy and heart failure of all severities. Developed by Spertus and colleagues in 2000, the KCCQ provides six disease-specific domains and a clinically summary score, making it ideal for comprehensive, domain-focused assessment in both clinical practice and research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John A. Spertus","subfamily":"cardiomyopathy and heart failure quality of life","year":"2000","type":"Self-report questionnaire"},"citations":[{"ref":"Green, C. P., Porter, C. B., Bresnahan, D. R., & Spertus, J. A. (2000). Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: a new health-related quality of life measure for heart failure. Journal of the American College of Cardiology, 35(5), 1245–1253.","type":"article","doi":"10.1016/S0735-1097(00)00531-3","isbn":null,"url":null}],"related":["minnesota-heart-failure","seattle-angina-questionnaire","new-york-heart-association-class","duke-activity-status-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kaplan-meier-analysis","name":"Kaplan-Meier Analysis","fullName":"Kaplan-Meier Survival Analysis","aliases":["KM analysis","KM estimator","product-limit estimator","Kaplan-Meier curve"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1958","originator":"Edward L. Kaplan and Paul Meier","url":"https://scholargate.app/en/epidemiology/kaplan-meier-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/kaplan-meier-analysis.md","definition":"Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward L. Kaplan and Paul Meier","year":"1958","type":"Nonparametric survival estimator","dataType":"Time-to-event data with censoring (survival times, event indicators)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.","type":"article","doi":"10.2307/2281868","isbn":null,"url":null},{"ref":"Kaplan–Meier estimator. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator"}],"related":["survival-analysis","cox-proportional-hazards","competing-risks-analysis","log-rank-test","cohort-study","randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kaplan-meier-estimator","name":"Kaplan-Meier Estimator","fullName":"Kaplan-Meier Survival Function Estimator","aliases":["KM estimator","product-limit estimator","Kaplan-Meier curve","survival curve estimator"],"domain":"statistics","family":"survival","subfamily":null,"year":1958,"originator":"Edward L. Kaplan and Paul Meier","url":"https://scholargate.app/en/statistics/kaplan-meier-estimator","markdownUrl":"https://scholargate.app/en/statistics/kaplan-meier-estimator.md","definition":"The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward L. Kaplan and Paul Meier","year":1958,"family":"Survival analysis","type":"Nonparametric estimator","handles_censoring":true,"parametric":false,"output":"Step-function survival curve S(t)","distribution_assumed":"None","estimand":"S(t) = P(T > t)"},"citations":[{"ref":"Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.","type":"article","doi":"10.1080/01621459.1958.10501452","isbn":null,"url":null},{"ref":"Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439856789","url":null},{"ref":"Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer.","type":"book","doi":null,"isbn":"978-1441966452","url":null}],"related":["log-rank-test","cox-proportional-hazards","nelson-aalen-estimator","accelerated-failure-time-model","competing-risks-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kaplan-meier","name":"Kaplan-Meier","fullName":"Kaplan-Meier Survival Estimator","aliases":["product-limit estimator","km curve","kaplan-meier sağkalım analizi"],"domain":"survival","family":"survival","subfamily":null,"year":1958,"originator":"Kaplan, E. L. & Meier, P.","url":"https://scholargate.app/en/survival/kaplan-meier","markdownUrl":"https://scholargate.app/en/survival/kaplan-meier.md","definition":"The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaplan, E. L. & Meier, P.","year":1958,"type":"Non-parametric survival estimator","handles":"Right-censoring"},"citations":[{"ref":"Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.","type":"article","doi":"10.1080/01621459.1958.10501452","isbn":null,"url":null},{"ref":"Kleinbaum, D. G. & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer.","type":"book","doi":null,"isbn":"978-1441966452","url":null}],"related":["cox-ph","nelson-aalen","log-rank-test","fine-gray-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"karl-fischer-titration","name":"Karl Fischer Titration","fullName":"Karl Fischer Titration","aliases":["KFT"],"domain":"food-science","family":"process-pipeline","subfamily":"Analytical Chemistry","year":"1935","originator":"Karl Fischer","url":"https://scholargate.app/en/food-science/karl-fischer-titration","markdownUrl":"https://scholargate.app/en/food-science/karl-fischer-titration.md","definition":"Karl Fischer Titration (KFT) is a precise analytical method for determining water content in food and pharmaceutical products. Developed by Karl Fischer in 1935, KFT uses a chemical reaction between water and an iodine-based titrant, allowing quantification of moisture with exceptional accuracy and sensitivity. KFT is the official gold-standard method for water determination in numerous food and pharmaceutical standards worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Fischer","subfamily":"Analytical Chemistry","year":"1935","type":"Titrimetric Water Determination"},"citations":[{"ref":"Karl Fischer. Neue Methode zur Maßstabbestimmung des Wassers in Flüssigkeiten und Gasen. Angewandte Chemie, 48(44), 394-396. (1935)","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Scholz, E. (2004). Karl Fischer titration (2nd ed.). Springer-Verlag.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Karl+Fischer+titration+%282nd+ed.%29+Scholz"}],"related":["hplc","dsc-gelatinization","accelerated-shelf-life-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"katz-independence-adl","name":"Katz Index of Independence in ADL","fullName":"Katz Index of Independence in Activities of Daily Living (ADL)","aliases":["Katz Index","Katz ADL Scale","Index of ADL"],"domain":"nursing","family":"process-pipeline","subfamily":"functional status assessment","year":"1963","originator":"Sidney Katz","url":"https://scholargate.app/en/nursing/katz-independence-adl","markdownUrl":"https://scholargate.app/en/nursing/katz-independence-adl.md","definition":"The Katz Index of Independence in Activities of Daily Living, developed by Sidney Katz and colleagues in 1963, is one of the earliest and most widely used tools for assessing functional status in older adults and persons with chronic illness. The scale evaluates six essential self-care activities (bathing, dressing, toileting, transfer, continence, feeding) through direct observation or interview and assigns an overall grade (A through G) reflecting the degree of independence. It remains a foundational instrument in geriatric assessment, rehabilitation medicine, and long-term care settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sidney Katz","subfamily":"functional status assessment","year":"1963","type":"Clinician-rated or observational functional assessment"},"citations":[{"ref":"Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A., & Jaffe, M. W. (1963). Studies of Illness in the Aged: The Index of ADL, a standardized measure of biological and psychosocial function. JAMA, 185(12), 914-919.","type":"article","doi":"10.1001/jama.1963.03060120024016","isbn":null,"url":null},{"ref":"Katz, S., Downs, T. D., Cash, H. R., & Grotz, R. C. (1970). Progress in development of the Index of ADL. Gerontologist, 10(1), 20-30.","type":"article","doi":"10.1093/geront/10.1_part_1.20","isbn":null,"url":null}],"related":["lawton-brody-iadl","zarit-caregiver-burden-scale","clinical-frailty-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"keetch-byram-drought-index","name":"Keetch-Byram Drought Index","fullName":"Keetch-Byram Drought Index","aliases":["KBDI","drought severity index"],"domain":"forestry","family":"process-pipeline","subfamily":"Fire Danger","year":"1968","originator":"John Keetch","url":"https://scholargate.app/en/forestry/keetch-byram-drought-index","markdownUrl":"https://scholargate.app/en/forestry/keetch-byram-drought-index.md","definition":"The Keetch-Byram Drought Index (KBDI) is a cumulative drought severity index used in fire danger rating systems to track long-term soil moisture depletion and drying trends. Developed in 1968 by Keetch and Byram, KBDI integrates daily temperature, precipitation, and prior drought state to produce a continuous index ranging from 0 (no drought, moist soil) to 800 (severe drought, very dry soil). KBDI is widely used in fire danger prediction and fire behavior modeling because soil moisture is a major driver of fuel drying and flammability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Keetch","subfamily":"Fire Danger","year":"1968","type":"drought index"},"citations":[{"ref":"Keetch, J. J., & Byram, G. M. (1968). A drought index for forest fire control. Research Paper SE-38, USDA Forest Service Southeastern Forest Experiment Station.","type":"article","doi":null,"isbn":null,"url":"https://www.fs.fed.us"},{"ref":"Burgan, R. E. (1988). 1988 revisions to the 1978 National Fire-Danger Rating System. Research Paper SE-273.","type":"article","doi":null,"isbn":null,"url":"https://www.fs.fed.us"}],"related":["fire-weather-index","rothermel-fire-model","fuel-moisture"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kelly-criterion","name":"Kelly Criterion","fullName":"Kelly Criterion for Optimal Position Sizing","aliases":["Kelly Formula","Optimal Bet Sizing"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Portfolio Theory","year":"1956","originator":"John L. Kelly Jr.","url":"https://scholargate.app/en/quantitative-finance/kelly-criterion","markdownUrl":"https://scholargate.app/en/quantitative-finance/kelly-criterion.md","definition":"The Kelly Criterion (1956) is a formula for optimal bet sizing that maximizes the long-run logarithmic growth of wealth. It specifies the optimal fraction of capital to risk on each trade based on win probability and payoff ratio. The criterion has become foundational in quantitative trading, portfolio management, and behavioral economics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John L. Kelly Jr.","subfamily":"Portfolio Theory","year":"1956","type":"Bet Sizing Framework"},"citations":[{"ref":"Kelly, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926.","type":"article","doi":"10.1002/j.1538-7305.1956.tb03809.x","isbn":null,"url":null},{"ref":"Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House.","type":"book","doi":null,"isbn":null,"url":"https://www.edwardothorp.com"}],"related":["risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kemeny-young","name":"KEMENY-YOUNG","fullName":"Kemeny-Young — Optimal rank aggregation minimising Kendall τ disagreement","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"1959","originator":"Kemeny, J. G.","url":"https://scholargate.app/en/decision-making/kemeny-young","markdownUrl":"https://scholargate.app/en/decision-making/kemeny-young.md","definition":"KEMENY-YOUNG (Kemeny-Young — Optimal rank aggregation minimising Kendall τ disagreement) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Kemeny, J. G. in 1959. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kemeny, J. G.","subfamily":"AggregationOperator","year":"1959","type":"Rank aggregation (Kemeny consensus, NP-hard optimisation)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Kemeny, J. G. (1959). Mathematics without numbers. Daedalus","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mathematics%20without%20numbers"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kemira","name":"KEMIRA","fullName":"KEmeny Median Indicator Ranks Accordance","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Krylovas, A. Zavadskas, E. K. Kosareva, N. Dadelo, S.","url":"https://scholargate.app/en/decision-making/kemira","markdownUrl":"https://scholargate.app/en/decision-making/kemira.md","definition":"KEMIRA (KEmeny Median Indicator Ranks Accordance) is a ranking multi-criteria decision-making (MCDM) method introduced by Krylovas, A. Zavadskas, E. K. Kosareva, N. Dadelo, S. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Krylovas, A. Zavadskas, E. K. Kosareva, N. Dadelo, S.","subfamily":"Ranking","year":"2014","type":"Group-of-criteria Kemeny-median weight elicitation + additive aggregation across two attribute groups","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Krylovas, A., Zavadskas, E. K., Kosareva, N., Dadelo, S. (2014). New KEMIRA method for determining priorities of the attributes in solving MCDM problem. International Journal of Information Technology & Decision Making","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=New+KEMIRA+method+for+determining+priorities+of+the+attributes+in+solving+MCDM+problem+Krylovas"}],"related":["topsis","vikor","wsm","wpm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kendall-tau","name":"Kendall Tau Correlation","fullName":"Kendall Tau Rank Correlation Coefficient","aliases":["Kendall's tau","Kendall tau-b","tau correlation","Kendall Tau Korelasyonu"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1938,"originator":"Maurice G. Kendall","url":"https://scholargate.app/en/statistics/kendall-tau","markdownUrl":"https://scholargate.app/en/statistics/kendall-tau.md","definition":"Kendall Tau is a nonparametric rank correlation coefficient introduced by Maurice G. Kendall in 1938 to measure the strength and direction of a monotone association between two ordinal or continuous variables. It is particularly suited to small samples and datasets containing many tied ranks, where the Spearman coefficient can be less stable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maurice G. Kendall","year":1938,"family":"Nonparametric correlation","type":"Rank-based association measure","outcome":"ordinal or continuous","parametric":false,"distribution":"Asymptotic normal (large n); exact permutation (small n)","minSample":10,"measureRange":"−1 to +1"},"citations":[{"ref":"Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1–2), 81–93.","type":"article","doi":"10.1093/biomet/30.1-2.81","isbn":null,"url":null}],"related":["spearman-correlation","pearson-correlation","mann-whitney-u","wilcoxon-signed-rank"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kendalls-tau","name":"Kendall's tau","fullName":"Kendall's Tau Rank Correlation Coefficient","aliases":["Kendall tau","Kendall rank correlation","tau-b","tau-c"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1938","originator":"Maurice G. Kendall","url":"https://scholargate.app/en/statistics/kendalls-tau","markdownUrl":"https://scholargate.app/en/statistics/kendalls-tau.md","definition":"Kendall's tau is a nonparametric measure of the ordinal association between two variables. It quantifies how consistently the relative ordering of one variable matches the ordering of another across all observation pairs, making it robust to outliers and suitable for ordinal or non-normally distributed data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maurice G. Kendall","year":"1938","type":"Nonparametric rank correlation","dataType":"Ordinal or continuous (ranked)","subfamily":"Classical statistics"},"citations":[{"ref":"Kendall, M. G. (1938). A new measure of rank correlation. Biometrika, 30(1/2), 81–93.","type":"article","doi":"10.1093/biomet/30.1-2.81","isbn":null,"url":null},{"ref":"Kendall, M. G. (1948). Rank Correlation Methods. Charles Griffin & Company.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kendall+Rank+Correlation+Methods+1948"}],"related":["spearman-correlation","pearson-correlation","mann-whitney-u-test","wilcoxon-signed-rank-test","roc-analysis","effect-size-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kentucky-inventory-mindfulness","name":"Kentucky Inventory of Mindfulness Skills","fullName":"Kentucky Inventory of Mindfulness Skills (KIMS)","aliases":["KIMS","KIMS-39"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"multidimensional-trait","year":"2004","originator":"Ruth A. Baer, Greg T. Smith, and Kristin B. Allen","url":"https://scholargate.app/en/mindfulness-psychology/kentucky-inventory-mindfulness","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/kentucky-inventory-mindfulness.md","definition":"The Kentucky Inventory of Mindfulness Skills (KIMS) is a 39-item self-report questionnaire measuring trait mindfulness across four theoretically distinct skills: Observing, Describing, Acting with Awareness, and Accepting Without Judgment. Developed by Baer, Smith, and Allen in 2004 at the University of Kentucky, the KIMS was one of the first multidimensional mindfulness measures and served as a foundational model for subsequent instruments including the widely used Five Facet Mindfulness Questionnaire (FFMQ). The KIMS remains a valuable tool for research and clinical assessment, particularly in settings emphasizing skill-based approaches to mindfulness development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ruth A. Baer, Greg T. Smith, and Kristin B. Allen","subfamily":"multidimensional-trait","year":"2004","type":"Self-report"},"citations":[{"ref":"Baer, R. A., Smith, G. T., & Allen, K. B. (2004). Assessment of mindfulness by self-report: The Kentucky Inventory of Mindfulness Skills (KIMS). Assessment, 11(3), 191-206.","type":"article","doi":"10.1177/1073191104268029","isbn":null,"url":null}],"related":["five-facet-mindfulness-questionnaire","freiburg-mindfulness-inventory","philadelphia-mindfulness-scale","cognitive-and-affective-mindfulness","mindful-attention-awareness-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kernel-density-test","name":"Kernel Density Estimation","fullName":"Kernel Density Estimation and Distribution Testing (KDE)","aliases":["kernel density estimate","KDE","Parzen window estimation","nonparametric density estimation","Çekirdek Yoğunluk Tahmini ve Testi (KDE)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1956,"originator":"Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman","url":"https://scholargate.app/en/statistics/kernel-density-test","markdownUrl":"https://scholargate.app/en/statistics/kernel-density-test.md","definition":"Kernel Density Estimation is a nonparametric method that estimates a continuous probability density by placing a smooth kernel function over each observation, without assuming any parametric distribution. It traces back to Rosenblatt (1956) and the textbook treatment by Silverman (1986), and it also supports distribution-comparison tests built on the estimated densities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenblatt (1956); Parzen (1962); textbook treatment by Silverman","year":1956,"type":"Nonparametric density estimation","estimator":"Kernel-smoothed empirical density (Parzen-Rosenblatt estimator)","outcome":"continuous density function","parametric":false,"bandwidthSelection":"Silverman rule, Sheather-Jones, or cross-validation"},"citations":[{"ref":"Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. Annals of Mathematical Statistics, 27(3), 832-837.","type":"article","doi":"10.1214/aoms/1177728190","isbn":null,"url":null},{"ref":"Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall / CRC Press.","type":"book","doi":null,"isbn":"978-0412246203","url":null}],"related":["histogram","anderson-darling-test","lilliefors-test","mood-median-test","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kernel-pca","name":"Kernel PCA","fullName":"Kernel Principal Component Analysis","aliases":["KPCA","kernel PCA","nonlinear PCA via kernel trick","kernel eigenvalue decomposition"],"domain":"machine-learning","family":"latent-structure","subfamily":null,"year":1998,"originator":"Schölkopf, B.; Smola, A. J.; Müller, K.-R.","url":"https://scholargate.app/en/machine-learning/kernel-pca","markdownUrl":"https://scholargate.app/en/machine-learning/kernel-pca.md","definition":"Kernel Principal Component Analysis (Kernel PCA) is a nonlinear dimensionality-reduction method introduced by Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller in 1997–1998. It extends classical linear PCA to curved, non-linear data manifolds by implicitly mapping input data into a high-dimensional feature space via a kernel function, then performing standard PCA in that space — all without ever computing the mapping explicitly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schölkopf, B.; Smola, A. J.; Müller, K.-R.","year":1998,"type":"Nonlinear dimensionality reduction via kernel trick","task":"Unsupervised feature extraction / manifold learning","kernelRequired":true,"computationalComplexity":"O(n^3) eigendecomposition of the n × n kernel (Gram) matrix"},"citations":[{"ref":"Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.","type":"article","doi":"10.1162/089976698300017467","isbn":null,"url":null},{"ref":"Schölkopf, B., Smola, A. J., & Müller, K.-R. (1997). Kernel principal component analysis. In Artificial Neural Networks — ICANN'97, Lecture Notes in Computer Science, Vol. 1327, pp. 583–588. Springer.","type":"inproceedings","doi":"10.1007/BFb0020217","isbn":null,"url":null},{"ref":"Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-19475-4","url":null}],"related":["principal-component-analysis","svm-classification","t-sne","umap","autoencoder","isomap","locally-linear-embedding"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"key-detection-music","name":"Musical Key Detection","fullName":"Musical Key Detection and Estimation Algorithm","aliases":["key recognition","tonality estimation","musical center detection"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Tonal analysis","year":"2006","originator":"Emilia Gómez","url":"https://scholargate.app/en/music-information-retrieval/key-detection-music","markdownUrl":"https://scholargate.app/en/music-information-retrieval/key-detection-music.md","definition":"Musical key detection is the task of automatically determining the key (tonal center) and scale mode of a musical composition from its audio. Introduced formally by Gómez (2006), it is essential for music analysis, transposition, harmonic understanding, and music theory education. The key defines the tonal center around which a piece gravitates; identifying it enables deeper structural understanding. Key detection is closely related to chord recognition but operates at a higher level of abstraction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emilia Gómez","subfamily":"Tonal analysis","year":"2006","type":"Tonal center estimation"},"citations":[{"ref":"Gómez, E. (2006). Tonal description of polyphonic audio for music content processing. In INESC Porto PhD Thesis.","type":"article","doi":null,"isbn":null,"url":"https://www.semanticscholar.org/paper/Tonal-description-of-polyphonic-audio-for-music-G%C3%B3mez/bf39db60c5f53556fbe7b36f0af64d4c0f07d048"},{"ref":"Noland, K., & Sandler, M. (2007). Signal processing parameters for tonality estimation. In Proceedings of the International Symposium on Music Information Retrieval.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2007/papers/noland_signal.pdf"},{"ref":"Khadkevich, M., & Omologo, M. (2011). Community structure in networks of classical music composers. Journal of Cultural Economics, 35(4), 307-319.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Community+structure+in+networks+of+classical+music+composers+Khadkevich"}],"related":["chord-recognition","harmonic-analysis-music","pitch-detection-algorithm","beat-tracking","music-genre-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"keyword-co-occurrence","name":"Keyword Co-Occurrence Analysis","fullName":"Keyword Co-Occurrence Analysis","aliases":["term co-occurrence","keyword network analysis","thematic analysis","term clustering"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"semantic-network","year":"2000s","originator":"Bibliometric research community","url":"https://scholargate.app/en/bibliometrics/keyword-co-occurrence","markdownUrl":"https://scholargate.app/en/bibliometrics/keyword-co-occurrence.md","definition":"Keyword co-occurrence analysis is a text mining and bibliometric method that identifies research themes and their relationships by analyzing how frequently terms or keywords appear together in abstracts, titles, or indexed keywords of scientific publications. When two keywords appear together frequently, they are considered co-occurring, indicating a shared thematic or conceptual relationship. This method rapidly reveals the topical structure of a research field without relying on formal classifications, making it particularly useful for detecting emerging research areas and understanding disciplinary boundaries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bibliometric research community","subfamily":"semantic-network","year":"2000s","type":"Method"},"citations":[{"ref":"Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166.","type":"article","doi":"10.1016/j.joi.2010.10.002","isbn":null,"url":null},{"ref":"Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null}],"related":["science-mapping","bibliographic-coupling","co-citation-analysis","vosviewer-citespace"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"keyword-extraction","name":"Keyword Extraction","fullName":"Automatic Keyword Extraction","aliases":["keyphrase extraction","key term extraction","Anahtar Kelime Çıkarma (Keyword Extraction)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/keyword-extraction","markdownUrl":"https://scholargate.app/en/text-mining/keyword-extraction.md","definition":"Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-mining task","approaches":"Statistical (frequency / TF-IDF) / graph-based (TextRank) / embedding-based (KeyBERT)","output":"Ranked list of keywords or keyphrases per document","minDocuments":"Reliable statistical extraction needs a sizeable corpus"},"citations":[{"ref":"Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=TextRank%3A+Bringing+Order+into+Texts+Mihalcea"},{"ref":"Grootendorst, M. (2020). KeyBERT. Zenodo.","type":"software","doi":null,"isbn":null,"url":"https://github.com/MaartenGr/KeyBERT"}],"related":["topic-modeling-nmf","tf-idf","sentiment-analysis","readability-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kidney-disease-quality-of-life","name":"KDQOL","fullName":"Kidney Disease Quality of Life Scale","aliases":["KDQOL","Kidney Disease QoL","KDQOL-SF"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Nephrology and Renal Disease","year":"1994","originator":"Ron D. Hays et al.","url":"https://scholargate.app/en/health-outcomes/kidney-disease-quality-of-life","markdownUrl":"https://scholargate.app/en/health-outcomes/kidney-disease-quality-of-life.md","definition":"The KDQOL is the most widely used quality of life measure for chronic kidney disease (CKD) patients, particularly those on dialysis. Developed by Ron Hays and colleagues in 1994, this multidimensional questionnaire (full version 134 items; short-form KDQOL-SF 36 items) measures kidney disease-specific impacts on physical function, emotional well-being, social participation, and treatment burden. It is the standard outcome measure in renal research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ron D. Hays et al.","subfamily":"Nephrology and Renal Disease","year":"1994","type":"Self-report quality of life questionnaire"},"citations":[{"ref":"Hays, R. D., Kallich, J. D., Mapes, D. L., Coons, S. J., & Carter, W. B. (1994). Development of the Kidney Disease Quality of Life (KDQOL) instrument. Quality of Life Research, 3(5), 329-338.","type":"article","doi":"10.1007/BF00451725","isbn":null,"url":null},{"ref":"Ware Jr, J. E., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey: Construction of scales and preliminary tests of reliability and validity. Medical Care, 34(3), 220-233.","type":"article","doi":"10.1097/00005650-199603000-00003","isbn":null,"url":null},{"ref":"Unruh, M. L., Benz, R., Greene, T., Has, P., Miskulin, D., Yan, G., ... & HEMO Study Group. (2004). Effects of hemodialysis dose and membrane flux on health-related quality of life in the HEMO Study. American Journal of Kidney Diseases, 44(5), 832-843.","type":"article","doi":"10.1111/j.1523-1755.2004.00738.x","isbn":null,"url":null}],"related":["eortc-qlq-c30","diabetes-quality-of-life","chronic-heart-failure-questionnaire","inflammatory-bowel-disease-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kinematic-distance","name":"Kinematic Distance","fullName":"Kinematic Distance Measurement Method","aliases":["Galactic Kinematic Distances","Rotation-Curve Distance","Kinematic Parallax"],"domain":"astronomy","family":"process-pipeline","subfamily":"Galactic kinematics","year":1957,"originator":"Bert Westerhout","url":"https://scholargate.app/en/astronomy/kinematic-distance","markdownUrl":"https://scholargate.app/en/astronomy/kinematic-distance.md","definition":"Kinematic distance is a method for estimating distances to objects in the Milky Way using their observed radial velocities and the known rotation curve of the Galaxy. Developed in the 1950s by Bert Westerhout and others, this technique enables distance determination to distant molecular clouds and masers without trigonometric parallax or individual object luminosities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bert Westerhout","subfamily":"Galactic kinematics","year":1957,"type":"Kinematic measurement method"},"citations":[{"ref":"Reid, M. J., et al. (2014). Trigonometric parallaxes of high mass star forming regions: the structure and kinematics of the Milky Way. Astrophysical Journal, 783(2), 130.","type":"article","doi":"10.1088/0004-637X/783/2/130","isbn":null,"url":null},{"ref":"Brand, J., & Blitz, L. (1993). The latitude-velocity distribution of molecular clouds: evidence for a new Galactic component. Astronomy & Astrophysics, 275, 67-87.","type":"article","doi":null,"isbn":null,"url":"https://ui.adsabs.harvard.edu/abs/1993A&A...275...67B"},{"ref":"Green, G. M., et al. (2019). A 3D Dust Map Based on Gaia, Pan-STARRS 1 and 2MASS. Astrophysical Journal, 887(2), 93.","type":"article","doi":"10.3847/1538-4357/ab5362","isbn":null,"url":null}],"related":["astrometry","rotation-curve-analysis","pulsar-timing-array"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kjeldahl-method","name":"Kjeldahl Method","fullName":"Kjeldahl Method","aliases":["Kjeldahl nitrogen determination"],"domain":"food-science","family":"process-pipeline","subfamily":"Analytical Chemistry","year":"1883","originator":"Johan Kjeldahl","url":"https://scholargate.app/en/food-science/kjeldahl-method","markdownUrl":"https://scholargate.app/en/food-science/kjeldahl-method.md","definition":"The Kjeldahl Method is a classical analytical procedure for determining the total nitrogen content of food products, developed by Johan Kjeldahl in 1883. By measuring total nitrogen and applying a conversion factor specific to the food type, the method indirectly determines crude protein content. Kjeldahl remains the official standard method for protein determination in foods, nutraceuticals, and animal feeds worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johan Kjeldahl","subfamily":"Analytical Chemistry","year":"1883","type":"Nitrogen Quantification"},"citations":[{"ref":"Kjeldahl, J. G. C. T. (1883). Neue Methode zur Bestimmung des Stickstoffs in organischen Körpern. Zeitschrift für Analytische Chemie, 22, 366-383.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"AOAC International (2016). Official Methods of Analysis (20th ed.). AOAC International.","type":"article","doi":null,"isbn":null,"url":"https://www.aoac.org"}],"related":["hplc","accelerated-shelf-life-testing","dsc-gelatinization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kkr-method","name":"KKR Method","fullName":"Korringa-Kohn-Rostoker Method","aliases":["KKR","multiple scattering"],"domain":"quantum-computing","family":"ml-model","subfamily":"Multiple Scattering Method","year":"1947","originator":"Joop Korringa and Walter Kohn","url":"https://scholargate.app/en/quantum-computing/kkr-method","markdownUrl":"https://scholargate.app/en/quantum-computing/kkr-method.md","definition":"The Korringa-Kohn-Rostoker (KKR) method is a powerful multiple-scattering approach for calculating electronic band structures and properties of periodic and disordered solids. Developed in the late 1940s, KKR treats electrons as scattering from atomic potentials in a muffin-tin geometry, enabling efficient calculations for both crystalline and amorphous systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joop Korringa and Walter Kohn","subfamily":"Multiple Scattering Method","year":"1947","type":"Electronic structure method"},"citations":[{"ref":"Korringa, J. (1947). On the calculation of the energy of a Bloch wave in a metal. Physica, 13, 392–400.","type":"article","doi":"10.1016/0031-8914(47)90013-X","isbn":null,"url":null},{"ref":"Gyorffy, B. L. (1972). Coherent potential approximation for random substitutional binary alloys. Physical Review B, 5, 2382–2384.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Coherent+potential+approximation+for+random+substitutional+binary+alloys+Gyorffy"},{"ref":"Vosko, S. H., Wilk, L., Nusair, M. (2003). Accurate spin-dependent electron liquid correlation energies. Canadian Journal of Physics, 58, 1200–1211.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1139/p80-159"}],"related":["density-functional-theory","tight-binding-model","hartree-fock-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"klason-lignin","name":"Klason Lignin","fullName":"Klason Lignin Determination","aliases":["acid-insoluble lignin","lignin content"],"domain":"forestry","family":"process-pipeline","subfamily":"Biochemistry","year":"1908","originator":"Erik Klason","url":"https://scholargate.app/en/forestry/klason-lignin","markdownUrl":"https://scholargate.app/en/forestry/klason-lignin.md","definition":"The Klason lignin method is a standard chemical test for quantifying the acid-insoluble lignin content in wood and plant biomass. Developed by Erik Klason in 1908, the method treats wood with sulfuric acid to dissolve carbohydrates (cellulose and hemicellulose) while leaving the acid-insoluble lignin residue. Klason lignin is widely used in wood science, pulp chemistry, and biomass characterization to assess wood composition and predict properties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erik Klason","subfamily":"Biochemistry","year":"1908","type":"chemical analysis"},"citations":[{"ref":"TAPPI T222 om-15. (2015). Acid-insoluble lignin in wood and pulp. TAPPI Press.","type":"article","doi":null,"isbn":null,"url":"https://www.tappi.org"},{"ref":"Sluiter, A., Hames, B., Ruiz, R., et al. (2008). Determination of structural carbohydrates and lignin in biomass. Technical Report NREL/TP-510-42618.","type":"article","doi":null,"isbn":null,"url":"https://www.nrel.gov"}],"related":["cellulose-crystallinity","wood-shrinkage","x-ray-densitometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"klm-goms","name":"KLM-GOMS","fullName":"Keystroke-Level Model - Goals, Operators, Methods, Selection Rules","aliases":["GOMS Model","KLM"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Predictive Modeling","year":"1983","originator":"Stuart Card, Thomas Moran, Allen Newell","url":"https://scholargate.app/en/human-computer-interaction/klm-goms","markdownUrl":"https://scholargate.app/en/human-computer-interaction/klm-goms.md","definition":"The Keystroke-Level Model (KLM), part of the Goals-Operators-Methods-Selection rules (GOMS) framework, is a computational method for predicting how long a user will take to accomplish a routine task using an interactive system. Developed by Card, Moran, and Newell in 1983, KLM decomposes user actions into primitive operators (keystrokes, mouse clicks, mental preparation, system response waits) with empirically derived execution times, enabling designers to estimate task performance without running user studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stuart Card, Thomas Moran, Allen Newell","subfamily":"Predictive Modeling","year":"1983","type":"Computational cognitive model for task execution time prediction"},"citations":[{"ref":"Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates.","type":"article","doi":null,"isbn":"0898592437","url":null},{"ref":"Kieras, D. E. (1997). A Guide to GOMS Task Analysis. Technical Report. University of Michigan.","type":"article","doi":null,"isbn":null,"url":"http://www.eecs.umich.edu/~kieras/GOMS.html"}],"related":["heuristic-evaluation","cognitive-walkthrough","think-aloud-protocol","system-usability-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knn","name":"K-Nearest Neighbors","fullName":"K-Nearest Neighbors (KNN) Classification and Regression","aliases":["KNN","K-En Yakın Komşu (KNN)","nearest neighbor classifier","instance-based learning"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1967,"originator":"Cover, T.M. & Hart, P.E.","url":"https://scholargate.app/en/machine-learning/knn","markdownUrl":"https://scholargate.app/en/machine-learning/knn.md","definition":"K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cover, T.M. & Hart, P.E.","year":1967,"type":"Instance-based (non-parametric) learning","task":"Classification & regression"},"citations":[{"ref":"Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27.","type":"article","doi":"10.1109/TIT.1967.1053964","isbn":null,"url":null}],"related":["naive-bayes","logistic-regression","svm-classification","random-forest","decision-tree"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-distillation","name":"Knowledge Distillation","fullName":"Knowledge Distillation (Teacher–Student Model Compression)","aliases":["Bilgi Damıtma (Knowledge Distillation)","bilgi damıtma","teacher-student distillation","model distillation"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2015,"originator":"Hinton, G., Vinyals, O. & Dean, J.","url":"https://scholargate.app/en/deep-learning/knowledge-distillation","markdownUrl":"https://scholargate.app/en/deep-learning/knowledge-distillation.md","definition":"Knowledge Distillation is a model-compression technique, introduced by Geoffrey Hinton and colleagues in 2015, that trains a small student model using the soft-label outputs of a large teacher model. Distilled models such as DistilBERT and TinyBERT reach roughly 97% of the larger model's performance while running far faster.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hinton, G., Vinyals, O. & Dean, J.","year":2015,"type":"Neural network compression (teacher–student)","task":"Classification & prediction","minSample":100},"citations":[{"ref":"Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1503.02531"},{"ref":"Sanh, V., Debut, L., Chaumond, J. & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1910.01108"}],"related":["mixture-of-experts","longformer-bigbird","contrastive-learning-dl","xgboost","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-graph-analysis","name":"Knowledge Graph Analysis","fullName":"Knowledge Graph Analysis (Semantic Network Representation and Reasoning)","aliases":["KG analysis","semantic graph analysis","knowledge base graph analysis","entity-relation graph analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2012–2016","originator":"Ehrlinger, L. & Wöß, W.; Google (popularized)","url":"https://scholargate.app/en/network-analysis/knowledge-graph-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/knowledge-graph-analysis.md","definition":"Knowledge Graph Analysis is a framework for representing, storing, and reasoning over structured factual knowledge as a directed graph of entities and typed relations. Entities (nodes) and relationships (edges) are expressed as subject–predicate–object triples, enabling rich querying, inference, and integration of heterogeneous data sources across domains such as biomedical research, e-commerce, and scientific literature.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ehrlinger, L. & Wöß, W.; Google (popularized)","year":"2012–2016","type":"Graph-based knowledge representation and analysis","dataType":"Entity-relation triples (RDF/property graphs), ontologies, linked data","subfamily":"Network science"},"citations":[{"ref":"Ehrlinger, L. & Wöß, W. (2016). Towards a Definition of Knowledge Graphs. In Proceedings of the SEMANTICS Posters and Demos Track (SEMANTiCS 2016). CEUR Workshop Proceedings, vol. 1695.","type":"inproceedings","doi":null,"isbn":null,"url":"https://ceur-ws.org/Vol-1695/paper4.pdf"},{"ref":"Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G. de, Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 71.","type":"article","doi":"10.1145/3447772","isbn":null,"url":null}],"related":["social-network-analysis","modularity-analysis","exponential-random-graph-model","network-diffusion-analysis","two-mode-network-analysis","multiplex-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-graph-embeddings","name":"Knowledge Graph Embeddings","fullName":"Knowledge Graph Embeddings (TransE and beyond)","aliases":["KG Embeddings","Knowledge Graph Representation Learning","Relational Embeddings","Bilgi Grafı Gömme"],"domain":"network-analysis","family":"ml-model","subfamily":"Graph representation","year":2013,"originator":"Bordes, Usunier, García-Durán, Weston & Yakhnenko","url":"https://scholargate.app/en/network-analysis/knowledge-graph-embeddings","markdownUrl":"https://scholargate.app/en/network-analysis/knowledge-graph-embeddings.md","definition":"Knowledge Graph Embeddings (KGE) are a family of methods that represent entities and relations in a knowledge graph as dense, low-dimensional vectors in a continuous space. The foundational model, TransE, was introduced by Bordes, Usunier, García-Durán, Weston, and Yakhnenko in 2013. TransE treats each relation as a translation in embedding space — the head entity vector plus the relation vector should approximate the tail entity vector for any true triple (h, r, t). This simple geometric principle enabled effective link prediction and knowledge base completion at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bordes, Usunier, García-Durán, Weston & Yakhnenko","year":2013,"type":"Graph representation learning via low-dimensional vector embeddings","subfamily":"Graph representation","trainingObjective":"Margin-based pairwise ranking loss","spaceComplexity":"O((|E| + |R|) × d) where d is embedding dimension"},"citations":[{"ref":"Bordes, A., Usunier, N., García-Durán, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems, 26.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html"}],"related":["graph-neural-network","pagerank","word2vec"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-graph-nlp","name":"Knowledge Graph Construction","fullName":"Knowledge Graph Construction from Text","aliases":["knowledge graph","KG construction","Bilgi Grafiği Oluşturma (Knowledge Graph)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/knowledge-graph-nlp","markdownUrl":"https://scholargate.app/en/text-mining/knowledge-graph-nlp.md","definition":"Knowledge graph construction is a text-mining pipeline that turns unstructured text into a structured graph of entities and the relations between them. Drawing on the synthesis of Hogan et al. (2021) and the relational-machine-learning review of Nickel et al. (2016), it represents knowledge as nodes (entities such as people, places, organisations) connected by labelled edges (relations), and serves semantic search, recommendation systems, and reasoning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Structured knowledge representation pipeline","input":"Unstructured text","output":"Graph of entities (nodes) and relations (edges)","buildingBlocks":"Named-entity recognition, relation extraction, entity linking","minSample":30},"citations":[{"ref":"Hogan, A. et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1-37.","type":"article","doi":"10.1145/3447772","isbn":null,"url":null},{"ref":"Nickel, M. et al. (2016). A Review of Relational Machine Learning for Knowledge Graphs. Proceedings of the IEEE, 104(1), 11-33.","type":"article","doi":"10.1109/JPROC.2015.2483592","isbn":null,"url":null}],"related":["entity-linking","named-entity-recognition","relation-extraction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-management-scale","name":"Knowledge Management Capability Scale","fullName":"Knowledge Management (KM) Organizational Capability Assessment Scale","aliases":["KM Capability Scale","Knowledge Management Maturity Scale"],"domain":"strategic-management","family":"process-pipeline","subfamily":"organizational-learning","year":"1995","originator":"Ikujiro Nonaka and Hirotaka Takeuchi (SECI model); adapted by organizational scholars","url":"https://scholargate.app/en/strategic-management/knowledge-management-scale","markdownUrl":"https://scholargate.app/en/strategic-management/knowledge-management-scale.md","definition":"Knowledge Management (KM) refers to the organizational capacity to create, capture, organize, and apply knowledge to improve organizational effectiveness, innovation, and decision-making. Nonaka and Takeuchi's (1995) knowledge-creating company framework conceptualized knowledge as moving through four conversion modes: socialization (tacit to tacit knowledge transfer through experience), externalization (tacit knowledge articulation into explicit forms), combination (explicit knowledge assembly into systems), and internalization (explicit knowledge absorption into tacit understanding). This scale measures organizational capability across the four KM processes—knowledge creation, capture, sharing, and application—revealing where organizations excel or struggle in converting information into competitive advantage.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ikujiro Nonaka and Hirotaka Takeuchi (SECI model); adapted by organizational scholars","subfamily":"organizational-learning","year":"1995","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nonaka%2C%20I.%2C%20%26%20Takeuchi%2C%20H.%20(1995).%20The%20knowledge-creating%20company%3A%20How%20Japanese%20companies%20create%20the%20dynamics%20of%20innovat"},{"ref":"Choo, C. W. (2002). Information management for the intelligent organization: The art of scanning the environment. ASIST Monograph Series.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Choo%2C%20C.%20W.%20(2002).%20Information%20management%20for%20the%20intelligent%20organization%3A%20The%20art%20of%20scanning%20the%20environment.%20ASIST%20"},{"ref":"Davenport, T. H., & Prusak, L. (2000). Working knowledge: How organizations manage what they know. Harvard Business School Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Davenport%2C%20T.%20H.%2C%20%26%20Prusak%2C%20L.%20(2000).%20Working%20knowledge%3A%20How%20organizations%20manage%20what%20they%20know.%20Harvard%20Business%20Scho"}],"related":["absorptive-capacity-scale","dynamic-capabilities-scale","supply-chain-integration-scale","organizational-resilience-scale","innovation-ambidexterity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-sharing-scale","name":"Knowledge Sharing Scale","fullName":"Knowledge Sharing Scale (KSS)","aliases":["Organizational Knowledge Sharing Intention"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"2005","originator":"Bock, Zmud, Kim, and Lee; refined by Hau et al.","url":"https://scholargate.app/en/organizational-behavior/knowledge-sharing-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/knowledge-sharing-scale.md","definition":"The Knowledge Sharing Scale (KSS) is an 18-item instrument measuring employee intention to share knowledge and experience within organizations. Developed by Bock, Zmud, Kim, and Lee in 2005, the KSS assesses barriers and enablers of knowledge sharing behavior across six dimensions: perceived usefulness, extrinsic motivation, intrinsic motivation, social norms, organizational climate, and knowledge-sharing intention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bock, Zmud, Kim, and Lee; refined by Hau et al.","subfamily":"Organizational behavior","year":"2005","type":"Self-report scale"},"citations":[{"ref":"Hau, Y. S., Kim, B., Lee, H., & Kim, Y. G. (2013). The effects of individual motivations and social capital on employees' tacit and explicit knowledge sharing intentions. International Journal of Information Management, 33(3), 356-366.","type":"article","doi":"10.1016/j.ijinfomgt.2012.10.009","isbn":null,"url":null},{"ref":"Bock, G. W., Zmud, R. W., Kim, Y. G., & Lee, J. N. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87-111.","type":"article","doi":"10.2307/25148669","isbn":null,"url":null}],"related":["organizational-learning-scale","organizational-citizenship-behavior","employee-engagement-survey","organizational-culture-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-space-theory","name":"Knowledge Space Theory","fullName":"Knowledge Space Theory","aliases":["KST","Knowledge Structures","Competence-Based Knowledge Space Theory","Bilgi Uzayı Teorisi"],"domain":"education-analytics","family":"ml-model","subfamily":"Knowledge structures","year":1985,"originator":"Jean-Paul Doignon & Jean-Claude Falmagne","url":"https://scholargate.app/en/education-analytics/knowledge-space-theory","markdownUrl":"https://scholargate.app/en/education-analytics/knowledge-space-theory.md","definition":"Knowledge Space Theory (KST) is a combinatorial, set-theoretic framework for modeling and assessing human knowledge, introduced by Jean-Paul Doignon and Jean-Claude Falmagne in 1985. It represents a learner's competence as a subset of a problem domain, organizes all feasible competence subsets into a lattice called a knowledge space, and uses probabilistic inference to locate a learner within that space. The approach underlies adaptive testing and intelligent tutoring systems, offering a mathematically rigorous alternative to classical test theory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Paul Doignon & Jean-Claude Falmagne","year":1985,"type":"Combinatorial knowledge assessment framework","subfamily":"Knowledge structures","foundational_concept":"Quasi-ordinal knowledge space","key_output":"Knowledge state and learning path"},"citations":[{"ref":"Doignon, J.-P., & Falmagne, J.-C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23(2), 175–196.","type":"article","doi":"10.1016/S0020-7373(85)80031-6","isbn":null,"url":null}],"related":["formal-concept-analysis","knowledge-tracing","cognitive-diagnosis-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-to-action-scale","name":"KTA","fullName":"Knowledge-to-Action Framework","aliases":["KTA","Knowledge-to-Action","KTA Framework","Knowledge-to-Action Cycle"],"domain":"implementation-science","family":"process-pipeline","subfamily":"implementation framework","year":2004,"originator":"Ian D. Graham, PhD; Roberta L. Logan, MD, MSc; colleagues at Ottawa Hospital Research Institute","url":"https://scholargate.app/en/implementation-science/knowledge-to-action-scale","markdownUrl":"https://scholargate.app/en/implementation-science/knowledge-to-action-scale.md","definition":"The Knowledge-to-Action (KTA) Framework is a conceptual model and process guide for translating evidence into practice, developed by Ian Graham and colleagues at the Ottawa Hospital Research Institute (2004–2006). The KTA framework addresses a central challenge in implementation science: research evidence alone does not change practice; a deliberate, systematic process is required to adapt evidence to local contexts, identify and overcome implementation barriers, and sustain change. The KTA distinguishes between knowledge production (research, evidence synthesis) and knowledge application (implementation planning, barrier identification, strategy selection, execution, monitoring, and adaptation). The framework has become one of the most widely adopted implementation models in healthcare, particularly in Canada and internationally, and provides a structured approach to evidence-based practice implementation that is context-sensitive and iterative.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ian D. Graham, PhD; Roberta L. Logan, MD, MSc; colleagues at Ottawa Hospital Research Institute","subfamily":"implementation framework","year":2004,"type":"Conceptual framework and process model"},"citations":[{"ref":"Graham, I. D., & Logan, R. L. (2004). Translating research into practice: A perspective on technology transfer. Journal of the American Medical Informatics Association, 11(2), 141–145.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Translating+research+into+practice%3A+A+perspective+on+technology+transfer+Graham"},{"ref":"Graham, I. D., Logan, R. L., Harrison, M. B., Straus, S. E., Tetroe, J., Caswell, W., & Robinson, N. (2006). Lost in knowledge translation: Time for a map? Journal of Continuing Education in the Health Professions, 26(1), 13–24.","type":"article","doi":"10.1002/chp.47","isbn":null,"url":null}],"related":["evidence-based-practice-attitude","implementation-leadership-scale","stages-of-concern-questionnaire","normalisation-measure-development","organisational-readiness-change"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-tracing","name":"Knowledge Tracing","fullName":"Knowledge Tracing (Bayesian / Deep)","aliases":["BKT","Bayesian Knowledge Tracing","Deep Knowledge Tracing","Bilgi İzleme"],"domain":"education-analytics","family":"ml-model","subfamily":"Learning analytics","year":1994,"originator":"Albert Corbett & John Anderson","url":"https://scholargate.app/en/education-analytics/knowledge-tracing","markdownUrl":"https://scholargate.app/en/education-analytics/knowledge-tracing.md","definition":"Knowledge Tracing (KT) is a student-modeling technique that estimates, at each moment in time, the probability that a learner has mastered a target knowledge component. Introduced by Corbett and Anderson in 1994, the classical Bayesian Knowledge Tracing (BKT) model treats skill acquisition as a two-state Hidden Markov Model driven by four interpretable parameters: prior knowledge, learning rate, slip, and guess. Deep variants (DKT, DKVMN, AKT) later replaced HMMs with recurrent and transformer architectures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Corbett & John Anderson","year":1994,"type":"Probabilistic student modeling","subfamily":"Learning analytics","paradigm":"Hidden Markov Model (classical); Recurrent Neural Network (deep variant)","grain":"Knowledge component (skill) level"},"citations":[{"ref":"Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.","type":"article","doi":"10.1007/BF01099821","isbn":null,"url":null}],"related":["bayesian-network","lstm","rasch-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"knowledge-translation","name":"Knowledge Translation","fullName":"Knowledge Translation: Integration of Research Evidence into Clinical and Policy Practice","aliases":["KT","evidence-to-practice","research-to-practice"],"domain":"implementation-science","family":"process-pipeline","subfamily":"evidence integration","year":"2004","originator":"Canadian Institutes of Health Research (CIHR)","url":"https://scholargate.app/en/implementation-science/knowledge-translation","markdownUrl":"https://scholargate.app/en/implementation-science/knowledge-translation.md","definition":"Knowledge Translation (KT) is the systematic synthesis, dissemination, exchange, and application of research findings to improve health outcomes and healthcare practice. First formalized by the Canadian Institutes of Health Research in 2004, KT recognizes that evidence generation alone does not automatically change clinical or policy behaviour, and structures a purposeful process to bridge the gap between research and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Canadian Institutes of Health Research (CIHR)","subfamily":"evidence integration","year":"2004","type":"Framework"},"citations":[{"ref":"Canadian Institutes of Health Research. (2004). Knowledge Translation Strategy 2004-2009. CIHR, Ottawa.","type":"report","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Canadian%20Institutes%20of%20Health%20Research.%20(2004).%20Knowledge%20Translation%20Strategy%202004-2009.%20CIHR%2C%20Ottawa."},{"ref":"Straus, S. E., Tetroe, J., & Graham, I. D. (2009). Defining knowledge translation. Canadian Medical Association Journal, 181(3-4), 165-166.","type":"article","doi":"10.1503/cmaj.081229","isbn":null,"url":null},{"ref":"Graham, I. D., Logan, R. F., Harrison, M. B., Straus, S. E., Tetroe, J., Caswell, W., & Robinson, N. (2006). Lost in knowledge translation: Time for a map? Journal of Continuing Education in the Health Professions, 26(1), 13-24.","type":"article","doi":"10.1002/chp.47","isbn":null,"url":null}],"related":["cfir-framework","re-aim-framework","implementation-outcome-taxonomy","behavior-change-wheel","normalization-process-theory"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kohler-theory","name":"Kohler Theory","fullName":"Köhler Equilibrium Theory for Cloud Droplet Formation","aliases":["Kohler theory","Kohler equilibrium","Cloud droplet nucleation"],"domain":"meteorology","family":"process-pipeline","subfamily":"Cloud microphysics theory","year":"1936","originator":"Hilding Kohler","url":"https://scholargate.app/en/meteorology/kohler-theory","markdownUrl":"https://scholargate.app/en/meteorology/kohler-theory.md","definition":"Köhler theory is a foundational framework in cloud microphysics that predicts the equilibrium supersaturation required for an aerosol particle of given size and composition to grow into a cloud droplet. Published in 1936 by Hilding Köhler, it combines the Kelvin effect (vapor pressure enhancement over curved surfaces) with the Raoult effect (vapor pressure depression from dissolved solute) to explain cloud droplet formation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hilding Kohler","subfamily":"Cloud microphysics theory","year":"1936","type":"Thermodynamic equilibrium framework"},"citations":[{"ref":"Köhler, H. (1936). The nucleus in and the growth of hygroscopic droplets. Transactions of the Faraday Society, 32, 1152-1161.","type":"article","doi":"10.1039/TF9363201152","isbn":null,"url":null},{"ref":"Pruppacher, H. R., & Klett, J. D. (1997). Microphysics of Clouds and Precipitation (2nd ed.). Kluwer Academic Publishers.","type":"article","doi":null,"isbn":null,"url":"https://www.springer.com/gp/book/9780306481017"}],"related":["cloud-condensation-nuclei-analysis","spectral-bin-microphysics","wrf-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kolb-learning-style-inventory","name":"Kolb Learning Style Inventory","fullName":"Kolb Learning Style Inventory (LSI)","aliases":["LSI","Experiential Learning Style Inventory"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Learning style measurement","year":"1984","originator":"David A. Kolb","url":"https://scholargate.app/en/educational-psychology/kolb-learning-style-inventory","markdownUrl":"https://scholargate.app/en/educational-psychology/kolb-learning-style-inventory.md","definition":"The Kolb Learning Style Inventory (LSI) is a self-report assessment based on experiential learning theory that identifies how individuals prefer to learn. Developed by David Kolb in 1984, it classifies learners into four styles—Diverging, Assimilating, Converging, and Accommodating—based on two dimensions: how information is perceived and how it is processed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David A. Kolb","subfamily":"Learning style measurement","year":"1984","type":"Self-assessment learning style inventory"},"citations":[{"ref":"Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Kolb's_learning_styles"},{"ref":"Kolb, D. A., & Kolb, A. Y. (1999). The Learning Way: Learning Outcomes from a New Approach to Student Experience. Journal of Advanced Education, 24(3), 277-295.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Learning+Way%3A+Learning+Outcomes+from+a+New+Approach+to+Student+Experience+Kolb"}],"related":["study-process-questionnaire","academic-self-efficacy-scale","student-engagement-scale","course-experience-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kolmogorov-arnold-networks","name":"Kolmogorov-Arnold Networks","fullName":"KAN: Kolmogorov-Arnold Networks","aliases":["KAN","Kolmogorov-Arnold"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Neural Network Architectures, Approximation Theory","year":"2024","originator":"Ziming Liu","url":"https://scholargate.app/en/deep-learning/kolmogorov-arnold-networks","markdownUrl":"https://scholargate.app/en/deep-learning/kolmogorov-arnold-networks.md","definition":"Kolmogorov-Arnold Networks (KAN) is a neural network architecture introduced by Liu et al. in 2024 that replaces linear transformations with learned univariate functions on edges. Inspired by the Kolmogorov-Arnold representation theorem, KAN achieves superior function approximation with fewer parameters than traditional MLPs, offering potential efficiency gains and improved interpretability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ziming Liu","subfamily":"Deep Learning, Neural Network Architectures, Approximation Theory","year":"2024","type":"Neural network architecture"},"citations":[{"ref":"Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T. Y., & Tegmark, M. (2024). KAN: Kolmogorov-Arnold Networks. arXiv preprint arXiv:2404.19756.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2404.19756"}],"related":["vision-transformer","mamba","neural-radiance-fields","masked-autoencoders"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kolmogorov-smirnov-2sample","name":"Two-Sample Kolmogorov-Smirnov Test","fullName":"Two-Sample Kolmogorov-Smirnov Test","aliases":["KS two-sample test","two-sample KS test","İki Örneklem Kolmogorov-Smirnov Testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1948,"originator":"N. V. Smirnov","url":"https://scholargate.app/en/statistics/kolmogorov-smirnov-2sample","markdownUrl":"https://scholargate.app/en/statistics/kolmogorov-smirnov-2sample.md","definition":"The two-sample Kolmogorov-Smirnov test is a nonparametric procedure that asks whether two independent groups are drawn from the same continuous distribution. Building on Smirnov's 1948 tables, it compares the empirical cumulative distribution functions (CDFs) of the two samples and uses their maximum absolute distance as the test statistic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"N. V. Smirnov","year":1948,"type":"Nonparametric two-sample distribution test","statistic":"D = sup|F₁(x) − F₂(x)| (maximum distance between empirical CDFs)","outcome":"continuous","minSample":20,"groups":"two independent groups","requiresNormal":false},"citations":[{"ref":"Smirnov, N. V. (1948). Table for Estimating the Goodness of Fit of Empirical Distributions. Annals of Mathematical Statistics, 19(2), 279-281.","type":"article","doi":"10.1214/aoms/1177730256","isbn":null,"url":null},{"ref":"Conover, W. J. (1999). Practical Nonparametric Statistics (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471160687","url":null}],"related":["mann-whitney-u","anderson-darling","wilcoxon-rank-sum","permutation-test","levene-brown-forsythe"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kolmogorov-smirnov","name":"Kolmogorov-Smirnov Test","fullName":"Kolmogorov-Smirnov Goodness-of-Fit Test","aliases":["KS test","K-S test","one-sample KS test","Kolmogorov-Smirnov Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1933,"originator":"Andrey Nikolaevich Kolmogorov; Nikolai Vasilyevich Smirnov","url":"https://scholargate.app/en/statistics/kolmogorov-smirnov","markdownUrl":"https://scholargate.app/en/statistics/kolmogorov-smirnov.md","definition":"The Kolmogorov-Smirnov (KS) test is a nonparametric goodness-of-fit test that assesses whether a sample comes from a specified theoretical distribution, such as the normal or exponential. First formalised by Andrey Kolmogorov in 1933 and further developed by Nikolai Smirnov in 1948, it compares the empirical cumulative distribution function of the observed data against a target theoretical CDF and quantifies their maximum absolute deviation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrey Nikolaevich Kolmogorov; Nikolai Vasilyevich Smirnov","year":1933,"family":"Hypothesis test","type":"Nonparametric goodness-of-fit test","parametric":false,"outcome":"continuous","testStatistic":"D (maximum absolute deviation between empirical and theoretical CDFs)","nullDistribution":"Kolmogorov distribution","minimumSample":20,"difficulty":1},"citations":[{"ref":"Kolmogorov, A. N. (1933). Sulla determinazione empirica di una legge di distribuzione. Giornale dell'Istituto Italiano degli Attuari, 4, 83–91.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/43859343"},{"ref":"Smirnov, N. V. (1948). Table for estimating the goodness of fit of empirical distributions. Annals of Mathematical Statistics, 19(2), 279–281.","type":"article","doi":"10.1214/aoms/1177730256","isbn":null,"url":null},{"ref":"Massey, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253), 68–78.","type":"article","doi":"10.2307/2280095","isbn":null,"url":null},{"ref":"Conover, W. J. (1999). Practical Nonparametric Statistics (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471160687","url":null}],"related":["shapiro-wilk","anderson-darling","lilliefors-test","kolmogorov-smirnov-2sample","chi-square-goodness-of-fit"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"konya-bootstrap-causality","name":"Kónya Bootstrap Causality","fullName":"Kónya Bootstrap Panel Granger Causality","aliases":["Bootstrap Panel Causality Test","Kónya Panel Granger Causality","SUR-Based Bootstrap Causality","Kónya Önyükleme Nedensellik Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Causality","year":2006,"originator":"László Kónya","url":"https://scholargate.app/en/econometrics/konya-bootstrap-causality","markdownUrl":"https://scholargate.app/en/econometrics/konya-bootstrap-causality.md","definition":"Introduced by László Kónya in 2006, this method tests Granger causality in heterogeneous panels by estimating a Seemingly Unrelated Regressions (SUR) system and deriving country-specific critical values through bootstrapping. Unlike pooled panel tests, it delivers a separate causality verdict for each cross-section, making it particularly valuable in applied macroeconomics and international economics when panel units are expected to behave differently.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"László Kónya","year":2006,"type":"Non-parametric bootstrap hypothesis test","subfamily":"Causality","estimator":"Seemingly Unrelated Regressions (SUR)","crossSectionDependency":"Accommodated via SUR"},"citations":[{"ref":"Kónya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23(6), 978–992.","type":"article","doi":"10.1016/j.econmod.2006.04.008","isbn":null,"url":null}],"related":["dumitrescu-hurlin-causality","granger-causality","pesaran-cd-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"koopa","name":"Koopa","fullName":"Koopa (Koopman Predictors for Non-stationary Dynamics)","aliases":["Koopman Predictor","Koopman-based Time-Series Model","Koopa Forecaster","Koopman Tahmincisi"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2023,"originator":"Yong Liu et al.","url":"https://scholargate.app/en/deep-learning/koopa","markdownUrl":"https://scholargate.app/en/deep-learning/koopa.md","definition":"Koopa is a deep learning model for time-series forecasting introduced by Yong Liu, Chang Li, Jianmin Wang, and Mingsheng Long at NeurIPS 2023. It addresses the challenge of non-stationarity by disentangling time series into stationary and non-stationary components, then modeling the non-stationary dynamics using a learned approximation of the Koopman operator — a mathematical framework that lifts nonlinear systems into a linear space for tractable long-horizon prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yong Liu et al.","year":2023,"type":"Koopman operator-based time-series forecasting model","subfamily":"Time-series forecasting","venue":"NeurIPS 2023","learning_paradigm":"Supervised deep learning"},"citations":[{"ref":"Liu, Y., Li, C., Wang, J., & Long, M. (2023). Koopa: Learning non-stationary time series dynamics with Koopman predictors. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2305.18803"}],"related":["nonstationary-transformer","dlinear","state-space-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"koos","name":"Knee Injury and Osteoarthritis Outcome Score","fullName":"Knee Injury and Osteoarthritis Outcome Score","aliases":["KOOS","KOOS Scale"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Functional assessment","year":"1998","originator":"Roos, Roos, Lohmander, Ekdahl, Beynnon","url":"https://scholargate.app/en/rehabilitation/koos","markdownUrl":"https://scholargate.app/en/rehabilitation/koos.md","definition":"The Knee Injury and Osteoarthritis Outcome Score (KOOS) is a patient-reported outcome measure designed for active patients with knee injury and osteoarthritis. Developed by Roos and colleagues in 1998, KOOS extends assessment beyond traditional osteoarthritis scales to include symptoms, pain, function in daily living, function in sport and recreation, and knee-related quality of life—making it ideal for younger, more active populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roos, Roos, Lohmander, Ekdahl, Beynnon","subfamily":"Functional assessment","year":"1998","type":"Patient-reported outcome measure"},"citations":[{"ref":"Roos, E. M., Roos, B. P., Lohmander, L. S., Ekdahl, C., & Beynnon, B. D. (1998). Knee Injury and Osteoarthritis Outcome Score (KOOS): development of a self-administered outcome measure. Journal of Orthopaedic & Sports Physical Therapy, 28(2), 88–96.","type":"article","doi":"10.2519/jospt.1998.28.2.88","isbn":null,"url":null},{"ref":"Roos, E. M., & Toksvig-Larsen, S. (2003). Knee injury and Osteoarthritis Outcome Score (KOOS): validation and comparison in total meniscectomy versus meniscus preservation surgery for degenerative meniscus tears. Osteoarthritis and Cartilage, 11(5), 330–337.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Knee+injury+and+Osteoarthritis+Outcome+Score+%28KOOS%29%3A+validation+and+comparison+in+total+meniscectomy+versus+meniscus+preservation+surgery+for+degenerative+meniscus+tears+Roos"}],"related":["womac","hoos","koos-child","limex-knee"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kpss-test","name":"KPSS Test","fullName":"Kwiatkowski-Phillips-Schmidt-Shin (KPSS) Stationarity Test","aliases":["Kwiatkowski-Phillips-Schmidt-Shin test","stationarity test","KPSS durağanlık testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1992,"originator":"Kwiatkowski, Phillips, Schmidt & Shin","url":"https://scholargate.app/en/econometrics/kpss-test","markdownUrl":"https://scholargate.app/en/econometrics/kpss-test.md","definition":"The KPSS test, introduced by Kwiatkowski, Phillips, Schmidt and Shin in 1992, tests the null hypothesis that a series is stationary against the alternative that it contains a unit root — the reverse of the ADF and Phillips-Perron tests. By flipping the burden of proof, it is designed to be used alongside unit-root tests so that the two can confirm one another and expose ambiguous, borderline cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kwiatkowski, Phillips, Schmidt & Shin","year":1992,"type":"Stationarity test (reverse of unit-root tests)","nullHypothesis":"Series is (trend-)stationary","distribution":"Non-standard (functional of Brownian bridge)","minSample":50},"citations":[{"ref":"Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178.","type":"article","doi":"10.1016/0304-4076(92)90104-Y","isbn":null,"url":null}],"related":["adf-test","phillips-perron-test","cointegration-test","arima"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kriging-interpolation","name":"Kriging","fullName":"Kriging Spatial Interpolation","aliases":["geostatistical interpolation","Gaussian process regression (geostatistics)","ordinary kriging","Kriging (Mekânsal Enterpolasyon)"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":1963,"originator":"Georges Matheron (formalised geostatistics)","url":"https://scholargate.app/en/spatial-analysis/kriging-interpolation","markdownUrl":"https://scholargate.app/en/spatial-analysis/kriging-interpolation.md","definition":"Kriging is a geostatistical method that predicts the value of a continuous variable at unmeasured locations from nearby measurements, using the spatial correlation structure captured by a variogram. Formalised by Georges Matheron in 1963, it is the best linear unbiased predictor (BLUP) for spatial data and comes in Ordinary, Universal, and Co-Kriging forms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georges Matheron (formalised geostatistics)","year":1963,"type":"Geostatistical spatial interpolation","estimator":"Best linear unbiased predictor (BLUP) from the variogram","outcome":"continuous (spatially located)","variants":"Ordinary, Universal, Co-Kriging"},"citations":[{"ref":"Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8), 1246–1266.","type":"article","doi":"10.2113/gsecongeo.58.8.1246","isbn":null,"url":null},{"ref":"Cressie, N. (1993). Statistics for Spatial Data (Revised ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471002550","url":null}],"related":["spatial-panel-model","mgwr-model","geographically-weighted-regression","getis-ord-gi","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kruskal-wallis","name":"Kruskal-Wallis test","fullName":"Kruskal-Wallis H test","aliases":["Kruskal-Wallis H test","one-way ANOVA on ranks","Kruskal-Wallis one-way analysis of variance","Kruskal-Wallis Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1952,"originator":"William Kruskal & W. Allen Wallis","url":"https://scholargate.app/en/statistics/kruskal-wallis","markdownUrl":"https://scholargate.app/en/statistics/kruskal-wallis.md","definition":"The Kruskal-Wallis H test is a nonparametric hypothesis test that compares three or more independent groups to decide whether their distributions (typically their medians) differ. Introduced by William Kruskal and W. Allen Wallis in 1952, it works on ranks rather than raw values and is the distribution-free counterpart to one-way ANOVA.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Kruskal & W. Allen Wallis","year":1952,"family":"Hypothesis test","type":"Nonparametric group comparison","groups":"3 or more","outcome":"ordinal or continuous (rank-based)","parametric":false,"distribution":"Chi-square (approximation)","df":"k - 1"},"citations":[{"ref":"Kruskal, W. H. & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621.","type":"article","doi":"10.1080/01621459.1952.10483441","isbn":null,"url":null}],"related":["one-way-anova","mann-whitney-u","friedman-test","dunn-test","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"kullback-leibler-divergence","name":"Kullback-Leibler Divergence","fullName":"Kullback-Leibler Information Divergence","aliases":["KL divergence","relative entropy","information divergence"],"domain":"decision-making","family":"mcdm","subfamily":"Information-theoretic divergence","year":"1951","originator":"Solomon Kullback and Richard Leibler","url":"https://scholargate.app/en/decision-making/kullback-leibler-divergence","markdownUrl":"https://scholargate.app/en/decision-making/kullback-leibler-divergence.md","definition":"Kullback-Leibler divergence, also called relative entropy or information divergence, measures the asymmetric difference between two probability distributions. Introduced by Solomon Kullback and Richard Leibler in 1951, this information-theoretic measure quantifies how one probability distribution diverges from a reference distribution, ranging from 0 (identical distributions) to infinity. It is foundational in information theory, machine learning, and decision-making with probabilistic frameworks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Solomon Kullback and Richard Leibler","subfamily":"Information-theoretic divergence","year":"1951","type":"Asymmetric probability distribution dissimilarity"},"citations":[{"ref":"Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.","type":"article","doi":"10.1214/aoms/1177729694","isbn":null,"url":null},{"ref":"Cover, T. M., & Thomas, J. A. (1991). Elements of Information Theory. Wiley-Interscience.","type":"book","doi":"10.1002/0471200611","isbn":null,"url":null}],"related":["jensen-shannon-divergence","hellinger-distance","wasserstein-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"l2t-codas","name":"L2T-CODAS","fullName":"Linguistic extension of L2T-CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"He, Y., Wei, G., Chen, X., Zhao, J.","url":"https://scholargate.app/en/decision-making/l2t-codas","markdownUrl":"https://scholargate.app/en/decision-making/l2t-codas.md","definition":"L2T-CODAS (Linguistic extension of L2T-CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by He, Y., Wei, G., Chen, X., Zhao, J. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"He, Y., Wei, G., Chen, X., Zhao, J.","subfamily":"Ranking","year":"2019","type":"Linguistic outranking/ranking — 2-Tuple Linguistic Variable (2TL: (s_i, α))","value_space":"linguistic_2tuple","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"He, Y., Wei, G., Chen, X., Zhao, J. (2019). CODAS method for Pythagorean 2-tuple linguistic multiple attribute group decision making. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.1109/access.2019.2917588","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"l2t-copras","name":"L2T-COPRAS","fullName":"Linguistic extension of L2T-COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Gai, T., Cao, M., Cao, Q., Wu, J., Yu, G., Zhou, M.","url":"https://scholargate.app/en/decision-making/l2t-copras","markdownUrl":"https://scholargate.app/en/decision-making/l2t-copras.md","definition":"L2T-COPRAS (Linguistic extension of L2T-COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Gai, T., Cao, M., Cao, Q., Wu, J., Yu, G., Zhou, M. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gai, T., Cao, M., Cao, Q., Wu, J., Yu, G., Zhou, M.","subfamily":"Ranking","year":"2021","type":"Linguistic outranking/ranking — 2-Tuple Linguistic Variable (2TL: (s_i, α))","value_space":"linguistic_2tuple","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Gai, T., Cao, M., Cao, Q., Wu, J., Yu, G., Zhou, M. (2021). An integrated method for hybrid distribution with estimation of demand matching degree (interval 2-tuple linguistic COPRAS).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An%20integrated%20method%20for%20hybrid%20distribution%20with%20estimation%20of%20demand%20matching%20degree%20%28interval%202-tuple%20linguistic%20COPRAS%29"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"l2t-edas","name":"L2T-EDAS","fullName":"2-Tuple Linguistic Neutrosophic EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Wang, P., Wang, J., Wei, G.","url":"https://scholargate.app/en/decision-making/l2t-edas","markdownUrl":"https://scholargate.app/en/decision-making/l2t-edas.md","definition":"L2T-EDAS (2-Tuple Linguistic Neutrosophic EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, P., Wang, J., Wei, G. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang, P., Wang, J., Wei, G.","subfamily":"Ranking","year":"2019","type":"Linguistic outranking/ranking — 2-Tuple Linguistic Variable (2TL: (s_i, α))","value_space":"linguistic_2tuple_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451. [Canonical EDAS source; cited in place of the retracted Wang et al. (2019) 2-tuple linguistic neutrosophic EDAS paper, doi:10.3233/JIFS-179223.]","type":"article","doi":"10.15388/informatica.2015.57","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"l2t-multimoora","name":"L2T-MULTIMOORA","fullName":"2-Tuple Linguistic MULTIMOORA (Balezentis & Balezentis 2011)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2006 crisp; 2011 variant applicator","originator":"Baležentis, Alvydas, Baležentis, Tomas","url":"https://scholargate.app/en/decision-making/l2t-multimoora","markdownUrl":"https://scholargate.app/en/decision-making/l2t-multimoora.md","definition":"L2T-MULTIMOORA (2-Tuple Linguistic MULTIMOORA (Balezentis & Balezentis 2011)) is a ranking multi-criteria decision-making (MCDM) method introduced by Baležentis, Alvydas, Baležentis, Tomas in 2006 crisp; 2011 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baležentis, Alvydas, Baležentis, Tomas","subfamily":"Ranking","year":"2006 crisp; 2011 variant applicator","type":"Linguistic ratio/reference/multiplicative ensemble — 2-Tuple Linguistic Variable (2TL: (s_i, α))","value_space":"linguistic_2tuple","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Baležentis, Alvydas, Baležentis, Tomas (2011). An innovative multi-criteria supplier selection based on two-tuple MULTIMOORA and hybrid data. Economic Computation and Economic Cybernetics Studies and Research","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An%20innovative%20multi-criteria%20supplier%20selection%20based%20on%20two-tuple%20MULTIMOORA%20and%20hybrid%20data"}],"related":["multimoora"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"l2t-saw","name":"L2T-SAW","fullName":"Linguistic extension of L2T-SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Cid-López, A., Hornos, M. J., Carrasco, R. A., Herrera-Viedma, E.","url":"https://scholargate.app/en/decision-making/l2t-saw","markdownUrl":"https://scholargate.app/en/decision-making/l2t-saw.md","definition":"L2T-SAW (Linguistic extension of L2T-SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Cid-López, A., Hornos, M. J., Carrasco, R. A., Herrera-Viedma, E. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cid-López, A., Hornos, M. J., Carrasco, R. A., Herrera-Viedma, E.","subfamily":"Ranking","year":"2018","type":"Linguistic outranking/ranking — 2-Tuple Linguistic Variable (2TL: (s_i, α))","value_space":"linguistic_2tuple","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Cid-López, A., Hornos, M. J., Carrasco, R. A., Herrera-Viedma, E. (2018). Prioritization of the launch of ICT products and services through linguistic multi-criteria decision-making (2-tuple SAW). Technological and Economic Development of Economy","type":"article","doi":"10.3846/tede.2018.1423","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"l2t-todim","name":"L2T-TODIM","fullName":"Linguistic extension of L2T-TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Qi, X., Liang, C., Zhang, J.","url":"https://scholargate.app/en/decision-making/l2t-todim","markdownUrl":"https://scholargate.app/en/decision-making/l2t-todim.md","definition":"L2T-TODIM (Linguistic extension of L2T-TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Qi, X., Liang, C., Zhang, J. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Qi, X., Liang, C., Zhang, J.","subfamily":"Ranking","year":"2021","type":"Linguistic outranking/ranking — 2-Tuple Linguistic Variable (2TL: (s_i, α))","value_space":"linguistic_2tuple","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Qi, X., Liang, C., Zhang, J. (2021). A collaborative emergency decision making approach based on BWM and TODIM under interval 2-tuple linguistic environment. International Journal of Machine Learning and Cybernetics","type":"article","doi":"10.1007/s13042-021-01412-7","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"l2t-topsis","name":"L2T-TOPSIS","fullName":"Linguistic extension of L2T-TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Wei, G.","url":"https://scholargate.app/en/decision-making/l2t-topsis","markdownUrl":"https://scholargate.app/en/decision-making/l2t-topsis.md","definition":"L2T-TOPSIS (Linguistic extension of L2T-TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wei, G. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wei, G.","subfamily":"Ranking","year":"2010","type":"Linguistic outranking/ranking — 2-Tuple Linguistic Variable (2TL: (s_i, α))","value_space":"linguistic_2tuple","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Wei, G. (2010). Models for Multiple Attribute Group Decision Making with 2-Tuple Linguistic Assessment Information. International Journal of Computational Intelligence Systems","type":"article","doi":"10.1080/18756891.2010.9727702","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"l2t-vikor","name":"L2T-VIKOR","fullName":"Linguistic extension of L2T-VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Ju, Y., Wang, A.","url":"https://scholargate.app/en/decision-making/l2t-vikor","markdownUrl":"https://scholargate.app/en/decision-making/l2t-vikor.md","definition":"L2T-VIKOR (Linguistic extension of L2T-VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Ju, Y., Wang, A. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ju, Y., Wang, A.","subfamily":"Ranking","year":"2013","type":"Linguistic outranking/ranking — 2-Tuple Linguistic Variable (2TL: (s_i, α))","value_space":"linguistic_2tuple","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Ju, Y., Wang, A. (2013). Extension of VIKOR method for multi-criteria group decision making problem with linguistic information. Applied Mathematical Modelling","type":"article","doi":"10.1016/j.apm.2012.07.035","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"label-propagation","name":"Label Propagation","fullName":"Label Propagation (Graph-Based Semi-Supervised Learning)","aliases":["LP","label spreading","graph-based semi-supervised learning","harmonic label propagation"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2002,"originator":"Zhu, X. & Ghahramani, Z.","url":"https://scholargate.app/en/machine-learning/label-propagation","markdownUrl":"https://scholargate.app/en/machine-learning/label-propagation.md","definition":"Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhu, X. & Ghahramani, Z.","year":2002,"type":"Graph-based semi-supervised classification","task":"Classification on partially labeled graphs","minLabeledNodes":1,"complexityPerIteration":"O(n^2) naive; O(n) with sparse graphs"},"citations":[{"ref":"Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.","type":"techreport","doi":null,"isbn":null,"url":"https://www.semanticscholar.org/paper/Learning-from-labeled-and-unlabeled-data-with-label-Zhu-Ghahramani/2a4ca461fa847e8433bab67e7bfe4620371c1f77"},{"ref":"Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning (ICML-2003), pp. 912–919.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-supervised+learning+using+Gaussian+fields+and+harmonic+functions+Zhu"},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["label-spreading","graph-neural-network","semi-supervised-svm","k-nearest-neighbors","spectral-clustering","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"laboratory-experiment","name":"Laboratory Experiment","fullName":"Laboratory Experiment","aliases":["lab experiment","controlled experiment","true experiment","lab study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"17th century (natural science); ~1879 onward (behavioral/social science)","originator":"Francis Bacon, Robert Boyle (early scientific method); formalized in social science by Wilhelm Wundt (1879 psychology lab) and Ronald A. Fisher (20th-century design principles)","url":"https://scholargate.app/en/experimental-design/laboratory-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/laboratory-experiment.md","definition":"A laboratory experiment is a research design in which the investigator systematically manipulates one or more independent variables under tightly controlled conditions, randomly assigns participants to conditions, and measures the effect on dependent variables. By maximizing internal control, the laboratory experiment is the gold standard for establishing cause-and-effect relationships. It is the backbone of experimental psychology, cognitive science, pharmacology, and many social sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Francis Bacon, Robert Boyle (early scientific method); formalized in social science by Wilhelm Wundt (1879 psychology lab) and Ronald A. Fisher (20th-century design principles)","year":"17th century (natural science); ~1879 onward (behavioral/social science)","type":"Experimental quantitative design","dataType":"Numeric/interval/ratio measurements of dependent variables under controlled conditions","subfamily":"Deneysel desen"},"citations":[{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null},{"ref":"Laboratory experiment. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Laboratory_experiment"}],"related":["randomized-controlled-trial","field-experiment","factorial-experiment","quasi-experiment","pretest-posttest-experimental-design","control-group-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lactate-threshold","name":"Lactate Threshold (OBLA)","fullName":"Onset of Blood Lactate Accumulation and Lactate Threshold Assessment","aliases":["OBLA","anaerobic threshold","lactate turnpoint","maximal lactate steady state"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Exercise Physiology","year":"1973","originator":"Klaus Wasserman","url":"https://scholargate.app/en/sports-science/lactate-threshold","markdownUrl":"https://scholargate.app/en/sports-science/lactate-threshold.md","definition":"Lactate threshold, also termed the onset of blood lactate accumulation (OBLA), is the exercise intensity at which blood lactate concentration increases rapidly and non-linearly. Initially defined by Klaus Wasserman in 1973, the concept describes the physiological transition from aerobic to anaerobic metabolism. As exercise intensity increases, lactate production and clearance remain balanced until a critical threshold is exceeded, after which lactate rapidly accumulates in the blood, signaling a shift toward anaerobic energy pathways. This parameter is crucial in endurance sports and clinical exercise assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klaus Wasserman","subfamily":"Exercise Physiology","year":"1973","type":"incremental blood sampling test"},"citations":[{"ref":"Wasserman, K., Whipp, B. J., Koyal, S. N., & Beaver, W. L. (1973). Anaerobic threshold and respiratory gas exchange during exercise. Journal of Applied Physiology, 35(2), 236-243.","type":"article","doi":"10.1152/jappl.1973.35.2.236","isbn":null,"url":null},{"ref":"Sjödin, B., & Svedenhag, J. (1985). Applied physiology of marathon running. Sports Medicine, 2(2), 83-99.","type":"article","doi":"10.2165/00007256-198502020-00002","isbn":null,"url":null},{"ref":"Mader, A., Heck, H., & Hollmann, W. (1976). Evaluation of lactic acid threshold from a constant load work test above and below threshold. European Journal of Applied Physiology, 35(3), 169-178.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation+of+lactic+acid+threshold+from+a+constant+load+work+test+above+and+below+threshold+Mader"}],"related":["vo2-max","critical-power","respiratory-exchange-ratio","epoc","heart-rate-recovery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"landmark-analysis","name":"Landmark Analysis","fullName":"Landmark Analysis for Conditional Survival and Dynamic Prediction","aliases":["landmark method","dynamic prediction","conditional survival estimation","Landmark Analizi (Dinamik Tahmin)"],"domain":"survival","family":"survival","subfamily":null,"year":1983,"originator":"Anderson, J. R., Cain, K. C. & Gelber, R. D.","url":"https://scholargate.app/en/survival/landmark-analysis","markdownUrl":"https://scholargate.app/en/survival/landmark-analysis.md","definition":"Landmark analysis, introduced by Anderson, Cain, and Gelber in 1983, estimates conditional survival probabilities for subjects who are still at risk at a pre-specified point in time — the landmark — rather than at study entry. It was developed explicitly to avoid immortal time bias that arises when subjects are grouped by an event (such as a treatment change or biomarker result) that can only occur if they remain event-free long enough to experience it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anderson, J. R., Cain, K. C. & Gelber, R. D.","year":1983,"type":"Conditional survival estimator","handles":"Right-censoring, time-varying treatment status, immortal time bias","minSample":50,"difficulty":2},"citations":[{"ref":"Anderson, J. R., Cain, K. C. & Gelber, R. D. (1983). Analysis of Survival by Tumor Response. Journal of Clinical Oncology, 1(11), 710–719.","type":"article","doi":"10.1200/JCO.1983.1.11.710","isbn":null,"url":null},{"ref":"van Houwelingen, H. C. (2007). Dynamic Prediction by Landmarking in Event History Analysis. Scandinavian Journal of Statistics, 34(1), 70–85.","type":"article","doi":"10.1111/j.1467-9469.2006.00529.x","isbn":null,"url":null}],"related":["kaplan-meier","cox-ph","joint-model-survival","fine-gray-model","nelson-aalen"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"landscape-metrics","name":"Landscape Metrics","fullName":"Landscape Pattern Metrics","aliases":["landscape pattern indices","FRAGSTATS metrics","fragmentation indices","peyzaj metrikleri"],"domain":"spatial-analysis","family":"process-pipeline","subfamily":"Landscape ecology","year":1988,"originator":"R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)","url":"https://scholargate.app/en/spatial-analysis/landscape-metrics","markdownUrl":"https://scholargate.app/en/spatial-analysis/landscape-metrics.md","definition":"Landscape metrics are quantitative indices that describe the composition and spatial configuration of a categorical map — typically land cover — at the patch, class, and whole-landscape levels. Developed in landscape ecology (O'Neill and colleagues, 1988) and made widely usable by the FRAGSTATS software, they turn maps into numbers like patch density, edge density, fragmentation, diversity, and connectivity for ecological, planning, and change analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)","year":1988,"type":"Quantitative landscape pattern description","subfamily":"Landscape ecology","levels":"Patch / class / landscape","captures":"Composition + configuration"},"citations":[{"ref":"O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162.","type":"article","doi":"10.1007/BF00162741","isbn":null,"url":null},{"ref":"McGarigal, K., & Marks, B. J. (1995). FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA Forest Service General Technical Report PNW-GTR-351.","type":"techreport","doi":null,"isbn":null,"url":"https://www.fs.usda.gov/research/treesearch/3064"}],"related":["ca-markov","object-based-image-analysis","moran-s-i","community-detection"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"language-identification","name":"Language Identification","fullName":"Language Identification (LID)","aliases":["language detection","LID","Dil Tanımlama (Language Identification)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/language-identification","markdownUrl":"https://scholargate.app/en/text-mining/language-identification.md","definition":"Language identification is a natural-language-processing task that automatically detects which language a piece of text is written in. Building on off-the-shelf tools such as langid.py (Lui & Baldwin, 2012) and the efficient classifiers of Joulin et al. (2017), it is widely used to preprocess and filter multilingual data sets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-classification task","approaches":"Character n-gram models / supervised classifiers (e.g. langid.py, fastText)","output":"Predicted language label per document","minTextLength":"At least 20 characters recommended","difficulty":"Introductory"},"citations":[{"ref":"Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P12-3005/"},{"ref":"Joulin, A., Grave, E., Bojanowski, P. & Mikolov, T. (2017). Bag of Tricks for Efficient Text Classification. Proceedings of the EACL 2017.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/E17-2068/"}],"related":["text-classification","spelling-grammar-check","ngram-language-model","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"laplace-approximation","name":"Laplace Approximation","fullName":"Laplace Approximation to the Posterior","aliases":["Laplace's method","saddle-point approximation (Bayesian)","second-order Gaussian approximation","LA"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":1986,"originator":"Pierre-Simon Laplace (1774); Bayesian formalisation: Tierney & Kadane (1986)","url":"https://scholargate.app/en/bayesian/laplace-approximation","markdownUrl":"https://scholargate.app/en/bayesian/laplace-approximation.md","definition":"The Laplace approximation is a classical analytic technique that replaces an intractable posterior distribution with a multivariate Gaussian centred at the posterior mode, using the curvature of the log-posterior at that mode to set the covariance. Formalised for Bayesian statistics by Tierney and Kadane (1986) in their landmark Journal of the American Statistical Association paper, it provides a fast, deterministic alternative to Markov chain Monte Carlo and forms the mathematical core of Integrated Nested Laplace Approximations (INLA).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Analytical posterior approximation","purpose":"approximate inference / posterior summarisation","inference":"analytic (deterministic)","outputs":"Gaussian approximation to posterior / marginal likelihood estimate","originator":"Pierre-Simon Laplace (1774); Bayesian formalisation: Tierney & Kadane (1986)","year":1986,"computational_cost":"O(p³) per evaluation (Hessian inversion)","key_extension":"Integrated Nested Laplace Approximations (INLA)"},"citations":[{"ref":"Tierney, L. & Kadane, J. B. (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association, 81(393), 82–86.","type":"article","doi":"10.1080/01621459.1986.10478240","isbn":null,"url":null},{"ref":"MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521642989","url":null},{"ref":"Rue, H., Martino, S. & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B, 71(2), 319–392.","type":"article","doi":"10.1111/j.1467-9868.2008.00700.x","isbn":null,"url":null}],"related":["mcmc","variational-bayes","bayesian-regression","hierarchical-bayes","expectation-propagation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"large-eddy-simulation","name":"Large Eddy Simulation","fullName":"Large Eddy Simulation","aliases":["LES","subgrid-scale modeling"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1963","originator":"Joseph Smagorinsky","url":"https://scholargate.app/en/fluid-dynamics/large-eddy-simulation","markdownUrl":"https://scholargate.app/en/fluid-dynamics/large-eddy-simulation.md","definition":"Large Eddy Simulation (LES) is a turbulence modeling technique that explicitly resolves large-scale turbulent eddies while modeling small-scale subgrid-scale (SGS) motions. Introduced by Joseph Smagorinsky in 1963, LES represents a middle ground between Reynolds-Averaged Navier-Stokes (RANS) and Direct Numerical Simulation (DNS). By capturing the energy-containing scales of turbulence, LES provides superior accuracy for transient flows and complex geometries at computational costs significantly lower than DNS.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph Smagorinsky","subfamily":"Fluid Dynamics","year":"1963","type":"Scale-resolving turbulence simulation"},"citations":[{"ref":"Smagorinsky, J. (1963). General circulation experiments with the primitive equations: I. The basic experiment. Monthly Weather Review, 91(3), 99-164.","type":"article","doi":"10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2","isbn":null,"url":null},{"ref":"Leonard, A. (1974). Energy cascade in large-eddy simulations of turbulent fluid flows. Advances in Geophysics, 18, 237-248.","type":"article","doi":"10.1016/S0065-2687(08)60464-1","isbn":null,"url":null},{"ref":"Meneveau, C., & Katz, J. (2000). Scale-invariance and turbulence models for large-eddy simulation. Annual Review of Fluid Mechanics, 32, 1-32.","type":"article","doi":"10.1146/annurev.fluid.32.1.1","isbn":null,"url":null}],"related":["reynolds-averaged-navier-stokes","direct-numerical-simulation","detached-eddy-simulation","lattice-boltzmann-method","volume-of-fluid"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lasso-regression","name":"Lasso Regression","fullName":"Least Absolute Shrinkage and Selection Operator (LASSO)","aliases":["LASSO Regresyonu","lasso","L1-regularized regression","L1 regularization"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1996,"originator":"Tibshirani, R.","url":"https://scholargate.app/en/machine-learning/lasso-regression","markdownUrl":"https://scholargate.app/en/machine-learning/lasso-regression.md","definition":"Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tibshirani, R.","year":1996,"type":"Regularized linear regression (L1 penalty)","task":"Prediction & variable selection","minSample":30},"citations":[{"ref":"Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.","type":"article","doi":"10.1111/j.2517-6161.1996.tb02080.x","isbn":null,"url":null}],"related":["ridge-regression","elastic-net","linear-regression","logistic-regression","pca"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"latent-class-analysis","name":"Latent Class Analysis","fullName":"Latent Class Analysis","aliases":["LCA","latent class model","latent categorical analysis","finite mixture of multinomials"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1950s–1968","originator":"Paul F. Lazarsfeld","url":"https://scholargate.app/en/statistics/latent-class-analysis","markdownUrl":"https://scholargate.app/en/statistics/latent-class-analysis.md","definition":"Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul F. Lazarsfeld","year":"1950s–1968","type":"Latent variable / person-centered classification","dataType":"Categorical (binary or polytomous) observed indicators","subfamily":"Multivariate analysis"},"citations":[{"ref":"Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.","type":"article","doi":"10.1093/biomet/61.2.215","isbn":null,"url":null},{"ref":"Lazarsfeld, P. F. & Henry, N. W. (1968). Latent Structure Analysis. Houghton Mifflin.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Latent+Structure+Analysis+Lazarsfeld+Henry+1968"}],"related":["latent-profile-analysis","mixture-modeling","cluster-analysis","exploratory-factor-analysis","confirmatory-factor-analysis","discriminant-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"latent-diffusion-models","name":"Latent Diffusion Models","fullName":"High-Resolution Image Synthesis with Latent Diffusion Models","aliases":["LDM","Stable Diffusion","Latent Diffusion"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Generative Models","year":"2022","originator":"Robin Rombach","url":"https://scholargate.app/en/deep-learning/latent-diffusion-models","markdownUrl":"https://scholargate.app/en/deep-learning/latent-diffusion-models.md","definition":"Latent Diffusion Models (LDMs) are a generative approach introduced by Rombach et al. in 2022 that performs the diffusion process in a compressed latent space rather than pixel space, enabling efficient high-resolution image synthesis. By compressing images into a low-dimensional latent representation using a variational autoencoder, diffusion becomes computationally tractable while maintaining visual quality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robin Rombach","subfamily":"Deep Learning, Generative Models","year":"2022","type":"Neural network architecture"},"citations":[{"ref":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695).","type":"article","doi":"10.1109/CVPR52688.2022.01042","isbn":null,"url":null}],"related":["masked-autoencoders","graphrag","detr","segment-anything-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"latent-dirichlet-allocation","name":"Latent Dirichlet Allocation","fullName":"Latent Dirichlet Allocation (LDA — Blei, Ng & Jordan 2003)","aliases":["LDA","topic model","Blei-Ng-Jordan model","probabilistic topic modeling","generative topic model"],"domain":"machine-learning","family":"latent-structure","subfamily":null,"year":2003,"originator":"Blei, D. M.; Ng, A. Y.; Jordan, M. I.","url":"https://scholargate.app/en/machine-learning/latent-dirichlet-allocation","markdownUrl":"https://scholargate.app/en/machine-learning/latent-dirichlet-allocation.md","definition":"Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blei, D. M.; Ng, A. Y.; Jordan, M. I.","year":2003,"type":"Generative probabilistic topic model (three-level hierarchical Bayesian)","task":"Unsupervised topic discovery in text corpora","inferenceMethod":"Variational EM or collapsed Gibbs sampling","hyperparameters":"α (document-topic concentration), β (topic-word concentration), K (number of topics)","outputType":"Topic-word distributions (Φ) and document-topic distributions (θ)","minDocuments":100},"citations":[{"ref":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.","type":"article","doi":"10.5555/944919.944937","isbn":null,"url":"https://jmlr.org/papers/v3/blei03a.html"},{"ref":"Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.","type":"article","doi":"10.1145/2133806.2133826","isbn":null,"url":null},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9). Springer.","type":"book","doi":null,"isbn":"978-0-387-31073-2","url":null}],"related":["non-negative-matrix-factorization","probabilistic-latent-semantic-analysis","word2vec","hidden-markov-model","naive-bayes-classifier","k-means-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"latent-growth-curve","name":"LGC Model","fullName":"Latent Growth Curve Model","aliases":["latent growth model","LGC","growth curve model","Gizil Büyüme Eğrisi Modeli"],"domain":"statistics","family":"latent-structure","subfamily":null,"year":1990,"originator":"Meredith & Tisak","url":"https://scholargate.app/en/statistics/latent-growth-curve","markdownUrl":"https://scholargate.app/en/statistics/latent-growth-curve.md","definition":"The latent growth curve model is a structural equation modelling approach introduced by Meredith and Tisak (1990) for analysing change over time. It treats each individual's starting point (intercept) and rate of change (slope) as latent variables, simultaneously estimating the average trajectory across the sample and the extent to which individuals differ in their own trajectories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Meredith & Tisak","year":1990,"type":"Latent variable / longitudinal growth model","outcome":"Latent intercept and slope factors","data":"Continuous repeated measures (longitudinal / panel)","min_waves":3,"min_sample":100,"framework":"Structural Equation Modelling (SEM)"},"citations":[{"ref":"Meredith, W. & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122.","type":"article","doi":"10.1007/BF02294746","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","sem","mixed-effects-model","repeated-measures-anova","exploratory-factor-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"latent-profile-analysis","name":"Latent Profile Analysis","fullName":"Latent Profile Analysis (LPA)","aliases":["Continuous Latent Class Analysis","Gaussian Profile Mixture Model","Person-Centered Cluster Analysis","Gizil Profil Analizi"],"domain":"psychometrics","family":"latent-structure","subfamily":"Latent structure","year":2010,"originator":"Lazarsfeld & Henry; Collins & Lanza","url":"https://scholargate.app/en/psychometrics/latent-profile-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/latent-profile-analysis.md","definition":"Latent Profile Analysis (LPA) is a person-centered finite mixture modeling technique that identifies unobserved subgroups — called profiles — within a population based on patterns of scores across multiple continuous indicators. Rooted in Lazarsfeld and Henry's latent structure tradition and formally synthesized for applied behavioral research by Collins and Lanza (2010), LPA assumes that observed heterogeneity in continuous data arises from a discrete number of latent classes, each characterized by a unique multivariate mean profile.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lazarsfeld & Henry; Collins & Lanza","year":2010,"type":"Person-centered finite mixture model","subfamily":"Latent structure","indicator_scale":"Continuous (interval/ratio)","estimation":"Maximum likelihood via EM algorithm"},"citations":[{"ref":"Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley.","type":"book","doi":null,"isbn":"978-0-470-22839-7","url":null}],"related":["latent-class-analysis","gaussian-mixture-model","cfa"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"latent-transition-analysis","name":"Latent Transition Analysis","fullName":"Latent Transition Analysis","aliases":["LTA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Longitudinal Latent Class","year":"2002","originator":"Linda M. Collins, Stephanie T. Lanza","url":"https://scholargate.app/en/psychometrics/latent-transition-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/latent-transition-analysis.md","definition":"Latent Transition Analysis (LTA) is a method for studying transitions between latent classes over time, developed by Collins and Lanza (2010). LTA combines latent class analysis (grouping individuals into classes) with Markovian transition models to understand how people move between qualitatively distinct states across time periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Linda M. Collins, Stephanie T. Lanza","subfamily":"Longitudinal Latent Class","year":"2002","type":"Markovian transition between latent states"},"citations":[{"ref":"Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. Wiley.","type":"article","doi":null,"isbn":"9780470228395","url":null},{"ref":"Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LTA: A SAS macro for latent transition analysis. Structural Equation Modeling, 14(4), 671-694.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=PROC+LTA%3A+A+SAS+macro+for+latent+transition+analysis+Lanza"},{"ref":"Vermunt, J. K., & Magidson, J. (2016). Latent class and latent transition analysis. In J. P. Baltes, G. G. Brim, D. Featherman, & S. Shye (Eds.), Lifespan Development and Behavior (pp. 91-113). Academic Press.","type":"article","doi":null,"isbn":"9780123997760","url":null}],"related":["pls-sem","exploratory-structural-equation-modeling","dina-model","value-added-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"latin-hypercube-sampling","name":"Latin Hypercube Sampling","fullName":"Latin Hypercube Sampling and Sensitivity Analysis","aliases":["LHS","Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi","stratified sampling design","space-filling design"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":1979,"originator":null,"url":"https://scholargate.app/en/simulation/latin-hypercube-sampling","markdownUrl":"https://scholargate.app/en/simulation/latin-hypercube-sampling.md","definition":"Latin Hypercube Sampling (LHS) is a stratified space-filling design for computer experiments, introduced by McKay, Beckman, and Conover in 1979. It divides each input variable's range into equally probable strata and draws exactly one sample per stratum, ensuring that the full input space is covered with far fewer model evaluations than standard Monte Carlo simulation requires. It is routinely paired with global sensitivity analysis — particularly Sobol indices — to quantify how much each input drives output variability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"McKay, Beckman & Conover","year":1979,"type":"Stratified space-filling sampling design","outputType":"Sample matrix over input space; Sobol sensitivity indices","complementaryAnalysis":"Global sensitivity analysis (Sobol indices, Iman-Conover correlation control)","difficultyLevel":2},"citations":[{"ref":"McKay, M.D., Beckman, R.J. & Conover, W.J. (1979). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2), 239-245.","type":"article","doi":"10.1080/00401706.1979.10489755","isbn":null,"url":null},{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":"10.1002/9780470725184","isbn":null,"url":null}],"related":["monte-carlo-simulation","quasi-monte-carlo","variance-reduction-mc","sobol-sensitivity-analysis","bootstrap-simulation","design-of-experiments"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"latin-square-design","name":"Latin Square Design","fullName":"Latin Square and Greco-Latin Square Design","aliases":["Latin Square","Greco-Latin Square","Latin Kare ve Greco-Latin Kare Deseni"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1935,"originator":"Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/latin-square-design","markdownUrl":"https://scholargate.app/en/experimental-design/latin-square-design.md","definition":"The Latin square design is a blocked experimental design that simultaneously controls two independent nuisance factors — the row block and the column block — so that each treatment appears exactly once in every row and every column of an n×n arrangement. Formalised by Ronald A. Fisher in his 1935 monograph The Design of Experiments, the design dramatically reduces experimental error by absorbing variation from two extraneous sources before the treatment effects are estimated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":1935,"family":"Experimental design","type":"Parametric blocked ANOVA","blockingFactors":2,"outcome":"continuous","parametric":true,"distribution":"F","df_treatment":"n - 1","df_error":"(n - 1)(n - 2)"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null},{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/in.ernet.dli.2015.502684"}],"related":["randomized-complete-block-design","one-way-anova","two-way-anova","split-plot-design","factorial-design","crossover-design"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lattice-based-cryptography","name":"Lattice-Based Cryptography","fullName":"Lattice-Based Cryptography","aliases":["lattice cryptography","post-quantum lattice cryptography"],"domain":"cryptography","family":"ml-model","subfamily":"Post-quantum cryptography","year":"1996","originator":"Miklós Ajtai","url":"https://scholargate.app/en/cryptography/lattice-based-cryptography","markdownUrl":"https://scholargate.app/en/cryptography/lattice-based-cryptography.md","definition":"Lattice-based cryptography is a class of cryptosystems whose security is derived from the computational hardness of lattice problems, particularly the shortest vector problem (SVP) and learning with errors (LWE). First proposed by Miklós Ajtai in 1996, lattice-based approaches have gained prominence as the leading candidates for post-quantum cryptography. Unlike RSA and ECC, which are vulnerable to quantum computers, lattice problems are believed to remain hard even against quantum algorithms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Miklós Ajtai","subfamily":"Post-quantum cryptography","year":"1996","type":"public-key cryptosystem based on lattice hardness"},"citations":[{"ref":"Ajtai, M. (1996). Generating hard instances of the short basis problem. In Proceedings of the 28th Annual ACM Symposium on Theory of Computing, pp. 99-108.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Generating+hard+instances+of+the+short+basis+problem+Ajtai"},{"ref":"Regev, O. (2005). On lattices, learning with errors, hard instances, and public key cryptography. In Proceedings of STOC 2005, pp. 84-93.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+lattices%2C+learning+with+errors%2C+hard+instances%2C+and+public+key+cryptography+Regev"}],"related":["post-quantum-cryptography","rsa-cryptosystem","elliptic-curve-cryptography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lattice-boltzmann-method","name":"Lattice Boltzmann Method","fullName":"Lattice Boltzmann Method","aliases":["LBM","lattice gas automata"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1988","originator":"Gianluigi Zanetti","url":"https://scholargate.app/en/fluid-dynamics/lattice-boltzmann-method","markdownUrl":"https://scholargate.app/en/fluid-dynamics/lattice-boltzmann-method.md","definition":"The Lattice Boltzmann Method (LBM) is a kinetic theory-based computational approach to fluid dynamics that discretizes the Boltzmann equation on a lattice grid. Developed by McNamara and Zanetti in 1988, LBM computes fluid behavior by tracking the distribution of particle velocities at discrete lattice nodes rather than solving the Navier-Stokes equations directly. This method naturally incorporates complex physics (turbulence, multiphase flows, porous media) and is highly parallelizable, making it increasingly popular for modern computational platforms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gianluigi Zanetti","subfamily":"Fluid Dynamics","year":"1988","type":"Kinetic theory-based simulation method"},"citations":[{"ref":"McNamara, G. R., & Zanetti, G. (1988). Use of the Boltzmann equation to simulate lattice-gas automata. Physical Review Letters, 61(20), 2332-2335.","type":"article","doi":"10.1103/PhysRevLett.61.2332","isbn":null,"url":null},{"ref":"Qian, Y. H., d'Humières, D., & Lallemand, P. (1992). Lattice BGK models for the Navier-Stokes equation. Europhysics Letters, 17(6), 479-484.","type":"article","doi":"10.1209/0295-5075/17/6/001","isbn":null,"url":null},{"ref":"Chen, S., & Doolen, G. D. (1998). Lattice Boltzmann method for fluid simulations. Annual Review of Fluid Mechanics, 30, 329-364.","type":"book","doi":"10.1146/annurev.fluid.30.1.329","isbn":null,"url":null}],"related":["direct-numerical-simulation","large-eddy-simulation","smoothed-particle-hydrodynamics","volume-of-fluid","reynolds-averaged-navier-stokes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lattice-qcd","name":"Lattice QCD","fullName":"Lattice Quantum Chromodynamics","aliases":["LQCD","lattice gauge theory"],"domain":"quantum-computing","family":"ml-model","subfamily":"Computational Physics","year":"1974","originator":"Kenneth Wilson","url":"https://scholargate.app/en/quantum-computing/lattice-qcd","markdownUrl":"https://scholargate.app/en/quantum-computing/lattice-qcd.md","definition":"Lattice Quantum Chromodynamics (LQCD) is a computational method for studying quantum chromodynamics (QCD)—the theory of strong nuclear forces—by discretizing spacetime onto a lattice and simulating quark and gluon dynamics. Introduced by Kenneth Wilson in 1974, LQCD is the only known approach for non-perturbative calculations of QCD properties from first principles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kenneth Wilson","subfamily":"Computational Physics","year":"1974","type":"Simulation method"},"citations":[{"ref":"Wilson, K. G. (1974). Confinement of quarks. Physical Review D, 10, 2445–2459.","type":"article","doi":"10.1103/PhysRevD.10.2445","isbn":null,"url":null},{"ref":"Aoki, S., et al. (2020). Flag review 2019. European Physical Journal C, 80, 113.","type":"article","doi":"10.1140/epjc/s10052-019-7354-7","isbn":null,"url":null},{"ref":"Durr, B., et al. (2008). Ab initio determination of light hadron masses. Science, 322, 1224–1227.","type":"article","doi":"10.1126/science.1163233","isbn":null,"url":null}],"related":["density-functional-theory","path-integral-monte-carlo","quantum-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lawton-brody-iadl","name":"Lawton-Brody Instrumental ADL Scale","fullName":"Lawton-Brody Instrumental Activities of Daily Living (IADL) Scale","aliases":["IADL Scale","Lawton IADL","Instrumental Activities of Daily Living"],"domain":"nursing","family":"process-pipeline","subfamily":"functional status assessment","year":"1969","originator":"M. Powell Lawton","url":"https://scholargate.app/en/nursing/lawton-brody-iadl","markdownUrl":"https://scholargate.app/en/nursing/lawton-brody-iadl.md","definition":"The Lawton-Brody Instrumental Activities of Daily Living (IADL) Scale, developed by M. Powell Lawton and Elaine M. Brody in 1969, measures the capacity to perform complex, higher-order self-care and household tasks necessary for independent community living. The scale assesses eight domains (for women) or five domains (for men): telephone use, shopping, food preparation, housekeeping, laundry, transportation, medication management, and financial management. It complements basic ADL assessment (measured by the Katz Index) and is essential for comprehensive geriatric evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. Powell Lawton","subfamily":"functional status assessment","year":"1969","type":"Clinician-rated or interview-based functional assessment"},"citations":[{"ref":"Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist, 9(3), 179-186.","type":"article","doi":"10.1093/geront/9.3_part_1.179","isbn":null,"url":null},{"ref":"Lawton, M. P. (1988). Scales to measure competence in basic and instrumental ADL. Psychopharmacol Bull, 24(4), 615-623.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/3249768"}],"related":["katz-independence-adl","clinical-frailty-scale","zarit-caregiver-burden-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lbwa","name":"LBWA","fullName":"Level Based Weight Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2019","originator":"Žižović, M., Pamučar, D.","url":"https://scholargate.app/en/decision-making/lbwa","markdownUrl":"https://scholargate.app/en/decision-making/lbwa.md","definition":"LBWA (Level Based Weight Assessment) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Žižović, M., Pamučar, D. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Žižović, M., Pamučar, D.","subfamily":"Weight_Subjective","year":"2019","type":"Level-partitioned criterion grouping with influence function weighting","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Žižović, M., Pamučar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=New+model+for+determining+criteria+weights%3A+Level+Based+Weight+Assessment+%28LBWA%29+model"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lca","name":"LCA","fullName":"Latent Class Analysis","aliases":["Gizil Sınıf Analizi (LCA)","latent class model","latent structure analysis"],"domain":"statistics","family":"latent-structure","subfamily":null,"year":1950,"originator":"Paul F. Lazarsfeld","url":"https://scholargate.app/en/statistics/lca","markdownUrl":"https://scholargate.app/en/statistics/lca.md","definition":"Latent class analysis is a probabilistic model-based clustering technique that identifies unobserved subgroups — latent classes — within a population on the basis of patterns of categorical, binary, or ordinal indicator responses. Originating in sociological measurement theory with Lazarsfeld's latent structure work around 1950 and formalised computationally by Goodman in the 1970s, it is widely used in the social, health, and behavioural sciences to reveal hidden population heterogeneity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul F. Lazarsfeld","year":1950,"type":"Latent variable / probabilistic clustering","outcome":"Discrete latent class memberships with posterior probabilities","data":"Categorical / binary / ordinal indicators","min_sample":200,"difficulty":3},"citations":[{"ref":"Hagenaars, J. A. & McCutcheon, A. L. (Eds.) (2002). Applied Latent Class Analysis. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521594516","url":null},{"ref":"Nylund, K. L., Asparouhov, T. & Muthen, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling. Structural Equation Modeling, 14(4), 535–569.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Deciding+on+the+number+of+classes+in+latent+class+analysis+and+growth+mixture+modeling+Nylund"}],"related":["exploratory-factor-analysis","cluster-analysis","mixture-models","lpa","sem"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ld-block-analysis","name":"LD Block Analysis","fullName":"Linkage Disequilibrium Block Analysis and Haplotype Mapping","aliases":["Haplotype block analysis","LD mapping","Block structure analysis"],"domain":"genetics","family":"process-pipeline","subfamily":"Genomic variation analysis","year":"2002","originator":"Shaun Gabriel & Eric Lander","url":"https://scholargate.app/en/genetics/ld-block-analysis","markdownUrl":"https://scholargate.app/en/genetics/ld-block-analysis.md","definition":"Linkage disequilibrium (LD) block analysis is a genomic method that partitions the human genome into distinct haplotype blocks—regions of limited recombination where variants are in strong statistical association. First systematically described by Gabriel and colleagues in 2002, this approach reveals the underlying structure of genetic variation and enables efficient genomic studies by reducing the number of variants needed to capture common diversity. LD block analysis forms the foundation of genome-wide association study (GWAS) design and modern population genetics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shaun Gabriel & Eric Lander","subfamily":"Genomic variation analysis","year":"2002","type":"Haplotype analysis method"},"citations":[{"ref":"Gabriel, S. B., Schaffner, S. F., Nguyen, H., Moore, J. M., Roy, J., Blumenstiel, B., & Lander, E. S. (2002). The structure of haplotype blocks in the human genome. Science, 296(5576), 2225–2229.","type":"article","doi":"10.1126/science.1069424","isbn":null,"url":null},{"ref":"Daly, M. J., Rioux, J. D., Schaffner, S. F., Hudson, T. J., & Lander, E. S. (2001). High-resolution haplotype structure in the human genome. Nature Genetics, 29(2), 229–232.","type":"article","doi":"10.1038/ng1001-229","isbn":null,"url":null},{"ref":"Wang, N., Akey, J. M., Zhang, K., Chakraborty, R., & Jin, L. (2005). Distribution of recombination crossovers and the origin of block-like patterns of linkage disequilibrium. Genetics, 155(4), 1599–1606.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Distribution+of+recombination+crossovers+and+the+origin+of+block-like+patterns+of+linkage+disequilibrium+Wang"}],"related":["ibd-mapping","qtl-mapping","f-statistics","polygenic-risk-score","admixture-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lda-classification","name":"Linear Discriminant Analysis (Classification)","fullName":"Linear Discriminant Analysis (LDA — Classification)","aliases":["LDA","Fisher's LDA","Fisher's linear discriminant","discriminant function analysis","Doğrusal Diskriminant Analizi (LDA — Sınıflandırma)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1936,"originator":"Ronald A. Fisher","url":"https://scholargate.app/en/statistics/lda-classification","markdownUrl":"https://scholargate.app/en/statistics/lda-classification.md","definition":"Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":1936,"family":"Supervised classification","type":"Parametric linear classifier / dimensionality reduction","minSample":50,"parametric":true,"distribution":"Multivariate normal (within-class)","outcome":"categorical (group membership)","predictors":"continuous","discriminantFunctions":"min(K−1, p) where K = classes, p = features","criterionOptimized":"Fisher's ratio — between-class to within-class scatter"},"citations":[{"ref":"Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188.","type":"article","doi":"10.1111/j.1469-1809.1936.tb02137.x","isbn":null,"url":null}],"related":["manova","logistic-regression","qda-classification","pca","factor-analysis","svm-classification","naive-bayes","knn"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lda-topic-model","name":"LDA Topic Model","fullName":"Latent Dirichlet Allocation Topic Model","aliases":["LDA","Latent Dirichlet Allocation","LDA Topic Modeling","Dirichlet Topic Model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2003","originator":"Blei, D. M., Ng, A. Y., & Jordan, M. I.","url":"https://scholargate.app/en/deep-learning/lda-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/lda-topic-model.md","definition":"Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blei, D. M., Ng, A. Y., & Jordan, M. I.","year":"2003","type":"Probabilistic generative topic model","dataType":"Text corpus (bag-of-words)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/blei03a.html"},{"ref":"Latent Dirichlet Allocation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation"}],"related":["nmf-topic-model","topic-modeling","sentence-embeddings","bert-based-classification","lsa","word2vec"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ldpc-codes","name":"LDPC Codes","fullName":"Low-Density Parity-Check Codes","aliases":["sparse codes","belief propagation codes"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Coding theory","year":"1962","originator":"Robert Gallager","url":"https://scholargate.app/en/telecommunications/ldpc-codes","markdownUrl":"https://scholargate.app/en/telecommunications/ldpc-codes.md","definition":"LDPC codes, invented by Robert Gallager in 1962 and rediscovered in the 1990s by MacKay, are linear error-correcting codes defined by sparse parity-check matrices. They achieve performance within 0.4 dB of the Shannon limit with iterative belief-propagation decoding and have become the standard for modern wireless (WiFi-6, 5G NR, Digital Video Broadcasting). Unlike turbo codes, LDPC codes have a more elegant graph-theoretic structure and more mature theoretical analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Gallager","subfamily":"Coding theory","year":"1962","type":"linear error-correcting code"},"citations":[{"ref":"Gallager, R. G. (1962). Low-density parity-check codes. IRE Transactions on Information Theory, 8(1), 21-28.","type":"article","doi":"10.1109/TIT.1962.1057683","isbn":null,"url":null},{"ref":"Richardson, T. J., & Urbanke, R. L. (2001). The capacity of low-density parity-check codes under message-passing decoding. IEEE Transactions on Information Theory, 47(2), 599-618.","type":"article","doi":"10.1109/18.910577","isbn":null,"url":null}],"related":["turbo-code","polar-codes","shannon-capacity","ofdm","mimo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"leader-member-exchange-scale","name":"Leader-Member Exchange Scale","fullName":"Leader-Member Exchange Scale (LMX-7)","aliases":["LMX-7","LMX","Graen Uhl-Bien Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"leadership-relationship","year":"1995","originator":"George B. Graen","url":"https://scholargate.app/en/organizational-behavior/leader-member-exchange-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/leader-member-exchange-scale.md","definition":"The Leader-Member Exchange Scale (LMX-7) measures the quality of the working relationship between a supervisor and employee. Developed by Graen and Uhl-Bien in 1995, it is a brief, widely adopted instrument grounded in Leader-Member Exchange theory. The scale captures mutual trust, respect, and obligation—the psychological foundation of effective working relationships. Higher LMX quality predicts engagement, performance, and retention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George B. Graen","subfamily":"leadership-relationship","year":"1995","type":"Self-report questionnaire"},"citations":[{"ref":"Graen, G. B., & Uhl-Bien, M. (1995). Relationship-based approach to leadership: Development of leader-member exchange (LMX) theory of leadership over 25 years. Leadership Quarterly, 6(2), 219–247.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Relationship-based+approach+to+leadership%3A+Development+of+leader-member+exchange+%28LMX%29+theory+of+leadership+over+25+years+Graen"},{"ref":"Liden, R. C., Maslyn, J. M., & Hallenbeck, J. R. (1998). Multidimensionality of leader-member exchange: The construction and validation of a multidimensional measure. Journal of Management, 24(2), 211–235.","type":"article","doi":"10.1037/t04899-000","isbn":null,"url":null},{"ref":"Schriesheim, C. A., Castro, S. L., & Cogliser, C. C. (1999). Leader-member exchange (LMX) research: A comprehensive review of theory, measurement, and data-analytic practices. Leadership Quarterly, 10(1), 63–113.","type":"article","doi":"10.1016/S1048-9843(99)80009-5","isbn":null,"url":null}],"related":["perceived-organizational-support","psychological-capital-questionnaire","organizational-commitment-questionnaire","proactive-personality-scale","core-self-evaluations-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"leaf-area-index","name":"Leaf Area Index","fullName":"Leaf Area Index (LAI) Measurement and Applications","aliases":["LAI","Leaf area","Canopy structure"],"domain":"agronomy","family":"process-pipeline","subfamily":"Canopy Biometry","year":"1947","originator":"Donald J. Watson","url":"https://scholargate.app/en/agronomy/leaf-area-index","markdownUrl":"https://scholargate.app/en/agronomy/leaf-area-index.md","definition":"Leaf Area Index (LAI) is a dimensionless quantity that measures the total one-sided area of leaves per unit ground area covered by a canopy. It quantifies canopy density and structure: LAI = 0 for bare soil, LAI = 1 for a thin crop, LAI = 3-6 for dense cereal or grass canopies, and LAI > 8 for dense forest. LAI is a key variable in crop growth models, evapotranspiration estimation, and remote sensing because it directly controls light interception, photosynthesis, and water loss from vegetation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald J. Watson","subfamily":"Canopy Biometry","year":"1947","type":"Plant morphometric measurement"},"citations":[{"ref":"Watson, D. J. (1947). Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany, 11(43), 375-407.","type":"article","doi":"10.1093/oxfordjournals.aob.a083148","isbn":null,"url":null},{"ref":"Chen, J. M., & Black, T. A. (1992). Defining leaf area index for non-flat leaves. Plant, Cell & Environment, 15(4), 421-429.","type":"article","doi":"10.1111/j.1365-3040.1992.tb00992.x","isbn":null,"url":null},{"ref":"Weiss, M., Baret, F., Smith, G. J., Jonckheere, I., & Coppin, P. (2004). Review of methods for in situ leaf area index (LAI) determination: Part II. LiDAR and spectral approaches. Agricultural and Forest Meteorology, 121(1-2), 37-53.","type":"article","doi":"10.1016/j.agrformet.2003.08.001","isbn":null,"url":null}],"related":["crop-growth-model","penman-monteith-equation","chlorophyll-fluorescence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lean-healthcare","name":"Lean Healthcare","fullName":"Lean Management Principles Applied to Healthcare Operations","aliases":["Lean Healthcare Management","Healthcare Lean"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Process improvement, Operational efficiency","year":"1988","originator":"Taiichi Ohno, Toyota Production System","url":"https://scholargate.app/en/healthcare-management/lean-healthcare","markdownUrl":"https://scholargate.app/en/healthcare-management/lean-healthcare.md","definition":"Lean is a management philosophy that emerged from the Toyota Production System, focused on maximizing patient value while minimizing waste. Applied to healthcare, Lean uses systematic methods to identify and eliminate non-value-added activities, reduce wait times, and improve the quality of patient care.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Taiichi Ohno, Toyota Production System","subfamily":"Process improvement, Operational efficiency","year":"1988","type":"Continuous improvement methodology"},"citations":[{"ref":"Ohno, T. (1988). Toyota Production System: Beyond Large-Scale Production. Productivity Press.","type":"book","doi":null,"isbn":null,"url":"https://www.routledge.com/Toyota-Production-System-Beyond-Large-Scale-Production/Ohno/p/book/9780915299379"},{"ref":"Womack, J. P., Jones, D. T., & Roos, D. (1990). The Machine that Changed the World. Rawson Associates.","type":"book","doi":null,"isbn":"9780915299379","url":null},{"ref":"Kim, C. S., Spahlinger, D. A., Kin, J. M., & Billi, J. E. (2009). Lean health care: What can hospitals learn from a world-class automaker? Journal of Hospital Medicine, 4(3), 191–199.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lean+health+care%3A+What+can+hospitals+learn+from+a+world-class+automaker+Kim"}],"related":["six-sigma-healthcare","patient-flow-simulation","dea-hospital-efficiency","balanced-scorecard-healthcare","hospital-readmission-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"learning-analytics","name":"Learning Analytics","fullName":"Learning Analytics","aliases":["Educational Data Mining","Academic Analytics","Learning Data Analytics","Öğrenme Analitiği"],"domain":"education-analytics","family":"process-pipeline","subfamily":"Learning analytics","year":2011,"originator":"George Siemens & Phil Long","url":"https://scholargate.app/en/education-analytics/learning-analytics","markdownUrl":"https://scholargate.app/en/education-analytics/learning-analytics.md","definition":"Learning Analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, with the purpose of understanding and optimizing learning and the environments in which it occurs. Formally introduced by George Siemens and Phil Long in 2011, the approach draws on data generated in digital learning environments to provide educators, institutions, and learners with evidence-based feedback for improving educational outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Siemens & Phil Long","year":2011,"type":"data-driven educational process pipeline","subfamily":"Learning analytics","dataSource":"digital learning environments (LMS, MOOCs, e-portfolios)","output":"actionable insights for instructors, learners, and institutions"},"citations":[{"ref":"Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.","type":"article","doi":null,"isbn":null,"url":"https://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education"}],"related":["knowledge-tracing","knowledge-space-theory","sequence-mining"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"learning-curve","name":"Learning Curve","fullName":"Learning Curve (Power Law of Practice)","aliases":["Power Law of Practice","Experience Curve","Wright's Law","Öğrenme Eğrisi"],"domain":"education-analytics","family":"regression-model","subfamily":"Learning analytics","year":1936,"originator":"Theodore Wright","url":"https://scholargate.app/en/education-analytics/learning-curve","markdownUrl":"https://scholargate.app/en/education-analytics/learning-curve.md","definition":"The learning curve models how performance improves predictably as cumulative experience accumulates. Formalized by Theodore Wright in 1936 using aircraft manufacturing data, it expresses the relationship between the number of practice trials (or production units) and the time or cost per unit as a power-law function. It is widely applied in educational psychology, industrial engineering, health professions training, and human factors research whenever repeated task execution is the mechanism of skill acquisition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Theodore Wright","year":1936,"type":"Power-law regression model","subfamily":"Learning analytics","data_requirement":"Cumulative production or practice trial counts with paired performance measurements","key_parameter":"Learning rate exponent (b)"},"citations":[{"ref":"Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the Aeronautical Sciences, 3(4), 122–128.","type":"article","doi":"10.2514/8.155","isbn":null,"url":null}],"related":["knowledge-tracing","learning-analytics","nonlinear-programming"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"least-cost-path","name":"Least-Cost Path","fullName":"Least-Cost Path / Cost-Distance Analysis","aliases":["cost-distance analysis","accumulated cost surface","least-cost corridor","en düşük maliyetli yol"],"domain":"spatial-analysis","family":"process-pipeline","subfamily":"Network/raster GIS","year":1994,"originator":"Edsger Dijkstra (shortest path); GIS cost-surface adaptation","url":"https://scholargate.app/en/spatial-analysis/least-cost-path","markdownUrl":"https://scholargate.app/en/spatial-analysis/least-cost-path.md","definition":"Least-cost path analysis finds the route between two locations that minimizes accumulated travel cost across a landscape, rather than minimizing straight-line distance. By encoding terrain, slope, land cover, and other frictions into a cost surface and accumulating cost outward from a source, it identifies optimal corridors for roads, pipelines, trails, power lines, and wildlife movement — a core raster-GIS technique built on Dijkstra's shortest-path logic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edsger Dijkstra (shortest path); GIS cost-surface adaptation","year":1994,"type":"Raster cost-surface routing","subfamily":"Network/raster GIS","input":"Cost (friction) surface","output":"Least-cost route / corridor"},"citations":[{"ref":"Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.","type":"article","doi":"10.1007/BF01386390","isbn":null,"url":null},{"ref":"Douglas, D. H. (1994). Least-cost path in GIS using an accumulated cost surface and slopelines. Cartographica, 31(3), 37–51.","type":"article","doi":"10.3138/D327-0323-2JUT-016M","isbn":null,"url":null}],"related":["location-allocation","kriging","gis-mcda","ca-markov"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"least-median-squares","name":"Least Median of Squares","fullName":"Least Median of Squares Regression","aliases":["LMS","least median of squares regression","en küçük medyan kareler (LMS)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1984,"originator":"Peter J. Rousseeuw","url":"https://scholargate.app/en/statistics/least-median-squares","markdownUrl":"https://scholargate.app/en/statistics/least-median-squares.md","definition":"Least Median of Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of minimising the sum of squared residuals like ordinary least squares, it minimises the median of the squared residuals, which lets the fit resist contamination by up to roughly 50% outliers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Rousseeuw","year":1984,"type":"Robust linear regression","estimator":"Minimises the median of squared residuals","breakdownPoint":"up to 50%","outcome":"continuous"},"citations":[{"ref":"Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.","type":"article","doi":"10.1080/01621459.1984.10477105","isbn":null,"url":null},{"ref":"Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust Statistics: The Approach Based on Influence Functions. Wiley.","type":"book","doi":null,"isbn":"978-0471735779","url":null}],"related":["ols-regression","quantile-regression","theil-sen-estimator","ransac-regression","least-trimmed-squares"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"least-trimmed-squares","name":"Least Trimmed Squares","fullName":"Least Trimmed Squares (LTS) Regression","aliases":["LTS","least trimmed squares regression","trimmed least squares","robust regression","En Küçük Kırpılmış Kareler (LTS)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1984,"originator":"Peter J. Rousseeuw","url":"https://scholargate.app/en/statistics/least-trimmed-squares","markdownUrl":"https://scholargate.app/en/statistics/least-trimmed-squares.md","definition":"Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Rousseeuw","year":1984,"type":"Robust linear regression","estimator":"Minimisation of the sum of the smallest h squared residuals","breakdownPoint":"up to 50%","outcome":"continuous"},"citations":[{"ref":"Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.","type":"article","doi":"10.1080/01621459.1984.10477105","isbn":null,"url":null},{"ref":"Rousseeuw, P. J., & Van Driessen, K. (2006). Computing LTS Regression for Large Data Sets. Data Mining and Knowledge Discovery, 12, 29-45.","type":"article","doi":"10.1007/s10618-005-0024-4","isbn":null,"url":null}],"related":["ols-regression","least-median-squares","ransac-regression","theil-sen-estimator","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lee-carter-model","name":"Lee-Carter Model","fullName":"Lee-Carter Mortality Forecasting Model","aliases":["LC Model","Lee-Carter Mortality Model","Singular Value Decomposition Mortality Model","Lee-Carter Ölümlülük Modeli"],"domain":"demography","family":"regression-model","subfamily":"Mortality modelling","year":1992,"originator":"Ronald Lee & Lawrence Carter","url":"https://scholargate.app/en/demography/lee-carter-model","markdownUrl":"https://scholargate.app/en/demography/lee-carter-model.md","definition":"The Lee-Carter model is a stochastic framework for modeling and forecasting age-specific mortality rates, introduced by Ronald Lee and Lawrence Carter in their landmark 1992 paper. It decomposes the logarithm of age-specific death rates into an age pattern of mortality, a time-varying index of mortality level, and an age-specific sensitivity of that index, then forecasts the time index using ARIMA time-series methods to generate probabilistic mortality projections.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Lee & Lawrence Carter","year":1992,"type":"Stochastic mortality forecasting model","subfamily":"Mortality modelling","data_requirement":"Age-specific death rates over multiple calendar years","core_method":"Singular value decomposition (SVD) plus ARIMA time-series forecasting"},"citations":[{"ref":"Lee, R. D., & Carter, L. R. (1992). Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87(419), 659–671.","type":"article","doi":"10.1080/01621459.1992.10475265","isbn":null,"url":null}],"related":["life-table","arima","principal-component-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lee-strazicich-test","name":"Lee-Strazicich Test","fullName":"Lee-Strazicich LM Unit-Root Test with Two Breaks","aliases":["LS Unit Root Test","Minimum LM Unit Root Test","Lee-Strazicich Two-Break Test","Lee-Strazicich LM Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Break unit-root tests","year":2003,"originator":"Junsoo Lee & Mark Strazicich","url":"https://scholargate.app/en/econometrics/lee-strazicich-test","markdownUrl":"https://scholargate.app/en/econometrics/lee-strazicich-test.md","definition":"The Lee-Strazicich (2003) test is a Lagrange Multiplier-based unit-root test that allows for two endogenous structural breaks under both the null and alternative hypotheses. Proposed by Junsoo Lee and Mark C. Strazicich, it corrects a fundamental flaw in earlier break-based tests such as Zivot-Andrews, where structural breaks were permitted only under the alternative. By incorporating breaks under the null, the LS test avoids spurious rejections and provides size-correct inference in the presence of level or trend shifts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Junsoo Lee & Mark Strazicich","year":2003,"type":"Lagrange Multiplier unit-root test with two endogenous structural breaks","subfamily":"Break unit-root tests","null_hypothesis":"Series has a unit root with two structural breaks","max_breaks":2},"citations":[{"ref":"Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089.","type":"article","doi":"10.1162/003465303772815961","isbn":null,"url":null}],"related":["zivot-andrews-test","lumsdaine-papell-test","adf-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"leeds-neuropathic-symptoms","name":"LANSS","fullName":"Leeds Assessment of Neuropathic Symptoms and Signs","aliases":["LANSS Pain Scale","Leeds Neuropathic Symptoms"],"domain":"neurology","family":"process-pipeline","subfamily":"neuropathic pain screening and assessment","year":"2001","originator":"Mark I. Bennett, University of Leeds","url":"https://scholargate.app/en/neurology/leeds-neuropathic-symptoms","markdownUrl":"https://scholargate.app/en/neurology/leeds-neuropathic-symptoms.md","definition":"The LANSS is a brief seven-item hybrid screening and diagnostic tool designed to differentiate neuropathic pain from non-neuropathic (nociceptive) pain. Developed by Mark Bennett at the University of Leeds in 2001, it combines five patient-reported symptom items with two clinician-performed neurological examination findings. With a sensitivity of 82% and specificity of 80%, LANSS is among the most accurate tools for identifying whether a patient's pain has a neuropathic component, making it invaluable in clinical practice and research settings where pain etiology classification is essential.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark I. Bennett, University of Leeds","subfamily":"neuropathic pain screening and assessment","year":"2001","type":"Hybrid questionnaire and clinician examination"},"citations":[{"ref":"Bennett, M. I. (2001). The LANSS Pain Scale: The Leeds Assessment of Neuropathic Symptoms and Signs. Pain, 92(1-2), 147-157.","type":"article","doi":"10.1016/S0304-3959(00)00482-6","isbn":null,"url":null}],"related":["neuropathic-pain-symptom-inventory","migraine-disability-assessment","stroke-specific-qol","alsfrs-r"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"legal-content-analysis","name":"Legal Content Analysis","fullName":"Legal Content Analysis","aliases":["LCA","legal text analysis","jurimetric content analysis","statutory content analysis"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1940s–1970s (applied systematically to legal texts)","originator":"Interdisciplinary; foundational content analysis by Harold Lasswell (1940s); applied to legal texts by empirical legal scholars from the 1970s onward","url":"https://scholargate.app/en/field-methods/legal-content-analysis","markdownUrl":"https://scholargate.app/en/field-methods/legal-content-analysis.md","definition":"Legal content analysis applies the systematic procedures of content analysis to legal texts — statutes, regulations, judicial opinions, treaties, and legal commentaries — in order to identify patterns, themes, and trends across a corpus of legal material. It bridges qualitative legal scholarship and quantitative social-science methods, enabling researchers to draw reproducible, evidence-based conclusions about how law is written, applied, or has changed over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Interdisciplinary; foundational content analysis by Harold Lasswell (1940s); applied to legal texts by empirical legal scholars from the 1970s onward","year":"1940s–1970s (applied systematically to legal texts)","type":"Systematic qualitative-quantitative text analysis","dataType":"Legal texts: statutes, regulations, court opinions, treaties, contracts, legal commentaries","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761915454","url":null},{"ref":"Nourse, V., & Shaffer, G. (2014). Varieties of New Legal Realism: Can a New World Order Prompt a New Legal Theory? Cornell Law Review, 95(1), 61–137.","type":"article","doi":null,"isbn":null,"url":"https://scholarship.law.cornell.edu/clr/vol95/iss1/2"}],"related":["doctrinal-legal-research","comparative-legal-analysis","case-law-analysis","content-analysis","discourse-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"legal-judgment-prediction","name":"Legal Judgment Prediction","fullName":"Legal Judgment Prediction using Machine Learning","aliases":["court outcome prediction","judicial decision prediction","legal AI forecasting"],"domain":"forensics","family":"process-pipeline","subfamily":"Machine learning and artificial intelligence","year":"2017","originator":"Daniel Katz","url":"https://scholargate.app/en/forensics/legal-judgment-prediction","markdownUrl":"https://scholargate.app/en/forensics/legal-judgment-prediction.md","definition":"Legal judgment prediction is a machine learning approach that forecasts court decisions and judicial outcomes based on case features, legal precedent, and judicial characteristics. Pioneered by Daniel Katz and colleagues in 2017 with their celebrated U.S. Supreme Court prediction model, this method applies supervised learning to large datasets of digitized court decisions to identify patterns in how judges decide cases. Legal judgment prediction has since expanded to appellate courts, trial courts, and international tribunals, enabling legal professionals to anticipate case outcomes and make strategic litigation decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel Katz","subfamily":"Machine learning and artificial intelligence","year":"2017","type":"Computational law and judicial decision prediction method"},"citations":[{"ref":"Katz, D. M., Bommarito, M. J., & Blackman, J. (2017). A general approach for predicting the behavior of the Supreme Court of the United States. PLOS One, 12(4), e0174698.","type":"article","doi":"10.1371/journal.pone.0174698","isbn":null,"url":null},{"ref":"Matz, D., & Spicer, J. (2019). Predicting judicial decisions of the European Court of Human Rights. Artificial Intelligence and Law, 27(2), 123-145.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/article/10.1007/s10506-019-09251-2"},{"ref":"Lage-Freitas, A., de Oliveira Santini, F., Praxedes Filho, P. H., & de Almeida Oliveira, A. (2022). Predicting Supreme Federal Court decisions by explainable machine learning. Frontiers in Artificial Intelligence, 4, 586561.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Predicting+Supreme+Federal+Court+decisions+by+explainable+machine+learning+Lage-Freitas"}],"related":["network-analysis-of-case-law","crime-linkage-analysis","geographic-profiling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lerchs-grossmann-algorithm","name":"Lerchs-Grossmann Algorithm","fullName":"Lerchs-Grossmann Algorithm for Open Pit Mine Design","aliases":["Lerchs-Grossmann Method","LG Algorithm"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Graph-based Optimization","year":"1965","originator":"Helmut Lerchs and Israel Grossmann","url":"https://scholargate.app/en/mining-engineering/lerchs-grossmann-algorithm","markdownUrl":"https://scholargate.app/en/mining-engineering/lerchs-grossmann-algorithm.md","definition":"The Lerchs-Grossmann Algorithm is a graph-theoretic method for determining the ultimate pit limit in open-pit mining operations. Introduced by Helmut Lerchs and Israel Grossmann in 1965, it maximizes the net present value of extracted ore while respecting slope stability constraints. This algorithm forms the theoretical foundation for most modern pit optimization software.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Helmut Lerchs and Israel Grossmann","subfamily":"Graph-based Optimization","year":"1965","type":"Graph-theoretic algorithm for pit limit optimization"},"citations":[{"ref":"Lerchs, H., & Grossmann, I. F. (1965). Optimum design of open-pit mines. Canadian Mining and Metallurgical Bulletin, 58(633), 47-54.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimum+design+of+open-pit+mines+Lerchs"},{"ref":"Johnson, T. B. (2014). Optimum pit limits - definition and computational procedures. Journal of the Australasian Institute of Mining and Metallurgy, 249, 21-28.","type":"article","doi":null,"isbn":null,"url":"https://www.ausimm.com.au/"}],"related":["pseudoflow","cut-off-grade","stope-layout","mine-ventilation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"leslie-matrix","name":"Leslie Matrix","fullName":"Leslie Matrix Population Projection","aliases":["Leslie model","age-structured population model","matrix population model","population dynamics"],"domain":"ecology","family":"process-pipeline","subfamily":"Demographic modeling","year":"1945","originator":"Patrick Leslie","url":"https://scholargate.app/en/ecology/leslie-matrix","markdownUrl":"https://scholargate.app/en/ecology/leslie-matrix.md","definition":"The Leslie matrix is a deterministic model of age-structured population dynamics, introduced by Patrick Leslie (1945). It projects population size and structure forward in time using age-specific fertility and survival rates. A Leslie matrix encodes these vital rates in a square matrix; multiplying the matrix by a population vector yields the population's composition at the next time step. This approach enables calculation of the population's asymptotic growth rate (λ), identification of stable age structure, and sensitivity analysis—understanding which vital rates most strongly influence population growth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patrick Leslie","subfamily":"Demographic modeling","year":"1945","type":"structured population dynamics"},"citations":[{"ref":"Leslie, P. H. (1945). On the use of matrices in certain population mathematics. Biometrika, 33(3), 183-212.","type":"article","doi":"10.1093/biomet/33.3.183","isbn":null,"url":null},{"ref":"Caswell, H. (2001). Matrix Population Models: Construction, Analysis, and Interpretation. Sinauer Associates, Sunderland, Massachusetts.","type":"book","doi":null,"isbn":null,"url":"https://global.oup.com/academic/product/matrix-population-models-9780878938134"},{"ref":"Easterling, M. R., Ellner, S. P., & Dixon, P. M. (2000). Size-specific sensitivity: applying a new structured population model. Ecology, 81(3), 694-708.","type":"article","doi":"10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2","isbn":null,"url":null}],"related":["population-viability-analysis","integral-projection-model","distance-sampling","siar-mixing-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lesson-study","name":"Lesson Study","fullName":"Lesson Study (Jugyou Kenkyuu)","aliases":["Jugyou Kenkyuu","LS","collaborative lesson research","teaching study"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Late 19th century Japan; international dissemination from 1999","originator":"Japanese elementary school teachers (formalized); introduced to Western research by James Stigler & James Hiebert","url":"https://scholargate.app/en/field-methods/lesson-study","markdownUrl":"https://scholargate.app/en/field-methods/lesson-study.md","definition":"Lesson study is a structured, cyclical form of professional development and educational research in which a team of teachers collaboratively plans a single 'research lesson,' observes it live in a classroom, analyzes student learning in detail, revises the lesson, and shares findings with the broader teaching community. Originating in Japanese elementary schools and brought to international attention by Stigler and Hiebert's 1999 comparative study, it has become one of the most widely adopted teacher-led inquiry methods worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Japanese elementary school teachers (formalized); introduced to Western research by James Stigler & James Hiebert","year":"Late 19th century Japan; international dissemination from 1999","type":"Collaborative practitioner inquiry / professional development research","dataType":"Classroom observation notes, video recordings, student work samples, teacher reflection journals","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Stigler, J. W., & Hiebert, J. (1999). The Teaching Gap: Best Ideas from the World's Teachers for Improving Education in the Classroom. Free Press.","type":"book","doi":null,"isbn":"978-0684852744","url":null},{"ref":"Lewis, C. C. (2002). Lesson Study: A Handbook of Teacher-Led Instructional Change. Research for Better Schools.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lesson+Study+A+Handbook+of+Teacher-Led+Instructional+Change+Lewis+2002"}],"related":["action-research","educational-action-research","classroom-observation","design-based-research","curriculum-analysis","teacher-professional-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"letter-to-editor","name":"Letter to the Editor","fullName":"Letter to the Editor (Rapid Communication and Response to Published Work)","aliases":["correspondence","editor response","rapid letter","technical comment"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Rapid communication","year":"1750","originator":"Academic journals (18th century onward)","url":"https://scholargate.app/en/academic-writing/letter-to-editor","markdownUrl":"https://scholargate.app/en/academic-writing/letter-to-editor.md","definition":"A letter to the editor is a brief, rapid communication (typically <500 words) published in academic journals, usually in response to a recently published article. Letters enable scholars to raise questions, offer corrections, present supporting or contrary evidence, or highlight implications of published work. Unlike full research articles, letters are faster to publish (weeks to months), making them valuable for timely scientific discourse. Letters are indexed in major databases (PubMed, Scopus, Web of Science) and count as publications, though carrying lower weight than original research articles. The letter format dates to the earliest academic journals and remains a vital vehicle for scholarly dialogue.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Academic journals (18th century onward)","subfamily":"Rapid communication","year":"1750","type":"Document Type"},"citations":[{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE.","type":"webpage","doi":null,"isbn":null,"url":"http://www.icmje.org"},{"ref":"American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). APA.","type":"book","doi":null,"isbn":"978-1-4338-3216-1","url":null},{"ref":"Committee on Publication Ethics (2023). Guidelines on Good Publication Practice. https://publicationethics.org","type":"webpage","doi":null,"isbn":null,"url":"https://publicationethics.org"}],"related":["original-research-article","editorial-commentary","peer-review-process","academic-debate"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"level-of-service-inventory","name":"LSI-R","fullName":"Level of Service Inventory-Revised","aliases":["LSI-R","LSI-R-SV","Andrews-Bonta Risk Assessment"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"offender-risk-and-need-assessment","year":"1995","originator":"D. A. Andrews, James Bonta","url":"https://scholargate.app/en/forensic-psychology/level-of-service-inventory","markdownUrl":"https://scholargate.app/en/forensic-psychology/level-of-service-inventory.md","definition":"The Level of Service Inventory-Revised (LSI-R) is a 54-item assessment instrument developed by Andrews and Bonta (1995) to measure offender risk level and criminogenic needs (dynamic risk factors related to criminal behavior) in criminal justice populations. It is grounded in the Risk-Need-Responsivity (RNR) model of offender rehabilitation and is widely used in correctional facilities, probation/parole services, and forensic settings to inform release decisions, supervision intensity, treatment prioritization, and rehabilitation planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. A. Andrews, James Bonta","subfamily":"offender-risk-and-need-assessment","year":"1995","type":"Interview-based / File-based"},"citations":[{"ref":"Andrews, D. A., & Bonta, J. (1995). The Level of Service Inventory-Revised. Department of Psychology, Carleton University.","type":"book","doi":null,"isbn":null,"url":"https://www.mhs.com/"},{"ref":"Bonta, J., Law, M., & Hanson, K. (2007). The prediction of criminal and violent recidivism among mentally disordered offenders: A meta-analysis. Psychological Bulletin, 123(2), 123–142.","type":"article","doi":"10.1037/0033-2909.123.2.123","isbn":null,"url":null}],"related":["hcr-20","violence-risk-appraisal-guide","saprof","psychopathy-checklist-screening"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"level-set-method","name":"Level Set Method","fullName":"Level Set Method","aliases":["Level-set","LSM","signed distance method"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1988","originator":"Stanley Osher","url":"https://scholargate.app/en/fluid-dynamics/level-set-method","markdownUrl":"https://scholargate.app/en/fluid-dynamics/level-set-method.md","definition":"The Level Set Method is an implicit interface tracking technique introduced by Osher and Sethian in 1988 for moving boundary problems and multiphase flows. Rather than explicitly tracking the interface, level sets represent it as the zero level set (contour) of a signed distance function φ. This approach elegantly handles topological changes, naturally computes interface curvature and normals, and integrates well with Eulerian solvers. Level sets have become essential for image processing, shape optimization, and interface-dominated fluid dynamics problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanley Osher","subfamily":"Fluid Dynamics","year":"1988","type":"Implicit interface tracking method"},"citations":[{"ref":"Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1), 12-49.","type":"article","doi":"10.1016/0021-9991(88)90002-2","isbn":null,"url":null},{"ref":"Sethian, J. A. (1996). Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press.","type":"article","doi":null,"isbn":"978-0521645577","url":null},{"ref":"Sussman, M., Smereka, P., & Osher, S. (1994). A level set approach for computing solutions to incompressible two-phase flow. Journal of Computational Physics, 114(1), 146-159.","type":"article","doi":"10.1006/jcph.1994.1155","isbn":null,"url":null}],"related":["volume-of-fluid","eulerian-lagrangian-model","large-eddy-simulation","direct-numerical-simulation","boundary-layer-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"levelized-cost-of-energy","name":"Levelized Cost of Energy","fullName":"Levelized Cost of Energy Analysis for Thermal Systems","aliases":["LCOE","levelized cost analysis"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Economic Analysis","year":"2009","originator":"Lazard","url":"https://scholargate.app/en/thermodynamics/levelized-cost-of-energy","markdownUrl":"https://scholargate.app/en/thermodynamics/levelized-cost-of-energy.md","definition":"Levelized Cost of Energy (LCOE) is a standardized metric that spreads the total lifecycle cost of an energy project over its lifetime energy output. It enables fair comparison of electricity generation technologies with different capital structures, operating costs, and lifetimes. LCOE is widely used for technology evaluation, investment decisions, and energy policy analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lazard","subfamily":"Economic Analysis","year":"2009","type":"Cost comparison framework"},"citations":[{"ref":"Lazard. (2023). Levelized Cost of Energy Analysis (v17.0). Lazard Ltd.","type":"report","doi":null,"isbn":null,"url":"https://www.lazard.com/perspective/levelized-cost-of-energy-2023/"},{"ref":"Cole, W., Frazier, A. W., & Augustine, C. (2020). Cost Projections for Utility-Scale Battery Storage. National Renewable Energy Laboratory (NREL), Technical Report NREL/TP-5700-75385.","type":"report","doi":null,"isbn":null,"url":"https://www.nrel.gov/docs/fy20/75385.pdf"}],"related":["exergoeconomic-analysis","exergoenvironmental-analysis","rankine-cycle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"levene-brown-forsythe","name":"Levene and Brown-Forsythe Test","fullName":"Levene and Brown-Forsythe Test for Equality of Variances","aliases":["Levene test","Brown-Forsythe test","homogeneity of variance test","Levene ve Brown-Forsythe Varyans Testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1960,"originator":"Howard Levene; Morton B. Brown and Alan B. Forsythe","url":"https://scholargate.app/en/statistics/levene-brown-forsythe","markdownUrl":"https://scholargate.app/en/statistics/levene-brown-forsythe.md","definition":"The Levene and Brown-Forsythe test checks whether two or more groups share the same variance (homogeneity of variance). Levene (1960) built the test on absolute deviations from each group mean, and Brown and Forsythe (1974) made it robust to non-normal data by centring on the group median instead.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Howard Levene; Morton B. Brown and Alan B. Forsythe","year":1960,"type":"Homogeneity of variance test (robust)","estimator":"F-test on absolute deviations from group centre (mean for Levene, median for Brown-Forsythe)","minSample":20,"requiresNormality":false},"citations":[{"ref":"Levene, H. (1960). Robust Tests for Equality of Variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling. Stanford University Press.","type":"book-chapter","doi":null,"isbn":null,"url":"https://archive.org/details/contributionstop0000unse"},{"ref":"Brown, M. B. & Forsythe, A. B. (1974). Robust Tests for the Equality of Variances. Journal of the American Statistical Association, 69(346), 364-367.","type":"article","doi":"10.1080/01621459.1974.10482955","isbn":null,"url":null}],"related":["bartlett-test","one-way-anova","welch-anova","permutation-test","bootstrap-inference"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"levenshtein-distance","name":"Levenshtein Distance","fullName":"Levenshtein Distance Metric","aliases":["edit distance","Damerau-Levenshtein distance"],"domain":"decision-making","family":"mcdm","subfamily":"String/sequence distance","year":"1966","originator":"Vladimir Levenshtein","url":"https://scholargate.app/en/decision-making/levenshtein-distance","markdownUrl":"https://scholargate.app/en/decision-making/levenshtein-distance.md","definition":"Levenshtein distance, also called edit distance, measures the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one string into another. Introduced by Vladimir Levenshtein in 1966, this metric is a true metric (satisfying all distance properties) and is fundamental in computational linguistics, spell checking, DNA sequence comparison, and record linkage. It ranges from 0 (identical strings) to the length of the longer string.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vladimir Levenshtein","subfamily":"String/sequence distance","year":"1966","type":"Edit distance metric"},"citations":[{"ref":"Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10, 707-710.","type":"article","doi":null,"isbn":null,"url":"http://www.sci.brooklyn.cuny.edu/~sklar/teaching/cis601/papers/levenshtein.pdf"},{"ref":"Damerau, F. J. (1964). A technique for computer detection and correction of spelling errors. Communications of the ACM, 7(3), 171-176.","type":"article","doi":"10.1145/363958.363994","isbn":null,"url":null}],"related":["dynamic-time-warping","jaccard-similarity","hamming-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"levin-lin-chu-test","name":"Levin-Lin-Chu Test","fullName":"Levin-Lin-Chu (LLC) Panel Unit-Root Test","aliases":["LLC Test","Panel Unit-Root Test (Homogeneous)","Levin-Lin Unit-Root Test","Panel Birim Kök Testi (LLC)"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Panel unit-root tests","year":2002,"originator":"Andrew Levin, Chien-Fu Lin & Chia-Shang Chu","url":"https://scholargate.app/en/econometrics/levin-lin-chu-test","markdownUrl":"https://scholargate.app/en/econometrics/levin-lin-chu-test.md","definition":"The Levin-Lin-Chu (LLC) test, introduced by Levin, Lin, and Chu (2002), is a first-generation panel unit-root test that pools cross-sectional information to test whether all units in a panel share a common autoregressive unit root. It is widely used in applied economics and finance when researchers work with balanced or near-balanced panels and require a powerful test against a homogeneous stationary alternative.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Levin, Chien-Fu Lin & Chia-Shang Chu","year":2002,"type":"Panel unit-root test (homogeneous alternative)","subfamily":"Panel unit-root tests","null_hypothesis":"All cross-sectional units share a common unit root (rho = 1)","distribution":"Standard normal (asymptotic), finite-sample corrections applied"},"citations":[{"ref":"Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24.","type":"article","doi":"10.1016/S0304-4076(01)00098-7","isbn":null,"url":null}],"related":["im-pesaran-shin-test","breitung-test","fisher-panel-unit-root-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lexical-decision-task","name":"Lexical Decision Task","fullName":"Lexical Decision Task","aliases":["Lexical Decision","Word Recognition Task","Lexicality Judgment"],"domain":"psychology","family":"hypothesis-test","subfamily":"Semantic Priming","year":"1971","originator":"David Meyer and Roger Schvaneveldt","url":"https://scholargate.app/en/psychology/lexical-decision-task","markdownUrl":"https://scholargate.app/en/psychology/lexical-decision-task.md","definition":"The Lexical Decision Task is a computerized measure of word recognition and semantic processing. Participants judge whether letter strings are real words or nonwords (pronounceable but meaningless letter combinations). Response times and accuracy reveal how quickly people access word meanings, how semantic relatedness facilitates recognition, and how word properties (frequency, length, concreteness) influence processing. The task is foundational in cognitive psychology and psycholinguistics for studying lexical representation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Meyer and Roger Schvaneveldt","subfamily":"Semantic Priming","year":"1971","type":"Decision task"},"citations":[{"ref":"Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90(2), 227-234.","type":"article","doi":"10.1037/h0031564","isbn":null,"url":null},{"ref":"Balota, D. A., Yap, M. J., Cortese, M. J., et al. (2007). The English Lexicon Project. Behavior Research Methods, 39(3), 445-459.","type":"article","doi":"10.3758/BF03193014","isbn":null,"url":null},{"ref":"Yap, M. J., Sibley, D. E., Balota, D. A., Ratcliff, R., & Rueckl, J. G. (2015). Insights into lexical processing via large-scale crowdsourcing. PLoS ONE, 10(3), e0119616.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Insights+into+lexical+processing+via+large-scale+crowdsourcing+Yap"}],"related":["semantic-priming","word-recognition","reaction-time-analysis","orthographic-processing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lexical-diversity","name":"Lexical Diversity","fullName":"Lexical Diversity Analysis","aliases":["lexical richness","vocabulary richness","Sözcüksel Çeşitlilik Analizi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/lexical-diversity","markdownUrl":"https://scholargate.app/en/text-mining/lexical-diversity.md","definition":"Lexical diversity analysis quantifies how varied the vocabulary of a text is — how rich an author's word choice is — using measures such as the type-token ratio (TTR), MTLD, vocd-D, and Yule's K. The MTLD and vocd-D measures were validated by McCarthy and Jarvis (2010), building on earlier work by Tweedie and Baayen (1998) on the stability of lexical-richness measures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Text quantification / lexical richness measurement","measures":"Type-token ratio (TTR), MTLD, Yule's K, vocd-D","input":"Tokenised text","output":"Lexical-diversity scores per text","minSample":10,"difficulty":"Introductory"},"citations":[{"ref":"McCarthy, P. M. & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381-392.","type":"article","doi":"10.3758/BRM.42.2.381","isbn":null,"url":null},{"ref":"Tweedie, F. J. & Baayen, R. H. (1998). How Variable May a Constant Be? Measures of Lexical Richness in Perspective. Computers and the Humanities, 32(5), 323-352.","type":"article","doi":"10.1023/A:1001749303137","isbn":null,"url":null}],"related":["sentiment-analysis","tf-idf","topic-modeling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lexical-substitution","name":"Lexical Substitution","fullName":"Lexical Substitution (Context-Sensitive Word Replacement)","aliases":["sözcüksel ikame","Sözcüksel İkame (Lexical Substitution)","context-aware synonym replacement","word-level paraphrasing"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2007","originator":"McCarthy & Navigli (SemEval shared task, 2007/2009)","url":"https://scholargate.app/en/text-mining/lexical-substitution","markdownUrl":"https://scholargate.app/en/text-mining/lexical-substitution.md","definition":"Lexical substitution is a natural-language-processing task — formalised by McCarthy and Navigli through the SemEval shared task series starting in 2007 — that replaces a target word in a sentence with a semantically equivalent alternative that preserves the meaning of the surrounding context. It draws on synonym resources such as WordNet or on distributional word embeddings and masked language models to generate and rank candidate replacements, and is used for text robustness testing, style adaptation, and training-data augmentation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McCarthy & Navigli (SemEval shared task, 2007/2009)","year":"2007","type":"NLP lexical-level text transformation","input":"Tokenised text corpus (cross-sectional or longitudinal)","output":"Corpus with target words replaced by context-appropriate alternatives","candidateSources":"WordNet synsets / distributional word embeddings / masked language models (e.g., BERT fill-mask)","difficulty":"Low (difficulty score 2/5)","minSample":"10 documents"},"citations":[{"ref":"McCarthy, D. & Navigli, R. (2009). The English Lexical Substitution Task. Language Resources and Evaluation, 43(2), 139-159.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/article/10.1007/s10579-009-9084-1"},{"ref":"Zhou, W. et al. (2019). BERT for Context-Aware Lexical Substitution. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 7557-7564.","type":"inproceedings","doi":null,"isbn":null,"url":"https://ojs.aaai.org/index.php/AAAI/article/view/4761"}],"related":["sentiment-analysis","text-augmentation","word-embeddings","bert-embeddings","paraphrase-detection","named-entity-recognition"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lexicographic-bwm","name":"Lexicographic Best Worst Method","fullName":"Lexicographic Best Worst Method (Lexicographic BWM)","aliases":["Lexicographic BWM"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Based on Rezaei's BWM framework and lexicographic optimization","url":"https://scholargate.app/en/decision-making/lexicographic-bwm","markdownUrl":"https://scholargate.app/en/decision-making/lexicographic-bwm.md","definition":"Lexicographic BWM combines the strengths of the Best Worst Method with lexicographic (sequential) optimization. Instead of weighting all criteria simultaneously, it assigns criteria to priority levels, solves the BWM for the highest-priority criteria first, then solves for lower-priority criteria while keeping the higher-priority weights fixed. This ensures that higher-priority criteria are never sacrificed to improve lower-priority ones.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Based on Rezaei's BWM framework and lexicographic optimization","subfamily":"Ranking","year":"2015","type":"Sequential best-worst comparisons with priority hierarchy"},"citations":[{"ref":"Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.","type":"article","doi":"10.1016/j.omega.2015.12.001","isbn":null,"url":null},{"ref":"Khanmohammadi, E., & Kazemi, M. (2019). A preference aggregation method based on the comparative advantage of each individual. Applied Soft Computing, 75, 298-310.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/j.asoc.2018.11.006"}],"related":["bwm","lexicographic-goal-programming","stratified-bwm","fuzzy-bwm","lexicographic-fuzzy-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lexicographic-goal-programming","name":"Lexicographic Goal Programming","fullName":"Lexicographic Goal Programming","aliases":["Lexicographic GP","LGP"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1961","originator":"Abraham Charnes and William W. Cooper","url":"https://scholargate.app/en/decision-making/lexicographic-goal-programming","markdownUrl":"https://scholargate.app/en/decision-making/lexicographic-goal-programming.md","definition":"Lexicographic Goal Programming (LGP) is a variant of goal programming introduced by Charnes and Cooper in the 1960s. It prioritizes multiple goals in a strict ordinal hierarchy, solving optimization problems sequentially: first achieve the highest-priority goal, then the second-highest while maintaining the first, and so on. This ensures that lower-priority goals are never pursued at the expense of higher-priority ones.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abraham Charnes and William W. Cooper","subfamily":"Ranking","year":"1961","type":"Sequential goal optimization with priority levels"},"citations":[{"ref":"Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. Management Science, 8(1), 38-91.","type":"article","doi":"10.2307/1909344","isbn":null,"url":null},{"ref":"Ijiri, Y. (1965). Management goals and accounting for control. North-Holland Publishing Co.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books?id=EH5SAAAAIAAJ"},{"ref":"Ignizio, J. P. (1976). Goal programming and extensions. Lexington Books.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Goal+programming+and+extensions+Ignizio"}],"related":["goal-programming","weighted-goal-programming","lexicographic-bwm","lexicographic-fuzzy-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lexicon-based-sentiment","name":"Lexicon-Based Sentiment Analysis","fullName":"Lexicon-Based Sentiment Analysis","aliases":["dictionary-based sentiment analysis","rule-based sentiment scoring","Sözlük Tabanlı Duygu Analizi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/lexicon-based-sentiment","markdownUrl":"https://scholargate.app/en/text-mining/lexicon-based-sentiment.md","definition":"Lexicon-based sentiment analysis computes sentiment at the word level using prebuilt sentiment dictionaries such as AFINN (Nielsen, 2011), SentiWordNet, VADER (Hutto & Gilbert, 2014), and the NRC Emotion Lexicon. It scores text by looking words up in a dictionary of charged terms, so it requires no labelled training data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Lexicon-based NLP sentiment-scoring task","lexicons":"AFINN / SentiWordNet / VADER / NRC Emotion Lexicon","trainingData":"Not required (unsupervised, dictionary-driven)","minSample":"10 documents","output":"Word-level and document-level sentiment scores"},"citations":[{"ref":"Nielsen, F.Å. (2011). A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs. Proceedings of the ESWC Workshop on 'Making Sense of Microposts'.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1103.2903"},{"ref":"Hutto, C.J. & Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 8(1), 216-225.","type":"article","doi":"10.1609/icwsm.v8i1.14550","isbn":null,"url":null}],"related":["sentiment-analysis","subjectivity-detection","text-complexity-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"libor-market-model","name":"Libor Market Model","fullName":"LIBOR Market Model (Brace-Gatarek-Musiela)","aliases":["BGM Model","LMM"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Market Models","year":"1997","originator":"Alan Brace, Dariusz Gatarek, and Marek Musiela","url":"https://scholargate.app/en/quantitative-finance/libor-market-model","markdownUrl":"https://scholargate.app/en/quantitative-finance/libor-market-model.md","definition":"The LIBOR Market Model (BGM), developed by Brace, Gatarek, and Musiela (1997), is a multi-factor interest rate model that directly models forward LIBOR rates as lognormal processes. Unlike short-rate models, LMM naturally prices caplets at the market level and is the industry standard for valuing caps, floors, and exotic interest rate derivatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Brace, Dariusz Gatarek, and Marek Musiela","subfamily":"Market Models","year":"1997","type":"Interest Rate Model"},"citations":[{"ref":"Brace, A., Gatarek, D., & Musiela, M. (1997). The market model of interest rate dynamics. Mathematical Finance, 7(2), 127-155.","type":"article","doi":"10.1111/1467-9965.00028","isbn":null,"url":null},{"ref":"Jamshidian, F. (1997). LIBOR and swap market models and measures. Finance and Stochastics, 1(4), 293-330.","type":"article","doi":"10.1007/s007800050026","isbn":null,"url":null}],"related":["hjm-framework","hull-white-model","risk-neutral-valuation","change-of-numeraire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lidar-analysis","name":"LiDAR Analysis","fullName":"LiDAR Point-Cloud Analysis","aliases":["Light Detection and Ranging","Airborne Laser Scanning","Terrestrial Laser Scanning","LiDAR Nokta Bulutu Analizi"],"domain":"remote-sensing","family":"process-pipeline","subfamily":"Remote sensing","year":2002,"originator":"Lefsky et al.","url":"https://scholargate.app/en/remote-sensing/lidar-analysis","markdownUrl":"https://scholargate.app/en/remote-sensing/lidar-analysis.md","definition":"LiDAR (Light Detection and Ranging) Point-Cloud Analysis is an active remote sensing technique that measures distances by emitting laser pulses and recording the time for returns to reach the sensor. First systematically applied to ecosystem science by Lefsky, Cohen, Parker, and Harding in 2002, LiDAR produces dense three-dimensional point clouds that encode the precise vertical and horizontal structure of vegetation, terrain, and built environments at resolutions unachievable by passive optical sensors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lefsky et al.","year":2002,"type":"Active remote sensing pipeline","subfamily":"Remote sensing","data_type":"3D point cloud","output":"Canopy/terrain structural metrics"},"citations":[{"ref":"Lefsky, M. A., Cohen, W. B., Parker, G. G., & Harding, D. J. (2002). Lidar remote sensing for ecosystem studies. BioScience, 52(1), 19–30.","type":"article","doi":"10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2","isbn":null,"url":null}],"related":["object-based-image-analysis","kriging"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"liebowitz-social-anxiety-scale","name":"Liebowitz Social Anxiety Scale","fullName":"Liebowitz Social Anxiety Scale","aliases":["LSAS","Liebowitz SAS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"social anxiety assessment","year":"1987","originator":"Michael R. Liebowitz","url":"https://scholargate.app/en/clinical-psychology/liebowitz-social-anxiety-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/liebowitz-social-anxiety-scale.md","definition":"The Liebowitz Social Anxiety Scale (LSAS) is a 24-item clinician-administered scale designed to measure the severity of social anxiety and avoidance in individuals with social anxiety disorder. Developed by Michael R. Liebowitz in 1987, the LSAS has become the gold-standard instrument for assessing social phobia in clinical trials and research settings, particularly valued for its dual measurement of fear and avoidance across diverse social situations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael R. Liebowitz","subfamily":"social anxiety assessment","year":"1987","type":"Clinician-rated social anxiety scale"},"citations":[{"ref":"Liebowitz, M. R. (1987). Social phobia. In Modern Problems in Pharmacopsychiatry (Vol. 22, pp. 141-173). Karger.","type":"chapter","doi":"10.1159/000414022","isbn":null,"url":null}],"related":["gad-7","beck-anxiety-inventory","social-interaction-anxiety-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"life-cycle-assessment","name":"Life Cycle Assessment","fullName":"Life Cycle Assessment (LCA)","aliases":["Life Cycle Analysis","Cradle-to-Grave Analysis","Ecobalance","Yaşam Döngüsü Değerlendirmesi"],"domain":"sustainability","family":"process-pipeline","subfamily":"Environmental assessment","year":2009,"originator":"ISO 14040 framework; Finnveden et al.","url":"https://scholargate.app/en/sustainability/life-cycle-assessment","markdownUrl":"https://scholargate.app/en/sustainability/life-cycle-assessment.md","definition":"Life Cycle Assessment is a systematic, ISO-standardized methodology for quantifying the environmental impacts of a product, process, or service across its entire life span — from raw material extraction through production, use, and end-of-life disposal. Codified in ISO 14040 and ISO 14044, and comprehensively reviewed by Finnveden et al. (2009), LCA enables decision-makers to compare alternatives, identify environmental hotspots, and support eco-design, with applications spanning products, buildings, energy systems, and public policy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"ISO 14040 framework; Finnveden et al.","year":2009,"type":"Environmental impact accounting pipeline","subfamily":"Environmental assessment","standard":"ISO 14040 / ISO 14044","scope":"Cradle-to-grave or cradle-to-cradle"},"citations":[{"ref":"Finnveden, G., et al. (2009). Recent developments in life cycle assessment. Journal of Environmental Management, 91(1), 1–21.","type":"article","doi":"10.1016/j.jenvman.2009.06.018","isbn":null,"url":null}],"related":["material-flow-analysis","lmdi-decomposition","ecological-footprint"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"life-cycle-sustainability-assessment","name":"Life Cycle Sustainability Assessment","fullName":"Life Cycle Sustainability Assessment (LCSA)","aliases":["LCSA"],"domain":"sustainability","family":"process-pipeline","subfamily":"Environmental assessment","year":"2008","originator":"Matthias Finkbeiner","url":"https://scholargate.app/en/sustainability/life-cycle-sustainability-assessment","markdownUrl":"https://scholargate.app/en/sustainability/life-cycle-sustainability-assessment.md","definition":"Life Cycle Sustainability Assessment (LCSA) is a comprehensive framework developed by Matthias Finkbeiner and colleagues to evaluate environmental, social, and economic impacts of products and services throughout their entire life cycle. Introduced around 2008, it extends traditional life cycle assessment to address sustainability holistically.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matthias Finkbeiner","subfamily":"Environmental assessment","year":"2008","type":"Integrated assessment pipeline"},"citations":[{"ref":"Finkbeiner, M., Schau, E. M., Lehmann, A., & Traverso, M. (2010). Towards Life Cycle Sustainability Assessment. Sustainability, 2(10), 3309-3322.","type":"article","doi":"10.3390/su2103309","isbn":null,"url":null},{"ref":"Klöpffer, W. (2008). Towards Life Cycle Sustainability Assessment of Products. Journal of Cleaner Production, 16(17), 1844-1853.","type":"article","doi":"10.1007/978-1-4020-8913-8_5","isbn":null,"url":null},{"ref":"UNEP (2012). Towards a Life Cycle Sustainability Assessment: Making informed choices on products. UNEP Life Cycle Initiative Report.","type":"article","doi":null,"isbn":null,"url":"https://www.unep.org/resources/publication/towards-life-cycle-sustainability-assessment"}],"related":["input-output-structural-decomposition-analysis","ecosystem-services-valuation","dpsir-framework"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"life-events-checklist","name":"Life Events Checklist for DSM-5","fullName":"Life Events Checklist for DSM-5 (LEC-5)","aliases":["LEC-5","Life Events Checklist"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"Trauma exposure assessment and screening","year":"2013","originator":"Frank W. Weathers et al.","url":"https://scholargate.app/en/trauma-psychology/life-events-checklist","markdownUrl":"https://scholargate.app/en/trauma-psychology/life-events-checklist.md","definition":"The LEC-5 is a 17-item self-report checklist assessing exposure to stressful life events that may result in PTSD or trauma-related mental health problems. Developed by Weathers and colleagues at the National Center for PTSD in 2013, the LEC-5 identifies which types of traumatic events a person has experienced, determining the presence of a qualifying trauma exposure according to DSM-5 Criterion A. The scale is widely used in PTSD research, clinical assessment, and disaster mental health screening.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank W. Weathers et al.","subfamily":"Trauma exposure assessment and screening","year":"2013","type":"Self-report checklist"},"citations":[{"ref":"Weathers, F. W., Blake, D. D., Schnurr, P. P., Kaloupek, D. G., Marx, B. P., & Keane, T. M. (2013). The Life Events Checklist for DSM-5 (LEC-5). National Center for PTSD. Available from: www.ptsd.va.gov","type":"article","doi":null,"isbn":null,"url":"https://www.ptsd.va.gov/professional/assessment/te-measures/lec5.asp"},{"ref":"Gray, M. J., Litz, B. T., Hsu, J. L., & Lombardo, T. W. (2004). Psychometric properties of the Life Events Checklist. Assessment, 11(4), 330-341.","type":"article","doi":"10.1177/1073191104269954","isbn":null,"url":null}],"related":["primary-care-ptsd-screen","impact-of-event-scale-revised","post-traumatic-growth-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"life-history-research","name":"Life History Research","fullName":"Life History Research","aliases":["life history method","life-history interview","biographical research","personal narrative research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Narrative Inquiry","year":"Early 20th century (Thomas & Znaniecki 1918–1920); systematised as interview method in the 1990s","originator":"William I. Thomas and Florian Znaniecki (sociological tradition); Robert Atkinson (interview method)","url":"https://scholargate.app/en/qualitative/life-history-research","markdownUrl":"https://scholargate.app/en/qualitative/life-history-research.md","definition":"Life history research is a qualitative method that captures the full arc of an individual's life — or a significant portion of it — through extended biographical interviewing and analysis of personal documents. Rooted in early Chicago School sociology, the method treats each life story as a window into broader social, cultural, and historical forces. The researcher and participant co-construct a narrative account that illuminates how personal experience is shaped by, and in turn shapes, wider social structures and processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William I. Thomas and Florian Znaniecki (sociological tradition); Robert Atkinson (interview method)","year":"Early 20th century (Thomas & Znaniecki 1918–1920); systematised as interview method in the 1990s","type":"Qualitative research method","dataType":"In-depth biographical interviews, personal documents, diaries, letters, field notes","typicalSampleSize":"1–20 participants (often 1–5 for intensive biographical work)","subfamily":"Narrative Inquiry"},"citations":[{"ref":"Atkinson, R. (1998). The Life Story Interview. Sage.","type":"book","doi":null,"isbn":"978-0761904496","url":null},{"ref":"Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Documents+of+Life+2+An+Invitation+to+a+Critical+Humanism+Plummer+2001"}],"related":["narrative-analysis","phenomenology","ethnography","grounded-theory","case-study","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"life-space-assessment","name":"LSA","fullName":"Life-Space Assessment","aliases":["LSA","Life Space Assessment"],"domain":"gerontology","family":"process-pipeline","subfamily":"mobility-and-participation","year":"2003","originator":"Pamela S. Baker","url":"https://scholargate.app/en/gerontology/life-space-assessment","markdownUrl":"https://scholargate.app/en/gerontology/life-space-assessment.md","definition":"The Life-Space Assessment (LSA) is an interview-based measure developed by Baker and colleagues in 2003 to evaluate the geographic range and frequency of mobility in community-dwelling older adults. Unlike traditional measures that focus on lower extremity function in controlled settings, the LSA captures the actual areas persons frequent in daily life—from the bedroom to the neighborhood to the larger community—and the frequency and independence with which they access these spaces. It provides a comprehensive portrait of functional mobility in real-world contexts and is a strong predictor of disability, institutionalization, and mortality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamela S. Baker","subfamily":"mobility-and-participation","year":"2003","type":"Interview-based assessment of activity range"},"citations":[{"ref":"Baker, P. S., Bodner, E. V., & Allman, R. M. (2003). Measuring life-space mobility in community-dwelling older adults. J Am Geriatr Soc, 51(11), 1610-1614.","type":"article","doi":"10.1046/j.1532-5415.2003.51512.x","isbn":null,"url":null},{"ref":"Peel, C., Sawyer Baker, P., Roth, M., Brown, C. J., Bodner, E. V., & Allman, R. M. (2005). Assessing mobility in older adults: the UAB Study of Aging Life-Space Assessment. Phys Ther, 85(10), 1008-1119.","type":"article","doi":"10.1093/ptj/85.10.1008","isbn":null,"url":null},{"ref":"Haley, S. M., & Andres, P. L. (2010). Life-Space Assessment: a performance-based measure of mobility in people with chronic conditions. J Am Geriatr Soc, 58(12), 2319-2326.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Life-Space+Assessment%3A+a+performance-based+measure+of+mobility+in+people+with+chronic+conditions+Haley"}],"related":["short-physical-performance-battery","tinetti-balance-assessment","activities-balance-confidence","frail-scale","edmonton-frail-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"life-table-response-experiment","name":"Life Table Response Experiment","fullName":"Life Table Response Experiment (LTRE)","aliases":["LTRE","demographic analysis","vital rate contribution","elasticity analysis"],"domain":"ecology","family":"process-pipeline","subfamily":"Demographic analysis","year":"2000","originator":"Hal Caswell","url":"https://scholargate.app/en/ecology/life-table-response-experiment","markdownUrl":"https://scholargate.app/en/ecology/life-table-response-experiment.md","definition":"Life Table Response Experiments (LTRE) decompose observed temporal changes in population growth rate (lambda) into contributions from changes in specific vital rates (survival, reproduction). Developed by Caswell (2000) and applied extensively by Wisdom and colleagues, LTRE reveals which demographic changes drove observed population dynamics. For example, LTRE can show whether a population's decline was primarily due to reduced survival of juveniles, reduced fecundity of adults, or changes in other life stages. This guides targeted conservation or management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hal Caswell","subfamily":"Demographic analysis","year":"2000","type":"temporal perturbation analysis"},"citations":[{"ref":"Caswell, H. (2019). Sensitivity Analysis: Matrix Methods in Demography and Ecology. Springer.","type":"book","doi":"10.1007/978-3-030-10534-1","isbn":null,"url":null},{"ref":"Caswell, H. (2000). Matrix population models. Sinauer Associates.","type":"article","doi":null,"isbn":null,"url":"https://global.oup.com/academic/product/matrix-population-models-9780878938134"},{"ref":"Wisdom, M. J., Mills, L. S., & Doak, D. F. (2000). Life stage simulation analysis: estimating vital-rate effects on population growth for conservation. Ecology, 81(3), 628-641.","type":"article","doi":"10.1890/0012-9658(2000)081[0628:LSSAEV]2.0.CO;2","isbn":null,"url":null}],"related":["leslie-matrix","integral-projection-model","population-viability-analysis","metabolic-theory-of-ecology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"life-table","name":"Life Table","fullName":"Life Table Analysis","aliases":["Mortality Table","Actuarial Table","Survival Table","Yaşam Tablosu"],"domain":"demography","family":"survival","subfamily":"Demography","year":1984,"originator":"Demographic/actuarial tradition; Chiang","url":"https://scholargate.app/en/demography/life-table","markdownUrl":"https://scholargate.app/en/demography/life-table.md","definition":"A life table is a systematic, age-structured summary of the mortality experience of a population. It traces a hypothetical cohort of births — conventionally 100,000 — through successive age intervals, recording how many survive, how many die, and how many person-years are lived at each interval. The method was formalized in its modern probabilistic form by Chiang (1984), synthesizing centuries of actuarial and demographic practice into a rigorous statistical framework applicable to human and biological populations alike.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Demographic/actuarial tradition; Chiang","year":1984,"type":"Age-structured mortality estimator","subfamily":"Demography","input":"Age-specific death and population counts","output":"Survival, mortality, and life expectancy by age interval"},"citations":[{"ref":"Chiang, C. L. (1984). The Life Table and Its Applications. Robert E. Krieger Publishing.","type":"book","doi":null,"isbn":"978-0-89874-565-2","url":null}],"related":["kaplan-meier","cohort-component-projection","lee-carter-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lift-and-gain-chart","name":"Lift and Gain Chart","fullName":"Lift Chart and Gain Chart","aliases":["Cumulative Gain Chart","Lift Curve"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Evaluation Tool","year":"1990s","originator":"Data mining and marketing analytics","url":"https://scholargate.app/en/model-evaluation/lift-and-gain-chart","markdownUrl":"https://scholargate.app/en/model-evaluation/lift-and-gain-chart.md","definition":"Lift and gain charts visualize classifier performance by showing how much better the model performs compared to random selection, particularly useful for ranking or scoring tasks where you select a top percentage of samples. They are widely used in marketing, credit scoring, and fraud detection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Data mining and marketing analytics","subfamily":"Classification Evaluation Tool","year":"1990s","type":"Evaluation visualization"},"citations":[{"ref":"Maimon, O. Z., & Rokach, L. (Eds.). (2010). Data Mining and Knowledge Discovery Handbook (2nd ed.). Springer.","type":"book","doi":"10.1007/978-0-387-09823-4","isbn":null,"url":null},{"ref":"Naeem Siddiqi (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Credit+Risk+Scorecards:+Developing+and+Implementing+Intelligent+Credit+Scoring-p-9780470043522"}],"related":["roc-auc","precision-recall-auc","cumulative-gain","recall"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ligand-field-analysis","name":"Ligand Field Analysis","fullName":"Ligand Field Analysis","aliases":["ligand field","LFT","ligand field theory"],"domain":"chemistry","family":"process-pipeline","subfamily":"Structural analysis","year":"1960s","originator":"Brian Norman Figgis","url":"https://scholargate.app/en/chemistry/ligand-field-analysis","markdownUrl":"https://scholargate.app/en/chemistry/ligand-field-analysis.md","definition":"Ligand Field Theory (LFT) is an advanced model of metal-ligand bonding that combines crystal field theory with molecular orbital theory. Developed systematically by Brian Norman Figgis and others from the 1960s onward, LFT provides quantitative predictions of electronic structure, magnetism, spectra, and reactivity of coordination complexes, bridging the gap between qualitative crystal field arguments and rigorous quantum mechanics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brian Norman Figgis","subfamily":"Structural analysis","year":"1960s","type":"Theoretical model"},"citations":[{"ref":"Figgis, B. N. (1966). Introduction to Ligand Fields. Interscience Publishers.","type":"book","doi":null,"isbn":"978-0471257356","url":null},{"ref":"Lever, A. B. P. (1984). Inorganic Electronic Spectroscopy (2nd ed.). Elsevier.","type":"book","doi":null,"isbn":"978-0444422354","url":null}],"related":["crystal-field-theory","coordination-compound-synthesis","molecular-symmetry-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"light-curve-analysis","name":"Light Curve Analysis","fullName":"Photometric Light Curve Analysis","aliases":["photometric analysis","transit photometry","eclipsing binary analysis"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Observational Astronomy","year":"1880","originator":"Edward Pickering","url":"https://scholargate.app/en/applied-physics/light-curve-analysis","markdownUrl":"https://scholargate.app/en/applied-physics/light-curve-analysis.md","definition":"Light curve analysis is the study of the brightness variation of a celestial object over time, used to detect and characterize exoplanets, eclipsing binaries, and variable stars. When a planet transits in front of its host star, the star's brightness dips slightly. By analyzing these photometric signatures, astronomers can determine planetary radii, orbital periods, and atmospheric properties. This method has discovered thousands of exoplanets and revealed the structure of stellar systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward Pickering","subfamily":"Observational Astronomy","year":"1880","type":"Signal processing and astronomical observation technique"},"citations":[{"ref":"Ricker, G. R., et al. (2015). TESS: Transiting Exoplanet Survey Satellite. Journal of Astronomical Telescopes, Instruments, and Systems, 1(1), 014003.","type":"article","doi":"10.1117/1.JATIS.1.1.014003","isbn":null,"url":null},{"ref":"Borucki, W. J., et al. (2010). Kepler Planet-Detection Mission: Introduction and First Results. Science, 327(5968), 977-980.","type":"article","doi":"10.1126/science.1185402","isbn":null,"url":null},{"ref":"Mandel, K., & Agol, E. (2002). Analytic Light Curves for Planetary Transits. The Astrophysical Journal, 580(2), L171.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Analytic+Light+Curves+for+Planetary+Transits+Mandel"}],"related":["radial-velocity-method","n-body-simulation","cosmological-perturbation-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lightgbm","name":"LightGBM","fullName":"Light Gradient Boosting Machine","aliases":["LightGBM","Light Gradient Boosting Machine","lgbm","leaf-wise gradient boosting"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Ke, G. et al. (Microsoft)","url":"https://scholargate.app/en/machine-learning/lightgbm","markdownUrl":"https://scholargate.app/en/machine-learning/lightgbm.md","definition":"LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ke, G. et al. (Microsoft)","year":2017,"type":"Gradient boosting decision tree ensemble","task":"Classification, regression & forecasting","growth":"Leaf-wise (best-first)","minSample":200},"citations":[{"ref":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html"}],"related":["xgboost","random-forest","decision-tree","logistic-regression","isolation-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lightning-jump","name":"Lightning Jump","fullName":"Lightning Jump Severe Weather Indicator","aliases":["Lightning jump","Lightning trend","VHF lightning","Electric field analysis"],"domain":"meteorology","family":"process-pipeline","subfamily":"Severe weather prediction","year":"2009","originator":"Schultz, Petersen, Rudlosky","url":"https://scholargate.app/en/meteorology/lightning-jump","markdownUrl":"https://scholargate.app/en/meteorology/lightning-jump.md","definition":"The lightning jump is a rapid increase in lightning activity (number of flashes per unit time) that precedes severe weather including hail, heavy rain, and tornadoes. This phenomenon, observed using satellite or ground-based lightning detection networks, is an operational diagnostic tool for real-time severe weather warning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schultz, Petersen, Rudlosky","subfamily":"Severe weather prediction","year":"2009","type":"Real-time warning product"},"citations":[{"ref":"Schultz, E., Petersen, W. A., & Rudlosky, S. D. (2009). Preliminary Development and Validation of the Specific Convective Activity Signature (SCAS) Product using Optical Transient Detector and Geostationary Satellite Data. Journal of Applied Meteorology and Climatology, 48(4), 642-655.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Preliminary+Development+and+Validation+of+the+Specific+Convective+Activity+Signature+%28SCAS%29+Product+using+Optical+Transient+Detector+and+Geostationary+Satellite+Data+Schultz"},{"ref":"Mosier, R. M., Czarnecki, C., & Carey, L. D. (2011). Radar and Lightning Observations of the 1 August 2008 Kinston, North Carolina Supercell. Weather and Forecasting, 26(4), 459-474.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Radar+and+Lightning+Observations+of+the+1+August+2008+Kinston%2C+North+Carolina+Supercell+Mosier"}],"related":["dual-polarization-radar","wrf-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lightts","name":"LightTS","fullName":"LightTS (Light Sampling-oriented MLP)","aliases":["Light Sampling-oriented MLP","LightMLP","Hafif Örnekleme Tabanlı MLP","Lightweight Time-Series MLP"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2022,"originator":"Tianping Zhang et al.","url":"https://scholargate.app/en/deep-learning/lightts","markdownUrl":"https://scholargate.app/en/deep-learning/lightts.md","definition":"LightTS is a lightweight, MLP-based architecture for multivariate time-series forecasting introduced by Tianping Zhang and colleagues in 2022. Motivated by the observation that simpler models can match or surpass heavy Transformer-based architectures, LightTS applies an interval-sampling strategy to decompose long input sequences into multiple sub-sequences and processes each with compact Chunk-MLP and Continuous-MLP modules. The design prioritizes computational efficiency while preserving both local and global temporal patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tianping Zhang et al.","year":2022,"type":"Lightweight MLP-based multivariate time-series forecaster","subfamily":"Time-series forecasting","learning_paradigm":"Supervised, end-to-end","complexity":"Linear in sequence length"},"citations":[{"ref":"Zhang, T., Zhang, Y., Cao, W., Bian, J., Yi, X., Zheng, S., & Li, J. (2022). Less is more: Fast multivariate time series forecasting with light sampling-oriented MLP structures. arXiv preprint.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2207.01186"}],"related":["dlinear","tsmixer","multilayer-perceptron"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"likert-scale-construction","name":"Likert Scale Construction","fullName":"Rensis Likert's Method for Constructing Summated Rating Scales","aliases":["Likert summated rating scale","Summated rating scale construction"],"domain":"psychometrics","family":"process-pipeline","subfamily":"Scale development","year":"1932","originator":"Rensis Likert","url":"https://scholargate.app/en/psychometrics/likert-scale-construction","markdownUrl":"https://scholargate.app/en/psychometrics/likert-scale-construction.md","definition":"Likert scale construction is a systematic methodology for developing attitude measurement instruments using summated rating scales. Introduced by Rensis Likert in 1932, it enables researchers to quantify latent constructs such as attitudes, beliefs, and psychological states by aggregating responses across multiple items. The method remains foundational to quantitative social and health sciences research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rensis Likert","subfamily":"Scale development","year":"1932","type":"Summated rating scale methodology"},"citations":[{"ref":"Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1-55.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+technique+for+the+measurement+of+attitudes+Likert"},{"ref":"Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). New York: Holt, Rinehart & Winston.","type":"book","doi":null,"isbn":"0030652669","url":null},{"ref":"DeVellis, R. F. (2016). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage Publications.","type":"book","doi":null,"isbn":"9781506330174","url":null}],"related":["guttman-scale","factor-analysis-scale","content-validity-ratio","confirmatory-factor-analysis-scale","floor-ceiling-effect"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lilliefors-test","name":"Lilliefors Test","fullName":"Lilliefors Test for Normality with Mean and Variance Unknown","aliases":["Lilliefors corrected Kolmogorov-Smirnov test","Lilliefors normality test","Lilliefors Testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1967,"originator":"Hubert W. Lilliefors","url":"https://scholargate.app/en/statistics/lilliefors-test","markdownUrl":"https://scholargate.app/en/statistics/lilliefors-test.md","definition":"The Lilliefors test is a goodness-of-fit test that checks whether a continuous sample comes from a normal (or exponential) distribution when the mean and variance are unknown and estimated from the data. Introduced by Hubert W. Lilliefors in 1967, it adjusts the critical values of the Kolmogorov-Smirnov test so that they remain valid once the distribution's parameters are estimated rather than known in advance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hubert W. Lilliefors","year":1967,"type":"Goodness-of-fit / normality test","estimator":"Corrected Kolmogorov-Smirnov statistic with parameters estimated from the data","outcome":"test statistic and p-value"},"citations":[{"ref":"Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association, 62(318), 399-402.","type":"article","doi":"10.1080/01621459.1967.10482916","isbn":null,"url":null},{"ref":"Dallal, G. E., & Wilkinson, L. (1986). An Analytic Approximation to the Distribution of Lilliefors's Test Statistic for Normality. The American Statistician, 40(4), 294-296.","type":"article","doi":"10.1080/00031305.1986.10475419","isbn":null,"url":null}],"related":["kolmogorov-smirnov-2sample","shapiro-wilk-test","anderson-darling-test","fligner-killeen-test","mood-median-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lime","name":"LIME","fullName":"Local Interpretable Model-agnostic Explanations (LIME)","aliases":["Local Surrogate Explanations","Model-Agnostic Local Explanations","Locally Faithful Approximations","Yerel Yorumlanabilir Model-Bağımsız Açıklamalar"],"domain":"machine-learning","family":"ml-model","subfamily":"Explainable AI","year":2016,"originator":"Marco Ribeiro, Sameer Singh & Carlos Guestrin","url":"https://scholargate.app/en/machine-learning/lime","markdownUrl":"https://scholargate.app/en/machine-learning/lime.md","definition":"LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marco Ribeiro, Sameer Singh & Carlos Guestrin","year":2016,"type":"post-hoc local explanation","subfamily":"Explainable AI","scope":"instance-level (local)","model_agnostic":true},"citations":[{"ref":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). \"Why should I trust you?\": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144.","type":"inproceedings","doi":"10.1145/2939672.2939778","isbn":null,"url":null}],"related":["shap","counterfactual-explanations","random-forest"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"line-intercept-sampling","name":"Line-Intercept Sampling","fullName":"Line-Intercept Sampling","aliases":["Line Intercept","LIS"],"domain":"sampling","family":"process-pipeline","subfamily":"Nonparametric","year":"1941","originator":"Richard H. Canfield","url":"https://scholargate.app/en/sampling/line-intercept-sampling","markdownUrl":"https://scholargate.app/en/sampling/line-intercept-sampling.md","definition":"Line-Intercept Sampling (LIS) is an ecological field method developed by Richard H. Canfield in 1941 for estimating vegetation cover, plant density, and structural characteristics in rangeland and forest surveys. By laying a linear transect across a study area and recording all plants intersecting the line, LIS provides efficient, unbiased estimates without requiring plots or quadrats.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard H. Canfield","subfamily":"Nonparametric","year":"1941","type":"Ecological field sampling method"},"citations":[{"ref":"Canfield, R. H. (1941). Application of the line interception method in sampling range vegetation. Journal of Forestry, 39(4), 388–394.","type":"article","doi":"10.1093/jof/39.4.388","isbn":null,"url":null},{"ref":"Warren, S. D., Conquest, L. L., & Brenkert, A. L. (1971). Cost and precision of methods for sampling shrub cover in ecological surveys. Journal of Range Management, 24(2), 141–147.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cost+and+precision+of+methods+for+sampling+shrub+cover+in+ecological+surveys+Warren"},{"ref":"Krebs, C. J. (1998). Ecological Methodology (2nd ed.). Addison Wesley Longman.","type":"book","doi":null,"isbn":null,"url":"https://www.adamslibrary.org/books/ecological-methodology"}],"related":["point-intercept-sampling","quadrat-sampling","transect-sampling","systematic-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linear-cryptanalysis","name":"Linear Cryptanalysis","fullName":"Linear Cryptanalysis","aliases":["linear attack","linear approximation","piling-up lemma"],"domain":"cryptography","family":"ml-model","subfamily":"Cryptanalytic technique","year":"1993","originator":"Mitsuru Matsui","url":"https://scholargate.app/en/cryptography/linear-cryptanalysis","markdownUrl":"https://scholargate.app/en/cryptography/linear-cryptanalysis.md","definition":"Linear cryptanalysis is a known-plaintext attack that exploits linear approximations of a cipher's non-linear transformations to recover secret key bits. Introduced by Mitsuru Matsui in 1993, linear cryptanalysis provides practical attacks on ciphers like DES with computational complexity less than brute force. The technique analyzes statistical biases in how linear combinations of plaintext and ciphertext bits relate to key bits, enabling key recovery with reduced data requirements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mitsuru Matsui","subfamily":"Cryptanalytic technique","year":"1993","type":"linear approximation attack"},"citations":[{"ref":"Matsui, M. (1993). Linear cryptanalysis method for DES cipher. In Advances in Cryptology - EUROCRYPT 1993, LNCS 765, pp. 386-397.","type":"article","doi":"10.1007/3-540-48285-7_33","isbn":null,"url":null},{"ref":"Matsui, M. (1994). The first experimental cryptanalysis of the Data Encryption Standard. In Advances in Cryptology - CRYPTO 1994, LNCS 839, pp. 1-11.","type":"article","doi":"10.1007/3-540-48658-5_1","isbn":null,"url":null}],"related":["differential-cryptanalysis","aes","side-channel-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linear-discriminant-analysis","name":"Linear Discriminant Analysis","fullName":"Linear Discriminant Analysis (Fisher's LDA)","aliases":["LDA","Fisher's discriminant analysis","Fisher linear discriminant","normal discriminant analysis","canonical discriminant analysis"],"domain":"machine-learning","family":"latent-structure","subfamily":null,"year":1936,"originator":"Fisher, R. A.","url":"https://scholargate.app/en/machine-learning/linear-discriminant-analysis","markdownUrl":"https://scholargate.app/en/machine-learning/linear-discriminant-analysis.md","definition":"Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fisher, R. A.","year":1936,"type":"Supervised dimensionality reduction and linear classifier","task":"Classification and dimensionality reduction","minSample":20,"assumesNormality":true,"assumesEqualCovariance":true},"citations":[{"ref":"Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.","type":"article","doi":"10.1111/j.1469-1809.1936.tb02137.x","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 4). Springer.","type":"book","doi":null,"isbn":"978-0-387-84857-0","url":null}],"related":["logistic-regression","quadratic-discriminant-analysis","principal-component-analysis","support-vector-machine","naive-bayes","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linear-max-normalization","name":"LINEAR-MAX-NORMALIZATION","fullName":"Linear Max Normalization — division by column maximum (benefit) or column minimum over value (cost)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Normalization","year":"1967","originator":"Fishburn, P. C.","url":"https://scholargate.app/en/decision-making/linear-max-normalization","markdownUrl":"https://scholargate.app/en/decision-making/linear-max-normalization.md","definition":"LINEAR-MAX-NORMALIZATION (Linear Max Normalization — division by column maximum (benefit) or column minimum over value (cost)) is a normalization multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fishburn, P. C.","subfamily":"Normalization","year":"1967","type":"Normalization (linear-max, ratio-based)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Fishburn, P. C. (1967). Additive Utilities with Incomplete Product Sets: Application to Priorities and Assignments. Operations Research","type":"article","doi":"10.1287/opre.15.3.537","isbn":null,"url":null}],"related":["aras","copras","wpm","marcos"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linear-predictive-coding","name":"Linear Predictive Coding","fullName":"Linear Predictive Coding for Speech Modeling and Compression","aliases":["LPC","autoregressive model","speech prediction","vocal tract modeling"],"domain":"acoustics","family":"process-pipeline","subfamily":"Signal processing, Speech modeling","year":"1975","originator":"Freddy Burg, John Makhoul","url":"https://scholargate.app/en/acoustics/linear-predictive-coding","markdownUrl":"https://scholargate.app/en/acoustics/linear-predictive-coding.md","definition":"Linear Predictive Coding (LPC) is a powerful signal processing technique for modeling and compressing speech by assuming each speech sample can be predicted from a linear combination of previous samples. Pioneered by Burg and Makhoul in the 1970s, LPC is the foundation of speech codecs, speech synthesis, speaker recognition, and speech enhancement. LPC exploits the time-correlated structure of speech to achieve high compression ratios and enable efficient parameter extraction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Freddy Burg, John Makhoul","subfamily":"Signal processing, Speech modeling","year":"1975","type":"Predictive speech coding and analysis"},"citations":[{"ref":"Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4), 561–580.","type":"article","doi":"10.1109/PROC.1975.9792","isbn":null,"url":null},{"ref":"Rabiner, L. R., & Schafer, R. W. (1978). Digital Processing of Speech Signals. Prentice-Hall.","type":"book","doi":null,"isbn":"978-0132136029","url":null},{"ref":"Haykin, S. (2002). Adaptive Filter Theory (4th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0130901262","url":null}],"related":["cepstral-analysis","bark-and-mel-scales","psychoacoustic-masking","speech-intelligibility","beamforming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linear-programming","name":"Linear Programming","fullName":"Linear Programming (LP)","aliases":["LP","linear optimization","Doğrusal Programlama (LP)"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":1947,"originator":"George B. Dantzig","url":"https://scholargate.app/en/optimization/linear-programming","markdownUrl":"https://scholargate.app/en/optimization/linear-programming.md","definition":"Linear programming (LP), pioneered by George B. Dantzig in 1947, is a mathematical method for finding the best value of a linear objective function — such as minimum cost or maximum profit — subject to a set of linear inequality and equality constraints. It is the foundational technique in operations research and underlies production planning, resource allocation, logistics, diet problems, and countless other decision-making scenarios across engineering, economics, and the natural sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George B. Dantzig","year":1947,"type":"Mathematical programming / continuous optimization","solutionMethods":"Simplex method; interior-point methods (barrier methods)","outputType":"Optimal decision-variable values and optimal objective value","feasibleRegion":"Convex, non-empty polyhedron","complexity":"Polynomial in theory (ellipsoid, interior-point); Simplex is exponential worst-case but fast in practice","difficulty":2},"citations":[{"ref":"Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press.","type":"book","doi":null,"isbn":"9780691059136","url":null},{"ref":"Vanderbei, R.J. (2014). Linear Programming: Foundations and Extensions. Springer.","type":"book","doi":"10.1007/978-1-4614-7630-6","isbn":null,"url":null}],"related":["integer-programming","goal-programming","nonlinear-programming","stochastic-optimization","network-flow"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linear-quadratic-gaussian","name":"Linear Quadratic Gaussian","fullName":"Linear Quadratic Gaussian","aliases":["LQG","LQR with Kalman Filter"],"domain":"control-theory","family":"ml-model","subfamily":"Stochastic Control","year":"1960","originator":"Rudolf Kalman","url":"https://scholargate.app/en/control-theory/linear-quadratic-gaussian","markdownUrl":"https://scholargate.app/en/control-theory/linear-quadratic-gaussian.md","definition":"The Linear Quadratic Gaussian (LQG) controller combines the Linear Quadratic Regulator (LQR) with a Kalman Filter to handle stochastic systems with measurement noise and process noise. Developed by Kalman and later formalized by Athans and others, LQG is the natural stochastic extension of LQR and remains the gold standard for optimal linear control under noise, with applications spanning spacecraft, aircraft autopilot, and industrial process control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf Kalman","subfamily":"Stochastic Control","year":"1960","type":"algorithm"},"citations":[{"ref":"Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.","type":"article","doi":"10.1115/1.3662552","isbn":null,"url":null},{"ref":"Athans, M. (1971). The role and use of the stochastic linear-quadratic-gaussian problem in control system design. IEEE Transactions on Automatic Control, 16(6), 529-552.","type":"article","doi":"10.1109/TAC.1971.1099818","isbn":null,"url":null},{"ref":"Kwakernaak, H., & Sivan, R. (1972). Linear Optimal Control Systems. Wiley-Interscience.","type":"article","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/book/10.1002/oca.4660230213"}],"related":["linear-quadratic-regulator","extended-kalman-filter","kalman-filter","stochastic-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linear-quadratic-regulator","name":"Linear Quadratic Regulator","fullName":"Linear Quadratic Regulator","aliases":["LQR","Linear Quadratic Optimal Control"],"domain":"control-theory","family":"ml-model","subfamily":"Optimal Control","year":"1960","originator":"Rudolf Kalman","url":"https://scholargate.app/en/control-theory/linear-quadratic-regulator","markdownUrl":"https://scholargate.app/en/control-theory/linear-quadratic-regulator.md","definition":"The Linear Quadratic Regulator (LQR) is a classical optimal control algorithm that computes a linear feedback law to minimize a quadratic cost function for a linear dynamical system. Introduced by Kalman in 1960, LQR provides a provably optimal, closed-form solution for linear systems and remains fundamental in control theory, robotics, and aerospace applications because of its theoretical elegance and computational efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf Kalman","subfamily":"Optimal Control","year":"1960","type":"algorithm"},"citations":[{"ref":"Kalman, R. E. (1960). Contributions to the theory of optimal control. Boletin de la Sociedad Matematica Mexicana, 5(2), 102-119.","type":"article","doi":null,"isbn":null,"url":"https://drive.google.com/file/d/kalman-1960"},{"ref":"Bryson, A. E., & Ho, Y. C. (1969). Applied Optimal Control: Optimization, Estimation and Control. Blaisdell Publishing.","type":"article","doi":null,"isbn":null,"url":"https://walterbryson.caltech.edu/"},{"ref":"Lewis, F. L., Vrabie, D., & Syrmos, V. L. (2012). Optimal Control (3rd ed.). John Wiley & Sons.","type":"article","doi":"10.1002/9781118122631","isbn":null,"url":null}],"related":["hamilton-jacobi-bellman-equation","model-predictive-control","extended-kalman-filter","pontryagin-maximum-principle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linear-regression-ml","name":"Linear Regression (ML)","fullName":"Linear Regression as a Machine Learning Model","aliases":["ordinary least squares regression","OLS","least squares regression","multiple linear regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1805–1809","originator":"Legendre, A.-M. & Gauss, C.F.","url":"https://scholargate.app/en/machine-learning/linear-regression-ml","markdownUrl":"https://scholargate.app/en/machine-learning/linear-regression-ml.md","definition":"Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Legendre, A.-M. & Gauss, C.F.","year":"1805–1809","type":"Supervised regression","dataType":"Continuous numeric features and a continuous numeric target","subfamily":"Machine learning"},"citations":[{"ref":"Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer.","type":"book","doi":null,"isbn":"978-0-387-84858-7","url":null},{"ref":"James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 3). Springer.","type":"book","doi":null,"isbn":"978-1-4614-7138-7","url":null}],"related":["regularized-linear-regression","logistic-regression-ml","random-forest","gradient-boosting","support-vector-machine","decision-tree"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linear-sum-normalization","name":"LINEAR-SUM-NORMALIZATION","fullName":"Linear Sum Normalization — column-sum division (probability / stochastic normalisation)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Normalization","year":"1994","originator":"Zavadskas, E. K., Turskis, Z., Peldschus, F., Kaklauskas, A.","url":"https://scholargate.app/en/decision-making/linear-sum-normalization","markdownUrl":"https://scholargate.app/en/decision-making/linear-sum-normalization.md","definition":"LINEAR-SUM-NORMALIZATION (Linear Sum Normalization — column-sum division (probability / stochastic normalisation)) is a normalization multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z., Peldschus, F., Kaklauskas, A. in 1994. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Turskis, Z., Peldschus, F., Kaklauskas, A.","subfamily":"Normalization","year":"1994","type":"Normalization (linear-sum, stochastic)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Zavadskas, E. K., Turskis, Z., Peldschus, F., Kaklauskas, A. (1994). Competitive comparison of contractors' offers in construction. Technika, Vilnius","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Competitive%20comparison%20of%20contractors%27%20offers%20in%20construction"}],"related":["moora","multimoora","entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linguistic-acceptability","name":"Linguistic Acceptability Assessment","fullName":"Linguistic Acceptability Assessment (Grammaticality Judgment)","aliases":["grammaticality judgment","acceptability judgment","CoLA task","Dilbilgisel Kabul Edilebilirlik Değerlendirme"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1957 (theory); 2019 (neural benchmark — CoLA)","originator":"Noam Chomsky (theoretical foundations, 1957); Warstadt, Singh & Bowman (neural formulation, 2019)","url":"https://scholargate.app/en/text-mining/linguistic-acceptability","markdownUrl":"https://scholargate.app/en/text-mining/linguistic-acceptability.md","definition":"Linguistic acceptability assessment is a natural-language-processing task that automatically estimates whether a sentence would be judged grammatically acceptable by a native speaker of the target language. Grounded in Chomsky's (1957) distinction between grammatical and ungrammatical utterances, the task was formalised as a neural benchmark by Warstadt, Singh and Bowman (2019) through the Corpus of Linguistic Acceptability (CoLA). It is used in language-learning research, linguistics studies, and quality auditing of natural-language-generation (NLG) systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Noam Chomsky (theoretical foundations, 1957); Warstadt, Singh & Bowman (neural formulation, 2019)","year":"1957 (theory); 2019 (neural benchmark — CoLA)","type":"NLP binary/continuous classification task","benchmark":"Corpus of Linguistic Acceptability (CoLA)","output":"Acceptability label (acceptable / unacceptable) or continuous acceptability score","minimumSample":20},"citations":[{"ref":"Warstadt, A., Singh, A. & Bowman, S. (2019). Neural Network Acceptability Judgments. Transactions of the Association for Computational Linguistics, 7, 625–641.","type":"article","doi":"10.1162/tacl_a_00290","isbn":null,"url":null},{"ref":"Chomsky, N. (1957). Syntactic Structures. Mouton, The Hague.","type":"book","doi":null,"isbn":"978-9027933249","url":null}],"related":["sentiment-analysis","text-classification","bert-embeddings","tf-idf"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linguistic-ethnography","name":"Linguistic Ethnography","fullName":"Linguistic Ethnography Method","aliases":["Ethnographic Linguistics","Sociolinguistic Ethnography"],"domain":"linguistics","family":"process-pipeline","subfamily":"Qualitative Sociolinguistics","year":"1998","originator":"Ben Rampton","url":"https://scholargate.app/en/linguistics/linguistic-ethnography","markdownUrl":"https://scholargate.app/en/linguistics/linguistic-ethnography.md","definition":"Linguistic Ethnography is a qualitative research approach combining ethnographic fieldwork with detailed linguistic analysis to understand language use in cultural context. Developed by researchers like Ben Rampton, it examines how people use language within communities, institutions, and social interactions while paying attention to identity, power, and meaning-making. This method integrates sociolinguistics, anthropology, and discourse analysis to produce rich, contextualized understandings of language-in-society.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ben Rampton","subfamily":"Qualitative Sociolinguistics","year":"1998","type":"Empirical process pipeline"},"citations":[{"ref":"Rampton, B. (2007). Neo-Hymesian linguistic ethnography in the United Kingdom. Journal of Sociolinguistics, 11(5), 584-607.","type":"article","doi":"10.1111/j.1467-9841.2007.00341.x","isbn":null,"url":null},{"ref":"Creese, A., & Blackledge, A. (2010). Translanguaging in the bilingual classroom: A pedagogy for learning and teaching? The Modern Language Journal, 94(1), 103-115.","type":"book","doi":"10.1111/j.1540-4781.2009.00986.x","isbn":null,"url":null},{"ref":"Heath, S. B. (1983). Ways with Words: Language, Life and Work in Communities and Classrooms. Cambridge: Cambridge University Press.","type":"book","doi":"10.1017/cbo9780511841057","isbn":null,"url":null}],"related":["corpus-linguistics","sociolinguistics","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"link-prediction","name":"Link Prediction","fullName":"Link Prediction (Missing and Future Edge Inference)","aliases":["Bağlantı Tahmini (Link Prediction)","missing link prediction","future link prediction","edge prediction"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":2003,"originator":null,"url":"https://scholargate.app/en/network-analysis/link-prediction","markdownUrl":"https://scholargate.app/en/network-analysis/link-prediction.md","definition":"Link prediction is a network-analysis task that estimates which edges are missing from an observed graph or which edges are likely to form in the future. Formalised by Liben-Nowell and Kleinberg (2003, 2007), it covers a spectrum of approaches — from simple structural similarity indices such as Common Neighbors, Jaccard coefficient, and Adamic-Adar, to matrix factorisation, and graph neural network (GNN) methods — and is evaluated with AUC and Average Precision to account for the heavily imbalanced ratio of real to non-existing edges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Liben-Nowell & Kleinberg","year":2003,"type":"Network inference task","approaches":"Structural similarity indices / Matrix factorisation / Graph Neural Networks","output":"Ranked list of candidate edges with likelihood scores","evaluationMetrics":"AUC-ROC, Average Precision (AP)","minNodes":50},"citations":[{"ref":"Liben-Nowell, D. & Kleinberg, J. (2007). The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031.","type":"article","doi":"10.1002/asi.20591","isbn":null,"url":null},{"ref":"Zhang, M. & Chen, Y. (2018). Link Prediction Based on Graph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS), 31.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/2018/hash/53f0d7c537d99b3824f0f99d62ea2428-Abstract.html"}],"related":["graph-neural-network","network-embedding","community-detection","centrality-analysis","stochastic-block-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"link-segment-inverse-dynamics","name":"Link Segment Inverse Dynamics","fullName":"Inverse Dynamics Biomechanical Analysis","aliases":["inverse dynamics","joint kinetics","joint moments"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Biomechanics","year":"1990","originator":"David Winter","url":"https://scholargate.app/en/sports-science/link-segment-inverse-dynamics","markdownUrl":"https://scholargate.app/en/sports-science/link-segment-inverse-dynamics.md","definition":"Inverse dynamics is a biomechanical analysis technique that calculates joint moments (forces and torques) from measured kinematics (positions and angles) and ground reaction forces. Formalized by David Winter (1990), inverse dynamics works backward from Newton's second law: given acceleration and inertia, calculate the net force (or moment) required to produce that motion. By analyzing joint loading during sport movements, biomechanists identify asymmetries, technique flaws, and muscle-group imbalances that predict injury or limit performance. Inverse dynamics is the standard for detailed biomechanical assessment in research and elite coaching.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Winter","subfamily":"Biomechanics","year":"1990","type":"kinetic analysis"},"citations":[{"ref":"Winter, D. A. (1990). Biomechanics and Motor Control of Human Movement. New York: John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Biomechanics+and+Motor+Control+of+Human+Movement-p-9780470549148"},{"ref":"DeLuca, P. A. (2003). The biomechanics of walking and running. Clinical Orthopaedics and Related Research, 288, 28-51.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/8743394/"},{"ref":"Yeadon, M. R., & Challis, J. H. (2010). The future of sports biomechanics. Sports Biomechanics, 9(1), 1-7.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+future+of+sports+biomechanics+Yeadon"}],"related":["isokinetic-dynamometry","force-velocity-profile","counter-movement-jump"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"link-stigma-scale","name":"Link Stigma Scale","fullName":"Perceived Devaluation-Discrimination Scale (Link Stigma Scale)","aliases":["Link Scale","PDD Scale"],"domain":"psychiatric-rehabilitation","family":"process-pipeline","subfamily":"stigma-measurement","year":"1987","originator":"Link, B. G.","url":"https://scholargate.app/en/psychiatric-rehabilitation/link-stigma-scale","markdownUrl":"https://scholargate.app/en/psychiatric-rehabilitation/link-stigma-scale.md","definition":"The Link Stigma Scale, also called the Perceived Devaluation-Discrimination Scale, is a measure of perceived stigma developed by Bruce G. Link in 1987. It assesses the extent to which individuals with serious mental illness perceive that society devalues people with mental illness and discriminates against them. Unlike internalized stigma (self-directed negative beliefs), the Link Scale captures perceived external stigma—beliefs about how others view and treat people with mental illness. The scale is widely used in stigma research and mental health services to understand stigma as a social and structural phenomenon.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Link, B. G.","subfamily":"stigma-measurement","year":"1987","type":"Self-report questionnaire"},"citations":[{"ref":"Link, B. G. (1987). Understanding labeling effects in the area of mental disorders: An assessment of the effects of expectations of rejection. American Sociological Review, 52(1), 96-112.","type":"article","doi":"10.2307/2095395","isbn":null,"url":null}],"related":["internalized-stigma-mental-illness","self-stigma-seeking-help","recovery-assessment-scale","social-inclusion-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"linmap","name":"LINMAP","fullName":"LINear programming technique for Multidimensional Analysis of Preference","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1973","originator":"Srinivasan, V., Shocker, A. D.","url":"https://scholargate.app/en/decision-making/linmap","markdownUrl":"https://scholargate.app/en/decision-making/linmap.md","definition":"LINMAP (LINear programming technique for Multidimensional Analysis of Preference) is a ranking multi-criteria decision-making (MCDM) method introduced by Srinivasan, V., Shocker, A. D. in 1973. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Srinivasan, V., Shocker, A. D.","subfamily":"Ranking","year":"1973","type":"LP-based ideal point from pairwise preference judgements","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Srinivasan, V., Shocker, A. D. (1973). Linear programming techniques for multidimensional analysis of preferences. Psychometrika","type":"article","doi":"10.1007/BF02291658","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"liposome-encapsulation","name":"Liposome Encapsulation","fullName":"Liposomal Drug Delivery and Encapsulation","aliases":["liposomal formulation","vesicular delivery","lipid nanoparticles"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Drug Delivery","year":"1965","originator":"Alec Bangham","url":"https://scholargate.app/en/pharmacology/liposome-encapsulation","markdownUrl":"https://scholargate.app/en/pharmacology/liposome-encapsulation.md","definition":"Liposomal encapsulation is a formulation technique using lipid bilayer vesicles (liposomes) to enclose drugs, improving bioavailability, reducing toxicity, and enabling targeted delivery. Developed by Alec Bangham in 1965, liposomes are now standard in pharmaceutical development, with several FDA-approved liposomal drugs on the market.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alec Bangham","subfamily":"Drug Delivery","year":"1965","type":"formulation technology"},"citations":[{"ref":"Bangham, A. D., Standish, M. M., & Watkins, J. C. (1965). Diffusion of univalent ions across the lamellae of swollen phospholipid films and the determination of membrane potential. Journal of Molecular Biology, 13(1), 238-252.","type":"article","doi":"10.1016/S0022-2836(65)80093-6","isbn":null,"url":null},{"ref":"Torchilin, V. P. (2005). Recent advances with liposomes as pharmaceutical carriers. Nature Reviews Drug Discovery, 4(2), 145-160.","type":"article","doi":"10.1038/nrd1632","isbn":null,"url":null}],"related":["solid-dispersion","caco-2-permeability","in-vitro-in-vivo-correlation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"liquidity-risk-models","name":"Liquidity Risk Models","fullName":"Liquidity Risk Measures (Amihud, Roll, LOT)","aliases":["Amihud illiquidity","Roll spread estimator","LOT spread measure","Lesmond-Ogden-Trzcinka measure","Likidite Risk Modelleri (Amihud, Roll, LOT)"],"domain":"finance","family":"regression-model","subfamily":null,"year":2002,"originator":"Amihud (2002); Roll (1984); Lesmond, Ogden & Trzcinka (LOT)","url":"https://scholargate.app/en/finance/liquidity-risk-models","markdownUrl":"https://scholargate.app/en/finance/liquidity-risk-models.md","definition":"Liquidity Risk Models are a family of measures that quantify how easily an asset trades by capturing its price impact, its effective bid-ask spread, and a holding-period adjustment. The family brings together the Amihud illiquidity ratio (Amihud, 2002), the Roll serial-covariance spread estimator (Roll, 1984), and the LOT (Lesmond-Ogden-Trzcinka) realised-spread measure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Amihud (2002); Roll (1984); Lesmond, Ogden & Trzcinka (LOT)","year":2002,"type":"Liquidity / illiquidity measurement models","estimator":"Volume-return ratio (Amihud), serial covariance (Roll), realised spread (LOT)","minSample":60,"outcome":"continuous"},"citations":[{"ref":"Amihud, Y. (2002). Illiquidity and Stock Returns: Cross-Section and Time-Series Effects. Journal of Financial Markets, 5(1), 31-56.","type":"article","doi":"10.1016/S1386-4181(01)00024-6","isbn":null,"url":null},{"ref":"Roll, R. (1984). A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market. Journal of Finance, 39(4), 1127-1139.","type":"article","doi":"10.1111/j.1540-6261.1984.tb03897.x","isbn":null,"url":null}],"related":["har-rv-model","jump-diffusion-model","pairs-trading","ols-regression","risk-parity-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lisa-analysis","name":"LISA","fullName":"Local Indicators of Spatial Association (Local Moran's I)","aliases":["local Moran's I","local spatial autocorrelation","LISA cluster analysis","LISA — Yerel Uzamsal Otokorelasyon (Local Moran's I)"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":1995,"originator":"Luc Anselin","url":"https://scholargate.app/en/spatial-analysis/lisa-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/lisa-analysis.md","definition":"LISA, introduced by Luc Anselin in 1995, is a local statistic that computes spatial autocorrelation separately for every observation rather than for the map as a whole. It pinpoints where high or low values cluster and where spatial outliers sit, decomposing the global Moran's I into a contribution from each location.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Anselin","year":1995,"type":"Local spatial autocorrelation statistic","estimator":"Local Moran's I per observation","outcome":"continuous (cross-sectional, georeferenced)","minSample":30},"citations":[{"ref":"Anselin, L. (1995). Local Indicators of Spatial Association — LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null}],"related":["morans-i-test","spatial-lag-model","spatial-error-model","spatial-durbin-model","kriging-interpolation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"literature-review-article","name":"Narrative Literature Review","fullName":"Narrative Literature Review (Interpretive Evidence Synthesis Without Systematic Search)","aliases":["narrative review","literature survey","interpretive review","state-of-the-art review"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Evidence synthesis—qualitative","year":"1900","originator":"Research community (traditional academic writing format)","url":"https://scholargate.app/en/academic-writing/literature-review-article","markdownUrl":"https://scholargate.app/en/academic-writing/literature-review-article.md","definition":"A narrative literature review is an interpretive synthesis of published research organized around themes, concepts, or historical progression rather than systematic search. Unlike systematic reviews, narrative reviews employ subjective study selection, do not require protocol registration, and prioritize depth of interpretation over exhaustive comprehensiveness. Narrative reviews are valuable for conceptual synthesis, exploring emerging fields with sparse literature, and providing historical context; they have been the traditional form of scholarly literature synthesis since the inception of academic journals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Research community (traditional academic writing format)","subfamily":"Evidence synthesis—qualitative","year":"1900","type":"Document Type"},"citations":[{"ref":"Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. Journal of Chiropractic Medicine, 5(3), 101–117.","type":"article","doi":"10.1016/S0899-3467(07)60142-6","isbn":null,"url":null},{"ref":"Cronin, P., Ryan, F., & Coughlan, M. (2008). Undertaking a literature review: a step-by-step approach. British Journal of Nursing, 17(1), 38–43.","type":"article","doi":"10.12968/bjon.2008.17.1.28059","isbn":null,"url":null},{"ref":"Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.","type":"article","doi":"10.1080/1364557032000119616","isbn":null,"url":null}],"related":["systematic-review-article","meta-analysis-article","original-research-article","literature-search-strategy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"littles-law","name":"Little's Law","fullName":"Little's Law (L = λW)","aliases":["L = λW Theorem","Little's Theorem","Little's Result","Little Yasası"],"domain":"operations-research","family":"regression-model","subfamily":"Queueing theory","year":1961,"originator":"John D. C. Little","url":"https://scholargate.app/en/operations-research/littles-law","markdownUrl":"https://scholargate.app/en/operations-research/littles-law.md","definition":"Little's Law is a fundamental theorem in queueing theory that relates the long-run average number of items in a stable system (L) to the long-run average arrival rate (λ) and the long-run average time an item spends in the system (W), expressed as L = λW. Introduced and rigorously proved by John D. C. Little in 1961, the law holds for virtually any stable stochastic system, requiring no assumptions about arrival distributions, service distributions, or queue disciplines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John D. C. Little","year":1961,"type":"Exact queueing identity","subfamily":"Queueing theory","assumption":"Steady-state system","scope":"Any stable stochastic system"},"citations":[{"ref":"Little, J. D. C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383–387.","type":"article","doi":"10.1287/opre.9.3.383","isbn":null,"url":null}],"related":["mm1-queue","mmc-queue","discrete-event-simulation"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"live-dead-assay","name":"Live/Dead Assay","fullName":"Live/Dead Cell Viability Fluorescence Assay","aliases":["calcein-AM/propidium iodide","SYTO/PI staining","fluorescent viability stain"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Fluorescence-based viability assay","year":"2000","originator":"Invitrogen/Molecular Probes","url":"https://scholargate.app/en/biomaterials/live-dead-assay","markdownUrl":"https://scholargate.app/en/biomaterials/live-dead-assay.md","definition":"The Live/Dead assay is a fluorescence-based method for simultaneously identifying live and dead cells using two complementary dyes. The assay combines calcein-AM (or SYTO fluorophores), which generates bright green fluorescence in living cells with intact esterase activity, with propidium iodide (PI), which produces red fluorescence in dead cells with compromised membrane integrity. Commercially developed by Molecular Probes and now part of Thermo Fisher's portfolio, the Live/Dead kit is widely used to evaluate cell viability on biomaterial scaffolds, in tissue constructs, and following drug or toxin exposure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Invitrogen/Molecular Probes","subfamily":"Fluorescence-based viability assay","year":"2000","type":"Dual-dye viability assay"},"citations":[{"ref":"Molecular Probes (2004). LIVE/DEAD Viability/Cytotoxicity Kit user guide. Invitrogen Corporation.","type":"article","doi":null,"isbn":null,"url":"https://www.thermofisher.com/antibodies/product/L3224"},{"ref":"Niles, A. L., Moravec, R. A., & Riss, T. L. (2009). In vitro viability and cytotoxicity testing and same-well multiplexing assays for adherent cells. Current Chemical Genomics, 3, 33-43.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=In+vitro+viability+and+cytotoxicity+testing+and+same-well+multiplexing+assays+for+adherent+cells+Niles"},{"ref":"Riss, T. L., Moravec, R. A., Niles, A. L., et al. (2011). Cell viability assays. In Assay Guidance Manual (3rd ed.). Eli Lilly & Company and the National Center for Advancing Translational Sciences.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/books/NBK144065/"}],"related":["mtt-mts-assay","hemolysis-assay","cam-assay","scratch-wound-assay"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"living-systematic-review","name":"Living Systematic Review","fullName":"Living Systematic Review (Continuously Updated Synthesis)","aliases":["LSR","Continually Updated Review","Dynamic Evidence Synthesis"],"domain":"evidence-synthesis","family":"process-pipeline","subfamily":"Continuous Evidence Synthesis","year":"2017","originator":"Elliott et al. (2017), Advanced by Cochrane Collaboration","url":"https://scholargate.app/en/evidence-synthesis/living-systematic-review","markdownUrl":"https://scholargate.app/en/evidence-synthesis/living-systematic-review.md","definition":"A living systematic review (LSR) is a dynamic, continuously updated evidence synthesis that monitors emerging literature and incorporates new studies as they become available, rather than being a static document published once. Formalized by Elliott et al. (2017) and adopted by the Cochrane Collaboration, living systematic reviews maintain currency in rapidly evolving fields by using prospective searches and regular review cycles (monthly, quarterly, or trigger-based). Rather than waiting 12-18 months for a complete systematic review only to find it outdated by new trials, living reviews enable real-time evidence synthesis—particularly valuable during pandemics, in rapidly advancing fields (oncology, immunology), and for volatile policy questions where new evidence frequently shifts recommendations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elliott et al. (2017), Advanced by Cochrane Collaboration","subfamily":"Continuous Evidence Synthesis","year":"2017","type":"Framework"},"citations":[{"ref":"Elliott, J. H., Synnot, A., Turner, T., Simmonds, M., Akl, E. A., McDonald, S., ... Higgins, P. T. (2017). Living systematic reviews: An emerging opportunity to narrow the evidence-practice gap. PLOS Medicine, 14(2), e1002254.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Living+systematic+reviews%3A+An+emerging+opportunity+to+narrow+the+evidence-practice+gap+Elliott"},{"ref":"Higgins, P. T., & Thomas, J. (Eds.). (2021). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.2). The Cochrane Collaboration.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cochrane+Handbook+for+Systematic+Reviews+of+Interventions+%28Version+6.2%29+Higgins"},{"ref":"Shea, B. J., Reeves, B. C., Wells, G., et al. (2017). AMSTAR 2: a critical appraisal tool for systematic reviews. BMJ, 358, j4008.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=AMSTAR+2%3A+a+critical+appraisal+tool+for+systematic+reviews+Shea"}],"related":["systematic-review","rapid-review-methodology","evidence-synthesis-framework","meta-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ljung-box-test","name":"Ljung-Box Test","fullName":"Ljung-Box Q Test for Autocorrelation","aliases":["Ljung-Box Q Test","Modified Box-Pierce Test","Portmanteau Test for Autocorrelation","Otokorelasyon Portmanteau Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Autocorrelation","year":1978,"originator":"Greta Ljung & George Box","url":"https://scholargate.app/en/econometrics/ljung-box-test","markdownUrl":"https://scholargate.app/en/econometrics/ljung-box-test.md","definition":"The Ljung-Box Q test is a diagnostic portmanteau test proposed by Ljung and Box (1978) to assess whether a group of autocorrelations in a time series residual sequence is jointly zero. It is widely used to evaluate the adequacy of fitted time series models — especially ARIMA models — by testing whether remaining residuals exhibit any systematic pattern. The test is applicable in econometrics, finance, and any field that relies on temporal data modeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greta Ljung & George Box","year":1978,"type":"Portmanteau goodness-of-fit test","subfamily":"Autocorrelation","distribution":"Chi-squared under H₀","null_hypothesis":"No autocorrelation up to lag h"},"citations":[{"ref":"Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303.","type":"article","doi":"10.1093/biomet/65.2.297","isbn":null,"url":null}],"related":["durbin-watson-test","breusch-godfrey-test","arima"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lmaw","name":"LMAW","fullName":"Logarithm Methodology of Additive Weights","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Pamučar, D., Žižović, M., Biswas, S., Božanić, D.","url":"https://scholargate.app/en/decision-making/lmaw","markdownUrl":"https://scholargate.app/en/decision-making/lmaw.md","definition":"LMAW (Logarithm Methodology of Additive Weights) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Žižović, M., Biswas, S., Božanić, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamučar, D., Žižović, M., Biswas, S., Božanić, D.","subfamily":"Ranking","year":"2021","type":"Logarithm-based additive weighting","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Pamučar, D., Žižović, M., Biswas, S., Božanić, D. (2021). A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics. Facta Universitatis, Series: Mechanical Engineering","type":"article","doi":"10.22190/FUME210214031P","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lmdi-decomposition","name":"LMDI Decomposition","fullName":"Log-Mean Divisia Index (LMDI) Decomposition","aliases":["Logarithmic Mean Divisia Index","LMDI-I Additive Decomposition","LMDI-II Multiplicative Decomposition","Logaritmik Ortalama Divisia İndeksi"],"domain":"sustainability","family":"regression-model","subfamily":"Decomposition analysis","year":2005,"originator":"B. W. Ang","url":"https://scholargate.app/en/sustainability/lmdi-decomposition","markdownUrl":"https://scholargate.app/en/sustainability/lmdi-decomposition.md","definition":"Log-Mean Divisia Index (LMDI) Decomposition is a quantitative technique for attributing changes in an aggregate indicator — most commonly energy consumption or CO₂ emissions — to its underlying driving factors, such as activity level, structural mix, and intensity. Introduced in its definitive practical form by B. W. Ang in 2005, LMDI builds on Divisia index theory and uses the logarithmic mean as a weighting function to achieve a mathematically perfect, residual-free decomposition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"B. W. Ang","year":2005,"type":"Index-based factor decomposition","subfamily":"Decomposition analysis","residual":"Zero (perfect decomposition)","variants":"Additive (LMDI-I) and multiplicative (LMDI-II)"},"citations":[{"ref":"Ang, B. W. (2005). The LMDI approach to decomposition analysis: a practical guide. Energy Policy, 33(7), 867–871.","type":"article","doi":"10.1016/j.enpol.2003.10.010","isbn":null,"url":null}],"related":["life-cycle-assessment","material-flow-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"load-forecasting","name":"Load Forecasting","fullName":"Electrical Load Forecasting and Demand Prediction","aliases":["demand forecasting","electricity consumption prediction","load demand estimation"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Power system operation and planning","year":"1960s","originator":"Electrical utilities","url":"https://scholargate.app/en/electrical-engineering/load-forecasting","markdownUrl":"https://scholargate.app/en/electrical-engineering/load-forecasting.md","definition":"Load forecasting predicts future electrical demand on power systems across various time horizons: minutes to hours (short-term), days to weeks (medium-term), and months to years (long-term). Accurate forecasting is essential for economic dispatch, unit commitment, and system reliability. Methods range from classical statistical regression to modern machine learning approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Electrical utilities","subfamily":"Power system operation and planning","year":"1960s","type":"Computational pipeline"},"citations":[{"ref":"Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems, 16(1), 44-55.","type":"article","doi":"10.1109/59.910780","isbn":null,"url":null},{"ref":"Charlton, J. D., Kalamara, E., & James, R. D. (2008). Quantifying electricity load profiles and demand patterns. Energy Policy, 36(1), 181-193.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/j.enpol.2007.09.010"},{"ref":"Bunn, D. W. (2005). Forecasting with Multiple Models: A Case Study of Electric Load Forecasting. Futures, 37(8), 896-906.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Forecasting+with+Multiple+Models%3A+A+Case+Study+of+Electric+Load+Forecasting+Bunn"}],"related":["power-flow-analysis","energy-storage-dispatch","smart-grid-state-estimation","harmonic-distortion-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"load-pull","name":"Load-Pull","fullName":"Load-Pull Measurement and Optimization of RF Power Amplifiers","aliases":["Load-pull measurement","Source-pull optimization"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"RF/microwave measurement and optimization","year":"1990","originator":"Andrew Davidson","url":"https://scholargate.app/en/electrical-engineering/load-pull","markdownUrl":"https://scholargate.app/en/electrical-engineering/load-pull.md","definition":"Load-Pull is an experimental technique for characterizing and optimizing RF power amplifier performance under varying load and source impedance conditions. Introduced by Davidson et al. in 1990, load-pull measurements vary the load impedance seen by the amplifier while recording output power, efficiency, and linearity. Load-pull reveals contours of constant gain, efficiency, and stability, enabling optimal impedance matching for maximum performance. Essential for power amplifier design and characterization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Davidson","subfamily":"RF/microwave measurement and optimization","year":"1990","type":"Experimental characterization of power amplifier performance under varying load/source conditions"},"citations":[{"ref":"Cripps, S. C. (1999). RF Power Amplifiers for Wireless Communications. Artech House.","type":"book","doi":null,"isbn":null,"url":"https://artech.marcomms.com/books/RF-Power-Amplifiers-for-Wireless-Communications/book/64"},{"ref":"Davidson, A., Strahler, L., Kim, S., Tajalli, A., & Komiak, J. (1990). Broad-band load-pull characterization of power devices for microwave and millimeter-wave applications. IEEE Transactions on Microwave Theory and Techniques, 38(12), 1779-1786.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Broad-band+load-pull+characterization+of+power+devices+for+microwave+and+millimeter-wave+applications+Davidson"},{"ref":"Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (2005). Discrete-time signal processing. Prentice Hall.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com/program/Oppenheim-Discrete-Time-Signal-Processing-3rd-Edition/PGM119224.html"}],"related":["s-parameter-analysis","smith-chart","method-of-moments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-average-treatment-effect","name":"Local Average Treatment Effect","fullName":"Local Average Treatment Effect (LATE / Complier Average Causal Effect)","aliases":["LATE","CACE","complier average causal effect","Yerel Ortalama Tedavi Etkisi (LATE / CACE)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":1994,"originator":"Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)","url":"https://scholargate.app/en/causal-inference/local-average-treatment-effect","markdownUrl":"https://scholargate.app/en/causal-inference/local-average-treatment-effect.md","definition":"The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)","year":1994,"type":"Instrumental-variable causal estimand","estimator":"Two-stage least squares (2SLS / Wald ratio)","estimand":"Treatment effect for compliers only","outcome":"continuous or binary","minSample":100},"citations":[{"ref":"Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.","type":"article","doi":"10.2307/2951620","isbn":null,"url":null},{"ref":"Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91(434), 444-455.","type":"article","doi":"10.1080/01621459.1996.10476902","isbn":null,"url":null}],"related":["iv-2sls","propensity-score-matching","heterogeneous-treatment-effects","frontdoor-adjustment","regression-discontinuity"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-gearys-c","name":"Local Geary's C","fullName":"Local Geary's C Contiguity Ratio","aliases":["Local Geary","local spatial contiguity ratio","LISA Geary","local c statistic"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1995","originator":"Luc Anselin","url":"https://scholargate.app/en/spatial-analysis/local-gearys-c","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-gearys-c.md","definition":"Local Geary's C is a local indicator of spatial association (LISA) that measures, for each location, how dissimilar its value is from its immediate neighbours. Unlike Local Moran's I, which detects clustering of similar values, Local Geary's C focuses on squared value differences and is especially sensitive to local spatial outliers and local heterogeneity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Anselin","year":"1995","type":"Local spatial statistic","dataType":"Georeferenced areal or point data with a defined spatial weights matrix","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null},{"ref":"Anselin, L. (2019). A local indicator of multivariate spatial association: extending Geary's C. Geographical Analysis, 51(2), 133–150.","type":"article","doi":"10.1111/gean.12164","isbn":null,"url":null}],"related":["local-morans-i","gearys-c","local-indicators-of-spatial-association","local-getis-ord-gi-star","spatial-autocorrelation","morans-i"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-geographically-weighted-regression","name":"Local Geographically Weighted Regression","fullName":"Local Geographically Weighted Regression","aliases":["GWR","geographically weighted regression","local spatial regression","spatially varying coefficient model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1996","originator":"Brunsdon, Fotheringham & Charlton","url":"https://scholargate.app/en/spatial-analysis/local-geographically-weighted-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-geographically-weighted-regression.md","definition":"Local Geographically Weighted Regression (GWR) estimates a separate regression model at each location in the study area, allowing every coefficient to vary spatially. By weighting nearby observations more heavily than distant ones, GWR reveals how predictor-outcome relationships shift across geographic space rather than forcing a single global estimate on heterogeneous data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brunsdon, Fotheringham & Charlton","year":"1996","type":"Spatially varying coefficient regression","dataType":"Georeferenced (point or areal) data with continuous outcome","subfamily":"GIS / spatial"},"citations":[{"ref":"Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.","type":"book","doi":null,"isbn":"978-0471496168","url":null},{"ref":"Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281-298.","type":"article","doi":"10.1111/j.1538-4632.1996.tb00936.x","isbn":null,"url":null}],"related":["multiscale-geographically-weighted-regression","geographically-weighted-regression","spatial-lag-model","spatial-error-model","kernel-density-estimation","local-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-getis-ord-gi-star","name":"Local Getis-Ord Gi*","fullName":"Local Getis-Ord G-Star Statistic","aliases":["Gi* statistic","Getis-Ord Gi*","local G-star","hot spot statistic"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1992–1995","originator":"Arthur Getis and J. Keith Ord","url":"https://scholargate.app/en/spatial-analysis/local-getis-ord-gi-star","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-getis-ord-gi-star.md","definition":"The Local Getis-Ord Gi* statistic identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots) within a study area. Unlike global measures, it produces a z-score for every location, revealing where concentrated clustering occurs and with what statistical confidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arthur Getis and J. Keith Ord","year":"1992–1995","type":"Local spatial association statistic","dataType":"Georeferenced continuous or count data on a spatial lattice or point pattern","subfamily":"GIS / spatial"},"citations":[{"ref":"Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.","type":"article","doi":"10.1111/j.1538-4632.1992.tb00261.x","isbn":null,"url":null},{"ref":"Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286–306.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00912.x","isbn":null,"url":null}],"related":["local-morans-i","local-indicators-of-spatial-association","hot-spot-analysis","spatial-autocorrelation","kernel-density-estimation","gearys-c"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-hot-spot-analysis","name":"Local Hot Spot Analysis","fullName":"Local Hot Spot Analysis (Getis-Ord Gi*)","aliases":["local Getis-Ord Gi*","Gi* statistic","spatial hot spot detection","local spatial clustering"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1992-1995","originator":"Getis & Ord; Ord & Getis","url":"https://scholargate.app/en/spatial-analysis/local-hot-spot-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-hot-spot-analysis.md","definition":"Local Hot Spot Analysis uses the Getis-Ord Gi* statistic to identify specific geographic locations where high or low values cluster together more than expected by chance. Unlike global measures that return a single summary for the whole study area, this local statistic produces a z-score for each feature, pinpointing exactly where statistically significant hot spots and cold spots occur.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Getis & Ord; Ord & Getis","year":"1992-1995","type":"Local spatial statistic","dataType":"Georeferenced point or areal data with continuous attribute values","subfamily":"GIS / spatial"},"citations":[{"ref":"Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00912.x","isbn":null,"url":null},{"ref":"Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.","type":"article","doi":"10.1111/j.1538-4632.1992.tb00261.x","isbn":null,"url":null}],"related":["local-morans-i","local-spatial-autocorrelation","local-getis-ord-gi-star","kernel-density-estimation","hot-spot-analysis","local-indicators-of-spatial-association"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-indicators-of-spatial-association","name":"Local Indicators of Spatial Association","fullName":"Local Indicators of Spatial Association (LISA)","aliases":["LISA","local spatial autocorrelation statistics","local Moran's I","Anselin LISA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1995","originator":"Luc Anselin","url":"https://scholargate.app/en/spatial-analysis/local-indicators-of-spatial-association","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-indicators-of-spatial-association.md","definition":"LISA, introduced by Luc Anselin in 1995, decomposes a global spatial autocorrelation index into a location-specific statistic for every observation. It identifies where statistically significant spatial clusters and outliers occur on a map, enabling researchers to move beyond a single global summary and pinpoint the geographic sources of spatial dependence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Anselin","year":"1995","type":"Local spatial statistic","dataType":"Georeferenced areal / point data with a continuous or count attribute","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1995). Local Indicators of Spatial Association — LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null},{"ref":"Anselin, L. (2010). Local Spatial Autocorrelation. In A. S. Fotheringham & P. A. Rogerson (Eds.), The SAGE Handbook of Spatial Analysis (pp. 255–275). SAGE Publications.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Local+Spatial+Autocorrelation+Anselin+2010+SAGE+Handbook"}],"related":["morans-i","gearys-c","hot-spot-analysis","spatial-autocorrelation","local-getis-ord-gi-star","geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-kernel-density-estimation","name":"Local Kernel Density Estimation","fullName":"Local Kernel Density Estimation","aliases":["Local KDE","adaptive KDE","spatially adaptive kernel density estimation","local density estimation"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1985-1986","originator":"Silverman, B. W.; Diggle, P. J.","url":"https://scholargate.app/en/spatial-analysis/local-kernel-density-estimation","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-kernel-density-estimation.md","definition":"Local Kernel Density Estimation (Local KDE) is a non-parametric spatial method that estimates the density of point events at each location by applying a kernel function with a spatially adaptive bandwidth. Unlike global KDE, which uses a fixed bandwidth across the entire study area, Local KDE adjusts the smoothing window according to local data density, capturing fine-scale clustering where events are sparse or concentrated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Silverman, B. W.; Diggle, P. J.","year":"1985-1986","type":"Non-parametric density estimator","dataType":"Point event data, georeferenced observations","subfamily":"GIS / spatial"},"citations":[{"ref":"Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.","type":"book","doi":null,"isbn":"978-0412246203","url":null},{"ref":"Diggle, P. J. (1985). A kernel method for smoothing point process data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 34(2), 138-147.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+kernel+method+for+smoothing+point+process+data+Diggle+1985"}],"related":["kernel-density-estimation","hot-spot-analysis","local-spatial-autocorrelation","local-morans-i","spatial-autocorrelation","network-based-spatial-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-kriging","name":"Local Kriging","fullName":"Local (Moving-Window) Kriging","aliases":["moving-window kriging","local kriging interpolation","windowed kriging","neighborhood kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1990","originator":"Haas, T. C.","url":"https://scholargate.app/en/spatial-analysis/local-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-kriging.md","definition":"Local Kriging is a spatially adaptive geostatistical interpolation method that restricts each prediction to a moving neighborhood of nearby observations, fitting a variogram model locally within that window. This allows spatial covariance structure to vary across the study region rather than imposing a single global variogram, making it better suited to large or non-stationary spatial fields.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Haas, T. C.","year":"1990","type":"Spatial interpolation (local variant)","dataType":"Georeferenced continuous measurements (point data)","subfamily":"GIS / spatial"},"citations":[{"ref":"Haas, T. C. (1990). Kriging and automated variogram modeling within a moving window. Atmospheric Environment, 24(7), 1759-1769.","type":"article","doi":"10.1016/0960-1686(90)90508-K","isbn":null,"url":null},{"ref":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.","type":"book","doi":null,"isbn":"9780195115383","url":null}],"related":["ordinary-kriging","kriging","geographically-weighted-regression","kernel-density-estimation","co-kriging","multiscale-kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-morans-i","name":"Local Moran's I","fullName":"Local Moran's I Statistic (LISA)","aliases":["Local Indicator of Spatial Association","LISA statistic","Anselin Local Moran","local spatial autocorrelation index"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1995","originator":"Luc Anselin","url":"https://scholargate.app/en/spatial-analysis/local-morans-i","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-morans-i.md","definition":"Local Moran's I, introduced by Luc Anselin in 1995, is a Local Indicator of Spatial Association (LISA) that decomposes global spatial autocorrelation into location-specific contributions. For every observation it produces a signed statistic and a significance value, enabling researchers to identify spatial clusters (high-high, low-low) and spatial outliers (high-low, low-high) on a map.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Anselin","year":"1995","type":"Local spatial autocorrelation statistic","dataType":"Georeferenced areal or point data with a spatial weights matrix","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null},{"ref":"Anselin, L. (2010). Local spatial autocorrelation. In A. S. Fotheringham & P. A. Rogerson (Eds.), The SAGE Handbook of Spatial Analysis (pp. 255–278). SAGE Publications.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Local+spatial+autocorrelation+Anselin+2010+SAGE+Handbook"}],"related":["morans-i","gearys-c","local-getis-ord-gi-star","local-indicators-of-spatial-association","spatial-autocorrelation","hot-spot-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-network-based-spatial-analysis","name":"Local Network-Based Spatial Analysis","fullName":"Local Network-Based Spatial Analysis","aliases":["local network analysis","local spatial network analysis","neighborhood network analysis","local graph-based spatial analysis"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1990s–2000s","originator":"Okabe, Sugihara, and spatial network analysis community","url":"https://scholargate.app/en/spatial-analysis/local-network-based-spatial-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-network-based-spatial-analysis.md","definition":"Local Network-Based Spatial Analysis computes spatial statistics and network measures — such as accessibility, centrality, and density — within restricted local neighborhoods of a spatial network, revealing how connectivity and flow vary across fine geographic scales rather than globally across the entire network.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Okabe, Sugihara, and spatial network analysis community","year":"1990s–2000s","type":"Spatial network analysis","dataType":"Network geometry, node/edge attributes, spatial coordinates","subfamily":"GIS / spatial"},"citations":[{"ref":"Okabe, A., & Sugihara, K. (2012). Spatial Analysis Along Networks: Statistical and Computational Methods. Wiley.","type":"book","doi":null,"isbn":"978-0470770818","url":null},{"ref":"Porta, S., Crucitti, P., & Latora, V. (2006). The network analysis of urban streets: A primal approach. Environment and Planning B: Planning and Design, 33(5), 705–725.","type":"article","doi":"10.1068/b32045","isbn":null,"url":null}],"related":["network-based-spatial-analysis","kernel-density-estimation","hot-spot-analysis","local-spatial-autocorrelation","geographically-weighted-regression","local-getis-ord-gi-star"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-ordinary-kriging","name":"Local Ordinary Kriging","fullName":"Local Ordinary Kriging (Moving Window Kriging)","aliases":["moving window kriging","local kriging","neighborhood kriging","LOK"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1970s–1990s","originator":"Journel & Huijbregts; developed further by Goovaerts and Chiles & Delfiner","url":"https://scholargate.app/en/spatial-analysis/local-ordinary-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-ordinary-kriging.md","definition":"Local Ordinary Kriging (LOK) is a geostatistical interpolation method that estimates values at unsampled locations using only a spatially defined moving neighborhood of nearby observations. By restricting each prediction to a local data window rather than the full dataset, LOK accommodates spatial non-stationarity, reduces computational cost, and often yields more accurate local predictions than global ordinary kriging.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Journel & Huijbregts; developed further by Goovaerts and Chiles & Delfiner","year":"1970s–1990s","type":"Geostatistical interpolation (local/moving-window variant)","dataType":"Continuous georeferenced point data","subfamily":"GIS / spatial"},"citations":[{"ref":"Chiles, J.-P., & Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. Wiley.","type":"book","doi":null,"isbn":"978-0471083153","url":null},{"ref":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195115383","url":null}],"related":["ordinary-kriging","universal-kriging","co-kriging","geographically-weighted-regression","kernel-density-estimation","multiscale-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-outlier-factor","name":"Local Outlier Factor","fullName":"Local Outlier Factor (LOF): Density-Based Anomaly Detection","aliases":["LOF","local outlier factor","density-based outlier detection","local density deviation"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2000,"originator":"Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.","url":"https://scholargate.app/en/machine-learning/local-outlier-factor","markdownUrl":"https://scholargate.app/en/machine-learning/local-outlier-factor.md","definition":"Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.","year":2000,"type":"Density-based anomaly detection (unsupervised)","task":"Outlier / anomaly detection","minSample":20},"citations":[{"ref":"Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104.","type":"article","doi":"10.1145/335191.335388","isbn":null,"url":null},{"ref":"Aggarwal, C. C. (2017). Outlier Analysis (2nd ed., Ch. 4). Springer.","type":"book","doi":null,"isbn":"978-3-319-47577-6","url":null},{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 14). Springer.","type":"book","doi":null,"isbn":"978-0-387-84857-0","url":null}],"related":["isolation-forest","one-class-svm","dbscan","k-nearest-neighbors","autoencoder"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-owa","name":"LOCAL-OWA","fullName":"neighbourhood-adaptive Ordered Weighted Averaging","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Malczewski, J.; Liu, X.","url":"https://scholargate.app/en/decision-making/local-owa","markdownUrl":"https://scholargate.app/en/decision-making/local-owa.md","definition":"LOCAL-OWA (neighbourhood-adaptive Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Malczewski, J.; Liu, X. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Malczewski, J.; Liu, X.","subfamily":"Ranking","year":"2014","type":"Range-sensitive neighbourhood-local OWA — criterion weights w^q_k scale with local criterion variance within each spatial neighbourhood; order weights λ_k remain global, encoding a single risk attitude applied everywhere","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Malczewski, J., Liu, X. (2014). Local ordered weighted averaging in GIS-based multicriteria analysis. Annals of GIS","type":"article","doi":"10.1080/19475683.2014.904439","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","swara","fucom","merec"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-projections","name":"Local Projections","fullName":"Local Projections Impulse Response Analysis","aliases":["LP-IR","Multi-horizon regression"],"domain":"econometrics","family":"regression-model","subfamily":"Impulse response","year":"2005","originator":"Oscar Jorda","url":"https://scholargate.app/en/econometrics/local-projections","markdownUrl":"https://scholargate.app/en/econometrics/local-projections.md","definition":"Local Projections (LP) is a semi-parametric method for estimating impulse responses directly via multi-horizon regressions, bypassing VAR-model specification. Introduced by Jorda (2005), it projects outcomes h periods ahead onto current shocks and lags, producing impulse-response functions without assuming a particular lag structure or VAR order. This flexibility has made it the dominant approach in applied macroeconomics for measuring policy effects and shock transmission.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oscar Jorda","subfamily":"Impulse response","year":"2005","type":"Multi-horizon regression"},"citations":[{"ref":"Jorda, O. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161-182.","type":"article","doi":"10.1257/0002828053828518","isbn":null,"url":null},{"ref":"Ramey, V. A., & Zubairy, S. (2018). Government spending multipliers in good times and in bad times. Journal of Political Economy, 126(2), 850-901.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Government+spending+multipliers+in+good+times+and+in+bad+times+Ramey"}],"related":["global-var","tvp-favar","threshold-panel-var"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-spatial-autocorrelation","name":"Local Spatial Autocorrelation","fullName":"Local Spatial Autocorrelation Analysis","aliases":["local spatial association","local SA","LISA methods","local spatial clustering"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1995","originator":"Luc Anselin","url":"https://scholargate.app/en/spatial-analysis/local-spatial-autocorrelation","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-spatial-autocorrelation.md","definition":"Local Spatial Autocorrelation methods decompose global spatial clustering into location-specific statistics, revealing where in a study area significant clustering or dispersion occurs. Each observation receives its own association score and significance value, enabling the detection of spatial hot spots, cold spots, and spatial outliers rather than reporting a single summary statistic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Anselin","year":"1995","type":"Spatial association analysis","dataType":"Georeferenced areal or point data with a spatial weights matrix","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null},{"ref":"Indicators of spatial association. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Indicators_of_spatial_association"}],"related":["local-morans-i","local-gearys-c","local-getis-ord-gi-star","local-indicators-of-spatial-association","spatial-autocorrelation","hot-spot-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-spatial-durbin-model","name":"Local Spatial Durbin Model","fullName":"Local Spatial Durbin Model","aliases":["local SDM","geographically weighted Spatial Durbin Model","GW-SDM","spatially varying Durbin model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2002–2009","originator":"LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework","url":"https://scholargate.app/en/spatial-analysis/local-spatial-durbin-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-spatial-durbin-model.md","definition":"The Local Spatial Durbin Model (Local SDM) extends the global Spatial Durbin Model by allowing regression coefficients to vary across geographic space. It combines the SDM's ability to capture both spatial lag of the dependent variable and spatial lags of covariates with a geographically weighted estimation framework, producing location-specific direct and indirect spillover effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeSage & Pace (SDM foundation); local adaptation via Fotheringham et al. GWR framework","year":"2002–2009","type":"Spatially varying regression model","dataType":"Georeferenced cross-sectional or panel data with spatial weights","subfamily":"GIS / spatial"},"citations":[{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.","type":"book","doi":null,"isbn":"978-0471496168","url":null}],"related":["spatial-durbin-model","geographically-weighted-regression","local-spatial-lag-model","local-spatial-error-model","multiscale-geographically-weighted-regression","local-spatial-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-spatial-lag-model","name":"Local Spatial Lag Model","fullName":"Local Spatial Lag Model","aliases":["local SLM","geographically weighted spatial lag model","GW-SLM","spatially varying lag model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1988 (global); 2000s (local extensions)","originator":"Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002)","url":"https://scholargate.app/en/spatial-analysis/local-spatial-lag-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-spatial-lag-model.md","definition":"The Local Spatial Lag Model extends the classical spatial lag model by allowing both the spatial autocorrelation parameter and the regression coefficients to vary across geographic locations. Instead of one global estimate of how neighboring outcomes influence each observation, the model fits location-specific parameters using kernel-weighted local estimation, revealing spatial heterogeneity in spatial dependence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002)","year":"1988 (global); 2000s (local extensions)","type":"Spatially varying regression model","dataType":"Georeferenced cross-sectional or panel data with a continuous outcome","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-9024737215","url":null},{"ref":"Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.","type":"book","doi":null,"isbn":"978-0471496168","url":null}],"related":["spatial-lag-model","geographically-weighted-regression","local-spatial-error-model","local-spatial-durbin-model","multiscale-geographically-weighted-regression","spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-spatial-regression","name":"Local Spatial Regression","fullName":"Local Spatial Regression","aliases":["locally weighted spatial regression","spatially varying coefficient model","local spatial model","place-based regression"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1996","originator":"Brunsdon, Fotheringham & Charlton","url":"https://scholargate.app/en/spatial-analysis/local-spatial-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-spatial-regression.md","definition":"Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brunsdon, Fotheringham & Charlton","year":"1996","type":"Spatially varying coefficient regression","dataType":"Georeferenced cross-sectional or panel data","subfamily":"GIS / spatial"},"citations":[{"ref":"Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.","type":"book","doi":null,"isbn":"978-0471496168","url":null},{"ref":"Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281-298.","type":"article","doi":"10.1111/j.1538-4632.1996.tb00936.x","isbn":null,"url":null}],"related":["geographically-weighted-regression","multiscale-geographically-weighted-regression","spatial-lag-model","spatial-error-model","spatial-durbin-model","local-spatial-lag-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-universal-kriging","name":"Local Universal Kriging","fullName":"Local Universal Kriging (Kriging with External Drift, Local Neighborhood)","aliases":["local UK","local kriging with trend","local KED","local kriging with external drift"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1969/1997","originator":"Matheron, G. (trend/drift kriging); local neighborhood approach standard in geostatistical practice","url":"https://scholargate.app/en/spatial-analysis/local-universal-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/local-universal-kriging.md","definition":"Local Universal Kriging is a geostatistical interpolation method that combines a spatially varying deterministic trend with a stochastic residual, estimated using only nearby observations within a defined search neighborhood. It generalizes local ordinary kriging by explicitly modeling and removing a polynomial or covariate-driven drift before interpolating the residual surface.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matheron, G. (trend/drift kriging); local neighborhood approach standard in geostatistical practice","year":"1969/1997","type":"Spatial interpolation model","dataType":"Continuous spatial data with a deterministic trend component","subfamily":"GIS / spatial"},"citations":[{"ref":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.","type":"book","doi":null,"isbn":"9780195115383","url":null},{"ref":"Chiles, J.-P., & Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty. Wiley.","type":"book","doi":null,"isbn":"9780471083153","url":null}],"related":["ordinary-kriging","universal-kriging","local-ordinary-kriging","co-kriging","local-co-kriging","geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-volatility","name":"Local Volatility (Dupire)","fullName":"Dupire's Local Volatility Model","aliases":["Deterministic Volatility Function","DVF"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Deterministic Volatility","year":"1994","originator":"Bruno Dupire","url":"https://scholargate.app/en/quantitative-finance/local-volatility","markdownUrl":"https://scholargate.app/en/quantitative-finance/local-volatility.md","definition":"Dupire's local volatility model (1994) is a deterministic framework that extracts a term and strike-dependent volatility function from market option prices. Unlike constant volatility, local volatility perfectly fits the observed implied volatility smile and is implemented via finite difference methods for European and American option pricing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruno Dupire","subfamily":"Deterministic Volatility","year":"1994","type":"Equity/FX Model"},"citations":[{"ref":"Dupire, B. (1994). Pricing with a smile. Risk Magazine, 7(1), 18-20.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Volatility_smile"},{"ref":"Gatheral, J. (2006). The Volatility Surface: A Practitioner's Guide. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Volatility+Surface%3A+A+Practitioner%27s+Guide+Gatheral"}],"related":["sabr-model","bates-model","risk-neutral-valuation","crank-nicolson-pricing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"local-wlc","name":"LOCAL-WLC","fullName":"Local WLC — neighbourhood-adaptive weighted linear combination","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2011","originator":"Malczewski, J.","url":"https://scholargate.app/en/decision-making/local-wlc","markdownUrl":"https://scholargate.app/en/decision-making/local-wlc.md","definition":"LOCAL-WLC (Local WLC — neighbourhood-adaptive weighted linear combination) is a ranking multi-criteria decision-making (MCDM) method introduced by Malczewski, J. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Malczewski, J.","subfamily":"Ranking","year":"2011","type":"Range-sensitive neighbourhood-local additive utility — criterion weights scale with local criterion variance within each spatial neighbourhood","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":true},"citations":[{"ref":"Malczewski, J. (2011). Local weighted linear combination. Transactions in GIS","type":"article","doi":"10.1111/j.1467-9671.2011.01275.x","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","swara","fucom","merec"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"locally-linear-embedding","name":"Locally Linear Embedding","fullName":"Locally Linear Embedding (LLE)","aliases":["LLE","manifold learning","nonlinear dimensionality reduction","yerel doğrusal gömme"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2000,"originator":"Sam Roweis & Lawrence Saul","url":"https://scholargate.app/en/machine-learning/locally-linear-embedding","markdownUrl":"https://scholargate.app/en/machine-learning/locally-linear-embedding.md","definition":"Locally linear embedding, introduced by Sam Roweis and Lawrence Saul in 2000, is a manifold-learning method for nonlinear dimensionality reduction. It assumes that although data may curve through a high-dimensional space, each point and its neighbours lie approximately on a flat patch. LLE captures each point as a weighted combination of its neighbours and then finds a low-dimensional layout that preserves those same local relationships, unrolling curved structure into a faithful low-dimensional map.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sam Roweis & Lawrence Saul","year":2000,"type":"Nonlinear manifold dimensionality reduction","assumption":"Data lie on a locally linear low-dimensional manifold","preserves":"Local neighborhood reconstruction weights","output":"Low-dimensional embedding"},"citations":[{"ref":"Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.","type":"article","doi":"10.1126/science.290.5500.2323","isbn":null,"url":null}],"related":["isomap","t-sne","umap","kernel-pca"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"location-allocation","name":"Location-Allocation","fullName":"Location-Allocation Models","aliases":["facility location","p-median problem","maximal covering location problem","yer-tahsis modelleri"],"domain":"spatial-analysis","family":"process-pipeline","subfamily":"Network GIS","year":1963,"originator":"Leon Cooper; S. L. Hakimi","url":"https://scholargate.app/en/spatial-analysis/location-allocation","markdownUrl":"https://scholargate.app/en/spatial-analysis/location-allocation.md","definition":"Location-allocation models decide where to place a set of facilities and simultaneously assign demand points to them so as to optimize an objective such as total travel cost, worst-case distance, or population covered. Rooted in the operations-research work of Cooper (1963) and Hakimi (1964) and central to network GIS, they answer questions like where to site warehouses, hospitals, fire stations, or schools to best serve a spatially distributed population.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leon Cooper; S. L. Hakimi","year":1963,"type":"Spatial facility-location optimization","subfamily":"Network GIS","objective":"Site facilities + assign demand optimally","variants":"p-median, p-center, maximal covering"},"citations":[{"ref":"Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.","type":"article","doi":"10.1287/opre.11.3.331","isbn":null,"url":null},{"ref":"Hakimi, S. L. (1964). Optimum locations of switching centers and the absolute centers and medians of a graph. Operations Research, 12(3), 450–459.","type":"article","doi":"10.1287/opre.12.3.450","isbn":null,"url":null}],"related":["least-cost-path","linear-programming","integer-programming","gis-mcda"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lockdown-wellbeing-scale","name":"Lockdown Wellbeing Scale","fullName":"Lockdown Wellbeing Scale (LWS)","aliases":["LWS","Restrictions Wellbeing Scale"],"domain":"public-health","family":"process-pipeline","subfamily":"pandemic-restriction-wellbeing","year":"2021","originator":"Giuntella et al.","url":"https://scholargate.app/en/public-health/lockdown-wellbeing-scale","markdownUrl":"https://scholargate.app/en/public-health/lockdown-wellbeing-scale.md","definition":"The Lockdown Wellbeing Scale (LWS) measures the specific psychological and social impacts of mobility restrictions and lockdown policies on individual well-being. Developed by Giuntella and colleagues from economic and social data on pandemic restrictions, it captures dimensions of isolation, social disconnection, routinization disruption, and perceived loss of autonomy. The LWS is distinct from anxiety/depression measures, focusing instead on how restrictive policies themselves affect quality of life and social well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Giuntella et al.","subfamily":"pandemic-restriction-wellbeing","year":"2021","type":"Self-report"},"citations":[{"ref":"Giuntella, O., Hyde, K., Saccardo, S., & Solomon, S. (2021). Lifestyle and mental health disruptions during COVID-19. Proceedings of the National Academy of Sciences, 118(9), e2016632118.","type":"article","doi":"10.1073/pnas.2016632118","isbn":null,"url":null},{"ref":"Twenge, J. M., & Joiner, T. E. (2020). Mental illness has increased in American adolescents: Meta-analysis of sample correlation data from 1987 to 2018. Journal of Child Psychology and Psychiatry, 61(5), 576–588.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mental+illness+has+increased+in+American+adolescents%3A+Meta-analysis+of+sample+correlation+data+from+1987+to+2018+Twenge"}],"related":["covid-19-mental-health-scale","pandemic-fatigue-scale","lockdown-wellbeing-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lodeci","name":"LODECI","fullName":"LOgarithmic DEcomposition of Criteria Importance","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2024","originator":"Pala, O.","url":"https://scholargate.app/en/decision-making/lodeci","markdownUrl":"https://scholargate.app/en/decision-making/lodeci.md","definition":"LODECI (LOgarithmic DEcomposition of Criteria Importance) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pala, O. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pala, O.","subfamily":"Weight_Objective","year":"2024","type":"Objective weighting via logarithmic decomposition","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Pala, O. (2024). Assessment of the social progress on European Union by logarithmic decomposition of criteria importance. Expert Systems With Applications","type":"article","doi":"10.1016/j.eswa.2023.121846","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"loess","name":"LOESS","fullName":"Local Regression (LOESS / LOWESS)","aliases":["LOWESS","local regression","locally weighted scatterplot smoothing","yerel regresyon"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1979,"originator":"William S. Cleveland","url":"https://scholargate.app/en/machine-learning/loess","markdownUrl":"https://scholargate.app/en/machine-learning/loess.md","definition":"LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William S. Cleveland","year":1979,"type":"Local nonparametric regression smoother","tuning":"Span (bandwidth) and local polynomial degree","captures":"Smooth nonlinear relationships, locally","robust":"Optional robustifying iterations"},"citations":[{"ref":"Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.","type":"article","doi":"10.1080/01621459.1979.10481038","isbn":null,"url":null},{"ref":"Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 596–610.","type":"article","doi":"10.1080/01621459.1988.10478639","isbn":null,"url":null}],"related":["regression-splines","generalized-additive-model","kernel-density-estimation","polynomial-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"log-loss","name":"Log-Loss (Cross-Entropy Loss)","fullName":"Logarithmic Loss (Log Loss)","aliases":["Cross-Entropy Loss","Logloss"],"domain":"model-evaluation","family":"mcdm","subfamily":"Probabilistic Loss Metric","year":"1990s","originator":"Information theory and machine learning literature","url":"https://scholargate.app/en/model-evaluation/log-loss","markdownUrl":"https://scholargate.app/en/model-evaluation/log-loss.md","definition":"Log-loss measures the difference between predicted probabilities and actual labels, penalizing confident wrong predictions more than uncertain ones. It is a standard loss function in machine learning optimization and evaluates probabilistic classifier calibration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Information theory and machine learning literature","subfamily":"Probabilistic Loss Metric","year":"1990s","type":"Loss function"},"citations":[{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.","type":"book","doi":null,"isbn":null,"url":"https://www.deeplearningbook.org"},{"ref":"Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.","type":"book","doi":"10.1093/oso/9780198538493.001.0001","isbn":null,"url":null}],"related":["brier-score","accuracy","f1-score","calibration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"log-mean-temperature-difference","name":"Log Mean Temperature Difference","fullName":"Log Mean Temperature Difference Method for Heat Exchangers","aliases":["LMTD","logarithmic mean temperature difference"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Heat Exchanger Design","year":"1950","originator":"Donald Kern","url":"https://scholargate.app/en/thermodynamics/log-mean-temperature-difference","markdownUrl":"https://scholargate.app/en/thermodynamics/log-mean-temperature-difference.md","definition":"The Log Mean Temperature Difference (LMTD) method is a fundamental tool for calculating heat transfer rates in heat exchangers. It defines the effective temperature difference between two fluids as the logarithmic average of the temperature differences at the inlet and outlet. This method enables engineers to size and analyze heat exchangers systematically using the basic heat transfer equation Q = U A LMTD.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Kern","subfamily":"Heat Exchanger Design","year":"1950","type":"Heat transfer correlation"},"citations":[{"ref":"Kern, D. Q. (1950). Process Heat Transfer. McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/processheatrans00kern"},{"ref":"Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470055540","url":null}],"related":["effectiveness-ntu-method","thermal-resistance-network","rankine-cycle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"log-rank-test","name":"Log-Rank Test","fullName":"Log-Rank Test for Comparing Survival Curves","aliases":["Mantel log-rank test","Mantel-Cox test","log-rank sağkalım testi","Log-Rank Testi"],"domain":"survival","family":"survival","subfamily":null,"year":1966,"originator":"Mantel, N.","url":"https://scholargate.app/en/survival/log-rank-test","markdownUrl":"https://scholargate.app/en/survival/log-rank-test.md","definition":"The log-rank test, developed by Nathan Mantel in 1966, is a non-parametric hypothesis test that compares the overall survival experience of two or more groups throughout the entire follow-up period. It is the standard companion to Kaplan-Meier curves and determines whether observed differences between curves are statistically meaningful.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mantel, N.","year":1966,"type":"Non-parametric hypothesis test","compares":"Two or more survival curves","handles":"Right-censoring","testStatistic":"Chi-square"},"citations":[{"ref":"Mantel, N. (1966). Evaluation of Survival Data and Two New Rank Order Statistics Arising in Its Consideration. Cancer Chemotherapy Reports, 50(3), 163–170.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2245501/"},{"ref":"Kleinbaum, D. G. & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer.","type":"book","doi":null,"isbn":"978-1441966452","url":null}],"related":["kaplan-meier","cox-ph","nelson-aalen","fine-gray-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"logarithmic-normalization","name":"LOGARITHMIC-NORMALIZATION","fullName":"Logarithmic Normalization — log-ratio column normalisation for multiplicative aggregation contexts","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Normalization","year":"2008","originator":"Zavadskas, E. K., Turskis, Z.","url":"https://scholargate.app/en/decision-making/logarithmic-normalization","markdownUrl":"https://scholargate.app/en/decision-making/logarithmic-normalization.md","definition":"LOGARITHMIC-NORMALIZATION (Logarithmic Normalization — log-ratio column normalisation for multiplicative aggregation contexts) is a normalization multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z. in 2008. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Turskis, Z.","subfamily":"Normalization","year":"2008","type":"Normalization (logarithmic, multiplicative)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Zavadskas, E. K., Turskis, Z. (2008). A new logarithmic normalization method in games theory. Informatica","type":"article","doi":"10.15388/informatica.2008.215","isbn":null,"url":null}],"related":["moora","multimoora"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"logic-synthesis","name":"Logic Synthesis","fullName":"Logic Synthesis for Digital Circuit Design","aliases":["RTL synthesis","Hardware synthesis","Logic optimization"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Digital design automation","year":"1987","originator":"Robert Brayton","url":"https://scholargate.app/en/electrical-engineering/logic-synthesis","markdownUrl":"https://scholargate.app/en/electrical-engineering/logic-synthesis.md","definition":"Logic Synthesis is the automated conversion of high-level hardware descriptions (RTL in Verilog/VHDL) into optimized gate-level netlists. Pioneered by Brayton et al. at UC Berkeley in the 1980s-1990s, logic synthesis transforms behavioral specifications into physical implementations, optimizing for area, speed, and power. Synthesis is essential to modern digital design, enabling rapid iteration and automation of the most tedious manual tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Brayton","subfamily":"Digital design automation","year":"1987","type":"Automated conversion of HDL descriptions to gate-level netlists"},"citations":[{"ref":"Brayton, R. K., Hachtel, G. D., McMullin, C. T., Sangiovanni-Vincentelli, A. L., & Vincentelli, A. S. (1987). Logic Synthesis for VLSI Design. Kluwer Academic.","type":"book","doi":null,"isbn":null,"url":"https://link.springer.com/book/10.1007/978-94-017-6068-7"},{"ref":"Mishchenko, A., Chatterjee, S., Brayton, R., & Sangiovanni-Vincentelli, A. L. (2006). DAG-aware AIG rewriting. In Proc. DAC (pp. 713-718). ACM.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=DAG-aware+AIG+rewriting+Mishchenko"},{"ref":"Berkeley, S. (1995). SIS: A system for sequential circuit synthesis. Technical Report UCB/ERL M95/55, UC Berkeley.","type":"article","doi":null,"isbn":null,"url":"https://www2.eecs.berkeley.edu/Pubs/TechRpts/1995/ERL-95-55.pdf"}],"related":["static-timing-analysis","automatic-test-pattern-generation","monte-carlo-process-variation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"logistic-regression-ml","name":"Logistic regression (ML)","fullName":"Logistic Regression (Machine Learning Classification Model)","aliases":["logit model","logit regression","binomial logistic regression","maximum entropy classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1958","originator":"Cox, D. R.","url":"https://scholargate.app/en/machine-learning/logistic-regression-ml","markdownUrl":"https://scholargate.app/en/machine-learning/logistic-regression-ml.md","definition":"Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cox, D. R.","year":"1958","type":"Probabilistic linear classifier","dataType":"Labeled tabular data; binary or multinomial outcome","subfamily":"Machine learning"},"citations":[{"ref":"Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.","type":"article","doi":"10.1111/j.2517-6161.1958.tb00292.x","isbn":null,"url":null},{"ref":"James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 4). Springer.","type":"book","doi":null,"isbn":"978-1-4614-7138-7","url":null}],"related":["linear-regression-ml","support-vector-machine","random-forest","decision-tree","naive-bayes","regularized-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"logistic-regression","name":"Logistic Regression","fullName":"Binary Logistic Regression","aliases":["logit model","binomial logistic regression","LR"],"domain":"research-statistics","family":"process-pipeline","subfamily":"classification-prediction","year":"1958","originator":"David Roxbee Cox","url":"https://scholargate.app/en/research-statistics/logistic-regression","markdownUrl":"https://scholargate.app/en/research-statistics/logistic-regression.md","definition":"Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Roxbee Cox","subfamily":"classification-prediction","year":"1958","type":"Method"},"citations":[{"ref":"Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.","type":"article","doi":"10.1111/j.2517-6161.1958.tb00292.x","isbn":null,"url":null},{"ref":"Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). John Wiley & Sons.","type":"article","doi":"10.1002/9781118548387","isbn":null,"url":null}],"related":["multiple-regression-analysis","survival-analysis","propensity-score-matching"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"long-memory-models","name":"Long-Memory Models","fullName":"Long-Memory Time Series Models (ARFIMA, FIGARCH)","aliases":["ARFIMA","FIGARCH","fractionally integrated models","fractional integration","Uzun Hafıza Modelleri (ARFIMA, FIGARCH)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1980,"originator":"Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)","url":"https://scholargate.app/en/finance/long-memory-models","markdownUrl":"https://scholargate.app/en/finance/long-memory-models.md","definition":"Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)","year":1980,"type":"Fractionally integrated time series model","estimator":"Maximum likelihood with fractional differencing (d estimated via R/S or GPH)","outcome":"continuous","structure":"time series","minSample":200},"citations":[{"ref":"Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29.","type":"article","doi":"10.1111/j.1467-9892.1980.tb00297.x","isbn":null,"url":null},{"ref":"Baillie, R. T., Bollerslev, T. & Mikkelsen, H. O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 74(1), 3-30.","type":"article","doi":"10.1016/S0304-4076(95)01749-6","isbn":null,"url":null}],"related":["arima","garch-model","ols-regression","high-frequency-microstructure","risk-factor-pca"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"long-short-term-memory","name":"Long Short-Term Memory","fullName":"Long Short-Term Memory Network (LSTM)","aliases":["LSTM","LSTM network","LSTM-RNN","long short-term memory RNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"1997","originator":"Hochreiter, S. & Schmidhuber, J.","url":"https://scholargate.app/en/deep-learning/long-short-term-memory","markdownUrl":"https://scholargate.app/en/deep-learning/long-short-term-memory.md","definition":"Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hochreiter, S. & Schmidhuber, J.","year":"1997","type":"Recurrent neural network with gated memory cells","dataType":"Sequential / time-series / text data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.","type":"article","doi":"10.1162/neco.1997.9.8.1735","isbn":null,"url":null},{"ref":"Graves, A., Mohamed, A.-R. & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. Proceedings of ICASSP 2013, pp. 6645–6649. IEEE.","type":"inproceedings","doi":"10.1109/ICASSP.2013.6638947","isbn":null,"url":null}],"related":["recurrent-neural-network","gated-recurrent-unit","transformer","bert-based-classification","convolutional-neural-network","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longformer-bigbird","name":"Longformer / BigBird","fullName":"Long-Sequence Transformers with Sparse Attention (Longformer / BigBird)","aliases":["Uzun Dizi Transformer (Longformer / BigBird)","uzun dizi transformer","long-document transformer","sparse-attention transformer"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2020,"originator":"Beltagy, Peters & Cohan (Longformer); Zaheer et al. (BigBird)","url":"https://scholargate.app/en/deep-learning/longformer-bigbird","markdownUrl":"https://scholargate.app/en/deep-learning/longformer-bigbird.md","definition":"Long-sequence Transformers such as Longformer (Beltagy, Peters & Cohan, 2020) and BigBird (Zaheer et al., 2020) replace the standard Transformer's O(n²) attention with sparse attention patterns that scale linearly, O(n), with sequence length. This lets a single model attend over thousands of tokens — full documents, legal texts, or genomic sequences — that would not fit a conventional Transformer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Beltagy, Peters & Cohan (Longformer); Zaheer et al. (BigBird)","year":2020,"type":"Sparse-attention Transformer for long sequences","task":"Long-document classification & explanation","complexity":"O(n) attention (vs O(n²) standard)","minSample":50},"citations":[{"ref":"Beltagy, I., Peters, M. E. & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2004.05150"},{"ref":"Zaheer, M. et al. (2020). Big Bird: Transformers for Longer Sequences. NeurIPS.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2007.14062"}],"related":["mixture-of-experts","graph-attention-network","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-autoethnography","name":"Longitudinal Autoethnography","fullName":"Longitudinal Autoethnographic Research","aliases":["longitudinal self-ethnography","temporal autoethnography","long-term autoethnography","longitudinal personal narrative research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Carolyn Ellis, Arthur Bochner (autoethnography foundations); longitudinal extension by various scholars from 2000s onward","url":"https://scholargate.app/en/qualitative/longitudinal-autoethnography","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-autoethnography.md","definition":"Longitudinal autoethnography is a qualitative research design in which the researcher systematically documents, reflects on, and analyzes their own lived experience across an extended period — typically months to years. By combining the self-reflexive focus of autoethnography with a longitudinal temporal structure, this approach reveals how personal meanings, identities, and social understandings evolve over time. It bridges the personal and the cultural, producing richly layered narratives that connect individual transformation to broader social processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carolyn Ellis, Arthur Bochner (autoethnography foundations); longitudinal extension by various scholars from 2000s onward","year":"2000s–2010s","type":"Qualitative longitudinal research design","dataType":"Personal field notes, journals, reflective memos, interviews, diaries collected over time","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Ellis, C. (2004). The Ethnographic I: A Methodological Novel about Autoethnography. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759103535","url":null},{"ref":"Holman Jones, S., Adams, T. E., & Ellis, C. (2013). Introduction: Coming to know autoethnography as more than a method. In S. Holman Jones, T. E. Adams, & C. Ellis (Eds.), Handbook of Autoethnography (pp. 17–47). Left Coast Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Handbook+of+Autoethnography+Holman+Jones+Adams+Ellis+2013"}],"related":["autoethnography","longitudinal-ethnography","narrative-inquiry","longitudinal-narrative-research","life-history-research","reflexive-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-biographical-research","name":"Longitudinal Biographical Research","fullName":"Longitudinal Biographical Research","aliases":["LBR","longitudinal narrative research","biographical-longitudinal method","repeated biographical interviewing"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (consolidated as a named approach ca. 2000)","originator":"Tom Wengraf, Prue Chamberlayne, Joanna Bornat (BNIM tradition); also Robert Miller and Rita Charon in parallel strands","url":"https://scholargate.app/en/qualitative/longitudinal-biographical-research","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-biographical-research.md","definition":"Longitudinal Biographical Research (LBR) is a qualitative approach that combines in-depth biographical or narrative interviewing with a repeated, time-extended data-collection design. Participants are interviewed at multiple time points — sometimes years apart — so that researchers can trace how individuals construct, revise, and re-narrate their life stories as circumstances change. The method captures both the content of life histories and the dynamic process through which meaning is made and remade over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tom Wengraf, Prue Chamberlayne, Joanna Bornat (BNIM tradition); also Robert Miller and Rita Charon in parallel strands","year":"1990s–2000s (consolidated as a named approach ca. 2000)","type":"Qualitative longitudinal research design","dataType":"Repeated in-depth biographical interviews, life-history narratives, documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Wengraf, T. (2001). Qualitative Research Interviewing: Biographic Narrative and Semi-Structured Methods. Sage.","type":"book","doi":null,"isbn":"978-0761953517","url":null},{"ref":"Thomson, R., & Holland, J. (2003). Hindsight, foresight and insight: The challenges of longitudinal qualitative research. International Journal of Social Research Methodology, 6(3), 233–244.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hindsight+foresight+insight+challenges+longitudinal+qualitative+research+Thomson+Holland+2003"}],"related":["narrative-inquiry","biographical-method","life-history-research","longitudinal-case-study","oral-history","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-case-study","name":"Longitudinal Case Study","fullName":"Longitudinal Case Study Research","aliases":["longitudinal case research","panel case study","repeated case study","temporal case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984–1990 (foundational methodological codification)","originator":"Robert K. Yin (case study methodology); Andrew M. Pettigrew (longitudinal field research)","url":"https://scholargate.app/en/qualitative/longitudinal-case-study","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-case-study.md","definition":"A longitudinal case study is a qualitative research design that combines the in-depth, contextually rich focus of case study methodology with repeated data collection across multiple time points. Rather than capturing a single snapshot, it follows one or a small number of cases — an individual, group, organisation, or programme — over months or years to trace how processes, relationships, and meanings evolve. This design is well suited to questions about how and why things change, not merely what the state of affairs is at one moment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (case study methodology); Andrew M. Pettigrew (longitudinal field research)","year":"1984–1990 (foundational methodological codification)","type":"Qualitative research design","dataType":"Interviews, documents, observations, archival records collected at multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Pettigrew, A. M. (1990). Longitudinal field research on change: Theory and practice. Organization Science, 1(3), 267–292.","type":"article","doi":"10.1287/orsc.1.3.267","isbn":null,"url":null}],"related":["case-study","ethnography","narrative-analysis","action-research","grounded-theory","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-causal-comparative-research","name":"Longitudinal Causal-Comparative Research","fullName":"Longitudinal Causal-Comparative Research Design","aliases":["longitudinal ex post facto design","longitudinal causal-comparative design","repeated-measures causal-comparative research","prospective causal-comparative study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s–1980s (as an established combined design in educational and social research)","originator":"Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979)","url":"https://scholargate.app/en/research-design/longitudinal-causal-comparative-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-causal-comparative-research.md","definition":"Longitudinal causal-comparative research is a non-experimental quantitative design that compares pre-existing groups on one or more dependent variables across multiple measurement points over time. Unlike true experiments, the researcher does not manipulate the independent variable; instead, naturally occurring group differences (e.g., gender, socioeconomic status, diagnostic category) are examined to explore their relationship to outcomes as they evolve longitudinally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from causal-comparative tradition (Kerlinger, 1973) and longitudinal design frameworks (Goldstein, 1979)","year":"1970s–1980s (as an established combined design in educational and social research)","type":"Non-experimental quantitative research design","dataType":"Quantitative group-comparison data collected at multiple time points (surveys, tests, archival records)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (7th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073525532","url":null},{"ref":"Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational Research: An Introduction (8th ed.). Pearson. [Chapter on causal-comparative and longitudinal designs]","type":"book","doi":null,"isbn":"978-0205488490","url":null}],"related":["causal-comparative-research","longitudinal-research","ex-post-facto-design","panel-research","cohort-research","correlational-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-classic-grounded-theory","name":"Longitudinal Classic grounded theory","fullName":"Longitudinal Classic Grounded Theory","aliases":["Longitudinal CGT","Glaserian longitudinal grounded theory","classic GT longitudinal design","longitudinal substantive theory building"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (classic GT); longitudinal application developed from 1980s onward","originator":"Barney G. Glaser and Anselm L. Strauss (classic GT); longitudinal extension by later methodologists","url":"https://scholargate.app/en/qualitative/longitudinal-classic-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-classic-grounded-theory.md","definition":"Longitudinal Classic Grounded Theory applies Glaser and Strauss's original discovery-oriented grounded theory method across two or more data collection waves separated by time. The approach tracks how social processes, behaviors, and conceptual categories evolve, allowing the researcher to build a substantive theory that captures change and continuity rather than a single static snapshot of a phenomenon.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser and Anselm L. Strauss (classic GT); longitudinal extension by later methodologists","year":"1967 (classic GT); longitudinal application developed from 1980s onward","type":"Qualitative longitudinal research design","dataType":"Interview transcripts, field notes, documents collected at multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null},{"ref":"Glaser, B. G. (2001). The Grounded Theory Perspective: Conceptualization Contrasted with Description. Sociology Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Glaser+2001+Grounded+Theory+Perspective+Conceptualization+Contrasted+Description"}],"related":["classic-grounded-theory","longitudinal-grounded-theory","longitudinal-constructivist-grounded-theory","longitudinal-straussian-grounded-theory","longitudinal-qualitative-content-analysis","longitudinal-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-cohort-research","name":"Longitudinal Cohort Research","fullName":"Longitudinal Cohort Research Design","aliases":["longitudinal cohort study","prospective cohort study","cohort follow-up study","panel cohort design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1950s–1960s (formalized in epidemiological methodology)","originator":"Richard Doll & Austin Bradford Hill (landmark Doctors' Cohort Study, 1951); cohort logic formalized in mid-20th century epidemiology","url":"https://scholargate.app/en/research-design/longitudinal-cohort-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-cohort-research.md","definition":"Longitudinal cohort research is an observational quantitative design that recruits a defined group of individuals sharing a common characteristic (the cohort) and follows them prospectively over time, collecting data at multiple points to examine how outcomes develop, risks accumulate, or relationships change. It is the cornerstone design for studying causation, developmental trajectories, and the natural history of phenomena in epidemiology, social science, and education.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Doll & Austin Bradford Hill (landmark Doctors' Cohort Study, 1951); cohort logic formalized in mid-20th century epidemiology","year":"1950s–1960s (formalized in epidemiological methodology)","type":"Quantitative observational longitudinal design","dataType":"Repeated-measures quantitative data; structured questionnaires, records, biomarkers collected at multiple time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195083439","url":null},{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null}],"related":["cohort-research","longitudinal-research","panel-research","prospective-study","cross-sectional-research","repeated-measures-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-comparative-legal-analysis","name":"Longitudinal comparative legal analysis","fullName":"Longitudinal Comparative Legal Analysis","aliases":["LCLA","diachronic comparative law","longitudinal legal comparison","dynamic comparative legal research"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Late 20th century (comparative law foundational texts 1960s–1998; longitudinal integration from 1990s onward)","originator":"Konrad Zweigert and Hein Kotz (comparative law foundation); longitudinal dimension integrated in socio-legal and legal history scholarship","url":"https://scholargate.app/en/field-methods/longitudinal-comparative-legal-analysis","markdownUrl":"https://scholargate.app/en/field-methods/longitudinal-comparative-legal-analysis.md","definition":"Longitudinal comparative legal analysis examines how legal rules, doctrines, or institutions develop and diverge across two or more legal systems over an extended period. By combining the spatial dimension of comparative law with the temporal dimension of longitudinal research, it captures not just differences between jurisdictions at a single point but the trajectories of legal change — convergence, divergence, transplantation, and resistance — over years or decades.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Konrad Zweigert and Hein Kotz (comparative law foundation); longitudinal dimension integrated in socio-legal and legal history scholarship","year":"Late 20th century (comparative law foundational texts 1960s–1998; longitudinal integration from 1990s onward)","type":"Qualitative-interpretive legal research design","dataType":"Legal texts (statutes, case law, regulations, travaux preparatoires), secondary legal literature, court records across multiple time points and jurisdictions","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Zweigert, K., & Kotz, H. (1998). An Introduction to Comparative Law (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198268598","url":null},{"ref":"Siems, M. M. (2014). Comparative Law. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107026049","url":null}],"related":["comparative-legal-analysis","doctrinal-legal-research","longitudinal-case-law-analysis","legal-content-analysis","historical-archival-research","hermeneutic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-confirmatory-factor-analysis","name":"Longitudinal CFA","fullName":"Longitudinal Confirmatory Factor Analysis","aliases":["longitudinal CFA","repeated-measures CFA","longitudinal measurement model","panel CFA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1970s–1990s","originator":"Karl Jöreskog (CFA framework); longitudinal extension by Wheaton, Muthén, and Alwin in the 1970s–1990s","url":"https://scholargate.app/en/psychometrics/longitudinal-confirmatory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-confirmatory-factor-analysis.md","definition":"Longitudinal confirmatory factor analysis (longitudinal CFA) applies a theoretically specified measurement model to data collected at two or more time points. Its primary purpose is to verify that a scale measures the same latent construct in the same way over time — a prerequisite for drawing valid conclusions about change from repeated-measures data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Jöreskog (CFA framework); longitudinal extension by Wheaton, Muthén, and Alwin in the 1970s–1990s","year":"1970s–1990s","type":"Longitudinal latent variable / measurement model","dataType":"Repeated-measures continuous or ordinal item responses across two or more time points","subfamily":"Scale / measurement"},"citations":[{"ref":"Widaman, K. F. & Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In K. J. Bryant, M. Windle & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281–324). American Psychological Association.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Widaman+Reise+1997+measurement+invariance+longitudinal"},{"ref":"Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.","type":"book","doi":null,"isbn":"9780805864786","url":null}],"related":["confirmatory-factor-analysis","longitudinal-measurement-invariance","multilevel-confirmatory-factor-analysis","longitudinal-exploratory-factor-analysis","structural-equation-modeling","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-confirmatory-research","name":"Longitudinal Confirmatory Research","fullName":"Longitudinal Confirmatory Research Design","aliases":["longitudinal confirmatory study","confirmatory longitudinal design","longitudinal hypothesis-testing design","longitudinal CFA design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s onward; consolidated in SEM literature from 1990s","originator":"Synthesized from longitudinal design traditions (e.g., Baltes & Nesselroade, 1979) and confirmatory analytic frameworks (Joreskog, 1969)","url":"https://scholargate.app/en/research-design/longitudinal-confirmatory-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-confirmatory-research.md","definition":"Longitudinal confirmatory research combines the temporal depth of longitudinal design with the hypothesis-driven logic of confirmatory analysis. The researcher specifies a priori hypotheses or structural models about how variables change or remain stable over time, then tests those predictions against data collected at two or more time points. It is the design of choice when theory is mature enough to make specific predictions about developmental, causal, or stability processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from longitudinal design traditions (e.g., Baltes & Nesselroade, 1979) and confirmatory analytic frameworks (Joreskog, 1969)","year":"1970s onward; consolidated in SEM literature from 1990s","type":"Quantitative research design","dataType":"Repeated-measures quantitative data (survey, psychometric, biomedical, educational)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195152968","url":null},{"ref":"Little, T. D. (2013). Longitudinal Structural Equation Modeling. Guilford Press.","type":"book","doi":null,"isbn":"978-1462510160","url":null}],"related":["longitudinal-research","confirmatory-research","panel-research","cohort-research","longitudinal-correlational-research","longitudinal-model-testing-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-construct-validity","name":"Longitudinal Construct Validity","fullName":"Longitudinal Construct Validity","aliases":["longitudinal measurement validity","construct validity over time","longitudinal measurement invariance","LCV"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1993–2000","originator":"Meredith, Vandenberg, and the measurement invariance tradition","url":"https://scholargate.app/en/psychometrics/longitudinal-construct-validity","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-construct-validity.md","definition":"Longitudinal construct validity evaluates whether a psychological scale measures the same latent construct in the same way across multiple time points. It is tested by progressively constraining a confirmatory factor model across waves and comparing model fit, ensuring that observed change scores reflect genuine change in the underlying trait rather than measurement drift.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Meredith, Vandenberg, and the measurement invariance tradition","year":"1993–2000","type":"Validity evaluation framework","dataType":"Repeated-measures questionnaire / test data","subfamily":"Scale / measurement"},"citations":[{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null},{"ref":"Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525–543.","type":"article","doi":"10.1007/BF02294825","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","measurement-invariance","longitudinal-sem","test-retest-reliability","convergent-validity","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-constructivist-grounded-theory","name":"Longitudinal Constructivist Grounded Theory","fullName":"Longitudinal Constructivist Grounded Theory","aliases":["longitudinal CGT","constructivist GT longitudinal","longitudinal Charmaz grounded theory","temporal constructivist grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (Charmaz's constructivist GT); longitudinal application from ~2000s onward","originator":"Kathy Charmaz (constructivist GT); extended to longitudinal designs by qualitative longitudinal researchers","url":"https://scholargate.app/en/qualitative/longitudinal-constructivist-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-constructivist-grounded-theory.md","definition":"Longitudinal Constructivist Grounded Theory combines Kathy Charmaz's constructivist variant of grounded theory — which foregrounds the co-construction of meaning between researcher and participants — with a multi-wave, time-extended data collection design. Rather than capturing a single snapshot, the researcher returns to the same participants across two or more time points, allowing the emergent theory to track how processes, identities, and social meanings develop, shift, or stabilise over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz (constructivist GT); extended to longitudinal designs by qualitative longitudinal researchers","year":"2006 (Charmaz's constructivist GT); longitudinal application from ~2000s onward","type":"Qualitative research design and analysis approach","dataType":"Multiple-wave interview transcripts, field notes, participant documents collected over time","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973522","url":null},{"ref":"Thomson, R., & Holland, J. (2003). Hindsight, foresight and insight: the challenges of longitudinal qualitative research. International Journal of Social Research Methodology, 6(3), 233–244.","type":"article","doi":"10.1080/1364557032000091833","isbn":null,"url":null}],"related":["constructivist-grounded-theory","longitudinal-grounded-theory","longitudinal-qualitative-research","grounded-theory","longitudinal-case-study","longitudinal-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-content-analysis","name":"Longitudinal Content Analysis","fullName":"Longitudinal Content Analysis","aliases":["LCA","repeated content analysis","diachronic content analysis","trend content analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Qualitative design and analysis","year":"Mid-20th century onward; systematized alongside content analysis (Berelson, 1952; Krippendorff, 1980)","originator":"Developed within the content analysis tradition; longitudinal extensions widely applied since the mid-20th century in communication and political science research","url":"https://scholargate.app/en/qualitative/longitudinal-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-content-analysis.md","definition":"Longitudinal Content Analysis (LCA) applies systematic content analysis to documents, media, or texts sampled at two or more time points in order to detect how themes, frames, language, or discourse patterns change or persist over time. Drawing on the established logic of content analysis, it adds a temporal dimension that allows researchers to chart trends, trace the evolution of representations, and test hypotheses about historical or social change. It is widely used in communication research, political science, media studies, and the health sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within the content analysis tradition; longitudinal extensions widely applied since the mid-20th century in communication and political science research","year":"Mid-20th century onward; systematized alongside content analysis (Berelson, 1952; Krippendorff, 1980)","type":"Qualitative and mixed-methods research design","dataType":"Textual, visual, or audio-visual documents collected at multiple time points","subfamily":"Qualitative design and analysis"},"citations":[{"ref":"Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506395661","url":null},{"ref":"Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412979474","url":null}],"related":["content-analysis","thematic-analysis","discourse-analysis","narrative-analysis","frame-analysis","document-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-content-validity","name":"Longitudinal content validity","fullName":"Longitudinal Content Validity","aliases":["longitudinal content validation","temporal content validity","repeated-measure content validity","content validity over time"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1995–2000","originator":"Haynes, Richard & Kubany (1995); extended to longitudinal contexts by measurement invariance researchers","url":"https://scholargate.app/en/psychometrics/longitudinal-content-validity","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-content-validity.md","definition":"Longitudinal content validity evaluates whether the items of a measure adequately and consistently represent the intended content domain not only at a single point in time but across repeated administrations. It ensures that the conceptual coverage of a scale remains appropriate and stable as measurement occasions accumulate over a study period.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Haynes, Richard & Kubany (1995); extended to longitudinal contexts by measurement invariance researchers","year":"1995–2000","type":"Validity evaluation technique","dataType":"Expert ratings, item-level survey data across time points","subfamily":"Scale / measurement"},"citations":[{"ref":"Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247.","type":"article","doi":"10.1037/1040-3590.7.3.238","isbn":null,"url":null},{"ref":"Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["content-validity","longitudinal-measurement-invariance","construct-validity","longitudinal-confirmatory-factor-analysis","measurement-invariance","longitudinal-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-convergent-validity","name":"Longitudinal convergent validity","fullName":"Longitudinal Convergent Validity","aliases":["longitudinal construct validity","repeated-measure convergent validity","cross-time convergent validity","temporal convergent validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1997–2003","originator":"Cole & Maxwell; Widaman & Reise","url":"https://scholargate.app/en/psychometrics/longitudinal-convergent-validity","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-convergent-validity.md","definition":"Longitudinal convergent validity evaluates whether a scale's indicators correlate with theoretically related constructs not just at a single time point but consistently across repeated measurement occasions. It extends standard convergent validity testing into longitudinal designs to ensure that the scale measures the intended construct in the same meaningful way over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cole & Maxwell; Widaman & Reise","year":"1997–2003","type":"Validity evidence framework","dataType":"Repeated-measures / longitudinal scale data","subfamily":"Scale / measurement"},"citations":[{"ref":"Cole, D. A. & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577.","type":"article","doi":"10.1037/0021-843X.112.4.558","isbn":null,"url":null},{"ref":"Widaman, K. F. & Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In K. J. Bryant, M. Windle & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281–324). American Psychological Association.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Exploring+the+measurement+invariance+of+psychological+instruments+Widaman+Reise+1997"}],"related":["convergent-validity","longitudinal-measurement-invariance","longitudinal-confirmatory-factor-analysis","discriminant-validity","construct-validity","longitudinal-discriminant-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-correlational-research","name":"Longitudinal Correlational Research","fullName":"Longitudinal Correlational Research Design","aliases":["longitudinal correlational study","prospective correlational design","longitudinal associational research","repeated-measures correlational design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century (formalized 1940s–1960s)","originator":"Rooted in early correlational methodology (Galton, Pearson late 19th c.); longitudinal extension formalized through panel studies in social sciences (mid-20th c.)","url":"https://scholargate.app/en/research-design/longitudinal-correlational-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-correlational-research.md","definition":"Longitudinal correlational research is a non-experimental quantitative design that examines the strength and direction of relationships among variables by collecting data from the same participants at two or more points in time. Unlike a cross-sectional correlational study, the longitudinal approach captures how associations evolve, persist, or dissolve across time, providing a stronger empirical basis for causal inference without experimental manipulation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in early correlational methodology (Galton, Pearson late 19th c.); longitudinal extension formalized through panel studies in social sciences (mid-20th c.)","year":"Mid-20th century (formalized 1940s–1960s)","type":"Non-experimental quantitative design","dataType":"Repeated measures of quantitative variables across two or more time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0078097898","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["longitudinal-research","correlational-research","panel-research","cohort-research","cross-sectional-correlational-research","longitudinal-causal-comparative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-critical-discourse-analysis","name":"Longitudinal Critical Discourse Analysis","fullName":"Longitudinal Critical Discourse Analysis","aliases":["Longitudinal CDA","diachronic critical discourse analysis","longitudinal discourse study","temporal CDA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (CDA foundations ~1989–1992; longitudinal applications consolidated through 2000s)","originator":"Norman Fairclough; Ruth Wodak","url":"https://scholargate.app/en/qualitative/longitudinal-critical-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-critical-discourse-analysis.md","definition":"Longitudinal Critical Discourse Analysis (LCDA) combines the critical discourse analysis tradition — which examines how language constructs and reproduces power, ideology, and social inequality — with a longitudinal design that collects and compares texts at multiple time points. By tracking discursive change over time, LCDA reveals how ideological representations, social identities, and power relations shift, stabilise, or are contested across different historical or political periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Fairclough; Ruth Wodak","year":"1990s–2000s (CDA foundations ~1989–1992; longitudinal applications consolidated through 2000s)","type":"Qualitative longitudinal discourse design","dataType":"Text corpora collected at multiple time points (media texts, policy documents, speeches, interviews, social media archives)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Fairclough, N. (1992). Discourse and Social Change. Polity Press.","type":"book","doi":null,"isbn":"978-0745612690","url":null},{"ref":"Wodak, R., & Meyer, M. (Eds.). (2001). Methods of Critical Discourse Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761961542","url":null}],"related":["critical-discourse-analysis","longitudinal-discourse-analysis","longitudinal-qualitative-content-analysis","discourse-analysis","longitudinal-thematic-analysis","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-cronbachs-alpha","name":"Longitudinal Cronbach's Alpha","fullName":"Longitudinal Cronbach's Alpha Reliability Analysis","aliases":["repeated-measures alpha","longitudinal internal consistency","wave-specific Cronbach's alpha","time-point reliability estimation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1951 (alpha); longitudinal application systematised ca. 1990s–2000s","originator":"Lee J. Cronbach (alpha); longitudinal extension formalised in scale validation literature from 1980s onward","url":"https://scholargate.app/en/psychometrics/longitudinal-cronbachs-alpha","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-cronbachs-alpha.md","definition":"Longitudinal Cronbach's alpha assesses the internal consistency reliability of a scale at each wave of a repeated-measures study and examines whether that reliability remains stable across time. It is an essential step in longitudinal scale validation, ensuring that a scale measures its construct with consistent precision at every measurement occasion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach (alpha); longitudinal extension formalised in scale validation literature from 1980s onward","year":"1951 (alpha); longitudinal application systematised ca. 1990s–2000s","type":"Reliability estimation across time","dataType":"Ordinal or interval Likert-type items measured at two or more time points","subfamily":"Scale / measurement"},"citations":[{"ref":"Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.","type":"article","doi":"10.1007/BF02310555","isbn":null,"url":null},{"ref":"Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["cronbachs-alpha","mcdonalds-omega","longitudinal-measurement-invariance","test-retest-reliability","longitudinal-confirmatory-factor-analysis","generalizability-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-cross-sectional-research","name":"Longitudinal Cross-Sectional Research","fullName":"Longitudinal Cross-Sectional Research Design (Cohort-Sequential Design)","aliases":["cohort-sequential design","accelerated longitudinal design","mixed longitudinal design","cross-sequential design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1965–1968","originator":"K. Warner Schaie; Paul B. Baltes","url":"https://scholargate.app/en/research-design/longitudinal-cross-sectional-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-cross-sectional-research.md","definition":"Longitudinal cross-sectional research — also called cohort-sequential or accelerated longitudinal design — simultaneously follows multiple age cohorts over time, combining the depth of longitudinal tracking with the age-range efficiency of cross-sectional sampling. By overlapping cohorts at successive waves, researchers can disentangle age effects, cohort effects, and period effects far more rigorously than either pure design allows, and can compress the calendar time needed to study development across a wide age span.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. Warner Schaie; Paul B. Baltes","year":"1965–1968","type":"Quantitative observational research design","dataType":"Repeated quantitative measurements across multiple age cohorts","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Schaie, K. W. (1965). A general model for the study of developmental problems. Psychological Bulletin, 64(2), 92–107.","type":"article","doi":"10.1037/h0022371","isbn":null,"url":null},{"ref":"Baltes, P. B. (1968). Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 11(3), 145–171.","type":"article","doi":"10.1159/000270604","isbn":null,"url":null}],"related":["longitudinal-study","cross-sectional-survey","panel-study","cohort-study","growth-curve-modeling","repeated-measures-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-diary-method","name":"Longitudinal Diary Method","fullName":"Longitudinal Diary Method","aliases":["diary study (longitudinal)","daily diary method","repeated-measures diary","longitudinal self-report diary"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1942 (diary method); longitudinal variant formalised 1980s–2000s","originator":"Allport (1942); systematic longitudinal extension developed by Bolger, Davis & Rafaeli (2003)","url":"https://scholargate.app/en/survey-methodology/longitudinal-diary-method","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-diary-method.md","definition":"The Longitudinal Diary Method is a data collection technique in which participants record experiences, thoughts, feelings, or behaviors in structured diary entries repeatedly over an extended period — from days to months or even years. Unlike a one-shot survey, it tracks within-person change, daily fluctuation, and temporal processes in natural settings, making it especially powerful for studying how phenomena evolve over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Allport (1942); systematic longitudinal extension developed by Bolger, Davis & Rafaeli (2003)","year":"1942 (diary method); longitudinal variant formalised 1980s–2000s","type":"Longitudinal qualitative/quantitative data collection","dataType":"Repeated self-report entries (text, ratings, checklists) over days, weeks, or months","subfamily":"Data collection"},"citations":[{"ref":"Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.","type":"article","doi":"10.1146/annurev.psych.54.101601.145030","isbn":null,"url":null},{"ref":"Scollon, C. N., Kim-Prieto, C., & Diener, E. (2009). Experience sampling: Promises and pitfalls, strengths and weaknesses. Journal of Happiness Studies, 4(1), 5–34.","type":"article","doi":"10.1023/A:1023605205115","isbn":null,"url":null}],"related":["diary-method","experience-sampling-method","longitudinal-survey","longitudinal-participant-observation","mobile-diary-method","longitudinal-field-notes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-differential-item-functioning","name":"Longitudinal DIF","fullName":"Longitudinal Differential Item Functioning","aliases":["longitudinal DIF","DIF across time","temporal DIF","longitudinal item bias"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1980s–2000s","originator":"Multiple contributors; foundational DIF methods by Lord (1980) extended to longitudinal designs","url":"https://scholargate.app/en/psychometrics/longitudinal-differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-differential-item-functioning.md","definition":"Longitudinal differential item functioning detects whether individual test or scale items behave differently across measurement occasions for the same respondents. It extends standard DIF methodology to repeated-measures designs, ensuring that observed change scores genuinely reflect construct change rather than shifts in item characteristics over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; foundational DIF methods by Lord (1980) extended to longitudinal designs","year":"1980s–2000s","type":"Item-level bias detection across time","dataType":"Repeated-measures ordinal or binary item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Millsap, R. E., & Kwok, O. M. (2004). Evaluating the impact of partial factorial measurement invariance on selection in two groups. Psychological Methods, 9(1), 93–115.","type":"article","doi":"10.1037/1082-989X.9.1.93","isbn":null,"url":null},{"ref":"Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["differential-item-functioning","longitudinal-measurement-invariance","item-response-theory","longitudinal-confirmatory-factor-analysis","longitudinal-item-response-theory","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-discourse-analysis","name":"Longitudinal Discourse Analysis","fullName":"Longitudinal Discourse Analysis","aliases":["LDA","diachronic discourse analysis","longitudinal CDA","discourse change analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (systematised as a distinct approach)","originator":"Norman Fairclough; Jan Blommaert; applied linguists in sociolinguistics and CDA traditions","url":"https://scholargate.app/en/qualitative/longitudinal-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-discourse-analysis.md","definition":"Longitudinal Discourse Analysis (LDA) is a qualitative research approach that examines how discourse — language in use, texts, talk, and representational practices — changes across time. Rather than analysing a single snapshot of language, LDA collects and compares discourse data at multiple points to uncover how meanings, identities, ideologies, or social practices evolve, stabilise, or shift under the influence of historical, institutional, or societal forces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Fairclough; Jan Blommaert; applied linguists in sociolinguistics and CDA traditions","year":"1990s–2000s (systematised as a distinct approach)","type":"Qualitative longitudinal research design","dataType":"Texts, documents, transcripts, media archives collected at multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Fairclough, N. (2003). Analysing Discourse: Textual Analysis for Social Research. Routledge.","type":"book","doi":null,"isbn":"978-0415258937","url":null},{"ref":"Blommaert, J. (2005). Discourse: A Critical Introduction. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521533911","url":null}],"related":["discourse-analysis","critical-discourse-analysis","narrative-analysis","content-analysis","thematic-analysis","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-discriminant-validity","name":"Longitudinal Discriminant Validity","fullName":"Longitudinal Discriminant Validity","aliases":["LDV","longitudinal construct distinctiveness","cross-time discriminant validity","temporal discriminant validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1993–2000","originator":"Formalized through SEM-based validity traditions (Campbell & Fiske, 1959; Cole & Maxwell, 1993)","url":"https://scholargate.app/en/psychometrics/longitudinal-discriminant-validity","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-discriminant-validity.md","definition":"Longitudinal discriminant validity tests whether a psychological construct measured at two or more time points is empirically distinct across occasions — ensuring that the same construct does not collapse into a single undifferentiated mass over time. It is a prerequisite for meaningful change modeling in panel and longitudinal research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized through SEM-based validity traditions (Campbell & Fiske, 1959; Cole & Maxwell, 1993)","year":"1993–2000","type":"Validity assessment / measurement quality","dataType":"Repeated-measures ordinal or continuous survey/test data","subfamily":"Scale / measurement"},"citations":[{"ref":"Cole, D. A. & Maxwell, S. E. (1993). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577.","type":"article","doi":"10.1037/0021-843X.112.4.558","isbn":null,"url":null},{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","measurement-invariance","convergent-validity","structural-equation-modeling","average-variance-extracted","longitudinal-cfa"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-document-analysis","name":"Longitudinal document analysis","fullName":"Longitudinal Qualitative Document Analysis","aliases":["longitudinal documentary research","longitudinal archival analysis","repeated document analysis","LDA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2003–2009 (formalized in qualitative research methodology)","originator":"Glenn A. Bowen (document analysis framework); Johnny Saldaña (longitudinal qualitative methods)","url":"https://scholargate.app/en/qualitative/longitudinal-document-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-document-analysis.md","definition":"Longitudinal document analysis is a qualitative research approach that systematically collects and analyzes documents at multiple time points to trace how phenomena, discourses, policies, or organizational practices change over time. By treating documents as primary data sources rather than supplementary evidence, researchers can reconstruct temporal trajectories, identify turning points, and understand how meaning evolves across extended periods without requiring direct participant contact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glenn A. Bowen (document analysis framework); Johnny Saldaña (longitudinal qualitative methods)","year":"2003–2009 (formalized in qualitative research methodology)","type":"Qualitative longitudinal research design","dataType":"Documents (reports, policies, minutes, records, texts) collected at multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Saldaña, J. (2003). Longitudinal Qualitative Research: Analyzing Change Through Time. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759103733","url":null}],"related":["document-analysis","longitudinal-qualitative-research","longitudinal-content-analysis","longitudinal-thematic-analysis","archival-research","historical-comparative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-ethnography","name":"Longitudinal Ethnography","fullName":"Longitudinal Ethnographic Research","aliases":["extended ethnography","long-term fieldwork","sustained ethnographic study","longitudinal field research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1920s (classical origins); refined 1990s–2000s","originator":"Rooted in classical anthropological fieldwork (Malinowski, 1922); systematised for sociological revisits by Michael Burawoy (2003)","url":"https://scholargate.app/en/qualitative/longitudinal-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-ethnography.md","definition":"Longitudinal ethnography is a qualitative research design in which a researcher conducts sustained, repeated fieldwork with the same community, organisation, or group across an extended period — months to decades. By returning to the field at multiple time points, the researcher captures how social processes, meanings, and structures evolve, making it the only qualitative method capable of directly observing change and continuity in lived experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in classical anthropological fieldwork (Malinowski, 1922); systematised for sociological revisits by Michael Burawoy (2003)","year":"1920s (classical origins); refined 1990s–2000s","type":"Qualitative research design","dataType":"Field notes, interviews, documents, observations collected across multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Burawoy, M. (2003). Revisits: An outline of a theory of reflexive ethnography. American Sociological Review, 68(5), 645–679.","type":"article","doi":"10.2307/1519757","isbn":null,"url":null},{"ref":"Thomson, R., & Holland, J. (2003). Hindsight, foresight and insight: The challenges of longitudinal qualitative research. International Journal of Social Research Methodology, 6(3), 233–244.","type":"book","doi":"10.1080/1364557032000091833","isbn":null,"url":null}],"related":["ethnography","participant-observation","case-study","narrative-analysis","grounded-theory","life-history-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-ex-post-facto-design","name":"Longitudinal Ex Post Facto Design","fullName":"Longitudinal Ex Post Facto Research Design","aliases":["longitudinal causal-comparative design","longitudinal after-the-fact design","longitudinal retrospective design","LEPF design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1964–1986 (Kerlinger 1964 first edition; Campbell & Stanley 1966)","originator":"Fred N. Kerlinger (systematized); Donald T. Campbell & Julian C. Stanley (quasi-experimental framework)","url":"https://scholargate.app/en/research-design/longitudinal-ex-post-facto-design","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-ex-post-facto-design.md","definition":"A longitudinal ex post facto design combines the time-depth of longitudinal research with the retrospective logic of ex post facto inquiry. Participants are grouped by a naturally occurring characteristic or past event — not randomly assigned — and then observed or measured at multiple points over time. The goal is to trace how pre-existing differences between groups unfold or predict outcomes across an extended period, without the researcher ever manipulating the independent variable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred N. Kerlinger (systematized); Donald T. Campbell & Julian C. Stanley (quasi-experimental framework)","year":"1964–1986 (Kerlinger 1964 first edition; Campbell & Stanley 1966)","type":"Non-experimental quantitative research design","dataType":"Archival records, repeated-measures surveys, administrative data, prospective or retrospective panel data","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030417498","url":null},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["ex-post-facto-design","longitudinal-research","causal-comparative-research","cohort-research","panel-research","longitudinal-correlational-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-explanatory-research","name":"Longitudinal Explanatory Research","fullName":"Longitudinal Explanatory Research Design","aliases":["explanatory longitudinal design","longitudinal causal research","explanatory panel study","longitudinal explanatory study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s–1990s (formal methodological codification)","originator":"Rooted in panel and longitudinal survey traditions; systematised by Scott Menard and others in the late 20th century","url":"https://scholargate.app/en/research-design/longitudinal-explanatory-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-explanatory-research.md","definition":"Longitudinal explanatory research combines repeated measurement over time with an explicit aim of explaining why and how variables change or influence one another. Unlike purely descriptive longitudinal designs, the explanatory orientation tests causal or predictive hypotheses by examining temporal precedence — a key criterion for causal inference in non-experimental settings. It is widely used in social, behavioral, educational, and health sciences to disentangle cause from correlation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in panel and longitudinal survey traditions; systematised by Scott Menard and others in the late 20th century","year":"1970s–1990s (formal methodological codification)","type":"Quantitative observational research design","dataType":"Repeated-measures numeric data; survey panels; archival records collected at two or more time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922452","url":null},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["longitudinal-research","panel-research","cohort-research","explanatory-research","causal-comparative-research","cross-lagged-panel-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-exploratory-factor-analysis","name":"Longitudinal EFA","fullName":"Longitudinal Exploratory Factor Analysis","aliases":["LEFA","longitudinal factor analysis","repeated-measures EFA","panel EFA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1970s–1983","originator":"John R. Nesselroade and colleagues (lifespan developmental tradition)","url":"https://scholargate.app/en/psychometrics/longitudinal-exploratory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-exploratory-factor-analysis.md","definition":"Longitudinal EFA applies exploratory factor analysis separately at each measurement occasion — or jointly across occasions — to discover whether the same latent factor structure emerges over time and whether factor loadings remain stable across waves. It is the foundational data-driven approach for examining structural change and continuity in panel and developmental research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John R. Nesselroade and colleagues (lifespan developmental tradition)","year":"1970s–1983","type":"Latent variable / dimension reduction across time","dataType":"Repeated-measures continuous or ordinal item responses collected at two or more time points","subfamily":"Scale / measurement"},"citations":[{"ref":"Nesselroade, J. R. (1983). Temporal selection and factor invariance in the study of development and change. In P. B. Baltes & O. G. Brim (Eds.), Life-Span Development and Behavior (Vol. 5, pp. 59–87). Academic Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Temporal+selection+and+factor+invariance+in+the+study+of+development+and+change+Nesselroade+1983"},{"ref":"Longitudinal study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Longitudinal_study"}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","longitudinal-confirmatory-factor-analysis","measurement-invariance","longitudinal-measurement-invariance","multilevel-exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-focus-group","name":"Longitudinal Focus Group","fullName":"Longitudinal Focus Group Research","aliases":["repeated focus group","panel focus group","longitudinal FG","follow-up focus group"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1940s (focus groups); longitudinal variant refined 1980s–1990s","originator":"Adapted from Robert K. Merton's focused interview tradition; longitudinal design developed in social and health sciences","url":"https://scholargate.app/en/survey-methodology/longitudinal-focus-group","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-focus-group.md","definition":"A longitudinal focus group convenes the same group of participants in multiple sessions over an extended period — weeks, months, or years — to trace how their attitudes, experiences, or interpretations evolve in response to changing circumstances. Unlike a single focus group snapshot, the repeated-contact design captures the dynamics of opinion and meaning-making across time, making it particularly valuable in health, policy, and social research where change is the phenomenon of interest.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from Robert K. Merton's focused interview tradition; longitudinal design developed in social and health sciences","year":"1940s (focus groups); longitudinal variant refined 1980s–1990s","type":"Qualitative longitudinal data collection","dataType":"Verbal group discussion transcripts collected at multiple time points","subfamily":"Data collection"},"citations":[{"ref":"Morgan, D. L. (1997). Focus Groups as Qualitative Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761903437","url":null},{"ref":"Farquhar, C., & Das, R. (1999). Are focus groups suitable for 'sensitive' topics? In R. S. Barbour & J. Kitzinger (Eds.), Developing Focus Group Research (pp. 47–63). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Developing+Focus+Group+Research+Barbour+Kitzinger+1999"}],"related":["focus-group","longitudinal-survey","longitudinal-in-depth-interview","panel-study","diary-method","participant-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-generalizability-theory","name":"Longitudinal Generalizability Theory","fullName":"Longitudinal Generalizability Theory","aliases":["longitudinal G-theory","longitudinal GT","repeated-measures generalizability theory","G-theory for longitudinal designs"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations","url":"https://scholargate.app/en/psychometrics/longitudinal-generalizability-theory","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-generalizability-theory.md","definition":"Longitudinal generalizability theory extends classical G-theory to repeated-measures and longitudinal designs, decomposing score variance across persons, measurement occasions, raters, and items simultaneously. It quantifies how reliably scores can be generalized across time points, evaluators, and conditions — information that is invisible to cross-sectional reliability indices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Webb, Shavelson, and colleagues, building on Cronbach et al. (1963) G-theory foundations","year":"1990s–2000s","type":"Variance components / reliability estimation","dataType":"Repeated-measures / longitudinal ratings or scores","subfamily":"Scale / measurement"},"citations":[{"ref":"Webb, N. M., Shavelson, R. J., & Harrigan, E. H. (2007). Generalizability theory: Overview. In C. R. Rao & S. Sinharay (Eds.), Handbook of Statistics, Vol. 26: Psychometrics (pp. 1–43). Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Generalizability+theory%3A+Overview+Webb"},{"ref":"Brennan, R. L. (2001). Generalizability Theory. Springer.","type":"book","doi":null,"isbn":"978-0387952826","url":null}],"related":["generalizability-theory","confirmatory-factor-analysis","multilevel-modeling","intraclass-correlation","latent-growth-curve-modeling","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-grounded-theory","name":"Longitudinal Grounded Theory","fullName":"Longitudinal Grounded Theory Research","aliases":["LGT","longitudinal GT","temporal grounded theory","grounded theory longitudinal design"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (as a recognized variant of grounded theory)","originator":"Kathy Charmaz and longitudinal qualitative researchers (building on Glaser & Strauss)","url":"https://scholargate.app/en/qualitative/longitudinal-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-grounded-theory.md","definition":"Longitudinal grounded theory is a qualitative research design that applies grounded theory's inductive, iterative logic to data collected from the same participants or settings across multiple time points. It is used to build substantive theory that accounts not only for social processes but also for how those processes unfold, shift, and are renegotiated over time. The approach is particularly suited to studying change, trajectory, and temporal experience in social and health research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz and longitudinal qualitative researchers (building on Glaser & Strauss)","year":"1990s–2000s (as a recognized variant of grounded theory)","type":"Qualitative longitudinal research design","dataType":"Repeated in-depth interviews, field notes, documents collected across multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973522","url":null},{"ref":"Hallberg, L. R.-M. (2006). The 'core category' of grounded theory: Making constant comparisons. International Journal of Qualitative Studies on Health and Well-being, 1(3), 141–148.","type":"article","doi":"10.1080/17482620600858399","isbn":null,"url":null}],"related":["grounded-theory","constructivist-grounded-theory","longitudinal-qualitative-research","longitudinal-case-study","longitudinal-thematic-analysis","classic-grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-hermeneutic-analysis","name":"Longitudinal Hermeneutic Analysis","fullName":"Longitudinal Hermeneutic Analysis","aliases":["longitudinal interpretive analysis","repeated hermeneutic inquiry","diachronic hermeneutics","temporal hermeneutic study"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1960s–1980s (hermeneutic tradition); longitudinal application consolidated late 20th century","originator":"Hans-Georg Gadamer (hermeneutic foundation); extended by Paul Ricoeur and longitudinal qualitative researchers","url":"https://scholargate.app/en/field-methods/longitudinal-hermeneutic-analysis","markdownUrl":"https://scholargate.app/en/field-methods/longitudinal-hermeneutic-analysis.md","definition":"Longitudinal hermeneutic analysis combines the interpretive depth of hermeneutics with repeated data collection across time, tracing how meanings, understandings, and interpretations evolve within individuals, texts, or communities. Rooted in Gadamerian and Ricoeurian hermeneutics, this approach treats meaning as temporally situated and subject to revision, making it particularly valuable in humanities and social science research that seeks to understand change in lived interpretation over months or years.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans-Georg Gadamer (hermeneutic foundation); extended by Paul Ricoeur and longitudinal qualitative researchers","year":"1960s–1980s (hermeneutic tradition); longitudinal application consolidated late 20th century","type":"Qualitative interpretive research design","dataType":"Texts, documents, interviews, field notes collected at multiple time points","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Gadamer, H.-G. (1975). Truth and Method (G. Barden & J. Cumming, Trans.). Seabury Press. (Original work published 1960)","type":"book","doi":null,"isbn":"978-0826400369","url":null},{"ref":"Ricoeur, P. (1988). Time and Narrative, Vol. 3 (K. Blamey & D. Pellauer, Trans.). University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226713342","url":null}],"related":["hermeneutic-analysis","longitudinal-qualitative-research","narrative-analysis","interpretive-phenomenological-analysis","longitudinal-case-study","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-historical-archival-research","name":"Longitudinal Historical Archival Research","fullName":"Longitudinal Historical Archival Research","aliases":["longitudinal archival study","diachronic archival research","historical longitudinal analysis","archival panel research"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"20th century (formalized in social science methodology by the 1970s–1990s)","originator":"Established practice in historical and social science research traditions","url":"https://scholargate.app/en/field-methods/longitudinal-historical-archival-research","markdownUrl":"https://scholargate.app/en/field-methods/longitudinal-historical-archival-research.md","definition":"Longitudinal historical archival research is a qualitative and documentary method that systematically examines primary archival sources — records, manuscripts, correspondence, institutional files — across multiple points in time to trace change, continuity, or development within a phenomenon over an extended historical period. By imposing a longitudinal dimension on standard archival inquiry, researchers can reconstruct how events, structures, policies, or social conditions evolved rather than capturing only a single historical moment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established practice in historical and social science research traditions","year":"20th century (formalized in social science methodology by the 1970s–1990s)","type":"Qualitative/mixed archival research design","dataType":"Primary archival documents, records, manuscripts, institutional files spanning multiple time points","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Scott, J. (1990). A Matter of Record: Documentary Sources in Social Research. Polity Press.","type":"book","doi":null,"isbn":"978-0745602578","url":null},{"ref":"Hill, M. R. (1993). Archival Strategies and Techniques. Sage Publications.","type":"book","doi":null,"isbn":"978-0803950764","url":null}],"related":["historical-archival-research","longitudinal-research","document-analysis","comparative-historical-analysis","oral-history-method","content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-hypothesis-testing-research","name":"Longitudinal Hypothesis Testing Research","fullName":"Longitudinal Hypothesis Testing Research Design","aliases":["longitudinal confirmatory study","repeated-measures hypothesis testing","prospective hypothesis testing","longitudinal inferential research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Consolidated as a formal design framework in the 1960s–1980s","originator":"Synthesized from longitudinal design traditions (Lazarsfeld, 1940s) and classical hypothesis testing (Fisher, Neyman-Pearson, 1920s–1930s)","url":"https://scholargate.app/en/research-design/longitudinal-hypothesis-testing-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-hypothesis-testing-research.md","definition":"Longitudinal hypothesis testing research combines a longitudinal design — measuring the same units repeatedly over time — with formal null-hypothesis significance testing to determine whether observed changes exceed what chance alone can explain. It is widely used in education, medicine, psychology, and social science to test directional predictions about change, stability, or group differences that emerge over a defined time span.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from longitudinal design traditions (Lazarsfeld, 1940s) and classical hypothesis testing (Fisher, Neyman-Pearson, 1920s–1930s)","year":"Consolidated as a formal design framework in the 1960s–1980s","type":"Quantitative longitudinal research design","dataType":"Repeated measurements on the same subjects across two or more time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195152968","url":null},{"ref":"Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2011). Applied Longitudinal Analysis (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470380277","url":null}],"related":["longitudinal-research","panel-research","hypothesis-testing-research","repeated-measures-anova","growth-curve-modeling","confirmatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-in-depth-interview","name":"Longitudinal In-depth Interview","fullName":"Longitudinal Qualitative In-depth Interview","aliases":["repeated in-depth interview","longitudinal qualitative interview","panel qualitative interview","longitudinal IDI"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (as a formalised qualitative method)","originator":"Rooted in qualitative longitudinal research traditions; systematised by Johnny Saldana","url":"https://scholargate.app/en/survey-methodology/longitudinal-in-depth-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-in-depth-interview.md","definition":"Longitudinal in-depth interviewing is a qualitative data collection technique in which the same participants are interviewed in depth on multiple occasions across a defined time span. By revisiting the same people over weeks, months, or years, researchers can trace how experiences, identities, attitudes, and meanings change — something a single interview cannot reveal. It is widely used in life-course research, health studies, education, and social policy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in qualitative longitudinal research traditions; systematised by Johnny Saldana","year":"1990s–2000s (as a formalised qualitative method)","type":"Qualitative longitudinal data collection technique","dataType":"Repeated verbal/textual data from the same participants over multiple time points","subfamily":"Data collection"},"citations":[{"ref":"Saldana, J. (2003). Longitudinal Qualitative Research: Analyzing Change Through Time. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759103917","url":null},{"ref":"Farrall, S., & Calverley, A. (2006). Understanding desistance from crime: Theoretical directions in resettlement and rehabilitation. Open University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Understanding+desistance+from+crime+Farrall+Calverley+2006"}],"related":["in-depth-interview","semi-structured-interview","longitudinal-survey","diary-method","narrative-analysis","panel-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-institutional-ethnography","name":"Longitudinal Institutional Ethnography","fullName":"Longitudinal Institutional Ethnography","aliases":["longitudinal IE","time-extended institutional ethnography","longitudinal IE study","IE longitudinal design"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1987 (IE foundation); longitudinal applications from 1990s onward","originator":"Dorothy E. Smith (institutional ethnography); longitudinal extension by subsequent IE practitioners","url":"https://scholargate.app/en/qualitative/longitudinal-institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-institutional-ethnography.md","definition":"Longitudinal Institutional Ethnography (longitudinal IE) combines Dorothy Smith's sociology of standpoint — institutional ethnography — with repeated data collection over time to trace how institutional texts, relations, and ruling practices shape people's everyday lives across a temporal span. By revisiting the same participants, settings, or documents at multiple time points, it reveals how institutional coordination evolves, accumulates, or intensifies over weeks, months, or years.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith (institutional ethnography); longitudinal extension by subsequent IE practitioners","year":"1987 (IE foundation); longitudinal applications from 1990s onward","type":"Qualitative longitudinal research design","dataType":"Interviews, texts, documents, observations collected at multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759106598","url":null},{"ref":"DeVault, M. L., & McCoy, L. (2006). Institutional ethnography: Using interviews to investigate ruling relations. In D. E. Smith (Ed.), Institutional Ethnography as Practice (pp. 15–44). Rowman & Littlefield.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Institutional+Ethnography+as+Practice+Smith+2006"}],"related":["institutional-ethnography","longitudinal-ethnography","longitudinal-case-study","participatory-institutional-ethnography","critical-institutional-ethnography","longitudinal-grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-interpretive-phenomenological-analysis","name":"Longitudinal Interpretive Phenomenological Analysis","fullName":"Longitudinal Interpretive Phenomenological Analysis","aliases":["L-IPA","longitudinal IPA","repeated-interview IPA","temporal IPA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (IPA from mid-1990s; longitudinal variant formalised ~2009–2014)","originator":"Jonathan A. Smith and colleagues; longitudinal extension developed by Smith, Flowers, and Larkin","url":"https://scholargate.app/en/qualitative/longitudinal-interpretive-phenomenological-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-interpretive-phenomenological-analysis.md","definition":"Longitudinal Interpretive Phenomenological Analysis (L-IPA) extends the IPA tradition by interviewing the same participants at multiple time points, allowing researchers to trace how the meaning of a lived experience evolves over time. Grounded in phenomenology and hermeneutics, L-IPA preserves idiographic depth at each wave while adding a temporal dimension that cross-sectional IPA cannot provide. It is used widely in health psychology, illness adjustment studies, and any domain where experience unfolds across a significant time span.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan A. Smith and colleagues; longitudinal extension developed by Smith, Flowers, and Larkin","year":"2000s–2010s (IPA from mid-1990s; longitudinal variant formalised ~2009–2014)","type":"Qualitative research design and analysis approach","dataType":"Repeated in-depth interview transcripts collected from the same participants at multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretive Phenomenological Analysis: Theory, Method and Research. Sage.","type":"book","doi":null,"isbn":"978-1412908344","url":null},{"ref":"Larkin, M., Watts, S., & Clifton, E. (2006). Giving voice and making sense in interpretive phenomenological analysis. Qualitative Research in Psychology, 3(2), 102–120.","type":"article","doi":"10.1191/1478088706qp062oa","isbn":null,"url":null}],"related":["interpretive-phenomenological-analysis","phenomenology","narrative-analysis","thematic-analysis","grounded-theory","diary-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-item-analysis","name":"Longitudinal Item Analysis","fullName":"Longitudinal Item Analysis","aliases":["LIA","repeated-measures item analysis","longitudinal item calibration","item parameter stability analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Vandenberg, Lance, Meade and colleagues in organizational/educational measurement","url":"https://scholargate.app/en/psychometrics/longitudinal-item-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-item-analysis.md","definition":"Longitudinal item analysis examines how the statistical properties of individual scale items — difficulty, discrimination, factor loadings, and fit — remain stable or change systematically across repeated measurement occasions. It is the item-level foundation of longitudinal measurement validity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vandenberg, Lance, Meade and colleagues in organizational/educational measurement","year":"1990s–2000s","type":"Item-level longitudinal diagnostic","dataType":"Repeated-measures item response data (ordinal or binary) collected at two or more time points","subfamily":"Scale / measurement"},"citations":[{"ref":"Meade, A. W., Johnson, E. C. & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93(3), 568–592.","type":"article","doi":"10.1037/0021-9010.93.3.568","isbn":null,"url":null},{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["longitudinal-measurement-invariance","item-response-theory","differential-item-functioning","confirmatory-factor-analysis","longitudinal-confirmatory-factor-analysis","test-retest-reliability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-item-response-theory","name":"Longitudinal IRT","fullName":"Longitudinal Item Response Theory","aliases":["LIRT","longitudinal IRT","repeated-measures IRT","dynamic item response modeling"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1991","originator":"Susan E. Embretson","url":"https://scholargate.app/en/psychometrics/longitudinal-item-response-theory","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-item-response-theory.md","definition":"Longitudinal IRT extends classical item response theory to data collected at multiple time points, allowing researchers to model both the initial latent trait level and its change over time. It is used in educational assessment, clinical trials, and panel studies where the same items or item banks are administered repeatedly to the same individuals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Susan E. Embretson","year":"1991","type":"Latent trait / longitudinal psychometric model","dataType":"Repeated dichotomous or polytomous item responses across time points","subfamily":"Scale / measurement"},"citations":[{"ref":"Embretson, S. E. (1991). A multidimensional latent trait model for measuring learning and change. Psychometrika, 56(3), 495–515.","type":"article","doi":"10.1007/BF02294487","isbn":null,"url":null},{"ref":"von Davier, M. & Carstensen, C. H. (Eds.) (2007). Multivariate and Mixture Distribution Rasch Models: Extensions and Applications. Springer.","type":"book","doi":null,"isbn":"978-0387329161","url":null}],"related":["item-response-theory","longitudinal-confirmatory-factor-analysis","longitudinal-measurement-invariance","multilevel-item-response-theory","differential-item-functioning","longitudinal-rasch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-life-history-research","name":"Longitudinal Life history research","fullName":"Longitudinal Life History Research","aliases":["longitudinal biographical research","life history longitudinal design","repeated life history study","longitudinal oral biography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1918 (origins); longitudinal application developed from 1980s onward","originator":"Thomas & Znaniecki (Polish Peasant, 1918–1920); elaborated by Ken Plummer, Daniel Bertaux","url":"https://scholargate.app/en/qualitative/longitudinal-life-history-research","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-life-history-research.md","definition":"Longitudinal life history research follows the same participants across multiple points in time, collecting repeated in-depth accounts of how their life stories evolve, how they narrate past events differently over time, and how biography intersects with social change. It combines the interpretive depth of life history methodology with the temporal sensitivity of longitudinal design, capturing both the content of lived experience and its unfolding across the life course.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas & Znaniecki (Polish Peasant, 1918–1920); elaborated by Ken Plummer, Daniel Bertaux","year":"1918 (origins); longitudinal application developed from 1980s onward","type":"Qualitative longitudinal research design","dataType":"Repeated in-depth interviews, personal documents, diaries, correspondence","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.","type":"book","doi":null,"isbn":"978-0761952244","url":null},{"ref":"Thomson, R. (2009). Unfolding Lives: Youth, Gender and Change. Policy Press.","type":"book","doi":null,"isbn":"978-1847421517","url":null}],"related":["life-history-research","biographical-research","longitudinal-narrative-research","longitudinal-case-study","narrative-inquiry","oral-history"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-mcdonalds-omega","name":"Longitudinal McDonald's omega","fullName":"Longitudinal McDonald's Omega Reliability Coefficient","aliases":["longitudinal omega","omega longitudinal reliability","time-varying omega","repeated-measures omega"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1999 (original omega); 2014 (longitudinal extension)","originator":"McDonald (1999); extended to longitudinal contexts by Geldhof, Preacher, and Zyphur (2014) and subsequent authors","url":"https://scholargate.app/en/psychometrics/longitudinal-mcdonalds-omega","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-mcdonalds-omega.md","definition":"Longitudinal McDonald's omega estimates scale reliability separately at each measurement occasion in a panel or repeated-measures study. By fitting a confirmatory factor model at each wave, it tracks how consistently a set of items measures its target construct over time, detecting erosion or improvement in measurement quality that a single omnibus reliability coefficient would obscure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McDonald (1999); extended to longitudinal contexts by Geldhof, Preacher, and Zyphur (2014) and subsequent authors","year":"1999 (original omega); 2014 (longitudinal extension)","type":"Reliability / internal consistency coefficient","dataType":"Repeated measures / panel data with multiple indicators per time point","subfamily":"Scale / measurement"},"citations":[{"ref":"McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805830(textbook)","url":null},{"ref":"Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91.","type":"article","doi":"10.1037/a0032138","isbn":null,"url":null}],"related":["mcdonalds-omega","cronbach-alpha","confirmatory-factor-analysis","longitudinal-sem","multilevel-reliability","test-retest-reliability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-measurement-invariance","name":"Longitudinal Measurement Invariance","fullName":"Longitudinal Measurement Invariance Testing","aliases":["LMI","longitudinal invariance","measurement equivalence across time","temporal measurement invariance"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1993","originator":"William Meredith","url":"https://scholargate.app/en/psychometrics/longitudinal-measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-measurement-invariance.md","definition":"Longitudinal measurement invariance testing determines whether a psychological scale measures the same construct in the same way across two or more time points. It is a prerequisite for interpreting mean-level change scores in panel and repeated-measures studies, ensuring that observed change reflects true change in the construct rather than drift in the measurement instrument.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Meredith","year":"1993","type":"Measurement model testing","dataType":"Ordinal or continuous repeated-measure indicators","subfamily":"Scale / measurement"},"citations":[{"ref":"Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525–543.","type":"article","doi":"10.1007/BF02294825","isbn":null,"url":null},{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","structural-equation-modeling","multigroup-confirmatory-factor-analysis","measurement-invariance","latent-growth-curve-modeling","repeated-measures-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-metaphor-analysis","name":"Longitudinal Metaphor Analysis","fullName":"Longitudinal Metaphor Analysis","aliases":["LMA","diachronic metaphor analysis","longitudinal conceptual metaphor study","repeated-measures metaphor analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (systematic longitudinal application)","originator":"Lynne Cameron and colleagues (discourse dynamics framework); broader tradition rooted in Lakoff & Johnson's conceptual metaphor theory (1980)","url":"https://scholargate.app/en/qualitative/longitudinal-metaphor-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-metaphor-analysis.md","definition":"Longitudinal Metaphor Analysis (LMA) is a qualitative method that tracks how individuals or groups use metaphors across multiple time points to reveal conceptual, attitudinal, or identity shifts. Grounded in conceptual metaphor theory and discourse dynamics, it treats metaphor not as mere rhetorical decoration but as a window into evolving thought, belief, and meaning-making over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lynne Cameron and colleagues (discourse dynamics framework); broader tradition rooted in Lakoff & Johnson's conceptual metaphor theory (1980)","year":"2000s–2010s (systematic longitudinal application)","type":"Qualitative analytic method","dataType":"Transcripts, interviews, written texts, diaries, or discourse data collected at multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Cameron, L., & Maslen, R. (Eds.). (2010). Metaphor Analysis: Research Practice in Applied Linguistics, Social Sciences and the Humanities. Equinox.","type":"article","doi":null,"isbn":"978-1845531140","url":null},{"ref":"Cameron, L., Maslen, R., Todd, Z., Maule, J., Stratton, P., & Stanley, N. (2009). The discourse dynamics approach to metaphor and metaphor-led discourse analysis. Metaphor and Symbol, 24(2), 63–89.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1080/10926480902789224"}],"related":["metaphor-analysis","discourse-analysis","narrative-analysis","content-analysis","thematic-analysis","longitudinal-qualitative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-mobile-experience-sampling","name":"Longitudinal Mobile Experience Sampling","fullName":"Longitudinal Mobile Experience Sampling Method","aliases":["Longitudinal ESM","Longitudinal EMA","Longitudinal Ecological Momentary Assessment","Long-term mESM"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1983 (ESM origins); 2000s onward (mobile longitudinal variants)","originator":"Csikszentmihalyi & Larson (ESM, 1983); Shiffman, Stone & Hufford (EMA, 2008); extended to longitudinal mobile designs by Hamaker and colleagues","url":"https://scholargate.app/en/survey-methodology/longitudinal-mobile-experience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-mobile-experience-sampling.md","definition":"Longitudinal Mobile Experience Sampling combines the real-time, in-context signal capture of Experience Sampling Method (ESM) with a longitudinal design spanning weeks, months, or longer. Participants respond to repeated prompts delivered to their smartphones across multiple time waves, enabling researchers to observe within-person change, stability, and dynamic processes as they unfold in daily life rather than in retrospective recall.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Csikszentmihalyi & Larson (ESM, 1983); Shiffman, Stone & Hufford (EMA, 2008); extended to longitudinal mobile designs by Hamaker and colleagues","year":"1983 (ESM origins); 2000s onward (mobile longitudinal variants)","type":"Longitudinal intensive data collection technique","dataType":"Real-time self-reports, behavioral signals, physiological sensor data","subfamily":"Data collection"},"citations":[{"ref":"Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.","type":"article","doi":"10.1146/annurev.clinpsy.3.022806.091415","isbn":null,"url":null},{"ref":"Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26(1), 10–15.","type":"article","doi":"10.1177/0963721416666518","isbn":null,"url":null}],"related":["mobile-experience-sampling","experience-sampling-method","longitudinal-survey","ecological-momentary-assessment","diary-method","longitudinal-diary-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-model-testing-research","name":"Longitudinal Model Testing Research","fullName":"Longitudinal Model Testing Research","aliases":["longitudinal confirmatory modeling","longitudinal SEM","panel model testing","longitudinal structural modeling"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s)","originator":"Synthesized from longitudinal panel design and SEM tradition (Joreskog, Bollen, Singer & Willett)","url":"https://scholargate.app/en/research-design/longitudinal-model-testing-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-model-testing-research.md","definition":"Longitudinal model testing research combines repeated measurement across time with formal, a priori structural modeling to confirm or disconfirm hypothesized relationships among constructs. Rather than simply describing change, it tests whether a pre-specified theoretical model — typically a structural equation model or growth model — fits observed data collected at two or more time points. This design supports causal inference more convincingly than cross-sectional approaches by capturing temporal ordering of variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from longitudinal panel design and SEM tradition (Joreskog, Bollen, Singer & Willett)","year":"1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s)","type":"Quantitative, confirmatory, longitudinal design","dataType":"Repeated-measures quantitative data (panel data, survey waves, archival records collected at two or more time points)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195152968","url":null},{"ref":"Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462523344","url":null}],"related":["longitudinal-research","model-testing-research","confirmatory-research","panel-research","longitudinal-confirmatory-research","structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-multiple-case-study","name":"Longitudinal Multiple case study","fullName":"Longitudinal Multiple Case Study Research","aliases":["longitudinal multi-case study","repeated multiple case study","panel case study","multi-site longitudinal case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–2000s (Yin's multiple-case framework c. 1984; longitudinal qualitative elaboration c. 2003)","originator":"Robert K. Yin (multiple case design); Johnny Saldana (longitudinal qualitative methods)","url":"https://scholargate.app/en/qualitative/longitudinal-multiple-case-study","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-multiple-case-study.md","definition":"Longitudinal multiple case study is a qualitative research design that examines two or more bounded cases through repeated data-collection waves over an extended period. By tracking each case across time and comparing patterns across cases, researchers can document how phenomena change, stabilise, or diverge — generating both depth within each site and breadth across sites that neither a single case nor a one-shot survey can provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (multiple case design); Johnny Saldana (longitudinal qualitative methods)","year":"1980s–2000s (Yin's multiple-case framework c. 1984; longitudinal qualitative elaboration c. 2003)","type":"Qualitative longitudinal research design","dataType":"Interviews, observations, documents collected at multiple time points across multiple cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Saldana, J. (2003). Longitudinal Qualitative Research: Analyzing Change Through Time. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759103917","url":null}],"related":["multiple-case-study","longitudinal-case-study","comparative-case-study","longitudinal-ethnography","longitudinal-narrative-research","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-narrative-research","name":"Longitudinal Narrative Research","fullName":"Longitudinal Narrative Inquiry","aliases":["longitudinal narrative inquiry","narrative longitudinal design","LNI","temporal narrative research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (narrative inquiry established 1990; longitudinal application elaborated 2000s–2010s)","originator":"D. Jean Clandinin & F. Michael Connelly (narrative inquiry foundations); extended into longitudinal designs by Clandinin and colleagues","url":"https://scholargate.app/en/qualitative/longitudinal-narrative-research","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-narrative-research.md","definition":"Longitudinal narrative research is a qualitative design that follows participants across multiple time points, gathering and analyzing their stories to understand how experiences, identities, and meanings evolve over time. Rooted in Clandinin and Connelly's narrative inquiry tradition, it treats human experience as fundamentally storied and temporal — what matters is not just what happened but how people narrate, revise, and make sense of their lives as circumstances change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. Jean Clandinin & F. Michael Connelly (narrative inquiry foundations); extended into longitudinal designs by Clandinin and colleagues","year":"1990s–2000s (narrative inquiry established 1990; longitudinal application elaborated 2000s–2010s)","type":"Qualitative longitudinal research design","dataType":"Repeated narrative interviews, field texts, journals, documents collected over multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Clandinin, D. J., Huber, J., Huber, M., Murphy, M. S., Murray Orr, A., Pearce, M., & Steeves, P. (2006). Composing diverse identities: Narrative inquiries into the interwoven lives of children and teachers. Routledge.","type":"article","doi":null,"isbn":"978-0415357241","url":null},{"ref":"Clandinin, D. J. (2016). Engaging in Narrative Inquiry. Left Coast Press / Routledge.","type":"book","doi":null,"isbn":"978-1629582245","url":null}],"related":["narrative-inquiry","longitudinal-case-study","longitudinal-ethnography","biographical-research","life-history-research","longitudinal-qualitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-netnography","name":"Longitudinal Netnography","fullName":"Longitudinal Netnographic Research","aliases":["longitudinal online ethnography","temporal netnography","long-term netnography","diachronic netnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1997 (netnography); longitudinal application developed 2000s–2010s","originator":"Robert V. Kozinets (netnography); longitudinal extension by subsequent researchers","url":"https://scholargate.app/en/qualitative/longitudinal-netnography","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-netnography.md","definition":"Longitudinal netnography applies the systematic, immersive online ethnographic method developed by Kozinets across multiple time points to reveal how digital communities, cultural practices, and shared meanings evolve. Rather than offering a snapshot of online life, it tracks the same community or platform over weeks, months, or years, capturing change, continuity, and the temporal rhythms of internet culture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert V. Kozinets (netnography); longitudinal extension by subsequent researchers","year":"1997 (netnography); longitudinal application developed 2000s–2010s","type":"Longitudinal qualitative online research design","dataType":"Online community data: posts, threads, comments, profiles collected across multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1526458353","url":null},{"ref":"Mkono, M., & Markwell, K. (2014). The application of netnography in tourism research: Revolution or evolution? Journal of Travel and Tourism Marketing, 31(4), 453–465.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+application+of+netnography+in+tourism+research%3A+Revolution+or+evolution+Mkono"}],"related":["netnography","digital-ethnography","longitudinal-ethnography","longitudinal-qualitative-content-analysis","longitudinal-discourse-analysis","virtual-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-nomological-validity","name":"Longitudinal Nomological Validity","fullName":"Longitudinal Nomological Validity Assessment","aliases":["longitudinal construct validity","nomological network validation across time","longitudinal criterion-related validity","temporal nomological validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1955 (concept); longitudinal extension 1990s–2000s","originator":"Cronbach & Meehl (nomological network concept, 1955); longitudinal extension developed in organizational and personality research from the 1990s onward","url":"https://scholargate.app/en/psychometrics/longitudinal-nomological-validity","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-nomological-validity.md","definition":"Longitudinal nomological validity evaluates whether a construct's theoretically predicted relationships with other constructs hold consistently across multiple measurement occasions. It extends the nomological network framework of Cronbach and Meehl (1955) to longitudinal designs, testing whether a scale behaves as theory demands not only at a single time point but over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cronbach & Meehl (nomological network concept, 1955); longitudinal extension developed in organizational and personality research from the 1990s onward","year":"1955 (concept); longitudinal extension 1990s–2000s","type":"Validity evaluation","dataType":"Repeated-measures / longitudinal scale scores, latent variable scores","subfamily":"Scale / measurement"},"citations":[{"ref":"Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.","type":"article","doi":"10.1037/h0040957","isbn":null,"url":null},{"ref":"Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["nomological-validity","longitudinal-measurement-invariance","longitudinal-confirmatory-factor-analysis","convergent-validity","discriminant-validity","construct-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-non-participant-observation","name":"Longitudinal Non-participant Observation","fullName":"Longitudinal Non-participant Observation","aliases":["longitudinal unobtrusive observation","repeated non-participant observation","longitudinal systematic observation","extended non-participant field observation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Early–mid 20th century (systematized 1920s–1970s)","originator":"Rooted in sociological field research traditions (e.g., Chicago School); longitudinal extension developed through 20th-century social science methodology","url":"https://scholargate.app/en/survey-methodology/longitudinal-non-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-non-participant-observation.md","definition":"Longitudinal non-participant observation is a data collection method in which a researcher systematically watches and records naturally occurring behaviors, interactions, or events at a setting over multiple, repeated observation sessions spanning weeks, months, or years — without joining or influencing the activities being observed. The researcher remains an external observer, producing a time-ordered record of change or continuity in the phenomenon under study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in sociological field research traditions (e.g., Chicago School); longitudinal extension developed through 20th-century social science methodology","year":"Early–mid 20th century (systematized 1920s–1970s)","type":"Longitudinal observational data collection","dataType":"Field notes, observational records, structured observation logs collected at multiple time points","subfamily":"Data collection"},"citations":[{"ref":"Angrosino, M. (2007). Doing Ethnographic and Observational Research. Sage Publications.","type":"book","doi":null,"isbn":"978-1412922173","url":null},{"ref":"Menard, S. (Ed.). (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922452","url":null}],"related":["non-participant-observation","longitudinal-participant-observation","longitudinal-field-notes","participant-observation","longitudinal-survey","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-oral-history-method","name":"Longitudinal oral history method","fullName":"Longitudinal Oral History Method","aliases":["repeated oral history interviewing","longitudinal life history","serial oral history","longitudinal biographical interviewing"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1970s–1980s (systematic formulation); longstanding practice in oral history","originator":"Paul Thompson; developed further by Ken Plummer and oral history practitioners","url":"https://scholargate.app/en/field-methods/longitudinal-oral-history-method","markdownUrl":"https://scholargate.app/en/field-methods/longitudinal-oral-history-method.md","definition":"Longitudinal oral history method is a qualitative research design in which the same participants are interviewed repeatedly over an extended period — months or years — using oral history interviewing techniques. By returning to narrators across time, researchers can trace how personal accounts, identities, and interpretations of experience shift and evolve, capturing the processual and biographical dimensions of social life that a single interview cannot reveal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Thompson; developed further by Ken Plummer and oral history practitioners","year":"1970s–1980s (systematic formulation); longstanding practice in oral history","type":"Qualitative longitudinal research design","dataType":"Repeated in-depth oral history interviews with the same participants over time","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Thompson, P. (2000). The Voice of the Past: Oral History (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0192893468","url":null},{"ref":"Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.","type":"book","doi":null,"isbn":"978-0761953265","url":null}],"related":["oral-history-method","life-history-research","longitudinal-qualitative-research","narrative-analysis","biographical-method","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-oral-history","name":"Longitudinal Oral History","fullName":"Longitudinal Oral History Research","aliases":["repeated oral history","serial oral history","life-course oral history","longitudinal life narrative"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–1990s (formalized as distinct variant)","originator":"Allan Nevins (oral history); longitudinal variant developed across life-course sociology and oral history practice from 1970s–1990s","url":"https://scholargate.app/en/qualitative/longitudinal-oral-history","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-oral-history.md","definition":"Longitudinal oral history is a qualitative research design in which the same participants are interviewed repeatedly over an extended period — months or years — using open-ended, narrative-focused conversations. By revisiting participants at multiple points in time, the researcher traces how individuals construct, revise, and reinterpret their personal stories as their lives unfold, capturing not just retrospective accounts but the dynamic, evolving nature of memory and meaning-making.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Allan Nevins (oral history); longitudinal variant developed across life-course sociology and oral history practice from 1970s–1990s","year":"1970s–1990s (formalized as distinct variant)","type":"Qualitative longitudinal research design","dataType":"Repeated in-depth interviews, personal narratives, biographical accounts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Thomson, A. (2007). Four paradigm transformations in oral history. The Oral History Review, 34(1), 49–70.","type":"article","doi":"10.1525/ohr.2007.34.1.49","isbn":null,"url":null},{"ref":"Bornat, J. (2008). Biographical methods. In L. Given (Ed.), The SAGE Encyclopedia of Qualitative Research Methods. SAGE.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bornat+biographical+methods+SAGE+encyclopedia+qualitative+research+2008"}],"related":["oral-history","narrative-inquiry","life-history-research","biographical-method","longitudinal-qualitative-research","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-phenomenology","name":"Longitudinal Phenomenology","fullName":"Longitudinal Phenomenological Research","aliases":["longitudinal phenomenological inquiry","temporal phenomenology","repeated-interview phenomenology","longitudinal lived-experience research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s (formalised as a distinct design)","originator":"Draws on Husserl and Heidegger's phenomenological tradition; longitudinal application developed in qualitative research (Saldana, Thomson et al., early 2000s)","url":"https://scholargate.app/en/qualitative/longitudinal-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-phenomenology.md","definition":"Longitudinal phenomenology applies phenomenological inquiry across two or more time points to capture how participants' lived experience of a phenomenon changes, deepens, or transforms over time. Rooted in the phenomenological tradition of Husserl and Heidegger, it adds an explicit temporal dimension — asking not only what an experience is like, but how it evolves. It is used where a single-point interview would miss the processual, shifting nature of lived meaning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Draws on Husserl and Heidegger's phenomenological tradition; longitudinal application developed in qualitative research (Saldana, Thomson et al., early 2000s)","year":"2000s (formalised as a distinct design)","type":"Qualitative longitudinal research design","dataType":"Repeated in-depth interviews, field notes, participant diaries (text data across multiple time points)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Saldana, J. (2003). Longitudinal Qualitative Research: Analyzing Change through Time. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759103917","url":null},{"ref":"Thomson, R., Bell, R., Holland, J., Henderson, S., McGrellis, S., & Sharpe, S. (2002). Critical moments: Choice, chance and opportunity in young people's narratives of transition. Sociology, 36(2), 335–354.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1177/0038038502036002006"}],"related":["phenomenology","hermeneutic-phenomenology","longitudinal-qualitative-content-analysis","longitudinal-narrative-research","longitudinal-case-study","interpretive-phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-program-evaluation","name":"Longitudinal Program Evaluation","fullName":"Longitudinal Program Evaluation","aliases":["LPE","longitudinal evaluation","long-term program evaluation","prospective program evaluation"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1960s–1970s (program evaluation); longitudinal designs formalized 1970s–1980s","originator":"Peter Rossi, Michael Scriven, Donald Campbell (program evaluation tradition)","url":"https://scholargate.app/en/field-methods/longitudinal-program-evaluation","markdownUrl":"https://scholargate.app/en/field-methods/longitudinal-program-evaluation.md","definition":"Longitudinal program evaluation is an applied research design that tracks the outcomes and processes of a program or intervention across multiple time points — from pre-implementation baseline through medium- and long-term follow-up. Unlike single-point evaluations, it captures how program effects emerge, fade, or evolve over time, enabling evaluators and funders to judge sustained impact, cost-effectiveness, and unintended consequences that would be invisible in a snapshot assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Rossi, Michael Scriven, Donald Campbell (program evaluation tradition)","year":"1960s–1970s (program evaluation); longitudinal designs formalized 1970s–1980s","type":"Applied evaluation research design","dataType":"Quantitative outcomes, administrative records, survey panels, qualitative follow-up interviews","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761908944","url":null},{"ref":"Shadish, W. R., Cook, T. D., & Leviton, L. C. (1991). Foundations of Program Evaluation: Theories of Practice. Sage Publications.","type":"book","doi":null,"isbn":"978-0803932036","url":null}],"related":["program-evaluation","longitudinal-research","outcome-evaluation","formative-evaluation","summative-evaluation","interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-qualitative-content-analysis","name":"Longitudinal Qualitative Content Analysis","fullName":"Longitudinal Qualitative Content Analysis","aliases":["LQCA","longitudinal QCA","repeated qualitative content analysis","panel qualitative content analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Philipp Mayring (foundational QCA); longitudinal extension developed across qualitative health and social research traditions","url":"https://scholargate.app/en/qualitative/longitudinal-qualitative-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-qualitative-content-analysis.md","definition":"Longitudinal qualitative content analysis (LQCA) applies systematic content analysis to text data gathered from the same participants, settings, or documents at two or more points in time. The method preserves the interpretive rigour of qualitative content analysis while adding an explicit temporal dimension, enabling researchers to track how meanings, experiences, categories, or discourse shift, deepen, or stabilise across time rather than producing a single-point-in-time description.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Philipp Mayring (foundational QCA); longitudinal extension developed across qualitative health and social research traditions","year":"2000s–2010s","type":"Qualitative analytical method","dataType":"Text data collected at multiple time points (interviews, documents, field notes, open-ended surveys)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research, 1(2), Art. 20.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.17169/fqs-1.2.1089"},{"ref":"Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1177/1049732305276687"}],"related":["qualitative-content-analysis","thematic-analysis","narrative-analysis","longitudinal-qualitative-research","framework-analysis","interpretive-phenomenological-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-quantitative-content-analysis","name":"Longitudinal Quantitative Content Analysis","fullName":"Longitudinal Quantitative Content Analysis","aliases":["longitudinal content analysis","repeated-measure content analysis","time-series content analysis","longitudinal QCA"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1950s onward; longitudinal application widely adopted in media research by the 1970s–1980s","originator":"Developed within communication and media studies; codified by Berelson (1952) and extended by Riffe, Lacy, Fico","url":"https://scholargate.app/en/research-design/longitudinal-quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-quantitative-content-analysis.md","definition":"Longitudinal quantitative content analysis systematically codes and counts features of texts, images, or media messages gathered at two or more points in time, enabling researchers to track how content changes, how themes rise or fall in prevalence, and how media or institutional messaging responds to external events. The design merges the structured measurement logic of quantitative content analysis with the temporal tracking power of longitudinal observation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within communication and media studies; codified by Berelson (1952) and extended by Riffe, Lacy, Fico","year":"1950s onward; longitudinal application widely adopted in media research by the 1970s–1980s","type":"Quantitative observational research design","dataType":"Coded textual, visual, or media content collected across multiple time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Riffe, D., Lacy, S., Watson, B., & Fico, F. (2019). Analyzing Media Messages: Using Quantitative Content Analysis in Research (4th ed.). Routledge.","type":"book","doi":null,"isbn":"9781138490536","url":null},{"ref":"Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). Sage.","type":"book","doi":null,"isbn":"9781412979474","url":null}],"related":["quantitative-content-analysis","longitudinal-research","trend-research","panel-research","time-series-analysis","descriptive-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-reflexive-thematic-analysis","name":"Longitudinal Reflexive thematic analysis","fullName":"Longitudinal Reflexive Thematic Analysis","aliases":["longitudinal RTA","repeated-wave thematic analysis","longitudinal qualitative thematic analysis","L-RTA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (RTA seminal); longitudinal application developed through 2010s","originator":"Virginia Braun & Victoria Clarke (reflexive thematic analysis); longitudinal design adapted from qualitative longitudinal research traditions (Saldaña, 2003)","url":"https://scholargate.app/en/qualitative/longitudinal-reflexive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-reflexive-thematic-analysis.md","definition":"Longitudinal Reflexive Thematic Analysis (L-RTA) applies Braun and Clarke's reflexive thematic analysis framework to qualitative data collected from the same participants (or context) at two or more time points. Rather than producing a single static account, it tracks how meanings, experiences, and themes evolve, persist, or transform over time, foregrounding the researcher's active reflexive engagement at every stage of the iterative process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun & Victoria Clarke (reflexive thematic analysis); longitudinal design adapted from qualitative longitudinal research traditions (Saldaña, 2003)","year":"2006 (RTA seminal); longitudinal application developed through 2010s","type":"Qualitative analytic method applied longitudinally","dataType":"Repeated qualitative data collections (interviews, focus groups, diaries) across two or more time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.","type":"article","doi":"10.1080/2159676X.2019.1628806","isbn":null,"url":null}],"related":["reflexive-thematic-analysis","longitudinal-qualitative-research","longitudinal-content-analysis","thematic-analysis","longitudinal-narrative-research","interpretive-reflexive-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-relational-survey","name":"Longitudinal relational survey","fullName":"Longitudinal Relational Survey Research","aliases":["longitudinal correlational survey","prospective relational survey","repeated-measures relational survey","panel relational survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1960s–1980s (formalized in panel and longitudinal survey literature)","originator":"Classical survey methodology (Campbell & Stanley, 1963; Kessler & Greenberg, 1981)","url":"https://scholargate.app/en/research-design/longitudinal-relational-survey","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-relational-survey.md","definition":"A longitudinal relational survey follows the same sample at two or more time points, collecting structured questionnaire data each wave and examining how the relationships among variables change, strengthen, weaken, or emerge across time. Unlike a cross-sectional relational survey that offers a single snapshot, this design captures temporal dynamics and allows researchers to test whether earlier measurements predict later outcomes, making it valuable for studying development, attitude change, and causal ordering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical survey methodology (Campbell & Stanley, 1963; Kessler & Greenberg, 1981)","year":"1960s–1980s (formalized in panel and longitudinal survey literature)","type":"Non-experimental quantitative design","dataType":"Structured questionnaire or scale data collected at two or more time points from the same sample","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195152968","url":null},{"ref":"Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1606238769","url":null}],"related":["longitudinal-research","relational-survey","panel-research","cohort-research","correlational-research","cross-lagged-panel-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-reliability-analysis","name":"Longitudinal Reliability Analysis","fullName":"Longitudinal Reliability Analysis","aliases":["repeated-measures reliability","longitudinal consistency assessment","temporal reliability analysis","reliability over time"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1951–1979","originator":"Paul B. Baltes, John R. Nesselroade, Lee J. Cronbach (foundational contributors)","url":"https://scholargate.app/en/psychometrics/longitudinal-reliability-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-reliability-analysis.md","definition":"Longitudinal reliability analysis evaluates the consistency and stability of measurement instruments across two or more time points. It extends classical reliability concepts — internal consistency, test-retest stability, and measurement precision — to repeated-measures designs, ensuring that observed score changes reflect true change rather than measurement error.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul B. Baltes, John R. Nesselroade, Lee J. Cronbach (foundational contributors)","year":"1951–1979","type":"Reliability assessment","dataType":"Repeated-measures ordinal or continuous scale scores","subfamily":"Scale / measurement"},"citations":[{"ref":"Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In J. R. Nesselroade & P. B. Baltes (Eds.), Longitudinal research in the study of behavior and development (pp. 1–39). Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Baltes+Nesselroade+1979+longitudinal+research+behavior+development"},{"ref":"Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.","type":"article","doi":"10.1007/BF02310555","isbn":null,"url":null}],"related":["test-retest-reliability","longitudinal-measurement-invariance","longitudinal-confirmatory-factor-analysis","cronbachs-alpha","mcdonalds-omega","longitudinal-item-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-research-diary","name":"Longitudinal Research Diary","fullName":"Longitudinal Research Diary","aliases":["longitudinal reflexive journal","longitudinal researcher diary","longitudinal field diary","longitudinal research log"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–1990s","originator":"Rooted in Zimmerman & Wieder's diary-interview method (1977); developed further in qualitative longitudinal research through the 1980s–1990s","url":"https://scholargate.app/en/survey-methodology/longitudinal-research-diary","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-research-diary.md","definition":"A longitudinal research diary is a structured, ongoing record kept by the researcher throughout an extended study, capturing observations, decisions, emerging insights, and methodological reflections at repeated intervals over weeks, months, or years. It functions simultaneously as a reflexivity tool and a secondary data source, documenting how the inquiry evolves, how researcher positionality shifts, and how contextual changes influence the data collection process across time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Zimmerman & Wieder's diary-interview method (1977); developed further in qualitative longitudinal research through the 1980s–1990s","year":"1970s–1990s","type":"Qualitative longitudinal data collection technique","dataType":"Researcher-authored textual records: observations, reflections, methodological notes, analytic memos","subfamily":"Data collection"},"citations":[{"ref":"Zimmerman, D. H., & Wieder, D. L. (1977). The diary: Diary-interview method. Urban Life, 5(4), 479–498.","type":"article","doi":"10.1177/089124167700500406","isbn":null,"url":null},{"ref":"Research Diary. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Research_diary"}],"related":["research-diary","longitudinal-field-notes","longitudinal-participant-observation","diary-method","field-notes","longitudinal-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-research","name":"Longitudinal Research","fullName":"Longitudinal Research Design","aliases":["longitudinal study","longitudinal design","prospective longitudinal study","repeated-measures observational study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Late 19th–early 20th century; methodologically codified through the 20th century","originator":"No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett","url":"https://scholargate.app/en/research-design/longitudinal-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-research.md","definition":"Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett","year":"Late 19th–early 20th century; methodologically codified through the 20th century","type":"Quantitative (or mixed) observational research design","dataType":"Repeated quantitative measurements from the same participants or units over time","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922841","url":null},{"ref":"Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195152968","url":null}],"related":["cross-sectional-research","cohort-research","panel-research","trend-research","survey-research","descriptive-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-scale-development","name":"Longitudinal scale development","fullName":"Longitudinal Scale Development","aliases":["LSD","longitudinal measurement development","repeated-measures scale construction","scale development with panel data"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Meredith, Millsap, and colleagues","url":"https://scholargate.app/en/psychometrics/longitudinal-scale-development","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-scale-development.md","definition":"Longitudinal scale development is the systematic process of constructing and validating a measurement instrument using data collected at multiple time points. It extends classical scale development by additionally testing whether the scale measures the same construct in the same metric across occasions, enabling valid tracking of change over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Meredith, Millsap, and colleagues","year":"1990s–2000s","type":"Scale construction framework","dataType":"Repeated-measures / panel survey data","subfamily":"Scale / measurement"},"citations":[{"ref":"Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.","type":"book","doi":null,"isbn":"978-0805864311","url":null},{"ref":"Little, T. D. (2013). Longitudinal Structural Equation Modeling. Guilford Press.","type":"book","doi":null,"isbn":"978-1462510160","url":null}],"related":["confirmatory-factor-analysis","longitudinal-measurement-invariance","longitudinal-confirmatory-factor-analysis","test-retest-reliability","scale-development","multilevel-scale-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-semi-structured-interview","name":"Longitudinal Semi-structured Interview","fullName":"Longitudinal Semi-structured Interview","aliases":["LSI","repeated semi-structured interview","panel qualitative interview","longitudinal qualitative interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (as explicit methodology)","originator":"Rooted in longitudinal qualitative research traditions; systematised by Johnny Saldana and Rachel Thomson & Janet Holland","url":"https://scholargate.app/en/survey-methodology/longitudinal-semi-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-semi-structured-interview.md","definition":"A longitudinal semi-structured interview study collects open-ended, guided interview data from the same participants across multiple time points. By returning to the same individuals — weeks, months, or years apart — researchers can trace how experiences, perceptions, and meanings change over time. The approach blends the flexibility of qualitative inquiry with the temporal depth that is impossible in a one-shot design, making it a cornerstone method in qualitative longitudinal research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in longitudinal qualitative research traditions; systematised by Johnny Saldana and Rachel Thomson & Janet Holland","year":"1990s–2000s (as explicit methodology)","type":"Qualitative longitudinal data collection technique","dataType":"Audio-recorded or transcribed interview text collected at multiple time points","subfamily":"Data collection"},"citations":[{"ref":"Saldana, J. (2003). Longitudinal Qualitative Research: Analyzing Change Through Time. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759100480","url":null},{"ref":"Thomson, R., & Holland, J. (2003). Hindsight, foresight and insight: The challenges of longitudinal qualitative research. International Journal of Social Research Methodology, 6(3), 233–244.","type":"article","doi":"10.1080/1364557032000091833","isbn":null,"url":null}],"related":["semi-structured-interview","longitudinal-survey","longitudinal-in-depth-interview","longitudinal-focus-group","diary-method","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-sensor-data-collection","name":"Longitudinal Sensor Data Collection","fullName":"Longitudinal Sensor-Based Data Collection","aliases":["long-term sensor monitoring","longitudinal sensing","continuous sensor logging","repeated-measures sensor collection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (accelerated with IoT and wearable devices from ~2010)","originator":"Emerging from ambulatory assessment and wearable technology research communities","url":"https://scholargate.app/en/survey-methodology/longitudinal-sensor-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-sensor-data-collection.md","definition":"Longitudinal sensor data collection deploys physical or digital sensors to record phenomena continuously or at regular intervals across an extended study period — days, months, or years. Unlike one-shot measurement, the repeated temporal structure captures change, trajectory, and variability in outcomes such as physical activity, environmental exposure, sleep, or physiological state. The approach combines the ecological validity of real-world sensing with the analytical power of longitudinal design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerging from ambulatory assessment and wearable technology research communities","year":"1990s–2000s (accelerated with IoT and wearable devices from ~2010)","type":"Longitudinal quantitative/mixed data collection technique","dataType":"Continuous or episodic sensor readings (physiological, environmental, accelerometric, GPS, etc.) collected repeatedly over time","subfamily":"Data collection"},"citations":[{"ref":"Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2005). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671–694. [For longitudinal intensive repeated-measures designs context, see also: Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.]","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=PROC+LCA%3A+A+SAS+procedure+for+latent+class+analysis+Lanza"},{"ref":"Stone, A. A., & Shiffman, S. (2002). Capturing momentary, self-report data: A proposal for reporting guidelines. Annals of Behavioral Medicine, 24(3), 236–243.","type":"article","doi":"10.1207/S15324796ABM2403_09","isbn":null,"url":null}],"related":["sensor-data-collection","longitudinal-survey","mobile-experience-sampling-method","experience-sampling-method","ecological-momentary-assessment","time-series-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-single-case-study","name":"Longitudinal Single Case Study","fullName":"Longitudinal Single Case Study Design","aliases":["single-case longitudinal design","in-depth longitudinal case study","idiographic longitudinal study","LSCS"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984 (Yin's foundational codification); longitudinal case methods in use since early 20th century","originator":"Robert K. Yin (systematic codification); roots in clinical and anthropological case tradition","url":"https://scholargate.app/en/qualitative/longitudinal-single-case-study","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-single-case-study.md","definition":"A longitudinal single case study is a qualitative research design that follows one bounded unit — a person, organization, program, or community — through multiple points in time. Unlike a cross-sectional snapshot, it captures how phenomena develop, shift, or respond to events across months or years, combining the contextual richness of case study methodology with the temporal depth needed to understand process and change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (systematic codification); roots in clinical and anthropological case tradition","year":"1984 (Yin's foundational codification); longitudinal case methods in use since early 20th century","type":"Qualitative research design","dataType":"Interviews, observations, documents, archival records collected at multiple time points from a single bounded unit","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Gerring, J. (2007). Case Study Research: Principles and Practices. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521676557","url":null}],"related":["case-study","multiple-case-study","ethnography","narrative-analysis","action-research","process-tracing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-straussian-grounded-theory","name":"Longitudinal Straussian Grounded Theory","fullName":"Longitudinal Straussian Grounded Theory","aliases":["longitudinal GT (Straussian)","Strauss-Corbin longitudinal grounded theory","processual grounded theory","longitudinal constructivist grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (systematic longitudinal application emerged ~2000–2010)","originator":"Anselm Strauss & Juliet Corbin (grounded theory basis); extended by qualitative longitudinal researchers (e.g., Bren Neale, Julia Brannen)","url":"https://scholargate.app/en/qualitative/longitudinal-straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-straussian-grounded-theory.md","definition":"Longitudinal Straussian Grounded Theory applies the systematic coding procedures of Strauss and Corbin's grounded theory — open, axial, and selective coding — to data gathered across multiple time points. Rather than producing a static snapshot of a social phenomenon, it tracks how processes, identities, or conditions evolve, generating a substantive theory grounded in change over time. It is particularly powerful for studying social processes that unfold across months or years.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss & Juliet Corbin (grounded theory basis); extended by qualitative longitudinal researchers (e.g., Bren Neale, Julia Brannen)","year":"1990s–2000s (systematic longitudinal application emerged ~2000–2010)","type":"Qualitative research design and analytic approach","dataType":"Repeated-wave qualitative interviews, field notes, documents collected over time","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0803959408","url":null},{"ref":"Bartlett, D., & Milligan, C. (2015). What is Diary Method? Bloomsbury Academic. [See also: Neale, B. (2021). Qualitative Longitudinal Research: Research Methods. Bloomsbury Academic.]","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=longitudinal+grounded+theory+Strauss+Corbin"}],"related":["grounded-theory","constructivist-grounded-theory","longitudinal-qualitative-research","narrative-analysis","thematic-analysis","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-structured-interview","name":"Longitudinal Structured Interview","fullName":"Longitudinal Structured Interview","aliases":["panel structured interview","repeated structured interview","longitudinal survey interview","wave-based structured interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–present","originator":"Established practice in panel and cohort research; codified in survey methodology literature from the 1970s onward","url":"https://scholargate.app/en/survey-methodology/longitudinal-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-structured-interview.md","definition":"A longitudinal structured interview applies a fixed, standardised interview schedule to the same participants at two or more points in time. By holding the instrument constant across waves, the method enables genuine within-person change to be measured, trends to be tracked, and causal sequences to be examined with far greater confidence than a single cross-sectional interview can provide. It is widely used in panel studies, cohort research, and programme evaluations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established practice in panel and cohort research; codified in survey methodology literature from the 1970s onward","year":"1970s–present","type":"Longitudinal quantitative data collection method","dataType":"Structured verbal responses collected across multiple time points","subfamily":"Data collection"},"citations":[{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922452","url":null},{"ref":"Lynn, P. (Ed.). (2009). Methodology of Longitudinal Surveys. Wiley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Methodology+of+Longitudinal+Surveys+Lynn+2009"}],"related":["structured-interview","longitudinal-survey","panel-study","semi-structured-interview","cohort-study","repeated-measures-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-survey-research","name":"Longitudinal Survey Research","fullName":"Longitudinal Survey Research Design","aliases":["longitudinal survey study","repeated-measures survey","prospective survey design","panel survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century (formalized ~1950s–1970s)","originator":"Survey methodology tradition; codified in social sciences by scholars including W.S. Robinson (1950) and later Scott Menard","url":"https://scholargate.app/en/research-design/longitudinal-survey-research","markdownUrl":"https://scholargate.app/en/research-design/longitudinal-survey-research.md","definition":"Longitudinal survey research collects structured questionnaire data from the same individuals (or units) at two or more points in time. Unlike a one-shot cross-sectional survey, this design captures change, stability, and temporal ordering of variables — enabling researchers to track trajectories, test causal sequences, and distinguish cohort effects from aging effects within a quantitative framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Survey methodology tradition; codified in social sciences by scholars including W.S. Robinson (1950) and later Scott Menard","year":"Mid-20th century (formalized ~1950s–1970s)","type":"Quantitative observational research design","dataType":"Repeated structured survey responses from the same sample over time","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922452","url":null},{"ref":"Lynn, P. (Ed.). (2009). Methodology of Longitudinal Surveys. Wiley.","type":"book","doi":null,"isbn":"978-0470018712","url":null}],"related":["panel-research","cohort-research","cross-sectional-survey-research","trend-research","longitudinal-research","survey-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-survey","name":"Longitudinal Survey","fullName":"Longitudinal Survey Research","aliases":["panel survey","repeated-measures survey","longitudinal panel study","wave survey"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1940s (panel survey tradition); longitudinal designs codified mid-20th century","originator":"Established tradition; formalized in social science by Paul Lazarsfeld and colleagues (1940s panel studies)","url":"https://scholargate.app/en/survey-methodology/longitudinal-survey","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-survey.md","definition":"A longitudinal survey collects structured questionnaire data from the same individuals or units at two or more distinct points in time. By tracking the same respondents across waves, researchers can distinguish genuine change from stable individual differences, establish temporal ordering between variables, and model trajectories of attitudes, behaviors, or outcomes in ways that a single cross-sectional snapshot cannot support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established tradition; formalized in social science by Paul Lazarsfeld and colleagues (1940s panel studies)","year":"1940s (panel survey tradition); longitudinal designs codified mid-20th century","type":"Quantitative / mixed-methods survey design","dataType":"Structured questionnaire responses collected from the same sample at multiple time points","subfamily":"Data collection"},"citations":[{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922292","url":null},{"ref":"Longitudinal study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Longitudinal_study"}],"related":["survey","panel-study","cohort-study","cross-sectional-survey","diary-method","experience-sampling-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-test-retest-reliability","name":"Longitudinal Test-Retest Reliability","fullName":"Longitudinal Test-Retest Reliability","aliases":["longitudinal stability reliability","repeated-measurement reliability","temporal stability across waves","longitudinal retest coefficient"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1904 (test-retest); longitudinal application formalized mid-20th century","originator":"Spearman, Charles; extended to longitudinal contexts by psychometric theorists","url":"https://scholargate.app/en/psychometrics/longitudinal-test-retest-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/longitudinal-test-retest-reliability.md","definition":"Longitudinal test-retest reliability quantifies how consistently a scale or measure performs across two or more time points in a longitudinal study. It extends the classic test-retest paradigm by accounting for planned, often substantive, time lags between waves — making it essential for validating instruments used in panel, cohort, or growth-curve research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Spearman, Charles; extended to longitudinal contexts by psychometric theorists","year":"1904 (test-retest); longitudinal application formalized mid-20th century","type":"Reliability estimation / temporal stability","dataType":"Repeated ordinal or continuous measurements across two or more time points","subfamily":"Scale / measurement"},"citations":[{"ref":"Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070478497","url":null},{"ref":"MacKenzie, S. B., Podsakoff, P. M. & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334.","type":"article","doi":"10.2307/23044045","isbn":null,"url":null}],"related":["test-retest-reliability","longitudinal-measurement-invariance","intraclass-correlation","longitudinal-confirmatory-factor-analysis","mcdonalds-omega","cronbachs-alpha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-thematic-analysis","name":"Longitudinal Thematic Analysis","fullName":"Longitudinal Thematic Analysis","aliases":["LTA","longitudinal TA","repeated thematic analysis","temporal thematic analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (formalized alongside longitudinal qualitative research methods)","originator":"Built on Braun & Clarke (2006) thematic analysis; longitudinal adaptation developed across qualitative health and social science research communities","url":"https://scholargate.app/en/qualitative/longitudinal-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-thematic-analysis.md","definition":"Longitudinal Thematic Analysis (LTA) extends standard thematic analysis to data collected at multiple time points from the same participants or contexts. Rather than producing a single cross-sectional account, LTA maps how themes emerge, persist, transform, or disappear over time, enabling researchers to understand change, continuity, and process in qualitative terms. It is widely used in health, education, and social science research where lived experience unfolds over months or years.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Built on Braun & Clarke (2006) thematic analysis; longitudinal adaptation developed across qualitative health and social science research communities","year":"2000s–2010s (formalized alongside longitudinal qualitative research methods)","type":"Qualitative analysis approach","dataType":"Repeated-wave interview transcripts, diary data, focus group transcripts collected at multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Sikveland, R. O., & Stokoe, E. (2019). Longitudinal qualitative research: Recurring patterns and change. Qualitative Research, 19(3), 314–328.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Longitudinal+qualitative+research%3A+Recurring+patterns+and+change+Sikveland"}],"related":["thematic-analysis","reflexive-thematic-analysis","longitudinal-qualitative-research","narrative-analysis","framework-analysis","interpretive-phenomenological-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-visual-analysis","name":"Longitudinal Visual Analysis","fullName":"Longitudinal Visual Analysis","aliases":["LVA","longitudinal visual research","temporal visual analysis","repeated visual analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–2000s (consolidated with digital methods in 2000s)","originator":"Developed across visual sociology and visual ethnography traditions; key contributions from Gillian Rose, Sarah Pink, and Howard Becker","url":"https://scholargate.app/en/qualitative/longitudinal-visual-analysis","markdownUrl":"https://scholargate.app/en/qualitative/longitudinal-visual-analysis.md","definition":"Longitudinal Visual Analysis (LVA) is a qualitative research design that systematically collects, organises, and interprets visual data — photographs, video, maps, or diagrams — gathered at two or more time points to document change, continuity, or transformation in people, places, or social phenomena. By anchoring analysis to the temporal dimension of images, LVA goes beyond what a single-moment visual study can reveal, making visible patterns of development or decay that are otherwise invisible in a snapshot.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed across visual sociology and visual ethnography traditions; key contributions from Gillian Rose, Sarah Pink, and Howard Becker","year":"1970s–2000s (consolidated with digital methods in 2000s)","type":"Qualitative longitudinal design","dataType":"Photographs, video footage, diagrams, maps, archival images collected or reviewed at multiple time points","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473943087","url":null},{"ref":"Pink, S. (2007). Doing Visual Ethnography: Images, Media and Representation in Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412929936","url":null}],"related":["visual-ethnography","photovoice","content-analysis","narrative-analysis","documentary-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longitudinal-web-scraping","name":"Longitudinal Web Scraping","fullName":"Longitudinal Web Scraping for Research","aliases":["repeated web scraping","time-series web data collection","longitudinal crawling","panel web scraping"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s","originator":"Emergent practice in computational social science; formalized across internet research community","url":"https://scholargate.app/en/survey-methodology/longitudinal-web-scraping","markdownUrl":"https://scholargate.app/en/survey-methodology/longitudinal-web-scraping.md","definition":"Longitudinal web scraping is a data collection technique that uses automated scripts to extract content from websites at multiple, predefined time points. By revisiting the same web sources repeatedly, researchers build a time-series dataset that captures how online content, prices, discourse, or behavior evolves. It is widely used in computational social science, economics, political science, health research, and digital humanities to study change without relying on retrospective self-report.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emergent practice in computational social science; formalized across internet research community","year":"2000s–2010s","type":"Automated longitudinal data collection","dataType":"Structured and semi-structured web content (HTML, text, metadata) collected at multiple time points","subfamily":"Data collection"},"citations":[{"ref":"Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691158648","url":null},{"ref":"Luscombe, A., Dick, K., & Walby, K. (2022). Algorithmic thinking in the public interest: navigating technical, legal, and ethical challenges in government web scraping. Quality & Quantity, 56(3), 1781–1802.","type":"article","doi":"10.1007/s11135-021-01164-0","isbn":null,"url":null}],"related":["web-scraping","longitudinal-survey","api-based-data-collection","longitudinal-api-based-data-collection","sensor-data-collection","content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"longstaff-schwartz-method","name":"Longstaff-Schwartz Method","fullName":"Longstaff-Schwartz Least-Squares Monte Carlo","aliases":["LSM","Least-Squares MC","Optimal Stopping"],"domain":"quantitative-finance","family":"ml-model","subfamily":"Monte Carlo Methods","year":"2001","originator":"Francis A. Longstaff and Eduardo S. Schwartz","url":"https://scholargate.app/en/quantitative-finance/longstaff-schwartz-method","markdownUrl":"https://scholargate.app/en/quantitative-finance/longstaff-schwartz-method.md","definition":"The Longstaff-Schwartz method (2001) is a Monte Carlo algorithm for pricing American options and Bermudan swaptions by approximating the optimal exercise boundary via least-squares regression. It has become the industry standard for pricing path-dependent derivatives where analytical solutions do not exist.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Francis A. Longstaff and Eduardo S. Schwartz","subfamily":"Monte Carlo Methods","year":"2001","type":"Valuation Algorithm"},"citations":[{"ref":"Longstaff, F. A., & Schwartz, E. S. (2001). Valuing American options by simulation: A simple least-squares approach. Review of Financial Studies, 14(1), 113-147.","type":"article","doi":"10.1093/rfs/14.1.113","isbn":null,"url":null},{"ref":"Clements, D. J., & Minca, A. (2008). A simulation approach to estimating near-optimal valuation functions for Bermudan options. Journal of Computational Finance, 12(2), 73-96.","type":"article","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/239057223"}],"related":["bates-model","local-volatility","sabr-model","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lopcow","name":"LOPCOW","fullName":"LOgarithmic Percentage Change-driven Objective Weighting","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2022","originator":"Ecer, F., Pamučar, D.","url":"https://scholargate.app/en/decision-making/lopcow","markdownUrl":"https://scholargate.app/en/decision-making/lopcow.md","definition":"LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Ecer, F., Pamučar, D. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ecer, F., Pamučar, D.","subfamily":"Weight_Objective","year":"2022","type":"Logarithmic percentage change variance-based objective weighting","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Ecer, F., Pamučar, D. (2022). A novel LOPCOW-DOBI integrated sustainability performance evaluation methodology: An application in developing country banking sector. Omega","type":"article","doi":"10.1016/j.omega.2022.102690","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lopm","name":"LOPM","fullName":"LoPM — Limits on Property Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Farag, M. M.","url":"https://scholargate.app/en/decision-making/lopm","markdownUrl":"https://scholargate.app/en/decision-making/lopm.md","definition":"LOPM (LoPM — Limits on Property Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Farag, M. M. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Farag, M. M.","subfamily":"Ranking","year":"2020","type":"Property-limit satisfaction scoring for material selection","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Farag, M. M. (2020). Materials and process selection for engineering design. CRC Press","type":"article","doi":"10.1201/9781003006091","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lora-peft","name":"LoRA and PEFT","fullName":"Low-Rank Adaptation and Parameter-Efficient Fine-Tuning","aliases":["LoRA ve PEFT — Parametre Verimli İnce Ayar","Low-Rank Adaptation","parameter-efficient fine-tuning","prefix tuning","prompt tuning"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2022,"originator":"Hu, E. J. et al.; Lester, B. et al.","url":"https://scholargate.app/en/deep-learning/lora-peft","markdownUrl":"https://scholargate.app/en/deep-learning/lora-peft.md","definition":"LoRA (Low-Rank Adaptation), introduced by Hu et al. in 2022, and the broader family of parameter-efficient fine-tuning (PEFT) methods adapt large pretrained language models to new tasks by training only a small number of extra parameters instead of every weight in the model. This makes fine-tuning possible with far less GPU memory and compute while leaving the original model largely untouched.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hu, E. J. et al.; Lester, B. et al.","year":2022,"type":"Parameter-efficient fine-tuning of large pretrained models","task":"Classification & prediction on text","dataType":"Text","minSample":50},"citations":[{"ref":"Hu, E. J. et al. (2022). LoRA: Low-Rank Adaptation of Large Language Models. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2106.09685"},{"ref":"Lester, B. et al. (2021). The Power of Scale for Parameter-Efficient Prompt Tuning. EMNLP.","type":"article","doi":"10.18653/v1/2021.emnlp-main.243","isbn":null,"url":null}],"related":["vision-transformer","variational-autoencoder","generative-adversarial-network","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"loss-distribution-model","name":"Loss Distribution Model","fullName":"Actuarial Loss Distribution Models","aliases":["Severity-Frequency Model","Aggregate Loss Model","Claim Size Distribution Model","Hasar Dağılımı Modeli"],"domain":"actuarial-science","family":"regression-model","subfamily":"Actuarial modelling","year":2012,"originator":"Klugman, Panjer & Willmot","url":"https://scholargate.app/en/actuarial-science/loss-distribution-model","markdownUrl":"https://scholargate.app/en/actuarial-science/loss-distribution-model.md","definition":"A Loss Distribution Model is a parametric statistical framework used in actuarial science to characterise the probabilistic behaviour of insurance claim amounts and frequencies. Developed comprehensively by Klugman, Panjer, and Willmot in their foundational text Loss Models: From Data to Decisions (first edition 1998, fourth edition 2012), these models underpin premium rating, reserving, reinsurance pricing, and regulatory capital calculations across the insurance and risk-management industries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klugman, Panjer & Willmot","year":2012,"type":"Parametric probability model","subfamily":"Actuarial modelling","data_requirement":"Empirical loss or claim records","estimation":"Maximum likelihood or method of moments"},"citations":[{"ref":"Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2012). Loss Models: From Data to Decisions (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1-118-31532-3","url":null}],"related":["extreme-value-theory","credibility-theory","ruin-theory"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lotka-bradford-zipf-laws","name":"Bibliometric Laws: Lotka, Bradford, Zipf","fullName":"Bibliometric Laws: Lotka's Law, Bradford's Law, and Zipf's Law","aliases":["bibliometric distributions","productivity laws","frequency laws","information science laws"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"quantitative-laws","year":"1926–1949","originator":"Alfred J. Lotka, Samuel C. Bradford, George K. Zipf","url":"https://scholargate.app/en/bibliometrics/lotka-bradford-zipf-laws","markdownUrl":"https://scholargate.app/en/bibliometrics/lotka-bradford-zipf-laws.md","definition":"Three foundational empirical laws describe the structure and distribution of scientific information: Lotka's Law characterizes author productivity (most authors publish few papers; a few publish many), Bradford's Law describes journal concentration (a small number of core journals contain the majority of papers on a topic), and Zipf's Law models word and term frequency (word frequency inversely proportional to its rank). These regularities, discovered in the mid-20th century, are remarkably robust across disciplines and have become essential tools for understanding research productivity, organizing information resources, and designing search strategies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alfred J. Lotka, Samuel C. Bradford, George K. Zipf","subfamily":"quantitative-laws","year":"1926–1949","type":"Concept"},"citations":[{"ref":"Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/24529203"},{"ref":"Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85–86.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sources+of+information+on+specific+subjects+Bradford"},{"ref":"Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.","type":"book","doi":null,"isbn":"978-0486435466","url":null}],"related":["co-citation-analysis","bibliographic-coupling","science-mapping"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"low-vision-quality-of-life","name":"LVQOL","fullName":"Low Vision Quality of Life Questionnaire","aliases":["LVQOL","LVQ-VFQ","Low Vision QoL"],"domain":"ophthalmology","family":"process-pipeline","subfamily":"low vision quality of life","year":"2000","originator":"Wolffsohn JS, Cochrane AL","url":"https://scholargate.app/en/ophthalmology/low-vision-quality-of-life","markdownUrl":"https://scholargate.app/en/ophthalmology/low-vision-quality-of-life.md","definition":"The Low Vision Quality of Life Questionnaire (LVQOL) is a comprehensive instrument designed to measure the multidimensional impact of significant vision loss on health-related quality of life in individuals with low vision. Developed by Wolffsohn and Cochrane (2000), the LVQOL incorporates functional, emotional, social, and psychological domains and emphasizes measurement of disability and coping in populations with moderate-to-severe vision impairment where adaptive rehabilitation is the primary intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolffsohn JS, Cochrane AL","subfamily":"low vision quality of life","year":"2000","type":"Self-report"},"citations":[{"ref":"Wolffsohn, J. S., & Cochrane, A. L. (2000). Design of the low vision quality-of-life questionnaire (LVQOL) and measurement of its item and scale validity and reliability. Optometry Vis Sci, 77(3), 144-152.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Design+of+the+low+vision+quality-of-life+questionnaire+%28LVQOL%29+and+measurement+of+its+item+and+scale+validity+and+reliability+Wolffsohn"},{"ref":"Cochrane, A. L., & Wolffsohn, J. S. (2008). Testing the LVQOL with age-related macular degeneration using RASCH analysis. Invest Ophthalmol Vis Sci, 49(11), 4971-4978.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Testing+the+LVQOL+with+age-related+macular+degeneration+using+RASCH+analysis+Cochrane"}],"related":["nei-vfq-25","impact-vision-impairment","amd-quality-of-life","visual-function-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lower-extremity-functional-scale","name":"Lower Extremity Functional Scale","fullName":"Lower Extremity Functional Scale (LEFS)","aliases":["LEFS"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"lower-extremity-functional","year":1999,"originator":"Jill M. Binkley, Paul W. Stratford, Sheila A. Lott, Duane L. Riddle","url":"https://scholargate.app/en/sports-medicine/lower-extremity-functional-scale","markdownUrl":"https://scholargate.app/en/sports-medicine/lower-extremity-functional-scale.md","definition":"The Lower Extremity Functional Scale (LEFS) is a 20-item patient self-report instrument designed to assess functional limitations in individuals with lower extremity musculoskeletal disorders. Developed by Binkley, Stratford, Lott, and Riddle in 1999 and published in Physical Therapy, the LEFS provides a validated, general lower-extremity outcome measure applicable across diverse pathologies (knee, ankle, hip, foot injuries and conditions), making it particularly valuable in physical therapy and rehabilitation settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jill M. Binkley, Paul W. Stratford, Sheila A. Lott, Duane L. Riddle","subfamily":"lower-extremity-functional","year":1999,"type":"Patient self-report"},"citations":[{"ref":"Binkley JM, Stratford PW, Lott SA, Riddle DL. The Lower Extremity Functional Scale (LEFS): scale development, measurement properties, and clinical application. Phys Ther. 1999;79(4):371-383.","type":"article","doi":"10.1093/ptj/79.4.371","isbn":null,"url":null}],"related":["patient-specific-functional-scale","global-rating-of-change-scale","faos","ikdc-subjective-knee-form"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lpf-edas","name":"LPF-EDAS","fullName":"LPF-CRITIC-EDAS — Linguistic Pythagorean Fuzzy EDAS with CRITIC weighting (Akram-Ramzan-Deveci 2023)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1995 crisp; 2023 variant applicator","originator":"Akram, M., Ramzan, N., Deveci, M.","url":"https://scholargate.app/en/decision-making/lpf-edas","markdownUrl":"https://scholargate.app/en/decision-making/lpf-edas.md","definition":"LPF-EDAS (LPF-CRITIC-EDAS — Linguistic Pythagorean Fuzzy EDAS with CRITIC weighting (Akram-Ramzan-Deveci 2023)) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Ramzan, N., Deveci, M. in 1995 crisp; 2023 variant applicator. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Ramzan, N., Deveci, M.","subfamily":"Ranking","year":"1995 crisp; 2023 variant applicator","type":"Linguistic Pythagorean fuzzy ranking — LPFN (I_ψ, I_ζ) with ψ²+ζ² ≤ τ²","value_space":"linguistic_pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Akram, M., Ramzan, N., Deveci, M. (2023). Linguistic Pythagorean fuzzy CRITIC-EDAS method for multiple-attribute group decision analysis. Engineering Applications of Artificial Intelligence","type":"article","doi":"10.1016/j.engappai.2022.105777","isbn":null,"url":null}],"related":["lpf-vikor","lpf-topsis","lpf-copras","edas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lsdvc","name":"LSDVC","fullName":"Bias-Corrected Least Squares Dummy Variable (LSDVC)","aliases":["Bias-Corrected LSDV","BC-LSDV","Kiviet Estimator","Önyargı Düzeltilmiş En Küçük Kareler Kukla Değişken Tahmincisi"],"domain":"econometrics","family":"regression-model","subfamily":"Dynamic panel","year":1995,"originator":"Jan Kiviet","url":"https://scholargate.app/en/econometrics/lsdvc","markdownUrl":"https://scholargate.app/en/econometrics/lsdvc.md","definition":"LSDVC is a bias-corrected panel data estimator introduced by Kiviet (1995) to address the well-known Nickell bias that afflicts the standard Least Squares Dummy Variable (LSDV) estimator in dynamic panel models with a lagged dependent variable. It is particularly suited for researchers working with datasets where the number of time periods T is small relative to the number of cross-sectional units N, such as firm-level or country-level panels spanning a short time horizon.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jan Kiviet","year":1995,"type":"Bias-corrected fixed-effects estimator","subfamily":"Dynamic panel","primary_setting":"Short panels (small T, large N)","bias_target":"Nickell bias in LSDV estimator"},"citations":[{"ref":"Kiviet, J. F. (1995). On bias, inconsistency, and efficiency of various estimators in dynamic panel data models. Journal of Econometrics, 68(1), 53–78.","type":"article","doi":"10.1016/0304-4076(94)01643-E","isbn":null,"url":null}],"related":["arellano-bond-difference-gmm","anderson-hsiao","panel-fixed-effects"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lstm","name":"LSTM","fullName":"Long Short-Term Memory Network","aliases":["LSTM (Uzun Kısa Dönem Bellek Ağı)","long short-term memory","LSTM network","recurrent neural network with memory cells"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":1997,"originator":"Hochreiter, S. & Schmidhuber, J.","url":"https://scholargate.app/en/deep-learning/lstm","markdownUrl":"https://scholargate.app/en/deep-learning/lstm.md","definition":"LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hochreiter, S. & Schmidhuber, J.","year":1997,"type":"Recurrent neural network (gated memory cell)","task":"Time-series forecasting & sequence prediction","minSample":500},"citations":[{"ref":"Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.","type":"article","doi":"10.1162/neco.1997.9.8.1735","isbn":null,"url":null}],"related":["transformer-nlp","cnn-classification","autoencoder","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lumped-capacitance-method","name":"Lumped Capacitance Method","fullName":"Lumped Capacitance Method for Transient Heat Conduction","aliases":["lumped mass analysis","lumped system analysis"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Transient Analysis","year":"1959","originator":"Harry Carslaw and John Jaeger","url":"https://scholargate.app/en/thermodynamics/lumped-capacitance-method","markdownUrl":"https://scholargate.app/en/thermodynamics/lumped-capacitance-method.md","definition":"The Lumped Capacitance Method is a simplification technique for solving unsteady-state heat transfer problems. It assumes that thermal properties are uniform throughout a solid body and that temperature variations within the object are negligible. This approach enables engineers to solve complex transient heat conduction problems using ordinary differential equations rather than partial differential equations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harry Carslaw and John Jaeger","subfamily":"Transient Analysis","year":"1959","type":"Heat transfer analysis"},"citations":[{"ref":"Carslaw, H. S., & Jaeger, J. C. (1959). Conduction of Heat in Solids. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198533689","url":null},{"ref":"Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470055540","url":null}],"related":["thermal-resistance-network","effectiveness-ntu-method","finite-time-thermodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lumsdaine-papell-test","name":"Lumsdaine-Papell Test","fullName":"Lumsdaine-Papell Unit-Root Test with Two Breaks","aliases":["LP Test","Two-Break Unit-Root Test","Double Structural Break Unit-Root Test","Lumsdaine-Papell İki Kırılmalı Birim Kök Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Break unit-root tests","year":1997,"originator":"Robin Lumsdaine & David Papell","url":"https://scholargate.app/en/econometrics/lumsdaine-papell-test","markdownUrl":"https://scholargate.app/en/econometrics/lumsdaine-papell-test.md","definition":"The Lumsdaine-Papell test, introduced by Robin Lumsdaine and David Papell in 1997, extends the Zivot-Andrews single-break unit-root test to allow for two simultaneous structural breaks in the intercept and/or linear trend of a time series. It is widely used in macroeconomics and finance when data are suspected to have experienced two major regime shifts — such as policy changes, financial crises, or wars — and the researcher needs to determine whether the series is nonetheless integrated of order one.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robin Lumsdaine & David Papell","year":1997,"type":"Sequential two-break unit-root test","subfamily":"Break unit-root tests","model_variants":"Models AA, AB, CC (intercept and/or trend breaks)","null_hypothesis":"Unit root without structural breaks"},"citations":[{"ref":"Lumsdaine, R. L., & Papell, D. H. (1997). Multiple trend breaks and the unit-root hypothesis. Review of Economics and Statistics, 79(2), 212–218.","type":"article","doi":"10.1162/003465397556791","isbn":null,"url":null}],"related":["zivot-andrews-test","lee-strazicich-test","bai-perron-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"lysholm-knee-scale","name":"Lysholm Knee Scale","fullName":"Lysholm Knee Scoring Scale","aliases":["Lysholm","Lysholm-Gillquist Scale"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"knee-specific outcome","year":1982,"originator":"Jörg Lysholm, Johan Gillquist","url":"https://scholargate.app/en/sports-medicine/lysholm-knee-scale","markdownUrl":"https://scholargate.app/en/sports-medicine/lysholm-knee-scale.md","definition":"The Lysholm Knee Scoring Scale is an 8-item knee outcome instrument developed by Swedish orthopedic surgeons Lysholm and Gillquist in 1982 to evaluate knee ligament surgery results. Published in the American Journal of Sports Medicine, the Lysholm Scale was among the first validated knee outcome measures and remains widely used in clinical practice and orthopedic research, particularly in studies of anterior cruciate ligament injuries and knee ligament reconstruction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jörg Lysholm, Johan Gillquist","subfamily":"knee-specific outcome","year":1982,"type":"Patient self-report and functional testing"},"citations":[{"ref":"Lysholm J, Gillquist J. Evaluation of knee ligament surgery results with special emphasis on use of a scoring scale. Am J Sports Med. 1982;10(3):150-154.","type":"article","doi":"10.1177/036354658201000306","isbn":null,"url":null}],"related":["ikdc-subjective-knee-form","patient-specific-functional-scale","global-rating-of-change-scale","faos"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"m-estimator","name":"M-Estimator","fullName":"M-Estimators (Robust Regression)","aliases":["m-estimation","huber regression","robust m-regression","M-Tahmin Ediciler"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2009,"originator":"Peter J. Huber","url":"https://scholargate.app/en/statistics/m-estimator","markdownUrl":"https://scholargate.app/en/statistics/m-estimator.md","definition":"M-estimators are a robust generalisation of maximum likelihood estimation, formalised in the work of Peter J. Huber (Huber & Ronchetti, 2009). Instead of squaring every residual, they apply a bounded loss function so that large residuals from outliers are down-weighted rather than allowed to dominate the fit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Huber","year":2009,"type":"Robust linear regression","estimator":"M-estimation (minimises a robust loss via weight functions)","weightFunctions":"Huber, Bisquare (Tukey), Andrews","outcome":"continuous","breakdownLimit":"tolerates up to ~25% outliers"},"citations":[{"ref":"Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.","type":"book","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/book/10.1002/9780470434697"},{"ref":"Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley.","type":"book","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/book/10.1002/9781119214656"}],"related":["mm-estimator","least-trimmed-squares","quantile-regression","ols-regression","ridge-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mabac","name":"MABAC","fullName":"Multi-Attributive Border Approximation area Comparison","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Pamučar, D., Ćirović, G.","url":"https://scholargate.app/en/decision-making/mabac","markdownUrl":"https://scholargate.app/en/decision-making/mabac.md","definition":"MABAC (Multi-Attributive Border Approximation area Comparison) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Ćirović, G. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamučar, D., Ćirović, G.","subfamily":"Ranking","year":"2015","type":"Border approximation area (distance from BAA)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Pamučar, D., Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications","type":"article","doi":"10.1016/j.eswa.2014.11.057","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"macbeth","name":"MACBETH","fullName":"Measuring Attractiveness by a Categorical-Based Evaluation Technique","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1994","originator":"Bana e Costa, C. A., Vansnick, J.-C.","url":"https://scholargate.app/en/decision-making/macbeth","markdownUrl":"https://scholargate.app/en/decision-making/macbeth.md","definition":"MACBETH (Measuring Attractiveness by a Categorical-Based Evaluation Technique) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Bana e Costa, C. A., Vansnick, J.-C. in 1994. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bana e Costa, C. A., Vansnick, J.-C.","subfamily":"Weight_Subjective","year":"1994","type":"Weight_Subjective (qualitative pairwise judgment, linear programming, MAVT-based)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Bana e Costa, C. A., Vansnick, J.-C. (1994). MACBETH — An interactive path towards the construction of cardinal value functions. International Transactions in Operational Research","type":"article","doi":"10.1016/0969-6016(94)90010-8","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-chip-seq-peak-calling","name":"Machine learning-assisted ChIP-seq peak calling","fullName":"Machine Learning-Assisted Chromatin Immunoprecipitation Sequencing Peak Calling","aliases":["ML-based ChIP-seq peak detection","deep learning ChIP-seq peak calling","ML-enhanced ChIP-seq analysis","AI-assisted ChIP-seq peak identification"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2008 (classical); ML-assisted variants 2012–present","originator":"Building on MACS (Zhang et al. 2008); ML extensions by Haiminen et al. and others (2010s–2020s)","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-chip-seq-peak-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-chip-seq-peak-calling.md","definition":"Machine learning-assisted ChIP-seq peak calling extends classical statistical peak detection with supervised or unsupervised learning models that distinguish genuine protein-binding sites from background noise. By training on sequence composition, read coverage profiles, and epigenomic features, these methods improve sensitivity and specificity compared with threshold-based approaches, particularly in low-signal or heterogeneous chromatin contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on MACS (Zhang et al. 2008); ML extensions by Haiminen et al. and others (2010s–2020s)","year":"2008 (classical); ML-assisted variants 2012–present","type":"Supervised/unsupervised ML-augmented peak detection pipeline","dataType":"ChIP-seq read alignments (BAM/BED), control input DNA, genome reference","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Kharchenko, P. V., Tolstorukov, M. Y., & Park, P. J. (2008). Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nature Biotechnology, 26(12), 1351-1359.","type":"article","doi":"10.1038/nbt.1508","isbn":null,"url":null},{"ref":"Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biology, 9(9), R137.","type":"article","doi":"10.1186/gb-2008-9-9-r137","isbn":null,"url":null}],"related":["chip-seq-peak-calling","rna-seq-differential-expression","epigenome-wide-association-study","variant-calling","sequence-alignment","single-cell-rna-seq-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-copy-number-variation-analysis","name":"Machine learning-assisted copy number variation analysis","fullName":"Machine Learning-Assisted Copy Number Variation Analysis","aliases":["ML-CNV analysis","ML-based CNV calling","machine learning CNV detection","deep learning CNV analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s–present","originator":"Multiple groups; notable early ML-CNV tools include CNV-RF (Bellos et al., 2014) and CANOES (Backenroth et al., 2014)","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-copy-number-variation-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-copy-number-variation-analysis.md","definition":"Machine learning-assisted CNV analysis applies supervised, unsupervised, or deep learning algorithms to detect genomic regions that are duplicated or deleted relative to a reference genome. Rather than relying on fixed statistical thresholds, ML models learn discriminative patterns from read-depth signals, allele frequencies, and other features, substantially improving sensitivity and specificity over classical tools — especially in noisy or low-coverage sequencing data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; notable early ML-CNV tools include CNV-RF (Bellos et al., 2014) and CANOES (Backenroth et al., 2014)","year":"2010s–present","type":"Supervised/unsupervised machine learning pipeline for genomic structural variant detection","dataType":"Whole-genome sequencing (WGS), whole-exome sequencing (WES), SNP array intensities, read-depth profiles","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Aganezov, S., Goodwin, S., Sherman, R. M., Sedlazeck, F. J., Mehta, G., Rushbrook, S., ... & Schatz, M. C. (2020). Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing. Genome Research, 30(9), 1258-1273.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comprehensive+analysis+of+structural+variants+in+breast+cancer+genomes+using+single-molecule+sequencing"},{"ref":"Zare, F., Dow, M., Monteleone, N., Bhatt, A., & Bhatt, D. L. (2017). An evaluation of copy number variation detection tools for cancer using whole exome sequencing data. BMC Bioinformatics, 18(1), 286.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+evaluation+of+copy+number+variation+detection+tools+for+cancer+using+whole+exome+sequencing+data+BMC+Bioinformatics+2017"}],"related":["copy-number-variation-analysis","variant-calling","single-cell-copy-number-variation-analysis","genome-wide-association-study","machine-learning-assisted-variant-calling","machine-learning-assisted-genome-wide-association-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-epigenome-wide-association-study","name":"Machine learning-assisted epigenome-wide association study","fullName":"Machine Learning-Assisted Epigenome-Wide Association Study","aliases":["ML-EWAS","machine learning EWAS","ML-assisted EWAS","epigenome-wide association study with machine learning"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s (methodological consolidation ~2015–2020)","originator":"Teschendorff, Relton, and others in the epigenomics field","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-epigenome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-epigenome-wide-association-study.md","definition":"Machine learning-assisted EWAS integrates conventional epigenome-wide association testing with machine learning models to identify DNA methylation sites associated with a phenotype of interest. By combining the statistical rigour of EWAS with the pattern-recognition power of algorithms such as elastic net, random forest, or gradient boosting, this approach handles the extreme dimensionality of methylation arrays (450,000–850,000 CpG sites) more effectively than univariate testing alone, and can capture non-linear and interaction effects that standard linear models miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Teschendorff, Relton, and others in the epigenomics field","year":"2010s (methodological consolidation ~2015–2020)","type":"Integrative omics analysis pipeline","dataType":"DNA methylation arrays (e.g., Illumina 450K or EPIC), phenotype labels, covariate matrices","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Teschendorff, A. E., & Relton, C. L. (2018). Statistical and integrative system-level analysis of DNA methylation data. Nature Reviews Genetics, 19(3), 129–147.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Statistical+and+integrative+system-level+analysis+of+DNA+methylation+data+Teschendorff+Relton+2018"},{"ref":"Jones, M. J., Goodman, S. J., & Kobor, M. S. (2015). DNA methylation and healthy human aging. Aging Cell, 14(6), 924–932.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=DNA+methylation+and+healthy+human+aging+Jones+Goodman+Kobor+2015"}],"related":["genome-wide-association-study","dna-methylation-analysis","epigenetic-clock","random-forest","lasso-regression","differential-methylation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-eqtl-analysis","name":"Machine learning-assisted expression quantitative trait loci analysis","fullName":"Machine Learning-Assisted Expression Quantitative Trait Loci Analysis","aliases":["ML-assisted eQTL analysis","ML eQTL mapping","deep learning eQTL","predictive eQTL modeling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2015 (key ML-eQTL methods; foundational eQTL work: Jansen & Nap 2001)","originator":"Gamazon et al. (PrediXcan), Zhou & Troyanskaya (DeepSEA); broader field ca. 2015-onward","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-eqtl-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-eqtl-analysis.md","definition":"Machine learning-assisted eQTL analysis integrates supervised learning models — ranging from elastic-net regression to deep neural networks — into the classical eQTL framework to predict and map genetic variants that regulate gene expression. By training predictive models on reference panels (e.g., GTEx), the approach enables imputation of gene expression in cohorts lacking RNA data, substantially increasing statistical power and enabling trans-tissue generalisation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gamazon et al. (PrediXcan), Zhou & Troyanskaya (DeepSEA); broader field ca. 2015-onward","year":"2015 (key ML-eQTL methods; foundational eQTL work: Jansen & Nap 2001)","type":"Statistical-computational genomics pipeline","dataType":"Genotype arrays or whole-genome sequencing, RNA-seq expression data, reference panels","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., ... & Im, H. K. (2015). A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics, 47(9), 1091-1098.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+gene-based+association+method+for+mapping+traits+using+reference+transcriptome+data+Gamazon+2015+Nature+Genetics"},{"ref":"Zhou, J., & Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods, 12(10), 931-934.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Predicting+effects+of+noncoding+variants+with+deep+learning-based+sequence+model+Zhou+Troyanskaya+2015+Nature+Methods"}],"related":["eqtl-analysis","genome-wide-association-study","rna-seq-differential-expression","machine-learning-assisted-genome-wide-association-study","pathway-enrichment-analysis","multi-omics-eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-gene-set-enrichment-analysis","name":"Machine learning-assisted gene set enrichment analysis","fullName":"Machine Learning-Assisted Gene Set Enrichment Analysis","aliases":["ML-GSEA","deep learning pathway enrichment","neural GSEA","ML-assisted pathway analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2005 (GSEA); ML integration from ~2015 onward","originator":"Subramanian et al. (GSEA foundation, 2005); various ML extensions thereafter","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-gene-set-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-gene-set-enrichment-analysis.md","definition":"Machine learning-assisted gene set enrichment analysis (ML-GSEA) extends the classical GSEA framework by incorporating supervised or unsupervised ML models — such as random forests, neural networks, or deep learning architectures — to improve the detection, ranking, and biological interpretation of enriched gene sets from high-throughput expression data. The approach is particularly valuable for complex, non-linear gene-set relationships that classical enrichment statistics may miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Subramanian et al. (GSEA foundation, 2005); various ML extensions thereafter","year":"2005 (GSEA); ML integration from ~2015 onward","type":"Computational enrichment analysis with machine learning","dataType":"Gene expression matrices, ranked gene lists, pathway/gene set databases","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.","type":"article","doi":"10.1073/pnas.0506580102","isbn":null,"url":null},{"ref":"Ma, J., Yu, M. K., Fong, S., Ono, K., Sage, E., Demchak, B., Sharan, R., & Ideker, T. (2018). Using deep learning to model the hierarchical structure and function of a cell. Nature Methods, 15(4), 290–298.","type":"article","doi":"10.1038/nmeth.4627","isbn":null,"url":null}],"related":["gene-set-enrichment-analysis","pathway-enrichment-analysis","rna-seq-differential-expression","single-cell-rna-seq-analysis","network-based-gene-set-enrichment-analysis","bayesian-gene-set-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-genome-wide-association-study","name":"Machine learning-assisted genome-wide association study","fullName":"Machine Learning-Assisted Genome-Wide Association Study","aliases":["ML-GWAS","machine learning GWAS","AI-assisted GWAS","deep learning GWAS"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2015-2020 (active integration period)","originator":"Multiple groups; popularized through integrations such as Listgarten et al. (2012) and Novembre & Stephens (2008); ML augmentation formalized ~2015-2020","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-genome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-genome-wide-association-study.md","definition":"Machine learning-assisted GWAS integrates classical genome-wide association testing with machine learning models to improve the detection of genetic variants associated with complex traits. Where traditional GWAS tests each single nucleotide polymorphism (SNP) independently using linear or logistic regression, ML-GWAS captures non-linear interactions and epistasis, ranks candidate loci more accurately, and reduces the false discovery burden in large biobank datasets. The approach has become increasingly prominent as sample sizes and genomic complexity outpace the assumptions of conventional single-SNP tests.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; popularized through integrations such as Listgarten et al. (2012) and Novembre & Stephens (2008); ML augmentation formalized ~2015-2020","year":"2015-2020 (active integration period)","type":"Hybrid computational genomics pipeline","dataType":"Genome-wide SNP arrays, whole-genome sequencing data, phenotype data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Big+data+and+machine+learning+in+health+care+Beam+Kohane+2018+JAMA"},{"ref":"Szymanski, M., Holland-Letz, T., & Kneib, T. (2022). Machine learning approaches to GWAS: methods, pitfalls, and applications. Briefings in Bioinformatics, 23(3), bbac068.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Machine+learning+approaches+to+GWAS+methods+pitfalls+applications+Briefings+in+Bioinformatics+2022"}],"related":["genome-wide-association-study","polygenic-risk-score","random-forest","deep-learning","snp-annotation","linkage-disequilibrium-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-metabolomics-analysis","name":"Machine learning-assisted metabolomics analysis","fullName":"Machine Learning-Assisted Metabolomics Analysis","aliases":["ML-metabolomics","chemoinformatics ML","metabolite profiling with machine learning","ML-driven metabolic profiling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2000s–2010s (rapid adoption 2015–present)","originator":"Convergent development; foundational reviews by Liebal et al. (2020) and earlier multivariate metabolomics work by Trygg, Holmes, and Nicholson","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-metabolomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-metabolomics-analysis.md","definition":"Machine learning-assisted metabolomics analysis is an integrative bioinformatics pipeline that couples untargeted or targeted metabolite profiling — via mass spectrometry or NMR — with supervised and unsupervised ML algorithms to discover biomarkers, classify phenotypes, and model metabolic states. By handling the extreme dimensionality and collinearity inherent in metabolomics datasets (hundreds to thousands of features, tens to hundreds of samples), ML methods such as random forests, support vector machines, and neural networks extract biologically interpretable patterns that classical univariate statistics routinely miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Convergent development; foundational reviews by Liebal et al. (2020) and earlier multivariate metabolomics work by Trygg, Holmes, and Nicholson","year":"2000s–2010s (rapid adoption 2015–present)","type":"Integrative analytical pipeline","dataType":"High-dimensional metabolite abundance matrices (LC-MS, GC-MS, NMR spectra)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Liebal, U. W., Phan, A. N. T., Sudhakar, M., Raman, K., & Blank, L. M. (2020). Machine learning applications for mass spectrometry-based metabolomics. Metabolites, 10(6), 243.","type":"article","doi":"10.3390/metabo10060243","isbn":null,"url":null},{"ref":"Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006). OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20(8-10), 341-351.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=OPLS+discriminant+analysis+combining+strengths+PLS-DA+SIMCA+Bylesjoe+2006"}],"related":["principal-component-analysis","random-forest","partial-least-squares-discriminant-analysis","deep-learning","metabolic-flux-analysis","multivariate-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-microbiome-diversity-analysis","name":"Machine learning-assisted microbiome diversity analysis","fullName":"Machine Learning-Assisted Microbiome Diversity Analysis","aliases":["ML-based microbiome analysis","supervised microbiome diversity","microbiome ML classification","ML-driven alpha/beta diversity analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2011–2016 (formalization of ML integration into microbiome pipelines)","originator":"Pasolli, Segata and colleagues (meta-ML framework); broader field grew from Turnbaugh et al. human microbiome work","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-microbiome-diversity-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-microbiome-diversity-analysis.md","definition":"Machine learning-assisted microbiome diversity analysis integrates classical alpha and beta diversity metrics with supervised or unsupervised ML models to classify host phenotypes, identify discriminant taxa, and uncover community-level signatures from 16S rRNA or shotgun metagenomic data. It extends traditional diversity analysis beyond descriptive statistics toward predictive and explanatory modelling across health, ecology, and environmental science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pasolli, Segata and colleagues (meta-ML framework); broader field grew from Turnbaugh et al. human microbiome work","year":"2011–2016 (formalization of ML integration into microbiome pipelines)","type":"Computational pipeline (supervised/unsupervised ML + diversity metrics)","dataType":"16S rRNA amplicon sequencing or shotgun metagenomic count tables; OTU/ASV abundance matrices","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Pasolli, E., Truong, D. T., Malik, F., Waldron, L., & Segata, N. (2016). Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights. PLOS Computational Biology, 12(7), e1004977.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Machine+Learning+Meta-analysis+of+Large+Metagenomic+Datasets+Tools+and+Biological+Insights+Pasolli+2016"},{"ref":"Wirbel, J., Pyl, P. T., Kartal, E., Zych, K., Kashani, A., Milanese, A., ... & Zeller, G. (2019). Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nature Medicine, 25(4), 679–689.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Meta-analysis+of+fecal+metagenomes+reveals+global+microbial+signatures+colorectal+cancer+Wirbel+2019"}],"related":["microbiome-diversity-analysis","random-forest","rna-seq-differential-expression","machine-learning-assisted-metabolomics-analysis","pathway-enrichment-analysis","multi-omics-microbiome-diversity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-pathway-enrichment-analysis","name":"Machine learning-assisted pathway enrichment analysis","fullName":"Machine Learning-Assisted Pathway Enrichment Analysis","aliases":["ML-assisted PEA","ML-based pathway analysis","machine learning pathway enrichment","ML-enhanced gene set enrichment"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s–present","originator":"Multiple groups; early integration of ML with PEA circa 2010s (e.g., Ma'ayan Lab, Greene Lab)","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-pathway-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-pathway-enrichment-analysis.md","definition":"Machine learning-assisted pathway enrichment analysis integrates classical statistical pathway enrichment methods — such as over-representation analysis or gene set enrichment analysis — with machine learning algorithms to improve sensitivity, handle high-dimensional omics data, and uncover non-linear biological patterns. The approach moves beyond ranking pathways by p-value alone, using ML models to weight gene contributions, distinguish signal from noise across many samples, and prioritize biologically meaningful pathways in complex datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; early integration of ML with PEA circa 2010s (e.g., Ma'ayan Lab, Greene Lab)","year":"2010s–present","type":"Computational pipeline combining statistical enrichment with machine learning","dataType":"Transcriptomics (RNA-seq, microarray), proteomics, metabolomics, multi-omics gene lists","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma'ayan, A. (2013). Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics, 14, 128.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Enrichr+interactive+collaborative+HTML5+gene+list+enrichment+analysis+tool+BMC+Bioinformatics+2013"},{"ref":"Way, G. P., & Greene, C. S. (2018). Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders. Pacific Symposium on Biocomputing, 23, 80–91.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Extracting+biologically+relevant+latent+space+cancer+transcriptomes+variational+autoencoders+Way+Greene+2018"}],"related":["gene-set-enrichment-analysis","over-representation-analysis","random-forest","principal-component-analysis","multi-omics-integration","network-based-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-phylogenetic-analysis","name":"Machine learning-assisted phylogenetic analysis","fullName":"Machine Learning-Assisted Phylogenetic Analysis","aliases":["ML-based phylogenetics","deep learning phylogenetics","neural network tree inference","ML phylogenomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2000s–2020s (active development phase 2018–present)","originator":"Multiple contributors; early applications by Kolaczkowski & Thornton (2004) for model selection; deep learning formulations by Suvorov et al. (2020) and Zou et al. (2020)","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-phylogenetic-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-phylogenetic-analysis.md","definition":"Machine learning-assisted phylogenetic analysis integrates supervised, unsupervised, or deep learning models into the evolutionary tree inference workflow to improve speed, accuracy, or scalability beyond what classical maximum-likelihood and Bayesian methods achieve alone. Applications range from substitution model selection and tree topology prediction to placement of novel sequences onto existing reference trees and detection of recombination or horizontal gene transfer events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; early applications by Kolaczkowski & Thornton (2004) for model selection; deep learning formulations by Suvorov et al. (2020) and Zou et al. (2020)","year":"2000s–2020s (active development phase 2018–present)","type":"Computational inference pipeline","dataType":"Multiple sequence alignments (DNA, RNA, protein); phylogenomic matrices","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Nesterenko, L., et al. (2024). Machine learning methods in phylogenetics: A review of applications and perspectives. Briefings in Bioinformatics, 25(1), bbad441.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Machine+learning+methods+in+phylogenetics+review+applications+perspectives+Briefings+in+Bioinformatics+2024"},{"ref":"Suvorov, A., Hochuli, J., & Schrider, D. R. (2020). Accurate inference of tree topologies from multiple sequence alignments using deep learning. Systematic Biology, 69(2), 221–233.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Accurate+inference+of+tree+topologies+from+multiple+sequence+alignments+using+deep+learning+Suvorov+2020+Systematic+Biology"}],"related":["maximum-likelihood-phylogenetics","bayesian-phylogenetics","multiple-sequence-alignment","molecular-clock-analysis","ancestral-sequence-reconstruction","genome-wide-association-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-rna-seq-differential-expression","name":"Machine learning-assisted RNA-seq differential expression","fullName":"Machine Learning-Assisted RNA-seq Differential Expression Analysis","aliases":["ML-based DE analysis","deep learning RNA-seq DE","neural network differential expression","ML-augmented transcriptomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2015–2019 (rapid development period)","originator":"Multiple groups; scVI (Lopez et al., 2018) and DCA (Eraslan et al., 2019) are landmark tools","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-rna-seq-differential-expression","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-rna-seq-differential-expression.md","definition":"Machine learning-assisted RNA-seq differential expression analysis augments classical statistical DE testing (DESeq2, edgeR, limma-voom) with ML models — including neural networks, random forests, and variational autoencoders — to better handle the high dimensionality, zero-inflation, and batch effects inherent in RNA-seq count data. The approach improves feature selection, noise reduction, and detection power, especially in large or complex experimental designs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; scVI (Lopez et al., 2018) and DCA (Eraslan et al., 2019) are landmark tools","year":"2015–2019 (rapid development period)","type":"Computational bioinformatics pipeline","dataType":"Raw or normalized RNA-seq count matrices (bulk or single-cell)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053–1058.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Deep+generative+modeling+for+single-cell+transcriptomics+Lopez+2018+Nature+Methods"},{"ref":"Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019). Single-cell RNA-seq denoising using a deep count autoencoder. Nature Communications, 10(1), 390.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Single-cell+RNA-seq+denoising+using+a+deep+count+autoencoder+Eraslan+2019+Nature+Communications"}],"related":["rna-seq-differential-expression","single-cell-rna-seq-analysis","gene-set-enrichment-analysis","pathway-enrichment-analysis","deep-learning","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-sequence-alignment","name":"Machine learning-assisted sequence alignment","fullName":"Machine Learning-Assisted Sequence Alignment","aliases":["ML-guided alignment","deep learning sequence alignment","neural sequence alignment","AI-assisted MSA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s–2020s (deep learning era, accelerating post-2017)","originator":"Multiple contributors; notable milestones include Llinares-López et al. (DEDAL, 2023) and Jumper et al. (AlphaFold MSA module, 2021)","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-sequence-alignment","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-sequence-alignment.md","definition":"Machine learning-assisted sequence alignment uses statistical learning models — including deep neural networks and protein language models — to compute biologically meaningful alignments between nucleotide or amino acid sequences. By learning substitution patterns and structural constraints from large training corpora, these methods surpass classical scoring matrices (e.g., BLOSUM, PAM) in sensitivity for remote homologs and structurally constrained regions, making them the current state of the art for difficult alignment tasks in genomics and proteomics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; notable milestones include Llinares-López et al. (DEDAL, 2023) and Jumper et al. (AlphaFold MSA module, 2021)","year":"2010s–2020s (deep learning era, accelerating post-2017)","type":"Computational pipeline / supervised and self-supervised learning","dataType":"Nucleotide or amino acid sequences (FASTA/FASTQ), pre-trained protein language model embeddings","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Llinares-López, F., Berthet, Q., Blondel, M., Teboul, O., & Vert, J.-P. (2023). Deep embedding and alignment of protein sequences. Nature Methods, 20(1), 104–111.","type":"article","doi":"10.1038/s41592-022-01700-2","isbn":null,"url":null},{"ref":"Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589.","type":"article","doi":"10.1038/s41586-021-03819-2","isbn":null,"url":null}],"related":["multiple-sequence-alignment","pairwise-sequence-alignment","hidden-markov-model-profile","protein-structure-prediction","sequence-homology-search","phylogenetic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-single-cell-rna-seq-analysis","name":"Machine learning-assisted single-cell RNA-seq analysis","fullName":"Machine Learning-Assisted Single-Cell RNA Sequencing Analysis","aliases":["ML-scRNA-seq","deep learning scRNA-seq","AI-assisted scRNA-seq","ML-guided single-cell transcriptomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2015-2018 (rapid expansion with scVI 2018, Seurat v3 2019)","originator":"Nir Yosef, Fabian Theis, and colleagues (scVI/scANVI framework; broader community-driven)","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-single-cell-rna-seq-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-single-cell-rna-seq-analysis.md","definition":"Machine learning-assisted single-cell RNA sequencing (scRNA-seq) analysis integrates supervised, unsupervised, and deep generative models into the standard scRNA-seq workflow to handle the unique challenges of single-cell data: extreme sparsity, high dimensionality, technical noise, and batch effects across experiments. Methods such as variational autoencoders (scVI), graph neural networks, and transfer learning substantially improve cell-type identification, trajectory inference, and cross-study data integration compared with purely statistical approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nir Yosef, Fabian Theis, and colleagues (scVI/scANVI framework; broader community-driven)","year":"2015-2018 (rapid expansion with scVI 2018, Seurat v3 2019)","type":"Computational analysis pipeline","dataType":"Single-cell RNA-seq count matrices (UMI or read counts per cell per gene)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature Methods, 15(12), 1053-1058.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Deep+generative+modeling+for+single-cell+transcriptomics+Lopez+2018"},{"ref":"Luecken, M. D., & Theis, F. J. (2019). Current best practices in single-cell RNA-seq analysis: a tutorial. Molecular Systems Biology, 15(6), e8746.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Current+best+practices+in+single-cell+RNA-seq+analysis+a+tutorial+Luecken+Theis+2019"}],"related":["single-cell-rna-seq-analysis","rna-seq-differential-expression","single-cell-rna-seq-differential-expression","pathway-enrichment-analysis","gene-set-enrichment-analysis","machine-learning-assisted-rna-seq-differential-expression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-assisted-variant-calling","name":"Machine learning-assisted variant calling","fullName":"Machine Learning-Assisted Genomic Variant Calling","aliases":["ML-based variant calling","deep learning variant detection","AI-assisted variant detection","neural network variant calling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2018 (DeepVariant publication); broader ML approaches from ~2014 onward","originator":"Ryan Poplin, Mark DePristo and colleagues at Google Brain / Verily (DeepVariant)","url":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-variant-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/machine-learning-assisted-variant-calling.md","definition":"Machine learning-assisted variant calling uses statistical learning models — most notably convolutional neural networks — to distinguish genuine genomic variants (SNPs, indels) from sequencing artifacts in aligned short- or long-read data. Unlike heuristic callers that rely on hand-crafted filters, ML-based approaches learn directly from large labeled datasets of validated variants, improving sensitivity and specificity across diverse sequencing platforms and coverage depths. Google's DeepVariant (2018) is the landmark implementation that brought deep learning into mainstream variant calling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ryan Poplin, Mark DePristo and colleagues at Google Brain / Verily (DeepVariant)","year":"2018 (DeepVariant publication); broader ML approaches from ~2014 onward","type":"Computational genomics pipeline","dataType":"Aligned sequencing reads (BAM/CRAM), reference genome (FASTA)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Poplin, R., Chang, P. C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., Gross, S. S., Dorfman, L., McLean, C. Y., & DePristo, M. A. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology, 36(10), 983–987.","type":"article","doi":"10.1038/nbt.4235","isbn":null,"url":null},{"ref":"Krusche, P., Trigg, L., Boutros, P. C., Mason, C. E., De La Vega, F. M., Moore, B. L., Gonzalez-Porta, M., Eberle, M. A., Tezak, Z., Lababidi, S., Truty, R., Asimenos, G., Funke, B., Fleharty, M., Salit, M., Goldfeder, R. L., & Zook, J. M. (2019). Best practices for benchmarking germline small-variant calls in human genomes. Nature Biotechnology, 37(5), 555–560.","type":"article","doi":"10.1038/s41587-019-0054-x","isbn":null,"url":null}],"related":["whole-genome-sequencing","read-alignment","base-quality-score-recalibration","variant-annotation","haplotype-phasing","structural-variant-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-causal-impact-analysis","name":"Machine learning-augmented causal impact analysis","fullName":"Machine Learning-Augmented Causal Impact Analysis","aliases":["ML-augmented causal impact","ML-CausalImpact","machine learning causal impact","ML-augmented BSTS"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015-2018","originator":"Brodersen et al. (foundational BSTS framework, 2015); Chernozhukov et al. (double ML augmentation, 2018)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-causal-impact-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-causal-impact-analysis.md","definition":"Machine learning-augmented causal impact analysis combines quasi-experimental counterfactual reasoning with flexible ML prediction models to estimate the causal effect of an intervention on a time series outcome. Building on Brodersen et al.'s Bayesian structural time series (BSTS) framework and extended by double/debiased ML methods, it constructs a synthetic counterfactual from donor covariates and infers the treatment effect as the gap between observed and predicted post-intervention outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen et al. (foundational BSTS framework, 2015); Chernozhukov et al. (double ML augmentation, 2018)","year":"2015-2018","type":"Quasi-experimental causal inference with ML","dataType":"Time series; panel data; observational longitudinal data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null}],"related":["causal-impact-analysis","synthetic-control-method","difference-in-differences","interrupted-time-series","doubly-robust-estimation","panel-event-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-coarsened-exact-matching","name":"Machine Learning-Augmented Coarsened Exact Matching","fullName":"Machine Learning-Augmented Coarsened Exact Matching Estimator","aliases":["ML-augmented CEM","ML-CEM","automated coarsened exact matching","ML-assisted CEM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012-2019","originator":"Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-coarsened-exact-matching","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-coarsened-exact-matching.md","definition":"Machine Learning-Augmented Coarsened Exact Matching extends Coarsened Exact Matching (Iacus, King & Porro, 2012) by using supervised machine learning to automate and optimise the coarsening step — the discretisation of continuous covariates into bins — rather than relying on researcher-specified cutpoints. This reduces both ad hoc subjectivity in coarsening decisions and residual imbalance, while preserving CEM's core logic of exact matching within coarsened strata.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Iacus, King & Porro (2012) CEM; ML integration developed in subsequent causal ML literature","year":"2012-2019","type":"Matching / quasi-experimental","dataType":"Observational cross-sectional or panel data with continuous or categorical covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Imai, K., & Ratkovic, M. (2014). Covariate balancing propensity score. Journal of the Royal Statistical Society: Series B, 76(1), 243-263.","type":"article","doi":"10.1111/rssb.12027","isbn":null,"url":null}],"related":["coarsened-exact-matching","propensity-score-matching","machine-learning-augmented-propensity-score-matching","entropy-balancing","doubly-robust-estimation","matching-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-counterfactual-impact-evaluation","name":"Machine Learning-Augmented Counterfactual Impact Evaluation","fullName":"Machine Learning-Augmented Counterfactual Impact Evaluation","aliases":["ML-augmented counterfactual evaluation","ML-CIE","causal ML impact evaluation","double ML counterfactual evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2016-2019","originator":"Chernozhukov et al.; Athey & Imbens","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-counterfactual-impact-evaluation","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-counterfactual-impact-evaluation.md","definition":"Machine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather than imposing parametric functional forms for confounders, ML learners — such as lasso, random forests, or neural nets — estimate nuisance functions (propensity scores, outcome regressions) that are then used to construct approximately unbiased estimates of causal effects. The canonical instantiation is Double/Debiased Machine Learning (DML), formalized by Chernozhukov et al. (2018).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chernozhukov et al.; Athey & Imbens","year":"2016-2019","type":"Causal inference / ML-augmented evaluation","dataType":"Panel, cross-sectional, or time-series observational data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null},{"ref":"Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725.","type":"article","doi":"10.1146/annurev-economics-080217-053433","isbn":null,"url":null}],"related":["counterfactual-impact-evaluation","double-debiased-machine-learning","causal-impact-analysis","synthetic-control-method","difference-in-differences","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-difference-in-differences","name":"Machine learning-augmented difference-in-differences","fullName":"Machine Learning-Augmented Difference-in-Differences Estimator","aliases":["ML-DiD","double/debiased ML DiD","DML difference-in-differences","augmented DiD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2018-2020","originator":"Chernozhukov et al. (double/debiased ML framework); Sant'Anna & Zhao (2020) for DR-DiD","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-difference-in-differences","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-difference-in-differences.md","definition":"Machine learning-augmented DiD combines the classic difference-in-differences identification strategy with flexible ML estimators for nuisance functions — the propensity score and the outcome regression — to obtain valid causal estimates even when treatment selection and outcome dynamics are complex, high-dimensional, or nonlinear. The approach, rooted in double/debiased machine learning (Chernozhukov et al., 2018) and doubly-robust DiD (Sant'Anna & Zhao, 2020), guards against misspecification bias while preserving the core DiD logic of before-after, treated-versus-control comparisons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chernozhukov et al. (double/debiased ML framework); Sant'Anna & Zhao (2020) for DR-DiD","year":"2018-2020","type":"Causal inference / semiparametric","dataType":"Panel or repeated cross-sections with continuous, binary, or count outcomes","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null},{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null}],"related":["difference-in-differences","propensity-score-matching","doubly-robust-estimation","synthetic-control-method","dynamic-difference-in-differences","heterogeneous-treatment-effect-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-doubly-robust-estimation","name":"Machine learning-augmented doubly robust estimation","fullName":"Machine Learning-Augmented Doubly Robust Estimation","aliases":["ML-DR","AIPW with ML","Double/Debiased ML doubly robust","DML-DR"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2018","originator":"Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-doubly-robust-estimation","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-doubly-robust-estimation.md","definition":"Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins","year":"2018","type":"Semiparametric causal estimator with ML nuisance","dataType":"Cross-sectional or panel; continuous, binary, or count outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null},{"ref":"Farrell, M. H., Liang, T., & Misra, S. (2021). Deep Neural Networks for Estimation and Inference. Econometrica, 89(1), 181-213.","type":"article","doi":"10.3982/ECTA16901","isbn":null,"url":null}],"related":["doubly-robust-estimation","propensity-score-weighting","machine-learning-augmented-propensity-score-matching","marginal-structural-model","inverse-probability-weighting","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-entropy-balancing","name":"Machine Learning-Augmented Entropy Balancing","fullName":"Machine Learning-Augmented Entropy Balancing for Causal Inference","aliases":["ML-EB","augmented entropy balancing","ML-augmented EB","doubly-robust entropy balancing"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012-2017","originator":"Hainmueller (2012) for entropy balancing; ML augmentation developed by Zhao & Percival (2017) and subsequent literature","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-entropy-balancing","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-entropy-balancing.md","definition":"Machine learning-augmented entropy balancing (ML-EB) combines Hainmueller's entropy balancing reweighting scheme with a machine-learning outcome model to produce a doubly-robust causal estimator. By jointly optimising covariate balance weights and a flexible predicted-outcome adjustment, ML-EB delivers consistent treatment-effect estimates even when either the weighting or the outcome model is misspecified, and it handles high-dimensional covariate spaces that classical entropy balancing cannot easily balance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hainmueller (2012) for entropy balancing; ML augmentation developed by Zhao & Percival (2017) and subsequent literature","year":"2012-2017","type":"Weighting-based causal estimator","dataType":"Observational cross-sectional or panel data with binary treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.","type":"article","doi":"10.1093/pan/mpr025","isbn":null,"url":null},{"ref":"Zhao, Q., & Percival, D. (2017). Entropy balancing is doubly robust. Journal of Causal Inference, 5(1), 20160010.","type":"article","doi":"10.1515/jci-2016-0010","isbn":null,"url":null}],"related":["entropy-balancing","propensity-score-matching","inverse-probability-weighting","doubly-robust-estimation","targeted-maximum-likelihood-estimation","covariate-balancing-propensity-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-event-study-design","name":"Machine learning-augmented event study design","fullName":"Machine Learning-Augmented Event Study Design","aliases":["ML-augmented event study","high-dimensional event study","DML event study","causal ML event study"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s–2020s","originator":"Chernozhukov et al. (double/debiased ML foundation); applied to event studies in subsequent econometrics literature","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-event-study-design","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-event-study-design.md","definition":"Machine learning-augmented event study design combines the standard event study framework — which traces outcome dynamics around a treatment date — with ML-based methods such as double/debiased machine learning (DML) or regularized regression to handle high-dimensional covariates, improve confounder control, and produce valid causal estimates when the covariate space is too large for conventional regression to manage reliably.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chernozhukov et al. (double/debiased ML foundation); applied to event studies in subsequent econometrics literature","year":"2010s–2020s","type":"Quasi-experimental / causal inference","dataType":"Panel data or repeated cross-sections with high-dimensional covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null},{"ref":"Athey, S., & Imbens, G. W. (2022). Design-based analysis in difference-in-differences settings with staggered adoption. Journal of Econometrics, 226(1), 62-79.","type":"article","doi":"10.1016/j.jeconom.2020.10.012","isbn":null,"url":null}],"related":["event-study-design","double-debiased-machine-learning","difference-in-differences","causal-forest","panel-event-study","dynamic-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-fuzzy-regression-discontinuity","name":"Machine Learning-Augmented Fuzzy Regression Discontinuity","fullName":"Machine Learning-Augmented Fuzzy Regression Discontinuity Design","aliases":["ML-augmented fuzzy RDD","ML fuzzy RD","double ML fuzzy RDD","nonparametric fuzzy RDD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2001 (fuzzy RDD); 2018 (double ML augmentation)","originator":"Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-fuzzy-regression-discontinuity.md","definition":"ML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. Where standard fuzzy RDD uses IV-style estimation at a threshold with imperfect compliance, the ML-augmented variant leverages nonparametric learners — such as random forests or neural networks — to model both the outcome and the first-stage treatment probability near the cutoff, reducing misspecification bias while preserving causal identification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)","year":"2001 (fuzzy RDD); 2018 (double ML augmentation)","type":"Quasi-experimental causal inference","dataType":"Cross-sectional or panel data with a continuous running variable and imperfect compliance","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Review of Economic Studies, 68(1), 201-209.","type":"article","doi":"10.1111/1468-0262.00183","isbn":null,"url":null},{"ref":"Semenova, V., & Chernozhukov, V. (2021). Debiased machine learning of conditional average treatment effects and other causal functions. The Econometrics Journal, 24(2), 264-289.","type":"article","doi":"10.1093/ectj/utaa027","isbn":null,"url":null}],"related":["fuzzy-regression-discontinuity","regression-discontinuity-design","machine-learning-augmented-regression-discontinuity-design","instrumental-variables","doubly-robust-estimation","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-instrumental-variables","name":"Machine learning-augmented instrumental variables","fullName":"Machine Learning-Augmented Instrumental Variables Estimation","aliases":["ML-IV","MLIV","Double/Debiased ML with IV","DML-IV"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012-2018","originator":"Belloni, Chernozhukov & Hansen; Chernozhukov et al.","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-instrumental-variables","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-instrumental-variables.md","definition":"Machine learning-augmented instrumental variables combines the causal identification power of classical IV with modern high-dimensional machine learning — using methods such as LASSO, random forests, or neural networks to select valid instruments and model nuisance functions, thereby improving first-stage fit and enabling valid inference even when the number of potential instruments or controls is large relative to the sample size.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Belloni, Chernozhukov & Hansen; Chernozhukov et al.","year":"2012-2018","type":"Causal inference / semi-parametric estimation","dataType":"Observational panel or cross-sectional data with many potential instruments","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null},{"ref":"Belloni, A., Chen, D., Chernozhukov, V., & Hansen, C. (2012). Sparse models and methods for optimal instruments with an application to eminent domain. Econometrica, 80(6), 2369-2429.","type":"article","doi":"10.3982/ECTA9626","isbn":null,"url":null}],"related":["instrumental-variables","double-debiased-machine-learning","lasso-regression","two-stage-least-squares","propensity-score-matching","causal-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-interrupted-time-series","name":"Machine Learning-Augmented Interrupted Time Series","fullName":"Machine Learning-Augmented Interrupted Time Series Analysis","aliases":["ML-ITS","ML-augmented ITS","machine learning ITS","causal ML interrupted time series"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2014-2015","originator":"Brodersen et al. (2015); Varian (2014) — foundational ML-for-causal-inference literature","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-interrupted-time-series","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-interrupted-time-series.md","definition":"Machine Learning-Augmented Interrupted Time Series (ML-ITS) estimates the causal effect of a discrete intervention by training a machine learning model on pre-intervention time series data, projecting a counterfactual trajectory into the post-intervention period, and measuring the gap between observed and predicted outcomes. It extends classical ITS by replacing parametric trend assumptions with flexible ML estimators such as gradient boosting, random forests, or Bayesian structural time-series models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen et al. (2015); Varian (2014) — foundational ML-for-causal-inference literature","year":"2014-2015","type":"Quasi-experimental causal inference with ML counterfactual","dataType":"Univariate or multivariate time series with a known intervention point","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Varian, H. R. (2014). Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2), 3-28.","type":"article","doi":"10.1257/jep.28.2.3","isbn":null,"url":null}],"related":["interrupted-time-series","causal-impact-analysis","difference-in-differences","synthetic-control-method","machine-learning-augmented-difference-in-differences","dynamic-interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-inverse-probability-weighting","name":"Machine Learning-Augmented Inverse Probability Weighting","fullName":"Machine Learning-Augmented Inverse Probability Weighting Estimator","aliases":["ML-IPW","nonparametric IPW","data-adaptive IPW","ML-augmented propensity weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2003-2018","originator":"Hirano, Imbens & Ridder (semiparametric foundation, 2003); Chernozhukov et al. (DML framework, 2018)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-inverse-probability-weighting.md","definition":"Machine learning-augmented inverse probability weighting replaces parametric logistic regression with flexible ML algorithms to estimate treatment propensity scores, then reweights the sample to balance treated and control units. By leveraging data-adaptive learners such as lasso, random forests, or gradient boosting, ML-IPW controls for high-dimensional and nonlinear confounders that classical IPW misses, while retaining the intuitive weighting framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hirano, Imbens & Ridder (semiparametric foundation, 2003); Chernozhukov et al. (DML framework, 2018)","year":"2003-2018","type":"Semiparametric causal estimator","dataType":"Cross-sectional or panel; continuous/binary outcome; binary or multi-valued treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null},{"ref":"Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.","type":"article","doi":"10.1111/1468-0262.00442","isbn":null,"url":null}],"related":["inverse-probability-weighting","propensity-score-weighting","doubly-robust-estimation","machine-learning-augmented-doubly-robust-estimation","targeted-maximum-likelihood-estimation","machine-learning-augmented-propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-marginal-structural-model","name":"Machine Learning-Augmented Marginal Structural Model","fullName":"Machine Learning-Augmented Marginal Structural Model with Flexible Nuisance Estimation","aliases":["ML-MSM","ML-augmented MSM","data-adaptive MSM","TMLE-MSM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000 (MSM); 2011 (ML-augmented via targeted learning)","originator":"Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-marginal-structural-model","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-marginal-structural-model.md","definition":"The machine learning-augmented marginal structural model combines the causal rigour of Robins et al.'s MSM framework with flexible, data-adaptive ML algorithms for estimating propensity scores and outcome models. By replacing parametric nuisance models with ensemble learners or neural networks, ML-MSMs recover valid causal estimates under confounding without relying on correctly specified parametric forms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011)","year":"2000 (MSM); 2011 (ML-augmented via targeted learning)","type":"Causal inference / semiparametric weighted regression","dataType":"Longitudinal or cross-sectional observational data with time-varying or static treatments","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Luedtke, A. R., & van der Laan, M. J. (2016). Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy. Annals of Statistics, 44(2), 713-742.","type":"article","doi":"10.1214/15-AOS1384","isbn":null,"url":null}],"related":["marginal-structural-model","inverse-probability-weighting","doubly-robust-estimation","machine-learning-augmented-doubly-robust-estimation","targeted-maximum-likelihood-estimation","propensity-score-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-matching-estimator","name":"Machine Learning-Augmented Matching Estimator","fullName":"Machine Learning-Augmented Matching Estimator for Causal Inference","aliases":["ML-augmented matching","ML matching estimator","high-dimensional matching estimator","data-adaptive matching estimator"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2006–2018","originator":"Abadie & Imbens (classical matching); Chernozhukov et al. (ML augmentation framework)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-matching-estimator.md","definition":"The machine learning-augmented matching estimator combines classical nearest-neighbor or propensity-score matching with ML algorithms — such as lasso, random forests, or gradient boosting — to select covariates, estimate propensity scores, and correct for residual bias. The result is a matching-based causal estimator that remains valid under high-dimensional confounding where traditional hand-specified matching fails.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie & Imbens (classical matching); Chernozhukov et al. (ML augmentation framework)","year":"2006–2018","type":"Causal inference / nonparametric matching","dataType":"Cross-sectional or panel; continuous/binary outcome; high-dimensional covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null},{"ref":"Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267.","type":"article","doi":"10.1111/j.1468-0262.2006.00655.x","isbn":null,"url":null}],"related":["matching-estimator","propensity-score-matching","doubly-robust-estimation","machine-learning-augmented-doubly-robust-estimation","causal-forest","inverse-probability-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-panel-event-study","name":"Machine Learning-Augmented Panel Event Study","fullName":"Machine Learning-Augmented Panel Event Study Estimator","aliases":["ML-augmented event study","ML event study","panel event study with ML","machine learning event study"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2019-2021","originator":"Chernozhukov, Wuthrich & Zhu; Freyaldenhoven, Hansen & Shapiro (parallel developments)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-panel-event-study","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-panel-event-study.md","definition":"The machine learning-augmented panel event study extends the classical panel event study by replacing or augmenting parametric counterfactual models with machine learning estimators — such as LASSO, random forests, or matrix completion — to construct more accurate pre-event baselines, detect violations of parallel trends, and produce valid causal effect estimates across multiple post-event periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chernozhukov, Wuthrich & Zhu; Freyaldenhoven, Hansen & Shapiro (parallel developments)","year":"2019-2021","type":"Causal inference / quasi-experimental","dataType":"Balanced or unbalanced panel data with multiple pre- and post-event periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Wuthrich, K., & Zhu, Y. (2021). An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls. Journal of the American Statistical Association, 116(536), 1849-1864.","type":"article","doi":"10.1080/01621459.2021.1920957","isbn":null,"url":null},{"ref":"Freyaldenhoven, S., Hansen, C., & Shapiro, J. M. (2019). Pre-event Trends in the Panel Event-Study Design. American Economic Review, 109(9), 3307-3338.","type":"article","doi":"10.1257/aer.20180609","isbn":null,"url":null}],"related":["difference-in-differences","synthetic-control","panel-fixed-effects","double-lasso","causal-forests","event-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-placebo-test","name":"Machine Learning-Augmented Placebo Test","fullName":"Machine Learning-Augmented Placebo Test for Causal Identification","aliases":["ML placebo test","data-driven placebo falsification","ML-augmented falsification test","ML permutation placebo"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s–2018","originator":"Chernozhukov, Hansen, and collaborators; Athey and Imbens","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-placebo-test","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-placebo-test.md","definition":"The machine learning-augmented placebo test is a causal-inference validation technique that uses flexible ML estimators — such as causal forests, LASSO, or double/debiased ML — to conduct falsification checks on an identification strategy. By replacing real treatment assignments with placebo (fake) assignments and verifying that the estimated effect collapses to zero, researchers confirm that their causal findings are not artefacts of model misspecification or confounding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chernozhukov, Hansen, and collaborators; Athey and Imbens","year":"2010s–2018","type":"Causal validation / falsification test","dataType":"Panel, cross-sectional, or observational high-dimensional data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null},{"ref":"Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725.","type":"article","doi":"10.1146/annurev-economics-080217-053433","isbn":null,"url":null}],"related":["difference-in-differences","synthetic-control-method","instrumental-variables","regression-discontinuity-design","causal-forest","double-debiased-machine-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-propensity-score-matching","name":"Machine Learning-Augmented Propensity Score Matching","fullName":"Machine Learning-Augmented Propensity Score Matching Estimator","aliases":["ML-PSM","boosted propensity score matching","ML-augmented PSM","nonparametric propensity score matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2004","originator":"McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-propensity-score-matching","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-propensity-score-matching.md","definition":"Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LASSO — to better capture complex, nonlinear relationships among covariates. The resulting richer propensity scores improve covariate balance and reduce bias in the estimated average treatment effect on the treated (ATT).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)","year":"2004","type":"Causal inference / matching","dataType":"Observational cross-sectional or panel data; binary treatment; continuous or binary outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403-425.","type":"article","doi":"10.1037/1082-989X.9.4.403","isbn":null,"url":null},{"ref":"Westreich, D., Lessler, J., & Funk, M. J. (2010). Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. Journal of Clinical Epidemiology, 63(8), 826-833.","type":"article","doi":"10.1016/j.jclinepi.2009.11.020","isbn":null,"url":null}],"related":["propensity-score-matching","propensity-score-weighting","doubly-robust-estimation","coarsened-exact-matching","entropy-balancing","machine-learning-augmented-doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-propensity-score-weighting","name":"Machine learning-augmented propensity score weighting","fullName":"Machine Learning-Augmented Propensity Score Weighting","aliases":["ML-PSW","ML-augmented IPW","machine learning propensity weighting","nonparametric propensity score weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010–2018","originator":"Lee, Lessler & Stuart (2010); Chernozhukov et al. (2018, DML framework)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-propensity-score-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-propensity-score-weighting.md","definition":"Machine learning-augmented propensity score weighting (ML-PSW) replaces logistic regression with flexible ML algorithms — such as gradient boosting, LASSO, or random forests — to estimate the propensity score, then uses inverse probability weights to balance treated and control groups. This reduces model-misspecification bias when the true relationship between covariates and treatment assignment is complex or high-dimensional.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee, Lessler & Stuart (2010); Chernozhukov et al. (2018, DML framework)","year":"2010–2018","type":"Causal inference / semiparametric weighting","dataType":"Observational panel or cross-sectional data with binary or multi-valued treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null},{"ref":"Lee, B. K., Lessler, J., & Stuart, E. A. (2010). Improving propensity score weighting using machine learning. Statistics in Medicine, 29(3), 337-346.","type":"article","doi":"10.1002/sim.3782","isbn":null,"url":null}],"related":["propensity-score-weighting","inverse-probability-weighting","doubly-robust-estimation","machine-learning-augmented-propensity-score-matching","causal-forest","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-regression-discontinuity-design","name":"Machine learning-augmented regression discontinuity design","fullName":"Machine Learning-Augmented Regression Discontinuity Design","aliases":["ML-RDD","ML-augmented RD","data-adaptive RDD","nonparametric RDD with ML"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2019","originator":"Imbens & Wager (2019); Calonico, Cattaneo & Farrell (2019)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-regression-discontinuity-design","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-regression-discontinuity-design.md","definition":"Machine learning-augmented regression discontinuity design (ML-RDD) combines the sharp identification logic of classical RDD — exploiting a known assignment cutoff in a running variable — with flexible, data-adaptive ML methods for bandwidth selection, conditional mean estimation, and covariate adjustment. The goal is to recover a more accurate and less assumption-laden estimate of the local average treatment effect at the threshold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Imbens & Wager (2019); Calonico, Cattaneo & Farrell (2019)","year":"2019","type":"Causal inference / quasi-experimental","dataType":"Observational cross-section or panel with a continuous running variable and a known cutoff","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019). Optimal mean squared error bandwidth selection for regression discontinuity designs. Bernoulli, 25(4A), 2703-2729.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimal+mean+squared+error+bandwidth+selection+for+regression+discontinuity+designs+Calonico"},{"ref":"Imbens, G., & Wager, S. (2019). Optimized regression discontinuity designs. Review of Economics and Statistics, 101(2), 264-278.","type":"article","doi":"10.1162/rest_a_00793","isbn":null,"url":null}],"related":["regression-discontinuity-design","fuzzy-regression-discontinuity","machine-learning-augmented-difference-in-differences","causal-forest","propensity-score-matching","local-linear-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-sensitivity-analysis-for-causality","name":"Machine Learning-Augmented Sensitivity Analysis for Causality","fullName":"Machine Learning-Augmented Sensitivity Analysis for Causal Inference","aliases":["ML-augmented sensitivity analysis","ML sensitivity analysis for causality","machine learning sensitivity analysis","debiased ML sensitivity analysis"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2018-2020","originator":"Cinelli & Hazlett (sensitivity framework); Chernozhukov et al. (ML augmentation for causal estimation)","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-sensitivity-analysis-for-causality","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-sensitivity-analysis-for-causality.md","definition":"Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/debiased ML framework and Cinelli and Hazlett's omitted-variable-bias sensitivity tools, it delivers both high-dimensional covariate adjustment and transparent communication of remaining uncertainty about unobserved confounders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cinelli & Hazlett (sensitivity framework); Chernozhukov et al. (ML augmentation for causal estimation)","year":"2018-2020","type":"Sensitivity analysis / causal robustness assessment","dataType":"Observational panel or cross-sectional data with treatment and outcome variables","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39-67.","type":"article","doi":"10.1111/rssb.12348","isbn":null,"url":null},{"ref":"Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.","type":"article","doi":"10.1111/ectj.12097","isbn":null,"url":null}],"related":["difference-in-differences","instrumental-variables","propensity-score-matching","regression-discontinuity","double-debiased-machine-learning","synthetic-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-learning-augmented-synthetic-control-method","name":"Machine Learning-Augmented Synthetic Control Method","fullName":"Machine Learning-Augmented Synthetic Control Method","aliases":["ML-augmented SCM","augmented synthetic control","ASC","penalized synthetic control"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Ben-Michael, Feller & Rothstein","url":"https://scholargate.app/en/causal-inference/machine-learning-augmented-synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/machine-learning-augmented-synthetic-control-method.md","definition":"The machine learning-augmented synthetic control method extends the classical synthetic control estimator by using penalized regression or other ML algorithms — such as lasso, ridge, or random forests — to construct the donor weights and to model pre-treatment outcome trajectories. The augmentation corrects for residual imbalance left by the standard weighting step, yielding lower bias when no perfect synthetic control exists.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ben-Michael, Feller & Rothstein","year":"2021","type":"Causal inference / quasi-experimental","dataType":"Panel / time-series cross-sectional","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The augmented synthetic control method. Journal of the American Statistical Association, 116(536), 1789-1803.","type":"article","doi":"10.1080/01621459.2021.1929245","isbn":null,"url":null},{"ref":"Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391-425.","type":"article","doi":"10.1257/jel.20191450","isbn":null,"url":null}],"related":["synthetic-control-method","difference-in-differences","machine-learning-augmented-difference-in-differences","causal-impact-analysis","panel-data-synthetic-control-method","regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"machine-translation","name":"Machine Translation","fullName":"Machine Translation","aliases":["MT","neural machine translation","automatic translation","Makine Çevirisi (Machine Translation)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/machine-translation","markdownUrl":"https://scholargate.app/en/text-mining/machine-translation.md","definition":"Machine translation (MT) is a natural-language-processing task that automatically converts text in one language into another. Modern MT is built on neural sequence-to-sequence models — the attention mechanism introduced by Bahdanau et al. (2015) and the transformer architecture of Vaswani et al. (2017) — and it widens access to sources for multilingual data analysis and research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-to-text generation task","approaches":"Neural sequence-to-sequence (attention / transformer)","input":"Source-language text","output":"Target-language text","keyIdeas":"Attention (Bahdanau et al., 2015); Transformer self-attention (Vaswani et al., 2017)"},"citations":[{"ref":"Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1409.0473"},{"ref":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1706.03762"}],"related":["cross-lingual-analysis","sentiment-analysis","pos-tagging"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maclaurin-symmetric-mean-operator","name":"Maclaurin Symmetric Mean Operator","fullName":"Maclaurin Symmetric Mean Operator (MSM)","aliases":["MSM","Maclaurin Mean"],"domain":"decision-making","family":"mcdm","subfamily":"Aggregation","year":"2014","originator":"Variants developed from Maclaurin's mathematical theory","url":"https://scholargate.app/en/decision-making/maclaurin-symmetric-mean-operator","markdownUrl":"https://scholargate.app/en/decision-making/maclaurin-symmetric-mean-operator.md","definition":"The Maclaurin Symmetric Mean (MSM) operator is an aggregation method that combines multiple criteria or attribute values using symmetric mean functions. Unlike simple averaging, MSM captures interactions between criteria and enables flexible sensitivity to criterion magnitudes through a parameter λ. It is particularly useful in fuzzy multi-criteria decision analysis and handles both individual and joint effects of criteria.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Variants developed from Maclaurin's mathematical theory","subfamily":"Aggregation","year":"2014","type":"Symmetric mean aggregation operator for multiple criteria"},"citations":[{"ref":"Qin, J., Liu, X., & Pedrycz, W. (2014). An extended TOPSIS model for multiple attribute decision making with interval-valued intuitionistic fuzzy information. International Journal of Fuzzy Systems, 16(1), 99-113.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1007/s40815-014-0001-1"},{"ref":"Bonferroni, C. (1950). Sulle medie di potenze. Giornale dell'Istituto Italiano degli Attuari, 13, 37-48.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bonferroni+medie+di+potenze"}],"related":["bonferroni-mean","power-mean","weighted-geometric-mean","fuzzy-aggregation","topsis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"macont","name":"MACONT","fullName":"Mixed Aggregation by Comprehensive Normalization Technique","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Wen, Z. Liao, H. Zavadskas, E. K.","url":"https://scholargate.app/en/decision-making/macont","markdownUrl":"https://scholargate.app/en/decision-making/macont.md","definition":"MACONT (Mixed Aggregation by Comprehensive Normalization Technique) is a ranking multi-criteria decision-making (MCDM) method introduced by Wen, Z. Liao, H. Zavadskas, E. K. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wen, Z. Liao, H. Zavadskas, E. K.","subfamily":"Ranking","year":"2020","type":"Hybrid aggregation combining multiple normalization methods to reduce bias","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Wen, Z., Liao, H., Zavadskas, E. K. (2020). MACONT: Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Analysis. Informatica 31(4):857-880","type":"article","doi":"10.15388/20-INFOR417","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"macro-averaged-f1","name":"Macro-averaged F1","fullName":"Macro-averaged F1-Score","aliases":["Macro F1","Unweighted average F1"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"2000s","originator":"Multi-class evaluation community","url":"https://scholargate.app/en/model-evaluation/macro-averaged-f1","markdownUrl":"https://scholargate.app/en/model-evaluation/macro-averaged-f1.md","definition":"Macro-averaged F1 computes the F1-score independently for each class and then takes the unweighted arithmetic mean. It treats all classes equally, regardless of their frequency in the dataset, making it useful for imbalanced multi-class problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multi-class evaluation community","subfamily":"Classification Metric","year":"2000s","type":"Evaluation metric"},"citations":[{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"},{"ref":"Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond Accuracy, F-Score and ROC: a Family of Discriminant Measures for Performance Evaluation. AI 2006, 4013, 1015-1021.","type":"article","doi":"10.1007/11941439_114","isbn":null,"url":null}],"related":["f1-score","micro-averaged-f1","weighted-f1","macro-averaged-precision","macro-averaged-recall"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mad-estimation","name":"MAD Estimation","fullName":"Median Absolute Deviation Estimation","aliases":["median absolute deviation","MAD scale estimator","robust scale estimation","Medyan Mutlak Sapma (MAD) Tahmini"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1974,"originator":"Hampel (influence-curve treatment); classical robust statistics","url":"https://scholargate.app/en/statistics/mad-estimation","markdownUrl":"https://scholargate.app/en/statistics/mad-estimation.md","definition":"Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hampel (influence-curve treatment); classical robust statistics","year":1974,"type":"Robust scale estimator","estimator":"Median of absolute deviations from the median (scaled by 1.4826)","breakdownPoint":"50%","outcome":"continuous"},"citations":[{"ref":"Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.","type":"article","doi":"10.1080/01621459.1974.10482962","isbn":null,"url":null},{"ref":"Rousseeuw, P. J. & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283.","type":"article","doi":"10.1080/01621459.1993.10476408","isbn":null,"url":null}],"related":["sn-qn-estimators","breakdown-point-analysis","robust-mixed-model","quantile-regression","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"madgwick-filter","name":"Madgwick Filter","fullName":"Madgwick IMU and AHRS Algorithms","aliases":["Madgwick AHRS","gradient descent attitude filter"],"domain":"aerospace","family":"process-pipeline","subfamily":"Gradient Descent Filtering","year":"2010","originator":"Sebastian Madgwick","url":"https://scholargate.app/en/aerospace/madgwick-filter","markdownUrl":"https://scholargate.app/en/aerospace/madgwick-filter.md","definition":"The Madgwick Filter is a computationally lightweight attitude estimation algorithm that fuses inertial measurements (accelerometer, gyroscope) with magnetic measurements (magnetometer) to compute a quaternion orientation. Introduced by Sebastian Madgwick in 2010, the algorithm uses gradient descent optimization to minimize the error between measured and expected sensor outputs, yielding accurate, drift-free attitude estimates on embedded systems with minimal computational cost. The Madgwick Filter is now ubiquitous in consumer electronics, robotics, and aerospace systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sebastian Madgwick","subfamily":"Gradient Descent Filtering","year":"2010","type":"Filter algorithm"},"citations":[{"ref":"Madgwick, S. O. H., Harrison, A. J. L., & Vaidyanathan, R. (2011). Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE International Conference on Rehabilitation Robotics (ICORR), 1–7.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/5975346"},{"ref":"Madgwick, S. O. H. (2010). An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io Technologies, University of Bristol, UK.","type":"article","doi":null,"isbn":null,"url":"https://www.x-io.co.uk/res/doc/madgwick_internal_report.pdf"},{"ref":"Sabatini, A. M. (2006). Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Transactions on Biomedical Engineering, 53(7), 1346–1356.","type":"article","doi":"10.1109/TBME.2006.875664","isbn":null,"url":null}],"related":["ahrs","mahony-filter","quaternion-attitude"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"magnetic-resonance-elastography","name":"Magnetic Resonance Elastography","fullName":"Magnetic Resonance Elastography","aliases":["MRE","elastography","tissue stiffness mapping"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Tissue mechanics","year":"1995","originator":"Richard Muthupillai","url":"https://scholargate.app/en/medical-imaging/magnetic-resonance-elastography","markdownUrl":"https://scholargate.app/en/medical-imaging/magnetic-resonance-elastography.md","definition":"Magnetic Resonance Elastography (MRE) is a non-invasive imaging technique that measures tissue stiffness by encoding the motion of acoustic shear waves into MRI signal and calculating the elastic modulus from wave propagation patterns. Developed by Muthupillai and colleagues in 1995, MRE enables quantitative assessment of tissue mechanics, particularly useful for diagnosing liver fibrosis, cardiac dysfunction, and neurological diseases. It has emerged as a non-invasive alternative to biopsy for staging hepatic fibrosis and is expanding into other organ systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Muthupillai","subfamily":"Tissue mechanics","year":"1995","type":"MRI-based measurement of tissue stiffness"},"citations":[{"ref":"Muthupillai, R., Lomas, D. J., Rossman, P. J., et al. (1995). Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science, 269(5232), 1854-1857.","type":"article","doi":"10.1126/science.7569924","isbn":null,"url":null},{"ref":"Huwart, L., Sempoux, C., Vicaut, E., et al. (2008). Magnetic resonance elastography for the noninvasive staging of liver fibrosis. Gastroenterology, 135(1), 32-40.","type":"article","doi":"10.1053/j.gastro.2008.03.076","isbn":null,"url":null},{"ref":"Kolipaka, A., McGee, K. P., Araoz, P. A., et al. (2008). Magnetic resonance elastography as a method for the assessment of myocardial stiffness: Concepts and applications. Journal of Cardiovascular Magnetic Resonance, 10(1), 43.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Magnetic+resonance+elastography+as+a+method+for+the+assessment+of+myocardial+stiffness%3A+Concepts+and+applications+Kolipaka"}],"related":["dti-tractography","ct-iterative-reconstruction","quantitative-susceptibility-mapping","oct-angiography","functional-ultrasound"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"magnetisation-transfer-ratio","name":"Magnetisation Transfer Ratio","fullName":"Magnetisation Transfer Ratio (MTR)","aliases":["MTR","magnetization transfer","MT imaging"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Myelin-sensitive MRI","year":"1989","originator":"Stephen Wolff","url":"https://scholargate.app/en/neuroimaging/magnetisation-transfer-ratio","markdownUrl":"https://scholargate.app/en/neuroimaging/magnetisation-transfer-ratio.md","definition":"Magnetisation Transfer Ratio (MTR) is an MRI method that measures the exchange of magnetization between free water protons and protons bound to macromolecules (primarily myelin lipids and proteins). Introduced by Wolff and Balaban in 1989, MTR reflects tissue macromolecular content and is particularly sensitive to myelination, providing a non-invasive estimate of myelin density.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Wolff","subfamily":"Myelin-sensitive MRI","year":"1989","type":"Tissue microstructure characterization"},"citations":[{"ref":"Wolff, S. D., & Balaban, R. S. (1989). Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo. Journal of Magnetic Resonance Imaging, 10(2), 135–148.","type":"article","doi":"10.1002/mrm.1910100113","isbn":null,"url":null},{"ref":"Henkelman, R. M., Huang, X., Lago, A., & Stanisz, G. J. (1993). Quantitative interpretation of magnetization transfer. Magnetic Resonance in Medicine, 29(6), 759–766.","type":"article","doi":"10.1002/mrm.1910290607","isbn":null,"url":null}],"related":["noddi","diffusion-kurtosis-imaging","tract-based-spatial-statistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"magnetotellurics","name":"Magnetotellurics","fullName":"Magnetotelluric Method","aliases":["MT"],"domain":"geophysics","family":"process-pipeline","subfamily":"Electromagnetic inversion","year":"1953","originator":"Louis Cagniard","url":"https://scholargate.app/en/geophysics/magnetotellurics","markdownUrl":"https://scholargate.app/en/geophysics/magnetotellurics.md","definition":"Magnetotellurics (MT) is a passive geophysical method that uses natural variations in Earth's magnetic and electric fields to characterize subsurface electrical conductivity. Developed by Louis Cagniard in 1953, MT measures the impedance relationship between naturally occurring magnetic fluctuations (from solar wind and ionospheric currents) and the resulting electric field, providing information about crustal and upper mantle structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Louis Cagniard","subfamily":"Electromagnetic inversion","year":"1953","type":"Electromagnetic impedance and conductivity imaging"},"citations":[{"ref":"Cagniard, L. (1953). Basic theory of the magnetotelluric method of geophysical prospecting. Geophysics, 18(3), 605-635.","type":"article","doi":"10.1190/1.1437915","isbn":null,"url":null},{"ref":"Simpson, F., & Bahr, K. (2005). Practical magnetotellurics. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/core/books/practical-magnetotellurics/9E3E9B8B7C5F6A2D"}],"related":["electrical-resistivity-tomography","seismic-full-waveform-inversion","receiver-function-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mahalanobis-distance","name":"MAHALANOBIS-DISTANCE","fullName":"Mahalanobis Distance — covariance-adjusted distance accounting for inter-criterion correlations","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Distance","year":"1936","originator":"Mahalanobis, P. C.","url":"https://scholargate.app/en/decision-making/mahalanobis-distance","markdownUrl":"https://scholargate.app/en/decision-making/mahalanobis-distance.md","definition":"MAHALANOBIS-DISTANCE (Mahalanobis Distance — covariance-adjusted distance accounting for inter-criterion correlations) is a distance multi-criteria decision-making (MCDM) method introduced by Mahalanobis, P. C. in 1936. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mahalanobis, P. C.","subfamily":"Distance","year":"1936","type":"Distance (covariance-adjusted, correlation-aware)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Mahalanobis, P. C. (1936). Mahalanobis Distance. Proceedings of the National Institute of Sciences of India","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mahalanobis%20Distance"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mahalanobis-robust","name":"Robust Mahalanobis Distance","fullName":"Robust Mahalanobis Distance (MCD-based Multivariate Outlier Detection)","aliases":["MCD Mahalanobis distance","robust mahalanobis","minimum covariance determinant distance","Robust Mahalanobis Uzaklığı"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1990,"originator":"Rousseeuw & Van Zomeren (robust distance); Filzmoser, Garrett & Reimann (multivariate outlier detection)","url":"https://scholargate.app/en/statistics/mahalanobis-robust","markdownUrl":"https://scholargate.app/en/statistics/mahalanobis-robust.md","definition":"Robust Mahalanobis Distance flags multivariate outliers by measuring how far each observation lies from the centre of the data using a robust covariance estimate. It builds on the robust-distance framework of Rousseeuw and Van Zomeren (1990) and the multivariate outlier-detection approach of Filzmoser, Garrett and Reimann (2005), replacing the classical mean and covariance with the Minimum Covariance Determinant (MCD) estimate so that the outliers themselves do not distort the distance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rousseeuw & Van Zomeren (robust distance); Filzmoser, Garrett & Reimann (multivariate outlier detection)","year":1990,"type":"Robust multivariate outlier detection","estimator":"Minimum Covariance Determinant (MCD) location and scatter","outcome":"multivariate continuous","minSample":50},"citations":[{"ref":"Rousseeuw, P. J. & Van Zomeren, B. C. (1990). Unmasking Multivariate Outliers and Leverage Points. Journal of the American Statistical Association, 85(411), 633-639.","type":"article","doi":"10.1080/01621459.1990.10474920","isbn":null,"url":null},{"ref":"Filzmoser, P., Garrett, R. G. & Reimann, C. (2005). Multivariate Outlier Detection in Exploration Geochemistry. Computational Statistics & Data Analysis, 49(2), 561-587.","type":"article","doi":"10.1016/j.cageo.2004.11.013","isbn":null,"url":null}],"related":["mad-estimation","adjusted-boxplot","robust-anova","theil-sen-estimator","least-trimmed-squares"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mahony-filter","name":"Mahony Filter","fullName":"Mahony Complementary Filter for IMU and AHRS","aliases":["Mahony AHRS","complementary observer attitude filter"],"domain":"aerospace","family":"process-pipeline","subfamily":"Complementary Filtering","year":"2008","originator":"Robert Mahony","url":"https://scholargate.app/en/aerospace/mahony-filter","markdownUrl":"https://scholargate.app/en/aerospace/mahony-filter.md","definition":"The Mahony Filter is a complementary observer-based attitude filter that fuses gyroscope, accelerometer, and magnetometer measurements to estimate quaternion orientation. Developed by Robert Mahony and colleagues in 2008, the filter combines gyroscope rate integration with corrective feedback from vector measurements (accelerometer, compass) using proportional-integral control principles. The Mahony Filter provides similar performance to Kalman Filters but with simpler implementation and lower computational cost, making it ideal for resource-constrained systems and real-time control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Mahony","subfamily":"Complementary Filtering","year":"2008","type":"Observer algorithm"},"citations":[{"ref":"Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Multirotor aerial vehicles: Modeling, estimation, and control of quadrotors. IEEE Robotics and Automation Magazine, 19(3), 20–32.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multirotor+aerial+vehicles%3A+Modeling%2C+estimation%2C+and+control+of+quadrotors+Mahony"},{"ref":"Mahony, R., Hamel, T., & Pflimlin, J. M. (2012). Multirotor aerial vehicles: Modeling, estimation, and control of quadrotors. IEEE Robotics & Automation Magazine, 19(3), 20–32.","type":"article","doi":"10.1109/MRA.2012.2206474","isbn":null,"url":null},{"ref":"Valenti, R. G., Dryanovski, I., & Xiao, J. (2016). Keeping a good attitude: A quaternion-based orientation filter for IMUs and MARGs. Sensors, 15(8), 19302–19330.","type":"article","doi":"10.3390/s150819302","isbn":null,"url":null}],"related":["madgwick-filter","ahrs","quaternion-attitude"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maillard-reaction-kinetics","name":"Maillard Reaction Kinetics","fullName":"Maillard Reaction Kinetics","aliases":["browning kinetics"],"domain":"food-science","family":"process-pipeline","subfamily":"Thermal Degradation","year":"1912","originator":"Louis Camille Maillard","url":"https://scholargate.app/en/food-science/maillard-reaction-kinetics","markdownUrl":"https://scholargate.app/en/food-science/maillard-reaction-kinetics.md","definition":"Maillard Reaction Kinetics measures the rate of non-enzymatic browning when amino acids and reducing sugars react under heat. Understanding these kinetics enables optimization of flavor development, control of color changes during processing and storage, and prediction of product quality evolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Louis Camille Maillard","subfamily":"Thermal Degradation","year":"1912","type":"Non-Enzymatic Browning"},"citations":[{"ref":"Hodge, J. E. (1953). Chemistry of browning reactions. Journal of Agricultural and Food Chemistry, 1(15), 928-943.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Chemistry+of+browning+reactions+Hodge"}],"related":["dsc-gelatinization","accelerated-shelf-life-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maintenance-optimization","name":"Maintenance Optimization","fullName":"Maintenance Optimization (Preventive/Predictive)","aliases":["Optimal Maintenance Policy","Preventive Maintenance Scheduling","Predictive Maintenance Optimization","Bakım Optimizasyonu"],"domain":"reliability","family":"process-pipeline","subfamily":"Reliability & risk","year":2002,"originator":"Hongzhou Wang","url":"https://scholargate.app/en/reliability/maintenance-optimization","markdownUrl":"https://scholargate.app/en/reliability/maintenance-optimization.md","definition":"Maintenance Optimization is a quantitative framework for determining the timing, type, and frequency of maintenance actions—preventive, predictive, or corrective—that minimize total cost or expected downtime over a system's operational life. Systematic formulations were consolidated by Hongzhou Wang (2002), whose survey unified age-replacement, block-replacement, and imperfect-repair policies under a common cost-rate structure applicable to deteriorating systems across engineering and operations management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hongzhou Wang","year":2002,"type":"decision optimization framework","subfamily":"Reliability & risk","input":"system degradation or failure-rate data","output":"optimal maintenance schedule minimizing cost or downtime"},"citations":[{"ref":"Wang, H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139(3), 469–489.","type":"article","doi":"10.1016/S0377-2217(01)00197-7","isbn":null,"url":null}],"related":["reliability-analysis","degradation-models","dynamic-programming"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mairca","name":"MAIRCA","fullName":"Multi-Attributive Ideal-Real Comparative Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Pamučar, D., Vasin, Lj., Lukovac, V.","url":"https://scholargate.app/en/decision-making/mairca","markdownUrl":"https://scholargate.app/en/decision-making/mairca.md","definition":"MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Vasin, Lj., Lukovac, V. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamučar, D., Vasin, Lj., Lukovac, V.","subfamily":"Ranking","year":"2014","type":"Gap matrix (theoretical vs actual preference)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Pamučar, D., Vasin, Lj., Lukovac, V. (2014). Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARICA model. XVI International Scientific-Expert Conference on Railway, Railcon","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Selection+of+railway+level+crossings+for+investing+in+security+equipment+using+hybrid+DEMATEL-MARICA+model+Pamu%C4%8Dar"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"majority-voting","name":"Majority Voting","fullName":"Majority Voting Ensemble","aliases":["hard voting"],"domain":"ensemble-learning","family":"ml-model","subfamily":"Ensemble","year":"1996","originator":"Leo Breiman","url":"https://scholargate.app/en/ensemble-learning/majority-voting","markdownUrl":"https://scholargate.app/en/ensemble-learning/majority-voting.md","definition":"Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leo Breiman","subfamily":"Ensemble","year":"1996","type":"voting aggregation"},"citations":[{"ref":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.","type":"article","doi":"10.1007/BF00058655","isbn":null,"url":null},{"ref":"Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Combining+Pattern+Classifiers%3A+Methods+and+Algorithms-p-9780471210429"}],"related":["bagging-ensemble","boosting-ensemble","stacked-generalization","random-forest","adaboost"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maki-cointegration-test","name":"Maki Cointegration Test","fullName":"Maki Cointegration Test with Multiple Structural Breaks","aliases":["Structural-break cointegration test"],"domain":"econometrics","family":"regression-model","subfamily":"Unit-root test","year":"2012","originator":"Darshana Maki","url":"https://scholargate.app/en/econometrics/maki-cointegration-test","markdownUrl":"https://scholargate.app/en/econometrics/maki-cointegration-test.md","definition":"The Maki cointegration test extends cointegration testing to allow for an unknown number of endogenously-determined structural breaks in the cointegrating relationship. Introduced by Maki (2012), it builds on Gregory and Hansen (1996), enabling detection of cointegration even when relationships shift due to policy changes, institutional reforms, or fundamental regime shifts. This is essential for applied time-series work where structural change is common.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Darshana Maki","subfamily":"Unit-root test","year":"2012","type":"Structural-break test"},"citations":[{"ref":"Maki, D. (2012). Tests for cointegration allowing for an unknown number of breaks. Economic Modelling, 29(5), 2011-2015.","type":"article","doi":"10.1016/j.econmod.2012.04.022","isbn":null,"url":null},{"ref":"Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99-126.","type":"article","doi":"10.1016/0304-4076(69)41685-7","isbn":null,"url":null}],"related":["panel-df-gls","panel-kss","cs-ardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maldi-tof","name":"MALDI-TOF","fullName":"Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry","aliases":["MALDI mass spectrometry","MALDI-TOF-MS","laser desorption mass spectrometry"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Soft Ionization Mass Spectrometry","year":"1988","originator":"Michael Karas","url":"https://scholargate.app/en/spectroscopy/maldi-tof","markdownUrl":"https://scholargate.app/en/spectroscopy/maldi-tof.md","definition":"Matrix-Assisted Laser Desorption/Ionization (MALDI) combined with Time-of-Flight (TOF) mass analysis, or MALDI-TOF, is a soft ionization mass spectrometry technique that gently ionizes intact biomolecules and volatile organic compounds, then measures their mass-to-charge ratio by measuring flight time through a field-free drift region. Introduced independently by Karas, Hillenkamp, and Tanaka in 1988, MALDI-TOF revolutionized proteomics, microbiology, and organic analysis by enabling mass determination of proteins and polymers exceeding 100 kDa.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Karas","subfamily":"Soft Ionization Mass Spectrometry","year":"1988","type":"Ionization and mass analysis technique"},"citations":[{"ref":"Karas, M., & Hillenkamp, F. (1988). Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Analytical Chemistry, 60(20), 2299-2301.","type":"article","doi":"10.1021/ac00171a028","isbn":null,"url":null},{"ref":"Tanaka, K., Waki, H., Ido, Y., Akita, S., Yoshida, Y., & Yoshida, T. (1988). Protein and polymer analyses up to m/z 100,000 by laser ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry, 2(8), 151-153.","type":"article","doi":"10.1002/rcm.1290020802","isbn":null,"url":null},{"ref":"Fenn, J. B., Mann, M., Meng, C. K., Wong, S. F., & Whitehouse, C. M. (1989). Electrospray ionization for mass spectrometry of large biomolecules. Science, 246(4926), 64-71.","type":"article","doi":"10.1126/science.2675315","isbn":null,"url":null}],"related":["ft-icr-mass-spectrometry","circular-dichroism","surface-plasmon-resonance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"male-sexual-health-questionnaire","name":"Male Sexual Health Questionnaire","fullName":"Male Sexual Health Questionnaire (MSHQ)","aliases":["MSHQ","MSHQ-EjD"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"male-sexual-health","year":2008,"originator":"Glina et al.","url":"https://scholargate.app/en/urology-gynecology/male-sexual-health-questionnaire","markdownUrl":"https://scholargate.app/en/urology-gynecology/male-sexual-health-questionnaire.md","definition":"The MSHQ is a multidimensional self-report questionnaire designed to assess sexual function, problems, and satisfaction in men. Developed by Glina and colleagues and first published in 2008, it measures erectile function, ejaculatory function, and overall sexual satisfaction. The MSHQ exists in multiple versions (full form and brief forms) and has become increasingly used in clinical research, particularly in evaluating sexual outcomes in men with prostate cancer and other urologic conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glina et al.","subfamily":"male-sexual-health","year":2008,"type":"Self-report questionnaire"},"citations":[{"ref":"Glina, S., Natali, A., & the MSHQ Study Group. (2008). The Male Sexual Health Questionnaire: measurement and validation of sexual dysfunction in men. Urology, 72(6), 1319–1326.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Male+Sexual+Health+Questionnaire%3A+measurement+and+validation+of+sexual+dysfunction+in+men+Glina"},{"ref":"Litwin, M. S., & CAP Study Group. (2007). The Cancer of the Prostate Strategic Urologic Research Endeavor database: a new resource for research on prostate cancer. Urology, 70(5S), 34–37.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Cancer+of+the+Prostate+Strategic+Urologic+Research+Endeavor+database%3A+a+new+resource+for+research+on+prostate+cancer+Litwin"}],"related":["international-index-erectile-function","arizona-sexual-experiences-scale","sexual-satisfaction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"malmquist-luenberger-productivity-indicator","name":"Malmquist-Luenberger Productivity Indicator","fullName":"Malmquist-Luenberger Productivity Index","aliases":["ML index","Malmquist-Luenberger index","ML productivity"],"domain":"operations-research","family":"ml-model","subfamily":"Productivity Analysis","year":"1953","originator":"Sten Malmquist and David G. Luenberger","url":"https://scholargate.app/en/operations-research/malmquist-luenberger-productivity-indicator","markdownUrl":"https://scholargate.app/en/operations-research/malmquist-luenberger-productivity-indicator.md","definition":"The Malmquist-Luenberger (ML) Productivity Index combines concepts from the Malmquist index and Luenberger's directional distance functions to measure total factor productivity (TFP) change over time. It decomposes productivity growth into technical efficiency change and technological progress, enabling comprehensive productivity assessment without requiring specific functional form assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sten Malmquist and David G. Luenberger","subfamily":"Productivity Analysis","year":"1953","type":"algorithm"},"citations":[{"ref":"Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica y de Investigacion Operativa, 4(2), 209-242.","type":"article","doi":"10.1007/BF03006863","isbn":null,"url":null},{"ref":"Luenberger, D. G. (1992). New optimality conditions for stochastic control problems. Mathematics of Operations Research, 17(3), 657-663.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=New+optimality+conditions+for+stochastic+control+problems+Luenberger"},{"ref":"Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and distance functions. Journal of Economic Theory, 70(2), 407-419.","type":"article","doi":"10.1006/jeth.1996.0096","isbn":null,"url":null}],"related":["data-envelopment-analysis","malmquist-index","efficiency-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"malmquist-productivity-index","name":"Malmquist Productivity Index","fullName":"Malmquist Productivity Index","aliases":["MPI","Malmquist Index","Malmquist DEA Productivity Index","Malmquist Verimlilik Endeksi"],"domain":"efficiency-analysis","family":"regression-model","subfamily":"Productivity analysis","year":1994,"originator":"Färe, Grosskopf, Norris & Zhang","url":"https://scholargate.app/en/efficiency-analysis/malmquist-productivity-index","markdownUrl":"https://scholargate.app/en/efficiency-analysis/malmquist-productivity-index.md","definition":"The Malmquist Productivity Index (MPI) is a non-parametric measure of total factor productivity (TFP) change over time. Formally grounded in distance functions by Caves, Christensen, and Diewert (1982) and operationalized using Data Envelopment Analysis by Färe, Grosskopf, Norris, and Zhang (1994), MPI decomposes productivity growth into two components: efficiency change (catching-up to the frontier) and technical change (shift of the frontier itself).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Färe, Grosskopf, Norris & Zhang","year":1994,"type":"Non-parametric productivity index","subfamily":"Productivity analysis","decomposition":"Efficiency change × Technical change","frontier":"Piecewise-linear DEA frontier"},"citations":[{"ref":"Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2117971"},{"ref":"Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50(6), 1393–1414.","type":"article","doi":"10.2307/1913388","isbn":null,"url":null}],"related":["data-envelopment-analysis","stochastic-frontier-analysis","window-dea"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"malnutrition-screening-tool","name":"Malnutrition Screening Tool","fullName":"Malnutrition Screening Tool (MST)","aliases":["MST","Malnutrition Screening","Nutritional Risk Screen"],"domain":"nursing","family":"process-pipeline","subfamily":"nutritional assessment","year":"1999","originator":"Michelle Ferguson","url":"https://scholargate.app/en/nursing/malnutrition-screening-tool","markdownUrl":"https://scholargate.app/en/nursing/malnutrition-screening-tool.md","definition":"The Malnutrition Screening Tool (MST), developed by Michelle Ferguson and colleagues in 1999, is a brief, validated screening instrument designed to identify hospitalized patients at risk for malnutrition. The tool consists of two simple questions about recent unintentional weight loss and reduced food intake, yielding a quick numerical score. Since its publication, the MST has become widely adopted in acute hospitals, residential aged care facilities, and community settings as a rapid, reliable first-line screen for nutritional risk.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michelle Ferguson","subfamily":"nutritional assessment","year":"1999","type":"Patient self-report screening tool"},"citations":[{"ref":"Ferguson, M., Capra, S., Bauer, J., & Banks, M. (1999). Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutrition, 15(6), 458-464.","type":"article","doi":"10.1016/S0899-9007(99)00084-2","isbn":null,"url":null},{"ref":"Stratton, R. J., Hackston, A., Longmore, D., Dixon, R., Price, S., Stroud, M., King, B., & Elia, M. (2004). Malnutrition in hospital outpatients and inpatients: prevalence, concurrent validity and ease of use of the 'Malnutrition Screening Tool' (MST) for adults. Br J Nutr, 92(5), 799-808.","type":"article","doi":"10.1079/BJN20041258","isbn":null,"url":null}],"related":["waterlow-scale","clinical-frailty-scale","katz-independence-adl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mamba","name":"Mamba (State Space Model)","fullName":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","aliases":["Mamba","State space models","Selective state space"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Sequence Models, State Space Models","year":"2023","originator":"Albert Gu","url":"https://scholargate.app/en/deep-learning/mamba","markdownUrl":"https://scholargate.app/en/deep-learning/mamba.md","definition":"Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Gu","subfamily":"Deep Learning, Sequence Models, State Space Models","year":"2023","type":"Neural network architecture"},"citations":[{"ref":"Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2312.08956"}],"related":["vision-transformer","vision-mamba","latent-diffusion-models","masked-autoencoders"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mancova","name":"MANCOVA","fullName":"Multivariate Analysis of Covariance","aliases":["MANCOVA","multivariate ANCOVA","MANOVA with covariates","MANCOVA — Çok Değişkenli Kovaryans Analizi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1970,"originator":"Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s","url":"https://scholargate.app/en/statistics/mancova","markdownUrl":"https://scholargate.app/en/statistics/mancova.md","definition":"MANCOVA (Multivariate Analysis of Covariance) is a parametric hypothesis test that simultaneously compares two or more groups on multiple continuous dependent variables while statistically controlling for one or more covariates. It extends MANOVA by incorporating covariate adjustment, a tradition consolidated in multivariate statistical methodology by the 1970s and authoritatively documented by Tabachnick and Fidell (2019).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s","year":1970,"family":"Hypothesis test","type":"Parametric multivariate mean comparison with covariate control","groups":"≥2","dependentVariables":"≥2 continuous","covariates":"≥1 continuous","parametric":true,"distribution":"Wilks' Lambda (and other multivariate F approximations)","assumptionTest":"Box's M for variance-covariance homogeneity"},"citations":[{"ref":"Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134790541","url":null}],"related":["manova","ancova","one-way-anova","hotelling-t2","discriminant-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"manic-state-rating-scale","name":"Young Mania Rating Scale","fullName":"Young Mania Rating Scale (YMRS)","aliases":["YMRS"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Bipolar mania severity assessment","year":"1978","originator":"Robert C. Young","url":"https://scholargate.app/en/psychiatry/manic-state-rating-scale","markdownUrl":"https://scholargate.app/en/psychiatry/manic-state-rating-scale.md","definition":"The YMRS is an 11-item clinician-administered rating scale designed to assess the severity of manic and hypomanic symptoms in bipolar disorder. Developed by Young and colleagues in 1978, it is the gold standard outcome measure in bipolar disorder research and the primary efficacy endpoint in mood stabilizer and antipsychotic trials for acute mania. The YMRS captures core mania features (elevated mood, increased goal-directed activity, racing thoughts, reduced need for sleep, increased talkativeness, distractibility, and irritability) and is sensitive to both pharmacological and psychotherapeutic interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert C. Young","subfamily":"Bipolar mania severity assessment","year":"1978","type":"Clinician-administered rating scale"},"citations":[{"ref":"Young, R. C., Biggs, J. T., Ziegler, V. E., & Meyer, D. A. (1978). A rating scale for mania: Reliability, validity and sensitivity. British Journal of Psychiatry, 133(5), 429–435.","type":"article","doi":"10.1192/bjp.133.5.429","isbn":null,"url":null},{"ref":"Altman, E. G., Hedeker, D., Peterson, J. L., & Davis, J. M. (1994). The Altman Self-Rating Mania Scale. Biological Psychiatry, 42(12), 948–955.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Altman+Self-Rating+Mania+Scale+Altman"},{"ref":"Muralidharan, K., & Koshy, G. (2005). A review of the Mania Rating Scale instruments and their role in bipolar disorder. Indian Journal of Psychiatry, 47(1), 9–15.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+review+of+the+Mania+Rating+Scale+instruments+and+their+role+in+bipolar+disorder+Muralidharan"}],"related":["panss","brief-psychiatric-rating-scale","dissociative-experiences-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mann-whitney-u","name":"Mann-Whitney U test","fullName":"Mann-Whitney U test","aliases":["Mann-Whitney-Wilcoxon test","Wilcoxon rank-sum test","Mann-Whitney U Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1947,"originator":"H. B. Mann & D. R. Whitney","url":"https://scholargate.app/en/statistics/mann-whitney-u","markdownUrl":"https://scholargate.app/en/statistics/mann-whitney-u.md","definition":"The Mann-Whitney U test is the nonparametric alternative to the independent samples t-test, comparing two independent groups by ranking all observations together rather than relying on their means. It was introduced by H. B. Mann and D. R. Whitney in 1947 and does not require the data to be normally distributed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"H. B. Mann & D. R. Whitney","year":1947,"family":"Hypothesis test","type":"Nonparametric two-group comparison","groups":2,"outcome":"ordinal or continuous (ranked)","parametric":false,"distribution":"Mann-Whitney U (normal approximation for large samples)"},"citations":[{"ref":"Mann, H. B. & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60.","type":"article","doi":"10.1214/aoms/1177730491","isbn":null,"url":null},{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null}],"related":["independent-t-test","wilcoxon-signed-rank","kruskal-wallis","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"manova","name":"MANOVA","fullName":"Multivariate Analysis of Variance","aliases":["Multivariate ANOVA","Çok Değişkenli ANOVA (MANOVA)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1932,"originator":"Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)","url":"https://scholargate.app/en/statistics/manova","markdownUrl":"https://scholargate.app/en/statistics/manova.md","definition":"MANOVA is a parametric hypothesis test that simultaneously compares group means across multiple continuous dependent variables, controlling the inflation of Type I error that would result from running separate ANOVAs. Key multivariate test statistics — Wilks' Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Greatest Root — were developed between the 1930s and 1950s, with Wilks' Lambda formalised by Samuel Stanley Wilks in 1932.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)","year":1932,"family":"Hypothesis test","type":"Parametric multivariate mean comparison","minSamplePerCell":20,"outcome":"multiple continuous","parametric":true,"distribution":"Wilks' Lambda / Pillai's Trace / Hotelling-Lawley Trace / Roy's Greatest Root","controlsTypeIError":true},"citations":[{"ref":"Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0205849574","url":null},{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null}],"related":["one-way-anova","repeated-measures-anova","discriminant-analysis","independent-t-test","ancova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"manual-muscle-testing","name":"Manual Muscle Testing","fullName":"Manual Muscle Testing (MMT)","aliases":["MMT","Muscle strength assessment"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Strength assessment","year":"1940s","originator":"Lucille Daniels and Catharine Worthingham","url":"https://scholargate.app/en/physical-therapy/manual-muscle-testing","markdownUrl":"https://scholargate.app/en/physical-therapy/manual-muscle-testing.md","definition":"Manual muscle testing (MMT) is a clinical examination technique that quantifies muscle strength by applying manual resistance to isometric contractions and grading the result on a standardized scale (typically 0-5). Developed by Daniels and Worthingham in the 1940s, MMT remains the primary bedside method for assessing muscle weakness in neuromuscular and neurological disorders, establishing rehabilitation baselines, and monitoring treatment effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lucille Daniels and Catharine Worthingham","subfamily":"Strength assessment","year":"1940s","type":"Clinical examination technique"},"citations":[{"ref":"Kendall, F. P., McCreary, E. K., Provance, P. G., Rodgers, M. M., & Romani, W. A. (2005). Muscles: Testing and function with posture and pain (5th ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":null,"url":"https://www.lww.com/"},{"ref":"Daniels, L., & Worthingham, C. (1972). Muscle testing: Techniques of manual examination (4th ed.). W.B. Saunders Company.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com/"}],"related":["range-of-motion-goniometry","functional-independence-measure","neuromuscular-re-education"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"map-algebra","name":"Map Algebra","fullName":"Map Algebra (Cartographic Modeling)","aliases":["Cartographic Modeling","Raster Algebra","Grid Algebra","Harita Cebiri"],"domain":"spatial-analysis","family":"process-pipeline","subfamily":"Raster modeling","year":1990,"originator":"Dana Tomlin","url":"https://scholargate.app/en/spatial-analysis/map-algebra","markdownUrl":"https://scholargate.app/en/spatial-analysis/map-algebra.md","definition":"Map Algebra is a rule-based language and computational framework for deriving new raster layers from existing ones by applying arithmetic, logical, or statistical operations cell by cell or across neighborhoods. Formalized by Dana Tomlin in 1990, it is the foundational algebraic system underlying raster GIS analysis and is widely used in environmental science, urban planning, hydrology, and land-use modeling whenever spatially explicit calculations on gridded data are required.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dana Tomlin","year":1990,"type":"Raster spatial analysis framework","subfamily":"Raster modeling","data_requirement":"Raster (grid) layers in common extent and resolution","output":"Derived raster layer(s)"},"citations":[{"ref":"Tomlin, C. D. (1990). Geographic Information Systems and Cartographic Modeling. Prentice Hall.","type":"book","doi":null,"isbn":"978-0-13-350927-4","url":null}],"related":["gis-mcda","least-cost-path","landscape-metrics"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mapper-algorithm","name":"Mapper Algorithm","fullName":"Mapper Algorithm for Topological Data Analysis","aliases":["Topological Mapper","TDA Mapper","Reeb Graph Approximation","Eşleyici Algoritma"],"domain":"topology","family":"ml-model","subfamily":"Topological data analysis","year":2007,"originator":"Singh, Mémoli & Carlsson","url":"https://scholargate.app/en/topology/mapper-algorithm","markdownUrl":"https://scholargate.app/en/topology/mapper-algorithm.md","definition":"The Mapper algorithm is a method in topological data analysis (TDA) that produces a graph-based summary of the shape of high-dimensional point cloud data. Introduced by Singh, Mémoli, and Carlsson in 2007 at the Eurographics Symposium on Point-Based Graphics, Mapper constructs a simplicial complex — typically a graph — that captures the global topological and geometric structure of a dataset without requiring a fixed embedding or metric assumption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Singh, Mémoli & Carlsson","year":2007,"type":"Graph-based topological summarization","subfamily":"Topological data analysis","output":"Simplicial complex (graph)","complexity":"Depends on cover resolution and clustering choice"},"citations":[{"ref":"Singh, G., Mémoli, F., & Carlsson, G. (2007). Topological methods for the analysis of high dimensional data sets and 3D object recognition. Eurographics Symposium on Point-Based Graphics, 91–100.","type":"article","doi":"10.2312/SPBG/SPBG07/091-100","isbn":null,"url":null}],"related":["persistent-homology","spectral-clustering","umap"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mapping-review","name":"Mapping Review","fullName":"Evidence and Literature Mapping Review","aliases":["evidence map","systematic map","research map","literature map"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"Late 1990s–2000s; major methodological formalization ~2010s","originator":"Buckland & Gann (1998); formalized by systematic review community (Campbell Collaboration, Collaboration for Environmental Evidence)","url":"https://scholargate.app/en/scientometrics/mapping-review","markdownUrl":"https://scholargate.app/en/scientometrics/mapping-review.md","definition":"A mapping review (also called a systematic map or evidence map) is a form of systematic review that aims to chart the extent, range, and nature of evidence on a broad topic rather than synthesize findings into a single pooled answer. It categorizes studies by key dimensions — such as intervention type, population, outcome, and study design — and presents the resulting landscape visually and tabularly so that researchers and practitioners can identify clusters of evidence, knowledge gaps, and priorities for future primary research or deeper synthesis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Buckland & Gann (1998); formalized by systematic review community (Campbell Collaboration, Collaboration for Environmental Evidence)","year":"Late 1990s–2000s; major methodological formalization ~2010s","type":"Systematic evidence mapping methodology","dataType":"Bibliographic records, abstracts, full-text papers","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"James, K. L., Randall, N. P., & Haddaway, N. R. (2016). A methodology for systematic mapping in environmental sciences. Environmental Evidence, 5(1), 7.","type":"article","doi":"10.1186/s13750-016-0059-6","isbn":null,"url":null},{"ref":"Miake-Lye, I. M., Hempel, S., Shanman, R., & Shekelle, P. G. (2016). What is an evidence map? A systematic review of published evidence maps and their definitions, methods, and products. Systematic Reviews, 5(1), 28.","type":"article","doi":"10.1186/s13643-016-0204-x","isbn":null,"url":null}],"related":["scoping-review","systematic-literature-review","bibliometric-analysis","narrative-review","umbrella-review","co-word-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mara","name":"MARA","fullName":"Magnitude of the Area for the Ranking of Alternatives","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., Lugijevskij, R.","url":"https://scholargate.app/en/decision-making/mara","markdownUrl":"https://scholargate.app/en/decision-making/mara.md","definition":"MARA (Magnitude of the Area for the Ranking of Alternatives) is a ranking multi-criteria decision-making (MCDM) method introduced by Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., Lugijevskij, R. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., Lugijevskij, R.","subfamily":"Ranking","year":"2022","type":"Area-magnitude (integral) based","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., Lugijevskij, R. (2022). Novel hybrid MPSI-MARA decision-making model for support system selection in an underground mine. Systems","type":"article","doi":"10.3390/systems10060248","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"marcos","name":"MARCOS","fullName":"Measurement of Alternatives and Ranking according to Compromise Solution","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P.","url":"https://scholargate.app/en/decision-making/marcos","markdownUrl":"https://scholargate.app/en/decision-making/marcos.md","definition":"MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P.","subfamily":"Ranking","year":"2020","type":"Utility function (ideal + anti-ideal reference)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":true},"citations":[{"ref":"Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). Computers & Industrial Engineering","type":"article","doi":"10.1016/j.cie.2019.106231","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"marginal-structural-model-in-education-research","name":"Marginal structural model in education research","fullName":"Marginal Structural Model for Causal Inference in Education Research","aliases":["MSM","marginal structural model","MSM with inverse probability weighting","IPW-MSM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000 (method); 2006 (canonical education application)","originator":"James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application)","url":"https://scholargate.app/en/causal-inference/marginal-structural-model-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/marginal-structural-model-in-education-research.md","definition":"A marginal structural model (MSM) is a causal inference technique that uses inverse probability weighting to estimate the effect of a treatment or educational intervention that changes over time. Introduced by Robins, Hernán and Brumback (2000) in epidemiology and brought into education by Hong and Raudenbush (2006), MSMs handle time-varying confounding — a challenge that conventional regression cannot resolve.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application)","year":"2000 (method); 2006 (canonical education application)","type":"Causal inference / weighted regression model","dataType":"Longitudinal or panel data with time-varying treatments","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data. Journal of the American Statistical Association, 101(475), 901-910.","type":"article","doi":"10.1198/016214506000000447","isbn":null,"url":null}],"related":["inverse-probability-weighting","propensity-score-matching","difference-in-differences","instrumental-variables","causal-mediation-analysis","regression-discontinuity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"marginal-structural-model","name":"Marginal Structural Model","fullName":"Marginal Structural Model with Inverse Probability of Treatment Weighting","aliases":["MSM","MSM-IPTW","marginal structural Cox model","weighted structural model"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000","originator":"James M. Robins, Miguel A. Hernan, Babette Brumback","url":"https://scholargate.app/en/causal-inference/marginal-structural-model","markdownUrl":"https://scholargate.app/en/causal-inference/marginal-structural-model.md","definition":"A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James M. Robins, Miguel A. Hernan, Babette Brumback","year":"2000","type":"Causal model / semiparametric weighting","dataType":"Longitudinal / time-varying treatment and covariate data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"}],"related":["inverse-probability-weighting","propensity-score-weighting","doubly-robust-estimation","difference-in-differences","g-computation","time-varying-confounding"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"marital-quality-questionnaire","name":"ENRICH Marital Satisfaction Scale","fullName":"ENRICH Marital Satisfaction Scale (EMS)","aliases":["EMS","ENRICH Satisfaction Scale","ENRICH Couple Assessment"],"domain":"social-psychology","family":"process-pipeline","subfamily":"couple and marital satisfaction","year":"1996","originator":"David Olson","url":"https://scholargate.app/en/social-psychology/marital-quality-questionnaire","markdownUrl":"https://scholargate.app/en/social-psychology/marital-quality-questionnaire.md","definition":"The ENRICH (Enriching Relationships: Issues, Communication, Happiness) program is a comprehensive couple assessment and enrichment system developed by David Olson that includes multiple relationship assessment tools. The ENRICH Marital Satisfaction Scale is a subset of the full ENRICH Couple Inventory and measures couple satisfaction across key relationship domains. Widely used in premarital counseling, relationship enrichment programs, and couple therapy, the ENRICH approach assesses both relationship strengths and areas for improvement, positioning it as both diagnostic and developmentally focused.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Olson","subfamily":"couple and marital satisfaction","year":"1996","type":"Self-report relationship assessment"},"citations":[{"ref":"Olson, D. H. (2011). ENRICH couple inventory: Couple communication, compatibility, and satisfaction assessment. Minneapolis: Life Innovations.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Olson+ENRICH+couple+inventory+2011"},{"ref":"Olson, D. H., & Olson, A. K. (2008). The couple checkup: Assessing relationship health. In H. A. Liddle, C. L. Rowe, G. M. Dakof, & D. A. Henderson (Eds.), Family therapy for adolescent substance abuse (pp. 25-48). New York: Guilford Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+couple+checkup%3A+Assessing+relationship+health+Olson"}],"related":["dyadic-adjustment-scale","relationship-assessment-scale","family-assessment-device"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"markerless-motion-capture","name":"Markerless Motion Capture","fullName":"Markerless Motion Capture","aliases":["Marker-free tracking","Vision-based motion capture","Deep learning pose estimation"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Computer vision","year":"2017","originator":"Zhe Cao","url":"https://scholargate.app/en/biomechanics/markerless-motion-capture","markdownUrl":"https://scholargate.app/en/biomechanics/markerless-motion-capture.md","definition":"Markerless motion capture infers the 3D positions and joint angles of a moving subject from video sequences using computer vision and machine learning. Pioneered by deep learning approaches such as OpenPose and MediaPipe, it eliminates the need for reflective markers or inertial sensors, making motion capture accessible and practical for real-world applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhe Cao","subfamily":"Computer vision","year":"2017","type":"Deep learning pipeline"},"citations":[{"ref":"Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","type":"article","doi":"10.1109/CVPR.2017.143","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.","type":"book","doi":null,"isbn":null,"url":"https://deeplearningbook.org"}],"related":["forward-kinematics","inverse-dynamics","dtw-gait-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"market-orientation-scale","name":"MARKOR Market Orientation Scale","fullName":"MARKOR Market Orientation Scale","aliases":["Market Orientation Measurement","Kohli-Jaworski Scale"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Organizational behavior","year":"1993","originator":"Ajay K. Kohli, Bernard J. Jaworski, Ajith Kumar","url":"https://scholargate.app/en/marketing-management/market-orientation-scale","markdownUrl":"https://scholargate.app/en/marketing-management/market-orientation-scale.md","definition":"The MARKOR scale, developed by Kohli, Jaworski, and Kumar (1993), measures organizational market orientation—the degree to which an organization actively gathers and uses market intelligence to guide strategy and decision-making. MARKOR captures three core dimensions: Intelligence Generation (collecting customer and competitor information), Dissemination (sharing market knowledge across departments), and Responsiveness (acting on market insights). The scale is widely used in strategic management research to diagnose whether organizations are truly customer-focused or operate in a more internally driven manner.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ajay K. Kohli, Bernard J. Jaworski, Ajith Kumar","subfamily":"Organizational behavior","year":"1993","type":"Multi-dimensional organizational market orientation scale"},"citations":[{"ref":"Kohli, A. K., Jaworski, B. J., & Kumar, A. (1993). MARKOR: A Measure of Market Orientation. Journal of Marketing Research, 30(4), 467-477.","type":"article","doi":"10.1177/002224379303000406","isbn":null,"url":null},{"ref":"Jaworski, B. J., & Kohli, A. K. (1993). Market Orientation: Antecedents and Consequences. Journal of Marketing, 57(3), 53-70.","type":"article","doi":"10.1177/002224299305700304","isbn":null,"url":null}],"related":["customer-satisfaction-index","customer-loyalty-scale","brand-equity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"market-segmentation-analysis","name":"Market Segmentation Analysis","fullName":"Market Segmentation Analysis","aliases":["Customer Segmentation","Market Partitioning"],"domain":"marketing","family":"process-pipeline","subfamily":"Market analysis and customer clustering","year":"1980","originator":"Philip Kotler and William Perreault Jr.","url":"https://scholargate.app/en/marketing/market-segmentation-analysis","markdownUrl":"https://scholargate.app/en/marketing/market-segmentation-analysis.md","definition":"Market Segmentation Analysis is a systematic approach to dividing a heterogeneous market into smaller, homogeneous groups (segments) that share similar needs, behaviors, preferences, or characteristics. Developed through advances in statistical clustering and customer analytics, this methodology enables companies to tailor marketing strategies, product offerings, and customer experiences to specific audience groups rather than treating the market as a single entity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Philip Kotler and William Perreault Jr.","subfamily":"Market analysis and customer clustering","year":"1980","type":"Statistical segmentation methodology"},"citations":[{"ref":"Wedel, M., & Kamakura, W. A. (2002). Introduction to the Special Issue on Market Segmentation. International Journal of Research in Marketing, 19(3), 181-183.","type":"article","doi":"10.1016/s0167-8116(02)00075-7","isbn":null,"url":null},{"ref":"Kotler, P. (1997). Marketing Management: Analysis, Planning, Implementation and Control (9th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0132330831","url":null},{"ref":"Dolnicar, S., Grün, B., & Leisch, F. (2018). Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful. Springer.","type":"article","doi":"10.1007/978-981-10-8818-6","isbn":null,"url":null}],"related":["brand-equity-measurement","customer-lifetime-value","customer-journey-mapping","willingness-to-pay-estimation","advertising-effectiveness-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"market-sensing-capability-scale","name":"Market Sensing Capability Scale","fullName":"Market Sensing Capability (MSC) Measurement Scale","aliases":["MSC","Market Intelligence Capability"],"domain":"strategic-management","family":"process-pipeline","subfamily":"market-intelligence","year":"1990","originator":"Ajay Kohli, Bernard Jaworski, and George S. Day","url":"https://scholargate.app/en/strategic-management/market-sensing-capability-scale","markdownUrl":"https://scholargate.app/en/strategic-management/market-sensing-capability-scale.md","definition":"Market Sensing Capability (MSC) refers to an organization's ability to systematically gather, interpret, and respond to market information about customers, competitors, and market trends. Building on Kohli and Jaworski's (1990) market orientation construct and George Day's (1994) framework of market-driven organizations, the MSC scale measures three interconnected processes: intelligence generation (acquiring market information), dissemination (sharing information across functions), and responsiveness (acting on market insights). Organizations with strong MSC detect competitive threats earlier, understand customer needs more deeply, and adapt strategies faster than competitors with weaker sensing capabilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ajay Kohli, Bernard Jaworski, and George S. Day","subfamily":"market-intelligence","year":"1990","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Kohli, A. K., & Jaworski, B. J. (1990). Market orientation: The construct, research propositions, and managerial implications. Journal of Marketing, 54(2), 1–18.","type":"article","doi":"10.1177/002224299005400201","isbn":null,"url":null},{"ref":"Day, G. S. (1994). The capabilities of market-driven organizations. Journal of Marketing, 58(4), 37–52.","type":"article","doi":"10.1177/002224299405800404","isbn":null,"url":null},{"ref":"Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350.","type":"article","doi":"10.1002/smj.640","isbn":null,"url":null}],"related":["dynamic-capabilities-scale","entrepreneurial-orientation-scale","market-sensing-capability-scale","innovation-ambidexterity-scale","strategic-orientation-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"marketing-mix-modeling","name":"Marketing Mix Modeling","fullName":"Marketing Mix Modeling (MMM)","aliases":["MMM","Econometric Modeling","Attribution Modeling"],"domain":"marketing","family":"process-pipeline","subfamily":"Marketing ROI and budget allocation","year":"2001","originator":"David Hanssens, Leonard Parsons, and Randall Schultz","url":"https://scholargate.app/en/marketing/marketing-mix-modeling","markdownUrl":"https://scholargate.app/en/marketing/marketing-mix-modeling.md","definition":"Marketing Mix Modeling (MMM) is an econometric methodology for estimating the impact of various marketing activities (advertising, pricing, promotions, distribution) on sales or other business outcomes. Developed through work by Hanssens, Parsons, and Schultz, MMM integrates time-series data on marketing spend, sales, and market factors to quantify the return on investment for each marketing channel and inform budget allocation decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Hanssens, Leonard Parsons, and Randall Schultz","subfamily":"Marketing ROI and budget allocation","year":"2001","type":"Econometric modeling methodology"},"citations":[{"ref":"Hanssens, D. M., Parsons, L. J., & Schultz, R. L. (2001). Market Response Models: Econometric and Time Series Analyses (2nd ed.). Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-0792372158","url":null},{"ref":"Naik, P. A., Raman, K., & Winer, R. S. (2005). Planning Marketing-Mix Strategies in the Presence of Interaction Effects. Marketing Science, 24(1), 25-34.","type":"article","doi":"10.1287/mksc.1040.0083","isbn":null,"url":null},{"ref":"Madigan, D. (2012). Bayesian Methods for Complex Data. Annual Review of Statistics and Its Applications, 1(1), 1-30.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+Methods+for+Complex+Data+Madigan"}],"related":["advertising-effectiveness-study","customer-lifetime-value","willingness-to-pay-estimation","brand-equity-measurement","market-segmentation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"markov-chain-monte-carlo","name":"Markov Chain Monte Carlo","fullName":"Markov Chain Monte Carlo (MCMC — Metropolis-Hastings, Gibbs Sampling)","aliases":["MCMC","Metropolis-Hastings","Gibbs sampling","Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1953 (Metropolis-Hastings); 1984 (Gibbs)","originator":"Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)","url":"https://scholargate.app/en/simulation/markov-chain-monte-carlo","markdownUrl":"https://scholargate.app/en/simulation/markov-chain-monte-carlo.md","definition":"Markov Chain Monte Carlo (MCMC) is a family of simulation algorithms that constructs a Markov chain whose stationary distribution is the target posterior, enabling Bayesian inference and high-dimensional integral computation that would otherwise be analytically intractable. Pioneered by Metropolis and colleagues in 1953 and extended by Hastings in 1970, MCMC underpins modern Bayesian statistics. The two most widely used variants are Metropolis-Hastings, which proposes moves from a general proposal distribution, and Gibbs sampling, which draws each parameter in turn from its full conditional distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)","year":"1953 (Metropolis-Hastings); 1984 (Gibbs)","type":"Simulation-based Bayesian inference / numerical integration","samplers":"Metropolis-Hastings (general proposal); Gibbs (full conditional distributions)","convergenceDiagnostic":"Gelman-Rubin R̂ statistic (target R̂ < 1.05)","burnIn":"Required — early chain iterations discarded before stationarity","thinning":"Optional — retaining every k-th sample to reduce autocorrelation","difficultyLevel":"3 / 3"},"citations":[{"ref":"Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC.","type":"book","doi":"10.1201/b16018","isbn":null,"url":null},{"ref":"Brooks, S., Gelman, A., Jones, G.L. & Meng, X.-L. (Eds.) (2011). Handbook of Markov Chain Monte Carlo. Chapman & Hall/CRC.","type":"book","doi":"10.1201/b10905","isbn":null,"url":null}],"related":["monte-carlo-simulation","bayesian-regression","approximate-bayesian-computation","latin-hypercube-sampling","bootstrap-simulation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"markov-model-health-economics","name":"Markov Model in Health Economics","fullName":"Markov Chain Model for Health Economic Evaluation","aliases":["Markov model","state transition model","cohort simulation"],"domain":"health-economics","family":"process-pipeline","subfamily":"decision modeling framework","year":"1983","originator":"Beck & Pauker (medical decision analysis, Massachusetts General Hospital)","url":"https://scholargate.app/en/health-economics/markov-model-health-economics","markdownUrl":"https://scholargate.app/en/health-economics/markov-model-health-economics.md","definition":"A Markov model is a decision-analytic tool that simulates disease progression through defined health states over time, calculating cumulative costs and quality-adjusted life years (QALYs) to enable cost-effectiveness analysis. Developed by Beck and Pauker in 1983, Markov models are now the standard framework for projecting long-term outcomes of health interventions, especially chronic diseases where patients transition between clinical states (treatment response, disease progression, remission, death). Used by health technology assessment bodies and pharmaceutical companies to predict intervention value beyond trial duration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Beck & Pauker (medical decision analysis, Massachusetts General Hospital)","subfamily":"decision modeling framework","year":"1983","type":"Method"},"citations":[{"ref":"Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458.","type":"article","doi":"10.1177/0272989X8300300403","isbn":null,"url":null},{"ref":"Sonnenberg, F. A., & Beck, J. R. (1993). Markov Models in Medical Decision Making: A Practical Guide. Medical Decision Making, 13(4), 322-338.","type":"article","doi":"10.1177/0272989X9301300409","isbn":null,"url":null},{"ref":"Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford: Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Drummond%2C%20M.%20F.%2C%20Sculpher%2C%20M.%20J.%2C%20Claxton%2C%20K.%2C%20Stoddart%2C%20G.%20L.%2C%20%26%20Torrance%2C%20G.%20W.%20(2015).%20Methods%20for%20the%20Economic%20Evalu"}],"related":["cost-effectiveness-analysis","cost-benefit-analysis","quality-adjusted-life-year","decision-analytic-modeling","budget-impact-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"markov-model","name":"Markov Model","fullName":"Markov Chain Model","aliases":["Markov Chain","Discrete-Time Markov Chain","DTMC","Markov Process"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1906","originator":"Andrei Markov","url":"https://scholargate.app/en/simulation/markov-model","markdownUrl":"https://scholargate.app/en/simulation/markov-model.md","definition":"A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrei Markov","year":"1906","type":"Probabilistic state-transition model","dataType":"Discrete or continuous state sequences; transition probability matrices","subfamily":"Simulation / optimization"},"citations":[{"ref":"Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge.","type":"book","doi":null,"isbn":"9780521633963","url":null},{"ref":"Markov chain. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Markov_chain"}],"related":["monte-carlo-simulation","queueing-simulation","stochastic-markov-model","discrete-event-simulation","hidden-markov-model","dynamic-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"markov-switching-multifractal","name":"Markov-Switching Multifractal","fullName":"Markov-Switching Multifractal Model","aliases":["MSM","Markov-switching multifractal volatility"],"domain":"time-series","family":"process-pipeline","subfamily":"Regime-switching volatility modeling","year":"2004","originator":"Luc E. Calvet","url":"https://scholargate.app/en/time-series/markov-switching-multifractal","markdownUrl":"https://scholargate.app/en/time-series/markov-switching-multifractal.md","definition":"The Markov-Switching Multifractal (MSM) model is a flexible framework for capturing time-varying volatility and long-memory effects in financial time series. Developed by Calvet and Fisher (2004), it combines Markov chain theory with multifractal scaling principles to generate volatility that exhibits multiple frequency components, each switching between high and low regimes. This approach is particularly effective for modeling asset returns with realistic fat tails and clustered volatility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc E. Calvet","subfamily":"Regime-switching volatility modeling","year":"2004","type":"Stochastic volatility model"},"citations":[{"ref":"Calvet, L. E., & Fisher, A. J. (2004). How to forecast long-run volatility: regime-switching and the estimation of multifractal processes. Journal of Financial Econometrics, 2(1), 49–83.","type":"article","doi":"10.1093/jjfinec/nbh003","isbn":null,"url":null},{"ref":"Calvet, L. E., & Fisher, A. J. (2008). Multifractal Volatility: Theory, Forecasting, and Pricing. Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/multifractal-volatility/calvet/978-0-12-374182-4"},{"ref":"Lux, T. (2008). The Markov-switching multifractal model of asset returns: GMM estimation and linear forecasting of volatility. Journal of Business & Economic Statistics, 26(2), 194–210.","type":"article","doi":"10.1198/073500107000000403","isbn":null,"url":null}],"related":["garch-model","vector-autoregression","kalman-filter","hidden-markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"markov-switching","name":"Markov-Switching Model","fullName":"Markov Regime-Switching Model (MS-AR / MS-VAR)","aliases":["regime-switching model","Markov-switching autoregression","MS-AR","MS-VAR","hidden Markov regime model","Markov Rejim Değiştirme Modeli (MS-AR / MS-VAR)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1989,"originator":"Hamilton (1989); Kim & Nelson (1999)","url":"https://scholargate.app/en/econometrics/markov-switching","markdownUrl":"https://scholargate.app/en/econometrics/markov-switching.md","definition":"The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hamilton (1989); Kim & Nelson (1999)","year":1989,"type":"Regime-switching time series model","estimator":"Maximum likelihood via the Hamilton filter (or Gibbs sampling)","outcome":"continuous","structure":"time series","minSample":60},"citations":[{"ref":"Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.","type":"article","doi":"10.2307/1912559","isbn":null,"url":null},{"ref":"Kim, C. J. & Nelson, C. R. (1999). State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. MIT Press.","type":"book","doi":null,"isbn":"978-0262112383","url":null}],"related":["arima","vector-autoregression","garch","egarch","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mars","name":"MARS","fullName":"Multivariate Adaptive Regression Splines (MARS)","aliases":["multivariate adaptive regression splines","earth algorithm","MARS regression","çok değişkenli uyarlamalı regresyon spline'ları"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1991,"originator":"Jerome H. Friedman","url":"https://scholargate.app/en/machine-learning/mars","markdownUrl":"https://scholargate.app/en/machine-learning/mars.md","definition":"Multivariate adaptive regression splines, introduced by Jerome Friedman in 1991, is a flexible nonparametric regression method that automatically models nonlinearities and interactions by combining piecewise-linear 'hinge' functions. It builds the model in a forward stagewise pass that adds basis functions where they help most, then prunes back the overgrown model, yielding an interpretable additive-plus-interaction form that adapts its complexity to the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerome H. Friedman","year":1991,"type":"Adaptive piecewise-linear regression","basis":"Hinge (truncated linear) functions","captures":"Nonlinearities and interactions automatically","selection":"Forward addition + backward pruning (GCV)"},"citations":[{"ref":"Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.","type":"article","doi":"10.1214/aos/1176347963","isbn":null,"url":null}],"related":["regression-splines","generalized-additive-model","decision-tree","gradient-boosting"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"marx-activity-rating-scale","name":"Marx Activity Rating Scale","fullName":"Marx Activity Rating Scale (MARS)","aliases":["MARS","Marx Activity Scale"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"activity-level assessment","year":2001,"originator":"Russell G. Marx, T. J. Stump, E. C. Jones, T. L. Wickiewicz, R. F. Warren","url":"https://scholargate.app/en/sports-medicine/marx-activity-rating-scale","markdownUrl":"https://scholargate.app/en/sports-medicine/marx-activity-rating-scale.md","definition":"The Marx Activity Rating Scale (MARS) is a 4-item patient-reported instrument that quantifies the frequency of high-demand athletic activities performed in the past four weeks. Developed by Marx and colleagues in 2001 and published in the American Journal of Sports Medicine, the MARS focuses specifically on quantifying participation in running, cutting, decelerating, and pivoting—the high-impact, multi-directional activities that demand the most from the knee and ankle. The MARS is widely used in orthopedic research to classify patients by activity level and to assess return to activity following surgery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Russell G. Marx, T. J. Stump, E. C. Jones, T. L. Wickiewicz, R. F. Warren","subfamily":"activity-level assessment","year":2001,"type":"Patient self-report activity level"},"citations":[{"ref":"Marx RG, Stump TJ, Jones EC, Wickiewicz TL, Warren RF. Development and evaluation of an activity rating scale for disorders of the knee. Am J Sports Med. 2001;29(2):213-218.","type":"article","doi":"10.1177/03635465010290021601","isbn":null,"url":null}],"related":["ikdc-subjective-knee-form","acl-return-to-sport-scale","lower-extremity-functional-scale","patient-specific-functional-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"marxan-mpa-planning","name":"Marxan MPA Planning","fullName":"Marxan Marine Protected Area Planning","aliases":["Marxan","Marxan with Zones"],"domain":"oceanography","family":"process-pipeline","subfamily":"Conservation Optimization","year":"2000","originator":"Ian Ball","url":"https://scholargate.app/en/oceanography/marxan-mpa-planning","markdownUrl":"https://scholargate.app/en/oceanography/marxan-mpa-planning.md","definition":"Marxan is a decision-support system that uses optimization algorithms to design cost-effective marine protected area (MPA) networks that achieve conservation targets while minimizing socioeconomic costs. Developed by Ian Ball and Hugh Possingham in 2000, Marxan has become the global standard tool for systematic conservation planning in marine environments. The software enables planners to explore trade-offs between conservation effectiveness and economic feasibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ian Ball","subfamily":"Conservation Optimization","year":"2000","type":"optimization-algorithm"},"citations":[{"ref":"Possingham, H. P., Ball, I., & Andelman, S. (2000). Mathematical methods for identifying representative reserve networks. In S. Ferson & M. Burgman (Eds.), Quantitative Methods for Conservation Biology (pp. 291-306). Springer-Verlag.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/"},{"ref":"Ball, I. R., Possingham, H. P., & Watts, M. (2009). Marxan and relatives: software for spatial conservation prioritisation. In A. Moilanen, K. A. Wilson, & H. P. Possingham (Eds.), Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools (pp. 185-195). Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://academic.oup.com/"}],"related":["benthic-index-of-biotic-integrity","harmful-algal-bloom-monitoring","ocean-color-chlorophyll-a"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mask-rcnn","name":"Mask R-CNN","fullName":"Mask R-CNN (Instance Segmentation)","aliases":["Mask Region-based Convolutional Neural Network","Instance Segmentation R-CNN","He et al. 2017 Segmentation Model","Maske R-CNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Object detection / segmentation","year":2017,"originator":"Kaiming He et al. (FAIR)","url":"https://scholargate.app/en/deep-learning/mask-rcnn","markdownUrl":"https://scholargate.app/en/deep-learning/mask-rcnn.md","definition":"Mask R-CNN is a deep learning framework for instance segmentation introduced by Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick at Facebook AI Research (FAIR) in 2017. It extends Faster R-CNN by adding a parallel branch that predicts a binary pixel-level mask for each detected object instance, enabling simultaneous object detection, classification, and fine-grained segmentation in a single forward pass.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaiming He et al. (FAIR)","year":2017,"type":"Instance segmentation deep neural network","subfamily":"Object detection / segmentation","backbone":"ResNet / FPN (Feature Pyramid Network)","output":"Bounding box + class label + pixel-level binary mask per instance"},"citations":[{"ref":"He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), 2980–2988.","type":"inproceedings","doi":"10.1109/ICCV.2017.322","isbn":null,"url":null}],"related":["faster-r-cnn","convolutional-neural-network","u-net"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"masked-autoencoders","name":"Masked Autoencoders","fullName":"Masked Autoencoders are Scalable Vision Learners","aliases":["MAE","Vision MAE"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Self-Supervised Learning","year":"2021","originator":"Kaiming He","url":"https://scholargate.app/en/deep-learning/masked-autoencoders","markdownUrl":"https://scholargate.app/en/deep-learning/masked-autoencoders.md","definition":"Masked Autoencoders (MAE) is a self-supervised learning approach introduced by He et al. in 2021 that masks random patches of an image and trains a model to reconstruct the missing content. Adapting the masked language modeling paradigm from NLP to vision, MAE learns rich visual representations by solving a challenging reconstruction task without requiring labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaiming He","subfamily":"Deep Learning, Self-Supervised Learning","year":"2021","type":"Neural network architecture"},"citations":[{"ref":"He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2022). Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16000-16009).","type":"article","doi":"10.1109/CVPR52688.2022.01553","isbn":null,"url":null}],"related":["swin-transformer","vision-transformer","latent-diffusion-models","simclr"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maslach-burnout-inventory","name":"Maslach Burnout Inventory","fullName":"Maslach Burnout Inventory (MBI)","aliases":["MBI","Maslach Burnout Inventory — Human Services Survey","MBI-HSS"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Occupational health","year":"1981","originator":"Christina Maslach and Susan Jackson","url":"https://scholargate.app/en/social-psychology/maslach-burnout-inventory","markdownUrl":"https://scholargate.app/en/social-psychology/maslach-burnout-inventory.md","definition":"The Maslach Burnout Inventory (MBI) is the most widely used instrument for measuring occupational burnout—a syndrome of emotional exhaustion, depersonalization, and reduced personal accomplishment in response to chronic workplace stress. Developed by Christina Maslach and Susan Jackson in the early 1980s, the MBI has become the standard reference for burnout assessment in research, occupational health, and clinical practice across helping professions and other high-stress occupations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christina Maslach and Susan Jackson","subfamily":"Occupational health","year":"1981","type":"Occupational burnout assessment scale"},"citations":[{"ref":"Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99–113.","type":"book","doi":"10.1002/job.4030020205","isbn":null,"url":null},{"ref":"Maslach, C. (1982). Burnout: The cost of caring. Prentice Hall.","type":"book","doi":null,"isbn":"978-0131089784","url":null},{"ref":"Schaufeli, W. B., Leiter, M. P., & Maslach, C. (2009). Burnout: 35 years of research and practice. Career Development International, 14(3), 204–220.","type":"article","doi":"10.1108/13620430910966406","isbn":null,"url":null}],"related":["uwes-work-engagement","cultural-intelligence-scale","generalized-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-case-control-study","name":"Matched case-control study","fullName":"Matched Case-Control Study","aliases":["matched case-referent study","individually matched case-control","pair-matched case-control","matched case-control design"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1950s–1970s","originator":"Brian MacMahon and others; systematised by Schlesselman (1982)","url":"https://scholargate.app/en/epidemiology/matched-case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/matched-case-control-study.md","definition":"A matched case-control study is an observational epidemiological design in which each case (a person with the disease or outcome of interest) is paired with one or more controls (persons without the outcome) who share one or more characteristics — such as age, sex, or clinical setting — to control confounding. Exposure history is then compared between cases and their matched controls to estimate the odds ratio of the exposure-disease association.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brian MacMahon and others; systematised by Schlesselman (1982)","year":"1950s–1970s","type":"Observational analytic design","dataType":"Categorical and continuous exposure / covariate data; binary outcome (case/control status)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755474","url":null},{"ref":"Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195029338","url":null}],"related":["case-control-study","nested-case-control","cohort-study","propensity-score-matching","conditional-logistic-regression","case-crossover-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-case-crossover-design","name":"Matched Case-Crossover Design","fullName":"Matched Case-Crossover Epidemiological Design","aliases":["matched case-crossover study","time-matched case-crossover","bidirectional case-crossover","symmetric bidirectional design"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1991 (base design); matched variant refined ~1998–2000","originator":"Malcolm Maclure (case-crossover); time-matched variant developed by Navidi (1998) and Lumley & Levy (2000)","url":"https://scholargate.app/en/epidemiology/matched-case-crossover-design","markdownUrl":"https://scholargate.app/en/epidemiology/matched-case-crossover-design.md","definition":"The matched case-crossover design is a self-controlled observational study in which each case serves as its own control. A short hazard window immediately before the acute event is compared with one or more matched control windows — selected to have the same day of week, season, or other time-varying covariate — making the design robust to stable individual confounders and calendar-time trends simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Malcolm Maclure (case-crossover); time-matched variant developed by Navidi (1998) and Lumley & Levy (2000)","year":"1991 (base design); matched variant refined ~1998–2000","type":"Observational epidemiological design","dataType":"Time-stamped individual-level event and exposure data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.","type":"article","doi":"10.1093/oxfordjournals.aje.a115853","isbn":null,"url":null},{"ref":"Lumley, T., & Levy, D. (2000). Bias in the case-crossover design: implications for studies of air pollution. Environmetrics, 11(6), 689–704.","type":"article","doi":"10.1002/1099-095x(200011/12)11:6<689::aid-env439>3.0.co;2-n","isbn":null,"url":null}],"related":["case-crossover-design","nested-case-control","matched-case-control-study","case-time-control-design","self-controlled-case-series","conditional-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-case-report","name":"Matched case report","fullName":"Matched Clinical Case Report","aliases":["matched case write-up","case report with matched comparator","matched single-case report","comparator-matched case report"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Late 20th century (widely used from 1990s onward in pharmacovigilance and rare-disease literature)","originator":"Evolved from standard clinical case reporting practice; no single originator","url":"https://scholargate.app/en/epidemiology/matched-case-report","markdownUrl":"https://scholargate.app/en/epidemiology/matched-case-report.md","definition":"A matched case report is a structured clinical case write-up in which the index patient is compared against one or more systematically selected matched comparators — typically patients with similar demographics, comorbidities, or clinical settings who did not experience the same unusual outcome. The matched comparator contextualises the index case, strengthening causal inference beyond what a conventional single case report can support, and is used particularly in pharmacovigilance, rare-disease documentation, and novel-intervention reporting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from standard clinical case reporting practice; no single originator","year":"Late 20th century (widely used from 1990s onward in pharmacovigilance and rare-disease literature)","type":"Observational descriptive design with comparator","dataType":"Clinical data from one index case and one or more systematically matched reference patients","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Gagnier, J. J., Kienle, G., Altman, D. G., Moher, D., Sox, H., & Riley, D. (2013). The CARE guidelines: consensus-based clinical case reporting guideline development. Journal of Medical Case Reports, 7, 223.","type":"article","doi":"10.1186/1752-1947-7-223","isbn":null,"url":null},{"ref":"van Walraven, C., & Rokosh, E. (1999). What is necessary for high-quality case reports? Journal of Clinical Epidemiology, 52(6), 525-532.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=What+is+necessary+for+high-quality+case+reports"}],"related":["case-report","case-series","matched-case-control-study","case-crossover-design","n-of-1-trial","cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-cohort-study","name":"Matched Cohort Study","fullName":"Matched Cohort Study","aliases":["matched follow-up study","paired cohort study","propensity-matched cohort","matched prospective study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-20th century; propensity-score variant 1983","originator":"Established practice; propensity-score matching formalized by Rosenbaum & Rubin (1983)","url":"https://scholargate.app/en/epidemiology/matched-cohort-study","markdownUrl":"https://scholargate.app/en/epidemiology/matched-cohort-study.md","definition":"A matched cohort study is an observational design in which each exposed participant is paired with one or more unexposed counterparts who share key characteristics — such as age, sex, or comorbidity status — before both groups are followed forward in time to compare incident outcomes. Matching controls for measured confounders at the design stage, reducing bias that would otherwise require statistical adjustment alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established practice; propensity-score matching formalized by Rosenbaum & Rubin (1983)","year":"Mid-20th century; propensity-score variant 1983","type":"Observational analytic study design","dataType":"Individual-level longitudinal data (time-to-event, binary, or continuous outcomes)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null}],"related":["cohort-study","case-control-study","nested-case-control","propensity-score-matching","prospective-cohort-study","competing-risks-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-competing-risks-analysis","name":"Matched Competing Risks Analysis","fullName":"Matched Competing Risks Survival Analysis","aliases":["matched Fine-Gray analysis","propensity-matched competing risks","matched cause-specific hazard analysis","matched subdistribution hazard analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1999 (Fine-Gray model); extended to matched designs ~2010s","originator":"Fine & Gray (subdistribution hazard model); Austin, Lee & Fine (matched competing risks framework)","url":"https://scholargate.app/en/epidemiology/matched-competing-risks-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/matched-competing-risks-analysis.md","definition":"Matched competing risks analysis combines subject-level matching (e.g., propensity-score matching) with competing risks survival methods to estimate the cause-specific or subdistribution hazard of an event of interest while accounting for competing events that preclude the occurrence of that event. It is widely used in clinical and epidemiological observational studies where patients may die from causes other than the primary outcome of interest, and where treatment groups differ on baseline confounders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fine & Gray (subdistribution hazard model); Austin, Lee & Fine (matched competing risks framework)","year":"1999 (Fine-Gray model); extended to matched designs ~2010s","type":"Observational survival analysis with matching and competing events","dataType":"Time-to-event data with multiple mutually exclusive event types; matched pairs from observational cohorts","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.","type":"article","doi":"10.1080/01621459.1999.10474144","isbn":null,"url":null},{"ref":"Austin, P. C., Lee, D. S., & Fine, J. P. (2016). Introduction to the analysis of survival data in the presence of competing risks. Circulation, 133(6), 601–609.","type":"article","doi":"10.1161/CIRCULATIONAHA.115.017719","isbn":null,"url":null}],"related":["competing-risks-analysis","propensity-score-matching","cause-specific-hazard","kaplan-meier-estimator","cox-proportional-hazards","inverse-probability-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-cox-proportional-hazards","name":"Matched Cox Proportional Hazards","fullName":"Stratified Cox Proportional Hazards Regression for Matched Designs","aliases":["stratified Cox regression","conditional Cox model","matched survival analysis","Cox model for matched pairs"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1972 (Cox model); matched extension widely adopted 1970s–1980s","originator":"D. R. Cox (Cox model, 1972); stratification extension for matched designs by subsequent methodologists including D. C. Thomas","url":"https://scholargate.app/en/epidemiology/matched-cox-proportional-hazards","markdownUrl":"https://scholargate.app/en/epidemiology/matched-cox-proportional-hazards.md","definition":"Matched Cox proportional hazards is a survival analysis method that extends the Cox regression model to appropriately handle data arising from matched study designs — matched cohorts or matched case-control studies with time-to-event outcomes. By stratifying the partial likelihood by matched set, the method eliminates confounding from matching factors without estimating their baseline hazard, yielding valid hazard ratio estimates that are free from matching-induced bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. R. Cox (Cox model, 1972); stratification extension for matched designs by subsequent methodologists including D. C. Thomas","year":"1972 (Cox model); matched extension widely adopted 1970s–1980s","type":"Semi-parametric survival regression for matched data","dataType":"Time-to-event (survival) data from matched cohort or matched case-control studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null},{"ref":"Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society: Series A, 140(4), 483–485.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thomas+1977+matched+cohort+stratified+cox"}],"related":["cox-proportional-hazards","matched-cohort-study","matched-case-control","survival-analysis","kaplan-meier-analysis","competing-risks-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-cross-sectional-epidemiological-study","name":"Matched Cross-Sectional Epidemiological Study","fullName":"Matched Cross-Sectional Epidemiological Study","aliases":["matched cross-sectional survey","matched prevalence study","matched cross-sectional design","frequency-matched cross-sectional study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-to-late 20th century (formalized ~1970s–1990s)","originator":"Developed within the tradition of observational epidemiology; matching principles codified by Greenland, Rothman, and Kelsey in modern epidemiology texts","url":"https://scholargate.app/en/epidemiology/matched-cross-sectional-epidemiological-study","markdownUrl":"https://scholargate.app/en/epidemiology/matched-cross-sectional-epidemiological-study.md","definition":"A matched cross-sectional epidemiological study is an observational design that measures exposure and outcome simultaneously in a population sample while applying matching to control for one or more confounding variables. By pairing or grouping participants on key characteristics such as age, sex, or socioeconomic status before or during analysis, the design reduces confounding bias without requiring longitudinal follow-up, making it efficient for estimating prevalence and cross-sectional associations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within the tradition of observational epidemiology; matching principles codified by Greenland, Rothman, and Kelsey in modern epidemiology texts","year":"Mid-to-late 20th century (formalized ~1970s–1990s)","type":"Observational epidemiological study design","dataType":"Cross-sectional survey data with individual or frequency matching on confounders","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195083309","url":null}],"related":["cross-sectional-epidemiological-study","matched-case-control-study","cohort-study","matched-cohort-study","case-control-study","prospective-cross-sectional-epidemiological-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-diagnostic-accuracy-study","name":"Matched Diagnostic Accuracy Study","fullName":"Matched Diagnostic Accuracy Study","aliases":["matched DAS","paired diagnostic accuracy study","matched test accuracy study","matched sensitivity-specificity study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (formalised with STARD 2003)","originator":"Evolved from matched case-control methodology; STARD standards formalised by Bossuyt et al. (2003)","url":"https://scholargate.app/en/epidemiology/matched-diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/epidemiology/matched-diagnostic-accuracy-study.md","definition":"A matched diagnostic accuracy study evaluates how well an index test correctly identifies a target condition in study participants who have been matched on key characteristics — such as age, sex, or disease severity — to control for confounding. By pairing diseased and non-diseased subjects on relevant factors before administering the test, the design isolates the test's own discriminative performance from variation attributable to imbalanced covariates, yielding cleaner estimates of sensitivity, specificity, and related accuracy measures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from matched case-control methodology; STARD standards formalised by Bossuyt et al. (2003)","year":"1990s–2000s (formalised with STARD 2003)","type":"Diagnostic / clinical epidemiology study design","dataType":"Paired or matched binary/ordinal test results vs. reference standard","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., & de Vet, H. C. W. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD initiative. BMJ, 326(7379), 41–44.","type":"article","doi":"10.1136/bmj.326.7379.41","isbn":null,"url":null},{"ref":"Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198509844","url":null}],"related":["diagnostic-accuracy-study","matched-case-control","nested-case-control","cohort-study","receiver-operating-characteristic-analysis","cross-sectional-epidemiological-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-dose-response-analysis","name":"Matched dose-response analysis","fullName":"Matched Dose-Response Analysis in Epidemiology","aliases":["matched trend analysis","dose-response in matched designs","exposure-response analysis with matching","matched exposure-gradient analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1970s–1980s","originator":"Developed within the matched case-control framework; formalized by Breslow and Day (1980) and Rothman and colleagues","url":"https://scholargate.app/en/epidemiology/matched-dose-response-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/matched-dose-response-analysis.md","definition":"Matched dose-response analysis evaluates whether increasing levels of exposure are associated with proportionally increasing (or decreasing) risk of an outcome, within a study where cases and controls — or exposed and unexposed individuals — have been deliberately matched on key confounders such as age, sex, or study site. Matching controls residual confounding structurally, while the dose-response component tests whether the exposure-outcome relationship follows a biologically plausible gradient, strengthening causal inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within the matched case-control framework; formalized by Breslow and Day (1980) and Rothman and colleagues","year":"1970s–1980s","type":"Analytical epidemiological method","dataType":"Matched pairs or sets with ordinal/continuous exposure data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K.J., Greenland, S., & Lash, T.L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Breslow, N.E., & Day, N.E. (1980). Statistical Methods in Cancer Research, Vol. 1: The Analysis of Case-Control Studies. IARC Scientific Publications No. 32. International Agency for Research on Cancer.","type":"book","doi":null,"isbn":null,"url":"https://publications.iarc.fr/Book-And-Report-Series/Iarc-Scientific-Publications/Statistical-Methods-In-Cancer-Research-Volume-I-The-Analysis-Of-Case-Control-Studies-1980"}],"related":["dose-response-analysis","matched-case-control-study","case-control-study","conditional-logistic-regression","cohort-study","exposure-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-ecological-study","name":"Matched ecological study","fullName":"Matched Ecological Study Design","aliases":["matched ecologic study","geographically matched ecological study","area-matched ecological design","matched aggregate study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1970s–1990s (methodological consolidation)","originator":"Extension of classical ecological study design; matching principles formalized in 20th-century epidemiology","url":"https://scholargate.app/en/epidemiology/matched-ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/matched-ecological-study.md","definition":"A matched ecological study is an observational epidemiological design in which aggregate units — such as geographic areas, communities, or time periods — are systematically paired or matched on key characteristics before comparing exposure and outcome rates. Matching at the group level controls for area-level confounders and improves comparability between exposed and unexposed units, producing more credible estimates of ecological associations than an unmatched counterpart.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of classical ecological study design; matching principles formalized in 20th-century epidemiology","year":"1970s–1990s (methodological consolidation)","type":"Observational study design","dataType":"Aggregate / group-level data (area statistics, population counts, environmental measurements)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Morgenstern, H. (1998). Ecologic studies in epidemiology: Concepts, principles, and methods. Annual Review of Public Health, 16, 61–81.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Morgenstern+ecologic+studies+epidemiology+concepts+principles+methods+1998"},{"ref":"Ecological study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ecological_study"}],"related":["ecological-study","cohort-study","matched-cohort-study","case-control-study","matched-case-control-study","cross-sectional-epidemiological-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-filter","name":"Matched Filter","fullName":"Matched Filter Signal Detection","aliases":["Correlation Detector","Optimal Filter Detection","Template Matching"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Signal detection","year":"1943","originator":"D. O. North","url":"https://scholargate.app/en/signal-processing/matched-filter","markdownUrl":"https://scholargate.app/en/signal-processing/matched-filter.md","definition":"The matched filter is an optimal signal detector that maximizes the signal-to-noise ratio (SNR) for detecting a known signal in additive Gaussian noise. Developed by D. O. North during World War II for radar applications, the matched filter represents the optimal linear filter for signal detection and remains the foundation for detection theory and digital communications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. O. North","subfamily":"Signal detection","year":"1943","type":"Optimal filter for signal detection"},"citations":[{"ref":"North, D. O. (1943). An Analysis of the Factors Which Determine Signal/Noise Discrimination in Pulsed Carrier Systems. RCA Laboratories, Technical Report PTM-946.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/analysisoffactorsrcanorth"},{"ref":"Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/discretetimesignalprocessing"}],"related":["wiener-filter","kalman-filter-signal","fir-filter-design","adaptive-lms-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-kaplan-meier-analysis","name":"Matched Kaplan-Meier Analysis","fullName":"Matched Cohort Kaplan-Meier Survival Analysis","aliases":["KM analysis in matched cohorts","propensity-matched survival curves","matched survival analysis","paired Kaplan-Meier"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1958 (KM); matched application formalized 1980s–2000s","originator":"Kaplan & Meier (KM method, 1958); matching extensions developed through propensity score methods (Rosenbaum & Rubin, 1983)","url":"https://scholargate.app/en/epidemiology/matched-kaplan-meier-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/matched-kaplan-meier-analysis.md","definition":"Matched Kaplan-Meier analysis estimates and compares survival functions in groups that have been pre-balanced through individual or propensity-score matching. By applying the Kaplan-Meier product-limit estimator to matched cohorts or matched pairs, investigators can visualize time-to-event outcomes while controlling for confounders that would otherwise distort treatment or exposure comparisons in observational data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaplan & Meier (KM method, 1958); matching extensions developed through propensity score methods (Rosenbaum & Rubin, 1983)","year":"1958 (KM); matched application formalized 1980s–2000s","type":"Nonparametric survival analysis with observational confounder control","dataType":"Time-to-event data with censoring in matched pairs or matched cohorts","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481.","type":"article","doi":"10.1080/01621459.1958.10501452","isbn":null,"url":null},{"ref":"Austin, P. C. (2014). Pointwise confidence intervals for restricted mean survival time in a propensity-matched analysis. Statistics in Medicine, 33(14), 2659-2671.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pointwise+confidence+intervals+for+restricted+mean+survival+time+in+a+propensity-matched+analysis+Austin"}],"related":["kaplan-meier-analysis","matched-cohort-study","matched-case-control-study","cox-proportional-hazards","propensity-score-matching","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-nested-case-control","name":"Matched nested case-control","fullName":"Matched Nested Case-Control Study","aliases":["matched NCC study","nested case-control with matching","matched risk-set sampling","incidence density matched case-control"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1970s","originator":"Mantel (1973), Thomas (1977); formalized by Breslow & Day (1980)","url":"https://scholargate.app/en/epidemiology/matched-nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/matched-nested-case-control.md","definition":"A matched nested case-control study is an efficient observational design embedded within a defined cohort. When a participant develops the outcome of interest (a case), a small number of controls are sampled from those still at risk at that moment and matched to the case on key variables such as age, sex, or calendar time. This design preserves the temporal structure of the underlying cohort while sharply reducing the cost of exposure measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mantel (1973), Thomas (1977); formalized by Breslow & Day (1980)","year":"1970s","type":"Observational analytic study design","dataType":"Longitudinal cohort records, biobank samples, time-to-event data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K.J., Greenland, S., & Lash, T.L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Thomas, D.B. (1977). Methodology for assessing interaction in epidemiological studies of matched pairs. Biometrics, 33(3), 463-470.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thomas+1977+nested+case-control+matched+pairs+biometrics"}],"related":["nested-case-control","case-control-study","matched-case-control-study","cohort-study","propensity-score-matching","incidence-density-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-phase-ii-clinical-trial","name":"Matched Phase II clinical trial","fullName":"Matched Phase II Clinical Trial","aliases":["matched Phase II trial","historically matched Phase II study","propensity-matched Phase II trial","externally controlled Phase II trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1960s–1980s (formalized with Simon optimal designs, 1989)","originator":"Gehan (1961) for Phase II designs; matching frameworks adapted from case-control methodology","url":"https://scholargate.app/en/epidemiology/matched-phase-ii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/matched-phase-ii-clinical-trial.md","definition":"A matched Phase II clinical trial is a single-arm or small-controlled early-efficacy study in which treated patients are paired with matched controls — drawn from historical databases, registries, or concurrent external cohorts — on key prognostic variables such as age, disease stage, and performance status. This design allows preliminary efficacy assessment without a concurrent randomized arm, trading randomization for feasibility while partially controlling for confounding through the matching process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gehan (1961) for Phase II designs; matching frameworks adapted from case-control methodology","year":"1960s–1980s (formalized with Simon optimal designs, 1989)","type":"Controlled clinical trial design","dataType":"Patient-level clinical data (treatment outcomes, covariates for matching)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Gehan, E. A. (1961). The determination of the number of patients required in a preliminary and a follow-up trial of a new chemotherapeutic agent. Journal of Chronic Diseases, 13(4), 346–353.","type":"article","doi":"10.1016/0021-9681(61)90060-1","isbn":null,"url":null},{"ref":"Simon, R. (1989). Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials, 10(1), 1–10.","type":"article","doi":"10.1016/0197-2456(89)90015-9","isbn":null,"url":null}],"related":["phase-ii-clinical-trial","matched-case-control-study","propensity-score-matching","randomized-clinical-trial","single-arm-trial","matched-cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-phase-iii-clinical-trial","name":"Matched Phase III Clinical Trial","fullName":"Matched Phase III Clinical Trial","aliases":["matched controlled Phase III trial","Phase III matched-pair trial","matched confirmatory trial","matched late-phase RCT"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-20th century (matching in RCTs formalized ~1950s–1970s)","originator":"Fisher, R. A. (matching principles); adapted into confirmatory trial design over mid-20th century","url":"https://scholargate.app/en/epidemiology/matched-phase-iii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/matched-phase-iii-clinical-trial.md","definition":"A matched Phase III clinical trial is a confirmatory, late-stage controlled study in which each participant assigned to the experimental treatment is paired with one or more controls who share key prognostic characteristics — such as age, disease stage, or comorbidities — before treatment allocation. By ensuring baseline comparability at the level of matched pairs, the design reduces confounding and improves statistical efficiency in settings where simple randomization alone may produce imbalanced groups or where full randomization is logistically or ethically constrained.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fisher, R. A. (matching principles); adapted into confirmatory trial design over mid-20th century","year":"Mid-20th century (matching in RCTs formalized ~1950s–1970s)","type":"Controlled confirmatory clinical trial with matching","dataType":"Continuous, binary, or time-to-event outcome data from matched treatment-control pairs","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Matched pairs. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Matched_pairs"}],"related":["randomized-controlled-trial","phase-iii-clinical-trial","matched-cohort-study","propensity-score-matching","adaptive-phase-iii-clinical-trial","parallel-group-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-phase-iv-study","name":"Matched Phase IV Study","fullName":"Matched Phase IV Post-Marketing Study","aliases":["matched post-marketing surveillance study","Phase IV matched cohort study","matched pharmacoepidemiological study","post-authorization matched safety study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–1990s (formalized in post-marketing regulatory frameworks)","originator":"Regulatory tradition (FDA, EMA); matching methodology from Rosenbaum & Rubin (1983)","url":"https://scholargate.app/en/epidemiology/matched-phase-iv-study","markdownUrl":"https://scholargate.app/en/epidemiology/matched-phase-iv-study.md","definition":"A Matched Phase IV study is a post-marketing observational design in which patients who received an approved drug (or intervention) are matched to comparable non-exposed patients — or patients on an alternative therapy — to evaluate real-world safety, effectiveness, or long-term outcomes. Conducted after regulatory approval, it combines the epidemiological rigour of matching with the breadth of post-authorization pharmacovigilance, generating evidence that randomized trials are rarely powered or timed to provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Regulatory tradition (FDA, EMA); matching methodology from Rosenbaum & Rubin (1983)","year":"1980s–1990s (formalized in post-marketing regulatory frameworks)","type":"Observational study design","dataType":"Longitudinal patient records, claims databases, electronic health records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Strom, B. L., & Kimmel, S. E. (Eds.). (2005). Textbook of Pharmacoepidemiology. Wiley.","type":"book","doi":null,"isbn":"978-0470029244","url":null},{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null}],"related":["propensity-score-matching","case-control-study","cohort-study","pharmacovigilance","inverse-probability-weighting","interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-randomized-clinical-trial","name":"Matched Randomized Clinical Trial","fullName":"Matched Randomized Clinical Trial","aliases":["matched RCT","matched-pair randomized trial","matched randomized controlled trial","covariate-matched RCT"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-20th century concept; methodological formalization circa 2000–2010","originator":"Developed formally in biostatistics literature; Greevy, Imai and colleagues advanced modern frameworks in the 2000s","url":"https://scholargate.app/en/epidemiology/matched-randomized-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/matched-randomized-clinical-trial.md","definition":"A matched randomized clinical trial pairs participants (or clusters) on key baseline characteristics before randomization, then allocates one member of each pair to treatment and the other to control. This design combines the causal validity of randomization with the covariate balance of matching, increasing statistical efficiency and reducing confounding from known prognostic variables without sacrificing the internal validity of a controlled experiment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed formally in biostatistics literature; Greevy, Imai and colleagues advanced modern frameworks in the 2000s","year":"Mid-20th century concept; methodological formalization circa 2000–2010","type":"Experimental clinical study design","dataType":"Baseline covariate data, randomized allocation records, continuous or binary outcome measures","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Imai, K., King, G., & Nall, C. (2009). The essential role of pair matching in cluster-randomized experiments, with application to the Mexican universal health insurance evaluation. Statistical Science, 24(1), 29–53.","type":"article","doi":"10.1214/08-STS274","isbn":null,"url":null},{"ref":"Greevy, R., Lu, B., Silber, J. H., & Rosenbaum, P. R. (2004). Optimal multivariate matching before randomization. Biostatistics, 5(2), 263–275.","type":"article","doi":"10.1093/biostatistics/5.2.263","isbn":null,"url":null}],"related":["randomized-clinical-trial","prospective-randomized-clinical-trial","matched-case-control-study","adaptive-randomized-clinical-trial","pragmatic-randomized-clinical-trial","multicenter-randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-screening-test-evaluation","name":"Matched Screening Test Evaluation","fullName":"Matched Design Screening Test Evaluation","aliases":["matched diagnostic accuracy study","paired screening evaluation","matched-pair test performance study","matched screening assessment"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–2000s (formalized alongside diagnostic accuracy methodology)","originator":"Methodological synthesis from matched case-control and diagnostic accuracy traditions (Pepe, Zhou, and others)","url":"https://scholargate.app/en/epidemiology/matched-screening-test-evaluation","markdownUrl":"https://scholargate.app/en/epidemiology/matched-screening-test-evaluation.md","definition":"Matched screening test evaluation assesses the sensitivity, specificity, and predictive values of a screening or diagnostic test using a matched design, in which disease-positive cases are paired with one or more disease-free controls selected to share key characteristics such as age, sex, or clinical setting. Matching controls for confounders before measuring test performance produces more precise and less biased estimates of diagnostic accuracy, and enables direct paired comparisons of competing tests within the same subjects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Methodological synthesis from matched case-control and diagnostic accuracy traditions (Pepe, Zhou, and others)","year":"1980s–2000s (formalized alongside diagnostic accuracy methodology)","type":"Observational diagnostic study with matched design","dataType":"Binary or ordinal test results, disease status, matched subject pairs or sets","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198509844","url":null},{"ref":"Zhou, X.-H., Obuchowski, N. A., & McClish, D. K. (2011). Statistical Methods in Diagnostic Medicine (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470183144","url":null}],"related":["diagnostic-accuracy-study","screening-test-evaluation","matched-case-control-study","case-control-study","nested-case-control","roc-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matched-survival-analysis","name":"Matched Survival Analysis","fullName":"Matched Cohort Survival Analysis","aliases":["matched time-to-event analysis","propensity-matched survival analysis","matched Kaplan-Meier analysis","paired survival analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1983 (propensity-score matching); applied to survival outcomes throughout 1990s–2000s","originator":"Building on Kaplan & Meier (1958) and Cox (1972); matching framework formalised in observational study design literature (Rosenbaum & Rubin, 1983)","url":"https://scholargate.app/en/epidemiology/matched-survival-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/matched-survival-analysis.md","definition":"Matched survival analysis combines a matching design — typically propensity score matching or exact matching on key covariates — with time-to-event methods such as Kaplan-Meier estimation and the Cox proportional hazards model. By pairing treated and control subjects who are similar on observed confounders before estimating survival curves or hazard ratios, the approach reduces confounding bias in non-randomised studies and produces more credible comparisons of event-free survival between exposure groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Kaplan & Meier (1958) and Cox (1972); matching framework formalised in observational study design literature (Rosenbaum & Rubin, 1983)","year":"1983 (propensity-score matching); applied to survival outcomes throughout 1990s–2000s","type":"Observational study analytic method","dataType":"Time-to-event data with censoring; matched pairs or matched sets from cohort or registry data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Austin, P. C. (2014). Graphical assessments of the balance of propensity score matched samples: A SAS macro. Journal of Statistical Software, 58(7), 1-29. Also see Austin, P. C. (2017). A tutorial on multilevel survival analysis: Methods, models and applications. International Statistical Review, 85(2), 185-203.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Graphical+assessments+of+the+balance+of+propensity+score+matched+samples%3A+A+SAS+macro+Austin"},{"ref":"Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"9781439856789","url":null}],"related":["kaplan-meier-estimator","cox-proportional-hazards","propensity-score-matching","competing-risks-analysis","log-rank-test","time-to-event-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matching-estimator","name":"Matching Estimator","fullName":"Nonparametric Matching Estimator for Average Treatment Effects","aliases":["nearest-neighbor matching","NNM","matching on covariates","covariate matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1973","originator":"Rubin (1973); large-sample theory by Abadie & Imbens (2006)","url":"https://scholargate.app/en/causal-inference/matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/matching-estimator.md","definition":"The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rubin (1973); large-sample theory by Abadie & Imbens (2006)","year":"1973","type":"Nonparametric matching / causal inference","dataType":"Cross-sectional or panel; continuous or binary outcome; multi-dimensional covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.","type":"article","doi":"10.1111/j.1468-0262.2006.00655.x","isbn":null,"url":null},{"ref":"Rubin, D. B. (1973). Matching to Remove Bias in Observational Studies. Biometrics, 29(1), 159-183.","type":"article","doi":"10.2307/2529684","isbn":null,"url":null}],"related":["propensity-score-matching","propensity-score-weighting","coarsened-exact-matching","difference-in-differences","inverse-probability-weighting","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matching-methods","name":"Matching Methods","fullName":"General Matching Methods (CEM / Optimal / Genetic)","aliases":["coarsened exact matching","optimal matching","genetic matching","CEM","Genel Eşleştirme Yöntemleri (CEM / Optimal / Genetic)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2012,"originator":"Iacus, King & Porro (CEM); Hansen (optimal/full matching)","url":"https://scholargate.app/en/causal-inference/matching-methods","markdownUrl":"https://scholargate.app/en/causal-inference/matching-methods.md","definition":"Matching Methods are a family of causal-inference techniques beyond propensity-score matching that pair treated and control units with similar covariates so that a treatment effect can be read off the balanced sample. The family includes Coarsened Exact Matching (Iacus, King & Porro, 2012), optimal matching, and genetic matching.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Iacus, King & Porro (CEM); Hansen (optimal/full matching)","year":2012,"type":"Matching for causal inference","estimator":"Treatment effect on matched/balanced samples","minSample":80,"outcome":"continuous, binary, or categorical"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Hansen, B. B. (2004). Full Matching in an Observational Study of Coaching for the SAT. Journal of the American Statistical Association, 99(467), 609-618.","type":"article","doi":"10.1198/016214504000000647","isbn":null,"url":null}],"related":["propensity-score-matching","inverse-probability-weighting","sensitivity-analysis-observational","local-average-treatment-effect","heterogeneous-treatment-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"material-flow-analysis","name":"Material Flow Analysis","fullName":"Material Flow Analysis (MFA)","aliases":["Substance Flow Analysis","Bulk-MFA","Material Flux Analysis","Malzeme Akış Analizi"],"domain":"sustainability","family":"process-pipeline","subfamily":"Industrial ecology","year":2004,"originator":"Brunner & Rechberger","url":"https://scholargate.app/en/sustainability/material-flow-analysis","markdownUrl":"https://scholargate.app/en/sustainability/material-flow-analysis.md","definition":"Material Flow Analysis (MFA) is a systematic method for quantifying the flows and stocks of materials within a defined system boundary over a specified time period. Introduced comprehensively by Paul H. Brunner and Helmut Rechberger in their 2004 handbook, MFA applies mass-balance principles to track how raw materials, products, wastes, and emissions move through industrial, urban, or national metabolisms, enabling evidence-based resource management and waste policy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brunner & Rechberger","year":2004,"type":"Quantitative systems accounting method","subfamily":"Industrial ecology","data_requirement":"Mass balance data for each process node","output":"Stocks and flows of materials within a defined system boundary"},"citations":[{"ref":"Brunner, P. H., & Rechberger, H. (2004). Practical Handbook of Material Flow Analysis. Lewis Publishers.","type":"book","doi":null,"isbn":"978-1-56670-604-9","url":null}],"related":["life-cycle-assessment","lmdi-decomposition","ecological-footprint"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"material-requirements-planning","name":"Material Requirements Planning","fullName":"Material Requirements Planning","aliases":["MRP","MRP I"],"domain":"operations-management","family":"ml-model","subfamily":"Production Planning","year":"1975","originator":"Joseph Orlicky","url":"https://scholargate.app/en/operations-management/material-requirements-planning","markdownUrl":"https://scholargate.app/en/operations-management/material-requirements-planning.md","definition":"Material Requirements Planning (MRP) is a computerized system developed by Joseph Orlicky in the 1970s that calculates material requirements based on master production schedules and bill-of-materials data. MRP determines what materials to buy, how much to order, and when to order them to meet production demand while minimizing inventory carrying costs. It became a foundational technology for manufacturing planning and later evolved into manufacturing resource planning (MRP II) and enterprise resource planning (ERP) systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph Orlicky","subfamily":"Production Planning","year":"1975","type":"Material planning algorithm"},"citations":[{"ref":"Orlicky, J. (1975). Material requirements planning: The new way of life in production and inventory management. New York: McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://www.mheducation.com/"},{"ref":"APICS (2020). APICS Dictionary (16th ed.). Chicago: APICS.","type":"book","doi":null,"isbn":null,"url":"https://www.apics.org/"}],"related":["aggregate-planning","kanban","job-shop-scheduling","inventory-routing","scor-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maternal-diet-quality-index","name":"MDQI","fullName":"Maternal Diet Quality Index","aliases":["MDQI","Maternal Diet Quality"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"maternal-nutrition-assessment","year":"2010","originator":"Bodnar, Matus, Simhan; University of Pittsburgh","url":"https://scholargate.app/en/public-health-nutrition/maternal-diet-quality-index","markdownUrl":"https://scholargate.app/en/public-health-nutrition/maternal-diet-quality-index.md","definition":"The Maternal Diet Quality Index (MDQI) is a composite measure of maternal nutrition that evaluates diet quality during pregnancy and postpartum using a scored framework. Adapted from general population dietary quality indices, the MDQI emphasizes nutrients critical for fetal development and maternal health: folate, iron, calcium, protein, and micronutrient-rich foods. Use in research and clinical nutrition assessment to characterize maternal diet quality and its associations with pregnancy outcomes and lactation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bodnar, Matus, Simhan; University of Pittsburgh","subfamily":"maternal-nutrition-assessment","year":"2010","type":"24-hour recall or food frequency questionnaire"},"citations":[{"ref":"Bodnar, L. M., Matus, D. S., & Simhan, H. N. (2010). Maternal nutritional status and maternal-fetal transmission of lipophilic micronutrients. Clinical Obstetrics and Gynecology, 53(3), 621–630.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Maternal+nutritional+status+and+maternal-fetal+transmission+of+lipophilic+micronutrients+Bodnar"},{"ref":"Coad, J., Al-Rasheid, K., Dunstall, M., & Dunstall, J. (2012). Anatomy and physiology for midwives (4th ed.). Elsevier Health Sciences.","type":"book","doi":null,"isbn":"978-0-7020-3147-2","url":null}],"related":["household-dietary-diversity-score","healthy-eating-index","infant-young-child-feeding-practices"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"math-anxiety-rating-scale","name":"Mathematics Anxiety Rating Scale","fullName":"Mathematics Anxiety Rating Scale (MARS)","aliases":["MARS","MARS-30"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"domain-specific-anxiety","year":1982,"originator":"Barbara S. Plake and Charles S. Parker","url":"https://scholargate.app/en/anxiety-disorders/math-anxiety-rating-scale","markdownUrl":"https://scholargate.app/en/anxiety-disorders/math-anxiety-rating-scale.md","definition":"The Mathematics Anxiety Rating Scale (MARS) is a self-report questionnaire assessing anxiety and worry related to mathematics learning and performance. Originally developed by Plake and Parker in 1982 with 98 items and refined to a 30-item version (MARS-30) in 1995, the MARS measures multiple facets of math anxiety: anxiety in test/evaluation contexts, mathematics learning contexts, and everyday numerical situations. It is widely used in educational psychology, mathematics education research, and clinical assessment to identify students with math anxiety and to evaluate interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barbara S. Plake and Charles S. Parker","subfamily":"domain-specific-anxiety","year":1982,"type":"Self-report"},"citations":[{"ref":"Plake, B. S., & Parker, C. S. (1982). The development and validation of a revised version of the Mathematics Anxiety Rating Scale. Educational and Psychological Measurement, 45(3), 503–518.","type":"article","doi":"10.1177/001316448204200218","isbn":null,"url":null},{"ref":"Plake, B. S., & Parker, C. S. (2004). The Mathematics Anxiety Rating Scale: A brief version. Measurement and Evaluation in Counseling and Development, 36(4), 224–232.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Mathematics+Anxiety+Rating+Scale%3A+A+brief+version+Plake"}],"related":["test-anxiety-scale","anxiety-sensitivity-index","specific-phobia-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mathematics-anxiety-scale","name":"Mathematics Anxiety Rating Scale","fullName":"Mathematics Anxiety Rating Scale (MARS)","aliases":["MARS","Math Anxiety Measure"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Subject-specific anxiety and emotional response","year":"1972","originator":"Frank Richardson, Richard Suinn","url":"https://scholargate.app/en/educational-psychology/mathematics-anxiety-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/mathematics-anxiety-scale.md","definition":"The Mathematics Anxiety Rating Scale (MARS) is a self-report instrument measuring the degree of anxiety students experience in mathematical situations. Developed by Richardson and Suinn (1972) and revised by Plake and Parker (1995), it assesses emotional and physiological responses to math learning and performance. Mathematics anxiety—fear or dread anticipating math tasks—significantly undermines achievement, particularly in STEM fields, and is a target for intervention in educational and clinical settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Richardson, Richard Suinn","subfamily":"Subject-specific anxiety and emotional response","year":"1972","type":"Domain-specific anxiety assessment"},"citations":[{"ref":"Richardson, F. C., & Suinn, R. M. (1972). The Mathematics Anxiety Rating Scale: Psychometric data. Journal of Counseling Psychology, 19(6), 551-554.","type":"article","doi":"10.1037/h0033456","isbn":null,"url":null},{"ref":"Plake, B. S., & Parker, C. S. (1995). Development and validation of a revised version of the Mathematics Anxiety Rating Scale. Journal of Educational Psychology, 87(2), 331-337.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+a+revised+version+of+the+Mathematics+Anxiety+Rating+Scale+Plake"}],"related":["academic-self-efficacy-scale","academic-motivation-scale","student-engagement-scale","critical-thinking-dispositions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matheuristics","name":"Matheuristics","fullName":"Matheuristics (Math Programming + Heuristics)","aliases":["Hybrid Metaheuristics","MIP-based Heuristics","Math-Programming Hybrids","Matematiksel Sezgisel Yöntemler"],"domain":"optimization","family":"process-pipeline","subfamily":"Metaheuristics","year":2009,"originator":"Maniezzo, Stützle & Voß","url":"https://scholargate.app/en/optimization/matheuristics","markdownUrl":"https://scholargate.app/en/optimization/matheuristics.md","definition":"Matheuristics is a class of hybrid optimization methods that tightly couple exact mathematical programming components—such as mixed-integer programming (MIP) solvers—with metaheuristic search procedures. Formally introduced and named by Maniezzo, Stützle, and Voß in 2009, the framework leverages the global-search capability of metaheuristics and the structural exploitation of mathematical programming to tackle large-scale combinatorial optimization problems that neither approach can solve effectively alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maniezzo, Stützle & Voß","year":2009,"type":"Hybrid optimization framework","subfamily":"Metaheuristics","paradigm":"Exact-heuristic hybridization","complexity":"Problem-dependent (NP-hard instances typical)"},"citations":[{"ref":"Maniezzo, V., Stützle, T., & Voß, S. (Eds.). (2009). Matheuristics: Hybridizing Metaheuristics and Mathematical Programming. Springer.","type":"book","doi":null,"isbn":"978-1-4419-1305-0","url":null}],"related":["integer-programming","hyper-heuristics","simheuristics"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matrix-completion","name":"Matrix Completion","fullName":"Low-Rank Matrix Completion","aliases":["Nuclear Norm Minimization","Collaborative Filtering via Low-Rank Recovery","Inductive Matrix Completion","Matris Tamamlama"],"domain":"machine-learning","family":"ml-model","subfamily":"Missing data","year":2009,"originator":"Emmanuel Candès & Benjamin Recht","url":"https://scholargate.app/en/machine-learning/matrix-completion","markdownUrl":"https://scholargate.app/en/machine-learning/matrix-completion.md","definition":"Matrix Completion is a technique for recovering a low-rank matrix from a small, possibly random subset of its entries. Introduced by Emmanuel Candès and Benjamin Recht in 2009, it reformulates the problem as nuclear norm minimization — a convex surrogate for rank minimization — and provides theoretical guarantees that exact recovery is achievable when entries are observed uniformly at random and the matrix satisfies an incoherence condition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emmanuel Candès & Benjamin Recht","year":2009,"type":"Convex low-rank recovery","subfamily":"Missing data","input":"Partially observed matrix","output":"Completed low-rank matrix"},"citations":[{"ref":"Candès, E. J., & Recht, B. (2009). Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6), 717–772.","type":"article","doi":"10.1007/s10208-009-9045-5","isbn":null,"url":null}],"related":["principal-component-analysis","mice-imputation","non-negative-matrix-factorization"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matrix-element-method","name":"Matrix Element Method","fullName":"Matrix Element Method for Physics Analysis","aliases":["MEM","matrix element calculation","amplitude evaluation"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Amplitude-based analysis","year":"1988","originator":"K. Kondo","url":"https://scholargate.app/en/particle-physics/matrix-element-method","markdownUrl":"https://scholargate.app/en/particle-physics/matrix-element-method.md","definition":"The Matrix Element Method (MEM) is a powerful analysis technique that leverages quantum field theory amplitudes to extract maximum physics information from individual events. By comparing observed detector signatures to predictions from matrix elements, MEM provides unbiased, model-independent measurements with excellent theoretical precision and sensitivity to new physics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. Kondo","subfamily":"Amplitude-based analysis","year":"1988","type":"Probability calculation framework"},"citations":[{"ref":"Kondo, K. (1988). Dynamical likelihood method for reconstruction of events produced by the top-quark pair in the lepton + jets channel at hadron colliders. Journal of the Physical Society of Japan, 57(12), 4126–4140.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dynamical+likelihood+method+for+reconstruction+of+events+produced+by+the+top-quark+pair+in+the+lepton+%2B+jets+channel+at+hadron+colliders+Kondo"},{"ref":"Campbell, J. M., Huston, J., & Krauss, F. (2011). The black book of the LHC: A physics guide. arXiv preprint arXiv:1005.3457. Journal of Physics: Conference Series, 1525(1), 012034.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1005.3457"},{"ref":"Martini, T., et al. (2015). Precision electroweak measurements and constraints on the Standard Model. Journal of High Energy Physics, 2015(12), 39.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Precision+electroweak+measurements+and+constraints+on+the+Standard+Model+Martini"}],"related":["feynman-diagram","effective-field-theory","vegas-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"matthews-correlation-coefficient","name":"Matthews Correlation Coefficient","fullName":"Matthews Correlation Coefficient (MCC)","aliases":["Phi Coefficient","Binary Classification Correlation"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"1975","originator":"Brian W. Matthews","url":"https://scholargate.app/en/model-evaluation/matthews-correlation-coefficient","markdownUrl":"https://scholargate.app/en/model-evaluation/matthews-correlation-coefficient.md","definition":"The Matthews Correlation Coefficient (MCC) is a correlation measure between predicted and actual binary classifications. It ranges from -1 to 1 and is considered one of the most reliable single-score metrics for evaluating binary classifiers, especially on imbalanced datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brian W. Matthews","subfamily":"Classification Metric","year":"1975","type":"Evaluation metric"},"citations":[{"ref":"Matthews, B. W. (1975). Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451.","type":"article","doi":"10.1016/0005-2795(75)90109-9","isbn":null,"url":null},{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"}],"related":["f1-score","balanced-accuracy","youdens-j-statistic","precision","recall"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maut","name":"MAUT","fullName":"Multi-Attribute Utility Theory","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1976","originator":"Keeney, R. L., Raiffa, H.","url":"https://scholargate.app/en/decision-making/maut","markdownUrl":"https://scholargate.app/en/decision-making/maut.md","definition":"MAUT (Multi-Attribute Utility Theory) is a ranking multi-criteria decision-making (MCDM) method introduced by Keeney, R. L., Raiffa, H. in 1976. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keeney, R. L., Raiffa, H.","subfamily":"Ranking","year":"1976","type":"Additive multi-attribute utility function","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Keeney, R. L., Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Trade-offs. Wiley","type":"article","doi":null,"isbn":"978-0-521-43883-4","url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maximum-covariance-analysis","name":"Maximum Covariance Analysis","fullName":"Maximum Covariance Analysis (MCA)","aliases":["MCA","Singular value decomposition","SVD analysis","Covariance analysis"],"domain":"meteorology","family":"process-pipeline","subfamily":"Statistical analysis","year":"1992","originator":"Bretherton, Wallace","url":"https://scholargate.app/en/meteorology/maximum-covariance-analysis","markdownUrl":"https://scholargate.app/en/meteorology/maximum-covariance-analysis.md","definition":"Maximum covariance analysis (MCA) is a statistical technique that identifies coupled patterns of variability between two spatially distributed fields (e.g., sea surface temperature and precipitation). Unlike EOF analysis which focuses on variance in a single field, MCA identifies spatial patterns that are maximally correlated between two different fields.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bretherton, Wallace","subfamily":"Statistical analysis","year":"1992","type":"Covariance decomposition method"},"citations":[{"ref":"Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., & Blade, I. (1992). The effective number of spatial degrees of freedom of a time-varying field. Journal of the Atmospheric Sciences, 49(11), 1063-1083.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+effective+number+of+spatial+degrees+of+freedom+of+a+time-varying+field+Bretherton"},{"ref":"Newman, M., Sardeshmukh, P. D., & Penland, C. (2016). Relative Contributions to Subseasonal Predictability: Bridging Medium-Range and Climate Time Scales. Journal of Climate, 29(15), 5629-5647.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Relative+Contributions+to+Subseasonal+Predictability%3A+Bridging+Medium-Range+and+Climate+Time+Scales+Newman"}],"related":["empirical-orthogonal-teleconnection","wrf-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maximum-likelihood-estimation","name":"Maximum Likelihood Estimation","fullName":"Maximum Likelihood Estimation","aliases":["MLE","maximum-likelihood estimator","ML estimation","Fisher's method of maximum likelihood"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1922,"originator":"R. A. Fisher","url":"https://scholargate.app/en/statistics/maximum-likelihood-estimation","markdownUrl":"https://scholargate.app/en/statistics/maximum-likelihood-estimation.md","definition":"Maximum Likelihood Estimation (MLE) is a general-purpose parametric method for estimating the unknown parameters of a statistical model by finding the parameter values that make the observed data most probable. Formalized by R. A. Fisher in his landmark 1922 paper in the Philosophical Transactions of the Royal Society, MLE has become the dominant parameter-estimation paradigm in modern statistics and is the foundational engine behind logistic regression, generalized linear models, structural equation modeling, and virtually all parametric inference procedures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher","year":1922,"family":"Parameter estimation","type":"Parametric point estimator","parametric":true,"estimand":"θ (parameter vector)","keyProperty":"Asymptotically unbiased, consistent, efficient (achieves Cramér–Rao lower bound asymptotically)","underlyingPrinciple":"Maximize the likelihood L(θ | x) = f(x | θ) over θ"},"citations":[{"ref":"Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368.","type":"article","doi":"10.1098/rsta.1922.0009","isbn":null,"url":null},{"ref":"Casella, G., & Berger, R. L. (2002). Statistical Inference (2nd ed.). Duxbury Press / Cengage Learning.","type":"book","doi":null,"isbn":"978-0534243128","url":null}],"related":["logistic-regression","linear-regression","generalized-linear-models","structural-equation-modeling","em-algorithm","bayesian-estimation","method-of-moments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maximum-power-point-tracking","name":"Maximum Power Point Tracking","fullName":"Maximum Power Point Tracking for Photovoltaic Systems","aliases":["MPPT","impedance matching"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Power Electronics","year":"2007","originator":"Trishan Esram","url":"https://scholargate.app/en/thermodynamics/maximum-power-point-tracking","markdownUrl":"https://scholargate.app/en/thermodynamics/maximum-power-point-tracking.md","definition":"Maximum Power Point Tracking (MPPT) is a control algorithm for photovoltaic and wind energy systems that continuously adjusts the electrical load to extract maximum power regardless of changing irradiance and temperature. Without MPPT, a solar panel or wind turbine operates below its power potential due to impedance mismatch with the load. MPPT boosts the annual energy yield by 15-25% depending on system and climate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Trishan Esram","subfamily":"Power Electronics","year":"2007","type":"Control algorithm"},"citations":[{"ref":"Villalva, M. G., Gazoli, J. R., & Ruppert Filho, E. (2009). Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Transactions on Power Electronics, 24(5), 1198-1208.","type":"article","doi":"10.1109/TPEL.2009.2013862","isbn":null,"url":null},{"ref":"Esram, T., & Chapman, P. L. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on Energy Conversion, 22(2), 439-449.","type":"article","doi":"10.1109/TEC.2006.874230","isbn":null,"url":null}],"related":["betz-limit","state-of-charge","levelized-cost-of-energy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"maximum-variation-sampling","name":"Maximum Variation Sampling","fullName":"Maximum Variation Purposive Sampling","aliases":["maximum variation sampling","maximum diversity sampling","MVS","heterogeneous sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1985 (Lincoln & Guba); elaborated 1990–2002 (Patton)","originator":"Lincoln & Guba; systematised by Michael Quinn Patton","url":"https://scholargate.app/en/survey-methodology/maximum-variation-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/maximum-variation-sampling.md","definition":"Maximum variation sampling is a purposive qualitative sampling strategy in which the researcher deliberately selects cases that span the widest possible range of variation on dimensions central to the study. The goal is not statistical representation but the identification of common patterns that cut across diverse cases as well as the documentation of the unique ways each context shapes the phenomenon under investigation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lincoln & Guba; systematised by Michael Quinn Patton","year":"1985 (Lincoln & Guba); elaborated 1990–2002 (Patton)","type":"Purposive qualitative sampling strategy","dataType":"Qualitative data — interviews, observations, documents","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. Chapter 5: Purposeful Sampling.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage.","type":"book","doi":null,"isbn":"978-0803924314","url":null}],"related":["purposive-sampling","theoretical-sampling","snowball-sampling","deviant-case-sampling","typical-case-sampling","stratified-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mayo-score-uc","name":"Mayo Score","fullName":"Mayo Score for Ulcerative Colitis Activity","aliases":["Mayo Clinic Score","UC Mayo Score"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"inflammatory-bowel-disease","year":"1987","originator":"Schroeder, K. W., Tremaine, W. J., and Ilstrup, D. M.","url":"https://scholargate.app/en/gastroenterology/mayo-score-uc","markdownUrl":"https://scholargate.app/en/gastroenterology/mayo-score-uc.md","definition":"The Mayo Score is a validated tool for assessing disease activity in ulcerative colitis, integrating clinical symptoms and endoscopic findings. Introduced by Schroeder and colleagues in 1987, it has become the reference standard for UC activity assessment in clinical trials and practice. The score combines stool frequency, rectal bleeding, overall physician global assessment, and endoscopic subscore into a single 0–12 scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schroeder, K. W., Tremaine, W. J., and Ilstrup, D. M.","subfamily":"inflammatory-bowel-disease","year":"1987","type":"Clinician-rated"},"citations":[{"ref":"Schroeder, K. W., Tremaine, W. J., & Ilstrup, D. M. (1987). Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. New England Journal of Medicine, 317(26), 1625–1629.","type":"article","doi":"10.1056/NEJM198712243172603","isbn":null,"url":null}],"related":["sccai","harvey-bradshaw-index","ibdq-short","cdai-crohns"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mccabe-thiele-method","name":"McCabe-Thiele Method","fullName":"McCabe-Thiele Graphical Method for Distillation Design","aliases":["McCabe-Thiele Diagram","Graphical Distillation Method"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Separation Process Design","year":"1925","originator":"Warren L. McCabe and Ernest W. Thiele","url":"https://scholargate.app/en/mining-engineering/mccabe-thiele-method","markdownUrl":"https://scholargate.app/en/mining-engineering/mccabe-thiele-method.md","definition":"The McCabe-Thiele Method, introduced by Warren L. McCabe and Ernest W. Thiele in 1925, is a graphical technique for designing and analyzing distillation columns. It predicts the number of theoretical plates (stages) needed to achieve a desired separation between light and heavy components. While primarily a chemical engineering tool, it applies to liquid-vapor separation problems in mining operations such as mercury recovery and rare earth element refining.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Warren L. McCabe and Ernest W. Thiele","subfamily":"Separation Process Design","year":"1925","type":"Graphical design method for distillation columns"},"citations":[{"ref":"McCabe, W. L., & Thiele, E. W. (1925). Graphical design of fractionating columns. Transactions of the American Institute of Chemical Engineers, 21, 30-60.","type":"article","doi":null,"isbn":null,"url":"https://aiche.onlinelibrary.wiley.com/"},{"ref":"Seader, J. D., Henley, E. J., & Roper, D. K. (2011). Separation process principles (3rd ed.). John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Separation+process+principles+%283rd+ed.%29+Seader"}],"related":["tromp-curve","rosin-rammler-distribution","washability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcdonald-kreitman-test","name":"McDonald-Kreitman Test","fullName":"McDonald-Kreitman Test for Detecting Adaptive Evolution","aliases":["MK test","Positive selection test"],"domain":"genetics","family":"process-pipeline","subfamily":"Selection testing","year":"1991","originator":"James McDonald & Martin Kreitman","url":"https://scholargate.app/en/genetics/mcdonald-kreitman-test","markdownUrl":"https://scholargate.app/en/genetics/mcdonald-kreitman-test.md","definition":"The McDonald-Kreitman (MK) test is a statistical method for detecting adaptive evolution by comparing ratios of synonymous and nonsynonymous substitutions within and between species. Developed by James McDonald and Martin Kreitman in 1991, this test exploits the key insight that neutral mutations accumulate at similar rates within and between species, while adaptive (nonsynonymous) substitutions should be enriched between species if they have been fixed by positive selection. The MK test has become a standard tool in molecular evolutionary biology for identifying genes under natural selection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James McDonald & Martin Kreitman","subfamily":"Selection testing","year":"1991","type":"Hypothesis test"},"citations":[{"ref":"McDonald, J. H., & Kreitman, M. (1991). Adaptive protein evolution at the Adh locus in Drosophila. Nature, 351(6328), 652–654.","type":"article","doi":"10.1038/351652a0","isbn":null,"url":null},{"ref":"Smith, N. G., & Eyre-Walker, A. (2002). Estimating the proportion of sites subject to positive selection across a large dataset. Genetics, 160(3), 1079–1086.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimating+the+proportion+of+sites+subject+to+positive+selection+across+a+large+dataset+Smith"},{"ref":"Charlesworth, B. (2010). The rate of adaptive evolution. Trends in Ecology & Evolution, 11(1), 22–26.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+rate+of+adaptive+evolution+Charlesworth"}],"related":["selection-sweep","hka-test","f-statistics","coalescent-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcdonald-omega","name":"McDonald's Omega","fullName":"McDonald's Hierarchical Omega (ωh)","aliases":["omega hierarchical","omega-h","bifactor omega","composite score validity coefficient","McDonald's Omega Hiyerarşik (ωh) — Kompozit Puan Geçerliliği"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1999,"originator":"Roderick P. McDonald","url":"https://scholargate.app/en/psychometrics/mcdonald-omega","markdownUrl":"https://scholargate.app/en/psychometrics/mcdonald-omega.md","definition":"McDonald's hierarchical omega (ωh) is a coefficient derived from a bifactor confirmatory factor model that quantifies what proportion of total-score variance is attributable to a single general factor rather than to group-specific factors or item-level error. Introduced by Roderick P. McDonald (1999) and elaborated for bifactor applications by Reise and colleagues (2013) and Rodriguez and colleagues (2016), it is the primary index used in psychometrics to evaluate whether a composite total score is a defensible summary of a multidimensional scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roderick P. McDonald","year":1999,"type":"Reliability / composite score validity coefficient","model":"Bifactor (hierarchical) confirmatory factor model","output":"ωh coefficient (0–1); ECV (Explained Common Variance)","data":"Ordinal or continuous item responses","min_sample":200,"threshold_omegah":"≥ 0.50 indicates composite adequately reflects the general factor","threshold_fit":"CFI ≥ 0.95 for the bifactor model"},"citations":[{"ref":"Reise, S. P., Scheines, R., Widaman, K. F. & Haviland, M. G. (2013). Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educational and Psychological Measurement, 73(1), 5–26.","type":"article","doi":"10.1177/0013164412449831","isbn":null,"url":null},{"ref":"Rodriguez, A., Reise, S. P. & Haviland, M. G. (2016). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137–150.","type":"article","doi":"10.1037/met0000045","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","exploratory-factor-analysis","cronbach-alpha","bifactor-model","sem"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcgill-pain-questionnaire","name":"McGill Pain Questionnaire","fullName":"McGill Pain Questionnaire (MPQ)","aliases":["MPQ","McGill Pain Index"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"multidimensional pain assessment","year":"1975","originator":"Ronald Melzack","url":"https://scholargate.app/en/pain-medicine/mcgill-pain-questionnaire","markdownUrl":"https://scholargate.app/en/pain-medicine/mcgill-pain-questionnaire.md","definition":"The McGill Pain Questionnaire (MPQ) is a multidimensional pain assessment instrument developed by Ronald Melzack in 1975. It measures pain across sensory, affective, and evaluative dimensions, allowing clinicians and researchers to capture the qualitative experience of pain beyond simple intensity ratings. The MPQ remains one of the most widely used pain assessment tools in clinical and research settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Melzack","subfamily":"multidimensional pain assessment","year":"1975","type":"Self-report questionnaire measuring multiple pain dimensions"},"citations":[{"ref":"Melzack, R. (1975). The McGill Pain Questionnaire: Major properties and scoring methods. Pain, 1(3), 277-299.","type":"article","doi":"10.1016/0304-3959(75)90044-5","isbn":null,"url":null},{"ref":"Melzack, R. (1987). The short-form McGill Pain Questionnaire. Pain, 30(2), 191-197.","type":"article","doi":"10.1016/0304-3959(87)91074-8","isbn":null,"url":null},{"ref":"Boureau, F., Doubrère, J.F., & Luu, M. (1990). Comparative study of validity and reliability of four French McGill Pain Questionnaire versions. Pain, 42(2), 169-184.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparative+study+of+validity+and+reliability+of+four+French+McGill+Pain+Questionnaire+versions+Boureau"}],"related":["pain-catastrophizing-scale","dallas-pain-questionnaire","neuropathic-pain-scale","pain-self-efficacy-questionnaire","pain-anxiety-symptoms-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcgill-quality-of-life","name":"McGill Quality of Life Questionnaire","fullName":"McGill Quality of Life Questionnaire (MQOL)","aliases":["MQOL"],"domain":"palliative-care","family":"process-pipeline","subfamily":"multidimensional-quality-of-life","year":"1995","originator":"Cohen, Mount, Strobel, and Bui","url":"https://scholargate.app/en/palliative-care/mcgill-quality-of-life","markdownUrl":"https://scholargate.app/en/palliative-care/mcgill-quality-of-life.md","definition":"The McGill Quality of Life Questionnaire (MQOL) is a 17-item, multidimensional self-report measure specifically developed for people with advanced cancer and other life-limiting illnesses. Created by Cohen, Mount, and colleagues at McGill University in 1995, the MQOL captures physical, functional, emotional, spiritual, and social dimensions of quality of life in a concise, patient-centered format. It has become a standard outcome measure in palliative care research, hospice quality improvement, and cancer centers internationally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cohen, Mount, Strobel, and Bui","subfamily":"multidimensional-quality-of-life","year":"1995","type":"Self-report"},"citations":[{"ref":"Cohen, S. R., Mount, B. M., Strobel, M. G., & Bui, F. (1995). The McGill Quality of Life Questionnaire: a measure of quality of life appropriate for people facing advanced cancer. Journal of Palliative Care, 11(3), 6–15.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/7499243"},{"ref":"Mount, B. M., Kearney, M., Cohen, S. R., Bui, F., & Strobel, M. G. (2007). The McGill Quality of Life Questionnaire: revised format. Journal of Palliative Medicine, 10(1), 167–172.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+McGill+Quality+of+Life+Questionnaire%3A+revised+format+Mount"}],"related":["spiritual-wellbeing-scale","facit-palliative","patient-dignity-inventory","palliative-performance-scale","good-death-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcmc-for-model-comparison","name":"MCMC for Model Comparison","fullName":"Markov Chain Monte Carlo for Bayesian Model Comparison","aliases":["reversible-jump MCMC","RJMCMC","marginal likelihood estimation via MCMC","Bayesian model selection via MCMC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1995","originator":"Peter J. Green (reversible-jump MCMC); Meng & Wong (bridge sampling)","url":"https://scholargate.app/en/bayesian/mcmc-for-model-comparison","markdownUrl":"https://scholargate.app/en/bayesian/mcmc-for-model-comparison.md","definition":"MCMC for model comparison uses Markov chain Monte Carlo algorithms to estimate the marginal likelihoods and Bayes factors needed to formally compare competing statistical models. Techniques such as reversible-jump MCMC and bridge sampling allow exploration across model spaces of different dimensionality, enabling fully Bayesian model selection and averaging.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Green (reversible-jump MCMC); Meng & Wong (bridge sampling)","year":"1995","type":"Bayesian computational method","dataType":"Any data suitable for Bayesian modeling","subfamily":"Bayesian / computational"},"citations":[{"ref":"Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–732.","type":"article","doi":"10.1093/biomet/82.4.711","isbn":null,"url":null},{"ref":"Meng, X.-L., & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistica Sinica, 6(4), 831–860.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/24306045"}],"related":["bayesian-model-averaging","mcmc","hamiltonian-monte-carlo","gibbs-sampling","bayesian-inference-for-model-comparison","approximate-bayesian-computation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcmc-with-measurement-error","name":"MCMC with Measurement Error","fullName":"Markov Chain Monte Carlo with Measurement Error Models","aliases":["MCMC errors-in-variables","Bayesian measurement error MCMC","MCMC misclassification model","Bayesian errors-in-variables"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993","originator":"Richardson & Gilks; Carroll, Ruppert & Stefanski","url":"https://scholargate.app/en/bayesian/mcmc-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/mcmc-with-measurement-error.md","definition":"MCMC with measurement error applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for the fact that covariates or outcomes are observed with error. By treating the true, unobserved values as latent variables and sampling their joint posterior alongside all other parameters, the method corrects for attenuation bias and produces valid inference even when some variables cannot be measured exactly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richardson & Gilks; Carroll, Ruppert & Stefanski","year":"1993","type":"Bayesian computational estimation","dataType":"Continuous or categorical data with fallibly measured covariates or outcomes","subfamily":"Bayesian / computational"},"citations":[{"ref":"Carroll, R. J., Ruppert, D., Stefanski, L. A. & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584886334","url":null},{"ref":"Richardson, S. & Gilks, W. R. (1993). A Bayesian approach to measurement error problems in epidemiology using conditional independence models. American Journal of Epidemiology, 138(6), 430-442.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Bayesian+approach+to+measurement+error+problems+in+epidemiology+using+conditional+independence+models"}],"related":["mcmc","bayesian-regression","gibbs-sampling","metropolis-hastings-with-measurement-error","bayesian-inference-with-measurement-error","hierarchical-bayesian-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcmc-with-missing-data","name":"MCMC with missing data","fullName":"Markov Chain Monte Carlo with Missing Data","aliases":["MCMC missing data","data augmentation MCMC","Bayesian multiple imputation","MCMC imputation"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1987","originator":"Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin","url":"https://scholargate.app/en/bayesian/mcmc-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/mcmc-with-missing-data.md","definition":"MCMC with missing data is a Bayesian computational strategy that treats unobserved values as additional unknown parameters. By alternating between sampling the missing values from their predictive distribution and sampling the model parameters from their posterior, the algorithm produces a valid joint posterior that fully accounts for uncertainty introduced by the missingness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin","year":"1987","type":"Bayesian computational method","dataType":"any data with MAR or MCAR missingness patterns","subfamily":"Bayesian / computational"},"citations":[{"ref":"Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471183860","url":null},{"ref":"Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528-540.","type":"article","doi":"10.1080/01621459.1987.10478458","isbn":null,"url":null}],"related":["gibbs-sampling","bayesian-inference-with-missing-data","multiple-imputation","hamiltonian-monte-carlo","metropolis-hastings-algorithm","bayesian-hierarchical-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcmc","name":"MCMC","fullName":"Markov Chain Monte Carlo","aliases":["markov chain monte carlo","MCMC sampling","MCMC (Markov Zinciri Monte Carlo)"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/bayesian/mcmc","markdownUrl":"https://scholargate.app/en/bayesian/mcmc.md","definition":"Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Posterior sampling algorithm","purpose":"prediction / exploration","var_types":"continuous / binary / categorical","min_sample":20,"requires_normality":false,"convergence_diagnostic":"R-hat < 1.1","outputs":"posterior samples / credible intervals"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Brooks, S., Gelman, A., Jones, G. & Meng, X.-L. (Eds.). (2011). Handbook of Markov Chain Monte Carlo. CRC Press.","type":"book","doi":null,"isbn":"978-1420079418","url":null}],"related":["bayesian-regression","hierarchical-bayes","bayesian-model-averaging","variational-inference"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcnemar-test","name":"McNemar's test","fullName":"McNemar's test","aliases":["McNemar chi-square test","test for correlated proportions","paired binary test","McNemar Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1947,"originator":"Quinn McNemar","url":"https://scholargate.app/en/statistics/mcnemar-test","markdownUrl":"https://scholargate.app/en/statistics/mcnemar-test.md","definition":"McNemar's test is a nonparametric hypothesis test that compares two paired (correlated) binary proportions, such as a yes/no measurement taken on the same subjects before and after an intervention. It was introduced by Quinn McNemar in 1947 and works on the 2×2 table of matched outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Quinn McNemar","year":1947,"family":"Hypothesis test","type":"Nonparametric test for paired binary data","groups":"2 paired conditions","outcome":"binary (2×2 table)","parametric":false,"distribution":"Chi-square","df":1},"citations":[{"ref":"McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153–157.","type":"article","doi":"10.1007/BF02295996","isbn":null,"url":null}],"related":["chi-square-test","cochran-q-test","binomial-test","paired-t-test","wilcoxon-signed-rank"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcp-penalized-regression","name":"MCP Penalized Regression","fullName":"Minimax Concave Penalty Penalized Regression","aliases":["MCP"],"domain":"psychometrics","family":"latent-structure","subfamily":"Variable Selection","year":"2010","originator":"Cun-Hui Zhang","url":"https://scholargate.app/en/psychometrics/mcp-penalized-regression","markdownUrl":"https://scholargate.app/en/psychometrics/mcp-penalized-regression.md","definition":"MCP (Minimax Concave Penalty) is a variable selection method developed by Zhang (2010) that uses a concave penalty function for automated feature selection. Like SCAD, MCP addresses bias in lasso by avoiding shrinkage of large coefficients, but uses a different penalty shape that is computationally simpler than SCAD.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cun-Hui Zhang","subfamily":"Variable Selection","year":"2010","type":"Penalized regression with minimax concave penalty"},"citations":[{"ref":"Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942.","type":"article","doi":"10.1214/09-AOS729","isbn":null,"url":null},{"ref":"Breheny, P., & Huang, J. (2011). Coordinate descent algorithms for nonconvex penalized regression. Annals of Applied Statistics, 5(1), 232-253.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Coordinate+descent+algorithms+for+nonconvex+penalized+regression+Breheny"},{"ref":"Zhang, C. H., & Zhang, T. (2012). A general theory of concave regularized M-estimators. Statistical Science, 27(4), 506-537.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+general+theory+of+concave+regularized+M-estimators+Zhang"}],"related":["scad-penalized-regression","pls-sem","exploratory-structural-equation-modeling","redundancy-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mcusum-chart","name":"MCUSUM Chart","fullName":"Multivariate CUSUM Control Chart","aliases":["Multivariate Cumulative Sum Chart","MCUSUM Control Chart","Crosier MCUSUM Scheme","Çok Değişkenli CUSUM Kontrol Grafiği"],"domain":"statistics","family":"process-pipeline","subfamily":"Statistical process control","year":1988,"originator":"Robert Crosier","url":"https://scholargate.app/en/statistics/mcusum-chart","markdownUrl":"https://scholargate.app/en/statistics/mcusum-chart.md","definition":"The Multivariate CUSUM (MCUSUM) Chart is a sequential monitoring scheme designed to detect small, persistent mean shifts in a process characterized by multiple correlated quality variables simultaneously. Introduced by Robert Crosier in 1988, it extends the classical univariate CUSUM principle to the multivariate setting by accumulating a vector-valued sum of deviations from the in-control mean, scaled by the process covariance structure, and comparing a scalar norm of that cumulative sum against a control limit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Crosier","year":1988,"type":"Multivariate sequential monitoring chart","subfamily":"Statistical process control","signal_statistic":"Cumulative sum of standardized multivariate deviations","reference_distribution":"Multivariate Normal"},"citations":[{"ref":"Crosier, R. B. (1988). Multivariate generalizations of cumulative sum quality-control schemes. Technometrics, 30(3), 291–303.","type":"article","doi":"10.2307/1270083","isbn":null,"url":null}],"related":["cusum-chart","mewma-chart","hotelling-t2-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mdq-mood-disorder","name":"Mood Disorder Questionnaire","fullName":"Mood Disorder Questionnaire (MDQ)","aliases":["MDQ"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Bipolar spectrum screening","year":"2000","originator":"Robert M. Hirschfeld and colleagues","url":"https://scholargate.app/en/clinical-psychology/mdq-mood-disorder","markdownUrl":"https://scholargate.app/en/clinical-psychology/mdq-mood-disorder.md","definition":"The Mood Disorder Questionnaire (MDQ) is a 13-item self-report screening instrument designed to identify individuals at risk for bipolar spectrum disorders. Developed by Hirschfeld and colleagues in 2000, the MDQ assesses symptoms of mania and hypomania as well as the clustering of symptoms into distinct episodes. It is widely used in primary care, psychiatric practice, and research to improve detection of bipolar disorder.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert M. Hirschfeld and colleagues","subfamily":"Bipolar spectrum screening","year":"2000","type":"Manic episode and bipolar disorder screening"},"citations":[{"ref":"Hirschfeld, R. M., Williams, J. B., Spitzer, R. L., Calabrese, J. R., Flynn, L., Keck, P. E., ... & Zajecka, J. (2000). Development and validation of a screening instrument for bipolar spectrum disorder: The Mood Disorder Questionnaire. American Journal of Psychiatry, 157(11), 1873-1875.","type":"article","doi":"10.1176/appi.ajp.157.11.1873","isbn":null,"url":null},{"ref":"Zimmerman, M., Galione, J. N., Chelminski, I., Young, D., Dalrymple, K., & Francione, C. (2008). Performance of the Mood Disorder Questionnaire screener for bipolar disorder in psychiatric outpatients. Bipolar Disorders, 10(1), 107-114.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Performance+of+the+Mood+Disorder+Questionnaire+screener+for+bipolar+disorder+in+psychiatric+outpatients+Zimmerman"}],"related":["hamilton-anxiety-rating-scale","hads","ces-d","k10-kessler","swls"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mean-absolute-error","name":"Mean Absolute Error","fullName":"Mean Absolute Error","aliases":["MAE","L1 error","mean absolute deviation"],"domain":"model-evaluation","family":"mcdm","subfamily":"Error metric","year":"1799","originator":"Pierre-Simon Laplace","url":"https://scholargate.app/en/model-evaluation/mean-absolute-error","markdownUrl":"https://scholargate.app/en/model-evaluation/mean-absolute-error.md","definition":"Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pierre-Simon Laplace","subfamily":"Error metric","year":"1799","type":"Robust distance-based metric"},"citations":[{"ref":"Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/trait-de-mcanique-cleste-1799"},{"ref":"Brossier, C. L. (1999). Consistency of trimmed and Winsorized L-estimators of location and scale. Journal of the American Statistical Association, 74(368), 813-821.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Consistency+of+trimmed+and+Winsorized+L-estimators+of+location+and+scale+Brossier"},{"ref":"Huber, P. J. (2009). Robust Statistics (2nd ed.). Hoboken, NJ: John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470129906","url":null}],"related":["root-mean-squared-error","mean-squared-error","mean-absolute-percentage-error","median-absolute-deviation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mean-absolute-percentage-error","name":"Mean Absolute Percentage Error","fullName":"Mean Absolute Percentage Error","aliases":["MAPE","mean absolute percentage deviation"],"domain":"model-evaluation","family":"mcdm","subfamily":"Relative error metric","year":"1985","originator":"J. Scott Armstrong","url":"https://scholargate.app/en/model-evaluation/mean-absolute-percentage-error","markdownUrl":"https://scholargate.app/en/model-evaluation/mean-absolute-percentage-error.md","definition":"Mean Absolute Percentage Error measures prediction accuracy as a percentage relative to actual values, expressing errors in units that are scale-independent and interpretable across datasets. Formalized by J. Scott Armstrong in 1985, MAPE is widely used in forecasting, supply chain, and business analytics where results must be communicated as percentage accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. Scott Armstrong","subfamily":"Relative error metric","year":"1985","type":"Percentage-based evaluation metric"},"citations":[{"ref":"Armstrong, J. S. (1985). Long-range forecasting: from crystal ball to computer (2nd ed.). New York: John Wiley & Sons.","type":"article","doi":null,"isbn":"978-0471082010","url":null},{"ref":"Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.","type":"article","doi":"10.1016/j.ijforecast.2006.03.001","isbn":null,"url":null},{"ref":"Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669-679.","type":"article","doi":"10.1016/j.ijforecast.2015.12.003","isbn":null,"url":null}],"related":["mean-absolute-error","root-mean-squared-error","symmetric-mape","mean-absolute-scaled-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mean-absolute-scaled-error","name":"Mean Absolute Scaled Error","fullName":"Mean Absolute Scaled Error","aliases":["MASE"],"domain":"model-evaluation","family":"mcdm","subfamily":"Scaled error metric","year":"2006","originator":"Rob J. Hyndman and Anne B. Koehler","url":"https://scholargate.app/en/model-evaluation/mean-absolute-scaled-error","markdownUrl":"https://scholargate.app/en/model-evaluation/mean-absolute-scaled-error.md","definition":"Mean Absolute Scaled Error is a scale-independent metric that measures prediction accuracy relative to a simple baseline (naive forecast). Introduced by Hyndman and Koehler (2006), MASE directly compares model performance to a reference method, overcoming limitations of MAPE and other percentage-based metrics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rob J. Hyndman and Anne B. Koehler","subfamily":"Scaled error metric","year":"2006","type":"Scale-independent baseline comparison metric"},"citations":[{"ref":"Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.","type":"article","doi":"10.1016/j.ijforecast.2006.03.001","isbn":null,"url":null},{"ref":"Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). Melbourne, Australia: OTexts.","type":"book","doi":null,"isbn":null,"url":"https://otexts.com/fpp3/"},{"ref":"Wang, X., & Petropoulos, F. (2016). To select or to combine? Forecasting from a thousand models. International Journal of Forecasting, 32(3), 594-606.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=To+select+or+to+combine+Wang"}],"related":["mean-absolute-error","mean-absolute-percentage-error","root-mean-squared-error","symmetric-mape"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mean-shift","name":"Mean Shift","fullName":"Mean Shift Clustering and Mode-Seeking Algorithm","aliases":["mean-shift clustering","mean shift mode seeking","kernel mean shift","nonparametric mode detection"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1975,"originator":"Fukunaga, K. & Hostetler, L. D.; extended by Comaniciu, D. & Meer, P.","url":"https://scholargate.app/en/machine-learning/mean-shift","markdownUrl":"https://scholargate.app/en/machine-learning/mean-shift.md","definition":"Mean Shift is a non-parametric, iterative mode-seeking algorithm that identifies clusters as the peaks of an underlying probability density function. Originally introduced by Fukunaga and Hostetler (1975) for gradient estimation in pattern recognition, it was substantially extended and popularized by Comaniciu and Meer (2002) for robust feature-space analysis and image segmentation. Unlike k-means, Mean Shift requires no prior specification of the number of clusters, deriving cluster structure entirely from the data density.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fukunaga, K. & Hostetler, L. D.; extended by Comaniciu, D. & Meer, P.","year":1975,"type":"Non-parametric mode-seeking / density-based clustering","task":"Clustering, image segmentation, feature-space analysis","numClustersRequired":false,"kernelDefault":"Epanechnikov or Gaussian","convergenceGuaranteed":true},"citations":[{"ref":"Fukunaga, K. & Hostetler, L. D. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21(1), 32–40.","type":"article","doi":"10.1109/TIT.1975.1055330","isbn":null,"url":null},{"ref":"Comaniciu, D. & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619.","type":"article","doi":"10.1109/34.1000236","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 14). Springer.","type":"book","doi":null,"isbn":"978-0-387-84858-7","url":null}],"related":["k-means","dbscan","gaussian-mixture-model","spectral-clustering","hierarchical-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mean-squared-error","name":"Mean Squared Error","fullName":"Mean Squared Error","aliases":["MSE","L2 error","quadratic error"],"domain":"model-evaluation","family":"mcdm","subfamily":"Error metric","year":"1809","originator":"Carl Friedrich Gauss","url":"https://scholargate.app/en/model-evaluation/mean-squared-error","markdownUrl":"https://scholargate.app/en/model-evaluation/mean-squared-error.md","definition":"Mean Squared Error is the foundational loss function for regression models, measuring the average squared deviation between predictions and observations. Originating from Gauss and Legendre's method of least squares (1805-1809), MSE is the basis for ordinary least squares regression and remains central to modern machine learning optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Friedrich Gauss","subfamily":"Error metric","year":"1809","type":"Squared-error loss function"},"citations":[{"ref":"Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/theoriamotus00gaus"},{"ref":"Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Paris: F. Didot.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/nouvellesmethod00legen"},{"ref":"Goodman, L. A. (1960). On the exact variance of products. Journal of the American Statistical Association, 55(292), 708-713.","type":"article","doi":"10.1080/01621459.1960.10483369","isbn":null,"url":null}],"related":["root-mean-squared-error","mean-absolute-error","r-squared","akaike-information-criterion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meaning-in-life-questionnaire","name":"Meaning in Life Questionnaire","fullName":"Meaning in Life Questionnaire","aliases":["MLQ"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"existential well-being","year":"2006","originator":"Michael Steger","url":"https://scholargate.app/en/positive-psychology/meaning-in-life-questionnaire","markdownUrl":"https://scholargate.app/en/positive-psychology/meaning-in-life-questionnaire.md","definition":"The Meaning in Life Questionnaire (MLQ) is a 10-item self-report measure developed by Steger and colleagues in 2006 to assess both the presence of meaning and the active search for meaning in life. It addresses a core existential dimension of well-being: the degree to which individuals experience their life as purposeful and meaningful. The MLQ distinguishes presence of meaning from search for meaning, revealing that growth and psychological adjustment can involve active meaning-seeking even when current meaning-presence is lower.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Steger","subfamily":"existential well-being","year":"2006","type":"Self-report questionnaire"},"citations":[{"ref":"Steger, M. F., Frazier, P., Kaler, M., & Oishi, S. (2006). The Meaning in Life Questionnaire: Assessing the presence of and search for meaning in life. Journal of Counseling Psychology, 53(1), 80–92.","type":"article","doi":"10.1037/0022-0167.53.1.80","isbn":null,"url":null}],"related":["flourishing-scale","who-5-wellbeing-index","perma-scale","hope-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"measurement-invariance","name":"Measurement Invariance","fullName":"Measurement Invariance Testing","aliases":["Factorial Invariance","Measurement Equivalence","Configural-Metric-Scalar Testing","Ölçüm Değişmezliği"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale validation","year":2000,"originator":"Vandenberg & Lance","url":"https://scholargate.app/en/psychometrics/measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/measurement-invariance.md","definition":"Measurement invariance testing is a sequence of nested confirmatory factor analysis (CFA) models that examines whether a psychological scale measures the same latent construct in the same way across distinct groups or time points. Systematized and popularized by Vandenberg and Lance (2000), the procedure tests a hierarchy of constraints — from identical factor patterns to identical item intercepts — so that researchers can justify meaningful group comparisons on latent means.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vandenberg & Lance","year":2000,"type":"Multi-group confirmatory factor analysis procedure","subfamily":"Scale validation","test_statistic":"Chi-square difference (Δχ²) and ΔCFI","software":"R (lavaan), Mplus, LISREL, AMOS"},"citations":[{"ref":"Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["cfa","sem","dif-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meat-quality-assessment","name":"Meat Quality Assessment","fullName":"Comprehensive Meat Quality Assessment and Grading","aliases":["carcass evaluation","meat grading","objective meat quality measurement"],"domain":"animal-science","family":"process-pipeline","subfamily":"Product quality assessment","year":"1920s","originator":"Meat Scientists and USDA","url":"https://scholargate.app/en/animal-science/meat-quality-assessment","markdownUrl":"https://scholargate.app/en/animal-science/meat-quality-assessment.md","definition":"Meat quality assessment is a systematic evaluation of carcass and meat characteristics that determine suitability for consumption and market value. Formalized by the USDA and meat scientists in the early 20th century, the practice integrates objective measurements—color, marbling, tenderness, water-holding capacity—with sensory evaluation to assign quality grades. Assessment guides pricing, processing decisions, and genetic selection to improve consumer satisfaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Meat Scientists and USDA","subfamily":"Product quality assessment","year":"1920s","type":"measurement and classification"},"citations":[{"ref":"American Meat Board. (1988). Nutritional information on meat. Journal of Food Science, 53(2), 398-407.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nutritional+information+on+meat+American"},{"ref":"Huff-Lonergan, E., & Lonergan, S. M. (2005). Mechanisms of water-holding capacity of meat: The role of postmortem biochemical and structural changes. Meat Science, 71(1), 194-204.","type":"article","doi":"10.1016/j.meatsci.2005.04.022","isbn":null,"url":null},{"ref":"Whipple, G., Koohmaraie, M., Dikeman, M. E., Crouse, J. D., Hunt, M. C., & Klemm, R. D. (1990). Evaluation of attributes that affect longissimus muscle tenderness in Bos taurus and Bos indicus cattle. Journal of Animal Science, 68(9), 2716-2728.","type":"article","doi":"10.2527/1990.6892716x","isbn":null,"url":null}],"related":["semen-quality-evaluation","body-condition-score-cattle","growth-curve-fitting-livestock"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"media-framing-analysis","name":"Media Framing Analysis","fullName":"Media Frames and Framing Effects Analysis","aliases":["frame analysis","news framing","discourse framing"],"domain":"media-studies","family":"process-pipeline","subfamily":"Qualitative media analysis","year":"1974","originator":"Erving Goffman, Robert Entman","url":"https://scholargate.app/en/media-studies/media-framing-analysis","markdownUrl":"https://scholargate.app/en/media-studies/media-framing-analysis.md","definition":"Media Framing Analysis is a systematic method for examining how news coverage and media messages organize and present information in ways that promote particular interpretations while obscuring others. Originating in Erving Goffman's sociological work (1974) and developed extensively by communication scholars like Robert Entman, the method decodes the frames—organizing principles and narrative structures—embedded in news reports, films, advertising, and public discourse. It reveals how media selections of what to emphasize, what to omit, and what narrative context to provide shape audience understanding of events and issues.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erving Goffman, Robert Entman","subfamily":"Qualitative media analysis","year":"1974","type":"Analytical method for identifying how media structures and presents information"},"citations":[{"ref":"Goffman, E. (1974). Frame Analysis: An Essay on the Organization of Experience. Harvard University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.harvard.edu/press"},{"ref":"Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51-58.","type":"article","doi":"10.1111/j.1460-2466.1993.tb01304.x","isbn":null,"url":null},{"ref":"Lakoff, G. (2004). Don't Think of an Elephant!: Know Your Values and Frame Your Arguments. Chelsea Green Publishing.","type":"book","doi":null,"isbn":null,"url":"https://chelseagreen.com"},{"ref":"Scheufele, D. A. (2000). Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass Communication & Society, 3(2-3), 297-316.","type":"article","doi":"10.1207/S15327825MCS0323_07","isbn":null,"url":null}],"related":["discourse-analysis-media","agenda-setting-analysis","film-narrative-analysis","reception-analysis","visual-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"media-literacy-questionnaire","name":"Media Literacy Questionnaire","fullName":"Media Literacy Questionnaire (MLQ)","aliases":["MLQ","Media Literacy Assessment"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"media-literacy-assessment","year":"2018","originator":"Rachel Wilson, Susan Luo, Yoong Wan Cheong, and Stella Tan","url":"https://scholargate.app/en/social-media-psychology/media-literacy-questionnaire","markdownUrl":"https://scholargate.app/en/social-media-psychology/media-literacy-questionnaire.md","definition":"The Media Literacy Questionnaire is a self-report instrument that assesses individuals' critical abilities regarding digital media: evaluating source credibility, identifying misinformation, recognizing advertising and algorithmic influence, and understanding media ownership and bias. Developed by Wilson and colleagues in 2018, it measures the cognitive and critical skills essential for navigating contemporary media environments effectively.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rachel Wilson, Susan Luo, Yoong Wan Cheong, and Stella Tan","subfamily":"media-literacy-assessment","year":"2018","type":"Self-report"},"citations":[{"ref":"Wilson, R., Luo, S., Cheong, Y. W., & Tan, S. H. (2018). The development of a media literacy questionnaire in the social media context. Asian Journal of Communication, 28(4), 426–444.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+development+of+a+media+literacy+questionnaire+in+the+social+media+context+Wilson"}],"related":["social-media-disorder-scale","social-comparison-scale-online","online-disinhibition-scale","digital-wellbeing-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"media-trust-scale","name":"Media Trust Scale","fullName":"Media Trust and Credibility Scale (MTS)","aliases":["MTS","Press Credibility Scale","News Media Confidence"],"domain":"political-psychology","family":"process-pipeline","subfamily":"institutional-attitudes","year":"1994","originator":"Mark D. West & Spiro Kiousis","url":"https://scholargate.app/en/political-psychology/media-trust-scale","markdownUrl":"https://scholargate.app/en/political-psychology/media-trust-scale.md","definition":"The Media Trust Scale measures audience confidence in news media credibility, including perceptions of accuracy, fairness, completeness, and journalists' motivations. Developed by West (1994) and extended by Kiousis (2001), the scale captures both medium-specific trust (trust in TV news vs. newspapers vs. online news) and outlet-specific trust (CNN vs. Fox News vs. BBC vs. local news). Media trust is central to understanding political polarization, misinformation vulnerability, and the functioning of the democratic public sphere, as low-trust populations reject news sources entirely, opening space for alternative information ecosystems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark D. West & Spiro Kiousis","subfamily":"institutional-attitudes","year":"1994","type":"Self-report"},"citations":[{"ref":"West, M. D. (1994). Validating a scale for the measurement of credibility: A covariance structure modeling approach. Journalism Quarterly, 71(1), 159-168.","type":"article","doi":"10.1177/107769909407100115","isbn":null,"url":null},{"ref":"Kiousis, S. (2001). Public trust or mistrust? Perceptions of media credibility in the information age. Mass Communication & Society, 4(4), 381-403.","type":"article","doi":"10.1207/S15327825MCS0404_4","isbn":null,"url":null},{"ref":"Pew Research Center. (2021). News consumption and media trust in an era of political polarization. Washington, DC: Pew Research Center.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Pew%20Research%20Center.%20(2021).%20News%20consumption%20and%20media%20trust%20in%20an%20era%20of%20political%20polarization.%20Washington%2C%20DC%3A%20Pew%20R"}],"related":["political-trust-scale","conspiracy-mentality-questionnaire","voter-cynicism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"median-ranking","name":"MEDIAN-RANKING","fullName":"Median ranking — per-alternative median rank","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"2024","originator":"Orakçı, E.","url":"https://scholargate.app/en/decision-making/median-ranking","markdownUrl":"https://scholargate.app/en/decision-making/median-ranking.md","definition":"MEDIAN-RANKING (Median ranking — per-alternative median rank) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Orakçı, E.","subfamily":"AggregationOperator","year":"2024","type":"Order statistic — column-wise median","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları","type":"article","doi":"10.58830/ozgur.pub623","isbn":null,"url":null}],"related":["borda","condorcet","copeland","dodgson","topsis","vikor","ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mediation-analysis","name":"Mediation Analysis","fullName":"Mediation Analysis (Baron-Kenny / Bootstrap)","aliases":["indirect effects analysis","path-based mediation","PROCESS macro mediation","Aracılık Analizi (Mediation / PROCESS)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1986,"originator":"Baron & Kenny","url":"https://scholargate.app/en/statistics/mediation-analysis","markdownUrl":"https://scholargate.app/en/statistics/mediation-analysis.md","definition":"Mediation analysis is a statistical procedure that tests whether the effect of an independent variable X on an outcome Y operates wholly or partly through a third variable M, called the mediator. Formalised by Baron and Kenny in 1986, it decomposes the total effect of X on Y into a direct path (c′) and an indirect path (a × b), quantifying how much of the relationship is carried by the mediating mechanism.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baron & Kenny","year":1986,"family":"Hypothesis test","type":"Indirect effects / path test","paths":"a (X→M), b (M→Y), c (total X→Y), c′ (direct X→Y)","indirectEffect":"a × b","inferenceMethod":"Bootstrap confidence interval (5000 resamples recommended)","minSample":50,"parametric":false,"outcome":"continuous (primary); binary or ordinal outcomes supported with extensions"},"citations":[{"ref":"Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+moderator-mediator+variable+distinction+in+social+psychological+research+Baron"},{"ref":"Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). The Guilford Press.","type":"book","doi":null,"isbn":"978-1462549030","url":null},{"ref":"Preacher, K. J. & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.","type":"article","doi":"10.3758/BRM.40.3.879","isbn":null,"url":null}],"related":["path-analysis","structural-equation-modeling","moderation-analysis","confirmatory-factor-analysis","multiple-regression","moderated-mediation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"medication-adherence-rating-scale","name":"Medication Adherence Rating Scale","fullName":"Medication Adherence Rating Scale (MARS)","aliases":["MARS"],"domain":"pharmacology","family":"process-pipeline","subfamily":"medication-adherence","year":"2000","originator":"Kathryn Thompson, Jayashri Kulkarni, and Anthony A. Sergejew","url":"https://scholargate.app/en/pharmacology/medication-adherence-rating-scale","markdownUrl":"https://scholargate.app/en/pharmacology/medication-adherence-rating-scale.md","definition":"The Medication Adherence Rating Scale (MARS) is a 10-item self-report measure developed by Thompson, Kulkarni, and Sergejew in 2000 to assess medication adherence behaviors and attitudes in psychiatric populations, particularly antipsychotic medication use. Although originally validated in schizophrenia, it has been successfully applied across diverse medical conditions including hypertension, diabetes, and chronic disease management, providing a quick, sensitive assessment of actual adherence frequency and admission of problematic medication-taking behaviors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathryn Thompson, Jayashri Kulkarni, and Anthony A. Sergejew","subfamily":"medication-adherence","year":"2000","type":"Self-report"},"citations":[{"ref":"Thompson, K., Kulkarni, J., & Sergejew, A. A. (2000). Reliability and validity of a new Medication Adherence Rating Scale (MARS) for the psychoses. Schizophrenia Research, 42(3), 241-247.","type":"article","doi":"10.1016/s0920-9964(99)00130-9","isbn":null,"url":null}],"related":["beliefs-medicines-questionnaire","drug-attitude-inventory","treatment-satisfaction-questionnaire-medication","self-efficacy-medication-adherence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"medication-reconciliation","name":"Medication Reconciliation","fullName":"Medication Reconciliation Process for Patient Safety","aliases":["Med Reconciliation","Medication List Verification","Drug-Drug Interaction Screening"],"domain":"nursing","family":"process-pipeline","subfamily":"Medication safety and error prevention","year":"2005","originator":"Institute of Medicine, The Joint Commission, and healthcare safety organizations","url":"https://scholargate.app/en/nursing/medication-reconciliation","markdownUrl":"https://scholargate.app/en/nursing/medication-reconciliation.md","definition":"Medication Reconciliation is a systematic process of identifying and resolving discrepancies between the medications a patient should be taking and what they are actually taking. Endorsed by The Joint Commission as a National Patient Safety Goal, medication reconciliation occurs at critical transition points such as hospital admission, transfer between units, and discharge. The process reduces medication errors and adverse drug events that can result from omissions, duplications, or interactions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Institute of Medicine, The Joint Commission, and healthcare safety organizations","subfamily":"Medication safety and error prevention","year":"2005","type":"Safety protocol"},"citations":[{"ref":"Institute of Medicine. (2006). Preventing Medication Errors. National Academies Press, Washington, DC.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/books/NBK25455/"},{"ref":"The Joint Commission. (2005). National Patient Safety Goals. Medication Reconciliation Standard NPSG.03.04.01.","type":"article","doi":null,"isbn":null,"url":"https://www.jointcommission.org/standards/national-patient-safety-goals/"}],"related":["cam-delirium-screening","nursing-sensitive-indicators","early-warning-score","patient-fall-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"medication-regimen-complexity","name":"Medication Regimen Complexity Index","fullName":"Medication Regimen Complexity Index (MRCI)","aliases":["MRCI"],"domain":"pharmacology","family":"process-pipeline","subfamily":"medication-complexity","year":"2012","originator":"Morgado, Rolo, and Castelo-Branco","url":"https://scholargate.app/en/pharmacology/medication-regimen-complexity","markdownUrl":"https://scholargate.app/en/pharmacology/medication-regimen-complexity.md","definition":"The Medication Regimen Complexity Index (MRCI) is a clinician-administered quantitative measure that objectively assesses the complexity of a patient's medication regimen based on the number of medications, frequency of dosing, and form of administration. Developed by Morgado, Rolo, and Castelo-Branco in 2012, the MRCI quantifies an important adherence barrier—the complexity of taking multiple medications with different schedules and administration routes. The MRCI is unique among adherence tools in that it measures an objective regimen characteristic (not patient behavior or belief), making it useful for deprescribing decisions, medication reconciliation, and identifying high-risk patients for non-adherence due to complexity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morgado, Rolo, and Castelo-Branco","subfamily":"medication-complexity","year":"2012","type":"Clinician-rated"},"citations":[{"ref":"Morgado, M., Rolo, S., & Castelo-Branco, M. (2012). Pharmacotherapy, 32(7), 652-660. (Original MRCI); Semla, T., & Beizer, J. (2018). American Geriatrics Society Beers Criteria for Potentially Inappropriate Medication Use in Older Adults. Journal of the American Geriatrics Society.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pharmacotherapy%2C+32%287%29%2C+652-660+Morgado"}],"related":["medication-adherence-rating-scale","treatment-satisfaction-questionnaire-medication","tablet-questionnaire","medication-understanding-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"medication-understanding-scale","name":"Medication Understanding and Use Self-Efficacy Scale","fullName":"Medication Understanding and Use Self-Efficacy Scale (MUSE-S)","aliases":["MUSE-S"],"domain":"pharmacology","family":"process-pipeline","subfamily":"medication-knowledge","year":"2009","originator":"Sunil Kripalani, Jill Risser, Monica E. Gatti, and Thomas A. Jacobson","url":"https://scholargate.app/en/pharmacology/medication-understanding-scale","markdownUrl":"https://scholargate.app/en/pharmacology/medication-understanding-scale.md","definition":"The Medication Understanding and Use Self-Efficacy Scale (MUSE-S) is a brief, patient-centered self-report measure assessing both knowledge and confidence regarding medication use. Developed by Kripalani and colleagues at Emory University in 2009, the MUSE-S evaluates whether patients understand their medications (what they are for, how to take them, important side effects) and feel confident managing them in daily life. This dual focus on knowledge and self-efficacy makes the MUSE-S particularly valuable for identifying education gaps, assessing health literacy barriers to medication adherence, and evaluating outcomes of medication counseling or education interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sunil Kripalani, Jill Risser, Monica E. Gatti, and Thomas A. Jacobson","subfamily":"medication-knowledge","year":"2009","type":"Self-report"},"citations":[{"ref":"Kripalani, S., Risser, J., Gatti, M. E., & Jacobson, T. A. (2009). Development and validation of a simple questionnaire to measure medication understanding. Medical Care, 47(3), 340-348.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+a+simple+questionnaire+to+measure+medication+understanding+Kripalani"}],"related":["medication-adherence-rating-scale","self-efficacy-medication-adherence","beliefs-medicines-questionnaire","treatment-satisfaction-questionnaire-medication"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mediterranean-diet-adherence","name":"MEDAS","fullName":"Mediterranean Diet Adherence Screener","aliases":["MEDAS","14-item MEDAS"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"dietary-pattern-assessment","year":2011,"originator":"Helmut Schröder, Montserrat Fitó, Ramón Estruch","url":"https://scholargate.app/en/nutritional-science/mediterranean-diet-adherence","markdownUrl":"https://scholargate.app/en/nutritional-science/mediterranean-diet-adherence.md","definition":"The Mediterranean Diet Adherence Screener is a 14-item food frequency questionnaire designed to rapidly assess adherence to the Mediterranean dietary pattern. Developed by Schröder and colleagues in 2011 and validated in the PREDIMED randomized controlled trial, it is one of the most widely used tools for measuring Mediterranean diet compliance in research and clinical practice. The MEDAS is particularly valuable for epidemiological studies, intervention trials, and cardiovascular disease prevention programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Helmut Schröder, Montserrat Fitó, Ramón Estruch","subfamily":"dietary-pattern-assessment","year":2011,"type":"Self-administered questionnaire"},"citations":[{"ref":"Schröder, H., Fitó, M., Estruch, R., et al. (2011). A short screener is valid for assessing Mediterranean diet adherence. The Journal of Nutrition, 141(6), 1140-1145.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+short+screener+is+valid+for+assessing+Mediterranean+diet+adherence+Schr%C3%B6der"},{"ref":"Estruch, R., Ros, E., Salas-Salvadó, J., et al. (2018). Primary prevention of cardiovascular disease with a Mediterranean diet supplemented with extra-virgin olive oil or nuts. The New England Journal of Medicine, 378(25), e34. [Republished after retraction of the 2013 article, doi:10.1056/NEJMoa1200303.]","type":"article","doi":"10.1056/NEJMoa1800389","isbn":null,"url":null}],"related":["dietary-quality-index","food-frequency-questionnaire","nutrition-self-efficacy-scale","mini-nutritional-assessment","intuitive-eating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meg-source-localization","name":"MEG Source Localization","fullName":"Magnetoencephalography Source Localization","aliases":["MEG localization","magnetic source imaging","MSI"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Inverse problem solution","year":"1972","originator":"David Cohen","url":"https://scholargate.app/en/neuroimaging/meg-source-localization","markdownUrl":"https://scholargate.app/en/neuroimaging/meg-source-localization.md","definition":"Magnetoencephalography (MEG) source localization is the inverse problem of estimating where in the brain neural currents originate from magnetic field measurements at the scalp. Introduced by David Cohen in 1972, MEG offers superior temporal resolution (milliseconds) and spatial specificity compared to EEG, as magnetic fields are less distorted by tissue conductivity, enabling researchers to pinpoint neural activity with high precision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Cohen","subfamily":"Inverse problem solution","year":"1972","type":"MEG neuroimaging analysis pipeline"},"citations":[{"ref":"Hauk, O., Friston, K. J., & Leff, A. (2019). Functional neuroimaging of language: understanding the complex relationships between localization and function. Journal of Neurolinguistics, 50, 236–250.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Functional+neuroimaging+of+language%3A+understanding+the+complex+relationships+between+localization+and+function+Hauk"},{"ref":"Halgren, E., Marinkovic, K., & Chauvel, P. (2006). Generators of the late cognitive potentials in auditory and visual oddball tasks. Electroencephalography and Clinical Neurophysiology, 106(2), 156–164.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Generators+of+the+late+cognitive+potentials+in+auditory+and+visual+oddball+tasks+Halgren"}],"related":["eloreta","event-related-potential-analysis","dynamic-causal-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"melasqol","name":"MelasQoL","fullName":"Melasma Quality of Life Scale","aliases":["Melasma-QoL"],"domain":"dermatology","family":"process-pipeline","subfamily":"disease-specific-quality-of-life","year":"2006","originator":"Cestari TF, Hexsel D","url":"https://scholargate.app/en/dermatology/melasqol","markdownUrl":"https://scholargate.app/en/dermatology/melasqol.md","definition":"MelasQoL is a disease-specific, patient-administered quality-of-life measure designed to assess the psychosocial burden of melasma, a common chronic disorder of symmetric facial hyperpigmentation. Developed by Cestari and colleagues in 2006, it captures the unique emotional and social impacts of a predominantly cosmetic condition that disproportionately affects women of color. MelasQoL is essential in clinical trials and observational studies of melasma treatments to ensure that efficacy encompasses meaningful quality-of-life outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cestari TF, Hexsel D","subfamily":"disease-specific-quality-of-life","year":"2006","type":"Self-report"},"citations":[{"ref":"Cestari TF, Hexsel D, Brandt FS, et al. Validation of a melasma quality of life questionnaire for Brazilian Portuguese language: the MelasQoL. Br J Dermatol. 2006;156(Suppl 3):13-20.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cestari+TF%2C+Hexsel+D%2C+Brandt+FS%2C+et+al.+Validation+of+a+melasma+quality+of+life+questionnaire+for+Brazilian+Portuguese+l+Cestari"},{"ref":"Pandya AG, Hynan LS, Bhore R, et al. Reliability and validity of the Melasma Area and Severity Index (MASI) and a new modified MASI scoring method. J Am Acad Dermatol. 2011;64(1):78-83.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pandya+AG%2C+Hynan+LS%2C+Bhore+R%2C+et+al.+Reliability+and+validity+of+the+Melasma+Area+and+Severity+Index+%28MASI%29+and+a+new+mo+Pandya"}],"related":["skindex-29","poem","dermatology-life-quality-index-children"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"melody-extraction","name":"Melody Extraction","fullName":"Melody Extraction Algorithm","aliases":["pitch contour extraction","melodic line extraction","f0 tracking"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Feature extraction","year":"2008","originator":"Anssi Klapuri","url":"https://scholargate.app/en/music-information-retrieval/melody-extraction","markdownUrl":"https://scholargate.app/en/music-information-retrieval/melody-extraction.md","definition":"Melody extraction is the task of automatically isolating the main melodic contour from polyphonic music recordings. It originated from music transcription research in the 2000s and addresses the core challenge of human pitch perception: identifying the perceptually dominant pitch when many instruments play simultaneously. Modern approaches use deep learning and are essential for music analysis, cover song detection, and music-to-lyrics alignment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anssi Klapuri","subfamily":"Feature extraction","year":"2008","type":"Polyphonic audio analysis"},"citations":[{"ref":"Salamon, J., & Gómez, E. (2014). Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing, 20(6), 1759-1770.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Melody+extraction+from+polyphonic+music+signals+using+pitch+contour+characteristics+Salamon"},{"ref":"Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337.","type":"article","doi":"10.1007/978-0-387-30441-0_20","isbn":null,"url":null},{"ref":"Bittner, R. M., McVicar, M., Salamon, J., & Ellis, D. P. (2017). An analysis of lead and accompaniment separation in polyphonic music. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2017/papers/018.pdf"}],"related":["pitch-detection-algorithm","automatic-music-transcription","harmonic-analysis-music","vocal-separation","music-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"member-checking","name":"Member Checking and Respondent Validation","fullName":"Participant Verification of Qualitative Findings","aliases":["member validation","respondent validation","participant feedback","credibility check"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"trustworthiness-assurance","year":"1985","originator":"Yvonna Lincoln and Egon Guba","url":"https://scholargate.app/en/qualitative-research/member-checking","markdownUrl":"https://scholargate.app/en/qualitative-research/member-checking.md","definition":"Member checking is a quality assurance procedure in qualitative research in which the researcher shares preliminary findings, interpretations, or analytical themes with research participants and asks whether the findings accurately reflect their perspectives and experiences. Developed by Lincoln and Guba (1985) as a trustworthiness criterion, member checking is considered a key method for ensuring credibility and reducing researcher misinterpretation. The goal is to verify that the researcher has understood participants correctly and that interpretations are grounded in participants' actual meaning-making, not the researcher's assumptions. Member checking can occur at different points in research (after individual interviews, after initial analysis, or after draft findings are written) and take different forms (individual feedback, group validation, interactive discussion).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yvonna Lincoln and Egon Guba","subfamily":"trustworthiness-assurance","year":"1985","type":"Method"},"citations":[{"ref":"Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. SAGE Publications.","type":"book","doi":null,"isbn":"978-0803924314","url":null},{"ref":"Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member checking: A tool to enhance trustworthiness or merely a nod to validation? Qualitative Health Research, 26(13), 1802-1811.","type":"article","doi":"10.1177/1049732316654870","isbn":null,"url":null},{"ref":"Carlson, J. A. (2010). Avoiding traps in member checking. The Qualitative Report, 15(5), 1102-1113.","type":"article","doi":null,"isbn":null,"url":"https://nsuworks.nova.edu/tqr/vol15/iss5/4"},{"ref":"Tong, A., Sainsbury, P., & Craig, J. (2007). Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care, 19(6), 349-357.","type":"article","doi":"10.1093/intqhc/mzm042","isbn":null,"url":null}],"related":["in-depth-interview-method","reflexivity-in-research","qualitative-rigor-criteria","participant-observation"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"memetic-algorithm","name":"Memetic Algorithm","fullName":"Memetic Algorithms (Hybrid Evolutionary + Local Search)","aliases":["Hybrid Evolutionary Algorithm","Cultural Algorithm (local-search variant)","Genetic Local Search","Memetik Algoritma"],"domain":"optimization","family":"process-pipeline","subfamily":"Metaheuristics","year":1989,"originator":"Pablo Moscato","url":"https://scholargate.app/en/optimization/memetic-algorithm","markdownUrl":"https://scholargate.app/en/optimization/memetic-algorithm.md","definition":"A Memetic Algorithm (MA) is a population-based metaheuristic that combines the global exploration of an evolutionary algorithm with the local exploitation of individual learning procedures. Introduced by Pablo Moscato in 1989 at Caltech, MAs draw on Richard Dawkins' concept of the meme — a unit of cultural transmission — to model the idea that solutions can improve not only through crossover and mutation but also through individual refinement within each generation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pablo Moscato","year":1989,"type":"Hybrid metaheuristic","subfamily":"Metaheuristics","inspiration":"Darwinian evolution + Dawkins meme concept","convergence":"Faster than pure GAs on many combinatorial problems"},"citations":[{"ref":"Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program Report 826.","type":"techreport","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/2354457"},{"ref":"Neri, F., & Cotta, C. (2012). Memetic algorithms and memetic computing optimization: A literature review. Swarm and Evolutionary Computation, 2, 1–14.","type":"article","doi":"10.1016/j.swevo.2011.11.003","isbn":null,"url":null}],"related":["genetic-algorithm","tabu-search","hyper-heuristics"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"memorial-symptom-assessment-scale","name":"MSAS","fullName":"Memorial Symptom Assessment Scale","aliases":["MSAS","MSAS-SF"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Comprehensive Multi-Symptom Assessment","year":"1994","originator":"Russell Portenoy","url":"https://scholargate.app/en/oncology-nursing/memorial-symptom-assessment-scale","markdownUrl":"https://scholargate.app/en/oncology-nursing/memorial-symptom-assessment-scale.md","definition":"The Memorial Symptom Assessment Scale is a comprehensive multisymptom instrument that captures both prevalence and distress of 32 cancer-related symptoms (full version) or 10 core symptoms (short form). Developed by Portenoy and colleagues at Memorial Sloan Kettering Cancer Center in 1994, the MSAS is designed for detailed symptom profiling in oncology research and clinical practice, enabling identification of symptom clusters and assessment of physical and psychological symptom burden separately.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Russell Portenoy","subfamily":"Comprehensive Multi-Symptom Assessment","year":"1994","type":"Patient self-report multisymptom prevalence and distress scale"},"citations":[{"ref":"Portenoy, R. K., Thaler, H. T., Kornblith, A. B., et al. (1994). The Memorial Symptom Assessment Scale: an instrument for the evaluation of symptom prevalence, characteristics and distress. Eur J Cancer, 30A(9), 1326–1336.","type":"article","doi":"10.1016/0959-8049(94)90182-1","isbn":null,"url":null},{"ref":"Cook, K. F., Broemling, L. D., Johnson, R. L., et al. (2007). Development and preliminary psychometric evaluation of the MSAS-GI: a brief symptom assessment for patients with gastrointestinal cancer. J Pain Symptom Manage, 34(3), 280–289.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+preliminary+psychometric+evaluation+of+the+MSAS-GI%3A+a+brief+symptom+assessment+for+patients+with+gastrointestinal+cancer+Cook"}],"related":["edmonton-symptom-assessment","distress-thermometer","brief-fatigue-inventory","fact-g","functional-living-index-cancer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mendelian-randomization","name":"Mendelian Randomization","fullName":"Mendelian Randomization Analysis","aliases":["MR"],"domain":"causal-inference","family":"regression-model","subfamily":"Causal","year":"1997","originator":"George Davey Smith","url":"https://scholargate.app/en/causal-inference/mendelian-randomization","markdownUrl":"https://scholargate.app/en/causal-inference/mendelian-randomization.md","definition":"Mendelian randomization is a method for estimating causal effects of exposures on outcomes using genetic variants as instrumental variables. Introduced by George Davey Smith in the 1990s, it exploits Mendel's law of segregation to remove confounding bias. It has become a cornerstone technique in epidemiological causal inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Davey Smith","subfamily":"Causal","year":"1997","type":"Genetic instrumental variable framework"},"citations":[{"ref":"Davey Smith, G., & Hemani, G. (2014). Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Molecular Genetics, 23(R1), R89-R98.","type":"article","doi":"10.1093/hmg/ddu328","isbn":null,"url":null},{"ref":"Hemani, G., Bowden, J., & Davey Smith, G. (2018). Evaluating the potential role of pleiotropy in Mendelian randomization studies. European Journal of Epidemiology, 33(9), 867-876.","type":"article","doi":"10.1093/hmg/ddy163","isbn":null,"url":null},{"ref":"Morrison, J., Knoblauch, N., Marcus, J. H., Stephens, M., & He, X. (2020). Mendelian randomization accounting for sample overlap. Nature Communications, 11(1), 574.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mendelian+randomization+accounting+for+sample+overlap+Morrison"}],"related":["instrumental-variable-analysis","two-stage-least-squares","regression-discontinuity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"menopause-rating-scale","name":"Menopause Rating Scale","fullName":"Menopause Rating Scale (MRS)","aliases":["MRS"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"menopause-symptoms","year":2000,"originator":"Heinemann et al.","url":"https://scholargate.app/en/urology-gynecology/menopause-rating-scale","markdownUrl":"https://scholargate.app/en/urology-gynecology/menopause-rating-scale.md","definition":"The MRS is an 11-item self-report symptom scale designed to assess the frequency and severity of menopausal symptoms including vasomotor complaints, psychologic symptoms, and urogenital manifestations. Developed by Heinemann and colleagues in Germany and first published in 2000, it has become the most widely used symptom measure in menopause research and clinical practice across 60+ countries and languages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heinemann et al.","subfamily":"menopause-symptoms","year":2000,"type":"Self-report symptom scale"},"citations":[{"ref":"Heinemann, K., Assmann, A., Möhner, S., & Schneider, H. P. (2004). The Menopause Rating Scale (MRS) as outcome measure for hormone replacement therapy. Menopause, 11(5), 571–578.","type":"article","doi":"10.1037/t35669-000","isbn":null,"url":null},{"ref":"Schneider, H. P., Heinemann, L. A., Rosemeier, H. P., Potthoff, P., & Behre, H. M. (2000). The Menopause Rating Scale (MRS): reliability, validity and cross-cultural comparability. Menopause International, 6(3), 145–161.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Menopause+Rating+Scale+%28MRS%29%3A+reliability%2C+validity+and+cross-cultural+comparability+Schneider"}],"related":["female-sexual-function-index","female-sexual-distress-scale","sexual-satisfaction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"menopause-specific-qol","name":"Menopause-Specific Quality of Life Questionnaire","fullName":"Menopause-Specific Quality of Life Questionnaire (MENQOL)","aliases":["MENQOL","Menopause Quality of Life"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"menopause-symptoms","year":1996,"originator":"Hilditch, J. R., Lewis, J., Peter, A., van Maris, B., Ross, A., Franssen, E., Guyatt, G. H.","url":"https://scholargate.app/en/obstetrics-gynecology/menopause-specific-qol","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/menopause-specific-qol.md","definition":"The Menopause-Specific Quality of Life Questionnaire (MENQOL) is a 29-item self-report instrument designed to assess quality of life in women experiencing menopausal symptoms. Developed by Hilditch and colleagues in 1996, the MENQOL captures four interrelated symptom domains: vasomotor symptoms (hot flushes, night sweats), psychosocial symptoms (mood, stress, anxiety), physical symptoms (fatigue, aches, sleep), and sexual symptoms. It is the primary menopause-specific outcome measure used in hormone replacement therapy trials and menopause research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hilditch, J. R., Lewis, J., Peter, A., van Maris, B., Ross, A., Franssen, E., Guyatt, G. H.","subfamily":"menopause-symptoms","year":1996,"type":"Self-report"},"citations":[{"ref":"Hilditch, J. R., Lewis, J., Peter, A., van Maris, B., Ross, A., Franssen, E., Guyatt, G. H., Lethargy, S., & Improvement, T. (1996). A menopause-specific quality of life questionnaire: development and psychometric properties. Maturitas, 24(3), 161-175.","type":"article","doi":"10.1016/S0378-5122(96)82006-8","isbn":null,"url":null}],"related":["pcosq","female-pelvic-pain-scale","premenstrual-symptoms-screening"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mental-health-continuum","name":"Mental Health Continuum Short Form","fullName":"Mental Health Continuum Short Form (MHC-SF)","aliases":["MHC-SF","Keyes Mental Health Continuum"],"domain":"psychiatric-rehabilitation","family":"process-pipeline","subfamily":"recovery-measurement","year":"2002","originator":"Keyes, C. L. M.","url":"https://scholargate.app/en/psychiatric-rehabilitation/mental-health-continuum","markdownUrl":"https://scholargate.app/en/psychiatric-rehabilitation/mental-health-continuum.md","definition":"The Mental Health Continuum Short Form (MHC-SF) is a 14-item measure assessing positive mental health and wellbeing across emotional, social, and psychological domains. Developed by Corey L. M. Keyes in 2002, the MHC-SF operationalizes the conceptualization of mental health as a continuum from languishing to flourishing, distinct from absence of mental illness. The scale captures life satisfaction, positive emotions, autonomy, personal growth, purpose, and social integration. The MHC-SF is widely used in population health research, clinical practice, and recovery-oriented mental health services.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keyes, C. L. M.","subfamily":"recovery-measurement","year":"2002","type":"Self-report questionnaire"},"citations":[{"ref":"Keyes, C. L. M. (2009). Atlanta: Brief description of the Mental Health Continuum Short Form (MHC-SF). Journal of Mental Health, 18(2), 113-123.","type":"article","doi":"10.1037/t30592-000","isbn":null,"url":null},{"ref":"Keyes, C. L. M. (2002). The mental health continuum: From languishing to flourishing in life. Journal of Health and Social Behavior, 43(2), 207-222.","type":"article","doi":"10.2307/3090197","isbn":null,"url":null}],"related":["recovery-assessment-scale","mental-health-recovery-measure","empowerment-scale-rogers","social-inclusion-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mental-toughness-questionnaire","name":"Mental Toughness Questionnaire","fullName":"Mental Toughness Questionnaire (MTQ48)","aliases":["MTQ48","Mental Toughness","4Cs"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"resilience-and-mental-toughness","year":"2002","originator":"Peter Clough, Keith Earle, David Sewell","url":"https://scholargate.app/en/sport-psychology/mental-toughness-questionnaire","markdownUrl":"https://scholargate.app/en/sport-psychology/mental-toughness-questionnaire.md","definition":"The MTQ48 is a 48-item instrument measuring mental toughness—the capacity to perform well under pressure, persist through adversity, maintain emotional control, and sustain commitment toward goals. Developed by Clough, Earle, and Sewell in 2002, the MTQ48 operationalizes mental toughness across four dimensions (the '4Cs': Control, Commitment, Challenge, and Confidence) and has become widely adopted in sport psychology, talent development, and organizational psychology for identifying and developing psychological resilience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Clough, Keith Earle, David Sewell","subfamily":"resilience-and-mental-toughness","year":"2002","type":"Self-report mental toughness and resilience questionnaire"},"citations":[{"ref":"Clough, P. J., Earle, K., & Sewell, D. (2002). Mental toughness: A definition and measured construct. Journal of Applied Sport Psychology, 14(3), 169–187.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mental+toughness%3A+A+definition+and+measured+construct+Clough"},{"ref":"Clough, P. J., Earle, K., Sewell, D., & Strycharczyk, D. (2012). Mental Toughness: The Mindset Behind Sporting Success. Bloomsbury Sport.","type":"book","doi":null,"isbn":null,"url":"https://books.bloomsbury.com/uk/mental-toughness-9781408195956.html"}],"related":["sport-confidence-inventory","competitive-state-anxiety-inventory","task-ego-orientation-sport","profile-of-mood-states"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"merec-g","name":"MEREC-G","fullName":"Method Based on the Removal Effects of Criteria - Generalized (MEREC-G)","aliases":["MEREC-G","Generalized MEREC"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Keshavarz Ghorabaee, Hosseinzadeh Lotfi et al.","url":"https://scholargate.app/en/decision-making/merec-g","markdownUrl":"https://scholargate.app/en/decision-making/merec-g.md","definition":"MEREC-G (Method Based on Removal Effects of Criteria - Generalized) is an objective weight derivation method that assigns weights based on the impact of removing each criterion from the decision analysis. The core idea is that important criteria, when removed, cause large changes in the final ranking. Generalized variants extend the original MEREC to various aggregation logic and decision contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keshavarz Ghorabaee, Hosseinzadeh Lotfi et al.","subfamily":"Ranking","year":"2021","type":"Objective weight derivation via removal impact assessment"},"citations":[{"ref":"Keshavarz Ghorabaee, M., Hosseinzadeh Lotfi, F., Behzadi, M., & Sałabun, W. (2021). MEREC: A new multi-criteria model to evaluate wind farm locations. Sustainability, 12(15), 6136.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MEREC%3A+A+new+multi-criteria+model+to+evaluate+wind+farm+locations+Keshavarz"},{"ref":"Pamučar, D., Ćirović, G., & Božanović-Kečan, S. (2021). A new model for determining weight coefficients of criteria in MCDM models: Full consistency method (FUCOM). Symmetry, 12(9), 1549.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.3390/sym12091549"}],"related":["merec","critic-m","entropy-method","variance-based-weighting","fucom"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"merec","name":"MEREC","fullName":"MEthod based on the Removal Effects of Criteria","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2021","originator":"Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Antucheviciene, J., Turskis, Z.","url":"https://scholargate.app/en/decision-making/merec","markdownUrl":"https://scholargate.app/en/decision-making/merec.md","definition":"MEREC (MEthod based on the Removal Effects of Criteria) is a weight objective multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Antucheviciene, J., Turskis, Z. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Antucheviciene, J., Turskis, Z.","subfamily":"Weight_Objective","year":"2021","type":"Removal-effect objective weighting (logarithmic utility)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Antucheviciene, J., Turskis, Z. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Informatica","type":"article","doi":"10.3390/sym13040525","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"merton-default-model","name":"Merton Default Model","fullName":"Merton Structural Default Model","aliases":["Structural Credit Model","Asset-to-Equity Model"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Structural Models","year":"1974","originator":"Robert C. Merton","url":"https://scholargate.app/en/quantitative-finance/merton-default-model","markdownUrl":"https://scholargate.app/en/quantitative-finance/merton-default-model.md","definition":"The Merton model (1974) is a structural approach to credit risk in which a firm defaults when its asset value falls below liabilities at maturity. Equity is viewed as a call option on firm value, and debt is an implicit short put position. The model links company fundamentals (asset volatility) to default probability and is foundational for modern credit risk measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert C. Merton","subfamily":"Structural Models","year":"1974","type":"Credit Risk Model"},"citations":[{"ref":"Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance, 29(2), 449-470.","type":"article","doi":"10.1111/j.1540-6261.1974.tb03058.x","isbn":null,"url":null},{"ref":"Vasicek, O. (2002). The distribution of losses on loan portfolios. Journal of Risk, 5(2), 15-25.","type":"article","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/228899496"}],"related":["credit-valuation-adjustment","debit-valuation-adjustment","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analysis-article","name":"Meta-Analysis","fullName":"Meta-Analysis (Statistical Synthesis and Pooling of Study Results)","aliases":["quantitative synthesis","meta-synthesis","pooled analysis","statistical integration"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Statistical synthesis","year":"1976","originator":"Glass (1976, term coining); Fisher and Pearson (statistical foundations)","url":"https://scholargate.app/en/academic-writing/meta-analysis-article","markdownUrl":"https://scholargate.app/en/academic-writing/meta-analysis-article.md","definition":"Meta-analysis is the statistical pooling of quantitative findings from multiple independent studies to produce a combined effect estimate. By aggregating data across studies, meta-analysis increases statistical power, reduces random error, and provides a precise summary of an intervention's effectiveness or an association's magnitude. Gene V. Glass coined the term in 1976, formalizing a technique that has become indispensable for evidence synthesis in medicine, psychology, education, and other evidence-based disciplines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glass (1976, term coining); Fisher and Pearson (statistical foundations)","subfamily":"Statistical synthesis","year":"1976","type":"Document Type"},"citations":[{"ref":"Page, M. J., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71.","type":"article","doi":"10.1136/bmj.n71","isbn":null,"url":null},{"ref":"Higgins, J. P., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558.","type":"article","doi":"10.1002/sim.1186","isbn":null,"url":null},{"ref":"Deeks, J. J., Higgins, J. P., & Altman, D. G. (2019). Analysing data and undertaking meta-analyses. In J. P. Higgins & J. Thomas (Eds.), Cochrane Handbook for Systematic Reviews of Interventions (Version 6.0). Cochrane.","type":"chapter","doi":null,"isbn":null,"url":"https://training.cochrane.org/handbook/current/chapter-10"}],"related":["systematic-review-article","forest-plot-interpretation","effect-size-statistics","heterogeneity-assessment","publication-bias"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-case-control-study","name":"Meta-analytic case-control study","fullName":"Meta-Analysis of Case-Control Studies","aliases":["pooled case-control analysis","case-control meta-analysis","meta-analytic case-control design","systematic pooled case-control"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–2000 (formalized with MOOSE reporting guidelines in 2000)","originator":"Systematic development attributed to multiple epidemiologists; MOOSE guidelines formalized by Stroup et al.","url":"https://scholargate.app/en/epidemiology/meta-analytic-case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-case-control-study.md","definition":"A meta-analytic case-control study systematically identifies, critically appraises, and quantitatively synthesizes data from multiple independent case-control studies examining the same exposure-disease relationship. By pooling odds ratios across studies, it yields a more precise and generalizable estimate of association than any single study can provide, while formally quantifying heterogeneity across populations, settings, and study periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Systematic development attributed to multiple epidemiologists; MOOSE guidelines formalized by Stroup et al.","year":"1980s–2000 (formalized with MOOSE reporting guidelines in 2000)","type":"Observational study synthesis","dataType":"Published or individual-level case-control study data (odds ratios, 2×2 tables)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Shapiro, S. (1994). Meta-analysis/Shmeta-analysis. American Journal of Epidemiology, 140(9), 771-778.","type":"article","doi":"10.1093/oxfordjournals.aje.a117324","isbn":null,"url":null},{"ref":"Stroup, D. F., Berlin, J. A., Morton, S. C., Olkin, I., Williamson, G. D., Rennie, D., ... & Thacker, S. B. (2000). Meta-analysis of observational studies in epidemiology: a proposal for reporting. JAMA, 283(15), 2008-2012.","type":"article","doi":"10.1001/jama.283.15.2008","isbn":null,"url":null}],"related":["case-control-study","systematic-review","meta-analysis","nested-case-control","prospective-case-control-study","matched-case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-case-crossover-design","name":"Meta-analytic case-crossover design","fullName":"Meta-Analysis of Case-Crossover Studies","aliases":["pooled case-crossover analysis","case-crossover meta-analysis","MACCO","systematic pooling of case-crossover studies"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1991 (base design); meta-analytic applications from late 1990s onward","originator":"Maclure (case-crossover basis, 1991); meta-analytic extension through environmental epidemiology consortia (1990s–2000s)","url":"https://scholargate.app/en/epidemiology/meta-analytic-case-crossover-design","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-case-crossover-design.md","definition":"The meta-analytic case-crossover design combines the within-person control structure of the case-crossover study with formal meta-analytic pooling across multiple studies. Each contributing study uses cases as their own controls by comparing exposure windows immediately preceding an acute event to matched reference windows in the same individual. The pooled approach synthesizes conditional odds ratios across studies, maximizing statistical power and generalizability — commonly applied to short-term environmental exposures such as air pollution, temperature extremes, and drug triggers of acute events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maclure (case-crossover basis, 1991); meta-analytic extension through environmental epidemiology consortia (1990s–2000s)","year":"1991 (base design); meta-analytic applications from late 1990s onward","type":"Observational epidemiological design with meta-analytic synthesis","dataType":"Individual-level or aggregate case-crossover effect estimates (conditional odds ratios) from multiple studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.","type":"article","doi":"10.1093/oxfordjournals.aje.a115853","isbn":null,"url":null},{"ref":"Bateson, T. F., & Schwartz, J. (2001). Selection bias and confounding in case-crossover analyses of environmental time-series data. Epidemiology, 12(6), 654–661.","type":"article","doi":"10.1097/00001648-200111000-00013","isbn":null,"url":null}],"related":["case-crossover-design","meta-analysis","systematic-review","time-series-analysis","nested-case-control","matched-case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-case-report","name":"Meta-analytic case report","fullName":"Meta-analytic Case Report Synthesis","aliases":["pooled case report analysis","systematic case report review","case report meta-analysis","MACR"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s–2010s (formalized methodology)","originator":"Developed iteratively in clinical epidemiology; formalized guidance by Murad et al. (2018)","url":"https://scholargate.app/en/epidemiology/meta-analytic-case-report","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-case-report.md","definition":"A meta-analytic case report is a secondary research methodology that systematically identifies, appraises, and quantitatively or qualitatively pools data from multiple published individual case reports on the same clinical phenomenon. It is used most often when randomized trials or cohort data are unavailable — particularly for rare diseases, uncommon drug reactions, or novel presentations — and transforms isolated anecdotal observations into a more robust aggregate picture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed iteratively in clinical epidemiology; formalized guidance by Murad et al. (2018)","year":"2000s–2010s (formalized methodology)","type":"Synthesis / secondary research design","dataType":"Published individual case reports (structured or free-text)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Murad, M. H., Sultan, S., Haffar, S., & Bazerbachi, F. (2018). Methodological quality and synthesis of case series and case reports. BMJ Evidence-Based Medicine, 23(2), 60–63.","type":"article","doi":"10.1136/bmjebm-2017-110853","isbn":null,"url":null},{"ref":"Nissen, T., & Wynn, R. (2014). The clinical case report: a review of its merits and limitations. BMC Research Notes, 7, 264.","type":"article","doi":"10.1186/1756-0500-7-264","isbn":null,"url":null}],"related":["case-report","systematic-review","meta-analysis","case-series","narrative-review","meta-analytic-case-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-case-series","name":"Meta-analytic Case Series","fullName":"Meta-analytic Pooling of Case Series Studies","aliases":["pooled case series","systematic review of case series","case series meta-analysis","aggregated case series"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s–2010s (formalization of methodology)","originator":"Developed iteratively in clinical epidemiology; methodological guidance formalized by Murad et al. and others in the 2000s–2010s","url":"https://scholargate.app/en/epidemiology/meta-analytic-case-series","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-case-series.md","definition":"A meta-analytic case series is an evidence-synthesis design that systematically identifies, appraises, and statistically pools outcome data from multiple single-arm case series on a defined clinical condition or intervention. It occupies a middle tier of evidence — above individual case reports and unsystematic series, but below pooled randomized trials — and is particularly valuable when experimental designs are ethically or practically unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed iteratively in clinical epidemiology; methodological guidance formalized by Murad et al. and others in the 2000s–2010s","year":"2000s–2010s (formalization of methodology)","type":"Evidence synthesis / meta-analytic method","dataType":"Aggregate or individual patient data from multiple case series","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Lovato, L. C., Hill, K., Hertert, S., Hunninghake, D. B., & Probstfield, J. L. (2002). Recruitment for controlled clinical trials: literature summary and annotated bibliography. Controlled Clinical Trials, 18(4), 328–352. [For meta-analytic approaches to non-randomised series see:] Murad, M. H., Sultan, S., Haffar, S., & Bazerbachi, F. (2018). Methodological quality and synthesis of case series and case reports. BMJ Evidence-Based Medicine, 23(2), 60–63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Recruitment+for+controlled+clinical+trials%3A+literature+summary+and+annotated+bibliography+Lovato"},{"ref":"Murad, M. H., Sultan, S., Haffar, S., & Bazerbachi, F. (2018). Methodological quality and synthesis of case series and case reports. BMJ Evidence-Based Medicine, 23(2), 60–63.","type":"article","doi":"10.1136/bmjebm-2017-110853","isbn":null,"url":null}],"related":["systematic-review","meta-analysis","case-series","pooled-analysis","narrative-synthesis","evidence-synthesis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-cohort-study","name":"Meta-analytic Cohort Study","fullName":"Meta-analytic Cohort Study","aliases":["cohort meta-analysis","pooled cohort analysis","meta-analysis of cohort studies","prospective cohort meta-analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–1990s (formalized practice)","originator":"Developed iteratively through epidemiological meta-analysis literature; Greenland, Berlin, Colditz among key contributors","url":"https://scholargate.app/en/epidemiology/meta-analytic-cohort-study","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-cohort-study.md","definition":"A meta-analytic cohort study systematically identifies, appraises, and statistically pools the findings of two or more independent cohort studies addressing the same exposure-outcome relationship. By combining large prospective datasets, it provides more precise risk estimates than any single cohort alone, makes dose-response patterns detectable, and enables subgroup analyses across diverse populations. It is the design of choice when cohort-level evidence exists but individual studies are underpowered or inconsistent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed iteratively through epidemiological meta-analysis literature; Greenland, Berlin, Colditz among key contributors","year":"1980s–1990s (formalized practice)","type":"Quantitative synthesis / observational epidemiology","dataType":"Summary statistics or individual participant data from two or more cohort studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301-1309.","type":"article","doi":"10.1093/oxfordjournals.aje.a116237","isbn":null,"url":null},{"ref":"Berlin, J. A., & Colditz, G. A. (1990). A meta-analysis of physical activity in the prevention of coronary heart disease. American Journal of Epidemiology, 132(4), 612-628.","type":"article","doi":"10.1093/oxfordjournals.aje.a115704","isbn":null,"url":null}],"related":["systematic-review","cohort-study","meta-analysis","pooled-analysis","prospective-study","dose-response-meta-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-competing-risks-analysis","name":"Meta-analytic competing risks analysis","fullName":"Meta-Analysis of Competing Risks Studies","aliases":["meta-analysis of competing risks","pooled competing risks analysis","systematic review competing risks"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s–2010s (formalized as a pooled approach)","originator":"Based on Fine & Gray (1999) competing risks framework; meta-analytic synthesis methods established through methodological literature (mid-2000s onward)","url":"https://scholargate.app/en/epidemiology/meta-analytic-competing-risks-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-competing-risks-analysis.md","definition":"Meta-analytic competing risks analysis pools results from multiple primary studies that each used a competing risks framework, allowing summary estimates of cause-specific or subdistribution hazard ratios and cumulative incidence functions. Because standard meta-analytic methods may misrepresent competing events, specialized pooling strategies are required that respect the subdistribution hazard structure introduced by Fine and Gray and the distinction between cause-specific and all-cause hazard models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Based on Fine & Gray (1999) competing risks framework; meta-analytic synthesis methods established through methodological literature (mid-2000s onward)","year":"2000s–2010s (formalized as a pooled approach)","type":"Systematic review / meta-analysis","dataType":"Published competing risks summary statistics (cause-specific hazard ratios, subdistribution hazard ratios, cumulative incidence functions)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Riley, R. D., Hayden, J. A., Steyerberg, E. W., et al. (2013). Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research. PLOS Medicine, 10(2), e1001380.","type":"article","doi":"10.1371/journal.pmed.1001380","isbn":null,"url":null},{"ref":"Wolkewitz, M., Cooper, B. S., Bonten, M. J., Barnett, A. G., & Schumacher, M. (2014). Interpreting and comparing risks in the presence of competing events. BMJ, 349, g5060.","type":"article","doi":"10.1136/bmj.g5060","isbn":null,"url":null}],"related":["competing-risks-analysis","fine-gray-competing-risks","meta-analytic-survival-analysis","meta-analytic-cohort-study","cox-proportional-hazards","kaplan-meier-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-cox-proportional-hazards","name":"Meta-analytic Cox proportional hazards","fullName":"Meta-analytic Cox Proportional Hazards Model","aliases":["pooled Cox regression meta-analysis","meta-Cox model","survival meta-analysis","Cox PH pooling"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1998–2007","originator":"Parmar, Torri & Stewart; Tierney et al.","url":"https://scholargate.app/en/epidemiology/meta-analytic-cox-proportional-hazards","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-cox-proportional-hazards.md","definition":"Meta-analytic Cox proportional hazards is a quantitative synthesis technique that pools log hazard ratios from multiple Cox regression survival analyses into a single, more precise estimate of the association between an exposure or treatment and a time-to-event outcome. It combines the inferential power of survival analysis with the evidence-aggregation logic of meta-analysis, making it the standard approach for summarising multi-study survival evidence in clinical and epidemiological research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Parmar, Torri & Stewart; Tierney et al.","year":"1998–2007","type":"Meta-analytic survival model","dataType":"Aggregate or individual-patient time-to-event data with log hazard ratios and standard errors from multiple studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Tierney, J. F., Stewart, L. A., Ghersi, D., Burdett, S., & Sydes, M. R. (2007). Practical methods for incorporating summary time-to-event data into meta-analysis. Trials, 8(1), 16.","type":"article","doi":"10.1186/1745-6215-8-16","isbn":null,"url":null},{"ref":"Parmar, M. K. B., Torri, V., & Stewart, L. (1998). Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Statistics in Medicine, 17(24), 2815–2834.","type":"article","doi":"10.1002/(SICI)1097-0258(19981230)17:24<2815::AID-SIM110>3.0.CO;2-8","isbn":null,"url":null}],"related":["cox-proportional-hazards","meta-analysis","individual-patient-data-meta-analysis","kaplan-meier-estimator","pooled-survival-analysis","hazard-ratio-meta-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-cross-sectional-epidemiological-study","name":"Meta-analytic cross-sectional epidemiological study","fullName":"Meta-Analysis of Cross-Sectional Epidemiological Studies","aliases":["pooled cross-sectional meta-analysis","prevalence meta-analysis","cross-sectional systematic review with meta-analysis","epidemiological prevalence synthesis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s–2010s (methodological consolidation)","originator":"Developed from the broader meta-analysis tradition (Glass, 1976); prevalence-specific pooling formalised by Barendregt et al. (2013)","url":"https://scholargate.app/en/epidemiology/meta-analytic-cross-sectional-epidemiological-study","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-cross-sectional-epidemiological-study.md","definition":"A meta-analytic cross-sectional epidemiological study systematically identifies and statistically pools prevalence or proportion estimates from multiple independent cross-sectional surveys. By combining data across studies — often using variance-stabilising transformations and random-effects models — it produces a more precise and generalisable estimate of disease burden, risk-factor frequency, or health behaviour prevalence in a defined population.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from the broader meta-analysis tradition (Glass, 1976); prevalence-specific pooling formalised by Barendregt et al. (2013)","year":"2000s–2010s (methodological consolidation)","type":"Quantitative synthesis design","dataType":"Aggregate prevalence or proportion data from multiple cross-sectional studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Barendregt, J. J., Doi, S. A., Lee, Y. Y., Norman, R. E., & Vos, T. (2013). Meta-analysis of prevalence. Journal of Epidemiology and Community Health, 67(11), 974-978.","type":"article","doi":"10.1136/jech-2013-203104","isbn":null,"url":null},{"ref":"Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions (2nd ed.). Wiley-Blackwell.","type":"book","doi":null,"isbn":"978-1119536956","url":null}],"related":["systematic-review","meta-analysis","cross-sectional-study","prevalence-study","random-effects-model","forest-plot"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-diagnostic-accuracy-study","name":"Meta-analytic Diagnostic Accuracy Study","fullName":"Meta-Analysis of Diagnostic Test Accuracy Studies","aliases":["DTA meta-analysis","diagnostic meta-analysis","systematic review of diagnostic accuracy","pooled diagnostic accuracy"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1993–2005 (foundational models)","originator":"Moses, Shapiro & Littenberg (SROC framework, 1993); Reitsma et al. (bivariate model, 2005)","url":"https://scholargate.app/en/epidemiology/meta-analytic-diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-diagnostic-accuracy-study.md","definition":"A meta-analytic diagnostic accuracy study systematically identifies and pools sensitivity and specificity data from multiple primary diagnostic test accuracy studies. Using the bivariate or hierarchical summary ROC (HSROC) model, it produces a joint summary of a test's ability to correctly classify diseased and non-diseased individuals across diverse clinical settings, accounting for the inherent trade-off between sensitivity and specificity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moses, Shapiro & Littenberg (SROC framework, 1993); Reitsma et al. (bivariate model, 2005)","year":"1993–2005 (foundational models)","type":"Quantitative systematic synthesis","dataType":"Contingency table data (TP, FP, FN, TN) from primary diagnostic accuracy studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Reitsma, J. B., Glas, A. S., Rutjes, A. W., Scholten, R. J., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10), 982–990.","type":"article","doi":"10.1016/j.jclinepi.2005.02.022","isbn":null,"url":null},{"ref":"Macaskill, P., Gatsonis, C., Deeks, J. J., Harbord, R. M., & Takwoingi, Y. (2010). Analysing and Presenting Results. In J. J. Deeks, P. M. Bossuyt, & C. Gatsonis (Eds.), Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. The Cochrane Collaboration.","type":"book","doi":null,"isbn":null,"url":"https://methods.cochrane.org/sdt/sites/methods.cochrane.org.sdt/files/public/uploads/Ch10_22Jan2010.pdf"}],"related":["diagnostic-accuracy-study","systematic-review","bivariate-meta-analysis","hierarchical-summary-roc","meta-analysis","screening-test-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-dose-response-analysis","name":"Meta-analytic dose-response analysis","fullName":"Meta-analytic Dose-Response Analysis","aliases":["dose-response meta-analysis","DRMA","pooled dose-response modeling","trend meta-analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1992","originator":"Sander Greenland & Matthew P. Longnecker","url":"https://scholargate.app/en/epidemiology/meta-analytic-dose-response-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-dose-response-analysis.md","definition":"Meta-analytic dose-response analysis pools summary statistics from multiple epidemiological studies to characterize how disease risk changes across ordered levels of an exposure. Rather than comparing a single high-exposure group against a reference, it reconstructs a continuous or categorical exposure-risk curve across the full range of doses, providing far richer evidence about the shape and magnitude of an association than any single study can supply.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sander Greenland & Matthew P. Longnecker","year":"1992","type":"Quantitative meta-analytic method","dataType":"Aggregated summary data (risk estimates, confidence intervals, exposure categories) from multiple studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301–1309.","type":"article","doi":"10.1093/oxfordjournals.aje.a116237","isbn":null,"url":null},{"ref":"Orsini, N., Li, R., Wolk, A., Khudyakov, P., & Spiegelman, D. (2012). Meta-analysis for linear and nonlinear dose-response relations: Examples, an evaluation of approximations, and software. American Journal of Epidemiology, 175(1), 66–73.","type":"article","doi":"10.1093/aje/kwr265","isbn":null,"url":null}],"related":["meta-analysis","systematic-review","restricted-cubic-splines","generalized-least-squares","exposure-response-modeling","network-meta-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-ecological-study","name":"Meta-analytic Ecological Study","fullName":"Meta-analytic Ecological Study","aliases":["ecological meta-analysis","aggregate-level meta-analysis","meta-analytic ecologic design","population-level meta-analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s","originator":"Morgenstern, Blettner, and colleagues in epidemiology methodology","url":"https://scholargate.app/en/epidemiology/meta-analytic-ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-ecological-study.md","definition":"A meta-analytic ecological study synthesises data from multiple populations or geographic units — rather than from individual patients — to estimate associations between exposures and health outcomes. By pooling aggregate-level statistics across studies or regions, it extends the reach of ecological reasoning to a wider evidence base, enabling detection of exposure-outcome relationships that single-population ecological analyses may miss due to limited variability or sample size.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morgenstern, Blettner, and colleagues in epidemiology methodology","year":"1990s","type":"Quantitative synthesis design","dataType":"Aggregate/group-level summary statistics from multiple studies or populations","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Blettner, M., Sauerbrei, W., Schlehofer, B., Scheuchenpflug, T., & Friedenreich, C. (1999). Traditional reviews, meta-analyses and pooled analyses in epidemiology. International Journal of Epidemiology, 28(1), 1–9.","type":"article","doi":"10.1093/ije/28.1.1","isbn":null,"url":null},{"ref":"Morgenstern, H. (1998). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 19, 61–87.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Ecologic+studies+in+epidemiology%3A+concepts%2C+principles%2C+and+methods+Morgenstern"}],"related":["meta-analysis","ecological-study","systematic-review","pooled-analysis","multilevel-modeling","geographic-epidemiology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-kaplan-meier-analysis","name":"Meta-analytic Kaplan-Meier analysis","fullName":"Meta-analytic Kaplan-Meier Survival Analysis","aliases":["KM meta-analysis","pooled Kaplan-Meier analysis","survival meta-analysis","IPD-KM meta-analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2007–2012 (systematic formalization)","originator":"Building on Kaplan & Meier (1958); meta-analytic extension formalized by Tierney et al. (2007) and Guyot et al. (2012)","url":"https://scholargate.app/en/epidemiology/meta-analytic-kaplan-meier-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-kaplan-meier-analysis.md","definition":"Meta-analytic Kaplan-Meier analysis synthesizes time-to-event data across multiple studies by pooling Kaplan-Meier survival estimates, either from reconstructed individual patient data or from summary statistics extracted from published curves. It produces a pooled survival function with confidence bands and enables formal heterogeneity testing across studies, offering higher statistical power and more generalizable survival estimates than any single study alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Kaplan & Meier (1958); meta-analytic extension formalized by Tierney et al. (2007) and Guyot et al. (2012)","year":"2007–2012 (systematic formalization)","type":"Quantitative meta-analytic method","dataType":"Published Kaplan-Meier curves, summary time-to-event statistics, or reconstructed individual patient data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Guyot, P., Ades, A. E., Ouwens, M. J., & Welton, N. J. (2012). Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Medical Research Methodology, 12, 9.","type":"article","doi":"10.1186/1471-2288-12-9","isbn":null,"url":null},{"ref":"Tierney, J. F., Stewart, L. A., Ghersi, D., Burdett, S., & Sydes, M. R. (2007). Practical methods for incorporating summary time-to-event data into meta-analysis. Trials, 8, 16.","type":"article","doi":"10.1186/1745-6215-8-16","isbn":null,"url":null}],"related":["kaplan-meier-analysis","survival-analysis","cox-proportional-hazards","competing-risks-analysis","meta-analytic-cox-proportional-hazards","systematic-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-nested-case-control","name":"Meta-analytic Nested Case-Control","fullName":"Meta-analytic Nested Case-Control Study","aliases":["MNCC","pooled nested case-control","meta-analysis of nested case-control studies","nested case-control meta-analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–2000s","originator":"Synthesis of Mantel-Haenszel methods and nested case-control design; formal pooling frameworks developed by Rothman, Greenland, and collaborative groups (e.g., IARC) through the 1980s–2000s","url":"https://scholargate.app/en/epidemiology/meta-analytic-nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-nested-case-control.md","definition":"Meta-analytic nested case-control analysis combines the efficiency advantages of the nested case-control design — in which cases and matched controls are sampled from a defined cohort — with the statistical power and generalisability gained by pooling estimates from multiple such studies. This approach is especially valuable in chronic-disease epidemiology where individual studies are often underpowered to detect modest exposure-outcome associations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesis of Mantel-Haenszel methods and nested case-control design; formal pooling frameworks developed by Rothman, Greenland, and collaborative groups (e.g., IARC) through the 1980s–2000s","year":"1980s–2000s","type":"Quantitative epidemiological synthesis","dataType":"Odds ratios and confidence intervals from multiple nested case-control studies within defined cohorts","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Hamling, J., Lee, P., Weitkunat, R., & Ambuhl, M. (2008). Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category. Statistics in Medicine, 27(7), 954–970.","type":"article","doi":"10.1002/sim.3013","isbn":null,"url":null}],"related":["nested-case-control","meta-analysis","case-cohort-study","systematic-review","pooled-analysis","cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-phase-i-clinical-trial","name":"Meta-analytic Phase I clinical trial","fullName":"Meta-analytic Approach to Phase I Clinical Trials","aliases":["meta-analytic dose-finding","MAP prior Phase I","MAPT design","Bayesian meta-analytic Phase I"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s–2010s","originator":"Neuenschwander, Capkun-Niggli, Branson, Spiegelhalter and colleagues","url":"https://scholargate.app/en/epidemiology/meta-analytic-phase-i-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-phase-i-clinical-trial.md","definition":"A meta-analytic Phase I clinical trial formally pools evidence from prior Phase I studies — using Bayesian or frequentist meta-analysis — to construct an informative prior (or summary estimate) for dose-toxicity relationships before or during a new first-in-human or early-phase study. The approach increases statistical efficiency, reduces the number of patients exposed to subtherapeutic or toxic doses, and accelerates dose selection by systematically leveraging all relevant historical dose-finding data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Neuenschwander, Capkun-Niggli, Branson, Spiegelhalter and colleagues","year":"2000s–2010s","type":"Bayesian meta-analytic dose-finding design","dataType":"Aggregated or individual patient data from historical Phase I trials (dose-toxicity, DLT rates, PK/PD parameters)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Neuenschwander, B., Capkun-Niggli, G., Branson, M., & Spiegelhalter, D. J. (2010). Summarizing historical information on controls in clinical trials. Clinical Trials, 7(1), 5–18.","type":"article","doi":"10.1177/1740774509356002","isbn":null,"url":null},{"ref":"Jaki, T., Clive, S., & Weir, C. J. (2013). Principles of dose finding studies in cancer: a comparison of trial designs. Cancer Chemotherapy and Pharmacology, 71(5), 1107–1114.","type":"article","doi":"10.1007/s00280-012-2059-8","isbn":null,"url":null}],"related":["continual-reassessment-method","3plus3-dose-escalation","bayesian-adaptive-design","network-meta-analysis","systematic-review","dose-response-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-phase-ii-clinical-trial","name":"Meta-analytic Phase II clinical trial","fullName":"Meta-analytic Phase II Clinical Trial Design","aliases":["MA-Phase II","meta-analytic single-arm trial","pooled Phase II design","Phase II meta-analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s–2010s","originator":"Developed within clinical epidemiology and oncology statistics; key contributions by Sutton, Warmuth, and colleagues","url":"https://scholargate.app/en/epidemiology/meta-analytic-phase-ii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-phase-ii-clinical-trial.md","definition":"A meta-analytic Phase II clinical trial integrates individual or aggregate data from multiple single-arm or small Phase II studies into a unified meta-analytic framework. Rather than relying on a single underpowered trial to screen for activity, this design pools evidence across comparable cohorts to obtain a more reliable estimate of treatment response, enabling better-informed go/no-go decisions before committing to a large Phase III randomized trial.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within clinical epidemiology and oncology statistics; key contributions by Sutton, Warmuth, and colleagues","year":"2000s–2010s","type":"Hybrid clinical trial / meta-analytic design","dataType":"Aggregate or individual-patient data from multiple Phase II trial cohorts","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Warmuth, M., & Hinzmann, B. (2013). Phase II trials in oncology: From the statistical design of trials to the meta-analysis of the results. Onkologie, 36(9), 555–564.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Phase+II+trials+in+oncology%3A+From+the+statistical+design+of+trials+to+the+meta-analysis+of+the+results+Warmuth"},{"ref":"Sutton, A. J., Cooper, N. J., Jones, D. R., Lambert, P. C., Thompson, J. R., & Abrams, K. R. (2007). Evidence-based sample size calculations based upon updated meta-analysis. Statistics in Medicine, 26(12), 2479–2500.","type":"article","doi":"10.1002/sim.2704","isbn":null,"url":null}],"related":["single-arm-clinical-trial","simon-two-stage-design","bayesian-adaptive-trial","network-meta-analysis","systematic-review","randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-phase-iii-clinical-trial","name":"Meta-analytic Phase III Clinical Trial","fullName":"Meta-analytic Synthesis of Phase III Clinical Trials","aliases":["Phase III meta-analysis","pooled Phase III analysis","systematic review of Phase III RCTs","confirmatory meta-analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1976 (meta-analysis); systematic application to Phase III RCTs from 1990s onward","originator":"Glass, G. V. (meta-analysis formalized); applied to Phase III trials via Cochrane Collaboration (Chalmers, Altman, Higgins)","url":"https://scholargate.app/en/epidemiology/meta-analytic-phase-iii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-phase-iii-clinical-trial.md","definition":"A meta-analytic Phase III clinical trial is a systematic, quantitative synthesis of multiple Phase III randomized controlled trials (RCTs) examining the same intervention. By pooling confirmatory trial data under a pre-registered protocol, the approach yields more precise effect estimates, resolves conflicting findings across trials, and supports regulatory or clinical guideline decisions with the highest level of evidence available in the evidence hierarchy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glass, G. V. (meta-analysis formalized); applied to Phase III trials via Cochrane Collaboration (Chalmers, Altman, Higgins)","year":"1976 (meta-analysis); systematic application to Phase III RCTs from 1990s onward","type":"Systematic quantitative evidence synthesis","dataType":"Aggregate or individual-level data from multiple Phase III randomized controlled trials","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Whitehead, A. (2002). Meta-Analysis of Controlled Clinical Trials. Wiley.","type":"book","doi":null,"isbn":"978-0471983705","url":null},{"ref":"Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2023). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.4). Cochrane.","type":"book","doi":null,"isbn":null,"url":"https://training.cochrane.org/handbook"}],"related":["meta-analysis","systematic-review","randomized-controlled-trial","network-meta-analysis","individual-patient-data-meta-analysis","bayesian-meta-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-phase-iv-study","name":"Meta-analytic Phase IV Study","fullName":"Meta-analytic Phase IV Post-marketing Study","aliases":["Phase IV meta-analysis","post-marketing meta-analysis","pharmacoepidemiologic meta-analysis","post-approval systematic review and meta-analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (formalised as regulatory requirement context grew)","originator":"Developed through the convergence of meta-analytic methods (Glass, 1976; Hedges & Olkin, 1985) and post-marketing pharmacoepidemiology frameworks","url":"https://scholargate.app/en/epidemiology/meta-analytic-phase-iv-study","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-phase-iv-study.md","definition":"A meta-analytic Phase IV study pools and quantitatively synthesises data from multiple Phase IV (post-marketing) sources — including observational cohorts, registries, spontaneous adverse-event databases, and post-approval randomised trials — to produce a single, more precise estimate of a drug or device's real-world effectiveness, safety, or utilisation pattern. By applying meta-analytic weighting to heterogeneous post-marketing evidence, it bridges the gap between tightly controlled pre-approval trials and the complexity of routine clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed through the convergence of meta-analytic methods (Glass, 1976; Hedges & Olkin, 1985) and post-marketing pharmacoepidemiology frameworks","year":"1990s–2000s (formalised as regulatory requirement context grew)","type":"Evidence synthesis applied to post-marketing observational and trial data","dataType":"Aggregate or individual-patient data from Phase IV observational studies, registries, spontaneous reporting databases, and post-approval RCTs","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Sutton, A. J., Abrams, K. R., Jones, D. R., Sheldon, T. A., & Song, F. (2000). Methods for Meta-Analysis in Medical Research. Wiley.","type":"book","doi":null,"isbn":"978-0471490661","url":null},{"ref":"Strom, B. L. (Ed.). (2005). Pharmacoepidemiology (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470860762","url":null}],"related":["systematic-review","meta-analysis","network-meta-analysis","pharmacovigilance","cohort-study","randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-randomized-clinical-trial","name":"Meta-analytic Randomized Clinical Trial","fullName":"Meta-analytic Randomized Clinical Trial","aliases":["meta-analytic RCT","MA-RCT","meta-analysis of RCTs","pooled randomized trial analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1976 (Glass coinage of meta-analysis); 1993 (Cochrane Collaboration formalization)","originator":"Gene V. Glass (meta-analysis method); Cochrane Collaboration (systematic RCT pooling standards)","url":"https://scholargate.app/en/epidemiology/meta-analytic-randomized-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-randomized-clinical-trial.md","definition":"A meta-analytic randomized clinical trial is a formal evidence-synthesis method that identifies, appraises, and statistically combines the results of multiple randomized clinical trials addressing the same clinical question. By pooling trial-level data, it produces a single, more precise estimate of treatment effect and quantifies between-trial heterogeneity, sitting at the apex of the evidence hierarchy for evaluating healthcare interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gene V. Glass (meta-analysis method); Cochrane Collaboration (systematic RCT pooling standards)","year":"1976 (Glass coinage of meta-analysis); 1993 (Cochrane Collaboration formalization)","type":"Quantitative evidence-synthesis design","dataType":"Aggregate or individual-level data from multiple randomized clinical trials","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions (2nd ed.). Wiley-Blackwell.","type":"book","doi":null,"isbn":"978-1119536628","url":null},{"ref":"Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Wiley.","type":"book","doi":null,"isbn":"978-0470057247","url":null}],"related":["systematic-review","randomized-controlled-trial","network-meta-analysis","bayesian-meta-analysis","individual-patient-data-meta-analysis","cochrane-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-screening-test-evaluation","name":"Meta-analytic Screening Test Evaluation","fullName":"Meta-analytic Evaluation of Screening and Diagnostic Tests","aliases":["diagnostic test accuracy meta-analysis","DTA meta-analysis","screening accuracy synthesis","meta-analytic DTA"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s (formal bivariate/HSROC framework ~2001–2005)","originator":"Reitsma et al. (bivariate model); Rutter & Gatsonis (HSROC model)","url":"https://scholargate.app/en/epidemiology/meta-analytic-screening-test-evaluation","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-screening-test-evaluation.md","definition":"Meta-analytic screening test evaluation is a quantitative evidence-synthesis approach that pools sensitivity, specificity, and related accuracy indices across multiple primary studies of the same screening or diagnostic test. It produces summary estimates of a test's ability to correctly identify disease-positive and disease-negative individuals, typically using the bivariate random-effects model or the Hierarchical Summary ROC (HSROC) framework, and visualises results with summary ROC curves and forest plots.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reitsma et al. (bivariate model); Rutter & Gatsonis (HSROC model)","year":"2000s (formal bivariate/HSROC framework ~2001–2005)","type":"Quantitative evidence-synthesis method","dataType":"2×2 contingency tables (TP, FP, FN, TN) from primary diagnostic accuracy studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Reitsma, J. B., Glas, A. S., Rutjes, A. W. S., Scholten, R. J. P. M., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10), 982–990.","type":"article","doi":"10.1016/j.jclinepi.2005.02.022","isbn":null,"url":null},{"ref":"Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. (2023). Cochrane.","type":"misc","doi":null,"isbn":null,"url":"https://training.cochrane.org/handbook-diagnostic-test-accuracy"}],"related":["systematic-review","bivariate-meta-analysis","roc-analysis","forest-plot","meta-regression","sroc-curve"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-analytic-survival-analysis","name":"Meta-analytic survival analysis","fullName":"Meta-analytic Survival Analysis","aliases":["meta-analysis of time-to-event data","pooled survival analysis","IPD survival meta-analysis","aggregate survival meta-analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (formalized ~1998)","originator":"Parmar, Torri & Stewart (statistical framework); broader IPD tradition developed by the Early Breast Cancer Trialists' Collaborative Group","url":"https://scholargate.app/en/epidemiology/meta-analytic-survival-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/meta-analytic-survival-analysis.md","definition":"Meta-analytic survival analysis is a quantitative synthesis method that pools hazard ratios and related time-to-event statistics from multiple independent studies to produce a single, more precise estimate of a treatment or exposure effect on survival outcomes such as overall survival, disease-free survival, or time to relapse. It can operate on aggregate published data or on individual patient data (IPD) contributed directly by study investigators.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Parmar, Torri & Stewart (statistical framework); broader IPD tradition developed by the Early Breast Cancer Trialists' Collaborative Group","year":"1990s–2000s (formalized ~1998)","type":"Quantitative synthesis / meta-analytic method","dataType":"Time-to-event (survival) summary statistics or individual patient data (IPD) from multiple studies","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Parmar, M. K. B., Torri, V., & Stewart, L. (1998). Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Statistics in Medicine, 17(24), 2815–2834.","type":"article","doi":"10.1002/(SICI)1097-0258(19981230)17:24<2815::AID-SIM110>3.0.CO;2-8","isbn":null,"url":null},{"ref":"Tierney, J. F., Stewart, L. A., Ghersi, D., Burdett, S., & Sydes, M. R. (2007). Practical methods for incorporating summary time-to-event data into meta-analysis. Trials, 8, 16.","type":"article","doi":"10.1186/1745-6215-8-16","isbn":null,"url":null}],"related":["survival-analysis","kaplan-meier-analysis","cox-proportional-hazards","competing-risks-analysis","systematic-review","individual-patient-data-meta-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-ethnography","name":"Meta-ethnography","fullName":"Meta-Ethnographic Synthesis","aliases":["qualitative meta-synthesis","interpretive synthesis","ethnographic synthesis","meta-ethnographic review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1988","originator":"George W. Noblit and R. Dwight Hare","url":"https://scholargate.app/en/scientometrics/meta-ethnography","markdownUrl":"https://scholargate.app/en/scientometrics/meta-ethnography.md","definition":"Meta-ethnography is a systematic method for synthesising findings across multiple qualitative studies by comparing and translating the conceptual frameworks and metaphors each study uses. Developed by Noblit and Hare in 1988, it produces a new interpretive account that goes beyond any single study, preserving the richness of qualitative data while generating broader theoretical insights. It is the most influential approach to qualitative evidence synthesis in health, social, and educational research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George W. Noblit and R. Dwight Hare","year":"1988","type":"Qualitative evidence synthesis method","dataType":"Published qualitative studies (ethnographies, interview-based research, case studies)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Noblit, G. W., & Hare, R. D. (1988). Meta-ethnography: Synthesizing qualitative studies. Sage.","type":"book","doi":null,"isbn":"978-0803930780","url":null},{"ref":"Campbell, R., Pound, P., Morgan, M., Daker-White, G., Britten, N., Pill, R., Yardley, L., Pope, C., & Donovan, J. (2011). Evaluating meta-ethnography: Systematic analysis and synthesis of qualitative research on access to primary healthcare for people with intellectual disabilities. Journal of Health Services Research & Policy, 16(1), 10-19.","type":"article","doi":"10.3310/hta15430","isbn":null,"url":null}],"related":["qualitative-meta-synthesis","systematic-literature-review","thematic-synthesis","narrative-synthesis","grounded-theory","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-regression-based-co-word-analysis","name":"Meta-Regression-Based Co-Word Analysis","fullName":"Meta-Regression-Based Co-Word Analysis","aliases":["MR-CWA","meta-regression co-word mapping","regression-weighted co-word analysis","co-word meta-regression"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2000s–2010s (hybrid application period)","originator":"Derived from Callon et al. (co-word analysis, 1983) and Glass (meta-regression lineage, 1976); hybrid application developed incrementally in scientometrics and evidence synthesis","url":"https://scholargate.app/en/scientometrics/meta-regression-based-co-word-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/meta-regression-based-co-word-analysis.md","definition":"Meta-regression-based co-word analysis is a hybrid scientometric technique that enriches traditional co-word mapping by weighting keyword co-occurrence networks with meta-regression-derived effect estimates. Instead of treating all documents as equally informative, the method uses statistical regression to incorporate study-level moderators — such as publication year, sample size, or methodological quality — into the co-occurrence structure, revealing how thematic clusters in a research field vary across moderator conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from Callon et al. (co-word analysis, 1983) and Glass (meta-regression lineage, 1976); hybrid application developed incrementally in scientometrics and evidence synthesis","year":"2000s–2010s (hybrid application period)","type":"Hybrid scientometric-statistical method","dataType":"Keyword co-occurrence matrices, effect sizes, moderator variables from published literature","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.","type":"article","doi":"10.1177/053901883022002003","isbn":null,"url":null},{"ref":"Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48.","type":"article","doi":"10.18637/jss.v036.i03","isbn":null,"url":null}],"related":["co-word-analysis","meta-analysis","meta-regression","bibliometric-analysis","systematic-review","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-regression-based-meta-analysis","name":"meta-regression-based meta-analysis","fullName":"Meta-Regression-Based Meta-Analysis","aliases":["meta-regression","meta-analytic regression","weighted regression meta-analysis","MR-MA"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1993–1999","originator":"Stephen G. Thompson & Simon J. Sharp (systematic framework); earlier work by Berlin, Longnecker & Greenland (1993)","url":"https://scholargate.app/en/scientometrics/meta-regression-based-meta-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/meta-regression-based-meta-analysis.md","definition":"Meta-regression-based meta-analysis extends standard meta-analysis by fitting a weighted regression model in which study-level characteristics (moderators) predict observed effect sizes. Rather than simply pooling effects, this approach asks why effects vary across studies — linking heterogeneity in outcomes to differences in population, intervention, design, or measurement features. It is the primary tool for explaining between-study variance in quantitative evidence synthesis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen G. Thompson & Simon J. Sharp (systematic framework); earlier work by Berlin, Longnecker & Greenland (1993)","year":"1993–1999","type":"Quantitative evidence synthesis with covariate modeling","dataType":"Aggregated study-level effect sizes and moderator variables from primary studies","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: a comparison of methods. Statistics in Medicine, 18(20), 2693–2708.","type":"article","doi":"10.1002/(SICI)1097-0258(19991030)18:20<2693::AID-SIM235>3.0.CO;2-V","isbn":null,"url":null},{"ref":"Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Wiley.","type":"book","doi":null,"isbn":"978-0470057247","url":null}],"related":["meta-analysis","systematic-literature-review","network-meta-analysis","sensitivity-analysis-based-meta-analysis","bibliometric-analysis","scoping-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-regression-based-rapid-review","name":"meta-regression-based rapid review","fullName":"Meta-Regression-Based Rapid Review","aliases":["rapid review with meta-regression","accelerated meta-regression review","rapid synthesis with meta-regression","RRMR"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2000s–2010s (convergence of rapid review and meta-regression)","originator":"Meta-regression: Simon Thompson & Stephen Sharp (1999); Rapid review methodology: Cochrane, WHO, and health technology assessment bodies (2000s onward)","url":"https://scholargate.app/en/scientometrics/meta-regression-based-rapid-review","markdownUrl":"https://scholargate.app/en/scientometrics/meta-regression-based-rapid-review.md","definition":"A meta-regression-based rapid review is an accelerated evidence synthesis that combines the time-efficient protocols of a rapid review with meta-regression analysis to identify which study-level or population-level characteristics explain variability in effect sizes across included studies. By streamlining search and screening steps without sacrificing the explanatory power of regression modeling, this approach delivers actionable heterogeneity insights under decision-making time constraints.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Meta-regression: Simon Thompson & Stephen Sharp (1999); Rapid review methodology: Cochrane, WHO, and health technology assessment bodies (2000s onward)","year":"2000s–2010s (convergence of rapid review and meta-regression)","type":"Quantitative evidence synthesis variant","dataType":"Aggregated effect size data from multiple primary studies (continuous or binary outcomes)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: A comparison of methods. Statistics in Medicine, 18(20), 2693–2708.","type":"article","doi":"10.1002/(SICI)1097-0258(19991030)18:20<2693::AID-SIM235>3.0.CO;2-V","isbn":null,"url":null},{"ref":"Tricco, A. C., Antony, J., Zarin, W., Strifler, L., Ghassemi, M., Ivory, J., Perrier, L., Hutton, B., Moher, D., & Straus, S. E. (2015). A scoping review of rapid review methods. BMC Medicine, 13, 224.","type":"article","doi":"10.1186/s12916-015-0465-6","isbn":null,"url":null}],"related":["rapid-review","meta-regression-based-meta-analysis","systematic-literature-review","network-meta-analysis","sensitivity-analysis-based-meta-analysis","scoping-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"meta-regression","name":"Meta-Regression","fullName":"Meta-Regression","aliases":["Meta-Analytic Regression","Weighted Regression in Meta-Analysis","Moderator Analysis","Meta-regresyon"],"domain":"meta-analysis","family":"regression-model","subfamily":"Evidence synthesis","year":2002,"originator":"Simon Thompson & Julian Higgins","url":"https://scholargate.app/en/meta-analysis/meta-regression","markdownUrl":"https://scholargate.app/en/meta-analysis/meta-regression.md","definition":"Meta-regression is a statistical technique that extends conventional meta-analysis by regressing study-level effect sizes on one or more study characteristics (moderators) to explain between-study heterogeneity. Formalized by Thompson and Higgins in 2002, it uses weighted least squares — weighting each study by the inverse of its variance — within a mixed-effects framework, allowing researchers to identify which study features systematically account for variation in observed effects across the literature.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Simon Thompson & Julian Higgins","year":2002,"type":"Weighted regression for effect-size heterogeneity","subfamily":"Evidence synthesis","input":"Study-level effect sizes with variance estimates","output":"Regression coefficients explaining between-study variance"},"citations":[{"ref":"Thompson, S. G., & Higgins, J. P. T. (2002). How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine, 21(11), 1559–1573.","type":"article","doi":"10.1002/sim.1187","isbn":null,"url":null}],"related":["meta-analysis","network-meta-analysis","weighted-least-squares"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"metabolic-theory-of-ecology","name":"Metabolic Theory of Ecology","fullName":"Metabolic Theory of Ecology (MTE)","aliases":["MTE","metabolic scaling","temperature-size rule","energy allocation"],"domain":"ecology","family":"process-pipeline","subfamily":"Bioenergetics","year":"2004","originator":"James Brown","url":"https://scholargate.app/en/ecology/metabolic-theory-of-ecology","markdownUrl":"https://scholargate.app/en/ecology/metabolic-theory-of-ecology.md","definition":"The Metabolic Theory of Ecology (MTE), developed by Brown and colleagues (2004), provides a unifying framework linking individual metabolic rate to ecological patterns across levels of organization (organisms, populations, ecosystems). MTE predicts how metabolic rate scales with body size (allometry) and temperature, and uses these scaling relationships to explain patterns in life history, population growth, community structure, and ecosystem dynamics. The theory is grounded in physics: metabolic rate is constrained by supply of resources (energy and nutrients) and demand determined by biochemical kinetics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Brown","subfamily":"Bioenergetics","year":"2004","type":"metabolic scaling theory"},"citations":[{"ref":"Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M., & West, G. B. (2004). Toward a metabolic basis of ecology. Ecology, 85(7), 1771-1789.","type":"article","doi":"10.1890/03-9000","isbn":null,"url":null},{"ref":"Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M., & Charnov, E. L. (2001). Effects of size and temperature on metabolic rate. Science, 293(5538), 2248-2251.","type":"article","doi":"10.1126/science.1061967","isbn":null,"url":null},{"ref":"Savage, V. M., Gillooly, J. F., Brown, J. H., West, G. B., & Charnov, E. L. (2004). Effects of body size and temperature on population growth. American Naturalist, 163(3), 429-441.","type":"article","doi":"10.1086/381872","isbn":null,"url":null}],"related":["leslie-matrix","integral-projection-model","food-web-topology","siar-mixing-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"metabolomics-analysis","name":"Metabolomics analysis","fullName":"Metabolomics Data Analysis","aliases":["metabolome profiling","metabolic profiling","metabonomics","metabolite profiling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"1998–2002","originator":"Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework)","url":"https://scholargate.app/en/bioinformatics/metabolomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/metabolomics-analysis.md","definition":"Metabolomics analysis is the large-scale, systematic measurement of small-molecule metabolites in a biological sample to characterise the metabolome — the complete set of metabolic intermediates and products present under defined conditions. By coupling high-throughput analytical platforms such as mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy with multivariate statistics and pathway databases, metabolomics bridges the genotype–phenotype gap and captures the downstream functional output of genes, transcripts, and proteins in real time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oliver et al. (coining of 'metabolomics'); Oliver Fiehn (systematic framework)","year":"1998–2002","type":"Quantitative omics pipeline","dataType":"Mass spectrometry (MS) or NMR spectral intensities; concentration matrices","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Fiehn, O. (2002). Metabolomics — the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2), 155–171.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Metabolomics+the+link+between+genotypes+and+phenotypes+Fiehn+2002"},{"ref":"Wishart, D. S., et al. (2022). HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Research, 50(D1), D622–D631.","type":"article","doi":"10.1093/nar/gkab1062","isbn":null,"url":null}],"related":["proteomics-analysis","pathway-enrichment-analysis","rna-seq-differential-expression","multi-omics-metabolomics-analysis","gene-set-enrichment-analysis","eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"metagenomic-binning","name":"Metagenomic Binning","fullName":"Metagenome Assembly and Genome Binning","aliases":["metagenomic assembly","genome binning","MAG recovery"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Metagenomics","year":"2011","originator":"Jillian Banfield","url":"https://scholargate.app/en/bioinformatics/metagenomic-binning","markdownUrl":"https://scholargate.app/en/bioinformatics/metagenomic-binning.md","definition":"Metagenomic binning partitions assembled contigs from complex microbial communities into distinct genome bins, each representing an individual organism or strain. Pioneered by Banfield and colleagues, this pipeline isolates single-organism genomes (metagenome-assembled genomes or MAGs) from environmental samples without requiring cultivated isolates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jillian Banfield","subfamily":"Metagenomics","year":"2011","type":"Sequence assembly and clustering pipeline"},"citations":[{"ref":"Kang, D. D., Froula, J., Egan, R., & Wang, Z. (2015). MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ, 3, e1165.","type":"article","doi":"10.7717/peerj.1165","isbn":null,"url":null},{"ref":"Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T., & Aluru, S. (2018). High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nature Communications, 9(1), 4045.","type":"article","doi":"10.1038/s41467-018-07641-9","isbn":null,"url":null},{"ref":"Sieber, C. M. K., Probst, A. J., Sharrar, A., Thomas, B. C., Hess, M., Tringe, S. G., & Banfield, J. F. (2018). Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nature Microbiology, 3(7), 836-843.","type":"article","doi":"10.1038/s41564-018-0171-1","isbn":null,"url":null}],"related":["hmmer-profile-search","crispr-screen-analysis","de-novo-transcriptome-assembly"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"metaphor-analysis","name":"Metaphor Analysis","fullName":"Metaphor Analysis","aliases":["Conceptual Metaphor Analysis","Metaphor Elicitation","Critical Metaphor Analysis","Linguistic Metaphor Analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Textual Analysis","year":"Theoretical foundation 1980; systematic research applications from 1990s onward","originator":"George Lakoff & Mark Johnson (Conceptual Metaphor Theory); Jonathan Charteris-Black (Critical Metaphor Analysis)","url":"https://scholargate.app/en/qualitative/metaphor-analysis","markdownUrl":"https://scholargate.app/en/qualitative/metaphor-analysis.md","definition":"Metaphor Analysis is a qualitative method that identifies, classifies, and interprets the metaphors embedded in language to reveal how speakers and writers conceptualise experience, construct meaning, and exercise ideological influence. Grounded in Lakoff and Johnson's Conceptual Metaphor Theory, it treats metaphor not as a literary decoration but as a fundamental cognitive structure — ARGUMENT IS WAR, TIME IS MONEY — that shapes how people think, reason, and act. It is widely applied in psychology, education, political discourse, health communication, and organisational research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Lakoff & Mark Johnson (Conceptual Metaphor Theory); Jonathan Charteris-Black (Critical Metaphor Analysis)","year":"Theoretical foundation 1980; systematic research applications from 1990s onward","type":"Qualitative research method","dataType":"Text (interviews, documents, speeches, written narratives, media corpora)","typicalSampleSize":"No fixed minimum; textual corpus ranging from single documents to hundreds of interview transcripts","subfamily":"Textual Analysis"},"citations":[{"ref":"Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226468013","url":null},{"ref":"Charteris-Black, J. (2004). Corpus Approaches to Critical Metaphor Analysis. Palgrave Macmillan.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Corpus+Approaches+to+Critical+Metaphor+Analysis+Charteris-Black+2004"}],"related":["discourse-analysis","content-analysis","thematic-analysis","narrative-analysis","phenomenology","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"method-of-moments-quantile-regression","name":"Method of Moments Quantile Regression","fullName":"Method of Moments for Quantile Regression","aliases":["GMM quantile regression"],"domain":"econometrics","family":"regression-model","subfamily":"Robust regression","year":"2004","originator":"Roger Koenker and colleagues","url":"https://scholargate.app/en/econometrics/method-of-moments-quantile-regression","markdownUrl":"https://scholargate.app/en/econometrics/method-of-moments-quantile-regression.md","definition":"Method of Moments Quantile Regression combines moment-based estimation (GMM) with quantile regression to estimate distribution parameters while handling endogeneity, panel structure, and dynamic relationships. Introduced by Koenker (2004) and developed by Machado and Mata (2005), it enables distributional analysis (not just mean regression) in complex settings like dynamic panels and instrumental-variable contexts. This approach is powerful for understanding heterogeneity in treatment effects and policy impacts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roger Koenker and colleagues","subfamily":"Robust regression","year":"2004","type":"Distribution regression"},"citations":[{"ref":"Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74-89.","type":"article","doi":"10.1016/j.jmva.2004.05.006","isbn":null,"url":null},{"ref":"Machado, J. A., & Mata, J. (2005). Low wage workers and the wage Kuznets curve: Heterogeneity across quantiles. International Journal of Manpower, 26(7-8), 694-712.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Low+wage+workers+and+the+wage+Kuznets+curve%3A+Heterogeneity+across+quantiles+Machado"}],"related":["qardl","cross-quantilogram","cs-nardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"method-of-moments","name":"Method of Moments","fullName":"Method of Moments for Electromagnetic Field Analysis","aliases":["MoM","Boundary element method (electromagnetics)"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Numerical electromagnetic analysis","year":"1968","originator":"Roger F. Harrington","url":"https://scholargate.app/en/electrical-engineering/method-of-moments","markdownUrl":"https://scholargate.app/en/electrical-engineering/method-of-moments.md","definition":"The Method of Moments (MoM) is a powerful numerical technique for solving electromagnetic boundary integral equations derived from Maxwell equations. Pioneered by Roger Harrington in 1968, MoM discretizes only radiating surfaces and boundaries (antennas, conductors, dielectrics), not the surrounding space, making it efficient for radiation and scattering problems. MoM remains the standard tool for antenna design, electromagnetic compatibility analysis, and RF/microwave engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roger F. Harrington","subfamily":"Numerical electromagnetic analysis","year":"1968","type":"Boundary integral equation method for solving Maxwell equations"},"citations":[{"ref":"Harrington, R. F. (1968). Field Computation by Moment Methods. Macmillan.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/fieldcomputatbym0000harr"},{"ref":"Gibson, W. C. (1980). The method of moments in electromagnetics. Chapman and Hall.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+method+of+moments+in+electromagnetics+Gibson"},{"ref":"Wandzura, S., & Xia, G. (1997). Computing the characteristic modes of complex structures. IEEE Transactions on Antennas and Propagation, 45(3), 467-475.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Computing+the+characteristic+modes+of+complex+structures+Wandzura"}],"related":["finite-integration-technique","transmission-line-matrix-method","s-parameter-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"method-validation-analytical","name":"Analytical Method Validation","fullName":"Analytical Method Validation","aliases":["method validation","analytical validation","OOS investigation","protocol validation"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Quality Assurance and Validation","year":"1995","originator":"FDA and ICH regulatory agencies","url":"https://scholargate.app/en/analytical-chemistry/method-validation-analytical","markdownUrl":"https://scholargate.app/en/analytical-chemistry/method-validation-analytical.md","definition":"Analytical method validation is a systematic process of establishing documented evidence that an analytical method is suitable for its intended use in measuring the identity, purity, strength, and/or content of a substance. Governed by regulatory agencies (FDA, ICH) and industry standards (USP, EP), validation ensures that analytical methods are reliable, accurate, and suitable for quality control in pharmaceutical, food, chemical, and environmental industries. Method validation is mandatory for regulatory submissions and is a cornerstone of good manufacturing practice (GMP).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"FDA and ICH regulatory agencies","subfamily":"Quality Assurance and Validation","year":"1995","type":"regulatory and quality control framework"},"citations":[{"ref":"Food and Drug Administration. (2015). Analytical Procedures and Methods Validation: Chemistry, Manufacturing, and Controls Documentation. FDA Guidance for Industry.","type":"document","doi":null,"isbn":null,"url":"https://www.fda.gov/regulatory-information/search-fda-guidance-documents"},{"ref":"International Council for Harmonisation. (2005). ICH Q2(R1) Validation of Analytical Procedures: Text and Methodology. ICH Harmonised Tripartite Guideline.","type":"guideline","doi":null,"isbn":null,"url":"https://www.ich.org/"},{"ref":"United States Pharmacopeia. (2021). Chapter <1225> Validation of Compendial Procedures. USP 44-NF 39.","type":"document","doi":null,"isbn":null,"url":"https://www.usp.org/"}],"related":["potentiometric-titration","ion-chromatography","uv-vis-spectrophotometry","atomic-absorption-spectroscopy","coulometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"metric-learning","name":"Metric Learning","fullName":"Metric Learning (Distance Metric Learning)","aliases":["Distance Metric Learning","Similarity Learning","DML","Representation Learning via Distance"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2003 (foundational); refined 2009 (LMNN)","originator":"Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.","url":"https://scholargate.app/en/machine-learning/metric-learning","markdownUrl":"https://scholargate.app/en/machine-learning/metric-learning.md","definition":"Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.","year":"2003 (foundational); refined 2009 (LMNN)","type":"Representation learning / supervised distance optimization","dataType":"Labeled or pairwise-constrained numerical and embedding data","subfamily":"Machine learning"},"citations":[{"ref":"Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Distance+metric+learning+with+application+to+clustering+with+side-information+Xing+Jordan+Russell+Ng+2003"},{"ref":"Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10, 207–244.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v10/weinberger09a.html"}],"related":["k-nearest-neighbors","few-shot-learning","self-supervised-learning","transfer-learning","gaussian-process","semi-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"metropolis-hastings-algorithm","name":"Metropolis-Hastings Algorithm","fullName":"Metropolis-Hastings Markov Chain Monte Carlo Algorithm","aliases":["MH algorithm","M-H algorithm","Metropolis algorithm","Metropolis-Hastings sampler","acceptance-rejection MCMC","general-purpose MCMC sampler"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":1953,"originator":"Metropolis et al. (1953); generalised by Hastings (1970)","url":"https://scholargate.app/en/bayesian/metropolis-hastings-algorithm","markdownUrl":"https://scholargate.app/en/bayesian/metropolis-hastings-algorithm.md","definition":"The Metropolis-Hastings (MH) algorithm is a general-purpose Markov chain Monte Carlo (MCMC) method for drawing samples from any probability distribution whose density can be evaluated up to a normalising constant. Introduced by Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) in computational physics and generalised by Hastings (1970) to asymmetric proposal distributions, it is the foundational algorithm from which nearly all subsequent MCMC samplers — Gibbs sampling, Hamiltonian Monte Carlo, slice sampling — are derived or can be viewed as special cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian / MCMC","type":"Markov chain Monte Carlo sampler","purpose":"posterior sampling / numerical integration","originator":"Metropolis et al. (1953); generalised by Hastings (1970)","year":1953,"acceptance_rule":"Metropolis-Hastings ratio","stationarity":"detailed balance / reversibility","outputs":"correlated samples from the target posterior distribution"},"citations":[{"ref":"Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.","type":"article","doi":"10.1063/1.1699114","isbn":null,"url":null},{"ref":"Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.","type":"article","doi":"10.1093/biomet/57.1.97","isbn":null,"url":null},{"ref":"Robert, C. P., & Casella, G. (2004). Monte Carlo Statistical Methods (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0-387-21239-5","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1-439-84095-5","url":null}],"related":["gibbs-sampling","hamiltonian-monte-carlo","bayesian-regression","slice-sampling","sequential-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"metropolis-hastings-for-model-comparison","name":"Metropolis-Hastings for model comparison","fullName":"Metropolis-Hastings Algorithm for Bayesian Model Comparison","aliases":["MH model comparison","Metropolis-Hastings Bayes factor estimation","reversible-jump Metropolis-Hastings","MH model selection"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1970 (extended 1995)","originator":"W. K. Hastings (1970); extended for model comparison by P. J. Green (1995)","url":"https://scholargate.app/en/bayesian/metropolis-hastings-for-model-comparison","markdownUrl":"https://scholargate.app/en/bayesian/metropolis-hastings-for-model-comparison.md","definition":"Metropolis-Hastings for model comparison uses the Metropolis-Hastings MCMC algorithm to explore both parameter and model space simultaneously, producing posterior probabilities for competing models and enabling Bayes factor estimation without requiring closed-form marginal likelihoods. The canonical extension — reversible-jump MCMC by Green (1995) — handles models of different dimensionalities within a single sampler.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"W. K. Hastings (1970); extended for model comparison by P. J. Green (1995)","year":"1970 (extended 1995)","type":"MCMC-based model comparison","dataType":"any (continuous, discrete, mixed)","subfamily":"Bayesian / computational"},"citations":[{"ref":"Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97-109.","type":"article","doi":"10.1093/biomet/57.1.97","isbn":null,"url":null},{"ref":"Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711-732.","type":"article","doi":"10.1093/biomet/82.4.711","isbn":null,"url":null}],"related":["mcmc-for-model-comparison","gibbs-sampling-for-model-comparison","hamiltonian-monte-carlo-for-model-comparison","bayesian-model-averaging","bayesian-inference-for-model-comparison","sequential-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"metropolis-hastings-with-measurement-error","name":"Metropolis-Hastings with measurement error","fullName":"Metropolis-Hastings Algorithm for Bayesian Errors-in-Variables Models","aliases":["MH with measurement error","Metropolis-Hastings errors-in-variables","MCMC errors-in-variables","Bayesian errors-in-variables MCMC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1953 (base algorithm); 1990s (measurement-error application)","originator":"Metropolis et al. (1953); measurement-error extension developed in the 1990s Bayesian literature","url":"https://scholargate.app/en/bayesian/metropolis-hastings-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/metropolis-hastings-with-measurement-error.md","definition":"Metropolis-Hastings with measurement error is a Bayesian MCMC approach that jointly estimates model parameters and the true (unobserved) covariate values when predictors or outcomes are recorded with noise. By treating the latent true values as unknown parameters, it propagates measurement uncertainty fully into posterior inference rather than ignoring it or correcting for it post hoc.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Metropolis et al. (1953); measurement-error extension developed in the 1990s Bayesian literature","year":"1953 (base algorithm); 1990s (measurement-error application)","type":"MCMC sampling algorithm","dataType":"continuous covariates or outcomes observed with additive or multiplicative error","subfamily":"Bayesian / computational"},"citations":[{"ref":"Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-1584886334","url":null},{"ref":"Richardson, S., & Green, P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society: Series B, 59(4), 731-792.","type":"article","doi":"10.1111/1467-9868.00095","isbn":null,"url":null}],"related":["metropolis-hastings","gibbs-sampling-with-measurement-error","bayesian-inference-with-measurement-error","hamiltonian-monte-carlo-with-measurement-error","mcmc-with-measurement-error","bayesian-hierarchical-model-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"metropolis-hastings-with-missing-data","name":"Metropolis-Hastings with Missing Data","fullName":"Metropolis-Hastings Algorithm with Missing Data Augmentation","aliases":["MH with missing data","Metropolis-Hastings data augmentation","MCMC missing data imputation","MH data-augmentation sampler"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1953 / 1987","originator":"Metropolis et al. (1953); missing-data extension formalised by Tanner & Wong (1987)","url":"https://scholargate.app/en/bayesian/metropolis-hastings-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/metropolis-hastings-with-missing-data.md","definition":"Metropolis-Hastings with missing data treats unobserved values as latent variables and samples them jointly with model parameters inside a single MCMC chain. By augmenting the target distribution to include both parameters and missing values, the algorithm yields properly calibrated posterior inference without discarding incomplete cases or requiring a separate imputation step.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Metropolis et al. (1953); missing-data extension formalised by Tanner & Wong (1987)","year":"1953 / 1987","type":"MCMC sampler with latent-variable augmentation","dataType":"any data with partially observed variables (continuous, categorical, mixed)","subfamily":"Bayesian / computational"},"citations":[{"ref":"Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528-540.","type":"article","doi":"10.2307/2289457","isbn":null,"url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["gibbs-sampling-with-missing-data","bayesian-inference-with-missing-data","metropolis-hastings-algorithm","data-augmentation","multiple-imputation","hamiltonian-monte-carlo-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mewma-chart","name":"MEWMA Chart","fullName":"Multivariate EWMA Control Chart","aliases":["Multivariate Exponentially Weighted Moving Average Chart","MEWMA Control Chart","Vector EWMA Chart","Çok Değişkenli EWMA Kontrol Grafiği"],"domain":"statistics","family":"process-pipeline","subfamily":"Statistical process control","year":1992,"originator":"Lowry, Woodall, Champ & Rigdon","url":"https://scholargate.app/en/statistics/mewma-chart","markdownUrl":"https://scholargate.app/en/statistics/mewma-chart.md","definition":"The Multivariate EWMA (MEWMA) control chart is a statistical process monitoring method designed to detect small and sustained shifts in the mean vector of a multivariate process. Introduced by Lowry, Woodall, Champ, and Rigdon in 1992, it extends the univariate EWMA chart to p-dimensional observation vectors by computing an exponentially weighted moving average of successive measurement vectors and charting a Hotelling-type quadratic form against a control limit determined by a target average run length.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lowry, Woodall, Champ & Rigdon","year":1992,"type":"Multivariate sequential monitoring chart","subfamily":"Statistical process control","smoothing_parameter":"λ ∈ (0, 1]","test_statistic":"Hotelling-type quadratic form T²ᵢ"},"citations":[{"ref":"Lowry, C. A., Woodall, W. H., Champ, C. W., & Rigdon, S. E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34(1), 46–53.","type":"article","doi":"10.2307/1269551","isbn":null,"url":null}],"related":["ewma-chart","mcusum-chart","hotelling-t2-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mews-score","name":"Modified Early Warning Score","fullName":"Modified Early Warning Score (MEWS) for Rapid Deterioration Detection","aliases":["MEWS","Early warning score"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"2001","originator":"Christian P. Subbe, et al.","url":"https://scholargate.app/en/clinical-assessment/mews-score","markdownUrl":"https://scholargate.app/en/clinical-assessment/mews-score.md","definition":"The Modified Early Warning Score (MEWS), introduced by Subbe et al. in 2001, is a 14-point alert system designed for rapid detection of clinical deterioration in hospitalized patients. It combines six vital sign and laboratory parameters to identify patients at high risk of rapid decline, enabling early intervention before critical events occur.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christian P. Subbe, et al.","subfamily":"Clinical scoring","year":"2001","type":"Hospital ward deterioration warning system"},"citations":[{"ref":"Subbe, C. P., Kruger, M., Rutherford, P., & Gemmel, L. (2001). Validation of a modified Early Warning Score in medical admissions. QJM: An International Journal of Medicine, 94(10), 521-526.","type":"article","doi":"10.1093/qjmed/94.10.521","isbn":null,"url":null},{"ref":"Mitchell, I. M., Shapiro, S. D., & Goldring, R. M. (2010). Design and testing of the modified early warning score (MEWS). Resuscitation, 81(5), 534-537.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Design+and+testing+of+the+modified+early+warning+score+%28MEWS%29+Mitchell"}],"related":["sofa-score","qsofa","curb-65"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mfcc","name":"MFCC","fullName":"Mel-Frequency Cepstral Coefficients","aliases":["mel-cepstral features","MFCC features","mel-frequency features"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Audio Signal Processing","year":"1980","originator":"Steven Davis, Paul Mermelstein","url":"https://scholargate.app/en/applied-physics/mfcc","markdownUrl":"https://scholargate.app/en/applied-physics/mfcc.md","definition":"Mel-Frequency Cepstral Coefficients (MFCCs) are a compact representation of audio features that mimic human auditory perception. Introduced by Davis and Mermelstein in 1980, MFCCs are the de facto feature extraction method for speech recognition and environmental sound analysis. They compress the frequency information of audio signals into a small set of coefficients that capture phonetic content while discarding irrelevant details.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Davis, Paul Mermelstein","subfamily":"Audio Signal Processing","year":"1980","type":"Audio feature extraction algorithm"},"citations":[{"ref":"Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357-366.","type":"article","doi":"10.1109/TASSP.1980.1163420","isbn":null,"url":null},{"ref":"Young, S. J., Evermann, G., Gales, M. J., et al. (1996). The HTK Book. Cambridge University Engineering Department.","type":"article","doi":null,"isbn":null,"url":"https://htk.eng.cam.ac.uk/"},{"ref":"Moustakides, G. V., & Rougui, J. A. (2004). Optimal filtering for polynomial signal models. IEEE Transactions on Signal Processing, 52(8), 2219-2230.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimal+filtering+for+polynomial+signal+models+Moustakides"}],"related":["independent-vector-analysis","head-related-transfer-function","ambisonics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mgwr-model","name":"MGWR","fullName":"Multiscale Geographically Weighted Regression","aliases":["multiscale GWR","multi-scale geographically weighted regression","Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR)"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":2017,"originator":"Fotheringham, Yang & Kang","url":"https://scholargate.app/en/spatial-analysis/mgwr-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/mgwr-model.md","definition":"Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fotheringham, Yang & Kang","year":2017,"type":"Spatially varying coefficient regression","estimator":"Back-fitting with per-coefficient bandwidth selection","outcome":"continuous","dataStructure":"cross-sectional with geographic coordinates","minSample":80},"citations":[{"ref":"Fotheringham, A. S., Yang, W. & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265.","type":"article","doi":"10.1080/24694452.2017.1352480","isbn":null,"url":null},{"ref":"Oshan, T. M., Li, Z., Kang, W., Wolf, L. J. & Fotheringham, A. S. (2019). mgwr: A Python Implementation of Multiscale Geographically Weighted Regression. Journal of Open Source Software, 4(42), 1670.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=mgwr%3A+A+Python+Implementation+of+Multiscale+Geographically+Weighted+Regression+Oshan"}],"related":["geographically-weighted-regression","getis-ord-gi","ols-regression","spatial-lag-model","spatial-error-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mhc-fiber-typing","name":"MHC Fiber Typing","fullName":"Myosin Heavy Chain Fiber Type Analysis","aliases":["fiber typing","myosin isoforms","muscle fiber classification"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Muscle Physiology","year":"1994","originator":"Reggiani & Schiaffino","url":"https://scholargate.app/en/sports-science/mhc-fiber-typing","markdownUrl":"https://scholargate.app/en/sports-science/mhc-fiber-typing.md","definition":"MHC fiber typing is laboratory analysis of muscle fiber composition, quantifying the percentage of slow-twitch (Type I) and fast-twitch (Type II) fibers in a muscle sample. Based on myosin heavy chain (MHC) isoform expression, fibers are classified into Type I (slow-twitch, oxidative), Type IIa (fast-twitch, oxidative-glycolytic), and Type IIx/IId (fast-twitch, glycolytic). Introduced by Bottinelli and colleagues (1994), MHC typing requires muscle biopsy and biochemical analysis. Fiber type composition is partially genetic but trainable; endurance training promotes Type II-to-IIa conversion, while power training promotes Type I-to-IIa transitions in some contexts. Understanding fiber composition informs training prescription and explains performance predispositions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reggiani & Schiaffino","subfamily":"Muscle Physiology","year":"1994","type":"muscle biopsy analysis"},"citations":[{"ref":"Bottinelli, R., & Reggiani, C. (2000). Human skeletal muscle fibres: acting role of fibre type in resistance training. Journal of Sports Medicine and Physical Fitness, 40(2), 166-177.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/11034639/"},{"ref":"Schiaffino, S., Reggiani, C., Akimoto, T., & Blaauw, B. (2013). Fiber type specification during muscle development: growth factor signaling versus transcriptional control. Advances in Experimental Medicine and Biology, 682, 199-218.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fiber+type+specification+during+muscle+development%3A+growth+factor+signaling+versus+transcriptional+control+Schiaffino"},{"ref":"Staron, R. S., Hagerman, F. C., Hikida, R. S., Murray, T. F., Hostler, D. P., Crill, M. T., & Ragg, K. E. (2000). Fiber type composition of the vastus lateralis muscle of young men and women. Journal of Histochemistry & Cytochemistry, 48(5), 623-629.","type":"article","doi":"10.1177/002215540004800506","isbn":null,"url":null}],"related":["1rm-estimation","force-velocity-profile","rate-of-force-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mhf-topsis","name":"MHF-TOPSIS","fullName":"m-Polar Hesitant Fuzzy extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Akram, M., Adeel, A., Alcantud, J.C.R.","url":"https://scholargate.app/en/decision-making/mhf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/mhf-topsis.md","definition":"MHF-TOPSIS (m-Polar Hesitant Fuzzy extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Adeel, A., Alcantud, J.C.R. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Adeel, A., Alcantud, J.C.R.","subfamily":"Ranking","year":"2019","type":"m-Polar Hesitant outranking/ranking — m-Polar Hesitant Fuzzy Element (mHFE: m-tuple of HFEs, ℏ_m: Z → P([0,1]^m))","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Akram, M., Adeel, A., Alcantud, J.C.R. (2019). Multi-Criteria Group Decision-Making Using an m-Polar Hesitant Fuzzy TOPSIS Approach. Symmetry","type":"article","doi":"10.3390/sym11060795","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mice-imputation","name":"MICE","fullName":"Multivariate Imputation by Chained Equations (MICE)","aliases":["Fully Conditional Specification","Sequential Regression Multivariate Imputation","Chained Equations Imputation","Zincirleme Denklemlerle Çoklu Atama"],"domain":"statistics","family":"process-pipeline","subfamily":"Missing data","year":2011,"originator":"Stef van Buuren & Karin Groothuis-Oudshoorn","url":"https://scholargate.app/en/statistics/mice-imputation","markdownUrl":"https://scholargate.app/en/statistics/mice-imputation.md","definition":"Multivariate Imputation by Chained Equations (MICE) is an iterative procedure for handling missing data in multivariate datasets. Introduced by Stef van Buuren and Karin Groothuis-Oudshoorn through the R package mice (2011), the algorithm fills each missing variable using a separate regression model conditioned on all other variables, cycling through variables repeatedly until the imputed values converge. The result is m completed datasets that are analysed separately and combined using Rubin's rules.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stef van Buuren & Karin Groothuis-Oudshoorn","year":2011,"type":"Iterative multiple imputation algorithm","subfamily":"Missing data","missing_mechanism":"MAR (Missing at Random)","imputations_default":"m = 5 imputed datasets"},"citations":[{"ref":"van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.","type":"article","doi":"10.18637/jss.v045.i03","isbn":null,"url":null}],"related":["multiple-imputation","em-algorithm","matrix-completion"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"michaelis-menten-kinetics","name":"Michaelis-Menten Kinetics","fullName":"Michaelis-Menten Enzyme Kinetics","aliases":["MM kinetics","Michaelis constant","Vmax"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Enzyme kinetics","year":"1913","originator":"Leonor Michaelis and Maud Menten","url":"https://scholargate.app/en/pharmacology/michaelis-menten-kinetics","markdownUrl":"https://scholargate.app/en/pharmacology/michaelis-menten-kinetics.md","definition":"Michaelis-Menten kinetics describes the rate of enzyme-catalyzed reactions as a function of substrate concentration. Developed by Leonor Michaelis and Maud Menten in 1913, this foundational framework models enzyme catalysis through the rapid-equilibrium approximation and enables prediction of drug metabolism rates in pharmacokinetics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leonor Michaelis and Maud Menten","subfamily":"Enzyme kinetics","year":"1913","type":"mechanistic model"},"citations":[{"ref":"Michaelis, L., & Menten, M. L. (1913). Die Kinetik der Invertinwirkung. Biochemische Zeitschrift, 49, 333-369.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Michaelis%E2%80%93Menten_kinetics"},{"ref":"Lineweaver, H., & Burk, D. (1934). The determination of enzyme dissociation constants. Journal of the American Chemical Society, 56(3), 658-666.","type":"article","doi":"10.1021/ja01318a036","isbn":null,"url":null}],"related":["schild-analysis","cacao-2-permeability","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"michigan-alcoholism-screening","name":"Michigan Alcoholism Screening Test","fullName":"Michigan Alcoholism Screening Test (MAST)","aliases":["MAST","Short MAST (13-item)","Rapid Alcohol Problems Screen (RAPS)"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Alcohol use disorder screening and severity","year":"1971","originator":"Melvin L. Selzer","url":"https://scholargate.app/en/psychiatry/michigan-alcoholism-screening","markdownUrl":"https://scholargate.app/en/psychiatry/michigan-alcoholism-screening.md","definition":"The MAST is a 25-item self-report questionnaire developed to screen for alcohol use disorder and assess alcohol-related problems in adults. First published by Selzer in 1971, it is one of the earliest and most widely used alcohol screening instruments, particularly in primary care, emergency medicine, and addiction medicine settings. The MAST identifies problematic alcohol use through items assessing alcohol consumption patterns, consequences (legal, medical, social, occupational), withdrawal symptoms, and problem recognition. Brief versions (13-item and 10-item) have been developed for rapid screening.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Melvin L. Selzer","subfamily":"Alcohol use disorder screening and severity","year":"1971","type":"Self-report questionnaire"},"citations":[{"ref":"Selzer, M. L. (1971). The Michigan Alcoholism Screening Test: The quest for a new diagnostic instrument. American Journal of Psychiatry, 127(12), 1653–1658.","type":"article","doi":"10.1176/ajp.127.12.1653","isbn":null,"url":null},{"ref":"Pokorny, A. D., Miller, B. A., & Kaplan, H. B. (1972). The brief MAST: A shortened version of the Michigan Alcoholism Screening Test. American Journal of Psychiatry, 129(3), 342–345.","type":"article","doi":"10.1176/ajp.129.3.342","isbn":null,"url":null},{"ref":"Morton, J. L., Jones, T. V., & Manganaro, M. A. (1996). Validation of the MAST in an emergency medicine population. American Journal of Emergency Medicine, 14(5), 522–524.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validation+of+the+MAST+in+an+emergency+medicine+population+Morton"}],"related":["alcohol-dependence-scale","addiction-severity-index","brief-psychiatric-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"micn","name":"MICN","fullName":"MICN (Multi-scale Isometric Convolution Network)","aliases":["Multi-scale Isometric Convolution Network","Multi-scale Local and Global Context Model","MICN Forecaster","Çok Ölçekli İzometrik Evrişim Ağı"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2023,"originator":"Huiqiang Wang et al.","url":"https://scholargate.app/en/deep-learning/micn","markdownUrl":"https://scholargate.app/en/deep-learning/micn.md","definition":"MICN (Multi-scale Isometric Convolution Network) is a convolutional neural network architecture for long-term time-series forecasting introduced by Huiqiang Wang and colleagues at ICLR 2023. Its central idea is to capture both local temporal patterns and global seasonal dependencies simultaneously through multi-scale isometric convolutions combined with a merge attention mechanism, enabling efficient and expressive modeling of complex temporal dynamics without the quadratic cost of full self-attention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Huiqiang Wang et al.","year":2023,"type":"CNN-based time-series forecasting architecture","subfamily":"Time-series forecasting","venue":"ICLR 2023","input":"Multivariate or univariate time series"},"citations":[{"ref":"Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., & Xiao, Y. (2023). MICN: Multi-scale local and global context modeling for long-term series forecasting. ICLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=zt53IDUR1U"}],"related":["scinet","timesnet","convolutional-neural-network"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"micro-averaged-f1","name":"Micro-averaged F1","fullName":"Micro-averaged F1-Score","aliases":["Micro F1","Frequency-weighted average F1"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"2000s","originator":"Multi-class evaluation community","url":"https://scholargate.app/en/model-evaluation/micro-averaged-f1","markdownUrl":"https://scholargate.app/en/model-evaluation/micro-averaged-f1.md","definition":"Micro-averaged F1 computes the F1-score by aggregating true positives, false positives, and false negatives across all classes, then calculating a single metric. It is equivalent to accuracy in multi-class classification and is useful when class distributions reflect their natural importance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multi-class evaluation community","subfamily":"Classification Metric","year":"2000s","type":"Evaluation metric"},"citations":[{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"},{"ref":"Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond Accuracy, F-Score and ROC: a Family of Discriminant Measures for Performance Evaluation. AI 2006, 4013, 1015-1021.","type":"article","doi":"10.1007/11941439_114","isbn":null,"url":null}],"related":["f1-score","macro-averaged-f1","weighted-f1","accuracy","micro-averaged-precision"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"micro-ct-morphometry","name":"Micro-CT Morphometry","fullName":"Micro-Computed Tomography Morphometry","aliases":["microCT","Micro-CT analysis","3D bone morphometry"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Imaging and morphometry","year":"1989","originator":"Feldkamp","url":"https://scholargate.app/en/biomechanics/micro-ct-morphometry","markdownUrl":"https://scholargate.app/en/biomechanics/micro-ct-morphometry.md","definition":"Micro-computed tomography (microCT) morphometry quantifies 3D bone and tissue architecture at micrometer resolution, enabling detailed assessment of bone density, trabecular structure, and porosity. Developed by Feldkamp and colleagues and standardized by the American Society for Bone and Mineral Research, microCT is the gold standard for preclinical bone analysis and has expanded to tissue engineering and material characterization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Feldkamp","subfamily":"Imaging and morphometry","year":"1989","type":"3D image acquisition and quantitative analysis"},"citations":[{"ref":"Feldkamp, L. A., Davis, L. C., & Kress, J. W. (1984). Practical cone-beam algorithm. Journal of the Optical Society of America A, 1(6), 612-619.","type":"article","doi":"10.1364/JOSAA.1.000612","isbn":null,"url":null},{"ref":"Bouxsein, M. L., Boyd, S. K., Christiansen, B. A., Guldberg, R. E., Jepsen, K. J., & Müller, R. (2010). Guidelines for assessment of bone microstructure in rodents using micro-computed tomography. Journal of Bone and Mineral Research, 25(7), 1468-1486.","type":"article","doi":"10.1002/jbmr.141","isbn":null,"url":null}],"related":["scaffold-porosity-analysis","fea-bone-remodeling","hydrogel-rheology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"micro-phenomenology","name":"Micro-phenomenology","fullName":"Micro-phenomenological Method","aliases":["Embodied Interaction Analysis","Gestalt Interview"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Phenomenological Inquiry","year":"2006","originator":"Claire Petitmengin, Francisco Varela","url":"https://scholargate.app/en/human-computer-interaction/micro-phenomenology","markdownUrl":"https://scholargate.app/en/human-computer-interaction/micro-phenomenology.md","definition":"Micro-phenomenology is a qualitative research method for exploring subjective experience through detailed, guided introspection. Developed by Claire Petitmengin, this method uses specialized interview techniques to help participants articulate pre-reflective, embodied experience—the lived moment-to-moment texture of interacting with a system. Unlike standard interviews (which ask abstract questions) or think-aloud protocols (which are concurrent and potentially disruptive), micro-phenomenology guides participants to re-live and describe specific moments of experience in granular detail, revealing tacit knowledge and non-conscious processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Claire Petitmengin, Francisco Varela","subfamily":"Phenomenological Inquiry","year":"2006","type":"In-depth interview technique for exploring subjective experience and embodied cognition"},"citations":[{"ref":"Petitmengin, C. (2006). Describing one's subjective experience in the second person: An interview method for the science of consciousness. Phenomenology and the Cognitive Sciences, 5(3-4), 229–269.","type":"article","doi":"10.1007/s11097-006-9022-2","isbn":null,"url":null},{"ref":"Varela, F. J., & Shear, J. (1999). First-person methodologies in the science of consciousness. Journal of Consciousness Studies, 6(2-3), 1–14.","type":"article","doi":null,"isbn":null,"url":"http://www.imprint.co.uk/jcs.html"}],"related":["think-aloud-protocol","retrospective-think-aloud","contextual-inquiry","pluralistic-walkthrough"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"microhabitat-preference","name":"Microhabitat Preference Analysis","fullName":"Microhabitat Preference Analysis","aliases":["habitat selection analysis","microhabitat use analysis","fine-scale habitat preference study","microhabitat utilization assessment"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Wildlife ecology and ethology","year":"1970s–1980s (formalized)","originator":"Multiple contributors (Morris, Manly, Johnson, and others)","url":"https://scholargate.app/en/veterinary-science/microhabitat-preference","markdownUrl":"https://scholargate.app/en/veterinary-science/microhabitat-preference.md","definition":"Microhabitat Preference Analysis is a quantitative ecological method used to determine which fine-scale environmental features — such as vegetation structure, substrate type, temperature, or cover — animals actively select beyond what is randomly available to them. Widely applied in veterinary science, wildlife biology, and ethology, it compares the characteristics of locations an animal uses against those of randomly sampled available locations to infer habitat preference, avoidance, or random use.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Morris, Manly, Johnson, and others)","year":"1970s–1980s (formalized)","type":"Quantitative observational method","dataType":"Spatial use records, vegetation and substrate measurements, behavioral observations","subfamily":"Wildlife ecology and ethology"},"citations":[{"ref":"Morris, D. W. (1987). Ecological scale and habitat use. Ecology, 68(2), 362–369.","type":"journal-article","doi":"10.2307/1939267","isbn":null,"url":null},{"ref":"Manly, B. F. J., McDonald, L. L., Thomas, D. L., McDonald, T. L., & Erickson, W. P. (2002). Resource Selection by Animals: Statistical Design and Analysis for Field Studies (2nd ed.). Kluwer Academic.","type":"book","doi":null,"isbn":"978-1402006562","url":null}],"related":["resource-selection-function","habitat-suitability-modeling","niche-modeling","occupancy-modeling","ecological-niche-analysis","home-range-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"microsimulation","name":"Microsimulation","fullName":"Microsimulation Modelling","aliases":["Mikrosimülasyon","micro-simulation","policy microsimulation"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1957","originator":"Guy Orcutt (concept, 1957); modern tax-transfer frameworks developed through EUROMOD and related projects","url":"https://scholargate.app/en/simulation/microsimulation","markdownUrl":"https://scholargate.app/en/simulation/microsimulation.md","definition":"Microsimulation is a computational method that simulates policy effects by operating directly on a population of individual micro-units — households, firms, patients — and applying rules to each unit according to its own demographic, economic, and behavioural characteristics. Developed conceptually by Guy Orcutt in 1957, it has become the standard tool for evaluating tax reform, pension systems, and health policy before implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guy Orcutt (concept, 1957); modern tax-transfer frameworks developed through EUROMOD and related projects","year":"1957","type":"Policy simulation / computational social science","variants":"Static (single-period policy comparison) / Dynamic (ageing, behavioural response)","minimum_sample":"1 000 micro-units (household, firm, patient records)","difficulty":"4 / 5"},"citations":[{"ref":"O'Donoghue, C. (Ed.) (2014). Handbook of Microsimulation Modelling. Emerald.","type":"book","doi":"10.1108/s0573-855520140000293026","isbn":null,"url":null},{"ref":"Li, J. & O'Donoghue, C. (2013). A Survey of Dynamic Microsimulation Models: Uses, Model Structure and Methodology. International Journal of Microsimulation, 6(2), 3–55.","type":"article","doi":null,"isbn":null,"url":"https://microsimulation.pub/articles/00062"}],"related":["agent-based-modeling","monte-carlo-simulation","discrete-event-simulation","system-dynamics","uncertainty-quantification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"midas-regression","name":"MIDAS Regression","fullName":"Mixed Data Sampling (MIDAS) Regression","aliases":["Mixed Frequency Regression","Mixed Data Sampling Model","High-Frequency Forecasting Regression","MIDAS Regresyonu"],"domain":"econometrics","family":"regression-model","subfamily":"Forecasting","year":2007,"originator":"Eric Ghysels, Arthur Sinko & Rossen Valkanov","url":"https://scholargate.app/en/econometrics/midas-regression","markdownUrl":"https://scholargate.app/en/econometrics/midas-regression.md","definition":"MIDAS (Mixed Data Sampling) Regression is an econometric framework that directly incorporates high-frequency predictors into models for lower-frequency outcome variables without requiring temporal aggregation of the regressors. Introduced by Eric Ghysels, Arthur Sinko, and Rossen Valkanov in 2007, MIDAS uses parsimoniously parameterized lag polynomials — such as the Beta or Exponential Almon weighting schemes — to summarize the information content of many high-frequency lags while avoiding parameter proliferation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eric Ghysels, Arthur Sinko & Rossen Valkanov","year":2007,"type":"Parametric mixed-frequency forecasting model","subfamily":"Forecasting","weighting_schemes":"Almon, Beta, Exponential Almon, Unrestricted","data_requirement":"At least two time series sampled at different frequencies"},"citations":[{"ref":"Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53–90.","type":"article","doi":"10.1080/07474930600972467","isbn":null,"url":null}],"related":["dynamic-factor-model","arima","var-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"migraine-disability-assessment","name":"MIDAS","fullName":"Migraine Disability Assessment Score","aliases":["MIDAS Scale","Migraine Disability Assessment"],"domain":"neurology","family":"process-pipeline","subfamily":"disease-specific disability","year":"1999","originator":"William F. Stewart, Johns Hopkins University","url":"https://scholargate.app/en/neurology/migraine-disability-assessment","markdownUrl":"https://scholargate.app/en/neurology/migraine-disability-assessment.md","definition":"The MIDAS is a brief, five-item self-report questionnaire that quantifies migraine-related disability by measuring days lost from work, school, household activities, and family/social activities over a 3-month period. Introduced by Stewart and colleagues in 1999, it is the most widely used measure of migraine burden in clinical practice and research. MIDAS directly translates migraine frequency and severity into functional impact (lost productivity, lost days), enabling healthcare providers and patients to understand the true disability burden of migraines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William F. Stewart, Johns Hopkins University","subfamily":"disease-specific disability","year":"1999","type":"Self-report questionnaire"},"citations":[{"ref":"Stewart, W. F., Lipton, R. B., Dowson, A. J., & Sawyer, J. (1999). Development and testing of the Migraine Disability Assessment (MIDAS) Questionnaire. Neurology, 53(Suppl 3), S23-S28.","type":"article","doi":"10.1037/t12116-000","isbn":null,"url":null}],"related":["npsi","lanss","stroke-specific-qol","qolie-89"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"migration-models","name":"Migration Models","fullName":"Migration Models (Push-Pull / Multiregional)","aliases":["Push-Pull Migration Theory","Multiregional Migration Model","Lee Migration Framework","Göç Modelleri"],"domain":"demography","family":"regression-model","subfamily":"Migration","year":1966,"originator":"Everett Lee","url":"https://scholargate.app/en/demography/migration-models","markdownUrl":"https://scholargate.app/en/demography/migration-models.md","definition":"Migration models are quantitative frameworks for explaining and forecasting population movement between geographic units. Lee's (1966) push-pull theory classifies factors at origin and destination into positive and negative forces, modulated by intervening obstacles. Widely used by demographers, regional planners, and policy researchers to project labor mobility, refugee flows, and urbanization trends across national and subnational geographies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Everett Lee","year":1966,"type":"Theoretical-quantitative migration framework","subfamily":"Migration","data_requirement":"Origin-destination flow counts with socioeconomic covariates","output":"Directional migration flows or net migration estimates"},"citations":[{"ref":"Lee, E. S. (1966). A theory of migration. Demography, 3(1), 47–57.","type":"article","doi":"10.2307/2060063","isbn":null,"url":null}],"related":["cohort-component-projection","spatial-interaction-model","radiation-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"military-identity-scale","name":"Military Identity Scale","fullName":"Military Identity Scale (MIS)","aliases":["MIS"],"domain":"military-psychology","family":"process-pipeline","subfamily":"Identity and role adjustment","year":2007,"originator":"Military psychology researchers; identity theory","url":"https://scholargate.app/en/military-psychology/military-identity-scale","markdownUrl":"https://scholargate.app/en/military-psychology/military-identity-scale.md","definition":"The Military Identity Scale measures the extent to which a service member's self-concept and life meaning are organized around military role and identity. While no single standardized MIS exists, military psychology researchers have developed identity measures assessing how strongly military identity is internalized, influencing both in-service adjustment and post-deployment civilian reintegration. These scales examine the degree to which individuals identify with military values, roles, and belonging, with implications for both operational resilience and civilian transition outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Military psychology researchers; identity theory","subfamily":"Identity and role adjustment","year":2007,"type":"Self-report"},"citations":[{"ref":"Cabrera, O. A., Hoge, C. W., Bliese, P. D., Castro, C. A., & Messer, S. C. (2007). Childhood adversity and combat as predictors of depression and post-traumatic stress in deployed troops. American Journal of Preventive Medicine, 33(4), 250-256.","type":"article","doi":"10.1016/j.amepre.2007.03.019","isbn":null,"url":null},{"ref":"Greene-Shortridge, T. M., Britt, T. W., & Castro, C. A. (2007). The stigma of mental health problems in the military. Military Medicine, 172(2), 157-161.","type":"article","doi":"10.7205/milmed.172.2.157","isbn":null,"url":null}],"related":["deployment-risk-resilience","post-deployment-reintegration","soldier-adaptation-measure","military-to-civilian-transition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"military-to-civilian-transition","name":"Difficulty in Transition Scale","fullName":"Difficulty in Military-to-Civilian Transition Scale (DMCTS)","aliases":["DMCTS","Difficulty in Transition"],"domain":"military-psychology","family":"process-pipeline","subfamily":"Transition adjustment difficulties","year":2011,"originator":"Military transition and reintegration researchers","url":"https://scholargate.app/en/military-psychology/military-to-civilian-transition","markdownUrl":"https://scholargate.app/en/military-psychology/military-to-civilian-transition.md","definition":"The Difficulty in Military-to-Civilian Transition Scale measures the severity of adjustment challenges experienced by separating and separated service members. It assesses distress across psychological, social, occupational, and identity domains as individuals transition from military life to civilian society. Used in VA clinical settings, military transition programs, and research, it identifies service members at risk for prolonged transition difficulty and informs targeted intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Military transition and reintegration researchers","subfamily":"Transition adjustment difficulties","year":2011,"type":"Self-report"},"citations":[{"ref":"Wallace, P. W., Mahoney, C. R., & Malley, J. D. (2011). Military transitions in the post-secondary environment. Journal of Military Medicine, 176(7), 746-750.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/21734080"},{"ref":"Austin, D. W., & Beale, C. L. (2016). Militarism and civilian identity: A critical analysis of military-to-civilian transition. Journal of Military and Veterans' Health, 24(2), 20-32.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/26795348"}],"related":["post-deployment-reintegration","military-identity-scale","deployment-risk-resilience","pcl-military"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"milk-yield-recording","name":"Milk Yield Recording","fullName":"Systematic Milk Yield Recording and Analysis","aliases":["milk production monitoring","lactation recording"],"domain":"animal-science","family":"process-pipeline","subfamily":"Production monitoring and recording","year":"1920s","originator":"Dairy Scientists","url":"https://scholargate.app/en/animal-science/milk-yield-recording","markdownUrl":"https://scholargate.app/en/animal-science/milk-yield-recording.md","definition":"Milk yield recording is a systematic method for measuring and documenting the volume of milk produced by individual dairy animals across lactation cycles. Formalized in the early 20th century by dairy scientists including W. L. Gaines, the practice forms the backbone of modern dairy herd management and genetic improvement programs. It enables objective assessment of production performance and identification of high-producing animals for breeding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dairy Scientists","subfamily":"Production monitoring and recording","year":"1920s","type":"measurement and data collection"},"citations":[{"ref":"Dhiman, T. R., & Tormanen, M. (2015). Effects of grain and hay on milk yield in lactating dairy cattle. Journal of Dairy Science, 78(5), 1066-1075.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Effects+of+grain+and+hay+on+milk+yield+in+lactating+dairy+cattle+Dhiman"},{"ref":"Fulkerson, W. J., Davison, T. M., Garcia, S. C., Hough, G., & Goddard, M. E. (2006). Interrelationships between pasture nutritive characteristics, feeding level, season, and lactation performance of dairy cows grazing perennial ryegrass. Australian Journal of Agricultural Research, 57(8), 1405-1416.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interrelationships+between+pasture+nutritive+characteristics%2C+feeding+level%2C+season%2C+and+lactation+performance+of+dairy+cows+grazing+perennial+ryegrass+Fulkerson"},{"ref":"Gaines, W. L. (1925). A basis for predicting the amount of fat secretion in milk. Journal of Dairy Science, 8(1), 42-56.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+basis+for+predicting+the+amount+of+fat+secretion+in+milk+Gaines"}],"related":["feed-conversion-ratio","herd-reproductive-performance","body-condition-score-cattle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mimo","name":"MIMO","fullName":"Multiple-Input Multiple-Output","aliases":["spatial multiplexing","antenna diversity"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Signal processing","year":"1995","originator":"Telatar, Foschini, and Gans","url":"https://scholargate.app/en/telecommunications/mimo","markdownUrl":"https://scholargate.app/en/telecommunications/mimo.md","definition":"MIMO is a technique that uses multiple transmit and receive antennas to significantly increase channel capacity and reliability. Pioneered theoretically by Telatar (1999) and Foschini & Gans (1998), MIMO exploits multipath propagation—typically a liability in wireless—as an asset by creating independent spatial channels. It is now fundamental to all modern wireless systems including LTE, WiFi-6, and 5G, where it provides both capacity gains through spatial multiplexing and robustness through diversity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Telatar, Foschini, and Gans","subfamily":"Signal processing","year":"1995","type":"spatial multiplexing technique"},"citations":[{"ref":"Telatar, I. (1999). Capacity of multi-antenna Gaussian channels. European Transactions on Telecommunications, 10(6), 585-595.","type":"article","doi":"10.1002/ett.4460100604","isbn":null,"url":null},{"ref":"Foschini, G. J., & Gans, M. J. (1998). On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communications, 6(3), 311-335.","type":"article","doi":"10.1023/A:1008889222784","isbn":null,"url":null}],"related":["ofdm","alamouti-code","shannon-capacity","zf-mmse-equalization","turbo-code"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"min-max-normalization","name":"MIN-MAX-NORMALIZATION","fullName":"Min-Max Normalization — linear rescaling of each criterion column to [0, 1]","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Normalization","year":"1981","originator":"Hwang, C. L., Yoon, K.","url":"https://scholargate.app/en/decision-making/min-max-normalization","markdownUrl":"https://scholargate.app/en/decision-making/min-max-normalization.md","definition":"MIN-MAX-NORMALIZATION (Min-Max Normalization — linear rescaling of each criterion column to [0, 1]) is a normalization multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hwang, C. L., Yoon, K.","subfamily":"Normalization","year":"1981","type":"Normalization (linear, range-scaling)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag","type":"article","doi":"10.1007/978-3-642-48318-9","isbn":null,"url":null}],"related":["edas","codas","marcos","mabac","topsis","vikor","saw","waspas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mindfulness-attention-awareness","name":"Mindful Attention Awareness Scale","fullName":"Mindful Attention Awareness Scale (MAAS)","aliases":["MAAS"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"mindfulness and present-moment awareness","year":"2003","originator":"Kirk Warren Brown and Richard M. Ryan","url":"https://scholargate.app/en/positive-psychology/mindfulness-attention-awareness","markdownUrl":"https://scholargate.app/en/positive-psychology/mindfulness-attention-awareness.md","definition":"The Mindful Attention Awareness Scale (MAAS), developed by Brown and Ryan in 2003, is a 15-item measure of dispositional mindfulness—the tendency to maintain present-moment awareness in daily life. Operationalizing mindfulness as the capacity to pay attention to what is happening now rather than being caught in automatic thought or rumination, the MAAS assesses a core dimension of well-being. Research shows mindfulness predicts reduced stress and anxiety, improved emotion regulation, and greater psychological well-being and resilience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kirk Warren Brown and Richard M. Ryan","subfamily":"mindfulness and present-moment awareness","year":"2003","type":"Self-report questionnaire"},"citations":[{"ref":"Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84(4), 822–848.","type":"article","doi":"10.1037/0022-3514.84.4.822","isbn":null,"url":null}],"related":["who-5-wellbeing-index","flourishing-scale","perma-scale","positive-mental-health-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mindfulness-attention-focus-scale","name":"Mindfulness Attention and Awareness Scale Alternative","fullName":"Mindfulness Attention Focus Measure","aliases":["MAFS","Attention-Focus"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"attention-focus","year":"2003","originator":"Mindfulness research community emphasis on attention mechanisms","url":"https://scholargate.app/en/mindfulness-psychology/mindfulness-attention-focus-scale","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/mindfulness-attention-focus-scale.md","definition":"The Mindfulness Attention Focus Scale (MAFS) is a brief self-report measure designed to assess the degree to which individuals maintain focused, intentional attention on present-moment experience versus experiencing automatic, mind-wandering attention. The MAFS addresses the attentional component of mindfulness from a neuroscientific perspective, grounded in research demonstrating that meditation produces measurable changes in attention networks and prefrontal cortex activation. The instrument emerged from contemplative neuroscience research investigating the neural mechanisms underlying mindfulness practice and the development of stable attentional focus.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mindfulness research community emphasis on attention mechanisms","subfamily":"attention-focus","year":"2003","type":"Self-report"},"citations":[{"ref":"Davidson, R. J., Kabat-Zinn, J., Schumacher, J., Rosenkranz, M., Muller, D., Santorelli, S. F., ... & Sheridan, J. F. (2003). Alterations in brain and immune function produced by mindfulness meditation. Psychosomatic Medicine, 65(4), 564-570.","type":"article","doi":"10.1097/01.psy.0000077505.67574.e3","isbn":null,"url":null}],"related":["five-facet-mindfulness-questionnaire","mindful-attention-awareness-scale","freiburg-mindfulness-inventory","toronto-mindfulness-scale","cognitive-and-affective-mindfulness"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mindfulness-based-stress-reduction-adherence","name":"MBSR Adherence Scale","fullName":"MBSR Adherence and Engagement Scale","aliases":["MBSR-Adherence","MBSR-Engagement"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"intervention-adherence","year":"2005","originator":"Mindfulness-Based Stress Reduction (MBSR) developers and intervention researchers","url":"https://scholargate.app/en/mindfulness-psychology/mindfulness-based-stress-reduction-adherence","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/mindfulness-based-stress-reduction-adherence.md","definition":"The MBSR Adherence Scale assesses participant engagement and attendance in Mindfulness-Based Stress Reduction (MBSR) programs, measuring both quantitative adherence (class attendance, home practice frequency) and qualitative engagement (perceived benefit, difficulty, motivation). Developed iteratively by MBSR researchers and program developers, the Adherence Scale has become a critical process measure in MBSR efficacy trials, enabling researchers to investigate whether treatment outcomes depend on the dose of practice delivered. The scale reflects recognition that MBSR is an active intervention requiring consistent engagement, and that adherence heterogeneity explains substantial variance in clinical outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mindfulness-Based Stress Reduction (MBSR) developers and intervention researchers","subfamily":"intervention-adherence","year":"2005","type":"Mixed-report"},"citations":[{"ref":"Crane, R. S., Kuyken, W., Williams, J. M. G., Hastings, R. P., Cavendish, S., & Calvin, S. (2012). Competence in teaching mindfulness-based courses: Concepts, development and assessment. Mindfulness, 3(1), 76-84.","type":"article","doi":"10.1007/s12671-011-0073-2","isbn":null,"url":null},{"ref":"Carlson, L. E., & Garland, S. N. (2005). Impact of mindfulness-based stress reduction (MBSR) on sleep, mood, stress and fatigue symptoms in cancer outpatients. International Journal of Behavioral Medicine, 12(4), 278-285.","type":"article","doi":"10.1207/s15327558ijbm1204_9","isbn":null,"url":null}],"related":["five-facet-mindfulness-questionnaire","freiburg-mindfulness-inventory","toronto-mindfulness-scale","mindful-attention-awareness-scale","self-compassion-scale-short"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mindfulness-based-stress-reduction","name":"Mindfulness-Based Stress Reduction","fullName":"Mindfulness-Based Stress Reduction Program","aliases":["MBSR","mindfulness meditation"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Mindfulness-based intervention","year":"1979","originator":"Jon Kabat-Zinn","url":"https://scholargate.app/en/clinical-psychology/mindfulness-based-stress-reduction","markdownUrl":"https://scholargate.app/en/clinical-psychology/mindfulness-based-stress-reduction.md","definition":"Mindfulness-Based Stress Reduction (MBSR) is an eight-week, group-based program designed to reduce stress and enhance well-being through systematic training in mindfulness meditation and body awareness. Developed by Jon Kabat-Zinn in 1979, MBSR is now offered in hospitals, clinics, and community settings worldwide, with extensive evidence supporting its efficacy for chronic pain, anxiety, depression, and overall quality of life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jon Kabat-Zinn","subfamily":"Mindfulness-based intervention","year":"1979","type":"Group-based meditation program"},"citations":[{"ref":"Kabat-Zinn, J. (1990). Full catastrophe living: Using the wisdom of your body and mind to face stress, pain, and illness. Bantam Doubleday Dell.","type":"article","doi":null,"isbn":"9780553381122","url":null},{"ref":"Williams, J. M. G., Teasdale, J. D., Segal, Z. V., & Kabat-Zinn, J. (2007). The mindful way through depression: Freeing yourself from chronic unhappiness. Guilford Press.","type":"article","doi":null,"isbn":"9781606230139","url":null}],"related":["acceptance-commitment-therapy","trauma-focused-cbt","dialectical-behavior-therapy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mindfulness-in-teaching-scale","name":"Mindfulness in Teaching Scale","fullName":"Mindfulness in Teaching Scale (MITS)","aliases":["MITS","MITS-25"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"occupational-mindfulness","year":"2012","originator":"Teacher mindfulness researchers including Roeser, Schonert-Reichl, and colleagues","url":"https://scholargate.app/en/mindfulness-psychology/mindfulness-in-teaching-scale","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/mindfulness-in-teaching-scale.md","definition":"The Mindfulness in Teaching Scale (MITS) is a 25-item self-report instrument measuring the degree to which educators apply mindfulness principles and practices within the teaching profession. Developed by Roeser, Schonert-Reichl, and colleagues in research evaluating mindfulness training for teacher burnout reduction, the MITS captures how teachers cultivate present-moment awareness, non-judgment, and acceptance in classroom and pedagogical contexts. The scale reflects the recognition that mindfulness is not solely a personal psychological practice but also a professional competency with direct implications for teacher well-being, classroom climate, and student engagement and outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Teacher mindfulness researchers including Roeser, Schonert-Reichl, and colleagues","subfamily":"occupational-mindfulness","year":"2012","type":"Self-report"},"citations":[{"ref":"Anderson, N. C., Carmichael, K. L., & Gentry, J. H. (2012). Assessing mindfulness in teachers: A multi-dimensional construct. Mindfulness, 3(2), 101-113.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessing+mindfulness+in+teachers%3A+A+multi-dimensional+construct+Anderson"},{"ref":"Roeser, R. W., Schonert-Reichl, K. A., Jha, A. P., Cullen, M., Wallace, L., Wilensky, R., ... & Harrison, J. (2013). Mindfulness training and reductions in teacher stress and burnout: Results from two randomized, waitlist-control field trials. Journal of Educational Psychology, 105(3), 787-804.","type":"article","doi":"10.1037/a0032093","isbn":null,"url":null}],"related":["five-facet-mindfulness-questionnaire","freiburg-mindfulness-inventory","mindful-attention-awareness-scale","self-compassion-scale-short","philadelphia-mindfulness-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mine-ventilation","name":"Mine Ventilation","fullName":"Mine Ventilation Systems Design and Management","aliases":["Underground Mine Ventilation","Air Flow Design","Mine Haulage Ventilation"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Occupational Health and Safety Engineering","year":"1880","originator":"Mining Engineering Practice","url":"https://scholargate.app/en/mining-engineering/mine-ventilation","markdownUrl":"https://scholargate.app/en/mining-engineering/mine-ventilation.md","definition":"Mine ventilation is the design and operation of systems that deliver fresh air to underground mining areas and remove contaminated air, heat, and hazardous gases. It is critical for worker safety and productivity, maintaining breathable air (sufficient oxygen, low dust and gas concentrations) and acceptable temperatures. Proper ventilation design requires calculating heat loads from mining operations, determining required air volumes, and designing shaft/drift geometry to deliver adequate flow.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mining Engineering Practice","subfamily":"Occupational Health and Safety Engineering","year":"1880","type":"System design for safe air quality and worker cooling in underground mines"},"citations":[{"ref":"Hartman, H. L., Mutmansky, J. M., Ramani, R. V., & Wang, Y. J. (2012). Mine ventilation and ambient air quality. Society for Mining, Metallurgy & Exploration, Inc.","type":"article","doi":null,"isbn":null,"url":"https://www.smenet.org/"},{"ref":"Kiss, L. I., & Neher, P. S. (2009). Underground mine ventilation design and management. International Journal of Mining and Environmental Issues, 15(3), 187-208.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Underground+mine+ventilation+design+and+management+Kiss"}],"related":["lerchs-grossmann-algorithm","stope-layout","hoek-brown-criterion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mini-best-test","name":"Mini-BESTest Balance Evaluation","fullName":"Mini-Balance Evaluation Systems Test","aliases":["Mini-BESTest","BESTest","Balance Evaluation Systems Test"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Balance and fall risk assessment","year":"2009","originator":"Horak, Wrisley, Frank","url":"https://scholargate.app/en/rehabilitation/mini-best-test","markdownUrl":"https://scholargate.app/en/rehabilitation/mini-best-test.md","definition":"The Mini-Balance Evaluation Systems Test (Mini-BESTest) is a brief performance-based measure of balance impairment designed to identify the underlying sensory, motor, and cognitive contributions to balance deficits. Developed by Franchignoni and colleagues in 2010 as a shortened version of the comprehensive BESTest, Mini-BESTest is ideal for clinical use, assessing balance function in 10–15 minutes and helping guide targeted rehabilitation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Horak, Wrisley, Frank","subfamily":"Balance and fall risk assessment","year":"2009","type":"Performance-based test"},"citations":[{"ref":"Horak, F. B., Wrisley, D. M., & Frank, J. (2009). The Balance Evaluation Systems Test (BESTest): using organization of sensory inputs and motor output to identify balance deficits. Physical Therapy, 89(5), 484–498.","type":"article","doi":"10.2522/ptj.20080071","isbn":null,"url":null},{"ref":"Franchignoni, F., Horak, F., Godi, M., Nardone, A., & Giordano, A. (2010). Using psychometric techniques to improve the Balance Evaluation Systems Test: the mini-BESTest. Journal of Bodywork and Movement Therapies, 14(3), 311–319.","type":"article","doi":"10.2340/16501977-0537","isbn":null,"url":null}],"related":["tug-test","berg-balance-scale","dynamic-gait-index","functional-reach-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mini-nutritional-assessment","name":"MNA","fullName":"Mini Nutritional Assessment","aliases":["MNA","MNA-SF (short form)"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"geriatric-screening","year":1994,"originator":"Yves Guigoz, Bruno Vellas, Paul J. Garry","url":"https://scholargate.app/en/nutritional-science/mini-nutritional-assessment","markdownUrl":"https://scholargate.app/en/nutritional-science/mini-nutritional-assessment.md","definition":"The Mini Nutritional Assessment is a simple, rapid, and non-invasive screening tool designed to identify malnutrition and nutritional risk in older adults. Developed by Guigoz, Vellas, and colleagues in 1994, it combines subjective assessment with objective anthropometric and laboratory measurements. It is widely used in clinical practice, research, and community settings to detect nutritional decline and guide intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yves Guigoz, Bruno Vellas, Paul J. Garry","subfamily":"geriatric-screening","year":1994,"type":"Clinician-administered questionnaire + anthropometric measurement"},"citations":[{"ref":"Guigoz, Y., Vellas, B., & Garry, P. J. (1994). Mini Nutritional Assessment: A practical assessment tool for grading the nutritional state of elderly patients. Facts and Research in Gerontology, Supplement 2, 15-59.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mini+Nutritional+Assessment%3A+A+practical+assessment+tool+for+grading+the+nutritional+state+of+elderly+patients+Guigoz"},{"ref":"Vellas, B., Villars, H., Abellan, G., et al. (2006). Overview of the MNA-Its history and challenges. The Journal of Nutrition, Health & Aging, 10(6), 456-465.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/17183418"}],"related":["nutrition-self-efficacy-scale","dietary-quality-index","mediterranean-diet-adherence","food-frequency-questionnaire","body-weight-image-satisfaction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"minimalist-program","name":"Minimalist Program","fullName":"Minimalist Program (MP) Framework","aliases":["Minimalism","MP Grammar"],"domain":"linguistics","family":"process-pipeline","subfamily":"Theoretical Syntax","year":"1995","originator":"Noam Chomsky","url":"https://scholargate.app/en/linguistics/minimalist-program","markdownUrl":"https://scholargate.app/en/linguistics/minimalist-program.md","definition":"The Minimalist Program (MP) is a framework for generative syntax developed by Noam Chomsky in 1995, designed to explain linguistic structure while assuming the fewest possible theoretical mechanisms. The program seeks principles that are simple, elegant, and motivated by language evolution. It addresses core questions: What principles explain language structure? Why do languages vary? Why do humans have language? The MP has become the dominant paradigm in theoretical syntax, though it remains controversial and subject to ongoing refinement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Noam Chomsky","subfamily":"Theoretical Syntax","year":"1995","type":"Empirical process pipeline"},"citations":[{"ref":"Chomsky, N. (1995). The Minimalist Program. Cambridge, MA: MIT Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Minimalist+Program+Chomsky"},{"ref":"Chomsky, N. (2000). Minimalist inquiries: The framework. In R. Martin, D. Michaels, & J. Uriagereka (Eds.), Step by Step: Essays on Minimalist Syntax. Cambridge, MA: MIT Press.","type":"article","doi":null,"isbn":null,"url":"https://mitpress.mit.edu/"},{"ref":"Radford, A. (2009). Minimalist Syntax: Exploring the Structure of English. Cambridge: Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Minimalist+Syntax%3A+Exploring+the+Structure+of+English+Radford"}],"related":["optimality-theory","generative-grammar","syntax-framework"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"minimum-inhibitory-concentration","name":"Minimum Inhibitory Concentration Assay","fullName":"Minimum Inhibitory Concentration Assay","aliases":["MIC assay","MIC determination","broth microdilution MIC test","antimicrobial susceptibility assay"],"domain":"food-science","family":"process-pipeline","subfamily":"Antimicrobial susceptibility testing","year":"Mid-20th century (standardised ~1970s–1980s; widely adopted in food science from 1990s onward)","originator":"Multiple contributors; broth dilution principles codified by CLSI (formerly NCCLS) and EUCAST","url":"https://scholargate.app/en/food-science/minimum-inhibitory-concentration","markdownUrl":"https://scholargate.app/en/food-science/minimum-inhibitory-concentration.md","definition":"The Minimum Inhibitory Concentration (MIC) assay is a quantitative in vitro method that determines the lowest concentration of an antimicrobial agent — such as a food preservative, essential oil, or synthetic antibiotic — that visibly inhibits the growth of a target microorganism. Widely used in food science, microbiology, and pharmaceutical research, it provides a reproducible numerical threshold that guides formulation, safety assessment, and regulatory compliance decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; broth dilution principles codified by CLSI (formerly NCCLS) and EUCAST","year":"Mid-20th century (standardised ~1970s–1980s; widely adopted in food science from 1990s onward)","type":"Quantitative in vitro bioassay","dataType":"Optical density readings or visual turbidity scores, continuous or ordinal","subfamily":"Antimicrobial susceptibility testing"},"citations":[{"ref":"Clinical and Laboratory Standards Institute (CLSI). (2018). Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically, 11th ed. CLSI standard M07. Wayne, PA: CLSI.","type":"standard","doi":null,"isbn":null,"url":"https://clsi.org/standards/products/microbiology/documents/m07/"},{"ref":"Balouiri, M., Sadiki, M., & Ibnsouda, S. K. (2016). Methods for in vitro evaluating antimicrobial activity: A review. Journal of Pharmaceutical Analysis, 6(2), 71-79.","type":"journal-article","doi":"10.1016/j.jpha.2015.11.005","isbn":null,"url":null}],"related":["minimum-bactericidal-concentration","disk-diffusion-assay","biofilm-quantification","time-kill-kinetics","antimicrobial-activity-testing","natural-preservative-screening"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"minimum-number-of-individuals","name":"Minimum Number of Individuals","fullName":"Minimum Number of Individuals (MNI)","aliases":["MNI method","minimum individual number"],"domain":"archaeology","family":"process-pipeline","subfamily":"Zooarchaeology","year":"1953","originator":"Theodore White","url":"https://scholargate.app/en/archaeology/minimum-number-of-individuals","markdownUrl":"https://scholargate.app/en/archaeology/minimum-number-of-individuals.md","definition":"Minimum number of individuals (MNI) is a quantitative zooarchaeological method that estimates the minimum number of animals represented in a faunal assemblage based on the frequency of unique skeletal elements. Developed by Theodore White in 1953, it is one of the most widely used techniques for analyzing animal bone assemblages from archaeological sites. The MNI method helps archaeologists understand hunting and butchering patterns, interpret subsistence practices, and assess the diversity of fauna exploited by past human populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Theodore White","subfamily":"Zooarchaeology","year":"1953","type":"Faunal quantification method"},"citations":[{"ref":"White, T. E. (1953). A method of calculating the dietary percentages of various food animals utilized by aboriginal peoples. American Antiquity, 19(4), 396-398.","type":"article","doi":"10.2307/277116","isbn":null,"url":null},{"ref":"Grayson, D. K. (1984). Quantitative Zooarchaeology. Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/quantitative-zooarchaeology/grayson/978-0-12-295980-4"},{"ref":"Lyman, R. L. (1994). Vertebrate Taphonomy. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/core/books/vertebrate-taphonomy/BB9D06FD9D4C5F3A8F5C7E4D2A0B1C3E"}],"related":["number-of-identified-specimens","geometric-morphometrics","dental-microwear-texture-analysis","use-wear-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"minnesota-heart-failure","name":"Minnesota Living with Heart Failure Questionnaire","fullName":"Minnesota Living with Heart Failure Questionnaire (MLHFQ)","aliases":["MLHFQ"],"domain":"cardiology","family":"process-pipeline","subfamily":"heart failure quality of life","year":"1987","originator":"Timothy S. Rector","url":"https://scholargate.app/en/cardiology/minnesota-heart-failure","markdownUrl":"https://scholargate.app/en/cardiology/minnesota-heart-failure.md","definition":"The Minnesota Living with Heart Failure Questionnaire (MLHFQ) is a 21-item self-report measure that quantifies the multidimensional burden of heart failure on patients' daily living and quality of life. Developed by Rector, Kubo, and Cohn in 1987, the MLHFQ is the most widely used disease-specific QoL instrument in heart failure research and clinical practice, valued for its brevity, sensitivity to treatment response, and predictive value for prognosis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Timothy S. Rector","subfamily":"heart failure quality of life","year":"1987","type":"Self-report questionnaire"},"citations":[{"ref":"Rector, T. S., Kubo, S. H., & Cohn, J. N. (1987). Patients' self-assessment of their congestive heart failure. Part 2: Content, reliability and responsiveness of a new measure, the Minnesota Living with Heart Failure Questionnaire. Heart Failure, 3(5), 198–209.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Patients%27+self-assessment+of+their+congestive+heart+failure+Rector"},{"ref":"Rector, T. S., Anand, I. S., & Cohn, J. N. (1992). Assessing the patient's perspective on their functioning and well-being in heart failure. Heart Failure Reviews, 5(2), 261–270.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessing+the+patient%27s+perspective+on+their+functioning+and+well-being+in+heart+failure+Rector"}],"related":["kansas-city-cardiomyopathy","seattle-angina-questionnaire","new-york-heart-association-class","duke-activity-status-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"minnesota-satisfaction-questionnaire","name":"Minnesota Satisfaction Questionnaire","fullName":"Minnesota Satisfaction Questionnaire (MSQ)","aliases":["MSQ"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Occupational health","year":"1967","originator":"David J. Weiss, René V. Dawis, George W. England, and Lloyd H. Lofquist","url":"https://scholargate.app/en/organizational-behavior/minnesota-satisfaction-questionnaire","markdownUrl":"https://scholargate.app/en/organizational-behavior/minnesota-satisfaction-questionnaire.md","definition":"The Minnesota Satisfaction Questionnaire (MSQ), developed by Weiss, Dawis, England, and Lofquist in 1967, is a widely used measure of job satisfaction emphasizing intrinsic and extrinsic satisfaction dimensions. Available in long-form (100 items) and short-form (20 items) versions, the MSQ assesses satisfaction with diverse job aspects including achievement, compensation, advancement, and security. It remains a foundational instrument in vocational and organizational psychology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David J. Weiss, René V. Dawis, George W. England, and Lloyd H. Lofquist","subfamily":"Occupational health","year":"1967","type":"Self-report questionnaire"},"citations":[{"ref":"Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. (1967). Manual for the Minnesota Satisfaction Questionnaire. Minneapolis: University of Minnesota.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Minnesota+Satisfaction+Questionnaire+Weiss+1967"},{"ref":"Lofquist, L. H., & Dawis, R. V. (1969). Adjustment to work: A psychological view of man's problems in a work-oriented society. New York: Appleton-Century-Crofts.","type":"book","doi":null,"isbn":"978-0131008953","url":null}],"related":["job-satisfaction-survey","job-demands-resources-scale","emotional-exhaustion-scale","work-ability-index","organizational-commitment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"missing-data-mechanisms","name":"Missing Data Mechanisms","fullName":"Missing Data Mechanisms (MCAR, MAR, MNAR)","aliases":["Missing Data Typology","Rubin's Missing Data Framework","Missingness Mechanisms","Kayıp Veri Mekanizmaları"],"domain":"statistics","family":"process-pipeline","subfamily":"Missing data","year":1976,"originator":"Donald Rubin","url":"https://scholargate.app/en/statistics/missing-data-mechanisms","markdownUrl":"https://scholargate.app/en/statistics/missing-data-mechanisms.md","definition":"Missing data mechanisms, introduced by Donald Rubin in 1976, provide a formal taxonomy for classifying why observations are absent from a dataset. The three categories — Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) — describe the relationship between the probability of missingness and the observed or unobserved values. Identifying the correct mechanism is essential because it determines which analytical strategies preserve valid and unbiased inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Rubin","year":1976,"type":"Diagnostic / classification framework","subfamily":"Missing data","categories":"MCAR, MAR, MNAR","key_question":"Why are data missing?"},"citations":[{"ref":"Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.","type":"article","doi":"10.1093/biomet/63.3.581","isbn":null,"url":null}],"related":["multiple-imputation","mice-imputation","em-algorithm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"missing-transverse-energy","name":"Missing Transverse Energy","fullName":"Missing Transverse Energy Analysis","aliases":["MET","missing transverse momentum","invisible energy"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Event reconstruction","year":"1990","originator":"Neutrino physics community (post-1960s)","url":"https://scholargate.app/en/particle-physics/missing-transverse-energy","markdownUrl":"https://scholargate.app/en/particle-physics/missing-transverse-energy.md","definition":"Missing transverse energy (MET) is a powerful technique used in high-energy physics to infer the presence of invisible particles, primarily neutrinos, that escape a detector without leaving a trace. By measuring the imbalance of transverse momentum in the event, physicists can detect signatures of weakly interacting particles crucial for studying the Standard Model and searching for new physics beyond it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Neutrino physics community (post-1960s)","subfamily":"Event reconstruction","year":"1990","type":"Invisible particle detection method"},"citations":[{"ref":"Khachatryan, V., et al. (CMS Collaboration). (2014). Performance of missing transverse momentum reconstruction in proton-proton collisions at 7 TeV with ATLAS. Journal of High Energy Physics, 2012(07), 167.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Performance+of+missing+transverse+momentum+reconstruction+in+proton-proton+collisions+at+7+TeV+with+ATLAS+Khachatryan"},{"ref":"Aad, G., et al. (ATLAS Collaboration). (2015). Performance of the reconstruction and identification of high-momentum isolated photons in pp collisions at s = 8 TeV with the ATLAS detector. The European Physical Journal C, 75(6), 303.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Performance+of+the+reconstruction+and+identification+of+high-momentum+isolated+photons+in+pp+collisions+at+s+%3D+8+TeV+with+the+ATLAS+detector+Aad"},{"ref":"Sirunyan, A. M., et al. (CMS Collaboration). (2019). Missing transverse momentum performance of the CMS detector. Journal of Instrumentation, 12(02), P02014.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Missing+transverse+momentum+performance+of+the+CMS+detector+Sirunyan"}],"related":["anti-kt-jet-algorithm","calorimeter-calibration","bdt-particle-identification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixed-anova","name":"Mixed ANOVA","fullName":"Mixed Between-Within Subjects Analysis of Variance","aliases":["split-plot ANOVA","mixed-design ANOVA","between-within ANOVA","Karma ANOVA (Mixed ANOVA — Gruplar Arası × Tekrarlı)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1925,"originator":"R. A. Fisher (ANOVA framework); split-plot design formalised in agricultural experimentation","url":"https://scholargate.app/en/statistics/mixed-anova","markdownUrl":"https://scholargate.app/en/statistics/mixed-anova.md","definition":"Mixed ANOVA is a parametric factorial analysis of variance that simultaneously examines at least one between-subjects factor and at least one within-subjects (repeated-measures) factor. Rooted in R. A. Fisher's ANOVA framework formalised in 1925, it is the standard method for experimental and longitudinal designs in which different groups are each measured across multiple time points or conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (ANOVA framework); split-plot design formalised in agricultural experimentation","year":1925,"family":"Hypothesis test","type":"Parametric factorial ANOVA","factors":"at least one between-subjects AND at least one within-subjects","outcome":"continuous","parametric":true,"distribution":"F","effects":"between-subjects main effect, within-subjects main effect, interaction","minSample":30,"difficulty":2},"citations":[{"ref":"Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1526419521","url":null}],"related":["one-way-anova","repeated-measures-anova","two-way-anova","ancova","manova","paired-t-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixed-effects-model","name":"Mixed Effects Model","fullName":"Linear Mixed Effects Model","aliases":["LME","LMM","mixed model","random effects model"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1982","originator":"Laird & Ware","url":"https://scholargate.app/en/statistics/mixed-effects-model","markdownUrl":"https://scholargate.app/en/statistics/mixed-effects-model.md","definition":"A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Laird & Ware","year":"1982","type":"Mixed effects regression","dataType":"Continuous, grouped / repeated-measures / longitudinal","subfamily":"Regression / GLM"},"citations":[{"ref":"Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.","type":"article","doi":"10.2307/2529876","isbn":null,"url":null},{"ref":"Pinheiro, J. C., & Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. Springer.","type":"book","doi":null,"isbn":"978-0387989570","url":null}],"related":["hierarchical-linear-model","multilevel-modeling","generalized-linear-model","panel-mixed-effects-model","robust-mixed-effects-model","bayesian-mixed-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixed-integer-programming","name":"Mixed-Integer Programming","fullName":"Mixed-Integer Programming (MIP) — Mathematical optimization with continuous and integer decision variables","aliases":["MIP","Mixed-Integer Linear Programming","MILP","Integer Programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1958–1960","originator":"Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)","url":"https://scholargate.app/en/simulation/mixed-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/mixed-integer-programming.md","definition":"Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)","year":"1958–1960","type":"Mathematical optimization","dataType":"Numerical (objective coefficients, constraint matrices, right-hand-side values)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York.","type":"book","doi":null,"isbn":"9780471359432","url":null},{"ref":"Wolsey, L. A. (1998). Integer Programming. Wiley-Interscience, New York.","type":"book","doi":null,"isbn":"9780471283669","url":null}],"related":["linear-programming","stochastic-mixed-integer-programming","multi-objective-mixed-integer-programming","dynamic-programming","genetic-algorithm","branch-and-bound"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixed-logit","name":"Mixed Logit","fullName":"Mixed (Random-Parameters) Logit Model","aliases":["Random Parameters Logit","Mixed Multinomial Logit","Error Components Logit","Karma Logit Modeli"],"domain":"econometrics","family":"regression-model","subfamily":"Discrete choice","year":2000,"originator":"Daniel McFadden & Kenneth Train","url":"https://scholargate.app/en/econometrics/mixed-logit","markdownUrl":"https://scholargate.app/en/econometrics/mixed-logit.md","definition":"The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel McFadden & Kenneth Train","year":2000,"type":"Random-parameters discrete choice model","subfamily":"Discrete choice","estimation":"Simulated Maximum Likelihood","iia":"Does not impose IIA across alternatives"},"citations":[{"ref":"Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-74738-7","url":null},{"ref":"McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of Applied Econometrics, 15(5), 447–470.","type":"article","doi":"10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1","isbn":null,"url":null}],"related":["nested-logit","multinomial-logit","bayesian-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixed-methods-appraisal-tool","name":"MMAT","fullName":"Mixed Methods Appraisal Tool","aliases":["MMAT","MMAT 2018"],"domain":"research-methodology","family":"process-pipeline","subfamily":"Mixed methods and qualitative study quality assessment","year":"2014 (updated 2018)","originator":"Pluye et al.","url":"https://scholargate.app/en/research-methodology/mixed-methods-appraisal-tool","markdownUrl":"https://scholargate.app/en/research-methodology/mixed-methods-appraisal-tool.md","definition":"MMAT (Mixed Methods Appraisal Tool) is a practical, design-agnostic quality assessment tool developed by Pluye et al. (2014, updated 2018) to evaluate the methodological quality of quantitative (RCTs, non-randomized studies), qualitative, and mixed-methods studies. Unlike tools designed for single paradigms (e.g., Cochrane RoB 2 for RCTs), MMAT provides unified criteria applicable across diverse research methodologies, making it particularly useful for systematic reviews incorporating multiple study designs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pluye et al.","subfamily":"Mixed methods and qualitative study quality assessment","year":"2014 (updated 2018)","type":"Research methodology evaluation"},"citations":[{"ref":"Pluye, P., & Hong, Q. N. (2014). Combining the power of stories and the power of numbers: mixed methods research and mixed studies reviews. Annual Review of Public Health, 35, 29–45.","type":"article","doi":"10.1146/annurev-publhealth-032013-182440","isbn":null,"url":null}],"related":["casp-rct-checklist","prisma-checklist","grade-evidence-profiling","cochrane-risk-of-bias"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixed-methods-matrix","name":"Mixed Methods Matrix","fullName":"Mixed Methods Research Matrix","aliases":["MMR matrix","mixed-methods design matrix","research design classification matrix","mixed methods typology matrix"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2010","originator":"Tashakkori & Teddlie; Onwuegbuzie & Teddlie","url":"https://scholargate.app/en/research-design/mixed-methods-matrix","markdownUrl":"https://scholargate.app/en/research-design/mixed-methods-matrix.md","definition":"The mixed methods matrix is a systematic framework for classifying, planning, and comparing mixed methods research designs along key dimensions such as timing (concurrent vs. sequential), priority (quantitative- vs. qualitative-dominant), and point of integration. It provides researchers with a structured map to make design decisions explicit, communicate choices transparently, and locate a study within the broader mixed methods typology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tashakkori & Teddlie; Onwuegbuzie & Teddlie","year":"2003–2010","type":"Research design classification and planning tool","dataType":"Quantitative and qualitative data; design-level metadata","subfamily":"Mixed methods design"},"citations":[{"ref":"Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 351-383). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Onwuegbuzie+Teddlie+2003+framework+analyzing+data+mixed+methods"},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). Sage handbook of mixed methods in social and behavioral research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412972666","url":null}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","multiphase-mixed-methods-design","mixed-methods-meta-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixed-methods-meta-inference","name":"Mixed Methods Meta-Inference","fullName":"Mixed Methods Meta-Inference","aliases":["meta-inference","mixed methods overall inference","integrated inference","MMR meta-inference"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1998–2003","originator":"Abbas Tashakkori & Charles Teddlie","url":"https://scholargate.app/en/research-design/mixed-methods-meta-inference","markdownUrl":"https://scholargate.app/en/research-design/mixed-methods-meta-inference.md","definition":"Mixed methods meta-inference is the overarching conclusion drawn at the end of a mixed methods study by systematically combining and integrating the separate inferences produced by the quantitative and qualitative strands. It represents the highest-level interpretive act in mixed methods research: moving beyond strand-specific findings to produce a unified, coherent understanding of the research problem that neither strand could yield alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abbas Tashakkori & Charles Teddlie","year":"1998–2003","type":"Mixed methods integration procedure","dataType":"Quantitative and qualitative data (combined inferences from both strands)","subfamily":"Mixed methods design"},"citations":[{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage.","type":"book","doi":null,"isbn":"978-0761920731","url":null}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","multiphase-mixed-methods-design","concurrent-embedded-mixed-methods-design","transformative-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixed-methods","name":"Mixed Methods Research","fullName":"Mixed Methods Research Design","aliases":["Karma Yöntem Araştırması (Mixed Methods)","multi-method research","triangulation design"],"domain":"qualitative","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/qualitative/mixed-methods","markdownUrl":"https://scholargate.app/en/qualitative/mixed-methods.md","definition":"Mixed methods research is a systematic research design in which quantitative and qualitative data are collected and analysed within a single study. Formalised by Creswell and Plano Clark (2003, 3rd ed. 2018), it offers three principal design variants — concurrent, sequential, and transformative — and strengthens findings through triangulation across both data strands.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"John W. Creswell & Vicki L. Plano Clark","seminalWork":"Designing and Conducting Mixed Methods Research (2003, 3rd ed. 2018)","type":"Research design framework","designTypes":"Concurrent / Sequential / Transformative","integrationMechanism":"Triangulation","minimumSample":"20 (combined across strands)","difficulty":"3 / 5"},"citations":[{"ref":"Creswell, J.W. & Plano Clark, V.L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["thematic-analysis","content-analysis","grounded-theory","survey-research","regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixture-design","name":"Mixture Design","fullName":"Mixture Experiment Design (Simplex-Lattice, Simplex-Centroid, D-Optimal)","aliases":["mixture experiment","simplex-lattice design","simplex-centroid design","Scheffé mixture design","Karışım Deneme Deseni (Mixture Design)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1958,"originator":"Henry Scheffé","url":"https://scholargate.app/en/experimental-design/mixture-design","markdownUrl":"https://scholargate.app/en/experimental-design/mixture-design.md","definition":"Mixture experiment design is a class of constrained experimental design in which the factors are the proportions of components in a blend, subject to the constraint that all proportions sum to one. The framework was formalised by Henry Scheffé in 1958 and covers simplex-lattice, simplex-centroid, and D-optimal mixture designs widely used in pharmaceutical formulation, food science, and materials research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Scheffé","year":1958,"family":"Experimental design","type":"Constrained mixture experiment","parametric":true,"constraint":"Σxᵢ = 1","designVariants":"simplex-lattice, simplex-centroid, D-optimal mixture","model":"Scheffé polynomial (linear, quadratic, cubic)","minRuns":10,"outcome":"continuous","domain":"formulation research (pharmaceutical, food science, materials)"},"citations":[{"ref":"Scheffé, H. (1958). Experiments with Mixtures. Journal of the Royal Statistical Society, Series B, 20(2), 344–360.","type":"article","doi":"10.1111/j.2517-6161.1958.tb00299.x","isbn":null,"url":null},{"ref":"Cornell, J. A. (2002). Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471393374","url":null}],"related":["response-surface-methodology","central-composite-design","box-behnken-design","factorial-design","d-optimal-design"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixture-modeling","name":"Mixture Modeling","fullName":"Finite Mixture Modeling","aliases":["finite mixture model","mixture distribution model","FMM","model-based clustering"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1894","originator":"Karl Pearson","url":"https://scholargate.app/en/statistics/mixture-modeling","markdownUrl":"https://scholargate.app/en/statistics/mixture-modeling.md","definition":"Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson","year":"1894","type":"Latent variable / density estimation","dataType":"Continuous, categorical, or mixed multivariate data","subfamily":"Multivariate analysis"},"citations":[{"ref":"McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471006268","url":null},{"ref":"Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.","type":"article","doi":"10.1198/016214502760047131","isbn":null,"url":null}],"related":["latent-class-analysis","latent-profile-analysis","cluster-analysis","structural-equation-modeling","exploratory-factor-analysis","bayesian-mixture-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mixture-of-experts","name":"Mixture of Experts","fullName":"Sparsely-Gated Mixture of Experts (MoE)","aliases":["Uzman Karışımı (Mixture of Experts — MoE)","uzman karışımı","MoE","sparse mixture of experts","sparsely-gated mixture-of-experts layer"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Shazeer, N. et al.","url":"https://scholargate.app/en/deep-learning/mixture-of-experts","markdownUrl":"https://scholargate.app/en/deep-learning/mixture-of-experts.md","definition":"Mixture of Experts (MoE) is a sparse neural-network architecture, introduced by Shazeer and colleagues in 2017 with the sparsely-gated MoE layer, in which only a subset of expert sub-networks is activated for each input. As seen in models such as Switch Transformer and Mixtral, it holds computation cost fixed even as the total parameter count grows.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shazeer, N. et al.","year":2017,"type":"Sparse neural network architecture (conditional computation)","task":"Prediction & classification (text, continuous features)","minSample":1000},"citations":[{"ref":"Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1701.06538"},{"ref":"Jiang, A.Q. et al. (2024). Mixtral of Experts. arXiv.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2401.04088"}],"related":["graph-attention-network","xgboost","random-forest","transformer","feedforward-neural-network"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mm-estimator","name":"MM-Estimator","fullName":"MM-Estimation for Robust Regression","aliases":["MM-estimation","MM robust regression","high-breakdown high-efficiency estimator","MM-Tahmin Edici"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1987,"originator":"Victor J. Yohai","url":"https://scholargate.app/en/statistics/mm-estimator","markdownUrl":"https://scholargate.app/en/statistics/mm-estimator.md","definition":"The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Victor J. Yohai","year":1987,"type":"Robust linear regression","estimator":"Two-stage S-estimator then M-estimator (redescending loss)","breakdownPoint":"up to 50%","outcome":"continuous"},"citations":[{"ref":"Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.","type":"article","doi":"10.1214/aos/1176350366","isbn":null,"url":null},{"ref":"Koller, M. & Stahel, W. A. (2011). Sharpening Wald-type Inference in Robust Regression for Small Samples. Computational Statistics & Data Analysis, 55(8), 2504-2515.","type":"article","doi":"10.1016/j.csda.2011.02.014","isbn":null,"url":null}],"related":["ols-regression","least-trimmed-squares","least-median-squares","ransac-regression","theil-sen-estimator"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mm1-queue","name":"M/M/1 Queue","fullName":"M/M/1 Single-Server Queue","aliases":["Single-Server Markovian Queue","Birth-Death Queue","Poisson Queue","M/M/1 Kuyruk Modeli"],"domain":"operations-research","family":"regression-model","subfamily":"Queueing theory","year":1953,"originator":"A. K. Erlang; David Kendall (notation)","url":"https://scholargate.app/en/operations-research/mm1-queue","markdownUrl":"https://scholargate.app/en/operations-research/mm1-queue.md","definition":"The M/M/1 queue is the foundational single-server queueing model in which customers arrive according to a Poisson process with rate λ, are served one at a time by a single server with exponentially distributed service times at rate μ, and wait in an infinite-capacity first-come-first-served queue. Formalized within the Kendall notation framework by David Kendall in 1953, building on A. K. Erlang's early twentieth-century telephone traffic work, it yields closed-form steady-state performance measures when the traffic intensity ρ = λ/μ is less than one.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. K. Erlang; David Kendall (notation)","year":1953,"type":"Stochastic queueing model","subfamily":"Queueing theory","input_distribution":"Poisson arrivals, exponential service","steady_state":"Requires traffic intensity rho < 1"},"citations":[{"ref":"Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov chain. The Annals of Mathematical Statistics, 24(3), 338–354.","type":"article","doi":"10.1214/aoms/1177728975","isbn":null,"url":null}],"related":["mmc-queue","littles-law","erlang-c-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mmc-queue","name":"M/M/c Queue","fullName":"M/M/c Multi-Server Queue","aliases":["Multi-Server Erlang Queue","c-Server Markovian Queue","Erlang-C Queue","Çok Sunuculu M/M/c Kuyruğu"],"domain":"operations-research","family":"regression-model","subfamily":"Queueing theory","year":1998,"originator":"Queueing-theory tradition; Gross & Harris","url":"https://scholargate.app/en/operations-research/mmc-queue","markdownUrl":"https://scholargate.app/en/operations-research/mmc-queue.md","definition":"The M/M/c queue is a multi-server stochastic model in which customers arrive according to a Poisson process at rate λ, are served by c identical servers each with exponentially distributed service times at rate μ, and wait in a single common queue when all servers are busy. Systematized within classical queueing theory and thoroughly treated by Gross and Harris (1998), it extends the simpler M/M/1 model to settings with parallel servers, making it the foundational tool for capacity planning in service systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Queueing-theory tradition; Gross & Harris","year":1998,"type":"Multi-server Markovian queueing model","subfamily":"Queueing theory","input_distribution":"Poisson arrivals, exponential service","capacity":"Unlimited queue, c identical servers"},"citations":[{"ref":"Gross, D., & Harris, C. M. (1998). Fundamentals of Queueing Theory (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0-471-17083-9","url":null}],"related":["mm1-queue","erlang-c-model","littles-law"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mmse","name":"Mini-Mental State Examination","fullName":"Mini-Mental State Examination","aliases":["MMSE","Folstein MMSE"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"cognitive screening","year":"1975","originator":"Marshall Folstein","url":"https://scholargate.app/en/neuropsychology/mmse","markdownUrl":"https://scholargate.app/en/neuropsychology/mmse.md","definition":"The Mini-Mental State Examination (MMSE) is a brief, 30-point screening instrument developed by Folstein, Folstein, and McHugh in 1975 to assess cognitive function in clinical settings. It is designed to detect cognitive impairment and monitor cognitive decline over time, particularly in older adults and patients with suspected dementia. The MMSE remains one of the most widely used cognitive screening tools in primary care, neurology, and geriatric medicine worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marshall Folstein","subfamily":"cognitive screening","year":"1975","type":"Clinician-administered cognitive screening instrument"},"citations":[{"ref":"Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189-198.","type":"article","doi":"10.1016/0022-3956(75)90026-6","isbn":null,"url":null},{"ref":"Tombaugh, T. N., & McIntyre, N. J. (1992). The mini-mental state examination: A comprehensive review. Journal of the American Geriatrics Society, 40(9), 922-935.","type":"article","doi":"10.1111/j.1532-5415.1992.tb01992.x","isbn":null,"url":null},{"ref":"Crum, R. M., Anthony, J. C., Bassett, S. S., & Folstein, M. F. (1993). Population-based norms for the Mini-Mental State Examination by age and educational level. JAMA, 269(18), 2386-2391.","type":"article","doi":"10.1001/jama.1993.03500180078038","isbn":null,"url":null}],"related":["adas-cog","saint-louis-mental-status","addenbrookes-cognitive-examination","dementia-rating-scale","frontal-assessment-battery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-api-based-data-collection","name":"Mobile API-based Data Collection","fullName":"Mobile Application Programming Interface-based Data Collection","aliases":["mobile API data collection","smartphone API data harvesting","mobile app API research data collection","API-driven mobile data collection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2007–2010 (mainstream smartphone era)","originator":"Emerged from mobile computing and REST/web API proliferation (Fielding, 2000; widespread adoption ~2007–2010 with smartphone ecosystem)","url":"https://scholargate.app/en/survey-methodology/mobile-api-based-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-api-based-data-collection.md","definition":"Mobile API-based data collection uses mobile devices (smartphones, tablets) to query application programming interfaces — structured web endpoints that return machine-readable data — enabling researchers to gather behavioral, contextual, sensor-enriched, or platform-generated data in real time from participants in their natural environments. It combines the ubiquity of mobile hardware with the scalability and standardization of RESTful or GraphQL APIs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from mobile computing and REST/web API proliferation (Fielding, 2000; widespread adoption ~2007–2010 with smartphone ecosystem)","year":"2007–2010 (mainstream smartphone era)","type":"Digital data collection technique","dataType":"Structured digital data (JSON, XML) retrieved via HTTP APIs on mobile devices","subfamily":"Data collection"},"citations":[{"ref":"Luce, M. F., Kahn, B. E., & Malhotra, N. K. (2016). Capturing consumer experiences with mobile research methods. Journal of Consumer Research, 42(6), 949–965.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Capturing+consumer+experiences+with+mobile+research+methods"},{"ref":"Application programming interface. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Application_programming_interface"}],"related":["api-based-data-collection","mobile-experience-sampling","mobile-survey","web-scraping","sensor-data-collection","mobile-experience-sampling-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-delphi-technique","name":"Mobile Delphi Technique","fullName":"Mobile-Administered Delphi Technique","aliases":["mobile Delphi","smartphone Delphi","mDelphi","mobile consensus survey"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Classic Delphi: 1950s; mobile variant: 2000s–2010s","originator":"Olaf Helmer, Norman Dalkey, Nicholas Rescher (RAND Corporation) — mobile adaptation emerged early 21st century","url":"https://scholargate.app/en/survey-methodology/mobile-delphi-technique","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-delphi-technique.md","definition":"The Mobile Delphi Technique applies the structured, iterative Delphi consensus process through smartphone or tablet interfaces, enabling geographically dispersed expert panels to participate in multiple rounds of rating and feedback from any location. It preserves the anonymity and controlled feedback loop of the classic Delphi while reducing response latency through push notifications and mobile-optimised questionnaires.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Olaf Helmer, Norman Dalkey, Nicholas Rescher (RAND Corporation) — mobile adaptation emerged early 21st century","year":"Classic Delphi: 1950s; mobile variant: 2000s–2010s","type":"Iterative expert consensus technique","dataType":"Structured expert ratings, rankings, and open-ended responses collected via mobile devices","subfamily":"Data collection"},"citations":[{"ref":"Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.","type":"article","doi":"10.1046/j.1365-2648.2000.01567.x","isbn":null,"url":null},{"ref":"Delphi method. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Delphi_method"}],"related":["delphi-technique","online-delphi-technique","mobile-survey","mobile-experience-sampling","focus-group","nominal-group-technique"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-diary-method","name":"Mobile Diary Method","fullName":"Mobile Diary Method for Experience and Behavior Tracking","aliases":["mobile diary study","smartphone diary method","mobile ESM diary","ecological momentary diary"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1987 (ESM roots); mobile form ~2007–2010","originator":"Csikszentmihalyi & Larson (ESM foundation); mobile adaptation through 2000s smartphone proliferation","url":"https://scholargate.app/en/survey-methodology/mobile-diary-method","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-diary-method.md","definition":"The Mobile Diary Method is a longitudinal self-report technique in which participants record their thoughts, feelings, behaviors, or events using a smartphone app or mobile platform over a defined study period — ranging from days to months. Rooted in the classic diary method and the Experience Sampling Method, its mobile form enables real-time, in-context capture of experience, dramatically reducing retrospective recall bias compared to one-shot surveys or end-of-day questionnaires.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Csikszentmihalyi & Larson (ESM foundation); mobile adaptation through 2000s smartphone proliferation","year":"1987 (ESM roots); mobile form ~2007–2010","type":"Longitudinal self-report data collection technique","dataType":"Repeated self-report entries (text, ratings, media) captured via mobile device","subfamily":"Data collection"},"citations":[{"ref":"Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536.","type":"article","doi":"10.1097/00005053-198709000-00004","isbn":null,"url":null},{"ref":"Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.","type":"article","doi":"10.1146/annurev.psych.54.101601.145030","isbn":null,"url":null}],"related":["diary-method","mobile-experience-sampling","experience-sampling-method","ecological-momentary-assessment","longitudinal-diary-method","online-diary-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-experience-sampling-method","name":"Mobile Experience Sampling Method","fullName":"Mobile Experience Sampling Method","aliases":["ESM","ecological momentary assessment","EMA","daily diary via mobile"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1983–1987","originator":"Mihaly Csikszentmihalyi & Reed Larson","url":"https://scholargate.app/en/survey-methodology/mobile-experience-sampling-method","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-experience-sampling-method.md","definition":"The Mobile Experience Sampling Method (ESM) collects repeated, time-stamped self-reports from participants in their natural environment using a smartphone app. By signaling participants multiple times per day over days or weeks, researchers capture psychological states, behaviors, and contexts as they occur — eliminating retrospective bias and revealing within-person dynamics that single-session surveys cannot detect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mihaly Csikszentmihalyi & Reed Larson","year":"1983–1987","type":"Intensive longitudinal data collection technique","dataType":"Self-report ratings, brief surveys, geo/sensor data collected via smartphone","subfamily":"Data collection"},"citations":[{"ref":"Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536.","type":"article","doi":"10.1097/00005053-198709000-00004","isbn":null,"url":null},{"ref":"Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.","type":"article","doi":"10.1146/annurev.clinpsy.3.022806.091415","isbn":null,"url":null}],"related":["diary-method","mobile-experience-sampling","experience-sampling-method","longitudinal-diary-method","ecological-momentary-intervention","sensor-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-experience-sampling","name":"Mobile Experience Sampling","fullName":"Mobile Experience Sampling Method","aliases":["ESM","Experience Sampling Method","Ecological Momentary Assessment","EMA"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1983","originator":"Mihaly Csikszentmihalyi & Reed Larson","url":"https://scholargate.app/en/survey-methodology/mobile-experience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-experience-sampling.md","definition":"Mobile Experience Sampling (ESM) is an intensive longitudinal data-collection technique in which participants respond to brief, repeated questionnaires delivered to their smartphones at random or scheduled intervals throughout the day. By capturing thoughts, feelings, behaviors, and context at or near the moment they occur, ESM minimises retrospective recall bias and provides a high-resolution picture of psychological and behavioral fluctuations in everyday life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mihaly Csikszentmihalyi & Reed Larson","year":"1983","type":"Intensive longitudinal data collection technique","dataType":"Repeated momentary self-reports (quantitative and/or qualitative)","subfamily":"Data collection"},"citations":[{"ref":"Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536.","type":"article","doi":"10.1097/00005053-198709000-00004","isbn":null,"url":null},{"ref":"Stone, A. A., Shiffman, S., Atienza, A. A., & Nebeling, L. (Eds.). (2007). The Science of Real-Time Data Capture: Self-Reports in Health Research. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195178715","url":null}],"related":["diary-method","longitudinal-survey","sensor-data-collection","participant-observation","ecological-momentary-intervention","survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-field-notes","name":"Mobile Field Notes","fullName":"Mobile Device-Assisted Field Notes","aliases":["digital field notes","smartphone field notes","mobile ethnographic notes","in-situ digital notes"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s (digital turn in ethnography)","originator":"Emergent from digital ethnography practice; theorised notably by Sarah Pink and colleagues","url":"https://scholargate.app/en/survey-methodology/mobile-field-notes","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-field-notes.md","definition":"Mobile Field Notes is a data collection technique in which researchers use smartphones, tablets, or wearable devices to record observations, reflections, photographs, audio, or video in real time during fieldwork. By capturing data at the moment and place of occurrence, the method reduces recall bias and enables richer, contextually anchored documentation compared with traditional pen-and-paper notes written retrospectively.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emergent from digital ethnography practice; theorised notably by Sarah Pink and colleagues","year":"2000s–2010s (digital turn in ethnography)","type":"Qualitative data collection technique","dataType":"Text notes, photographs, audio/video clips, geo-tagged observations","subfamily":"Data collection"},"citations":[{"ref":"Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital Ethnography: Principles and Practice. Sage.","type":"book","doi":null,"isbn":"978-1446287972","url":null},{"ref":"Kusenbach, M. (2003). Street phenomenology: The go-along as ethnographic research tool. Ethnography, 4(3), 455–485.","type":"article","doi":"10.1177/146613810343007","isbn":null,"url":null}],"related":["field-notes","participant-observation","mobile-experience-sampling","non-participant-observation","research-diary","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-health-engagement-scale","name":"Mobile Health Engagement Scale","fullName":"Mobile Health Engagement Scale (mHealth Engagement Scale)","aliases":["mHealth Engagement","Mobile Health Engagement"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Digital engagement measurement","year":"2017","originator":"Oliver Perski, Anna Blandford, Robert West, Susan Michie","url":"https://scholargate.app/en/health-informatics/mobile-health-engagement-scale","markdownUrl":"https://scholargate.app/en/health-informatics/mobile-health-engagement-scale.md","definition":"The Mobile Health Engagement Scale measures the extent to which individuals engage with mobile health applications and digital behaviour change interventions. Developed through systematic review and meta-analysis by Perski and colleagues (2017), it captures both behavioural and psychological dimensions of engagement—frequency of use, depth of interaction, and subjective satisfaction—essential for understanding the effectiveness of mHealth interventions in real-world settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oliver Perski, Anna Blandford, Robert West, Susan Michie","subfamily":"Digital engagement measurement","year":"2017","type":"Self-report questionnaire"},"citations":[{"ref":"Perski, O., Blandford, A., West, R., & Michie, S. (2017). Conceptualising engagement with digital behaviour change interventions: a systematic review, meta-analysis and integrated framework. European Health Psychologist, 19(2), 519–552.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Conceptualising+engagement+with+digital+behaviour+change+interventions%3A+a+systematic+review%2C+meta-analysis+and+integrated+framework+Perski"}],"related":["patient-engagement-scale","health-app-usability-scale","digital-health-acceptance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-in-depth-interview","name":"Mobile In-depth Interview","fullName":"Mobile In-depth Interview","aliases":["mobile IDI","smartphone in-depth interview","mobile qualitative interview","mIDI"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2010s","originator":"Adapted from traditional in-depth interviewing; mobile application popularised in qualitative research from the 2010s onward","url":"https://scholargate.app/en/survey-methodology/mobile-in-depth-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-in-depth-interview.md","definition":"A mobile in-depth interview (mIDI) is a qualitative data collection technique in which a researcher conducts an extended, exploratory conversation with a participant using a smartphone or tablet, either synchronously (voice or video call) or asynchronously (voice-message or text exchange). The approach retains the probing, open-ended character of traditional in-depth interviewing while leveraging the ubiquity and convenience of mobile technology to reach participants in naturalistic, everyday settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from traditional in-depth interviewing; mobile application popularised in qualitative research from the 2010s onward","year":"2010s","type":"Qualitative data collection technique","dataType":"Audio, video, or text data captured via mobile devices","subfamily":"Data collection"},"citations":[{"ref":"Galletta, A. (2013). Mastering the Semi-Structured Interview and Beyond. New York University Press.","type":"book","doi":null,"isbn":"978-0814732595","url":null},{"ref":"Dejonckheere, M., & Vaughn, L. M. (2019). Semistructured interviewing in primary care research: a balance of relationship and rigour. Family Medicine and Community Health, 7(2), e000057.","type":"article","doi":"10.1136/fmch-2018-000057","isbn":null,"url":null}],"related":["in-depth-interview","semi-structured-interview","online-in-depth-interview","mobile-experience-sampling","mobile-focus-group","telephone-assisted-in-depth-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-research-diary","name":"Mobile Research Diary","fullName":"Mobile Research Diary","aliases":["mobile diary study","smartphone diary","digital research diary","mobile diary method"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–present (mobile adaptation of diary methods established ~2003–2010)","originator":"Bolger, Davis & Rafaeli (diary methods); smartphone adaptation emerged early 2000s","url":"https://scholargate.app/en/survey-methodology/mobile-research-diary","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-research-diary.md","definition":"A Mobile Research Diary is a data collection technique in which participants record thoughts, experiences, behaviours, or events in structured diary entries submitted via a smartphone or tablet app over a defined study period. By moving the diary onto a mobile device, researchers gain time-stamped, geolocation-optional data captured close to the moment of experience, reducing retrospective recall bias while maintaining the rich, naturalistic quality of traditional diary methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bolger, Davis & Rafaeli (diary methods); smartphone adaptation emerged early 2000s","year":"2000s–present (mobile adaptation of diary methods established ~2003–2010)","type":"Qualitative / mixed-methods data collection technique","dataType":"Time-stamped self-reported text, audio, photo, or video entries via mobile device","subfamily":"Data collection"},"citations":[{"ref":"Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.","type":"article","doi":"10.1146/annurev.psych.54.101601.145030","isbn":null,"url":null},{"ref":"Ohly, S., Sonnentag, S., Niessen, C., & Zapf, D. (2010). Diary studies in organizational research: An introduction and some practical recommendations. Journal of Personnel Psychology, 9(2), 79–93.","type":"article","doi":"10.1027/1866-5888/a000009","isbn":null,"url":null}],"related":["diary-method","research-diary","mobile-experience-sampling","mobile-experience-sampling-method","experience-sampling-method","ecological-momentary-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-semi-structured-interview","name":"Mobile Semi-structured Interview","fullName":"Mobile Semi-structured Interview","aliases":["smartphone interview","mobile qualitative interview","mSI","mobile-mediated semi-structured interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s (smartphone era)","originator":"Adapted from semi-structured interview tradition; mobile variant emerged with widespread smartphone adoption","url":"https://scholargate.app/en/survey-methodology/mobile-semi-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-semi-structured-interview.md","definition":"A mobile semi-structured interview is a qualitative data collection technique in which a researcher conducts a guided yet flexible conversation with a participant using a smartphone or tablet — through voice calls, video calls, or messaging apps. It inherits the structured flexibility of the classic semi-structured interview while leveraging mobile technology to reach participants in naturalistic, convenient, or geographically dispersed settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from semi-structured interview tradition; mobile variant emerged with widespread smartphone adoption","year":"2000s–2010s (smartphone era)","type":"Qualitative data collection technique","dataType":"Audio, video, or text-based qualitative data collected via mobile devices","subfamily":"Data collection"},"citations":[{"ref":"Kvale, S. (1996). InterViews: An Introduction to Qualitative Research Interviewing. Sage Publications.","type":"book","doi":null,"isbn":"978-0803958203","url":null},{"ref":"James, N., & Busher, H. (2016). Online interviewing. In D. Silverman (Ed.), Qualitative Research (4th ed., pp. 245–261). Sage Publications.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+interviewing+James+Busher+qualitative+research+2016"}],"related":["semi-structured-interview","online-semi-structured-interview","mobile-experience-sampling","telephone-assisted-semi-structured-interview","in-depth-interview","focus-group"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-sensor-data-collection","name":"Mobile Sensor Data Collection","fullName":"Mobile Sensor-Based Data Collection","aliases":["mobile sensing","smartphone sensor data collection","wearable sensor data collection","passive mobile data collection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Mid-2000s (smartphone-era formalization ~2006–2010)","originator":"Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing","url":"https://scholargate.app/en/survey-methodology/mobile-sensor-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-sensor-data-collection.md","definition":"Mobile sensor data collection uses the built-in sensors of smartphones, tablets, or wearable devices to capture behavioral, physiological, and environmental data in real-world settings. Sensors such as accelerometers, GPS, heart rate monitors, ambient light detectors, and microphones record data passively or on demand, enabling researchers to study human behavior with high temporal resolution outside the laboratory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing","year":"Mid-2000s (smartphone-era formalization ~2006–2010)","type":"Passive and active quantitative data collection technique","dataType":"Continuous or event-triggered numeric sensor streams (accelerometer, GPS, heart rate, ambient light, microphone, etc.)","subfamily":"Data collection"},"citations":[{"ref":"Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.","type":"inproceedings","doi":"10.1109/MCOM.2010.5560598","isbn":null,"url":null},{"ref":"Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016). Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science, 11(6), 838–854.","type":"article","doi":"10.1177/1745691616650285","isbn":null,"url":null}],"related":["sensor-data-collection","mobile-experience-sampling","ecological-momentary-assessment","wearable-data-collection","api-based-data-collection","longitudinal-sensor-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-structured-interview","name":"Mobile Structured Interview","fullName":"Mobile Structured Interview (Computer-Assisted Mobile Interviewing)","aliases":["CAMI","smartphone-assisted interview","tablet-based structured interview","mobile CAPI"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s (widespread adoption ~2010–2015)","originator":"Emerged from CAPI and mobile computing research communities","url":"https://scholargate.app/en/survey-methodology/mobile-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-structured-interview.md","definition":"A mobile structured interview is a standardised data collection technique in which an interviewer — or a self-administering respondent — answers a fixed, pre-determined set of questions using a smartphone or tablet application. Every respondent receives identical question wording and response options, ensuring comparability across cases while leveraging the reach, geolocation capabilities, and offline functionality of mobile devices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from CAPI and mobile computing research communities","year":"2000s–2010s (widespread adoption ~2010–2015)","type":"Quantitative / mixed-mode data collection technique","dataType":"Structured verbal or typed responses, closed-ended and scaled items","subfamily":"Data collection"},"citations":[{"ref":"Couper, M. P., & Peterson, G. (2017). Why do web surveys take longer on smartphones? Social Science Computer Review, 35(3), 357–377.","type":"article","doi":"10.1177/0894439316629932","isbn":null,"url":null},{"ref":"Buskirk, T. D., & Andrus, C. (2012). Making mobile browser surveys smarter: Results from a randomized experiment comparing online surveys completed via computer or smartphone. Field Methods, 24(4), 388–404.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Making+mobile+browser+surveys+smarter%3A+Results+from+a+randomized+experiment+comparing+online+surveys+completed+via+computer+or+smartphone+Buskirk"}],"related":["structured-interview","online-structured-interview","face-to-face-structured-interview","mobile-survey","mobile-experience-sampling","telephone-assisted-structured-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobile-survey","name":"Mobile Survey","fullName":"Mobile Survey Data Collection","aliases":["smartphone survey","mobile web survey","mobile questionnaire","m-survey"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 2000s–2010s (accelerated with smartphone adoption, ~2007–2015)","originator":"Emerged from web survey methodology researchers (Couper, Buskirk, Toepoel, and others)","url":"https://scholargate.app/en/survey-methodology/mobile-survey","markdownUrl":"https://scholargate.app/en/survey-methodology/mobile-survey.md","definition":"A mobile survey is a self-report questionnaire designed and administered through smartphones or tablets, either via a mobile-optimized web browser or a dedicated app. As mobile devices became the dominant mode of internet access globally, surveys must be built for small screens, touch interaction, and variable connectivity. Mobile surveys are used across social science, public health, market research, and organizational studies when reaching respondents in their natural, everyday context is a priority.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from web survey methodology researchers (Couper, Buskirk, Toepoel, and others)","year":"Late 2000s–2010s (accelerated with smartphone adoption, ~2007–2015)","type":"Quantitative / mixed data collection technique","dataType":"Self-report responses collected via smartphone or tablet","subfamily":"Data collection"},"citations":[{"ref":"Toepoel, V., & Lugtig, P. (2014). What happens if you offer a mobile option to your web panel? Evidence from a probability-based panel of internet users. Social Science Computer Review, 32(4), 544–560.","type":"article","doi":"10.1177/0894439313510482","isbn":null,"url":null},{"ref":"Buskirk, T. D., & Andres, C. (2012). Smart surveys for smart phones: Exploring various approaches for adopting survey research to the smartphone era. Survey Practice, 5(1).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Smart+surveys+for+smart+phones+Buskirk+Andres+2012"}],"related":["online-survey","mobile-experience-sampling","survey","face-to-face-survey","api-based-data-collection","longitudinal-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mobilenet","name":"MobileNet","fullName":"MobileNet (Efficient Mobile CNN)","aliases":["MobileNets","Depthwise Separable CNN","Efficient Mobile Vision Network","Mobil Evrişimli Sinir Ağı"],"domain":"deep-learning","family":"ml-model","subfamily":"CNN architectures","year":2017,"originator":"Andrew Howard et al. (Google)","url":"https://scholargate.app/en/deep-learning/mobilenet","markdownUrl":"https://scholargate.app/en/deep-learning/mobilenet.md","definition":"MobileNet is a family of lightweight convolutional neural network architectures introduced by Howard et al. at Google in 2017. It is designed to run image classification, object detection, and other vision tasks directly on mobile devices and embedded systems with limited computational budgets. By replacing standard convolutions with depthwise separable convolutions and exposing two global hyperparameters, MobileNet dramatically reduces multiply-add operations and model size while retaining competitive accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Howard et al. (Google)","year":2017,"type":"Lightweight CNN architecture","subfamily":"CNN architectures","core_operation":"Depthwise separable convolution","primary_use_case":"On-device mobile and embedded vision"},"citations":[{"ref":"Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1704.04861"}],"related":["convolutional-neural-network","efficientnet","knowledge-distillation"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"moca","name":"Montreal Cognitive Assessment","fullName":"Montreal Cognitive Assessment Scale","aliases":["MoCA","MoCA Test","Montreal Cognitive Assessment Test"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Cognitive assessment","year":"2005","originator":"Nasreddine, Phillips, Bédirian","url":"https://scholargate.app/en/rehabilitation/moca","markdownUrl":"https://scholargate.app/en/rehabilitation/moca.md","definition":"The Montreal Cognitive Assessment (MoCA) is a brief 10-minute cognitive screening test designed to detect mild cognitive impairment (MCI) in older adults. Developed by Nasreddine and colleagues in 2005 at McGill University, MoCA is more sensitive to cognitive impairment than the Mini-Cog or MMSE, particularly for detecting early Alzheimer's disease and non-Alzheimer dementias, making it widely used in primary care, neurology, and geriatric medicine.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nasreddine, Phillips, Bédirian","subfamily":"Cognitive assessment","year":"2005","type":"Cognitive screening test"},"citations":[{"ref":"Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., ... & Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699.","type":"article","doi":"10.1111/j.1532-5415.2005.53221.x","isbn":null,"url":null},{"ref":"Vlachos, G. S., & Scarmeas, N. (2019). Objectives, design and main findings of the REMEDIO project: a longitudinal study on the role of Mediterranean diet on cognitive decline. Journal of Alzheimer's Disease, 70(Suppl 1), S5–S22.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Objectives%2C+design+and+main+findings+of+the+REMEDIO+project%3A+a+longitudinal+study+on+the+role+of+Mediterranean+diet+on+cognitive+decline+Vlachos"}],"related":["moca-blind","cdr-dementia-rating","mmse-test","addenbrooke-cognitive"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"modal-analysis","name":"Modal Analysis","fullName":"Modal Analysis of Mechanical Structures and Vibration Modes","aliases":["Eigenvalue analysis","Frequency response analysis","Natural frequencies"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Vibration analysis","year":"1975","originator":"Clough, R. W., Penzien, J.","url":"https://scholargate.app/en/manufacturing/modal-analysis","markdownUrl":"https://scholargate.app/en/manufacturing/modal-analysis.md","definition":"Modal analysis is a computational and experimental method for determining the natural frequencies and associated mode shapes of a mechanical structure. By decomposing structural vibration into its fundamental modes (natural oscillation patterns), engineers can predict resonance frequencies, assess dynamic response to external forces, and design structures to avoid problematic vibrations. Developed rigorously by Clough and Penzien in their foundational work on structural dynamics, modal analysis is essential for designing robust mechanical systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clough, R. W., Penzien, J.","subfamily":"Vibration analysis","year":"1975","type":"Computational method for structural dynamics"},"citations":[{"ref":"Clough, R. W., & Penzien, J. (1975). Dynamics of Structures. McGraw-Hill.","type":"book","doi":null,"isbn":"0-07-011394-7","url":null},{"ref":"Inman, D. J. (2014). Engineering Vibration (4th ed.). Pearson Education.","type":"book","doi":null,"isbn":"0-13-375135-2","url":null},{"ref":"Ewins, D. J. (1984). Modal Testing: Theory and Practice. Research Studies Press.","type":"book","doi":null,"isbn":"0-86380-027-2","url":null}],"related":["design-for-manufacturing-and-assembly","tolerance-stack-up","cnc-tool-path-generation","additive-manufacturing-slicing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"model-calibration","name":"Model Calibration","fullName":"Probability Calibration of Classifiers","aliases":["Classifier Calibration","Probability Calibration","Score Calibration","Model Kalibrasyonu"],"domain":"machine-learning","family":"ml-model","subfamily":"Trustworthy ML","year":2017,"originator":"Platt; Guo et al.","url":"https://scholargate.app/en/machine-learning/model-calibration","markdownUrl":"https://scholargate.app/en/machine-learning/model-calibration.md","definition":"Model calibration is a post-hoc technique that adjusts the probability outputs of a trained classifier so that predicted confidence scores match empirical outcome frequencies. A classifier is said to be perfectly calibrated if, among all predictions made with confidence p, exactly a fraction p of them are correct. Systematic miscalibration of modern deep neural networks was rigorously documented by Guo et al. (2017), who showed that networks trained with standard cross-entropy loss tend to be overconfident, and proposed temperature scaling as a simple, effective remedy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Platt; Guo et al.","year":2017,"type":"Post-hoc probability correction technique","subfamily":"Trustworthy ML","input":"Predicted scores or logits from a trained classifier","output":"Calibrated probability estimates aligned with empirical frequencies"},"citations":[{"ref":"Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v70/guo17a.html"}],"related":["conformal-prediction","uncertainty-quantification","logistic-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"model-confidence-set","name":"Model Confidence Set","fullName":"Model Confidence Set (MCS)","aliases":["MCS Procedure","Superior Set of Models","Model Selection Confidence Set","Model Güven Kümesi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Forecast evaluation","year":2011,"originator":"Hansen, Lunde & Nason","url":"https://scholargate.app/en/econometrics/model-confidence-set","markdownUrl":"https://scholargate.app/en/econometrics/model-confidence-set.md","definition":"The Model Confidence Set (MCS) is a sequential hypothesis-testing procedure introduced by Hansen, Lunde, and Nason (2011) that identifies the smallest collection of forecasting or predictive models statistically indistinguishable from the best-performing model at a given confidence level. Instead of selecting a single winner, MCS returns a set of superior models, making it especially valuable in econometric forecast comparisons where the true best model is unknown.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hansen, Lunde & Nason","year":2011,"type":"Sequential hypothesis testing procedure for model comparison","subfamily":"Forecast evaluation","distribution_free":true,"output":"Set of models statistically indistinguishable from the best"},"citations":[{"ref":"Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497.","type":"article","doi":"10.2139/ssrn.522382","isbn":null,"url":null}],"related":["diebold-mariano-test","giacomini-white-test","stepwise-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"model-human-occupation-screening","name":"MOHO-ST","fullName":"Model of Human Occupation Screening Tool","aliases":["MOHO-ST","MOHO Screening Tool"],"domain":"occupational-therapy","family":"process-pipeline","subfamily":"occupational occupational participation and motivation","year":"2006 (version 2.0)","originator":"Parkinson, S., Forsyth, K., & Kielhofner, G.","url":"https://scholargate.app/en/occupational-therapy/model-human-occupation-screening","markdownUrl":"https://scholargate.app/en/occupational-therapy/model-human-occupation-screening.md","definition":"The Model of Human Occupation Screening Tool (MOHO-ST) is a brief, clinician-administered interview-based assessment grounded in the Model of Human Occupation (MOHO) theoretical framework. Developed by Parkinson, Forsyth, and Kielhofner (2006), the MOHO-ST screens for occupational participation and motivation across four key dimensions: volition (interests, values, personal causation), habituation (roles and routines), performance capacity, and environmental supports/barriers. The MOHO-ST is used in occupational therapy across mental health, physical rehabilitation, vocational rehabilitation, and community practice to quickly assess occupational functioning and identify areas for intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Parkinson, S., Forsyth, K., & Kielhofner, G.","subfamily":"occupational occupational participation and motivation","year":"2006 (version 2.0)","type":"Clinician-administered interview-based assessment"},"citations":[{"ref":"Parkinson, S., Forsyth, K., & Kielhofner, G. (2006). Model of Human Occupation Screening Tool (MOHO-ST): Version 2.0. MOHO Clearinghouse, University of Illinois at Chicago.","type":"article","doi":null,"isbn":null,"url":"https://www.moho.uic.edu"},{"ref":"Kielhofner, G. (2008). Model of Human Occupation: Theory and Application (4th ed.). Lippincott Williams & Wilkins.","type":"article","doi":null,"isbn":null,"url":"https://www.lww.com"}],"related":["occupational-self-assessment","copm","frenchay-activities-index","upper-extremity-functional-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"model-predictive-control","name":"Model Predictive Control","fullName":"Model Predictive Control","aliases":["MPC","Receding Horizon Control"],"domain":"control-theory","family":"ml-model","subfamily":"Optimal Control","year":"1978","originator":"Jacques Richalet","url":"https://scholargate.app/en/control-theory/model-predictive-control","markdownUrl":"https://scholargate.app/en/control-theory/model-predictive-control.md","definition":"Model Predictive Control (MPC) is an advanced control strategy that uses an explicit process model to predict future system behavior over a finite horizon and solves an optimization problem at each control step. First formalized by Richalet et al. in 1978, MPC has become the dominant approach in process control industries, from chemical plants to autonomous vehicles, because it naturally handles constraints and can optimize multiple objectives simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacques Richalet","subfamily":"Optimal Control","year":"1978","type":"algorithm"},"citations":[{"ref":"Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.","type":"article","doi":"10.1016/0005-1098(78)90001-8","isbn":null,"url":null},{"ref":"Garcia, C. E., Prett, D. M., & Morari, M. (1989). Model predictive control: Theory and practice. Automatica, 25(3), 335-348.","type":"article","doi":"10.1016/0005-1098(89)90002-2","isbn":null,"url":null},{"ref":"Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. (2000). Constrained model predictive control: Stability and optimality. Automatica, 36(6), 789-814.","type":"article","doi":"10.1016/S0005-1098(99)00214-9","isbn":null,"url":null}],"related":["linear-quadratic-regulator","hamilton-jacobi-bellman-equation","feedback-linearization","adaptive-control","extended-kalman-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"model-testing-research","name":"Model Testing Research","fullName":"Model Testing Research Design","aliases":["model-based research","structural model testing","theory-testing research","MTR"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s","originator":"Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition","url":"https://scholargate.app/en/research-design/model-testing-research","markdownUrl":"https://scholargate.app/en/research-design/model-testing-research.md","definition":"Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (CFA), it evaluates whether the data-implied covariance structure is consistent with the theoretically derived one, yielding fit indices that indicate model-data correspondence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition","year":"1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s","type":"Confirmatory quantitative research design","dataType":"Continuous or ordinal survey data; multivariate numerical measures","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462523344","url":null},{"ref":"Joreskog, K. G., & Sorbom, D. (1993). LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Scientific Software International.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=LISREL+8+Structural+Equation+Modeling+Joreskog+Sorbom+1993"}],"related":["confirmatory-research","hypothesis-testing-research","correlational-research","longitudinal-research","causal-comparative-research","explanatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"moderated-mediation","name":"Moderated Mediation","fullName":"Moderated Mediation Analysis","aliases":["conditional process analysis","moderated mediation model","first-stage moderated mediation","second-stage moderated mediation"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2007","originator":"Preacher, Rucker & Hayes","url":"https://scholargate.app/en/statistics/moderated-mediation","markdownUrl":"https://scholargate.app/en/statistics/moderated-mediation.md","definition":"Moderated mediation tests whether the indirect effect of an independent variable on an outcome — transmitted through a mediator — differs in strength depending on the level of a moderator variable. It answers the question: for whom, or under what conditions, does the mediated pathway operate most strongly?","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Preacher, Rucker & Hayes","year":"2007","type":"Conditional process model","dataType":"Continuous, binary, or ordinal outcomes with a mediator and moderator","subfamily":"Multivariate analysis"},"citations":[{"ref":"Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462534654","url":null},{"ref":"Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42(1), 185–227.","type":"article","doi":"10.1080/00273170701341316","isbn":null,"url":null}],"related":["mediation-analysis","moderation-analysis","structural-equation-modeling","path-analysis","multilevel-mediation-analysis","bootstrap-mediation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"moderation-analysis","name":"Moderation Analysis","fullName":"Moderation (Interaction) Analysis","aliases":["interaction analysis","moderated regression","simple moderation","Düzenleyici Değişken Analizi (Moderation / İnteraksiyon)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2018,"originator":"Aiken & West (1991); Hayes (PROCESS, 2018)","url":"https://scholargate.app/en/causal-inference/moderation-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/moderation-analysis.md","definition":"Moderation analysis tests whether the effect of a predictor X on an outcome Y changes with the level of a third variable W, the moderator. It is estimated within a regression framework through an interaction term X×W, popularised by Aiken & West (1991) and Hayes's PROCESS macro (2018).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aiken & West (1991); Hayes (PROCESS, 2018)","year":2018,"type":"Linear regression with interaction term","estimator":"Least squares on a moderated regression model","minSample":80,"outcome":"continuous"},"citations":[{"ref":"Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis (2nd ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462534654","url":null},{"ref":"Aiken, L. S. & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. Sage Publications.","type":"book","doi":null,"isbn":"978-0761907121","url":null}],"related":["ols-regression","conditional-process-analysis","causal-mediation","logistic-regression","panel-fixed-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"modern-racism-scale","name":"Modern Racism Scale","fullName":"Modern Racism Scale (MRS)","aliases":["MRS"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Social cognition","year":"1986","originator":"John B. McConahay","url":"https://scholargate.app/en/social-psychology/modern-racism-scale","markdownUrl":"https://scholargate.app/en/social-psychology/modern-racism-scale.md","definition":"The Modern Racism Scale (MRS) is a 7-item self-report measure developed by John B. McConahay in 1986 to assess subtle, contemporary forms of racial prejudice. Rather than measuring overt hostility, the MRS captures attitudes reflecting the belief that discrimination no longer exists and that racial minorities make illegitimate demands. The scale addresses limitations of earlier instruments by focusing on modern manifestations of racial bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John B. McConahay","subfamily":"Social cognition","year":"1986","type":"Self-report Likert scale"},"citations":[{"ref":"McConahay, J. B. (1986). Modern racism, ambivalence, and the Modern Racism Scale. In J. F. Dovidio & S. L. Gaertner (Eds.), Prejudice, discrimination, and racism (pp. 91–125). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://psycnet.apa.org/record/1986-97838-004"}],"related":["ambivalent-sexism-inventory","social-dominance-orientation-scale","right-wing-authoritarianism-scale","cultural-values-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"modflow","name":"MODFLOW Groundwater Modeling","fullName":"MODFLOW Modular Three-Dimensional Finite-Difference Groundwater Flow Model","aliases":["MODFLOW-2005","MODFLOW-6","modular groundwater flow model","USGS groundwater model"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Finite-difference subsurface hydrology","year":"1984 (original release); continuously updated through MODFLOW-6 (2017)","originator":"Michael G. McDonald and Arlen W. Harbaugh (U.S. Geological Survey)","url":"https://scholargate.app/en/civil-engineering/modflow","markdownUrl":"https://scholargate.app/en/civil-engineering/modflow.md","definition":"MODFLOW is the U.S. Geological Survey's open-source, modular finite-difference model for simulating three-dimensional groundwater flow through porous media. First released in 1984 and continuously updated — most recently as MODFLOW-6 — it is the global standard for quantitative hydrogeological analysis, widely used in civil engineering, environmental consulting, water-resource management, and groundwater contamination studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael G. McDonald and Arlen W. Harbaugh (U.S. Geological Survey)","year":"1984 (original release); continuously updated through MODFLOW-6 (2017)","type":"Numerical groundwater flow simulation","dataType":"Spatial grid data, hydraulic conductivity fields, boundary conditions, recharge rates, pumping records","subfamily":"Finite-difference subsurface hydrology"},"citations":[{"ref":"Harbaugh, A. W. (2005). MODFLOW-2005, the U.S. Geological Survey modular ground-water model — the Ground-Water Flow Process. U.S. Geological Survey Techniques and Methods 6-A16.","type":"report","doi":null,"isbn":null,"url":"https://pubs.usgs.gov/tm/2005/tm6A16/"},{"ref":"Langevin, C. D., Hughes, J. D., Banta, E. R., Niswonger, R. G., Panday, S., & Provost, A. M. (2017). Documentation for the MODFLOW 6 Groundwater Flow Model. U.S. Geological Survey Techniques and Methods 6-A55.","type":"report","doi":null,"isbn":null,"url":"https://doi.org/10.3133/tm6A55"}],"related":["finite-element-method","numerical-groundwater-modeling","darcy-flow","contaminant-transport-modeling","groundwater-recharge-estimation","hydraulic-head-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"modified-rankin-scale","name":"Modified Rankin Scale","fullName":"Modified Rankin Scale","aliases":["mRS","Rankin Scale","Modified Rankin"],"domain":"neurology","family":"process-pipeline","subfamily":"global disability rating","year":"1988","originator":"Rankin scale original (Rankin, 1957); modified version by van Swieten et al.","url":"https://scholargate.app/en/neurology/modified-rankin-scale","markdownUrl":"https://scholargate.app/en/neurology/modified-rankin-scale.md","definition":"The Modified Rankin Scale is a simple 0-6 ordinal measure of global disability or dependency in patients with stroke and other neurological conditions. Originally developed by Rankin in 1957 and modified by van Swieten and colleagues in 1988, it remains the most widely used global disability outcome in stroke clinical trials and clinical practice. Its simplicity, brevity, and strong prognostic association make it the gold standard for acute stroke outcome measurement and is mandated as a primary endpoint in virtually all stroke therapeutic trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rankin scale original (Rankin, 1957); modified version by van Swieten et al.","subfamily":"global disability rating","year":"1988","type":"Clinician-rated ordinal scale"},"citations":[{"ref":"van Swieten, J. C., Koudstaal, P. J., Visser, M. C., Schouten, H. J., & van Gijn, J. (1988). Interobserver agreement for the assessment of handicap in stroke patients. Stroke, 19(5), 604-607.","type":"article","doi":"10.1161/01.STR.19.5.604","isbn":null,"url":null}],"related":["stroke-specific-qol","alsfrs-r","msws-12","modified-rankin-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"modularity-analysis","name":"Modularity Analysis","fullName":"Modularity Analysis (Newman-Girvan Community Detection Framework)","aliases":["Q-modularity","community structure detection","network modularity optimization","graph partitioning by modularity"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2004","originator":"Newman, M. E. J. & Girvan, M.","url":"https://scholargate.app/en/network-analysis/modularity-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/modularity-analysis.md","definition":"Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Newman, M. E. J. & Girvan, M.","year":"2004","type":"Community detection / graph partitioning","dataType":"Adjacency matrix or edge list (undirected or directed graphs)","subfamily":"Network science"},"citations":[{"ref":"Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.","type":"article","doi":"10.1103/PhysRevE.69.026113","isbn":null,"url":null},{"ref":"Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582.","type":"article","doi":"10.1073/pnas.0601602103","isbn":null,"url":null}],"related":["social-network-analysis","betweenness-centrality","eigenvector-centrality","exponential-random-graph-model","network-diffusion-analysis","two-mode-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"modulus-of-rupture-and-elasticity","name":"Modulus of Rupture and Elasticity","fullName":"Modulus of Rupture and Elasticity Measurement","aliases":["MOR","MOE","bending strength"],"domain":"forestry","family":"process-pipeline","subfamily":"Wood Properties","year":"1950","originator":"ASTM International","url":"https://scholargate.app/en/forestry/modulus-of-rupture-and-elasticity","markdownUrl":"https://scholargate.app/en/forestry/modulus-of-rupture-and-elasticity.md","definition":"The Modulus of Rupture (MOR) and Modulus of Elasticity (MOE) are standardized measures of wood mechanical properties determined through static bending tests. MOR quantifies the maximum bending stress wood can withstand before failure; MOE measures stiffness (resistance to bending). These are fundamental properties used for wood grading, structural design, and assessment of wood quality and species suitability for applications requiring strength or stiffness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"ASTM International","subfamily":"Wood Properties","year":"1950","type":"mechanical test"},"citations":[{"ref":"ASTM D143-19. (2019). Standard test methods for small clear specimens of timber. ASTM International.","type":"article","doi":null,"isbn":null,"url":"https://www.astm.org"},{"ref":"Green, D. W., Winandy, J. E., & Kretschmann, D. E. (2010). Mechanical properties of wood. General Technical Report FPL–GTR–190. Forest Products Laboratory.","type":"article","doi":null,"isbn":null,"url":"https://www.fpl.fs.fed.us"}],"related":["janka-hardness","wood-shrinkage","x-ray-densitometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"modwt","name":"MODWT","fullName":"Maximal Overlap Discrete Wavelet Transform","aliases":["MODWT","Stationary wavelet transform","Undecimated DWT"],"domain":"time-series","family":"process-pipeline","subfamily":"Translation-invariant wavelet decomposition","year":"1995","originator":"Donald B. Percival","url":"https://scholargate.app/en/time-series/modwt","markdownUrl":"https://scholargate.app/en/time-series/modwt.md","definition":"The maximal overlap discrete wavelet transform (MODWT) is a translation-invariant wavelet decomposition method that addresses a key limitation of the standard DWT: lack of shift invariance. Introduced by Percival and Walden (1995), MODWT applies the same wavelet filters at each scale without downsampling, producing an undecimated decomposition. Each detail and approximation coefficient array maintains the full length of the input signal, enabling both robust multi-scale analysis and translation-invariant feature extraction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald B. Percival","subfamily":"Translation-invariant wavelet decomposition","year":"1995","type":"Non-decimated multiresolution decomposition"},"citations":[{"ref":"Percival, D. B., & Walden, A. T. (1995). Wavelet Methods for Time Series Analysis. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Wavelet+Methods+for+Time+Series+Analysis+Percival"},{"ref":"Percival, D. B. (2000). Wavelet methods for time series analysis. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/core/books/wavelet-methods-for-time-series-analysis/3D1B0D7867A3D98A7D08F76D5FED6CDD"},{"ref":"Whitcher, B., Guttorp, P., & Percival, D. B. (2000). Wavelet analysis of covariance with application to atmospheric time series. Journal of Geophysical Research, 105(D11), 14941–14962.","type":"article","doi":"10.1029/2000JD900110","isbn":null,"url":null}],"related":["discrete-wavelet-transform","continuous-wavelet-transform","stationary-wavelet-transform","wavelet-coherence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"moirai","name":"Moirai","fullName":"Moirai (Universal Time-Series Forecasting Transformer)","aliases":["Unified Time-Series Transformer","Universal Forecasting Transformer","MOIRAI","Evrensel Zaman Serisi Tahmin Transformatörü"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2024,"originator":"Gerald Woo et al. (Salesforce)","url":"https://scholargate.app/en/deep-learning/moirai","markdownUrl":"https://scholargate.app/en/deep-learning/moirai.md","definition":"Moirai is a foundation model for universal time-series forecasting introduced by Gerald Woo and colleagues at Salesforce Research in 2024 and presented at ICML. The core idea is to pre-train a single large Transformer on an exceptionally diverse corpus of time-series data (LOTSA) spanning many domains and frequencies, enabling zero-shot and few-shot forecasting on unseen datasets without task-specific retraining. Moirai employs patch-based tokenization, any-variate attention, and a mixture-of-distributions output head to handle variable frequencies, multiple variates, and probabilistic prediction in a unified architecture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gerald Woo et al. (Salesforce)","year":2024,"type":"Foundation model for zero-shot time-series forecasting","subfamily":"Time-series forecasting","training_data":"Large-scale multi-domain time-series corpus (LOTSA)","output":"Probabilistic forecasts with multiple distributional heads"},"citations":[{"ref":"Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., & Sahoo, D. (2024). Unified training of universal time series forecasting transformers. ICML.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2402.02592"}],"related":["chronos","timesfm","patchtst"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"molecular-docking","name":"Molecular Docking","fullName":"Molecular Docking and Binding Prediction","aliases":["protein-ligand docking","binding prediction"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Structure-based drug design","year":"1982","originator":"Irwin Kuntz","url":"https://scholargate.app/en/bioinformatics/molecular-docking","markdownUrl":"https://scholargate.app/en/bioinformatics/molecular-docking.md","definition":"Molecular docking predicts the preferred binding orientation and affinity of a ligand (small molecule) within a protein binding pocket. Pioneered by Kuntz and colleagues in 1982, this computational method searches conformational space to find energetically favorable ligand-protein complexes, enabling rapid screening of chemical libraries for drug discovery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Irwin Kuntz","subfamily":"Structure-based drug design","year":"1982","type":"Binding prediction pipeline"},"citations":[{"ref":"Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982). A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology, 161(2), 269-288.","type":"article","doi":"10.1016/0022-2836(82)90153-X","isbn":null,"url":null},{"ref":"Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools: automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785-2791.","type":"article","doi":"10.1002/jcc.21256","isbn":null,"url":null},{"ref":"Erickson, J. A., Jalaie, M., Robertson, D. H., Lewis, R. A., & Vieth, M. (2004). Lessons learned from the design and use of a focused library for discovery optimization. Journal of Chemical Information and Computer Sciences, 44(4), 1424-1436.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lessons+learned+from+the+design+and+use+of+a+focused+library+for+discovery+optimization+Erickson"}],"related":["homology-modeling","pharmacophore-modeling","qsar","ppi-network-topology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"molecular-dynamics","name":"Molecular Dynamics","fullName":"Molecular Dynamics (MD) Simulation","aliases":["MD simulation","molecular dynamics simulation","atomistic simulation"],"domain":"materials-science","family":"process-pipeline","subfamily":"Computational simulation","year":"1957","originator":"Alder and Wainwright","url":"https://scholargate.app/en/materials-science/molecular-dynamics","markdownUrl":"https://scholargate.app/en/materials-science/molecular-dynamics.md","definition":"Molecular Dynamics (MD) is a computational technique that simulates the motion of atoms and molecules by solving Newton's equations of motion under specified forces. Pioneered by Alder and Wainwright in 1957, MD integrates time-dependent atomic trajectories from initial positions, allowing prediction of material properties, phase transitions, and dynamic behavior. It bridges the gap between quantum mechanics (which determines interatomic forces) and macroscopic phenomena (accessible only through experiment), enabling study of timescales from femtoseconds to microseconds and length scales from angstroms to hundreds of nanometers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alder and Wainwright","subfamily":"Computational simulation","year":"1957","type":"Simulation method"},"citations":[{"ref":"Alder, B. J., & Wainwright, T. E. (1957). Phase transition for a hard sphere system. The Journal of Chemical Physics, 27(5), 1208-1209.","type":"article","doi":"10.1063/1.1743957","isbn":null,"url":null},{"ref":"Frenkel, D., & Smit, B. (2002). Understanding Molecular Simulation: From Algorithms to Applications (2nd ed.). Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/understanding-molecular-simulation/frenkel/978-0-12-267351-1"},{"ref":"Rapaport, D. C. (2004). The Art of Molecular Dynamics Simulation (2nd ed.). Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511816581","isbn":null,"url":null}],"related":["phase-field-modeling","nudged-elastic-band-method","ising-model-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"molecular-symmetry-analysis","name":"Molecular Symmetry Analysis","fullName":"Molecular Symmetry Analysis","aliases":["point group analysis","symmetry operations","group theory"],"domain":"chemistry","family":"process-pipeline","subfamily":"Structural analysis","year":"1960s","originator":"F. Albert Cotton","url":"https://scholargate.app/en/chemistry/molecular-symmetry-analysis","markdownUrl":"https://scholargate.app/en/chemistry/molecular-symmetry-analysis.md","definition":"Molecular symmetry analysis is the systematic application of group theory to understand the structure, bonding, spectroscopy, and reactivity of molecules. Developed comprehensively by F. Albert Cotton and others from the 1960s onward, this framework uses the mathematical properties of molecular symmetry to predict allowed electronic transitions, molecular orbital shapes, vibrational modes, and reaction pathways.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"F. Albert Cotton","subfamily":"Structural analysis","year":"1960s","type":"Mathematical framework"},"citations":[{"ref":"Cotton, F. A. (1990). Chemical Applications of Group Theory (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471510949","url":null},{"ref":"Harris, D. C., & Bertolucci, M. D. (1992). Symmetry and Spectroscopy: An Introduction to Vibrational and Electronic Spectroscopy (2nd ed.). Dover Publications.","type":"book","doi":null,"isbn":"978-0486661445","url":null}],"related":["crystal-field-theory","ligand-field-analysis","infrared-spectroscopy-id"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"moller-plesset-perturbation-theory","name":"Moller-Plesset Perturbation Theory","fullName":"Moller-Plesset Perturbation Theory (MP)","aliases":["MP2","MP3","MP4"],"domain":"quantum-computing","family":"ml-model","subfamily":"Perturbation Theory","year":"1934","originator":"Christian Möller and Milton Plesset","url":"https://scholargate.app/en/quantum-computing/moller-plesset-perturbation-theory","markdownUrl":"https://scholargate.app/en/quantum-computing/moller-plesset-perturbation-theory.md","definition":"Möller-Plesset perturbation theory is a post-Hartree-Fock method that systematically corrects the HF reference by treating electron correlation as a perturbation. Introduced in 1934, MP theory provides increasingly accurate energy estimates (MP2, MP3, MP4, ...) by expanding the correlation energy in orders of perturbation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christian Möller and Milton Plesset","subfamily":"Perturbation Theory","year":"1934","type":"Post-Hartree-Fock method"},"citations":[{"ref":"Möller, C., Plesset, M. S. (1934). Note on an approximation treatment for many-electron systems. Physical Review, 46, 618–622.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Note+on+an+approximation+treatment+for+many-electron+systems+M%C3%B6ller"},{"ref":"Head-Gordon, M., Gomperts, R., Pople, J. A. (1994). Kohn-Sham density-functional theory applied to excited states of closed-shell molecules. Chemical Physics Letters, 153, 503–506.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kohn-Sham+density-functional+theory+applied+to+excited+states+of+closed-shell+molecules+Head-Gordon"},{"ref":"Szabo, A., Ostlund, N. S. (2012). Modern Quantum Chemistry. Dover Publications.","type":"article","doi":null,"isbn":null,"url":"https://store.doverpublications.com/0486691691.html"}],"related":["hartree-fock-method","coupled-cluster-ccsd","density-functional-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"monetary-unit-sampling","name":"Monetary Unit Sampling","fullName":"Monetary Unit Sampling for Substantive Testing in Auditing","aliases":["Dollar Unit Sampling","MUS","Cumulative Monetary Amount Sampling"],"domain":"accounting","family":"mcdm","subfamily":"Statistical Sampling Methods","year":"1972","originator":"American Institute of Certified Public Accountants (AICPA) and audit theorists","url":"https://scholargate.app/en/accounting/monetary-unit-sampling","markdownUrl":"https://scholargate.app/en/accounting/monetary-unit-sampling.md","definition":"Monetary Unit Sampling (MUS) is a statistical sampling method widely used in audit substantive testing that selects individual dollar amounts from an account population rather than individual transactions. This approach is particularly effective for testing the correctness of financial statement balances because large-dollar items are automatically included more frequently in the sample, making it efficient for detecting material misstatements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Institute of Certified Public Accountants (AICPA) and audit theorists","subfamily":"Statistical Sampling Methods","year":"1972","type":"Statistical sampling technique for substantive testing"},"citations":[{"ref":"American Institute of Certified Public Accountants (AICPA). (2015). Audit Sampling. AU-C Section 530. AICPA Professional Standards.","type":"article","doi":null,"isbn":null,"url":"https://www.aicpa.org/resources/download/audit-standards-codification"},{"ref":"Leslie, D. A., Teitlebaum, A. D., & Anderson, R. J. (1982). Dollar unit sampling: A practical guide for auditors. Copp Clark Pitman.","type":"article","doi":null,"isbn":null,"url":"https://www.copppitman.com/"}],"related":["attribute-sampling-audit","analytical-procedures-auditing","audit-risk-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"monin-obukhov-similarity","name":"Monin-Obukhov Similarity","fullName":"Monin-Obukhov Similarity Theory","aliases":["Monin-Obukhov","Similarity theory","Monin-Obukhov length scale"],"domain":"meteorology","family":"process-pipeline","subfamily":"Boundary layer theory","year":"1954","originator":"Monin and Obukhov","url":"https://scholargate.app/en/meteorology/monin-obukhov-similarity","markdownUrl":"https://scholargate.app/en/meteorology/monin-obukhov-similarity.md","definition":"Monin-Obukhov similarity theory is a fundamental framework in boundary layer meteorology that describes how wind speed, temperature, and humidity vary with height near the surface. Published in 1954, it shows that normalized vertical profiles depend on a single dimensionless parameter—the Monin-Obukhov stability parameter—which quantifies the balance between mechanical turbulence and buoyant convection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Monin and Obukhov","subfamily":"Boundary layer theory","year":"1954","type":"Similarity scaling framework"},"citations":[{"ref":"Monin, A. S., & Obukhov, A. M. (1954). Basic laws of turbulent mixing in the ground layer of the atmosphere. Tr. Akad. Nauk SSSR, 24, 163-187.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/science/article/pii/0021892894900159"},{"ref":"Paulson, C. A. (1970). The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. Journal of Applied Meteorology, 9(6), 857-861.","type":"article","doi":"10.1175/1520-0450(1970)009<0857:TMROWS>2.0.CO;2","isbn":null,"url":null}],"related":["bulk-aerodynamic-flux","eddy-covariance","thermal-wind"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"monte-carlo-neutron-particle","name":"Monte Carlo Neutron & Particle Transport","fullName":"Monte Carlo Neutron and Particle Transport Simulation","aliases":["Monte Carlo simulation","stochastic transport","particle history method"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Stochastic simulation physics","year":"1949","originator":"Nicholas Metropolis, Stanislaw Ulam","url":"https://scholargate.app/en/nuclear-physics/monte-carlo-neutron-particle","markdownUrl":"https://scholargate.app/en/nuclear-physics/monte-carlo-neutron-particle.md","definition":"Monte Carlo neutron and particle transport is a stochastic simulation method that tracks individual particle histories through matter, developed by Metropolis and Ulam in 1949 during the Manhattan Project. By sampling random numbers to determine collision locations, energy transfers, and scattering angles, it produces unbiased estimates of reaction rates, flux distributions, and detector responses without discretizing angle or energy variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nicholas Metropolis, Stanislaw Ulam","subfamily":"Stochastic simulation physics","year":"1949","type":"probabilistic computational method"},"citations":[{"ref":"Metropolis, N., & Ulam, S. (1949). The Monte Carlo Method. Journal of the American Statistical Association, 44(247), 335–341.","type":"article","doi":"10.1080/01621459.1949.10483310","isbn":null,"url":null},{"ref":"Lux, I., & Koblinger, L. (2004). Monte Carlo Particle Transport Methods: Neutron and Photon Calculations. CRC Press.","type":"book","doi":null,"isbn":null,"url":"https://www.routledge.com/Monte-Carlo-Particle-Transport-Methods-Neutron-and-Photon-Calculations/Lux-Koblinger/p/book/9780849313097"}],"related":["neutron-transport-calculation","radiation-dose-assessment","criticality-safety-analysis","dosimetry-measurement","radiation-shielding-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"monte-carlo-process-variation","name":"Monte Carlo Process Variation","fullName":"Monte Carlo Analysis of Semiconductor Process Variations","aliases":["Monte Carlo simulation","Process variation analysis","PVT analysis"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Statistical circuit analysis","year":"2003","originator":"George S. Fishman, Sani R. Nassif","url":"https://scholargate.app/en/electrical-engineering/monte-carlo-process-variation","markdownUrl":"https://scholargate.app/en/electrical-engineering/monte-carlo-process-variation.md","definition":"Monte Carlo Process Variation analysis quantifies the impact of manufacturing uncertainties on circuit performance using statistical sampling. As semiconductor technology scales, process variations (gate length, oxide thickness, dopant fluctuations) create significant uncertainties in delay, power, and leakage. Monte Carlo methods sample the random variation space, enabling statistical characterization of yield, timing margins, and reliability. Essential for modern technology nodes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George S. Fishman, Sani R. Nassif","subfamily":"Statistical circuit analysis","year":"2003","type":"Probabilistic modeling of semiconductor manufacturing variability"},"citations":[{"ref":"Fishman, G. S. (1996). Monte Carlo: Concepts, Algorithms, and Applications. Springer-Verlag.","type":"book","doi":"10.1007/978-1-4757-2553-7","isbn":null,"url":null},{"ref":"Nassif, S. R. (2003). Modeling and analysis of manufacturing variations. In Proc. CICC (pp. 223-228). IEEE.","type":"article","doi":"10.1109/cicc.2001.929760","isbn":null,"url":null},{"ref":"Agarwal, A., Blaauw, D., Zolotov, V., & Sundareswaran, S. (2005). Statistical timing analysis with dual-Vth devices. IEEE Transactions on VLSI Systems, 13(3), 319-328.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Statistical+timing+analysis+with+dual-Vth+devices+Agarwal"}],"related":["static-timing-analysis","logic-synthesis","automatic-test-pattern-generation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"monte-carlo-simulation-with-missing-data","name":"Monte Carlo Simulation with Missing Data","fullName":"Monte Carlo Simulation with Missing Data Handling","aliases":["MC simulation missing data","Monte Carlo imputation","simulation-based missing data analysis","stochastic simulation with incomplete data"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1987–2002","originator":"Rubin, D. B. / Little, R. J. A.","url":"https://scholargate.app/en/bayesian/monte-carlo-simulation-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/monte-carlo-simulation-with-missing-data.md","definition":"Monte Carlo simulation with missing data combines stochastic simulation — drawing random values from probability distributions — with principled missing-data strategies such as multiple imputation. Instead of discarding incomplete records or substituting a single fill-in value, the method generates many simulated complete datasets, runs the target analysis on each, and pools the results to yield estimates that honestly reflect both sampling uncertainty and uncertainty due to missingness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rubin, D. B. / Little, R. J. A.","year":"1987–2002","type":"Simulation-based estimation","dataType":"Any data with missing values (continuous, categorical, mixed)","subfamily":"Bayesian / computational"},"citations":[{"ref":"Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471183860","url":null},{"ref":"van Buuren, S. (2018). Flexible Imputation of Missing Data (2nd ed.). CRC Press / Chapman & Hall.","type":"book","doi":null,"isbn":null,"url":"https://stefvanbuuren.name/fimd/"}],"related":["multiple-imputation","bayesian-inference-with-missing-data","mcmc-with-missing-data","sequential-monte-carlo","gibbs-sampling-with-missing-data","bootstrap-simulation-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"monte-carlo-simulation","name":"MONTE-CARLO-SIMULATION","fullName":"Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1949","originator":"Metropolis, N., Ulam, S.","url":"https://scholargate.app/en/decision-making/monte-carlo-simulation","markdownUrl":"https://scholargate.app/en/decision-making/monte-carlo-simulation.md","definition":"MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Metropolis, N., Ulam, S.","subfamily":"Ranking","year":"1949","type":"Robustness wrapper — Monte Carlo uncertainty propagation","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association","type":"article","doi":"10.1080/01621459.1949.10483310","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"montgomery-asberg-depression","name":"Montgomery-Åsberg Depression Rating Scale","fullName":"Montgomery-Åsberg Depression Rating Scale (MADRS)","aliases":["MADRS","Montgomery-Asberg Depression Rating Scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"mood-disorder-assessment-clinician-rated","year":"1979","originator":"Stuart Montgomery & Marie Åsberg","url":"https://scholargate.app/en/clinical-psychology/montgomery-asberg-depression","markdownUrl":"https://scholargate.app/en/clinical-psychology/montgomery-asberg-depression.md","definition":"The Montgomery-Åsberg Depression Rating Scale is a 10-item clinician-rated assessment designed by Stuart Montgomery and Marie Åsberg in 1979 to measure depression severity and track treatment response. Published in the British Journal of Psychiatry, the MADRS was developed as an alternative to longer instruments like the Hamilton Depression Rating Scale, emphasizing items most sensitive to treatment change. It has become a primary outcome measure in antidepressant trials and is widely used in both research and clinical practice across psychiatry, primary care, and medical specialty settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stuart Montgomery & Marie Åsberg","subfamily":"mood-disorder-assessment-clinician-rated","year":"1979","type":"Clinician-rated interview scale"},"citations":[{"ref":"Montgomery, S. A., & Åsberg, M. (1979). A new depression scale designed to be sensitive to change. British Journal of Psychiatry, 134, 382–389.","type":"article","doi":"10.1192/bjp.134.4.382","isbn":null,"url":null},{"ref":"Snaith, R. P. (1993). The concepts of mild depression. British Journal of Psychiatry, 150(3), 387–393.","type":"article","doi":"10.1192/bjp.150.3.387","isbn":null,"url":null},{"ref":"Faries, D. E., Pontén, M., Gregor, K. L., & Montgomery, S. A. (2000). Responsiveness of the Montgomery-Åsberg Depression Rating Scale. International Clinical Psychopharmacology, 15(6), 340–347.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Responsiveness+of+the+Montgomery-%C3%85sberg+Depression+Rating+Scale+Faries"}],"related":["phq-9","bdi-ii","hamilton-depression-rating-scale","quick-inventory-depressive","clinical-global-impressions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mood-median-test","name":"Mood's Median Test","fullName":"Mood's Median Test for k Groups","aliases":["median test","Brown-Mood median test","Mood Medyan Testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1954,"originator":"A. M. Mood","url":"https://scholargate.app/en/statistics/mood-median-test","markdownUrl":"https://scholargate.app/en/statistics/mood-median-test.md","definition":"Mood's median test is a nonparametric procedure that compares the medians of k independent groups by counting how many observations in each group fall above and below the pooled (grand) median, then applying a chi-square test to the resulting 2×k contingency table. It traces to A. M. Mood's 1954 work on nonparametric two-sample tests.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. M. Mood","year":1954,"type":"Nonparametric median comparison","estimator":"Chi-square on a 2×k above/below-median contingency table","outcome":"continuous or ordinal","groups":"k independent groups"},"citations":[{"ref":"Mood, A. M. (1954). On the Asymptotic Efficiency of Certain Nonparametric Two-Sample Tests. Annals of Mathematical Statistics, 25(3), 514-522.","type":"article","doi":"10.1214/aoms/1177728719","isbn":null,"url":null},{"ref":"Hollander, M., Wolfe, D. A., & Chicken, E. (2014). Nonparametric Statistical Methods (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470387375","url":null}],"related":["kruskal-wallis-test","mann-whitney-u-test","conover-iman-test","kolmogorov-smirnov-2sample","levene-brown-forsythe"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"moora","name":"MOORA","fullName":"Multi-Objective Optimisation by Ratio Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2006","originator":"Brauers, W. K. M., Zavadskas, E. K.","url":"https://scholargate.app/en/decision-making/moora","markdownUrl":"https://scholargate.app/en/decision-making/moora.md","definition":"MOORA (Multi-Objective Optimisation by Ratio Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Brauers, W. K. M., Zavadskas, E. K. in 2006. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brauers, W. K. M., Zavadskas, E. K.","subfamily":"Ranking","year":"2006","type":"Ratio system + reference point (vector normalisation)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":true},"citations":[{"ref":"Brauers, W. K. M., Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The%20MOORA%20method%20and%20its%20application%20to%20privatization%20in%20a%20transition%20economy"}],"related":["multimoora","ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"moosra","name":"MOOSRA","fullName":"Multi-Objective Optimization on the basis of Simple Ratio Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Sarkar, A. Panja, S. C. Das, D. Sarkar, B.","url":"https://scholargate.app/en/decision-making/moosra","markdownUrl":"https://scholargate.app/en/decision-making/moosra.md","definition":"MOOSRA (Multi-Objective Optimization on the basis of Simple Ratio Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Sarkar, A. Panja, S. C. Das, D. Sarkar, B. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sarkar, A. Panja, S. C. Das, D. Sarkar, B.","subfamily":"Ranking","year":"2015","type":"Ratio of benefit to cost weighted normalized scores","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Sarkar, A., Panja, S. C., Das, D., Sarkar, B. (2015). Developing an efficient decision support system for non-traditional machining process selection. Journal of Intelligent Manufacturing","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Developing+an+efficient+decision+support+system+for+non-traditional+machining+process+selection+Sarkar"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"moral-injury-events-scale","name":"Moral Injury Events Scale","fullName":"Moral Injury Events Scale (MIES)","aliases":["MIES"],"domain":"military-psychology","family":"process-pipeline","subfamily":"Moral injury assessment","year":2013,"originator":"Nash, Marino Carper, Mills, Au, Goldsmith, & Litz","url":"https://scholargate.app/en/military-psychology/moral-injury-events-scale","markdownUrl":"https://scholargate.app/en/military-psychology/moral-injury-events-scale.md","definition":"The MIES is a 9-item self-report measure assessing exposure to morally injurious events in military personnel. Developed by Nash and colleagues in 2013, it captures three dimensions: perpetration (committing acts that violate personal values), betrayal (witnessing leaders/unit members violate moral standards), and observed moral violations. The MIES measures moral injury burden distinct from PTSD and is increasingly used in veteran mental health screening and treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nash, Marino Carper, Mills, Au, Goldsmith, & Litz","subfamily":"Moral injury assessment","year":2013,"type":"Self-report"},"citations":[{"ref":"Nash, W. P., Marino Carper, T. L., Mills, M. A., Au, T. A., Goldsmith, A. A., & Litz, B. T. (2013). Psychometric evaluation of the Moral Injury Events Scale. Clinical Psychology & Psychotherapy, 20(3), 249-263.","type":"article","doi":"10.7205/milmed-d-13-00017","isbn":null,"url":null},{"ref":"Litz, B. T., Stein, N. B., Delaney, E., Lebowitz, L., Nash, W. P., Silva, C., & Maguen, S. (2009). Moral injury and moral repair in war veterans: A preliminary model and intervention strategy. Clinical Psychology Review, 29(8), 695-706.","type":"article","doi":"10.1016/j.cpr.2009.07.003","isbn":null,"url":null}],"related":["pcl-military","combat-exposure-scale","post-deployment-reintegration","peritraumatic-distress-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"morans-i-test","name":"Moran's I","fullName":"Moran's I Spatial Autocorrelation Test","aliases":["global Moran's I","spatial autocorrelation test","Moran's I Uzamsal Otokorelasyon Testi"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":1950,"originator":"Patrick A. P. Moran","url":"https://scholargate.app/en/spatial-analysis/morans-i-test","markdownUrl":"https://scholargate.app/en/spatial-analysis/morans-i-test.md","definition":"Moran's I is a global statistic, introduced by Patrick Moran in 1950, that measures whether and how a continuous variable is spatially autocorrelated across mapped units. A positive value signals clustering of similar values, a negative value signals a dispersed (checkerboard) pattern, and it is most often used as a diagnostic before moving to spatial regression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patrick A. P. Moran","year":1950,"type":"Global spatial autocorrelation statistic","estimator":"Permutation / analytical inference on a weighted cross-product","outcome":"continuous","minSample":30},"citations":[{"ref":"Moran, P.A.P. (1950). Notes on Continuous Stochastic Phenomena. Biometrika, 37(1/2), 17–23.","type":"article","doi":"10.2307/2332142","isbn":null,"url":null},{"ref":"Cliff, A.D. & Ord, J.K. (1981). Spatial Processes: Models and Applications. Pion.","type":"book","doi":null,"isbn":"978-0850860818","url":null}],"related":["lisa-analysis","spatial-lag-model","spatial-error-model","spatial-durbin-model","pearson-correlation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"morans-i","name":"Moran's I","fullName":"Moran's Index of Spatial Autocorrelation","aliases":["Moran's I statistic","global Moran's I","spatial autocorrelation index","Moran index"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1950","originator":"Patrick A. P. Moran","url":"https://scholargate.app/en/spatial-analysis/morans-i","markdownUrl":"https://scholargate.app/en/spatial-analysis/morans-i.md","definition":"Moran's I is the standard global statistic for detecting spatial autocorrelation: whether nearby locations tend to share similar values. The index ranges from approximately −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering), allowing researchers to test whether a geographic pattern differs from complete spatial randomness with a single, interpretable number.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patrick A. P. Moran","year":"1950","type":"Spatial autocorrelation statistic","dataType":"Georeferenced continuous or count data with a spatial weights matrix","subfamily":"GIS / spatial"},"citations":[{"ref":"Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.","type":"article","doi":"10.2307/2332142","isbn":null,"url":null},{"ref":"Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion.","type":"book","doi":null,"isbn":"9780850860818","url":null}],"related":["spatial-autocorrelation","gearys-c","local-indicators-of-spatial-association","local-morans-i","local-getis-ord-gi-star","geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"morisky-medication-adherence","name":"Morisky Medication Adherence Scale","fullName":"Morisky Medication Adherence Scale (MMAS)","aliases":["MMAS","Morisky Scale","Medication Adherence Scale"],"domain":"nursing","family":"process-pipeline","subfamily":"behavioral health assessment","year":"1986","originator":"Donald E. Morisky","url":"https://scholargate.app/en/nursing/morisky-medication-adherence","markdownUrl":"https://scholargate.app/en/nursing/morisky-medication-adherence.md","definition":"The Morisky Medication Adherence Scale is a brief, validated tool developed by Donald Morisky in 1986 to measure patients' adherence to prescribed medications. Originally created to assess hypertension medication compliance, it has since become a standard screening instrument across chronic disease management, primary care, and pharmaceutical research. The scale is valued for its brevity, ease of administration, and predictive validity for clinical outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald E. Morisky","subfamily":"behavioral health assessment","year":"1986","type":"Patient self-report questionnaire"},"citations":[{"ref":"Morisky, D. E., Green, L. W., & Levine, D. M. (1986). Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care, 24(1), 67-74.","type":"article","doi":"10.1097/00005650-198601000-00007","isbn":null,"url":null}],"related":["zarit-caregiver-burden-scale","clinical-frailty-scale","medication-adherence-barrier-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"morphological-analysis","name":"Morphological Analysis","fullName":"Morphological Analysis and Stemming","aliases":["stemming","lemmatization","Morfolojik Analiz ve Kök Bulma"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1980,"originator":"M.F. Porter (Porter stemmer)","url":"https://scholargate.app/en/text-mining/morphological-analysis","markdownUrl":"https://scholargate.app/en/text-mining/morphological-analysis.md","definition":"Morphological analysis splits words into their stems and affixes so that different surface forms of the same word can be treated as one. It covers two complementary approaches — rule-based stemming, such as the Porter (1980) and Snowball algorithms, and dictionary-aware lemmatization — and is a critical text-normalisation step for agglutinative languages such as Turkish and Arabic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M.F. Porter (Porter stemmer)","year":1980,"type":"Text-normalisation preprocessing task","approaches":"Stemming (Porter, Snowball) / lemmatization","output":"Word stems or dictionary lemmas","minSample":10,"difficulty":"Introductory"},"citations":[{"ref":"Porter, M.F. (1980). An Algorithm for Suffix Stripping. Program, 14(3), 130-137.","type":"article","doi":"10.1108/eb046814","isbn":null,"url":null},{"ref":"Schmid, H. (1994). Probabilistic Part-of-Speech Tagging Using Decision Trees. Proceedings of the International Conference on New Methods in Language Processing (NEMLAP).","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.cis.lmu.de/~schmid/tools/TreeTagger/data/tree-tagger1.pdf"}],"related":["sentiment-analysis","tf-idf","language-identification","text-segmentation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"morse-fall-scale","name":"Morse Fall Scale","fullName":"Morse Fall Scale for Fall Risk Assessment","aliases":["MFS","Morse Scale","Fall Risk Index"],"domain":"nursing","family":"process-pipeline","subfamily":"Fall risk assessment and prevention","year":"1987","originator":"Janice M. Morse","url":"https://scholargate.app/en/nursing/morse-fall-scale","markdownUrl":"https://scholargate.app/en/nursing/morse-fall-scale.md","definition":"The Morse Fall Scale (MFS) is a brief, reliable tool for assessing the risk of falling in hospitalized patients. Developed by Janice M. Morse through research identifying characteristics of fall-prone patients, the MFS evaluates six specific risk factors: history of falling, secondary diagnoses, ambulatory aids, intravenous therapy, gait, and mental status. The scale's simplicity, short administration time, and strong predictive validity have made it one of the most widely adopted fall risk assessment instruments in acute care settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Janice M. Morse","subfamily":"Fall risk assessment and prevention","year":"1987","type":"Risk assessment scale"},"citations":[{"ref":"Morse, J. M., Tylko, S. J., & Dixon, H. A. (1987). Characteristics of the fall-prone patient. The Gerontologist, 27(4), 516-522.","type":"article","doi":"10.1093/geront/27.4.516","isbn":null,"url":null},{"ref":"Morse, J. M. (1997). Preventing patient falls: establishing a fall intervention program. New York: Springer Publishing Company.","type":"article","doi":null,"isbn":null,"url":"https://www.springer.com/"}],"related":["patient-fall-risk-assessment","care-dependency-scale","nursing-sensitive-indicators","early-warning-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mos-social-support-survey","name":"MOS Social Support Survey","fullName":"Medical Outcomes Study Social Support Survey","aliases":["MOS-SS","Medical Outcomes Study Support Scale"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Functional and social support assessment","year":"1991","originator":"Cathy D. Sherbourne and Alice L. Stewart","url":"https://scholargate.app/en/health-measurement/mos-social-support-survey","markdownUrl":"https://scholargate.app/en/health-measurement/mos-social-support-survey.md","definition":"The Medical Outcomes Study Social Support Survey (MOS-SS) is a 19-item self-report measure of social support developed by Sherbourne and Stewart in 1991. It assesses functional aspects of social relationships—emotional, informational, tangible, and social companionship support—relevant to health outcomes in diverse populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cathy D. Sherbourne and Alice L. Stewart","subfamily":"Functional and social support assessment","year":"1991","type":"Social support perception measurement"},"citations":[{"ref":"Sherbourne, C. D., & Stewart, A. L. (1991). The MOS Social Support Survey. Social Science & Medicine, 32(6), 705–714.","type":"article","doi":"10.1016/0277-9536(91)90150-B","isbn":null,"url":null},{"ref":"Stewart, A. L., Hays, R. D., & Ware, J. E. (1988). The MOS Short-Form General Health Survey: reliability and validity in a patient population. Medical Care, 26(7), 724–735.","type":"article","doi":"10.1097/00005650-198807000-00007","isbn":null,"url":null},{"ref":"Cohen, S. (1992). Stress, social support, and disorder. In S. H. Friedman (Ed.), Hostility, coping, and health (pp. 109–128). American Psychological Association.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Stress%2C+social+support%2C+and+disorder+Cohen"}],"related":["sf-36","whoqol-bref","haq-disability-index","promis","duke-health-profile"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mosaic-plagiarism","name":"Mosaic Plagiarism","fullName":"Mosaic Plagiarism: Blending Copied Phrases With Original Text Without Attribution","aliases":["patch-writing","patchwork plagiarism","incremental plagiarism"],"domain":"research-ethics","family":"process-pipeline","subfamily":"plagiarism-detection-and-prevention","year":"1990s","originator":"Academic integrity framework (modern definition)","url":"https://scholargate.app/en/research-ethics/mosaic-plagiarism","markdownUrl":"https://scholargate.app/en/research-ethics/mosaic-plagiarism.md","definition":"Mosaic plagiarism, also called patch-writing, occurs when an author mixes copied phrases and sentences from a source with original text, rearranges material from multiple sources, or interweaves paraphrased and verbatim passages without proper citation or quotation marks. It is difficult to detect because the copied portions are interspersed with original writing, creating a surface appearance of original work.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Academic integrity framework (modern definition)","subfamily":"plagiarism-detection-and-prevention","year":"1990s","type":"Concept"},"citations":[{"ref":"Roig, M. (2015). Avoiding plagiarism, self-plagiarism, and other questionable writing practices: A guide to ethical writing. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products/plagiarism"},{"ref":"Harris, R. A. (2004). Using sources effectively: Strengthening your writing. Pyrczak Publishing.","type":"article","doi":null,"isbn":"9781884585127","url":null},{"ref":"Steneck, N. H. (2007). Introduction to the responsible conduct of research. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products"}],"related":["verbatim-plagiarism","paraphrasing-plagiarism","similarity-vs-plagiarism","turnitin-ithenticate"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"motivational-interviewing","name":"Motivational Interviewing","fullName":"Motivational Interviewing Technique","aliases":["MI","motivational enhancement"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Motivational intervention","year":"1991","originator":"William R. Miller, Stephen Rollnick","url":"https://scholargate.app/en/clinical-psychology/motivational-interviewing","markdownUrl":"https://scholargate.app/en/clinical-psychology/motivational-interviewing.md","definition":"Motivational Interviewing (MI) is a client-centered counseling approach designed to elicit and strengthen intrinsic motivation for behavioral change. Developed by William R. Miller and Stephen Rollnick in 1991, MI has been extensively applied to substance use disorders, health behavior change, mental health treatment engagement, and numerous other areas where ambivalence about change is a primary obstacle.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William R. Miller, Stephen Rollnick","subfamily":"Motivational intervention","year":"1991","type":"Client-centered counseling approach"},"citations":[{"ref":"Miller, W. R., & Rollnick, S. (2002). Motivational interviewing: Preparing people for change (2nd ed.). Guilford Press.","type":"article","doi":null,"isbn":"9781572305632","url":null},{"ref":"Miller, W. R., & Rollnick, S. (2009). Ten things that motivational interviewing is not. Behavioural and Cognitive Psychotherapy, 37(2), 129–140.","type":"article","doi":"10.1017/S1352465809005128","isbn":null,"url":null}],"related":["cognitive-behavioral-therapy-assessment","acceptance-commitment-therapy","trauma-focused-cbt"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"motor-assessment-scale","name":"MAS","fullName":"Motor Assessment Scale","aliases":["MAS"],"domain":"occupational-therapy","family":"process-pipeline","subfamily":"post-stroke motor recovery","year":"1985","originator":"Carr, J. H., Shepherd, R. B., Nordholm, L., & Lynne, D.","url":"https://scholargate.app/en/occupational-therapy/motor-assessment-scale","markdownUrl":"https://scholargate.app/en/occupational-therapy/motor-assessment-scale.md","definition":"The Motor Assessment Scale (MAS) is a clinician-rated, performance-based measure of motor function specifically developed for stroke survivors. Created by Carr, Shepherd, and colleagues (1985) at the University of Sydney, the MAS evaluates 8 fundamental motor tasks reflecting functional mobility and motor control relevant to post-stroke recovery. The MAS has become a standard outcome measure in stroke rehabilitation research and clinical practice, widely used to assess and track motor recovery following acute and chronic stroke.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carr, J. H., Shepherd, R. B., Nordholm, L., & Lynne, D.","subfamily":"post-stroke motor recovery","year":"1985","type":"Performance-based, clinician-rated observation"},"citations":[{"ref":"Carr, J. H., Shepherd, R. B., Nordholm, L., & Lynne, D. (1985). Investigation of a new motor assessment scale for stroke patients. Physical Therapy, 65(2), 175-180.","type":"article","doi":"10.1093/ptj/65.2.175","isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/3969398"},{"ref":"Carr, J. H., & Shepherd, R. B. (2010). Neurological rehabilitation: Optimizing motor performance (2nd ed.). Churchill Livingstone Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"}],"related":["wolf-motor-function-test","nine-hole-peg-test","upper-extremity-functional-scale","motor-assessment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"motor-drive-efficiency-analysis","name":"Motor Drive Efficiency Analysis","fullName":"Motor Drive Efficiency Analysis and Loss Assessment","aliases":["motor efficiency assessment","drive system losses","motor performance evaluation"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Rotating machinery and motor system efficiency","year":"1970s","originator":"Electrical machinery manufacturers","url":"https://scholargate.app/en/electrical-engineering/motor-drive-efficiency-analysis","markdownUrl":"https://scholargate.app/en/electrical-engineering/motor-drive-efficiency-analysis.md","definition":"Motor drive efficiency analysis quantifies energy losses in electrical motors and variable frequency drive (VFD) systems, which together comprise the largest industrial electrical load. Methods assess copper losses in windings, core (iron) losses, mechanical losses, and converter losses to identify efficiency improvements and estimate annual energy and cost savings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Electrical machinery manufacturers","subfamily":"Rotating machinery and motor system efficiency","year":"1970s","type":"Computational pipeline"},"citations":[{"ref":"IEEE Std 841-2023: IEEE Guide for the Selection and Use of AC Electric Motors with Adjustable Speed Drives.","type":"standard","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/10071625"},{"ref":"U.S. Department of Energy (2019). Motor System Efficiency: Industrial Assessment Center Data.","type":"report","doi":null,"isbn":null,"url":"https://www.energy.gov"},{"ref":"Deephansuwan, P., & Areerak, K. L. (2018). Review on three phase induction motor efficiency improvement. Applied Mechanics and Materials, 893, 249-257.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Review+on+three+phase+induction+motor+efficiency+improvement+Deephansuwan"}],"related":["power-quality-assessment","harmonic-distortion-analysis","power-flow-analysis","protection-relay-coordination"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"moving-average-model","name":"Moving Average Model","fullName":"Moving Average Time Series Model","aliases":["MA model","MA(q) process","moving-average process","Box-Jenkins MA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970","originator":"Box and Jenkins","url":"https://scholargate.app/en/econometrics/moving-average-model","markdownUrl":"https://scholargate.app/en/econometrics/moving-average-model.md","definition":"The Moving Average model of order q — written MA(q) — expresses the current value of a time series as a linear combination of the current and past random shocks (innovations). Unlike the AR model which uses lagged values of the series itself, the MA model uses lagged error terms, making it well-suited for capturing short-lived disturbances that dissipate over q periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box and Jenkins","year":"1970","type":"Linear time series model","dataType":"Univariate time series (continuous, equally spaced observations)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day.","type":"book","doi":null,"isbn":"978-0130607744","url":null},{"ref":"Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691042893","url":null}],"related":["autoregressive-model","arma-model","arima-model","sarima-model","vector-autoregression","exponential-smoothing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpf-dombi-wa","name":"MPF-DOMBI-WA","fullName":"m-Polar Fuzzy Dombi Weighted Averaging / Geometric MCDM (Akram, Yaqoob, Ali & Chammam 2020 / Akram & Adeel 2023 Ch. 8) — single-DM m-PF MCDM ranking via Dombi t-conorm/t-norm-based aggregation operators (mFDWA primary, mFDWG companion) followed by m-PF score-based descending ordering","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"2020","originator":"Akram, M., Yaqoob, N., Ali, G., Chammam, W.","url":"https://scholargate.app/en/decision-making/mpf-dombi-wa","markdownUrl":"https://scholargate.app/en/decision-making/mpf-dombi-wa.md","definition":"MPF-DOMBI-WA (m-Polar Fuzzy Dombi Weighted Averaging / Geometric MCDM (Akram, Yaqoob, Ali & Chammam 2020 / Akram & Adeel 2023 Ch. 8) — single-DM m-PF MCDM ranking via Dombi t-conorm/t-norm-based aggregation operators (mFDWA primary, mFDWG companion) followed by m-PF score-based descending ordering) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Akram, M., Yaqoob, N., Ali, G., Chammam, W. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Yaqoob, N., Ali, G., Chammam, W.","subfamily":"AggregationOperator","year":"2020","type":"Dombi-norm aggregation operator MCDM — Dombi sum/product on m-PF numbers (Eq. 4), weighted averaging (mFDWA, Eq. 6) and weighted geometric (mFDWG, Def. 9) operators, m-PF score function S(ζ)=(1/m)Σ p_i◦ζ (Eq. 1) ranking","value_space":"m_polar","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Akram, M., Yaqoob, N., Ali, G., Chammam, W. (2020). Extensions of Dombi Aggregation Operators for Decision Making under m-Polar Fuzzy Information. Journal of Mathematics (Hindawi)","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Extensions+of+Dombi+Aggregation+Operators+for+Decision+Making+under+m-Polar+Fuzzy+Information+Akram"}],"related":["ahp","entropy","bwm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpf-electre-i","name":"MPF-ELECTRE-I","fullName":"m-Polar Fuzzy extension of ELECTRE-I (Akram, Waseem & Liu 2019)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2019","originator":"Akram, M., Waseem, N., Liu, P.","url":"https://scholargate.app/en/decision-making/mpf-electre-i","markdownUrl":"https://scholargate.app/en/decision-making/mpf-electre-i.md","definition":"MPF-ELECTRE-I (m-Polar Fuzzy extension of ELECTRE-I (Akram, Waseem & Liu 2019)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Waseem, N., Liu, P. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Waseem, N., Liu, P.","subfamily":"Outranking","year":"2019","type":"Outranking — m-PF concordance/discordance with aggregate dominance graph","value_space":"m_polar","uncertainty":"epistemic","compensation":"none","rank_reversal":true},"citations":[{"ref":"Akram, M., Waseem, N., Liu, P. (2019). Novel approach in decision making with m-polar fuzzy ELECTRE-I. International Journal of Fuzzy Systems","type":"article","doi":"10.1007/s40815-019-00608-y","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpf-electre-ii","name":"MPF-ELECTRE-II","fullName":"m-Polar Fuzzy extension of ELECTRE-II for multi-criteria group decision making (Akram & Adeel 2023)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2023","originator":"Akram, M., Adeel, A.","url":"https://scholargate.app/en/decision-making/mpf-electre-ii","markdownUrl":"https://scholargate.app/en/decision-making/mpf-electre-ii.md","definition":"MPF-ELECTRE-II (m-Polar Fuzzy extension of ELECTRE-II for multi-criteria group decision making (Akram & Adeel 2023)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Adeel, A. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Adeel, A.","subfamily":"Outranking","year":"2023","type":"Outranking — m-PF strong/weak relations with five concordance & discordance thresholds and forward/reverse/average iterative ranking","value_space":"m_polar","uncertainty":"epistemic","compensation":"none","rank_reversal":true},"citations":[{"ref":"Akram, M., Adeel, A. (2023). MCDM Methods with Multi-polar Fuzzy Information — Chapter 4, §4.4 The m-Polar Fuzzy ELECTRE II Method. Studies in Fuzziness and Soft Computing, vol. 430, Springer Nature","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MCDM+Methods+with+Multi-polar+Fuzzy+Information+%E2%80%94+Chapter+4%2C+%C2%A74.4+The+m-Polar+Fuzzy+ELECTRE+II+Method+Akram"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpf-electre-iii","name":"MPF-ELECTRE-III","fullName":"m-Polar Fuzzy extension of ELECTRE-III with pseudo-criterion thresholds, Shannon-entropy objective weights and Li–Wang net credibility ranking (Akram & Adeel 2023)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2023","originator":"Akram, M., Adeel, A.","url":"https://scholargate.app/en/decision-making/mpf-electre-iii","markdownUrl":"https://scholargate.app/en/decision-making/mpf-electre-iii.md","definition":"MPF-ELECTRE-III (m-Polar Fuzzy extension of ELECTRE-III with pseudo-criterion thresholds, Shannon-entropy objective weights and Li–Wang net credibility ranking (Akram & Adeel 2023)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Adeel, A. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Adeel, A.","subfamily":"Outranking","year":"2023","type":"Pseudo-criterion outranking — q/p/ν thresholds, Shannon-entropy objective weights, partial concordance/discordance and Li–Wang credibility-based net ranking","value_space":"m_polar","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Akram, M., Adeel, A. (2023). MCDM Methods with Multi-polar Fuzzy Information — Chapter 5, §5.2 An m-Polar Fuzzy ELECTRE III Method. Studies in Fuzziness and Soft Computing, vol. 430, Springer Nature","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MCDM+Methods+with+Multi-polar+Fuzzy+Information+%E2%80%94+Chapter+5%2C+%C2%A75.2+An+m-Polar+Fuzzy+ELECTRE+III+Method+Akram"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpf-electre-iv","name":"MPF-ELECTRE-IV","fullName":"m-Polar Fuzzy extension of ELECTRE-IV with weight-free pseudo-criterion outranking, five dominance classes (quasi/canonical/pseudo/sub/veto), Vallée–Zielniewicz credibility levels and Belton–Stewart ascending+descending distillation (Akram & Adeel 2023)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2023","originator":"Akram, M., Adeel, A.","url":"https://scholargate.app/en/decision-making/mpf-electre-iv","markdownUrl":"https://scholargate.app/en/decision-making/mpf-electre-iv.md","definition":"MPF-ELECTRE-IV (m-Polar Fuzzy extension of ELECTRE-IV with weight-free pseudo-criterion outranking, five dominance classes (quasi/canonical/pseudo/sub/veto), Vallée–Zielniewicz credibility levels and Belton–Stewart ascending+descending distillation (Akram & Adeel 2023)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Adeel, A. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Adeel, A.","subfamily":"Outranking","year":"2023","type":"Weight-free pseudo-criterion outranking — q/p/ν thresholds, dominance-class counts (NP, NQ, NI, NE), Vallée–Zielniewicz ζ ∈ {1, 0.8, 0.6, 0.4, 0.2, 0} credibility, two-way distillation pre-orders intersected to a final pre-order","value_space":"m_polar","uncertainty":"epistemic","compensation":"none","rank_reversal":true},"citations":[{"ref":"Akram, M., Adeel, A. (2023). MCDM Methods with Multi-polar Fuzzy Information — Chapter 6, §6.2 An m-Polar Fuzzy ELECTRE IV Method. Studies in Fuzziness and Soft Computing, vol. 430, Springer Nature","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MCDM+Methods+with+Multi-polar+Fuzzy+Information+%E2%80%94+Chapter+6%2C+%C2%A76.2+An+m-Polar+Fuzzy+ELECTRE+IV+Method+Akram"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpf-hf-topsis","name":"MPF-HF-TOPSIS","fullName":"m-Polar Hesitant Fuzzy TOPSIS (Akram, Adeel & Alcantud 2019, Symmetry 11(6):795) — multi-criteria group decision-making by extending TOPSIS to the m-polar hesitant fuzzy (mHF) set framework; pole-wise mHPIS/mHNIS extraction, mHF Euclidean distance and closeness coefficient ranking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Akram, M., Adeel, A., Alcantud, J. C. R.","url":"https://scholargate.app/en/decision-making/mpf-hf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/mpf-hf-topsis.md","definition":"MPF-HF-TOPSIS (m-Polar Hesitant Fuzzy TOPSIS (Akram, Adeel & Alcantud 2019, Symmetry 11(6):795) — multi-criteria group decision-making by extending TOPSIS to the m-polar hesitant fuzzy (mHF) set framework; pole-wise mHPIS/mHNIS extraction, mHF Euclidean distance and closeness coefficient ranking) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Adeel, A., Alcantud, J. C. R. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Adeel, A., Alcantud, J. C. R.","subfamily":"Ranking","year":"2019","type":"Distance-based ranking — m-polar hesitant fuzzy TOPSIS — pole-wise max/min ideals on a weighted mHF decision matrix (Eqs. 1–2), mHF Euclidean distance (Eqs. 3–4), closeness coefficient E_j' (Eq. 5)","value_space":"m_polar","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Akram, M., Adeel, A., Alcantud, J. C. R. (2019). Multi-Criteria Group Decision-Making Using an m-Polar Hesitant Fuzzy TOPSIS Approach. Symmetry (MDPI)","type":"article","doi":"10.3390/sym11060795","isbn":null,"url":null}],"related":["ahp","entropy","bwm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpf-promethee","name":"MPF-PROMETHEE","fullName":"m-Polar Fuzzy extension of PROMETHEE I/II with AHP-derived crisp weights, six Brans–Vincke generalized criteria preference functions, and positive/negative/net outranking flows (Akram, Shumaiza & Alcantud 2020 / Akram & Adeel 2023 Ch. 7)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2020","originator":"Akram, M., Shumaiza, Alcantud, J. C. R.","url":"https://scholargate.app/en/decision-making/mpf-promethee","markdownUrl":"https://scholargate.app/en/decision-making/mpf-promethee.md","definition":"MPF-PROMETHEE (m-Polar Fuzzy extension of PROMETHEE I/II with AHP-derived crisp weights, six Brans–Vincke generalized criteria preference functions, and positive/negative/net outranking flows (Akram, Shumaiza & Alcantud 2020 / Akram & Adeel 2023 Ch. 7)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Shumaiza, Alcantud, J. C. R. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M., Shumaiza, Alcantud, J. C. R.","subfamily":"Outranking","year":"2020","type":"Outranking-flow PROMETHEE — six generalized criteria preference functions (I–VI), AHP-assisted scalar weights, positive/negative/net outranking flows, PROMETHEE I partial pre-order + PROMETHEE II complete order","value_space":"m_polar","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Akram, M., Shumaiza, Alcantud, J. C. R. (2020). An m-Polar Fuzzy PROMETHEE Approach for AHP-Assisted Group Decision-Making. Mathematical and Computational Applications (MDPI)","type":"article","doi":"10.3390/mca25020026","isbn":null,"url":null}],"related":["ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpf-topsis-ling","name":"MPF-TOPSIS-LING","fullName":"m-Polar Fuzzy Linguistic TOPSIS for MCGDM (Adeel, Akram & Koam 2019, Symmetry 11(6):735) — multi-criteria group decision-making via m-polar fuzzy linguistic variables (mFLV), expert-aggregated m-PF linguistic decision matrix, aggregated linguistic-term-set weights, m-PF linguistic positive/negative ideal solutions (mPIS / mNIS), m-PF linguistic Euclidean distances, relative closeness coefficient E'_j descending ranking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Adeel, A., Akram, M., Koam, A. N. A.","url":"https://scholargate.app/en/decision-making/mpf-topsis-ling","markdownUrl":"https://scholargate.app/en/decision-making/mpf-topsis-ling.md","definition":"MPF-TOPSIS-LING (m-Polar Fuzzy Linguistic TOPSIS for MCGDM (Adeel, Akram & Koam 2019, Symmetry 11(6):735) — multi-criteria group decision-making via m-polar fuzzy linguistic variables (mFLV), expert-aggregated m-PF linguistic decision matrix, aggregated linguistic-term-set weights, m-PF linguistic positive/negative ideal solutions (mPIS / mNIS), m-PF linguistic Euclidean distances, relative closeness coefficient E'_j descending ranking) is a ranking multi-criteria decision-making (MCDM) method introduced by Adeel, A., Akram, M., Koam, A. N. A. in 2019. It turns a decision matrix of alternative","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adeel, A., Akram, M., Koam, A. N. A.","subfamily":"Ranking","year":"2019","type":"Distance-based group-decision MCDM under m-polar fuzzy linguistic information — aggregated m-PF linguistic decision matrix d'^i_jk = (1/r) Σ_l d^l,i_jk, weighted matrix e^i_jk = w'_k d^i_jk, m-PF linguistic Euclidean distances (Eqs. 3–4) to mPIS / mNIS (Eqs. 1–2), closeness coefficient E'_j (Eq. 5) descending ranking","value_space":"m_polar","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Adeel, A., Akram, M., Koam, A. N. A. (2019). Group Decision-Making Based on m-Polar Fuzzy Linguistic TOPSIS Method. Symmetry (MDPI)","type":"article","doi":"10.3390/sym11060735","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpls","name":"MPLS","fullName":"Multiprotocol Label Switching","aliases":["label switching","traffic engineering"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Routing/forwarding","year":"2001","originator":"IETF MPLS Working Group","url":"https://scholargate.app/en/telecommunications/mpls","markdownUrl":"https://scholargate.app/en/telecommunications/mpls.md","definition":"Multiprotocol Label Switching (MPLS) is a forwarding paradigm that prepends a short label to packets, enabling routers to make forwarding decisions based on the label rather than IP destination address. Introduced by IETF (2001), MPLS was designed to enable traffic engineering, VPN creation, and fast rerouting in IP networks. While MPLS complexity is high, it remains foundational in service provider backbones for traffic engineering and Quality of Service (QoS) provisioning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"IETF MPLS Working Group","subfamily":"Routing/forwarding","year":"2001","type":"label-based forwarding paradigm"},"citations":[{"ref":"Rosen, E. C., Viswanathan, A., & Callon, R. (2001). Multiprotocol Label Switching Architecture. RFC 3031.","type":"article","doi":null,"isbn":null,"url":"https://www.ietf.org"},{"ref":"Awduche, D. O., Malcolm, J., Agogbua, J., et al. (2002). Requirements for Traffic Engineering over MPLS. RFC 2702.","type":"article","doi":null,"isbn":null,"url":"https://www.ietf.org"}],"related":["bgp","ospf","diffserv","software-defined-networking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mpsi","name":"MPSI","fullName":"Modified PSI method for objective weighting (Modified Preference Selection Index weighting)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2010","originator":"Maniya, K., Bhatt, M. G.","url":"https://scholargate.app/en/decision-making/mpsi","markdownUrl":"https://scholargate.app/en/decision-making/mpsi.md","definition":"MPSI (Modified PSI method for objective weighting (Modified Preference Selection Index weighting)) is a weight objective multi-criteria decision-making (MCDM) method introduced by Maniya, K., Bhatt, M. G. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maniya, K., Bhatt, M. G.","subfamily":"Weight_Objective","year":"2010","type":"Modified PSI variance-based objective weighting (weight extraction from PSI preference variation)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Maniya, K., Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design","type":"article","doi":"10.1016/j.matdes.2009.11.020","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mrc-dyspnoea-scale","name":"MRC Dyspnoea","fullName":"Medical Research Council Dyspnoea Scale","aliases":["MRC","MRC Dyspnea","Modified Borg"],"domain":"pulmonology","family":"process-pipeline","subfamily":"dyspnea-grading","year":"1959","originator":"Medical Research Council (UK)","url":"https://scholargate.app/en/pulmonology/mrc-dyspnoea-scale","markdownUrl":"https://scholargate.app/en/pulmonology/mrc-dyspnoea-scale.md","definition":"The MRC Dyspnoea Scale is a simple 5-grade ordinal classification of dyspnea severity based on the exertional threshold at which breathlessness limits activity. Developed by the UK Medical Research Council (MRC) in 1959, it remains one of the most widely used dyspnea assessments globally due to its brevity, ease of administration, and strong prognostic correlation in chronic obstructive pulmonary disease and other chronic respiratory diseases. The scale is used in clinical practice, epidemiological surveys, and longitudinal disease monitoring to grade symptom severity and guide treatment intensity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Medical Research Council (UK)","subfamily":"dyspnea-grading","year":"1959","type":"Clinician or self-rated ordinal scale"},"citations":[{"ref":"Van Swieten, J. C., Koudstaal, P. J., Visser, M. C., Schouten, H. J., & van Gijn, J. (1988). Interobserver agreement for the assessment of handicap in stroke patients. Stroke, 19(5), 604-607.","type":"article","doi":"10.1161/01.STR.19.5.604","isbn":null,"url":null},{"ref":"Bestall, J. C., Paul, E. A., Garrod, R., Garnham, R., Jones, P. W., & Wedzicha, J. A. (1999). Usefulness of the Medical Research Council (MRC) dyspnoea scale as a measure of disability in patients with chronic obstructive pulmonary disease. Thorax, 54(7), 581-586.","type":"article","doi":"10.1136/thx.54.7.581","isbn":null,"url":null}],"related":["st-george-respiratory-questionnaire","chronic-respiratory-disease-questionnaire","asthma-control-questionnaire","breathlessness-cough-sputum-scale","sinonasal-outcome-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"msfc","name":"MSFC","fullName":"Multiple Sclerosis Functional Composite","aliases":["MS Functional Composite"],"domain":"neurology","family":"process-pipeline","subfamily":"Multiple sclerosis functional assessment","year":"1999","originator":"Gary Cutter, Richard Rudick, and NMSS Consortium","url":"https://scholargate.app/en/neurology/msfc","markdownUrl":"https://scholargate.app/en/neurology/msfc.md","definition":"The Multiple Sclerosis Functional Composite (MSFC) is an objective, performance-based assessment of MS-related disability capturing three key functional domains: lower extremity mobility, upper extremity coordination, and cognitive/processing speed. Developed in 1999 by the National MS Society and adopted widely in clinical trials, the MSFC provides quantifiable endpoints complementing the Expanded Disability Status Scale (EDSS). The three-component design addresses EDSS limitations by including cognition and standardizing measurement via timed tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gary Cutter, Richard Rudick, and NMSS Consortium","subfamily":"Multiple sclerosis functional assessment","year":"1999","type":"Clinician-administered performance test"},"citations":[{"ref":"Cutter, G. R., Baier, M. L., Rudick, R. A., et al. (1999). Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Multiple Sclerosis, 5(4), 244-250.","type":"article","doi":"10.1093/brain/122.5.871","isbn":null,"url":null}],"related":["edss-multiple-sclerosis","updrs","rivermead-mobility-index","msfc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"msqol-54","name":"MSQOL-54","fullName":"Multiple Sclerosis Quality of Life-54","aliases":["MS QoL-54"],"domain":"neurology","family":"process-pipeline","subfamily":"disease-specific quality of life","year":"1995","originator":"Barbara G. Vickrey, UCLA","url":"https://scholargate.app/en/neurology/msqol-54","markdownUrl":"https://scholargate.app/en/neurology/msqol-54.md","definition":"The MSQOL-54 is a disease-specific quality-of-life instrument designed to assess the physical and mental burden of multiple sclerosis on patients' daily functioning and well-being. Developed by Vickrey and colleagues in 1995, it combines the widely-used SF-36 generic health questionnaire with 18 MS-specific items to provide comprehensive measurement of QoL in MS populations. This scale is a cornerstone tool in MS clinical research and patient monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barbara G. Vickrey, UCLA","subfamily":"disease-specific quality of life","year":"1995","type":"Self-report questionnaire"},"citations":[{"ref":"Vickrey, B. G., Hays, R. D., Genovese, B. J., Myers, L. W., & Ellison, G. W. (1995). Outcomes in Multiple Sclerosis: The Multiple Sclerosis Quality of Life-54 Scale. Health Psychology, 14(1), 34-42.","type":"article","doi":"10.1037/t53653-000","isbn":null,"url":null}],"related":["qolie-89","stroke-specific-qol","msws-12","modified-rankin-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"msws-12","name":"MSWS-12","fullName":"Multiple Sclerosis Walking Scale-12","aliases":["MS Walking Scale-12","MSWS"],"domain":"neurology","family":"process-pipeline","subfamily":"disease-specific walking disability","year":"2003","originator":"Jeremy C. Hobart, University of Plymouth","url":"https://scholargate.app/en/neurology/msws-12","markdownUrl":"https://scholargate.app/en/neurology/msws-12.md","definition":"The MSWS-12 is a brief, patient-reported outcome measure specifically designed to assess the impact of multiple sclerosis on walking ability and limitation. Developed by Hobart and colleagues in 2003, this 12-item scale captures both the physical difficulty and functional consequences of MS-related gait impairment. It is highly responsive to change and widely used in MS clinical trials and practice as a primary outcome measure for assessing intervention effects on mobility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeremy C. Hobart, University of Plymouth","subfamily":"disease-specific walking disability","year":"2003","type":"Self-report questionnaire"},"citations":[{"ref":"Hobart, J. C., Riazi, A., Lamping, D. L., Fitzpatrick, R., & Thompson, A. J. (2003). Measuring the impact of MS on walking ability: The 12-Item MS Walking Scale (MSWS-12). Neurology, 60(1), 31-36.","type":"article","doi":"10.1212/WNL.60.1.31","isbn":null,"url":null}],"related":["msqol-54","modified-rankin-scale","timed-25-foot-walk","stroke-specific-qol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mtt-mts-assay","name":"MTT/MTS Assay","fullName":"MTT/MTS Cell Viability and Proliferation Assay","aliases":["3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide","tetrazolium assay","mitochondrial activity assay"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Cell viability assessment","year":"1983","originator":"Tatsuro Mosmann","url":"https://scholargate.app/en/biomaterials/mtt-mts-assay","markdownUrl":"https://scholargate.app/en/biomaterials/mtt-mts-assay.md","definition":"The MTT assay, introduced by Tatsuro Mosmann in 1983, is a colorimetric method for quantifying cell viability and proliferation by measuring mitochondrial metabolic activity. The method detects the conversion of the water-soluble tetrazolium salt MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) by active mitochondria, producing an insoluble purple formazan precipitate proportional to the number of viable cells. The related MTS assay, which does not require solubilization, offers improved kinetics and is now widely adopted in both academic research and pharmaceutical development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tatsuro Mosmann","subfamily":"Cell viability assessment","year":"1983","type":"Colorimetric assay"},"citations":[{"ref":"Mosmann, T. (1983). Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. Journal of Immunological Methods, 65(1-2), 55-63.","type":"article","doi":"10.1016/0022-1759(83)90303-4","isbn":null,"url":null},{"ref":"Slade, P. G. (1999). MTS tetrazolium compound (abstract). Methods in Cell Biology, 63, 65-72.","type":"article","doi":null,"isbn":null,"url":"https://www.promega.com/products/cell-health-assays/cell-viability-and-proliferation/mts-assay/"},{"ref":"Riss, T. L., Moravec, R. A., Niles, A. L., et al. (2004). Cell viability assays. In Assay Guidance Manual. Eli Lilly & Company and the National Center for Advancing Translational Sciences.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/books/NBK144065/"}],"related":["live-dead-assay","hemolysis-assay","scratch-wound-assay","electrospinning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mueller-stokes-calculus","name":"Mueller-Stokes Calculus","fullName":"Mueller-Stokes Calculus for Polarization","aliases":["Mueller matrix method","Stokes parameters","Mueller calculus"],"domain":"optics","family":"process-pipeline","subfamily":"Polarization","year":"1852","originator":"George Gabriel Stokes and Hans Mueller","url":"https://scholargate.app/en/optics/mueller-stokes-calculus","markdownUrl":"https://scholargate.app/en/optics/mueller-stokes-calculus.md","definition":"Mueller-Stokes calculus is a mathematical framework for describing and analyzing the polarization properties of light, including partially polarized and unpolarized light. Grounded in George Gabriel Stokes' 1852 work on polarization parameters and extended by Hans Mueller in 1948, this formalism uses the four-component Stokes vector and the 4×4 Mueller matrix to track how optical systems transform polarization states.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Gabriel Stokes and Hans Mueller","subfamily":"Polarization","year":"1852","type":"Vector-matrix formalism"},"citations":[{"ref":"Stokes, G. G. (1852). On the composition and resolution of streams of polarized light from different sources. Transactions of the Cambridge Philosophical Society, 9, 399-416.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/"},{"ref":"Mueller, H. (1948). The foundations of optics. Journal of the Optical Society of America, 38(8), 661-644.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+foundations+of+optics+Mueller"},{"ref":"Goldstein, D. H. (2003). Polarized Light (2nd ed.). Marcel Dekker.","type":"book","doi":null,"isbn":null,"url":"https://www.dekker.com/"}],"related":["jones-calculus","fourier-optics","interferogram-fringe-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-arm-experiment","name":"Multi-arm experiment","fullName":"Multi-Arm Experimental Design","aliases":["multi-arm trial","multiple-arm experiment","multi-group experiment","many-arm design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Experimental design","year":"1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs","originator":"Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)","url":"https://scholargate.app/en/experimental-design/multi-arm-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/multi-arm-experiment.md","definition":"A multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)","year":"1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs","type":"Experimental design","dataType":"Continuous, binary, or time-to-event outcome data across three or more treatment arms","subfamily":"Experimental design"},"citations":[{"ref":"Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256.","type":"article","doi":"10.1002/sim.1430","isbn":null,"url":null},{"ref":"Multi-arm bandit. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Multi-armed_bandit"}],"related":["factorial-experiment","adaptive-experiment","randomized-controlled-trial","crossover-randomized-controlled-trial","ab-test","cluster-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-document-summarization","name":"Multi-Document Summarization","fullName":"Multi-Document Summarization","aliases":["MDS","Çok Belgeli Özetleme (Multi-Document Summarization)","multi-source summarization"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/multi-document-summarization","markdownUrl":"https://scholargate.app/en/text-mining/multi-document-summarization.md","definition":"Multi-document summarization (MDS) is a natural-language-processing task that condenses a cluster of related documents into a single comprehensive, coherent, and non-redundant summary. Formally described by Erkan and Radev (2004) through the LexRank algorithm, MDS is used in news cluster analysis, systematic literature reviews, and research synthesis to give readers a unified view of information spread across multiple sources.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-summarization task","approaches":"Extractive (graph-based, e.g., LexRank) / Abstractive (seq2seq, transformer-based)","minimumDocuments":"5 related documents","output":"Comprehensive, coherent, non-redundant summary covering the document set","keyChallenges":"Redundancy management, cross-document coreference, information fusion"},"citations":[{"ref":"Erkan, G. & Radev, D.R. (2004). LexRank: Graph-Based Lexical Centrality as Salience in Text Summarization. Journal of Artificial Intelligence Research, 22, 457-479.","type":"article","doi":null,"isbn":null,"url":"https://www.jair.org/index.php/jair/article/view/10396"},{"ref":"Liu, P.J. et al. (2018). Generating Wikipedia by Summarizing Long Sequences. International Conference on Learning Representations (ICLR).","type":"conference","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=Hyg0vbWC-"}],"related":["sentiment-analysis","text-classification","topic-modeling","tf-idf","bert-embeddings","extractive-summarization","abstractive-summarization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-confirmatory-factor-analysis","name":"Multi-group confirmatory factor analysis","fullName":"Multi-Group Confirmatory Factor Analysis","aliases":["MG-CFA","multi-group CFA","measurement invariance testing","multi-sample CFA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1971","originator":"Karl Jöreskog","url":"https://scholargate.app/en/psychometrics/multi-group-confirmatory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-confirmatory-factor-analysis.md","definition":"Multi-group confirmatory factor analysis tests whether a measurement model holds equivalently across two or more groups — such as cultures, genders, or time points. By imposing increasingly stringent equality constraints and comparing model fit, it determines whether comparisons of latent mean scores are justified.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Jöreskog","year":"1971","type":"Measurement model / invariance test","dataType":"Continuous or ordinal indicators from two or more groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null},{"ref":"Millsap, R. E. (2011). Statistical Approaches to Measurement Equivalence. Routledge.","type":"book","doi":null,"isbn":"978-0805859447","url":null}],"related":["confirmatory-factor-analysis","measurement-invariance","exploratory-factor-analysis","structural-equation-modeling","differential-item-functioning","multi-group-exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-content-validity","name":"Multi-group content validity","fullName":"Multi-group Content Validity","aliases":["multi-group CVI","cross-group content validity","subgroup content validity index","multi-panel content validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1986–2006","originator":"Lynn (1986); extended by Polit & Beck (2006)","url":"https://scholargate.app/en/psychometrics/multi-group-content-validity","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-content-validity.md","definition":"Multi-group content validity extends the standard content validity index (CVI) procedure by computing and comparing item- and scale-level validity indices across two or more distinct expert panels or subgroups. It ensures that a scale's items are judged as relevant and representative not only overall but also within each subgroup of interest, supporting cross-group generalizability of the instrument.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lynn (1986); extended by Polit & Beck (2006)","year":"1986–2006","type":"Validity assessment / expert judgment aggregation","dataType":"Ordinal ratings from multiple expert panels or subgroups","subfamily":"Scale / measurement"},"citations":[{"ref":"Polit, D. F. & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497.","type":"article","doi":"10.1002/nur.20147","isbn":null,"url":null},{"ref":"Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–385.","type":"article","doi":"10.1097/00006199-198611000-00017","isbn":null,"url":null}],"related":["content-validity-index","confirmatory-factor-analysis","measurement-invariance","delphi-method","inter-rater-reliability","scale-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-convergent-validity","name":"Multi-group convergent validity","fullName":"Multi-group Convergent Validity Assessment","aliases":["cross-group convergent validity","multi-sample convergent validity","MGCFA convergent validity","AVE across groups"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1981 / 2000","originator":"Fornell & Larcker (convergent validity criteria); Vandenberg & Lance (multi-group extension)","url":"https://scholargate.app/en/psychometrics/multi-group-convergent-validity","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-convergent-validity.md","definition":"Multi-group convergent validity examines whether items purported to measure the same latent construct relate strongly to that construct consistently across distinct subgroups such as demographic categories, cultures, or experimental conditions. It extends single-sample convergent validity checks into a comparative multi-group confirmatory factor analysis framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fornell & Larcker (convergent validity criteria); Vandenberg & Lance (multi-group extension)","year":"1981 / 2000","type":"Validity assessment procedure","dataType":"Ordinal or continuous scale items; multi-group CFA output","subfamily":"Scale / measurement"},"citations":[{"ref":"Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.","type":"article","doi":"10.1177/002224378101800104","isbn":null,"url":null},{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["convergent-validity","discriminant-validity","multi-group-confirmatory-factor-analysis","multi-group-measurement-invariance","confirmatory-factor-analysis","multi-group-discriminant-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-cronbachs-alpha","name":"Multi-group Cronbach's alpha","fullName":"Multi-group Cronbach's Alpha Reliability Analysis","aliases":["group-stratified alpha","cross-group alpha comparison","subgroup internal consistency","MG-alpha"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1951 (alpha); multi-group application from 1980s onward","originator":"Lee J. Cronbach (alpha); multi-group extension in cross-cultural and measurement invariance research","url":"https://scholargate.app/en/psychometrics/multi-group-cronbachs-alpha","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-cronbachs-alpha.md","definition":"Multi-group Cronbach's alpha estimates and compares the internal consistency reliability of a scale separately within each of two or more defined subgroups. It is used in cross-cultural, demographic, and comparative psychometric research to establish that a scale measures its construct with equivalent precision across groups before making cross-group comparisons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach (alpha); multi-group extension in cross-cultural and measurement invariance research","year":"1951 (alpha); multi-group application from 1980s onward","type":"Reliability / internal consistency comparison","dataType":"Ordinal or interval item responses across two or more defined groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.","type":"article","doi":"10.1007/BF02310555","isbn":null,"url":null},{"ref":"van de Schoot, R., Lugtig, P., & Hox, J. (2012). A checklist for testing measurement invariance. European Journal of Developmental Psychology, 9(4), 486–492.","type":"article","doi":"10.1080/17405629.2012.686740","isbn":null,"url":null}],"related":["cronbachs-alpha","mcdonalds-omega","multi-group-reliability-analysis","multi-group-confirmatory-factor-analysis","multi-group-measurement-invariance","differential-item-functioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-differential-item-functioning","name":"Multi-group Differential Item Functioning","fullName":"Multi-group Differential Item Functioning","aliases":["MG-DIF","multi-group DIF","differential item functioning across groups","multiple-group DIF analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1980s-1990s","originator":"Shealy & Stout (SIBTEST framework); Lord (IRT-based DIF)","url":"https://scholargate.app/en/psychometrics/multi-group-differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-differential-item-functioning.md","definition":"Multi-group differential item functioning examines whether test or scale items function equivalently across three or more distinct groups — such as gender, ethnicity, or country — after matching respondents on the underlying trait being measured. Items that behave differently across groups threaten fair measurement and valid score comparisons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shealy & Stout (SIBTEST framework); Lord (IRT-based DIF)","year":"1980s-1990s","type":"Measurement bias detection","dataType":"Dichotomous or polytomous item responses across three or more groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Millsap, R. E. (2012). Statistical Approaches to Measurement Invariance. Routledge.","type":"book","doi":null,"isbn":"978-1848728936","url":null},{"ref":"Magis, D., Beland, S., Tuerlinckx, F., & De Boeck, P. (2010). A general framework and an R package for the detection of dichotomous differential item functioning. Behavior Research Methods, 42(3), 847-862.","type":"article","doi":"10.3758/BRM.42.3.847","isbn":null,"url":null}],"related":["differential-item-functioning","multi-group-measurement-invariance","item-response-theory","multi-group-item-response-theory","confirmatory-factor-analysis","multi-group-confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-discriminant-validity","name":"Multi-group discriminant validity","fullName":"Multi-group Discriminant Validity Assessment","aliases":["cross-group discriminant validity","multi-sample discriminant validity","MGDV","discriminant validity across groups"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1981 (foundational criterion); multi-group extension 1990s–2000s","originator":"Fornell & Larcker (for the AVE-based criterion); extended to multi-group settings by the SEM invariance literature","url":"https://scholargate.app/en/psychometrics/multi-group-discriminant-validity","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-discriminant-validity.md","definition":"Multi-group discriminant validity assessment tests whether constructs measured by a scale are empirically distinct not just in one sample but consistently across two or more groups (e.g., cultures, genders, age cohorts). It extends standard discriminant validity criteria — such as the AVE rule and the HTMT ratio — into a multi-group confirmatory factor analysis framework to verify that conceptual distinctness is replicable across subpopulations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fornell & Larcker (for the AVE-based criterion); extended to multi-group settings by the SEM invariance literature","year":"1981 (foundational criterion); multi-group extension 1990s–2000s","type":"Validity assessment / model comparison","dataType":"Ordinal or continuous indicators from two or more groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.","type":"article","doi":"10.1177/002224378101800104","isbn":null,"url":null},{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["discriminant-validity","convergent-validity","multi-group-confirmatory-factor-analysis","multi-group-measurement-invariance","construct-validity","average-variance-extracted"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-exploratory-factor-analysis","name":"Multi-group EFA","fullName":"Multi-group Exploratory Factor Analysis","aliases":["MGEFA","multi-sample exploratory factor analysis","simultaneous EFA across groups","exploratory factor analysis with multiple groups"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1981","originator":"Muthén & Christoffersson","url":"https://scholargate.app/en/psychometrics/multi-group-exploratory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-exploratory-factor-analysis.md","definition":"Multi-group exploratory factor analysis estimates the latent factor structure of a set of items separately within each of two or more groups and then examines whether the discovered structures are consistent across groups. It is used to explore dimensionality before imposing invariance constraints, and to diagnose group-specific factor patterns that would invalidate cross-group comparisons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Muthén & Christoffersson","year":"1981","type":"Latent variable / multi-group dimension reduction","dataType":"Continuous or ordinal item responses across two or more groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Muthén, B. & Christoffersson, A. (1981). Simultaneous factor analysis of dichotomous variables in several groups. Psychometrika, 46(4), 407–419.","type":"article","doi":"10.1007/BF02293798","isbn":null,"url":null},{"ref":"Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111–150.","type":"article","doi":"10.1207/S15327906MBR3601_05","isbn":null,"url":null}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","multi-group-confirmatory-factor-analysis","measurement-invariance","differential-item-functioning","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-generalizability-theory","name":"Multi-group Generalizability Theory","fullName":"Multi-group Generalizability Theory","aliases":["MG G-theory","multi-group G-theory","generalizability theory across groups","cross-group G-study"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1963–2001","originator":"Lee J. Cronbach and colleagues (Cronbach, Gleser, Nanda, Rajaratnam), extended to multi-group contexts by Brennan and others","url":"https://scholargate.app/en/psychometrics/multi-group-generalizability-theory","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-generalizability-theory.md","definition":"Multi-group generalizability theory (MG G-theory) extends classical generalizability theory to estimate and compare variance components — attributable to persons, items, raters, occasions, and their interactions — simultaneously across two or more defined groups. It reveals whether a measurement procedure is equally reliable and generalizable for every group studied, supporting fair and equitable score interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach and colleagues (Cronbach, Gleser, Nanda, Rajaratnam), extended to multi-group contexts by Brennan and others","year":"1963–2001","type":"Variance component / reliability generalization","dataType":"Continuous or polytomous ratings/scores nested in facets (raters, occasions, items) across groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Brennan, R. L. (2001). Generalizability Theory. Springer.","type":"book","doi":null,"isbn":"978-0387952826","url":null},{"ref":"Shavelson, R. J. & Webb, N. M. (1989). Generalizability theory: 1973–1988. British Journal of Mathematical and Statistical Psychology, 42(1), 3–27.","type":"article","doi":"10.1037/0003-066x.44.6.922","isbn":null,"url":null}],"related":["generalizability-theory","multi-group-confirmatory-factor-analysis","multi-group-measurement-invariance","multi-group-reliability-analysis","multi-group-cronbachs-alpha","multi-group-rasch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-item-analysis","name":"Multi-group item analysis","fullName":"Multi-Group Item Analysis","aliases":["MGIA","group-comparative item analysis","subgroup item analysis","cross-group item analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1986","originator":"Classical test theory tradition; systematised by Crocker & Algina (1986)","url":"https://scholargate.app/en/psychometrics/multi-group-item-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-item-analysis.md","definition":"Multi-group item analysis computes classical item statistics — difficulty, discrimination, and corrected item-total correlations — separately for each subgroup in a sample and then compares those statistics across groups. It is a standard diagnostic step in scale development and test fairness evaluation, revealing items that behave differently for men versus women, across age cohorts, or across cultural groups before more formal DIF testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical test theory tradition; systematised by Crocker & Algina (1986)","year":"1986","type":"Comparative item-level analysis","dataType":"Dichotomous or polytomous item responses across two or more known groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Crocker, L. & Algina, J. (1986). Introduction to Classical and Modern Test Theory. Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030616341","url":null},{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null}],"related":["differential-item-functioning","multi-group-reliability-analysis","multi-group-confirmatory-factor-analysis","item-response-theory","multi-group-measurement-invariance","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-item-response-theory","name":"Multi-group item response theory","fullName":"Multi-Group Item Response Theory","aliases":["MG-IRT","multiple-group IRT","multi-group latent trait model","IRT across groups"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s","originator":"Multiple contributors; formalized by Birnbaum (1968) for IRT; multi-group extensions developed through 1980s–1990s","url":"https://scholargate.app/en/psychometrics/multi-group-item-response-theory","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-item-response-theory.md","definition":"Multi-group item response theory fits IRT models simultaneously across two or more defined groups — such as males and females, or different cultural samples — to determine whether item parameters are invariant across those groups. It is the primary IRT-based framework for testing measurement equivalence and detecting differential item functioning (DIF) at the model level.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; formalized by Birnbaum (1968) for IRT; multi-group extensions developed through 1980s–1990s","year":"1990s","type":"Latent trait / measurement invariance","dataType":"Dichotomous or polytomous item responses across defined groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null},{"ref":"Kim, S.-H. & Cohen, A. S. (1998). Detection of differential item functioning under the graded response model with the likelihood ratio test. Applied Psychological Measurement, 22(4), 345–355.","type":"article","doi":"10.1177/014662169802200403","isbn":null,"url":null}],"related":["item-response-theory","differential-item-functioning","multi-group-confirmatory-factor-analysis","multi-group-measurement-invariance","confirmatory-factor-analysis","multi-group-rasch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-mcdonalds-omega","name":"Multi-group McDonald's omega","fullName":"Multi-group McDonald's Omega Reliability Analysis","aliases":["multi-group omega","omega across groups","group-comparative omega","MG-omega"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1999 (multi-group extension: 2000s–2010s)","originator":"Roderick P. McDonald","url":"https://scholargate.app/en/psychometrics/multi-group-mcdonalds-omega","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-mcdonalds-omega.md","definition":"Multi-group McDonald's omega estimates and compares the reliability of a scale across two or more distinct groups. Rooted in confirmatory factor analysis, it uses the factor loadings and unique variances from each group's measurement model to compute omega, then tests whether reliability is statistically equivalent across groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roderick P. McDonald","year":"1999 (multi-group extension: 2000s–2010s)","type":"Reliability coefficient (multi-group extension)","dataType":"Ordinal or continuous item scores across two or more groups","subfamily":"Scale / measurement"},"citations":[{"ref":"McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805830408","url":null},{"ref":"Hayes, A. F. & Coutts, J. J. (2020). Use omega rather than Cronbach's alpha for estimating reliability. But … Communication Methods and Measures, 14(1), 1–24.","type":"article","doi":"10.1080/19312458.2020.1718629","isbn":null,"url":null}],"related":["mcdonalds-omega","cronbachs-alpha","multi-group-cronbachs-alpha","multi-group-confirmatory-factor-analysis","multi-group-measurement-invariance","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-measurement-invariance","name":"Multi-group measurement invariance","fullName":"Multi-group Measurement Invariance Testing","aliases":["measurement invariance","factorial invariance","cross-group invariance","MI testing"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1971–1993","originator":"Jöreskog, K. G. (1971); Meredith, W. (1993)","url":"https://scholargate.app/en/psychometrics/multi-group-measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-measurement-invariance.md","definition":"Multi-group measurement invariance testing examines whether a latent construct is measured in the same way across two or more distinct groups — such as cultures, genders, or age cohorts. It is a prerequisite for meaningful group comparisons of latent means or relationships, ensuring that observed score differences reflect true differences rather than measurement artifacts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jöreskog, K. G. (1971); Meredith, W. (1993)","year":"1971–1993","type":"Model comparison / hypothesis testing","dataType":"Ordinal or continuous item responses across two or more groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null},{"ref":"Putnick, D. L. & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90.","type":"article","doi":"10.1016/j.dr.2016.06.004","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","multi-group-confirmatory-factor-analysis","differential-item-functioning","structural-equation-modeling","exploratory-factor-analysis","multi-group-exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-rasch-model","name":"Multi-group Rasch model","fullName":"Multi-group Rasch Model","aliases":["MG-Rasch","Rasch measurement invariance","multi-group 1PL IRT","cross-group Rasch analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1960 (Rasch); 1980s–1990s (multi-group extensions)","originator":"Georg Rasch (single-group); extended to multi-group applications by Fischer, Molenaar, and others","url":"https://scholargate.app/en/psychometrics/multi-group-rasch-model","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-rasch-model.md","definition":"The multi-group Rasch model fits the one-parameter logistic item response model simultaneously across two or more distinct groups, testing whether item difficulty parameters are invariant across groups. It is the primary psychometric tool for establishing that a scale measures the same latent trait with the same metric in each group, a prerequisite for meaningful score comparisons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georg Rasch (single-group); extended to multi-group applications by Fischer, Molenaar, and others","year":"1960 (Rasch); 1980s–1990s (multi-group extensions)","type":"Item response model / measurement invariance test","dataType":"Dichotomous or polytomous item responses from two or more identified groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Fischer, G. H. & Molenaar, I. W. (Eds.) (1995). Rasch Models: Foundations, Recent Developments, and Applications. Springer.","type":"book","doi":null,"isbn":"978-0387944296","url":null},{"ref":"Andrich, D. (1988). Rasch Models for Measurement. Sage Publications.","type":"book","doi":null,"isbn":"978-0803927414","url":null}],"related":["rasch-model","item-response-theory","multi-group-confirmatory-factor-analysis","differential-item-functioning","multi-group-measurement-invariance","multi-group-item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-reliability-analysis","name":"Multi-group Reliability Analysis","fullName":"Multi-group Reliability Analysis","aliases":["reliability comparison across groups","group-specific reliability estimation","multi-sample reliability analysis","cross-group internal consistency"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Classical test theory traditions; synthesized in modern practice by Vandenberg & Lance (2000) and Sijtsma (2009)","url":"https://scholargate.app/en/psychometrics/multi-group-reliability-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-reliability-analysis.md","definition":"Multi-group reliability analysis estimates internal consistency or stability coefficients separately within each group and then formally compares them to determine whether a scale functions with equal precision across populations. It is a foundational step in cross-group measurement research, typically carried out alongside or prior to measurement invariance testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical test theory traditions; synthesized in modern practice by Vandenberg & Lance (2000) and Sijtsma (2009)","year":"1990s–2000s","type":"Reliability estimation and comparison","dataType":"Ordinal or interval item responses from two or more distinct groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null},{"ref":"Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach's alpha. Psychometrika, 74(1), 107–120.","type":"article","doi":"10.1007/s11336-008-9101-0","isbn":null,"url":null}],"related":["cronbachs-alpha","mcdonalds-omega","multi-group-measurement-invariance","multi-group-confirmatory-factor-analysis","generalizability-theory","multi-group-cronbachs-alpha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-scale-development","name":"Multi-group scale development","fullName":"Multi-Group Scale Development","aliases":["MGSD","cross-group scale development","multi-sample scale development","comparative scale construction"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1971 (multi-group CFA); 2000 (applied synthesis for scale development)","originator":"Jöreskog, K. G. (multi-group SEM framework); systematised for scale development by Vandenberg & Lance (2000)","url":"https://scholargate.app/en/psychometrics/multi-group-scale-development","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-scale-development.md","definition":"Multi-group scale development constructs and validates a measurement scale simultaneously across two or more distinct populations or groups. The approach integrates standard item generation and factor-analytic procedures with a systematic hierarchy of measurement invariance tests to ensure that the resulting scale measures the same construct in the same way in every target group.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jöreskog, K. G. (multi-group SEM framework); systematised for scale development by Vandenberg & Lance (2000)","year":"1971 (multi-group CFA); 2000 (applied synthesis for scale development)","type":"Scale development / measurement model testing","dataType":"Ordinal or continuous item-level responses from two or more groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null},{"ref":"Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.","type":"book","doi":null,"isbn":"978-0805864656","url":null}],"related":["confirmatory-factor-analysis","multi-group-confirmatory-factor-analysis","multi-group-measurement-invariance","scale-development","exploratory-factor-analysis","differential-item-functioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-group-test-retest-reliability","name":"Multi-group test-retest reliability","fullName":"Multi-group Test-Retest Reliability Analysis","aliases":["multi-group temporal stability","cross-group test-retest reliability","group-comparative retest reliability","multi-sample temporal consistency"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1979–2000","originator":"Systematic multi-group extensions developed alongside measurement invariance frameworks (Vandenberg & Lance, 2000); intraclass correlation foundation in Shrout & Fleiss (1979)","url":"https://scholargate.app/en/psychometrics/multi-group-test-retest-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/multi-group-test-retest-reliability.md","definition":"Multi-group test-retest reliability evaluates whether a measure produces stable scores across time separately for two or more defined groups — such as different genders, age cohorts, or clinical populations — and determines whether the degree of that temporal stability is equivalent across those groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Systematic multi-group extensions developed alongside measurement invariance frameworks (Vandenberg & Lance, 2000); intraclass correlation foundation in Shrout & Fleiss (1979)","year":"1979–2000","type":"Reliability estimation across groups","dataType":"Repeated-measures scores from two or more defined groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Shrout, P. E. & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.","type":"article","doi":"10.1037/0033-2909.86.2.420","isbn":null,"url":null},{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["test-retest-reliability","multi-group-measurement-invariance","multi-group-cronbachs-alpha","intraclass-correlation","confirmatory-factor-analysis","multi-group-confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-layer-perceptron","name":"Multi-layer Perceptron","fullName":"Multi-layer Perceptron (Feedforward Neural Network with Backpropagation)","aliases":["MLP","feedforward neural network","fully connected neural network","artificial neural network","dense neural network"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1986,"originator":"Rumelhart, D. E., Hinton, G. E., & Williams, R. J.","url":"https://scholargate.app/en/machine-learning/multi-layer-perceptron","markdownUrl":"https://scholargate.app/en/machine-learning/multi-layer-perceptron.md","definition":"The Multi-layer Perceptron (MLP) is a feedforward neural network architecture trained by backpropagation, formalised by Rumelhart, Hinton, and Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons with nonlinear activation functions, and an output layer, the MLP can approximate any continuous function to arbitrary accuracy and serves as the conceptual bridge between classical machine learning and modern deep learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rumelhart, D. E., Hinton, G. E., & Williams, R. J.","year":1986,"type":"Feedforward neural network (supervised learning)","task":"Classification, regression, function approximation","minSample":100},"citations":[{"ref":"Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.","type":"article","doi":"10.1038/323533a0","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 6–7). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 5). Springer.","type":"book","doi":null,"isbn":"978-0-387-31073-2","url":null}],"related":["logistic-regression","random-forest","xgboost","convolutional-neural-network","recurrent-neural-network","support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-level-cluster-sampling","name":"Multi-level Cluster Sampling","fullName":"Multi-level Cluster Sampling","aliases":["hierarchical cluster sampling","nested cluster sampling","multi-stage cluster sampling","clustered multilevel sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1950s-1970s (cluster sampling); multilevel extension formalized 1980s-1990s","originator":"W. G. Cochran (cluster sampling foundations); extended into multilevel contexts by survey methodologists","url":"https://scholargate.app/en/survey-methodology/multi-level-cluster-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-level-cluster-sampling.md","definition":"Multi-level cluster sampling is a probability sampling design for hierarchically structured populations — such as students nested within classrooms within schools within districts. Clusters are randomly selected at each level of the hierarchy before individual units are sampled within the final-level clusters. The design mirrors the natural nesting of real-world populations and enables efficient large-scale data collection while supporting multilevel statistical analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"W. G. Cochran (cluster sampling foundations); extended into multilevel contexts by survey methodologists","year":"1950s-1970s (cluster sampling); multilevel extension formalized 1980s-1990s","type":"Probability sampling design","dataType":"Quantitative; nested/hierarchical population data","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1849202008","url":null}],"related":["cluster-sampling","multistage-sampling","stratified-sampling","proportional-cluster-sampling","systematic-sampling","simple-random-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-level-convenience-sampling","name":"Multi-level Convenience Sampling","fullName":"Multi-level Convenience Sampling","aliases":["hierarchical convenience sampling","nested convenience sampling","multilevel accessibility sampling","multi-tier convenience sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1980s–1990s (concurrent with multilevel modeling development)","originator":"Emerged from multilevel/hierarchical research traditions","url":"https://scholargate.app/en/survey-methodology/multi-level-convenience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-level-convenience-sampling.md","definition":"Multi-level convenience sampling is a non-probability approach in which units are selected by convenience at each of two or more nested levels of a hierarchy — for example, recruiting whatever schools agree to participate and then enrolling all available students within those schools. It is widely used in organizational, educational, and health research where the researcher has limited control over access but must respect the nested structure of the population.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from multilevel/hierarchical research traditions","year":"1980s–1990s (concurrent with multilevel modeling development)","type":"Non-probability sampling design","dataType":"Any data collected at two or more nested organizational or social levels","subfamily":"Sampling"},"citations":[{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728462","url":null},{"ref":"Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4.","type":"article","doi":"10.11648/j.ajtas.20160501.11","isbn":null,"url":null}],"related":["convenience-sampling","multistage-sampling","cluster-sampling","purposive-sampling","multi-level-cluster-sampling","multi-level-stratified-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-level-maximum-variation-sampling","name":"Multi-level Maximum Variation Sampling","fullName":"Multi-level Maximum Variation Sampling","aliases":["hierarchical maximum variation sampling","nested maximum diversity sampling","multi-tier purposive variation sampling","MLMVS"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s","originator":"Synthesized from Patton's maximum variation sampling (1990) and multi-level survey design traditions","url":"https://scholargate.app/en/survey-methodology/multi-level-maximum-variation-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-level-maximum-variation-sampling.md","definition":"Multi-level maximum variation sampling is a purposive strategy that deliberately selects cases at two or more nested organizational levels — such as schools within districts, or patients within clinics — while maximizing heterogeneity on key dimensions at each level. The aim is to capture the full range of variation within a hierarchically structured population so that patterns common across diverse contexts can be identified and context-specific differences can be documented with credibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Patton's maximum variation sampling (1990) and multi-level survey design traditions","year":"1990s–2000s","type":"Purposive qualitative/mixed-methods sampling design","dataType":"Qualitative interviews, observations, or mixed-methods data at two or more nested organizational levels","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Chapter 5: Maximum variation sampling and purposeful sampling strategies]","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Bryman, A. (2016). Social Research Methods (5th ed.). Oxford University Press. [Multi-level and purposive sampling in mixed and qualitative designs]","type":"book","doi":null,"isbn":"978-0198745082","url":null}],"related":["maximum-variation-sampling","purposive-sampling","multistage-sampling","stratified-sampling","theoretical-sampling","multi-level-purposive-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-level-purposive-sampling","name":"Multi-level Purposive Sampling","fullName":"Multi-level Purposive Sampling","aliases":["hierarchical purposive sampling","nested purposive sampling","multi-tier purposive sampling","multi-site purposive sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1980s–1990s","originator":"Derived from Patton's purposive sampling framework; formalized in multi-site qualitative and mixed-methods research","url":"https://scholargate.app/en/survey-methodology/multi-level-purposive-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-level-purposive-sampling.md","definition":"Multi-level purposive sampling applies purposive selection criteria at two or more nested levels of a research hierarchy — for instance, first selecting sites or organizations, then selecting participants within each site. This layered approach allows researchers to align the theoretical logic of purposive sampling with the real-world structure of complex, hierarchical populations, making it especially valuable in multi-site qualitative studies and mixed-methods research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from Patton's purposive sampling framework; formalized in multi-site qualitative and mixed-methods research","year":"1980s–1990s","type":"Non-probability sampling strategy","dataType":"Qualitative or mixed-methods data from hierarchically nested units","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative Data Analysis: A Methods Sourcebook (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1452257877","url":null}],"related":["purposive-sampling","multistage-sampling","maximum-variation-sampling","theoretical-sampling","cluster-sampling","stratified-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-level-stratified-sampling","name":"Multi-level Stratified Sampling","fullName":"Multi-level Stratified Sampling","aliases":["hierarchical stratified sampling","nested stratified sampling","multilevel stratified design","stratified multilevel sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1950s–1970s","originator":"Formalized by Leslie Kish and William G. Cochran in the mid-20th century survey sampling literature","url":"https://scholargate.app/en/survey-methodology/multi-level-stratified-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-level-stratified-sampling.md","definition":"Multi-level stratified sampling applies stratification at two or more hierarchical levels of a nested population structure — for example, first stratifying geographic regions, then stratifying schools within each region, then stratifying classrooms within each school. This layered control over the composition of the sample at every level reduces variance and supports analysis at each level of the hierarchy, making it a powerful design for large-scale educational, epidemiological, and organizational surveys.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized by Leslie Kish and William G. Cochran in the mid-20th century survey sampling literature","year":"1950s–1970s","type":"Probability sampling design","dataType":"Hierarchically structured populations (e.g., students within schools, employees within firms)","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471489009","url":null}],"related":["stratified-sampling","multistage-sampling","cluster-sampling","proportional-stratified-sampling","systematic-sampling","multi-level-cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-level-typical-case-sampling","name":"Multi-level Typical Case Sampling","fullName":"Multi-level Typical Case Sampling","aliases":["multilevel typical case selection","hierarchical typical case sampling","nested typical case sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s","originator":"Draws on Patton (typical case sampling) and multilevel research traditions (Hox, Raudenbush)","url":"https://scholargate.app/en/survey-methodology/multi-level-typical-case-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-level-typical-case-sampling.md","definition":"Multi-level typical case sampling is a purposive strategy that selects representative, average-profile units at each level of a hierarchical structure — for example, typical classrooms within typical schools, or typical employees within typical departments. It is used when the research goal is to describe or illustrate the ordinary functioning of a nested phenomenon rather than to capture its extremes or full variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Draws on Patton (typical case sampling) and multilevel research traditions (Hox, Raudenbush)","year":"1990s–2000s","type":"Purposive sampling strategy","dataType":"Nested/hierarchical data (e.g., students within schools, employees within organizations)","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728462","url":null}],"related":["typical-case-sampling","purposive-sampling","multi-level-purposive-sampling","multistage-sampling","maximum-variation-sampling","multi-level-cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-level-weighted-sampling","name":"Multi-level weighted sampling","fullName":"Multi-level Weighted Sampling","aliases":["hierarchical weighted sampling","nested weighted sampling","multilevel probability weighting","weighted hierarchical sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1960s–1980s (developed alongside large-scale survey programs)","originator":"Leslie Kish (probability sampling theory); complex survey methodologists","url":"https://scholargate.app/en/survey-methodology/multi-level-weighted-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-level-weighted-sampling.md","definition":"Multi-level weighted sampling is a probability-based survey design that draws samples from hierarchically nested populations — such as students within classrooms within schools within districts — and assigns design weights at each level to account for unequal selection probabilities. The resulting weighted data enable unbiased population-level inference despite the complex, non-proportional structure of the sampling frame. It is the backbone of major international assessments such as PISA and TIMSS.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leslie Kish (probability sampling theory); complex survey methodologists","year":"1960s–1980s (developed alongside large-scale survey programs)","type":"Probability sampling design","dataType":"Hierarchically structured populations (e.g., students within schools within districts)","subfamily":"Sampling"},"citations":[{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons. New York.","type":"book","doi":null,"isbn":"978-0471109495","url":null},{"ref":"Skinner, C. J., Holt, D., & Smith, T. M. F. (Eds.). (1989). Analysis of Complex Surveys. John Wiley & Sons. Chichester.","type":"book","doi":null,"isbn":"978-0471918455","url":null}],"related":["multistage-sampling","stratified-sampling","cluster-sampling","weighted-sampling","proportional-stratified-sampling","systematic-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-agent-based-modeling","name":"Multi-objective agent-based modeling","fullName":"Multi-Objective Agent-Based Modeling","aliases":["MO-ABM","Multi-objective ABM","Pareto-based agent-based modeling","Multi-objective agent simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2001-2006","originator":"Deb, K.; Tesfatsion, L. et al.","url":"https://scholargate.app/en/simulation/multi-objective-agent-based-modeling","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-agent-based-modeling.md","definition":"Multi-Objective Agent-Based Modeling (MO-ABM) couples agent-based simulation with multi-objective optimization to simultaneously optimize several conflicting performance criteria across complex adaptive systems. Autonomous agents interact according to behavioral rules while an optimizer searches for parameter configurations that achieve Pareto-optimal trade-offs among competing system-level goals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Deb, K.; Tesfatsion, L. et al.","year":"2001-2006","type":"Simulation-optimization hybrid","dataType":"Agent behavioral rules, performance objectives, environment parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester.","type":"book","doi":null,"isbn":"9780471873396","url":null},{"ref":"Tesfatsion, L., Judd, K. L. (Eds.) (2006). Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics. North-Holland, Amsterdam.","type":"book","doi":null,"isbn":"9780444512536","url":null}],"related":["agent-based-modeling","multi-objective-optimization","nsga-ii","multi-objective-genetic-algorithm","stochastic-agent-based-modeling","multi-objective-system-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-ant-colony-optimization","name":"Multi-objective ant colony optimization","fullName":"Multi-Objective Ant Colony Optimization (MOACO)","aliases":["MOACO","Multi-Objective ACO","Pareto Ant Colony Optimization","Multi-objective ACO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1999","originator":"Gambardella, Taillard & Agazzi; Dorigo & Stützle","url":"https://scholargate.app/en/simulation/multi-objective-ant-colony-optimization","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-ant-colony-optimization.md","definition":"Multi-Objective Ant Colony Optimization (MOACO) is a swarm-intelligence metaheuristic that extends the classic Ant Colony Optimization framework to simultaneously optimize two or more conflicting objectives. Artificial ants construct candidate solutions guided by pheromone trails and heuristic information, progressively building an archive of Pareto-optimal solutions rather than converging to a single best answer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gambardella, Taillard & Agazzi; Dorigo & Stützle","year":"1999","type":"Population-based metaheuristic","dataType":"Combinatorial / continuous solution spaces with multiple conflicting objectives","subfamily":"Simulation / optimization"},"citations":[{"ref":"Gambardella, L. M., Taillard, E., & Agazzi, G. (1999). MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. In D. Corne, M. Dorigo, & F. Glover (Eds.), New Ideas in Optimization (pp. 63–76). McGraw-Hill.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MACS-VRPTW+Multiple+ant+colony+system+vehicle+routing+Gambardella+1999"},{"ref":"Dorigo, M., & Stützle, T. (2004). Ant Colony Optimization. MIT Press.","type":"book","doi":null,"isbn":"9780262042192","url":null}],"related":["ant-colony-optimization","multi-objective-genetic-algorithm","nsga-ii","multi-objective-particle-swarm-optimization","multi-objective-simulated-annealing","pareto-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-cellular-automata","name":"Multi-objective cellular automata","fullName":"Multi-Objective Cellular Automata — Simulation-based spatial optimization with multiple competing objectives","aliases":["MOCA","Multi-objective CA","Multi-criteria cellular automata","MO-CA"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Various (Liu et al., White & Engelen, Clarke et al.)","url":"https://scholargate.app/en/simulation/multi-objective-cellular-automata","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-cellular-automata.md","definition":"Multi-Objective Cellular Automata (MOCA) couples the bottom-up spatial dynamics of cellular automata with multi-objective optimization to simultaneously pursue competing goals — such as maximizing urban compactness while minimizing ecosystem loss. Each grid cell updates its state based on transition rules that are calibrated or steered to satisfy a Pareto-optimal trade-off among two or more objectives, making the method widely used in land-use change simulation, urban growth modeling, and spatial planning under conflicting demands.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Liu et al., White & Engelen, Clarke et al.)","year":"1990s–2000s","type":"Hybrid simulation-optimization","dataType":"Spatial / raster grid, categorical or continuous state values","subfamily":"Simulation / optimization"},"citations":[{"ref":"Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., Pei, F. (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94-116.","type":"article","doi":"10.1016/j.landurbplan.2017.09.019","isbn":null,"url":null},{"ref":"Jantz, C. A., Goetz, S. J., Shelley, M. K. (2004). Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environment and Planning B: Planning and Design, 31(2), 251-271.","type":"article","doi":"10.1068/b2983","isbn":null,"url":null}],"related":["cellular-automata","multi-objective-optimization","agent-based-modeling","nsga-ii","multi-objective-genetic-algorithm","system-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-discrete-event-simulation","name":"Multi-objective discrete-event simulation","fullName":"Multi-Objective Discrete-Event Simulation","aliases":["MO-DES","Multi-objective DES","Pareto-based discrete-event simulation","DES with multi-objective optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Various (DES: Tocher 1963; multi-objective integration: 1990s–2000s OR literature)","url":"https://scholargate.app/en/simulation/multi-objective-discrete-event-simulation","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-discrete-event-simulation.md","definition":"Multi-Objective Discrete-Event Simulation (MO-DES) couples a discrete-event simulation engine with multi-objective optimization to explore trade-offs among two or more conflicting performance measures — such as throughput, cost, and waiting time — across stochastic, time-ordered process models. It is widely applied in manufacturing, logistics, healthcare, and service system design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (DES: Tocher 1963; multi-objective integration: 1990s–2000s OR literature)","year":"1990s–2000s","type":"Simulation-optimization hybrid","dataType":"Event logs, process parameters, performance metrics","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kleijnen, J. P. C., & Gaury, E. (2003). Short-term robustness of production management systems: A case study. European Journal of Operational Research, 148(2), 452–465.","type":"article","doi":"10.1016/s0377-2217(02)00437-x","isbn":null,"url":null},{"ref":"Discrete-event simulation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Discrete-event_simulation"}],"related":["discrete-event-simulation","multi-objective-optimization","nsga-ii","monte-carlo-simulation","stochastic-discrete-event-simulation","multi-objective-system-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-dynamic-programming","name":"Multi-objective dynamic programming","fullName":"Multi-Objective Dynamic Programming","aliases":["MODP","Multi-criteria dynamic programming","Vector dynamic programming","Pareto dynamic programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957-1975","originator":"Extension of Bellman (1957); formalized by multiple authors from 1970s onward","url":"https://scholargate.app/en/simulation/multi-objective-dynamic-programming","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-dynamic-programming.md","definition":"Multi-Objective Dynamic Programming (MODP) extends Bellman's classical dynamic programming to settings where a decision-maker must optimize several competing objectives simultaneously across a sequence of stages. Rather than a single optimal policy, it produces a Pareto-optimal set of policies — each representing a distinct trade-off profile — by propagating vector-valued value functions backward through the state space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Bellman (1957); formalized by multiple authors from 1970s onward","year":"1957-1975","type":"Exact optimization — recursive multi-objective decomposition","dataType":"Sequential decision states, numeric rewards/costs on multiple objectives","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780691079516","url":null},{"ref":"Daellenbach, H. G., & Flood, R. L. (1992). Multi-objective dynamic programming. European Journal of Operational Research, 56(2), 215-225.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multi-objective+dynamic+programming+Daellenbach"}],"related":["multi-objective-optimization","dynamic-programming","multi-objective-linear-programming","nsga-ii","stochastic-dynamic-programming","multi-objective-genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-genetic-algorithm","name":"Multi-objective genetic algorithm","fullName":"Multi-Objective Genetic Algorithm (MOGA)","aliases":["MOGA","Multi-objective GA","Evolutionary multi-objective optimization","EMO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1984","originator":"Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)","url":"https://scholargate.app/en/simulation/multi-objective-genetic-algorithm","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-genetic-algorithm.md","definition":"A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)","year":"1984","type":"Population-based evolutionary optimizer","dataType":"Continuous, discrete, or mixed decision variables with multiple objective functions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.","type":"book","doi":null,"isbn":"9780201157673","url":null},{"ref":"Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.","type":"article","doi":"10.1109/4235.996017","isbn":null,"url":null}],"related":["nsga-ii","multi-objective-optimization","genetic-algorithm","multi-objective-particle-swarm-optimization","multi-objective-simulated-annealing","pareto-front-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-goal-programming","name":"Multi-objective goal programming","fullName":"Multi-Objective Goal Programming","aliases":["MOGP","Multi-goal programming","Vector goal programming","Multi-criteria goal programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1961","originator":"Charnes, A. and Cooper, W. W.","url":"https://scholargate.app/en/simulation/multi-objective-goal-programming","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-goal-programming.md","definition":"Multi-Objective Goal Programming (MOGP) is a mathematical programming technique that simultaneously pursues several aspirational targets by minimizing weighted deviations from each goal. Rooted in Charnes and Cooper's original goal programming framework (1961), MOGP extends it to handle multiple competing objectives, making it indispensable in operations research, supply chain design, resource allocation, and policy analysis where decision-makers must satisfy — or come close to — multiple conflicting requirements at once.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charnes, A. and Cooper, W. W.","year":"1961","type":"Mathematical programming / multi-criteria optimization","dataType":"Quantitative targets and weights; linear or nonlinear constraints","subfamily":"Simulation / optimization"},"citations":[{"ref":"Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York.","type":"book","doi":null,"isbn":"978-0471148258","url":null},{"ref":"Jones, D., Tamiz, M. (2010). Practical Goal Programming. Springer, New York.","type":"book","doi":"10.1007/978-1-4419-5771-9","isbn":null,"url":null}],"related":["goal-programming","multi-objective-optimization","multi-objective-linear-programming","lexicographic-goal-programming","weighted-goal-programming","nsga-ii"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-linear-programming","name":"Multi-objective linear programming","fullName":"Multi-Objective Linear Programming (MOLP)","aliases":["MOLP","Vector Linear Programming","Multi-criteria LP","Linear Vector Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1955–1986","originator":"Steuer, R. E.; Charnes, A.; Cooper, W. W.","url":"https://scholargate.app/en/simulation/multi-objective-linear-programming","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-linear-programming.md","definition":"Multi-Objective Linear Programming (MOLP) extends classical linear programming to handle several conflicting linear objective functions simultaneously over a feasible region defined by linear constraints. Instead of a single optimal solution, MOLP produces a Pareto-efficient frontier from which a decision-maker selects a preferred trade-off. It is foundational to operations research and management science for resource allocation, planning, and design problems with competing goals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steuer, R. E.; Charnes, A.; Cooper, W. W.","year":"1955–1986","type":"Mathematical optimization / vector optimization","dataType":"Continuous numerical variables, linear objective functions, linear constraints","subfamily":"Simulation / optimization"},"citations":[{"ref":"Steuer, R. E. (1986). Multiple Criteria Optimization: Theory, Computation, and Application. John Wiley & Sons, New York.","type":"book","doi":null,"isbn":"9780471888468","url":null},{"ref":"Chankong, V., Haimes, Y. Y. (1983). Multiobjective Decision Making: Theory and Methodology. North-Holland, New York.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multiobjective+Decision+Making+Theory+and+Methodology+Chankong+Haimes+1983"}],"related":["goal-programming","multi-objective-optimization","linear-programming","nsga-ii","weighted-sum-model","pareto-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-markov-model","name":"Multi-objective Markov Model","fullName":"Multi-objective Markov Decision Process Model","aliases":["MOMDP","Multi-objective MDP","Multi-criteria Markov Decision Process","MO-Markov Model"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2006","originator":"Chatterjee, K., Majumdar, R., Henzinger, T. A. (formal; survey: Roijers et al.)","url":"https://scholargate.app/en/simulation/multi-objective-markov-model","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-markov-model.md","definition":"A Multi-objective Markov Model (MOMDP) extends classical Markov Decision Processes to settings where an agent must optimize several reward signals simultaneously. Instead of a single optimal policy, the model produces a Pareto-optimal set of policies, enabling decision-makers to navigate trade-offs between competing goals such as cost, risk, and throughput over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chatterjee, K., Majumdar, R., Henzinger, T. A. (formal; survey: Roijers et al.)","year":"2006","type":"Stochastic sequential decision model with multiple objectives","dataType":"Transition probabilities, state spaces, multi-dimensional reward vectors","subfamily":"Simulation / optimization"},"citations":[{"ref":"Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48, 67–113.","type":"article","doi":"10.1613/jair.3987","isbn":null,"url":null},{"ref":"Chatterjee, K., Majumdar, R., & Henzinger, T. A. (2006). Markov decision processes with multiple objectives. In Proceedings of STACS 2006, Lecture Notes in Computer Science, vol. 3884, pp. 325–336. Springer, Berlin.","type":"inproceedings","doi":"10.1007/11672142_26","isbn":null,"url":null}],"related":["markov-model","multi-objective-optimization","stochastic-dynamic-programming","multi-objective-dynamic-programming","scenario-analysis","stochastic-markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-microsimulation","name":"Multi-objective microsimulation","fullName":"Multi-objective Microsimulation — Policy evaluation across simultaneous competing objectives","aliases":["MO-Microsim","Multi-criteria microsimulation","Multi-objective policy microsimulation","MOMS"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957 (microsimulation); 2000s (multi-objective extension)","originator":"Orcutt, G. H. (microsimulation); multi-objective extension developed by policy modeling community","url":"https://scholargate.app/en/simulation/multi-objective-microsimulation","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-microsimulation.md","definition":"Multi-objective microsimulation extends the classic microsimulation framework by simultaneously tracking and optimizing several competing policy objectives — such as efficiency, equity, fiscal cost, and social welfare — across a heterogeneous population of individual units. It produces a Pareto frontier of policy options rather than a single recommended solution, enabling transparent tradeoff analysis for complex policy decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Orcutt, G. H. (microsimulation); multi-objective extension developed by policy modeling community","year":"1957 (microsimulation); 2000s (multi-objective extension)","type":"Simulation-based policy evaluation","dataType":"Individual-level microdata, longitudinal administrative records, survey data","subfamily":"Simulation / optimization"},"citations":[{"ref":"Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39(2), 116-123.","type":"article","doi":"10.2307/1928528","isbn":null,"url":null},{"ref":"Dekkers, G., & Belloni, P. (2015). Combining microsimulation and policy analysis: toward a multi-objective welfare approach. International Journal of Microsimulation, 8(1), 20-49.","type":"article","doi":null,"isbn":null,"url":"https://microsimulation.pub/articles/00072"}],"related":["microsimulation","multi-objective-optimization","monte-carlo-simulation","scenario-analysis","stochastic-microsimulation","agent-based-microsimulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-mixed-integer-programming","name":"Multi-objective mixed-integer programming","fullName":"Multi-Objective Mixed-Integer Programming","aliases":["MO-MIP","Multi-criteria MIP","MOMIP","Multi-objective MILP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1980s–2000s","originator":"Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization","url":"https://scholargate.app/en/simulation/multi-objective-mixed-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-mixed-integer-programming.md","definition":"Multi-Objective Mixed-Integer Programming (MO-MIP) is an optimization framework that simultaneously optimizes two or more conflicting objective functions subject to linear or nonlinear constraints, where some decision variables are restricted to integer values and others are continuous. It is widely applied in engineering design, supply chain planning, resource allocation, and scheduling problems that require discrete choices alongside continuous quantities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization","year":"1980s–2000s","type":"Mathematical optimization","dataType":"Numerical decision variables (continuous and integer), objective function coefficients, constraints","subfamily":"Simulation / optimization"},"citations":[{"ref":"Ehrgott, M. (2005). Multicriteria Optimization (2nd ed.). Springer, Berlin.","type":"book","doi":null,"isbn":"9783540213987","url":null},{"ref":"Mavrotas, G. (2009). Effective implementation of the epsilon-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2), 455-465.","type":"article","doi":"10.1016/j.amc.2009.03.037","isbn":null,"url":null}],"related":["mixed-integer-programming","multi-objective-optimization","multi-objective-linear-programming","multi-objective-dynamic-programming","nsga-ii","multi-objective-goal-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-optimization","name":"Multi-Objective Optimization","fullName":"Multi-Objective Optimization (MOO) — simultaneous optimization of two or more conflicting objective functions","aliases":["MOO","Multi-Criteria Optimization","Vector Optimization","Pareto Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1896 (concept); 1989–2002 (evolutionary algorithms era)","originator":"Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.","url":"https://scholargate.app/en/simulation/multi-objective-optimization","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-optimization.md","definition":"Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.","year":"1896 (concept); 1989–2002 (evolutionary algorithms era)","type":"Optimization framework","dataType":"Continuous, discrete, or mixed decision variables; multiple objective function values","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester.","type":"book","doi":null,"isbn":"9780471873396","url":null},{"ref":"Multi-objective optimization. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Multi-objective_optimization"}],"related":["nsga-ii","pareto-analysis","goal-programming","genetic-algorithm","mixed-integer-programming","weighted-sum-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-particle-swarm-optimization","name":"Multi-objective particle swarm optimization","fullName":"Multi-Objective Particle Swarm Optimization (MOPSO)","aliases":["MOPSO","Multi-objective PSO","Pareto PSO","Vector-evaluated PSO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2004","originator":"Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.","url":"https://scholargate.app/en/simulation/multi-objective-particle-swarm-optimization","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-particle-swarm-optimization.md","definition":"Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of candidate solutions toward the true Pareto front without requiring a priori preference information.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S.","year":"2004","type":"Population-based swarm metaheuristic","dataType":"Continuous decision variables, multiple objective functions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Coello Coello, C. A., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 256–279.","type":"article","doi":"10.1109/TEVC.2004.826067","isbn":null,"url":null},{"ref":"Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks (ICNN), Perth, Australia, 4, 1942–1948.","type":"inproceedings","doi":"10.1109/ICNN.1995.488968","isbn":null,"url":null}],"related":["nsga-ii","multi-objective-genetic-algorithm","multi-objective-simulated-annealing","particle-swarm-optimization","multi-objective-ant-colony-optimization","multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-queueing-simulation","name":"Multi-objective Queueing Simulation","fullName":"Multi-objective Queueing Simulation — Simultaneous optimization of competing performance metrics in simulated queuing systems","aliases":["MOQS","Multi-criteria Queueing Simulation","Multi-objective Queue Optimization","Pareto Queueing Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Operations research community (Banks, Deb, and related authors)","url":"https://scholargate.app/en/simulation/multi-objective-queueing-simulation","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-queueing-simulation.md","definition":"Multi-objective queueing simulation combines discrete-event queueing models with multi-objective optimization to simultaneously evaluate and optimize conflicting performance metrics — such as average wait time, server utilization, throughput, and service cost — across a simulated queuing system. It produces a Pareto front of non-dominated solutions rather than a single optimal point, enabling decision-makers to understand trade-offs explicitly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Operations research community (Banks, Deb, and related authors)","year":"1990s–2000s","type":"Simulation-based multi-objective optimization","dataType":"Queueing system parameters (arrival rates, service rates, capacity, costs)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Pearson Prentice Hall.","type":"book","doi":null,"isbn":"9780136062127","url":null},{"ref":"Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons.","type":"book","doi":null,"isbn":"9780471873396","url":null}],"related":["multi-objective-optimization","queueing-simulation","discrete-event-simulation","nsga-ii","multi-objective-discrete-event-simulation","pareto-front-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-scenario-analysis","name":"Multi-objective Scenario Analysis","fullName":"Multi-objective Scenario Analysis — Evaluating alternative futures across multiple competing objectives","aliases":["MOSA","Multi-criteria scenario analysis","Multi-objective futures analysis","MO-scenario analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2013 (integrated framework); scenario analysis roots: 1967","originator":"Stewart, French & Rios (integration formalized); scenario analysis roots: Kahn & Wiener (1967)","url":"https://scholargate.app/en/simulation/multi-objective-scenario-analysis","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-scenario-analysis.md","definition":"Multi-objective Scenario Analysis (MOSA) is a structured method that constructs a set of plausible future scenarios and evaluates each scenario against multiple competing objectives or criteria. By making trade-offs explicit across objectives and across possible futures, it supports strategic decisions where uncertainty about the future and conflicts between goals co-exist. It is widely applied in energy planning, climate adaptation, public policy, and corporate strategy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stewart, French & Rios (integration formalized); scenario analysis roots: Kahn & Wiener (1967)","year":"2013 (integrated framework); scenario analysis roots: 1967","type":"Structured qualitative-quantitative hybrid","dataType":"Scenario narratives, performance matrices, objective weights","subfamily":"Simulation / optimization"},"citations":[{"ref":"Stewart, T. J., French, S., & Rios, J. (2013). Integrating multicriteria decision analysis and scenario planning: Review and extension. Omega, 41(4), 679-688.","type":"article","doi":"10.1016/j.omega.2012.09.003","isbn":null,"url":null},{"ref":"Schoemaker, P. J. H. (1995). Scenario planning: A tool for strategic thinking. Sloan Management Review, 36(2), 25-40.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Scenario+planning+a+tool+for+strategic+thinking+Schoemaker+1995"}],"related":["scenario-analysis","multi-objective-optimization","multi-criteria-decision-analysis","sensitivity-analysis","stochastic-scenario-analysis","policy-scenario-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-sensitivity-analysis","name":"Multi-objective sensitivity analysis","fullName":"Multi-Objective Sensitivity Analysis","aliases":["MOSA","Multi-criteria sensitivity analysis","Pareto sensitivity analysis","Multi-objective SA"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Evolved from classical sensitivity analysis (Saltelli et al.) combined with multi-objective optimization (Pareto, 1896)","url":"https://scholargate.app/en/simulation/multi-objective-sensitivity-analysis","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-sensitivity-analysis.md","definition":"Multi-Objective Sensitivity Analysis (MOSA) examines how changes in model parameters, weights, or assumptions affect an entire set of competing objectives simultaneously. Rather than asking how a single output shifts, MOSA tracks changes in the Pareto front or trade-off surface, revealing which parameters most destabilize multi-objective solutions and where decision-maker choices are robust versus fragile.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from classical sensitivity analysis (Saltelli et al.) combined with multi-objective optimization (Pareto, 1896)","year":"1990s–2000s","type":"Analytical technique — parametric sensitivity across multiple objectives","dataType":"Numerical model parameters, objective function values, Pareto-optimal solutions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley, Chichester.","type":"book","doi":null,"isbn":"9780470059975","url":null},{"ref":"Ehrgott, M. (2005). Multicriteria Optimization (2nd ed.). Springer, Berlin.","type":"book","doi":"10.1007/3-540-27659-9","isbn":null,"url":null}],"related":["sensitivity-analysis","multi-objective-optimization","pareto-front-analysis","scenario-analysis","monte-carlo-simulation","multi-objective-goal-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-simulated-annealing","name":"Multi-objective simulated annealing","fullName":"Multi-Objective Simulated Annealing","aliases":["MOSA","Multi-Criteria Simulated Annealing","Pareto Simulated Annealing","PSA"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1992–1998","originator":"Serafini, P.; Czyzak, P. and Jaszkiewicz, A.","url":"https://scholargate.app/en/simulation/multi-objective-simulated-annealing","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-simulated-annealing.md","definition":"Multi-Objective Simulated Annealing (MOSA) extends the classical simulated annealing metaheuristic to problems with two or more conflicting objective functions. Instead of converging to a single optimum, MOSA explores the solution space stochastically and maintains an archive of non-dominated (Pareto-optimal) solutions, offering decision-makers a diverse trade-off front rather than one prescribed answer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Serafini, P.; Czyzak, P. and Jaszkiewicz, A.","year":"1992–1998","type":"Metaheuristic / Pareto-based optimizer","dataType":"Continuous or combinatorial decision variables with multiple objective functions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Czyzak, P., Jaszkiewicz, A. (1998). Pareto simulated annealing — a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis, 7(1), 34–47.","type":"article","doi":"10.1007/978-3-642-59132-7_33","isbn":null,"url":null},{"ref":"Serafini, P. (1992). Simulated annealing for multi-objective optimization problems. In Proceedings of the Tenth International Conference on Multiple Criteria Decision Making, Taipei, Taiwan, pp. 87–96.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Simulated+annealing+for+multi-objective+optimization+problems+Serafini+1992"}],"related":["simulated-annealing","multi-objective-optimization","nsga-ii","multi-objective-genetic-algorithm","multi-objective-tabu-search","multi-objective-particle-swarm-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-system-dynamics","name":"Multi-objective system dynamics","fullName":"Multi-Objective System Dynamics","aliases":["MOSD","Multi-criteria SD","Multi-objective SD modeling","System dynamics with multiple objectives"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1961 (SD); multi-objective extensions from 1990s onward","originator":"Forrester, J. W. (System Dynamics); multi-objective extension by various authors","url":"https://scholargate.app/en/simulation/multi-objective-system-dynamics","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-system-dynamics.md","definition":"Multi-Objective System Dynamics (MOSD) couples the feedback-loop simulation power of System Dynamics with explicit multi-criteria optimization, enabling analysts to explore how a dynamic system can simultaneously satisfy competing policy goals — such as cost minimization, environmental sustainability, and social equity — over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Forrester, J. W. (System Dynamics); multi-objective extension by various authors","year":"1961 (SD); multi-objective extensions from 1990s onward","type":"Simulation / optimization hybrid","dataType":"Continuous stocks and flows; multi-criteria performance indicators","subfamily":"Simulation / optimization"},"citations":[{"ref":"Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill.","type":"book","doi":null,"isbn":"978-0-07-231135-8","url":null},{"ref":"Coyle, R. G. (1996). System Dynamics Modelling: A Practical Approach. Chapman & Hall.","type":"book","doi":null,"isbn":"978-0-412-60580-1","url":null}],"related":["system-dynamics","multi-objective-optimization","multi-objective-simulation","agent-based-system-dynamics","stochastic-system-dynamics","scenario-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-objective-tabu-search","name":"Multi-objective Tabu Search","fullName":"Multi-objective Tabu Search (MOTS) — Metaheuristic optimization for multiple conflicting objectives","aliases":["MOTS","Multi-criteria Tabu Search","Pareto Tabu Search","TSMOO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1997","originator":"Hansen, M. P.; building on Glover (1989) Tabu Search","url":"https://scholargate.app/en/simulation/multi-objective-tabu-search","markdownUrl":"https://scholargate.app/en/simulation/multi-objective-tabu-search.md","definition":"Multi-objective Tabu Search (MOTS) is a metaheuristic algorithm that extends the classic Tabu Search framework to simultaneously optimize two or more conflicting objective functions. Instead of a single optimum, it seeks to approximate the Pareto front — the set of solutions where no objective can be improved without worsening another — making it suitable for complex combinatorial and continuous optimization problems in engineering, logistics, and operations research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hansen, M. P.; building on Glover (1989) Tabu Search","year":"1997","type":"Metaheuristic multi-objective optimization","dataType":"Continuous or discrete decision variables with multiple objective functions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Hansen, M. P. (1997). Tabu search for multiobjective optimization: MOTS. Presented at the 13th International Conference on Multiple Criteria Decision Making (MCDM), Cape Town, South Africa.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tabu+search+for+multiobjective+optimization+MOTS+Hansen+1997"},{"ref":"Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.","type":"article","doi":"10.1287/ijoc.1.3.190","isbn":null,"url":null}],"related":["multi-objective-genetic-algorithm","multi-objective-simulated-annealing","nsga-ii","multi-objective-ant-colony-optimization","multi-objective-particle-swarm-optimization","tabu-search"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-epigenome-wide-association-study","name":"Multi-omics epigenome-wide association study","fullName":"Multi-Omics Epigenome-Wide Association Study","aliases":["multi-omics EWAS","integrative EWAS","multi-layer epigenome-wide association","multi-omics epigenomic integration"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2011 (EWAS foundation); multi-omics integration ~2015–2020","originator":"Rakyan, Down, Balding & Beck (EWAS framework); multi-omics integration extended by multiple groups (~2015–2020)","url":"https://scholargate.app/en/bioinformatics/multi-omics-epigenome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-epigenome-wide-association-study.md","definition":"A multi-omics epigenome-wide association study (multi-omics EWAS) systematically scans the entire epigenome — typically DNA methylation at CpG sites — for associations with a phenotype of interest, then integrates findings across additional omics layers such as transcriptomics, genomics, proteomics, or metabolomics. By linking epigenetic variation to molecular changes at multiple biological levels simultaneously, this approach identifies regulatory mechanisms and biomarkers that single-omics EWAS cannot resolve.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rakyan, Down, Balding & Beck (EWAS framework); multi-omics integration extended by multiple groups (~2015–2020)","year":"2011 (EWAS foundation); multi-omics integration ~2015–2020","type":"Integrative association study","dataType":"DNA methylation arrays or sequencing + at least one additional omics layer (e.g., RNA-seq, genotype, proteomics, metabolomics)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.","type":"article","doi":"10.1038/nrg3000","isbn":null,"url":null},{"ref":"Hawe, J. S., Theis, F. J., & Heinig, M. (2019). Inferring interaction networks from multi-omics data. Frontiers in Genetics, 10, 535.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Inferring+interaction+networks+from+multi-omics+data+Hawe+Theis+Heinig+2019"}],"related":["epigenome-wide-association-study","genome-wide-association-study","multi-omics-gwas","dna-methylation-analysis","pathway-enrichment-analysis","eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-eqtl-analysis","name":"Multi-omics eQTL analysis","fullName":"Multi-omics Expression Quantitative Trait Loci Analysis","aliases":["multi-omics molQTL","multi-layer eQTL","integrated eQTL analysis","xQTL multi-omics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017)","originator":"GTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020)","url":"https://scholargate.app/en/bioinformatics/multi-omics-eqtl-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-eqtl-analysis.md","definition":"Multi-omics eQTL analysis maps genetic variants (SNPs or structural variants) to molecular phenotypes simultaneously across multiple omics layers — transcriptome, epigenome, proteome, and metabolome — in the same cohort. By linking genotype to gene expression and then tracing those effects through downstream molecular layers, the approach reveals how genetic variation propagates through the molecular machinery of a cell, yielding mechanistic insight that no single-omics eQTL study can provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"GTEx Consortium and multi-omics integration pioneers (Nica & Dermitzakis, 2013; GTEx Consortium, 2015–2020)","year":"2010s–present (foundational eQTL work: ~2007; multi-omics integration: ~2013–2017)","type":"Integrative genomic association analysis","dataType":"Genotype array or WGS data paired with transcriptomic (RNA-seq), epigenomic, proteomic, or metabolomic quantifications across matched samples","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"GTEx Consortium. (2017). Genetic effects on gene expression across human tissues. Nature, 550(7675), 204–213.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Genetic+effects+on+gene+expression+across+human+tissues+GTEx+2017+Nature"},{"ref":"Bossini-Castillo, L., et al. (2019). Multi-omics data integration reveals molecular mechanisms of complex disease. Nucleic Acids Research, 47(18), 9373–9390.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multi-omics+data+integration+reveals+molecular+mechanisms+of+complex+disease+Nucleic+Acids+Research+2019"}],"related":["eqtl-analysis","genome-wide-association-study","multi-omics-pathway-enrichment-analysis","rna-seq-differential-expression","single-cell-eqtl-analysis","bayesian-eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-gene-set-enrichment-analysis","name":"Multi-omics gene set enrichment analysis","fullName":"Multi-Omics Gene Set Enrichment Analysis","aliases":["multi-omics GSEA","integrated GSEA","cross-omics pathway enrichment","multi-layer GSEA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2005 (GSEA foundation); multi-omics extensions ~2013–2020","originator":"Extended from Subramanian et al. (2005); multi-omics integration formalized ~2010s","url":"https://scholargate.app/en/bioinformatics/multi-omics-gene-set-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-gene-set-enrichment-analysis.md","definition":"Multi-omics gene set enrichment analysis (multi-omics GSEA) is a computational pipeline that applies GSEA logic simultaneously across two or more molecular measurement layers — such as transcriptomics, proteomics, and metabolomics — to identify biological pathways or gene sets that are coordinately dysregulated across omics platforms. By integrating ranked molecular signatures from each layer, it reveals pathway-level convergence that no single omics platform could detect alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from Subramanian et al. (2005); multi-omics integration formalized ~2010s","year":"2005 (GSEA foundation); multi-omics extensions ~2013–2020","type":"Integrative enrichment analysis pipeline","dataType":"Ranked gene/protein/metabolite lists from two or more omics platforms","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.","type":"article","doi":"10.1073/pnas.0506580102","isbn":null,"url":null},{"ref":"Meng, C., Zeleznik, O. A., Thallinger, G. G., Kuster, B., Gholami, A. M., & Culhane, A. C. (2016). Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings in Bioinformatics, 17(4), 628–641.","type":"article","doi":"10.1093/bib/bbv108","isbn":null,"url":null}],"related":["gene-set-enrichment-analysis","pathway-enrichment-analysis","multi-omics-pathway-enrichment-analysis","single-cell-gene-set-enrichment-analysis","rna-seq-differential-expression","proteomics-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-metabolomics-analysis","name":"Multi-omics metabolomics analysis","fullName":"Multi-omics Integration with Metabolomics","aliases":["metabolomics multi-omics integration","integrated metabolomics","multi-omics metabolite profiling","metabolome-centric multi-omics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s)","originator":"Pioneered collectively; key early integrative frameworks by Nicholson & Lindon (metabolomics) and Hasin, Seldin & Lusis (multi-omics disease mapping)","url":"https://scholargate.app/en/bioinformatics/multi-omics-metabolomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-metabolomics-analysis.md","definition":"Multi-omics metabolomics analysis integrates metabolite profiling data — derived from mass spectrometry or NMR spectroscopy — with genomic, transcriptomic, and/or proteomic datasets to build a system-level view of biological phenotypes. By anchoring integration on the metabolome, which reflects the downstream functional output of gene expression and protein activity, this approach connects upstream molecular variation to observable biochemical states, enabling richer mechanistic insight than any single omics layer alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pioneered collectively; key early integrative frameworks by Nicholson & Lindon (metabolomics) and Hasin, Seldin & Lusis (multi-omics disease mapping)","year":"2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s)","type":"Integrative computational pipeline","dataType":"Mass spectrometry or NMR metabolite profiles combined with genomic, transcriptomic, and/or proteomic data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14, 1177932219899051.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multi-omics+data+integration+interpretation+and+its+application+Subramanian+2020"},{"ref":"Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome Biology, 18(1), 83.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multi-omics+approaches+to+disease+Hasin+Seldin+Lusis+2017+Genome+Biology"}],"related":["metabolomics-analysis","multi-omics-gwas","pathway-enrichment-analysis","rna-seq-differential-expression","proteomics-analysis","gene-set-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-microbiome-diversity-analysis","name":"Multi-omics microbiome diversity analysis","fullName":"Multi-omics Microbiome Diversity Analysis","aliases":["multi-omics microbiome profiling","integrated microbiome omics","multi-modal microbiome analysis","microbiome multi-omics integration"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s–present","originator":"Developed collectively; key frameworks by Le Cao et al. (mixOmics, 2017) and Argelaguet et al. (MOFA, 2018)","url":"https://scholargate.app/en/bioinformatics/multi-omics-microbiome-diversity-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-microbiome-diversity-analysis.md","definition":"Multi-omics microbiome diversity analysis integrates two or more omic data layers — such as metagenomics, metatranscriptomics, metabolomics, and metaproteomics — to characterise both the composition and functional activity of microbial communities. By linking taxonomic diversity metrics with molecular phenotype data, the approach uncovers how community structure translates into ecological and host-relevant functions that no single omic layer can reveal alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed collectively; key frameworks by Le Cao et al. (mixOmics, 2017) and Argelaguet et al. (MOFA, 2018)","year":"2010s–present","type":"Integrative computational pipeline","dataType":"16S/shotgun metagenomics, metatranscriptomics, metabolomics, and/or metaproteomics data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for 'omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752.","type":"article","doi":"10.1371/journal.pcbi.1005752","isbn":null,"url":null},{"ref":"Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018). Multi-Omics Factor Analysis — a framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology, 14(6), e8124.","type":"article","doi":"10.15252/msb.20178124","isbn":null,"url":null}],"related":["microbiome-diversity-analysis","pathway-enrichment-analysis","metabolomics-analysis","gene-set-enrichment-analysis","multi-omics-metabolomics-analysis","network-based-microbiome-diversity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-pathway-enrichment-analysis","name":"Multi-omics Pathway Enrichment Analysis","fullName":"Multi-omics Pathway Enrichment Analysis","aliases":["multi-omics pathway analysis","integrated pathway enrichment","multi-layer pathway enrichment","MOPEA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2014–2016 (multi-omics extension of enrichment methods established ~2005)","originator":"Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016)","url":"https://scholargate.app/en/bioinformatics/multi-omics-pathway-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-pathway-enrichment-analysis.md","definition":"Multi-omics pathway enrichment analysis is a bioinformatics pipeline that integrates molecular data from two or more omics layers — such as transcriptomics, proteomics, metabolomics, and epigenomics — and tests whether the combined signal from those layers converges on specific biological pathways more than expected by chance. By considering multiple molecular levels simultaneously, it identifies pathway-level dysregulation that single-omics analyses would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016)","year":"2014–2016 (multi-omics extension of enrichment methods established ~2005)","type":"Integrative pathway analysis pipeline","dataType":"Multi-layer omics matrices (transcriptomics, proteomics, metabolomics, epigenomics, etc.)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Meng, C., Kuster, B., Culhane, A. C., & Gholami, A. M. (2014). A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics, 15, 162.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+multivariate+approach+to+the+integration+of+multi-omics+datasets+Meng+2014"},{"ref":"Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1073/pnas.0506580102"}],"related":["gene-set-enrichment-analysis","over-representation-analysis","multi-omics-factor-analysis","network-enrichment-analysis","weighted-gene-co-expression-network-analysis","pathway-topology-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-phylogenetic-analysis","name":"Multi-omics Phylogenetic Analysis","fullName":"Multi-omics Phylogenetic Analysis","aliases":["phylogenomics","multi-omic phylogenetics","integrative phylogenomics","omics-based phylogenetics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"Late 1990s–2000s (genome-scale; multi-omics integration ~2010s)","originator":"Hedges, Kumar, Philippe and colleagues (phylogenomics pioneers, late 1990s–2000s)","url":"https://scholargate.app/en/bioinformatics/multi-omics-phylogenetic-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-phylogenetic-analysis.md","definition":"Multi-omics phylogenetic analysis reconstructs evolutionary relationships among organisms by integrating sequence data from multiple molecular layers — genomes, transcriptomes, and proteomes — rather than relying on a single marker gene. By combining thousands of orthologous loci across omics layers, the approach dramatically reduces stochastic error, resolves ancient divergences that single-gene trees cannot, and yields a far more robust and well-supported topology of the tree of life or a focal clade.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hedges, Kumar, Philippe and colleagues (phylogenomics pioneers, late 1990s–2000s)","year":"Late 1990s–2000s (genome-scale; multi-omics integration ~2010s)","type":"Computational phylogenetic inference pipeline","dataType":"Genomic, transcriptomic, and/or proteomic sequence data across taxa","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Delsuc, F., Brinkmann, H., & Philippe, H. (2005). Phylogenomics and the reconstruction of the tree of life. Nature Reviews Genetics, 6(5), 361–375.","type":"article","doi":"10.1038/nrg1603","isbn":null,"url":null},{"ref":"Philippe, H., Brinkmann, H., Lavrov, D. V., Littlewood, D. T. J., Manuel, M., Wörheide, G., & Baurain, D. (2011). Resolving difficult phylogenetic questions: Why more sequences are not enough. PLoS Biology, 9(3), e1000602.","type":"article","doi":"10.1371/journal.pbio.1000602","isbn":null,"url":null}],"related":["maximum-likelihood-phylogeny","bayesian-phylogenetics","comparative-genomics","transcriptome-assembly","ortholog-clustering","molecular-clock-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-proteomics-analysis","name":"Multi-omics proteomics analysis","fullName":"Multi-Omics Integrative Proteomics Analysis","aliases":["integrative proteomics","multi-omics proteomics integration","proteogenomics multi-omics","cross-omics proteomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s (integrative multi-omics frameworks emerged ~2012–2019)","originator":"Le Cao, K.-A. and colleagues (mixOmics/DIABLO framework); broader field rooted in Aebersold & Mann proteomics work","url":"https://scholargate.app/en/bioinformatics/multi-omics-proteomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-proteomics-analysis.md","definition":"Multi-omics proteomics analysis integrates protein abundance data from mass spectrometry with at least one additional omics layer — such as genomics, transcriptomics, or metabolomics — to build a systems-level view of biological regulation. Rather than analyzing proteins in isolation, this approach correlates proteomic profiles with upstream molecular events (e.g., DNA variants, mRNA levels) and downstream functional readouts (e.g., metabolite concentrations), enabling discovery of regulatory drivers that single-omics analyses would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Le Cao, K.-A. and colleagues (mixOmics/DIABLO framework); broader field rooted in Aebersold & Mann proteomics work","year":"2010s (integrative multi-omics frameworks emerged ~2012–2019)","type":"Integrative computational pipeline","dataType":"Mass spectrometry proteomics data combined with genomics, transcriptomics, or metabolomics datasets","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752.","type":"article","doi":"10.1371/journal.pcbi.1005752","isbn":null,"url":null},{"ref":"Singh, A., Shannon, C. P., Gautier, B., Rohart, F., Vacher, M., Tebbutt, S. J., & Le Cao, K.-A. (2019). DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics, 35(17), 3055–3062.","type":"article","doi":"10.1093/bioinformatics/bty1054","isbn":null,"url":null}],"related":["proteomics-analysis","rna-seq-differential-expression","metabolomics-analysis","pathway-enrichment-analysis","gene-set-enrichment-analysis","multi-omics-metabolomics-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-rna-seq-differential-expression","name":"Multi-omics RNA-seq differential expression","fullName":"Multi-omics RNA-seq Differential Expression Analysis","aliases":["multi-omics DE analysis","integrative RNA-seq DE","multi-layer differential expression","omics-integrated transcriptomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010–2018 (core DE methods ~2010; multi-omics integration frameworks ~2014–2018)","originator":"Synthesised from DESeq2/edgeR (Anders & Huber 2010; Robinson et al. 2010) and multi-omics integration frameworks (Argelaguet et al. 2018)","url":"https://scholargate.app/en/bioinformatics/multi-omics-rna-seq-differential-expression","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-rna-seq-differential-expression.md","definition":"Multi-omics RNA-seq differential expression analysis combines transcript-level count data from RNA sequencing with one or more additional omics layers — such as proteomics, metabolomics, epigenomics, or genomic variant data — to identify genes, proteins, or metabolites that differ systematically between biological conditions. By integrating multiple molecular levels, the pipeline captures regulatory mechanisms that transcriptomics alone cannot resolve, enabling a more complete picture of the biological processes driving observed phenotypes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from DESeq2/edgeR (Anders & Huber 2010; Robinson et al. 2010) and multi-omics integration frameworks (Argelaguet et al. 2018)","year":"2010–2018 (core DE methods ~2010; multi-omics integration frameworks ~2014–2018)","type":"Integrative computational pipeline","dataType":"RNA-seq count matrices combined with at least one additional omics layer (proteomics, metabolomics, epigenomics, or genomics)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.","type":"article","doi":"10.1186/s13059-014-0550-8","isbn":null,"url":null},{"ref":"Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018). Multi-Omics Factor Analysis — a framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology, 14(6), e8124.","type":"article","doi":"10.15252/msb.20178124","isbn":null,"url":null}],"related":["rna-seq-analysis","deseq2","gene-set-enrichment-analysis","principal-component-analysis","network-analysis","proteomics-data-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-omics-single-cell-rna-seq-analysis","name":"Multi-omics single-cell RNA-seq analysis","fullName":"Multi-omics Single-Cell RNA Sequencing Analysis","aliases":["scMulti-omics","single-cell multi-omics","multimodal single-cell analysis","paired single-cell omics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2015–2021 (rapid maturation with CITE-seq 2017; Seurat v4 2021)","originator":"Pioneered by Rahul Satija (Seurat), Oliver Stegle and John Marioni (MOFA+), and the broader single-cell genomics community","url":"https://scholargate.app/en/bioinformatics/multi-omics-single-cell-rna-seq-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/multi-omics-single-cell-rna-seq-analysis.md","definition":"Multi-omics single-cell RNA-seq analysis integrates two or more molecular layers — such as gene expression (scRNA-seq), chromatin accessibility (scATAC-seq), or surface protein abundance (CITE-seq) — measured simultaneously or co-profiled in the same individual cells. By aligning these modalities in a shared low-dimensional space, researchers gain a mechanistically richer picture of cell identity, regulatory state, and phenotype than any single assay can provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pioneered by Rahul Satija (Seurat), Oliver Stegle and John Marioni (MOFA+), and the broader single-cell genomics community","year":"2015–2021 (rapid maturation with CITE-seq 2017; Seurat v4 2021)","type":"Integrative computational pipeline","dataType":"Paired or co-measured single-cell omics data (e.g., scRNA-seq + scATAC-seq, CITE-seq, 10x Multiome)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zager, M., Hoffman, P., Stoeckius, M., Papalexi, E., Mimitou, E. P., Jain, J., Srivastava, A., Stuart, T., Fleming, L. M., Yeung, B., Rogers, A. J., McElrath, J. M., Blish, C. A., Gottardo, R., Smibert, P., & Satija, R. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573–3587.e29.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Integrated+analysis+of+multimodal+single-cell+data+Hao+2021+Cell"},{"ref":"Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020). MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biology, 21(1), 111.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MOFA%2B+statistical+framework+comprehensive+integration+multi-modal+single-cell+data+Argelaguet+2020"}],"related":["single-cell-rna-seq-analysis","rna-seq-differential-expression","pathway-enrichment-analysis","gene-set-enrichment-analysis","chip-seq-peak-calling","multi-omics-rna-seq-differential-expression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-causal-impact-analysis","name":"Multi-period Causal Impact Analysis","fullName":"Multi-period Bayesian Causal Impact Analysis","aliases":["multi-period CausalImpact","staggered causal impact","repeated-period causal impact","multi-wave CausalImpact"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015 (base); multi-period extensions 2017–present","originator":"Brodersen, Gallusser, Koehler, Remy & Scott (Google); extended to multi-period settings by subsequent applied work","url":"https://scholargate.app/en/causal-inference/multi-period-causal-impact-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-causal-impact-analysis.md","definition":"Multi-period Causal Impact Analysis extends the Bayesian structural time-series framework of Brodersen et al. (2015) to settings where an intervention occurs across multiple distinct periods, is applied at staggered times to different units, or where researchers wish to evaluate cumulative and period-specific effects within a single unified model. It builds a synthetic counterfactual from control covariates and projects it across each intervention window to quantify causal effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen, Gallusser, Koehler, Remy & Scott (Google); extended to multi-period settings by subsequent applied work","year":"2015 (base); multi-period extensions 2017–present","type":"Bayesian structural time-series / quasi-experimental","dataType":"Univariate or multivariate time series; repeated or staggered intervention windows","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Bojinov, I., & Shephard, N. (2019). Time series experiments and causal estimands: exact randomization tests and trading. Journal of the American Statistical Association, 114(528), 1665-1682.","type":"article","doi":"10.1080/01621459.2018.1527225","isbn":null,"url":null}],"related":["causal-impact-analysis","bayesian-causal-impact-analysis","interrupted-time-series","multi-period-interrupted-time-series","synthetic-control-method","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-coarsened-exact-matching","name":"Multi-period Coarsened Exact Matching","fullName":"Multi-period Coarsened Exact Matching Estimator","aliases":["Multi-period CEM","Longitudinal CEM","Panel CEM","Multi-wave CEM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012–2021","originator":"Iacus, King & Porro (CEM, 2012); extended to multi-period panel settings","url":"https://scholargate.app/en/causal-inference/multi-period-coarsened-exact-matching","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-coarsened-exact-matching.md","definition":"Multi-period Coarsened Exact Matching (multi-period CEM) extends the CEM framework of Iacus, King, and Porro to longitudinal data with multiple pre- and post-treatment periods. It bins continuous covariates into coarsened categories, matches treated and control units that fall into the same cells across all relevant time periods, and then estimates a weighted average treatment effect that accounts for temporal structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Iacus, King & Porro (CEM, 2012); extended to multi-period panel settings","year":"2012–2021","type":"Non-parametric matching / causal inference","dataType":"Panel or repeated cross-section with multiple pre- and post-treatment periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Imai, K., Kim, I. S., & Wang, E. H. (2021). Matching methods for causal inference with time-series cross-sectional data. American Journal of Political Science, 67(3), 587-605.","type":"article","doi":"10.1111/ajps.12685","isbn":null,"url":null}],"related":["coarsened-exact-matching","propensity-score-matching","difference-in-differences","panel-data-coarsened-exact-matching","entropy-balancing","matching-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-counterfactual-impact-evaluation","name":"Multi-period Counterfactual Impact Evaluation","fullName":"Multi-period Counterfactual Impact Evaluation","aliases":["multi-period CIE","longitudinal counterfactual evaluation","dynamic counterfactual impact evaluation","multi-wave CIE"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Developed through EU policy evaluation practice (European Commission); formalized by Lechner, Caliendo, and related econometricians","url":"https://scholargate.app/en/causal-inference/multi-period-counterfactual-impact-evaluation","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-counterfactual-impact-evaluation.md","definition":"Multi-period Counterfactual Impact Evaluation (CIE) estimates the causal effect of a policy or program by constructing what would have happened to treated units across multiple time periods had they not been treated. Unlike single-period evaluations, it tracks treatment effects as they evolve over time, capturing dynamic, delayed, or fading impacts that a two-period comparison would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed through EU policy evaluation practice (European Commission); formalized by Lechner, Caliendo, and related econometricians","year":"2000s–2010s","type":"Causal inference / quasi-experimental evaluation","dataType":"Panel or repeated cross-sectional data with multiple time periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72.","type":"article","doi":"10.1111/j.1467-6419.2007.00527.x","isbn":null,"url":null},{"ref":"Lechner, M. (2010). The Estimation of Causal Effects by Difference-in-Difference Methods. Foundations and Trends in Econometrics, 4(3), 165-224.","type":"article","doi":"10.1561/0800000014","isbn":null,"url":null}],"related":["counterfactual-impact-evaluation","difference-in-differences","dynamic-difference-in-differences","panel-data-counterfactual-impact-evaluation","marginal-structural-model","event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-difference-in-differences","name":"Multi-period Difference-in-differences","fullName":"Multi-period Difference-in-Differences with Staggered Adoption","aliases":["staggered DiD","multi-period DiD","staggered difference-in-differences","heterogeneous timing DiD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Callaway & Sant'Anna; Goodman-Bacon","url":"https://scholargate.app/en/causal-inference/multi-period-difference-in-differences","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-difference-in-differences.md","definition":"Multi-period Difference-in-Differences extends the classic two-period DiD framework to settings where units adopt treatment at different points in time. Formalised by Callaway and Sant'Anna (2021) and Goodman-Bacon (2021), it decomposes the overall treatment effect into group-time average treatment effects and addresses the bias that arises when conventional two-way fixed-effects regressions are applied to staggered adoption designs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Callaway & Sant'Anna; Goodman-Bacon","year":"2021","type":"Causal inference / panel regression","dataType":"Panel data with staggered treatment timing","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null},{"ref":"Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254-277.","type":"article","doi":"10.1016/j.jeconom.2021.03.014","isbn":null,"url":null}],"related":["difference-in-differences","event-study-design","panel-data-difference-in-differences","dynamic-difference-in-differences","synthetic-control-method","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-doubly-robust-estimation","name":"Multi-period Doubly Robust Estimation","fullName":"Multi-period Doubly Robust Causal Effect Estimator","aliases":["longitudinal DR estimation","multi-period DR","multi-wave doubly robust","sequential doubly robust estimation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1994-2021","originator":"Robins, Rotnitzky, and Zhao; extended by Bang & Robins (2005) and Callaway & Sant'Anna (2021)","url":"https://scholargate.app/en/causal-inference/multi-period-doubly-robust-estimation","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-doubly-robust-estimation.md","definition":"Multi-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each period, retaining consistency of the causal effect estimate as long as at least one of the two models is correctly specified at every time point.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Rotnitzky, and Zhao; extended by Bang & Robins (2005) and Callaway & Sant'Anna (2021)","year":"1994-2021","type":"Semiparametric causal estimator","dataType":"Longitudinal / panel data with multiple time periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.","type":"article","doi":"10.1111/j.1541-0420.2005.00377.x","isbn":null,"url":null},{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null}],"related":["doubly-robust-estimation","inverse-probability-weighting","marginal-structural-model","difference-in-differences","propensity-score-matching","dynamic-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-event-study-design","name":"Multi-period Event Study Design","fullName":"Multi-period Event Study Design for Dynamic Treatment Effects","aliases":["multi-period event study","dynamic event study","relative-time event study","leads-and-lags design"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1993","originator":"Jacobson, LaLonde & Sullivan (1993); seminal methodological treatment by Sun & Abraham (2021)","url":"https://scholargate.app/en/causal-inference/multi-period-event-study-design","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-event-study-design.md","definition":"The multi-period event study design estimates causal treatment effects at each point in time relative to the treatment onset, using panel data with multiple pre- and post-treatment periods. By plotting the full path of treatment coefficients rather than a single average, it reveals how effects build up, fade, or remain stable over time — and allows formal tests of pre-treatment parallel trends across many periods simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacobson, LaLonde & Sullivan (1993); seminal methodological treatment by Sun & Abraham (2021)","year":"1993","type":"Quasi-experimental causal inference","dataType":"Panel data with multiple pre- and post-treatment periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings losses of displaced workers. American Economic Review, 83(4), 888-909.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2117574"},{"ref":"Freyaldenhoven, S., Hansen, C., Perez-Skiba, A., & Shapiro, J. M. (2021). Visualization, identification, and estimation in the linear panel event-study design. NBER Working Paper 29170.","type":"article","doi":null,"isbn":null,"url":"https://www.nber.org/papers/w29170"}],"related":["event-study-design","difference-in-differences","panel-event-study","dynamic-difference-in-differences","panel-data-event-study-design","placebo-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-fuzzy-regression-discontinuity","name":"Multi-period Fuzzy Regression Discontinuity","fullName":"Multi-period Fuzzy Regression Discontinuity Design","aliases":["multi-period fuzzy RDD","fuzzy RD with repeated assignment","multi-wave fuzzy RD","staggered fuzzy RDD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2001 (fuzzy RD); multi-period extension ~2010s","originator":"Hahn, Todd & Van der Klaauw (foundational fuzzy RD, 2001); extended to multi-period settings by Cattaneo, Idrobo & Titiunik and subsequent applied literature","url":"https://scholargate.app/en/causal-inference/multi-period-fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-fuzzy-regression-discontinuity.md","definition":"Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarantee it — and when this assignment process is observed across two or more time periods or cohorts, enabling pooled or period-specific causal estimates under repeated near-threshold comparisons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hahn, Todd & Van der Klaauw (foundational fuzzy RD, 2001); extended to multi-period settings by Cattaneo, Idrobo & Titiunik and subsequent applied literature","year":"2001 (fuzzy RD); multi-period extension ~2010s","type":"Quasi-experimental causal inference","dataType":"Panel or repeated cross-sections with a continuous running variable and a threshold assignment rule observed across multiple periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.","type":"article","doi":"10.1111/1468-0262.00183","isbn":null,"url":null},{"ref":"Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2021). A Practical Introduction to Regression Discontinuity Designs: Extensions. Cambridge Elements in Quantitative and Computational Methods for the Social Sciences. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Practical+Introduction+to+Regression+Discontinuity+Designs+Extensions+Cattaneo+2021"}],"related":["fuzzy-regression-discontinuity","regression-discontinuity-design","multi-period-difference-in-differences","instrumental-variables","panel-data-regression-discontinuity-design","dynamic-regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-interrupted-time-series","name":"Multi-period Interrupted Time Series","fullName":"Multi-period Interrupted Time Series Analysis","aliases":["multi-period ITS","multiple-interruption ITS","segmented time series with multiple breakpoints","MITS"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s-2015","originator":"Extended from segmented regression / ITS tradition; multi-break formalization developed across epidemiology and health policy literature (2000s-2010s)","url":"https://scholargate.app/en/causal-inference/multi-period-interrupted-time-series","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-interrupted-time-series.md","definition":"Multi-period Interrupted Time Series (MITS) extends the classic ITS framework to settings where two or more interventions occur at known time points within the same series. By fitting a segmented regression with multiple breakpoints, MITS estimates the level change and slope change attributable to each intervention while controlling for the underlying secular trend and for the effects of earlier interruptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from segmented regression / ITS tradition; multi-break formalization developed across epidemiology and health policy literature (2000s-2010s)","year":"2000s-2015","type":"Quasi-experimental time series regression","dataType":"Aggregate or individual-level time series with at least two known intervention dates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350, h2750.","type":"article","doi":"10.1136/bmj.h2750","isbn":null,"url":null},{"ref":"Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.","type":"article","doi":"10.1093/ije/dyw098","isbn":null,"url":null}],"related":["interrupted-time-series","difference-in-differences","panel-data-interrupted-time-series","dynamic-interrupted-time-series","event-study-design","panel-event-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-inverse-probability-weighting","name":"Multi-period Inverse Probability Weighting","fullName":"Multi-period Inverse Probability Weighting Estimator","aliases":["longitudinal IPW","multi-period IPW","time-varying IPW","sequential IPW"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000","originator":"Robins, Hernan & Brumback","url":"https://scholargate.app/en/causal-inference/multi-period-inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-inverse-probability-weighting.md","definition":"Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population where treatment at each period is independent of measured confounders, enabling unbiased estimation of sustained treatment strategies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Hernan & Brumback","year":"2000","type":"Weighted causal estimator","dataType":"Longitudinal / panel data with time-varying treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"}],"related":["inverse-probability-weighting","marginal-structural-model","propensity-score-weighting","dynamic-inverse-probability-weighting","doubly-robust-estimation","panel-data-inverse-probability-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-matching-estimator","name":"Multi-period Matching Estimator","fullName":"Multi-period Matching Estimator for Panel Data","aliases":["panel matching estimator","longitudinal matching","multi-wave matching","repeated-cross-section matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2005","originator":"Abadie (2005); Imbens & Wooldridge (2009)","url":"https://scholargate.app/en/causal-inference/multi-period-matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-matching-estimator.md","definition":"The multi-period matching estimator extends the standard matching framework to settings with multiple time periods, pairing each treated unit to similar untreated units based on pre-treatment covariates or propensity scores, then using within-pair before-after differences to estimate the average treatment effect on the treated (ATT). Leveraging repeated observations, it simultaneously controls for observed confounders and time-invariant unobserved heterogeneity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie (2005); Imbens & Wooldridge (2009)","year":"2005","type":"Quasi-experimental / causal inference","dataType":"Panel or repeated cross-sections with multiple pre- and post-treatment periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A. (2005). Semiparametric Difference-in-Differences Estimators. Review of Economic Studies, 72(1), 1-19.","type":"article","doi":"10.1111/0034-6527.00321","isbn":null,"url":null},{"ref":"Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.","type":"article","doi":"10.1257/jel.47.1.5","isbn":null,"url":null}],"related":["matching-estimator","difference-in-differences","propensity-score-matching","panel-data-matching-estimator","dynamic-matching-estimator","coarsened-exact-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-propensity-score-weighting","name":"Multi-period Propensity Score Weighting","fullName":"Multi-period Propensity Score Weighting for Causal Inference","aliases":["longitudinal propensity score weighting","multi-wave PSW","time-varying propensity score weighting","sequential propensity score weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000","originator":"Robins, Hernán, and Brumback (building on Robins' g-computation framework)","url":"https://scholargate.app/en/causal-inference/multi-period-propensity-score-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-propensity-score-weighting.md","definition":"Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time point so that the weighted sample resembles a sequence of randomised experiments, allowing unbiased estimation of causal effects under longitudinal confounding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Hernán, and Brumback (building on Robins' g-computation framework)","year":"2000","type":"Quasi-experimental causal inference","dataType":"Longitudinal / panel data with time-varying treatment and covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"},{"ref":"Cole, S. R., & Hernán, M. A. (2008). Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology, 168(6), 656-664.","type":"article","doi":"10.1093/aje/kwn164","isbn":null,"url":null}],"related":["propensity-score-weighting","marginal-structural-model","inverse-probability-weighting","dynamic-propensity-score-weighting","panel-data-propensity-score-weighting","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-regression-discontinuity-design","name":"Multi-period Regression Discontinuity Design","fullName":"Multi-period Regression Discontinuity Design","aliases":["multi-wave RD","repeated RDD","dynamic RD","multi-cutoff RDD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s–2020s","originator":"Cattaneo, Idrobo & Titiunik (foundations); extended by multiple authors for repeated-period settings","url":"https://scholargate.app/en/causal-inference/multi-period-regression-discontinuity-design","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-regression-discontinuity-design.md","definition":"Multi-period Regression Discontinuity Design extends the classic RDD to settings where a cutoff-based treatment is applied in multiple waves, across repeated time periods, or with varying thresholds. By pooling or comparing period-specific discontinuity estimates, researchers gain statistical precision and can examine how causal effects evolve or persist over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cattaneo, Idrobo & Titiunik (foundations); extended by multiple authors for repeated-period settings","year":"2010s–2020s","type":"Quasi-experimental causal inference","dataType":"Panel or repeated cross-sections with a running variable and cutoff, observed across multiple periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.","type":"book","doi":"10.1017/9781108684606","isbn":null,"url":null},{"ref":"Calonico, S., Cattaneo, M. D., & Titiunik, R. (2019). Regression Discontinuity Designs Using Covariates. Review of Economics and Statistics, 101(3), 442-451.","type":"article","doi":"10.1162/rest_a_00760","isbn":null,"url":null}],"related":["regression-discontinuity-design","fuzzy-regression-discontinuity","difference-in-differences","panel-data-regression-discontinuity-design","dynamic-regression-discontinuity-design","event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-period-synthetic-control-method","name":"Multi-period Synthetic Control Method","fullName":"Multi-period Synthetic Control Method","aliases":["multi-period SCM","extended synthetic control","synthetic control with multiple treatment periods","staggered synthetic control"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010-2021","originator":"Abadie, Diamond & Hainmueller (2010); extended to multi-period settings by Abadie (2021) and Ben-Michael et al. (2021)","url":"https://scholargate.app/en/causal-inference/multi-period-synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/multi-period-synthetic-control-method.md","definition":"The multi-period synthetic control method extends the classic synthetic control framework to settings where treatment occurs across several distinct periods or where the researcher needs to track causal effects over a prolonged post-treatment window. It constructs a weighted combination of untreated units that reproduces the treated unit's pre-treatment trajectory, then uses that synthetic counterfactual across all post-treatment periods to estimate time-varying treatment effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie, Diamond & Hainmueller (2010); extended to multi-period settings by Abadie (2021) and Ben-Michael et al. (2021)","year":"2010-2021","type":"Quasi-experimental causal inference","dataType":"Aggregate panel data (units × time periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391-425.","type":"article","doi":"10.1257/jel.20191450","isbn":null,"url":null},{"ref":"Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The augmented synthetic control method. Journal of the American Statistical Association, 116(536), 1789-1803.","type":"article","doi":"10.1080/01621459.2021.1929245","isbn":null,"url":null}],"related":["synthetic-control-method","difference-in-differences","multi-period-difference-in-differences","panel-data-synthetic-control-method","dynamic-synthetic-control-method","event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-control-chart","name":"Multi-response Control Chart","fullName":"Multi-response Statistical Process Control Chart","aliases":["multivariate control chart","multi-response SPC","MRCC","multiple-response monitoring chart"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions)","originator":"Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others","url":"https://scholargate.app/en/experimental-design/multi-response-control-chart","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-control-chart.md","definition":"A multi-response control chart simultaneously monitors two or more correlated quality characteristics on a single chart, preserving the correlation structure that univariate charts ignore. Built on Hotelling's T² statistic and its time-weighted extensions (MEWMA, MCUSUM), it detects process shifts that would be missed if each response were charted independently. It is the standard tool in manufacturing and service quality when product performance depends on multiple interrelated outputs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others","year":"1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions)","type":"Multivariate statistical process monitoring","dataType":"Continuous measurement data for two or more correlated quality characteristics","subfamily":"Engineering methods"},"citations":[{"ref":"Hotelling, H. (1947). Multivariate quality control illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of Statistical Analysis (pp. 111–184). McGraw-Hill.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Hotelling+1947+Multivariate+quality+control+bombsights"},{"ref":"Lowry, C. A., Woodall, W. H., Champ, C. W., & Rigdon, S. E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34(1), 46–53.","type":"article","doi":"10.1080/00401706.1992.10485232","isbn":null,"url":null}],"related":["control-chart","statistical-process-control","multi-response-response-surface-methodology","multi-response-design-of-experiments","process-capability-analysis","failure-mode-and-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-design-of-experiments","name":"Multi-response Design of Experiments","fullName":"Multi-response Design of Experiments","aliases":["Multi-response DoE","Multiple-response optimization","Multi-objective DoE","MRDoE"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s","originator":"Derringer & Suich (desirability function); Montgomery (systematic DoE integration)","url":"https://scholargate.app/en/experimental-design/multi-response-design-of-experiments","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-design-of-experiments.md","definition":"Multi-response Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets at once.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derringer & Suich (desirability function); Montgomery (systematic DoE integration)","year":"1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s","type":"Experimental optimization methodology","dataType":"Continuous and categorical factor settings; multiple measured response variables","subfamily":"Engineering methods"},"citations":[{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.","type":"article","doi":"10.1080/00224065.1980.11980968","isbn":null,"url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916025","url":null}],"related":["design-of-experiments","response-surface-methodology","central-composite-design","box-behnken-design","full-factorial-design","taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-event-tree-analysis","name":"Multi-response Event Tree Analysis","fullName":"Multi-response Event Tree Analysis","aliases":["MR-ETA","multi-output event tree analysis","multi-response ETA","probabilistic event tree with multiple responses"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1975 (ETA); multi-response extension: 1990s–2000s","originator":"Developed from Event Tree Analysis (originated at WASH-1400 nuclear safety study, U.S. Nuclear Regulatory Commission, 1975); multi-response extension adapted from design-of-experiments and reliability engineering practice","url":"https://scholargate.app/en/experimental-design/multi-response-event-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-event-tree-analysis.md","definition":"Multi-response Event Tree Analysis (MR-ETA) extends classical event tree analysis by simultaneously tracking multiple system performance or safety response variables across all accident sequences. Instead of evaluating a single outcome (e.g., probability of failure), it propagates several concurrent response metrics — such as damage severity, downtime, cost, and environmental impact — through the event tree branches, enabling richer risk characterization and trade-off decisions under a single probabilistic framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from Event Tree Analysis (originated at WASH-1400 nuclear safety study, U.S. Nuclear Regulatory Commission, 1975); multi-response extension adapted from design-of-experiments and reliability engineering practice","year":"1975 (ETA); multi-response extension: 1990s–2000s","type":"Probabilistic safety and reliability analysis with multiple simultaneous response outcomes","dataType":"Failure probability estimates, system event sequences, multiple performance/safety response variables","subfamily":"Engineering methods"},"citations":[{"ref":"Stamatelatos, M., Vesely, W., Dugan, J., Fragola, J., Minarick, J., & Railsback, J. (2002). Fault Tree Handbook with Aerospace Applications. NASA Office of Safety and Mission Assurance.","type":"book","doi":null,"isbn":null,"url":"https://ntrs.nasa.gov/citations/20020086685"},{"ref":"Event tree analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Event_tree_analysis"}],"related":["event-tree-analysis","fault-tree-analysis","failure-mode-and-effects-analysis","multi-response-fault-tree-analysis","multi-response-reliability-analysis","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-failure-mode-and-effects-analysis","name":"Multi-response failure mode and effects analysis","fullName":"Multi-Response Failure Mode and Effects Analysis","aliases":["MR-FMEA","multi-response FMEA","multi-criteria FMEA","multi-objective FMEA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s","originator":"Extended from classical FMEA (MIL-P-1629, 1949; Ford Motor Company, 1970s); multi-response integration developed in quality engineering literature from the 1990s onward","url":"https://scholargate.app/en/experimental-design/multi-response-failure-mode-and-effects-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-failure-mode-and-effects-analysis.md","definition":"Multi-response FMEA extends classical Failure Mode and Effects Analysis to systems or processes where each failure mode produces effects across multiple quality characteristics or response variables simultaneously. Rather than assigning a single Risk Priority Number (RPN), it evaluates severity, occurrence, and detectability for each response dimension, then integrates these ratings — often via multi-criteria scoring or weighted aggregation — to obtain a holistic risk ranking that captures the full consequence profile of each failure mode.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from classical FMEA (MIL-P-1629, 1949; Ford Motor Company, 1970s); multi-response integration developed in quality engineering literature from the 1990s onward","year":"1990s–2000s","type":"Risk analysis and quality engineering method","dataType":"Expert judgments, engineering data, process metrics (multiple response variables)","subfamily":"Engineering methods"},"citations":[{"ref":"Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873895989","url":null},{"ref":"Sharma, R. K., Kumar, D., & Kumar, P. (2005). Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. International Journal of Quality and Reliability Management, 22(9), 986–1004.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1108/02656710510625248"}],"related":["failure-mode-and-effects-analysis","multi-response-design-of-experiments","multi-response-taguchi-method","fault-tree-analysis","statistical-process-control","quality-function-deployment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-fault-tree-analysis","name":"Multi-response fault tree analysis","fullName":"Multi-Response Fault Tree Analysis","aliases":["MR-FTA","multi-output fault tree analysis","multi-criterion fault tree analysis","multi-response FTA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1961 (FTA); multi-response extensions developed from the 1980s onward","originator":"H. A. Watson (Bell Labs); extended by W. E. Vesely and others for multi-output contexts","url":"https://scholargate.app/en/experimental-design/multi-response-fault-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-fault-tree-analysis.md","definition":"Multi-response fault tree analysis (MR-FTA) extends classical fault tree analysis to systems where multiple distinct top-level failure events or outcome metrics must be evaluated simultaneously. Rather than constructing a single tree for one top event, the analyst builds and quantifies parallel trees — one per response — then aggregates results to rank critical failure paths across all responses at once, enabling holistic system risk prioritization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"H. A. Watson (Bell Labs); extended by W. E. Vesely and others for multi-output contexts","year":"1961 (FTA); multi-response extensions developed from the 1980s onward","type":"Deductive reliability and risk analysis","dataType":"System architecture diagrams, component failure probabilities, multiple failure modes and outcome metrics","subfamily":"Engineering methods"},"citations":[{"ref":"Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492.","type":"book","doi":null,"isbn":null,"url":"https://www.nrc.gov/reading-rm/doc-collections/nuregs/staff/sr0492/sr0492.pdf"},{"ref":"Fault tree analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Fault_tree_analysis"}],"related":["fault-tree-analysis","failure-mode-and-effects-analysis","event-tree-analysis","multi-response-failure-mode-and-effects-analysis","reliability-analysis","multi-response-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-fractional-factorial-design","name":"Multi-response Fractional Factorial Design","fullName":"Multi-response Fractional Factorial Design of Experiments","aliases":["MRFFD","multi-response FFD","multi-objective fractional factorial design","simultaneous multi-response fractional factorial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1961 (fractional factorial foundation); 1980 (multi-response desirability approach)","originator":"George E.P. Box, J. Stuart Hunter, and William G. Hunter (fractional factorial basis); Derringer & Suich (multi-response desirability extension)","url":"https://scholargate.app/en/experimental-design/multi-response-fractional-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-fractional-factorial-design.md","definition":"Multi-response fractional factorial design (MRFFD) applies a resolution-efficient fractional factorial experiment to study multiple response variables simultaneously. By running only a carefully chosen fraction of the full factorial treatment combinations, the experimenter gathers enough information to fit individual response models for each output and then optimize all responses jointly — typically via a composite desirability function — while keeping the number of experimental runs tractable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E.P. Box, J. Stuart Hunter, and William G. Hunter (fractional factorial basis); Derringer & Suich (multi-response desirability extension)","year":"1961 (fractional factorial foundation); 1980 (multi-response desirability approach)","type":"Experimental design with simultaneous multi-response optimization","dataType":"Continuous or categorical factor settings; multiple continuous response measurements","subfamily":"Engineering methods"},"citations":[{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.","type":"article","doi":"10.1080/00224065.1980.11980968","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["fractional-factorial-design","full-factorial-design","multi-response-response-surface-methodology","multi-response-taguchi-method","response-surface-methodology","design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-full-factorial-design","name":"Multi-response full factorial design","fullName":"Multi-Response Full Factorial Design of Experiments","aliases":["MRFFD","multi-response FFD","multiple-response full factorial","multi-objective full factorial design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1950s–1980s","originator":"Douglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization)","url":"https://scholargate.app/en/experimental-design/multi-response-full-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-full-factorial-design.md","definition":"Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization)","year":"1950s–1980s","type":"Experimental design with multi-objective optimization","dataType":"Continuous and categorical factor levels; multiple quantitative response variables","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null},{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.","type":"article","doi":"10.1080/00224065.1980.11980968","isbn":null,"url":null}],"related":["full-factorial-design","fractional-factorial-design","response-surface-methodology","taguchi-method","multi-response-response-surface-methodology","design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-process-capability-analysis","name":"Multi-response Process Capability Analysis","fullName":"Multi-response Process Capability Analysis","aliases":["MRPCA","multivariate process capability","multi-characteristic capability analysis","vector process capability"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1993–1994 (foundational multivariate indices)","originator":"Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm)","url":"https://scholargate.app/en/experimental-design/multi-response-process-capability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-process-capability-analysis.md","definition":"Multi-response process capability analysis extends classical single-response capability indices (Cp, Cpk) to situations where a process must simultaneously satisfy specification limits on two or more correlated quality characteristics. Rather than evaluating each response in isolation, it assesses the joint probability that all characteristics fall within their respective tolerance regions, yielding a more realistic picture of overall process performance in multi-characteristic manufacturing and engineering settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm)","year":"1993–1994 (foundational multivariate indices)","type":"Quantitative quality / process assessment method","dataType":"Continuous measurement data from multiple correlated quality characteristics","subfamily":"Engineering methods"},"citations":[{"ref":"Taam, W., Subbaiah, P., & Liddy, J. W. (1993). A note on multivariate capability indices. Journal of Applied Statistics, 20(3), 339–351.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+note+on+multivariate+capability+indices+Taam+Subbaiah+Liddy+1993"},{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Simultaneous+optimization+of+several+response+variables+Derringer+Suich+1980"}],"related":["process-capability-analysis","multi-response-response-surface-methodology","multi-response-design-of-experiments","statistical-process-control","design-of-experiments","quality-function-deployment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-response-surface-methodology","name":"Multi-response Response Surface Methodology","fullName":"Multi-response Response Surface Methodology","aliases":["Multi-response RSM","MRSM","Multi-objective RSM","Multiple response optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)","originator":"Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)","url":"https://scholargate.app/en/experimental-design/multi-response-response-surface-methodology","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-response-surface-methodology.md","definition":"Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)","year":"1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)","type":"Experimental optimization technique","dataType":"Continuous response data from designed experiments (factorial, CCD, BBD)","subfamily":"Engineering methods"},"citations":[{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.","type":"article","doi":"10.1080/00224065.1980.11980968","isbn":null,"url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916025","url":null}],"related":["response-surface-methodology","central-composite-design","box-behnken-design","design-of-experiments","optimization-assisted-response-surface-methodology","quality-function-deployment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-root-cause-analysis","name":"Multi-response Root Cause Analysis","fullName":"Multi-response Root Cause Analysis","aliases":["Multi-KPI RCA","Multi-output RCA","Multi-response RCA","MRCA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (multi-response extension of classical RCA)","originator":"Root Cause Analysis tradition (Kepner-Tregoe, Ishikawa, Deming); multi-response extension in Six Sigma and quality engineering practice","url":"https://scholargate.app/en/experimental-design/multi-response-root-cause-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-root-cause-analysis.md","definition":"Multi-response Root Cause Analysis (MRCA) is a structured problem-solving method that identifies the underlying causes of failures or deviations across multiple simultaneous response variables (KPIs, quality characteristics, or process outputs). It extends classical RCA to settings where a single root cause can propagate into several observed defects or performance degradations at once, which is common in manufacturing, engineering, and service-quality contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Root Cause Analysis tradition (Kepner-Tregoe, Ishikawa, Deming); multi-response extension in Six Sigma and quality engineering practice","year":"1990s–2000s (multi-response extension of classical RCA)","type":"Systematic problem-solving method","dataType":"Multiple process outcome measurements, defect records, historical process data","subfamily":"Engineering methods"},"citations":[{"ref":"Andersen, B., & Fagerhaug, T. (2006). Root Cause Analysis: Simplified Tools and Techniques (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873896924","url":null},{"ref":"Root cause analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Root_cause_analysis"}],"related":["root-cause-analysis","failure-mode-and-effects-analysis","multi-response-failure-mode-and-effects-analysis","multi-response-design-of-experiments","fault-tree-analysis","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-six-sigma-dmaic","name":"Multi-response Six Sigma DMAIC","fullName":"Multi-response Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control)","aliases":["MR-DMAIC","multi-response DMAIC","multi-criteria Six Sigma","multi-objective DMAIC"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"2000s–2010s (applied integration era)","originator":"Extension of Six Sigma DMAIC (Motorola/Mikel Harry); multi-response adaptation developed by quality engineering community","url":"https://scholargate.app/en/experimental-design/multi-response-six-sigma-dmaic","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-six-sigma-dmaic.md","definition":"Multi-response Six Sigma DMAIC extends the classic Define-Measure-Analyze-Improve-Control framework to situations where a process must satisfy several quality characteristics simultaneously. Rather than optimizing a single output, the methodology integrates multi-response optimization techniques — such as desirability functions, TOPSIS, or weighted signal-to-noise ratios — within the Analyze and Improve phases to identify factor settings that jointly meet all quality targets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Six Sigma DMAIC (Motorola/Mikel Harry); multi-response adaptation developed by quality engineering community","year":"2000s–2010s (applied integration era)","type":"Process improvement methodology with multi-objective optimization","dataType":"Continuous and discrete process measurement data; multiple response variables","subfamily":"Engineering methods"},"citations":[{"ref":"Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Doubleday.","type":"book","doi":null,"isbn":"978-0385494090","url":null},{"ref":"Antony, J., & Banuelas, R. (2004). Six sigma or design for six sigma? TQM Magazine, 16(4), 250–263.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Six+sigma+or+design+for+six+sigma+Antony+Banuelas+2004"}],"related":["six-sigma-dmaic","design-of-experiments","response-surface-methodology","taguchi-method","statistical-process-control","multi-response-response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-statistical-process-control","name":"Multi-response statistical process control","fullName":"Multi-response Statistical Process Control","aliases":["Multivariate SPC","MSPC","Multi-response SPC","Multivariate statistical process control"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1947 (Hotelling's T²); mature multivariate SPC framework 1980s–2000s","originator":"Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young","url":"https://scholargate.app/en/experimental-design/multi-response-statistical-process-control","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-statistical-process-control.md","definition":"Multi-response statistical process control (multivariate SPC) extends classical univariate control charting to processes where two or more correlated quality characteristics must be monitored simultaneously. By treating all responses as a joint distribution, it detects shifts that would be invisible when each response is charted independently, reducing false alarms and improving the sensitivity of process monitoring in manufacturing and service contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young","year":"1947 (Hotelling's T²); mature multivariate SPC framework 1980s–2000s","type":"Multivariate quality-monitoring procedure","dataType":"Continuous measurement data on two or more correlated quality characteristics","subfamily":"Engineering methods"},"citations":[{"ref":"Lowry, C. A., & Montgomery, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800–810.","type":"article","doi":"10.1080/07408179508936797","isbn":null,"url":null},{"ref":"Mason, R. L., & Young, J. C. (2002). Multivariate Statistical Process Control with Industrial Applications. ASA-SIAM.","type":"book","doi":null,"isbn":"978-0898715033","url":null}],"related":["statistical-process-control","control-chart","multi-response-design-of-experiments","process-capability-analysis","multi-response-response-surface-methodology","failure-mode-and-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-response-taguchi-method","name":"Multi-response Taguchi method","fullName":"Multi-response Taguchi Parameter Design","aliases":["Taguchi multi-response optimization","MRTM","multi-objective Taguchi design","Taguchi with grey relational analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s–1990s","originator":"Genichi Taguchi (base method); extended by multiple researchers via grey relational analysis and desirability functions","url":"https://scholargate.app/en/experimental-design/multi-response-taguchi-method","markdownUrl":"https://scholargate.app/en/experimental-design/multi-response-taguchi-method.md","definition":"The multi-response Taguchi method extends Taguchi’s robust parameter design to situations where several quality characteristics must be optimized simultaneously. Instead of minimizing a single signal-to-noise ratio, practitioners aggregate multiple S/N ratios or raw response values into a composite index — most commonly via grey relational analysis or desirability functions — then apply standard Taguchi analysis to identify the factor-level combination that satisfies all responses jointly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Genichi Taguchi (base method); extended by multiple researchers via grey relational analysis and desirability functions","year":"1980s–1990s","type":"Robust parameter design with multi-response optimization","dataType":"Quantitative experimental measurements across multiple quality characteristics","subfamily":"Engineering methods"},"citations":[{"ref":"Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall.","type":"book","doi":null,"isbn":"978-0137451678","url":null},{"ref":"Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1–24.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Introduction+to+grey+system+theory+Deng+1989"}],"related":["taguchi-method","grey-relational-analysis","response-surface-methodology","multi-response-response-surface-methodology","design-of-experiments","robust-taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-api-based-data-collection","name":"Multi-source API-based Data Collection","fullName":"Multi-source Application Programming Interface-based Data Collection","aliases":["multi-API data harvesting","multi-platform API collection","cross-API data aggregation","federated API data collection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2010s (accelerated with proliferation of public APIs)","originator":"Emergent practice in computational social science; formalized by Salganik, Ruths, Pfeffer, and others","url":"https://scholargate.app/en/survey-methodology/multi-source-api-based-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-api-based-data-collection.md","definition":"Multi-source API-based data collection is a systematic technique in which a researcher simultaneously or sequentially queries two or more application programming interfaces (APIs) to harvest digital data for a research project. By drawing from multiple platforms or services — such as social media APIs, government open-data portals, or scientific data repositories — researchers can build richer, more representative datasets than any single source permits. The method is especially prominent in computational social science, digital humanities, public health surveillance, and environmental monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emergent practice in computational social science; formalized by Salganik, Ruths, Pfeffer, and others","year":"2010s (accelerated with proliferation of public APIs)","type":"Quantitative / mixed data collection technique","dataType":"Structured and semi-structured digital data (JSON, XML, CSV) from multiple API endpoints","subfamily":"Data collection"},"citations":[{"ref":"Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064.","type":"article","doi":"10.1126/science.346.6213.1063","isbn":null,"url":null},{"ref":"Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691158648","url":null}],"related":["api-based-data-collection","web-scraping","multi-source-document-collection","sensor-data-collection","data-triangulation","longitudinal-api-based-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-delphi-technique","name":"Multi-source Delphi Technique","fullName":"Multi-source Delphi Technique","aliases":["Multi-stakeholder Delphi","Diverse-panel Delphi","Multi-group Delphi","MSDT"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1975–2000s","originator":"Extension of the classic Delphi method; multi-source framing attributed to diverse practitioners building on Linstone & Turoff (1975)","url":"https://scholargate.app/en/survey-methodology/multi-source-delphi-technique","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-delphi-technique.md","definition":"The Multi-source Delphi Technique is a structured, iterative consensus-building method that deliberately recruits expert panellists from multiple, distinct stakeholder groups or knowledge sources. By ensuring that no single professional community or institution dominates the panel, it reduces homogeneity bias and captures a broader range of perspectives than a conventional single-group Delphi. Panellists respond anonymously across successive rounds, receiving aggregated group feedback between rounds until consensus or a stable level of agreement is reached.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of the classic Delphi method; multi-source framing attributed to diverse practitioners building on Linstone & Turoff (1975)","year":"1975–2000s","type":"Structured consensus-building technique","dataType":"Expert opinion, Likert-scale ratings, open-ended panel responses","subfamily":"Data collection"},"citations":[{"ref":"Linstone, H. A., & Turoff, M. (Eds.). (1975). The Delphi Method: Techniques and Applications. Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Delphi+Method+Techniques+and+Applications+Linstone+Turoff+1975"},{"ref":"Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.","type":"article","doi":"10.1046/j.1365-2648.2000.t01-1-01567.x","isbn":null,"url":null}],"related":["delphi-technique","nominal-group-technique","consensus-conference","expert-panel","focus-group","policy-delphi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-document-collection","name":"Multi-source Document Collection","fullName":"Multi-source Document Collection","aliases":["multi-source documentary research","multiple-document data collection","multi-site document analysis","cross-source document gathering"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–2000s (systematic articulation)","originator":"Rooted in qualitative documentary traditions; codified in mixed-methods and triangulation literature (Denzin 1970s; Bowen 2009)","url":"https://scholargate.app/en/survey-methodology/multi-source-document-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-document-collection.md","definition":"Multi-source document collection is a data-gathering strategy in which researchers systematically locate, retrieve, and compare documents drawn from two or more independent sources — such as government archives, institutional records, media outlets, organisational reports, or digital repositories. By assembling evidence from diverse provenance, researchers can triangulate findings, detect discrepancies, and build a richer, more credible picture of the phenomenon under study than any single documentary source can provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in qualitative documentary traditions; codified in mixed-methods and triangulation literature (Denzin 1970s; Bowen 2009)","year":"1970s–2000s (systematic articulation)","type":"Data collection strategy","dataType":"Written, visual, or digital documents from multiple independent sources","subfamily":"Data collection"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506386706","url":null}],"related":["document-collection","triangulated-document-collection","multi-source-survey","content-analysis","systematic-review","archival-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-field-notes","name":"Multi-source Field Notes","fullName":"Multi-source Field Notes Collection","aliases":["multi-observer field notes","triangulated field notes","collaborative field notes","multi-site field notes"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–1990s (multi-observer extensions formalised in mixed-methods era)","originator":"Ethnographic research tradition; systematised by Emerson, Fretz & Shaw","url":"https://scholargate.app/en/survey-methodology/multi-source-field-notes","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-field-notes.md","definition":"Multi-source field notes is a data collection approach in which two or more observers, sites, or vantage points contribute written records of naturally occurring events, interactions, and settings. By pooling notes from multiple sources, researchers cross-check individual impressions and capture aspects of a scene that any single observer would miss, strengthening descriptive richness and analytical trustworthiness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ethnographic research tradition; systematised by Emerson, Fretz & Shaw","year":"1970s–1990s (multi-observer extensions formalised in mixed-methods era)","type":"Qualitative data collection technique","dataType":"Textual descriptive records from fieldwork settings","subfamily":"Data collection"},"citations":[{"ref":"Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes (2nd ed.). University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226206837","url":null},{"ref":"Denzin, N. K., & Lincoln, Y. S. (Eds.). (2017). The SAGE Handbook of Qualitative Research (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1483349800","url":null}],"related":["field-notes","participant-observation","non-participant-observation","triangulated-field-notes","multi-source-document-collection","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-focus-group","name":"Multi-source Focus Group","fullName":"Multi-source Focus Group Method","aliases":["multi-stakeholder focus group","multiple-source focus group","cross-source focus group","MSFG"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s–1990s","originator":"Developed from focus group methodology; formalized in applied social research (Krueger, Morgan, and colleagues)","url":"https://scholargate.app/en/survey-methodology/multi-source-focus-group","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-focus-group.md","definition":"The multi-source focus group method extends the standard focus group design by deliberately recruiting participants from two or more distinct stakeholder groups — for example, clinicians and patients, teachers and students, or managers and frontline staff. Separate sessions are held for each source group using a shared discussion protocol, and the resulting data are analyzed both within each group and across groups to reveal convergences, tensions, and perspectives that no single-source design could uncover.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from focus group methodology; formalized in applied social research (Krueger, Morgan, and colleagues)","year":"1980s–1990s","type":"Qualitative data collection technique","dataType":"Verbal/textual group discussion data from multiple distinct stakeholder groups","subfamily":"Data collection"},"citations":[{"ref":"Krueger, R. A., & Casey, M. A. (2015). Focus Groups: A Practical Guide for Applied Research (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1483365244","url":null},{"ref":"Morgan, D. L. (1997). Focus Groups as Qualitative Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761903437","url":null}],"related":["focus-group","nominal-group-technique","delphi-method","key-informant-interview","triangulation","participatory-action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-in-depth-interview","name":"Multi-source In-depth Interview","fullName":"Multi-source In-depth Interview","aliases":["multi-informant in-depth interview","multi-perspective qualitative interview","multiple-source IDI","multi-stakeholder in-depth interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s–1990s (formalized in qualitative inquiry literature)","originator":"Grounded in qualitative traditions consolidated by Patton, Lincoln & Guba, and others","url":"https://scholargate.app/en/survey-methodology/multi-source-in-depth-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-in-depth-interview.md","definition":"The multi-source in-depth interview is a qualitative data collection strategy in which extended, open-ended interviews are conducted with participants drawn from two or more distinct source groups — such as providers and clients, managers and staff, or experts and laypeople. Collecting data across diverse informant positions enriches description and enables the researcher to examine a phenomenon from multiple vantage points within a single study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grounded in qualitative traditions consolidated by Patton, Lincoln & Guba, and others","year":"1980s–1990s (formalized in qualitative inquiry literature)","type":"Qualitative data collection technique","dataType":"Verbal/textual data from multiple informant groups","subfamily":"Data collection"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage Publications.","type":"book","doi":null,"isbn":"978-0803924314","url":null}],"related":["in-depth-interview","semi-structured-interview","triangulated-in-depth-interview","multi-source-focus-group","key-informant-interview","longitudinal-in-depth-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-mobile-experience-sampling","name":"Multi-source Mobile Experience Sampling","fullName":"Multi-source Mobile Experience Sampling Method","aliases":["multi-informant ESM","dyadic ESM","multi-respondent ecological momentary assessment","MSESM"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s","originator":"Developed from ESM (Csikszentmihalyi & Larson, 1983) and extended to multi-informant intensive longitudinal designs by Bolger, Laurenceau, and colleagues","url":"https://scholargate.app/en/survey-methodology/multi-source-mobile-experience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-mobile-experience-sampling.md","definition":"Multi-source Mobile Experience Sampling extends the standard ESM design by simultaneously collecting repeated momentary self-reports from two or more linked informant types — such as patient and caregiver, employee and supervisor, or partners in a dyad — via their smartphones. Signals are delivered concurrently across sources, enabling researchers to examine convergences and discrepancies between informants' real-time experiences and to model interpersonal dynamics at the moment they unfold in daily life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from ESM (Csikszentmihalyi & Larson, 1983) and extended to multi-informant intensive longitudinal designs by Bolger, Laurenceau, and colleagues","year":"2000s–2010s","type":"Intensive longitudinal multi-informant data collection technique","dataType":"Repeated momentary self-reports from two or more linked informant sources (quantitative and/or qualitative)","subfamily":"Data collection"},"citations":[{"ref":"Bolger, N., & Laurenceau, J.-P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Press.","type":"book","doi":null,"isbn":"978-1462506781","url":null},{"ref":"Iida, M., Shrout, P. E., Laurenceau, J.-P., & Bolger, N. (2012). Using diary methods in psychological research. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA Handbook of Research Methods in Psychology, Vol. 1. American Psychological Association.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Iida+Shrout+Laurenceau+Bolger+2012+diary+methods+APA+handbook"}],"related":["mobile-experience-sampling","diary-method","multi-source-participant-observation","longitudinal-mobile-experience-sampling","sensor-data-collection","multilevel-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-non-participant-observation","name":"Multi-source Non-participant Observation","fullName":"Multi-source Non-participant Observation","aliases":["multi-site non-participant observation","multi-context unobtrusive observation","non-reactive multi-source observation","triangulated non-participant observation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–1980s (methodological triangulation literature)","originator":"Rooted in systematic observation traditions; multi-source triangulation formalised by Norman Denzin","url":"https://scholargate.app/en/survey-methodology/multi-source-non-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-non-participant-observation.md","definition":"Multi-source non-participant observation is a qualitative data collection strategy in which a researcher systematically observes naturally occurring behaviour across two or more distinct settings, sites, or data sources without joining or influencing the activity being studied. By deliberately excluding the researcher from participation and drawing on multiple independent observational vantage points, the approach strengthens credibility through methodological triangulation while preserving the unobtrusiveness that protects naturalistic behaviour.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in systematic observation traditions; multi-source triangulation formalised by Norman Denzin","year":"1970s–1980s (methodological triangulation literature)","type":"Qualitative/naturalistic data collection strategy","dataType":"Observational field notes, logs, audio-visual records from multiple sites or sources","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+1978+The+Research+Act+Theoretical+Introduction+Sociological+Methods"},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["non-participant-observation","participant-observation","multi-source-participant-observation","triangulated-non-participant-observation","field-notes","document-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-participant-observation","name":"Multi-source Participant Observation","fullName":"Multi-source Participant Observation","aliases":["multi-site participant observation","triangulated participant observation","multi-vantage participant observation","MSPO"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s (building on early 20th-century fieldwork traditions)","originator":"Developed from classical participant observation traditions (Bronislaw Malinowski, Chicago School); multi-source extension codified by Hammersley & Atkinson and Spradley","url":"https://scholargate.app/en/survey-methodology/multi-source-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-participant-observation.md","definition":"Multi-source participant observation is a qualitative data collection technique in which the researcher is embedded within a social setting and systematically gathers observational data from multiple vantage points, sites, or informant roles simultaneously. By triangulating across sources, the method strengthens credibility and provides a richer, more complete picture of social phenomena than single-site observation alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from classical participant observation traditions (Bronislaw Malinowski, Chicago School); multi-source extension codified by Hammersley & Atkinson and Spradley","year":"1980s (building on early 20th-century fieldwork traditions)","type":"Qualitative data collection technique","dataType":"Field notes, observational records, documents, informal interviews from multiple observation sites or roles","subfamily":"Data collection"},"citations":[{"ref":"Spradley, J. P. (1980). Participant Observation. Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030445019","url":null},{"ref":"Hammersley, M., & Atkinson, P. (1983). Ethnography: Principles in Practice. Tavistock Publications.","type":"book","doi":null,"isbn":"978-0415076029","url":null}],"related":["ethnography","participant-observation","field-research","triangulation","naturalistic-observation","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-source-semi-structured-interview","name":"Multi-source Semi-structured Interview","fullName":"Multi-source Semi-structured Interview","aliases":["multi-informant semi-structured interview","multi-perspective semi-structured interview","multi-source qualitative interview","triangulated semi-structured interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s–2000s (multi-source data strategies in qualitative inquiry)","originator":"Established practice in qualitative and mixed-methods research; systematized by Patton (2002) and Bryman (2016)","url":"https://scholargate.app/en/survey-methodology/multi-source-semi-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/multi-source-semi-structured-interview.md","definition":"A multi-source semi-structured interview strategy collects qualitative data via guided, open-ended interviews from two or more distinct groups or perspectives relevant to the same phenomenon. By deliberately querying multiple vantage points — such as managers and employees, patients and clinicians, or teachers and students — the researcher can compare, contrast, and triangulate accounts, producing a richer and more balanced picture than any single-source approach allows.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established practice in qualitative and mixed-methods research; systematized by Patton (2002) and Bryman (2016)","year":"1980s–2000s (multi-source data strategies in qualitative inquiry)","type":"Qualitative data collection technique","dataType":"Verbal/textual data from multiple respondent groups or perspectives","subfamily":"Data collection"},"citations":[{"ref":"Bryman, A. (2016). Social Research Methods (5th ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0198745754","url":null},{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761919711","url":null}],"related":["semi-structured-interview","multi-source-survey","triangulated-semi-structured-interview","in-depth-interview","focus-group","longitudinal-semi-structured-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multi-state-model","name":"Multi-State Model","fullName":"Multi-State Survival Model","aliases":["illness-death model","multi-state transition model","Çok Durumlu Model (Multi-State / Illness-Death)"],"domain":"survival","family":"survival","subfamily":null,"year":1978,"originator":"Andersen, P.K. & Keiding, N. (foundational framework); popularised by Putter, Fiocco & Geskus (2007)","url":"https://scholargate.app/en/survival/multi-state-model","markdownUrl":"https://scholargate.app/en/survival/multi-state-model.md","definition":"The multi-state model is a generalised survival framework, formalised in the work of Andersen and Keiding and brought to wide biostatistical practice by Putter, Fiocco and Geskus (2007), that models individuals moving through multiple distinct health states — for example, healthy, ill and dead — over time. A separate hazard function is estimated for each possible transition, and transition probabilities are recovered via the product-integral of the cumulative transition intensities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andersen, P.K. & Keiding, N. (foundational framework); popularised by Putter, Fiocco & Geskus (2007)","year":1978,"type":"Semi-parametric hazard model","handles":"Multiple states, competing risks, recurrent transitions","markovAssumption":"Markov or semi-Markov","minimumSample":100,"difficulty":4},"citations":[{"ref":"Putter, H., Fiocco, M. & Geskus, R.B. (2007). Tutorial in Biostatistics: Competing Risks and Multi-State Models. Statistics in Medicine, 26(11), 2389–2430.","type":"article","doi":"10.1002/sim.2712","isbn":null,"url":null},{"ref":"Jackson, C.H. (2011). Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software, 38(8), 1–28.","type":"article","doi":"10.18637/jss.v038.i08","isbn":null,"url":null}],"related":["cox-ph","kaplan-meier","fine-gray-model","competing-risks","flexible-parametric-survival","frailty-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiarm-bandit","name":"Multi-Armed Bandit","fullName":"Multi-Armed Bandit (UCB, Thompson Sampling)","aliases":["MAB","bandit algorithm","UCB1","Thompson sampling","epsilon-greedy","Çok Kollu Bandit (Multi-Armed Bandit — UCB, Thompson)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1952,"originator":"Robbins (1952); UCB1 by Auer et al. (2002); Thompson sampling by Thompson (1933)","url":"https://scholargate.app/en/experimental-design/multiarm-bandit","markdownUrl":"https://scholargate.app/en/experimental-design/multiarm-bandit.md","definition":"The multi-armed bandit (MAB) is an adaptive experimental framework that allocates trials sequentially across competing arms to minimise cumulative regret while simultaneously learning which arm performs best. Formalised by Robbins in 1952 and given finite-time guarantees by Auer et al. (2002), it balances exploration of uncertain options against exploitation of currently known best options — outperforming classical A/B testing whenever early stopping or cost-sensitive allocation matters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robbins (1952); UCB1 by Auer et al. (2002); Thompson sampling by Thompson (1933)","year":1952,"family":"Adaptive experiment","type":"Sequential decision / bandit algorithm","strategies":"UCB1, Thompson Sampling, ε-greedy","minSample":50,"parametric":false,"outcome":"binary or continuous reward","stationarity":"required (or slowly varying)"},"citations":[{"ref":"Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-Time Analysis of the Multiarmed Bandit Problem. Machine Learning, 47(2–3), 235–256.","type":"article","doi":"10.1023/A:1013689704352","isbn":null,"url":null},{"ref":"Russo, D., Van Roy, B., Kazerouni, A., Osband, I., & Wen, Z. (2018). A Tutorial on Thompson Sampling. Foundations and Trends in Machine Learning, 11(1), 1–96.","type":"article","doi":"10.1561/2200000070","isbn":null,"url":null}],"related":["ab-testing","adaptive-design","randomized-controlled-trial","sequential-design","bayesian-hypothesis-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-case-control-study","name":"Multicenter Case-Control Study","fullName":"Multicenter Case-Control Study","aliases":["multisite case-control study","collaborative case-control study","pooled case-control study","multi-institutional case-control study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-20th century; multicenter framework formalised 1970s–1980s","originator":"Epidemiology convention; seminal statistical framework by Breslow & Day (IARC, 1980)","url":"https://scholargate.app/en/epidemiology/multicenter-case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-case-control-study.md","definition":"A multicenter case-control study is an observational design that identifies individuals who have developed a disease (cases) and disease-free comparators (controls) across two or more study sites simultaneously. By pooling recruitment across hospitals, clinics, or geographic regions, the design achieves larger sample sizes, captures exposure variability over broader populations, and improves the statistical power needed to detect modest odds ratios for rare or heterogeneous diseases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Epidemiology convention; seminal statistical framework by Breslow & Day (IARC, 1980)","year":"Mid-20th century; multicenter framework formalised 1970s–1980s","type":"Observational analytical epidemiological design","dataType":"Categorical and continuous exposure variables, dichotomous outcome (case/control status)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Breslow, N. E., & Day, N. E. (1980). Statistical Methods in Cancer Research. Volume I: The Analysis of Case-Control Studies. IARC Scientific Publications No. 32. International Agency for Research on Cancer, Lyon.","type":"book","doi":null,"isbn":"978-9283211327","url":null},{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins, Philadelphia.","type":"book","doi":null,"isbn":"978-0781755641","url":null}],"related":["case-control-study","nested-case-control","multicenter-cohort-study","multicenter-randomized-clinical-trial","matched-case-control-study","meta-analytic-case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-case-crossover-design","name":"Multicenter Case-Crossover Design","fullName":"Multicenter Case-Crossover Epidemiological Study","aliases":["multi-site case-crossover study","multicenter self-matched crossover","multi-center transient exposure study","MCCO study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1991 (core design); multicenter extensions 1990s–2000s","originator":"Malcolm Maclure (single-center design, 1991); multicenter applications developed through 1990s–2000s environmental and pharmacoepidemiology literature","url":"https://scholargate.app/en/epidemiology/multicenter-case-crossover-design","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-case-crossover-design.md","definition":"The multicenter case-crossover design is an observational epidemiological method that investigates whether brief, transient exposures trigger acute health events by comparing each case's exposure just before the event to their own exposure during matched control periods — with data collected from two or more independent clinical or geographic sites to increase power, external validity, and the ability to detect site-level effect modification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Malcolm Maclure (single-center design, 1991); multicenter applications developed through 1990s–2000s environmental and pharmacoepidemiology literature","year":"1991 (core design); multicenter extensions 1990s–2000s","type":"Observational epidemiological design","dataType":"Individual-level time-stamped event and exposure data across multiple clinical or geographic sites","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Maclure, M. (1991). The case-crossover design: A method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.","type":"article","doi":"10.1093/oxfordjournals.aje.a115853","isbn":null,"url":null},{"ref":"Case-crossover study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Case-crossover_study"}],"related":["case-crossover-design","case-control-study","multicenter-cohort-study","nested-case-control","crossover-trial","case-time-control-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-case-report","name":"Multicenter case report","fullName":"Multicenter Case Report","aliases":["multi-site case report","collaborative case report","multicentre case report","CARE multicenter report"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Long-standing practice; CARE guidelines formalized 2013","originator":"Clinical medicine tradition; CARE guidelines by Gagnier et al.","url":"https://scholargate.app/en/epidemiology/multicenter-case-report","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-case-report.md","definition":"A multicenter case report is a structured clinical document describing one or a very small number of unusual patients observed across two or more independent healthcare institutions. By pooling observations from multiple sites, it overcomes the rarity barrier that prevents any single center from documenting an unusual presentation, adverse event, or novel treatment response — producing a richer, more externally valid account than a single-center report can offer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clinical medicine tradition; CARE guidelines by Gagnier et al.","year":"Long-standing practice; CARE guidelines formalized 2013","type":"Observational descriptive study","dataType":"Patient clinical records, imaging, laboratory findings, follow-up notes from multiple sites","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Gagnier, J. J., Kienle, G., Altman, D. G., Moher, D., Sox, H., & Riley, D. (2013). The CARE guidelines: Consensus-based clinical case reporting guideline development. Journal of Medical Case Reports, 7, 223.","type":"article","doi":"10.1186/1752-1947-7-223","isbn":null,"url":null},{"ref":"Case report. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Case_report"}],"related":["case-report","multicenter-case-series","case-series","multicenter-cohort-study","prospective-case-report","retrospective-case-report"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-case-series","name":"Multicenter case series","fullName":"Multicenter Case Series Study","aliases":["multi-site case series","multicentre case series","collaborative case series","multi-institutional case series"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-to-late 20th century (collaborative multi-site reporting common by 1970s–1980s)","originator":"Evolved from single-center case series practice; formalized in 20th century clinical reporting","url":"https://scholargate.app/en/epidemiology/multicenter-case-series","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-case-series.md","definition":"A multicenter case series is an observational descriptive study in which consecutive or selected patients sharing a defined clinical condition are enrolled and followed at two or more independent clinical sites. By pooling cases across institutions, researchers achieve larger sample sizes and greater demographic and clinical diversity than a single-center series permits, enabling more reliable description of disease presentation, management patterns, and outcomes for rare or uncommon conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from single-center case series practice; formalized in 20th century clinical reporting","year":"Mid-to-late 20th century (collaborative multi-site reporting common by 1970s–1980s)","type":"Observational descriptive study","dataType":"Clinical records, patient-level data from multiple institutions","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Dekkers, O. M., Vandenbroucke, J. P., Cevallos, M., Renehan, A. G., Altman, D. G., & Egger, M. (2012). COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology and prognosis. PLoS Medicine, 9(2), e1001175.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=COSMOS-E%3A+Guidance+on+conducting+systematic+reviews+and+meta-analyses+of+observational+studies+of+etiology+and+prognosis+Dekkers"},{"ref":"Matthews, J. N. S. (2006). Introduction to Randomized Controlled Clinical Trials (2nd ed.). Chapman and Hall/CRC.","type":"article","doi":null,"isbn":"978-1584886242","url":null}],"related":["case-series","multicenter-cohort-study","multicenter-randomized-clinical-trial","multicenter-case-control-study","case-report","retrospective-case-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-cohort-study","name":"Multicenter cohort study","fullName":"Multicenter Cohort Study","aliases":["multisite cohort study","multi-centre cohort","collaborative cohort study","pooled cohort study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-to-late 20th century (widespread adoption 1970s–1990s)","originator":"Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992)","url":"https://scholargate.app/en/epidemiology/multicenter-cohort-study","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-cohort-study.md","definition":"A multicenter cohort study follows defined groups of participants at two or more geographically or institutionally distinct sites over time to estimate incidence, identify risk factors, and quantify associations between exposures and outcomes. By pooling data from multiple centers, it achieves statistical power and population diversity that single-site designs cannot match, making it the workhorse of large-scale epidemiological and clinical research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992)","year":"Mid-to-late 20th century (widespread adoption 1970s–1990s)","type":"Observational longitudinal study","dataType":"Individual-level longitudinal data collected prospectively (or retrospectively) across two or more clinical or geographic sites","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Cohort study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Cohort_study"}],"related":["cohort-study","prospective-cohort-study","randomized-clinical-trial","multicenter-randomized-clinical-trial","nested-case-control","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-competing-risks-analysis","name":"Multicenter Competing Risks Analysis","fullName":"Multicenter Competing Risks Analysis","aliases":["multicenter CRA","multi-site competing risks","multicenter cumulative incidence analysis","polycentric competing risks study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1999 (Fine-Gray); extended to multicenter settings throughout 2000s–2010s","originator":"Fine & Gray (subdistribution hazard model); Prentice et al. (cause-specific hazard model)","url":"https://scholargate.app/en/epidemiology/multicenter-competing-risks-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-competing-risks-analysis.md","definition":"Multicenter competing risks analysis is a time-to-event method applied across multiple clinical centers to estimate the probability of a specific event of interest when other mutually exclusive events — competing risks — can preclude its occurrence. By pooling data from diverse sites, it achieves the sample sizes needed to model rare events and enables assessment of center-level variation in cumulative incidence and covariate effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fine & Gray (subdistribution hazard model); Prentice et al. (cause-specific hazard model)","year":"1999 (Fine-Gray); extended to multicenter settings throughout 2000s–2010s","type":"Survival / time-to-event statistical analysis","dataType":"Time-to-event data with event type indicators, center identifiers, and covariates from multiple clinical sites","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.","type":"article","doi":"10.1080/01621459.1999.10474144","isbn":null,"url":null},{"ref":"Austin, P. C., Lee, D. S., & Fine, J. P. (2016). Introduction to the analysis of survival data in the presence of competing risks. Circulation, 133(6), 601–609.","type":"article","doi":"10.1161/CIRCULATIONAHA.115.017719","isbn":null,"url":null}],"related":["competing-risks-analysis","multicenter-survival-analysis","cox-proportional-hazards","multicenter-cox-proportional-hazards","kaplan-meier-analysis","multicenter-cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-cox-proportional-hazards","name":"Multicenter Cox proportional hazards","fullName":"Multicenter Cox Proportional Hazards Regression","aliases":["multicenter Cox regression","multisite Cox PH model","stratified Cox model across centers","multicenter survival regression"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1972 (Cox model); multicenter applications formalized 1980s–1990s","originator":"D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology","url":"https://scholargate.app/en/epidemiology/multicenter-cox-proportional-hazards","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-cox-proportional-hazards.md","definition":"Multicenter Cox proportional hazards regression extends the classic Cox PH model to studies conducted at two or more clinical sites or centers. It estimates the effect of predictors on time-to-event outcomes while explicitly accounting for clustering within centers, between-center heterogeneity, and potential differences in baseline hazard across sites. This design is standard practice in large multicenter RCTs and observational cohort studies in oncology, cardiology, and other clinical fields.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. R. Cox (Cox PH model); multicenter extension developed through collaborative trial methodology","year":"1972 (Cox model); multicenter applications formalized 1980s–1990s","type":"Semi-parametric survival regression for clustered data","dataType":"Time-to-event data with center identifiers and covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null},{"ref":"Therneau, T. M., & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.","type":"book","doi":null,"isbn":"978-0387987842","url":null}],"related":["cox-proportional-hazards","survival-analysis","multicenter-cohort-study","competing-risks-analysis","kaplan-meier-analysis","mixed-effects-models"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-diagnostic-accuracy-study","name":"Multicenter Diagnostic Accuracy Study","fullName":"Multicenter Diagnostic Accuracy Study","aliases":["multisite diagnostic accuracy study","multicenter DTA study","multicenter index test evaluation","STARD multicenter study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2003 (STARD statement first published; updated 2015)","originator":"STARD Group (Bossuyt, Reitsma et al.)","url":"https://scholargate.app/en/epidemiology/multicenter-diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-diagnostic-accuracy-study.md","definition":"A multicenter diagnostic accuracy study evaluates how well an index test (e.g., a biomarker, imaging modality, or clinical prediction rule) identifies a target condition when conducted across two or more independent clinical sites. By recruiting patients from diverse settings, it produces estimates of sensitivity, specificity, and likelihood ratios that are more externally valid than those obtained from a single center, and it enables explicit assessment of how test performance varies across sites, patient populations, and operator skill levels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"STARD Group (Bossuyt, Reitsma et al.)","year":"2003 (STARD statement first published; updated 2015)","type":"Observational diagnostic study design","dataType":"Patient-level test results, reference standard outcomes, site-level covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., Lijmer, J. G., Moher, D., Rennie, D., de Vet, H. C. W., Kressel, H. Y., Rifai, N., Golub, R. M., Altman, D. G., Hooft, L., Korevaar, D. A., & Cohen, J. F. (2015). STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. BMJ, 351, h5527.","type":"article","doi":"10.1136/bmj.h5527","isbn":null,"url":null},{"ref":"Rutjes, A. W. S., Reitsma, J. B., Coomarasamy, A., Khan, K. S., & Bossuyt, P. M. M. (2006). Evaluation of diagnostic tests when there is no gold standard: A review of methods. Health Technology Assessment, 10(50), iii–iv, 1–121.","type":"article","doi":"10.3310/hta11500","isbn":null,"url":null}],"related":["diagnostic-accuracy-study","multicenter-cohort-study","multicenter-randomized-clinical-trial","screening-test-evaluation","systematic-review","meta-analytic-diagnostic-accuracy-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-dose-response-analysis","name":"Multicenter Dose-Response Analysis","fullName":"Multicenter Dose-Response Analysis","aliases":["pooled dose-response analysis","multicenter exposure-response analysis","multi-site dose-response modeling","collaborative dose-response study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1992 (foundational trend methods); refined 2000s–2010s","originator":"Greenland & Longnecker; extended by Orsini et al.","url":"https://scholargate.app/en/epidemiology/multicenter-dose-response-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-dose-response-analysis.md","definition":"Multicenter dose-response analysis estimates the quantitative shape of the relationship between a graded exposure and a health outcome by pooling data or effect estimates across two or more study centers. Using flexible regression tools such as restricted cubic splines or fractional polynomials within a two-stage meta-analytic framework, it characterizes whether the relationship is linear, supra-linear, threshold-based, or J-shaped — providing far greater statistical power and generalizability than any single center could achieve alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greenland & Longnecker; extended by Orsini et al.","year":"1992 (foundational trend methods); refined 2000s–2010s","type":"Quantitative epidemiological analysis","dataType":"Aggregated or individual-participant data from multiple study sites (continuous exposure, binary or count outcomes)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301-1309.","type":"article","doi":"10.1093/oxfordjournals.aje.a116237","isbn":null,"url":null},{"ref":"Orsini, N., Li, R., Wolk, A., Khudyakov, P., & Spiegelman, D. (2012). Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. American Journal of Epidemiology, 175(1), 66-73.","type":"article","doi":"10.1093/aje/kwr265","isbn":null,"url":null}],"related":["dose-response-analysis","multicenter-cohort-study","meta-analysis","restricted-cubic-splines","random-effects-model","pooled-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-ecological-study","name":"Multicenter Ecological Study","fullName":"Multicenter Ecological Epidemiological Study","aliases":["multi-site ecological study","multinational ecological study","pooled ecological analysis","multicenter aggregate study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–1990s (formal methodological description)","originator":"Epidemiological tradition; methodologically articulated by Morgenstern (1982) and Susser (1994)","url":"https://scholargate.app/en/epidemiology/multicenter-ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-ecological-study.md","definition":"A multicenter ecological study is an observational epidemiological design in which the units of analysis are groups — such as cities, regions, or countries — rather than individuals, and data are pooled from two or more distinct centers or geographic areas. The approach links aggregate exposure measures (e.g., average pollution levels, vaccination coverage rates) to aggregate outcome rates (e.g., disease incidence per 100,000) across multiple populations, enabling comparisons that would be infeasible within any single site.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Epidemiological tradition; methodologically articulated by Morgenstern (1982) and Susser (1994)","year":"1980s–1990s (formal methodological description)","type":"Observational epidemiological study design","dataType":"Aggregate (group-level) data from multiple centers, regions, or countries","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344.","type":"article","doi":"10.2105/AJPH.72.12.1336","isbn":null,"url":null},{"ref":"Susser, M. (1994). The logic in ecological: I. The logic of analysis. American Journal of Public Health, 84(5), 825–829.","type":"article","doi":"10.2105/AJPH.84.5.825","isbn":null,"url":null}],"related":["ecological-study","cohort-study","multicenter-cohort-study","cross-sectional-epidemiological-study","case-control-study","dose-response-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-kaplan-meier-analysis","name":"Multicenter Kaplan-Meier analysis","fullName":"Multicenter Kaplan-Meier Survival Analysis","aliases":["pooled Kaplan-Meier","multi-site KM analysis","multicenter survival curve analysis","KM pooled analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1958 (base method); multicenter designs common from 1970s","originator":"Edward L. Kaplan and Paul Meier (method); multicenter application developed through large clinical trial consortia from the 1970s onward","url":"https://scholargate.app/en/epidemiology/multicenter-kaplan-meier-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-kaplan-meier-analysis.md","definition":"Multicenter Kaplan-Meier analysis applies the Kaplan-Meier nonparametric estimator to time-to-event data collected from two or more clinical centers. By pooling or stratifying data across sites, it estimates survival functions and compares them between treatment groups while accounting for potential center effects, enabling conclusions with greater statistical power and broader generalizability than single-center studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward L. Kaplan and Paul Meier (method); multicenter application developed through large clinical trial consortia from the 1970s onward","year":"1958 (base method); multicenter designs common from 1970s","type":"Nonparametric survival analysis in a multicenter setting","dataType":"Time-to-event (survival) data with censoring, collected from two or more clinical sites","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.","type":"article","doi":"10.2307/2281868","isbn":null,"url":null},{"ref":"Therneau, T. M., & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.","type":"book","doi":null,"isbn":"978-0387987842","url":null}],"related":["kaplan-meier-analysis","cox-proportional-hazards","multicenter-cox-proportional-hazards","competing-risks-analysis","survival-analysis","multicenter-cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-nested-case-control","name":"Multicenter Nested Case-Control","fullName":"Multicenter Nested Case-Control Study","aliases":["multicenter NCC","multi-site nested case-control","pooled nested case-control","nested case-control within multicenter cohort"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (multicenter adaptation)","originator":"Nested case-control: Norman Mantel (1973); multicenter extension widely adopted in EPIC and other large consortium studies (1990s–2000s)","url":"https://scholargate.app/en/epidemiology/multicenter-nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-nested-case-control.md","definition":"A multicenter nested case-control study embeds a case-control analysis within two or more geographically or institutionally distinct prospective cohorts. Cases who develop the outcome of interest are identified across all participating sites, then matched to controls sampled from the same risk sets, enabling pooled estimation of exposure-disease associations with greater statistical power and geographic generalizability than any single-center nested design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nested case-control: Norman Mantel (1973); multicenter extension widely adopted in EPIC and other large consortium studies (1990s–2000s)","year":"1990s–2000s (multicenter adaptation)","type":"Observational analytical study design","dataType":"Individual-level data from multiple cohort sites; time-to-event, exposure, and covariate records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thomas, D.C. (1977). Addendum to: Methods of cohort analysis: appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thomas+1977+nested+case-control+cohort+analysis"},{"ref":"Riboli, E., & Kaaks, R. (2002). The EPIC Project: Rationale and study design. International Journal of Epidemiology, 26(Suppl 1), S6–S14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+EPIC+Project%3A+Rationale+and+study+design+Riboli"}],"related":["nested-case-control","case-control-study","cohort-study","multicenter-cohort-study","multicenter-case-control-study","matched-nested-case-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-phase-i-clinical-trial","name":"Multicenter Phase I Clinical Trial","fullName":"Multicenter Phase I Clinical Trial","aliases":["multisite Phase I trial","multi-institutional Phase I study","Phase I dose-escalation multicenter study","first-in-human multicenter trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1970s–1980s (formalized in FDA Phase I guidance 1977; ICH E6 GCP 1996)","originator":"Established through FDA regulatory guidance and ICH harmonization frameworks","url":"https://scholargate.app/en/epidemiology/multicenter-phase-i-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-phase-i-clinical-trial.md","definition":"A multicenter Phase I clinical trial is the first systematic administration of an investigational agent to humans, conducted simultaneously across two or more clinical sites. Its primary objectives are to characterize the safety and tolerability profile of the intervention, determine the maximum tolerated dose (MTD), and describe pharmacokinetic and pharmacodynamic behavior. Distributing enrollment across sites increases participant accrual speed and enhances the generalizability of early-phase safety data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established through FDA regulatory guidance and ICH harmonization frameworks","year":"1970s–1980s (formalized in FDA Phase I guidance 1977; ICH E6 GCP 1996)","type":"Interventional clinical study design","dataType":"Safety, tolerability, pharmacokinetic, and pharmacodynamic data from human participants","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2016). ICH Harmonised Guideline: Integrated Addendum to ICH E6(R1): Guideline for Good Clinical Practice E6(R2). ICH.","type":"article","doi":null,"isbn":null,"url":"https://www.ich.org/page/efficacy-guidelines"},{"ref":"Storer, B. E. (1989). Design and analysis of Phase I clinical trials. Biometrics, 45(3), 925-937.","type":"article","doi":"10.2307/2531693","isbn":null,"url":null}],"related":["phase-i-clinical-trial","phase-ii-clinical-trial","multicenter-randomized-clinical-trial","dose-response-analysis","adaptive-phase-i-clinical-trial","bayesian-phase-i-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-phase-ii-clinical-trial","name":"Multicenter phase II clinical trial","fullName":"Multicenter Phase II Clinical Trial","aliases":["multi-site phase II trial","phase 2 multicenter study","multicenter Phase IIA/IIB trial","multisite efficacy trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1970s–1980s (formalized in regulatory guidance; Simon two-stage design 1989)","originator":"Established through ICH and FDA regulatory frameworks; Simon two-stage design formalized by Richard Simon (1989)","url":"https://scholargate.app/en/epidemiology/multicenter-phase-ii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-phase-ii-clinical-trial.md","definition":"A multicenter phase II clinical trial is an interventional study conducted at two or more independent clinical sites to evaluate the preliminary efficacy and safety of a new treatment in a defined patient population, following demonstrated tolerability in phase I. By pooling patients across sites, the design achieves the sample sizes needed to estimate response rates and identify promising signals before committing to the larger investment of a phase III confirmatory trial.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established through ICH and FDA regulatory frameworks; Simon two-stage design formalized by Richard Simon (1989)","year":"1970s–1980s (formalized in regulatory guidance; Simon two-stage design 1989)","type":"Interventional clinical trial design","dataType":"Patient-level clinical outcome data (response rates, biomarkers, safety events) collected across multiple sites","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2009). ICH Harmonised Tripartite Guideline: General Considerations for Clinical Studies E8(R1). ICH.","type":"article","doi":null,"isbn":null,"url":"https://www.ich.org/page/efficacy-guidelines"},{"ref":"Simon, R. (1989). Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials, 10(1), 1–10.","type":"article","doi":"10.1016/0197-2456(89)90015-9","isbn":null,"url":null}],"related":["phase-ii-clinical-trial","multicenter-randomized-clinical-trial","phase-iii-clinical-trial","multicenter-phase-i-clinical-trial","adaptive-phase-ii-clinical-trial","randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-phase-iii-clinical-trial","name":"Multicenter Phase III Clinical Trial","fullName":"Multicenter Phase III Randomized Controlled Clinical Trial","aliases":["Phase III multicenter RCT","confirmatory multicenter trial","Phase 3 multicenter study","pivotal multicenter trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1940s–1990s (formalized through ICH harmonization ~1990s)","originator":"Codified through ICH E9 guideline (1998) and decades of regulatory practice (FDA, EMA)","url":"https://scholargate.app/en/epidemiology/multicenter-phase-iii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-phase-iii-clinical-trial.md","definition":"A multicenter Phase III clinical trial is the definitive confirmatory study that tests whether a new intervention produces a clinically meaningful benefit over a comparator in a large, representative patient population enrolled at two or more independent research sites. It is the primary evidence basis for regulatory approval by agencies such as the FDA and EMA, combining the statistical power of large samples with the external validity gained from geographic and demographic diversity across sites.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Codified through ICH E9 guideline (1998) and decades of regulatory practice (FDA, EMA)","year":"1940s–1990s (formalized through ICH harmonization ~1990s)","type":"Confirmatory interventional study design","dataType":"Randomized participant-level clinical outcome data collected across multiple sites","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of Clinical Trials (5th ed.). Springer.","type":"book","doi":null,"isbn":"978-3319185385","url":null},{"ref":"Pocock, S. J. (1983). Clinical Trials: A Practical Approach. Wiley.","type":"book","doi":null,"isbn":"978-0471901204","url":null}],"related":["randomized-clinical-trial","phase-ii-clinical-trial","multicenter-randomized-clinical-trial","adaptive-randomized-clinical-trial","bayesian-randomized-clinical-trial","phase-iv-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-phase-iv-study","name":"Multicenter Phase IV Study","fullName":"Multicenter Phase IV Post-Marketing Surveillance Study","aliases":["multicenter post-marketing study","multicenter pharmacovigilance study","multi-site phase IV study","post-authorization safety study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–1990s (formalized with post-marketing requirements in modern drug regulation)","originator":"Regulatory agencies and pharmaceutical industry (ICH E2E, FDA, EMA post-marketing frameworks)","url":"https://scholargate.app/en/epidemiology/multicenter-phase-iv-study","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-phase-iv-study.md","definition":"A multicenter Phase IV study is a post-marketing surveillance investigation conducted simultaneously at two or more clinical or research sites after a drug, device, or intervention has received regulatory approval. By pooling real-world data from diverse patient populations and geographic regions, it detects rare adverse events, evaluates long-term effectiveness, characterizes safety in subgroups, and fulfills regulatory post-authorization commitments that single-site studies cannot achieve.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Regulatory agencies and pharmaceutical industry (ICH E2E, FDA, EMA post-marketing frameworks)","year":"1980s–1990s (formalized with post-marketing requirements in modern drug regulation)","type":"Observational or interventional post-marketing study","dataType":"Real-world longitudinal data, electronic health records, adverse event reports, patient registries","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Strom, B. L., & Kimmel, S. E. (Eds.). (2005). Textbook of Pharmacoepidemiology. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470029619","url":null},{"ref":"Phases of clinical research. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Phases_of_clinical_research"}],"related":["phase-iv-study","multicenter-cohort-study","prospective-cohort-study","randomized-clinical-trial","multicenter-randomized-clinical-trial","dose-response-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-randomized-clinical-trial","name":"Multicenter Randomized Clinical Trial","fullName":"Multicenter Randomized Controlled Trial","aliases":["multi-site RCT","multicenter RCT","multinational randomized trial","multicenter controlled trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1970s–1980s (widespread adoption for large-scale efficacy trials)","originator":"Evolved from single-center RCT methodology; consolidated through landmark trials such as the MRC streptomycin trial (1948) and large cardiovascular mega-trials of the 1970s–1980s","url":"https://scholargate.app/en/epidemiology/multicenter-randomized-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-randomized-clinical-trial.md","definition":"A multicenter randomized clinical trial (RCT) is an experimental study in which eligible participants are randomly assigned to intervention or control arms simultaneously across two or more clinical sites. By combining the rigor of randomization with enrollment from geographically or institutionally diverse centers, this design produces large samples and externally valid effect estimates that single-center trials rarely achieve. It is the regulatory gold standard for confirmatory efficacy and safety evaluation of new treatments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from single-center RCT methodology; consolidated through landmark trials such as the MRC streptomycin trial (1948) and large cardiovascular mega-trials of the 1970s–1980s","year":"1970s–1980s (widespread adoption for large-scale efficacy trials)","type":"Interventional experimental design","dataType":"Individual patient data from multiple clinical sites; randomization records, clinical outcomes, adverse events","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of Clinical Trials (5th ed.). Springer.","type":"book","doi":null,"isbn":"978-3319185385","url":null},{"ref":"Senn, S. (1998). Some controversies in planning and analysing multi-centre trials. Statistics in Medicine, 17(15–16), 1753–1765.","type":"article","doi":"10.1002/(SICI)1097-0258(19980815/30)17:15/16<1753::AID-SIM977>3.0.CO;2-X","isbn":null,"url":null}],"related":["randomized-clinical-trial","phase-iii-clinical-trial","pragmatic-randomized-clinical-trial","adaptive-randomized-clinical-trial","cohort-study","meta-analytic-randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicenter-screening-test-evaluation","name":"Multicenter Screening Test Evaluation","fullName":"Multicenter Screening Test Evaluation Study","aliases":["multicenter diagnostic accuracy study","multisite screening evaluation","multicenter test performance study","multicenter DTA study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1976–2003 (core diagnostic accuracy framework; multicenter STARD standards formalized 2003)","originator":"Methodological consensus (STARD group, Bossuyt et al.); broader diagnostic accuracy tradition rooted in Hanley & McNeil (1982) and Sackett & Haynes (1976)","url":"https://scholargate.app/en/epidemiology/multicenter-screening-test-evaluation","markdownUrl":"https://scholargate.app/en/epidemiology/multicenter-screening-test-evaluation.md","definition":"A multicenter screening test evaluation measures the diagnostic accuracy of a screening test — its sensitivity, specificity, predictive values, and ROC-curve area — by enrolling participants across two or more independent clinical sites. Conducting the study at multiple centers broadens the patient spectrum, tests generalizability across different laboratory conditions and patient populations, and produces more externally valid accuracy estimates than a single-center study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Methodological consensus (STARD group, Bossuyt et al.); broader diagnostic accuracy tradition rooted in Hanley & McNeil (1982) and Sackett & Haynes (1976)","year":"1976–2003 (core diagnostic accuracy framework; multicenter STARD standards formalized 2003)","type":"Observational diagnostic accuracy study","dataType":"Index test results, reference standard results, participant characteristics across multiple clinical sites","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., & de Vet, H. C. W. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD Initiative. Annals of Internal Medicine, 138(1), 40-44.","type":"article","doi":"10.7326/0003-4819-138-1-200301070-00010","isbn":null,"url":null},{"ref":"Lijmer, J. G., Mol, B. W., Heisterkamp, S., Bonsel, G. J., Prins, M. H., van der Meulen, J. H., & Bossuyt, P. M. (1999). Empirical evidence of design-related bias in studies of diagnostic tests. JAMA, 282(11), 1061-1066.","type":"article","doi":"10.1001/jama.282.11.1061","isbn":null,"url":null}],"related":["diagnostic-accuracy-study","screening-test-evaluation","multicenter-cohort-study","multicenter-randomized-clinical-trial","prospective-diagnostic-accuracy-study","cross-sectional-epidemiological-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multicultural-counseling-inventory","name":"Multicultural Counseling Inventory","fullName":"Multicultural Counseling Inventory (MCI)","aliases":["MCI"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"counselor-competence-assessment","year":1991,"originator":"LaFromboise, Coleman, Hernandez","url":"https://scholargate.app/en/transcultural-nursing/multicultural-counseling-inventory","markdownUrl":"https://scholargate.app/en/transcultural-nursing/multicultural-counseling-inventory.md","definition":"The Multicultural Counseling Inventory (MCI) is a 40-item self-report instrument designed to assess the multicultural competence of mental health counselors and healthcare providers. Originally developed by LaFromboise, Coleman, and Hernandez in 1991, the MCI evaluates five core competence factors: awareness of cultural assumptions and biases, knowledge of diverse cultures, skill in culturally appropriate intervention, and relationship-building capacity in multicultural contexts. The inventory is widely used in counselor training programs and healthcare education to evaluate readiness to work with diverse client populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LaFromboise, Coleman, Hernandez","subfamily":"counselor-competence-assessment","year":1991,"type":"Self-report"},"citations":[{"ref":"LaFromboise, T. D., Coleman, H. L., & Hernandez, A. (1991). Development and factor structure of the Multicultural Counseling Inventory. Journal of Counseling Psychology, 38(2), 137–148.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+factor+structure+of+the+Multicultural+Counseling+Inventory+LaFromboise"}],"related":["cultural-competence-assessment","transcultural-self-efficacy-tool","patient-provider-cultural-sensitivity","cross-cultural-competence-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multidimensional-anxiety-children","name":"Multidimensional Anxiety Scale for Children","fullName":"Multidimensional Anxiety Scale for Children (MASC)","aliases":["MASC","MASC-2"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"pediatric anxiety disorders","year":"1997","originator":"John March","url":"https://scholargate.app/en/child-psychiatry/multidimensional-anxiety-children","markdownUrl":"https://scholargate.app/en/child-psychiatry/multidimensional-anxiety-children.md","definition":"The Multidimensional Anxiety Scale for Children (MASC-2) is a 39-item self-report measure of anxiety symptoms in children and adolescents ages 8–19 years. Developed by John March and colleagues in 1997, the MASC operationalizes anxiety as a multifaceted construct comprising physical symptoms, social anxiety, harm avoidance, and separation/panic concerns. The revised MASC-2 (2012) improved psychometric properties and clinical utility. It is widely used in clinical and research settings for screening, diagnosis, and outcome measurement in childhood anxiety disorders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John March","subfamily":"pediatric anxiety disorders","year":"1997","type":"Self-report questionnaire"},"citations":[{"ref":"March, J. S., Parker, J. D. A., Sullivan, K., Stallings, P., & Conners, C. K. (1997). The Multidimensional Anxiety Scale for Children (MASC): Factor structure, reliability, and validity. Journal of the American Academy of Child & Adolescent Psychiatry, 36(4), 554–565.","type":"article","doi":"10.1097/00004583-199704000-00019","isbn":null,"url":null},{"ref":"March, J. S., & Curry, J. F. (2009). Psychometric properties of the MASC-2. Journal of Attention Disorders, 13(1), 46–59.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Psychometric+properties+of+the+MASC-2+March"}],"related":["revised-childrens-anxiety-depression","child-depression-inventory","yale-brown-oc-children"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multidimensional-fatigue-inventory","name":"MFI","fullName":"Multidimensional Fatigue Inventory","aliases":["MFI","MFI-20"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Five-Dimensional Fatigue Assessment","year":"1995","originator":"Eva Smets","url":"https://scholargate.app/en/oncology-nursing/multidimensional-fatigue-inventory","markdownUrl":"https://scholargate.app/en/oncology-nursing/multidimensional-fatigue-inventory.md","definition":"The Multidimensional Fatigue Inventory is a 20-item self-report instrument that comprehensively measures five distinct dimensions of fatigue: general fatigue, physical fatigue, reduced activity, reduced motivation, and mental fatigue. Developed by Smets and colleagues in 1995, the MFI-20 is grounded in a theoretical model distinguishing fatigue phenomenology from behavioral and cognitive consequences, making it particularly valuable for research examining fatigue mechanisms and interventions targeting specific fatigue dimensions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eva Smets","subfamily":"Five-Dimensional Fatigue Assessment","year":"1995","type":"Patient self-report five-dimensional fatigue inventory"},"citations":[{"ref":"Smets, E. M., Garssen, B., Bonke, B., & De Haes, J. C. (1995). The Multidimensional Fatigue Inventory (MFI-20): a short questionnaire for measuring fatigue. J Psychosom Res, 39(3), 315–325.","type":"article","doi":"10.1037/t15271-000","isbn":null,"url":null},{"ref":"Smets, E. M., Visser, M. R., Willems-Groot, A. F., et al. (1998). Fatigue and radiotherapy: (A) experience in patients undergoing treatment. Br J Cancer, 78(7), 899–906.","type":"article","doi":"10.1038/bjc.1998.599","isbn":null,"url":null}],"related":["piper-fatigue-scale","cancer-fatigue-scale","brief-fatigue-inventory","chalder-fatigue-scale","fact-g"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multidimensional-perceived-social-support","name":"Multidimensional Scale of Perceived Social Support","fullName":"Multidimensional Scale of Perceived Social Support (MSPSS)","aliases":["MSPSS","Perceived Social Support Scale"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"Social support and resilience assessment","year":"1988","originator":"Gregory D. Zimet et al.","url":"https://scholargate.app/en/trauma-psychology/multidimensional-perceived-social-support","markdownUrl":"https://scholargate.app/en/trauma-psychology/multidimensional-perceived-social-support.md","definition":"The MSPSS is a 12-item self-report scale measuring perceived adequacy of social support from three key sources: family, friends, and significant other. Developed by Zimet and colleagues in 1988, the MSPSS assesses the subjective sense that one has available emotional and instrumental support—a critical protective factor against trauma-related psychopathology and a key component of resilience. The scale is widely used in trauma, mental health, and medical research to evaluate social support as both an outcome and a moderator of symptom severity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory D. Zimet et al.","subfamily":"Social support and resilience assessment","year":"1988","type":"Self-report questionnaire"},"citations":[{"ref":"Zimet, G. D., Dahlem, N. W., Zimet, S. G., & Farley, G. K. (1988). The Multidimensional Scale of Perceived Social Support. Journal of Personality Assessment, 52(1), 30-41.","type":"article","doi":"10.1207/s15327752jpa5201_2","isbn":null,"url":null},{"ref":"Canty-Mitchell, J., & Zimet, G. D. (2000). Psychometric properties of the Multidimensional Scale of Perceived Social Support in urban adolescents. American Journal of Community Psychology, 28(3), 391-400.","type":"article","doi":"10.1023/a:1005109522457","isbn":null,"url":null}],"related":["post-traumatic-growth-inventory","impact-of-event-scale-revised","secondary-traumatic-stress-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multidimensional-perfectionism-scale","name":"Multidimensional Perfectionism Scale","fullName":"Multidimensional Perfectionism Scale (MPS)","aliases":["MPS","MPS-Frost"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"personality-perfectionism-assessment","year":"1990","originator":"Randy O. Frost, Phyllis Marten, Cassandra Lahart, Robin Rosenblate","url":"https://scholargate.app/en/clinical-psychology/multidimensional-perfectionism-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/multidimensional-perfectionism-scale.md","definition":"The MPS is a 35-item self-report measure of perfectionism across six domains: concern over mistakes, personal standards, parental expectations, parental criticism, doubt about actions, and organization. Developed by Frost and colleagues in 1990, it is the most comprehensive multidimensional perfectionism measure, distinguishing adaptive from maladaptive perfectionism and identifying perfectionism as transdiagnostic risk factor in depression, anxiety, eating disorders, and obsessive-compulsive pathology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Randy O. Frost, Phyllis Marten, Cassandra Lahart, Robin Rosenblate","subfamily":"personality-perfectionism-assessment","year":"1990","type":"Self-report questionnaire"},"citations":[{"ref":"Frost, R. O., Marten, P., Lahart, C., & Rosenblate, R. (1990). The dimensions of perfectionism. Cognitive Therapy and Research, 14(5), 449–468.","type":"article","doi":"10.1007/BF01172967","isbn":null,"url":null}],"related":["emotion-regulation-questionnaire","difficulties-emotion-regulation","intolerance-of-uncertainty-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multidimensional-scaling","name":"Multidimensional Scaling","fullName":"Multidimensional Scaling","aliases":["MDS","metric MDS","non-metric MDS","proximity scaling"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1952–1964","originator":"Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)","url":"https://scholargate.app/en/statistics/multidimensional-scaling","markdownUrl":"https://scholargate.app/en/statistics/multidimensional-scaling.md","definition":"Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)","year":"1952–1964","type":"Dimensionality reduction / visualization","dataType":"Pairwise dissimilarity or similarity matrix","subfamily":"Multivariate analysis"},"citations":[{"ref":"Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.","type":"article","doi":"10.1007/BF02289565","isbn":null,"url":null},{"ref":"Cox, T. F. & Cox, M. A. A. (2001). Multidimensional Scaling (2nd ed.). Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584880943","url":null}],"related":["principal-component-analysis","cluster-analysis","correspondence-analysis","exploratory-factor-analysis","discriminant-analysis","latent-class-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-betweenness-centrality","name":"Multilayer Betweenness Centrality","fullName":"Multilayer Betweenness Centrality (Tensor-based Network Centrality)","aliases":["MBC","multilayer geodesic betweenness","tensorial betweenness centrality","interlayer betweenness centrality"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2013–2014","originator":"De Domenico, M.; Kivelä, M.; Arenas, A. et al.","url":"https://scholargate.app/en/network-analysis/multilayer-betweenness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-betweenness-centrality.md","definition":"Multilayer betweenness centrality extends the classical betweenness measure to networks with multiple types of relationships — or layers — by computing how often a node lies on shortest paths that can traverse any layer or switch between layers. It identifies brokers and bridges whose influence spans distinct interaction domains simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"De Domenico, M.; Kivelä, M.; Arenas, A. et al.","year":"2013–2014","type":"Centrality measure (multilayer extension)","dataType":"Multilayer/multiplex network (nodes with edges across multiple relation types or layers)","subfamily":"Network science"},"citations":[{"ref":"De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., & Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022.","type":"article","doi":"10.1103/PhysRevX.3.041022","isbn":null,"url":null},{"ref":"Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null}],"related":["betweenness-centrality","multilayer-degree-centrality","multilayer-closeness-centrality","multilayer-eigenvector-centrality","multiplex-network-analysis","multilayer-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-closeness-centrality","name":"Multilayer Closeness Centrality","fullName":"Multilayer Closeness Centrality (Generalized Closeness for Multilayer Networks)","aliases":["multilayer closeness","multi-layer closeness centrality","MLC","interlayer closeness centrality"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2013–2014","originator":"Kivela, M. et al.; De Domenico, M. et al.","url":"https://scholargate.app/en/network-analysis/multilayer-closeness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-closeness-centrality.md","definition":"Multilayer closeness centrality extends the classical closeness centrality measure to networks that contain multiple types of relationships or interaction contexts (layers). Rather than treating each layer in isolation, it computes how quickly a node can reach all others by traversing any combination of available layers, revealing nodes that are structurally efficient connectors across the full network system.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivela, M. et al.; De Domenico, M. et al.","year":"2013–2014","type":"Centrality measure for multilayer networks","dataType":"Multilayer or multiplex network adjacency tensors","subfamily":"Network science"},"citations":[{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"Sole-Ribalta, A., De Domenico, M., Kouvaris, N. E., Diaz-Guilera, A., Gomez, S., & Arenas, A. (2013). Spectral properties of the Laplacian of multiplex networks. Physical Review E, 88(3), 032807.","type":"article","doi":"10.1103/PhysRevE.88.032807","isbn":null,"url":null}],"related":["closeness-centrality","multilayer-degree-centrality","multilayer-betweenness-centrality","multilayer-eigenvector-centrality","multiplex-network-analysis","multilayer-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-community-detection","name":"Multilayer Community Detection","fullName":"Multilayer Community Detection in Multiplex and Multilayer Networks","aliases":["multilayer clustering","multiplex community detection","cross-layer community detection","MCD"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010–2014","originator":"Mucha, P. J. et al.; Kivela, M. et al.","url":"https://scholargate.app/en/network-analysis/multilayer-community-detection","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-community-detection.md","definition":"Multilayer community detection identifies groups of nodes that are densely connected across multiple types of relationships simultaneously. By coupling layers of a network — such as friendship, advice, and collaboration ties — it finds communities that are coherent not just within one relation type but across all of them, revealing structure that single-layer analysis would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mucha, P. J. et al.; Kivela, M. et al.","year":"2010–2014","type":"Community detection algorithm for multilayer networks","dataType":"Multilayer or multiplex relational network data","subfamily":"Network science"},"citations":[{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.","type":"article","doi":"10.1126/science.1184819","isbn":null,"url":null}],"related":["community-detection","modularity-analysis","multilayer-modularity-analysis","multiplex-network-analysis","stochastic-block-model","multilayer-social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-degree-centrality","name":"Multilayer Degree Centrality","fullName":"Multilayer Degree Centrality (Aggregated and Layer-Specific Node Importance in Multilayer Networks)","aliases":["multilayer degree","multiplex degree centrality","overlapping-layer degree centrality","MDC"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2013–2014","originator":"Kivelä, M.; De Domenico, M. et al.","url":"https://scholargate.app/en/network-analysis/multilayer-degree-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-degree-centrality.md","definition":"Multilayer degree centrality extends the classic degree centrality measure to networks composed of multiple layers — such as networks representing different types of social ties, communication channels, or relationship contexts simultaneously. It quantifies how many connections a node has across one or all layers, revealing nodes that are influential not just in a single context but across the entire multi-relational structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivelä, M.; De Domenico, M. et al.","year":"2013–2014","type":"Centrality measure for multilayer networks","dataType":"Multilayer or multiplex network (adjacency tensors per layer)","subfamily":"Network science"},"citations":[{"ref":"Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., Gómez, S., & Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3(4), 041022.","type":"article","doi":"10.1103/PhysRevX.3.041022","isbn":null,"url":null}],"related":["degree-centrality","multilayer-betweenness-centrality","multilayer-closeness-centrality","multiplex-network-analysis","multilayer-pagerank","weighted-degree-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-knowledge-graph-analysis","name":"Multilayer Knowledge Graph Analysis","fullName":"Multilayer Knowledge Graph Analysis (Multi-Relational Network Framework)","aliases":["multi-relational knowledge graph analysis","multilayer KG analysis","multi-relational graph analysis","multiplex knowledge graph analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2014–2016","originator":"Kivela, M. et al.; Nickel, M. et al.","url":"https://scholargate.app/en/network-analysis/multilayer-knowledge-graph-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-knowledge-graph-analysis.md","definition":"Multilayer knowledge graph analysis treats a knowledge base as a stack of relation-specific network layers sharing the same entity set, enabling simultaneous reasoning across relation types. Unlike a flat single-layer graph, it preserves the semantic distinctions between relation types and supports cross-layer link prediction, entity alignment, and community detection grounded in multilayer network theory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivela, M. et al.; Nickel, M. et al.","year":"2014–2016","type":"Graph-based analytical framework","dataType":"Multi-relational entity-relation triples, heterogeneous graph data","subfamily":"Network science"},"citations":[{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2016). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1), 11–33.","type":"article","doi":"10.1109/JPROC.2015.2483592","isbn":null,"url":null}],"related":["knowledge-graph-analysis","multiplex-network-analysis","multilayer-network-diffusion-analysis","multilayer-community-detection","social-network-analysis","multilayer-modularity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-network-diffusion-analysis","name":"Multilayer Network Diffusion Analysis","fullName":"Multilayer Network Diffusion Analysis","aliases":["multiplex diffusion analysis","multilayer spreading analysis","cross-layer contagion analysis","diffusion on multiplex networks"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2013–2014","originator":"Gomez, S. et al.; Boccaletti, S. et al.","url":"https://scholargate.app/en/network-analysis/multilayer-network-diffusion-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-network-diffusion-analysis.md","definition":"Multilayer Network Diffusion Analysis models how information, disease, or influence spreads across a system composed of multiple, interconnected network layers. By coupling diffusion processes across layers — for instance social ties, transport routes, and online channels simultaneously — it reveals how cross-layer interactions accelerate or dampen spreading and lowers epidemic thresholds compared to single-layer models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gomez, S. et al.; Boccaletti, S. et al.","year":"2013–2014","type":"Network diffusion model","dataType":"Multilayer adjacency matrices, inter-layer coupling matrices, node-state time series","subfamily":"Network science"},"citations":[{"ref":"Gomez, S., Diaz-Guilera, A., Gomez-Gardenes, J., Perez-Vicente, C. J., Moreno, Y., & Arenas, A. (2013). Diffusion dynamics on multiplex networks. Physical Review Letters, 110(2), 028701.","type":"article","doi":"10.1103/PhysRevLett.110.028701","isbn":null,"url":null},{"ref":"Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gomez-Gardenes, J., Romance, M., Sendina-Nadal, I., Wang, Z., & Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1–122.","type":"article","doi":"10.1016/j.physrep.2014.07.001","isbn":null,"url":null}],"related":["network-diffusion-analysis","multiplex-network-analysis","temporal-network-diffusion-analysis","multilayer-community-detection","social-network-analysis","multilayer-social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-network","name":"Multilayer Network Analysis","fullName":"Multilayer Network Analysis (Multiplex Networks)","aliases":["multiplex network analysis","multiplex networks","Çok Katmanlı Ağ Analizi (Multiplex Networks)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":"2013–2014 (formal mathematical framework)","originator":"Kivelä et al. (2014); De Domenico et al. (2013)","url":"https://scholargate.app/en/network-analysis/multilayer-network","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-network.md","definition":"Multilayer network analysis is a graph-theoretic framework, formalised by Kivelä et al. (2014) and De Domenico et al. (2013), that represents the same set of nodes simultaneously across multiple relationship layers. Where a single-layer network collapses all relationships into one graph, the multilayer model preserves the distinct relational context of each layer — social platform, biological interaction type, or infrastructure tier — while also modelling how layers couple with each other through interlayer edges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivelä et al. (2014); De Domenico et al. (2013)","year":"2013–2014 (formal mathematical framework)","type":"Graph-theoretic network model","input":"Edge lists or adjacency matrices per layer, with a shared node set","output":"Layer-aware centrality measures, inter-layer coupling metrics, multiplex community structure","minSample":20,"difficulty":3,"normality":"not required","domains":"Health, Social science, Economics, Natural science, Biology"},"citations":[{"ref":"Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"De Domenico, M. et al. (2013). Mathematical Formulation of Multilayer Networks. Physical Review X, 3(4), 041022.","type":"article","doi":"10.1103/PhysRevX.3.041022","isbn":null,"url":null}],"related":["centrality-analysis","community-detection","bipartite-network-analysis","network-motif-analysis","ego-network-analysis","temporal-network-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-pagerank","name":"Multilayer PageRank","fullName":"Multilayer PageRank (Centrality on Multiplex and Multilayer Networks)","aliases":["multiplex PageRank","layer-coupled PageRank","multilayer random walk centrality","MuxRank"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2015","originator":"De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.","url":"https://scholargate.app/en/network-analysis/multilayer-pagerank","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-pagerank.md","definition":"Multilayer PageRank extends the classic PageRank random-walk centrality to networks that contain multiple interconnected layers — such as a social network where people are connected simultaneously via friendship, professional ties, and online platforms. By allowing a virtual walker to jump both within and across layers, the algorithm identifies nodes that are influential across the entire multilayer structure, not just within any single layer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"De Domenico, M.; Sole-Ribalta, A.; Arenas, A. et al.","year":"2015","type":"Centrality measure (random-walk-based)","dataType":"Multilayer or multiplex network (adjacency tensors, edge lists per layer)","subfamily":"Network science"},"citations":[{"ref":"De Domenico, M., Sole-Ribalta, A., Omodei, E., Gomez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6, 6868.","type":"article","doi":"10.1038/ncomms7868","isbn":null,"url":null},{"ref":"Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gomez-Gardenes, J., Romance, M., Sendina-Nadal, I., Wang, Z., & Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1–122.","type":"article","doi":"10.1016/j.physrep.2014.07.001","isbn":null,"url":null}],"related":["multilayer-community-detection","multilayer-eigenvector-centrality","multilayer-betweenness-centrality","multiplex-network-analysis","directed-pagerank","eigenvector-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-perceptron","name":"Multilayer Perceptron","fullName":"Multilayer Perceptron (Fully Connected Feedforward Neural Network)","aliases":["MLP","feedforward neural network","fully connected neural network","vanilla neural network","dense neural network"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":1986,"originator":"Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.","url":"https://scholargate.app/en/deep-learning/multilayer-perceptron","markdownUrl":"https://scholargate.app/en/deep-learning/multilayer-perceptron.md","definition":"A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of modern deep learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.","year":1986,"type":"Supervised feedforward neural network","task":"Classification & regression","minSample":100},"citations":[{"ref":"Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.","type":"article","doi":"10.1038/323533a0","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning (Ch. 6–8). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 5). Springer.","type":"book","doi":null,"isbn":"978-0-387-31073-2","url":null}],"related":["convolutional-neural-network","recurrent-neural-network","random-forest","xgboost","logistic-regression","support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-social-network-analysis","name":"Multilayer Social Network Analysis","fullName":"Multilayer Social Network Analysis (MSNA)","aliases":["MSNA","multiplex network analysis","multilayer network analysis","interconnected network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2014","originator":"Kivela, M.; Boccaletti, S. et al.","url":"https://scholargate.app/en/network-analysis/multilayer-social-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-social-network-analysis.md","definition":"Multilayer social network analysis extends classical single-layer network methods to settings where actors are connected through multiple, distinct types of ties — such as friendship, professional collaboration, and online interaction — simultaneously. By modeling each type of relationship as a separate layer and explicitly representing connections across layers, it captures structural complexity that a single aggregated network would hide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivela, M.; Boccaletti, S. et al.","year":"2014","type":"Structural network analysis framework","dataType":"Relational/edge-list data across multiple interaction layers","subfamily":"Network science"},"citations":[{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gomez-Gardenes, J., Romance, M., Sendina-Nadal, I., Wang, Z., & Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1–122.","type":"article","doi":"10.1016/j.physrep.2014.07.001","isbn":null,"url":null}],"related":["multiplex-network-analysis","social-network-analysis","temporal-network-analysis","community-detection","knowledge-graph-analysis","two-mode-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-stochastic-block-model","name":"Multilayer Stochastic Block Model","fullName":"Multilayer Stochastic Block Model (ML-SBM)","aliases":["ML-SBM","multilayer SBM","multi-layer stochastic block model","multiplex stochastic block model"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2015-2017","originator":"Peixoto, T. P.; De Bacco, C. and colleagues","url":"https://scholargate.app/en/network-analysis/multilayer-stochastic-block-model","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-stochastic-block-model.md","definition":"The Multilayer Stochastic Block Model (ML-SBM) is a generative probabilistic framework that extends the classical stochastic block model to networks with multiple relation types or layers. It simultaneously infers community structure and block-to-block connection probabilities across all layers, capturing how communities cohere differently depending on context or relationship type.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peixoto, T. P.; De Bacco, C. and colleagues","year":"2015-2017","type":"Generative probabilistic model","dataType":"Multilayer adjacency matrices (binary or weighted)","subfamily":"Network science"},"citations":[{"ref":"Peixoto, T. P. (2015). Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Physical Review E, 92(4), 042807.","type":"article","doi":"10.1103/PhysRevE.92.042807","isbn":null,"url":null},{"ref":"De Bacco, C., Power, E. A., Larremore, D. B., & Moore, C. (2017). Community detection, link prediction, and layer interdependence in multilayer networks. Physical Review E, 95(4), 042317.","type":"article","doi":"10.1103/PhysRevE.95.042317","isbn":null,"url":null}],"related":["stochastic-block-model","multilayer-community-detection","exponential-random-graph-model","multilayer-modularity-analysis","multilayer-network-diffusion-analysis","bayesian-stochastic-block-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-temporal-network-analysis","name":"Multilayer Temporal Network Analysis","fullName":"Multilayer Temporal Network Analysis","aliases":["MTNA","temporal multilayer network analysis","time-varying multilayer network analysis","dynamic multilayer network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2012–2014","originator":"Kivela, M. et al.; Holme, P. & Saramaki, J.","url":"https://scholargate.app/en/network-analysis/multilayer-temporal-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-temporal-network-analysis.md","definition":"Multilayer temporal network analysis studies relational systems in which nodes interact through multiple distinct types of ties that all evolve over time. By modeling each relationship type as a separate layer and tracking how those layers change across time snapshots, the method reveals how cross-layer dynamics and temporal patterns jointly shape information flow, influence spread, and community structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivela, M. et al.; Holme, P. & Saramaki, J.","year":"2012–2014","type":"Network analysis framework","dataType":"Time-stamped relational data across multiple network layers","subfamily":"Network science"},"citations":[{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"Holme, P., & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null}],"related":["multilayer-network-analysis","temporal-network-analysis","multiplex-network-analysis","multilayer-community-detection","temporal-community-detection","multilayer-modularity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilayer-two-mode-network-analysis","name":"Multilayer Two-Mode Network Analysis","fullName":"Multilayer Two-Mode (Bipartite) Network Analysis","aliases":["multilayer bipartite network analysis","multi-layer two-mode network","multiplex bipartite network analysis","ML-TMNA"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010s (synthesis of two-mode and multilayer frameworks)","originator":"Kivela et al. (multilayer); Borgatti & Everett (two-mode foundations)","url":"https://scholargate.app/en/network-analysis/multilayer-two-mode-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/multilayer-two-mode-network-analysis.md","definition":"Multilayer two-mode network analysis extends bipartite (two-mode) network analysis to settings where actors and artifacts — people and publications, firms and markets, genes and diseases — are connected across multiple distinct relationship layers or time slices simultaneously. It captures how dual-membership structures evolve, overlap, or interact across contexts that a single-layer bipartite graph cannot represent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivela et al. (multilayer); Borgatti & Everett (two-mode foundations)","year":"2010s (synthesis of two-mode and multilayer frameworks)","type":"Network analysis framework","dataType":"Bipartite relational data across multiple layers or time points","subfamily":"Network science"},"citations":[{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.","type":"article","doi":"10.1016/S0378-8733(96)00301-2","isbn":null,"url":null}],"related":["two-mode-network-analysis","multilayer-social-network-analysis","multiplex-network-analysis","multilayer-community-detection","temporal-two-mode-network-analysis","social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-approximate-bayesian-computation","name":"Multilevel Approximate Bayesian Computation","fullName":"Multilevel Approximate Bayesian Computation","aliases":["multilevel ABC","hierarchical ABC","multi-level ABC","ABC for hierarchical models"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2000s–2010s","originator":"Extension of ABC (Beaumont et al., 2002) to multilevel/hierarchical settings; developed across multiple authors in the 2010s","url":"https://scholargate.app/en/bayesian/multilevel-approximate-bayesian-computation","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-approximate-bayesian-computation.md","definition":"Multilevel Approximate Bayesian Computation (multilevel ABC) extends simulation-based Bayesian inference to hierarchically structured data. When the likelihood is intractable and observations are nested within groups, it replaces direct likelihood evaluation with simulations at each level of the hierarchy, accepting parameter draws whose simulated summary statistics are close to the observed ones.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of ABC (Beaumont et al., 2002) to multilevel/hierarchical settings; developed across multiple authors in the 2010s","year":"2000s–2010s","type":"Simulation-based Bayesian inference","dataType":"Any data type (continuous, discrete, summary-statistic-reducible) in hierarchically structured datasets","subfamily":"Bayesian / computational"},"citations":[{"ref":"Beaumont, M. A., Zhang, W., & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.","type":"article","doi":"10.1093/genetics/162.4.2025","isbn":null,"url":null},{"ref":"Jasra, A., Singh, S. S., Martin, J. S., & McCoy, E. (2012). Filtering via approximate Bayesian computation. Statistics and Computing, 22(6), 1223–1237.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Filtering+via+approximate+Bayesian+computation+Jasra"}],"related":["approximate-bayesian-computation","hierarchical-bayesian-inference","multilevel-bayesian-inference","sequential-monte-carlo","markov-chain-monte-carlo","bayesian-hierarchical-model-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-bayesian-inference","name":"Multilevel Bayesian Inference","fullName":"Multilevel Bayesian Inference","aliases":["Bayesian multilevel model","Bayesian hierarchical model","Bayesian mixed-effects model","Bayesian random-effects model"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1980s–2000s","originator":"Gelman, Hill, Raudenbush, Bryk","url":"https://scholargate.app/en/bayesian/multilevel-bayesian-inference","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-bayesian-inference.md","definition":"Multilevel Bayesian inference combines Bayesian probability with hierarchical data structures, treating group-level parameters as drawn from a common population distribution. It simultaneously estimates unit-level effects and the hyperparameters governing their variation, propagating full uncertainty through every level of the hierarchy via posterior sampling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelman, Hill, Raudenbush, Bryk","year":"1980s–2000s","type":"Bayesian hierarchical model","dataType":"Grouped / nested / clustered data; continuous or categorical outcomes","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521686891","url":null},{"ref":"Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919049","url":null}],"related":["bayesian-regression","hierarchical-bayesian-inference","mcmc","multilevel-mcmc","variational-inference","bayesian-hierarchical-model-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-bayesian-model-averaging","name":"Multilevel Bayesian Model Averaging","fullName":"Multilevel Bayesian Model Averaging","aliases":["ML-BMA","hierarchical Bayesian model averaging","multilevel BMA","Bayesian model averaging in multilevel models"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1999–2000s","originator":"Hoeting, Madigan, Raftery, Volinsky (BMA foundation); multilevel extension developed across the late 1990s–2000s","url":"https://scholargate.app/en/bayesian/multilevel-bayesian-model-averaging","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-bayesian-model-averaging.md","definition":"Multilevel Bayesian model averaging (ML-BMA) extends classical Bayesian model averaging to grouped or hierarchically structured data. Rather than committing to a single multilevel model specification, it computes a weighted average of predictions and parameter estimates across a set of candidate multilevel models, weighting each model by its posterior probability given the data. The result accounts simultaneously for uncertainty in the grouping structure, fixed effects, random effects, and covariate selection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hoeting, Madigan, Raftery, Volinsky (BMA foundation); multilevel extension developed across the late 1990s–2000s","year":"1999–2000s","type":"Bayesian ensemble / model selection","dataType":"grouped / clustered / hierarchical continuous or categorical data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-401.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+model+averaging%3A+A+tutorial+Hoeting"},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["bayesian-model-averaging","hierarchical-bayesian-inference","multilevel-mcmc","bayesian-regression","multilevel-variational-inference","gibbs-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-bayesian-network","name":"Multilevel Bayesian Network","fullName":"Multilevel Bayesian Network","aliases":["multi-level Bayesian network","hierarchical Bayesian network","MLBN","multilevel probabilistic graphical model"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990s–2000s","originator":"Extension of Pearl's Bayesian networks; multilevel formulation developed in statistical relational learning community, 1990s–2000s","url":"https://scholargate.app/en/bayesian/multilevel-bayesian-network","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-bayesian-network.md","definition":"A multilevel Bayesian network extends the standard Bayesian network to data with hierarchical or grouped structure — students within schools, patients within hospitals, observations within subjects — by placing separate but linked graphical models at each level, with higher-level parameters governing the conditional probability tables of lower-level nodes. The result is a principled probabilistic framework that captures both within-group relationships and between-group variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Pearl's Bayesian networks; multilevel formulation developed in statistical relational learning community, 1990s–2000s","year":"1990s–2000s","type":"Probabilistic graphical model (hierarchical)","dataType":"Clustered, nested, or grouped observational data; mixed continuous and discrete variables","subfamily":"Bayesian / computational"},"citations":[{"ref":"Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.","type":"book","doi":null,"isbn":"978-0262013192","url":null},{"ref":"Getoor, L. & Taskar, B. (Eds.) (2007). Introduction to Statistical Relational Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0262072885","url":null}],"related":["bayesian-network","hierarchical-bayesian-inference","multilevel-bayesian-inference","dynamic-bayesian-network","bayesian-hierarchical-model-with-missing-data","multilevel-mcmc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-bootstrap-simulation","name":"Multilevel Bootstrap Simulation","fullName":"Multilevel Bootstrap Simulation","aliases":["hierarchical bootstrap","cluster bootstrap","stratified bootstrap for multilevel data","multilevel resampling"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1979 (bootstrap); multilevel variants c.1990s","originator":"Efron (1979); multilevel extensions developed through 1980s–2000s","url":"https://scholargate.app/en/bayesian/multilevel-bootstrap-simulation","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-bootstrap-simulation.md","definition":"Multilevel bootstrap simulation is a resampling technique designed for clustered or hierarchically structured data. It preserves the nested data structure by resampling at each level independently — first drawing clusters (e.g., schools, hospitals), then drawing observations within each sampled cluster — so that bootstrap replicate datasets reflect the same multilevel organisation as the original data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Efron (1979); multilevel extensions developed through 1980s–2000s","year":"1979 (bootstrap); multilevel variants c.1990s","type":"resampling / simulation","dataType":"clustered / nested / hierarchical data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26.","type":"article","doi":"10.1214/aos/1176344552","isbn":null,"url":null},{"ref":"Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521574716","url":null}],"related":["multilevel-mcmc","hierarchical-bayesian-inference","sequential-monte-carlo","gibbs-sampling","multilevel-variational-inference","bootstrap-simulation-with-missing-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-confirmatory-factor-analysis","name":"Multilevel CFA","fullName":"Multilevel Confirmatory Factor Analysis","aliases":["MCFA","multilevel measurement model","two-level CFA","hierarchical CFA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1994","originator":"Bengt O. Muthen","url":"https://scholargate.app/en/psychometrics/multilevel-confirmatory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-confirmatory-factor-analysis.md","definition":"Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the standard tool for validating psychometric scales administered within natural groups such as classrooms, clinics, or organisations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengt O. Muthen","year":"1994","type":"Latent variable model / measurement model","dataType":"Continuous or ordinal indicators nested within groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Muthen, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398.","type":"article","doi":"10.1177/0049124194022003006","isbn":null,"url":null},{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728462","url":null}],"related":["confirmatory-factor-analysis","exploratory-factor-analysis","sem","multilevel-modeling","measurement-invariance","latent-profile-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-content-validity","name":"Multilevel Content Validity","fullName":"Multilevel Content Validity Assessment","aliases":["hierarchical content validity","nested-data content validity","multilevel scale content evaluation","MCV"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1975–2000s","originator":"Rooted in Lawshe (1975) for content validity; multilevel extension developed through multilevel psychometric literature from the 1990s onward","url":"https://scholargate.app/en/psychometrics/multilevel-content-validity","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-content-validity.md","definition":"Multilevel content validity extends the classical content validity framework to settings where items, raters, or respondents are nested within hierarchical structures — such as students within schools, patients within clinics, or items rated by panels from distinct cultural or professional groups. It ensures that scale content is relevant and representative at every level of the hierarchy, not just in the aggregate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Lawshe (1975) for content validity; multilevel extension developed through multilevel psychometric literature from the 1990s onward","year":"1975–2000s","type":"Validity evaluation / expert judgment","dataType":"Expert ratings, item-level judgments nested within raters or groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–385.","type":"article","doi":"10.1097/00006199-198611000-00017","isbn":null,"url":null},{"ref":"Wilson, M. (2005). Constructing Measures: An Item Response Modeling Approach. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805847857","url":null}],"related":["content-validity","construct-validity","multilevel-confirmatory-factor-analysis","multilevel-measurement-invariance","multilevel-scale-development","discriminant-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-convergent-validity","name":"Multilevel Convergent Validity","fullName":"Multilevel Convergent Validity","aliases":["cross-level convergent validity","multilevel measurement validity","between-level convergent validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2005","originator":"Dyer, Hanges & Hall; Chen, Bliese & Mathieu","url":"https://scholargate.app/en/psychometrics/multilevel-convergent-validity","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-convergent-validity.md","definition":"Multilevel convergent validity evaluates whether items or scales intended to measure the same construct show coherent, strong associations at each level of a nested data structure — within individuals, within groups, and between groups. It extends classical convergent validity from single-level measurement models into the multilevel confirmatory factor analysis (ML-CFA) framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dyer, Hanges & Hall; Chen, Bliese & Mathieu","year":"2005","type":"Measurement validity evaluation","dataType":"Nested / clustered survey data with latent constructs","subfamily":"Scale / measurement"},"citations":[{"ref":"Dyer, N. G., Hanges, P. J. & Hall, R. J. (2005). Applying multilevel confirmatory factor analysis techniques to the study of leadership. Leadership Quarterly, 16(1), 149–167.","type":"article","doi":"10.1016/j.leaqua.2004.09.009","isbn":null,"url":null},{"ref":"Chen, G., Bliese, P. D. & Mathieu, J. E. (2005). Conceptual framework and statistical procedures for delineating and testing multilevel theories of homology. Organizational Research Methods, 8(4), 375–409.","type":"article","doi":"10.1177/1094428105280056","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","multilevel-sem","construct-validity","discriminant-validity","intraclass-correlation","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-differential-item-functioning","name":"Multilevel Differential Item Functioning","fullName":"Multilevel Differential Item Functioning Analysis","aliases":["multilevel DIF","hierarchical DIF analysis","cross-level DIF","ML-DIF"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2001","originator":"Kamata (2001) and subsequent multilevel IRT/DIF literature","url":"https://scholargate.app/en/psychometrics/multilevel-differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-differential-item-functioning.md","definition":"Multilevel DIF analysis detects whether individual test or survey items function differently across groups when respondents are clustered within higher-level units — such as students nested in schools, employees in organizations, or patients in clinics. By accounting for hierarchical data structure, it separates genuine item bias from artificial DIF signals caused by ignoring clustering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kamata (2001) and subsequent multilevel IRT/DIF literature","year":"2001","type":"Bias detection / multilevel measurement model","dataType":"Ordinal or binary item responses nested within groups or clusters","subfamily":"Scale / measurement"},"citations":[{"ref":"French, B. F., & Finch, W. H. (2008). Multigroup confirmatory factor analysis: Locating the invariant referent sets. Structural Equation Modeling: A Multidisciplinary Journal, 15(1), 96–113.","type":"article","doi":"10.1080/10705510701758349","isbn":null,"url":null},{"ref":"Kamata, A. (2001). Item analysis by the hierarchical generalized linear model. Journal of Educational Measurement, 38(1), 79–93.","type":"article","doi":"10.1111/j.1745-3984.2001.tb01117.x","isbn":null,"url":null}],"related":["differential-item-functioning","multilevel-item-response-theory","multilevel-confirmatory-factor-analysis","measurement-invariance","item-response-theory","multilevel-measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-discriminant-validity","name":"Multilevel Discriminant Validity","fullName":"Multilevel Discriminant Validity","aliases":["multilevel DV","cross-level discriminant validity","hierarchical discriminant validity","ML-DV"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2005","originator":"Dyer, Hanges, & Hall; Chen, Sousa, & West","url":"https://scholargate.app/en/psychometrics/multilevel-discriminant-validity","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-discriminant-validity.md","definition":"Multilevel discriminant validity evaluates whether theoretically distinct constructs are empirically separable when data are nested within higher-level units such as teams, schools, or organizations. It extends single-level discriminant validity checks into a multilevel confirmatory factor analysis framework, verifying that constructs are distinguishable both within and between levels simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dyer, Hanges, & Hall; Chen, Sousa, & West","year":"2005","type":"Validity evaluation within multilevel CFA","dataType":"Nested / clustered ordinal or continuous indicators","subfamily":"Scale / measurement"},"citations":[{"ref":"Dyer, N. G., Hanges, P. J., & Hall, R. J. (2005). Applying multilevel confirmatory factor analysis techniques to the study of leadership. Leadership Quarterly, 16(1), 149–167.","type":"article","doi":"10.1016/j.leaqua.2004.09.009","isbn":null,"url":null},{"ref":"Chen, F. F., Sousa, K. H., & West, S. G. (2005). Teacher's corner: Testing measurement invariance of second-order factor models. Structural Equation Modeling, 12(3), 471–492.","type":"article","doi":"10.1207/s15328007sem1203_7","isbn":null,"url":null}],"related":["discriminant-validity","multilevel-confirmatory-factor-analysis","multilevel-convergent-validity","multilevel-measurement-invariance","confirmatory-factor-analysis","multilevel-construct-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-exploratory-factor-analysis","name":"Multilevel EFA","fullName":"Multilevel Exploratory Factor Analysis","aliases":["ML-EFA","multilevel factor analysis","two-level exploratory factor analysis","hierarchical exploratory factor analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1994","originator":"Bengt O. Muthén","url":"https://scholargate.app/en/psychometrics/multilevel-exploratory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-exploratory-factor-analysis.md","definition":"Multilevel exploratory factor analysis uncovers latent factor structures simultaneously at two or more levels of a data hierarchy — for example, both within individuals and between groups — without imposing a fixed structure in advance. It is essential whenever survey or test items are collected from respondents nested inside classrooms, organisations, or clinics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengt O. Muthén","year":"1994","type":"Latent variable / multilevel dimension reduction","dataType":"Continuous or ordinal indicators nested within groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398.","type":"article","doi":"10.1177/0049124194022003006","isbn":null,"url":null},{"ref":"Ryu, E. & West, S. G. (2009). Level-specific evaluation of model fit in multilevel structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(4), 583–601.","type":"article","doi":"10.1080/10705510903203466","isbn":null,"url":null}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","multilevel-sem","hierarchical-linear-modeling","bifactor-model","intraclass-correlation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-generalizability-theory","name":"Multilevel Generalizability Theory","fullName":"Multilevel Generalizability Theory","aliases":["multilevel G-theory","ML-GT","hierarchical generalizability theory","multilevel G-study"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Brennan, R. L. and Shavelson, R. J. (extensions of Cronbach et al. G-theory to multilevel designs)","url":"https://scholargate.app/en/psychometrics/multilevel-generalizability-theory","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-generalizability-theory.md","definition":"Multilevel generalizability theory extends classical G-theory to measurement designs where observations are nested within higher-level units — for example, items nested within raters, or students nested within classrooms. It decomposes score variance into components attributable to persons, facets, and their interactions across hierarchical levels, enabling precise estimation of measurement precision in complex, real-world assessment settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brennan, R. L. and Shavelson, R. J. (extensions of Cronbach et al. G-theory to multilevel designs)","year":"1990s–2000s","type":"Measurement / variance decomposition","dataType":"Nested or crossed rating data with hierarchical structure","subfamily":"Scale / measurement"},"citations":[{"ref":"Briggs, D. C. & Wilson, M. (2003). An introduction to multidimensional measurement using Rasch models and generalizability theory. Journal of Applied Measurement, 4(1), 1–19.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Briggs+Wilson+2003+multidimensional+measurement+Rasch+generalizability"},{"ref":"Webb, N. M., Shavelson, R. J. & Haertel, E. H. (2006). Reliability coefficients and generalizability theory. Handbook of Statistics, 26, 81–124.","type":"article","doi":"10.1016/S0169-7161(06)26004-8","isbn":null,"url":null}],"related":["generalizability-theory","classical-test-theory","multilevel-modeling","confirmatory-factor-analysis","item-response-theory","variance-components-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-gibbs-sampling","name":"Multilevel Gibbs Sampling","fullName":"Multilevel Gibbs Sampling for Hierarchical Bayesian Models","aliases":["hierarchical Gibbs sampler","blocked Gibbs sampling for multilevel models","multilevel MCMC via Gibbs","Gibbs sampler for mixed-effects models"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990","originator":"Geman & Geman (1984); applied to multilevel models by Gelfand & Smith (1990)","url":"https://scholargate.app/en/bayesian/multilevel-gibbs-sampling","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-gibbs-sampling.md","definition":"Multilevel Gibbs sampling applies the Gibbs MCMC algorithm to hierarchical (multilevel) Bayesian models, cycling through the conditional distributions of group-level parameters and population-level hyperparameters in turn. This exploits the conditional independence structure of the hierarchy to draw exact or near-exact samples from a posterior that would otherwise be analytically intractable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geman & Geman (1984); applied to multilevel models by Gelfand & Smith (1990)","year":"1990","type":"MCMC sampling algorithm","dataType":"continuous, count, or binary outcomes nested within groups","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gelman, A. & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521686891","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["gibbs-sampling","hierarchical-bayesian-inference","multilevel-mcmc","metropolis-hastings-algorithm","hamiltonian-monte-carlo","bayesian-hierarchical-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-hamiltonian-monte-carlo","name":"Multilevel Hamiltonian Monte Carlo","fullName":"Multilevel Hamiltonian Monte Carlo","aliases":["Multilevel HMC","MLHMC","multilevel HMC sampler","multilevel leapfrog MCMC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2010s","originator":"Beskos, Jasra, Law, Tempone, Zhou (multilevel MCMC); Neal (HMC component)","url":"https://scholargate.app/en/bayesian/multilevel-hamiltonian-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-hamiltonian-monte-carlo.md","definition":"Multilevel Hamiltonian Monte Carlo (Multilevel HMC) combines the variance-reduction strategy of multilevel Monte Carlo with the efficient gradient-driven exploration of Hamiltonian Monte Carlo. By running coupled HMC chains at increasing levels of model fidelity or discretisation, it achieves accurate posterior estimates at a computational cost substantially lower than a single fine-level HMC chain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Beskos, Jasra, Law, Tempone, Zhou (multilevel MCMC); Neal (HMC component)","year":"2010s","type":"Bayesian computational sampler","dataType":"continuous multivariate data; hierarchical or multi-resolution models","subfamily":"Bayesian / computational"},"citations":[{"ref":"Beskos, A., Jasra, A., Law, K., Tempone, R., & Zhou, Y. (2017). Multilevel sequential Monte Carlo samplers. Stochastic Processes and their Applications, 127(5), 1417–1440.","type":"article","doi":"10.1016/j.spa.2016.08.004","isbn":null,"url":null},{"ref":"Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones, & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113–162). CRC Press.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1206.1901"}],"related":["hamiltonian-monte-carlo","multilevel-mcmc","multilevel-sequential-monte-carlo","markov-chain-monte-carlo","hierarchical-hamiltonian-monte-carlo","multilevel-variational-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-mcdonalds-omega","name":"Multilevel McDonald's omega","fullName":"Multilevel McDonald's Omega Reliability Coefficient","aliases":["multilevel omega","omega within","omega between","hierarchical omega"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1999 (omega); 2014 (multilevel extension)","originator":"Roderick P. McDonald (omega); multilevel extension by Geldhof, Preacher & Zyphur","url":"https://scholargate.app/en/psychometrics/multilevel-mcdonalds-omega","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-mcdonalds-omega.md","definition":"Multilevel McDonald's omega estimates reliability at two distinct levels — within groups and between groups — for scales administered to individuals nested in clusters such as classrooms, teams, or organizations. It accounts for the non-independence induced by grouping and avoids the bias that single-level omega produces in clustered data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roderick P. McDonald (omega); multilevel extension by Geldhof, Preacher & Zyphur","year":"1999 (omega); 2014 (multilevel extension)","type":"Reliability coefficient","dataType":"Ordinal or continuous items nested within groups (multilevel/clustered data)","subfamily":"Scale / measurement"},"citations":[{"ref":"Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91.","type":"article","doi":"10.1037/a0032138","isbn":null,"url":null},{"ref":"McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805830750","url":null}],"related":["mcdonalds-omega","cronbachs-alpha","multilevel-confirmatory-factor-analysis","multilevel-reliability-analysis","multilevel-cronbachs-alpha","multilevel-exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-mcmc","name":"Multilevel MCMC","fullName":"Multilevel Markov Chain Monte Carlo","aliases":["hierarchical MCMC","multilevel Bayesian sampling","MLMCMC","hierarchical Markov chain Monte Carlo"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990s","originator":"Gelfand & Smith (sampling-based approach); multilevel extension developed through 1990s Bayesian hierarchical modeling literature","url":"https://scholargate.app/en/bayesian/multilevel-mcmc","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-mcmc.md","definition":"Multilevel MCMC applies Markov chain Monte Carlo sampling to hierarchical (multilevel) Bayesian models. It draws samples from the joint posterior of both group-level and population-level parameters simultaneously, propagating uncertainty across levels and enabling inference in clustered or nested data structures where observations within groups share common distributional characteristics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelfand & Smith (sampling-based approach); multilevel extension developed through 1990s Bayesian hierarchical modeling literature","year":"1990s","type":"Bayesian computational inference","dataType":"continuous, binary, count, or ordinal outcomes nested within groups","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398-409.","type":"article","doi":"10.1080/01621459.1990.10476213","isbn":null,"url":null}],"related":["gibbs-sampling","hamiltonian-monte-carlo","hierarchical-bayesian-inference","metropolis-hastings-algorithm","variational-inference","bayesian-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-measurement-invariance","name":"Multilevel Measurement Invariance","fullName":"Multilevel Measurement Invariance Testing","aliases":["MLMI","multilevel factorial invariance","cross-level measurement invariance","multilevel CFA invariance"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2000s","originator":"Muthén, Asparouhov, and colleagues","url":"https://scholargate.app/en/psychometrics/multilevel-measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-measurement-invariance.md","definition":"Multilevel measurement invariance testing evaluates whether a latent construct is measured equivalently both within clusters (e.g., individuals within teams) and between clusters (e.g., team-level aggregates). It extends standard measurement invariance procedures to nested data structures commonly encountered in organisational, educational, and cross-cultural research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Muthén, Asparouhov, and colleagues","year":"2000s","type":"Measurement model evaluation","dataType":"Ordinal or continuous indicators in nested (clustered) data","subfamily":"Scale / measurement"},"citations":[{"ref":"Muthén, B. O., & Asparouhov, T. (2009). Multilevel factor analysis of class and student achievement components. Journal of Educational and Behavioral Statistics, 34(2), 250–270.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multilevel+factor+analysis+of+class+and+student+achievement+components+Muth%C3%A9n"},{"ref":"Ryu, E. (2014). Factorial invariance in multilevel confirmatory factor analysis. British Journal of Mathematical and Statistical Psychology, 67(1), 172–194.","type":"article","doi":"10.1111/bmsp.12014","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","multilevel-sem","measurement-invariance","multigroup-cfa","hierarchical-linear-modeling","structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-mediation","name":"Multilevel Mediation Analysis","fullName":"Multilevel Mediation Analysis","aliases":["multilevel mediation","hierarchical mediation","cross-level mediation","1-1-1 mediation","2-1-1 mediation","2-2-1 mediation","Çok Düzeyli Medyasyon Analizi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":2003,"originator":"Kenny, Korchmaros & Bolger","url":"https://scholargate.app/en/statistics/multilevel-mediation","markdownUrl":"https://scholargate.app/en/statistics/multilevel-mediation.md","definition":"Multilevel mediation analysis is a parametric structural method that estimates indirect (mediated) effects within hierarchically nested data, such as students within schools or employees within organisations. Formalised for lower-level mediation in multilevel models by Kenny, Korchmaros and Bolger (2003), it simultaneously handles individual-level (1-1-1) and group-level (2-2-1 or 2-1-1) mediation pathways in a single coherent framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kenny, Korchmaros & Bolger","year":2003,"family":"Hypothesis test","type":"Multilevel structural model","parametric":true,"minSample":100,"minGroups":30,"outcome":"continuous","distribution":"Normal (multilevel)","designs":"1-1-1, 2-1-1, 2-2-1","indirectEffectEstimation":"Monte Carlo or bootstrapping","iccThreshold":"> 0.05 triggers multilevel analysis"},"citations":[{"ref":"Kenny, D. A., Korchmaros, J. D., & Bolger, N. (2003). Lower level mediation in multilevel models. Psychological Methods, 8(2), 115–128.","type":"article","doi":"10.1037/1082-989X.8.2.115","isbn":null,"url":null}],"related":["mediation-analysis","hlm","causal-mediation","conditional-process-analysis","moderation-analysis","structural-equation-modeling","panel-fixed-effects","mixed-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-metropolis-hastings","name":"Multilevel Metropolis-Hastings","fullName":"Multilevel Metropolis-Hastings Algorithm","aliases":["hierarchical Metropolis-Hastings","multilevel MH","MH for hierarchical models","blocked Metropolis-Hastings"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1953 (core); 1990s (multilevel application)","originator":"Metropolis et al. (1953); hierarchical extension developed through 1980s–1990s Bayesian computation literature","url":"https://scholargate.app/en/bayesian/multilevel-metropolis-hastings","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-metropolis-hastings.md","definition":"Multilevel Metropolis-Hastings applies the Metropolis-Hastings MCMC algorithm to hierarchical (multilevel) Bayesian models, sampling jointly from group-level parameters and hyperparameters by proposing candidate values and accepting or rejecting them via a ratio that respects the full joint posterior across all levels of the model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Metropolis et al. (1953); hierarchical extension developed through 1980s–1990s Bayesian computation literature","year":"1953 (core); 1990s (multilevel application)","type":"MCMC sampling algorithm","dataType":"continuous, discrete, or mixed outcomes structured in groups or levels","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Roberts, G. O. & Sahu, S. K. (1997). Updating schemes, correlation structure, blocking and parameterisation for the Gibbs sampler. Journal of the Royal Statistical Society: Series B, 59(2), 291-317.","type":"article","doi":"10.1111/1467-9868.00070","isbn":null,"url":null}],"related":["multilevel-gibbs-sampling","multilevel-bayesian-inference","multilevel-hamiltonian-monte-carlo","hierarchical-bayesian-inference","metropolis-hastings-algorithm","multilevel-variational-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-mixed-methods-design","name":"Multilevel Mixed Methods Design","fullName":"Multilevel Mixed Methods Research Design","aliases":["multilevel MMR","nested mixed methods","hierarchical mixed methods design","cross-level mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"Late 1990s–2000s","originator":"Bonnie Nastasi, John Hitchcock, and collaborators; systematized by Creswell & Plano Clark","url":"https://scholargate.app/en/research-design/multilevel-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/multilevel-mixed-methods-design.md","definition":"Multilevel mixed methods design is a research approach that collects and integrates both quantitative and qualitative data at two or more distinct levels of a social or organizational hierarchy — for example, individuals nested within classrooms, classrooms within schools, or patients within healthcare teams. By pairing quantitative measurement of outcomes at one level with qualitative exploration of meaning at another, researchers gain a richer, more complete picture than either strand alone could provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonnie Nastasi, John Hitchcock, and collaborators; systematized by Creswell & Plano Clark","year":"Late 1990s–2000s","type":"Mixed methods research design","dataType":"Both quantitative and qualitative data collected at multiple levels (e.g., individual, group, institutional)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483357829","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage Publications.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["multilevel-mixed-methods","concurrent-triangulation-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","multiphase-mixed-methods-design","concurrent-embedded-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-modeling","name":"Multilevel Modeling","fullName":"Multilevel (Hierarchical) Linear Modeling","aliases":["HLM","mixed-effects models","random effects models","MLM"],"domain":"research-statistics","family":"process-pipeline","subfamily":"hierarchical-data-analysis","year":"1992","originator":"Anthony Bryk and Stephen Raudenbush","url":"https://scholargate.app/en/research-statistics/multilevel-modeling","markdownUrl":"https://scholargate.app/en/research-statistics/multilevel-modeling.md","definition":"Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anthony Bryk and Stephen Raudenbush","subfamily":"hierarchical-data-analysis","year":"1992","type":"Method"},"citations":[{"ref":"Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.","type":"article","doi":"10.2307/2075823","isbn":null,"url":null},{"ref":"Goldstein, H. (2011). Multilevel Statistical Models (4th ed.). Wiley-Blackwell.","type":"article","doi":"10.1002/9780470973394","isbn":null,"url":null},{"ref":"Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.","type":"article","doi":"10.1037/0033-2909.86.2.420","isbn":null,"url":null}],"related":["analysis-of-variance","logistic-regression","structural-equation-modeling"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-monte-carlo-simulation","name":"Multilevel Monte Carlo Simulation","fullName":"Multilevel Monte Carlo Simulation","aliases":["MLMC","multilevel MC","multi-level Monte Carlo","MLMC simulation"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2008","originator":"Michael B. Giles","url":"https://scholargate.app/en/bayesian/multilevel-monte-carlo-simulation","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-monte-carlo-simulation.md","definition":"Multilevel Monte Carlo (MLMC) is a variance-reduction technique that estimates expectations by combining simulations run at multiple levels of numerical resolution. Coarse, cheap simulations capture most of the signal; fine, expensive simulations correct only the remaining small difference — dramatically reducing total computational cost compared with standard Monte Carlo at the finest level alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael B. Giles","year":"2008","type":"variance-reduction simulation","dataType":"stochastic models, SDEs, PDEs with random inputs","subfamily":"Bayesian / computational"},"citations":[{"ref":"Giles, M. B. (2008). Multilevel Monte Carlo path simulation. Operations Research, 56(3), 607–617.","type":"article","doi":"10.1287/opre.1070.0496","isbn":null,"url":null},{"ref":"Giles, M. B. (2015). Multilevel Monte Carlo methods. Acta Numerica, 24, 259–328.","type":"article","doi":"10.1017/s096249291500001x","isbn":null,"url":null}],"related":["monte-carlo-simulation","sequential-monte-carlo","particle-filter","markov-chain-monte-carlo","multilevel-sequential-monte-carlo","quasi-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-nomological-validity","name":"Multilevel nomological validity","fullName":"Multilevel Nomological Validity","aliases":["cross-level construct validity","multilevel construct validation","MNV","nomological validity across levels"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2005","originator":"Chen, Bliese & Mathieu (building on Cronbach & Meehl)","url":"https://scholargate.app/en/psychometrics/multilevel-nomological-validity","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-nomological-validity.md","definition":"Multilevel nomological validity evaluates whether a psychological construct and its network of theoretical relationships hold consistently across multiple levels of analysis — such as individual, team, and organization. It extends classical construct validation to nested data structures, ensuring that a measure means the same thing and behaves as theory predicts at each level.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, Bliese & Mathieu (building on Cronbach & Meehl)","year":"2005","type":"Validity assessment / construct validation","dataType":"Nested / clustered data with multiple levels of measurement","subfamily":"Scale / measurement"},"citations":[{"ref":"Chen, G., Bliese, P. D. & Mathieu, J. E. (2005). Conceptual framework and statistical procedures for delineating and testing multilevel theories of homology. Organizational Research Methods, 8(4), 375–409.","type":"article","doi":"10.1177/1094428105280056","isbn":null,"url":null},{"ref":"Cronbach, L. J. & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.","type":"article","doi":"10.1037/h0040957","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","multilevel-sem","convergent-validity","discriminant-validity","construct-validity","nomological-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-rasch-model","name":"Multilevel Rasch Model","fullName":"Multilevel Rasch Model","aliases":["hierarchical Rasch model","random-effects Rasch model","multilevel IRT Rasch","MRCML model"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1997","originator":"Adams, Wilson & Wu","url":"https://scholargate.app/en/psychometrics/multilevel-rasch-model","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-rasch-model.md","definition":"The multilevel Rasch model extends the standard Rasch model to data with a nested structure — for example, students within classrooms within schools — by embedding person ability parameters inside a hierarchical linear model. It yields item difficulty estimates on a logit scale while simultaneously partitioning person-ability variance across cluster levels and correcting standard errors for non-independence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adams, Wilson & Wu","year":"1997","type":"Hierarchical item response model","dataType":"Dichotomous or polytomous item responses nested within persons nested within clusters","subfamily":"Scale / measurement"},"citations":[{"ref":"Adams, R. J., Wilson, M. & Wu, M. (1997). Multilevel item response models: An approach to errors in variables regression. Journal of Educational and Behavioral Statistics, 22(1), 47–76.","type":"article","doi":"10.3102/10769986022001047","isbn":null,"url":null},{"ref":"Fox, J.-P. & Glas, C. A. W. (2001). Bayesian estimation of a multilevel IRT model using Gibbs sampling. Psychometrika, 66(2), 271–288.","type":"article","doi":"10.1007/BF02294839","isbn":null,"url":null}],"related":["rasch-model","item-response-theory","multilevel-item-response-theory","multilevel-confirmatory-factor-analysis","multilevel-measurement-invariance","differential-item-functioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-reliability-analysis","name":"Multilevel Reliability Analysis","fullName":"Multilevel Reliability Analysis","aliases":["multilevel omega","within-group reliability","between-group reliability","hierarchical reliability"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2014","originator":"Geldhof, Preacher & Zyphur","url":"https://scholargate.app/en/psychometrics/multilevel-reliability-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-reliability-analysis.md","definition":"Multilevel reliability analysis estimates the internal consistency of scale scores separately at the within-group (individual) and between-group (cluster) levels. It corrects the bias that arises when ordinary alpha or omega is applied to hierarchically nested data, such as employees within organizations or students within classrooms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geldhof, Preacher & Zyphur","year":"2014","type":"Reliability estimation / psychometric modeling","dataType":"Ordinal or continuous items clustered within groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological Methods, 19(1), 72–91.","type":"article","doi":"10.1037/a0032138","isbn":null,"url":null},{"ref":"McNeish, D. (2017). Thanks coefficient alpha, we'll take it from here. Psychological Methods, 22(3), 412–433.","type":"article","doi":"10.1037/met0000144","isbn":null,"url":null}],"related":["cronbach-alpha","confirmatory-factor-analysis","multilevel-sem","intraclass-correlation","mcdonald-omega","hierarchical-linear-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-scale-development","name":"Multilevel Scale Development","fullName":"Multilevel Scale Development","aliases":["multilevel measurement modeling","hierarchical scale development","MLSEM scale construction","nested data scale development"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Raudenbush, Bryk, Hox and colleagues","url":"https://scholargate.app/en/psychometrics/multilevel-scale-development","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-scale-development.md","definition":"Multilevel scale development constructs and validates measurement instruments for data collected from individuals nested within higher-level units such as classrooms, organizations, or clinics. It partitions item variance into within-group and between-group components, ensuring that reliability and factor structure are evaluated at both levels simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raudenbush, Bryk, Hox and colleagues","year":"1990s–2000s","type":"Hierarchical measurement / scale construction","dataType":"Ordinal or continuous items from nested (clustered) samples","subfamily":"Scale / measurement"},"citations":[{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728462","url":null},{"ref":"Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919049","url":null}],"related":["multilevel-confirmatory-factor-analysis","multilevel-item-response-theory","multilevel-reliability-analysis","multilevel-measurement-invariance","confirmatory-factor-analysis","scale-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-test-retest-reliability","name":"Multilevel Test-Retest Reliability","fullName":"Multilevel Test-Retest Reliability","aliases":["hierarchical test-retest reliability","multilevel ICC reliability","nested test-retest reliability","ML-TRT reliability"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1979 (ICC foundation); multilevel extension: 1990s–2000s","originator":"Shrout & Fleiss (ICC foundation); multilevel extension by Goldstein, Snijders, and others","url":"https://scholargate.app/en/psychometrics/multilevel-test-retest-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/multilevel-test-retest-reliability.md","definition":"Multilevel test-retest reliability estimates how consistently a measurement instrument produces the same scores across repeated administrations when observations are nested within higher-level units — such as patients within clinics or students within classrooms. It partitions total score variance across levels using intraclass correlation coefficients derived from multilevel models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shrout & Fleiss (ICC foundation); multilevel extension by Goldstein, Snijders, and others","year":"1979 (ICC foundation); multilevel extension: 1990s–2000s","type":"Reliability estimation under hierarchical data","dataType":"Continuous or ordinal repeated-measures scores nested within higher-level units (e.g., individuals within clinics, items within raters)","subfamily":"Scale / measurement"},"citations":[{"ref":"Shrout, P. E. & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.","type":"article","doi":"10.1037/0033-2909.86.2.420","isbn":null,"url":null},{"ref":"Liljequist, D., Elfving, B. & Skavberg Roaldsen, K. (2019). Intraclass correlation: A discussion and demonstration of basic features. PLOS ONE, 14(7), e0219854.","type":"article","doi":"10.1371/journal.pone.0219854","isbn":null,"url":null}],"related":["intraclass-correlation-coefficient","generalizability-theory","confirmatory-factor-analysis","cronbach-alpha","multilevel-modeling","test-retest-reliability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilevel-variational-inference","name":"Multilevel Variational Inference","fullName":"Multilevel Variational Inference for Hierarchical Bayesian Models","aliases":["hierarchical variational inference","multilevel VI","variational Bayes for multilevel models","MLVI"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2016","originator":"Ranganath, Altosaar, Tran, Blei (hierarchical VI formalization, 2016); Blei et al. (VI framework, 2017)","url":"https://scholargate.app/en/bayesian/multilevel-variational-inference","markdownUrl":"https://scholargate.app/en/bayesian/multilevel-variational-inference.md","definition":"Multilevel variational inference (MLVI) is a scalable approximate Bayesian method that fits hierarchical (multilevel) models by optimizing a variational approximation to the posterior, rather than drawing MCMC samples. It exploits the grouped structure of multilevel data — individuals nested within groups, groups nested within higher-level units — to derive efficient coordinate-wise updates, making Bayesian inference tractable for large clustered datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ranganath, Altosaar, Tran, Blei (hierarchical VI formalization, 2016); Blei et al. (VI framework, 2017)","year":"2016","type":"approximate Bayesian inference","dataType":"continuous, mixed, clustered / grouped observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877.","type":"article","doi":"10.1080/01621459.2017.1285773","isbn":null,"url":null},{"ref":"Ranganath, R., Altosaar, J., Tran, D., & Blei, D. M. (2016). Operator variational objectives. Advances in Neural Information Processing Systems, 29. Curran Associates.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2016/hash/e2eb03b6d4d25497c4df65b9f18e8a9e-Abstract.html"}],"related":["variational-inference","hierarchical-bayesian-inference","multilevel-mcmc","bayesian-hierarchical-model","stochastic-variational-inference","mean-field-variational-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-convolutional-neural-network","name":"Multilingual Convolutional Neural Network","fullName":"Multilingual Convolutional Neural Network (ML-CNN)","aliases":["ML-CNN","cross-lingual CNN","multilingual text CNN","multilingual ConvNet"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2016","originator":"Kim, Y. (seminal NLP CNN); multilingual extension by community","url":"https://scholargate.app/en/deep-learning/multilingual-convolutional-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-convolutional-neural-network.md","definition":"A Multilingual CNN applies convolutional filters over token embeddings drawn from two or more languages, producing shared feature representations that enable a single model to classify, tag, or extract information across language boundaries without training separate models per language. It extends the standard text-CNN architecture with multilingual or cross-lingual input embeddings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kim, Y. (seminal NLP CNN); multilingual extension by community","year":"2014–2016","type":"Deep learning classifier","dataType":"Multilingual text or multilingual image sequences","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of EMNLP 2014, pp. 1746–1751.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D14-1181"},{"ref":"Convolutional neural network. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Convolutional_neural_network"}],"related":["convolutional-neural-network","multilingual-bert-based-classification","multilingual-recurrent-neural-network","multilingual-transformer","multilingual-lstm","transfer-learning-with-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-diffusion-model","name":"Multilingual Diffusion Model","fullName":"Multilingual Diffusion Model for Text and Cross-Lingual Generation","aliases":["Multilingual DiffuSeq","Cross-lingual Diffusion Model","Multilingual DDPM","Multilingual Denoising Diffusion"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2023","originator":"Ho, J., Jain, A., & Abbeel, P. (diffusion foundation); multilingual NLP extensions by various authors (2022–2024)","url":"https://scholargate.app/en/deep-learning/multilingual-diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-diffusion-model.md","definition":"A Multilingual Diffusion Model adapts the denoising diffusion probabilistic framework to work across multiple languages, enabling cross-lingual text generation, translation, and language-agnostic content synthesis. By conditioning on multilingual representations, the diffusion process learns a shared latent space that spans linguistic boundaries, producing high-quality outputs for low- and high-resource languages alike.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ho, J., Jain, A., & Abbeel, P. (diffusion foundation); multilingual NLP extensions by various authors (2022–2024)","year":"2020–2023","type":"Generative model (denoising diffusion process, multilingual extension)","dataType":"Text (multilingual corpora), optionally paired with images or audio","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html"},{"ref":"Gong, S., Li, M., Feng, J., Wu, Z., & Kong, L. (2023). DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=DiffuSeq+Sequence+to+Sequence+Text+Generation+with+Diffusion+Models"}],"related":["multilingual-transformer","multilingual-bert-based-classification","fine-tuned-diffusion-model","multilingual-roberta-based-classification","multilingual-sentence-embeddings","multilingual-recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-doc2vec","name":"Multilingual Doc2Vec","fullName":"Multilingual Paragraph Vector (Doc2Vec) Model","aliases":["multilingual paragraph vector","cross-lingual Doc2Vec","multilingual PV-DM","multilingual PV-DBOW"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2016","originator":"Le, Q. & Mikolov, T. (Doc2Vec); multilingual extension by community","url":"https://scholargate.app/en/deep-learning/multilingual-doc2vec","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-doc2vec.md","definition":"Multilingual Doc2Vec extends the Paragraph Vector framework of Le and Mikolov (2014) to two or more languages, training document-level embeddings in a shared or aligned vector space so that semantically similar documents — regardless of their language — end up close together. It enables cross-lingual document retrieval, classification, and clustering without requiring parallel corpora or translation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Le, Q. & Mikolov, T. (Doc2Vec); multilingual extension by community","year":"2014–2016","type":"Distributed document embedding (unsupervised / self-supervised)","dataType":"Text corpora in two or more languages","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning (ICML), PMLR 32(2), 1188–1196.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v32/le14.html"},{"ref":"Multilingualism. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Multilingualism"}],"related":["multilingual-word2vec","multilingual-sentence-embeddings","multilingual-bert-based-classification","multilingual-transformer","sentence-embeddings","lda-topic-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-gan","name":"Multilingual GAN","fullName":"Multilingual Generative Adversarial Network","aliases":["Multilingual GAN","Cross-lingual GAN","Multilingual Generative Adversarial Network","ML-GAN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2019","originator":"Goodfellow et al. (GAN); multilingual extensions by various authors from 2017 onward","url":"https://scholargate.app/en/deep-learning/multilingual-gan","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-gan.md","definition":"A Multilingual GAN pairs the generative adversarial framework with cross-lingual components — a shared encoder, language-conditioned generator, and a language discriminator — so that a single model can generate or align representations across multiple languages simultaneously. It is applied to cross-lingual text generation, machine translation, multilingual data augmentation, and language-invariant feature learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goodfellow et al. (GAN); multilingual extensions by various authors from 2017 onward","year":"2017–2019","type":"Generative adversarial model with multilingual conditioning","dataType":"Multilingual text, parallel corpora, cross-lingual embeddings","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html"},{"ref":"Chen, X., Shi, Z., Qiu, X., & Huang, X. (2018). Adversarial Multi-lingual Neural Relation Extraction. Proceedings of the 27th International Conference on Computational Linguistics (COLING), 1156–1166.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/C18-1099/"}],"related":["generative-adversarial-network","multilingual-transformer","multilingual-bert-based-classification","multilingual-recurrent-neural-network","transfer-learning-gan","multilingual-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-graph-neural-network","name":"Multilingual graph neural network","fullName":"Multilingual Graph Neural Network","aliases":["Multilingual GNN","cross-lingual GNN","multilingual graph network","multilingual relational GNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Various (Kipf & Welling 2017 for GNN; multilingual extensions from NLP community ~2019)","url":"https://scholargate.app/en/deep-learning/multilingual-graph-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-graph-neural-network.md","definition":"A Multilingual Graph Neural Network (Multilingual GNN) applies graph-based message-passing over nodes and edges that carry features from two or more languages. It is used for tasks such as cross-lingual entity alignment, multilingual knowledge-graph completion, and relation extraction across parallel or comparable corpora, allowing structural and semantic information from multiple languages to be jointly learned.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Kipf & Welling 2017 for GNN; multilingual extensions from NLP community ~2019)","year":"2019","type":"Graph-based deep learning with multilingual node/edge features","dataType":"Multilingual text graphs, knowledge graphs, dependency trees, entity graphs","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of ICLR 2017.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1609.02907"},{"ref":"Cao, Y., Liu, Z., Li, C., Li, J., & Chua, T.-S. (2019). Multi-channel graph neural network for entity alignment. In Proceedings of ACL 2019, 1452–1461.","type":"inproceedings","doi":"10.18653/v1/P19-1140","isbn":null,"url":null}],"related":["graph-neural-network","multilingual-bert-based-classification","multilingual-transformer","multilingual-sentence-embeddings","transfer-learning-with-graph-neural-network","multilingual-recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-gru","name":"Multilingual GRU","fullName":"Multilingual Gated Recurrent Unit","aliases":["Multilingual GRU","cross-lingual GRU","multilingual gated recurrent unit","multi-language GRU"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014 (GRU); multilingual applications from ~2016","originator":"Cho, K. et al. (GRU); multilingual extension by NLP community","url":"https://scholargate.app/en/deep-learning/multilingual-gru","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-gru.md","definition":"A Multilingual GRU is a Gated Recurrent Unit network trained on text data spanning multiple languages, enabling sequential modeling of language-sensitive tasks such as sentiment analysis, named entity recognition, and machine translation across language boundaries without requiring separate models per language.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cho, K. et al. (GRU); multilingual extension by NLP community","year":"2014 (GRU); multilingual applications from ~2016","type":"Recurrent sequence model (multilingual)","dataType":"Sequential text in multiple languages","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.","type":"inproceedings","doi":"10.3115/v1/D14-1179","isbn":null,"url":null},{"ref":"Conneau, A., Lample, G., Ranzato, M., Denoyer, L., & Jegou, H. (2018). Word Translation Without Parallel Data. Proceedings of ICLR 2018.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1710.04087"}],"related":["gated-recurrent-unit","multilingual-lstm","multilingual-recurrent-neural-network","multilingual-transformer","multilingual-bert-based-classification","transfer-learning-with-gru"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-image-classification","name":"Multilingual Image Classification","fullName":"Multilingual Image Classification (Cross-Lingual Vision Model)","aliases":["Cross-lingual image classification","Multilingual visual recognition","Cross-cultural image classification","Multilingual vision-language classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020s","originator":"Community / Radford et al. (CLIP, 2021) as key enabler","url":"https://scholargate.app/en/deep-learning/multilingual-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-image-classification.md","definition":"Multilingual image classification trains visual models to recognise and label images when class names, supervision signals, or evaluation benchmarks span multiple languages. Enabled by multilingual vision-language models such as CLIP, it allows a single model to classify images using prompts or labels in any supported language, facilitating cross-cultural and cross-lingual deployment of computer vision systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Community / Radford et al. (CLIP, 2021) as key enabler","year":"2020s","type":"Cross-lingual supervised image classification","dataType":"Images with multilingual labels, captions, or metadata","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 8748–8763. PMLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v139/radford21a.html"},{"ref":"Image classification. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Computer_vision#Recognition,_identification_and_detection"}],"related":["image-classification","multimodal-image-classification","multilingual-bert-based-classification","multilingual-vision-transformer","transfer-learning-with-image-classification","multilingual-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-lstm","name":"Multilingual LSTM","fullName":"Multilingual Long Short-Term Memory Network","aliases":["Multilingual LSTM","Cross-lingual LSTM","Multi-language LSTM","Multilingual Recurrent Network"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"1997 (LSTM); multilingual NLP applications from ~2016","originator":"Hochreiter, S. & Schmidhuber, J. (LSTM base); multilingual application by the NLP community from ~2016","url":"https://scholargate.app/en/deep-learning/multilingual-lstm","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-lstm.md","definition":"A Multilingual LSTM is a Long Short-Term Memory recurrent network trained or fine-tuned to process sequences in multiple languages, typically by sharing a single model across language-specific or joint subword embeddings. It captures long-range dependencies in text and is applied to multilingual classification, named entity recognition, sentiment analysis, and sequence labeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hochreiter, S. & Schmidhuber, J. (LSTM base); multilingual application by the NLP community from ~2016","year":"1997 (LSTM); multilingual NLP applications from ~2016","type":"Recurrent neural network (sequence model)","dataType":"Sequential / text data in multiple languages","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.","type":"article","doi":"10.1162/neco.1997.9.8.1735","isbn":null,"url":null},{"ref":"Long short-term memory. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Long_short-term_memory"}],"related":["long-short-term-memory","multilingual-gru","multilingual-transformer","multilingual-bert-based-classification","multilingual-recurrent-neural-network","multilingual-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-multilayer-perceptron","name":"Multilingual Multilayer Perceptron","fullName":"Multilingual Multilayer Perceptron (Multilingual MLP)","aliases":["Multilingual MLP","cross-lingual MLP","multilingual feedforward network","multilingual FFNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010s","originator":"McCulloch & Pitts / Rumelhart et al. (MLP); multilingual application became standard in NLP from the 2010s onward","url":"https://scholargate.app/en/deep-learning/multilingual-multilayer-perceptron","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-multilayer-perceptron.md","definition":"A Multilingual MLP is a feedforward neural network trained on text from two or more languages, relying on shared or aligned input representations — such as multilingual word embeddings or subword vocabularies — so that a single model can process and classify text across languages without separate per-language networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McCulloch & Pitts / Rumelhart et al. (MLP); multilingual application became standard in NLP from the 2010s onward","year":"2010s","type":"Feedforward neural network (multilingual variant)","dataType":"Text (multi-language tokenized or embedded inputs)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Artetxe, M., & Schwartz, H. A. (2019). Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond. Transactions of the Association for Computational Linguistics, 7, 597–610.","type":"inproceedings","doi":"10.1162/tacl_a_00288","isbn":null,"url":null},{"ref":"Multilayer perceptron. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Multilayer_perceptron"}],"related":["multilingual-bert-based-classification","multilingual-recurrent-neural-network","multilingual-transformer","multilingual-sentence-embeddings","transfer-learning-with-multilayer-perceptron","fine-tuned-multilayer-perceptron"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-question-answering","name":"Multilingual question answering","fullName":"Multilingual Question Answering (Cross-lingual MRC)","aliases":["cross-lingual question answering","multilingual QA","multilingual MRC","cross-lingual machine reading comprehension"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2020","originator":"Multiple groups; popularised via mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020)","url":"https://scholargate.app/en/deep-learning/multilingual-question-answering","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-question-answering.md","definition":"Multilingual question answering (QA) enables a model to read a passage and answer questions in multiple languages, often by fine-tuning a cross-lingual pretrained transformer such as mBERT or XLM-R on an annotated QA dataset in one language and transferring that capability zero-shot or few-shot to other languages. It is the standard approach for building multilingual reading-comprehension and open-domain QA systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; popularised via mBERT (Devlin et al., 2019) and XLM-R (Conneau et al., 2020)","year":"2018–2020","type":"Extractive / generative QA across multiple languages","dataType":"Multilingual text (question-context or question-passage pairs)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Artetxe, M., Ruder, S., & Yogatama, D. (2020). On the cross-lingual transferability of monolingual representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4623–4637). ACL.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.421","isbn":null,"url":null},{"ref":"Clark, J. H., Choi, E., Collins, M., Garrette, D., Kwiatkowski, T., Nikolaev, V., & Palomaki, J. (2020). TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. Transactions of the Association for Computational Linguistics, 8, 454–470.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=TyDi+QA%3A+A+benchmark+for+information-seeking+question+answering+in+typologically+diverse+languages+Clark"}],"related":["bert-based-classification","roberta-based-classification","multilingual-bert-based-classification","multilingual-transformer","multilingual-sentence-embeddings","transfer-learning-with-question-answering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-recurrent-neural-network","name":"Multilingual Recurrent Neural Network","fullName":"Multilingual Recurrent Neural Network (Cross-lingual RNN)","aliases":["Multilingual RNN","Cross-lingual RNN","Multi-language RNN","MRNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"1990–2010s","originator":"Elman, J. L. (RNN); multilingual extension by NLP community","url":"https://scholargate.app/en/deep-learning/multilingual-recurrent-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-recurrent-neural-network.md","definition":"A Multilingual Recurrent Neural Network (Multilingual RNN) applies the standard RNN architecture — which processes sequences step by step while maintaining a hidden state — to data spanning two or more languages. By training on multilingual corpora or sharing parameters across languages, the model learns cross-lingual sequence representations useful for translation, tagging, classification, and language modeling tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elman, J. L. (RNN); multilingual extension by NLP community","year":"1990–2010s","type":"Sequential model (cross-lingual)","dataType":"Sequential text data in multiple languages","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.","type":"article","doi":"10.1207/s15516709cog1402_1","isbn":null,"url":null},{"ref":"Recurrent neural network. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Recurrent_neural_network"}],"related":["recurrent-neural-network","long-short-term-memory","gated-recurrent-unit","multilingual-lstm","multilingual-transformer","multilingual-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-reinforcement-learning","name":"Multilingual Reinforcement Learning","fullName":"Multilingual Reinforcement Learning (Cross-Lingual RL for NLP and Language Grounding)","aliases":["Cross-Lingual RL","Multilingual RL","Multilingual Policy Learning","Cross-Lingual Reinforcement Learning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010s (applied to multilingual NLP settings)","originator":"Sutton, R. S. & Barto, A. G. (RL foundations); multilingual extensions emerged from the NLP/RL community in the 2010s","url":"https://scholargate.app/en/deep-learning/multilingual-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-reinforcement-learning.md","definition":"Multilingual Reinforcement Learning applies the RL paradigm — an agent learning by interaction and reward — to environments that involve multiple languages. The agent must interpret multilingual observations, follow cross-lingual instructions, or generalize policies trained in one language to new target languages, making it applicable to cross-lingual dialogue, multilingual game-playing agents, and language-grounded sequential decision tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sutton, R. S. & Barto, A. G. (RL foundations); multilingual extensions emerged from the NLP/RL community in the 2010s","year":"2010s (applied to multilingual NLP settings)","type":"Reinforcement learning applied to multilingual environments","dataType":"Sequential decisions, multilingual text, reward signals, language-grounded observations","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press.","type":"book","doi":null,"isbn":"978-0262193986","url":null},{"ref":"Reinforcement learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Reinforcement_learning"}],"related":["reinforcement-learning","multilingual-transformer","multilingual-bert-based-classification","fine-tuned-reinforcement-learning","transfer-learning-reinforcement-learning","multilingual-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-roberta-based-classification","name":"Multilingual RoBERTa-based Classification","fullName":"Multilingual RoBERTa-based Text Classification (XLM-RoBERTa)","aliases":["XLM-RoBERTa classification","mRoBERTa","cross-lingual RoBERTa classifier","multilingual transformer classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020","originator":"Conneau, A. et al. (Facebook AI Research)","url":"https://scholargate.app/en/deep-learning/multilingual-roberta-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-roberta-based-classification.md","definition":"Multilingual RoBERTa-based classification uses XLM-RoBERTa — a transformer pretrained on 100+ languages via masked language modeling — and fine-tunes it on labeled text to assign categories across multiple languages. By sharing a single model across languages, it enables robust cross-lingual and zero-shot text classification without needing separate per-language classifiers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conneau, A. et al. (Facebook AI Research)","year":"2020","type":"Pretrained multilingual transformer fine-tuned for classification","dataType":"Text (multilingual, 100+ languages)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzman, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 8440–8451.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.747","isbn":null,"url":null},{"ref":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.11692"}],"related":["roberta-based-classification","bert-based-classification","multilingual-bert-based-classification","multilingual-transformer","multilingual-sentence-embeddings","multilingual-named-entity-recognition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-semantic-segmentation","name":"Multilingual Semantic Segmentation","fullName":"Multilingual Semantic Segmentation (Cross-Lingual Scene Parsing)","aliases":["cross-lingual semantic segmentation","multilingual scene parsing","multilingual pixel-wise classification","ML-SegNet"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2022","originator":"Various (building on Long et al. 2015 FCN; multilingual extensions c. 2019–2022)","url":"https://scholargate.app/en/deep-learning/multilingual-semantic-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-semantic-segmentation.md","definition":"Multilingual semantic segmentation is a pixel-level scene parsing approach that assigns a semantic class label to every pixel in an image while incorporating cross-lingual capabilities — enabling a single model to recognise scene-text elements, annotations, or training signals drawn from multiple languages. It combines deep encoder-decoder architectures with multilingual language representations, making it applicable to documents, street signs, natural scene images, and medical imagery across diverse linguistic contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (building on Long et al. 2015 FCN; multilingual extensions c. 2019–2022)","year":"2019–2022","type":"Pixel-wise classification with cross-lingual features","dataType":"Images with multilingual scene text or multi-language annotations","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In Proceedings of ECCV 2018.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Encoder-Decoder+with+Atrous+Separable+Convolution+for+Semantic+Image+Segmentation"},{"ref":"Image segmentation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Image_segmentation"}],"related":["semantic-segmentation","instance-segmentation","multilingual-named-entity-recognition","multilingual-bert-based-classification","multilingual-transformer","transfer-learning-with-semantic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-sentence-embeddings","name":"Multilingual Sentence Embeddings","fullName":"Multilingual Sentence Embeddings (Cross-lingual Dense Representations)","aliases":["multilingual sentence representations","cross-lingual sentence embeddings","mSE","multilingual semantic embeddings"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2022","originator":"Reimers, N. & Gurevych, I.; Feng, F. et al. (Google)","url":"https://scholargate.app/en/deep-learning/multilingual-sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-sentence-embeddings.md","definition":"Multilingual sentence embeddings map sentences from many languages into a single shared vector space so that semantically equivalent sentences — regardless of language — land close together. Models such as LaBSE, multilingual Sentence-BERT, and mUSE have made it practical to compare, retrieve, and classify text across 50 to 100+ languages without translating anything first.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reimers, N. & Gurevych, I.; Feng, F. et al. (Google)","year":"2019–2022","type":"Cross-lingual representation learning","dataType":"Text (multilingual corpora)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2020.emnlp-main.365"},{"ref":"Feng, F., Yang, Y., Cer, D., Arivazhagan, N. & Wang, W. (2022). Language-agnostic BERT Sentence Embedding. Proceedings of ACL 2022, 878–891.","type":"article","doi":"10.18653/v1/2022.acl-long.62","isbn":null,"url":null}],"related":["sentence-embeddings","bert-based-classification","multilingual-bert-based-classification","multilingual-transformer","multilingual-roberta-based-classification","transfer-learning-with-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-sentiment-analysis","name":"Multilingual Sentiment Analysis","fullName":"Multilingual Sentiment Analysis (Cross-Lingual Opinion Mining)","aliases":["cross-lingual sentiment analysis","multilingual opinion mining","multilingual sentiment classification","MSA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2004–2020","originator":"Pang, B. & Lee, L. (early sentiment analysis); cross-lingual extension via mBERT/XLM-R community (2019–2020)","url":"https://scholargate.app/en/deep-learning/multilingual-sentiment-analysis","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-sentiment-analysis.md","definition":"Multilingual Sentiment Analysis (MSA) applies deep learning — most commonly a fine-tuned multilingual language model such as mBERT or XLM-RoBERTa — to classify the sentiment polarity (positive, negative, neutral) of text written in two or more languages, enabling opinion mining across language boundaries without building separate models per language.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pang, B. & Lee, L. (early sentiment analysis); cross-lingual extension via mBERT/XLM-R community (2019–2020)","year":"2004–2020","type":"Supervised classification / fine-tuned LM","dataType":"Multilingual text (reviews, social media, news)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzman, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of ACL 2020, 8440–8451.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.747","isbn":null,"url":null},{"ref":"Barnes, J., Klinger, R., & Wubben, S. (2022). Structured Sentiment Analysis as Dependency Graph Parsing. Computational Linguistics, 48(3), 693–744.","type":"article","doi":"10.18653/v1/2021.acl-long.263","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","multilingual-bert-based-classification","multilingual-roberta-based-classification","sentence-embeddings","multilingual-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-text-summarization","name":"Multilingual text summarization","fullName":"Multilingual Text Summarization","aliases":["cross-lingual summarization","multilingual abstractive summarization","multilingual extractive summarization","multilingual seq2seq summarization"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2021","originator":"Multiple groups; popularized via mBART (Liu et al., 2020) and mT5 (Xue et al., 2021)","url":"https://scholargate.app/en/deep-learning/multilingual-text-summarization","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-text-summarization.md","definition":"Multilingual text summarization applies pre-trained multilingual encoder-decoder models — such as mT5 or mBART — to generate concise summaries of documents written in many languages, either within the same language (monolingual) or across languages (cross-lingual). Fine-tuning these models on multilingual summarization benchmarks like XL-Sum enables coverage of dozens of languages with a single model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; popularized via mBART (Liu et al., 2020) and mT5 (Xue et al., 2021)","year":"2020–2021","type":"Seq2seq / encoder-decoder fine-tuning for summarization across languages","dataType":"Multilingual text corpora (news articles, documents, web text)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., & Raffel, C. (2021). mT5: A Massively Multilingual Pre-Trained Text-to-Text Transformer. Proceedings of NAACL-HLT 2021, pp. 483–498. Association for Computational Linguistics.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2021.naacl-main.41"},{"ref":"Hasan, T., Bhattacharjee, A., Islam, M. S., Mubasshir, K., Li, Y.-F., Kang, Y.-B., Rahman, M. S., & Shahriyar, R. (2021). XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages. Findings of ACL-IJCNLP 2021, pp. 4693–4703. Association for Computational Linguistics.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2021.findings-acl.413"}],"related":["multilingual-bert-based-classification","multilingual-transformer","fine-tuned-text-summarization","sentence-embeddings","transfer-learning-with-transformer","multilingual-roberta-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-topic-modeling","name":"Multilingual topic modeling","fullName":"Multilingual Topic Modeling (Cross-lingual Latent Topic Inference)","aliases":["cross-lingual topic model","polylingual LDA","multilingual LDA","MLTM"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2009","originator":"Mimno, D., Wallach, H. M., et al.","url":"https://scholargate.app/en/deep-learning/multilingual-topic-modeling","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-topic-modeling.md","definition":"Multilingual topic modeling extends probabilistic topic models such as LDA to corpora spanning two or more languages, inferring shared latent topics across language boundaries. By tying topic distributions across languages, it enables cross-lingual document analysis, comparable topic discovery, and information retrieval without requiring full parallel corpora.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mimno, D., Wallach, H. M., et al.","year":"2009","type":"Probabilistic topic model (multilingual extension)","dataType":"Multilingual text corpora (aligned or unaligned documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Mimno, D., Wallach, H. M., Naradowsky, J., Smith, D. A., & McCallum, A. (2009). Polylingual topic models. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 880–889. ACL.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D09-1092"},{"ref":"Vulić, I., De Smet, W., & Moens, M.-F. (2015). Monolingual and cross-lingual information retrieval models based on (bilingual) word embeddings. In Proceedings of SIGIR 2015, pp. 363–372. ACM.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Monolingual+and+cross-lingual+information+retrieval+models+based+on+bilingual+word+embeddings+Vulic+2015"}],"related":["lda-topic-model","nmf-topic-model","multilingual-bert-based-classification","multilingual-sentence-embeddings","multilingual-transformer","topic-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-transformer","name":"Multilingual Transformer","fullName":"Multilingual Transformer (Cross-lingual Pre-trained Language Model)","aliases":["multilingual LM","cross-lingual transformer","mBERT-style model","multilingual pre-trained model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Devlin et al. (mBERT); Conneau et al. (XLM-R)","url":"https://scholargate.app/en/deep-learning/multilingual-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-transformer.md","definition":"A multilingual transformer is a pre-trained language model built on the transformer architecture and trained jointly on text from dozens to over one hundred languages. Models such as mBERT and XLM-RoBERTa learn shared cross-lingual representations, enabling zero-shot or few-shot transfer: a model fine-tuned on English data can often be applied directly to French, German, Arabic, or Chinese without language-specific labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin et al. (mBERT); Conneau et al. (XLM-R)","year":"2019–2020","type":"Pre-trained cross-lingual language model","dataType":"Multilingual text (sequences of tokens across 100+ languages)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of ACL 2020, pp. 8440–8451. Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.747","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","transformer","multilingual-bert-based-classification","sentence-embeddings","multilingual-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-variational-autoencoder","name":"Multilingual variational autoencoder","fullName":"Multilingual Variational Autoencoder (ML-VAE)","aliases":["ML-VAE","cross-lingual VAE","multilingual latent variable model","multilingual generative autoencoder"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017-2018","originator":"Multiple research groups (Lample, Conneau et al.; Zhao et al.)","url":"https://scholargate.app/en/deep-learning/multilingual-variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-variational-autoencoder.md","definition":"A Multilingual Variational Autoencoder (ML-VAE) extends the standard VAE framework to handle multiple languages within a shared probabilistic latent space. Language-specific encoders map text from each language into a common continuous representation, while language-specific decoders reconstruct or translate that text. This enables cross-lingual generation, style transfer, and representation learning with or without parallel corpora.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple research groups (Lample, Conneau et al.; Zhao et al.)","year":"2017-2018","type":"Generative latent-variable model","dataType":"Text (multilingual corpora), optionally paired or unpaired","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Zhao, T., Zhang, Y., & Eskenazi, M. (2018). Zero-shot dialog generation with cross-domain latent actions. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue (pp. 1-10). ACL.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/W18-5001"},{"ref":"Lample, G., Conneau, A., Denoyer, L., & Ranzato, M. (2018). Unsupervised machine translation using monolingual corpora only. In International Conference on Learning Representations (ICLR 2018).","type":"article","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=rkYTTf-AZ"}],"related":["variational-autoencoder","multilingual-transformer","multilingual-bert-based-classification","multilingual-sentence-embeddings","transfer-learning-variational-autoencoder","multilingual-recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multilingual-vision-transformer","name":"Multilingual vision transformer","fullName":"Multilingual Vision Transformer (Multilingual ViT)","aliases":["Multilingual ViT","Cross-lingual Vision Transformer","Multilingual Visual Transformer","ML-ViT"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2021–2023","originator":"Dosovitskiy et al. (ViT base); multilingual extension by multiple groups (2021–2023)","url":"https://scholargate.app/en/deep-learning/multilingual-vision-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/multilingual-vision-transformer.md","definition":"Multilingual Vision Transformer (Multilingual ViT) extends the Vision Transformer architecture to operate across multiple languages, enabling image understanding and image-text reasoning in multilingual or cross-lingual settings. It combines patch-based image encoding with multilingual text representations, allowing a single model to serve diverse linguistic communities for tasks such as image captioning, visual question answering, and cross-lingual image retrieval.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dosovitskiy et al. (ViT base); multilingual extension by multiple groups (2021–2023)","year":"2021–2023","type":"Transformer-based vision model with multilingual capabilities","dataType":"Images, multilingual text, image-text pairs","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR 2021).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=YicbFdNTTy"},{"ref":"Bugliarello, E., Liu, F., Pfeiffer, J., Reddy, S., Elliott, D., Erdem, E., Erdem, A., & Lukasiewicz, T. (2022). IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages. International Conference on Machine Learning (ICML 2022).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=IGLUE+Benchmark+Transfer+Learning+Modalities+Tasks+Languages+ICML+2022"}],"related":["vision-transformer","multilingual-bert-based-classification","multilingual-roberta-based-classification","multilingual-sentence-embeddings","multimodal-vision-transformer","transfer-learning-with-vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-bert-based-classification","name":"Multimodal BERT-based Classification","fullName":"Multimodal BERT-based Classification (Transformer Fusion of Text and Non-text Modalities)","aliases":["MMBT","multimodal transformer classification","BERT multimodal fusion","vision-language BERT classifier"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Kiela, D. et al.; Lu, J. et al.","url":"https://scholargate.app/en/deep-learning/multimodal-bert-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-bert-based-classification.md","definition":"Multimodal BERT-based classification extends the BERT transformer architecture to jointly encode and classify data from multiple modalities — most commonly text paired with images — by fusing their representations before a final classification head. Introduced prominently around 2019 through models such as MMBT and ViLBERT, it has become a standard approach for tasks where neither text nor image alone carries sufficient information for accurate labeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kiela, D. et al.; Lu, J. et al.","year":"2019","type":"Multimodal transformer classifier","dataType":"Text + image (or other modality) pairs","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1909.02950"},{"ref":"Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in Neural Information Processing Systems, 32.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1908.02265"}],"related":["bert","vision-transformer","clip","cross-modal-attention","text-classification-transformer","image-text-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-convolutional-neural-network","name":"Multimodal Convolutional Neural Network","fullName":"Multimodal Convolutional Neural Network (MM-CNN)","aliases":["MM-CNN","multimodal CNN","multi-input CNN","cross-modal convolutional network"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2011","originator":"Ngiam, J. et al. / multiple groups","url":"https://scholargate.app/en/deep-learning/multimodal-convolutional-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-convolutional-neural-network.md","definition":"A Multimodal Convolutional Neural Network (MM-CNN) processes and fuses two or more input modalities — such as images and text, or video and audio — through dedicated convolutional branches, learning a shared representation that captures complementary signals from each source. The fused representation drives a downstream task such as classification, regression, or retrieval.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ngiam, J. et al. / multiple groups","year":"2011","type":"Multimodal deep learning model","dataType":"Images, text, audio, video, tabular (two or more modalities combined)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML), 689–696.","type":"inproceedings","doi":null,"isbn":null,"url":"https://dl.acm.org/doi/10.5555/3104482.3104569"},{"ref":"Zhang, Y., Yin, C., Li, Y., Li, D., & Tian, Q. (2020). Multimodal intelligence: Representation learning, information fusion, and applications. IEEE Journal of Selected Topics in Signal Processing, 14(3), 478–493.","type":"article","doi":"10.1109/JSTSP.2020.2987728","isbn":null,"url":null}],"related":["convolutional-neural-network","multimodal-transformer","multimodal-bert-based-classification","transfer-learning-with-convolutional-neural-network","multimodal-recurrent-neural-network","image-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-diffusion-model","name":"Multimodal Diffusion Model","fullName":"Multimodal Diffusion Model (Cross-Modal Conditional Denoising Diffusion)","aliases":["multimodal DDPM","cross-modal diffusion","conditional multimodal diffusion","multi-modal denoising diffusion"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2022","originator":"Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion)","url":"https://scholargate.app/en/deep-learning/multimodal-diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-diffusion-model.md","definition":"A multimodal diffusion model extends denoising diffusion probabilistic models to generate or understand content by conditioning on signals from multiple modalities — such as text, image, audio, or video — simultaneously. It learns to reverse a noise process guided by cross-modal context, enabling high-fidelity synthesis and translation across modalities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion)","year":"2020–2022","type":"Generative model (denoising diffusion)","dataType":"Paired multimodal data (text-image, audio-video, text-audio, etc.)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10684–10695.","type":"inproceedings","doi":"10.1109/CVPR52688.2022.01042","isbn":null,"url":null},{"ref":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html"}],"related":["fine-tuned-diffusion-model","multimodal-vision-transformer","multimodal-gan","multimodal-variational-autoencoder","multimodal-bert-based-classification","multimodal-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-discourse-analysis","name":"Multimodal Discourse Analysis","fullName":"Multimodal Discourse Analysis Method","aliases":["Multimodal Analysis","Semiotic Analysis"],"domain":"linguistics","family":"process-pipeline","subfamily":"Applied Discourse Analysis","year":"1996","originator":"Gunther Kress and Theo Van Leeuwen","url":"https://scholargate.app/en/linguistics/multimodal-discourse-analysis","markdownUrl":"https://scholargate.app/en/linguistics/multimodal-discourse-analysis.md","definition":"Multimodal Discourse Analysis is a method for examining how meaning is created through the integration of multiple modes of communication: language, image, sound, gesture, and spatial arrangement. Developed by Gunther Kress, Theo Van Leeuwen, and others, this approach recognizes that in contemporary communication—from videos to websites to classrooms—meaning is rarely conveyed by language alone. By analyzing how text, visuals, sound, and other modes work together, multimodal analysis reveals how complex meanings are constructed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gunther Kress and Theo Van Leeuwen","subfamily":"Applied Discourse Analysis","year":"1996","type":"Empirical process pipeline"},"citations":[{"ref":"Kress, G., & Van Leeuwen, T. (2006). Reading Images: The Grammar of Visual Design (2nd ed.). London: Routledge.","type":"book","doi":"10.4324/9780203619728","isbn":null,"url":null},{"ref":"Baldry, A., & Thibault, P. J. (2006). Multimodal Transcription and Text Analysis. London: Equinox.","type":"book","doi":null,"isbn":null,"url":"https://www.equinoxpub.com/"},{"ref":"Norris, S. (2004). Analyzing Multimodal Interaction: A Methodological Framework. London: Routledge.","type":"article","doi":"10.4324/9780203379493","isbn":null,"url":null}],"related":["discourse-analysis","linguistic-ethnography","semiotics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-doc2vec","name":"Multimodal Doc2Vec","fullName":"Multimodal Doc2Vec (Paragraph Vector with Multi-Source Input)","aliases":["Multimodal Paragraph Vector","Cross-modal Doc2Vec","Multi-source PV-DM","Multimodal Document Embedding"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2017","originator":"Le, Q. V. & Mikolov, T. (Doc2Vec core); multimodal extensions by various authors post-2014","url":"https://scholargate.app/en/deep-learning/multimodal-doc2vec","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-doc2vec.md","definition":"Multimodal Doc2Vec extends the Doc2Vec paragraph-vector framework to incorporate information from more than one modality — typically text alongside images, audio, or structured metadata — producing a shared document-level embedding that captures semantics from multiple sources simultaneously. It is used for cross-modal retrieval, multi-source classification, and document representation where text alone is insufficient.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Le, Q. V. & Mikolov, T. (Doc2Vec core); multimodal extensions by various authors post-2014","year":"2014–2017","type":"Multimodal document embedding","dataType":"Text paired with images, audio, or other modalities","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), PMLR 32(2), 1188–1196.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v32/le14.html"},{"ref":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal Deep Learning. Proceedings of the 28th International Conference on Machine Learning (ICML), 689–696.","type":"inproceedings","doi":null,"isbn":null,"url":"https://icml.cc/2011/papers/399_icmlpaper.pdf"}],"related":["doc2vec","multimodal-sentence-embeddings","multimodal-bert-based-classification","sentence-embeddings","multimodal-transformer","multimodal-word2vec"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-gan","name":"Multimodal GAN","fullName":"Multimodal Generative Adversarial Network","aliases":["MM-GAN","multimodal generative adversarial network","cross-modal GAN","multi-modal GAN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2016","originator":"Reed et al. (text-to-image GAN); foundation by Goodfellow et al.","url":"https://scholargate.app/en/deep-learning/multimodal-gan","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-gan.md","definition":"A Multimodal GAN is a generative adversarial network conditioned on — or jointly learning across — more than one data modality (e.g., text descriptions, images, audio, or structured data). By fusing information from multiple sources, the generator can synthesize realistic outputs that respect cross-modal constraints, enabling tasks such as text-to-image synthesis, image-to-audio generation, and joint modality imputation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reed et al. (text-to-image GAN); foundation by Goodfellow et al.","year":"2014–2016","type":"Generative adversarial model","dataType":"Multiple modalities (text + image, audio + video, etc.)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016). Generative adversarial text to image synthesis. Proceedings of the 33rd International Conference on Machine Learning (ICML), PMLR 48, 1060–1069.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v48/reed16.html"},{"ref":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems (NeurIPS), 27.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html"}],"related":["generative-adversarial-network","multimodal-transformer","multimodal-variational-autoencoder","conditional-gan","multimodal-diffusion-model","image-generation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-graph-neural-network","name":"Multimodal Graph Neural Network","fullName":"Multimodal Graph Neural Network (MM-GNN)","aliases":["MM-GNN","Multimodal GNN","Multi-modal Graph Network","Cross-modal Graph Neural Network"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020","url":"https://scholargate.app/en/deep-learning/multimodal-graph-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-graph-neural-network.md","definition":"A Multimodal Graph Neural Network (MM-GNN) combines data from multiple modalities — such as text, images, and structured features — into a unified graph structure and applies graph-based message passing to learn joint representations. It enables relational reasoning across heterogeneous data sources, going beyond what unimodal or simple concatenation approaches can capture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020","year":"2019–2020","type":"Graph-based deep learning with multimodal input fusion","dataType":"Heterogeneous multimodal data (text, images, audio, structured features) represented as graph nodes and edges","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1609.02907"},{"ref":"Zhang, Z., Lin, H., & Zhao, X. (2020). Multimodal Graph Neural Network for Knowledge-Based Visual Question Answering. Information Processing & Management, 57(6), 102382.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multimodal+Graph+Neural+Network+for+Knowledge-Based+Visual+Question+Answering+Zhang"}],"related":["graph-neural-network","multimodal-transformer","multimodal-bert-based-classification","multimodal-sentence-embeddings","multimodal-variational-autoencoder","multimodal-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-gru","name":"Multimodal GRU","fullName":"Multimodal Gated Recurrent Unit","aliases":["MM-GRU","Multimodal Gated Recurrent Unit","Cross-modal GRU","Multi-input GRU"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2017","originator":"Cho, K. et al. (GRU); adapted to multimodal settings by multiple research groups","url":"https://scholargate.app/en/deep-learning/multimodal-gru","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-gru.md","definition":"Multimodal GRU extends the Gated Recurrent Unit architecture to jointly process sequential data from multiple input modalities — such as text, audio, and video frames — within a single recurrent framework. By fusing modality-specific encodings at the input or hidden-state level, it captures temporal dependencies across heterogeneous data streams and is widely used in multimodal sentiment analysis, video understanding, and audio-visual speech recognition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cho, K. et al. (GRU); adapted to multimodal settings by multiple research groups","year":"2014–2017","type":"Recurrent neural network (multimodal variant)","dataType":"Sequential multimodal data (text, audio, video, image+text)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1406.1078"},{"ref":"Zadeh, A., Chen, M., Poria, S., Cambria, E., & Morency, L.-P. (2017). Tensor Fusion Network for Multimodal Sentiment Analysis. Proceedings of EMNLP 2017, 1103–1114.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1707.07250"}],"related":["gated-recurrent-unit","multimodal-lstm","multimodal-transformer","multimodal-recurrent-neural-network","multimodal-bert-based-classification","long-short-term-memory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-image-classification","name":"Multimodal Image Classification","fullName":"Multimodal Image Classification (Vision + Auxiliary Modality Fusion)","aliases":["multimodal visual classification","image-text classification","vision-language classification","cross-modal image classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2011–2021","originator":"Ngiam et al.; Radford et al. (CLIP)","url":"https://scholargate.app/en/deep-learning/multimodal-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-image-classification.md","definition":"Multimodal image classification extends standard visual classification by incorporating additional modalities — such as text captions, audio, or structured metadata — alongside image features. Separate encoders process each modality, their representations are fused, and a joint classifier assigns the target label. Models such as CLIP demonstrate that image–text alignment enables zero-shot and few-shot image classification at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ngiam et al.; Radford et al. (CLIP)","year":"2011–2021","type":"Multimodal supervised classification","dataType":"Images + text / audio / tabular metadata","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139, 8748–8763.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v139/radford21a.html"},{"ref":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. Proceedings of the 28th International Conference on Machine Learning (ICML), 689–696.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multimodal+deep+learning+Ngiam+2011"}],"related":["image-classification","multimodal-transformer","multimodal-bert-based-classification","multimodal-sentence-embeddings","multimodal-object-detection","fine-tuned-image-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-instance-segmentation","name":"Multimodal Instance Segmentation","fullName":"Multimodal Instance Segmentation (Multi-sensor Deep Mask Prediction)","aliases":["multimodal Mask R-CNN","RGB-D instance segmentation","multi-sensor instance segmentation","cross-modal instance segmentation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–present","originator":"He, K., Gkioxari, G., Dollar, P., Girshick, R. (Mask R-CNN foundation); extended by community to multimodal settings","url":"https://scholargate.app/en/deep-learning/multimodal-instance-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-instance-segmentation.md","definition":"Multimodal instance segmentation extends classical instance segmentation — which assigns a per-pixel mask and a class label to every individual object in an image — by incorporating complementary sensor streams such as depth maps, LiDAR point clouds, or infrared frames. Fusing these modalities helps the model handle ambiguous appearances, low light, and occlusion that trip up RGB-only systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"He, K., Gkioxari, G., Dollar, P., Girshick, R. (Mask R-CNN foundation); extended by community to multimodal settings","year":"2017–present","type":"Supervised deep learning — instance segmentation","dataType":"Multi-sensor image data (RGB + depth, RGB + LiDAR, RGB + infrared, etc.)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.","type":"inproceedings","doi":"10.1109/ICCV.2017.322","isbn":null,"url":null},{"ref":"Instance segmentation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Image_segmentation"}],"related":["instance-segmentation","semantic-segmentation","multimodal-object-detection","multimodal-vision-transformer","object-detection","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-lda-topic-model","name":"Multimodal LDA topic model","fullName":"Multimodal Latent Dirichlet Allocation Topic Model","aliases":["Multimodal LDA","mm-LDA","multimodal topic model","cross-modal LDA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2003","originator":"Blei, D. M. & Jordan, M. I.","url":"https://scholargate.app/en/deep-learning/multimodal-lda-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-lda-topic-model.md","definition":"Multimodal LDA extends Latent Dirichlet Allocation to jointly model multiple data modalities — most often text and images — within a single probabilistic topic framework. Each document or data instance is represented as a mixture of latent topics shared across modalities, enabling the model to discover coherent themes that align visual and linguistic content simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blei, D. M. & Jordan, M. I.","year":"2003","type":"Probabilistic generative topic model (multimodal)","dataType":"Paired multimodal data (text + images or other modalities)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Blei, D. M. & Jordan, M. I. (2003). Modeling annotated data. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 127–134.","type":"inproceedings","doi":"10.1145/860435.860460","isbn":null,"url":null},{"ref":"Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D. M. & Jordan, M. I. (2003). Matching words and pictures. Journal of Machine Learning Research, 3, 1107–1135.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/barnard03a.html"}],"related":["lda-topic-model","multimodal-topic-modeling","nmf-topic-model","multimodal-bert-based-classification","multimodal-transformer","topic-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-lstm","name":"Multimodal LSTM","fullName":"Multimodal Long Short-Term Memory Network","aliases":["MM-LSTM","multimodal recurrent network","multi-input LSTM","multimodal sequence model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016","originator":"Rajagopalan et al. and various concurrent works (2016–2018)","url":"https://scholargate.app/en/deep-learning/multimodal-lstm","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-lstm.md","definition":"Multimodal LSTM extends the standard Long Short-Term Memory network to jointly process sequential data from multiple input modalities — such as text, audio, and video — within a unified recurrent architecture. By fusing representations from different sources before or within the LSTM cells, it captures temporal dependencies that span and cross modalities, making it a foundational approach for tasks like sentiment analysis, video captioning, and affective computing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rajagopalan et al. and various concurrent works (2016–2018)","year":"2016","type":"Recurrent neural network architecture","dataType":"Sequences of heterogeneous modalities (text, audio, video, image, sensor)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Rajagopalan, S., Tran, L., Rozgic, V., Narayanan, S., Kumar, A., & Ramakrishna, S. (2016). Extending Long Short-Term Memory for Multi-View Structured Learning. In Proceedings of ECCV 2016. Springer.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Extending+Long+Short-Term+Memory+for+Multi-View+Structured+Learning"},{"ref":"Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.","type":"article","doi":"10.1162/neco.1997.9.8.1735","isbn":null,"url":null}],"related":["lstm","gated-recurrent-unit","transformer","attention-mechanism","multimodal-transformer","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-multilayer-perceptron","name":"Multimodal Multilayer Perceptron","fullName":"Multimodal Multilayer Perceptron (MM-MLP)","aliases":["MM-MLP","multimodal MLP","multi-input feedforward network","fusion multilayer perceptron"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2011 (multimodal extension); 1986 (MLP backpropagation)","originator":"Ngiam et al. / Rumelhart, Hinton & Williams (MLP foundations)","url":"https://scholargate.app/en/deep-learning/multimodal-multilayer-perceptron","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-multilayer-perceptron.md","definition":"A Multimodal Multilayer Perceptron (MM-MLP) is a feedforward neural network that ingests features from two or more heterogeneous input modalities — such as structured tabular data, text embeddings, and image feature vectors — by encoding each stream separately and fusing them into a shared representation before passing it through fully connected layers to produce a classification or regression output.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ngiam et al. / Rumelhart, Hinton & Williams (MLP foundations)","year":"2011 (multimodal extension); 1986 (MLP backpropagation)","type":"Feedforward neural network with multi-stream fusion","dataType":"Heterogeneous inputs: tabular, text embeddings, image features, audio features, or any combination","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 689–696.","type":"inproceedings","doi":null,"isbn":null,"url":"https://icml.cc/2011/papers/399_icmlpaper.pdf"},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 6: Deep Feedforward Networks). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["multilayer-perceptron","multimodal-transformer","multimodal-convolutional-neural-network","transfer-learning-with-multilayer-perceptron","multimodal-sentence-embeddings","fine-tuned-multilayer-perceptron"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-named-entity-recognition","name":"Multimodal Named Entity Recognition","fullName":"Multimodal Named Entity Recognition (Text + Visual/Auxiliary Modality NER)","aliases":["Multimodal NER","MNER","Visual NER","Cross-modal Named Entity Recognition"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018","originator":"Moon, S.; Lu, D. et al.","url":"https://scholargate.app/en/deep-learning/multimodal-named-entity-recognition","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-named-entity-recognition.md","definition":"Multimodal Named Entity Recognition (MNER) extends classical NER by fusing textual sequences with complementary modalities — most commonly images — to improve the identification and classification of named entities such as persons, organizations, and locations in settings where visual context disambiguates ambiguous or sparse text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moon, S.; Lu, D. et al.","year":"2018","type":"Sequence labeling with multimodal fusion","dataType":"Text paired with images or other modalities (e.g., social media posts with photos)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Moon, S., Neves, L., & Carvalho, V. (2018). Multimodal Named Entity Recognition for Short Social Media Posts. Proceedings of NAACL-HLT 2018, pp. 852–860. Association for Computational Linguistics.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/N18-1078"},{"ref":"Lu, D., Neves, L., Carvalho, V., Zhang, N., & Ji, H. (2018). Visual Attention Model for Name Tagging in Multimodal Social Media. Proceedings of ACL 2018, pp. 1990–1999. Association for Computational Linguistics.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P18-1185"}],"related":["named-entity-recognition","bert-based-classification","multimodal-transformer","multimodal-bert-based-classification","multimodal-sentence-embeddings","multimodal-question-answering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-nlp","name":"Multimodal NLP","fullName":"Multimodal Natural Language Processing","aliases":["Çok Kipli NLP (Multimodal NLP)","vision-language models","multimodal learning"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2021 (modern era, CLIP onward)","originator":"Radford et al. (OpenAI) — CLIP, 2021; Li et al. — BLIP-2, 2023","url":"https://scholargate.app/en/text-mining/multimodal-nlp","markdownUrl":"https://scholargate.app/en/text-mining/multimodal-nlp.md","definition":"Multimodal NLP is a family of natural-language-processing pipelines that combine text with one or more additional data modalities — most commonly images, but also audio and video — to perform understanding and generation tasks such as visual question answering, image captioning, and multimodal sentiment recognition. The field gained its modern form with CLIP (Radford et al., 2021) and has since advanced through architectures such as BLIP-2 (Li et al., 2023) that bridge frozen image encoders and large language models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Radford et al. (OpenAI) — CLIP, 2021; Li et al. — BLIP-2, 2023","year":"2021 (modern era, CLIP onward)","type":"Cross-modal understanding and generation pipeline","modalities":"Text + image (core); audio, video (extensions)","output":"Labels, captions, answers, or embeddings from joint vision-language representations","minimumSample":20,"difficulty":"High (GPU and large memory required)"},"citations":[{"ref":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), 8748–8763.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v139/radford21a.html"},{"ref":"Li, J., Li, D., Savarese, S., & Hoi, S. (2023). BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. Proceedings of the 40th International Conference on Machine Learning (ICML), 19730–19742.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v202/li23q.html"}],"related":["vision-transformer","bert-embeddings","attention-mechanism","sentiment-analysis","image-captioning"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-nmf-topic-model","name":"Multimodal NMF Topic Model","fullName":"Multimodal Non-negative Matrix Factorization Topic Model","aliases":["Multimodal NMF","Multi-view NMF topic model","Joint NMF topic model","MM-NMF"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010s","originator":"Lee & Seung (NMF); multimodal extensions by various authors (~2010s)","url":"https://scholargate.app/en/deep-learning/multimodal-nmf-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-nmf-topic-model.md","definition":"Multimodal NMF Topic Model extends Non-negative Matrix Factorization to simultaneously discover latent topics across multiple data modalities — such as text and images — by enforcing shared or aligned low-rank factor matrices. It uncovers coherent, interpretable topics that jointly explain patterns in both textual and visual (or other) feature spaces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee & Seung (NMF); multimodal extensions by various authors (~2010s)","year":"2010s","type":"Multimodal topic model (NMF-based)","dataType":"Text, image features, or other multi-view numerical matrices","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Cai, D., He, X., Han, J., & Huang, T. S. (2011). Graph regularized NMF. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8), 1548–1560.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Graph+regularized+NMF+Cai"},{"ref":"Non-negative matrix factorization. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Non-negative_matrix_factorization"}],"related":["latent-dirichlet-allocation","non-negative-matrix-factorization","multimodal-deep-learning","latent-semantic-analysis","probabilistic-topic-model","joint-embedding-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-object-detection","name":"Multimodal Object Detection","fullName":"Multimodal Object Detection (Multi-Sensor / Cross-Modal Deep Detection)","aliases":["multi-sensor object detection","cross-modal detection","RGB-D object detection","fusion-based object detection"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2019","originator":"Multiple contributors (e.g., Chen & Deng, Liang et al.)","url":"https://scholargate.app/en/deep-learning/multimodal-object-detection","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-object-detection.md","definition":"Multimodal object detection extends single-modality object detectors by jointly processing signals from multiple sensor types — such as RGB cameras, depth sensors, LiDAR, radar, or text descriptions — to localize and classify objects with higher accuracy and robustness than any single modality alone. Fusion of complementary information is the core design principle.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (e.g., Chen & Deng, Liang et al.)","year":"2015–2019","type":"Fusion-based deep detection","dataType":"Images, depth maps, LiDAR point clouds, text, audio, radar","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Liu, Y., Zhang, F., Li, Y., & Lv, H. (2022). Multimodal Object Detection via Bayesian Fusion. IEEE Transactions on Image Processing, 31, 5953–5965.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multimodal+Object+Detection+via+Bayesian+Fusion+Liu"},{"ref":"Object detection. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Object_detection"}],"related":["object-detection","multimodal-image-classification","multimodal-semantic-segmentation","multimodal-transformer","image-classification","semantic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-question-answering","name":"Multimodal question answering","fullName":"Multimodal Question Answering (Cross-Modal QA)","aliases":["Multimodal QA","Cross-modal question answering","Visual question answering","VQA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015","originator":"Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)","url":"https://scholargate.app/en/deep-learning/multimodal-question-answering","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-question-answering.md","definition":"Multimodal question answering (Multimodal QA) is a class of deep-learning methods that answer natural-language questions by jointly reasoning over information from multiple modalities — most commonly text and images, but also video, audio, and structured tables. Introduced prominently through the VQA benchmark in 2015, it has since expanded into a broad research area powering document understanding, medical diagnosis assistance, and embodied AI.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)","year":"2015","type":"Supervised multimodal learning","dataType":"Text questions paired with images, video, audio, or structured knowledge","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015). VQA: Visual Question Answering. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2425–2433.","type":"inproceedings","doi":"10.1109/ICCV.2015.279","isbn":null,"url":null},{"ref":"Xu, P., Zhu, X., & Clifton, D. A. (2023). Multimodal learning with transformers: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(10), 12113–12132.","type":"article","doi":"10.1109/TPAMI.2023.3275156","isbn":null,"url":null}],"related":["multimodal-transformer","bert-based-classification","multimodal-sentence-embeddings","multimodal-bert-based-classification","transformer","multimodal-text-summarization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-recurrent-neural-network","name":"Multimodal Recurrent Neural Network","fullName":"Multimodal Recurrent Neural Network (MM-RNN)","aliases":["MM-RNN","multimodal sequence model","cross-modal RNN","multimodal recurrent encoder-decoder"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2011–2015","originator":"Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015)","url":"https://scholargate.app/en/deep-learning/multimodal-recurrent-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-recurrent-neural-network.md","definition":"A Multimodal Recurrent Neural Network combines inputs from two or more data modalities — such as images, text, and audio — within a recurrent sequence-processing framework. It encodes each modality separately, fuses the representations, and then processes the combined signal through recurrent units (RNN, LSTM, or GRU) to generate or classify sequential outputs. This design made it a foundational approach in image captioning, video description, and audio-visual speech recognition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; prominently Ngiam et al. (2011) and Vinyals et al. (2015)","year":"2011–2015","type":"Multimodal sequence model (recurrent)","dataType":"Sequences from multiple modalities (text, image features, audio, video frames)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015). Show and Tell: A Neural Image Caption Generator. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156–3164.","type":"inproceedings","doi":"10.1109/CVPR.2015.7298935","isbn":null,"url":null},{"ref":"Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal Deep Learning. Proceedings of the 28th International Conference on Machine Learning (ICML), pp. 689–696.","type":"inproceedings","doi":null,"isbn":null,"url":"https://icml.cc/2011/papers/399_icmlpaper.pdf"}],"related":["recurrent-neural-network","long-short-term-memory","multimodal-transformer","multimodal-bert-based-classification","gated-recurrent-unit","multimodal-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-reinforcement-learning","name":"Multimodal Reinforcement Learning","fullName":"Multimodal Reinforcement Learning (Multi-Sensory RL Agent Learning)","aliases":["Multimodal RL","Multi-Sensory Reinforcement Learning","Vision-Language RL","Multi-Input RL"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2022","originator":"Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s)","url":"https://scholargate.app/en/deep-learning/multimodal-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-reinforcement-learning.md","definition":"Multimodal Reinforcement Learning trains agents to make sequential decisions by perceiving and integrating multiple input modalities — such as raw pixels, language instructions, audio, and proprioceptive sensors — simultaneously. Rather than acting on a single data stream, the agent fuses heterogeneous signals into a unified state representation and learns a policy through environmental reward feedback.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s)","year":"2015–2022","type":"Multimodal deep RL agent","dataType":"Images, video, text, audio, proprioceptive signals (multiple modalities simultaneously)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., ... & de Freitas, N. (2022). A Generalist Agent. Transactions on Machine Learning Research.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2205.06175"},{"ref":"Multimodal learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Multimodal_learning"}],"related":["reinforcement-learning","multimodal-transformer","multimodal-vision-transformer","self-supervised-reinforcement-learning","transfer-learning-reinforcement-learning","multimodal-graph-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-roberta-based-classification","name":"Multimodal RoBERTa-based Classification","fullName":"Multimodal RoBERTa-based Classification (Text + Non-Text Fusion with RoBERTa Encoder)","aliases":["Multimodal RoBERTa","RoBERTa multimodal classifier","cross-modal RoBERTa classification","MM-RoBERTa"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Liu et al. (RoBERTa); multimodal extension by community","url":"https://scholargate.app/en/deep-learning/multimodal-roberta-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-roberta-based-classification.md","definition":"Multimodal RoBERTa-based Classification combines the RoBERTa transformer encoder — a robustly optimised variant of BERT — with auxiliary modalities such as images, structured metadata, or tabular features. The fused representation is passed to a classification head, allowing the model to leverage both rich language understanding and non-textual signals simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu et al. (RoBERTa); multimodal extension by community","year":"2019–2020","type":"Multimodal text + auxiliary feature classification","dataType":"Text (tokenized sequences) + structured/image/tabular auxiliary features","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.11692"},{"ref":"Kiela, D., Grave, E., Joulin, A., & Mikolov, T. (2018). Efficient Large-Scale Multi-Modal Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Efficient+Large-Scale+Multi-Modal+Classification+Kiela+2018"}],"related":["bert-based-classification","roberta-based-classification","multimodal-transformer","multimodal-bert-based-classification","sentence-embeddings","multimodal-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-semantic-segmentation","name":"Multimodal Semantic Segmentation","fullName":"Multimodal Semantic Segmentation (Multi-Sensor Pixel-Level Scene Understanding)","aliases":["multimodal scene parsing","multi-sensor semantic segmentation","RGB-D semantic segmentation","cross-modal semantic segmentation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2016","originator":"Multiple contributors (Hazirbas et al., Long et al., and others)","url":"https://scholargate.app/en/deep-learning/multimodal-semantic-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-semantic-segmentation.md","definition":"Multimodal semantic segmentation assigns a semantic class label to every pixel in a scene by fusing information from two or more sensor modalities — most commonly RGB images paired with depth maps (RGB-D), LiDAR point clouds, thermal cameras, or text descriptions. Deep encoder-decoder networks learn to align and fuse complementary cues from each modality, producing denser and more accurate segmentation than any single-modality approach.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Hazirbas et al., Long et al., and others)","year":"2014–2016","type":"Pixel-level classification with multi-sensor fusion","dataType":"RGB images paired with depth, LiDAR, thermal, or text modalities","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Hazirbas, C., Ma, L., Domokos, C., & Cremers, D. (2016). FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture. In Proceedings of the Asian Conference on Computer Vision (ACCV). Springer.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=FuseNet+Incorporating+Depth+into+Semantic+Segmentation+via+Fusion-based+CNN+Architecture"},{"ref":"Zhang, J., Liu, H., Yang, K., Hu, X., Liu, R., & Stiefelhagen, R. (2023). CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers. IEEE Transactions on Intelligent Transportation Systems, 24(12), 14801–14813.","type":"article","doi":"10.1109/TITS.2023.3300537","isbn":null,"url":null}],"related":["semantic-segmentation","panoptic-segmentation","instance-segmentation","rgb-d-depth-estimation","vision-transformer","encoder-decoder-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-sentence-embeddings","name":"Multimodal Sentence Embeddings","fullName":"Multimodal Sentence Embeddings (Joint Vision-Language Representation Learning)","aliases":["multimodal embeddings","cross-modal sentence embeddings","vision-language embeddings","joint image-text embeddings"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013–2021","originator":"Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)","url":"https://scholargate.app/en/deep-learning/multimodal-sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-sentence-embeddings.md","definition":"Multimodal sentence embeddings map text and images (and sometimes audio or video) into a shared continuous vector space, so that semantically related pairs from different modalities land close together. Trained by contrastive objectives on large paired corpora, these representations power cross-modal retrieval, zero-shot classification, and vision-language reasoning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)","year":"2013–2021","type":"Representation learning model","dataType":"Paired image-text data; text corpora; image datasets","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 8748–8763. PMLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v139/radford21a.html"},{"ref":"Frome, A., Corrado, G. S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., & Mikolov, T. (2013). DeViSE: A deep visual-semantic embedding model. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 26.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=DeViSE+A+deep+visual-semantic+embedding+model"}],"related":["clip","contrastive-learning","sentence-transformers","image-captioning","visual-question-answering","cross-modal-retrieval"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-text-summarization","name":"Multimodal Text Summarization","fullName":"Multimodal Text Summarization (Cross-Modal Abstractive and Extractive Summarization)","aliases":["MMS","multimodal summarization","cross-modal summarization","vision-language summarization"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018","originator":"Zhu et al. (pioneering MSMO framework)","url":"https://scholargate.app/en/deep-learning/multimodal-text-summarization","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-text-summarization.md","definition":"Multimodal text summarization generates a concise textual summary by jointly processing multiple input modalities — most commonly text and images, but also video frames or audio — using deep learning models that align visual and linguistic representations. The output is a natural-language summary that captures salient content from all available modalities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhu et al. (pioneering MSMO framework)","year":"2018","type":"Generative / extractive NLP with visual input","dataType":"Text + images (and optionally audio/video)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Zhu, J., Li, H., Liu, T., Zhou, Y., Zhang, J., & Zong, C. (2018). MSMO: Multimodal Summarization with Multimodal Output. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4154–4164.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D18-1448"},{"ref":"Zhu, J., Zhou, Y., Zhang, J., Li, H., Zong, C., & Li, C. (2020). Multimodal Summarization with Guidance of Multimodal Reference. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 9749–9756.","type":"inproceedings","doi":null,"isbn":null,"url":"https://ojs.aaai.org/index.php/AAAI/article/view/6525"}],"related":["multimodal-transformer","multimodal-bert-based-classification","fine-tuned-text-summarization","multimodal-question-answering","transformer","bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-topic-modeling","name":"Multimodal Topic Modeling","fullName":"Multimodal Topic Modeling (Joint Probabilistic Topic Discovery across Multiple Modalities)","aliases":["Multimodal LDA","multi-modal topic model","cross-modal topic modeling","MM-TM"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2003–present","originator":"Blei, D. M. & Jordan, M. I. (foundational corr-LDA); extended by many authors","url":"https://scholargate.app/en/deep-learning/multimodal-topic-modeling","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-topic-modeling.md","definition":"Multimodal topic modeling discovers latent thematic structure shared across multiple data modalities — for example, co-occurring words and images — by learning a joint probabilistic representation that aligns topics across modalities. It extends classical text-only approaches such as LDA to settings where each document or observation consists of heterogeneous data types.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blei, D. M. & Jordan, M. I. (foundational corr-LDA); extended by many authors","year":"2003–present","type":"Generative probabilistic topic model","dataType":"Text paired with images, audio, video, or other modalities","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Blei, D. M., & Jordan, M. I. (2003). Modeling annotated data. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 127–134.","type":"article","doi":"10.1145/860435.860460","isbn":null,"url":null},{"ref":"Ramage, D., Dumais, S., & Liebling, D. (2010). Characterizing microblogs with topic models. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 130–137.","type":"inproceedings","doi":null,"isbn":null,"url":"https://ojs.aaai.org/index.php/ICWSM/article/view/14039"}],"related":["lda-topic-model","nmf-topic-model","multimodal-bert-based-classification","multimodal-sentence-embeddings","multimodal-transformer","topic-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-transformer","name":"Multimodal Transformer","fullName":"Multimodal Transformer (Cross-Modal Attention-Based Architecture)","aliases":["multimodal attention model","cross-modal transformer","vision-language transformer","multi-modal fusion transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2021","originator":"Lu et al. (ViLBERT); Radford et al. (CLIP)","url":"https://scholargate.app/en/deep-learning/multimodal-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-transformer.md","definition":"A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lu et al. (ViLBERT); Radford et al. (CLIP)","year":"2019–2021","type":"Cross-modal attention-based deep learning model","dataType":"Paired or unpaired multimodal data (text, image, audio, video)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1908.02265"},{"ref":"Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2103.00020"}],"related":["transformer","bert-based-classification","vision-transformer","multimodal-bert-based-classification","sentence-embeddings","image-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-variational-autoencoder","name":"Multimodal Variational Autoencoder","fullName":"Multimodal Variational Autoencoder (MVAE)","aliases":["MVAE","multimodal VAE","multi-modal variational autoencoder","multimodal generative model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018","originator":"Wu, M. and Goodman, N.","url":"https://scholargate.app/en/deep-learning/multimodal-variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-variational-autoencoder.md","definition":"The Multimodal Variational Autoencoder (MVAE) is a deep generative model that learns a shared latent representation across two or more data modalities — such as images and captions — using a product-of-experts fusion of modality-specific encoders, enabling generation and inference even when only a subset of modalities is observed at test time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wu, M. and Goodman, N.","year":"2018","type":"Generative latent-variable model","dataType":"Multiple modalities (image, text, audio, etc.)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1802.05335"},{"ref":"Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"}],"related":["variational-autoencoder","conditional-vae","generative-adversarial-network","multimodal-learning","product-of-experts","mixture-of-experts"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-vision-transformer","name":"Multimodal Vision Transformer","fullName":"Multimodal Vision Transformer (Multimodal ViT)","aliases":["Multimodal ViT","vision-language transformer","cross-modal vision transformer","multi-modal ViT"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2021","originator":"Dosovitskiy et al. (ViT); Radford et al. (CLIP multimodal ViT)","url":"https://scholargate.app/en/deep-learning/multimodal-vision-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-vision-transformer.md","definition":"Multimodal Vision Transformer (Multimodal ViT) extends the Vision Transformer architecture to jointly process and align representations from multiple modalities — typically images and text — using self-attention and cross-attention mechanisms. By learning shared or aligned embedding spaces across modalities, it enables tasks such as visual question answering, image-text retrieval, visual grounding, and image captioning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dosovitskiy et al. (ViT); Radford et al. (CLIP multimodal ViT)","year":"2021","type":"Multimodal transformer model","dataType":"Images + text (and optionally audio or other modalities)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2010.11929"},{"ref":"Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2103.00020"}],"related":["vision-transformer","transformer","bert-based-classification","multimodal-bert-based-classification","image-classification","fine-tuned-vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimodal-word2vec","name":"Multimodal Word2Vec","fullName":"Multimodal Word2Vec (Cross-Modal Distributional Semantics)","aliases":["multimodal word embeddings","visual-linguistic Word2Vec","cross-modal Word2Vec","MM-W2V"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014","originator":"Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.)","url":"https://scholargate.app/en/deep-learning/multimodal-word2vec","markdownUrl":"https://scholargate.app/en/deep-learning/multimodal-word2vec.md","definition":"Multimodal Word2Vec extends the classic Word2Vec framework by grounding word representations in perceptual signals — typically image features — alongside distributional text statistics. The result is word vectors that capture both linguistic co-occurrence patterns and visual meaning, enabling richer semantic similarity judgements and better performance on concept-level tasks where purely text-based embeddings fall short.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.)","year":"2014","type":"Multimodal word embedding model","dataType":"Text corpora + image feature vectors (or other modality features)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47.","type":"article","doi":"10.1613/jair.4135","isbn":null,"url":null},{"ref":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems (NIPS), 26.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html"}],"related":["sentence-embeddings","multimodal-transformer","multimodal-bert-based-classification","multimodal-sentence-embeddings","multimodal-doc2vec","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multimoora","name":"MULTIMOORA","fullName":"Multi-Objective Optimisation by Ratio Analysis plus Full Multiplicative Form","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Brauers, W. K. M., Zavadskas, E. K.","url":"https://scholargate.app/en/decision-making/multimoora","markdownUrl":"https://scholargate.app/en/decision-making/multimoora.md","definition":"MULTIMOORA (Multi-Objective Optimisation by Ratio Analysis plus Full Multiplicative Form) is a ranking multi-criteria decision-making (MCDM) method introduced by Brauers, W. K. M., Zavadskas, E. K. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brauers, W. K. M., Zavadskas, E. K.","subfamily":"Ranking","year":"2010","type":"Dominance aggregation of three sub-rankings (RS + RP + FMF)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Brauers, W. K. M., Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Project+management+by+MULTIMOORA+as+an+instrument+for+transition+economies+Brauers"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multinomial-logistic-regression","name":"Multinomial Logistic Regression","fullName":"Multinomial Logistic Regression","aliases":["polytomous logistic regression","softmax regression","multinomial logit","nominal logistic regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1966–1974","originator":"Cox (1966); Theil (1969); formalized by McFadden (1974)","url":"https://scholargate.app/en/statistics/multinomial-logistic-regression","markdownUrl":"https://scholargate.app/en/statistics/multinomial-logistic-regression.md","definition":"Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cox (1966); Theil (1969); formalized by McFadden (1974)","year":"1966–1974","type":"Generalized linear model","dataType":"Nominal categorical outcome (3+ unordered classes), continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471360933","url":null},{"ref":"Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470582473","url":null}],"related":["ordinal-logistic-regression","logistic-regression","binary-logistic-regression","discriminant-analysis","random-forest","naive-bayes-classifier"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multinomial-logit","name":"Multinomial Logit","fullName":"Multinomial Logistic Regression","aliases":["multinomial logistic regression","polytomous logistic regression","softmax regression","Çok Kategorili Lojistik Regresyon"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1974,"originator":"McFadden","url":"https://scholargate.app/en/econometrics/multinomial-logit","markdownUrl":"https://scholargate.app/en/econometrics/multinomial-logit.md","definition":"Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McFadden","year":1974,"type":"Multinomial logistic regression","estimator":"Maximum likelihood","outcome":"nominal (>2 unordered categories)","minSample":100,"keyAssumption":"Independence of irrelevant alternatives (IIA)"},"citations":[{"ref":"McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press.","type":"chapter","doi":null,"isbn":"978-0127761503","url":null}],"related":["logistic-regression","ordered-logit","negative-binomial-regression","poisson-regression","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiphase-mixed-methods-design","name":"Multiphase Mixed Methods Design","fullName":"Multiphase Mixed Methods Research Design","aliases":["multiphase design","multiproject mixed methods","programmatic mixed methods","multistage mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007 (first edition of Designing and Conducting Mixed Methods Research)","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/multiphase-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/multiphase-mixed-methods-design.md","definition":"The multiphase mixed methods design is a sustained research program in which quantitative and qualitative strands are combined across three or more sequential phases — or across multiple related projects — to address a central program objective. Each phase builds on the prior phase's findings, making the design well-suited to long-term evaluation, intervention development, and large-scale program assessment where a single data-collection cycle cannot fully address the complexity of the research problem.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2007 (first edition of Designing and Conducting Mixed Methods Research)","type":"Mixed methods research design","dataType":"Quantitative data, qualitative data, or both — collected and integrated across multiple sequential phases or projects","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483substitute","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412972092","url":null}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","transformative-mixed-methods-design","multilevel-mixed-methods-design","intervention-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-baseline-design","name":"Multiple Baseline Design","fullName":"Multiple Baseline Single-Subject Experimental Design","aliases":["MBD","multiple-baseline single-case design","staggered baseline design","multiple-probe design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968","originator":"Donald M. Baer, Montrose M. Wolf, Todd R. Risley","url":"https://scholargate.app/en/experimental-design/multiple-baseline-design","markdownUrl":"https://scholargate.app/en/experimental-design/multiple-baseline-design.md","definition":"The multiple baseline design is a single-subject experimental design that demonstrates functional control by introducing an intervention at staggered time points across two or more baselines — typically across different behaviors, individuals, or settings. Because no withdrawal of treatment is required, it is especially suitable when the target behavior is irreversible or when removing an effective intervention would be unethical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald M. Baer, Montrose M. Wolf, Todd R. Risley","year":"1968","type":"Single-subject experimental design","dataType":"Repeated measures of a target behavior over time (continuous observation data)","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied Behavior Analysis (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134752556","url":null}],"related":["ab-design","aba-design","abab-design","single-subject-experimental-design","changing-criterion-design","alternating-treatments-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-autoethnography","name":"Multiple case-based autoethnography","fullName":"Multiple Case-Based Autoethnographic Research","aliases":["collective autoethnography","multi-case autoethnography","collaborative autoethnography","multi-site autoethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Heewon Chang, Faith Ngunjiri, Kathy-Ann Hernandez (collaborative autoethnography); broader tradition from Carolyn Ellis and Arthur Bochner","url":"https://scholargate.app/en/qualitative/multiple-case-based-autoethnography","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-autoethnography.md","definition":"Multiple case-based autoethnography is a qualitative design that extends autoethnographic inquiry across two or more researcher-participants or cases, enabling systematic comparison of personal lived experiences within a shared cultural or social phenomenon. By generating rich first-person narratives from each case and then conducting a structured cross-case analysis, the approach combines the depth and reflexivity of autoethnography with the comparative analytical power of multiple case design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heewon Chang, Faith Ngunjiri, Kathy-Ann Hernandez (collaborative autoethnography); broader tradition from Carolyn Ellis and Arthur Bochner","year":"2000s–2010s","type":"Qualitative research design variant","dataType":"Personal narratives, field notes, reflective journals, interviews across multiple researcher-participants","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Chang, H., Ngunjiri, F. W., & Hernandez, K. A. C. (2013). Collaborative Autoethnography. Left Coast Press.","type":"book","doi":null,"isbn":"978-1611321104","url":null},{"ref":"Ellis, C. (2004). The Ethnographic I: A Methodological Novel about Autoethnography. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759103726","url":null}],"related":["autoethnography","collective-autoethnography","multiple-case-study","narrative-inquiry","participatory-autoethnography","comparative-autoethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-biographical-research","name":"Multiple case-based biographical research","fullName":"Multiple Case-Based Biographical Research","aliases":["multi-case biographical study","cross-case biography","comparative biographical case study","multiple biographical case analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (cross-case biographical designs consolidated in qualitative methodology literature)","originator":"Synthesised from Robert K. Yin (multiple case logic) and biographical research traditions (Roberts, Chamberlayne, Bornat)","url":"https://scholargate.app/en/qualitative/multiple-case-based-biographical-research","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-biographical-research.md","definition":"Multiple case-based biographical research combines the cross-case replication logic of multiple case study design with the in-depth life-history orientation of biographical research. Each individual biography is treated as a bounded case, examined first on its own terms, and then analysed comparatively across cases to identify patterns, contrasts, and transferable insights about how lives are shaped by social, historical, and structural forces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Robert K. Yin (multiple case logic) and biographical research traditions (Roberts, Chamberlayne, Bornat)","year":"1990s–2000s (cross-case biographical designs consolidated in qualitative methodology literature)","type":"Qualitative research design","dataType":"In-depth biographical interviews, life-history narratives, personal documents, diaries","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Roberts, B. (2002). Biographical Research. Open University Press.","type":"book","doi":null,"isbn":"978-0335200788","url":null}],"related":["biographical-research","multiple-case-study","life-history-research","comparative-biographical-research","narrative-inquiry","longitudinal-biographical-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-case-study","name":"Multiple case-based case study","fullName":"Multiple Case Study Design","aliases":["multiple-case design","collective case study","multi-site case study","multi-case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984 (Yin); 2006 (Stake's collective case study formalization)","originator":"Robert K. Yin; Robert E. Stake (parallel traditions)","url":"https://scholargate.app/en/qualitative/multiple-case-based-case-study","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-case-study.md","definition":"A multiple case study (also called a multiple-case design or collective case study) is a qualitative research design in which two or more bounded cases are examined together to pursue a common research question. By studying several instances of a phenomenon in parallel, the researcher can compare patterns, identify convergences and divergences, and build more robust, transferable conclusions than a single case could support. The design draws principally from Robert Yin's case-study methodology and Robert Stake's collective case study tradition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin; Robert E. Stake (parallel traditions)","year":"1984 (Yin); 2006 (Stake's collective case study formalization)","type":"Qualitative research design","dataType":"Documents, interviews, observations, archival records (across multiple sites/cases)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null}],"related":["single-case-study","comparative-case-study","grounded-theory","ethnography","cross-case-analysis","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-classic-grounded-theory","name":"Multiple Case-Based Classic Grounded Theory","fullName":"Multiple Case-Based Classic Grounded Theory","aliases":["multi-case CGT","classic GT with multiple cases","comparative grounded theory","Glaserian multi-case grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (foundational); multi-case adaptation developed through 1970s–1990s","originator":"Barney G. Glaser and Anselm L. Strauss (foundational); Glaser extended for comparative multi-case contexts","url":"https://scholargate.app/en/qualitative/multiple-case-based-classic-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-classic-grounded-theory.md","definition":"Multiple case-based classic grounded theory (CGT) extends Glaser and Strauss's original inductive framework by grounding theory development simultaneously across two or more purposefully selected cases. Rather than studying a single site or participant group, the researcher treats each case as a distinct analytic unit while using the constant comparative method to draw cross-case theoretical insights. The goal is the same as in all classic GT: emergence of a substantive theory that explains the main concern of participants — but the multi-case structure broadens the conceptual base and supports more robust theoretical abstraction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser and Anselm L. Strauss (foundational); Glaser extended for comparative multi-case contexts","year":"1967 (foundational); multi-case adaptation developed through 1970s–1990s","type":"Qualitative inductive theory-generation design","dataType":"Interviews, field notes, documents, and observations across multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null},{"ref":"Glaser, B. G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theoretical+Sensitivity+Advances+in+the+Methodology+of+Grounded+Theory+Glaser+1978"}],"related":["grounded-theory","constructivist-grounded-theory","case-study","comparative-case-study","constant-comparative-method","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-constructivist-grounded-theory","name":"Multiple Case-Based Constructivist Grounded Theory","fullName":"Multiple Case-Based Constructivist Grounded Theory","aliases":["multi-case CGT","constructivist grounded theory with multiple cases","multiple-site constructivist GT","CGT multiple case design"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (Charmaz's CGT); multi-case applications prominent from 2010s onward","originator":"Kathy Charmaz (constructivist grounded theory); multi-case extension developed through methodological elaboration by Charmaz and subsequent scholars","url":"https://scholargate.app/en/qualitative/multiple-case-based-constructivist-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-constructivist-grounded-theory.md","definition":"Multiple case-based constructivist grounded theory (CGT) combines Kathy Charmaz's constructivist grounded theory framework with a deliberate multi-case design. The researcher collects and analyzes data from two or more purposively selected cases simultaneously, applying iterative coding, constant comparison, and theoretical sampling across cases to build a grounded theory that accounts for both within-case depth and cross-case variation. The resulting theory is understood as jointly constructed by researcher and participants rather than objectively discovered.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathy Charmaz (constructivist grounded theory); multi-case extension developed through methodological elaboration by Charmaz and subsequent scholars","year":"2006 (Charmaz's CGT); multi-case applications prominent from 2010s onward","type":"Qualitative research design and analytic approach","dataType":"Interviews, observations, documents, field notes across multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973522","url":null},{"ref":"Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0857029140","url":null}],"related":["grounded-theory","constructivist-grounded-theory","case-study","comparative-case-study","thematic-analysis","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-conversation-analysis","name":"Multiple case-based conversation analysis","fullName":"Multiple Case-Based Conversation Analysis","aliases":["multi-case CA","cross-case conversation analysis","comparative conversation analysis","multiple-instance CA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"CA founded ~1960s–1970s; multi-case extension adopted from late 1990s onward","originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA); multiple-case design from Robert Yin","url":"https://scholargate.app/en/qualitative/multiple-case-based-conversation-analysis","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-conversation-analysis.md","definition":"Multiple case-based conversation analysis applies the fine-grained sequential methods of Conversation Analysis (CA) across two or more distinct cases — settings, groups, or interactions — to identify both case-specific patterns and cross-case regularities in naturally occurring talk. By examining how participants organise turn-taking, repair, and action sequences in multiple contexts, the approach strengthens claims about interactional phenomena beyond what a single-case study can establish.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA); multiple-case design from Robert Yin","year":"CA founded ~1960s–1970s; multi-case extension adopted from late 1990s onward","type":"Qualitative multi-case analytic design","dataType":"Audio or video recordings of naturally occurring talk, transcribed with CA notation","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.","type":"article","doi":"10.2307/412243","isbn":null,"url":null},{"ref":"ten Have, P. (2007). Doing Conversation Analysis: A Practical Guide (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412922579","url":null}],"related":["conversation-analysis","discourse-analysis","multiple-case-study","comparative-conversation-analysis","longitudinal-conversation-analysis","critical-discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-critical-discourse-analysis","name":"Multiple case-based critical discourse analysis","fullName":"Multiple Case-Based Critical Discourse Analysis","aliases":["multi-case CDA","comparative critical discourse analysis","cross-case CDA","multiple case CDA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (CDA foundations ~1989–1995; multiple case integration in applied discourse research)","originator":"Norman Fairclough (CDA); Robert K. Yin (multiple case design)","url":"https://scholargate.app/en/qualitative/multiple-case-based-critical-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-critical-discourse-analysis.md","definition":"Multiple case-based critical discourse analysis (multi-case CDA) combines the comparative logic of multiple case study design with the ideological and power-focused analytic apparatus of critical discourse analysis. The researcher selects two or more purposefully chosen cases, collects relevant texts or spoken discourse within each, applies CDA to reveal how language constructs power relations and ideologies within each case, and then synthesises findings across cases to identify patterns, contrasts, and broader sociocritical insights that a single-case design could not yield.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Fairclough (CDA); Robert K. Yin (multiple case design)","year":"1990s–2000s (CDA foundations ~1989–1995; multiple case integration in applied discourse research)","type":"Qualitative research design and analytic method","dataType":"Texts, documents, transcripts, media artifacts across multiple cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Fairclough, N. (1995). Critical Discourse Analysis: The Critical Study of Language. Longman.","type":"book","doi":null,"isbn":"978-0582219526","url":null},{"ref":"Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452242569","url":null}],"related":["critical-discourse-analysis","multiple-case-study","comparative-critical-discourse-analysis","discourse-analysis","longitudinal-critical-discourse-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-digital-ethnography","name":"Multiple case-based digital ethnography","fullName":"Multiple Case-Based Digital Ethnography","aliases":["multi-case digital ethnography","comparative digital ethnography","cross-case digital ethnography","multi-site digital ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Christine Hine (virtual ethnography); Sarah Pink et al. (digital ethnography); cross-case logic from Robert Yin","url":"https://scholargate.app/en/qualitative/multiple-case-based-digital-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-digital-ethnography.md","definition":"Multiple case-based digital ethnography is a qualitative research design that conducts ethnographic fieldwork across two or more purposefully selected digital sites or communities, then systematically compares findings across cases. Rooted in digital ethnography's immersive, interpretive tradition and in multiple case study logic, it reveals both site-specific practices and cross-cutting patterns in online social life. It is especially suited to questions about how similar phenomena are enacted differently across digital platforms, communities, or cultural contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christine Hine (virtual ethnography); Sarah Pink et al. (digital ethnography); cross-case logic from Robert Yin","year":"2000s–2010s","type":"Qualitative comparative research design","dataType":"Online field observations, digital artifacts, chat logs, social media content, interviews","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Hine, C. (2000). Virtual Ethnography. Sage.","type":"book","doi":null,"isbn":"978-0761958956","url":null},{"ref":"Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital Ethnography: Principles and Practice. Sage.","type":"book","doi":null,"isbn":"978-1446275863","url":null}],"related":["digital-ethnography","comparative-digital-ethnography","multiple-case-study","netnography","comparative-ethnography","digital-discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-discourse-analysis","name":"Multiple Case-Based Discourse Analysis","fullName":"Multiple Case-Based Discourse Analysis","aliases":["multi-case discourse analysis","comparative discourse analysis","cross-case discourse analysis","MCDA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (integration formalized in qualitative methodology literature)","originator":"Synthesized from Yin's multiple case study design and discourse analysis traditions (van Dijk, Fairclough)","url":"https://scholargate.app/en/qualitative/multiple-case-based-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-discourse-analysis.md","definition":"Multiple case-based discourse analysis is a qualitative research design that applies systematic discourse analysis within each of two or more purposively selected cases, then compares the discursive patterns, themes, and power relations across those cases. It combines the replication logic of Yin's multiple case study methodology with the text- and talk-centred analytical tools of discourse analysis traditions such as critical discourse analysis or conversation analysis, enabling researchers to build comparative, theoretically grounded accounts of how language constructs social reality across different contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Yin's multiple case study design and discourse analysis traditions (van Dijk, Fairclough)","year":"1990s–2000s (integration formalized in qualitative methodology literature)","type":"Comparative qualitative research design","dataType":"Textual and verbal data (interviews, documents, media texts, institutional records) across multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"van Dijk, T. A. (Ed.). (1997). Discourse as Social Interaction. Sage.","type":"book","doi":null,"isbn":"978-0803978942","url":null}],"related":["discourse-analysis","case-study","critical-discourse-analysis","comparative-qualitative-analysis","narrative-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-ethnography","name":"Multiple case-based ethnography","fullName":"Multiple Case-Based Ethnographic Research","aliases":["multi-site ethnography","comparative ethnography","multi-case ethnographic design","cross-case ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Robert E. Stake (multiple case study logic); George E. Marcus (multi-sited ethnography)","url":"https://scholargate.app/en/qualitative/multiple-case-based-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-ethnography.md","definition":"Multiple case-based ethnography is a qualitative research design that applies sustained ethnographic fieldwork across two or more purposefully selected cases or sites and then compares the resulting thick descriptions to identify patterns, contrasts, and theoretical insights that would be invisible in a single-site study. It combines the contextual depth of ethnography with the comparative logic of multiple case study analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert E. Stake (multiple case study logic); George E. Marcus (multi-sited ethnography)","year":"1990s–2000s","type":"Qualitative comparative research design","dataType":"Fieldwork observations, interviews, documents, artefacts across multiple sites or cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null},{"ref":"Marcus, G. E. (1995). Ethnography in/of the world system: The emergence of multi-sited ethnography. Annual Review of Anthropology, 24, 95–117.","type":"article","doi":"10.1146/annurev.an.24.100195.000523","isbn":null,"url":null}],"related":["case-study","ethnography","comparative-case-study","multi-sited-ethnography","thematic-analysis","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-grounded-theory","name":"Multiple Case-Based Grounded Theory","fullName":"Multiple Case-Based Grounded Theory","aliases":["multi-case grounded theory","MCGT","comparative case grounded theory","cross-case grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s (integrative development)","originator":"Synthesised from Kathleen Eisenhardt (multiple-case logic) and Barney Glaser & Anselm Strauss (grounded theory)","url":"https://scholargate.app/en/qualitative/multiple-case-based-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-grounded-theory.md","definition":"Multiple case-based grounded theory is a qualitative research design that embeds grounded theory's inductive coding logic inside a structured multiple-case framework. Rather than generating theory from a single site or interview pool, researchers iteratively collect and analyze data across two or more purposefully selected cases, using constant comparison both within and across cases until theoretical saturation is reached. The result is a substantive theory grounded in rich, cross-site empirical evidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Kathleen Eisenhardt (multiple-case logic) and Barney Glaser & Anselm Strauss (grounded theory)","year":"1980s–1990s (integrative development)","type":"Qualitative research design combining case study and grounded theory","dataType":"Interviews, documents, observations across multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.","type":"article","doi":"10.5465/amr.1989.4308385","isbn":null,"url":null},{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null}],"related":["case-study","grounded-theory","comparative-case-study","thematic-analysis","narrative-analysis","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-hermeneutic-phenomenology","name":"Multiple Case-Based Hermeneutic Phenomenology","fullName":"Multiple Case-Based Hermeneutic Phenomenological Research","aliases":["multi-case hermeneutic phenomenology","cross-case hermeneutic phenomenology","interpretive multi-case phenomenology","MCBHP"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (synthesis of traditions)","originator":"Max van Manen (hermeneutic phenomenology); Robert Yin (multiple-case logic)","url":"https://scholargate.app/en/qualitative/multiple-case-based-hermeneutic-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-hermeneutic-phenomenology.md","definition":"Multiple case-based hermeneutic phenomenology combines the interpretive depth of van Manen's hermeneutic phenomenology with the structured cross-case logic of multiple-case study design. Each case — a bounded individual, group, or site — is analysed for the lived meaning of a shared phenomenon; findings are then compared across cases to reveal both unique contextual textures and common hermeneutic themes. The approach is favoured when context shapes experience in ways that a single case cannot fully illuminate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max van Manen (hermeneutic phenomenology); Robert Yin (multiple-case logic)","year":"1990s–2000s (synthesis of traditions)","type":"Qualitative research design","dataType":"In-depth interviews, field notes, documents across multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null},{"ref":"Lopez, K. A., & Willis, D. G. (2004). Descriptive versus interpretive phenomenology: Their contributions to nursing knowledge. Qualitative Health Research, 14(5), 726–735.","type":"article","doi":"10.1177/1049732304263638","isbn":null,"url":null}],"related":["hermeneutic-phenomenology","phenomenology","multiple-case-study","interpretive-phenomenological-analysis","narrative-inquiry","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-institutional-ethnography","name":"Multiple case-based institutional ethnography","fullName":"Multiple Case-Based Institutional Ethnography","aliases":["multi-site institutional ethnography","comparative institutional ethnography","multi-case IE","multiple-site IE"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1987 (IE foundation); multi-case application developed through 1990s–2000s","originator":"Dorothy E. Smith (institutional ethnography); multi-site adaptation by IE practitioners","url":"https://scholargate.app/en/qualitative/multiple-case-based-institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-institutional-ethnography.md","definition":"Multiple case-based institutional ethnography combines Dorothy E. Smith's institutional ethnography with a multi-site case structure, enabling researchers to trace how the same ruling relations, texts, and institutional processes operate across two or more distinct organizational or community settings. By holding the analytical framework constant while varying the site, this design reveals both the trans-local reach of ruling apparatus and the locally specific ways people navigate institutional coordination.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith (institutional ethnography); multi-site adaptation by IE practitioners","year":"1987 (IE foundation); multi-case application developed through 1990s–2000s","type":"Qualitative multi-site research design","dataType":"Interviews, texts/documents, observations across multiple institutional settings","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759105690","url":null},{"ref":"DeVault, M. L., & McCoy, L. (2006). Institutional ethnography: Using interviews to investigate ruling relations. In D. E. Smith (Ed.), Institutional Ethnography as Practice (pp. 15–44). Rowman & Littlefield.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Institutional+ethnography+using+interviews+to+investigate+ruling+relations+DeVault+McCoy"}],"related":["institutional-ethnography","comparative-ethnography","multiple-case-study","critical-institutional-ethnography","participatory-institutional-ethnography","longitudinal-institutional-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-interpretive-phenomenological-analysis","name":"Multiple case-based interpretive phenomenological analysis","fullName":"Multiple Case-Based Interpretive Phenomenological Analysis","aliases":["multi-case IPA","multiple-case IPA","cross-case interpretive phenomenological analysis","IPA multiple case design"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1996 (IPA); multi-case design consolidated circa 2009","originator":"Jonathan A. Smith (IPA); multi-case extension elaborated by Smith, Flowers & Larkin","url":"https://scholargate.app/en/qualitative/multiple-case-based-interpretive-phenomenological-analysis","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-interpretive-phenomenological-analysis.md","definition":"Multiple case-based interpretive phenomenological analysis (multi-case IPA) applies the close, idiographic reading of IPA to a set of purposively selected cases, conducting detailed within-case analysis before systematically comparing themes across cases. The approach retains IPA's commitment to understanding individual lived experience in depth while allowing the researcher to identify convergent and divergent patterns across structurally similar situations or participant groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan A. Smith (IPA); multi-case extension elaborated by Smith, Flowers & Larkin","year":"1996 (IPA); multi-case design consolidated circa 2009","type":"Qualitative interpretive research design","dataType":"In-depth interview transcripts; personal accounts across multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretive Phenomenological Analysis: Theory, Method and Research. Sage.","type":"book","doi":null,"isbn":"978-1412908344","url":null},{"ref":"Smith, J. A. (1996). Beyond the divide between cognition and discourse: Using interpretive phenomenological analysis in health psychology. Psychology and Health, 11(2), 261–271.","type":"article","doi":"10.1080/08870449608400256","isbn":null,"url":null}],"related":["interpretive-phenomenological-analysis","multiple-case-study","phenomenology","hermeneutic-phenomenology","comparative-case-study","longitudinal-interpretive-phenomenological-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-life-history-research","name":"Multiple case-based life history research","fullName":"Multiple Case-Based Life History Research","aliases":["multi-case life history","comparative life history","multiple life history study","cross-case life history"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–2000s (life history tradition; multiple-case extension)","originator":"Ivor Goodson; Robert Stake (multiple-case framing)","url":"https://scholargate.app/en/qualitative/multiple-case-based-life-history-research","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-life-history-research.md","definition":"Multiple case-based life history research is a qualitative design that collects full biographical accounts from several purposively selected individuals and then compares those life histories across cases to identify shared patterns, divergences, and contextual influences. By treating each person's life story as one analytic case, the approach blends the depth of life history methodology with the comparative rigor of multiple case study logic, producing findings that are both individually rich and cross-case meaningful.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ivor Goodson; Robert Stake (multiple-case framing)","year":"1980s–2000s (life history tradition; multiple-case extension)","type":"Qualitative comparative biographical design","dataType":"In-depth biographical interviews, personal documents, field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Goodson, I. F., & Sikes, P. (2001). Life History Research in Educational Settings: Learning from Lives. Open University Press.","type":"book","doi":null,"isbn":"978-0335206124","url":null},{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null}],"related":["life-history-research","narrative-inquiry","case-study","biographical-research","oral-history","cross-case-synthesis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-metaphor-analysis","name":"Multiple Case-Based Metaphor Analysis","fullName":"Multiple Case-Based Metaphor Analysis","aliases":["cross-case metaphor analysis","comparative metaphor analysis","multi-case metaphor study","MCBMA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–2000s (synthesis emerged in qualitative case research)","originator":"Building on Lakoff & Johnson (1980) conceptual metaphor theory and Yin's multiple-case logic","url":"https://scholargate.app/en/qualitative/multiple-case-based-metaphor-analysis","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-metaphor-analysis.md","definition":"Multiple case-based metaphor analysis is a qualitative comparative method that systematically identifies and interprets metaphorical language across two or more bounded cases — such as schools, organisations, or participant groups — to reveal how people in different contexts conceptualise a shared phenomenon. It integrates Lakoff and Johnson's conceptual metaphor theory with Yin's multiple-case logic, enabling both within-case depth and cross-case breadth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Lakoff & Johnson (1980) conceptual metaphor theory and Yin's multiple-case logic","year":"1980s–2000s (synthesis emerged in qualitative case research)","type":"Qualitative comparative design","dataType":"Texts, interviews, documents from multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226468013","url":null},{"ref":"Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452242569","url":null}],"related":["metaphor-analysis","case-study","thematic-analysis","narrative-analysis","content-analysis","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-narrative-inquiry","name":"Multiple case-based narrative inquiry","fullName":"Multiple Case-Based Narrative Inquiry","aliases":["multi-case narrative inquiry","cross-case narrative research","comparative narrative inquiry","multi-site narrative inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s (synthesis of Clandinin & Connelly 2000 with multiple case study design)","originator":"D. Jean Clandinin & F. Michael Connelly (narrative inquiry); Robert K. Yin (multiple case logic)","url":"https://scholargate.app/en/qualitative/multiple-case-based-narrative-inquiry","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-narrative-inquiry.md","definition":"Multiple case-based narrative inquiry is a qualitative research design that applies narrative inquiry — the study of human experience through story — across two or more purposively selected cases. Each case is treated as a bounded narrative unit, enabling both within-case depth and cross-case comparison. The approach draws on Clandinin and Connelly's narrative inquiry tradition while adopting the replication logic of multiple case design to build richer, more transferable understandings of how people narrate and make meaning of their experiences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. Jean Clandinin & F. Michael Connelly (narrative inquiry); Robert K. Yin (multiple case logic)","year":"2000s (synthesis of Clandinin & Connelly 2000 with multiple case study design)","type":"Qualitative research design","dataType":"Interview narratives, life stories, field texts, documents from multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787943523","url":null},{"ref":"Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["narrative-inquiry","comparative-case-study","multiple-case-study","longitudinal-narrative-research","cross-case-analysis","interpretive-narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-netnography","name":"Multiple case-based netnography","fullName":"Multiple Case-Based Netnography","aliases":["multi-site netnography","comparative netnography","multiple case netnography","multi-community netnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Robert V. Kozinets (netnography); multiple-case extension draws on Yin's case study logic","url":"https://scholargate.app/en/qualitative/multiple-case-based-netnography","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-netnography.md","definition":"Multiple case-based netnography combines Kozinets's netnographic method — an ethnographic adaptation for online communities — with Yin's multiple case study logic. The researcher systematically collects and interprets naturalistic digital data from two or more distinct online communities or platforms, then conducts within-case analyses and a structured cross-case comparison to identify both shared patterns and context-specific differences. The design is especially powerful for understanding how cultural meanings, consumer practices, or social dynamics vary across different digital contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert V. Kozinets (netnography); multiple-case extension draws on Yin's case study logic","year":"2000s–2010s","type":"Qualitative comparative research design","dataType":"Online community data (posts, threads, profiles, user-generated content) from two or more distinct digital communities or platforms","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.","type":"book","doi":null,"isbn":"978-1847875228","url":null},{"ref":"Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452242569","url":null}],"related":["netnography","case-study","ethnography","virtual-ethnography","comparative-qualitative-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-oral-history","name":"Multiple case-based oral history","fullName":"Multiple Case-Based Oral History Research","aliases":["multi-case oral history","comparative oral history","cross-case oral history","multi-site oral history"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–1990s (convergence of oral history and case study traditions)","originator":"Alessandro Portelli (oral history theory); Robert K. Yin (multiple case logic)","url":"https://scholargate.app/en/qualitative/multiple-case-based-oral-history","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-oral-history.md","definition":"Multiple case-based oral history is a qualitative research design that embeds oral history interviews within a multiple-case framework. Rather than collecting testimonies from a single community or site, the researcher deliberately selects two or more distinct cases — communities, cohorts, organisations, or geographic sites — gathers in-depth oral testimonies within each, and then conducts systematic cross-case comparison to identify both shared and divergent historical experiences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alessandro Portelli (oral history theory); Robert K. Yin (multiple case logic)","year":"1970s–1990s (convergence of oral history and case study traditions)","type":"Qualitative multi-case research design","dataType":"In-depth oral history interviews, recorded testimonies, archival documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Portelli, A. (1997). The Battle of Valle Giulia: Oral History and the Art of Dialogue. University of Wisconsin Press.","type":"book","doi":null,"isbn":"978-0299153045","url":null},{"ref":"Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452242569","url":null}],"related":["oral-history","comparative-oral-history","multiple-case-study","longitudinal-oral-history","narrative-inquiry","life-history-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-phenomenology","name":"Multiple case-based phenomenology","fullName":"Multiple Case-Based Phenomenological Research","aliases":["multi-case phenomenology","cross-case phenomenological study","phenomenological multiple case study","comparative phenomenological case inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Synthesis drawing on Robert Stake (multiple case study) and Edmund Husserl / Clark Moustakas (phenomenology)","url":"https://scholargate.app/en/qualitative/multiple-case-based-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-phenomenology.md","definition":"Multiple case-based phenomenology combines the bounded, comparative logic of multiple case study design with the lived-experience focus of phenomenological inquiry. The researcher selects two or more distinct cases — individuals, sites, or groups — who share the same target phenomenon, conducts phenomenological analysis within each case, and then synthesises findings across cases to identify both shared essential structures and case-specific variations. The result is richer and more transferable than a single-case phenomenological study while remaining grounded in the depth that phenomenology demands.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesis drawing on Robert Stake (multiple case study) and Edmund Husserl / Clark Moustakas (phenomenology)","year":"1990s–2000s","type":"Qualitative research design","dataType":"In-depth interviews, focus groups, reflective accounts across multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null},{"ref":"Moustakas, C. (1994). Phenomenological Research Methods. Sage.","type":"book","doi":null,"isbn":"978-0803957466","url":null}],"related":["phenomenology","multiple-case-study","comparative-phenomenology","hermeneutic-phenomenology","interpretive-phenomenological-analysis","longitudinal-phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-reflexive-thematic-analysis","name":"Multiple Case-Based Reflexive Thematic Analysis","fullName":"Multiple Case-Based Reflexive Thematic Analysis","aliases":["MC-RTA","multi-case reflexive thematic analysis","cross-case reflexive thematic analysis","multiple case RTA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (RTA origins); 2010s onward (combined application)","originator":"Braun & Clarke (reflexive thematic analysis); Yin (multiple case study framework)","url":"https://scholargate.app/en/qualitative/multiple-case-based-reflexive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-reflexive-thematic-analysis.md","definition":"Multiple case-based reflexive thematic analysis integrates Braun and Clarke's reflexive thematic analysis (RTA) with a multiple case study framework. Qualitative data are collected from two or more bounded cases, RTA is applied within each case to generate case-specific themes, and the themes are then compared and synthesised across cases. The approach preserves the depth and interpretive richness of RTA while enabling cross-case pattern recognition that single-case designs cannot provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Braun & Clarke (reflexive thematic analysis); Yin (multiple case study framework)","year":"2006 (RTA origins); 2010s onward (combined application)","type":"Qualitative research design and analysis approach","dataType":"Qualitative text data (interviews, documents, field notes) from two or more bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. Sage.","type":"book","doi":null,"isbn":"978-1473953246","url":null},{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["reflexive-thematic-analysis","thematic-analysis","multiple-case-study","case-study","narrative-analysis","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-semiotic-analysis","name":"Multiple Case-Based Semiotic Analysis","fullName":"Multiple Case-Based Semiotic Analysis","aliases":["multi-case semiotic analysis","comparative semiotic case study","cross-case semiotic inquiry","MCSA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s (consolidation in communication and marketing research)","originator":"Synthesised from Peircean/Saussurean semiotics and Yin's multiple case study logic; Floch (1990) is a key applied exemplar","url":"https://scholargate.app/en/qualitative/multiple-case-based-semiotic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-semiotic-analysis.md","definition":"Multiple case-based semiotic analysis is a qualitative research design that applies semiotic frameworks — the systematic study of signs, codes, and meaning-making — across two or more purposively selected cases. By combining the comparative logic of multiple case study research with the interpretive tools of semiotics (structural, Peircean, or Greimasian), it enables researchers to uncover how meaning is constructed and varied across distinct cultural, organisational, or communicative contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Peircean/Saussurean semiotics and Yin's multiple case study logic; Floch (1990) is a key applied exemplar","year":"1980s–1990s (consolidation in communication and marketing research)","type":"Qualitative comparative research design","dataType":"Texts, images, advertisements, artefacts, media outputs (sign systems)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Floch, J.-M. (1990). Semiotique, marketing et communication: sous les signes, les strategies. Presses Universitaires de France. [English translation: Semiotics, Marketing and Communication, Palgrave Macmillan, 2001.]","type":"book","doi":null,"isbn":"978-0333776858","url":null},{"ref":"Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452242569","url":null}],"related":["case-study","semiotic-analysis","discourse-analysis","content-analysis","narrative-analysis","comparative-qualitative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-single-case-study","name":"Multiple Case-Based Single Case Study","fullName":"Multiple Case-Based Single Case Study (Embedded Single-Case Design)","aliases":["embedded single-case study","single-case embedded design","holistic embedded case study","single case with multiple units of analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1984 (Yin first edition); 1995 (Stake)","originator":"Robert K. Yin (embedded case design); Robert E. Stake (case study methodology)","url":"https://scholargate.app/en/qualitative/multiple-case-based-single-case-study","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-single-case-study.md","definition":"A multiple case-based single case study — also called an embedded single-case design — is a qualitative strategy in which a researcher investigates one bounded case (the primary unit of analysis) by systematically examining multiple embedded sub-units within it. Rather than studying several separate cases for comparison, the design focuses all analytical attention on one overarching case while using variation across its internal sub-units to build a richer, more robust understanding of that single case.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (embedded case design); Robert E. Stake (case study methodology)","year":"1984 (Yin first edition); 1995 (Stake)","type":"Qualitative case study design","dataType":"Documents, interviews, observations, archival records","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (1995). The Art of Case Study Research. Sage.","type":"book","doi":null,"isbn":"978-0803957671","url":null}],"related":["case-study","multiple-case-study","ethnography","phenomenology","narrative-inquiry","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-straussian-grounded-theory","name":"Multiple case-based Straussian grounded theory","fullName":"Multiple Case-Based Straussian Grounded Theory","aliases":["multi-case Straussian GT","Strauss-Corbin grounded theory across cases","multiple-site Straussian grounded theory","multi-case GT (Strauss & Corbin)"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s (synthesis of Strauss & Corbin 1990 and multi-case design conventions)","originator":"Anselm Strauss & Juliet Corbin (Straussian GT); multiple-case design formalized by Robert K. Yin and Kathleen Eisenhardt","url":"https://scholargate.app/en/qualitative/multiple-case-based-straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-straussian-grounded-theory.md","definition":"Multiple case-based Straussian grounded theory combines Strauss and Corbin's systematic coding procedures — open, axial, and selective coding — with a multiple case design in which the same grounded theory analysis is conducted across two or more purposively selected cases. The approach aims to generate a mid-range theory grounded in rich, cross-case qualitative data while capitalizing on the comparative leverage offered by multiple sites or units, ultimately producing a theory with broader scope and stronger transferability than a single-case grounded theory study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss & Juliet Corbin (Straussian GT); multiple-case design formalized by Robert K. Yin and Kathleen Eisenhardt","year":"1990s (synthesis of Strauss & Corbin 1990 and multi-case design conventions)","type":"Qualitative research design and analytic strategy","dataType":"Interviews, observations, documents collected across two or more purposively selected cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932500","url":null},{"ref":"Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532-550.","type":"article","doi":"10.5465/amr.1989.4308385","isbn":null,"url":null}],"related":["straussian-grounded-theory","constructivist-grounded-theory","multiple-case-study","comparative-grounded-theory","classic-grounded-theory","longitudinal-grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-thematic-analysis","name":"Multiple Case-Based Thematic Analysis","fullName":"Multiple Case-Based Thematic Analysis","aliases":["cross-case thematic analysis","multi-case thematic analysis","comparative thematic analysis","MCBTA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (integration period)","originator":"Synthesized from Braun & Clarke (thematic analysis) and Yin (multiple case study design)","url":"https://scholargate.app/en/qualitative/multiple-case-based-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-thematic-analysis.md","definition":"Multiple case-based thematic analysis (MCBTA) is a qualitative design that applies thematic analysis sequentially within each case and then comparatively across cases. It combines the bounded, contextual focus of multiple case study methodology with the systematic coding and theme-development procedures of Braun and Clarke's thematic analysis, enabling researchers to identify both case-specific patterns and shared themes that hold across contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Braun & Clarke (thematic analysis) and Yin (multiple case study design)","year":"2000s–2010s (integration period)","type":"Qualitative comparative design","dataType":"Interview transcripts, documents, field notes across multiple bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452242569","url":null}],"related":["thematic-analysis","case-study","comparative-qualitative-analysis","cross-case-synthesis","narrative-analysis","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-based-visual-analysis","name":"Multiple Case-Based Visual Analysis","fullName":"Multiple Case-Based Visual Analysis","aliases":["multi-case visual analysis","comparative visual case study","cross-case image analysis","MCVA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (convergence of case study and visual research traditions)","originator":"Synthesised from Robert E. Stake (multiple case design) and Gillian Rose / visual methodologies scholars","url":"https://scholargate.app/en/qualitative/multiple-case-based-visual-analysis","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-based-visual-analysis.md","definition":"Multiple case-based visual analysis is a qualitative design that systematically examines visual materials — photographs, drawings, maps, video stills, or image-rich documents — across two or more purposefully selected cases. By combining Robert Stake's multiple case study logic with visual analysis frameworks, it enables researchers to identify both case-specific visual meanings and cross-case patterns, producing richer comparative insights than either method yields alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Robert E. Stake (multiple case design) and Gillian Rose / visual methodologies scholars","year":"2000s–2010s (convergence of case study and visual research traditions)","type":"Qualitative comparative research design","dataType":"Visual materials (photographs, drawings, video stills, maps, documents with images) across two or more bounded cases","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null},{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473942028","url":null}],"related":["case-study","visual-analysis","multimodal-analysis","comparative-case-study","content-analysis","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-design-based-research","name":"Multiple-case design-based research","fullName":"Multiple-Case Design-Based Research","aliases":["multi-site DBR","multi-case design experiment","multiple-site design research","MCDBR"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1992 (DBR); multiple-case variant codified through 2000s–2010s","originator":"Ann Brown and Allan Collins (DBR origins); multiple-case extension developed by the DBR Collective and scholars such as Jan Herrington and Thomas Reeves","url":"https://scholargate.app/en/field-methods/multiple-case-design-based-research","markdownUrl":"https://scholargate.app/en/field-methods/multiple-case-design-based-research.md","definition":"Multiple-case design-based research (MCDBR) is an interventionist methodology drawn from the learning sciences and education research. It extends single-site design-based research by implementing and iteratively refining an educational intervention across two or more distinct sites, contexts, or participant groups simultaneously or sequentially. The cross-case structure strengthens theoretical transferability and exposes context-dependent variations that a single site could never reveal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ann Brown and Allan Collins (DBR origins); multiple-case extension developed by the DBR Collective and scholars such as Jan Herrington and Thomas Reeves","year":"1992 (DBR); multiple-case variant codified through 2000s–2010s","type":"Interventionist qualitative/mixed-methods design","dataType":"Observations, interviews, artefacts, iterative implementation records from multiple sites","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. Journal of the Learning Sciences, 2(2), 141–178.","type":"article","doi":"10.1207/s15327809jls0202_2","isbn":null,"url":null},{"ref":"Herrington, J., McKenney, S., Reeves, T., & Oliver, R. (2011). Design-based research and doctoral students: Guidelines for preparing a dissertation proposal. In Proceedings of EdMedia 2007: World Conference on Educational Multimedia, Hypermedia and Telecommunications. Association for the Advancement of Computing in Education (AACE).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Design-based+research+and+doctoral+students+guidelines+Herrington+McKenney+Reeves+2011"}],"related":["design-based-research","case-study","action-research","participatory-action-research","mixed-methods-research","comparative-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-lesson-study","name":"Multiple-case Lesson Study","fullName":"Multiple-Case Lesson Study","aliases":["multi-site lesson study","cross-case lesson study","collaborative lesson research (multi-case)","MCLS"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1999–2002 (Western formalization); Japanese origins 19th century","originator":"Japanese education tradition; systematized in Western research by Catherine Lewis, James Stigler, and James Hiebert","url":"https://scholargate.app/en/field-methods/multiple-case-lesson-study","markdownUrl":"https://scholargate.app/en/field-methods/multiple-case-lesson-study.md","definition":"Multiple-case lesson study extends the Japanese lesson study cycle — collaborative planning, live observation, and structured debrief of a single research lesson — across two or more independent cases (schools, classrooms, or teacher teams). By replicating and comparing the cycle at multiple sites, researchers can distinguish context-specific findings from those that generalize across settings, producing richer evidence about effective instructional practices in humanities and social science domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Japanese education tradition; systematized in Western research by Catherine Lewis, James Stigler, and James Hiebert","year":"1999–2002 (Western formalization); Japanese origins 19th century","type":"Collaborative qualitative research design","dataType":"Observation notes, video recordings, student work samples, teacher reflective logs","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Lewis, C. C. (2002). Lesson Study: A Handbook of Teacher-Led Instructional Change. Research for Better Schools.","type":"book","doi":null,"isbn":"978-0944536483","url":null},{"ref":"Stigler, J. W., & Hiebert, J. (1999). The Teaching Gap: Best Ideas from the World's Teachers for Improving Education in the Classroom. Free Press.","type":"book","doi":null,"isbn":"978-0684852744","url":null}],"related":["lesson-study","case-study","action-research","design-based-research","participatory-action-research","comparative-case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-case-study","name":"Multiple-Case Study","fullName":"Multiple-Case (Comparative) Study Design","aliases":["comparative case study","multi-site case study","collective case study","cross-case analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Case Study","year":"1980s–1990s (Yin's first edition 1984; Stake's collective case study concept 1995)","originator":"Robert K. Yin (systematic replication logic); Robert E. Stake (naturalistic/collective case tradition)","url":"https://scholargate.app/en/qualitative/multiple-case-study","markdownUrl":"https://scholargate.app/en/qualitative/multiple-case-study.md","definition":"Multiple-case study design investigates two or more bounded real-world cases using the same research protocol, then compares findings across cases to identify patterns, contrasts, and explanatory insights that a single case could not produce. Developed primarily through Robert Yin's replication logic and Robert Stake's collective case tradition, the approach is particularly powerful when a researcher needs to determine whether a phenomenon occurs under varied conditions or to test an emerging theoretical explanation against rival contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (systematic replication logic); Robert E. Stake (naturalistic/collective case tradition)","year":"1980s–1990s (Yin's first edition 1984; Stake's collective case study concept 1995)","type":"Qualitative research method","dataType":"Interviews, documents, observations, archival records (multiple sources per case)","typicalSampleSize":"2–10 cases (each case may involve multiple participants or sites)","subfamily":"Case Study"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.","type":"book","doi":null,"isbn":"978-1593852481","url":null}],"related":["case-study","ethnography","grounded-theory","action-research","mixed-methods","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-comparisons-problem","name":"Multiple Comparisons Problem","fullName":"The Multiple Comparisons Problem and Statistical Correction Methods","aliases":["multiple testing","family-wise error","p-value adjustment","false discovery rate"],"domain":"research-statistics","family":"process-pipeline","subfamily":"statistical-inference","year":1935,"originator":"Carlo Bonferroni; Benjamini & Hochberg","url":"https://scholargate.app/en/research-statistics/multiple-comparisons-problem","markdownUrl":"https://scholargate.app/en/research-statistics/multiple-comparisons-problem.md","definition":"When conducting multiple statistical tests, the probability of obtaining at least one false positive by chance increases with the number of tests. The multiple comparisons problem (also called the multiplicity problem) occurs because if you conduct 100 hypothesis tests at α = 0.05, you expect ~5 false positives by chance alone, even if all null hypotheses are true. Correction methods—Bonferroni, Benjamini-Hochberg false discovery rate (FDR), and others—adjust the significance threshold or p-values to control error rates. This concept is critical for research integrity and has profound implications for exploratory science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carlo Bonferroni; Benjamini & Hochberg","subfamily":"statistical-inference","year":1935,"type":"Concept"},"citations":[{"ref":"Bonferroni, C. E. (1935). Il calcolo dei coefficienti di correlazione nel caso di variabilità di gruppi. Instituto Italiano di Statistica.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Bonferroni_correction"},{"ref":"Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57(1), 289–300.","type":"article","doi":"10.1111/j.2517-6161.1995.tb02031.x","isbn":null,"url":null},{"ref":"Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124.","type":"article","doi":"10.1371/journal.pmed.0020124","isbn":null,"url":null}],"related":["p-value-significance","type-i-type-ii-error","null-hypothesis","publication-bias"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-correspondence-analysis","name":"Multiple Correspondence Analysis","fullName":"Multiple Correspondence Analysis (MCA)","aliases":["MCA","Homogeneity Analysis","Multiple Nominal Component Analysis","Çoklu Uyum Analizi"],"domain":"statistics","family":"latent-structure","subfamily":"Dimensionality reduction","year":2006,"originator":"Greenacre & Blasius","url":"https://scholargate.app/en/statistics/multiple-correspondence-analysis","markdownUrl":"https://scholargate.app/en/statistics/multiple-correspondence-analysis.md","definition":"Multiple Correspondence Analysis (MCA) is a multivariate ordination technique designed to explore and visualize associations among three or more categorical variables simultaneously. By mapping both observations and variable categories onto a shared low-dimensional space, MCA reveals hidden structure in nominal or ordinal survey data. The method was comprehensively systematized and extended by Michael Greenacre and Jorg Blasius in their 2006 edited volume, building on earlier geometric data analysis traditions developed in France by Jean-Paul Benzecri during the 1960s and 1970s.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greenacre & Blasius","year":2006,"type":"Multivariate exploratory ordination","subfamily":"Dimensionality reduction","input":"Categorical (nominal/ordinal) variables","output":"Low-dimensional map of categories and observations"},"citations":[{"ref":"Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1-58488-628-0","url":null}],"related":["correspondence-analysis","principal-component-analysis","biplot"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-factor-analysis","name":"Multiple Factor Analysis","fullName":"Multiple Factor Analysis","aliases":["MFA","MFA multiple"],"domain":"psychometrics","family":"latent-structure","subfamily":"Multivariate Analysis","year":"1985","originator":"Brigitte Escofier, Jérôme Pagès","url":"https://scholargate.app/en/psychometrics/multiple-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/multiple-factor-analysis.md","definition":"Multiple Factor Analysis (MFA) is a dimension reduction technique developed by Escofier and Pagès (1985) for analyzing multiple groups of variables measured on the same observations. MFA balances the influence of each variable group to provide a unified view of how observations relate across multiple perspectives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brigitte Escofier, Jérôme Pagès","subfamily":"Multivariate Analysis","year":"1985","type":"Multiblock dimension reduction"},"citations":[{"ref":"Escofier, B., & Pagès, J. (1985). Analyses factorielles simples et multiples : Objectifs, méthodes et interprétation. Dunod.","type":"article","doi":null,"isbn":"9782040116835","url":null},{"ref":"Pagès, J. (2004). Multiple Factor Analysis by Example Using R. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"9781482234700","url":null},{"ref":"Abdi, H., & Valentin, D. (2013). Multiple Factor Analysis. John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multiple+Factor+Analysis+Abdi"}],"related":["pls-sem","exploratory-structural-equation-modeling","redundancy-analysis","fuzzy-anova","latent-transition-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-imputation","name":"Multiple Imputation","fullName":"Multiple Imputation by Chained Equations (MICE)","aliases":["MICE","Multivariate Imputation by Chained Equations","Çoklu Atama (Multiple Imputation — MICE)"],"domain":"statistics","family":"process-pipeline","subfamily":null,"year":1987,"originator":"Donald B. Rubin","url":"https://scholargate.app/en/statistics/multiple-imputation","markdownUrl":"https://scholargate.app/en/statistics/multiple-imputation.md","definition":"Multiple Imputation (MI), formally introduced by Donald B. Rubin in 1987, is a principled statistical procedure for handling missing data. Rather than replacing each missing value once, MI fills the gaps m times — each time drawing plausible values from the posterior predictive distribution of the missing data — producing m complete datasets. Each dataset is analysed independently, and the results are combined into a single set of estimates using Rubin's pooling rules. The MICE variant (Multivariate Imputation by Chained Equations), popularised by van Buuren and Groothuis-Oudshoorn (2011), extends the approach to mixed variable types by imputing each variable in turn through a sequence of conditional regression models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald B. Rubin","year":1987,"type":"Missing-data handling procedure","mechanism":"MAR or MCAR required; MNAR requires sensitivity analysis","imputations":"m ≥ 5 datasets (commonly m ≥ 100 × missing fraction)","pooling":"Rubin's rules","minimumSample":30,"difficulty":"Moderate (level 2 of 5)"},"citations":[{"ref":"Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.","type":"book","doi":"10.1002/9780470316696","isbn":null,"url":null},{"ref":"van Buuren, S. & Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67.","type":"article","doi":null,"isbn":null,"url":"https://www.jstatsoft.org/article/view/v045i03"}],"related":["listwise-deletion","single-imputation","expectation-maximization","missing-data-analysis","propensity-score-matching"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-linear-regression","name":"Multiple Linear Regression","fullName":"Multiple Linear Regression (Ordinary Least Squares)","aliases":["MLR","OLS regression","multiple regression","linear regression with multiple predictors","multivariate linear regression"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1886,"originator":"Francis Galton; formalized by Karl Pearson","url":"https://scholargate.app/en/statistics/multiple-linear-regression","markdownUrl":"https://scholargate.app/en/statistics/multiple-linear-regression.md","definition":"Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Francis Galton; formalized by Karl Pearson","year":1886,"family":"Regression model","type":"Parametric linear model","estimator":"Ordinary Least Squares (OLS)","outcome":"continuous","parametric":true,"predictors":"two or more continuous or dummy-coded","distribution":"Normal (residuals)","assumptions":"linearity, independence, homoscedasticity, normality of residuals, no perfect multicollinearity"},"citations":[{"ref":"Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.","type":"article","doi":"10.2307/2841583","isbn":null,"url":null},{"ref":"Pearson, K., & Lee, A. (1908). On the generalised probable error in multiple normal correlation. Biometrika, 6(1), 59–68.","type":"article","doi":"10.1093/biomet/6.1.59","isbn":null,"url":null},{"ref":"Draper, N. R., & Smith, H. (1966). Applied Regression Analysis (1st ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"9780471221708","url":null},{"ref":"Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"9780470542811","url":null}],"related":["simple-linear-regression","logistic-regression","ridge-regression","lasso-regression","polynomial-regression","stepwise-regression","one-way-anova","ancova","principal-component-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiple-regression-analysis","name":"Multiple Regression Analysis","fullName":"Multiple Linear Regression","aliases":["MLR","multivariate regression","linear regression"],"domain":"research-statistics","family":"process-pipeline","subfamily":"predictive-modeling","year":"1801","originator":"Carl Friedrich Gauss","url":"https://scholargate.app/en/research-statistics/multiple-regression-analysis","markdownUrl":"https://scholargate.app/en/research-statistics/multiple-regression-analysis.md","definition":"Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Friedrich Gauss","subfamily":"predictive-modeling","year":"1801","type":"Method"},"citations":[{"ref":"Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Applied+Regression+Analysis+Draper"},{"ref":"Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (1992). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Applied+Multiple+Regression%2FCorrelation+Analysis+for+the+Behavioral+Sciences+%282nd+ed.%29+Cohen"},{"ref":"Marquardt, D. W. (1980). You should standardize the independent variables in your regression models. Discussion of a paper by G. David Knottnerus. Journal of the American Statistical Association, 75(369), 87–91.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=You+should+standardize+the+independent+variables+in+your+regression+models+Marquardt"}],"related":["analysis-of-variance","logistic-regression","factor-analysis","structural-equation-modeling"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiplex-network-analysis","name":"Multiplex Network Analysis","fullName":"Multiplex Network Analysis (Multi-Layer Network Analysis with Shared Node Sets)","aliases":["multiplex networks","multi-layer network analysis","multilayer network analysis","MNA"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2014","originator":"Kivela, M.; Boccaletti, S. et al.","url":"https://scholargate.app/en/network-analysis/multiplex-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/multiplex-network-analysis.md","definition":"Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivela, M.; Boccaletti, S. et al.","year":"2014","type":"Structural network model","dataType":"Relational / edge-list data across multiple relation types","subfamily":"Network science"},"citations":[{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gomez-Gardenes, J., Romance, M., Sendina-Nadal, I., Wang, Z., & Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544(1), 1–122.","type":"article","doi":"10.1016/j.physrep.2014.07.001","isbn":null,"url":null}],"related":["social-network-analysis","two-mode-network-analysis","multilayer-social-network-analysis","community-detection","betweenness-centrality","network-diffusion-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiscale-geographically-weighted-regression","name":"Multiscale Geographically Weighted Regression","fullName":"Multiscale Geographically Weighted Regression","aliases":["MGWR","multiscale GWR","multi-scale geographically weighted regression","variable-bandwidth GWR"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2017","originator":"A. Stewart Fotheringham, Wei Yang, and Wei Kang","url":"https://scholargate.app/en/spatial-analysis/multiscale-geographically-weighted-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/multiscale-geographically-weighted-regression.md","definition":"Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. Stewart Fotheringham, Wei Yang, and Wei Kang","year":"2017","type":"Local spatial regression","dataType":"Georeferenced cross-sectional or panel data with continuous outcome","subfamily":"GIS / spatial"},"citations":[{"ref":"Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.","type":"article","doi":"10.1080/24694452.2017.1352480","isbn":null,"url":null},{"ref":"Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), 269.","type":"article","doi":"10.3390/ijgi8060269","isbn":null,"url":null}],"related":["geographically-weighted-regression","spatial-lag-model","spatial-error-model","local-spatial-regression","spatial-durbin-model","kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiscale-getis-ord-gi","name":"Multiscale Getis-Ord Gi*","fullName":"Multiscale Getis-Ord Gi* Hot Spot Analysis","aliases":["multi-distance Gi*","multiscale hot spot analysis","multi-bandwidth Getis-Ord","scale-varying Gi*"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1995 (Gi* basis); multiscale application 2000s onward","originator":"Ord & Getis (1995); multiscale extension developed in applied spatial analysis practice","url":"https://scholargate.app/en/spatial-analysis/multiscale-getis-ord-gi","markdownUrl":"https://scholargate.app/en/spatial-analysis/multiscale-getis-ord-gi.md","definition":"Multiscale Getis-Ord Gi* extends the classic local hot spot statistic by computing Gi* z-scores across a range of spatial distance bands or neighborhood sizes. This reveals whether clusters of high or low values are scale-dependent — appearing only at fine local scales, only at broad regional scales, or persistently across all scales — providing richer spatial intelligence than a single-bandwidth analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ord & Getis (1995); multiscale extension developed in applied spatial analysis practice","year":"1995 (Gi* basis); multiscale application 2000s onward","type":"Local spatial statistic (multiscale)","dataType":"Georeferenced point or polygon data with a continuous attribute","subfamily":"GIS / spatial"},"citations":[{"ref":"Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00912.x","isbn":null,"url":null},{"ref":"Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.","type":"book","doi":null,"isbn":"978-0471496168","url":null}],"related":["local-getis-ord-gi-star","hot-spot-analysis","multiscale-geographically-weighted-regression","kernel-density-estimation","local-morans-i","spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiscale-morans-i","name":"Multiscale Moran's I","fullName":"Multiscale Moran's I Spatial Autocorrelation","aliases":["multi-scale Moran's I","spatial correlogram Moran","Moran correlogram","multiscale spatial autocorrelation"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1950 (base); multiscale variant 1980s-1990s","originator":"P. A. P. Moran (base statistic, 1950); multiscale extension developed through spatial ecology and geography literature","url":"https://scholargate.app/en/spatial-analysis/multiscale-morans-i","markdownUrl":"https://scholargate.app/en/spatial-analysis/multiscale-morans-i.md","definition":"Multiscale Moran's I extends the classic global Moran's I statistic by computing spatial autocorrelation across a series of distance lags or spatial scales. The resulting spatial correlogram reveals at which geographic scales clusters or dispersions of a variable exist, offering richer insight than a single summary statistic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"P. A. P. Moran (base statistic, 1950); multiscale extension developed through spatial ecology and geography literature","year":"1950 (base); multiscale variant 1980s-1990s","type":"Spatial autocorrelation statistic","dataType":"Georeferenced continuous or count variables; spatial weights matrix at multiple lag distances","subfamily":"GIS / spatial"},"citations":[{"ref":"Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1-2), 17-23.","type":"article","doi":"10.2307/2332142","isbn":null,"url":null},{"ref":"Legendre, P., & Fortin, M.-J. (1989). Spatial pattern and ecological analysis. Vegetatio, 80(2), 107-138.","type":"article","doi":"10.1007/BF00048036","isbn":null,"url":null}],"related":["morans-i","spatial-autocorrelation","local-morans-i","gearys-c","multiscale-geographically-weighted-regression","local-indicators-of-spatial-association"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multiscale-spatial-autocorrelation","name":"Multiscale Spatial Autocorrelation","fullName":"Multiscale Spatial Autocorrelation Analysis","aliases":["multi-scale spatial autocorrelation","scale-decomposed spatial autocorrelation","multiscale Moran analysis","MSA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2002","originator":"Borcard & Legendre; Csillag & Kabos","url":"https://scholargate.app/en/spatial-analysis/multiscale-spatial-autocorrelation","markdownUrl":"https://scholargate.app/en/spatial-analysis/multiscale-spatial-autocorrelation.md","definition":"Multiscale spatial autocorrelation extends classical spatial autocorrelation analysis by computing and comparing autocorrelation statistics (such as Moran's I) across a range of spatial scales simultaneously. This reveals at which geographic distances or resolutions spatial clustering or dispersion is strongest, providing a richer picture than a single global measure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Borcard & Legendre; Csillag & Kabos","year":"2002","type":"Spatial autocorrelation decomposition","dataType":"Georeferenced continuous or count data with spatial coordinates","subfamily":"GIS / spatial"},"citations":[{"ref":"Borcard, D., & Legendre, P. (2002). All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153(1-2), 51-68.","type":"article","doi":"10.1016/S0304-3800(01)00501-4","isbn":null,"url":null},{"ref":"Csillag, F., & Kabos, S. (2002). Wavelets, boundaries, and the spatial analysis of landscape pattern. Ecoscience, 9(2), 177-190.","type":"article","doi":"10.1080/11956860.2002.11682704","isbn":null,"url":null}],"related":["spatial-autocorrelation","morans-i","local-spatial-autocorrelation","geographically-weighted-regression","multiscale-geographically-weighted-regression","local-indicators-of-spatial-association"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multistage-sampling","name":"Multistage Sampling","fullName":"Multistage Cluster Sampling","aliases":["multistage cluster sampling","multi-stage sampling","nested sampling","hierarchical sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1950s–1960s (formalized in Kish 1965 and Cochran 1977)","originator":"Leslie Kish; William G. Cochran","url":"https://scholargate.app/en/survey-methodology/multistage-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/multistage-sampling.md","definition":"Multistage sampling is a probability-based design that selects a sample by working through two or more successive levels of a population hierarchy — for example, first selecting regions, then districts within those regions, then households within those districts. It makes large-scale surveys practical when a complete population list is unavailable or when the population is geographically dispersed, by concentrating fieldwork within a manageable number of sampled units at each stage.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leslie Kish; William G. Cochran","year":"1950s–1960s (formalized in Kish 1965 and Cochran 1977)","type":"Probability sampling design","dataType":"Population lists or maps organized in hierarchical units (e.g., regions, districts, households)","subfamily":"Sampling"},"citations":[{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471109495","url":null},{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null}],"related":["cluster-sampling","stratified-sampling","systematic-sampling","simple-random-sampling","proportional-multistage-sampling","probability-proportional-to-size-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multitask-learning","name":"Multitask Learning","fullName":"Multitask Learning","aliases":["MTL","Joint Learning","Shared Representation Learning","Çok Görevli Öğrenme"],"domain":"deep-learning","family":"ml-model","subfamily":"Training paradigms","year":1997,"originator":"Rich Caruana","url":"https://scholargate.app/en/deep-learning/multitask-learning","markdownUrl":"https://scholargate.app/en/deep-learning/multitask-learning.md","definition":"Multitask Learning (MTL) is a machine learning paradigm in which a model is trained simultaneously on multiple related tasks, sharing representations across them to improve generalization. Introduced formally by Rich Caruana in 1997, MTL draws on the intuition that auxiliary tasks act as inductive bias, providing extra supervision signals that help the shared layers learn richer, more robust feature representations than single-task training would yield.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rich Caruana","year":1997,"type":"Inductive transfer method","subfamily":"Training paradigms","learning_mode":"Supervised / semi-supervised","parameter_sharing":"Hard or soft"},"citations":[{"ref":"Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.","type":"article","doi":"10.1023/A:1007379606734","isbn":null,"url":null}],"related":["transfer-learning","knowledge-distillation","curriculum-learning"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-causal-comparative-research","name":"Multivariate Causal-Comparative Research","fullName":"Multivariate Causal-Comparative Research Design","aliases":["multivariate causal-comparative design","MANOVA causal-comparative study","multi-outcome ex post facto research","multivariate ex post facto design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century onward; multivariate extension systematized 1970s–1990s","originator":"Extension of causal-comparative tradition (cf. Chapin, 1947; Gay, Mills & Airasian)","url":"https://scholargate.app/en/research-design/multivariate-causal-comparative-research","markdownUrl":"https://scholargate.app/en/research-design/multivariate-causal-comparative-research.md","definition":"Multivariate causal-comparative research is a quantitative, non-experimental design that investigates whether pre-existing group differences (defined by a naturally occurring categorical variable) are associated with differences across multiple outcome variables considered simultaneously. By extending the classic causal-comparative framework to several dependent variables at once, it reduces Type I error inflation and captures the correlated structure of outcomes that univariate comparisons would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of causal-comparative tradition (cf. Chapin, 1947; Gay, Mills & Airasian)","year":"Mid-20th century onward; multivariate extension systematized 1970s–1990s","type":"Quantitative non-experimental comparative design","dataType":"Numerical scores on multiple dependent and/or independent variables","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-1260085594","url":null},{"ref":"Gay, L. R., Mills, G. E., & Airasian, P. W. (2012). Educational Research: Competencies for Analysis and Applications (10th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0132613170","url":null}],"related":["causal-comparative-research","multivariate-correlational-research","ex-post-facto-design","manova","discriminant-analysis","longitudinal-causal-comparative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-cohort-research","name":"Multivariate Cohort Research","fullName":"Multivariate Cohort Research Design","aliases":["multivariate cohort study","cohort study with multivariate analysis","multivariable cohort design","multivariate longitudinal cohort"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1950s–1970s (cohort methods); multivariate extensions prominent from 1970s onward","originator":"Epidemiology and biostatistics tradition; advanced by Rothman, Breslow, and colleagues","url":"https://scholargate.app/en/research-design/multivariate-cohort-research","markdownUrl":"https://scholargate.app/en/research-design/multivariate-cohort-research.md","definition":"Multivariate cohort research follows a defined group of individuals forward in time, collecting data on multiple exposures, outcomes, and covariates simultaneously. By applying multivariate statistical models — such as Cox regression, mixed-effects models, or structural equation models — researchers can disentangle the independent contributions of several predictors to one or more outcomes while controlling for confounders. The design is widely used in epidemiology, public health, psychology, and social sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Epidemiology and biostatistics tradition; advanced by Rothman, Breslow, and colleagues","year":"1950s–1970s (cohort methods); multivariate extensions prominent from 1970s onward","type":"Observational quantitative research design","dataType":"Repeated-measures numeric data; time-to-event data; multiple continuous and categorical variables","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Vittinghoff, E., Glidden, D. V., Shiboski, S. C., & McCulloch, C. E. (2012). Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-1461413523","url":null}],"related":["cohort-research","longitudinal-research","multivariate-longitudinal-research","panel-research","causal-comparative-research","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-correlational-research","name":"Multivariate Correlational Research","fullName":"Multivariate Correlational Research Design","aliases":["multivariate correlational design","multivariate relational research","multiple-variable correlational study","multivariate associational research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s","originator":"Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others","url":"https://scholargate.app/en/research-design/multivariate-correlational-research","markdownUrl":"https://scholargate.app/en/research-design/multivariate-correlational-research.md","definition":"Multivariate correlational research is a non-experimental quantitative design that examines the simultaneous associations among three or more variables. Rather than manipulating conditions, the researcher measures naturally occurring variables and uses techniques such as multiple regression, canonical correlation, or structural equation modeling to map the pattern and strength of their interrelationships. It is the dominant design when the goal is to understand how a set of predictors jointly relates to one or more outcome variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from Galton and Pearson's bivariate correlation work, extended to multivariate contexts by R.A. Fisher, Harold Hotelling, and others","year":"1920s–1930s (multivariate extensions); consolidated in applied social science by 1970s","type":"Non-experimental quantitative research design","dataType":"Continuous, ordinal, or categorical measured variables (no manipulation)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134790541","url":null},{"ref":"Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.). Lawrence Erlbaum.","type":"book","doi":null,"isbn":"978-0805822236","url":null}],"related":["correlational-research","multiple-regression","structural-equation-modeling","canonical-correlation","multivariate-descriptive-research","path-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-cross-sectional-research","name":"Multivariate Cross-Sectional Research","fullName":"Multivariate Cross-Sectional Research Design","aliases":["multivariate survey design","multi-variable cross-sectional study","MXSR","multivariate observational study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1960s–1970s (formalized with widespread multivariate methods)","originator":"Developed from the convergence of survey methodology (Kerlinger) and multivariate statistics (Tabachnick, Fidell)","url":"https://scholargate.app/en/research-design/multivariate-cross-sectional-research","markdownUrl":"https://scholargate.app/en/research-design/multivariate-cross-sectional-research.md","definition":"Multivariate cross-sectional research collects data on multiple variables from a defined population at a single point in time and uses multivariate statistical techniques — such as multiple regression, MANOVA, factor analysis, or structural equation modeling — to examine simultaneous relationships among those variables. It combines the efficiency of a cross-sectional snapshot with the analytical power to handle complex, multi-variable research questions in a single study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from the convergence of survey methodology (Kerlinger) and multivariate statistics (Tabachnick, Fidell)","year":"1960s–1970s (formalized with widespread multivariate methods)","type":"Quantitative observational design","dataType":"Structured survey/questionnaire data, multiple continuous or categorical variables","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N., & Lee, H. B. (2000). Foundations of Behavioral Research (4th ed.). Harcourt College Publishers.","type":"book","doi":null,"isbn":"978-0155078970","url":null},{"ref":"Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134790541","url":null}],"related":["cross-sectional-research","multivariate-correlational-research","multivariate-descriptive-research","longitudinal-research","survey-research","correlational-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-explanatory-research","name":"Multivariate Explanatory Research","fullName":"Multivariate Explanatory Research Design","aliases":["multivariate explanatory design","explanatory multivariate research","multivariate causal-explanatory study","MER"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-to-late 20th century (consolidated ~1960s–1980s)","originator":"Rooted in the multivariate statistics tradition (R.A. Fisher, Harold Hotelling) combined with explanatory research design conventions codified by Kerlinger and others","url":"https://scholargate.app/en/research-design/multivariate-explanatory-research","markdownUrl":"https://scholargate.app/en/research-design/multivariate-explanatory-research.md","definition":"Multivariate explanatory research is a quantitative design that simultaneously examines multiple independent variables to explain variance in one or more outcomes. Rather than describing what exists or simply correlating pairs of variables, it seeks causal or structural explanations by testing theoretically grounded models with techniques such as multiple regression, MANOVA, or structural equation modeling on survey, administrative, or observational numeric data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in the multivariate statistics tradition (R.A. Fisher, Harold Hotelling) combined with explanatory research design conventions codified by Kerlinger and others","year":"Mid-to-late 20th century (consolidated ~1960s–1980s)","type":"Quantitative research design","dataType":"Continuous, ordinal, or categorical numeric data across multiple variables","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1473756540","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["multivariate-correlational-research","multivariate-confirmatory-research","explanatory-research","causal-comparative-research","structural-equation-modeling","multiple-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-exploratory-quantitative-research","name":"Multivariate Exploratory Quantitative Research","fullName":"Multivariate Exploratory Quantitative Research Design","aliases":["multivariate exploratory design","exploratory multivariate analysis","multivariate data exploration","MEQ research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1930s–1960s (foundational multivariate methods); codified in research design literature from the 1980s onward","originator":"Hair, Tabachnick, and colleagues (canonical synthesis); roots in Fisher, Hotelling, and Thurstone (early 20th century)","url":"https://scholargate.app/en/research-design/multivariate-exploratory-quantitative-research","markdownUrl":"https://scholargate.app/en/research-design/multivariate-exploratory-quantitative-research.md","definition":"Multivariate exploratory quantitative research is a design in which researchers simultaneously examine multiple quantitative variables without imposing a predetermined structural model, using techniques such as exploratory factor analysis, cluster analysis, or principal component analysis to detect latent patterns, natural groupings, or underlying dimensions in the data. The goal is discovery and pattern recognition rather than hypothesis confirmation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hair, Tabachnick, and colleagues (canonical synthesis); roots in Fisher, Hotelling, and Thurstone (early 20th century)","year":"1930s–1960s (foundational multivariate methods); codified in research design literature from the 1980s onward","type":"Quantitative research design","dataType":"Continuous, ordinal, or categorical quantitative data; multiple variables measured simultaneously","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1473756540","url":null},{"ref":"Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134790541","url":null}],"related":["exploratory-factor-analysis","cluster-analysis","principal-component-analysis","exploratory-quantitative-research","multivariate-correlational-research","confirmatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-longitudinal-research","name":"Multivariate Longitudinal Research","fullName":"Multivariate Longitudinal Research Design","aliases":["longitudinal multivariate design","MLR","multivariate panel study","multivariate repeated-measures design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s–1980s (formalized in behavioral sciences literature)","originator":"Nesselroade, Baltes, and the developmental/behavioral sciences tradition","url":"https://scholargate.app/en/research-design/multivariate-longitudinal-research","markdownUrl":"https://scholargate.app/en/research-design/multivariate-longitudinal-research.md","definition":"Multivariate longitudinal research is a quantitative observational design that follows the same units — individuals, groups, or organizations — across two or more time points while measuring several outcome and predictor variables simultaneously. By combining the temporal dimension of longitudinal tracking with multivariate statistical analysis, it allows researchers to examine how a system of variables co-evolves, how early measures predict later outcomes across multiple domains, and whether relationships among variables are stable or change over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nesselroade, Baltes, and the developmental/behavioral sciences tradition","year":"1970s–1980s (formalized in behavioral sciences literature)","type":"Quantitative observational research design","dataType":"Continuous and categorical quantitative measures collected at multiple time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Nesselroade, J. R., & Baltes, P. B. (Eds.). (1979). Longitudinal Research in the Study of Behavior and Development. Academic Press.","type":"book","doi":null,"isbn":"978-0125154505","url":null},{"ref":"Bijleveld, C. C. J. H., van der Kamp, L. J. T., Mooijaart, A., van der Kloot, W. A., van der Leeden, R., & van der Burg, E. (1998). Longitudinal Data Analysis: Designs, Models and Methods. Sage.","type":"book","doi":null,"isbn":"978-0761953371","url":null}],"related":["longitudinal-research","panel-research","cohort-research","cross-sectional-research","multilevel-modeling","structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-model-testing-research","name":"Multivariate Model Testing Research","fullName":"Multivariate Model Testing Research Design","aliases":["multivariate model testing","multivariate structural testing","multivariate confirmatory modeling","MVMT research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s–1980s (multivariate model testing as a distinct approach)","originator":"Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis)","url":"https://scholargate.app/en/research-design/multivariate-model-testing-research","markdownUrl":"https://scholargate.app/en/research-design/multivariate-model-testing-research.md","definition":"Multivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis)","year":"1970s–1980s (multivariate model testing as a distinct approach)","type":"Quantitative confirmatory research design","dataType":"Continuous, ordinal, or categorical multivariate data (multiple measured variables)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134790541","url":null},{"ref":"Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462523344","url":null}],"related":["structural-equation-modeling","confirmatory-factor-analysis","multivariate-correlational-research","model-testing-research","path-analysis","multivariate-hypothesis-testing-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-panel-research","name":"Multivariate Panel Research","fullName":"Multivariate Panel Research Design","aliases":["multivariate panel data analysis","panel data multivariate modeling","multi-outcome panel study","longitudinal multivariate panel design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1960s–1980s (econometrics); broader social-science uptake 1990s–2000s","originator":"Econometric tradition; formalized by Cheng Hsiao and Badi Baltagi","url":"https://scholargate.app/en/research-design/multivariate-panel-research","markdownUrl":"https://scholargate.app/en/research-design/multivariate-panel-research.md","definition":"Multivariate panel research combines the repeated-measurement structure of panel data — the same subjects observed at multiple time points — with the simultaneous analysis of two or more outcome or predictor variables. By modeling joint trajectories across units and time, it controls for unobserved individual heterogeneity while capturing the interplay among variables, making it one of the most powerful non-experimental designs available for causal and predictive inference in the social, behavioral, and economic sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Econometric tradition; formalized by Cheng Hsiao and Badi Baltagi","year":"1960s–1980s (econometrics); broader social-science uptake 1990s–2000s","type":"Quantitative panel research design","dataType":"Repeated measurements of multiple continuous, ordinal, or count variables across the same units over time","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null},{"ref":"Baltagi, B. H. (2008). Econometric Analysis of Panel Data (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470518861","url":null}],"related":["panel-research","longitudinal-research","multivariate-longitudinal-research","correlational-research","structural-equation-modeling","multilevel-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-pattern-analysis","name":"Multivariate Pattern Analysis","fullName":"Multivariate Pattern Analysis (MVPA)","aliases":["MVPA","brain decoding","pattern classification"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Machine learning decoding","year":"2001","originator":"James V. Haxby","url":"https://scholargate.app/en/neuroimaging/multivariate-pattern-analysis","markdownUrl":"https://scholargate.app/en/neuroimaging/multivariate-pattern-analysis.md","definition":"Multivariate Pattern Analysis (MVPA) is a machine learning approach to fMRI that decodes cognitive states, stimuli, or behavior from whole-brain spatial patterns of neural activity. Pioneered by Haxby and colleagues in 2001, MVPA treats fMRI as a classification problem: can a trained decoder predict what a person is perceiving or thinking based solely on their brain activity pattern?","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James V. Haxby","subfamily":"Machine learning decoding","year":"2001","type":"fMRI pattern classification pipeline"},"citations":[{"ref":"Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430.","type":"article","doi":"10.1016/j.tics.2006.07.005","isbn":null,"url":null},{"ref":"Haxby, J. V., Gobbini, M. I., Furey, M. L., et al. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293(5539), 2425–2430.","type":"article","doi":"10.1126/science.1063736","isbn":null,"url":null}],"related":["representational-similarity-analysis","voxel-based-morphometry","graph-brain-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-quantitative-content-analysis","name":"Multivariate Quantitative Content Analysis","fullName":"Multivariate Quantitative Content Analysis","aliases":["multivariate QCA","multivariate content analysis","MQCA","multivariate text analysis"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1969–2000s","originator":"Rooted in Holsti (1969) and Neuendorf (2002); multivariate extensions developed in communication and political science research from the 1970s onward","url":"https://scholargate.app/en/research-design/multivariate-quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/multivariate-quantitative-content-analysis.md","definition":"Multivariate quantitative content analysis (MQCA) is a systematic, replicable approach to measuring multiple attributes of communication content simultaneously and examining how those attributes relate to each other or to external variables. It extends standard content analysis by applying multivariate statistical techniques — such as factor analysis, cluster analysis, regression, or MANOVA — to coded content data, enabling researchers to uncover complex patterns across many variables at once.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Holsti (1969) and Neuendorf (2002); multivariate extensions developed in communication and political science research from the 1970s onward","year":"1969–2000s","type":"Quantitative research design","dataType":"Coded textual, visual, or audio content units with multiple measured variables","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage Publications.","type":"book","doi":null,"isbn":"978-0761919773","url":null},{"ref":"Holsti, O. R. (1969). Content Analysis for the Social Sciences and Humanities. Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Content+Analysis+for+the+Social+Sciences+and+Humanities+Holsti+1969"}],"related":["quantitative-content-analysis","multivariate-correlational-research","comparative-quantitative-content-analysis","longitudinal-quantitative-content-analysis","structural-equation-modeling","factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"multivariate-regression","name":"Multivariate Regression","fullName":"Multivariate Multiple Linear Regression","aliases":["multivariate multiple regression","MLR with multiple dependent variables","multiple-outcome regression","Çok Değişkenli Regresyon (MLR — Çoklu DV)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2007,"originator":"Johnson & Wichern (textbook treatment); classical multivariate least squares","url":"https://scholargate.app/en/statistics/multivariate-regression","markdownUrl":"https://scholargate.app/en/statistics/multivariate-regression.md","definition":"Multivariate regression is a linear regression method that predicts several continuous dependent variables at the same time from a shared set of predictors. As developed in standard treatments such as Johnson and Wichern's Applied Multivariate Statistical Analysis (2007), each response equation can be fitted by ordinary least squares while the covariance structure of the residuals is used for joint testing across outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johnson & Wichern (textbook treatment); classical multivariate least squares","year":2007,"type":"Multivariate linear regression","estimator":"Equation-by-equation least squares with joint residual covariance","outcome":"multiple continuous responses","minSample":50},"citations":[{"ref":"Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0131877153","url":null}],"related":["ols-regression","mancova","hotelling-t2","ridge-regression","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mundlak-chamberlain","name":"Mundlak-Chamberlain","fullName":"Mundlak-Chamberlain Correlated Random Effects","aliases":["Correlated Random Effects","CRE Estimator","Mundlak Device","Korelasyonlu Rassal Etkiler"],"domain":"econometrics","family":"regression-model","subfamily":"Static panel","year":1978,"originator":"Yair Mundlak; Gary Chamberlain","url":"https://scholargate.app/en/econometrics/mundlak-chamberlain","markdownUrl":"https://scholargate.app/en/econometrics/mundlak-chamberlain.md","definition":"The Mundlak-Chamberlain correlated random effects (CRE) estimator, introduced by Mundlak (1978) and extended by Chamberlain (1982), is a panel data technique that reconciles the fixed effects and random effects approaches by explicitly modelling the correlation between unobserved individual heterogeneity and the observed regressors. By including within-group means of time-varying covariates as additional regressors in a random effects framework, CRE yields estimates numerically equivalent to the within (fixed effects) estimator while permitting identification of time-invariant variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yair Mundlak; Gary Chamberlain","year":1978,"type":"Panel data estimator","subfamily":"Static panel","data_requirement":"Balanced or unbalanced panel","software":"Stata (xtreg, mundlak), R (plm)"},"citations":[{"ref":"Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica, 46(1), 69–85.","type":"article","doi":"10.2307/1913646","isbn":null,"url":null}],"related":["panel-fixed-effects","random-effects-model","hausman-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"muscle-synergy-analysis","name":"Muscle Synergy Analysis","fullName":"Muscle Synergy Analysis","aliases":["Motor synergy","Synergy extraction","Motor primitives"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Motor control","year":"1999","originator":"Marc Tresch","url":"https://scholargate.app/en/biomechanics/muscle-synergy-analysis","markdownUrl":"https://scholargate.app/en/biomechanics/muscle-synergy-analysis.md","definition":"Muscle synergy analysis decomposes complex motor behavior into a small set of coactivated muscle groups (synergies or motor primitives). Pioneered by Marc Tresch and colleagues studying frog motor control, this approach reveals how the nervous system simplifies the control of many muscles by organizing them into task-relevant combinations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marc Tresch","subfamily":"Motor control","year":"1999","type":"Dimensionality reduction and pattern extraction"},"citations":[{"ref":"Tresch, M. C., Saltiel, P., Bizzi, E., & Bizzi, E. (1999). The construction of movement by the spinal cord. Nature Neuroscience, 2(2), 162-167.","type":"article","doi":"10.1038/5721","isbn":null,"url":null},{"ref":"Lee, D. D., & Seung, H. S. (2016). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788-791.","type":"article","doi":"10.1038/44565","isbn":null,"url":null}],"related":["inverse-dynamics","emg-envelope","dtw-gait-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"music-genre-classification","name":"Music Genre Classification","fullName":"Music Genre Classification Algorithm","aliases":["genre recognition","music categorization","style classification"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Classification","year":"2002","originator":"George Tzanetakis","url":"https://scholargate.app/en/music-information-retrieval/music-genre-classification","markdownUrl":"https://scholargate.app/en/music-information-retrieval/music-genre-classification.md","definition":"Music genre classification is the task of automatically assigning genre labels (rock, jazz, classical, pop, etc.) to audio recordings. Introduced formally by Tzanetakis and Cook (2002), it is one of the earliest and most studied music information retrieval problems. It remains critical for music discovery, recommendation systems, digital library organization, and music streaming services. Modern systems achieve high accuracy on standard datasets using deep learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Tzanetakis","subfamily":"Classification","year":"2002","type":"Audio feature-based classification"},"citations":[{"ref":"Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302.","type":"article","doi":"10.1109/tsa.2002.800560","isbn":null,"url":null},{"ref":"Sturm, B. L. (2014). The state of the art ten years after A comparison of document content analysis approaches for genre classification of musical audio signals. Journal of the American Society for Information Science and Technology, 65(9), 1757-1766.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+state+of+the+art+ten+years+after+A+comparison+of+document+content+analysis+approaches+for+genre+classification+of+musical+audio+signals+Sturm"},{"ref":"Costa, Y. M., Oliveira, L. S., & Silla Jr, C. N. (2014). An evaluation of convolutional neural networks for music classification using mel-frequency cepstral coefficients. In Proceedings of the International Joint Conference on Neural Networks.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1904.06971"}],"related":["beat-tracking","music-similarity-measure","music-segmentation","timbre-analysis","automatic-music-transcription"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"music-segmentation","name":"Music Segmentation","fullName":"Music Segmentation and Structure Detection Algorithm","aliases":["structural segmentation","music structure analysis","section boundary detection"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Structure analysis","year":"2001","originator":"Masataka Goto","url":"https://scholargate.app/en/music-information-retrieval/music-segmentation","markdownUrl":"https://scholargate.app/en/music-information-retrieval/music-segmentation.md","definition":"Music segmentation is the task of dividing a musical recording into distinct structural sections (e.g., verse, chorus, bridge, pre-chorus, outro). Introduced by Goto (2001), it identifies major structural boundaries and labels sections according to musical form. Segmentation is essential for music understanding, audio editing, and composition analysis. It enables higher-level tasks like cover song identification and song structure-aware music generation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Masataka Goto","subfamily":"Structure analysis","year":"2001","type":"Audio structural analysis"},"citations":[{"ref":"Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval.","type":"article","doi":null,"isbn":null,"url":"https://ismir2003.ismir.net/papers/goto_ismir2001.pdf"},{"ref":"Levy, M., & Sandler, M. (2008). Structural segmentation of musical audio by constrained clustering. IEEE Transactions on Audio, Speech, and Language Processing, 16(2), 318-326.","type":"article","doi":"10.1109/tasl.2007.910781","isbn":null,"url":null},{"ref":"McVicar, M., Santos-Rodríguez, R., Ni, Y., & De Bie, T. (2014). Automatic annotation of musical key and time signature from audio using Hidden Markov Models. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2014/papers/272.pdf"}],"related":["beat-tracking","music-genre-classification","chord-recognition","tempo-estimation","melody-extraction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"music-similarity-measure","name":"Music Similarity Measure","fullName":"Music Similarity Distance and Measure Algorithm","aliases":["music distance metric","timbral similarity","content-based similarity"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Distance metrics and similarity","year":"2001","originator":"Beth Logan","url":"https://scholargate.app/en/music-information-retrieval/music-similarity-measure","markdownUrl":"https://scholargate.app/en/music-information-retrieval/music-similarity-measure.md","definition":"Music similarity measures are computational methods for assessing how musically related two audio recordings are. Introduced by Logan (2001), similarity measures enable content-based music recommendation, playlist generation, and music discovery. Unlike fingerprinting, which identifies the same song, similarity measures gauge stylistic, timbral, and structural resemblance between different songs. Measures can be acoustic (comparing spectral features), high-level (genre, mood), or hybrid.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Beth Logan","subfamily":"Distance metrics and similarity","year":"2001","type":"Content-based audio similarity"},"citations":[{"ref":"Logan, B., & Salomon, A. (2001). A music similarity function based on song structure. In Proceedings of the International Conference on Music Information Retrieval.","type":"article","doi":null,"isbn":null,"url":"https://ismir2001.ismir.net/pdf/logan.pdf"},{"ref":"Mandel, M. I., & Ellis, D. P. (2005). Song-level features and support vector machines for music classification. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2005/papers/003.pdf"},{"ref":"Serra, X., Gómez, E., Herrera, P., & Gómez, P. (2008). Chroma binary similarity and local alignment for cover song identification. IEEE Transactions on Audio, Speech, and Language Processing, 16(5), 1029-1037.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Chroma+binary+similarity+and+local+alignment+for+cover+song+identification+Serra"}],"related":["music-genre-classification","audio-fingerprinting","timbre-analysis","beat-tracking","chord-recognition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"music-therapy-assessment-tool","name":"Music Therapy Assessment Tool","fullName":"Music Therapy Assessment Tool","aliases":["MTAT","Music Therapy Outcome Measures"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"Music-based therapeutic interventions","year":"2008","originator":"Hanson, B.; Clark, M.; Plante, W.","url":"https://scholargate.app/en/integrative-medicine/music-therapy-assessment-tool","markdownUrl":"https://scholargate.app/en/integrative-medicine/music-therapy-assessment-tool.md","definition":"The MTAT is a comprehensive assessment instrument for measuring client outcomes and music therapist competency in music therapy. Developed by Hanson and colleagues, it operationalizes music therapy impact across emotional, social, behavioral, and physiological domains, suitable for diverse populations including psychiatric, pediatric, geriatric, and neurological populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hanson, B.; Clark, M.; Plante, W.","subfamily":"Music-based therapeutic interventions","year":"2008","type":"Multi-method: client self-report, clinician observation, behavioral coding"},"citations":[{"ref":"Thaut, M. H. (2005). Rhythm, music, and the brain: Scientific foundations and clinical applications. New York: Routledge.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Thaut%2C%20M.%20H.%20(2005).%20Rhythm%2C%20music%2C%20and%20the%20brain%3A%20Scientific%20foundations%20and%20clinical%20applications.%20New%20York%3A%20Routledge"},{"ref":"Hanson, B., Clark, M., & Plante, W. (2008). A music therapy assessment tool for psychiatric patients. Journal of Music Therapy, 45(3), 341–359.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+music+therapy+assessment+tool+for+psychiatric+patients+Hanson"},{"ref":"World Federation of Music Therapy. (2011). Music therapy assessment and outcome measures. Nordoff-Robbins Foundation.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=World%20Federation%20of%20Music%20Therapy.%20(2011).%20Music%20therapy%20assessment%20and%20outcome%20measures.%20Nordoff-Robbins%20Foundation."}],"related":["therapeutic-touch-assessment","holistic-caring-inventory","spiritual-care-competence-scale","attitudes-cam-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"muskingum-routing","name":"Muskingum Routing","fullName":"Muskingum Method for Flood Routing","aliases":["Flood routing","Stream flow attenuation","Hydrologic routing"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Hydrology","year":"1938","originator":"George McCarthy","url":"https://scholargate.app/en/civil-engineering/muskingum-routing","markdownUrl":"https://scholargate.app/en/civil-engineering/muskingum-routing.md","definition":"The Muskingum method is a hydrologic flood routing technique that predicts how a flood wave attenuates (reduces in peak) and spreads as it travels down a river reach. Developed by McCarthy in 1938 for the US Army Corps of Engineers, the method is simple enough for hand calculations while capturing the essential physics of flood propagation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George McCarthy","subfamily":"Hydrology","year":"1938","type":"Hydrologic method for flood attenuation in rivers"},"citations":[{"ref":"McCarthy, G. T. (1938). The Unit Hydrograph and Flood Routing. US Army Corps of Engineers Document 608.","type":"article","doi":null,"isbn":null,"url":"https://www.usace.army.mil"},{"ref":"Cunge, J. A. (1969). On the subject of a flood propagation computation method (Muskingum method). Journal of Hydraulic Research, 7(2), 205-230.","type":"article","doi":"10.1080/00221686909500264","isbn":null,"url":null},{"ref":"Chow, V. T., Maidment, D. R., & Mays, L. W. (1988). Applied Hydrology. McGraw-Hill.","type":"book","doi":null,"isbn":"0-07-010810-2","url":null}],"related":["unit-hydrograph","modflow","traffic-flow"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"must-malnutrition","name":"MUST Malnutrition Universal Screening Tool","fullName":"Malnutrition Universal Screening Tool (MUST)","aliases":["MUST","Malnutrition screening"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"2003","originator":"Marinos Elia","url":"https://scholargate.app/en/clinical-assessment/must-malnutrition","markdownUrl":"https://scholargate.app/en/clinical-assessment/must-malnutrition.md","definition":"The Malnutrition Universal Screening Tool (MUST), developed by Elia and endorsed by BAPEN (British Association for Parenteral and Enteral Nutrition), is a rapid 3-component screening tool for identifying adults at risk of malnutrition in hospital and community settings. It is based on BMI, unintentional weight loss, and acute disease severity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marinos Elia","subfamily":"Clinical scoring","year":"2003","type":"Universal malnutrition screening"},"citations":[{"ref":"Elia, M. (2003). Screening for malnutrition: a multidisciplinary responsibility. Development and use of the Malnutrition Universal Screening Tool (MUST) for adults. BAPEN (British Association for Parenteral and Enteral Nutrition).","type":"article","doi":null,"isbn":null,"url":"https://www.bapen.org.uk/screening-for-malnutrition/must-toolkit"},{"ref":"Stratton, R. J., Hackston, A., Longmore, D., et al. (2004). Malnutrition in hospital outpatients and inpatients: prevalence, concurrent validity and ease of use of the Malnutrition Universal Screening Tool (MUST) for adults. British Journal of Nutrition, 92(5), 799-808.","type":"article","doi":"10.1079/BJN20041258","isbn":null,"url":null}],"related":["nrs-2002-nutritional-risk","apache-ii-score","glasgow-blatchford-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"mutation-testing","name":"Mutation Testing","fullName":"Mutation Testing for Test Adequacy","aliases":["mutation analysis","mutant testing","fault injection"],"domain":"numerical-methods","family":"ml-model","subfamily":"Software Testing","year":"1978","originator":"Richard DeMillo, Richard Lipton, and Frederick Sayward","url":"https://scholargate.app/en/numerical-methods/mutation-testing","markdownUrl":"https://scholargate.app/en/numerical-methods/mutation-testing.md","definition":"Mutation Testing is a fault-injection technique developed by DeMillo, Lipton, and Sayward in 1978 that evaluates test suite effectiveness by introducing small, deliberate bugs (mutations) into source code and checking if tests catch them. A test suite that kills (detects) all mutants is stronger than one that achieves high code coverage without killing mutants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard DeMillo, Richard Lipton, and Frederick Sayward","subfamily":"Software Testing","year":"1978","type":"Test effectiveness evaluation"},"citations":[{"ref":"DeMillo, R. A., Lipton, R. J., & Sayward, F. G. (1978). Hints on test data selection: Help for the practicing programmer. IEEE Computer, 11(4), 34–41.","type":"article","doi":"10.1109/C-M.1978.218136","isbn":null,"url":null},{"ref":"Just, R., Jalali, D., Inozemtseva, L., Ernst, M. D., & Holmes, R. (2014). Are mutants killed by tests? How test suite composition affects the effectiveness of mutation testing. Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Are+mutants+killed+by+tests+Just"},{"ref":"Jia, Y., & Harman, M. (2010). An analysis and survey of the development of mutation testing. IEEE Transactions on Software Engineering, 37(5), 649–678.","type":"article","doi":"10.1109/TSE.2010.62","isbn":null,"url":null}],"related":["test-coverage","fault-injection","software-testing","regression-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-aras","name":"N-ARAS","fullName":"Neutrosophic ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Adalı, Esra Aytaç Öztaş, Tayfun Özçil, Abdullah Öztaş, Gülin Zeynep Tuş, Ayşegül","url":"https://scholargate.app/en/decision-making/n-aras","markdownUrl":"https://scholargate.app/en/decision-making/n-aras.md","definition":"N-ARAS (Neutrosophic ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Adalı, Esra Aytaç Öztaş, Tayfun Özçil, Abdullah Öztaş, Gülin Zeynep Tuş, Ayşegül in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adalı, Esra Aytaç Öztaş, Tayfun Özçil, Abdullah Öztaş, Gülin Zeynep Tuş, Ayşegül","subfamily":"Ranking","year":"2023","type":"Single-valued neutrosophic extension of ARAS","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Adalı, Esra Aytaç, Öztaş, Tayfun, Özçil, Abdullah, Öztaş, Gülin Zeynep, Tuş, Ayşegül (2023). A New Multi-Criteria Decision-Making Method Under Neutrosophic Environment: ARAS Method With Single-Valued Neutrosophic Numbers. International Journal of Information Technology & Decision Making","type":"article","doi":"10.1142/s0219622022500456","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-aroman","name":"N-AROMAN","fullName":"Neutrosophic extension of AROMAN","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Bošković et al.","url":"https://scholargate.app/en/decision-making/n-aroman","markdownUrl":"https://scholargate.app/en/decision-making/n-aroman.md","definition":"N-AROMAN (Neutrosophic extension of AROMAN) is a ranking multi-criteria decision-making (MCDM) method introduced by Bošković et al. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bošković et al.","subfamily":"Ranking","year":"2023","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Bošković et al. (2023). Neutrosophic Alternative Ranking Order Method Accounting for two-step Normalization. IEEE Access","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Neutrosophic+Alternative+Ranking+Order+Method+Accounting+for+two-step+Normalization+Bo%C5%A1kovi%C4%87"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-beatsx","name":"N-BEATSx","fullName":"N-BEATSx: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting","aliases":["N-BEATSx","NBEATS-x"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Time Series Forecasting","year":"2023","originator":"Cristian Challu","url":"https://scholargate.app/en/deep-learning/n-beatsx","markdownUrl":"https://scholargate.app/en/deep-learning/n-beatsx.md","definition":"N-BEATSx is an extension of the N-BEATS neural time series forecasting model that incorporates exogenous (external) variables through a cross-learner architecture. Published in 2023, N-BEATSx improves upon N-BEATS by enabling the model to leverage additional features beyond the historical time series values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cristian Challu","subfamily":"Deep Learning, Time Series Forecasting","year":"2023","type":"Neural network architecture"},"citations":[{"ref":"Challu, C., Olivares, K. Q., Oreshkin, B., Garza, F., Mergenthaler-Canseco, M., & Dubrawski, A. (2023). N-BEATSx: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. In ICLR 2023 Workshop on Multimodal Learning for Science (p. 4).","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2305.18840"}],"related":["timegpt","mamba","vision-mamba","spatial-temporal-gcn"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-body-simulation","name":"N-Body Simulation","fullName":"N-Body Simulation","aliases":["gravitational N-body problem","many-body simulation"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Computational Physics","year":"1687","originator":"Isaac Newton","url":"https://scholargate.app/en/applied-physics/n-body-simulation","markdownUrl":"https://scholargate.app/en/applied-physics/n-body-simulation.md","definition":"N-body simulation is a computational method for modeling the dynamics of a system of particles under mutual gravitational forces. Originating from Newton's laws of motion and gravitation, it solves the fundamental equations of celestial mechanics. This technique is essential for understanding planetary orbits, star cluster evolution, and cosmological structure formation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Isaac Newton","subfamily":"Computational Physics","year":"1687","type":"Computational simulation algorithm"},"citations":[{"ref":"Poincaré, H. (1892). Les méthodes nouvelles de la mécanique céleste. Gauthier-Villars.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/lesmthodesnouv01poinrich"},{"ref":"Newton, I. (1687). Philosophiæ Naturalis Principia Mathematica. Royal Society.","type":"book","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Principia_Mathematica"},{"ref":"Aarseth, S. J. (1985). Direct methods for N-body simulations. In Multiple Time Scales (pp. 377-418). Springer.","type":"article","doi":"10.1016/b978-0-12-123420-1.50017-3","isbn":null,"url":null}],"related":["orbit-determination","gravity-assist","hohmann-transfer","light-curve-analysis","cosmological-perturbation-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-cocoso","name":"N-COCOSO","fullName":"Neutrosophic CoCoSo","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2024","originator":"Nabeeh, N. A. Sallam, K. M.","url":"https://scholargate.app/en/decision-making/n-cocoso","markdownUrl":"https://scholargate.app/en/decision-making/n-cocoso.md","definition":"N-COCOSO (Neutrosophic CoCoSo) is a ranking multi-criteria decision-making (MCDM) method introduced by Nabeeh, N. A. Sallam, K. M. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nabeeh, N. A. Sallam, K. M.","subfamily":"Ranking","year":"2024","type":"Single-valued neutrosophic extension of CoCoSo","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Nabeeh, N. A., Sallam, K. M. (2024). A Combined Compromise Solution (CoCoSo) of MCDM Problems for Selection of Medical Best Bearing Ring. Neutrosophic Optimization and Intelligent Systems","type":"article","doi":"10.61356/j.nois.2024.16089","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-codas","name":"N-CODAS","fullName":"Neutrosophic extension of CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Pamučar, D., Badi, I., Sanja, K., Obradović, R. — NOTE: paper uses LNN (Linguistic NS), not SVNN. SVNN CODAS originator unknown.","url":"https://scholargate.app/en/decision-making/n-codas","markdownUrl":"https://scholargate.app/en/decision-making/n-codas.md","definition":"N-CODAS (Neutrosophic extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Badi, I., Sanja, K., Obradović, R. — NOTE: paper uses LNN (Linguistic NS), not SVNN. SVNN CODAS originator unknown. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamučar, D., Badi, I., Sanja, K., Obradović, R. — NOTE: paper uses LNN (Linguistic NS), not SVNN. SVNN CODAS originator unknown.","subfamily":"Ranking","year":"2018","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"(). N-CODAS — SVN extension; \"Yüksel 2020\" anchor UNCONFIRMED per LVR.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=N-CODAS%20%E2%80%94%20SVN%20extension%3B%20%22Y%C3%BCksel%202020%22%20anchor%20UNCONFIRMED%20per%20LVR"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-copras","name":"N-COPRAS","fullName":"COPRAS-IN — COPRAS with Interval Neutrosophic Numbers (INN)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Şahin, R.","url":"https://scholargate.app/en/decision-making/n-copras","markdownUrl":"https://scholargate.app/en/decision-making/n-copras.md","definition":"N-COPRAS (COPRAS-IN — COPRAS with Interval Neutrosophic Numbers (INN)) is a ranking multi-criteria decision-making (MCDM) method introduced by Şahin, R. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Şahin, R.","subfamily":"Ranking","year":"2019","type":"Interval Neutrosophic ranking — INN: <[T_L,T_U],[I_L,I_U],[F_L,F_U]> with 0 ≤ T_U+I_U+F_U ≤ 3","value_space":"interval_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Şahin, R. (2019). COPRAS Method with Neutrosophic Sets. Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets, Studies in Fuzziness and Soft Computing, vol 369, Springer, Cham","type":"article","doi":"10.1007/978-3-030-00045-5_19","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","merec","copras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-dnma","name":"N-DNMA","fullName":"Neutrosophic DNMA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":null,"originator":"See source","url":"https://scholargate.app/en/decision-making/n-dnma","markdownUrl":"https://scholargate.app/en/decision-making/n-dnma.md","definition":"N-DNMA (Neutrosophic DNMA) is a ranking multi-criteria decision-making (MCDM) method. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"See source","subfamily":"Ranking","type":"Single-valued neutrosophic extension of DNMA","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"(). N-DNMA — Internal Extension (no SVN-DNMA paper in literature). Complex & Intelligent Systems","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=N-DNMA%20%E2%80%94%20Internal%20Extension%20%28no%20SVN-DNMA%20paper%20in%20literature%29"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-edas","name":"N-EDAS","fullName":"Neutrosophic extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Stanujkić, D., Karabašević, D., Popović, G., Pamučar, D., Stević, Ž., Zavadskas, E. K., Smarandache, F.","url":"https://scholargate.app/en/decision-making/n-edas","markdownUrl":"https://scholargate.app/en/decision-making/n-edas.md","definition":"N-EDAS (Neutrosophic extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkić, D., Karabašević, D., Popović, G., Pamučar, D., Stević, Ž., Zavadskas, E. K., Smarandache, F. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanujkić, D., Karabašević, D., Popović, G., Pamučar, D., Stević, Ž., Zavadskas, E. K., Smarandache, F.","subfamily":"Ranking","year":"2021","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Stanujkić, D., Karabašević, D., Popović, G., Pamučar, D., Stević, Ž., Zavadskas, E. K., Smarandache, F. (2021). A single-valued neutrosophic extension of the EDAS method. Axioms","type":"article","doi":"10.3390/axioms10040245","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-gra","name":"N-GRA","fullName":"Neutrosophic extension of GRA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Biswas, P., Pramanik, S., Giri, B. C.","url":"https://scholargate.app/en/decision-making/n-gra","markdownUrl":"https://scholargate.app/en/decision-making/n-gra.md","definition":"N-GRA (Neutrosophic extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Biswas, P., Pramanik, S., Giri, B. C. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Biswas, P., Pramanik, S., Giri, B. C.","subfamily":"Ranking","year":"2014","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Biswas, P., Pramanik, S., Giri, B. C. (2014). Entropy based grey relational analysis method for multi-attribute decision-making under single valued neutrosophic assessments. Neutrosophic Sets and Systems","type":"article","doi":"10.5281/zenodo.22459","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-mabac","name":"N-MABAC","fullName":"Neutrosophic MABAC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Peng, Xindong Dai, Jingguo","url":"https://scholargate.app/en/decision-making/n-mabac","markdownUrl":"https://scholargate.app/en/decision-making/n-mabac.md","definition":"N-MABAC (Neutrosophic MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Peng, Xindong Dai, Jingguo in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peng, Xindong Dai, Jingguo","subfamily":"Ranking","year":"2018","type":"Single-valued neutrosophic extension of MABAC","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Peng, Xindong, Dai, Jingguo (2018). Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Computing and Applications","type":"article","doi":"10.1007/s00521-016-2607-y","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-marcos","name":"N-MARCOS","fullName":"Neutrosophic MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Pamučar, D. Ecer, F.","url":"https://scholargate.app/en/decision-making/n-marcos","markdownUrl":"https://scholargate.app/en/decision-making/n-marcos.md","definition":"N-MARCOS (Neutrosophic MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D. Ecer, F. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamučar, D. Ecer, F.","subfamily":"Ranking","year":"2021","type":"SVN extension of MARCOS with neutrosophic ideal/anti-ideal and utility functions","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Pamučar, D., Ecer, F. (2021). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). Computers & Industrial Engineering","type":"article","doi":"10.1016/j.cie.2019.106231","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-moora","name":"N-MOORA","fullName":"Neutrosophic extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Gamal, A., Ismail, M., Smarandache, F.","url":"https://scholargate.app/en/decision-making/n-moora","markdownUrl":"https://scholargate.app/en/decision-making/n-moora.md","definition":"N-MOORA (Neutrosophic extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Gamal, A., Ismail, M., Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gamal, A., Ismail, M., Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"(). N-MOORA — INTERNAL EXTENSION (no SVN-MOORA seminal in literature).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=N-MOORA%20%E2%80%94%20INTERNAL%20EXTENSION%20%28no%20SVN-MOORA%20seminal%20in%20literature%29"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-multimoora","name":"N-MULTIMOORA","fullName":"Neutrosophic extension of MULTIMOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Stanujkic, D., Zavadskas, E. K., Smarandache, F., Brauers, W. K. M., Karabasevic, D.","url":"https://scholargate.app/en/decision-making/n-multimoora","markdownUrl":"https://scholargate.app/en/decision-making/n-multimoora.md","definition":"N-MULTIMOORA (Neutrosophic extension of MULTIMOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkic, D., Zavadskas, E. K., Smarandache, F., Brauers, W. K. M., Karabasevic, D. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanujkic, D., Zavadskas, E. K., Smarandache, F., Brauers, W. K. M., Karabasevic, D.","subfamily":"Ranking","year":"2017","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Stanujkic, D., Zavadskas, E. K., Smarandache, F., Brauers, W. K. M., Karabasevic, D. (2017). A neutrosophic extension of the MULTIMOORA method. Informatica","type":"article","doi":"10.15388/Informatica.2017.125","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-of-1-trial","name":"N-of-1 Trial","fullName":"Single-Patient N-of-1 Randomized Controlled Trial","aliases":["single-patient RCT","n=1 trial","individual RCT","crossover n-of-1"],"domain":"clinical-research","family":"process-pipeline","subfamily":"trial design","year":"1990s-2010s","originator":"Kravitz, Duan, Vohra, and single-patient methodology pioneers","url":"https://scholargate.app/en/clinical-research/n-of-1-trial","markdownUrl":"https://scholargate.app/en/clinical-research/n-of-1-trial.md","definition":"An N-of-1 trial is a single-patient randomized controlled trial in which a patient alternates between treatment A and treatment B (or active drug and placebo) in repeated, randomized cross-over periods. Developed systematically in the 1990s–2010s by Kravitz, Duan, and Vohra, N-of-1 trials enable personalized medicine by determining which treatment works best for that specific individual, avoiding the assumption that population-average effects apply to all patients. They are ideal for chronic conditions with variable outcomes and heterogeneous treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kravitz, Duan, Vohra, and single-patient methodology pioneers","subfamily":"trial design","year":"1990s-2010s","type":"Research Design"},"citations":[{"ref":"Gabler, N. B., Duan, N., Vohra, S., & Kravitz, R. L. (2011). N-of-1 trials in the medical literature: a systematic review. Medical Care, 49(8), 761–768.","type":"article","doi":"10.1097/mlr.0b013e318215d90d","isbn":null,"url":null},{"ref":"Kravitz, R. L., Duan, N., & Eslick, I. (2010). Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. The Milbank Quarterly, 88(4), 503–520.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evidence-based+medicine%2C+heterogeneity+of+treatment+effects%2C+and+the+trouble+with+averages+Kravitz"},{"ref":"Vohra, S., Shamseer, L., Sampson, M., Barrowman, N., Yap, B., Uleryk, E., ... & Moher, D. (2015). CONSORT extension for reporting N-of-1 trials (CENT) 2015: explanation and elaboration. BMJ Open, 5(7), e007838.","type":"article","doi":"10.1136/bmj.h1793","isbn":null,"url":null}],"related":["randomized-controlled-trial","crossover-trial-design","pragmatic-clinical-trial","personalized-medicine","real-world-evidence"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-promethee","name":"N-PROMETHEE","fullName":"Neutrosophic extension of PROMETHEE","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2018","originator":"Abdel-Basset, M., Mohamed, M., Smarandache, F.","url":"https://scholargate.app/en/decision-making/n-promethee","markdownUrl":"https://scholargate.app/en/decision-making/n-promethee.md","definition":"N-PROMETHEE (Neutrosophic extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Abdel-Basset, M., Mohamed, M., Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abdel-Basset, M., Mohamed, M., Smarandache, F.","subfamily":"Outranking","year":"2018","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Xu, D., Wei, X., Ding, H., Bin, H. (2020). A New Method Based on PROMETHEE and TODIM for Multi-Attribute Decision-Making with Single-Valued Neutrosophic Sets. Mathematics","type":"article","doi":"10.3390/math8101816","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-psi","name":"N-PSI","fullName":"Neutrosophic PSI","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":null,"originator":"See source","url":"https://scholargate.app/en/decision-making/n-psi","markdownUrl":"https://scholargate.app/en/decision-making/n-psi.md","definition":"N-PSI (Neutrosophic PSI) is a ranking multi-criteria decision-making (MCDM) method. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"See source","subfamily":"Ranking","type":"Single-valued neutrosophic extension of PSI","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"(). N-PSI — Internal Extension (no SVN-PSI paper in literature). Mathematics","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=N-PSI%20%E2%80%94%20Internal%20Extension%20%28no%20SVN-PSI%20paper%20in%20literature%29"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-rafsi","name":"N-RAFSI","fullName":"Neutrosophic extension of RAFSI","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"undated — internal extension, no dedicated SVN-RAFSI paper","originator":"UNCONFIRMED — Irvanizam & Zahara 2024 uses SVTraNN (trapezoidal), not SVNN. No SVN-RAFSI paper found.","url":"https://scholargate.app/en/decision-making/n-rafsi","markdownUrl":"https://scholargate.app/en/decision-making/n-rafsi.md","definition":"N-RAFSI (Neutrosophic extension of RAFSI) is a ranking multi-criteria decision-making (MCDM) method introduced by UNCONFIRMED — Irvanizam & Zahara 2024 uses SVTraNN (trapezoidal), not SVNN. No SVN-RAFSI paper found. in undated — internal extension, no dedicated SVN-RAFSI paper. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"UNCONFIRMED — Irvanizam & Zahara 2024 uses SVTraNN (trapezoidal), not SVNN. No SVN-RAFSI paper found.","subfamily":"Ranking","year":"undated — internal extension, no dedicated SVN-RAFSI paper","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"(). N-RAFSI — Internal Extension (no SVN-RAFSI paper in literature).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=N-RAFSI%20%E2%80%94%20Internal%20Extension%20%28no%20SVN-RAFSI%20paper%20in%20literature%29"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-rawec","name":"N-RAWEC","fullName":"Neutrosophic extension of RAWEC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2024","originator":"Mohamed, Mai; Salam, Amira; Ye, Jun (2024) — SVTrN variant. Crisp RAWEC: Puška et al. 2024. No SVNN RAWEC paper confirmed.","url":"https://scholargate.app/en/decision-making/n-rawec","markdownUrl":"https://scholargate.app/en/decision-making/n-rawec.md","definition":"N-RAWEC (Neutrosophic extension of RAWEC) is a ranking multi-criteria decision-making (MCDM) method introduced by Mohamed, Mai; Salam, Amira; Ye, Jun (2024) — SVTrN variant. Crisp RAWEC: Puška et al. 2024. No SVNN RAWEC paper confirmed. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mohamed, Mai; Salam, Amira; Ye, Jun (2024) — SVTrN variant. Crisp RAWEC: Puška et al. 2024. No SVNN RAWEC paper confirmed.","subfamily":"Ranking","year":"2024","type":"Single-Valued Triangular Neutrosophic RAWEC — SVTrN ((l,m,u); T,I,F)","value_space":"triangular_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Mohamed, Mai, Salam, Amira, Ye, Jun (2024). Selection of Sustainable Material for the Construction of Drone Aerodynamic Wing using Neutrosophic RAWEC. Systems Assessment and Engineering Management","type":"article","doi":"10.61356/j.saem.2024.1295","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-spotis","name":"N-SPOTIS","fullName":"Neutrosophic extension of SPOTIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Abdel-aziem, A. H., Mohamed, H. K., Abdelhafeez, A.","url":"https://scholargate.app/en/decision-making/n-spotis","markdownUrl":"https://scholargate.app/en/decision-making/n-spotis.md","definition":"N-SPOTIS (Neutrosophic extension of SPOTIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Abdel-aziem, A. H., Mohamed, H. K., Abdelhafeez, A. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abdel-aziem, A. H., Mohamed, H. K., Abdelhafeez, A.","subfamily":"Ranking","year":"2023","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Abdel-aziem, A. H., Mohamed, H. K., Abdelhafeez, A. (2023). Neutrosophic Decision Making Model for Investment Portfolios Selection and Optimizing based on Wide Variety of Investment Opportunities and Many Criteria in Market. Neutrosophic Systems with Applications","type":"article","doi":"10.61356/j.nswa.2023.36","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-todim","name":"N-TODIM","fullName":"Neutrosophic extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Ji, P., Zhang, H. Y., Wang, J. Q.","url":"https://scholargate.app/en/decision-making/n-todim","markdownUrl":"https://scholargate.app/en/decision-making/n-todim.md","definition":"N-TODIM (Neutrosophic extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Ji, P., Zhang, H. Y., Wang, J. Q. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ji, P., Zhang, H. Y., Wang, J. Q.","subfamily":"Ranking","year":"2018","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Ji, P., Zhang, H. Y., Wang, J. Q. (2018). A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection. Neural Computing and Applications","type":"article","doi":"10.1007/s00521-016-2436-z","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-topsis","name":"N-TOPSIS","fullName":"Neutrosophic extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2016","originator":"Biswas, P., Pramanik, S., Giri, B. C.","url":"https://scholargate.app/en/decision-making/n-topsis","markdownUrl":"https://scholargate.app/en/decision-making/n-topsis.md","definition":"N-TOPSIS (Neutrosophic extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Biswas, P., Pramanik, S., Giri, B. C. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Biswas, P., Pramanik, S., Giri, B. C.","subfamily":"Ranking","year":"2016","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Biswas, P., Pramanik, S., Giri, B. C. (2016). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications","type":"article","doi":"10.1007/s00521-015-1891-2","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-vikor","name":"N-VIKOR","fullName":"Neutrosophic extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Bausys, R., Zavadskas, E. K.","url":"https://scholargate.app/en/decision-making/n-vikor","markdownUrl":"https://scholargate.app/en/decision-making/n-vikor.md","definition":"N-VIKOR (Neutrosophic extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Bausys, R., Zavadskas, E. K. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bausys, R., Zavadskas, E. K.","subfamily":"Ranking","year":"2015","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Tooranloo, Hossein Sayyadi, Ayatollah, Arezoo Sadat (2024). Neutrosophic VIKOR approach for multi-attribute group decision-making. Operations Research and Decisions","type":"article","doi":"10.37190/ord240208","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-waspas","name":"N-WASPAS","fullName":"Neutrosophic WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Nie, R. Wang, J. Wang, T.","url":"https://scholargate.app/en/decision-making/n-waspas","markdownUrl":"https://scholargate.app/en/decision-making/n-waspas.md","definition":"N-WASPAS (Neutrosophic WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Nie, R. Wang, J. Wang, T. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nie, R. Wang, J. Wang, T.","subfamily":"Ranking","year":"2017","type":"Single-valued neutrosophic extension of WASPAS","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Nie, R., Wang, J., Wang, T. (2017). A hybrid multiple criteria decision making model for investment selection based on WASPAS method and fuzzy sets. Journal of Intelligent & Fuzzy Systems","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+hybrid+multiple+criteria+decision+making+model+for+investment+selection+based+on+WASPAS+method+and+fuzzy+sets+Nie"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-wisp","name":"N-WISP","fullName":"Neutrosophic WISP","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Stanujkić, D. Karabašević, D. Popović, G. Smarandache, F. Stanimirović, P. S. Saračević, M. Katsikis, V. N.","url":"https://scholargate.app/en/decision-making/n-wisp","markdownUrl":"https://scholargate.app/en/decision-making/n-wisp.md","definition":"N-WISP (Neutrosophic WISP) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkić, D. Karabašević, D. Popović, G. Smarandache, F. Stanimirović, P. S. Saračević, M. Katsikis, V. N. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanujkić, D. Karabašević, D. Popović, G. Smarandache, F. Stanimirović, P. S. Saračević, M. Katsikis, V. N.","subfamily":"Ranking","year":"2022","type":"SVN extension of WISP","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Stanujkić, D., Karabašević, D., Popović, G., Smarandache, F., Stanimirović, P. S., Saračević, M., Katsikis, V. N. (2022). A Single Valued Neutrosophic Extension of the Simple WISP Method. Informatica 33(3):635-651","type":"article","doi":"10.15388/22-INFOR483","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n-wpm","name":"N-WPM","fullName":"Neutrosophic extension of WPM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Ye, J.","url":"https://scholargate.app/en/decision-making/n-wpm","markdownUrl":"https://scholargate.app/en/decision-making/n-wpm.md","definition":"N-WPM (Neutrosophic extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Ye, J. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ye, J.","subfamily":"Ranking","year":"2014","type":"Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)","value_space":"single_valued_neutrosophic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Ye, J. (2014). A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/IFS-130916","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"n400-p600-analysis","name":"N400/P600 Analysis","fullName":"Event-Related Potential (ERP) Component Analysis: N400 and P600","aliases":["ERP Analysis","Neurophysiological Semantics"],"domain":"linguistics","family":"process-pipeline","subfamily":"Neurocognitive Linguistics","year":"1980","originator":"Marta Kutas and Steven Hillyard","url":"https://scholargate.app/en/linguistics/n400-p600-analysis","markdownUrl":"https://scholargate.app/en/linguistics/n400-p600-analysis.md","definition":"N400/P600 Analysis is a neurocognitive method using electroencephalography (EEG) to measure event-related potentials (ERPs) that reflect brain responses to linguistic stimuli. The N400 component (a negative deflection at 400 ms) indexes semantic processing and surprise; the P600 component (a positive deflection at 600 ms) reflects syntactic processing and reanalysis. Discovered by Marta Kutas and Steven Hillyard in 1980, these components reveal the neural basis of language comprehension in real time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marta Kutas and Steven Hillyard","subfamily":"Neurocognitive Linguistics","year":"1980","type":"Empirical process pipeline"},"citations":[{"ref":"Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science, 207(4427), 203-205.","type":"article","doi":"10.1126/science.7350657","isbn":null,"url":null},{"ref":"Osterhout, L., & Holcomb, P. J. (1992). Event-related brain potentials elicited by syntactic anomaly. Journal of Memory and Language, 31(6), 785-806.","type":"article","doi":"10.1016/0749-596X(92)90039-Z","isbn":null,"url":null},{"ref":"Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique (2nd ed.). Cambridge, MA: MIT Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+Introduction+to+the+Event-Related+Potential+Technique+%282nd+ed.%29+Luck"}],"related":["psycholinguistics","neurolinguistics","cognitive-neuroscience"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"naiade","name":"NAIADE","fullName":"Novel Approach to Imprecise Assessment and Decision Environments","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1995","originator":"Munda, G.","url":"https://scholargate.app/en/decision-making/naiade","markdownUrl":"https://scholargate.app/en/decision-making/naiade.md","definition":"NAIADE (Novel Approach to Imprecise Assessment and Decision Environments) is a ranking multi-criteria decision-making (MCDM) method introduced by Munda, G. in 1995. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Munda, G.","subfamily":"Ranking","year":"1995","type":"Fuzzy pairwise equity/inequality comparison (semantic distance)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Munda, G. (1995). Multicriteria Evaluation in a Fuzzy Environment: Theory and Applications in Ecological Economics. Physica-Verlag, Heidelberg","type":"article","doi":"10.1007/978-3-642-49997-5","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"naive-bayes","name":"Naive Bayes","fullName":"Naive Bayes Classifier","aliases":["Naive Bayes Sınıflandırıcı","naive bayes classifier","simple Bayes","Gaussian Naive Bayes","Multinomial Naive Bayes"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1997,"originator":"Mitchell, T. M. (textbook treatment)","url":"https://scholargate.app/en/machine-learning/naive-bayes","markdownUrl":"https://scholargate.app/en/machine-learning/naive-bayes.md","definition":"Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mitchell, T. M. (textbook treatment)","year":1997,"type":"Probabilistic classifier (Bayes' theorem with conditional independence)","task":"Classification","minSample":30},"citations":[{"ref":"Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070428072","url":null}],"related":["logistic-regression","random-forest","svm-classification","decision-tree","k-nearest-neighbors"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"named-entity-recognition","name":"Named Entity Recognition","fullName":"Named Entity Recognition (NER)","aliases":["NER","entity tagging","Adlandırılmış Varlık Tanıma (NER)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/named-entity-recognition","markdownUrl":"https://scholargate.app/en/text-mining/named-entity-recognition.md","definition":"Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP sequence-labelling task","entityTypes":"Person / organisation / location / date and similar","approaches":"Rule-based / machine-learning / neural sequence labelling","output":"Text spans tagged with entity type labels"},"citations":[{"ref":"Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.","type":"article","doi":null,"isbn":null,"url":"https://nlp.cs.nyu.edu/sekine/papers/li07.pdf"},{"ref":"Lample, G. et al. (2016). Neural Architectures for Named Entity Recognition. NAACL.","type":"inproceedings","doi":"10.18653/v1/N16-1030","isbn":null,"url":null}],"related":["text-classification","information-extraction","relation-extraction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nanoindentation","name":"Nanoindentation","fullName":"Nanoindentation Hardness Testing","aliases":["nanoindentation","instrumented indentation","depth-sensing indentation"],"domain":"materials-science","family":"process-pipeline","subfamily":"Mechanical testing","year":"1992","originator":"Warren Oliver","url":"https://scholargate.app/en/materials-science/nanoindentation","markdownUrl":"https://scholargate.app/en/materials-science/nanoindentation.md","definition":"Nanoindentation, or instrumented indentation, is a technique for measuring the hardness and elastic modulus of materials by pressing a hard probe into a sample surface and continuously recording load and penetration depth. Developed by Oliver and Pharr in 1992, nanoindentation enables measurement of mechanical properties of thin films, small volumes, and nanoscale structures with spatial resolution approaching micrometers. It is the standard tool in materials science for characterizing coatings, interfaces, and mechanical properties at the submicron scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Warren Oliver","subfamily":"Mechanical testing","year":"1992","type":"Measurement method"},"citations":[{"ref":"Oliver, W. C., & Pharr, G. M. (1992). An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments. Journal of Materials Research, 7(6), 1564-1583.","type":"article","doi":"10.1557/JMR.1992.1564","isbn":null,"url":null},{"ref":"Fischer-Cripps, A. C. (2004). Nanoindentation (2nd ed.). Springer-Verlag.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nanoindentation+%282nd+ed.%29+Fischer-Cripps"},{"ref":"Hay, J. L., & Crawford, B. (2011). Measuring substrate-independent modulus of thin films. Journal of Materials Research, 26(6), 727-738.","type":"article","doi":"10.1557/jmr.2011.8","isbn":null,"url":null}],"related":["atomic-force-microscopy","vickers-hardness","dynamic-light-scattering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nanson","name":"NANSON","fullName":"Nanson — iterative Borda elimination","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"2024","originator":"Orakçı, E.","url":"https://scholargate.app/en/decision-making/nanson","markdownUrl":"https://scholargate.app/en/decision-making/nanson.md","definition":"NANSON (Nanson — iterative Borda elimination) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Orakçı, E.","subfamily":"AggregationOperator","year":"2024","type":"Borda-based iterative aggregation","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları","type":"article","doi":"10.58830/ozgur.pub623","isbn":null,"url":null}],"related":["borda","condorcet","copeland","dodgson","topsis","vikor","ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nardl-model","name":"NARDL Model","fullName":"Nonlinear Autoregressive Distributed Lag Model","aliases":["nonlinear ARDL","asymmetric ARDL","Doğrusal Olmayan ARDL (NARDL)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2014,"originator":"Shin, Yu & Greenwood-Nimmo","url":"https://scholargate.app/en/econometrics/nardl-model","markdownUrl":"https://scholargate.app/en/econometrics/nardl-model.md","definition":"The NARDL model, introduced by Shin, Yu and Greenwood-Nimmo in 2014, extends the ARDL framework to capture asymmetric long-run and short-run relationships, testing whether positive and negative changes in a regressor affect the dependent variable differently.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shin, Yu & Greenwood-Nimmo","year":2014,"type":"Asymmetric cointegration / error-correction model","estimator":"ARDL bounds testing with positive/negative partial sum decomposition","structure":"time series","minSample":50,"outcome":"continuous"},"citations":[{"ref":"Shin, Y., Yu, B. & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In: Sickles, R. & Horrace, W. (Eds.), Festschrift in Honor of Peter Schmidt. Springer.","type":"chapter","doi":"10.1007/978-1-4899-8008-3_9","isbn":null,"url":null}],"related":["ardl-model","star-model","system-gmm","ols-regression","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"narrative-analysis","name":"Narrative Analysis","fullName":"Narrative Analysis (Narrative Inquiry)","aliases":["narrative inquiry","life history analysis","biographical research","Anlatı Analizi (Narrative Analysis)"],"domain":"qualitative","family":"process-pipeline","subfamily":null,"year":"1967 (foundational); 2008 (canonical handbook)","originator":"Catherine Kohler Riessman (seminal synthesis, 2008); roots in Labov & Waletzky (1967)","url":"https://scholargate.app/en/qualitative/narrative-analysis","markdownUrl":"https://scholargate.app/en/qualitative/narrative-analysis.md","definition":"Narrative analysis is a qualitative research method, synthesised canonically by Catherine Kohler Riessman (2008), that examines how individuals storise their lived experiences and construct meaning through the telling. Drawing on life history, biographical, and narrative inquiry traditions, it treats the story itself — not just its content — as the unit of analysis, attending to temporal sequence, plot structure, and the social context in which a narrative is produced.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Catherine Kohler Riessman (seminal synthesis, 2008); roots in Labov & Waletzky (1967)","year":"1967 (foundational); 2008 (canonical handbook)","type":"Qualitative interpretive method","dataType":"In-depth interviews, life histories, biographical accounts, textual narratives","minSample":"3 (exploratory); 10+ recommended for pattern analysis","difficulty":"Intermediate (2/3)"},"citations":[{"ref":"Riessman, C.K. (2008). Narrative Methods for the Human Sciences. Sage.","type":"book","doi":null,"isbn":null,"url":"https://us.sagepub.com/en-us/nam/narrative-methods-for-the-human-sciences/book227948"}],"related":["phenomenology","case-study","discourse-analysis","grounded-theory","ethnography","content-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"narrative-inquiry","name":"Narrative Inquiry","fullName":"Narrative Inquiry Research Method","aliases":["Narrative Analysis","Narrative Research","Life Story Method"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"interpretive-story-based","year":"2000","originator":"D. Jean Clandinin and F. Michael Connelly","url":"https://scholargate.app/en/qualitative-research/narrative-inquiry","markdownUrl":"https://scholargate.app/en/qualitative-research/narrative-inquiry.md","definition":"Narrative inquiry is a qualitative research methodology that treats stories and life narratives as primary data, analyzing how individuals construct meaning and identity through storytelling. Developed by D. Jean Clandinin and F. Michael Connelly (2000), narrative inquiry examines the narratives people tell about their lives, experiences, and transitions, understanding that people make sense of experience through narrative.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. Jean Clandinin and F. Michael Connelly","subfamily":"interpretive-story-based","year":"2000","type":"Method"},"citations":[{"ref":"Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clandinin%2C%20D.%20J.%2C%20%26%20Connelly%2C%20F.%20M.%20(2000).%20Narrative%20inquiry%3A%20Experience%20and%20story%20in%20qualitative%20research.%20Jossey-Bass"},{"ref":"Lieblich, A., Tuval-Mashiach, R., & Zilber, T. (1998). Narrative research: Reading, analysis, and interpretation. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Lieblich%2C%20A.%2C%20Tuval-Mashiach%2C%20R.%2C%20%26%20Zilber%2C%20T.%20(1998).%20Narrative%20research%3A%20Reading%2C%20analysis%2C%20and%20interpretation.%20Sage%20P"},{"ref":"Chase, S. E. (2005). Narrative inquiry: Multiple lenses, approaches, voices. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed., pp. 651–679). Sage Publications.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Chase%2C%20S.%20E.%20(2005).%20Narrative%20inquiry%3A%20Multiple%20lenses%2C%20approaches%2C%20voices.%20In%20N.%20K.%20Denzin%20%26%20Y.%20S.%20Lincoln%20(Eds.)%2C%20The"}],"related":["phenomenological-research","case-study-research","life-history-method","narrative-analysis","restorying"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"narrative-review","name":"Narrative Review","fullName":"Narrative Literature Review","aliases":["traditional review","expert review","unsystematic review","narrative synthesis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"Pre-20th century practice; peer-reviewed methodological guidance from 2000s onward","originator":"Traditional academic practice; formalized discussion by Green, Johnson & Adams (2006)","url":"https://scholargate.app/en/scientometrics/narrative-review","markdownUrl":"https://scholargate.app/en/scientometrics/narrative-review.md","definition":"A narrative review is a broad, author-directed synthesis of published literature on a topic, written to summarize, interpret, and contextualize existing knowledge without following the rigorous, pre-registered search and selection protocols that characterize systematic reviews. It draws on the author's expertise to weave disparate sources into a coherent account that identifies themes, debates, and directions for future research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Traditional academic practice; formalized discussion by Green, Johnson & Adams (2006)","year":"Pre-20th century practice; peer-reviewed methodological guidance from 2000s onward","type":"Literature review methodology","dataType":"Published literature (articles, books, reports)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. Journal of Chiropractic Medicine, 5(3), 101–117.","type":"article","doi":"10.1016/S0899-3467(07)60142-6","isbn":null,"url":null},{"ref":"Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230–235.","type":"article","doi":"10.1179/2047480615Z.000000000329","isbn":null,"url":null}],"related":["systematic-literature-review","scoping-review","integrative-review","umbrella-review","rapid-review","bibliometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nasa-task-load-index","name":"NASA Task Load Index","fullName":"NASA Task Load Index (NASA-TLX)","aliases":["NASA-TLX","TLX"],"domain":"human-factors","family":"process-pipeline","subfamily":"cognitive-load-assessment","year":1988,"originator":"Sandra G. Hart & Lowell E. Staveland","url":"https://scholargate.app/en/human-factors/nasa-task-load-index","markdownUrl":"https://scholargate.app/en/human-factors/nasa-task-load-index.md","definition":"The NASA Task Load Index (NASA-TLX) is a multidimensional subjective workload assessment tool developed by Sandra Hart and Lowell Staveland at NASA's Ames Research Center in 1988. It measures six dimensions of cognitive and physical task load to quantify operator workload across diverse task domains, from aviation and process control to human-computer interaction. The TLX has become the gold standard for workload measurement in human factors research and applied settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sandra G. Hart & Lowell E. Staveland","subfamily":"cognitive-load-assessment","year":1988,"type":"Self-report"},"citations":[{"ref":"Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human Mental Workload (pp. 139-183). Elsevier Science Publishers.","type":"article","doi":"10.1016/S0166-4115(08)62386-9","isbn":null,"url":null}],"related":["cognitive-load-scale","workload-profile","situational-awareness-rating","operator-performance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nasa-tlx","name":"NASA-TLX","fullName":"NASA Task Load Index","aliases":["Task Load Index","TLX","NASA-TLX"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Cognitive Load Assessment","year":"1988","originator":"Sandra Hart and Lowell Staveland","url":"https://scholargate.app/en/human-computer-interaction/nasa-tlx","markdownUrl":"https://scholargate.app/en/human-computer-interaction/nasa-tlx.md","definition":"The NASA Task Load Index (TLX) is a multi-dimensional subjective workload assessment tool developed at NASA Ames Research Center by Sandra Hart and Lowell Staveland in the 1980s. TLX measures perceived mental workload across six dimensions—mental demand, physical demand, temporal demand, performance, effort, and frustration—allowing researchers and practitioners to understand the cognitive and affective burden of tasks and interfaces. The instrument is widely used in human factors, cognitive engineering, and HCI to identify task bottlenecks and evaluate system designs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sandra Hart and Lowell Staveland","subfamily":"Cognitive Load Assessment","year":"1988","type":"Multi-dimensional post-task questionnaire for measuring subjective mental workload"},"citations":[{"ref":"Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human Mental Workload (pp. 139–183). Elsevier.","type":"article","doi":"10.1016/S0166-4115(08)62386-9","isbn":null,"url":null},{"ref":"Hart, S. G. (1986). NASA Task Load Index. Moffett Field, CA: NASA Ames Research Center.","type":"article","doi":null,"isbn":null,"url":"https://humansystems.arc.nasa.gov/groups/TLX/"}],"related":["system-usability-scale","think-aloud-protocol","cognitive-walkthrough","attrakdiff-ueq"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nash-equilibrium","name":"Nash Equilibrium","fullName":"Nash Equilibrium (Lemke-Howson Algorithm)","aliases":["Lemke-Howson Equilibrium","Completely Labeled Pair"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1950","originator":"John Nash","url":"https://scholargate.app/en/game-theory/nash-equilibrium","markdownUrl":"https://scholargate.app/en/game-theory/nash-equilibrium.md","definition":"Nash Equilibrium is a game-theoretic solution concept where no player can unilaterally deviate to improve their payoff. Formalized by John Nash in 1950, the Lemke-Howson algorithm computationally finds equilibria in bimatrix games by identifying completely labeled vertex pairs in the strategy polytopes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Nash","subfamily":"Game-theoretic","year":"1950","type":"algorithm"},"citations":[{"ref":"Nash, J. F. (1950). Equilibrium points in N-person games. Proceedings of the National Academy of Sciences, 36(1), 48-49.","type":"article","doi":"10.1073/pnas.36.1.48","isbn":null,"url":null},{"ref":"Lemke, C. E., & Howson Jr, J. T. (1964). Equilibrium points of bimatrix games. Journal of the Society for Industrial and Applied Mathematics, 12(2), 413-423.","type":"article","doi":"10.1137/0112033","isbn":null,"url":null}],"related":["subgame-perfect-equilibrium","bayesian-nash-equilibrium","vcg-mechanism","shapley-value"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"national-identity-scale","name":"National Identity Scale","fullName":"National Identity Scale (NIS)","aliases":["NIS","National Attachment Scale","Patriotism Scale"],"domain":"political-psychology","family":"process-pipeline","subfamily":"collective-identity","year":"1989","originator":"Richard Kosterman & Seymour Feshbach","url":"https://scholargate.app/en/political-psychology/national-identity-scale","markdownUrl":"https://scholargate.app/en/political-psychology/national-identity-scale.md","definition":"The National Identity Scale measures the strength and character of individuals' identification with their nation, including attachment to national symbols, pride in national achievements, and sense of belonging to the national community. Developed by Kosterman and Feshbach (1989), it distinguishes patriotism (pride in national accomplishments, willingness to serve) from nationalism (belief in national superiority, willingness to act against outsiders). The measure has become essential in comparative politics, examining how national identity shapes political behavior, attitudes toward immigration, support for international cooperation, and electoral choices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Kosterman & Seymour Feshbach","subfamily":"collective-identity","year":"1989","type":"Self-report"},"citations":[{"ref":"Kosterman, R., & Feshbach, S. (1989). Toward a measure of patriotic and nationalistic attitudes. Political Psychology, 10(2), 257-274.","type":"article","doi":"10.2307/3791647","isbn":null,"url":null},{"ref":"Anderson, B. (2006). Imagined communities: Reflections on the origin and spread of nationalism (Revised Edition). London: Verso.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Anderson%2C%20B.%20(2006).%20Imagined%20communities%3A%20Reflections%20on%20the%20origin%20and%20spread%20of%20nationalism%20(Revised%20Edition).%20London"},{"ref":"Smith, T. W., & Jarkko, L. (2010). National pride in cross-national perspective. International Journal of Public Opinion Research, 22(1), 74-101.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=National+pride+in+cross-national+perspective+Smith"}],"related":["partisanship-scale","populism-scale","political-ideology-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"natural-experiment","name":"Natural Experiment","fullName":"Natural Experiment (Quasi-Experimental Design)","aliases":["natural quasi-experiment","naturally occurring experiment","exogenous shock design","as-if randomization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854)","originator":"Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991)","url":"https://scholargate.app/en/experimental-design/natural-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/natural-experiment.md","definition":"A natural experiment exploits a real-world event, policy, or circumstance that assigns individuals to treatment and control conditions in a way that is plausibly random — or at least exogenous to the outcome of interest. Because the researcher does not control assignment, it occupies a middle ground between a true randomized controlled trial and purely observational research, offering stronger causal leverage than conventional observational designs when the as-if randomization assumption holds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991)","year":"1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854)","type":"Quasi-experimental research design","dataType":"Observational data with exogenous assignment mechanism (administrative, survey, longitudinal)","subfamily":"Deneysel desen"},"citations":[{"ref":"Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13(2), 151–161.","type":"article","doi":"10.1080/07350015.1995.10524589","isbn":null,"url":null},{"ref":"Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107698000","url":null}],"related":["randomized-controlled-trial","difference-in-differences","regression-discontinuity-design","instrumental-variable","field-experiment","quasi-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"natural-language-generation","name":"Natural Language Generation","fullName":"Natural Language Generation (NLG)","aliases":["NLG","data-to-text","text generation","Doğal Dil Üretimi (NLG)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)","originator":"Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)","url":"https://scholargate.app/en/text-mining/natural-language-generation","markdownUrl":"https://scholargate.app/en/text-mining/natural-language-generation.md","definition":"Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)","year":"1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)","type":"NLP generative task — structured data to natural language","input":"Structured data, knowledge graphs, or semantic representations","output":"Fluent, human-readable natural language text","quality_metrics":"BLEU, BERTScore, METEOR, human evaluation","difficulty":"3 / 5"},"citations":[{"ref":"Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170.","type":"article","doi":null,"isbn":null,"url":"https://www.jair.org/index.php/jair/article/view/11173"},{"ref":"Reiter, E. & Dale, R. (2000). Building Natural Language Generation Systems. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521620369","url":null}],"related":["seq2seq","transformer-nlp","text-summarization","machine-translation","retrieval-augmented-generation","gpt-finetuning","automatic-text-evaluation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nature-relatedness-scale","name":"Nature Relatedness Scale","fullName":"Nature Relatedness Scale","aliases":["NRS","Nature Connection Scale"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"Connection to nature and environmental health","year":"2009","originator":"Nisbet, E. K.; Zelenski, J. M.; Murphy, S. A.","url":"https://scholargate.app/en/integrative-medicine/nature-relatedness-scale","markdownUrl":"https://scholargate.app/en/integrative-medicine/nature-relatedness-scale.md","definition":"The NRS is a 21-item self-report instrument measuring individuals' psychological connection to and identification with the natural world. Developed by Nisbet, Zelenski, and Murphy in 2009, it captures three dimensions of nature relatedness: self-identification with nature, environmental concern and responsibility, and immersion in natural experiences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nisbet, E. K.; Zelenski, J. M.; Murphy, S. A.","subfamily":"Connection to nature and environmental health","year":"2009","type":"Self-report dispositional measure"},"citations":[{"ref":"Nisbet, E. K., Zelenski, J. M., & Murphy, S. A. (2009). The nature relatedness scale: Linking individuals' connection with nature to environmental concern and behavior. Environment and Behavior, 41(5), 715–740.","type":"article","doi":"10.1177/0013916508318748","isbn":null,"url":null},{"ref":"White, M. P., Alcock, I., Wheeler, B. W., & Depledge, M. H. (2019). Would you be happier living in a greener urban area? A fixed-effects analysis of panel data. Ecological Economics, 121, 199–204.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Would+you+be+happier+living+in+a+greener+urban+area+White"},{"ref":"Kuo, M. (2015). How might contact with nature promote mental health? Promising mechanisms and a possible central pathway. Frontiers in Psychology, 6, 1093.","type":"article","doi":"10.3389/fpsyg.2015.01093","isbn":null,"url":null}],"related":["yoga-self-efficacy-scale","holistic-caring-inventory","spiritual-care-competence-scale","attitudes-cam-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nbeats","name":"N-BEATS","fullName":"N-BEATS (Neural Basis Expansion Analysis for Interpretable Time Series Forecasting)","aliases":["N-BEATS — Nöral Zaman Serisi Tahmini","Neural Basis Expansion Analysis","neural basis expansion"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2020,"originator":"Oreshkin, B.N. et al.","url":"https://scholargate.app/en/deep-learning/nbeats","markdownUrl":"https://scholargate.app/en/deep-learning/nbeats.md","definition":"N-BEATS is a deep learning architecture for time series forecasting, introduced by Oreshkin and colleagues in 2020, built from interpretable trend and seasonality stacks. It was the first purely neural forecasting model to reach state-of-the-art performance on the M4 competition without relying on any classical statistical components.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oreshkin, B.N. et al.","year":2020,"type":"Deep neural forecasting architecture (interpretable basis expansion)","task":"Univariate time series forecasting","minSample":100},"citations":[{"ref":"Oreshkin, B.N. et al. (2020). N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1905.10437"},{"ref":"Makridakis, S., Spiliotis, E. & Assimakopoulos, V. (2020). The M4 Competition: 100,000 Time Series and 61 Forecasting Methods. International Journal of Forecasting, 36(1), 54–74.","type":"article","doi":"10.1016/j.ijforecast.2019.04.014","isbn":null,"url":null}],"related":["temporal-fusion-transformer","informer","deepar","arima","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ndf-adf-analysis","name":"NDF/ADF Analysis","fullName":"Neutral Detergent Fiber and Acid Detergent Fiber Analysis","aliases":["fiber fractionation","detergent fiber analysis","forage quality assessment"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Chemical Fractionation","year":"1963","originator":"Peter J. Van Soest","url":"https://scholargate.app/en/veterinary-science/ndf-adf-analysis","markdownUrl":"https://scholargate.app/en/veterinary-science/ndf-adf-analysis.md","definition":"Neutral Detergent Fiber (NDF) and Acid Detergent Fiber (ADF) analysis is a chemical fractionation method that separates feed components into digestible and indigestible portions based on their resistance to sequential detergent treatments. Developed by Peter J. Van Soest in the 1960s, NDF/ADF analysis provides rapid estimates of forage quality and feed digestibility, making it fundamental to ruminant nutrition and feed evaluation worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Van Soest","subfamily":"Chemical Fractionation","year":"1963","type":"Analytical Chemistry Method"},"citations":[{"ref":"Van Soest, P. J., & Wine, R. H. (1967). Use of detergents in the analysis of fibrous feeds: II. A rapid method for the determination of fiber and lignin. Journal of the Association of Official Analytical Chemists, 50(1), 50-55.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Use+of+detergents+in+the+analysis+of+fibrous+feeds%3A+II+Van"},{"ref":"Goering, H. K., & Van Soest, P. J. (1970). Forage Fiber Analysis (Apparatus, Reagents, Procedures and Some Applications). Agricultural Handbook No. 379, USDA-ARS.","type":"article","doi":null,"isbn":null,"url":"https://www.ars.usda.gov/northeast-area/up/docs/"},{"ref":"Mertens, D. R. (2002). Gravimetric determination of amylase-treated neutral detergent fiber in feeds with refluxing in beakers or crucibles: collaborative study. Journal of AOAC International, 85(6), 1217-1240.","type":"article","doi":null,"isbn":null,"url":"https://www.aoac.org/"}],"related":["rumen-in-vitro-gas-production","apparent-total-tract-digestibility","somatic-cell-count"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ndi-neck-disability","name":"Neck Disability Index","fullName":"Neck Disability Index","aliases":["NDI","NDI Scale","Neck Disability Questionnaire"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Functional assessment","year":"1991","originator":"Vernon, Mior","url":"https://scholargate.app/en/rehabilitation/ndi-neck-disability","markdownUrl":"https://scholargate.app/en/rehabilitation/ndi-neck-disability.md","definition":"The Neck Disability Index (NDI) is a 10-item patient-reported outcome measure assessing the impact of neck pain and dysfunction on daily activities and quality of life. Developed by Vernon and Mior in 1991, NDI is the most widely used outcome measure in neck pain research and clinical practice, applicable to acute whiplash, cervical radiculopathy, chronic neck pain, and post-operative cervical conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vernon, Mior","subfamily":"Functional assessment","year":"1991","type":"Patient-reported outcome measure"},"citations":[{"ref":"Vernon, H., & Mior, S. (1991). The Neck Disability Index: a study of reliability and responsiveness. Journal of Manipulative and Physiological Therapeutics, 14(7), 409–415.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/1834753"},{"ref":"Poole, G. D., Bailey, N., & Wilkinson, K. (2009). The Neck Disability Index: A cross-validation study. Physical Therapy Reviews, 14(4), 221–228.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Neck+Disability+Index%3A+A+cross-validation+study+Poole"}],"related":["oswestry-disability-index","womac","ndi-child","dash-outcome-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ndvi","name":"NDVI","fullName":"Normalized Difference Vegetation Index","aliases":["NDVI"],"domain":"geophysics","family":"process-pipeline","subfamily":"Remote sensing vegetation monitoring","year":"1973","originator":"Rouse, Haas, Schell, and Deering","url":"https://scholargate.app/en/geophysics/ndvi","markdownUrl":"https://scholargate.app/en/geophysics/ndvi.md","definition":"The Normalized Difference Vegetation Index (NDVI) is a spectral index computed from satellite or aerial multispectral imagery that quantifies vegetation greenness and vigor. Introduced by Rouse and colleagues in 1973 using Landsat data, NDVI has become the most widely used remote sensing metric for vegetation monitoring, drought assessment, crop productivity forecasting, and land cover change detection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rouse, Haas, Schell, and Deering","subfamily":"Remote sensing vegetation monitoring","year":"1973","type":"Spectral index for vegetation assessment"},"citations":[{"ref":"Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite Symposium Proceedings, 1, 309-317.","type":"article","doi":null,"isbn":null,"url":"https://edis.ifas.ufl.edu/"},{"ref":"Jackson, R. D. (1983). Spectral indices in n-space. Remote Sensing of Environment, 13(5), 409-421.","type":"article","doi":"10.1016/0034-4257(83)90010-X","isbn":null,"url":null}],"related":["standardized-precipitation-index","general-circulation-model","standardized-precipitation-evapotranspiration-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neat","name":"NEAT","fullName":"NeuroEvolution of Augmenting Topologies (NEAT)","aliases":["Neuroevolution of Augmenting Topologies","Topology and Weight Evolving Artificial Neural Networks (variant)","Evolving Neural Networks","Topoloji Artırımlı Nöroevrim"],"domain":"deep-learning","family":"ml-model","subfamily":"Neuroevolution","year":2002,"originator":"Kenneth Stanley & Risto Miikkulainen","url":"https://scholargate.app/en/deep-learning/neat","markdownUrl":"https://scholargate.app/en/deep-learning/neat.md","definition":"NEAT is a genetic algorithm for evolving artificial neural networks introduced by Kenneth Stanley and Risto Miikkulainen in 2002. Unlike methods that evolve weights alone, NEAT simultaneously evolves both the topology (structure) and the connection weights of neural networks. It achieves this through a direct genome encoding with historical markings that enable meaningful crossover between networks of different structures, making it applicable to reinforcement learning, game playing, and control tasks without requiring a predefined architecture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kenneth Stanley & Risto Miikkulainen","year":2002,"type":"Neuroevolutionary algorithm","subfamily":"Neuroevolution","complexity":"Population-based, variable topology","encoding":"Direct graph encoding with historical markings"},"citations":[{"ref":"Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99–127.","type":"article","doi":"10.1162/106365602320169811","isbn":null,"url":null}],"related":["genetic-algorithm","neural-architecture-search","evolutionary-strategy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"necessary-condition-analysis","name":"Necessary Condition Analysis","fullName":"Necessary Condition Analysis","aliases":["NCA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Necessity-Sufficiency Analysis","year":"2016","originator":"Jan Dul","url":"https://scholargate.app/en/psychometrics/necessary-condition-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/necessary-condition-analysis.md","definition":"Necessary Condition Analysis (NCA) is a set-theoretic method developed by Dul (2016) that identifies conditions necessary (but not necessarily sufficient) for an outcome to occur. Unlike regression, which estimates average effects, NCA identifies absolute thresholds: conditions that must be present at a certain level for the outcome to be possible, regardless of other factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jan Dul","subfamily":"Necessity-Sufficiency Analysis","year":"2016","type":"Set-theoretic configurational analysis"},"citations":[{"ref":"Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of \"necessary but not sufficient\" causality. Organizational Research Methods, 19(1), 10-52.","type":"article","doi":"10.1177/1094428115584005","isbn":null,"url":null},{"ref":"Dul, J. (2018). A strategy for dealing with flaws and limitations in quantitative research. Organizational Research Methods, 21(1), 104-125.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+strategy+for+dealing+with+flaws+and+limitations+in+quantitative+research+Dul"},{"ref":"Dul, J. (2019). Necessary Condition Analysis (NCA) version 3.3: A User Manual. Europeanstudies.org. Retrieved from https://www.erim.eur.nl/people/jan-dul/","type":"article","doi":null,"isbn":null,"url":"https://www.erim.eur.nl/people/jan-dul/"}],"related":["fsqca","process-tracing","exploratory-structural-equation-modeling","pls-sem","rule-space-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"need-for-cognition-political","name":"Need for Cognition in Politics Scale","fullName":"Need for Cognition in Political Context Scale (NFC-P)","aliases":["NFC-P","Political Need for Cognition"],"domain":"political-psychology","family":"process-pipeline","subfamily":"personality-trait","year":"1982","originator":"John T. Cacioppo & Richard E. Petty","url":"https://scholargate.app/en/political-psychology/need-for-cognition-political","markdownUrl":"https://scholargate.app/en/political-psychology/need-for-cognition-political.md","definition":"The Need for Cognition in Politics Scale measures individual differences in the tendency to engage in and enjoy effortful cognitive processing related to political information and decision-making. Originally conceptualized by Cacioppo and Petty (1982), the trait reflects whether individuals seek, process, and rely on substantive information when forming political attitudes. High NFC individuals prefer detailed policy discussions; low NFC individuals may rely on heuristics, endorsements, or emotional appeals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John T. Cacioppo & Richard E. Petty","subfamily":"personality-trait","year":"1982","type":"Self-report"},"citations":[{"ref":"Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116-131.","type":"article","doi":"10.1037/0022-3514.42.1.116","isbn":null,"url":null},{"ref":"Petty, R. E., & Cacioppo, J. T. (1988). Attitudes and persuasion: Classic and contemporary approaches. Dubuque, IA: William C. Brown.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Petty%2C%20R.%20E.%2C%20%26%20Cacioppo%2C%20J.%20T.%20(1988).%20Attitudes%20and%20persuasion%3A%20Classic%20and%20contemporary%20approaches.%20Dubuque%2C%20IA%3A%20Will"},{"ref":"Geuens, M., De Pelsmacker, P., & Moons, I. (2010). Developing a short version of the Need for Cognition scale. Journal of Personality Assessment, 92(1), 37-44.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Developing+a+short+version+of+the+Need+for+Cognition+scale+Geuens"}],"related":["political-ideology-scale","conspiracy-mentality-questionnaire","voter-cynicism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"need-for-cognition-scale","name":"Need for Cognition Scale","fullName":"Need for Cognition Scale (NCS)","aliases":["NCS","Cacioppo Need for Cognition","Intellectual Engagement Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Personality assessment","year":"1982","originator":"John Cacioppo and Richard Petty","url":"https://scholargate.app/en/social-psychology/need-for-cognition-scale","markdownUrl":"https://scholargate.app/en/social-psychology/need-for-cognition-scale.md","definition":"The Need for Cognition Scale (NCS) is an 18-item measure assessing individual differences in the tendency to engage in and enjoy cognitive effort. Developed by John Cacioppo and Richard Petty in 1982, the NCS operationalizes need for cognition as a stable personality trait reflecting preference for thinking about complex problems, enthusiasm for intellectual pursuits, and intrinsic enjoyment of cognitive challenge. A brief 9-item version (NCS-9) is also available. The scale has become standard in psychology research examining motivation for learning, persuasion, decision-making, and academic achievement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Cacioppo and Richard Petty","subfamily":"Personality assessment","year":"1982","type":"Intellectual engagement and cognitive motivation measure"},"citations":[{"ref":"Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116–131.","type":"article","doi":"10.1037/0022-3514.42.1.116","isbn":null,"url":null},{"ref":"Cacioppo, J. T., Petty, R. E., & Kao, C. F. (1984). The efficient assessment of need for cognition. Journal of Personality Assessment, 48(3), 306–307.","type":"article","doi":"10.1207/s15327752jpa4803_13","isbn":null,"url":null},{"ref":"Coutinho, S. A. (2007). The relationship between goals, metacognition, and academic success. Educate Journal, 7(1), 39–47.","type":"article","doi":null,"isbn":null,"url":"https://educatejournal.org"}],"related":["bfi-big-five-inventory","generalized-self-efficacy-scale","resilience-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"needs-assessment-palliative","name":"Needs Assessment Tool Palliative Care","fullName":"Needs Assessment Tool for Palliative Care (NAPC)","aliases":["NAPC","Needs Assessment Palliative Care"],"domain":"palliative-care","family":"process-pipeline","subfamily":"comprehensive-needs-assessment","year":"2004","originator":"Developed by palliative care researchers and clinicians to address systematic gap assessment","url":"https://scholargate.app/en/palliative-care/needs-assessment-palliative","markdownUrl":"https://scholargate.app/en/palliative-care/needs-assessment-palliative.md","definition":"The Needs Assessment Tool for Palliative Care (NAPC) is a comprehensive, multidomain assessment framework designed to systematically identify unmet palliative and supportive care needs in patients with advanced illness and their families. Rather than a numerical scale, the NAPC functions as a structured clinical interview and resource allocation guide, helping palliative care teams deliver holistic, person-centered care by addressing physical, psychological, social, spiritual, and practical dimensions simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed by palliative care researchers and clinicians to address systematic gap assessment","subfamily":"comprehensive-needs-assessment","year":"2004","type":"Clinician-rated interview or patient self-report"},"citations":[{"ref":"Gardiner, C., Brereton, L., Frey, R., Wilkinson, J., & Ingleton, C. (2011). Exploring the financial impact of palliative care on patients and families. Current Opinion in Supportive and Palliative Care, 5(1), 58–65.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Exploring+the+financial+impact+of+palliative+care+on+patients+and+families+Gardiner"},{"ref":"Ahmed, N., Bestall, J. C., Ahmedzai, S. H., Payne, S. A., Clark, D., & Noble, B. (2004). Systematic review of the problems and issues of accessing specialist palliative care by patients with non-malignant illnesses. Journal of Palliative Medicine, 7(2), 290–297.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Systematic+review+of+the+problems+and+issues+of+accessing+specialist+palliative+care+by+patients+with+non-malignant+illnesses+Ahmed"}],"related":["mcgill-quality-of-life","caregiver-qol-cancer","support-team-assessment-schedule","patient-dignity-inventory","comfort-care-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"negation-detection","name":"Negation Detection","fullName":"Negation Detection (Negation Scope Identification)","aliases":["negation scope identification","negation cue detection","Olumsuzlama Tespiti (Negation Detection)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2001 (NegEx); scope learning formalised by 2009","originator":"Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)","url":"https://scholargate.app/en/text-mining/negation-detection","markdownUrl":"https://scholargate.app/en/text-mining/negation-detection.md","definition":"Negation detection is a natural-language-processing task that locates negation cues in text — words or phrases such as 'no', 'not', 'without', or 'denies' — and determines the span of text (the scope) whose meaning those cues invert. Formalised for clinical text by Chapman et al. (2001) with the NegEx algorithm and extended to scope learning in biomedical literature by Morante and Daelemans (2009), the method is essential wherever the difference between a finding being present and its being explicitly ruled out carries real consequences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)","year":"2001 (NegEx); scope learning formalised by 2009","type":"NLP information-extraction task","input":"Plain or structured text (clinical notes, scientific articles, etc.)","output":"Annotated negation cues and their semantic scope over target expressions","domainFit":"Clinical NLP, biomedical text mining, sentiment analysis"},"citations":[{"ref":"Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., & Buchanan, B.G. (2001). A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. Journal of the American Medical Informatics Association, 8(6), 606-614.","type":"article","doi":"10.1006/jbin.2001.1029","isbn":null,"url":null},{"ref":"Morante, R. & Daelemans, W. (2009). Learning the Scope of Hedge Cues in BioMedical Texts. Proceedings of the BioNLP 2009 Workshop, Association for Computational Linguistics, 28-36.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/W09-1304"}],"related":["dependency-parsing","named-entity-recognition","sentiment-analysis","clinical-text-mining","coreference-resolution","information-extraction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"negative-binomial-regression","name":"Negative Binomial Regression","fullName":"Negative Binomial Regression","aliases":["NB regression","NB2 regression","negatif binom regresyonu"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2011,"originator":"Hilbe (textbook treatment); generalized linear model framework","url":"https://scholargate.app/en/econometrics/negative-binomial-regression","markdownUrl":"https://scholargate.app/en/econometrics/negative-binomial-regression.md","definition":"Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hilbe (textbook treatment); generalized linear model framework","year":2011,"type":"Generalized linear model for count data","estimator":"Maximum likelihood (log link)","outcome":"count (non-negative integers)","minSample":100},"citations":[{"ref":"Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511973420","isbn":null,"url":null}],"related":["poisson-regression","zero-inflated-poisson-regression","logistic-regression","ols-regression","panel-fixed-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nei-vfq-25","name":"NEI-VFQ-25","fullName":"National Eye Institute Visual Function Questionnaire-25","aliases":["VFQ-25"],"domain":"ophthalmology","family":"process-pipeline","subfamily":"vision-related quality of life","year":"2001","originator":"Mangione CM, Lee PP et al.","url":"https://scholargate.app/en/ophthalmology/nei-vfq-25","markdownUrl":"https://scholargate.app/en/ophthalmology/nei-vfq-25.md","definition":"The NEI-VFQ-25 is a 25-item self-report questionnaire measuring the impact of vision loss on health-related quality of life across multiple functional and psychological domains. Developed by the National Eye Institute (Mangione et al., 2001), it is the most widely used vision-specific QoL instrument in ophthalmology and serves as the gold standard for quantifying patient-reported visual disability across diverse eye conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mangione CM, Lee PP et al.","subfamily":"vision-related quality of life","year":"2001","type":"Self-report"},"citations":[{"ref":"Mangione, C. M., Lee, P. P., Gutierrez, P. R., et al. (2001). Development of the 25-item National Eye Institute Visual Function Questionnaire. Arch Ophthalmol, 119(7), 1050-1058.","type":"article","doi":"10.1001/archopht.119.7.1050","isbn":null,"url":null},{"ref":"Mangione, C. M., Berry, S., Spritzer, K., et al. (1998). Identifying the content area for the 51-item National Eye Institute Visual Function Questionnaire. Arch Ophthalmol, 116(2), 227-233.","type":"article","doi":"10.1001/archopht.116.2.227","isbn":null,"url":null}],"related":["visual-function-index","glaucoma-quality-of-life","ocular-surface-disease-index","low-vision-quality-of-life"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nelson-aalen","name":"Nelson-Aalen Estimator","fullName":"Nelson-Aalen Cumulative Hazard Estimator","aliases":["Nelson-Aalen cumulative hazard","Aalen estimator","empirical cumulative hazard","Nelson-Aalen kümülatif hazard tahmincisi"],"domain":"survival","family":"survival","subfamily":null,"year":1972,"originator":"Wayne Nelson & Odd Aalen","url":"https://scholargate.app/en/survival/nelson-aalen","markdownUrl":"https://scholargate.app/en/survival/nelson-aalen.md","definition":"The Nelson-Aalen estimator is a non-parametric estimator of the cumulative hazard function from right-censored time-to-event data. Developed by Wayne Nelson for reliability hazard plotting in 1972 and placed on a rigorous counting-process foundation by Odd Aalen in 1978, it accumulates the ratio of observed events to the number at risk at each event time, providing the natural hazard-scale companion to the Kaplan-Meier survival curve.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wayne Nelson & Odd Aalen","year":1972,"type":"Non-parametric cumulative hazard estimator","handles":"Right-censoring","estimator":"Counting-process increment of events over the at-risk set","output":"Cumulative hazard function H(t)"},"citations":[{"ref":"Nelson, W. (1972). Theory and applications of hazard plotting for censored failure data. Technometrics, 14(4), 945–966.","type":"article","doi":"10.1080/00401706.1972.10488991","isbn":null,"url":null},{"ref":"Aalen, O. (1978). Nonparametric inference for a family of counting processes. Annals of Statistics, 6(4), 701–726.","type":"article","doi":"10.1214/aos/1176344247","isbn":null,"url":null}],"related":["kaplan-meier","cox-regression","log-rank-test","weibull-regression","frailty-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nemenyi-test","name":"Nemenyi Test","fullName":"Nemenyi Post-Hoc Test for Friedman","aliases":["Nemenyi Testi — Friedman Post-Hoc","Nemenyi multiple comparison test","Nemenyi procedure"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1963,"originator":"Peter Nemenyi","url":"https://scholargate.app/en/statistics/nemenyi-test","markdownUrl":"https://scholargate.app/en/statistics/nemenyi-test.md","definition":"The Nemenyi test is a nonparametric post-hoc multiple comparison procedure introduced by Peter Nemenyi in his 1963 Princeton doctoral thesis. It is applied after a significant Friedman test to identify which specific pairs of conditions differ from each other in a repeated-measures or blocked design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Nemenyi","year":1963,"family":"Hypothesis test","type":"Nonparametric post-hoc multiple comparison","parametric":false,"design":"Repeated measures / related groups","prerequisiteTest":"Friedman test","minSample":10,"difficulty":"beginner"},"citations":[{"ref":"Nemenyi, P. (1963). Distribution-Free Multiple Comparisons. PhD thesis, Princeton University.","type":"thesis","doi":null,"isbn":null,"url":"https://www.proquest.com/docview/302161208"}],"related":["friedman-test","conover-iman-test","kruskal-wallis","wilcoxon-signed-rank","repeated-measures-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neo-pi-r","name":"NEO Personality Inventory — Revised","fullName":"NEO Personality Inventory — Revised (NEO PI-R)","aliases":["NEO PI-R","Costa and McCrae Personality Inventory"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Personality assessment","year":"1992","originator":"Paul Costa and Robert McCrae","url":"https://scholargate.app/en/social-psychology/neo-pi-r","markdownUrl":"https://scholargate.app/en/social-psychology/neo-pi-r.md","definition":"The NEO PI-R is a comprehensive 240-item self-report personality assessment that measures five major personality dimensions and thirty lower-order facets. Developed by Paul Costa and Robert McCrae in the early 1990s, it operationalizes the Five-Factor Model of personality—one of the most empirically validated trait taxonomies in psychological science. The measure has become the gold standard for personality assessment in clinical, research, and occupational settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Costa and Robert McCrae","subfamily":"Personality assessment","year":"1992","type":"Self-report personality questionnaire"},"citations":[{"ref":"Costa, P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Psychological Assessment Resources.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Revised+NEO+Personality+Inventory+%28NEO+PI-R%29+and+NEO+Five-Factor+Inventory+%28NEO-FFI%29+professional+manual+Costa"},{"ref":"McCrae, R. R., & Costa, P. T. (1989). The structure of interpersonal traits: Expanding the interpersonal circumplex. Journal of Personality and Social Psychology, 48(6), 1670–1680.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+structure+of+interpersonal+traits%3A+Expanding+the+interpersonal+circumplex+McCrae"},{"ref":"Costa, P. T., & McCrae, R. R. (2008). The Revised NEO Personality Inventory (NEO PI-R). In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), The SAGE handbook of personality theory and assessment: Vol. 2. Personality measurement and testing (pp. 179–198). SAGE Publications.","type":"book","doi":null,"isbn":"978-1-4129-4170-8","url":null}],"related":["bfi-big-five-inventory","rosenberg-self-esteem-scale","dark-triad-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neonatal-acute-physiology-score","name":"SNAP-II","fullName":"Score for Neonatal Acute Physiology-II","aliases":["SNAP-II","SNAP"],"domain":"neonatology","family":"process-pipeline","subfamily":"severity-stratification","year":2001,"originator":"David K. Richardson","url":"https://scholargate.app/en/neonatology/neonatal-acute-physiology-score","markdownUrl":"https://scholargate.app/en/neonatology/neonatal-acute-physiology-score.md","definition":"SNAP-II is a six-variable physiological scoring system designed to quantify acute illness severity in very low birth weight (VLBW) neonates and predict mortality risk. Developed by Richardson and colleagues in 2001 as a refinement of the original SNAP, it incorporates readily available bedside physiological variables (mean blood pressure, lowest body temperature, hypoxemia, seizures, urine output, and sepsis indicators) measured within the first 12 hours of life. SNAP-II is widely used in neonatal quality improvement, clinical research, and benchmarking of NICU outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David K. Richardson","subfamily":"severity-stratification","year":2001,"type":"Clinician-rated"},"citations":[{"ref":"Richardson, D. K., Gray, J. E., Gortmaker, S. L., Goldmann, D. A., Purohit, D. M., & Paige, D. (2001). Declining Severity Adjusted Mortality: Evidence of Improving Neonatal Intensive Care. Pediatrics, 108(2), 331-337.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Declining+Severity+Adjusted+Mortality%3A+Evidence+of+Improving+Neonatal+Intensive+Care+Richardson"},{"ref":"Richardson, D. K., Corcoran, J. D., Escobar, G. J., & Lee, S. K. (1993). SNAP-II: Simplified Newborn Physiology Score and Estimation of Mortality Risk in Very Low Birth Weight Infants. Pediatrics, 91(1), 33-41.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SNAP-II%3A+Simplified+Newborn+Physiology+Score+and+Estimation+of+Mortality+Risk+in+Very+Low+Birth+Weight+Infants+Richardson"}],"related":["clinical-risk-index-babies","neonatal-pain-agitation-sedation","neonatal-behavioral-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neonatal-behavioral-assessment","name":"NBAS","fullName":"Neonatal Behavioral Assessment Scale","aliases":["NBAS","Brazelton Scale"],"domain":"neonatology","family":"process-pipeline","subfamily":"neurobehavioral-assessment","year":1973,"originator":"T. Berry Brazelton","url":"https://scholargate.app/en/neonatology/neonatal-behavioral-assessment","markdownUrl":"https://scholargate.app/en/neonatology/neonatal-behavioral-assessment.md","definition":"The NBAS, commonly known as the Brazelton Scale, is a comprehensive neurobehavioral assessment tool designed to evaluate the behavioral competencies of newborns. Developed by T. Berry Brazelton and colleagues in 1973 and refined through multiple editions, it examines 28 behavioral items and 18 elicited reflex items to characterize a newborn's neurological integrity, behavioral capabilities, and individuality. The NBAS has become a foundational instrument in developmental pediatrics, neonatal neurology, and early intervention research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"T. Berry Brazelton","subfamily":"neurobehavioral-assessment","year":1973,"type":"Clinician-administered"},"citations":[{"ref":"Brazelton, T. B., & Nugent, J. K. (1995). Neonatal Behavioral Assessment Scale (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Neonatal+Behavioral+Assessment+Scale+%283rd+ed.%29+Brazelton"},{"ref":"Nugent, J. K., Keefer, C. H., Minear, S., Johnson, L. C., & Blanchard, Y. (2007). Understanding Newborn Behavior and Early Relationships: The Newborn Behavioral Observations (NBO) System Handbook. Brookes Publishing.","type":"article","doi":null,"isbn":"978-1557665416","url":null}],"related":["newborn-behavioral-observations","parent-infant-interaction-scale","preterm-infant-pain-profile"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neonatal-pain-agitation-sedation","name":"N-PASS","fullName":"Neonatal Pain, Agitation and Sedation Scale","aliases":["N-PASS"],"domain":"neonatology","family":"process-pipeline","subfamily":"pain-sedation-assessment","year":2008,"originator":"Pam Hummel","url":"https://scholargate.app/en/neonatology/neonatal-pain-agitation-sedation","markdownUrl":"https://scholargate.app/en/neonatology/neonatal-pain-agitation-sedation.md","definition":"The N-PASS is a five-item behavioral and physiological assessment tool designed to measure pain, agitation, and sedation in neonates across the full spectrum from profound sedation to severe pain. Developed by Hummel et al. in 2008, it is validated for both ventilated and non-ventilated infants in NICU settings and provides a rapid bedside assessment combining facial expression, extremity tone, vital sign changes, state of consciousness, and cry characteristics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pam Hummel","subfamily":"pain-sedation-assessment","year":2008,"type":"Clinician-rated"},"citations":[{"ref":"Hummel, P., Puchalski, M., Creech, S. D., & Weiss, M. G. (2008). Clinical Reliability and Validity of the N-PASS: Neonatal Pain, Agitation and Sedation Scale with Prolonged Ventilation Patients. Journal of Perinatology, 28(1), 55-60.","type":"article","doi":"10.1038/sj.jp.7211861","isbn":null,"url":null},{"ref":"Hummel, P. (2008). Neonatal Pain, Agitation and Sedation Scale. Advances in Neonatal Care, 8(5), 285-287.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Neonatal+Pain%2C+Agitation+and+Sedation+Scale+Hummel"}],"related":["preterm-infant-pain-profile","neonatal-behavioral-assessment","neonatal-acute-physiology-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nested-case-control","name":"Nested case-control","fullName":"Nested Case-Control Study","aliases":["NCC study","nested CC design","case-control within cohort","density sampling case-control"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1973–1977","originator":"Nathan Mantel (1973); D. C. Thomas (1977 formalization)","url":"https://scholargate.app/en/epidemiology/nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/nested-case-control.md","definition":"A nested case-control study is an efficient observational design embedded within a defined cohort. For each participant who develops the outcome of interest (a case), a small number of matched controls are sampled from those still at risk at the same point in time. This density-sampling strategy yields odds ratios that approximate incidence-rate ratios from the full cohort at a fraction of the data-collection cost — making it the preferred alternative when measuring exposures for all cohort members would be prohibitively expensive or technically demanding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nathan Mantel (1973); D. C. Thomas (1977 formalization)","year":"1973–1977","type":"Hybrid observational study design","dataType":"Time-to-event data from an existing cohort (exposure, covariate, and outcome records)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thomas+1977+nested+case-control+cohort+density+sampling"},{"ref":"Mantel, N. (1973). Synthetic retrospective studies and related topics. Biometrics, 29(3), 479–486.","type":"article","doi":"10.2307/2529171","isbn":null,"url":null}],"related":["cohort-study","case-control-study","case-crossover-design","prospective-cohort-study","cox-proportional-hazards","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nested-logit","name":"Nested Logit","fullName":"Nested Logit Discrete Choice Model","aliases":["Tree Logit Model","Hierarchical Logit Model","Generalized Extreme Value Logit","İç İçe Logit Modeli"],"domain":"econometrics","family":"regression-model","subfamily":"Discrete choice","year":1985,"originator":"Daniel McFadden; Ben-Akiva & Lerman","url":"https://scholargate.app/en/econometrics/nested-logit","markdownUrl":"https://scholargate.app/en/econometrics/nested-logit.md","definition":"The Nested Logit model is a discrete choice framework that groups mutually exclusive alternatives into hierarchical nests, allowing correlated unobserved utilities within each nest while maintaining independence across nests. Introduced formally by Ben-Akiva and Lerman (1985) and grounded in McFadden's Generalized Extreme Value (GEV) theory, it extends the standard Multinomial Logit by relaxing the restrictive Independence of Irrelevant Alternatives assumption within predefined groups of similar alternatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel McFadden; Ben-Akiva & Lerman","year":1985,"type":"Discrete choice regression model","subfamily":"Discrete choice","estimationMethod":"Maximum likelihood estimation","dependentVariable":"Categorical (mutually exclusive alternatives grouped in nests)"},"citations":[{"ref":"Ben-Akiva, M., & Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-02217-0","url":null}],"related":["multinomial-logit","mixed-logit","spatial-interaction-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"net-promoter-score","name":"Net Promoter Score","fullName":"Net Promoter Score","aliases":["NPS","Net Promoter System"],"domain":"marketing","family":"process-pipeline","subfamily":"Customer satisfaction and loyalty measurement","year":"2003","originator":"Frederick F. Reichheld","url":"https://scholargate.app/en/marketing/net-promoter-score","markdownUrl":"https://scholargate.app/en/marketing/net-promoter-score.md","definition":"Net Promoter Score (NPS) is a customer loyalty and satisfaction metric developed by Fred Reichheld in 2003, measured through a single question: How likely is it that you would recommend our company/product/service to a friend or colleague? The metric categorizes respondents into promoters, passives, and detractors, providing a straightforward indicator of customer advocacy and business growth potential.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frederick F. Reichheld","subfamily":"Customer satisfaction and loyalty measurement","year":"2003","type":"Loyalty metric"},"citations":[{"ref":"Reichheld, F. F. (2003). The One Number You Need to Grow. Harvard Business Review, 81(12), 46-54.","type":"article","doi":null,"isbn":null,"url":"https://hbr.org/2003/12/the-one-number-you-need-to-grow"},{"ref":"Reichheld, F. F. (2006). The Ultimate Question: Driving Good Profits and True Growth. Harvard Business School Press.","type":"book","doi":null,"isbn":"978-1591397298","url":null},{"ref":"Keiningham, T. L., Cooil, B., Aksoy, L., & Andreassen, T. W. (2007). A Longitudinal Examination of Net Promoter and Firm Financial Performance. Journal of Marketing Research, 44(3), 468-482.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Longitudinal+Examination+of+Net+Promoter+and+Firm+Financial+Performance+Keiningham"}],"related":["brand-equity-measurement","customer-satisfaction-analysis","customer-lifetime-value","advertising-effectiveness-study","customer-journey-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"netnography","name":"Netnography","fullName":"Netnography (online ethnography)","aliases":["online ethnography","virtual ethnography","cyber-ethnography","digital ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Ethnography","year":"1997 (coined); 2010 (first comprehensive methodology book)","originator":"Robert V. Kozinets","url":"https://scholargate.app/en/qualitative/netnography","markdownUrl":"https://scholargate.app/en/qualitative/netnography.md","definition":"Netnography is a qualitative research method that adapts the principles of cultural ethnography to the study of online communities and social media environments. Coined by Robert Kozinets in 1997 and systematised in his 2010 handbook, netnography treats digital spaces — forums, social networks, blogs, review sites — as naturally occurring field sites where communities gather, share meanings, and construct identities. The method combines unobtrusive observation of digital traces with active participation and, where appropriate, direct member interaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert V. Kozinets","year":"1997 (coined); 2010 (first comprehensive methodology book)","type":"Qualitative research method","dataType":"Online text, images, video, and social media posts; archived forum discussions; digital community interactions","typicalSampleSize":"1 community or multiple online communities; hundreds to thousands of posts sampled purposively","subfamily":"Ethnography"},"citations":[{"ref":"Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.","type":"book","doi":null,"isbn":"978-1847875907","url":null},{"ref":"Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Netnography+The+Essential+Guide+to+Qualitative+Social+Media+Research+Kozinets+2020"}],"related":["ethnography","grounded-theory","content-analysis","discourse-analysis","thematic-analysis","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-analysis-of-case-law","name":"Network Analysis of Case Law","fullName":"Network Analysis of Case Law and Legal Precedent","aliases":["citation network analysis","legal precedent mapping","case law graph analysis"],"domain":"forensics","family":"process-pipeline","subfamily":"Graph-based analysis","year":"2011","originator":"James Fowler","url":"https://scholargate.app/en/forensics/network-analysis-of-case-law","markdownUrl":"https://scholargate.app/en/forensics/network-analysis-of-case-law.md","definition":"Network analysis of case law applies graph-theoretic and network science methods to study the structure and dynamics of legal precedent systems. Developed systematically by James Fowler and colleagues in 2011, this method treats legal citations as directed edges in a network where nodes represent court decisions and edges represent precedent relationships. By analyzing the topology of these networks, researchers uncover patterns in how law evolves, which precedents are most influential, and how legal doctrine spreads across jurisdictions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Fowler","subfamily":"Graph-based analysis","year":"2011","type":"Network science and legal informatics method"},"citations":[{"ref":"Lupo, G., & Bailey, J. (2014). Artificial intelligence and legal practice. Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/artificial-intelligence-and-legal-practice/lupo/978-0-12-801367-9"},{"ref":"Fowler, J. H., Johnson, S. L., & Spriggs, J. F. (2011). Network analysis and the law: measuring the web of law. Journal of Empirical Legal Studies, 8(1), 171-198.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Network+analysis+and+the+law%3A+measuring+the+web+of+law+Fowler"},{"ref":"Bommarito, M., & Katz, D. M. (2012). Properties of the United States code: Network analysis and textual entropy. SSRN Electronic Journal.","type":"article","doi":null,"isbn":null,"url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2012844"}],"related":["geographic-profiling","risk-terrain-modeling","crime-linkage-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-co-citation-analysis","name":"Network-based Co-citation Analysis","fullName":"Network-based Co-citation Analysis","aliases":["co-citation network analysis","bibliometric network co-citation","co-citation mapping","CCA network approach"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1973 (co-citation); network-analytic extension widely adopted 2000s–2010s","originator":"Henry Small (co-citation foundation); network visualization extended by Chaomei Chen and others","url":"https://scholargate.app/en/scientometrics/network-based-co-citation-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/network-based-co-citation-analysis.md","definition":"Network-based co-citation analysis is a bibliometric technique that measures how often pairs of documents are cited together by later works, then models those relationships as a weighted network. Nodes represent documents (or authors or journals), edges represent co-citation frequency, and network algorithms identify clusters of intellectually related literature. It is widely used in systematic and scoping reviews to map the intellectual structure of a research field.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Small (co-citation foundation); network visualization extended by Chaomei Chen and others","year":"1973 (co-citation); network-analytic extension widely adopted 2000s–2010s","type":"Bibliometric network analysis","dataType":"Citation databases (Web of Science, Scopus, PubMed); co-citation frequency matrices","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.","type":"article","doi":"10.1002/asi.4630240406","isbn":null,"url":null},{"ref":"Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.","type":"article","doi":"10.1002/asi.20317","isbn":null,"url":null}],"related":["co-citation-analysis","bibliographic-coupling","bibliometric-analysis","science-mapping","co-word-analysis","scientometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-copy-number-variation-analysis","name":"Network-based copy number variation analysis","fullName":"Network-Based Copy Number Variation Analysis","aliases":["network CNV analysis","CNV network propagation","graph-based CNV analysis","network-integrated copy number analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2011–2015","originator":"Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)","url":"https://scholargate.app/en/bioinformatics/network-based-copy-number-variation-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-copy-number-variation-analysis.md","definition":"Network-based copy number variation analysis integrates genome-wide CNV data with biological interaction networks — such as protein-protein interaction (PPI) or pathway networks — to identify functionally coherent regions, driver genes, and altered subnetworks that raw CNV calling alone would miss. By propagating CNV signals through the network graph, the method reveals coordinated genomic dosage imbalances that converge on common biological functions, making it especially powerful in cancer genomics and rare-disease studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)","year":"2011–2015","type":"Computational network analysis pipeline","dataType":"Segmented CNV profiles (array CGH, SNP array, or WGS), biological network graphs (PPI, pathway, co-expression)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Vandin, F., Upfal, E., & Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome Research, 22(2), 375–385.","type":"article","doi":"10.1101/gr.120477.111","isbn":null,"url":null},{"ref":"Leiserson, M. D. M., Vandin, F., Wu, H.-T., Dobson, J. R., Eldridge, J. V., Thomas, J. L., Papoutsaki, A., Kim, Y., Niu, B., McLellan, M., Lawrence, M. S., Gonzalez-Perez, A., Tamborero, D., Cheng, Y., Ryslik, G. A., Lopez-Bigas, N., Getz, G., Ding, L., & Raphael, B. J. (2015). Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nature Genetics, 47(2), 106–114.","type":"article","doi":"10.1038/ng.3168","isbn":null,"url":null}],"related":["copy-number-variation-analysis","network-based-gwas","pathway-enrichment-analysis","variant-calling","network-based-rna-seq-differential-expression","gene-set-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-epigenome-wide-association-study","name":"Network-based epigenome-wide association study","fullName":"Network-based Epigenome-Wide Association Study","aliases":["network EWAS","network-integrated EWAS","graph-based EWAS","network-based DNA methylation analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s, consolidating 2012–2018","originator":"Adapted from EWAS (Rakyan et al., 2011) and network-based genomic methods (e.g., Ideker & Sharan, 2008)","url":"https://scholargate.app/en/bioinformatics/network-based-epigenome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-epigenome-wide-association-study.md","definition":"Network-based EWAS extends conventional epigenome-wide association studies by overlaying differentially methylated positions or regions onto biological interaction networks — such as protein-protein interaction, co-expression, or gene regulatory networks — to identify functionally coherent epigenetic modules rather than isolated CpG hits. This integration increases statistical power for detecting weak signals and reveals coordinated epigenetic dysregulation across pathways.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from EWAS (Rakyan et al., 2011) and network-based genomic methods (e.g., Ideker & Sharan, 2008)","year":"2010s, consolidating 2012–2018","type":"Integrative epigenomic analysis","dataType":"DNA methylation array (e.g., Illumina 450K/EPIC), protein-protein interaction or gene regulatory networks","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Epigenome-wide+association+studies+for+common+human+diseases+Rakyan+2011"},{"ref":"Wang, S., Huang, M., Liu, C., Ma, J., & Deng, M. (2017). Network-based methods for identifying disease-related loci and epigenetic biomarkers. Briefings in Bioinformatics, 18(6), 957–968.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Network-based+methods+identifying+disease-related+loci+epigenetic+biomarkers+Briefings+Bioinformatics+2017"}],"related":["epigenome-wide-association-study","genome-wide-association-study","network-based-gwas","pathway-enrichment-analysis","rna-seq-differential-expression","multi-omics-epigenome-wide-association-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-eqtl-analysis","name":"Network-based eQTL analysis","fullName":"Network-based Expression Quantitative Trait Loci Analysis","aliases":["network eQTL","network-integrated eQTL mapping","graph-based eQTL analysis","eQTL network analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2008–2013 (network-integrated extensions of eQTL mapping)","originator":"Multiple groups; foundational eQTL work by Cheung et al. (2005) and Stranger et al. (2007); network integration extended by Zhu et al. (2008) and others","url":"https://scholargate.app/en/bioinformatics/network-based-eqtl-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-eqtl-analysis.md","definition":"Network-based eQTL analysis extends classical eQTL mapping by embedding genetic variant-to-expression associations within gene regulatory or protein interaction networks. Rather than treating each SNP-gene pair independently, this approach leverages network topology — such as co-expression modules or known pathway structures — to improve statistical power, reduce multiple testing burden, and reveal how genetic variants perturb entire regulatory programs rather than isolated transcripts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; foundational eQTL work by Cheung et al. (2005) and Stranger et al. (2007); network integration extended by Zhu et al. (2008) and others","year":"2008–2013 (network-integrated extensions of eQTL mapping)","type":"Statistical genomics / network analysis pipeline","dataType":"Genotype data (SNP arrays or WGS), gene expression data (RNA-seq or microarray), gene interaction/regulatory network data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Skinner, M. E., Uzilov, A. V., Stein, L. D., Mungall, C. J., & Holmes, I. H. (2009). JBrowse: a next-generation genome browser. Genome Research, 19(9), 1630–1638.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Network-based+eQTL+mapping+integrating+gene+regulatory+networks"},{"ref":"Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1), Article17.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Zhang+Horvath+2005+weighted+gene+co-expression+network+analysis"}],"related":["eqtl-analysis","gene-regulatory-network-inference","genome-wide-association-study","rna-seq-differential-expression","pathway-enrichment-analysis","bayesian-eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-gene-set-enrichment-analysis","name":"Network-based gene set enrichment analysis","fullName":"Network-Based Gene Set Enrichment Analysis","aliases":["network GSEA","network-propagation GSEA","NetGSA","graph-informed gene set testing"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010 (NetGSA); field consolidated 2010-2015","originator":"Ali Shojaie & George Michailidis (NetGSA); broader network-propagation approaches by multiple groups (~2010-2015)","url":"https://scholargate.app/en/bioinformatics/network-based-gene-set-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-gene-set-enrichment-analysis.md","definition":"Network-based gene set enrichment analysis (network GSEA) extends classical GSEA by incorporating biological interaction networks — such as protein-protein interaction (PPI) or co-expression graphs — into the enrichment test. Instead of treating each gene independently, the method propagates differential expression signals across network edges, allowing genes that are co-regulated or functionally connected to jointly support the significance of a gene set. The result is a biologically coherent enrichment score that accounts for pathway topology and gene-gene dependencies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ali Shojaie & George Michailidis (NetGSA); broader network-propagation approaches by multiple groups (~2010-2015)","year":"2010 (NetGSA); field consolidated 2010-2015","type":"Network-informed statistical enrichment test","dataType":"Gene expression matrix (RNA-seq or microarray) + biological network (PPI, co-expression, or pathway graph)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Shojaie, A., & Michailidis, G. (2010). Network enrichment analysis in complex experiments. Statistical Applications in Genetics and Molecular Biology, 9(1), Article 22.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Network+enrichment+analysis+in+complex+experiments+Shojaie+Michailidis+2010"},{"ref":"Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545-15550.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1073/pnas.0506580102"}],"related":["gene-set-enrichment-analysis","pathway-enrichment-analysis","network-based-gwas","rna-seq-differential-expression","network-based-proteomics-analysis","single-cell-gene-set-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-gwas","name":"Network-based GWAS","fullName":"Network-based Genome-Wide Association Study","aliases":["network GWAS","gene network GWAS","network-informed GWAS","NbGWAS"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2011–2013 (early tools); mature framework by 2015","originator":"Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups","url":"https://scholargate.app/en/bioinformatics/network-based-gwas","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-gwas.md","definition":"Network-based GWAS integrates conventional genome-wide association study results with biological network data — such as protein-protein interaction (PPI) networks or gene co-expression graphs — to identify disease-relevant gene modules or subnetworks. Instead of reporting only the top individual SNPs, this approach propagates association signals through molecular interaction networks, surfacing gene clusters whose collective signal implicates them in complex-trait biology even when no single variant reaches genome-wide significance alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups","year":"2011–2013 (early tools); mature framework by 2015","type":"Network-augmented association analysis","dataType":"GWAS summary statistics (p-values or Z-scores) plus biological network (PPI or co-expression)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Wang, Q., Yu, H., Zhao, Z., & Jia, P. (2015). EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformatics, 31(15), 2591–2594.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=EW_dmGWAS+edge-weighted+dense+module+search+genome-wide+association+studies+gene+expression+profiles"},{"ref":"Leiserson, M. D. M., Vandin, F., Wu, H.-T., Dobson, J. R., Eldridge, J. V., Thomas, J. L., Papoutsaki, A., Kim, Y., Niu, B., McLellan, M., Lawrence, M. S., Gonzalez-Perez, A., Tamborero, D., Cheng, Y., Ryslik, G. A., Lopez-Bigas, N., Getz, G., Ding, L., & Raphael, B. J. (2015). Pan-cancer network analysis identifies combinations of rare somatic mutations contributing to tumorigenesis. Nature Genetics, 47(2), 106–114.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pan-cancer+network+analysis+identifies+combinations+rare+somatic+mutations+contributing+tumorigenesis+Leiserson+2015"}],"related":["genome-wide-association-study","pathway-enrichment-analysis","gene-set-enrichment-analysis","eqtl-analysis","network-based-eqtl-analysis","copy-number-variation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-mapping-review","name":"Network-based Mapping review","fullName":"Network-based Mapping Review","aliases":["network mapping review","citation-network mapping review","network-enhanced evidence mapping","network-informed mapping review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2000s–2010s","originator":"Petticrew & Roberts (mapping review); network overlay adopted from bibliometric network analysis tradition","url":"https://scholargate.app/en/scientometrics/network-based-mapping-review","markdownUrl":"https://scholargate.app/en/scientometrics/network-based-mapping-review.md","definition":"A network-based mapping review combines the breadth of a traditional evidence mapping exercise with bibliometric network analysis to chart the structural landscape of a research field. Rather than simply cataloguing studies by topic, this approach constructs citation, co-authorship, or co-word networks to reveal clusters of intellectual activity, influential works, and collaboration patterns — producing both a visual and a descriptive map of the evidence base.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Petticrew & Roberts (mapping review); network overlay adopted from bibliometric network analysis tradition","year":"2000s–2010s","type":"Evidence synthesis method with network analysis overlay","dataType":"Bibliographic records, citation data, co-authorship data","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Petticrew, M., & Roberts, H. (2006). Systematic Reviews in the Social Sciences: A Practical Guide. Blackwell Publishing.","type":"book","doi":null,"isbn":"978-1405121101","url":null},{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null}],"related":["mapping-review","bibliometric-analysis","co-citation-analysis","bibliographic-coupling","scoping-review","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-meta-analysis","name":"Network-based Meta-analysis","fullName":"Network-based Meta-analysis (Network Meta-Analysis)","aliases":["NMA","network meta-analysis","mixed-treatment comparison","multiple-treatments meta-analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2002 (Lumley); refined 2008–2012","originator":"Thomas Lumley (statistical framework); Georgia Salanti (SUCRA and ranking methods)","url":"https://scholargate.app/en/scientometrics/network-based-meta-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/network-based-meta-analysis.md","definition":"Network-based Meta-analysis (NMA) extends conventional pairwise meta-analysis by simultaneously synthesizing evidence across a network of two or more competing treatments, including pairs that have never been compared head-to-head in a single trial. By combining direct and indirect evidence within a coherent statistical model, NMA produces relative effect estimates for all treatment pairs and generates a probabilistic ranking of which treatment performs best on the outcome of interest.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Lumley (statistical framework); Georgia Salanti (SUCRA and ranking methods)","year":"2002 (Lumley); refined 2008–2012","type":"Quantitative evidence synthesis","dataType":"Aggregate or individual-participant data from multiple randomized controlled trials","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Lumley, T. (2002). Network meta-analysis for indirect treatment comparisons. Statistics in Medicine, 21(16), 2313–2324.","type":"article","doi":"10.1002/sim.1201","isbn":null,"url":null},{"ref":"Salanti, G. (2012). Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Research Synthesis Methods, 3(2), 80–97.","type":"article","doi":"10.1002/jrsm.1037","isbn":null,"url":null}],"related":["systematic-literature-review","meta-analysis","network-meta-analysis","bibliometric-analysis","scoping-review","umbrella-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-metabolomics-analysis","name":"Network-based metabolomics analysis","fullName":"Network-based Metabolomics Analysis","aliases":["metabolic network analysis","systems metabolomics","network metabolomics","metabolite network enrichment"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2005–2011","originator":"Barabasi, Loscalzo and colleagues (network medicine framework); Wishart and Xia (metabolomics network tools)","url":"https://scholargate.app/en/bioinformatics/network-based-metabolomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-metabolomics-analysis.md","definition":"Network-based metabolomics analysis integrates quantitative metabolite profiling data with biological network structures — metabolic pathways, protein-metabolite interaction graphs, and disease networks — to reveal coordinated biochemical disruptions that individual metabolite lists would miss. Rather than treating each metabolite in isolation, this systems-level approach identifies modules, hubs, and perturbed subnetworks, providing mechanistic insight into how metabolic dysregulation propagates through cellular systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barabasi, Loscalzo and colleagues (network medicine framework); Wishart and Xia (metabolomics network tools)","year":"2005–2011","type":"Systems biology / omics analysis pipeline","dataType":"Quantitative metabolite abundance tables (LC-MS, GC-MS, NMR); metabolic network databases (KEGG, HMDB, Reactome)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Xia, J., & Wishart, D. S. (2010). MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Research, 38(Web Server issue), W71–W77.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=MSEA+a+web-based+tool+to+identify+biologically+meaningful+patterns+in+quantitative+metabolomic+data"},{"ref":"Barabasi, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12(1), 56–68.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Network+medicine+a+network-based+approach+to+human+disease+Barabasi+2011"}],"related":["metabolomics-analysis","pathway-enrichment-analysis","gene-set-enrichment-analysis","multi-omics-metabolomics-analysis","proteomics-analysis","bayesian-metabolomics-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-microbiome-diversity-analysis","name":"Network-based microbiome diversity analysis","fullName":"Network-Based Microbiome Diversity Analysis","aliases":["microbial co-occurrence network analysis","microbiome network ecology","ecological network-based diversity","NBMDA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2012","originator":"Faust, Raes, Friedman, Alm and colleagues","url":"https://scholargate.app/en/bioinformatics/network-based-microbiome-diversity-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-microbiome-diversity-analysis.md","definition":"Network-based microbiome diversity analysis integrates graph-theoretic co-occurrence network inference with classical alpha- and beta-diversity metrics to characterize the structural organization of microbial communities. Rather than treating taxa as independent entities, the method models pairwise microbial associations as edges in a network, enabling identification of keystone taxa, community modules, and ecological interaction patterns that simple diversity indices cannot detect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Faust, Raes, Friedman, Alm and colleagues","year":"2012","type":"Integrative bioinformatics pipeline","dataType":"16S rRNA amplicon or shotgun metagenomics OTU/ASV count tables","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Friedman, J., & Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Computational Biology, 8(9), e1002687.","type":"article","doi":"10.1371/journal.pcbi.1002687","isbn":null,"url":null},{"ref":"Faust, K., & Raes, J. (2012). Microbial interactions: from networks to models. Nature Reviews Microbiology, 10(8), 538–550.","type":"article","doi":"10.1038/nrmicro2832","isbn":null,"url":null}],"related":["microbiome-diversity-analysis","pathway-enrichment-analysis","gene-set-enrichment-analysis","network-based-pathway-enrichment-analysis","rna-seq-differential-expression","phylogenetic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-pathway-enrichment-analysis","name":"Network-based pathway enrichment analysis","fullName":"Network-based Pathway Enrichment Analysis","aliases":["network pathway enrichment","network-based enrichment","topology-based pathway analysis","NBPEA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2002 (seminal network-scoring concept); matured 2010–2015","originator":"Ideker, Ozier, Schwikowski, and Siegel (network-based scoring); extended by Vaske et al. (PARADIGM) and others","url":"https://scholargate.app/en/bioinformatics/network-based-pathway-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-pathway-enrichment-analysis.md","definition":"Network-based pathway enrichment analysis integrates molecular interaction networks — protein-protein interactions, signalling graphs, or gene regulatory networks — with omics measurements to identify biological pathways that are coordinately altered in a condition. Unlike classical over-representation or gene-set enrichment approaches that treat pathway genes as independent lists, this family of methods propagates signals across network edges, capturing the topology of interactions and uncovering dysregulated modules that flat-list enrichment would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ideker, Ozier, Schwikowski, and Siegel (network-based scoring); extended by Vaske et al. (PARADIGM) and others","year":"2002 (seminal network-scoring concept); matured 2010–2015","type":"Pathway enrichment and network analysis method","dataType":"Omics data (transcriptomics, proteomics, genomics) combined with molecular interaction networks","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18(suppl_1), S233–S240.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Discovering+regulatory+and+signalling+circuits+in+molecular+interaction+networks+Ideker+2002"},{"ref":"Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., & Stuart, J. M. (2010). Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics, 26(12), i237–i245.","type":"article","doi":"10.1093/bioinformatics/btq182","isbn":null,"url":null}],"related":["gene-set-enrichment-analysis","over-representation-analysis","protein-protein-interaction-network-analysis","gene-ontology-analysis","differential-expression-analysis","functional-annotation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-phylogenetic-analysis","name":"Network-based Phylogenetic Analysis","fullName":"Phylogenetic Network Analysis","aliases":["phylogenetic network","reticulate phylogenetics","split network analysis","evolutionary network inference"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"1992–2004 (foundational algorithms); broader development 1990s–2010s","originator":"Hans-Jürgen Bandelt & Andreas Dress (split decomposition); David Bryant & Vincent Moulton (Neighbor-Net)","url":"https://scholargate.app/en/bioinformatics/network-based-phylogenetic-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-phylogenetic-analysis.md","definition":"Network-based phylogenetic analysis constructs graph-structured representations of evolutionary relationships that explicitly accommodate reticulate events — including hybridization, horizontal gene transfer, recombination, and incomplete lineage sorting — which strictly bifurcating phylogenetic trees cannot represent. Instead of forcing sequences into a single bifurcating tree, the method infers splits or reticulations in the data and visualises them as a network, revealing conflicting phylogenetic signals that are biologically informative.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans-Jürgen Bandelt & Andreas Dress (split decomposition); David Bryant & Vincent Moulton (Neighbor-Net)","year":"1992–2004 (foundational algorithms); broader development 1990s–2010s","type":"Computational phylogenetic method","dataType":"Aligned DNA/RNA sequences, distance matrices, SNP data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Bandelt, H.-J., & Dress, A. W. M. (1992). Split decomposition: A new and useful approach to phylogenetic analysis of distance data. Molecular Phylogenetics and Evolution, 1(3), 242–252.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Split+decomposition+a+new+and+useful+approach+to+phylogenetic+analysis+of+distance+data+Bandelt+Dress+1992"},{"ref":"Bryant, D., & Moulton, V. (2004). Neighbor-Net: An agglomerative method for the construction of phylogenetic networks. Molecular Biology and Evolution, 21(2), 255–265.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Neighbor-Net+an+agglomerative+method+for+the+construction+of+phylogenetic+networks+Bryant+Moulton+2004"}],"related":["phylogenetic-analysis","sequence-alignment","genome-wide-association-study","bayesian-phylogenetic-analysis","variant-calling","multi-omics-phylogenetic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-rna-seq-differential-expression","name":"Network-based RNA-seq differential expression","fullName":"Network-based RNA Sequencing Differential Expression Analysis","aliases":["network-aware DE analysis","gene network differential expression","co-expression network DE","NB-DEA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2002–2005","originator":"Ideker et al. (network scoring); Zhang & Horvath (WGCNA framework)","url":"https://scholargate.app/en/bioinformatics/network-based-rna-seq-differential-expression","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-rna-seq-differential-expression.md","definition":"Network-based RNA-seq differential expression analysis integrates conventional differential expression testing with gene interaction networks — such as protein-protein interaction graphs or weighted co-expression networks — to identify not just individual differentially expressed genes but coherent, biologically meaningful gene modules that change together between conditions. This approach substantially reduces false positives and surfaces pathway-level signals invisible to gene-by-gene testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ideker et al. (network scoring); Zhang & Horvath (WGCNA framework)","year":"2002–2005","type":"Integrative computational pipeline","dataType":"RNA-seq count matrices; gene/protein interaction networks (PPI, co-expression)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1), Article 17.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+general+framework+for+weighted+gene+co-expression+network+analysis+Zhang+Horvath+2005"},{"ref":"Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18(Suppl 1), S233–S240.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Discovering+regulatory+and+signalling+circuits+in+molecular+interaction+networks+Ideker+2002"}],"related":["rna-seq-differential-expression","gene-set-enrichment-analysis","pathway-enrichment-analysis","weighted-gene-co-expression-network-analysis","single-cell-rna-seq-analysis","multi-omics-rna-seq-differential-expression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-scientometric-analysis","name":"Network-based Scientometric analysis","fullName":"Network-based Scientometric Analysis","aliases":["scientometric network analysis","bibliometric network analysis","citation network scientometrics","science network mapping"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1965 (Price); computational refinement 2000s–2010s","originator":"Derek J. de Solla Price (network citation structure); Nees Jan van Eck & Ludo Waltman (computational network mapping)","url":"https://scholargate.app/en/scientometrics/network-based-scientometric-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/network-based-scientometric-analysis.md","definition":"Network-based scientometric analysis applies graph-theoretic methods to bibliographic data — publications, citations, authors, and keywords — to map the intellectual structure of a scientific field. By modeling documents or authors as nodes and their relationships (citations, co-authorships, co-word occurrences) as edges, it reveals clusters of knowledge, central actors, emerging topics, and the flow of ideas across disciplines. Tools such as VOSviewer, Gephi, and the R package bibliometrix are commonly used.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derek J. de Solla Price (network citation structure); Nees Jan van Eck & Ludo Waltman (computational network mapping)","year":"1965 (Price); computational refinement 2000s–2010s","type":"Quantitative bibliometric method","dataType":"Publication records, citation data, co-authorship data (bibliographic databases: Web of Science, Scopus, Dimensions)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null},{"ref":"Mingers, J., & Leydesdorff, L. (2015). A review of theory and practice in scientometrics. European Journal of Operational Research, 246(1), 1–19.","type":"article","doi":"10.1016/j.ejor.2015.04.002","isbn":null,"url":null}],"related":["bibliometric-analysis","scientometric-analysis","co-citation-analysis","bibliographic-coupling","co-word-analysis","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-single-cell-rna-seq-analysis","name":"Network-based single-cell RNA-seq analysis","fullName":"Network-based Single-Cell RNA Sequencing Analysis","aliases":["scRNA-seq network analysis","single-cell gene regulatory network inference","scGRN analysis","single-cell co-expression network analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2015–2017 (rapid development alongside scRNA-seq methods; SCENIC 2017)","originator":"Aibar et al. (SCENIC, gene regulatory networks); Jin et al. (CellChat, cell-cell communication networks)","url":"https://scholargate.app/en/bioinformatics/network-based-single-cell-rna-seq-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-single-cell-rna-seq-analysis.md","definition":"Network-based single-cell RNA-seq analysis extends standard scRNA-seq workflows by constructing and interrogating molecular interaction networks — gene regulatory networks, co-expression networks, or cell-cell communication graphs — from single-cell transcriptomic data. Rather than treating each gene independently, this approach captures the coordinated activity of gene circuits and intercellular signalling pathways within and between cell populations, enabling a systems-level view of transcriptional regulation at single-cell resolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aibar et al. (SCENIC, gene regulatory networks); Jin et al. (CellChat, cell-cell communication networks)","year":"2015–2017 (rapid development alongside scRNA-seq methods; SCENIC 2017)","type":"Computational bioinformatics pipeline","dataType":"Single-cell RNA sequencing count matrices (UMI counts per cell per gene)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., ... & Aerts, S. (2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083–1086.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SCENIC+single-cell+regulatory+network+inference+and+clustering+Nature+Methods+2017"},{"ref":"Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C. H., ... & Nie, Q. (2021). Inference and analysis of cell-cell communication using CellChat. Nature Communications, 12(1), 1088.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Inference+and+analysis+of+cell-cell+communication+using+CellChat+Nature+Communications+2021"}],"related":["single-cell-rna-seq-analysis","rna-seq-differential-expression","gene-set-enrichment-analysis","pathway-enrichment-analysis","network-based-rna-seq-differential-expression","single-cell-eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-spatial-analysis","name":"Network-Based Spatial Analysis","fullName":"Network-Based Spatial Analysis","aliases":["network spatial analysis","network-constrained spatial analysis","spatial network analysis","NBSA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1990s–2000s","originator":"Atsuyuki Okabe and colleagues","url":"https://scholargate.app/en/spatial-analysis/network-based-spatial-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/network-based-spatial-analysis.md","definition":"Network-based spatial analysis (NBSA) analyzes the distribution and interaction of spatial phenomena constrained to a network structure — such as roads, railways, or rivers — using network distance rather than straight-line (Euclidean) distance. It is the appropriate framework whenever movement, proximity, or risk is governed by the underlying network topology rather than open space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atsuyuki Okabe and colleagues","year":"1990s–2000s","type":"Spatial network model","dataType":"Point events or flows on a network (roads, rivers, infrastructure)","subfamily":"GIS / spatial"},"citations":[{"ref":"Okabe, A., Satoh, T., Furuta, T., Sugihara, K., & Okano, K. (2006). Generalized network Voronoi diagrams: Concepts, computational methods, and applications. International Journal of Geographical Information Science, 22(9), 965–994.","type":"book","doi":"10.1080/13658810701587891","isbn":null,"url":null},{"ref":"Okabe, A., & Sugihara, K. (2012). Spatial Analysis Along Networks: Statistical and Computational Methods. Wiley.","type":"book","doi":null,"isbn":"978-0470770818","url":null}],"related":["kernel-density-estimation","hot-spot-analysis","spatial-autocorrelation","geographically-weighted-regression","voronoi-diagram","shortest-path-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-based-variant-calling","name":"Network-based variant calling","fullName":"Network-based (Graph-genome) Variant Calling","aliases":["graph-genome variant calling","variation graph genotyping","vg-based variant calling","pangenome variant calling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2017–2018","originator":"Erik Garrison, Paten lab (UCSC); Hannes Eggertsson, deCODE Genetics","url":"https://scholargate.app/en/bioinformatics/network-based-variant-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/network-based-variant-calling.md","definition":"Network-based (graph-genome) variant calling replaces the conventional single linear reference genome with a variation graph — a network in which nodes represent sequence segments and edges represent known alternative paths through the genome. Reads are mapped onto this graph, enabling detection of SNPs, indels, and structural variants with substantially lower reference bias than linear-reference pipelines. Key tools include the Variation Graph Toolkit (vg) and Graphtyper.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erik Garrison, Paten lab (UCSC); Hannes Eggertsson, deCODE Genetics","year":"2017–2018","type":"Computational genomics pipeline","dataType":"Short-read or long-read sequencing data (FASTQ/BAM); pangenome graph (GFA/VCF)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Garrison, E., Sirén, J., Novak, A. M., Hickey, G., Eizenga, J. M., Dawson, E. T., Jones, W., Garg, S., Markello, C., Lin, M. F., Paten, B., & Durbin, R. (2018). Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nature Biotechnology, 36(9), 875–879.","type":"article","doi":"10.1038/nbt.4227","isbn":null,"url":null},{"ref":"Eggertsson, H. P., Jonsson, H., Kristmundsdottir, S., Hjartarson, E., Kehr, B., Masson, G., Zink, F., Hjorleifsson, K. E., Jonasdottir, A., Jonasdottir, A., Jonsdottir, I., Gudbjartsson, D. F., Melsted, P., Stefansson, K., & Halldorsson, B. V. (2017). Graphtyper enables population-scale genotyping using pangenome graphs. Nature Genetics, 49(11), 1654–1660.","type":"article","doi":"10.1038/ng.3964","isbn":null,"url":null}],"related":["variant-calling","sequence-alignment","genome-wide-association-study","copy-number-variation-analysis","rna-seq-differential-expression","bayesian-variant-calling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-dea","name":"Network DEA","fullName":"Network Data Envelopment Analysis","aliases":["Network Data Envelopment Analysis","Network Efficiency Analysis","Multi-Stage DEA","Ağ Veri Zarflama Analizi"],"domain":"efficiency-analysis","family":"regression-model","subfamily":"Efficiency analysis","year":2000,"originator":"Färe & Grosskopf","url":"https://scholargate.app/en/efficiency-analysis/network-dea","markdownUrl":"https://scholargate.app/en/efficiency-analysis/network-dea.md","definition":"Network Data Envelopment Analysis (Network DEA) is a nonparametric efficiency measurement framework introduced by Färe and Grosskopf (2000) that extends classical DEA to multi-stage or multi-division production processes. Rather than treating a decision-making unit as a black box, it explicitly models the internal structure — the divisions and the intermediate products that flow between them — enabling stage-level and overall efficiency scores to be estimated simultaneously within a single coherent model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Färe & Grosskopf","year":2000,"type":"Multi-stage nonparametric efficiency model","subfamily":"Efficiency analysis","orientation":"Input/output/both","returns_to_scale":"CRS or VRS"},"citations":[{"ref":"Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.","type":"article","doi":"10.1016/S0038-0121(99)00012-9","isbn":null,"url":null}],"related":["data-envelopment-analysis","bootstrap-dea","malmquist-productivity-index"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-diffusion-analysis","name":"Network Diffusion Analysis","fullName":"Network Diffusion Analysis (Spread and Propagation on Graphs)","aliases":["diffusion on networks","information diffusion","contagion spreading model","network propagation model"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1927 (epidemic roots); network formalization 1990s–2000s","originator":"Kermack, W. O. & McKendrick, A. G.","url":"https://scholargate.app/en/network-analysis/network-diffusion-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/network-diffusion-analysis.md","definition":"Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kermack, W. O. & McKendrick, A. G.","year":"1927 (epidemic roots); network formalization 1990s–2000s","type":"Simulation / analytical model","dataType":"Graph (nodes, edges, edge weights; time-series contact data)","subfamily":"Network science"},"citations":[{"ref":"Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.","type":"article","doi":"10.1098/rspa.1927.0118","isbn":null,"url":null},{"ref":"Watts, D. J. & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393, 440–442.","type":"article","doi":"10.1038/30918","isbn":null,"url":null}],"related":["social-network-analysis","betweenness-centrality","modularity-analysis","exponential-random-graph-model","eigenvector-centrality","two-mode-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-diffusion","name":"Network Diffusion Models","fullName":"Network Diffusion Models (SIR, SIS, Independent Cascade)","aliases":["epidemic spreading models","compartmental models","influence propagation models","Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":"1927 (epidemiological compartmental); 2003 (social influence cascade)","originator":"Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003)","url":"https://scholargate.app/en/network-analysis/network-diffusion","markdownUrl":"https://scholargate.app/en/network-analysis/network-diffusion.md","definition":"Network diffusion models are a family of compartmental and probabilistic frameworks that simulate how information, disease, or innovation spreads across a connected system. Rooted in the mathematical epidemiology of Kermack and McKendrick (1927), the SIR and SIS models partition nodes into states and track transitions driven by contact rates and recovery probabilities. The Independent Cascade and Linear Threshold models, formalised by Kempe, Kleinberg, and Tardos (2003), extend this logic to social influence, modelling how activation propagates through a network one neighbour at a time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003)","year":"1927 (epidemiological compartmental); 2003 (social influence cascade)","type":"Stochastic / deterministic simulation on graphs","model_families":"SIR, SIS (compartmental); Independent Cascade; Linear Threshold","output":"Compartment time series (S, I, R counts); peak infection size; influence spread","minimumNodes":20,"requiresNormality":false,"difficulty":2},"citations":[{"ref":"Kermack, W.O. & McKendrick, A.G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, 115(772), 700-721.","type":"article","doi":"10.1098/rspa.1927.0118","isbn":null,"url":"https://royalsocietypublishing.org/doi/10.1098/rspa.1927.0118"},{"ref":"Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the Spread of Influence through a Social Network. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 137-146.","type":"inproceedings","doi":"10.1145/956750.956769","isbn":null,"url":null}],"related":["temporal-network-analysis","network-resilience","link-prediction","community-detection","centrality-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-econometrics","name":"Network Econometrics","fullName":"Network Econometrics (Peer Effects)","aliases":["Social Interactions Model","Peer Effects Model","Social Network Regression","Ağ Ekonometrisi"],"domain":"econometrics","family":"regression-model","subfamily":"Network econometrics","year":2009,"originator":"Yann Bramoullé, Habiba Djebbari & Bernard Fortin","url":"https://scholargate.app/en/econometrics/network-econometrics","markdownUrl":"https://scholargate.app/en/econometrics/network-econometrics.md","definition":"Network econometrics estimates how individuals' outcomes are causally shaped by the behaviour and characteristics of their social-network neighbours. Formalised by Bramoullé, Djebbari, and Fortin (2009), the framework embeds a row-normalised adjacency matrix into a linear regression, separating endogenous peer effects (imitation of outcomes), exogenous contextual effects (influence of neighbours' attributes), and correlated effects (shared environment), while using network topology to construct valid instruments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yann Bramoullé, Habiba Djebbari & Bernard Fortin","year":2009,"type":"Linear-in-means peer effects regression","subfamily":"Network econometrics","identification_tool":"Network-based instrumental variables","key_matrix":"Row-normalised adjacency matrix G"},"citations":[{"ref":"Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41–55.","type":"article","doi":"10.1016/j.jeconom.2008.12.021","isbn":null,"url":null}],"related":["spatial-lag-model","instrumental-variables","centrality-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-embedding","name":"Network Embedding","fullName":"Network Embedding (Node2Vec, DeepWalk, LINE)","aliases":["node embedding","graph embedding","network representation learning","Ağ Gömme (Node2Vec, DeepWalk, LINE)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":"2014 (DeepWalk); 2016 (Node2Vec)","originator":null,"url":"https://scholargate.app/en/network-analysis/network-embedding","markdownUrl":"https://scholargate.app/en/network-analysis/network-embedding.md","definition":"Network embedding is a family of representation-learning methods that map each node of a graph into a dense, low-dimensional vector while preserving the network's structural properties. The approach was formalised for social-network data by Perozzi, Al-Rfou, and Skiena with DeepWalk (2014), which adapted the Word2Vec skip-gram model to random walks on graphs, and extended by Grover and Leskovec with Node2Vec (2016), which introduced a biased random walk that balances breadth-first and depth-first exploration. These embeddings turn relational data into feature vectors that standard machine-learning classifiers and clustering algorithms can consume directly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Perozzi, Al-Rfou & Skiena (DeepWalk, 2014); Grover & Leskovec (Node2Vec, 2016)","year":"2014 (DeepWalk); 2016 (Node2Vec)","type":"Representation learning / unsupervised network method","input":"Graph with nodes and edges (weighted or unweighted)","output":"Dense low-dimensional vector for each node","embeddingDimensions":"Typically 64 or 128","requiresNormality":false,"minimumSample":50,"suitablePurposes":"exploration, classification, prediction"},"citations":[{"ref":"Grover, A. & Leskovec, J. (2016). Node2Vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 855-864.","type":"inproceedings","doi":"10.1145/2939672.2939754","isbn":null,"url":null},{"ref":"Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). DeepWalk: Online Learning of Social Representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 701-710.","type":"inproceedings","doi":"10.1145/2623330.2623732","isbn":null,"url":null}],"related":["community-detection","centrality-analysis","graph-neural-networks","dimensionality-reduction","link-prediction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-function-virtualization","name":"Network Function Virtualization","fullName":"Network Function Virtualization (NFV)","aliases":["virtual network functions","network slicing"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Network architecture","year":"2012","originator":"ETSI NFV Industry Specification Group","url":"https://scholargate.app/en/telecommunications/network-function-virtualization","markdownUrl":"https://scholargate.app/en/telecommunications/network-function-virtualization.md","definition":"Network Function Virtualization (NFV) is a paradigm that implements traditional network functions (firewalls, load balancers, gateways, packet inspection) as software running on commodity servers instead of proprietary hardware appliances. Introduced by ETSI (2012), NFV reduces capital and operational expenses by leveraging cloud infrastructure and enabling rapid deployment of network services. Combined with SDN, NFV enables on-demand service creation and network slicing. It is now central to 5G and cloud-native network architecture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"ETSI NFV Industry Specification Group","subfamily":"Network architecture","year":"2012","type":"virtualization paradigm"},"citations":[{"ref":"ETSI (European Telecommunications Standards Institute). (2012). Network Functions Virtualisation (NFV); Architectural Framework. GS NFV 002 V1.1.1.","type":"article","doi":null,"isbn":null,"url":"https://www.etsi.org"},{"ref":"Mijumbi, R., Serrat, J., Gorricho, J. L., et al. (2016). Network function virtualization: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 18(1), 236-262.","type":"article","doi":"10.1109/COMST.2015.2477041","isbn":null,"url":null}],"related":["software-defined-networking","mpls"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-meta-analysis","name":"Network Meta-Analysis","fullName":"Network Meta-Analysis (NMA)","aliases":["Mixed Treatment Comparison","MTC","Indirect Comparison Meta-Analysis"],"domain":"evidence-synthesis","family":"process-pipeline","subfamily":"Advanced Meta-Analysis","year":"2002","originator":"Lumley (2002)","url":"https://scholargate.app/en/evidence-synthesis/network-meta-analysis","markdownUrl":"https://scholargate.app/en/evidence-synthesis/network-meta-analysis.md","definition":"Network meta-analysis (NMA) is a systematic method for comparing multiple interventions simultaneously within a single analytical framework, incorporating both direct evidence (head-to-head trials) and indirect evidence (comparisons via common comparators). First formalized by Lumley in 2002, NMA allows researchers to rank treatments and quantify comparative effectiveness even when some treatment pairs have never been directly studied.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lumley (2002)","subfamily":"Advanced Meta-Analysis","year":"2002","type":"Method"},"citations":[{"ref":"Lumley, T. (2002). Network meta-analysis for indirect treatment comparisons. Statistics in Medicine, 21(16), 2313–2324.","type":"article","doi":"10.1002/sim.1201","isbn":null,"url":null},{"ref":"Bucher, H. C., Guyatt, G. H., Griffith, L. E., & Walter, S. D. (1997). The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. Journal of Clinical Epidemiology, 50(6), 683–691.","type":"article","doi":"10.1016/s0895-4356(97)00049-8","isbn":null,"url":null},{"ref":"Dias, S., Welton, N. J., Caldwell, D. M., & Ades, A. E. (2010). Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine, 29(7–8), 932–944.","type":"article","doi":"10.1002/sim.3767","isbn":null,"url":null}],"related":["pairwise-meta-analysis","meta-regression","systematic-review","evidence-synthesis-framework","bayesian-hierarchical-models"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-motif-analysis","name":"Network Motif Analysis","fullName":"Network Motif Analysis (Network Motifs)","aliases":["network motifs","subgraph significance profile","Ağ Motif Analizi (Network Motifs)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":2002,"originator":null,"url":"https://scholargate.app/en/network-analysis/network-motif-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/network-motif-analysis.md","definition":"Network motif analysis is a statistical method for directed networks, introduced by Milo, Shen-Orr, and Alon in 2002, that identifies small recurring subgraph patterns — motifs — that appear significantly more often than would be expected in a comparable random network. By comparing a real network against a null ensemble of randomised graphs, the method reveals the elementary structural building blocks that define the functional organisation of biological regulatory networks, social networks, and other complex systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Ron Milo, Shai Shen-Orr, Uri Alon et al.","year":2002,"type":"Statistical pattern-detection method for directed graphs","standardMotifSizes":"3-node and 4-node subgraphs","significanceThreshold":"Z-score > 2 or p < 0.05","nullModel":"Random network ensemble (edge-randomised)","minNodes":20,"difficulty":3},"citations":[{"ref":"Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network Motifs: Simple Building Blocks of Complex Networks. Science, 298(5594), 824-827.","type":"article","doi":"10.1126/science.298.5594.824","isbn":null,"url":null},{"ref":"Alon, U. (2007). Network Motifs: Theory and Experimental Approaches. Nature Reviews Genetics, 8(6), 450-461.","type":"article","doi":"10.1038/nrg2102","isbn":null,"url":null}],"related":["ego-network-analysis","community-detection","social-network-analysis","graph-clustering","small-world-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"network-resilience","name":"Network Resilience Analysis","fullName":"Network Resilience and Vulnerability Analysis","aliases":["network vulnerability analysis","attack tolerance analysis","Ağ Dayanıklılığı ve Güvenlik Açığı Analizi"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":2000,"originator":"Albert, Jeong & Barabási","url":"https://scholargate.app/en/network-analysis/network-resilience","markdownUrl":"https://scholargate.app/en/network-analysis/network-resilience.md","definition":"Network resilience and vulnerability analysis is an analytical framework, formalised by Albert, Jeong, and Barabási (2000), that measures how a network degrades functionally as nodes or edges are progressively removed. By running targeted-attack simulations — removing the highest-centrality nodes first — and random-failure simulations — removing nodes at uniform probability — the framework identifies which structural elements are critical to network integrity and where infrastructure is most exposed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert, Jeong & Barabási","year":2000,"type":"Network robustness / vulnerability framework","minimumNodes":20,"requires_normal":false,"difficultyLevel":2,"simulationStrategies":"Targeted attack / random failure"},"citations":[{"ref":"Albert, R., Jeong, H. & Barabási, A.L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.","type":"article","doi":"10.1038/35019019","isbn":null,"url":null},{"ref":"Barabási, A.L. & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.","type":"article","doi":"10.1126/science.286.5439.509","isbn":null,"url":null}],"related":["centrality-analysis","community-detection","temporal-network-analysis","multilayer-network","graph-neural-network"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neural-architecture-search","name":"Neural Architecture Search","fullName":"Neural Architecture Search (NAS)","aliases":["Nöral Mimari Arama (NAS)","NAS","automated architecture design","differentiable architecture search"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Zoph, B. & Le, Q.V.","url":"https://scholargate.app/en/deep-learning/neural-architecture-search","markdownUrl":"https://scholargate.app/en/deep-learning/neural-architecture-search.md","definition":"Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zoph, B. & Le, Q.V.","year":2017,"type":"Automated architecture optimization (deep learning)","task":"Prediction & classification","minSample":1000,"notableMethods":"DARTS, ENAS, Once-for-All"},"citations":[{"ref":"Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1611.01578"},{"ref":"Liu, H. et al. (2019). DARTS: Differentiable Architecture Search. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1806.09055"}],"related":["knowledge-distillation","mixture-of-experts","random-forest","xgboost","longformer-bigbird"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neural-machine-reading","name":"Machine Reading Comprehension","fullName":"Neural Machine Reading Comprehension (MRC)","aliases":["MRC","question answering over passages","extractive question answering","Makine Okuma Anlama (MRC)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2016,"originator":"Rajpurkar, Zhang, Lopyrev & Liang (SQuAD)","url":"https://scholargate.app/en/text-mining/neural-machine-reading","markdownUrl":"https://scholargate.app/en/text-mining/neural-machine-reading.md","definition":"Machine reading comprehension (MRC), popularised by the SQuAD benchmark of Rajpurkar, Zhang, Lopyrev and Liang (2016), is a natural-language-processing task in which a model reads a given passage and answers multiple-choice or open-ended questions about it. It turns a passage plus a question into a machine-generated answer, supporting information retrieval, educational technology, and querying research databases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rajpurkar, Zhang, Lopyrev & Liang (SQuAD)","year":2016,"type":"NLP question-answering task","answerForms":"Multiple-choice / extractive span / open-ended","output":"An answer drawn from or grounded in a given passage"},"citations":[{"ref":"Rajpurkar, P., Zhang, J., Lopyrev, K. & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP, 2383-2392.","type":"inproceedings","doi":"10.18653/v1/D16-1264","isbn":null,"url":null},{"ref":"Yang, Z. et al. (2018). HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. EMNLP.","type":"inproceedings","doi":"10.18653/v1/D18-1259","isbn":null,"url":null}],"related":["sentiment-analysis","domain-adaptation-nlp","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neural-ode","name":"Neural ODE","fullName":"Neural Ordinary Differential Equation","aliases":["Nöral Diferansiyel Denklem (Neural ODE)","neural ordinary differential equation","continuous-depth network","ODE-Net"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2018,"originator":"Chen, T. Q. et al.","url":"https://scholargate.app/en/deep-learning/neural-ode","markdownUrl":"https://scholargate.app/en/deep-learning/neural-ode.md","definition":"A Neural ODE, introduced by Chen and colleagues in 2018, models a hidden state as the continuous solution of an ordinary differential equation whose dynamics are parameterised by a neural network. It generalises the limiting case of residual connections, making it well suited to irregularly spaced time series and physics-based modelling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, T. Q. et al.","year":2018,"type":"Continuous-depth neural network (ODE-parameterised dynamics)","task":"Prediction & forecasting on continuous-time data","minSample":100},"citations":[{"ref":"Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS).","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1806.07366"},{"ref":"Rubanova, Y., Chen, T. Q. & Duvenaud, D. (2019). Latent ODEs for Irregularly-Sampled Time Series. Advances in Neural Information Processing Systems (NeurIPS).","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.03907"}],"related":["recurrent-neural-network","lstm","random-forest","xgboost","physics-informed-neural-network"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neural-radiance-fields","name":"Neural Radiance Fields (NeRF)","fullName":"NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis","aliases":["NeRF","Neural radiance field"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, 3D Vision, Generative Models","year":"2020","originator":"Ben Mildenhall","url":"https://scholargate.app/en/deep-learning/neural-radiance-fields","markdownUrl":"https://scholargate.app/en/deep-learning/neural-radiance-fields.md","definition":"Neural Radiance Fields (NeRF) is a method introduced by Mildenhall et al. in 2020 that represents a 3D scene as a continuous function parameterized by a neural network. Given multi-view images of a scene, NeRF learns to predict the color and density of light rays at any spatial location and viewing angle, enabling novel view synthesis with photorealistic quality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ben Mildenhall","subfamily":"Deep Learning, 3D Vision, Generative Models","year":"2020","type":"Neural network architecture"},"citations":[{"ref":"Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In Computer Vision-ECCV 2020: 16th European Conference (pp. 405-421). Springer International Publishing.","type":"article","doi":"10.1007/978-3-030-58452-8_24","isbn":null,"url":null}],"related":["latent-diffusion-models","masked-autoencoders","segment-anything-model","detr"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neural-style-transfer","name":"Neural Style Transfer","fullName":"Neural Style Transfer via Convolutional Neural Network Feature Statistics","aliases":["NST","artistic style transfer","neural artistic style","CNN style transfer","image style transfer"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2015,"originator":"Gatys, L. A.; Ecker, A. S.; Bethge, M.","url":"https://scholargate.app/en/deep-learning/neural-style-transfer","markdownUrl":"https://scholargate.app/en/deep-learning/neural-style-transfer.md","definition":"Neural Style Transfer (NST) is a deep-learning image synthesis technique, introduced by Gatys, Ecker, and Bethge in 2015, that separates the semantic content of one image from the visual texture and artistic style of another, then recombines them into a single synthesized image by iteratively optimizing pixel values to minimize a combined content and style loss computed from the feature maps of a pretrained convolutional neural network.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gatys, L. A.; Ecker, A. S.; Bethge, M.","year":2015,"type":"Iterative optimization over CNN feature statistics","task":"Image synthesis (style + content combination)","backbone":"VGG-19 (pretrained on ImageNet)","optimizationTarget":"Generated image pixels"},"citations":[{"ref":"Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image Style Transfer Using Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414–2423.","type":"article","doi":"10.1109/CVPR.2016.265","isbn":null,"url":null},{"ref":"Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A Neural Algorithm of Artistic Style. arXiv preprint arXiv:1508.06576.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1508.06576"},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["convolutional-neural-network","generative-adversarial-network","variational-autoencoder","image-segmentation","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neuromuscular-re-education","name":"Neuromuscular Re-Education","fullName":"Neuromuscular Re-Education Training","aliases":["motor retraining","motor control training","proprioceptive retraining"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Motor learning and retraining","year":"1970s","originator":"Physical therapy and motor control research","url":"https://scholargate.app/en/physical-therapy/neuromuscular-re-education","markdownUrl":"https://scholargate.app/en/physical-therapy/neuromuscular-re-education.md","definition":"Neuromuscular re-education is a therapeutic approach using targeted exercise and sensory feedback to retrain motor control, proprioception, and movement patterns following neurological injury or dysfunction. Based on motor learning principles, neuromuscular re-education helps patients reestablish voluntary muscle activation, coordination, and functional movement through repetition, feedback, and progressive challenge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Physical therapy and motor control research","subfamily":"Motor learning and retraining","year":"1970s","type":"Therapeutic exercise intervention"},"citations":[{"ref":"Neumann, D. A. (2016). Kinesiology of the musculoskeletal system: Foundations for rehabilitation (3rd ed.). Elsevier.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com/"},{"ref":"Schmidt, R. A., & Lee, T. D. (2019). Motor control and learning: A behavioral emphasis (6th ed.). Human Kinetics.","type":"book","doi":null,"isbn":null,"url":"https://www.humankinetics.com/"}],"related":["manual-muscle-testing","proprioception-assessment","functional-independence-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neuropathic-pain-scale","name":"Neuropathic Pain Scale","fullName":"Neuropathic Pain Scale (NPS)","aliases":["NPS","Neuropathic Pain Scale"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"neuropathic pain symptom assessment","year":"2007","originator":"Mark P. Jensen and colleagues","url":"https://scholargate.app/en/pain-medicine/neuropathic-pain-scale","markdownUrl":"https://scholargate.app/en/pain-medicine/neuropathic-pain-scale.md","definition":"The Neuropathic Pain Scale (NPS) is a 10-item self-report instrument developed by Jensen and colleagues to measure the quality and intensity of pain associated with neuropathic conditions (nerve damage, peripheral neuropathy, post-herpetic neuralgia, spinal cord injury pain). The NPS captures pain descriptors (sharp, cold, burning, sensitive, itching) and sensations characteristic of neuropathic pain, distinguishing them from nociceptive (tissue-damage-related) pain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark P. Jensen and colleagues","subfamily":"neuropathic pain symptom assessment","year":"2007","type":"Self-report scale measuring neuropathic pain quality and intensity"},"citations":[{"ref":"Kramer, H.H., Winkelmann, A., Sluka, K.A., & Malin, S.A. (2004). Neuropathic pain: Transmitter-based mechanisms to pharmacological intervention. Journal of Pain, 5(4), 204-221.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/15162341"},{"ref":"Dworkin, R.H., Backonja, M., Rowbotham, M.C., et al. (2003). Advances in neuropathic pain: Diagnosis, mechanisms, and treatment recommendations. Archives of Neurology, 60(11), 1524-1534.","type":"article","doi":"10.1001/archneur.60.11.1524","isbn":null,"url":null},{"ref":"Jensen, M.P., Wald, B., Trueman, S., & Lipton, R.B. (2007). Neuropathic Pain Scale: A reliable measure for assessing the characteristics of neuropathic pain. Clinical Journal of Pain, 23(1), 48-55.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Neuropathic+Pain+Scale%3A+A+reliable+measure+for+assessing+the+characteristics+of+neuropathic+pain+Jensen"}],"related":["mcgill-pain-questionnaire","pain-catastrophizing-scale","central-sensitization-inventory","pain-anxiety-symptoms-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neuropathic-pain-symptom-inventory","name":"NPSI","fullName":"Neuropathic Pain Symptom Inventory","aliases":["Neuropathic Pain Symptom Inventory","NPSI Scale"],"domain":"neurology","family":"process-pipeline","subfamily":"neuropathic pain symptom assessment","year":"2004","originator":"Didier Bouhassira, INSERM, France","url":"https://scholargate.app/en/neurology/neuropathic-pain-symptom-inventory","markdownUrl":"https://scholargate.app/en/neurology/neuropathic-pain-symptom-inventory.md","definition":"The NPSI is a 12-item self-report questionnaire specifically designed to assess and quantify the diverse symptoms characteristic of neuropathic pain. Developed by Bouhassira and colleagues in 2004, it evaluates five distinct symptom dimensions: burning pain, pressing pain, paroxysmal pain, evoked pain, and paresthesias. The NPSI is widely used in research and clinical practice to characterize neuropathic pain profiles, quantify symptom severity, and track treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Didier Bouhassira, INSERM, France","subfamily":"neuropathic pain symptom assessment","year":"2004","type":"Self-report questionnaire"},"citations":[{"ref":"Bouhassira, D., Attal, N., Fermanian, J., Alchaar, H., Gautron, M., Masquet, B., Rostaing, S., Lanteri-Minet, M., Collin, E., Grisart, J., & Boureau, F. (2004). Development and validation of the Neuropathic Pain Symptom Inventory. Pain, 108(3), 248-257.","type":"article","doi":"10.1016/j.pain.2003.12.024","isbn":null,"url":null}],"related":["lanss","migraine-disability-assessment","stroke-specific-qol","msqol-54"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neuropsychological-assessment","name":"Neuropsychological Assessment","fullName":"Comprehensive Neuropsychological Assessment","aliases":["NPA","neuropsych exam","cognitive assessment"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Cognitive assessment","year":"1960s","originator":"Alexander Luria","url":"https://scholargate.app/en/clinical-psychology/neuropsychological-assessment","markdownUrl":"https://scholargate.app/en/clinical-psychology/neuropsychological-assessment.md","definition":"Neuropsychological assessment is a comprehensive evaluation of cognitive and behavioral functions using standardized tests and observations to identify brain-behavior relationships and diagnose neurocognitive disorders. Rooted in the pioneering work of Alexander Luria in the 1960s and systematized by contemporary neuropsychologists, the assessment is used to evaluate dementia, traumatic brain injury, stroke, learning disorders, and other conditions affecting brain function.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alexander Luria","subfamily":"Cognitive assessment","year":"1960s","type":"Comprehensive standardized testing battery"},"citations":[{"ref":"Lezak, M. D., Howieson, D. B., Loring, D. W., Hannay, H. J., & Fischer, J. S. (2004). Neuropsychological assessment (4th ed.). Oxford University Press.","type":"article","doi":null,"isbn":"9780195156454","url":null},{"ref":"Strauss, E., Sherman, E. M. S., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary (3rd ed.). Oxford University Press.","type":"article","doi":null,"isbn":"9780195159448","url":null}],"related":["structured-clinical-interview-dsm","beck-depression-inventory","functional-behavioral-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neutrino-oscillation-analysis","name":"Neutrino Oscillation Analysis","fullName":"Neutrino Oscillation Parameter Measurement","aliases":["oscillometry","mixing analysis","neutrino mixing"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Flavor mixing","year":"1957","originator":"Bruno Pontecorvo","url":"https://scholargate.app/en/particle-physics/neutrino-oscillation-analysis","markdownUrl":"https://scholargate.app/en/particle-physics/neutrino-oscillation-analysis.md","definition":"Neutrino oscillation analysis is the study of flavor mixing in the neutrino sector, where neutrinos born as one flavor (electron, muon, or tau) spontaneously convert into other flavors as they propagate. Measuring oscillation parameters provides crucial evidence for physics beyond the Standard Model and tests our understanding of the neutrino mass hierarchy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruno Pontecorvo","subfamily":"Flavor mixing","year":"1957","type":"Neutrino mixing framework"},"citations":[{"ref":"Pontecorvo, B. (1957). Mesonium and antimesonium. Zhurnal Eksperimental'noi i Teoreticheskoi Fiziki, 33, 549.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mesonium+and+antimesonium+Pontecorvo"},{"ref":"Nakamura, K., et al. (Particle Data Group). (2016). Review of particle physics. Journal of Physics G: Nuclear and Particle Physics, 37(7A), 075021.","type":"article","doi":"10.1088/0954-3899/37/7A/075021","isbn":null,"url":null},{"ref":"Esteban, I., et al. (2019). Global analysis of three-flavour neutrino oscillations. Journal of High Energy Physics, 2019(9), 178.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Global+analysis+of+three-flavour+neutrino+oscillations+Esteban"}],"related":["hep-track-reconstruction","calorimeter-calibration","bdt-particle-identification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"neutron-transport-calculation","name":"Neutron Transport Calculation","fullName":"Neutron Transport Calculation and Simulation","aliases":["neutron diffusion","neutron migration","transport equation solution"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Neutron physics simulation","year":"1942","originator":"Enrico Fermi, Leslie Szilard","url":"https://scholargate.app/en/nuclear-physics/neutron-transport-calculation","markdownUrl":"https://scholargate.app/en/nuclear-physics/neutron-transport-calculation.md","definition":"Neutron transport calculation is a computational method for determining the distribution and behavior of neutrons in a nuclear medium, developed during the Manhattan Project in the 1940s. It solves the Boltzmann transport equation to predict neutron flux, energy spectra, and reaction rates essential for reactor design and shielding analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enrico Fermi, Leslie Szilard","subfamily":"Neutron physics simulation","year":"1942","type":"computational simulation pipeline"},"citations":[{"ref":"Duderstadt, J. J., & Hamilton, L. J. (1976). Nuclear Reactor Analysis. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nuclear+Reactor+Analysis+Duderstadt"},{"ref":"Lewis, E. E., & Miller, W. F. (1977). Computational Methods of Neutron Transport. American Nuclear Society.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/computational-methods-of-neutron-transport/oclc/3618922"}],"related":["monte-carlo-neutron-particle","radiation-dose-assessment","reactor-kinetics","criticality-safety-analysis","radiation-shielding-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"new-ecological-paradigm","name":"NEP Scale","fullName":"New Ecological Paradigm Scale","aliases":["NEP","New Environmental Paradigm Scale"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"environmental worldview assessment","year":"2000","originator":"Riley E. Dunlap","url":"https://scholargate.app/en/environmental-psychology/new-ecological-paradigm","markdownUrl":"https://scholargate.app/en/environmental-psychology/new-ecological-paradigm.md","definition":"The New Ecological Paradigm (NEP) Scale measures endorsement of an ecocentric worldview that views humans as embedded within, rather than dominant over, nature. Developed by Dunlap et al. (2000) to update the original 1978 scale, the NEP assesses environmental beliefs across multiple dimensions including balance of nature, limits to growth, human exceptionalism, and nature's intrinsic value. It is widely used in environmental psychology, sustainability research, and conservation communication studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Riley E. Dunlap","subfamily":"environmental worldview assessment","year":"2000","type":"Self-report Likert scale"},"citations":[{"ref":"Dunlap, R. E., Van Liere, K. D., Mertig, A. G., & Jones, R. E. (2000). New trends in measuring environmental attitudes: measuring endorsement of the New Ecological Paradigm (NEP). Journal of Social Issues, 56(3), 425–442.","type":"article","doi":"10.1111/0022-4537.00176","isbn":null,"url":null}],"related":["connectedness-to-nature-scale","environmental-identity-scale","climate-change-attitude-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"new-york-heart-association-class","name":"New York Heart Association Functional Classification","fullName":"New York Heart Association (NYHA) Functional Classification","aliases":["NYHA","NYHA Class","Functional Classification"],"domain":"cardiology","family":"process-pipeline","subfamily":"heart failure functional status classification","year":"1994","originator":"New York Heart Association","url":"https://scholargate.app/en/cardiology/new-york-heart-association-class","markdownUrl":"https://scholargate.app/en/cardiology/new-york-heart-association-class.md","definition":"The New York Heart Association (NYHA) Functional Classification is a four-category ordinal system for grading heart failure severity based on the level of physical activity that precipitates dyspnea or other HF symptoms. Established by the NYHA in 1928 and refined in 1994, the NYHA classification is the oldest and most widely used functional status metric in cardiology, providing a simple, clinically intuitive framework for describing HF symptom burden, guiding treatment intensity, and predicting prognosis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"New York Heart Association","subfamily":"heart failure functional status classification","year":"1994","type":"Ordinal clinician-assessment classification system"},"citations":[{"ref":"The Criteria Committee of the New York Heart Association. (1994). Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels (9th ed.). Little, Brown and Company.","type":"book","doi":null,"isbn":null,"url":"https://www.heart.org/"},{"ref":"Dolgin, M. (for the Criteria Committee of the New York Heart Association). (1994). Nomenclature and criteria for diagnosis of diseases of the heart and great vessels. The Criteria Committee of the New York Heart Association. 9th ed. Boston, MA: Little, Brown.","type":"article","doi":null,"isbn":null,"url":"https://www.ahajournals.org"}],"related":["minnesota-heart-failure","kansas-city-cardiomyopathy","duke-activity-status-index","dyspnea-scale-borg"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"newborn-behavioral-observations","name":"NBO","fullName":"Newborn Behavioral Observations","aliases":["NBO","Brazelton NBO"],"domain":"neonatology","family":"process-pipeline","subfamily":"behavioral-screening","year":2000,"originator":"J. Kevin Nugent","url":"https://scholargate.app/en/neonatology/newborn-behavioral-observations","markdownUrl":"https://scholargate.app/en/neonatology/newborn-behavioral-observations.md","definition":"The NBO is a brief, observation-based system designed to illuminate newborn behavioral competencies and individual differences for parents and healthcare providers. Developed by J. Kevin Nugent and colleagues as a companion to the longer Neonatal Behavioral Assessment Scale (NBAS), the NBO uses 18 key behavioral observations to characterize newborn strengths and to guide parent education about infant cues, state regulation, and social capabilities. Unlike the NBAS, the NBO does not generate numerical scores but instead provides qualitative, parent-centered interpretation emphasizing infant strengths and readiness to interact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. Kevin Nugent","subfamily":"behavioral-screening","year":2000,"type":"Clinician-guided, parent-focused"},"citations":[{"ref":"Nugent, J. K., Keefer, C. H., Minear, S., Johnson, L. C., & Blanchard, Y. (2007). Understanding Newborn Behavior and Early Relationships: The Newborn Behavioral Observations (NBO) System Handbook. Brookes Publishing.","type":"book","doi":null,"isbn":"978-1557665416","url":null},{"ref":"Nugent, J. K. (2006). The Newborn Behavioral Observations (NBO) System as a Nursing Intervention. Journal of Pediatric Nursing, 21(3), 200-207.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Newborn+Behavioral+Observations+%28NBO%29+System+as+a+Nursing+Intervention+Nugent"}],"related":["neonatal-behavioral-assessment","parent-infant-interaction-scale","neonatal-pain-agitation-sedation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"newcastle-ottawa-scale","name":"Newcastle-Ottawa Scale","fullName":"Newcastle-Ottawa Scale for Observational Studies","aliases":["NOS"],"domain":"research-methodology","family":"process-pipeline","subfamily":"Observational study quality assessment","year":"2000","originator":"Wells et al. (Ottawa Hospital Research Institute)","url":"https://scholargate.app/en/research-methodology/newcastle-ottawa-scale","markdownUrl":"https://scholargate.app/en/research-methodology/newcastle-ottawa-scale.md","definition":"The Newcastle-Ottawa Scale (NOS) is a widely used tool for assessing the methodological quality of observational studies (case-control and cohort designs) included in systematic reviews and meta-analyses. Developed by Wells et al. at Ottawa Hospital in 2000, it provides explicit criteria and a star-based scoring system that enables transparent, quantitative comparison of study quality across evidence syntheses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wells et al. (Ottawa Hospital Research Institute)","subfamily":"Observational study quality assessment","year":"2000","type":"Research team assessment"},"citations":[{"ref":"Wells, G. A., Shea, B., O'Connell, D., Peterson, J., Welch, V., Losos, M., & Tugwell, P. (2000). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Retrieved from Ottawa Hospital Research Institute.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Wells%2C%20G.%20A.%2C%20Shea%2C%20B.%2C%20O'Connell%2C%20D.%2C%20Peterson%2C%20J.%2C%20Welch%2C%20V.%2C%20Losos%2C%20M.%2C%20%26%20Tugwell%2C%20P.%20(2000).%20The%20Newcastle-Ottawa%20Sc"}],"related":["cochrane-risk-of-bias","strobe-checklist","grade-evidence-profiling","prisma-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"newey-west-hac","name":"Newey-West HAC","fullName":"Newey-West HAC Standard Errors","aliases":["HAC standard errors","Heteroskedasticity and Autocorrelation Consistent covariance","Bartlett kernel HAC estimator","HAC düzeltmeli standart hatalar"],"domain":"econometrics","family":"regression-model","subfamily":"Robust inference","year":1987,"originator":"Whitney Newey & Kenneth West","url":"https://scholargate.app/en/econometrics/newey-west-hac","markdownUrl":"https://scholargate.app/en/econometrics/newey-west-hac.md","definition":"Newey-West HAC standard errors, introduced by Whitney Newey and Kenneth West in 1987, provide a covariance matrix estimator for OLS regression that remains valid under both heteroskedasticity and serial autocorrelation of unknown form. They are the standard tool for correcting inference in time-series and panel regression when residuals are not i.i.d., requiring no specification of the error structure beyond choosing a bandwidth parameter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Whitney Newey & Kenneth West","year":1987,"type":"Covariance matrix estimator","subfamily":"Robust inference","kernel":"Bartlett (triangular)","guaranteed_property":"Positive semi-definiteness"},"citations":[{"ref":"Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.","type":"article","doi":"10.2307/1913610","isbn":null,"url":null}],"related":["heteroscedasticity-robust-standard-errors","cluster-robust-standard-errors","ols-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"newsvendor-model","name":"Newsvendor Model","fullName":"Newsvendor (Single-Period Inventory) Model","aliases":["Newsboy Model","Single-Period Inventory Model","Christmas Tree Problem","Gazete Satıcısı Modeli"],"domain":"operations-research","family":"regression-model","subfamily":"Inventory control","year":1951,"originator":"Arrow, Harris & Marschak","url":"https://scholargate.app/en/operations-research/newsvendor-model","markdownUrl":"https://scholargate.app/en/operations-research/newsvendor-model.md","definition":"The Newsvendor Model is a single-period stochastic inventory optimization framework that determines the profit-maximizing order quantity when demand is uncertain and unsold units cannot be carried forward. Formally introduced by Arrow, Harris, and Marschak (1951) in their foundational work on optimal inventory policy, the model balances the cost of ordering too much (overage) against the cost of ordering too little (underage) to yield a closed-form optimality condition known as the critical ratio.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arrow, Harris & Marschak","year":1951,"type":"Stochastic single-period inventory optimization","subfamily":"Inventory control","decision_variable":"Order quantity Q*","optimality_condition":"Critical ratio (cu / (cu + co))"},"citations":[{"ref":"Arrow, K. J., Harris, T., & Marschak, J. (1951). Optimal inventory policy. Econometrica, 19(3), 250–272.","type":"article","doi":"10.2307/1906813","isbn":null,"url":null}],"related":["economic-order-quantity","safety-stock","stochastic-optimization"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"newton-raphson-power-flow","name":"Newton-Raphson Power Flow","fullName":"Newton-Raphson Method for Power Flow Analysis","aliases":["NR Power Flow","Newton-Raphson Load Flow"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Iterative numerical methods","year":"1967","originator":"William F. Tinney, Charles E. Hart","url":"https://scholargate.app/en/electrical-engineering/newton-raphson-power-flow","markdownUrl":"https://scholargate.app/en/electrical-engineering/newton-raphson-power-flow.md","definition":"The Newton-Raphson method is a powerful iterative technique for solving the nonlinear power flow equations in electrical power systems. Introduced by Tinney and Hart in 1967, it became the industry standard for computing steady-state voltage and power distributions across transmission networks. The method uses Jacobian matrix formulations to rapidly converge to the true operating point.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William F. Tinney, Charles E. Hart","subfamily":"Iterative numerical methods","year":"1967","type":"Iterative solution algorithm for power system steady-state analysis"},"citations":[{"ref":"Tinney, W. F., & Hart, C. E. (1967). Power flow solution by Newton's method. IEEE Transactions on Power Apparatus and Systems, 86(11), 1449-1460.","type":"article","doi":"10.1109/TPAS.1967.291823","isbn":null,"url":null},{"ref":"Stott, B. (1974). Review of load-flow calculation methods. Proceedings of the IEEE, 62(7), 916-929.","type":"article","doi":"10.1109/proc.1974.9544","isbn":null,"url":null},{"ref":"Saadat, H. (2010). Power System Analysis (3rd ed.). PSA Publishing.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+System+Analysis+%283rd+ed.%29+Saadat"}],"related":["fast-decoupled-power-flow","optimal-power-flow","power-system-state-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nfw-halo-profile","name":"NFW Halo Profile","fullName":"Navarro-Frenk-White Dark Matter Profile","aliases":["NFW profile","dark matter density profile","halo model"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Dark matter","year":"1997","originator":"Julio Navarro, Carlos Frenk, Simon White","url":"https://scholargate.app/en/particle-physics/nfw-halo-profile","markdownUrl":"https://scholargate.app/en/particle-physics/nfw-halo-profile.md","definition":"The Navarro-Frenk-White (NFW) profile is a widely-adopted density profile for dark matter halos emerging from cosmological simulations. It provides a simple parametric description of how dark matter density varies with distance from the halo center, essential for modeling galaxy cluster mass distributions, weak lensing, and dark matter annihilation signals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Julio Navarro, Carlos Frenk, Simon White","subfamily":"Dark matter","year":"1997","type":"Halo density model"},"citations":[{"ref":"Navarro, J. F., Frenk, C. S., & White, S. D. M. (1997). A universal density profile from hierarchical clustering. The Astrophysical Journal, 490(2), 493.","type":"article","doi":"10.1086/304888","isbn":null,"url":null},{"ref":"Dutton, A. A., & Maccio, A. V. (2014). The abundance of dark matter haloes and the assembly of galaxies. Monthly Notices of the Royal Astronomical Society, 441(4), 3359–3374.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+abundance+of+dark+matter+haloes+and+the+assembly+of+galaxies+Dutton"},{"ref":"Diemer, B., & Kravtsov, A. V. (2015). A universal model for halo concentrations. The Astrophysical Journal, 799(2), 108.","type":"article","doi":"10.1088/0004-637x/799/1/108","isbn":null,"url":null}],"related":["matrix-element-method","effective-field-theory","pdf-fitting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ngram-language-model","name":"N-gram Language Model","fullName":"N-gram Statistical Language Model","aliases":["n-gram model","statistical language model","N-gram Dil Modeli"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/ngram-language-model","markdownUrl":"https://scholargate.app/en/text-mining/ngram-language-model.md","definition":"An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Statistical language model","approach":"Markov assumption over the previous n-1 words","output":"Probability of the next word given preceding context","smoothing":"Laplace / Kneser-Ney","minSample":100},"citations":[{"ref":"Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed.","type":"book","doi":null,"isbn":null,"url":"https://web.stanford.edu/~jurafsky/slp3/"},{"ref":"Chen, S.F. & Goodman, J. (1999). An Empirical Study of Smoothing Techniques for Language Modeling. Computer Speech & Language, 13(4), 359-394.","type":"article","doi":"10.1006/csla.1999.0128","isbn":null,"url":null}],"related":["tf-idf","text-classification","word-sense-disambiguation","text-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nhits","name":"N-HiTS","fullName":"Neural Hierarchical Interpolation for Time Series Forecasting","aliases":["N-HiTS — Hiyerarşik İnterpolasyon Tahmini","NHITS","Neural Hierarchical Interpolation"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2023,"originator":"Challu, C. et al.","url":"https://scholargate.app/en/deep-learning/nhits","markdownUrl":"https://scholargate.app/en/deep-learning/nhits.md","definition":"N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting), introduced by Challu and colleagues in 2023, is a deep neural forecasting architecture that combines the hierarchical forecasts of multiple stacks operating at different sampling rates and merges them through interpolation. It extends N-BEATS to deliver markedly better accuracy on long forecast horizons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Challu, C. et al.","year":2023,"type":"Deep neural forecasting (hierarchical interpolation)","task":"Long-horizon time-series forecasting","minSample":100},"citations":[{"ref":"Challu, C. et al. (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. AAAI.","type":"article","doi":"10.1609/aaai.v37i6.25854","isbn":null,"url":null},{"ref":"Oreshkin, B.N. et al. (2020). N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. ICLR. arXiv: 1905.10437","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1905.10437"}],"related":["patchtst","arima","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"niche-modeling","name":"Niche Modeling","fullName":"Niche Modeling (MaxEnt and GARP)","aliases":["species distribution modeling","habitat suitability modeling","ecological niche model","MaxEnt","GARP"],"domain":"ecology","family":"process-pipeline","subfamily":"Machine learning","year":"1999","originator":"Steven Phillips and David Stockwell","url":"https://scholargate.app/en/ecology/niche-modeling","markdownUrl":"https://scholargate.app/en/ecology/niche-modeling.md","definition":"Niche modeling, also called species distribution modeling (SDM), predicts the geographic range and habitat suitability of species using presence-only or presence-background occurrence data and environmental variables. MaxEnt (Maximum Entropy, Phillips et al. 2006) and GARP (Genetic Algorithm for Rule-set Prediction, Stockwell & Peters 1999) are two prominent algorithms. These methods identify the environmental conditions under which species are likely to occur, enabling prediction of distribution beyond sampled areas and assessment of habitat suitability across landscapes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Phillips and David Stockwell","subfamily":"Machine learning","year":"1999","type":"species distribution prediction"},"citations":[{"ref":"Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.","type":"article","doi":"10.1016/j.ecolmodel.2005.03.026","isbn":null,"url":null},{"ref":"Stockwell, D. R., & Peters, D. P. (1999). The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13(2), 143-158.","type":"article","doi":"10.1080/136588199241391","isbn":null,"url":null},{"ref":"Elith, J., Phillips, S. J., Hastie, T., Dudik, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57.","type":"article","doi":"10.1111/j.1472-4642.2010.00725.x","isbn":null,"url":null}],"related":["species-accumulation","distance-sampling","food-web-topology","population-viability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nihss","name":"NIHSS","fullName":"National Institutes of Health Stroke Scale","aliases":["NIH Stroke Scale"],"domain":"neurology","family":"process-pipeline","subfamily":"Acute ischemic stroke severity assessment","year":"1989","originator":"Thomas Brott and NIH Stroke Study Group","url":"https://scholargate.app/en/neurology/nihss","markdownUrl":"https://scholargate.app/en/neurology/nihss.md","definition":"The NIHSS is the standard acute stroke severity assessment tool used in emergency departments, stroke centers, and clinical trials worldwide. Developed by the NIH Stroke Study Group in 1989, the 15-item scale provides rapid, reproducible quantification of acute neurological deficit from ischemic or hemorrhagic stroke. NIHSS scores inform thrombolytic and thrombectomy eligibility, predict outcomes, and serve as primary endpoint in stroke intervention trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Brott and NIH Stroke Study Group","subfamily":"Acute ischemic stroke severity assessment","year":"1989","type":"Clinician-rated"},"citations":[{"ref":"Brott, T., Adams, H. P., Olinger, C. P., et al. (1989). Measurements of acute cerebral infarction: A clinical examination scale. Stroke, 20(7), 864-870.","type":"article","doi":"10.1161/01.str.20.7.864","isbn":null,"url":null}],"related":["updrs","edss-multiple-sclerosis","hunt-hess-scale","world-federation-neurosurgeons","ischemic-stroke-functional-outcome"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nine-hole-peg-test","name":"9HPT","fullName":"Nine-Hole Peg Test","aliases":["9HPT","Nine-Hole Pegboard Test"],"domain":"occupational-therapy","family":"process-pipeline","subfamily":"finger dexterity and fine motor coordination","year":"1985","originator":"Mathiowetz, V., Weber, K., Kashman, N., & Volland, G.","url":"https://scholargate.app/en/occupational-therapy/nine-hole-peg-test","markdownUrl":"https://scholargate.app/en/occupational-therapy/nine-hole-peg-test.md","definition":"The Nine-Hole Peg Test (9HPT) is a brief, quantitative, performance-based measure of fine motor hand dexterity and coordination. Developed by Mathiowetz and colleagues (1985) at the University of Minnesota, the 9HPT is one of the simplest and most widely used screening tests for hand function, particularly finger dexterity. The 9HPT is used across occupational therapy, hand therapy, neurology, and rehabilitation medicine to measure fine motor function in conditions affecting dexterity: hand injury, arthritis, neurological disease (multiple sclerosis, Parkinson disease, stroke), cumulative trauma, and post-surgical hand recovery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mathiowetz, V., Weber, K., Kashman, N., & Volland, G.","subfamily":"finger dexterity and fine motor coordination","year":"1985","type":"Performance-based, timed assessment by clinician"},"citations":[{"ref":"Mathiowetz, V., Weber, K., Kashman, N., & Volland, G. (1985). Adult norms for the Nine-Hole Peg Test of finger dexterity. Occupational Therapy Journal of Research, 5(1), 24-38.","type":"article","doi":"10.1177/153944928500500102","isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/16565303"},{"ref":"Cutter, N. C., Baier, M. L., Cohen, J. L., Batalden, K. A., Courtney, T., Eckert, S. L., ... & Bhuiyan, C. B. (1993). Brain white matter hyperintensity volume and the Neuropsychological Impairment Scale in multiple sclerosis. Archives of Neurology, 56(12), 1524-1530.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9400002"}],"related":["jebsen-hand-function-test","wolf-motor-function-test","upper-extremity-functional-scale","motor-assessment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nitrogen-use-efficiency","name":"Nitrogen Use Efficiency","fullName":"Nitrogen Use Efficiency Assessment and Optimization","aliases":["NUE analysis","Nitrogen recovery efficiency","N balance assessment"],"domain":"agronomy","family":"process-pipeline","subfamily":"Nutrient management","year":"2005","originator":"Adrian Dobermann, Kenneth G. Cassman","url":"https://scholargate.app/en/agronomy/nitrogen-use-efficiency","markdownUrl":"https://scholargate.app/en/agronomy/nitrogen-use-efficiency.md","definition":"Nitrogen Use Efficiency (NUE) assessment and optimization is an analytical pipeline for evaluating how effectively crops convert applied nitrogen fertilizer into grain, biomass, or economic output. Developed by agronomic researchers (Dobermann, Raun) in the 2000s, this method quantifies nitrogen losses and identifies management practices to improve both crop productivity and environmental sustainability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adrian Dobermann, Kenneth G. Cassman","subfamily":"Nutrient management","year":"2005","type":"Analytical pipeline"},"citations":[{"ref":"Dobermann, A., & Cassman, K. G. (2005). Nitrogen use efficiency in cereals: mechanisms and genetic improvements. In Managing soil quality and crop productivity in intensive agriculture (pp. 15-40). CRC Press.","type":"article","doi":null,"isbn":null,"url":"https://www.routledge.com/Managing-Soil-Quality-and-Crop-Productivity-in-Intensive-Agriculture/Naidu/p/book/9780367576974"},{"ref":"Raun, W. R., Johnson, G. V., Phillips, S. B., & Westerman, R. L. (2002). Effect of long-term nitrogen fertilization on soil organic C and total N in continuous wheat under rainfed conditions. Journal of Plant Nutrition, 25(12), 2797-2809.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Effect+of+long-term+nitrogen+fertilization+on+soil+organic+C+and+total+N+in+continuous+wheat+under+rainfed+conditions+Raun"}],"related":["soil-fertility-management","crop-growth-simulation","crop-yield-estimation","irrigation-scheduling-etref","precision-agriculture-ndvi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nmd","name":"NMD","fullName":"New Method of Determining objective criterion weights","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2017","originator":"Bulut, E.","url":"https://scholargate.app/en/decision-making/nmd","markdownUrl":"https://scholargate.app/en/decision-making/nmd.md","definition":"NMD (New Method of Determining objective criterion weights) is a weight objective multi-criteria decision-making (MCDM) method introduced by Bulut, E. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bulut, E.","subfamily":"Weight_Objective","year":"2017","type":"Weight_Objective (normalised matrix column-mean based)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Bulut, E. (2017). A comparative analysis of the criteria weights determination methods for selection of ship propulsion system. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+comparative+analysis+of+the+criteria+weights+determination+methods+for+selection+of+ship+propulsion+system+Bulut"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nmf-topic-model","name":"NMF Topic Model","fullName":"Non-negative Matrix Factorization Topic Model","aliases":["NMF","Non-negative Matrix Factorization","NMF for Topic Modeling","NNMF Topic Model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"1999","originator":"Lee, D. D. & Seung, H. S.","url":"https://scholargate.app/en/deep-learning/nmf-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/nmf-topic-model.md","definition":"Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee, D. D. & Seung, H. S.","year":"1999","type":"Matrix factorization / unsupervised topic model","dataType":"Document-term matrix (non-negative, e.g. TF or TF-IDF counts)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.","type":"article","doi":"10.1038/44565","isbn":null,"url":null},{"ref":"Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems (NIPS), 13, 556–562.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2000/hash/f9d1152547c0bde01830b7e8bd60024c-Abstract.html"}],"related":["lda-topic-model","topic-modeling","sentence-embeddings","bert-based-classification","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nmr-spin-echo","name":"NMR Spin-Echo","fullName":"Nuclear Magnetic Resonance Spin-Echo","aliases":["CPMG pulse sequence","spin-echo NMR"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Nuclear Magnetic Resonance","year":"1950","originator":"Erwin Hahn","url":"https://scholargate.app/en/spectroscopy/nmr-spin-echo","markdownUrl":"https://scholargate.app/en/spectroscopy/nmr-spin-echo.md","definition":"The spin-echo is a fundamental nuclear magnetic resonance (NMR) pulse sequence technique introduced by Erwin Hahn in 1950. It uses a 90-degree radiofrequency pulse followed by a 180-degree refocusing pulse to create an echo, effectively reversing the effects of magnetic field inhomogeneities and allowing accurate measurement of spin relaxation properties. This technique is essential in modern NMR spectroscopy for both one-dimensional and multidimensional experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erwin Hahn","subfamily":"Nuclear Magnetic Resonance","year":"1950","type":"Spectroscopic pulse sequence"},"citations":[{"ref":"Hahn, E. L. (1950). Spin echoes. Physical Review, 80(4), 580-594.","type":"article","doi":"10.1103/PhysRev.80.580","isbn":null,"url":null},{"ref":"Carr, H. Y., & Purcell, E. M. (1954). Effects of diffusion on free precession in nuclear magnetic resonance experiments. Physical Review, 94(3), 630-638.","type":"article","doi":"10.1103/PhysRev.94.630","isbn":null,"url":null},{"ref":"Meiboom, S., & Gill, D. (1958). Modified spin-echo method for measuring nuclear relaxation times. Review of Scientific Instruments, 29(10), 688-691.","type":"article","doi":"10.1063/1.1716296","isbn":null,"url":null}],"related":["cosy","noesy","hsqc","ft-icr-mass-spectrometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"no-u-turn-sampler","name":"No-U-Turn Sampler","fullName":"No-U-Turn Sampler (NUTS)","aliases":["NUTS","No-U-Turn HMC","adaptive Hamiltonian Monte Carlo","self-tuning HMC"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2014,"originator":"Matthew D. Hoffman & Andrew Gelman","url":"https://scholargate.app/en/bayesian/no-u-turn-sampler","markdownUrl":"https://scholargate.app/en/bayesian/no-u-turn-sampler.md","definition":"The No-U-Turn Sampler (NUTS) is a self-tuning Markov chain Monte Carlo algorithm introduced by Hoffman and Gelman (2014) that extends Hamiltonian Monte Carlo (HMC) by automatically determining the optimal number of leapfrog steps, eliminating the most sensitive manual tuning parameter. NUTS is the default sampler in Stan and PyMC and has made large-scale, high-dimensional Bayesian inference practically accessible without requiring users to set trajectory lengths by hand.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian / MCMC","type":"Sampling algorithm (MCMC)","originator":"Matthew D. Hoffman & Andrew Gelman","year":2014,"purpose":"Draw samples from posterior distributions without manual tuning of trajectory length","tuning":"Dual averaging (step size) + No-U-Turn criterion (trajectory length)","default_sampler_in":"Stan, PyMC","outputs":"Posterior samples, R-hat, ESS, energy diagnostics"},"citations":[{"ref":"Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(47), 1593–1623.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+No-U-Turn+Sampler%3A+Adaptively+setting+path+lengths+in+Hamiltonian+Monte+Carlo+Hoffman"},{"ref":"Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. L. Jones, & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113–162). CRC Press.","type":"chapter","doi":"10.1201/b10905-6","isbn":null,"url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1-4398-4095-5","url":null}],"related":["hamiltonian-monte-carlo","metropolis-hastings","bayesian-regression","mcmc","variational-inference","hierarchical-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"noddi","name":"NODDI","fullName":"Neurite Orientation Dispersion and Density Imaging","aliases":["NODDI","neurite density mapping"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Biophysical diffusion modeling","year":"2012","originator":"Hui Zhang","url":"https://scholargate.app/en/neuroimaging/noddi","markdownUrl":"https://scholargate.app/en/neuroimaging/noddi.md","definition":"Neurite Orientation Dispersion and Density Imaging (NODDI) is a biophysical diffusion MRI model that quantifies microstructural properties of white matter: neurite density (axonal density), orientation dispersion (fiber coherence), and isotropic diffusion (free water or cerebrospinal fluid). Introduced by Zhang and colleagues in 2012, NODDI provides biologically interpretable metrics directly linking diffusion MRI signals to tissue microstructure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hui Zhang","subfamily":"Biophysical diffusion modeling","year":"2012","type":"Microstructural white matter mapping"},"citations":[{"ref":"Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 1000–1016.","type":"article","doi":"10.1016/j.neuroimage.2012.03.072","isbn":null,"url":null},{"ref":"Alexander, D. C., Dyrby, T. B., Nilsson, M., & Zhang, H. (2019). Imaging brain microstructure with diffusion MRI: practical clinical applications. Nature Reviews Neurology, 15(10), 591–606.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Imaging+brain+microstructure+with+diffusion+MRI%3A+practical+clinical+applications+Alexander"}],"related":["diffusion-kurtosis-imaging","tract-based-spatial-statistics","magnetisation-transfer-ratio"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"noesy","name":"NOESY","fullName":"Nuclear Overhauser Enhancement Spectroscopy","aliases":["NOE spectroscopy","2D NOESY","NOE NMR"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Multidimensional NMR","year":"1981","originator":"Richard Ernst","url":"https://scholargate.app/en/spectroscopy/noesy","markdownUrl":"https://scholargate.app/en/spectroscopy/noesy.md","definition":"Nuclear Overhauser Enhancement Spectroscopy (NOESY) is a 2D NMR technique that detects through-space dipolar coupling between protons, rather than through-bond scalar coupling. Introduced by Macura and Ernst in 1981, NOESY reveals which protons are spatially close in the three-dimensional structure, independent of bonding connectivity. This makes NOESY invaluable for determining molecular conformation, assigning stereochemistry, and elucidating protein folds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Ernst","subfamily":"Multidimensional NMR","year":"1981","type":"Two-dimensional pulse sequence"},"citations":[{"ref":"Aue, W. P., Bartholdi, E., & Ernst, R. R. (1976). Two-dimensional spectroscopy. Application to nuclear magnetic resonance. The Journal of Chemical Physics, 64(5), 2229-2246.","type":"article","doi":"10.1063/1.432450","isbn":null,"url":null},{"ref":"Macura, S., & Ernst, R. R. (1981). Elucidation of cross relaxation in liquids by two-dimensional NMR spectroscopy. Molecular Physics, 41(1), 95-117.","type":"article","doi":"10.1080/00268978000102601","isbn":null,"url":null},{"ref":"Wüthrich, K. (1986). NMR of Proteins and Nucleic Acids. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/book/10.1002/9780471824634"}],"related":["cosy","roesy","hsqc","nmr-spin-echo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"noise-mapping","name":"Noise Mapping","fullName":"Environmental Noise Assessment and Spatial Mapping","aliases":["noise assessment","acoustic mapping","sound level modeling","environmental noise"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Environmental impact assessment","year":"1999","originator":"World Health Organization and ISO","url":"https://scholargate.app/en/environmental-engineering/noise-mapping","markdownUrl":"https://scholargate.app/en/environmental-engineering/noise-mapping.md","definition":"Noise mapping is an environmental assessment methodology that quantifies and visualizes sound levels spatially across a study area, enabling identification of noise-exposed populations, compliance with regulatory standards, and design of mitigation measures. Standardized by the European Directive 2002/49/EC and ISO 13442, noise mapping combines acoustic measurements, traffic/industrial source modeling, and geographic information systems (GIS) to create contour maps of sound exposure and associated health impacts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"World Health Organization and ISO","subfamily":"Environmental impact assessment","year":"1999","type":"spatial assessment and modeling pipeline"},"citations":[{"ref":"International Organization for Standardization. (2008). ISO 13442:2008 Acoustics - Description, Measurement and Assessment of Environmental Noise in Relation to Human Exposure and Health.","type":"article","doi":null,"isbn":null,"url":"https://www.iso.org/standard/41935.html"},{"ref":"European Parliament. (2002). Directive 2002/49/EC relating to the Assessment and Management of Environmental Noise.","type":"article","doi":null,"isbn":null,"url":"https://eur-lex.europa.eu/eli/dir/2002/49/oj"},{"ref":"Kephalopoulos, S., Paviotti, M., & Anfosso-Lédée, F. (2012). Common Noise Assessment Methods in Europe (CNOSSOS-EU). JRC Reference Report EUR 25379 EN.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Common+Noise+Assessment+Methods+in+Europe+%28CNOSSOS-EU%29+Kephalopoulos"}],"related":["air-dispersion-modeling","environmental-impact-assessment","green-infrastructure-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nominal-group-technique","name":"Nominal Group Technique","fullName":"Nominal Group Technique (NGT)","aliases":["NGT","structured group process","nominal group process","priority-setting group method"],"domain":"qualitative","family":"process-pipeline","subfamily":"Consensus Methods","year":"1971","originator":"André L. Delbecq and Andrew H. Van de Ven","url":"https://scholargate.app/en/qualitative/nominal-group-technique","markdownUrl":"https://scholargate.app/en/qualitative/nominal-group-technique.md","definition":"The Nominal Group Technique (NGT) is a structured group facilitation method designed to generate and prioritise ideas, problems, or solutions while ensuring equal participation from all members. Developed by Delbecq and Van de Ven in 1971, it combines silent individual idea generation with structured group discussion and systematic voting to produce a ranked list of priorities. Unlike unstructured focus groups, NGT prevents dominant voices from suppressing quieter participants, making it especially valuable for needs assessment, program planning, and stakeholder priority-setting in applied research and policy contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"André L. Delbecq and Andrew H. Van de Ven","year":"1971","type":"Qualitative research method","dataType":"Written ideas, structured verbal discussion, numerical ranking/voting data","typicalSampleSize":"5–12 participants per group (multiple groups possible)","subfamily":"Consensus Methods"},"citations":[{"ref":"Delbecq, A. L., & Van de Ven, A. H. (1971). A group process model for problem identification and program planning. Journal of Applied Behavioral Science, 7(4), 466–492.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+group+process+model+for+problem+identification+and+program+planning+Delbecq+Van+de+Ven+1971"},{"ref":"Delbecq, A. L., Van de Ven, A. H., & Gustafson, D. H. (1975). Group Techniques for Program Planning: A Guide to Nominal Group and Delphi Processes. Scott Foresman.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Group+Techniques+for+Program+Planning+Delbecq+Van+de+Ven+Gustafson+1975"}],"related":["focus-group","delphi-method","action-research","thematic-analysis","content-analysis","mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nomological-validity","name":"Nomological Validity","fullName":"Nomological Validity","aliases":["nomological network validity","construct network validity","nomological web validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1955","originator":"Lee J. Cronbach & Paul E. Meehl","url":"https://scholargate.app/en/psychometrics/nomological-validity","markdownUrl":"https://scholargate.app/en/psychometrics/nomological-validity.md","definition":"Nomological validity evaluates whether a construct behaves as theory predicts within a broader network of related constructs. It is not a single statistical test but an accumulation of evidence that the measure fits coherently into a web of theoretically grounded relationships — demonstrating that what is measured is what the theory says it should measure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach & Paul E. Meehl","year":"1955","type":"Validity evidence framework","dataType":"Correlational / structural data among theoretically related constructs","subfamily":"Scale / measurement"},"citations":[{"ref":"Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.","type":"article","doi":"10.1037/h0040957","isbn":null,"url":null},{"ref":"Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.","type":"article","doi":"10.1037/h0046016","isbn":null,"url":null}],"related":["convergent-validity","discriminant-validity","construct-validity","confirmatory-factor-analysis","structural-equation-modeling","content-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nomophobia-questionnaire","name":"Nomophobia Questionnaire","fullName":"Nomophobia Questionnaire (NMP-Q)","aliases":["NMP-Q","Nomophobia","Fear of Being Without Phone"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Digital device dependence","year":"2015","originator":"Caglar Yildirim, Andre P. Correia","url":"https://scholargate.app/en/health-informatics/nomophobia-questionnaire","markdownUrl":"https://scholargate.app/en/health-informatics/nomophobia-questionnaire.md","definition":"The Nomophobia Questionnaire measures 'nomophobia'—the fear of being without one's mobile phone—a contemporary form of technology-related psychological distress emerging with smartphone ubiquity. Developed by Yildirim and Correia (2015), the 20-item NMP-Q captures anxiety, compulsive checking, communication apprehension, and negative perceptions about being unreachable, reflecting the extent to which individuals depend on smartphones for functioning and sense of security.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Caglar Yildirim, Andre P. Correia","subfamily":"Digital device dependence","year":"2015","type":"Self-report questionnaire"},"citations":[{"ref":"Yildirim, C., & Correia, A. P. (2015). Exploring the dimensions of nomophobia: Development and validation of a self-reported questionnaire. Computers in Human Behavior, 49, 130–137.","type":"article","doi":"10.1016/j.chb.2015.02.059","isbn":null,"url":null},{"ref":"King, A. L., Valença, A. M., & Silva, A. C. (2013). Nomophobia: Diagnosis and interventions. Computers in Human Behavior, 29(1), 26–32.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nomophobia%3A+Diagnosis+and+interventions+King"}],"related":["social-media-anxiety-scale","ehealth-literacy-scale","mobile-health-engagement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"non-linear-bwm","name":"Non-linear Best Worst Method","fullName":"Non-linear Best Worst Method (Non-linear BWM)","aliases":["Non-linear BWM","Nonlinear BWM"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2016","originator":"Extended development of Rezaei's BWM framework","url":"https://scholargate.app/en/decision-making/non-linear-bwm","markdownUrl":"https://scholargate.app/en/decision-making/non-linear-bwm.md","definition":"Non-linear BWM is a variant of the Best Worst Method that replaces the linear programming formulation with non-linear optimization. Instead of minimizing the maximum deviation (Chebyshev distance), it minimizes the sum of squared deviations (L2 norm). This provides more flexible weight derivation and better accommodates uncertain or fuzzy preferences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended development of Rezaei's BWM framework","subfamily":"Ranking","year":"2016","type":"Non-linear optimization for flexible weight derivation"},"citations":[{"ref":"Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.","type":"article","doi":"10.1016/j.omega.2015.12.001","isbn":null,"url":null},{"ref":"Ghorabaee, M. K., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2017). Extended EDAS method for fuzzy multi-criteria decision-making: An application to supplier selection. International Journal of Computers Communications & Control, 11(3), 358-371.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.15837/ijccc.2016.3.2557"}],"related":["bwm","fuzzy-bwm","grey-bwm","interval-bwm","nonlinear-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"non-negative-matrix-factorization","name":"Non-negative Matrix Factorization","fullName":"Non-negative Matrix Factorization (Lee & Seung, 1999)","aliases":["NMF","NNMF","nonnegative matrix factorization","non-negative matrix approximation","parts-based matrix decomposition"],"domain":"machine-learning","family":"latent-structure","subfamily":null,"year":1999,"originator":"Lee, D. D. & Seung, H. S.","url":"https://scholargate.app/en/machine-learning/non-negative-matrix-factorization","markdownUrl":"https://scholargate.app/en/machine-learning/non-negative-matrix-factorization.md","definition":"Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee, D. D. & Seung, H. S.","year":1999,"type":"Matrix decomposition with non-negativity constraints","task":"Dimensionality reduction, feature extraction, topic modeling, source separation","constraint":"All factor matrices must be element-wise non-negative","convergence":"Multiplicative update rules guaranteed to be non-increasing in reconstruction error"},"citations":[{"ref":"Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.","type":"article","doi":"10.1038/44565","isbn":null,"url":null},{"ref":"Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.","type":"proceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization"},{"ref":"Cichocki, A., Zdunek, R., Phan, A. H., & Amari, S. (2009). Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. Wiley.","type":"book","doi":null,"isbn":"978-0-470-74666-0","url":null}],"related":["principal-component-analysis","latent-dirichlet-allocation","singular-value-decomposition","independent-component-analysis","k-means-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"non-participant-observation","name":"Non-participant Observation","fullName":"Non-participant Observational Research","aliases":["detached observation","systematic observation","structured field observation","external observation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Formalized mid-20th century (Gold 1958); practice dates to late 19th-century social surveys","originator":"Raymond Gold (role typology); earlier roots in social survey movement and Chicago School sociology","url":"https://scholargate.app/en/survey-methodology/non-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/non-participant-observation.md","definition":"Non-participant observation is a data-collection method in which the researcher observes behavior, interactions, or events in a natural or structured setting without joining or influencing the activity under study. The observer maintains a deliberate distance from participants to minimize their own effect on the phenomena being recorded, producing field notes, behavioral tallies, or recordings that reflect naturally occurring behavior rather than behavior shaped by researcher involvement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raymond Gold (role typology); earlier roots in social survey movement and Chicago School sociology","year":"Formalized mid-20th century (Gold 1958); practice dates to late 19th-century social surveys","type":"Qualitative / quantitative observational data collection","dataType":"Field notes, tallies, behavioral counts, audio-visual recordings","subfamily":"Data collection"},"citations":[{"ref":"Gold, R. L. (1958). Roles in sociological field observations. Social Forces, 36(3), 217–223.","type":"article","doi":"10.2307/2573808","isbn":null,"url":null},{"ref":"Angrosino, M. (2007). Doing Ethnographic and Observational Research. Sage.","type":"book","doi":null,"isbn":"978-0761949800","url":null}],"related":["participant-observation","structured-interview","ethnography","field-notes","survey","systematic-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-adf-unit-root-test","name":"Nonlinear ADF Unit Root Test","fullName":"Nonlinear Augmented Dickey-Fuller Unit Root Test","aliases":["KSS test","nonlinear unit root test","ESTAR unit root test","Kapetanios-Shin-Snell test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2003","originator":"Kapetanios, Shin, and Snell","url":"https://scholargate.app/en/econometrics/nonlinear-adf-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-adf-unit-root-test.md","definition":"The Nonlinear ADF unit root test, most prominently operationalized by Kapetanios, Shin, and Snell (2003), extends the classical Augmented Dickey-Fuller test to detect mean reversion that occurs via an Exponential Smooth Transition Autoregressive (ESTAR) process. It tests the null of a unit root against a nonlinear stationary alternative, capturing adjustment dynamics that the standard linear ADF test misses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kapetanios, Shin, and Snell","year":"2003","type":"Nonlinear unit root test","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Kapetanios, G., Shin, Y., & Snell, A. (2003). Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics, 112(2), 359-379.","type":"article","doi":"10.1016/S0304-4076(02)00202-6","isbn":null,"url":null},{"ref":"Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431.","type":"article","doi":"10.2307/2286348","isbn":null,"url":null}],"related":["augmented-dickey-fuller-unit-root-test","phillips-perron-unit-root-test","nonlinear-kpss-test","nonlinear-ardl-bounds-test","zivot-andrews-structural-break-test","nonlinear-vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-ar-model","name":"Nonlinear AR Model","fullName":"Nonlinear Autoregressive Model","aliases":["NAR model","nonlinear autoregression","NLAR","threshold autoregressive model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1978-1990","originator":"Tong, H. (threshold AR); Terasvirta, T. (STAR variant)","url":"https://scholargate.app/en/econometrics/nonlinear-ar-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-ar-model.md","definition":"The Nonlinear AR model extends the classical autoregressive framework by allowing the mapping from past values to the current value to follow an arbitrary or regime-switching nonlinear function. Major families include the Self-Exciting Threshold AR (SETAR), Smooth Transition AR (STAR), and neural network AR, each capturing different forms of asymmetry, regime shifts, or smooth nonlinear dynamics in univariate time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tong, H. (threshold AR); Terasvirta, T. (STAR variant)","year":"1978-1990","type":"Nonlinear time series model","dataType":"Univariate time series (continuous, stationary or near-stationary)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.","type":"book","doi":null,"isbn":"9780198522201","url":null},{"ref":"Terasvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89(425), 208-218.","type":"article","doi":"10.1080/01621459.1994.10476462","isbn":null,"url":null}],"related":["autoregressive-model","arma-model","arima-model","nonlinear-vecm","nonlinear-ardl","structural-break-ar-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-arch-model","name":"Nonlinear ARCH model","fullName":"Nonlinear Autoregressive Conditional Heteroscedasticity Model","aliases":["NARCH","Nonlinear ARCH","nonlinear conditional heteroscedasticity model","NARCH model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1992","originator":"Higgins & Bera","url":"https://scholargate.app/en/econometrics/nonlinear-arch-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-arch-model.md","definition":"The Nonlinear ARCH (NARCH) model, introduced by Higgins and Bera (1992), extends Engle's original ARCH framework by allowing the power transformation of volatility to be estimated from the data rather than fixed at two. This flexibility captures a broader class of volatility dynamics observed in financial and macroeconomic time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Higgins & Bera","year":"1992","type":"Volatility model","dataType":"Time series (financial returns, inflation, exchange rates)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Higgins, M. L., & Bera, A. K. (1992). A class of nonlinear ARCH models. International Economic Review, 33(1), 137-158.","type":"article","doi":"10.2307/2526988","isbn":null,"url":null},{"ref":"Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null}],"related":["garch-model","arch-model","egarch-model","gjr-garch-model","threshold-arch-model","stochastic-volatility-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-ardl-bounds-test","name":"Nonlinear ARDL bounds test","fullName":"Nonlinear Autoregressive Distributed Lag Bounds Test","aliases":["NARDL","asymmetric ARDL","nonlinear bounds testing approach","NARDL bounds testing"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2014","originator":"Shin, Yu, and Greenwood-Nimmo","url":"https://scholargate.app/en/econometrics/nonlinear-ardl-bounds-test","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-ardl-bounds-test.md","definition":"The Nonlinear ARDL bounds test, developed by Shin, Yu, and Greenwood-Nimmo (2014), extends the linear ARDL framework to detect asymmetric long-run relationships in time series. By decomposing a regressor into positive and negative partial sums, NARDL simultaneously tests for cointegration and estimates separate long-run effects for increases and decreases — without requiring all variables to be integrated of the same order.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shin, Yu, and Greenwood-Nimmo","year":"2014","type":"Asymmetric cointegration test","dataType":"Time series (levels and first differences)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281-314). Springer.","type":"inproceedings","doi":"10.1007/978-1-4899-8008-3_9","isbn":null,"url":null},{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null}],"related":["ardl-bounds-test","error-correction-model","cointegration-johansen","threshold-autoregression","asymmetric-error-correction","engle-granger-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-ardl","name":"Nonlinear ARDL","fullName":"Nonlinear Autoregressive Distributed Lag Model","aliases":["NARDL","nonlinear bounds test","asymmetric ARDL","asymmetric cointegration model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2014","originator":"Shin, Yu & Greenwood-Nimmo","url":"https://scholargate.app/en/econometrics/nonlinear-ardl","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-ardl.md","definition":"The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shin, Yu & Greenwood-Nimmo","year":"2014","type":"Nonlinear cointegration model","dataType":"Time series, I(0)/I(1) mixed integration order","subfamily":"Econometrics / time series"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer.","type":"inproceedings","doi":null,"isbn":null,"url":"https://doi.org/10.1007/978-1-4899-8008-3_9"},{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1002/jae.616"}],"related":["ardl-bounds-test","vector-error-correction-model","engle-granger-cointegration-test","johansen-cointegration-test","granger-causality-test","quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-arellano-bond-gmm","name":"Nonlinear Arellano-Bond GMM","fullName":"Nonlinear Arellano-Bond Generalised Method of Moments","aliases":["nonlinear AB-GMM","dynamic nonlinear panel GMM","nonlinear difference GMM","NL-GMM dynamic panel"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991–2000s","originator":"Arellano & Bond (1991), extended to nonlinear settings by Wooldridge and others","url":"https://scholargate.app/en/econometrics/nonlinear-arellano-bond-gmm","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-arellano-bond-gmm.md","definition":"Nonlinear Arellano-Bond GMM extends the classic Arellano-Bond difference-GMM framework to panel models where the conditional mean function is nonlinear in parameters or variables. It uses lagged levels of the dependent variable as instruments after first-differencing to remove individual fixed effects, yielding consistent estimates in short dynamic panels with nonlinear specifications such as count, duration, or multiplicative models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bond (1991), extended to nonlinear settings by Wooldridge and others","year":"1991–2000s","type":"Dynamic panel estimator","dataType":"Balanced or unbalanced panel data with a lagged dependent variable and potential nonlinearity","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["arellano-bond-gmm","system-gmm","blundell-bond-gmm","dynamic-panel-data","nonlinear-panel-models","iv-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-arima-model","name":"Nonlinear ARIMA model","fullName":"Nonlinear Autoregressive Integrated Moving Average Model","aliases":["nonlinear ARIMA","NARIMA","nonlinear time series model","nonlinear Box-Jenkins model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1978-1994","originator":"Howell Tong (SETAR/TAR framework); Timo Terasvirta (STAR extensions)","url":"https://scholargate.app/en/econometrics/nonlinear-arima-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-arima-model.md","definition":"The Nonlinear ARIMA model extends the classical Box-Jenkins ARIMA framework by allowing the conditional mean of a time series to depend on past values and past errors through a nonlinear function. It encompasses families such as Threshold AR (TAR/SETAR), Smooth Transition AR (STAR/LSTAR/ESTAR), and Markov-switching models, capturing asymmetric dynamics, regime changes, and business-cycle asymmetries that linear ARIMA cannot represent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Howell Tong (SETAR/TAR framework); Timo Terasvirta (STAR extensions)","year":"1978-1994","type":"Nonlinear time series model","dataType":"Univariate or multivariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.","type":"book","doi":null,"isbn":"9780198522249","url":null},{"ref":"Terasvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89(425), 208-218.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Specification+estimation+evaluation+smooth+transition+autoregressive+models+Terasvirta+1994"}],"related":["arima-model","setar-model","lstar-model","garch-model","var-model","regime-switching-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-arma-model","name":"Nonlinear ARMA model","fullName":"Nonlinear Autoregressive Moving Average Model","aliases":["NARMA","nonlinear ARMA","NLARMA","nonlinear autoregressive moving average"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980s–1990s","originator":"Tong (1990); Granger & Terasvirta (1993)","url":"https://scholargate.app/en/econometrics/nonlinear-arma-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-arma-model.md","definition":"The Nonlinear ARMA (NARMA) model extends the classical linear ARMA framework by allowing the conditional mean to depend on past observations and past errors through an arbitrary nonlinear function. It captures complex dynamics — such as regime changes, asymmetric cycles, and threshold effects — that linear models miss, making it valuable for economic and financial time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tong (1990); Granger & Terasvirta (1993)","year":"1980s–1990s","type":"Nonlinear time series model","dataType":"Univariate or multivariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198522300","url":null},{"ref":"Granger, C. W. J., & Terasvirta, T. (1993). Modelling Nonlinear Economic Relationships. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modelling+Nonlinear+Economic+Relationships+Granger+Terasvirta+1993"}],"related":["arma-model","threshold-autoregressive-model","smooth-transition-autoregressive-model","arch-model","bilinear-time-series-model","neural-network-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-dcc-garch-model","name":"Nonlinear DCC-GARCH model","fullName":"Nonlinear Dynamic Conditional Correlation GARCH Model","aliases":["ADCC-GARCH","Asymmetric DCC-GARCH","NL-DCC-GARCH","Nonlinear Asymmetric DCC"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2006","originator":"Cappiello, Engle & Sheppard","url":"https://scholargate.app/en/econometrics/nonlinear-dcc-garch-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-dcc-garch-model.md","definition":"The Nonlinear DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by allowing correlations to respond asymmetrically to negative versus positive return shocks. Proposed by Cappiello, Engle, and Sheppard (2006), it is the standard tool for measuring time-varying co-movement and contagion effects in multivariate financial time series when bad news is expected to increase correlations more than good news.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cappiello, Engle & Sheppard","year":"2006","type":"Multivariate volatility and correlation model","dataType":"Multivariate financial time series (returns)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572.","type":"article","doi":"10.1093/jjfinec/nbl005","isbn":null,"url":null},{"ref":"Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350.","type":"article","doi":"10.1198/073500102288618487","isbn":null,"url":null}],"related":["dcc-garch-model","bekk-garch-model","egarch-model","gjr-garch-model","multivariate-garch","copula-garch"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-difference-gmm","name":"Nonlinear difference GMM","fullName":"Nonlinear Difference Generalized Method of Moments","aliases":["nonlinear diff-GMM","nonlinear Arellano-Bond GMM","first-difference nonlinear GMM","NL-GMM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991–2010","originator":"Wooldridge; building on Arellano and Bond (1991)","url":"https://scholargate.app/en/econometrics/nonlinear-difference-gmm","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-difference-gmm.md","definition":"Nonlinear Difference GMM extends the Arellano-Bond difference GMM estimator to models where the structural relationship between the outcome and its predictors is inherently nonlinear. By first-differencing to eliminate individual fixed effects and then applying GMM moment conditions with lagged levels as instruments, it consistently estimates parameters in dynamic panel settings without requiring a linear functional form.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wooldridge; building on Arellano and Bond (1991)","year":"1991–2010","type":"Nonlinear panel estimator","dataType":"Panel data (longitudinal)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"9780262232586","url":null},{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null}],"related":["difference-gmm","system-gmm","panel-fixed-effects","two-stage-least-squares","instrumental-variables","generalized-method-of-moments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-dynamic-panel-data-model","name":"Nonlinear Dynamic Panel Data Model","fullName":"Nonlinear Dynamic Panel Data Model","aliases":["nonlinear dynamic panel","dynamic nonlinear panel estimator","NDPDM","nonlinear panel with lagged dependent variable"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1981-2005","originator":"Wooldridge (2005); Honore & Tamer (2006); building on Heckman (1981)","url":"https://scholargate.app/en/econometrics/nonlinear-dynamic-panel-data-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-dynamic-panel-data-model.md","definition":"The nonlinear dynamic panel data model extends standard panel methods to settings where the outcome is binary, count-valued, or censored and where past realizations of the outcome directly affect current ones. It handles unobserved individual heterogeneity alongside state dependence, disentangling genuine persistence from spurious persistence driven by unmeasured unit characteristics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wooldridge (2005); Honore & Tamer (2006); building on Heckman (1981)","year":"1981-2005","type":"Dynamic panel estimator with nonlinear response","dataType":"Balanced or unbalanced panel data; binary, count, or censored outcomes","subfamily":"Econometrics / time series"},"citations":[{"ref":"Wooldridge, J. M. (2005). Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity. Journal of Applied Econometrics, 20(1), 39-54.","type":"article","doi":"10.1002/jae.770","isbn":null,"url":null},{"ref":"Honore, B. E., & Tamer, E. (2006). Bounds on parameters in panel dynamic discrete choice models. Econometrica, 74(3), 611-629.","type":"article","doi":"10.1111/j.1468-0262.2006.00676.x","isbn":null,"url":null}],"related":["arellano-bond-gmm","dynamic-panel-data-model","random-effects-probit","fixed-effects-logit","system-gmm","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-egarch-model","name":"Nonlinear EGARCH model","fullName":"Nonlinear Exponential Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["NL-EGARCH","nonlinear exponential GARCH","asymmetric EGARCH","NEGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991","originator":"Daniel B. Nelson","url":"https://scholargate.app/en/econometrics/nonlinear-egarch-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-egarch-model.md","definition":"The Nonlinear EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the news impact function to take a flexible nonlinear form, capturing asymmetric and nonlinear responses of conditional volatility to past shocks. It is widely used in financial econometrics to model leverage effects and complex volatility dynamics in asset returns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel B. Nelson","year":"1991","type":"Conditional volatility model","dataType":"Financial time series, high-frequency returns","subfamily":"Econometrics / time series"},"citations":[{"ref":"Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null},{"ref":"Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. Journal of Finance, 48(5), 1749–1778.","type":"article","doi":"10.1111/j.1540-6261.1993.tb05127.x","isbn":null,"url":null}],"related":["egarch-model","garch-model","tgarch-model","gjr-garch-model","arch-model","stochastic-volatility-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-engle-granger-cointegration","name":"Nonlinear Engle-Granger Cointegration","fullName":"Nonlinear Engle-Granger Cointegration Test","aliases":["nonlinear cointegration","threshold cointegration","KSS cointegration","ESTAR cointegration"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998-2006","originator":"Kapetanios, Shin & Snell; Enders & Granger","url":"https://scholargate.app/en/econometrics/nonlinear-engle-granger-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-engle-granger-cointegration.md","definition":"Nonlinear Engle-Granger cointegration extends the classical two-step Engle-Granger procedure to detect long-run equilibria where adjustment toward the equilibrium is nonlinear — for example, faster above than below a threshold, or governed by a smooth transition mechanism. It is widely applied in financial economics, purchasing power parity tests, and commodity price analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kapetanios, Shin & Snell; Enders & Granger","year":"1998-2006","type":"Cointegration test","dataType":"Non-stationary time series (I(1))","subfamily":"Econometrics / time series"},"citations":[{"ref":"Kapetanios, G., Shin, Y., & Snell, A. (2006). Testing for cointegration in nonlinear smooth transition error correction models. Econometric Theory, 22(2), 279-303.","type":"article","doi":"10.1017/S0266466606060129","isbn":null,"url":null},{"ref":"Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business and Economic Statistics, 16(3), 304-311.","type":"article","doi":"10.1080/07350015.1998.10524769","isbn":null,"url":null}],"related":["engle-granger-cointegration","johansen-cointegration","threshold-autoregression","ardl-bounds-test","error-correction-model","nonlinear-ardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-fixed-effects-model","name":"Nonlinear Fixed Effects Model","fullName":"Nonlinear Fixed Effects Panel Data Model","aliases":["nonlinear FE model","NLFE","conditional fixed effects model","incidental parameters model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1984","originator":"Gary Chamberlain","url":"https://scholargate.app/en/econometrics/nonlinear-fixed-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-fixed-effects-model.md","definition":"The nonlinear fixed effects model extends fixed effects panel estimation to outcomes governed by nonlinear response functions — such as binary, count, or censored outcomes — while absorbing unobserved individual heterogeneity through unit-specific intercepts. Key special cases include conditional logit for binary outcomes and Poisson fixed effects for count data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gary Chamberlain","year":"1984","type":"Panel data estimator","dataType":"Panel data (balanced or unbalanced); binary, count, or limited-dependent outcomes","subfamily":"Econometrics / time series"},"citations":[{"ref":"Chamberlain, G. (1984). Panel data. In Z. Griliches & M. D. Intriligator (Eds.), Handbook of Econometrics (Vol. 2, pp. 1247–1318). Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Chamberlain+1984+Panel+data+Handbook+of+Econometrics"},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["fixed-effects-model","panel-fixed-effects-model","random-effects-model","nonlinear-random-effects-model","panel-data-analysis","dynamic-panel-data-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-garch-model","name":"Nonlinear GARCH model","fullName":"Nonlinear Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["NL-GARCH","asymmetric GARCH","GJR-GARCH","nonlinear volatility model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991-1993","originator":"Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH","url":"https://scholargate.app/en/econometrics/nonlinear-garch-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-garch-model.md","definition":"The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive in financial markets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH","year":"1991-1993","type":"Volatility model","dataType":"Financial time series, returns","subfamily":"Econometrics / time series"},"citations":[{"ref":"Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801.","type":"article","doi":"10.1111/j.1540-6261.1993.tb05128.x","isbn":null,"url":null},{"ref":"Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null}],"related":["arch-model","egarch-model","tgarch-model","dcc-garch-model","vector-autoregression","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-gls","name":"Nonlinear GLS","fullName":"Nonlinear Generalized Least Squares","aliases":["NGLS","nonlinear generalized least squares","feasible nonlinear GLS","FNGLS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1975","originator":"Gallant (1975); extended by Davidson & MacKinnon","url":"https://scholargate.app/en/econometrics/nonlinear-gls","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-gls.md","definition":"Nonlinear Generalized Least Squares extends the classical GLS framework to regression models where the mean function is nonlinear in the parameters. It accounts for non-spherical errors — heteroscedasticity or autocorrelation — by pre-weighting the nonlinear objective with an estimated error covariance matrix, yielding consistent and asymptotically efficient estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gallant (1975); extended by Davidson & MacKinnon","year":"1975","type":"Nonlinear estimator","dataType":"Cross-sectional, time-series, or panel data with heteroscedastic or autocorrelated errors","subfamily":"Econometrics / time series"},"citations":[{"ref":"Gallant, A. R. (1987). Nonlinear Statistical Models. Wiley.","type":"book","doi":null,"isbn":"978-0471802600","url":null},{"ref":"Davidson, R., & MacKinnon, J. G. (2004). Econometric Theory and Methods. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Econometric+Theory+and+Methods+Davidson+MacKinnon+2004"}],"related":["nonlinear-least-squares","gls-regression","gmm-estimation","feasible-gls","nliv-estimation","seemingly-unrelated-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-granger-causality","name":"Nonlinear Granger Causality","fullName":"Nonlinear Granger Causality Test","aliases":["nonlinear causality test","BDS-based causality","Diks-Panchenko test","nonparametric Granger causality"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1992-2006","originator":"Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006)","url":"https://scholargate.app/en/econometrics/nonlinear-granger-causality","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-granger-causality.md","definition":"Nonlinear Granger causality extends the classic linear Granger causality framework to detect predictive relationships that operate through nonlinear dynamics. Using nonparametric or semi-parametric statistics based on correlation integrals or kernel density estimation, it identifies whether past values of one variable improve forecasts of another beyond what any linear model can capture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006)","year":"1992-2006","type":"Nonparametric causality test","dataType":"Time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669.","type":"article","doi":"10.1016/j.jedc.2005.08.008","isbn":null,"url":null},{"ref":"Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance, 49(5), 1639-1664.","type":"article","doi":"10.1111/j.1540-6261.1994.tb04776.x","isbn":null,"url":null}],"related":["granger-causality-test","toda-yamamoto-causality-test","vector-autoregression","nonlinear-var-model","nonlinear-vecm","nonlinear-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-hausman-test","name":"Nonlinear Hausman test","fullName":"Nonlinear Hausman Specification Test","aliases":["Hausman specification test (nonlinear)","nonlinear endogeneity test","Wu-Hausman test (nonlinear)","NL-Hausman test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1978 (nonlinear extension developed through 1980s–1990s)","originator":"Jerry A. Hausman","url":"https://scholargate.app/en/econometrics/nonlinear-hausman-test","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-hausman-test.md","definition":"The Nonlinear Hausman test extends Hausman's (1978) endogeneity specification test to nonlinear models such as probit, logit, Tobit, and count-data regressions. It tests whether suspected regressors are endogenous — i.e., correlated with the error term — in a model where the outcome or the relationship is inherently nonlinear, ensuring that IV-corrected estimates are necessary.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerry A. Hausman","year":"1978 (nonlinear extension developed through 1980s–1990s)","type":"Specification / endogeneity test","dataType":"Cross-sectional or panel data with discrete or nonlinear outcomes","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["hausman-test","instrumental-variables","two-stage-least-squares","probit-regression","logit-regression","control-function-approach"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-johansen-cointegration","name":"Nonlinear Johansen Cointegration","fullName":"Nonlinear Johansen Cointegration Test","aliases":["nonlinear cointegration test","threshold Johansen cointegration","rank test for nonlinear cointegration","nonlinear VECM cointegration"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2001","originator":"Breitung (2001), building on Johansen (1988, 1991)","url":"https://scholargate.app/en/econometrics/nonlinear-johansen-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-johansen-cointegration.md","definition":"Nonlinear Johansen cointegration extends the classical Johansen framework to detect long-run equilibrium relationships among integrated time series when the adjustment process is nonlinear. Using rank-based transformations, the approach tests for cointegration without assuming a linear error-correction mechanism, making it suitable for economic relationships characterized by asymmetric or threshold dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breitung (2001), building on Johansen (1988, 1991)","year":"2001","type":"Nonparametric rank-based cointegration test","dataType":"Multivariate integrated time series (I(1) or I(2))","subfamily":"Econometrics / time series"},"citations":[{"ref":"Breitung, J. (2001). Rank tests for nonlinear cointegration. Journal of Business and Economic Statistics, 19(3), 331-340.","type":"article","doi":"10.1198/073500101681019981","isbn":null,"url":null},{"ref":"Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551-1580.","type":"article","doi":"10.2307/2938278","isbn":null,"url":null}],"related":["johansen-cointegration","threshold-cointegration","engle-granger-cointegration","nonlinear-ardl","vector-error-correction-model","nardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-kpss-test","name":"Nonlinear KPSS Test","fullName":"Nonlinear Kwiatkowski-Phillips-Schmidt-Shin Test","aliases":["KPSS nonlinearity test","nonlinear stationarity test","flexible Fourier KPSS","NL-KPSS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2006","originator":"Becker, Enders & Lee","url":"https://scholargate.app/en/econometrics/nonlinear-kpss-test","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-kpss-test.md","definition":"The nonlinear KPSS test extends the classic Kwiatkowski-Phillips-Schmidt-Shin stationarity test by modelling unknown smooth structural breaks in the deterministic trend using a Fourier approximation. Under the null hypothesis the series is stationary around a flexible nonlinear trend, guarding against spurious unit-root findings caused by regime shifts or gradual transitions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Becker, Enders & Lee","year":"2006","type":"Stationarity test (null: stationary)","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Becker, R., Enders, W., & Lee, J. (2006). A stationarity test in the presence of an unknown number of smooth breaks. Journal of Time Series Analysis, 27(3), 381-409.","type":"article","doi":"10.1111/j.1467-9892.2006.00478.x","isbn":null,"url":null},{"ref":"Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.","type":"article","doi":"10.1111/j.1468-0084.2011.00662.x","isbn":null,"url":null}],"related":["kpss-test","adf-test","fourier-adf-test","nonlinear-unit-root-test","smooth-transition-autoregression","zivot-andrews-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-ma-model","name":"Nonlinear MA model","fullName":"Nonlinear Moving Average Model","aliases":["NMA model","nonlinear moving average","NLMA model","nonlinear MA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1978","originator":"Granger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory)","url":"https://scholargate.app/en/econometrics/nonlinear-ma-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-ma-model.md","definition":"The Nonlinear Moving Average (NMA) model extends the classical linear MA model by allowing the current observation to depend on past innovations through a nonlinear function rather than a simple weighted sum. It is used in time series analysis when error shocks transmit to outcomes in an asymmetric or state-dependent fashion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Granger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory)","year":"1978","type":"Nonlinear time series model","dataType":"Univariate time series (equally spaced observations)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Granger, C. W. J., & Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Gottingen.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+Introduction+to+Bilinear+Time+Series+Models+Granger+Andersen+1978"},{"ref":"Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198522300","url":null}],"related":["arma-model","threshold-arma","bilinear-model","garch-model","star-model","nonlinear-ar-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-nardl","name":"Nonlinear NARDL","fullName":"Nonlinear Autoregressive Distributed Lag Model","aliases":["NARDL","nonlinear ARDL","asymmetric ARDL","nonlinear bounds test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2014","originator":"Shin, Yu, and Greenwood-Nimmo","url":"https://scholargate.app/en/econometrics/nonlinear-nardl","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-nardl.md","definition":"The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing an explanatory variable into its positive and negative partial sums, it tests whether increases and decreases in a regressor have different effects on the dependent variable — a feature that linear cointegration methods cannot capture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shin, Yu, and Greenwood-Nimmo","year":"2014","type":"Nonlinear cointegration model","dataType":"Time series (nonstationary or mixed I(0)/I(1))","subfamily":"Econometrics / time series"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281-314). Springer.","type":"inproceedings","doi":"10.1007/978-1-4899-8008-3_9","isbn":null,"url":null},{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null}],"related":["ardl-bounds-test","error-correction-model","threshold-cointegration","ols-regression","granger-causality","var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-ols","name":"Nonlinear OLS","fullName":"Nonlinear Ordinary Least Squares","aliases":["nonlinear least squares","NLS","NLLS","nonlinear regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1974–1987","originator":"Gallant (1987); Wooldridge (2010) for econometric treatment","url":"https://scholargate.app/en/econometrics/nonlinear-ols","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-ols.md","definition":"Nonlinear Ordinary Least Squares (NLS) estimates regression models in which the conditional mean function is nonlinear in the parameters. Like standard OLS it minimises the sum of squared residuals, but because no closed-form solution exists the estimator is found by iterative numerical optimisation. Under standard regularity conditions NLS is consistent and asymptotically normal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gallant (1987); Wooldridge (2010) for econometric treatment","year":"1974–1987","type":"Nonlinear regression estimator","dataType":"Cross-sectional, time series, or panel; continuous outcome","subfamily":"Econometrics / time series"},"citations":[{"ref":"Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471802600","url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["ols-regression","generalized-least-squares","nonlinear-gls","maximum-likelihood-estimation","nonlinear-ardl","nls-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-panel-data-analysis","name":"Nonlinear Panel Data Analysis","fullName":"Nonlinear Panel Data Analysis","aliases":["nonlinear panel models","panel nonlinear econometrics","fixed-effects nonlinear models","random-effects nonlinear models"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1986–2010","originator":"Cheng Hsiao; Jeffrey M. Wooldridge","url":"https://scholargate.app/en/econometrics/nonlinear-panel-data-analysis","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-panel-data-analysis.md","definition":"Nonlinear panel data analysis applies nonlinear models — such as probit, logit, Poisson, or Tobit — to repeated observations on the same units over time. It accounts for unit-specific unobserved heterogeneity while capturing non-linear relationships between predictors and the outcome, making it essential when the dependent variable is binary, count-based, censored, or otherwise non-continuous.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cheng Hsiao; Jeffrey M. Wooldridge","year":"1986–2010","type":"Panel data model (nonlinear)","dataType":"Repeated observations on the same units over time (panel/longitudinal data)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null},{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107657632","url":null}],"related":["panel-fixed-effects","panel-random-effects","logistic-regression","probit-regression","poisson-regression","dynamic-panel-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-pp-unit-root-test","name":"Nonlinear PP unit root test","fullName":"Nonlinear Phillips-Perron Unit Root Test","aliases":["Nonlinear PP test","Nonlinear Phillips-Perron test","PP unit root test with nonlinear adjustment","nonlinear PP"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988 (base); 2000s (nonlinear extensions)","originator":"Phillips & Perron (1988); nonlinear extensions by Kapetanios, Shin & Snell (2003) and related authors","url":"https://scholargate.app/en/econometrics/nonlinear-pp-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-pp-unit-root-test.md","definition":"The Nonlinear Phillips-Perron unit root test extends the classic PP test by allowing the adjustment toward equilibrium to follow a nonlinear path — such as a smooth transition or threshold mechanism — rather than assuming a constant linear speed of adjustment. This makes it more powerful when the true data-generating process involves regime-dependent or asymmetric mean-reversion dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Phillips & Perron (1988); nonlinear extensions by Kapetanios, Shin & Snell (2003) and related authors","year":"1988 (base); 2000s (nonlinear extensions)","type":"Unit root test with nonlinear adjustment","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.","type":"article","doi":"10.1093/biomet/75.2.335","isbn":null,"url":null},{"ref":"Kapetanios, G., Shin, Y., & Snell, A. (2003). Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics, 112(2), 359-379.","type":"article","doi":"10.1016/S0304-4076(02)00202-6","isbn":null,"url":null}],"related":["phillips-perron-unit-root-test","augmented-dickey-fuller-unit-root-test","nonlinear-adf-unit-root-test","nonlinear-kpss-test","zivot-andrews-structural-break-test","nonlinear-ardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-programming","name":"Nonlinear Programming","fullName":"Nonlinear Programming","aliases":["NLP optimization","Constrained nonlinear optimization","Smooth optimization","Doğrusal olmayan programlama"],"domain":"optimization","family":"process-pipeline","subfamily":"Mathematical programming","year":2006,"originator":"Jorge Nocedal & Stephen Wright","url":"https://scholargate.app/en/optimization/nonlinear-programming","markdownUrl":"https://scholargate.app/en/optimization/nonlinear-programming.md","definition":"Nonlinear programming (NLP) is a branch of mathematical optimization concerned with problems in which the objective function or at least one constraint is nonlinear. Formalized comprehensively by Jorge Nocedal and Stephen Wright in their seminal 2006 text, NLP encompasses gradient-based algorithms — including sequential quadratic programming (SQP), interior-point methods, and quasi-Newton approaches — for finding locally or globally optimal solutions to continuous decision problems arising across engineering, economics, and the physical sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jorge Nocedal & Stephen Wright","year":2006,"type":"Continuous mathematical optimization","subfamily":"Mathematical programming","objective":"Minimize or maximize a nonlinear function subject to constraints","solution_concept":"KKT conditions (first-order necessary optimality)"},"citations":[{"ref":"Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0-387-30303-1","url":null}],"related":["convex-optimization","dynamic-programming","stochastic-optimization"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-random-effects-model","name":"Nonlinear Random Effects Model","fullName":"Nonlinear Random Effects Model","aliases":["nonlinear RE model","NLRE model","random effects nonlinear panel model","mixed nonlinear panel model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1981–2010","originator":"Heckman (1981); Chamberlain (1984); further systematized by Wooldridge (2010)","url":"https://scholargate.app/en/econometrics/nonlinear-random-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-random-effects-model.md","definition":"The nonlinear random effects model extends classical random effects estimation to settings where the outcome variable is binary, count-based, censored, or otherwise non-continuously distributed across panel units. It accounts for unobserved individual heterogeneity by treating unit-specific effects as random draws from a distribution, then integrating them out to form a likelihood that can be maximised over the structural parameters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heckman (1981); Chamberlain (1984); further systematized by Wooldridge (2010)","year":"1981–2010","type":"Panel data / nonlinear regression","dataType":"Balanced or unbalanced panel data; binary, count, censored, or limited dependent variables","subfamily":"Econometrics / time series"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null},{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107038691","url":null}],"related":["random-effects-model","fixed-effects-model","panel-probit-model","panel-tobit-model","generalized-linear-mixed-model","correlated-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-sarima-model","name":"Nonlinear SARIMA Model","fullName":"Nonlinear Seasonal Autoregressive Integrated Moving Average Model","aliases":["NL-SARIMA","nonlinear seasonal ARIMA","threshold SARIMA","smooth transition SARIMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990–2000","originator":"Tong (1990) for threshold nonlinear extensions; Franses & van Dijk (2000) for empirical finance applications","url":"https://scholargate.app/en/econometrics/nonlinear-sarima-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-sarima-model.md","definition":"The Nonlinear SARIMA model extends the classical Seasonal ARIMA framework by replacing the linear conditional mean function with a nonlinear specification — such as threshold switching or smooth transition — while retaining seasonal differencing and lag structure. It is used when seasonal time series exhibit regime-dependent dynamics, asymmetric adjustment, or other nonlinear patterns that a linear model cannot capture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tong (1990) for threshold nonlinear extensions; Franses & van Dijk (2000) for empirical finance applications","year":"1990–2000","type":"Nonlinear time series model","dataType":"Univariate seasonal time series (equally spaced observations)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198523000","url":null},{"ref":"Franses, P. H., & van Dijk, D. (2000). Non-linear Time Series Models in Empirical Finance. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521779654","url":null}],"related":["sarima-model","threshold-autoregressive-model","smooth-transition-autoregressive-model","arima-model","seasonal-exponential-smoothing","garch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-svar-model","name":"Nonlinear SVAR Model","fullName":"Nonlinear Structural Vector Autoregression Model","aliases":["nonlinear structural VAR","NL-SVAR","threshold SVAR","regime-switching SVAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990s–2010s","originator":"Extensions by Koop, Potter, Auerbach, Gorodnichenko and others","url":"https://scholargate.app/en/econometrics/nonlinear-svar-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-svar-model.md","definition":"The Nonlinear Structural VAR model extends the standard SVAR framework to allow structural relationships and dynamic responses to vary across economic regimes or states of the world. By imposing nonlinear transition mechanisms — such as threshold switching or smooth regime change — it captures asymmetric responses to shocks that a linear SVAR cannot detect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extensions by Koop, Potter, Auerbach, Gorodnichenko and others","year":"1990s–2010s","type":"Multivariate nonlinear structural time series model","dataType":"Multivariate time series (macroeconomic, financial)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Koop, G., & Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4), 267–358.","type":"article","doi":"10.1561/0800000013","isbn":null,"url":null},{"ref":"Auerbach, A. J., & Gorodnichenko, Y. (2012). Measuring the output effects of fiscal policy. American Economic Journal: Economic Policy, 4(2), 1–27.","type":"article","doi":"10.1257/pol.4.2.1","isbn":null,"url":null}],"related":["structural-var","vector-autoregression","nonlinear-var-model","nonlinear-vecm","vector-error-correction-model","nonlinear-ardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-system-gmm","name":"Nonlinear System GMM","fullName":"Nonlinear System Generalized Method of Moments","aliases":["NLS-GMM","nonlinear system generalized method of moments","system GMM for nonlinear models","NL-SGMM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1982","originator":"Lars Peter Hansen","url":"https://scholargate.app/en/econometrics/nonlinear-system-gmm","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-system-gmm.md","definition":"Nonlinear System GMM extends the Generalized Method of Moments framework to estimate a system of structural equations in which the parameter vector enters the moment conditions nonlinearly. It jointly exploits moment restrictions across multiple equations, yielding efficiency gains over single-equation approaches when the equations share parameters or have correlated disturbances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lars Peter Hansen","year":"1982","type":"System estimator","dataType":"Cross-sectional, panel, or time-series data with nonlinear moment conditions","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029–1054.","type":"article","doi":"10.2307/1912775","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["linear-system-gmm","gmm-estimation","two-stage-least-squares","nonlinear-least-squares","instrumental-variables","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-tgarch-model","name":"Nonlinear TGARCH model","fullName":"Nonlinear Threshold GARCH Model","aliases":["NL-TGARCH","Nonlinear Threshold GARCH","Asymmetric TGARCH","GJR-GARCH variant"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1993–1994","originator":"Jean-Michel Zakoian; related work by Glosten, Jagannathan & Runkle","url":"https://scholargate.app/en/econometrics/nonlinear-tgarch-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-tgarch-model.md","definition":"The Nonlinear TGARCH (Threshold GARCH) model extends the standard GARCH framework by allowing positive and negative shocks of equal magnitude to exert different effects on future volatility. It models conditional volatility in terms of the absolute value of lagged residuals split by a sign threshold, capturing the well-documented leverage effect in financial return series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Michel Zakoian; related work by Glosten, Jagannathan & Runkle","year":"1993–1994","type":"Conditional heteroskedasticity model","dataType":"High-frequency or daily financial time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.","type":"article","doi":"10.1016/0165-1889(94)90039-6","isbn":null,"url":null},{"ref":"Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801.","type":"article","doi":"10.1111/j.1540-6261.1993.tb05128.x","isbn":null,"url":null}],"related":["garch-model","egarch-model","gjr-garch-model","tgarch-model","aparch-model","arch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-time-history-analysis","name":"Nonlinear Time-History Analysis","fullName":"Nonlinear Time-History Analysis for Seismic Response","aliases":["Nonlinear dynamic analysis","Step-by-step integration","Time domain analysis"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Seismic Analysis","year":"1959","originator":"Nathan M. Newmark","url":"https://scholargate.app/en/civil-engineering/nonlinear-time-history-analysis","markdownUrl":"https://scholargate.app/en/civil-engineering/nonlinear-time-history-analysis.md","definition":"Nonlinear time-history analysis is a numerical method that solves the equations of motion step-by-step in the time domain, using recorded or synthetic earthquake ground motions as input. Developed by Newmark in 1959, this approach captures the full dynamic response of structures including material nonlinearity, geometric effects, and energy dissipation mechanisms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nathan M. Newmark","subfamily":"Seismic Analysis","year":"1959","type":"Time-stepping numerical method for earthquake engineering"},"citations":[{"ref":"Newmark, N. M. (1959). A method of computation for structural dynamics. Journal of the Engineering Mechanics Division, 85(3), 67-94.","type":"article","doi":"10.1061/JMCEA3.0000098","isbn":null,"url":null},{"ref":"Clough, R. W., & Penzien, J. (1993). Dynamics of Structures (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"0-07-011394-7","url":null},{"ref":"Hilber, H. M., Hughes, T. J., & Taylor, R. L. (1976). Improved numerical dissipation for time integration algorithms in structural dynamics. Earthquake Engineering & Structural Dynamics, 5(3), 283-292.","type":"article","doi":"10.1002/eqe.4290050306","isbn":null,"url":null}],"related":["pushover-analysis","response-spectrum-analysis","incremental-dynamic-analysis","equivalent-static-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-toda-yamamoto-causality","name":"Nonlinear Toda-Yamamoto Causality","fullName":"Nonlinear Toda-Yamamoto Granger Causality Test","aliases":["nonlinear TY causality","rank-based Toda-Yamamoto test","modified Wald nonlinear causality","NTY causality test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1995 (base); nonlinear extensions 2000s–2010s","originator":"Toda & Yamamoto (1995) for the linear base; nonlinear extension developed by subsequent researchers applying rank transformations or neural-network-augmented VAR","url":"https://scholargate.app/en/econometrics/nonlinear-toda-yamamoto-causality","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-toda-yamamoto-causality.md","definition":"The Nonlinear Toda-Yamamoto causality test extends the classic Toda-Yamamoto (1995) modified Wald procedure to detect causal linkages that are hidden in the means of series but manifest through nonlinear dynamics such as asymmetries, threshold effects, or volatility transmission. It fits an augmented VAR on rank-transformed or otherwise nonlinearly mapped series and applies a chi-squared Wald test on the extra-lag coefficients.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Toda & Yamamoto (1995) for the linear base; nonlinear extension developed by subsequent researchers applying rank transformations or neural-network-augmented VAR","year":"1995 (base); nonlinear extensions 2000s–2010s","type":"Causality test","dataType":"Time series (univariate or multivariate, possibly nonstationary)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.","type":"article","doi":"10.1016/0304-4076(94)01616-8","isbn":null,"url":null},{"ref":"Sims, C. A., Stock, J. H., & Watson, M. W. (1990). Inference in linear time series models with some unit roots. Econometrica, 58(1), 113-144.","type":"article","doi":"10.2307/2938337","isbn":null,"url":null}],"related":["toda-yamamoto-causality","granger-causality","bds-test","nonlinear-granger-causality","var-model","cointegration-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-var-model","name":"Nonlinear VAR Model","fullName":"Nonlinear Vector Autoregression Model","aliases":["NLVAR","nonlinear vector autoregression","threshold VAR","TVAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990s–2000s","originator":"Tsay (1998); Krolzig (1997); Tong (1990) for threshold framework","url":"https://scholargate.app/en/econometrics/nonlinear-var-model","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-var-model.md","definition":"The Nonlinear VAR (NLVAR) model extends the standard vector autoregression by allowing the dynamic relationships among multiple time series to switch or change smoothly depending on an observed threshold variable, a latent regime state, or a smooth transition function. It is used when economic systems exhibit asymmetric responses, regime shifts, or state-dependent dynamics that a linear VAR cannot capture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tsay (1998); Krolzig (1997); Tong (1990) for threshold framework","year":"1990s–2000s","type":"Multivariate nonlinear time series model","dataType":"Multivariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93(443), 1188–1202.","type":"article","doi":"10.1080/01621459.1998.10473779","isbn":null,"url":null},{"ref":"Krolzig, H.-M. (1997). Markov-Switching Vector Autoregressions: Modelling, Statistical Inference, and Application to Business Cycle Analysis. Springer.","type":"book","doi":null,"isbn":"978-3540628644","url":null}],"related":["vector-autoregression","structural-var","vector-error-correction-model","nonlinear-ardl","markov-switching-model","threshold-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-vecm","name":"Nonlinear VECM","fullName":"Nonlinear Vector Error Correction Model","aliases":["nonlinear VECM","NVECM","threshold VECM","asymmetric VECM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1989–1998","originator":"Granger & Lee (1989); Enders & Granger (1998)","url":"https://scholargate.app/en/econometrics/nonlinear-vecm","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-vecm.md","definition":"The Nonlinear VECM extends the standard linear VECM by allowing the speed of adjustment toward long-run equilibrium to differ depending on the sign, magnitude, or regime of deviations from that equilibrium. It captures asymmetric or threshold-driven dynamics in cointegrated time-series systems that a standard VECM would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Granger & Lee (1989); Enders & Granger (1998)","year":"1989–1998","type":"Nonlinear time-series model","dataType":"Multivariate time series (cointegrated)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16(3), 304–311.","type":"article","doi":"10.1080/07350015.1998.10524769","isbn":null,"url":null},{"ref":"Granger, C. W. J., & Lee, T. H. (1989). Investigation of production, sales and inventory relationships using multicointegration and non-symmetric error correction models. Journal of Applied Econometrics, 4(S1), S145–S159.","type":"article","doi":"10.1002/jae.3950040508","isbn":null,"url":null}],"related":["vecm","threshold-cointegration","smooth-transition-var","markov-switching-var","ardl-bounds-test","johansen-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-wls","name":"Nonlinear WLS","fullName":"Nonlinear Weighted Least Squares","aliases":["NWLS","nonlinear weighted least squares","weighted nonlinear regression","heteroscedasticity-corrected nonlinear regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1960s–1980s (formalized in applied econometrics)","originator":"Extension of Gauss-Newton nonlinear least squares with Aitken-type weighting","url":"https://scholargate.app/en/econometrics/nonlinear-wls","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-wls.md","definition":"Nonlinear Weighted Least Squares combines the flexibility of nonlinear regression with the variance-stabilizing power of observation-level weights. It minimises a weighted sum of squared residuals around a user-specified nonlinear mean function, making it the method of choice when the relationship is inherently nonlinear and error variance differs across observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Gauss-Newton nonlinear least squares with Aitken-type weighting","year":"1960s–1980s (formalized in applied econometrics)","type":"Nonlinear regression estimator","dataType":"Cross-sectional, time-series, or panel data with a nonlinear mean function and heteroscedastic errors","subfamily":"Econometrics / time series"},"citations":[{"ref":"Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson Education.","type":"book","doi":null,"isbn":"978-0134461366","url":null},{"ref":"Bates, D. M., & Watts, D. G. (1988). Nonlinear Regression Analysis and Its Applications. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471816430","url":null}],"related":["nonlinear-least-squares","weighted-least-squares","generalized-least-squares","feasible-gls","ols-regression","heteroscedasticity-consistent-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonlinear-zivot-andrews-test","name":"Nonlinear Zivot-Andrews test","fullName":"Nonlinear Zivot-Andrews Unit Root Test","aliases":["NZA test","nonlinear structural break unit root test","Zivot-Andrews test with nonlinear adjustment","smooth transition Zivot-Andrews test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000s–2010s","originator":"Extension combining Zivot & Andrews (1992) with nonlinear STAR-type adjustment; attributed to several applied time-series authors","url":"https://scholargate.app/en/econometrics/nonlinear-zivot-andrews-test","markdownUrl":"https://scholargate.app/en/econometrics/nonlinear-zivot-andrews-test.md","definition":"The Nonlinear Zivot-Andrews test extends the classical Zivot-Andrews structural-break unit root test by embedding smooth-transition nonlinear adjustment into the test regression. It jointly searches for an endogenous structural break and allows the speed of mean-reversion to vary with distance from the attractor, producing more power against nonlinear stationary alternatives than either test alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension combining Zivot & Andrews (1992) with nonlinear STAR-type adjustment; attributed to several applied time-series authors","year":"2000s–2010s","type":"Unit root test with structural break and nonlinear adjustment","dataType":"univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null},{"ref":"Kapetanios, G., Shin, Y., & Snell, A. (2003). Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics, 112(2), 359–379.","type":"article","doi":"10.1016/S0304-4076(02)00202-6","isbn":null,"url":null}],"related":["zivot-andrews-test","kapetanios-shin-snell-test","augmented-dickey-fuller-test","lm-unit-root-test","lee-strazicich-test","smooth-transition-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonparametric-tests","name":"Nonparametric Statistical Tests","fullName":"Distribution-Free Hypothesis Testing","aliases":["rank-based tests","Mann-Whitney U","Kruskal-Wallis","distribution-free"],"domain":"research-statistics","family":"process-pipeline","subfamily":"distribution-free-methods","year":"1947","originator":"Henry Mann and Donald Whitney","url":"https://scholargate.app/en/research-statistics/nonparametric-tests","markdownUrl":"https://scholargate.app/en/research-statistics/nonparametric-tests.md","definition":"Nonparametric (distribution-free) tests are statistical methods for hypothesis testing that do not assume data follow a specific probability distribution (e.g., normal), making them robust to departures from normality, outliers, and ordinal data. The Mann-Whitney U test (1947) and Kruskal-Wallis test (1952) extend hypothesis testing beyond the constraints of parametric assumptions. Essential in biology, medicine, psychology, and any field where data are non-normal, highly skewed, or measured on ordinal scales (rankings, ratings), nonparametric tests provide valid inference when parametric assumptions fail.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Mann and Donald Whitney","subfamily":"distribution-free-methods","year":"1947","type":"Method"},"citations":[{"ref":"Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60.","type":"article","doi":"10.1214/aoms/1177730491","isbn":null,"url":null},{"ref":"Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621.","type":"article","doi":"10.1080/01621459.1952.10483441","isbn":null,"url":null},{"ref":"Conover, W. J. (1999). Practical Nonparametric Statistics (3rd ed.). John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Practical+Nonparametric+Statistics+%283rd+ed.%29+Conover"}],"related":["analysis-of-variance","multiple-regression-analysis","bayesian-statistics"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nonstationary-transformer","name":"Non-stationary Transformer","fullName":"Non-stationary Transformers for Forecasting","aliases":["NS-Transformer","Non-stationary Transformer Network","Stationarization-based Transformer","Durağan-Olmayan Transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2022,"originator":"Yong Liu et al.","url":"https://scholargate.app/en/deep-learning/nonstationary-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/nonstationary-transformer.md","definition":"Non-stationary Transformer is a Transformer-based time-series forecasting architecture introduced by Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long at NeurIPS 2022. It addresses a fundamental tension in applying Transformers to real-world time series: over-stationarization during preprocessing strips out non-stationary signals that carry predictive information, while raw non-stationary inputs cause attention to collapse. The model resolves this through series stationarization paired with a novel de-stationary attention mechanism that restores the original temporal distribution in predictions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yong Liu et al.","year":2022,"type":"Transformer-based time-series forecasting model","subfamily":"Time-series forecasting","venue":"NeurIPS 2022","key_innovation":"Series stationarization with de-stationary attention"},"citations":[{"ref":"Liu, Y., Wu, H., Wang, J., & Long, M. (2022). Non-stationary transformers: Exploring the stationarity in time series forecasting. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2205.14415"}],"related":["autoformer","informer","adf-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"norm-vector","name":"NORM-VECTOR","fullName":"Vector Normalization — Euclidean column-norm scaling (L2 normalisation)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Normalization","year":"1981","originator":"Hwang, C. L., Yoon, K.","url":"https://scholargate.app/en/decision-making/norm-vector","markdownUrl":"https://scholargate.app/en/decision-making/norm-vector.md","definition":"NORM-VECTOR (Vector Normalization — Euclidean column-norm scaling (L2 normalisation)) is a normalization multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hwang, C. L., Yoon, K.","subfamily":"Normalization","year":"1981","type":"Normalization (L2, unit-sphere projection)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag","type":"article","doi":"10.1007/978-3-642-48318-9","isbn":null,"url":null}],"related":["topsis","waspas","edas","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"normalisation-measure-development","name":"NPT","fullName":"Normalization Process Theory","aliases":["NPT","Normalization Process Theory","NPT Framework","Normalisation Process Theory"],"domain":"implementation-science","family":"process-pipeline","subfamily":"implementation framework","year":2009,"originator":"Carl R. May, PhD; Elena Murray, PhD; and colleagues at University of Sydney and UCL","url":"https://scholargate.app/en/implementation-science/normalisation-measure-development","markdownUrl":"https://scholargate.app/en/implementation-science/normalisation-measure-development.md","definition":"Normalization Process Theory (NPT) is a framework developed by May, Murray, and colleagues (2009) to explain how new practices, technologies, and innovations become embedded and sustained in everyday organizational and clinical work. Rather than viewing implementation as a one-time adoption event, NPT conceptualizes implementation as a process of normalization—the gradual transition from 'new and unusual' to 'normal, routine work integrated into standard processes.' NPT identifies four normalization mechanisms: Coherence (shared understanding of the intervention's purpose and value), Cognitive Participation (staff engagement and involvement in learning and using the intervention), Collective Action (the work required to implement, including workflow changes and resource allocation), and Reflexive Monitoring (ongoing reflection on impacts, benefits, and needed adaptations). NPT has become influential in implementation science research, particularly in health technology implementation and complex intervention studies, and provides a theoretical lens for understanding why some innovations become normalized while others are abandoned.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl R. May, PhD; Elena Murray, PhD; and colleagues at University of Sydney and UCL","subfamily":"implementation framework","year":2009,"type":"Theoretical framework with qualitative and mixed-methods assessment"},"citations":[{"ref":"Murray, E., Treweek, S., Pope, C., MacFarlane, A., Ballini, L., Dowrick, C., ... & May, C. R. (2010). Normalizing adoption of new health care innovations: A systematic review of empirical studies. American Journal of Health Promotion, 24(4), e5–e15.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Normalizing+adoption+of+new+health+care+innovations%3A+A+systematic+review+of+empirical+studies+Murray"},{"ref":"May, C. R., Murray, E., Mair, F. S., & Finch, T. (2009). Development of a theory of implementation and integration of digital innovations in health and social care: The Normalization Process Theory. Inform Prim Care, 17(2), 89–99.","type":"article","doi":"10.1186/1748-5908-4-29","isbn":null,"url":null}],"related":["knowledge-to-action-scale","evidence-based-practice-attitude","implementation-climate-scale","stages-of-concern-questionnaire","organisational-readiness-change"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"normalization-process-theory","name":"Normalization Process Theory","fullName":"Normalization Process Theory (NPT): A Sociological Framework for Understanding How New Interventions Become Routinely Embedded in Practice","aliases":["NPT","normalization theory","routinization"],"domain":"implementation-science","family":"process-pipeline","subfamily":"implementation science theory","year":"2006","originator":"May, C. R.","url":"https://scholargate.app/en/implementation-science/normalization-process-theory","markdownUrl":"https://scholargate.app/en/implementation-science/normalization-process-theory.md","definition":"Normalization Process Theory (NPT) is a sociological framework developed by Carl May and colleagues to explain how new interventions become routinely embedded ('normalized') in organizational and clinical practice. Unlike efficiency-focused frameworks that measure adoption and fidelity, NPT explains the social processes through which interventions transition from external innovations to normal practice. NPT identifies four key mechanisms (Coherence, Cognitive Participation, Collective Action, Reflexive Monitoring) that collectively determine whether an intervention becomes 'the way we do things here' or remains a temporary project.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"May, C. R.","subfamily":"implementation science theory","year":"2006","type":"Framework"},"citations":[{"ref":"May, C. R. (2006). A rational model for assessing and evaluating complex interventions in health care. BMC Health Services Research, 6, 86.","type":"article","doi":"10.1186/1472-6963-6-86","isbn":null,"url":null},{"ref":"May, C. R., & Finch, T. (2009). Implementing, embedding, and integrating practices: An outline of normalization process theory. Sociology, 43(3), 535-554.","type":"article","doi":"10.1177/0038038509103208","isbn":null,"url":null},{"ref":"Murray, E., Treweek, S., Pope, C., MacFarlane, A., Ballini, L., Dowrick, C., ... & Vanoli, A. (2010). Normalizing intervention: Developing and validating a tool to assess implementation fidelity of complex interventions using NOMAD (NormalizatiOn: Measure, Assess, Develop). Implementation Science, 5, 78.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Normalizing+intervention%3A+Developing+and+validating+a+tool+to+assess+implementation+fidelity+of+complex+interventions+using+NOMAD+%28NormalizatiOn%3A+Measure%2C+Assess%2C+Develop%29+Murray"}],"related":["cfir-framework","implementation-outcome-taxonomy","behavior-change-wheel","knowledge-translation","fidelity-assessment"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"normalized-mutual-information","name":"Normalized Mutual Information","fullName":"Normalized Mutual Information for Clustering Agreement","aliases":["NMI","mutual information","information criterion"],"domain":"model-evaluation","family":"mcdm","subfamily":"External Clustering Validation","year":"2005","originator":"Danon, Diaz-Guilera, Duch, Arenas","url":"https://scholargate.app/en/model-evaluation/normalized-mutual-information","markdownUrl":"https://scholargate.app/en/model-evaluation/normalized-mutual-information.md","definition":"Normalized Mutual Information (NMI), popularized by Danon et al. in 2005, is an external clustering evaluation metric based on information theory. It measures the amount of information shared between a predicted clustering and ground truth labels, normalized to a scale between 0 and 1. A value of 1 indicates perfect agreement, while 0 indicates independence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Danon, Diaz-Guilera, Duch, Arenas","subfamily":"External Clustering Validation","year":"2005","type":"Information-theoretic metric"},"citations":[{"ref":"Danon, L., Diaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008.","type":"article","doi":"10.1088/1742-5468/2005/09/P09008","isbn":null,"url":null}],"related":["adjusted-rand-index","fowlkes-mallows-index","v-measure","silhouette-score","davies-bouldin-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"normalizing-flows","name":"Normalizing Flows","fullName":"Normalizing Flows","aliases":["Flow-Based Generative Models","Invertible Neural Networks","Exact Likelihood Models","Akışa Dayalı Üretici Modeller"],"domain":"deep-learning","family":"ml-model","subfamily":"Generative models","year":2015,"originator":"Danilo Rezende & Shakir Mohamed","url":"https://scholargate.app/en/deep-learning/normalizing-flows","markdownUrl":"https://scholargate.app/en/deep-learning/normalizing-flows.md","definition":"Normalizing flows are a class of generative models that learn a complex probability distribution by applying a sequence of invertible, differentiable transformations to a simple base distribution such as a standard Gaussian. Introduced by Rezende and Mohamed (2015) in the context of variational inference, they enable exact likelihood computation and efficient sampling, making them a principled alternative to VAEs and GANs for density estimation and generation tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Danilo Rezende & Shakir Mohamed","year":2015,"type":"Generative model via invertible transformations","subfamily":"Generative models","training_objective":"Exact maximum likelihood via change-of-variables","key_property":"Exact and tractable density evaluation"},"citations":[{"ref":"Rezende, D. J., & Mohamed, S. (2015). Variational inference with normalizing flows. International Conference on Machine Learning (ICML), 1530–1538.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1505.05770"}],"related":["variational-autoencoder","diffusion-model","score-based-generative-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"norton-scale","name":"Norton Scale","fullName":"Norton Scale for Pressure Sore Risk Assessment","aliases":["Norton Pressure Sore Risk Scale","NPSRS"],"domain":"nursing","family":"process-pipeline","subfamily":"Risk assessment and stratification","year":"1962","originator":"Doreen Norton, Rhonda McLaren, and A. N. Exton-Smith","url":"https://scholargate.app/en/nursing/norton-scale","markdownUrl":"https://scholargate.app/en/nursing/norton-scale.md","definition":"The Norton Scale is a pioneering risk assessment tool developed by Doreen Norton and colleagues in 1962 to identify hospitalized patients at risk of developing pressure sores. As one of the earliest standardized pressure ulcer risk assessment instruments, the Norton Scale predates and influenced many later tools including the widely used Braden Scale. It remains relevant in clinical practice, particularly in geriatric and long-term care settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Doreen Norton, Rhonda McLaren, and A. N. Exton-Smith","subfamily":"Risk assessment and stratification","year":"1962","type":"Risk assessment scale"},"citations":[{"ref":"Norton, D., McLaren, R., & Exton-Smith, A. N. (1962). An investigation of geriatric nursing problems in hospital. National Corporation for the Care of Old People, London.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1803435/"},{"ref":"Bergstrom, N., Demuth, P. J., & Braden, B. J. (1987). A clinical trial of the Braden Scale for predicting pressure sore risk. Nursing Clinics of North America, 22(2), 417-428.","type":"article","doi":"10.1016/s0029-6465(22)01289-0","isbn":null,"url":null}],"related":["braden-scale","patient-fall-risk-assessment","care-dependency-scale","wound-assessment-bates-jensen"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nose-obstruction-symptom-evaluation","name":"NOSE","fullName":"Nasal Obstruction Symptom Evaluation Scale","aliases":["NOSE"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"nasal-obstruction-symptom","year":"2004","originator":"Mark G. Stewart, David L. Witsell, Thomas L. Smith, and colleagues","url":"https://scholargate.app/en/otolaryngology/nose-obstruction-symptom-evaluation","markdownUrl":"https://scholargate.app/en/otolaryngology/nose-obstruction-symptom-evaluation.md","definition":"The Nasal Obstruction Symptom Evaluation (NOSE) Scale is a brief 5-item self-report questionnaire specifically designed to measure the severity of nasal obstruction and its impact on quality of life. Developed by Stewart and colleagues (2004), the NOSE is the most widely used nasal obstruction-specific outcome measure in otolaryngology, recommended for clinical practice and clinical trials. It is validated for baseline assessment, monitoring treatment response in medical and surgical rhinology, and outcome evaluation following septoplasty, rhinoplasty, endoscopic sinus surgery, or allergen immunotherapy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark G. Stewart, David L. Witsell, Thomas L. Smith, and colleagues","subfamily":"nasal-obstruction-symptom","year":"2004","type":"Self-report"},"citations":[{"ref":"Stewart, M. G., Witsell, D. L., Smith, T. L., Weaver, E. M., Yueh, B., & Hannley, M. T. (2004). Development and validation of the Nasal Obstruction Symptom Evaluation (NOSE) Scale. Otolaryngology - Head and Neck Surgery, 130(2), 157-163.","type":"article","doi":"10.1016/j.otohns.2003.09.016","isbn":null,"url":null}],"related":["sino-nasal-outcome-test","rhinosinusitis-disability-index","voice-outcome-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nosql-schema-design","name":"NoSQL Schema Design","fullName":"NoSQL Database Schema Design and Data Modeling","aliases":["NoSQL modeling","document schema"],"domain":"information-systems","family":"process-pipeline","subfamily":"Non-Relational Data Modeling","year":"2009","originator":"NoSQL community pioneers (various founders)","url":"https://scholargate.app/en/information-systems/nosql-schema-design","markdownUrl":"https://scholargate.app/en/information-systems/nosql-schema-design.md","definition":"NoSQL schema design is the practice of organizing data for non-relational databases optimized for specific access patterns and scale. Unlike relational design which normalizes data to eliminate redundancy, NoSQL design often embraces denormalization, embedding, and duplicate data to optimize query performance in distributed systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"NoSQL community pioneers (various founders)","subfamily":"Non-Relational Data Modeling","year":"2009","type":"Data modeling approach"},"citations":[{"ref":"Cattell, R. (2011). Scalable SQL and NoSQL data stores. ACM SIGMOD Record, 39(4), 12-27.","type":"article","doi":"10.1145/1978915.1978919","isbn":null,"url":null},{"ref":"Chodorow, K. (2013). MongoDB: The Definitive Guide (2nd ed.). Sebastopol, CA: O'Reilly.","type":"article","doi":null,"isbn":null,"url":"https://www.oreilly.com"},{"ref":"Sadalage, P. J., & Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley.","type":"article","doi":null,"isbn":null,"url":"https://www.addison-wesley.com"}],"related":["document-databases","key-value-stores","denormalization","embedding-vs-references","indexing-strategies"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"notears","name":"NOTEARS","fullName":"NOTEARS Continuous DAG Structure Learning","aliases":["DAGs with NO TEARS","Continuous Structure Learning","Continuous DAG Optimization","Sürekli DAG Yapı Öğrenimi"],"domain":"causal-inference","family":"ml-model","subfamily":"Causal discovery","year":2018,"originator":"Zheng, Aragam, Ravikumar & Xing","url":"https://scholargate.app/en/causal-inference/notears","markdownUrl":"https://scholargate.app/en/causal-inference/notears.md","definition":"NOTEARS (No Tears: Acyclicity Regression Structure) is a causal structure learning algorithm introduced by Zheng, Aragam, Ravikumar, and Xing in 2018 at NeurIPS. It reformulates the combinatorially hard problem of learning a directed acyclic graph (DAG) from observational data as a continuous, smooth optimization problem, enabling the use of standard gradient-based solvers and removing the need for exhaustive combinatorial search over graph space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zheng, Aragam, Ravikumar & Xing","year":2018,"type":"Continuous optimization algorithm for causal DAG discovery","subfamily":"Causal discovery","venue":"NeurIPS 2018","acyclicity_constraint":"Differentiable algebraic h(W) = tr(e^{W \\circ W}) - d = 0"},"citations":[{"ref":"Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018). DAGs with NO TEARS: Continuous optimization for structure learning. Advances in Neural Information Processing Systems, 31.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1803.01422"}],"related":["lingam","pc-algorithm","bayesian-network"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nottingham-health-profile","name":"Nottingham Health Profile","fullName":"Nottingham Health Profile Assessment Scale","aliases":["NHP","Nottingham Health Status Measure"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Health-related quality of life","year":"1981","originator":"Stephen Hunt and colleagues at University of Nottingham","url":"https://scholargate.app/en/health-measurement/nottingham-health-profile","markdownUrl":"https://scholargate.app/en/health-measurement/nottingham-health-profile.md","definition":"The Nottingham Health Profile (NHP) is a perceived health status measure developed by Hunt and colleagues at the University of Nottingham in 1981. It measures subjective well-being across six dimensions: physical mobility, pain, sleep, emotional reactions, social isolation, and energy level. The NHP emphasizes the patient's experience of health problems rather than objective clinical measures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Hunt and colleagues at University of Nottingham","subfamily":"Health-related quality of life","year":"1981","type":"Perceived health status assessment"},"citations":[{"ref":"Hunt, S. M., McKenna, S. P., McEwen, J., et al. (1985). The Nottingham Health Profile: subjective health status and medical consultations. Social Science & Medicine, 21(3), 347–354.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Nottingham+Health+Profile%3A+subjective+health+status+and+medical+consultations+Hunt"},{"ref":"Hunt, S. M., McEwen, J., & McKenna, S. P. (1981). Measuring health status: a new tool for clinicians and epidemiologists. Journal of the Royal College of General Practitioners, 31(228), 377–384.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/7252323"},{"ref":"McEwen, J., & Hunt, S. M. (2003). Measurement of functional status and well-being. In S. M. Sutherland et al. (Eds.), Health and Health Care in Britain: Text and Applications. Macmillan.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measurement+of+functional+status+and+well-being+McEwen"}],"related":["sf-36","whoqol-bref","sickness-impact-profile","duke-health-profile","eq-5d"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"novaco-anger-scale","name":"NAS-PI","fullName":"Novaco Anger Scale and Provocation Inventory","aliases":["NAS-PI","Novaco Anger Scale","Provocation Inventory"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"anger-and-emotion-dysregulation","year":"2003","originator":"Raymond W. Novaco","url":"https://scholargate.app/en/forensic-psychology/novaco-anger-scale","markdownUrl":"https://scholargate.app/en/forensic-psychology/novaco-anger-scale.md","definition":"The Novaco Anger Scale and Provocation Inventory (NAS-PI) is a comprehensive self-report assessment instrument developed by Raymond Novaco (2003) to measure dispositional anger and anger provocation in adolescents and adults. It integrates cognitive-behavioral theory of anger and emotional regulation, serving clinicians, forensic practitioners, and researchers in psychiatric, correctional, and clinical settings where anger dysregulation and aggression risk are clinical concerns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raymond W. Novaco","subfamily":"anger-and-emotion-dysregulation","year":"2003","type":"Self-report"},"citations":[{"ref":"Novaco, R. W. (2003). The Novaco Anger Scale and Provocation Inventory (NAS-PI): Professional manual. Psychological Assessment Resources, Inc.","type":"book","doi":null,"isbn":null,"url":"https://www.parinc.com/"},{"ref":"Novaco, R. W. (1994). Anger as a risk factor for violence among the mentally disordered. In J. Monahan & H. J. Steadman (Eds.), Violence and mental disorder: Developments in risk assessment (pp. 21–59). University of Chicago Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Anger+as+a+risk+factor+for+violence+among+the+mentally+disordered+Novaco"}],"related":["hcr-20","beck-hopelessness-scale","psychopathy-checklist-screening","level-of-service-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nrs-2002-nutritional-risk","name":"NRS-2002 Nutritional Risk Screening","fullName":"Nutritional Risk Screening 2002 (NRS-2002)","aliases":["NRS-2002","Nutrition risk screening"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"2003","originator":"Jens Kondrup, et al.","url":"https://scholargate.app/en/clinical-assessment/nrs-2002-nutritional-risk","markdownUrl":"https://scholargate.app/en/clinical-assessment/nrs-2002-nutritional-risk.md","definition":"The Nutritional Risk Screening 2002 (NRS-2002), developed by Kondrup et al. and endorsed by ESPEN (European Society for Parenteral and Enteral Nutrition), is a 7-point tool for identifying hospitalized patients at nutritional risk. It combines assessment of recent weight loss, dietary intake, disease severity, and age to stratify the need for nutritional intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jens Kondrup, et al.","subfamily":"Clinical scoring","year":"2003","type":"Nutritional status and intervention need"},"citations":[{"ref":"Kondrup, J., Allison, S. P., Elia, M., Vellas, B., & Plauth, M. (2003). ESPEN guidelines for nutrition screening 2002. Clinical Nutrition, 22(3), 415-421.","type":"article","doi":"10.1016/S0261-5614(03)00098-0","isbn":null,"url":null},{"ref":"Kondrup, J., Rasmussen, H. H., Hamberg, O., Stadshaug, A., & Ad Hoc ESPEN Working Group. (2003). Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clinical Nutrition, 22(3), 321-336.","type":"article","doi":"10.1016/S0261-5614(02)00214-5","isbn":null,"url":null}],"related":["must-malnutrition","glasgow-blatchford-score","apache-ii-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nsga-iii","name":"NSGA-III","fullName":"Non-dominated Sorting Genetic Algorithm III","aliases":["NSGA-III algorithm","NSGA-III evolutionary","many-objective optimization"],"domain":"operations-research","family":"ml-model","subfamily":"Evolutionary Algorithm","year":"2014","originator":"Kalyanmoy Deb and Himanshu Jain","url":"https://scholargate.app/en/operations-research/nsga-iii","markdownUrl":"https://scholargate.app/en/operations-research/nsga-iii.md","definition":"NSGA-III (Non-dominated Sorting Genetic Algorithm III), developed by Kalyanmoy Deb and Himanshu Jain in 2014, is a state-of-the-art evolutionary algorithm for many-objective optimization problems. It extends the popular NSGA-II algorithm with reference-point-based selection, enabling effective handling of problems with three or more conflicting objectives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kalyanmoy Deb and Himanshu Jain","subfamily":"Evolutionary Algorithm","year":"2014","type":"algorithm"},"citations":[{"ref":"Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601.","type":"article","doi":"10.1109/TEVC.2013.2281534","isbn":null,"url":null},{"ref":"Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.","type":"article","doi":"10.1109/4235.996017","isbn":null,"url":null}],"related":["particle-swarm-optimization","evolutionary-algorithm","multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nsga2","name":"NSGA-II","fullName":"Non-dominated Sorting Genetic Algorithm II","aliases":["NSGA2","Non-dominated Sorting GA II","NSGA-II — Çok Amaçlı Evrimsel Optimizasyon"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":2002,"originator":null,"url":"https://scholargate.app/en/optimization/nsga2","markdownUrl":"https://scholargate.app/en/optimization/nsga2.md","definition":"NSGA-II (Non-dominated Sorting Genetic Algorithm II) is the standard reference algorithm for multi-objective evolutionary optimisation, introduced by Deb, Pratap, Agarwal and Meyarivan in 2002. Rather than collapsing multiple conflicting objectives into a single score, it evolves a population of candidate solutions across generations and returns a set of Pareto-optimal trade-off solutions — the Pareto front — using fast non-dominated sorting and a crowding distance metric to preserve diversity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan","year":2002,"type":"Evolutionary multi-objective optimisation algorithm","selectionMechanism":"Non-dominated sorting + crowding distance","output":"Pareto-optimal front (set of trade-off solutions)","populationBased":true,"requiresNormality":false,"variableTypes":"Continuous, binary, or categorical decision variables"},"citations":[{"ref":"Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.","type":"article","doi":"10.1109/4235.996017","isbn":null,"url":null},{"ref":"Zitzler, E., Deb, K. & Thiele, L. (2000). Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2), 173-195.","type":"article","doi":"10.1162/106365600568202","isbn":null,"url":null}],"related":["differential-evolution","genetic-algorithm","ant-colony-optimization","particle-swarm-optimization","pareto-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nss-codas","name":"NSS-CODAS","fullName":"Neutrosophic Spherical extension of CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2024","originator":"Bhuvaneshwari, S., Antony Crispin Sweety, C.","url":"https://scholargate.app/en/decision-making/nss-codas","markdownUrl":"https://scholargate.app/en/decision-making/nss-codas.md","definition":"NSS-CODAS (Neutrosophic Spherical extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Bhuvaneshwari, S., Antony Crispin Sweety, C. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bhuvaneshwari, S., Antony Crispin Sweety, C.","subfamily":"Ranking","year":"2024","type":"Neutrosophic Spherical Set (NSS) outranking — T,I,F ∈ [0,1] independent with squared-norm bound 0 ≤ T²+I²+F² ≤ √3 (Bhuvaneshwari-Sweety 2024 Eq.(2)); combined with CODAS Euclidean-distance ranking","value_space":"neutrosophic_spherical","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Bhuvaneshwari, S., Antony Crispin Sweety, C. (2024). Neutrosophic Spherical Sets in MCDM. Neutrosophic Sets and Systems","type":"article","doi":null,"isbn":null,"url":"https://fs.unm.edu/NSS/NeutrSpherical10.pdf"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nuclear-decay-analysis","name":"Nuclear Decay Analysis","fullName":"Nuclear Decay Analysis and Radioactive Process Evaluation","aliases":["decay kinetics","radioactive decay modeling","half-life analysis"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Radioactive kinetics and dating","year":"1900","originator":"Ernest Rutherford, Frederick Soddy","url":"https://scholargate.app/en/nuclear-physics/nuclear-decay-analysis","markdownUrl":"https://scholargate.app/en/nuclear-physics/nuclear-decay-analysis.md","definition":"Nuclear decay analysis is the systematic study of radioactive transformation processes, originating from Rutherford and Soddy's work in the early 1900s. It quantifies the rate and modes of nuclear disintegration using decay constants, half-lives, and branching ratios to predict activity evolution, date samples via radiometric methods, and assess the long-term hazard from radioactive materials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ernest Rutherford, Frederick Soddy","subfamily":"Radioactive kinetics and dating","year":"1900","type":"analytical process model"},"citations":[{"ref":"Evans, R. D. (1955). The Atomic Nucleus. McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/the-atomic-nucleus/oclc/523842"},{"ref":"Knoll, G. F. (2010). Radiation Detection and Measurement (4th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Radiation+Detection+and+Measurement+%284th+ed.%29+Knoll"}],"related":["activation-analysis","dosimetry-measurement","radioactive-waste-classification","reactor-kinetics","radiation-dose-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nuclear-fuel-cycle-analysis","name":"Nuclear Fuel Cycle Analysis","fullName":"Nuclear Fuel Cycle Analysis and Material Flow Assessment","aliases":["fuel cycle modeling","material accounting","energy lifecycle assessment"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Nuclear energy systems analysis","year":"1942","originator":"Enrico Fermi, Alvin Weinberg","url":"https://scholargate.app/en/nuclear-physics/nuclear-fuel-cycle-analysis","markdownUrl":"https://scholargate.app/en/nuclear-physics/nuclear-fuel-cycle-analysis.md","definition":"Nuclear fuel cycle analysis is a comprehensive assessment of uranium and plutonium flows from extraction through enrichment, power generation, and waste management, originating from Fermi's controlled nuclear reaction. It quantifies resource requirements, energy balances, greenhouse gas emissions, and waste streams to evaluate nuclear energy sustainability, proliferation risk, and economic viability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enrico Fermi, Alvin Weinberg","subfamily":"Nuclear energy systems analysis","year":"1942","type":"system-level material and energy accounting"},"citations":[{"ref":"International Atomic Energy Agency (2021). Nuclear Fuel Cycle Information System (NFCIS). IAEA-NDS-3/Rev.2.","type":"report","doi":null,"isbn":null,"url":"https://www-nds.iaea.org/nfcis/"},{"ref":"Cochran, T. B., Paine, C. E., Feiveson, H. A., & von Hippel, F. N. (2010). Fast Breeder Reactor Development in the U.S.: A Comparative Failure. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/fast-breeder-reactor-development-in-the-us-a-comparative-failure/oclc/646733866"}],"related":["nuclear-decay-analysis","criticality-safety-analysis","reactor-kinetics","radiation-dose-assessment","radioactive-waste-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nucleophilic-substitution-sn","name":"Nucleophilic Substitution Analysis","fullName":"Nucleophilic Substitution Reaction Analysis","aliases":["SN1","SN2","nucleophilic substitution","SN reaction"],"domain":"chemistry","family":"process-pipeline","subfamily":"Synthesis","year":"1937","originator":"Edward Hughes & Christopher Ingold","url":"https://scholargate.app/en/chemistry/nucleophilic-substitution-sn","markdownUrl":"https://scholargate.app/en/chemistry/nucleophilic-substitution-sn.md","definition":"Nucleophilic substitution reaction analysis is the systematic study of how nucleophiles attack electrophilic carbons (or other atoms), displacing leaving groups and forming new bonds. Formalized by Hughes, Ingold, and Winstein from the 1930s onward, this framework distinguishes mechanistic pathways (SN1 vs. SN2) and enables chemists to predict outcomes, optimize conditions, and design synthetic routes using substitution reactions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward Hughes & Christopher Ingold","subfamily":"Synthesis","year":"1937","type":"Mechanistic framework"},"citations":[{"ref":"Hughes, E. D., & Ingold, C. K. (1937). Mechanism of substitution at a saturated carbon atom. Part IV. A discussion of relative reactivities in different solvents. Journal of the Chemical Society, 527–537.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mechanism+of+substitution+at+a+saturated+carbon+atom+Hughes"},{"ref":"Winstein, S., & Grunwald, E. (1955). The correlation of solvolysis rates. III. t-butyl chloride in a wide range of solvent mixtures. Journal of the American Chemical Society, 77(12), 3191–3207.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+correlation+of+solvolysis+rates+Winstein"}],"related":["substitution-reaction-kinetics","redox-reaction-mechanism","synthesis-route-planning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nudged-elastic-band-method","name":"Nudged Elastic Band Method","fullName":"Nudged Elastic Band Method (NEB)","aliases":["NEB","elastic band method","transition-path finding"],"domain":"materials-science","family":"process-pipeline","subfamily":"Transition-path finding","year":"1998","originator":"Hannes Jónsson","url":"https://scholargate.app/en/materials-science/nudged-elastic-band-method","markdownUrl":"https://scholargate.app/en/materials-science/nudged-elastic-band-method.md","definition":"The Nudged Elastic Band (NEB) method is a computational technique for finding minimum-energy transition paths between stable atomic configurations and estimating activation barriers. Developed by Jónsson, Mills, and Jacobsen in 1998, NEB connects initial and final states with a chain of images (configurations) held together by artificial springs, then optimizes the chain to trace a reaction pathway. The climbing-image variant, introduced by Henkelman in 2000, further refines the saddle point. NEB is the standard tool in materials science and chemistry for modeling diffusion, defect formation, and chemical reactions at the atomic scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hannes Jónsson","subfamily":"Transition-path finding","year":"1998","type":"Optimization method"},"citations":[{"ref":"Jonsson, H., Mills, G., & Jacobsen, K. W. (1998). Nudged elastic band method for finding minimum energy paths of transitions. Classical and Quantum Dynamics in Condensed Phase Simulations. World Scientific.","type":"article","doi":null,"isbn":null,"url":"https://www.worldscientific.com/worldscibooks/10.1142/3856"},{"ref":"Henkelman, G., Uberuaga, B. P., & Jonsson, H. (2000). A climbing image nudged elastic band method for finding saddle points and minimum energy paths. The Journal of Chemical Physics, 113(22), 9901-9904.","type":"article","doi":"10.1063/1.1329672","isbn":null,"url":null},{"ref":"Sheppard, D., Terrell, R., & Henkelman, G. (2008). Optimization methods for finding minimum energy paths. The Journal of Chemical Physics, 128(13), 134106.","type":"article","doi":"10.1063/1.2841941","isbn":null,"url":null}],"related":["molecular-dynamics","phase-field-modeling","finite-element-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"null-hypothesis","name":"Null Hypothesis Testing","fullName":"Null Hypothesis Significance Testing (NHST) and Hypothesis Formulation","aliases":["NHST","hypothesis formulation","null hypothesis","alternative hypothesis","two-tailed test"],"domain":"research-statistics","family":"process-pipeline","subfamily":"hypothesis-testing","year":1925,"originator":"Ronald Fisher; Neyman & Pearson","url":"https://scholargate.app/en/research-statistics/null-hypothesis","markdownUrl":"https://scholargate.app/en/research-statistics/null-hypothesis.md","definition":"Null Hypothesis Significance Testing (NHST) is the dominant statistical framework in empirical research. The null hypothesis (H₀) represents the default assumption—typically 'no effect' or 'no difference'—while the alternative hypothesis (H₁) represents the claim being tested. The test calculates the probability of observing the data given H₀ is true (p-value); if p is very small, H₀ is rejected in favor of H₁. Formulated by Ronald Fisher and extended by Neyman and Pearson in the early 20th century, NHST is foundational to confirmatory research but has been widely critiqued for misuse and misinterpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Fisher; Neyman & Pearson","subfamily":"hypothesis-testing","year":1925,"type":"Concept"},"citations":[{"ref":"Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/statisticalmeth00fish"},{"ref":"Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, 231, 289–337.","type":"article","doi":"10.1098/rsta.1933.0009","isbn":null,"url":null},{"ref":"Gigerenzer, G., & Marewski, J. N. (2015). Surrogate Science: The Idol of a Universal Method for Scientific Inference. Journal of Management, 41(2), 421–440.","type":"article","doi":"10.1177/0149206314547522","isbn":null,"url":null}],"related":["p-value-significance","type-i-type-ii-error","statistical-power","confidence-interval"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"number-of-identified-specimens","name":"Number of Identified Specimens","fullName":"Number of Identified Specimens (NISP)","aliases":["NISP method","specimen count"],"domain":"archaeology","family":"process-pipeline","subfamily":"Zooarchaeology","year":"1971","originator":"R. E. Chaplin","url":"https://scholargate.app/en/archaeology/number-of-identified-specimens","markdownUrl":"https://scholargate.app/en/archaeology/number-of-identified-specimens.md","definition":"Number of identified specimens (NISP) is a fundamental zooarchaeological method that quantifies the abundance of faunal remains by counting all identifiable bone fragments or specimens in an assemblage. Formalized by R. E. Chaplin and later refined by Donald Grayson and others, NISP is the most straightforward and widely used quantification metric in zooarchaeology. Despite its simplicity, NISP is sensitive to both cultural and taphonomic factors that affect preservation, fragmentation, and identification of bone assemblages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. E. Chaplin","subfamily":"Zooarchaeology","year":"1971","type":"Faunal quantification method"},"citations":[{"ref":"Chaplin, R. E. (1971). The Study of Animal Bones from Archaeological Sites. Seminar Press.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/study-of-animal-bones-from-archaeological-sites/oclc/264893850"},{"ref":"Grayson, D. K. (1984). Quantitative Zooarchaeology. Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/quantitative-zooarchaeology/grayson/978-0-12-295980-4"},{"ref":"Lyman, R. L. (2008). Quantitative Paleozoology. University of Chicago Press.","type":"book","doi":"10.1017/cbo9780511813863","isbn":null,"url":null}],"related":["minimum-number-of-individuals","geometric-morphometrics","use-wear-analysis","dental-microwear-texture-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"numeric-rating-scale-pain","name":"Numeric Rating Scale for Pain","fullName":"Numeric Rating Scale - Pain Intensity","aliases":["NRS","NRS-11","NRS-101"],"domain":"health-services","family":"process-pipeline","subfamily":"Single-item pain intensity rating","year":"1986","originator":"Mark P. Jensen and colleagues","url":"https://scholargate.app/en/health-services/numeric-rating-scale-pain","markdownUrl":"https://scholargate.app/en/health-services/numeric-rating-scale-pain.md","definition":"The Numeric Rating Scale (NRS) is a single-item, self-report measure of pain intensity developed by Jensen and colleagues in 1986. Patients rate their pain on an 11-point scale (0-10) where 0 represents no pain and 10 represents the worst pain imaginable. The NRS is among the most widely used pain severity measures in clinical practice and research due to its simplicity, rapid administration, and robust measurement properties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark P. Jensen and colleagues","subfamily":"Single-item pain intensity rating","year":"1986","type":"Unidimensional pain severity measurement"},"citations":[{"ref":"Jensen, M. P., Karoly, P., & Braver, S. (1986). The measurement of clinical pain intensity: a comparison of six methods. Pain, 27(3), 297-307.","type":"article","doi":"10.1016/0304-3959(86)90228-9","isbn":null,"url":null},{"ref":"Jensen, M. P., Turner, J. A., & Romano, J. M. (2001). Changes in beliefs, catastrophizing, and coping are associated with improvement in multidisciplinary pain treatment. Journal of Consulting and Clinical Psychology, 69(4), 655-662.","type":"article","doi":"10.1037/0022-006X.69.4.655","isbn":null,"url":null},{"ref":"Hawker, G. A., Mian, S. O., Kendzerska, T., & French, M. (2011). Measures of adult pain: Visual Analog Scale for Pain (VAS), Numeric Rating Scale for Pain (NRS), McGill Pain Questionnaire (MPQ). Arthritis Care & Research, 63(S11), S240-S252.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measures+of+adult+pain%3A+Visual+Analog+Scale+for+Pain+%28VAS%29%2C+Numeric+Rating+Scale+for+Pain+%28NRS%29%2C+McGill+Pain+Questionnaire+%28MPQ%29+Hawker"}],"related":["brief-pain-inventory","visual-analog-scale","mcgill-pain-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nuremberg-code","name":"Nuremberg Code","fullName":"Nuremberg Code of Ethics for Human Experimentation","aliases":["Code of Nuremberg","Ten Principles"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-frameworks","year":"1947","originator":"International Military Tribunal at Nuremberg (Allied Powers)","url":"https://scholargate.app/en/research-ethics/nuremberg-code","markdownUrl":"https://scholargate.app/en/research-ethics/nuremberg-code.md","definition":"The Nuremberg Code (1947) is the first international ethical code governing human experimentation, established by the International Military Tribunal at Nuremberg following trials of Nazi physicians for conducting torture and unethical experiments on concentration camp prisoners. Its ten principles, led by absolute requirement for voluntary informed consent, became the foundation for all modern research ethics governance and remain the gold standard for protecting research subjects from exploitation and abuse.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Military Tribunal at Nuremberg (Allied Powers)","subfamily":"ethical-frameworks","year":"1947","type":"Framework"},"citations":[{"ref":"Nuremberg Military Tribunal. (1947). Trials of War Criminals before the Nuremberg Military Tribunals under Control Council Law No. 10. United States Government Printing Office.","type":"legal","doi":null,"isbn":null,"url":"https://history.nih.gov/about/what-we-do/history/histories/the-nuremberg-code"},{"ref":"National Institutes of Health. (2017). The Nuremberg Code. Office of History.","type":"article","doi":null,"isbn":null,"url":"https://history.nih.gov/about/what-we-do/history/histories/the-nuremberg-code"}],"related":["declaration-of-helsinki","belmont-report","informed-consent-research","research-misconduct"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nurse-work-environment-scale","name":"Practice Environment Scale of the Nursing Work Index","fullName":"Practice Environment Scale of the Nursing Work Index (PES-NWI)","aliases":["PES-NWI","NWI-R"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"work-environment-nursing","year":"2002","originator":"Ellen T. Lake (University of Pennsylvania School of Nursing), based on foundational work by Kramer and Hafner (1989)","url":"https://scholargate.app/en/healthcare-management/nurse-work-environment-scale","markdownUrl":"https://scholargate.app/en/healthcare-management/nurse-work-environment-scale.md","definition":"The Practice Environment Scale of the Nursing Work Index (PES-NWI) is a 31-item instrument designed to measure nurses' perceptions of their practice environment, particularly factors related to autonomy, control over practice, and organizational support. Developed by Lake in 2002 and based on foundational work by Kramer and Hafner, the PES-NWI assesses five key domains: nursing foundations for quality care, staffing and resource adequacy, collegial nurse–physician relationships, nurse manager ability and support, and organizational support for nursing. It is widely used in hospital quality and nursing research to identify environmental factors associated with nurse satisfaction, retention, and patient safety outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ellen T. Lake (University of Pennsylvania School of Nursing), based on foundational work by Kramer and Hafner (1989)","subfamily":"work-environment-nursing","year":"2002","type":"Self-report"},"citations":[{"ref":"Lake, E. T. (2002). Development of the Practice Environment Scale of the Nursing Work Index. Research in Nursing & Health, 25(3), 176–188.","type":"article","doi":"10.1002/nur.10032","isbn":null,"url":null},{"ref":"Aiken, L. H., Sloane, D. M., Lake, E. T., Sochalski, J., & Weber, A. L. (2008). The Practice Environment Scale of the Nursing Work Index: Five country comparison. Nursing Research, 46(5), 298–308.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Practice+Environment+Scale+of+the+Nursing+Work+Index%3A+Five+country+comparison+Aiken"},{"ref":"Lake, E. T. (2007). The nursing practice environment: measurement and evidence. Medical Care Research and Review, 64(2 Suppl), 104S–122S.","type":"article","doi":"10.1177/1077558707299253","isbn":null,"url":null}],"related":["safety-attitudes-questionnaire","hospital-survey-patient-safety","patient-safety-climate-scale","healthcare-teamwork-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nursing-clinical-competence-scale","name":"NCCS","fullName":"Nursing Clinical Competence Scale","aliases":["Clinical Competence Scale","Nursing Skills Assessment","Competency Rating Scale"],"domain":"health-education","family":"process-pipeline","subfamily":"clinical-competence-assessment","year":"2009","originator":"Rozahn Walt & Charmaine van der Walt","url":"https://scholargate.app/en/health-education/nursing-clinical-competence-scale","markdownUrl":"https://scholargate.app/en/health-education/nursing-clinical-competence-scale.md","definition":"The NCCS is a multidimensional self-assessment and clinician-rated instrument measuring nursing students' perceived and observed clinical competence across technical, interpersonal, and cognitive domains. Developed by Walt and van der Walt in 2009, the scale evaluates students' mastery of fundamental nursing skills, critical thinking, communication, and professional judgment. It is used in nursing education to monitor competence development, identify learning gaps, and predict readiness for licensure examinations (e.g., NCLEX-RN).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rozahn Walt & Charmaine van der Walt","subfamily":"clinical-competence-assessment","year":"2009","type":"Self-report and clinician-rated scale"},"citations":[{"ref":"Walt, R. & van der Walt, C. (2009). The nursing clinical competence scale: Development and psychometric testing of a self-assessment instrument. Nurse Educ Today 29(6): 610–616.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+nursing+clinical+competence+scale%3A+Development+and+psychometric+testing+of+a+self-assessment+instrument+Walt"},{"ref":"Kostovich, C. T. & Oswald, S. L. (2016). An exploratory study of valid mechanisms for evaluating NCLEX-RN readiness in undergraduate nursing students. Nurse Educ Today 36(1): 375–381.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+exploratory+study+of+valid+mechanisms+for+evaluating+NCLEX-RN+readiness+in+undergraduate+nursing+students+Kostovich"}],"related":["clinical-learning-environment-scale","clinical-teaching-quality-scale","professional-identity-scale","simulation-debriefing-quality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nursing-sensitive-indicators","name":"Nursing-Sensitive Indicators","fullName":"Nursing-Sensitive Quality Indicators for Healthcare Performance","aliases":["NSI","Nursing Quality Metrics","Hospital-Acquired Complication Indicators"],"domain":"nursing","family":"process-pipeline","subfamily":"Quality measurement and performance evaluation","year":"1994","originator":"American Nurses Association (ANA)","url":"https://scholargate.app/en/nursing/nursing-sensitive-indicators","markdownUrl":"https://scholargate.app/en/nursing/nursing-sensitive-indicators.md","definition":"Nursing-Sensitive Indicators are quality metrics that measure healthcare outcomes significantly influenced by nursing care. Developed by the American Nurses Association (ANA) and maintained through the National Database of Nursing Quality Indicators (NDNQI), these indicators assess hospital-acquired complications, staffing levels, nurse-sensitive outcomes, and other dimensions of care quality. They serve as benchmarking tools for evaluating nursing practice effectiveness and organizational performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Nurses Association (ANA)","subfamily":"Quality measurement and performance evaluation","year":"1994","type":"Quality indicator set"},"citations":[{"ref":"American Nurses Association. (2001). National Database of Nursing Quality Indicators (NDNQI). Journal of Nursing Administration, 31(5), 255-260.","type":"article","doi":null,"isbn":null,"url":"https://www.na-dbnqi.org/"},{"ref":"Needleman, J., Buerhaus, P., Paucek, K., Leibson, C. L., & Chiang, Y. P. (2011). The value of nurse staffing in hospitals. Medical Care, 49(2), 249-256.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+value+of+nurse+staffing+in+hospitals+Needleman"}],"related":["braden-scale","cam-delirium-screening","nursing-workload-nems","early-warning-score","patient-fall-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nursing-workload-nems","name":"Nursing Workload NEMS","fullName":"Nine Equivalents of Nursing Manpower Use Score","aliases":["NEMS","Nursing Workload Score","Intensive Care Workload Assessment"],"domain":"nursing","family":"process-pipeline","subfamily":"Workload measurement and staffing assessment","year":"1997","originator":"Dick R. Miranda, Rui Moreno, and Guido Iapichino","url":"https://scholargate.app/en/nursing/nursing-workload-nems","markdownUrl":"https://scholargate.app/en/nursing/nursing-workload-nems.md","definition":"The Nine Equivalents of Nursing Manpower Use Score (NEMS) is a validated assessment instrument specifically designed to quantify nursing workload in intensive care unit (ICU) settings. Developed by Miranda, Moreno, and Iapichino, NEMS measures the intensity of nursing care required based on therapeutic interventions and patient monitoring activities. Unlike severity of illness scores that focus on mortality risk, NEMS directly measures the nursing effort expended, making it particularly useful for ICU staffing decisions and workload documentation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dick R. Miranda, Rui Moreno, and Guido Iapichino","subfamily":"Workload measurement and staffing assessment","year":"1997","type":"Workload assessment tool"},"citations":[{"ref":"Miranda, D. R., Moreno, R., & Iapichino, G. (1997). Nine equivalents of nursing manpower use score (NEMS). Intensive Care Medicine, 23(7), 760-765.","type":"article","doi":"10.1007/s001340050406","isbn":null,"url":null},{"ref":"Cullen, D. J., Civetta, J. M., Briggs, B. A., & Ferrara, L. C. (1974). Therapeutic Intervention Scoring System (TISS): A method for quantitative comparison of patient acuity. Critical Care Medicine, 2(2), 57-60.","type":"article","doi":"10.1097/00003246-197403000-00001","isbn":null,"url":null}],"related":["nursing-sensitive-indicators","care-dependency-scale","early-warning-score","patient-fall-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nutrition-literacy-scale","name":"NLAI","fullName":"Nutrition Literacy Assessment Instrument","aliases":["NLAI","Nutrition Literacy Assessment","Nutrition Literacy"],"domain":"public-health-nutrition","family":"process-pipeline","subfamily":"nutrition-knowledge-assessment","year":"2007","originator":"Diamond; Rothman; Nutrition Literacy Research","url":"https://scholargate.app/en/public-health-nutrition/nutrition-literacy-scale","markdownUrl":"https://scholargate.app/en/public-health-nutrition/nutrition-literacy-scale.md","definition":"The NLAI is a 26-item validated instrument measuring nutrition literacy—the ability to understand nutrition information and use it to make healthy food choices. Developed by Diamond and refined through validation studies by Rothman and colleagues, the NLAI evaluates comprehension of nutrition labels, understanding of portion sizes, knowledge of daily values, and ability to interpret nutrient claims. Nutrition literacy is a critical determinant of dietary quality and health outcomes, particularly in populations with low general literacy or limited numeracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diamond; Rothman; Nutrition Literacy Research","subfamily":"nutrition-knowledge-assessment","year":"2007","type":"Validated questionnaire; knowledge and comprehension"},"citations":[{"ref":"Diamond, M. R. (2007). Development of a reliable and valid nutrition literacy assessment instrument. Journal of Nutrition Education and Behavior, 39(Suppl 5), S26–S30.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+of+a+reliable+and+valid+nutrition+literacy+assessment+instrument+Diamond"},{"ref":"Rothman, R. L., Housam, R., Weiss, H., et al. (2006). Patient understanding of food labels: the role of literacy and numeracy. American Journal of Preventive Medicine, 31(5), 391–398.","type":"article","doi":"10.1016/j.amepre.2006.07.025","isbn":null,"url":null}],"related":["household-dietary-diversity-score","healthy-eating-index","maternal-diet-quality-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nutrition-self-efficacy-scale","name":"DASES","fullName":"Diabetes Self-Efficacy Scale / Nutrition Self-Efficacy Scale","aliases":["DASES","diabetes-self-efficacy","nutrition-efficacy"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"self-efficacy-confidence","year":2003,"originator":"Kate Lorig, Philip L. Ritter, Farrokh Alavifard (Stanford Patient Education Center)","url":"https://scholargate.app/en/nutritional-science/nutrition-self-efficacy-scale","markdownUrl":"https://scholargate.app/en/nutritional-science/nutrition-self-efficacy-scale.md","definition":"The Nutrition Self-Efficacy Scale, sometimes called the Diabetes Self-Efficacy Scale (DASES), is an 8-item instrument measuring confidence in performing diet-related behaviors and self-management skills. Developed by Lorig and colleagues at the Stanford Patient Education Center in 2003, it is based on self-efficacy theory and measures respondents' confidence in their ability to eat healthily, manage portions, choose healthful foods, and overcome dietary barriers. The scale is used in diabetes care, weight management, and general nutrition intervention research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kate Lorig, Philip L. Ritter, Farrokh Alavifard (Stanford Patient Education Center)","subfamily":"self-efficacy-confidence","year":2003,"type":"Self-report confidence scale"},"citations":[{"ref":"Lorig, K., Ritter, P. L., Villa, F., & Piette, J. D. (2009). Spanish language diabetes self-management with and without automated telephone reinforcement: two randomized trials. Diabetes Care, 32(3), 408-414.","type":"article","doi":"10.2337/dc07-1313","isbn":null,"url":null},{"ref":"Stanford Patient Education Center. (2003). Chronic Disease Self-Efficacy Scales. Stanford University School of Medicine.","type":"book","doi":null,"isbn":null,"url":"https://patient.stanford.edu/about/services/cdsmp"}],"related":["mini-nutritional-assessment","dietary-quality-index","mediterranean-diet-adherence","intuitive-eating-scale","dutch-eating-behavior-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"nvivo-atlas-qualitative","name":"NVivo and ATLAS.ti for Qualitative Analysis","fullName":"Computer-Assisted Qualitative Data Analysis Software (CAQDAS)","aliases":["CAQDAS","QDA software","qualitative analysis software","NVivo","ATLAS.ti"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"analysis-tool","year":"1999","originator":"QSR International (NVivo) and Scientific Software-Citational (ATLAS.ti)","url":"https://scholargate.app/en/qualitative-research/nvivo-atlas-qualitative","markdownUrl":"https://scholargate.app/en/qualitative-research/nvivo-atlas-qualitative.md","definition":"NVivo and ATLAS.ti are Computer-Assisted Qualitative Data Analysis Software (CAQDAS) programs that facilitate coding, organizing, and analyzing qualitative data—including text (transcripts, documents), images, video, and audio. NVivo, developed by QSR International, is widely used in academic research and supports data organization, coding, memo-writing, retrieval, and analysis visualizations. ATLAS.ti, developed by Scientific Software-Citational, emphasizes hermeneutic interpretation and network visualization. Both tools were introduced in the late 1990s and have become standard across disciplines. CAQDAS is not analysis itself—the researcher must make analytical decisions—but rather augments human analysis by managing large data volumes, organizing codes systematically, tracking analysis decisions, and generating visualizations. These tools improve transparency and rigor in qualitative research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"QSR International (NVivo) and Scientific Software-Citational (ATLAS.ti)","subfamily":"analysis-tool","year":"1999","type":"Tool"},"citations":[{"ref":"Lewins, A., & Silver, C. (2007). Using Software in Qualitative Research: A Step-by-Step Guide. SAGE Publications.","type":"article","doi":null,"isbn":"978-1412903653","url":null},{"ref":"Friese, S. (2019). Qualitative Data Analysis with ATLAS.ti (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1526424944","url":null},{"ref":"Bazeley, P., & Jackson, K. (2013). Qualitative Data Analysis with NVivo (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1446257098","url":null},{"ref":"Hoover, S. M., Polasek, D. W., & Shuart-Faris, N. (2012). Qualitative software and rigor in systematic reviews. Journal of Electronic Resources in Medical Libraries, 9(3), 194-204.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Qualitative+software+and+rigor+in+systematic+reviews+Hoover"}],"related":["document-analysis","qualitative-synthesis-methods","saturation-in-qualitative","participant-observation"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"object-based-image-analysis","name":"Object-Based Image Analysis","fullName":"Object-Based Image Analysis (OBIA)","aliases":["Geographic Object-Based Image Analysis","GEOBIA","Object-Oriented Image Analysis","Nesne Tabanlı Görüntü Analizi"],"domain":"remote-sensing","family":"process-pipeline","subfamily":"Remote sensing","year":2010,"originator":"Thomas Blaschke","url":"https://scholargate.app/en/remote-sensing/object-based-image-analysis","markdownUrl":"https://scholargate.app/en/remote-sensing/object-based-image-analysis.md","definition":"Object-Based Image Analysis (OBIA) is a remote sensing image processing paradigm that groups pixels into meaningful image objects before classification, rather than analysing each pixel independently. Formally articulated and consolidated by Thomas Blaschke in his landmark 2010 ISPRS review, OBIA draws on multiresolution segmentation algorithms and combines spectral, spatial, contextual, and textural object attributes to produce semantically rich land-cover maps from high-resolution imagery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Blaschke","year":2010,"type":"Image segmentation and classification pipeline","subfamily":"Remote sensing","input":"High-resolution raster imagery (multispectral, hyperspectral, LiDAR)","output":"Semantically labeled image objects (polygons)"},"citations":[{"ref":"Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.","type":"article","doi":"10.1016/j.isprsjprs.2009.06.004","isbn":null,"url":null}],"related":["pixel-based-classification","change-detection","landscape-metrics"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"object-detection","name":"Object Detection","fullName":"Object Detection (Region-Based and Anchor-Free Deep Neural Network Models)","aliases":["visual object detection","image object localization","region-based object detection","bounding-box detection"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2016","originator":"Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)","url":"https://scholargate.app/en/deep-learning/object-detection","markdownUrl":"https://scholargate.app/en/deep-learning/object-detection.md","definition":"Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)","year":"2014–2016","type":"Supervised deep learning (region proposal or single-shot)","dataType":"Images (annotated with bounding boxes and class labels)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587.","type":"inproceedings","doi":"10.1109/CVPR.2014.81","isbn":null,"url":null},{"ref":"Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788.","type":"inproceedings","doi":"10.1109/CVPR.2016.91","isbn":null,"url":null}],"related":["image-classification","semantic-segmentation","instance-segmentation","convolutional-neural-network","fine-tuned-object-detection","transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"obsidian-hydration-dating","name":"Obsidian Hydration Dating","fullName":"Obsidian Hydration Dating (OHD)","aliases":["OHD","obsidian hydration method"],"domain":"archaeology","family":"process-pipeline","subfamily":"Chemical Kinetics","year":"1960","originator":"Irving Friedman","url":"https://scholargate.app/en/archaeology/obsidian-hydration-dating","markdownUrl":"https://scholargate.app/en/archaeology/obsidian-hydration-dating.md","definition":"Obsidian hydration dating (OHD) is a chronometric method that determines the age of obsidian artifacts by measuring the thickness of a hydration layer formed on their exposed surfaces. Developed by Irving Friedman and Robert Smith in 1960, it is based on the principle that fresh obsidian surfaces absorb water from the surrounding environment at a measurable rate. The method is particularly valuable in archaeology for dating volcanic glass tools and other obsidian objects, especially in regions where obsidian was commonly used for cutting and scraping implements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Irving Friedman","subfamily":"Chemical Kinetics","year":"1960","type":"Hydration layer dating technique"},"citations":[{"ref":"Friedman, I., & Smith, R. L. (1960). A new dating method using obsidian: Part 1, the surface rind method. Journal of Geophysical Research, 65(4), 1287-1291.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+dating+method+using+obsidian%3A+Part+1%2C+the+surface+rind+method+Friedman"},{"ref":"Ericson, J. E., & Berger, R. (1975). Kinetic energy of ionizing radiation as a determinant of the obsidian hydration rate. Nature, 254(5496), 55-56.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kinetic+energy+of+ionizing+radiation+as+a+determinant+of+the+obsidian+hydration+rate+Ericson"},{"ref":"Mazer, J. J., Bates, J. K., Stevenson, C. M., & Clelland, J. G. (1991). Obsidian-water reactions: Measuring extremely low water contents in obsidian. Geochimica et Cosmochimica Acta, 55(2), 395-405.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Obsidian-water+reactions%3A+Measuring+extremely+low+water+contents+in+obsidian+Mazer"}],"related":["thermoluminescence-dating","optically-stimulated-luminescence-dating","archaeomagnetic-dating","radiocarbon-dating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"occlusal-analysis","name":"Occlusal Analysis","fullName":"Occlusal Examination and Analysis","aliases":["occlusion assessment","bite analysis","centric relation recording"],"domain":"dentistry","family":"process-pipeline","subfamily":"Prosthodontics and TMD management","year":"1899 (Angle's classification); 1950s+ (modern analysis)","originator":"Multiple innovators (Angle, Posselt, Dawson, Okeson)","url":"https://scholargate.app/en/dentistry/occlusal-analysis","markdownUrl":"https://scholargate.app/en/dentistry/occlusal-analysis.md","definition":"Occlusal analysis is a systematic clinical and instrumental examination that evaluates the relationships between the maxillary and mandibular teeth, the temporomandibular joint, and the muscles of mastication. Comprehensive occlusal analysis informs diagnosis of malocclusion, temporomandibular disorders, and guides prosthodontic and orthodontic treatment planning. The analysis integrates static occlusal relationships (centric relation, centric occlusion) with dynamic occlusal patterns (jaw movements) to assess functional harmony and identify occlusal interferences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple innovators (Angle, Posselt, Dawson, Okeson)","subfamily":"Prosthodontics and TMD management","year":"1899 (Angle's classification); 1950s+ (modern analysis)","type":"Clinical and instrumental examination"},"citations":[{"ref":"Okeson, J. P. (2020). Management of temporomandibular disorders and occlusion (8th ed.). Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/management-of-temporomandibular-disorders-and-occlusion/okeson/978-0-323-56657-0"},{"ref":"Rugh, J. D., & Harlan, J. (1988). Nocturnal masseter muscle activity and symptoms of temporomandibular disorders. Journal of Prosthodontic Research, 60(4), 329-334.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nocturnal+masseter+muscle+activity+and+symptoms+of+temporomandibular+disorders+Rugh"},{"ref":"Ash, M. M., & Nelson, S. J. (2003). Wheeler's dental anatomy, physiology and occlusion (8th ed.). WB Saunders.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/wheelers-dental-anatomy-physiology-and-occlusion/ash/978-0-7216-9382-8"}],"related":["temporomandibular-joint-analysis","orthodontic-cephalometry","tooth-mobility-assessment","periodontal-probing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"occupational-exposure-questionnaire","name":"Occupational Exposure Questionnaire","fullName":"Occupational Exposure Questionnaire (OEQ)","aliases":["OEQ"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-hazard-assessment","year":"2007","originator":"NIOSH; Occupational Epidemiology Community","url":"https://scholargate.app/en/occupational-health/occupational-exposure-questionnaire","markdownUrl":"https://scholargate.app/en/occupational-health/occupational-exposure-questionnaire.md","definition":"The Occupational Exposure Questionnaire (OEQ) systematically documents workers' exposure to physical, chemical, biological, ergonomic, and psychosocial hazards in their occupational roles. Used by occupational health practitioners and researchers, the OEQ captures frequency, duration, and intensity of hazard exposure, enabling identification of high-risk workers, validation of job exposure matrices, and epidemiological investigation of occupational disease. The OEQ is foundational for occupational health surveillance and regulatory compliance (OSHA, EPA, HSE standards).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"NIOSH; Occupational Epidemiology Community","subfamily":"occupational-hazard-assessment","year":"2007","type":"Self-report"},"citations":[{"ref":"National Institute for Occupational Safety and Health (NIOSH). (2007). Exposure assessment: A handbook for conducting occupational health surveys. DHHS (NIOSH) Publication No. 2007-154.","type":"article","doi":null,"isbn":null,"url":"https://www.cdc.gov/niosh"},{"ref":"Checkoway, H., Pearce, N., & Kriebel, D. (1989). Research methods in occupational epidemiology. Oxford University Press.","type":"article","doi":null,"isbn":"978-0-195-04156-5","url":null}],"related":["workplace-violence-scale","sexual-harassment-experiences-questionnaire","occupational-fatigue-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"occupational-fatigue-scale","name":"Occupational Fatigue Exhaustion Recovery Scale","fullName":"Occupational Fatigue Exhaustion Recovery Scale (OFER)","aliases":["OFER","Occupational Fatigue Scale"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-stress","year":"2006","originator":"Winwood, Bakker, & Liss-Malone","url":"https://scholargate.app/en/occupational-health/occupational-fatigue-scale","markdownUrl":"https://scholargate.app/en/occupational-health/occupational-fatigue-scale.md","definition":"The Occupational Fatigue Exhaustion Recovery Scale (OFER) measures worker fatigue across three dimensions: acute fatigue (tiredness after the current work period), chronic fatigue (accumulated exhaustion over weeks or months), and inter-shift recovery (ability to recuperate between work shifts). Developed by Winwood and colleagues, the OFER distinguishes between short-term fatigue (recoverable) and long-term exhaustion (requiring intervention), making it essential for identifying workers at risk of injury, burnout, and occupational health decline in high-demand roles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Winwood, Bakker, & Liss-Malone","subfamily":"occupational-stress","year":"2006","type":"Self-report"},"citations":[{"ref":"Winwood, P. C., Bakker, A. B., & Winwood, L. M. (2006). Do the effort–reward imbalance model and the demand control model measure occupational fatigue? A claims analysis of occupational health data. J Occup Environ Med, 48(11), 1112–1120.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Do+the+effort%E2%80%93reward+imbalance+model+and+the+demand+control+model+measure+occupational+fatigue+Winwood"},{"ref":"Winwood, P. C., Winwood, L. M., & Liss-Malone, S. (2007). Development and validation of a scale to measure occupational exhaustion. J Occup Environ Med, 49(8), 864–873.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+a+scale+to+measure+occupational+exhaustion+Winwood"}],"related":["psychosocial-safety-climate-scale","occupational-exposure-questionnaire","workplace-ostracism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"occupational-self-assessment","name":"OSA","fullName":"Occupational Self-Assessment","aliases":["OSA"],"domain":"occupational-therapy","family":"process-pipeline","subfamily":"occupational functioning and self-perception","year":"2006 (OSA v2)","originator":"Baron, K., Kielhofner, G., & colleagues (Model of Human Occupation framework)","url":"https://scholargate.app/en/occupational-therapy/occupational-self-assessment","markdownUrl":"https://scholargate.app/en/occupational-therapy/occupational-self-assessment.md","definition":"The Occupational Self-Assessment (OSA) is a client-centered, reflective tool designed to measure an individual's perception of occupational functioning and identify areas of occupational concern or goals. Developed by Baron, Kielhofner, and colleagues within the Model of Human Occupation (MOHO) framework, the OSA integrates competence self-rating with importance rating, revealing the gap between what the client can do and what matters to them. The OSA is used in occupational therapy across mental health, physical rehabilitation, aging, and developmental disability to identify therapy goals and monitor changes in occupational functioning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baron, K., Kielhofner, G., & colleagues (Model of Human Occupation framework)","subfamily":"occupational functioning and self-perception","year":"2006 (OSA v2)","type":"Self-report questionnaire and importance rating"},"citations":[{"ref":"Baron, K., Kielhofner, G., Iyenger, A., Goldhammer, V., & Wolenski, J. (2006). The Occupational Self Assessment (OSA) (2nd ed.). MOHO Clearinghouse, University of Illinois at Chicago.","type":"article","doi":null,"isbn":null,"url":"https://www.moho.uic.edu"},{"ref":"Kielhofner, G., & Henry, A. D. (1988). Development and investigation of the occupational performance history interview. American Journal of Occupational Therapy, 42(8), 489-498.","type":"article","doi":"10.5014/ajot.42.8.489","isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/3195667"}],"related":["copm","occupational-self-assessment","frenchay-activities-index","upper-extremity-functional-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"occupational-stress-index","name":"Occupational Stress Index","fullName":"Occupational Stress Index (OSI)","aliases":["OSI","Osipow Scale","Job Stress Measure"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"occupational-health","year":"1987","originator":"Samuel H. Osipow","url":"https://scholargate.app/en/organizational-behavior/occupational-stress-index","markdownUrl":"https://scholargate.app/en/organizational-behavior/occupational-stress-index.md","definition":"The Occupational Stress Index (OSI) is a comprehensive self-report measure of job-related stress and coping resources. Developed by Osipow and Spokane in 1987, the 140-item scale (abbreviated versions also exist) captures role overload, role boundary, role insufficiency, role ambiguity, physical environment demands, and coping resources. The OSI is grounded in stress and coping theory and predicts health outcomes, performance, and retention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Samuel H. Osipow","subfamily":"occupational-health","year":"1987","type":"Self-report questionnaire"},"citations":[{"ref":"Osipow, S. H., & Spokane, A. R. (1987). Occupational Stress Inventory manual (Rev. ed.). Psychological Assessment Resources.","type":"book","doi":null,"isbn":"978-0911216929","url":null},{"ref":"Spokane, A. R., Ohlund, B., Luchetta, E., & Meir, E. I. (1997). Validity of the Occupational Stress Inventory: Comparative and factorial validity with measures of job characteristics. Journal of Career Assessment, 5(3), 343–355.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validity+of+the+Occupational+Stress+Inventory%3A+Comparative+and+factorial+validity+with+measures+of+job+characteristics+Spokane"},{"ref":"Pearlman, D. S., Hartman, E. A., & O'Neill, C. B. (2000). Occupational stress: A review and critique of three major approaches. Journal of Organizational Behavior, 21(8), 915–933.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Occupational+stress%3A+A+review+and+critique+of+three+major+approaches+Pearlman"}],"related":["workplace-bullying-questionnaire","perceived-organizational-support","job-descriptive-index","psychological-capital-questionnaire","leader-member-exchange-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ocean-atmosphere-coupled-model","name":"Ocean-Atmosphere Coupled Model","fullName":"Ocean-Atmosphere Coupled Model","aliases":["AOGCM"],"domain":"geophysics","family":"process-pipeline","subfamily":"Climate simulation and modeling","year":"1975","originator":"Syukuro Manabe, Kirk Bryan, and others","url":"https://scholargate.app/en/geophysics/ocean-atmosphere-coupled-model","markdownUrl":"https://scholargate.app/en/geophysics/ocean-atmosphere-coupled-model.md","definition":"An Ocean-Atmosphere Coupled Model (AOGCM) is a comprehensive climate simulation that couples dynamic general circulation models of the atmosphere and ocean with explicit exchange of heat, momentum, and moisture at the interface. Developed by Manabe, Bryan, and colleagues in the 1970s, coupled models are essential for simulating climate change, ocean circulation changes, and climate-ocean interactions over decadal to centennial timescales.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Syukuro Manabe, Kirk Bryan, and others","subfamily":"Climate simulation and modeling","year":"1975","type":"Coupled atmosphere-ocean climate system simulation"},"citations":[{"ref":"Manabe, S., Bryan, K., & Spelman, M. J. (1975). A global ocean-atmosphere climate model with seasonal variation for future studies of climate sensitivity. Journal of Physical Oceanography, 5(1), 3-29.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+global+ocean-atmosphere+climate+model+with+seasonal+variation+for+future+studies+of+climate+sensitivity+Manabe"},{"ref":"Knutti, R., & Sedláček, J. (2013). Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change, 3(4), 369-373.","type":"article","doi":"10.1038/nclimate1716","isbn":null,"url":null}],"related":["general-circulation-model","swat-model","hec-ras"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ocean-color-chlorophyll-a","name":"Ocean Color Chlorophyll-a","fullName":"Ocean Color Chlorophyll-a Remote Sensing","aliases":["Chlorophyll-a Retrieval","Ocean Productivity Monitoring"],"domain":"oceanography","family":"process-pipeline","subfamily":"Remote Sensing","year":"1978","originator":"Remote Sensing Community","url":"https://scholargate.app/en/oceanography/ocean-color-chlorophyll-a","markdownUrl":"https://scholargate.app/en/oceanography/ocean-color-chlorophyll-a.md","definition":"Ocean color remote sensing is the primary global method for retrieving seawater chlorophyll-a concentrations and phytoplankton productivity from satellite sensors. Based on bio-optical principles established in the 1970s, ocean color algorithms convert satellite spectral reflectance measurements into estimates of chlorophyll-a pigment concentration. This method enables global-scale, real-time monitoring of oceanic primary productivity and plankton dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Remote Sensing Community","subfamily":"Remote Sensing","year":"1978","type":"bio-optical"},"citations":[{"ref":"Gordon, H. R., & Morel, A. Y. (1983). Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. Springer-Verlag.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/"},{"ref":"Behrenfeld, M. J., & Falkowski, P. G. (2001). A consumer's guide to phytoplankton primary productivity models. Limnology and Oceanography, 46(7), 1639-1654.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+consumer%27s+guide+to+phytoplankton+primary+productivity+models+Behrenfeld"}],"related":["harmful-algal-bloom-monitoring","ctd-profiling","phytoplankton-size-class"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oci-r","name":"Obsessive-Compulsive Inventory","fullName":"Obsessive-Compulsive Inventory-Revised (OCI-R)","aliases":["OCI-R","OCI"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Obsessive-compulsive disorder assessment","year":"2002","originator":"Edna B. Foa and colleagues","url":"https://scholargate.app/en/clinical-psychology/oci-r","markdownUrl":"https://scholargate.app/en/clinical-psychology/oci-r.md","definition":"The Obsessive-Compulsive Inventory-Revised (OCI-R) is an 18-item self-report measure of obsessive-compulsive disorder (OCD) symptoms. Developed by Foa and colleagues in 2002, the OCI-R is a revised and shortened version of the original OCI. It assesses six dimensions of OCD: obsessing, hoarding, neutralizing, contamination, responsibility, and symmetry. The OCI-R has become the standard self-report outcome measure in OCD research and treatment trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edna B. Foa and colleagues","subfamily":"Obsessive-compulsive disorder assessment","year":"2002","type":"OCD symptom severity measurement"},"citations":[{"ref":"Foa, E. B., Huppert, J. D., Leiberg, S., Langner, R., Kichic, R., Hajcak, G., & Salkovskis, P. M. (2002). The Obsessive-Compulsive Inventory: Development and validation of a short version. Psychological Assessment, 14(4), 485-496.","type":"article","doi":"10.1037/1040-3590.14.4.485","isbn":null,"url":null},{"ref":"Grisham, J. R., Fullana, M. A., Mataix-Cols, D., Moffitt, T. E., Caspi, A., & Foa, E. B. (2008). Risk factors prospectively associated with adult obsessive-compulsive symptoms: A comparison of two birth cohorts assessed at different life stages. American Journal of Psychiatry, 165(12), 1552-1560.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Risk+factors+prospectively+associated+with+adult+obsessive-compulsive+symptoms%3A+A+comparison+of+two+birth+cohorts+assessed+at+different+life+stages+Grisham"}],"related":["spin-social-phobia","hads","das-21","k10-kessler","hamilton-anxiety-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ocra","name":"OCRA","fullName":"Operational Competitiveness Rating","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1994","originator":"Parkan, C.","url":"https://scholargate.app/en/decision-making/ocra","markdownUrl":"https://scholargate.app/en/decision-making/ocra.md","definition":"OCRA (Operational Competitiveness Rating) is a ranking multi-criteria decision-making (MCDM) method introduced by Parkan, C. in 1994. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Parkan, C.","subfamily":"Ranking","year":"1994","type":"Preference rating (input/output separation)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Parkan, C. (1994). Operational competitiveness ratings of production units. Managerial and Decision Economics","type":"article","doi":"10.1002/mde.4090150303","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oct-angiography","name":"OCT Angiography","fullName":"Optical Coherence Tomography Angiography","aliases":["OCTA","OCT-A"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Non-invasive imaging","year":"2012","originator":"Yali Jia","url":"https://scholargate.app/en/medical-imaging/oct-angiography","markdownUrl":"https://scholargate.app/en/medical-imaging/oct-angiography.md","definition":"Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that visualizes the microvasculature in the retina and choroid by detecting motion contrast from flowing blood. Developed by Jia and colleagues in 2012, OCTA uses repeated OCT scans of the same tissue location to identify blood flow based on the decorrelation signal. It has become a critical diagnostic tool in ophthalmology for detecting retinal and macular diseases without requiring fluorescein injection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yali Jia","subfamily":"Non-invasive imaging","year":"2012","type":"Optical imaging technique for vasculature visualization"},"citations":[{"ref":"Jia, Y., Tan, O., Tokayer, J., et al. (2012). Split-spectrum amplitude-decorrelation angiography with optical coherence tomography. Optics Express, 20(4), 4710-4725.","type":"article","doi":"10.1364/OE.20.004710","isbn":null,"url":null},{"ref":"Wang, R. K., An, L. (2012). Doppler optical micro-angiography for volumetric imaging of vascular perfusion in vivo. Optics Express, 14(17), 7881-7895.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Doppler+optical+micro-angiography+for+volumetric+imaging+of+vascular+perfusion+in+vivo+Wang"},{"ref":"Spaide, R. F., Fujimoto, J. G., Waheed, N. K. (2015). Image artifacts in optical coherence tomography angiography. Retina, 35(11), 2163-2180.","type":"article","doi":"10.1097/IAE.0000000000000765","isbn":null,"url":null}],"related":["pet-kinetic-modeling","ct-iterative-reconstruction","dti-tractography","quantitative-susceptibility-mapping","radiomics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ocular-surface-disease-index","name":"OSDI","fullName":"Ocular Surface Disease Index","aliases":["OSDI","Ocular Surface Index"],"domain":"ophthalmology","family":"process-pipeline","subfamily":"dry eye / ocular surface disease","year":"2000","originator":"Schiffman RM, Christianson MD et al.","url":"https://scholargate.app/en/ophthalmology/ocular-surface-disease-index","markdownUrl":"https://scholargate.app/en/ophthalmology/ocular-surface-disease-index.md","definition":"The OSDI is a 12-item symptom questionnaire designed to screen for and grade the severity of dry eye disease and other ocular surface disorders. Developed by Schiffman, Christianson, and colleagues (2000), it quantifies patient-reported ocular irritation and visual function limitations across frequency and impact domains. The OSDI is the most widely used screening tool for dry eye in clinical practice, clinical trials, and epidemiological studies worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schiffman RM, Christianson MD et al.","subfamily":"dry eye / ocular surface disease","year":"2000","type":"Self-report"},"citations":[{"ref":"Schiffman, R. M., Christianson, M. D., Jacobsen, G., Hirsch, J. D., & Reis, B. L. (2000). Reliability and validity of the Ocular Surface Disease Index. Arch Ophthalmol, 118(5), 615-621.","type":"article","doi":"10.1001/archopht.118.5.615","isbn":null,"url":null},{"ref":"Begley, C. G., Chalmers, R. L., Abetz, L., & Vensel, B. (2003). The relationship between habitual patient-reported symptoms and clinical signs among patients with dry eye of varying severity. Invest Ophthalmol Vis Sci, 44(10), 4753-4761.","type":"article","doi":"10.1167/iovs.03-0270","isbn":null,"url":null}],"related":["nei-vfq-25","visual-function-index","impact-vision-impairment","low-vision-quality-of-life"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ofdm","name":"OFDM","fullName":"Orthogonal Frequency Division Multiplexing","aliases":["multicarrier modulation"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Signal processing","year":"1971","originator":"Weinstein and Ebert","url":"https://scholargate.app/en/telecommunications/ofdm","markdownUrl":"https://scholargate.app/en/telecommunications/ofdm.md","definition":"OFDM is a multicarrier modulation technique that divides a wideband channel into many narrowband orthogonal subcarriers. Introduced by Weinstein and Ebert in 1971, it exploits the duality between time and frequency domains to efficiently use spectrum while mitigating intersymbol interference in frequency-selective channels. OFDM is now the standard for high-speed wireless systems including WiFi, cellular LTE, and digital broadcasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Weinstein and Ebert","subfamily":"Signal processing","year":"1971","type":"multicarrier modulation scheme"},"citations":[{"ref":"Weinstein, S. B., & Ebert, P. M. (1971). Data transmission by frequency-division multiplexing using the discrete Fourier transform. IEEE Transactions on Communication Technology, 19(5), 628-634.","type":"article","doi":"10.1109/TCOM.1971.1090705","isbn":null,"url":null},{"ref":"Alves, H., Nouri, M., & Latva-aho, M. (2015). Performance analysis of orthogonal frequency division multiplexing for wireless networks. IEEE Access, 3, 1627-1640.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Performance+analysis+of+orthogonal+frequency+division+multiplexing+for+wireless+networks+Alves"}],"related":["mimo","alamouti-code","zf-mmse-equalization","turbo-code","shannon-capacity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ohip-14","name":"OHIP-14","fullName":"Oral Health Impact Profile-14","aliases":["OHIP","Oral Health Impact Profile"],"domain":"dentistry","family":"process-pipeline","subfamily":"oral-health-quality-of-life","year":"1997","originator":"Geraint D. Slade","url":"https://scholargate.app/en/dentistry/ohip-14","markdownUrl":"https://scholargate.app/en/dentistry/ohip-14.md","definition":"The OHIP-14 is a 14-item, validated instrument measuring the impact of oral conditions on quality of life and functional well-being. Developed by Slade in 1997, it is a shortened form of the original 49-item OHIP and has become the gold standard for assessing oral health-related quality of life in clinical research and practice. It captures patient-centred outcomes across functional, psychological, and social dimensions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geraint D. Slade","subfamily":"oral-health-quality-of-life","year":"1997","type":"Self-report questionnaire"},"citations":[{"ref":"Slade, G. D. (1997). Derivation and validation of a short-form oral health impact profile. Community Dentistry and Oral Epidemiology, 25(4), 284-290.","type":"article","doi":"10.1111/j.1600-0528.1997.tb00941.x","isbn":null,"url":null}],"related":["xerostomia-inventory","dental-anxiety-modified-scale","child-oral-health-qol","oral-impacts-daily-performance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"okumura-hata-model","name":"Okumura-Hata Model","fullName":"Okumura-Hata Path Loss Prediction Model","aliases":["path loss model","propagation prediction"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Propagation modeling","year":"1968","originator":"Masahiro Okumura and Masahiro Hata","url":"https://scholargate.app/en/telecommunications/okumura-hata-model","markdownUrl":"https://scholargate.app/en/telecommunications/okumura-hata-model.md","definition":"The Okumura-Hata model is an empirical propagation model for predicting path loss in mobile radio systems. Developed by Okumura (1968) and mathematically formalized by Hata (1980), it is one of the most widely used models for cellular network planning. The model predicts median path loss as a function of frequency, distance, and antenna heights, with environment-specific correction factors. Despite its age, the Okumura-Hata model remains a standard in 2G/3G planning and is often used as a baseline for more sophisticated models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Masahiro Okumura and Masahiro Hata","subfamily":"Propagation modeling","year":"1968","type":"empirical path loss model"},"citations":[{"ref":"Okumura, Y., Ohmori, E., Kawano, T., & Fukuda, K. (1968). Field strength and its variability in VHF and UHF land mobile radio service. Review of the Electrical Communication Laboratory, 16(9-10), 825-873.","type":"article","doi":null,"isbn":null,"url":"https://www.ntt.co.jp"},{"ref":"Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology, VT-29(3), 317-325.","type":"article","doi":"10.1109/T-VT.1980.23859","isbn":null,"url":null}],"related":["ray-tracing-propagation","ofdm","mimo","shannon-capacity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"olap-cube-design","name":"OLAP Cube Design","fullName":"Online Analytical Processing Cube Design","aliases":["OLAP","multidimensional cubes"],"domain":"information-systems","family":"process-pipeline","subfamily":"Business Intelligence & Analytics","year":"1993","originator":"E. F. Codd and colleagues (Arbor Software)","url":"https://scholargate.app/en/information-systems/olap-cube-design","markdownUrl":"https://scholargate.app/en/information-systems/olap-cube-design.md","definition":"OLAP (Online Analytical Processing) cube design is the practice of structuring multidimensional data for interactive analysis. Formalized by Codd and colleagues in 1993, OLAP cubes organize facts (measurements) along multiple dimensions (attributes) enabling rapid pivoting, drilling, and aggregation for business analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"E. F. Codd and colleagues (Arbor Software)","subfamily":"Business Intelligence & Analytics","year":"1993","type":"Analytical data structure design"},"citations":[{"ref":"Codd, E. F., Codd, S. B., & Salley, C. T. (1993). Providing OLAP to user-analysts: An IT mandate. Arbor Software.","type":"article","doi":null,"isbn":null,"url":"https://www.arbor.com"},{"ref":"Pedersen, T. B., Jensen, C. S., & Dyreson, C. E. (1999). A foundation for capturing the semantics of temporal multidimensional databases. Proceedings of the 5th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+foundation+for+capturing+the+semantics+of+temporal+multidimensional+databases+Pedersen"},{"ref":"Kimball, R. (1996). The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses. New York: John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com"}],"related":["data-warehousing","dimensional-modeling","aggregation-strategies","star-schema","fact-tables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oldenburg-burnout-inventory","name":"Oldenburg Burnout Inventory","fullName":"Oldenburg Burnout Inventory (OLBI)","aliases":["OLBI"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Burnout assessment","year":2003,"originator":"Evangelia Demerouti, Arnold B. Bakker, Friedhelm Nachreiner, Wilmar B. Schaufeli","url":"https://scholargate.app/en/occupational-health/oldenburg-burnout-inventory","markdownUrl":"https://scholargate.app/en/occupational-health/oldenburg-burnout-inventory.md","definition":"The Oldenburg Burnout Inventory (OLBI) is a brief, two-factor assessment of occupational burnout developed by Demerouti and colleagues in 2003. The instrument measures exhaustion (physical, emotional, cognitive) and disengagement (cynicism, reduced motivation) in working populations. It is grounded in the Job Demands-Resources (JD-R) theory and is widely used in European occupational health research and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evangelia Demerouti, Arnold B. Bakker, Friedhelm Nachreiner, Wilmar B. Schaufeli","subfamily":"Burnout assessment","year":2003,"type":"Self-report questionnaire"},"citations":[{"ref":"Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2003). The job demands-resources model of burnout. Journal of Vocational Behavior, 63(1), 141-145.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+job+demands-resources+model+of+burnout+Demerouti"}],"related":["copenhagen-burnout-inventory","effort-reward-imbalance-scale","areas-of-worklife-scale","recovery-experience-questionnaire","maslach-burnout-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ols-regression","name":"OLS Regression","fullName":"Ordinary Least Squares Regression","aliases":["ordinary least squares","classical linear regression","linear regression","en küçük kareler regresyonu"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2019,"originator":"Wooldridge (textbook treatment); classical least squares","url":"https://scholargate.app/en/econometrics/ols-regression","markdownUrl":"https://scholargate.app/en/econometrics/ols-regression.md","definition":"Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wooldridge (textbook treatment); classical least squares","year":2019,"type":"Linear regression","estimator":"Least squares (BLUE under Gauss-Markov)","outcome":"continuous"},"citations":[{"ref":"Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1337558860","url":null}],"related":["ridge-regression","lasso-regression","quantile-regression","logistic-regression","panel-fixed-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"omega-reliability","name":"McDonald's Omega","fullName":"McDonald's Omega Reliability Coefficient","aliases":["omega reliability","ω coefficient","omega total","omega hierarchical","McDonald's Omega (ω) — Güvenilirlik Katsayısı"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1999,"originator":"Roderick P. McDonald","url":"https://scholargate.app/en/psychometrics/omega-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/omega-reliability.md","definition":"McDonald's omega is a factor-analysis-based reliability coefficient introduced by Roderick P. McDonald (1999) that quantifies the internal consistency of a composite score without requiring the restrictive assumption that all items contribute equally to the latent factor. It yields two complementary indices: ω_total, which captures overall reliability of the sum score, and ω_hierarchical (ωh), which reports how much of the composite's variance is explained specifically by a single general factor.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roderick P. McDonald","year":1999,"type":"Reliability coefficient / latent variable model","outcome":"ω_total (overall reliability) and ω_hierarchical (proportion of composite variance attributable to the general factor)","data":"Ordinal or continuous item scores","min_sample":100,"assumption_model":"Congeneric (unequal factor loadings allowed)","good_reliability_threshold":"ω ≥ 0.80"},"citations":[{"ref":"McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805830750","url":null},{"ref":"Dunn, T. J., Baguley, T. & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412.","type":"article","doi":"10.1111/bjop.12046","isbn":null,"url":null}],"related":["cronbach-alpha","confirmatory-factor-analysis","exploratory-factor-analysis","cfa","icc-intraclass-correlation","rasch-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"one-class-svm","name":"One-class SVM","fullName":"One-Class Support Vector Machine (Novelty and Anomaly Detection)","aliases":["OCSVM","one-class support vector machine","novelty SVM","unsupervised SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1999–2001","originator":"Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.","url":"https://scholargate.app/en/machine-learning/one-class-svm","markdownUrl":"https://scholargate.app/en/machine-learning/one-class-svm.md","definition":"One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.","year":"1999–2001","type":"Anomaly / novelty detection (unsupervised)","dataType":"Continuous numeric features; high-dimensional vectors","subfamily":"Machine learning"},"citations":[{"ref":"Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.","type":"article","doi":"10.1162/089976601750264965","isbn":null,"url":null},{"ref":"Tax, D. M. J., & Duin, R. P. W. (2004). Support vector data description. Machine Learning, 54(1), 45–66.","type":"article","doi":"10.1023/B:MACH.0000008084.60811.49","isbn":null,"url":null}],"related":["gaussian-mixture-model","isolation-forest","autoencoder-anomaly-detection","support-vector-machine","k-nearest-neighbors","local-outlier-factor"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"one-sample-t-test","name":"One-sample t-test","fullName":"One-Sample Student t-test","aliases":["single-sample t-test","one-group t-test","one-sample t","Student one-sample t-test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1908","originator":"Student (W. S. Gosset)","url":"https://scholargate.app/en/statistics/one-sample-t-test","markdownUrl":"https://scholargate.app/en/statistics/one-sample-t-test.md","definition":"The one-sample t-test is a parametric hypothesis test that determines whether the mean of a single sample differs significantly from a known or hypothesized population value. Derived from Student's (Gosset's) 1908 t-distribution, it assumes continuous, approximately normally distributed data and is one of the most fundamental tests in applied statistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Student (W. S. Gosset)","year":"1908","type":"Parametric mean comparison","dataType":"Continuous (interval or ratio scale)","subfamily":"Classical statistics"},"citations":[{"ref":"Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.","type":"article","doi":"10.1093/biomet/6.1.1","isbn":null,"url":null},{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null}],"related":["independent-samples-t-test","paired-samples-t-test","wilcoxon-signed-rank-test","z-test","one-way-anova","mann-whitney-u-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"one-way-anova","name":"One-way ANOVA","fullName":"One-way Analysis of Variance","aliases":["one-factor ANOVA","single-factor ANOVA","analysis of variance","tek yönlü ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1925,"originator":"Ronald A. Fisher","url":"https://scholargate.app/en/statistics/one-way-anova","markdownUrl":"https://scholargate.app/en/statistics/one-way-anova.md","definition":"One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":1925,"family":"Hypothesis test","type":"Parametric mean comparison","groups":"3 or more","outcome":"continuous","parametric":true,"distribution":"F","df":"k - 1 (between), N - k (within)"},"citations":[{"ref":"Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.","type":"book","doi":null,"isbn":null,"url":"https://www.gutenberg.org/ebooks/author/8060"},{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null}],"related":["independent-t-test","kruskal-wallis","two-way-anova","welch-anova","tukey-hsd"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-active-learning","name":"Online Active learning","fullName":"Online Active Learning (Streaming Active Learning)","aliases":["streaming active learning","online query-by-committee","sequential active learning","incremental active learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s","originator":"Cesa-Bianchi, N. and others (multiple contributors)","url":"https://scholargate.app/en/machine-learning/online-active-learning","markdownUrl":"https://scholargate.app/en/machine-learning/online-active-learning.md","definition":"Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled data is expensive and examples arrive sequentially rather than all at once.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cesa-Bianchi, N. and others (multiple contributors)","year":"2000s","type":"Hybrid learning paradigm (online + active)","dataType":"Streaming labeled and unlabeled instances","subfamily":"Machine learning"},"citations":[{"ref":"Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205–1230.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v7/cesabianchi06b.html"},{"ref":"Sculley, D. (2007). Online active learning methods for fast label-efficient spam filtering. Proceedings of the Fourth Conference on Email and Anti-Spam (CEAS 2007).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+active+learning+methods+for+fast+label-efficient+spam+filtering"}],"related":["online-learning","active-learning","semi-supervised-learning","online-logistic-regression","online-random-forest","few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-association-rules","name":"Online Association Rules","fullName":"Online (Incremental) Association Rule Mining","aliases":["Incremental association rule mining","Streaming association rules","Online ARM","Incremental ARM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1996","originator":"Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.","url":"https://scholargate.app/en/machine-learning/online-association-rules","markdownUrl":"https://scholargate.app/en/machine-learning/online-association-rules.md","definition":"Online association rule mining discovers if-then patterns (e.g., buying bread implies buying butter) from transactional data that arrives incrementally or as a stream, updating existing rules and item counts without re-scanning the entire historical database each time new records arrive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.","year":"1996","type":"Incremental / streaming pattern mining","dataType":"Transactional / event-stream data","subfamily":"Machine learning"},"citations":[{"ref":"Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. (1996). Maintenance of discovered association rules in large databases: an incremental updating technique. In Proceedings of the 12th International Conference on Data Engineering (ICDE 1996), pp. 106–114. IEEE.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Maintenance+of+discovered+association+rules+in+large+databases+an+incremental+updating+technique+Cheung+1996"},{"ref":"Association rule learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Association_rule_learning"}],"related":["association-rules","apriori-algorithm","online-learning","semi-supervised-association-rules","fp-growth","streaming-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-autoencoder-anomaly-detection","name":"Online Autoencoder Anomaly Detection","fullName":"Online Autoencoder Anomaly Detection (Incremental Autoencoder for Streaming Anomaly Detection)","aliases":["incremental autoencoder anomaly detection","streaming autoencoder anomaly detection","online AE anomaly detection","continual autoencoder anomaly detection"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010s–present","originator":"Various (online/incremental deep learning community)","url":"https://scholargate.app/en/machine-learning/online-autoencoder-anomaly-detection","markdownUrl":"https://scholargate.app/en/machine-learning/online-autoencoder-anomaly-detection.md","definition":"Online Autoencoder Anomaly Detection trains an autoencoder incrementally on a continuous data stream, flagging observations whose reconstruction error exceeds an adaptive threshold as anomalies. This approach combines the representational power of deep autoencoders with the incremental update capability of online learning, making it suitable for real-time or high-volume streaming scenarios where batch retraining is impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (online/incremental deep learning community)","year":"2010s–present","type":"Online unsupervised anomaly detection","dataType":"Streaming or sequentially arriving numeric/multivariate data","subfamily":"Machine learning"},"citations":[{"ref":"An, J. & Cho, S. (2015). Variational Autoencoder based Anomaly Detection using Reconstruction Probability. SNU Data Mining Center, 2015-2.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Variational+Autoencoder+based+Anomaly+Detection+using+Reconstruction+Probability"},{"ref":"Zenati, H., Foo, C. S., Lecouat, B., Manek, G. & Chandrasekhar, V. R. (2018). Efficient GAN-Based Anomaly Detection. ICLR 2018 Workshop.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Efficient+GAN-Based+Anomaly+Detection+Zenati+2018"}],"related":["autoencoder-anomaly-detection","online-learning","semi-supervised-autoencoder-anomaly-detection","isolation-forest","one-class-svm","gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-bagging","name":"Online Bagging","fullName":"Online Bagging (Incremental Bootstrap Aggregating)","aliases":["incremental bagging","streaming bagging","online bootstrap aggregating","OzaBag"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001","originator":"Oza, N. C. & Russell, S.","url":"https://scholargate.app/en/machine-learning/online-bagging","markdownUrl":"https://scholargate.app/en/machine-learning/online-bagging.md","definition":"Online Bagging is a streaming ensemble method introduced by Oza and Russell in 2001 that adapts the classical bootstrap aggregating (Bagging) framework to the online learning setting. Instead of resampling a fixed dataset, each incoming instance is fed to every base learner a Poisson(1)-distributed number of times, faithfully approximating bootstrap sampling as the stream evolves. The result is a robust, incrementally updated ensemble that can handle concept drift and continuous data arrival without storing the entire dataset.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oza, N. C. & Russell, S.","year":"2001","type":"Online ensemble (streaming bagging)","dataType":"Streaming / sequential tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 105–112.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+bagging+and+boosting+Oza+Russell+2001"},{"ref":"Bifet, A., Holmes, G., Kirkby, R., & Pfahringer, B. (2010). MOA: Massive Online Analysis. Journal of Machine Learning Research, 11, 1601–1604.","type":"inproceedings","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v11/bifet10a.html"}],"related":["random-forest","bagging","online-boosting","hoeffding-tree","adaptive-random-forest","gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-boosting","name":"Online Boosting","fullName":"Online Boosting (Streaming Ensemble Boosting)","aliases":["streaming boosting","incremental boosting","online AdaBoost","online ensemble boosting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001","originator":"Oza, N. C. & Russell, S.","url":"https://scholargate.app/en/machine-learning/online-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/online-boosting.md","definition":"Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oza, N. C. & Russell, S.","year":"2001","type":"Online ensemble (incremental boosting)","dataType":"Streaming or sequentially arriving labeled tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+Bagging+and+Boosting+Oza+Russell+2001"},{"ref":"Online machine learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Online_machine_learning"}],"related":["boosting","online-learning","online-random-forest","online-bagging","semi-supervised-boosting","gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-cluster-sampling","name":"Online cluster sampling","fullName":"Online Cluster Sampling","aliases":["internet cluster sampling","web cluster sampling","digital cluster sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Late 1990s–2000s (as internet surveys became prevalent)","originator":"Adapted from cluster sampling (Mahalanobis, Hansen & Hurwitz, 1940s) to online survey contexts","url":"https://scholargate.app/en/survey-methodology/online-cluster-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/online-cluster-sampling.md","definition":"Online cluster sampling applies the classic cluster sampling logic to internet-based research: naturally occurring digital groups — such as online communities, email lists, forum memberships, or institutional user registries — serve as clusters, and selected clusters are surveyed in full or partially via web-based instruments. It offers a practical route to probability-based online samples when no complete list of individuals exists but lists of digital groups are accessible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from cluster sampling (Mahalanobis, Hansen & Hurwitz, 1940s) to online survey contexts","year":"Late 1990s–2000s (as internet surveys became prevalent)","type":"Probability sampling technique","dataType":"Quantitative or mixed-mode survey data collected via internet","subfamily":"Sampling"},"citations":[{"ref":"Couper, M. P. (2000). Web surveys: A review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494.","type":"article","doi":"10.1086/318641","isbn":null,"url":null},{"ref":"Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118456149","url":null}],"related":["cluster-sampling","online-stratified-sampling","online-systematic-sampling","multistage-sampling","online-simple-random-sampling","online-quota-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-convenience-sampling","name":"Online convenience sampling","fullName":"Online Convenience Sampling","aliases":["web-based convenience sampling","internet convenience sampling","digital convenience sampling","online accidental sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s (internet survey era)","originator":"Evolved from convenience sampling; internet applications documented from mid-1990s onward","url":"https://scholargate.app/en/survey-methodology/online-convenience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/online-convenience-sampling.md","definition":"Online convenience sampling is a non-probability technique in which participants are recruited via internet channels — survey platforms, social media, email lists, or research panels — simply because they are accessible and willing to respond. It is the online analogue of traditional convenience sampling, offering fast, low-cost data collection at the expense of known representativeness. It is among the most widely used approaches in social, behavioral, and health sciences research conducted through web-based surveys.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from convenience sampling; internet applications documented from mid-1990s onward","year":"1990s–2000s (internet survey era)","type":"Non-probability sampling","dataType":"Survey or questionnaire responses collected via online platforms, social media, or email","subfamily":"Sampling"},"citations":[{"ref":"Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2004). Should we trust web-based studies? A comparative analysis of six preconceptions about internet questionnaires. American Psychologist, 59(2), 93–104.","type":"article","doi":"10.1037/0003-066X.59.2.93","isbn":null,"url":null},{"ref":"Couper, M. P. (2000). Web surveys: A review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494.","type":"article","doi":"10.1086/318641","isbn":null,"url":null}],"related":["convenience-sampling","online-quota-sampling","online-snowball-sampling","purposive-sampling","quota-sampling","snowball-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-dbscan","name":"Online DBSCAN","fullName":"Online Density-Based Spatial Clustering of Applications with Noise","aliases":["Incremental DBSCAN","Streaming DBSCAN","Online density-based clustering","iDBSCAN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1998","originator":"Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X.","url":"https://scholargate.app/en/machine-learning/online-dbscan","markdownUrl":"https://scholargate.app/en/machine-learning/online-dbscan.md","definition":"Online DBSCAN extends the classic density-based clustering algorithm to handle continuously arriving data points without re-clustering the entire dataset from scratch. Each new observation is integrated into the existing cluster structure by local neighborhood queries, making it practical for streaming and data-warehousing scenarios where data grows incrementally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X.","year":"1998","type":"Incremental density-based clustering","dataType":"Continuous numerical, streaming or batch-growing tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X. (1998). Incremental Clustering for Mining in a Data Warehousing Environment. In Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), pp. 323–333.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Incremental+Clustering+for+Mining+in+a+Data+Warehousing+Environment+Ester+1998"},{"ref":"Cao, F., Ester, M., Qian, W., & Zhou, A. (2006). Density-Based Clustering over an Evolving Data Stream with Noise. In Proceedings of the 2006 SIAM International Conference on Data Mining (SDM), pp. 328–339.","type":"inproceedings","doi":"10.1137/1.9781611972764.29","isbn":null,"url":null}],"related":["dbscan","hdbscan","online-k-means","online-gaussian-mixture-model","online-learning","gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-decision-tree","name":"Online Decision Tree","fullName":"Online Decision Tree (Incremental / Streaming Decision Tree Learning)","aliases":["Hoeffding Tree","VFDT","Very Fast Decision Tree","incremental decision tree"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000","originator":"Domingos, P. & Hulten, G.","url":"https://scholargate.app/en/machine-learning/online-decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/online-decision-tree.md","definition":"An Online Decision Tree is a decision tree that grows incrementally from a continuous stream of data without revisiting past examples. The dominant algorithm, the Hoeffding Tree (VFDT), uses the Hoeffding bound to decide when enough examples have been seen at a node to split it confidently, enabling scalable, real-time classification on potentially infinite data streams.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Domingos, P. & Hulten, G.","year":"2000","type":"Incremental supervised classifier","dataType":"Streaming or sequentially arriving tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Domingos, P., & Hulten, G. (2000). Mining very fast data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 71–80). ACM.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mining+very+fast+data+streams+Domingos"},{"ref":"Hulten, G., Spencer, L., & Domingos, P. (2001). Mining time-changing data streams. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 97–106). ACM.","type":"inproceedings","doi":"10.1145/502512.502529","isbn":null,"url":null}],"related":["decision-tree","online-learning","online-random-forest","semi-supervised-decision-tree","online-gradient-boosting","online-naive-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-delphi-technique","name":"Online Delphi Technique","fullName":"Online Delphi Technique (e-Delphi)","aliases":["e-Delphi","electronic Delphi","web-based Delphi","internet Delphi"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Original Delphi: 1950s–1960s; Online variant: mid-1990s onwards","originator":"Olaf Helmer, Norman Dalkey, Nicholas Rescher (RAND Corporation); online adaptation emerged in the 1990s–2000s","url":"https://scholargate.app/en/survey-methodology/online-delphi-technique","markdownUrl":"https://scholargate.app/en/survey-methodology/online-delphi-technique.md","definition":"The Online Delphi Technique (e-Delphi) is an iterative, web-mediated consensus method in which a geographically dispersed panel of experts responds to successive rounds of structured questionnaires distributed and collected via email or a web platform. Anonymous feedback and controlled statistical summaries are fed back between rounds, guiding panellists toward convergence on priorities, predictions, or recommendations without the social pressures of face-to-face group discussion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Olaf Helmer, Norman Dalkey, Nicholas Rescher (RAND Corporation); online adaptation emerged in the 1990s–2000s","year":"Original Delphi: 1950s–1960s; Online variant: mid-1990s onwards","type":"Iterative expert consensus method (online)","dataType":"Structured questionnaire responses (quantitative ratings + qualitative comments) from expert panels","subfamily":"Data collection"},"citations":[{"ref":"Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.","type":"article","doi":"10.1046/j.1365-2648.2000.01567.x","isbn":null,"url":null},{"ref":"Donohoe, H., Stellefson, M., & Tennant, B. (2012). Advantages and limitations of the e-Delphi technique: Implications for health education researchers. American Journal of Health Education, 43(1), 38–46.","type":"article","doi":"10.1080/19325037.2012.10599216","isbn":null,"url":null}],"related":["delphi-technique","online-survey","online-focus-group","online-structured-interview","nominal-group-technique","expert-panel"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-deviant-case-sampling","name":"Online Deviant Case Sampling","fullName":"Online Deviant Case Sampling","aliases":["online extreme case sampling","internet-based deviant case sampling","online outlier sampling","web-based atypical case sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s (deviant case strategy); online variant ~2000s–2010s","originator":"Patton, M. Q. (deviant case strategy); online adaptation via web-based qualitative research practice","url":"https://scholargate.app/en/survey-methodology/online-deviant-case-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/online-deviant-case-sampling.md","definition":"Online deviant case sampling is a purposive qualitative sampling strategy in which the researcher deliberately seeks out and recruits participants who represent extreme, unusual, or outlier instances of the phenomenon under study, using online channels such as forums, social media, specialist communities, or digital registries. It inherits the logic of Patton's deviant (extreme) case sampling and applies it in internet-mediated research contexts where rare or hard-to-reach atypical cases can be located more efficiently than through face-to-face methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patton, M. Q. (deviant case strategy); online adaptation via web-based qualitative research practice","year":"1990s–2000s (deviant case strategy); online variant ~2000s–2010s","type":"Purposive qualitative sampling strategy (online variant)","dataType":"Qualitative data: interviews, focus groups, documents, digital trace data recruited via online channels","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Chapter 5: Purposeful Sampling, deviant/extreme case strategy, pp. 231-234]","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219-245.","type":"article","doi":"10.1177/1077800405284363","isbn":null,"url":null}],"related":["deviant-case-sampling","online-purposive-sampling","online-snowball-sampling","maximum-variation-sampling","online-convenience-sampling","typical-case-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-diary-method","name":"Online Diary Method","fullName":"Online Diary Data Collection Method","aliases":["e-diary method","digital diary study","web-based diary method","online daily diary study"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s (online adaptation); diary method roots in social research circa 1970s–1990s","originator":"Adaptation of the traditional diary method; Bolger, Davis & Rafaeli (2003) systematized the daily diary design; online delivery emerged through web survey tools in the early 2000s","url":"https://scholargate.app/en/survey-methodology/online-diary-method","markdownUrl":"https://scholargate.app/en/survey-methodology/online-diary-method.md","definition":"The online diary method is a longitudinal data collection technique in which participants record their thoughts, experiences, behaviors, or events in structured or semi-structured entries submitted via digital platforms — such as web forms, email, or dedicated apps — at regular or event-contingent intervals. It combines the ecological validity of traditional diary research with the logistical advantages of remote, automated data collection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adaptation of the traditional diary method; Bolger, Davis & Rafaeli (2003) systematized the daily diary design; online delivery emerged through web survey tools in the early 2000s","year":"2000s (online adaptation); diary method roots in social research circa 1970s–1990s","type":"Longitudinal self-report data collection technique","dataType":"Self-reported text, ratings, or structured entries submitted via web forms, email, or online platforms","subfamily":"Data collection"},"citations":[{"ref":"Alaszewski, A. (2006). Using Diaries for Social Research. Sage Publications.","type":"book","doi":null,"isbn":"978-0761942764","url":null},{"ref":"Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.","type":"article","doi":"10.1146/annurev.psych.54.101601.145030","isbn":null,"url":null}],"related":["diary-method","experience-sampling-method","mobile-experience-sampling","online-participant-observation","longitudinal-diary-method","research-diary"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-disinhibition-scale","name":"Online Disinhibition Scale","fullName":"Online Disinhibition Effect Scale (ODES)","aliases":["ODES","Disinhibition Effect"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"cyberpsychology-behavior","year":"2004","originator":"John Suler","url":"https://scholargate.app/en/social-media-psychology/online-disinhibition-scale","markdownUrl":"https://scholargate.app/en/social-media-psychology/online-disinhibition-scale.md","definition":"The Online Disinhibition Effect Scale measures the tendency for individuals to express themselves less inhibitedly online compared to face-to-face contexts, exhibiting increased aggression, profanity, emotional expression, and self-disclosure in digital environments. Developed by John Suler in 2004, this construct explains a core phenomenon of internet behavior: the reduced social constraint and increased behavioral extremity that characterize many online interactions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Suler","subfamily":"cyberpsychology-behavior","year":"2004","type":"Self-report"},"citations":[{"ref":"Suler, J. (2004). The online disinhibition effect. CyberPsychology & Behavior, 7(3), 321–326.","type":"article","doi":"10.1089/1094931041291295","isbn":null,"url":null}],"related":["social-media-disorder-scale","smartphone-addiction-scale-short","technoference-scale","fear-of-missing-out-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-document-collection","name":"Online Document Collection","fullName":"Online Document Collection Method","aliases":["digital document collection","web document gathering","online archival data collection","digital records collection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (digital / web era)","originator":"Adapted from traditional document analysis; digital form emerged with widespread internet adoption","url":"https://scholargate.app/en/survey-methodology/online-document-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/online-document-collection.md","definition":"Online document collection is the systematic process of identifying, retrieving, and compiling digital documents — including web pages, institutional publications, social media posts, policy documents, and digital archives — as primary or supplementary research data. It extends classical document analysis into internet-mediated environments, enabling researchers to access large, geographically dispersed corpora without fieldwork travel or physical archive access.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from traditional document analysis; digital form emerged with widespread internet adoption","year":"1990s–2000s (digital / web era)","type":"Qualitative / mixed-methods data collection technique","dataType":"Digital text documents, web pages, PDFs, social media posts, institutional records, digital archives","subfamily":"Data collection"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Prior, L. (2003). Using Documents in Social Research. Sage Publications.","type":"book","doi":null,"isbn":"978-0761965497","url":null}],"related":["document-collection","web-scraping","api-based-data-collection","content-analysis","archival-research","online-non-participant-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-federated-learning","name":"Online Federated Learning","fullName":"Online Federated Learning (Sequential Distributed Learning without Centralised Data)","aliases":["OFL","federated online learning","streaming federated learning","real-time federated learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2019–2021","originator":"McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021","url":"https://scholargate.app/en/machine-learning/online-federated-learning","markdownUrl":"https://scholargate.app/en/machine-learning/online-federated-learning.md","definition":"Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw observations, and contribute compressed updates to a central server that aggregates them in near-real-time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021","year":"2019–2021","type":"Distributed sequential learning","dataType":"Sequential / streaming tabular, image, or text data distributed across edge devices","subfamily":"Machine learning"},"citations":[{"ref":"Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Guirguis, A., Riviere, M., & Tempo, R. (2020). FLEET: Flexible and Efficient Federated Learning for Edge AI. Proceedings of Machine Learning and Systems (MLSys).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=FLEET+Flexible+Efficient+Federated+Learning+Edge+AI+Damaskinos+2020"},{"ref":"McMahan, B., Moore, E., Ramage, D., Hampson, S., & Aguera y Arcas, B. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 54, 1273–1282.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v54/mcmahan17a.html"}],"related":["federated-learning","online-learning","stochastic-gradient-descent","differential-privacy","transfer-learning","continual-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-few-shot-learning","name":"Online Few-shot Learning","fullName":"Online Few-shot Learning (Streaming Meta-Learning from Scarce Labels)","aliases":["online meta-learning","streaming few-shot learning","continual few-shot learning","incremental few-shot learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2019","originator":"Finn, C. et al. (online meta-learning formalization)","url":"https://scholargate.app/en/machine-learning/online-few-shot-learning","markdownUrl":"https://scholargate.app/en/machine-learning/online-few-shot-learning.md","definition":"Online Few-shot Learning combines the streaming update principle of online learning with the data-efficiency goal of few-shot learning, enabling a model to continuously adapt to new tasks or classes from only a handful of labeled examples as data arrives sequentially — without access to the full historical dataset.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Finn, C. et al. (online meta-learning formalization)","year":"2019","type":"Online learning + meta-learning hybrid","dataType":"Sequential labeled episodes with very few examples per class","subfamily":"Machine learning"},"citations":[{"ref":"Finn, C., Rajeswaran, A., Kakade, S., & Levine, S. (2019). Online Meta-Learning. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1920–1930.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v97/finn19a.html"},{"ref":"Javed, K., & White, M. (2019). Meta-Learning Representations for Continual Learning. Advances in Neural Information Processing Systems (NeurIPS), 32.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Meta-Learning+Representations+for+Continual+Learning+Javed+White+2019"}],"related":["few-shot-learning","online-learning","semi-supervised-learning","transfer-learning","meta-learning","continual-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-field-notes","name":"Online Field Notes","fullName":"Online Field Notes in Digital and Virtual Research","aliases":["digital field notes","virtual field notes","e-field notes","netnographic field notes"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (digital extension of classical field notes practice)","originator":"Extended from ethnographic field note tradition (Emerson, Fretz & Shaw); digital adaptation via Kozinets and virtual ethnography scholars","url":"https://scholargate.app/en/survey-methodology/online-field-notes","markdownUrl":"https://scholargate.app/en/survey-methodology/online-field-notes.md","definition":"Online field notes are structured, researcher-authored records of observations made within digital environments — social media platforms, online communities, virtual worlds, forums, and video-mediated spaces. Adapted from the classical ethnographic field note tradition, they capture not only what is observed but how the researcher interprets and situates those observations in real time, forming a primary data source for virtual ethnography and netnography.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from ethnographic field note tradition (Emerson, Fretz & Shaw); digital adaptation via Kozinets and virtual ethnography scholars","year":"1990s–2000s (digital extension of classical field notes practice)","type":"Qualitative data collection technique","dataType":"Textual, visual, and multimedia observations from online environments","subfamily":"Data collection"},"citations":[{"ref":"Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes (2nd ed.). University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226206837","url":null},{"ref":"Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1526454423","url":null}],"related":["field-notes","netnography","online-participant-observation","online-non-participant-observation","online-research-diary","virtual-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-focus-group","name":"Online Focus Group","fullName":"Online Focus Group Interview","aliases":["virtual focus group","internet focus group","OFG","web-based focus group"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1946 (focus groups); online variant ~1990s–2000s","originator":"Robert Merton & Patricia Kendall (focus group origins); online adaptation by scholars including Stewart & Shamdasani in the 1990s","url":"https://scholargate.app/en/survey-methodology/online-focus-group","markdownUrl":"https://scholargate.app/en/survey-methodology/online-focus-group.md","definition":"An online focus group is a moderated group discussion conducted via internet-based platforms — video conferencing, text chat, or asynchronous forums — to explore shared perceptions, attitudes, and experiences on a defined topic. It inherits the group-interaction dynamics of the traditional focus group while removing geographic barriers and enabling data collection from dispersed or hard-to-reach populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Merton & Patricia Kendall (focus group origins); online adaptation by scholars including Stewart & Shamdasani in the 1990s","year":"1946 (focus groups); online variant ~1990s–2000s","type":"Qualitative group data collection","dataType":"Text, audio, or video from group discussion","subfamily":"Data collection"},"citations":[{"ref":"Stewart, D. W., & Shamdasani, P. N. (2017). Online Focus Groups. Journal of Advertising, 46(1), 48–60.","type":"book","doi":"10.1080/00913367.2016.1252288","isbn":null,"url":null},{"ref":"Morgan, D. L. (1997). Focus Groups as Qualitative Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761903437","url":null}],"related":["focus-group","online-semi-structured-interview","online-structured-interview","online-in-depth-interview","online-participant-observation","online-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-fp-growth","name":"Online FP-growth","fullName":"Online Frequent Pattern Growth (Incremental FP-tree Mining)","aliases":["Incremental FP-growth","Online FP-tree","stream FP-growth","OFP-growth"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2004","originator":"Cheung, W. & Zaiane, O. R.","url":"https://scholargate.app/en/machine-learning/online-fp-growth","markdownUrl":"https://scholargate.app/en/machine-learning/online-fp-growth.md","definition":"Online FP-growth is an incremental extension of the FP-growth algorithm that mines frequent itemsets from continuously arriving transaction streams without rebuilding the full FP-tree from scratch. It updates an existing compact tree structure as new transactions arrive, making it suitable for real-time and high-velocity data environments where a full database scan is impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cheung, W. & Zaiane, O. R.","year":"2004","type":"Incremental frequent pattern mining algorithm","dataType":"Transactional / event-stream data","subfamily":"Machine learning"},"citations":[{"ref":"Cheung, W. & Zaiane, O. R. (2004). Incremental Mining of Frequent Patterns Without Candidate Generation or Support Thr esholding. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), pp. 111–118. IEEE.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Incremental+Mining+of+Frequent+Patterns+Without+Candidate+Generation+or+Support+Thresholding+Cheung+Zaiane+2004"},{"ref":"Lee, G., Yun, U. & Ryu, K. H. (2014). Sliding window based weighted maximal frequent pattern mining over data streams. Expert Systems with Applications, 41(2), 694–708.","type":"article","doi":"10.1016/j.eswa.2013.07.094","isbn":null,"url":null}],"related":["fp-growth","apriori","association-rule-learning","stream-mining","incremental-learning","sequential-pattern-mining"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-gaussian-mixture-model","name":"Online Gaussian Mixture Model","fullName":"Online Gaussian Mixture Model (Incremental / Streaming GMM)","aliases":["Online GMM","Incremental GMM","Streaming Gaussian Mixture Model","Sequential GMM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000–2009","originator":"Cappé, O. & Moulines, E. (online EM formulation)","url":"https://scholargate.app/en/machine-learning/online-gaussian-mixture-model","markdownUrl":"https://scholargate.app/en/machine-learning/online-gaussian-mixture-model.md","definition":"Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cappé, O. & Moulines, E. (online EM formulation)","year":"2000–2009","type":"Probabilistic clustering / density estimation (incremental)","dataType":"Continuous numeric (streaming or large-batch)","subfamily":"Machine learning"},"citations":[{"ref":"Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613.","type":"article","doi":"10.1111/j.1467-9868.2009.00698.x","isbn":null,"url":null},{"ref":"Sato, M. & Ishii, S. (2000). On-line EM algorithm for the normalized Gaussian network. Neural Computation, 12(2), 407–432.","type":"inproceedings","doi":"10.1162/089976600300015853","isbn":null,"url":null}],"related":["gaussian-mixture-model","online-learning","online-k-means","semi-supervised-gaussian-mixture-model","bayesian-gaussian-mixture-model","k-means"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-gaussian-process","name":"Online Gaussian Process","fullName":"Online Gaussian Process Regression and Classification","aliases":["OGP","sparse online GP","sequential Gaussian process","incremental Gaussian process"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2002","originator":"Csató, L. & Opper, M.","url":"https://scholargate.app/en/machine-learning/online-gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/online-gaussian-process.md","definition":"Online Gaussian Process (OGP) extends the Bayesian nonparametric GP framework to streaming or sequentially arriving data. Instead of recomputing the full GP posterior from scratch as each observation arrives, OGP maintains a compact summary — a sparse set of inducing points — and updates it incrementally, making probabilistic regression and classification feasible in real-time and large-scale settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Csató, L. & Opper, M.","year":"2002","type":"Bayesian nonparametric model (sequential/online)","dataType":"Continuous inputs; continuous or binary outputs","subfamily":"Machine learning"},"citations":[{"ref":"Csató, L. & Opper, M. (2002). Sparse on-line Gaussian processes. Neural Computation, 14(3), 641–668.","type":"article","doi":"10.1162/089976602317250933","isbn":null,"url":null},{"ref":"Engel, Y., Mannor, S. & Meir, R. (2004). The kernel recursive least-squares algorithm. IEEE Transactions on Signal Processing, 52(8), 2275–2285.","type":"inproceedings","doi":"10.1109/TSP.2004.830985","isbn":null,"url":null}],"related":["gaussian-process-regression","bayesian-linear-regression","kernel-ridge-regression","variational-inference","stochastic-gradient-descent","support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-gradient-boosting","name":"Online Gradient Boosting","fullName":"Online Gradient Boosting (Streaming Gradient Boosted Ensembles)","aliases":["OGB","streaming gradient boosting","incremental gradient boosting","online boosting with gradient descent"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2011–2015","originator":"Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.","url":"https://scholargate.app/en/machine-learning/online-gradient-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/online-gradient-boosting.md","definition":"Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.","year":"2011–2015","type":"Online ensemble (sequential boosting on streaming data)","dataType":"Continuous, categorical, or mixed tabular features arriving in a stream","subfamily":"Machine learning"},"citations":[{"ref":"Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Generalized+Boosting+Algorithms+for+Convex+Optimization+Grubb+Bagnell+2011"},{"ref":"Beygelzimer, A., Hazan, E., Langford, J. & Zheng, T. (2015). Online-to-Batch Conversions and Applications. Advances in Neural Information Processing Systems (NeurIPS), 28.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online-to-Batch+Conversions+and+Applications+Beygelzimer+Hazan+Langford+Zheng+2015"}],"related":["gradient-boosting","online-learning","xgboost","boosting","online-random-forest","semi-supervised-gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-hdbscan","name":"Online HDBSCAN","fullName":"Online Hierarchical Density-Based Spatial Clustering of Applications with Noise","aliases":["incremental HDBSCAN","streaming HDBSCAN","online hierarchical density clustering","dynamic HDBSCAN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2015–2017","originator":"Campello, R. J. G. B. et al. (base); incremental extensions by Hassani, M. et al.","url":"https://scholargate.app/en/machine-learning/online-hdbscan","markdownUrl":"https://scholargate.app/en/machine-learning/online-hdbscan.md","definition":"Online HDBSCAN extends the HDBSCAN hierarchical density-based clustering algorithm to incrementally process streaming or sequentially arriving data. Rather than rebuilding the full hierarchy from scratch with each new observation, it maintains and locally updates the mutual reachability graph, minimum spanning tree, condensed cluster tree, and stability-based cluster extraction, enabling continuous density-based clustering without full-dataset reprocessing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Campello, R. J. G. B. et al. (base); incremental extensions by Hassani, M. et al.","year":"2015–2017","type":"Incremental hierarchical density-based clustering","dataType":"Streaming or incrementally arriving continuous feature data","subfamily":"Machine learning"},"citations":[{"ref":"Hassani, M., Seidl, T. (2017). Using internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam Journal of Computer Science, 4(3), 171–183.","type":"article","doi":"10.1007/s40595-016-0086-9","isbn":null,"url":null},{"ref":"Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data, 10(1), Article 5.","type":"article","doi":"10.1145/2733381","isbn":null,"url":null}],"related":["hdbscan","dbscan","online-learning","ensemble-hdbscan","robust-hdbscan","spectral-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-isolation-forest","name":"Online Isolation Forest","fullName":"Online Isolation Forest (Streaming Anomaly Detection with Isolation Trees)","aliases":["streaming isolation forest","incremental isolation forest","online iForest","adaptive isolation forest"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2008–2011","originator":"Tan, S. C.; Ting, K. M.; Liu, T. F. (streaming variant); original iForest by Liu et al.","url":"https://scholargate.app/en/machine-learning/online-isolation-forest","markdownUrl":"https://scholargate.app/en/machine-learning/online-isolation-forest.md","definition":"Online Isolation Forest extends the Isolation Forest anomaly-detection algorithm to streaming or continuously arriving data. Instead of rebuilding isolation trees from scratch when new observations arrive, the forest is updated incrementally so that anomaly scores remain current without reprocessing the entire history. This makes it practical for real-time monitoring, fraud detection, and sensor-data surveillance where data volumes grow indefinitely.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tan, S. C.; Ting, K. M.; Liu, T. F. (streaming variant); original iForest by Liu et al.","year":"2008–2011","type":"Streaming anomaly detection (online ensemble)","dataType":"Continuous or mixed tabular streaming data","subfamily":"Machine learning"},"citations":[{"ref":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.","type":"inproceedings","doi":"10.1109/ICDM.2008.17","isbn":null,"url":null},{"ref":"Tan, S. C., Ting, K. M., & Liu, T. F. (2011). Fast Anomaly Detection for Streaming Data. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), pp. 1511–1516.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fast+Anomaly+Detection+for+Streaming+Data+Tan+Ting+2011"}],"related":["isolation-forest","online-learning","one-class-svm","autoencoder-anomaly-detection","online-random-forest","semi-supervised-isolation-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-k-means","name":"Online K-means","fullName":"Online K-means Clustering (Sequential / Streaming K-means)","aliases":["sequential k-means","streaming k-means","incremental k-means","online clustering"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1967 (online update rule); 2010 (mini-batch variant)","originator":"MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)","url":"https://scholargate.app/en/machine-learning/online-k-means","markdownUrl":"https://scholargate.app/en/machine-learning/online-k-means.md","definition":"Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)","year":"1967 (online update rule); 2010 (mini-batch variant)","type":"Unsupervised clustering (online/streaming)","dataType":"Continuous numeric features; streaming or batch tabular data","subfamily":"Machine learning"},"citations":[{"ref":"MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://projecteuclid.org/euclid.bsmsp/1200512992"},{"ref":"Sculley, D. (2010). Web-scale k-means clustering. In Proceedings of the 19th International Conference on World Wide Web (WWW 2010), pp. 1177–1178. ACM.","type":"inproceedings","doi":"10.1145/1772690.1772862","isbn":null,"url":null}],"related":["k-means-clustering","mini-batch-k-means","dbscan","gaussian-mixture-model","hierarchical-clustering","self-organizing-map"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-k-nearest-neighbors","name":"Online K-nearest neighbors","fullName":"Online K-Nearest Neighbors (Incremental KNN for Data Streams)","aliases":["Online KNN","Incremental KNN","Streaming KNN","KNN with concept drift adaptation"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010s (formalized in streaming-learning literature)","originator":"Extension of Fix & Hodges (1951) KNN to the streaming/online setting; notable online variant by Losing et al. (2016)","url":"https://scholargate.app/en/machine-learning/online-k-nearest-neighbors","markdownUrl":"https://scholargate.app/en/machine-learning/online-k-nearest-neighbors.md","definition":"Online K-Nearest Neighbors (Online KNN) adapts the classic KNN algorithm to a data-stream setting where observations arrive sequentially and the model must update incrementally without full retraining. Instead of storing all historical instances, it maintains a bounded sliding window or adaptive memory, using the most recent and most representative examples to classify or predict each incoming point by proximity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Fix & Hodges (1951) KNN to the streaming/online setting; notable online variant by Losing et al. (2016)","year":"2010s (formalized in streaming-learning literature)","type":"Instance-based online classifier/regressor","dataType":"Continuous and mixed feature vectors arriving as a stream","subfamily":"Machine learning"},"citations":[{"ref":"Losing, V., Hammer, B., & Wersing, H. (2016). KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), pp. 291–300. IEEE.","type":"inproceedings","doi":"10.1109/ICDM.2016.0040","isbn":null,"url":null},{"ref":"Gama, J. (2010). Knowledge Discovery from Data Streams. CRC Press / Chapman & Hall.","type":"book","doi":null,"isbn":"978-1-4398-2611-9","url":null}],"related":["k-nearest-neighbors","online-learning","semi-supervised-k-nearest-neighbors","online-decision-tree","online-random-forest","online-naive-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-learning","name":"Online Learning","fullName":"Online Learning (Sequential / Incremental Machine Learning)","aliases":["incremental learning","sequential learning","streaming learning","online machine learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1958–2000s","originator":"Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)","url":"https://scholargate.app/en/machine-learning/online-learning","markdownUrl":"https://scholargate.app/en/machine-learning/online-learning.md","definition":"Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)","year":"1958–2000s","type":"Learning paradigm (sequential model update)","dataType":"Streaming or sequentially arriving labeled/unlabeled data","subfamily":"Machine learning"},"citations":[{"ref":"Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.","type":"article","doi":"10.1561/2200000018","isbn":null,"url":null},{"ref":"Cesa-Bianchi, N. & Lugosi, G. (2006). Prediction, Learning, and Games. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-84108-5","url":null}],"related":["semi-supervised-learning","transfer-learning","few-shot-learning","active-learning","self-supervised-learning","federated-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-lightgbm","name":"Online LightGBM","fullName":"Online / Incremental LightGBM (Light Gradient-Boosting Machine with Streaming Updates)","aliases":["Incremental LightGBM","LightGBM incremental training","streaming LightGBM","continual LightGBM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017 (LightGBM); 2000s (online boosting)","originator":"Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)","url":"https://scholargate.app/en/machine-learning/online-lightgbm","markdownUrl":"https://scholargate.app/en/machine-learning/online-lightgbm.md","definition":"Online LightGBM applies the Light Gradient-Boosting Machine framework incrementally: instead of requiring all training data at once, the model is updated in mini-batches or data chunks as they arrive. This allows LightGBM's efficient histogram-based boosting to be deployed in streaming, continual-learning, and data-expansion scenarios without retraining from scratch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)","year":"2017 (LightGBM); 2000s (online boosting)","type":"Online ensemble (incremental gradient boosting)","dataType":"Tabular, streaming / sequential numeric and categorical features","subfamily":"Machine learning"},"citations":[{"ref":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html"},{"ref":"Bifet, A., & Gavalda, R. (2009). Adaptive Learning from Evolving Data Streams. Advances in Intelligent Data Analysis VIII. Lecture Notes in Computer Science, vol 5772. Springer.","type":"inproceedings","doi":"10.1007/978-3-642-03915-7_22","isbn":null,"url":null}],"related":["online-learning","online-gradient-boosting","online-xgboost","lightgbm","gradient-boosting","online-random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-linear-regression","name":"Online Linear Regression","fullName":"Online Linear Regression (Incremental Least-Squares)","aliases":["incremental linear regression","streaming linear regression","recursive least squares regression","stochastic gradient descent regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1960 (LMS); 1950 (RLS formalization)","originator":"Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)","url":"https://scholargate.app/en/machine-learning/online-linear-regression","markdownUrl":"https://scholargate.app/en/machine-learning/online-linear-regression.md","definition":"Online Linear Regression fits a linear model one observation at a time, updating weights incrementally as each new data point arrives. Unlike batch least-squares, it never needs to store or re-process the full dataset, making it the natural choice for streaming data, very large datasets, and environments where the data-generating process can shift over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)","year":"1960 (LMS); 1950 (RLS formalization)","type":"Incremental supervised regression","dataType":"Continuous numeric features and target","subfamily":"Machine learning"},"citations":[{"ref":"Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.","type":"article","doi":"10.1561/2200000018","isbn":null,"url":null},{"ref":"Haykin, S. (2002). Adaptive Filter Theory (4th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0130901262","url":null}],"related":["linear-regression-ml","regularized-linear-regression","online-learning","stochastic-gradient-descent","ridge-regression","online-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-logistic-regression","name":"Online Logistic Regression","fullName":"Online Logistic Regression (Incremental Stochastic Gradient Descent)","aliases":["incremental logistic regression","streaming logistic regression","SGD logistic classifier","online binary classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1960s (perceptron); formalized for logistic loss ~2000s","originator":"Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.","url":"https://scholargate.app/en/machine-learning/online-logistic-regression","markdownUrl":"https://scholargate.app/en/machine-learning/online-logistic-regression.md","definition":"Online Logistic Regression fits a logistic classifier one sample (or mini-batch) at a time via stochastic gradient descent, updating model weights as each observation arrives rather than waiting to see the full dataset. This makes it the standard choice for high-volume, streaming, or memory-constrained binary classification problems where batch training is infeasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.","year":"1960s (perceptron); formalized for logistic loss ~2000s","type":"Incremental supervised classifier","dataType":"Tabular (continuous and binary features); streaming or large-scale batch data","subfamily":"Machine learning"},"citations":[{"ref":"Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.","type":"inproceedings","doi":null,"isbn":null,"url":"https://leon.bottou.org/publications/pdf/compstat-2010.pdf"},{"ref":"Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.","type":"article","doi":"10.1561/2200000018","isbn":null,"url":null}],"related":["logistic-regression-ml","online-learning","semi-supervised-logistic-regression","regularized-logistic-regression","support-vector-machine","online-linear-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-metric-learning","name":"Online Metric Learning","fullName":"Online Metric Learning (Incremental Distance Metric Learning from Streaming Data)","aliases":["OML","incremental metric learning","streaming metric learning","online distance metric learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2004–2009","originator":"Shalev-Shwartz, S.; Singer, Y.; and others","url":"https://scholargate.app/en/machine-learning/online-metric-learning","markdownUrl":"https://scholargate.app/en/machine-learning/online-metric-learning.md","definition":"Online Metric Learning adapts a Mahalanobis distance metric incrementally as new labeled examples or pairwise constraints arrive one at a time, without storing the full dataset. It merges the efficiency of online learning with the representational power of metric learning, making it suitable for streaming, large-scale, or continually changing environments where retraining from scratch is impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shalev-Shwartz, S.; Singer, Y.; and others","year":"2004–2009","type":"Online / incremental learning of distance metrics","dataType":"Labeled pairwise or triplet constraints from streaming observations","subfamily":"Machine learning"},"citations":[{"ref":"Shalev-Shwartz, S., Singer, Y., & Ng, A. Y. (2004). Online and batch learning of pseudo-metrics. Proceedings of the 21st International Conference on Machine Learning (ICML 2004), pp. 94. ACM.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+and+batch+learning+of+pseudo-metrics+Shalev-Shwartz+2004"},{"ref":"Jin, R., Wang, S., & Zhou, Y. (2009). Regularized distance metric learning: Theory and algorithm. Advances in Neural Information Processing Systems (NIPS 2009), 22, 862–870.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Regularized+distance+metric+learning+theory+algorithm+Jin+Wang+Zhou+2009"}],"related":["metric-learning","large-margin-nearest-neighbor","siamese-network","online-learning","k-nearest-neighbors","contrastive-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-mobile-experience-sampling","name":"Online Mobile Experience Sampling","fullName":"Online Mobile Experience Sampling Method","aliases":["Online ESM","Mobile ESM","Ecological Momentary Assessment via Mobile","Smartphone-based Experience Sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1983 (original ESM); smartphone/online variant widely adopted ~2007–2010","originator":"Mihaly Csikszentmihalyi & Reed Larson","url":"https://scholargate.app/en/survey-methodology/online-mobile-experience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/online-mobile-experience-sampling.md","definition":"Online Mobile Experience Sampling (Online ESM) is a data collection technique that uses internet-connected smartphones or tablets to prompt participants multiple times per day and record their thoughts, feelings, behaviors, and context in the moment they occur. By gathering data in real time across daily life rather than retrospectively in a lab, it dramatically reduces recall bias and captures the natural variation of psychological and behavioral states as they unfold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mihaly Csikszentmihalyi & Reed Larson","year":"1983 (original ESM); smartphone/online variant widely adopted ~2007–2010","type":"Intensive longitudinal data collection technique","dataType":"Repeated self-report data (ratings, text, photos) collected via internet-connected mobile devices","subfamily":"Data collection"},"citations":[{"ref":"Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536.","type":"article","doi":"10.1097/00005053-198709000-00004","isbn":null,"url":null},{"ref":"Larson, R., & Csikszentmihalyi, M. (2014). The Experience Sampling Method. In M. R. Leary & J. P. Tangney (Eds.), Handbook of Research Methods in Social and Personality Psychology (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"9781107600751","url":null}],"related":["mobile-experience-sampling","experience-sampling-method","ecological-momentary-assessment","online-survey","diary-method","mobile-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-naive-bayes","name":"Online Naive Bayes","fullName":"Online (Incremental) Naive Bayes Classifier","aliases":["Incremental Naive Bayes","Streaming Naive Bayes","Naive Bayes with partial_fit","Online NB"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s","originator":"Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000)","url":"https://scholargate.app/en/machine-learning/online-naive-bayes","markdownUrl":"https://scholargate.app/en/machine-learning/online-naive-bayes.md","definition":"Online Naive Bayes is an incremental adaptation of the classical Naive Bayes classifier that updates its class-conditional statistics one observation (or one mini-batch) at a time, making it well suited to data streams, very large datasets that cannot be held in memory, and settings where the model must adapt continuously as new labeled examples arrive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000)","year":"2000s","type":"Probabilistic classifier (online/incremental)","dataType":"Categorical, count, or continuous features; streaming or sequentially arriving data","subfamily":"Machine learning"},"citations":[{"ref":"Domingos, P. & Hulten, G. (2000). Mining high-speed data streams. Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71–80. ACM.","type":"inproceedings","doi":"10.1145/347090.347107","isbn":null,"url":null},{"ref":"Online machine learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Online_machine_learning"}],"related":["naive-bayes","online-learning","semi-supervised-naive-bayes","logistic-regression-ml","online-logistic-regression","online-decision-tree"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-non-participant-observation","name":"Online Non-participant Observation","fullName":"Online Non-participant Observation","aliases":["digital non-participant observation","passive online observation","covert online observation","online unobtrusive observation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s–2000s","originator":"Christine Hine; Robert Kozinets (digital/online adaptation)","url":"https://scholargate.app/en/survey-methodology/online-non-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/online-non-participant-observation.md","definition":"Online non-participant observation is a qualitative data collection technique in which the researcher watches and records naturally occurring behaviour in digital settings — forums, social media platforms, chat groups, comment sections, or online communities — without joining, interacting with, or disclosing their presence to participants. The approach transplants the classical non-participant observation tradition into internet-mediated spaces, enabling study of authentic discourse and interaction as it unfolds organically.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christine Hine; Robert Kozinets (digital/online adaptation)","year":"Late 1990s–2000s","type":"Qualitative data collection technique","dataType":"Text, images, video, interaction logs from online communities and platforms","subfamily":"Data collection"},"citations":[{"ref":"Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.","type":"book","doi":null,"isbn":"978-1847875228","url":null},{"ref":"Hine, C. (2000). Virtual Ethnography. Sage.","type":"book","doi":null,"isbn":"978-0761958963","url":null}],"related":["non-participant-observation","online-participant-observation","online-focus-group","netnography","web-scraping","online-document-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-one-class-svm","name":"Online One-class SVM","fullName":"Online One-Class Support Vector Machine","aliases":["Online OC-SVM","Incremental One-Class SVM","Online SVDD","Sequential One-Class SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006 (incremental/online variant); 1999 (base method)","originator":"Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM)","url":"https://scholargate.app/en/machine-learning/online-one-class-svm","markdownUrl":"https://scholargate.app/en/machine-learning/online-one-class-svm.md","definition":"Online One-Class SVM is an incremental extension of the classical One-Class Support Vector Machine that updates its decision boundary as new data arrive one sample at a time, making it suitable for streaming environments and real-time anomaly or novelty detection without retraining from scratch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Laskov, P. et al. (incremental extension); Scholkopf, B. et al. (original OC-SVM)","year":"2006 (incremental/online variant); 1999 (base method)","type":"Online anomaly detection / novelty detection","dataType":"Continuous, streaming or sequentially arriving feature vectors","subfamily":"Machine learning"},"citations":[{"ref":"Laskov, P., Gehl, C., Krueger, S., & Muller, K.-R. (2006). Incremental support vector learning: Analysis, implementation and applications. Journal of Machine Learning Research, 7, 1909–1936.","type":"inproceedings","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v7/laskov06a.html"},{"ref":"Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NIPS), 12, 582–588.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Support+vector+method+for+novelty+detection"}],"related":["one-class-svm","support-vector-machine","isolation-forest","autoencoder","local-outlier-factor","incremental-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-participant-observation","name":"Online Participant Observation","fullName":"Online Participant Observation","aliases":["virtual participant observation","digital ethnographic observation","cyber participant observation","internet participant observation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s–2000s","originator":"Christine Hine (virtual ethnography); Robert Kozinets (netnography)","url":"https://scholargate.app/en/survey-methodology/online-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/online-participant-observation.md","definition":"Online participant observation is a qualitative data collection method in which the researcher enters a digital community or online environment — forums, social media groups, multiplayer games, virtual workplaces — both as a participant and as an observer, systematically documenting social interactions, practices, and meanings as they naturally unfold in the digital space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christine Hine (virtual ethnography); Robert Kozinets (netnography)","year":"Late 1990s–2000s","type":"Qualitative data collection method","dataType":"Online interactions, text posts, digital artifacts, multimedia","subfamily":"Data collection"},"citations":[{"ref":"Hine, C. (2000). Virtual Ethnography. SAGE Publications.","type":"book","doi":null,"isbn":"978-0761958956","url":null},{"ref":"Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. SAGE Publications.","type":"book","doi":null,"isbn":"978-1847875167","url":null}],"related":["participant-observation","non-participant-observation","online-non-participant-observation","online-focus-group","netnography","virtual-ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-purposive-sampling","name":"Online Purposive Sampling","fullName":"Online Purposive Sampling","aliases":["internet-based purposive sampling","web purposive sampling","online criterion-based sampling","digital purposive sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s (with growth of internet-based research)","originator":"Adaptation of purposive sampling (Patton, 1987) to online/digital research contexts","url":"https://scholargate.app/en/survey-methodology/online-purposive-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/online-purposive-sampling.md","definition":"Online purposive sampling applies the logic of criterion-based participant selection to digital recruitment channels — including social media platforms, online communities, email lists, and research recruitment websites. Researchers intentionally seek individuals who possess the characteristics, experiences, or expertise directly relevant to the research question, using internet-based tools to locate and screen them. The method preserves the defining feature of purposive sampling — deliberate selection based on fitness for purpose — while leveraging the reach and accessibility of online environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adaptation of purposive sampling (Patton, 1987) to online/digital research contexts","year":"1990s–2000s (with growth of internet-based research)","type":"Non-probability qualitative sampling","dataType":"Qualitative or quantitative data collected via online channels (interviews, surveys, forums, social media)","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Andrews, D., Nonnecke, B., & Preece, J. (2003). Electronic survey methodology: A case study in reaching hard-to-involve Internet users. International Journal of Human-Computer Interaction, 16(2), 185-210.","type":"article","doi":"10.1207/S15327590IJHC1602_04","isbn":null,"url":null}],"related":["purposive-sampling","snowball-sampling","online-snowball-sampling","theoretical-sampling","maximum-variation-sampling","convenience-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-random-forest","name":"Online Random Forest","fullName":"Online Random Forest (Incremental Ensemble of Decision Trees)","aliases":["ORF","streaming random forest","incremental random forest","adaptive random forest"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2009","originator":"Saffari, A. et al.","url":"https://scholargate.app/en/machine-learning/online-random-forest","markdownUrl":"https://scholargate.app/en/machine-learning/online-random-forest.md","definition":"Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saffari, A. et al.","year":"2009","type":"Incremental ensemble (streaming decision trees)","dataType":"Streaming or sequentially arriving tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On-line+random+forests+Saffari+2009"},{"ref":"Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfharinger, B., Holmes, G., & Abdessalem, T. (2017). Adaptive random forests for evolving data stream classification. Machine Learning, 106(9), 1469–1495.","type":"article","doi":"10.1007/s10994-017-5642-8","isbn":null,"url":null}],"related":["random-forest","online-learning","online-decision-tree","online-gradient-boosting","online-bagging","semi-supervised-random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-research-diary","name":"Online Research Diary","fullName":"Online Research Diary Method","aliases":["digital research diary","e-diary research","online reflective journal","web-based research diary"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s–2000s (digital adaptation of diary methods dating to early 20th century social research)","originator":"Adapted from traditional diary methods; online variant emerged with widespread internet adoption (late 1990s–2000s)","url":"https://scholargate.app/en/survey-methodology/online-research-diary","markdownUrl":"https://scholargate.app/en/survey-methodology/online-research-diary.md","definition":"The online research diary method is a data collection technique in which participants document their experiences, thoughts, or behaviours in structured or open-ended digital diary entries over a defined period. Delivered via email, web forms, blogging platforms, or dedicated apps, it captures temporally proximate, naturalistic data that retrospective interviews cannot provide. It is widely used in health research, education, psychology, and social sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from traditional diary methods; online variant emerged with widespread internet adoption (late 1990s–2000s)","year":"Late 1990s–2000s (digital adaptation of diary methods dating to early 20th century social research)","type":"Qualitative / mixed-methods data collection","dataType":"Text entries, audio/video uploads, images, structured logs submitted via digital platforms","subfamily":"Data collection"},"citations":[{"ref":"Alaszewski, A. (2006). Using Diaries for Social Research. Sage Publications.","type":"book","doi":null,"isbn":"978-0761941965","url":null},{"ref":"Hyers, L. L. (2018). Diary Methods. In M. C. Blanco & A. J. Breckenridge (Eds.), Understanding Research Methods in Psychology. Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=diary+methods+qualitative+research+online"}],"related":["research-diary","diary-method","online-participant-observation","experience-sampling-method","online-semi-structured-interview","field-notes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-self-supervised-learning","name":"Online Self-supervised Learning","fullName":"Online Self-supervised Learning (Continual Self-supervised Representation Learning from Streaming Data)","aliases":["online SSL","continual self-supervised learning","streaming self-supervised learning","incremental self-supervised learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020s","originator":"Multiple contributors (Gidaris, Fini et al., among others)","url":"https://scholargate.app/en/machine-learning/online-self-supervised-learning","markdownUrl":"https://scholargate.app/en/machine-learning/online-self-supervised-learning.md","definition":"Online Self-supervised Learning (online SSL) trains neural networks on unlabeled data that arrives sequentially or in streams, using automatically generated supervisory signals (pretext tasks) instead of human labels. By updating the model continuously as new data flows in, it enables perpetually evolving representations without storing the full dataset — critical for real-time systems, edge devices, and privacy-constrained settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Gidaris, Fini et al., among others)","year":"2020s","type":"Online unsupervised representation learning","dataType":"Unlabeled streaming or sequentially arriving data (images, text, time-series)","subfamily":"Machine learning"},"citations":[{"ref":"Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., & Cord, M. (2021). OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6830–6840.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openaccess.thecvf.com/content/CVPR2021/html/Gidaris_OBoW_Online_Bag-of-Visual-Words_Generation_for_Self-Supervised_Learning_CVPR_2021_paper.html"},{"ref":"Fini, E., Da Costa, V. G. T., Alameda-Pineda, X., Ricci, E., Alahari, K., & Mairal, J. (2022). Self-Supervised Models are Continual Learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9621–9630.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openaccess.thecvf.com/content/CVPR2022/html/Fini_Self-Supervised_Models_Are_Continual_Learners_CVPR_2022_paper.html"}],"related":["self-supervised-learning","contrastive-learning","continual-learning","online-learning","transfer-learning","representation-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-semi-structured-interview","name":"Online Semi-structured Interview","fullName":"Online Semi-structured Interview","aliases":["virtual semi-structured interview","remote semi-structured interview","online qualitative interview","video-mediated semi-structured interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s–2000s (systematic treatment by ~2010)","originator":"Adapted from face-to-face semi-structured interviewing; online variant emerged with internet adoption in research (Salmons, Mann, Stewart)","url":"https://scholargate.app/en/survey-methodology/online-semi-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/online-semi-structured-interview.md","definition":"An online semi-structured interview is a qualitative data collection technique in which a researcher conducts a guided but flexible conversation with a participant over a digital medium — video call, telephone, chat, or email — using a prepared interview guide with open-ended questions while remaining free to probe, reorder, or add follow-up questions as the dialogue unfolds. It combines the accessibility of remote communication with the depth and adaptability of semi-structured inquiry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from face-to-face semi-structured interviewing; online variant emerged with internet adoption in research (Salmons, Mann, Stewart)","year":"Late 1990s–2000s (systematic treatment by ~2010)","type":"Qualitative data collection technique","dataType":"Text transcripts, audio/video recordings from synchronous or asynchronous online exchanges","subfamily":"Data collection"},"citations":[{"ref":"Brinkmann, S., & Kvale, S. (2015). InterViews: Learning the Craft of Qualitative Research Interviewing (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1452203867","url":null},{"ref":"Salmons, J. (2014). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483332673","url":null}],"related":["semi-structured-interview","online-in-depth-interview","online-structured-interview","online-focus-group","online-survey","face-to-face-semi-structured-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-semi-supervised-learning","name":"Online Semi-supervised learning","fullName":"Online Semi-supervised Learning (Stream-based Learning with Partial Labels)","aliases":["stream-based semi-supervised learning","incremental semi-supervised learning","online SSL","semi-supervised online learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)","url":"https://scholargate.app/en/machine-learning/online-semi-supervised-learning","markdownUrl":"https://scholargate.app/en/machine-learning/online-semi-supervised-learning.md","definition":"Online semi-supervised learning combines the incremental, one-pass nature of online learning with the ability to exploit unlabeled data alongside sparse labeled observations. It is designed for settings where data arrives as a stream and obtaining labels for every instance is expensive or impractical — such as real-time classification of web content, sensor readings, or social media posts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)","year":"2000s–2010s","type":"Incremental / stream-based semi-supervised learning framework","dataType":"Sequential/streaming data with mixed labeled and unlabeled observations","subfamily":"Machine learning"},"citations":[{"ref":"Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 393–407. Springer.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+manifold+regularization+A+new+learning+setting+and+empirical+study+Goldberg+2008"},{"ref":"Semi-supervised learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Semi-supervised_learning"}],"related":["semi-supervised-learning","online-learning","self-supervised-learning","active-learning","transfer-learning","label-propagation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-sensor-data-collection","name":"Online Sensor Data Collection","fullName":"Online (Networked) Sensor Data Collection","aliases":["networked sensor data collection","IoT data collection","remote sensor monitoring","wireless sensor data acquisition"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000)","originator":"Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000)","url":"https://scholargate.app/en/survey-methodology/online-sensor-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/online-sensor-data-collection.md","definition":"Online sensor data collection is a systematic technique for gathering continuous or event-triggered measurements from physical sensors that transmit readings in real time over a network — the internet, a local wireless network, or a dedicated IoT protocol. It is used widely in environmental monitoring, health informatics, smart-city research, industrial systems, and behavioral science to capture objective, high-frequency data without requiring manual recording by participants or observers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000)","year":"Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000)","type":"Quantitative / mixed-mode data collection technique","dataType":"Continuous or event-triggered numeric/binary readings transmitted over a network","subfamily":"Data collection"},"citations":[{"ref":"Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422.","type":"article","doi":"10.1016/S1389-1286(01)00302-4","isbn":null,"url":null},{"ref":"Wireless sensor network. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Wireless_sensor_network"}],"related":["sensor-data-collection","mobile-sensor-data-collection","api-based-data-collection","mobile-experience-sampling","web-scraping","longitudinal-sensor-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-simple-random-sampling","name":"Online simple random sampling","fullName":"Online Simple Random Sampling","aliases":["web simple random sampling","internet SRS","digital random sampling","online SRS"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Late 1990s–2000s (digital adaptation)","originator":"Adapted from classical simple random sampling (Neyman, 1934) for web/digital survey contexts; operationalised by survey methodology researchers from the late 1990s onward","url":"https://scholargate.app/en/survey-methodology/online-simple-random-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/online-simple-random-sampling.md","definition":"Online simple random sampling applies the logic of classical simple random sampling (SRS) to digital data collection: every member of a defined online population has an equal and independent probability of being selected, and the survey is administered via web platform, email link, or online panel. The approach combines the statistical rigour of probability sampling with the speed and cost advantages of internet-based survey delivery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from classical simple random sampling (Neyman, 1934) for web/digital survey contexts; operationalised by survey methodology researchers from the late 1990s onward","year":"Late 1990s–2000s (digital adaptation)","type":"Probability sampling design","dataType":"Quantitative; digital survey responses","subfamily":"Sampling"},"citations":[{"ref":"Couper, M. P. (2008). Designing Effective Web Surveys. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521700535","url":null},{"ref":"Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118456149","url":null}],"related":["simple-random-sampling","stratified-sampling","online-stratified-sampling","systematic-sampling","online-cluster-sampling","quota-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-social-comparison-scale","name":"Iowa-Netherlands Social Comparison Orientation Scale","fullName":"Iowa-Netherlands Comparison Orientation Measure","aliases":["INCOM","Gibbons-Buunk"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"social-comparison-psychology","year":"1999","originator":"Frederick X. Gibbons and Bram P. Buunk","url":"https://scholargate.app/en/social-media-psychology/online-social-comparison-scale","markdownUrl":"https://scholargate.app/en/social-media-psychology/online-social-comparison-scale.md","definition":"The Iowa-Netherlands Comparison Orientation Measure (INCOM) is an 11-item self-report scale that assesses individual differences in the tendency to engage in social comparison—comparing oneself to others on abilities, attributes, and outcomes. Developed by Gibbons and Buunk in 1999, it captures both upward comparison (to those perceived as superior) and downward comparison (to those perceived as inferior), providing a trait-like measure of this fundamental social motivation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frederick X. Gibbons and Bram P. Buunk","subfamily":"social-comparison-psychology","year":"1999","type":"Self-report"},"citations":[{"ref":"Gibbons, F. X., & Buunk, B. P. (1999). Individual differences in social comparison: Development of a scale of social comparison orientation. Journal of Personality and Social Psychology, 76(1), 129–142.","type":"article","doi":"10.1037/0022-3514.76.1.129","isbn":null,"url":null}],"related":["fear-of-missing-out-scale","social-media-disorder-scale","social-comparison-scale-online","passive-social-media-use-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-social-support-scale","name":"Online Social Support Scale","fullName":"Online Social Support Scale (OSSS)","aliases":["OSSS","Online Social Support","Internet Social Support"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Digital social connection and support","year":"2011","originator":"Joana Vilelas, Carla Tomás; Emma Nick, David Cole et al.","url":"https://scholargate.app/en/health-informatics/online-social-support-scale","markdownUrl":"https://scholargate.app/en/health-informatics/online-social-support-scale.md","definition":"The Online Social Support Scale measures the perceived availability and quality of emotional, informational, and practical support received through digital channels—social media, online communities, forums, messaging apps, and digital platforms. Developed by Vilelas and Tomás (2011) for patients with chronic illness and refined by Nick and colleagues (2017), the scale recognizes that social support increasingly flows through digital networks, particularly for geographically dispersed, stigmatized, or medically complex populations who benefit from asynchronous, text-based support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joana Vilelas, Carla Tomás; Emma Nick, David Cole et al.","subfamily":"Digital social connection and support","year":"2011","type":"Self-report questionnaire"},"citations":[{"ref":"Vilelas, J. M., & Tomás, C. C. (2011). Internet social support: An instrument for studying virtual communities of patients with fibromyalgia. Computers, Informatics, Nursing, 29(10), 576–585.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Internet+social+support%3A+An+instrument+for+studying+virtual+communities+of+patients+with+fibromyalgia+Vilelas"},{"ref":"Nick, E. A., Cole, D. A., Cho, S. J., & Smith, D. K. (2018). The online social support scale: Measure development and validation. Psychological Assessment, 30(9), 1127–1143.","type":"article","doi":"10.1037/pas0000558","isbn":null,"url":null}],"related":["patient-engagement-scale","social-media-anxiety-scale","ehealth-literacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-structured-interview","name":"Online Structured Interview","fullName":"Online Structured Interview","aliases":["web-based structured interview","virtual structured interview","digital structured interview","e-interview (structured)"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s–2000s (with widespread adoption post-2010)","originator":"Emerged from structured interview methodology adapted for internet-mediated communication","url":"https://scholargate.app/en/survey-methodology/online-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/online-structured-interview.md","definition":"An online structured interview applies the classical structured interview protocol — a fixed set of predetermined questions asked in a fixed order — via internet-mediated channels such as video conferencing, synchronous chat, or email. Every participant receives the exact same questions, enabling systematic comparison across respondents while eliminating geographic barriers. It combines the standardization benefits of face-to-face structured interviewing with the reach, cost efficiency, and scheduling flexibility of online data collection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from structured interview methodology adapted for internet-mediated communication","year":"Late 1990s–2000s (with widespread adoption post-2010)","type":"Quantitative/standardized data collection technique","dataType":"Closed-ended and fixed-format verbal or typed responses","subfamily":"Data collection"},"citations":[{"ref":"Salmons, J. (2015). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452283500","url":null},{"ref":"Couper, M. P. (2000). Web surveys: A review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494.","type":"article","doi":"10.1086/318641","isbn":null,"url":null}],"related":["structured-interview","online-survey","online-semi-structured-interview","online-in-depth-interview","online-focus-group","computer-assisted-interviewing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-support-vector-machine","name":"Online Support Vector Machine","fullName":"Online Support Vector Machine (Incremental SVM for Streaming Data)","aliases":["Online SVM","Incremental SVM","LASVM","Pegasos SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2005–2011","originator":"Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM)","url":"https://scholargate.app/en/machine-learning/online-support-vector-machine","markdownUrl":"https://scholargate.app/en/machine-learning/online-support-vector-machine.md","definition":"Online SVM adapts the classical support vector machine to streaming or sequentially arriving data by updating the decision boundary one example at a time rather than solving a global quadratic program. Algorithms such as Pegasos and LASVM make this tractable at large scale, preserving the margin-maximising spirit of SVMs with sub-linear time per update.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM)","year":"2005–2011","type":"Online kernel classifier","dataType":"Continuous, binary, or encoded categorical features; binary or multiclass labels","subfamily":"Machine learning"},"citations":[{"ref":"Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3–30.","type":"article","doi":"10.1007/s10107-010-0420-4","isbn":null,"url":null},{"ref":"Bordes, A., Ertekin, S., Weston, J., & Bottou, L. (2005). Fast kernel classifiers with online and active learning. Journal of Machine Learning Research, 6, 1579–1619.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v6/bordes05a.html"}],"related":["support-vector-machine","online-learning","online-logistic-regression","online-gradient-boosting","sgd-classifier","kernel-perceptron"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-survey","name":"Online Survey","fullName":"Online Survey Research","aliases":["web survey","internet survey","e-survey","computer-assisted web interviewing"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Mid-1990s (widespread scholarly adoption ~1995–2000)","originator":"Mick P. Couper, Don A. Dillman (early systematic frameworks)","url":"https://scholargate.app/en/survey-methodology/online-survey","markdownUrl":"https://scholargate.app/en/survey-methodology/online-survey.md","definition":"An online survey is a structured data collection instrument hosted on a web platform and completed by respondents via internet-connected devices. It enables large-scale, geographically dispersed data gathering at low cost and with rapid turnaround. Respondents self-administer the questionnaire at their convenience, which reduces interviewer bias and permits automatic data capture. Online surveys are the dominant mode of survey research in social, behavioural, health, and market research today.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mick P. Couper, Don A. Dillman (early systematic frameworks)","year":"Mid-1990s (widespread scholarly adoption ~1995–2000)","type":"Quantitative / mixed-methods data collection technique","dataType":"Self-reported structured responses (Likert scales, multiple choice, open text)","subfamily":"Data collection"},"citations":[{"ref":"Couper, M. P. (2000). Web surveys: A review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494.","type":"article","doi":"10.1086/318641","isbn":null,"url":null},{"ref":"Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118456149","url":null}],"related":["survey","structured-interview","mobile-survey","face-to-face-survey","delphi-technique","longitudinal-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-systematic-sampling","name":"Online Systematic Sampling","fullName":"Online Systematic Sampling","aliases":["web systematic sampling","digital systematic sampling","interval sampling online","e-survey systematic sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Late 1990s–2000s (web survey era)","originator":"Adapted from classical systematic sampling (Madow & Madow, 1944) for web survey contexts","url":"https://scholargate.app/en/survey-methodology/online-systematic-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/online-systematic-sampling.md","definition":"Online systematic sampling applies the classical every-k-th-element rule to digital survey contexts — selecting respondents from a web panel, membership database, or visitor stream at a fixed interval. It combines the operational simplicity of systematic sampling with the reach and speed of online data collection, producing a roughly representative sample without requiring complex randomisation infrastructure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from classical systematic sampling (Madow & Madow, 1944) for web survey contexts","year":"Late 1990s–2000s (web survey era)","type":"Probability sampling design","dataType":"Digital survey responses; web panel records; visitor or membership lists","subfamily":"Sampling"},"citations":[{"ref":"Couper, M. P. (2008). Designing Effective Web Surveys. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521713528","url":null},{"ref":"Lohr, S. L. (2022). Sampling: Design and Analysis (3rd ed.). CRC Press / Chapman & Hall.","type":"book","doi":null,"isbn":"978-0367279509","url":null}],"related":["systematic-sampling","online-simple-random-sampling","online-stratified-sampling","online-cluster-sampling","online-quota-sampling","proportional-systematic-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-theoretical-sampling","name":"Online theoretical sampling","fullName":"Online Theoretical Sampling","aliases":["internet-based theoretical sampling","digital theoretical sampling","web-based theoretical sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1967 (theoretical sampling); online adaptation ~2000s–2010s","originator":"Glaser & Strauss (theoretical sampling); adapted to online contexts by internet qualitative researchers","url":"https://scholargate.app/en/survey-methodology/online-theoretical-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/online-theoretical-sampling.md","definition":"Online theoretical sampling applies the logic of theoretical sampling — selecting participants or data sources based on emerging theory rather than predetermined criteria — within digital environments. Researchers iteratively recruit from online communities, forums, social media, or virtual networks, guided at each step by conceptual gaps identified during concurrent analysis. It is most commonly used in grounded theory studies conducted wholly or partially over the internet.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glaser & Strauss (theoretical sampling); adapted to online contexts by internet qualitative researchers","year":"1967 (theoretical sampling); online adaptation ~2000s–2010s","type":"Qualitative sampling strategy","dataType":"Qualitative data from online sources (forums, social media, virtual communities, online interviews)","subfamily":"Sampling"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null},{"ref":"Salmons, J. (2015). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483332673","url":null}],"related":["theoretical-sampling","snowball-sampling","online-snowball-sampling","purposive-sampling","grounded-theory","online-purposive-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-transfer-learning","name":"Online Transfer learning","fullName":"Online Transfer Learning (Streaming Transfer Learning)","aliases":["OTL","streaming transfer learning","incremental transfer learning","online domain adaptation"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010","originator":"Zhao, P. & Hoi, S. C. H.","url":"https://scholargate.app/en/machine-learning/online-transfer-learning","markdownUrl":"https://scholargate.app/en/machine-learning/online-transfer-learning.md","definition":"Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled target datasets upfront.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhao, P. & Hoi, S. C. H.","year":"2010","type":"Online learning with source-domain knowledge transfer","dataType":"Sequential/streaming labeled and unlabeled instances, with source-domain data","subfamily":"Machine learning"},"citations":[{"ref":"Zhao, P., & Hoi, S. C. H. (2010). OTL: A Framework of Online Transfer Learning. In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 1231–1238. Omnipress.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=OTL+A+Framework+of+Online+Transfer+Learning+Zhao+Hoi+2010"},{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["transfer-learning","online-learning","semi-supervised-learning","domain-adaptation","few-shot-learning","meta-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-trust-scale","name":"Online Trust Scale","fullName":"Online Consumer Trust Scale","aliases":["Consumer Trust","Web Trust"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"2000","originator":"Walker & Johnson; Jarvenpaa et al.","url":"https://scholargate.app/en/information-systems/online-trust-scale","markdownUrl":"https://scholargate.app/en/information-systems/online-trust-scale.md","definition":"The Online Trust Scale measures consumer confidence and belief in online platforms (e-commerce websites, digital services, social platforms). Developed by researchers including Jarvenpaa, Tractinsky, and Vitale (2000) and Walker and Johnson (2006), this scale captures dimensions of perceived security, vendor reliability, privacy protection, and transaction confidence. Online trust is a critical antecedent to behavioral intention and actual purchase/usage in digital commerce contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walker & Johnson; Jarvenpaa et al.","subfamily":"Technology adoption","year":"2000","type":"Likert-scale trust measure"},"citations":[{"ref":"Walker, D., & Johnson, R. (2006). Why consumers use online and visit physical stores. Journal of Retailing and Consumer Services, 13(2), 143-151.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Why+consumers+use+online+and+visit+physical+stores+Walker"},{"ref":"Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1(1-2), 45-71.","type":"article","doi":"10.1023/A:1019104520776","isbn":null,"url":null}],"related":["technology-readiness-index","tam-questionnaire","elearning-satisfaction-scale","social-media-engagement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-voting-ensemble","name":"Online Voting Ensemble","fullName":"Online Voting Ensemble (Incremental Majority-Vote Ensemble for Data Streams)","aliases":["streaming voting ensemble","incremental voting ensemble","online majority-vote ensemble","data-stream voting classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001–2009","originator":"Oza, N. C. & Russell, S.; extended by Bifet et al.","url":"https://scholargate.app/en/machine-learning/online-voting-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/online-voting-ensemble.md","definition":"Online Voting Ensemble is an incremental ensemble method that maintains a pool of base classifiers — each updated continuously on arriving data — and combines their predictions through a weighted or unweighted majority vote. Designed for data streams, it adapts to non-stationary distributions without retraining from scratch, making it well-suited to real-time classification tasks where data arrives sequentially and concept drift may occur.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oza, N. C. & Russell, S.; extended by Bifet et al.","year":"2001–2009","type":"Online ensemble (incremental majority vote)","dataType":"Streaming / sequential tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 229–236.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+bagging+and+boosting+Oza+Russell+2001"},{"ref":"Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., & Gavaldà, R. (2009). New ensemble methods for evolving data streams. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 139–148.","type":"inproceedings","doi":"10.1145/1557019.1557041","isbn":null,"url":null}],"related":["voting-ensemble","online-learning","online-bagging","online-boosting","online-random-forest","semi-supervised-voting-ensemble"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"online-weighted-sampling","name":"Online Weighted Sampling","fullName":"Online Weighted Sampling","aliases":["web-based weighted sampling","internet survey weighting","online panel weighting","weighted internet sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Late 1990s–2000s","originator":"Survey methodology practitioners; systematized via probability-based online panels (e.g., Knowledge Networks, founded late 1990s)","url":"https://scholargate.app/en/survey-methodology/online-weighted-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/online-weighted-sampling.md","definition":"Online weighted sampling is the practice of recruiting respondents via internet platforms and then applying statistical weights to correct for unequal selection probabilities, coverage gaps, and differential non-response. It enables researchers to draw valid population inferences from web surveys by compensating for the structural biases inherent in online recruitment — including the fact that not all members of a target population have equal internet access or equal likelihood of joining a panel.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Survey methodology practitioners; systematized via probability-based online panels (e.g., Knowledge Networks, founded late 1990s)","year":"Late 1990s–2000s","type":"Probability-adjusted online sampling technique","dataType":"Quantitative survey data collected via internet platforms","subfamily":"Sampling"},"citations":[{"ref":"Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118456149","url":null},{"ref":"Bethlehem, J., & Biffignandi, S. (2012). Handbook of Web Surveys. Wiley.","type":"article","doi":null,"isbn":"978-0470603567","url":null}],"related":["weighted-sampling","online-stratified-sampling","quota-sampling","online-simple-random-sampling","post-stratification","propensity-score-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"open-access-publishing","name":"Open Access Publishing Models","fullName":"Open Access Publishing Models and Licensing","aliases":["OA Publishing","Gold Open Access","Green Open Access","Diamond OA"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"publishing-models","year":"2002","originator":"Budapest Open Access Initiative (2002); open science movement","url":"https://scholargate.app/en/publication-ethics/open-access-publishing","markdownUrl":"https://scholargate.app/en/publication-ethics/open-access-publishing.md","definition":"Open access (OA) publishing removes subscription paywalls, making research freely available to all readers online without subscription fees. The Budapest Open Access Initiative (2002) defined OA as the right to read, download, copy, distribute, print, search, and link research freely. Multiple OA models exist: Gold OA (immediate free access, often author-funded via APCs), Green OA (free self-archiving in repositories), and Diamond OA (free to both authors and readers). OA expands research impact, enables global participation in science, and aligns with public funding mandates. However, OA models vary in sustainability and are sometimes exploited by predatory publishers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Budapest Open Access Initiative (2002); open science movement","subfamily":"publishing-models","year":"2002","type":"Standard"},"citations":[{"ref":"Budapest Open Access Initiative (2002, revised 2012). Budapest Open Access Initiative.","type":"webpage","doi":null,"isbn":null,"url":"https://www.budapestopenaccessinitiative.org/"},{"ref":"Suber, P. (2012). Open Access. MIT Press.","type":"book","doi":"10.7551/mitpress/9286.001.0001","isbn":null,"url":null},{"ref":"Directory of Open Access Journals (2023). DOAJ. Public database of open access journals.","type":"webpage","doi":null,"isbn":null,"url":"https://doaj.org/"}],"related":["predatory-journals","peer-review-process","preprint-servers","cope-guidelines"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"open-coding","name":"Open Coding","fullName":"Open Coding","aliases":["initial coding","open categorisation","substantive coding"],"domain":"qualitative","family":"process-pipeline","subfamily":"Qualitative Coding","year":"1967 (Glaser & Strauss); refined 1990 (Strauss & Corbin)","originator":"Barney G. Glaser & Anselm L. Strauss (classic grounded theory); elaborated by Anselm Strauss & Juliet Corbin","url":"https://scholargate.app/en/qualitative/open-coding","markdownUrl":"https://scholargate.app/en/qualitative/open-coding.md","definition":"Open coding is the first, exploratory phase of qualitative data analysis in which raw text — interviews, field notes, or documents — is broken into discrete segments and labelled with short descriptive codes. Developed within grounded theory by Glaser and Strauss and later elaborated by Strauss and Corbin, the procedure is deliberately open and inductive: the analyst reads line-by-line without imposing a predetermined framework, allowing concepts to emerge directly from the data. The resulting codes are then compared and grouped into provisional categories that become the building blocks for subsequent, more selective analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney G. Glaser & Anselm L. Strauss (classic grounded theory); elaborated by Anselm Strauss & Juliet Corbin","year":"1967 (Glaser & Strauss); refined 1990 (Strauss & Corbin)","type":"Qualitative research method","dataType":"Interview transcripts, field notes, documents, observational records","typicalSampleSize":"15–30 interviews or equivalent text units","subfamily":"Qualitative Coding"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0803959408","url":null},{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Constructing+Grounded+Theory+A+Practical+Guide+Through+Qualitative+Analysis+Charmaz+2006"}],"related":["grounded-theory","thematic-analysis","content-analysis","narrative-analysis","discourse-analysis","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"open-information-extraction","name":"Open Information Extraction","fullName":"Open Information Extraction (Open IE)","aliases":["Open IE","OpenIE","open relation extraction","Açık Bilgi Çıkarma (Open IE)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2007,"originator":"Banko, Cafarella, Soderland, Broadhead & Etzioni","url":"https://scholargate.app/en/text-mining/open-information-extraction","markdownUrl":"https://scholargate.app/en/text-mining/open-information-extraction.md","definition":"Open Information Extraction (Open IE) is a text-mining task that automatically extracts subject-relation-object triples from text without requiring a predefined relation schema. Introduced by Banko and colleagues (2007) for extraction over the open web, it converts free-running text into structured assertions used to build knowledge graphs and to mine large text collections.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Banko, Cafarella, Soderland, Broadhead & Etzioni","year":2007,"type":"Schema-free relation-extraction task","output":"Subject-relation-object triples","minSample":20,"difficulty":"3 / 5"},"citations":[{"ref":"Banko, M., Cafarella, M. J., Soderland, S., Broadhead, M. & Etzioni, O. (2007). Open Information Extraction from the Web. Proceedings of IJCAI 2007, 2670-2676.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.ijcai.org/Proceedings/07/Papers/429.pdf"},{"ref":"Mausam (2016). Open Information Extraction Systems and Downstream Applications. Proceedings of IJCAI 2016, 4074-4077.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.ijcai.org/Proceedings/16/Papers/604.pdf"}],"related":["named-entity-recognition","entity-linking","knowledge-graph","constituency-parsing"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"operator-performance-scale","name":"Operator Performance Assessment Scale","fullName":"Operator Performance Assessment Scale (OPAS)","aliases":["OPAS","Performance Rating Scale"],"domain":"human-factors","family":"process-pipeline","subfamily":"performance-assessment","year":1993,"originator":"William W. Wierwille, Frank T. Eggemeier","url":"https://scholargate.app/en/human-factors/operator-performance-scale","markdownUrl":"https://scholargate.app/en/human-factors/operator-performance-scale.md","definition":"The Operator Performance Assessment Scale (OPAS), formalized by Wierwille and Eggemeier in 1993, is a structured rating method for assessing operator task performance on multiple dimensions (primary task accuracy, secondary task accuracy, task completion time, error rate, procedure adherence) in applied settings. OPAS bridges subjective workload perception (NASA-TLX, situational awareness) and objective behavioral metrics by capturing expert judgment of performance quality across multiple performance channels, enabling holistic evaluation of how well operators managed task demands.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William W. Wierwille, Frank T. Eggemeier","subfamily":"performance-assessment","year":1993,"type":"Observer-rated / Self-rated"},"citations":[{"ref":"Wierwille, W. W., & Eggemeier, F. T. (1993). Recommendations for mental workload measurement in a test and evaluation environment. Human Factors, 35(2), 263–281.","type":"article","doi":"10.1177/001872089303500205","isbn":null,"url":null},{"ref":"Vidulich, M. A., & Tsang, P. S. (1988). The role of output modality in performance and mental workload during concurrent spatial and verbal tasks. Human Factors, 30(5), 613–623.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+role+of+output+modality+in+performance+and+mental+workload+during+concurrent+spatial+and+verbal+tasks+Vidulich"}],"related":["situational-awareness-rating","workload-profile","nasa-task-load-index","human-error-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"opinion-mining","name":"Opinion Mining","fullName":"Opinion Mining (Aspect-Based Sentiment Extraction)","aliases":["aspect-based sentiment analysis","opinion extraction","Görüş Madenciliği (Opinion Mining)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2012,"originator":"Bing Liu","url":"https://scholargate.app/en/text-mining/opinion-mining","markdownUrl":"https://scholargate.app/en/text-mining/opinion-mining.md","definition":"Opinion mining is a natural-language-processing task that systematically extracts and analyses user opinions about a product, service, or topic — identifying the specific features (aspects) being discussed, the sentiment expressed toward each, and the opinion holders. Consolidated by Bing Liu (2012), it goes beyond a single document-level label to produce structured aspect–opinion–holder records.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bing Liu","year":2012,"type":"NLP information-extraction task","output":"Aspect–opinion–holder triples with polarity","minSample":50,"varType":"Text data"},"citations":[{"ref":"Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool.","type":"book","doi":"10.2200/S00416ED1V01Y201204HLT016","isbn":null,"url":null},{"ref":"Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.","type":"article","doi":"10.1561/1500000011","isbn":null,"url":null}],"related":["sentiment-analysis","argument-mining","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"opioid-risk-tool","name":"ORT","fullName":"Opioid Risk Tool","aliases":["ORT"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"opioid-risk-assessment","year":"2005","originator":"Webster, Webster","url":"https://scholargate.app/en/addiction-medicine/opioid-risk-tool","markdownUrl":"https://scholargate.app/en/addiction-medicine/opioid-risk-tool.md","definition":"The ORT is a brief, 10-item self-report screening instrument designed to identify patients at elevated risk for opioid misuse, addiction, or aberrant drug-related behaviors prior to initiating opioid therapy. Developed by Webster and Webster in 2005, it stratifies patients into low, moderate, and high risk categories based on personal and family history of substance abuse, psychiatric comorbidity, and psychosocial factors. The ORT is widely used in pain management and primary care settings to guide shared decision-making and risk mitigation strategies when prescribing opioids.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Webster, Webster","subfamily":"opioid-risk-assessment","year":"2005","type":"Self-report"},"citations":[{"ref":"Webster, L. R., & Webster, R. M. (2005). Predicting aberrant behaviors in opioid-treated patients: preliminary validation of the Opioid Risk Tool. Pain Medicine, 6(6), 432–442.","type":"article","doi":"10.1111/j.1526-4637.2005.00072.x","isbn":null,"url":null}],"related":["dudit","sadq","brief-addiction-monitor","substance-abuse-subtle-screening"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optical-flow-lucas-kanade","name":"Lucas-Kanade Optical Flow","fullName":"Lucas-Kanade Optical Flow Estimation","aliases":["Lucas-Kanade method","Sparse optical flow"],"domain":"computer-vision","family":"ml-model","subfamily":"Motion estimation","year":"1981","originator":"Bruce Lucas and Takeo Kanade","url":"https://scholargate.app/en/computer-vision/optical-flow-lucas-kanade","markdownUrl":"https://scholargate.app/en/computer-vision/optical-flow-lucas-kanade.md","definition":"The Lucas-Kanade method, introduced by Bruce Lucas and Takeo Kanade in 1981, is a foundational technique for estimating optical flow—the apparent motion of objects in image sequences. By computing pixel-level motion vectors, the Lucas-Kanade algorithm tracks feature displacements between consecutive frames, enabling object tracking, motion estimation, and video analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruce Lucas and Takeo Kanade","subfamily":"Motion estimation","year":"1981","type":"Optical flow and tracking"},"citations":[{"ref":"Lucas, B. D., & Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI), 674–679.","type":"article","doi":null,"isbn":null,"url":"https://ri.cmu.edu/pub_files/pub3/lucas_b_d_1981_2.pdf"},{"ref":"Bouguet, J. Y. (2001). Pyramidal implementation of the Lucas Kanade feature tracker. OpenCV Documentation.","type":"article","doi":null,"isbn":null,"url":"https://docs.opencv.org/3.4/d7/d8b/tutorial_py_lucas_kanade.html"}],"related":["harris-corner-detection","sift-feature-detection","scale-space-theory","template-matching","blob-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optically-stimulated-luminescence-dating","name":"Optically Stimulated Luminescence Dating","fullName":"Optically Stimulated Luminescence Dating (OSL)","aliases":["OSL dating","optical dating"],"domain":"archaeology","family":"process-pipeline","subfamily":"Radiometric","year":"1985","originator":"David Huntley","url":"https://scholargate.app/en/archaeology/optically-stimulated-luminescence-dating","markdownUrl":"https://scholargate.app/en/archaeology/optically-stimulated-luminescence-dating.md","definition":"Optically stimulated luminescence (OSL) dating is a chronometric method that determines the age of sedimentary materials by measuring light-induced electron release from mineral grains. Developed by David Huntley and colleagues in the 1980s, it measures the time elapsed since sediment was last exposed to sunlight. This technique is widely used in archaeology, geology, and paleoenvironmental studies to date deposits ranging from a few decades to several hundred thousand years old.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Huntley","subfamily":"Radiometric","year":"1985","type":"Luminescence dating technique"},"citations":[{"ref":"Huntley, D. J., Godfrey-Smith, D. I., & Thewalt, M. L. (1985). Thermoluminescence dating of ocean sediments. Canadian Journal of Earth Sciences, 22(3), 423-427.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thermoluminescence+dating+of+ocean+sediments+Huntley"},{"ref":"Rhodes, E. J. (1994). Optical dating of quartz using the 320 nm TL emission. Radiation Measurements, 23(2-3), 371-378.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optical+dating+of+quartz+using+the+320+nm+TL+emission+Rhodes"},{"ref":"Wintle, A. G., & Murray, A. S. (2006). A review of quartz optically stimulated luminescence characteristics and their relevance to single-aliquot regeneration dating protocols. Radiation Measurements, 41(4), 369-391.","type":"article","doi":"10.1016/j.radmeas.2005.11.001","isbn":null,"url":null}],"related":["thermoluminescence-dating","electron-spin-resonance-dating","uranium-thorium-dating","archaeomagnetic-dating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optics","name":"OPTICS","fullName":"OPTICS: Ordering Points To Identify the Clustering Structure","aliases":["OPTICS","Ordering Points To Identify the Clustering Structure","density-based clustering with reachability plot","generalized DBSCAN"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1999,"originator":"Ankerst, M.; Breunig, M. M.; Kriegel, H.-P.; Sander, J.","url":"https://scholargate.app/en/machine-learning/optics","markdownUrl":"https://scholargate.app/en/machine-learning/optics.md","definition":"OPTICS (Ordering Points To Identify the Clustering Structure) is a density-based clustering algorithm introduced by Ankerst, Breunig, Kriegel, and Sander in 1999. It generalizes DBSCAN by processing points in an ordering that encodes the full density-based cluster structure of a dataset, enabling the detection of clusters of varying densities through a reachability plot rather than requiring a fixed global density threshold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ankerst, M.; Breunig, M. M.; Kriegel, H.-P.; Sander, J.","year":1999,"type":"Density-based clustering (reachability ordering)","task":"Unsupervised clustering with varying-density support","minSample":5,"parameters":"minPts (minimum neighbourhood size), epsilon (upper bound on neighbourhood radius)"},"citations":[{"ref":"Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2), 49–60.","type":"article","doi":"10.1145/304181.304187","isbn":null,"url":null},{"ref":"Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), 226–231.","type":"article","doi":null,"isbn":null,"url":"https://dl.acm.org/doi/10.5555/3001460.3001507"},{"ref":"Aggarwal, C. C., & Reddy, C. K. (Eds.) (2013). Data Clustering: Algorithms and Applications (Ch. 4). CRC Press.","type":"book","doi":null,"isbn":"978-1-4665-5821-2","url":null}],"related":["dbscan","hdbscan","kmeans","hierarchical-clustering","gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimal-design","name":"Optimal Experimental Design","fullName":"Optimal Experimental Design (D-Optimal, I-Optimal)","aliases":["D-Optimal Design","I-Optimal Design","Computer-Generated Design","Optimal Deneme Deseni (D-Optimal, I-Optimal)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1972,"originator":"V. V. Fedorov","url":"https://scholargate.app/en/experimental-design/optimal-design","markdownUrl":"https://scholargate.app/en/experimental-design/optimal-design.md","definition":"Optimal experimental design is a computer-aided approach to constructing experiments that maximises statistical efficiency for a given model and run budget. Formalised by V. V. Fedorov in 1972, it selects experimental points from a candidate set so that the information matrix M = X'X is optimised according to a chosen criterion — most commonly D-optimality (maximising the determinant) or I-optimality (minimising average prediction variance). It is the preferred strategy whenever classical designs such as central composite or Box-Behnken cannot be applied because the experimental region is constrained or factor ranges are irregular.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"V. V. Fedorov","year":1972,"family":"Experimental Design","type":"Computer-aided optimal design","parametric":true,"variants":"D-Optimal, I-Optimal","minRuns":10,"suitableFor":"constrained regions, irregular factor spaces","algorithmicBasis":"Coordinate exchange, simulated annealing"},"citations":[{"ref":"Fedorov, V.V. (1972). Theory of Optimal Experiments. Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/theory-of-optimal-experiments/oclc/545526"},{"ref":"Atkinson, A.C., Donev, A.N., & Tobias, R.D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press.","type":"book","doi":null,"isbn":"978-0199296606","url":null}],"related":["response-surface-methodology","central-composite-design","box-behnken-design","plackett-burman","factorial-design","d-optimal-design"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimal-power-flow","name":"Optimal Power Flow","fullName":"Optimal Power Flow for Economic and Security-Constrained Dispatch","aliases":["OPF","Economic Dispatch with Constraints"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Constrained optimization","year":"1962","originator":"Jean Carpentier","url":"https://scholargate.app/en/electrical-engineering/optimal-power-flow","markdownUrl":"https://scholargate.app/en/electrical-engineering/optimal-power-flow.md","definition":"Optimal Power Flow (OPF) is a fundamental optimization framework for computing the most economical and secure operating point of an electrical power system. Introduced by Jean Carpentier in 1962, OPF minimizes operational costs (fuel, losses, or other expenses) while satisfying physical and operational constraints. Modern electric grids depend on OPF for real-time economic dispatch, security analysis, and planning, making it one of the most important problems in power systems engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean Carpentier","subfamily":"Constrained optimization","year":"1962","type":"Nonlinear constrained optimization for power system operation"},"citations":[{"ref":"Carpentier, J. (1962). Contribution à l'étude du dispatching économique. Bulletin de la Société Française des Électriciens, 8(3), 431-447.","type":"article","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/282820025"},{"ref":"Momoh, J. A. (2014). Optimal Power Flow Solutions. IEEE Transactions on Power Systems, 62(2), 576-585.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimal+Power+Flow+Solutions+Momoh"},{"ref":"Alsac, O., & Stott, B. (1974). Optimal load flow with steady-state security. IEEE Transactions on Power Apparatus and Systems, 93(3), 745-751.","type":"article","doi":"10.1109/TPAS.1974.293972","isbn":null,"url":null}],"related":["newton-raphson-power-flow","economic-dispatch","unit-commitment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimality-theory","name":"Optimality Theory","fullName":"Optimality Theory (OT) Framework","aliases":["OT","Constraint-Based Phonology"],"domain":"linguistics","family":"process-pipeline","subfamily":"Theoretical Phonology","year":"1993","originator":"Alan Prince and Paul Smolensky","url":"https://scholargate.app/en/linguistics/optimality-theory","markdownUrl":"https://scholargate.app/en/linguistics/optimality-theory.md","definition":"Optimality Theory (OT) is a constraint-based framework for modeling phonology and syntax, developed by Alan Prince and Paul Smolensky in 1993. The core idea is that languages produce the optimal output that best satisfies a ranked hierarchy of universal constraints. Rather than listing rules, OT explains linguistic phenomena as solutions to conflicting pressures—sounds and structures emerge as the least bad compromise among competing demands. This framework has revolutionized phonological theory and is widely applied to morphophonology, segmental and suprasegmental analysis, and cross-linguistic variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Prince and Paul Smolensky","subfamily":"Theoretical Phonology","year":"1993","type":"Empirical process pipeline"},"citations":[{"ref":"Prince, A., & Smolensky, P. (1993). Optimality Theory: Constraint Interaction in Generative Grammar. Blackwell Publishers.","type":"article","doi":null,"isbn":null,"url":"https://www.blackwellpublishing.com/"},{"ref":"Kager, R. (1999). Optimality Theory. Cambridge: Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511812408","isbn":null,"url":null},{"ref":"McCarthy, J. D. (2008). Doing Optimality Theory: Applying Theory to Data. Malden, MA: Blackwell.","type":"book","doi":null,"isbn":null,"url":"https://www.blackwellpublishing.com/default.asp"}],"related":["minimalist-program","phonological-analysis","constraint-based-grammar"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimism-life-orientation-test","name":"Life Orientation Test Revised","fullName":"Life Orientation Test – Revised (LOT-R)","aliases":["LOT-R"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"dispositional optimism","year":"1994","originator":"Michael Scheier and Charles Carver","url":"https://scholargate.app/en/positive-psychology/optimism-life-orientation-test","markdownUrl":"https://scholargate.app/en/positive-psychology/optimism-life-orientation-test.md","definition":"The Life Orientation Test – Revised (LOT-R) is a 10-item measure of dispositional optimism developed by Scheier, Carver, and Bridges in 1994. It assesses the general expectancy that good things (versus bad things) will happen in the future. Optimism, as measured by the LOT-R, predicts coping success, health outcomes, and psychological well-being independent of self-efficacy or other personality factors. The revised version addressed psychometric concerns in the original 1985 LOT, improving clarity and reducing item ambiguity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Scheier and Charles Carver","subfamily":"dispositional optimism","year":"1994","type":"Self-report questionnaire"},"citations":[{"ref":"Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing coping strategies from coping styles: A health psychological perspective. Journal of Personality and Social Psychology, 67(6), 1061–1078.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Distinguishing+coping+strategies+from+coping+styles%3A+A+health+psychological+perspective+Scheier"}],"related":["hope-scale","flourishing-scale","perma-scale","subjective-wellbeing-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-box-behnken-design","name":"Optimization-assisted Box-Behnken design","fullName":"Optimization-Assisted Box-Behnken Design","aliases":["BBD with optimization","Box-Behnken design optimization","RSM-BBD optimization","Box-Behnken response optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960 (BBD); optimization integration established 1980s–1990s","originator":"Box & Behnken (design); Derringer & Suich (desirability optimization)","url":"https://scholargate.app/en/experimental-design/optimization-assisted-box-behnken-design","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-box-behnken-design.md","definition":"Optimization-assisted Box-Behnken design (BBD) combines the Box-Behnken three-level experimental design with a formal optimization step to locate factor settings that maximize, minimize, or hit a target for one or more responses. BBD fits a second-order response surface model using fewer runs than a full factorial, and the optimization stage — typically via desirability functions or numerical search — then exploits that fitted model to identify the true optimum within the experimental region.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Behnken (design); Derringer & Suich (desirability optimization)","year":"1960 (BBD); optimization integration established 1980s–1990s","type":"Experimental design with post-modeling optimization","dataType":"Continuous numeric response data from designed experiments","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.","type":"article","doi":"10.1080/00401706.1960.10489912","isbn":null,"url":null},{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.","type":"article","doi":"10.1080/00224065.1980.11980968","isbn":null,"url":null}],"related":["box-behnken-design","central-composite-design","response-surface-methodology","optimization-assisted-central-composite-design","optimization-assisted-response-surface-methodology","taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-central-composite-design","name":"Optimization-assisted central composite design","fullName":"Optimization-Assisted Central Composite Design","aliases":["CCD with optimization","optimized CCD","RSM-CCD optimization","central composite design with response optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1951 (CCD); optimization coupling formalized 1970s–1990s","originator":"Box & Wilson (CCD, 1951); optimization integration by Myers, Montgomery & colleagues","url":"https://scholargate.app/en/experimental-design/optimization-assisted-central-composite-design","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-central-composite-design.md","definition":"Optimization-assisted central composite design (CCD) combines the rotatable, second-order experimental layout of central composite design with mathematical optimization algorithms — typically desirability functions, response surface optimization, or metaheuristics — to find the factor settings that simultaneously maximize, minimize, or hit target values for one or more response variables. It is the most widely applied response-surface optimization workflow in chemical, pharmaceutical, food science, and manufacturing engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Wilson (CCD, 1951); optimization integration by Myers, Montgomery & colleagues","year":"1951 (CCD); optimization coupling formalized 1970s–1990s","type":"Experimental design with mathematical optimization","dataType":"Continuous process factors and quantitative response variables","subfamily":"Engineering methods"},"citations":[{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470174463","url":null},{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.","type":"article","doi":"10.1080/00224065.1980.11980968","isbn":null,"url":null}],"related":["central-composite-design","response-surface-methodology","box-behnken-design","full-factorial-design","fractional-factorial-design","taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-design-of-experiments","name":"Optimization-assisted design of experiments","fullName":"Optimization-Assisted Design of Experiments","aliases":["OA-DoE","DoE with optimization","optimization-integrated DoE","multi-objective experimental optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980 (desirability approach); broader integration through 1990s–2000s","originator":"Derringer & Suich (desirability function); extended by Myers, Montgomery, and Anderson-Cook","url":"https://scholargate.app/en/experimental-design/optimization-assisted-design-of-experiments","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-design-of-experiments.md","definition":"Optimization-assisted design of experiments (OA-DoE) couples a structured experimental plan with a mathematical optimization engine to locate factor settings that simultaneously satisfy multiple response objectives. Rather than stopping at fitting a response surface model, the analyst applies desirability functions, genetic algorithms, or other optimizers to the fitted model to identify the global or near-global optimum across all responses of interest.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derringer & Suich (desirability function); extended by Myers, Montgomery, and Anderson-Cook","year":"1980 (desirability approach); broader integration through 1990s–2000s","type":"Hybrid experimental-optimization method","dataType":"Continuous response variables from designed experiments (numerical)","subfamily":"Engineering methods"},"citations":[{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.","type":"article","doi":"10.1080/00224065.1980.11980968","isbn":null,"url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916018","url":null}],"related":["design-of-experiments","response-surface-methodology","central-composite-design","box-behnken-design","taguchi-method","full-factorial-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-event-tree-analysis","name":"Optimization-assisted event tree analysis","fullName":"Optimization-Assisted Event Tree Analysis","aliases":["OA-ETA","optimization-integrated ETA","optimization-enhanced event tree analysis","ETA with optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1975 (ETA); optimization integration ~1990s–2000s","originator":"Event tree analysis originated at the U.S. Nuclear Regulatory Commission (WASH-1400, 1975); optimization integration developed through risk engineering literature from the 1990s onward","url":"https://scholargate.app/en/experimental-design/optimization-assisted-event-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-event-tree-analysis.md","definition":"Optimization-assisted event tree analysis couples the structured probability logic of classical event tree analysis (ETA) with an optimization layer — typically mathematical programming or metaheuristic search — to identify the best combination of safety barriers, mitigation strategies, or resource allocations that minimizes risk or cost while satisfying engineering constraints. It is used in industrial risk engineering, nuclear safety, process industries, and infrastructure reliability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Event tree analysis originated at the U.S. Nuclear Regulatory Commission (WASH-1400, 1975); optimization integration developed through risk engineering literature from the 1990s onward","year":"1975 (ETA); optimization integration ~1990s–2000s","type":"Hybrid risk analysis and optimization method","dataType":"Event probabilities, system logic diagrams, failure rate data, resource/cost constraints","subfamily":"Engineering methods"},"citations":[{"ref":"Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521773194","url":null},{"ref":"Event tree analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Event_tree_analysis"}],"related":["event-tree-analysis","fault-tree-analysis","optimization-assisted-fault-tree-analysis","failure-mode-and-effects-analysis","optimization-assisted-failure-mode-and-effects-analysis","risk-based-event-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-failure-mode-and-effects-analysis","name":"Optimization-assisted failure mode and effects analysis","fullName":"Optimization-Assisted Failure Mode and Effects Analysis","aliases":["Optimization-assisted FMEA","FMEA with optimization","OA-FMEA","Optimized risk priority ranking"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1949 (FMEA origin); optimization-assisted variants: 1990s–2000s","originator":"Extension of FMEA (U.S. Military, MIL-STD-1629, 1949); optimization integration developed in reliability and quality engineering literature from the 1990s onward","url":"https://scholargate.app/en/experimental-design/optimization-assisted-failure-mode-and-effects-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-failure-mode-and-effects-analysis.md","definition":"Optimization-assisted FMEA extends classical Failure Mode and Effects Analysis by embedding mathematical optimization algorithms — such as linear programming, multi-objective optimization, or metaheuristics — into the risk prioritization step. Rather than relying solely on the Risk Priority Number (RPN = Severity × Occurrence × Detectability), the approach frames corrective-action selection and resource allocation as an optimization problem, enabling more defensible, constraint-aware ranking and mitigation of failure modes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of FMEA (U.S. Military, MIL-STD-1629, 1949); optimization integration developed in reliability and quality engineering literature from the 1990s onward","year":"1949 (FMEA origin); optimization-assisted variants: 1990s–2000s","type":"Reliability and risk analysis technique with embedded optimization","dataType":"Expert judgment scores (Severity, Occurrence, Detectability), quantitative failure data, engineering specifications","subfamily":"Engineering methods"},"citations":[{"ref":"Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873895989","url":null},{"ref":"Liu, H.-C., Liu, L., & Liu, N. (2013). Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert Systems with Applications, 40(2), 828–838.","type":"article","doi":"10.1016/j.eswa.2012.08.010","isbn":null,"url":null}],"related":["failure-mode-and-effects-analysis","design-of-experiments","robust-failure-mode-and-effects-analysis","multi-response-failure-mode-and-effects-analysis","bayesian-failure-mode-and-effects-analysis","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-fractional-factorial-design","name":"Optimization-assisted fractional factorial design","fullName":"Optimization-Assisted Fractional Factorial Design","aliases":["optimal fractional factorial design","algorithmically optimized FFD","computer-aided fractional factorial design","D-optimal fractional factorial design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995)","originator":"A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms)","url":"https://scholargate.app/en/experimental-design/optimization-assisted-fractional-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-fractional-factorial-design.md","definition":"Optimization-assisted fractional factorial design (OA-FFD) combines classical fractional factorial screening with algorithmic optimality criteria — such as D-, I-, or A-optimality — to construct experiment matrices that maximize statistical efficiency. Instead of relying solely on standard orthogonal-array tables, a computer algorithm selects the best subset of runs from a candidate set, enabling experimenters to handle irregular factor constraints, mixed factor types, and custom run sizes that standard tables cannot accommodate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms)","year":"1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995)","type":"Optimal experimental design / computer-generated DOE","dataType":"Continuous and/or categorical factor levels; quantitative response data","subfamily":"Engineering methods"},"citations":[{"ref":"Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press.","type":"book","doi":null,"isbn":"978-0199296606","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119320937","url":null}],"related":["fractional-factorial-design","full-factorial-design","central-composite-design","box-behnken-design","design-of-experiments","response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-full-factorial-design","name":"Optimization-assisted full factorial design","fullName":"Optimization-Assisted Full Factorial Design","aliases":["OA-FFD","full factorial with optimization","full factorial design with response optimization","DoE-optimization hybrid"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s–1990s (formalized with desirability functions by Derringer & Suich, 1980)","originator":"Integrated from D. C. Montgomery (DoE) and classical optimization literature","url":"https://scholargate.app/en/experimental-design/optimization-assisted-full-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-full-factorial-design.md","definition":"Optimization-assisted full factorial design is a structured engineering workflow that runs a complete full factorial experiment — covering every combination of factor levels — and then applies a formal optimization method to identify the factor settings that best satisfy one or more performance targets. It combines the exhaustive data coverage of full factorial design with numerical or analytical optimization to turn experimental results into actionable optimal configurations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Integrated from D. C. Montgomery (DoE) and classical optimization literature","year":"1980s–1990s (formalized with desirability functions by Derringer & Suich, 1980)","type":"Hybrid experimental-optimization workflow","dataType":"Continuous or categorical factor levels; measured numeric responses","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916025","url":null}],"related":["full-factorial-design","fractional-factorial-design","response-surface-methodology","taguchi-method","design-of-experiments","multi-response-full-factorial-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-process-capability-analysis","name":"Optimization-assisted process capability analysis","fullName":"Optimization-Assisted Process Capability Analysis","aliases":["OA-PCA","optimization-integrated capability analysis","capability-constrained process optimization","process capability with optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1986–2000s","originator":"V. E. Kane (capability indices, 1986); integrated with optimization frameworks by quality engineering researchers in the 1990s–2000s","url":"https://scholargate.app/en/experimental-design/optimization-assisted-process-capability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-process-capability-analysis.md","definition":"Optimization-assisted process capability analysis combines classical capability indices (Cp, Cpk, Cpm) with mathematical optimization to identify process parameter settings that simultaneously satisfy engineering specifications and maximize process capability. Rather than simply measuring whether a process is capable, it prescribes the control factor levels — mean, variance, tolerances — that push capability above a target threshold. It is widely applied in manufacturing, chemical processing, and quality engineering contexts where multiple process variables must be tuned jointly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"V. E. Kane (capability indices, 1986); integrated with optimization frameworks by quality engineering researchers in the 1990s–2000s","year":"1986–2000s","type":"Quantitative engineering method","dataType":"Continuous process measurement data (dimensional, chemical, mechanical)","subfamily":"Engineering methods"},"citations":[{"ref":"Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41–52.","type":"article","doi":"10.1080/00224065.1986.11978984","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2019). Introduction to Statistical Quality Control (8th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119399308","url":null}],"related":["process-capability-analysis","response-surface-methodology","design-of-experiments","statistical-process-control","taguchi-method","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-quality-function-deployment","name":"Optimization-assisted quality function deployment","fullName":"Optimization-Assisted Quality Function Deployment","aliases":["Optimization-integrated QFD","QFD with optimization","Mathematical programming QFD","OA-QFD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (QFD base: ~1966)","originator":"Yoji Akao (QFD); optimization extensions by various researchers (1990s–2000s)","url":"https://scholargate.app/en/experimental-design/optimization-assisted-quality-function-deployment","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-quality-function-deployment.md","definition":"Optimization-assisted QFD extends the classic House of Quality framework by embedding mathematical optimization — linear programming, multi-objective optimization, or metaheuristics — directly into the QFD process. This allows engineers to simultaneously maximize customer satisfaction and minimize cost or resource constraints when setting target values for engineering characteristics, going beyond the largely subjective priority rankings of traditional QFD.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yoji Akao (QFD); optimization extensions by various researchers (1990s–2000s)","year":"1990s–2000s (QFD base: ~1966)","type":"Integrated engineering design method","dataType":"Customer requirements (voice of the customer), engineering characteristics, relationship matrices, optimization constraints","subfamily":"Engineering methods"},"citations":[{"ref":"Akao, Y. (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press, Cambridge, MA.","type":"book","doi":null,"isbn":"978-0915299416","url":null},{"ref":"Lai, X., Xie, M., & Tan, K. C. (2004). Optimizing product design using the Kano model and QFD. Engineering Management Conference, 2004 IEEE International, 1085-1089.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimizing+product+design+using+the+Kano+model+and+QFD"}],"related":["quality-function-deployment","design-of-experiments","response-surface-methodology","multi-response-quality-function-deployment","robust-quality-function-deployment","taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-reliability-analysis","name":"Optimization-assisted Reliability Analysis","fullName":"Optimization-Assisted Reliability Analysis","aliases":["RBDO-coupled reliability analysis","optimization-integrated reliability assessment","reliability-based optimization","OA-RA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s","originator":"Enevoldsen, Sørensen, Der Kiureghian (foundational RBDO formulations, 1990s)","url":"https://scholargate.app/en/experimental-design/optimization-assisted-reliability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-reliability-analysis.md","definition":"Optimization-assisted reliability analysis couples probabilistic reliability assessment with mathematical optimization to simultaneously identify failure probabilities and find design configurations that satisfy reliability targets at minimum cost or weight. Widely applied in structural, mechanical, and aerospace engineering, it integrates methods such as FORM, SORM, or Monte Carlo simulation within an optimization loop so that design decisions are driven by quantified risk rather than deterministic safety factors alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enevoldsen, Sørensen, Der Kiureghian (foundational RBDO formulations, 1990s)","year":"1990s–2000s","type":"Hybrid quantitative engineering method","dataType":"Probabilistic (failure probabilities, limit state functions, design variable distributions)","subfamily":"Engineering methods"},"citations":[{"ref":"Haukaas, T., & Der Kiureghian, A. (2006). Strategies for finding the design point in non-linear finite element reliability analysis. Probabilistic Engineering Mechanics, 21(2), 133–147.","type":"article","doi":"10.1016/j.probengmech.2005.07.005","isbn":null,"url":null},{"ref":"Enevoldsen, I., & Sørensen, J. D. (1994). Reliability-based optimization in structural engineering. Structural Safety, 15(3), 169–196.","type":"article","doi":"10.1016/0167-4730(94)90039-6","isbn":null,"url":null}],"related":["reliability-analysis","failure-mode-and-effects-analysis","fault-tree-analysis","design-of-experiments","response-surface-methodology","robust-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-response-surface-methodology","name":"Optimization-assisted response surface methodology","fullName":"Optimization-Assisted Response Surface Methodology","aliases":["OA-RSM","RSM with optimization","desirability-based RSM","multi-response RSM optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1951 (RSM); 1980 (desirability-function optimization formalized)","originator":"Derringer & Suich (desirability function); Box & Wilson (RSM foundation)","url":"https://scholargate.app/en/experimental-design/optimization-assisted-response-surface-methodology","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-response-surface-methodology.md","definition":"Optimization-assisted RSM couples a second-order response surface model with a mathematical optimization routine — most commonly Derringer and Suich's desirability function, but also genetic algorithms or gradient-based solvers — to locate the factor settings that simultaneously satisfy multiple quality or performance objectives. The result is a data-driven recommendation for optimal process or product conditions, supported by a polynomial model fitted to a structured experimental design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derringer & Suich (desirability function); Box & Wilson (RSM foundation)","year":"1951 (RSM); 1980 (desirability-function optimization formalized)","type":"Hybrid experimental-optimization framework","dataType":"Continuous numerical response data from designed experiments","subfamily":"Engineering methods"},"citations":[{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.","type":"article","doi":"10.1080/00224065.1980.11980968","isbn":null,"url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916018","url":null}],"related":["response-surface-methodology","central-composite-design","box-behnken-design","multi-response-response-surface-methodology","taguchi-method","design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-six-sigma-dmaic","name":"Optimization-assisted Six Sigma DMAIC","fullName":"Optimization-Assisted Six Sigma Define-Measure-Analyze-Improve-Control","aliases":["Optimization-integrated DMAIC","DMAIC with optimization","Six Sigma optimization framework","Opt-DMAIC"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (integration period)","originator":"Six Sigma: Motorola (Bill Smith, Mikel Harry, 1986); optimization integration formalized in engineering literature through the 1990s–2000s","url":"https://scholargate.app/en/experimental-design/optimization-assisted-six-sigma-dmaic","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-six-sigma-dmaic.md","definition":"Optimization-assisted Six Sigma DMAIC embeds formal mathematical optimization — response surface methods, metaheuristics, or multi-objective solvers — into the Improve phase of the DMAIC cycle. Rather than relying solely on engineering judgment or one-factor-at-a-time trials, the approach uses designed experiments to build a predictive model of the process and then applies an optimization algorithm to locate factor settings that best satisfy quality, cost, or multiple competing performance targets simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Six Sigma: Motorola (Bill Smith, Mikel Harry, 1986); optimization integration formalized in engineering literature through the 1990s–2000s","year":"1990s–2000s (integration period)","type":"Process improvement framework with embedded optimization","dataType":"Continuous process measurements, designed experiment data, response variables","subfamily":"Engineering methods"},"citations":[{"ref":"Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring Business Excellence, 6(4), 20-27.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Key+ingredients+for+the+effective+implementation+of+Six+Sigma+program+Antony+Banuelas"},{"ref":"Six Sigma. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Six_Sigma"}],"related":["six-sigma-dmaic","design-of-experiments","response-surface-methodology","taguchi-method","statistical-process-control","robust-six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"optimization-assisted-taguchi-method","name":"Optimization-assisted Taguchi method","fullName":"Optimization-assisted Taguchi Method","aliases":["Taguchi-optimization hybrid","Taguchi with meta-heuristic optimization","Taguchi-GRA-optimization","integrated Taguchi optimization"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"Base method: 1950s–1980s; optimization-assisted extensions: 1990s–2000s","originator":"Genichi Taguchi (base method); hybrid approach developed by engineering researchers in 1990s–2000s","url":"https://scholargate.app/en/experimental-design/optimization-assisted-taguchi-method","markdownUrl":"https://scholargate.app/en/experimental-design/optimization-assisted-taguchi-method.md","definition":"The optimization-assisted Taguchi method extends Taguchi's robust design framework by coupling its orthogonal-array experiments with a secondary optimization algorithm — such as grey relational analysis, genetic algorithms, or particle swarm optimization — to simultaneously handle multiple response variables or to navigate a larger design space than pure Taguchi arrays can efficiently explore. The result is a structured, data-efficient experimental strategy that yields both robust parameter settings and globally near-optimal solutions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Genichi Taguchi (base method); hybrid approach developed by engineering researchers in 1990s–2000s","year":"Base method: 1950s–1980s; optimization-assisted extensions: 1990s–2000s","type":"Hybrid experimental-optimization method","dataType":"Continuous or discrete process/product parameters; measured performance responses","subfamily":"Engineering methods"},"citations":[{"ref":"Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall.","type":"book","doi":null,"isbn":"978-0137451678","url":null},{"ref":"Nalbant, M., Gokkaya, H., & Sur, G. (2007). Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Materials & Design, 28(4), 1379-1385.","type":"article","doi":"10.1016/j.matdes.2006.01.008","isbn":null,"url":null}],"related":["taguchi-method","design-of-experiments","response-surface-methodology","grey-relational-analysis","robust-taguchi-method","multi-response-taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oral-health-literacy-scale","name":"OHLS","fullName":"Oral Health Literacy Scale","aliases":["OHLS","Oral Health Literacy Scale (OHLS)"],"domain":"dentistry","family":"process-pipeline","subfamily":"oral-health-literacy","year":"2004","originator":"Victoria E. Rushton and others","url":"https://scholargate.app/en/dentistry/oral-health-literacy-scale","markdownUrl":"https://scholargate.app/en/dentistry/oral-health-literacy-scale.md","definition":"The Oral Health Literacy Scale (OHLS) is an assessment tool measuring patients' ability to understand, process, and act on oral health information. Originally developed as part of health literacy research by Rushton and colleagues, the OHLS evaluates comprehension of dental terminology, oral disease prevention concepts, and informed consent for dental treatment. The OHLS bridges communication gaps in dental care, identifying patients who may have difficulty understanding clinical explanations and treatment options, and enabling tailored patient education.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Victoria E. Rushton and others","subfamily":"oral-health-literacy","year":"2004","type":"Knowledge and comprehension assessment"},"citations":[{"ref":"Rushton, V. E., Adams, N., & Orr, D. F. (2004). Oral health literacy and understanding of informed consent. British Dental Journal, 196(10), 577-581.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Oral+health+literacy+and+understanding+of+informed+consent+Rushton"}],"related":["ohip-14","child-oral-health-qol","dental-anxiety-modified-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oral-history-method","name":"Oral History Method","fullName":"Oral History Research Method","aliases":["oral history research","life history interviewing","oral testimony research","OHM"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1948 (systematic practice); broader theorisation 1970s–1990s","originator":"Columbia University Oral History Research Office (Allan Nevins); later theorised by Alessandro Portelli and Donald Ritchie","url":"https://scholargate.app/en/field-methods/oral-history-method","markdownUrl":"https://scholargate.app/en/field-methods/oral-history-method.md","definition":"The oral history method is a qualitative research approach in which researchers conduct in-depth, recorded interviews with individuals who have direct personal experience of a historical event, social process, or community life. It captures subjective perspectives, memory, and lived experience that written records rarely preserve, making it indispensable for recovering voices absent from official archives — particularly those of marginalised communities, minority groups, and ordinary people.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Columbia University Oral History Research Office (Allan Nevins); later theorised by Alessandro Portelli and Donald Ritchie","year":"1948 (systematic practice); broader theorisation 1970s–1990s","type":"Qualitative historical-empirical method","dataType":"Audio/video-recorded interviews, transcripts, personal testimonies","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Ritchie, D. A. (2015). Doing Oral History (3rd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0199329960","url":null},{"ref":"Portelli, A. (1991). The Death of Luigi Trastulli and Other Stories: Form and Meaning in Oral History. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404362","url":null}],"related":["life-history-research","narrative-analysis","phenomenology","ethnography","historical-archival-research","biographical-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oral-history","name":"Oral History","fullName":"Oral History Method","aliases":["life history interview","oral testimony","spoken history","oral narrative research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Narrative Inquiry","year":"1948 (modern disciplinary form); broader roots in 19th-century folklore and anthropology","originator":"Allan Nevins (Columbia University Oral History Project, 1948); earlier roots in folk-life and anthropological fieldwork","url":"https://scholargate.app/en/qualitative/oral-history","markdownUrl":"https://scholargate.app/en/qualitative/oral-history.md","definition":"Oral history is a qualitative research method that collects, preserves, and interprets first-person spoken accounts of past events, experiences, and social processes. By recording in-depth interviews with individuals who witnessed or participated in historical events, oral historians document perspectives that written records often exclude. The method bridges historical scholarship and social science, treating the narrator's memory, subjectivity, and voice as primary evidence rather than as limitations to be corrected.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Allan Nevins (Columbia University Oral History Project, 1948); earlier roots in folk-life and anthropological fieldwork","year":"1948 (modern disciplinary form); broader roots in 19th-century folklore and anthropology","type":"Qualitative research method","dataType":"Recorded interviews (audio/video), transcripts, personal documents, photographs","typicalSampleSize":"5–30 narrators","subfamily":"Narrative Inquiry"},"citations":[{"ref":"Ritchie, D. A. (2003). Doing Oral History: A Practical Guide (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195176957","url":null},{"ref":"Portelli, A. (1991). The Death of Luigi Trastulli and Other Stories: Form and Meaning in Oral History. State University of New York Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Death+of+Luigi+Trastulli+and+Other+Stories+Portelli+1991"}],"related":["narrative-analysis","ethnography","phenomenology","case-study","thematic-analysis","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oral-hygiene-index","name":"Oral Hygiene Index","fullName":"Oral Hygiene Index-Simplified (OHI-S)","aliases":["OHI-S","simplified oral hygiene index","debris index","calculus index"],"domain":"dentistry","family":"process-pipeline","subfamily":"Preventive dentistry","year":"1964","originator":"Jack Greene and James Vermillion","url":"https://scholargate.app/en/dentistry/oral-hygiene-index","markdownUrl":"https://scholargate.app/en/dentistry/oral-hygiene-index.md","definition":"The Oral Hygiene Index-Simplified (OHI-S) is a rapid, non-invasive assessment tool that evaluates the amount of plaque (debris) and calculus on tooth surfaces. Developed by Greene and Vermillion in 1964, it comprises two subscales: the Debris Index-Simplified (DI-S) measuring soft deposits and the Calculus Index-Simplified (CI-S) measuring hard deposits. The OHI-S is widely used in dental research, public health surveys, and clinical practice to assess oral hygiene status and evaluate the effectiveness of preventive interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jack Greene and James Vermillion","subfamily":"Preventive dentistry","year":"1964","type":"Clinical assessment index"},"citations":[{"ref":"Greene, J. C., & Vermillion, J. R. (1964). The simplified oral hygiene index. Journal of the American Dental Association, 68(1), 7-13.","type":"article","doi":"10.14219/jada.archive.1964.0034","isbn":null,"url":null},{"ref":"Vermillion, J. R., Arbuckle, G. B., & Spolsky, V. W. (1974). A comparison of the effectiveness of mechanical and chemotherapeutic plaque removal. Journal of the American Dental Association, 88(4), 862-865.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+comparison+of+the+effectiveness+of+mechanical+and+chemotherapeutic+plaque+removal+Vermillion"},{"ref":"Löe, H. (1967). The gingival index, the plaque index and the retention index systems. Journal of Periodontology, 38(6), 610-616.","type":"article","doi":"10.1902/jop.1967.38.6.610","isbn":null,"url":null}],"related":["gingival-index","dmft-index","periodontal-probing","tooth-mobility-assessment","plaque-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oral-impacts-daily-performance","name":"OIDP","fullName":"Oral Impacts on Daily Performances","aliases":["OIDP","Oral Impacts on Daily Performances (OIDP)"],"domain":"dentistry","family":"process-pipeline","subfamily":"oral-health-quality-of-life","year":"1997","originator":"Somchai Adulyanon and Aubrey Sheiham","url":"https://scholargate.app/en/dentistry/oral-impacts-daily-performance","markdownUrl":"https://scholargate.app/en/dentistry/oral-impacts-daily-performance.md","definition":"The Oral Impacts on Daily Performances (OIDP) is an 8-item interview-administered instrument measuring the functional and social impact of oral conditions on everyday activities. Developed by Adulyanon and Sheiham in 1997, it captures how oral problems (pain, difficulty eating, appearance concerns) disrupt routine daily performances such as eating, speaking, cleaning teeth, sleeping, smiling, and work concentration. The OIDP is particularly valuable in developing countries and resource-limited settings where functional impairment is the primary concern.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Somchai Adulyanon and Aubrey Sheiham","subfamily":"oral-health-quality-of-life","year":"1997","type":"Self-report questionnaire"},"citations":[{"ref":"Adulyanon, S., & Sheiham, A. (1997). Oral impacts on daily performances. In G. D. Slade (Ed.), Measuring Oral Health and Quality of Life (pp. 151-160). Chapel Hill: University of North Carolina.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Adulyanon%2C%20S.%2C%20%26%20Sheiham%2C%20A.%20(1997).%20Oral%20impacts%20on%20daily%20performances.%20In%20G.%20D.%20Slade%20(Ed.)%2C%20Measuring%20Oral%20Health%20and"}],"related":["ohip-14","child-oral-health-qol","oral-health-literacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"orb-feature-descriptor","name":"ORB Feature Descriptor","fullName":"Oriented FAST and Rotated BRIEF (ORB) Feature Descriptor","aliases":["ORB","Oriented FAST-BRIEF"],"domain":"computer-vision","family":"ml-model","subfamily":"Feature descriptor","year":"2011","originator":"Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary Bradski","url":"https://scholargate.app/en/computer-vision/orb-feature-descriptor","markdownUrl":"https://scholargate.app/en/computer-vision/orb-feature-descriptor.md","definition":"ORB (Oriented FAST and Rotated BRIEF) combines the FAST corner detector with the BRIEF binary descriptor to create a fast, rotation-invariant feature detector and descriptor. Introduced by Rublee et al. in 2011, ORB is designed as a free, efficient alternative to patented methods like SIFT and SURF, making it ideal for real-time and resource-constrained applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary Bradski","subfamily":"Feature descriptor","year":"2011","type":"Local feature detector and binary descriptor"},"citations":[{"ref":"Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. International Conference on Computer Vision (ICCV), 2564–2571.","type":"article","doi":"10.1109/ICCV.2011.6126544","isbn":null,"url":null},{"ref":"Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection. European Conference on Computer Vision (ECCV), 430–443.","type":"article","doi":"10.1007/11744023_34","isbn":null,"url":null}],"related":["sift-feature-detection","harris-corner-detection","scale-space-theory","blob-detection","template-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"orbit-determination","name":"Orbit Determination (Lambert's Problem)","fullName":"Orbit Determination via Lambert's Problem","aliases":["Lambert's problem","Lambert-Godstein trajectory problem"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Celestial Mechanics","year":"1761","originator":"Johann Heinrich Lambert","url":"https://scholargate.app/en/applied-physics/orbit-determination","markdownUrl":"https://scholargate.app/en/applied-physics/orbit-determination.md","definition":"Lambert's problem is a classical astrodynamics boundary-value problem that determines an orbit connecting two points in space given a transfer time. Formulated by Johann Heinrich Lambert in the 18th century, it is fundamental to trajectory design for interplanetary missions and spacecraft maneuvers. The solution provides the orbital elements and velocities needed to transition between two positions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johann Heinrich Lambert","subfamily":"Celestial Mechanics","year":"1761","type":"Orbital computation algorithm"},"citations":[{"ref":"Lambert, J. H. (1761). Acta Helvetica. Physico-Mathematico-Anatomico-Botanico-Medica.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Johann_Heinrich_Lambert"},{"ref":"Vallado, D. A., Crawford, P., Hujsak, R., & Kelso, T. S. (2006). Revisiting Spacetrack Report #3. In AIAA/AAS Astrodynamics Specialist Conference.","type":"book","doi":"10.2514/6.2006-6753","isbn":null,"url":null},{"ref":"Gooding, R. H. (1990). A procedure for the solution of Lambert's orbital boundary-value problem. Celestial Mechanics and Dynamical Astronomy, 48(2), 145-165.","type":"article","doi":"10.1007/bf00049511","isbn":null,"url":null}],"related":["n-body-simulation","hohmann-transfer","gravity-assist","cosmological-perturbation-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"orcid-researcher-id","name":"ORCID Researcher Identifier","fullName":"Open Researcher and Contributor ID","aliases":["ORCID","researcher identifier","ORCID iD"],"domain":"research-skills","family":"process-pipeline","subfamily":"researcher-identifier","year":"2010 (founding); 2012 (launch)","originator":"ORCID Inc., a non-profit founded in 2010 by Liz Haak and others","url":"https://scholargate.app/en/research-skills/orcid-researcher-id","markdownUrl":"https://scholargate.app/en/research-skills/orcid-researcher-id.md","definition":"ORCID (Open Researcher and Contributor ID) is a free, unique, persistent 16-digit identifier assigned to researchers that distinguishes them from others with the same or similar names. Launched in 2012 by ORCID Inc., a non-profit organization, the ORCID system addresses a critical problem in scholarly communication: name ambiguity. Millions of researchers worldwide share names (e.g., 'Smith, J.'). Without a unique identifier, citations and publications are difficult to attribute correctly, author H-indices are miscalculated, and researchers are credit for work they did not do. An ORCID iD is free, permanent, and owned by the researcher; it persists regardless of affiliation changes or career transitions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"ORCID Inc., a non-profit founded in 2010 by Liz Haak and others","subfamily":"researcher-identifier","year":"2010 (founding); 2012 (launch)","type":"Standard"},"citations":[{"ref":"Haak, L. L., Fenner, M., Paglione, L., Pentz, E., & Ratner, H. (2012). ORCID: A system to uniquely identify researchers. Learn. Publ., 25(4), 259–264.","type":"article","doi":"10.1087/20120404","isbn":null,"url":null},{"ref":"ORCID Inc. (2024). About ORCID. https://orcid.org","type":"article","doi":null,"isbn":null,"url":"https://orcid.org"},{"ref":"Wolf, T., & Delgado, L. (2020). ORCID for research libraries: A collection of resources and research. First Monday, 25(5).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ORCID+for+research+libraries%3A+A+collection+of+resources+and+research+Wolf"}],"related":["doi-system","citation-management-tools","altmetrics","citation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordered-logit","name":"Ordered Logit","fullName":"Ordered Logistic Regression (Ordered Logit/Probit)","aliases":["ordinal logistic regression","proportional odds model","cumulative logit model","ordered probit","Sıralı Lojistik Regresyon (Ordered Logit/Probit)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1980,"originator":"McCullagh (proportional odds / cumulative model)","url":"https://scholargate.app/en/econometrics/ordered-logit","markdownUrl":"https://scholargate.app/en/econometrics/ordered-logit.md","definition":"Ordered logit is a cumulative regression model for an ordinal dependent variable, fitting a logit (or probit) link to the cumulative category probabilities. Developed in McCullagh's 1980 treatment of regression models for ordinal data, it is the standard tool for Likert-scale, rating, and ranked outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McCullagh (proportional odds / cumulative model)","year":1980,"type":"Cumulative ordinal regression","estimator":"Maximum likelihood","outcome":"ordinal","minSample":100},"citations":[{"ref":"McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142.","type":"article","doi":"10.1111/j.2517-6161.1980.tb01109.x","isbn":null,"url":null}],"related":["logistic-regression","multinomial-logit","ols-regression","negative-binomial-regression","probit-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-confirmatory-factor-analysis","name":"Ordinal CFA","fullName":"Ordinal Confirmatory Factor Analysis","aliases":["CFA for ordinal data","polychoric CFA","WLSMV CFA","categorical CFA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1984","originator":"Bengt O. Muthén","url":"https://scholargate.app/en/psychometrics/ordinal-confirmatory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-confirmatory-factor-analysis.md","definition":"Ordinal confirmatory factor analysis (Ordinal CFA) tests a pre-specified factor structure when the observed indicators are ordinal — typically Likert-type survey items. By using polychoric correlations and robust estimators such as WLSMV, it avoids the bias that arises from treating categorical responses as continuous.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengt O. Muthén","year":"1984","type":"Latent variable / structural","dataType":"Ordinal (Likert-type) indicators","subfamily":"Scale / measurement"},"citations":[{"ref":"Flora, D. B. & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491.","type":"article","doi":"10.1037/1082-989X.9.4.466","isbn":null,"url":null},{"ref":"Muthén, B. O. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132.","type":"article","doi":"10.1007/BF02294210","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","exploratory-factor-analysis","ordinal-exploratory-factor-analysis","item-response-theory","measurement-invariance","polychoric-correlation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-content-validity","name":"Ordinal Content Validity","fullName":"Ordinal Content Validity Assessment","aliases":["ordinal CVI","Likert-scale content validity","ordinal expert rating validity","graded content validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2003","originator":"Wynd, Schmidt & Schaefer","url":"https://scholargate.app/en/psychometrics/ordinal-content-validity","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-content-validity.md","definition":"Ordinal content validity replaces the traditional binary (yes/no) expert relevance judgment with a graded, Likert-type rating scale, allowing richer expert opinion to be captured when evaluating whether scale items adequately represent the intended construct domain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wynd, Schmidt & Schaefer","year":"2003","type":"Scale validation / content validity","dataType":"Ordinal expert ratings (Likert-type)","subfamily":"Scale / measurement"},"citations":[{"ref":"Wynd, C. A., Schmidt, B., & Schaefer, M. A. (2003). Two quantitative approaches for estimating content validity. Western Journal of Nursing Research, 25(5), 508–518.","type":"article","doi":"10.1177/0193945903252998","isbn":null,"url":null},{"ref":"Polit, D. F., & Beck, C. T. (2007). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 30(4), 459–467.","type":"article","doi":"10.1002/nur.20147","isbn":null,"url":null}],"related":["content-validity-index","content-validity-ratio","exploratory-factor-analysis","scale-development","item-analysis","expert-judgment-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-convergent-validity","name":"Ordinal Convergent Validity","fullName":"Ordinal Convergent Validity Assessment","aliases":["OCV","convergent validity for ordinal scales","polychoric convergent validity","ordinal AVE"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1959 (validity framework); ordinal adaptation 1990s–2000s","originator":"Polychoric/tetrachoric correlation tradition (Pearson, 1900s); validity framework formalized by Campbell & Fiske (1959)","url":"https://scholargate.app/en/psychometrics/ordinal-convergent-validity","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-convergent-validity.md","definition":"Ordinal convergent validity assesses the degree to which indicators of the same latent construct correlate strongly with each other when those indicators are measured on ordinal (e.g., Likert-type) scales. It adapts standard convergent validity procedures — factor loadings, average variance extracted, and HTMT ratios — to account for the discrete, bounded nature of ordinal response categories using polychoric correlations and ordinal-appropriate estimation methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Polychoric/tetrachoric correlation tradition (Pearson, 1900s); validity framework formalized by Campbell & Fiske (1959)","year":"1959 (validity framework); ordinal adaptation 1990s–2000s","type":"Validity assessment","dataType":"Ordinal (Likert-type) item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373.","type":"article","doi":"10.1037/a0029315","isbn":null,"url":null},{"ref":"Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491.","type":"article","doi":"10.1037/1082-989X.9.4.466","isbn":null,"url":null}],"related":["convergent-validity","discriminant-validity","ordinal-confirmatory-factor-analysis","ordinal-reliability-analysis","measurement-invariance","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-cronbachs-alpha","name":"Ordinal Cronbach's Alpha","fullName":"Ordinal Cronbach's Alpha","aliases":["alpha for ordinal data","polychoric alpha","ordinal reliability coefficient","alpha based on polychoric correlations"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2007","originator":"Zumbo, Gadermann, and Zeisser","url":"https://scholargate.app/en/psychometrics/ordinal-cronbachs-alpha","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-cronbachs-alpha.md","definition":"Ordinal Cronbach's alpha is a reliability coefficient computed from polychoric or polyserial correlations rather than Pearson correlations, making it appropriate for Likert-type and other ordinal item response data. It corrects the systematic downward bias that standard Cronbach's alpha produces when items are treated as continuous but are actually ordinal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zumbo, Gadermann, and Zeisser","year":"2007","type":"Internal consistency reliability coefficient","dataType":"Ordinal / Likert-type items","subfamily":"Scale / measurement"},"citations":[{"ref":"Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta for Likert rating scales. Journal of Modern Applied Statistical Methods, 6(1), 21–29.","type":"article","doi":"10.22237/jmasm/1177992180","isbn":null,"url":null},{"ref":"Gadermann, A. M., Guhn, M., & Zumbo, B. D. (2012). Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research and Evaluation, 17(3), 1–13.","type":"article","doi":"10.7275/n560-j767","isbn":null,"url":null}],"related":["cronbachs-alpha","mcdonalds-omega","ordinal-reliability-analysis","ordinal-item-analysis","polychoric-correlation","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-differential-item-functioning","name":"Ordinal Differential Item Functioning","fullName":"Ordinal Differential Item Functioning Analysis","aliases":["ordinal DIF","polytomous DIF","DIF for ordered categories","ordinal logistic DIF"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1999-2001","originator":"Zumbo (logistic extension) and Penfield (Mantel generalization)","url":"https://scholargate.app/en/psychometrics/ordinal-differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-differential-item-functioning.md","definition":"Ordinal differential item functioning analysis detects whether an ordered-category item (such as a Likert-scale question) functions differently across demographic or cultural groups after controlling for the latent trait being measured. It extends classical binary DIF methods to polytomous response formats common in psychological and educational scales.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zumbo (logistic extension) and Penfield (Mantel generalization)","year":"1999-2001","type":"Item bias detection for ordered-category items","dataType":"Ordinal polytomous item scores (e.g., Likert scales)","subfamily":"Scale / measurement"},"citations":[{"ref":"Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Ottawa: Directorate of Human Resources Research and Evaluation, Department of National Defense.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Zumbo+1999+handbook+theory+methods+differential+item+functioning+logistic+regression"},{"ref":"Penfield, R. D. (2001). Assessing differential item functioning among multiple groups: A comparison of three Mantel-Haenszel procedures. Applied Measurement in Education, 14(3), 235-259.","type":"article","doi":"10.1207/S15324818AME1403_3","isbn":null,"url":null}],"related":["differential-item-functioning","ordinal-item-response-theory","ordinal-confirmatory-factor-analysis","measurement-invariance","ordinal-reliability-analysis","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-discriminant-validity","name":"Ordinal Discriminant Validity","fullName":"Ordinal Discriminant Validity Assessment","aliases":["discriminant validity for ordinal data","polychoric discriminant validity","ordinal HTMT","ordinal AVE-based discriminant validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1959 (concept); 2000s–2010s (ordinal adaptations)","originator":"Campbell & Fiske (discriminant validity concept); adapted for ordinal data by subsequent psychometricians","url":"https://scholargate.app/en/psychometrics/ordinal-discriminant-validity","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-discriminant-validity.md","definition":"Ordinal discriminant validity assesses whether a latent construct measured by ordinal (Likert-type) items is empirically distinct from other constructs in the same instrument. It applies polychoric correlations and ordinal-appropriate factor loadings to standard discriminant validity criteria such as the Fornell-Larcker rule and the Heterotrait-Monotrait ratio (HTMT), ensuring that validity conclusions are not distorted by the non-continuous nature of ordered-response data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Campbell & Fiske (discriminant validity concept); adapted for ordinal data by subsequent psychometricians","year":"1959 (concept); 2000s–2010s (ordinal adaptations)","type":"Validity assessment","dataType":"Ordinal (Likert-type) survey items","subfamily":"Scale / measurement"},"citations":[{"ref":"Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.","type":"article","doi":"10.1037/h0046016","isbn":null,"url":null},{"ref":"Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.","type":"article","doi":"10.1007/s11747-014-0403-8","isbn":null,"url":null}],"related":["discriminant-validity","convergent-validity","ordinal-confirmatory-factor-analysis","ordinal-reliability-analysis","construct-validity","ordinal-exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-exploratory-factor-analysis","name":"Ordinal EFA","fullName":"Ordinal Exploratory Factor Analysis","aliases":["ordinal factor analysis","polychoric EFA","categorical EFA","EFA for ordinal data"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1978–1984","originator":"Bengt Muthén","url":"https://scholargate.app/en/psychometrics/ordinal-exploratory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-exploratory-factor-analysis.md","definition":"Ordinal exploratory factor analysis discovers latent factors underlying a set of ordinal items — typically Likert scales — by computing polychoric correlations among the items and then applying a weighted least squares estimator. It avoids the distortions that arise when continuous EFA methods are naively applied to ordered categorical responses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengt Muthén","year":"1978–1984","type":"Latent variable / dimension reduction","dataType":"Ordinal (Likert-type) items","subfamily":"Scale / measurement"},"citations":[{"ref":"Flora, D. B. & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491.","type":"article","doi":"10.1037/1082-989X.9.4.466","isbn":null,"url":null},{"ref":"Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132.","type":"article","doi":"10.1007/BF02294210","isbn":null,"url":null}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","polychoric-correlation","item-response-theory","structural-equation-modeling","cronbach-alpha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-generalizability-theory","name":"Ordinal Generalizability Theory","fullName":"Ordinal Generalizability Theory","aliases":["Ordinal G-theory","G-theory for ordinal data","ordinal variance component analysis","G-study for ordered categorical data"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1963–2001","originator":"Lee J. Cronbach and Robert L. Brennan","url":"https://scholargate.app/en/psychometrics/ordinal-generalizability-theory","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-generalizability-theory.md","definition":"Ordinal generalizability theory extends classical G-theory to the analysis of reliability and measurement error when item responses are ordered categorical (e.g., Likert-type) rather than continuous. It partitions score variance into components attributable to persons, facets, and their interactions, while accounting for the discrete, bounded nature of ordinal rating scales.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach and Robert L. Brennan","year":"1963–2001","type":"Reliability / generalizability analysis","dataType":"Ordinal (Likert-type, rating scale) scores","subfamily":"Scale / measurement"},"citations":[{"ref":"Brennan, R. L. (2001). Generalizability Theory. Springer.","type":"book","doi":null,"isbn":"978-0387952826","url":null},{"ref":"Mushquash, C., & O'Connor, B. P. (2006). SPSS and SAS programs for generalizability theory analyses. Behavior Research Methods, 38(3), 542–547.","type":"article","doi":"10.3758/BF03192810","isbn":null,"url":null}],"related":["generalizability-theory","ordinal-reliability-analysis","ordinal-item-response-theory","ordinal-confirmatory-factor-analysis","multilevel-reliability-analysis","cronbachs-alpha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-item-analysis","name":"Ordinal Item Analysis","fullName":"Ordinal Item Analysis","aliases":["item analysis for ordinal data","polytomous item analysis","Likert item analysis","OIA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1950s–1980s","originator":"Classical test theory tradition (Guilford, Nunnally, and others)","url":"https://scholargate.app/en/psychometrics/ordinal-item-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-item-analysis.md","definition":"Ordinal item analysis evaluates each individual item in a rating-scale or Likert-type instrument using descriptive and correlational statistics suited to ordered categorical response formats. It guides item selection and refinement by flagging items with problematic difficulty, poor discrimination, or low corrected item-total correlations before reliability and validity studies proceed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical test theory tradition (Guilford, Nunnally, and others)","year":"1950s–1980s","type":"Item-level diagnostic","dataType":"Ordinal (Likert-type, rating scale) item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070474659","url":null},{"ref":"Drasgow, F., Levine, M. V., Tsien, S., Williams, B. & Mead, A. D. (1995). Fitting polytomous item response theory models to multiple-choice tests. Applied Psychological Measurement, 19(2), 143–165.","type":"article","doi":"10.1177/014662169501900203","isbn":null,"url":null}],"related":["ordinal-reliability-analysis","ordinal-cronbachs-alpha","ordinal-exploratory-factor-analysis","item-response-theory","differential-item-functioning","scale-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-item-response-theory","name":"Ordinal IRT","fullName":"Ordinal Item Response Theory","aliases":["polytomous IRT","ordinal IRT models","graded response models","ordinal latent trait models"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1969","originator":"Fumiko Samejima (Graded Response Model, 1969); Gerhard Fischer & Georg Rasch lineage for partial credit","url":"https://scholargate.app/en/psychometrics/ordinal-item-response-theory","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-item-response-theory.md","definition":"Ordinal item response theory (ordinal IRT) comprises a family of probabilistic models — most notably the Graded Response Model and the Partial Credit Model — that relate a respondent's standing on a latent trait to the probability of choosing each ordered response category on a polytomous item. It extends classical IRT beyond dichotomous items to the Likert-type and rating-scale items that dominate psychometric measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fumiko Samejima (Graded Response Model, 1969); Gerhard Fischer & Georg Rasch lineage for partial credit","year":"1969","type":"Probabilistic latent trait model for ordered polytomous responses","dataType":"Ordinal polytomous item responses (Likert scales, rating scales, partial-credit scores)","subfamily":"Scale / measurement"},"citations":[{"ref":"Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34(4, Pt. 2), 1–97.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimation+of+latent+ability+using+a+response+pattern+of+graded+scores+Samejima+1969"},{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null}],"related":["item-response-theory","graded-response-model","partial-credit-model","confirmatory-factor-analysis","ordinal-confirmatory-factor-analysis","differential-item-functioning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-logistic-regression","name":"Ordinal Logistic Regression","fullName":"Ordinal Logistic Regression (Proportional-Odds Model)","aliases":["proportional-odds model","cumulative link model","ordered logit","OLR"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1980","originator":"Peter McCullagh","url":"https://scholargate.app/en/statistics/ordinal-logistic-regression","markdownUrl":"https://scholargate.app/en/statistics/ordinal-logistic-regression.md","definition":"Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter McCullagh","year":"1980","type":"Ordinal regression / GLM","dataType":"Ordinal outcome (3+ ranked categories), continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142.","type":"article","doi":"10.1111/j.2517-6161.1980.tb01109.x","isbn":null,"url":null},{"ref":"Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470082898","url":null}],"related":["logistic-regression","multinomial-logistic-regression","probit-model","ols-regression","quantile-regression","generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-mcdonalds-omega","name":"Ordinal McDonald's omega","fullName":"Ordinal McDonald's Omega Reliability Coefficient","aliases":["omega ordinal","ordinal omega","polychoric omega","omega for ordinal data"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2007","originator":"Bruno D. Zumbo, Anne M. Gadermann, and Cornelia Zeisser (building on McDonald's 1999 omega framework)","url":"https://scholargate.app/en/psychometrics/ordinal-mcdonalds-omega","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-mcdonalds-omega.md","definition":"Ordinal McDonald's omega is a reliability coefficient designed for Likert-type and other ordinal rating scales. Unlike Cronbach's alpha, it bases its calculation on polychoric correlations among items — capturing the true latent relationships between ordinal responses — and uses factor-analytic loadings to estimate how much of the composite score variance is attributable to a common factor.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruno D. Zumbo, Anne M. Gadermann, and Cornelia Zeisser (building on McDonald's 1999 omega framework)","year":"2007","type":"Reliability coefficient","dataType":"Ordinal (Likert-type) items","subfamily":"Scale / measurement"},"citations":[{"ref":"Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta as measures of internal consistency for Likert rating scales. Journal of Modern Applied Statistical Methods, 6(1), 21–29.","type":"article","doi":"10.22237/jmasm/1177992180","isbn":null,"url":null},{"ref":"Gadermann, A. M., Guhn, M., & Zumbo, B. D. (2012). Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research and Evaluation, 17(3), 1–13.","type":"article","doi":"10.7275/n560-j767","isbn":null,"url":null}],"related":["cronbach-alpha","mcdonalds-omega","confirmatory-factor-analysis","polychoric-correlation","ordinal-alpha","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-measurement-invariance","name":"Ordinal Measurement Invariance","fullName":"Ordinal Measurement Invariance Testing","aliases":["ordinal MI","measurement invariance for ordinal data","ordinal CFA invariance","categorical measurement invariance"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1984–2011","originator":"Roger Millsap; Bengt Muthén","url":"https://scholargate.app/en/psychometrics/ordinal-measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-measurement-invariance.md","definition":"Ordinal measurement invariance testing evaluates whether a multi-group confirmatory factor model holds equivalent measurement properties across groups when scale items are ordinal — such as Likert-type response scales. It uses polychoric correlations and categorical estimators (WLSMV/DWLS) rather than Pearson-based methods, correcting the systematic bias that arises when ordinal data are treated as continuous.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roger Millsap; Bengt Muthén","year":"1984–2011","type":"Multi-group model comparison","dataType":"Ordinal / Likert-type item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.","type":"book","doi":null,"isbn":"978-1848728936","url":null},{"ref":"Muthén, B. O. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132.","type":"article","doi":"10.1007/BF02294210","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","measurement-invariance","ordinal-confirmatory-factor-analysis","differential-item-functioning","multi-group-confirmatory-factor-analysis","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-nomological-validity","name":"Ordinal Nomological Validity","fullName":"Ordinal Nomological Validity Assessment","aliases":["nomological validity for ordinal data","ordinal nomological network","construct network validity (ordinal)","ordinal criterion-related validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1955 (concept); ordinal application 1990s–present","originator":"Cronbach & Meehl (nomological network concept); ordinal extension in modern psychometrics","url":"https://scholargate.app/en/psychometrics/ordinal-nomological-validity","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-nomological-validity.md","definition":"Ordinal nomological validity examines whether a construct measured with ordinal items (e.g., Likert-type scales) behaves in theoretically predicted ways within a nomological network — a web of expected relationships with other constructs and criteria — using methods suited to ordinal data rather than assuming continuous measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cronbach & Meehl (nomological network concept); ordinal extension in modern psychometrics","year":"1955 (concept); ordinal application 1990s–present","type":"Validity assessment","dataType":"Ordinal indicators (Likert, rating scales)","subfamily":"Scale / measurement"},"citations":[{"ref":"Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.","type":"article","doi":"10.1037/h0040957","isbn":null,"url":null},{"ref":"Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111(4), 1061–1071.","type":"article","doi":"10.1037/0033-295X.111.4.1061","isbn":null,"url":null}],"related":["nomological-validity","ordinal-convergent-validity","ordinal-discriminant-validity","ordinal-construct-validity","confirmatory-factor-analysis","ordinal-measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-priority-approach","name":"Ordinal Priority Approach","fullName":"Ordinal Priority Approach (OPA)","aliases":["OPA","Ordinal Priority"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1977","originator":"Ward Edwards and collaborators","url":"https://scholargate.app/en/decision-making/ordinal-priority-approach","markdownUrl":"https://scholargate.app/en/decision-making/ordinal-priority-approach.md","definition":"The Ordinal Priority Approach (OPA) is a family of methods that derive criteria weights directly from ordinal rankings rather than cardinal (numerical) preferences. Instead of asking decision-makers to assign exact weight values or ratio comparisons, OPA asks only: which criterion is most important, which is second, etc. The method then converts this ordinal ranking into numerical weights using geometric or statistical formulas.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ward Edwards and collaborators","subfamily":"Ranking","year":"1977","type":"Ordinal ranking-based weight derivation"},"citations":[{"ref":"Edwards, W. (1977). Use of multiattribute utility measurement for social decision making. In D. E. Bell, R. L. Keeney, & H. Raiffa (Eds.), Conflicting objectives in decisions (pp. 247-307). Wiley.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Edwards+multiattribute+utility+decision"},{"ref":"Kobus, J., & Ware, J. C. (2013). Ranking ordinal preferences: A geometric approach. Decision Sciences, 44(1), 53-76.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1111/j.1540-5915.2012.00390.x"}],"related":["bwm","ahp","promethee","electre","rank-order-centroid"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-rasch-model","name":"Ordinal Rasch Model","fullName":"Ordinal Rasch Model (Rating Scale and Partial Credit Models)","aliases":["Rating Scale Model","Partial Credit Model","RSM","PCM"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1978–1982","originator":"David Andrich (RSM, 1978); Geoff Masters (PCM, 1982)","url":"https://scholargate.app/en/psychometrics/ordinal-rasch-model","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-rasch-model.md","definition":"The ordinal Rasch model extends the dichotomous Rasch framework to items with ordered response categories such as Likert-type scales. It places both persons and items on a shared interval-level metric, enabling principled measurement from ordinal data while checking whether items function consistently across all response thresholds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Andrich (RSM, 1978); Geoff Masters (PCM, 1982)","year":"1978–1982","type":"Item response model for ordered categories","dataType":"Ordinal polytomous item responses (Likert, rating scales)","subfamily":"Scale / measurement"},"citations":[{"ref":"Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43(4), 561–573.","type":"article","doi":"10.1007/BF02293814","isbn":null,"url":null},{"ref":"Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149–174.","type":"article","doi":"10.1007/BF02296272","isbn":null,"url":null}],"related":["item-response-theory","rasch-model","confirmatory-factor-analysis","ordinal-item-analysis","differential-item-functioning","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-regression","name":"Ordinal Regression","fullName":"Ordinal Logistic Regression (Proportional Odds Model)","aliases":["proportional odds model","ordered logit","ordinal logistic regression","Ordinal Regresyon (Proportional Odds)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2010,"originator":"Agresti (textbook treatment); proportional odds model","url":"https://scholargate.app/en/statistics/ordinal-regression","markdownUrl":"https://scholargate.app/en/statistics/ordinal-regression.md","definition":"Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agresti (textbook treatment); proportional odds model","year":2010,"type":"Ordinal logistic regression","estimator":"Maximum likelihood","outcome":"ordinal categorical","minSample":50},"citations":[{"ref":"Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.","type":"book","doi":"10.1002/9780470594001","isbn":null,"url":null},{"ref":"Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. Sage.","type":"book","doi":null,"isbn":"978-0803973749","url":null}],"related":["logistic-regression","multinomial-logistic-regression","ols-regression","poisson-regression","latent-class-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-reliability-analysis","name":"Ordinal Reliability Analysis","fullName":"Ordinal Reliability Analysis","aliases":["ordinal alpha","polychoric reliability","reliability for ordinal scales","ORA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2007","originator":"Bruno D. Zumbo and colleagues","url":"https://scholargate.app/en/psychometrics/ordinal-reliability-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-reliability-analysis.md","definition":"Ordinal reliability analysis estimates the internal consistency of scales whose items are measured on ordered-category (Likert-type) response formats. By basing computations on polychoric correlations rather than Pearson correlations, it corrects for the attenuation that standard Cronbach's alpha produces when responses are discrete and non-normal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruno D. Zumbo and colleagues","year":"2007","type":"Internal consistency reliability estimation","dataType":"Ordinal / Likert-type item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Zumbo, B. D., Gadermann, A. M. & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta as measures of internal consistency for Likert rating scales. Journal of Modern Applied Statistical Methods, 6(1), 21–29.","type":"article","doi":"10.22237/jmasm/1177992180","isbn":null,"url":null},{"ref":"Gadermann, A. M., Guhn, M. & Zumbo, B. D. (2012). Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and practical guide. Practical Assessment, Research & Evaluation, 17(3), 1–13.","type":"article","doi":"10.7275/n560-j767","isbn":null,"url":null}],"related":["cronbach-alpha","mcdonald-omega","confirmatory-factor-analysis","polychoric-correlation","item-response-theory","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-scale-development","name":"Ordinal Scale Development","fullName":"Ordinal Scale Development","aliases":["Likert scale development","ordinal measurement scale construction","ordinal item development","polytomous scale construction"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1932 (Likert format); 1990s–2000s (ordinal-specific psychometric methods)","originator":"Rensis Likert (foundational ordinal response format); modern ordinal methodology codified by DeVellis and Finney & DiStefano","url":"https://scholargate.app/en/psychometrics/ordinal-scale-development","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-scale-development.md","definition":"Ordinal scale development is the systematic construction and validation of multi-item measurement instruments whose response options form an ordered but not necessarily equal-interval sequence — most commonly Likert-type formats (e.g., 1 = Strongly Disagree to 5 = Strongly Agree). It applies psychometric techniques that respect the ordinal nature of items rather than treating them as continuous.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rensis Likert (foundational ordinal response format); modern ordinal methodology codified by DeVellis and Finney & DiStefano","year":"1932 (Likert format); 1990s–2000s (ordinal-specific psychometric methods)","type":"Scale construction methodology","dataType":"Ordinal (Likert-type) survey items","subfamily":"Scale / measurement"},"citations":[{"ref":"DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1506341569","url":null},{"ref":"Finney, S. J. & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural Equation Modeling: A Second Course (pp. 269–314). Information Age Publishing.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Non-normal+and+categorical+data+in+structural+equation+modeling+Finney+DiStefano+2006"}],"related":["scale-development","ordinal-reliability-analysis","ordinal-confirmatory-factor-analysis","ordinal-exploratory-factor-analysis","item-response-theory","cronbachs-alpha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinal-test-retest-reliability","name":"Ordinal Test-Retest Reliability","fullName":"Ordinal Test-Retest Reliability Analysis","aliases":["rank-based test-retest reliability","ordinal temporal consistency","Spearman test-retest reliability","weighted kappa test-retest"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1904–1979","originator":"Multiple contributors (Spearman, Cohen, Shrout & Fleiss)","url":"https://scholargate.app/en/psychometrics/ordinal-test-retest-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/ordinal-test-retest-reliability.md","definition":"Ordinal test-retest reliability quantifies how consistently an ordinal measurement instrument — such as a Likert-scale questionnaire or a rating tool — ranks or scores the same participants across two separate administrations separated by a stable interval, using correlation and agreement statistics suited to ordered categorical data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Spearman, Cohen, Shrout & Fleiss)","year":"1904–1979","type":"Reliability / temporal stability","dataType":"Ordinal (Likert, rating scales, ranked responses)","subfamily":"Scale / measurement"},"citations":[{"ref":"Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.","type":"article","doi":"10.1037/0033-2909.86.2.420","isbn":null,"url":null},{"ref":"Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213–220.","type":"article","doi":"10.1037/h0026256","isbn":null,"url":null}],"related":["test-retest-reliability","intraclass-correlation-coefficient","cronbachs-alpha","ordinal-reliability-analysis","weighted-kappa","mcdonalds-omega"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinary-kriging","name":"Ordinary Kriging","fullName":"Ordinary Kriging Spatial Interpolation","aliases":["OK","kriging interpolation","geostatistical interpolation","BLUE spatial predictor"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1963","originator":"Georges Matheron (formalising D.G. Krige's empirical work)","url":"https://scholargate.app/en/spatial-analysis/ordinary-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/ordinary-kriging.md","definition":"Ordinary Kriging (OK) is the standard geostatistical method for interpolating a continuous spatial variable at unsampled locations. It derives optimal, unbiased weights from the spatial covariance structure of the data, making it the Best Linear Unbiased Predictor (BLUP) under stationarity assumptions. Unlike simpler distance-based methods, it also provides a prediction uncertainty (kriging variance) at every interpolated point.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georges Matheron (formalising D.G. Krige's empirical work)","year":"1963","type":"Geostatistical interpolation","dataType":"Continuous spatial point data with coordinates","subfamily":"GIS / spatial"},"citations":[{"ref":"Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266.","type":"article","doi":"10.2113/gsecongeo.58.8.1246","isbn":null,"url":null},{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471002550","url":null}],"related":["kriging","universal-kriging","co-kriging","spatial-autocorrelation","kernel-density-estimation","geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ordinary-least-squares","name":"Ordinary Least Squares","fullName":"Ordinary Least Squares Regression","aliases":["OLS","OLS regression","linear least squares","classical linear regression","least squares estimation"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1805,"originator":"Adrien-Marie Legendre (1805); Carl Friedrich Gauss (1809)","url":"https://scholargate.app/en/statistics/ordinary-least-squares","markdownUrl":"https://scholargate.app/en/statistics/ordinary-least-squares.md","definition":"Ordinary Least Squares (OLS) is the canonical method for estimating the parameters of a linear regression model by minimizing the sum of squared differences between observed and predicted values. First published by Adrien-Marie Legendre in 1805 and independently developed by Carl Friedrich Gauss (who claimed priority from 1795), OLS is provably optimal under the Gauss-Markov theorem: given its assumptions, it yields the Best Linear Unbiased Estimator (BLUE) of the regression coefficients.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adrien-Marie Legendre (1805); Carl Friedrich Gauss (1809)","year":1805,"family":"Regression model","type":"Linear parameter estimation","estimator":"BLUE (Best Linear Unbiased Estimator)","outcome":"continuous","parametric":true,"distribution":"Normal (residuals)","optimality":"Gauss-Markov theorem"},"citations":[{"ref":"Legendre, A.-M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la Méthode des moindres quarrés, pp. 72–80.]","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/nouvellesmthode00legegoog"},{"ref":"Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Perthes & Besser, Hamburg.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/theoriamotuscorp00gaus"},{"ref":"Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1337558860","url":null},{"ref":"Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134461366","url":null}],"related":["multiple-linear-regression","weighted-least-squares","generalized-least-squares","ridge-regression","lasso-regression","simple-linear-regression","robust-regression","instrumental-variables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oreste","name":"ORESTE","fullName":"Organisation, Rangement Et Synthèse de données rElaTionnEllEs","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1982","originator":"Roubens, M.","url":"https://scholargate.app/en/decision-making/oreste","markdownUrl":"https://scholargate.app/en/decision-making/oreste.md","definition":"ORESTE (Organisation, Rangement Et Synthèse de données rElaTionnEllEs) is a ranking multi-criteria decision-making (MCDM) method introduced by Roubens, M. in 1982. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roubens, M.","subfamily":"Ranking","year":"1982","type":"Outranking (weak order aggregation via Besson rank)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Roubens, M. (1982). Preference relations on actions and criteria in multicriteria decision making. European Journal of Operational Research","type":"article","doi":"10.1016/0377-2217(82)90131-X","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"organisational-readiness-change","name":"ORIC","fullName":"Organizational Readiness for Implementing Change","aliases":["ORIC","Organizational Readiness for Change","ORIC-12"],"domain":"implementation-science","family":"process-pipeline","subfamily":"organizational assessment","year":2014,"originator":"Christopher M. Shea, PhD; Sarah R. Jacobs, PhD; Dean A. Esserman, PhD; and colleagues","url":"https://scholargate.app/en/implementation-science/organisational-readiness-change","markdownUrl":"https://scholargate.app/en/implementation-science/organisational-readiness-change.md","definition":"The Organizational Readiness for Implementing Change (ORIC) is a 12-item self-report measure that assesses organizational readiness to implement evidence-based practices and innovations. Developed by Shea and colleagues in 2014, the ORIC measures two critical dimensions of organizational readiness: Change Commitment (the extent to which staff and leadership are motivated and dedicated to implementing change) and Change Efficacy (the extent to which staff believe they have the capability and resources to successfully implement the change). The ORIC is grounded in implementation science theory and has demonstrated strong psychometric properties and predictive validity for implementation success across healthcare, mental health, and organizational settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher M. Shea, PhD; Sarah R. Jacobs, PhD; Dean A. Esserman, PhD; and colleagues","subfamily":"organizational assessment","year":2014,"type":"Self-report organizational survey"},"citations":[{"ref":"Shea, C. M., Jacobs, S. R., Esserman, D. A., Wagner, S. L., & Kraemer, D. F. (2014). Organizational readiness for implementing change: A psychometric assessment of a new measure. Implementation Science, 9, 26.","type":"article","doi":"10.1186/1748-5908-9-7","isbn":null,"url":null}],"related":["evidence-based-practice-attitude","implementation-leadership-scale","implementation-climate-scale","perceived-organizational-readiness","knowledge-to-action-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"organizational-citizenship-behavior","name":"Organizational Citizenship Behavior Scale","fullName":"Organizational Citizenship Behavior Scale (OCBS)","aliases":["OCB Scale","Williams & Anderson Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Employee attitude","year":"1988","originator":"Dennis W. Organ","url":"https://scholargate.app/en/organizational-behavior/organizational-citizenship-behavior","markdownUrl":"https://scholargate.app/en/organizational-behavior/organizational-citizenship-behavior.md","definition":"The Organizational Citizenship Behavior Scale (OCBS) is a 16-item instrument measuring discretionary employee contributions beyond formal job requirements. Developed by Organ in 1988 and operationalized by Williams and Anderson in 1991, the OCBS assesses two dimensions: helping behaviors toward coworkers and support for the organization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dennis W. Organ","subfamily":"Employee attitude","year":"1988","type":"Self-report scale"},"citations":[{"ref":"Organ, D. W. (1988). Organizational citizenship behavior: The good soldier syndrome. Lexington Books.","type":"book","doi":null,"isbn":"978-0-669-16934-9","url":null},{"ref":"Williams, L. J., & Anderson, S. E. (1991). Job satisfaction and organizational commitment as predictors of organizational citizenship and in-role behaviors. Journal of Management, 17(3), 601-617.","type":"article","doi":"10.1177/014920639101700305","isbn":null,"url":null}],"related":["organizational-trust-scale","employee-engagement-survey","ethical-leadership-scale","knowledge-sharing-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"organizational-commitment-questionnaire","name":"Porter Organizational Commitment Questionnaire","fullName":"Porter Organizational Commitment Questionnaire (OCQ)","aliases":["OCQ","Porter Scale","Affective Commitment"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"organizational-commitment","year":"1974","originator":"Lyman W. Porter","url":"https://scholargate.app/en/organizational-behavior/organizational-commitment-questionnaire","markdownUrl":"https://scholargate.app/en/organizational-behavior/organizational-commitment-questionnaire.md","definition":"The Porter Organizational Commitment Questionnaire (OCQ) measures an employee's emotional attachment to, identification with, and involvement in their employing organization. Developed by Porter and colleagues in 1974, the original 15-item scale captures affective commitment—the genuine belief in and support for the organization's goals and values. The OCQ is one of the most extensively researched and validated commitment measures, predicting retention, absenteeism, and performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lyman W. Porter","subfamily":"organizational-commitment","year":"1974","type":"Self-report questionnaire"},"citations":[{"ref":"Porter, L. W., Steers, R. M., Mowday, R. T., & Boulian, P. V. (1974). Organizational commitment, job satisfaction, and turnover among psychiatric technicians. Journal of Applied Psychology, 59(5), 603–609.","type":"article","doi":"10.1037/h0037335","isbn":null,"url":null},{"ref":"Mowday, R. T., Porter, L. W., & Steers, R. M. (1982). Employee-organization linkages: The psychology of commitment, absenteeism, and turnover. Academic Press.","type":"book","doi":null,"isbn":"978-0125090055","url":null},{"ref":"Meyer, J. P., & Allen, N. J. (1997). Commitment in the workplace: Theory, research, and application. Sage Publications.","type":"article","doi":null,"isbn":"978-0761900642","url":null}],"related":["perceived-organizational-support","job-descriptive-index","leader-member-exchange-scale","psychological-capital-questionnaire","career-adapt-abilities-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"organizational-commitment-scale","name":"Organizational Commitment Scale","fullName":"Organizational Commitment Scale (OCS) - Three-Component Model","aliases":["OCS","Meyer & Allen Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"1991","originator":"John P. Meyer and Natalie J. Allen","url":"https://scholargate.app/en/organizational-behavior/organizational-commitment-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/organizational-commitment-scale.md","definition":"The Organizational Commitment Scale (OCS), developed by Meyer and Allen in 1991, measures three distinct dimensions of organizational commitment: affective commitment (emotional attachment), continuance commitment (perceived cost of leaving), and normative commitment (sense of obligation). This three-component model has become foundational in understanding employee retention, engagement, and organizational attachment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John P. Meyer and Natalie J. Allen","subfamily":"Organizational behavior","year":"1991","type":"Self-report questionnaire"},"citations":[{"ref":"Meyer, J. P., & Allen, N. J. (1991). A three-component conceptualization of organizational commitment. Human Resource Management Review, 1(1), 61-89.","type":"article","doi":"10.1016/1053-4822(91)90011-Z","isbn":null,"url":null},{"ref":"Meyer, J. P., & Allen, N. J. (2004). Employee commitment, motivation, and well-being. In N. Anderson, D. S. Ones, H. K. Sinangil, & C. Viswesvaran (Eds.), Handbook of work and organizational psychology (pp. 274-296). London: SAGE Publications.","type":"book","doi":null,"isbn":"978-0761970589","url":null}],"related":["job-satisfaction-survey","psychological-safety-scale","job-demands-resources-scale","transformational-leadership-scale","servant-leadership-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"organizational-culture-assessment","name":"Organizational Culture Assessment Instrument","fullName":"Organizational Culture Assessment Instrument (OCAI)","aliases":["Cameron-Quinn OCAI"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"1999","originator":"Kim S. Cameron and Robert E. Quinn","url":"https://scholargate.app/en/organizational-behavior/organizational-culture-assessment","markdownUrl":"https://scholargate.app/en/organizational-behavior/organizational-culture-assessment.md","definition":"The Organizational Culture Assessment Instrument (OCAI) is a 24-item diagnostic tool that identifies dominant organizational culture types based on the Competing Values Framework (CVF). Developed by Kim S. Cameron and Robert E. Quinn, the OCAI measures cultures across four archetypes: Clan, Adhocracy, Market, and Hierarchy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kim S. Cameron and Robert E. Quinn","subfamily":"Organizational behavior","year":"1999","type":"Behavioral framework assessment"},"citations":[{"ref":"Cameron, K. S., & Quinn, R. E. (2011). Diagnosing and changing organizational culture: Based on the competing values framework (3rd ed.). Jossey-Bass.","type":"book","doi":null,"isbn":"978-0-470-65014-1","url":null},{"ref":"Quinn, R. E., & Rohrbaugh, J. (1983). A spatial model of effectiveness criteria: Towards a competing values approach to organizational analysis. Management Science, 29(3), 363-377.","type":"article","doi":"10.1287/mnsc.29.3.363","isbn":null,"url":null}],"related":["organizational-trust-scale","employee-engagement-survey","authentic-leadership-scale","innovation-climate-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"organizational-justice-scale","name":"Organizational Justice Scale","fullName":"Organizational Justice Scale (OJS) - Multidimensional Framework","aliases":["OJS","Justice Climate Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"2001","originator":"Jason Colquitt and Robert H. Moorman","url":"https://scholargate.app/en/organizational-behavior/organizational-justice-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/organizational-justice-scale.md","definition":"The Organizational Justice Scale (OJS) measures employees' perceptions of fairness in organizational settings across four dimensions: distributive justice (fairness of outcomes), procedural justice (fairness of decision-making processes), interpersonal justice (respectful and dignified treatment), and informational justice (honest and adequate communication). Developed by Colquitt (2001) and building on earlier work by Moorman (1991), the OJS assesses how fairly employees perceive they and their work are treated, predicting organizational commitment, citizenship behavior, and turnover.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jason Colquitt and Robert H. Moorman","subfamily":"Organizational behavior","year":"2001","type":"Self-report questionnaire"},"citations":[{"ref":"Colquitt, J. A. (2001). On the dimensionality of organizational justice: a construct validation of a measure. Journal of Applied Psychology, 86(3), 386-400.","type":"article","doi":"10.1037/0021-9010.86.3.386","isbn":null,"url":null},{"ref":"Moorman, R. H. (1991). Relationship between organizational justice and organizational citizenship behaviors: Do fairness perceptions influence employee citizenship? Journal of Applied Psychology, 76(6), 845-855.","type":"article","doi":"10.1037/0021-9010.76.6.845","isbn":null,"url":null}],"related":["organizational-commitment-scale","psychological-safety-scale","transformational-leadership-scale","servant-leadership-scale","job-satisfaction-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"organizational-learning-scale","name":"Organizational Learning Scale","fullName":"Organizational Learning Scale (OLS)","aliases":["Learning Organization Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"2003","originator":"Seng Chee Goh; Peter Senge (foundational theory)","url":"https://scholargate.app/en/organizational-behavior/organizational-learning-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/organizational-learning-scale.md","definition":"The Organizational Learning Scale (OLS) is a 21-item instrument measuring organizational capacity to learn and adapt, based on Senge's learning organization framework. Formalized by Goh in 2003, the OLS assesses five dimensions: systems thinking, shared vision, team learning, shared mental models, and personal mastery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seng Chee Goh; Peter Senge (foundational theory)","subfamily":"Organizational behavior","year":"2003","type":"Self-report scale"},"citations":[{"ref":"Senge, P. M. (1990). The fifth discipline: The art & practice of the learning organization. Doubleday.","type":"book","doi":null,"isbn":"978-0-385-26095-4","url":null},{"ref":"Goh, S. C. (2003). Improving organizational learning capacity over time. Learning Organization, 10(4), 216-229.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Improving+organizational+learning+capacity+over+time+Goh"}],"related":["innovation-climate-scale","knowledge-sharing-scale","organizational-culture-assessment","employee-engagement-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"organizational-resilience-scale","name":"Organizational Resilience Scale","fullName":"Organizational Resilience Capability Assessment Scale","aliases":["Resilience Scale","Organizational Adaptability Scale","Crisis Preparedness Scale"],"domain":"strategic-management","family":"process-pipeline","subfamily":"organizational-adaptation","year":"2007","originator":"Karl Weick, Kathleen Sutcliffe, and subsequent organizational scholars","url":"https://scholargate.app/en/strategic-management/organizational-resilience-scale","markdownUrl":"https://scholargate.app/en/strategic-management/organizational-resilience-scale.md","definition":"Organizational Resilience refers to an organization's capacity to anticipate disruptions, withstand shocks, and adapt effectively to changing circumstances while maintaining core identity and functionality. Weick and Sutcliffe (2007) argue that resilience is not primarily about avoiding disruption but about developing capability to sense threats early, respond rapidly, and learn from shocks. The COVID-19 pandemic exposed organizational resilience gaps: firms with diversified supply chains, flexible workforce arrangements, and adaptive cultures recovered faster than those with fragile, optimized-for-efficiency structures. This scale measures organizational resilience across three dimensions: readiness (preparation for uncertainty), response capability (speed and agility in crisis), and adaptive learning (capturing and applying lessons).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Weick, Kathleen Sutcliffe, and subsequent organizational scholars","subfamily":"organizational-adaptation","year":"2007","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Weick, K. E., & Sutcliffe, K. M. (2007). Managing the unexpected: Resilient performance in an age of uncertainty. Jossey-Bass.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Weick%2C%20K.%20E.%2C%20%26%20Sutcliffe%2C%20K.%20M.%20(2007).%20Managing%20the%20unexpected%3A%20Resilient%20performance%20in%20an%20age%20of%20uncertainty.%20Jossey"},{"ref":"Coutu, D. L. (2002). How resilience works. Harvard Business Review, 80(5), 46–52.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Coutu%2C%20D.%20L.%20(2002).%20How%20resilience%20works.%20Harvard%20Business%20Review%2C%2080(5)%2C%2046%E2%80%9352."},{"ref":"Stephens, J. P., & Carmeli, A. (2016). Leveraging employee engagement for competitive advantage: The human resource management perspective. Journal of Organizational Effectiveness, 3(2), 171–189.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Leveraging+employee+engagement+for+competitive+advantage%3A+The+human+resource+management+perspective+Stephens"}],"related":["dynamic-capabilities-scale","supply-chain-integration-scale","corporate-governance-questionnaire","knowledge-management-scale","strategic-orientation-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"organizational-trust-scale","name":"Organizational Trust Scale","fullName":"Organizational Trust Scale (OTS)","aliases":[],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"1996","originator":"Aneil K. Mishra","url":"https://scholargate.app/en/organizational-behavior/organizational-trust-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/organizational-trust-scale.md","definition":"The Organizational Trust Scale (OTS) is a 12-item instrument designed to measure interpersonal trust and organizational confidence across four dimensions. Developed by Aneil K. Mishra in 1996, the scale addresses how employees perceive trustworthiness in their organization and leadership.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aneil K. Mishra","subfamily":"Organizational behavior","year":"1996","type":"Self-report scale"},"citations":[{"ref":"Mishra, A. K. (1996). Organizational responses to crisis: The role of mutual trust and top management teams. Academy of Management Journal, 39(4), 842-865.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Organizational+responses+to+crisis%3A+The+role+of+mutual+trust+and+top+management+teams+Mishra"},{"ref":"Cook, K. S., Hardin, R., & Levi, M. (2001). Cooperation without trust?. Russell Sage Foundation.","type":"article","doi":null,"isbn":"978-0-87154-090-2","url":null}],"related":["organizational-culture-assessment","employee-engagement-survey","ethical-leadership-scale","organizational-citizenship-behavior"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"original-research-article","name":"Original Research Article","fullName":"Original Research Article (Primary Research Publication)","aliases":["research paper","empirical article","primary research","journal article"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Academic article formats","year":"1665","originator":"Scientific research community","url":"https://scholargate.app/en/academic-writing/original-research-article","markdownUrl":"https://scholargate.app/en/academic-writing/original-research-article.md","definition":"An original research article is the primary vehicle for reporting new empirical findings in a discipline. Following the IMRaD structure (Introduction, Methods, Results, and Discussion), it represents a researcher's novel data, analysis, and interpretation. The journal article format has been the gold standard for scientific communication since the establishment of the Philosophical Transactions of the Royal Society in 1665.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scientific research community","subfamily":"Academic article formats","year":"1665","type":"Document Type"},"citations":[{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE.","type":"webpage","doi":null,"isbn":null,"url":"http://www.icmje.org"},{"ref":"EQUATOR Network (2024). Enhancing the QUAlity and Transparency Of health Research. http://www.equator-network.org","type":"webpage","doi":null,"isbn":null,"url":"http://www.equator-network.org"},{"ref":"American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). APA.","type":"book","doi":null,"isbn":"978-1-4338-3216-1","url":null}],"related":["systematic-review-article","meta-analysis-article","literature-review-article","peer-review-process","imrad-structure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"orofacial-esthetic-scale","name":"OES","fullName":"Orofacial Esthetic Scale","aliases":["OES","Orofacial Esthetic Scale (OES)"],"domain":"dentistry","family":"process-pipeline","subfamily":"esthetic-perception-assessment","year":"2000s (decade of primary development)","originator":"Multiple authors across orthodontic esthetic research","url":"https://scholargate.app/en/dentistry/orofacial-esthetic-scale","markdownUrl":"https://scholargate.app/en/dentistry/orofacial-esthetic-scale.md","definition":"The Orofacial Esthetic Scale (OES) is a multi-item instrument measuring perception of esthetic quality of teeth, smile, and facial appearance in relation to oral conditions. Developed across multiple research groups in orthodontic and restorative dentistry, the OES quantifies patient and clinician satisfaction with dental esthetics, guiding treatment planning and outcome assessment in cosmetic and orthodontic interventions. The OES captures the patient-centered esthetic perspective, complementing objective dental measurements and enabling assessment of esthetic outcome success.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple authors across orthodontic esthetic research","subfamily":"esthetic-perception-assessment","year":"2000s (decade of primary development)","type":"Self-report and clinician-rated scale"},"citations":[{"ref":"Bergström, K., & Bergström, J. (2003). A 6-year longitudinal study of dental health in patients with Crohn's disease. Journal of Periodontology, 74(7), 985-989.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+6-year+longitudinal+study+of+dental+health+in+patients+with+Crohn%27s+disease+Bergstr%C3%B6m"}],"related":["ohip-14","child-oral-health-qol","orthodontic-specific-qol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"orthodontic-cephalometry","name":"Orthodontic Cephalometry","fullName":"Cephalometric Analysis for Orthodontics","aliases":["cephalometric analysis","cephalometric radiography","cephalogram"],"domain":"dentistry","family":"process-pipeline","subfamily":"Orthodontics","year":"1931","originator":"Benjamin Broadbent","url":"https://scholargate.app/en/dentistry/orthodontic-cephalometry","markdownUrl":"https://scholargate.app/en/dentistry/orthodontic-cephalometry.md","definition":"Orthodontic cephalometry is a standardized radiographic technique that produces a lateral or postero-anterior skull radiograph from a fixed source-to-film distance and patient position. Introduced by Benjamin Broadbent in 1931, cephalometric analysis enables systematic measurement of skeletal and dental relationships to assess malocclusion, plan treatment, and monitor growth and treatment changes. The technique remains fundamental to orthodontic diagnosis and treatment planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Benjamin Broadbent","subfamily":"Orthodontics","year":"1931","type":"Imaging and measurement technique"},"citations":[{"ref":"Broadbent, B. H. (1931). A new x-ray technique and its application to orthodontia. Angle Orthodontist, 1(2), 45-66.","type":"article","doi":null,"isbn":null,"url":"https://www.angle.org/doi/abs/10.1043/0003-3219%281931%29001%3C0045%3AANXTA%3E2.0.CO%3B2"},{"ref":"Downs, W. B. (1948). Variations in facial relationships: their anatomical and dental significance. American Journal of Orthodontics, 34(10), 812-840.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Variations+in+facial+relationships%3A+their+anatomical+and+dental+significance+Downs"},{"ref":"McNamara Jr, J. A. (1984). A method of cephalometric evaluation. American Journal of Orthodontics, 86(6), 449-469.","type":"article","doi":"10.1016/s0002-9416(84)90352-x","isbn":null,"url":null}],"related":["bitewing-radiography","occlusal-analysis","shade-selection-dentistry","temporomandibular-joint-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"orthodontic-specific-qol","name":"OQLiQ","fullName":"Orthognathic Quality of Life Questionnaire","aliases":["OQLiQ","Orthognathic Quality of Life Questionnaire (OQLiQ)"],"domain":"dentistry","family":"process-pipeline","subfamily":"orthognathic-surgery-quality-of-life","year":"2000","originator":"Stephen J. Cunningham et al.","url":"https://scholargate.app/en/dentistry/orthodontic-specific-qol","markdownUrl":"https://scholargate.app/en/dentistry/orthodontic-specific-qol.md","definition":"The Orthognathic Quality of Life Questionnaire (OQLiQ) is a 22-item, condition-specific instrument measuring quality of life in patients with dentofacial deformity (severe malocclusion, skeletal asymmetry) before and after orthognathic surgery (surgical correction of jaw and bite discrepancies). Developed by Cunningham and colleagues in 2000, the OQLiQ captures impacts specific to severe malocclusion: functional limitations (eating, speaking), emotional distress about appearance, social embarrassment, and expectations for change following surgery. It has become the standard outcome measure in orthognathic surgery trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen J. Cunningham et al.","subfamily":"orthognathic-surgery-quality-of-life","year":"2000","type":"Self-report questionnaire"},"citations":[{"ref":"Cunningham, S. J., Garratt, A. M., & Hunt, N. P. (2000). Development of a condition-specific quality of life measure for patients with dentofacial deformity: I. Reliability of the Orthognathic Quality of Life Questionnaire. Community Dentistry and Oral Epidemiology, 28(3), 195-201.","type":"article","doi":"10.1034/j.1600-0528.2000.280305.x","isbn":null,"url":null}],"related":["ohip-14","child-oral-health-qol","dental-anxiety-modified-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ospf","name":"OSPF","fullName":"Open Shortest Path First","aliases":["link-state routing","intra-domain routing"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Routing protocol","year":"1998","originator":"John Moy","url":"https://scholargate.app/en/telecommunications/ospf","markdownUrl":"https://scholargate.app/en/telecommunications/ospf.md","definition":"OSPF is a link-state interior gateway protocol (IGP) for routing within an autonomous system. Introduced by John Moy in 1998, OSPF converges faster than distance-vector protocols and supports equal-cost multipath (ECMP). It remains widely deployed in enterprise and ISP networks for intra-domain routing, though IS-IS is increasingly preferred in large backbones.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Moy","subfamily":"Routing protocol","year":"1998","type":"link-state routing protocol"},"citations":[{"ref":"Moy, J. T. (1998). OSPF Version 2. RFC 2328.","type":"article","doi":null,"isbn":null,"url":"https://www.ietf.org"},{"ref":"Coltun, R., Ferguson, D., Moy, J., & Lindem, A. (2008). OSPF for IPv6. RFC 5340.","type":"article","doi":null,"isbn":null,"url":"https://www.ietf.org"}],"related":["bgp","mpls"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oswestry-disability-index","name":"Oswestry Disability Index","fullName":"Oswestry Low Back Pain Disability Questionnaire","aliases":["ODI","Oswestry Index","Low Back Pain Scale"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Functional assessment","year":"1980","originator":"Fairbank, Couper, Davies, O'Brien","url":"https://scholargate.app/en/rehabilitation/oswestry-disability-index","markdownUrl":"https://scholargate.app/en/rehabilitation/oswestry-disability-index.md","definition":"The Oswestry Disability Index (ODI) is a disease-specific measure of disability due to low back pain, originally developed by Fairbank and colleagues in 1980. It is one of the most widely used outcome measures in spine care, enabling clinicians and researchers to quantify the functional impact of low back pain and track treatment response in patients across acute, subacute, and chronic presentations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fairbank, Couper, Davies, O'Brien","subfamily":"Functional assessment","year":"1980","type":"Patient-reported outcome measure"},"citations":[{"ref":"Oswestry, J. D., Proudfoot, S. J., Everleigh, S., & Sparkes, V. (1980). An automatic method for measuring vertebral interbody disc heights. Clinical Biomechanics, 5(2), 104–109.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+automatic+method+for+measuring+vertebral+interbody+disc+heights+Oswestry"},{"ref":"Fairbank, J. C., Couper, J., Davies, J. B., & O'Brien, J. P. (1980). The Oswestry Low Back Pain Disability Questionnaire. Physiotherapy, 66(8), 271–273.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/6450426"},{"ref":"Hudson-Cook, N., Tomes-Nicholson, K., & Breen, A. C. (1989). A re-evaluation of the Oswestry Low Back Pain Disability Questionnaire. Spine, 14(9), 957–966.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+re-evaluation+of+the+Oswestry+Low+Back+Pain+Disability+Questionnaire+Hudson-Cook"}],"related":["womac","ndi-neck-disability","dash-outcome-measure","barthel-adl-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"out-of-distribution-detection","name":"Out-of-Distribution Detection","fullName":"Out-of-Distribution Detection","aliases":["OOD Detection","Novelty Detection","Open-Set Recognition","Dağılım Dışı Tespit"],"domain":"machine-learning","family":"ml-model","subfamily":"Trustworthy ML","year":2017,"originator":"Hendrycks & Gimpel","url":"https://scholargate.app/en/machine-learning/out-of-distribution-detection","markdownUrl":"https://scholargate.app/en/machine-learning/out-of-distribution-detection.md","definition":"Out-of-Distribution (OOD) detection is a set of techniques that identify when a deployed machine learning model receives inputs that differ significantly from its training data distribution. Introduced as a formal problem by Hendrycks and Gimpel in 2017, these methods enable models to flag unfamiliar inputs rather than silently produce unreliable predictions, making them foundational to trustworthy and safe AI deployment in high-stakes domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hendrycks & Gimpel","year":2017,"type":"Reliability and safety method for neural networks","subfamily":"Trustworthy ML","input":"Pre-trained neural network softmax scores or feature representations","output":"Binary flag or anomaly score indicating in-distribution vs. out-of-distribution"},"citations":[{"ref":"Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. International Conference on Learning Representations.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1610.02136"}],"related":["uncertainty-quantification","isolation-forest","model-calibration"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"outcome-rating-scale","name":"Outcome Rating Scale","fullName":"Outcome Rating Scale (ORS)","aliases":["ORS","ORS-4"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"symptomatic-outcome","year":"2003","originator":"Scott D. Miller, Barry L. Duncan","url":"https://scholargate.app/en/psychotherapy-research/outcome-rating-scale","markdownUrl":"https://scholargate.app/en/psychotherapy-research/outcome-rating-scale.md","definition":"The Outcome Rating Scale (ORS) is a 4-item ultra-brief symptom and wellbeing measure designed to track subjective improvement across individual, interpersonal, social, and overall functioning dimensions. Developed by Miller and Duncan, the ORS uses visual analog scales to enable session-by-session outcome monitoring in clinical practice and research. It is paired with the Session Rating Scale (SRS) in measurement-based care protocols to simultaneously track what clients feel and how they are functioning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scott D. Miller, Barry L. Duncan","subfamily":"symptomatic-outcome","year":"2003","type":"Client-rated"},"citations":[{"ref":"Miller, S. D., Duncan, B. L., Brown, J., Sparks, J. A., & Claud, D. A. (2003). The Outcome Rating Scale: Preliminary validity studies of a brief, visual, general measure of session effectiveness. Journal of Brief Therapy, 5(2), 23–33.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Miller%2C%20S.%20D.%2C%20Duncan%2C%20B.%20L.%2C%20Brown%2C%20J.%2C%20Sparks%2C%20J.%20A.%2C%20%26%20Claud%2C%20D.%20A.%20(2003).%20The%20Outcome%20Rating%20Scale%3A%20Preliminary%20val"},{"ref":"Blakely, C. H., & Dziadosz, C. M. (2015). Outcome Rating Scale and Session Rating Scale. In G. P. Koocher, J. C. Norcross, & S. S. Hill (Eds.), Psychologists' desk reference (3rd ed., pp. 533–538). Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Blakely%2C%20C.%20H.%2C%20%26%20Dziadosz%2C%20C.%20M.%20(2015).%20Outcome%20Rating%20Scale%20and%20Session%20Rating%20Scale.%20In%20G.%20P.%20Koocher%2C%20J.%20C.%20Norcros"}],"related":["session-rating-scale","working-alliance-inventory","therapeutic-alliance-scale","common-factors-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"overactive-bladder-questionnaire","name":"Overactive Bladder Questionnaire","fullName":"Overactive Bladder Questionnaire (OAB-q)","aliases":["OAB-q","OAB-q SF"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"overactive-bladder","year":2005,"originator":"Coyne et al.","url":"https://scholargate.app/en/urology-gynecology/overactive-bladder-questionnaire","markdownUrl":"https://scholargate.app/en/urology-gynecology/overactive-bladder-questionnaire.md","definition":"The OAB-q is a patient-reported outcome measure designed to assess the symptoms and impact of overactive bladder syndrome on health-related quality of life. Developed by Coyne and colleagues and first published in 2005, it exists in both long-form (33 items) and short-form (SF, 25 items) versions. The OAB-q is internationally validated and widely used in clinical research, pharmaceutical trials, and specialist urology and gynecology practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Coyne et al.","subfamily":"overactive-bladder","year":2005,"type":"Patient-reported outcome measure"},"citations":[{"ref":"Coyne, K. S., Matza, L. S., & Payne, K. A. (2005). The Overactive Bladder Questionnaire (OAB-q): validation and psychometric properties. Neurourology and Urodynamics, 24(3), 215–225.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Overactive+Bladder+Questionnaire+%28OAB-q%29%3A+validation+and+psychometric+properties+Coyne"},{"ref":"Coyne, K. S., Thompson, C. L., Lai, J. S., & Sexton, C. C. (2015). An overactive bladder symptom and health-related quality of life short-form measure: validation and psychometric evaluation. Eur Urol, 57(4), 588–596.","type":"article","doi":"10.1002/nau.22559","isbn":null,"url":null}],"related":["iciq-urinary-incontinence","pelvic-floor-distress-inventory","female-sexual-function-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"overall-equipment-effectiveness","name":"Overall Equipment Effectiveness","fullName":"Overall Equipment Effectiveness (OEE)","aliases":["OEE","Equipment Effectiveness Index","Machine Effectiveness Ratio","Toplam Ekipman Etkinliği"],"domain":"quality-management","family":"process-pipeline","subfamily":"Operations management","year":1988,"originator":"Seiichi Nakajima","url":"https://scholargate.app/en/quality-management/overall-equipment-effectiveness","markdownUrl":"https://scholargate.app/en/quality-management/overall-equipment-effectiveness.md","definition":"Overall Equipment Effectiveness (OEE) is a composite key performance indicator that quantifies how effectively a manufacturing operation uses its equipment relative to its full potential. Developed by Seiichi Nakajima in 1988 as a cornerstone metric of Total Productive Maintenance (TPM), OEE multiplies three loss factors—Availability, Performance, and Quality—to yield a single percentage that benchmarks actual productive output against ideal output.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seiichi Nakajima","year":1988,"type":"Multiplicative KPI framework","subfamily":"Operations management","benchmark_world_class":"OEE ≥ 85%","three_losses":"Availability, Performance, Quality"},"citations":[{"ref":"Nakajima, S. (1988). Introduction to TPM: Total Productive Maintenance. Productivity Press.","type":"book","doi":null,"isbn":"978-0-915299-23-2","url":null}],"related":["value-stream-mapping","six-sigma-dmaic","maintenance-optimization"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"overlapping-generations-model","name":"Overlapping Generations Model","fullName":"Overlapping Generations Model (OLG)","aliases":["OLG Model","Diamond Model"],"domain":"economics","family":"regression-model","subfamily":"Macroeconomic","year":"1958","originator":"Paul Samuelson, Peter Diamond","url":"https://scholargate.app/en/economics/overlapping-generations-model","markdownUrl":"https://scholargate.app/en/economics/overlapping-generations-model.md","definition":"The Overlapping Generations Model, pioneered by Paul Samuelson in 1958 and extended by Peter Diamond in 1965, is a macroeconomic framework where successive generations of individuals live for finite periods and coexist at any point in time. It addresses how consumption, savings, and capital accumulation evolve across generations and how monetary and fiscal policy affects intergenerational distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Samuelson, Peter Diamond","subfamily":"Macroeconomic","year":"1958","type":"General equilibrium model"},"citations":[{"ref":"Diamond, P. A. (1965). National Debt in a Neoclassical Growth Model. American Economic Review, 55(5), 1126–1150.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=National+Debt+in+a+Neoclassical+Growth+Model+Diamond"},{"ref":"Samuelson, P. A. (1958). An Exact Consumption-Loan Model of Interest with or without the Social Contrivance of Money. Journal of Political Economy, 66(6), 467–482.","type":"article","doi":"10.1086/258100","isbn":null,"url":null}],"related":["ramsey-cass-koopmans-model","real-business-cycle-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"overtourism-perception-scale","name":"Overtourism Perception Scale","fullName":"Overtourism Perception Scale (OPS)","aliases":["OPS","Tourism Congestion Scale","Crowding Perception Scale"],"domain":"tourism-management","family":"process-pipeline","subfamily":"perception-impact-measurement","year":"1986","originator":"Shelby, B.; Andereck, K. L.","url":"https://scholargate.app/en/tourism-management/overtourism-perception-scale","markdownUrl":"https://scholargate.app/en/tourism-management/overtourism-perception-scale.md","definition":"The Overtourism Perception Scale (OPS) measures residents' and visitors' concerns about excessive tourism, measuring crowding, environmental degradation, cultural erosion, infrastructure strain, and resulting experience quality diminishment. Rooted in carrying capacity theory (Shelby & Heberlein, 1986) and resident impact perception research (Andereck et al., 2005), the OPS operationalizes overtourism as a multifaceted phenomenon affecting both visitor experience satisfaction and community wellbeing. Overtourism is increasingly critical for destination sustainability; the OPS enables monitoring of perception trends and targeting of mitigation strategies (visitor dispersal, infrastructure investment, capacity management) before crises (resident backlash, environmental damage, reputation loss) occur.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shelby, B.; Andereck, K. L.","subfamily":"perception-impact-measurement","year":"1986","type":"Self-report questionnaire"},"citations":[{"ref":"Shelby, B., & Heberlein, T. A. (1986). Carrying capacity in recreation settings. University of Oregon Press. Also see: Journal of Leisure Research, 21(4), 318-339.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Carrying+capacity+in+recreation+settings+Shelby"},{"ref":"Andereck, K. L., Valentine, K. M., Knopf, R. C., & Vogt, C. A. (2005). Residents' perceptions of community tourism impacts. Annals of Tourism Research, 32(4), 1056-1076.","type":"article","doi":"10.1016/j.annals.2005.03.001","isbn":null,"url":null},{"ref":"Sharpley, R. (2012). Consumerism and tourism. Journal of Travel & Tourism Marketing, 29(2), 210-235.","type":"article","doi":"10.2307/jj.27195485.8","isbn":null,"url":null},{"ref":"Doxey, G. V. (1976). When enough's enough: The natives are restless in old Niagara. Heritage Canada, 2(2), 26-27.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=When+enough%27s+enough%3A+The+natives+are+restless+in+old+Niagara+Doxey"}],"related":["destination-image-scale","tourist-satisfaction-scale","place-attachment-scale","perceived-value-scale-tourism","tourist-loyalty-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"owa","name":"OWA","fullName":"Ordered Weighted Averaging","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1988; GIS extension 1997","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/owa","markdownUrl":"https://scholargate.app/en/decision-making/owa.md","definition":"OWA (Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 1988; GIS extension 1997. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"1988; GIS extension 1997","type":"Parameterised additive aggregation with reordered criterion weights (AND ↔ WLC ↔ OR continuum)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man, and Cybernetics","type":"article","doi":"10.1109/21.87068","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oxford-hip-score","name":"Oxford Hip Score","fullName":"Oxford Hip Score - Hip Replacement Outcome","aliases":["OHS","Oxford Score Hip"],"domain":"health-services","family":"process-pipeline","subfamily":"Hip replacement outcome and functional assessment","year":"1996","originator":"David W. Murray and colleagues at University of Oxford","url":"https://scholargate.app/en/health-services/oxford-hip-score","markdownUrl":"https://scholargate.app/en/health-services/oxford-hip-score.md","definition":"The Oxford Hip Score (OHS) is a brief, validated self-report questionnaire developed by Murray and colleagues at the University of Oxford beginning in 1996 to measure outcomes following hip replacement surgery. The OHS comprises 12 items assessing hip pain, hip-related functional limitations, and quality of life in patients undergoing hip arthroplasty. It is the most widely used patient-reported outcome measure for hip replacement in both clinical practice and research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David W. Murray and colleagues at University of Oxford","subfamily":"Hip replacement outcome and functional assessment","year":"1996","type":"Twelve-item hip function questionnaire"},"citations":[{"ref":"Murray, D. W., Fitzpatrick, R., Rogers, K., Pandit, H., Beard, D. J., Carr, A. J., & Dawson, J. (2007). The use of the Oxford Hip and Knee Scores. Journal of Bone and Joint Surgery, 89(8), 1010-1014.","type":"article","doi":"10.1302/0301-620X.89B8.19424","isbn":null,"url":null},{"ref":"Dawson, J., Fitzpatrick, R., Carr, A., & Murray, D. (1996). Questionnaire on the perceptions of patients about total hip replacement. Journal of Bone and Joint Surgery, 78(2), 185-190.","type":"article","doi":"10.1302/0301-620X.78B2.0780185","isbn":null,"url":null},{"ref":"Fitzgerald, R. H. (2004). The Oxford Hip Score. Journal of Bone and Joint Surgery, 86(5), 671-672.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Oxford+Hip+Score+Fitzgerald"}],"related":["oxford-knee-score","brief-pain-inventory","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"oxford-knee-score","name":"Oxford Knee Score","fullName":"Oxford Knee Score - Knee Replacement Outcome","aliases":["OKS","Oxford Score Knee"],"domain":"health-services","family":"process-pipeline","subfamily":"Knee replacement outcome and functional assessment","year":"1998","originator":"David W. Murray and colleagues at University of Oxford","url":"https://scholargate.app/en/health-services/oxford-knee-score","markdownUrl":"https://scholargate.app/en/health-services/oxford-knee-score.md","definition":"The Oxford Knee Score (OKS) is a brief, validated self-report questionnaire developed by Murray and colleagues at the University of Oxford in 1998 to measure outcomes following knee replacement surgery. The OKS comprises 12 items assessing knee pain, knee-related functional limitations, and quality of life in patients undergoing total knee arthroplasty. It is the primary patient-reported outcome measure for knee replacement in international orthopedic research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David W. Murray and colleagues at University of Oxford","subfamily":"Knee replacement outcome and functional assessment","year":"1998","type":"Twelve-item knee function questionnaire"},"citations":[{"ref":"Dawson, J., Fitzpatrick, R., Murray, D., & Carr, A. (1998). Questionnaire on the perceptions of patients about total knee replacement. Journal of Bone and Joint Surgery, 80(1), 63-69.","type":"article","doi":"10.1302/0301-620X.80B1.0800063","isbn":null,"url":null},{"ref":"Jenkinson, C., Coulter, A., & Bruster, S. (2002). The Picker Patient Experience Questionnaire: development and validation using data from in-patient surveys in five countries. International Journal for Quality in Health Care, 14(5), 353-358.","type":"article","doi":"10.1093/intqhc/14.5.353","isbn":null,"url":null},{"ref":"Beard, D. J., Harris, K., Dawson, J., Doll, H., Murray, D. W., Carr, A. J., & Price, A. J. (2015). Meaningful changes for the Oxford Hip and Knee Scores after joint replacement surgery. Journal of Clinical Epidemiology, 68(1), 73-79.","type":"article","doi":"10.1016/j.jclinepi.2014.08.009","isbn":null,"url":null}],"related":["oxford-hip-score","brief-pain-inventory","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-aras","name":"P-ARAS","fullName":"Plithogenic extension of P-ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-aras","markdownUrl":"https://scholargate.app/en/decision-making/p-aras.md","definition":"P-ARAS (Plithogenic extension of P-ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-aroman","name":"P-AROMAN","fullName":"Plithogenic extension of P-AROMAN","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Bošković et al.","url":"https://scholargate.app/en/decision-making/p-aroman","markdownUrl":"https://scholargate.app/en/decision-making/p-aroman.md","definition":"P-AROMAN (Plithogenic extension of P-AROMAN) is a ranking multi-criteria decision-making (MCDM) method introduced by Bošković et al. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bošković et al.","subfamily":"Ranking","year":"2023","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Bošković et al. (2023). Plithogenic Alternative Ranking Order Method Accounting for two-step Normalization. Plithogenic Logic and Computation","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogenic+Alternative+Ranking+Order+Method+Accounting+for+two-step+Normalization+Bo%C5%A1kovi%C4%87"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-cocoso","name":"P-COCOSO","fullName":"P-CoCoSo — Plithogenic extension of P-COCOSO","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-cocoso","markdownUrl":"https://scholargate.app/en/decision-making/p-cocoso.md","definition":"P-COCOSO (P-CoCoSo — Plithogenic extension of P-COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-codas","name":"P-CODAS","fullName":"Plithogenic extension of P-CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-codas","markdownUrl":"https://scholargate.app/en/decision-making/p-codas.md","definition":"P-CODAS (Plithogenic extension of P-CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-copras","name":"P-COPRAS","fullName":"Plithogenic extension of P-COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-copras","markdownUrl":"https://scholargate.app/en/decision-making/p-copras.md","definition":"P-COPRAS (Plithogenic extension of P-COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-dnma","name":"P-DNMA","fullName":"Plithogenic extension of P-DNMA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-dnma","markdownUrl":"https://scholargate.app/en/decision-making/p-dnma.md","definition":"P-DNMA (Plithogenic extension of P-DNMA) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
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{"slug":"p-promethee","name":"P-PROMETHEE","fullName":"Plithogenic extension of P-PROMETHEE","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2019","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-promethee","markdownUrl":"https://scholargate.app/en/decision-making/p-promethee.md","definition":"P-PROMETHEE (Plithogenic extension of P-PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Outranking","year":"2019","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Smarandache, F. (2019). Plithogenic MCDM methods. Infinite Study","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogenic%20MCDM%20methods"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
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{"slug":"p-spotis","name":"P-SPOTIS","fullName":"Plithogenic extension of P-SPOTIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-spotis","markdownUrl":"https://scholargate.app/en/decision-making/p-spotis.md","definition":"P-SPOTIS (Plithogenic extension of P-SPOTIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-todim","name":"P-TODIM","fullName":"Plithogenic extension of P-TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-todim","markdownUrl":"https://scholargate.app/en/decision-making/p-todim.md","definition":"P-TODIM (Plithogenic extension of P-TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-topsis","name":"P-TOPSIS","fullName":"Plithogenic extension of P-TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-topsis","markdownUrl":"https://scholargate.app/en/decision-making/p-topsis.md","definition":"P-TOPSIS (Plithogenic extension of P-TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-value-significance","name":"P-Value and Statistical Significance","fullName":"P-Value and the Concept of Statistical Significance in Hypothesis Testing","aliases":["p-value","significance test","statistical significance","alpha level"],"domain":"research-statistics","family":"process-pipeline","subfamily":"hypothesis-testing","year":1925,"originator":"Ronald Fisher","url":"https://scholargate.app/en/research-statistics/p-value-significance","markdownUrl":"https://scholargate.app/en/research-statistics/p-value-significance.md","definition":"The p-value is the probability of observing data as extreme as or more extreme than what was actually observed, assuming the null hypothesis is true. Introduced by Ronald Fisher in 1925, it is the foundation of frequentist hypothesis testing. Statistical significance is declared when the p-value falls below a pre-specified threshold (alpha level, typically 0.05).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Fisher","subfamily":"hypothesis-testing","year":1925,"type":"Concept"},"citations":[{"ref":"Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/statisticalmeth00fish"},{"ref":"Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, 231, 289–337.","type":"article","doi":"10.1098/rsta.1933.0009","isbn":null,"url":null},{"ref":"Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129–133.","type":"article","doi":"10.1080/00031305.2016.1154108","isbn":null,"url":null}],"related":["null-hypothesis","type-i-type-ii-error","effect-size","statistical-power","multiple-comparisons-problem"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-vikor","name":"P-VIKOR","fullName":"Plithogenic extension of P-VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-vikor","markdownUrl":"https://scholargate.app/en/decision-making/p-vikor.md","definition":"P-VIKOR (Plithogenic extension of P-VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-waspas","name":"P-WASPAS","fullName":"Plithogenic extension of P-WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-waspas","markdownUrl":"https://scholargate.app/en/decision-making/p-waspas.md","definition":"P-WASPAS (Plithogenic extension of P-WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-wisp","name":"P-WISP","fullName":"Plithogenic extension of P-WISP","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-wisp","markdownUrl":"https://scholargate.app/en/decision-making/p-wisp.md","definition":"P-WISP (Plithogenic extension of P-WISP) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"p-wpm","name":"P-WPM","fullName":"Plithogenic extension of P-WPM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Smarandache, F.","url":"https://scholargate.app/en/decision-making/p-wpm","markdownUrl":"https://scholargate.app/en/decision-making/p-wpm.md","definition":"P-WPM (Plithogenic extension of P-WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Smarandache, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smarandache, F.","subfamily":"Ranking","year":"2018","type":"Plithogenic outranking/ranking — Plithogenic Set (PltS: attribute values with contradiction degrees)","value_space":"plithogenic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Smarandache, F. (2018). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons Publishing, Brussels","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Plithogeny%2C%20Plithogenic%20Set%2C%20Logic%2C%20Probability%2C%20and%20Statistics"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pac-qol","name":"Patient Assessment of Constipation Quality of Life","fullName":"Patient Assessment of Constipation Quality of Life Questionnaire","aliases":["PAC-QoL","PAC-Q"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"gastrointestinal-symptom-burden","year":"2005","originator":"Marquis, P., De La Loge, C., Dubois, D., et al.","url":"https://scholargate.app/en/gastroenterology/pac-qol","markdownUrl":"https://scholargate.app/en/gastroenterology/pac-qol.md","definition":"The Patient Assessment of Constipation Quality of Life (PAC-QoL) is a validated, patient-reported outcome measure designed to assess the impact of functional constipation on physical, psychological, and social well-being. Developed by Marquis and colleagues in 2005, the PAC-QoL comprises 28 items organized into four domains: Physical Discomfort, Psychosocial Discomfort, Worries and Concerns, and Satisfaction. The PAC-QoL is responsive to treatment and widely used in constipation clinical trials and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marquis, P., De La Loge, C., Dubois, D., et al.","subfamily":"gastrointestinal-symptom-burden","year":"2005","type":"Self-report"},"citations":[{"ref":"Marquis, P., De La Loge, C., Dubois, D., McDermott, A., & Chassany, O. (2005). Development and validation of the Patient Assessment of Constipation-Quality of Life questionnaire. Scandinavian Journal of Gastroenterology, 40(5), 540–551.","type":"article","doi":"10.1080/00365520510012208","isbn":null,"url":null}],"related":["rome-iv-ibs-criteria","gcsi","gerd-hrql","ibdq-short"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"page-test","name":"Page's L Test","fullName":"Page's L Test for Ordered Alternatives","aliases":["page trend test","page ordered alternatives test","Page L Testi — Sıralı Alternatifler"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1963,"originator":"Ellis Batten Page","url":"https://scholargate.app/en/statistics/page-test","markdownUrl":"https://scholargate.app/en/statistics/page-test.md","definition":"Page's L test is a nonparametric hypothesis test designed for repeated-measures (randomized complete block) designs in which the researcher has a specific, pre-stated ordering hypothesis across k ≥ 3 conditions. Introduced by Ellis Batten Page in 1963, it is more powerful than the Friedman test when the alternative hypothesis specifies a monotone trend rather than a general difference among conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ellis Batten Page","year":1963,"family":"Hypothesis test","type":"Nonparametric trend test","groups":"k ≥ 3","outcome":"continuous or ordinal","parametric":false,"design":"Repeated measures / randomized complete block","statistic":"L (Page's L)","alternativeHypothesis":"Ordered (directional)"},"citations":[{"ref":"Page, E. B. (1963). Ordered hypotheses for multiple treatments: a significance test for linear ranks. Journal of the American Statistical Association, 58(301), 216–230.","type":"article","doi":"10.1080/01621459.1963.10500843","isbn":null,"url":null},{"ref":"Hollander, M. & Wolfe, D. A. (1999). Nonparametric Statistical Methods (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471190455","url":null}],"related":["friedman-test","jonckheere-terpstra-test","kruskal-wallis-test","wilcoxon-signed-rank-test","kendall-w"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pagerank","name":"PageRank","fullName":"PageRank Centrality","aliases":["Google PageRank","Random Surfer Model","Link-Based Ranking","PageRank Merkeziliği"],"domain":"network-analysis","family":"ml-model","subfamily":"Centrality","year":1999,"originator":"Page, Brin, Motwani & Winograd","url":"https://scholargate.app/en/network-analysis/pagerank","markdownUrl":"https://scholargate.app/en/network-analysis/pagerank.md","definition":"PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Google search engine and remains one of the most influential algorithms in network science and information retrieval.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Page, Brin, Motwani & Winograd","year":1999,"type":"Iterative link-based centrality algorithm","subfamily":"Centrality","convergence":"Power iteration until score change < tolerance","damping_factor":"Typically set to 0.85"},"citations":[{"ref":"Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.","type":"article","doi":null,"isbn":null,"url":"http://ilpubs.stanford.edu:8090/422/"}],"related":["centrality-analysis","hits","knowledge-graph-embeddings"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pain-anxiety-symptoms-scale","name":"Pain Anxiety Symptoms Scale","fullName":"Pain Anxiety Symptoms Scale (PASS)","aliases":["PASS","Anxiety Symptoms Scale"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"pain-related anxiety and fear-avoidance","year":"1996","originator":"Gordon J.G. Asmundson and colleagues","url":"https://scholargate.app/en/pain-medicine/pain-anxiety-symptoms-scale","markdownUrl":"https://scholargate.app/en/pain-medicine/pain-anxiety-symptoms-scale.md","definition":"The Pain Anxiety Symptoms Scale (PASS) is a 20-item self-report instrument developed by Asmundson and colleagues in 1996 to measure anxiety symptoms specifically related to pain. The PASS captures fear of pain, avoidance behaviors, cognitive anxiety, and physiological anxiety responses that commonly accompany chronic pain and contribute to disability through fear-avoidance mechanisms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon J.G. Asmundson and colleagues","subfamily":"pain-related anxiety and fear-avoidance","year":"1996","type":"Self-report scale measuring anxiety symptoms in response to pain"},"citations":[{"ref":"McWilliams, L.A., Asmundson, G.J., & Gauthier, N. (2006). Pain anxiety symptoms scale: Brief 20-item version (PASS-20). Journal of Pain, 7(7), 479-485.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pain+anxiety+symptoms+scale%3A+Brief+20-item+version+%28PASS-20%29+McWilliams"},{"ref":"Asmundson, G.J., Norton, P.J., & Norton, G.R. (1999). Beyond pain: The role of fear and avoidance in chronicity. Clinical Psychology Review, 19(1), 97-119.","type":"article","doi":"10.1016/S0272-7358(98)00034-8","isbn":null,"url":null},{"ref":"McCracken, L.M., & Dhingra, L. (2002). A short version of the Pain Anxiety Symptoms Scale (PASS-20): Preliminary development and validity. Pain Research & Management, 7(1), 45-50.","type":"article","doi":"10.1155/2002/517163","isbn":null,"url":null}],"related":["pain-catastrophizing-scale","central-sensitization-inventory","pain-self-efficacy-questionnaire","chronic-pain-acceptance-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pain-assessment-behavioral-scale","name":"Behavioral Pain Scale","fullName":"Behavioral Pain Scale (BPS) for Critically Ill Patients","aliases":["BPS","Behavioral assessment","ICU pain scale"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"2001","originator":"Jean-Francois Payen, et al.","url":"https://scholargate.app/en/clinical-assessment/pain-assessment-behavioral-scale","markdownUrl":"https://scholargate.app/en/clinical-assessment/pain-assessment-behavioral-scale.md","definition":"The Behavioral Pain Scale (BPS), developed by Payen et al. in 2001, is a 12-point tool designed to assess pain in critically ill sedated or paralyzed patients who cannot communicate verbally. It evaluates facial expressions, upper limb movements, and ventilator compliance to quantify pain intensity despite sedation or neuromuscular blockade.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Francois Payen, et al.","subfamily":"Clinical scoring","year":"2001","type":"Pain assessment in sedated patients"},"citations":[{"ref":"Payen, J. F., Bru, O., Bosson, J. L., et al. (2001). Assessing pain in critically ill sedated patients by using a behavioral pain scale. Critical Care Medicine, 29(12), 2258-2263.","type":"article","doi":"10.1097/00003246-200112000-00004","isbn":null,"url":null},{"ref":"Gelinas, C., Fillion, L., Puntillo, K. A., Viens, C., & Fortier, M. (2006). Validation of the Critical-Care Pain Observation Tool in adult patients. American Journal of Critical Care, 15(4), 420-427.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/16823021"}],"related":["visual-analog-scale-pain","richmond-agitation-sedation","glasgow-coma-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pain-assessment-nrs","name":"Numerical Rating Scale for Pain","fullName":"Numerical Rating Scale for Pain Assessment","aliases":["NRS","NRS-11","Numeric Pain Rating Scale","Pain Intensity Scale"],"domain":"nursing","family":"process-pipeline","subfamily":"Symptom assessment and pain measurement","year":"1978","originator":"Multiple researchers (Downie, Leatham, et al.)","url":"https://scholargate.app/en/nursing/pain-assessment-nrs","markdownUrl":"https://scholargate.app/en/nursing/pain-assessment-nrs.md","definition":"The Numerical Rating Scale (NRS) is a simple, widely used tool for assessing subjective pain intensity in patients. Patients rate their pain on a scale from 0 to 10, where 0 represents no pain and 10 represents the worst pain imaginable. The NRS is one of the most frequently used pain assessment instruments in clinical practice due to its brevity, ease of administration, and strong psychometric properties across diverse patient populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple researchers (Downie, Leatham, et al.)","subfamily":"Symptom assessment and pain measurement","year":"1978","type":"Assessment scale"},"citations":[{"ref":"Herr, K., Coyne, P. J., Key, T., et al. (2011). Pain assessment in the nonverbal patient: Position statement with clinical practice recommendations. Pain Management Nursing, 7(2), 44-52.","type":"article","doi":"10.1016/j.pmn.2006.02.003","isbn":null,"url":null},{"ref":"Williamson, A., & Hoggart, B. (2005). Pain: a review of three commonly used pain rating scales. Journal of Clinical Nursing, 14(7), 798-804.","type":"article","doi":"10.1111/j.1365-2702.2005.01121.x","isbn":null,"url":null}],"related":["cam-delirium-screening","wound-assessment-bates-jensen","nursing-sensitive-indicators","patient-fall-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pain-catastrophizing-scale","name":"Pain Catastrophizing Scale","fullName":"Pain Catastrophizing Scale (PCS)","aliases":["PCS","Catastrophizing Scale"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"pain-related cognition and emotion","year":"1995","originator":"Michael J. Sullivan and Steven R. Bishop","url":"https://scholargate.app/en/pain-medicine/pain-catastrophizing-scale","markdownUrl":"https://scholargate.app/en/pain-medicine/pain-catastrophizing-scale.md","definition":"The Pain Catastrophizing Scale (PCS) is a 13-item self-report questionnaire developed by Sullivan, Bishop, and Pivik in 1995 to measure catastrophic thinking about pain—the tendency to magnify pain threat, ruminate about pain, and feel helpless in response to pain. Elevated catastrophizing predicts worse pain outcomes and is a key treatment target in cognitive-behavioral pain management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael J. Sullivan and Steven R. Bishop","subfamily":"pain-related cognition and emotion","year":"1995","type":"Self-report questionnaire measuring catastrophic thinking about pain"},"citations":[{"ref":"Sullivan, M.J., Bishop, S.R., & Pivik, J. (1995). The Pain Catastrophizing Scale: Development and validation. Psychological Assessment, 7(4), 524-532.","type":"article","doi":"10.1037/1040-3590.7.4.524","isbn":null,"url":null},{"ref":"Sullivan, M.J.L., Thorn, B., Haythornthwaite, J.A., et al. (2001). Theoretical perspectives on the relation between catastrophizing and pain. Clinical Journal of Pain, 17(1), 52-64.","type":"article","doi":"10.1097/00002508-200103000-00008","isbn":null,"url":null},{"ref":"Osman, A., Barrios, F.X., Gutierrez, P.M., et al. (2000). The Pain Catastrophizing Scale: Further psychometric evaluation with adult samples. Journal of Behavioral Medicine, 23(4), 351-365.","type":"article","doi":"10.1023/A:1005548801037","isbn":null,"url":null}],"related":["pain-anxiety-symptoms-scale","mcgill-pain-questionnaire","pain-self-efficacy-questionnaire","central-sensitization-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pain-self-efficacy-questionnaire","name":"Pain Self-Efficacy Questionnaire","fullName":"Pain Self-Efficacy Questionnaire (PSEQ)","aliases":["PSEQ","Self-Efficacy Questionnaire"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"pain-related self-efficacy and coping","year":"1989","originator":"Michael K. Nicholas","url":"https://scholargate.app/en/pain-medicine/pain-self-efficacy-questionnaire","markdownUrl":"https://scholargate.app/en/pain-medicine/pain-self-efficacy-questionnaire.md","definition":"The Pain Self-Efficacy Questionnaire (PSEQ) is a 10-item self-report instrument developed by Nicholas in 1989 to measure self-efficacy beliefs—a person's confidence in their ability to manage pain and function despite pain. Higher PSEQ scores predict better pain outcomes, less disability, and greater treatment success, making it a key measure in pain rehabilitation and psychological intervention research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael K. Nicholas","subfamily":"pain-related self-efficacy and coping","year":"1989","type":"Self-report questionnaire measuring self-efficacy beliefs about managing chronic pain"},"citations":[{"ref":"Nicholas, M.K. (1989). Self-efficacy and chronic pain. The American Psychological Association Annual Convention, New Orleans, LA.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/1850444"},{"ref":"Nicholas, M.K., McArthur, G.D., Coulton, S., & Ashworth, M.A. (2007). Development and testing of a revised version of the Pain Self-Efficacy Questionnaire. In: Pain Medicine Clinical Update, 18, 5-7.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+testing+of+a+revised+version+of+the+Pain+Self-Efficacy+Questionnaire+Nicholas"},{"ref":"Anderson, K.O., Dowds, B.N., Pelletz, R.E., Edwards, W.T., & Peeters-Asdourian, C. (1995). Development and initial validation of a scale to measure self-efficacy beliefs in patients with chronic pain. Pain, 63(1), 77-84.","type":"article","doi":"10.1016/0304-3959(95)00021-J","isbn":null,"url":null}],"related":["pain-catastrophizing-scale","pain-anxiety-symptoms-scale","roland-morris-disability","chronic-pain-acceptance-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"paired-samples-t-test","name":"Paired samples t-test","fullName":"Paired Samples t-test","aliases":["dependent t-test","matched pairs t-test","repeated measures t-test","within-subjects t-test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1908","originator":"Student (W. S. Gosset)","url":"https://scholargate.app/en/statistics/paired-samples-t-test","markdownUrl":"https://scholargate.app/en/statistics/paired-samples-t-test.md","definition":"The paired samples t-test is a parametric hypothesis test that compares the means of two related measurements from the same subjects or matched pairs to determine whether the average difference is significantly different from zero. It leverages the dependency between observations to produce a more powerful test than its independent-samples counterpart.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Student (W. S. Gosset)","year":"1908","type":"Parametric mean comparison","dataType":"Continuous paired observations","subfamily":"Classical statistics"},"citations":[{"ref":"Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.","type":"article","doi":"10.1093/biomet/6.1.1","isbn":null,"url":null},{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null}],"related":["independent-samples-t-test","wilcoxon-signed-rank-test","one-sample-t-test","repeated-measures-anova","mann-whitney-u-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"paired-t-test","name":"Paired t-test","fullName":"Paired Samples t-test","aliases":["dependent samples t-test","repeated measures t-test","matched-pairs t-test","eşleştirilmiş örneklem t-testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1908,"originator":"Student (W. S. Gosset)","url":"https://scholargate.app/en/statistics/paired-t-test","markdownUrl":"https://scholargate.app/en/statistics/paired-t-test.md","definition":"The paired samples t-test is a parametric hypothesis test that compares two measurements taken on the same subjects — such as a before and after reading — to decide whether the average change differs from zero. It rests on the t-distribution introduced by Student (W. S. Gosset) in 1908 and works on the within-subject difference scores rather than the raw measurements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Student (W. S. Gosset)","year":1908,"family":"Hypothesis test","type":"Parametric mean comparison (paired)","groups":2,"outcome":"continuous","parametric":true,"distribution":"Student t","df":"n - 1"},"citations":[{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null},{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":"10.4324/9780203771587","isbn":null,"url":null}],"related":["independent-t-test","wilcoxon-signed-rank","repeated-measures-anova","one-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pairs-trading","name":"Pairs Trading","fullName":"Pairs Trading / Statistical Arbitrage Strategy","aliases":["statistical arbitrage","relative-value arbitrage","mean-reversion pairs strategy","Çift Alım-Satım Stratejisi (Pairs Trading / Statistical Arbitrage)"],"domain":"finance","family":"regression-model","subfamily":null,"year":2006,"originator":"Gatev, Goetzmann & Rouwenhorst (empirical rule); Vidyamurthy (quantitative framing)","url":"https://scholargate.app/en/finance/pairs-trading","markdownUrl":"https://scholargate.app/en/finance/pairs-trading.md","definition":"Pairs trading is a quantitative trading strategy that takes a long-short position on two cointegrated assets when the gap (spread) between their prices shows mean reversion. It was popularised as a relative-value arbitrage rule by Gatev, Goetzmann and Rouwenhorst (2006) and framed quantitatively by Vidyamurthy (2004).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gatev, Goetzmann & Rouwenhorst (empirical rule); Vidyamurthy (quantitative framing)","year":2006,"type":"Cointegration-based mean-reversion trading strategy","estimator":"Cointegrating regression (hedge ratio) + Ornstein-Uhlenbeck mean-reversion","structure":"time series","minSample":250,"difficulty":3},"citations":[{"ref":"Gatev, E., Goetzmann, W. N. & Rouwenhorst, K. G. (2006). Pairs Trading: Performance of a Relative-Value Arbitrage Rule. Review of Financial Studies, 19(3), 797–827.","type":"article","doi":"10.1093/rfs/hhj020","isbn":null,"url":null},{"ref":"Vidyamurthy, G. (2004). Pairs Trading: Quantitative Methods and Analysis. Wiley.","type":"book","doi":null,"isbn":"978-0471460671","url":null}],"related":["ols-regression","har-rv-model","risk-parity-model","tail-risk-measures","wavelet-finance"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"paleomagnetic-analysis","name":"Paleomagnetic Analysis","fullName":"Paleomagnetic Analysis","aliases":["Paleomagnetism"],"domain":"geophysics","family":"process-pipeline","subfamily":"Paleomagnetic reconstruction and dating","year":"1953","originator":"Ronald Fisher and contributors","url":"https://scholargate.app/en/geophysics/paleomagnetic-analysis","markdownUrl":"https://scholargate.app/en/geophysics/paleomagnetic-analysis.md","definition":"Paleomagnetic analysis is the study of remnant magnetization in rocks and sediments to determine the direction and magnitude of the Earth's ancient magnetic field and to establish the ages and tectonic histories of crustal rocks. Formalized by Fisher (1953) and Butler (1992), paleomagnetism underpins plate tectonics plate reconstruction, magnetostratigraphic dating, and paleoclimate studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Fisher and contributors","subfamily":"Paleomagnetic reconstruction and dating","year":"1953","type":"Analysis of remnant magnetization in rocks for chronology and tectonics"},"citations":[{"ref":"Fisher, R. A. (1953). Dispersion on a sphere. Proceedings of the Royal Society of London, 217(1130), 295-305.","type":"article","doi":"10.1098/rspa.1953.0064","isbn":null,"url":null},{"ref":"Butler, R. F. (1992). Paleomagnetism: Magnetic domains to geological terranes. Blackwell Scientific Publications.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Paleomagnetism%3A+Magnetic+Domains+to+Geological+Terranes-p-9780865420731"}],"related":["radiocarbon-dating","isotope-ratio-mass-spectrometry","receiver-function-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"paleomagnetism-analysis","name":"Paleomagnetism Analysis","fullName":"Paleomagnetism Analysis","aliases":["paleomagnetic dating","magnetostratigraphy","paleomagnetic remanence"],"domain":"geoscience","family":"process-pipeline","subfamily":"Geomagnetic reversal chronology","year":"1906","originator":"Bernard Brunhes and Motonori Matuyama","url":"https://scholargate.app/en/geoscience/paleomagnetism-analysis","markdownUrl":"https://scholargate.app/en/geoscience/paleomagnetism-analysis.md","definition":"Paleomagnetism analysis is the study of ancient magnetic properties of rocks, measuring fossil magnetization to determine paleomagnetic field history and assign geological ages. Pioneered by Brunhes (1906) and systematized by Tauxe (2010), this method reveals geomagnetic reversals, polar wander paths, and paleomagnetic chronology independent of fossil biostratigraphy. Analysis integrates laboratory rock magnetism with field sampling to build high-resolution timescales and constrain plate motion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernard Brunhes and Motonori Matuyama","subfamily":"Geomagnetic reversal chronology","year":"1906","type":"temporal constraint pipeline"},"citations":[{"ref":"Butler, R. F. (1992). Paleomagnetism: Magnetic Domains to Geologic Terranes. Blackwell Scientific Publications.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Cande, S. C., & Kent, D. V. (1995). Revised calibration of the geomagnetic polarity time scale for the Late Cretaceous and Cenozoic. Journal of Geophysical Research, 100(B4), 6093–6095.","type":"article","doi":"10.1029/94JB03098","isbn":null,"url":null},{"ref":"Tauxe, L. (2010). Essentials of Paleomagnetism (1st ed.). University of California Press.","type":"book","doi":null,"isbn":null,"url":"https://ucpress.edu"}],"related":["geochronological-dating","stratigraphic-correlation","basin-subsidence-analysis","paleoenvironmental-analysis","geochemical-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"palliative-performance-scale","name":"Palliative Performance Scale","fullName":"Palliative Performance Scale (PPS)","aliases":["PPS"],"domain":"palliative-care","family":"process-pipeline","subfamily":"functional-status","year":"1996","originator":"Anderson, Downing, and colleagues","url":"https://scholargate.app/en/palliative-care/palliative-performance-scale","markdownUrl":"https://scholargate.app/en/palliative-care/palliative-performance-scale.md","definition":"The Palliative Performance Scale (PPS) is an 11-point clinician-rated functional assessment tool for patients with advanced, life-limiting illness. Developed by Anderson and colleagues in 1996, it measures overall performance status from 100% (normal) to 0% (death), integrating five domains of functional decline. The PPS is widely used in palliative care, hospice, and oncology settings to guide treatment intensity, prognostication, and care planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anderson, Downing, and colleagues","subfamily":"functional-status","year":"1996","type":"Clinician-rated"},"citations":[{"ref":"Anderson, F., Downing, G. M., Hill, J., Casorso, L., & Lerch, N. (1996). Palliative Performance Scale: A new tool. J Palliat Care, 12(1), 5–11.","type":"article","doi":"10.1177/082585979601200102","isbn":null,"url":null},{"ref":"Glare, P. A., Semple, D., & Staquet, M. J. (2011). Palliative Performance Scale. In A. G. Liptak (Ed.), Palliative care: Core skills and clinical competencies (2nd ed., pp. 421–428). Saunders.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/8945616"}],"related":["spiritual-wellbeing-scale","mcgill-quality-of-life","comfort-care-checklist","patient-dignity-inventory","facit-palliative"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"palynology","name":"Palynology","fullName":"Palynology — The Scientific Study of Pollen and Spores","aliases":["pollen analysis","spore analysis","palynostratigraphy","aerobiology pollen study"],"domain":"agronomy","family":"process-pipeline","subfamily":"Biological and geological trace analysis","year":"Early 20th century (von Post 1916; formal discipline consolidated by mid-20th century)","originator":"Multiple contributors (Lennart von Post pioneered quantitative pollen analysis ~1916)","url":"https://scholargate.app/en/agronomy/palynology","markdownUrl":"https://scholargate.app/en/agronomy/palynology.md","definition":"Palynology is the scientific study of pollen grains and plant spores — microscopic structures that are chemically resistant and preserve well in sediment, soil, peat, ice, and other matrices. In agronomy, palynology is applied to reconstruct past vegetation and land-use histories, monitor crop pollination dynamics, trace the botanical origin of honey, assess aeroallergen loads, and support plant breeding programmes. It bridges botany, ecology, archaeology, and environmental science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Lennart von Post pioneered quantitative pollen analysis ~1916)","year":"Early 20th century (von Post 1916; formal discipline consolidated by mid-20th century)","type":"Laboratory pipeline — morphological identification and quantitative counting","dataType":"Pollen grains and spores (microscopic biological specimens from sediment, soil, air, or honey samples)","subfamily":"Biological and geological trace analysis"},"citations":[{"ref":"Faegri, K., & Iversen, J. (1989). Textbook of Pollen Analysis (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471919681","url":null},{"ref":"Moore, P. D., Webb, J. A., & Collinson, M. E. (1991). Pollen Analysis (2nd ed.). Blackwell Scientific Publications.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/pollen-analysis/oclc/23625177"}],"related":["carbon-dating","sediment-core-analysis","vegetation-mapping","phenology","aerobiology","archaeobotany"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pam","name":"PAM","fullName":"Polygons Area Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Various authors","url":"https://scholargate.app/en/decision-making/pam","markdownUrl":"https://scholargate.app/en/decision-making/pam.md","definition":"PAM (Polygons Area Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Various authors in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various authors","subfamily":"Ranking","year":"2010","type":"Geometric aggregation via polygon area on equal-angle axes","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Various authors (2010). Polygons Area Method for MCDM. Various journals","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Polygons%20Area%20Method%20for%20MCDM"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pamssem-i","name":"PAMSSEM-I","fullName":"PAMSSEM I — Procédure d'Agrégation Multicritère","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1996","originator":"Martel, J. M., Aouni, B.","url":"https://scholargate.app/en/decision-making/pamssem-i","markdownUrl":"https://scholargate.app/en/decision-making/pamssem-i.md","definition":"PAMSSEM-I (PAMSSEM I — Procédure d'Agrégation Multicritère) is a outranking multi-criteria decision-making (MCDM) method introduced by Martel, J. M., Aouni, B. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martel, J. M., Aouni, B.","subfamily":"Outranking","year":"1996","type":"Outranking (partial ranking)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Martel, J. M., Aouni, B. (1996). Incorporating the decision-maker's preferences in the goal-programming model with fuzzy goal values. Journal of the Operational Research Society","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Incorporating%20the%20decision-maker%27s%20preferences%20in%20the%20goal-programming%20model%20with%20fuzzy%20goal%20values"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pamssem-ii","name":"PAMSSEM-II","fullName":"PAMSSEM II — Total ranking variant","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1996","originator":"Martel, J. M., Aouni, B.","url":"https://scholargate.app/en/decision-making/pamssem-ii","markdownUrl":"https://scholargate.app/en/decision-making/pamssem-ii.md","definition":"PAMSSEM-II (PAMSSEM II — Total ranking variant) is a outranking multi-criteria decision-making (MCDM) method introduced by Martel, J. M., Aouni, B. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martel, J. M., Aouni, B.","subfamily":"Outranking","year":"1996","type":"Outranking (partial ranking)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Martel, J. M., Aouni, B. (1996). Incorporating the decision-maker's preferences in the goal-programming model with fuzzy goal values. Journal of the Operational Research Society","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Incorporating%20the%20decision-maker%27s%20preferences%20in%20the%20goal-programming%20model%20with%20fuzzy%20goal%20values"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pan-tompkins-qrs-detection","name":"Pan-Tompkins QRS Detection","fullName":"Pan-Tompkins QRS Detection Algorithm","aliases":["QRS detection","R-peak detection","Heartbeat detection"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Biomedical signal processing","year":"1985","originator":"Jiapu Pan","url":"https://scholargate.app/en/biomechanics/pan-tompkins-qrs-detection","markdownUrl":"https://scholargate.app/en/biomechanics/pan-tompkins-qrs-detection.md","definition":"The Pan-Tompkins algorithm is a real-time QRS detection method for electrocardiograms (ECGs) that identifies the R-peaks (ventricular depolarization) and QRS complexes from continuous cardiac waveforms. Published by Jiapu Pan and Willis Tompkins in 1985, it remains a standard reference for ECG processing and is widely implemented in clinical monitoring systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jiapu Pan","subfamily":"Biomedical signal processing","year":"1985","type":"Digital signal processing pipeline"},"citations":[{"ref":"Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, BME-32(3), 230-236.","type":"article","doi":"10.1109/TBME.1985.325532","isbn":null,"url":null},{"ref":"Clifford, G. D., Azuaje, F., & McSharry, P. E. (2006). ECG statistics, noise, artifacts, and missing data. Advanced Methods and Tools for ECG Data Analysis, 1, 1-41.","type":"article","doi":null,"isbn":null,"url":"https://mitpress.mit.edu"}],"related":["heart-rate-variability","photoplethysmography","windkessel-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panas","name":"Positive and Negative Affect Schedule","fullName":"Positive and Negative Affect Schedule (PANAS)","aliases":["PANAS","PANAS-X"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Self-report questionnaire","year":"1988","originator":"David Watson, Lee Anna Clark, and Auke Tellegen","url":"https://scholargate.app/en/clinical-psychology/panas","markdownUrl":"https://scholargate.app/en/clinical-psychology/panas.md","definition":"The Positive and Negative Affect Schedule (PANAS) is a brief, efficient self-report measure of mood and emotional affect. Developed by Watson, Clark, and Tellegen in 1988, it assesses two independent dimensions: positive affect (enthusiasm, attentiveness, interest) and negative affect (distress, anxiety, anger). The 20-item standard version is one of the most widely used instruments for measuring emotion in research and clinical contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Watson, Lee Anna Clark, and Auke Tellegen","subfamily":"Self-report questionnaire","year":"1988","type":"Mood and affect self-assessment"},"citations":[{"ref":"Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063-1070.","type":"article","doi":"10.1037/0022-3514.54.6.1063","isbn":null,"url":null},{"ref":"Watson, D., & Clark, L. A. (1999). The PANAS-X: Manual for the positive and negative affect schedule—expanded form. University of Iowa.","type":"article","doi":null,"isbn":null,"url":"https://homepage.uiowa.edu/~davwatson/panas-x.html"}],"related":["hamilton-anxiety-rating-scale","swls","dass-21","hads","ces-d"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pandemic-fatigue-scale","name":"Pandemic Fatigue Scale","fullName":"Pandemic Fatigue Scale (PFS)","aliases":["PFS","COVID Fatigue Scale"],"domain":"public-health","family":"process-pipeline","subfamily":"pandemic-behavioral-fatigue","year":"2020","originator":"Restrepo et al.","url":"https://scholargate.app/en/public-health/pandemic-fatigue-scale","markdownUrl":"https://scholargate.app/en/public-health/pandemic-fatigue-scale.md","definition":"The Pandemic Fatigue Scale (PFS) measures psychological exhaustion and reduced motivation to maintain protective behaviors during prolonged pandemics. Developed by Restrepo and colleagues, it captures the phenomenon whereby individuals progressively abandon preventive measures (distancing, mask-wearing, testing) despite ongoing transmission risk, driven by 'fatigue' or loss of motivation rather than reduced threat perception. The PFS has become essential for monitoring behavioral adherence trends and explaining divergence between risk and protective behavior during multi-wave pandemics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Restrepo et al.","subfamily":"pandemic-behavioral-fatigue","year":"2020","type":"Self-report"},"citations":[{"ref":"Restrepo, A., Pfeil, J., & Farias, M. (2021). Pandemic fatigue and the risk of SARS-CoV-2 transmission in a representative US sample. Nature Medicine, 27(6), 1093–1101.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pandemic+fatigue+and+the+risk+of+SARS-CoV-2+transmission+in+a+representative+US+sample+Restrepo"},{"ref":"Solis-Moreira, R., Arriaga, J., Gutierrez, R., & Loayza, M. (2021). Pandemic Fatigue Scale: Development and psychometric validation. Frontiers in Psychology, 12, 714606.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pandemic+Fatigue+Scale%3A+Development+and+psychometric+validation+Solis-Moreira"}],"related":["covid-19-mental-health-scale","health-protective-behavior-scale","covid-19-anxiety-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pandemic-grief-scale","name":"Pandemic Grief Scale","fullName":"Pandemic Grief Scale (PGS)","aliases":["PGS","COVID Grief Scale"],"domain":"public-health","family":"process-pipeline","subfamily":"pandemic-bereavement","year":"2021","originator":"Zisook et al.","url":"https://scholargate.app/en/public-health/pandemic-grief-scale","markdownUrl":"https://scholargate.app/en/public-health/pandemic-grief-scale.md","definition":"The Pandemic Grief Scale (PGS) is a brief screening instrument assessing grief reactions specific to death losses during COVID-19. Developed by Zisook and colleagues in 2021, it adapts the Inventory of Complicated Grief (ICG) items to pandemic bereavement contexts, measuring both typical grief responses and complicated grief symptoms. The PGS recognizes that pandemic deaths—often characterized by social isolation, restricted funeral rituals, and constrained grieving practices—create unique bereavement trajectories requiring tailored support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zisook et al.","subfamily":"pandemic-bereavement","year":"2021","type":"Self-report"},"citations":[{"ref":"Zisook, S., Iglewicz, A., Avanzato, M. R., Maglione, J., Glorioso, D., Zetumer, S., ... & Shuchter, S. R. (2021). Bereavement, complicated grief, and depression: A report from the experience registry of bereavement in adults. Journal of Affective Disorders Reports, 6, 100255.","type":"article","doi":"10.1016/b978-0-12-800136-3.00018-1","isbn":null,"url":null}],"related":["covid-19-mental-health-scale","covid-19-anxiety-scale","pandemic-fatigue-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-adf-unit-root-test","name":"Panel ADF Unit Root Test","fullName":"Panel Augmented Dickey-Fuller Unit Root Test","aliases":["Panel ADF test","IPS test","Im-Pesaran-Shin test","panel unit root test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002–2003","originator":"Im, Pesaran & Shin (2003); Levin, Lin & Chu (2002)","url":"https://scholargate.app/en/econometrics/panel-adf-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/panel-adf-unit-root-test.md","definition":"The Panel Augmented Dickey-Fuller (Panel ADF) unit root test extends the classical ADF framework to panel datasets. By pooling information across cross-sectional units it achieves substantially higher power than single-series ADF tests, allowing researchers to determine whether time-series variables are stationary or integrated of order one before modelling long-run relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Im, Pesaran & Shin (2003); Levin, Lin & Chu (2002)","year":"2002–2003","type":"Unit root / stationarity test","dataType":"Balanced or unbalanced panel (multiple cross-sections observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.","type":"article","doi":"10.1016/S0304-4076(03)00092-7","isbn":null,"url":null},{"ref":"Levin, A., Lin, C.-F., & Chu, C.-S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24.","type":"article","doi":"10.1016/S0304-4076(01)00098-7","isbn":null,"url":null}],"related":["augmented-dickey-fuller-unit-root-test","panel-pp-unit-root-test","panel-kpss-test","panel-engle-granger-cointegration","panel-johansen-cointegration","panel-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-ar-model","name":"Panel AR model","fullName":"Panel Autoregressive Model","aliases":["panel autoregressive model","PAR model","AR model for panel data","panel AR(p)"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980s-2000s","originator":"Hsiao, C.; Arellano, M.","url":"https://scholargate.app/en/econometrics/panel-ar-model","markdownUrl":"https://scholargate.app/en/econometrics/panel-ar-model.md","definition":"The Panel AR model extends the classical univariate autoregressive model to panel data, capturing how each unit's own past values predict its current value while controlling for unobserved individual heterogeneity through fixed or random effects. It is foundational for modelling dynamic persistence in micro or macro panel datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hsiao, C.; Arellano, M.","year":"1980s-2000s","type":"Autoregressive time-series model for panel data","dataType":"Balanced or unbalanced panel (cross-sectional units observed over multiple time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null},{"ref":"Arellano, M. (2003). Panel Data Econometrics. Oxford University Press.","type":"book","doi":null,"isbn":"978-0199245284","url":null}],"related":["panel-arma-model","panel-arima-model","panel-var-model","panel-dynamic-panel-data-model","arellano-bond-gmm-estimator","fixed-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-ardl-bounds-test","name":"Panel ARDL Bounds Test","fullName":"Panel Autoregressive Distributed Lag Bounds Testing Approach","aliases":["Panel ARDL","Panel bounds testing","Panel ARDL cointegration","Panel PSS bounds test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2001","originator":"Pesaran, Shin & Smith","url":"https://scholargate.app/en/econometrics/panel-ardl-bounds-test","markdownUrl":"https://scholargate.app/en/econometrics/panel-ardl-bounds-test.md","definition":"The Panel ARDL Bounds Test extends the Pesaran, Shin and Smith (2001) bounds testing procedure to panel data, allowing researchers to test for long-run cointegrating relationships between variables without requiring all series to be integrated of the same order. It is widely used in macro-panel studies where variables may be I(0), I(1), or a mixture of both.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pesaran, Shin & Smith","year":"2001","type":"Bounds test for cointegration","dataType":"Panel time series (macro/micro panel)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null},{"ref":"Pesaran, M. H., & Pesaran, B. (1997). Working with Microfit 4.0: Interactive Econometric Analysis. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pesaran+Pesaran+1997+Working+with+Microfit+Interactive+Econometric+Analysis"}],"related":["panel-engle-granger-cointegration","panel-johansen-cointegration","panel-vecm","panel-granger-causality","nonlinear-ardl","panel-nardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-arellano-bond-gmm","name":"Panel Arellano-Bond GMM","fullName":"Panel Data Arellano-Bond Generalized Method of Moments Estimator","aliases":["Arellano-Bond GMM","AB-GMM","difference GMM estimator","dynamic panel GMM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991","originator":"Manuel Arellano and Stephen Bond","url":"https://scholargate.app/en/econometrics/panel-arellano-bond-gmm","markdownUrl":"https://scholargate.app/en/econometrics/panel-arellano-bond-gmm.md","definition":"The Arellano-Bond GMM estimator addresses the two core problems of dynamic panel models — individual fixed effects correlated with the regressors, and the endogeneity introduced by a lagged dependent variable — by first-differencing to remove fixed effects and then using lagged levels of the dependent variable as internal instruments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Manuel Arellano and Stephen Bond","year":"1991","type":"Dynamic panel GMM estimator","dataType":"Balanced or unbalanced panel data with a lagged dependent variable","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86–136.","type":"article","doi":"10.1177/1536867X0900900106","isbn":null,"url":null}],"related":["arellano-bond-gmm-estimator","panel-system-gmm","panel-difference-gmm","dynamic-panel-data-model","panel-fixed-effects-model","panel-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-arima-model","name":"Panel ARIMA model","fullName":"Panel Autoregressive Integrated Moving Average Model","aliases":["Panel ARIMA","ARIMA for panel data","cross-sectional ARIMA","multi-unit ARIMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970s–2000s","originator":"Extension of Box-Jenkins ARIMA (Box & Jenkins, 1970) to panel settings; formalised in panel econometrics literature (Hsiao, 2003)","url":"https://scholargate.app/en/econometrics/panel-arima-model","markdownUrl":"https://scholargate.app/en/econometrics/panel-arima-model.md","definition":"The Panel ARIMA model extends the classical Box-Jenkins ARIMA framework to panel data, fitting autoregressive integrated moving-average dynamics to multiple cross-sectional units observed over time. It accommodates unit-specific short-run dynamics and non-stationarity, making it suitable for forecasting and dynamic analysis when both cross-sectional and temporal dimensions are present.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Box-Jenkins ARIMA (Box & Jenkins, 1970) to panel settings; formalised in panel econometrics literature (Hsiao, 2003)","year":"1970s–2000s","type":"Time-series model applied to panel data","dataType":"Balanced or unbalanced panel (multiple units, multiple time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null},{"ref":"Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118675021","url":null}],"related":["arima-model","panel-data-analysis","panel-var-model","panel-ardl-bounds-test","vector-autoregression","panel-ar-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-arma-model","name":"Panel ARMA model","fullName":"Panel Autoregressive Moving Average Model","aliases":["Panel ARMA","ARMA panel model","panel autoregressive moving average","cross-sectional ARMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980s–2000s","originator":"Baltagi, Hsiao and related panel data literature","url":"https://scholargate.app/en/econometrics/panel-arma-model","markdownUrl":"https://scholargate.app/en/econometrics/panel-arma-model.md","definition":"The Panel ARMA model extends the classical Autoregressive Moving Average (ARMA) framework to panel data, allowing each cross-sectional unit to carry an individual effect while the within-unit error dynamics follow an ARMA(p, q) process. It captures both autocorrelation and moving-average dependence in panel residuals, yielding efficient estimates when the error structure is correctly specified.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baltagi, Hsiao and related panel data literature","year":"1980s–2000s","type":"Panel time series model","dataType":"Balanced or unbalanced panel data with temporal ordering","subfamily":"Econometrics / time series"},"citations":[{"ref":"Baltagi, B. H. (2008). Econometric Analysis of Panel Data (4th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470518861","url":null},{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null}],"related":["panel-ar-model","panel-ma-model","arma-model","panel-data-analysis","panel-fixed-effects-model","vector-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-causal-comparative-research","name":"Panel-based Causal-Comparative Research","fullName":"Panel-based Causal-Comparative Research Design","aliases":["panel causal-comparative design","longitudinal ex post facto research","panel ex post facto study","repeated-measures causal-comparative study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gozlemsel desen","year":"1950s–1980s (formalized across educational and social science methodology literature)","originator":"Building on causal-comparative tradition (John W. Best, 1959) extended to panel data structures in social and educational research","url":"https://scholargate.app/en/research-design/panel-based-causal-comparative-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-causal-comparative-research.md","definition":"Panel-based causal-comparative research is a quantitative observational design that tracks the same sample of participants or units across multiple time points and then compares pre-existing groups to identify differences in outcomes. By combining the temporal depth of a panel structure with the group-contrast logic of causal-comparative (ex post facto) methodology, it allows researchers to examine how naturally occurring conditions — such as treatment exposure, policy changes, or demographic characteristics — relate to outcomes over time, without experimental random assignment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on causal-comparative tradition (John W. Best, 1959) extended to panel data structures in social and educational research","year":"1950s–1980s (formalized across educational and social science methodology literature)","type":"Quantitative observational research design","dataType":"Repeated measurements of the same units (individuals, schools, organizations) across at least two time points","subfamily":"Tarama ve gozlemsel desen"},"citations":[{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-1260087840","url":null},{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107038691","url":null}],"related":["causal-comparative-research","longitudinal-research","panel-study","cohort-study","fixed-effects-model","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-cohort-research","name":"Panel-based Cohort Research","fullName":"Panel-based Cohort Research Design","aliases":["panel cohort study","longitudinal panel cohort","cohort panel design","panel longitudinal study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century (formalized ~1950s–1970s)","originator":"Developed through convergence of epidemiological cohort methodology and social science panel survey traditions","url":"https://scholargate.app/en/research-design/panel-based-cohort-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-cohort-research.md","definition":"Panel-based cohort research is a longitudinal observational design that follows a defined group of individuals — the cohort — across multiple repeated measurement waves, collecting structured quantitative data at each wave. It merges the epidemiological strength of cohort tracking (a group sharing a common characteristic or entry point) with the panel study convention of standardized, repeated-contact data collection. The design enables analysis of change over time within individuals while supporting causal inference about exposure-outcome relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed through convergence of epidemiological cohort methodology and social science panel survey traditions","year":"Mid-20th century (formalized ~1950s–1970s)","type":"Quantitative longitudinal observational design","dataType":"Repeated structured measurements on the same individuals over multiple time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107038691","url":null},{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null}],"related":["cohort-study","panel-study","longitudinal-research","prospective-study","repeated-measures-design","survey-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-confirmatory-research","name":"Panel-based Confirmatory Research","fullName":"Panel-Based Confirmatory Research Design","aliases":["confirmatory panel design","longitudinal confirmatory study","panel confirmatory analysis","PBCR"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1960s–1980s (formalization of panel methods with confirmatory inference)","originator":"Multiple contributors; panel data analysis formalized by Yair Mundlak, Zvi Griliches, and Edwin Kuh in the 1960s–1970s; confirmatory integration developed across econometrics and SEM traditions","url":"https://scholargate.app/en/research-design/panel-based-confirmatory-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-confirmatory-research.md","definition":"Panel-based confirmatory research combines the longitudinal power of panel data — repeated observations of the same units over time — with a pre-specified, hypothesis-driven analytic framework. Instead of exploring patterns post-hoc, the researcher commits to theoretical propositions before data collection and uses the panel structure to test causal or directional claims while controlling for unobserved time-invariant confounders. It is widely used in economics, sociology, epidemiology, and organizational research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; panel data analysis formalized by Yair Mundlak, Zvi Griliches, and Edwin Kuh in the 1960s–1970s; confirmatory integration developed across econometrics and SEM traditions","year":"1960s–1980s (formalization of panel methods with confirmatory inference)","type":"Quantitative longitudinal research design","dataType":"Repeated-measures quantitative data from the same units (individuals, firms, countries) observed across two or more time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["longitudinal-research","confirmatory-factor-analysis","structural-equation-modeling","cross-lagged-panel-model","fixed-effects-model","repeated-measures-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-correlational-research","name":"Panel-based correlational research","fullName":"Panel-based Correlational Research Design","aliases":["panel correlational study","longitudinal correlational panel","panel survey research","repeated-measures correlational design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s–1980s (formal panel analysis methods)","originator":"Panel methodology systematized by economists and sociologists, notably Kessler & Greenberg (1981) and Cheng Hsiao (1986)","url":"https://scholargate.app/en/research-design/panel-based-correlational-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-correlational-research.md","definition":"Panel-based correlational research follows the same individuals, organizations, or units across multiple time points and quantifies associations among variables within that longitudinal structure. Unlike a one-shot correlational survey, the panel design captures temporal ordering and within-unit change, enabling researchers to test whether earlier values of one variable predict later values of another while statistically controlling for stable individual differences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Panel methodology systematized by economists and sociologists, notably Kessler & Greenberg (1981) and Cheng Hsiao (1986)","year":"1970s–1980s (formal panel analysis methods)","type":"Quantitative observational design","dataType":"Repeated measures from the same participants or units across two or more time points (panel/longitudinal survey data)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kessler, R. C., & Greenberg, D. F. (1981). Linear Panel Analysis: Models of Quantitative Change. Academic Press.","type":"article","doi":null,"isbn":"9780124053502","url":null},{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"9780521522717","url":null}],"related":["longitudinal-survey","cross-lagged-panel-model","cohort-study","time-series-analysis","repeated-measures-anova","correlational-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-cross-sectional-research","name":"Panel-based cross-sectional research","fullName":"Panel-Based Cross-Sectional Research Design","aliases":["panel cross-sectional survey","rotating panel cross-section","repeated cross-section panel","cross-sectional panel design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1940s–1960s (formalized in social survey methodology)","originator":"Panel survey methodology developed from large-scale government and social survey programs (e.g., University of Michigan Survey Research Center, 1940s–1950s)","url":"https://scholargate.app/en/research-design/panel-based-cross-sectional-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-cross-sectional-research.md","definition":"Panel-based cross-sectional research draws repeated cross-sectional measurements from a pre-recruited standing panel rather than sampling fresh respondents each time. This hybrid design preserves the snapshot character of classic cross-sectional surveys while gaining speed, cost efficiency, and comparability across waves. It is widely used in social, health, and market research whenever population-level estimates are needed quickly and repeatedly without full longitudinal tracking of the same individuals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Panel survey methodology developed from large-scale government and social survey programs (e.g., University of Michigan Survey Research Center, 1940s–1950s)","year":"1940s–1960s (formalized in social survey methodology)","type":"Quantitative observational design","dataType":"Structured survey data; repeated or rotating samples drawn from a defined panel","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kasprzyk, D., Duncan, G., Kalton, G., & Singh, M. P. (Eds.). (1989). Panel Surveys. John Wiley & Sons.","type":"article","doi":null,"isbn":"978-0471622635","url":null},{"ref":"Lynn, P. (Ed.). (2009). Methodology of Longitudinal Surveys. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Methodology+of+Longitudinal+Surveys+Lynn"}],"related":["longitudinal-research","cross-sectional-survey","cohort-study","repeated-measures-design","survey-research","time-series-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-descriptive-research","name":"Panel-based Descriptive Research","fullName":"Panel-based Descriptive Research Design","aliases":["descriptive panel study","panel survey descriptive design","repeated cross-sectional descriptive panel","panel descriptive research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1940s–1960s","originator":"Developed within survey methodology and social science panel traditions (Lazarsfeld, Kish, and others)","url":"https://scholargate.app/en/research-design/panel-based-descriptive-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-descriptive-research.md","definition":"Panel-based descriptive research follows the same set of individuals, households, or organizations across multiple time points and uses that repeated-measures structure to describe how variables, distributions, and patterns change over time — without imposing an experimental manipulation or testing causal hypotheses. It is distinguished from cross-sectional descriptive research by its capacity to document intra-individual change, and from explanatory panel research by its goal of accurate description rather than causal modelling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within survey methodology and social science panel traditions (Lazarsfeld, Kish, and others)","year":"1940s–1960s","type":"Quantitative observational research design","dataType":"Repeated measurements on the same sample units (survey, administrative, or registry data)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922827","url":null},{"ref":"Kish, L. (1965). Survey Sampling. Wiley.","type":"book","doi":null,"isbn":"978-0471489009","url":null}],"related":["panel-research","longitudinal-research","descriptive-research","cross-sectional-research","survey-research","cohort-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-ex-post-facto-design","name":"Panel-based ex post facto design","fullName":"Panel-Based Ex Post Facto Research Design","aliases":["panel ex post facto study","longitudinal causal-comparative design","retrospective panel design","panel causal-comparative study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1950s–1970s (synthesized from ex post facto tradition and panel survey research)","originator":"Developed from Kerlinger's ex post facto framework combined with panel survey methodology (Lazarsfeld, Kerlinger)","url":"https://scholargate.app/en/research-design/panel-based-ex-post-facto-design","markdownUrl":"https://scholargate.app/en/research-design/panel-based-ex-post-facto-design.md","definition":"A panel-based ex post facto design tracks the same group of participants across multiple time points to examine how pre-existing differences in an independent variable — one the researcher did not manipulate — are associated with changes in an outcome over time. It merges the temporal depth of panel methodology with the causal-comparative logic of ex post facto research, enabling stronger causal inference than a single cross-sectional snapshot while remaining fully non-experimental.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from Kerlinger's ex post facto framework combined with panel survey methodology (Lazarsfeld, Kerlinger)","year":"1950s–1970s (synthesized from ex post facto tradition and panel survey research)","type":"Non-experimental longitudinal observational design","dataType":"Quantitative repeated-measures data collected from the same panel at multiple time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030417511","url":null},{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922452","url":null}],"related":["panel-study","ex-post-facto-design","causal-comparative-design","longitudinal-design","cohort-study","cross-lagged-panel-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-exploratory-quantitative-research","name":"Panel-based exploratory quantitative research","fullName":"Panel-Based Exploratory Quantitative Research","aliases":["exploratory panel study","panel survey design","longitudinal exploratory survey","repeated-measures exploratory design"],"domain":"research-design","family":"process-pipeline","subfamily":"Survey and observational design","year":"1940s–1960s (formalized in social sciences)","originator":"Rooted in panel survey methodology developed broadly in social science (Lazarsfeld, 1940s; Kish, 1965)","url":"https://scholargate.app/en/research-design/panel-based-exploratory-quantitative-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-exploratory-quantitative-research.md","definition":"Panel-based exploratory quantitative research tracks the same sample of participants across multiple measurement points to discover patterns, relationships, and change processes that a single snapshot cannot reveal. Because the research goal is exploratory — uncovering structure rather than testing a predetermined hypothesis — the design is especially valuable in emerging topic areas where theory is underdeveloped and the relevant variables are not yet well understood.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in panel survey methodology developed broadly in social science (Lazarsfeld, 1940s; Kish, 1965)","year":"1940s–1960s (formalized in social sciences)","type":"Quantitative observational research design","dataType":"Repeated quantitative measurements from the same respondents over time (survey, questionnaire, structured observation)","subfamily":"Survey and observational design"},"citations":[{"ref":"Lynn, P. (Ed.). (2009). Methodology of Longitudinal Surveys. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470018712","url":null},{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922452","url":null}],"related":["longitudinal-survey","cross-sectional-survey","cohort-study","time-series-analysis","repeated-measures-design","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-model-testing-research","name":"Panel-based Model Testing Research","fullName":"Panel-based Model Testing Research Design","aliases":["panel SEM","longitudinal model testing","panel structural equation modeling","panel-based hypothesis testing"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1970s–1980s (panel econometrics and SEM matured in parallel)","originator":"Developed across econometrics (Hsiao, Hausman) and psychometrics (Jöreskog, Bollen)","url":"https://scholargate.app/en/research-design/panel-based-model-testing-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-model-testing-research.md","definition":"Panel-based model testing research combines the longitudinal power of panel survey designs with the confirmatory rigor of structural model testing — such as structural equation modeling (SEM), path analysis, or confirmatory factor analysis — applied to data collected from the same units (individuals, firms, countries) across multiple time points. This approach enables researchers to test theoretically specified causal and mediation structures while controlling for unobserved unit-level heterogeneity and examining how relationships unfold over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed across econometrics (Hsiao, Hausman) and psychometrics (Jöreskog, Bollen)","year":"1970s–1980s (panel econometrics and SEM matured in parallel)","type":"Quantitative longitudinal research design","dataType":"Repeated-measures panel data (same units observed across multiple time points)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Bollen, K. A. (1989). Structural Equations with Latent Variables. Wiley.","type":"book","doi":null,"isbn":"978-0471011712","url":null},{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107657632","url":null}],"related":["structural-equation-modeling","confirmatory-factor-analysis","longitudinal-research","cross-lagged-panel-model","multilevel-modeling","latent-growth-curve-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-observational-quantitative-research","name":"Panel-based Observational Quantitative Research","fullName":"Panel-based Observational Quantitative Research Design","aliases":["panel observational study","longitudinal observational panel design","panel survey research","repeated-measures observational design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1960s–1980s (formalized in econometrics); widely adopted in social sciences by 1990s","originator":"Established through econometrics literature; foundational contributions by Cheng Hsiao, Zvi Griliches, and Marc Nerlove","url":"https://scholargate.app/en/research-design/panel-based-observational-quantitative-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-observational-quantitative-research.md","definition":"Panel-based observational quantitative research follows the same individuals, organizations, or units across two or more time points without experimentally manipulating any condition. By combining the within-unit depth of longitudinal tracking with the numerical precision of quantitative measurement, it enables researchers to study change over time, detect lagged effects, and control for stable unobserved characteristics — all while maintaining the ethical simplicity of pure observation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established through econometrics literature; foundational contributions by Cheng Hsiao, Zvi Griliches, and Marc Nerlove","year":"1960s–1980s (formalized in econometrics); widely adopted in social sciences by 1990s","type":"Quantitative observational longitudinal design","dataType":"Repeated numeric measurements on the same units across multiple time points (panel/longitudinal data)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["longitudinal-survey","cohort-study","fixed-effects-model","random-effects-model","cross-sectional-survey","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-quantitative-content-analysis","name":"Panel-based quantitative content analysis","fullName":"Panel-Based Quantitative Content Analysis","aliases":["longitudinal content analysis","repeated-measures content analysis","panel content analysis","tracking content analysis"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1950s–1980s (formalized in communication research)","originator":"Synthesized from Berelson's content analysis tradition and panel study methodology","url":"https://scholargate.app/en/research-design/panel-based-quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/panel-based-quantitative-content-analysis.md","definition":"Panel-based quantitative content analysis applies systematic, numeric coding of media or textual content to the same fixed panel of sources at multiple time points. By holding the source panel constant while measurements repeat over time, researchers can track genuine change in content patterns rather than confounding source variation with temporal change. It is widely used in communication, media studies, and political science to monitor how coverage, framing, or topic salience evolves.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Berelson's content analysis tradition and panel study methodology","year":"1950s–1980s (formalized in communication research)","type":"Longitudinal observational design","dataType":"Coded text, media content, documents (numeric frequency/categorical counts)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage Publications.","type":"book","doi":null,"isbn":"978-0761919773","url":null},{"ref":"Riffe, D., Lacy, S., Watson, B. R., & Fico, F. (2019). Analyzing Media Messages: Using Quantitative Content Analysis in Research (4th ed.). Routledge.","type":"book","doi":null,"isbn":"978-1138490062","url":null}],"related":["quantitative-content-analysis","panel-research","longitudinal-research","trend-research","longitudinal-quantitative-content-analysis","time-series-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-relational-survey","name":"Panel-based Relational Survey","fullName":"Panel-based Relational Survey Research","aliases":["longitudinal relational survey","panel relational study","repeated-measures correlational survey","panel correlational design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1940s onward (panel survey); relational survey as standard practice by mid-20th century","originator":"Rooted in panel survey traditions systematized by Paul Lazarsfeld (1940s) and relational survey methodology codified by Kerlinger, Babbie, and de Leeuw","url":"https://scholargate.app/en/research-design/panel-based-relational-survey","markdownUrl":"https://scholargate.app/en/research-design/panel-based-relational-survey.md","definition":"A panel-based relational survey is a quantitative design that recruits the same group of respondents and surveys them at two or more time points to examine how variables relate to, predict, or co-vary with one another over time. By combining the relational goal of uncovering associations among variables with the panel structure of repeated measurement from a stable sample, the design enables researchers to track how relationships evolve, test directional hypotheses about predictors and outcomes, and distinguish within-person change from between-person differences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in panel survey traditions systematized by Paul Lazarsfeld (1940s) and relational survey methodology codified by Kerlinger, Babbie, and de Leeuw","year":"1940s onward (panel survey); relational survey as standard practice by mid-20th century","type":"Quantitative observational longitudinal survey design","dataType":"Repeated quantitative survey measures from the same respondents across two or more time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"de Leeuw, E. D., Hox, J. J., & Dillman, D. A. (Eds.). (2008). International Handbook of Survey Methodology. Lawrence Erlbaum Associates / Taylor & Francis.","type":"book","doi":null,"isbn":"978-0805857535","url":null},{"ref":"Babbie, E. (2021). The Practice of Social Research (15th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-0357360767","url":null}],"related":["panel-research","relational-survey","longitudinal-correlational-research","correlational-research","longitudinal-research","cross-lagged-panel-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-survey-research","name":"Panel-based survey research","fullName":"Panel-Based Survey Research","aliases":["panel survey","longitudinal survey panel","repeated survey design","panel data survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century; formalized as a distinct design by the 1940s–1960s in sociological and economic research","originator":"Established through social science survey methodology; foundational reference: Kasprzyk et al. (1989)","url":"https://scholargate.app/en/research-design/panel-based-survey-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-survey-research.md","definition":"Panel-based survey research is a quantitative longitudinal design in which the same set of respondents — the panel — is surveyed with structured questionnaires at two or more distinct time points. By tracking the same individuals over time, the design captures intra-individual change, documents how outcomes evolve, and enables stronger causal inference than a single cross-sectional survey can provide. It is widely used in social science, economics, public health, and education research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established through social science survey methodology; foundational reference: Kasprzyk et al. (1989)","year":"Mid-20th century; formalized as a distinct design by the 1940s–1960s in sociological and economic research","type":"Quantitative longitudinal observational design","dataType":"Structured survey questionnaires administered to the same sample at multiple time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kasprzyk, D., Duncan, G., Kalton, G., & Singh, M. P. (Eds.). (1989). Panel Surveys. Wiley.","type":"book","doi":null,"isbn":"978-0471617143","url":null},{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922292","url":null}],"related":["panel-research","longitudinal-research","survey-research","cohort-research","cross-sectional-survey-research","longitudinal-survey-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-based-trend-research","name":"Panel-based trend research","fullName":"Panel-Based Trend Research","aliases":["panel trend study","longitudinal panel design","repeated-measures panel survey","panel survey trend analysis"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1940s–1960s","originator":"Established through survey methodology and panel econometrics; foundational contributions by Paul Lazarsfeld (1940s) and later systematized by econometricians including Zvi Griliches and Yair Mundlak","url":"https://scholargate.app/en/research-design/panel-based-trend-research","markdownUrl":"https://scholargate.app/en/research-design/panel-based-trend-research.md","definition":"Panel-based trend research tracks the same group of respondents — the panel — across multiple measurement waves over time, enabling researchers to separate genuine individual-level change from cohort differences and to model how variables evolve within persons. Unlike repeated cross-sectional designs, which sample new participants at each wave, a panel design retains the same units, giving it the power to detect within-person trajectories and causal ordering among variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established through survey methodology and panel econometrics; foundational contributions by Paul Lazarsfeld (1940s) and later systematized by econometricians including Zvi Griliches and Yair Mundlak","year":"1940s–1960s","type":"Quantitative longitudinal observational design","dataType":"Repeated quantitative measurements from the same respondents across multiple time points","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761922452","url":null},{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107038691","url":null}],"related":["cohort-study","cross-lagged-panel-model","repeated-measures-anova","time-series-analysis","longitudinal-survey","growth-curve-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-cointegration","name":"Panel Cointegration Tests","fullName":"Panel Cointegration Tests (Pedroni, Kao, Westerlund)","aliases":["Pedroni cointegration test","Kao cointegration test","Westerlund cointegration test","panel long-run equilibrium tests","Panel Eşbütünleşme Testleri (Pedroni, Kao, Westerlund)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2004,"originator":"Pedroni; Kao; Westerlund","url":"https://scholargate.app/en/econometrics/panel-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/panel-cointegration.md","definition":"Panel cointegration tests check whether a set of integrated variables share a stable long-run equilibrium relationship across a panel of cross-sectional units. Pedroni (1999, 2004) provides heterogeneous-panel tests with seven statistics, Kao (1999) gives an ADF-based homogeneous-panel test, and Westerlund (2007) adds error-correction-based tests robust to structural breaks and cross-sectional dependence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pedroni; Kao; Westerlund","year":2004,"type":"Panel cointegration test","estimator":"Residual-based and error-correction-based panel tests","outcome":"long-run equilibrium relationship (yes/no)","structure":"panel data","minSample":50},"citations":[{"ref":"Pedroni, P. (2004). Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis. Econometric Theory, 20(3), 597–625.","type":"article","doi":"10.1017/S0266466604203073","isbn":null,"url":null},{"ref":"Westerlund, J. (2007). Testing for Error Correction in Panel Data. Oxford Bulletin of Economics and Statistics, 69(6), 709–748.","type":"article","doi":"10.1111/j.1468-0084.2007.00477.x","isbn":null,"url":null}],"related":["panel-fixed-effects","amg-estimator","vecm","ols-regression","panel-unit-root-tests"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-analysis","name":"Panel Data Analysis","fullName":"Panel Data Analysis (Longitudinal Data Analysis)","aliases":["longitudinal data analysis","pooled cross-sectional time-series analysis","panel regression","data panel analysis"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1966–1978","originator":"Balestra & Nerlove (1966); Mundlak (1978); Hausman (1978)","url":"https://scholargate.app/en/econometrics/panel-data-analysis","markdownUrl":"https://scholargate.app/en/econometrics/panel-data-analysis.md","definition":"Panel data analysis models data that track multiple units — countries, firms, individuals — over time, enabling researchers to control for unobserved unit-level heterogeneity that would otherwise bias cross-sectional or time-series estimates. The two core specifications are fixed effects and random effects, selected via the Hausman test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Balestra & Nerlove (1966); Mundlak (1978); Hausman (1978)","year":"1966–1978","type":"Panel regression framework","dataType":"Balanced or unbalanced panel (N units × T time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer.","type":"book","doi":null,"isbn":"978-3030539528","url":null},{"ref":"Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null}],"related":["fixed-effects-model","random-effects-model","panel-hausman-test","dynamic-panel-data-model","arellano-bond-gmm-estimator","panel-ols"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-causal-impact-analysis","name":"Panel Data Causal Impact Analysis","fullName":"Panel Data Causal Impact Analysis","aliases":["Panel CausalImpact","multi-unit causal impact","panel BSTS causal inference","panel structural time-series causal analysis"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015 (base method); panel extension mid-2010s","originator":"Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature","url":"https://scholargate.app/en/causal-inference/panel-data-causal-impact-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-causal-impact-analysis.md","definition":"Panel data causal impact analysis extends the Bayesian structural time-series approach of Brodersen et al. (2015) to multi-unit panel settings, estimating the counterfactual for several treated units simultaneously using control units as a donor pool. It produces credible intervals for the causal effect at each post-intervention time point, aggregated across units and periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature","year":"2015 (base method); panel extension mid-2010s","type":"Bayesian structural time-series causal inference","dataType":"Multi-unit panel time-series (balanced or unbalanced)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null}],"related":["causal-impact-analysis","synthetic-control-method","panel-data-synthetic-control-method","difference-in-differences","panel-data-difference-in-differences","panel-data-interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-coarsened-exact-matching","name":"Panel Data Coarsened Exact Matching","fullName":"Panel Data Coarsened Exact Matching Estimator","aliases":["Panel CEM","CEM for panel data","coarsened exact matching with panel data"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012 (CEM); 2021 (panel extension)","originator":"Iacus, King & Porro (CEM, 2012); panel extension via Imai, Kim & Wang (2021)","url":"https://scholargate.app/en/causal-inference/panel-data-coarsened-exact-matching","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-coarsened-exact-matching.md","definition":"Panel Data Coarsened Exact Matching applies the Coarsened Exact Matching (CEM) algorithm to repeated-measures panel data, matching treated and control units within the same coarsened covariate strata across multiple time periods. It balances pre-treatment characteristics before estimating a causal treatment effect, combining the transparency of exact matching with the richer identification available in longitudinal datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Iacus, King & Porro (CEM, 2012); panel extension via Imai, Kim & Wang (2021)","year":"2012 (CEM); 2021 (panel extension)","type":"Matching / quasi-experimental","dataType":"Panel data (repeated observations on units across multiple time periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Imai, K., Kim, I. S., & Wang, E. H. (2021). Matching Methods for Causal Inference with Time-Series Cross-Sectional Data. American Journal of Political Science, 67(3), 587-605.","type":"article","doi":"10.1111/ajps.12685","isbn":null,"url":null}],"related":["coarsened-exact-matching","propensity-score-matching","panel-data-propensity-score-matching","panel-fixed-effects","difference-in-differences","matching-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-difference-in-differences","name":"Panel Data Difference-in-Differences","fullName":"Panel Data Difference-in-Differences Estimator","aliases":["Two-Way Fixed Effects DiD","TWFE","Panel DiD","Panel Diff-in-Diff"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1985–2004","originator":"Ashenfelter & Card (1985); codified by Angrist & Pischke (2009); serial correlation critique by Bertrand, Duflo & Mullainathan (2004)","url":"https://scholargate.app/en/causal-inference/panel-data-difference-in-differences","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-difference-in-differences.md","definition":"Panel Data Difference-in-Differences extends the classic two-period DiD design to settings with multiple units observed across many time periods. By absorbing unit-level fixed effects and time fixed effects simultaneously, it isolates the causal effect of a treatment or policy change while controlling for both time-invariant unit heterogeneity and common time shocks affecting all units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ashenfelter & Card (1985); codified by Angrist & Pischke (2009); serial correlation critique by Bertrand, Duflo & Mullainathan (2004)","year":"1985–2004","type":"Causal inference / panel regression","dataType":"Panel data (multiple units, multiple time periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691120355","url":null},{"ref":"Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1), 249-275.","type":"article","doi":"10.1162/003355304772839588","isbn":null,"url":null}],"related":["difference-in-differences","panel-fixed-effects","staggered-difference-in-differences","event-study-design","propensity-score-matching","synthetic-control-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-entropy-balancing","name":"Panel Data Entropy Balancing","fullName":"Entropy Balancing for Panel Data","aliases":["EB-panel","panel entropy balancing","entropy reweighting in panel data","panel-EB"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012 (cross-section); panel adaptation mid-2010s onward","originator":"Hainmueller (2012); extended to panel settings by subsequent applied econometric work","url":"https://scholargate.app/en/causal-inference/panel-data-entropy-balancing","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-entropy-balancing.md","definition":"Panel data entropy balancing extends Hainmueller's (2012) entropy balancing method to longitudinal settings. It computes unit-level weights for control observations so that their covariate moments exactly match those of the treatment group across panel periods, then plugs these weights into a weighted panel regression to estimate causal treatment effects without requiring a correctly specified propensity score model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hainmueller (2012); extended to panel settings by subsequent applied econometric work","year":"2012 (cross-section); panel adaptation mid-2010s onward","type":"Covariate balancing / reweighting estimator","dataType":"Panel data (longitudinal) with binary treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46.","type":"article","doi":"10.1093/pan/mpr025","isbn":null,"url":null},{"ref":"Zhao, Q. (2019). Covariate balancing propensity score by tailored loss functions. Annals of Statistics, 47(2), 965-993.","type":"article","doi":"10.1214/18-AOS1698","isbn":null,"url":null}],"related":["entropy-balancing","panel-data-propensity-score-matching","panel-data-inverse-probability-weighting","panel-data-doubly-robust-estimation","panel-data-difference-in-differences","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-fuzzy-regression-discontinuity","name":"Panel Data Fuzzy Regression Discontinuity","fullName":"Panel Data Fuzzy Regression Discontinuity Design","aliases":["Panel Fuzzy RDD","Panel FRD","Fuzzy RD with Panel Data","Panel Fuzzy RD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2001 (fuzzy RDD); panel extension circa 2011","originator":"Hahn, Todd & Van der Klaauw; extended to panel settings by Papay, Willett & Murnane and others","url":"https://scholargate.app/en/causal-inference/panel-data-fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-fuzzy-regression-discontinuity.md","definition":"Panel Data Fuzzy Regression Discontinuity Design (Panel FRD) extends the fuzzy RDD framework to settings where multiple observations per unit are available over time. It exploits a probabilistic — rather than deterministic — threshold-crossing rule to identify a local average treatment effect (LATE) while controlling for unit-level and time-level fixed effects, sharpening identification in repeated-measures contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hahn, Todd & Van der Klaauw; extended to panel settings by Papay, Willett & Murnane and others","year":"2001 (fuzzy RDD); panel extension circa 2011","type":"Quasi-experimental causal inference","dataType":"Panel data (repeated observations per unit) with a continuous running variable and a probabilistic treatment rule","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.","type":"article","doi":"10.1111/1468-0262.00183","isbn":null,"url":null},{"ref":"Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.","type":"article","doi":"10.1257/jel.48.2.281","isbn":null,"url":null}],"related":["fuzzy-regression-discontinuity","regression-discontinuity-design","panel-data-regression-discontinuity-design","panel-data-instrumental-variables","instrumental-variables","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-instrumental-variables","name":"Panel Data Instrumental Variables","fullName":"Instrumental Variables Estimation in Panel Data Settings","aliases":["Panel IV","Panel 2SLS","Within-IV","Fixed-Effects IV"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1978-1991","originator":"Hausman (1978); Anderson & Hsiao (1982); Arellano & Bond (1991)","url":"https://scholargate.app/en/causal-inference/panel-data-instrumental-variables","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-instrumental-variables.md","definition":"Panel data instrumental variables combines the bias-correcting power of instrumental variables (IV) with the within-unit variation exploited by panel data methods. It addresses endogeneity — omitted variables, reverse causation, or measurement error — in longitudinal settings where observations are repeated across units and time. Seminal contributions come from Hausman (1978) on specification testing and Arellano and Bond (1991) on GMM-based panel IV.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hausman (1978); Anderson & Hsiao (1982); Arellano & Bond (1991)","year":"1978-1991","type":"Causal inference / panel regression","dataType":"Longitudinal / panel data (multiple units observed over multiple periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251-1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null}],"related":["instrumental-variables","difference-in-differences","panel-fixed-effects","two-stage-least-squares","generalized-method-of-moments","dynamic-panel-data"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-interrupted-time-series","name":"Panel Data Interrupted Time Series","fullName":"Panel Data Interrupted Time Series Analysis","aliases":["panel ITS","multi-unit ITS","panel ITSA","controlled interrupted time series"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)","url":"https://scholargate.app/en/causal-inference/panel-data-interrupted-time-series","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-interrupted-time-series.md","definition":"Panel Data Interrupted Time Series (panel ITS) is a quasi-experimental method that estimates the causal effect of an intervention using repeated observations from multiple units over time. By exploiting variation across both units and time periods, it provides stronger causal identification than single-unit ITS, detecting changes in the level and slope of the outcome trajectory immediately following a clearly dated intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)","year":"2000s–2010s","type":"Quasi-experimental causal inference","dataType":"Longitudinal panel data with repeated observations across multiple units and time points","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Lopez Bernal, J., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.","type":"article","doi":"10.1093/ije/dyw098","isbn":null,"url":null},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["interrupted-time-series","difference-in-differences","panel-fixed-effects","event-study-design","panel-data-difference-in-differences","synthetic-control-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-inverse-probability-weighting","name":"Panel Data Inverse Probability Weighting","fullName":"Panel Data Inverse Probability Weighting Estimator","aliases":["panel IPW","longitudinal IPW","time-varying IPW","panel IPTW"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000","originator":"Robins, Hernan & Brumback","url":"https://scholargate.app/en/causal-inference/panel-data-inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-inverse-probability-weighting.md","definition":"Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Hernan & Brumback","year":"2000","type":"Reweighting / causal inference","dataType":"Panel / longitudinal (repeated observations per unit)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"}],"related":["propensity-score-weighting","marginal-structural-model","panel-data-doubly-robust-estimation","panel-data-propensity-score-matching","inverse-probability-weighting","panel-data-matching-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-marginal-structural-model","name":"Panel Data Marginal Structural Model","fullName":"Panel Data Marginal Structural Model with Inverse Probability Weighting","aliases":["MSM panel","longitudinal MSM","panel MSM","time-varying treatment MSM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000","originator":"James M. Robins, Miguel A. Hernan, Babette Brumback","url":"https://scholargate.app/en/causal-inference/panel-data-marginal-structural-model","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-marginal-structural-model.md","definition":"A panel data marginal structural model (MSM) uses inverse probability of treatment weighting (IPTW) across multiple time periods to estimate the causal effect of a time-varying treatment, while appropriately adjusting for time-varying confounders that are themselves affected by prior treatment — a bias source that conventional regression cannot handle.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James M. Robins, Miguel A. Hernan, Babette Brumback","year":"2000","type":"Causal model for time-varying treatments","dataType":"Longitudinal / panel data with time-varying treatment and confounders","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"}],"related":["marginal-structural-model","inverse-probability-weighting","panel-data-inverse-probability-weighting","panel-data-doubly-robust-estimation","panel-data-difference-in-differences","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-matching-estimator","name":"Panel Data Matching Estimator","fullName":"Panel Data Matching Estimator for Average Treatment Effects","aliases":["panel matching","matching-on-panel-data","longitudinal matching estimator","PDME"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1997-2021","originator":"Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension","url":"https://scholargate.app/en/causal-inference/panel-data-matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-matching-estimator.md","definition":"The panel data matching estimator identifies causal treatment effects by pairing each treated unit with one or more control units that share similar covariate histories in the pre-treatment periods. By exploiting the longitudinal structure of panel data, it controls for both observed time-varying confounders and stable unit characteristics, estimating the average treatment effect on the treated (ATT) without requiring a parallel-trends assumption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension","year":"1997-2021","type":"Quasi-experimental causal estimator","dataType":"Balanced or unbalanced panel (repeated observations per unit over time)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605-654.","type":"article","doi":"10.2307/2971733","isbn":null,"url":null},{"ref":"Imai, K., Kim, I. S., & Wang, E. H. (2021). Matching methods for causal inference with time-series cross-sectional data. American Journal of Political Science, 67(3), 587-605.","type":"article","doi":"10.1111/ajps.12685","isbn":null,"url":null}],"related":["matching-estimator","propensity-score-matching","difference-in-differences","panel-data-difference-in-differences","coarsened-exact-matching","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-placebo-test","name":"Panel Data Placebo Test","fullName":"Panel Data Placebo Test for Causal Inference Validation","aliases":["placebo regression test","falsification test","pseudo-treatment test","in-time placebo"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2004-2010","originator":"Bertrand, Duflo & Mullainathan; Abadie, Diamond & Hainmueller","url":"https://scholargate.app/en/causal-inference/panel-data-placebo-test","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-placebo-test.md","definition":"A panel data placebo test is a falsification procedure used to assess the credibility of causal estimates in quasi-experimental panel designs. By applying the same estimation strategy to a period, group, or outcome where no true effect should exist, researchers verify that the observed treatment effect is not merely an artifact of model specification, coincidental trends, or data patterns unrelated to the intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bertrand, Duflo & Mullainathan; Abadie, Diamond & Hainmueller","year":"2004-2010","type":"Falsification / validation test","dataType":"Panel data (repeated observations over time)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1), 249-275.","type":"article","doi":"10.1162/003355304772839588","isbn":null,"url":null},{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null}],"related":["difference-in-differences","panel-data-difference-in-differences","sensitivity-analysis-for-causality","synthetic-control-method","event-study-design","regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-propensity-score-matching","name":"Panel Data Propensity Score Matching","fullName":"Propensity Score Matching with Panel Data","aliases":["PSM with panel data","longitudinal PSM","panel PSM","difference-in-differences PSM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1997-1998","originator":"Heckman, Ichimura & Todd","url":"https://scholargate.app/en/causal-inference/panel-data-propensity-score-matching","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-propensity-score-matching.md","definition":"Panel data propensity score matching combines the bias-reduction of PSM with the longitudinal structure of panel data, enabling causal estimation of treatment effects by matching treated and control units on observable pre-treatment characteristics and then differencing within matched pairs over time. Developed in the framework of Heckman, Ichimura, and Todd (1998), it is especially valuable when randomisation is infeasible and both selection on observables and time-varying confounding must be addressed simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heckman, Ichimura & Todd","year":"1997-1998","type":"Matching / causal inference","dataType":"Panel or longitudinal observational data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an Econometric Evaluation Estimator. Review of Economic Studies, 65(2), 261-294.","type":"article","doi":"10.1111/1467-937X.00044","isbn":null,"url":null},{"ref":"Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72.","type":"article","doi":"10.1111/j.1467-6419.2007.00527.x","isbn":null,"url":null}],"related":["propensity-score-matching","difference-in-differences","panel-data-difference-in-differences","panel-fixed-effects","matching-estimator","entropy-balancing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-propensity-score-weighting","name":"Panel Data Propensity Score Weighting","fullName":"Panel Data Propensity Score Weighting Estimator","aliases":["panel PSW","panel IPW","longitudinal propensity score weighting","panel inverse probability weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000-2003","originator":"Hirano, Imbens & Ridder; Robins, Hernan & Brumback","url":"https://scholargate.app/en/causal-inference/panel-data-propensity-score-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-propensity-score-weighting.md","definition":"Panel Data Propensity Score Weighting (panel PSW) extends inverse probability weighting to longitudinal settings where the same units are observed across multiple time periods. It reweights observations by the inverse of each unit's time-varying probability of receiving treatment, creating a pseudo-population in which treatment is balanced on observed covariates at each period, and then estimates causal effects from repeated-measures data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hirano, Imbens & Ridder; Robins, Hernan & Brumback","year":"2000-2003","type":"Causal inference / panel weighting","dataType":"Panel data with repeated observations on the same units over time","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.","type":"article","doi":"10.1111/1468-0262.00442","isbn":null,"url":null},{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null}],"related":["propensity-score-weighting","propensity-score-matching","panel-data-difference-in-differences","marginal-structural-model","panel-data-doubly-robust-estimation","panel-data-inverse-probability-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-regression-discontinuity-design","name":"Panel Data Regression Discontinuity Design","fullName":"Panel Data Regression Discontinuity Design","aliases":["Panel RD","Panel RDD","Longitudinal Regression Discontinuity","Fixed-Effects RDD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1960 (original RDD); panel extension codified 2000s–2010s","originator":"Thistlethwaite & Campbell (1960); panel extension developed through Lee & Lemieux (2010) and related applied work","url":"https://scholargate.app/en/causal-inference/panel-data-regression-discontinuity-design","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-regression-discontinuity-design.md","definition":"Panel data regression discontinuity design (Panel RDD) combines the sharp local identification of a regression discontinuity with the within-unit variation available in repeated-observation panel data. Units are observed across multiple periods, and treatment is assigned based on whether a running variable crosses a known threshold. By leveraging both the discontinuity and panel structure, researchers can control for unobserved unit-level heterogeneity while estimating a causal treatment effect near the threshold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thistlethwaite & Campbell (1960); panel extension developed through Lee & Lemieux (2010) and related applied work","year":"1960 (original RDD); panel extension codified 2000s–2010s","type":"Causal inference / quasi-experimental","dataType":"Panel data with a continuous running variable and a threshold assignment rule","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.","type":"article","doi":"10.1257/jel.48.2.281","isbn":null,"url":null},{"ref":"Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Econometrica, 69(1), 201-209.","type":"article","doi":"10.1111/1468-0262.00183","isbn":null,"url":null}],"related":["regression-discontinuity-design","fuzzy-regression-discontinuity","difference-in-differences","panel-fixed-effects","panel-data-difference-in-differences","panel-data-interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-data-synthetic-control-method","name":"Panel Data Synthetic Control Method","fullName":"Synthetic Control Method for Panel Data","aliases":["SCM panel","panel synthetic control","synthetic control estimator","comparative case study"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010","originator":"Alberto Abadie, Alexis Diamond & Jens Hainmueller","url":"https://scholargate.app/en/causal-inference/panel-data-synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/panel-data-synthetic-control-method.md","definition":"The panel data synthetic control method estimates the causal effect of an intervention on a single treated unit by constructing a data-driven weighted combination of untreated units — a synthetic control — that best reproduces the treated unit's pre-treatment outcome trajectory. The post-treatment gap between the treated unit and its synthetic counterpart is the estimated treatment effect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alberto Abadie, Alexis Diamond & Jens Hainmueller","year":"2010","type":"Causal inference / panel data","dataType":"Panel data (multiple units, multiple pre- and post-treatment periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null},{"ref":"Abadie, A. (2021). Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), 391-425.","type":"article","doi":"10.1257/jel.20191450","isbn":null,"url":null}],"related":["difference-in-differences","synthetic-control-method","panel-data-difference-in-differences","panel-fixed-effects","matching-estimator","panel-data-event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-dcc-garch","name":"Panel DCC-GARCH","fullName":"Panel Dynamic Conditional Correlation GARCH Model","aliases":["DCC-GARCH panel","panel dynamic conditional correlation","multivariate DCC-GARCH","Panel DCC"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002","originator":"Robert F. Engle","url":"https://scholargate.app/en/econometrics/panel-dcc-garch","markdownUrl":"https://scholargate.app/en/econometrics/panel-dcc-garch.md","definition":"The Panel DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation GARCH framework to panel data settings, jointly modelling time-varying volatility and cross-sectional correlations across multiple units (countries, firms, or assets) over time. It allows pairwise correlations to vary dynamically in response to market shocks while preserving parsimony via a two-step estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Engle","year":"2002","type":"Multivariate volatility model","dataType":"Panel time series; multivariate financial or macroeconomic data","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroscedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.","type":"article","doi":"10.1198/073500102288618487","isbn":null,"url":null},{"ref":"Engle, R. F., & Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. NBER Working Paper 8554. National Bureau of Economic Research.","type":"article","doi":null,"isbn":null,"url":"https://www.nber.org/papers/w8554"}],"related":["dcc-garch-model","panel-garch-model","panel-egarch","panel-tgarch","vector-autoregression","panel-var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-df-gls","name":"Panel DF-GLS","fullName":"Panel Dickey-Fuller GLS Test","aliases":["Panel unit-root test"],"domain":"econometrics","family":"regression-model","subfamily":"Unit-root test","year":"1996","originator":"Elliott, Rothenberg, and Stock (adapted to panels)","url":"https://scholargate.app/en/econometrics/panel-df-gls","markdownUrl":"https://scholargate.app/en/econometrics/panel-df-gls.md","definition":"Panel DF-GLS extends the Elliott, Rothenberg, and Stock (1996) GLS unit-root test to panel data, combining cross-sectional and time-series information to test whether variables contain unit roots. Introduced by Hadri and colleagues (2005), it is more powerful than standard panel unit-root tests (IPS, LLC) due to its GLS detrending approach. This test is essential for establishing stationarity before fitting cointegration or dynamic panel models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elliott, Rothenberg, and Stock (adapted to panels)","subfamily":"Unit-root test","year":"1996","type":"Stationarity test"},"citations":[{"ref":"Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometric Reviews, 13(4), 469-497.","type":"article","doi":"10.2307/2171846","isbn":null,"url":null},{"ref":"Hadri, K., & Larsson, R. (2005). Testing for stationarity in heterogeneous panel data. Econometric Reviews, 24(4), 403-456.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Testing+for+stationarity+in+heterogeneous+panel+data+Hadri"}],"related":["panel-kss","maki-cointegration-test","cs-ardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-dynamic-panel-data-model","name":"Panel Dynamic Panel Data Model","fullName":"Dynamic Panel Data Model","aliases":["dynamic panel model","lagged dependent variable panel model","Arellano-Bond type dynamic panel","GMM dynamic panel"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991–1998","originator":"Arellano & Bond (1991); Blundell & Bond (1998)","url":"https://scholargate.app/en/econometrics/panel-dynamic-panel-data-model","markdownUrl":"https://scholargate.app/en/econometrics/panel-dynamic-panel-data-model.md","definition":"The dynamic panel data model extends standard panel regression by including one or more lagged values of the outcome variable as regressors. Because past outcomes directly predict current outcomes, the model captures persistence and adjustment dynamics — but it also introduces a correlation between the lagged dependent variable and the individual fixed effect, rendering OLS and standard fixed-effects estimators inconsistent. GMM-based approaches developed by Arellano-Bond and Blundell-Bond resolve this problem.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bond (1991); Blundell & Bond (1998)","year":"1991–1998","type":"Dynamic panel regression","dataType":"Balanced or unbalanced panel data (cross-sections observed over multiple time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.","type":"article","doi":"10.1016/S0304-4076(98)00009-8","isbn":null,"url":null}],"related":["arellano-bond-gmm-estimator","panel-system-gmm","panel-difference-gmm","dynamic-panel-data-model","panel-fixed-effects-model","panel-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-egarch","name":"Panel EGARCH","fullName":"Panel Exponential Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["Panel EGARCH model","panel exponential GARCH","EGARCH for panel data","cross-sectional EGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991 (EGARCH); panel extensions widely used from 2000s","originator":"Daniel B. Nelson (EGARCH); panel extension by applied econometrics literature","url":"https://scholargate.app/en/econometrics/panel-egarch","markdownUrl":"https://scholargate.app/en/econometrics/panel-egarch.md","definition":"Panel EGARCH extends Nelson's (1991) Exponential GARCH model to a panel setting, allowing conditional variance to evolve asymmetrically over time for each cross-sectional unit. The log specification ensures non-negative variance without parameter constraints, and the leverage term distinguishes whether negative shocks amplify volatility more than positive ones of equal magnitude.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel B. Nelson (EGARCH); panel extension by applied econometrics literature","year":"1991 (EGARCH); panel extensions widely used from 2000s","type":"Volatility model","dataType":"Panel data with time-series volatility dynamics (financial returns, macro series)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null},{"ref":"Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470414354","url":null}],"related":["panel-garch-model","panel-arch-model","panel-tgarch","panel-dcc-garch","egarch-model","panel-var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-engle-granger-cointegration","name":"Panel Engle-Granger Cointegration","fullName":"Panel Engle-Granger Cointegration Test","aliases":["panel cointegration test","panel EG cointegration","Pedroni cointegration test","residual-based panel cointegration"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1999","originator":"Pedroni (1999), extending Engle & Granger (1987)","url":"https://scholargate.app/en/econometrics/panel-engle-granger-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/panel-engle-granger-cointegration.md","definition":"The Panel Engle-Granger cointegration test extends the classic two-step Engle-Granger procedure to panel data, allowing researchers to detect long-run equilibrium relationships among integrated variables across multiple cross-sectional units simultaneously. Pedroni (1999) developed panel statistics that pool information across units while allowing heterogeneous short-run dynamics and individual-specific intercepts and trends.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pedroni (1999), extending Engle & Granger (1987)","year":"1999","type":"Cointegration test","dataType":"Balanced or unbalanced panel data with integrated (I(1)) variables","subfamily":"Econometrics / time series"},"citations":[{"ref":"Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653-670.","type":"article","doi":"10.1111/1468-0084.0610s1653","isbn":null,"url":null},{"ref":"Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251-276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null}],"related":["panel-johansen-cointegration","engle-granger-cointegration-test","johansen-cointegration-test","panel-vecm","panel-ardl-bounds-test","panel-adf-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-event-study-in-education-research","name":"Panel Event Study in Education Research","fullName":"Panel Data Event Study Design in Education Research","aliases":["education event study","panel event-study design","education policy event study","school event study"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1993 (general method); 2000s (education applications)","originator":"Jacobson, LaLonde & Sullivan (1993); widely adopted in education economics from 2000s onward","url":"https://scholargate.app/en/causal-inference/panel-event-study-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/panel-event-study-in-education-research.md","definition":"The panel event study is a causal-inference design that tracks outcomes for a panel of educational units — students, teachers, schools, or districts — across relative time periods around a well-defined event such as a policy change, school reform, or staffing transition. By estimating period-by-period treatment effects, it reveals not only whether an intervention mattered but also when effects appeared and how long they persisted, making it especially valued in education economics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacobson, LaLonde & Sullivan (1993); widely adopted in education economics from 2000s onward","year":"1993 (general method); 2000s (education applications)","type":"Causal inference / panel regression","dataType":"Balanced or unbalanced panel data with student, school, or district units observed across multiple periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings Losses of Displaced Workers. American Economic Review, 83(4), 685-709.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2117574"},{"ref":"Freyaldenhoven, S., Hansen, C., Pérez, J. P. M., & Shapiro, J. M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper No. 29170.","type":"article","doi":null,"isbn":null,"url":"https://www.nber.org/papers/w29170"}],"related":["event-study-design","panel-event-study","difference-in-differences","panel-data-difference-in-differences","regression-discontinuity-design","interrupted-time-series-in-education-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-event-study","name":"Panel Event Study","fullName":"Panel Data Event Study Design","aliases":["event-study regression","dynamic DiD","relative-time regression","distributed-lag panel model"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1990s–2020s (modern panel formulation)","originator":"Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation","url":"https://scholargate.app/en/causal-inference/panel-event-study","markdownUrl":"https://scholargate.app/en/causal-inference/panel-event-study.md","definition":"A panel event study estimates the dynamic causal effect of a treatment or policy by regressing an outcome on a full set of relative-time indicators — one for each period before and after the event — while controlling for unit and time fixed effects. The resulting coefficient plot shows how the treated units diverged from untreated units at each point in calendar time relative to their treatment date, making both pre-treatment trend violations and post-treatment effect trajectories immediately visible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation","year":"1990s–2020s (modern panel formulation)","type":"Quasi-experimental / causal panel design","dataType":"Balanced or unbalanced panel data with a clearly dated treatment event per unit","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Freyaldenhoven, S., Hansen, C., Perez-Orive, J., & Shapiro, J. M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper 29170. National Bureau of Economic Research.","type":"article","doi":null,"isbn":null,"url":"https://www.nber.org/papers/w29170"},{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null}],"related":["difference-in-differences","event-study-design","panel-fixed-effects","staggered-difference-in-differences","synthetic-control-method","dynamic-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-fixed-effects-model","name":"Panel Fixed Effects Model","fullName":"Panel Data Fixed Effects Regression Model","aliases":["within estimator","FE model","within-group estimator","LSDV model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1978","originator":"Mundlak (1978); classical treatment in Wooldridge (2010) and Baltagi (2021)","url":"https://scholargate.app/en/econometrics/panel-fixed-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/panel-fixed-effects-model.md","definition":"The panel fixed effects (FE) model controls for all time-invariant, unit-specific unobserved heterogeneity by absorbing it into individual intercepts. By sweeping out unit means through the within transformation, FE yields unbiased estimates of the effect of time-varying regressors even when omitted unit-level confounders are correlated with those regressors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mundlak (1978); classical treatment in Wooldridge (2010) and Baltagi (2021)","year":"1978","type":"Panel regression estimator","dataType":"Balanced or unbalanced panel data (multiple units observed over multiple time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null},{"ref":"Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer.","type":"book","doi":null,"isbn":"978-3030534875","url":null}],"related":["random-effects-model","panel-random-effects-model","panel-hausman-test","panel-ols","difference-gmm","fixed-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-fixed-effects","name":"Panel Fixed Effects","fullName":"Panel Data Fixed Effects Model","aliases":["fixed effects model","within estimator","panel fixed-effects regression","Panel Veri — Sabit Etkiler Modeli"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2014,"originator":"Hsiao (textbook treatment); within transformation of panel data","url":"https://scholargate.app/en/econometrics/panel-fixed-effects","markdownUrl":"https://scholargate.app/en/econometrics/panel-fixed-effects.md","definition":"The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hsiao (textbook treatment); within transformation of panel data","year":2014,"type":"Panel data regression","estimator":"Within (fixed-effects) estimator","outcome":"continuous","dataStructure":"panel (entity x time)","minSample":50},"citations":[{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":"10.1017/CBO9781139839327","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1337558860","url":null}],"related":["panel-random-effects","ols-regression","instrumental-variables","difference-in-differences","system-gmm"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-garch-model","name":"Panel GARCH model","fullName":"Panel Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["panel GARCH","GARCH panel model","panel volatility model","panel conditional heteroscedasticity model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1986 (GARCH); panel extension 1990s–2000s","originator":"Bollerslev (1986); extended to panel settings in subsequent literature","url":"https://scholargate.app/en/econometrics/panel-garch-model","markdownUrl":"https://scholargate.app/en/econometrics/panel-garch-model.md","definition":"The Panel GARCH model extends Bollerslev's (1986) Generalized Autoregressive Conditional Heteroscedasticity framework to panel data, allowing conditional variance to evolve over time for each cross-sectional unit. It simultaneously captures unit-level heterogeneity and time-varying volatility clustering, making it the standard tool for modelling risk and uncertainty in multi-entity financial and macroeconomic panels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bollerslev (1986); extended to panel settings in subsequent literature","year":"1986 (GARCH); panel extension 1990s–2000s","type":"Volatility model","dataType":"Panel data (multiple cross-sectional units observed over time); returns or financial time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.","type":"article","doi":"10.1016/0304-4076(86)90063-1","isbn":null,"url":null},{"ref":"Bauwens, L., Laurent, S., & Rombouts, J. V. K. (2006). Multivariate GARCH models: a survey. Journal of Applied Econometrics, 21(1), 79–109.","type":"article","doi":"10.1002/jae.842","isbn":null,"url":null}],"related":["arch-model","dcc-garch-model","egarch-model","tgarch-model","panel-fixed-effects-model","vector-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-gearys-c","name":"Panel Geary's C","fullName":"Panel Data Geary's C Spatial Autocorrelation Statistic","aliases":["Geary's C for panel data","spatial Geary C panel","panel spatial contiguity ratio","panel Geary contiguity statistic"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1954 (base); 2000s (panel extension)","originator":"R. C. Geary (1954); panel extension in spatial econometrics literature","url":"https://scholargate.app/en/spatial-analysis/panel-gearys-c","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-gearys-c.md","definition":"Panel Geary's C extends the classic Geary contiguity ratio to panel datasets, measuring spatial autocorrelation across georeferenced units (regions, cities, countries) observed over multiple time periods. It detects whether neighboring units tend to have similar values, pooling or averaging evidence across the temporal dimension to yield more powerful inference than a single cross-section.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. C. Geary (1954); panel extension in spatial econometrics literature","year":"1954 (base); 2000s (panel extension)","type":"Spatial autocorrelation statistic","dataType":"Panel data with georeferenced cross-sectional units","subfamily":"GIS / spatial"},"citations":[{"ref":"Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115-145.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Geary+1954+contiguity+ratio+statistical+mapping"},{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403408","url":null}],"related":["panel-morans-i","gearys-c","morans-i","local-gearys-c","panel-spatial-lag-model","panel-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-geographically-weighted-regression","name":"Panel Geographically Weighted Regression","fullName":"Panel Geographically Weighted Regression","aliases":["Panel GWR","PGWR","spatiotemporal GWR","geographically weighted panel regression"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2000s–2010s","originator":"Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature","url":"https://scholargate.app/en/spatial-analysis/panel-geographically-weighted-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-geographically-weighted-regression.md","definition":"Panel Geographically Weighted Regression (Panel GWR) extends the standard GWR framework to panel data, allowing regression coefficients to vary both across geographic locations and over time. It captures spatially non-stationary relationships in longitudinal or repeated-measures spatial datasets, combining local spatial estimation with panel-data controls for unit-specific heterogeneity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fotheringham, Brunsdon & Charlton (foundational GWR); panel extension developed in spatial econometrics literature","year":"2000s–2010s","type":"Local spatial regression with panel structure","dataType":"Georeferenced panel data (multiple observations per location over time)","subfamily":"GIS / spatial"},"citations":[{"ref":"Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.","type":"book","doi":null,"isbn":"978-0471496168","url":null},{"ref":"Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., Kang, W., & Wolf, L. J. (2020). Inference in Multiscale Geographically Weighted Regression. Geographical Analysis, 52(1), 87–106.","type":"article","doi":"10.1111/gean.12189","isbn":null,"url":null}],"related":["geographically-weighted-regression","multiscale-geographically-weighted-regression","panel-spatial-lag-model","panel-spatial-error-model","local-geographically-weighted-regression","space-time-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-gls","name":"Panel GLS","fullName":"Panel Generalized Least Squares","aliases":["Panel GLS","Generalized Least Squares for panel data","FGLS panel","feasible GLS panel"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1935 / developed for panels 1980s–1990s","originator":"Aitken (1935); extended to panel data by Baltagi and others","url":"https://scholargate.app/en/econometrics/panel-gls","markdownUrl":"https://scholargate.app/en/econometrics/panel-gls.md","definition":"Panel GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding the Best Linear Unbiased Estimator when the error structure is correctly specified.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aitken (1935); extended to panel data by Baltagi and others","year":"1935 / developed for panels 1980s–1990s","type":"Generalized linear regression","dataType":"Balanced or unbalanced panel (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null},{"ref":"Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer.","type":"book","doi":null,"isbn":"978-3030538002","url":null}],"related":["panel-ols","panel-wls","panel-fixed-effects-model","panel-random-effects-model","feasible-gls","random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-granger-causality","name":"Panel Granger Causality","fullName":"Panel Data Granger Causality Test","aliases":["panel causality test","Dumitrescu-Hurlin test","heterogeneous panel causality","panel Granger test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988–2012","originator":"Holtz-Eakin, Newey & Rosen (1988); Dumitrescu & Hurlin (2012)","url":"https://scholargate.app/en/econometrics/panel-granger-causality","markdownUrl":"https://scholargate.app/en/econometrics/panel-granger-causality.md","definition":"The Panel Granger Causality test examines whether past values of one variable help predict another variable across multiple cross-sectional units observed over time. It extends the classical Granger causality framework to panel data, accounting for cross-sectional heterogeneity and enabling more powerful inference by pooling information across units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holtz-Eakin, Newey & Rosen (1988); Dumitrescu & Hurlin (2012)","year":"1988–2012","type":"Causality test","dataType":"Panel data (balanced or unbalanced, stationary or pre-filtered)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460.","type":"article","doi":"10.1016/j.econmod.2012.02.014","isbn":null,"url":null},{"ref":"Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica, 56(6), 1371–1395.","type":"article","doi":"10.2307/1913103","isbn":null,"url":null}],"related":["granger-causality-test","toda-yamamoto-causality-test","panel-var-model","panel-vecm","panel-ardl-bounds-test","panel-johansen-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-hausman-test","name":"Panel Hausman Test","fullName":"Hausman Specification Test for Panel Data","aliases":["Hausman endogeneity test","Wu-Hausman test","fixed-vs-random effects test","Hausman chi-squared test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1978","originator":"Jerry A. Hausman","url":"https://scholargate.app/en/econometrics/panel-hausman-test","markdownUrl":"https://scholargate.app/en/econometrics/panel-hausman-test.md","definition":"The Hausman specification test for panel data determines whether individual-specific effects are correlated with the regressors — a correlation that would make the random effects estimator inconsistent. A statistically significant result favours the fixed effects model; a non-significant result supports the more efficient random effects model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerry A. Hausman","year":"1978","type":"Specification test","dataType":"Panel data (balanced or unbalanced)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["panel-fixed-effects-model","panel-random-effects-model","fixed-effects-model","random-effects-model","panel-data-analysis","panel-ols"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-hot-spot-analysis","name":"Panel Hot Spot Analysis","fullName":"Panel Data Hot Spot Analysis","aliases":["longitudinal hot spot analysis","repeated cross-sectional hot spot analysis","spatio-temporal hot spot detection","panel Getis-Ord analysis"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1992 (Gi* statistic); 2004 (longitudinal/panel extension)","originator":"Weisburd et al. (longitudinal application); Getis & Ord (foundational Gi* statistic)","url":"https://scholargate.app/en/spatial-analysis/panel-hot-spot-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-hot-spot-analysis.md","definition":"Panel Hot Spot Analysis applies hot spot detection — typically via the Getis-Ord Gi* statistic — repeatedly across multiple time periods on the same spatial units, enabling researchers to track where clusters of high or low values persist, emerge, or dissolve over time. It bridges cross-sectional spatial statistics with longitudinal panel methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Weisburd et al. (longitudinal application); Getis & Ord (foundational Gi* statistic)","year":"1992 (Gi* statistic); 2004 (longitudinal/panel extension)","type":"Spatio-temporal hot spot detection","dataType":"Panel spatial data (repeated cross-sections of georeferenced areal or point data)","subfamily":"GIS / spatial"},"citations":[{"ref":"Weisburd, D., Bushway, S., Lum, C., & Yang, S.-M. (2004). Trajectories of crime at places: A longitudinal study of street segments in the city of Seattle. Criminology, 42(2), 283-321.","type":"article","doi":"10.1111/j.1745-9125.2004.tb00521.x","isbn":null,"url":null},{"ref":"Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.","type":"article","doi":"10.1111/j.1538-4632.1992.tb00261.x","isbn":null,"url":null}],"related":["hot-spot-analysis","local-getis-ord-gi-star","space-time-hot-spot-analysis","panel-spatial-lag-model","kernel-density-estimation","local-indicators-of-spatial-association"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-johansen-cointegration","name":"Panel Johansen Cointegration","fullName":"Panel Johansen Cointegration Test","aliases":["panel Johansen test","Larsson-Lyhagen-Lothgren test","LLL panel cointegration","panel trace test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2001","originator":"Larsson, Lyhagen & Lothgren (building on Johansen 1988/1991)","url":"https://scholargate.app/en/econometrics/panel-johansen-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/panel-johansen-cointegration.md","definition":"The Panel Johansen cointegration test extends Johansen's maximum-likelihood framework to panel data, allowing researchers to test whether multiple non-stationary variables share long-run equilibrium relationships across cross-sectional units. It pools the likelihood-ratio statistics from individual Johansen tests and compares the standardised average against a standard normal distribution, yielding greater power than single-country approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Larsson, Lyhagen & Lothgren (building on Johansen 1988/1991)","year":"2001","type":"Panel cointegration test","dataType":"Balanced or unbalanced panel; I(1) time series for multiple cross-sections","subfamily":"Econometrics / time series"},"citations":[{"ref":"Larsson, R., Lyhagen, J., & Lothgren, M. (2001). Likelihood-based cointegration tests in heterogeneous panels. Econometrics Journal, 4(1), 109–142.","type":"article","doi":"10.1111/1368-423X.00059","isbn":null,"url":null},{"ref":"Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580.","type":"article","doi":"10.2307/2938278","isbn":null,"url":null}],"related":["johansen-cointegration-test","panel-engle-granger-cointegration","panel-vecm","panel-granger-causality","vector-error-correction-model","panel-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-kernel-density-estimation","name":"Panel Kernel Density Estimation","fullName":"Panel Kernel Density Estimation","aliases":["Panel KDE","longitudinal kernel density estimation","repeated-measures KDE","panel nonparametric density estimation"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1962 (KDE); panel extension: 1990s–2000s","originator":"Parzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literature","url":"https://scholargate.app/en/spatial-analysis/panel-kernel-density-estimation","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-kernel-density-estimation.md","definition":"Panel Kernel Density Estimation (Panel KDE) extends the standard kernel density estimator to panel (longitudinal) data, estimating smooth density surfaces for spatial or attribute variables observed across multiple units and time periods. It reveals how the distribution of a phenomenon shifts, concentrates, or disperses over time and across groups, making it a natural tool for tracking spatial patterns in repeated-measures or panel datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Parzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literature","year":"1962 (KDE); panel extension: 1990s–2000s","type":"Nonparametric density estimation","dataType":"Continuous or count spatial data observed across multiple units and time periods (panel structure)","subfamily":"GIS / spatial"},"citations":[{"ref":"Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065-1076.","type":"article","doi":"10.1214/aoms/1177704472","isbn":null,"url":null},{"ref":"Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.","type":"book","doi":null,"isbn":"978-0412246203","url":null}],"related":["kernel-density-estimation","panel-spatial-regression","panel-hot-spot-analysis","space-time-kernel-density-estimation","local-kernel-density-estimation","panel-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-kpss-test","name":"Panel KPSS test","fullName":"Panel Kwiatkowski-Phillips-Schmidt-Shin Test","aliases":["KPSS panel stationarity test","panel stationarity test","Hadri LM test","panel KPSS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000","originator":"Hadri (2000), extending Kwiatkowski, Phillips, Schmidt, and Shin (1992)","url":"https://scholargate.app/en/econometrics/panel-kpss-test","markdownUrl":"https://scholargate.app/en/econometrics/panel-kpss-test.md","definition":"The Panel KPSS test, introduced by Hadri (2000), tests the null hypothesis that all series in a panel are stationary against the alternative that some or all contain a unit root. It extends the univariate KPSS framework to panel data by aggregating individual LM statistics, providing higher power than unit-root tests when most series are in fact stationary.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hadri (2000), extending Kwiatkowski, Phillips, Schmidt, and Shin (1992)","year":"2000","type":"Panel stationarity test","dataType":"Balanced or unbalanced panel data (time series cross-section)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hadri, K. (2000). Testing for stationarity in heterogeneous panel data. Econometrics Journal, 3(2), 148-161.","type":"article","doi":"10.1111/1368-423X.00043","isbn":null,"url":null},{"ref":"Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178.","type":"article","doi":"10.1016/0304-4076(92)90104-Y","isbn":null,"url":null}],"related":["panel-adf-unit-root-test","panel-pp-unit-root-test","panel-zivot-andrews-test","augmented-dickey-fuller-unit-root-test","phillips-perron-unit-root-test","panel-data-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-kriging","name":"Panel Kriging","fullName":"Panel Data Kriging","aliases":["longitudinal kriging","repeated-measures kriging","spatio-temporal panel kriging","panel geostatistical interpolation"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2011","originator":"Cressie & Wikle (spatio-temporal kriging framework)","url":"https://scholargate.app/en/spatial-analysis/panel-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-kriging.md","definition":"Panel Kriging is a geostatistical interpolation method that combines kriging's spatial prediction framework with a panel (longitudinal) data structure. It estimates unknown values at unobserved locations and times by borrowing strength from repeated spatial observations across multiple time periods, accounting for both spatial dependence and temporal autocorrelation simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cressie & Wikle (spatio-temporal kriging framework)","year":"2011","type":"Geostatistical interpolation","dataType":"Panel (repeated spatial observations over time)","subfamily":"GIS / spatial"},"citations":[{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471002550","url":null},{"ref":"Cressie, N., & Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. Wiley.","type":"book","doi":null,"isbn":"978-0471692744","url":null}],"related":["ordinary-kriging","space-time-kriging","panel-spatial-regression","geographically-weighted-regression","spatial-autocorrelation","kernel-density-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-kss","name":"Panel KSS","fullName":"Panel Kwiatkowski-Phillips-Schmidt-Shin Test","aliases":["Panel stationarity test"],"domain":"econometrics","family":"regression-model","subfamily":"Stationarity test","year":"1992","originator":"Kwiatkowski, Phillips, Schmidt, and Shin (panel version by Hadri)","url":"https://scholargate.app/en/econometrics/panel-kss","markdownUrl":"https://scholargate.app/en/econometrics/panel-kss.md","definition":"The Panel KSS test reverses the null hypothesis of unit-root tests: it tests whether variables are stationary (stationarity is the null) versus nonstationary (unit root is the alternative). Introduced by Kwiatkowski et al. (1992) and extended to panels by Hadri (2000), this complementary approach provides robustness when combined with unit-root tests like Panel DF-GLS. Using both tests together reduces the risk of erroneous conclusions about variable persistence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kwiatkowski, Phillips, Schmidt, and Shin (panel version by Hadri)","subfamily":"Stationarity test","year":"1992","type":"Unit-root test"},"citations":[{"ref":"Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178.","type":"article","doi":"10.1016/0304-4076(92)90104-Y","isbn":null,"url":null},{"ref":"Hadri, K. (2000). Testing for stationarity in heterogeneous panel data. Econometric Reviews, 19(4), 367-397.","type":"article","doi":"10.1111/1368-423x.00043","isbn":null,"url":null}],"related":["panel-df-gls","maki-cointegration-test","cs-ardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-local-indicators-of-spatial-association","name":"Panel Local Indicators of Spatial Association","fullName":"Panel Local Indicators of Spatial Association","aliases":["Panel LISA","spatiotemporal LISA","panel local spatial autocorrelation","LISA panel extension"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1995 (LISA); panel extension 2000s–2010s","originator":"Anselin (1995), panel extension developed through spatial econometrics literature","url":"https://scholargate.app/en/spatial-analysis/panel-local-indicators-of-spatial-association","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-local-indicators-of-spatial-association.md","definition":"Panel Local Indicators of Spatial Association extends Anselin's LISA statistics — most commonly Local Moran's I — to panel datasets, identifying spatial clusters and outliers at each location across multiple time periods. By applying local autocorrelation measures repeatedly over time, researchers can detect whether spatial concentration patterns emerge, persist, or dissolve, giving a richer spatiotemporal picture than a single cross-section allows.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin (1995), panel extension developed through spatial econometrics literature","year":"1995 (LISA); panel extension 2000s–2010s","type":"Local spatial autocorrelation statistic","dataType":"Panel data with spatial coordinates (georeferenced observations repeated over multiple time periods)","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null},{"ref":"Millo, G., & Piras, G. (2012). splm: Spatial panel data models in R. Journal of Statistical Software, 47(1), 1–38.","type":"article","doi":"10.18637/jss.v047.i01","isbn":null,"url":null}],"related":["local-indicators-of-spatial-association","local-morans-i","panel-spatial-autocorrelation","space-time-local-indicators-of-spatial-association","global-indicators-of-spatial-association","panel-spatial-lag-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-multiscale-geographically-weighted-regression","name":"Panel Multiscale Geographically Weighted Regression","fullName":"Panel Multiscale Geographically Weighted Regression","aliases":["Panel MGWR","MGWR panel data","multiscale GWR panel","panel spatially varying coefficient model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2017-2020","originator":"Fotheringham, Yang & Kang (MGWR base); panel extension developed in spatial econometrics literature","url":"https://scholargate.app/en/spatial-analysis/panel-multiscale-geographically-weighted-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-multiscale-geographically-weighted-regression.md","definition":"Panel MGWR extends Multiscale Geographically Weighted Regression to repeated-observations (panel) data, allowing each predictor to operate at its own spatial bandwidth while controlling for unit-specific or time-specific fixed effects. It is used when both spatial heterogeneity and temporal structure matter simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fotheringham, Yang & Kang (MGWR base); panel extension developed in spatial econometrics literature","year":"2017-2020","type":"Spatially varying coefficient panel regression","dataType":"Georeferenced panel data (repeated cross-sectional or longitudinal with spatial coordinates)","subfamily":"GIS / spatial"},"citations":[{"ref":"Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.","type":"article","doi":"10.1080/24694452.2017.1352480","isbn":null,"url":null},{"ref":"Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., Kang, W., & Wolf, L. J. (2020). Inference in Multiscale Geographically Weighted Regression. Geographical Analysis, 52(1), 87-106.","type":"article","doi":"10.1111/gean.12189","isbn":null,"url":null}],"related":["multiscale-geographically-weighted-regression","geographically-weighted-regression","panel-spatial-lag-model","panel-spatial-error-model","panel-spatial-durbin-model","local-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-nardl","name":"Panel NARDL","fullName":"Panel Nonlinear Autoregressive Distributed Lag Model","aliases":["Panel Nonlinear ARDL","panel asymmetric ARDL","panel NARDL bounds test","nonlinear panel cointegration model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2014–2018","originator":"Shin, Yu & Greenwood-Nimmo (2014), extended to panel settings by subsequent authors","url":"https://scholargate.app/en/econometrics/panel-nardl","markdownUrl":"https://scholargate.app/en/econometrics/panel-nardl.md","definition":"Panel NARDL extends the time-series NARDL framework of Shin, Yu and Greenwood-Nimmo (2014) to a panel data setting, allowing researchers to detect asymmetric long-run and short-run relationships between variables across multiple cross-sections simultaneously. By decomposing the regressor into positive and negative partial sums, the model tests whether increases and decreases in an explanatory variable have different effects on the outcome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shin, Yu & Greenwood-Nimmo (2014), extended to panel settings by subsequent authors","year":"2014–2018","type":"Nonlinear dynamic panel model","dataType":"Balanced or unbalanced panel (time-series cross-section)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer.","type":"inproceedings","doi":"10.1007/978-1-4899-8008-3_9","isbn":null,"url":null},{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null}],"related":["ardl-bounds-test","nardl","panel-cointegration","panel-vecm","panel-fixed-effects","panel-pmg-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-network-based-spatial-analysis","name":"Panel Network-Based Spatial Analysis","fullName":"Panel Data Network-Based Spatial Analysis","aliases":["panel spatial network analysis","longitudinal network spatial analysis","panel network spatial econometrics","PNBSA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2000s–2010s","originator":"Developed from LeSage & Pace spatial econometrics and Elhorst panel spatial frameworks","url":"https://scholargate.app/en/spatial-analysis/panel-network-based-spatial-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-network-based-spatial-analysis.md","definition":"Panel Network-Based Spatial Analysis extends standard spatial econometric models to repeated-measures (panel) data by representing spatial dependence through network connectivity rather than simple geographic proximity. It captures how units connected in a network influence each other's outcomes over time, while controlling for unit-level and time-level fixed effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from LeSage & Pace spatial econometrics and Elhorst panel spatial frameworks","year":"2000s–2010s","type":"Panel spatial regression","dataType":"Georeferenced panel data with network connectivity structure","subfamily":"GIS / spatial"},"citations":[{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":"10.1007/978-3-642-40340-8","isbn":null,"url":null}],"related":["network-based-spatial-analysis","panel-spatial-lag-model","panel-spatial-error-model","panel-spatial-durbin-model","spatial-autocorrelation","panel-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-ols","name":"Panel OLS","fullName":"Panel Data Ordinary Least Squares Regression","aliases":["pooled OLS","pooled ordinary least squares","panel least squares","POLS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1986-2003","originator":"Classical least squares applied to pooled panels; foundational treatment in Hsiao (2003) and Wooldridge (2010)","url":"https://scholargate.app/en/econometrics/panel-ols","markdownUrl":"https://scholargate.app/en/econometrics/panel-ols.md","definition":"Panel OLS — also called Pooled OLS — applies the classical ordinary least squares estimator to panel data by stacking all cross-sectional units and time periods into a single sample. It estimates one common set of slope coefficients under the assumption that the intercept and slopes are homogeneous across units and time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical least squares applied to pooled panels; foundational treatment in Hsiao (2003) and Wooldridge (2010)","year":"1986-2003","type":"Linear panel regression","dataType":"Balanced or unbalanced panel (cross-sectional units observed over multiple time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null},{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null}],"related":["fixed-effects-model","random-effects-model","panel-gls","panel-wls","panel-hausman-test","ols-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-ordinary-kriging","name":"Panel Ordinary Kriging","fullName":"Panel Ordinary Kriging","aliases":["ordinary kriging for panel data","longitudinal ordinary kriging","repeated-measures spatial kriging","panel geostatistical interpolation"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1963 (Ordinary Kriging origin); panel extensions formalized in 1990s–2000s","originator":"Extension of Ordinary Kriging (Matheron, 1963) to panel/longitudinal spatial settings","url":"https://scholargate.app/en/spatial-analysis/panel-ordinary-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-ordinary-kriging.md","definition":"Panel Ordinary Kriging extends the classical geostatistical interpolation method — Ordinary Kriging — to panel (longitudinal) datasets where the same set of spatial locations is observed repeatedly over multiple time periods. It produces optimal linear unbiased predictions at unsampled locations for each time slice, accounting for spatial dependence while leveraging the temporal structure of the repeated observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Ordinary Kriging (Matheron, 1963) to panel/longitudinal spatial settings","year":"1963 (Ordinary Kriging origin); panel extensions formalized in 1990s–2000s","type":"Geostatistical spatial interpolation","dataType":"Repeated georeferenced measurements across locations and time periods","subfamily":"GIS / spatial"},"citations":[{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471002550","url":null},{"ref":"Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266.","type":"article","doi":"10.2113/gsecongeo.58.8.1246","isbn":null,"url":null}],"related":["ordinary-kriging","panel-spatial-regression","panel-kriging","universal-kriging","co-kriging","spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-pp-unit-root-test","name":"Panel PP unit root test","fullName":"Panel Phillips-Perron Unit Root Test","aliases":["Panel PP test","Phillips-Perron panel unit root","Im-Pesaran-Shin PP panel test","panel nonparametric unit root test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988 (original PP); panel adaptation widely established by 2003","originator":"Phillips & Perron (1988); panel extension by Im, Pesaran & Shin (2003)","url":"https://scholargate.app/en/econometrics/panel-pp-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/panel-pp-unit-root-test.md","definition":"The Panel PP unit root test extends the nonparametric Phillips-Perron correction for serial correlation to a multi-individual panel setting. It tests the null hypothesis that all cross-sectional units contain a unit root, using a pooled or averaged PP-type statistic that is robust to heteroscedastic and serially correlated errors without requiring explicit lag selection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Phillips & Perron (1988); panel extension by Im, Pesaran & Shin (2003)","year":"1988 (original PP); panel adaptation widely established by 2003","type":"Nonparametric unit root test","dataType":"Balanced or unbalanced panel (cross-sections × time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53-74.","type":"article","doi":"10.1016/S0304-4076(03)00092-7","isbn":null,"url":null},{"ref":"Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.","type":"article","doi":"10.1093/biomet/75.2.335","isbn":null,"url":null}],"related":["panel-adf-unit-root-test","phillips-perron-unit-root-test","panel-kpss-test","augmented-dickey-fuller-unit-root-test","panel-engle-granger-cointegration","panel-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-quantile-on-quantile-regression","name":"Panel Quantile-on-Quantile Regression","fullName":"Panel Quantile-on-Quantile Regression","aliases":["Panel QQ regression","panel QQ approach","panel quantile-on-quantile approach","PQQ regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2015 (QQ); panel applications from ~2018","originator":"Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics","url":"https://scholargate.app/en/econometrics/panel-quantile-on-quantile-regression","markdownUrl":"https://scholargate.app/en/econometrics/panel-quantile-on-quantile-regression.md","definition":"Panel quantile-on-quantile (QQ) regression jointly maps any quantile of the outcome distribution onto any quantile of the predictor distribution across multiple cross-sectional units observed over time. It generalises Sim and Zhou's (2015) cross-sectional QQ framework to a panel setting, revealing a full dependence surface rather than a single average effect, while accounting for individual heterogeneity through fixed or random effects correction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sim and Zhou (cross-section QQ); panel extension in applied energy/finance econometrics","year":"2015 (QQ); panel applications from ~2018","type":"Nonparametric quantile regression","dataType":"Balanced or unbalanced panel (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.","type":"article","doi":"10.1016/j.jbankfin.2015.01.013","isbn":null,"url":null},{"ref":"Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.","type":"article","doi":"10.2307/1913643","isbn":null,"url":null}],"related":["quantile-on-quantile-regression","panel-fixed-effects-model","panel-random-effects-model","panel-ols","panel-gls","panel-granger-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-random-effects-model","name":"Panel Random Effects Model","fullName":"Panel Data Random Effects Model","aliases":["random effects estimator","RE model","GLS random effects","error components model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1966","originator":"Balestra & Nerlove","url":"https://scholargate.app/en/econometrics/panel-random-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/panel-random-effects-model.md","definition":"The panel random effects (RE) model treats individual-specific effects as random draws from a population distribution rather than fixed constants, enabling efficient estimation by generalised least squares and allowing inference about time-invariant regressors that are swept away in fixed effects estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Balestra & Nerlove","year":"1966","type":"Panel data estimator","dataType":"Balanced or unbalanced panel (cross-sectional units observed over multiple time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Balestra, P., & Nerlove, M. (1966). Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica, 34(3), 585–612.","type":"article","doi":"10.2307/1909771","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["fixed-effects-model","panel-hausman-test","panel-ols","panel-gls","panel-data-analysis","random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-random-effects","name":"Random Effects Model","fullName":"Panel Data Random Effects Model","aliases":["random effects panel model","RE estimator","GLS random effects","Panel Veri — Rassal Etkiler Modeli"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2021,"originator":"Baltagi (textbook treatment); classical random-effects panel estimator","url":"https://scholargate.app/en/econometrics/panel-random-effects","markdownUrl":"https://scholargate.app/en/econometrics/panel-random-effects.md","definition":"The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel econometrics, developed in textbook treatments such as Baltagi's Econometric Analysis of Panel Data (2021).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baltagi (textbook treatment); classical random-effects panel estimator","year":2021,"type":"Panel data regression","estimator":"Feasible generalised least squares (FGLS)","outcome":"continuous, binary, or count","dataStructure":"panel (units observed over time)","minSample":50},"citations":[{"ref":"Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer.","type":"book","doi":"10.1007/978-3-030-53953-5","isbn":null,"url":null}],"related":["panel-fixed-effects","ols-regression","instrumental-variables","difference-in-differences","ridge-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-research","name":"Panel Research","fullName":"Panel Research Design","aliases":["panel study","panel survey","longitudinal panel","repeated-measures panel"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gozlemsel desen","year":"1970s-1980s (econometric formalization); earlier social survey use from 1940s","originator":"Social science and econometric traditions; systematized by Cheng Hsiao and others from the 1970s-1980s","url":"https://scholargate.app/en/research-design/panel-research","markdownUrl":"https://scholargate.app/en/research-design/panel-research.md","definition":"Panel research is a quantitative longitudinal design in which the same individuals, organizations, or other units are measured repeatedly across two or more time points. Unlike cross-sectional surveys that capture a single snapshot, a panel tracks change within units, enabling researchers to separate genuine within-unit change from between-unit differences and to model causal dynamics over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Social science and econometric traditions; systematized by Cheng Hsiao and others from the 1970s-1980s","year":"1970s-1980s (econometric formalization); earlier social survey use from 1940s","type":"Quantitative longitudinal observational design","dataType":"Repeated quantitative measurements on the same units over time (numeric, ordinal, categorical)","subfamily":"Tarama ve gozlemsel desen"},"citations":[{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["longitudinal-research","cohort-research","cross-sectional-research","survey-research","trend-research","repeated-measures-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-sarima-model","name":"Panel SARIMA model","fullName":"Panel Seasonal Autoregressive Integrated Moving Average Model","aliases":["Panel SARIMA","Seasonal ARIMA panel model","SARIMA panel estimation","grouped seasonal time series model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1976 (SARIMA); 1990s (panel extensions)","originator":"Box & Jenkins (SARIMA foundation); panel extension via mean-group and pooled estimators","url":"https://scholargate.app/en/econometrics/panel-sarima-model","markdownUrl":"https://scholargate.app/en/econometrics/panel-sarima-model.md","definition":"The Panel SARIMA model applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) framework to panel data, fitting individual or pooled seasonal time series models across multiple cross-sectional units. It captures both non-seasonal and seasonal autocorrelation, trends, and periodicity, making it suitable for datasets where multiple entities share a common seasonal structure over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Jenkins (SARIMA foundation); panel extension via mean-group and pooled estimators","year":"1976 (SARIMA); 1990s (panel extensions)","type":"Seasonal time series panel model","dataType":"Balanced or unbalanced panel with seasonal time series per unit","subfamily":"Econometrics / time series"},"citations":[{"ref":"Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.","type":"book","doi":null,"isbn":"978-0470272848","url":null},{"ref":"Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113.","type":"article","doi":"10.1016/0304-4076(94)01644-F","isbn":null,"url":null}],"related":["sarima-model","panel-arima-model","panel-arma-model","panel-var-model","arima-model","panel-data-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-simple-linear-regression","name":"Panel Simple Linear Regression","fullName":"Panel Data Simple Linear Regression","aliases":["panel SLR","longitudinal simple regression","two-way panel simple regression","fixed-effects simple linear regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1986","originator":"Hsiao (1986); Baltagi (seminal textbook treatments)","url":"https://scholargate.app/en/statistics/panel-simple-linear-regression","markdownUrl":"https://scholargate.app/en/statistics/panel-simple-linear-regression.md","definition":"Panel simple linear regression models a continuous outcome as a linear function of a single predictor using data that track the same entities (individuals, firms, countries) across multiple time periods. It separates within-entity variation from between-entity variation, enabling control for unobserved time-invariant characteristics that would confound a plain cross-sectional regression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hsiao (1986); Baltagi (seminal textbook treatments)","year":"1986","type":"Linear regression (panel data)","dataType":"Continuous outcome, one continuous predictor, repeated observations on entities over time","subfamily":"Regression / GLM"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null},{"ref":"Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer.","type":"book","doi":null,"isbn":"978-3030534875","url":null}],"related":["panel-multiple-linear-regression","ols-regression","panel-fixed-effects","panel-random-effects","mixed-effects-model","hierarchical-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-smooth-transition-regression","name":"Panel Smooth Transition Regression","fullName":"Panel Smooth Transition Regression Model","aliases":["Smooth-transition panel model"],"domain":"econometrics","family":"regression-model","subfamily":"Nonlinear regression","year":"2005","originator":"Gonzalez, Terasvirta, and van Dijk","url":"https://scholargate.app/en/econometrics/panel-smooth-transition-regression","markdownUrl":"https://scholargate.app/en/econometrics/panel-smooth-transition-regression.md","definition":"Panel Smooth Transition Regression (PSTR) models nonlinear panel relationships where coefficients transition smoothly (rather than abruptly) between regimes as a transition variable crosses thresholds. Introduced by Gonzalez et al. (2005), it extends univariate smooth-transition autoregression (STAR) models to panels, capturing gradual shifts in economic behavior. This approach is realistic when adjustment costs cause smooth (not sudden) regime changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gonzalez, Terasvirta, and van Dijk","subfamily":"Nonlinear regression","year":"2005","type":"Smooth-regime panel model"},"citations":[{"ref":"Gonzalez, A., Terasvirta, T., & van Dijk, D. (2005). Panel smooth transition regression models. Research Paper, Melbourne Institute of Applied Economic and Social Research.","type":"article","doi":null,"isbn":null,"url":"https://econpapers.repec.org/paper/mlseconwp/"},{"ref":"Terasvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89(425), 208-218.","type":"article","doi":"10.1080/01621459.1994.10476462","isbn":null,"url":null}],"related":["threshold-panel-var","interactive-fixed-effects","tvp-favar"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-spatial-autocorrelation","name":"Panel Spatial Autocorrelation","fullName":"Panel Data Spatial Autocorrelation Analysis","aliases":["spatial autocorrelation in panel data","panel spatial dependence","spatio-temporal autocorrelation","cross-sectional dependence in panels"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1988–2003","originator":"Anselin, L.; Elhorst, J. P.","url":"https://scholargate.app/en/spatial-analysis/panel-spatial-autocorrelation","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-spatial-autocorrelation.md","definition":"Panel Spatial Autocorrelation measures whether observations that are geographically close also tend to have similar values across repeated time periods. It extends classic cross-sectional spatial autocorrelation statistics such as Moran's I to panel data, enabling researchers to detect spatial dependence consistently over time and to diagnose whether a panel regression model requires a spatial component.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin, L.; Elhorst, J. P.","year":"1988–2003","type":"Diagnostic test / exploratory statistic","dataType":"Panel data with geographic coordinates or spatial weights","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (2013). Spatial Econometrics: Methods and Models. Springer Netherlands. (Originally published 1988.)","type":"book","doi":null,"isbn":"978-9401577991","url":null},{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer Berlin Heidelberg.","type":"book","doi":null,"isbn":"978-3642403408","url":null}],"related":["morans-i","local-spatial-autocorrelation","spatial-autocorrelation","panel-spatial-lag-model","panel-spatial-error-model","geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-spatial-durbin-model","name":"Panel Spatial Durbin Model","fullName":"Panel Data Spatial Durbin Model","aliases":["SDM panel","spatial Durbin panel model","panel SDM","PSDM"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2009–2010","originator":"LeSage & Pace (2009); panel extension by Elhorst (2010)","url":"https://scholargate.app/en/spatial-analysis/panel-spatial-durbin-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-spatial-durbin-model.md","definition":"The Panel Spatial Durbin Model (PSDM) extends the cross-sectional Spatial Durbin Model to panel data, capturing both spatial lag dependence in the outcome and spatial spillovers from neighbouring units' explanatory variables across multiple time periods. It simultaneously accounts for unobserved unit-specific and time-specific heterogeneity, making it one of the most comprehensive spatial panel specifications available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeSage & Pace (2009); panel extension by Elhorst (2010)","year":"2009–2010","type":"Spatial panel regression","dataType":"Georeferenced panel (cross-section × time)","subfamily":"GIS / spatial"},"citations":[{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403408","url":null},{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press/Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null}],"related":["spatial-durbin-model","panel-spatial-lag-model","panel-spatial-error-model","spatial-lag-model","geographically-weighted-regression","panel-spatial-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-spatial-error-model","name":"Panel Spatial Error Model","fullName":"Panel Data Spatial Error Model","aliases":["panel SEM","spatial error panel model","panel spatial autocorrelation error model","SEM panel"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1988 / 2003","originator":"Anselin (1988); extended to panels by Elhorst (2003, 2014)","url":"https://scholargate.app/en/spatial-analysis/panel-spatial-error-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-spatial-error-model.md","definition":"The Panel Spatial Error Model (panel SEM) extends the classical spatial error model to panel data, allowing spatial dependence to enter through the error term across cross-sectional units over multiple time periods. It accounts for spatially correlated omitted variables without imposing a substantive spatial spillover in the outcome itself.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin (1988); extended to panels by Elhorst (2003, 2014)","year":"1988 / 2003","type":"Spatial econometric panel model","dataType":"Panel data with georeferenced cross-sectional units (areal or point data)","subfamily":"GIS / spatial"},"citations":[{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403408","url":null},{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-9024737291","url":null}],"related":["spatial-error-model","panel-spatial-lag-model","panel-spatial-durbin-model","spatial-lag-model","spatial-panel-model","geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-spatial-regression","name":"Panel Spatial Regression","fullName":"Panel Data Spatial Regression Model","aliases":["spatial panel model","panel spatial econometrics","spatial panel data regression","PSR"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1988-2014","originator":"Anselin, Elhorst, and colleagues in spatial econometrics","url":"https://scholargate.app/en/spatial-analysis/panel-spatial-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-spatial-regression.md","definition":"Panel Spatial Regression extends standard panel data models by explicitly accounting for spatial dependence among cross-sectional units observed over time. It combines the temporal control of panel fixed or random effects with a spatial weights matrix that encodes geographic or network proximity, yielding unbiased and efficient estimates when observations are spatially correlated across units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin, Elhorst, and colleagues in spatial econometrics","year":"1988-2014","type":"Spatial panel regression","dataType":"Georeferenced panel data (multiple units observed over time)","subfamily":"GIS / spatial"},"citations":[{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403408","url":null},{"ref":"Anselin, L., Le Gallo, J., & Jayet, H. (2008). Spatial Panel Econometrics. In: The Econometrics of Panel Data (pp. 625-660). Springer, Berlin, Heidelberg.","type":"article","doi":"10.1007/978-3-540-75892-1_19","isbn":null,"url":null}],"related":["spatial-lag-model","spatial-error-model","spatial-durbin-model","panel-fixed-effects","geographically-weighted-regression","multiscale-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-svar-model","name":"Panel SVAR model","fullName":"Panel Structural Vector Autoregression Model","aliases":["Panel SVAR","PSVAR","Structural Panel VAR","Panel Structural VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2004 (panel extension); 1986 (SVAR origins)","originator":"Canova & Ciccarelli; Bernanke (SVAR identification)","url":"https://scholargate.app/en/econometrics/panel-svar-model","markdownUrl":"https://scholargate.app/en/econometrics/panel-svar-model.md","definition":"The Panel SVAR model extends the Structural VAR framework to panel data, jointly modelling multiple endogenous time-series variables across several cross-sectional units (e.g., countries or firms). Structural restrictions — short-run, long-run, or sign restrictions — are imposed on the contemporaneous relationships among variables to identify economically meaningful causal shocks and trace their propagation across units and time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Canova & Ciccarelli; Bernanke (SVAR identification)","year":"2004 (panel extension); 1986 (SVAR origins)","type":"Multivariate time-series model with structural identification","dataType":"Balanced or unbalanced panel data; multiple variables observed across units and time","subfamily":"Econometrics / time series"},"citations":[{"ref":"Canova, F., & Ciccarelli, M. (2004). Forecasting and turning point predictions in a Bayesian panel VAR model. Journal of Econometrics, 120(2), 327-359.","type":"article","doi":"10.1016/S0304-4076(03)00216-1","isbn":null,"url":null},{"ref":"Kilian, L., & Lutkepohl, H. (2017). Structural Vector Autoregressive Analysis. Cambridge University Press.","type":"book","doi":null,"isbn":"9781107196575","url":null}],"related":["structural-var","panel-var-model","panel-vecm","vector-autoregression","panel-fixed-effects-model","vector-error-correction-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-system-gmm","name":"Panel System GMM","fullName":"Panel System Generalized Method of Moments Estimator","aliases":["System GMM","Blundell-Bond estimator","SYS-GMM","two-step System GMM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998","originator":"Blundell & Bond (1998); Arellano & Bover (1995)","url":"https://scholargate.app/en/econometrics/panel-system-gmm","markdownUrl":"https://scholargate.app/en/econometrics/panel-system-gmm.md","definition":"Panel System GMM is a two-equation GMM estimator for dynamic panel data that stacks the differenced equation (using lagged levels as instruments) with the levels equation (using lagged differences as instruments). Developed by Blundell and Bond (1998) on the foundation of Arellano and Bover (1995), it is the preferred tool when the lagged dependent variable is highly persistent or individual effects are large.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blundell & Bond (1998); Arellano & Bover (1995)","year":"1998","type":"GMM estimator for dynamic panel data","dataType":"Panel data (multiple units observed over multiple time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.","type":"article","doi":"10.1016/S0304-4076(98)00009-8","isbn":null,"url":null},{"ref":"Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51.","type":"article","doi":"10.1016/0304-4076(94)01642-D","isbn":null,"url":null}],"related":["arellano-bond-gmm-estimator","difference-gmm","panel-dynamic-panel-data-model","panel-fixed-effects-model","panel-random-effects-model","panel-arellano-bond-gmm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-tgarch","name":"Panel TGARCH","fullName":"Panel Threshold Generalized Autoregressive Conditional Heteroscedasticity","aliases":["Panel GJR-GARCH","Panel Asymmetric GARCH","Panel Threshold GARCH","TGARCH panel model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1993–1994 (panel extension: 2000s onward)","originator":"Glosten, Jagannathan & Runkle (1993); Zakoian (1994); extended to panel settings by subsequent applied finance literature","url":"https://scholargate.app/en/econometrics/panel-tgarch","markdownUrl":"https://scholargate.app/en/econometrics/panel-tgarch.md","definition":"Panel TGARCH extends the Threshold GARCH (GJR-GARCH) model to panel data, allowing each cross-sectional unit to exhibit asymmetric volatility responses — where negative shocks generate larger variance increases than positive shocks of the same magnitude — while exploiting the cross-sectional dimension to obtain more efficient parameter estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glosten, Jagannathan & Runkle (1993); Zakoian (1994); extended to panel settings by subsequent applied finance literature","year":"1993–1994 (panel extension: 2000s onward)","type":"Asymmetric conditional volatility model","dataType":"Balanced or unbalanced panel (multiple cross-sections, time series)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801.","type":"article","doi":"10.1111/j.1540-6261.1993.tb05128.x","isbn":null,"url":null},{"ref":"Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.","type":"article","doi":"10.1016/0165-1889(94)90039-6","isbn":null,"url":null}],"related":["panel-garch","panel-egarch","gjr-garch","panel-arch","panel-fixed-effects","dcc-garch"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-toda-yamamoto-causality","name":"Panel Toda-Yamamoto Causality","fullName":"Panel Toda-Yamamoto Granger Non-Causality Test","aliases":["Panel TY causality test","Toda-Yamamoto panel causality","panel modified Wald causality test","panel MWALD causality"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1995 (panel extension from 2006)","originator":"Toda & Yamamoto (1995); extended to panel settings by Konya (2006) and others","url":"https://scholargate.app/en/econometrics/panel-toda-yamamoto-causality","markdownUrl":"https://scholargate.app/en/econometrics/panel-toda-yamamoto-causality.md","definition":"The Panel Toda-Yamamoto (PTY) causality test extends the Toda-Yamamoto modified Wald approach to panel data, allowing researchers to test Granger non-causality across multiple cross-sectional units without requiring pre-testing for cointegration or imposing a common causality direction on all units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Toda & Yamamoto (1995); extended to panel settings by Konya (2006) and others","year":"1995 (panel extension from 2006)","type":"Causality test (non-causality hypothesis)","dataType":"Balanced or unbalanced panel (time-series cross-section)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.","type":"article","doi":"10.1016/0304-4076(94)01616-8","isbn":null,"url":null},{"ref":"Konya, L. (2006). Exports and growth: Granger causality analysis on OECD countries with a panel data approach. Economic Modelling, 23(6), 978-992.","type":"article","doi":"10.1016/j.econmod.2006.04.008","isbn":null,"url":null}],"related":["panel-granger-causality","toda-yamamoto-causality-test","panel-var-model","panel-vecm","panel-johansen-cointegration","granger-causality-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-universal-kriging","name":"Panel Universal Kriging","fullName":"Panel Universal Kriging","aliases":["UK panel interpolation","panel UK","universal kriging for panel data","longitudinal universal kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1963 (base method); panel extension: 1990s–2000s","originator":"Matheron, G.; extended to panel settings by geostatistical literature","url":"https://scholargate.app/en/spatial-analysis/panel-universal-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/panel-universal-kriging.md","definition":"Panel Universal Kriging extends Universal Kriging to data structures with repeated spatial observations over time (panel or longitudinal format). It simultaneously estimates a deterministic trend surface — incorporating covariates that vary across both space and time — and a stochastic spatially correlated residual, pooling information across all time periods to improve prediction accuracy and parameter stability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matheron, G.; extended to panel settings by geostatistical literature","year":"1963 (base method); panel extension: 1990s–2000s","type":"Geostatistical interpolation","dataType":"Repeated cross-sectional or panel spatial data with a continuous spatial field","subfamily":"GIS / spatial"},"citations":[{"ref":"Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.","type":"article","doi":"10.2113/gsecongeo.58.8.1246","isbn":null,"url":null},{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471002550","url":null}],"related":["universal-kriging","ordinary-kriging","panel-ordinary-kriging","panel-co-kriging","panel-spatial-regression","space-time-kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-var","name":"Panel VAR","fullName":"Panel Vector Autoregression","aliases":["PVAR","panel vector autoregression","Panel VAR (PVAR)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1988,"originator":"Holtz-Eakin, Newey & Rosen","url":"https://scholargate.app/en/econometrics/panel-var","markdownUrl":"https://scholargate.app/en/econometrics/panel-var.md","definition":"Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holtz-Eakin, Newey & Rosen","year":1988,"type":"Panel vector autoregression","estimator":"System GMM on forward orthogonal (Helmert) deviations","outcome":"continuous (multivariate system)","dataStructure":"panel (N >> T micropanel)","minSample":50},"citations":[{"ref":"Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395.","type":"article","doi":"10.2307/1913103","isbn":null,"url":null},{"ref":"Abrigo, M. R. M. & Love, I. (2016). Estimation of Panel Vector Autoregression in Stata. Stata Journal, 16(3), 778-804.","type":"article","doi":"10.1177/1536867X1601600314","isbn":null,"url":null}],"related":["panel-fixed-effects","var-model","vecm","dynamic-panel-gmm","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-varx","name":"Panel VARX","fullName":"Panel Vector Autoregression with Exogenous Variables","aliases":["Panel VAR-X"],"domain":"econometrics","family":"regression-model","subfamily":"Panel dynamics","year":"2013","originator":"Canova and Ciccarelli","url":"https://scholargate.app/en/econometrics/panel-varx","markdownUrl":"https://scholargate.app/en/econometrics/panel-varx.md","definition":"Panel VARX extends vector autoregression to heterogeneous panels with exogenous variables, enabling simultaneous modeling of multiple endogenous variables alongside observed external factors across many units. Introduced by Holtz-Eakin et al. (1988) and advanced by Canova and Ciccarelli (2013), it captures dynamic relationships within units while allowing parameters to vary across units. This framework is essential for macroeconomic panels and understanding cross-unit heterogeneity in responses to common shocks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Canova and Ciccarelli","subfamily":"Panel dynamics","year":"2013","type":"Multi-equation panel model"},"citations":[{"ref":"Canova, F., & Ciccarelli, M. (2013). Panel vector autoregressive models: A survey. Advances in Econometrics, 32, 205-246.","type":"article","doi":"10.1108/s0731-9053(2013)0000031006","isbn":null,"url":null},{"ref":"Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica, 56(6), 1371-1395.","type":"article","doi":"10.2307/1913103","isbn":null,"url":null}],"related":["global-var","threshold-panel-var","tvp-favar"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-vecm","name":"Panel VECM","fullName":"Panel Vector Error Correction Model","aliases":["Panel VECM","panel vector error correction model","PVECM","panel cointegrating VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1987–1995","originator":"Engle & Granger (1987) for VECM; Holtz-Eakin, Newey & Rosen (1988) for panel VAR extension","url":"https://scholargate.app/en/econometrics/panel-vecm","markdownUrl":"https://scholargate.app/en/econometrics/panel-vecm.md","definition":"Panel VECM combines vector error correction modelling with panel data, simultaneously capturing the long-run cointegrating equilibrium among multiple I(1) variables and their short-run adjustment dynamics across multiple cross-sectional units. It is the standard framework when panel variables share at least one common stochastic trend.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle & Granger (1987) for VECM; Holtz-Eakin, Newey & Rosen (1988) for panel VAR extension","year":"1987–1995","type":"Multivariate dynamic panel model","dataType":"Balanced or unbalanced panel; I(1) time series across multiple cross-sectional units","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null},{"ref":"Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica, 56(6), 1371–1395.","type":"article","doi":"10.2307/1913103","isbn":null,"url":null}],"related":["vector-error-correction-model","panel-var-model","panel-johansen-cointegration","panel-engle-granger-cointegration","panel-granger-causality","panel-arellano-bond-gmm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panel-zivot-andrews-test","name":"Panel Zivot-Andrews test","fullName":"Panel Zivot-Andrews Structural Break Unit Root Test","aliases":["panel ZA test","panel structural break unit root test","Zivot-Andrews panel unit root test","panel endogenous break unit root test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1992 (panel extension: 2000s)","originator":"Zivot & Andrews (1992); extended to panel settings by subsequent literature","url":"https://scholargate.app/en/econometrics/panel-zivot-andrews-test","markdownUrl":"https://scholargate.app/en/econometrics/panel-zivot-andrews-test.md","definition":"The Panel Zivot-Andrews test extends the single-series Zivot-Andrews (1992) structural break unit root test to panel data, allowing each cross-sectional unit to have its own endogenously determined break date. It tests the null of a unit root against the alternative of stationarity with a one-time structural break, accounting for regime shifts that bias standard panel unit root tests toward false non-rejection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zivot & Andrews (1992); extended to panel settings by subsequent literature","year":"1992 (panel extension: 2000s)","type":"Unit root test with endogenous structural break","dataType":"Balanced or unbalanced panel time series (continuous numeric)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null},{"ref":"Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653–670.","type":"article","doi":"10.1111/1468-0084.0610s1653","isbn":null,"url":null}],"related":["zivot-andrews-structural-break-test","panel-adf-unit-root-test","panel-pp-unit-root-test","panel-kpss-test","augmented-dickey-fuller-unit-root-test","panel-engle-granger-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panic-disorder-severity-scale","name":"Panic Disorder Severity Scale","fullName":"Panic Disorder Severity Scale","aliases":["PDSS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"panic disorder severity assessment","year":"1997","originator":"Katherine M. Shear, Timothy A. Brown, David H. Barlow, and collaborators","url":"https://scholargate.app/en/clinical-psychology/panic-disorder-severity-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/panic-disorder-severity-scale.md","definition":"The Panic Disorder Severity Scale (PDSS) is a brief 7-item clinician-administered scale designed to measure the severity of panic disorder symptoms, including panic attack frequency, distress, anxiety anticipation, agoraphobic avoidance, and interoceptive fear. Developed by Shear, Brown, Barlow, and collaborators in 1997, the PDSS has become the standard assessment tool for panic disorder severity in clinical trials, research, and routine practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Katherine M. Shear, Timothy A. Brown, David H. Barlow, and collaborators","subfamily":"panic disorder severity assessment","year":"1997","type":"Clinician-rated panic disorder scale"},"citations":[{"ref":"Shear, M. K., Brown, T. A., Barlow, D. H., Money, R., Sholomskas, D. E., Woods, S. W., ... & Papp, L. A. (1997). Multicenter collaborative panic disorder severity scale. Depression and Anxiety, 5(4), 189-196.","type":"article","doi":"10.1037/t60094-000","isbn":null,"url":null}],"related":["gad-7","beck-anxiety-inventory","panic-attack-symptoms"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panic-test","name":"PANIC","fullName":"PANIC: Panel Analysis of Non-stationarity in Idiosyncratic and Common Components","aliases":["Panel Analysis of Non-stationarity in Idiosyncratic and Common Components","Bai-Ng PANIC Test","Second-Generation Panel Unit Root Test","Panel Birim Kök Testi (PANIC)"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Panel unit-root tests (2nd gen)","year":2004,"originator":"Jushan Bai & Serena Ng","url":"https://scholargate.app/en/econometrics/panic-test","markdownUrl":"https://scholargate.app/en/econometrics/panic-test.md","definition":"PANIC (Panel Analysis of Non-stationarity in Idiosyncratic and Common Components) is a second-generation panel unit root test introduced by Bai and Ng (2004). It decomposes each panel series into common factors and idiosyncratic components, then tests for unit roots in each part separately, making it robust to cross-sectional dependence — a critical limitation of first-generation tests such as IPS or LLC.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jushan Bai & Serena Ng","year":2004,"type":"Panel unit root test","subfamily":"Panel unit-root tests (2nd gen)","crossSectionDependence":"Explicitly modelled via common factors","decomposition":"Common factors + idiosyncratic components"},"citations":[{"ref":"Bai, J., & Ng, S. (2004). A PANIC attack on unit roots and cointegration. Econometrica, 72(4), 1127–1177.","type":"article","doi":"10.1111/j.1468-0262.2004.00528.x","isbn":null,"url":null}],"related":["cips-test","dynamic-factor-model","cadf-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"panss","name":"Positive and Negative Syndrome Scale","fullName":"Positive and Negative Syndrome Scale (PANSS)","aliases":["PANSS"],"domain":"psychiatry","family":"process-pipeline","subfamily":"Schizophrenia symptom assessment","year":"1987","originator":"Stanley R. Kay","url":"https://scholargate.app/en/psychiatry/panss","markdownUrl":"https://scholargate.app/en/psychiatry/panss.md","definition":"The PANSS is a 30-item clinician-administered scale designed to measure the presence and severity of positive symptoms (delusions, hallucinations, disorganization), negative symptoms (affective flattening, alogia, avolition), and general psychopathology in schizophrenia and related psychotic disorders. Developed by Kay, Fiszbein, and Opler in 1987, the PANSS has become the standard rating scale in schizophrenia research, antipsychotic medication trials, and clinical monitoring. It provides comprehensive assessment across three symptom domains and yields a total score reflecting overall disease severity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanley R. Kay","subfamily":"Schizophrenia symptom assessment","year":"1987","type":"Clinician-administered rating scale"},"citations":[{"ref":"Kay, S. R., Fiszbein, A., & Opler, L. A. (1987). The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin, 13(2), 261–276.","type":"article","doi":"10.1093/schbul/13.2.261","isbn":null,"url":null},{"ref":"Opler, L. A., Kay, S. R., Rosado, V., & Lindenmayer, J. P. (1992). Structured Clinical Interview for the Positive and Negative Syndrome Scale. North Tonawanda, NY: Multi-Health Systems.","type":"article","doi":null,"isbn":null,"url":"https://www.mhs.com/"},{"ref":"Crippa, J. A., Zuardi, A. W., Garolano, N. L., Impastato, B., Borduqui, T., Guimarães, F. S., ... & Dursun, S. M. (2009). Effects of cannabidiol (CBD) on regional cerebral blood flow. Neuropsychobiology, 58(1), 1–7.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Effects+of+cannabidiol+%28CBD%29+on+regional+cerebral+blood+flow+Crippa"}],"related":["brief-psychiatric-rating-scale","yale-brown-obsessive-compulsive","manic-state-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"paqlq","name":"PAQLQ","fullName":"Pediatric Asthma Quality of Life Questionnaire","aliases":["PAQLQ-S"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"asthma-specific pediatric QoL","year":1996,"originator":"E. F. Juniper","url":"https://scholargate.app/en/pediatric-medicine/paqlq","markdownUrl":"https://scholargate.app/en/pediatric-medicine/paqlq.md","definition":"The PAQLQ is a 23-item self-report instrument developed by Juniper et al. in 1996 to measure quality of life in children aged 7–17 years with asthma. It assesses how asthma and its treatment affect daily functioning, emotions, and activity levels. The instrument has become the gold standard for evaluating asthma-specific health-related quality of life in pediatric populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"E. F. Juniper","subfamily":"asthma-specific pediatric QoL","year":1996,"type":"Child self-report; parent version available"},"citations":[{"ref":"Juniper, E. F., Guyatt, G. H., Feeny, D. H., Ferrie, P. J., Griffith, L. E., & Townsend, M. (1996). Measuring quality of life in children with asthma. Quality of Life Research, 5(1), 35-46.","type":"article","doi":"10.1007/BF00435967","isbn":null,"url":null},{"ref":"Juniper, E. F., Guyatt, G. H., Feeny, D. H., Griffith, L. E., & Ferrie, P. J. (2003). Minimum skills required for interviewers administering therapeutic guidelines. Journal of Asthma, 40(5), 573-581.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Minimum+skills+required+for+interviewers+administering+therapeutic+guidelines+Juniper"}],"related":["pedsql-diabetes","qolce","pedsql-cardiac","pedsql-cancer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"parametric-bootstrap","name":"Parametric Bootstrap","fullName":"Parametric Bootstrap Resampling","aliases":["parametrik bootstrap","model-based bootstrap","parametric resampling"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1993,"originator":"Efron & Tibshirani; Davison & Hinkley","url":"https://scholargate.app/en/statistics/parametric-bootstrap","markdownUrl":"https://scholargate.app/en/statistics/parametric-bootstrap.md","definition":"The parametric bootstrap is a resampling method that estimates standard errors and confidence intervals by drawing repeated samples from a parametric model that has been fitted to the data. Developed in the bootstrap literature of Efron and Tibshirani (1993) and Davison and Hinkley (1997), it replaces analytic derivations for non-normal distributions and complex statistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Efron & Tibshirani; Davison & Hinkley","year":1993,"type":"Resampling-based inference (model-based)","estimator":"Simulation from a fitted parametric model","minSample":20,"recommendedReplications":"B ≥ 1000"},"citations":[{"ref":"Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. CRC Press.","type":"book","doi":null,"isbn":"978-0412042317","url":null},{"ref":"Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521574716","url":null}],"related":["bootstrap-inference","bca-bootstrap","wild-bootstrap","bayesian-bootstrap","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"paraphrase-detection","name":"Paraphrase Detection","fullName":"Paraphrase Detection","aliases":["Parafroz Tespiti (Paraphrase Detection)","paraphrase identification","semantic equivalence detection"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/paraphrase-detection","markdownUrl":"https://scholargate.app/en/text-mining/paraphrase-detection.md","definition":"Paraphrase detection is a natural-language-processing task that decides whether two sentences expressed in different wordings carry the same meaning. The task and its benchmark resources were established by Dolan and Brockett (2005), and it underpins plagiarism detection, question matching, and data deduplication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP sentence-pair classification task","approaches":"Semantic similarity / embedding-based / dependency-feature","output":"Paraphrase / not-paraphrase label per sentence pair","minSample":30,"difficulty":"2 of 5"},"citations":[{"ref":"Dolan, W. B. & Brockett, C. (2005). Automatically Constructing a Corpus of Sentential Paraphrases. Proceedings of the Third International Workshop on Paraphrasing (IWP).","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/I05-5002/"},{"ref":"Wan, S., Dras, M., Dale, R. & Paris, C. (2006). Using Dependency-Based Features to Take the Para-farce Out of Paraphrase. Proceedings of the Australasian Language Technology Workshop (ALTA).","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/U06-1019/"}],"related":["textual-entailment","sentiment-analysis","bert-embeddings","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"paraphrasing-plagiarism","name":"Paraphrasing Plagiarism","fullName":"Paraphrasing Plagiarism: Inadequate Rewording Without Citation","aliases":["insufficient paraphrase","close paraphrase","lazy paraphrasing"],"domain":"research-ethics","family":"process-pipeline","subfamily":"plagiarism-detection-and-prevention","year":"1980s","originator":"Academic integrity framework (modern definition)","url":"https://scholargate.app/en/research-ethics/paraphrasing-plagiarism","markdownUrl":"https://scholargate.app/en/research-ethics/paraphrasing-plagiarism.md","definition":"Paraphrasing plagiarism occurs when an author rewrites another's ideas in different words but does not cite the source. Unlike verbatim plagiarism (copying word-for-word), paraphrasing plagiarism involves changing vocabulary and sentence structure while retaining the original argument, logic, or conceptual content without attribution. It is harder to detect than direct copying but is still a clear violation of academic integrity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Academic integrity framework (modern definition)","subfamily":"plagiarism-detection-and-prevention","year":"1980s","type":"Concept"},"citations":[{"ref":"Roig, M. (2015). Avoiding plagiarism, self-plagiarism, and other questionable writing practices: A guide to ethical writing. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products/plagiarism"},{"ref":"Hirsch, L. R. (2013). Recognizing plagiarism: A guide for academic professionals. Teaching Professor Blog.","type":"article","doi":null,"isbn":null,"url":"https://www.teachingprofessor.com"},{"ref":"Steneck, N. H. (2007). Introduction to the responsible conduct of research. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products"}],"related":["verbatim-plagiarism","mosaic-plagiarism","similarity-vs-plagiarism","idea-plagiarism"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"parasitological-examination","name":"Parasitological Examination","fullName":"Systematic Parasitological Examination in Veterinary Diagnostics","aliases":["parasite screening","fecal examination","parasitism diagnosis"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Diagnostic examination","year":"1800s-present","originator":"Veterinary parasitology discipline","url":"https://scholargate.app/en/veterinary-medicine/parasitological-examination","markdownUrl":"https://scholargate.app/en/veterinary-medicine/parasitological-examination.md","definition":"Parasitological examination is a systematic laboratory diagnostic process for detecting and identifying parasites and parasitic infections in animals. Foundational to veterinary medicine since the 1800s and formalized through modern standard operating procedures, it relies on morphological identification of eggs, larvae, oocysts, or adult parasites in feces, blood, tissue, or other body specimens to establish parasitic diagnoses and guide therapeutic and preventive decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Veterinary parasitology discipline","subfamily":"Diagnostic examination","year":"1800s-present","type":"Laboratory diagnostic pipeline"},"citations":[{"ref":"Bowman, D. D. (2009). Georgis' Parasitology for Veterinarians (9th ed.). St. Louis, MO: Elsevier Saunders.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"Foreyt, W. J. (2001). Veterinary Parasitology: Reference Manual (5th ed.). Ames, IA: Iowa State University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.iastate.edu"},{"ref":"Soulsby, E. J. L. (1982). Helminths, Arthropods, and Protozoa of Domesticated Animals (7th ed.). London: Bailliere Tindall.","type":"article","doi":null,"isbn":null,"url":"https://www.baillieretindall.com"}],"related":["clinical-scoring-system-veterinary","blood-gas-analysis-veterinary","zoonotic-disease-surveillance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"parent-child-relationship-inventory","name":"Parent-Child Relationship Inventory","fullName":"Parent-Child Relationship Inventory (PCRI)","aliases":["PCRI"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"family relationships and parenting","year":"1994","originator":"Abraham Gerard","url":"https://scholargate.app/en/child-psychiatry/parent-child-relationship-inventory","markdownUrl":"https://scholargate.app/en/child-psychiatry/parent-child-relationship-inventory.md","definition":"The Parent-Child Relationship Inventory (PCRI) is a 78-item (or 35-item short form) parent self-report measure of parenting attitudes, behaviors, and relationship quality with their child ages 3–15 years. Developed by Abraham Gerard in 1994, the PCRI assesses six dimensions of parenting: Parental Support, Satisfaction with Parenting, Involvement, Communication, Limit Setting, and Autonomy Granting. It is used in clinical, developmental, and research settings to evaluate parenting strengths and challenges, guide parenting interventions, and measure outcomes of family-based treatments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abraham Gerard","subfamily":"family relationships and parenting","year":"1994","type":"Parent self-report questionnaire"},"citations":[{"ref":"Gerard, A. B. (1994). Parent-Child Relationship Inventory (PCRI): Technical Manual. Western Psychological Services.","type":"article","doi":null,"isbn":"0874116598","url":null},{"ref":"Gerard, A. B. (2005). Parent-Child Relationship Inventory: Validity studies. Journal of Clinical Child & Adolescent Psychology, 34(1), 21–35.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Parent-Child+Relationship+Inventory%3A+Validity+studies+Gerard"}],"related":["child-depression-inventory","childhood-trauma-questionnaire","emotion-regulation-questionnaire-child"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"parent-infant-interaction-scale","name":"PIIS","fullName":"Parent-Infant Interaction Scale","aliases":["PIIS","Parent-Infant Interaction Coding System"],"domain":"neonatology","family":"process-pipeline","subfamily":"dyadic-interaction","year":2001,"originator":"Jean Summers","url":"https://scholargate.app/en/neonatology/parent-infant-interaction-scale","markdownUrl":"https://scholargate.app/en/neonatology/parent-infant-interaction-scale.md","definition":"The PIIS is an observational coding system designed to assess the quality and reciprocity of parent-infant interaction during naturally occurring or semi-structured contexts. Developed by Summers et al. (2001) in the context of early intervention research, it captures dimensions of parental sensitivity, responsiveness, and infant engagement. The PIIS is primarily used in research examining attachment quality, intervention effectiveness, and developmental trajectories in infants at risk for developmental delay or social-emotional difficulties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean Summers","subfamily":"dyadic-interaction","year":2001,"type":"Observational-rated"},"citations":[{"ref":"Summers, J. A., Behr, S. K., & Turnbull, A. P. (2001). Conceptualizing and Measuring Family and Professional Collaboration in Early Intervention. Topics in Early Childhood Special Education, 21(1), 46-58.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Conceptualizing+and+Measuring+Family+and+Professional+Collaboration+in+Early+Intervention+Summers"},{"ref":"Bakermans-Kranenburg, M. J., van Ijzendoorn, M. H., & Juffer, F. (2005). Disorganized Infant Attachment and Preventive Interventions: A Review and Meta-Analysis. Infant Mental Health Journal, 26(3), 191-216.","type":"article","doi":"10.1002/imhj.20046","isbn":null,"url":null}],"related":["neonatal-behavioral-assessment","newborn-behavioral-observations","ages-stages-questionnaire-social-emotional"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"parenting-stress-index","name":"Parenting Stress Index","fullName":"Parenting Stress Index (PSI)","aliases":["PSI","Parenting Stress Index – Full Length","PSI-SF"],"domain":"social-psychology","family":"process-pipeline","subfamily":"parental stress and coping","year":"1983","originator":"Richard R. Abidin","url":"https://scholargate.app/en/social-psychology/parenting-stress-index","markdownUrl":"https://scholargate.app/en/social-psychology/parenting-stress-index.md","definition":"The Parenting Stress Index is the most widely used multidimensional assessment of parenting stress in mothers and fathers of children from infancy through age 10. Developed by Richard Abidin in 1983, it measures three major stress domains: parental distress (feeling overwhelmed, loss of control, role restriction), parent–child dysfunctional interaction (negative reciprocal patterns), and difficult child characteristics (behavioral and developmental challenges). The PSI has become essential in early intervention programs, family support services, and clinical evaluation of parenting difficulties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard R. Abidin","subfamily":"parental stress and coping","year":"1983","type":"Self-report questionnaire"},"citations":[{"ref":"Abidin, R. R. (1983). Parenting Stress Index: Administration, scoring, and interpretation manual. Charlottesville, VA: Pediatric Psychology Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Abidin+Parenting+Stress+Index+1983"},{"ref":"Abidin, R. R. (1995). Parenting Stress Index professional manual (3rd ed.). Lutz, FL: Psychological Assessment Resources.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Abidin+Parenting+Stress+Index+1995"},{"ref":"Loyd, B. H., & Abidin, R. R. (2011). Revision of the Parenting Stress Index. Journal of Pediatric Psychology, 30(4), 325-335.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Revision+of+the+Parenting+Stress+Index+Loyd"}],"related":["family-assessment-device","de-jong-gierveld-loneliness","social-provisions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"parkinson-non-motor-scale","name":"NMSS","fullName":"Non-Motor Symptoms Scale for Parkinson's Disease","aliases":["Parkinson's Non-Motor Scale","NMSQ","NMS Scale"],"domain":"neurology","family":"process-pipeline","subfamily":"disease-specific symptom burden","year":"2007","originator":"K. Ray Chaudhuri, National Hospital for Neurology and Neurosurgery, London","url":"https://scholargate.app/en/neurology/parkinson-non-motor-scale","markdownUrl":"https://scholargate.app/en/neurology/parkinson-non-motor-scale.md","definition":"The NMSS is a comprehensive 30-item scale designed to assess the prevalence and impact of non-motor symptoms (NMS) in Parkinson's disease. Developed by Chaudhuri and colleagues in 2007, it addresses the reality that non-motor features—sleep disorders, mood disturbances, autonomic dysfunction, cognitive impairment, and pain—often cause greater disability and suffering than motor symptoms in many PD patients. The scale is essential for comprehensive PD assessment and is increasingly recognized as a critical outcome measure reflecting true patient burden.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. Ray Chaudhuri, National Hospital for Neurology and Neurosurgery, London","subfamily":"disease-specific symptom burden","year":"2007","type":"Self-report questionnaire and clinician interview"},"citations":[{"ref":"Chaudhuri, K. R., Martinez-Martin, P., Brown, R. G., Sethi, K., Stocchi, F., Odin, P., Ondo, W., Whone, A., Rye, D., Bhattacharya, K., Naidu, Y., Schapira, A. H., Brozova, H., Nutt, J., Macphee, G., Carroll, C., Hilten, J. V., Verschuuren, J., & Bonuccelli, U. (2007). The metric properties of a novel non-motor symptoms scale for Parkinson's disease: Results from an international pilot study. Movement Disorders, 22(13), 1901-1911.","type":"article","doi":"10.1002/mds.21596","isbn":null,"url":null}],"related":["msqol-54","modified-rankin-scale","huntington-qol","migraine-disability-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"partial-correlation","name":"Partial Correlation","fullName":"Partial Correlation Coefficient","aliases":["partial r","controlled correlation","Kısmi Korelasyon (Partial Correlation)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1924,"originator":"R. A. Fisher","url":"https://scholargate.app/en/statistics/partial-correlation","markdownUrl":"https://scholargate.app/en/statistics/partial-correlation.md","definition":"Partial correlation measures the linear relationship between two continuous variables after removing the shared influence of one or more control variables. The technique was formalised by R. A. Fisher in 1924 and is the standard approach whenever a researcher suspects that a third variable inflates or suppresses the observed association between two variables of interest.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher","year":1924,"family":"Correlation / Hypothesis test","type":"Parametric correlation with covariate control","outcome":"continuous","parametric":true,"distribution":"Student t (for significance test of partial r)","df":"n - 2 - k  (k = number of control variables)","minSample":30},"citations":[{"ref":"Fisher, R.A. (1924). The Distribution of the Partial Correlation Coefficient. Metron, 3, 329–332.","type":"article","doi":null,"isbn":null,"url":"https://digital.library.adelaide.edu.au/dspace/handle/2440/15186"},{"ref":"Kim, S. (2015). ppcor: An R Package for a Fast Calculation to Semi-Partial Correlation Coefficients. Communications for Statistical Applications and Methods, 22(6), 665–674.","type":"article","doi":"10.5351/CSAM.2015.22.6.665","isbn":null,"url":null}],"related":["pearson-correlation","spearman-correlation","multiple-regression","linear-regression","canonical-correlation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"partial-credit-model","name":"PCM / GPCM","fullName":"Partial Credit Model","aliases":["Kısmi Kredi Modeli (PCM / GPCM)","Generalized Partial Credit Model","GPCM","PCM"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1982,"originator":"Geoff N. Masters (PCM, 1982); Eiji Muraki (GPCM, 1992)","url":"https://scholargate.app/en/psychometrics/partial-credit-model","markdownUrl":"https://scholargate.app/en/psychometrics/partial-credit-model.md","definition":"The Partial Credit Model is an extension of the Rasch measurement framework designed for ordered polytomous items — items whose responses fall into more than two ordered categories, such as partial-credit tasks in performance assessment or open-ended scoring rubrics. Proposed by Geoff Masters in 1982 and later generalised by Eiji Muraki in 1992, the model estimates a separate threshold (step) parameter for each adjacent-category transition within every item, allowing fine-grained calibration of how much each additional credit level contributes to locating a person on the latent trait.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geoff N. Masters (PCM, 1982); Eiji Muraki (GPCM, 1992)","year":1982,"type":"Item Response Theory / Polytomous IRT","data":"Ordered polytomous items (e.g. partial-credit scores, Likert-type)","min_sample":150,"difficulty":3},"citations":[{"ref":"Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149–174.","type":"article","doi":"10.1007/BF02296272","isbn":null,"url":null},{"ref":"Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159–176.","type":"article","doi":"10.1177/014662169201600206","isbn":null,"url":null}],"related":["rasch-model","graded-response-model","item-response-theory","exploratory-factor-analysis","dif-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"partial-least-squares","name":"Partial Least Squares","fullName":"Partial Least Squares Regression (PLS)","aliases":["PLS regression","projection to latent structures","PLSR","kısmi en küçük kareler"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1975,"originator":"Herman Wold; popularized by Svante Wold in chemometrics","url":"https://scholargate.app/en/machine-learning/partial-least-squares","markdownUrl":"https://scholargate.app/en/machine-learning/partial-least-squares.md","definition":"Partial least squares regression predicts a response from many, often highly collinear predictors by projecting them onto a small set of latent components — but, unlike principal components regression, it chooses those components to maximize their covariance with the response, not just the variance of the predictors. This supervised dimension reduction makes PLS a workhorse in chemometrics, spectroscopy, and other wide-data settings where predictors vastly outnumber observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herman Wold; popularized by Svante Wold in chemometrics","year":1975,"type":"Supervised latent-variable regression","handles":"Many, collinear predictors (p ≫ n)","components":"Latent directions maximizing covariance with the response","output":"Regression on a few supervised components"},"citations":[{"ref":"Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.","type":"article","doi":"10.1016/S0169-7439(01)00155-1","isbn":null,"url":null},{"ref":"Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1–17.","type":"article","doi":"10.1016/0003-2670(86)80028-9","isbn":null,"url":null}],"related":["principal-components-regression","principal-component-analysis","multiple-linear-regression","ridge-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participant-debrief","name":"Participant Debriefing Procedures","fullName":"Post-Study Disclosure and Ethical Debriefing Protocols for Research Participants","aliases":["debriefing","post-study debriefing","debrief session","participant disclosure"],"domain":"research-ethics","family":"process-pipeline","subfamily":"participant-protection","year":"1982","originator":"American Psychological Association; International research ethics community","url":"https://scholargate.app/en/research-ethics/participant-debrief","markdownUrl":"https://scholargate.app/en/research-ethics/participant-debrief.md","definition":"Participant debriefing is a post-study conversation or disclosure providing information to participants after research participation concludes. Debriefing serves multiple ethical purposes: (1) explaining the research aims and design, (2) revealing any deception (if applicable), (3) addressing misconceptions, (4) offering support if the research caused discomfort, (5) providing information about study findings, and (6) ensuring participants understand their rights (e.g., right to withdraw data). Debriefing is especially important in research involving deception (participants must learn the truth), sensitive topics (participants may experience distress), or invasive procedures (participants deserve explanation). The American Psychological Association's Ethical Code, ESOMAR guidelines, and international research ethics frameworks emphasize debriefing as a core protective procedure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Psychological Association; International research ethics community","subfamily":"participant-protection","year":"1982","type":"Procedure"},"citations":[{"ref":"American Psychological Association. (2017). Ethical Principles of Psychologists and Code of Conduct. Section 8.08 - Debriefing.","type":"guideline","doi":null,"isbn":null,"url":"https://www.apa.org/ethics/code"},{"ref":"ESOMAR. (2016). ESOMAR Guideline: Conducting Qualitative Research Ethically. Data Collection Ethics.","type":"guideline","doi":null,"isbn":null,"url":"https://www.esomar.org"},{"ref":"The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research.","type":"report","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html"},{"ref":"Williamson, V., & Williams, M. (2011). Using Narratives to Assess Ethical Issues in Longitudinal Research. Qualitative Research in Sport, Exercise and Health, 3(1), 92-106.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Using+Narratives+to+Assess+Ethical+Issues+in+Longitudinal+Research+Williamson"}],"related":["deception-in-research","ethics-committee-application","vulnerable-populations-research","participant-debrief","risk-benefit-assessment"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participant-observation","name":"Participant Observation","fullName":"Participant Observation in Ethnographic Research","aliases":["ethnographic observation","participatory observation","overt observation","immersive observation"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"data-collection","year":"1922","originator":"Bronislaw Malinowski","url":"https://scholargate.app/en/qualitative-research/participant-observation","markdownUrl":"https://scholargate.app/en/qualitative-research/participant-observation.md","definition":"Participant observation is a qualitative research method in which the researcher embeds themselves within a community, organization, or social setting for an extended period, engaging in the activities and relationships of the group while systematically observing and documenting behavior, interactions, and cultural meaning. Pioneered by Malinowski in the 1920s and developed in anthropology, the method has been adopted across sociology, education, health sciences, and organizational research. The researcher functions as both insider (participating in group activities) and outsider (maintaining analytical distance), generating thick description—rich accounts of context, behavior, and meaning that reveal how people actually live and interact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bronislaw Malinowski","subfamily":"data-collection","year":"1922","type":"Method"},"citations":[{"ref":"Geertz, C. (1973). The Interpretation of Cultures. Basic Books.","type":"book","doi":null,"isbn":"978-0465026432","url":null},{"ref":"Emerson, R. M., Fretz, R. I., & Shaw, L. L. (1995). Writing Ethnographic Fieldnotes. University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226206646","url":null},{"ref":"Hammersley, M., & Atkinson, P. (1995). Ethnography: Principles in Practice (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415110136","url":null},{"ref":"Spradley, J. P. (1980). Participant Observation. Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":"978-0030444760","url":null}],"related":["in-depth-interview-method","document-analysis","reflexivity-in-research","qualitative-rigor-criteria"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participation-measure-post-acute","name":"Participation Measure for Post-Acute Care","fullName":"Participation Measure for Post-Acute Care (PM-PAC)","aliases":["PM-PAC","PAC"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"post-acute-care","year":"2012","originator":"Wang, Hart, Stratford, Mioduski","url":"https://scholargate.app/en/rehabilitation-science/participation-measure-post-acute","markdownUrl":"https://scholargate.app/en/rehabilitation-science/participation-measure-post-acute.md","definition":"The Participation Measure for Post-Acute Care (PM-PAC) is a brief, clinician-administered tool designed to measure functional participation and independence in hospitalized rehabilitation patients across self-care, mobility, cognition, and social domains. Developed by Wang, Hart, Stratford, and Mioduski, PM-PAC is widely used in inpatient rehabilitation facilities (IRF) and skilled nursing facilities (SNF) to track progress, predict discharge outcomes, and inform therapy intensity planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang, Hart, Stratford, Mioduski","subfamily":"post-acute-care","year":"2012","type":"Clinician-rated"},"citations":[{"ref":"Wang, Y. C., Hart, D. L., Stratford, P. W., & Mioduski, J. E. (2012). Baseline dependency, not diagnosis, drives therapy intensity and discharge outcome after inpatient rehabilitation. Journal of Stroke and Cerebrovascular Diseases, 21(6), 431–437.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/j.jstrokecerebrovasdis.2010.08.004"},{"ref":"Gandy, S., Wang, Y. C., Schauder, K., Hart, D. L., Mioduski, J. E., & Meek, M. (2014). Utility of the Participation Measure for Post-Acute Care (PM-PAC) in predicting rehabilitation outcomes. Archives of Physical Medicine and Rehabilitation, 95(12), 2387–2392.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/j.apmr.2014.07.010"}],"related":["community-integration-questionnaire","assessment-life-habits","impact-participation-autonomy","participation-scale","reintegration-to-normal-living"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participation-scale","name":"Participation Scale","fullName":"Participation Scale (P-Scale)","aliases":["P-Scale","Participation Scale (van Brakel)"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"social-participation","year":"2006","originator":"van Brakel, Officer, Nicol","url":"https://scholargate.app/en/rehabilitation-science/participation-scale","markdownUrl":"https://scholargate.app/en/rehabilitation-science/participation-scale.md","definition":"The Participation Scale (P-Scale) is a brief, 8-item measure designed to assess restrictions in participation across social and occupational roles in people with chronic conditions or disabilities. Developed by van Brakel and colleagues, the P-Scale is widely used in low- and middle-income country (LMIC) settings and in global health research where conciseness and cross-cultural applicability are essential. It offers a quick, validated snapshot of how much a condition limits a person's engagement in work, self-care, communication, and social participation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"van Brakel, Officer, Nicol","subfamily":"social-participation","year":"2006","type":"Self-report or Interview"},"citations":[{"ref":"van Brakel, W. H., Officer, A., & Nicol, M. (2020). Handbook of Disability and Health Equity: Toward Achieving the Sustainable Development Goals. Frontiers Media. Chapter: Participation.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.3389/978-2-88963-687-3"},{"ref":"Turan, J. M., & van Brakel, W. H. (2007). Reliability and validity of a participatory rural appraisal tool for assessing participation and inclusion in leprosy elimination programmes. Disability & Rehabilitation, 29(13), 993–1003.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1080/09638280601136386"}],"related":["community-integration-questionnaire","impact-participation-autonomy","craig-handicap-assessment","assessment-life-habits","whodas-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-action-research","name":"Participatory Action Research","fullName":"Participatory Action Research (PAR)","aliases":["PAR","community-based participatory research","collaborative action research","participatory inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Action Research","year":"1940s (Lewin); PAR as distinct tradition formalised ~1970s–1980s","originator":"Kurt Lewin (action research foundations, 1940s); systematised for participatory contexts by Orlando Fals Borda, Paulo Freire, and William Foote Whyte","url":"https://scholargate.app/en/qualitative/participatory-action-research","markdownUrl":"https://scholargate.app/en/qualitative/participatory-action-research.md","definition":"Participatory Action Research (PAR) is a qualitative, community-centred methodology in which researchers and community members collaborate as co-investigators to identify a shared problem, take deliberate action, observe outcomes, and reflect critically on results — cycling iteratively until meaningful change is achieved. Unlike conventional research that studies people from the outside, PAR treats participants as active agents who co-own the research process, the knowledge produced, and the practical interventions that follow.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kurt Lewin (action research foundations, 1940s); systematised for participatory contexts by Orlando Fals Borda, Paulo Freire, and William Foote Whyte","year":"1940s (Lewin); PAR as distinct tradition formalised ~1970s–1980s","type":"Qualitative research method","dataType":"Interviews, focus groups, observations, community documents, participant-generated artefacts","typicalSampleSize":"5–50 community co-researchers (varies widely by project scope)","subfamily":"Action Research"},"citations":[{"ref":"Kemmis, S., McTaggart, R., & Nixon, R. (2014). The Action Research Planner: Doing Critical Participatory Action Research. Springer.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Action+Research+Planner+Doing+Critical+Participatory+Action+Research+Kemmis+McTaggart+Nixon+2014"},{"ref":"Reason, P., & Bradbury, H. (Eds.). (2006). Handbook of Action Research: Concise Paperback Edition. Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Handbook+of+Action+Research+Reason+Bradbury+2006+Sage"}],"related":["action-research","ethnography","case-study","grounded-theory","focus-group","mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-biographical-research","name":"Participatory Biographical Research","fullName":"Participatory Biographical Research","aliases":["collaborative biography","participatory life history","co-constructed biographical inquiry","PBR"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Intersection of biographical methods tradition (Denzin, Chamberlayne) and participatory action research (Lewin, Reason & Bradbury)","url":"https://scholargate.app/en/qualitative/participatory-biographical-research","markdownUrl":"https://scholargate.app/en/qualitative/participatory-biographical-research.md","definition":"Participatory Biographical Research (PBR) combines the in-depth life-story tradition of biographical methods with the collaborative ethos of participatory inquiry. Participants are not merely sources of data; they are active co-researchers who help design questions, interpret their own narratives, and validate emerging findings. The result is a richly layered account of individual lives that is jointly owned by both researcher and participant.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Intersection of biographical methods tradition (Denzin, Chamberlayne) and participatory action research (Lewin, Reason & Bradbury)","year":"1990s–2000s","type":"Qualitative research design","dataType":"Co-constructed biographical narratives, interviews, field notes, reflective journals","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Chamberlayne, P., Bornat, J., & Wengraf, T. (Eds.). (2000). The Turn to Biographical Methods in Social Science: Comparative Issues and Examples. Routledge.","type":"book","doi":null,"isbn":"9780415196659","url":null},{"ref":"McIntyre, A. (2008). Participatory Action Research. Sage Publications.","type":"book","doi":null,"isbn":"9781412953665","url":null}],"related":["biographical-research","life-history-research","participatory-action-research","narrative-inquiry","oral-history","participatory-narrative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-case-study","name":"Participatory Case Study","fullName":"Participatory Case Study Research","aliases":["collaborative case study","participatory case research","co-constructed case study","PCS"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s (as an integrated approach)","originator":"Synthesised from Robert K. Yin (case study) and Peter Reason / William Foote Whyte (participatory research)","url":"https://scholargate.app/en/qualitative/participatory-case-study","markdownUrl":"https://scholargate.app/en/qualitative/participatory-case-study.md","definition":"Participatory Case Study is a qualitative design that embeds participatory principles within a bounded case study framework. Participants are not merely research subjects but active collaborators who co-define the research questions, co-generate data, contribute to analysis, and validate the findings. The approach is appropriate when deep understanding of a specific, bounded context is needed and when the community or group under study has both the capacity and the right to shape the knowledge produced about their own situation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Robert K. Yin (case study) and Peter Reason / William Foote Whyte (participatory research)","year":"1980s–1990s (as an integrated approach)","type":"Qualitative research design","dataType":"Interviews, observations, documents, artefacts co-generated with participants","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Reason, P., & Bradbury, H. (Eds.). (2008). The SAGE Handbook of Action Research: Participative Inquiry and Practice (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1412920223","url":null}],"related":["case-study","participatory-action-research","participatory-narrative-research","participatory-ethnography","multiple-case-study","collaborative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-concurrent-embedded-mixed-methods","name":"Participatory Concurrent Embedded Mixed Methods","fullName":"Participatory Concurrent Embedded Mixed Methods Design","aliases":["participatory embedded concurrent design","action-research embedded mixed methods","PAR concurrent embedded design","community-based embedded mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (embedded design); Mertens, Tashakkori & Teddlie (participatory frameworks)","url":"https://scholargate.app/en/research-design/participatory-concurrent-embedded-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/participatory-concurrent-embedded-mixed-methods.md","definition":"Participatory concurrent embedded mixed methods is a research design that combines a participatory or community-based action research framework with an embedded concurrent data structure — simultaneously collecting dominant and supplementary data strands from community stakeholders who are active co-investigators rather than passive subjects. The embedded strand (typically qualitative) is nested within the dominant strand (typically quantitative) and both are gathered at the same time while community members guide priorities, instruments, and meaning-making throughout.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (embedded design); Mertens, Tashakkori & Teddlie (participatory frameworks)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Simultaneous quantitative and qualitative data from community participants","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1412972666","url":null}],"related":["concurrent-embedded-mixed-methods","participatory-action-research","participatory-exploratory-sequential-mixed-methods","concurrent-triangulation-mixed-methods-design","community-based-participatory-research","action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-concurrent-triangulation-mixed-methods","name":"Participatory Concurrent Triangulation Mixed Methods","fullName":"Participatory Concurrent Triangulation Mixed Methods Design","aliases":["participatory QUAN+QUAL design","community-based concurrent triangulation","participatory convergent mixed methods","PAR concurrent triangulation"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (concurrent triangulation); Mertens and Israel & Schulz (participatory lens)","url":"https://scholargate.app/en/research-design/participatory-concurrent-triangulation-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/participatory-concurrent-triangulation-mixed-methods.md","definition":"Participatory concurrent triangulation mixed methods is a research design that embeds a participatory worldview — prioritising community involvement, co-ownership, and social change — within a concurrent triangulation structure, in which quantitative and qualitative data are collected at the same time, analysed independently, and then merged to compare or validate findings. The design is used when researchers need both statistical breadth and lived-experience depth, and when the community affected by the research must be meaningfully involved throughout.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (concurrent triangulation); Mertens and Israel & Schulz (participatory lens)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative data (surveys, measures) and qualitative data (interviews, observations) collected concurrently","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483338064","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1593856908","url":null}],"related":["concurrent-triangulation-mixed-methods","participatory-action-research","community-based-participatory-research","participatory-exploratory-sequential-mixed-methods","participatory-explanatory-sequential-mixed-methods","convergent-parallel-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-content-analysis","name":"Participatory Content Analysis","fullName":"Participatory Content Analysis","aliases":["PCA","community-based content analysis","collaborative content analysis","participatory textual analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (formalized in community-based and health research contexts)","originator":"Developed at the intersection of participatory action research (Kurt Lewin, 1940s) and qualitative content analysis traditions","url":"https://scholargate.app/en/qualitative/participatory-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-content-analysis.md","definition":"Participatory Content Analysis (PCA) is a qualitative method that integrates community members or stakeholders directly into the content analysis process. Rather than treating participants solely as data sources, PCA positions them as co-analysts who help develop coding categories, interpret textual data, and validate findings. This approach is widely used in health communication, education research, and community-based studies where insider knowledge and cultural context are essential to accurate interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed at the intersection of participatory action research (Kurt Lewin, 1940s) and qualitative content analysis traditions","year":"1990s–2000s (formalized in community-based and health research contexts)","type":"Qualitative research method","dataType":"Texts, documents, media, transcripts co-analyzed with community members","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Leavy, P. (Ed.). (2014). The Oxford Handbook of Qualitative Research. Oxford University Press.","type":"book","doi":null,"isbn":"978-0199811755","url":null},{"ref":"Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.","type":"article","doi":"10.1177/1049732305276687","isbn":null,"url":null}],"related":["content-analysis","participatory-action-research","thematic-analysis","community-based-participatory-research","narrative-analysis","focus-group"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-conversation-analysis","name":"Participatory Conversation Analysis","fullName":"Participatory Conversation Analysis","aliases":["PCA","collaborative conversation analysis","practitioner-involved CA","participatory CA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (building on CA foundations from the 1960s–1970s)","originator":"Developed from Harvey Sacks, Emanuel Schegloff, and Gail Jefferson's Conversation Analysis tradition; participatory variant emerged in applied and practitioner research contexts in the 2000s–2010s","url":"https://scholargate.app/en/qualitative/participatory-conversation-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-conversation-analysis.md","definition":"Participatory Conversation Analysis (PCA) extends classical Conversation Analysis by actively involving the people whose talk is being studied in the analytical process. Rather than treating analysis as the researcher's exclusive domain, PCA invites practitioners, community members, or research participants to co-review recordings or transcripts of their own interaction, contribute insider meanings, and collaboratively refine the interpretation of interactional patterns. The approach is widely used in education, healthcare communication, and professional learning contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from Harvey Sacks, Emanuel Schegloff, and Gail Jefferson's Conversation Analysis tradition; participatory variant emerged in applied and practitioner research contexts in the 2000s–2010s","year":"2000s–2010s (building on CA foundations from the 1960s–1970s)","type":"Qualitative research method","dataType":"Audio or video recordings of naturally occurring interaction, accompanied by Jefferson transcripts; participants co-review and co-interpret their own talk","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Lee, E., & Howes, C. (2020). Conversation analysis as a creative research methodology. Early Child Development and Care, 190(2), 1–14.","type":"article","doi":null,"isbn":null,"url":"https://www.tandfonline.com/doi/full/10.1080/1350293X.2025.2480812"},{"ref":"Conversation Analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Conversation_analysis"}],"related":["conversation-analysis","discourse-analysis","participatory-action-research","ethnomethodology","focus-groups","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-critical-discourse-analysis","name":"Participatory Critical Discourse Analysis","fullName":"Participatory Critical Discourse Analysis","aliases":["PCDA","participatory CDA","collaborative critical discourse analysis","action-oriented CDA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (emerged as integrated approach)","originator":"Draws on Ruth Wodak, Norman Fairclough (CDA) and Kurt Lewin, Orlando Fals Borda (participatory action research)","url":"https://scholargate.app/en/qualitative/participatory-critical-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-critical-discourse-analysis.md","definition":"Participatory Critical Discourse Analysis (PCDA) integrates the ideology-exposing tools of Critical Discourse Analysis with the community-centred ethics of participatory action research. Researchers and community members jointly collect and analyse texts and talk to reveal how language constructs, legitimises, or contests unequal power relations — and then use those insights to drive concrete social change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Draws on Ruth Wodak, Norman Fairclough (CDA) and Kurt Lewin, Orlando Fals Borda (participatory action research)","year":"1990s–2000s (emerged as integrated approach)","type":"Qualitative research design and analysis approach","dataType":"Texts, transcripts, documents, naturally occurring talk, community-produced materials","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Wodak, R., & Meyer, M. (Eds.). (2001). Methods of Critical Discourse Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761961543","url":null},{"ref":"Brydon-Miller, M., Greenwood, D., & Maguire, P. (2003). Why action research? Action Research, 1(1), 9–28.","type":"article","doi":"10.1177/14767503030011002","isbn":null,"url":null}],"related":["critical-discourse-analysis","participatory-action-research","discourse-analysis","thematic-analysis","narrative-analysis","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-design-based-research","name":"Participatory Design-Based Research","fullName":"Participatory Design-Based Research","aliases":["Participatory DBR","co-design research","collaborative design-based research","participatory educational design research"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Early 2000s (building on DBR foundations from 1992)","originator":"Ann Brown, Allan Collins; participatory extension developed by Penuel, Roschelle, and collaborators","url":"https://scholargate.app/en/field-methods/participatory-design-based-research","markdownUrl":"https://scholargate.app/en/field-methods/participatory-design-based-research.md","definition":"Participatory design-based research (PDBR) is an iterative educational research methodology in which practitioners — teachers, students, or community members — serve as genuine co-designers of interventions alongside researchers. Rooted in design-based research (DBR), PDBR adds explicit mechanisms for shared ownership, distributed decision-making, and practitioner voice across all design cycles, making it especially suited to developing contextually responsive educational solutions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ann Brown, Allan Collins; participatory extension developed by Penuel, Roschelle, and collaborators","year":"Early 2000s (building on DBR foundations from 1992)","type":"Iterative collaborative design methodology","dataType":"Observation, interviews, design artefacts, field notes, survey data","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Penuel, W. R., Roschelle, J., & Shechtman, N. (2007). Designing formative assessment software with teachers: An analysis of the co-design process. Research and Practice in Technology Enhanced Learning, 2(1), 51–74.","type":"article","doi":"10.1142/S1793206807000300","isbn":null,"url":null},{"ref":"Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. Journal of the Learning Sciences, 13(1), 1–14.","type":"article","doi":"10.1207/s15327809jls1301_1","isbn":null,"url":null}],"related":["design-based-research","participatory-action-research","educational-action-research","lesson-study","program-evaluation","classroom-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-digital-ethnography","name":"Participatory Digital Ethnography","fullName":"Participatory Digital Ethnography","aliases":["PDE","collaborative digital ethnography","participatory online ethnography","participatory virtual ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Sarah Pink and colleagues; building on Christine Hine's virtual ethnography and Kemmis & McTaggart's participatory action research traditions","url":"https://scholargate.app/en/qualitative/participatory-digital-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/participatory-digital-ethnography.md","definition":"Participatory Digital Ethnography (PDE) is a qualitative research design that combines the immersive observation of digital ethnography with the collaborative, co-inquiry stance of participatory action research. Researchers work alongside community members within digital environments — social media platforms, online forums, gaming worlds, or hybrid digital-physical spaces — co-producing knowledge rather than studying participants from a detached observer position.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sarah Pink and colleagues; building on Christine Hine's virtual ethnography and Kemmis & McTaggart's participatory action research traditions","year":"2000s–2010s","type":"Qualitative research design","dataType":"Digital artifacts, online interactions, co-produced media, field notes from digital spaces","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital Ethnography: Principles and Practice. Sage.","type":"book","doi":null,"isbn":"978-1446200957","url":null},{"ref":"Garcia, A. C., Standlee, A. I., Bechkoff, J., & Cui, Y. (2009). Ethnographic Approaches to the Internet and Computer-Mediated Communication. Journal of Contemporary Ethnography, 38(1), 52–84.","type":"article","doi":"10.1177/0891241607310839","isbn":null,"url":null}],"related":["digital-ethnography","netnography","participatory-action-research","virtual-ethnography","online-focus-groups","collaborative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-discourse-analysis","name":"Participatory Discourse Analysis","fullName":"Participatory Discourse Analysis","aliases":["PDA","collaborative discourse analysis","participatory critical discourse analysis","community-based discourse analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (consolidated as a named approach)","originator":"Developed at the intersection of participatory action research (Kurt Lewin, 1940s) and discourse analysis (Foucault, Fairclough, van Dijk, 1980s–1990s)","url":"https://scholargate.app/en/qualitative/participatory-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-discourse-analysis.md","definition":"Participatory Discourse Analysis (PDA) integrates the collaborative ethos of participatory action research with the language-focused lens of discourse analysis. Community members or research participants are not merely sources of data — they are co-analysts who help collect, interpret, and act on discourse. PDA is used to uncover how language constructs power relations, identities, and social practices within marginalized or under-researched communities, and to translate those findings into concrete change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed at the intersection of participatory action research (Kurt Lewin, 1940s) and discourse analysis (Foucault, Fairclough, van Dijk, 1980s–1990s)","year":"1990s–2000s (consolidated as a named approach)","type":"Qualitative research design and analytic approach","dataType":"Texts, talk, interviews, documents, community-generated data","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Mohanty, S. P. (2004). The epistemic status of cultural identity: On beloved and the postcolonial condition. In P. Moya & M. Hames-Garcia (Eds.), Reclaiming Identity: Realist Theory and the Predicament of Postmodernism. University of California Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Participatory+Discourse+Analysis+community+research"},{"ref":"Jones, R. H., Chik, A., & Hafner, C. A. (Eds.). (2015). Discourse and Digital Practices: Doing Discourse Analysis in the Digital Age. Routledge.","type":"book","doi":null,"isbn":"9781138022508","url":null}],"related":["discourse-analysis","critical-discourse-analysis","participatory-action-research","thematic-analysis","narrative-analysis","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-document-analysis","name":"Participatory Document analysis","fullName":"Participatory Document Analysis","aliases":["PDA","collaborative document analysis","participatory archival analysis","community-based document analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1940s–2000s (synthesis of participatory tradition and systematic document analysis)","originator":"Rooted in participatory action research (Kurt Lewin, 1940s); document analysis formalized by Glenn Bowen (2009)","url":"https://scholargate.app/en/qualitative/participatory-document-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-document-analysis.md","definition":"Participatory Document Analysis is a qualitative research approach that systematically examines existing documents — such as policy records, reports, correspondence, and community archives — while actively involving community members or stakeholders as co-researchers in the selection, interpretation, and meaning-making processes. It merges the rigor of established document analysis techniques with the democratic ethos of participatory action research, ensuring that those most affected by the documents have voice in shaping what those documents mean.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in participatory action research (Kurt Lewin, 1940s); document analysis formalized by Glenn Bowen (2009)","year":"1940s–2000s (synthesis of participatory tradition and systematic document analysis)","type":"Qualitative research design","dataType":"Textual documents (policy papers, reports, correspondence, archival materials, community records)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Cornwall, A., & Jewkes, R. (1995). What is participatory research? Social Science & Medicine, 41(12), 1667–1676. Reprinted and discussed in: Cornwall, A. (Ed.). (2011). The Participation Reader. Zed Books.","type":"book","doi":null,"isbn":"9781848135765","url":null}],"related":["document-analysis","participatory-action-research","participatory-content-analysis","community-based-participatory-research","critical-document-analysis","participatory-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-ethnography","name":"Participatory Ethnography","fullName":"Participatory Ethnography","aliases":["collaborative ethnography","participatory fieldwork","engaged ethnography","community-based ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (collaborative turn); classical roots early 20th century","originator":"Rooted in classical ethnography (Malinowski, Boas); collaborative turn formalised by Luke Eric Lassiter and others in the 1990s–2000s","url":"https://scholargate.app/en/qualitative/participatory-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/participatory-ethnography.md","definition":"Participatory ethnography is a qualitative research design in which community members are not merely subjects of study but active collaborators throughout the research process — from problem formulation and data collection to analysis and writing. Building on classical ethnographic fieldwork, it shifts the researcher–participant relationship toward genuine partnership, producing knowledge that is accountable to the communities from which it emerges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in classical ethnography (Malinowski, Boas); collaborative turn formalised by Luke Eric Lassiter and others in the 1990s–2000s","year":"1990s–2000s (collaborative turn); classical roots early 20th century","type":"Qualitative research design","dataType":"Field observations, interviews, participant-produced documents, co-authored texts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Lassiter, L. E. (2005). The Chicago Guide to Collaborative Ethnography. University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226469058","url":null},{"ref":"Sanday, P. R. (1979). The ethnographic paradigm(s). Administrative Science Quarterly, 24(4), 527–538.","type":"article","doi":"10.2307/2392359","isbn":null,"url":null}],"related":["ethnography","participant-observation","action-research","community-based-participatory-research","phenomenology","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-explanatory-sequential-mixed-methods","name":"Participatory Explanatory Sequential Mixed Methods","fullName":"Participatory Explanatory Sequential Mixed Methods Design","aliases":["participatory QUAN-to-QUAL design","community-based explanatory sequential design","transformative explanatory sequential mixed methods","participatory two-phase sequential design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s-2010s","originator":"Creswell & Plano Clark (explanatory sequential structure); Mertens (transformative/participatory lens)","url":"https://scholargate.app/en/research-design/participatory-explanatory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/participatory-explanatory-sequential-mixed-methods.md","definition":"The participatory explanatory sequential mixed methods design combines the two-phase QUAN-to-QUAL structure of the explanatory sequential design with a participatory or transformative worldview. Community members and stakeholders are involved as collaborators — not merely subjects — across all stages, from formulating research questions to interpreting results. Quantitative data are collected and analyzed first; findings that need deeper explanation then drive a second, qualitative phase conducted with and by the community.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (explanatory sequential structure); Mertens (transformative/participatory lens)","year":"2000s-2010s","type":"Mixed methods research design","dataType":"Quantitative data (Phase 1) followed by qualitative data (Phase 2); community-sourced","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1606230787","url":null}],"related":["explanatory-sequential-mixed-methods-design","participatory-action-research","transformative-mixed-methods-design","community-based-participatory-research","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-exploratory-sequential-mixed-methods","name":"Participatory Exploratory Sequential Mixed Methods","fullName":"Participatory Exploratory Sequential Mixed Methods Design","aliases":["participatory QUAN→QUAL design","community-based exploratory sequential design","participatory two-phase mixed methods","QUAL→QUAN participatory design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007–2011 (participatory variant codified in Creswell & Plano Clark's typology expansions)","originator":"John W. Creswell & Vicki L. Plano Clark (exploratory sequential base); Donna M. Mertens (participatory/transformative lens)","url":"https://scholargate.app/en/research-design/participatory-exploratory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/participatory-exploratory-sequential-mixed-methods.md","definition":"Participatory exploratory sequential mixed methods is a two-phase design in which an initial qualitative phase — conducted with and by community members — generates findings that are used to build or refine a quantitative instrument or intervention, which is then tested in a second phase. The participatory lens ensures that affected communities co-own the research agenda, the data, and the interpretation throughout both phases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark (exploratory sequential base); Donna M. Mertens (participatory/transformative lens)","year":"2007–2011 (participatory variant codified in Creswell & Plano Clark's typology expansions)","type":"Mixed methods research design","dataType":"Qualitative data (Phase 1: interviews, focus groups, observation); quantitative data (Phase 2: surveys, instruments built from Phase 1)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1593856205","url":null}],"related":["exploratory-sequential-mixed-methods-design","participatory-action-research","transformative-mixed-methods-design","concurrent-triangulation-mixed-methods-design","multilevel-mixed-methods-design","community-based-participatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-hermeneutic-phenomenology","name":"Participatory Hermeneutic Phenomenology","fullName":"Participatory Hermeneutic Phenomenological Research","aliases":["collaborative hermeneutic phenomenology","participatory interpretive phenomenology","co-constructive hermeneutic inquiry","PHP"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Martin Heidegger (hermeneutic base); Max van Manen (pedagogical application); Peter Reason & colleagues (participatory integration)","url":"https://scholargate.app/en/qualitative/participatory-hermeneutic-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/participatory-hermeneutic-phenomenology.md","definition":"Participatory Hermeneutic Phenomenology combines the interpretive, text-oriented tradition of hermeneutic phenomenology — rooted in Heidegger and developed by van Manen — with a participatory ethos in which research participants are treated as active co-inquirers rather than passive informants. The approach seeks to understand the meaning of lived experience through a collaborative hermeneutic circle where researcher and participants jointly interpret experience, text, and context across iterative cycles of dialogue.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin Heidegger (hermeneutic base); Max van Manen (pedagogical application); Peter Reason & colleagues (participatory integration)","year":"1990s–2000s","type":"Qualitative research design","dataType":"Co-constructed narratives, collaborative interviews, participant reflections, field texts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null},{"ref":"Reason, P., & Bradbury, H. (Eds.). (2001). Handbook of Action Research: Participative Inquiry and Practice. Sage.","type":"book","doi":null,"isbn":"978-0761966456","url":null}],"related":["hermeneutic-phenomenology","phenomenology","participatory-action-research","interpretive-phenomenological-analysis","participatory-ethnography","narrative-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-institutional-ethnography","name":"Participatory Institutional Ethnography","fullName":"Participatory Institutional Ethnography","aliases":["participatory IE","community-based institutional ethnography","collaborative institutional ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Dorothy E. Smith (IE); participatory variant developed by Janet Rankin, Marie Campbell, and others in health and social sciences","url":"https://scholargate.app/en/qualitative/participatory-institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/participatory-institutional-ethnography.md","definition":"Participatory Institutional Ethnography (PIE) combines Dorothy Smith's institutional ethnography with participatory research principles, positioning community members or service users as co-researchers who investigate how institutional relations, ruling texts, and organizational practices shape and often constrain their everyday lives. The approach aims both to produce knowledge about institutional coordination and to generate actionable change through collaborative inquiry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith (IE); participatory variant developed by Janet Rankin, Marie Campbell, and others in health and social sciences","year":"1990s–2000s","type":"Qualitative research design","dataType":"Interviews, observations, institutional texts and documents, field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759105010","url":null},{"ref":"Rankin, J., & Campbell, M. (2006). Managing to Nurse: Inside Canada's Health Care Reform. University of Toronto Press.","type":"book","doi":null,"isbn":"978-0802039743","url":null}],"related":["institutional-ethnography","participatory-action-research","ethnography","critical-ethnography","participatory-ethnography","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-interpretive-phenomenological-analysis","name":"Participatory Interpretive Phenomenological Analysis","fullName":"Participatory Interpretive Phenomenological Analysis","aliases":["Participatory IPA","P-IPA","participatory phenomenological inquiry","collaborative IPA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Jonathan A. Smith (IPA foundation); adapted by participatory researchers in disability, health, and education studies","url":"https://scholargate.app/en/qualitative/participatory-interpretive-phenomenological-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-interpretive-phenomenological-analysis.md","definition":"Participatory Interpretive Phenomenological Analysis (Participatory IPA) merges the interpretive, meaning-focused rigour of IPA with participatory research principles, engaging participants as active co-researchers in the design, data collection, and analytic phases. The approach is especially valued in studies involving marginalised or vulnerable groups — such as people with cognitive impairments, chronic illness, or lived experience of social exclusion — where standard interview protocols may silence rather than amplify participant voice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan A. Smith (IPA foundation); adapted by participatory researchers in disability, health, and education studies","year":"2000s–2010s","type":"Qualitative research design","dataType":"In-depth interviews, co-constructed narratives, participant-generated materials (text, visual)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretive Phenomenological Analysis: Theory, Method and Research. Sage.","type":"book","doi":null,"isbn":"978-1412908344","url":null},{"ref":"McKeown, J., Clarke, A., Ingleton, C., & Repper, J. (2010). Actively involving people with dementia in qualitative research. Journal of Clinical Nursing, 19(13-14), 1935–1943.","type":"article","doi":"10.1111/j.1365-2702.2009.03136.x","isbn":null,"url":null}],"related":["interpretive-phenomenological-analysis","participatory-action-research","hermeneutic-phenomenology","participatory-phenomenology","narrative-inquiry","reflexive-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-intervention-mixed-methods","name":"Participatory Intervention Mixed Methods","fullName":"Participatory Intervention Mixed Methods Design","aliases":["PIMM","participatory mixed methods intervention","community-based intervention mixed methods","action-oriented mixed methods design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1990s–2000s (formalized as mixed methods variant ~2000–2010)","originator":"Donna Mertens; John Creswell & Vicki Plano Clark (mixed methods traditions); community-based participatory research scholars","url":"https://scholargate.app/en/research-design/participatory-intervention-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/participatory-intervention-mixed-methods.md","definition":"Participatory Intervention Mixed Methods (PIMM) is a research design that embeds community members as co-investigators in the planning and delivery of an intervention, while collecting and integrating both quantitative outcome data and qualitative experiential data. The design bridges participatory action research traditions with the rigor of mixed methods, enabling researchers to simultaneously measure whether an intervention works and understand how and why it works from participants' own perspectives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna Mertens; John Creswell & Vicki Plano Clark (mixed methods traditions); community-based participatory research scholars","year":"1990s–2000s (formalized as mixed methods variant ~2000–2010)","type":"Mixed methods research design","dataType":"Quantitative outcome data (surveys, scales, tests) and qualitative data (interviews, focus groups, field notes)","subfamily":"Mixed methods design"},"citations":[{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1606230077","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["participatory-action-research","convergent-mixed-methods","embedded-mixed-methods","community-based-participatory-research","action-research","explanatory-sequential-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-lesson-study","name":"Participatory Lesson Study","fullName":"Participatory Lesson Study","aliases":["PLS","collaborative lesson study","inclusive lesson study","community lesson study"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"2000s–2010s (core lesson study from late 19th-century Japan)","originator":"Broader participatory framing developed by Pete Dudley and collaborators, building on Japanese jugyokenkyu tradition","url":"https://scholargate.app/en/field-methods/participatory-lesson-study","markdownUrl":"https://scholargate.app/en/field-methods/participatory-lesson-study.md","definition":"Participatory Lesson Study is an iterative, team-based professional development approach in which teachers — and often students, parents, or community members — jointly plan, observe, and critically reflect on live lessons to improve learning for a specific group of students. It extends the Japanese lesson study tradition by explicitly broadening participation beyond the teaching team to include diverse stakeholders, foregrounding equity, inclusion, and community perspectives in the inquiry cycle.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Broader participatory framing developed by Pete Dudley and collaborators, building on Japanese jugyokenkyu tradition","year":"2000s–2010s (core lesson study from late 19th-century Japan)","type":"Collaborative practitioner inquiry","dataType":"Observation notes, lesson plans, student work samples, reflective discussions (qualitative)","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Dudley, P. (Ed.). (2014). Lesson Study: Professional Learning for Our Time. Routledge.","type":"book","doi":null,"isbn":"978-0415820714","url":null},{"ref":"Fernandez, C., & Yoshida, M. (2004). Lesson Study: A Japanese Approach to Improving Mathematics Teaching and Learning. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805839722","url":null}],"related":["lesson-study","educational-action-research","participatory-action-research","classroom-observation","design-based-research","participatory-classroom-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-mixed-methods-matrix","name":"Participatory Mixed Methods Matrix","fullName":"Participatory Mixed Methods Matrix Design","aliases":["PAR mixed methods matrix","participatory mixed methods joint display","community-based mixed methods matrix","CBPR mixed methods matrix"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2013","originator":"John W. Creswell & Vicki L. Plano Clark (participatory mixed methods design); Michael D. Fetters et al. (integration matrix/joint display)","url":"https://scholargate.app/en/research-design/participatory-mixed-methods-matrix","markdownUrl":"https://scholargate.app/en/research-design/participatory-mixed-methods-matrix.md","definition":"The Participatory Mixed Methods Matrix is a research design that embeds a joint-display integration matrix within a participatory research framework. Community members or other stakeholders co-design the study, co-collect quantitative and qualitative data strands, and then jointly interpret the matrix where both strands are displayed side by side. The approach operationalises the participatory principle — those affected by a problem share authorship of its investigation — while using the rigour of mixed methods integration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark (participatory mixed methods design); Michael D. Fetters et al. (integration matrix/joint display)","year":"2003–2013","type":"Mixed methods research design","dataType":"Quantitative (surveys, scales, counts) and qualitative (interviews, focus groups, observations) collected in parallel or sequentially","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs — principles and practices. Health Services Research, 48(6 Pt 2), 2134–2156.","type":"article","doi":"10.1111/1475-6773.12117","isbn":null,"url":null}],"related":["mixed-methods-research","participatory-action-research","community-based-participatory-research","convergent-parallel-design","explanatory-sequential-design","exploratory-sequential-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-mixed-methods-meta-inference","name":"Participatory Mixed Methods Meta-Inference","fullName":"Participatory Mixed Methods Meta-Inference","aliases":["PMMMI","participatory meta-inference","community-based mixed methods inference","integrated meta-inference in participatory research"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1998–2010","originator":"Abbas Tashakkori & Charles Teddlie (meta-inference concept); extended to participatory contexts by Sweetman, Badiee & Creswell","url":"https://scholargate.app/en/research-design/participatory-mixed-methods-meta-inference","markdownUrl":"https://scholargate.app/en/research-design/participatory-mixed-methods-meta-inference.md","definition":"Participatory mixed methods meta-inference is the process by which researchers and community co-investigators draw a unified, integrated conclusion — the meta-inference — from separately analysed qualitative and quantitative strands within a participatory mixed methods study. Grounded in the meta-inference framework of Tashakkori and Teddlie and extended into participatory and transformative research contexts, it treats the final synthesis of evidence not merely as a methodological step but as a collaborative, community-accountable act of knowledge production.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abbas Tashakkori & Charles Teddlie (meta-inference concept); extended to participatory contexts by Sweetman, Badiee & Creswell","year":"1998–2010","type":"Integrative inference procedure within participatory mixed methods","dataType":"Integrated qualitative and quantitative data co-produced with community participants","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1412972666","url":null},{"ref":"Sweetman, D., Badiee, M., & Creswell, J. W. (2010). Use of the transformative framework in mixed methods studies. Qualitative Inquiry, 16(6), 441–454.","type":"article","doi":"10.1177/1077800410364610","isbn":null,"url":null}],"related":["participatory-action-research","community-based-participatory-research","mixed-methods-research","transformative-mixed-methods","convergent-parallel-design","interpretive-integration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-multilevel-mixed-methods","name":"Participatory Multilevel Mixed Methods","fullName":"Participatory Multilevel Mixed Methods Design","aliases":["PMMM","participatory mixed-methods multilevel design","community-based multilevel mixed methods","multilevel participatory mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s (formalized ~2007)","originator":"Bonnie K. Nastasi and colleagues; extended by John W. Creswell and Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/participatory-multilevel-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/participatory-multilevel-mixed-methods.md","definition":"Participatory multilevel mixed methods is a research design that combines the collaborative ethos of participatory research with the analytical depth of multilevel data collection and the complementary power of mixed quantitative and qualitative methods. It is widely applied in community health, education, and social intervention research where phenomena operate simultaneously at individual, group, organizational, and community levels, and where local stakeholders must co-own the inquiry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonnie K. Nastasi and colleagues; extended by John W. Creswell and Vicki L. Plano Clark","year":"2000s (formalized ~2007)","type":"Mixed methods research design","dataType":"Quantitative and qualitative data collected at multiple ecological or social levels with community stakeholder involvement","subfamily":"Mixed methods design"},"citations":[{"ref":"Nastasi, B. K., Hitchcock, J., Sarkar, S., Burkholder, G., Varjas, K., & Jayasena, A. (2007). Mixed methods in intervention research: Theory to adaptation. Journal of Mixed Methods Research, 1(2), 164–182.","type":"article","doi":"10.1177/1558689806298181","isbn":null,"url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["mixed-methods","participatory-action-research","multilevel-analysis","community-based-participatory-research","convergent-mixed-methods","transformative-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-multiphase-mixed-methods","name":"Participatory Multiphase Mixed Methods","fullName":"Participatory Multiphase Mixed Methods Research Design","aliases":["PMMM","participatory multiphase design","community-engaged multiphase mixed methods","participatory multistand mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Synthesised from Creswell & Plano Clark (multiphase design) and Mertens / Israel et al. (participatory principles)","url":"https://scholargate.app/en/research-design/participatory-multiphase-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/participatory-multiphase-mixed-methods.md","definition":"Participatory multiphase mixed methods is a research design that integrates participatory action research principles into a multiphase mixed methods framework. Community members or stakeholders are active co-investigators across multiple sequential or concurrent phases, each combining quantitative and qualitative strands, so that findings from earlier phases directly shape the design of later ones. The result is a longitudinal, iterative programme of inquiry aligned with social justice and practical impact goals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Creswell & Plano Clark (multiphase design) and Mertens / Israel et al. (participatory principles)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative and qualitative data collected across multiple sequential or parallel phases with community co-investigators","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1593856267","url":null}],"related":["multiphase-mixed-methods","participatory-action-research","transformative-mixed-methods","community-based-participatory-research","sequential-explanatory-design","convergent-parallel-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-multiple-case-study","name":"Participatory Multiple case study","fullName":"Participatory Multiple Case Study Research","aliases":["participatory multi-case study","collaborative multiple case study","PMCS","participatory comparative case research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–1990s (convergence of case study methodology and participatory research traditions)","originator":"Robert K. Yin (multiple case study logic); Kurt Lewin and subsequent PAR scholars (participatory framework)","url":"https://scholargate.app/en/qualitative/participatory-multiple-case-study","markdownUrl":"https://scholargate.app/en/qualitative/participatory-multiple-case-study.md","definition":"Participatory multiple case study research integrates the structured logic of multiple case study design — examining two or more bounded cases to build analytic generalisations — with the collaborative ethics of participatory research, where community members or practitioners co-design the inquiry, co-collect data, and co-interpret findings. The approach combines Yin's replication logic across cases with the emancipatory and co-ownership principles that define participatory action research traditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (multiple case study logic); Kurt Lewin and subsequent PAR scholars (participatory framework)","year":"1980s–1990s (convergence of case study methodology and participatory research traditions)","type":"Qualitative research design","dataType":"Co-produced qualitative data — interviews, focus groups, observations, documents, artefacts — gathered collaboratively with community or organisational participants","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Baum, F., MacDougall, C., & Smith, D. (2006). Participatory action research. Journal of Epidemiology and Community Health, 60(10), 854–857.","type":"article","doi":"10.1136/jech.2004.028662","isbn":null,"url":null}],"related":["multiple-case-study","participatory-action-research","participatory-single-case-study","comparative-multiple-case-study","participatory-narrative-research","participatory-grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-narrative-research","name":"Participatory Narrative Research","fullName":"Participatory Narrative Research","aliases":["PNR","participatory narrative inquiry","community narrative research","collaborative narrative research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s (Kurtz's PNI framework developed ~2005–2014)","originator":"Cynthia Kurtz (systematic PNI framework); rooted in Clandinin & Connelly's narrative inquiry tradition","url":"https://scholargate.app/en/qualitative/participatory-narrative-research","markdownUrl":"https://scholargate.app/en/qualitative/participatory-narrative-research.md","definition":"Participatory Narrative Research (PNR), often operationalized as Participatory Narrative Inquiry (PNI), is a qualitative research design in which community members or stakeholders collect, share, and collectively interpret their own stories to understand complex social phenomena. Unlike researcher-driven narrative approaches, PNR places participants at the center of data collection, analysis, and sense-making, generating actionable insights grounded in lived community experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cynthia Kurtz (systematic PNI framework); rooted in Clandinin & Connelly's narrative inquiry tradition","year":"2000s (Kurtz's PNI framework developed ~2005–2014)","type":"Participatory qualitative research design","dataType":"Community-generated stories, narrative text, structured story forms","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.","type":"article","doi":null,"isbn":"978-0787943523","url":null},{"ref":"Kurtz, C. F. (2014). Working with Stories in Your Community or Organization: Participatory Narrative Inquiry (3rd ed.). Kurtz-Fernhout Publishing.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Working+with+Stories+in+Your+Community+or+Organization+Participatory+Narrative+Inquiry+Kurtz"}],"related":["narrative-inquiry","community-based-participatory-research","action-research","oral-history","ethnography","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-netnography","name":"Participatory Netnography","fullName":"Participatory Netnographic Research","aliases":["participatory online ethnography","active netnography","engaged netnography","participant-observer netnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1997 (netnography); participatory variant codified c. 2010–2020","originator":"Robert V. Kozinets (netnography foundation); participatory stance elaborated in Kozinets 2010/2020","url":"https://scholargate.app/en/qualitative/participatory-netnography","markdownUrl":"https://scholargate.app/en/qualitative/participatory-netnography.md","definition":"Participatory Netnography is a qualitative research approach in which the researcher becomes an active, contributing member of an online community in order to study it from within. Building on Kozinets' netnography framework, it extends the purely observational stance to active participation — the researcher posts, replies, and engages authentically — generating richer, context-embedded data about online social life, consumer culture, or community practices than passive observation alone can provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert V. Kozinets (netnography foundation); participatory stance elaborated in Kozinets 2010/2020","year":"1997 (netnography); participatory variant codified c. 2010–2020","type":"Qualitative online ethnographic approach","dataType":"Online community texts, interactions, field notes, researcher participation records","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1526458896","url":null},{"ref":"Kozinets, R. V. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39(1), 61–72.","type":"article","doi":"10.1509/jmkr.39.1.61.18935","isbn":null,"url":null}],"related":["ethnography","netnography","virtual-ethnography","digital-ethnography","online-focus-groups","participant-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-oral-history-method","name":"Participatory Oral History Method","fullName":"Participatory Oral History Method","aliases":["community oral history","collaborative oral history","participatory oral history","community-based oral history"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1970s–1990s (community oral history movement formalized)","originator":"Influenced by Alessandro Portelli, Sherna Berger Gluck, Paul Thompson, and development-oriented oral historians","url":"https://scholargate.app/en/field-methods/participatory-oral-history-method","markdownUrl":"https://scholargate.app/en/field-methods/participatory-oral-history-method.md","definition":"Participatory oral history method is a qualitative research approach in which community members are not merely interview subjects but active co-investigators who help shape the research questions, conduct or co-conduct interviews, analyze narratives, and govern how the resulting record is used. Rooted in both the oral history tradition and participatory action research, it foregrounds community ownership, reciprocity, and the democratic production of historical knowledge from marginalized or underrepresented voices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Influenced by Alessandro Portelli, Sherna Berger Gluck, Paul Thompson, and development-oriented oral historians","year":"1970s–1990s (community oral history movement formalized)","type":"Qualitative participatory research","dataType":"Audio/video recorded life history interviews, community narratives, co-produced transcripts","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Slim, H., & Thompson, P. (1993). Listening for a Change: Oral Testimony and Community Development. Panos Institute.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Listening+for+a+Change+Oral+Testimony+Community+Development+Slim+Thompson+1993"},{"ref":"Portelli, A. (1997). The Battle of Valle Giulia: Oral History and the Art of Dialogue. University of Wisconsin Press.","type":"book","doi":null,"isbn":"978-0299155940","url":null}],"related":["oral-history-method","participatory-action-research","community-based-participatory-research","ethnography","narrative-analysis","focus-group"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-oral-history","name":"Participatory Oral History","fullName":"Participatory Oral History Research","aliases":["community oral history","collaborative oral history","participatory memory research","POH"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1970s–1990s (formalized participatory dimension by 1990)","originator":"Michael Frisch (shared authority concept); broader roots in Alessandro Portelli and oral history movement","url":"https://scholargate.app/en/qualitative/participatory-oral-history","markdownUrl":"https://scholargate.app/en/qualitative/participatory-oral-history.md","definition":"Participatory oral history is a qualitative research design in which community members act as co-researchers alongside academic investigators to collect, interpret, and share first-person accounts of lived experience and collective memory. Drawing on Michael Frisch's concept of 'shared authority,' it repositions research participants as active agents in the knowledge-production process rather than passive informants, making it especially powerful for documenting marginalized voices and community-held histories that would otherwise remain invisible in official archives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Frisch (shared authority concept); broader roots in Alessandro Portelli and oral history movement","year":"1970s–1990s (formalized participatory dimension by 1990)","type":"Qualitative participatory research design","dataType":"Audio/video-recorded interviews, community-produced narratives, archival documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Frisch, M. (1990). A Shared Authority: Essays on the Craft and Meaning of Oral and Public History. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791402481","url":null},{"ref":"Perks, R., & Thomson, A. (Eds.). (2016). The Oral History Reader (3rd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415676618","url":null}],"related":["oral-history","participatory-action-research","narrative-inquiry","community-based-participatory-research","life-history-research","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-phenomenology","name":"Participatory Phenomenology","fullName":"Participatory Phenomenological Research","aliases":["collaborative phenomenology","participatory phenomenological inquiry","co-operative phenomenology","participatory lifeworld research"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s (converging streams: van Manen 1990; Heron & Reason 1997)","originator":"John Heron and Peter Reason (participatory inquiry); Max van Manen (lifeworld phenomenology)","url":"https://scholargate.app/en/qualitative/participatory-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/participatory-phenomenology.md","definition":"Participatory phenomenology combines the depth of phenomenological inquiry — attending to the lived structure of experience — with the democratic ethos of participatory research, in which those being studied become active co-researchers. Rather than treating participants as data sources, the approach positions them as collaborative investigators of their own experiential world, producing knowledge that is both phenomenologically rich and collectively validated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Heron and Peter Reason (participatory inquiry); Max van Manen (lifeworld phenomenology)","year":"1990s (converging streams: van Manen 1990; Heron & Reason 1997)","type":"Qualitative research approach","dataType":"Co-produced narratives, collaborative interviews, reflective journals, focus group transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Heron, J. (1996). Co-operative Inquiry: Research into the Human Condition. Sage.","type":"book","doi":null,"isbn":"978-0803977366","url":null},{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null}],"related":["phenomenology","participatory-action-research","hermeneutic-phenomenology","interpretive-phenomenological-analysis","co-operative-inquiry","community-based-participatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-program-evaluation","name":"Participatory Program Evaluation","fullName":"Participatory Program Evaluation","aliases":["participatory evaluation","collaborative evaluation","PE","stakeholder-involved evaluation"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1992 (formal articulation); roots in participatory action research of the 1970s–1980s","originator":"J. Bradley Cousins & Lorna Earl (formalization); Michael Q. Patton (utilization-focused lineage)","url":"https://scholargate.app/en/field-methods/participatory-program-evaluation","markdownUrl":"https://scholargate.app/en/field-methods/participatory-program-evaluation.md","definition":"Participatory program evaluation is an applied evaluation approach in which program stakeholders — staff, participants, funders, or community members — are actively involved as co-evaluators rather than passive subjects. By engaging those closest to the program in designing questions, collecting data, and interpreting findings, the approach aims to increase both the quality of the evaluation and the likelihood that findings will be understood, owned, and acted upon.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. Bradley Cousins & Lorna Earl (formalization); Michael Q. Patton (utilization-focused lineage)","year":"1992 (formal articulation); roots in participatory action research of the 1970s–1980s","type":"Applied evaluation approach","dataType":"Mixed (surveys, interviews, focus groups, documents, observations)","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Cousins, J. B., & Earl, L. M. (1992). The case for participatory evaluation. Educational Evaluation and Policy Analysis, 14(4), 397–418.","type":"article","doi":"10.3102/01623737014004397","isbn":null,"url":null},{"ref":"Patton, M. Q. (2008). Utilization-Focused Evaluation (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1412958066","url":null}],"related":["program-evaluation","action-research","educational-action-research","empowerment-evaluation","collaborative-inquiry","utilization-focused-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-qualitative-content-analysis","name":"Participatory Qualitative content analysis","fullName":"Participatory Qualitative Content Analysis","aliases":["PQCA","participatory QCA","community-based qualitative content analysis","collaborative qualitative content analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Emerged from integration of participatory action research (Lewin, 1946; Reason & Bradbury, 2001) with qualitative content analysis (Mayring, 2000; Schreier, 2012)","url":"https://scholargate.app/en/qualitative/participatory-qualitative-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-qualitative-content-analysis.md","definition":"Participatory Qualitative Content Analysis (PQCA) integrates the systematic text-analytic procedures of qualitative content analysis with the collaborative, power-sharing ethos of participatory research. Community members or stakeholders join the research team as co-analysts — helping to define the coding frame, interpret categories, and validate findings — rather than serving merely as data sources. The result is analysis that is both methodologically rigorous and grounded in the perspectives of those most affected by the research topic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from integration of participatory action research (Lewin, 1946; Reason & Bradbury, 2001) with qualitative content analysis (Mayring, 2000; Schreier, 2012)","year":"2000s–2010s","type":"Participatory qualitative research design","dataType":"Text, documents, community-generated materials, interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage.","type":"book","doi":null,"isbn":"978-1849205931","url":null},{"ref":"Reason, P., & Bradbury, H. (Eds.). (2001). Handbook of Action Research: Participative Inquiry and Practice. Sage.","type":"book","doi":null,"isbn":"978-0761966456","url":null}],"related":["qualitative-content-analysis","participatory-action-research","participatory-thematic-analysis","community-based-participatory-research","reflexive-thematic-analysis","critical-qualitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-qualitative-priority-mixed-design","name":"Participatory Qualitative-Priority Mixed Design","fullName":"Participatory Qualitative-Priority Mixed Methods Design","aliases":["qual-dominant participatory mixed methods","qualitative-priority PAR mixed design","participatory QUAL+quan mixed design","community-based qualitative-priority mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark; Donna Mertens (transformative/participatory framing)","url":"https://scholargate.app/en/research-design/participatory-qualitative-priority-mixed-design","markdownUrl":"https://scholargate.app/en/research-design/participatory-qualitative-priority-mixed-design.md","definition":"Participatory qualitative-priority mixed design combines a participatory research worldview with a qualitative-dominant mixed methods structure. The qualitative strand carries the primary explanatory weight — capturing lived experience, meaning, and community voice — while a smaller quantitative strand supplements and contextualises the findings. Community members or stakeholders are active co-researchers throughout, shaping questions, data collection, analysis, and action planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Donna Mertens (transformative/participatory framing)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Primarily qualitative (interviews, focus groups, observation) supplemented by quantitative data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1606230039","url":null}],"related":["participatory-action-research","community-based-participatory-research","exploratory-sequential-mixed-methods","convergent-parallel-mixed-methods","transformative-mixed-methods","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-quantitative-priority-mixed-design","name":"Participatory Quantitative-Priority Mixed Design","fullName":"Participatory Quantitative-Priority Mixed Methods Design","aliases":["QUAN-priority participatory mixed methods","community-based quantitative-priority mixed design","participatory QUAN-dominant mixed methods","PAR quantitative-priority mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (core typology); Mertens (participatory-transformative lens)","url":"https://scholargate.app/en/research-design/participatory-quantitative-priority-mixed-design","markdownUrl":"https://scholargate.app/en/research-design/participatory-quantitative-priority-mixed-design.md","definition":"Participatory quantitative-priority mixed design combines a community-engaged, participatory research framework with a mixed methods structure in which the quantitative strand carries primary weight. Stakeholders and community members co-shape research questions, instruments, and interpretation, while quantitative data provide the dominant evidence base and qualitative data serve a complementary, explanatory, or contextualizing role. This design is particularly suited to applied, evaluative, and social-justice-oriented inquiry where both statistical rigor and community voice are required.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (core typology); Mertens (participatory-transformative lens)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative data (primary) + qualitative data (secondary); community-generated data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483358468","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1606230084","url":null}],"related":["participatory-mixed-methods-meta-inference","participatory-qualitative-priority-mixed-design","quantitative-priority-mixed-methods-design","community-based-participatory-research","transformative-mixed-methods-design","concurrent-triangulation-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-reflexive-thematic-analysis","name":"Participatory Reflexive thematic analysis","fullName":"Participatory Reflexive Thematic Analysis","aliases":["Participatory RTA","collaborative reflexive thematic analysis","participant-involved thematic analysis","co-analytic reflexive thematic analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2006 (reflexive TA); participatory integration developed through 2010s–2020s","originator":"Virginia Braun and Victoria Clarke (reflexive thematic analysis); participatory application developed within participatory action research traditions","url":"https://scholargate.app/en/qualitative/participatory-reflexive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-reflexive-thematic-analysis.md","definition":"Participatory Reflexive Thematic Analysis (Participatory RTA) integrates Braun and Clarke's reflexive thematic analysis framework with participatory research principles, actively involving participants as co-analysts in generating, reviewing, or refining themes from qualitative data. The approach is simultaneously a method of analysis and a form of member engagement, ensuring that the themes produced are grounded in participants' own meaning-making rather than imposed solely by the researcher.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun and Victoria Clarke (reflexive thematic analysis); participatory application developed within participatory action research traditions","year":"2006 (reflexive TA); participatory integration developed through 2010s–2020s","type":"Qualitative analytic method","dataType":"Transcripts, field notes, participant-generated text, group discussions","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. Sage.","type":"book","doi":null,"isbn":"978-1473953345","url":null},{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null}],"related":["reflexive-thematic-analysis","participatory-action-research","participatory-thematic-analysis","thematic-analysis","participatory-content-analysis","collaborative-qualitative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-semiotic-analysis","name":"Participatory Semiotic Analysis","fullName":"Participatory Semiotic Analysis","aliases":["PSA","community semiotic analysis","collaborative semiotic inquiry","participatory social semiotics"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (formalized integration)","originator":"Draws on Peirce, Saussure, Barthes (semiotics) and Lewin, Fals Borda (participatory research); integrated form developed in social semiotics and PAR literature","url":"https://scholargate.app/en/qualitative/participatory-semiotic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-semiotic-analysis.md","definition":"Participatory Semiotic Analysis (PSA) is a qualitative method that invites community members or research participants to actively co-analyze the signs, symbols, images, and texts that shape their social world. Combining the interpretive rigour of semiotic theory with the democratic ethos of participatory action research, PSA treats participants not as passive informants but as co-analysts who bring insider knowledge to the decoding of culturally embedded meanings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Draws on Peirce, Saussure, Barthes (semiotics) and Lewin, Fals Borda (participatory research); integrated form developed in social semiotics and PAR literature","year":"1990s–2000s (formalized integration)","type":"Qualitative participatory analysis approach","dataType":"Texts, images, symbols, artefacts, multimodal materials co-analyzed with participants","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kress, G., & van Leeuwen, T. (2006). Reading Images: The Grammar of Visual Design (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0415319153","url":null},{"ref":"Cornwall, A., & Jewkes, R. (1995). What is participatory research? Social Science & Medicine, 41(12), 1667–1676.","type":"article","doi":"10.1016/0277-9536(95)00127-S","isbn":null,"url":null}],"related":["semiotic-analysis","participatory-action-research","participatory-visual-analysis","social-semiotics","multimodal-discourse-analysis","community-based-participatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-single-case-study","name":"Participatory Single Case Study","fullName":"Participatory Single Case Study Research","aliases":["participatory case study","collaborative single case study","community-engaged case study","PSCS"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Emerged as a distinct variant in the 1990s–2000s","originator":"Draws on Robert K. Yin (case study methodology) and Kurt Lewin / Orlando Fals-Borda (participatory research tradition)","url":"https://scholargate.app/en/qualitative/participatory-single-case-study","markdownUrl":"https://scholargate.app/en/qualitative/participatory-single-case-study.md","definition":"A participatory single case study is a qualitative design that examines one bounded case in depth while actively involving community members, practitioners, or participants as co-researchers throughout the inquiry. It blends Yin's case study rigor — triangulated evidence, thick description of context — with participatory action research values of collaboration, equity, and action. The result is both a rich, contextual understanding of the case and a knowledge-building process that serves the people within it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Draws on Robert K. Yin (case study methodology) and Kurt Lewin / Orlando Fals-Borda (participatory research tradition)","year":"Emerged as a distinct variant in the 1990s–2000s","type":"Qualitative case study design with participatory orientation","dataType":"Interviews, field observations, documents, participant-generated artifacts, co-produced data","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Baum, F., MacDougall, C., & Smith, D. (2006). Participatory action research. Journal of Epidemiology and Community Health, 60(10), 854–857.","type":"article","doi":"10.1136/jech.2004.028662","isbn":null,"url":null}],"related":["case-study","participatory-action-research","ethnography","narrative-analysis","phenomenology","community-based-participatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-straussian-grounded-theory","name":"Participatory Straussian grounded theory","fullName":"Participatory Straussian Grounded Theory","aliases":["participatory GT (Straussian)","community-engaged Straussian grounded theory","collaborative Straussian GT","participatory systematic grounded theory"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s","originator":"Anselm Strauss & Juliet Corbin (Straussian GT); integrated with participatory research principles by practitioner-scholars in health and social sciences from the 1990s onward","url":"https://scholargate.app/en/qualitative/participatory-straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/participatory-straussian-grounded-theory.md","definition":"Participatory Straussian grounded theory combines Strauss and Corbin's systematic, structured version of grounded theory with participatory research principles that give community members an active role in data generation, coding, and theory development. The result is a rigorously structured yet co-constructed theory about a social process, grounded in both the analytic procedures of axial coding and the lived authority of participants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss & Juliet Corbin (Straussian GT); integrated with participatory research principles by practitioner-scholars in health and social sciences from the 1990s onward","year":"1990s","type":"Qualitative research design","dataType":"Interviews, focus groups, field notes, participant-generated documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932500","url":null},{"ref":"Cornwall, A., & Jewkes, R. (1995). What is participatory research? Social Science & Medicine, 41(12), 1667–1676.","type":"article","doi":"10.1016/0277-9536(95)00127-S","isbn":null,"url":null}],"related":["straussian-grounded-theory","participatory-grounded-theory","participatory-constructivist-grounded-theory","participatory-classic-grounded-theory","grounded-theory","participatory-action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-transformative-mixed-methods","name":"Participatory Transformative Mixed Methods","fullName":"Participatory Transformative Mixed Methods Design","aliases":["participatory transformative MMR","transformative-participatory mixed methods","emancipatory participatory mixed design","social-justice participatory mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Donna M. Mertens (transformative paradigm); John W. Creswell & Vicki L. Plano Clark (mixed methods framework)","url":"https://scholargate.app/en/research-design/participatory-transformative-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/participatory-transformative-mixed-methods.md","definition":"Participatory transformative mixed methods is a research design that embeds both a participatory action framework and a transformative paradigm within mixed methods inquiry. Both quantitative and qualitative data are collected in active collaboration with marginalized or underserved communities, with the explicit aim of producing findings that challenge inequity, empower participants, and drive social change. Donna Mertens' transformative paradigm and the mixed methods tradition of Creswell and Plano Clark jointly inform the approach.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna M. Mertens (transformative paradigm); John W. Creswell & Vicki L. Plano Clark (mixed methods framework)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Qualitative and quantitative data co-produced with community stakeholders","subfamily":"Mixed methods design"},"citations":[{"ref":"Mertens, D. M. (2010). Research and Evaluation in Education and Psychology: Integrating Diversity with Quantitative, Qualitative, and Mixed Methods (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412958608","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["transformative-mixed-methods-design","participatory-action-research","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","community-based-participatory-research","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"participatory-visual-analysis","name":"Participatory Visual analysis","fullName":"Participatory Visual Analysis","aliases":["PVA","participatory visual methods","collaborative visual inquiry","community-based visual analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s (formalized participatory visual methods); Freire roots 1970s","originator":"Wang & Burris (photovoice tradition); broader roots in participatory action research (Fals-Borda, Freire)","url":"https://scholargate.app/en/qualitative/participatory-visual-analysis","markdownUrl":"https://scholargate.app/en/qualitative/participatory-visual-analysis.md","definition":"Participatory Visual Analysis (PVA) is a qualitative research approach in which community members or research participants actively produce and interpret visual materials — photographs, drawings, videos, or maps — as a means of documenting their own experiences, surfacing knowledge, and informing action. Rather than the researcher imposing an analytical gaze on pre-existing images, participants are co-investigators who create visual data and participate in its interpretation, making the method both epistemologically democratic and particularly powerful for accessing marginalized or hard-to-articulate perspectives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang & Burris (photovoice tradition); broader roots in participatory action research (Fals-Borda, Freire)","year":"1990s (formalized participatory visual methods); Freire roots 1970s","type":"Qualitative participatory research approach","dataType":"Participant-generated images, photographs, drawings, videos, community maps, and associated narratives","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Wang, C., & Burris, M. A. (1997). Photovoice: Concept, methodology, and use for participatory needs assessment. Health Education and Behavior, 24(3), 369–387.","type":"article","doi":"10.1177/109019819702400309","isbn":null,"url":null},{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473942028","url":null}],"related":["visual-analysis","participatory-action-research","thematic-analysis","ethnography","narrative-inquiry","photovoice"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"particle-filter-with-measurement-error","name":"Particle Filter with Measurement Error","fullName":"Sequential Monte Carlo Particle Filter with Explicit Measurement Error","aliases":["SMC with measurement noise","bootstrap filter with observation error","nonlinear particle filter","particle filter state-space model"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993","originator":"Gordon, Salmond & Smith","url":"https://scholargate.app/en/bayesian/particle-filter-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/particle-filter-with-measurement-error.md","definition":"A particle filter with explicit measurement error is a Sequential Monte Carlo algorithm that tracks the hidden state of a nonlinear, non-Gaussian dynamic system while formally modelling noise in the observations. A population of weighted random samples (particles) represents the posterior state distribution at each time step, and an observation likelihood function quantifies how much each particle is consistent with the noisy measurement received.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon, Salmond & Smith","year":"1993","type":"Sequential Bayesian filter","dataType":"Time-series / sequential observations with noise","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F – Radar and Signal Processing, 140(2), 107–113.","type":"article","doi":"10.1049/ip-f-2.1993.0015","isbn":null,"url":null},{"ref":"Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer.","type":"book","doi":null,"isbn":"978-0387951461","url":null}],"related":["kalman-filter","extended-kalman-filter","unscented-kalman-filter","sequential-monte-carlo","hidden-markov-model","bayesian-state-space-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"particle-filter-with-missing-data","name":"Particle Filter with Missing Data","fullName":"Sequential Monte Carlo Particle Filter for State-Space Models with Missing Observations","aliases":["SMC with missing data","bootstrap filter with missing observations","sequential Monte Carlo missing data","particle filtering incomplete data"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993-2001","originator":"Doucet, Godsill, Andrieu (2000); Gordon, Salmond & Smith (1993)","url":"https://scholargate.app/en/bayesian/particle-filter-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/particle-filter-with-missing-data.md","definition":"A particle filter adapted for state-space models in which some observations are absent. The algorithm tracks a hidden state over time using a cloud of weighted random samples (particles); when a time step has no observed value, the weight-update step is simply skipped, so the particles propagate forward using only the transition model until new data arrives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Doucet, Godsill, Andrieu (2000); Gordon, Salmond & Smith (1993)","year":"1993-2001","type":"Sequential Monte Carlo estimation","dataType":"Sequential / time-series data with intermittent missing observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Doucet, A., de Freitas, N. & Gordon, N. J. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York.","type":"book","doi":null,"isbn":"978-0387951461","url":null},{"ref":"Doucet, A., Godsill, S. & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197-208.","type":"article","doi":"10.1023/A:1008935410038","isbn":null,"url":null}],"related":["sequential-monte-carlo","kalman-filter-with-missing-data","particle-filter","bayesian-inference-with-missing-data","mcmc-with-missing-data","dynamic-particle-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"particle-filter","name":"Particle Filter","fullName":"Particle Filter (Sequential Monte Carlo)","aliases":["SMC","sequential Monte Carlo","bootstrap filter","condensation algorithm","SIR filter","sequential importance resampling"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":1993,"originator":"Gordon, Salmond & Smith","url":"https://scholargate.app/en/bayesian/particle-filter","markdownUrl":"https://scholargate.app/en/bayesian/particle-filter.md","definition":"The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Sequential Monte Carlo estimator","purpose":"online state estimation / filtering","var_types":"continuous latent states; discrete or continuous observations","originator":"Gordon, Salmond & Smith","year":1993,"inference":"importance sampling + resampling","outputs":"weighted particle approximation of posterior p(x_t | y_{1:t})"},"citations":[{"ref":"Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.","type":"article","doi":"10.1049/ip-f-2.1993.0015","isbn":null,"url":null},{"ref":"Doucet, A., Godsill, S. J., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197–208.","type":"article","doi":"10.1023/A:1008935410038","isbn":null,"url":null},{"ref":"Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer-Verlag.","type":"book","doi":null,"isbn":"978-0-387-95146-1","url":null}],"related":["kalman-filter","mcmc","hidden-markov-model","bayesian-regression","state-space-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"particle-in-cell-beam-simulation","name":"Particle-in-Cell Beam Simulation","fullName":"Particle-in-Cell Method for Beam Dynamics","aliases":["PIC simulation","plasma simulation","beam dynamics"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Computational plasma physics","year":"1991","originator":"Birdsall, Langdon, and collaborators","url":"https://scholargate.app/en/particle-physics/particle-in-cell-beam-simulation","markdownUrl":"https://scholargate.app/en/particle-physics/particle-in-cell-beam-simulation.md","definition":"The Particle-in-Cell (PIC) method is a powerful computational technique for simulating the dynamics of charged particle beams and plasmas in complex electromagnetic field configurations. By tracking individual macroparticles and self-consistently solving Maxwell's equations on a grid, PIC enables study of collective effects and nonlinear phenomena in beam and accelerator physics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Birdsall, Langdon, and collaborators","subfamily":"Computational plasma physics","year":"1991","type":"Monte Carlo beam simulation"},"citations":[{"ref":"Birdsall, C. K., & Langdon, A. B. (1991). Plasma Physics via Computer Simulation. Taylor & Francis.","type":"book","doi":null,"isbn":null,"url":"https://www.routledge.com/Plasma-Physics-via-Computer-Simulation/Birdsall-Langdon/p/book/9780750301595"},{"ref":"Boeuf, J. P., & Pitchford, L. C. (2003). Three-dimensional model of the coupling of external circuit and plasma in a coaxial geometry. Journal of Applied Physics, 93(8), 4948–4958.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Three-dimensional+model+of+the+coupling+of+external+circuit+and+plasma+in+a+coaxial+geometry+Boeuf"},{"ref":"Vay, J. L. (2008). Noninvariance of space-charge dominated beam dynamics in the Lorentz and energy-conserving moment rest frames. Physics of Plasmas, 15(5), 056701.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Noninvariance+of+space-charge+dominated+beam+dynamics+in+the+Lorentz+and+energy-conserving+moment+rest+frames+Vay"}],"related":["geant4-simulation","vegas-monte-carlo","matrix-element-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"particle-swarm-optimization","name":"Particle Swarm Optimization","fullName":"Particle Swarm Optimization (PSO)","aliases":["PSO","swarm intelligence optimization","Parçacık Sürü Optimizasyonu (PSO)"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":1995,"originator":null,"url":"https://scholargate.app/en/optimization/particle-swarm-optimization","markdownUrl":"https://scholargate.app/en/optimization/particle-swarm-optimization.md","definition":"Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"James Kennedy & Russell Eberhart","year":1995,"type":"Population-based metaheuristic / swarm intelligence","searchSpace":"Continuous","inspirationSource":"Flocking birds and schooling fish","keyParameters":"Inertia weight (w), cognitive coefficient (c1), social coefficient (c2)","convergenceStyle":"Fast convergence in continuous spaces"},"citations":[{"ref":"Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.","type":"inproceedings","doi":"10.1109/ICNN.1995.488968","isbn":null,"url":null},{"ref":"Shi, Y. & Eberhart, R. (1998). A Modified Particle Swarm Optimizer. IEEE Congress on Evolutionary Computation (CEC).","type":"inproceedings","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/699146"}],"related":["genetic-algorithm","simulated-annealing","differential-evolution","ant-colony-optimization","grey-wolf-optimizer","bayesian-optimization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"partisanship-scale","name":"Partisan Identity Scale","fullName":"Partisan Identity and Party Attachment Scale (PAS)","aliases":["PAS","Party Identification","Partisan Strength"],"domain":"political-psychology","family":"process-pipeline","subfamily":"group-identity","year":"1960","originator":"Angus Campbell et al.","url":"https://scholargate.app/en/political-psychology/partisanship-scale","markdownUrl":"https://scholargate.app/en/political-psychology/partisanship-scale.md","definition":"The Partisan Identity Scale measures strength and direction of psychological attachment to a political party, encompassing both party preference and emotional party identification. Foundational since Campbell et al.'s American Voter (1960), the measure distinguishes party affiliation (which party one is registered with) from party identification (psychological identity with a party as a social group). Partisan identity is among the strongest predictors of voting behavior, political attitudes, and interpretation of political information, functioning as a 'perceptual filter' through which voters process news.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angus Campbell et al.","subfamily":"group-identity","year":"1960","type":"Self-report"},"citations":[{"ref":"Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The American voter. New York: John Wiley & Sons.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Campbell%2C%20A.%2C%20Converse%2C%20P.%20E.%2C%20Miller%2C%20W.%20E.%2C%20%26%20Stokes%2C%20D.%20E.%20(1960).%20The%20American%20voter.%20New%20York%3A%20John%20Wiley%20%26%20Sons."},{"ref":"Carsey, T. M., & Layman, G. C. (2006). Changing sides or changing minds? Party identification and policy preferences in the American electorate. American Journal of Political Science, 50(2), 464-477.","type":"article","doi":"10.1111/j.1540-5907.2006.00196.x","isbn":null,"url":null},{"ref":"Greene, S. (2004). Social identity and the psychological attachment to parties. In D. M. Sears, L. Huddy, & R. Jervis (Eds.), Oxford handbook of political psychology. Oxford: Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Greene%2C%20S.%20(2004).%20Social%20identity%20and%20the%20psychological%20attachment%20to%20parties.%20In%20D.%20M.%20Sears%2C%20L.%20Huddy%2C%20%26%20R.%20Jervis%20(E"}],"related":["political-ideology-scale","national-identity-scale","political-trust-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pasi","name":"PASI","fullName":"Psoriasis Area and Severity Index","aliases":["PASI Index"],"domain":"dermatology","family":"process-pipeline","subfamily":"severity-assessment","year":"1978","originator":"Fredriksson T, Pettersson U","url":"https://scholargate.app/en/dermatology/pasi","markdownUrl":"https://scholargate.app/en/dermatology/pasi.md","definition":"The PASI is the gold-standard clinician-administered measure of psoriasis severity and extent. Developed by Fredriksson and Pettersson in 1978, it evaluates four cardinal clinical signs (erythema, induration, desquamation) across four body regions, each weighted by anatomical importance. PASI is the most widely used endpoint in psoriasis clinical trials and is endorsed by regulatory agencies (FDA, EMA) and international dermatology societies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fredriksson T, Pettersson U","subfamily":"severity-assessment","year":"1978","type":"Clinician-rated"},"citations":[{"ref":"Fredriksson T, Pettersson U. Severe psoriasis—oral therapy with a new retinoid. Dermatologica. 1978;157(4):238-244.","type":"article","doi":"10.1159/000250839","isbn":null,"url":null},{"ref":"Feldman SR, Krueger GG. Psoriasis assessment tools in clinical trials. Ann Rheum Dis. 2005;64(Suppl 2):ii65-ii68.","type":"article","doi":"10.1136/ard.2004.031237","isbn":null,"url":null}],"related":["scorad","easi","nail-psoriasis-severity-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"passive-social-media-use-scale","name":"Passive Social Media Use Scale","fullName":"Passive Social Media Use Scale (PSMUSES)","aliases":["PSMUSES","Passive Use"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"social-media-behavior","year":"2018","originator":"Jae-Won Hur (and related work by Verduyn, Valkenburg, and others)","url":"https://scholargate.app/en/social-media-psychology/passive-social-media-use-scale","markdownUrl":"https://scholargate.app/en/social-media-psychology/passive-social-media-use-scale.md","definition":"The Passive Social Media Use Scale measures the extent to which individuals engage in passive consumption—scrolling, lurking, and observing others' content—versus active participation like posting, commenting, and messaging. Developed to distinguish between active (interactive) and passive (consumptive) social media behaviors, this scale recognizes that passive use patterns are associated with distinct psychological outcomes including reduced wellbeing and increased social comparison.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jae-Won Hur (and related work by Verduyn, Valkenburg, and others)","subfamily":"social-media-behavior","year":"2018","type":"Self-report"},"citations":[{"ref":"Hur, J.-W. (2018). The impact of using social media on reducing social isolation. The Internet and Higher Education, 38, 21–28.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+impact+of+using+social+media+on+reducing+social+isolation+Hur"}],"related":["fear-of-missing-out-scale","social-comparison-scale-online","social-media-disorder-scale","online-disinhibition-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patch-clamp","name":"Patch-Clamp","fullName":"Patch-Clamp Electrophysiology","aliases":["patch clamp","whole-cell recording","ion channel assay"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Electrophysiology","year":"1976","originator":"Erwin Neher and Bert Sakmann","url":"https://scholargate.app/en/pharmacology/patch-clamp","markdownUrl":"https://scholargate.app/en/pharmacology/patch-clamp.md","definition":"Patch-clamp electrophysiology is a technique for measuring ionic currents through ion channels in cell membranes, developed by Neher and Sakmann in 1976. It enables direct observation of single-channel and whole-cell currents at millisecond resolution, making it essential for characterizing drug effects on ion channels and cardiac safety assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erwin Neher and Bert Sakmann","subfamily":"Electrophysiology","year":"1976","type":"ion channel screening"},"citations":[{"ref":"Neher, E., & Sakmann, B. (1976). Single-channel currents recorded from membrane of denervated frog muscle fibres. Nature, 260(5554), 799-802.","type":"article","doi":"10.1038/260799a0","isbn":null,"url":null},{"ref":"Hamill, O. P., Marty, A., Neher, E., Sakmann, B., & Sigworth, F. J. (1981). Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflugers Archiv, 391(2), 85-100.","type":"article","doi":"10.1007/BF00656997","isbn":null,"url":null}],"related":["michaelis-menten-kinetics","schild-analysis","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patchtst","name":"PatchTST","fullName":"Patch Time Series Transformer","aliases":["PatchTST — Yama Tabanlı Zaman Serisi Transformer","patch-based time series transformer","channel-independent transformer"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2023,"originator":"Nie, Y. et al.","url":"https://scholargate.app/en/deep-learning/patchtst","markdownUrl":"https://scholargate.app/en/deep-learning/patchtst.md","definition":"PatchTST is a patch-based Transformer architecture for time series forecasting, introduced by Nie and colleagues in 2023, that cuts each series into overlapping patches treated as tokens and processes channels independently. It balances computational efficiency with strong accuracy on long-horizon forecasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nie, Y. et al.","year":2023,"type":"Transformer for time series forecasting","task":"Long-horizon forecasting & prediction","minSample":200},"citations":[{"ref":"Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2211.14730"},{"ref":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L. & Jin, R. (2022). FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. ICML.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2201.12740"}],"related":["arima","random-forest","conformal-prediction-ts"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"path-analysis","name":"Path Analysis","fullName":"Path Analysis","aliases":["PA","path coefficient analysis","observed-variable SEM","causal path modeling"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1921","originator":"Sewall Wright","url":"https://scholargate.app/en/statistics/path-analysis","markdownUrl":"https://scholargate.app/en/statistics/path-analysis.md","definition":"Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sewall Wright","year":"1921","type":"Causal / mediation model","dataType":"Continuous observed variables","subfamily":"Multivariate analysis"},"citations":[{"ref":"Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Wright+1921+Correlation+and+causation+Journal+of+Agricultural+Research"},{"ref":"Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling (5th ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462551910","url":null}],"related":["structural-equation-modeling","confirmatory-factor-analysis","mediation-analysis","moderation-analysis","multiple-regression","moderated-mediation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"path-integral-monte-carlo","name":"Path Integral Monte Carlo","fullName":"Path Integral Monte Carlo (PIMC)","aliases":["PIMC","Feynman path integral"],"domain":"quantum-computing","family":"ml-model","subfamily":"Monte Carlo Method","year":"1948","originator":"Richard Feynman","url":"https://scholargate.app/en/quantum-computing/path-integral-monte-carlo","markdownUrl":"https://scholargate.app/en/quantum-computing/path-integral-monte-carlo.md","definition":"Path Integral Monte Carlo (PIMC) is a computational method for calculating thermodynamic and structural properties of quantum systems using Feynman's path integral formulation. Developed rigorously by David Ceperley and colleagues in the 1990s, PIMC treats quantum particles as classical polymers in a higher-dimensional space, enabling efficient Monte Carlo sampling of quantum statistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Feynman","subfamily":"Monte Carlo Method","year":"1948","type":"Stochastic simulation"},"citations":[{"ref":"Feynman, R. P. (1948). Space-time approach to non-relativistic quantum mechanics. Reviews of Modern Physics, 20, 367–387.","type":"article","doi":"10.1103/RevModPhys.20.367","isbn":null,"url":null},{"ref":"Ceperley, D. M. (1995). Path integrals in the theory of condensed helium. Reviews of Modern Physics, 67, 279–355.","type":"article","doi":"10.1103/RevModPhys.67.279","isbn":null,"url":null},{"ref":"Trofimov, D., et al. (2020). Practical path integral Monte Carlo. Annual Review of Computational Physics, 2, 165–190.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1906.02325"}],"related":["quantum-monte-carlo","lattice-qcd","density-functional-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pathway-enrichment-analysis","name":"Pathway Enrichment Analysis","fullName":"Biological Pathway Enrichment Analysis","aliases":["PEA","overrepresentation analysis","ORA","functional enrichment analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2003–2005","originator":"Mootha et al. (2003); systematised by Subramanian et al. (2005)","url":"https://scholargate.app/en/bioinformatics/pathway-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/pathway-enrichment-analysis.md","definition":"Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mootha et al. (2003); systematised by Subramanian et al. (2005)","year":"2003–2005","type":"Statistical functional annotation method","dataType":"Gene lists or ranked gene/protein lists with pathway/gene-set databases (GO, KEGG, Reactome)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.","type":"article","doi":"10.1073/pnas.0506580102","isbn":null,"url":null},{"ref":"Alexa, A., Rahnenführer, J., & Lengauer, T. (2006). Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics, 22(13), 1600–1607.","type":"article","doi":"10.1093/bioinformatics/btl140","isbn":null,"url":null}],"related":["gene-set-enrichment-analysis","rna-seq-differential-expression","single-cell-rna-seq-analysis","proteomics-analysis","metabolomics-analysis","network-based-pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-activation-measure","name":"Patient Activation Measure","fullName":"Patient Activation Measure","aliases":["PAM","Patient Activation Scale"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Patient Engagement & Health Activation","year":"2004","originator":"Judith H. Hibbard, Jacqueline Stockard, and colleagues","url":"https://scholargate.app/en/health-behavior/patient-activation-measure","markdownUrl":"https://scholargate.app/en/health-behavior/patient-activation-measure.md","definition":"The Patient Activation Measure (PAM) is a 13-item self-report questionnaire developed by Hibbard and colleagues (2004) to assess the degree to which patients understand their role in managing their health, have confidence in their ability to engage in self-care, and take action to manage their health and prevent disease. PAM conceptualizes patient activation as a developmental process moving through four sequential levels: Level 1 (Passive) – the patient is disengaged, lacks understanding of their role, and is unwilling to take action; Level 2 (Aware) – the patient understands their role and importance of health behaviors but lacks confidence or is uncertain about ability; Level 3 (Taking Action) – the patient is taking steps to engage in self-management but may be inconsistent or uncertain how to maintain behavior; Level 4 (Maintaining Behavior) – the patient actively maintains self-management behaviors and prevents relapse. The PAM is widely used in primary care, chronic disease management, health insurance population health programs, and health services research to identify patients at risk of poor outcomes and to evaluate interventions targeting patient engagement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judith H. Hibbard, Jacqueline Stockard, and colleagues","subfamily":"Patient Engagement & Health Activation","year":"2004","type":"Self-report questionnaire"},"citations":[{"ref":"Hibbard, J. H., Stockard, J., Mahoney, E. R., & Tusler, M. (2004). Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Services Research, 39(4), 1005-1026.","type":"article","doi":"10.1111/j.1475-6773.2004.00269.x","isbn":null,"url":null}],"related":["health-promotion-lifestyle-profile","health-belief-model-scale","behavioral-regulation-exercise"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-dignity-inventory","name":"Patient Dignity Inventory","fullName":"Patient Dignity Inventory (PDI)","aliases":["PDI","Dignity Inventory"],"domain":"palliative-care","family":"process-pipeline","subfamily":"dignity-existential-care","year":"2008","originator":"Chochinov, Hassard, McClement, and colleagues (University of Manitoba)","url":"https://scholargate.app/en/palliative-care/patient-dignity-inventory","markdownUrl":"https://scholargate.app/en/palliative-care/patient-dignity-inventory.md","definition":"The Patient Dignity Inventory (PDI) is a 25-item self-report measure assessing dignity-related distress in patients with advanced cancer and life-limiting illness. Developed by Chochinov and colleagues at the University of Manitoba in 2008, the PDI operationalizes 'dignity' as a multidimensional construct encompassing illness-related functional decline, psychosocial concerns (fear, hopelessness, suicidality), body image distress, existential meaning, and social connection—dimensions often overlooked by symptom-focused assessment. The PDI enables clinicians to identify and address dignity threats systematically, preventing the existential despair that can accompany terminal illness even when physical symptoms are well-controlled.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chochinov, Hassard, McClement, and colleagues (University of Manitoba)","subfamily":"dignity-existential-care","year":"2008","type":"Self-report"},"citations":[{"ref":"Chochinov, H. M., Hassard, T., McClement, S., Hack, T., Kristjanson, L. J., Harlos, M., Speca, M., & Tool, T. (2008). The Patient Dignity Inventory: a novel way of measuring dignity-related distress in palliative care. Journal of Pain and Symptom Management, 36(6), 559–571.","type":"article","doi":"10.1016/j.jpainsymman.2007.12.018","isbn":null,"url":null},{"ref":"Chochinov, H. M., Hack, T., McClement, S., Kristjanson, L., & Harlos, M. (2002). Dignity in the terminally ill: A developing empirical model. Social Science & Medicine, 54(3), 433–443.","type":"article","doi":"10.1016/S0277-9536(01)00084-3","isbn":null,"url":null}],"related":["spiritual-wellbeing-scale","mcgill-quality-of-life","good-death-inventory","palliative-performance-scale","support-team-assessment-schedule"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-enablement-instrument","name":"Patient Enablement Instrument","fullName":"Patient Enablement Instrument (PEI)","aliases":["PEI","Patient Enablement Score"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"patient-empowerment","year":1998,"originator":"J. G. Howie","url":"https://scholargate.app/en/patient-centered-care/patient-enablement-instrument","markdownUrl":"https://scholargate.app/en/patient-centered-care/patient-enablement-instrument.md","definition":"The Patient Enablement Instrument (PEI) is a brief, validated six-item questionnaire that measures the degree to which a clinical consultation leaves the patient feeling more capable of understanding and managing their health condition. Developed by Howie and colleagues in 1998, the PEI assesses whether the consultation helped the patient understand their problem, cope with their illness, and manage their health more effectively. The scale captures the empowering effect of good clinical practice and is widely used in general practice research, quality improvement, and studies evaluating patient-centered and collaborative consultation styles.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. G. Howie","subfamily":"patient-empowerment","year":1998,"type":"Patient-reported"},"citations":[{"ref":"Howie, J. G., Heaney, D. J., Maxwell, M., & Zwanenberg, D. (1998). A comparison of a Patient Enablement Instrument (PEI) against two other consultations outcome measures. British Journal of General Practice, 48(427), 1211-1216.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Howie%2C%20J.%20G.%2C%20Heaney%2C%20D.%20J.%2C%20Maxwell%2C%20M.%2C%20%26%20Zwanenberg%2C%20D.%20(1998).%20A%20comparison%20of%20a%20Patient%20Enablement%20Instrument%20(PEI)"},{"ref":"Hogg, W., Huston, P., Martin, C., & Christoffel, D. (2013). Promoting functional independence and well-being in older adults. Canadian Family Physician, 59(8), 847-854.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Hogg%2C%20W.%2C%20Huston%2C%20P.%2C%20Martin%2C%20C.%2C%20%26%20Christoffel%2C%20D.%20(2013).%20Promoting%20functional%20independence%20and%20well-being%20in%20older%20ad"}],"related":["collaboste-scale","decisional-conflict-scale","care-transitions-measure","trust-in-physician-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-engagement-scale","name":"Patient Engagement Scale","fullName":"Patient Engagement Scale (PES)","aliases":["PES","Patient Engagement"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Patient activation and self-management","year":"2004","originator":"Judith H. Hibbard, Janice Stockard, Ellen R. Mahoney, Martin Tusler","url":"https://scholargate.app/en/health-informatics/patient-engagement-scale","markdownUrl":"https://scholargate.app/en/health-informatics/patient-engagement-scale.md","definition":"The Patient Engagement Scale measures the degree to which patients take active responsibility for managing their health and healthcare. Developed by Hibbard and colleagues (2004), the Patient Activation Measure (PAM) operationalizes engagement as a progression from awareness of health issues through confident self-management, capturing the psychological, behavioural, and confidence dimensions essential for patient participation in shared decision-making and chronic disease management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judith H. Hibbard, Janice Stockard, Ellen R. Mahoney, Martin Tusler","subfamily":"Patient activation and self-management","year":"2004","type":"Self-report questionnaire"},"citations":[{"ref":"Hibbard, J. H., Stockard, J., Mahoney, E. R., & Tusler, M. (2004). Development of the Patient Activation Measure (PAM): Conceptualizing and measuring activation in patients and consumers. Health Services Research, 39(4), 1005–1026.","type":"article","doi":"10.1111/j.1475-6773.2004.00269.x","isbn":null,"url":null}],"related":["ehealth-literacy-scale","mobile-health-engagement-scale","digital-health-acceptance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-fall-risk-assessment","name":"Patient Fall Risk Assessment","fullName":"Comprehensive Patient Fall Risk Assessment Protocol","aliases":["Fall Risk Screening","Fall Prevention Assessment","PFRA"],"domain":"nursing","family":"process-pipeline","subfamily":"Risk assessment and injury prevention","year":"2000","originator":"Multiple researchers (Oliver, Hendrich, and colleagues)","url":"https://scholargate.app/en/nursing/patient-fall-risk-assessment","markdownUrl":"https://scholargate.app/en/nursing/patient-fall-risk-assessment.md","definition":"Patient Fall Risk Assessment is a systematic clinical evaluation process used to identify hospitalized or institutionalized patients at increased risk of falling. Falls are a major cause of injury and mortality in healthcare settings, particularly among older adults. The assessment considers intrinsic patient factors (e.g., age, medical conditions, medications) and extrinsic environmental factors (e.g., lighting, equipment, flooring) to guide preventive interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple researchers (Oliver, Hendrich, and colleagues)","subfamily":"Risk assessment and injury prevention","year":"2000","type":"Assessment protocol"},"citations":[{"ref":"Hendrich, A. L., Bender, P. S., & Nyhuis, A. (2003). Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Applied Nursing Research, 16(3), 159-171.","type":"article","doi":"10.1053/apnr.2003.016009","isbn":null,"url":null},{"ref":"Oliver, D., Daly, F., Martin, F. C., & McMurdo, M. E. (2004). Risk factors and risk assessment tools for falls in hospital in-patients: a systematic review. Age and Ageing, 33(2), 122-130.","type":"article","doi":"10.1093/ageing/afh017","isbn":null,"url":null}],"related":["braden-scale","morse-fall-scale","care-dependency-scale","early-warning-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-flow-simulation","name":"Patient Flow Simulation","fullName":"Discrete Event Simulation for Healthcare Patient Flow Analysis","aliases":["Healthcare DES","Patient Movement Simulation"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Simulation, Process modeling","year":"1990","originator":"Operations research and management science","url":"https://scholargate.app/en/healthcare-management/patient-flow-simulation","markdownUrl":"https://scholargate.app/en/healthcare-management/patient-flow-simulation.md","definition":"Discrete Event Simulation (DES) is a computational technique that models the movement of patients through healthcare facilities by simulating individual patient journeys and interactions with resources (staff, beds, equipment). DES allows realistic representation of complex, stochastic healthcare processes and supports 'what-if' analysis without disrupting live operations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Operations research and management science","subfamily":"Simulation, Process modeling","year":"1990","type":"Discrete event simulation technique"},"citations":[{"ref":"Pidd, M. (1992). Computer Simulation in Management Science (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"9780471939314","url":null},{"ref":"Sokolowski, J. A., & Banks, C. M. (2009). Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modeling+and+Simulation+Fundamentals%3A+Theoretical+Underpinnings+and+Practical+Domains+Sokolowski"},{"ref":"Gunal, M. M., & Pidd, M. (2010). Discrete event simulation for performance modelling in health care: A review of the literature. Journal of Simulation, 4(1), 42–51.","type":"article","doi":"10.1057/jos.2009.25","isbn":null,"url":null}],"related":["queuing-theory-healthcare","hospital-bed-occupancy-model","lean-healthcare","six-sigma-healthcare","staffing-ratio-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-global-impression-change","name":"Patient Global Impression of Change","fullName":"Patient Global Impression of Change (PGIC)","aliases":["PGIC","Patient Global Impression of Change Scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"global-treatment-outcome","year":"1976","originator":"William Guy","url":"https://scholargate.app/en/clinical-psychology/patient-global-impression-change","markdownUrl":"https://scholargate.app/en/clinical-psychology/patient-global-impression-change.md","definition":"The Patient Global Impression of Change is a single-item, seven-point rating scale asking patients to report their overall impression of change since treatment initiation. Originally published by William Guy in the ECDEU Assessment Manual in 1976, the PGIC has become a standard co-primary endpoint in clinical trials assessing treatment efficacy. The scale is endorsed by the FDA as a patient-reported outcome measure for demonstrating clinical benefit. Despite its simplicity, the PGIC captures patients' holistic perception of improvement—integrating symptom reduction, functional recovery, and subjective well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Guy","subfamily":"global-treatment-outcome","year":"1976","type":"Self-report single-item rating"},"citations":[{"ref":"Guy, W. (1976). ECDEU Assessment Manual for Psychopharmacology. Rockville, MD: National Institute of Mental Health, US Department of Health, Education, and Welfare.","type":"book","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/25375819"},{"ref":"Farnik, M., & Pierzchala, W. (2012). Instruments to assess fatigue in neurology. Neurological Sciences, 33(5), 1015–1020.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Instruments+to+assess+fatigue+in+neurology+Farnik"},{"ref":"FDA. (2009). Guidance for Industry on Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. Center for Drug Evaluation and Research (CDER).","type":"article","doi":null,"isbn":null,"url":"https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-reported-outcome-measures-use-medical-product-development-support-labeling-claims"}],"related":["phq-9","quick-inventory-depressive","sheehan-disability-scale","clinical-global-impressions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-health-questionnaire-2","name":"Patient Health Questionnaire-2","fullName":"Patient Health Questionnaire-2 - Depression Screening","aliases":["PHQ-2","Patient Health Questionnaire"],"domain":"health-services","family":"process-pipeline","subfamily":"Major depressive disorder screening","year":"2003","originator":"Kurt Kroenke, Robert L. Spitzer, and Janet B. Williams","url":"https://scholargate.app/en/health-services/patient-health-questionnaire-2","markdownUrl":"https://scholargate.app/en/health-services/patient-health-questionnaire-2.md","definition":"The Patient Health Questionnaire-2 (PHQ-2) is an ultra-brief, validated two-item screening instrument developed by Kroenke and colleagues in 2003 to identify major depression in primary care and medical populations. The PHQ-2 assesses the two cardinal symptoms of depression (depressed mood and anhedonia) over the past two weeks using a 0-3 frequency scale. It is the shortest validated depression screening tool enabling routine use in time-constrained clinical settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kurt Kroenke, Robert L. Spitzer, and Janet B. Williams","subfamily":"Major depressive disorder screening","year":"2003","type":"Two-item depression screening instrument"},"citations":[{"ref":"Kroenke, K., Spitzer, R. L., & Williams, J. B. (2003). The Patient Health Questionnaire-2: validity of a two-item depression screener. Medical Care, 41(11), 1284-1292.","type":"article","doi":"10.1097/01.MLR.0000093487.78664.3C","isbn":null,"url":null},{"ref":"Spitzer, R. L., Kroenke, K., & Williams, J. B. (1999). Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA, 282(18), 1737-1744.","type":"article","doi":"10.1001/jama.282.18.1737","isbn":null,"url":null},{"ref":"Li, C., Friedman, B., Conwell, Y., & Fiscella, K. (2007). Validity of the Patient Health Questionnaire 2 (PHQ-2) in identifying major depression in older people. Journal of the American Geriatrics Society, 55(4), 596-602.","type":"article","doi":"10.1111/j.1532-5415.2007.01103.x","isbn":null,"url":null}],"related":["brief-pain-inventory","epworth-sleepiness-scale","dast-10"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-provider-cultural-sensitivity","name":"Patient-Provider Cultural Sensitivity Scale","fullName":"Patient-Provider Cultural Sensitivity Scale (PPCSS)","aliases":["PPCSS"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"patient-centeredness-cultural-assessment","year":2008,"originator":"Dogba, Foley","url":"https://scholargate.app/en/transcultural-nursing/patient-provider-cultural-sensitivity","markdownUrl":"https://scholargate.app/en/transcultural-nursing/patient-provider-cultural-sensitivity.md","definition":"The Patient-Provider Cultural Sensitivity Scale (PPCSS) is a measure designed to assess the degree to which healthcare providers demonstrate cultural sensitivity and respect in clinical encounters. The instrument evaluates provider behaviors and attitudes that honor patients' cultural identities, values, and preferences, including active listening, non-judgmental communication, incorporation of cultural health beliefs into care planning, and shared decision-making. The PPCSS can be completed by both patients (rating provider behavior) and providers (self-rating), making it a valuable tool for evaluating patient-centered, culturally responsive care.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dogba, Foley","subfamily":"patient-centeredness-cultural-assessment","year":2008,"type":"Self-report"},"citations":[{"ref":"Dogba, M. J., & Foley, R. (2008). Patient preferences for involvement in healthcare decision-making: A comparison of interprofessional joint decision-making and traditional medical approaches. Health Expectations, 11(1), 68–76.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Patient+preferences+for+involvement+in+healthcare+decision-making%3A+A+comparison+of+interprofessional+joint+decision-making+and+traditional+medical+approaches+Dogba"}],"related":["cultural-competence-assessment","cultural-humility-scale","patient-provider-cultural-sensitivity","racism-and-life-experiences-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-reported-communication-scale","name":"PRCS","fullName":"Patient-Reported Communication Scale","aliases":["PRCS Clinician Communication","Communication Quality Scale"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"clinician-communication","year":2009,"originator":"Marianne Haskard Zolnierek, Roxane Dimateo","url":"https://scholargate.app/en/patient-centered-care/patient-reported-communication-scale","markdownUrl":"https://scholargate.app/en/patient-centered-care/patient-reported-communication-scale.md","definition":"The Patient-Reported Communication Scale (PRCS) is a brief, validated instrument that measures patients' perceptions of clinician communication quality in healthcare encounters. Developed through meta-analytic research by Haskard Zolnierek and DiMatteo, the PRCS assesses key dimensions of effective patient-clinician communication: clarity of explanations, listening, showing respect and empathy, and addressing patient concerns. The scale is used to evaluate clinician communication competence, identify training needs, and correlate communication quality with patient adherence, satisfaction, and health outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marianne Haskard Zolnierek, Roxane Dimateo","subfamily":"clinician-communication","year":2009,"type":"Patient-reported"},"citations":[{"ref":"Haskard Zolnierek, K. B., & DiMatteo, M. R. (2009). Physician communication and patient adherence to treatment: a meta-analysis. Medical Care, 47(8), 826-834.","type":"article","doi":"10.1097/mlr.0b013e31819a5acc","isbn":null,"url":null},{"ref":"Street, R. L., Jr., Makoul, G., Arora, N. K., & Epstein, R. M. (2009). How does communication heal? Pathways linking clinician–patient communication to health outcomes. Patient Education and Counseling, 74(3), 295-301.","type":"article","doi":"10.1016/j.pec.2008.11.015","isbn":null,"url":null}],"related":["collaboste-scale","trust-in-physician-scale","patient-enablement-instrument","care-transitions-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-reported-experience-measure","name":"Patient Reported Experience Measure Framework","fullName":"Patient Reported Experience Measure (PREM) Framework","aliases":["PREM"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"patient-experience-framework","year":"2015","originator":"NHS England Quality Improvement and Health Quality Improvement Partnership, based on internationally recognized patient experience methodology","url":"https://scholargate.app/en/healthcare-management/patient-reported-experience-measure","markdownUrl":"https://scholargate.app/en/healthcare-management/patient-reported-experience-measure.md","definition":"The Patient Reported Experience Measure (PREM) framework is a methodological approach for systematically collecting, analyzing, and acting on patient feedback about healthcare experiences. Unlike HCAHPS, which is a specific, standardized survey, PREM is a flexible framework that can be adapted to different care settings, patient populations, and organizational contexts. PREM encompasses structured patient surveys, interviews, focus groups, and real-time feedback mechanisms, all aimed at capturing patient-centered perspectives on care quality, communication, responsiveness, and dignity. PREMs are used alongside Patient Reported Outcome Measures (PROMs, which assess health status changes) to provide a complete picture of care from the patient perspective.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"NHS England Quality Improvement and Health Quality Improvement Partnership, based on internationally recognized patient experience methodology","subfamily":"patient-experience-framework","year":"2015","type":"Self-report (patient-reported)"},"citations":[{"ref":"NHS England National Archives and Health Quality Improvement Partnership. (2019). Patient Reported Experience Measures (PREMs): A Resource for Commissioners. National Health Service, United Kingdom.","type":"report","doi":null,"isbn":null,"url":"https://www.england.nhs.uk/publication/patient-reported-experience-measures/"},{"ref":"Black, N., Barron, D., & Jenkinson, C. (2013). How to use patient experience surveys in practice. Journal of Health Services Research and Policy, 16(3), 173–177.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+to+use+patient+experience+surveys+in+practice+Black"},{"ref":"Gibbons, E., & Fitzpatrick, R. (2014). Recent developments in measuring outcomes in routine clinical practice. Journal of Health Services Research & Policy, 19(3), 177–182.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Recent+developments+in+measuring+outcomes+in+routine+clinical+practice+Gibbons"}],"related":["hospital-consumer-assessment","patient-safety-climate-scale","safety-attitudes-questionnaire","nurse-work-environment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-safety-climate-scale","name":"Patient Safety Climate in Healthcare Organizations","fullName":"Patient Safety Climate Scale (PSCS)","aliases":["PSCS"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"organizational-safety-culture","year":"2005","originator":"Colla, J. B., Bracken, A. C., Kinney, L. M., and colleagues","url":"https://scholargate.app/en/healthcare-management/patient-safety-climate-scale","markdownUrl":"https://scholargate.app/en/healthcare-management/patient-safety-climate-scale.md","definition":"The Patient Safety Climate Scale (PSCS) is a focused, brief assessment tool designed to measure staff perceptions of the safety climate within a specific healthcare unit or department. Unlike broader safety culture instruments, the PSCS concentrates on the immediate work environment—how safety is prioritized at the team and unit level, whether staff feel supported in reporting concerns, and whether leadership demonstrates commitment to preventing harm. The PSCS has been used in hospitals, ambulatory centers, and long-term care facilities to rapidly assess readiness for safety initiatives or to track improvements following targeted interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Colla, J. B., Bracken, A. C., Kinney, L. M., and colleagues","subfamily":"organizational-safety-culture","year":"2005","type":"Self-report"},"citations":[{"ref":"Blegen, M. A., Gearhart, S., O'Brien, R., Sehgal, N. L., & Alldredge, B. K. (2004). AHRQ's Hospital Survey on Patient Safety Culture: Psychometric analyses. Journal of Patient Safety, 5(3), 139–144.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=AHRQ%27s+Hospital+Survey+on+Patient+Safety+Culture%3A+Psychometric+analyses+Blegen"},{"ref":"Colla, J. B., Bracken, A. C., Kinney, L. M., & Weeks, W. B. (2005). Measuring patient safety climate: a review of surveys. Quality & Safety in Health Care, 14(5), 364–366.","type":"article","doi":"10.1136/qshc.2005.014217","isbn":null,"url":null},{"ref":"Merlo, J., Gerdtham, U. G., Lynch, J., Beckman, A., & Lithman, T. (2009). Inverse care law in an area with universal health coverage. Scandinavian Journal of Public Health, 34(3), 270–276.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Inverse+care+law+in+an+area+with+universal+health+coverage+Merlo"}],"related":["safety-attitudes-questionnaire","hospital-survey-patient-safety","nurse-work-environment-scale","healthcare-teamwork-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-safety-competence-scale","name":"PSCS","fullName":"Patient Safety Competence Self-Evaluation Scale","aliases":["Safety Competence Scale","Patient Safety Awareness","Safety Culture Assessment"],"domain":"health-education","family":"process-pipeline","subfamily":"patient-safety-education","year":"2012","originator":"Lachman et al.; adapted from Reason's error theory","url":"https://scholargate.app/en/health-education/patient-safety-competence-scale","markdownUrl":"https://scholargate.app/en/health-education/patient-safety-competence-scale.md","definition":"The PSCS is a self-report instrument measuring healthcare students' and professionals' self-perceived competence in patient safety practices, safety awareness, and safety culture engagement. Developed by Lachman and informed by James Reason's theoretical framework of human error and systems thinking, the PSCS evaluates the degree to which individuals understand safety principles, recognize hazards, report incidents, collaborate on safety issues, and contribute to a culture of safety. The scale is used in healthcare education and quality improvement to assess baseline safety competence, evaluate safety training effectiveness, and identify gaps in safety culture understanding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lachman et al.; adapted from Reason's error theory","subfamily":"patient-safety-education","year":"2012","type":"Self-assessment questionnaire"},"citations":[{"ref":"Reason, J. (2000). Human error: Models and management. BMJ 320(7237): 768–770.","type":"article","doi":"10.1136/bmj.320.7237.768","isbn":null,"url":null},{"ref":"Lachman, V. D. (2012). Patient and nurse safety: Culture of safety. Medsurg Nurs 21(6): 379–382.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/pubmed/23431685"}],"related":["nursing-clinical-competence-scale","clinical-learning-environment-scale","interprofessional-collaboration-scale","professional-identity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-satisfaction-cam","name":"Patient Satisfaction with CAM Scale","fullName":"Patient Satisfaction with Complementary and Alternative Medicine Scale","aliases":["PSCS","PSCS-CAM"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"Patient satisfaction with CAM services","year":"1998","originator":"Margolis, S. A.; Glassman, S.; Wicks, R.","url":"https://scholargate.app/en/integrative-medicine/patient-satisfaction-cam","markdownUrl":"https://scholargate.app/en/integrative-medicine/patient-satisfaction-cam.md","definition":"The PSCS is a patient-report instrument measuring satisfaction with complementary and alternative medicine services, including acupuncture, herbal medicine, massage, and other modalities. Developed by Margolis and colleagues in 1998, it captures dimensions of satisfaction specific to CAM practice—practitioner communication, efficacy expectations, cost concerns, and interpersonal warmth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Margolis, S. A.; Glassman, S.; Wicks, R.","subfamily":"Patient satisfaction with CAM services","year":"1998","type":"Self-report patient satisfaction scale"},"citations":[{"ref":"Margolis, S. A., Glassman, S., & Wicks, R. (1998). Measuring satisfaction of acupuncture and Chinese medicine patients using a newly developed patient satisfaction scale. Alternative Therapies in Health and Medicine, 4(4), 54–60.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Margolis%2C%20S.%20A.%2C%20Glassman%2C%20S.%2C%20%26%20Wicks%2C%20R.%20(1998).%20Measuring%20satisfaction%20of%20acupuncture%20and%20Chinese%20medicine%20patients%20u"},{"ref":"Garrow, D., & Egede, L. E. (2006). Association between complementary and alternative medicine use, preventive care practices, and health care utilization. Archives of Internal Medicine, 166(7), 713–718.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Association+between+complementary+and+alternative+medicine+use%2C+preventive+care+practices%2C+and+health+care+utilization+Garrow"}],"related":["cam-use-questionnaire","attitudes-cam-scale","integrative-medicine-attitudes","patient-satisfaction-cam"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-satisfaction-questionnaire","name":"Patient Satisfaction Questionnaire","fullName":"Patient Satisfaction Questionnaire - General","aliases":["PSQ","PSQ-18"],"domain":"health-services","family":"process-pipeline","subfamily":"Patient satisfaction and experience measurement","year":"1983","originator":"John E. Ware Jr. and Alvin Tarlov","url":"https://scholargate.app/en/health-services/patient-satisfaction-questionnaire","markdownUrl":"https://scholargate.app/en/health-services/patient-satisfaction-questionnaire.md","definition":"The Patient Satisfaction Questionnaire (PSQ) is a psychometrically validated self-report instrument developed by Ware and colleagues beginning in 1983 to measure patient satisfaction with medical care. The PSQ-18, a shortened version, comprises 18 items assessing general dimensions of healthcare satisfaction including accessibility, provider interaction, and trust.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John E. Ware Jr. and Alvin Tarlov","subfamily":"Patient satisfaction and experience measurement","year":"1983","type":"Multi-item satisfaction questionnaire"},"citations":[{"ref":"Ware, J. E., Snyder, M. K., Wright, W. R., & Davies, A. R. (1983). Defining and measuring patient satisfaction with medical care. Evaluation and Program Planning, 6(3-4), 247-263.","type":"article","doi":"10.1016/0149-7189(83)90005-8","isbn":null,"url":null},{"ref":"Marshall, G. N., Hays, R. D., Sherbourne, C. D., & Wells, K. B. (1993). The structure of patient satisfaction with outpatient medical care. Medical Care, 31(12), 1174-1187.","type":"article","doi":"10.1037//1040-3590.5.4.477","isbn":null,"url":null},{"ref":"Ware, J. E., Hays, R. D., & Marshall, G. N. (1996). Validation of a brief instrument measuring patient satisfaction with medical care. Medical Care, 34(2), 120-131.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validation+of+a+brief+instrument+measuring+patient+satisfaction+with+medical+care+Ware"}],"related":["cahps-survey","brief-pain-inventory","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-specific-functional-scale","name":"Patient-Specific Functional Scale","fullName":"Patient-Specific Functional Scale (PSFS)","aliases":["PSFS"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"patient-centered functional assessment","year":1995,"originator":"Paul W. Stratford, Gill Westaway, Colin Gill, Jill M. Binkley","url":"https://scholargate.app/en/sports-medicine/patient-specific-functional-scale","markdownUrl":"https://scholargate.app/en/sports-medicine/patient-specific-functional-scale.md","definition":"The Patient-Specific Functional Scale (PSFS) is a unique, individualized outcome instrument that captures patient-identified functional limitations and tracks change in those specific activities. Developed by Stratford and colleagues in 1995 and published in Physiotherapy Canada, the PSFS revolutionized patient-centered assessment by allowing each patient to identify and rate the three to five activities most important to them, rather than answering predetermined questions. This approach ensures relevance and maximizes the instrument's sensitivity to clinically meaningful change in patient-valued outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul W. Stratford, Gill Westaway, Colin Gill, Jill M. Binkley","subfamily":"patient-centered functional assessment","year":1995,"type":"Patient self-report"},"citations":[{"ref":"Stratford PW, Gill C, Westaway MD, Binkley JM. Assessing disability and change on individual patients: a report of a patient-specific measure. Physiother Can. 1995;47(4):258-263.","type":"article","doi":"10.3138/ptc.47.4.258","isbn":null,"url":null}],"related":["global-rating-of-change-scale","lower-extremity-functional-scale","ikdc-subjective-knee-form","faos"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"patient-therapist-agreement","name":"Patient-Therapist Agreement Scale","fullName":"Patient-Therapist Agreement Scale (PTAS)","aliases":["PTAS","Goal Agreement Scale"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"goal-agreement","year":"1965","originator":"Edward H. Nash, Robert Hoehn-Saric","url":"https://scholargate.app/en/psychotherapy-research/patient-therapist-agreement","markdownUrl":"https://scholargate.app/en/psychotherapy-research/patient-therapist-agreement.md","definition":"The Patient-Therapist Agreement Scale (PTAS) measures the degree to which client and therapist agree on therapy goals, treatment focus, and expected treatment duration—a core component of the therapeutic alliance. Developed by Nash and colleagues in their foundational study of psychotherapy preparation, the PTAS operationalizes the principle that shared understanding of 'what we're working on and how long it will take' predicts engagement and outcome. It is used primarily in research and training to assess goal alignment and identify mismatches that may undermine treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward H. Nash, Robert Hoehn-Saric","subfamily":"goal-agreement","year":"1965","type":"Client/Therapist-rated"},"citations":[{"ref":"Nash, E. H., Hoehn-Saric, R., Battle, C. C., Stone, A. R., Imber, S. D., & Frank, J. D. (1965). Systemic preparation of patients for psychotherapy: Effects on therapy behavior and outcome. Journal of Psychiatric Research, 2(4), 267–281.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Systemic+preparation+of+patients+for+psychotherapy%3A+Effects+on+therapy+behavior+and+outcome+Nash"},{"ref":"Gelso, C. J., & Carter, J. A. (1985). The relationship in counseling and psychotherapy: Components, consequences, and theoretical antecedents. The Counseling Psychologist, 13(2), 155–243.","type":"article","doi":"10.1177/0011000085132001","isbn":null,"url":null}],"related":["working-alliance-inventory","session-rating-scale","therapeutic-alliance-scale","outcome-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pavement-me-design","name":"Pavement ME Design","fullName":"Mechanistic-Empirical Pavement Design Guide","aliases":["MEPDG","Pavement design","Fatigue and rutting"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Transportation engineering","year":"2008","originator":"AASHTO (American Association of State Highway and Transportation Officials)","url":"https://scholargate.app/en/civil-engineering/pavement-me-design","markdownUrl":"https://scholargate.app/en/civil-engineering/pavement-me-design.md","definition":"The Mechanistic-Empirical Pavement Design Guide (MEPDG or Pavement ME) is a modern method for designing asphalt pavements that predicts performance (rutting, cracking) using mechanistic stress analysis combined with empirical distress models. Developed by AASHTO in 2008 as a successor to the 1993 AASHTO Empirical Guide, this approach provides better accuracy and enables climate-based, site-specific design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"AASHTO (American Association of State Highway and Transportation Officials)","subfamily":"Transportation engineering","year":"2008","type":"Performance-prediction model for asphalt pavement design"},"citations":[{"ref":"AASHTO (2008). Mechanistic-Empirical Pavement Design Guide: A Manual of Practice. American Association of State Highway and Transportation Officials.","type":"article","doi":null,"isbn":null,"url":"https://www.aashto.org"},{"ref":"Wang, D., Refsdal, G., & Creighton, A. (2010). Calibration of Pavement ME pavement performance equations at the project level. Transportation Research Record, 2153, 12-20.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Calibration+of+Pavement+ME+pavement+performance+equations+at+the+project+level+Wang"},{"ref":"American Institute of Asphalt Pavement Association (2015). Asphalt Pavement Design Guide. Report TIS-20(R18).","type":"article","doi":null,"isbn":null,"url":"https://www.asphaltpavement.org"}],"related":["terzaghi-consolidation","traffic-flow","unit-hydrograph"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pca-weight","name":"PCA-WEIGHT","fullName":"PCA Weighting — Principal Component Analysis based objective weighting","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"1901","originator":"Pearson, K.","url":"https://scholargate.app/en/decision-making/pca-weight","markdownUrl":"https://scholargate.app/en/decision-making/pca-weight.md","definition":"PCA-WEIGHT (PCA Weighting — Principal Component Analysis based objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pearson, K. in 1901. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pearson, K.","subfamily":"Weight_Objective","year":"1901","type":"Weight_Objective (PCA variance explained, eigenvector-based)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine","type":"article","doi":"10.1080/14786440109462720","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pca","name":"Principal Component Analysis","fullName":"Principal Component Analysis (PCA)","aliases":["Temel Bileşenler Analizi (PCA)","PCA","principal components analysis","Karhunen-Loève transform"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2002,"originator":"Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)","url":"https://scholargate.app/en/machine-learning/pca","markdownUrl":"https://scholargate.app/en/machine-learning/pca.md","definition":"Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)","year":2002,"type":"Unsupervised dimensionality reduction","task":"Dimensionality reduction & exploration","minSample":30},"citations":[{"ref":"Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.","type":"book","doi":"10.1007/b98835","isbn":null,"url":null}],"related":["kmeans-clustering","factor-analysis","lasso-regression","linear-regression","hierarchical-clustering"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pcl-5","name":"PTSD Checklist for DSM-5","fullName":"PTSD Checklist for DSM-5 (PCL-5)","aliases":["PCL-5","PTSD Checklist"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Trauma and PTSD assessment","year":"2013","originator":"Frank W. Weathers, Brett T. Litz, and Terence M. Keane","url":"https://scholargate.app/en/clinical-psychology/pcl-5","markdownUrl":"https://scholargate.app/en/clinical-psychology/pcl-5.md","definition":"The PTSD Checklist for DSM-5 (PCL-5) is a 20-item self-report measure of posttraumatic stress disorder (PTSD) symptoms aligned with DSM-5 diagnostic criteria. Developed by Weathers, Litz, and Keane, it is the gold standard screening and outcome measure for PTSD in military, veteran, and civilian trauma populations. The PCL-5 assesses four symptom clusters: re-experiencing, avoidance, negative alterations in cognition and mood, and hyperarousal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank W. Weathers, Brett T. Litz, and Terence M. Keane","subfamily":"Trauma and PTSD assessment","year":"2013","type":"Self-report PTSD symptom measure"},"citations":[{"ref":"Blevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K., & Domino, J. L. (2015). The Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation. Journal of Traumatic Stress, 28(6), 489-498.","type":"article","doi":"10.1002/jts.22059","isbn":null,"url":null},{"ref":"Weathers, F. W., Litz, B. T., Keane, T. M., Palmieri, P. A., Marx, B. P., & Schnurr, P. P. (2013). The PTSD Checklist for DSM-5 (PCL-5). National Center for PTSD.","type":"article","doi":null,"isbn":null,"url":"https://www.ptsd.va.gov/professional/assessment/adult-int/pcl5.asp"}],"related":["caps-5-ptsd","hamilton-anxiety-rating-scale","hads","dass-21","gds-geriatric-depression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pcl-military","name":"PTSD Checklist Military Version","fullName":"PTSD Checklist for DSM-IV (Military Version)","aliases":["PCL-M","PCL-Military"],"domain":"military-psychology","family":"process-pipeline","subfamily":"PTSD assessment","year":1993,"originator":"Weathers, Litz, Herman, Huska, & Keane","url":"https://scholargate.app/en/military-psychology/pcl-military","markdownUrl":"https://scholargate.app/en/military-psychology/pcl-military.md","definition":"The PCL-M is a 17-item self-report inventory measuring PTSD symptom severity in military personnel. Developed by Weathers and colleagues in 1993, it directly corresponds to DSM-IV diagnostic criteria. It is widely used in military, veteran, and trauma-exposed populations for screening and monitoring treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Weathers, Litz, Herman, Huska, & Keane","subfamily":"PTSD assessment","year":1993,"type":"Self-report"},"citations":[{"ref":"Weathers, F. W., Litz, B. T., Herman, D. S., Huska, J. A., & Keane, T. M. (1993). The PTSD Checklist (PCL): Reliability and diagnostic utility. Journal of Traumatic Stress, 6(4), 1-6.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+PTSD+Checklist+%28PCL%29%3A+Reliability+and+diagnostic+utility+Weathers"},{"ref":"Ruggiero, K. J., Ben, K. L., & Scotti, J. R. (2003). Knowledge, correct conditional probability, and choice of denominators in assessing children's understanding of AIDS transmission. Journal of Pediatric Psychology, 28(1), 45-52.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/12490632"},{"ref":"Blanchard, E. B., Jones-Alexander, J., Buckley, T. C., & Forneris, C. A. (1996). Psychometric properties of the PTSD Checklist (PCL). Behaviour Research and Therapy, 34(8), 669-673.","type":"article","doi":"10.1016/0005-7967(96)00033-2","isbn":null,"url":null}],"related":["combat-exposure-scale","moral-injury-events-scale","peritraumatic-distress-inventory","deployment-risk-resilience"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pcosq","name":"Polycystic Ovary Syndrome Questionnaire","fullName":"Polycystic Ovary Syndrome Questionnaire (PCOSQ)","aliases":["PCOSQ","PCOS Quality of Life"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"endocrine-reproductive-health","year":1998,"originator":"Cronin, L., Guyatt, G., Griffith, L., Wong, E., Azziz, R., Young, D., & Kjerulff, K.","url":"https://scholargate.app/en/obstetrics-gynecology/pcosq","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/pcosq.md","definition":"The Polycystic Ovary Syndrome Questionnaire (PCOSQ) is a 26-item self-report instrument developed to measure quality of life in women with polycystic ovary syndrome (PCOS). Created by Cronin and colleagues in 1998, the PCOSQ is disease-specific, assessing five dimensions uniquely relevant to PCOS: emotional effects, body hair, weight, infertility, and menstrual problems. It enables clinicians and researchers to evaluate the multidimensional impact of PCOS and monitor outcomes of lifestyle, hormonal, and reproductive interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cronin, L., Guyatt, G., Griffith, L., Wong, E., Azziz, R., Young, D., & Kjerulff, K.","subfamily":"endocrine-reproductive-health","year":1998,"type":"Self-report"},"citations":[{"ref":"Cronin, L., Guyatt, G., Griffith, L., Wong, E., Azziz, R., Young, D., & Kjerulff, K. (1998). Development of a health-related quality-of-life questionnaire (PCOSQ) for women with polycystic ovary syndrome (PCOS). The Journal of Clinical Epidemiology, 51(11), 1235-1246.","type":"article","doi":"10.1210/jcem.83.6.4990","isbn":null,"url":null}],"related":["menopause-specific-qol","female-pelvic-pain-scale","perinatal-anxiety-screening-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pdf-fitting","name":"PDF Fitting","fullName":"Parton Distribution Function Fitting","aliases":["PDF","structure function","parton model"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Hadron structure","year":"1969","originator":"James Bjorken and collaborators","url":"https://scholargate.app/en/particle-physics/pdf-fitting","markdownUrl":"https://scholargate.app/en/particle-physics/pdf-fitting.md","definition":"Parton Distribution Function (PDF) fitting is the process of determining the probability distributions of quarks and gluons inside hadrons using high-energy collision data. PDFs are fundamental inputs to all hadron collider phenomenology, essential for predicting cross-sections, designing triggers, and interpreting new physics searches at the Large Hadron Collider.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Bjorken and collaborators","subfamily":"Hadron structure","year":"1969","type":"QCD framework"},"citations":[{"ref":"Bjorken, J. D. (1969). Asymptotic sum rules at infinite momentum. Physical Review, 179(5), 1547.","type":"article","doi":"10.1103/PhysRev.179.1547","isbn":null,"url":null},{"ref":"Alekhin, S., et al. (2014). PDF4LHC recommendations for LHC Run II. The European Physical Journal C, 75(7), 304.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=PDF4LHC+recommendations+for+LHC+Run+II+Alekhin"},{"ref":"Bailey, S., et al. (2020). Parton distributions for the LHC Run II. The European Physical Journal C, 76(7), 391.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Parton+distributions+for+the+LHC+Run+II+Bailey"}],"related":["renormalization-group-equations","vegas-monte-carlo","matrix-element-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pdq-39","name":"PDQ-39","fullName":"Parkinson's Disease Questionnaire-39","aliases":["PDQ-39","Parkinson's Disease Questionnaire","PDQ"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Neurological Movement Disorders","year":"1997","originator":"Crispin Jenkinson et al.","url":"https://scholargate.app/en/health-outcomes/pdq-39","markdownUrl":"https://scholargate.app/en/health-outcomes/pdq-39.md","definition":"The PDQ-39 is the most widely used patient-reported outcome measure for Parkinson's disease quality of life. Developed by Crispin Jenkinson and colleagues in 1997, this 39-item self-report questionnaire comprehensively assesses how Parkinson's symptoms affect daily functioning, emotional well-being, stigma, social support, and cognitive-communication abilities. It is the recommended instrument in major Parkinson's disease clinical trials and forms a core component of outcome measurement in movement disorders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Crispin Jenkinson et al.","subfamily":"Neurological Movement Disorders","year":"1997","type":"Self-report quality of life questionnaire"},"citations":[{"ref":"Jenkinson, C., Fitzpatrick, R., Peto, V., Greenhall, R., & Hyman, N. (1997). The Parkinson's Disease Questionnaire (PDQ-39): Development and validation of a Parkinson's disease summary index score. Age and Ageing, 26(5), 353-357.","type":"article","doi":"10.1093/ageing/26.5.353","isbn":null,"url":null},{"ref":"Peto, V., Jenkinson, C., Fitzpatrick, R., & Greenhall, R. (1995). The development and validation of a short measure of functioning and well-being for individuals with Parkinson's disease. Quality of Life Research, 4(3), 241-248.","type":"article","doi":"10.1007/BF02260863","isbn":null,"url":null},{"ref":"Martínez-Martín, P., Rodríguez-Blázquez, C., Alvarez, M., Arakaki, T., Bergareche, A., Chade, A., ... & Grupo Centros Colaboradores de la Sociedad Española de Neurología para la validación de escalas en neurología. (2009). Expanded and independent validation of the Movement Disorder Society-sponsored unified Parkinson's disease rating scale (MDS-UPDRS). Journal of Parkinson's Disease, 3(3), 271-283.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/23938258"}],"related":["eortc-qlq-c30","dlqi","fibromyalgia-impact-questionnaire","chronic-heart-failure-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pearson-correlation","name":"Pearson Correlation","fullName":"Pearson Product-Moment Correlation Coefficient","aliases":["pearson r","product-moment correlation","bivariate correlation","Pearson Korelasyon Analizi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1895,"originator":"Karl Pearson","url":"https://scholargate.app/en/statistics/pearson-correlation","markdownUrl":"https://scholargate.app/en/statistics/pearson-correlation.md","definition":"The Pearson product-moment correlation coefficient (r) is a parametric measure of the direction and strength of the linear association between two continuous variables. Introduced by Karl Pearson in 1895, it remains the most widely used bivariate correlation statistic in the social, health, and natural sciences. The coefficient ranges from −1 (perfect negative linear relationship) to +1 (perfect positive), with 0 indicating no linear association.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson","year":1895,"family":"Hypothesis test","type":"Parametric correlation","variables":2,"outcome":"continuous","parametric":true,"outputRange":"[-1, 1]","effectSizeInterpretation":"|r| < 0.30 weak, 0.30–0.49 moderate, >= 0.50 strong"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":"10.4324/9780203771587","isbn":null,"url":null},{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null}],"related":["spearman-correlation","simple-linear-regression","kendall-tau","partial-correlation","multiple-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pediatric-migraine-qol","name":"PedMIDAS","fullName":"PedMIDAS: Pediatric Migraine Disability Assessment Scale","aliases":["Pediatric Migraine Disability Assessment","MIDAS","MIDAS Pediatric"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"headache/migraine pediatric functional disability","year":2001,"originator":"Amy D. Hershey","url":"https://scholargate.app/en/pediatric-medicine/pediatric-migraine-qol","markdownUrl":"https://scholargate.app/en/pediatric-medicine/pediatric-migraine-qol.md","definition":"The PedMIDAS is a brief 6-item parent-report (with child input for older youth) instrument developed by Hershey et al. in 2001 to quantify migraine-related functional disability in children and adolescents. Rather than measuring pain intensity or headache frequency, the PedMIDAS focuses on the ultimate impact of migraine on daily life: how many days of school, sports/play, and family activities does the child miss or participate in only with significant limitation? This outcome-focused approach makes it particularly useful for assessing the real-world burden of migraine in children and evaluating treatment effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Amy D. Hershey","subfamily":"headache/migraine pediatric functional disability","year":2001,"type":"Parent-report and/or child self-report of missed activities"},"citations":[{"ref":"Hershey, A. D., Powers, S. W., Vockell, A. L., LeCates, S., & Ellinwood, E. H. (2001). PedMIDAS: Development of a questionnaire to assess disabilities in migrainous children. Headache, 41(6), 556-563.","type":"article","doi":"10.1212/wnl.57.11.2034","isbn":null,"url":null},{"ref":"Powers, S. W., Patton, S. R., Hommel, K. A., & Hershey, A. D. (2004). Quality of life in pediatric migraine: clinical impact and comparison of generic and headache-specific instruments. Headache, 43(5), 499-508.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quality+of+life+in+pediatric+migraine%3A+clinical+impact+and+comparison+of+generic+and+headache-specific+instruments+Powers"}],"related":["paqlq","pedsql-diabetes","child-health-questionnaire","qolce"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pediatric-quality-of-life-pedsql","name":"PedsQL Pediatric Quality of Life","fullName":"Pediatric Quality of Life Inventory (PedsQL)","aliases":["PedsQL","PedsQL 4.0","PedsQL Generic Core Scales"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"Health-related quality of life","year":"2001","originator":"James Varni","url":"https://scholargate.app/en/developmental-assessment/pediatric-quality-of-life-pedsql","markdownUrl":"https://scholargate.app/en/developmental-assessment/pediatric-quality-of-life-pedsql.md","definition":"The Pediatric Quality of Life Inventory (PedsQL), developed by James Varni in 2001, is a validated, evidence-based patient-reported outcome measure assessing health-related quality of life in children and adolescents aged 2–18 years. Available in generic core scales and disease-specific modules, it measures physical, emotional, social, and school functioning, making it ideal for clinical research, outcome measurement, and population health surveillance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Varni","subfamily":"Health-related quality of life","year":"2001","type":"Pediatric health-related quality of life inventory"},"citations":[{"ref":"Varni, J. W., Seid, M., & Rode, C. A. (2001). The PedsQL: Measurement model for the pediatric quality of life inventory. Journal of Clinical Child & Adolescent Psychology, 30(2), 130-141.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+PedsQL%3A+Measurement+model+for+the+pediatric+quality+of+life+inventory+Varni"},{"ref":"Varni, J. W., Seid, M., & Kurtin, P. S. (2007). PedsQL 4.0: Reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales. Medical Care, 39(8), 800-812.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=PedsQL+4.0%3A+Reliability+and+validity+of+the+Pediatric+Quality+of+Life+Inventory+version+4.0+generic+core+scales+Varni"}],"related":["bayley-scales","ages-stages-questionnaire","cbcl-child-behavior","school-engagement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pediatric-sleep-questionnaire","name":"Pediatric Sleep Questionnaire","fullName":"Pediatric Sleep Questionnaire (PSQ)","aliases":["PSQ","PSQ-22"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"pediatric sleep medicine","year":"2000","originator":"Ronald Chervin","url":"https://scholargate.app/en/child-psychiatry/pediatric-sleep-questionnaire","markdownUrl":"https://scholargate.app/en/child-psychiatry/pediatric-sleep-questionnaire.md","definition":"The Pediatric Sleep Questionnaire (PSQ) is a 22–24 item parent-report screening tool for sleep-disordered breathing and associated daytime dysfunction in children ages 2–18 years. Developed by Ronald Chervin at the University of Michigan in 2000, the PSQ measures three domains: symptoms of obstructive sleep apnea (snoring, witnessed apneas, gasping), daytime sleepiness and behavioral consequences, and sleep behavior problems (parasomnias, restlessness). It is widely used in pediatric primary care, ENT, and sleep medicine settings to identify children at risk for clinically significant sleep disorders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Chervin","subfamily":"pediatric sleep medicine","year":"2000","type":"Parent-report screening questionnaire"},"citations":[{"ref":"Chervin, R. D., Hedger, K., Dillon, J. E., & Pituch, K. J. (2000). Pediatric sleep questionnaire (PSQ): Validity and reliability of scales for sleep-disordered breathing, snoring, sleepiness, and sleep behavior. Sleep Medicine, 1(1), 21–32.","type":"article","doi":"10.1016/S1389-9457(99)00009-X","isbn":null,"url":null},{"ref":"Chervin, R. D., Weatherly, R. A., Garetz, S. L., Ruzicka, D. L., Giordani, B. J., Hodges, E. K., . . . Dillon, J. E. (2007). Pediatric sleep questionnaire: Prediction of sleep apnea and outcomes. Archives of Otolaryngology–Head & Neck Surgery, 133(3), 216–222.","type":"article","doi":"10.1001/archotol.133.3.216","isbn":null,"url":null}],"related":["child-depression-inventory","emotion-regulation-questionnaire-child","revised-childrens-anxiety-depression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pedogenesis-model","name":"Pedogenesis Modeling","fullName":"Pedogenesis Modeling: Quantitative Simulation of Soil Formation Processes","aliases":["soil formation modeling","soil genesis simulation","pedogenic process modeling","quantitative pedology"],"domain":"agronomy","family":"process-pipeline","subfamily":"Quantitative pedology and soil systems modeling","year":"1941 (Jenny's factorial model); process-based numerical models from 1990s onward","originator":"Hans Jenny (foundational framework); later extended by multiple contributors including Simonson, Hoosbeek, and Bryant","url":"https://scholargate.app/en/agronomy/pedogenesis-model","markdownUrl":"https://scholargate.app/en/agronomy/pedogenesis-model.md","definition":"Pedogenesis modeling is a quantitative method used in agronomy and soil science to simulate the processes by which soils form and evolve over time. Rooted in Hans Jenny's 1941 factorial framework — soil as a function of climate, organisms, relief, parent material, and time — modern approaches translate these conceptual drivers into coupled numerical process equations, allowing researchers to reconstruct past soil states and project future soil properties under changing land use or climate scenarios.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans Jenny (foundational framework); later extended by multiple contributors including Simonson, Hoosbeek, and Bryant","year":"1941 (Jenny's factorial model); process-based numerical models from 1990s onward","type":"Quantitative process-based simulation model","dataType":"Soil profile data, climate records, topographic data, parent material properties, vegetation/land-use data","subfamily":"Quantitative pedology and soil systems modeling"},"citations":[{"ref":"Minasny, B., Finke, P., Stockmann, U., Vanwalleghem, T., & McBratney, A. B. (2015). Resolving the integral connection between pedogenesis and landscape evolution. Earth-Science Reviews, 150, 102–120.","type":"article","doi":"10.1016/j.earscirev.2015.07.004","isbn":null,"url":null},{"ref":"Jenny, H. (1941). Factors of Soil Formation: A System of Quantitative Pedology. McGraw-Hill, New York.","type":"book","doi":null,"isbn":null,"url":"https://www.biodiversitylibrary.org/item/148555"}],"related":["soil-water-balance","watershed-hydrology-model","land-use-change-analysis","digital-soil-mapping","erosion-model","carbon-sequestration-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pedsql-cancer","name":"PedsQL Cancer Module","fullName":"Pediatric Quality of Life Inventory—Cancer Module","aliases":["PedsQL 3.0 Cancer"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"cancer-specific pediatric quality of life","year":2002,"originator":"James W. Varni","url":"https://scholargate.app/en/pediatric-medicine/pedsql-cancer","markdownUrl":"https://scholargate.app/en/pediatric-medicine/pedsql-cancer.md","definition":"The PedsQL Cancer Module is a 31-item disease-specific instrument developed by Varni et al. in 2002 to measure quality of life in children and adolescents with cancer aged 2–18 years. It captures treatment burden (nausea, vomiting, pain, hair loss), cancer-related worry, cognitive concerns, and emotional and social impacts of diagnosis and treatment. Used alongside the PedsQL Generic Core Scales, it provides comprehensive assessment of both cancer-specific and general health-related quality of life during active treatment, survivorship, and end-of-life care.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James W. Varni","subfamily":"cancer-specific pediatric quality of life","year":2002,"type":"Child self-report and parent proxy"},"citations":[{"ref":"Varni, J. W., Burwinkle, T. M., Katz, E. R., Meeske, K., & Dickinson, R. P. (2002). The PedsQL in pediatric cancer: Reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales, multidimensional fatigue scale, and cancer module. Cancer, 94(7), 2090-2106.","type":"article","doi":"10.1037/t70689-000","isbn":null,"url":null},{"ref":"Varni, J. W., Seid, M., & Rode, C. A. (2000). The PedsQL: Measurement model for the Pediatric Quality of Life Inventory. Medical Care, 37(2), 126-139.","type":"article","doi":"10.1097/00005650-199902000-00003","isbn":null,"url":null}],"related":["pedsql-diabetes","paqlq","qolce","pedsql-cardiac","pedsql-sickle-cell"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pedsql-cardiac","name":"PedsQL Cardiac Module","fullName":"Pediatric Quality of Life Inventory—Cardiac Module","aliases":["PedsQL 3.0 Cardiac"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"cardiac-specific pediatric quality of life","year":2005,"originator":"James W. Varni","url":"https://scholargate.app/en/pediatric-medicine/pedsql-cardiac","markdownUrl":"https://scholargate.app/en/pediatric-medicine/pedsql-cardiac.md","definition":"The PedsQL Cardiac Module is a disease-specific instrument developed by Varni et al. in the mid-2000s to measure quality of life in children and adolescents with cardiac disease aged 2–18 years. Measuring across domains including cardiac symptom impact, activity limitations, and cardiac-related worry, it captures how congenital heart disease, acquired heart disease, and cardiac treatment affect daily functioning and well-being. Used alongside the PedsQL Generic Core Scales, it provides comprehensive assessment of cardiac-specific and general health-related quality of life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James W. Varni","subfamily":"cardiac-specific pediatric quality of life","year":2005,"type":"Child self-report and parent proxy"},"citations":[{"ref":"Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). The PedsQL as a pediatric patient-reported outcome: Reliability and validity of the PedsQL Generic Core Scales and PedsQL 4.0 Multidimensional Fatigue Scale in a National US Sample of Children Reported Health. Journal of Clinical Psychology in Medical Settings, 14(3), 206-214.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+PedsQL+as+a+pediatric+patient-reported+outcome%3A+Reliability+and+validity+of+the+PedsQL+Generic+Core+Scales+and+PedsQL+4.0+Multidimensional+Fatigue+Scale+in+a+National+US+Sample+of+Children+Reporte"},{"ref":"Uzark, K., Jones, K., Slusher, J., Limbers, C. A., Burwinkle, T. M., & Varni, J. W. (2012). Quality of life in children and adolescents with heart disease. Congenital Heart Disease, 7(1), 17-25.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quality+of+life+in+children+and+adolescents+with+heart+disease+Uzark"}],"related":["pedsql-diabetes","pedsql-cancer","paqlq","pedsql-sickle-cell"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pedsql-diabetes","name":"PedsQL Diabetes Module","fullName":"Pediatric Quality of Life Inventory—Diabetes Module","aliases":["PedsQL 3.0 Diabetes"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"diabetes-specific pediatric QoL","year":2003,"originator":"James W. Varni","url":"https://scholargate.app/en/pediatric-medicine/pedsql-diabetes","markdownUrl":"https://scholargate.app/en/pediatric-medicine/pedsql-diabetes.md","definition":"The PedsQL Diabetes Module is a 28-item disease-specific instrument developed by Varni et al. in 2003 to measure quality of life in children and adolescents with type 1 and type 2 diabetes. It captures the impact of diabetes management, glucose monitoring, and disease-related worry on daily functioning. The module is paired with the PedsQL Generic Core Scales, enabling both disease-specific and general health-related quality of life assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James W. Varni","subfamily":"diabetes-specific pediatric QoL","year":2003,"type":"Child self-report and parent proxy"},"citations":[{"ref":"Varni, J. W., Burwinkle, T. M., Jacobs, J. R., Gottschalk, M., Kaufman, F., & Jones, K. L. (2003). The PedsQL in type 1 and type 2 diabetes: Reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales and type 1 Diabetes Module. Diabetes Care, 26(3), 631-637.","type":"article","doi":"10.2337/diacare.26.3.631","isbn":null,"url":null},{"ref":"Varni, J. W., Seid, M., & Rode, C. A. (2000). The PedsQL: Measurement model for the Pediatric Quality of Life Inventory. Medical Care, 37(2), 126-139.","type":"article","doi":"10.1097/00005650-199902000-00003","isbn":null,"url":null}],"related":["paqlq","qolce","pedsql-cancer","pedsql-cardiac","pedsql-sickle-cell"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pedsql-sickle-cell","name":"PedsQL Sickle Cell Module","fullName":"Pediatric Quality of Life Inventory—Sickle Cell Disease Module","aliases":["PedsQL 3.0 Sickle Cell"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"hematologic disease pediatric quality of life","year":2012,"originator":"James W. Varni","url":"https://scholargate.app/en/pediatric-medicine/pedsql-sickle-cell","markdownUrl":"https://scholargate.app/en/pediatric-medicine/pedsql-sickle-cell.md","definition":"The PedsQL Sickle Cell Disease Module is a disease-specific instrument developed by Varni et al. in 2012 to measure quality of life in children and adolescents with sickle cell disease aged 2–18 years. Measuring across domains including pain and symptoms, functional limitations, school impact, and disease-related worry, it captures how sickle cell disease and its complications affect daily life and well-being. Used alongside the PedsQL Generic Core Scales, it provides comprehensive assessment of sickle cell disease-specific and general health-related quality of life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James W. Varni","subfamily":"hematologic disease pediatric quality of life","year":2012,"type":"Child self-report and parent proxy"},"citations":[{"ref":"Varni, J. W., Limbers, C. A., Bryant, W. P., & Wilson, D. P. (2012). The PedsQL in pediatric sickle cell disease: Measurement model, factor structure, and reliability and validity of the pediatric quality of life inventory sickle cell disease module. Journal of Health Psychology, 17(7), 1012-1024.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+PedsQL+in+pediatric+sickle+cell+disease%3A+Measurement+model%2C+factor+structure%2C+and+reliability+and+validity+of+the+pediatric+quality+of+life+inventory+sickle+cell+disease+module+Varni"},{"ref":"Klingel, M., Kamps, R., Douwes Dekker, H. M., Grobbee, D. E., Hartman, A., & Moll, A. C. (2012). Sickle cell disease and quality of life. International Journal of Environmental Research and Public Health, 15(8), 1638.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sickle+cell+disease+and+quality+of+life+Klingel"}],"related":["pedsql-diabetes","pedsql-cancer","pedsql-cardiac","paqlq"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"peer-learning-scale","name":"Peer Learning Scale","fullName":"Peer Learning Scale (PLS)","aliases":["PLS"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"collaborative-learning-impact","year":"2000s","originator":"Various; based on collaborative learning theory","url":"https://scholargate.app/en/educational-psychology/peer-learning-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/peer-learning-scale.md","definition":"The Peer Learning Scale measures the extent and quality of collaborative learning experiences among students, capturing the frequency of peer interaction, perceived support from peers, quality of peer feedback, and learning gains from collaboration. Grounded in social-constructivist theory and decades of research on collaborative learning, the PLS assesses a critical dimension of the modern learning environment: peer interaction is not incidental but a core mechanism of learning through explanation, feedback, and distributed cognition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various; based on collaborative learning theory","subfamily":"collaborative-learning-impact","year":"2000s","type":"Self-report questionnaire or observation"},"citations":[{"ref":"Topping, K. J. (2009). Peer assessment. Theory into Practice, 48(1), 20–27.","type":"article","doi":"10.1080/00405840802577569","isbn":null,"url":null},{"ref":"Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=peer+learning+collaborative+learning"}],"related":["classroom-environment-scale","academic-resilience-scale","study-skills-assessment","academic-help-seeking-scale","university-student-satisfaction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"peer-review-process","name":"Peer Review Process","fullName":"Peer Review Process in Academic Publishing","aliases":["Peer Reviewing","Manuscript Evaluation","Scholarly Review"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"publication-process","year":"1665","originator":"Scientific publishing community; formalized by journals and COPE","url":"https://scholargate.app/en/publication-ethics/peer-review-process","markdownUrl":"https://scholargate.app/en/publication-ethics/peer-review-process.md","definition":"Peer review is the process by which manuscripts are evaluated by experts in the same field before publication in academic journals. Reviewers assess the manuscript's scientific merit, methodology, clarity, and contribution to the field. Established in 1665 with the first scientific journal (Philosophical Transactions of the Royal Society), peer review remains the gold standard for quality control in academic publishing. Despite ongoing criticism and proposals for alternatives, peer review continues to filter low-quality and unethical work, though it is imperfect and sometimes slow.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scientific publishing community; formalized by journals and COPE","subfamily":"publication-process","year":"1665","type":"Process"},"citations":[{"ref":"Committee on Publication Ethics (2023). COPE Guidelines: Ethical Guidelines for Peer Reviewers. COPE.","type":"webpage","doi":null,"isbn":null,"url":"https://publicationethics.org/"},{"ref":"Shamoo, A. E., & Resnik, D. B. (2009). Responsible Conduct of Research (2nd ed.). Oxford University Press.","type":"article","doi":"10.1093/acprof:oso/9780195368246.001.0001","isbn":null,"url":null},{"ref":"Bailey, J. H., Mehlman, M. J., Rath, D. P., & Garrison, R. H. (2006). Perspectives on Peer Review. Journal of Scholarly Publishing, 37(2), 120–135.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Perspectives+on+Peer+Review+Bailey"}],"related":["icmje-authorship-criteria","plagiarism-in-research","cope-guidelines","retraction-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pelvic-floor-distress-inventory","name":"Pelvic Floor Distress Inventory","fullName":"Pelvic Floor Distress Inventory (PFDI)","aliases":["PFDI","PFDI-20"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"pelvic-floor-disorder","year":2001,"originator":"Barber et al.","url":"https://scholargate.app/en/urology-gynecology/pelvic-floor-distress-inventory","markdownUrl":"https://scholargate.app/en/urology-gynecology/pelvic-floor-distress-inventory.md","definition":"The PFDI is a condition-specific quality-of-life measure designed to assess symptom distress across the spectrum of pelvic floor disorders, including urinary incontinence, pelvic organ prolapse, and fecal incontinence. Originally published by Barber and colleagues in 2001 with 93 items, the 20-item short form (PFDI-20) was later developed to improve clinical feasibility while maintaining measurement precision. It is now the standard outcome measure in pelvic floor disorder research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barber et al.","subfamily":"pelvic-floor-disorder","year":2001,"type":"Condition-specific quality-of-life questionnaire"},"citations":[{"ref":"Barber, M. D., Kuchibhatla, M. N., Pieper, C. F., & Bump, R. C. (2001). Psychometric evaluation of 2 comprehensive condition-specific quality of life instruments for women with pelvic floor disorders. American Journal of Obstetrics and Gynecology, 185(6), 1388–1395.","type":"article","doi":"10.1067/mob.2001.118659","isbn":null,"url":null},{"ref":"Barber, M. D., Walters, M. D., & Bump, R. C. (2005). Short forms of two condition-specific quality-of-life questionnaires for women with pelvic floor disorders (PFDI-20 and PFIQ-7). American Journal of Obstetrics and Gynecology, 193(1), 103–113.","type":"article","doi":"10.1016/j.ajog.2004.12.025","isbn":null,"url":null}],"related":["iciq-urinary-incontinence","overactive-bladder-questionnaire","female-sexual-function-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"penetration-testing-methodology","name":"Penetration Testing Methodology","fullName":"Systematic Penetration Testing Framework and Exploitation Methodology","aliases":["Pen Testing","Ethical Hacking","Security Testing"],"domain":"cryptography","family":"process-pipeline","subfamily":"Security testing and attack simulation","year":"2008","originator":"National Institute of Standards and Technology (NIST), OWASP","url":"https://scholargate.app/en/cryptography/penetration-testing-methodology","markdownUrl":"https://scholargate.app/en/cryptography/penetration-testing-methodology.md","definition":"Penetration testing is an authorized, controlled simulated attack on systems, networks, and applications to evaluate their security defenses. Unlike vulnerability assessment (which identifies weaknesses), penetration testing actively exploits vulnerabilities to demonstrate real-world impact, confirm exploitability, and assess an organization's incident response capabilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"National Institute of Standards and Technology (NIST), OWASP","subfamily":"Security testing and attack simulation","year":"2008","type":"Authorized security exploit and assessment"},"citations":[{"ref":"National Institute of Standards and Technology (2008). Penetration Testing and Security Testing. NIST Special Publication 800-115.","type":"report","doi":null,"isbn":null,"url":"https://csrc.nist.gov/publications/detail/sp/800-115/final"},{"ref":"OWASP (2023). OWASP Testing Guide v4.2. OWASP Foundation.","type":"report","doi":null,"isbn":null,"url":"https://owasp.org/www-project-web-security-testing-guide"},{"ref":"Tenable (2023). Nessus Professional: Automated Vulnerability Assessment and Exploitation. Technical Report.","type":"article","doi":null,"isbn":null,"url":"https://www.tenable.com"}],"related":["vulnerability-assessment","intrusion-detection-system","tls-protocol-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"peng-robinson-equation-of-state","name":"Peng-Robinson Equation of State","fullName":"Peng-Robinson Cubic Equation of State for Fluids","aliases":["PR-EOS","Peng-Robinson model"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Thermodynamic Modeling","year":"1976","originator":"Ding-Yu Peng and David Bernard Robinson","url":"https://scholargate.app/en/applied-physics/peng-robinson-equation-of-state","markdownUrl":"https://scholargate.app/en/applied-physics/peng-robinson-equation-of-state.md","definition":"The Peng-Robinson equation of state is a cubic model that describes the thermodynamic properties of pure fluids and mixtures. Introduced by Ding-Yu Peng and David Bernard Robinson in 1976, it improves upon earlier models (van der Waals, Redlich-Kwong) by better predicting compressibility factors and phase equilibria, especially near the critical point. It is widely used in petroleum engineering, chemical process design, and natural gas calculations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ding-Yu Peng and David Bernard Robinson","subfamily":"Thermodynamic Modeling","year":"1976","type":"Equation of state; thermodynamic property correlation"},"citations":[{"ref":"Peng, D. Y., & Robinson, D. B. (1976). A new two-constant equation of state. Industrial & Engineering Chemistry Fundamentals, 15(1), 59-64.","type":"article","doi":"10.1021/i160057a011","isbn":null,"url":null},{"ref":"Reid, R. C., Prausnitz, J. M., & Sherwood, T. K. (1987). The Properties of Gases and Liquids (4th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0-07-051798-8","url":null},{"ref":"Soave, G. (1972). Equilibrium constants from a modified Redlich-Kwong equation of state. Chemical Engineering Science, 27(6), 1197-1203.","type":"article","doi":"10.1016/0009-2509(72)80096-4","isbn":null,"url":null}],"related":["unifac","pinch-analysis","cstr-model","pfr-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"penman-monteith-equation","name":"Penman-Monteith Equation","fullName":"Penman-Monteith Equation for Evapotranspiration","aliases":["PM Equation","FAO-56 PM","Evapotranspiration Model"],"domain":"agronomy","family":"process-pipeline","subfamily":"Boundary Layer Biophysics","year":"1948-1965","originator":"Howard Latimer Penman, John Monteith","url":"https://scholargate.app/en/agronomy/penman-monteith-equation","markdownUrl":"https://scholargate.app/en/agronomy/penman-monteith-equation.md","definition":"The Penman-Monteith equation is a mechanistic model for estimating evapotranspiration (ET), the combined loss of water from soil and plant canopies to the atmosphere. First proposed by Penman (1948) for bare soil and water surfaces, then extended by Monteith (1965) to incorporate plant resistance to water vapor diffusion, it has become the international standard for water balance studies, crop water requirement calculation, and hydrological modeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Howard Latimer Penman, John Monteith","subfamily":"Boundary Layer Biophysics","year":"1948-1965","type":"Mechanistic evapotranspiration model"},"citations":[{"ref":"Penman, H. L. (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society A, 193(1032), 120-145.","type":"article","doi":"10.1098/rspa.1948.0037","isbn":null,"url":null},{"ref":"Monteith, J. L. (1965). Evaporation and environment. Symposia of the Society for Experimental Biology, 19, 205-234.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/j.ctt1h9t8h6"},{"ref":"Allen, R. G., Pereira, L. S., Raes, D., Smith, M., & Hargreaves, G. H. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56, Rome: FAO.","type":"article","doi":null,"isbn":null,"url":"http://www.fao.org/3/X0490E/x0490e00.htm"}],"related":["crop-growth-model","soil-moisture-curve","canopy-interception"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"penn-state-worry-questionnaire","name":"Penn State Worry Questionnaire","fullName":"Penn State Worry Questionnaire","aliases":["PSWQ"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"worry-specific assessment","year":"1990","originator":"Thomas J. Meyer, Marta L. Miller, Robin L. Metzger, Thomas D. Borkovec","url":"https://scholargate.app/en/clinical-psychology/penn-state-worry-questionnaire","markdownUrl":"https://scholargate.app/en/clinical-psychology/penn-state-worry-questionnaire.md","definition":"The Penn State Worry Questionnaire (PSWQ) is a 16-item self-report instrument specifically designed to measure the trait dimension of worry—the tendency to worry excessively across situations. Developed by Meyer, Miller, Metzger, and Borkovec in 1990, the PSWQ has become the standard instrument for assessing worry as a transdiagnostic symptom dimension in clinical and research settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas J. Meyer, Marta L. Miller, Robin L. Metzger, Thomas D. Borkovec","subfamily":"worry-specific assessment","year":"1990","type":"Self-report worry questionnaire"},"citations":[{"ref":"Meyer, T. J., Miller, M. L., Metzger, R. L., & Borkovec, T. D. (1990). Development and validation of the Penn State Worry Questionnaire. Behaviour Research and Therapy, 28(6), 487-495.","type":"article","doi":"10.1016/0005-7967(90)90135-6","isbn":null,"url":null}],"related":["gad-7","state-trait-anxiety-inventory","beck-anxiety-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"perceived-organizational-readiness","name":"PORAS","fullName":"Perceived Organizational Readiness for Assisting the System","aliases":["PORAS","Perceived Organizational Readiness","Perceived Readiness Scale"],"domain":"implementation-science","family":"process-pipeline","subfamily":"organizational assessment","year":2009,"originator":"Christopher D. Helfrich, PhD; Ying-Fang Li, PhD; Neil D. Sharp, MD; colleagues at Veterans Affairs and University of Washington","url":"https://scholargate.app/en/implementation-science/perceived-organizational-readiness","markdownUrl":"https://scholargate.app/en/implementation-science/perceived-organizational-readiness.md","definition":"The Perceived Organizational Readiness for Assisting the System (PORAS) is a 19-item self-report measure developed by Helfrich and colleagues to assess organizational readiness to implement health information technology systems and other healthcare innovations. Grounded in Weiner's theory of organizational readiness for change, the PORAS measures four dimensions of readiness: Valence (perceived importance of the change to the organization), Motivation (organizational commitment and drive to implement), Resource Adequacy (availability of financial, human, and technical resources), and Change Efficacy (staff belief in organizational capability to successfully implement). While originally developed for health IT implementation, the PORAS framework and scale are applicable to broader healthcare innovations and evidence-based practice implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher D. Helfrich, PhD; Ying-Fang Li, PhD; Neil D. Sharp, MD; colleagues at Veterans Affairs and University of Washington","subfamily":"organizational assessment","year":2009,"type":"Self-report organizational survey"},"citations":[{"ref":"Helfrich, C. D., Li, Y. F., Sharp, N. D., & Sales, A. E. (2009). Organizational readiness to change assessment (ORCA): Development of an instrument based on the perspectives of health care professionals. Journal of the American Medical Informatics Association, 16(4), 523–530.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Organizational+readiness+to+change+assessment+%28ORCA%29%3A+Development+of+an+instrument+based+on+the+perspectives+of+health+care+professionals+Helfrich"},{"ref":"Weiner, B. J. (2009). A theory of organizational readiness for change. Implementation Science, 4, 67.","type":"article","doi":"10.1186/1748-5908-4-67","isbn":null,"url":null}],"related":["organisational-readiness-change","evidence-based-practice-attitude","implementation-climate-scale","implementation-leadership-scale","knowledge-to-action-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"perceived-organizational-support","name":"Perceived Organizational Support Scale","fullName":"Perceived Organizational Support Scale (POSS)","aliases":["POSS","POS Scale","Eisenberger Organizational Support"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"organizational-climate","year":"1986","originator":"Robert Eisenberger","url":"https://scholargate.app/en/organizational-behavior/perceived-organizational-support","markdownUrl":"https://scholargate.app/en/organizational-behavior/perceived-organizational-support.md","definition":"The Perceived Organizational Support Scale (POSS) measures employees' beliefs about the degree to which their employing organization values their contributions and cares about their well-being. Developed by Eisenberger and colleagues in 1986, it is a foundational construct in organizational psychology that predicts employee engagement, commitment, and performance. The scale is grounded in social exchange theory and reciprocity norms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Eisenberger","subfamily":"organizational-climate","year":"1986","type":"Self-report questionnaire"},"citations":[{"ref":"Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71(3), 500–507.","type":"article","doi":"10.1037/0021-9010.71.3.500","isbn":null,"url":null},{"ref":"Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: A review of the literature. Journal of Applied Psychology, 87(4), 698–714.","type":"article","doi":"10.1037/0021-9010.87.4.698","isbn":null,"url":null},{"ref":"Caesens, G., & Stinglhamber, F. (2014). The relationship between perceived organizational support and work engagement: The role of self-efficacy and its outcomes. European Review of Applied Psychology, 64(5), 259–267.","type":"article","doi":"10.1016/j.erap.2014.08.002","isbn":null,"url":null}],"related":["leader-member-exchange-scale","psychological-capital-questionnaire","organizational-commitment-questionnaire","proactive-personality-scale","occupational-stress-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"perceived-stress-reactivity-scale","name":"Perceived Stress Reactivity Scale","fullName":"Perceived Stress Reactivity Scale (PSRS)","aliases":["PSRS","Stress Reactivity Scale"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"Individual differences in stress responsiveness and vulnerability","year":"2009","originator":"Paul L. Hewitt & Gordon L. Flett","url":"https://scholargate.app/en/trauma-psychology/perceived-stress-reactivity-scale","markdownUrl":"https://scholargate.app/en/trauma-psychology/perceived-stress-reactivity-scale.md","definition":"The PSRS is an 8-item self-report scale measuring individual differences in perceived reactivity to stressful situations—the subjective sense of being easily stressed, emotionally reactive, or overwhelmed by demands. Developed by Hewitt and colleagues in the context of perfectionism and stress research, the PSRS captures a trait-like tendency toward heightened stress reactivity, often termed stress sensitivity or emotional reactivity. The scale is used in clinical and research settings to identify individuals at risk for stress-related psychopathology and to measure changes in stress responsiveness over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul L. Hewitt & Gordon L. Flett","subfamily":"Individual differences in stress responsiveness and vulnerability","year":"2009","type":"Self-report questionnaire"},"citations":[{"ref":"Hewitt, P. L., Flett, G. L., Mikail, S. F., & Singh, R. (2016). Perfectionism and stress processes in psychopathology. In G. L. Flett & P. L. Hewitt (Eds.), Perfectionism and psychological distress (pp. 255-284). Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Perfectionism+and+stress+processes+in+psychopathology+Hewitt"},{"ref":"Eisler, I. (2009). The empirical and theoretical base of family therapy and multiple family day treatment for adolescent anorexia nervosa. Journal of Family Therapy, 17(3), 353-372.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+empirical+and+theoretical+base+of+family+therapy+and+multiple+family+day+treatment+for+adolescent+anorexia+nervosa+Eisler"}],"related":["impact-of-event-scale-revised","secondary-traumatic-stress-scale","burnout-assessment-tool"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"perceived-stress-scale","name":"Perceived Stress Scale","fullName":"Perceived Stress Scale (PSS)","aliases":["PSS"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Occupational health","year":"1983","originator":"Sheldon Cohen, Tom Kamarck, and Robin Mermelstein","url":"https://scholargate.app/en/organizational-behavior/perceived-stress-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/perceived-stress-scale.md","definition":"The Perceived Stress Scale (PSS), developed by Cohen, Kamarck, and Mermelstein in 1983, is the most widely used measure of subjective stress in research and clinical practice. Available in 10-item (PSS-10) and 14-item (PSS-14) versions, the PSS assesses the degree to which individuals perceive situations as unpredictable, uncontrollable, and overwhelming. The scale captures stress as a result of how people interpret and react to life events rather than the events themselves.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sheldon Cohen, Tom Kamarck, and Robin Mermelstein","subfamily":"Occupational health","year":"1983","type":"Self-report questionnaire"},"citations":[{"ref":"Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385-396.","type":"article","doi":"10.2307/2136404","isbn":null,"url":null},{"ref":"Cohen, S., & Williamson, G. (1994). Perceived stress in a probability sample of the United States. In S. Spacapan & S. Oskamp (Eds.), The social psychology of health: Claremont Symposium on Applied Social Psychology (pp. 31-67). Thousand Oaks, CA: SAGE Publications.","type":"chapter","doi":null,"isbn":"978-0803951746","url":null}],"related":["job-demands-resources-scale","emotional-exhaustion-scale","work-ability-index","organizational-commitment-scale","job-satisfaction-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"perceived-value-scale-tourism","name":"Perceived Value Scale for Tourism","fullName":"Perceived Value Scale for Tourism (PVST)","aliases":["PVST","Tourism Perceived Value"],"domain":"tourism-management","family":"process-pipeline","subfamily":"value-perception-measurement","year":"1988","originator":"Zeithaml, V. A.; Petrick, J. F.","url":"https://scholargate.app/en/tourism-management/perceived-value-scale-tourism","markdownUrl":"https://scholargate.app/en/tourism-management/perceived-value-scale-tourism.md","definition":"The Perceived Value Scale for Tourism (PVST) measures visitors' judgments of whether tourism experiences deliver fair value—balancing perceived benefits (quality of experience, emotional satisfaction, novelty) against perceived costs (monetary price, time investment, effort). Rooted in Zeithaml's value perception theory (1988) and extended by Petrick (2002) to leisure contexts, the PVST operationalizes value as multidimensional (not price alone), capturing emotional and relative components alongside financial fairness. Value perception is a critical satisfaction driver and predictor of repeat visitation and word-of-mouth, particularly for experiences with high upfront investment and uncertain return.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zeithaml, V. A.; Petrick, J. F.","subfamily":"value-perception-measurement","year":"1988","type":"Self-report questionnaire"},"citations":[{"ref":"Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.","type":"article","doi":"10.1177/002224298805200302","isbn":null,"url":null},{"ref":"Williams, M. R., & Attaway, J. S. (2001). Exploring salespersons' customer orientation as a mediator of organizational culture's influence on buyer-seller relationships. Journal of Personal Selling and Sales Management, 16(4), 33-52.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Exploring+salespersons%27+customer+orientation+as+a+mediator+of+organizational+culture%27s+influence+on+buyer-seller+relationships+Williams"},{"ref":"Petrick, J. F. (2002). Development of a multi-dimensional scale for measuring the perceived value of a service. Journal of Leisure Research, 34(2), 119-134.","type":"article","doi":"10.1080/00222216.2002.11949965","isbn":null,"url":null},{"ref":"Sanchez, J., Callarisa, L., Rodríguez, R. M., & Moliner, M. A. (2006). Perceived value of the purchase of a tourism product. Tourism Management, 27(3), 394-409.","type":"article","doi":"10.1016/j.tourman.2004.11.007","isbn":null,"url":null}],"related":["tourist-satisfaction-scale","hotel-service-quality-scale","destination-image-scale","travel-motivation-scale","tourist-loyalty-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"perceptual-preference-mapping","name":"Perceptual and Preference Mapping","fullName":"Perceptual and Preference Mapping","aliases":["Perceptual Mapping","Preference Mapping","Attribute-Based Mapping","Algısal Haritalama"],"domain":"statistics","family":"latent-structure","subfamily":"Perceptual mapping","year":1979,"originator":"John Hauser & Frank Koppelman","url":"https://scholargate.app/en/statistics/perceptual-preference-mapping","markdownUrl":"https://scholargate.app/en/statistics/perceptual-preference-mapping.md","definition":"Perceptual and preference mapping is a family of multivariate techniques that simultaneously positions competing objects—brands, products, or stimuli—and respondent preferences within a common low-dimensional space. Introduced systematically by Hauser and Koppelman (1979), the approach lets researchers visualize how consumers perceive attribute-level similarities among objects and which attributes drive individual or segment-level choice. It is widely used in market research, sensory science, and strategic positioning analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Hauser & Frank Koppelman","year":1979,"type":"Multivariate spatial representation","subfamily":"Perceptual mapping","data_input":"Attribute ratings or similarity judgments","output":"Low-dimensional joint space of objects and preferences"},"citations":[{"ref":"Hauser, J. R., & Koppelman, F. S. (1979). Alternative perceptual mapping techniques: Relative accuracy and usefulness. Journal of Marketing Research, 16(4), 495–506.","type":"article","doi":"10.1177/002224377901600406","isbn":null,"url":null}],"related":["correspondence-analysis","multidimensional-scaling","biplot"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"perceptual-voice-quality-scale","name":"GRBAS Voice Perceptual Scale","fullName":"GRBAS Scale (Grade, Roughness, Breathiness, Asthenia, Strain)","aliases":["GRBAS","GRBASI","Voice Perceptual Rating"],"domain":"speech-language-pathology","family":"process-pipeline","subfamily":"voice quality perceptual rating","year":"1981","originator":"Hirano, M.","url":"https://scholargate.app/en/speech-language-pathology/perceptual-voice-quality-scale","markdownUrl":"https://scholargate.app/en/speech-language-pathology/perceptual-voice-quality-scale.md","definition":"The GRBAS Scale (Grade, Roughness, Breathiness, Asthenia, Strain) is a clinician-rated perceptual assessment tool for classifying voice quality across five distinct vocal dimensions. Developed by Hirano in 1981, GRBAS provides a standardized language for voice clinicians and physicians to describe dysphonia characteristics (e.g., rough voice, breathy voice, weak voice) using ordinal subscales. GRBAS is foundational in voice pathology education and remains widely used in clinical and research settings despite modern objective measures like acoustic analysis and laryngeal imaging.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hirano, M.","subfamily":"voice quality perceptual rating","year":"1981","type":"Clinician-rated"},"citations":[{"ref":"Hirano, M. (1981). Clinical Examination of Voice. Vienna: Springer-Verlag.","type":"article","doi":null,"isbn":"978-3-7091-4621-5","url":null},{"ref":"Debruyne, F., Decoster, W., Van Gorp, G., & Verheggen, R. (1997). Perceptual Evaluation of Voice Disorders. Acta Oto-Laryngologica, 117(S527), 34–38.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Perceptual+Evaluation+of+Voice+Disorders+Debruyne"},{"ref":"Karnell, M. P., Melton, S. D., Childers, D. G., & Hicks, D. M. (1991). Use of Nasofiberscope and Stroboscopy in Teaching Perceptual Voice Evaluation. Journal of Voice, 5(3), 236–241.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Use+of+Nasofiberscope+and+Stroboscopy+in+Teaching+Perceptual+Voice+Evaluation+Karnell"}],"related":["voice-handicap-index","boston-aphasia-severity","dysphagia-outcome-severity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"perinatal-anxiety-screening-scale","name":"Perinatal Anxiety Screening Scale","fullName":"Perinatal Anxiety Screening Scale (PASS)","aliases":["PASS"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"perinatal-mental-health","year":2014,"originator":"Somerville et al.","url":"https://scholargate.app/en/obstetrics-gynecology/perinatal-anxiety-screening-scale","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/perinatal-anxiety-screening-scale.md","definition":"The Perinatal Anxiety Screening Scale (PASS) is a 31-item self-report instrument designed to screen for anxiety symptoms during pregnancy and the postpartum period. Developed by Somerville and colleagues in 2014, it addresses the clinical need for a brief, validated tool that captures the full spectrum of perinatal anxiety disorders, including generalized anxiety, panic, obsessive-compulsive features, and posttraumatic stress in the context of childbirth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Somerville et al.","subfamily":"perinatal-mental-health","year":2014,"type":"Self-report"},"citations":[{"ref":"Somerville, S., Dedman, K., Hagan, R., Oxnam, E., Wettinger, M., Byrne, S., Coo, S., Doherty, D. A., & Papagaroufalis, K. (2014). The Perinatal Anxiety Screening Scale (PASS): development and preliminary validation. Archives of Women's Mental Health, 17(5), 443-454.","type":"article","doi":"10.1007/s00737-014-0425-8","isbn":null,"url":null}],"related":["pregnancy-related-anxiety-questionnaire","postpartum-bonding-questionnaire","antepartum-depression-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"periodontal-probing","name":"Periodontal Probing","fullName":"Periodontal Probing Depth Assessment","aliases":["probing depth measurement","pocket depth assessment"],"domain":"dentistry","family":"process-pipeline","subfamily":"Periodontics","year":"1957","originator":"American Academy of Periodontology","url":"https://scholargate.app/en/dentistry/periodontal-probing","markdownUrl":"https://scholargate.app/en/dentistry/periodontal-probing.md","definition":"Periodontal probing is a clinical assessment technique that measures the depth of gingival crevices and periodontal pockets to diagnose periodontal disease. Introduced by the American Academy of Periodontology in the mid-20th century, it remains the gold standard for assessing periodontal health status. The procedure evaluates the clinical attachment level and recession depth to identify inflammation, attachment loss, and disease progression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"American Academy of Periodontology","subfamily":"Periodontics","year":"1957","type":"Clinical measurement procedure"},"citations":[{"ref":"Armitage, G. C. (1999). Development of a classification system for periodontal diseases and conditions. Annals of Periodontology, 4(1), 1-6.","type":"article","doi":"10.1902/annals.1999.4.1.1","isbn":null,"url":null},{"ref":"Jeffcoat, M. K. (1992). The etiology and pathogenesis of periodontal diseases are multifactorial. The Journal of the American Dental Association, 123(5), 85-89.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+etiology+and+pathogenesis+of+periodontal+diseases+are+multifactorial+Jeffcoat"},{"ref":"Page, R. C., & Kornman, K. S. (1997). The pathogenesis of human periodontitis: an introduction. Periodontology 2000, 14(1), 9-11.","type":"article","doi":"10.1111/j.1600-0757.1997.tb00189.x","isbn":null,"url":null}],"related":["dmft-index","gingival-index","dental-erosion-index","bone-density-dental","tooth-mobility-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"peritraumatic-distress-inventory","name":"Peritraumatic Distress Inventory","fullName":"Peritraumatic Distress Inventory (PDI)","aliases":["PDI","Peri-Traumatic Distress Inventory"],"domain":"military-psychology","family":"process-pipeline","subfamily":"Acute stress assessment","year":2001,"originator":"Brunet, Akerib, & Birmes","url":"https://scholargate.app/en/military-psychology/peritraumatic-distress-inventory","markdownUrl":"https://scholargate.app/en/military-psychology/peritraumatic-distress-inventory.md","definition":"The PDI is a 13-item self-report measure assessing the emotional, physical, and cognitive distress experienced during or immediately after a traumatic event. Developed by Brunet, Akerib, and Birmes in 2001, it captures acute peritraumatic responses (dissociation, fear, confusion) that predict risk for chronic PTSD. It is widely used in emergency medicine, military medical systems, and trauma research to identify acutely traumatized individuals at high risk for persistent psychological injury.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brunet, Akerib, & Birmes","subfamily":"Acute stress assessment","year":2001,"type":"Self-report (retrospective to trauma)"},"citations":[{"ref":"Brunet, A., Akerib, V., & Birmes, P. (2001). Don't forget initial symptoms of acute stress disorder: Evaluation of a simple stack of criteria. Journal of Nervous and Mental Disease, 189(7), 460-466.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Don%27t+forget+initial+symptoms+of+acute+stress+disorder%3A+Evaluation+of+a+simple+stack+of+criteria+Brunet"},{"ref":"Birmes, P., Brunet, A., Carreno, I., Ducassé, J. L., Charlet, J. P., Lauque, D., ... & Schmitt, L. (2003). The predictive power of peritraumatic dissociation and acute stress symptoms for posttraumatic stress symptoms: A three-month follow-up study. Journal of Nervous and Mental Disease, 191(5), 300-304.","type":"article","doi":"10.1176/appi.ajp.160.7.1337","isbn":null,"url":null}],"related":["pcl-military","combat-exposure-scale","post-deployment-reintegration","soldier-adaptation-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"perma-scale","name":"PERMA Profiler","fullName":"PERMA-Profiler","aliases":["PERMA Profiler","PERMA Model"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"multidimensional flourishing","year":"2016","originator":"James Butler and Margaret Kern","url":"https://scholargate.app/en/positive-psychology/perma-scale","markdownUrl":"https://scholargate.app/en/positive-psychology/perma-scale.md","definition":"The PERMA-Profiler is a 23-item multidimensional measure of flourishing developed by Butler and Kern in 2016 based on Seligman's PERMA model of positive psychology. It assesses five core domains of human flourishing—Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment—plus Negative Emotion and Loneliness as contextual factors. This instrument bridges the theoretical PERMA framework with practical measurement, enabling comprehensive assessment of psychological well-being across multiple life dimensions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Butler and Margaret Kern","subfamily":"multidimensional flourishing","year":"2016","type":"Self-report questionnaire"},"citations":[{"ref":"Butler, J., & Kern, M. L. (2016). The PERMA-Profiler: A brief multidimensional measure of flourishing. International Journal of Wellbeing, 6(3), 1–48.","type":"article","doi":"10.5502/ijw.v6i3.526","isbn":null,"url":null}],"related":["flourishing-scale","who-5-wellbeing-index","positive-mental-health-scale","meaning-in-life-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"permutation-test","name":"Permutation Test","fullName":"Permutation (Randomization) Test","aliases":["randomization test","exact permutation test","re-randomization test","Permütasyon Testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2005,"originator":"Good (2005); Edgington & Onghena (2007); resampling tradition","url":"https://scholargate.app/en/statistics/permutation-test","markdownUrl":"https://scholargate.app/en/statistics/permutation-test.md","definition":"The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Good (2005); Edgington & Onghena (2007); resampling tradition","year":2005,"type":"Nonparametric resampling test","estimator":"Empirical permutation distribution of the test statistic","outcome":"exact p-value","minSample":10,"distributionFree":true},"citations":[{"ref":"Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387202792","url":null},{"ref":"Edgington, E. S., & Onghena, P. (2007). Randomization Tests (4th ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1584885894","url":null}],"related":["bootstrap-inference","jackknife","trimmed-mean-test","robust-correlation","winsorized-estimation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"persistent-homology","name":"Persistent Homology","fullName":"Persistent Homology (Topological Data Analysis)","aliases":["Topological Persistence","Persistence Barcodes","Persistent Betti Numbers","Kalıcı Homoloji"],"domain":"topology","family":"ml-model","subfamily":"Topological data analysis","year":2002,"originator":"Edelsbrunner, Letscher & Zomorodian","url":"https://scholargate.app/en/topology/persistent-homology","markdownUrl":"https://scholargate.app/en/topology/persistent-homology.md","definition":"Persistent homology is a method in topological data analysis that quantifies the multi-scale topological structure of data by tracking connected components, loops, and voids as a scale parameter varies. Introduced by Edelsbrunner, Letscher, and Zomorodian in 2002, it encodes topological features through their birth and death scales, producing persistence diagrams or barcodes that serve as compact, coordinate-free descriptors of shape. The approach is robust to noise and provides a mathematically rigorous bridge between discrete data and algebraic topology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edelsbrunner, Letscher & Zomorodian","year":2002,"type":"Topological feature extraction algorithm","subfamily":"Topological data analysis","output":"Persistence diagrams / barcodes","parameter":"Filtration scale parameter ε"},"citations":[{"ref":"Edelsbrunner, H., Letscher, D., & Zomorodian, A. (2002). Topological persistence and simplification. Discrete & Computational Geometry, 28(4), 511–533.","type":"article","doi":"10.1007/s00454-002-2885-2","isbn":null,"url":null},{"ref":"Carlsson, G. (2009). Topology and data. Bulletin of the American Mathematical Society, 46(2), 255–308.","type":"article","doi":"10.1090/S0273-0979-09-01249-X","isbn":null,"url":null}],"related":["mapper-algorithm","umap","locally-linear-embedding"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"person-centred-care-assessment","name":"Person-Centred Care Assessment Tool","fullName":"Person-Centred Care Assessment Tool (PCAT)","aliases":["PCAT-SV","PCC Assessment Scale"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"person-centered-care","year":2010,"originator":"Brendan McCormack, David Edvardsson","url":"https://scholargate.app/en/patient-centered-care/person-centred-care-assessment","markdownUrl":"https://scholargate.app/en/patient-centered-care/person-centred-care-assessment.md","definition":"The Person-Centred Care Assessment Tool (PCAT) is an observational and staff-report instrument designed to evaluate the degree to which healthcare services and interactions embody person-centered care principles. Developed by Brendan McCormack and David Edvardsson, the PCAT assesses key dimensions of person-centered practice: knowing the person, being respectful, engaging authentically, taking a holistic view, and adapting care to individual values and preferences. The tool has been widely used in nursing homes, dementia care, hospital wards, and community health settings to evaluate care environment quality and identify opportunities for person-centered transformation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brendan McCormack, David Edvardsson","subfamily":"person-centered-care","year":2010,"type":"Staff-rated or Mixed"},"citations":[{"ref":"McCormack, B., Eley, D., Prideaux, D., & Jackson, D. (2010). Blending critical realism and hermeneutics in a PhD research: Researching person-centred care. Qualitative Research Journal, 10(1), 42-54.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Blending+critical+realism+and+hermeneutics+in+a+PhD+research%3A+Researching+person-centred+care+McCormack"},{"ref":"Edvardsson, D., Winblad, B., & Sandman, P. O. (2008). Person-centred care of people with severe Alzheimer's disease: current status and ways forward. The Lancet Neurology, 7(4), 362-367.","type":"article","doi":"10.1016/s1474-4422(08)70063-2","isbn":null,"url":null}],"related":["collaboste-scale","patient-enablement-instrument","care-transitions-measure","trust-in-physician-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"personal-recovery-questionnaire","name":"Questionnaire about the Process of Recovery","fullName":"Questionnaire about the Process of Recovery (QPR)","aliases":["QPR","Neil-QPR","Process of Recovery Questionnaire"],"domain":"psychiatric-rehabilitation","family":"process-pipeline","subfamily":"recovery-measurement","year":"2009","originator":"Neil, S. T., Kilbride, M., Pitt, L., et al.","url":"https://scholargate.app/en/psychiatric-rehabilitation/personal-recovery-questionnaire","markdownUrl":"https://scholargate.app/en/psychiatric-rehabilitation/personal-recovery-questionnaire.md","definition":"The Questionnaire about the Process of Recovery (QPR), also called the 'Neil-QPR,' is a 22-item self-report measure assessing subjective recovery processes in individuals with serious mental illness, particularly schizophrenia and related disorders. Developed by Stephen T. Neil, Matthias Kilbride, Leonie Pitt, and colleagues in 2009, the QPR captures dimensions central to lived experience of recovery: awareness of mental illness and strengths, motivation to pursue recovery goals, effective coping strategies, hope, and self-esteem. Unlike scales measuring recovery outcomes, the QPR emphasizes recovery as an active process—the psychological and behavioral work individuals undertake.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Neil, S. T., Kilbride, M., Pitt, L., et al.","subfamily":"recovery-measurement","year":"2009","type":"Self-report questionnaire"},"citations":[{"ref":"Neil, S. T., Kilbride, M., Pitt, L., Nothard, S., Welford, P., Sellwood, W., & Bebbington, P. (2009). The questionnaire about the process of recovery (QPR): A measurement tool developed in collaboration with service users. Schizophrenia Bulletin, 35(2), 403-413.","type":"article","doi":"10.1080/17522430902913450","isbn":null,"url":null}],"related":["recovery-assessment-scale","mental-health-recovery-measure","empowerment-scale-rogers","personal-recovery-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"personality-assessment-mmpi","name":"MMPI Personality Assessment","fullName":"Minnesota Multiphasic Personality Inventory Assessment","aliases":["MMPI-2","MMPI-2-RF","personality inventory"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Personality assessment","year":"1943","originator":"Starke R. Hathaway, J. Charnley McKinley","url":"https://scholargate.app/en/clinical-psychology/personality-assessment-mmpi","markdownUrl":"https://scholargate.app/en/clinical-psychology/personality-assessment-mmpi.md","definition":"The Minnesota Multiphasic Personality Inventory (MMPI) is a 567-item standardized self-report inventory designed to assess personality traits, psychopathology, and behavioral tendencies in adults. Originally published in 1943 and revised as the MMPI-2 in 1989 and the MMPI-2-RF in 2008, the MMPI remains the most widely used and researched objective personality assessment instrument in clinical, forensic, and industrial settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Starke R. Hathaway, J. Charnley McKinley","subfamily":"Personality assessment","year":"1943","type":"Objective standardized personality inventory"},"citations":[{"ref":"Butcher, J. N., Dahlstrom, W. G., Graham, J. R., Tellegen, A., & Kaemmer, B. (2001). MMPI-2: Manual for administration, scoring, and interpretation (Rev. ed.). University of Minnesota Press.","type":"article","doi":null,"isbn":"9780816631926","url":null},{"ref":"Ben-Porath, Y. S., & Tellegen, A. (2008). MMPI-2-RF: Manual for administration, scoring, and interpretation. University of Minnesota Press.","type":"article","doi":null,"isbn":"9780816631940","url":null}],"related":["structured-clinical-interview-dsm","neuropsychological-assessment","cognitive-behavioral-therapy-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pesaran-cd-test","name":"Pesaran CD Test","fullName":"Pesaran Cross-Sectional Dependence (CD) Test","aliases":["CD Test","Cross-Sectional Dependence Test","Pesaran General CD Test","Kesitsel Bağımlılık Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Cross-sectional dependence","year":2021,"originator":"M. Hashem Pesaran","url":"https://scholargate.app/en/econometrics/pesaran-cd-test","markdownUrl":"https://scholargate.app/en/econometrics/pesaran-cd-test.md","definition":"The Pesaran CD test is a general diagnostic procedure for detecting cross-sectional dependence in panel data models. Developed by M. Hashem Pesaran (2021), it is applicable to both balanced and unbalanced panels with large N and T, and retains validity under heterogeneous slope coefficients. The test is widely adopted in empirical economics, finance, and political economy as a prerequisite check before selecting appropriate estimators or unit-root tests for panel datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. Hashem Pesaran","year":2021,"type":"Non-parametric diagnostic test","subfamily":"Cross-sectional dependence","null_hypothesis":"Cross-sectional independence","applicable_panels":"Balanced and unbalanced panels"},"citations":[{"ref":"Pesaran, M. H. (2021). General diagnostic tests for cross-sectional dependence in panels. Empirical Economics, 60(1), 13–50.","type":"article","doi":"10.1007/s00181-020-01875-7","isbn":null,"url":null}],"related":["frees-test","cips-test","breusch-godfrey-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pesaran-timmermann-test","name":"Pesaran-Timmermann Test","fullName":"Pesaran-Timmermann Test of Directional Predictive Accuracy","aliases":["PT Test","Directional Accuracy Test","Nonparametric Predictive Performance Test","Pesaran-Timmermann Yön Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Forecast evaluation","year":1992,"originator":"M. Hashem Pesaran & Allan Timmermann","url":"https://scholargate.app/en/econometrics/pesaran-timmermann-test","markdownUrl":"https://scholargate.app/en/econometrics/pesaran-timmermann-test.md","definition":"Introduced by Pesaran and Timmermann (1992), the PT test is a nonparametric procedure that evaluates whether a forecasting model correctly predicts the direction (sign) of a target variable more often than would be expected by chance. It is widely used in financial econometrics and macroeconomic forecasting to assess the practical utility of a model beyond simple error metrics, particularly when the economic cost of getting the direction wrong is high.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. Hashem Pesaran & Allan Timmermann","year":1992,"type":"Nonparametric one-sided test","subfamily":"Forecast evaluation","null_hypothesis":"Forecasts and realizations are independently distributed (no directional predictability)","asymptotic_distribution":"Standard normal under the null"},"citations":[{"ref":"Pesaran, M. H., & Timmermann, A. (1992). A simple nonparametric test of predictive performance. Journal of Business & Economic Statistics, 10(4), 461–465.","type":"article","doi":"10.1080/07350015.1992.10509922","isbn":null,"url":null}],"related":["diebold-mariano-test","runs-test","sign-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pesticide-efficacy-trial","name":"Pesticide Efficacy Trial","fullName":"Field Trial Design and Efficacy Assessment for Pest Control Products","aliases":["Fungicide efficacy test","Insecticide trial","Disease control evaluation"],"domain":"agronomy","family":"process-pipeline","subfamily":"Integrated pest management","year":"2010","originator":"EPPO (European and Mediterranean Plant Protection Organization)","url":"https://scholargate.app/en/agronomy/pesticide-efficacy-trial","markdownUrl":"https://scholargate.app/en/agronomy/pesticide-efficacy-trial.md","definition":"Pesticide Efficacy Trial is an experimental design and analysis pipeline for evaluating the effectiveness of fungicides, insecticides, and other plant protection products under field or greenhouse conditions. Standardized by EPPO and IOBC, this method quantifies pest or disease control and informs regulatory approval, product comparison, and farmer decision-making.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"EPPO (European and Mediterranean Plant Protection Organization)","subfamily":"Integrated pest management","year":"2010","type":"Experimental design pipeline"},"citations":[{"ref":"European and Mediterranean Plant Protection Organization (2010). Standard procedures for efficacy evaluation of plant protection products. EPPO Bulletin 40(3), 313-328.","type":"article","doi":null,"isbn":null,"url":"https://www.eppo.int/standards/standards-setting"},{"ref":"International Organization for Biological Control (2007). Quality control of biological pest control agents. IOBC/WPRS Bulletin 30(5), 1-54.","type":"article","doi":null,"isbn":null,"url":"https://www.iobc-wprs.org/"}],"related":["weed-density-mapping","crop-growth-simulation","crop-yield-estimation","nitrogen-use-efficiency","phenological-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pet-kinetic-modeling","name":"PET Kinetic Modeling","fullName":"Positron Emission Tomography Kinetic Modeling","aliases":["PET pharmacokinetics","Dynamic PET","PET compartmental modeling"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Quantitative imaging","year":"1983","originator":"Christoph Patlak","url":"https://scholargate.app/en/medical-imaging/pet-kinetic-modeling","markdownUrl":"https://scholargate.app/en/medical-imaging/pet-kinetic-modeling.md","definition":"PET kinetic modeling is a quantitative analysis technique that tracks the temporal behavior of radioactive tracers in tissue to extract physiological parameters such as blood flow, metabolic rate, and receptor density. Established by Patlak, Logan, and Gunn in the 1980s and 1990s, kinetic modeling transforms raw PET time-activity curves into interpretable biological measures. It is widely used in neurology, oncology, and cardiology to assess disease severity, treatment response, and regional tissue function.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christoph Patlak","subfamily":"Quantitative imaging","year":"1983","type":"Mathematical framework for tracer kinetics in PET imaging"},"citations":[{"ref":"Patlak, C. S., Blasberg, R. G., Fenstermacher, J. D. (1983). Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Journal of Cerebral Blood Flow & Metabolism, 3(1), 1-7.","type":"article","doi":"10.1038/jcbfm.1983.1","isbn":null,"url":null},{"ref":"Logan, J., Fowler, J. S., Christman, D. R., et al. (2000). Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C]cocaine PET studies in human subjects. Journal of Cerebral Blood Flow & Metabolism, 10(5), 740-747.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Graphical+analysis+of+reversible+radioligand+binding+from+time-activity+measurements+applied+to+%5BN-11C%5Dcocaine+PET+studies+in+human+subjects+Logan"},{"ref":"Gunn, R. N., Gunn, S. R., Cunningham, V. J. (2001). Positron emission tomography compartmental models. Journal of Cerebral Blood Flow & Metabolism, 21(6), 635-652.","type":"article","doi":"10.1097/00004647-200106000-00002","isbn":null,"url":null}],"related":["oct-angiography","dti-tractography","quantitative-susceptibility-mapping","ct-iterative-reconstruction","radiomics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"petrographic-analysis","name":"Petrographic Analysis","fullName":"Petrographic Analysis","aliases":["microscopy analysis","thin section analysis","modal composition determination"],"domain":"geoscience","family":"process-pipeline","subfamily":"Microscopic rock analysis","year":"1858","originator":"Henry Clifton Sorby","url":"https://scholargate.app/en/geoscience/petrographic-analysis","markdownUrl":"https://scholargate.app/en/geoscience/petrographic-analysis.md","definition":"Petrographic analysis is the microscopic examination of rock thin sections to determine mineral composition, grain size, texture, and diagenetic alteration. Pioneered by Sorby in 1858, this method remains the gold standard for understanding lithology and has evolved to include quantitative image analysis and cathodoluminescence. Petrographic data anchor well-log interpretation, validate seismic velocity models, and constrain paleoenvironmental and diagenetic histories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Clifton Sorby","subfamily":"Microscopic rock analysis","year":"1858","type":"compositional characterization pipeline"},"citations":[{"ref":"Tucker, M. E. (2003). Sedimentary Rocks in the Field: A Color Guide (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sedimentary+Rocks+in+the+Field%3A+A+Color+Guide+%283rd+ed.%29+Tucker"},{"ref":"Shelley, D. (1985). Diagenesis of Shales and Competent Interbeds: Petrographic and Geochemical Evidence. Journal of the Geological Society, 142(6), 1003–1021.","type":"book","doi":null,"isbn":null,"url":"https://jgs.lyellcollection.org"},{"ref":"Folk, R. L. (1954). The distinction between grain size and mineral composition in sedimentary rock nomenclature. Journal of Geology, 62(4), 344–359.","type":"article","doi":"10.1086/626171","isbn":null,"url":null}],"related":["well-log-analysis","stratigraphic-correlation","geologic-mapping","rock-mass-classification","basin-subsidence-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-aras","name":"PF-ARAS","fullName":"Pythagorean extension of ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-aras","markdownUrl":"https://scholargate.app/en/decision-making/pf-aras.md","definition":"PF-ARAS (Pythagorean extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-cocoso","name":"PF-COCOSO","fullName":"PF-CoCoSo — Pythagorean extension of COCOSO","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-cocoso","markdownUrl":"https://scholargate.app/en/decision-making/pf-cocoso.md","definition":"PF-COCOSO (PF-CoCoSo — Pythagorean extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-codas","name":"PF-CODAS","fullName":"Pythagorean extension of CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-codas","markdownUrl":"https://scholargate.app/en/decision-making/pf-codas.md","definition":"PF-CODAS (Pythagorean extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-copras","name":"PF-COPRAS","fullName":"Pythagorean extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-copras","markdownUrl":"https://scholargate.app/en/decision-making/pf-copras.md","definition":"PF-COPRAS (Pythagorean extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-edas","name":"PF-EDAS","fullName":"Pythagorean extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-edas","markdownUrl":"https://scholargate.app/en/decision-making/pf-edas.md","definition":"PF-EDAS (Pythagorean extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-electre-ii","name":"PF-ELECTRE-II","fullName":"Pythagorean fuzzy ELECTRE-II for group MCDM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2021","originator":"Akram, M.; Ilyas, F.; Garg, H.","url":"https://scholargate.app/en/decision-making/pf-electre-ii","markdownUrl":"https://scholargate.app/en/decision-making/pf-electre-ii.md","definition":"PF-ELECTRE-II (Pythagorean fuzzy ELECTRE-II for group MCDM) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M.; Ilyas, F.; Garg, H. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M.; Ilyas, F.; Garg, H.","subfamily":"Outranking","year":"2021","type":"Pythagorean outranking — Pythagorean Fuzzy Number (PFN: μ, ν, π; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Akram, M., Ilyas, F., Garg, H. (2021). ELECTRE-II method for group decision-making in Pythagorean fuzzy environment. Applied Intelligence","type":"article","doi":"10.1007/s10489-021-02200-0","isbn":null,"url":null}],"related":["pf-ahp","pf-swara","pf-bwm","pf-dematel","pf-entropy","electre-ii","fuzzy-electre-ii"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-gra","name":"PF-GRA","fullName":"Pythagorean extension of GRA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-gra","markdownUrl":"https://scholargate.app/en/decision-making/pf-gra.md","definition":"PF-GRA (Pythagorean extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-mabac","name":"PF-MABAC","fullName":"Pythagorean extension of MABAC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-mabac","markdownUrl":"https://scholargate.app/en/decision-making/pf-mabac.md","definition":"PF-MABAC (Pythagorean extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-marcos","name":"PF-MARCOS","fullName":"Pythagorean extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-marcos","markdownUrl":"https://scholargate.app/en/decision-making/pf-marcos.md","definition":"PF-MARCOS (Pythagorean extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-moora","name":"PF-MOORA","fullName":"Pythagorean extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-moora","markdownUrl":"https://scholargate.app/en/decision-making/pf-moora.md","definition":"PF-MOORA (Pythagorean extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-promethee","name":"PF-PROMETHEE","fullName":"Pythagorean extension of PROMETHEE","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-promethee","markdownUrl":"https://scholargate.app/en/decision-making/pf-promethee.md","definition":"PF-PROMETHEE (Pythagorean extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Outranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-saw","name":"PF-SAW","fullName":"Pythagorean extension of SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-saw","markdownUrl":"https://scholargate.app/en/decision-making/pf-saw.md","definition":"PF-SAW (Pythagorean extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-todim","name":"PF-TODIM","fullName":"Pythagorean extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2016","originator":"Ren, P., Xu, Z., Gou, X.","url":"https://scholargate.app/en/decision-making/pf-todim","markdownUrl":"https://scholargate.app/en/decision-making/pf-todim.md","definition":"PF-TODIM (Pythagorean extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Ren, P., Xu, Z., Gou, X. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ren, P., Xu, Z., Gou, X.","subfamily":"Ranking","year":"2016","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Ren, P., Xu, Z., Gou, X. (2016). Pythagorean Fuzzy TODIM Approach to Multi-Criteria Decision Making. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2015.12.020","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-topsis","name":"PF-TOPSIS","fullName":"Pythagorean extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Zhang, X., Xu, Z.","url":"https://scholargate.app/en/decision-making/pf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/pf-topsis.md","definition":"PF-TOPSIS (Pythagorean extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhang, X., Xu, Z. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhang, X., Xu, Z.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zhang, X., Xu, Z. (2014). Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets. International Journal of Intelligent Systems","type":"article","doi":"10.1002/int.21676","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-vikor","name":"PF-VIKOR","fullName":"Pythagorean extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2024","originator":"Abbas, S., Hussain, Z., Hussain, Z., Ali, I., Mudabar, S.M.","url":"https://scholargate.app/en/decision-making/pf-vikor","markdownUrl":"https://scholargate.app/en/decision-making/pf-vikor.md","definition":"PF-VIKOR (Pythagorean extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Abbas, S., Hussain, Z., Hussain, Z., Ali, I., Mudabar, S.M. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abbas, S., Hussain, Z., Hussain, Z., Ali, I., Mudabar, S.M.","subfamily":"Ranking","year":"2024","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Abbas, S., Hussain, Z., Hussain, Z., Ali, I., Mudabar, S.M. (2024). Advanced Entropy Models in Pythagorean Fuzzy Sets: Revolutionizing Multi-Criteria Decision Making with PF-VIKOR. Fuzzy Economic Review","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Advanced+Entropy+Models+in+Pythagorean+Fuzzy+Sets%3A+Revolutionizing+Multi-Criteria+Decision+Making+with+PF-VIKOR+Abbas"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-waspas","name":"PF-WASPAS","fullName":"Pythagorean extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-waspas","markdownUrl":"https://scholargate.app/en/decision-making/pf-waspas.md","definition":"PF-WASPAS (Pythagorean extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pf-wpm","name":"PF-WPM","fullName":"Pythagorean extension of WPM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/pf-wpm","markdownUrl":"https://scholargate.app/en/decision-making/pf-wpm.md","definition":"PF-WPM (Pythagorean extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2014","type":"Pythagorean outranking/ranking — Pythagorean Fuzzy Number (PFN: μ, ν; μ²+ν² ≤ 1)","value_space":"pythagorean","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2013.2278989","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pfr-model","name":"PFR Model","fullName":"Plug Flow Reactor Model","aliases":["ideal tubular reactor","plug-flow model","PFR"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Reactor Engineering","year":"1962","originator":"Octave Levenspiel","url":"https://scholargate.app/en/applied-physics/pfr-model","markdownUrl":"https://scholargate.app/en/applied-physics/pfr-model.md","definition":"The PFR (Plug Flow Reactor) model describes the behavior of a tubular reactor in which fluid elements move through as distinct plugs with no axial mixing. Fluid at the inlet is freshly unreacted; as it travels downstream, reactions progress. This idealized model, formalized by Octave Levenspiel alongside CSTR theory, is the opposite extreme: while CSTRs are fully mixed, PFRs have no axial mixing. In practice, PFRs achieve higher conversion than CSTRs for the same residence time and are widely used in the chemical and petroleum industries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Octave Levenspiel","subfamily":"Reactor Engineering","year":"1962","type":"Mathematical model for plug-flow reactor"},"citations":[{"ref":"Levenspiel, O. (1999). Chemical Reaction Engineering (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-471-25424-9","url":null},{"ref":"Fogler, H. S. (2016). Elements of Chemical Reaction Engineering (5th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0-13-388928-8","url":null},{"ref":"Schmidt, L. D. (2005). The Engineering of Chemical Reactions (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0-195-10490-0","url":null}],"related":["cstr-model","reactive-distillation","adsorption-isotherm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pharmacokinetic-compartment-model","name":"Pharmacokinetic Compartment Model","fullName":"Compartmental Pharmacokinetic Models","aliases":["Mammillary Compartment Model","Multi-Compartment PK Model","Compartmental Analysis","Farmakokinetik Kompartman Modeli"],"domain":"pharmacometrics","family":"regression-model","subfamily":"Pharmacokinetics","year":1982,"originator":"Gibaldi & Perrier","url":"https://scholargate.app/en/pharmacometrics/pharmacokinetic-compartment-model","markdownUrl":"https://scholargate.app/en/pharmacometrics/pharmacokinetic-compartment-model.md","definition":"The pharmacokinetic compartment model represents the body as one or more hypothetical compartments interconnected by first-order rate processes, describing how a drug is absorbed, distributed, and eliminated over time. Systematized by Gibaldi and Perrier in 1982, these models use ordinary differential equations to characterize plasma concentration-time profiles. They are the cornerstone of drug development, dosage regimen design, and regulatory submission pharmacokinetic analyses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gibaldi & Perrier","year":1982,"type":"Deterministic ODE-based pharmacokinetic model","subfamily":"Pharmacokinetics","estimationMethod":"Nonlinear least squares or maximum likelihood","parameterCount":"2 (one-compartment) to 6+ (two-compartment)"},"citations":[{"ref":"Gibaldi, M., & Perrier, D. (1982). Pharmacokinetics (2nd ed.). Marcel Dekker.","type":"book","doi":null,"isbn":"978-0-8247-1042-2","url":null}],"related":["emax-model","population-pharmacokinetics","bioequivalence-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pharmacophore-modeling","name":"Pharmacophore Modeling","fullName":"Pharmacophore-based Ligand Design and Virtual Screening","aliases":["pharmacophore pattern recognition","3D pharmacophore"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Ligand-based drug design","year":"1977","originator":"Peter Gund","url":"https://scholargate.app/en/bioinformatics/pharmacophore-modeling","markdownUrl":"https://scholargate.app/en/bioinformatics/pharmacophore-modeling.md","definition":"Pharmacophore modeling identifies the spatial arrangement of molecular features (hydrogen bond donors, acceptors, aromatic rings) that are essential for biological activity. Introduced by Gund in 1977, this ligand-based method creates a three-dimensional pattern that can screen chemical libraries and design new active compounds without requiring receptor structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Gund","subfamily":"Ligand-based drug design","year":"1977","type":"Pattern-based virtual screening pipeline"},"citations":[{"ref":"Wermuth, C. G., Ganellin, C. R., Lindberg, P., & Mitscher, L. A. (1998). Glossary of terms used in medicinal chemistry. Pure and Applied Chemistry, 70(5), 1129-1143.","type":"article","doi":"10.1351/pac199870051129","isbn":null,"url":null},{"ref":"Ohno, K. & Ueda, Y. (2006). Modern photochemistry of organic compounds. Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Leung, S. C., Bodkin, M., von Delft, F., & Morris, G. M. (2012). SiteMap: a tool for identifying and characterizing binding sites in protein structures. Journal of Chemical Information and Modeling, 52(11), 3008-3020.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SiteMap%3A+a+tool+for+identifying+and+characterizing+binding+sites+in+protein+structures+Leung"}],"related":["molecular-docking","qsar","homology-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pharmacovigilance-prr-ror","name":"Pharmacovigilance PRR/ROR","fullName":"Proportional Reporting Ratio and Reporting Odds Ratio for Pharmacovigilance","aliases":["PRR","ROR","signal detection","adverse event monitoring"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Pharmacoepidemiology","year":"2002","originator":"Arne Melander and colleagues","url":"https://scholargate.app/en/pharmacology/pharmacovigilance-prr-ror","markdownUrl":"https://scholargate.app/en/pharmacology/pharmacovigilance-prr-ror.md","definition":"Proportional Reporting Ratio (PRR) and Reporting Odds Ratio (ROR) are statistical methods for detecting safety signals in spontaneous adverse event reporting databases. Developed and formalized by researchers in the early 2000s, these measures identify drug-adverse event associations that warrant further investigation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arne Melander and colleagues","subfamily":"Pharmacoepidemiology","year":"2002","type":"safety signal detection"},"citations":[{"ref":"Szarfman, A., Tonning, J. M., Doraiswamy, P. M., & Osgood, D. J. (2002). Pharmacovigilance in the post-marketing setting: establishing causal links between drugs and adverse events. Drug Safety, 25(9), 619-631.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pharmacovigilance+in+the+post-marketing+setting%3A+establishing+causal+links+between+drugs+and+adverse+events+Szarfman"},{"ref":"van Puijenbroek, E. P., Bate, A., Leufkens, H. G., Lindquist, M., Orre, R., & Egberts, A. C. (2002). A comparison of measures of disproportionality for signal detection in spontaneous adverse drug reaction reporting. Pharmacoepidemiology and Drug Safety, 11(1), 3-10.","type":"article","doi":"10.1002/pds.668","isbn":null,"url":null}],"related":["caco-2-permeability","pharmacovigilance-prr-ror","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phase-field-modeling","name":"Phase-Field Modeling","fullName":"Phase-Field Modeling (PFM)","aliases":["phase-field method","diffuse interface method"],"domain":"materials-science","family":"process-pipeline","subfamily":"Continuum simulation","year":"1958","originator":"John W. Cahn","url":"https://scholargate.app/en/materials-science/phase-field-modeling","markdownUrl":"https://scholargate.app/en/materials-science/phase-field-modeling.md","definition":"Phase-Field Modeling (PFM) is a continuum computational method for simulating microstructure evolution, phase transitions, and interfacial dynamics without explicitly tracking moving boundaries. Developed from Cahn-Ginzburg-Landau theory in the 1950s, PFM represents distinct phases through continuous order parameters that vary smoothly over diffuse interfaces. This approach elegantly handles topological changes (nucleation, coalescence, pinch-off), complex interface geometries, and strongly coupled multiphysics. It is the dominant method for studying dendritic growth, spinodal decomposition, grain evolution, and reactive transport in materials science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Cahn","subfamily":"Continuum simulation","year":"1958","type":"Simulation method"},"citations":[{"ref":"Cahn, J. W. (1958). Free energy of a nonuniform system: Interfacial free energy. The Journal of Chemical Physics, 28(2), 258-267.","type":"article","doi":"10.1063/1.1744102","isbn":null,"url":null},{"ref":"Ginzburg, V. L., & Landau, L. D. (1950). Theory of superconductivity. Zhurnal Eksperimental'noi i Teoreticheskoi Fiziki, 20, 1064.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ginzburg%E2%80%93Landau_theory"},{"ref":"Wang, S. L., Sekerka, R. F., Wheeler, A. A., Murray, B. T., Coriell, S. R., Braun, R. J., & McFadden, G. B. (2010). Thermodynamically-consistent phase-field models for solidification. Physica D, 69(3-4), 189-200.","type":"book","doi":"10.1016/0167-2789(93)90189-8","isbn":null,"url":null}],"related":["molecular-dynamics","finite-element-analysis","calphad"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phase-i-clinical-trial","name":"Phase I Clinical Trial","fullName":"Phase I Clinical Trial (First-in-Human / Dose-Escalation Study)","aliases":["Phase 1 trial","first-in-human study","FIH study","dose-escalation study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1960s (formal regulatory framework established ~1963–1970s)","originator":"Regulatory and clinical pharmacology community; formalized in U.S. FDA IND regulations (1963) and ICH guidelines","url":"https://scholargate.app/en/epidemiology/phase-i-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/phase-i-clinical-trial.md","definition":"A Phase I clinical trial is the first stage of human testing for a new drug, biologic, or intervention. Its primary objective is to evaluate safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) rather than therapeutic efficacy. Small cohorts of participants — typically healthy volunteers or patients with advanced disease — receive sequentially increasing doses to identify the maximum tolerated dose (MTD) and the dose-limiting toxicities (DLTs) that define the boundary for subsequent trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Regulatory and clinical pharmacology community; formalized in U.S. FDA IND regulations (1963) and ICH guidelines","year":"1960s (formal regulatory framework established ~1963–1970s)","type":"Interventional clinical study design","dataType":"Safety events, pharmacokinetic (PK) measurements, dose-level assignments, adverse event grading (CTCAE)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Storer, B. E. (1989). Design and analysis of phase I clinical trials. Biometrics, 45(3), 925–937.","type":"article","doi":"10.2307/2531693","isbn":null,"url":null},{"ref":"International Council for Harmonisation (ICH). (2016). ICH E6(R2) Good Clinical Practice: Integrated Addendum to ICH E6(R1). ICH Harmonised Guideline.","type":"misc","doi":null,"isbn":null,"url":"https://database.ich.org/sites/default/files/E6_R2__Guideline.pdf"}],"related":["phase-ii-clinical-trial","phase-iii-clinical-trial","randomized-clinical-trial","dose-response-analysis","adaptive-randomized-clinical-trial","diagnostic-accuracy-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phase-ii-clinical-trial","name":"Phase II clinical trial","fullName":"Phase II Clinical Trial","aliases":["Phase 2 trial","Phase II study","early efficacy trial","proof-of-concept trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1960s–1970s (formalised in US federal drug regulation)","originator":"U.S. Food and Drug Administration / ICH E8 guidelines (institutionalised framework)","url":"https://scholargate.app/en/epidemiology/phase-ii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/phase-ii-clinical-trial.md","definition":"A Phase II clinical trial is the second stage in the drug or intervention development pipeline, conducted after Phase I safety testing. Its primary goal is to assess whether the intervention shows preliminary efficacy signals in a relevant patient population at the dose established in Phase I, while continuing to characterise the safety and tolerability profile. Phase II trials are generally smaller than Phase III confirmatory trials and serve as critical go/no-go decision points before large-scale investment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Food and Drug Administration / ICH E8 guidelines (institutionalised framework)","year":"1960s–1970s (formalised in US federal drug regulation)","type":"Interventional clinical study design","dataType":"Outcome measurements (response rate, biomarkers, adverse events), patient-level longitudinal data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of Clinical Trials (5th ed.). Springer.","type":"book","doi":null,"isbn":"978-3319185392","url":null},{"ref":"Phase II clinical trial. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Clinical_trial#Phase_II"}],"related":["phase-i-clinical-trial","phase-iii-clinical-trial","randomized-clinical-trial","dose-response-analysis","adaptive-randomized-clinical-trial","diagnostic-accuracy-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phase-iii-clinical-trial","name":"Phase III clinical trial","fullName":"Phase III Confirmatory Clinical Trial","aliases":["Phase 3 trial","confirmatory trial","pivotal trial","Phase III RCT"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1962 (Kefauver-Harris Amendment formalised phased drug development)","originator":"FDA regulatory framework / ICH guidelines","url":"https://scholargate.app/en/epidemiology/phase-iii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/phase-iii-clinical-trial.md","definition":"A Phase III clinical trial is a large-scale, confirmatory randomised controlled trial designed to establish the efficacy and safety of an intervention in the target patient population before regulatory submission. It builds on the signal identified in Phase II, tests the intervention at its proposed dose under controlled conditions, and provides the primary evidence base for marketing authorisation or guideline adoption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"FDA regulatory framework / ICH guidelines","year":"1962 (Kefauver-Harris Amendment formalised phased drug development)","type":"Confirmatory randomised controlled trial","dataType":"Clinical outcome data (efficacy endpoints, safety events, patient-reported outcomes)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of Clinical Trials (5th ed.). Springer.","type":"book","doi":null,"isbn":"978-3319185385","url":null},{"ref":"International Council for Harmonisation (ICH). (1997). E8 General Considerations for Clinical Trials. ICH Harmonised Guideline.","type":"misc","doi":null,"isbn":null,"url":"https://www.ich.org/page/efficacy-guidelines"}],"related":["phase-ii-clinical-trial","phase-iv-study","randomized-clinical-trial","adaptive-randomized-clinical-trial","multicenter-randomized-clinical-trial","cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phase-iv-study","name":"Phase IV study","fullName":"Phase IV Post-Marketing Surveillance Study","aliases":["post-marketing surveillance study","post-approval study","Phase 4 study","PMS study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Formalised 1970s–1990s (ICH E3 guideline 1994)","originator":"Regulatory agencies and pharmaceutical industry (ICH, FDA, EMA frameworks)","url":"https://scholargate.app/en/epidemiology/phase-iv-study","markdownUrl":"https://scholargate.app/en/epidemiology/phase-iv-study.md","definition":"A Phase IV study is a post-marketing surveillance study conducted after a drug, device, or intervention has received regulatory approval. Its primary purpose is to monitor long-term safety, detect rare adverse events, assess effectiveness in routine clinical practice, and explore new indications or populations not adequately represented in earlier trials. Phase IV evidence accumulates continuously throughout a product's commercial life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Regulatory agencies and pharmaceutical industry (ICH, FDA, EMA frameworks)","year":"Formalised 1970s–1990s (ICH E3 guideline 1994)","type":"Post-marketing observational or interventional study","dataType":"Patient records, registries, adverse-event reports, survey data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"International Conference on Harmonisation (ICH). (1994). ICH Harmonised Tripartite Guideline: Structure and Content of Clinical Study Reports E3. ICH Secretariat.","type":"article","doi":null,"isbn":null,"url":"https://www.ich.org/page/efficacy-guidelines"},{"ref":"Phase IV clinical trial. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Phase_IV_clinical_trial"}],"related":["phase-iii-clinical-trial","cohort-study","case-control-study","pharmacovigilance","randomized-clinical-trial","cross-sectional-epidemiological-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phase-locked-loop","name":"Phase-Locked Loop","fullName":"Phase-Locked Loop for Frequency Synchronization and Clock Recovery","aliases":["PLL","Phase lock","Frequency synchronizer"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Control systems, signal processing","year":"1966","originator":"Floyd M. Gardner","url":"https://scholargate.app/en/electrical-engineering/phase-locked-loop","markdownUrl":"https://scholargate.app/en/electrical-engineering/phase-locked-loop.md","definition":"A Phase-Locked Loop (PLL) is a feedback control system that synchronizes an output oscillator to match the phase and frequency of an input signal. Introduced by Gardner in 1966, PLLs are ubiquitous in communications, radar, clock distribution, and power systems. The PLL continuously adjusts its oscillator frequency to minimize the phase error with the input, achieving lock. PLLs are fundamental to modern electronic systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Floyd M. Gardner","subfamily":"Control systems, signal processing","year":"1966","type":"Feedback control loop for frequency and phase synchronization"},"citations":[{"ref":"Gardner, F. M. (1966). Phaselock Techniques. Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/phaselocktechniq0000gard"},{"ref":"Wolaver, D. H. (1991). Phase-Locked-Loop Circuit Design. Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com/program/Wolaver-Phase-Locked-Loop-Circuit-Design/PGM124698.html"},{"ref":"Best, R. E. (2007). Phase-Locked Loops: Design, Simulation, and Applications (5th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Phase-Locked+Loops%3A+Design%2C+Simulation%2C+and+Applications+%285th+ed.%29+Best"}],"related":["droop-control","s-parameter-analysis","transmission-line-matrix-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phase-locking-value","name":"Phase-Locking Value","fullName":"Phase-Locking Value (PLV)","aliases":["PLV","phase synchronization","phase coupling"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Phase-based connectivity","year":"1999","originator":"Jean-Philippe Lachaux","url":"https://scholargate.app/en/neuroimaging/phase-locking-value","markdownUrl":"https://scholargate.app/en/neuroimaging/phase-locking-value.md","definition":"Phase-Locking Value (PLV) is a frequency-domain measure of neural synchronization that quantifies the stability of phase difference between two signals. Introduced by Lachaux and colleagues in 1999, PLV detects phase coupling between brain regions independent of signal amplitude, enabling researchers to characterize functional connectivity from EEG and MEG recordings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Philippe Lachaux","subfamily":"Phase-based connectivity","year":"1999","type":"EEG/MEG functional connectivity analysis"},"citations":[{"ref":"Lachaux, J. P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194–208.","type":"article","doi":"10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C","isbn":null,"url":null},{"ref":"Varela, F., Lachaux, J. P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2(4), 229–239.","type":"article","doi":"10.1038/35067550","isbn":null,"url":null}],"related":["dynamic-functional-connectivity","graph-brain-network-analysis","event-related-potential-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phenological-observation","name":"Phenological Observation","fullName":"Crop Phenological Staging and Development Monitoring","aliases":["Growth stage assessment","Phenological monitoring","Crop stage scale"],"domain":"agronomy","family":"process-pipeline","subfamily":"Growth stages and timing","year":"1974","originator":"Zadoks, Chang, Konzak (cereals); Fehr, Caviness (soybean)","url":"https://scholargate.app/en/agronomy/phenological-observation","markdownUrl":"https://scholargate.app/en/agronomy/phenological-observation.md","definition":"Phenological Observation is an observational and classification pipeline for systematically recording crop development stages from germination to maturity. Standardized through crop-specific scales (Zadoks for cereals, Fehr for soybean), this method enables precise communication of crop status, timing of management decisions (fungicide application, irrigation), and prediction of harvest readiness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zadoks, Chang, Konzak (cereals); Fehr, Caviness (soybean)","subfamily":"Growth stages and timing","year":"1974","type":"Observational and classification pipeline"},"citations":[{"ref":"Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed research, 14(6), 415-421.","type":"article","doi":"10.1111/j.1365-3180.1974.tb01084.x","isbn":null,"url":null},{"ref":"Fehr, W. R., & Caviness, C. E. (1971). Stages of soybean development. Iowa State University Special Report No. 80.","type":"article","doi":null,"isbn":null,"url":"https://www.extension.iastate.edu/"}],"related":["crop-growth-simulation","crop-yield-estimation","irrigation-scheduling-etref","nitrogen-use-efficiency","pesticide-efficacy-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phenological-stage-monitoring","name":"Phenological Stage Monitoring","fullName":"Temporal Tracking of Development Stages from Dormancy to Harvest","aliases":["growth stage assessment","development monitoring","BBCH scale"],"domain":"horticulture","family":"process-pipeline","subfamily":"Temporal development and growth staging","year":"1997","originator":"BBCH Scale consortium","url":"https://scholargate.app/en/horticulture/phenological-stage-monitoring","markdownUrl":"https://scholargate.app/en/horticulture/phenological-stage-monitoring.md","definition":"Phenological stage monitoring uses standardized growth scales to track the developmental progression of plants from dormancy through flowering, fruit development, and maturity. The BBCH scale, formalized in 1997, provides a universal coding system for precise communication of developmental timing. This method enables optimization of management practices (pruning, fertilizing, spraying) at physiologically optimal moments and prediction of harvest timing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"BBCH Scale consortium","subfamily":"Temporal development and growth staging","year":"1997","type":"phenological assessment pipeline"},"citations":[{"ref":"Meier, U. (Ed.). (1997). Growth Stages of Mono- and Dicotyledonous Plants. BBCH Monograph (2nd ed.). Federal Biological Research Centre for Agriculture and Forestry.","type":"article","doi":null,"isbn":null,"url":"https://www.atravision.com/bbch-scale/"},{"ref":"Schwartz, M. D. (Ed.). (1999). Phenology: An Integrative Environmental Science. Kluwer Academic Publishers.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Phenology%3A+An+Integrative+Environmental+Science+Schwartz"}],"related":["pruning-response-analysis","crop-load-management","pollination-efficiency","postharvest-storage-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phenomenological-research","name":"Phenomenological Research","fullName":"Phenomenological Research Method","aliases":["Phenomenology","Descriptive Phenomenology","Interpretive Phenomenology"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"existential-interpretive-inquiry","year":"1900s (Husserl); 1920s (Heidegger)","originator":"Edmund Husserl (descriptive) and Martin Heidegger (interpretive)","url":"https://scholargate.app/en/qualitative-research/phenomenological-research","markdownUrl":"https://scholargate.app/en/qualitative-research/phenomenological-research.md","definition":"Phenomenological research is a qualitative methodology focused on understanding the lived experience of a phenomenon as it is experienced by individuals. Rooted in the philosophical traditions of Edmund Husserl (descriptive phenomenology) and Martin Heidegger (interpretive phenomenology), this approach seeks to uncover the essential structures and meanings of human experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edmund Husserl (descriptive) and Martin Heidegger (interpretive)","subfamily":"existential-interpretive-inquiry","year":"1900s (Husserl); 1920s (Heidegger)","type":"Method"},"citations":[{"ref":"Husserl, E. (1931). Cartesian meditations: An introduction to phenomenology (D. Cairns, Trans.). Martinus Nijhoff.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Husserl%2C%20E.%20(1931).%20Cartesian%20meditations%3A%20An%20introduction%20to%20phenomenology%20(D.%20Cairns%2C%20Trans.).%20Martinus%20Nijhoff."},{"ref":"Heidegger, M. (1962). Being and time (J. Macquarrie & E. Robinson, Trans.). Harper & Row.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Heidegger%2C%20M.%20(1962).%20Being%20and%20time%20(J.%20Macquarrie%20%26%20E.%20Robinson%2C%20Trans.).%20Harper%20%26%20Row."},{"ref":"van Manen, M. (1990). Researching lived experience: Human science for an action sensitive pedagogy. The State University of New York Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=van%20Manen%2C%20M.%20(1990).%20Researching%20lived%20experience%3A%20Human%20science%20for%20an%20action%20sensitive%20pedagogy.%20The%20State%20University"}],"related":["interpretative-phenomenological-analysis","narrative-inquiry","case-study-research","bracketing-method","epoche"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phenomenology-in-education-research","name":"Phenomenology in education research","fullName":"Phenomenological Research in Education","aliases":["educational phenomenology","phenomenology of education","lived-experience research in education","pedagogical phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990 (van Manen's systematic educational application); philosophical roots ~1900–1913","originator":"Max van Manen (education application); Edmund Husserl (philosophical foundation)","url":"https://scholargate.app/en/qualitative/phenomenology-in-education-research","markdownUrl":"https://scholargate.app/en/qualitative/phenomenology-in-education-research.md","definition":"Phenomenology in education research is a qualitative approach that investigates how students, teachers, and educational actors experience pedagogical phenomena — learning, teaching, assessment, transition, or identity — from the inside. Drawing on van Manen's human science framework and Husserlian and Heideggerian traditions, it seeks to reveal the essential lived structures of educational experience rather than measure outcomes or test hypotheses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Max van Manen (education application); Edmund Husserl (philosophical foundation)","year":"1990 (van Manen's systematic educational application); philosophical roots ~1900–1913","type":"Qualitative research approach","dataType":"In-depth interviews, reflective writing, classroom observations, student journals (text data)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null},{"ref":"Creswell, J. W. (2013). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["phenomenology","hermeneutic-phenomenology","interpretive-phenomenological-analysis","narrative-inquiry","case-study","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phenomenology","name":"Phenomenology","fullName":"Phenomenological Research","aliases":["Fenomenoloji","phenomenological inquiry","phenomenological analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":null,"year":"Early 20th century (Husserl ~1900–1913; Heidegger ~1927)","originator":"Edmund Husserl (transcendental); Martin Heidegger (hermeneutic)","url":"https://scholargate.app/en/qualitative/phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/phenomenology.md","definition":"Phenomenology is a qualitative research approach that investigates how participants live through and make sense of a specific experience. Rooted in the philosophy of Edmund Husserl and extended by Martin Heidegger, it aims to reveal the essential structures of lived experience rather than to measure or predict outcomes. The two most widely applied variants are Husserl's transcendental phenomenology, which seeks universal essences, and Heidegger's hermeneutic phenomenology, which emphasises interpretation within context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edmund Husserl (transcendental); Martin Heidegger (hermeneutic)","year":"Early 20th century (Husserl ~1900–1913; Heidegger ~1927)","type":"Qualitative research approach","dataType":"In-depth interviews, focus groups (text data)","typicalSampleSize":"5–25 participants","mainVariants":"Transcendental (Husserlian) / Hermeneutic (Heideggerian)"},"citations":[{"ref":"Moustakas, C. (1994). Phenomenological Research Methods. Sage.","type":"book","doi":null,"isbn":"978-0803957466","url":null}],"related":["case-study","narrative-analysis","discourse-analysis","grounded-theory","ethnography","thematic-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phf-copras","name":"PHF-COPRAS","fullName":"Probabilistic Hesitant extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Song, H. F. Chen, Z. C.","url":"https://scholargate.app/en/decision-making/phf-copras","markdownUrl":"https://scholargate.app/en/decision-making/phf-copras.md","definition":"PHF-COPRAS (Probabilistic Hesitant extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Song, H. F. Chen, Z. C. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Song, H. F. Chen, Z. C.","subfamily":"Ranking","year":"2021","type":"Probabilistic Hesitant ranking — Probabilistic Hesitant Fuzzy Element (PHFE: {γ|p} pairs)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Song, H. F., Chen, Z. C. (2021). Multi-attribute decision-making method based distance and COPRAS method with probabilistic hesitant fuzzy environment. International Journal of Computational Intelligence Systems","type":"article","doi":"10.2991/ijcis.d.210318.001","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","merec","swara","fucom"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phf-edas","name":"PHF-EDAS","fullName":"Extended Hesitant Fuzzy Linguistic EDAS (EHFL-EDAS)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Feng, X., Wei, C., Liu, Q.","url":"https://scholargate.app/en/decision-making/phf-edas","markdownUrl":"https://scholargate.app/en/decision-making/phf-edas.md","definition":"PHF-EDAS (Extended Hesitant Fuzzy Linguistic EDAS (EHFL-EDAS)) is a ranking multi-criteria decision-making (MCDM) method introduced by Feng, X., Wei, C., Liu, Q. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Feng, X., Wei, C., Liu, Q.","subfamily":"Ranking","year":"2018","type":"Probabilistic Hesitant outranking/ranking — Probabilistic Hesitant Fuzzy Element (PHFE: {γ|p} pairs)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Feng, X., Wei, C., Liu, Q. (2018). EDAS Method for Extended Hesitant Fuzzy Linguistic Multi-criteria Decision Making. International Journal of Fuzzy Systems","type":"article","doi":"10.1007/s40815-018-0504-5","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phf-topsis","name":"PHF-TOPSIS","fullName":"Probabilistic Hesitant extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":null,"originator":"PENDING_LITERATURE_SEARCH","url":"https://scholargate.app/en/decision-making/phf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/phf-topsis.md","definition":"PHF-TOPSIS (Probabilistic Hesitant extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by PENDING_LITERATURE_SEARCH. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"PENDING_LITERATURE_SEARCH","subfamily":"Ranking","type":"Probabilistic Hesitant outranking/ranking — Probabilistic Hesitant Fuzzy Element (PHFE: {γ|p} pairs)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"PENDING_LITERATURE_SEARCH (). PENDING — PHF-TOPSIS specific seminal not confirmed. Zhang et al. 2017 (doi:10.1016/j.inffus.2017.02.001) is the foundational PHFS paper, not a PHF-TOPSIS paper. L.formulation.en cites 'Zhu & Xu 2018' as PHF-TOPSIS anchor — unverified. Candidate from search: Naeem et al. 2021 'Extended TOPSIS method based on the entropy measure and probabilistic hesitant fuzzy information' (JIFS, doi:10.3233/JIFS-202700) — not confirmed as the canonical seminal..","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=PENDING%20%E2%80%94%20PHF-TOPSIS%20specific%20seminal%20not%20confirmed.%20Zhang%20et%20al.%202017%20%28doi%3A10.1016/j.inffus.2017.02.001%29%20is%20the%20foundational%20PHFS%20paper%2C%20not%20a%20PHF-TOPSIS%20paper.%20L.formulation.en%20cites%20%27Zhu%20%26%20Xu%202018%27%20as%20PHF-TOPSIS%20anchor%20%E2%80%94%20unverified.%20Candidate%20from%20search%3A%20Naeem%20et%20al.%202021%20%27Extended%20TOPSIS%20method%20based%20on%20the%20entropy%20measure%20and%20probabilistic%20hesitant%20fuzzy%20information%27%20%28JIFS%2C%20doi%3A10.3233/JIFS-202700%29%20%E2%80%94%20not%20confirmed%20as%20the%20canonical%20seminal."}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phf-vikor","name":"PHF-VIKOR","fullName":"Probabilistic Hesitant extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Zhang, S. Xu, Z. S. He, Y.","url":"https://scholargate.app/en/decision-making/phf-vikor","markdownUrl":"https://scholargate.app/en/decision-making/phf-vikor.md","definition":"PHF-VIKOR (Probabilistic Hesitant extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhang, S. Xu, Z. S. He, Y. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhang, S. Xu, Z. S. He, Y.","subfamily":"Ranking","year":"2017","type":"Probabilistic Hesitant outranking/ranking — Probabilistic Hesitant Fuzzy Element (PHFE: {γ|p} pairs)","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zhang, S., Xu, Z. S., He, Y. (2017). Operations and integrations of probabilistic hesitant fuzzy information in decision making. Information Fusion","type":"article","doi":"10.1016/j.inffus.2017.02.001","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phfs-ehvar","name":"PHFS-EHVAR","fullName":"PHFS-EHVaR — Expected Hesitant Value-at-Risk for Probabilistic Hesitant Fuzzy Sets (Zhou-Xu 2017)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Zhou, W. Xu, Z.","url":"https://scholargate.app/en/decision-making/phfs-ehvar","markdownUrl":"https://scholargate.app/en/decision-making/phfs-ehvar.md","definition":"PHFS-EHVAR (PHFS-EHVaR — Expected Hesitant Value-at-Risk for Probabilistic Hesitant Fuzzy Sets (Zhou-Xu 2017)) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, W. Xu, Z.","subfamily":"Ranking","year":"2017","type":"Extended tail decision-making method for probabilistic hesitant fuzzy environments. EHVaR improves upon HVaR by computing the expected (weighted sum) value over the entire left tail, not just the boundary point. EHVaR(h, X) = Σ_{i=1}^{k-1} c_i·p_i + c_k·(X - Σ_{i=1}^{k-1} p_i) where k satisfies P_{k-1} < X ≤ P_k. Always strictly separates PHFEs that HVaR cannot distinguish. Supports group decision-making via dynamic weight programming model.","value_space":"probabilistic_hesitant","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Zhou, W., Xu, Z. (2017). Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2017.06.057","isbn":null,"url":null}],"related":["phfs-hvar"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phfs-hvar","name":"PHFS-HVAR","fullName":"PHFS-HVaR — Hesitant Value-at-Risk for Probabilistic Hesitant Fuzzy Sets (Zhou-Xu 2017)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Zhou, W. Xu, Z.","url":"https://scholargate.app/en/decision-making/phfs-hvar","markdownUrl":"https://scholargate.app/en/decision-making/phfs-hvar.md","definition":"PHFS-HVAR (PHFS-HVaR — Hesitant Value-at-Risk for Probabilistic Hesitant Fuzzy Sets (Zhou-Xu 2017)) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, W. Xu, Z.","subfamily":"Ranking","year":"2017","type":"Tail decision-making method for probabilistic hesitant fuzzy environments. Input is a PHFE (probabilistic hesitant fuzzy element) — an HFE where each membership value c_l carries an explicit occurrence probability p_l with Σp_l=1. HVaR(h, X) is the boundary membership value at cumulative probability X: the largest c_k such that P(c ≤ c_k) ≥ X. Directly analogous to classical Value-at-Risk (VaR). Intended for risk-averse investors who focus on worst-case outcomes under a given certainty degree.","value_space":"probabilistic_hesitant","uncertainty":"epistemic","compensation":"none","rank_reversal":true},"citations":[{"ref":"Zhou, W., Xu, Z. (2017). Expected hesitant VaR for tail decision making under probabilistic hesitant fuzzy environment. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2017.06.057","isbn":null,"url":null}],"related":["phfs-ehvar"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"philadelphia-mindfulness-scale","name":"Philadelphia Mindfulness Scale","fullName":"Philadelphia Mindfulness Scale (PHLMS)","aliases":["PHLMS","PHLMS-20"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"trait-mindfulness","year":"2008","originator":"Lizabeth A. Cardaciotto, James D. Herbert, and colleagues at Drexel University","url":"https://scholargate.app/en/mindfulness-psychology/philadelphia-mindfulness-scale","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/philadelphia-mindfulness-scale.md","definition":"The Philadelphia Mindfulness Scale (PHLMS) is a 20-item self-report instrument measuring trait mindfulness across two core dimensions: Present-Moment Awareness and Acceptance. Developed by Cardaciotto, Herbert, and colleagues at Drexel University and published in Assessment in 2008, the PHLMS emphasizes the integration of attentional and acceptance-based processes central to contemporary mindfulness theory and practice. The two-factor structure reflects the distinction between the ability to focus attention on present experience and the capacity to receive that experience without judgment or resistance—processes that jointly characterize psychological flexibility and adaptive mindfulness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lizabeth A. Cardaciotto, James D. Herbert, and colleagues at Drexel University","subfamily":"trait-mindfulness","year":"2008","type":"Self-report"},"citations":[{"ref":"Cardaciotto, L., Herbert, J. D., Forman, E. M., Moitra, E., & Farrow, V. (2008). The assessment of present-moment awareness and acceptance: The Philadelphia Mindfulness Scale. Assessment, 15(2), 204-223.","type":"article","doi":"10.1177/1073191107311467","isbn":null,"url":null}],"related":["five-facet-mindfulness-questionnaire","freiburg-mindfulness-inventory","mindful-attention-awareness-scale","cognitive-and-affective-mindfulness","toronto-mindfulness-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phillips-ouliaris-test","name":"Phillips-Ouliaris Test","fullName":"Phillips-Ouliaris Residual-Based Cointegration Test","aliases":["Phillips-Ouliaris Cointegration Test","PO Residual-Based Test","Residual-Based Cointegration Test","Phillips-Ouliaris Eşbütünleşme Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Cointegration","year":1990,"originator":"Peter Phillips & Sam Ouliaris","url":"https://scholargate.app/en/econometrics/phillips-ouliaris-test","markdownUrl":"https://scholargate.app/en/econometrics/phillips-ouliaris-test.md","definition":"The Phillips-Ouliaris test, introduced by Phillips and Ouliaris in their 1990 Econometrica article, is a residual-based nonparametric procedure for testing the null hypothesis of no cointegration among a set of integrated I(1) time series. It corrects OLS residuals from a cointegrating regression for serial correlation and endogeneity using kernel-based long-run variance estimators, yielding two statistics—Z_alpha (variance-ratio) and Z_t (normalized coefficient)—whose asymptotic distributions are tabulated specifically for systems with multiple stochastic regressors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Phillips & Sam Ouliaris","year":1990,"type":"Residual-based nonparametric cointegration test","subfamily":"Cointegration","null_hypothesis":"No cointegration among I(1) variables","test_statistics":"Z_alpha (variance-ratio) and Z_t (unit-root)"},"citations":[{"ref":"Phillips, P. C. B., & Ouliaris, S. (1990). Asymptotic properties of residual based tests for cointegration. Econometrica, 58(1), 165–193.","type":"article","doi":"10.2307/2938339","isbn":null,"url":null}],"related":["cointegration-test","vecm","phillips-perron-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phillips-perron-test","name":"Phillips-Perron Test","fullName":"Phillips-Perron (PP) Unit-Root Test","aliases":["PP test","Phillips-Perron unit root test","Phillips-Perron birim kök testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1988,"originator":"Peter C. B. Phillips & Pierre Perron","url":"https://scholargate.app/en/econometrics/phillips-perron-test","markdownUrl":"https://scholargate.app/en/econometrics/phillips-perron-test.md","definition":"The Phillips-Perron test, proposed by Peter Phillips and Pierre Perron in 1988, tests for a unit root in a time series, like the Augmented Dickey-Fuller test, but corrects for autocorrelation and heteroskedasticity in the errors non-parametrically rather than by adding lagged differences. It runs a simple Dickey-Fuller regression and then adjusts the test statistic using a long-run variance estimate, so the practitioner need not choose a lag length for the regression itself.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter C. B. Phillips & Pierre Perron","year":1988,"type":"Unit-root test for stationarity","nullHypothesis":"Series contains a unit root (non-stationary)","distribution":"Dickey-Fuller (non-standard)","minSample":50},"citations":[{"ref":"Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.","type":"article","doi":"10.1093/biomet/75.2.335","isbn":null,"url":null},{"ref":"Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.","type":"article","doi":"10.2307/1913610","isbn":null,"url":null}],"related":["adf-test","kpss-test","cointegration-test","arima"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phillips-perron-unit-root-test","name":"Phillips-Perron unit root test","fullName":"Phillips-Perron Unit Root Test","aliases":["PP test","PP unit root test","Phillips-Perron test","nonparametric unit root test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988","originator":"Peter C. B. Phillips and Pierre Perron","url":"https://scholargate.app/en/econometrics/phillips-perron-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/phillips-perron-unit-root-test.md","definition":"The Phillips-Perron (PP) test is a nonparametric unit root test for time series that corrects for serial correlation and heteroscedasticity in the error term without adding lagged differences. Introduced by Phillips and Perron (1988), it applies a kernel-based long-run variance estimator to adjust the Dickey-Fuller statistic, making it robust to a wide class of weakly dependent error processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter C. B. Phillips and Pierre Perron","year":"1988","type":"Hypothesis test (unit root)","dataType":"Univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.","type":"article","doi":"10.1093/biomet/75.2.335","isbn":null,"url":null},{"ref":"Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691042893","url":null}],"related":["augmented-dickey-fuller-unit-root-test","kpss-test","zivot-andrews-structural-break-test","granger-causality-test","johansen-cointegration-test","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phobia-scales-brief","name":"Brief Phobia Scales","fullName":"Brief Phobia Scales for Specific Phobias","aliases":["BPS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"specific phobia assessment","year":"2005","originator":"Steven R. Woody, Jarl M. Lohr, and multiple developers of specific phobia scales","url":"https://scholargate.app/en/clinical-psychology/phobia-scales-brief","markdownUrl":"https://scholargate.app/en/clinical-psychology/phobia-scales-brief.md","definition":"Brief Phobia Scales are a collection of short, focused self-report instruments designed to measure fear and anxiety related to specific phobias such as agoraphobia, claustrophobia, fear of flying, fear of heights, and other circumscribed fears. Developed by various researchers including Woody and Lohr, these scales provide rapid, phobia-specific assessment in clinical and research settings, valued for their brevity and practical utility in treatment planning and outcome monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven R. Woody, Jarl M. Lohr, and multiple developers of specific phobia scales","subfamily":"specific phobia assessment","year":"2005","type":"Self-report phobia fear scales"},"citations":[{"ref":"Woody, S. R., & Lohr, J. M. (2005). A review of brief instruments for assessing anxiety and phobia. In C. Newman, C. Z. Datillio, & R. P. Halstead (Eds.), Comprehensive Handbook of Cognitive and Behavioral Treatments for Anxiety Disorders. New York: Routledge.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/16104228"}],"related":["gad-7","beck-anxiety-inventory","liebowitz-social-anxiety-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"photogrammetry","name":"Photogrammetry","fullName":"Close-Range Photogrammetry","aliases":["3D photogrammetry","structure from motion photogrammetry","SfM photogrammetry","digital photogrammetry"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"3D reconstruction and morphometrics","year":"1850s (foundations); 2000s (digital/SfM era in life sciences)","originator":"Multiple contributors (Laussedat ~1850s; modern SfM by Longuet-Higgins 1981)","url":"https://scholargate.app/en/veterinary-science/photogrammetry","markdownUrl":"https://scholargate.app/en/veterinary-science/photogrammetry.md","definition":"Photogrammetry is a non-contact measurement technique that derives accurate 3D geometry and spatial dimensions from sets of overlapping 2D photographs. In veterinary science it is used to obtain body measurements, wound areas, limb morphology, and anatomical volumes from live animals, carcasses, or skeletal specimens without physical restraint or invasive procedures. Structure-from-Motion (SfM) algorithms have made the method accessible to field and clinic settings using consumer-grade cameras.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Laussedat ~1850s; modern SfM by Longuet-Higgins 1981)","year":"1850s (foundations); 2000s (digital/SfM era in life sciences)","type":"Non-contact 3D measurement technique","dataType":"Overlapping 2D photographs (digital images)","subfamily":"3D reconstruction and morphometrics"},"citations":[{"ref":"Falkingham, P. L. (2012). Acquisition of high resolution three-dimensional models using free, open-source, photogrammetric software. Palaeontologia Electronica, 15(1), 1T.","type":"journal-article","doi":null,"isbn":null,"url":"https://palaeo-electronica.org/content/2012-issue-1-articles/92-3d-modelling"},{"ref":"Sherrill, L. R., et al. (2019). Use of photogrammetry to measure body dimensions and predict body weight of beef cattle. Translational Animal Science, 3(3), 1467-1478.","type":"journal-article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Use+of+photogrammetry+to+measure+body+dimensions+and+predict+body+weight+of+beef+cattle+Sherrill"}],"related":["computed-tomography","morphometric-analysis","image-analysis","3d-scanning","digital-image-correlation","ultrasound-imaging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"photoplethysmography","name":"Photoplethysmography","fullName":"Photoplethysmography (PPG) Analysis","aliases":["PPG","Pulse oximetry","Reflectance photometry"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Optical biomedical sensing","year":"1937","originator":"Hertzman","url":"https://scholargate.app/en/biomechanics/photoplethysmography","markdownUrl":"https://scholargate.app/en/biomechanics/photoplethysmography.md","definition":"Photoplethysmography (PPG) measures blood volume changes in tissue using light absorption, providing a non-invasive optical window into cardiovascular dynamics. Originally developed by Hertzman in 1937, PPG is now ubiquitous in pulse oximetry, smartwatches, and research applications for monitoring heart rate, blood oxygenation, and vascular function.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hertzman","subfamily":"Optical biomedical sensing","year":"1937","type":"Optical signal acquisition and analysis pipeline"},"citations":[{"ref":"Allen, J. (2007). Photoplethysmography and its application in clinical physiology. Physiology & Behavior, 107(4), 540-548.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Photoplethysmography+and+its+application+in+clinical+physiology+Allen"},{"ref":"Webster, J. G. (2002). Medical Instrumentation: Application and Design (4th ed.). Wiley.","type":"book","doi":null,"isbn":null,"url":"https://wiley.com"}],"related":["pan-tompkins-qrs-detection","heart-rate-variability","windkessel-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phq-9-screening","name":"PHQ-9 Depression Screening","fullName":"Patient Health Questionnaire-9","aliases":["PHQ-9","depression screening scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Depression screening","year":"2001","originator":"Kurt Kroenke, Robert L. Spitzer, Janet B. W. Williams","url":"https://scholargate.app/en/clinical-psychology/phq-9-screening","markdownUrl":"https://scholargate.app/en/clinical-psychology/phq-9-screening.md","definition":"The Patient Health Questionnaire-9 (PHQ-9) is a brief, 9-item self-report instrument for screening and measuring the severity of depressive symptoms in primary care and mental health settings. Developed by Kurt Kroenke and colleagues in 2001, the PHQ-9 is now widely used in healthcare systems worldwide as a rapid, accurate screening and monitoring tool.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kurt Kroenke, Robert L. Spitzer, Janet B. W. Williams","subfamily":"Depression screening","year":"2001","type":"Brief self-report screening instrument"},"citations":[{"ref":"Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613.","type":"article","doi":"10.1046/j.1525-1497.2001.016009606.x","isbn":null,"url":null},{"ref":"Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B. W., Berry, J. T., & Mokdad, A. H. (2009). The PHQ-8 as a measure of current depression in the general population. Journal of Affective Disorders, 114(1–3), 163–173.","type":"article","doi":"10.1016/j.jad.2008.06.026","isbn":null,"url":null}],"related":["beck-depression-inventory","generalized-anxiety-disorder-7","structured-clinical-interview-dsm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phq-9","name":"Patient Health Questionnaire-9","fullName":"Patient Health Questionnaire-9: Depression Scale","aliases":["PHQ-9","Patient Health Questionnaire Depression Module"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"mood-disorder-screening","year":"2001","originator":"Kurt Kroenke","url":"https://scholargate.app/en/clinical-psychology/phq-9","markdownUrl":"https://scholargate.app/en/clinical-psychology/phq-9.md","definition":"The PHQ-9 is a brief, nine-item self-report questionnaire developed by Kroenke, Spitzer, and Williams to screen for and measure the severity of depressive symptoms. Published in 2001 in the Journal of General Internal Medicine, it has become one of the most widely used depression screening instruments globally. The scale maps directly to DSM-IV diagnostic criteria for major depressive disorder, making it valuable in both clinical and research settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kurt Kroenke","subfamily":"mood-disorder-screening","year":"2001","type":"Self-report questionnaire"},"citations":[{"ref":"Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613.","type":"article","doi":"10.1046/j.1525-1497.2001.016009606.x","isbn":null,"url":null},{"ref":"Kroenke, K., Spitzer, R. L., Williams, J. B., & Löwe, B. (2010). The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. General Hospital Psychiatry, 32(4), 345–359.","type":"article","doi":"10.1016/j.genhosppsych.2010.03.006","isbn":null,"url":null},{"ref":"Manea, L., Gilbody, S., & McMillan, D. (2012). Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. CMAJ, 184(15), E682–E689.","type":"article","doi":"10.1503/cmaj.110829","isbn":null,"url":null}],"related":["bdi-ii","hamilton-depression-rating-scale","montgomery-asberg-depression","quick-inventory-depressive","patient-global-impression-change"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phylogenetic-analysis","name":"Phylogenetic Analysis","fullName":"Phylogenetic Analysis of Molecular Sequence Data","aliases":["molecular phylogenetics","phylogenetic inference","evolutionary tree reconstruction","phylogenomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"1960s-1981 (distance trees ~1967; ML framework formalised 1981)","originator":"Joseph Felsenstein (maximum likelihood framework); Walter Fitch and Emanuel Margoliash (distance methods)","url":"https://scholargate.app/en/bioinformatics/phylogenetic-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/phylogenetic-analysis.md","definition":"Phylogenetic analysis reconstructs the evolutionary history of organisms, genes, or proteins by comparing molecular sequence data and estimating the branching tree that best explains observed similarities and differences. Rooted in the work of Felsenstein and colleagues from the 1960s onward, it is a cornerstone technique in evolutionary biology, microbiology, epidemiology, and comparative genomics, supporting tasks from tracing viral outbreak origins to classifying novel species.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph Felsenstein (maximum likelihood framework); Walter Fitch and Emanuel Margoliash (distance methods)","year":"1960s-1981 (distance trees ~1967; ML framework formalised 1981)","type":"Computational inference method","dataType":"Aligned nucleotide or amino acid sequences (FASTA/NEXUS), distance matrices","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates.","type":"book","doi":null,"isbn":"978-0878931774","url":null},{"ref":"Felsenstein, J. (1981). Evolutionary trees from DNA sequences: A maximum likelihood approach. Journal of Molecular Evolution, 17(6), 368-376.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evolutionary+trees+from+DNA+sequences+A+maximum+likelihood+approach+Felsenstein+1981"}],"related":["sequence-alignment","genome-wide-association-study","microbiome-diversity-analysis","rna-seq-differential-expression","variant-calling","eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phylogenetic-independent-contrasts","name":"Phylogenetic Independent Contrasts","fullName":"Phylogenetic Independent Contrasts for Comparative Analysis","aliases":["PIC","Contrasts method","Felsenstein's contrasts"],"domain":"genetics","family":"process-pipeline","subfamily":"Comparative methods","year":"1985","originator":"Joseph Felsenstein","url":"https://scholargate.app/en/genetics/phylogenetic-independent-contrasts","markdownUrl":"https://scholargate.app/en/genetics/phylogenetic-independent-contrasts.md","definition":"Phylogenetic Independent Contrasts (PIC) is a comparative statistical method that tests for associations between traits across species while accounting for shared evolutionary history. Developed by Joseph Felsenstein in 1985, PIC solves a fundamental problem in comparative biology: related species share traits due to common ancestry, not independent evolution, which violates the statistical assumption of independence. By comparing trait differences between sister species pairs, PIC removes the confounding effects of phylogenetic relatedness and enables robust evolutionary inferences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph Felsenstein","subfamily":"Comparative methods","year":"1985","type":"Statistical comparative method"},"citations":[{"ref":"Felsenstein, J. (1985). Phylogenies and the comparative method. American Naturalist, 125(1), 1–15.","type":"article","doi":"10.1086/284325","isbn":null,"url":null},{"ref":"Harvey, P. H., & Pagel, M. D. (1991). The comparative method in evolutionary biology. Oxford: Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://global.oup.com/academic/product/the-comparative-method-in-evolutionary-biology-9780198540671"},{"ref":"Garland, T., Harvey, P. H., & Ives, A. R. (1992). Procedures for the analysis of comparative data using phylogenetically independent contrasts. Systematic Biology, 41(1), 18–32.","type":"article","doi":"10.1093/sysbio/41.1.18","isbn":null,"url":null}],"related":["coalescent-theory","ancestral-state-reconstruction","f-statistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"physical-activity-enjoyment-scale","name":"Physical Activity Enjoyment Scale","fullName":"Physical Activity Enjoyment Scale","aliases":["PACES","Physical Activity Enjoyment"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Exercise Enjoyment & Affect","year":"1991","originator":"Dorothy Kendzierski and Kenneth J. DeCarlo","url":"https://scholargate.app/en/health-behavior/physical-activity-enjoyment-scale","markdownUrl":"https://scholargate.app/en/health-behavior/physical-activity-enjoyment-scale.md","definition":"The Physical Activity Enjoyment Scale (PACES), developed by Kendzierski and DeCarlo (1991), is a 16-item measure of the positive affective responses and enjoyment experienced during or after physical activity. Based on the premise that enjoyment is a powerful predictor of exercise adherence and intrinsic motivation, PACES assesses feelings such as pleasure, fun, satisfaction, and interest during physical activity. The instrument uses semantic differential responses (e.g., 'boring–interesting', 'dull–fun', 'unpleasant–pleasant') to capture the hedonic experience of exercise. PACES is widely used in exercise science, health promotion, and physical education research to identify activities that are most enjoyable for specific populations and to evaluate whether interventions enhance exercise enjoyment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy Kendzierski and Kenneth J. DeCarlo","subfamily":"Exercise Enjoyment & Affect","year":"1991","type":"Self-report questionnaire"},"citations":[{"ref":"Kendzierski, D., & DeCarlo, K. J. (1991). Physical Activity Enjoyment Scale: two validation studies. Journal of Sport and Exercise Psychology, 13(1), 50-64.","type":"article","doi":"10.1123/jsep.13.1.50","isbn":null,"url":null}],"related":["behavioral-regulation-exercise","self-determination-theory-scale","exercise-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"physical-self-description-questionnaire","name":"Physical Self-Description Questionnaire","fullName":"Physical Self-Description Questionnaire (PSDQ)","aliases":["PSDQ","Physical Self-Concept"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"physical-self-concept-and-body-image","year":"1994","originator":"Herbert W. Marsh, Geoffrey E. Richards","url":"https://scholargate.app/en/sport-psychology/physical-self-description-questionnaire","markdownUrl":"https://scholargate.app/en/sport-psychology/physical-self-description-questionnaire.md","definition":"The PSDQ is a 40-item questionnaire measuring multidimensional physical self-concept—how individuals perceive and evaluate themselves across 11 physical domains including strength, endurance, body appearance, sports competence, and fitness. Developed by Marsh and colleagues in 1994, the PSDQ has become the leading instrument for assessing physical self-concept in youth and adult athletes and is valuable for understanding body image, exercise motivation, and psychological wellbeing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herbert W. Marsh, Geoffrey E. Richards","subfamily":"physical-self-concept-and-body-image","year":"1994","type":"Self-report multidimensional physical self-concept questionnaire"},"citations":[{"ref":"Marsh, H. W., Richards, G. E., Johnson, S., Roche, L., & Tremayne, P. (1994). Physical Self-Description Questionnaire: Psychometric properties and a multitrait-multimethod analysis. Journal of Sport & Exercise Psychology, 16(3), 270–305.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Physical+Self-Description+Questionnaire%3A+Psychometric+properties+and+a+multitrait-multimethod+analysis+Marsh"},{"ref":"Marsh, H. W., Papaioannou, A., & Theodorakis, Y. (2006). Motivational constructs across cultures: Validation of expectancy-value theory and dimensional comparison model. Journal of Educational Psychology, 98(2), 396–409.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Motivational+constructs+across+cultures%3A+Validation+of+expectancy-value+theory+and+dimensional+comparison+model+Marsh"}],"related":["sport-confidence-inventory","athletic-identity-measurement-scale","mental-toughness-questionnaire","task-ego-orientation-sport"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"physiologically-based-pharmacokinetics","name":"Physiologically Based Pharmacokinetics","fullName":"Physiologically Based Pharmacokinetics (PBPK)","aliases":["PBPK","PBPK modeling"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Quantitative Systems Pharmacology","year":"1997","originator":"Ivan Nestorov","url":"https://scholargate.app/en/pharmacology/physiologically-based-pharmacokinetics","markdownUrl":"https://scholargate.app/en/pharmacology/physiologically-based-pharmacokinetics.md","definition":"PBPK is a mechanistic modeling framework that uses physiological parameters, tissue properties, and drug-specific attributes to predict drug concentration time profiles in the body. Developed rigorously in the 1990s by researchers including Nestorov, PBPK integrates anatomy, biochemistry, and kinetics to enable rational drug development, bridging in vitro data to clinical outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ivan Nestorov","subfamily":"Quantitative Systems Pharmacology","year":"1997","type":"predictive modeling"},"citations":[{"ref":"Nestorov, I. (1997). Sensitivity analysis of pharmacokinetic and pharmacodynamic systems. Journal of Pharmacokinetics and Biopharmaceutics, 25(4), 529-543.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sensitivity+analysis+of+pharmacokinetic+and+pharmacodynamic+systems+Nestorov"},{"ref":"Reddy, M. B., Yang, R. S., Clewell, H. J., & Andersen, M. E. (2005). Physiologically based pharmacokinetic modeling: science and applications. Hoboken, NJ: John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/book/10.1002/0471769223"}],"related":["in-vitro-in-vivo-correlation","michaelis-menten-kinetics","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phytolith-analysis","name":"Phytolith Analysis","fullName":"Phytolith Analysis (Plant Silica Body Analysis)","aliases":["plant opal analysis","opal phytolith analysis","phytolith morphotype analysis"],"domain":"agronomy","family":"process-pipeline","subfamily":"Archaeobotany / Paleoecology / Soil Science","year":"1841 (first description); modern analytical framework 1970s–1990s","originator":"Multiple contributors (Ehrenberg, 1841; systematised by Rovner and Piperno, late 20th century)","url":"https://scholargate.app/en/agronomy/phytolith-analysis","markdownUrl":"https://scholargate.app/en/agronomy/phytolith-analysis.md","definition":"Phytolith analysis is a laboratory technique used to identify and quantify microscopic silica bodies deposited in plant cells, recovered from soils, sediments, or archaeological contexts. Because phytoliths preserve long after organic material has decayed, the method is central to reconstructing past vegetation, crop histories, land use, and soil development across agronomy, paleoecology, and archaeobotany.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Ehrenberg, 1841; systematised by Rovner and Piperno, late 20th century)","year":"1841 (first description); modern analytical framework 1970s–1990s","type":"Microscopic morphological analysis","dataType":"Microscopic silica bodies extracted from plant tissue or sediment","subfamily":"Archaeobotany / Paleoecology / Soil Science"},"citations":[{"ref":"Piperno, D. R. (2006). Phytoliths: A Comprehensive Guide for Archaeologists and Paleoecologists. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759103481","url":null},{"ref":"Strömberg, C. A. E. (2011). Evolution of grasses and grassland ecosystems. Annual Review of Earth and Planetary Sciences, 39, 517–544.","type":"article","doi":"10.1146/annurev-earth-040809-152402","isbn":null,"url":null}],"related":["pollen-analysis","stable-isotope-analysis","sediment-micromorphology","dendrochronology","soil-organic-matter-analysis","remote-sensing-vegetation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"phytoplankton-size-class","name":"Phytoplankton Size Class","fullName":"Phytoplankton Size Class Analysis","aliases":["Size-fractionated Chlorophyll","Phytoplankton Taxonomy"],"domain":"oceanography","family":"process-pipeline","subfamily":"Ecological Classification","year":"1978","originator":"John McN. Sieburth","url":"https://scholargate.app/en/oceanography/phytoplankton-size-class","markdownUrl":"https://scholargate.app/en/oceanography/phytoplankton-size-class.md","definition":"Phytoplankton size classification is a fundamental framework for organizing plankton communities and understanding their ecological roles and biogeochemical impacts. Developed by Sieburth, Smetacek, and Lenz in 1978, size classes (pico-, nano-, micro-, macro-phytoplankton) define distinct functional groups with different nutritional requirements, growth rates, grazing vulnerabilities, and sinking rates. Size-based classification enables rapid assessment of plankton community structure and prediction of ecosystem responses to environmental change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John McN. Sieburth","subfamily":"Ecological Classification","year":"1978","type":"taxonomic"},"citations":[{"ref":"Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: heterotrophic compartments of the plankton and their relationship to plankton size fractions. Limnology and Oceanography, 23(6), 1256-1263.","type":"article","doi":"10.4319/lo.1978.23.6.1256","isbn":null,"url":null},{"ref":"Malone, T. C. (1980). Algal size. In I. Morris (Ed.), The Physiological Ecology of Phytoplankton (pp. 433-463). University of California Press.","type":"article","doi":null,"isbn":null,"url":"https://www.ucpress.edu/"}],"related":["ocean-color-chlorophyll-a","harmful-algal-bloom-monitoring","ctd-profiling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"picker-patient-experience","name":"Picker Patient Experience Questionnaire","fullName":"Picker Patient Experience Questionnaire","aliases":["Picker PPE","Picker Institute Survey"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"patient-experience","year":2002,"originator":"Picker Institute","url":"https://scholargate.app/en/patient-centered-care/picker-patient-experience","markdownUrl":"https://scholargate.app/en/patient-centered-care/picker-patient-experience.md","definition":"The Picker Patient Experience Questionnaire is a comprehensive, validated instrument developed by the Picker Institute to measure the quality of the patient experience across multiple dimensions of healthcare delivery. Administered post-discharge or post-encounter, it assesses ten domains of patient-centered care: respect and dignity, information and communication, emotional support, involvement in decisions, continuity of care, coordination, access, physical comfort, emotional needs, and overall experience. The Picker questionnaire has become a standard measure in health systems internationally for evaluating and improving patient experience and has been used extensively in both inpatient and outpatient settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Picker Institute","subfamily":"patient-experience","year":2002,"type":"Patient-reported"},"citations":[{"ref":"Jenkinson, C., Coulter, A., Bruster, S., Richards, N., & Chandola, T. (2002). Patients' experiences and satisfaction with health care: results of a questionnaire study of specific aspects of care. British Medical Journal, 324(7329), 860-864.","type":"article","doi":"10.1136/qhc.11.4.335","isbn":null,"url":null},{"ref":"Coulter, A., Jenkinson, C., Jenkinson, C. (2005). European patient satisfaction with health care and control over health. European Journal of Public Health, 15(3), 282-290.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=European+patient+satisfaction+with+health+care+and+control+over+health+Coulter"}],"related":["collaboste-scale","patient-reported-communication-scale","patient-enablement-instrument","care-transitions-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"picrosirius-red-staining","name":"Picrosirius Red Staining","fullName":"Picrosirius Red Collagen Visualization Assay","aliases":["sirius red","collagen staining","fibrillar collagen assay"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Extracellular matrix characterization","year":"1978","originator":"Junqueira, Bignolas, Brentani","url":"https://scholargate.app/en/biomaterials/picrosirius-red-staining","markdownUrl":"https://scholargate.app/en/biomaterials/picrosirius-red-staining.md","definition":"Picrosirius red (acid red 80) is a direct dye for collagen that binds specifically to the triple helix structure of fibrillar collagens and allows direct visualization and quantification under light and polarized light microscopy. Introduced by Junqueira and colleagues in 1978, picrosirius red staining has become the gold standard for assessing collagen deposition and organization in tissue sections, scaffolds, and cell cultures. The key advantage is that picrosirius red-stained collagen exhibits birefringence under polarized light, enabling researchers to visualize not only the amount of collagen but also its degree of organization and fibril maturity—information crucial for evaluating bone, cartilage, skin, and tendon engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Junqueira, Bignolas, Brentani","subfamily":"Extracellular matrix characterization","year":"1978","type":"Staining assay"},"citations":[{"ref":"Junqueira, L. C. U., Bignolas, G., & Brentani, R. R. (1978). Picrosirius staining plus polarization microscopy, a specific method for collagen detection in tissue sections. Histochemical Journal, 11(4), 447-455.","type":"article","doi":"10.1007/BF01002772","isbn":null,"url":null},{"ref":"Whittaker, P., Kloner, R. A., Boughner, D. R., & Pickering, J. G. (1994). Quantitative assessment of myocardial collagen with picrosirius red staining and circularly polarized light microscopy. Basic Research in Cardiology, 89(6), 476-484.","type":"article","doi":"10.1007/bf00788278","isbn":null,"url":null},{"ref":"Bickel, M., Touzel, R., & Buisson, A. C. (2011). Isolation and characterization of collagen types from fish skin. Comparative Biochemistry and Physiology, 160(2), 147-154.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Isolation+and+characterization+of+collagen+types+from+fish+skin+Bickel"}],"related":["alizarin-red-staining","dynamic-mechanical-analysis","swelling-and-degradation","electrospinning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-aras","name":"PIF-ARAS","fullName":"PiF-ARAS — Picture Fuzzy extension of ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-aras","markdownUrl":"https://scholargate.app/en/decision-making/pif-aras.md","definition":"PIF-ARAS (PiF-ARAS — Picture Fuzzy extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture utility-degree ranking — Picture Fuzzy Number (PiFN: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-artasi","name":"PIF-ARTASI","fullName":"Picture Fuzzy ARTASI (Kara et al. 2024)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2024","originator":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D.","url":"https://scholargate.app/en/decision-making/pif-artasi","markdownUrl":"https://scholargate.app/en/decision-making/pif-artasi.md","definition":"PIF-ARTASI (Picture Fuzzy ARTASI (Kara et al. 2024)) is a ranking multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D.","subfamily":"Ranking","year":"2024","type":"Picture outranking/ranking — PiFS (μ, η, ν; μ+η+ν ≤ 1) + adaptive standardized intervals","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. (2024). A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2024.111826","isbn":null,"url":null}],"related":["pif-cimas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-cimas-artasi","name":"PIF-CIMAS-ARTASI","fullName":"Hybrid Picture Fuzzy weighting (CIMAS) + ranking (ARTASI)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Hybrid","year":"2024","originator":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D.","url":"https://scholargate.app/en/decision-making/pif-cimas-artasi","markdownUrl":"https://scholargate.app/en/decision-making/pif-cimas-artasi.md","definition":"PIF-CIMAS-ARTASI (Hybrid Picture Fuzzy weighting (CIMAS) + ranking (ARTASI)) is a hybrid multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D.","subfamily":"Hybrid","year":"2024","type":"Two-stage MAGDM: PIF-CIMAS criterion weights → PIF-ARTASI alternative ranking","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. (2024). A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2024.111826","isbn":null,"url":null}],"related":["pif-cimas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-cimas","name":"PIF-CIMAS","fullName":"Picture Fuzzy variant of CIMAS (Kara et al. 2024)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2024","originator":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D.","url":"https://scholargate.app/en/decision-making/pif-cimas","markdownUrl":"https://scholargate.app/en/decision-making/pif-cimas.md","definition":"PIF-CIMAS (Picture Fuzzy variant of CIMAS (Kara et al. 2024)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D.","subfamily":"Weight_Subjective","year":"2024","type":"Picture Fuzzy criteria weighting via expert assessment (CIMAS) — PFWA aggregation","value_space":"picture","uncertainty":"epistemic","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. (2024). A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2024.111826","isbn":null,"url":null}],"related":["pif-artasi","pif-cimas-artasi","pif-codas","pif-copras","pif-edas","pif-marcos","pif-moora","pif-saw"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-codas","name":"PIF-CODAS","fullName":"PiF-CODAS — Picture extension of CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-codas","markdownUrl":"https://scholargate.app/en/decision-making/pif-codas.md","definition":"PIF-CODAS (PiF-CODAS — Picture extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture distance-based ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["pif-ahp","pfs-lbwa","pfs-swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-copras","name":"PIF-COPRAS","fullName":"PiF-COPRAS — Picture extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C.","url":"https://scholargate.app/en/decision-making/pif-copras","markdownUrl":"https://scholargate.app/en/decision-making/pif-copras.md","definition":"PIF-COPRAS (PiF-COPRAS — Picture extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C.","subfamily":"Ranking","year":"2013","type":"Picture outranking/ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Cuong, B. C. (2013). Picture fuzzy sets — first results, parts I and II. Seminar on Neuro-Fuzzy Systems with Applications, Institute of Mathematics, Hanoi (preprint); reprinted as Cuong (2014), J. Comput. Sci. Cybernet. 30, 409–420","type":"article","doi":"10.15625/1813-9663/30/4/5032","isbn":null,"url":null}],"related":["pif-cimas","copras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-dombi","name":"PIF-DOMBI","fullName":"Picture Fuzzy Dombi Aggregation Operators for MADM (Jana, Senapati, Pal & Yager 2019)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"2019","originator":"Jana, C. Senapati, T. Pal, M. Yager, R. R.","url":"https://scholargate.app/en/decision-making/pif-dombi","markdownUrl":"https://scholargate.app/en/decision-making/pif-dombi.md","definition":"PIF-DOMBI (Picture Fuzzy Dombi Aggregation Operators for MADM (Jana, Senapati, Pal & Yager 2019)) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Jana, C. Senapati, T. Pal, M. Yager, R. R. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jana, C. Senapati, T. Pal, M. Yager, R. R.","subfamily":"AggregationOperator","year":"2019","type":"Dombi t-norm/t-conorm aggregation on Picture Fuzzy Numbers (PFN: μ,η,ν; μ+η+ν≤1). PFDWA (arithmetic, Def.13/Eq.2) primary, PFDWG (geometric, Def.16/Eq.7) companion. Ranking by score Ê(T)=(1+μ-ν)/2 (Def.6) with accuracy L̂(T)=μ-ν tie-break (Def.7).","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Jana, C., Senapati, T., Pal, M., Yager, R. R. (2019). Picture fuzzy Dombi aggregation operators: Application to MADM process. Applied Soft Computing","type":"article","doi":"10.1016/j.asoc.2018.10.021","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-edas","name":"PIF-EDAS","fullName":"PiF-EDAS — Picture extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-edas","markdownUrl":"https://scholargate.app/en/decision-making/pif-edas.md","definition":"PIF-EDAS (PiF-EDAS — Picture extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture outranking/ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["pif-cimas","edas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-marcos","name":"PIF-MARCOS","fullName":"PiF-MARCOS — Picture extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-marcos","markdownUrl":"https://scholargate.app/en/decision-making/pif-marcos.md","definition":"PIF-MARCOS (PiF-MARCOS — Picture extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture outranking/ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["pif-cimas","marcos"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-moora","name":"PIF-MOORA","fullName":"PiF-MOORA — Picture extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-moora","markdownUrl":"https://scholargate.app/en/decision-making/pif-moora.md","definition":"PIF-MOORA (PiF-MOORA — Picture extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture outranking/ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["pif-cimas","moora"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-saw","name":"PIF-SAW","fullName":"PiF-SAW — Picture extension of SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-saw","markdownUrl":"https://scholargate.app/en/decision-making/pif-saw.md","definition":"PIF-SAW (PiF-SAW — Picture extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture outranking/ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["pif-cimas","saw"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-todim","name":"PIF-TODIM","fullName":"PiF-TODIM — Picture extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-todim","markdownUrl":"https://scholargate.app/en/decision-making/pif-todim.md","definition":"PIF-TODIM (PiF-TODIM — Picture extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture outranking/ranking — Picture Fuzzy Number (PiFN: ⟨μ, η, ν⟩; μ+η+ν ≤ 1) with prospect-theory loss aversion","value_space":"picture","uncertainty":"epistemic","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["pif-cimas","pif-ahp","pif-swara","todim"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-topsis","name":"PIF-TOPSIS","fullName":"PiF-TOPSIS — Picture extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-topsis","markdownUrl":"https://scholargate.app/en/decision-making/pif-topsis.md","definition":"PIF-TOPSIS (PiF-TOPSIS — Picture extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture outranking/ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["pif-cimas","pif-ahp","pif-swara","topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-vikor","name":"PIF-VIKOR","fullName":"PiF-VIKOR — Picture extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-vikor","markdownUrl":"https://scholargate.app/en/decision-making/pif-vikor.md","definition":"PIF-VIKOR (PiF-VIKOR — Picture extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture outranking/ranking — Picture Fuzzy Set (PiFS: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["pif-cimas","vikor"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pif-waspas","name":"PIF-WASPAS","fullName":"PiF-WASPAS — Picture Fuzzy extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Cuong, B. C., Kreinovich, V.","url":"https://scholargate.app/en/decision-making/pif-waspas","markdownUrl":"https://scholargate.app/en/decision-making/pif-waspas.md","definition":"PIF-WASPAS (PiF-WASPAS — Picture Fuzzy extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Cuong, B. C., Kreinovich, V. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuong, B. C., Kreinovich, V.","subfamily":"Ranking","year":"2013","type":"Picture WSM-WPM hybrid ranking — Picture Fuzzy Number (PiFN: μ, η, ν; μ+η+ν ≤ 1)","value_space":"picture","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Cuong, B. C., Kreinovich, V. (2013). Picture fuzzy sets — A new concept for computational intelligence problems. 2013 Third World Congress on Information and Communication Technologies (WICT 2013)","type":"article","doi":"10.1109/WICT.2013.7113099","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-ab-design","name":"Pilot AB Design","fullName":"Pilot AB Single-Subject Experimental Design","aliases":["pilot AB phase design","preliminary AB design","exploratory AB single-case design","feasibility AB design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s (AB design); pilot framing formalized in practice by 1980s–1990s","originator":"Murray Sidman; Baer, Wolf & Risley (AB logic); pilot application emergent from single-subject research practice","url":"https://scholargate.app/en/experimental-design/pilot-ab-design","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-ab-design.md","definition":"A pilot AB design applies the two-phase baseline-then-intervention structure of the AB single-subject design in an explicitly exploratory or feasibility mode — before committing to a more rigorous reversal or multiple-baseline study. The researcher collects repeated baseline (A) and intervention (B) data from one or a few individuals primarily to test measurement procedures, estimate effect size, verify data stability, and determine whether a stronger single-case design is warranted and feasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray Sidman; Baer, Wolf & Risley (AB logic); pilot application emergent from single-subject research practice","year":"1960s (AB design); pilot framing formalized in practice by 1980s–1990s","type":"Single-subject pilot experimental design","dataType":"Repeatedly measured behavioral or outcome data over time (individual participant)","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91-97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null}],"related":["ab-design","aba-design","abab-design","multiple-baseline-design","single-subject-experimental-design","pilot-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-ab-test","name":"Pilot A/B Test","fullName":"Pilot A/B Test (Preliminary Split-Test Experiment)","aliases":["pilot split test","feasibility A/B test","preliminary A/B experiment","pilot randomized comparison"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2000s–2010s (formalized in digital experimentation literature)","originator":"Derived from pilot study methodology (Kraemer et al., 2006) applied to A/B testing practice","url":"https://scholargate.app/en/experimental-design/pilot-ab-test","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-ab-test.md","definition":"A Pilot A/B test is a small-scale, preliminary split-test experiment run before a full A/B test to assess feasibility, estimate effect sizes, detect operational problems, and validate measurement instruments. Participants are randomly assigned to a control condition (A) and a treatment condition (B), but the study is explicitly underpowered — its purpose is to inform the design of the definitive test, not to yield a conclusive comparison.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from pilot study methodology (Kraemer et al., 2006) applied to A/B testing practice","year":"2000s–2010s (formalized in digital experimentation literature)","type":"Experimental design — feasibility study","dataType":"Continuous, binary, or ordinal outcome measurements from two randomly assigned groups","subfamily":"Deneysel desen"},"citations":[{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: The what, why and how. BMC Medical Research Methodology, 10(1), 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null},{"ref":"Kohavi, R., Longbotham, R., Sommerfield, D., & Henne, R. M. (2009). Controlled experiments on the web: Survey and practical guide. Data Mining and Knowledge Discovery, 18(1), 140-181.","type":"article","doi":"10.1007/s10618-008-0114-1","isbn":null,"url":null}],"related":["ab-test","pilot-randomized-controlled-trial","multi-arm-experiment","adaptive-ab-test","pragmatic-ab-test","factorial-ab-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-aba-design","name":"Pilot ABA Design","fullName":"Pilot ABA Reversal Design","aliases":["Pilot ABA reversal design","Pilot withdrawal design","Pilot single-subject reversal design","Feasibility ABA design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s (ABA design); pilot adaptation in clinical and behavioral research from 1980s onward","originator":"Murray Sidman (ABA reversal logic); pilot study methodology broadly attributed to clinical trial traditions","url":"https://scholargate.app/en/experimental-design/pilot-aba-design","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-aba-design.md","definition":"The Pilot ABA Design is a small-scale single-subject experiment that applies the ABA reversal structure — baseline, intervention, withdrawal — to test the feasibility, acceptability, and preliminary effect of an intervention before committing to a full-scale study. It provides early evidence of whether the treatment produces a detectable change and whether the reversal is ethically and practically achievable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray Sidman (ABA reversal logic); pilot study methodology broadly attributed to clinical trial traditions","year":"1960s (ABA design); pilot adaptation in clinical and behavioral research from 1980s onward","type":"Single-subject experimental pilot design","dataType":"Repeated-measures behavioral or clinical outcome data on a single participant or small series","subfamily":"Deneysel desen"},"citations":[{"ref":"Sidman, M. (1960). Tactics of Scientific Research: Evaluating Experimental Data in Psychology. Basic Books.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tactics+of+Scientific+Research+Sidman+1960"},{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., ... & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null}],"related":["aba-design","abab-design","pilot-ab-design","multiple-baseline-design","single-subject-experimental-design","pilot-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-abab-design","name":"Pilot ABAB Design","fullName":"Pilot ABAB Reversal Design","aliases":["pilot reversal design","feasibility ABAB design","pilot withdrawal design","pilot single-subject reversal"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Experimental design","year":"1960s (ABAB base); pilot application codified c. 2000s","originator":"Derived from ABAB reversal design (Sidman, 1960); pilot framing formalized in behavioral intervention feasibility literature (late 20th–early 21st century)","url":"https://scholargate.app/en/experimental-design/pilot-abab-design","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-abab-design.md","definition":"A Pilot ABAB design is a small-scale feasibility trial of the ABAB reversal design, conducted with one or a few participants to test whether an intervention produces reliable behavior change under alternating baseline and treatment conditions before committing resources to a larger study. It combines the internal-validity logic of the ABAB reversal with the limited scope and preliminary aims of a pilot investigation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from ABAB reversal design (Sidman, 1960); pilot framing formalized in behavioral intervention feasibility literature (late 20th–early 21st century)","year":"1960s (ABAB base); pilot application codified c. 2000s","type":"Single-subject experimental feasibility design","dataType":"Repeated behavioral observations (continuous or interval recording) per individual participant","subfamily":"Experimental design"},"citations":[{"ref":"Byiers, B. J., Reichle, J., & Symons, F. J. (2012). Single-subject experimental design for evidence-based practice. American Journal of Speech-Language Pathology, 21(4), 397–414.","type":"article","doi":"10.1044/1058-0360(2012/11-0036)","isbn":null,"url":null},{"ref":"Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Allyn & Bacon.","type":"book","doi":null,"isbn":"978-0205474554","url":null}],"related":["abab-design","aba-design","ab-design","multiple-baseline-design","pilot-randomized-controlled-trial","single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-cluster-sampling","name":"Pilot Cluster Sampling","fullName":"Pilot Cluster Sampling","aliases":["pilot area sampling","feasibility cluster sample","preliminary cluster survey","pilot cluster survey"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century (cluster sampling foundations); 2000s (pilot study formalization)","originator":"Rooted in W. G. Cochran's cluster sampling theory (1953) combined with pilot-study methodology formalized by Lancaster, Dodd & Williamson (2004) and Thabane et al. (2010)","url":"https://scholargate.app/en/survey-methodology/pilot-cluster-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-cluster-sampling.md","definition":"Pilot cluster sampling is the application of a cluster sampling protocol on a small, preliminary scale to evaluate the feasibility, logistics, and parameter estimates needed before committing to a full-scale cluster survey. A subset of clusters is randomly selected and fully surveyed, yielding estimates of the intraclass correlation (ICC), design effect, recruitment rates, and operational costs. These findings directly inform the sample size and cluster allocation of the definitive survey.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in W. G. Cochran's cluster sampling theory (1953) combined with pilot-study methodology formalized by Lancaster, Dodd & Williamson (2004) and Thabane et al. (2010)","year":"Mid-20th century (cluster sampling foundations); 2000s (pilot study formalization)","type":"Probability sampling feasibility design","dataType":"Quantitative or mixed; unit-level measurements within naturally occurring groups; recruitment and logistical feasibility metrics","subfamily":"Sampling"},"citations":[{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null},{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471162407","url":null}],"related":["cluster-sampling","pilot-stratified-sampling","pilot-systematic-sampling","multistage-sampling","adaptive-cluster-sampling","proportional-cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-control-group-experimental-design","name":"Pilot Control Group Experimental Design","fullName":"Pilot Study with Control Group Experimental Design","aliases":["pilot controlled experiment","pilot RCT feasibility study","small-scale controlled trial","pilot control group study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-20th century; widely formalized by 1980s–2000s","originator":"Established through clinical and behavioral research traditions; formalized by Bradford Hill and colleagues in mid-20th century trial methodology","url":"https://scholargate.app/en/experimental-design/pilot-control-group-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-control-group-experimental-design.md","definition":"A pilot control group experimental design is a small-scale, preliminary experiment that includes both a treatment group and a control group, conducted before the main study to test whether the full trial is feasible. It produces early effect-size estimates, identifies protocol problems, and confirms that random (or systematic) assignment to conditions is workable — all while generating a genuine comparison between treated and untreated participants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established through clinical and behavioral research traditions; formalized by Bradford Hill and colleagues in mid-20th century trial methodology","year":"Mid-20th century; widely formalized by 1980s–2000s","type":"Experimental design (pilot/feasibility variant)","dataType":"Quantitative (continuous, ordinal, or count outcomes from intervention and control participants)","subfamily":"Deneysel desen"},"citations":[{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10, 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null},{"ref":"Campbell, M., Fitzpatrick, R., Haines, A., Kinmonth, A. L., Sandercock, P., Spiegelhalter, D., & Tyrer, P. (2000). Framework for design and evaluation of complex interventions to improve health. BMJ, 321(7262), 694–696.","type":"article","doi":"10.1136/bmj.321.7262.694","isbn":null,"url":null}],"related":["control-group-experimental-design","pilot-randomized-controlled-trial","pretest-posttest-experimental-design","randomized-controlled-trial","feasibility-study","blocked-control-group-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-deviant-case-sampling","name":"Pilot Deviant Case Sampling","fullName":"Pilot Deviant Case Sampling","aliases":["pilot extreme case sampling","pilot outlier sampling","pilot atypical case sampling","pilot deviant-case pilot sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"2002 (Patton codification); pilot study tradition mid-20th century","originator":"Patton (deviant case sampling); pilot study methodology attributed broadly to pre-main-study practice traditions","url":"https://scholargate.app/en/survey-methodology/pilot-deviant-case-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-deviant-case-sampling.md","definition":"Pilot deviant case sampling is a purposive sampling strategy applied during a pilot phase in which participants are deliberately selected because they represent extreme, unusual, or atypical instances of the phenomenon under study. Rather than seeking representative participants for the pilot run, the researcher intentionally recruits outlier cases to probe the boundaries of research instruments, interview guides, or data collection protocols before the main study begins.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patton (deviant case sampling); pilot study methodology attributed broadly to pre-main-study practice traditions","year":"2002 (Patton codification); pilot study tradition mid-20th century","type":"Purposive sampling variant applied in pilot phase","dataType":"Qualitative or mixed-methods data from interviews, observations, or instruments","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. [Chapter 5: Sampling Strategies, pp. 230–246 on deviant/extreme case sampling]","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"van Teijlingen, E. R., & Hundley, V. (2002). The importance of pilot studies. Nursing Standard, 16(40), 33–36.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+importance+of+pilot+studies"}],"related":["deviant-case-sampling","pilot-purposive-sampling","pilot-maximum-variation-sampling","extreme-case-sampling","purposive-sampling","pilot-typical-case-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-factorial-experiment","name":"Pilot Factorial Experiment","fullName":"Pilot Factorial Experiment","aliases":["preliminary factorial study","pilot factorial design","small-scale factorial trial","feasibility factorial experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1930s (Fisher); pilot application conventions developed mid-20th century","originator":"R. A. Fisher (factorial design foundations); formalized in experimental statistics literature","url":"https://scholargate.app/en/experimental-design/pilot-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-factorial-experiment.md","definition":"A pilot factorial experiment is a small-scale, preliminary study that employs a factorial structure to simultaneously vary two or more factors across a limited number of experimental units. Its purpose is not to deliver definitive conclusions but to estimate effect sizes, within-group variance, and factor interactions, and to test logistical feasibility before committing resources to a full-scale factorial experiment. It is widely used in behavioral sciences, engineering, agriculture, and clinical research as an essential planning step.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (factorial design foundations); formalized in experimental statistics literature","year":"1930s (Fisher); pilot application conventions developed mid-20th century","type":"Preliminary experimental design","dataType":"Continuous or categorical outcome measures; multiple independent factors","subfamily":"Deneysel desen"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null},{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471718130","url":null}],"related":["full-factorial-design","fractional-factorial-design","two-level-factorial-design","response-surface-methodology","randomized-controlled-trial","pilot-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-field-experiment","name":"Pilot Field Experiment","fullName":"Pilot Field Experiment","aliases":["pilot field trial","small-scale field experiment","feasibility field experiment","exploratory field experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-20th century (systematised 1960s–1990s)","originator":"Rooted in Campbell & Stanley (1966) experimental design tradition; formalised in clinical and social research through the 20th century","url":"https://scholargate.app/en/experimental-design/pilot-field-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-field-experiment.md","definition":"A pilot field experiment is a small-scale, preliminary version of a planned full field experiment conducted in a naturalistic setting. It tests whether the intervention, randomisation procedure, measurement instruments, and logistical protocols are feasible before committing to a full-scale study. Results inform sample size calculations, refine treatment protocols, and identify procedural risks — saving resources and improving the quality of the definitive study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Campbell & Stanley (1966) experimental design tradition; formalised in clinical and social research through the 20th century","year":"Mid-20th century (systematised 1960s–1990s)","type":"Experimental design","dataType":"Quantitative (outcome measures, effect sizes, attrition rates); mixed methods possible","subfamily":"Deneysel desen"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research. Rand McNally.","type":"article","doi":null,"isbn":"978-0395307878","url":null},{"ref":"Leon, A. C., Davis, L. L., & Kraemer, H. C. (2011). The role and interpretation of pilot studies in clinical research. Journal of Psychiatric Research, 45(5), 626–629.","type":"article","doi":"10.1016/j.jpsychires.2010.10.008","isbn":null,"url":null}],"related":["randomized-controlled-trial","quasi-experiment","field-experiment","feasibility-study","pretest-posttest-design","cluster-randomized-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-fractional-factorial-experiment","name":"Pilot Fractional Factorial Experiment","fullName":"Pilot Fractional Factorial Experiment","aliases":["pilot FFE","screening pilot design","pilot fractional factorial","pilot FF screening study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1950s–1960s (fractional factorial foundation); pilot study integration formalized in 20th century DOE practice","originator":"Box, Hunter & Hunter (fractional factorial); pilot study concept developed broadly in industrial and clinical experimentation","url":"https://scholargate.app/en/experimental-design/pilot-fractional-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-fractional-factorial-experiment.md","definition":"A pilot fractional factorial experiment is a small-scale preliminary study that uses a fractional factorial design — testing only a subset of all possible factor combinations — to screen multiple factors simultaneously before committing to a full-scale investigation. It provides early estimates of effect sizes, variance, and feasibility at substantially reduced cost and participant burden compared to a full factorial pilot or a full-scale trial.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box, Hunter & Hunter (fractional factorial); pilot study concept developed broadly in industrial and clinical experimentation","year":"1950s–1960s (fractional factorial foundation); pilot study integration formalized in 20th century DOE practice","type":"Experimental screening design (pilot phase)","dataType":"Continuous or categorical outcome measurements from a small experimental run","subfamily":"Deneysel desen"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null},{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: The what, why and how. BMC Medical Research Methodology, 10(1), 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null}],"related":["fractional-factorial-experiment","full-factorial-experiment","pilot-randomized-controlled-trial","factorial-experiment","response-surface-methodology","plackett-burman-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-full-factorial-experiment","name":"Pilot full factorial experiment","fullName":"Pilot Full Factorial Experiment","aliases":["pilot factorial design","pilot 2^k design","pilot complete factorial experiment","screening factorial pilot"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1920s (Fisher); pilot usage formalised mid-20th century","originator":"R. A. Fisher (full factorial foundations); pilot application codified in applied DOE literature (Box, Hunter & Hunter; Montgomery)","url":"https://scholargate.app/en/experimental-design/pilot-full-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-full-factorial-experiment.md","definition":"A pilot full factorial experiment is a small-scale, complete crossing of all selected factors at all their levels, run before a definitive study to gather preliminary effect estimates, assess variability, and verify experimental logistics. It retains the complete combinatorial structure of a full factorial design — every combination of factor levels is tested — but is intentionally limited in scope (fewer replicates, narrower factor ranges) to conserve resources while maximising learning about factor effects and interactions before committing to a larger investigation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (full factorial foundations); pilot application codified in applied DOE literature (Box, Hunter & Hunter; Montgomery)","year":"1920s (Fisher); pilot usage formalised mid-20th century","type":"Experimental design (pilot/screening phase)","dataType":"Continuous or categorical outcome measurements from a controlled experiment","subfamily":"Deneysel desen"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null},{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471718130","url":null}],"related":["full-factorial-experiment","fractional-factorial-experiment","two-level-factorial-design","response-surface-methodology","one-factor-at-a-time","randomized-complete-block-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-multi-arm-experiment","name":"Pilot Multi-Arm Experiment","fullName":"Pilot Multi-Arm Experimental Design","aliases":["pilot multi-arm trial","feasibility multi-arm study","pilot parallel-arm experiment","preliminary multi-arm experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1990s–2000s","originator":"Evolved from clinical trial methodology; consolidated in the 1990s–2000s","url":"https://scholargate.app/en/experimental-design/pilot-multi-arm-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-multi-arm-experiment.md","definition":"A pilot multi-arm experiment is a small-scale preliminary trial that tests the feasibility, logistics, and parameter estimates needed to plan a full-scale multi-arm study. It simultaneously evaluates two or more active treatment arms alongside a control, providing early evidence on recruitment rates, retention, protocol adherence, variability, and likely effect sizes before committing to a resource-intensive definitive experiment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from clinical trial methodology; consolidated in the 1990s–2000s","year":"1990s–2000s","type":"Experimental design (pilot/feasibility)","dataType":"Continuous, categorical, or binary outcome measures; feasibility indicators","subfamily":"Deneysel desen"},"citations":[{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: The what, why and how. BMC Medical Research Methodology, 10(1), 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null},{"ref":"Chow, S.-C., & Liu, J.-P. (2008). Design and Analysis of Clinical Trials: Concepts and Methodologies (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471249858","url":null}],"related":["multi-arm-experiment","pilot-randomized-controlled-trial","adaptive-multi-arm-experiment","factorial-experiment","pilot-field-experiment","randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-multistage-sampling","name":"Pilot Multistage Sampling","fullName":"Pilot Multistage Sampling","aliases":["pilot MSS","multistage pilot sampling","trial multistage sampling","multistage sampling pilot test"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century onward","originator":"Survey methodology tradition; formalized in Kish (1965) and later survey practice literature","url":"https://scholargate.app/en/survey-methodology/pilot-multistage-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-multistage-sampling.md","definition":"Pilot multistage sampling applies a small-scale trial run of a multistage sampling design before committing to the full fieldwork. The researcher draws a mini-version of the hierarchical sample — typically spanning the same stages (e.g., regions, then clusters, then individuals) — to test frame quality, stage-transition procedures, and variance estimates, then uses those findings to calibrate the main sampling plan.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Survey methodology tradition; formalized in Kish (1965) and later survey practice literature","year":"Mid-20th century onward","type":"Probability sampling with pilot validation phase","dataType":"Survey data (quantitative or mixed); frame documents, cluster lists","subfamily":"Sampling"},"citations":[{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471109495","url":null},{"ref":"Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470465462","url":null}],"related":["multistage-sampling","stratified-sampling","cluster-sampling","systematic-sampling","pilot-stratified-sampling","pilot-cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-natural-experiment","name":"Pilot Natural Experiment","fullName":"Pilot Natural Experiment Design","aliases":["feasibility natural experiment","preliminary natural experiment","pilot quasi-experiment","exploratory natural experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2000s–2010s (as formalized practice)","originator":"Combination of natural experiment tradition (Dunning, Angrist, Pischke) and pilot study methodology","url":"https://scholargate.app/en/experimental-design/pilot-natural-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-natural-experiment.md","definition":"A pilot natural experiment is a small-scale preliminary study that exploits an existing exogenous event or policy variation to test whether a full natural experiment is viable. It preserves the core logic of natural experiments — using real-world discontinuities to approximate causal inference — while explicitly scoping the work to assess data availability, group comparability, effect detectability, and procedural feasibility before committing resources to a larger study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Combination of natural experiment tradition (Dunning, Angrist, Pischke) and pilot study methodology","year":"2000s–2010s (as formalized practice)","type":"Quasi-experimental feasibility design","dataType":"Observational data with exogenous variation; administrative records, surveys, secondary data","subfamily":"Deneysel desen"},"citations":[{"ref":"Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.","type":"book","doi":null,"isbn":"9781107017412","url":null},{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., ... & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null}],"related":["natural-experiment","pilot-randomized-controlled-trial","pilot-field-experiment","quasi-experimental-design","difference-in-differences","regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-pretest-posttest-experimental-design","name":"Pilot pretest-posttest experimental design","fullName":"Pilot Pretest-Posttest Experimental Design","aliases":["pilot pretest-posttest study","small-scale pretest-posttest trial","feasibility pretest-posttest design","pilot two-group pretest-posttest"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1963 (formal codification by Campbell & Stanley); pilot trials widely used from mid-20th century onward","originator":"Donald T. Campbell and Julian C. Stanley (experimental design codification); pilot study concept widely credited to early 20th-century clinical trial practice","url":"https://scholargate.app/en/experimental-design/pilot-pretest-posttest-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-pretest-posttest-experimental-design.md","definition":"A pilot pretest-posttest experimental design is a small-scale, preliminary study that applies a pretest-posttest measurement framework to a limited sample before a full-scale experiment. Its primary goals are to assess the feasibility of procedures, estimate effect sizes for power analysis, identify instrument problems, and uncover logistical barriers — all before committing to the cost and scale of a definitive trial. It combines the internal-validity advantages of repeated measurement with the pragmatic scope of a pilot study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald T. Campbell and Julian C. Stanley (experimental design codification); pilot study concept widely credited to early 20th-century clinical trial practice","year":"1963 (formal codification by Campbell & Stanley); pilot trials widely used from mid-20th century onward","type":"Experimental / feasibility design","dataType":"Quantitative (continuous or ordinal outcome measures collected at pre- and post-intervention time points)","subfamily":"Deneysel desen"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. In N. L. Gage (Ed.), Handbook of Research on Teaching (pp. 171–246). Rand McNally.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+quasi-experimental+designs+for+research+Campbell+Stanley+1963"},{"ref":"Leon, A. C., Davis, L. L., & Kraemer, H. C. (2011). The role and interpretation of pilot studies in clinical research. Journal of Psychiatric Research, 45(5), 626–629.","type":"article","doi":"10.1016/j.jpsychires.2010.10.008","isbn":null,"url":null}],"related":["pretest-posttest-control-group-design","randomized-controlled-trial","quasi-experimental-design","one-group-pretest-posttest-design","feasibility-study","repeated-measures-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-randomized-controlled-trial","name":"Pilot Randomized Controlled Trial","fullName":"Pilot Randomized Controlled Trial","aliases":["pilot RCT","feasibility RCT","pilot trial","preliminary RCT"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1990s–2000s (methodological formalization)","originator":"Formalized through clinical trials methodology community","url":"https://scholargate.app/en/experimental-design/pilot-randomized-controlled-trial","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-randomized-controlled-trial.md","definition":"A pilot randomized controlled trial (pilot RCT) is a small-scale, fully randomized experiment conducted before a definitive RCT to test the feasibility of study procedures, estimate key parameters such as recruitment rates and effect-size variability, and identify practical barriers. It uses the same randomization, intervention, and measurement protocol as the planned full trial but on a fraction of the target sample. The goal is not to confirm efficacy but to refine and justify the main trial design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized through clinical trials methodology community","year":"1990s–2000s (methodological formalization)","type":"Experimental feasibility design","dataType":"Continuous, categorical, and ordinal outcome data; recruitment and retention rates","subfamily":"Deneysel desen"},"citations":[{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., ... & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null},{"ref":"Lancaster, G. A., Dodd, S., & Williamson, P. R. (2004). Design and analysis of pilot studies: recommendations for good practice. Journal of Evaluation in Clinical Practice, 10(2), 307-312.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Design+and+analysis+of+pilot+studies+recommendations+for+good+practice+Lancaster+2004"}],"related":["randomized-controlled-trial","feasibility-study","adaptive-randomized-controlled-trial","cluster-randomized-controlled-trial","crossover-randomized-controlled-trial","factorial-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-single-subject-experimental-design","name":"Pilot Single-Subject Experimental Design","fullName":"Pilot Single-Subject Experimental Design","aliases":["pilot SSED","pilot N-of-1 experiment","pilot single-case experimental design","pilot SCED"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2000s–2010s (as an explicitly named piloting strategy for SSED)","originator":"Derived from single-subject experimental design traditions (Sidman, 1960; Kazdin) and pilot study methodology (Lancaster, Dodd, Williamson, 2004; Thabane et al., 2010)","url":"https://scholargate.app/en/experimental-design/pilot-single-subject-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-single-subject-experimental-design.md","definition":"A pilot single-subject experimental design (pilot SSED) is a small-scale feasibility trial applied to one or very few individuals, combining the repeated-measurement logic of single-subject experimental design with the explicit preparatory aims of a pilot study. It is used to test an intervention protocol, measurement procedures, and design logistics before committing to a full-scale single-case or group experiment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from single-subject experimental design traditions (Sidman, 1960; Kazdin) and pilot study methodology (Lancaster, Dodd, Williamson, 2004; Thabane et al., 2010)","year":"2000s–2010s (as an explicitly named piloting strategy for SSED)","type":"Pilot experimental design","dataType":"Repeated behavioral or outcome measurements on one or very few individuals","subfamily":"Deneysel desen"},"citations":[{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., ... & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null},{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null}],"related":["single-subject-experimental-design","ab-design","aba-design","multiple-baseline-design","pilot-randomized-controlled-trial","adaptive-single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-solomon-four-group-design","name":"Pilot Solomon Four-Group Design","fullName":"Pilot Solomon Four-Group Experimental Design","aliases":["Pilot S4GD","Solomon four-group pilot study","pilot four-group pretest-posttest control design","pilot SFGD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1949 (Solomon design); pilot usage documented in experimental methodology literature from 1960s onward","originator":"Richard L. Solomon (base design); pilot application is a standard methodological practice","url":"https://scholargate.app/en/experimental-design/pilot-solomon-four-group-design","markdownUrl":"https://scholargate.app/en/experimental-design/pilot-solomon-four-group-design.md","definition":"The Pilot Solomon Four-Group Design is a small-scale, preliminary implementation of the Solomon four-group experimental design. Its purpose is to test the feasibility and logistics of the full design before committing to a resource-intensive main study. The Solomon four-group design, introduced by Richard L. Solomon in 1949, controls for pretest sensitisation by using four groups — two that receive a pretest and two that do not — crossed with treatment and control conditions. Piloting this design allows researchers to estimate effect sizes, detect procedural problems, and verify that the pretest does not unduly influence posttest scores.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard L. Solomon (base design); pilot application is a standard methodological practice","year":"1949 (Solomon design); pilot usage documented in experimental methodology literature from 1960s onward","type":"Experimental design — pilot phase","dataType":"Continuous or categorical outcome measures; pretest and posttest scores","subfamily":"Deneysel desen"},"citations":[{"ref":"Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.","type":"article","doi":"10.1037/h0062958","isbn":null,"url":null},{"ref":"Braver, M. C. W., & Braver, S. L. (1988). Statistical treatment of the Solomon four-group design: A meta-analytic approach. Psychological Bulletin, 104(1), 150–154.","type":"article","doi":"10.1037/0033-2909.104.1.150","isbn":null,"url":null}],"related":["solomon-four-group-design","pretest-posttest-control-group-design","randomized-controlled-trial","pilot-study","factorial-design","quasi-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-tested-api-based-data-collection","name":"Pilot-tested API-based data collection","fullName":"Pilot-tested API-based Data Collection","aliases":["pilot API data collection","pre-tested API harvesting","API data collection pilot study","pilot-validated API scraping"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s","originator":"Convergence of survey pilot-testing tradition (Presser et al., 2004) and computational social science API methods (Salganik, 2018)","url":"https://scholargate.app/en/survey-methodology/pilot-tested-api-based-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-tested-api-based-data-collection.md","definition":"Pilot-tested API-based data collection is a structured digital data-gathering approach in which a researcher designs an API query or harvesting script and then runs a small-scale trial before executing the full collection. The pilot phase exposes authentication issues, rate-limit constraints, schema inconsistencies, and coverage gaps, enabling targeted refinements that protect the integrity and completeness of the final dataset. It bridges the software-engineering practice of integration testing with the social-science tradition of instrument pre-testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Convergence of survey pilot-testing tradition (Presser et al., 2004) and computational social science API methods (Salganik, 2018)","year":"2000s–2010s","type":"Applied data-collection variant","dataType":"Structured digital data retrieved via application programming interfaces (JSON, XML, CSV)","subfamily":"Data collection"},"citations":[{"ref":"Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691158648","url":null},{"ref":"Presser, S., Couper, M. P., Lessler, J. T., Martin, E., Martin, J., Rothgeb, J. M., & Singer, E. (2004). Methods for testing and evaluating survey questions. Public Opinion Quarterly, 68(1), 109–130.","type":"article","doi":"10.1093/poq/nfh008","isbn":null,"url":null}],"related":["api-based-data-collection","pilot-tested-survey","web-scraping","pilot-tested-web-scraping","mobile-api-based-data-collection","online-api-based-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-tested-experiment-log","name":"Pilot-tested experiment log","fullName":"Pilot-tested Experiment Log","aliases":["pilot-tested lab journal","pilot-tested research logbook","validated experiment diary","pre-tested lab log"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"19th–20th century (lab notebooks); pilot-testing conventions codified mid-20th century","originator":"Scientific research community (laboratory practice); pilot-testing formalized by survey and experimental methodologists","url":"https://scholargate.app/en/survey-methodology/pilot-tested-experiment-log","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-tested-experiment-log.md","definition":"A pilot-tested experiment log is a structured research instrument — a systematic journal of experimental procedures, observations, and results — that has been trialed with a small representative sample before full deployment. The pilot phase identifies ambiguous recording fields, impractical time demands, or inconsistent terminology, enabling targeted revisions that improve the log's reliability and completeness before the main data-collection phase begins.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scientific research community (laboratory practice); pilot-testing formalized by survey and experimental methodologists","year":"19th–20th century (lab notebooks); pilot-testing conventions codified mid-20th century","type":"Instrument-validation + structured data collection","dataType":"Structured observational and procedural records; qualitative and quantitative entries","subfamily":"Data collection"},"citations":[{"ref":"Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. Journal of the Learning Sciences, 13(1), 1–14.","type":"article","doi":"10.1207/s15327809jls1301_1","isbn":null,"url":null},{"ref":"Lab notebook. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Lab_notebook"}],"related":["research-diary","field-notes","pilot-tested-diary-method","longitudinal-research-diary","sensor-data-collection","document-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-tested-field-notes","name":"Pilot-tested field notes","fullName":"Pilot-tested Field Notes Protocol","aliases":["pre-validated field notes","pilot field observation notes","trial-tested observational notes"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Field notes: early 20th century; pilot-testing protocols formalised mid-20th century","originator":"Ethnographic tradition (Bronislaw Malinowski, Robert Emerson et al.); pilot testing practice generalised across social sciences","url":"https://scholargate.app/en/survey-methodology/pilot-tested-field-notes","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-tested-field-notes.md","definition":"Pilot-tested field notes combine the classical ethnographic practice of systematic observational recording with a deliberate pre-validation phase. Before the main data collection begins, the researcher conducts one or more trial observation sessions to test and refine the note-taking protocol — assessing categories, focus areas, and recording conventions — so that the main fieldwork captures relevant data consistently and completely.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ethnographic tradition (Bronislaw Malinowski, Robert Emerson et al.); pilot testing practice generalised across social sciences","year":"Field notes: early 20th century; pilot-testing protocols formalised mid-20th century","type":"Qualitative data collection technique","dataType":"Observational text data (written field notes, jottings, descriptive and reflective entries)","subfamily":"Data collection"},"citations":[{"ref":"Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes (2nd ed.). University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226206837","url":null},{"ref":"Van Maanen, J. (2011). Tales of the Field: On Writing Ethnography (2nd ed.). University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226849645","url":null}],"related":["field-notes","non-participant-observation","participant-observation","pilot-tested-participant-observation","ethnography","pilot-tested-diary-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-tested-in-depth-interview","name":"Pilot-tested In-depth Interview","fullName":"Pilot-tested In-depth Interview","aliases":["pretested in-depth interview","pilot in-depth interview","pilot-tested IDI","pilot interview protocol"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (pilot testing of qualitative instruments formalized)","originator":"Rooted in interview methodology tradition; pilot testing practice systematized by Kvale and others","url":"https://scholargate.app/en/survey-methodology/pilot-tested-in-depth-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-tested-in-depth-interview.md","definition":"A pilot-tested in-depth interview is a qualitative data collection approach in which the interview guide is administered to a small number of participants before the main study, specifically to identify ambiguous questions, refine probes, estimate session duration, and verify that the protocol elicits rich, relevant narratives. The revised guide is then used in the full data collection phase, improving data quality and interview consistency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in interview methodology tradition; pilot testing practice systematized by Kvale and others","year":"1990s–2000s (pilot testing of qualitative instruments formalized)","type":"Qualitative data collection technique","dataType":"Verbal/narrative data from individual interviews","subfamily":"Data collection"},"citations":[{"ref":"Kvale, S., & Brinkmann, S. (2009). InterViews: Learning the Craft of Qualitative Research Interviewing (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761925422","url":null},{"ref":"van Teijlingen, E., & Hundley, V. (2002). The importance of pilot studies. Nursing Standard, 16(40), 33-36.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+importance+of+pilot+studies+van+Teijlingen+Hundley+2002"}],"related":["in-depth-interview","semi-structured-interview","pilot-tested-semi-structured-interview","pilot-tested-focus-group","cognitive-interviewing","face-to-face-in-depth-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-tested-mobile-experience-sampling","name":"Pilot-tested mobile experience sampling","fullName":"Pilot-tested Mobile Experience Sampling Method","aliases":["pilot-tested mobile ESM","pretested mESM","validated mobile experience sampling","mobile ESM with pilot phase"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s (mobile ESM); pilot-testing practice codified in 2010s","originator":"Reed Larson & Mihaly Csikszentmihalyi (ESM); mobile adaptation developed across 2000s–2010s","url":"https://scholargate.app/en/survey-methodology/pilot-tested-mobile-experience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-tested-mobile-experience-sampling.md","definition":"Pilot-tested mobile experience sampling (mESM) is a data collection approach that combines smartphone-delivered, real-time self-report prompts — the Experience Sampling Method — with a structured pilot phase to validate the instrument, signal timing, burden level, and response quality before full deployment. The pilot phase is not optional decoration; it is the core quality gate that separates a rigorously validated mESM study from an ad hoc one.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reed Larson & Mihaly Csikszentmihalyi (ESM); mobile adaptation developed across 2000s–2010s","year":"2000s–2010s (mobile ESM); pilot-testing practice codified in 2010s","type":"Data collection technique","dataType":"Real-time self-report data collected via smartphone prompts","subfamily":"Data collection"},"citations":[{"ref":"Larson, R., & Csikszentmihalyi, M. (1983). The experience sampling method. New Directions for Methodology of Social and Behavioral Science, 15, 41–56.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+experience+sampling+method+Larson+Csikszentmihalyi+1983"},{"ref":"van Berkel, N., Ferreira, D., & Kostakos, V. (2017). The experience sampling method on mobile devices. ACM Computing Surveys, 50(6), 1–40.","type":"article","doi":"10.1145/3123988","isbn":null,"url":null}],"related":["mobile-experience-sampling","experience-sampling-method","pilot-tested-survey","ecological-momentary-assessment","diary-method","sensor-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-tested-semi-structured-interview","name":"Pilot-tested Semi-structured Interview","fullName":"Pilot-tested Semi-structured Interview Protocol","aliases":["pilot semi-structured interview","pre-tested qualitative interview","pilot interview protocol","trial semi-structured interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–1980s (as formalised pilot testing of qualitative instruments)","originator":"Standard qualitative methods practice; systematised in social research methodology literature","url":"https://scholargate.app/en/survey-methodology/pilot-tested-semi-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-tested-semi-structured-interview.md","definition":"A pilot-tested semi-structured interview combines the flexibility of semi-structured interviewing — a guide of open-ended questions allowing conversational depth — with a mandatory pre-study pilot phase in which the guide is trialled on a small subset of participants or informants. The pilot reveals ambiguous questions, poor sequencing, and missing topics before the main data collection begins, substantially strengthening the validity and efficiency of the final instrument.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Standard qualitative methods practice; systematised in social research methodology literature","year":"1970s–1980s (as formalised pilot testing of qualitative instruments)","type":"Qualitative data collection technique with pre-validation phase","dataType":"Audio/video recordings and transcripts from semi-structured interviews","subfamily":"Data collection"},"citations":[{"ref":"Bryman, A. (2016). Social Research Methods (5th ed.). Oxford University Press.","type":"book","doi":null,"isbn":"9780198714965","url":null},{"ref":"van Teijlingen, E., & Hundley, V. (2001). The importance of pilot studies. Social Research Update, 35, 1–4.","type":"article","doi":null,"isbn":null,"url":"https://sru.soc.surrey.ac.uk/SRU35.html"}],"related":["semi-structured-interview","pilot-tested-structured-interview","pilot-tested-in-depth-interview","cognitive-interviewing","face-to-face-semi-structured-interview","online-semi-structured-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-tested-sensor-data-collection","name":"Pilot-tested Sensor Data Collection","fullName":"Pilot-tested Sensor Data Collection","aliases":["sensor pilot study","sensor pre-deployment testing","instrument validation with sensors","sensor calibration pilot"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (formalized with proliferation of digital sensing technologies)","originator":"General research methods practice; sensor pilot testing codified through IoT and environmental monitoring literature","url":"https://scholargate.app/en/survey-methodology/pilot-tested-sensor-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-tested-sensor-data-collection.md","definition":"Pilot-tested sensor data collection is a structured data gathering approach in which sensor instruments — hardware or software-based devices that measure physical, environmental, physiological, or behavioral signals — are deployed in a small-scale trial before the main study. The pilot phase verifies sensor accuracy, communication reliability, data format consistency, and placement adequacy, allowing researchers to identify and correct technical problems before full-scale data collection begins.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"General research methods practice; sensor pilot testing codified through IoT and environmental monitoring literature","year":"1990s–2000s (formalized with proliferation of digital sensing technologies)","type":"Data collection procedure with pre-deployment validation phase","dataType":"Quantitative continuous or discrete measurements (physical, environmental, physiological, or behavioral sensor readings)","subfamily":"Data collection"},"citations":[{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1506386706","url":null},{"ref":"Lajnef, N., Chatti, M., Chakrabartty, S., Rhimi, M., & Bhatt, P. (2015). Health monitoring of civil infrastructures by wireless sensor networks. ISRN Civil Engineering, 2012, 1–14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=pilot+testing+sensor+data+collection+validation+deployment"}],"related":["sensor-data-collection","pilot-tested-field-notes","mobile-sensor-data-collection","longitudinal-sensor-data-collection","remote-sensor-data-collection","api-based-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-tested-survey","name":"Pilot-tested Survey","fullName":"Pilot-tested Survey","aliases":["pre-tested survey","survey pre-testing","questionnaire pilot study","survey field test"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Widely formalised from the 1970s-1980s","originator":"Systematic practice codified by Jean M. Converse and Stanley Presser","url":"https://scholargate.app/en/survey-methodology/pilot-tested-survey","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-tested-survey.md","definition":"A pilot-tested survey is a structured questionnaire that has been administered to a small, representative sample before the main data-collection phase. The purpose is to detect problems with wording, response options, skip logic, or timing, allowing the researcher to refine the instrument before it reaches the full sample. Pilot testing is not a separate research design; it is a quality-assurance step embedded within survey methodology that substantially reduces measurement error.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Systematic practice codified by Jean M. Converse and Stanley Presser","year":"Widely formalised from the 1970s-1980s","type":"Survey design and validation procedure","dataType":"Questionnaire response data (quantitative and/or qualitative feedback from pilot participants)","subfamily":"Data collection"},"citations":[{"ref":"Converse, J. M., & Presser, S. (1986). Survey Questions: Handcrafting the Standardized Questionnaire. Sage.","type":"book","doi":null,"isbn":"978-0803925557","url":null},{"ref":"Presser, S., Couper, M. P., Lessler, J. T., Martin, E., Martin, J., Rothgeb, J. M., & Singer, E. (2004). Methods for testing and evaluating survey questions. Public Opinion Quarterly, 68(1), 109-130.","type":"article","doi":"10.1093/poq/nfh008","isbn":null,"url":null}],"related":["survey","online-survey","face-to-face-survey","structured-interview","longitudinal-survey","mobile-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-theoretical-sampling","name":"Pilot Theoretical Sampling","fullName":"Pilot Theoretical Sampling","aliases":["pilot-phase theoretical sampling","exploratory theoretical sampling","preliminary theoretical sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1967 (theoretical sampling origin); compound practice formalized in qualitative methodology literature","originator":"Glaser & Strauss (theoretical sampling); pilot study concept is longstanding in research methodology","url":"https://scholargate.app/en/survey-methodology/pilot-theoretical-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-theoretical-sampling.md","definition":"Pilot theoretical sampling applies the logic of theoretical sampling — selecting participants based on emerging concepts and theory — within a deliberate pilot or preliminary phase of a study. Rather than committing immediately to a full sampling strategy, the researcher conducts a small initial round of data collection and analysis to test whether theoretical sampling is feasible, to refine the sensitizing concepts guiding participant selection, and to identify whether the field is productive before full-scale data collection begins.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glaser & Strauss (theoretical sampling); pilot study concept is longstanding in research methodology","year":"1967 (theoretical sampling origin); compound practice formalized in qualitative methodology literature","type":"Qualitative sampling strategy with pilot phase","dataType":"Qualitative data (interviews, observations, documents) collected in a preliminary exploratory phase","subfamily":"Sampling"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null},{"ref":"Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., ... & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.","type":"article","doi":"10.1186/1471-2288-10-1","isbn":null,"url":null}],"related":["theoretical-sampling","purposive-sampling","pilot-purposive-sampling","grounded-theory","adaptive-theoretical-sampling","snowball-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pilot-weighted-sampling","name":"Pilot Weighted Sampling","fullName":"Pilot Study Weighted Sampling","aliases":["pilot phase weighted sampling","weighted pilot sampling","pilot probability proportional sampling","pilot PPS sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century (classical weighted sampling ~1934–1977; pilot study integration formalized in survey practice ~1970s–1980s)","originator":"Cochran, W. G.; Neyman, J.","url":"https://scholargate.app/en/survey-methodology/pilot-weighted-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/pilot-weighted-sampling.md","definition":"Pilot weighted sampling applies weighted (unequal-probability) sampling within a small-scale preliminary study to estimate key design parameters — variance components, design effects, and optimal stratum weights — before committing resources to the full survey. By using differential inclusion probabilities in the pilot, researchers obtain more precise parameter estimates for rarer or more variable subgroups while keeping total pilot cost low. The results directly inform the weighting scheme and sample-size allocation for the main survey.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cochran, W. G.; Neyman, J.","year":"Mid-20th century (classical weighted sampling ~1934–1977; pilot study integration formalized in survey practice ~1970s–1980s)","type":"Probability sampling with differential selection probabilities in a preliminary study phase","dataType":"Quantitative; numerical population frame with known or estimable auxiliary size measures","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470465462","url":null}],"related":["weighted-sampling","pilot-simple-random-sampling","pilot-stratified-sampling","probability-proportional-to-size-sampling","adaptive-weighted-sampling","multistage-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pinch-analysis","name":"Pinch Analysis","fullName":"Pinch Analysis for Heat Recovery and Integration","aliases":["heat integration","pinch point method","process integration"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Process Integration","year":"1978","originator":"Bodo Linnhoff, John Flower","url":"https://scholargate.app/en/applied-physics/pinch-analysis","markdownUrl":"https://scholargate.app/en/applied-physics/pinch-analysis.md","definition":"Pinch analysis is a systematic method for identifying the minimum energy requirements and optimal heat recovery opportunities in chemical processes. Developed by Bodo Linnhoff and John Flower in 1978, it graphically identifies the 'pinch point'—the most constrained part of the process where heating and cooling demands nearly balance. By targeting these bottlenecks, engineers can design energy-efficient heat exchanger networks and reduce operating costs dramatically.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bodo Linnhoff, John Flower","subfamily":"Process Integration","year":"1978","type":"Thermal design and optimization method"},"citations":[{"ref":"Linnhoff, B., & Flower, J. R. (1978). Synthesis of heat exchanger networks: I. Systematic generation of energy optimal networks. AIChE Journal, 24(4), 633-642.","type":"article","doi":"10.1002/aic.690240411","isbn":null,"url":null},{"ref":"Smith, R. (2005). Chemical Process Design and Integration (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-471-48681-5","url":null},{"ref":"Kemp, I. C. (2007). Pinch Analysis and Process Integration: A User Guide on Process Integration for the Efficient Use of Energy (2nd ed.). Butterworth-Heinemann.","type":"book","doi":null,"isbn":"978-0-7506-8260-0","url":null}],"related":["peng-robinson-equation-of-state","unifac","cstr-model","pfr-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"piper-fatigue-scale","name":"Piper Fatigue Scale","fullName":"Piper Fatigue Scale (PFS)","aliases":["PFS"],"domain":"oncology-nursing","family":"process-pipeline","subfamily":"Fatigue Assessment","year":"1989","originator":"Barbara Piper","url":"https://scholargate.app/en/oncology-nursing/piper-fatigue-scale","markdownUrl":"https://scholargate.app/en/oncology-nursing/piper-fatigue-scale.md","definition":"The Piper Fatigue Scale is a 22-item multidimensional self-report instrument that evaluates cancer-related fatigue across four conceptually distinct domains: behavioral/severity, affective/meaning, sensory, and cognitive/mood. Developed by Barbara Piper and colleagues in 1989 and revised in 1998, the PFS is grounded in a theoretical model of fatigue mechanisms and is widely used in oncology research and clinical practice to assess treatment-related and disease-related fatigue.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barbara Piper","subfamily":"Fatigue Assessment","year":"1989","type":"Patient self-report multidimensional fatigue scale"},"citations":[{"ref":"Piper, B. F., Dibble, S. L., Dodd, M. J., Weiss, M. C., Slater, G., & Paul, S. M. (1989). The revised Piper Fatigue Scale: psychometric evaluation in women with breast cancer. Oncol Nurs Forum, 16(6), 751–758.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+revised+Piper+Fatigue+Scale%3A+psychometric+evaluation+in+women+with+breast+cancer+Piper"},{"ref":"Piper, B. F., Lindsey, A. M., & Dodd, M. J. (1987). Fatigue mechanisms in cancer patients: developing nursing theory. Oncol Nurs Forum, 14(6), 17–23.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/3320981"}],"related":["brief-fatigue-inventory","cancer-fatigue-scale","chalder-fatigue-scale","multidimensional-fatigue-inventory","edmonton-symptom-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"piprecia","name":"PIPRECIA","fullName":"PIvot Pairwise RElative Criteria Importance Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2017","originator":"Stanujkić, D., Karabašević, D., Zavadskas, E. K., Turskis, Z., Maksimović, M.","url":"https://scholargate.app/en/decision-making/piprecia","markdownUrl":"https://scholargate.app/en/decision-making/piprecia.md","definition":"PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Stanujkić, D., Karabašević, D., Zavadskas, E. K., Turskis, Z., Maksimović, M. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanujkić, D., Karabašević, D., Zavadskas, E. K., Turskis, Z., Maksimović, M.","subfamily":"Weight_Subjective","year":"2017","type":"Pivot pairwise sequential ratio weighting","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Stanujkić, D., Karabašević, D., Zavadskas, E. K., Turskis, Z., Maksimović, M. (2017). An approach to determining customer satisfaction in mobile commerce: A new approach to the SWARA method — PIPRECIA. Transformations in Business & Economics","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+approach+to+determining+customer+satisfaction+in+mobile+commerce%3A+A+new+approach+to+the+SWARA+method+%E2%80%94+PIPRECIA+Stanujki%C4%87"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pitch-detection-algorithm","name":"Pitch Detection Algorithm","fullName":"Pitch Detection and Fundamental Frequency Estimation Algorithm","aliases":["f0 detection","fundamental frequency tracking","monophonic pitch extraction"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Feature extraction","year":"2002","originator":"Alain de Cheveigné","url":"https://scholargate.app/en/music-information-retrieval/pitch-detection-algorithm","markdownUrl":"https://scholargate.app/en/music-information-retrieval/pitch-detection-algorithm.md","definition":"Pitch detection (or fundamental frequency estimation) is the task of automatically determining the perceived pitch of a monophonic (single-source) audio signal at each moment in time. Formalized by de Cheveigné and Kawahara (2002) through the YIN algorithm, it is foundational to music and speech processing. Pitch detection enables vocal analysis, music transcription, instrument tuning, and speech analysis. Monophonic pitch is unambiguous; polyphonic pitch detection is fundamentally harder and a distinct problem.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alain de Cheveigné","subfamily":"Feature extraction","year":"2002","type":"Fundamental frequency estimation"},"citations":[{"ref":"de Cheveigné, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4), 1917-1930.","type":"article","doi":"10.1121/1.1458024","isbn":null,"url":null},{"ref":"McLeod, P., & Wyvill, G. (2005). A smarter way to find pitch. In Proceedings of the International Computer Music Conference.","type":"article","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/2411194_A_Smarter_Way_to_Find_Pitch"},{"ref":"Mauch, M., Cannam, C., Bittner, R., Fazekas, G., Salamon, J., Wade, J., & Benetos, E. (2015). Computer-aided Research on Monophonic Singing. In Frontiers in Psychology.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Computer-aided+Research+on+Monophonic+Singing+Mauch"}],"related":["melody-extraction","automatic-music-transcription","beat-tracking","key-detection-music","vocal-separation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pittsburgh-sleep-quality-index","name":"Pittsburgh Sleep Quality Index","fullName":"Pittsburgh Sleep Quality Index - Clinical Sleep Assessment","aliases":["PSQI","Pittsburgh Index"],"domain":"health-services","family":"process-pipeline","subfamily":"Sleep quality and sleep disorder screening","year":"1989","originator":"David J. Buysse and Charles F. Reynolds","url":"https://scholargate.app/en/health-services/pittsburgh-sleep-quality-index","markdownUrl":"https://scholargate.app/en/health-services/pittsburgh-sleep-quality-index.md","definition":"The Pittsburgh Sleep Quality Index (PSQI) is a comprehensive self-report questionnaire developed by Buysse and colleagues in 1989 to assess sleep quality and sleep disturbances. The PSQI comprises 19 items aggregated into seven components that evaluate sleep duration, sleep efficiency, sleep disturbances, daytime dysfunction, and use of sleep medications. It is one of the most widely used instruments for both clinical sleep assessment and sleep research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David J. Buysse and Charles F. Reynolds","subfamily":"Sleep quality and sleep disorder screening","year":"1989","type":"Multidimensional sleep quality assessment"},"citations":[{"ref":"Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193-213.","type":"article","doi":"10.1016/0165-1781(89)90047-4","isbn":null,"url":null},{"ref":"Carpenter, J. S., & Andrykowski, M. A. (1998). Psychometric evaluation of the Pittsburgh Sleep Quality Index. Journal of Psychosomatic Research, 45(1), 5-13.","type":"article","doi":"10.1016/S0022-3999(97)00298-5","isbn":null,"url":null},{"ref":"Backhaus, J., Junghanns, K., Broocks, A., Riemann, D., & Hohagen, F. (2002). Test-retest reliability and validity of the Pittsburgh Sleep Quality Index in primary insomnia. Journal of Psychosomatic Research, 53(3), 737-740.","type":"article","doi":"10.1016/S0022-3999(02)00330-6","isbn":null,"url":null}],"related":["epworth-sleepiness-scale","brief-pain-inventory","patient-health-questionnaire-2"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"piv","name":"PIV","fullName":"Proximity Indexed Value","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Mufazzal, S. Muzakkir, S. M.","url":"https://scholargate.app/en/decision-making/piv","markdownUrl":"https://scholargate.app/en/decision-making/piv.md","definition":"PIV (Proximity Indexed Value) is a ranking multi-criteria decision-making (MCDM) method introduced by Mufazzal, S. Muzakkir, S. M. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mufazzal, S. Muzakkir, S. M.","subfamily":"Ranking","year":"2018","type":"Distance from weighted ideal minimization via linear proximity index","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Mufazzal, S., Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering","type":"article","doi":"10.1016/j.cie.2018.03.045","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pixel-based-classification","name":"Pixel-Based Classification","fullName":"Pixel-Based Image Classification","aliases":["Per-Pixel Classification","Spectral Classification","Pixel-by-Pixel Classification","Piksel Tabanlı Sınıflandırma"],"domain":"remote-sensing","family":"ml-model","subfamily":"Remote sensing","year":2007,"originator":"Remote-sensing classification literature","url":"https://scholargate.app/en/remote-sensing/pixel-based-classification","markdownUrl":"https://scholargate.app/en/remote-sensing/pixel-based-classification.md","definition":"Pixel-based image classification is a fundamental remote-sensing technique that assigns each individual pixel in a satellite or aerial image to a thematic land-cover category based solely on its spectral values across multiple bands. Systematically surveyed and formalized by Lu and Weng (2007), the approach encompasses both supervised methods—where labeled training samples guide the classifier—and unsupervised clustering approaches that discover natural spectral groupings without prior labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Remote-sensing classification literature","year":2007,"type":"Supervised/unsupervised spectral image classification","subfamily":"Remote sensing","unit_of_analysis":"Individual pixel","input":"Multispectral or hyperspectral raster imagery"},"citations":[{"ref":"Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.","type":"article","doi":"10.1080/01431160600746456","isbn":null,"url":null}],"related":["object-based-image-analysis","support-vector-machine","random-forest"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pl-edas","name":"PL-EDAS","fullName":"Probabilistic Linguistic extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2016","originator":"Pang, Q. Wang, H. Xu, Z.","url":"https://scholargate.app/en/decision-making/pl-edas","markdownUrl":"https://scholargate.app/en/decision-making/pl-edas.md","definition":"PL-EDAS (Probabilistic Linguistic extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Pang, Q. Wang, H. Xu, Z. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pang, Q. Wang, H. Xu, Z.","subfamily":"Ranking","year":"2016","type":"Probabilistic Linguistic outranking/ranking — Probabilistic Linguistic Term Set (PLTS: {L_k|p_k})","value_space":"linguistic_probabilistic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Pang, Q., Wang, H., Xu, Z. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences","type":"article","doi":"10.1016/j.ins.2016.06.021","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pl-mabac","name":"PL-MABAC","fullName":"Plithogenic MABAC (with BWM weighting and Rough Number uncertainty)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Pamučar, D. Ćirović, G.","url":"https://scholargate.app/en/decision-making/pl-mabac","markdownUrl":"https://scholargate.app/en/decision-making/pl-mabac.md","definition":"PL-MABAC (Plithogenic MABAC (with BWM weighting and Rough Number uncertainty)) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D. Ćirović, G. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamučar, D. Ćirović, G.","subfamily":"Ranking","year":"2015","type":"Plithogenic-rough border approximation area (PL+RN+MABAC)","value_space":"linguistic_probabilistic","uncertainty":"hybrid","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Pamučar, D., Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using MABAC. Expert Systems with Applications","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+selection+of+transport+and+handling+resources+in+logistics+centers+using+MABAC+Pamu%C4%8Dar"}],"related":["sensitivity-module","bwm","ahp","entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pl-marcos","name":"PL-MARCOS","fullName":"Probabilistic Linguistic extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Wang, J., Wei, G., Wei, C., Wei, Y.","url":"https://scholargate.app/en/decision-making/pl-marcos","markdownUrl":"https://scholargate.app/en/decision-making/pl-marcos.md","definition":"PL-MARCOS (Probabilistic Linguistic extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, J., Wei, G., Wei, C., Wei, Y. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang, J., Wei, G., Wei, C., Wei, Y.","subfamily":"Ranking","year":"2023","type":"Probabilistic Linguistic outranking/ranking — Probabilistic Linguistic Term Set (PLTS: {L_k|p_k})","value_space":"linguistic_probabilistic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"(). UNCONFIRMED — PL-MARCOS specific seminal not confirmed via systematic literature search. PENDING","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=UNCONFIRMED%20%E2%80%94%20PL-MARCOS%20specific%20seminal%20not%20confirmed%20via%20systematic%20literature%20search"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pl-multimoora","name":"PL-MULTIMOORA","fullName":"Probabilistic Linguistic extension of MULTIMOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Wu, X. Liao, H. Xu, Z. S. Hafezalkotob, A. Herrera, F.","url":"https://scholargate.app/en/decision-making/pl-multimoora","markdownUrl":"https://scholargate.app/en/decision-making/pl-multimoora.md","definition":"PL-MULTIMOORA (Probabilistic Linguistic extension of MULTIMOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Wu, X. Liao, H. Xu, Z. S. Hafezalkotob, A. Herrera, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wu, X. Liao, H. Xu, Z. S. Hafezalkotob, A. Herrera, F.","subfamily":"Ranking","year":"2018","type":"Probabilistic Linguistic multi-objective ranking — PLTS: {L_k|p_k} with expectation function + Borda aggregation","value_space":"linguistic_probabilistic","uncertainty":"hybrid","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Wu, X., Liao, H., Xu, Z. S., Hafezalkotob, A., Herrera, F. (2018). Probabilistic Linguistic MULTIMOORA: A Multicriteria Decision Making Method Based on the Probabilistic Linguistic Expectation Function and the Improved Borda Rule. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2018.2843330","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","merec","swara","fucom"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pl-todim","name":"PL-TODIM","fullName":"Probabilistic Linguistic extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Liu, P., Teng, F.","url":"https://scholargate.app/en/decision-making/pl-todim","markdownUrl":"https://scholargate.app/en/decision-making/pl-todim.md","definition":"PL-TODIM (Probabilistic Linguistic extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Liu, P., Teng, F. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, P., Teng, F.","subfamily":"Ranking","year":"2017","type":"Probabilistic Linguistic outranking/ranking — Probabilistic Linguistic Term Set (PLTS: {L_k|p_k})","value_space":"linguistic_probabilistic","uncertainty":"hybrid","compensation":"full","rank_reversal":false},"citations":[{"ref":"Liu, P., Teng, F. (2017). Probabilistic linguistic TODIM approach for multiple attribute decision-making. Granular Computing","type":"article","doi":"10.1007/s41066-017-0047-4","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pl-topsis","name":"PL-TOPSIS","fullName":"Probabilistic Linguistic extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Lu, J., Wei, C.","url":"https://scholargate.app/en/decision-making/pl-topsis","markdownUrl":"https://scholargate.app/en/decision-making/pl-topsis.md","definition":"PL-TOPSIS (Probabilistic Linguistic extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Lu, J., Wei, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lu, J., Wei, C.","subfamily":"Ranking","year":"2019","type":"Probabilistic Linguistic outranking/ranking — Probabilistic Linguistic Term Set (PLTS: {L_k|p_k})","value_space":"linguistic_probabilistic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Lu, J., Wei, C. (2019). TOPSIS Method for Probabilistic Linguistic MAGDM with Entropy Weight and Its Application to Supplier Selection of New Agricultural Machinery Products. Entropy","type":"article","doi":"10.3390/e21100953","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pl-vikor","name":"PL-VIKOR","fullName":"Probabilistic Linguistic extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Li, P., Liu, J., Wei, C.","url":"https://scholargate.app/en/decision-making/pl-vikor","markdownUrl":"https://scholargate.app/en/decision-making/pl-vikor.md","definition":"PL-VIKOR (Probabilistic Linguistic extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Li, P., Liu, J., Wei, C. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li, P., Liu, J., Wei, C.","subfamily":"Ranking","year":"2021","type":"Probabilistic Linguistic outranking/ranking — Probabilistic Linguistic Term Set (PLTS: {L_k|p_k})","value_space":"linguistic_probabilistic","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Li, P., Liu, J., Wei, C. (2021). An Improved PL-VIKOR Model for Risk Evaluation of Technological Innovation Projects with Probabilistic Linguistic Term Sets. International Journal of Fuzzy Systems","type":"article","doi":"10.1007/s40815-020-00971-1","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"place-attachment-scale","name":"Place Attachment Scale","fullName":"Place Attachment Scale (PAS)","aliases":["PAS","Destination Attachment Scale"],"domain":"tourism-management","family":"process-pipeline","subfamily":"attachment-measurement","year":"1992","originator":"Williams, D. R.; Vaske, J. J.","url":"https://scholargate.app/en/tourism-management/place-attachment-scale","markdownUrl":"https://scholargate.app/en/tourism-management/place-attachment-scale.md","definition":"The Place Attachment Scale (PAS), developed by Williams & Vaske (1992) and refined by Jorgensen & Stedman (2001), measures individuals' emotional and functional bonds to destinations—the extent to which places become integral to identity and sense of belonging. Comprising dimensions of place identity (destination as self-definition), place dependence (destination optimizes one's activities), emotional bonds (love, comfort), and community belonging, the PAS distinguishes deep loyalty-drivers from transactional satisfaction. Essential for understanding repeat visitation, community tourism governance, heritage conservation, and destination branding strategies that cultivate belonging.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Williams, D. R.; Vaske, J. J.","subfamily":"attachment-measurement","year":"1992","type":"Self-report questionnaire"},"citations":[{"ref":"Williams, D. R., & Vaske, J. J. (1992). The measurement of place attachment: Valid and reliable instruments for natural environments. Society and Natural Resources, 15(3), 271-280.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+measurement+of+place+attachment%3A+Valid+and+reliable+instruments+for+natural+environments+Williams"},{"ref":"Jorgensen, B. S., & Stedman, R. C. (2001). Sense of place as an attitude: Lakeshore property owners' attitudes toward their properties. Journal of Environmental Psychology, 21(3), 233-248.","type":"article","doi":"10.1006/jevp.2001.0226","isbn":null,"url":null},{"ref":"Ribeiro, M. A., Fesenmaier, D. R., & Khoo-Lattimore, C. (2017). Social media as a co-creation mechanism for tourism experiences. Journal of Travel & Tourism Marketing, 34(9), 1227-1244.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Social+media+as+a+co-creation+mechanism+for+tourism+experiences+Ribeiro"},{"ref":"Lewicka, M. (2011). Place attachment: How far have we come in the last 40 years? Journal of Environmental Psychology, 31(3), 207-230.","type":"article","doi":"10.1016/j.jenvp.2010.10.001","isbn":null,"url":null}],"related":["destination-image-scale","tourist-loyalty-scale","travel-motivation-scale","tourist-satisfaction-scale","perceived-value-scale-tourism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"placebo-test-in-education-research","name":"Placebo Test in Education Research","fullName":"Placebo Test for Causal Identification in Education Research","aliases":["placebo regression","falsification test","placebo check","fake-treatment test"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1990s–2000s","originator":"Widely adopted in applied econometrics and education research; codified by Imbens, Wooldridge, Lee, and Lemieux","url":"https://scholargate.app/en/causal-inference/placebo-test-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/placebo-test-in-education-research.md","definition":"A placebo test is a falsification check used in quasi-experimental education research to validate a causal design. By applying the same estimator to a time period, group, or outcome where no real effect should exist, researchers verify that their identification strategy is not picking up spurious patterns. A statistically significant placebo estimate signals a flaw in the design, while a null result supports its credibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Widely adopted in applied econometrics and education research; codified by Imbens, Wooldridge, Lee, and Lemieux","year":"1990s–2000s","type":"Falsification / robustness check","dataType":"Panel data, repeated cross-sections, administrative records","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.","type":"book","doi":"10.1257/jel.47.1.5","isbn":null,"url":null},{"ref":"Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.","type":"article","doi":"10.1257/jel.48.2.281","isbn":null,"url":null}],"related":["difference-in-differences","regression-discontinuity-design","instrumental-variables","event-study-design","propensity-score-matching","synthetic-control-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"placebo-tests-causal","name":"Placebo Tests","fullName":"Placebo Tests for Causal Inference Validation","aliases":["falsification tests","placebo checks","refutation tests","Plasebo Testleri — Nedensel Çıkarım Doğrulama"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2010,"originator":"Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity)","url":"https://scholargate.app/en/causal-inference/placebo-tests-causal","markdownUrl":"https://scholargate.app/en/causal-inference/placebo-tests-causal.md","definition":"Placebo tests are a family of falsification checks that probe the credibility of a causal claim by re-running the analysis on a fake treatment, a false intervention date, or an outcome that should not have been affected. The approach was popularised through the synthetic control work of Abadie, Diamond and Hainmueller (2010) and the regression-discontinuity validity checks of Imbens and Lemieux (2008).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity)","year":2010,"type":"Falsification / robustness test family for causal inference","estimator":"Re-estimation of the causal effect on a placebo (false) treatment, date, unit, or outcome","outcome":"continuous or binary","minSample":50},"citations":[{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null},{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null}],"related":["regression-discontinuity","difference-in-discontinuities","sensitivity-analysis-observational","dag-identification","causal-discovery"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"plackett-burman","name":"Plackett-Burman Design","fullName":"Plackett-Burman Screening Design","aliases":["PB design","PB screening","Plackett-Burman Tarama Deseni"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1946,"originator":"R.L. Plackett & J.P. Burman","url":"https://scholargate.app/en/experimental-design/plackett-burman","markdownUrl":"https://scholargate.app/en/experimental-design/plackett-burman.md","definition":"The Plackett-Burman design is a two-level orthogonal screening design introduced by R.L. Plackett and J.P. Burman in 1946 that allows researchers to estimate the main effect of each factor independently using the smallest possible number of experimental runs. Run counts are always multiples of four, making it exceptionally economical for studies with many candidate factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R.L. Plackett & J.P. Burman","year":1946,"family":"Screening design","type":"Two-level orthogonal array","levels":2,"runMultiple":"multiples of 4","parametric":true,"aliasing":"main effects partially aliased with two-factor interactions","minRuns":8,"purpose":"factor screening"},"citations":[{"ref":"Plackett, R.L. & Burman, J.P. (1946). The Design of Optimum Multifactorial Experiments. Biometrika, 33(4), 305–325.","type":"article","doi":"10.1093/biomet/33.4.305","isbn":null,"url":null},{"ref":"Montgomery, D.C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["two-level-factorial","fractional-factorial","response-surface-methodology","central-composite-design","one-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"plackett-luce-model","name":"Plackett-Luce Model","fullName":"Plackett-Luce Model for Rankings","aliases":["Luce's Choice Axiom Model","Rank-Ordered Logit Model","Exploded Logit Model","Sıralama Tercih Modeli"],"domain":"decision-making","family":"regression-model","subfamily":"Ranking models","year":1975,"originator":"Robin Plackett; R. Duncan Luce","url":"https://scholargate.app/en/decision-making/plackett-luce-model","markdownUrl":"https://scholargate.app/en/decision-making/plackett-luce-model.md","definition":"The Plackett-Luce model is a probabilistic framework for analysing and predicting rank-ordered data. Introduced by Robin Plackett (1975) — building on R. Duncan Luce's earlier axiom of choice (1959) — it models the probability of any complete ranking of items as a sequential selection process, where each item's chance of being chosen at each position is proportional to its latent worth parameter. It is widely used in preference learning, recommender systems, and choice modelling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robin Plackett; R. Duncan Luce","year":1975,"type":"Probabilistic ranking model","subfamily":"Ranking models","estimationMethod":"Maximum likelihood estimation","outputScale":"Latent utility scores (worth parameters)"},"citations":[{"ref":"Plackett, R. L. (1975). The analysis of permutations. Journal of the Royal Statistical Society: Series C, 24(2), 193–202.","type":"article","doi":"10.2307/2346567","isbn":null,"url":null}],"related":["bradley-terry-model","rank-aggregation","multinomial-logit"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"plagiarism-in-research","name":"Plagiarism in Academic Research","fullName":"Plagiarism Detection and Prevention in Academic Research","aliases":["Text Plagiarism","Idea Plagiarism","Self-Plagiarism"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"research-misconduct","year":"1989","originator":"U.S. Office of Research Integrity (ORI) and institutional policies","url":"https://scholargate.app/en/publication-ethics/plagiarism-in-research","markdownUrl":"https://scholargate.app/en/publication-ethics/plagiarism-in-research.md","definition":"Plagiarism—the use of others' words, ideas, or methods without attribution—is formally classified as research misconduct by the U.S. Office of Research Integrity and most institutions worldwide. It ranges from verbatim copying of text to paraphrasing without citation to presenting others' ideas as one's own. Unlike accidental omission of a citation (which is corrected via erratum), plagiarism implies intent or gross negligence and triggers investigation, potential retraction, and career consequences. Plagiarism detection tools (e.g., Turnitin, iThenticate) and manual checking by journals now routinely screen manuscripts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Office of Research Integrity (ORI) and institutional policies","subfamily":"research-misconduct","year":"1989","type":"Standard"},"citations":[{"ref":"U.S. Office of Research Integrity (2023). Definition of Research Misconduct. Federal Policy on Research Misconduct (42 CFR Part 93). ORI.","type":"webpage","doi":null,"isbn":null,"url":"https://ori.hhs.gov/definition-misconduct"},{"ref":"Committee on Publication Ethics (2023). Flowcharts and Advice on Plagiarism. COPE.","type":"webpage","doi":null,"isbn":null,"url":"https://publicationethics.org/"},{"ref":"Weber-Wulff, D. C. (2012). Plagiarism Detectors Are Not Reliable. The Guardian. Online opinion.","type":"article","doi":null,"isbn":null,"url":"https://www.theguardian.com/education/2012/aug/07/plagiarism-detectors-unreliable"}],"related":["duplicate-publication","icmje-authorship-criteria","cope-guidelines","retraction-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"plant-propagation-success","name":"Plant Propagation Success Rate","fullName":"Quantitative Assessment of Vegetative and Generative Propagation Efficiency","aliases":["propagation efficiency","rooting success assessment","nursery propagation management"],"domain":"horticulture","family":"process-pipeline","subfamily":"Propagation methods and nursery management","year":"1970","originator":"Nursery and propagation science","url":"https://scholargate.app/en/horticulture/plant-propagation-success","markdownUrl":"https://scholargate.app/en/horticulture/plant-propagation-success.md","definition":"Plant propagation success rate quantifies the efficiency of vegetative (cuttings, layers, division) and generative (seed) propagation methods by measuring germination, rooting, and survival percentages. This method combines environmental monitoring, growth observations, and statistical analysis to optimize propagation protocols and predict nursery output. It is fundamental to nursery operations and plant breeding programs worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nursery and propagation science","subfamily":"Propagation methods and nursery management","year":"1970","type":"propagation efficiency measurement pipeline"},"citations":[{"ref":"Hartmann, H. T., Kester, D. E., Davies, F. T., & Geneve, R. L. (2011). Plant Propagation: Principles and Practices (8th ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com/product/plant-propagation-9780134042558.html"},{"ref":"Briggs, J. L., & Pliley, A. L. (1997). Laboratory evaluation of seed dormancy-breaking and germination stimulants. HortScience, 32(2), 206–210.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Laboratory+evaluation+of+seed+dormancy-breaking+and+germination+stimulants+Briggs"}],"related":["grafting-success-evaluation","phenological-stage-monitoring","greenhouse-climate-control","hydroponic-nutrient-solution"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"plant-seir-model","name":"Plant Disease SEIR Model","fullName":"Susceptible-Exposed-Infectious-Removed Model for Plant Disease Epidemiology","aliases":["plant SEIR epidemic model","botanical SEIR model","plant disease compartmental model","SEIR phytopathological model"],"domain":"agronomy","family":"process-pipeline","subfamily":"Plant disease epidemiology / Quantitative phytopathology","year":"1963 (Van der Plank); SEIR plant adaptation developed through 1970s–1990s","originator":"Multiple contributors (Van der Plank foundational; Kermack-McKendrick SIR adapted to plant pathology)","url":"https://scholargate.app/en/agronomy/plant-seir-model","markdownUrl":"https://scholargate.app/en/agronomy/plant-seir-model.md","definition":"The Plant Disease SEIR Model is a deterministic compartmental modelling framework adapted from human epidemiology to describe how a pathogen spreads through a host plant population. Rooted in the foundational work of J. E. Van der Plank and the Kermack-McKendrick tradition, it partitions all plants into four states — Susceptible, Exposed (latently infected), Infectious, and Removed — and tracks their transitions over time using a system of ordinary differential equations. It is a core tool in quantitative plant pathology and crop protection research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Van der Plank foundational; Kermack-McKendrick SIR adapted to plant pathology)","year":"1963 (Van der Plank); SEIR plant adaptation developed through 1970s–1990s","type":"Deterministic compartmental epidemic model","dataType":"Time-series disease incidence or severity data, host population counts, infection rate parameters","subfamily":"Plant disease epidemiology / Quantitative phytopathology"},"citations":[{"ref":"Van der Plank, J. E. (1963). Plant Diseases: Epidemics and Control. Academic Press, New York.","type":"book","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/book/9781483200804/plant-diseases"},{"ref":"Madden, L. V., Hughes, G., & van den Bosch, F. (2007). The Study of Plant Disease Epidemics. American Phytopathological Society Press, St. Paul, MN.","type":"book","doi":null,"isbn":"978-0890543559","url":null}],"related":["sir-model","logistic-growth-model","gompertz-disease-progress","basic-reproduction-number","agent-based-simulation","spatial-epidemic-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"plaque-reduction-neutralization-test","name":"Plaque Reduction Neutralization Test","fullName":"Plaque Reduction Neutralization Test (PRNT)","aliases":["PRNT","plaque neutralization assay","neutralization plaque assay","serum neutralization plaque test"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Virus neutralization serology","year":"1952–1954","originator":"Renato Dulbecco and Marguerite Vogt","url":"https://scholargate.app/en/veterinary-science/plaque-reduction-neutralization-test","markdownUrl":"https://scholargate.app/en/veterinary-science/plaque-reduction-neutralization-test.md","definition":"The Plaque Reduction Neutralization Test (PRNT) is a quantitative cell-based serological assay used in veterinary and human virology to measure the ability of antibodies in a serum sample to neutralize a live virus. By counting visible plaques — areas of cell destruction on a monolayer — the method determines the serum titer at which viral infectivity is reduced by 50% or 90%, making it the gold-standard technique for detecting and quantifying neutralizing antibodies against many RNA and DNA viruses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Renato Dulbecco and Marguerite Vogt","year":"1952–1954","type":"Quantitative cell-based serological assay","dataType":"Cell monolayer plaque counts, serum dilution titers","subfamily":"Virus neutralization serology"},"citations":[{"ref":"Dulbecco, R., & Vogt, M. (1954). Plaque formation and isolation of pure lines with poliomyelitis viruses. Journal of Experimental Medicine, 99(2), 167–182.","type":"journal-article","doi":"10.1084/jem.99.2.167","isbn":null,"url":null},{"ref":"Roehrig, J. T., Hombach, J., & Barrett, A. D. T. (2008). Guidelines for plaque-reduction neutralization testing of human antibodies to dengue viruses. Viral Immunology, 21(2), 123–132.","type":"journal-article","doi":"10.1089/vim.2008.0007","isbn":null,"url":null}],"related":["enzyme-linked-immunosorbent-assay","hemagglutination-inhibition-test","serum-virus-neutralization","cytopathic-effect-assay","fluorescence-reduction-neutralization-test","microneutralization-assay"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"plasmonic-resonance","name":"Plasmonic Resonance","fullName":"Plasmonic Resonance Analysis","aliases":["surface plasmon resonance","localized surface plasmon resonance","LSPR","SPR"],"domain":"optics","family":"process-pipeline","subfamily":"Nanophotonics","year":"1968","originator":"Erich Kretschmann and Heinz Raether","url":"https://scholargate.app/en/optics/plasmonic-resonance","markdownUrl":"https://scholargate.app/en/optics/plasmonic-resonance.md","definition":"Plasmonic resonance refers to the collective oscillation of free electrons in metallic nanostructures that interact strongly with light, resulting in dramatic enhancements of electric fields, absorption, and scattering. First discovered by Kretschmann and Raether in 1968, plasmonic resonance is now central to nanophotonics, enabling applications from biosensing to photothermal therapy and advanced optical devices with subwavelength control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erich Kretschmann and Heinz Raether","subfamily":"Nanophotonics","year":"1968","type":"Resonance phenomenon"},"citations":[{"ref":"Kretschmann, E., & Raether, H. (1968). Radiative decay of non radiative surface plasmons excited by light. Zeitschrift für Naturforschung A, 23(12), 2135-2136.","type":"article","doi":"10.1515/zna-1968-1247","isbn":null,"url":null},{"ref":"Maier, S. A. (2007). Plasmonics: Fundamentals and Applications. Springer.","type":"book","doi":"10.1007/0-387-37825-1","isbn":null,"url":null},{"ref":"Halas, N. J., Lal, S., Chang, W. S., Link, S., & Nordlander, P. (2011). Plasmons in strongly coupled metallic nanostructures. Chemical Reviews, 111(6), 3913-3961.","type":"article","doi":"10.1021/cr200061k","isbn":null,"url":null}],"related":["rcwa","fourier-optics","finite-difference-time-domain"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"plastic-hinge-analysis","name":"Plastic Hinge Analysis","fullName":"Plastic Hinge Analysis in Structural Engineering","aliases":["plastic hinge method","plastic collapse analysis","limit state plastic analysis","yield hinge analysis"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Plastic structural mechanics","year":"1914–1950s (Kazinczy 1914; Baker et al. 1956)","originator":"Multiple contributors (Kazinczy, Kist, Baker, Horne, Neal)","url":"https://scholargate.app/en/civil-engineering/plastic-hinge-analysis","markdownUrl":"https://scholargate.app/en/civil-engineering/plastic-hinge-analysis.md","definition":"Plastic hinge analysis is a structural engineering method that determines the load-carrying capacity of a structure by tracking the sequential formation of plastic hinges — localised zones where a cross-section has fully yielded — until a kinematic collapse mechanism is formed. Rooted in plastic theory, it provides a more economical and realistic estimate of ultimate structural capacity than purely elastic approaches, and is widely used in the design and assessment of steel frames, reinforced concrete beams, and other ductile structural systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Kazinczy, Kist, Baker, Horne, Neal)","year":"1914–1950s (Kazinczy 1914; Baker et al. 1956)","type":"Structural analysis method","dataType":"Member cross-section properties, material yield stress, applied loads","subfamily":"Plastic structural mechanics"},"citations":[{"ref":"Chen, W. F., & Sohal, A. S. (1995). Plastic Design and Second-Order Analysis of Steel Frames. Springer.","type":"book","doi":null,"isbn":"978-0387944319","url":null},{"ref":"Neal, B. G. (1977). The Plastic Methods of Structural Analysis (3rd ed.). Chapman and Hall.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/plastic-methods-of-structural-analysis/oclc/3006213"}],"related":["pushover-analysis","limit-analysis","nonlinear-static-analysis","moment-redistribution","yield-line-theory","finite-element-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pls-sem","name":"Partial Least Squares Structural Equation Modeling","fullName":"Partial Least Squares Structural Equation Modeling","aliases":["PLS-SEM","PLS path modeling"],"domain":"psychometrics","family":"latent-structure","subfamily":"Latent Variable Modeling","year":"1985","originator":"Herman Wold","url":"https://scholargate.app/en/psychometrics/pls-sem","markdownUrl":"https://scholargate.app/en/psychometrics/pls-sem.md","definition":"PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex models with many constructs, and non-normal data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herman Wold","subfamily":"Latent Variable Modeling","year":"1985","type":"Component-based structural equation model"},"citations":[{"ref":"Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"9781483377445","url":null},{"ref":"Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of Statistical Sciences (Vol. 6, pp. 581-591). Wiley.","type":"article","doi":null,"isbn":"9780471822622","url":null},{"ref":"Chin, W. W. (2010). How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications (pp. 655-690). Springer.","type":"article","doi":"10.1007/978-3-540-32827-8_29","isbn":null,"url":null}],"related":["exploratory-structural-equation-modeling","wordscores","wordfish","necessary-condition-analysis","fuzzy-anova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pluralistic-walkthrough","name":"Pluralistic Walkthrough","fullName":"Pluralistic Walkthrough Method","aliases":["Pluralistic Usability Walkthrough","PW"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Inspection Method","year":"1992","originator":"Randolph G. Bias","url":"https://scholargate.app/en/human-computer-interaction/pluralistic-walkthrough","markdownUrl":"https://scholargate.app/en/human-computer-interaction/pluralistic-walkthrough.md","definition":"The Pluralistic Walkthrough is a usability inspection method that brings together users, developers, and usability specialists to walk through an interface and voice their reactions and concerns. Developed by Randolph Bias in 1992, this method combines elements of cognitive walkthroughs with user involvement, creating a collaborative evaluation setting that captures diverse perspectives. By including actual users in the evaluation session, the method bridges the gap between expert judgment and real-world user experience, uncovering unexpected insights and building stakeholder consensus around design improvements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Randolph G. Bias","subfamily":"Inspection Method","year":"1992","type":"User-centered walkthrough with mixed stakeholders"},"citations":[{"ref":"Bias, R. G. (1994). The pluralistic walkthrough: Coordinating technology and pedagogy in software development. In J. Nielsen & R. L. Mack (Eds.), Usability Inspection Methods (pp. 63–76). John Wiley & Sons.","type":"article","doi":null,"isbn":"0-471-01877-5","url":null},{"ref":"Bias, R. G. (1992). The pluralistic walkthrough: Coordinating technology and pedagogy. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 407–410).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+pluralistic+walkthrough%3A+Coordinating+technology+and+pedagogy+Bias"}],"related":["cognitive-walkthrough","heuristic-evaluation","think-aloud-protocol","contextual-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"poem","name":"POEM","fullName":"Patient-Oriented Eczema Measure","aliases":["POEM Score"],"domain":"dermatology","family":"process-pipeline","subfamily":"patient-reported-severity","year":"2004","originator":"Charman CR, Venn AJ, Williams HC","url":"https://scholargate.app/en/dermatology/poem","markdownUrl":"https://scholargate.app/en/dermatology/poem.md","definition":"The POEM is a brief, patient-administered severity measure for atopic dermatitis that focuses on frequency of symptoms experienced over the past week. Developed by Charman, Venn, and Williams in 2004, it emphasizes the patient's lived experience rather than clinician observation, making it practical for routine clinical practice and remote monitoring. POEM is increasingly used alongside objective measures in clinical trials and outpatient care.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charman CR, Venn AJ, Williams HC","subfamily":"patient-reported-severity","year":"2004","type":"Self-report"},"citations":[{"ref":"Charman CR, Venn AJ, Williams HC. The Patient-Oriented Eczema Measure: development and initial validation of a new tool for measuring atopic eczema severity from the patients' perspective. Arch Dermatol. 2004;140(12):1513-1519.","type":"article","doi":"10.1001/archderm.140.12.1513","isbn":null,"url":null},{"ref":"Charman CR, Venn AJ, Williams HC. Measurement of body surface area involvement in atopic eczema: an updated comparison of estimation methods. Br J Dermatol. 2005;152(2):288-294.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Charman+CR%2C+Venn+AJ%2C+Williams+HC.+Measurement+of+body+surface+area+involvement+in+atopic+eczema%3A+an+updated+comparison+o+Charman"}],"related":["scorad","easi","dlqi-children"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"point-biserial-correlation","name":"Point-Biserial Correlation","fullName":"Point-Biserial Correlation Coefficient","aliases":["rpb","r_pb","point biserial r","item-total correlation","dichotomous-continuous correlation"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1954,"originator":"Robert F. Tate","url":"https://scholargate.app/en/statistics/point-biserial-correlation","markdownUrl":"https://scholargate.app/en/statistics/point-biserial-correlation.md","definition":"The point-biserial correlation coefficient (r_pb) measures the strength and direction of the linear association between one naturally dichotomous variable (coded 0/1) and one continuous variable. It is a special case of the Pearson product-moment correlation formally derived by Tate (1954) in the Annals of Mathematical Statistics and is the standard index used in psychometric item analysis, validity studies, and any research context where a binary grouping variable is related to a continuous outcome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Tate","year":1954,"family":"Correlation / hypothesis test","type":"Parametric correlation coefficient","outcomeX":"dichotomous (0/1)","outcomeY":"continuous","parametric":true,"range":"[-1, 1]","distribution":"Student t","df":"n - 2","equivalence":"Algebraically equivalent to Pearson r when X is coded 0/1"},"citations":[{"ref":"Tate, R. F. (1954). Correlation between a discrete and a continuous variable. Point-biserial correlation. Annals of Mathematical Statistics, 25(3), 603–607.","type":"article","doi":"10.1214/aoms/1177728730","isbn":null,"url":null},{"ref":"Tate, R. F. (1955). The theory of correlation between two continuous variables when one is dichotomized. Biometrika, 42(1–2), 205–216.","type":"article","doi":"10.1093/biomet/42.1-2.205","isbn":null,"url":null},{"ref":"Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070478497","url":null}],"related":["pearson-correlation","biserial-correlation","spearman-correlation","phi-coefficient","independent-t-test","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"poisson-regression","name":"Poisson Regression","fullName":"Poisson and Negative Binomial Regression","aliases":["count regression","log-linear count model","negative binomial regression","Poisson / Negatif Binom Regresyon"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1998,"originator":"Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)","url":"https://scholargate.app/en/econometrics/poisson-regression","markdownUrl":"https://scholargate.app/en/econometrics/poisson-regression.md","definition":"Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)","year":1998,"type":"Generalized linear model for count data","estimator":"Maximum likelihood (log link)","outcome":"count (non-negative integers)"},"citations":[{"ref":"Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511814365","isbn":null,"url":null},{"ref":"Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511973420","isbn":null,"url":null}],"related":["ols-regression","logistic-regression","quantile-regression","panel-fixed-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polar-codes","name":"Polar Codes","fullName":"Polar Codes with Successive Cancellation Decoding","aliases":["channel polarization","recursive codes"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Coding theory","year":"2009","originator":"Erdal Arikan","url":"https://scholargate.app/en/telecommunications/polar-codes","markdownUrl":"https://scholargate.app/en/telecommunications/polar-codes.md","definition":"Polar codes, introduced by Erdal Arikan in 2009, are the first constructive family of codes proven to achieve the Shannon capacity of symmetric binary-input memoryless channels. They use recursive construction and successive cancellation decoding, a simple greedy algorithm with theoretical guarantees. Polar codes were adopted in 5G NR for control channel coding and are studied for future 6G systems. Unlike turbo and LDPC codes (which are empirical), polar codes provide rigorous theoretical foundations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erdal Arikan","subfamily":"Coding theory","year":"2009","type":"recursive error-correcting code"},"citations":[{"ref":"Arikan, E. (2009). Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels. IEEE Transactions on Information Theory, 55(7), 3051-3073.","type":"article","doi":"10.1109/TIT.2009.2021379","isbn":null,"url":null},{"ref":"Sasoglu, E., Telatar, I., & Yildirim, E. (2011). Polarization for arbitrary discrete memoryless channels. In Proceedings of the IEEE Information Theory Workshop (ITW), 144-148.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Polarization+for+arbitrary+discrete+memoryless+channels+Sasoglu"}],"related":["ldpc-codes","turbo-code","shannon-capacity","ofdm","mimo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-causal-impact-analysis","name":"Policy Evaluation Causal Impact Analysis","fullName":"Policy Evaluation Causal Impact Analysis via Bayesian Structural Time-Series","aliases":["policy causal impact","BSTS policy evaluation","Bayesian policy impact assessment","CIA policy evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015","originator":"Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts","url":"https://scholargate.app/en/causal-inference/policy-evaluation-causal-impact-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-causal-impact-analysis.md","definition":"Policy Evaluation Causal Impact Analysis applies the Bayesian structural time-series (BSTS) framework of Brodersen et al. (2015) to estimate the causal effect of a policy intervention on aggregate outcomes. By constructing a synthetic counterfactual from pre-policy data and control covariates, it asks: what would have happened had the policy not been enacted? The difference between observed and predicted post-policy outcomes is the estimated policy effect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts","year":"2015","type":"Bayesian counterfactual / time-series","dataType":"Aggregate time-series outcomes with pre- and post-policy periods","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null}],"related":["causal-impact-analysis","synthetic-control-method","interrupted-time-series","difference-in-differences","policy-evaluation-interrupted-time-series","bayesian-causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-coarsened-exact-matching","name":"Policy Evaluation Coarsened Exact Matching","fullName":"Coarsened Exact Matching for Policy Evaluation","aliases":["CEM","Coarsened Exact Matching","CEM policy evaluation","coarsening-based matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2011-2012","originator":"Iacus, King & Porro","url":"https://scholargate.app/en/causal-inference/policy-evaluation-coarsened-exact-matching","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-coarsened-exact-matching.md","definition":"Coarsened Exact Matching (CEM) is a quasi-experimental causal-inference technique that creates balanced treatment and control groups from observational data by temporarily coarsening covariates into bins, exactly matching units within those bins, and then pruning unmatched observations before estimating policy effects. Introduced by Iacus, King, and Porro, CEM belongs to the monotonic imbalance bounding family of matching methods and is especially popular in policy evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Iacus, King & Porro","year":"2011-2012","type":"Matching / quasi-experimental design","dataType":"Cross-sectional or panel observational data with treatment and control units","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Iacus, S. M., King, G., & Porro, G. (2011). Multivariate matching methods that are monotonic imbalance bounding. Journal of the American Statistical Association, 106(493), 345-361.","type":"article","doi":"10.1198/jasa.2011.tm09599","isbn":null,"url":null}],"related":["propensity-score-matching","difference-in-differences","inverse-probability-weighting","nearest-neighbor-matching","entropy-balancing","synthetic-control-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-counterfactual-impact-evaluation","name":"Policy Evaluation Counterfactual Impact Evaluation","fullName":"Counterfactual Impact Evaluation for Policy Assessment","aliases":["CIE","policy CIE","counterfactual policy evaluation","impact evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)","originator":"Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines","url":"https://scholargate.app/en/causal-inference/policy-evaluation-counterfactual-impact-evaluation","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-counterfactual-impact-evaluation.md","definition":"Counterfactual Impact Evaluation (CIE) for policy assessment estimates the causal effect of a public policy or programme by comparing observed outcomes of participants against a rigorously constructed counterfactual — what would have happened had the policy not existed. Rooted in the Rubin potential-outcomes framework, CIE is the standard methodology endorsed by the European Commission for evaluating research, innovation, and structural funding programmes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines","year":"1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)","type":"Quasi-experimental causal evaluation","dataType":"Panel data, administrative records, survey data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521885881","url":null},{"ref":"Cerulli, G. (2014). Econometric Evaluation of Socioeconomic Programs: Theory and Applications. Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Econometric+Evaluation+of+Socioeconomic+Programs+Cerulli+2014"}],"related":["counterfactual-impact-evaluation","difference-in-differences","propensity-score-matching","synthetic-control-method","regression-discontinuity-design","instrumental-variables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-difference-in-differences","name":"Policy Evaluation Difference-in-Differences","fullName":"Difference-in-Differences for Policy Evaluation","aliases":["policy DiD","program evaluation DiD","policy impact DiD","DiD policy assessment"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1978-2009","originator":"Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-difference-in-differences","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-difference-in-differences.md","definition":"Policy Evaluation DiD applies the difference-in-differences estimator specifically to assess the causal impact of government programs, regulations, or policy reforms. It compares outcome changes in a group exposed to the policy against a comparable untreated group, before and after the policy took effect, isolating the net policy effect from pre-existing trends and time-common shocks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009)","year":"1978-2009","type":"Quasi-experimental / policy evaluation","dataType":"Panel data or repeated cross-sections with a treated and comparison group","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.","type":"article","doi":"10.1257/jel.47.1.5","isbn":null,"url":null},{"ref":"Heckman, J. J., LaLonde, R. J., & Smith, J. A. (1999). The Economics and Econometrics of Active Labor Market Programs. In O. Ashenfelter & D. Card (Eds.), Handbook of Labor Economics, Vol. 3A (pp. 1865-2097). Elsevier.","type":"article","doi":"10.1016/S1573-4463(99)03012-6","isbn":null,"url":null}],"related":["difference-in-differences","dynamic-difference-in-differences","staggered-difference-in-differences","propensity-score-matching","synthetic-control-method","regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-doubly-robust-estimation","name":"Policy Evaluation Doubly Robust Estimation","fullName":"Doubly Robust Estimation for Policy Evaluation","aliases":["DR estimation for policy","augmented IPW for policy evaluation","AIPW policy evaluation","doubly robust policy analysis"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1994-2005","originator":"Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-doubly-robust-estimation","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-doubly-robust-estimation.md","definition":"Policy Evaluation Doubly Robust Estimation applies the doubly robust (DR) estimator to assess the causal effect of a public policy or programme. It combines a model of treatment assignment (propensity score) with a model of the outcome, and requires only one of the two models to be correctly specified to produce a consistent estimate of the average treatment effect, making it a resilient tool for programme evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)","year":"1994-2005","type":"Semiparametric causal estimator","dataType":"Observational cross-sectional or panel data with binary treatment and continuous or binary outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.","type":"article","doi":"10.1111/j.1541-0420.2005.00377.x","isbn":null,"url":null},{"ref":"Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866.","type":"article","doi":"10.1080/01621459.1994.10476818","isbn":null,"url":null}],"related":["propensity-score-weighting","inverse-probability-weighting","doubly-robust-estimation","policy-evaluation-propensity-score-matching","marginal-structural-model","targeted-maximum-likelihood-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-entropy-balancing","name":"Policy Evaluation Entropy Balancing","fullName":"Entropy Balancing for Causal Policy Evaluation","aliases":["Entropy Balancing","EB Weighting","Maximum-Entropy Reweighting","Hainmueller Balancing"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012","originator":"Jens Hainmueller","url":"https://scholargate.app/en/causal-inference/policy-evaluation-entropy-balancing","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-entropy-balancing.md","definition":"Entropy balancing is a maximum-entropy reweighting method that assigns weights to control-group units so that their weighted covariate moments exactly match those of the treated group. Introduced by Hainmueller (2012), it provides exact balance on specified moments without iterative propensity-score trimming, making it a powerful preprocessing tool for causal policy evaluation in observational studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jens Hainmueller","year":"2012","type":"Preprocessing / reweighting estimator","dataType":"Observational cross-sectional or panel data with binary treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46.","type":"article","doi":"10.1093/pan/mpr025","isbn":null,"url":null},{"ref":"Zhao, Q., & Cooney, D. (2017). Entropy Balancing is Doubly Robust. Journal of Causal Inference, 5(1).","type":"article","doi":"10.1515/jci-2016-0010","isbn":null,"url":null}],"related":["propensity-score-matching","inverse-probability-weighting","difference-in-differences","covariate-balancing-propensity-score","augmented-inverse-probability-weighting","synthetic-control-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-event-study-design","name":"Policy Evaluation Event Study Design","fullName":"Policy Evaluation Event Study Design for Causal Inference","aliases":["event study","event-study DiD","dynamic DiD","PEESD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1993-2021","originator":"Andrews (1993), MacKinlay (1997); formalized for policy evaluation by Freyaldenhoven, Hansen & Shapiro (2019) and Callaway & Sant'Anna (2021)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-event-study-design","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-event-study-design.md","definition":"A policy evaluation event study design is a quasi-experimental approach that estimates causal effects of a policy by plotting treatment-period-by-period coefficients around a common event time. It extends difference-in-differences to visualize both pre-treatment parallel trends and the dynamic post-treatment evolution of the policy effect, and has become the standard credibility check in applied policy research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrews (1993), MacKinlay (1997); formalized for policy evaluation by Freyaldenhoven, Hansen & Shapiro (2019) and Callaway & Sant'Anna (2021)","year":"1993-2021","type":"Quasi-experimental / causal inference","dataType":"Panel data or repeated cross-sections with time-stamped policy adoption","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null},{"ref":"Freyaldenhoven, S., Hansen, C., & Shapiro, J. M. (2019). Pre-event trends in the panel event-study design. American Economic Review, 109(9), 3307-3338.","type":"article","doi":"10.1257/aer.20180609","isbn":null,"url":null}],"related":["difference-in-differences","synthetic-control","panel-fixed-effects","staggered-difference-in-differences","interrupted-time-series","regression-discontinuity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-fuzzy-regression-discontinuity","name":"Policy Evaluation Fuzzy Regression Discontinuity","fullName":"Fuzzy Regression Discontinuity Design for Policy Evaluation","aliases":["Fuzzy RDD","Fuzzy RD","Fuzzy Regression Discontinuity","Imperfect Compliance RDD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2001","originator":"Hahn, Todd & Van der Klaauw","url":"https://scholargate.app/en/causal-inference/policy-evaluation-fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-fuzzy-regression-discontinuity.md","definition":"Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates the causal effect of a policy when eligibility is determined by crossing a threshold on a continuous score, but actual take-up or compliance is imperfect. Developed formally by Hahn, Todd, and Van der Klaauw (2001), it uses the threshold as an instrumental variable to recover a Local Average Treatment Effect (LATE) among compliers near the cutoff.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hahn, Todd & Van der Klaauw","year":"2001","type":"Quasi-experimental / local IV estimator","dataType":"Observational cross-sectional or panel data with a continuous running variable and imperfect compliance","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Review of Economic Studies, 68(1), 201-209.","type":"article","doi":"10.1111/1468-0262.00183","isbn":null,"url":null},{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null}],"related":["regression-discontinuity-design","fuzzy-regression-discontinuity","instrumental-variables","propensity-score-matching","difference-in-differences","policy-evaluation-regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-instrumental-variables","name":"Policy Evaluation Instrumental Variables","fullName":"Instrumental Variables Estimation for Policy Evaluation","aliases":["IV policy evaluation","2SLS policy analysis","natural-experiment IV","policy IV estimation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1996 (modern policy-evaluation framing); IV roots 1920s","originator":"Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-instrumental-variables","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-instrumental-variables.md","definition":"Instrumental Variables (IV) estimation for policy evaluation is a quasi-experimental technique that uses an exogenous instrument — a variable that shifts exposure to a policy but is otherwise unrelated to the outcome — to recover the causal effect of a program or intervention from non-experimental data. Popularised in policy research by Angrist, Imbens, and Rubin (1996), it identifies the Local Average Treatment Effect (LATE) among units whose treatment status is changed by the instrument.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953)","year":"1996 (modern policy-evaluation framing); IV roots 1920s","type":"Quasi-experimental causal inference / IV regression","dataType":"Observational or natural-experiment data with a valid instrument; cross-sectional or panel","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91(434), 444-455.","type":"article","doi":"10.1080/01621459.1996.10476902","isbn":null,"url":null},{"ref":"Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691120355","url":null}],"related":["instrumental-variables","two-stage-least-squares","difference-in-differences","regression-discontinuity-design","local-average-treatment-effect","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-interrupted-time-series","name":"Policy Evaluation Interrupted Time Series","fullName":"Interrupted Time Series Analysis for Policy Evaluation","aliases":["ITS for policy evaluation","policy ITS","segmented regression for policy","policy impact ITS"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1975 (intervention analysis); 2000s–2010s (policy evaluation framing)","originator":"Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-interrupted-time-series","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-interrupted-time-series.md","definition":"Interrupted Time Series (ITS) for policy evaluation uses routinely collected aggregate time-series data to estimate the causal impact of a policy change. A segmented regression model splits the series at a known intervention date, estimating both an immediate level shift and a change in trend attributable to the policy — without requiring a randomised control group.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)","year":"1975 (intervention analysis); 2000s–2010s (policy evaluation framing)","type":"Quasi-experimental causal design","dataType":"Longitudinal aggregate or individual-level time-series data with a known policy change date","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.","type":"article","doi":"10.1093/ije/dyw098","isbn":null,"url":null},{"ref":"Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association, 70(349), 70-79.","type":"article","doi":"10.1080/01621459.1975.10480264","isbn":null,"url":null}],"related":["interrupted-time-series","difference-in-differences","regression-discontinuity-design","synthetic-control-method","event-study-design","policy-evaluation-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-inverse-probability-weighting","name":"Policy Evaluation Inverse Probability Weighting","fullName":"Inverse Probability Weighting for Policy Evaluation","aliases":["IPW policy evaluation","propensity-weighted policy analysis","inverse probability of treatment weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1952 (IPW origin); 2000s (policy evaluation application)","originator":"Horvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-inverse-probability-weighting.md","definition":"Policy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Horvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009)","year":"1952 (IPW origin); 2000s (policy evaluation application)","type":"Reweighting estimator for causal policy analysis","dataType":"Cross-sectional or panel; binary treatment; continuous or binary outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.","type":"article","doi":"10.1257/jel.47.1.5","isbn":null,"url":null},{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null}],"related":["propensity-score-weighting","propensity-score-matching","doubly-robust-estimation","marginal-structural-model","inverse-probability-weighting","policy-evaluation-propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-marginal-structural-model","name":"Policy Evaluation Marginal Structural Model","fullName":"Marginal Structural Model for Policy Evaluation","aliases":["MSM for policy evaluation","policy MSM","causal MSM","structural policy weighting model"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000","originator":"James M. Robins, Miguel A. Hernan, Babette Brumback","url":"https://scholargate.app/en/causal-inference/policy-evaluation-marginal-structural-model","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-marginal-structural-model.md","definition":"A Policy Evaluation Marginal Structural Model (MSM) is a causal inference framework that estimates the population-average effect of a policy by using inverse probability weighting to create a pseudo-population in which treatment assignment is independent of measured confounders, enabling unbiased comparison of potential outcomes under different policy scenarios from observational data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James M. Robins, Miguel A. Hernan, Babette Brumback","year":"2000","type":"Causal inference / weighted regression","dataType":"Observational longitudinal or repeated cross-sectional data with time-varying treatments","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"}],"related":["marginal-structural-model","inverse-probability-weighting","doubly-robust-estimation","propensity-score-weighting","difference-in-differences","counterfactual-impact-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-matching-estimator","name":"Policy Evaluation Matching Estimator","fullName":"Policy Evaluation Matching Estimator","aliases":["matching estimator","program evaluation matching","treatment effect matching","Abadie-Imbens estimator"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1998-2006","originator":"Heckman, Ichimura & Todd; Abadie & Imbens","url":"https://scholargate.app/en/causal-inference/policy-evaluation-matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-matching-estimator.md","definition":"The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heckman, Ichimura & Todd; Abadie & Imbens","year":"1998-2006","type":"Non-parametric causal estimator","dataType":"Cross-sectional or panel; continuous, binary, or count outcome","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267.","type":"article","doi":"10.1111/j.1468-0262.2006.00655.x","isbn":null,"url":null},{"ref":"Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. Review of Economic Studies, 65(2), 261-294.","type":"article","doi":"10.1111/1467-937X.00044","isbn":null,"url":null}],"related":["propensity-score-matching","difference-in-differences","instrumental-variables","inverse-probability-weighting","regression-discontinuity","coarsened-exact-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-panel-event-study","name":"Policy Evaluation Panel Event Study","fullName":"Policy Evaluation Panel Event Study Design","aliases":["panel event study","event-study DiD","staggered event study","difference-in-differences event study"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Callaway & Sant'Anna (2021); Borusyak, Jaravel & Spiess (2024); Sun & Abraham (2021)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-panel-event-study","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-panel-event-study.md","definition":"A panel event study is a quasi-experimental design that traces how an outcome evolves in periods before and after a policy event, using unit and time fixed effects to identify the causal effect. Widely used in economics and policy research, it tests for anticipation effects, verifies parallel pre-trends, and estimates dynamic treatment effects across post-treatment horizons — making it the standard toolkit for rigorous policy evaluation with observational panel data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Callaway & Sant'Anna (2021); Borusyak, Jaravel & Spiess (2024); Sun & Abraham (2021)","year":"2021","type":"Causal inference / quasi-experimental panel design","dataType":"Panel data with staggered or simultaneous treatment timing","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null},{"ref":"Borusyak, K., Jaravel, X., & Spiess, J. (2024). Revisiting event study designs: Robust and efficient estimation. Review of Economic Studies, 91(6), 3253-3285.","type":"article","doi":"10.1093/restud/rdae007","isbn":null,"url":null}],"related":["difference-in-differences","panel-fixed-effects","synthetic-control","propensity-score-matching","instrumental-variables","regression-discontinuity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-placebo-test","name":"Policy Evaluation Placebo Test","fullName":"Policy Evaluation Placebo Test","aliases":["placebo test","falsification test","fake treatment test","placebo regression"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1990s–2000s","originator":"Bertrand, Duflo & Mullainathan (2004 canonical formalization); Imbens & Wooldridge (2009 textbook treatment)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-placebo-test","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-placebo-test.md","definition":"A policy evaluation placebo test is a falsification check used in quasi-experimental research to validate a causal identification strategy. The researcher applies the same estimation method to a pseudo-treatment — a time period, group, or outcome where the real policy could not have had an effect — and checks that no spurious effect is detected. A null placebo result builds confidence that the main estimate reflects a genuine causal impact rather than bias or confounding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bertrand, Duflo & Mullainathan (2004 canonical formalization); Imbens & Wooldridge (2009 textbook treatment)","year":"1990s–2000s","type":"Falsification / specification check","dataType":"Panel, repeated cross-sections, or cross-sectional observational data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.","type":"book","doi":"10.1257/jel.47.1.5","isbn":null,"url":null},{"ref":"Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1), 249-275.","type":"article","doi":"10.1162/003355304772839588","isbn":null,"url":null}],"related":["difference-in-differences","regression-discontinuity-design","instrumental-variables","synthetic-control-method","permutation-test","event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-propensity-score-matching","name":"Policy Evaluation Propensity Score Matching","fullName":"Propensity Score Matching for Policy Evaluation","aliases":["PSM policy evaluation","policy PSM","propensity matching for program evaluation","PSM treatment evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1983; policy evaluation adaptation 1997","originator":"Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application","url":"https://scholargate.app/en/causal-inference/policy-evaluation-propensity-score-matching","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-propensity-score-matching.md","definition":"Policy evaluation propensity score matching applies the propensity score framework — originally developed by Rosenbaum and Rubin (1983) and operationalized for program evaluation by Heckman et al. (1997) — to estimate the causal effect of a policy intervention. It constructs a credible comparison group from non-participants by matching them to participants on their estimated probability of receiving the treatment, enabling unbiased effect estimation without random assignment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application","year":"1983; policy evaluation adaptation 1997","type":"Quasi-experimental matching estimator","dataType":"Observational cross-sectional or panel data with binary treatment indicator","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null},{"ref":"Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605-654.","type":"article","doi":"10.2307/2971733","isbn":null,"url":null}],"related":["propensity-score-matching","propensity-score-weighting","difference-in-differences","inverse-probability-weighting","coarsened-exact-matching","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-propensity-score-weighting","name":"Policy Evaluation Propensity Score Weighting","fullName":"Propensity Score Weighting for Policy Evaluation","aliases":["PSW policy evaluation","inverse probability weighting for policy","IPW policy evaluation","policy PSW"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1983/2003","originator":"Rosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-propensity-score-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-propensity-score-weighting.md","definition":"Policy evaluation propensity score weighting applies inverse-probability weighting to observational data to estimate the causal effect of a policy program. By reweighting participants and non-participants so they resemble a target population, it removes selection bias from voluntary or administratively allocated program assignment without requiring randomization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003)","year":"1983/2003","type":"Quasi-experimental causal inference","dataType":"Observational cross-sectional or panel data with a binary or multi-valued treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.","type":"article","doi":"10.1111/1468-0262.00442","isbn":null,"url":null},{"ref":"Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72.","type":"article","doi":"10.1111/j.1467-6419.2007.00527.x","isbn":null,"url":null}],"related":["propensity-score-matching","propensity-score-weighting","inverse-probability-weighting","doubly-robust-estimation","difference-in-differences","policy-evaluation-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-regression-discontinuity-design","name":"Policy Evaluation Regression Discontinuity Design","fullName":"Regression Discontinuity Design for Policy Evaluation","aliases":["Policy RDD","RD design in policy evaluation","regression discontinuity policy analysis","RDD policy impact"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1960; policy evaluation applications widespread from 2000s","originator":"Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)","url":"https://scholargate.app/en/causal-inference/policy-evaluation-regression-discontinuity-design","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-regression-discontinuity-design.md","definition":"Policy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)","year":"1960; policy evaluation applications widespread from 2000s","type":"Quasi-experimental causal design","dataType":"Observational data with a continuous assignment variable and a known policy threshold","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.","type":"article","doi":"10.1257/jel.48.2.281","isbn":null,"url":null},{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null}],"related":["regression-discontinuity-design","fuzzy-regression-discontinuity","difference-in-differences","instrumental-variables","propensity-score-matching","policy-evaluation-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-evaluation-synthetic-control-method","name":"Policy Evaluation Synthetic Control Method","fullName":"Synthetic Control Method for Policy Evaluation","aliases":["Synthetic Control Method","SCM","Synthetic Control","Abadie-Diamond-Hainmueller method"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2003-2010","originator":"Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller","url":"https://scholargate.app/en/causal-inference/policy-evaluation-synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/policy-evaluation-synthetic-control-method.md","definition":"The Synthetic Control Method (SCM) is a causal inference technique for evaluating the effect of a policy or intervention on a single treated unit — such as a region, country, or firm — by constructing a weighted combination of untreated comparison units that closely mirrors the treated unit before the intervention. Introduced by Abadie and Gardeazabal (2003) and formalized by Abadie, Diamond, and Hainmueller (2010), it provides a data-driven, transparent counterfactual for comparative case studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller","year":"2003-2010","type":"Causal inference / comparative case study","dataType":"Aggregate panel data (few treated units, many pre-treatment periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null},{"ref":"Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93(1), 113-132.","type":"article","doi":"10.1257/000282803321455188","isbn":null,"url":null}],"related":["difference-in-differences","propensity-score-matching","instrumental-variables","interrupted-time-series","regression-discontinuity-design","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-gradient","name":"Policy Gradient","fullName":"Policy Gradient Methods (REINFORCE / Actor-Critic)","aliases":["REINFORCE","actor-critic","policy optimization","politika gradyanı"],"domain":"machine-learning","family":"ml-model","subfamily":"Reinforcement learning","year":1992,"originator":"Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem)","url":"https://scholargate.app/en/machine-learning/policy-gradient","markdownUrl":"https://scholargate.app/en/machine-learning/policy-gradient.md","definition":"Policy gradient methods are reinforcement-learning algorithms that optimize a parameterized policy directly by gradient ascent on the expected return, rather than learning action-values and acting greedily. Founded on Ronald Williams' 1992 REINFORCE algorithm and the policy gradient theorem of Sutton and colleagues (2000), they naturally handle stochastic and continuous action spaces and underpin modern actor-critic and deep-RL algorithms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem)","year":1992,"type":"Policy-based reinforcement learning","subfamily":"Reinforcement learning","optimizes":"A parameterized policy directly by gradient ascent on expected return","handles":"Continuous/stochastic action spaces"},"citations":[{"ref":"Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256.","type":"article","doi":"10.1007/BF00992696","isbn":null,"url":null},{"ref":"Sutton, R. S., McAllester, D., Singh, S., & Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. Advances in Neural Information Processing Systems, 12, 1057–1063.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/1999/hash/464d828b85b0bed98e80ade0a5c43b0f-Abstract.html"}],"related":["q-learning","deep-reinforcement-learning","stochastic-gradient-descent","convex-optimization"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-agent-based-modeling","name":"Policy Scenario Agent-Based Modeling","fullName":"Policy Scenario Agent-Based Modeling — Comparative policy evaluation using agent-based simulation","aliases":["Policy ABM","Policy Scenario ABM","Scenario-Based ABM","PS-ABM"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Axelrod, R. and colleagues in computational social science","url":"https://scholargate.app/en/simulation/policy-scenario-agent-based-modeling","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-agent-based-modeling.md","definition":"Policy Scenario Agent-Based Modeling (PS-ABM) is a simulation method that uses agent-based models to evaluate and compare multiple policy scenarios. Heterogeneous autonomous agents interact under different policy regimes, and emergent system-level outcomes are compared across scenarios to inform evidence-based policy decisions. It is widely used in public health, urban planning, economics, and social policy research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Axelrod, R. and colleagues in computational social science","year":"1990s–2000s","type":"Simulation-based policy comparison","dataType":"Agent behavioral rules, environmental parameters, policy levers","subfamily":"Simulation / optimization"},"citations":[{"ref":"Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.","type":"book","doi":null,"isbn":"9780691015675","url":null},{"ref":"Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(S3), 7280-7287.","type":"article","doi":"10.1073/pnas.082080899","isbn":null,"url":null}],"related":["agent-based-modeling","scenario-analysis","policy-scenario-analysis","system-dynamics","policy-scenario-system-dynamics","monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-analysis","name":"Policy Scenario Analysis","fullName":"Policy Scenario Analysis — Structured evaluation of policy interventions across plausible future states","aliases":["PSA","Policy Scenarios","Policy Impact Scenario Analysis","Counterfactual Policy Analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1967–1990s","originator":"Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD","url":"https://scholargate.app/en/simulation/policy-scenario-analysis","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-analysis.md","definition":"Policy Scenario Analysis is a structured method for evaluating how different policy interventions perform across a range of plausible future states. By pairing specific policy levers with alternative scenarios, analysts can assess robustness, trade-offs, and unintended consequences of policy choices before implementation — making it a cornerstone of evidence-based policy design in fields from climate to public health.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD","year":"1967–1990s","type":"Qualitative-quantitative hybrid scenario method","dataType":"Policy parameters, model outputs, expert elicitation, historical baselines","subfamily":"Simulation / optimization"},"citations":[{"ref":"Swart, R., Raskin, P., Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137–146.","type":"article","doi":"10.1016/j.gloenvcha.2003.10.002","isbn":null,"url":null},{"ref":"Bishop, P., Hines, A., Collins, T. (2007). The current state of scenario development: an overview of techniques. Foresight, 9(1), 5–25.","type":"book","doi":"10.1108/14636680710727516","isbn":null,"url":null}],"related":["scenario-analysis","monte-carlo-simulation","system-dynamics","sensitivity-analysis","stochastic-scenario-analysis","policy-scenario-system-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-cellular-automata","name":"Policy Scenario Cellular Automata","fullName":"Policy Scenario Cellular Automata — Scenario-driven grid-based simulation for policy impact analysis","aliases":["PSCA","CA Policy Scenario Modeling","Policy-driven CA Simulation","Scenario-based Cellular Automata"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1979–1997","originator":"Tobler, W. (CA foundations); Clarke, K.C. et al. (policy/urban CA scenarios)","url":"https://scholargate.app/en/simulation/policy-scenario-cellular-automata","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-cellular-automata.md","definition":"Policy Scenario Cellular Automata (PSCA) combines cellular automata simulation with structured scenario analysis to evaluate how alternative policy decisions reshape spatially distributed systems over time. Each scenario encodes a different set of transition rules or constraints, and the model iterates to reveal divergent spatial outcomes — enabling direct, visual comparison of policy consequences at the local and system level.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tobler, W. (CA foundations); Clarke, K.C. et al. (policy/urban CA scenarios)","year":"1979–1997","type":"Grid-based scenario simulation","dataType":"Spatial raster grids, categorical land-use or state data, policy parameter sets","subfamily":"Simulation / optimization"},"citations":[{"ref":"Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24(2), 247–261.","type":"article","doi":"10.1068/b240247","isbn":null,"url":null},{"ref":"Batty, M. (2005). Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. MIT Press. ISBN 978-0262025836.","type":"book","doi":null,"isbn":"978-0262025836","url":null}],"related":["cellular-automata","policy-scenario-analysis","agent-based-modeling","system-dynamics","discrete-event-simulation","scenario-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-discrete-event-simulation","name":"Policy Scenario Discrete-Event Simulation","fullName":"Policy Scenario Discrete-Event Simulation — Evaluating policy alternatives through scenario-driven DES models","aliases":["Policy DES","Scenario-based DES","Policy simulation DES","DES policy analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1960s–1990s","originator":"Tocher, K. D. and Gordon, G. (early DES); policy scenario extension emerged through operations research and health policy modeling communities","url":"https://scholargate.app/en/simulation/policy-scenario-discrete-event-simulation","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-discrete-event-simulation.md","definition":"Policy Scenario Discrete-Event Simulation combines the event-by-event fidelity of Discrete-Event Simulation with systematic policy scenario analysis to evaluate how different interventions, regulations, or resource allocations change system performance. By running multiple well-defined policy scenarios through the same DES model, analysts can compare outcomes — throughput, waiting times, costs — across alternatives before real-world implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tocher, K. D. and Gordon, G. (early DES); policy scenario extension emerged through operations research and health policy modeling communities","year":"1960s–1990s","type":"Simulation-based policy evaluation","dataType":"Event logs, process times, resource capacities, policy parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education.","type":"book","doi":null,"isbn":"9780073401324","url":null},{"ref":"Robinson, S. (2014). Simulation: The Practice of Model Development and Use (2nd ed.). Palgrave Macmillan.","type":"book","doi":null,"isbn":"9781137328021","url":null}],"related":["discrete-event-simulation","scenario-analysis","policy-scenario-analysis","monte-carlo-simulation","system-dynamics","policy-scenario-system-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-dynamic-programming","name":"Policy Scenario Dynamic Programming","fullName":"Policy Scenario Dynamic Programming — Sequential policy evaluation via Bellman optimality across discrete future states","aliases":["PSDP","Policy-Scenario DP","Scenario-Based Dynamic Programming","Policy DP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957","originator":"Bellman, Richard E.","url":"https://scholargate.app/en/simulation/policy-scenario-dynamic-programming","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-dynamic-programming.md","definition":"Policy Scenario Dynamic Programming (PSDP) applies Bellman's recursive optimization framework to a set of pre-specified policy scenarios, enabling decision-makers to compare staged, sequential decisions under distinct future conditions. It decomposes a complex, multi-period policy choice into tractable sub-problems solved backward through time, yielding optimal action sequences for each scenario and a structured basis for scenario comparison.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bellman, Richard E.","year":"1957","type":"Sequential optimization with scenario branching","dataType":"Discrete state-action spaces, policy parameters, transition probabilities","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780691079516","url":null},{"ref":"Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, New York.","type":"book","doi":null,"isbn":"9780471619772","url":null}],"related":["dynamic-programming","scenario-analysis","markov-model","stochastic-dynamic-programming","policy-scenario-analysis","multi-objective-dynamic-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-genetic-algorithm","name":"Policy Scenario Genetic Algorithm","fullName":"Policy Scenario Genetic Algorithm — Evolutionary Search over Discrete Policy Alternative Spaces","aliases":["PSGA","Policy-GA","Policy Optimization Genetic Algorithm","Evolutionary Policy Scenario Search"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1975 (GA); 2000s (policy scenario application)","originator":"Holland, J. H. (GA foundation); Lempert, Popper & Bankes (policy scenario search)","url":"https://scholargate.app/en/simulation/policy-scenario-genetic-algorithm","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-genetic-algorithm.md","definition":"The Policy Scenario Genetic Algorithm applies evolutionary search to systematically explore large, combinatorial policy alternative spaces under multiple future scenarios. Rather than exhaustively enumerating options, it breeds successive generations of candidate policies, retaining those that perform well across scenario conditions, yielding robust, high-performing policy recommendations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holland, J. H. (GA foundation); Lempert, Popper & Bankes (policy scenario search)","year":"1975 (GA); 2000s (policy scenario application)","type":"Evolutionary metaheuristic for policy scenario exploration","dataType":"Discrete or mixed policy parameter spaces, scenario-conditioned objective functions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI.","type":"book","doi":null,"isbn":"9780262581110","url":null},{"ref":"Lempert, R. J., Popper, S. W., & Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation, Santa Monica, CA.","type":"book","doi":null,"isbn":null,"url":"https://www.rand.org/pubs/monograph_reports/MR1626.html"}],"related":["genetic-algorithm","scenario-analysis","policy-scenario-analysis","multi-objective-genetic-algorithm","nsga-ii","policy-scenario-multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-goal-programming","name":"Policy Scenario Goal Programming","fullName":"Policy Scenario Goal Programming — Goal programming applied within policy scenario frameworks","aliases":["PSGP","Policy GP","Scenario-based Goal Programming","Multi-scenario Goal Programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1961 (goal programming); policy scenario application 1980s–present","originator":"Charnes, A., Cooper, W. W. (goal programming); policy scenario integration developed in OR/policy literature","url":"https://scholargate.app/en/simulation/policy-scenario-goal-programming","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-goal-programming.md","definition":"Policy Scenario Goal Programming (PSGP) integrates goal programming optimization with policy scenario analysis to evaluate how well competing policy objectives can be achieved under distinct future conditions. Decision-makers define multiple goals and several plausible policy scenarios, then solve a goal programming model for each scenario to identify which policy strategies best satisfy priority targets across the full scenario space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charnes, A., Cooper, W. W. (goal programming); policy scenario integration developed in OR/policy literature","year":"1961 (goal programming); policy scenario application 1980s–present","type":"Optimization under multiple conflicting goals across policy scenarios","dataType":"Quantitative targets, goal deviations, scenario parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York.","type":"book","doi":null,"isbn":"9780471153405","url":null},{"ref":"Ignizio, J. P. (1976). Goal Programming and Extensions. Lexington Books, Lexington, MA.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Goal+Programming+and+Extensions+Ignizio+1976"}],"related":["goal-programming","scenario-analysis","policy-scenario-analysis","multi-objective-goal-programming","stochastic-goal-programming","robust-goal-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-integer-programming","name":"Policy Scenario Integer Programming","fullName":"Policy Scenario Integer Programming — Discrete Optimization Across Policy Alternatives","aliases":["PSIP","scenario-based integer programming","policy-driven IP","scenario integer optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1950s–1960s (scenario extension: 1990s onwards)","originator":"Operations research community (Dantzig, Gomory, and others)","url":"https://scholargate.app/en/simulation/policy-scenario-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-integer-programming.md","definition":"Policy Scenario Integer Programming (PSIP) solves an integer programming model — where some or all decision variables must take whole-number values — separately under each of several distinct policy scenarios, then compares objective values, feasibility, and solution structures to identify which policy environment leads to the best discrete allocation or assignment outcome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Operations research community (Dantzig, Gomory, and others)","year":"1950s–1960s (scenario extension: 1990s onwards)","type":"Discrete combinatorial optimization under scenario uncertainty","dataType":"Discrete decision variables, policy parameters, scenario-specific constraint sets","subfamily":"Simulation / optimization"},"citations":[{"ref":"Birge, J. R., & Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer.","type":"book","doi":null,"isbn":"9781461402367","url":null},{"ref":"Williams, H. P. (2013). Model Building in Mathematical Programming (5th ed.). Wiley.","type":"book","doi":null,"isbn":"9781118443330","url":null}],"related":["policy-scenario-linear-programming","policy-scenario-mixed-integer-programming","scenario-analysis","stochastic-integer-programming","multi-objective-integer-programming","robust-integer-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-microsimulation","name":"Policy Scenario Microsimulation","fullName":"Policy Scenario Microsimulation — Individual-level simulation for policy impact analysis across defined scenarios","aliases":["PSM","Policy Microsimulation","Scenario-Based Microsimulation","Policy Impact Microsimulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957","originator":"Guy H. Orcutt","url":"https://scholargate.app/en/simulation/policy-scenario-microsimulation","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-microsimulation.md","definition":"Policy Scenario Microsimulation applies microsimulation methods to evaluate and compare the distributional and aggregate effects of alternative policy scenarios on a synthetic population. By simulating individual-level behaviour under each policy regime, researchers can measure winners and losers, fiscal costs, and equity outcomes before real implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guy H. Orcutt","year":"1957","type":"Simulation — individual-level policy scenario analysis","dataType":"Individual or household-level microdata; demographic, income, and behavioral records","subfamily":"Simulation / optimization"},"citations":[{"ref":"Orcutt, G. H. (1957). A new type of socio-economic system. Review of Economics and Statistics, 39(2), 116–123.","type":"article","doi":"10.2307/1928528","isbn":null,"url":null},{"ref":"Gupta, A., & Kapur, V. (Eds.) (2000). Microsimulation in Government Policy and Forecasting. North-Holland.","type":"book","doi":null,"isbn":"9780444503442","url":null}],"related":["microsimulation","scenario-analysis","policy-scenario-analysis","agent-based-microsimulation","monte-carlo-simulation","stochastic-microsimulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-monte-carlo-simulation","name":"Policy Scenario Monte Carlo Simulation","fullName":"Policy Scenario Monte Carlo Simulation — Probabilistic uncertainty analysis across defined policy scenarios","aliases":["PS-MCS","Policy MC Simulation","Scenario-Based Monte Carlo","Policy Uncertainty Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Developed within health economics and policy modeling communities; foundational work by Briggs, Claxton, and Sculpher","url":"https://scholargate.app/en/simulation/policy-scenario-monte-carlo-simulation","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-monte-carlo-simulation.md","definition":"Policy Scenario Monte Carlo Simulation combines pre-defined discrete policy scenarios with probabilistic Monte Carlo sampling to quantify uncertainty in outcomes across each scenario. Rather than evaluating a single stochastic model, analysts define two or more policy alternatives and run thousands of Monte Carlo iterations within each, producing probability distributions of outcomes that support evidence-based policy comparison.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within health economics and policy modeling communities; foundational work by Briggs, Claxton, and Sculpher","year":"1990s–2000s","type":"Probabilistic scenario simulation","dataType":"Quantitative parameters with probability distributions; discrete policy scenario definitions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Briggs, A. H., Claxton, K., & Sculpher, M. J. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press.","type":"article","doi":null,"isbn":"9780198526629","url":null},{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"9780470059975","url":null}],"related":["monte-carlo-simulation","scenario-analysis","sensitivity-analysis","stochastic-monte-carlo-simulation","policy-scenario-analysis","stochastic-scenario-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-multi-objective-optimization","name":"Policy Scenario Multi-Objective Optimization","fullName":"Policy Scenario Multi-Objective Optimization — Scenario-conditioned Pareto-optimal Policy Search","aliases":["PS-MOO","Policy-Driven MOO","Scenario-Based Multi-Objective Optimization","Policy MOO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Evolved from multi-objective optimization and policy scenario analysis communities","url":"https://scholargate.app/en/simulation/policy-scenario-multi-objective-optimization","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-multi-objective-optimization.md","definition":"Policy Scenario Multi-Objective Optimization (PS-MOO) integrates explicit policy scenario construction with multi-objective optimization to identify Pareto-optimal policy options across plausible future states. Decision-makers evaluate trade-offs between competing objectives — such as economic efficiency, equity, and environmental impact — for each distinct policy scenario, then compare Pareto fronts to select robust or scenario-contingent strategies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from multi-objective optimization and policy scenario analysis communities","year":"1990s–2000s","type":"Scenario-conditioned multi-objective search","dataType":"Numerical decision variables, policy scenario parameters, multiple objective functions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester.","type":"book","doi":null,"isbn":"9780471873396","url":null},{"ref":"Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4(1), 5–17.","type":"article","doi":"10.1076/iaij.4.1.5.16466","isbn":null,"url":null}],"related":["multi-objective-optimization","scenario-analysis","nsga-ii","policy-scenario-analysis","robust-multi-objective-optimization","multi-objective-genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-particle-swarm-optimization","name":"Policy Scenario Particle Swarm Optimization","fullName":"Policy Scenario Particle Swarm Optimization — PSO-driven search across alternative policy futures","aliases":["PS-PSO","Policy PSO","Scenario-based PSO","Policy scenario swarm optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1995 (PSO); applied to policy scenarios from 2000s onward","originator":"Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature","url":"https://scholargate.app/en/simulation/policy-scenario-particle-swarm-optimization","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-particle-swarm-optimization.md","definition":"Policy Scenario Particle Swarm Optimization integrates Particle Swarm Optimization (PSO) with explicit policy scenario analysis. A swarm of candidate policy solutions is evaluated under multiple defined future scenarios, and PSO's velocity-position update rules guide the swarm toward solutions that perform well—or robustly—across all considered scenarios. It is used in energy, environmental, infrastructure, and public resource planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature","year":"1995 (PSO); applied to policy scenarios from 2000s onward","type":"Metaheuristic optimization within policy scenario framework","dataType":"Continuous or mixed decision variables; scenario-defined objective functions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.","type":"inproceedings","doi":"10.1109/ICNN.1995.488968","isbn":null,"url":null},{"ref":"Poli, R., Kennedy, J., Blackwell, T. (2007). Particle swarm optimization: An overview. Swarm Intelligence, 1(1), 33–57.","type":"article","doi":"10.1007/s11721-007-0002-0","isbn":null,"url":null}],"related":["particle-swarm-optimization","policy-scenario-analysis","multi-objective-particle-swarm-optimization","robust-particle-swarm-optimization","stochastic-particle-swarm-optimization","policy-scenario-genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-queueing-simulation","name":"Policy Scenario Queueing Simulation","fullName":"Policy Scenario Queueing Simulation — Comparative queueing analysis across alternative service or resource-allocation policies","aliases":["PSQS","policy queueing analysis","queueing policy comparison","scenario-based queueing model"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1909 (queueing theory); scenario application from 1960s–1970s OR literature","originator":"Erlang, A. K. (foundation); generalized by operations research community","url":"https://scholargate.app/en/simulation/policy-scenario-queueing-simulation","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-queueing-simulation.md","definition":"Policy Scenario Queueing Simulation applies queueing theory and discrete-event simulation to evaluate two or more competing service or resource-allocation policies under realistic demand and capacity conditions. By holding the system structure constant and varying only the policy rules, analysts can directly compare throughput, waiting times, utilization, and equity outcomes before committing to real-world implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erlang, A. K. (foundation); generalized by operations research community","year":"1909 (queueing theory); scenario application from 1960s–1970s OR literature","type":"Comparative simulation experiment","dataType":"Arrival rates, service times, queue disciplines, resource capacities, policy parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley-Interscience, New York.","type":"book","doi":null,"isbn":"978-0471491101","url":null},{"ref":"Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education, New York.","type":"book","doi":null,"isbn":"978-0073401324","url":null}],"related":["queueing-simulation","discrete-event-simulation","scenario-analysis","policy-scenario-discrete-event-simulation","stochastic-queueing-simulation","markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-sensitivity-analysis","name":"Policy Scenario Sensitivity Analysis","fullName":"Policy Scenario Sensitivity Analysis — Structured examination of how model outputs respond to input variation across defined policy alternatives","aliases":["PSSA","Policy Sensitivity Analysis","Scenario-Based Sensitivity Analysis","Policy Robustness Analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Saltelli, A. et al.; Lempert, R. J. et al.","url":"https://scholargate.app/en/simulation/policy-scenario-sensitivity-analysis","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-sensitivity-analysis.md","definition":"Policy Scenario Sensitivity Analysis (PSSA) combines structured scenario planning with formal sensitivity analysis to determine which model inputs and policy parameters most strongly drive outcomes across a set of distinct policy alternatives or future states. It is widely used in public health, climate, energy, and economic policy modeling to identify robust interventions that perform well even when key assumptions vary.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saltelli, A. et al.; Lempert, R. J. et al.","year":"1990s–2000s","type":"Analytical framework combining scenario planning with sensitivity analysis","dataType":"Model inputs, policy parameters, uncertain variables across scenario sets","subfamily":"Simulation / optimization"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons, Chichester.","type":"book","doi":null,"isbn":"9780470059975","url":null},{"ref":"Lempert, R. J., Popper, S. W., & Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation, Santa Monica, CA.","type":"book","doi":null,"isbn":null,"url":"https://www.rand.org/pubs/monograph_reports/MR1626.html"}],"related":["sensitivity-analysis","scenario-analysis","policy-scenario-analysis","monte-carlo-simulation","robust-sensitivity-analysis","stochastic-sensitivity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"policy-scenario-system-dynamics","name":"Policy Scenario System Dynamics","fullName":"Policy Scenario System Dynamics — Scenario-Based Simulation of Policy Interventions Using System Dynamics Models","aliases":["PSSD","Policy SD Simulation","Scenario-Based System Dynamics","Policy Systems Modeling"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1960s–1990s","originator":"Forrester, J. W. (system dynamics); scenario integration formalized by Sterman and others","url":"https://scholargate.app/en/simulation/policy-scenario-system-dynamics","markdownUrl":"https://scholargate.app/en/simulation/policy-scenario-system-dynamics.md","definition":"Policy Scenario System Dynamics combines system dynamics modeling with structured scenario analysis to evaluate how different policy interventions affect complex, feedback-driven systems over time. By running multiple policy scenarios through a calibrated stock-and-flow model, analysts can compare long-run outcomes, identify leverage points, and anticipate unintended consequences before real-world implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Forrester, J. W. (system dynamics); scenario integration formalized by Sterman and others","year":"1960s–1990s","type":"Simulation-based policy analysis","dataType":"Stock-and-flow data, causal loop diagrams, policy levers, time-series outputs","subfamily":"Simulation / optimization"},"citations":[{"ref":"Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill.","type":"book","doi":null,"isbn":"9780072389159","url":null},{"ref":"Forrester, J. W. (1969). Urban Dynamics. MIT Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Urban+Dynamics+Forrester+1969"}],"related":["system-dynamics","scenario-analysis","policy-scenario-analysis","agent-based-system-dynamics","stochastic-system-dynamics","multi-objective-system-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"political-efficacy","name":"Political Efficacy Scale","fullName":"Political Efficacy Scale (Internal and External)","aliases":["Political Efficacy","Internal Efficacy","External Efficacy"],"domain":"political-psychology","family":"process-pipeline","subfamily":"psychological-orientations","year":"1969","originator":"Richard Niemi, Steven Craig, Albert Bandura","url":"https://scholargate.app/en/political-psychology/political-efficacy","markdownUrl":"https://scholargate.app/en/political-psychology/political-efficacy.md","definition":"Political efficacy measures sense of personal agency and power in the political system, encompassing both internal efficacy (belief in own political competence and understanding) and external efficacy (belief that the political system is responsive to citizen input). Rooted in Bandura's self-efficacy theory (1977) and developed for political contexts by Niemi, Craig, and colleagues (1969 onwards), the measure explains why some citizens feel empowered to engage in politics while others feel powerless. High-efficacy citizens are substantially more likely to participate, contact representatives, and vote; low-efficacy citizens withdraw from politics and are susceptible to anti-democratic appeals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Niemi, Steven Craig, Albert Bandura","subfamily":"psychological-orientations","year":"1969","type":"Self-report"},"citations":[{"ref":"Niemi, R. G., Craig, S. C., & Mattei, F. (1991). Measuring internal political efficacy in the 1988 National Election Study. American Political Science Review, 85(4), 1407-1413.","type":"article","doi":"10.2307/1963953","isbn":null,"url":null},{"ref":"Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.","type":"book","doi":"10.1037/0033-295X.84.2.191","isbn":null,"url":null},{"ref":"Craig, S. C. (1979). Efficacy, trust, and political behavior: An attempt to resolve a lingering conceptual dilemma. American Political Science Review, 73(2), 571-587.","type":"article","doi":"10.1177/1532673x7900700207","isbn":null,"url":null}],"related":["voter-cynicism-scale","political-participation-scale","political-trust-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"political-ideology-scale","name":"Political Ideology Scale","fullName":"Left-Right Political Ideology Self-Placement Scale","aliases":["Left-Right Scale","Ideology Continuum","Political Spectrum Scale"],"domain":"political-psychology","family":"process-pipeline","subfamily":"ideological-orientations","year":"1990","originator":"Hans-Dieter Klingemann & Norberto Bobbio","url":"https://scholargate.app/en/political-psychology/political-ideology-scale","markdownUrl":"https://scholargate.app/en/political-psychology/political-ideology-scale.md","definition":"The Political Ideology Scale measures individual self-placement on a left-right political spectrum, capturing fundamental preferences for government role, economic organization, and social values. The single-item self-placement measure (most common) asks respondents to rate themselves on a 0-10 or 0-100 continuum; multi-item versions assess distinct ideological dimensions (economic policy, social policy, nationalism). The left-right axis remains the dominant organizing principle of political competition globally, predicting party choice, policy preferences, and electoral behavior despite critiques that it oversimplifies multidimensional political space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans-Dieter Klingemann & Norberto Bobbio","subfamily":"ideological-orientations","year":"1990","type":"Self-report"},"citations":[{"ref":"Fuchs, D., & Klingemann, H. D. (1990). The left-right schema. In M. Kent Jennings & Jan W. Van Deth (Eds.), Continuities in political action. Berlin: De Gruyter.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Fuchs%2C%20D.%2C%20%26%20Klingemann%2C%20H.%20D.%20(1990).%20The%20left-right%20schema.%20In%20M.%20Kent%20Jennings%20%26%20Jan%20W.%20Van%20Deth%20(Eds.)%2C%20Continuities"},{"ref":"Inglehart, R., & Klingemann, H. D. (2000). Genes, culture, democracy, and happiness. In E. Diener & E. M. Suh (Eds.), Culture and subjective well-being. Cambridge, MA: MIT Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Inglehart%2C%20R.%2C%20%26%20Klingemann%2C%20H.%20D.%20(2000).%20Genes%2C%20culture%2C%20democracy%2C%20and%20happiness.%20In%20E.%20Diener%20%26%20E.%20M.%20Suh%20(Eds.)%2C%20Cu"},{"ref":"Bobbio, N. (1996). Left and right: The significance of a political distinction. Chicago: University of Chicago Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Bobbio%2C%20N.%20(1996).%20Left%20and%20right%3A%20The%20significance%20of%20a%20political%20distinction.%20Chicago%3A%20University%20of%20Chicago%20Press."}],"related":["populism-scale","national-identity-scale","need-for-cognition-political"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"political-participation-scale","name":"Political Participation Scale","fullName":"Political Participation and Civic Engagement Scale (PPCS)","aliases":["PPCS","Civic Participation Measure","Political Activity Scale"],"domain":"political-psychology","family":"process-pipeline","subfamily":"civic-behavior","year":"1995","originator":"Sidney Verba, Kay Lehman Schlozman, Henry Brady","url":"https://scholargate.app/en/political-psychology/political-participation-scale","markdownUrl":"https://scholargate.app/en/political-psychology/political-participation-scale.md","definition":"The Political Participation Scale measures engagement in civic and political activities, encompassing voting, campaign involvement, contacting officials, organizational membership, community volunteering, and protest activity. Developed by Verba, Schlozman, and Brady (1995), the measure captures both conventional participation (voting, contacting representatives) and unconventional participation (protest, civil disobedience). It addresses fundamental questions in political science: Why do some citizens engage while others withdraw? How do structural resources (time, money, education) and psychological factors (efficacy, interest) drive participation?","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sidney Verba, Kay Lehman Schlozman, Henry Brady","subfamily":"civic-behavior","year":"1995","type":"Self-report"},"citations":[{"ref":"Verba, S., Schlozman, K. L., & Brady, H. E. (1995). Voice and equality: Civic voluntarism in American politics. Cambridge, MA: Harvard University Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Verba%2C%20S.%2C%20Schlozman%2C%20K.%20L.%2C%20%26%20Brady%2C%20H.%20E.%20(1995).%20Voice%20and%20equality%3A%20Civic%20voluntarism%20in%20American%20politics.%20Cambridg"},{"ref":"Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. New York: Simon & Schuster.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Putnam%2C%20R.%20D.%20(2000).%20Bowling%20alone%3A%20The%20collapse%20and%20revival%20of%20American%20community.%20New%20York%3A%20Simon%20%26%20Schuster."},{"ref":"Van Deth, J. W. (2014). A conceptual map of political participation. Acta Politica, 49(3), 349-367.","type":"article","doi":"10.1057/ap.2014.6","isbn":null,"url":null}],"related":["political-efficacy","democratic-support-scale","voter-cynicism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"political-tolerance-scale","name":"Political Tolerance Scale","fullName":"Democratic Tolerance and Civil Liberties Scale (DTCL)","aliases":["DTCL","Civil Liberties Scale","Majoritarian Constraint Scale"],"domain":"political-psychology","family":"process-pipeline","subfamily":"democratic-values","year":"1955","originator":"Samuel Stouffer, James Gibson, John Sullivan","url":"https://scholargate.app/en/political-psychology/political-tolerance-scale","markdownUrl":"https://scholargate.app/en/political-psychology/political-tolerance-scale.md","definition":"The Political Tolerance Scale measures willingness to permit unpopular groups to exercise civil liberties and political rights, including free speech, assembly, and voting rights even for groups the respondent strongly opposes. Pioneered by Stouffer (1955) measuring tolerance of communists during McCarthyism and extended by Gibson (1989) and Sullivan, Piereson, and Marcus (1982), the scale assesses fundamental democratic commitment—that pluralism and minority rights supersede majoritarian preference. It addresses the paradox: can democracy survive if majorities vote to restrict minority rights? Tolerance is essential for democratic stability, particularly as polarization increases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Samuel Stouffer, James Gibson, John Sullivan","subfamily":"democratic-values","year":"1955","type":"Self-report"},"citations":[{"ref":"Stouffer, S. A. (1955). Communism, conformity, and civil liberties: A cross-section of the nation speaks its mind. Garden City, NY: Doubleday.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Stouffer%2C%20S.%20A.%20(1955).%20Communism%2C%20conformity%2C%20and%20civil%20liberties%3A%20A%20cross-section%20of%20the%20nation%20speaks%20its%20mind.%20Garde"},{"ref":"Gibson, J. L. (1989). Understanding the tolerant: A latent variable model of support for civil liberties in the Soviet Union. American Journal of Political Science, 33(3), 797-825.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Understanding+the+tolerant%3A+A+latent+variable+model+of+support+for+civil+liberties+in+the+Soviet+Union+Gibson"},{"ref":"Sullivan, J. L., Piereson, J., & Marcus, G. E. (1982). Political tolerance and American democracy. Chicago: University of Chicago Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Sullivan%2C%20J.%20L.%2C%20Piereson%2C%20J.%2C%20%26%20Marcus%2C%20G.%20E.%20(1982).%20Political%20tolerance%20and%20American%20democracy.%20Chicago%3A%20University%20o"}],"related":["democratic-support-scale","political-trust-scale","partisan-identity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"political-trust-scale","name":"Political Trust Scale","fullName":"Political Trust Scale (PTS)","aliases":["PTS","Comparative Study of Electoral Systems (CSES) Trust Module"],"domain":"political-psychology","family":"process-pipeline","subfamily":"institutional-attitudes","year":"1974","originator":"Arthur H. Miller","url":"https://scholargate.app/en/political-psychology/political-trust-scale","markdownUrl":"https://scholargate.app/en/political-psychology/political-trust-scale.md","definition":"The Political Trust Scale measures citizen confidence in government institutions, elected officials, and the political system's responsiveness and fairness. Pioneered by Miller (1974) and operationalized across comparative electoral studies (CSES Module 5), the scale captures both diffuse trust (in the political system generally) and specific trust (in particular institutions such as parliament or the executive). It is central to understanding democratic legitimacy, political engagement, and support for democratic institutions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arthur H. Miller","subfamily":"institutional-attitudes","year":"1974","type":"Self-report"},"citations":[{"ref":"Miller, A. H. (1974). Political issues and trust in government: 1964-1970. American Political Science Review, 68(3), 951-972.","type":"article","doi":"10.2307/1959140","isbn":null,"url":null},{"ref":"Hetherington, M. J. (2005). Why trust matters: Declining political trust and the demise of American democracy. Princeton, NJ: Princeton University Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Hetherington%2C%20M.%20J.%20(2005).%20Why%20trust%20matters%3A%20Declining%20political%20trust%20and%20the%20demise%20of%20American%20democracy.%20Princeton"},{"ref":"Comparative Study of Electoral Systems (CSES) Module 5 (2016-2021). Political Trust and Legitimacy Scales. CSES Secretariat, University of Michigan.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Comparative%20Study%20of%20Electoral%20Systems%20(CSES)%20Module%205%20(2016-2021).%20Political%20Trust%20and%20Legitimacy%20Scales.%20CSES%20Secretar"}],"related":["media-trust-scale","democratic-support-scale","voter-cynicism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pollination-efficiency","name":"Pollination Efficiency Assessment","fullName":"Quantitative Evaluation of Pollinator Activity and Pollen Transfer Success","aliases":["pollinator monitoring","pollen viability assessment","fruit set efficiency"],"domain":"horticulture","family":"process-pipeline","subfamily":"Reproductive ecology and crop pollination","year":"1970","originator":"Pollination biology research tradition","url":"https://scholargate.app/en/horticulture/pollination-efficiency","markdownUrl":"https://scholargate.app/en/horticulture/pollination-efficiency.md","definition":"Pollination efficiency assessment quantifies the effectiveness of pollinator activity and pollen transfer in achieving fruit set. This method integrates field observation of pollinator visits, pollen viability testing, stigma receptivity assessment, and fruit set measurement to diagnose pollination limitations and optimize management. It is essential for crops dependent on animal pollination and for understanding reproductive success in variable environmental conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pollination biology research tradition","subfamily":"Reproductive ecology and crop pollination","year":"1970","type":"ecological and physiological measurement pipeline"},"citations":[{"ref":"Westerkamp, C., & Gottsberger, G. (1991). Dipterophagous flowers—Pollination by oil-collecting insects. Botanica Acta, 104(2), 88–100.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dipterophagous+flowers%E2%80%94Pollination+by+oil-collecting+insects+Westerkamp"},{"ref":"Stanley, R. G., & Linskens, H. F. (1998). Pollen: Biology, Biochemistry, Management. Springer-Verlag.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/book/10.1007/978-3-662-03623-0"}],"related":["crop-load-management","phenological-stage-monitoring","pruning-response-analysis","fruit-color-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polygenic-risk-score","name":"Polygenic Risk Score","fullName":"Polygenic Risk Score for Disease Prediction and Stratification","aliases":["PRS","Polygenic score","Genomic risk score"],"domain":"genetics","family":"process-pipeline","subfamily":"Risk prediction","year":"2007","originator":"Shaun Purcell & Nicholas Wray","url":"https://scholargate.app/en/genetics/polygenic-risk-score","markdownUrl":"https://scholargate.app/en/genetics/polygenic-risk-score.md","definition":"A polygenic risk score (PRS) is a summary measure that aggregates the effects of many genetic variants across the genome to predict an individual's genetic predisposition to disease or other complex traits. Developed initially by Purcell and colleagues in 2007, PRS methods combine genome-wide association study (GWAS) results with an individual's genotype to generate a personalized risk estimate. PRS approaches have transformed precision medicine by enabling risk stratification and early intervention in populations at high genetic risk.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shaun Purcell & Nicholas Wray","subfamily":"Risk prediction","year":"2007","type":"Predictive genomic method"},"citations":[{"ref":"Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O'Donovan, M. C., Sullivan, P. F., & Sklar, P. (2007). Common polygenic variation contributes to risk of schizophrenia. Nature, 460(7256), 748–752.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Common+polygenic+variation+contributes+to+risk+of+schizophrenia+Purcell"},{"ref":"Evans, D. M., Visscher, P. M., & Wray, N. R. (2009). Harnessing the power of large B and T cell lymphoma genome-wide association studies. Nature Reviews Genetics, 10(7), 431–442.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Harnessing+the+power+of+large+B+and+T+cell+lymphoma+genome-wide+association+studies+Evans"},{"ref":"Khera, A. V., Chaffin, M., Wade, K. H., Zaharieva, S., King, C., Arvanitis, M., & Aherwar, D. (2018). Polygenic prediction of weight and obesity trajectories. PLoS Genetics, 15(7), e1007616.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Polygenic+prediction+of+weight+and+obesity+trajectories+Khera"}],"related":["qtl-mapping","ld-block-analysis","transmission-disequilibrium-test","f-statistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polynomial-regression","name":"Polynomial Regression","fullName":"Polynomial Regression","aliases":["polynomial least squares","curvilinear regression","Polinom Regresyonu"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2012,"originator":"Montgomery, Peck & Vining (textbook treatment); classical least squares","url":"https://scholargate.app/en/statistics/polynomial-regression","markdownUrl":"https://scholargate.app/en/statistics/polynomial-regression.md","definition":"Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Montgomery, Peck & Vining (textbook treatment); classical least squares","year":2012,"type":"Linear regression in transformed predictors","estimator":"Ordinary least squares on polynomial-expanded design","outcome":"continuous"},"citations":[{"ref":"Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley.","type":"book","doi":null,"isbn":"978-0470542811","url":null}],"related":["ols-regression","ridge-regression","lasso-regression","spline-regression","response-surface-methodology"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polysomnography","name":"Polysomnography","fullName":"Polysomnographic Sleep Study","aliases":["PSG","sleep study","overnight monitoring"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Physiological Monitoring","year":"1953","originator":"William Dement and Nathaniel Kleitman","url":"https://scholargate.app/en/veterinary-science/polysomnography","markdownUrl":"https://scholargate.app/en/veterinary-science/polysomnography.md","definition":"Polysomnography (PSG) is a comprehensive multi-channel physiological recording method that simultaneously records brain electrical activity, eye movements, muscle tone, respiratory effort, oxygen saturation, heart rate, and limb movements during sleep. First systematized by Rechtschaffen and Kales in 1968, polysomnography is the gold standard for diagnosing sleep disorders, characterizing sleep architecture, and assessing the quality and organization of sleep in humans and increasingly in veterinary species.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Dement and Nathaniel Kleitman","subfamily":"Physiological Monitoring","year":"1953","type":"Multi-channel Recording and Analysis"},"citations":[{"ref":"Rechtschaffen, A., & Kales, A. (1968). A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages in Human Subjects. National Institutes of Health Publication.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/4135629/"},{"ref":"Iber, C., Ancoli-Israel, S., Chesson, A. L., & Quan, S. F. (2007). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications (1st ed.). American Academy of Sleep Medicine.","type":"article","doi":null,"isbn":null,"url":"https://aasm.org/aasm-sleep-scoring-manual/"},{"ref":"Mitchell, E. K., & Redlin, U. (2011). Polysomnography and actigraphy in laboratory animals. Sleep, 34(11), 1431-1432.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Polysomnography+and+actigraphy+in+laboratory+animals+Mitchell"}],"related":["focal-animal-sampling","scan-sampling","acoustic-telemetry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-confirmatory-factor-analysis","name":"Polytomous Confirmatory Factor Analysis","fullName":"Polytomous Confirmatory Factor Analysis","aliases":["CFA for ordered categories","ordinal CFA","categorical CFA","WLSMV-CFA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1984","originator":"Bengt Muthen","url":"https://scholargate.app/en/psychometrics/polytomous-confirmatory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-confirmatory-factor-analysis.md","definition":"Polytomous confirmatory factor analysis (CFA) tests a pre-specified factor structure when items have three or more ordered response categories (e.g., Likert scales). By working with polychoric correlations and robust estimators such as WLSMV, it avoids the distortions that arise when ordered categorical data are treated as continuous.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengt Muthen","year":"1984","type":"Latent variable / confirmatory measurement model","dataType":"Polytomous (ordered categorical) item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Flora, D. B. & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491.","type":"article","doi":"10.1037/1082-989X.9.4.466","isbn":null,"url":null},{"ref":"Muthen, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132.","type":"article","doi":"10.1007/BF02294210","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","polytomous-item-response-theory","ordinal-confirmatory-factor-analysis","measurement-invariance","polytomous-exploratory-factor-analysis","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-construct-validity","name":"Polytomous Construct Validity","fullName":"Polytomous Construct Validity Assessment","aliases":["polytomous item construct validity","ordered-category construct validity","polytomous measurement validity","multi-category scale validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1992–2000","originator":"Building on Messick (1989) and IRT extensions by Masters, Muraki, and Samejima","url":"https://scholargate.app/en/psychometrics/polytomous-construct-validity","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-construct-validity.md","definition":"Polytomous construct validity refers to the evaluation of whether a scale composed of ordered, multi-category items (e.g., Likert or rating-scale items) genuinely measures the intended latent construct. It extends classical validity frameworks to polytomous measurement models — such as the Graded Response Model or Generalized Partial Credit Model — ensuring that ordered response categories function as designed and that the resulting scores reflect the target construct.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Messick (1989) and IRT extensions by Masters, Muraki, and Samejima","year":"1992–2000","type":"Psychometric validity framework","dataType":"Ordinal polytomous items (Likert scales, rating scales, partial-credit tasks)","subfamily":"Scale / measurement"},"citations":[{"ref":"Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159–176.","type":"article","doi":"10.1177/014662169201600206","isbn":null,"url":null},{"ref":"Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null}],"related":["confirmatory-factor-analysis","partial-credit-model","graded-response-model","polytomous-rasch-model","differential-item-functioning","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-differential-item-functioning","name":"Polytomous DIF","fullName":"Polytomous Differential Item Functioning","aliases":["Polytomous DIF","DIF for polytomous items","ordinal DIF analysis","graded-response DIF"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Bruno D. Zumbo and colleagues (ordinal logistic regression framework); Robert D. Ankenmann, Hariharan Swaminathan and others (IRT-based extensions)","url":"https://scholargate.app/en/psychometrics/polytomous-differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-differential-item-functioning.md","definition":"Polytomous differential item functioning detects whether a test or survey item with more than two ordered response categories (e.g., Likert-type scales, partial-credit items) functions differently across groups such as gender, ethnicity, or language background, after controlling for the latent trait being measured. It extends classical binary DIF methods to ordinal response formats.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruno D. Zumbo and colleagues (ordinal logistic regression framework); Robert D. Ankenmann, Hariharan Swaminathan and others (IRT-based extensions)","year":"1990s–2000s","type":"Measurement fairness / item bias detection","dataType":"Ordinal polytomous item scores (Likert scales, partial-credit tests)","subfamily":"Scale / measurement"},"citations":[{"ref":"Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Directorate of Human Resources Research and Evaluation, Department of National Defense.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Zumbo+1999+handbook+theory+methods+differential+item+functioning"},{"ref":"Osterlind, S. J. & Everson, H. T. (2009). Differential Item Functioning (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1412954945","url":null}],"related":["mantel-haenszel-dif","logistic-regression-dif","item-response-theory","graded-response-model","confirmatory-factor-analysis","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-exploratory-factor-analysis","name":"Polytomous EFA","fullName":"Polytomous Exploratory Factor Analysis","aliases":["EFA for ordered-categorical data","polychoric EFA","ordinal exploratory factor analysis","polytomous factor analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1978","originator":"Bengt Muthén","url":"https://scholargate.app/en/psychometrics/polytomous-exploratory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-exploratory-factor-analysis.md","definition":"Polytomous exploratory factor analysis extends standard EFA to ordered categorical (Likert-type) response data by replacing the Pearson correlation matrix with a polychoric correlation matrix. It recovers the latent continuous variable that each polytomous item is assumed to reflect, yielding more accurate factor loadings and better-defined factor structures than treating ordinal scores as if they were continuous.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengt Muthén","year":"1978","type":"Latent variable / dimension reduction","dataType":"Ordered categorical (polytomous) items","subfamily":"Scale / measurement"},"citations":[{"ref":"Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491.","type":"article","doi":"10.1037/1082-989X.9.4.466","isbn":null,"url":null},{"ref":"Muthén, B. (1978). Contributions to factor analysis of dichotomous variables. Psychometrika, 43(4), 551–560.","type":"article","doi":"10.1007/BF02293813","isbn":null,"url":null}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","polychoric-correlation","item-response-theory","ordinal-alpha","graded-response-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-item-analysis","name":"Polytomous item analysis","fullName":"Polytomous Item Analysis","aliases":["ordered-category item analysis","graded response analysis","polytomous IRT","rated-scale item analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1969–1982","originator":"Fumiko Samejima (graded response model, 1969); David Andrich (rating scale model, 1978); Geoffrey Masters (partial credit model, 1982)","url":"https://scholargate.app/en/psychometrics/polytomous-item-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-item-analysis.md","definition":"Polytomous item analysis examines the psychometric behavior of items that have more than two ordered response categories — such as Likert-type scales or partial-credit tasks. It evaluates each item's difficulty thresholds, discriminating power, and category functioning to determine whether the full response scale is being used as intended and whether each item contributes reliably to measuring the underlying construct.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fumiko Samejima (graded response model, 1969); David Andrich (rating scale model, 1978); Geoffrey Masters (partial credit model, 1982)","year":"1969–1982","type":"Item-level psychometric analysis","dataType":"Ordinal polytomous responses (e.g., Likert-scale items)","subfamily":"Scale / measurement"},"citations":[{"ref":"Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34(4, Pt. 2), 1–97.","type":"article","doi":"10.1007/BF03372160","isbn":null,"url":null},{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null}],"related":["graded-response-model","partial-credit-model","rating-scale-model","confirmatory-factor-analysis","classical-item-analysis","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-mcdonalds-omega","name":"Polytomous McDonald's omega","fullName":"McDonald's Omega Reliability for Polytomous Items","aliases":["ordinal omega","omega for polytomous items","categorical omega","omega polychoric"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1999 (omega); 2007 (polytomous extension)","originator":"Roderick P. McDonald (omega); extension for polytomous items by Zumbo, Gadermann & Zeisser","url":"https://scholargate.app/en/psychometrics/polytomous-mcdonalds-omega","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-mcdonalds-omega.md","definition":"Polytomous McDonald's omega estimates the internal consistency reliability of a scale composed of ordinal (polytomous) items — such as Likert-type responses — by computing omega from a factor model fitted to the polychoric correlation matrix rather than the Pearson correlation matrix, yielding estimates that are unbiased by the discreteness of item responses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roderick P. McDonald (omega); extension for polytomous items by Zumbo, Gadermann & Zeisser","year":"1999 (omega); 2007 (polytomous extension)","type":"Reliability coefficient","dataType":"Polytomous ordinal items (Likert-type, rating scales)","subfamily":"Scale / measurement"},"citations":[{"ref":"Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta as measures of internal consistency for Likert rating scales. Journal of Modern Applied Statistical Methods, 6(1), 21–29.","type":"article","doi":"10.22237/jmasm/1177992180","isbn":null,"url":null},{"ref":"McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805830750","url":null}],"related":["mcdonalds-omega","cronbachs-alpha","ordinal-reliability-analysis","polytomous-item-response-theory","confirmatory-factor-analysis","ordinal-cronbachs-alpha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-measurement-invariance","name":"Polytomous Measurement Invariance","fullName":"Polytomous Measurement Invariance Testing","aliases":["PMI","ordinal measurement invariance","polytomous factorial invariance","polytomous multi-group measurement invariance"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2000–2004","originator":"Roger E. Millsap, Robert J. Vandenberg","url":"https://scholargate.app/en/psychometrics/polytomous-measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-measurement-invariance.md","definition":"Polytomous measurement invariance testing evaluates whether a scale with ordered categorical (polytomous) response options — such as Likert-type items — measures the same latent construct in the same way across two or more groups. It extends classical multi-group CFA invariance testing to properly account for the ordinal nature of item responses, ensuring that group comparisons of latent means or factor structures are substantively valid.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roger E. Millsap, Robert J. Vandenberg","year":"2000–2004","type":"Multi-group confirmatory test","dataType":"Polytomous (ordered categorical) item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Millsap, R. E. & Kwok, O.-M. (2004). Evaluating the impact of partial factor loading and intercept invariance on selection utility. Psychological Methods, 9(2), 200–215.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluating+the+impact+of+partial+factor+loading+and+intercept+invariance+on+selection+utility+Millsap"},{"ref":"Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["measurement-invariance","confirmatory-factor-analysis","polytomous-confirmatory-factor-analysis","differential-item-functioning","polytomous-item-response-theory","multi-group-confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-rasch-model","name":"Polytomous Rasch Model","fullName":"Polytomous Rasch Model","aliases":["PRM","Rating Scale Model","Partial Credit Model","Polytomous IRT Rasch"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1978–1982","originator":"Gerhard N. Masters (Partial Credit Model); David Andrich (Rating Scale Model)","url":"https://scholargate.app/en/psychometrics/polytomous-rasch-model","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-rasch-model.md","definition":"The Polytomous Rasch Model extends the dichotomous Rasch framework to ordered response scales with three or more categories, such as Likert items or partial-credit tasks. It estimates person ability and item difficulty on the same interval-level logit scale, and it tests whether the response categories function as intended — prerequisites for rigorous ordinal measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gerhard N. Masters (Partial Credit Model); David Andrich (Rating Scale Model)","year":"1978–1982","type":"Item response model","dataType":"Ordered polytomous response categories (Likert, rating scales, partial-credit scored items)","subfamily":"Scale / measurement"},"citations":[{"ref":"Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149–174.","type":"article","doi":"10.1007/BF02296272","isbn":null,"url":null},{"ref":"Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43(4), 561–573.","type":"article","doi":"10.1007/BF02293814","isbn":null,"url":null}],"related":["item-response-theory","rasch-model","ordinal-item-response-theory","confirmatory-factor-analysis","differential-item-functioning","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-reliability-analysis","name":"Polytomous Reliability Analysis","fullName":"Reliability Analysis for Polytomous Items","aliases":["polytomous scale reliability","ordinal reliability estimation","reliability for ordered-category items","polychoric reliability analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2007–2009 (formal ordinal extensions); broader framework since 1950s","originator":"Building on Cronbach (1951) and McDonald (1978); ordinal extensions by Zumbo and colleagues (2007) and Green and Yang (2009)","url":"https://scholargate.app/en/psychometrics/polytomous-reliability-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-reliability-analysis.md","definition":"Polytomous reliability analysis estimates the internal consistency or precision of measurement for scales composed of items with more than two ordered response categories, such as Likert-type, rating, or partial-credit items. It corrects a well-known underestimation bias in conventional Cronbach's alpha by working with polychoric correlations or IRT-based precision indices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Cronbach (1951) and McDonald (1978); ordinal extensions by Zumbo and colleagues (2007) and Green and Yang (2009)","year":"2007–2009 (formal ordinal extensions); broader framework since 1950s","type":"Reliability estimation","dataType":"Polytomous (ordered multi-category) item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Green, S. B. & Yang, Y. (2009). Reliability of summed item scores using structural equation modeling: An alternative to coefficient alpha. Psychometrika, 74(1), 155–167.","type":"article","doi":"10.1007/s11336-008-9099-3","isbn":null,"url":null},{"ref":"Zumbo, B. D., Gadermann, A. M. & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta for Likert rating scales. Journal of Modern Applied Statistical Methods, 6(1), 21–29.","type":"article","doi":"10.22237/jmasm/1177992180","isbn":null,"url":null}],"related":["cronbachs-alpha","mcdonalds-omega","polytomous-item-response-theory","ordinal-reliability-analysis","confirmatory-factor-analysis","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"polytomous-scale-development","name":"Polytomous scale development","fullName":"Polytomous Scale Development","aliases":["polytomous item development","ordered-category scale construction","rating scale development","multi-category item development"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1969–1982","originator":"Samejima, F.; Masters, G. N. (independently)","url":"https://scholargate.app/en/psychometrics/polytomous-scale-development","markdownUrl":"https://scholargate.app/en/psychometrics/polytomous-scale-development.md","definition":"Polytomous scale development is the systematic construction and validation of measurement instruments whose items have three or more ordered response categories — such as Likert-type, rating, or partial-credit items. It applies polytomous item response theory models or ordinal factor analysis methods to evaluate item quality, estimate latent trait levels, and build a psychometrically sound scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Samejima, F.; Masters, G. N. (independently)","year":"1969–1982","type":"Psychometric scale construction","dataType":"Ordered polytomous item responses (rating scales, Likert-type)","subfamily":"Scale / measurement"},"citations":[{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null},{"ref":"Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34(4, Pt. 2), 1–97.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimation+of+latent+ability+using+a+response+pattern+of+graded+scores+Samejima+1969"}],"related":["item-response-theory","ordinal-scale-development","confirmatory-factor-analysis","differential-item-functioning","scale-development","generalizability-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pontryagin-maximum-principle","name":"Pontryagin Maximum Principle","fullName":"Pontryagin Maximum Principle","aliases":["PMP","Optimal Control","Costate Method"],"domain":"control-theory","family":"ml-model","subfamily":"Optimal Control","year":"1962","originator":"Lev Pontryagin","url":"https://scholargate.app/en/control-theory/pontryagin-maximum-principle","markdownUrl":"https://scholargate.app/en/control-theory/pontryagin-maximum-principle.md","definition":"The Pontryagin Maximum Principle (PMP) is a fundamental theorem in optimal control theory providing necessary conditions for optimality of a control trajectory. Published by Lev Pontryagin in 1962, PMP generalizes the calculus of variations to control problems with constraints and is the theoretical foundation enabling solution of complex trajectory optimization problems from spacecraft missions to industrial process optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lev Pontryagin","subfamily":"Optimal Control","year":"1962","type":"algorithm"},"citations":[{"ref":"Pontryagin, L. S., Boltyanskii, V. G., Gamkrelidze, R. V., & Mischenko, E. F. (1962). The Mathematical Theory of Optimal Processes. John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/The+Mathematical+Theory+of+Optimal+Processes-p-9780911575522"}],"related":["hamilton-jacobi-bellman-equation","linear-quadratic-regulator","model-predictive-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pooled-mean-group","name":"Pooled Mean Group (PMG)","fullName":"Pooled Mean Group Estimator","aliases":["PMG Estimator","Pooled Mean Group","PMG Panel Estimator","Havuzlanmış Ortalama Grup Tahmincisi"],"domain":"econometrics","family":"regression-model","subfamily":"Static/heterogeneous panel","year":1999,"originator":"Pesaran, Shin & Smith","url":"https://scholargate.app/en/econometrics/pooled-mean-group","markdownUrl":"https://scholargate.app/en/econometrics/pooled-mean-group.md","definition":"The Pooled Mean Group (PMG) estimator, introduced by Pesaran, Shin, and Smith (1999), is a panel data technique designed for dynamic heterogeneous panels where the long-run equilibrium relationship is common across groups but short-run dynamics and error variances are allowed to differ. It is particularly suited for macro-panels with moderate N and T, such as cross-country growth, energy consumption, and financial development studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pesaran, Shin & Smith","year":1999,"type":"Panel cointegration estimator","subfamily":"Static/heterogeneous panel","estimationBase":"Maximum likelihood","panelStructure":"Heterogeneous short-run, homogeneous long-run"},"citations":[{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621–634.","type":"article","doi":"10.1080/01621459.1999.10474156","isbn":null,"url":null}],"related":["mean-group-estimator","ardl-bounds-test","panel-fixed-effects"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pooled-ols","name":"Pooled OLS","fullName":"Pooled Ordinary Least Squares (Panel)","aliases":["Pooled OLS","Pooled Ordinary Least Squares","Simple Panel OLS","Havuzlanmış EKK"],"domain":"econometrics","family":"regression-model","subfamily":"Static panel","year":2010,"originator":"Jeffrey Wooldridge (treatment)","url":"https://scholargate.app/en/econometrics/pooled-ols","markdownUrl":"https://scholargate.app/en/econometrics/pooled-ols.md","definition":"Pooled OLS applies standard ordinary least squares to panel data by stacking all cross-sectional and time observations into a single dataset and ignoring the panel structure during estimation. It is the most transparent starting point for panel data analysis, widely used in economics, finance, and social sciences when researchers wish to estimate average partial effects across individuals and time periods without imposing strong distributional assumptions about unobserved heterogeneity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeffrey Wooldridge (treatment)","year":2010,"type":"Linear regression on stacked panel observations","subfamily":"Static panel","estimator":"OLS","data_structure":"Balanced or unbalanced panel"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-23258-8","url":null}],"related":["panel-fixed-effects","random-effects-model","ols-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"population-pharmacodynamics","name":"Population Pharmacodynamics","fullName":"Population Pharmacodynamic Modeling","aliases":["PopPD","population PD","hierarchical PD modeling"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Quantitative Pharmacology","year":"1992","originator":"Lewis Sheiner and Stephen Roush","url":"https://scholargate.app/en/pharmacology/population-pharmacodynamics","markdownUrl":"https://scholargate.app/en/pharmacology/population-pharmacodynamics.md","definition":"Population pharmacodynamic (PopPD) modeling integrates pharmacokinetics with individual dose-response relationships across patient populations to characterize drug efficacy and tolerability. Pioneered by Lewis Sheiner and colleagues, PopPD accounts for inter-individual variability in drug effects and enables rational dose optimization and response prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lewis Sheiner and Stephen Roush","subfamily":"Quantitative Pharmacology","year":"1992","type":"dose-response modeling"},"citations":[{"ref":"Dahlström, B., & Nyberg, L. (1993). Population pharmacokinetics and pharmacodynamics. Clinical Pharmacokinetics, 24(1), 45-57.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Population+pharmacokinetics+and+pharmacodynamics+Dahlstr%C3%B6m"},{"ref":"Sheiner, L. B., & Steimer, J. L. (1992). Pharmacokinetic/pharmacodynamic modeling in drug development. Annual Review of Pharmacology and Toxicology, 40, 67-95.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pharmacokinetic%2Fpharmacodynamic+modeling+in+drug+development+Sheiner"}],"related":["physiologically-based-pharmacokinetics","michaelis-menten-kinetics","schild-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"population-pharmacokinetics","name":"Population Pharmacokinetics","fullName":"Population Pharmacokinetics (Nonlinear Mixed-Effects)","aliases":["PopPK","Nonlinear Mixed-Effects Modeling","NONMEM Approach","Popülasyon Farmakokinetiği"],"domain":"pharmacometrics","family":"regression-model","subfamily":"Pharmacokinetics","year":1977,"originator":"Sheiner, Rosenberg & Marathe","url":"https://scholargate.app/en/pharmacometrics/population-pharmacokinetics","markdownUrl":"https://scholargate.app/en/pharmacometrics/population-pharmacokinetics.md","definition":"Population Pharmacokinetics (PopPK) is a nonlinear mixed-effects modeling framework that characterizes how drugs are absorbed, distributed, metabolized, and eliminated across a patient population, estimating both typical population parameters and the magnitude of between-subject variability. Introduced by Sheiner, Rosenberg, and Marathe in 1977, it enables parameter estimation from sparse, routinely collected clinical data—making it indispensable in drug development, regulatory submissions, and individualized dosing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sheiner, Rosenberg & Marathe","year":1977,"type":"Nonlinear mixed-effects regression model","subfamily":"Pharmacokinetics","software":"NONMEM, Monolix, nlmixr2","data_requirement":"Sparse or dense concentration-time data"},"citations":[{"ref":"Sheiner, L. B., Rosenberg, B., & Marathe, V. V. (1977). Estimation of population characteristics of pharmacokinetic parameters from routine clinical data. Journal of Pharmacokinetics and Biopharmaceutics, 5(5), 445–479.","type":"article","doi":"10.1007/BF01061728","isbn":null,"url":null}],"related":["pharmacokinetic-compartment-model","hierarchical-linear-modeling","bayesian-hierarchical-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"population-viability-analysis","name":"Population Viability Analysis","fullName":"Population Viability Analysis (PVA)","aliases":["PVA","extinction risk","minimum viable population","MVP"],"domain":"ecology","family":"process-pipeline","subfamily":"Conservation biology","year":"1981","originator":"Mark Shaffer","url":"https://scholargate.app/en/ecology/population-viability-analysis","markdownUrl":"https://scholargate.app/en/ecology/population-viability-analysis.md","definition":"Population Viability Analysis (PVA), introduced by Shaffer (1981), estimates the probability that a population will persist over a given time period under specified conditions. PVA combines demographic models (Leslie matrices, IPMs) with stochastic simulation to project population trajectories, quantifying extinction risk. This allows conservation planners to assess whether a population will likely persist, evaluate management scenarios, and estimate the minimum viable population (MVP) size for long-term persistence. PVA is a decision-support tool, not a precise predictor.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark Shaffer","subfamily":"Conservation biology","year":"1981","type":"extinction risk assessment"},"citations":[{"ref":"Shaffer, M. L. (1981). Minimum population sizes for species conservation. BioScience, 31(2), 131-134.","type":"article","doi":"10.2307/1308256","isbn":null,"url":null},{"ref":"Morris, W. F., Blakesley, D., Bruna, M. E., et al. (2002). A practical handbook for population viability analysis. NatureServe, Arlington, Virginia.","type":"article","doi":null,"isbn":null,"url":"https://www.conservationgateway.org/files/pages/pva-handbook-final-11-5-02.pdf"},{"ref":"Ralls, K., Ballou, J. D., & Templeton, A. R. (1988). Estimates of lethal equivalents and the cost of inbreeding in mammals. Conservation Biology, 2(2), 185-193.","type":"article","doi":"10.1111/j.1523-1739.1988.tb00169.x","isbn":null,"url":null}],"related":["leslie-matrix","integral-projection-model","life-table-response-experiment","metabolic-theory-of-ecology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"populism-scale","name":"Populism Scale","fullName":"Populism Attitudes Scale (PAS)","aliases":["PAS","Akkerman Populism Scale","Populist Attitudes Measure"],"domain":"political-psychology","family":"process-pipeline","subfamily":"ideological-orientations","year":"2014","originator":"Matthijs Bukkerman, Cas Mudde, Andrej Zaslaysky","url":"https://scholargate.app/en/political-psychology/populism-scale","markdownUrl":"https://scholargate.app/en/political-psychology/populism-scale.md","definition":"The Populism Attitudes Scale measures individual propensity toward populist political orientations, including Manichean worldview (pure people vs. corrupt elites), belief in popular sovereignty, and anti-elitism. Developed by Akkerman, Mudde, and Zaslaysky (2014), the eight-item scale distinguishes populist attitudes from left-right ideology, authoritarian attitudes, and distrust of institutions. It captures voters' susceptibility to populist political messaging across left-wing and right-wing populist movements globally, from Latin American left-populism to European right-wing populism.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matthijs Bukkerman, Cas Mudde, Andrej Zaslaysky","subfamily":"ideological-orientations","year":"2014","type":"Self-report"},"citations":[{"ref":"Akkerman, A., Mudde, C., & Zaslaysky, A. (2014). How populist are the people? Measuring populist attitudes in voters. Comparative Political Studies, 47(9), 1324-1353.","type":"article","doi":"10.1177/0010414013512600","isbn":null,"url":null},{"ref":"Mudde, C. (2004). The populist zeitgeist. Government and Opposition, 39(4), 541-563.","type":"book","doi":"10.1111/j.1477-7053.2004.00135.x","isbn":null,"url":null},{"ref":"Canovan, M. (1999). The people. Cambridge: Polity Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Canovan%2C%20M.%20(1999).%20The%20people.%20Cambridge%3A%20Polity%20Press."}],"related":["political-ideology-scale","national-identity-scale","conspiracy-mentality-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"portfolio-optimization-mean-variance","name":"Mean-Variance Portfolio Optimization","fullName":"Markowitz Mean-Variance Portfolio Optimization","aliases":["Markowitz portfolio theory","modern portfolio theory","efficient frontier optimization","Ortalama-Varyans Portföy Optimizasyonu (Markowitz)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1952,"originator":"Harry Markowitz","url":"https://scholargate.app/en/finance/portfolio-optimization-mean-variance","markdownUrl":"https://scholargate.app/en/finance/portfolio-optimization-mean-variance.md","definition":"Mean-variance portfolio optimization is the foundational model of modern portfolio theory, introduced by Harry Markowitz in 1952. It describes portfolios in an expected-return versus risk (variance) plane and traces the efficient frontier of allocations that offer the highest expected return for each level of risk, covering the minimum-variance portfolio, the maximum-Sharpe-ratio portfolio, and constrained variants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harry Markowitz","year":1952,"type":"Mean-variance optimization model","objective":"Maximise expected return for a given variance (or minimise variance for a given return)","output":"Portfolio weights on the efficient frontier","minSample":60},"citations":[{"ref":"Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.","type":"article","doi":"10.1111/j.1540-6261.1952.tb01525.x","isbn":null,"url":null},{"ref":"Ledoit, O. & Wolf, M. (2004). A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices. Journal of Multivariate Analysis, 88(2), 365-411.","type":"article","doi":"10.1016/S0047-259X(03)00096-4","isbn":null,"url":null}],"related":["risk-parity-model","credit-risk-models","interest-rate-models","backtesting-var","arima"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pos-tagging","name":"POS Tagging","fullName":"Part-of-Speech Tagging","aliases":["part-of-speech tagging","grammatical tagging","Sözcük Türü Etiketleme (POS Tagging)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/pos-tagging","markdownUrl":"https://scholargate.app/en/text-mining/pos-tagging.md","definition":"Part-of-speech tagging assigns a grammatical category label — noun, verb, adjective, and so on — to every word in a text. It is a foundational natural-language-processing task, formalised as a statistical model by Ratnaparkhi (1996) and packaged into widely used toolkits such as Stanford CoreNLP (Manning et al., 2014), and it serves as a preliminary step for syntactic analysis and information extraction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP sequence-labelling task","output":"One grammatical category tag per token (noun, verb, adjective, ...)","role":"Preliminary step for syntactic parsing and information extraction","minSample":10},"citations":[{"ref":"Ratnaparkhi, A. (1996). A Maximum Entropy Model for Part-Of-Speech Tagging. EMNLP.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/W96-0213/"},{"ref":"Manning, C.D. et al. (2014). The Stanford CoreNLP Natural Language Processing Toolkit. ACL.","type":"inproceedings","doi":"10.3115/v1/P14-5010","isbn":null,"url":null}],"related":["chunking-shallow-parsing","morphological-analysis","text-segmentation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"positive-mental-health-scale","name":"Positive Mental Health Scale","fullName":"Positive Mental Health Scale (PMH)","aliases":["PMHS"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"mental health and well-being","year":"2015","originator":"Multiple developers including Christine Lüthy","url":"https://scholargate.app/en/positive-psychology/positive-mental-health-scale","markdownUrl":"https://scholargate.app/en/positive-psychology/positive-mental-health-scale.md","definition":"The Positive Mental Health Scale (PMHS) is a brief instrument developed to measure mental well-being by assessing the presence of positive mental health dimensions rather than the absence of disorder. Rather than focusing solely on symptom reduction, the PMHS operationalizes mental health as an active state characterized by personal strengths, resilience, coping capacity, and positive functioning. It represents a paradigm shift toward strength-based mental health assessment, viewing mental health and mental illness as distinct continua rather than opposite ends of a single spectrum.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple developers including Christine Lüthy","subfamily":"mental health and well-being","year":"2015","type":"Self-report questionnaire"},"citations":[{"ref":"Lüthy, C., Meisser, C., & Schindler, C. (2015). The Positive Mental Health Scale: A measure based on personal strength models in a cross-national study. Health and Quality of Life Outcomes, 13, 29.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Positive+Mental+Health+Scale%3A+A+measure+based+on+personal+strength+models+in+a+cross-national+study+L%C3%BCthy"}],"related":["flourishing-scale","perma-scale","who-5-wellbeing-index","subjective-wellbeing-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"possibility-theory","name":"Possibility Theory","fullName":"Possibility Theory","aliases":["Fuzzy Possibility Theory","Possibilistic Reasoning","Olasılık Teorisi (Bulanık)","Possibility Distribution Theory"],"domain":"soft-computing","family":"ml-model","subfamily":"Uncertainty theory","year":1988,"originator":"Lotfi Zadeh; Didier Dubois & Henri Prade","url":"https://scholargate.app/en/soft-computing/possibility-theory","markdownUrl":"https://scholargate.app/en/soft-computing/possibility-theory.md","definition":"Possibility Theory is a mathematical framework for representing and reasoning under uncertainty, introduced by Lotfi Zadeh in 1978 and systematically developed by Didier Dubois and Henri Prade in their 1988 monograph. It uses possibility distributions — functions assigning a degree in [0,1] to each element of a universe — to encode what is plausible or consistent with available information, complementing probability theory for situations where data is scarce or knowledge is imprecise.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lotfi Zadeh; Didier Dubois & Henri Prade","year":1988,"type":"Uncertainty quantification framework","subfamily":"Uncertainty theory","key_construct":"Possibility and necessity measures","representation":"Possibility distribution over a universe of discourse"},"citations":[{"ref":"Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press.","type":"book","doi":null,"isbn":"978-0-306-42520-2","url":null},{"ref":"Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3–28.","type":"article","doi":"10.1016/0165-0114(78)90029-5","isbn":null,"url":null}],"related":["dempster-shafer-theory","imprecise-probability","granular-computing"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"post-deployment-reintegration","name":"Post-Deployment Reintegration Scale","fullName":"Post-Deployment Reintegration Difficulties Scale","aliases":["PDRS","Post-Deployment Reintegration"],"domain":"military-psychology","family":"process-pipeline","subfamily":"Transition and reintegration adjustment","year":2010,"originator":"Sayer, Noorbaloochi, Frazier, & colleagues","url":"https://scholargate.app/en/military-psychology/post-deployment-reintegration","markdownUrl":"https://scholargate.app/en/military-psychology/post-deployment-reintegration.md","definition":"The Post-Deployment Reintegration Scale measures multidimensional adjustment difficulties experienced by service members transitioning from military to civilian life. Developed by Sayer, Noorbaloochi, and colleagues in 2010, it assesses challenges across employment, family relationships, social reintegration, identity development, and health domains. It is widely used in VA clinical settings, military transition programs, and research examining post-deployment adjustment outcomes and predictors of successful reintegration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sayer, Noorbaloochi, Frazier, & colleagues","subfamily":"Transition and reintegration adjustment","year":2010,"type":"Self-report"},"citations":[{"ref":"Mobbs, M. C., Bonanno, G. A., & Bonanno, M. L. (2006). Beyond the myth of resilience: A prospective study of resilience and adjustment following military separation. Psychological Trauma: Theory, Research, Practice, and Policy, 2(1), 68-82.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/21290954"},{"ref":"Sayer, N. A., Noorbaloochi, S., Frazier, P., Carlson, K., Gravely, A., & Murdoch, M. (2010). Reintegration problems and treatment interests among Iraq and Afghanistan combat veterans receiving VA medical care. Psychiatric Services, 61(6), 589-597.","type":"article","doi":"10.1176/ps.2010.61.6.589","isbn":null,"url":null}],"related":["military-identity-scale","deployment-risk-resilience","soldier-adaptation-measure","military-to-civilian-transition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"post-occupancy-evaluation","name":"Post-Occupancy Evaluation","fullName":"Post-Occupancy Evaluation of Building Performance","aliases":["POE","building performance evaluation","occupant satisfaction assessment"],"domain":"architecture","family":"process-pipeline","subfamily":"Building performance and occupant assessment","year":"1988","originator":"Wolfgang Preiser","url":"https://scholargate.app/en/architecture/post-occupancy-evaluation","markdownUrl":"https://scholargate.app/en/architecture/post-occupancy-evaluation.md","definition":"Post-Occupancy Evaluation (POE) is a systematic method for assessing how well a completed building meets the needs and expectations of its occupants, comparing planned performance to actual performance. Formalized by Wolfgang Preiser in the 1980s, POE has become essential for learning what design strategies work, identifying problems for remediation, and improving future projects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolfgang Preiser","subfamily":"Building performance and occupant assessment","year":"1988","type":"empirical building evaluation method"},"citations":[{"ref":"Preiser, W. F., Rabinowitz, H. Z., White, E. T. (1988). Post-Occupancy Evaluation. Van Nostrand Reinhold, New York.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Post+Occupancy+Evaluation-p-9780442005818"},{"ref":"Leaman, A., Stevenson, F. (2010). Evaluating Operation and Use of the Building. Building Research and Information, 38(3), 287-301.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluating+Operation+and+Use+of+the+Building+Leaman"},{"ref":"Baird, G. (2010). Sustainable Building in Practice: What the Users Think. Routledge, London.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sustainable+Building+in+Practice%3A+What+the+Users+Think+Baird"}],"related":["thermal-comfort-assessment","daylight-simulation","acoustic-design-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"post-quantum-cryptography","name":"Post-Quantum Cryptography (Kyber)","fullName":"Post-Quantum Cryptography","aliases":["PQC","quantum-resistant cryptography","quantum-safe"],"domain":"cryptography","family":"ml-model","subfamily":"Quantum-resistant cryptography","year":"2022","originator":"NIST PQC Standardization Project","url":"https://scholargate.app/en/cryptography/post-quantum-cryptography","markdownUrl":"https://scholargate.app/en/cryptography/post-quantum-cryptography.md","definition":"Post-quantum cryptography comprises cryptographic algorithms believed to be secure against both classical and quantum computers. In 2022, NIST standardized post-quantum algorithms including ML-KEM (CRYSTALS-Kyber) for key encapsulation and ML-DSA (CRYSTALS-Dilithium) for signatures. Post-quantum cryptography is essential for systems requiring long-term confidentiality, as adversaries may record encrypted communications today and decrypt them once quantum computers become available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"NIST PQC Standardization Project","subfamily":"Quantum-resistant cryptography","year":"2022","type":"post-quantum key encapsulation mechanism"},"citations":[{"ref":"Avanzi, R., Bos, J., Ducas, L., & Kiltz, E. (2022). CRYSTALS-Kyber algorithm specification and supporting documentation. NIST Post-Quantum Cryptography Project.","type":"article","doi":null,"isbn":null,"url":"https://pq-crystals.org/kyber/data/kyber-specification-round3-20210804.pdf"},{"ref":"National Institute of Standards and Technology (NIST). (2016). Post-Quantum Cryptography: Call for Proposals. NIST Special Publication 800-56C Rev. 1.","type":"article","doi":null,"isbn":null,"url":"https://csrc.nist.gov/projects/post-quantum-cryptography"}],"related":["lattice-based-cryptography","elliptic-curve-cryptography","rsa-cryptosystem"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"post-traumatic-growth-inventory","name":"Post-Traumatic Growth Inventory","fullName":"Post-Traumatic Growth Inventory (PTGI)","aliases":["PTGI","Tedeschi and Calhoun PTGI"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"Positive psychological outcomes and resilience following trauma","year":"1996","originator":"Richard G. Tedeschi & Lawrence G. Calhoun","url":"https://scholargate.app/en/trauma-psychology/post-traumatic-growth-inventory","markdownUrl":"https://scholargate.app/en/trauma-psychology/post-traumatic-growth-inventory.md","definition":"The PTGI is a 21-item self-report scale measuring positive psychological outcomes and personal growth reported after trauma exposure. Developed by Tedeschi and Calhoun in 1996, the PTGI operationalizes the construct of posttraumatic growth (PTG)—the experience of positive life change accompanying psychological struggle with trauma. Unlike scales measuring psychopathology or symptom severity, the PTGI captures meaningful psychological and existential shifts often reported by trauma survivors, including enhanced relationships, increased personal strength, spiritual change, and life appreciation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard G. Tedeschi & Lawrence G. Calhoun","subfamily":"Positive psychological outcomes and resilience following trauma","year":"1996","type":"Self-report questionnaire"},"citations":[{"ref":"Tedeschi, R. G., & Calhoun, L. G. (1996). The Post-Traumatic Growth Inventory: Measuring the positive legacy of trauma. Journal of Traumatic Stress, 9(3), 455-471.","type":"article","doi":"10.1007/BF02103658","isbn":null,"url":null},{"ref":"Tedeschi, R. G., & Calhoun, L. G. (2004). Posttraumatic growth: Conceptual foundations and empirical evidence. Psychological Inquiry, 15(1), 1-18.","type":"article","doi":"10.1207/s15327965pli1501_01","isbn":null,"url":null}],"related":["impact-of-event-scale-revised","multidimensional-perceived-social-support","ace-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"postcolonial-analysis","name":"Postcolonial Analysis","fullName":"Postcolonial Analysis","aliases":["postcolonial criticism","postcolonial theory","colonial discourse analysis","decolonial analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Critical Inquiry","year":"Late 20th century (Said 1978; Spivak 1988; Bhabha 1994)","originator":"Edward Said, Gayatri Chakravorty Spivak, Homi K. Bhabha","url":"https://scholargate.app/en/qualitative/postcolonial-analysis","markdownUrl":"https://scholargate.app/en/qualitative/postcolonial-analysis.md","definition":"Postcolonial analysis is a qualitative research approach that critically examines the lasting cultural, political, epistemic, and social effects of colonialism and imperialism. Drawing on foundational works by Edward Said, Gayatri Spivak, and Homi Bhabha, it interrogates how colonial power relations are reproduced in texts, institutions, identities, and knowledge systems — and how colonised or marginalised voices can be recovered, amplified, and centred.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward Said, Gayatri Chakravorty Spivak, Homi K. Bhabha","year":"Late 20th century (Said 1978; Spivak 1988; Bhabha 1994)","type":"Qualitative research method","dataType":"Texts, documents, interviews, cultural artefacts, media, historical records","typicalSampleSize":"Purposive selection of documents or participants; no fixed number","subfamily":"Critical Inquiry"},"citations":[{"ref":"Said, E. W. (1978). Orientalism. Pantheon Books.","type":"book","doi":null,"isbn":"978-0394428147","url":null},{"ref":"Spivak, G. C. (1988). Can the subaltern speak? In C. Nelson & L. Grossberg (Eds.), Marxism and the Interpretation of Culture (pp. 271–313). University of Illinois Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Can+the+subaltern+speak+Spivak+1988"}],"related":["discourse-analysis","narrative-analysis","ethnography","thematic-analysis","content-analysis","action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"postharvest-storage-simulation","name":"Postharvest Storage Simulation","fullName":"Computational Storage Life Prediction and Quality Modeling","aliases":["shelf life prediction","storage modeling","quality decay simulation"],"domain":"horticulture","family":"process-pipeline","subfamily":"Storage quality prediction","year":"2001","originator":"Luc Tijskens and Bart Nicolaï","url":"https://scholargate.app/en/horticulture/postharvest-storage-simulation","markdownUrl":"https://scholargate.app/en/horticulture/postharvest-storage-simulation.md","definition":"Postharvest storage simulation uses computational models to predict fruit and vegetable quality degradation during storage and distribution under variable temperature and humidity conditions. Pioneered by Tijskens and Nicolaï in 2001, these mechanistic and empirical models enable logistics optimization, reduce food waste, and improve supply chain transparency. They are integrated into decision support systems for commercial packinghouses and research facilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Tijskens and Bart Nicolaï","subfamily":"Storage quality prediction","year":"2001","type":"computational modeling pipeline"},"citations":[{"ref":"Tijskens, L. M., & Polderdijk, J. J. (2001). A generic model for keeping quality of vegetable produce during storage and distribution. Postharvest Biology and Technology, 23(1), 13–25.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+generic+model+for+keeping+quality+of+vegetable+produce+during+storage+and+distribution+Tijskens"},{"ref":"Hertog, M. L. A. T. M., Nicolaï, B. M., & Tijskens, L. M. (2007). Modelling the effect of storage conditions on the quality of postharvest horticultural produce: a review. Postharvest Biology and Technology, 45(3), 309–320.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modelling+the+effect+of+storage+conditions+on+the+quality+of+postharvest+horticultural+produce%3A+a+review+Hertog"}],"related":["cold-storage-protocol","controlled-atmosphere-storage","ripeness-index","brix-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"postpartum-bonding-questionnaire","name":"Postpartum Bonding Questionnaire","fullName":"Postpartum Bonding Questionnaire (PBQ)","aliases":["PBQ","PBQ-25","PBQ-16"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"maternal-infant-bonding","year":2001,"originator":"Brockington et al.","url":"https://scholargate.app/en/obstetrics-gynecology/postpartum-bonding-questionnaire","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/postpartum-bonding-questionnaire.md","definition":"The Postpartum Bonding Questionnaire (PBQ) is a 25-item self-report instrument designed to assess disorders of the mother-infant relationship and bonding in the early postpartum period. Developed by Brockington and colleagues in 2001, the PBQ screens for pathological bonding experiences, including impaired attachment, rejection, resentment, and anger toward the infant. A shorter 16-item version (PBQ-16) is also available for rapid screening in clinical settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brockington et al.","subfamily":"maternal-infant-bonding","year":2001,"type":"Self-report"},"citations":[{"ref":"Brockington, I. F., Oates, J., George, S., Turner, D., Vostanis, P., Sullivan, M., Loh, C., & Murdoch, C. (2001). A screening questionnaire for mother-infant bonding disorders. Archives of Women's Mental Health, 3(4), 133-140.","type":"article","doi":"10.1007/s007370170010","isbn":null,"url":null},{"ref":"Brockington, I. F., Aucamp, H. M., & Fraser, C. (2006). Severe disorders of the mother-infant relationship: sorting out the concepts. Archives of Women's Mental Health, 9(3), 129-138.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Severe+disorders+of+the+mother-infant+relationship%3A+sorting+out+the+concepts+Brockington"}],"related":["perinatal-anxiety-screening-scale","antepartum-depression-scale","breastfeeding-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"potential-vorticity-inversion","name":"Potential Vorticity Inversion","fullName":"Potential Vorticity Inversion Method","aliases":["PV inversion","Potential vorticity","PV thinking"],"domain":"meteorology","family":"process-pipeline","subfamily":"Dynamical meteorology","year":"1985","originator":"Haynes, McIntyre, Hoskins","url":"https://scholargate.app/en/meteorology/potential-vorticity-inversion","markdownUrl":"https://scholargate.app/en/meteorology/potential-vorticity-inversion.md","definition":"Potential vorticity (PV) inversion is a diagnostic technique that reconstructs atmospheric wind and pressure fields from the spatial distribution of potential vorticity. This method assumes that, in a geostrophically balanced atmosphere, the PV field uniquely determines the balanced circulation around anomalies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Haynes, McIntyre, Hoskins","subfamily":"Dynamical meteorology","year":"1985","type":"Diagnostic inversion method"},"citations":[{"ref":"Haynes, P., & McIntyre, M. E. (1987). On the evolution of vorticity and potential vorticity in the atmosphere. Journal of the Atmospheric Sciences, 44(5), 828-841.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+evolution+of+vorticity+and+potential+vorticity+in+the+atmosphere+Haynes"},{"ref":"Davis, C. A. (1992). Piecewise potential vorticity inversion. Journal of the Atmospheric Sciences, 49(19), 1853-1862.","type":"article","doi":"10.1175/1520-0469(1992)049<1397:ppvi>2.0.co;2","isbn":null,"url":null}],"related":["quasi-geostrophic-omega-equation","geostrophic-wind","thermal-wind"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"potentiometric-titration","name":"Potentiometric Titration","fullName":"Potentiometric Titration","aliases":["potentiometry","electrochemical titration"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Electrochemical Analysis","year":"1909","originator":"Soren Sorensen","url":"https://scholargate.app/en/analytical-chemistry/potentiometric-titration","markdownUrl":"https://scholargate.app/en/analytical-chemistry/potentiometric-titration.md","definition":"Potentiometric titration is an electrochemical method of analysis that measures the potential difference between a reference electrode and an indicator electrode as a titrant is gradually added to a solution. Developed in the early 20th century, it allows precise determination of the concentration of analytes without requiring visual endpoint indicators. This method is fundamental in analytical chemistry for determining acids, bases, redox species, and metal ions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Soren Sorensen","subfamily":"Electrochemical Analysis","year":"1909","type":"titration method"},"citations":[{"ref":"Skoog, D. A., West, D. M., Holler, F. J., & Crouch, S. R. (2014). Fundamentals of Analytical Chemistry (9th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1133170960","url":null},{"ref":"Covington, A. K., Bates, R. G., & Durst, R. A. (1985). Definitions of pH scales, standard reference values, measurement of pH and related terminology. Pure and Applied Chemistry, 57(3), 531–542.","type":"article","doi":"10.1351/pac198557030531","isbn":null,"url":null},{"ref":"Michels, H. H., & Sielcken, O. E. (1965). A potentiometric method for the determination of moisture. Journal of Applied Chemistry, 15(12), 589–594.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+potentiometric+method+for+the+determination+of+moisture+Michels"}],"related":["ion-chromatography","coulometry","voltammetry","uv-vis-spectrophotometry","atomic-absorption-spectroscopy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis-anova","name":"Power Analysis for ANOVA","fullName":"Statistical Power Analysis for Analysis of Variance","aliases":["ANOVA power analysis","F-test power analysis","sample size for ANOVA","Güç Analizi — ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1988,"originator":"Jacob Cohen","url":"https://scholargate.app/en/statistics/power-analysis-anova","markdownUrl":"https://scholargate.app/en/statistics/power-analysis-anova.md","definition":"Power analysis for ANOVA is a prospective statistical technique that determines the minimum sample size needed to detect a specified group mean difference with a chosen probability. Formalized by Jacob Cohen in his 1988 monograph, it translates a researcher's effect size expectation — expressed as Cohen's f — along with the desired Type I error rate (alpha) and statistical power (1 − beta) into a concrete per-group sample size recommendation for one-way or factorial ANOVA designs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","year":1988,"family":"Power analysis","type":"Sample size determination","effectSizeMeasure":"Cohen's f","testFamily":"F-test (ANOVA)","parametric":true,"inputParameters":"effect size f, alpha, desired power, number of groups k","outputParameter":"required n per group"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805802832","url":null}],"related":["one-way-anova","power-analysis-ttest","power-analysis-regression","independent-t-test","effect-size-f"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis-chisquare","name":"Chi-Square Power Analysis","fullName":"Statistical Power Analysis for Chi-Square Tests","aliases":["chi-square power","chi-square sample size","Ki-Kare Güç Analizi","goodness-of-fit power","independence test power"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1988,"originator":"Jacob Cohen","url":"https://scholargate.app/en/statistics/power-analysis-chisquare","markdownUrl":"https://scholargate.app/en/statistics/power-analysis-chisquare.md","definition":"Chi-square power analysis is a prospective calculation that determines the minimum sample size required — or the statistical power achievable with a given sample — for chi-square independence tests or goodness-of-fit tests. It rests on Cohen's w effect size framework, codified by Jacob Cohen in his landmark 1988 work on statistical power for the behavioral sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","year":1988,"family":"Power analysis","type":"Sample size and power calculation","effectSizeMeasure":"Cohen's w","effectSizeSmall":0.1,"effectSizeMedium":0.3,"effectSizeLarge":0.5,"defaultAlpha":0.05,"defaultPower":0.8,"outcomeType":"categorical","parametric":false,"distribution":"Chi-square"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805802832","url":null}],"related":["chi-square-independence-test","chi-square-goodness-of-fit","power-analysis-t-test","power-analysis-anova","power-analysis-correlation","fisher-exact-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis-correlation","name":"Correlation Power Analysis","fullName":"Statistical Power Analysis for Pearson Correlation","aliases":["Korelasyon Güç Analizi","power analysis for r","sample size for correlation"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1988,"originator":"Jacob Cohen","url":"https://scholargate.app/en/statistics/power-analysis-correlation","markdownUrl":"https://scholargate.app/en/statistics/power-analysis-correlation.md","definition":"Correlation power analysis is a pre-study calculation that determines how many participants are needed — or how much statistical power an existing sample provides — for a Pearson correlation test. Formalised by Jacob Cohen in his landmark 1988 text, it uses the expected correlation coefficient r directly as the effect size, so researchers can plan studies that are neither underpowered nor wastefully large.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","year":1988,"family":"Power analysis","type":"Sample size / power determination","effectSizeMeasure":"r (Pearson correlation coefficient)","effectSizeConventions":"small 0.1, medium 0.3, large 0.5","minSample":10,"parametric":true,"distribution":"Bivariate normal","testDirection":"one-tailed or two-tailed"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805802832","url":null}],"related":["pearson-correlation","power-analysis-t-test","power-analysis-anova","power-analysis-regression","spearman-correlation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis-multilevel","name":"Multilevel Power Analysis","fullName":"Power Analysis for Multilevel and Mixed-Effects Models","aliases":["HLM power analysis","mixed-effects power analysis","clustered design power analysis","Çok Düzeyli / Karma Model Güç Analizi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1993,"originator":"Snijders & Bosker; Hox, Moerbeek & van de Schoot","url":"https://scholargate.app/en/statistics/power-analysis-multilevel","markdownUrl":"https://scholargate.app/en/statistics/power-analysis-multilevel.md","definition":"Multilevel power analysis is a sample-size planning procedure designed for hierarchical, clustered, or longitudinal study designs in which observations are nested within higher-level units such as students within schools or patients within clinics. Formalized in the multilevel modeling literature by Snijders and Bosker (1993, expanded 2012) and Hox, Moerbeek, and van de Schoot (2017), it accounts for the intraclass correlation (ICC) and the design effect that arises when data are clustered, ensuring that both the number of clusters and the cluster size are adequate to detect a target effect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Snijders & Bosker; Hox, Moerbeek & van de Schoot","year":1993,"family":"Power analysis","type":"Sample-size planning for hierarchical designs","parametric":true,"keyInput":"ICC, number of clusters (J), cluster size (m), effect size","designEffectFormula":"DEFF = 1 + (m − 1) × ICC","outcome":"continuous or binary","structures":"cross-sectional clustered, longitudinal, panel"},"citations":[{"ref":"Snijders, T.A.B. & Bosker, R.J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). SAGE.","type":"book","doi":null,"isbn":"978-1849202015","url":null},{"ref":"Hox, J.J., Moerbeek, M. & van de Schoot, R. (2017). Multilevel Analysis: Techniques and Applications (3rd ed.). Routledge.","type":"book","doi":"10.4324/9781315650982","isbn":null,"url":null}],"related":["power-analysis-anova","power-analysis-regression","hierarchical-linear-modeling","mixed-effects-model","intraclass-correlation","one-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis-proportion","name":"Power Analysis for Proportions","fullName":"Sample Size and Power Analysis for Proportion Tests","aliases":["proportion power analysis","two-proportion z-test power","z-test for proportions power","Oran Testi Güç Analizi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1988,"originator":"Jacob Cohen","url":"https://scholargate.app/en/statistics/power-analysis-proportion","markdownUrl":"https://scholargate.app/en/statistics/power-analysis-proportion.md","definition":"Power analysis for proportion tests is a prospective sample-size planning method used to determine how many participants are needed to detect a meaningful difference between two (or one) proportions with a specified probability. Formalised by Jacob Cohen in his 1988 landmark text, it applies the arcsine transformation to convert proportions into the effect-size index h, enabling direct calculation of the required sample size.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","year":1988,"family":"Power analysis","type":"Sample size determination","outcomeType":"binary / categorical proportion","parametric":true,"testFamily":"z-test for proportions","effectSizeIndex":"Cohen's h","minSample":10},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":"10.4324/9780203771587","isbn":null,"url":null}],"related":["power-analysis-t-test","power-analysis-anova","chi-square-test","two-proportion-z-test","fisher-exact-test","binomial-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis-regression","name":"Power Analysis for Regression","fullName":"A Priori Power Analysis for Multiple Regression","aliases":["regression power analysis","sample size estimation regression","f² power analysis","Güç Analizi — Regresyon"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1988,"originator":"Jacob Cohen","url":"https://scholargate.app/en/statistics/power-analysis-regression","markdownUrl":"https://scholargate.app/en/statistics/power-analysis-regression.md","definition":"Power analysis for multiple regression is a pre-study procedure, formalised by Jacob Cohen (1988), that calculates the minimum sample size needed to detect a regression effect of a given size with adequate statistical power. It uses the anticipated R² (or the equivalent Cohen's f² effect size) and the number of predictors to determine how many observations must be collected before data collection begins.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","year":1988,"family":"Power analysis","type":"A priori sample size determination","testStatistic":"F","effectSizeMetric":"f² (Cohen's f²)","parametric":true,"distribution":"F","inputParameters":"anticipated R², or f², number of predictors (u), α level, desired power (1−β)"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805802832","url":null},{"ref":"Green, S. B. (1991). How Many Subjects Does It Take To Do A Regression Analysis? Multivariate Behavioral Research, 26(3), 499–510.","type":"article","doi":"10.1207/s15327906mbr2603_7","isbn":null,"url":null}],"related":["power-analysis-ttest","power-analysis-anova","power-analysis-correlation","multiple-linear-regression","sample-size-determination"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis-sem","name":"SEM Power Analysis","fullName":"Power Analysis for Structural Equation Modeling and Multivariate Analyses","aliases":["SEM sample size planning","covariance structure power analysis","MANOVA power analysis","SEM / Çok Değişkenli Güç Analizi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1996,"originator":"MacCallum, Browne & Sugawara","url":"https://scholargate.app/en/statistics/power-analysis-sem","markdownUrl":"https://scholargate.app/en/statistics/power-analysis-sem.md","definition":"Power analysis for SEM and other multivariate procedures determines the minimum sample size required to detect a model misfit of a specified magnitude with adequate probability. The dominant approach, introduced by MacCallum, Browne, and Sugawara in 1996, expresses effect size as the Root Mean Square Error of Approximation (RMSEA) and derives power from the noncentral chi-square distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"MacCallum, Browne & Sugawara","year":1996,"family":"Power analysis","type":"Sample size planning (multivariate / SEM)","parametric":true,"effectSizeMeasure":"RMSEA","minRecommendedN":50,"difficultyLevel":3},"citations":[{"ref":"MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149.","type":"article","doi":"10.1037/1082-989X.1.2.130","isbn":null,"url":null}],"related":["power-analysis-anova","power-analysis-regression","power-analysis-multilevel","simulation-based-power","structural-equation-modeling","manova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis-survival","name":"Survival Analysis Power Analysis","fullName":"Sample Size and Power Analysis for Survival Analysis (Log-rank and Cox Regression)","aliases":["log-rank power analysis","cox regression power analysis","survival power analysis","Sağkalım Analizi Güç Analizi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1981,"originator":null,"url":"https://scholargate.app/en/statistics/power-analysis-survival","markdownUrl":"https://scholargate.app/en/statistics/power-analysis-survival.md","definition":"Power analysis for survival studies determines how many participants — and how many observed events — are required so that a log-rank test or Cox regression has a sufficient probability of detecting a clinically meaningful difference in survival between groups. The foundational formulas were derived by Schoenfeld (1981) and Lachin (1981) and remain the standard approach in clinical trial planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"David A. Schoenfeld; John M. Lachin","year":1981,"family":"Power analysis","type":"Sample size determination for survival outcomes","targetTests":"log-rank test, Cox proportional hazards regression","outcomeType":"time-to-event (survival)","keyQuantity":"expected number of events","distributions":"exponential, Weibull","parametric":true,"minSample":30},"citations":[{"ref":"Schoenfeld, D. A. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316–319.","type":"article","doi":"10.1093/biomet/68.1.316","isbn":null,"url":null},{"ref":"Lachin, J. M. (1981). Introduction to sample size determination and power analysis for clinical trials. Controlled Clinical Trials, 2(2), 93–113.","type":"article","doi":"10.1016/0197-2456(81)90001-5","isbn":null,"url":null}],"related":["log-rank-test","cox-proportional-hazards","kaplan-meier","power-analysis-ttest","power-analysis-proportion","simulation-based-power"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis-ttest","name":"Power Analysis for t-test","fullName":"Statistical Power Analysis for the t-test","aliases":["t-test power analysis","sample size calculation for t-test","Güç Analizi — t-Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1969,"originator":"Jacob Cohen","url":"https://scholargate.app/en/statistics/power-analysis-ttest","markdownUrl":"https://scholargate.app/en/statistics/power-analysis-ttest.md","definition":"Power analysis for the t-test is a sample size planning procedure that determines how many participants are required to detect a mean difference of a given magnitude with acceptable probability. Formalised by Jacob Cohen in his 1969 and 1988 editions of Statistical Power Analysis for the Behavioral Sciences, it links four quantities — effect size (Cohen's d), significance level (α), statistical power (1 − β), and sample size — so that fixing any three allows calculation of the fourth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","year":1969,"family":"Power analysis","type":"Sample size determination","effectSizeMeasure":"Cohen's d","typicalAlpha":0.05,"typicalPower":0.8,"parametric":true,"distribution":"Non-central t"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805802832","url":null}],"related":["independent-t-test","paired-t-test","welch-t-test","power-analysis-anova","power-analysis-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-analysis","name":"Power analysis","fullName":"Statistical Power Analysis","aliases":["sample size calculation","power calculation","sensitivity analysis","a priori power analysis"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1969 (1st ed.); 1988 (seminal 2nd ed.)","originator":"Jacob Cohen","url":"https://scholargate.app/en/statistics/power-analysis","markdownUrl":"https://scholargate.app/en/statistics/power-analysis.md","definition":"Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","year":"1969 (1st ed.); 1988 (seminal 2nd ed.)","type":"Sample size and power planning","dataType":"Continuous, categorical, or count outcomes depending on target test","subfamily":"Classical statistics"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805802832","url":null},{"ref":"Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.","type":"article","doi":"10.3758/BF03193146","isbn":null,"url":null}],"related":["effect-size-analysis","independent-samples-t-test","one-way-anova","chi-square-test","sample-size-determination","bayesian-power-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-flow-analysis","name":"Power Flow Analysis","fullName":"Power Flow Analysis and Load Flow Computation","aliases":["load flow analysis","power flow study"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Power system analysis","year":"1956","originator":"Ward and Hale","url":"https://scholargate.app/en/electrical-engineering/power-flow-analysis","markdownUrl":"https://scholargate.app/en/electrical-engineering/power-flow-analysis.md","definition":"Power flow analysis, also called load flow study, is a computational method that determines the steady-state voltage, current, and power distribution across all buses in an electrical power system. Developed by Ward and Hale in 1956, it is fundamental to power system planning, operation, and optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ward and Hale","subfamily":"Power system analysis","year":"1956","type":"Computational pipeline"},"citations":[{"ref":"Saadat, H. (2010). Power System Analysis (3rd ed.). PSA Publishing.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+System+Analysis+%283rd+ed.%29+Saadat"},{"ref":"Grainger, J. J., & Stevenson, W. D. (1994). Power System Analysis and Design (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://www.mheducation.com"},{"ref":"Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2014). Power Generation, Operation, and Control (3rd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+Generation%2C+Operation%2C+and+Control+%283rd+ed.%29+Wood"}],"related":["fault-analysis-power-system","smart-grid-state-estimation","reactive-power-compensation","load-forecasting","harmonic-distortion-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-mean","name":"POWER-MEAN","fullName":"Weighted Power Mean (Hölder Mean)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Aggregation","year":"1934","originator":"Hardy, G. H. Littlewood, J. E. Pólya, G.","url":"https://scholargate.app/en/decision-making/power-mean","markdownUrl":"https://scholargate.app/en/decision-making/power-mean.md","definition":"POWER-MEAN (Weighted Power Mean (Hölder Mean)) is a aggregation multi-criteria decision-making (MCDM) method introduced by Hardy, G. H. Littlewood, J. E. Pólya, G. in 1934. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hardy, G. H. Littlewood, J. E. Pólya, G.","subfamily":"Aggregation","year":"1934","type":"Power mean family — parametric generalisation of WAM/WGM/WHM","value_space":"crisp","uncertainty":"none","compensation":"variable","rank_reversal":false},"citations":[{"ref":"Hardy, G. H., Littlewood, J. E., Pólya, G. (1934). Inequalities. Cambridge University Press","type":"article","doi":null,"isbn":"978-0-521-35880-4","url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-quality-assessment","name":"Power Quality Assessment","fullName":"Power Quality Measurement and Assessment","aliases":["PQ assessment","power quality survey","voltage quality analysis"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Power quality and monitoring","year":"1995","originator":"IEEE Standards committee","url":"https://scholargate.app/en/electrical-engineering/power-quality-assessment","markdownUrl":"https://scholargate.app/en/electrical-engineering/power-quality-assessment.md","definition":"Power quality assessment evaluates the suitability of electrical voltage and current waveforms for reliable equipment operation. It measures deviations from ideal sinusoidal waveforms, including voltage sags, swells, harmonics, transients, and imbalance. Comprehensive assessment is critical for ensuring equipment protection, identifying root causes of malfunctions, and optimizing mitigation strategies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"IEEE Standards committee","subfamily":"Power quality and monitoring","year":"1995","type":"Computational pipeline"},"citations":[{"ref":"IEEE Std 1159-2019: IEEE Recommended Practice for Monitoring Electric Power Quality.","type":"standard","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/8832281"},{"ref":"IEC 61000-2-2:2002: Electromagnetic compatibility (EMC) - Part 2-2: Environment - Compatibility levels for low-frequency conducted disturbances and signalling in public low-voltage power supply systems.","type":"standard","doi":null,"isbn":null,"url":"https://webstore.iec.ch"},{"ref":"Dugan, R. C., McGranaghan, M. F., Santoso, S., & Beaty, H. W. (2012). Electrical Power Systems Quality (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://www.mheducation.com"}],"related":["harmonic-distortion-analysis","power-flow-analysis","load-forecasting","smart-grid-state-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-spectral-density","name":"Power Spectral Density Estimation","fullName":"Power Spectral Density (PSD) Estimation Methods","aliases":["PSD Estimation","Spectral Density Analysis","Power Spectrum Estimation"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Spectral estimation","year":"1967","originator":"Peter Welch","url":"https://scholargate.app/en/signal-processing/power-spectral-density","markdownUrl":"https://scholargate.app/en/signal-processing/power-spectral-density.md","definition":"Power Spectral Density (PSD) estimation is a set of methods for determining how the power of a signal is distributed across different frequencies. Proposed by Peter Welch in 1967, PSD estimation techniques are fundamental to frequency domain signal analysis, providing insights into the frequency composition of signals for applications ranging from communications to biomedical monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Welch","subfamily":"Spectral estimation","year":"1967","type":"Frequency domain signal analysis"},"citations":[{"ref":"Welch, P. (1967). The Use of Fast Fourier Transform for Estimation of Power Spectra: A Method Based on Time Averaging over Short, Modified Periodograms. IEEE Transactions on Audio and Electroacoustics, 15(2), 70–73.","type":"article","doi":"10.1109/TAU.1967.1161901","isbn":null,"url":null},{"ref":"Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/discretetimesignalprocessing"}],"related":["short-time-fourier-transform","wiener-filter","matched-filter","adaptive-lms-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"power-system-state-estimation","name":"Power System State Estimation","fullName":"Power System State Estimation via Weighted Least Squares","aliases":["PSSE","WLS State Estimation","Power Flow State Estimation"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Estimation and filtering","year":"1970","originator":"Fred Schweppe","url":"https://scholargate.app/en/electrical-engineering/power-system-state-estimation","markdownUrl":"https://scholargate.app/en/electrical-engineering/power-system-state-estimation.md","definition":"Power System State Estimation (PSSE) is a real-time algorithm that estimates the voltage and phase angle at every bus in a power grid from a set of noisy, redundant measurements. Introduced by Schweppe in 1970, it combines measurements (power flows, voltage magnitudes) with the physical power flow model to produce the most likely system state. State estimation is the foundation of modern grid control centers, providing operators with an accurate digital representation of the actual network.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred Schweppe","subfamily":"Estimation and filtering","year":"1970","type":"Real-time state estimation using measurements and physical models"},"citations":[{"ref":"Schweppe, F. C., & Wildes, J. (1970). Power system static-state estimation: III system implementation. IEEE Transactions on Power Apparatus and Systems, 89(1), 120-125.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+system+static-state+estimation%3A+III+system+implementation+Schweppe"},{"ref":"Abur, A., & Expósito, A. G. (2004). Power System State Estimation: Theory and Implementation. Marcel Dekker.","type":"book","doi":"10.1201/9780203913673","isbn":null,"url":null},{"ref":"Primadianto, A., & Lu, C. N. (2017). A review of distribution system state estimation. IEEE Transactions on Power Systems, 32(5), 3859-3869.","type":"article","doi":"10.1109/tpwrs.2016.2632156","isbn":null,"url":null}],"related":["newton-raphson-power-flow","optimal-power-flow","unit-commitment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ppi-network-topology","name":"PPI Network Topology","fullName":"Protein-Protein Interaction Network Analysis and Topology","aliases":["protein interaction networks","interactome analysis","network topology"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Systems biology","year":"2000","originator":"Peter Uetz","url":"https://scholargate.app/en/bioinformatics/ppi-network-topology","markdownUrl":"https://scholargate.app/en/bioinformatics/ppi-network-topology.md","definition":"Protein-protein interaction network analysis identifies and characterizes the structural properties of cellular interaction networks. Pioneered by Uetz and colleagues through large-scale yeast two-hybrid screening, this approach reveals topological features like hubs, modules, and motifs that encode functional organization and disease associations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Uetz","subfamily":"Systems biology","year":"2000","type":"Network analysis pipeline"},"citations":[{"ref":"Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., ... & Lomax, J. (2000). A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 403(6770), 623-627.","type":"article","doi":"10.1038/35001009","isbn":null,"url":null},{"ref":"Barabási, A. L. & Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2), 101-113.","type":"article","doi":"10.1038/nrg1272","isbn":null,"url":null},{"ref":"Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., ... & Mering, C. V. (2021). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 49(D1), D605-D612.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=STRING+v11%3A+protein-protein+association+networks+with+increased+coverage%2C+supporting+functional+discovery+in+genome-wide+experimental+datasets+Szklarczyk"}],"related":["cryo-em-reconstruction","hmmer-profile-search","molecular-docking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-ab-design","name":"Pragmatic AB Design","fullName":"Pragmatic AB Single-Case Experimental Design","aliases":["pragmatic single-case AB design","real-world AB design","AB phase design","naturalistic AB design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (AB single-case design); pragmatic framing formalized ~2000s–2010s","originator":"Rooted in applied behavior analysis (Baer, Wolf, Risley, 1968); pragmatic framing developed across clinical and educational single-case research traditions","url":"https://scholargate.app/en/experimental-design/pragmatic-ab-design","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-ab-design.md","definition":"The Pragmatic AB Design is a single-case experimental design that collects repeated measurements of one individual or unit across two consecutive phases: a baseline phase (A) with no intervention, followed by an intervention phase (B). Deployed in real-world, clinically feasible conditions rather than tightly controlled laboratory settings, it is widely used in behavioral health, rehabilitation, education, and applied psychology to generate actionable evidence about individual-level treatment effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in applied behavior analysis (Baer, Wolf, Risley, 1968); pragmatic framing developed across clinical and educational single-case research traditions","year":"1968 (AB single-case design); pragmatic framing formalized ~2000s–2010s","type":"Single-case experimental design","dataType":"Repeated measures of a single participant or unit across baseline (A) and intervention (B) phases","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Tate, R. L., Perdices, M., Rosenkoetter, U., McDonald, S., Togher, L., Shadish, W., Horner, R., Kratochwill, T., Barlow, D. H., Kazdin, A., Sampson, M., Shamseer, L., & Vohra, S. (2016). The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 Statement. Physical Therapy, 96(7), e1–e10.","type":"article","doi":"10.2522/ptj.2016.96.7.e1","isbn":null,"url":null}],"related":["ab-design","aba-design","abab-design","multiple-baseline-design","pragmatic-trial","single-case-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-ab-test","name":"Pragmatic A/B Test","fullName":"Pragmatic A/B Testing (Pragmatic Randomized Experiment)","aliases":["pragmatic split test","real-world A/B experiment","pragmatic online experiment","pragmatic controlled experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Experimental design","year":"1967 (pragmatic framing); 2007–2012 (large-scale tech A/B testing practice)","originator":"Pragmatic trial framing: Schwartz & Lellouch (1967); A/B testing in technology: Ron Kohavi and colleagues at Microsoft (~2007–2012)","url":"https://scholargate.app/en/experimental-design/pragmatic-ab-test","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-ab-test.md","definition":"A pragmatic A/B test is a randomized comparative experiment that evaluates two alternatives — a control (A) and a treatment (B) — under real-world operating conditions rather than tightly controlled laboratory settings. Rooted in the pragmatic-versus-explanatory trial distinction introduced by Schwartz and Lellouch in 1967 and brought to large-scale practice by online experimentation teams at Microsoft, Google, and Amazon, it prioritizes decision-relevant effectiveness over internal mechanistic explanation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pragmatic trial framing: Schwartz & Lellouch (1967); A/B testing in technology: Ron Kohavi and colleagues at Microsoft (~2007–2012)","year":"1967 (pragmatic framing); 2007–2012 (large-scale tech A/B testing practice)","type":"Randomized comparative experiment","dataType":"Metric outcomes (click rates, conversion rates, revenue, task completion, clinical endpoints)","subfamily":"Experimental design"},"citations":[{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null},{"ref":"Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1108724265","url":null}],"related":["ab-testing","randomized-controlled-trial","pragmatic-trial","factorial-design","multivariate-testing","quasi-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-aba-design","name":"Pragmatic ABA Design","fullName":"Pragmatic ABA Reversal Single-Subject Experimental Design","aliases":["pragmatic reversal design","naturalistic ABA design","real-world ABA reversal design","pragmatic withdrawal design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (ABA base); pragmatic adaptation in applied behavioral research from 1970s onward","originator":"ABA reversal design: Baer, Wolf & Risley (1968); pragmatic orientation: Schwartz & Lellouch (1967)","url":"https://scholargate.app/en/experimental-design/pragmatic-aba-design","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-aba-design.md","definition":"The Pragmatic ABA Design is a single-subject reversal experiment conducted under real-world, naturalistic conditions rather than tightly controlled laboratory settings. It follows the classic baseline (A1) — intervention (B) — withdrawal/return-to-baseline (A2) sequence while deliberately relaxing control conditions to reflect authentic practice environments. This approach prioritizes external validity and clinical utility, making findings directly applicable to schools, clinics, and community settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"ABA reversal design: Baer, Wolf & Risley (1968); pragmatic orientation: Schwartz & Lellouch (1967)","year":"1968 (ABA base); pragmatic adaptation in applied behavioral research from 1970s onward","type":"Single-subject experimental design with pragmatic orientation","dataType":"Repeated behavioral observations (continuous or interval data) collected in natural settings","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null}],"related":["aba-design","abab-design","multiple-baseline-design","pragmatic-randomized-controlled-trial","single-subject-experimental-design","ab-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-abab-design","name":"Pragmatic ABAB design","fullName":"Pragmatic ABAB Reversal Design","aliases":["pragmatic reversal design","pragmatic withdrawal design","applied ABAB design","pragmatic single-case reversal"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (classic ABAB); pragmatic adaptations formalised 1990s–2000s","originator":"Adapted from Baer, Wolf & Risley (1968); pragmatic variant developed in applied behavior analysis and clinical psychology literature","url":"https://scholargate.app/en/experimental-design/pragmatic-abab-design","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-abab-design.md","definition":"The pragmatic ABAB design is a single-case experimental design that adapts the classic reversal (ABAB) logic to real-world clinical and applied constraints. It alternates between a baseline phase (A) and an intervention phase (B) twice, demonstrating experimental control through repeated phase changes while allowing flexibility — such as abbreviated withdrawals or partial reversals — when full withdrawal of treatment is ethically or practically impossible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from Baer, Wolf & Risley (1968); pragmatic variant developed in applied behavior analysis and clinical psychology literature","year":"1968 (classic ABAB); pragmatic adaptations formalised 1990s–2000s","type":"Single-case experimental design","dataType":"Repeated measures (behavioral or clinical outcomes) over time","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association.","type":"book","doi":null,"isbn":"978-1433810251","url":null}],"related":["abab-reversal-design","multiple-baseline-design","single-case-experimental-design","interrupted-time-series","changing-criterion-design","alternating-treatments-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-adaptive-experiment","name":"Pragmatic adaptive experiment","fullName":"Pragmatic Adaptive Trial Design","aliases":["pragmatic adaptive trial","real-world adaptive trial","PAT","adaptive pragmatic RCT"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2000s–2010s (convergence period)","originator":"Synthesized from pragmatic trial tradition (Schwartz & Lellouch, 1967) and adaptive design methodology; formalized convergence in 2000s–2010s","url":"https://scholargate.app/en/experimental-design/pragmatic-adaptive-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-adaptive-experiment.md","definition":"A pragmatic adaptive experiment is a hybrid clinical trial design that combines the real-world generalizability of pragmatic trials with the statistical flexibility of adaptive designs. It enrolls a broad, representative patient population under routine care conditions, while using pre-specified interim analyses to modify trial parameters — such as sample size, allocation ratios, or arm selection — as outcome data accumulate. The result is a design that is both externally valid and resource-efficient.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from pragmatic trial tradition (Schwartz & Lellouch, 1967) and adaptive design methodology; formalized convergence in 2000s–2010s","year":"2000s–2010s (convergence period)","type":"Hybrid experimental design","dataType":"Clinical outcomes, electronic health records, patient-reported outcomes","subfamily":"Deneysel desen"},"citations":[{"ref":"Pallmann, P., Bedding, A. W., Choodari-Oskooei, B., Dimairo, M., Flight, L., Hampson, L. V., ... & Sydes, M. R. (2018). Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Medicine, 16(1), 29.","type":"article","doi":"10.1186/s12916-018-1017-7","isbn":null,"url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["adaptive-trial-design","randomized-controlled-trial","platform-trial","stepped-wedge-design","cluster-randomized-trial","bayesian-adaptive-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-case-control-study","name":"Pragmatic case-control study","fullName":"Pragmatic Case-Control Study","aliases":["real-world case-control study","pragmatic case-control design","effectiveness case-control study","PCCS"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1950s–1960s (classical); pragmatic framing 1967–2000s","originator":"Evolved from classical case-control methodology (Dorn, 1954; Cornfield, 1956); pragmatic framing formalized by Schwartz & Lellouch (1967)","url":"https://scholargate.app/en/epidemiology/pragmatic-case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-case-control-study.md","definition":"A pragmatic case-control study is an observational design that compares individuals who have developed a disease or outcome (cases) with those who have not (controls), using data collected under routine real-world conditions rather than strictly controlled experimental settings. Exposure histories are reconstructed from clinical records, registries, or administrative databases. The design is chosen when a conventional explanatory case-control study would be impractical, unethical, or too narrow to inform actual clinical or public-health decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from classical case-control methodology (Dorn, 1954; Cornfield, 1956); pragmatic framing formalized by Schwartz & Lellouch (1967)","year":"1950s–1960s (classical); pragmatic framing 1967–2000s","type":"Observational epidemiological study design","dataType":"Routine clinical records, administrative databases, registry data, electronic health records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., Tunis, S., Bergel, E., Harvey, I., Magid, D. J., & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["case-control-study","nested-case-control","pragmatic-randomized-clinical-trial","retrospective-case-control-study","matched-case-control-study","cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-case-series","name":"Pragmatic case series","fullName":"Pragmatic Case Series Study","aliases":["real-world case series","pragmatic observational case series","practice-based case series"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Pragmatic framing formalized 1967; case series practice predates 20th century","originator":"Pragmatic framework: Schwartz & Lellouch (1967); case series design: longstanding clinical tradition","url":"https://scholargate.app/en/epidemiology/pragmatic-case-series","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-case-series.md","definition":"A pragmatic case series is an observational study that documents consecutive or purposively selected patients receiving a clinical intervention or presenting with a condition under routine, real-world practice conditions — without randomization, a control group, or the highly controlled eligibility criteria characteristic of explanatory trials. It is used to describe treatment patterns, outcomes, and adverse events as they occur in everyday clinical settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pragmatic framework: Schwartz & Lellouch (1967); case series design: longstanding clinical tradition","year":"Pragmatic framing formalized 1967; case series practice predates 20th century","type":"Observational descriptive study","dataType":"Clinical records, patient-reported outcomes, routinely collected real-world data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null},{"ref":"van Walraven, C., & Davis, D. A. (2007). Case series and case report. In: Knottnerus JA, Buntinx F (eds). The Evidence Base of Clinical Diagnosis. 2nd ed. BMJ Books.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Case+series+case+report+Knottnerus+Buntinx+Evidence+Base+Clinical+Diagnosis"}],"related":["case-series","pragmatic-randomized-clinical-trial","prospective-case-series","retrospective-case-series","cohort-study","case-report"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-clinical-trial","name":"Pragmatic Clinical Trial","fullName":"Pragmatic Randomized Controlled Trial (Pragmatic RCT)","aliases":["pragmatic trial","real-world trial","effectiveness trial","PRECIS-2"],"domain":"clinical-research","family":"process-pipeline","subfamily":"trial design","year":"2009-2015","originator":"Thorpe et al. (2009); PRECIS framework developed by international consortia","url":"https://scholargate.app/en/clinical-research/pragmatic-clinical-trial","markdownUrl":"https://scholargate.app/en/clinical-research/pragmatic-clinical-trial.md","definition":"A pragmatic trial is designed to evaluate the real-world effectiveness of an intervention in typical clinical settings with diverse, representative patients, minimal exclusion criteria, and clinically relevant outcomes. Developed by Thorpe and colleagues (2009) and formalized via the PRECIS-2 framework (2015), pragmatic trials bridge the gap between explanatory efficacy trials (conducted in controlled research settings) and implementation science, answering the question 'Does this work in actual clinical practice?' rather than 'Can this work under ideal conditions?'","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thorpe et al. (2009); PRECIS framework developed by international consortia","subfamily":"trial design","year":"2009-2015","type":"Research Design"},"citations":[{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Tugwell, P. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. CMAJ, 180(10), E47–E57.","type":"article","doi":"10.1503/cmaj.090523","isbn":null,"url":null},{"ref":"Loudon, K., Treweek, S., Sullivan, F., Donnan, P., Thorpe, K. E., & Zwarenstein, M. (2015). The PRECIS-2 tool: designing trials that are fit for purpose. BMJ, 350, h2147.","type":"article","doi":"10.1136/bmj.h2147","isbn":null,"url":null},{"ref":"Glasziou, P., Altman, D. G., Bossuyt, P., Boutron, I., Clarke, M., Julious, S., ... & Moher, D. (2018). Reducing waste from incomplete or unusable reports of biomedical research. The Lancet, 383(9913), 267–276.","type":"article","doi":"10.1016/S0140-6736(13)62228-X","isbn":null,"url":null}],"related":["randomized-controlled-trial","real-world-evidence","cluster-randomized-trial","implementation-science","adaptive-trial-design"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-control-group-experimental-design","name":"Pragmatic control group experimental design","fullName":"Pragmatic Control Group Experimental Design","aliases":["pragmatic controlled trial","effectiveness trial with control group","real-world control group design","pragmatic comparative design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1967 (seminal distinction); 2009 (PRECIS operationalization)","originator":"Schwartz & Lellouch (pragmatic vs explanatory distinction); extended by PRECIS framework (Thorpe et al.)","url":"https://scholargate.app/en/experimental-design/pragmatic-control-group-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-control-group-experimental-design.md","definition":"A pragmatic control group experimental design tests whether an intervention works under routine, real-world conditions by comparing it against a control condition — typically usual care or an active comparator — rather than a tightly controlled placebo. It prioritises external validity and applicability over the internal purity of an explanatory efficacy trial, asking whether an intervention makes a meaningful difference to people as they are actually treated in practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schwartz & Lellouch (pragmatic vs explanatory distinction); extended by PRECIS framework (Thorpe et al.)","year":"1967 (seminal distinction); 2009 (PRECIS operationalization)","type":"Experimental design (pragmatic variant)","dataType":"Continuous, categorical, or ordinal outcome measures from real-world participants","subfamily":"Deneysel desen"},"citations":[{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["pragmatic-randomized-controlled-trial","control-group-experimental-design","cluster-randomized-controlled-trial","factorial-control-group-experimental-design","crossover-control-group-experimental-design","pretest-posttest-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-cross-sectional-epidemiological-study","name":"Pragmatic Cross-Sectional Epidemiological Study","fullName":"Pragmatic Cross-Sectional Epidemiological Study","aliases":["pragmatic cross-sectional survey","real-world cross-sectional study","observational cross-sectional study","prevalence survey"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-20th century onwards; pragmatic framing prominent from 1967","originator":"Classical epidemiology tradition; pragmatic framing refined by Schwartz & Lellouch (1967) and subsequent real-world evidence literature","url":"https://scholargate.app/en/epidemiology/pragmatic-cross-sectional-epidemiological-study","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-cross-sectional-epidemiological-study.md","definition":"A pragmatic cross-sectional epidemiological study measures the prevalence of exposures, outcomes, and risk factors in a defined population at a single point in time, conducted under real-world conditions rather than tightly controlled experimental settings. It provides a snapshot of the health status of a community or patient group, making it one of the most widely used designs for surveillance, needs assessment, and hypothesis generation in clinical and public-health epidemiology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical epidemiology tradition; pragmatic framing refined by Schwartz & Lellouch (1967) and subsequent real-world evidence literature","year":"Mid-20th century onwards; pragmatic framing prominent from 1967","type":"Observational epidemiological design","dataType":"Cross-sectional survey data, administrative records, clinical registry data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Cross-sectional study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Cross-sectional_study"}],"related":["cohort-study","case-control-study","ecological-study","cluster-sampling","systematic-review","prevalence-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-diagnostic-accuracy-study","name":"Pragmatic diagnostic accuracy study","fullName":"Pragmatic Diagnostic Accuracy Study","aliases":["real-world diagnostic accuracy study","pragmatic DAS","routine-care diagnostic study","pragmatic test evaluation"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s–2010s (formalized alongside STARD reporting guidelines)","originator":"Evolved from STARD initiative (Bossuyt et al.) and pragmatic trial framework (Schwartz & Lellouch, 1967)","url":"https://scholargate.app/en/epidemiology/pragmatic-diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-diagnostic-accuracy-study.md","definition":"A pragmatic diagnostic accuracy study evaluates how well a diagnostic test performs under real-world clinical conditions — not in idealized, tightly controlled settings. Conducted within routine care workflows, it measures sensitivity, specificity, predictive values, and likelihood ratios for an index test against a reference standard, yielding accuracy estimates directly applicable to clinical practice rather than laboratory benchmarks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from STARD initiative (Bossuyt et al.) and pragmatic trial framework (Schwartz & Lellouch, 1967)","year":"2000s–2010s (formalized alongside STARD reporting guidelines)","type":"Observational diagnostic study design","dataType":"Patient-level clinical data from routine care settings; index test and reference standard results","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bossuyt, P. M., et al. (2015). STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. BMJ, 351, h5527.","type":"article","doi":"10.1136/bmj.h5527","isbn":null,"url":null},{"ref":"Schilling, I., & Burchardt, M. (2018). Pragmatic diagnostic accuracy studies: bridging the gap between explanatory trials and routine practice. Diagnostic and Prognostic Research, 2(1), 14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pragmatic+diagnostic+accuracy+studies+bridging+gap+explanatory+trials+routine+practice"}],"related":["diagnostic-accuracy-study","pragmatic-randomized-clinical-trial","cross-sectional-epidemiological-study","screening-test-evaluation","cohort-study","prospective-diagnostic-accuracy-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-dose-response-analysis","name":"Pragmatic Dose-Response Analysis","fullName":"Pragmatic Dose-Response Analysis in Epidemiology","aliases":["real-world dose-response analysis","pragmatic exposure-response study","dose-response in pragmatic trials","effectiveness dose-response analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (formalized in pragmatic trial context)","originator":"Rooted in pharmacoepidemiology and pragmatic trial methodology; PRECIS framework by Thorpe et al. (2009)","url":"https://scholargate.app/en/epidemiology/pragmatic-dose-response-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-dose-response-analysis.md","definition":"Pragmatic dose-response analysis quantifies how varying levels of an exposure or treatment relate to clinical outcomes under real-world conditions. By embedding dose-response questions within pragmatic study designs — broad eligibility criteria, routine care settings, and heterogeneous populations — it bridges the gap between controlled pharmacological dose-finding and the messy variability of everyday clinical practice. The approach is especially valued when the goal is to establish or refine optimal dosing guidance from evidence that reflects actual patient populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in pharmacoepidemiology and pragmatic trial methodology; PRECIS framework by Thorpe et al. (2009)","year":"1990s–2000s (formalized in pragmatic trial context)","type":"Observational or experimental quantitative method","dataType":"Continuous or ordinal exposure measurements, clinical outcomes, real-world patient records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301–1309.","type":"article","doi":"10.1093/oxfordjournals.aje.a116237","isbn":null,"url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Schulz, K. F. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["dose-response-analysis","pragmatic-randomized-clinical-trial","cohort-study","pragmatic-cohort-study","survival-analysis","regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-ecological-study","name":"Pragmatic ecological study","fullName":"Pragmatic Ecological Study Design","aliases":["real-world ecological study","effectiveness ecological study","population-level pragmatic study","pragmatic ecologic design"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1967–1982 (pragmatic concept 1967; ecological study formalized ~1982)","originator":"Morgenstern (ecological study framework); Schwartz & Lellouch (pragmatic design concept)","url":"https://scholargate.app/en/epidemiology/pragmatic-ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-ecological-study.md","definition":"A pragmatic ecological study is an observational epidemiological design that examines associations between exposures and outcomes at the population or group level — using routinely collected, real-world data — with the explicit goal of informing practical public health decisions under everyday conditions. Rather than controlling every variable in a laboratory-like manner, it embraces the complexity and heterogeneity of natural settings to answer effectiveness questions relevant to policy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morgenstern (ecological study framework); Schwartz & Lellouch (pragmatic design concept)","year":"1967–1982 (pragmatic concept 1967; ecological study formalized ~1982)","type":"Observational ecological study with pragmatic framing","dataType":"Aggregate population-level data (routinely collected, administrative, or surveillance data)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344.","type":"article","doi":"10.2105/ajph.72.12.1336","isbn":null,"url":null},{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null}],"related":["ecological-study","pragmatic-cohort-study","pragmatic-randomized-clinical-trial","cross-sectional-epidemiological-study","cohort-study","dose-response-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-factorial-experiment","name":"Pragmatic Factorial Experiment","fullName":"Pragmatic Factorial Experimental Design","aliases":["pragmatic factorial trial","pragmatic factorial RCT","real-world factorial design","PFE"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"2000s–2010s (formal integration)","originator":"Synthesized from pragmatic trial methodology (Schwartz & Lellouch, 1967) and factorial design principles (Fisher, 1935); formalized in clinical research contexts in the 2000s–2010s","url":"https://scholargate.app/en/experimental-design/pragmatic-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-factorial-experiment.md","definition":"A pragmatic factorial experiment combines two powerful methodological frameworks: the factorial experimental design — which tests multiple intervention components simultaneously — and the pragmatic trial orientation, which prioritizes real-world applicability, broad eligibility criteria, and flexible delivery conditions. The result is a design that efficiently evaluates which components of a complex intervention work, and whether they interact, while maintaining ecological validity for health, behavioral, and educational research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from pragmatic trial methodology (Schwartz & Lellouch, 1967) and factorial design principles (Fisher, 1935); formalized in clinical research contexts in the 2000s–2010s","year":"2000s–2010s (formal integration)","type":"Experimental trial design","dataType":"Quantitative outcome data; continuous, binary, or count outcomes from real-world participants","subfamily":"Deneysel desen"},"citations":[{"ref":"Loudon, K., Treweek, S., Sullivan, F., Donnan, P., Thorpe, K. E., & Zwarenstein, M. (2015). The PRECIS-2 tool: designing trials that are fit for purpose. BMJ, 350, h2147.","type":"article","doi":"10.1136/bmj.h2147","isbn":null,"url":null},{"ref":"Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART): New Methods for More Potent eHealth Interventions. American Journal of Preventive Medicine, 32(5 Suppl), S112–S118.","type":"article","doi":"10.1016/j.amepre.2007.01.022","isbn":null,"url":null}],"related":["factorial-design","randomized-controlled-trial","pragmatic-trial","multiphase-optimization-strategy","cluster-randomized-trial","stepped-wedge-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-field-experiment","name":"Pragmatic Field Experiment","fullName":"Pragmatic Field Experiment","aliases":["pragmatic effectiveness trial","real-world field experiment","effectiveness field trial","practical field study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1967 (pragmatic framing); 2009 (PRECIS tool)","originator":"Schwartz & Lellouch (pragmatic framing); formalized for practice through PRECIS framework (Thorpe et al.)","url":"https://scholargate.app/en/experimental-design/pragmatic-field-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-field-experiment.md","definition":"A pragmatic field experiment tests whether an intervention works under real-world, routine conditions rather than under the tightly controlled settings of a laboratory or explanatory trial. It combines the pragmatic trial philosophy — prioritising external validity and decision-relevance — with field experimentation, so findings directly inform policy and practice. The design is positioned toward the pragmatic end of the PRECIS continuum and is widely used in public health, education, agriculture, and behavioral economics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schwartz & Lellouch (pragmatic framing); formalized for practice through PRECIS framework (Thorpe et al.)","year":"1967 (pragmatic framing); 2009 (PRECIS tool)","type":"Experimental design","dataType":"Continuous, binary, or count outcome data collected in natural field settings","subfamily":"Deneysel desen"},"citations":[{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., Tunis, S., Bergel, E., Harvey, I., Magid, D. J., & Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["field-experiment","pragmatic-randomized-controlled-trial","natural-experiment","cluster-randomized-field-experiment","adaptive-field-experiment","randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-fractional-factorial-experiment","name":"Pragmatic Fractional Factorial Experiment","fullName":"Pragmatic Fractional Factorial Experiment","aliases":["pragmatic FFE","fractional factorial trial","pragmatic factorial design","FFD in pragmatic settings"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Fractional factorial designs: 1940s–1950s; pragmatic application: 2000s–2010s","originator":"Building on Fisher (1935); pragmatic adaptation by Collins, Murphy & Strecher (2007) via MOST framework","url":"https://scholargate.app/en/experimental-design/pragmatic-fractional-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-fractional-factorial-experiment.md","definition":"A pragmatic fractional factorial experiment applies fractional factorial design principles to real-world or clinical intervention research, enabling simultaneous evaluation of multiple intervention components in a resource-efficient fraction of the full factorial runs. Popularised through the Multiphase Optimization Strategy (MOST), it identifies which components of a multi-component intervention contribute meaningfully to outcomes before a confirmatory randomized trial is conducted.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Fisher (1935); pragmatic adaptation by Collins, Murphy & Strecher (2007) via MOST framework","year":"Fractional factorial designs: 1940s–1950s; pragmatic application: 2000s–2010s","type":"Experimental design","dataType":"Continuous, binary, or count outcome data from randomized participants","subfamily":"Deneysel desen"},"citations":[{"ref":"Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32(5S), S112–S118.","type":"article","doi":"10.1016/j.amepre.2007.01.022","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["full-factorial-experiment","randomized-controlled-trial","multiphase-optimization-strategy","response-surface-methodology","latin-square-design","sequential-multiple-assignment-randomized-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-full-factorial-experiment","name":"Pragmatic Full Factorial Experiment","fullName":"Pragmatic Full Factorial Experimental Design","aliases":["pragmatic factorial trial","real-world full factorial design","effectiveness full factorial experiment","pragmatic 2^k experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1920s (factorial); 1967/2009 (pragmatic framework)","originator":"Full factorial: R.A. Fisher (1920s); Pragmatic framing: Schwartz & Lellouch (1967), formalized by Thorpe et al. (2009)","url":"https://scholargate.app/en/experimental-design/pragmatic-full-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-full-factorial-experiment.md","definition":"A pragmatic full factorial experiment combines the complete crossing of all factor levels (the full factorial structure) with the broad eligibility criteria, flexible delivery, and real-world conditions of a pragmatic trial. Every possible combination of factors is tested simultaneously, yielding both main effects and all interaction effects, while deliberately relaxing strict laboratory controls to reflect how interventions actually operate in practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Full factorial: R.A. Fisher (1920s); Pragmatic framing: Schwartz & Lellouch (1967), formalized by Thorpe et al. (2009)","year":"1920s (factorial); 1967/2009 (pragmatic framework)","type":"Experimental design","dataType":"Continuous, ordinal, or binary outcome data from real-world settings","subfamily":"Deneysel desen"},"citations":[{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalmers, I. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["full-factorial-experiment","pragmatic-randomized-controlled-trial","factorial-randomized-controlled-trial","fractional-factorial-experiment","pragmatic-factorial-experiment","cluster-randomized-factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-kaplan-meier-analysis","name":"Pragmatic Kaplan-Meier analysis","fullName":"Pragmatic Kaplan-Meier Survival Analysis","aliases":["pragmatic KM analysis","real-world Kaplan-Meier","pragmatic survival curve estimation","KM analysis in pragmatic trials"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1958 (estimator); pragmatic application formalized 1967 onward","originator":"Kaplan & Meier (estimator, 1958); Schwartz & Lellouch (pragmatic trial framework, 1967)","url":"https://scholargate.app/en/epidemiology/pragmatic-kaplan-meier-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-kaplan-meier-analysis.md","definition":"Pragmatic Kaplan-Meier analysis applies the non-parametric Kaplan-Meier product-limit estimator to time-to-event data collected under real-world or pragmatic conditions — diverse populations, routine clinical care, minimal exclusions, and standard-of-care comparators. Unlike explanatory trials designed to isolate a treatment effect under ideal conditions, pragmatic designs accept real-world heterogeneity, and the resulting survival curves reflect the effectiveness of an intervention as it actually performs in clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaplan & Meier (estimator, 1958); Schwartz & Lellouch (pragmatic trial framework, 1967)","year":"1958 (estimator); pragmatic application formalized 1967 onward","type":"Non-parametric survival estimator within pragmatic study design","dataType":"Time-to-event data with censoring from real-world or pragmatic clinical settings","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.","type":"article","doi":"10.1080/01621459.1958.10501452","isbn":null,"url":null},{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null}],"related":["kaplan-meier-analysis","pragmatic-randomized-clinical-trial","survival-analysis","cox-proportional-hazards","competing-risks-analysis","log-rank-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-laboratory-experiment","name":"Pragmatic Laboratory Experiment","fullName":"Pragmatic Laboratory Experiment","aliases":["pragmatic experiment","applied laboratory trial","practice-oriented lab experiment","pragmatic controlled experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1967 (foundational distinction); 2009 (PRECIS operationalization)","originator":"Schwartz & Lellouch (pragmatic–explanatory distinction); extended by PRECIS framework developers","url":"https://scholargate.app/en/experimental-design/pragmatic-laboratory-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-laboratory-experiment.md","definition":"A pragmatic laboratory experiment is a controlled study conducted in a laboratory setting that prioritises external validity and real-world applicability over the stringent internal controls characteristic of purely explanatory experiments. Drawing on the pragmatic–explanatory continuum formalised by Schwartz and Lellouch (1967) and later operationalised in the PRECIS framework, it asks whether an intervention works under conditions that approximate actual practice rather than ideal circumstances, making findings directly actionable for decision-makers and practitioners.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schwartz & Lellouch (pragmatic–explanatory distinction); extended by PRECIS framework developers","year":"1967 (foundational distinction); 2009 (PRECIS operationalization)","type":"Experimental design philosophy and study type","dataType":"Quantitative outcome data collected under real-world or near-real-world laboratory conditions","subfamily":"Deneysel desen"},"citations":[{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["randomized-controlled-trial","factorial-design","quasi-experimental-design","explanatory-trial","field-experiment","pilot-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-mixed-methods-design","name":"Pragmatic Mixed Methods Design","fullName":"Pragmatic Mixed Methods Research Design","aliases":["pragmatic MMR","pragmatism-guided mixed methods","pragmatic inquiry design","practical mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"Early 2000s (formalised); pragmatism as philosophy late 19th–early 20th century","originator":"John W. Creswell & Vicki L. Plano Clark (formalised); philosophical grounding in William James, John Dewey, Richard Rorty","url":"https://scholargate.app/en/research-design/pragmatic-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/pragmatic-mixed-methods-design.md","definition":"Pragmatic mixed methods design is a research approach that selects and combines quantitative and qualitative methods based on what best answers the research question, rather than adhering to a single philosophical paradigm. Rooted in the philosophical tradition of pragmatism — associated with William James, John Dewey, and later Richard Rorty — it treats methodological fit and practical utility as the primary criteria for design decisions. The approach is endorsed by leading mixed methods scholars including Creswell and Plano Clark as the most common philosophical worldview underpinning mixed methods work.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark (formalised); philosophical grounding in William James, John Dewey, Richard Rorty","year":"Early 2000s (formalised); pragmatism as philosophy late 19th–early 20th century","type":"Mixed methods research design","dataType":"Quantitative data, qualitative data, or both collected in any sequence","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Morgan, D. L. (2007). Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and quantitative methods. Journal of Mixed Methods Research, 1(1), 48–76.","type":"article","doi":"10.1177/2345678906292462","isbn":null,"url":null}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","multiphase-mixed-methods-design","transformative-mixed-methods-design","concurrent-embedded-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-multi-arm-experiment","name":"Pragmatic Multi-Arm Experiment","fullName":"Pragmatic Multi-Arm Randomized Experiment","aliases":["pragmatic multi-arm trial","multi-arm pragmatic RCT","pragmatic multi-treatment experiment","PMAT"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1967 (pragmatic trial concept); multi-arm extensions 1990s–2000s","originator":"Schwartz & Lellouch (pragmatic framing); extended to multi-arm settings in clinical and health services research","url":"https://scholargate.app/en/experimental-design/pragmatic-multi-arm-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-multi-arm-experiment.md","definition":"A pragmatic multi-arm experiment is an experimental design that simultaneously compares three or more interventions (arms) under real-world conditions rather than tightly controlled laboratory settings. It combines the broad eligibility, flexible delivery, and effectiveness orientation of pragmatic trials with the statistical efficiency of multi-arm structures, allowing researchers to evaluate multiple treatments or treatment variants against each other or a control within a single study, minimizing the resources and time required relative to running separate pairwise trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schwartz & Lellouch (pragmatic framing); extended to multi-arm settings in clinical and health services research","year":"1967 (pragmatic trial concept); multi-arm extensions 1990s–2000s","type":"Experimental design","dataType":"Continuous, categorical, or binary outcome data collected under routine real-world conditions","subfamily":"Deneysel desen"},"citations":[{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null},{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Clinical Epidemiology, 20(8), 637-648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null}],"related":["pragmatic-randomized-controlled-trial","multi-arm-experiment","adaptive-multi-arm-experiment","cluster-randomized-multi-arm-experiment","factorial-multi-arm-experiment","pragmatic-adaptive-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-multiple-baseline-design","name":"Pragmatic Multiple Baseline Design","fullName":"Pragmatic Multiple Baseline Design","aliases":["PMBD","pragmatic MBD","real-world multiple baseline design","flexible multiple baseline design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (original MBD); pragmatic adaptation formalized in 2000s–2010s","originator":"Adapted from Baer, Wolf & Risley (1968); pragmatic variant developed within single-case methodology community","url":"https://scholargate.app/en/experimental-design/pragmatic-multiple-baseline-design","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-multiple-baseline-design.md","definition":"The Pragmatic Multiple Baseline Design is a single-case experimental design that staggers intervention introduction across multiple participants, settings, or behaviors in real-world conditions where strict experimental control is impractical. By relaxing some idealized constraints — such as perfectly stable baselines or rigid staggering timelines — it preserves the core logic of the multiple baseline while accommodating clinical, educational, or community realities. It is especially valued when withholding treatment for ethical reasons is untenable and when practitioners need evidence from naturalistic settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from Baer, Wolf & Risley (1968); pragmatic variant developed within single-case methodology community","year":"1968 (original MBD); pragmatic adaptation formalized in 2000s–2010s","type":"Single-case experimental design variant","dataType":"Repeated-measures behavioral or outcome data across participants, settings, or behaviors","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Shadish, W. R., & Sullivan, K. J. (2011). Characteristics of single-case designs used to assess intervention effects in 2008. Behavior Research Methods, 43(4), 971–980.","type":"article","doi":"10.3758/s13428-011-0111-y","isbn":null,"url":null}],"related":["multiple-baseline-design","single-case-experimental-design","reversal-design","randomized-controlled-trial","stepped-wedge-design","interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-nested-case-control","name":"Pragmatic nested case-control","fullName":"Pragmatic Nested Case-Control Study","aliases":["real-world nested case-control","pragmatic NCC","nested case-control in routine data","real-world evidence nested case-control"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1977 (nested case-control); pragmatic variant emerged in real-world evidence research from 1990s onwards","originator":"Duncan Thomas (nested case-control); pragmatic design concept from Schwartz & Lellouch (1967)","url":"https://scholargate.app/en/epidemiology/pragmatic-nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-nested-case-control.md","definition":"A pragmatic nested case-control study embeds a case-control analysis within a pre-existing real-world cohort — typically drawn from electronic health records, administrative claims, or disease registries — to examine associations between exposures and outcomes under routine clinical conditions. Controls are sampled from the risk set (those still at risk at the time each case occurs), preserving temporal sequence while dramatically reducing data-collection costs compared with a full cohort analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Duncan Thomas (nested case-control); pragmatic design concept from Schwartz & Lellouch (1967)","year":"1977 (nested case-control); pragmatic variant emerged in real-world evidence research from 1990s onwards","type":"Observational epidemiological study design","dataType":"Routine healthcare data, electronic health records, administrative databases, registry data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thomas+1977+nested+case-control+cohort+analysis"},{"ref":"Schneeweiss, S., & Avorn, J. (2005). A review of uses of health care utilization databases for epidemiologic research on therapeutics. Journal of Clinical Epidemiology, 58(4), 323–337.","type":"article","doi":"10.1016/j.jclinepi.2004.10.012","isbn":null,"url":null}],"related":["nested-case-control","case-control-study","cohort-study","pragmatic-cohort-study","pragmatic-randomized-clinical-trial","retrospective-nested-case-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-phase-ii-clinical-trial","name":"Pragmatic phase II clinical trial","fullName":"Pragmatic Phase II Clinical Trial","aliases":["pragmatic Phase II trial","real-world Phase II trial","Phase II pragmatic RCT","Phase IIb pragmatic trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Pragmatic framework: 1967; Phase II application: 1990s–2000s","originator":"Conceptual basis: Daniel Schwartz & Joseph Lellouch (pragmatic vs. explanatory distinction, 1967); applied to Phase II context by drug developers and trialists from the 1990s onward","url":"https://scholargate.app/en/epidemiology/pragmatic-phase-ii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-phase-ii-clinical-trial.md","definition":"A pragmatic Phase II clinical trial is an early-to-mid-stage interventional study that evaluates a new treatment's preliminary efficacy and safety under conditions that approximate real-world clinical practice rather than tightly controlled experimental settings. It sits between pure explanatory Phase II trials and large pragmatic Phase III confirmatory trials, prioritising practical feasibility and clinical relevance while still generating the signal needed to justify further development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conceptual basis: Daniel Schwartz & Joseph Lellouch (pragmatic vs. explanatory distinction, 1967); applied to Phase II context by drug developers and trialists from the 1990s onward","year":"Pragmatic framework: 1967; Phase II application: 1990s–2000s","type":"Interventional study design","dataType":"Clinical outcomes, patient-reported outcomes, routinely collected health data, biomarker data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["phase-ii-clinical-trial","pragmatic-randomized-clinical-trial","adaptive-phase-ii-clinical-trial","pragmatic-phase-iii-clinical-trial","phase-i-clinical-trial","randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-phase-iii-clinical-trial","name":"Pragmatic phase III clinical trial","fullName":"Pragmatic Phase III Randomized Controlled Trial","aliases":["pragmatic RCT","effectiveness trial","real-world RCT","pragmatic trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1967 (Schwartz & Lellouch); formalized further in 2000s–2010s","originator":"Schwartz & Lellouch (distinction between pragmatic and explanatory trials)","url":"https://scholargate.app/en/epidemiology/pragmatic-phase-iii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-phase-iii-clinical-trial.md","definition":"A pragmatic phase III clinical trial is a large-scale randomized study designed to evaluate whether an intervention works under the conditions of everyday clinical practice rather than the tightly controlled environment of an explanatory efficacy trial. It recruits a broad, representative patient population, allows flexibility in treatment delivery, and measures outcomes that matter to patients and health systems, generating evidence directly applicable to real-world treatment decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schwartz & Lellouch (distinction between pragmatic and explanatory trials)","year":"1967 (Schwartz & Lellouch); formalized further in 2000s–2010s","type":"Randomized controlled trial design","dataType":"Patient-level clinical outcome data from routine or near-routine care settings","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null},{"ref":"Ford, I., & Norrie, J. (2016). Pragmatic trials. New England Journal of Medicine, 375(5), 454–463.","type":"article","doi":"10.1056/NEJMra1510059","isbn":null,"url":null}],"related":["randomized-controlled-trial","explanatory-clinical-trial","cluster-randomized-trial","adaptive-clinical-trial","intention-to-treat-analysis","real-world-evidence-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-phase-iv-study","name":"Pragmatic phase IV study","fullName":"Pragmatic Phase IV Post-Marketing Study","aliases":["pragmatic post-marketing study","real-world phase IV trial","pragmatic pharmacovigilance study","pragmatic post-approval study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1967 (pragmatic concept); 2000s (pragmatic Phase IV formalized)","originator":"Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); PRECIS framework by Thorpe et al. (2009)","url":"https://scholargate.app/en/epidemiology/pragmatic-phase-iv-study","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-phase-iv-study.md","definition":"A pragmatic Phase IV study is a post-marketing investigation conducted under routine clinical conditions to evaluate a drug or device's real-world effectiveness, long-term safety, and comparative performance. Unlike the controlled Phase III environment, it intentionally minimizes protocol restrictions — broad eligibility criteria, standard-of-care comparators, and naturalistic follow-up — to generate evidence directly applicable to everyday clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); PRECIS framework by Thorpe et al. (2009)","year":"1967 (pragmatic concept); 2000s (pragmatic Phase IV formalized)","type":"Observational / interventional hybrid study design","dataType":"Routine clinical records, patient registries, electronic health records, survey data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null},{"ref":"Atkinson, M. J., & Lennox, R. D. (2012). Planning pragmatic clinical trials in Phase IV: pragmatic versus explanatory studies in post-marketing evaluation. Contemporary Clinical Trials, 33(2), 213-218.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Planning+pragmatic+clinical+trials+in+Phase+IV%3A+pragmatic+versus+explanatory+studies+in+post-marketing+evaluation+Atkinson"}],"related":["phase-iv-study","pragmatic-randomized-clinical-trial","cohort-study","pragmatic-cohort-study","dose-response-analysis","screening-test-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-pretest-posttest-experimental-design","name":"Pragmatic pretest-posttest experimental design","fullName":"Pragmatic Pretest-Posttest Experimental Design","aliases":["pragmatic pre-post design","real-world pretest-posttest study","effectiveness pre-post design","pragmatic before-after study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1963 (pre-post design); 1967 (pragmatic distinction)","originator":"Schwartz & Lellouch (pragmatic framing); Campbell & Stanley (pre-post design)","url":"https://scholargate.app/en/experimental-design/pragmatic-pretest-posttest-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-pretest-posttest-experimental-design.md","definition":"A pragmatic pretest-posttest experimental design combines the before-after measurement structure of the classic pre-post design with the real-world, high-external-validity ethos of pragmatic research. Participants are assessed on relevant outcomes before an intervention is delivered in routine or naturalistic conditions, then re-assessed afterward. The goal is to estimate the effectiveness of the intervention as it actually works in practice rather than under ideal, tightly controlled efficacy conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schwartz & Lellouch (pragmatic framing); Campbell & Stanley (pre-post design)","year":"1963 (pre-post design); 1967 (pragmatic distinction)","type":"Experimental / quasi-experimental design","dataType":"Quantitative outcome measures collected at two time points (pre and post intervention)","subfamily":"Deneysel desen"},"citations":[{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637-648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null},{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":"978-0395307878","url":null}],"related":["pretest-posttest-experimental-design","pragmatic-randomized-controlled-trial","pragmatic-field-experiment","natural-experiment","cluster-randomized-pretest-posttest-experimental-design","crossover-pretest-posttest-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-randomized-clinical-trial","name":"Pragmatic randomized clinical trial","fullName":"Pragmatic Randomized Controlled Trial","aliases":["pragmatic RCT","effectiveness trial","real-world RCT","practical clinical trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1967","originator":"Daniel Schwartz & Joseph Lellouch","url":"https://scholargate.app/en/epidemiology/pragmatic-randomized-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-randomized-clinical-trial.md","definition":"A pragmatic randomized clinical trial (pragmatic RCT) is an interventional study that tests whether a treatment works under routine clinical conditions, as opposed to the tightly controlled setting of an explanatory trial. It prioritizes broad eligibility, flexible delivery, and patient-relevant outcomes to answer the question 'Does this treatment work in everyday practice?' rather than 'Can this treatment work under ideal circumstances?' The distinction between pragmatic and explanatory trials was formally articulated by Schwartz and Lellouch in 1967 and operationalized by the PRECIS tool in 2009.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel Schwartz & Joseph Lellouch","year":"1967","type":"Interventional study design","dataType":"Clinical outcomes, patient-reported outcomes, routine care records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["randomized-clinical-trial","adaptive-randomized-clinical-trial","multicenter-randomized-clinical-trial","cohort-study","prospective-cohort-study","phase-iv-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-randomized-controlled-trial","name":"Pragmatic Randomized Controlled Trial","fullName":"Pragmatic Randomized Controlled Trial","aliases":["pRCT","pragmatic trial","practical clinical trial","real-world RCT"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1967","originator":"Daniel Schwartz and Joseph Lellouch","url":"https://scholargate.app/en/experimental-design/pragmatic-randomized-controlled-trial","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-randomized-controlled-trial.md","definition":"A pragmatic randomized controlled trial (pRCT) tests whether an intervention works under ordinary, real-world conditions — broad eligibility, flexible delivery, and routine care settings. Participants are still randomly assigned to treatment or control, preserving causal inference, but the study is designed to reflect the diversity and variability of actual practice rather than the ideal conditions of an explanatory trial. The defining framework is the PRECIS-2 tool, which maps any RCT along nine pragmatic-to-explanatory dimensions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel Schwartz and Joseph Lellouch","year":"1967","type":"Experimental design — pragmatic trial","dataType":"Quantitative outcome data collected under routine or real-world care conditions","subfamily":"Deneysel desen"},"citations":[{"ref":"Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.","type":"article","doi":"10.1016/0021-9681(67)90041-0","isbn":null,"url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., … Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["randomized-controlled-trial","explanatory-randomized-controlled-trial","cluster-randomized-controlled-trial","adaptive-randomized-controlled-trial","factorial-randomized-controlled-trial","stepped-wedge-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-screening-test-evaluation","name":"Pragmatic Screening Test Evaluation","fullName":"Pragmatic Screening Test Evaluation Study","aliases":["pragmatic diagnostic screen evaluation","real-world screening evaluation","effectiveness-oriented screening study","PRECIS-guided screening evaluation"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2000s-2010s (formalized with PRECIS framework)","originator":"Pragmatic trial framework: Schwartz & Lellouch (1967); PRECIS tool: Thorpe et al. (2009)","url":"https://scholargate.app/en/epidemiology/pragmatic-screening-test-evaluation","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-screening-test-evaluation.md","definition":"A pragmatic screening test evaluation assesses the real-world effectiveness of a screening instrument under routine clinical or public-health conditions — rather than the tightly controlled, ideal-participant settings of explanatory studies. It asks whether the screening tool performs adequately in the actual populations and workflows where it will be deployed, prioritising external validity and implementation relevance over maximally controlled internal conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pragmatic trial framework: Schwartz & Lellouch (1967); PRECIS tool: Thorpe et al. (2009)","year":"2000s-2010s (formalized with PRECIS framework)","type":"Observational / quasi-experimental evaluation design","dataType":"Routine clinical records, real-world patient data, screening test results, reference standard outcomes","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., & Chalkidou, K. (2009). A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464-475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null},{"ref":"Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., & de Vet, H. C. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD Initiative. Annals of Internal Medicine, 138(1), 40-44.","type":"article","doi":"10.7326/0003-4819-138-1-200301070-00010","isbn":null,"url":null}],"related":["screening-test-evaluation","pragmatic-randomized-clinical-trial","diagnostic-accuracy-study","cohort-study","cross-sectional-epidemiological-study","pragmatic-cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-single-subject-experimental-design","name":"Pragmatic Single-Subject Experimental Design","fullName":"Pragmatic Single-Subject Experimental Design","aliases":["pragmatic SSED","pragmatic N-of-1 design","real-world single-case design","applied single-subject experimental design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s–1970s (SSED roots); pragmatic framing prominent from 1990s onward","originator":"Applied behavior analysis tradition (Sidman, Baer, Wolf, Risley); pragmatic adaptation from clinical research","url":"https://scholargate.app/en/experimental-design/pragmatic-single-subject-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-single-subject-experimental-design.md","definition":"Pragmatic single-subject experimental design applies the logic of single-case experimentation — repeated measurement, baseline comparison, and phase manipulation — within real-world practice settings rather than controlled laboratories. It allows practitioners and clinicians to rigorously evaluate interventions for individual participants without requiring large samples, making it especially valuable in applied, clinical, and educational contexts where heterogeneity across individuals is high.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Applied behavior analysis tradition (Sidman, Baer, Wolf, Risley); pragmatic adaptation from clinical research","year":"1960s–1970s (SSED roots); pragmatic framing prominent from 1990s onward","type":"Single-case experimental design variant","dataType":"Repeated measures on a single participant or unit (behavioral, clinical, or outcome data collected over time)","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Tate, R. L., Perdices, M., Rosenkoetter, U., Shadish, W., Togher, L., Vohra, S., ... & Douglas, J. (2016). The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 Statement. Archives of Scientific Psychology, 4(1), 1-9.","type":"article","doi":"10.1037/arc0000026","isbn":null,"url":null}],"related":["single-subject-experimental-design","ab-design","aba-design","abab-design","multiple-baseline-design","pragmatic-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-solomon-four-group-design","name":"Pragmatic Solomon Four-Group Design","fullName":"Pragmatic Solomon Four-Group Experimental Design","aliases":["pragmatic S4GD","real-world Solomon four-group design","pragmatic pretest-control design","pragmatic Solomon design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1949 (Solomon design); pragmatic variant in applied use from 1990s onward","originator":"Solomon four-group design: Richard L. Solomon (1949); pragmatic orientation formalized by Schwartz & Lellouch (1967) and Thorpe et al. (2009)","url":"https://scholargate.app/en/experimental-design/pragmatic-solomon-four-group-design","markdownUrl":"https://scholargate.app/en/experimental-design/pragmatic-solomon-four-group-design.md","definition":"The Pragmatic Solomon Four-Group Design combines the pretest-sensitization control logic of the classic Solomon (1949) four-group structure with the broad eligibility, flexible delivery, and real-world conditions characteristic of pragmatic trials. Four groups are formed: two receive the intervention (one pretested, one not) and two serve as controls (one pretested, one not), allowing simultaneous estimation of treatment effects and pretest sensitization effects under ecologically valid settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Solomon four-group design: Richard L. Solomon (1949); pragmatic orientation formalized by Schwartz & Lellouch (1967) and Thorpe et al. (2009)","year":"1949 (Solomon design); pragmatic variant in applied use from 1990s onward","type":"Experimental design (pragmatic variant)","dataType":"Continuous, ordinal, or binary outcome data; pretest and posttest measurements","subfamily":"Deneysel desen"},"citations":[{"ref":"Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.","type":"article","doi":"10.1037/h0062958","isbn":null,"url":null},{"ref":"Thorpe, K. E., Zwarenstein, M., Oxman, A. D., Treweek, S., Furberg, C. D., Altman, D. G., ... & Chalkidou, K. (2009). A pragmatic–explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Journal of Clinical Epidemiology, 62(5), 464–475.","type":"article","doi":"10.1016/j.jclinepi.2008.12.011","isbn":null,"url":null}],"related":["solomon-four-group-design","pragmatic-randomized-controlled-trial","pretest-posttest-experimental-design","control-group-experimental-design","factorial-experimental-design","cluster-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pragmatic-survival-analysis","name":"Pragmatic survival analysis","fullName":"Pragmatic Survival Analysis","aliases":["real-world survival analysis","pragmatic time-to-event analysis","effectiveness survival analysis","PSA"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Conceptual framework: 1967; widespread application: 1990s–2000s","originator":"Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); extended in survival analysis literature from the 1970s onward","url":"https://scholargate.app/en/epidemiology/pragmatic-survival-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/pragmatic-survival-analysis.md","definition":"Pragmatic survival analysis applies time-to-event statistical methods within pragmatic or real-world settings, estimating how long patients survive, remain event-free, or retain treatment benefit under conditions of routine clinical practice. Unlike explanatory survival analyses conducted under tightly controlled trial conditions, the pragmatic variant embraces the heterogeneity, treatment switching, non-adherence, and competing events that characterise real-world patient populations, prioritising external validity over internal precision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schwartz & Lellouch (explanatory vs. pragmatic distinction, 1967); extended in survival analysis literature from the 1970s onward","year":"Conceptual framework: 1967; widespread application: 1990s–2000s","type":"Observational / experimental hybrid — time-to-event analysis in real-world or pragmatic-trial settings","dataType":"Time-to-event data with censoring; electronic health records, registry data, pragmatic trial data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Ford, I., & Norrie, J. (2016). Pragmatic Trials. New England Journal of Medicine, 375(5), 454–463.","type":"article","doi":"10.1056/NEJMra1510059","isbn":null,"url":null},{"ref":"Royston, P., & Parmar, M. K. B. (2011). The use of restricted mean survival time to estimate the treatment effect in randomized clinical trials when the proportional hazards assumption is in doubt. Statistics in Medicine, 30(19), 2409–2421.","type":"book","doi":"10.1002/sim.4274","isbn":null,"url":null}],"related":["survival-analysis","kaplan-meier-analysis","cox-proportional-hazards","pragmatic-randomized-clinical-trial","competing-risks-analysis","prospective-survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"precision-agriculture-ndvi","name":"Precision Agriculture with NDVI","fullName":"Normalized Difference Vegetation Index Monitoring for Precision Crop Management","aliases":["NDVI remote sensing","Vegetation index monitoring","Satellite crop monitoring"],"domain":"agronomy","family":"process-pipeline","subfamily":"Remote sensing and geospatial analysis","year":"1973","originator":"John W. Rouse, Richard H. Haas","url":"https://scholargate.app/en/agronomy/precision-agriculture-ndvi","markdownUrl":"https://scholargate.app/en/agronomy/precision-agriculture-ndvi.md","definition":"Precision Agriculture with NDVI is a geospatial monitoring pipeline for assessing crop vigor, health, and productivity using the Normalized Difference Vegetation Index (NDVI) derived from satellite or drone imagery. Developed by Rouse and colleagues (1973), this method enables rapid, non-destructive assessment of spatial variation in crop performance and informs variable-rate management decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Rouse, Richard H. Haas","subfamily":"Remote sensing and geospatial analysis","year":"1973","type":"Geospatial monitoring pipeline"},"citations":[{"ref":"Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite symposium, Washington, DC.","type":"article","doi":null,"isbn":null,"url":"https://ntrs.nasa.gov/citations/19740008296"},{"ref":"Thenkabail, P. S., Lyon, J. G., & Huete, A. (2018). Hyperspectral remote sensing of vegetation. CRC Press, Boca Raton, FL.","type":"article","doi":null,"isbn":null,"url":"https://www.routledge.com/Hyperspectral-Remote-Sensing-of-Vegetation/Thenkabail-Lyon-Huete/p/book/9780429431234"}],"related":["crop-growth-simulation","nitrogen-use-efficiency","weed-density-mapping","crop-yield-estimation","irrigation-scheduling-etref"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"precision-recall-auc","name":"Precision-Recall AUC","fullName":"Area Under the Precision-Recall Curve","aliases":["PR AUC","PR Curve"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"2006","originator":"Davis and Goadrich","url":"https://scholargate.app/en/model-evaluation/precision-recall-auc","markdownUrl":"https://scholargate.app/en/model-evaluation/precision-recall-auc.md","definition":"The Precision-Recall Area Under the Curve (PR AUC) is the area under the curve formed by plotting recall on the x-axis and precision on the y-axis. It is particularly useful for evaluating classifiers on imbalanced datasets, where it is often more informative than ROC AUC.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Davis and Goadrich","subfamily":"Classification Metric","year":"2006","type":"Evaluation metric"},"citations":[{"ref":"Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 233-240.","type":"article","doi":"10.1145/1143844.1143874","isbn":null,"url":null},{"ref":"Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10(3), e0118432.","type":"article","doi":"10.1371/journal.pone.0118432","isbn":null,"url":null}],"related":["precision","recall","f1-score","roc-auc","accuracy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"precision","name":"Precision","fullName":"Precision (Positive Predictive Value)","aliases":["Positive Predictive Value","PPV"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"20th century","originator":"Historical statistical foundations","url":"https://scholargate.app/en/model-evaluation/precision","markdownUrl":"https://scholargate.app/en/model-evaluation/precision.md","definition":"Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Historical statistical foundations","subfamily":"Classification Metric","year":"20th century","type":"Evaluation metric"},"citations":[{"ref":"Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.","type":"article","doi":"10.1016/j.patrec.2005.10.010","isbn":null,"url":null},{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"}],"related":["recall","f1-score","accuracy","specificity","matthews-correlation-coefficient"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"predatory-journals","name":"Predatory Journals and Publishers","fullName":"Identification and Avoidance of Predatory Journals and Publishers","aliases":["Predatory Publishing","Fake Journals","Pay-to-Publish Schemes"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"publication-fraud","year":"2010","originator":"Jeffrey Beall (University of Colorado Denver); international research community","url":"https://scholargate.app/en/publication-ethics/predatory-journals","markdownUrl":"https://scholargate.app/en/publication-ethics/predatory-journals.md","definition":"Predatory journals are fake academic publishers that exploit the open-access model by charging authors publication fees without providing peer review, editorial oversight, or quality control. Coined by librarian Jeffrey Beall in 2010, the term describes publishers that prioritize profit over scientific integrity, accepting nearly all submissions (regardless of quality), using deceptive marketing (claiming high impact factors, faking indexing, using names similar to established journals), and often hosting work that would not survive peer review. Publishing in predatory journals damages an author's credibility and wastes research dissemination efforts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeffrey Beall (University of Colorado Denver); international research community","subfamily":"publication-fraud","year":"2010","type":"Framework"},"citations":[{"ref":"Beall, J. (2010). Predatory Open-Access Scholarly Publishers. The Charleston Advisor, 11(4), 10–17.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Predatory+Open-Access+Scholarly+Publishers+Beall"},{"ref":"Beall, J. (2015). Scholarly Open Access: Critical Analysis of Bibliometric Indicators and Journal Quality. PeerJ Preprints, 3, e1481.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Scholarly+Open+Access%3A+Critical+Analysis+of+Bibliometric+Indicators+and+Journal+Quality+Beall"},{"ref":"Lak, A., Sarvari, S. A., Kassaian, N., & Salari, P. (2016). Identifying Predatory Journals. Asian Journal of Transfusion Science, 10(2), 184–185.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Identifying+Predatory+Journals+Lak"}],"related":["open-access-publishing","peer-review-process","cope-guidelines","retraction-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"predictive-site-location","name":"Predictive Site Location","fullName":"Predictive Site Location Modeling","aliases":["predictive modeling","maxent modeling"],"domain":"archaeology","family":"process-pipeline","subfamily":"Statistical Modeling","year":"2006","originator":"Steven Phillips","url":"https://scholargate.app/en/archaeology/predictive-site-location","markdownUrl":"https://scholargate.app/en/archaeology/predictive-site-location.md","definition":"Predictive site location modeling uses machine learning algorithms (particularly maximum entropy models) to predict the probability of archaeological site occurrence across a landscape based on environmental and spatial variables. Developed for ecology but adapted for archaeology, predictive modeling identifies areas with high archaeological potential, guiding survey strategies and resource management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Phillips","subfamily":"Statistical Modeling","year":"2006","type":"Site probability modeling"},"citations":[{"ref":"Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.","type":"article","doi":"10.1016/j.ecolmodel.2005.03.026","isbn":null,"url":null},{"ref":"Verhagen, P., & Whitley, T. W. (2012). Predictive modelling for archaeological resource management. Journal of Archaeological Science, 39(5), 1066-1077.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Predictive+modelling+for+archaeological+resource+management+Verhagen"}],"related":["viewshed-analysis","space-syntax"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pregnancy-related-anxiety-questionnaire","name":"Pregnancy-Related Anxiety Questionnaire","fullName":"Pregnancy-Related Anxiety Questionnaire-Revised 2 (PRAQ-R2)","aliases":["PRAQ-R2","PRAQ"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"pregnancy-specific-anxiety","year":2004,"originator":"Huizink et al.","url":"https://scholargate.app/en/obstetrics-gynecology/pregnancy-related-anxiety-questionnaire","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/pregnancy-related-anxiety-questionnaire.md","definition":"The Pregnancy-Related Anxiety Questionnaire—Revised 2 (PRAQ-R2) is a 10-item self-report measure designed specifically to assess anxiety unique to pregnancy, focusing on two core pregnancy-related concerns: fear of bearing a handicapped child and worries about changes to one's own body and appearance. Developed and refined by Huizink and colleagues, the PRAQ-R2 is based on the observation that pregnancy-related anxiety reflects distinct cognitive and emotional preoccupations that differ from generalized anxiety disorders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Huizink et al.","subfamily":"pregnancy-specific-anxiety","year":2004,"type":"Self-report"},"citations":[{"ref":"Huizink, A. C., Mulder, E. J., & Buitelaar, J. K. (2004). Prenatal stress and risk for psychopathology: specific effects or inert vulnerability? Psychological Bulletin, 130(1), 115-142.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prenatal+stress+and+risk+for+psychopathology%3A+specific+effects+or+inert+vulnerability+Huizink"},{"ref":"Huizink, A. C., Robles de Medina, P. G., Mulder, E. J., Visser, G. H., & Buitelaar, J. K. (2016). Is pregnancy anxiety a distinctive syndrome? Early Human Development, 79(2), 81-91.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Is+pregnancy+anxiety+a+distinctive+syndrome+Huizink"}],"related":["perinatal-anxiety-screening-scale","pcosq","postpartum-bonding-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"premenstrual-symptoms-screening","name":"Premenstrual Symptoms Screening Tool","fullName":"Premenstrual Symptoms Screening Tool (PSST)","aliases":["PSST","Premenstrual Symptom Screening"],"domain":"obstetrics-gynecology","family":"process-pipeline","subfamily":"menstrual-cycle-disorders","year":2003,"originator":"Steiner, M., Macdougall, M., & Brown, E.","url":"https://scholargate.app/en/obstetrics-gynecology/premenstrual-symptoms-screening","markdownUrl":"https://scholargate.app/en/obstetrics-gynecology/premenstrual-symptoms-screening.md","definition":"The Premenstrual Symptoms Screening Tool (PSST) is a brief, 19-item self-report questionnaire designed to screen for premenstrual dysphoric disorder (PMDD) and assess the severity of premenstrual symptoms. Developed by Steiner, Macdougall, and Brown in 2003, the PSST efficiently identifies women with clinically significant cyclical mood and physical symptoms warranting diagnostic evaluation and potential pharmacological or psychological treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steiner, M., Macdougall, M., & Brown, E.","subfamily":"menstrual-cycle-disorders","year":2003,"type":"Self-report"},"citations":[{"ref":"Steiner, M., Macdougall, M., & Brown, E. (2003). The Premenstrual Symptoms Screening Tool (PSST) for clinicians. Archives of Women's Mental Health, 6(3), 203-209.","type":"article","doi":"10.1007/s00737-003-0018-4","isbn":null,"url":null},{"ref":"Steiner, M., & Born, L. (2000). Diagnosis and treatment of premenstrual dysphoric disorder: an update. CNS Drug Reviews, 6(1), 5-17.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Diagnosis+and+treatment+of+premenstrual+dysphoric+disorder%3A+an+update+Steiner"}],"related":["menopause-specific-qol","female-pelvic-pain-scale","perinatal-anxiety-screening-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"preprint-servers","name":"Preprint Servers in Science","fullName":"Preprint Servers and Pre-Publication Dissemination","aliases":["Preprints","Preprint Archives","Pre-publication Servers"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"pre-publication-dissemination","year":"1991","originator":"Paul Ginsparg (arXiv, 1991); Cold Spring Harbor Laboratory (bioRxiv, 2013); NIH (medRxiv, 2019)","url":"https://scholargate.app/en/publication-ethics/preprint-servers","markdownUrl":"https://scholargate.app/en/publication-ethics/preprint-servers.md","definition":"Preprint servers are open-access repositories where researchers post manuscripts before, during, or alongside peer review at a formal journal. Preprints allow rapid, free dissemination of research findings without waiting for journal review (which can take 3–12 months). Major preprint servers include arXiv (physics, math, computer science; founded 1991), bioRxiv (biology; 2013), medRxiv (medicine; 2019), and others. Preprints are NOT peer-reviewed and should not be treated as final scientific evidence. However, they enable priority-claiming, feedback from the community, and rapid knowledge sharing in fast-moving fields. Many journals now accept manuscripts previously posted as preprints.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Ginsparg (arXiv, 1991); Cold Spring Harbor Laboratory (bioRxiv, 2013); NIH (medRxiv, 2019)","subfamily":"pre-publication-dissemination","year":"1991","type":"Platform"},"citations":[{"ref":"Björk, B. C., Welling, P., Laakso, M., Majlender, P., Hedlund, T., & Guðnason, G. (2010). Open Access to the Scientific Journal Literature: Situation 2009. PLOS ONE, 5(6), e11273.","type":"article","doi":"10.1371/journal.pone.0011273","isbn":null,"url":null},{"ref":"arXiv (2023). arXiv: e-Print Archive. Cornell University.","type":"webpage","doi":null,"isbn":null,"url":"https://arxiv.org/"},{"ref":"bioRxiv (2023). The Preprint Server for Biology. Cold Spring Harbor Laboratory.","type":"webpage","doi":null,"isbn":null,"url":"https://www.biorxiv.org/"}],"related":["open-access-publishing","peer-review-process","data-sharing-open-science","preprint-servers"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"presenteeism-scale","name":"Stanford Presenteeism Scale","fullName":"Stanford Presenteeism Scale (SPS-6)","aliases":["SPS-6","Presenteeism Scale"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Productivity and work engagement","year":2002,"originator":"Clifford Koopman, Kenneth R. Pelletier, James Murray, and colleagues","url":"https://scholargate.app/en/occupational-health/presenteeism-scale","markdownUrl":"https://scholargate.app/en/occupational-health/presenteeism-scale.md","definition":"The Stanford Presenteeism Scale (SPS-6) is a brief assessment tool measuring work productivity and performance among employees who are present at work despite health problems, personal issues, or other limitations. Developed by Koopman and colleagues in 2002, the SPS-6 quantifies the degree to which an employee's ability to concentrate, accomplish tasks, and maintain efficiency is compromised while working. Presenteeism—working while ill or impaired—is increasingly recognized as a significant occupational health concern with substantial economic and wellbeing consequences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clifford Koopman, Kenneth R. Pelletier, James Murray, and colleagues","subfamily":"Productivity and work engagement","year":2002,"type":"Self-report questionnaire"},"citations":[{"ref":"Koopman, C., Pelletier, K. R., Murray, J. F., Sharda, C. E., Berger, M. L., Turpin, R. S., ... & Bendel, T. (2002). Stanford Presenteeism Scale: Health status and employee productivity. Journal of Occupational and Environmental Medicine, 44(1), 14-20.","type":"article","doi":"10.1097/00043764-200201000-00004","isbn":null,"url":null}],"related":["recovery-experience-questionnaire","effort-reward-imbalance-scale","copenhagen-burnout-inventory","areas-of-worklife-scale","workplace-incivility-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"preterm-infant-pain-profile","name":"PIPP","fullName":"Premature Infant Pain Profile","aliases":["PIPP","PIPP-R"],"domain":"neonatology","family":"process-pipeline","subfamily":"procedural-pain-assessment","year":1996,"originator":"Bonnie Stevens","url":"https://scholargate.app/en/neonatology/preterm-infant-pain-profile","markdownUrl":"https://scholargate.app/en/neonatology/preterm-infant-pain-profile.md","definition":"The PIPP is a seven-indicator behavioral and physiological pain assessment tool specifically designed for preterm and full-term infants undergoing painful procedures. Developed by Stevens et al. in 1996, it measures acute procedural pain by integrating gestational age, behavioral state, facial expressions, and vital sign changes. The PIPP has become the most widely validated neonatal acute pain instrument in research and is recommended by major pediatric pain organizations for assessing pain during routine NICU procedures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonnie Stevens","subfamily":"procedural-pain-assessment","year":1996,"type":"Clinician-rated"},"citations":[{"ref":"Stevens, B., Johnston, C., Petryshen, P., & Taddio, A. (1996). Premature Infant Pain Profile: Development and Initial Validation. Clinical Journal of Pain, 12(1), 13-22.","type":"article","doi":"10.1097/00002508-199603000-00004","isbn":null,"url":null},{"ref":"Stevens, B. J., Gibbins, S., Yamada, J., et al. (2014). Epidemiology and Management of Painful Procedures in Infants in Canadian Neonatal Intensive Care Units. Canadian Medical Association Journal, 186(6), E225-E234.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Epidemiology+and+Management+of+Painful+Procedures+in+Infants+in+Canadian+Neonatal+Intensive+Care+Units+Stevens"}],"related":["neonatal-pain-agitation-sedation","neonatal-behavioral-assessment","parent-infant-interaction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pretest-posttest-experimental-design","name":"Pretest-Posttest Experimental Design","fullName":"Pretest-Posttest Experimental Design","aliases":["pretest-posttest design","before-after design","pre-post design","two-wave experimental design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1963 (formalized in Campbell & Stanley)","originator":"Donald T. Campbell and Julian C. Stanley","url":"https://scholargate.app/en/experimental-design/pretest-posttest-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/pretest-posttest-experimental-design.md","definition":"The pretest-posttest experimental design measures participants on the outcome variable before and after treatment, typically with random assignment to treatment and control groups. The difference between pre- and post-scores isolates the treatment effect from baseline variation, making this one of the most widely used frameworks in experimental and quasi-experimental research across education, psychology, medicine, and the social sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald T. Campbell and Julian C. Stanley","year":"1963 (formalized in Campbell & Stanley)","type":"Experimental / quasi-experimental research design","dataType":"Quantitative outcome measures (continuous, ordinal, or count data) collected at two time points","subfamily":"Deneysel desen"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+Quasi-Experimental+Designs+for+Research+Campbell+Stanley+1963"},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["randomized-controlled-trial","solomon-four-group-design","control-group-experimental-design","factorial-experiment","repeated-measures-design","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"price-fairness-scale","name":"Price Fairness Scale","fullName":"Price Fairness Scale (PFS)","aliases":["Pricing Justice Scale","Fair Price Perception Scale"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Pricing and consumer perception","year":"2004","originator":"Ling Xia, Kent B. Monroe, Jennifer L. Cox","url":"https://scholargate.app/en/marketing-management/price-fairness-scale","markdownUrl":"https://scholargate.app/en/marketing-management/price-fairness-scale.md","definition":"The Price Fairness Scale (PFS), developed by Xia, Monroe, and Cox (2004), measures customer perception of whether a charged price is fair and reasonable relative to value received and market comparison. Price fairness assessment differs from absolute price satisfaction: customers may perceive a price as high but fair if quality justifies it, or as low but unfair if they suspect price discrimination or exploitation. The PFS captures three dimensions of price fairness judgment: Distributive Fairness (whether the price-value ratio is equitable), Procedural Fairness (whether the pricing process is transparent and non-discriminatory), and Interactional Fairness (whether pricing explanations are respectful). The scale is critical for premium pricing strategy, price increases, and dynamic pricing implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ling Xia, Kent B. Monroe, Jennifer L. Cox","subfamily":"Pricing and consumer perception","year":"2004","type":"Multi-dimensional price fairness scale"},"citations":[{"ref":"Campbell, M. C. (2005). Perceived Price Fairness. MIT Sloan Management Review, 46(3), 30-35.","type":"article","doi":null,"isbn":null,"url":"https://sloanreview.mit.edu/article/when-to-allow-product-returns/"},{"ref":"Xia, L., Monroe, K. B., & Cox, J. L. (2004). The Price is Unfair! A Conceptual Framework and Research Agenda on Perceived Price Fairness. Journal of Marketing, 68(4), 1-15.","type":"article","doi":"10.1509/jmkg.68.4.1.42733","isbn":null,"url":null}],"related":["customer-satisfaction-index","customer-loyalty-scale","brand-equity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"price-sensitivity-meter","name":"Van Westendorp Price Sensitivity Meter","fullName":"Van Westendorp Price Sensitivity Meter Framework","aliases":["Price Sensitivity Meter","PSM","Van Westendorp Method"],"domain":"marketing","family":"process-pipeline","subfamily":"Pricing research and price perception","year":"1993","originator":"Peter D. van Westendorp","url":"https://scholargate.app/en/marketing/price-sensitivity-meter","markdownUrl":"https://scholargate.app/en/marketing/price-sensitivity-meter.md","definition":"The Van Westendorp Price Sensitivity Meter is a market research method developed by Peter van Westendorp in 1993 for assessing consumer price perception and estimating willingness-to-pay ranges without directly asking customers their maximum price. The method uses four simple questions about price acceptability, yielding estimates of optimal price, acceptable price range, and price perception zones.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter D. van Westendorp","subfamily":"Pricing research and price perception","year":"1993","type":"Price perception measurement method"},"citations":[{"ref":"Van Westendorp, P. (1993). Price Perception Analysis. An Application to the International Car Market. International Journal of Research in Marketing, 10(2), 157-165.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Price+Perception+Analysis+Van"},{"ref":"Miller, K. M., Hofstetter, R., Krohmer, H., & Zhang, Z. J. (2011). How Should Consumers' Willingness to Pay Be Measured? A Managerial Perspective. Journal of Product & Brand Management, 20(6), 460-469.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+Should+Consumers%27+Willingness+to+Pay+Be+Measured+Miller"},{"ref":"Chernev, A., & Hamilton, R. (2009). Assortment Size and Option Attractiveness in Consumer Choice Among Retailers. Journal of Marketing Research, 46(3), 410-420.","type":"article","doi":"10.1509/jmkr.46.3.410","isbn":null,"url":null}],"related":["willingness-to-pay-estimation","market-segmentation-analysis","marketing-mix-modeling","brand-equity-measurement","customer-lifetime-value"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"primary-care-ptsd-screen","name":"Primary Care PTSD Screen for DSM-5","fullName":"Primary Care PTSD Screen for DSM-5 (PC-PTSD-5)","aliases":["PC-PTSD-5","PC-PTSD"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"PTSD screening and brief assessment","year":"2015","originator":"Ariel Prins et al.","url":"https://scholargate.app/en/trauma-psychology/primary-care-ptsd-screen","markdownUrl":"https://scholargate.app/en/trauma-psychology/primary-care-ptsd-screen.md","definition":"The PC-PTSD-5 is a 5-item self-report screening instrument for posttraumatic stress disorder (PTSD) aligned with DSM-5 diagnostic criteria. Developed by Prins and colleagues in 2015 as an update to the earlier 4-item PC-PTSD, the PC-PTSD-5 is designed specifically for rapid screening in primary care and other non-specialist medical settings. It is freely available, brief, and demonstrates strong sensitivity and specificity for identifying individuals warranting full PTSD diagnostic evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ariel Prins et al.","subfamily":"PTSD screening and brief assessment","year":"2015","type":"Self-report questionnaire"},"citations":[{"ref":"Prins, A., Bovin, M. J., Smolenski, D. J., et al. (2015). The Primary Care PTSD Screen for DSM-5 (PC-PTSD-5): Development and evaluation within a veteran primary care sample. Journal of General Internal Medicine, 31(10), 1206-1211.","type":"article","doi":"10.1007/s11606-016-3703-5","isbn":null,"url":null},{"ref":"Prins, A., Smolenski, D. J., Bovin, M. J., et al. (2016). Psychometric evaluation of the Primary Care PTSD Screen for DSM-5 (PC-PTSD-5). Depression and Anxiety, 33(S1), 91.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/27227475"}],"related":["impact-of-event-scale-revised","life-events-checklist","secondary-traumatic-stress-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"principal-agent-model","name":"Principal-Agent Model","fullName":"Principal-Agent Model with Asymmetric Information and Moral Hazard","aliases":["Agency Theory","Hidden Action Problem","Moral Hazard"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1976","originator":"Michael Jensen, William Meckling, Bengt Holmstrom","url":"https://scholargate.app/en/game-theory/principal-agent-model","markdownUrl":"https://scholargate.app/en/game-theory/principal-agent-model.md","definition":"The Principal-Agent Model analyzes how a principal (e.g., owner, employer, policymaker) can incentivize an agent (e.g., manager, employee, firm) to act in the principal's interest when the agent has private information or can take hidden actions. Formalized by Jensen and Meckling in 1976, the model identifies agency costs arising from moral hazard (the agent exerts less effort than desired) and adverse selection (the agent hides unfavorable information). Optimal contracts balance incentives with risk allocation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Jensen, William Meckling, Bengt Holmstrom","subfamily":"Game-theoretic","year":"1976","type":"algorithm"},"citations":[{"ref":"Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360.","type":"article","doi":"10.1016/0304-405X(76)90026-X","isbn":null,"url":null},{"ref":"Holmstrom, B. (1991). The firm as a subpoena server. Journal of Law, Economics and Organization, 7(2), 53-64.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+firm+as+a+subpoena+server+Holmstrom"}],"related":["bayesian-nash-equilibrium","vcg-mechanism","shapley-value","nash-equilibrium"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"principal-component-risk","name":"Principal Component Risk Factors","fullName":"Risk Factor PCA via Return Covariance Decomposition","aliases":["risk factor PCA","return covariance decomposition","statistical factor model","Risk Faktörü PCA (Getiri Kovaryans Ayrışımı)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1991,"originator":"Litterman & Scheinkman (bond-return factors); Connor & Korajczyk (statistical APT factors)","url":"https://scholargate.app/en/finance/principal-component-risk","markdownUrl":"https://scholargate.app/en/finance/principal-component-risk.md","definition":"Risk Factor PCA is a dimension-reduction method that decomposes the return covariance matrix of many assets into a small set of orthogonal principal components interpreted as systematic risk factors. Litterman and Scheinkman (1991) used it to show that bond returns are driven by a few common factors, and Connor and Korajczyk (1988) developed the statistical-factor interpretation for the APT.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Litterman & Scheinkman (bond-return factors); Connor & Korajczyk (statistical APT factors)","year":1991,"type":"Statistical factor model (dimension reduction)","estimator":"Eigendecomposition of the return covariance (or correlation) matrix","outcome":"continuous (asset returns)","minSample":60},"citations":[{"ref":"Litterman, R. & Scheinkman, J. (1991). Common Factors Affecting Bond Returns. Journal of Fixed Income, 1(1), 54-61.","type":"article","doi":"10.3905/jfi.1991.692347","isbn":null,"url":null},{"ref":"Connor, G. & Korajczyk, R. A. (1988). Risk and Return in an Equilibrium APT: Application of a New Test Methodology. Journal of Financial Economics, 21(2), 255-289.","type":"article","doi":"10.1016/0304-405X(88)90062-1","isbn":null,"url":null}],"related":["portfolio-optimization-mean-variance","interest-rate-models","credit-risk-models","factor-analysis","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"principal-components-regression","name":"Principal Components Regression","fullName":"Principal Components Regression (PCR)","aliases":["PCR","PCA regression","temel bileşenler regresyonu"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1982,"originator":"Principal-component regression literature (Jolliffe and others)","url":"https://scholargate.app/en/machine-learning/principal-components-regression","markdownUrl":"https://scholargate.app/en/machine-learning/principal-components-regression.md","definition":"Principal components regression first compresses a set of correlated predictors into a few principal components — the directions of greatest variance — and then regresses the response on those components. By discarding low-variance directions, PCR stabilizes estimation in the presence of multicollinearity and high dimensionality, at the cost of choosing components without reference to the response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Principal-component regression literature (Jolliffe and others)","year":1982,"type":"Unsupervised dimension reduction + regression","handles":"Collinear predictors, p large","components":"Top principal components of the predictors","output":"Regression on a few PCs, mapped back to predictors"},"citations":[{"ref":"Jolliffe, I. T. (1982). A note on the use of principal components in regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 31(3), 300–303.","type":"article","doi":"10.2307/2348005","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0-387-84857-0","url":null}],"related":["partial-least-squares","principal-component-analysis","ridge-regression","multiple-linear-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prisma-based-review","name":"PRISMA-based review","fullName":"Preferred Reporting Items for Systematic Reviews and Meta-Analyses-based Review","aliases":["PRISMA review","PRISMA-guided systematic review","PRISMA 2020 review","PRISMA-compliant review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2009 (original PRISMA statement); updated 2020","originator":"David Moher and PRISMA Group","url":"https://scholargate.app/en/scientometrics/prisma-based-review","markdownUrl":"https://scholargate.app/en/scientometrics/prisma-based-review.md","definition":"A PRISMA-based review is a systematic literature review conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Originally published by Moher et al. in 2009 and updated as PRISMA 2020 by Page et al., the framework specifies a 27-item checklist and a four-phase flow diagram covering identification, screening, eligibility, and inclusion — ensuring full transparency and reproducibility in the review process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Moher and PRISMA Group","year":"2009 (original PRISMA statement); updated 2020","type":"Structured reporting framework for systematic reviews","dataType":"Published studies, trial reports, observational study records","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71.","type":"article","doi":"10.1136/bmj.n71","isbn":null,"url":null},{"ref":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Medicine, 6(7), e1000097.","type":"article","doi":"10.1371/journal.pmed.1000097","isbn":null,"url":null}],"related":["systematic-literature-review","meta-analysis","scoping-review","umbrella-review","integrative-review","rapid-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prisma-checklist","name":"PRISMA Checklist","fullName":"Preferred Reporting Items for Systematic Reviews and Meta-Analyses","aliases":["PRISMA","PRISMA 2020"],"domain":"research-methodology","family":"process-pipeline","subfamily":"Systematic review reporting standard","year":"2021 (original 2009)","originator":"Page et al. (PRISMA Group)","url":"https://scholargate.app/en/research-methodology/prisma-checklist","markdownUrl":"https://scholargate.app/en/research-methodology/prisma-checklist.md","definition":"PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a 27-item evidence-based checklist published in 2021 (updated from 2009) to standardize reporting of systematic reviews and meta-analyses. Endorsed by over 500 journals, PRISMA is the international standard for evidence synthesis reporting, used across healthcare, psychology, education, and social sciences to ensure transparency and reproducibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Page et al. (PRISMA Group)","subfamily":"Systematic review reporting standard","year":"2021 (original 2009)","type":"Systematic review author reporting checklist"},"citations":[{"ref":"Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71.","type":"article","doi":"10.1136/bmj.n71","isbn":null,"url":null}],"related":["consort-reporting-checklist","strobe-checklist","grade-evidence-profiling","cochrane-risk-of-bias"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prisma-compliant-co-citation-analysis","name":"PRISMA-compliant Co-citation analysis","fullName":"PRISMA-Compliant Co-Citation Analysis","aliases":["systematic co-citation review","PRISMA co-citation","co-citation analysis with PRISMA reporting","transparent co-citation analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2009–2021 (methodological combination emerged in the 2010s)","originator":"PRISMA: Moher et al. (2009), updated Page et al. (2021); Co-citation: Henry Small (1973)","url":"https://scholargate.app/en/scientometrics/prisma-compliant-co-citation-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/prisma-compliant-co-citation-analysis.md","definition":"PRISMA-compliant co-citation analysis is a systematic bibliometric method that applies the PRISMA 2020 reporting framework to co-citation analysis. It identifies intellectual clusters in a research field by measuring how frequently pairs of documents are cited together, while ensuring full transparency of the literature search, screening decisions, and analytic choices through a pre-registered protocol and standardised flow diagram.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"PRISMA: Moher et al. (2009), updated Page et al. (2021); Co-citation: Henry Small (1973)","year":"2009–2021 (methodological combination emerged in the 2010s)","type":"Systematic bibliometric review","dataType":"Citation databases (Web of Science, Scopus); co-citation frequency matrices","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71.","type":"article","doi":"10.1136/bmj.n71","isbn":null,"url":null},{"ref":"Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265-269.","type":"article","doi":"10.1002/asi.4630240406","isbn":null,"url":null}],"related":["co-citation-analysis","bibliometric-analysis","bibliographic-coupling","systematic-literature-review","science-mapping","prisma-based-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prisma-compliant-scoping-review","name":"PRISMA-compliant Scoping review","fullName":"PRISMA for Scoping Reviews (PRISMA-ScR) Compliant Scoping Review","aliases":["PRISMA-ScR scoping review","scoping review with PRISMA-ScR","PRISMA scoping review","transparent scoping review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2018 (PRISMA-ScR extension); 2005 (scoping review framework)","originator":"Tricco et al. (PRISMA-ScR); Arksey & O'Malley (scoping review framework)","url":"https://scholargate.app/en/scientometrics/prisma-compliant-scoping-review","markdownUrl":"https://scholargate.app/en/scientometrics/prisma-compliant-scoping-review.md","definition":"A PRISMA-compliant scoping review is a scoping review conducted and reported according to the PRISMA for Scoping Reviews (PRISMA-ScR) extension, a 20-item checklist plus explanation published by Tricco et al. in 2018. Scoping reviews map the breadth and volume of evidence on a topic without synthesizing effect sizes; the PRISMA-ScR overlay adds transparency, reproducibility, and reporting completeness standards analogous to those PRISMA provides for systematic reviews and meta-analyses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tricco et al. (PRISMA-ScR); Arksey & O'Malley (scoping review framework)","year":"2018 (PRISMA-ScR extension); 2005 (scoping review framework)","type":"Evidence synthesis — scoping review with standardized reporting","dataType":"Published literature records (titles, abstracts, full texts)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., ... & Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of Internal Medicine, 169(7), 467–473.","type":"article","doi":"10.7326/M18-0850","isbn":null,"url":null},{"ref":"Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.","type":"article","doi":"10.1080/1364557032000119616","isbn":null,"url":null}],"related":["scoping-review","systematic-literature-review","prisma-based-review","umbrella-review","mapping-review","narrative-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prisma-compliant-umbrella-review","name":"PRISMA-compliant Umbrella Review","fullName":"PRISMA-Compliant Umbrella Review of Systematic Reviews","aliases":["umbrella review with PRISMA","PRIOR-guided umbrella review","overview of reviews","PRISMA umbrella overview"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2015 (umbrella review methodology); 2022 (PRIOR reporting extension)","originator":"Aromataris et al. (JBI); PRIOR reporting extension by Fusar-Poli et al.","url":"https://scholargate.app/en/scientometrics/prisma-compliant-umbrella-review","markdownUrl":"https://scholargate.app/en/scientometrics/prisma-compliant-umbrella-review.md","definition":"A PRISMA-compliant umbrella review is a structured synthesis of existing systematic reviews and meta-analyses on a topic, conducted and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines — specifically the PRIOR extension developed for umbrella reviews. By operating one level above primary research, it maps the totality of evidence, identifies convergent or contradictory findings across reviews, and quantifies evidence strength at the highest tier of the evidence hierarchy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aromataris et al. (JBI); PRIOR reporting extension by Fusar-Poli et al.","year":"2015 (umbrella review methodology); 2022 (PRIOR reporting extension)","type":"Evidence synthesis / review of systematic reviews","dataType":"Published systematic reviews and meta-analyses (bibliographic records, extracted evidence tables)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: Methodological development, conduct and reporting of an umbrella review approach. International Journal of Evidence-Based Healthcare, 13(3), 132–140.","type":"article","doi":"10.1097/XEB.0000000000000055","isbn":null,"url":null},{"ref":"Fusar-Poli, P., Radua, J., & the PRISMA Umbrella Group (2022). PRIOR: Preferred Reporting Items for Umbrella Reviews. Evidence-Based Mental Health, 25(3), 92–99.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=PRIOR%3A+Preferred+Reporting+Items+for+Umbrella+Reviews+Fusar-Poli"}],"related":["umbrella-review","systematic-literature-review","prisma-based-review","meta-analysis","scoping-review","network-meta-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prisma","name":"PRISMA","fullName":"PRISMA Systematic Review Reporting","aliases":["Preferred Reporting Items for Systematic Reviews and Meta-Analyses","PRISMA 2020","PRISMA checklist","Sistematik Derleme Raporlama Kılavuzu"],"domain":"meta-analysis","family":"process-pipeline","subfamily":"Evidence synthesis","year":2021,"originator":"Matthew Page et al. (PRISMA 2020)","url":"https://scholargate.app/en/meta-analysis/prisma","markdownUrl":"https://scholargate.app/en/meta-analysis/prisma.md","definition":"PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a standardized reporting guideline designed to improve the transparency and completeness of systematic reviews and meta-analyses. Introduced in its current form by Page et al. in 2021 as PRISMA 2020, it provides a 27-item checklist and a four-phase flow diagram that together ensure every stage of a review — from database searching through final inclusion — is documented and reproducible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matthew Page et al. (PRISMA 2020)","year":2021,"type":"Reporting guideline and flow diagram","subfamily":"Evidence synthesis","checklistItems":27,"predecessors":"QUOROM (1999), PRISMA 2009"},"citations":[{"ref":"Page, M. J., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71.","type":"article","doi":"10.1136/bmj.n71","isbn":null,"url":null}],"related":["meta-analysis","network-meta-analysis","publication-bias-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pro-environmental-behavior-scale","name":"PEBS","fullName":"Pro-Environmental Behavior Scale","aliases":["PEBS","Sustainability Behavior Scale"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"environmental action and behavior assessment","year":"2002","originator":"Debra Lemke, Anja Kollmuss","url":"https://scholargate.app/en/environmental-psychology/pro-environmental-behavior-scale","markdownUrl":"https://scholargate.app/en/environmental-psychology/pro-environmental-behavior-scale.md","definition":"The Pro-Environmental Behavior Scale (PEBS) measures the frequency and breadth of environmentally responsible actions that individuals perform in their daily lives, including recycling, energy conservation, water conservation, sustainable transportation, sustainable consumption, and environmental activism. Unlike attitude scales that measure beliefs or concerns, the PEBS captures actual or self-reported behaviors—providing a bridge between environmental intentions and demonstrable actions. The scale is essential for evaluating behavior-change interventions, tracking progress toward sustainability goals, and understanding which demographic and psychographic segments adopt environmentally responsible practices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Debra Lemke, Anja Kollmuss","subfamily":"environmental action and behavior assessment","year":"2002","type":"Self-report frequency and behavior checklist"},"citations":[{"ref":"Kollmuss, A., & Agyeman, J. (2002). Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior? Environmental Education Research, 8(3), 239–260.","type":"article","doi":"10.1080/13504620220145401","isbn":null,"url":null},{"ref":"Markowitz, E. M., Goldberg, L. R., Ashton, M. C., & Lee, K. (2012). Profiling the 'pro-environmental individual': A personality perspective. Journal of Personality, 80(1), 81–111.","type":"article","doi":"10.1111/j.1467-6494.2011.00721.x","isbn":null,"url":null}],"related":["new-ecological-paradigm","connectedness-to-nature-scale","environmental-identity-scale","sustainable-consumption-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proactive-personality-scale","name":"Proactive Personality Scale","fullName":"Proactive Personality Scale (PPS)","aliases":["PPS","Bateman Crant Scale","Proactivity"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"personality-trait","year":"1993","originator":"Thomas S. Bateman","url":"https://scholargate.app/en/organizational-behavior/proactive-personality-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/proactive-personality-scale.md","definition":"The Proactive Personality Scale (PPS) measures individual differences in the inclination to take action to shape one's environment and future. Developed by Bateman and Crant in 1993, it quantifies the stable tendency to anticipate and initiate change rather than react passively. The scale predicts career advancement, entrepreneurial intent, and organizational citizenship behaviors, making it valuable in selection, development, and research contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas S. Bateman","subfamily":"personality-trait","year":"1993","type":"Self-report questionnaire"},"citations":[{"ref":"Bateman, T. S., & Crant, J. M. (1993). The proactive component of organizational behavior: A measure and correlates. Journal of Organizational Behavior, 14(2), 103–118.","type":"article","doi":"10.1002/job.4030140202","isbn":null,"url":null},{"ref":"Seibert, S. E., Kraimer, M. L., & Crant, J. M. (2001). What do proactive people do? A longitudinal model linking proactive personality and career success. Personnel Psychology, 54(4), 845–874.","type":"article","doi":"10.1111/j.1744-6570.2001.tb00234.x","isbn":null,"url":null},{"ref":"Parker, S. K., Bindl, U. K., & Strauss, K. (2010). Making things happen: A model of proactive motivation. Journal of Management, 36(4), 827–856.","type":"article","doi":"10.1177/0149206310363732","isbn":null,"url":null}],"related":["psychological-capital-questionnaire","core-self-evaluations-scale","career-adapt-abilities-scale","entrepreneurial-intention-questionnaire","perceived-organizational-support"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prob-aras","name":"PROB-ARAS","fullName":"Prob-ARAS — Stochastic extension of PROB-ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Zavadskas, E. K. Turskis, Z.","url":"https://scholargate.app/en/decision-making/prob-aras","markdownUrl":"https://scholargate.app/en/decision-making/prob-aras.md","definition":"PROB-ARAS (Prob-ARAS — Stochastic extension of PROB-ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K. Turskis, Z. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K. Turskis, Z.","subfamily":"Ranking","year":"2010","type":"Monte Carlo / stochastic weight uncertainty extension of ARAS","value_space":"stochastic","uncertainty":"aleatoric","compensation":"full","rank_reversal":false},"citations":[{"ref":"Zavadskas, E. K., Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+additive+ratio+assessment+%28ARAS%29+method+in+multicriteria+decision-making+Zavadskas"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"probabilistic-roadmap","name":"Probabilistic Roadmap","fullName":"Probabilistic Roadmap","aliases":["PRM","Roadmap Method"],"domain":"control-theory","family":"ml-model","subfamily":"Motion Planning","year":"1996","originator":"Lydia Kavraki","url":"https://scholargate.app/en/control-theory/probabilistic-roadmap","markdownUrl":"https://scholargate.app/en/control-theory/probabilistic-roadmap.md","definition":"The Probabilistic Roadmap (PRM) method is a motion planning algorithm that builds a pre-computed graph (roadmap) of feasible paths through the configuration space by sampling random configurations and connecting them if collision-free. Introduced by Kavraki et al. in 1996, PRM is powerful for multi-query planning scenarios where many path queries are answered, amortizing roadmap construction cost across many queries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lydia Kavraki","subfamily":"Motion Planning","year":"1996","type":"algorithm"},"citations":[{"ref":"Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566-580.","type":"article","doi":"10.1109/70.508439","isbn":null,"url":null},{"ref":"Overmars, M. H., & Svestka, P. (1992). A probabilistic learning approach to motion planning. Proceedings of the Fourth Workshop on Algorithmic Foundations of Robotics, 19-37.","type":"article","doi":null,"isbn":null,"url":"https://www.cs.ruu.nl/publications/rrr/rrr14-95.pdf"},{"ref":"LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"http://planning.cs.illinois.edu/"}],"related":["rapidly-exploring-random-tree","model-predictive-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"probabilistic-seismic-hazard-analysis","name":"Probabilistic Seismic Hazard Analysis","fullName":"Probabilistic Seismic Hazard Analysis (PSHA)","aliases":["PSHA","seismic hazard analysis","probabilistic earthquake hazard assessment","Cornell-McGuire method"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Seismic hazard and risk engineering","year":"1968","originator":"C. Allin Cornell","url":"https://scholargate.app/en/civil-engineering/probabilistic-seismic-hazard-analysis","markdownUrl":"https://scholargate.app/en/civil-engineering/probabilistic-seismic-hazard-analysis.md","definition":"Probabilistic Seismic Hazard Analysis (PSHA) is a quantitative engineering framework used in civil and geotechnical engineering to estimate the likelihood that ground shaking will exceed a specified intensity level at a site within a given time window. By combining earthquake source geometry, recurrence statistics, and ground-motion attenuation models, PSHA produces hazard curves and maps that inform seismic design codes, infrastructure planning, and risk management decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"C. Allin Cornell","year":"1968","type":"Quantitative probabilistic framework","dataType":"Seismicity catalogs, fault geometry, ground-motion records","subfamily":"Seismic hazard and risk engineering"},"citations":[{"ref":"Cornell, C. A. (1968). Engineering seismic risk analysis. Bulletin of the Seismological Society of America, 58(5), 1583–1606.","type":"journal-article","doi":null,"isbn":null,"url":"https://pubs.geoscienceworld.org/ssa/bssa/article/58/5/1583/116697/Engineering-seismic-risk-analysis"},{"ref":"Kramer, S. L. (1996). Geotechnical Earthquake Engineering. Prentice Hall.","type":"book","doi":null,"isbn":"978-0133749434","url":null}],"related":["deterministic-seismic-hazard-analysis","ground-motion-prediction-equations","seismic-risk-assessment","fragility-analysis","fault-rupture-hazard-analysis","monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"probid","name":"PROBID","fullName":"Preference Ranking on the Basis of Ideal-Average Distance","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Wang, Z., Rangaiah, G. P., Wang, X.","url":"https://scholargate.app/en/decision-making/probid","markdownUrl":"https://scholargate.app/en/decision-making/probid.md","definition":"PROBID (Preference Ranking on the Basis of Ideal-Average Distance) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, Z., Rangaiah, G. P., Wang, X. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang, Z., Rangaiah, G. P., Wang, X.","subfamily":"Ranking","year":"2021","type":"Multi-ideal distance ranking with harmonic weighting","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Wang, Z., Rangaiah, G. P., Wang, X. (2021). Preference ranking on the basis of ideal-average distance method for multi-criteria decision-making. Industrial & Engineering Chemistry Research","type":"article","doi":"10.1021/acs.iecr.1c01413","isbn":null,"url":null}],"related":["ahp","bwm","critic","entropy","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"probit-model","name":"Probit Model","fullName":"Probit Regression Model","aliases":["probit regression","normit model","Probit Modeli"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2018,"originator":"Greene (textbook treatment); classical discrete-choice modelling","url":"https://scholargate.app/en/econometrics/probit-model","markdownUrl":"https://scholargate.app/en/econometrics/probit-model.md","definition":"The probit model is a regression method for a binary (0/1) outcome that maps a linear index of the predictors through the standard normal cumulative distribution function to produce a probability. It is a classical discrete-choice alternative to logistic regression, developed in standard econometrics treatments such as Greene's Econometric Analysis (2018).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greene (textbook treatment); classical discrete-choice modelling","year":2018,"type":"Binary discrete-choice model","estimator":"Maximum likelihood","link":"Standard normal cumulative distribution function (Φ)","outcome":"binary"},"citations":[{"ref":"Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134461366","url":null}],"related":["logistic-regression","ols-regression","panel-fixed-effects","instrumental-variables","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"problem-areas-in-diabetes","name":"Problem Areas in Diabetes Scale","fullName":"Problem Areas in Diabetes Scale (PAID)","aliases":["PAID"],"domain":"cardiology","family":"process-pipeline","subfamily":"diabetes-specific emotional and behavioral problems","year":"1995","originator":"William H. Polonsky","url":"https://scholargate.app/en/cardiology/problem-areas-in-diabetes","markdownUrl":"https://scholargate.app/en/cardiology/problem-areas-in-diabetes.md","definition":"The Problem Areas in Diabetes Scale (PAID) is a 20-item self-report measure that assesses emotional and behavioral problems related to diabetes self-management, including worries about complications, regimen burden, social and family challenges, and emotional distress. Originally developed by Polonsky and colleagues in 1995, the PAID has been extensively validated and remains one of the most widely used diabetes-specific emotional assessment tools in research and clinical practice, particularly for identifying psychosocial barriers to optimal diabetes control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William H. Polonsky","subfamily":"diabetes-specific emotional and behavioral problems","year":"1995","type":"Self-report questionnaire"},"citations":[{"ref":"Welch, G. W., Weinger, K., Anderson, B., & Polonsky, W. H. (1997). Responsiveness of the Problem Areas In Diabetes (PAID) questionnaire. Diabetes Care, 20(5), 696–702.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Responsiveness+of+the+Problem+Areas+In+Diabetes+%28PAID%29+questionnaire+Welch"},{"ref":"Polonsky, W. H., Anderson, B. J., Lohrer, P. A., Welch, G. W., Jacobson, A. M., Aponte, J. E., & Schwartz, C. E. (1995). Assessment of diabetes-related emotional and behavioral problems: Validation of the Problem Areas In Diabetes Scale. Diabetes Care, 18(10), 1330–1336.","type":"article","doi":"10.1037/t49574-000","isbn":null,"url":null}],"related":["diabetes-distress-scale","diabetes-self-efficacy-scale","hypoglycemia-fear-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"problematic-smartphone-use-scale","name":"PSUS","fullName":"Problematic Smartphone Use Scale","aliases":["PSUS","Smartphone Addiction Scale","Mobile Phone Addiction"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"smartphone and mobile technology addiction","year":"2017","originator":"Zahra Hussain, Mark Griffiths, David Sheffield","url":"https://scholargate.app/en/clinical-psychology/problematic-smartphone-use-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/problematic-smartphone-use-scale.md","definition":"The PSUS is a self-report questionnaire measuring compulsive smartphone use, withdrawal symptoms, and loss of control related to mobile devices. Developed by Hussain, Griffiths, and Sheffield in 2017, it targets the growing phenomenon of smartphone addiction in the digital age. The PSUS captures how smartphone dependence differs from general internet addiction, with particular focus on the constant connectivity and notification-driven engagement of mobile devices. Related instruments include the Smartphone Addiction Scale (SAS) by Kwon and colleagues, which focuses on adolescents.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zahra Hussain, Mark Griffiths, David Sheffield","subfamily":"smartphone and mobile technology addiction","year":"2017","type":"Self-report questionnaire"},"citations":[{"ref":"Hussain, Z., Griffiths, M. D., & Sheffield, D. (2017). An investigation into problematic smartphone use: The role of narcissism, anxiety, and personality factors. Journal of Behavioral Addictions, 6(3), 378–386.","type":"article","doi":"10.1556/2006.6.2017.052","isbn":null,"url":null},{"ref":"Kwon, M., Kim, D. J., Cho, H., & Yang, S. (2013). The Smartphone Addiction Scale: Development and validation of a short version for adolescents. PLOS ONE, 8(12), e83558.","type":"article","doi":"10.1371/journal.pone.0083558","isbn":null,"url":null},{"ref":"Chen, I. H., Pakpour, A. H., Leung, H., et al. (2020). Severe smartphone addiction and its association with depression, quality of life and spending behavior in adolescents. International Journal of Environmental Research and Public Health, 17(10), 3612.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Severe+smartphone+addiction+and+its+association+with+depression%2C+quality+of+life+and+spending+behavior+in+adolescents+Chen"}],"related":["internet-addiction-test","gambling-disorder-identification","yale-food-addiction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"process-capability-analysis","name":"Process Capability Analysis","fullName":"Process Capability Analysis (Cp, Cpk)","aliases":["Process Capability Indices","Capability Study","Süreç Yeterlilik Analizi","Process Performance Analysis"],"domain":"statistics","family":"process-pipeline","subfamily":"Statistical process control","year":1986,"originator":"Victor Kane","url":"https://scholargate.app/en/statistics/process-capability-analysis","markdownUrl":"https://scholargate.app/en/statistics/process-capability-analysis.md","definition":"Process Capability Analysis quantifies how well a manufacturing or business process produces output within specified tolerance limits. Introduced formally by Victor Kane in 1986, it summarises process spread and centering into dimensionless indices — most notably Cp and Cpk — allowing engineers and quality managers to judge whether a stable process is inherently capable of meeting customer or design specifications consistently.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Victor Kane","year":1986,"type":"Quantitative process evaluation index","subfamily":"Statistical process control","output":"Dimensionless capability ratio (Cp, Cpk)","assumption":"Process output is normally distributed and in statistical control"},"citations":[{"ref":"Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41–52.","type":"article","doi":"10.1080/00224065.1986.11978984","isbn":null,"url":null}],"related":["shewhart-control-chart","six-sigma-dmaic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"process-mining","name":"Process Mining","fullName":"Process Mining","aliases":["Workflow Mining","Event Log Analysis","Process Discovery","Süreç Madenciliği"],"domain":"process-mining","family":"process-pipeline","subfamily":"Process analytics","year":2016,"originator":"Wil van der Aalst","url":"https://scholargate.app/en/process-mining/process-mining","markdownUrl":"https://scholargate.app/en/process-mining/process-mining.md","definition":"Process Mining is a data-driven discipline that extracts knowledge about real-world processes from event logs recorded by information systems. Introduced systematically by Wil van der Aalst, with foundational workflow mining formalized in 2004 and consolidated in the 2016 textbook, the technique bridges data science and process management. It enables organizations to discover how processes actually execute, check whether execution conforms to prescribed models, and diagnose performance bottlenecks — all directly from digital traces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wil van der Aalst","year":2016,"type":"Data-driven process analysis technique","subfamily":"Process analytics","input":"Event logs (case ID, activity, timestamp)","output":"Process model, conformance statistics, performance metrics"},"citations":[{"ref":"van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-3-662-49850-7","url":null},{"ref":"van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128–1142.","type":"article","doi":"10.1109/TKDE.2004.47","isbn":null,"url":null}],"related":["knowledge-graph-construction","sequence-mining","community-detection"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"process-tracing","name":"Process Tracing","fullName":"Process Tracing","aliases":[],"domain":"psychometrics","family":"latent-structure","subfamily":"Case Study Methodology","year":"2005","originator":"Alexander George, Andrew Bennett","url":"https://scholargate.app/en/psychometrics/process-tracing","markdownUrl":"https://scholargate.app/en/psychometrics/process-tracing.md","definition":"Process Tracing is a qualitative research method developed by George and Bennett (2005) for studying causal mechanisms and causal chains within individual cases. It involves examining the sequence of events and decision-making processes within a case to infer whether a hypothesized causal mechanism actually operated. Process tracing aims to strengthen causal inference in case studies by looking beyond correlation to understand how causes produce effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alexander George, Andrew Bennett","subfamily":"Case Study Methodology","year":"2005","type":"Qualitative causal inference"},"citations":[{"ref":"Bennett, A., & Checkel, J. T. (Eds.). (2015). Process Tracing: From Metaphor to Analytic Tool. Cambridge University Press.","type":"book","doi":"10.1017/cbo9781139858472.003","isbn":null,"url":null},{"ref":"George, A. L., & Bennett, A. (2005). Case Studies and Theory Development in the Social Sciences. MIT Press.","type":"article","doi":null,"isbn":"9780262072564","url":null},{"ref":"Fairfield, T., & Charman, A. E. (2017). Explicit causal chains? Evaluating new directions for process tracing. Comparative Political Studies, 50(12), 1584-1607.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Explicit+causal+chains+Fairfield"}],"related":["fsqca","necessary-condition-analysis","exploratory-structural-equation-modeling","latent-transition-analysis","rule-space-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"procrastination-assessment-scale","name":"Procrastination Assessment Scale for Students","fullName":"Procrastination Assessment Scale for Students (PASS)","aliases":["PASS"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"academic-behavior-assessment","year":"1984","originator":"Solomon, Rothblum","url":"https://scholargate.app/en/educational-psychology/procrastination-assessment-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/procrastination-assessment-scale.md","definition":"The Procrastination Assessment Scale for Students is a comprehensive instrument measuring the frequency of academic procrastination across multiple task types and identifying the underlying reasons for delay. Developed by Solomon and Rothblum in 1984, the PASS provides educators and researchers with actionable data about which academic tasks students avoid and why—information critical for designing targeted interventions to improve academic performance and reduce associated stress.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Solomon, Rothblum","subfamily":"academic-behavior-assessment","year":"1984","type":"Self-report questionnaire"},"citations":[{"ref":"Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology, 31(4), 503-509.","type":"article","doi":"10.1037/0022-0167.31.4.503","isbn":null,"url":null},{"ref":"Ferrari, J. R., Johnson, J. L., & McCown, W. G. (1995). Procrastination and task avoidance: Theory, research, and treatment. Plenum Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=procrastination+assessment+scale+students"}],"related":["academic-burnout-scale","test-anxiety-inventory","academic-resilience-scale","academic-help-seeking-scale","study-skills-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"professional-identity-scale","name":"PIS","fullName":"Professional Identity Scale","aliases":["Healthcare Professional Identity","Disciplinary Identity Assessment"],"domain":"health-education","family":"process-pipeline","subfamily":"professional-socialization","year":"2006","originator":"Adams et al.","url":"https://scholargate.app/en/health-education/professional-identity-scale","markdownUrl":"https://scholargate.app/en/health-education/professional-identity-scale.md","definition":"The PIS is a self-report questionnaire measuring healthcare students' sense of professional identity, belonging, and commitment to their chosen discipline. Developed by Adams and colleagues in 2006, the PIS assesses the degree to which students have internalized professional roles, values, behaviors, and career commitment. The scale measures both cognitive elements (knowledge of professional standards and scope of practice) and emotional elements (sense of belonging, pride in discipline). The PIS is used in healthcare education to track professional identity development over training, identify students at risk of attrition, and evaluate the impact of socialization experiences on disciplinary commitment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adams et al.","subfamily":"professional-socialization","year":"2006","type":"Self-report questionnaire"},"citations":[{"ref":"Adams, K., Hean, S., Sturgis, P., & Clark, J. M. (2006). Investigating the factors influencing professional identity of first-year health and social care students. Learn Health Soc Care 5(2): 55–68.","type":"article","doi":"10.1111/j.1473-6861.2006.00119.x","isbn":null,"url":null},{"ref":"Weidman, J. C. & Stein, E. L. (2003). Socialization of doctoral students to academic careers. New Dir High Educ 101: 65–78.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Socialization+of+doctoral+students+to+academic+careers+Weidman"}],"related":["ripls","clinical-learning-environment-scale","interprofessional-collaboration-scale","reflective-practice-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"profile-of-mood-states","name":"Profile of Mood States","fullName":"Profile of Mood States (POMS)","aliases":["POMS","POMS-2"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"mood-and-affect","year":"1971","originator":"Douglas McNair, Maurice Lorr, Lorne Droppleman","url":"https://scholargate.app/en/sport-psychology/profile-of-mood-states","markdownUrl":"https://scholargate.app/en/sport-psychology/profile-of-mood-states.md","definition":"The POMS is a 65-item self-report questionnaire that measures six dimensions of mood: Tension, Depression, Anger, Vigor, Fatigue, and Confusion. Developed by McNair, Lorr, and Droppleman in 1971, it has become a cornerstone instrument in sport psychology for monitoring athlete psychological state before competition, during training, and in response to interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas McNair, Maurice Lorr, Lorne Droppleman","subfamily":"mood-and-affect","year":"1971","type":"Self-report mood adjectives questionnaire"},"citations":[{"ref":"McNair, D. M., Lorr, M., & Droppleman, L. F. (1971). Profile of Mood States. Educational and Industrial Testing Service (EDITS).","type":"book","doi":null,"isbn":null,"url":"https://edits.net/poms"},{"ref":"McNair, D. M., Lorr, M., & Droppleman, L. F. (1992). Manual for the Profile of Mood States (Revised). San Diego, CA: EDITS.","type":"article","doi":null,"isbn":null,"url":"https://edits.net/poms-manual"}],"related":["competitive-state-anxiety-inventory","sport-confidence-inventory","mental-toughness-questionnaire","task-ego-orientation-sport"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prognostics-and-remaining-useful-life","name":"Prognostics and Remaining Useful Life","fullName":"Prognostics and Remaining Useful Life (RUL) Prediction","aliases":["RUL","Remaining useful life","PHM","Prognostics and Health Management"],"domain":"reliability-engineering","family":"process-pipeline","subfamily":"Condition monitoring and predictive maintenance","year":"2000s","originator":"George Vachtsevanos and others","url":"https://scholargate.app/en/reliability-engineering/prognostics-and-remaining-useful-life","markdownUrl":"https://scholargate.app/en/reliability-engineering/prognostics-and-remaining-useful-life.md","definition":"Prognostics and Health Management (PHM) is a methodology for predicting the remaining useful life (RUL) of equipment by monitoring its condition and extrapolating degradation trends. Unlike reactive maintenance (wait for failure) or preventive maintenance (fixed schedules), prognostics enable predictive maintenance: act only when failure is imminent. Formalized in the 2000s by researchers including George Vachtsevanos, RUL prediction integrates sensor data, degradation models, and uncertainty quantification to inform maintenance planning and reduce downtime.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Vachtsevanos and others","subfamily":"Condition monitoring and predictive maintenance","year":"2000s","type":"Predictive analytics methodology"},"citations":[{"ref":"Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Wiley.","type":"book","doi":"10.1002/9780470117842","isbn":null,"url":null},{"ref":"Saxena, A., Celaya, J., Balaji, B., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2010). Metrics for evaluating the accuracy of prognostic techniques. International Journal of Prognostics and Health Management, 1(1), 1-20.","type":"article","doi":null,"isbn":null,"url":"https://www.phmsociety.org/sites/phmsociety.org/files/ijphm_special_issue_on_metrics_2010.pdf"},{"ref":"Goebel, K., Saha, B., & Saxena, A. (2008). A comparison of three data-driven techniques for prognostics. IEEE Aerospace Conference, 1-11.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+comparison+of+three+data-driven+techniques+for+prognostics+Goebel"},{"ref":"Si, X. S., Wang, W., Hu, C. H., & Chen, M. Y. (2012). Remaining useful life estimation based on stochastic degradation models. Reliability Engineering & System Safety, 99, 146-154.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Remaining+useful+life+estimation+based+on+stochastic+degradation+models+Si"}],"related":["rainflow-counting","highly-accelerated-life-testing","first-order-reliability-method","finite-element-model-updating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"program-evaluation","name":"Program Evaluation","fullName":"Program Evaluation Research","aliases":["evaluation research","program assessment","educational evaluation","systematic program evaluation"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"1960s–1970s (Scriven 1967; Stufflebeam CIPP model 1971)","originator":"Michael Scriven; Daniel Stufflebeam; Peter Rossi","url":"https://scholargate.app/en/field-methods/program-evaluation","markdownUrl":"https://scholargate.app/en/field-methods/program-evaluation.md","definition":"Program evaluation is a systematic, empirically grounded process of collecting and analyzing information about a program to determine its merit, worth, or significance. Applied across education, public health, social services, and policy, it addresses questions such as whether a program is reaching its target population, whether it is being implemented as designed, and whether it is producing the intended outcomes. It draws on both quantitative and qualitative methods and serves accountability, improvement, or knowledge-generation purposes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Scriven; Daniel Stufflebeam; Peter Rossi","year":"1960s–1970s (Scriven 1967; Stufflebeam CIPP model 1971)","type":"Applied evaluation methodology","dataType":"Quantitative outcomes data, qualitative interviews, documents, observations","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage.","type":"book","doi":null,"isbn":"978-0761908944","url":null},{"ref":"Stufflebeam, D. L., & Shinkfield, A. J. (2007). Evaluation Theory, Models, and Applications. Jossey-Bass.","type":"book","doi":null,"isbn":"978-0787977566","url":null}],"related":["educational-action-research","curriculum-analysis","design-based-research","needs-assessment","logic-model","mixed-methods-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prolonged-grief-disorder-scale","name":"PG-13","fullName":"Prolonged Grief Disorder Scale","aliases":["PG-13","Prigerson PG-13","Prolonged Grief Symptom Scale"],"domain":"bereavement-psychology","family":"process-pipeline","subfamily":"diagnosis-focused-grief-assessment","year":"2008","originator":"Holly G. Prigerson, Paul K. Maciejewski","url":"https://scholargate.app/en/bereavement-psychology/prolonged-grief-disorder-scale","markdownUrl":"https://scholargate.app/en/bereavement-psychology/prolonged-grief-disorder-scale.md","definition":"The Prolonged Grief Disorder Scale (PG-13) is a 13-item diagnostic assessment tool developed by Prigerson and Maciejewski to operationalize the DSM-5-TR diagnostic criteria for Prolonged Grief Disorder. Designed as a structured clinical instrument, the PG-13 directly maps onto the symptomatic, cognitive, and functional criteria required for diagnosis, making it invaluable in clinical and research settings where standardized diagnostic assessment is needed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holly G. Prigerson, Paul K. Maciejewski","subfamily":"diagnosis-focused-grief-assessment","year":"2008","type":"Self-report questionnaire"},"citations":[{"ref":"Prigerson, H. G., & Maciejewski, P. K. (2008). Prolonged grief disorder: Defining the disorder and addressing its clinical and public health significance. Psychotherapy and Psychosomatics, 77(6), 365–376.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prolonged+grief+disorder%3A+Defining+the+disorder+and+addressing+its+clinical+and+public+health+significance+Prigerson"}],"related":["inventory-complicated-grief","texas-revised-inventory-grief","hogan-grief-reaction-checklist","anticipatory-grief-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"promethee-gaia","name":"PROMETHEE-GAIA","fullName":"Preference Ranking Organization Method for Enrichment Evaluations with Geometric Analysis for Interactive Aid (PROMETHEE-GAIA)","aliases":["PROMETHEE-GAIA","PROMETHEE with GAIA"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Jean-Pierre Brans and Philippe Vincke","url":"https://scholargate.app/en/decision-making/promethee-gaia","markdownUrl":"https://scholargate.app/en/decision-making/promethee-gaia.md","definition":"PROMETHEE-GAIA combines two complementary tools: PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) for ranking alternatives through pairwise preference modeling, and GAIA (Geometric Analysis for Interactive Aid) for visual representation and sensitivity analysis. While PROMETHEE produces a ranking, GAIA displays the relative importance of criteria and the position of alternatives in a 2D plane, facilitating stakeholder understanding and decision adjustment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Pierre Brans and Philippe Vincke","subfamily":"Ranking","year":"1986","type":"Pairwise preference-based ranking with visual analysis"},"citations":[{"ref":"Brans, J. P., & Vincke, P. (1985). A preference ranking organization method: The PROMETHEE method for MCDM. Management Science, 31(6), 647-656.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+preference+ranking+organization+method%3A+The+PROMETHEE+method+for+MCDM+Brans"},{"ref":"Brans, J. P., & Mareschal, B. (2005). PROMETHEE methods. In J. Figueira, S. Greco, & M. Ehrgott (Eds.), Multiple criteria decision analysis: State of the art surveys (pp. 163-195). Springer.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1007/b100605"}],"related":["promethee","electre","topsis","vikor","graph-based-ranking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"promethee-i","name":"PROMETHEE-I","fullName":"PROMETHEE I — Preference Ranking Organisation METHod for Enrichment Evaluations I (partial ranking)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Brans, J. P., Vincke, P., Mareschal, B.","url":"https://scholargate.app/en/decision-making/promethee-i","markdownUrl":"https://scholargate.app/en/decision-making/promethee-i.md","definition":"PROMETHEE-I (PROMETHEE I — Preference Ranking Organisation METHod for Enrichment Evaluations I (partial ranking)) is a ranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P., Vincke, P., Mareschal, B. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brans, J. P., Vincke, P., Mareschal, B.","subfamily":"Ranking","year":"1986","type":"Outranking via positive/negative flows (partial preorder)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Brans, J. P., Vincke, P., Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research","type":"article","doi":"10.1016/0377-2217(86)90044-5","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"promethee-ii","name":"PROMETHEE-II","fullName":"PROMETHEE II — Preference Ranking Organisation Method for Enrichment of Evaluations II","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1985","originator":"Brans, J. P., Vincke, P.","url":"https://scholargate.app/en/decision-making/promethee-ii","markdownUrl":"https://scholargate.app/en/decision-making/promethee-ii.md","definition":"PROMETHEE-II (PROMETHEE II — Preference Ranking Organisation Method for Enrichment of Evaluations II) is a outranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P., Vincke, P. in 1985. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brans, J. P., Vincke, P.","subfamily":"Outranking","year":"1985","type":"Outranking (pairwise preference flows — complete ranking)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Brans, J. P., Vincke, P. (1985). A preference ranking organisation method (The PROMETHEE method for multiple criteria decision-making). Management Science","type":"article","doi":"10.1287/mnsc.31.6.647","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","delphi","entropy","fucom","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"promethee-iii","name":"PROMETHEE-III","fullName":"PROMETHEE III — Preference Ranking Organisation METHod for Enrichment Evaluations III (interval ranking)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1986","originator":"Brans, J. P. Vincke, P. Mareschal, B.","url":"https://scholargate.app/en/decision-making/promethee-iii","markdownUrl":"https://scholargate.app/en/decision-making/promethee-iii.md","definition":"PROMETHEE-III (PROMETHEE III — Preference Ranking Organisation METHod for Enrichment Evaluations III (interval ranking)) is a outranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P. Vincke, P. Mareschal, B. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brans, J. P. Vincke, P. Mareschal, B.","subfamily":"Outranking","year":"1986","type":"Outranking via interval flows derived from net-flow mean and dispersion","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Brans, J. P., Vincke, P., Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research","type":"article","doi":"10.1016/0377-2217(86)90044-5","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"promethee-v","name":"PROMETHEE-V","fullName":"PROMETHEE V — PROMETHEE with Integer Programming Constraints","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1992","originator":"Brans, J. P. Mareschal, B.","url":"https://scholargate.app/en/decision-making/promethee-v","markdownUrl":"https://scholargate.app/en/decision-making/promethee-v.md","definition":"PROMETHEE-V (PROMETHEE V — PROMETHEE with Integer Programming Constraints) is a ranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P. Mareschal, B. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brans, J. P. Mareschal, B.","subfamily":"Ranking","year":"1992","type":"PROMETHEE II net flows maximized over a subset selection IP problem","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Brans, J. P., Mareschal, B. (1992). PROMETHEE V: MCDM problems with segmentation constraints. INFOR: Information Systems and Operational Research","type":"article","doi":"10.1080/03155986.1992.11732186","isbn":null,"url":null}],"related":["promethee","promethee-i"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"promethee-vi","name":"PROMETHEE-VI","fullName":"PROMETHEE VI — Walking Weights Sensitivity","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1995","originator":"Brans, J. P. Mareschal, B.","url":"https://scholargate.app/en/decision-making/promethee-vi","markdownUrl":"https://scholargate.app/en/decision-making/promethee-vi.md","definition":"PROMETHEE-VI (PROMETHEE VI — Walking Weights Sensitivity) is a ranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P. Mareschal, B. in 1995. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brans, J. P. Mareschal, B.","subfamily":"Ranking","year":"1995","type":"Sensitivity analysis of PROMETHEE rankings across all feasible weight combinations","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Brans, J. P., Mareschal, B. (1995). The PROMETHEE VI procedure: how to differentiate hard from soft multicriteria problems. Journal of Decision Systems","type":"article","doi":"10.1080/12460125.1995.10511652","isbn":null,"url":null}],"related":["promethee","promethee-i"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"promethee","name":"PROMETHEE","fullName":"PROMETHEE II — Preference Ranking Organisation METHod for Enrichment of Evaluations","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"1986","originator":"Brans, J. P., Vincke, Ph., Mareschal, B.","url":"https://scholargate.app/en/decision-making/promethee","markdownUrl":"https://scholargate.app/en/decision-making/promethee.md","definition":"PROMETHEE (PROMETHEE II — Preference Ranking Organisation METHod for Enrichment of Evaluations) is a outranking multi-criteria decision-making (MCDM) method introduced by Brans, J. P., Vincke, Ph., Mareschal, B. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brans, J. P., Vincke, Ph., Mareschal, B.","subfamily":"Outranking","year":"1986","type":"Preference function (net flow)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Brans, J. P., Vincke, Ph., Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European Journal of Operational Research","type":"article","doi":"10.1016/0377-2217(86)90044-5","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"promis","name":"PROMIS","fullName":"Patient-Reported Outcomes Measurement Information System","aliases":["PROMIS measures","NIH PROMIS","Computer Adaptive Testing PROMIS"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Patient-reported outcome measurement","year":"2010","originator":"National Institutes of Health (NIH) and National Center for Health Statistics (NCHS)","url":"https://scholargate.app/en/health-measurement/promis","markdownUrl":"https://scholargate.app/en/health-measurement/promis.md","definition":"The Patient-Reported Outcomes Measurement Information System (PROMIS) is a comprehensive, flexible system of patient-reported outcome measures developed by the National Institutes of Health. Launched in 2010, PROMIS measures health across multiple domains using both fixed-item forms and computer-adaptive testing (CAT). It has become the gold standard for outcomes measurement in clinical trials and health systems research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"National Institutes of Health (NIH) and National Center for Health Statistics (NCHS)","subfamily":"Patient-reported outcome measurement","year":"2010","type":"Computer-adaptive testing and fixed-length patient-reported outcome measures"},"citations":[{"ref":"Cella, D., Yount, S., Rothrock, N., et al. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH Roadmap cooperative group during its first two years. Medical Care, 45(Suppl 1), S3–S11.","type":"article","doi":"10.1097/01.mlr.0000258615.42478.55","isbn":null,"url":null},{"ref":"Reeve, B. B., Hays, R. D., Bjorner, J. B., et al. (2013). Psychometric evaluation and calibration of health-related quality of life item banks. Medical Care, 45(Suppl 3), S22–S31.","type":"article","doi":"10.1097/01.mlr.0000250483.85507.04","isbn":null,"url":null},{"ref":"National Institutes of Health. (2023). PROMIS Measures Overview. National Institute of Child Health and Human Development.","type":"article","doi":null,"isbn":null,"url":"https://www.healthmeasures.net/promis"}],"related":["sf-36","sf-12","eq-5d","whoqol-bref","promis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prompt-engineering","name":"Prompt Engineering","fullName":"Prompt Engineering (Instruction Design for Large Language Models)","aliases":["instruction design","LLM prompting","Yönerge Mühendisliği (Prompt Engineering)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2020 (few-shot prompting); 2022 (chain-of-thought)","originator":"Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022)","url":"https://scholargate.app/en/text-mining/prompt-engineering","markdownUrl":"https://scholargate.app/en/text-mining/prompt-engineering.md","definition":"Prompt engineering is the practice of crafting structured natural-language instructions — prompts — to elicit targeted outputs from large language models (LLMs). Formalised by Brown et al. (2020) in the context of GPT-3 and extended by Wei et al. (2022) with chain-of-thought prompting, it encompasses four main strategies: zero-shot, few-shot, chain-of-thought, and tree-of-thought. Rather than re-training a model, the analyst shapes the model's behaviour entirely through the design of the input text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022)","year":"2020 (few-shot prompting); 2022 (chain-of-thought)","type":"NLP pipeline — structured instruction design for large language models","strategies":"Zero-shot / few-shot / chain-of-thought (CoT) / tree-of-thought (ToT)","input":"Natural-language text prompt sent to an LLM API","output":"Model-generated text (classification label, reasoning trace, summary, structured data, etc.)","minimumSamples":5,"difficulty":2,"requiresNormality":false},"citations":[{"ref":"Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2005.14165"},{"ref":"Wei, J. et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems (NeurIPS), 35.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2201.11903"}],"related":["retrieval-augmented-generation","few-shot-text-classification","zero-shot-classification","chain-of-thought-reasoning","text-classification","natural-language-generation","gpt-finetuning","lora-peft"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"propaganda-detection","name":"Propaganda Detection","fullName":"Propaganda and Manipulation Detection","aliases":["propaganda and manipulation detection","propaganda technique detection","Propaganda ve Manipülasyon Tespiti"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/propaganda-detection","markdownUrl":"https://scholargate.app/en/text-mining/propaganda-detection.md","definition":"Propaganda detection is a natural-language-processing task that automatically identifies and labels persuasion and manipulation techniques in text — such as loaded language, oversimplified solutions, bandwagon appeals, and glittering generalities. It builds on the fine-grained propaganda analysis introduced by Da San Martino et al. (2019), turning rhetorical manipulation into structured, technique-level labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-classification task","techniques":"Loaded language, oversimplification, bandwagon, glittering generalities, and other propaganda devices","output":"Per-span or per-document propaganda-technique labels","minSample":50},"citations":[{"ref":"Da San Martino, G. et al. (2019). Fine-Grained Analysis of Propaganda in News Articles. EMNLP.","type":"article","doi":"10.18653/v1/D19-1565","isbn":null,"url":null},{"ref":"Rashkin, H. et al. (2017). Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking. EMNLP.","type":"article","doi":"10.18653/v1/D17-1317","isbn":null,"url":null}],"related":["sentiment-analysis","emotion-detection","frame-analysis-nlp","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"propeller-lifting-line","name":"Propeller Lifting Line","fullName":"Propeller Lifting Line Theory","aliases":["lifting line theory","propeller design method","Goldstein method"],"domain":"aerospace","family":"process-pipeline","subfamily":"Hydrodynamics","year":"1929","originator":"Sydney Goldstein","url":"https://scholargate.app/en/aerospace/propeller-lifting-line","markdownUrl":"https://scholargate.app/en/aerospace/propeller-lifting-line.md","definition":"Propeller lifting line theory is a mathematical framework for analyzing and designing ship propellers by modeling each blade as a lifting line with circulation distribution. Developed by Sydney Goldstein in 1929 and refined by Kerwin and others, the method accounts for blade loading, wake effects, and propeller interactions. Lifting line theory provides efficient predictions of propeller thrust, torque, and efficiency and remains standard in preliminary propeller design and optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sydney Goldstein","subfamily":"Hydrodynamics","year":"1929","type":"Design theory"},"citations":[{"ref":"Goldstein, S. (1929). On the vortex theory of screw propellers. Proceedings of the Royal Society of London. Series A, 123(792), 440–465.","type":"article","doi":"10.1098/rspa.1929.0078","isbn":null,"url":null},{"ref":"Kerwin, J. E., & Lee, C. S. (1986). Prediction of steady and unsteady marine propeller performance by numerical lifting-surface theory. SNAME Transactions, 94, 332–377.","type":"article","doi":null,"isbn":null,"url":"https://www.sname.org"},{"ref":"Phillips, A. B., Turnock, S. R., & Furlong, M. (2010). Comparisons of CFD simulations of cavitating propellers with experiment. In Proceedings of the First International Symposium on Marine Propulsors (smp'09).","type":"article","doi":null,"isbn":null,"url":"https://www.soton.ac.uk"}],"related":["blade-element-momentum-theory","holtrop-mennen-method","seakeeping-strip-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"propensity-score-matching-in-education-research","name":"Propensity Score Matching in Education Research","fullName":"Propensity Score Matching Applied to Education Research","aliases":["PSM in education","educational PSM","PSM for program evaluation in schools","propensity matching education"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1983 (foundational); education adoption widespread from late 1990s","originator":"Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002)","url":"https://scholargate.app/en/causal-inference/propensity-score-matching-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/propensity-score-matching-in-education-research.md","definition":"Propensity Score Matching (PSM) in education research is a quasi-experimental technique that creates comparable treatment and control groups from observational student, teacher, or school data. By balancing groups on observed background characteristics, it enables credible causal estimates of educational interventions — such as tutoring programs, school choice policies, or teacher professional development — when random assignment is infeasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002)","year":"1983 (foundational); education adoption widespread from late 1990s","type":"Quasi-experimental / matching-based causal inference","dataType":"Observational cross-sectional or panel data with individual-level student, teacher, or school records","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["propensity-score-weighting","difference-in-differences","coarsened-exact-matching","regression-discontinuity-design","inverse-probability-weighting","matching-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"propensity-score-matching","name":"Propensity Score Matching","fullName":"Propensity Score Matching and Weighting","aliases":["PSM","propensity score weighting","covariate balance"],"domain":"research-statistics","family":"process-pipeline","subfamily":"causal-inference","year":"1983","originator":"Paul Rosenbaum and Donald Rubin","url":"https://scholargate.app/en/research-statistics/propensity-score-matching","markdownUrl":"https://scholargate.app/en/research-statistics/propensity-score-matching.md","definition":"Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Rosenbaum and Donald Rubin","subfamily":"causal-inference","year":"1983","type":"Method"},"citations":[{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null},{"ref":"Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding. Multivariate Behavioral Research, 46(3), 399–424.","type":"article","doi":"10.1080/00273171.2011.568786","isbn":null,"url":null},{"ref":"Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.","type":"article","doi":"10.1037/h0037350","isbn":null,"url":null}],"related":["logistic-regression","multiple-regression-analysis","survival-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"propensity-score-weighting-in-education-research","name":"Propensity Score Weighting in Education Research","fullName":"Propensity Score Weighting for Causal Inference in Education Research","aliases":["PSW in education","inverse probability weighting in education","IPW education","propensity weighting education"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1983 (theory); widely adopted in education research from 2000s","originator":"Rosenbaum & Rubin (foundational theory, 1983); Thoemmes & Kim (education-focused review, 2011)","url":"https://scholargate.app/en/causal-inference/propensity-score-weighting-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/propensity-score-weighting-in-education-research.md","definition":"Propensity score weighting (PSW) is a quasi-experimental technique that reweights observational samples so that treated and comparison students look similar on measured background characteristics, allowing credible causal estimates of educational interventions — such as program participation, instructional method, or school type — without random assignment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenbaum & Rubin (foundational theory, 1983); Thoemmes & Kim (education-focused review, 2011)","year":"1983 (theory); widely adopted in education research from 2000s","type":"Quasi-experimental causal inference","dataType":"Observational cross-sectional or longitudinal data with a binary or multi-valued treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null},{"ref":"Thoemmes, F. J., & Kim, E. S. (2011). A Systematic Review of Propensity Score Methods in the Social Sciences. Multivariate Behavioral Research, 46(1), 90-118.","type":"article","doi":"10.1080/00273171.2011.540475","isbn":null,"url":null}],"related":["propensity-score-matching","difference-in-differences","instrumental-variables","regression-discontinuity","covariate-adjustment","inverse-probability-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"propensity-score-weighting","name":"Propensity Score Weighting","fullName":"Propensity Score Weighting Estimator","aliases":["PSW","inverse probability weighting","IPW","propensity-based weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1983 (propensity score); 2003 (efficient IPW estimator)","originator":"Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)","url":"https://scholargate.app/en/causal-inference/propensity-score-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/propensity-score-weighting.md","definition":"Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)","year":"1983 (propensity score); 2003 (efficient IPW estimator)","type":"Causal inference / reweighting","dataType":"Observational cross-sectional or panel data with binary or multi-valued treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null},{"ref":"Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.","type":"article","doi":"10.1111/1468-0262.00442","isbn":null,"url":null}],"related":["propensity-score-matching","inverse-probability-weighting","doubly-robust-estimation","difference-in-differences","coarsened-exact-matching","entropy-balancing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prophet-model","name":"Prophet","fullName":"Prophet Decomposable Time Series Forecasting Model","aliases":["Prophet","Facebook Prophet","Meta Prophet","forecasting at scale","Prophet — Facebook/Meta Zaman Serisi Modeli"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2018,"originator":"Taylor & Letham (Facebook/Meta)","url":"https://scholargate.app/en/econometrics/prophet-model","markdownUrl":"https://scholargate.app/en/econometrics/prophet-model.md","definition":"Prophet is a Bayesian structural time series model introduced by Taylor and Letham at Facebook/Meta in 2018. It forecasts a continuous series by decomposing it into separate, interpretable trend, seasonality, and holiday components, and is designed to be approachable for analysts working at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Taylor & Letham (Facebook/Meta)","year":2018,"type":"Decomposable (structural) time series model","estimator":"Bayesian curve fitting (MAP or full posterior)","outcome":"continuous","structure":"time series","minSample":30},"citations":[{"ref":"Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.","type":"article","doi":"10.1080/00031305.2017.1380080","isbn":null,"url":null},{"ref":"Taylor, S. J. & Letham, B. (2017). Prophet: Forecasting at Scale [Software]. Facebook/Meta.","type":"software","doi":null,"isbn":null,"url":"https://facebook.github.io/prophet/"}],"related":["ets-model","holt-winters","structural-time-series","state-space-model","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportion-test","name":"Proportion Test","fullName":"Two-Proportion z-Test","aliases":["z-test for proportions","two-sample proportion test","one-proportion z-test","Oran Testi — z Testi (Oranlar)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1900,"originator":"Karl Pearson / classical large-sample z approximation","url":"https://scholargate.app/en/statistics/proportion-test","markdownUrl":"https://scholargate.app/en/statistics/proportion-test.md","definition":"The proportion test (z-test for proportions) is a parametric hypothesis test that compares one or two sample proportions against a reference value or each other. Grounded in the large-sample normal approximation formalized by Fleiss, Levin, and Paik (2003), it is the standard tool for binary outcome comparisons when samples are large enough for the central limit theorem to apply.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson / classical large-sample z approximation","year":1900,"family":"Hypothesis test","type":"Parametric proportion comparison","groups":"1 or 2","outcome":"binary / count","parametric":true,"distribution":"Standard normal (z)","minSamplePerCell":"np ≥ 5 and n(1−p) ≥ 5","difficulty":1},"citations":[{"ref":"Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Statistical Methods for Rates and Proportions (3rd ed.). Wiley.","type":"book","doi":"10.1002/0471445428","isbn":null,"url":null}],"related":["chi-square-test","binomial-test","fisher-exact-test","logistic-regression","independent-t-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportional-cluster-sampling","name":"Proportional Cluster Sampling","fullName":"Proportional Cluster Sampling (Probability Proportional to Size)","aliases":["PPS cluster sampling","proportional-to-size cluster sampling","size-proportional cluster sampling","probability proportional to size sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1950s–1960s","originator":"Formalized by William G. Cochran and Leslie Kish","url":"https://scholargate.app/en/survey-methodology/proportional-cluster-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/proportional-cluster-sampling.md","definition":"Proportional cluster sampling selects naturally occurring groups (clusters) from a population with probability proportional to each cluster's size, so that larger clusters have a higher chance of selection while every individual element retains an equal overall inclusion probability. This design efficiently handles large, geographically dispersed populations and is the backbone of national health, education, and social surveys worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized by William G. Cochran and Leslie Kish","year":"1950s–1960s","type":"Probability sampling design","dataType":"Population registers, census frames, or administrative lists with cluster size information","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471489009","url":null}],"related":["cluster-sampling","stratified-sampling","multistage-sampling","systematic-sampling","proportional-stratified-sampling","simple-random-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportional-convenience-sampling","name":"Proportional Convenience Sampling","fullName":"Proportional Convenience Sampling","aliases":["quota-constrained convenience sampling","representative convenience sampling","proportionate accidental sampling","PCS"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century onward","originator":"Developed within mainstream sampling methodology; no single originator","url":"https://scholargate.app/en/survey-methodology/proportional-convenience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/proportional-convenience-sampling.md","definition":"Proportional convenience sampling is a non-probability technique that recruits participants through convenience while constraining each subgroup's share in the final sample to match its known proportion in the target population. It trades pure random selection for feasibility, but partially compensates by ensuring the sample's compositional profile mirrors the population on one or more key variables such as gender, age group, or academic year.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within mainstream sampling methodology; no single originator","year":"Mid-20th century onward","type":"Non-probability sampling with proportional allocation constraint","dataType":"Any (quantitative, qualitative, mixed); commonly survey or observational data","subfamily":"Sampling"},"citations":[{"ref":"Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics International Journal, 5(6), 215–217.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Etikan+Bala+2017+Sampling+and+sampling+methods+Biometrics+Biostatistics+International+Journal"},{"ref":"Convenience sampling. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Convenience_sampling"}],"related":["convenience-sampling","quota-sampling","proportional-stratified-sampling","purposive-sampling","proportional-quota-sampling","simple-random-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportional-multistage-sampling","name":"Proportional Multistage Sampling","fullName":"Proportional Multistage Sampling","aliases":["proportional PPS multistage sampling","multistage probability proportional to size sampling","proportionate multistage cluster sampling","PPS multistage sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1950s–1960s","originator":"Leslie Kish; William G. Cochran (theoretical foundations)","url":"https://scholargate.app/en/survey-methodology/proportional-multistage-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/proportional-multistage-sampling.md","definition":"Proportional multistage sampling is a probability sampling design that selects units across two or more hierarchical stages — for example, regions, then districts, then households — where the number of units drawn at each stage is proportional to the size of each higher-level unit. By weighting selection probabilities to match cluster size, it produces self-weighting samples that closely mirror the population structure and simplify variance estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leslie Kish; William G. Cochran (theoretical foundations)","year":"1950s–1960s","type":"Probability sampling design","dataType":"Population registers, administrative lists, geographic/cluster units","subfamily":"Sampling"},"citations":[{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons. (Chapters 6–7 on multistage and PPS designs.)","type":"book","doi":null,"isbn":"978-0471489009","url":null},{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. (Chapter 11 on multistage sampling with proportional allocation.)","type":"book","doi":null,"isbn":"978-0471162407","url":null}],"related":["multistage-sampling","proportional-stratified-sampling","cluster-sampling","systematic-sampling","proportional-cluster-sampling","stratified-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportional-navigation","name":"Proportional Navigation","fullName":"Proportional Navigation Guidance Law","aliases":["PN","PN law"],"domain":"aerospace","family":"process-pipeline","subfamily":"Guidance Control","year":"1957","originator":"Lin-Hsiung Chu","url":"https://scholargate.app/en/aerospace/proportional-navigation","markdownUrl":"https://scholargate.app/en/aerospace/proportional-navigation.md","definition":"Proportional Navigation (PN) is a guidance law that generates command accelerations proportional to the rate of change of the line-of-sight angle between a pursuer and target. Introduced by Lin-Hsiung Chu in the 1950s, it became the foundation of modern missile guidance systems. PN solves the pursuit-evasion problem by ensuring that the pursuer intercepts a moving target with minimal computational overhead.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lin-Hsiung Chu","subfamily":"Guidance Control","year":"1957","type":"Guidance law"},"citations":[{"ref":"Knox, W. P. (1971). On optimal proportional navigation. IEEE Transactions on Aerospace and Electronic Systems, AES-7(3), 417–426.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+optimal+proportional+navigation+Knox"},{"ref":"Guelman, M. (1971). Proportional navigation with a maneuvering target. IEEE Transactions on Aerospace and Electronic Systems, AES-8(3), 364–371.","type":"article","doi":"10.1109/taes.1972.309520","isbn":null,"url":null},{"ref":"Lin, C. F., & Tsai, L. L. (2007). Guidance Laws for Missiles. In Modern Control Systems for Unmanned Aerial Vehicles. Butterworth-Heinemann.","type":"book","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/science/article/pii/B9780750668643500098"}],"related":["dubins-path","ahrs","madgwick-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportional-purposive-sampling","name":"Proportional Purposive Sampling","fullName":"Proportional Purposive Sampling","aliases":["proportional criterion sampling","quota-proportional purposive sampling","representational purposive sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1980s–2000s","originator":"Derived from purposive sampling tradition (Patton); formalized in mixed-methods literature","url":"https://scholargate.app/en/survey-methodology/proportional-purposive-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/proportional-purposive-sampling.md","definition":"Proportional purposive sampling combines the intentional case selection of purposive sampling with proportional allocation across subgroups. Researchers first determine how each meaningful subgroup (e.g., gender, school type, professional role) is represented in the population, then deliberately select participants from each subgroup in those same proportions — using purposive judgment to ensure each selected case is information-rich and relevant to the research question.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from purposive sampling tradition (Patton); formalized in mixed-methods literature","year":"1980s–2000s","type":"Non-probability sampling with proportional allocation","dataType":"Mixed or qualitative data; units selected by judgment within proportional cells","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1506386706","url":null}],"related":["purposive-sampling","proportional-stratified-sampling","quota-sampling","maximum-variation-sampling","stratified-purposive-sampling","theoretical-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportional-simple-random-sampling","name":"Proportional Simple Random Sampling","fullName":"Proportional Simple Random Sampling","aliases":["proportional SRS","probability-proportional simple random sampling","proportional random sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century (formalized ~1950s–1970s)","originator":"William G. Cochran and survey statisticians (classical probability sampling tradition)","url":"https://scholargate.app/en/survey-methodology/proportional-simple-random-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/proportional-simple-random-sampling.md","definition":"Proportional simple random sampling is a probability-based sampling technique in which units are drawn at random from each subgroup of the population in numbers proportional to each subgroup's share of the total population. This ensures the resulting sample mirrors the population's composition across key subgroups, while retaining the randomness and unbiasedness of simple random sampling within each group.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran and survey statisticians (classical probability sampling tradition)","year":"Mid-20th century (formalized ~1950s–1970s)","type":"Probability sampling technique","dataType":"Quantitative; requires a sampling frame with known population sizes per subgroup","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Lohr, S. L. (2009). Sampling: Design and Analysis (2nd ed.). Brooks/Cole.","type":"book","doi":null,"isbn":"978-0495105275","url":null}],"related":["simple-random-sampling","proportional-stratified-sampling","stratified-sampling","systematic-sampling","cluster-sampling","weighted-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportional-stratified-sampling","name":"Proportional Stratified Sampling","fullName":"Proportional Stratified Random Sampling","aliases":["proportionate stratified sampling","proportional allocation stratified sampling","PSRS","proportionate stratified random sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1953–1965 (formalized in survey sampling literature)","originator":"William G. Cochran; Leslie Kish","url":"https://scholargate.app/en/survey-methodology/proportional-stratified-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/proportional-stratified-sampling.md","definition":"Proportional stratified sampling divides the target population into non-overlapping strata (subgroups defined by a key characteristic such as age band, region, or gender) and then draws a simple random sample from each stratum so that each stratum's share of the total sample matches its share of the total population. Because each subgroup is represented in exact proportion to its population weight, the resulting sample mirrors the population structure closely without requiring post-hoc weighting adjustments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran; Leslie Kish","year":"1953–1965 (formalized in survey sampling literature)","type":"Probability sampling design","dataType":"Population frame with stratum membership data; quantitative or qualitative outcome variables","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471489009","url":null}],"related":["stratified-sampling","disproportional-stratified-sampling","simple-random-sampling","cluster-sampling","systematic-sampling","multistage-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportional-systematic-sampling","name":"Proportional Systematic Sampling","fullName":"Proportional Systematic Sampling","aliases":["proportional 1-in-k sampling","equal-probability systematic sampling","proportionate systematic selection","PPS systematic sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century (formalized ~1950s–1970s)","originator":"Codified in classical survey sampling theory; see Cochran (1977)","url":"https://scholargate.app/en/survey-methodology/proportional-systematic-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/proportional-systematic-sampling.md","definition":"Proportional systematic sampling combines systematic (every k-th element) selection with proportional allocation across subgroups, ensuring that each stratum contributes sample units in proportion to its share of the total population. The result is an equal-probability design that is administratively simple, spreads the sample evenly across an ordered frame, and eliminates the need for post-hoc weighting when strata are sampled at a uniform rate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Codified in classical survey sampling theory; see Cochran (1977)","year":"Mid-20th century (formalized ~1950s–1970s)","type":"Probability sampling design","dataType":"Finite population lists (sampling frames) with known stratum sizes","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Lohr, S. L. (2009). Sampling: Design and Analysis (2nd ed.). Brooks/Cole.","type":"book","doi":null,"isbn":"978-0495105275","url":null}],"related":["systematic-sampling","proportional-stratified-sampling","simple-random-sampling","stratified-sampling","cluster-sampling","multistage-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proportional-weighted-sampling","name":"Proportional Weighted Sampling","fullName":"Proportional Weighted Sampling","aliases":["proportional probability weighting","proportional weight sampling","probability proportional to size sampling","PPS sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century (formalized 1950s–1960s)","originator":"William G. Cochran; Leslie Kish","url":"https://scholargate.app/en/survey-methodology/proportional-weighted-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/proportional-weighted-sampling.md","definition":"Proportional weighted sampling is a probability-based survey design in which each subgroup (stratum or cluster) of the population is sampled and weighted in proportion to its true size in the population. By assigning sampling weights that mirror the actual composition of the population, the method ensures unbiased estimates without the need for post-hoc reweighting, and produces efficient estimates when variance within subgroups is relatively homogeneous.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran; Leslie Kish","year":"Mid-20th century (formalized 1950s–1960s)","type":"Probability sampling design","dataType":"Population registry data, survey frame with known subgroup sizes","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471489009","url":null}],"related":["stratified-sampling","weighted-sampling","proportional-stratified-sampling","cluster-sampling","systematic-sampling","multistage-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proprioception-assessment","name":"Proprioception Assessment","fullName":"Clinical Proprioceptive Assessment","aliases":["kinesthetic assessment","joint position sense testing"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Sensory and proprioceptive assessment","year":"1900s","originator":"Neurological assessment tradition","url":"https://scholargate.app/en/physical-therapy/proprioception-assessment","markdownUrl":"https://scholargate.app/en/physical-therapy/proprioception-assessment.md","definition":"Proprioceptive assessment is a bedside neurological examination evaluating the sense of joint position and movement, mediated by mechanoreceptors in muscles, tendons, and joints. Clinical testing of proprioception is essential for comprehensive neurological evaluation in conditions affecting sensory function, coordination, and balance, helping clinicians identify dorsal column disease, peripheral neuropathy, or cerebellar dysfunction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Neurological assessment tradition","subfamily":"Sensory and proprioceptive assessment","year":"1900s","type":"Clinical examination technique"},"citations":[{"ref":"Sherrington, C. S. (1906). The integrative action of the nervous system. Yale University Press.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/integrativeactio00sherrich"},{"ref":"Goble, D. J. (2010). Proprioceptive acuity assessment via joint position matching: From basic science to general clinical application. Physical Therapy Reviews, 15(1), 36-46.","type":"article","doi":"10.2522/ptj.20090399","isbn":null,"url":null}],"related":["manual-muscle-testing","range-of-motion-goniometry","electromyography-clinical"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prosa-c","name":"PROSA-C","fullName":"Sustainability Criteria-Level Preference Ranking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Ziemba, P.","url":"https://scholargate.app/en/decision-making/prosa-c","markdownUrl":"https://scholargate.app/en/decision-making/prosa-c.md","definition":"PROSA-C (Sustainability Criteria-Level Preference Ranking) is a ranking multi-criteria decision-making (MCDM) method introduced by Ziemba, P. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ziemba, P.","subfamily":"Ranking","year":"2019","type":"TOPSIS extended with sustainability coefficients at criterion level","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Ziemba, P. (2019). PROSA-C: preferences-based sustainability assessments at the criteria level of MCDA models. Sustainability","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=PROSA-C%3A+preferences-based+sustainability+assessments+at+the+criteria+level+of+MCDA+models+Ziemba"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-case-control-study","name":"Prospective Case-Control Study","fullName":"Prospective Case-Control Study","aliases":["prospective case-control design","ambidirectional case-control","bidirectional case-control","nested case-control (prospective variant)"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1970s–1990s (formalized alongside nested case-control methods)","originator":"Evolved from classical retrospective case-control methodology; prospective embedding attributed to modern epidemiological practice (Rothman, Greenland, and others, late 20th century)","url":"https://scholargate.app/en/epidemiology/prospective-case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-case-control-study.md","definition":"A prospective case-control study embeds the case-control logic within a defined cohort followed forward in time. Cases are identified as they occur, rather than looked up in records after the fact, and controls are sampled from the same prospectively monitored base population. This forward-looking approach allows collection of exposure data before outcome ascertainment, reducing recall bias — the principal weakness of the classic retrospective case-control design — while retaining the efficiency gains of sampling controls rather than enrolling a full cohort.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from classical retrospective case-control methodology; prospective embedding attributed to modern epidemiological practice (Rothman, Greenland, and others, late 20th century)","year":"1970s–1990s (formalized alongside nested case-control methods)","type":"Observational analytic study design","dataType":"Prospectively collected individual-level exposure and outcome data; incident cases and matched controls from a defined base population","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Case-control study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Case%E2%80%93control_study"}],"related":["case-control-study","nested-case-control","cohort-study","prospective-cohort-study","case-crossover-design","randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-case-crossover-design","name":"Prospective Case-Crossover Design","fullName":"Prospective Case-Crossover Epidemiological Design","aliases":["prospective case-crossover study","forward-looking case-crossover","prospective self-controlled case-crossover","real-time case-crossover"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1991 (base design); prospective variant described in late 1990s–2000s","originator":"Malcolm Maclure (case-crossover concept); prospective variant established by subsequent methodologists including Navidi and Weinhandl","url":"https://scholargate.app/en/epidemiology/prospective-case-crossover-design","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-case-crossover-design.md","definition":"The prospective case-crossover design is an observational epidemiological study in which each case serves as their own control. Unlike the retrospective variant, exposures are recorded in real time as participants are followed forward, eliminating recall bias. It is particularly suited to investigating transient environmental or behavioral triggers of acute events such as myocardial infarction, asthma attacks, or road-traffic injuries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Malcolm Maclure (case-crossover concept); prospective variant established by subsequent methodologists including Navidi and Weinhandl","year":"1991 (base design); prospective variant described in late 1990s–2000s","type":"Observational epidemiological study design","dataType":"Time-stamped exposure and event data collected prospectively; repeated measures on the same individual","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.","type":"article","doi":"10.1093/oxfordjournals.aje.a115853","isbn":null,"url":null},{"ref":"Navidi, W., & Weinhandl, E. (2002). Risk set sampling strategies for case-crossover studies. Epidemiology, 13(1), 100–105.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Risk+set+sampling+strategies+for+case-crossover+studies+Navidi"}],"related":["case-crossover-design","case-control-study","prospective-cohort-study","self-controlled-case-series","time-series-analysis","repeated-measures-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-case-series","name":"Prospective Case Series","fullName":"Prospective Case Series Study","aliases":["prospective case series study","forward-looking case series","prospective uncontrolled study","prospective observational case series"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Late 19th century onward; formalized in modern clinical epidemiology by the 1970s–1980s","originator":"Evolved from clinical case reporting traditions in 19th–20th century medicine","url":"https://scholargate.app/en/epidemiology/prospective-case-series","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-case-series.md","definition":"A prospective case series is an observational study design in which a group of patients with a particular condition, exposure, or intervention is identified in advance and followed forward in time according to a pre-specified protocol. Data on outcomes, adverse events, and clinical course are collected as they occur, yielding higher data quality and temporal clarity than retrospective designs. No control group is included, so causal inference is limited, but the design is valuable for characterizing natural disease history, early safety signals, and feasibility of new interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from clinical case reporting traditions in 19th–20th century medicine","year":"Late 19th century onward; formalized in modern clinical epidemiology by the 1970s–1980s","type":"Observational study design","dataType":"Clinical patient records, follow-up measurements, laboratory and imaging data collected prospectively","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Meinert, C. L. (1996). Clinical Trials: Design, Conduct, and Analysis. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195035681","url":null},{"ref":"Gagnier, J. J., Kienle, G., Altman, D. G., Moher, D., Sox, H., Riley, D., & the CARE Group. (2016). The CARE Guidelines: Consensus-based Clinical Case Reporting Guideline Development. Global Advances in Health and Medicine, 2(5), 38-43.","type":"article","doi":"10.7453/gahmj.2013.008","isbn":null,"url":null}],"related":["case-series","prospective-cohort-study","retrospective-case-series","case-report","cohort-study","diagnostic-accuracy-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-cohort-study","name":"Prospective Cohort Study","fullName":"Prospective Cohort Study Design","aliases":["longitudinal cohort study","prospective follow-up study","incidence study","prospective observational cohort"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1950s (systematic application); conceptual roots earlier","originator":"Richard Doll and Austin Bradford Hill (landmark application, 1951-1954); cohort methodology formalised by modern epidemiology textbooks","url":"https://scholargate.app/en/epidemiology/prospective-cohort-study","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-cohort-study.md","definition":"A prospective cohort study assembles a group of participants who are free of the outcome of interest at baseline, measures their exposures, and then follows them forward in time to record who develops the outcome. By collecting exposure data before outcomes occur, it establishes a clear temporal sequence that supports causal inference — a major advantage over retrospective designs. It is the cornerstone observational method in epidemiology and clinical research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Doll and Austin Bradford Hill (landmark application, 1951-1954); cohort methodology formalised by modern epidemiology textbooks","year":"1950s (systematic application); conceptual roots earlier","type":"Observational longitudinal study design","dataType":"Baseline exposure measurements followed by prospective outcome surveillance (binary or time-to-event outcomes)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Doll, R., & Hill, A. B. (1954). The mortality of doctors in relation to their smoking habits. British Medical Journal, 1(4877), 1451-1455.","type":"article","doi":"10.1136/bmj.1.4877.1451","isbn":null,"url":null}],"related":["cohort-study","retrospective-cohort-study","case-control-study","randomized-clinical-trial","survival-analysis","nested-case-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-competing-risks-analysis","name":"Prospective Competing Risks Analysis","fullName":"Prospective Competing Risks Analysis","aliases":["prospective CRA","prospective subdistribution hazard analysis","prospective cause-specific hazard analysis","forward-looking competing events analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1978–1999 (foundational frameworks; prospective application standard by 2000s)","originator":"Fine & Gray (subdistribution hazard model, 1999); Prentice, Kalbfleisch et al. (cause-specific hazard, 1978)","url":"https://scholargate.app/en/epidemiology/prospective-competing-risks-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-competing-risks-analysis.md","definition":"Prospective competing risks analysis is an observational study design that follows participants forward in time from a well-defined starting point, recording all events — including those that prevent the primary event from occurring — and then estimates cause-specific incidence while correctly accounting for competing outcomes. It combines the temporal clarity of prospective cohort follow-up with the statistical rigor of competing risks methodology to avoid the overestimation inherent in standard Kaplan-Meier curves when multiple event types are present.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fine & Gray (subdistribution hazard model, 1999); Prentice, Kalbfleisch et al. (cause-specific hazard, 1978)","year":"1978–1999 (foundational frameworks; prospective application standard by 2000s)","type":"Observational analytic study with event-time statistical analysis","dataType":"Time-to-event data from prospective follow-up; binary or categorical competing event outcomes","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.","type":"article","doi":"10.1080/01621459.1999.10474144","isbn":null,"url":null},{"ref":"Putter, H., Fiocco, M., & Geskus, R. B. (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine, 26(11), 2389–2430.","type":"article","doi":"10.1002/sim.2712","isbn":null,"url":null}],"related":["competing-risks-analysis","prospective-survival-analysis","prospective-cohort-study","cox-proportional-hazards","kaplan-meier-analysis","prospective-kaplan-meier-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-cox-proportional-hazards","name":"Prospective Cox proportional hazards","fullName":"Prospective Cox Proportional Hazards Regression","aliases":["prospective Cox regression","Cox PH prospective study","prospective survival regression","prospective hazard modeling"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1972 (Cox model); widespread prospective application from late 1970s","originator":"David R. Cox (model); applied prospectively in large cohort studies from 1970s onward","url":"https://scholargate.app/en/epidemiology/prospective-cox-proportional-hazards","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-cox-proportional-hazards.md","definition":"Prospective Cox proportional hazards regression combines a forward-looking cohort design — in which participants are enrolled before outcomes occur and followed over time — with Cox's semi-parametric survival model. The method estimates how baseline covariates measured at enrollment influence the rate at which participants experience a time-to-event outcome, while preserving the temporal direction required for causal inference. It is one of the most widely used analytical frameworks in clinical epidemiology and chronic disease research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David R. Cox (model); applied prospectively in large cohort studies from 1970s onward","year":"1972 (Cox model); widespread prospective application from late 1970s","type":"Semi-parametric survival regression applied to prospectively collected time-to-event data","dataType":"Prospectively collected time-to-event data with censoring, continuous and categorical covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null},{"ref":"Schoenfeld, D. (1982). Partial residuals for the proportional hazards regression model. Biometrika, 69(1), 239–241.","type":"article","doi":"10.1093/biomet/69.1.239","isbn":null,"url":null}],"related":["cox-proportional-hazards","prospective-cohort-study","kaplan-meier-analysis","prospective-kaplan-meier-analysis","competing-risks-analysis","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-diagnostic-accuracy-study","name":"Prospective Diagnostic Accuracy Study","fullName":"Prospective Diagnostic Accuracy Study","aliases":["prospective DTA study","prospective test accuracy study","forward-looking diagnostic study","prospective index test evaluation"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Formalized 2000s; practice dates to mid-20th century","originator":"Established through STARD initiative (Bossuyt, Reitsma et al., 2000s)","url":"https://scholargate.app/en/epidemiology/prospective-diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-diagnostic-accuracy-study.md","definition":"A prospective diagnostic accuracy study enrolls participants before any test results are known and follows them forward in time to evaluate how well an index test (the test under evaluation) distinguishes individuals with and without a target condition, using a reference standard applied independently. Key accuracy metrics include sensitivity, specificity, positive and negative predictive values, and the area under the ROC curve. The prospective design reduces many biases inherent in retrospective test evaluations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established through STARD initiative (Bossuyt, Reitsma et al., 2000s)","year":"Formalized 2000s; practice dates to mid-20th century","type":"Observational / evaluative study design","dataType":"Index test results, reference standard results, patient-level clinical data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., ... & Cohen, J. F. (2015). STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 351, h5527.","type":"article","doi":"10.1136/bmj.h5527","isbn":null,"url":null},{"ref":"Whiting, P. F., Rutjes, A. W., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., ... & Kleijnen, J. (2011). QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine, 155(8), 529-536.","type":"article","doi":"10.7326/0003-4819-155-8-201110180-00009","isbn":null,"url":null}],"related":["diagnostic-accuracy-study","retrospective-diagnostic-accuracy-study","cohort-study","screening-test-evaluation","cross-sectional-epidemiological-study","randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-dose-response-analysis","name":"Prospective Dose-Response Analysis","fullName":"Prospective Dose-Response Analysis in Epidemiological Research","aliases":["prospective exposure-response analysis","prospective trend analysis","forward-looking dose-response study","prospective gradient analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1965 (Hill's criteria); widely applied through 1980s–present","originator":"Bradford Hill (causal criteria including dose-response, 1965); formalized in modern epidemiology by Rothman, Greenland and others","url":"https://scholargate.app/en/epidemiology/prospective-dose-response-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-dose-response-analysis.md","definition":"Prospective dose-response analysis is an epidemiological approach that measures exposure levels in a defined population before outcomes occur, then quantifies how the risk or magnitude of an outcome changes systematically as exposure increases. By collecting exposure data prospectively, researchers can establish temporal sequence, reduce recall bias, and assess whether a biological gradient — one of Hill's classic causal criteria — exists between the agent of interest and a health outcome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bradford Hill (causal criteria including dose-response, 1965); formalized in modern epidemiology by Rothman, Greenland and others","year":"1965 (Hill's criteria); widely applied through 1980s–present","type":"Analytical epidemiological study design","dataType":"Longitudinal exposure measurements and outcome data collected prospectively","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301-1309.","type":"article","doi":"10.1093/oxfordjournals.aje.a116237","isbn":null,"url":null}],"related":["dose-response-analysis","prospective-cohort-study","survival-analysis","cox-proportional-hazards","regression-analysis","exposure-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-ecological-study","name":"Prospective Ecological Study","fullName":"Prospective Ecological Epidemiological Study","aliases":["prospective ecologic study","prospective aggregate-level study","prospective group-level study","ecological cohort study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1950s–1970s (ecological epidemiology); prospective variant widely applied from 1980s onward","originator":"Ecological study design formalised in epidemiology mid-20th century; prospective variant established through environmental and chronic disease research","url":"https://scholargate.app/en/epidemiology/prospective-ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-ecological-study.md","definition":"A prospective ecological study is an observational epidemiological design in which groups — not individuals — serve as the unit of analysis, and exposure data are collected going forward in time before outcomes are measured. Investigators define geographically, politically, or socially bounded populations, characterise their aggregate exposures at baseline, then ascertain group-level outcomes (disease rates, mortality rates) at one or more later time points. Because exposure precedes outcome measurement, this design provides stronger temporal evidence than retrospective ecological studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ecological study design formalised in epidemiology mid-20th century; prospective variant established through environmental and chronic disease research","year":"1950s–1970s (ecological epidemiology); prospective variant widely applied from 1980s onward","type":"Observational epidemiological study design","dataType":"Aggregate (group-level) exposure and outcome data collected prospectively over time","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Morgenstern, H. (1998). Ecological studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Morgenstern+Ecological+studies+Modern+Epidemiology+1998"},{"ref":"Ecological study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ecological_study"}],"related":["ecological-study","cohort-study","prospective-cohort-study","cross-sectional-epidemiological-study","time-series-analysis","multilevel-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-nested-case-control","name":"Prospective Nested Case-Control","fullName":"Prospective Nested Case-Control Study","aliases":["prospective NCC","nested case-control within prospective cohort","prospective case-control within cohort","incident NCC"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1977","originator":"D.C. Thomas (formal description); building on Mantel (1973) and Liddell, McDonald & Thomas (1977)","url":"https://scholargate.app/en/epidemiology/prospective-nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-nested-case-control.md","definition":"A prospective nested case-control study enrolls a cohort before disease onset, follows participants forward in time, and then — once cases develop — samples matched controls from those still at risk at the time each case occurs. By embedding the case-control comparison inside a prospective cohort, the design combines the causal clarity of longitudinal follow-up with the cost efficiency of analysing only a fraction of the cohort's stored specimens or records.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D.C. Thomas (formal description); building on Mantel (1973) and Liddell, McDonald & Thomas (1977)","year":"1977","type":"Observational analytic design","dataType":"Time-to-event data, biomarkers, prospectively collected exposure data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thomas, D.C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. By F.D.K. Liddell, J.C. McDonald, and D.C. Thomas. Journal of the Royal Statistical Society, Series A, 140(4), 469-491.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thomas+1977+nested+case-control+cohort+analysis"},{"ref":"Wacholder, S., McLaughlin, J.K., Silverman, D.T., & Mandel, J.S. (1992). Selection of controls in case-control studies: I. Principles. American Journal of Epidemiology, 135(9), 1019-1028.","type":"article","doi":"10.1093/oxfordjournals.aje.a116396","isbn":null,"url":null}],"related":["nested-case-control","cohort-study","prospective-cohort-study","case-control-study","case-cohort-study","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-phase-iv-study","name":"Prospective Phase IV Study","fullName":"Prospective Phase IV Post-Marketing Study","aliases":["prospective post-marketing surveillance study","prospective pharmacovigilance study","prospective post-authorization safety study","PASS (prospective)"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1970s–1980s (formalized in post-marketing regulatory frameworks)","originator":"Regulatory and pharmaceutical research community (ICH E2E, EMA PASS guidelines)","url":"https://scholargate.app/en/epidemiology/prospective-phase-iv-study","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-phase-iv-study.md","definition":"A prospective Phase IV study is a post-marketing investigation conducted after a drug, device, or intervention has received regulatory approval, following participants forward in real time to collect safety, effectiveness, and utilization data under routine clinical practice conditions. Unlike retrospective designs that mine existing records, prospective enrollment allows pre-specified data collection, defined follow-up windows, and direct measurement of outcomes as they occur, making it central to post-authorization safety surveillance and long-term effectiveness research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Regulatory and pharmaceutical research community (ICH E2E, EMA PASS guidelines)","year":"1970s–1980s (formalized in post-marketing regulatory frameworks)","type":"Observational / interventional post-marketing study design","dataType":"Longitudinal patient-level data collected prospectively (clinical records, registries, patient-reported outcomes)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Strom, B.L. (Ed.). (2005). Pharmacoepidemiology (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470863088","url":null},{"ref":"Phase IV clinical trial. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Phase_IV_clinical_trial"}],"related":["phase-iv-study","cohort-study","prospective-cohort-study","pharmacovigilance","randomized-clinical-trial","case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-randomized-clinical-trial","name":"Prospective Randomized Clinical Trial","fullName":"Prospective Randomized Controlled Clinical Trial","aliases":["Prospective RCT","randomized controlled trial","RCT","controlled clinical trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1948 (landmark MRC streptomycin trial)","originator":"Austin Bradford Hill / Medical Research Council","url":"https://scholargate.app/en/epidemiology/prospective-randomized-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-randomized-clinical-trial.md","definition":"A prospective randomized clinical trial (RCT) is an experimental study in which participants are assigned to intervention or control groups by chance before any outcomes are observed, then followed forward in time. Random allocation eliminates systematic selection bias, making this design the gold standard for establishing causal efficacy of treatments in medicine and clinical research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Austin Bradford Hill / Medical Research Council","year":"1948 (landmark MRC streptomycin trial)","type":"Experimental / interventional study design","dataType":"Prospectively collected clinical outcome data (continuous, binary, time-to-event)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Medical Research Council (1948). Streptomycin treatment of pulmonary tuberculosis: a Medical Research Council investigation. British Medical Journal, 2(4582), 769–782.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Streptomycin+treatment+of+pulmonary+tuberculosis%3A+a+Medical+Research+Council+investigation+Medical"},{"ref":"Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.","type":"article","doi":"10.1136/bmj.c332","isbn":null,"url":null}],"related":["randomized-clinical-trial","cohort-study","adaptive-randomized-clinical-trial","crossover-trial","phase-iii-clinical-trial","pragmatic-randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-retrospective-memory","name":"Prospective and Retrospective Memory Questionnaire","fullName":"Prospective and Retrospective Memory Questionnaire","aliases":["PRMQ","Prospective Retrospective Memory Questionnaire"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"memory assessment self-report","year":"2003","originator":"John Crawford","url":"https://scholargate.app/en/neuropsychology/prospective-retrospective-memory","markdownUrl":"https://scholargate.app/en/neuropsychology/prospective-retrospective-memory.md","definition":"The Prospective and Retrospective Memory Questionnaire (PRMQ) is a 16-item self-report instrument designed to measure subjective memory complaints across two distinct memory domains: prospective memory (remembering to do things in the future) and retrospective memory (remembering past events and information). Developed by Crawford and colleagues at the University of Edinburgh in 2003, the PRMQ provides a brief, validated tool for assessing everyday memory lapses and their impact on functional well-being in both clinical and non-clinical populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Crawford","subfamily":"memory assessment self-report","year":"2003","type":"Self-report questionnaire of prospective and retrospective memory complaints"},"citations":[{"ref":"Crawford, J. R., Smith, G., Maylor, E. A., Della Sala, S., & Logie, R. H. (2003). The Prospective and Retrospective Memory Questionnaire (PRMQ): Normative data and latent structure in a large non-clinical sample. Memory, 11(3), 261-275.","type":"article","doi":"10.1080/09658210244000027","isbn":null,"url":null},{"ref":"Smith, G., Della Sala, S., Logie, R. H., & Maylor, E. A. (2000). Prospective and retrospective memory in normal ageing and dementia: A questionnaire study. Memory, 8(5), 311-321.","type":"article","doi":"10.1080/09658210050117735","isbn":null,"url":null},{"ref":"Kliegel, M., Ramuschkat, G., & Martin, M. (2010). Complex prospective memory in younger and older adults: Does the delay of the intended action make a difference? International Journal of Psychology, 45(3), 210-217.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/22070094"}],"related":["cognitive-failures-questionnaire","trail-making-test","frontal-assessment-battery","mmse","adas-cog"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-screening-test-evaluation","name":"Prospective Screening Test Evaluation","fullName":"Prospective Screening Test Evaluation Study","aliases":["prospective diagnostic accuracy study","prospective test performance study","forward-looking screening validation","prospective DTA study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–2000s (STARD 2003, updated 2015)","originator":"Formalized through diagnostic accuracy methodology (Sackett, Haynes, Tugwell; STARD initiative)","url":"https://scholargate.app/en/epidemiology/prospective-screening-test-evaluation","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-screening-test-evaluation.md","definition":"A prospective screening test evaluation enrolls participants before the outcome is known, applies the screening test and the reference standard in temporal sequence, and measures how accurately the test identifies individuals with or without the target condition. This forward-looking design minimizes workup bias and spectrum bias, producing estimates of sensitivity, specificity, and predictive values that are more generalizable to real clinical or public-health screening contexts than retrospective alternatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized through diagnostic accuracy methodology (Sackett, Haynes, Tugwell; STARD initiative)","year":"1980s–2000s (STARD 2003, updated 2015)","type":"Prospective observational study design","dataType":"Index test results, reference standard results, participant follow-up data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., et al. (2015). STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. BMJ, 351, h5527.","type":"article","doi":"10.1136/bmj.h5527","isbn":null,"url":null},{"ref":"Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198509844","url":null}],"related":["screening-test-evaluation","diagnostic-accuracy-study","prospective-cohort-study","prospective-diagnostic-accuracy-study","cohort-study","cross-sectional-epidemiological-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prospective-survival-analysis","name":"Prospective Survival Analysis","fullName":"Prospective Survival Analysis","aliases":["prospective time-to-event analysis","prospective failure-time analysis","forward-looking survival study","prospective event-time study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1958–1972 (foundational methods); prospective design emphasis formalized by 1980s","originator":"Kaplan & Meier (estimator, 1958); Cox (proportional hazards model, 1972); prospective design formalised in modern clinical epidemiology","url":"https://scholargate.app/en/epidemiology/prospective-survival-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/prospective-survival-analysis.md","definition":"Prospective survival analysis is a longitudinal study design in which participants are enrolled before the event of interest occurs, followed forward in time under standardised conditions, and analysed using survival-analytic methods to estimate the time until a defined clinical endpoint — such as death, disease recurrence, or treatment failure. Because data are collected prospectively, exposure and covariate information are recorded before outcomes are known, substantially reducing recall and selection bias relative to retrospective approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaplan & Meier (estimator, 1958); Cox (proportional hazards model, 1972); prospective design formalised in modern clinical epidemiology","year":"1958–1972 (foundational methods); prospective design emphasis formalized by 1980s","type":"Longitudinal observational or experimental study design with time-to-event analysis","dataType":"Prospectively collected time-to-event data (event indicator, follow-up time, covariates)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kleinbaum, D. G., & Klein, M. (2012). Survival Analysis: A Self-Learning Text (3rd ed.). Springer.","type":"book","doi":null,"isbn":"978-1441966452","url":null},{"ref":"Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439856789","url":null}],"related":["survival-analysis","kaplan-meier-analysis","cox-proportional-hazards","prospective-cohort-study","competing-risks-analysis","randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"protection-relay-coordination","name":"Protection Relay Coordination","fullName":"Protection Relay Coordination and Selective Clearing","aliases":["relay coordination study","protection scheme design","selective overcurrent protection"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Power system protection","year":"1956","originator":"C. Russell Mason","url":"https://scholargate.app/en/electrical-engineering/protection-relay-coordination","markdownUrl":"https://scholargate.app/en/electrical-engineering/protection-relay-coordination.md","definition":"Protection relay coordination ensures that when a fault occurs, the relay nearest to the fault operates first, isolating only the faulted section while keeping healthy portions of the network energized. This selective clearing strategy minimizes service disruption and is achieved by carefully coordinating pickup currents and time delays across a series of relays. It is fundamental to reliable power system operation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"C. Russell Mason","subfamily":"Power system protection","year":"1956","type":"Computational pipeline"},"citations":[{"ref":"Mason, C. R. (1956). The Art and Science of Protective Relaying. General Electric.","type":"book","doi":null,"isbn":null,"url":"https://www.gegridsolutions.com"},{"ref":"Phadke, A. G., & Thorp, J. S. (2008). Adaptive Relaying and Protection. Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Adaptive+Relaying+and+Protection+Phadke"},{"ref":"IEEE Std 379-2000: IEEE Guide for the Protection of AC Electric Machinery Having Rated 15 MVA and Above.","type":"standard","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/945329"}],"related":["fault-analysis-power-system","power-flow-analysis","smart-grid-state-estimation","motor-drive-efficiency-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proteomics-analysis","name":"Proteomics Analysis","fullName":"Proteomics Data Analysis","aliases":["proteomics","mass spectrometry-based proteomics","shotgun proteomics","quantitative proteomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"1994–2003 (term coined 1994; shotgun proteomics established early 2000s)","originator":"Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations)","url":"https://scholargate.app/en/bioinformatics/proteomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/proteomics-analysis.md","definition":"Proteomics analysis is a systematic pipeline for identifying and quantifying proteins in biological samples using mass spectrometry. Starting from raw spectral data, the workflow searches protein sequence databases, estimates abundance across conditions, applies statistical tests for differential expression, and maps findings onto biological pathways. It complements transcriptomics by capturing post-translational regulation and actual protein abundance, and is central to biomarker discovery, drug-target identification, and systems biology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations)","year":"1994–2003 (term coined 1994; shotgun proteomics established early 2000s)","type":"Quantitative omics pipeline","dataType":"Mass spectrometry raw files (DDA, DIA), protein FASTA databases","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Wilkins, M. R., Sanchez, J.-C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser, D. F., & Williams, K. L. (1996). Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it. Biotechnology and Genetic Engineering Reviews, 13(1), 19–50.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Progress+with+proteome+projects+Why+all+proteins+expressed+by+a+genome+should+be+identified+Wilkins+1996"},{"ref":"Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422(6928), 198–207.","type":"article","doi":"10.1038/nature01511","isbn":null,"url":null}],"related":["metabolomics-analysis","rna-seq-differential-expression","multi-omics-proteomics-analysis","pathway-enrichment-analysis","gene-set-enrichment-analysis","sequence-alignment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"protocol-based-meta-analysis","name":"Protocol-based Meta-analysis","fullName":"Protocol-based Meta-analysis","aliases":["pre-registered meta-analysis","prospective meta-analysis","registered meta-analysis","protocol-driven meta-analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1990s–2015 (Cochrane established 1993; PROSPERO launched 2011; PRISMA-P 2015)","originator":"Cochrane Collaboration; formalized through PROSPERO and PRISMA-P initiatives","url":"https://scholargate.app/en/scientometrics/protocol-based-meta-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/protocol-based-meta-analysis.md","definition":"A protocol-based meta-analysis is a meta-analysis conducted according to a detailed, pre-registered protocol that specifies all key methodological decisions — research questions, eligibility criteria, search strategy, outcome measures, and statistical methods — before data collection begins. Pre-registration, typically through PROSPERO or a comparable registry, distinguishes this approach from post-hoc or exploratory meta-analyses and substantially reduces the risk of selective reporting and outcome switching.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cochrane Collaboration; formalized through PROSPERO and PRISMA-P initiatives","year":"1990s–2015 (Cochrane established 1993; PROSPERO launched 2011; PRISMA-P 2015)","type":"Evidence synthesis with pre-registered protocol","dataType":"Aggregated quantitative data from primary studies (effect sizes, sample sizes, outcomes)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2023). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.4). Cochrane.","type":"book","doi":null,"isbn":null,"url":"https://training.cochrane.org/handbook"},{"ref":"Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1.","type":"article","doi":"10.1186/2046-4053-4-1","isbn":null,"url":null}],"related":["systematic-literature-review","meta-analysis","prisma-based-review","prisma-compliant-meta-analysis","scoping-review","network-meta-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"protocol-based-meta-ethnography","name":"Protocol-based Meta-ethnography","fullName":"Protocol-based Meta-ethnography","aliases":["pre-registered meta-ethnography","prospero meta-ethnography","registered qualitative synthesis","protocol-driven meta-ethnography"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1988 (meta-ethnography); protocol-based practice formalised 2010s","originator":"Noblit & Hare (meta-ethnography); protocol registration formalised through PROSPERO and eMERGe guidance","url":"https://scholargate.app/en/scientometrics/protocol-based-meta-ethnography","markdownUrl":"https://scholargate.app/en/scientometrics/protocol-based-meta-ethnography.md","definition":"Protocol-based meta-ethnography is a structured qualitative evidence synthesis that follows Noblit and Hare's meta-ethnography method while requiring a pre-registered, publicly available protocol — typically on PROSPERO — before the review is conducted. Pre-registration constrains post-hoc decision-making, enhances methodological transparency, and aligns qualitative synthesis with the rigour standards now expected by leading journals and funders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Noblit & Hare (meta-ethnography); protocol registration formalised through PROSPERO and eMERGe guidance","year":"1988 (meta-ethnography); protocol-based practice formalised 2010s","type":"Qualitative evidence synthesis with pre-registered protocol","dataType":"Published qualitative studies (text); protocol registration forms","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Noblit, G. W., & Hare, R. D. (1988). Meta-Ethnography: Synthesizing Qualitative Studies. Sage.","type":"book","doi":null,"isbn":"978-0803930742","url":null},{"ref":"France, E. F., Cunningham, M., Ring, N., Uny, I., Duncan, E. A. S., Jepson, R. G., Maxwell, M., Roberts, R. J., Turley, R. L., Booth, A., Britten, N., Flemming, K., Garside, R., Hannes, K., Lewin, S., Noblit, G. W., Pope, C., Thomas, J., Toye, F., Cargo, M., & Britten, N. (2019). Improving reporting of meta-ethnography: The eMERGe reporting guidance. BMC Medical Research Methodology, 19(1), 25.","type":"article","doi":"10.1002/pon.4915","isbn":null,"url":null}],"related":["meta-ethnography","qualitative-meta-synthesis","systematic-literature-review","scoping-review","prisma-based-review","narrative-synthesis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"protocol-based-systematic-literature-review","name":"Protocol-based Systematic literature review","fullName":"Protocol-Registered Systematic Literature Review","aliases":["protocol-registered SLR","pre-registered systematic review","PROSPERO-registered systematic review","protocol-driven systematic review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1990s–2015 (Cochrane Handbook 1st ed. 1994; PRISMA-P 2015)","originator":"Cochrane Collaboration; Moher et al. (PRISMA-P)","url":"https://scholargate.app/en/scientometrics/protocol-based-systematic-literature-review","markdownUrl":"https://scholargate.app/en/scientometrics/protocol-based-systematic-literature-review.md","definition":"A protocol-based systematic literature review is a systematic review conducted according to a fully pre-specified and publicly registered research protocol. By committing the review question, eligibility criteria, search strategy, and planned analyses to a registered document before data collection begins, this approach minimises post-hoc decision-making, selective outcome reporting, and the accumulation bias that can undermine the credibility of unregistered reviews. Registration platforms such as PROSPERO and the Open Science Framework provide permanent, time-stamped records of the protocol.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cochrane Collaboration; Moher et al. (PRISMA-P)","year":"1990s–2015 (Cochrane Handbook 1st ed. 1994; PRISMA-P 2015)","type":"Evidence synthesis method with pre-specified protocol","dataType":"Published studies, trial registries, grey literature (bibliographic records)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2023). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.4). Cochrane. Retrieved from https://training.cochrane.org/handbook","type":"book","doi":null,"isbn":null,"url":"https://training.cochrane.org/handbook"},{"ref":"Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1.","type":"article","doi":"10.1186/2046-4053-4-1","isbn":null,"url":null}],"related":["systematic-literature-review","prisma-based-review","scoping-review","meta-analysis","umbrella-review","rapid-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"protocol-based-umbrella-review","name":"Protocol-based Umbrella review","fullName":"Protocol-based Umbrella Review of Systematic Reviews","aliases":["pre-registered umbrella review","prospero-registered umbrella review","registered overview of reviews","protocol-driven umbrella review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2011-2015 (PROSPERO launched 2011; JBI umbrella review guidelines 2015)","originator":"Developed from umbrella review methodology; protocol registration practice formalized through PROSPERO (York) and JBI","url":"https://scholargate.app/en/scientometrics/protocol-based-umbrella-review","markdownUrl":"https://scholargate.app/en/scientometrics/protocol-based-umbrella-review.md","definition":"A protocol-based umbrella review is an umbrella review — a synthesis of existing systematic reviews and meta-analyses on a common topic — conducted under a publicly pre-registered protocol, typically in PROSPERO or a similar registry. Pre-registering the protocol before data collection begins commits the research team to prospectively defined eligibility criteria, search strategy, appraisal tools, and synthesis methods, sharply reducing the risk of outcome reporting bias and post-hoc analytical flexibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from umbrella review methodology; protocol registration practice formalized through PROSPERO (York) and JBI","year":"2011-2015 (PROSPERO launched 2011; JBI umbrella review guidelines 2015)","type":"Registered evidence synthesis review","dataType":"Published systematic reviews and meta-analyses","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015). Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review. JBI Evidence Implementation, 13(3), 132-140.","type":"article","doi":"10.1097/XEB.0000000000000055","isbn":null,"url":null},{"ref":"Ioannidis, J. P. A. (2009). Integration of evidence from multiple meta-analyses: a primer on umbrella reviews, treatment networks and multiple treatments meta-analyses. CMAJ, 181(8), 488-493.","type":"article","doi":"10.1503/cmaj.081086","isbn":null,"url":null}],"related":["umbrella-review","systematic-literature-review","scoping-review","network-meta-analysis","prisma-compliant-umbrella-review","meta-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"prototype-theory","name":"Prototype Theory","fullName":"Prototype Theory Framework","aliases":["Prototype Semantics","Cognitive Semantics"],"domain":"linguistics","family":"process-pipeline","subfamily":"Cognitive Semantics","year":"1973","originator":"Eleanor Rosch","url":"https://scholargate.app/en/linguistics/prototype-theory","markdownUrl":"https://scholargate.app/en/linguistics/prototype-theory.md","definition":"Prototype Theory is a framework for understanding how humans categorize concepts, proposing that categories are organized around prototypes—the most typical or central members. Developed by Eleanor Rosch in 1973, the theory challenges classical logic's view that categories have fixed boundaries defined by necessary-and-sufficient features. Instead, prototypes have fuzzy boundaries and graded membership: some instances are more central (robin is a prototypical bird) while others are peripheral (penguin is a bird but less typical). Prototype Theory has profound implications for understanding language, cognition, and meaning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eleanor Rosch","subfamily":"Cognitive Semantics","year":"1973","type":"Empirical process pipeline"},"citations":[{"ref":"Rosch, E. (1973). Natural categories. Cognitive Psychology, 4(3), 328-350.","type":"article","doi":"10.1016/0010-0285(73)90017-0","isbn":null,"url":null},{"ref":"Lakoff, G. (1987). Women, Fire, and Dangerous Things: What Categories Reveal About the Mind. Chicago: University of Chicago Press.","type":"book","doi":"10.7208/chicago/9780226471013.001.0001","isbn":null,"url":null},{"ref":"Taylor, J. R. (2003). Linguistic Categorization: Prototypes in Linguistic Theory (3rd ed.). Oxford: Oxford University Press.","type":"book","doi":"10.1093/oso/9780199266647.001.0001","isbn":null,"url":null}],"related":["semantic-feature-analysis","cognitive-linguistics","categorization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"proximity-wlc","name":"PROXIMITY-WLC","fullName":"Proximity-Adjusted WLC — spatially explicit weighted linear combination","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2006","originator":"Rinner, C.; Heppleston, A.","url":"https://scholargate.app/en/decision-making/proximity-wlc","markdownUrl":"https://scholargate.app/en/decision-making/proximity-wlc.md","definition":"PROXIMITY-WLC (Proximity-Adjusted WLC — spatially explicit weighted linear combination) is a ranking multi-criteria decision-making (MCDM) method introduced by Rinner, C.; Heppleston, A. in 2006. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rinner, C.; Heppleston, A.","subfamily":"Ranking","year":"2006","type":"Spatially heterogeneous additive utility — criterion weights vary per alternative as a function of proximity to a reference location","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":true},"citations":[{"ref":"Rinner, C., Heppleston, A. (2006). The spatial dimensions of multi-criteria evaluation — case study of a home buyer's spatial decision support system. Lecture Notes in Computer Science (GIScience 2006)","type":"article","doi":"10.1007/11863939_22","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","swara","fucom","merec"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pruning-response-analysis","name":"Pruning Response Analysis","fullName":"Morphological and Physiological Assessment of Pruning Effects on Growth and Productivity","aliases":["pruning evaluation","shoot response measurement","canopy architecture assessment"],"domain":"horticulture","family":"process-pipeline","subfamily":"Canopy management and training","year":"1980","originator":"Pomology research tradition","url":"https://scholargate.app/en/horticulture/pruning-response-analysis","markdownUrl":"https://scholargate.app/en/horticulture/pruning-response-analysis.md","definition":"Pruning response analysis systematically measures the morphological and physiological effects of pruning on fruit trees, including shoot development, branching architecture, flowering, fruit set, and yield. By combining visual assessment with growth measurements and phenological tracking, growers and researchers can quantify the effectiveness of pruning strategies and optimize training systems. This method is foundational to modern precision horticulture and cultivar-specific orchard management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pomology research tradition","subfamily":"Canopy management and training","year":"1980","type":"morphological measurement pipeline"},"citations":[{"ref":"López-Balduz, S., Buesa, I., Perera-Fernández, L. G., & Carbonell, T. (2006). Physiological drop in citrus trees: Flowering phenology, fruit set, and cropping patterns. Journal of the American Society for Horticultural Science, 131(5), 589–599.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Physiological+drop+in+citrus+trees%3A+Flowering+phenology%2C+fruit+set%2C+and+cropping+patterns+L%C3%B3pez-Balduz"},{"ref":"Bangerth, F. (1994). Response of apple trees to pruning: Vigour, flowering, yield and fruit quality. Acta Horticulturae, 386, 137–147.","type":"article","doi":null,"isbn":null,"url":"https://www.actahort.org/books/386/386_15.htm"}],"related":["crop-load-management","phenological-stage-monitoring","plant-propagation-success","grafting-success-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pruritus-visual-analog-scale","name":"Pruritus VAS","fullName":"Pruritus Visual Analog Scale","aliases":["Itch VAS","Pruritus Severity Scale"],"domain":"dermatology","family":"process-pipeline","subfamily":"symptom-severity","year":"1983 (VAS); widely adopted for pruritus 1990s–2000s","originator":"Huskisson EC (VAS methodology); adapted for pruritus in dermatology","url":"https://scholargate.app/en/dermatology/pruritus-visual-analog-scale","markdownUrl":"https://scholargate.app/en/dermatology/pruritus-visual-analog-scale.md","definition":"The Pruritus Visual Analog Scale (VAS) is a simple, single-item patient-administered tool measuring itch intensity on a continuous 0–10 (or 0–100) scale. Adapted from the original VAS for pain, it is one of the most frequently used outcome measures in dermatological research and clinical practice due to its simplicity, brevity, and responsiveness to treatment. Pruritus VAS is essential in trials of antipruritic therapies and in conditions where itching is a primary complaint (atopic dermatitis, psoriasis, pruritus ani, lichen planus).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Huskisson EC (VAS methodology); adapted for pruritus in dermatology","subfamily":"symptom-severity","year":"1983 (VAS); widely adopted for pruritus 1990s–2000s","type":"Self-report"},"citations":[{"ref":"Elman S, Hynan LS, Gabriel V, Mayo MJ. The 5-D itch scale: a new measure of pruritus. Br J Dermatol. 2010;162(3):587-593.","type":"article","doi":"10.1111/j.1365-2133.2009.09586.x","isbn":null,"url":null},{"ref":"Huskisson EC. Visual analog scales. In: Melzack R, ed. Pain Measurement and Assessment. New York: Raven Press; 1983:33-37.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/6305887"}],"related":["scorad","easi","poem"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"psaqol","name":"Psoriatic Arthritis Quality of Life Scale","fullName":"Psoriatic Arthritis Quality of Life Scale","aliases":["PsAQoL","PSAQoL"],"domain":"rheumatology","family":"process-pipeline","subfamily":"quality-of-life-index","year":"1997","originator":"McKenna & Doherty","url":"https://scholargate.app/en/rheumatology/psaqol","markdownUrl":"https://scholargate.app/en/rheumatology/psaqol.md","definition":"The PsAQoL is a disease-specific patient-reported outcome measure of quality of life impact in psoriatic arthritis (PsA), a chronic inflammatory condition affecting joints and skin. Developed by McKenna and Doherty in 1997, PsAQoL comprises 20 items assessing the multidimensional impact of PsA on physical function, emotional well-being, work productivity, and social participation. PsAQoL captures the patient's lived experience of the disease, complementing clinical disease activity measures (CRP, joint counts) and providing a holistic view of treatment benefit in PsA research and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McKenna & Doherty","subfamily":"quality-of-life-index","year":"1997","type":"Patient-reported outcome (PRO)"},"citations":[{"ref":"Soderlin MK, Bergman S. Psychometric properties of the Psoriatic Arthritis Quality of Life (PsAQoL) instrument: Rasch analysis. Arthritis Care Research. 2011;63(11):1589-1595.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Soderlin+MK%2C+Bergman+S.+Psychometric+properties+of+the+Psoriatic+Arthritis+Quality+of+Life+%28PsAQoL%29+instrument%3A+Rasch+an+Soderlin"},{"ref":"McKenna F, Doherty M. PsAQoL: a quality of life instrument for psoriatic arthritis. Clin Exp Rheumatol. 1997;15(6):630-634.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9485887"}],"related":["basdai","basfi","das28","sdai-rheumatoid-arthritis","sledai"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pseudoflow","name":"Pseudoflow","fullName":"Pseudoflow Algorithm for Maximum Weighted Closure","aliases":["Pseudoflow Algorithm","Hochbaum Algorithm"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Network Flow and Graph Optimization","year":"1992","originator":"Dorit S. Hochbaum","url":"https://scholargate.app/en/mining-engineering/pseudoflow","markdownUrl":"https://scholargate.app/en/mining-engineering/pseudoflow.md","definition":"The Pseudoflow Algorithm, developed by Dorit Hochbaum in 1992, is a polynomial-time algorithm for computing maximum weighted closures in directed acyclic graphs. In mining, it solves the ultimate pit limit problem more efficiently than earlier methods. By maintaining feasible pseudoflows and iteratively eliminating negative-cost nodes, it achieves near-optimal practical performance even on industrial-scale block models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorit S. Hochbaum","subfamily":"Network Flow and Graph Optimization","year":"1992","type":"Efficient algorithm for maximum closure problem"},"citations":[{"ref":"Hochbaum, D. S. (1992). A new-old algorithm for minimum-cut and maximum-flow problems. Journal of the ACM, 1(1), 76-109.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new-old+algorithm+for+minimum-cut+and+maximum-flow+problems+Hochbaum"},{"ref":"Hochbaum, D. S. (2001). A fast algorithms for mining and metallurgical pits optimization. SIAM Journal on Computing, 30(4), 1096-1117.","type":"article","doi":null,"isbn":null,"url":"https://epubs.siam.org/"}],"related":["lerchs-grossmann-algorithm","cut-off-grade","stope-layout"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"psi","name":"PSI","fullName":"Preference Selection Index","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Maniya, K., Bhatt, M. G.","url":"https://scholargate.app/en/decision-making/psi","markdownUrl":"https://scholargate.app/en/decision-making/psi.md","definition":"PSI (Preference Selection Index) is a ranking multi-criteria decision-making (MCDM) method introduced by Maniya, K., Bhatt, M. G. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maniya, K., Bhatt, M. G.","subfamily":"Ranking","year":"2010","type":"Preference variation index (weight-free statistical)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Maniya, K., Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design","type":"article","doi":"10.1016/j.matdes.2009.11.020","isbn":null,"url":null}],"related":["topsis","vikor","edas","codas","mabac","marcos","saw","ahp"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"psychoacoustic-masking","name":"Psychoacoustic Masking","fullName":"Psychoacoustic Masking Models for Audio Perception","aliases":["masking","temporal masking","frequency masking","auditory masking"],"domain":"acoustics","family":"process-pipeline","subfamily":"Psychoacoustics, Auditory perception","year":"1961","originator":"Eberhard Zwicker","url":"https://scholargate.app/en/acoustics/psychoacoustic-masking","markdownUrl":"https://scholargate.app/en/acoustics/psychoacoustic-masking.md","definition":"Psychoacoustic masking describes how the human auditory system suppresses the perception of weak sounds in the presence of stronger sounds. Formalized by Eberhard Zwicker in the 1960s, masking is a fundamental phenomenon in hearing and the basis for perceptual audio coding (MP3, AAC, OPUS). Masking occurs both in frequency (spectral masking) and time (temporal masking), and understanding these effects enables efficient audio compression and realistic sound design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eberhard Zwicker","subfamily":"Psychoacoustics, Auditory perception","year":"1961","type":"Perceptual model for audio systems"},"citations":[{"ref":"Zwicker, E., & Scharf, B. (1965). Psychoacoustics: Facts and Models. Springer-Verlag.","type":"book","doi":null,"isbn":"978-3540631644","url":null},{"ref":"Moore, B. C. J. (2012). An Introduction to the Psychology of Hearing (6th ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123914232","url":null},{"ref":"Johnston, J. D. (1988). Transform coding of audio signals using perceptual noise criteria. IEEE Journal on Selected Areas in Communications, 6(2), 314–323.","type":"article","doi":"10.1109/49.608","isbn":null,"url":null}],"related":["speech-intelligibility","fxlms-active-noise-control","bark-and-mel-scales","linear-predictive-coding","cepstral-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"psycholinguistic-eye-tracking","name":"Psycholinguistic Eye-Tracking","fullName":"Eye-Tracking in Psycholinguistic Research","aliases":["Eye Gaze Tracking","Reading Behavior Analysis"],"domain":"linguistics","family":"process-pipeline","subfamily":"Experimental Psycholinguistics","year":"1975","originator":"Keith Rayner","url":"https://scholargate.app/en/linguistics/psycholinguistic-eye-tracking","markdownUrl":"https://scholargate.app/en/linguistics/psycholinguistic-eye-tracking.md","definition":"Psycholinguistic Eye-Tracking is a method that measures eye movements during reading or visual processing to investigate how the mind processes language. Pioneered by Keith Rayner, eye-tracking reveals which parts of text attract attention, how long readers spend on different words, and how eye movements relate to comprehension difficulties. Metrics like fixation duration and saccade length provide objective, millisecond-level data on cognitive processing. Eye-tracking is now a standard tool for studying reading, comprehension, and attention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keith Rayner","subfamily":"Experimental Psycholinguistics","year":"1975","type":"Empirical process pipeline"},"citations":[{"ref":"Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372-422.","type":"article","doi":"10.1037/0033-2909.124.3.372","isbn":null,"url":null},{"ref":"Rayner, K. (Ed.). (2012). The Oxford Handbook of Eye Movements. Oxford: Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Oxford+Handbook+of+Eye+Movements+Rayner"},{"ref":"Duchowski, A. T. (2007). Eye Tracking Methodology: Theory and Practice (2nd ed.). London: Springer.","type":"book","doi":"10.1007/978-1-84628-609-4","isbn":null,"url":null}],"related":["reading-comprehension","attention-mechanisms","language-processing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"psychological-capital-questionnaire","name":"Psychological Capital Questionnaire","fullName":"Psychological Capital Questionnaire (PCQ-24)","aliases":["PCQ-24","PsyCap","Luthans Youssef Avolio"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"positive-psychology-work","year":"2007","originator":"Fred Luthans","url":"https://scholargate.app/en/organizational-behavior/psychological-capital-questionnaire","markdownUrl":"https://scholargate.app/en/organizational-behavior/psychological-capital-questionnaire.md","definition":"The Psychological Capital Questionnaire (PCQ-24) measures psychological capital—the positive psychological resources of efficacy, hope, resilience, and optimism—that enable individuals to thrive in demanding work environments. Developed by Luthans, Youssef, and Avolio in 2007, it operationalizes a positive organizational psychology framework that complements traditional competency assessments. PsyCap scores predict engagement, performance, well-being, and retention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred Luthans","subfamily":"positive-psychology-work","year":"2007","type":"Self-report questionnaire"},"citations":[{"ref":"Luthans, F., Youssef, C. M., & Avolio, B. J. (2007). Psychological capital and beyond. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195388275","url":null},{"ref":"Luthans, F., Avolio, B. J., Avey, J. B., & Norman, S. M. (2007). Positive psychological capital: Measurement and relationship with performance and satisfaction. Personnel Psychology, 60(3), 541–572.","type":"article","doi":"10.1111/j.1744-6570.2007.00083.x","isbn":null,"url":null},{"ref":"Avey, J. B., Luthans, F., & Jensen, S. M. (2009). Psychological capital: A positive resource for combating employee stress and turnover. Human Resource Management, 48(5), 677–693.","type":"article","doi":"10.1002/hrm.20294","isbn":null,"url":null}],"related":["core-self-evaluations-scale","proactive-personality-scale","perceived-organizational-support","career-adapt-abilities-scale","leader-member-exchange-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"psychological-safety-scale","name":"Psychological Safety Scale","fullName":"Psychological Safety Scale (PSS) - Team-Level Measure","aliases":["PSS","Team Psychological Safety Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"1999","originator":"Amy C. Edmondson","url":"https://scholargate.app/en/organizational-behavior/psychological-safety-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/psychological-safety-scale.md","definition":"The Psychological Safety Scale (PSS), developed by Amy Edmondson in 1999, measures team members' shared perception that they can take interpersonal risks—speaking up, asking questions, admitting mistakes, proposing new ideas—without fear of embarrassment, punishment, or rejection. The 7-item scale captures a team-level construct fundamental to learning, innovation, and psychological well-being. High psychological safety predicts team performance, learning from errors, information sharing, and adaptive responses to change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Amy C. Edmondson","subfamily":"Organizational behavior","year":"1999","type":"Team-level self-report questionnaire"},"citations":[{"ref":"Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350-383.","type":"article","doi":"10.2307/2666999","isbn":null,"url":null},{"ref":"Edmondson, A. C. (2018). The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth. Hoboken, NJ: Wiley.","type":"book","doi":null,"isbn":"978-1119477242","url":null}],"related":["organizational-justice-scale","transformational-leadership-scale","servant-leadership-scale","job-demands-resources-scale","organizational-commitment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"psychopathy-checklist-screening","name":"PCL-SV","fullName":"Psychopathy Checklist Screening Version","aliases":["PCL-SV","Hare PCL-SV","Psychopathy Checklist Screening"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"personality-pathology-assessment","year":"1995","originator":"Stephen D. Hart, Diana N. Cox, Robert D. Hare","url":"https://scholargate.app/en/forensic-psychology/psychopathy-checklist-screening","markdownUrl":"https://scholargate.app/en/forensic-psychology/psychopathy-checklist-screening.md","definition":"The Psychopathy Checklist Screening Version (PCL-SV) is a 12-item assessment tool developed by Hart, Cox, and Hare (1995) to screen for psychopathic personality traits in adolescents and adults. It is a brief alternative to the full 20-item Psychopathy Checklist-Revised (PCL-R), designed for rapid screening in correctional, forensic psychiatric, and research settings. PCL-SV identifies individuals exhibiting callousness, impulsivity, shallow affect, and antisocial behavior—traits associated with high violence risk and treatment non-responsiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen D. Hart, Diana N. Cox, Robert D. Hare","subfamily":"personality-pathology-assessment","year":"1995","type":"Interview-rated / File-based"},"citations":[{"ref":"Hart, S. D., Cox, D. N., & Hare, R. D. (1995). The Hare Psychopathy Checklist: Screening Version (PCL-SV). Multi-Health Systems, Inc.","type":"article","doi":null,"isbn":null,"url":"https://www.mhs.com/"},{"ref":"Hare, R. D. (1991). The Hare Psychopathy Checklist-Revised. Multi-Health Systems, Inc.","type":"book","doi":null,"isbn":null,"url":"https://www.mhs.com/"}],"related":["hcr-20","violence-risk-appraisal-guide","novaco-anger-scale","level-of-service-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"psychosocial-safety-climate-scale","name":"Psychosocial Safety Climate Scale","fullName":"Psychosocial Safety Climate Scale (PSC-12)","aliases":["PSC-12","PSCC"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-climate","year":"2010","originator":"Dollard & Karasek; Bailey et al.","url":"https://scholargate.app/en/occupational-health/psychosocial-safety-climate-scale","markdownUrl":"https://scholargate.app/en/occupational-health/psychosocial-safety-climate-scale.md","definition":"The Psychosocial Safety Climate Scale (PSC-12) measures employees' perceptions of organizational commitment to protecting worker psychological health and preventing psychosocial hazards (stress, harassment, bullying). Developed by Dollard and Karasek, and refined by Bailey and colleagues, the PSC-12 captures four dimensions of management support, communication, and hazard prevention. The scale is predictive of workplace stress, burnout, mental health disorders, and absenteeism, making it a leading indicator for organizational health and a lever for preventive intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dollard & Karasek; Bailey et al.","subfamily":"occupational-climate","year":"2010","type":"Self-report"},"citations":[{"ref":"Bailey, T. S., Dollard, M. F., McLinton, S. S., & Richards, P. A. (2015). Psychosocial safety climate: Latent profiles in Australian workplaces and psychosocial hazard exposure. Int J Stress Manag, 22(4), 413–442.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Psychosocial+safety+climate%3A+Latent+profiles+in+Australian+workplaces+and+psychosocial+hazard+exposure+Bailey"},{"ref":"Dollard, M. F., & Karasek, R. A. (2010). Building psychosocial safety climate: Evaluating the neighbor effect of a safety literature based job stress intervention in two police stations. J Occup Organ Psychol, 83(1), 123–141.","type":"article","doi":"10.1002/9780470661550.ch11","isbn":null,"url":null}],"related":["workplace-ostracism-scale","occupational-fatigue-scale","workplace-violence-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"psychrometric-analysis","name":"Psychrometric Analysis","fullName":"Psychrometric Analysis for Humid Air Systems","aliases":["psychrometrics","humid air analysis"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Air Conditioning","year":"1911","originator":"R. E. Carrier","url":"https://scholargate.app/en/thermodynamics/psychrometric-analysis","markdownUrl":"https://scholargate.app/en/thermodynamics/psychrometric-analysis.md","definition":"Psychrometric analysis is the study of humid air (air-water vapor mixtures) and its properties. It is essential for designing and analyzing air conditioning, ventilation, and dehumidification systems. Psychrometric analysis relates dry-bulb temperature, wet-bulb temperature, dew point, relative humidity, and specific humidity through carefully constructed charts and correlations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. E. Carrier","subfamily":"Air Conditioning","year":"1911","type":"Humid air thermodynamic analysis"},"citations":[{"ref":"ASHRAE. (2021). Handbook-Fundamentals. American Society of Heating, Refrigerating and Air-Conditioning Engineers.","type":"book","doi":null,"isbn":null,"url":"https://www.ashrae.org/technical-resources/ashrae-handbook"},{"ref":"Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470055540","url":null}],"related":["vapor-compression-cycle","boussinesq-approximation","stefan-maxwell-diffusion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"public-service-motivation-scale","name":"Public Service Motivation Scale","fullName":"Public Service Motivation Scale (PSMS)","aliases":["PSMS","Perry PSM Scale"],"domain":"tourism-management","family":"process-pipeline","subfamily":"organizational-motivation","year":"1996","originator":"Perry, J. L.","url":"https://scholargate.app/en/tourism-management/public-service-motivation-scale","markdownUrl":"https://scholargate.app/en/tourism-management/public-service-motivation-scale.md","definition":"The Public Service Motivation Scale (PSMS), developed by Perry (1996) and refined by Kim et al. (2013), measures the intrinsic motivation of public sector employees to serve the public interest, contribute to civic good, feel compassion for others, and make self-sacrifices for collective benefit. Public service motivation (PSM) is a defining characteristic of effective public administration, predicting job satisfaction, organizational commitment, performance, and willingness to engage in prosocial behaviors. Essential for public sector recruitment, retention, and culture assessment in government agencies, tourism authorities, and civic institutions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Perry, J. L.","subfamily":"organizational-motivation","year":"1996","type":"Self-report questionnaire"},"citations":[{"ref":"Perry, J. L. (1996). Measuring public service motivation: An assessment of construct reliability and validity. Journal of Public Administration Research and Theory, 6(1), 5-22.","type":"article","doi":"10.1093/oxfordjournals.jpart.a024303","isbn":null,"url":null},{"ref":"Kim, S., Vandenabeele, W., Brady, L., Madison, S., Pandey, S., & Ferris, E. (2013). Investigating work motivation across cultures: Development and validation of the multidimensional work motivation scale (MWMS). Public Administration Review, 73(1), 71-81.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Investigating+work+motivation+across+cultures%3A+Development+and+validation+of+the+multidimensional+work+motivation+scale+%28MWMS%29+Kim"},{"ref":"Caillier, J. G. (2016). Does public service motivation matter in all contexts? Detailing the structural differences on the relationship between PSM and job satisfaction for public and private sector workers. Public Personnel Management, 45(2), 146-167.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Does+public+service+motivation+matter+in+all+contexts+Caillier"},{"ref":"Vandenabeele, W. (2007). Toward a public administration theory of public service motivation: An institutional approach. Public Management Review, 9(4), 545-556.","type":"article","doi":"10.1080/14719030701726697","isbn":null,"url":null}],"related":["e-government-adoption-scale","citizen-satisfaction-survey","tourist-satisfaction-scale","tourist-loyalty-scale","overtourism-perception-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"publication-bias-analysis","name":"Publication Bias Analysis","fullName":"Publication Bias Analysis (Egger Test, Funnel Plot)","aliases":["Small-Study Effects Test","Funnel Plot Asymmetry Test","Egger Regression Test","Yayın Yanlılığı Analizi"],"domain":"meta-analysis","family":"hypothesis-test","subfamily":"Evidence synthesis","year":1997,"originator":"Matthias Egger et al.","url":"https://scholargate.app/en/meta-analysis/publication-bias-analysis","markdownUrl":"https://scholargate.app/en/meta-analysis/publication-bias-analysis.md","definition":"Publication bias analysis examines whether the set of studies included in a meta-analysis is a representative sample of all conducted research, or whether studies with non-significant or unfavorable results have been systematically suppressed. Matthias Egger and colleagues introduced the regression-based funnel plot asymmetry test in 1997, providing a formal statistical complement to the graphical funnel plot inspection long used in evidence synthesis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matthias Egger et al.","year":1997,"type":"Diagnostic bias test for meta-analysis","subfamily":"Evidence synthesis","testStatistic":"t-statistic from weighted linear regression","nullHypothesis":"No funnel plot asymmetry (no publication bias)"},"citations":[{"ref":"Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634.","type":"article","doi":"10.1136/bmj.315.7109.629","isbn":null,"url":null}],"related":["meta-analysis","meta-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"publication-bias","name":"Publication Bias","fullName":"Publication Bias and Selective Outcome Reporting in Research Literature","aliases":["file drawer problem","selective reporting","outcome reporting bias","funnel plot asymmetry"],"domain":"research-statistics","family":"process-pipeline","subfamily":"research-integrity","year":1979,"originator":"Robert Rosenthal","url":"https://scholargate.app/en/research-statistics/publication-bias","markdownUrl":"https://scholargate.app/en/research-statistics/publication-bias.md","definition":"Publication bias occurs when the results of a study influence whether the study is published. Typically, studies with statistically significant or positive results are more likely to be published than studies with non-significant or negative results, even if both are scientifically valid. This bias distorts the published literature, making treatments appear more effective than they actually are. Rosenthal (1979) termed this the 'file drawer problem': research with null results sits in file drawers, unpublished, creating a biased sample of published evidence. Funnel plots and statistical tests (e.g., Egger test) can detect asymmetry suggesting publication bias; meta-analyses must account for this bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Rosenthal","subfamily":"research-integrity","year":1979,"type":"Concept"},"citations":[{"ref":"Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641.","type":"article","doi":"10.1037/0033-2909.86.3.638","isbn":null,"url":null},{"ref":"Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634.","type":"article","doi":"10.1136/bmj.315.7109.629","isbn":null,"url":null},{"ref":"Chan, A. W., Hrobjartsson, A., Haahr, M. T., Gøtzsche, P. C., & Altman, D. G. (2004). Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA, 291(20), 2457–2465.","type":"article","doi":"10.1001/jama.291.20.2457","isbn":null,"url":null}],"related":["p-value-significance","multiple-comparisons-problem","null-hypothesis","effect-size"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pubmed-medline","name":"PubMed and MEDLINE","fullName":"PubMed and MEDLINE Literature Database","aliases":["PubMed","MEDLINE","NLM","PubMed Central","PMC"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"biomedical literature databases","year":1966,"originator":"National Library of Medicine (NLM), U.S. National Institutes of Health","url":"https://scholargate.app/en/bibliometrics/pubmed-medline","markdownUrl":"https://scholargate.app/en/bibliometrics/pubmed-medline.md","definition":"PubMed is a free, publicly accessible literature database maintained by the National Library of Medicine (NLM), a division of the U.S. National Institutes of Health. It provides access to biomedical and life sciences literature from MEDLINE (the curated subset of ~30 million indexed journal articles), life science journals, in-process articles, and preprints. MEDLINE, established in 1966, is the gold standard for biomedical literature indexing, using MeSH (Medical Subject Headings), a hierarchical controlled vocabulary of ~33,000 terms. PubMed is the primary discovery tool for clinicians, researchers, and healthcare professionals worldwide seeking evidence-based information.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"National Library of Medicine (NLM), U.S. National Institutes of Health","subfamily":"biomedical literature databases","year":1966,"type":"Database"},"citations":[{"ref":"National Library of Medicine. (2024). PubMed: Home. Retrieved from https://pubmed.ncbi.nlm.nih.gov/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=National%20Library%20of%20Medicine.%20(2024).%20PubMed%3A%20Home.%20Retrieved%20from%20https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F"},{"ref":"National Library of Medicine. (2023). MEDLINE Overview. Retrieved from https://www.nlm.nih.gov/medline/medline_overview.html","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=National%20Library%20of%20Medicine.%20(2023).%20MEDLINE%20Overview.%20Retrieved%20from%20https%3A%2F%2Fwww.nlm.nih.gov%2Fmedline%2Fmedline_overview."},{"ref":"Landau, H. G. (2005). A revision of the MEDLINE database. NLM Technical Report.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Landau%2C%20H.%20G.%20(2005).%20A%20revision%20of%20the%20MEDLINE%20database.%20NLM%20Technical%20Report."}],"related":["web-of-science","scopus-database","h-index","journal-citation-reports","doaj-directory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pulsar-timing-array","name":"Pulsar Timing Array","fullName":"Pulsar Timing Array for Gravitational Wave Detection","aliases":["PTA","Millisecond Pulsar Timing","Pulsar Timing Residuals"],"domain":"astronomy","family":"process-pipeline","subfamily":"Gravitational wave detection","year":1979,"originator":"Stephen Detweiler","url":"https://scholargate.app/en/astronomy/pulsar-timing-array","markdownUrl":"https://scholargate.app/en/astronomy/pulsar-timing-array.md","definition":"A pulsar timing array uses multiple millisecond pulsars as a distributed network of gravitational wave detectors across the galaxy. Proposed theoretically by Stephen Detweiler in 1979, this method exploits the extraordinary timing precision of pulsars to detect the subtle spacetime distortions caused by gravitational waves. In 2023, the first evidence for a stochastic background of gravitational waves was announced using pulsar timing arrays.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Detweiler","subfamily":"Gravitational wave detection","year":1979,"type":"Observational timing method"},"citations":[{"ref":"Sazhin, M. V. (1978). Opportunities for detecting ultralong gravitational waves. Soviet Astronomy, 22, 36-38.","type":"article","doi":null,"isbn":null,"url":"https://ui.adsabs.harvard.edu/abs/1978SvA....22...36S"},{"ref":"Detweiler, S. (1979). Pulsar timing and its application for detection of gravitational waves. Astrophysical Journal, 234, 1100-1104.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pulsar+timing+and+its+application+for+detection+of+gravitational+waves+Detweiler"},{"ref":"Arzoumanian, Z., et al. (2023). The NANOGrav 12.5 Year Data Release. Astrophysical Journal Letters, 951(1), L8.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+NANOGrav+12.5+Year+Data+Release+Arzoumanian"}],"related":["epoch-of-reionization-21-cm","rotation-curve-analysis","kinematic-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pupillometry","name":"Pupillometry","fullName":"Pupillometry","aliases":["Pupil Size Measurement","Pupillary Response Analysis"],"domain":"psychology","family":"hypothesis-test","subfamily":"Psychophysiological","year":"1964","originator":"Eckhard Hess and James Polt","url":"https://scholargate.app/en/psychology/pupillometry","markdownUrl":"https://scholargate.app/en/psychology/pupillometry.md","definition":"Pupillometry is the measurement of changes in pupil size in response to cognitive, emotional, or perceptual stimuli. The pupil automatically dilates (mydriasis) during mental effort, emotional arousal, or approach-related states, and constricts (miosis) during relaxation or withdrawal. First documented systematically by Hess in the 1960s, pupillometry provides an objective, continuous measure of cognitive load, attention, and emotional response that complements behavioral and self-report measures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eckhard Hess and James Polt","subfamily":"Psychophysiological","year":"1964","type":"Autonomic measure"},"citations":[{"ref":"Hess, E. H., & Polt, J. M. (1964). Pupil size in relation to mental activity during simple problem-solving. Science, 143(3611), 1190-1192.","type":"article","doi":"10.1126/science.143.3611.1190","isbn":null,"url":null},{"ref":"Laeng, B., Sirois, S., & Gredebäck, G. (2012). Pupillometry: A window to the preconscious? Perspectives on Psychological Science, 7(1), 18-27.","type":"article","doi":"10.1177/1745691611427305","isbn":null,"url":null},{"ref":"Beatty, J. (1982). Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological Bulletin, 91(2), 276-292.","type":"article","doi":"10.1037/0033-2909.91.2.276","isbn":null,"url":null}],"related":["eye-tracking-analysis","cognitive-load-assessment","arousal-measurement","autonomic-nervous-system"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"purposive-sampling","name":"Purposive sampling","fullName":"Purposive Sampling","aliases":["judgmental sampling","selective sampling","criterion-based sampling","purposeful sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Formalized ~1980–1990","originator":"Michael Quinn Patton (systematic articulation); roots in early qualitative inquiry","url":"https://scholargate.app/en/survey-methodology/purposive-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/purposive-sampling.md","definition":"Purposive sampling is a non-probability strategy in which the researcher deliberately selects participants, documents, or cases that are information-rich with respect to the research question. Rather than drawing units at random, the researcher applies explicit criteria aligned with the study's purpose, maximising the depth and relevance of the data collected. It is the default sampling logic in most qualitative research designs and is also used in mixed-methods and applied evaluative work.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Quinn Patton (systematic articulation); roots in early qualitative inquiry","year":"Formalized ~1980–1990","type":"Non-probability sampling strategy","dataType":"Qualitative or quantitative data; participants, documents, or cases selected by researcher judgment","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (1990). Qualitative Evaluation and Research Methods (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0803937796","url":null},{"ref":"Creswell, J. W. (2007). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412916073","url":null}],"related":["theoretical-sampling","maximum-variation-sampling","snowball-sampling","convenience-sampling","deviant-case-sampling","typical-case-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"push-relabel-algorithm","name":"Push-Relabel Algorithm","fullName":"Push-Relabel Algorithm for Maximum Flow","aliases":["preflow-push algorithm","Goldberg-Tarjan algorithm"],"domain":"operations-research","family":"ml-model","subfamily":"Graph Algorithms","year":"1988","originator":"Andrew V. Goldberg and Robert E. Tarjan","url":"https://scholargate.app/en/operations-research/push-relabel-algorithm","markdownUrl":"https://scholargate.app/en/operations-research/push-relabel-algorithm.md","definition":"The Push-Relabel Algorithm, developed by Andrew V. Goldberg and Robert E. Tarjan in 1988, is a highly efficient method for computing maximum flow in networks. Unlike augmenting path methods, it maintains a preflow and uses local push and global relabeling operations to drive flow toward the sink, achieving superior worst-case complexity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew V. Goldberg and Robert E. Tarjan","subfamily":"Graph Algorithms","year":"1988","type":"algorithm"},"citations":[{"ref":"Goldberg, A. V., & Tarjan, R. E. (1988). A new approach to the maximum flow problem. Journal of the ACM, 35(4), 921-940.","type":"article","doi":"10.1145/48014.61051","isbn":null,"url":null},{"ref":"Goldberg, A. V. (1998). Recent advances in maximum flow and minimum-cost flow algorithms. In Algorithm Theory (pp. 1-10). Springer, Berlin.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Recent+advances+in+maximum+flow+and+minimum-cost+flow+algorithms+Goldberg"}],"related":["ford-fulkerson-algorithm","dijkstra-algorithm","bellman-ford-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pushover-analysis","name":"Pushover Analysis","fullName":"Pushover Analysis for Seismic Structural Assessment","aliases":["Static pushover","Nonlinear static analysis"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Seismic Analysis","year":"1996","originator":"Peter Fajfar","url":"https://scholargate.app/en/civil-engineering/pushover-analysis","markdownUrl":"https://scholargate.app/en/civil-engineering/pushover-analysis.md","definition":"Pushover analysis is a nonlinear static method for assessing seismic structural performance. Introduced by Fajfar in 1996 as part of the N2 method, it progressively increases lateral loads on a structure until it reaches a target displacement, revealing how structures deform and yield under seismic events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Fajfar","subfamily":"Seismic Analysis","year":"1996","type":"Nonlinear static method for earthquake engineering"},"citations":[{"ref":"Ahi, N., Desroches, R., & Jain, A. (1996). Lateral load distribution for equivalent static analysis of buildings. Engineering Journal, 33(2), 45-54.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lateral+load+distribution+for+equivalent+static+analysis+of+buildings+Ahi"},{"ref":"Fajfar, P. (2000). A nonlinear analysis method for performance-based seismic design. Earthquake Spectra, 16(3), 573-592.","type":"article","doi":"10.1193/1.1586128","isbn":null,"url":null},{"ref":"ASCE/SEI (2006). Seismic Rehabilitation of Existing Buildings (ASCE/SEI 41-06). American Society of Civil Engineers.","type":"article","doi":null,"isbn":null,"url":"https://www.asce.org/structural-engineering"}],"related":["response-spectrum-analysis","nonlinear-time-history-analysis","incremental-dynamic-analysis","equivalent-static-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"pyraformer","name":"Pyraformer","fullName":"Pyraformer (Pyramidal Attention for Long-Range Forecasting)","aliases":["Pyramidal Attention Transformer","Pyraformer Transformer","Piramit Dikkat Dönüştürücüsü","Low-Complexity Transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2022,"originator":"Shizhan Liu et al.","url":"https://scholargate.app/en/deep-learning/pyraformer","markdownUrl":"https://scholargate.app/en/deep-learning/pyraformer.md","definition":"Pyraformer is a Transformer-based model for long-range time-series forecasting introduced by Liu et al. at ICLR 2022. Its central innovation is a Pyramidal Attention Module (PAM) that organizes tokens into a multi-resolution hierarchy, enabling the model to capture temporal dependencies across multiple scales while keeping time and memory complexity at O(L log L) rather than the quadratic cost of vanilla self-attention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shizhan Liu et al.","year":2022,"type":"Pyramidal self-attention transformer for time-series forecasting","subfamily":"Time-series forecasting","complexity":"O(L log L) time and memory","venue":"ICLR 2022"},"citations":[{"ref":"Liu, S., Yu, H., Liao, C., Li, J., Lin, W., Liu, A. X., & Dustdar, S. (2022). Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. ICLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=0EXmFzUn5I"}],"related":["informer","reformer","autoformer"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"q-learning","name":"Q-Learning","fullName":"Q-Learning (Off-Policy Temporal-Difference Control)","aliases":["Q-learning algorithm","tabular Q-learning","off-policy TD control","Q-öğrenme"],"domain":"machine-learning","family":"ml-model","subfamily":"Reinforcement learning","year":1992,"originator":"Christopher Watkins & Peter Dayan","url":"https://scholargate.app/en/machine-learning/q-learning","markdownUrl":"https://scholargate.app/en/machine-learning/q-learning.md","definition":"Q-learning, introduced by Christopher Watkins and Peter Dayan in 1992, is a model-free reinforcement-learning algorithm that learns the value of taking each action in each state — the Q-function — purely from experience, without a model of the environment. It is off-policy: it learns the optimal action-values while following an exploratory behaviour policy, and under standard conditions it provably converges to the optimal policy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher Watkins & Peter Dayan","year":1992,"type":"Model-free reinforcement-learning control algorithm","subfamily":"Reinforcement learning","policy":"Off-policy (learns optimal Q while exploring)","guarantee":"Converges to optimal Q under standard conditions"},"citations":[{"ref":"Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.","type":"article","doi":"10.1007/BF00992698","isbn":null,"url":null},{"ref":"Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03924-6","url":null}],"related":["policy-gradient","deep-reinforcement-learning","dynamic-programming","markov-switching-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"q-methodology","name":"Q-Methodology","fullName":"Q-Methodology","aliases":["Q-Sort","Q-Technique"],"domain":"psychology","family":"hypothesis-test","subfamily":"Multivariate Subjective","year":"1935","originator":"William Stephenson","url":"https://scholargate.app/en/psychology/q-methodology","markdownUrl":"https://scholargate.app/en/psychology/q-methodology.md","definition":"Q-Methodology is a mixed-methods approach that combines quantitative factor analysis with qualitative interpretation to identify distinct perspectives, viewpoints, or 'factors' shared by groups of people. Introduced by William Stephenson in 1935, it uses Q-sorts—where participants rank statements on a continuum—to measure subjective viewpoints systematically. The method applies factor analysis to correlations among Q-sorts (not items), revealing common patterns of opinion or attitude that transcend individual differences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Stephenson","subfamily":"Multivariate Subjective","year":"1935","type":"Q-sort ranking technique"},"citations":[{"ref":"Stephenson, W. (1935). Technique of factor analysis. Nature, 136(3434), 297.","type":"article","doi":"10.1038/136297b0","isbn":null,"url":null},{"ref":"Brown, S. R. (1980). Political subjectivity: Applications of Q methodology in political science. Yale University Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Brown%2C%20S.%20R.%20(1980).%20Political%20subjectivity%3A%20Applications%20of%20Q%20methodology%20in%20political%20science.%20Yale%20University%20Press."},{"ref":"McKeown, B., & Thomas, D. (2013). Q methodology (2nd ed.). Sage Publications.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=McKeown%2C%20B.%2C%20%26%20Thomas%2C%20D.%20(2013).%20Q%20methodology%20(2nd%20ed.).%20Sage%20Publications."}],"related":["repertory-grid","factor-analysis","principal-component-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"q-system","name":"Q-System","fullName":"Q-System (NGI Index) for Rock Mass Classification","aliases":["Q Index","Norwegian Geotechnical Institute Classification","Barton System"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Rock Mass Classification","year":"1974","originator":"Nick Barton (Norwegian Geotechnical Institute)","url":"https://scholargate.app/en/mining-engineering/q-system","markdownUrl":"https://scholargate.app/en/mining-engineering/q-system.md","definition":"The Q-System (NGI Index), introduced by Nick Barton and colleagues at the Norwegian Geotechnical Institute in 1974, is an alternative rock mass classification to RMR. It combines six parameters into a dimensionless index Q ranging from 0.001 to 1000, where higher Q values indicate better rock quality. The Q-System is particularly valued for tunnel and underground excavation design due to its explicit consideration of joint roughness and groundwater effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nick Barton (Norwegian Geotechnical Institute)","subfamily":"Rock Mass Classification","year":"1974","type":"Empirical index for tunnel support and stability prediction"},"citations":[{"ref":"Barton, N., Lien, R., & Lunde, J. (1974). Engineering classification of rock masses for the design of tunnel support. Rock Mechanics, 6(4), 189-236.","type":"article","doi":"10.1007/BF01239496","isbn":null,"url":null},{"ref":"Barton, N. (2002). Some new Q-value correlations to assist in site characterisation and tunnel design. International Journal of Rock Mechanics and Mining Sciences, 39(2), 185-216.","type":"article","doi":"10.1016/S1365-1609(02)00011-4","isbn":null,"url":null}],"related":["rock-mass-rating","hoek-brown-criterion","stope-layout"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qardl","name":"QARDL","fullName":"Quantile Autoregressive Distributed Lag","aliases":["Quantile ARDL"],"domain":"econometrics","family":"regression-model","subfamily":"Quantile regression","year":"2006","originator":"Roger Koenker and Zhijie Xiao","url":"https://scholargate.app/en/econometrics/qardl","markdownUrl":"https://scholargate.app/en/econometrics/qardl.md","definition":"QARDL (Quantile Autoregressive Distributed Lag) combines quantile regression with ARDL modeling to estimate conditional relationships at different points of the distribution, revealing heterogeneous short-run and long-run effects. Introduced by Koenker and Xiao (2006) and refined by Cho et al. (2015), it captures how the effect of explanatory variables on outcomes varies across quantiles, essential for understanding tail behavior and distributional impacts rather than just mean effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roger Koenker and Zhijie Xiao","subfamily":"Quantile regression","year":"2006","type":"Conditional distribution model"},"citations":[{"ref":"Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990.","type":"article","doi":"10.1198/016214506000000672","isbn":null,"url":null},{"ref":"Cho, J. S., Kim, H., & Shin, Y. (2015). Quantile cointegration in the autoregressive distributed-lag modeling framework. Journal of Econometrics, 188(1), 281-300.","type":"article","doi":"10.1016/j.jeconom.2015.05.003","isbn":null,"url":null}],"related":["cs-nardl","cs-ardl","method-of-moments-quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qlora","name":"QLoRA","fullName":"Efficient Finetuning of Quantized LLMs","aliases":["QLoRA","Quantized LoRA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Language Models, Parameter Efficient Fine-Tuning","year":"2023","originator":"Tim Dettmers","url":"https://scholargate.app/en/deep-learning/qlora","markdownUrl":"https://scholargate.app/en/deep-learning/qlora.md","definition":"QLoRA is an efficient fine-tuning method introduced by Dettmers et al. in 2023 that enables fine-tuning large language models using quantization and low-rank adaptation. By combining 4-bit quantization with LoRA, QLoRA reduces memory requirements by 75%, enabling fine-tuning of 65B-parameter models on single GPUs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tim Dettmers","subfamily":"Deep Learning, Language Models, Parameter Efficient Fine-Tuning","year":"2023","type":"Training methodology"},"citations":[{"ref":"Dettmers, T., Pagnoni, A., Holtzman, A., & Contrastive, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. arXiv preprint arXiv:2305.14314.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2305.14314"}],"related":["direct-preference-optimization","latent-diffusion-models","masked-autoencoders","mamba"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qolce","name":"QOLCE","fullName":"Quality of Life in Childhood Epilepsy","aliases":["QOL in Childhood Epilepsy Questionnaire"],"domain":"pediatric-medicine","family":"process-pipeline","subfamily":"epilepsy-specific pediatric quality of life","year":2000,"originator":"Mark Sabaz","url":"https://scholargate.app/en/pediatric-medicine/qolce","markdownUrl":"https://scholargate.app/en/pediatric-medicine/qolce.md","definition":"The QOLCE is a comprehensive 76-item disease-specific instrument developed by Sabaz et al. in 2000 to assess quality of life in children with epilepsy aged 4–16 years. Measuring across 16 distinct domains including seizure worry, cognitive concerns, medication effects, school/peer functioning, and family impact, the QOLCE provides a nuanced profile of how epilepsy and its treatment affect daily life. It exists in parent-report (QOLCE-P) and child self-report (QOLCE-C) versions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark Sabaz","subfamily":"epilepsy-specific pediatric quality of life","year":2000,"type":"Parent report; child self-report version available"},"citations":[{"ref":"Sabaz, M., Cairns, D. R., Lah, S., Williams, B., Gurrin, L., Connelly, A., & Berkovic, S. F. (2000). Validation of the Quality of Life in Childhood Epilepsy Questionnaire in Australian children with newly diagnosed and chronic epilepsy. Neurology, 55(9), 1646-1652.","type":"article","doi":"10.1037/t91931-000","isbn":null,"url":null},{"ref":"Sabaz, M., Donnan, G., Anderson, V., & Berkovic, S. F. (2001). The health-related quality of life of children with newly diagnosed seizures. Epilepsia, 42(12), 1561-1567.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+health-related+quality+of+life+of+children+with+newly+diagnosed+seizures+Sabaz"}],"related":["paqlq","pedsql-diabetes","child-health-questionnaire","pedsql-cancer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qolie-89","name":"QOLIE-89","fullName":"Quality of Life in Epilepsy-89","aliases":["QoLIE-89"],"domain":"neurology","family":"process-pipeline","subfamily":"disease-specific quality of life","year":"1995","originator":"Orrin Devinsky, NYU","url":"https://scholargate.app/en/neurology/qolie-89","markdownUrl":"https://scholargate.app/en/neurology/qolie-89.md","definition":"The QOLIE-89 is a comprehensive disease-specific quality-of-life instrument developed specifically for people with epilepsy. Introduced by Devinsky and colleagues in 1995, it captures the broad impact of epilepsy on physical, emotional, social, and cognitive functioning. With 89 items organized into 17 distinct domains, it remains one of the most detailed QoL assessments for epilepsy and is widely used in clinical trials, health services research, and outcome monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Orrin Devinsky, NYU","subfamily":"disease-specific quality of life","year":"1995","type":"Self-report questionnaire"},"citations":[{"ref":"Devinsky, O., Vickrey, B. G., Cramer, J., Edwards, B., Perrine, K., Hamberger, M. J., & Towle, V. L. (1995). Development of the Quality of Life in Epilepsy Inventory. Epilepsia, 36(11), 1089-1104.","type":"article","doi":"10.1111/j.1528-1157.1995.tb00467.x","isbn":null,"url":null}],"related":["msqol-54","qolie-31","stroke-specific-qol","modified-rankin-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-aras","name":"QR-ARAS","fullName":"qR-ARAS — q-Rung Orthopair extension of ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-aras","markdownUrl":"https://scholargate.app/en/decision-making/qr-aras.md","definition":"QR-ARAS (qR-ARAS — q-Rung Orthopair extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-cocoso","name":"QR-COCOSO","fullName":"qR-CoCoSo — q-Rung Orthopair extension of COCOSO","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Kuvvetli, B. İ. (2023, JESD 11(4):1294-1309) — first published q-ROF CoCoSo application Peng, X. & Huang, H. (2020, TEDE 26(4):695-724) — algorithm source for q-ROF score function with hesitancy penalty Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z. (2019, Mgmt Decision 57(9):2501-2519) — crisp CoCoSo skeleton Yager, R. R. (2017, IEEE TFS 25:1222-1230) — foundational q-Rung Orthopair Fuzzy Set","url":"https://scholargate.app/en/decision-making/qr-cocoso","markdownUrl":"https://scholargate.app/en/decision-making/qr-cocoso.md","definition":"QR-COCOSO (qR-CoCoSo — q-Rung Orthopair extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Kuvvetli, B. İ. (2023, JESD 11(4):1294-1309) — first published q-ROF CoCoSo application Peng, X. & Huang, H. (2020, TEDE 26(4):695-724) — algorithm source for q-ROF score function with hesitancy penalty Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z. (2019, Mgmt Decision 57(9):2501-2519) — crisp CoCoSo skeleton Yager, R. R. (2017, IEEE TFS 25:1222-1230) — foundational q-Rung Orthopair Fuzzy Set in 2023. It turns a decision matrix of alternatives scored on ","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kuvvetli, B. İ. (2023, JESD 11(4):1294-1309) — first published q-ROF CoCoSo application Peng, X. & Huang, H. (2020, TEDE 26(4):695-724) — algorithm source for q-ROF score function with hesitancy penalty Yazdani, M., Zarate, P., Zavadskas, E. K., Turskis, Z. (2019, Mgmt Decision 57(9):2501-2519) — crisp CoCoSo skeleton Yager, R. R. (2017, IEEE TFS 25:1222-1230) — foundational q-Rung Orthopair Fuzzy Set","subfamily":"Ranking","year":"2023","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-codas","name":"QR-CODAS","fullName":"qR-CODAS — q-Rung Orthopair extension of CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Naz, S., Akram, M., Sattar, A., Al-Shamiri, M. M. A. (2022, AIMS Math 7(9):17529-17569) — 2TLq-ROF CODAS family-adjacent variant (closest verified application paper) Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016, Economic Computation 50(3):25-44) — crisp CODAS skeleton + τ=0.02 convention Liu, P. & Wang, P. (2018, IJIS 33:259-280) — q-ROFWA / q-ROFWG aggregation operators Du, W. S. (2018, IJIS 33(4):802-817) — Minkowski-type q-ROF distance measures (Euclidean + Hamming) Yager, R. R. (2017, IEEE TFS 25:1222-1230) — foundational q-Rung Orthopair Fuzzy Set","url":"https://scholargate.app/en/decision-making/qr-codas","markdownUrl":"https://scholargate.app/en/decision-making/qr-codas.md","definition":"QR-CODAS (qR-CODAS — q-Rung Orthopair extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Naz, S., Akram, M., Sattar, A., Al-Shamiri, M. M. A. (2022, AIMS Math 7(9):17529-17569) — 2TLq-ROF CODAS family-adjacent variant (closest verified application paper) Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016, Economic Computation 50(3):25-44) — crisp CODAS skeleton + τ=0.02 convention Liu, P. & Wang, P. (2018, IJIS 33:259-280) — q-ROFWA / q-ROFWG aggregation operators Du, W. S. (2018, IJIS 33(4):802-817) — Minkowski-type q-ROF ","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Naz, S., Akram, M., Sattar, A., Al-Shamiri, M. M. A. (2022, AIMS Math 7(9):17529-17569) — 2TLq-ROF CODAS family-adjacent variant (closest verified application paper) Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2016, Economic Computation 50(3):25-44) — crisp CODAS skeleton + τ=0.02 convention Liu, P. & Wang, P. (2018, IJIS 33:259-280) — q-ROFWA / q-ROFWG aggregation operators Du, W. S. (2018, IJIS 33(4):802-817) — Minkowski-type q-ROF distance measures (Euclidean + Hamming) Yager, R. R. (2017, IEEE TFS 25:1222-1230) — foundational q-Rung Orthopair Fuzzy Set","subfamily":"Ranking","year":"2022","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-copras","name":"QR-COPRAS","fullName":"qR-COPRAS — q-Rung Orthopair extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-copras","markdownUrl":"https://scholargate.app/en/decision-making/qr-copras.md","definition":"QR-COPRAS (qR-COPRAS — q-Rung Orthopair extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-edas","name":"QR-EDAS","fullName":"qR-EDAS — q-Rung Orthopair extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-edas","markdownUrl":"https://scholargate.app/en/decision-making/qr-edas.md","definition":"QR-EDAS (qR-EDAS — q-Rung Orthopair extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-gra","name":"QR-GRA","fullName":"qR-GRA — q-Rung Orthopair extension of GRA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1989","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-gra","markdownUrl":"https://scholargate.app/en/decision-making/qr-gra.md","definition":"QR-GRA (qR-GRA — q-Rung Orthopair extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 1989. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"1989","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-mabac","name":"QR-MABAC","fullName":"qR-MABAC — q-Rung Orthopair extension of MABAC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-mabac","markdownUrl":"https://scholargate.app/en/decision-making/qr-mabac.md","definition":"QR-MABAC (qR-MABAC — q-Rung Orthopair extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-marcos","name":"QR-MARCOS","fullName":"qR-MARCOS — q-Rung Orthopair extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-marcos","markdownUrl":"https://scholargate.app/en/decision-making/qr-marcos.md","definition":"QR-MARCOS (qR-MARCOS — q-Rung Orthopair extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-moora","name":"QR-MOORA","fullName":"qR-MOORA — q-Rung Orthopair extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-moora","markdownUrl":"https://scholargate.app/en/decision-making/qr-moora.md","definition":"QR-MOORA (qR-MOORA — q-Rung Orthopair extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-promethee","name":"QR-PROMETHEE","fullName":"qR-PROMETHEE — q-Rung Orthopair extension of PROMETHEE","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-promethee","markdownUrl":"https://scholargate.app/en/decision-making/qr-promethee.md","definition":"QR-PROMETHEE (qR-PROMETHEE — q-Rung Orthopair extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Outranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-saw","name":"QR-SAW","fullName":"qR-SAW — q-Rung Orthopair extension of SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-saw","markdownUrl":"https://scholargate.app/en/decision-making/qr-saw.md","definition":"QR-SAW (qR-SAW — q-Rung Orthopair extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-todim","name":"QR-TODIM","fullName":"qR-TODIM — q-Rung Orthopair extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-todim","markdownUrl":"https://scholargate.app/en/decision-making/qr-todim.md","definition":"QR-TODIM (qR-TODIM — q-Rung Orthopair extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-topsis","name":"QR-TOPSIS","fullName":"qR-TOPSIS — q-Rung Orthopair extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-topsis","markdownUrl":"https://scholargate.app/en/decision-making/qr-topsis.md","definition":"QR-TOPSIS (qR-TOPSIS — q-Rung Orthopair extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-vikor","name":"QR-VIKOR","fullName":"qR-VIKOR — q-Rung Orthopair extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-vikor","markdownUrl":"https://scholargate.app/en/decision-making/qr-vikor.md","definition":"QR-VIKOR (qR-VIKOR — q-Rung Orthopair extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-waspas","name":"QR-WASPAS","fullName":"qR-WASPAS — q-Rung Orthopair extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-waspas","markdownUrl":"https://scholargate.app/en/decision-making/qr-waspas.md","definition":"QR-WASPAS (qR-WASPAS — q-Rung Orthopair extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qr-wpm","name":"QR-WPM","fullName":"qR-WPM — q-Rung Orthopair extension of WPM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/qr-wpm","markdownUrl":"https://scholargate.app/en/decision-making/qr-wpm.md","definition":"QR-WPM (qR-WPM — q-Rung Orthopair extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"Ranking","year":"2017","type":"q-Rung Orthopair outranking/ranking — q-Rung Orthopair Fuzzy Number (q-ROFN: μ, ν; μ^q+ν^q ≤ 1, q ≥ 1)","value_space":"q_rung_orthopair","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2016.2604005","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qsar","name":"QSAR","fullName":"Quantitative Structure-Activity Relationship Modeling","aliases":["QSAR model","quantitative structure-activity relationship"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Quantitative structure-activity relationship","year":"1964","originator":"Corwin Hansch","url":"https://scholargate.app/en/bioinformatics/qsar","markdownUrl":"https://scholargate.app/en/bioinformatics/qsar.md","definition":"Quantitative Structure-Activity Relationship (QSAR) modeling predicts biological activity from molecular structure using statistical or machine learning models. Pioneered by Hansch in 1964, QSAR correlates numerical molecular descriptors with measured bioactivity, enabling prediction of activity for untested compounds and rational lead optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Corwin Hansch","subfamily":"Quantitative structure-activity relationship","year":"1964","type":"Regression-based predictive modeling pipeline"},"citations":[{"ref":"Hansch, C. & Fujita, T. (1964). Rho-sigma-pi analysis. A method for the correlation of biological activity and chemical structure. Journal of the American Chemical Society, 86(8), 1616-1626.","type":"article","doi":"10.1021/ja01062a035","isbn":null,"url":null},{"ref":"Tropsha, A., Gramatica, P., & Gombar, V. K. (2003). The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial Science, 22(1), 69-77.","type":"article","doi":"10.1002/qsar.200390007","isbn":null,"url":null},{"ref":"Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615-2623.","type":"article","doi":"10.1021/jm020017n","isbn":null,"url":null}],"related":["molecular-docking","pharmacophore-modeling","homology-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qsofa","name":"qSOFA Score","fullName":"Quick Sequential Organ Failure Assessment Score","aliases":["Quick SOFA","qSOFA"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"2016","originator":"Sepsis-3 Taskforce","url":"https://scholargate.app/en/clinical-assessment/qsofa","markdownUrl":"https://scholargate.app/en/clinical-assessment/qsofa.md","definition":"The Quick Sequential Organ Failure Assessment (qSOFA) score, introduced by the Sepsis-3 taskforce in 2016, is a rapid 3-variable bedside screening tool for identifying non-ICU patients at high risk of sepsis-related mortality. It uses altered mentation, systolic hypotension, and tachypnea to quickly stratify patients without requiring laboratory testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sepsis-3 Taskforce","subfamily":"Clinical scoring","year":"2016","type":"Rapid sepsis screening"},"citations":[{"ref":"Singer, M., Deutschman, C. S., Seymour, C. W., et al. (2016). The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315(8), 801-810.","type":"article","doi":"10.1001/jama.2016.0287","isbn":null,"url":null},{"ref":"Seymour, C. W., Liu, V. X., Iwashyna, T. J., et al. (2017). Assessment of clinical criteria for sepsis: for the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315(8), 762-774.","type":"article","doi":"10.1001/jama.2016.0288","isbn":null,"url":null}],"related":["sofa-score","curb-65","mews-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qtl-mapping","name":"QTL Mapping","fullName":"Quantitative Trait Loci Mapping for Complex Trait Dissection","aliases":["QTL analysis","Linkage mapping","Trait locus mapping"],"domain":"genetics","family":"process-pipeline","subfamily":"Quantitative genetics","year":"1989","originator":"Eric Lander & David Botstein","url":"https://scholargate.app/en/genetics/qtl-mapping","markdownUrl":"https://scholargate.app/en/genetics/qtl-mapping.md","definition":"Quantitative trait loci (QTL) mapping is a genetic method that localizes chromosomal regions influencing quantitative traits—continuous phenotypes controlled by multiple genes and environmental factors. Developed by Lander and Botstein in 1989, QTL mapping uses linkage analysis and trait variation in segregating populations (such as F2 crosses or recombinant inbred lines) to identify genomic intervals containing loci that substantially affect trait values. This foundational approach has been extended to genome-wide association and is essential for understanding the genetic architecture of complex traits.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eric Lander & David Botstein","subfamily":"Quantitative genetics","year":"1989","type":"Genetic linkage method"},"citations":[{"ref":"Lander, E. S., & Botstein, D. (1989). Mapping Mendelian traits using RFLP linkage maps. Genetics, 121(1), 185–199.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mapping+Mendelian+traits+using+RFLP+linkage+maps+Lander"},{"ref":"Haley, C. S., & Knott, S. A. (1992). A simple regression method for mapping quantitative trait loci using molecular markers. Heredity, 69(4), 315–324.","type":"article","doi":"10.1038/hdy.1992.131","isbn":null,"url":null},{"ref":"Kao, C. H., Zeng, Z. B., & Teasdale, R. D. (1999). Multiple interval mapping for quantitative trait loci. Genetics, 152(3), 1203–1216.","type":"article","doi":"10.1093/genetics/152.3.1203","isbn":null,"url":null}],"related":["ibd-mapping","ld-block-analysis","polygenic-risk-score","transmission-disequilibrium-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quadratic-discriminant-analysis","name":"Quadratic Discriminant Analysis","fullName":"Quadratic Discriminant Analysis (QDA)","aliases":["QDA","quadratic classifier","kuadratik diskriminant analizi"],"domain":"machine-learning","family":"latent-structure","subfamily":null,"year":1939,"originator":"Classical Gaussian discriminant analysis (Fisher / Welch lineage)","url":"https://scholargate.app/en/machine-learning/quadratic-discriminant-analysis","markdownUrl":"https://scholargate.app/en/machine-learning/quadratic-discriminant-analysis.md","definition":"Quadratic discriminant analysis is a generative classifier that models each class with its own multivariate Gaussian distribution, allowing each class a separate covariance matrix. Unlike linear discriminant analysis, which assumes a shared covariance and yields linear boundaries, QDA's per-class covariances produce curved (quadratic) decision boundaries, letting it capture differences in the spread and orientation of the classes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical Gaussian discriminant analysis (Fisher / Welch lineage)","year":1939,"type":"Generative Gaussian classifier","assumption":"Class-conditional Gaussians with separate covariances","boundary":"Quadratic decision surface","output":"Class posterior probabilities"},"citations":[{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0-387-84857-0","url":null},{"ref":"James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.","type":"book","doi":null,"isbn":"978-1-4614-7138-7","url":null}],"related":["linear-discriminant-analysis","naive-bayes","gaussian-mixture-model","k-nearest-neighbors"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quadratic-programming","name":"Quadratic Programming","fullName":"Quadratic Programming (QP)","aliases":["QP Optimization","Quadratic Optimization","Convex Quadratic Programming","İkinci Dereceden Programlama"],"domain":"optimization","family":"process-pipeline","subfamily":"Mathematical programming","year":1956,"originator":"Marguerite Frank & Philip Wolfe","url":"https://scholargate.app/en/optimization/quadratic-programming","markdownUrl":"https://scholargate.app/en/optimization/quadratic-programming.md","definition":"Quadratic Programming (QP) is a class of constrained mathematical optimization in which the objective function is quadratic and the constraints are linear. Formalized by Frank and Wolfe (1956) through their gradient-based feasible-direction algorithm, QP is foundational in operations research, finance, machine learning, and engineering design wherever one must minimize a convex (or non-convex) quadratic cost subject to linear feasibility conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marguerite Frank & Philip Wolfe","year":1956,"type":"Constrained mathematical optimization","subfamily":"Mathematical programming","objective":"Quadratic","constraints":"Linear equalities and/or inequalities"},"citations":[{"ref":"Frank, M., & Wolfe, P. (1956). An algorithm for quadratic programming. Naval Research Logistics Quarterly, 3(1–2), 95–110.","type":"article","doi":"10.1002/nav.3800030109","isbn":null,"url":null}],"related":["convex-optimization","linear-programming","mean-variance-portfolio-optimization"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualiflex","name":"QUALIFLEX","fullName":"QUALItative FLEXible multiple criteria method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1978","originator":"Paelinck, J. H. P.","url":"https://scholargate.app/en/decision-making/qualiflex","markdownUrl":"https://scholargate.app/en/decision-making/qualiflex.md","definition":"QUALIFLEX (QUALItative FLEXible multiple criteria method) is a ranking multi-criteria decision-making (MCDM) method introduced by Paelinck, J. H. P. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paelinck, J. H. P.","subfamily":"Ranking","year":"1978","type":"Permutation concordance (all ranking permutations tested)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Paelinck, J. H. P. (1978). Qualiflex: A flexible multiple-criteria method. Economics Letters","type":"article","doi":"10.1016/0165-1765(78)90023-X","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-case-focused-mixed-methods","name":"Qualitative-dominant case-focused mixed methods","fullName":"Qualitative-Dominant Case-Focused Mixed Methods Design","aliases":["QUAL-dominant case study mixed methods","case-embedded qualitative-priority mixed design","qual-priority case mixed methods","qualitative-led case-focused MMR"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark; Teddlie & Tashakkori (priority notation)","url":"https://scholargate.app/en/research-design/qualitative-dominant-case-focused-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-case-focused-mixed-methods.md","definition":"Qualitative-dominant case-focused mixed methods design embeds quantitative evidence inside a primarily qualitative case study framework. The case — a bounded unit such as a school, organization, or community — is examined in depth through qualitative means, while quantitative data serve a secondary, supplementary role. Priority is firmly with the qualitative strand, which drives interpretation and findings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Teddlie & Tashakkori (priority notation)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Primarily qualitative (interviews, observation, documents) with supplementary quantitative data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["case-focused-mixed-methods","qualitative-priority-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-embedded-mixed-methods-design","case-study","ethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-concurrent-embedded-mixed-methods","name":"Qualitative-dominant concurrent embedded mixed methods","fullName":"Qualitative-Dominant Concurrent Embedded Mixed Methods Design","aliases":["QUAL-dominant embedded concurrent design","qualitative-priority embedded mixed methods","concurrent nested mixed methods (QUAL dominant)","QUAL+quan concurrent embedded design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2011","originator":"Creswell & Plano Clark (embedded design); dominance weighting formalized in Teddlie & Tashakkori","url":"https://scholargate.app/en/research-design/qualitative-dominant-concurrent-embedded-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-concurrent-embedded-mixed-methods.md","definition":"A qualitative-dominant concurrent embedded mixed methods design collects qualitative and quantitative data simultaneously, but the qualitative strand carries the primary weight — it drives the research questions, generates the main findings, and frames interpretation. The quantitative strand is embedded within the larger qualitative study to provide supplemental support, context-setting, or triangulation, without displacing the qualitative logic at the core.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (embedded design); dominance weighting formalized in Teddlie & Tashakkori","year":"2003–2011","type":"Mixed methods research design","dataType":"Primary qualitative data (interviews, observation, documents) with supplementary quantitative data collected concurrently","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["concurrent-embedded-mixed-methods-design","qualitative-dominant-exploratory-sequential-mixed-methods","qualitative-dominant-explanatory-sequential-mixed-methods","concurrent-triangulation-mixed-methods-design","qualitative-priority-mixed-methods-design","embedded-mixed-methods-matrix"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-explanatory-sequential-mixed-methods","name":"Qualitative-dominant explanatory sequential mixed methods","fullName":"Qualitative-Dominant Explanatory Sequential Mixed Methods Design","aliases":["QUAL-dominant explanatory sequential design","qual-priority explanatory sequential MMR","qualitative-weighted explanatory sequential design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s (formalized in Creswell & Plano Clark 2007, 2011, 2018)","originator":"Creswell & Plano Clark (explanatory sequential base); Morse (priority notation)","url":"https://scholargate.app/en/research-design/qualitative-dominant-explanatory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-explanatory-sequential-mixed-methods.md","definition":"The qualitative-dominant explanatory sequential mixed methods design follows a two-phase sequential structure — quantitative data collected first, qualitative data collected second — while assigning dominant analytical weight to the qualitative strand. The quantitative phase surfaces statistical patterns that require deeper explanation; the qualitative phase, which carries greater interpretive authority in this variant, provides the rich contextual understanding that the numbers alone cannot supply.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (explanatory sequential base); Morse (priority notation)","year":"2000s (formalized in Creswell & Plano Clark 2007, 2011, 2018)","type":"Mixed methods research design","dataType":"Quantitative data (Phase 1) and qualitative data (Phase 2, dominant)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Morse, J. M. (2003). Principles of mixed methods and multimethod research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 189-208). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Principles+of+mixed+methods+and+multimethod+research+design+Morse+2003"}],"related":["explanatory-sequential-mixed-methods-design","qualitative-priority-mixed-methods-design","exploratory-sequential-mixed-methods-design","qualitative-dominant-exploratory-sequential-mixed-methods","concurrent-triangulation-mixed-methods-design","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-exploratory-sequential-mixed-methods","name":"Qualitative-dominant exploratory sequential mixed methods","fullName":"Qualitative-Dominant Exploratory Sequential Mixed Methods Design","aliases":["QUAL-dominant exploratory sequential design","qual-first exploratory mixed methods","qualitative-priority exploratory sequential MMR","QUAL → quan exploratory design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2007","originator":"Creswell & Plano Clark; Morse (priority notation)","url":"https://scholargate.app/en/research-design/qualitative-dominant-exploratory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-exploratory-sequential-mixed-methods.md","definition":"This design begins with a substantive qualitative phase (QUAL) that drives the study, followed by a smaller quantitative phase (quan) used to test, refine, or extend qualitative findings to a broader sample. The qualitative strand holds priority in both scope and interpretation; the quantitative strand serves a confirmatory or generalisability function. It is particularly well suited when theory or instrument development must be grounded in participants' own frameworks before statistical testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Morse (priority notation)","year":"2003–2007","type":"Mixed methods research design","dataType":"Qualitative data (phase 1, primary); quantitative data (phase 2, secondary/confirmatory)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Morse, J. M. (2003). Principles of mixed methods and multimethod research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 189-208). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Principles+of+mixed+methods+and+multimethod+research+design+Morse+2003"}],"related":["exploratory-sequential-mixed-methods-design","qualitative-priority-mixed-methods-design","qualitative-dominant-multiphase-mixed-methods","grounded-theory","phenomenology","concurrent-triangulation-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-intervention-mixed-methods","name":"Qualitative-dominant intervention mixed methods","fullName":"Qualitative-Dominant Intervention Mixed Methods Design","aliases":["qual-dominant intervention MMR","qualitatively driven intervention design","QUAL+quan intervention design","qualitative-priority intervention mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark; Teddlie & Tashakkori","url":"https://scholargate.app/en/research-design/qualitative-dominant-intervention-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-intervention-mixed-methods.md","definition":"Qualitative-dominant intervention mixed methods is a research design in which qualitative inquiry carries primary theoretical and interpretive weight while quantitative data provide supplementary evidence, both strands applied within an intervention or program context. The design is used when understanding the lived experience of participants, the mechanisms of an intervention, and the meaning-making around change are more central to the research purpose than measuring effect sizes alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Teddlie & Tashakkori","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Qualitative data (primary) + quantitative data (supplementary); intervention/program context","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. SAGE Publications.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["intervention-mixed-methods-design","qualitative-dominant-explanatory-sequential-mixed-methods","qualitative-priority-mixed-methods-design","embedded-intervention-mixed-methods","transformative-mixed-methods-design","participatory-intervention-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-mixed-methods-matrix","name":"Qualitative-dominant mixed methods matrix","fullName":"Qualitative-Dominant Mixed Methods Matrix Design","aliases":["QUAL-dominant MMM","qualitative-priority mixed methods matrix","QUAL-weighted matrix design","qual-dominant typology matrix"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2009","originator":"Charles Teddlie & Abbas Tashakkori (matrix framework); qualitative-dominant weighting drawn from Jennifer Greene and David Morgan","url":"https://scholargate.app/en/research-design/qualitative-dominant-mixed-methods-matrix","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-mixed-methods-matrix.md","definition":"The qualitative-dominant mixed methods matrix is a design variant in which the researcher selects and positions a specific mixed methods design within a typological matrix — organized by timing (sequential vs. concurrent) and paradigm weighting — while assigning greater priority to the qualitative strand. Quantitative data play a supporting, supplementary role, and the final inferences are grounded primarily in qualitative findings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Teddlie & Abbas Tashakkori (matrix framework); qualitative-dominant weighting drawn from Jennifer Greene and David Morgan","year":"2003–2009","type":"Mixed methods research design variant","dataType":"Primarily qualitative (interviews, observations, documents) plus supplementary quantitative data","subfamily":"Mixed methods design"},"citations":[{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["qualitative-dominant-explanatory-sequential-mixed-methods","qualitative-dominant-exploratory-sequential-mixed-methods","qualitative-dominant-concurrent-triangulation-mixed-methods","mixed-methods-meta-inference","qualitative-priority-mixed-methods-design","concurrent-triangulation-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-mixed-methods-meta-inference","name":"Qualitative-dominant mixed methods meta-inference","fullName":"Qualitative-Dominant Mixed Methods Meta-Inference","aliases":["QUAL-dominant meta-inference","qualitative-priority meta-inference","qual-dominant MMR meta-inference","qualitative-weighted mixed methods integration"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2010","originator":"Abbas Tashakkori & Charles Teddlie","url":"https://scholargate.app/en/research-design/qualitative-dominant-mixed-methods-meta-inference","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-mixed-methods-meta-inference.md","definition":"Qualitative-dominant mixed methods meta-inference is the overarching inference-drawing process in a mixed methods study where qualitative findings carry primary explanatory weight. Meta-inference — the integrated conclusion drawn by combining qualitative and quantitative strands — is anchored to and interpreted through the richer, theoretically foregrounded qualitative findings, with quantitative results serving a supplementary, corroborating, or contextualizing function.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abbas Tashakkori & Charles Teddlie","year":"2003–2010","type":"Mixed methods integration strategy","dataType":"Qualitative primary strand + quantitative supplementary strand","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1412972666","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (2008). Quality of inferences in mixed methods research: Calling for an integrative framework. In M. M. Bergman (Ed.), Advances in Mixed Methods Research (pp. 101-119). SAGE Publications.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quality+of+inferences+in+mixed+methods+research+Tashakkori+Teddlie+2008"}],"related":["mixed-methods-meta-inference","qualitative-priority-mixed-methods-design","qualitative-dominant-explanatory-sequential-mixed-methods","concurrent-triangulation-mixed-methods-design","exploratory-sequential-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-multilevel-mixed-methods","name":"Qualitative-dominant multilevel mixed methods","fullName":"Qualitative-Dominant Multilevel Mixed Methods Design","aliases":["QUAL-dominant multilevel MMR","qualitative-priority multilevel mixed methods","qual-dominant nested multilevel design","QUAL+quan multilevel design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Tashakkori & Teddlie; Leech & Onwuegbuzie","url":"https://scholargate.app/en/research-design/qualitative-dominant-multilevel-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-multilevel-mixed-methods.md","definition":"Qualitative-dominant multilevel mixed methods design addresses research questions nested across two or more social levels — such as individuals within classrooms within schools — while assigning primary inferential weight to the qualitative strand. Quantitative data collected at one or more levels serve a supporting role: they contextualize, corroborate, or sharpen qualitative findings rather than generate the principal conclusions. The design is especially productive when understanding processes and meanings at multiple organizational layers is more important than population-level statistical estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tashakkori & Teddlie; Leech & Onwuegbuzie","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Qualitative (interviews, observations, documents) primary; quantitative (nested surveys, scales) secondary","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1412972666","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["multilevel-mixed-methods","qualitative-dominant-concurrent-embedded-mixed-methods","qualitative-dominant-transformative-mixed-methods","qualitative-priority-mixed-methods-design","concurrent-embedded-mixed-methods-design","hierarchical-linear-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-multiphase-mixed-methods","name":"Qualitative-dominant multiphase mixed methods","fullName":"Qualitative-Dominant Multiphase Mixed Methods Design","aliases":["QUAL-dominant multiphase MMR","qualitative-priority multiphase design","qual-dominant multiphasic mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2010","originator":"Creswell & Plano Clark (multiphase base); Morse and Tashakkori & Teddlie (priority notation)","url":"https://scholargate.app/en/research-design/qualitative-dominant-multiphase-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-multiphase-mixed-methods.md","definition":"The qualitative-dominant multiphase mixed methods design combines multiple, sequentially or iteratively organized phases across a research program, with qualitative inquiry holding explicit priority. Quantitative data are collected in one or more supporting phases to supplement, refine, or validate the dominant qualitative strands. The design is common in longitudinal program evaluations, theory-building projects, and community-based participatory research where deep contextual understanding is the primary aim.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (multiphase base); Morse and Tashakkori & Teddlie (priority notation)","year":"2003–2010","type":"Mixed methods research design","dataType":"Qualitative data (primary) and quantitative data (supporting), collected across multiple sequential or iterative phases","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412972666","url":null}],"related":["multiphase-mixed-methods-design","qualitative-priority-mixed-methods-design","exploratory-sequential-mixed-methods-design","explanatory-sequential-mixed-methods-design","qualitative-dominant-exploratory-sequential-mixed-methods","embedded-multiphase-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-pragmatic-mixed-methods","name":"Qualitative-dominant pragmatic mixed methods","fullName":"Qualitative-Dominant Pragmatic Mixed Methods Design","aliases":["QUAL-dominant pragmatic MMR","qualitative-priority pragmatic mixed methods","qual-dominant pragmatic design","QUAL+quan pragmatic"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1990s–2010s","originator":"David L. Morgan; John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/qualitative-dominant-pragmatic-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-pragmatic-mixed-methods.md","definition":"Qualitative-dominant pragmatic mixed methods is a research design in which a qualitative strand carries the primary weight of the inquiry, while a smaller quantitative component adds breadth or corroboration. Grounded in pragmatism as its philosophical framework, the design treats questions of data type, sequence, and integration as practical choices driven by the research problem rather than by methodological ideology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David L. Morgan; John W. Creswell & Vicki L. Plano Clark","year":"1990s–2010s","type":"Mixed methods research design","dataType":"Primary qualitative data (interviews, observation, documents) supplemented by quantitative data (surveys, counts, scales)","subfamily":"Mixed methods design"},"citations":[{"ref":"Morgan, D. L. (2014). Integrating Qualitative and Quantitative Methods: A Pragmatic Approach. Sage.","type":"book","doi":null,"isbn":"978-1452204949","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["qualitative-dominant-explanatory-sequential-mixed-methods","qualitative-dominant-exploratory-sequential-mixed-methods","pragmatic-mixed-methods-design","qualitative-priority-mixed-methods-design","exploratory-sequential-mixed-methods-design","transformative-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-dominant-transformative-mixed-methods","name":"Qualitative-dominant transformative mixed methods","fullName":"Qualitative-Dominant Transformative Mixed Methods Design","aliases":["QUAL-dominant transformative MMR","qualitative-priority transformative design","QUAL+ transformative mixed methods","transformative mixed methods with qualitative priority"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2010","originator":"Donna M. Mertens (transformative framework); John W. Creswell & Vicki L. Plano Clark (weighting typology)","url":"https://scholargate.app/en/research-design/qualitative-dominant-transformative-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/qualitative-dominant-transformative-mixed-methods.md","definition":"Qualitative-dominant transformative mixed methods is a mixed methods research design in which qualitative data carry the primary evidential weight while quantitative data serve a supplementary role, and the entire inquiry is governed by a transformative theoretical framework — one committed to social justice, equity, and the amplification of marginalized voices. The design produces rich contextual understanding alongside statistical corroboration to advance emancipatory or advocacy-oriented goals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna M. Mertens (transformative framework); John W. Creswell & Vicki L. Plano Clark (weighting typology)","year":"2003–2010","type":"Mixed methods research design","dataType":"Primarily qualitative (interviews, focus groups, observation, documents) supplemented by quantitative data","subfamily":"Mixed methods design"},"citations":[{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1593856267","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["transformative-mixed-methods-design","qualitative-priority-mixed-methods-design","participatory-mixed-methods-meta-inference","concurrent-triangulation-mixed-methods-design","exploratory-sequential-mixed-methods-design","embedded-transformative-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-meta-synthesis","name":"Qualitative Meta-Synthesis","fullName":"Qualitative Meta-Synthesis (Thematic Meta-Synthesis)","aliases":["Qualitative Evidence Synthesis","Thematic Synthesis","Metasynthesis","Qualitative Systematic Review"],"domain":"evidence-synthesis","family":"process-pipeline","subfamily":"Qualitative Evidence Synthesis","year":"2007","originator":"Sandelowski & Barroso (2007), Popularized by Thomas & Harden (2008)","url":"https://scholargate.app/en/evidence-synthesis/qualitative-meta-synthesis","markdownUrl":"https://scholargate.app/en/evidence-synthesis/qualitative-meta-synthesis.md","definition":"Qualitative meta-synthesis is a systematic method for synthesizing findings from multiple qualitative research studies (interviews, focus groups, ethnographies) to develop integrated interpretations and theoretical insights. Formalized by Sandelowski and Barroso (2007) and popularized by Thomas and Harden (2008), qualitative meta-synthesis preserves the rich, contextual, interpretive nature of qualitative evidence while enabling broader conclusions across multiple studies. Unlike quantitative meta-analysis, which pools numbers, qualitative meta-synthesis synthesizes themes, meanings, and conceptual insights—answering questions like 'How do cancer patients experience treatment side effects?' or 'What factors shape patient engagement with preventive health programs?' across multiple studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sandelowski & Barroso (2007), Popularized by Thomas & Harden (2008)","subfamily":"Qualitative Evidence Synthesis","year":"2007","type":"Framework"},"citations":[{"ref":"Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, 45.","type":"article","doi":"10.1186/1471-2288-8-45","isbn":null,"url":null},{"ref":"Sandelowski, M., & Barroso, J. (2007). Handbook for Synthesizing Qualitative Research. Springer Publishing Company.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Sandelowski%2C%20M.%2C%20%26%20Barroso%2C%20J.%20(2007).%20Handbook%20for%20Synthesizing%20Qualitative%20Research.%20Springer%20Publishing%20Company."},{"ref":"Britten, N., Campbell, R., Pope, C., Donovan, J., Morgan, M., & Pill, R. (2002). Using meta ethnography to synthesise qualitative research: a worked example. Journal of Health Services Research & Policy, 7(4), 209–215.","type":"article","doi":"10.1258/135581902320432732","isbn":null,"url":null}],"related":["systematic-review","thematic-analysis","qualitative-research-methods","realist-synthesis","evidence-synthesis-framework"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-priority-mixed-methods-design","name":"Qualitative-priority mixed methods design","fullName":"Qualitative-Priority Mixed Methods Research Design","aliases":["QUAL-dominant mixed methods","qualitative-dominant mixed design","qual-priority MMR","qualitative-weighted mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1991–2003 (formalized in mixed methods typologies)","originator":"Janice Morse; John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/qualitative-priority-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/qualitative-priority-mixed-methods-design.md","definition":"Qualitative-priority mixed methods design is a mixed methods approach in which qualitative inquiry carries the greater weight — in terms of volume, analytical depth, and interpretive authority — while a supplementary quantitative strand provides supporting evidence. The design acknowledges that the phenomenon under study is best understood through meaning-making, lived experience, or social processes, with numbers used to corroborate or contextualize, not to dominate, the research story.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Janice Morse; John W. Creswell & Vicki L. Plano Clark","year":"1991–2003 (formalized in mixed methods typologies)","type":"Mixed methods research design","dataType":"Primarily qualitative (text, interviews, observations) supplemented by quantitative (numeric) data","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Morse, J. M., & Niehaus, L. (2009). Mixed Method Design: Principles and Procedures. Left Coast Press.","type":"book","doi":null,"isbn":"978-1598741889","url":null}],"related":["exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","quantitative-priority-mixed-methods-design","transformative-mixed-methods-design","grounded-theory","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-research-overview","name":"Qualitative Research Overview","fullName":"Qualitative Research: Approaches, Methods, and Analysis","aliases":["qualitative inquiry","exploratory research","interpretive research"],"domain":"research-methodology","family":"process-pipeline","subfamily":"exploratory research methodology","year":"1900","originator":"Ethnographers (Boas), grounded theorists (Glaser & Strauss, 1967), phenomenologists, and interpretivists (1900s–1980s)","url":"https://scholargate.app/en/research-methodology/qualitative-research-overview","markdownUrl":"https://scholargate.app/en/research-methodology/qualitative-research-overview.md","definition":"Qualitative research is a systematic inquiry into human experiences, meanings, behaviors, and contexts using non-numerical data (words, text, images, observations). Unlike quantitative research, which seeks to measure variables and test hypotheses numerically, qualitative research prioritizes depth, contextual richness, and understanding of 'how' and 'why.' Major approaches include phenomenology (lived experiences), grounded theory (theory development from data), ethnography (cultural understanding through immersion), case study (in-depth investigation of a specific case), and narrative inquiry (personal stories). Creswell (2017), Braun and Clarke (2006), and Patton (2015) provide contemporary frameworks for qualitative design, data collection, and analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ethnographers (Boas), grounded theorists (Glaser & Strauss, 1967), phenomenologists, and interpretivists (1900s–1980s)","subfamily":"exploratory research methodology","year":"1900","type":"Framework"},"citations":[{"ref":"Creswell, J. W. (2017). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). SAGE Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Creswell%2C%20J.%20W.%20(2017).%20Qualitative%20Inquiry%20and%20Research%20Design%3A%20Choosing%20Among%20Five%20Approaches%20(4th%20ed.).%20SAGE%20Publicat"},{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Patton, M. Q. (2015). Qualitative Research & Evaluation Methods (4th ed.). SAGE Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Patton%2C%20M.%20Q.%20(2015).%20Qualitative%20Research%20%26%20Evaluation%20Methods%20(4th%20ed.).%20SAGE%20Publications."}],"related":["research-design-types","data-collection-methods","research-question-formulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-rigor-criteria","name":"Trustworthiness Criteria in Qualitative Research","fullName":"Lincoln and Guba's Framework for Assessing Qualitative Research Rigor","aliases":["trustworthiness criteria","credibility","dependability","confirmability","transferability","qualitative quality"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"quality-assurance","year":"1985","originator":"Yvonna Lincoln and Egon Guba","url":"https://scholargate.app/en/qualitative-research/qualitative-rigor-criteria","markdownUrl":"https://scholargate.app/en/qualitative-research/qualitative-rigor-criteria.md","definition":"Trustworthiness is a framework for evaluating the quality and rigor of qualitative research, developed by Lincoln and Guba (1985) as an alternative to quantitative criteria (internal validity, external validity, reliability, objectivity). The framework comprises five criteria: credibility (findings are accurate and grounded in data), transferability (findings apply to other contexts), dependability (findings are consistent and defensible), confirmability (findings reflect the data and participants' perspectives, not researcher bias), and authenticity (research reflects diverse viewpoints and promotes understanding). This framework has become standard for assessing qualitative research across disciplines and guides researchers in designing and reporting rigorous qualitative studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yvonna Lincoln and Egon Guba","subfamily":"quality-assurance","year":"1985","type":"Framework"},"citations":[{"ref":"Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. SAGE Publications.","type":"book","doi":null,"isbn":"978-0803924314","url":null},{"ref":"Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of Qualitative Research (pp. 105-117). SAGE Publications.","type":"article","doi":null,"isbn":"978-0803950671","url":null},{"ref":"Tracy, S. J. (2010). Qualitative quality: Eight big-tent criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837-851.","type":"article","doi":"10.1177/1077800410383121","isbn":null,"url":null},{"ref":"Whittemore, R., Chase, S. K., & Mandle, C. L. (2001). Validity in qualitative research. Qualitative Health Research, 11(4), 522-537.","type":"article","doi":"10.1177/104973201129119299","isbn":null,"url":null}],"related":["member-checking","reflexivity-in-research","participant-observation","in-depth-interview-method"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"qualitative-synthesis-methods","name":"Qualitative Evidence Synthesis Methods","fullName":"Systematic Review and Meta-Synthesis of Qualitative Research","aliases":["qualitative meta-synthesis","meta-ethnography","thematic synthesis","systematic review of qualitative studies"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"synthesis-method","year":"1988","originator":"George Noblit and Dwight Hare","url":"https://scholargate.app/en/qualitative-research/qualitative-synthesis-methods","markdownUrl":"https://scholargate.app/en/qualitative-research/qualitative-synthesis-methods.md","definition":"Qualitative evidence synthesis (QES) is a systematic method for combining and interpreting findings from multiple qualitative research studies to generate higher-level understanding and theory. Different approaches—meta-ethnography, thematic synthesis, meta-narrative review, critical interpretive synthesis—each have distinct philosophical underpinnings and analytical procedures. Introduced by Noblit and Hare (1988) with meta-ethnography, qualitative synthesis has evolved alongside systematic reviews of quantitative research. Unlike quantitative meta-analysis, which pools numerical effect sizes, qualitative synthesis integrates concepts, themes, and interpretations from primary studies, identifying patterns, conflicts, and emergent theory. QES is increasingly used in health research, social sciences, and education to understand complex phenomena, translate research into practice, and identify gaps in evidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Noblit and Dwight Hare","subfamily":"synthesis-method","year":"1988","type":"Method"},"citations":[{"ref":"Noblit, G. W., & Hare, R. D. (1988). Meta-ethnography: Synthesizing Qualitative Studies. SAGE Publications.","type":"book","doi":null,"isbn":"978-0803931725","url":null},{"ref":"Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8(1), 45.","type":"article","doi":"10.1186/1471-2288-8-45","isbn":null,"url":null},{"ref":"Sandelowski, M., Docherty, S., & Emden, C. (2006). Qualitative metasynthesis: Issues and techniques. Research in Nursing & Health, 20(4), 365-371.","type":"article","doi":"10.1002/(SICI)1098-240X(199708)20:4<365::AID-NUR9>3.0.CO;2-E","isbn":null,"url":null},{"ref":"Britten, N., Campbell, R., Pope, C., Donovan, J., Morgan, M., & Pill, R. (2002). Using meta ethnography to synthesise qualitative research: A worked example. Journal of Health Services Research & Policy, 7(4), 209-215.","type":"article","doi":"10.1258/135581902320432732","isbn":null,"url":null}],"related":["document-analysis","thematic-analysis","qualitative-rigor-criteria","nvivo-atlas-qualitative"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quality-adjusted-life-year","name":"Quality-Adjusted Life Year","fullName":"Quality-Adjusted Life Year (QALY)","aliases":["QALY","health utility measure"],"domain":"health-economics","family":"process-pipeline","subfamily":"health utility measurement","year":"1985","originator":"Alan Williams (Health Economics Research Centre, Oxford University)","url":"https://scholargate.app/en/health-economics/quality-adjusted-life-year","markdownUrl":"https://scholargate.app/en/health-economics/quality-adjusted-life-year.md","definition":"A QALY measures health benefit as utility weight (0 = death, 1 = perfect health) multiplied by time lived. Developed by Alan Williams in 1985, QALYs enable comparison of disparate health interventions on a common metric. Used globally by health technology assessment bodies—NICE (UK), HAS (France), CADTH (Canada), WHO—to decide which treatments deserve public funding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Williams (Health Economics Research Centre, Oxford University)","subfamily":"health utility measurement","year":"1985","type":"Method"},"citations":[{"ref":"Kind, P. (1989). The EuroQol instrument: an index of health-related quality of life. In B. Teeling Smith (Ed.), Measuring health: a practical approach. Chichester: Wiley.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Kind%2C%20P.%20(1989).%20The%20EuroQol%20instrument%3A%20an%20index%20of%20health-related%20quality%20of%20life.%20In%20B.%20Teeling%20Smith%20(Ed.)%2C%20Measurin"},{"ref":"Weinstein, M. C., & Stason, W. B. (1976). Hypertension: A Policy Perspective. Cambridge, MA: Harvard University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Weinstein%2C%20M.%20C.%2C%20%26%20Stason%2C%20W.%20B.%20(1976).%20Hypertension%3A%20A%20Policy%20Perspective.%20Cambridge%2C%20MA%3A%20Harvard%20University%20Press."},{"ref":"Brooks, R. (1996). EuroQol: the current state of play. Health Policy, 37(1), 53-72.","type":"article","doi":"10.1016/0168-8510(96)00822-6","isbn":null,"url":null}],"related":["disability-adjusted-life-year","cost-effectiveness-analysis","markov-model-health-economics","decision-analytic-modeling","willingness-to-pay"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quality-function-deployment","name":"Quality Function Deployment","fullName":"Quality Function Deployment (House of Quality)","aliases":["QFD","House of Quality","customer-driven engineering","voice of the customer matrix"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1966 (Japan); popularised in the West ~1988","originator":"Yoji Akao","url":"https://scholargate.app/en/experimental-design/quality-function-deployment","markdownUrl":"https://scholargate.app/en/experimental-design/quality-function-deployment.md","definition":"Quality Function Deployment (QFD) is a structured method for translating customer needs — the voice of the customer — into specific technical requirements at every stage of product or service development. Originating in Japan in the 1960s, QFD uses a matrix-based tool called the House of Quality to make customer priorities visible, link them to engineering parameters, expose trade-offs, and maintain focus on what customers actually value throughout the design process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yoji Akao","year":"1966 (Japan); popularised in the West ~1988","type":"Structured quality planning and product design method","dataType":"Customer requirements (qualitative), engineering metrics (quantitative), competitive benchmarking data","subfamily":"Engineering methods"},"citations":[{"ref":"Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press.","type":"book","doi":null,"isbn":"978-0915299416","url":null},{"ref":"Hauser, J. R., & Clausing, D. (1988). The house of quality. Harvard Business Review, 66(3), 63–73.","type":"article","doi":null,"isbn":null,"url":"https://hbr.org/1988/05/the-house-of-quality"}],"related":["failure-mode-and-effects-analysis","design-of-experiments","taguchi-method","statistical-process-control","six-sigma-dmaic","benchmarking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quandt-andrews-test","name":"Quandt-Andrews Test","fullName":"Quandt-Andrews (sup-Wald) Unknown Breakpoint Test","aliases":["sup-Wald Test","Andrews Breakpoint Test","Unknown Structural Break Test","Quandt Likelihood Ratio Test"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Structural break","year":1993,"originator":"Donald Andrews","url":"https://scholargate.app/en/econometrics/quandt-andrews-test","markdownUrl":"https://scholargate.app/en/econometrics/quandt-andrews-test.md","definition":"The Quandt-Andrews test, formalized by Andrews (1993), detects structural breaks in regression parameters when the breakpoint date is unknown a priori. It sweeps all candidate break dates within a trimmed interior of the sample, computes a Wald (or LM/LR) statistic at each candidate, and reports the supremum of those statistics. Applied economists and time-series analysts use it to test whether coefficients remain stable across a full estimation window without needing to specify when the break occurred.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Andrews","year":1993,"type":"Supremum test for structural change","subfamily":"Structural break","null_hypothesis":"Parameter stability across the full sample","distribution":"Non-standard; tabulated by Andrews (1993)"},"citations":[{"ref":"Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4), 821–856.","type":"article","doi":"10.2307/2951764","isbn":null,"url":null}],"related":["chow-test","bai-perron-test","cusum-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantile-on-quantile-regression","name":"Quantile-on-Quantile Regression","fullName":"Quantile-on-Quantile Regression","aliases":["QQ regression","QQ approach","quantile-on-quantile approach","nonparametric quantile regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2015","originator":"Sim and Zhou","url":"https://scholargate.app/en/econometrics/quantile-on-quantile-regression","markdownUrl":"https://scholargate.app/en/econometrics/quantile-on-quantile-regression.md","definition":"Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-dimensional surface of slope coefficients indexed by both the quantile of the outcome and the quantile of the predictor, revealing heterogeneous and asymmetric dependency structures invisible to standard regression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sim and Zhou","year":"2015","type":"Nonparametric quantile regression","dataType":"Cross-sectional, time series, panel","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.","type":"article","doi":"10.1016/j.jbankfin.2015.01.013","isbn":null,"url":null},{"ref":"Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.","type":"article","doi":"10.2307/1913643","isbn":null,"url":null}],"related":["quantile-regression","nonlinear-ardl","arma-model","vector-autoregression","granger-causality-test","dcc-garch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantile-regression-np","name":"Nonparametric Quantile Regression","fullName":"Quantile Regression (Nonparametric Variants)","aliases":["quantile regression","median regression","distribution-free quantile regression","Kantil Regresyon (Nonparametric Varyantlar)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1978,"originator":"Koenker & Bassett","url":"https://scholargate.app/en/statistics/quantile-regression-np","markdownUrl":"https://scholargate.app/en/statistics/quantile-regression-np.md","definition":"Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Koenker & Bassett","year":1978,"type":"Quantile regression (nonparametric variants)","estimator":"Minimisation of the asymmetric (pinball/check) loss","outcome":"continuous","normality":"not required","minSample":50,"difficulty":2},"citations":[{"ref":"Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.","type":"article","doi":"10.2307/1913643","isbn":null,"url":null},{"ref":"Koenker, R. (2005). Quantile Regression. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521608275","url":null}],"related":["ols-regression","theil-sen-estimator","kernel-density-test","ridge-regression","lasso-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantile-regression","name":"Quantile Regression","fullName":"Quantile Regression","aliases":["conditional quantile regression","regression quantiles","Kantil Regresyon"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1978,"originator":"Koenker & Bassett","url":"https://scholargate.app/en/econometrics/quantile-regression","markdownUrl":"https://scholargate.app/en/econometrics/quantile-regression.md","definition":"Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Koenker & Bassett","year":1978,"type":"Conditional quantile regression","estimator":"Minimisation of asymmetrically weighted absolute residuals (check loss)","outcome":"continuous / count","minSample":50},"citations":[{"ref":"Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.","type":"article","doi":"10.2307/1913643","isbn":null,"url":null},{"ref":"Koenker, R. (2005). Quantile Regression. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511754098","isbn":null,"url":null}],"related":["ols-regression","ridge-regression","lasso-regression","poisson-regression","panel-fixed-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantile-var","name":"Quantile VAR","fullName":"Quantile Vector Autoregression","aliases":["Quantile-based impulse response"],"domain":"econometrics","family":"regression-model","subfamily":"Quantile dynamics","year":"2006","originator":"Koenker and Xiao","url":"https://scholargate.app/en/econometrics/quantile-var","markdownUrl":"https://scholargate.app/en/econometrics/quantile-var.md","definition":"Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behavior and contagion effects invisible to mean-based VAR analysis. This is essential for risk management and understanding how crises propagate differently than normal times.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Koenker and Xiao","subfamily":"Quantile dynamics","year":"2006","type":"Distribution impulse response"},"citations":[{"ref":"Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990.","type":"article","doi":"10.1198/016214506000000672","isbn":null,"url":null},{"ref":"White, H., Kim, T. H., & Manganelli, S. (2015). VAR for VaR: Measuring tail dependence using multivariate regression quantiles. Journal of Econometrics, 187(1), 169-188.","type":"article","doi":"10.1016/j.jeconom.2015.02.004","isbn":null,"url":null}],"related":["qardl","cross-quantilogram","method-of-moments-quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-content-analysis","name":"Quantitative Content Analysis","fullName":"Quantitative Content Analysis","aliases":["QCA","manifest content analysis","systematic content analysis","frequency-based content analysis"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1950s (Berelson 1952; Krippendorff 1980/2004)","originator":"Bernard Berelson; later systematised by Klaus Krippendorff","url":"https://scholargate.app/en/research-design/quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/quantitative-content-analysis.md","definition":"Quantitative content analysis is a systematic, replicable method for converting the manifest content of text, images, or other recorded communication into numerical data. By applying a pre-specified codebook to a defined corpus and counting or scaling the resulting categories, researchers obtain frequency distributions, proportions, and relationships that can be subjected to standard statistical tests. It is the dominant method for large-scale, objective analysis of media, documents, social media posts, policy texts, and similar materials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernard Berelson; later systematised by Klaus Krippendorff","year":"1950s (Berelson 1952; Krippendorff 1980/2004)","type":"Quantitative observational research method","dataType":"Text, images, audio, video — any recorded communication coded into numeric categories","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761915454","url":null},{"ref":"Berelson, B. (1952). Content Analysis in Communication Research. Free Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Berelson+Content+Analysis+in+Communication+Research+1952"}],"related":["survey-research","descriptive-research","correlational-research","observational-quantitative-research","longitudinal-quantitative-content-analysis","comparative-quantitative-content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-descriptive-analysis","name":"Quantitative Descriptive Analysis","fullName":"Quantitative Descriptive Analysis (QDA)","aliases":["QDA"],"domain":"food-science","family":"process-pipeline","subfamily":"Sensory Evaluation","year":"1974","originator":"Herbert Stone","url":"https://scholargate.app/en/food-science/quantitative-descriptive-analysis","markdownUrl":"https://scholargate.app/en/food-science/quantitative-descriptive-analysis.md","definition":"Quantitative Descriptive Analysis (QDA) is a comprehensive sensory evaluation method developed by Stone and colleagues in the 1970s that uses a trained panel to describe the intensity of sensory attributes in food products. QDA provides detailed, quantitative profiles of flavor, aroma, texture, and appearance, allowing researchers and product developers to characterize and compare products objectively.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herbert Stone","subfamily":"Sensory Evaluation","year":"1974","type":"Descriptive Analysis Method"},"citations":[{"ref":"Stone, H., Bleibaum, R. N., & Thomas, H. A. (2012). Sensory evaluation practices (4th ed.). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"Lawless, H. T., & Heymann, H. (2010). Sensory evaluation of food: Principles and practices (2nd ed.). Springer.","type":"article","doi":"10.1007/978-1-4419-6488-5","isbn":null,"url":null}],"related":["texture-profile-analysis","temporal-dominance-of-sensations","just-about-right-scaling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-case-focused-mixed-methods","name":"Quantitative-dominant case-focused mixed methods","fullName":"Quantitative-Dominant Case-Focused Mixed Methods Design","aliases":["QUAN-dominant case study MMR","quantitatively weighted case mixed methods","dominant-status case-centered mixed design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2007","originator":"Tashakkori & Teddlie (dominant-status typology); Creswell & Plano Clark (design taxonomy)","url":"https://scholargate.app/en/research-design/quantitative-dominant-case-focused-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-case-focused-mixed-methods.md","definition":"Quantitative-dominant case-focused mixed methods organizes a study around one or more clearly bounded cases while assigning primary weight and inferential authority to quantitative data. Qualitative data are collected within the same case boundaries and serve an augmenting, explanatory, or contextual role rather than an equal one. The design is ideal when a case (a school, organization, community, or patient cohort) is the unit of analysis and the core research questions require measurable outcomes that qualitative evidence then helps interpret or explain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tashakkori & Teddlie (dominant-status typology); Creswell & Plano Clark (design taxonomy)","year":"2003–2007","type":"Mixed methods research design","dataType":"Quantitative (primary: surveys, tests, structured instruments) + qualitative (secondary: interviews, documents, observations) within one or more bounded cases","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412972666","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344452","url":null}],"related":["case-focused-mixed-methods-design","quantitative-dominant-concurrent-embedded-mixed-methods","quantitative-dominant-explanatory-sequential-mixed-methods","explanatory-sequential-mixed-methods-design","concurrent-embedded-mixed-methods-design","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-concurrent-embedded-mixed-methods","name":"Quantitative-dominant concurrent embedded mixed methods","fullName":"Quantitative-Dominant Concurrent Embedded Mixed Methods Design","aliases":["QUAN-dominant embedded design","concurrent embedded design (QUAN priority)","quantitative-primary embedded mixed methods","QUAN+qual embedded design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2007","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/quantitative-dominant-concurrent-embedded-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-concurrent-embedded-mixed-methods.md","definition":"A mixed methods design in which a dominant quantitative study (survey, experiment, or other large-scale numeric inquiry) is conducted simultaneously with a smaller, embedded qualitative component. The qualitative strand serves a secondary, supporting role — such as explaining mechanisms, capturing participant experience, or monitoring implementation — while the quantitative strand drives the primary research questions and conclusions. Both strands run concurrently rather than sequentially.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2003–2007","type":"Mixed methods research design","dataType":"Primarily quantitative (surveys, experiments, tests) with embedded qualitative data (interviews, observations)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). Advanced mixed methods research designs. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 209–240). Sage Publications.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Advanced+mixed+methods+research+designs+Creswell+Plano+Clark+2003"}],"related":["concurrent-embedded-mixed-methods-design","quantitative-dominant-explanatory-sequential-mixed-methods","concurrent-triangulation-mixed-methods-design","embedded-mixed-methods-matrix","multilevel-mixed-methods-design","quantitative-priority-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-concurrent-triangulation-mixed-methods","name":"Quantitative-dominant concurrent triangulation mixed methods","fullName":"Quantitative-Dominant Concurrent Triangulation Mixed Methods Design","aliases":["QUAN-dominant triangulation design","concurrent triangulation with quantitative priority","QUAN+qual triangulation design","dominant-status triangulation design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1998–2011","originator":"David L. Morgan; John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/quantitative-dominant-concurrent-triangulation-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-concurrent-triangulation-mixed-methods.md","definition":"The quantitative-dominant concurrent triangulation mixed methods design collects quantitative (QUAN) and qualitative (qual) data simultaneously, with quantitative data carrying the primary weight. The two strands are analyzed independently and then compared or merged to triangulate findings, with the smaller qualitative strand serving to corroborate, elaborate, or nuance the quantitative results. The explicit QUAN priority means that the research questions, sampling logic, and conclusions are primarily anchored in the quantitative component.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David L. Morgan; John W. Creswell & Vicki L. Plano Clark","year":"1998–2011","type":"Mixed methods research design","dataType":"Quantitative (primary) and qualitative (secondary) data collected concurrently","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null},{"ref":"Morgan, D. L. (1998). Practical strategies for combining qualitative and quantitative methods: Applications to health research. Qualitative Health Research, 8(3), 362–376.","type":"article","doi":"10.1177/104973239800800307","isbn":null,"url":null}],"related":["concurrent-triangulation-mixed-methods-design","quantitative-dominant-concurrent-embedded-mixed-methods","explanatory-sequential-mixed-methods-design","qualitative-dominant-concurrent-triangulation-mixed-methods","concurrent-mixed-methods-meta-inference","quantitative-priority-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-explanatory-sequential-mixed-methods","name":"Quantitative-dominant explanatory sequential mixed methods","fullName":"Quantitative-Dominant Explanatory Sequential Mixed Methods Design","aliases":["QUAN-dominant explanatory sequential design","quan-priority explanatory sequential MMR","quantitative-dominant QUAN→qual design","weighted explanatory sequential mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007 (first edition of Designing and Conducting Mixed Methods Research)","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/quantitative-dominant-explanatory-sequential-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-explanatory-sequential-mixed-methods.md","definition":"The quantitative-dominant explanatory sequential mixed methods design is a two-phase mixed methods approach in which a larger, primary quantitative study is conducted first, followed by a smaller, secondary qualitative phase that explains, elaborates, or contextualises the quantitative results. Quantitative evidence carries the greater weight in answering the research questions, while qualitative data provide interpretive depth for puzzling or unexpected statistical findings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2007 (first edition of Designing and Conducting Mixed Methods Research)","type":"Mixed methods research design variant","dataType":"Quantitative data (primary) and qualitative data (secondary/follow-up)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452274614","url":null}],"related":["explanatory-sequential-mixed-methods-design","quantitative-dominant-exploratory-sequential-mixed-methods","quantitative-priority-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-intervention-mixed-methods","name":"Quantitative-dominant intervention mixed methods","fullName":"Quantitative-Dominant Intervention Mixed Methods Design","aliases":["QUAN-dominant intervention MMD","quantitatively weighted intervention mixed methods","QUAN+qual intervention design","quantitative-priority intervention mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007–2011 (codified in Creswell & Plano Clark editions)","originator":"Creswell & Plano Clark (intervention MMD); weighting framework systematized in mixed methods typologies","url":"https://scholargate.app/en/research-design/quantitative-dominant-intervention-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-intervention-mixed-methods.md","definition":"Quantitative-dominant intervention mixed methods design embeds a qualitative component within a predominantly quantitative intervention study — typically a randomized controlled trial or quasi-experiment — where the quantitative strand carries the primary weight in determining efficacy, while the qualitative strand explains the processes, mechanisms, or participant experiences that illuminate why and how the intervention works.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (intervention MMD); weighting framework systematized in mixed methods typologies","year":"2007–2011 (codified in Creswell & Plano Clark editions)","type":"Mixed methods research design","dataType":"Primarily quantitative (outcome/efficacy data); supplementary qualitative (process/experience data)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Brundisini, F., Giacomini, M., DeJean, D., Vanstone, M., Winsor, S., & Smith, A. (2013). Chronic disease patients' experiences with accessing health care in rural and remote areas: A systematic review and qualitative meta-synthesis. Ontario Health Technology Assessment Series, 13(15), 1–33.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3817950/"}],"related":["intervention-mixed-methods-design","explanatory-sequential-mixed-methods-design","concurrent-embedded-mixed-methods-design","randomized-controlled-trial","quantitative-priority-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-mixed-methods-meta-inference","name":"Quantitative-dominant mixed methods meta-inference","fullName":"Quantitative-Dominant Mixed Methods Meta-Inference","aliases":["QUAN-dominant meta-inference","quantitatively weighted meta-inference","QUAN-priority integration inference","quantitative-weighted mixed inference"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2007","originator":"Tashakkori & Teddlie (meta-inference concept); Creswell & Plano Clark (dominance weighting framework)","url":"https://scholargate.app/en/research-design/quantitative-dominant-mixed-methods-meta-inference","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-mixed-methods-meta-inference.md","definition":"Quantitative-dominant mixed methods meta-inference is an integration procedure in which the researcher draws an overarching conclusion by combining inferences from both quantitative and qualitative strands, while explicitly assigning greater evidential weight to the quantitative results. The qualitative strand serves a supporting, elaborating, or contextualizing role rather than an equal voice in the final interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tashakkori & Teddlie (meta-inference concept); Creswell & Plano Clark (dominance weighting framework)","year":"2003–2007","type":"Mixed methods integration procedure","dataType":"Combined quantitative (primary) and qualitative (secondary) datasets","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage.","type":"book","doi":null,"isbn":"978-0761920731","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["mixed-methods-meta-inference","quantitative-dominant-multilevel-mixed-methods","quantitative-priority-mixed-methods-design","explanatory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","mixed-methods-matrix"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-multilevel-mixed-methods","name":"Quantitative-dominant multilevel mixed methods","fullName":"Quantitative-Dominant Multilevel Mixed Methods Design","aliases":["QUAN-dominant multilevel MMR","multilevel mixed methods with quantitative priority","QUAN-priority multilevel design","dominant-status multilevel mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2010","originator":"Tashakkori & Teddlie (multilevel MMR); dominant-status typology formalized by Morse (1991) and elaborated by Tashakkori & Teddlie","url":"https://scholargate.app/en/research-design/quantitative-dominant-multilevel-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-multilevel-mixed-methods.md","definition":"Quantitative-dominant multilevel mixed methods design is a mixed methods approach in which quantitative inquiry carries the primary evidential weight while qualitative data play an auxiliary, illuminating role, and both strands are applied across two or more hierarchically nested levels of analysis — for example, students within classrooms within schools. The design is suited to research questions that require both statistical modeling of nested structures and contextual understanding of how those structures operate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tashakkori & Teddlie (multilevel MMR); dominant-status typology formalized by Morse (1991) and elaborated by Tashakkori & Teddlie","year":"2003–2010","type":"Mixed methods research design","dataType":"Nested quantitative data (surveys, tests, administrative records) supplemented by qualitative data (interviews, documents) at one or more levels","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1412972666","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["multilevel-mixed-methods-design","explanatory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","quantitative-priority-mixed-methods-design","concurrent-embedded-mixed-methods-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-multiphase-mixed-methods","name":"Quantitative-dominant multiphase mixed methods","fullName":"Quantitative-Dominant Multiphase Mixed Methods Design","aliases":["QUAN-dominant multiphase MMR","quantitatively driven multiphase design","multiphase mixed methods with quantitative priority","QUAN-priority multiphase design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (multiphase framework); Tashakkori & Teddlie (priority notation)","url":"https://scholargate.app/en/research-design/quantitative-dominant-multiphase-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-multiphase-mixed-methods.md","definition":"A quantitative-dominant multiphase mixed methods design conducts a series of distinct research phases — at least two, often three or more — in which quantitative data and analyses bear the primary weight of answering the research questions, while qualitative components serve a supporting or explanatory role. Phases are linked by explicit integration points where findings from one phase inform the design or interpretation of the next.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (multiphase framework); Tashakkori & Teddlie (priority notation)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative (primary) and qualitative (secondary) data across multiple phases","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Designing+and+Conducting+Mixed+Methods+Research+Creswell+Plano+Clark+2018"},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). Sage Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412972666","url":null}],"related":["multiphase-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","quantitative-priority-mixed-methods-design","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-pragmatic-mixed-methods","name":"Quantitative-dominant pragmatic mixed methods","fullName":"Quantitative-Dominant Pragmatic Mixed Methods Design","aliases":["QUAN-dominant pragmatic MMR","pragmatic quantitative-priority mixed design","quan-priority pragmatic design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"1998–2010","originator":"Tashakkori & Teddlie (mixed methods paradigm discourse); pragmatic strand systematized by Morgan and Creswell","url":"https://scholargate.app/en/research-design/quantitative-dominant-pragmatic-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-pragmatic-mixed-methods.md","definition":"A mixed methods design in which quantitative data and analysis carry the primary explanatory weight while a smaller qualitative component provides contextual depth. Grounded in philosophical pragmatism, design decisions — including timing, sequencing, and the scope of each strand — are driven by what best answers the research question rather than by adherence to a single paradigmatic tradition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tashakkori & Teddlie (mixed methods paradigm discourse); pragmatic strand systematized by Morgan and Creswell","year":"1998–2010","type":"Mixed methods research design","dataType":"Primarily quantitative (surveys, tests, administrative records); supplemented by qualitative (interviews, observations)","subfamily":"Mixed methods design"},"citations":[{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1412972666","url":null},{"ref":"Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1506386706","url":null}],"related":["pragmatic-mixed-methods-design","quantitative-priority-mixed-methods-design","explanatory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","multilevel-mixed-methods-design","mixed-methods-meta-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-dominant-transformative-mixed-methods","name":"Quantitative-dominant transformative mixed methods","fullName":"Quantitative-Dominant Transformative Mixed Methods Design","aliases":["QUAN-dominant transformative MMR","transformative mixed methods (quantitative priority)","transformative QUAN-priority design","advocacy-framed mixed methods (quantitative emphasis)"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2007 (Mertens); systematised 2011 (Creswell & Plano Clark)","originator":"Donna M. Mertens (transformative paradigm); John W. Creswell & Vicki L. Plano Clark (mixed methods typology)","url":"https://scholargate.app/en/research-design/quantitative-dominant-transformative-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/quantitative-dominant-transformative-mixed-methods.md","definition":"Quantitative-dominant transformative mixed methods design embeds a transformative theoretical lens — such as feminist, critical race, or disability theory — as the overarching framework of a study while assigning greater weight and priority to quantitative data collection and analysis. Qualitative data play a secondary, supplementary role, typically contextualising or deepening statistical findings. The design is common in social justice, equity, and advocacy-oriented applied research where large-scale measurement is essential but must be grounded in an explicit commitment to marginalized communities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna M. Mertens (transformative paradigm); John W. Creswell & Vicki L. Plano Clark (mixed methods typology)","year":"2003–2007 (Mertens); systematised 2011 (Creswell & Plano Clark)","type":"Mixed methods research design","dataType":"Quantitative data (primary) + qualitative data (secondary/supplementary)","subfamily":"Mixed methods design"},"citations":[{"ref":"Mertens, D. M. (2007). Transformative paradigm: Mixed methods and social justice. Journal of Mixed Methods Research, 1(3), 212–225.","type":"article","doi":"10.1177/1558689807302811","isbn":null,"url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412975179","url":null}],"related":["transformative-mixed-methods-design","quantitative-dominant-explanatory-sequential-mixed-methods","quantitative-priority-mixed-methods-design","concurrent-triangulation-mixed-methods-design","participatory-concurrent-embedded-mixed-methods","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-priority-mixed-methods-design","name":"Quantitative-priority mixed methods design","fullName":"Quantitative-Priority Mixed Methods Research Design","aliases":["QUAN-dominant mixed methods","quantitative-dominant mixed methods","quan-priority design","quantitative-first mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2009","originator":"Creswell & Plano Clark; Teddlie & Tashakkori","url":"https://scholargate.app/en/research-design/quantitative-priority-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/quantitative-priority-mixed-methods-design.md","definition":"Quantitative-priority mixed methods design is a research approach in which quantitative data and analysis carry the primary explanatory weight, while qualitative data play a supplementary or corroborating role. The researcher collects and analyzes quantitative data first (or concurrently with greater emphasis), then uses qualitative findings to elaborate, explain, or contextualize the statistical results. Priority and sequence together define where integration occurs and how each strand informs the other.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Teddlie & Tashakkori","year":"2003–2009","type":"Mixed methods research design","dataType":"Primarily quantitative (surveys, instruments, experiments); supplementary qualitative (interviews, documents)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["explanatory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","concurrent-embedded-mixed-methods-design","qualitative-priority-mixed-methods-design","multiphase-mixed-methods-design","pragmatic-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantitative-susceptibility-mapping","name":"Quantitative Susceptibility Mapping","fullName":"Quantitative Susceptibility Mapping","aliases":["QSM","susceptibility-weighted imaging"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Magnetic resonance imaging","year":"2015","originator":"Yong Wang","url":"https://scholargate.app/en/medical-imaging/quantitative-susceptibility-mapping","markdownUrl":"https://scholargate.app/en/medical-imaging/quantitative-susceptibility-mapping.md","definition":"Quantitative Susceptibility Mapping (QSM) is a post-processing technique that converts MRI phase data into quantitative susceptibility values, enabling direct visualization and measurement of tissue magnetic properties. Developed by Wang, Liu, and colleagues, QSM transforms phase shifts caused by differences in magnetic susceptibility between tissues into tissue-specific biomarkers. It has revolutionized the sensitivity of MRI to iron, calcium, and other paramagnetic and diamagnetic substances, making it valuable in neurodegenerative disease diagnosis and tissue characterization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yong Wang","subfamily":"Magnetic resonance imaging","year":"2015","type":"Quantitative MRI contrast mechanism"},"citations":[{"ref":"Wang, Y., Liu, T. (2015). Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker. Magnetic Resonance in Medicine, 73(1), 82-101.","type":"article","doi":"10.1002/mrm.25358","isbn":null,"url":null},{"ref":"Liu, T., Spincemaille, P., de Rochefort, L., et al. (2009). Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from undersample phase data. Magnetic Resonance in Medicine, 66(3), 579-592.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Calculation+of+susceptibility+through+multiple+orientation+sampling+%28COSMOS%29%3A+a+method+for+conditioning+the+inverse+problem+from+undersample+phase+data+Liu"},{"ref":"Haacke, E. M., Xu, Y., Cheng, Y. C., Reichenbach, J. R. (2004). Susceptibility weighted imaging (SWI). Magnetic Resonance in Medicine, 52(3), 612-618.","type":"article","doi":"10.1002/mrm.20198","isbn":null,"url":null}],"related":["dti-tractography","ct-iterative-reconstruction","pet-kinetic-modeling","oct-angiography","functional-ultrasound"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantum-approximate-optimization-algorithm","name":"Quantum Approximate Optimization Algorithm","fullName":"Quantum Approximate Optimization Algorithm (QAOA)","aliases":["QAOA","quantum alternating operator ansatz"],"domain":"quantum-computing","family":"ml-model","subfamily":"Variational Algorithm","year":"2014","originator":"Edward Farhi","url":"https://scholargate.app/en/quantum-computing/quantum-approximate-optimization-algorithm","markdownUrl":"https://scholargate.app/en/quantum-computing/quantum-approximate-optimization-algorithm.md","definition":"The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm designed to solve combinatorial optimization problems on near-term quantum devices. Introduced by Farhi, Goldstone, and Gutmann in 2014, QAOA encodes optimization problems into quantum circuits and uses classical optimization to tune circuit parameters, aiming to find approximately optimal solutions for problems like MaxCut, graph coloring, and scheduling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward Farhi","subfamily":"Variational Algorithm","year":"2014","type":"Hybrid quantum-classical algorithm"},"citations":[{"ref":"Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.","type":"article","doi":"10.48550/arXiv.1411.4028","isbn":null,"url":null},{"ref":"Zhou, L., Wang, S. T., Choi, S., et al. (2020). Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices. Physical Review X, 10, 021067.","type":"article","doi":"10.1103/PhysRevX.10.021067","isbn":null,"url":null},{"ref":"Hadfield, S., Wang, Z., O'Gorman, B., et al. (2019). From the Ising model to QAOA: A quantum optimization algorithm from the physicist's perspective. Algorithms, 12, 34.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=From+the+Ising+model+to+QAOA%3A+A+quantum+optimization+algorithm+from+the+physicist%27s+perspective+Hadfield"}],"related":["variational-quantum-eigensolver","quantum-phase-estimation","quantum-monte-carlo","grovers-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantum-key-distribution","name":"Quantum Key Distribution (BB84)","fullName":"Quantum Key Distribution BB84 Protocol","aliases":["BB84","quantum cryptography"],"domain":"quantum-computing","family":"ml-model","subfamily":"Quantum Cryptography","year":"1984","originator":"Charles Bennett and Gilles Brassard","url":"https://scholargate.app/en/quantum-computing/quantum-key-distribution","markdownUrl":"https://scholargate.app/en/quantum-computing/quantum-key-distribution.md","definition":"Quantum Key Distribution (QKD) BB84 is a cryptographic protocol allowing two parties to establish a shared secret key using quantum mechanics. Proposed by Bennett and Brassard in 1984, BB84 provides information-theoretic security: an eavesdropper's presence is guaranteed to be detected, and the secret key is provably secure against unlimited computational power.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Bennett and Gilles Brassard","subfamily":"Quantum Cryptography","year":"1984","type":"Cryptographic protocol"},"citations":[{"ref":"Bennett, C. H., Brassard, G. (1984). Quantum cryptography: public key distribution and coin tossing. Proceedings of IEEE International Conference on Computers, Systems, and Signal Processing, 175–179.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2003.06557"},{"ref":"Shor, P. W., Preskill, J. (2000). Simple proof of security of the BB84 quantum key distribution protocol. Physical Review Letters, 85, 441–444.","type":"article","doi":"10.1103/PhysRevLett.85.441","isbn":null,"url":null},{"ref":"Renner, R. (2008). Security of quantum key distribution. arXiv preprint arXiv:0512258.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/quant-ph/0512258"}],"related":["shors-algorithm","quantum-teleportation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantum-monte-carlo","name":"Quantum Monte Carlo","fullName":"Quantum Monte Carlo (QMC)","aliases":["QMC","variational Monte Carlo","diffusion Monte Carlo"],"domain":"quantum-computing","family":"ml-model","subfamily":"Stochastic Method","year":"1953","originator":"Nicholas Metropolis and colleagues","url":"https://scholargate.app/en/quantum-computing/quantum-monte-carlo","markdownUrl":"https://scholargate.app/en/quantum-computing/quantum-monte-carlo.md","definition":"Quantum Monte Carlo (QMC) is a stochastic computational method for computing ground state properties of quantum many-body systems. Combining classical Monte Carlo sampling with quantum mechanics, QMC approaches are among the most accurate methods available for electronic structure and condensed matter physics, achieving sub-percent accuracy for many systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nicholas Metropolis and colleagues","subfamily":"Stochastic Method","year":"1953","type":"Monte Carlo simulation"},"citations":[{"ref":"Metropolis, N., Rosenbluth, A. W., et al. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.","type":"article","doi":"10.1063/1.1699114","isbn":null,"url":null},{"ref":"Reynolds, P. J., Tobochnik, J., Gould, H. (1990). Diffusion quantum Monte Carlo. Computers in Physics, 4, 662–668.","type":"article","doi":"10.1063/1.4822960","isbn":null,"url":null},{"ref":"Needs, R. J., et al. (2020). Variational and diffusion quantum Monte Carlo calculations with the CASINO code. The Journal of Chemical Physics, 152, 154106.","type":"article","doi":"10.1063/1.5144288","isbn":null,"url":null}],"related":["density-functional-theory","hartree-fock-method","path-integral-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantum-phase-estimation","name":"Quantum Phase Estimation","fullName":"Quantum Phase Estimation (QPE)","aliases":["QPE","phase kickback"],"domain":"quantum-computing","family":"ml-model","subfamily":"Quantum Algorithm","year":"1995","originator":"Alexei Kitaev","url":"https://scholargate.app/en/quantum-computing/quantum-phase-estimation","markdownUrl":"https://scholargate.app/en/quantum-computing/quantum-phase-estimation.md","definition":"Quantum Phase Estimation (QPE) is a fundamental quantum subroutine that estimates the eigenvalues of a unitary operator. Developed by Alexei Kitaev in 1995, QPE combines controlled unitary evolution with the quantum Fourier transform to extract eigenvalues from quantum states with exponential precision scaling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alexei Kitaev","subfamily":"Quantum Algorithm","year":"1995","type":"Subroutine algorithm"},"citations":[{"ref":"Kitaev, A. Y. (1995). Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant-ph/9511026.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/quant-ph/9511026"},{"ref":"Cleve, R., Ekert, A., Macchiavello, C., Mosca, M. (1998). Quantum algorithms revisited. Proceedings of the Royal Society A, 454, 339–354.","type":"article","doi":"10.1098/rspa.1998.0164","isbn":null,"url":null},{"ref":"Aspuru-Guzik, A., Love, P. J., Love, P. J. (2005). Simulated quantum computation of molecular energies. Science, 309, 1704–1707.","type":"article","doi":"10.1126/science.1113479","isbn":null,"url":null}],"related":["variational-quantum-eigensolver","grovers-algorithm","quantum-approximate-optimization-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantum-svm","name":"Quantum SVM","fullName":"Quantum Support Vector Machine","aliases":["QSVM","quantum kernel"],"domain":"quantum-computing","family":"ml-model","subfamily":"Quantum Machine Learning","year":"2014","originator":"Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd","url":"https://scholargate.app/en/quantum-computing/quantum-svm","markdownUrl":"https://scholargate.app/en/quantum-computing/quantum-svm.md","definition":"Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm combining quantum feature spaces with classical SVM training. Proposed by Rebentrost et al. in 2014, QSVM leverages quantum processors to compute kernel functions, potentially offering speedup for classification problems while remaining practical on near-term quantum devices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd","subfamily":"Quantum Machine Learning","year":"2014","type":"Machine learning algorithm"},"citations":[{"ref":"Rebentrost, P., Mohseni, M., Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113, 130503.","type":"article","doi":"10.1103/PhysRevLett.113.130503","isbn":null,"url":null},{"ref":"Havlíček, V., Córcoles, A. D., Temme, K., et al. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567, 209–212.","type":"article","doi":"10.1038/s41586-019-0980-2","isbn":null,"url":null},{"ref":"Liu, Y., Arunachalam, S., Temme, K. (2021). A rigorous and robust quantum speed-up in supervised machine learning. arXiv preprint arXiv:2010.07471.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2010.07471"}],"related":["variational-quantum-eigensolver","quantum-approximate-optimization-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quantum-teleportation","name":"Quantum Teleportation","fullName":"Quantum Teleportation Protocol","aliases":["teleportation","entanglement-assisted communication"],"domain":"quantum-computing","family":"ml-model","subfamily":"Quantum Communication","year":"1993","originator":"Charles Bennett and colleagues","url":"https://scholargate.app/en/quantum-computing/quantum-teleportation","markdownUrl":"https://scholargate.app/en/quantum-computing/quantum-teleportation.md","definition":"Quantum Teleportation is a protocol for transferring an unknown quantum state between distant parties using entanglement and classical communication. Discovered by Bennett et al. in 1993, teleportation violates no fundamental principles but demonstrates the power of entanglement: an unknown quantum state can be reconstructed at a distant location without ever being transmitted.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Bennett and colleagues","subfamily":"Quantum Communication","year":"1993","type":"Communication protocol"},"citations":[{"ref":"Bennett, C. H., Brassard, G., Crépeau, C., Jozsa, R., Peres, A., Wootters, W. K. (1993). Teleporting an unknown quantum state via dual classical and Einstein-Podolsky-Rosen channels. Physical Review Letters, 70, 1895–1899.","type":"article","doi":"10.1103/PhysRevLett.70.1895","isbn":null,"url":null},{"ref":"Bouwmeester, D., et al. (1997). Experimental quantum teleportation. Nature, 390, 575–579.","type":"article","doi":"10.1038/37539","isbn":null,"url":null},{"ref":"Ma, X. S., et al. (2012). Quantum teleportation over 143 kilometres using active feed-forward. Nature, 489, 269–273.","type":"article","doi":"10.1038/nature11472","isbn":null,"url":null}],"related":["quantum-key-distribution","quantum-entanglement","surface-code-quantum-error-correction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quasi-geostrophic-omega-equation","name":"Quasi-Geostrophic Omega Equation","fullName":"Quasi-Geostrophic Omega Equation for Vertical Motion","aliases":["QG omega equation","Quasi-geostrophic dynamics","Vertical motion prediction"],"domain":"meteorology","family":"process-pipeline","subfamily":"Quasi-geostrophic theory","year":"1970s","originator":"Trenberth, Omaga","url":"https://scholargate.app/en/meteorology/quasi-geostrophic-omega-equation","markdownUrl":"https://scholargate.app/en/meteorology/quasi-geostrophic-omega-equation.md","definition":"The quasi-geostrophic (QG) omega equation is a fundamental diagnostic equation in synoptic meteorology that relates vertical motion (omega = dP/dt) to horizontal temperature and vorticity fields. It predicts where air rises and sinks based on the geostrophic flow structure without explicitly solving for vertical velocity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Trenberth, Omaga","subfamily":"Quasi-geostrophic theory","year":"1970s","type":"Diagnostic equation for vertical motion"},"citations":[{"ref":"Holton, J. R. (2004). An Introduction to Dynamic Meteorology (4th ed.). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/an-introduction-to-dynamic-meteorology/holton/978-0-12-354966-1"},{"ref":"Navarra, A., & Simmons, A. J. (2006). Instability of a truncated Eady model. Journal of the Atmospheric Sciences, 51(7), 1033-1051.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Instability+of+a+truncated+Eady+model+Navarra"}],"related":["geostrophic-wind","thermal-wind","potential-vorticity-inversion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quaternion-attitude","name":"Quaternion Attitude","fullName":"Quaternion Attitude Representation and Kinematics","aliases":["quaternion representation","attitude kinematics","q-vector"],"domain":"aerospace","family":"process-pipeline","subfamily":"Representation","year":"1843","originator":"William Hamilton (quaternions), aerospace engineers","url":"https://scholargate.app/en/aerospace/quaternion-attitude","markdownUrl":"https://scholargate.app/en/aerospace/quaternion-attitude.md","definition":"Quaternion attitude representation is a mathematical framework for describing three-dimensional rotations using four-dimensional vectors (quaternions). Superior to Euler angles due to the absence of singularities (gimbal lock), quaternions are the standard representation in modern attitude estimation, spacecraft control, and 3D computer graphics. Quaternion kinematics elegantly expresses how attitude evolves under angular velocity measurements from gyroscopes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Hamilton (quaternions), aerospace engineers","subfamily":"Representation","year":"1843","type":"Mathematical framework"},"citations":[{"ref":"Shuster, M. D. (1993). A survey of attitude representations. Journal of the Astronautical Sciences, 41(4), 439–517.","type":"article","doi":null,"isbn":null,"url":"https://www.jsass.or.jp"},{"ref":"Titterton, D. H., & Weston, J. L. (2004). Strapdown Inertial Navigation Technology (2nd ed.). Institution of Engineering and Technology.","type":"book","doi":"10.1049/PBRA017E","isbn":null,"url":null},{"ref":"Savage, P. G. (2000). Strapdown inertial navigation integration algorithm design. Part 1: Attitude algorithms. Journal of Guidance, Control, and Dynamics, 21(1), 19–28.","type":"article","doi":"10.2514/2.4228","isbn":null,"url":null}],"related":["ahrs","madgwick-filter","mahony-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"query-optimization","name":"Query Optimization","fullName":"Database Query Optimization","aliases":[],"domain":"information-systems","family":"process-pipeline","subfamily":"Query Processing & Execution","year":"1979","originator":"IBM San Jose Research Laboratory","url":"https://scholargate.app/en/information-systems/query-optimization","markdownUrl":"https://scholargate.app/en/information-systems/query-optimization.md","definition":"Query optimization is a critical process in database management that transforms high-level SQL queries into efficient execution plans. Developed systematically by IBM researchers in the late 1970s, it aims to minimize execution time, disk I/O, and resource consumption by selecting the most effective access paths and join strategies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"IBM San Jose Research Laboratory","subfamily":"Query Processing & Execution","year":"1979","type":"Database optimization technique"},"citations":[{"ref":"Jarke, M., & Koch, J. (1984). Query optimization in database systems. ACM Computing Surveys, 16(2), 111-152.","type":"article","doi":"10.1145/356924.356928","isbn":null,"url":null},{"ref":"Selinger, P. G., Astrahan, M. M., Chamberlin, D. D., Lorie, R. A., & Price, T. G. (1979). Access path selection in a relational database management system. Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data, 23-34.","type":"article","doi":"10.1145/582095.582099","isbn":null,"url":null},{"ref":"Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book (2nd ed.). Pearson Education.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com"}],"related":["database-normalization","indexing-strategy","execution-plan-analysis","cost-estimation","join-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quest-scale-religion","name":"Quest Scale","fullName":"Quest Scale of Religious Orientation","aliases":["Quest Scale","Religious Quest"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"religious orientation and maturity","year":1976,"originator":"Daniel C. Batson & W. Larry Ventis","url":"https://scholargate.app/en/psychology-of-religion/quest-scale-religion","markdownUrl":"https://scholargate.app/en/psychology-of-religion/quest-scale-religion.md","definition":"The Quest Scale, developed by Batson and Ventis (1976), is a 12-item self-report measure of a third religious orientation beyond Allport and Ross's intrinsic and extrinsic religiosity. The 'quest' orientation reflects an open, questioning approach to religion: someone who views faith as an ongoing journey of exploration and doubt rather than a settled worldview or instrumental tool. High quest scorers embrace existential uncertainty, seek genuine answers to life's deepest questions, and are comfortable with religious doubt and revision. The scale has become important in understanding mature religious development and predicting prosocial behavior, openness, and psychological flexibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel C. Batson & W. Larry Ventis","subfamily":"religious orientation and maturity","year":1976,"type":"Self-report"},"citations":[{"ref":"Batson, C. D., & Ventis, W. L. (1982). The Religious Experience: A Social-Psychological Perspective. Oxford University Press. ISBN: 9780195030761.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books/about/The_Religious_Experience.html?id=xz4yAQAAIAAJ"},{"ref":"Batson, C. D., Schoenrade, P. A., & Ventis, W. L. (1993). Religion and the Individual: A Social-Psychological Perspective. Oxford University Press. ISBN: 9780195089073.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books/about/Religion_and_the_Individual.html?id=UhMvAQAAIAAJ"}],"related":["intrinsic-extrinsic-religiosity","daily-spiritual-experience-scale","duke-religion-index","systems-belief-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"question-answering","name":"Question Answering","fullName":"Question Answering (QA)","aliases":["QA","machine reading comprehension","Soru Cevaplama (Question Answering)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/question-answering","markdownUrl":"https://scholargate.app/en/text-mining/question-answering.md","definition":"Question answering is a natural-language-processing task that automatically answers natural-language questions grounded in a given context passage, using either extractive or generative approaches. The task was crystallised by the SQuAD benchmark of Rajpurkar et al. (2016), and later models such as XLNet (Yang et al., 2019) pushed reading-comprehension accuracy higher.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-comprehension task","approaches":"Extractive / generative","input":"A natural-language question plus a context passage","output":"An answer in natural language","benchmark":"SQuAD (Rajpurkar et al., 2016)"},"citations":[{"ref":"Rajpurkar, P. et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP.","type":"inproceedings","doi":"10.18653/v1/D16-1264","isbn":null,"url":null},{"ref":"Yang, Z. et al. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2019/hash/dc6a7e655d7e5840e66733e9ee67cc69-Abstract.html"}],"related":["sentiment-analysis","machine-translation","named-entity-recognition","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"queueing-simulation","name":"Queueing Simulation","fullName":"Queueing Simulation — Stochastic Simulation of Waiting-Line Systems","aliases":["Queue Simulation","Queuing Theory Simulation","Waiting-Line Simulation","DES-Queue"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1909","originator":"Agner Krarup Erlang","url":"https://scholargate.app/en/simulation/queueing-simulation","markdownUrl":"https://scholargate.app/en/simulation/queueing-simulation.md","definition":"Queueing Simulation combines classical queueing theory with discrete-event simulation to model systems where entities arrive, wait for service, and depart. It predicts performance metrics such as average waiting time, queue length, and server utilization, enabling capacity planning and bottleneck identification across service, manufacturing, healthcare, and network systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agner Krarup Erlang","year":"1909","type":"Stochastic simulation / analytical modeling","dataType":"Arrival times, service times, queue discipline parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley-Interscience, New York.","type":"book","doi":null,"isbn":"978-0471491101","url":null},{"ref":"Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education, New York.","type":"book","doi":null,"isbn":"978-0073401324","url":null}],"related":["discrete-event-simulation","monte-carlo-simulation","markov-model","system-dynamics","agent-based-modeling","stochastic-queueing-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"queuing-theory-healthcare","name":"Queuing Theory in Healthcare","fullName":"Queuing Theory for Healthcare Service Management and Wait Time Analysis","aliases":["Healthcare Queuing","Queue Management Healthcare"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Operations research, Service management","year":"1909","originator":"Agner Krarup Erlang","url":"https://scholargate.app/en/healthcare-management/queuing-theory-healthcare","markdownUrl":"https://scholargate.app/en/healthcare-management/queuing-theory-healthcare.md","definition":"Queuing theory is a mathematical discipline that models waiting lines, service capacity, and customer (patient) flow. Developed initially by Agner Erlang for telecommunications in 1909, it has been extensively applied to healthcare to analyze and optimize emergency departments, outpatient clinics, surgical suites, and diagnostic service centers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agner Krarup Erlang","subfamily":"Operations research, Service management","year":"1909","type":"Stochastic modeling and optimization technique"},"citations":[{"ref":"Erlang, A. K. (1909). The theory of probabilities and telephone conversations. Nyt Tidsskrift for Matematik, 20(B), 33–39.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Erlang_(unit)"},{"ref":"Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their application to the theory of failures. Annals of Mathematical Statistics, 24(3), 338–354.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Stochastic+processes+occurring+in+the+theory+of+queues+and+their+application+to+the+theory+of+failures+Kendall"},{"ref":"Gross, D., Shortle, J. F., Thompson, J. M., & Harris, C. M. (2008). Fundamentals of Queuing Theory (4th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fundamentals+of+Queuing+Theory+%284th+ed.%29+Gross"}],"related":["patient-flow-simulation","hospital-bed-occupancy-model","lean-healthcare","staffing-ratio-analysis","dea-hospital-efficiency"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quick-inventory-depressive","name":"Quick Inventory of Depressive Symptomatology","fullName":"Quick Inventory of Depressive Symptomatology (QIDS)","aliases":["QIDS","Quick Inventory of Depressive Symptomatology-Self Report","QIDS-SR"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"mood-disorder-assessment","year":"2003","originator":"A. John Rush","url":"https://scholargate.app/en/clinical-psychology/quick-inventory-depressive","markdownUrl":"https://scholargate.app/en/clinical-psychology/quick-inventory-depressive.md","definition":"The Quick Inventory of Depressive Symptomatology is a 16-item assessment designed by A. John Rush and colleagues to efficiently measure the severity of depressive symptoms in adults. Published in Biological Psychiatry in 2003, the QIDS exists in both self-report (QIDS-SR) and clinician-rated (QIDS-C) versions. It was developed as a brief alternative to the longer Inventory of Depressive Symptomatology (IDS, 30 items) while maintaining comprehensive coverage of DSM-IV depressive symptoms. The QIDS has become a standard outcome measure in treatment research, particularly in large comparative effectiveness trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. John Rush","subfamily":"mood-disorder-assessment","year":"2003","type":"Self-report or clinician-administered questionnaire"},"citations":[{"ref":"Rush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow, B., Klein, D. N., & Ninan, P. T. (2003). The 16-item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biological Psychiatry, 54(5), 573–583.","type":"article","doi":"10.1016/S0006-3223(02)01866-8","isbn":null,"url":null},{"ref":"Trivedi, M. H., Rush, A. J., Ibrahim, H. M., Carmody, T. J., Biggs, M. M., Suppes, T., & Crismon, M. L. (2004). The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) for the evaluation of major depressive disorder. Depression and Anxiety, 20(3), 123–126.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Inventory+of+Depressive+Symptomatology%2C+Clinician+Rating+%28IDS-C%29+and+Self-Report+%28IDS-SR%29%2C+and+the+Quick+Inventory+of+Depressive+Symptomatology%2C+Clinician+Rating+%28QIDS-C%29+and+Self-Report+%28QIDS-SR%29"},{"ref":"Berndt, E. R., Koran, L. M., Roness, L. A., Trivedi, M. H., Herman, B. K., & Lee, J. C. (2000). Lost human productivity from US cancer mortality. Journal of Clinical Psychiatry, 61(9), 630–636.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lost+human+productivity+from+US+cancer+mortality+Berndt"}],"related":["phq-9","bdi-ii","montgomery-asberg-depression","patient-global-impression-change"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"quota-sampling","name":"Quota Sampling","fullName":"Quota Sampling","aliases":["quota-controlled sampling","quota selection","non-probability quota sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1930s","originator":"Developed in market research and opinion polling, notably applied by George Gallup in the 1930s","url":"https://scholargate.app/en/survey-methodology/quota-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/quota-sampling.md","definition":"Quota sampling is a non-probability technique in which the researcher pre-specifies how many units to recruit from each subgroup (quota cell) defined by one or more control variables such as age, gender, or occupation. Interviewers or data collectors then use their own judgment to find and enroll participants until each cell is filled. The method guarantees the sample mirrors the population on the control variables but does not provide the randomness needed for classical statistical inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed in market research and opinion polling, notably applied by George Gallup in the 1930s","year":"1930s","type":"Non-probability sampling design","dataType":"Categorical population characteristics; survey or interview data","subfamily":"Sampling"},"citations":[{"ref":"Moser, C. A., & Kalton, G. (1972). Survey Methods in Social Investigation (2nd ed.). Heinemann.","type":"book","doi":null,"isbn":"978-0435827496","url":null},{"ref":"Quota sampling. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Quota_sampling"}],"related":["stratified-sampling","purposive-sampling","convenience-sampling","proportional-stratified-sampling","cluster-sampling","systematic-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"r-squared","name":"R-squared","fullName":"Coefficient of Determination","aliases":["R²","coefficient of determination","r2 score"],"domain":"model-evaluation","family":"mcdm","subfamily":"Regression evaluation","year":"1896","originator":"Karl Pearson","url":"https://scholargate.app/en/model-evaluation/r-squared","markdownUrl":"https://scholargate.app/en/model-evaluation/r-squared.md","definition":"The coefficient of determination, denoted R², measures the proportion of variance in the dependent variable explained by the independent variables in a regression model. Introduced by Karl Pearson in the late 19th century, R² is one of the most widely used metrics for assessing how well a model fits observed data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson","subfamily":"Regression evaluation","year":"1896","type":"Goodness-of-fit metric"},"citations":[{"ref":"Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mathematical+contributions+to+the+theory+of+evolution+Pearson"},{"ref":"Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.","type":"article","doi":"10.1080/14786440109462720","isbn":null,"url":null},{"ref":"Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society A, 222, 309-368.","type":"article","doi":"10.1098/rsta.1922.0009","isbn":null,"url":null}],"related":["adjusted-r-squared","root-mean-squared-error","mean-absolute-error","akaike-information-criterion","bayesian-information-criterion"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"racism-and-life-experiences-scale","name":"Racism and Life Experiences Scales","fullName":"Racism and Life Experiences Scales (RaLES)","aliases":["RaLES"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"discrimination-stress-assessment","year":2000,"originator":"Harrell, S. P.","url":"https://scholargate.app/en/transcultural-nursing/racism-and-life-experiences-scale","markdownUrl":"https://scholargate.app/en/transcultural-nursing/racism-and-life-experiences-scale.md","definition":"The Racism and Life Experiences Scales (RaLES) are a multidimensional assessment designed to measure the frequency and intensity of racism-related stress experienced by people of color. Developed by Harrell in 2000, the RaLES operationalize racism not as a single phenomenon but as a constellation of stressors across multiple life domains—individual encounters, collective experiences, institutional discrimination, and historical trauma. The instrument is used in health research to evaluate the psychosocial burden of racism and to understand mechanisms linking discrimination to mental and physical health disparities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harrell, S. P.","subfamily":"discrimination-stress-assessment","year":2000,"type":"Self-report"},"citations":[{"ref":"Harrell, S. P. (2000). A multidimensional conceptualization of racism-related stress: Implications for the well-being of people of color. American Journal of Orthopsychiatry, 70(1), 42–57.","type":"article","doi":"10.1037/h0087722","isbn":null,"url":null}],"related":["cultural-competence-assessment","social-distance-scale","ethnic-identity-scale","cultural-humility-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radial-velocity-method","name":"Radial Velocity Method","fullName":"Radial Velocity Exoplanet Detection Method","aliases":["Doppler method","spectroscopic velocity measurement"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Observational Astronomy","year":"1844","originator":"Friedrich Wilhelm Bessel","url":"https://scholargate.app/en/applied-physics/radial-velocity-method","markdownUrl":"https://scholargate.app/en/applied-physics/radial-velocity-method.md","definition":"The radial velocity method detects exoplanets by measuring the Doppler shift of a star's spectral lines caused by gravitational tugging from orbiting planets. When a planet orbits a star, the star wobbles slightly toward and away from Earth, creating periodic shifts in its light spectrum. First proposed by Friedrich Wilhelm Bessel in the 19th century and successfully applied to exoplanet detection in 1995, this method has discovered nearly half of all known exoplanets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Friedrich Wilhelm Bessel","subfamily":"Observational Astronomy","year":"1844","type":"Spectroscopic measurement technique"},"citations":[{"ref":"Mayor, M., & Queloz, D. (1995). A Jupiter-mass companion to a solar-type star. Nature, 378(6555), 355-359.","type":"article","doi":"10.1038/378355a0","isbn":null,"url":null},{"ref":"Campbell, B., Walker, G. A., & Yang, S. (1988). A Search for Substellar Companions to Solar-type Stars. The Astrophysical Journal, 331, 902.","type":"article","doi":"10.1086/166608","isbn":null,"url":null},{"ref":"Pepe, F., et al. (2014). Exoplanet research with the HARPS-N spectrograph at the Telescopio Nazionale Galileo. Astronomy & Astrophysics, 534, A58.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Exoplanet+research+with+the+HARPS-N+spectrograph+at+the+Telescopio+Nazionale+Galileo+Pepe"}],"related":["light-curve-analysis","n-body-simulation","orbit-determination"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radiation-dose-assessment","name":"Radiation Dose Assessment","fullName":"Radiation Dose Assessment and Evaluation","aliases":["dose calculation","exposure assessment","radiation hazard evaluation"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Radiation health physics","year":"1928","originator":"International Commission on Radiological Protection (ICRP)","url":"https://scholargate.app/en/nuclear-physics/radiation-dose-assessment","markdownUrl":"https://scholargate.app/en/nuclear-physics/radiation-dose-assessment.md","definition":"Radiation dose assessment is a systematic evaluation of human exposure to ionizing radiation from external or internal sources, formalized by the International Commission on Radiological Protection (ICRP) in the late 20th century. It combines radiation transport calculations with biological effect models to quantify absorbed dose, equivalent dose, and effective dose for worker safety and public health protection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Commission on Radiological Protection (ICRP)","subfamily":"Radiation health physics","year":"1928","type":"computational health assessment pipeline"},"citations":[{"ref":"International Commission on Radiological Protection (2007). The 2007 Recommendations of the ICRP. Publication 103. Annals of the ICRP, 37(2–4).","type":"report","doi":null,"isbn":null,"url":"https://www.icrp.org/publication.asp?id=The%202007%20Recommendations%20of%20the%20ICRP"},{"ref":"Shultis, J. K., & Faw, R. E. (2007). Fundamentals of Nuclear Science and Engineering. CRC Press.","type":"book","doi":"10.1201/b12824","isbn":null,"url":null}],"related":["dosimetry-measurement","radiation-protection-optimization","neutron-transport-calculation","radiation-shielding-design","monte-carlo-neutron-particle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radiation-model","name":"Radiation Model","fullName":"Radiation Model of Mobility and Migration","aliases":["Radiation Law of Human Mobility","Parameter-free Mobility Model","Simini Radiation Model","Radyasyon Modeli"],"domain":"spatial-analysis","family":"regression-model","subfamily":"Spatial interaction","year":2012,"originator":"Filippo Simini et al.","url":"https://scholargate.app/en/spatial-analysis/radiation-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/radiation-model.md","definition":"The Radiation Model, introduced by Simini et al. in 2012, is a parameter-free model for predicting human mobility and migration flows between geographic locations. Drawing an analogy from radiation physics, it predicts trip volumes based solely on population sizes at origin and destination, and the intervening population within the circle connecting them. It has been widely applied to commuting flows, migration, and epidemic spreading.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Filippo Simini et al.","year":2012,"type":"Parameter-free spatial interaction model","subfamily":"Spatial interaction","data_requirement":"Population counts at origin and destination","calibration":"No free parameters required"},"citations":[{"ref":"Simini, F., González, M. C., Maritan, A., & Barabási, A.-L. (2012). A universal model for mobility and migration patterns. Nature, 484, 96–100.","type":"article","doi":"10.1038/nature10856","isbn":null,"url":null}],"related":["spatial-interaction-model","huff-model","migration-models"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radiation-protection-optimization","name":"Radiation Protection Optimization","fullName":"Radiation Protection Optimization and Risk-Based Management","aliases":["ALARA optimization","health physics planning","dose optimization"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Health physics planning and optimization","year":"1977","originator":"International Commission on Radiological Protection (ICRP)","url":"https://scholargate.app/en/nuclear-physics/radiation-protection-optimization","markdownUrl":"https://scholargate.app/en/nuclear-physics/radiation-protection-optimization.md","definition":"Radiation protection optimization is a systematic approach to design and manage exposure reduction strategies using risk-benefit analysis, codified by the ICRP in the principle of As Low As Reasonably Achievable (ALARA) in 1977. By balancing radiation dose reduction against cost, effort, and societal benefit, it guides practical protection decisions in medical imaging, occupational settings, and environmental remediation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Commission on Radiological Protection (ICRP)","subfamily":"Health physics planning and optimization","year":"1977","type":"optimization methodology"},"citations":[{"ref":"International Commission on Radiological Protection (2007). The 2007 Recommendations of the ICRP. Publication 103. Annals of the ICRP, 37(2–4).","type":"report","doi":null,"isbn":null,"url":"https://www.icrp.org/publication.asp?id=The%202007%20Recommendations%20of%20the%20ICRP"},{"ref":"Cember, H., & Johnson, T. E. (2009). Introduction to Health Physics (4th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Introduction+to+Health+Physics+%284th+ed.%29+Cember"}],"related":["radiation-dose-assessment","radiation-shielding-design","dosimetry-measurement","monte-carlo-neutron-particle","reactor-kinetics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radiation-shielding-design","name":"Radiation Shielding Design","fullName":"Radiation Shielding Design and Optimization","aliases":["shield analysis","attenuation design","dose reduction engineering"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Protective design and hazard mitigation","year":"1898","originator":"Ernest Rutherford, Pierre Curie","url":"https://scholargate.app/en/nuclear-physics/radiation-shielding-design","markdownUrl":"https://scholargate.app/en/nuclear-physics/radiation-shielding-design.md","definition":"Radiation shielding design is an engineering discipline that uses physics-based calculations and materials selection to reduce radiation exposure to acceptable levels, originating from Curie and Rutherford's early radiation studies in the 1890s. By combining attenuation theory, source characterization, and dose modeling, it determines material composition, thickness, and geometry to protect workers, the public, and sensitive equipment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ernest Rutherford, Pierre Curie","subfamily":"Protective design and hazard mitigation","year":"1898","type":"engineering design methodology"},"citations":[{"ref":"Cember, H., & Johnson, T. E. (2009). Introduction to Health Physics (4th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Introduction+to+Health+Physics+%284th+ed.%29+Cember"},{"ref":"International Commission on Radiation Units and Measurements (1993). Stopping Powers and Ranges for Protons and Alpha Particles. ICRU Report 49.","type":"report","doi":null,"isbn":null,"url":"https://www.icru.org/report-49"}],"related":["radiation-dose-assessment","monte-carlo-neutron-particle","neutron-transport-calculation","criticality-safety-analysis","dosimetry-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radiative-transfer","name":"Radiative Transfer","fullName":"Radiative Transfer Modeling in Astrophysics","aliases":["RT Modeling","Radiative Transport","Light Transport Simulation"],"domain":"astronomy","family":"process-pipeline","subfamily":"Theoretical modeling","year":1978,"originator":"Dimitri Mihalas","url":"https://scholargate.app/en/astronomy/radiative-transfer","markdownUrl":"https://scholargate.app/en/astronomy/radiative-transfer.md","definition":"Radiative transfer is the mathematical treatment of how light propagates through matter, including absorption, emission, and scattering. Central to astrophysics and stellar atmosphere modeling, radiative transfer calculations translate physical conditions (density, temperature, composition) into observable spectra and colors, bridging theory and observation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dimitri Mihalas","subfamily":"Theoretical modeling","year":1978,"type":"Computational simulation method"},"citations":[{"ref":"Mihalas, D. (1978). Stellar Atmospheres (2nd ed.). San Francisco: W.H. Freeman.","type":"article","doi":null,"isbn":"0716703742","url":null},{"ref":"Lucy, L. B. (1999). A Monte Carlo method for radiative transfer. Astrophysical Journal, 544(2), 889-906.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Monte+Carlo+method+for+radiative+transfer+Lucy"},{"ref":"Robitaille, T. P., et al. (2011). YSO-VISION: self-consistent stellar atmosphere and disk modeling of young stellar objects. Astronomy & Astrophysics, 545, A47.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=YSO-VISION%3A+self-consistent+stellar+atmosphere+and+disk+modeling+of+young+stellar+objects+Robitaille"}],"related":["sed-fitting","stellar-population-synthesis","exoplanet-transmission-spectroscopy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radioactive-waste-classification","name":"Radioactive Waste Classification","fullName":"Radioactive Waste Classification and Characterization","aliases":["waste categorization","hazard stratification","material disposition"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Nuclear waste management","year":"1960","originator":"International Atomic Energy Agency (IAEA)","url":"https://scholargate.app/en/nuclear-physics/radioactive-waste-classification","markdownUrl":"https://scholargate.app/en/nuclear-physics/radioactive-waste-classification.md","definition":"Radioactive waste classification is a systematic framework for categorizing radioactive materials based on activity, heat generation, and long-term hazard potential, developed by the IAEA. It stratifies waste into classes (exempt, very low-level, low-level, intermediate-level, high-level) to determine appropriate management pathways—from near-surface disposal to deep geological repositories—ensuring environmental protection and regulatory compliance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Atomic Energy Agency (IAEA)","subfamily":"Nuclear waste management","year":"1960","type":"regulatory classification framework"},"citations":[{"ref":"International Atomic Energy Agency (2009). Classification of Radioactive Waste. IAEA Safety Standards Series No. GSG-1.","type":"report","doi":null,"isbn":null,"url":"https://www.iaea.org/publications/8329"},{"ref":"U.S. Nuclear Regulatory Commission (2019). Waste Classification and Disposal. 10 CFR Part 61.","type":"report","doi":null,"isbn":null,"url":"https://www.nrc.gov/about-nrc/regulatory/reg-guides/waste.html"}],"related":["nuclear-decay-analysis","radiation-dose-assessment","radiation-protection-optimization","criticality-safety-analysis","dosimetry-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radiocarbon-dating","name":"Radiocarbon Dating","fullName":"Radiocarbon Dating","aliases":["¹⁴C dating","Carbon-14 dating"],"domain":"geophysics","family":"process-pipeline","subfamily":"Radiometric dating","year":"1949","originator":"Willard Libby","url":"https://scholargate.app/en/geophysics/radiocarbon-dating","markdownUrl":"https://scholargate.app/en/geophysics/radiocarbon-dating.md","definition":"Radiocarbon dating is a radiometric technique that determines the age of organic materials by measuring the radioactive decay of ¹⁴C (carbon-14), a rare isotope produced in the atmosphere by cosmic ray interactions. Developed by Willard Libby in 1949, radiocarbon dating became a foundational method in archaeology, paleoclimate studies, and geology, enabling dating of organic materials from the past ~50,000 years with typical precision of ±50–100 years.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Willard Libby","subfamily":"Radiometric dating","year":"1949","type":"Chronometric method based on ¹⁴C decay"},"citations":[{"ref":"Libby, W. F. (1949). Radiocarbon dating. University of Chicago Press.","type":"article","doi":null,"isbn":null,"url":"https://www.press.uchicago.edu/"},{"ref":"Reimer, P. J., et al. (2020). The IntCal20 radiocarbon calibration curve. Radiocarbon, 62(4), 725-757.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+IntCal20+radiocarbon+calibration+curve+Reimer"}],"related":["paleomagnetic-analysis","isotope-ratio-mass-spectrometry","standardized-precipitation-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radiographic-assessment-veterinary","name":"Radiographic Assessment in Veterinary Medicine","fullName":"Systematic Radiographic Assessment and Diagnostic Imaging Interpretation in Veterinary Medicine","aliases":["X-ray diagnosis","radiological assessment","radiographic interpretation"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Diagnostic imaging","year":"1896-present","originator":"Veterinary diagnostic radiology","url":"https://scholargate.app/en/veterinary-medicine/radiographic-assessment-veterinary","markdownUrl":"https://scholargate.app/en/veterinary-medicine/radiographic-assessment-veterinary.md","definition":"Radiographic assessment is a systematic diagnostic imaging method using X-rays to create two-dimensional images of internal structures, facilitating detection of skeletal, thoracic, and abdominal pathology. Since the discovery of X-rays in 1896 and their early adoption in veterinary medicine, radiography has remained foundational to veterinary diagnostics. Modern radiographic assessment combines conventional techniques with advanced processing and systematic interpretation protocols to maximize diagnostic accuracy and clinical impact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Veterinary diagnostic radiology","subfamily":"Diagnostic imaging","year":"1896-present","type":"Diagnostic imaging pipeline"},"citations":[{"ref":"Thrall, D. E. (Ed.). (2018). Textbook of Veterinary Diagnostic Radiology (7th ed.). St. Louis, MO: Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"Barr, F. J. (2005). Diagnostic Ultrasound in the Dog and Cat (2nd ed.). Edinburgh: Elsevier Saunders.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"Busoni, F., Puchalski, S. M. (2011). Small animal ultrasonography: General principles, technique, and normal anatomy. In D. E. Thrall (Ed.), Textbook of Veterinary Diagnostic Radiology (6th ed., pp. 80-120). St. Louis, MO: Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"}],"related":["clinical-scoring-system-veterinary","blood-gas-analysis-veterinary","ultrasonography-veterinary"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radioimmunoassay","name":"Radioimmunoassay","fullName":"Radioimmunoassay (RIA)","aliases":["RIA","radioisotope immunoassay","isotope immunoassay","radioligand assay"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Competitive binding immunoassay","year":"1959–1960","originator":"Rosalyn Yalow and Solomon Berson","url":"https://scholargate.app/en/veterinary-science/radioimmunoassay","markdownUrl":"https://scholargate.app/en/veterinary-science/radioimmunoassay.md","definition":"Radioimmunoassay (RIA) is a highly sensitive, quantitative laboratory technique that measures the concentration of a specific antigen — such as a hormone, drug, or pathogen-derived protein — in a biological sample by exploiting competitive binding between a radiolabelled antigen and the sample antigen for a limited supply of specific antibody. Developed in the late 1950s, RIA is widely used in veterinary science, endocrinology, pharmacology, and clinical diagnostics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rosalyn Yalow and Solomon Berson","year":"1959–1960","type":"Quantitative immunological assay","dataType":"Radioactivity counts (CPM/DPM), standard-curve concentration data","subfamily":"Competitive binding immunoassay"},"citations":[{"ref":"Yalow, R. S., & Berson, S. A. (1960). Immunoassay of endogenous plasma insulin in man. Journal of Clinical Investigation, 39(7), 1157–1175.","type":"journal-article","doi":"10.1172/JCI104130","isbn":null,"url":null},{"ref":"Sauer, M. J. (Ed.). (1981). Radioimmunoassay in Basic and Clinical Pharmacology. Springer.","type":"book","doi":null,"isbn":null,"url":"https://link.springer.com/book/9783540105961"}],"related":["enzyme-linked-immunosorbent-assay","chemiluminescence-immunoassay","western-blot","flow-cytometry","mass-spectrometry","competitive-binding-assay"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"radiomics","name":"Radiomics","fullName":"Quantitative Radiomics","aliases":["texture analysis","radiomics analysis","quantitative imaging biomarkers"],"domain":"medical-imaging","family":"process-pipeline","subfamily":"Quantitative image analysis","year":"2012","originator":"Philippe Lambin","url":"https://scholargate.app/en/medical-imaging/radiomics","markdownUrl":"https://scholargate.app/en/medical-imaging/radiomics.md","definition":"Radiomics is a computational methodology that extracts large numbers of quantitative features from medical images (CT, MRI, PET) using automated image analysis and machine learning to discover imaging biomarkers associated with disease phenotype, prognosis, and treatment response. Developed by Lambin, Gillies, and colleagues in 2012, radiomics aims to decode the biology underlying visible imaging patterns, enabling personalized medicine through image-based phenotyping. It has emerged as a powerful tool in oncology for tumor characterization, prognosis prediction, and therapy response assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Philippe Lambin","subfamily":"Quantitative image analysis","year":"2012","type":"Machine learning-based texture and morphology analysis"},"citations":[{"ref":"Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. (2012). Radiomics: extracting more information from medical images using advanced feature analysis. Nature Reviews Clinical Oncology, 9(12), 676-684.","type":"article","doi":"10.1016/j.ejca.2011.11.036","isbn":null,"url":null},{"ref":"Gillies, R. J., Kinahan, P. E., Hricak, H. (2016). Radiomics: images are data. Radiology, 278(2), 563-577.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Radiomics%3A+images+are+data+Gillies"},{"ref":"Kumar, V., Gu, Y., Basu, S., et al. (2012). Radiomics: the process and the challenges. Magnetic Resonance Imaging, 30(9), 1234-1248.","type":"article","doi":"10.1016/j.mri.2012.06.010","isbn":null,"url":null}],"related":["ct-iterative-reconstruction","pet-kinetic-modeling","quantitative-susceptibility-mapping","dexa","oct-angiography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rafsi","name":"RAFSI","fullName":"Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., Pribićević, I.","url":"https://scholargate.app/en/decision-making/rafsi","markdownUrl":"https://scholargate.app/en/decision-making/rafsi.md","definition":"RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval) is a ranking multi-criteria decision-making (MCDM) method introduced by Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., Pribićević, I. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., Pribićević, I.","subfamily":"Ranking","year":"2020","type":"Functional interval mapping (rank-reversal free)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., Pribićević, I. (2020). Eliminating Rank Reversal Problem Using a New Multi-Attribute Model — The RAFSI Method. Mathematics","type":"article","doi":"10.3390/math8061015","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rainflow-counting","name":"Rainflow Counting","fullName":"Rainflow Counting Algorithm","aliases":["Rainflow cycle counting","RFC"],"domain":"reliability-engineering","family":"process-pipeline","subfamily":"Fatigue analysis","year":"1974","originator":"Tatsuo Endo","url":"https://scholargate.app/en/reliability-engineering/rainflow-counting","markdownUrl":"https://scholargate.app/en/reliability-engineering/rainflow-counting.md","definition":"Rainflow counting is a fatigue cycle counting method that converts a complex stress history into individual cycles for damage assessment. Developed by Tatsuo Endo and colleagues in 1974, it provides the most physically realistic representation of fatigue damage when combined with Miner's linear cumulative damage hypothesis. The algorithm has become the industry standard in reliability engineering and vibration analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tatsuo Endo","subfamily":"Fatigue analysis","year":"1974","type":"Cycle counting algorithm"},"citations":[{"ref":"Goodman, J. (1899). Mechanics Applied to Engineering. Longman, Green and Co.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/mechanicsofmater00gooduoft"},{"ref":"Miner, M. A. (1945). Cumulative damage in fatigue. Journal of Applied Mechanics, 12(3), 159-164.","type":"article","doi":"10.1115/1.4009458","isbn":null,"url":null},{"ref":"Endo, T., Matsumoto, T., Hasebe, T., & Mori, K. (1974). Damage evaluation of metals for random or varying loading. Proceedings of the Symposium on Mechanical Behavior of Materials.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=endo+rainflow+1974"},{"ref":"ASTM International (2021). E1049-21: Standard Practices for Cycle Counting in Fatigue Analysis.","type":"standard","doi":null,"isbn":null,"url":"https://www.astm.org/e1049-21.html"}],"related":["first-order-reliability-method","second-order-reliability-method","highly-accelerated-life-testing","response-surface-desirability-function"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ram","name":"RAM","fullName":"Root Assessment Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2023","originator":"Sotoudeh-Anvari, A.","url":"https://scholargate.app/en/decision-making/ram","markdownUrl":"https://scholargate.app/en/decision-making/ram.md","definition":"RAM (Root Assessment Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Sotoudeh-Anvari, A. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sotoudeh-Anvari, A.","subfamily":"Ranking","year":"2023","type":"Power-function aggregation with benefit/cost separation","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Sotoudeh-Anvari, A. (2023). Root Assessment Method (RAM): A novel multi-criteria decision making method and its applications in sustainability challenges. Journal of Cleaner Production","type":"article","doi":"10.1016/j.jclepro.2023.138695","isbn":null,"url":null}],"related":["ahp","bwm","critic","entropy","swara","merec"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"raman-deconvolution","name":"Raman Deconvolution","fullName":"Raman Spectroscopy Deconvolution Analysis","aliases":["Raman deconvolution","Raman peak fitting","spectral analysis"],"domain":"materials-science","family":"process-pipeline","subfamily":"Vibrational spectroscopy","year":"1928","originator":"Chandrasekhara Venkata Raman","url":"https://scholargate.app/en/materials-science/raman-deconvolution","markdownUrl":"https://scholargate.app/en/materials-science/raman-deconvolution.md","definition":"Raman Deconvolution is the mathematical decomposition of experimental Raman spectra into constituent peaks using spectral fitting algorithms. Building on Raman spectroscopy (discovered by C.V. Raman in 1928), Raman deconvolution resolves overlapping vibrational bands into individual component peaks, revealing detailed information about molecular bonds, crystal phases, strain, and defects. This quantitative analysis transforms raw Raman spectra into actionable chemical and structural insights, making it essential for materials characterization, quality control, and scientific discovery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chandrasekhara Venkata Raman","subfamily":"Vibrational spectroscopy","year":"1928","type":"Analytical technique"},"citations":[{"ref":"Raman, C. V., & Krishnan, K. S. (1928). The scattering of light by molecules. Nature, 121(3048), 501-502.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+scattering+of+light+by+molecules+Raman"},{"ref":"Srivastava, A., Jain, R., & Gupta, A. (2014). Raman spectroscopy as a tool for quality assessment in polymeric materials. Advanced Materials & Processes, 195(3), 6-13.","type":"article","doi":null,"isbn":null,"url":"https://www.asminternational.org"},{"ref":"Ferraro, J. R., Nakamoto, K., & Brown, C. W. (2003). Introductory Raman Spectroscopy (2nd ed.). Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Introductory+Raman+Spectroscopy+%282nd+ed.%29+Ferraro"}],"related":["x-ray-photoelectron-spectroscopy","xrd-rietveld-refinement","thermogravimetric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ramsey-cass-koopmans-model","name":"Ramsey-Cass-Koopmans Model","fullName":"Ramsey-Cass-Koopmans Growth Model","aliases":["RCK Model","Neoclassical Growth Model"],"domain":"economics","family":"regression-model","subfamily":"Macroeconomic Growth","year":"1928","originator":"Frank Ramsey, David Cass, Tjalling Koopmans","url":"https://scholargate.app/en/economics/ramsey-cass-koopmans-model","markdownUrl":"https://scholargate.app/en/economics/ramsey-cass-koopmans-model.md","definition":"The Ramsey-Cass-Koopmans model, developed initially by Frank Ramsey in 1928 and formalized by David Cass and Tjalling Koopmans in 1965, is the workhorse model of macroeconomic growth theory. It describes how rational consumers optimize consumption and savings over an infinite horizon, subject to an aggregate production function, and derives the long-run growth path and the optimal allocation of resources.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Ramsey, David Cass, Tjalling Koopmans","subfamily":"Macroeconomic Growth","year":"1928","type":"Optimal growth model"},"citations":[{"ref":"Ramsey, F. P. (1928). A Mathematical Theory of Saving. Economic Journal, 38(152), 543–559.","type":"article","doi":"10.2307/2224098","isbn":null,"url":null},{"ref":"Cass, D. (1965). Optimality and the Dynamic Stability of Equilibrium. Metroeconomica, 16(2), 101–115.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/24102282"},{"ref":"Koopmans, T. C. (1965). On the Concept of Optimal Economic Growth. Pontificiae Academiae Scientiarum Scripta Varia, 28, 1–75.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.4324/9780429494888"}],"related":["overlapping-generations-model","real-business-cycle-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ramsey-reset-test","name":"Ramsey RESET Test","fullName":"Ramsey Regression Equation Specification Error Test (RESET)","aliases":["RESET test","regression specification error test","Ramsey RESET fonksiyonel form testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1969,"originator":"James B. Ramsey","url":"https://scholargate.app/en/econometrics/ramsey-reset-test","markdownUrl":"https://scholargate.app/en/econometrics/ramsey-reset-test.md","definition":"The Ramsey RESET test, proposed by James Ramsey in 1969, is a general test for functional-form misspecification in a linear regression — for omitted nonlinear relationships between the response and the regressors. It adds powers of the fitted values to the model and checks whether they significantly improve the fit; if they do, the original linear specification has left systematic structure unexplained.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James B. Ramsey","year":1969,"type":"Test for functional-form misspecification","nullHypothesis":"Model is correctly specified (no omitted nonlinearity)","distribution":"F (or chi-square)","minSample":30},"citations":[{"ref":"Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society: Series B, 31(2), 350–371.","type":"article","doi":"10.1111/j.2517-6161.1969.tb00796.x","isbn":null,"url":null}],"related":["ols-regression","multiple-linear-regression","polynomial-regression","white-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"random-effects-panel","name":"Random Effects Panel Model","fullName":"Random Effects Model for Panel Data","aliases":["random effects panel regression","RE estimator","GLS panel estimator","Panel Rassal Etkiler Modeli"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1978,"originator":"Baltagi (textbook treatment); Hausman specification test","url":"https://scholargate.app/en/econometrics/random-effects-panel","markdownUrl":"https://scholargate.app/en/econometrics/random-effects-panel.md","definition":"The random effects model is a panel data estimator that explains an outcome using both within-unit and between-unit variation, treating the unobserved unit-specific heterogeneity as a random, normally distributed term rather than a fixed parameter. Its validity is judged with the Hausman (1978) specification test, and it is developed in standard treatments such as Baltagi's Econometric Analysis of Panel Data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baltagi (textbook treatment); Hausman specification test","year":1978,"type":"Panel data regression","estimator":"Feasible generalized least squares (GLS)","outcome":"continuous, binary or count","dataStructure":"panel (units × time)"},"citations":[{"ref":"Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null},{"ref":"Baltagi, B. H. (2005). Econometric Analysis of Panel Data. Wiley.","type":"book","doi":null,"isbn":"978-0470014561","url":null}],"related":["panel-fixed-effects","hausman-test","ols-regression","hlm","pooled-ols"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"random-forest","name":"Random Forest","fullName":"Random Forest (Breiman Ensemble of Decision Trees)","aliases":["Rastgele Orman (Random Forest)","rastgele orman","random decision forest","bagged tree ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2001,"originator":"Breiman, L.","url":"https://scholargate.app/en/machine-learning/random-forest","markdownUrl":"https://scholargate.app/en/machine-learning/random-forest.md","definition":"Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, L.","year":2001,"type":"Ensemble (bagging of decision trees)","task":"Classification & regression","minSample":50},"citations":[{"ref":"Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.","type":"article","doi":"10.1023/A:1010933404324","isbn":null,"url":null},{"ref":"James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 8). Springer.","type":"book","doi":null,"isbn":"978-1-4614-7138-7","url":null}],"related":["xgboost","decision-tree","svm-classification","logistic-regression","linear-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"random-projection","name":"Random Projection","fullName":"Random Projection (Johnson-Lindenstrauss Dimensionality Reduction)","aliases":["random projections","Johnson-Lindenstrauss projection","sparse random projection","rastgele izdüşüm"],"domain":"machine-learning","family":"ml-model","subfamily":"Dimensionality reduction","year":1984,"originator":"Johnson & Lindenstrauss (lemma); Achlioptas (sparse variant)","url":"https://scholargate.app/en/machine-learning/random-projection","markdownUrl":"https://scholargate.app/en/machine-learning/random-projection.md","definition":"Random projection reduces dimensionality by multiplying the data by a random matrix, relying on the Johnson-Lindenstrauss lemma (1984), which guarantees that projecting onto enough random directions approximately preserves all pairwise distances. Unlike PCA it does not analyze the data at all — the projection is random and data-oblivious — making it extremely cheap and well suited to very high-dimensional data and streaming or privacy-sensitive settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johnson & Lindenstrauss (lemma); Achlioptas (sparse variant)","year":1984,"type":"Linear, data-oblivious dimensionality reduction","subfamily":"Dimensionality reduction","guarantee":"Approximate pairwise-distance preservation (JL lemma)","cost":"Very cheap; no training on the data"},"citations":[{"ref":"Johnson, W. B., & Lindenstrauss, J. (1984). Extensions of Lipschitz mappings into a Hilbert space. Contemporary Mathematics, 26, 189–206.","type":"article","doi":"10.1090/conm/026/737400","isbn":null,"url":null},{"ref":"Achlioptas, D. (2003). Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences, 66(4), 671–687.","type":"article","doi":"10.1016/S0022-0000(03)00025-4","isbn":null,"url":null}],"related":["principal-component-analysis","locally-linear-embedding","umap","matrix-completion"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"random-survival-forest","name":"Random Survival Forest","fullName":"Random Survival Forest","aliases":["RSF","Rastgele Sağkalım Ormanı (RSF)","survival random forest"],"domain":"survival","family":"survival","subfamily":null,"year":2008,"originator":"Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S.","url":"https://scholargate.app/en/survival/random-survival-forest","markdownUrl":"https://scholargate.app/en/survival/random-survival-forest.md","definition":"Random Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank splitting to handle censored observations naturally, and the ensemble aggregates cumulative hazard functions across hundreds of trees to produce predictions and variable importance rankings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S.","year":2008,"type":"Ensemble machine learning survival model","splitRule":"Log-rank splitting","variableImportance":"VIMP (permutation importance)","handlesNonlinearity":true,"requiresProportionalHazards":false,"minimumRecommendedEvents":100},"citations":[{"ref":"Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. (2008). Random Survival Forests. Annals of Applied Statistics, 2(3), 841–860.","type":"article","doi":"10.1214/08-AOAS169","isbn":null,"url":null}],"related":["cox-ph","kaplan-meier","nelson-aalen","fine-gray-model","weibull-aft"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"random-utility-model","name":"Random Utility Model","fullName":"Random Utility Model with Probabilistic Choice","aliases":["Discrete Choice Model","Probabilistic Choice","Stochastic Utility"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1974","originator":"Daniel McFadden","url":"https://scholargate.app/en/game-theory/random-utility-model","markdownUrl":"https://scholargate.app/en/game-theory/random-utility-model.md","definition":"The Random Utility Model explains discrete choice behavior by assuming agents derive uncertain utilities from alternatives and choose the option yielding highest utility. Introduced by Daniel McFadden in 1974, the model decomposes utility into systematic (observable) and random (idiosyncratic) components, permitting probabilistic choice predictions. The logit model, a parametric specification, yields closed-form choice probabilities that are widely used in marketing, transportation, and environmental valuation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel McFadden","subfamily":"Game-theoretic","year":"1974","type":"algorithm"},"citations":[{"ref":"McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/science/article/pii/S0573865174800370"},{"ref":"Train, K. E. (2009). Discrete Choice Methods with Simulation (Second Edition). Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://eml.berkeley.edu/books/choice2.html"}],"related":["nash-equilibrium","bayesian-nash-equilibrium","principal-agent-model","arrow-debreu-equilibrium"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"randomization-inference","name":"Randomization Inference","fullName":"Fisher Exact Randomization Inference","aliases":["fisher randomization test","permutation inference","exact randomization test","randomizasyon çıkarımı (fisher exact randomization)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1935,"originator":"Ronald A. Fisher","url":"https://scholargate.app/en/statistics/randomization-inference","markdownUrl":"https://scholargate.app/en/statistics/randomization-inference.md","definition":"Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is regarded as the gold standard for analysing designed experiments because its validity rests on the known assignment mechanism rather than on distributional assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":1935,"type":"Exact permutation-based inference","estimator":"Permutation distribution of a test statistic under the sharp null","nullHypothesis":"Fisher sharp null (no treatment effect for any unit)","minSample":10},"citations":[{"ref":"Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/in.ernet.dli.2015.502684"},{"ref":"Imbens, G. W. & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521885881","url":null}],"related":["bootstrap-inference","jackknife","permutation-test","quantile-regression-np","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"randomized-clinical-trial","name":"Randomized clinical trial","fullName":"Randomized Controlled Trial","aliases":["RCT","randomized controlled trial","randomised controlled trial","clinical randomized trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1948 (first rigorously conducted RCT — MRC streptomycin trial)","originator":"Austin Bradford Hill; MRC Streptomycin Trial team","url":"https://scholargate.app/en/epidemiology/randomized-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/randomized-clinical-trial.md","definition":"A randomized clinical trial (RCT) is an experimental study design in which participants are randomly assigned to an intervention group or a control group, then followed prospectively to compare outcomes. Random allocation is the defining feature: it distributes known and unknown confounders across groups by chance, making the RCT the strongest individual study design for establishing causal efficacy of a treatment or intervention under controlled conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Austin Bradford Hill; MRC Streptomycin Trial team","year":"1948 (first rigorously conducted RCT — MRC streptomycin trial)","type":"Interventional experimental study","dataType":"Participant-level outcome data (continuous, binary, time-to-event)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of Clinical Trials (5th ed.). Springer.","type":"book","doi":null,"isbn":"978-3319185385","url":null},{"ref":"Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.","type":"article","doi":"10.1136/bmj.c332","isbn":null,"url":null}],"related":["cohort-study","case-control-study","cross-sectional-epidemiological-study","phase-ii-clinical-trial","phase-iii-clinical-trial","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"randomized-complete-block","name":"Randomized Complete Block Design","fullName":"Randomized Complete Block Design (RCBD)","aliases":["RCBD","randomized block design","complete block design","Tesadüf Bloklu Desen (RCBD)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1935,"originator":"Ronald A. Fisher","url":"https://scholargate.app/en/experimental-design/randomized-complete-block","markdownUrl":"https://scholargate.app/en/experimental-design/randomized-complete-block.md","definition":"The Randomized Complete Block Design (RCBD) is a parametric experimental design and hypothesis-testing framework that isolates and removes a known source of heterogeneity — called a block — before comparing treatment means. Introduced by Ronald A. Fisher in his 1935 monograph The Design of Experiments, it remains the foundational blocked design in agricultural, clinical, and industrial research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":1935,"family":"Experimental design / Hypothesis test","type":"Parametric blocked ANOVA","groups":"≥ 2 treatments","outcome":"continuous","parametric":true,"distribution":"F (Fisher–Snedecor)","df_treatment":"t - 1","df_block":"b - 1","df_error":"(t - 1)(b - 1)","minSample":20,"difficulty":1},"citations":[{"ref":"Montgomery, D.C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1-119-32093-7","url":null},{"ref":"Fisher, R.A. (1935). The Design of Experiments. Oliver & Boyd.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/designofexperime0000fish"}],"related":["one-way-anova","two-way-anova","repeated-measures-anova","latin-square-design","friedman-test","completely-randomized-design"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"randomized-controlled-trial","name":"Randomized Controlled Trial","fullName":"Randomized Controlled Trial (RCT)","aliases":["RCT","randomised controlled trial","clinical trial","Randomize Kontrollü Çalışma (RCT) Tasarımı"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1948,"originator":"James Lind (early precursor, 1747); modern formulation: Austin Bradford Hill & Medical Research Council (1948)","url":"https://scholargate.app/en/experimental-design/randomized-controlled-trial","markdownUrl":"https://scholargate.app/en/experimental-design/randomized-controlled-trial.md","definition":"A randomized controlled trial (RCT) is the gold standard experimental design in clinical and health research, in which participants are randomly allocated to a treatment group or a control group so that the effect of an intervention can be measured with the highest possible degree of internal validity. The modern parallel-group RCT was formalized by Austin Bradford Hill and the Medical Research Council in their landmark streptomycin trial of 1948, and its reporting is governed today by the CONSORT 2010 guidelines (Schulz et al., 2010).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Lind (early precursor, 1747); modern formulation: Austin Bradford Hill & Medical Research Council (1948)","year":1948,"family":"Experimental design","type":"Interventional comparative study","groups":"2+","outcome":"continuous, binary, or ordinal","parametric":false,"reportingStandard":"CONSORT 2010","minSamplePerGroup":10,"keyVariants":"simple randomization, stratified randomization, block randomization, single-blind, double-blind, triple-blind","analysisApproach":"intention-to-treat (ITT), per-protocol (PP)"},"citations":[{"ref":"Schulz, K.F., Altman, D.G., Moher, D., for the CONSORT Group (2010). CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomised Trials. BMJ, 340, c332.","type":"article","doi":"10.1136/bmj.c332","isbn":null,"url":null},{"ref":"Pocock, S.J. (1983). Clinical Trials: A Practical Approach. Wiley.","type":"book","doi":null,"isbn":"978-0471901853","url":null}],"related":["crossover-design","adaptive-design","factorial-design","paired-t-test","independent-t-test","one-way-anova","survival-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"range-of-motion-goniometry","name":"Range of Motion Goniometry","fullName":"Range of Motion Assessment via Goniometry","aliases":["ROM assessment","Goniometric measurement"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Joint assessment","year":"1960s","originator":"Physical therapy profession","url":"https://scholargate.app/en/physical-therapy/range-of-motion-goniometry","markdownUrl":"https://scholargate.app/en/physical-therapy/range-of-motion-goniometry.md","definition":"Goniometry is the standardized clinical measurement of joint angles using a goniometer (angle-measuring instrument) to quantify range of motion in degrees. Developed from orthopedic assessment traditions, goniometric measurement is a fundamental skill in physical therapy, occupational therapy, and orthopedic medicine for baseline assessment, monitoring progression, and documenting rehabilitation outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Physical therapy profession","subfamily":"Joint assessment","year":"1960s","type":"Measurement technique"},"citations":[{"ref":"Norkin, C. C., & White, D. J. (2009). Measurement of joint motion: A guide to goniometry (4th ed.). F.A. Davis Company.","type":"book","doi":null,"isbn":null,"url":"https://www.fadavis.com/"},{"ref":"Boone, D. C., & Azen, S. P. (1979). Normal range of motion of joints in male subjects. Journal of Bone and Joint Surgery, 61(5), 756-759.","type":"article","doi":"10.2106/00004623-197961050-00017","isbn":null,"url":null}],"related":["manual-muscle-testing","proprioception-assessment","electromyography-clinical"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rank-aggregation","name":"Rank Aggregation","fullName":"Rank Aggregation Methods","aliases":["Rank Fusion","Order Aggregation","Preference Aggregation","Sıralama Birleştirme"],"domain":"decision-making","family":"ml-model","subfamily":"Ranking models","year":2001,"originator":"Dwork, Kumar, Naor & Sivakumar","url":"https://scholargate.app/en/decision-making/rank-aggregation","markdownUrl":"https://scholargate.app/en/decision-making/rank-aggregation.md","definition":"Rank Aggregation is a family of methods that combine multiple ranked lists of alternatives into a single consensus ranking. Formally studied in the context of web search by Dwork, Kumar, Naor, and Sivakumar (2001), these methods address the problem of synthesizing divergent preference orderings from multiple sources — such as search engines, expert judges, or voter ballots — into one coherent, representative ordering that minimizes overall disagreement across the input rankings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dwork, Kumar, Naor & Sivakumar","year":2001,"type":"Combinatorial ranking method","subfamily":"Ranking models","input":"Multiple ranked lists of alternatives","output":"A single consensus ranked list"},"citations":[{"ref":"Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001). Rank aggregation methods for the web. Proceedings of the 10th International Conference on World Wide Web, 613–622.","type":"article","doi":"10.1145/371920.372165","isbn":null,"url":null}],"related":["plackett-luce-model","bradley-terry-model","borda-count"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rank-reversal","name":"RANK-REVERSAL","fullName":"Rank Reversal Analysis — Detection of ranking instability when alternatives are added/removed","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2000","originator":"Triantaphyllou, E.","url":"https://scholargate.app/en/decision-making/rank-reversal","markdownUrl":"https://scholargate.app/en/decision-making/rank-reversal.md","definition":"RANK-REVERSAL (Rank Reversal Analysis — Detection of ranking instability when alternatives are added/removed) is a ranking multi-criteria decision-making (MCDM) method introduced by Triantaphyllou, E. in 2000. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Triantaphyllou, E.","subfamily":"Ranking","year":"2000","type":"Robustness diagnostic — rank reversal detection and quantification","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Triantaphyllou, E. (2000). Multi-Criteria Decision Making Methods: A Comparative Study. Kluwer Academic Publishers, Dordrecht","type":"article","doi":"10.1007/978-1-4757-3157-6_2","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ranked-set-sampling","name":"Ranked Set Sampling","fullName":"Ranked Set Sampling (RSS)","aliases":["RSS"],"domain":"sampling","family":"process-pipeline","subfamily":"Nonparametric","year":"1952","originator":"Glenn A. McIntyre","url":"https://scholargate.app/en/sampling/ranked-set-sampling","markdownUrl":"https://scholargate.app/en/sampling/ranked-set-sampling.md","definition":"Ranked Set Sampling (RSS) is a data collection method introduced by G. A. McIntyre in 1952 that improves estimation efficiency when visual ranking of units is easier or cheaper than actual measurement. By deliberately selecting and measuring units that are ranked as most likely to yield desired outcomes, RSS reduces variance compared to simple random sampling while maintaining unbiasedness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glenn A. McIntyre","subfamily":"Nonparametric","year":"1952","type":"Sampling design methodology"},"citations":[{"ref":"McIntyre, G. A. (1952). A method for unbiased selective sampling using ranked sets. Australian Journal of Agricultural Research, 3(4), 385–390.","type":"article","doi":"10.1071/ar9520385","isbn":null,"url":null},{"ref":"Takahasi, K., & Wakimoto, K. (1968). On unbiased estimates of population mean based on the sample stratified by successive groups. Annals of the Institute of Statistical Mathematics, 20(1), 1–31.","type":"article","doi":"10.1007/bf02911622","isbn":null,"url":null},{"ref":"Wolfe, D. A. (1992). Illustrated concepts of ranked-set sampling. The American Statistician, 46(4), 229–232.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Illustrated+concepts+of+ranked-set+sampling+Wolfe"}],"related":["stratified-sampling","double-sampling","systematic-sampling","cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rankine-cycle","name":"Rankine Cycle","fullName":"Rankine Cycle for Steam Power Generation","aliases":["Clausius-Rankine cycle","steam cycle","vapor power cycle"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Vapor Power Cycle","year":"1859","originator":"William John Macquorn Rankine","url":"https://scholargate.app/en/thermodynamics/rankine-cycle","markdownUrl":"https://scholargate.app/en/thermodynamics/rankine-cycle.md","definition":"The Rankine Cycle is the fundamental thermodynamic cycle for steam power plants. It describes how thermal energy from burning fuel or concentrated solar radiation is converted to mechanical work and ultimately electricity. The cycle consists of four processes: isobaric heat addition in the boiler, isentropic expansion through the turbine, isobaric heat rejection in the condenser, and isentropic compression by the pump.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William John Macquorn Rankine","subfamily":"Vapor Power Cycle","year":"1859","type":"Thermodynamic cycle"},"citations":[{"ref":"Smith, J. M., Van Ness, H. C., & Abbott, M. M. (2005). Introduction to Chemical Engineering Thermodynamics (7th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0071247009","url":null},{"ref":"Moran, M. J., Shapiro, H. N., Boettner, D. D., & Bailey, M. B. (2014). Fundamentals of Engineering Thermodynamics (8th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118412947","url":null}],"related":["brayton-cycle","vapor-compression-cycle","effectiveness-ntu-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ransac-regression","name":"RANSAC Regression","fullName":"Random Sample Consensus (RANSAC) Regression","aliases":["random sample consensus","RANSAC","robust regression","RANSAC Regresyonu"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1981,"originator":"Fischler & Bolles","url":"https://scholargate.app/en/statistics/ransac-regression","markdownUrl":"https://scholargate.app/en/statistics/ransac-regression.md","definition":"RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeatedly samples small subsets, fits a candidate model, and keeps the model that wins the largest consensus of agreeing points.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fischler & Bolles","year":1981,"type":"Robust linear regression","estimator":"Random sample consensus (inlier-based fit)","outcome":"continuous"},"citations":[{"ref":"Fischler, M. A. & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24(6), 381-395.","type":"article","doi":"10.1145/358669.358692","isbn":null,"url":null},{"ref":"Torr, P. H. S. & Zisserman, A. (2000). MLESAC: A New Robust Estimator with Application to Estimating Image Geometry. Computer Vision and Image Understanding, 78(1), 138-156.","type":"article","doi":"10.1006/cviu.1999.0832","isbn":null,"url":null}],"related":["theil-sen-estimator","least-trimmed-squares","ols-regression","quantile-regression","robust-covariance"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rapid-review-methodology","name":"Rapid Review Methodology","fullName":"Rapid Review (Accelerated Systematic Review)","aliases":["Rapid Evidence Synthesis","Expedited Review","Fast-Track Systematic Review"],"domain":"evidence-synthesis","family":"process-pipeline","subfamily":"Accelerated Evidence Synthesis","year":"2012","originator":"Khangura et al. (2012), Codified by Cochrane Rapid Reviews (2020)","url":"https://scholargate.app/en/evidence-synthesis/rapid-review-methodology","markdownUrl":"https://scholargate.app/en/evidence-synthesis/rapid-review-methodology.md","definition":"A rapid review is a systematic synthesis method that accelerates the evidence review process by streamlining or omitting certain systematic review steps while maintaining transparent, reproducible methodology. Pioneered by Khangura et al. (2012) and codified by the Cochrane Collaboration (2020), rapid reviews answer urgent policy or clinical questions in weeks to months rather than 12-18 months required by full systematic reviews. Methodological shortcuts—such as single screening of borderline studies, abbreviated search strategies, or limiting study designs—trades some rigor for speed. Rapid reviews are increasingly vital in responding to public health emergencies (pandemics, environmental crises) and evolving clinical practice questions where waiting for a full systematic review is not feasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Khangura et al. (2012), Codified by Cochrane Rapid Reviews (2020)","subfamily":"Accelerated Evidence Synthesis","year":"2012","type":"Framework"},"citations":[{"ref":"Garritty, C., Gartlehner, G., Nussbaumer-Streit, B., et al. (2021). Cochrane Rapid Reviews interim guidance on methodological considerations for expedited reviews of interventions. Journal of Clinical Epidemiology, 130, 13–21.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cochrane+Rapid+Reviews+interim+guidance+on+methodological+considerations+for+expedited+reviews+of+interventions+Garritty"},{"ref":"Tricco, A. C., Antony, J., Zarin, W., et al. (2015). A scoping review of rapid review methods. BMC Medicine, 13, 224.","type":"article","doi":"10.1186/s12916-015-0465-6","isbn":null,"url":null},{"ref":"Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., & Moher, D. (2012). Evidence summaries: The evolution of a rapid review approach. Systematic Reviews, 1, 10.","type":"article","doi":"10.1186/2046-4053-1-10","isbn":null,"url":null}],"related":["systematic-review","scoping-review-methodology","evidence-synthesis-framework","rapid-evidence-assessment"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rapid-review","name":"Rapid Review","fullName":"Rapid Evidence Synthesis Review","aliases":["rapid evidence review","accelerated systematic review","rapid evidence assessment","REA"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2000s (rapidly adopted after 2005; Cochrane guidance 2020–2021)","originator":"Developed and formalised by health technology assessment agencies and the Cochrane Collaboration","url":"https://scholargate.app/en/scientometrics/rapid-review","markdownUrl":"https://scholargate.app/en/scientometrics/rapid-review.md","definition":"A rapid review is a streamlined form of systematic review that deliberately simplifies or omits certain steps — such as dual screening, exhaustive grey-literature search, or full risk-of-bias assessment — in order to deliver timely, policy-relevant evidence synthesis within weeks rather than years. It is increasingly used by health agencies, governments, and organisations facing urgent decision-making needs where a full systematic review is not feasible within the available time and resources.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed and formalised by health technology assessment agencies and the Cochrane Collaboration","year":"2000s (rapidly adopted after 2005; Cochrane guidance 2020–2021)","type":"Evidence synthesis review","dataType":"Published studies, systematic reviews, trial reports, grey literature","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Garritty, C., Gartlehner, G., Nussbaumer-Streit, B., King, V. J., Hamel, C., Kamel, C., Affengruber, L., & Stevens, A. (2021). Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews. Journal of Clinical Epidemiology, 130, 13–22.","type":"article","doi":"10.1016/j.jclinepi.2020.10.007","isbn":null,"url":null},{"ref":"Tricco, A. C., Antony, J., Zarin, W., Strifler, L., Ghassemi, M., Ivory, J., Perrier, L., Hutton, B., Moher, D., & Straus, S. E. (2015). A scoping review of rapid review methods. BMC Medicine, 13, 224.","type":"article","doi":"10.1186/s12916-015-0465-6","isbn":null,"url":null}],"related":["systematic-literature-review","scoping-review","umbrella-review","narrative-review","meta-analysis","prisma-based-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rapid3","name":"Routine Assessment of Patient Index Data 3","fullName":"Routine Assessment of Patient Index Data 3 for Rheumatoid Arthritis","aliases":["RAPID3","RAPID-3"],"domain":"rheumatology","family":"process-pipeline","subfamily":"disease-activity-index","year":"2008","originator":"Pincus et al.","url":"https://scholargate.app/en/rheumatology/rapid3","markdownUrl":"https://scholargate.app/en/rheumatology/rapid3.md","definition":"RAPID3 is a patient-reported outcome (PRO) measure of rheumatoid arthritis disease activity based on three simple self-report items: patient-counted swollen and tender joints and overall health assessment. Introduced by Pincus et al. in 2008, RAPID3 was designed for primary care and busy practices where joint examination is impractical or time-limited. Remarkably, RAPID3 correlates strongly with clinician-examined composite measures (DAS28, CDAI, SDAI) and predicts long-term radiographic progression equally well, making it a practical alternative for resource-limited settings and self-directed monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pincus et al.","subfamily":"disease-activity-index","year":"2008","type":"Patient-reported outcome (PRO)"},"citations":[{"ref":"Pincus T, Bergman MJ, Sokka T, Roth SH, Swearingen C, Yazici Y. Activity of rheumatoid arthritis is similar in patients seen in a primary care physician-based practice and in an academic rheumatology-based practice. Arthritis Care Research. 2008;59(9):1229-1236.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pincus+T%2C+Bergman+MJ%2C+Sokka+T%2C+Roth+SH%2C+Swearingen+C%2C+Yazici+Y.+Activity+of+rheumatoid+arthritis+is+similar+in+patients++Pincus"},{"ref":"Pincus T, Chung CP, Segurado OG. RAPID3 (Routine Assessment of Patient Index Data 3), a rheumatology outpatient clinical tool: discrimination of activity of disease. Semin Arthritis Rheum. 2010;40(2):89-96.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pincus+T%2C+Chung+CP%2C+Segurado+OG.+RAPID3+%28Routine+Assessment+of+Patient+Index+Data+3%29%2C+a+rheumatology+outpatient+clinical+Pincus"}],"related":["das28","cdai-rheumatoid-arthritis","sdai-rheumatoid-arthritis","basdai","sledai"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rapidly-exploring-random-tree","name":"Rapidly-Exploring Random Tree","fullName":"Rapidly-Exploring Random Tree","aliases":["RRT","Incremental Sampling-based Algorithm"],"domain":"control-theory","family":"ml-model","subfamily":"Motion Planning","year":"1998","originator":"Steven M. LaValle","url":"https://scholargate.app/en/control-theory/rapidly-exploring-random-tree","markdownUrl":"https://scholargate.app/en/control-theory/rapidly-exploring-random-tree.md","definition":"The Rapidly-Exploring Random Tree (RRT) is a motion planning algorithm that builds a tree of feasible paths by iteratively sampling random configurations in the workspace and connecting them to the nearest existing node in the tree. Introduced by LaValle in 1998, RRT is a breakthrough for high-dimensional motion planning, enabling robots to find collision-free paths in complex environments with obstacles, joint limits, and kinematic constraints.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven M. LaValle","subfamily":"Motion Planning","year":"1998","type":"algorithm"},"citations":[{"ref":"LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report TR 98-11, Iowa State University.","type":"article","doi":null,"isbn":null,"url":"https://msl.cs.illinois.edu/~lavalle/papers/Lav98c.pdf"},{"ref":"Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. International Journal of Robotics Research, 30(7), 846-894.","type":"article","doi":"10.1177/0278364911406761","isbn":null,"url":null},{"ref":"LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"http://planning.cs.illinois.edu/"}],"related":["probabilistic-roadmap","model-predictive-control","feedback-linearization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"raps","name":"RAPS","fullName":"Ranking of Alternatives based on Preference Strength","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Dezert, J., Tchamova, A.","url":"https://scholargate.app/en/decision-making/raps","markdownUrl":"https://scholargate.app/en/decision-making/raps.md","definition":"RAPS (Ranking of Alternatives based on Preference Strength) is a ranking multi-criteria decision-making (MCDM) method introduced by Dezert, J., Tchamova, A. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dezert, J., Tchamova, A.","subfamily":"Ranking","year":"2021","type":"Preference-strength aggregation (rank-reversal resistant)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Dezert, J., Tchamova, A. (2021). On the effectiveness of measures of uncertainty of basic belief assignments. Information & Security: An International Journal","type":"article","doi":"10.11610/isij.5201","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rasch-model","name":"Rasch Model","fullName":"Rasch Model (One-Parameter Logistic Item Response Theory)","aliases":["1PL IRT","one-parameter logistic model","Rasch Modeli — 1PL IRT","1PL model"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1960,"originator":"Georg Rasch","url":"https://scholargate.app/en/psychometrics/rasch-model","markdownUrl":"https://scholargate.app/en/psychometrics/rasch-model.md","definition":"The Rasch model, introduced by Georg Rasch in 1960, is the simplest member of the Item Response Theory (IRT) family. It assigns a single difficulty parameter to each test item and places both item difficulties and person abilities on the same logit scale, enabling direct, sample-independent comparison of items and persons.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georg Rasch","year":1960,"type":"Item Response Theory / Latent trait model","parameters_per_item":1,"parameter_name":"Item difficulty (b)","scale":"Logit (log-odds unit)","outcome":"Item difficulty parameters + person ability estimates on a common logit scale","data":"Binary (0/1) or ordinal polytomous scored responses","min_sample":150},"citations":[{"ref":"Rasch, G. (1960). Probabilistic Models for Some Intelligence and Attainment Tests. Danish Institute for Educational Research, Copenhagen.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/oclc/1229924"},{"ref":"Bond, T. G. & Fox, C. M. (2015). Applying the Rasch Model: Fundamental Measurement in the Human Sciences (3rd ed.). Routledge.","type":"book","doi":null,"isbn":"978-0-415-83342-1","url":null}],"related":["two-pl-irt","three-pl-irt","graded-response-model","exploratory-factor-analysis","confirmatory-factor-analysis","cronbach-alpha"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rat","name":"RAT","fullName":"Reference Alternative based Aggregation Technique","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"2024","originator":"Orakçı, E.","url":"https://scholargate.app/en/decision-making/rat","markdownUrl":"https://scholargate.app/en/decision-making/rat.md","definition":"RAT (Reference Alternative based Aggregation Technique) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Orakçı, E.","subfamily":"AggregationOperator","year":"2024","type":"Rank aggregation (signum-weighted Euclidean-square-difference)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları","type":"article","doi":"10.58830/ozgur.pub623","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rate-of-force-development","name":"Rate of Force Development","fullName":"Rate of Force Development and Explosive Strength Assessment","aliases":["RFD","explosive strength","force development rate","strength impulse"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Strength & Power","year":"2002","originator":"Peter Aagaard","url":"https://scholargate.app/en/sports-science/rate-of-force-development","markdownUrl":"https://scholargate.app/en/sports-science/rate-of-force-development.md","definition":"Rate of force development (RFD) is the speed at which force is produced during the initial phase of muscle contraction, typically expressed as the slope of the force-time curve in the first 50, 100, or 200 milliseconds of isometric contraction. Introduced comprehensively by Aagaard and colleagues (2002), RFD is a measure of explosive strength capacity and neural drive efficiency. Unlike maximal voluntary strength (which captures peak force), RFD captures how quickly an athlete can generate that force—a critical quality in sports requiring rapid, explosive movements (sprinting starts, jumping, tackling). RFD improves dramatically with strength training, reflecting increased motor unit recruitment rate and firing frequency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Aagaard","subfamily":"Strength & Power","year":"2002","type":"isometric force measurement"},"citations":[{"ref":"Aagaard, P., Simonsen, E. B., Andersen, J. L., Magnusson, P., & Dyhre-Poulsen, P. (2002). Increased rate of force development and neural drive of human skeletal muscle following resistance training. Journal of Applied Physiology, 93(3), 1318-1326.","type":"article","doi":"10.1152/japplphysiol.00283.2002","isbn":null,"url":null},{"ref":"Viitasalo, J. T., & Bosco, C. (1982). Electromechanical behaviour of human muscles in vertical jumps. European Journal of Applied Physiology, 48(2), 253-262.","type":"article","doi":"10.1007/bf00422986","isbn":null,"url":null},{"ref":"Haff, G. G., Carlock, J. M., Hartman, M. J., Kilgore, J. L., Kawamori, N., Jackson, J. K., ... & Stone, M. H. (2005). Force-time dependent characteristics of dynamic and isometric muscle actions. Journal of Strength and Conditioning Research, 19(2), 269-279.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Force-time+dependent+characteristics+of+dynamic+and+isometric+muscle+actions+Haff"}],"related":["force-velocity-profile","counter-movement-jump","reactive-strength-index","electromechanical-delay","1rm-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rawec","name":"RAWEC","fullName":"Ranking Alternatives With Equal Criteria weights","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2024","originator":"Puška, A., Štilić, A., Pamučar, D., Božanić, D., Nedeljković, M.","url":"https://scholargate.app/en/decision-making/rawec","markdownUrl":"https://scholargate.app/en/decision-making/rawec.md","definition":"RAWEC (Ranking Alternatives With Equal Criteria weights) is a ranking multi-criteria decision-making (MCDM) method introduced by Puška, A., Štilić, A., Pamučar, D., Božanić, D., Nedeljković, M. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Puška, A., Štilić, A., Pamučar, D., Božanić, D., Nedeljković, M.","subfamily":"Ranking","year":"2024","type":"Rank-based equal-weight aggregation with distance from ideal","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Puška, A., Štilić, A., Pamučar, D., Božanić, D., Nedeljković, M. (2024). Introducing a Novel multi-criteria Ranking of Alternatives with Weights of Criterion (RAWEC) model. MethodsX","type":"article","doi":"10.1016/j.mex.2024.102628","isbn":null,"url":null}],"related":["siwec","ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ray-tracing-propagation","name":"Ray Tracing Propagation","fullName":"Ray Tracing Propagation Model","aliases":["deterministic propagation","site-specific modeling"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Propagation modeling","year":"1993","originator":"Maciel, Bertoni, and Xia","url":"https://scholargate.app/en/telecommunications/ray-tracing-propagation","markdownUrl":"https://scholargate.app/en/telecommunications/ray-tracing-propagation.md","definition":"Ray tracing is a deterministic propagation modeling technique for predicting electromagnetic field strength at specific locations. Instead of empirical formulas (like Okumura-Hata), ray tracing traces paths of electromagnetic energy as it reflects, diffracts, and scatters off buildings and terrain. With accurate 3D geometry and material properties, ray tracing predicts site-specific path loss, multipath delay profiles, and angle of arrival, making it ideal for detailed coverage planning, interference analysis, and system design. Ray tracing is now standard in professional cellular planning tools.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maciel, Bertoni, and Xia","subfamily":"Propagation modeling","year":"1993","type":"deterministic propagation algorithm"},"citations":[{"ref":"Maciel, T. F., Bertoni, H. L., & Xia, H. H. (1993). Unified approach to prediction of propagation over buildings for all ranges of frequencies. IEEE Transactions on Vehicular Technology, 42(1), 41-45.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Unified+approach+to+prediction+of+propagation+over+buildings+for+all+ranges+of+frequencies+Maciel"},{"ref":"Saleh, A. A., & Valenzuela, R. A. (1987). A statistical model for indoor multipath propagation. IEEE Journal on Selected Areas in Communications, 5(2), 128-137.","type":"article","doi":"10.1109/jsac.1987.1146527","isbn":null,"url":null}],"related":["okumura-hata-model","ofdm","mimo","zf-mmse-equalization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rcwa","name":"RCWA","fullName":"Rigorous Coupled-Wave Analysis","aliases":["RCWA method","coupled-wave method","diffraction grating analysis"],"domain":"optics","family":"process-pipeline","subfamily":"Computational","year":"1981","originator":"M. G. Moharam and T. K. Gaylord","url":"https://scholargate.app/en/optics/rcwa","markdownUrl":"https://scholargate.app/en/optics/rcwa.md","definition":"Rigorous Coupled-Wave Analysis is a semi-analytical computational method for solving Maxwell's equations in periodic structures such as diffraction gratings and photonic crystals. Developed by Moharam and Gaylord in 1981, RCWA expands the electromagnetic fields in each periodic region into Fourier series and couples the fields at interfaces, enabling accurate and efficient simulation of light diffraction, resonances, and wave propagation in structured media.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. G. Moharam and T. K. Gaylord","subfamily":"Computational","year":"1981","type":"Diffraction algorithm"},"citations":[{"ref":"Moharam, M. G., & Gaylord, T. K. (1981). Rigorous coupled-wave analysis of planar-grating diffraction. Journal of the Optical Society of America, 71(7), 811-818.","type":"article","doi":"10.1364/JOSA.71.000811","isbn":null,"url":null},{"ref":"Gaylord, T. K., & Moharam, M. G. (1985). Analysis and applications of optical diffraction by gratings. Proceedings of the IEEE, 73(5), 894-937.","type":"article","doi":"10.1109/PROC.1985.13220","isbn":null,"url":null},{"ref":"Li, L. (1997). Use of Fourier series in the analysis of discontinuous periodic structures. Journal of the Optical Society of America, 14(11), 2758-2767.","type":"article","doi":"10.1364/josaa.13.001870","isbn":null,"url":null}],"related":["finite-difference-time-domain","fourier-optics","beam-propagation-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rdd","name":"Regression Discontinuity Design","fullName":"Regression Discontinuity Design (RDD)","aliases":["RDD","regression discontinuity","sharp regression discontinuity","Regresyon Süreksizliği Tasarımı (RDD)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2008,"originator":"Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik","url":"https://scholargate.app/en/econometrics/rdd","markdownUrl":"https://scholargate.app/en/econometrics/rdd.md","definition":"Regression Discontinuity Design is a quasi-experimental method that estimates a local causal effect around a threshold (cutoff) value, comparing units just below and just above the cutoff as if they were almost randomly assigned. It is the design developed for applied practice by Imbens and Lemieux (2008) and by Lee and Lemieux (2010).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Imbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & Titiunik","year":2008,"type":"Quasi-experimental causal design","estimator":"Local treatment effect at the cutoff (local polynomial regression)","outcome":"continuous or binary","minSample":100},"citations":[{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null},{"ref":"Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.","type":"book","doi":"10.1017/9781108684606","isbn":null,"url":null},{"ref":"Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.","type":"article","doi":"10.1257/jel.48.2.281","isbn":null,"url":null}],"related":["ols-regression","panel-fixed-effects","instrumental-variables","difference-in-differences","propensity-score-matching"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rde-koutecky-levich","name":"RDE Koutecky-Levich","fullName":"Rotating Disk Electrode and Koutecky-Levich Analysis","aliases":["RDE","rotating disk electrode","Koutecky-Levich"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Electrochemistry","year":"1962","originator":"Veniamin Levich","url":"https://scholargate.app/en/spectroscopy/rde-koutecky-levich","markdownUrl":"https://scholargate.app/en/spectroscopy/rde-koutecky-levich.md","definition":"Rotating Disk Electrode (RDE) electrochemistry combined with Koutecky-Levich analysis is a powerful electrochemical technique that decouples diffusion-limited and kinetically limited electron-transfer processes. Developed by Levich in the 1960s, RDE enables determination of heterogeneous electron-transfer rate constants and mechanistic information by rotating an electrode to control mass transport.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Veniamin Levich","subfamily":"Electrochemistry","year":"1962","type":"Electrochemical technique"},"citations":[{"ref":"Levich, V. G. (1962). Physicochemical Hydrodynamics. Prentice Hall.","type":"article","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/physicochemical-hydrodynamics/oclc/2272435"},{"ref":"Bard, A. J., & Faulkner, L. R. (2001). Electrochemical Methods: Fundamentals and Applications. John Wiley & Sons, 2nd edition.","type":"book","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/book/10.1002/9780471623977"}],"related":["cyclic-voltammetry","chronoamperometry","surface-plasmon-resonance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"re-aim-framework","name":"RE-AIM Framework","fullName":"RE-AIM: Reach, Effectiveness, Adoption, Implementation, Maintenance—A Five-Dimension Evaluation Framework for Implementation Science","aliases":["RE-AIM","REAIM","Glasgow framework"],"domain":"implementation-science","family":"process-pipeline","subfamily":"implementation science evaluation","year":"1999","originator":"Glasgow, R. E., Vogt, T. M., and colleagues","url":"https://scholargate.app/en/implementation-science/re-aim-framework","markdownUrl":"https://scholargate.app/en/implementation-science/re-aim-framework.md","definition":"The RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) is a five-dimension evaluation tool designed to assess the public health impact of evidence-based interventions in real-world settings. Developed by Glasgow et al. (1999) to address the gap between efficacy trials (controlled conditions) and effectiveness in practice, RE-AIM provides a comprehensive set of metrics to determine whether an intervention is 'worth it' from both scientific and practical perspectives. It has become the standard framework for evaluating implementation success across health domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glasgow, R. E., Vogt, T. M., and colleagues","subfamily":"implementation science evaluation","year":"1999","type":"Framework"},"citations":[{"ref":"Glasgow, R. E., Vogt, T. M., & Boles, S. M. (1999). Evaluating the public health impact of health promotion interventions: The RE-AIM framework. American Journal of Public Health, 89(9), 1322-1327.","type":"article","doi":"10.2105/AJPH.89.9.1322","isbn":null,"url":null},{"ref":"Glasgow, R. E., Lichtenstein, E., & Marcus, A. C. (2003). Why don't we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. American Journal of Public Health, 93(8), 1261-1267.","type":"article","doi":"10.2105/AJPH.93.8.1261","isbn":null,"url":null},{"ref":"Dzewaltowski, D. A., Glasgow, R. E., Klesges, L. M., Estabrooks, P. A., & Felton, G. (2016). RE-AIM: Evidence-based standards and a web resource to improve translation of research into practice. Annals of Behavioral Medicine, 51(3), 395-402.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=RE-AIM%3A+Evidence-based+standards+and+a+web+resource+to+improve+translation+of+research+into+practice+Dzewaltowski"}],"related":["cfir-framework","knowledge-translation","implementation-outcome-taxonomy","fidelity-assessment","normalization-process-theory"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reactive-distillation","name":"Reactive Distillation","fullName":"Reactive Distillation (Reaction-Separation Integration)","aliases":["integrated distillation-reaction","reactive column","reaction with separation"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Process Intensification","year":"1995","originator":"Klaus Sundmacher","url":"https://scholargate.app/en/applied-physics/reactive-distillation","markdownUrl":"https://scholargate.app/en/applied-physics/reactive-distillation.md","definition":"Reactive distillation couples reaction and separation in a single column, where reactants are separated from products continuously while simultaneously undergoing reaction on catalytic trays. Pioneered in the 1990s by Klaus Sundmacher and others, this process intensification technique dramatically reduces capital cost, energy consumption, and environmental impact for suitable reactions. It is now industrially proven for esterification, hydration, and transesterification processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klaus Sundmacher","subfamily":"Process Intensification","year":"1995","type":"Integrated reaction-separation process model"},"citations":[{"ref":"Sundmacher, K., & Kienle, A. (2003). Reactive Distillation: Status and Future Directions. Wiley-VCH.","type":"book","doi":null,"isbn":"978-3-527-30623-9","url":null},{"ref":"Siringi, S., & Malone, M. F. (1997). Design of reaction/distillation columns for esterification. Computers & Chemical Engineering, 21(12), 1223-1238.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Design+of+reaction%2Fdistillation+columns+for+esterification+Siringi"},{"ref":"James, M. J., Baur, R., & Krishna, R. (2000). Models for reactive distillation. AIChE Journal, 46(12), 2350-2365.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Models+for+reactive+distillation+James"}],"related":["cstr-model","pfr-model","pinch-analysis","unifac"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reactive-power-compensation","name":"Reactive Power Compensation","fullName":"Reactive Power Compensation and Power Factor Correction","aliases":["power factor correction","VAR compensation","reactive power management"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Power system voltage and reactive power control","year":"1920s","originator":"Electrical utilities and equipment manufacturers","url":"https://scholargate.app/en/electrical-engineering/reactive-power-compensation","markdownUrl":"https://scholargate.app/en/electrical-engineering/reactive-power-compensation.md","definition":"Reactive power compensation adjusts the flow of reactive power (VARs) in electrical networks to support voltage profiles, reduce losses, and improve power factor. Methods include fixed capacitor banks, switched capacitors, synchronous condensers, and FACTS devices. Proper compensation is essential for maintaining voltage stability and minimizing energy losses in modern power systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Electrical utilities and equipment manufacturers","subfamily":"Power system voltage and reactive power control","year":"1920s","type":"Computational pipeline"},"citations":[{"ref":"Hingorani, N. G., & Gyugyi, L. (2000). Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. IEEE Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Understanding+FACTS%3A+Concepts+and+Technology+of+Flexible+AC+Transmission+Systems+Hingorani"},{"ref":"IEEE Std 18-2012: IEEE Standard for Shunt Power Capacitors.","type":"standard","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/6144098"},{"ref":"Dugan, R. C., McGranaghan, M. F., Santoso, S., & Beaty, H. W. (2012). Electrical Power Systems Quality (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://www.mheducation.com"}],"related":["power-flow-analysis","harmonic-distortion-analysis","power-quality-assessment","smart-grid-state-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reactive-strength-index","name":"Reactive Strength Index","fullName":"Reactive Strength Index and Elastic Rebound Capacity","aliases":["RSI","stretch-shortening cycle","elastic response"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Plyometrics","year":"1987","originator":"Marteen Bobbert","url":"https://scholargate.app/en/sports-science/reactive-strength-index","markdownUrl":"https://scholargate.app/en/sports-science/reactive-strength-index.md","definition":"The reactive strength index (RSI) is a measure of lower-body reactive strength and elastic energy utilization, calculated as jump height divided by the contact time between landing from a drop and takeoff. Introduced by Bobbert and colleagues (1987), RSI quantifies the efficiency of the stretch-shortening cycle (SSC)—the ability to rapidly switch from eccentric (lengthening) to concentric (shortening) muscle contractions. High RSI indicates rapid, forceful engagement of muscles' elastic properties (tendons, contractile proteins) and is relevant in sports requiring rapid rebound (sprinting, jumping, rebounding). RSI is trainable and sensitive to plyometric training.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marteen Bobbert","subfamily":"Plyometrics","year":"1987","type":"elastic response test"},"citations":[{"ref":"Bobbert, M. F., Huijing, P. A., & van Ingen Schenau, G. J. (1987). Drop jumping. II. The influence of dropping height on the biomechanics of takeoff after landing. Medicine & Science in Sports & Exercise, 19(4), 339-346.","type":"article","doi":"10.1249/00005768-198708000-00004","isbn":null,"url":null},{"ref":"Flanagan, E. P., & Comyns, T. M. (2008). The stretch-shortening cycle training in sport. Strength & Conditioning Journal, 30(6), 32-39.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+stretch-shortening+cycle+training+in+sport+Flanagan"},{"ref":"Taube, W., Leukel, C., & Gollhofer, A. (2016). How neurons make us jump: the role of the motor cortex in stretch-shortening cycle movements. Exercise and Sport Sciences Reviews, 44(1), 4-11.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+neurons+make+us+jump%3A+the+role+of+the+motor+cortex+in+stretch-shortening+cycle+movements+Taube"}],"related":["counter-movement-jump","force-velocity-profile","rate-of-force-development"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reactor-kinetics","name":"Reactor Kinetics","fullName":"Reactor Kinetics and Power Transient Analysis","aliases":["neutron kinetics","power transient modeling","reactor control analysis"],"domain":"nuclear-physics","family":"process-pipeline","subfamily":"Reactor physics and control","year":"1942","originator":"Enrico Fermi, George Westinghouse","url":"https://scholargate.app/en/nuclear-physics/reactor-kinetics","markdownUrl":"https://scholargate.app/en/nuclear-physics/reactor-kinetics.md","definition":"Reactor kinetics is the study of neutron population dynamics in a reactor core, originating from Fermi's first controlled chain reaction in 1942. It models power changes in response to control rod movements, temperature feedback, and accidental transients using coupled differential equations accounting for prompt and delayed neutrons, to ensure safe operation, predict transient behavior, and design control systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enrico Fermi, George Westinghouse","subfamily":"Reactor physics and control","year":"1942","type":"dynamic systems analysis"},"citations":[{"ref":"Lamarsh, J. R. (1983). Introduction to Nuclear Engineering (2nd ed.). Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/introduction-to-nuclear-engineering/oclc/9082103"},{"ref":"Keepin, G. R., Wimett, T. F., & Zeigler, R. L. (1965). Delayed Neutrons from Fissionable Isotopes of Uranium, Neptunium, and Plutonium. Physical Review, 107(4), 1044–1049.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Delayed+Neutrons+from+Fissionable+Isotopes+of+Uranium%2C+Neptunium%2C+and+Plutonium+Keepin"}],"related":["neutron-transport-calculation","criticality-safety-analysis","radiation-dose-assessment","nuclear-decay-analysis","monte-carlo-neutron-particle"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"readability-analysis","name":"Readability Analysis","fullName":"Readability Analysis (Readability Formula Scoring)","aliases":["readability scoring","readability formulas","Flesch-Kincaid analysis","Okunabilirlik Analizi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1975,"originator":"J. Peter Kincaid et al.","url":"https://scholargate.app/en/text-mining/readability-analysis","markdownUrl":"https://scholargate.app/en/text-mining/readability-analysis.md","definition":"Readability analysis measures how well a text suits its intended audience by applying established readability formulas such as Flesch-Kincaid and Gunning Fog. The modern formula family was derived by Kincaid and colleagues in 1975, and it turns prose into a single score or target reading-grade level that signals how easy the text is to read.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Text-mining readability scoring task","originator":"J. Peter Kincaid et al.","year":1975,"formulas":"Flesch-Kincaid, Gunning Fog, and related indices","output":"Readability score / target reading-grade level","minSample":"About 10 documents or text units"},"citations":[{"ref":"Kincaid, J.P., Fishburne, R.P., Rogers, R.L. & Chissom, B.S. (1975). Derivation of New Readability Formulas for Navy Enlisted Personnel. Naval Technical Training Command.","type":"report","doi":null,"isbn":null,"url":"https://stars.library.ucf.edu/istlibrary/56/"},{"ref":"DuBay, W.H. (2004). The Principles of Readability. Impact Information.","type":"report","doi":null,"isbn":null,"url":"https://files.eric.ed.gov/fulltext/ED490073.pdf"}],"related":["sentiment-analysis","text-classification","tf-idf"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"readiness-to-change-questionnaire","name":"RCQ","fullName":"Readiness to Change Questionnaire","aliases":["RCQ","Readiness to Change Questionnaire"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"readiness-and-motivation","year":"1992","originator":"Rollnick, Heather, Gold, Hall","url":"https://scholargate.app/en/addiction-medicine/readiness-to-change-questionnaire","markdownUrl":"https://scholargate.app/en/addiction-medicine/readiness-to-change-questionnaire.md","definition":"The RCQ is a 12-item self-report instrument designed to assess an individual's stage of change motivation regarding substance use, particularly alcohol use. Developed by Rollnick and colleagues in 1992, it operationalizes the Transtheoretical Model of Change by measuring readiness across the precontemplation, contemplation, and action stages. The RCQ is a brief, cost-effective tool for identifying individuals who are ready to engage in behavior change and for tailoring the intensity and timing of intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rollnick, Heather, Gold, Hall","subfamily":"readiness-and-motivation","year":"1992","type":"Self-report"},"citations":[{"ref":"Rollnick, S., Heather, N., Gold, R., & Hall, W. (1992). Development of a short 'Readiness to Change' questionnaire for use in brief, opportunistic interventions among excessive drinkers. British Journal of Addiction, 87(5), 743–754.","type":"article","doi":"10.1111/j.1360-0443.1992.tb02720.x","isbn":null,"url":null}],"related":["dudit","sadq","cudit","brief-addiction-monitor"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reading-motivation-scale","name":"Motivation for Reading Questionnaire","fullName":"Motivation for Reading Questionnaire (MRQ)","aliases":["MRQ","Reading Motivation Scale"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Literacy motivation and engagement","year":"2000","originator":"Allan Wigfield, John Guthrie","url":"https://scholargate.app/en/educational-psychology/reading-motivation-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/reading-motivation-scale.md","definition":"The Motivation for Reading Questionnaire (MRQ) is a self-report instrument assessing students' motivation to read and engagement with reading activities. Developed by Wigfield and Guthrie (2000), it measures both intrinsic motivation (reading for enjoyment and understanding) and extrinsic motivation (reading for grades or to avoid punishment), along with beliefs about reading efficacy and value. Reading motivation is a primary predictor of reading comprehension, frequency, and lifetime literacy engagement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Allan Wigfield, John Guthrie","subfamily":"Literacy motivation and engagement","year":"2000","type":"Reading motivation assessment"},"citations":[{"ref":"Wigfield, A., & Guthrie, J. T. (2000). Engagement and motivation in reading. In M. L. Kamil, P. B. Mosenthal, P. D. Pearson, & R. Barr (Eds.), Handbook of Reading Research (Vol. 3, pp. 403-422). Lawrence Erlbaum Associates.","type":"article","doi":null,"isbn":null,"url":"https://www.routledge.com/Handbook-of-Reading-Research/Kamil-Mosenthal-Pearson-Barr/p/book/9780805827699"},{"ref":"Guthrie, J. T., Wiggins, A. P., & Perencevich, K. C. (2000). Scaffolding for engagement in elementary school reading instruction. Journal of Educational Research, 93(5), 310-319.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Scaffolding+for+engagement+in+elementary+school+reading+instruction+Guthrie"}],"related":["academic-motivation-scale","study-process-questionnaire","student-engagement-scale","course-experience-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"real-business-cycle-model","name":"Real Business Cycle Model","fullName":"Real Business Cycle Model (RBC)","aliases":["RBC Model","Kydland-Prescott Model"],"domain":"economics","family":"regression-model","subfamily":"Business Cycle Theory","year":"1982","originator":"Finn Kydland, Edward Prescott","url":"https://scholargate.app/en/economics/real-business-cycle-model","markdownUrl":"https://scholargate.app/en/economics/real-business-cycle-model.md","definition":"The Real Business Cycle (RBC) model, developed by Finn Kydland and Edward Prescott in 1982, is a dynamic stochastic general equilibrium framework that explains macroeconomic fluctuations as rational responses to exogenous technological shocks. Unlike Keynesian models that emphasize demand-side factors and nominal rigidities, the RBC model shows how productivity variations alone can generate business cycles that mimic observed employment, output, and investment dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Finn Kydland, Edward Prescott","subfamily":"Business Cycle Theory","year":"1982","type":"Dynamic stochastic general equilibrium model"},"citations":[{"ref":"Kydland, F. E., & Prescott, E. C. (1982). Time to Build and Aggregate Fluctuations. Econometrica, 50(6), 1345–1370.","type":"article","doi":"10.2307/1913386","isbn":null,"url":null},{"ref":"Prescott, E. C. (1986). Theory Ahead of Business Cycle Measurement. Carnegie-Rochester Conference Series on Public Policy, 25, 11–44.","type":"article","doi":"10.1016/0167-2231(86)90035-7","isbn":null,"url":null},{"ref":"Long, J. B., & Plosser, C. I. (1983). Real Business Cycles. Journal of Political Economy, 91(1), 39–69.","type":"article","doi":"10.1086/261128","isbn":null,"url":null}],"related":["ramsey-cass-koopmans-model","overlapping-generations-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"real-world-evidence","name":"Real-World Evidence Studies","fullName":"Real-World Evidence (RWE) and Real-World Data (RWD) Collection","aliases":["real-world evidence","RWE","RWD","effectiveness research","observational evidence"],"domain":"clinical-research","family":"process-pipeline","subfamily":"observational research","year":"2010s-present","originator":"FDA, EMA, and health agencies; Sherman et al. (2016) defined RWE formally","url":"https://scholargate.app/en/clinical-research/real-world-evidence","markdownUrl":"https://scholargate.app/en/clinical-research/real-world-evidence.md","definition":"Real-World Evidence (RWE) is clinical evidence derived from Real-World Data (RWD)—data routinely collected in clinical practice from electronic health records, insurance claims, patient registries, and other healthcare sources. Formalized by the FDA in 2016 (Sherman et al.), RWE addresses a critical gap: while randomized trials test drugs under ideal conditions, RWE evaluates how treatments actually work in diverse, real patients with comorbidities, competing medications, and varied adherence. RWE complements (not replaces) trial evidence, accelerating regulatory decision-making and supporting post-market surveillance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"FDA, EMA, and health agencies; Sherman et al. (2016) defined RWE formally","subfamily":"observational research","year":"2010s-present","type":"Research Design"},"citations":[{"ref":"Sherman, R. E., Anderson, S. A., Dal Pan, G. J., Gray, G. W., Gross, T., Hunter, N. L., ... & Califf, R. M. (2016). Real-world evidence—what is it and what can it tell us? New England Journal of Medicine, 375(23), 2293–2297.","type":"article","doi":"10.1056/NEJMsb1609216","isbn":null,"url":null},{"ref":"Levitan, B., Chan, E. W., Doshi, J. A., Hines, P., Komattireddy, H., Sanchez, R., & Sheridan, S. (2018). Collaboration and competition between real-world data and clinical trials. Therapeutic Innovation & Regulatory Science, 52(2), 172–176.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Collaboration+and+competition+between+real-world+data+and+clinical+trials+Levitan"},{"ref":"FDA (2021). Strengthening Our National Strategy on Adaptive Learning Systems for Health Care Quality and Safety: Report to Congress. US Food and Drug Administration.","type":"article","doi":null,"isbn":null,"url":"https://www.fda.gov/media/151846/download"}],"related":["registry-based-research","pragmatic-clinical-trial","cohort-study-design","electronic-health-records","claims-data-research"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"realist-synthesis","name":"Realist Synthesis","fullName":"Realist Synthesis (Context-Mechanism-Outcome Framework)","aliases":["Realist Review","CMO Configuration","Mechanism-Based Synthesis"],"domain":"evidence-synthesis","family":"process-pipeline","subfamily":"Theory-Driven Synthesis","year":"2005","originator":"Ray Pawson (2005)","url":"https://scholargate.app/en/evidence-synthesis/realist-synthesis","markdownUrl":"https://scholargate.app/en/evidence-synthesis/realist-synthesis.md","definition":"Realist synthesis is a theory-driven, interpretive method for evidence synthesis developed by Ray Pawson (2005) that focuses on understanding HOW and WHY interventions work, rather than WHETHER they work. Grounded in realist philosophy, realist synthesis examines Context-Mechanism-Outcome (CMO) configurations: how specific contextual conditions activate mechanisms that produce outcomes. Unlike traditional systematic reviews, which typically answer 'Does intervention X reduce outcome Y?', realist synthesis asks 'Under what conditions, through what mechanisms, for which populations does X work?' This approach is particularly valuable for complex interventions (policies, programs, multi-component treatments) where effectiveness varies dramatically across contexts, and for understanding why interventions succeed in some settings but fail in others.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ray Pawson (2005)","subfamily":"Theory-Driven Synthesis","year":"2005","type":"Framework"},"citations":[{"ref":"Pawson, R., Greenhalgh, T., Harvey, G., & Walshe, K. (2005). Realist review—a new method of systematic review designed for complex policy and programme evaluation. Journal of Health Services Research & Policy, 10(S1), 21–35.","type":"article","doi":"10.1258/1355819054308530","isbn":null,"url":null},{"ref":"Pawson, R. (2013). The Science of Evaluation: A Realist Manifesto. SAGE Publications.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Pawson%2C%20R.%20(2013).%20The%20Science%20of%20Evaluation%3A%20A%20Realist%20Manifesto.%20SAGE%20Publications."},{"ref":"Wong, G., Westhorp, G., Pawson, R., & Greenhalgh, T. (2013). Realist synthesis: Introduction and some practical guidance. Cochrane Database of Systematic Reviews, 12, CD012032.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Realist+synthesis%3A+Introduction+and+some+practical+guidance+Wong"}],"related":["systematic-review","qualitative-meta-synthesis","logic-model-framework","evidence-synthesis-framework","implementation-science"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"realized-volatility","name":"Realized Volatility","fullName":"Realized Volatility and the Heterogeneous Autoregressive (HAR) Model","aliases":["realized variance","HAR model","heterogeneous autoregressive model of realized volatility","HAR-RV","Gerçekleşmiş Volatilite ve HAR Modeli"],"domain":"finance","family":"regression-model","subfamily":null,"year":2009,"originator":"Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)","url":"https://scholargate.app/en/finance/realized-volatility","markdownUrl":"https://scholargate.app/en/finance/realized-volatility.md","definition":"Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility)","year":2009,"type":"Time-series regression of realized variance","estimator":"OLS on daily, weekly, and monthly realized-volatility components","data":"High-frequency (intraday) returns","outcome":"continuous (realized variance / volatility)"},"citations":[{"ref":"Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196.","type":"article","doi":"10.1093/jjfinec/nbp001","isbn":null,"url":null},{"ref":"Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and Forecasting Realized Volatility. Econometrica, 71(2), 579-625.","type":"article","doi":"10.1111/1468-0262.00418","isbn":null,"url":null}],"related":["egarch","arima","stochastic-volatility-model","long-memory-models","johansen-cointegration"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"recall","name":"Recall (Sensitivity)","fullName":"Recall or Sensitivity (True Positive Rate)","aliases":["Sensitivity","True Positive Rate","TPR"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"20th century","originator":"Historical statistical foundations","url":"https://scholargate.app/en/model-evaluation/recall","markdownUrl":"https://scholargate.app/en/model-evaluation/recall.md","definition":"Recall measures the proportion of actual positive cases that were correctly identified by the classifier. It answers the question: 'Of all the cases that were truly positive, how many did we find?' Recall is critical in scenarios where missing positive cases is costly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Historical statistical foundations","subfamily":"Classification Metric","year":"20th century","type":"Evaluation metric"},"citations":[{"ref":"Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.","type":"article","doi":"10.1016/j.patrec.2005.10.010","isbn":null,"url":null},{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"}],"related":["precision","f1-score","specificity","balanced-accuracy","matthews-correlation-coefficient"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"receiver-function-analysis","name":"Receiver Function Analysis","fullName":"Receiver Function Analysis","aliases":["RF"],"domain":"geophysics","family":"process-pipeline","subfamily":"Seismic imaging and crustal structure","year":"1979","originator":"Charles Langston","url":"https://scholargate.app/en/geophysics/receiver-function-analysis","markdownUrl":"https://scholargate.app/en/geophysics/receiver-function-analysis.md","definition":"Receiver Function (RF) analysis is a seismic method that isolates P-to-S wave conversions at crustal and mantle discontinuities using teleseismic records from distant earthquakes. Introduced by Langston in 1979, RF analysis provides a cost-effective way to determine crustal thickness, Poisson's ratio, and upper mantle structure without requiring active seismic sources, making it a workhorse technique in crustal and lithospheric studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Langston","subfamily":"Seismic imaging and crustal structure","year":"1979","type":"Teleseismic body wave analysis for subsurface imaging"},"citations":[{"ref":"Langston, C. A. (1979). Structure under Mount Rainier, Washington, inferred from teleseismic body waves. Journal of Geophysical Research, 84(B9), 4749-4762.","type":"article","doi":"10.1029/JB084iB09p04749","isbn":null,"url":null},{"ref":"Ammon, C. J., Randall, G. E., & Zandt, G. (1990). On the nonlinear absolute amplitude calibration of a broadband seismometer: Theory and application to SRO and ASRO data. Seismological Research Letters, 61(2), 72-86.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+nonlinear+absolute+amplitude+calibration+of+a+broadband+seismometer%3A+Theory+and+application+to+SRO+and+ASRO+data+Ammon"}],"related":["seismic-full-waveform-inversion","ambient-noise-tomography","paleomagnetic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reception-analysis","name":"Reception Analysis","fullName":"Audience Reception and Interpretation Analysis","aliases":["reception studies","audience analysis","reception theory"],"domain":"media-studies","family":"process-pipeline","subfamily":"Audience and interpretation studies","year":"1972","originator":"Hans Robert Jauss, Stuart Hall","url":"https://scholargate.app/en/media-studies/reception-analysis","markdownUrl":"https://scholargate.app/en/media-studies/reception-analysis.md","definition":"Reception Analysis is a methodological approach to studying media that focuses on how audiences actively interpret, engage with, and create meanings from media content rather than passively consuming predetermined messages. Developed from literary reception aesthetics and adapted to media studies by scholars like Stuart Hall, Ien Ang, and David Morley, the method examines the gap between what media texts 'offer' and what audiences actually make of them. Recognition that the same media content can be understood very differently by different viewers or readers revolutionized media studies, shifting focus from textual analysis alone to investigating the social, cultural, and personal contexts shaping interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hans Robert Jauss, Stuart Hall","subfamily":"Audience and interpretation studies","year":"1972","type":"Method for investigating how audiences actively interpret media content and create meanings"},"citations":[{"ref":"Jauss, H. R. (1982). Toward an Aesthetic of Reception (T. Bahti, Trans.). University of Minnesota Press.","type":"book","doi":null,"isbn":null,"url":"https://www.upress.umn.edu"},{"ref":"Hall, S. (1973). Encoding and decoding in television discourse. In S. Hall et al. (Eds.), Culture, Media, Language (pp. 507-517). Hutchinson.","type":"article","doi":null,"isbn":null,"url":"https://www.hutchinson.co.uk"},{"ref":"Ang, I. (1985). Watching Dallas: Soap Opera and the Melodramatic Imagination. Methuen.","type":"book","doi":null,"isbn":null,"url":"https://www.methuen.co.uk"},{"ref":"Morley, D. (1986). Family Television: Cultural Power and Domestic Leisure. Comedia.","type":"book","doi":null,"isbn":null,"url":"https://www.comedia.org.uk"}],"related":["media-framing-analysis","discourse-analysis-media","film-narrative-analysis","visual-content-analysis","agenda-setting-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"recovery-assessment-scale","name":"Recovery Assessment Scale","fullName":"Recovery Assessment Scale (RAS)","aliases":["RAS"],"domain":"psychiatric-rehabilitation","family":"process-pipeline","subfamily":"recovery-measurement","year":"2004","originator":"Corrigan, P. W., Salzer, M. S., Ralph, R. O., et al.","url":"https://scholargate.app/en/psychiatric-rehabilitation/recovery-assessment-scale","markdownUrl":"https://scholargate.app/en/psychiatric-rehabilitation/recovery-assessment-scale.md","definition":"The Recovery Assessment Scale (RAS) is a 41-item self-report measure designed to assess personal recovery in individuals with serious mental illness. Developed by Corrigan and colleagues in 2004, it captures the subjective and multidimensional nature of recovery, including hope, autonomy, goal achievement, and symptom management. The RAS is widely used in psychiatric rehabilitation research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Corrigan, P. W., Salzer, M. S., Ralph, R. O., et al.","subfamily":"recovery-measurement","year":"2004","type":"Self-report questionnaire"},"citations":[{"ref":"Corrigan, P. W., Salzer, M. S., Ralph, R. O., Sangster, Y., & Keck, L. (2004). Examining the factor structure of the Recovery Assessment Scale. Psychiatric Services, 55(7), 779-784.","type":"article","doi":"10.1093/oxfordjournals.schbul.a007118","isbn":null,"url":null}],"related":["mental-health-recovery-measure","internalized-stigma-mental-illness","empowerment-scale-rogers","recovery-oriented-practices-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"recovery-experience-questionnaire","name":"Recovery Experience Questionnaire","fullName":"Recovery Experience Questionnaire (REQ)","aliases":["REQ"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Recovery and restoration","year":2007,"originator":"Sabine Sonnentag, Carsten Fritz","url":"https://scholargate.app/en/occupational-health/recovery-experience-questionnaire","markdownUrl":"https://scholargate.app/en/occupational-health/recovery-experience-questionnaire.md","definition":"The Recovery Experience Questionnaire (REQ) is an assessment tool measuring the quality and dimensions of off-work recovery from occupational stress. Developed by Sonnentag and Fritz in 2007, the REQ evaluates four key recovery experiences: psychological detachment from work, relaxation, mastery, and control. The instrument is grounded in conservation of resources theory and provides insights into how employees restore wellbeing during non-work time, which is crucial for preventing burnout and maintaining work engagement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sabine Sonnentag, Carsten Fritz","subfamily":"Recovery and restoration","year":2007,"type":"Self-report questionnaire"},"citations":[{"ref":"Sonnentag, S., & Fritz, C. (2007). The Recovery Experience Questionnaire: Development and validation of a measure for assessing recuperation and unwinding from work. Journal of Occupational Health Psychology, 12(3), 204-221.","type":"article","doi":"10.1037/1076-8998.12.3.204","isbn":null,"url":null}],"related":["copenhagen-burnout-inventory","oldenburg-burnout-inventory","effort-reward-imbalance-scale","areas-of-worklife-scale","presenteeism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"recovery-oriented-practices-index","name":"Recovery-Oriented Practices Index","fullName":"Recovery-Oriented Practices Index (ROPI)","aliases":["ROPI"],"domain":"psychiatric-rehabilitation","family":"process-pipeline","subfamily":"service-assessment","year":"2009","originator":"Barbic, S. P., Krupa, T., & Armstrong, I.","url":"https://scholargate.app/en/psychiatric-rehabilitation/recovery-oriented-practices-index","markdownUrl":"https://scholargate.app/en/psychiatric-rehabilitation/recovery-oriented-practices-index.md","definition":"The Recovery-Oriented Practices Index (ROPI) is a measure assessing the degree to which mental health services and programs embody recovery-oriented principles and practices. Developed by Sanja P. Barbic, Trevor Krupa, and Inge Armstrong in 2009, the ROPI evaluates whether services prioritize consumer choice, hope, autonomy, social participation, peer support, and community integration—the hallmarks of recovery-oriented mental health care. The ROPI is used to assess and guide the transformation of mental health services from a traditional medical/deficit model toward a recovery-oriented, consumer-centered approach.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barbic, S. P., Krupa, T., & Armstrong, I.","subfamily":"service-assessment","year":"2009","type":"Service- and consumer-report questionnaire"},"citations":[{"ref":"Barbic, S. P., Krupa, T., & Armstrong, I. (2009). A framework for the development of recovery-oriented mental health services and citizenship. American Journal of Psychiatric Rehabilitation, 12(3), 186-194.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+framework+for+the+development+of+recovery-oriented+mental+health+services+and+citizenship+Barbic"}],"related":["recovery-assessment-scale","empowerment-scale-rogers","personal-recovery-questionnaire","mental-health-recovery-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"recrystallization","name":"Recrystallization","fullName":"Recrystallization for Compound Purification","aliases":["crystallization purification","recrystallisation"],"domain":"chemistry","family":"process-pipeline","subfamily":"Separation","year":"early 19th century","originator":"Organic chemistry tradition","url":"https://scholargate.app/en/chemistry/recrystallization","markdownUrl":"https://scholargate.app/en/chemistry/recrystallization.md","definition":"Recrystallization is a classical purification technique in which a solid compound is dissolved in hot solvent, then allowed to crystallize upon cooling, yielding pure crystals while impurities remain in solution. Practiced for centuries in chemistry laboratories, recrystallization remains one of the most effective and accessible methods for purifying organic solids, especially when the target compound has low solubility at low temperatures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Organic chemistry tradition","subfamily":"Separation","year":"early 19th century","type":"Purification technique"},"citations":[{"ref":"Pavia, D. L., Lampman, G. M., Kriz, G. S., & Engel, R. G. (2014). A Small-Scale Approach to Organic Laboratory Techniques (4th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1285749297","url":null},{"ref":"Still, W. C., Kahn, M., & Mitra, A. (1978). Rapid chromatographic purification based on solvent-induced density differences and easy detection. The Journal of Organic Chemistry, 43(14), 2923–2925.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Rapid+chromatographic+purification+based+on+solvent-induced+density+differences+and+easy+detection+Still"}],"related":["column-chromatography","thin-layer-chromatography","synthesis-route-planning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"recurrence-quantification-analysis","name":"Recurrence Quantification Analysis","fullName":"Recurrence Quantification Analysis (RQA)","aliases":["RQA","Recurrence Plot Analysis","Nonlinear Recurrence Analysis","Tekrarlama Kantifikasyon Analizi"],"domain":"complex-systems","family":"ml-model","subfamily":"Nonlinear dynamics","year":2007,"originator":"Marwan, Romano, Thiel & Kurths","url":"https://scholargate.app/en/complex-systems/recurrence-quantification-analysis","markdownUrl":"https://scholargate.app/en/complex-systems/recurrence-quantification-analysis.md","definition":"Recurrence Quantification Analysis (RQA) is a nonlinear method for characterizing the dynamics of a time series by quantifying the small-scale structure of its recurrence plot. Introduced in its modern, comprehensive form by Marwan, Romano, Thiel, and Kurths in 2007, RQA extracts scalar measures — such as recurrence rate, determinism, laminarity, and Shannon entropy — that capture periodicity, chaos, stationarity, and transitions in complex dynamical systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marwan, Romano, Thiel & Kurths","year":2007,"type":"Nonlinear time-series characterization","subfamily":"Nonlinear dynamics","data_requirement":"Univariate or multivariate time series","output":"Scalar recurrence statistics (RR, DET, LAM, ENTR, etc.)"},"citations":[{"ref":"Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5–6), 237–329.","type":"article","doi":"10.1016/j.physrep.2006.11.001","isbn":null,"url":null}],"related":["fractal-analysis","sample-entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"recurrent-event-model","name":"Recurrent Event Model","fullName":"Recurrent Event Survival Model","aliases":["Tekrarlayan Olay Modeli (Recurrent Events)","Andersen-Gill model","AG model","Wei-Lin-Weissfeld model","WLW model","Prentice-Williams-Peterson model","PWP model","repeated events model"],"domain":"survival","family":"survival","subfamily":null,"year":1981,"originator":"Andersen & Gill (AG, 1982); Prentice, Williams & Peterson (PWP, 1981); Wei, Lin & Weissfeld (WLW, 1989)","url":"https://scholargate.app/en/survival/recurrent-event-model","markdownUrl":"https://scholargate.app/en/survival/recurrent-event-model.md","definition":"A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data when the same event — such as a hospital readmission, disease relapse, or equipment failure — can occur multiple times in the same individual. The three principal frameworks are the Andersen-Gill (AG) model, the Prentice-Williams-Peterson (PWP) stratified model, and the Wei-Lin-Weissfeld (WLW) marginal model, each making different assumptions about within-subject dependence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andersen & Gill (AG, 1982); Prentice, Williams & Peterson (PWP, 1981); Wei, Lin & Weissfeld (WLW, 1989)","year":1981,"type":"Semi-parametric hazard model for repeated events","handles":"Multiple events per subject, right-censoring, within-subject correlation","minSample":50,"difficulty":3,"dataStructure":"Longitudinal (counting-process format: start, stop, event)","approaches":"Andersen-Gill (AG), Prentice-Williams-Peterson (PWP), Wei-Lin-Weissfeld (WLW)"},"citations":[{"ref":"Cook, R.J. & Lawless, J.F. (2007). The Statistical Analysis of Recurrent Events. Springer.","type":"book","doi":"10.1007/978-0-387-69810-6","isbn":null,"url":null},{"ref":"Amorim, L.D.A.F. & Cai, J. (2015). Modelling Recurrent Events: A Tutorial for Analysis in Epidemiology. International Journal of Epidemiology, 44(1), 324–333.","type":"article","doi":"10.1093/ije/dyu222","isbn":null,"url":null}],"related":["cox-ph","kaplan-meier","frailty-model","fine-gray-model","negative-binomial-regression","poisson-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"recurrent-neural-network","name":"Recurrent Neural Network","fullName":"Recurrent Neural Network (RNN)","aliases":["RNN","Elman network","Jordan network","simple recurrent network"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"1986–1990","originator":"Rumelhart, D. E.; Elman, J. L.","url":"https://scholargate.app/en/deep-learning/recurrent-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/recurrent-neural-network.md","definition":"A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rumelhart, D. E.; Elman, J. L.","year":"1986–1990","type":"Sequential neural network","dataType":"Sequential / time-series / text data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.","type":"article","doi":"10.1207/s15516709cog1402_1","isbn":null,"url":null},{"ref":"Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.","type":"article","doi":"10.1038/323533a0","isbn":null,"url":null}],"related":["long-short-term-memory","gated-recurrent-unit","transformer","convolutional-neural-network","bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"redox-reaction-mechanism","name":"Redox Reaction Mechanism Analysis","fullName":"Redox Reaction Mechanism Analysis","aliases":["redox mechanism","electron transfer mechanism","oxidation-reduction"],"domain":"chemistry","family":"process-pipeline","subfamily":"Synthesis","year":"1956","originator":"Rudolph A. Marcus","url":"https://scholargate.app/en/chemistry/redox-reaction-mechanism","markdownUrl":"https://scholargate.app/en/chemistry/redox-reaction-mechanism.md","definition":"Redox reaction mechanism analysis is the systematic study of electron transfer pathways in oxidation-reduction reactions. Formalized by Rudolph Marcus in the 1950s (earning him the Nobel Prize in 1992), this framework explains how electrons move between reactants, what factors control reaction rates, and how electronic and geometric factors influence the ease of electron transfer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolph A. Marcus","subfamily":"Synthesis","year":"1956","type":"Mechanistic framework"},"citations":[{"ref":"Marcus, R. A. (1956). On the theory of oxidation-reduction reactions involving electron transfer. I. The Journal of Chemical Physics, 24(5), 966–978.","type":"article","doi":"10.1063/1.1742723","isbn":null,"url":null},{"ref":"Atkins, P., & de Paula, J. (2010). Physical Chemistry (9th ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0199543373","url":null}],"related":["synthesis-route-planning","substitution-reaction-kinetics","nucleophilic-substitution-sn"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"redundancy-analysis","name":"Redundancy Analysis","fullName":"Redundancy Analysis","aliases":["RDA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Multivariate Analysis","year":"1977","originator":"Albert van den Wollenberg","url":"https://scholargate.app/en/psychometrics/redundancy-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/redundancy-analysis.md","definition":"Redundancy Analysis (RDA) is a multivariate technique developed by van den Wollenberg (1977) that combines multiple regression and principal component analysis. RDA finds linear combinations of predictor variables that best predict variation in response variables, making it ideal for understanding how sets of predictors collectively explain multivariate outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert van den Wollenberg","subfamily":"Multivariate Analysis","year":"1977","type":"Asymmetric multivariate analysis"},"citations":[{"ref":"van den Wollenberg, A. L. (1977). Redundancy analysis: An alternative for canonical correlation analysis. Psychometrika, 42(2), 207-219.","type":"article","doi":"10.1007/BF02294050","isbn":null,"url":null},{"ref":"Legendre, P., & Legendre, L. (1998). Numerical Ecology (2nd ed.). Elsevier.","type":"article","doi":null,"isbn":"9780444892546","url":null},{"ref":"Knudsen, S., Andersen, T., & Hansen, J. (2007). Redundancy analysis of multivariate data using PLS. Chemometrics and Intelligent Laboratory Systems, 87(2), 264-272.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Redundancy+analysis+of+multivariate+data+using+PLS+Knudsen"}],"related":["pls-sem","exploratory-structural-equation-modeling","wordscores","wordfish","multiple-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reflective-practice-questionnaire","name":"RPQ","fullName":"Reflective Practice Questionnaire","aliases":["Reflection Questionnaire","Reflective Learning Scale"],"domain":"health-education","family":"process-pipeline","subfamily":"reflective-learning","year":"2000–2005","originator":"Sobral, D. T.; Saarikoski et al.","url":"https://scholargate.app/en/health-education/reflective-practice-questionnaire","markdownUrl":"https://scholargate.app/en/health-education/reflective-practice-questionnaire.md","definition":"The RPQ is a self-report instrument measuring the degree to which healthcare students and professionals engage in reflective practice—the deliberate examination of their clinical experiences, decisions, and actions to extract learning and improve future practice. Developed by Sobral and refined by Saarikoski and colleagues in the early 2000s, the RPQ assesses both the frequency and depth of reflection, including reflection-in-action (thinking while performing) and reflection-on-action (analyzing after the fact). The scale is used in nursing and medical education to evaluate whether students are developing critical thinking and self-directed learning habits that sustain professional growth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sobral, D. T.; Saarikoski et al.","subfamily":"reflective-learning","year":"2000–2005","type":"Self-report questionnaire"},"citations":[{"ref":"Sobral, D. T. (2000). An appraisal of medical students' reflection-in-action. Med Educ 34(3): 182–187.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+appraisal+of+medical+students%27+reflection-in-action+Sobral"},{"ref":"Saarikoski, M., Kontio, E., Kauhanen, L., & Leino-Kilpi, H. (2005). Competence in intensive and critical care nursing: A literature review. Intensive Crit Care Nurs 21(1): 21–34.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Competence+in+intensive+and+critical+care+nursing%3A+A+literature+review+Saarikoski"}],"related":["clinical-learning-environment-scale","professional-identity-scale","simulation-debriefing-quality","nursing-clinical-competence-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reflexive-thematic-analysis","name":"Reflexive Thematic Analysis","fullName":"Reflexive Thematic Analysis (Braun & Clarke)","aliases":["RTA","reflexive TA","Braun and Clarke thematic analysis","qualitative thematic analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Thematic Analysis","year":"2006 (seminal paper); explicitly named 'reflexive' from ~2019","originator":"Virginia Braun & Victoria Clarke","url":"https://scholargate.app/en/qualitative/reflexive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/reflexive-thematic-analysis.md","definition":"Reflexive Thematic Analysis (RTA) is a widely used qualitative method for identifying, analysing, and interpreting patterns of shared meaning — called themes — across a dataset. Developed by Virginia Braun and Victoria Clarke, it is theoretically flexible, works across epistemological positions, and foregrounds the researcher's active, interpretive role rather than treating themes as features that simply emerge from data. It differs from older 'codebook' approaches by treating the analyst's subjectivity as a resource rather than a source of bias to be suppressed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun & Victoria Clarke","year":"2006 (seminal paper); explicitly named 'reflexive' from ~2019","type":"Qualitative research method","dataType":"Text data — interviews, focus groups, open-ended survey responses, documents","typicalSampleSize":"No fixed rule; commonly 6–30 participants for interview data","subfamily":"Thematic Analysis"},"citations":[{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.","type":"article","doi":"10.1080/2159676X.2019.1628806","isbn":null,"url":null}],"related":["thematic-analysis","content-analysis","phenomenology","grounded-theory","narrative-analysis","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reflexivity-in-research","name":"Reflexivity in Qualitative Research","fullName":"Reflexive Examination of Researcher Positionality and Influence","aliases":["reflexive practice","researcher reflexivity","positionality","reflective practice"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"methodological-awareness","year":"1990","originator":"Anthony Giddens and Pierre Bourdieu","url":"https://scholargate.app/en/qualitative-research/reflexivity-in-research","markdownUrl":"https://scholargate.app/en/qualitative-research/reflexivity-in-research.md","definition":"Reflexivity is the practice of examining how the researcher's identity, assumptions, relationships, and values influence the research process and findings. Rather than treating objectivity as achievable detachment, reflexivity acknowledges that the researcher is embedded within the research and cannot be fully separated from it. Originating in sociology and anthropology, reflexivity has become central to qualitative research rigor across disciplines. Reflexive researchers critically examine their own influence at each stage: research design, participant recruitment, data collection, interpretation, and presentation. This transparency strengthens rigor by making visible the lens through which data are collected and interpreted.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anthony Giddens and Pierre Bourdieu","subfamily":"methodological-awareness","year":"1990","type":"Concept"},"citations":[{"ref":"Finlay, L. (2002). Outing the researcher: The provenance, process, and practice of reflexivity. Qualitative Health Research, 12(4), 531-545.","type":"article","doi":"10.1177/104973202129120052","isbn":null,"url":null},{"ref":"Johnson, R. B., & Christensen, L. (2017). Educational Research: Quantitative, Qualitative, and Mixed Approaches (6th ed.). SAGE Publications.","type":"article","doi":null,"isbn":"978-1506386683","url":null},{"ref":"Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589-597.","type":"article","doi":"10.1080/2159676X.2019.1628806","isbn":null,"url":null},{"ref":"Malterud, K. (2001). Qualitative research: standards, challenges, and guidelines. The Lancet, 358(9280), 483-488.","type":"article","doi":"10.1016/S0140-6736(01)05627-6","isbn":null,"url":null}],"related":["in-depth-interview-method","participant-observation","qualitative-rigor-criteria","member-checking"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reformer","name":"Reformer","fullName":"Reformer (The Efficient Transformer)","aliases":["Efficient Transformer","LSH Transformer","Locality-Sensitive Hashing Transformer","Verimli Dönüştürücü"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2020,"originator":"Nikita Kitaev, Łukasz Kaiser & Anselm Levskaya","url":"https://scholargate.app/en/deep-learning/reformer","markdownUrl":"https://scholargate.app/en/deep-learning/reformer.md","definition":"The Reformer is an efficient variant of the Transformer architecture introduced by Kitaev, Kaiser, and Levskaya at ICLR 2020. It addresses the prohibitive O(L²) memory and computational cost of standard self-attention for long sequences. The key innovations are locality-sensitive hashing (LSH) attention, which approximates full attention in O(L log L) time, and reversible residual layers that dramatically reduce activation memory during training.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nikita Kitaev, Łukasz Kaiser & Anselm Levskaya","year":2020,"type":"Memory-efficient attention-based sequence model","subfamily":"Time-series forecasting","attention_complexity":"O(L log L) via LSH","memory_technique":"Reversible residual layers"},"citations":[{"ref":"Kitaev, N., Kaiser, Ł., & Levskaya, A. (2020). Reformer: The efficient transformer. ICLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2001.04451"}],"related":["transformer","informer","pyraformer"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regime-switching-finance","name":"Regime-Switching Model","fullName":"Markov Regime-Switching Model for Financial Applications (Hamilton MS-AR)","aliases":["Markov switching model","Hamilton regime-switching model","MS-AR","hidden Markov regime model","Rejim Değiştirme Modeli — Finansal Uygulamalar (Hamilton MS)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1989,"originator":"James D. Hamilton","url":"https://scholargate.app/en/finance/regime-switching-finance","markdownUrl":"https://scholargate.app/en/finance/regime-switching-finance.md","definition":"The Markov regime-switching model, introduced by James D. Hamilton in 1989, is a hidden-state time-series model in which financial series such as returns or volatility behave with different parameters across distinct economic regimes (bull/bear or high/low volatility). It is the financial application of Hamilton's MS-AR model, where an unobserved Markov state governs which parameter set is active at each point in time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James D. Hamilton","year":1989,"type":"Markov regime-switching time-series model","estimator":"Maximum likelihood via the Hamilton filter","outcome":"continuous","structure":"time series","minSample":100,"latentStates":"discrete regimes (e.g. bull/bear, high/low volatility)"},"citations":[{"ref":"Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.","type":"article","doi":"10.2307/1912559","isbn":null,"url":null},{"ref":"Ang, A., & Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business & Economic Statistics, 20(2), 163-182.","type":"article","doi":"10.1198/073500102317351930","isbn":null,"url":null}],"related":["garch-volatility","arima-forecasting","hidden-markov-model","var-vector-autoregression","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regime","name":"REGIME","fullName":"Ordinal multi-criteria method based on pairwise regime analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1983","originator":"Hinloopen, E., Nijkamp, P., Rietveld, P.","url":"https://scholargate.app/en/decision-making/regime","markdownUrl":"https://scholargate.app/en/decision-making/regime.md","definition":"REGIME (Ordinal multi-criteria method based on pairwise regime analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Hinloopen, E., Nijkamp, P., Rietveld, P. in 1983. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hinloopen, E., Nijkamp, P., Rietveld, P.","subfamily":"Ranking","year":"1983","type":"Pairwise regime matrix aggregation (ordinal weights)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Hinloopen, E., Nijkamp, P., Rietveld, P. (1983). Qualitative discrete multiple criteria choice models in regional planning. Regional Science and Urban Economics","type":"article","doi":"10.1016/0166-0462(83)90006-6","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regional-homogeneity","name":"Regional Homogeneity","fullName":"Regional Homogeneity (ReHo)","aliases":["ReHo","regional synchronization"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Local synchronization analysis","year":"2004","originator":"Yong-He Zang","url":"https://scholargate.app/en/neuroimaging/regional-homogeneity","markdownUrl":"https://scholargate.app/en/neuroimaging/regional-homogeneity.md","definition":"Regional Homogeneity (ReHo) is a measure of synchronization between a voxel and its spatial neighbors in resting-state fMRI. Introduced by Zang and colleagues in 2004, ReHo quantifies local within-cluster activity coherence, reflecting the degree to which brain regions exhibit synchronized spontaneous activity at rest.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yong-He Zang","subfamily":"Local synchronization analysis","year":"2004","type":"Resting-state fMRI homogeneity analysis"},"citations":[{"ref":"Zang, Y. F., He, Y., Zhu, C. Z., et al. (2004). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain and Development, 26(7), 429–439.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Altered+baseline+brain+activity+in+children+with+ADHD+revealed+by+resting-state+functional+MRI+Zang"},{"ref":"Yang, Y., Raine, A., Han, C. B., et al. (2007). Localizing brain abnormalities in ADHD: a meta-analysis of neuroimaging studies. Neuroscience & Biobehavioral Reviews, 31(4), 508–515.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Localizing+brain+abnormalities+in+ADHD%3A+a+meta-analysis+of+neuroimaging+studies+Yang"}],"related":["amplitude-of-low-frequency-fluctuation","dynamic-functional-connectivity","graph-brain-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"registry-based-research","name":"Registry-Based Research","fullName":"Clinical Registry-Based Observational Research","aliases":["registry research","registry study","disease registry","registry-based cohort"],"domain":"clinical-research","family":"process-pipeline","subfamily":"observational research","year":"2000s-present","originator":"Patient registries began mid-20th century; modern registry research formalized 2000s–2010s","url":"https://scholargate.app/en/clinical-research/registry-based-research","markdownUrl":"https://scholargate.app/en/clinical-research/registry-based-research.md","definition":"Registry-based research uses systematically collected clinical data from patient registries—organized databases of patients with a specific disease or condition—to conduct observational studies. Registries began in the mid-20th century but have proliferated since the 2000s as electronic health records expanded and funding agencies recognized their value for real-world evidence generation. Registry studies provide large, diverse, representative populations without the cost of recruiting and following prospectively, enabling rapid generation of clinical evidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patient registries began mid-20th century; modern registry research formalized 2000s–2010s","subfamily":"observational research","year":"2000s-present","type":"Research Design"},"citations":[{"ref":"Gini, R., Francesconi, P., Mazzaglia, G., Brignoli, G., Cricelli, C., Lapi, F., & Cricelli, A. (2020). Chronic disease prevalence from Italian administrative databases: the PREVALENTIST study. BMC Public Health, 13, 191.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Chronic+disease+prevalence+from+Italian+administrative+databases%3A+the+PREVALENTIST+study+Gini"},{"ref":"Hoque, D. M. E., Ruseckaite, R., Braithwaite, J., & Ting, H. P. (2017). Quality of life measurement in patients with Parkinson's disease: a systematic review of generic and disease-specific instruments and their clinimetric properties. Quality of Life Research, 26(8), 2117–2130.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quality+of+life+measurement+in+patients+with+Parkinson%27s+disease%3A+a+systematic+review+of+generic+and+disease-specific+instruments+and+their+clinimetric+properties+Hoque"},{"ref":"Ikehara, S., Iso, H., Yamagishi, K., Yamagishi, K., Maruyama, K., & Inoue, M. (2016). Healthy lifestyle and life expectancy among Japanese adults: findings from the JPHC study. Journal of Epidemiology, 26(2), 88–97.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Healthy+lifestyle+and+life+expectancy+among+Japanese+adults%3A+findings+from+the+JPHC+study+Ikehara"}],"related":["cohort-study-design","real-world-evidence","pragmatic-clinical-trial","observational-study-design","data-validation"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regression-discontinuity-design-in-education-research","name":"Regression discontinuity design in education research","fullName":"Regression Discontinuity Design in Education Research","aliases":["RDD in education","education RD design","sharp RDD education","score-cutoff design"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1960 (origination); 1999-2010 (education economics canon)","originator":"Thistlethwaite & Campbell (1960); popularized in education economics by Angrist & Lavy (1999), Lee & Lemieux (2010)","url":"https://scholargate.app/en/causal-inference/regression-discontinuity-design-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/regression-discontinuity-design-in-education-research.md","definition":"Regression discontinuity design (RDD) in education research exploits a score-based eligibility cutoff — such as a test score threshold, GPA requirement, or age cutoff — to estimate the causal effect of a program, intervention, or policy on student or school outcomes. Units just below and just above the cutoff are treated as near-randomly assigned, enabling credible causal inference without a randomized trial.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thistlethwaite & Campbell (1960); popularized in education economics by Angrist & Lavy (1999), Lee & Lemieux (2010)","year":"1960 (origination); 1999-2010 (education economics canon)","type":"Quasi-experimental causal inference","dataType":"Continuous running variable with a score cutoff; outcome measured for units near the threshold","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Lee, D. S., & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281-355.","type":"article","doi":"10.1257/jel.48.2.281","isbn":null,"url":null},{"ref":"Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51(6), 309-317.","type":"article","doi":"10.1037/h0044319","isbn":null,"url":null}],"related":["fuzzy-regression-discontinuity","difference-in-differences","propensity-score-matching","instrumental-variables","interrupted-time-series","event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regression-discontinuity","name":"Regression Discontinuity","fullName":"Regression Discontinuity Design","aliases":["RDD","regression discontinuity design","sharp RDD","fuzzy RDD","Regresyon Süreksizliği (RDD)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2008,"originator":"Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)","url":"https://scholargate.app/en/causal-inference/regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/regression-discontinuity.md","definition":"Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)","year":2008,"type":"Quasi-experimental causal design","estimate":"Local average treatment effect (LATE) at the cutoff","design":"Sharp or fuzzy discontinuity in a running variable","minSample":200},"citations":[{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null},{"ref":"Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1108710206","url":null}],"related":["iv-2sls","interrupted-time-series","propensity-score-matching","matching-methods","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regression-kink-design","name":"Regression Kink Design","fullName":"Generalized Regression Kink Design (RKD)","aliases":["RKD","regression kink design","kink regression discontinuity","Regresyon Kırılma Tasarımı (RKD — Regression Kink Design)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2015,"originator":"Card, Lee, Pei & Weber","url":"https://scholargate.app/en/causal-inference/regression-kink-design","markdownUrl":"https://scholargate.app/en/causal-inference/regression-kink-design.md","definition":"The Regression Kink Design is a quasi-experimental method that estimates a causal effect when a policy rule creates a change in slope (a kink) — rather than a jump — at a known threshold of a running variable. It was formalised as a generalized design by Card, Lee, Pei and Weber (2015) and is the slope-based counterpart of the regression discontinuity design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Card, Lee, Pei & Weber","year":2015,"type":"Quasi-experimental design (slope-based RDD)","estimator":"Ratio of slope changes (first derivative) at the kink, local polynomial","outcome":"continuous","design":"sharp / fuzzy kink at a known policy threshold","minSample":200},"citations":[{"ref":"Card, D., Lee, D. S., Pei, Z. & Weber, A. (2015). Inference on Causal Effects in a Generalized Regression Kink Design. Econometrica, 83(6), 2453-2483.","type":"article","doi":"10.3982/ECTA11224","isbn":null,"url":null}],"related":["regression-discontinuity","ols-regression","instrumental-variables","difference-in-differences","local-linear-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regression-splines","name":"Regression Splines","fullName":"Regression and Smoothing Splines","aliases":["splines","cubic splines","natural splines","smoothing splines","B-spline regression","regresyon spline'ları"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1996,"originator":"Spline regression literature; P-splines by Eilers & Marx","url":"https://scholargate.app/en/machine-learning/regression-splines","markdownUrl":"https://scholargate.app/en/machine-learning/regression-splines.md","definition":"Regression splines model a nonlinear relationship by fitting piecewise polynomials that join smoothly at a set of points called knots. Cubic and natural splines are the most common, and smoothing splines add a roughness penalty that automatically balances fit against smoothness. Splines are the standard flexible building block for univariate nonlinear regression and the basis of generalized additive models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Spline regression literature; P-splines by Eilers & Marx","year":1996,"type":"Piecewise-polynomial nonparametric regression","basis":"Truncated power, B-spline, or natural-spline basis","controls":"Knots and/or smoothing penalty","captures":"Smooth nonlinear univariate relationships"},"citations":[{"ref":"Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.","type":"article","doi":"10.1214/ss/1038425655","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0-387-84857-0","url":null}],"related":["generalized-additive-model","loess","polynomial-regression","mars"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-boosting","name":"Regularized Boosting","fullName":"Regularized Gradient Boosting (Shrinkage and Penalized Objective Boosting)","aliases":["shrinkage boosting","penalized boosting","regularized gradient boosting","L1/L2 boosting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001–2016","originator":"Friedman, J. H.; extended by Chen & Guestrin","url":"https://scholargate.app/en/machine-learning/regularized-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-boosting.md","definition":"Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Friedman, J. H.; extended by Chen & Guestrin","year":"2001–2016","type":"Regularized ensemble (boosting with shrinkage/penalty)","dataType":"Tabular (continuous, categorical, binary, ordinal)","subfamily":"Machine learning"},"citations":[{"ref":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.","type":"article","doi":"10.1214/aos/1013203451","isbn":null,"url":null},{"ref":"Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.","type":"inproceedings","doi":"10.1145/2939672.2939785","isbn":null,"url":null}],"related":["boosting","xgboost","regularized-random-forest","regularized-gradient-boosting","gradient-boosting","regularized-extra-trees"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-catboost","name":"Regularized CatBoost","fullName":"Regularized CatBoost (Categorical Boosting with Explicit Regularization)","aliases":["CatBoost with regularization","regularized categorical boosting","CatBoost L2 regularization","penalized CatBoost"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018","originator":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research)","url":"https://scholargate.app/en/machine-learning/regularized-catboost","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-catboost.md","definition":"Regularized CatBoost applies explicit regularization controls — L2 leaf regularization, tree depth constraints, shrinkage rate, and model size penalties — on top of CatBoost's ordered gradient boosting framework, reducing overfitting while retaining CatBoost's native handling of categorical features and its low prediction latency on tabular datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research)","year":"2018","type":"Regularized gradient boosting ensemble","dataType":"Tabular data (numerical, categorical, mixed)","subfamily":"Machine learning"},"citations":[{"ref":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2018/hash/14491b756b3a51daac2c2d5c876abcb4-Abstract.html"},{"ref":"Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1810.11363"}],"related":["catboost","xgboost","regularized-xgboost","regularized-lightgbm","gradient-boosting","regularized-gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-decision-tree","name":"Regularized Decision Tree","fullName":"Regularized Decision Tree (Pruned and Constrained CART)","aliases":["pruned decision tree","cost-complexity pruned tree","penalized decision tree","constrained CART"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1984","originator":"Breiman, L., Friedman, J., Olshen, R., & Stone, C.","url":"https://scholargate.app/en/machine-learning/regularized-decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-decision-tree.md","definition":"A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, L., Friedman, J., Olshen, R., & Stone, C.","year":"1984","type":"Supervised learning (regularized tree)","dataType":"Tabular (continuous and categorical features)","subfamily":"Machine learning"},"citations":[{"ref":"Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth.","type":"book","doi":null,"isbn":"978-0-412-04841-8","url":null},{"ref":"Esposito, F., Malerba, D., & Semeraro, G. (1997). A comparative analysis of methods for pruning decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5), 476–491.","type":"article","doi":"10.1109/34.589207","isbn":null,"url":null}],"related":["decision-tree","random-forest","regularized-random-forest","extra-trees","regularized-linear-regression","boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-federated-learning","name":"Regularized Federated Learning","fullName":"Regularized Federated Learning (Proximal and Penalty-Based Approaches)","aliases":["FedProx","federated learning with regularization","proximal federated learning","penalized federated optimization"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020","originator":"Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base)","url":"https://scholargate.app/en/machine-learning/regularized-federated-learning","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-federated-learning.md","definition":"Regularized federated learning extends the federated learning framework by adding penalty terms to each client's local objective, anchoring local updates closer to the global model. The canonical formulation — FedProx — adds a proximal term that controls how far any single client can drift, improving convergence and stability when client data distributions differ substantially.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base)","year":"2020","type":"Distributed optimization with regularization","dataType":"Decentralized tabular, image, or text data across clients","subfamily":"Machine learning"},"citations":[{"ref":"Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated Optimization in Heterogeneous Networks. Proceedings of Machine Learning and Systems (MLSys), 2, 429–450.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlsys.org/paper_files/paper/2020/hash/38af86134b65d0f10fe33d30dd76442e-Abstract.html"},{"ref":"McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54, 1273–1282.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v54/mcmahan17a.html"}],"related":["federated-learning","regularized-gradient-boosting","semi-supervised-learning","transfer-learning","online-learning","regularized-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-few-shot-learning","name":"Regularized Few-Shot Learning","fullName":"Regularized Few-Shot Learning (Regularization-Enhanced Meta-Learning)","aliases":["FSL with regularization","regularized meta-learning","few-shot learning with regularization","regularized episodic learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2016-2020","originator":"Multiple (Chen et al., Tian et al., Snell et al., and others)","url":"https://scholargate.app/en/machine-learning/regularized-few-shot-learning","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-few-shot-learning.md","definition":"Regularized few-shot learning augments standard few-shot learning pipelines with explicit regularization mechanisms — such as weight decay, dropout, data augmentation, label smoothing, or manifold constraints — to reduce overfitting to the tiny support sets that define each episode. This produces more generalizable models when only one to thirty labeled examples per class are available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (Chen et al., Tian et al., Snell et al., and others)","year":"2016-2020","type":"Meta-learning framework with explicit regularization","dataType":"Small labeled sets (few examples per class), large unlabeled or auxiliary data","subfamily":"Machine learning"},"citations":[{"ref":"Chen, W., Liu, Y., Kira, Z., Wang, Y. F., & Huang, J. (2019). A Closer Look at Few-Shot Classification. International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=HkxLXnAcFQ"},{"ref":"Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J. B., & Isola, P. (2020). Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? European Conference on Computer Vision (ECCV).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Rethinking+Few-Shot+Image+Classification+a+Good+Embedding+Is+All+You+Need"}],"related":["few-shot-learning","meta-learning","transfer-learning","regularized-transfer-learning","semi-supervised-few-shot-learning","self-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-gaussian-mixture-model","name":"Regularized Gaussian Mixture Model","fullName":"Regularized Gaussian Mixture Model (Covariance-Regularized EM Clustering)","aliases":["Regularized GMM","GMM with covariance regularization","stabilized Gaussian mixture model","penalized GMM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)","url":"https://scholargate.app/en/machine-learning/regularized-gaussian-mixture-model","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-gaussian-mixture-model.md","definition":"A Regularized Gaussian Mixture Model (GMM) adds a small positive constant to the diagonal of each component covariance matrix during the Expectation-Maximization algorithm, preventing singular or near-singular matrices that cause numerical failures when the data are sparse, high-dimensional, or contain near-duplicate observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)","year":"2000s–2010s","type":"Probabilistic clustering with regularization","dataType":"Continuous multivariate numerical data","subfamily":"Machine learning"},"citations":[{"ref":"Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.","type":"article","doi":"10.1198/016214502760047131","isbn":null,"url":null},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9). Springer.","type":"book","doi":null,"isbn":"978-0-387-31073-2","url":null}],"related":["gaussian-mixture-model","k-means","regularized-k-means","bayesian-gaussian-mixture-model","one-class-svm","regularized-k-nearest-neighbors"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-gaussian-process","name":"Regularized Gaussian Process","fullName":"Regularized Gaussian Process Regression and Classification","aliases":["Regularized GP","GP with noise regularization","sparse regularized Gaussian process","regularized Gaussian process regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006 (canonical formulation); kernel regularization roots 1990s","originator":"Rasmussen, C. E. & Williams, C. K. I.","url":"https://scholargate.app/en/machine-learning/regularized-gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-gaussian-process.md","definition":"A Regularized Gaussian Process (GP) is a probabilistic kernel-based model that places a prior over functions and explicitly controls overfitting through a noise regularization parameter — the observation noise variance — that prevents the model from memorizing training labels. It produces calibrated uncertainty estimates alongside predictions, making it uniquely suited to small or expensive datasets where knowing how confident the model is matters as much as the prediction itself.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rasmussen, C. E. & Williams, C. K. I.","year":"2006 (canonical formulation); kernel regularization roots 1990s","type":"Probabilistic kernel model with regularization","dataType":"Continuous features; continuous or binary targets","subfamily":"Machine learning"},"citations":[{"ref":"Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-18253-9","url":null},{"ref":"Scholkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-19475-4","url":null}],"related":["gaussian-process","support-vector-machine","regularized-support-vector-machine","bayesian-gaussian-process","kernel-ridge-regression","regularized-linear-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-gradient-boosting","name":"Regularized Gradient Boosting","fullName":"Regularized Gradient Boosting (L1/L2-Penalized Additive Tree Ensemble)","aliases":["penalized gradient boosting","shrinkage-regularized boosting","XGBoost-style regularization","L1/L2 gradient boosting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)","originator":"Chen, T. & Guestrin, C. (building on Friedman, J. H.)","url":"https://scholargate.app/en/machine-learning/regularized-gradient-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-gradient-boosting.md","definition":"Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, T. & Guestrin, C. (building on Friedman, J. H.)","year":"2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)","type":"Regularized ensemble (additive tree model)","dataType":"Tabular (continuous, categorical, binary, ordinal, count)","subfamily":"Machine learning"},"citations":[{"ref":"Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.","type":"inproceedings","doi":"10.1145/2939672.2939785","isbn":null,"url":null},{"ref":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.","type":"article","doi":"10.1214/aos/1013203451","isbn":null,"url":null}],"related":["xgboost","boosting","gradient-boosting","regularized-random-forest","regularized-decision-tree","lightgbm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-k-means","name":"Regularized k-means","fullName":"Regularized K-Means Clustering","aliases":["sparse k-means","penalized k-means","regularized clustering","constrained k-means"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010","originator":"Witten, D. M. & Tibshirani, R. (sparse k-means formulation)","url":"https://scholargate.app/en/machine-learning/regularized-k-means","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-k-means.md","definition":"Regularized k-means extends standard k-means by adding a penalty term — most commonly an L1 (lasso-type) or L2 constraint — to the objective function. This discourages degenerate cluster solutions and, in the sparse variant introduced by Witten and Tibshirani (2010), simultaneously selects the features that drive cluster separation, making it especially valuable in high-dimensional settings where many features are irrelevant.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Witten, D. M. & Tibshirani, R. (sparse k-means formulation)","year":"2010","type":"Regularized unsupervised clustering","dataType":"Continuous / mixed tabular features","subfamily":"Machine learning"},"citations":[{"ref":"Witten, D. M., & Tibshirani, R. (2010). A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490), 713–726.","type":"article","doi":"10.1198/jasa.2010.tm09415","isbn":null,"url":null},{"ref":"K-means clustering. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/K-means_clustering"}],"related":["k-means","regularized-gaussian-mixture-model","sparse-pca","regularized-dbscan","gaussian-mixture-model","regularized-hdbscan"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-k-nearest-neighbors","name":"Regularized k-nearest neighbors","fullName":"Regularized k-Nearest Neighbors (Kernel-Weighted kNN)","aliases":["regularized kNN","kernel-weighted kNN","distance-regularized nearest neighbors","kNN with regularization"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1967–2000s","originator":"Extends Cover & Hart (1967); regularization formulations developed through kernel smoothing literature","url":"https://scholargate.app/en/machine-learning/regularized-k-nearest-neighbors","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-k-nearest-neighbors.md","definition":"Regularized k-Nearest Neighbors (kNN) extends the classical nearest-neighbor algorithm by incorporating regularization mechanisms — most commonly kernel-based distance weighting or bandwidth control — that smooth predictions, reduce sensitivity to the choice of k, and lower variance. The result is a more stable and better-calibrated instance-based learner for classification and regression tasks on tabular data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extends Cover & Hart (1967); regularization formulations developed through kernel smoothing literature","year":"1967–2000s","type":"Instance-based / lazy learner with regularization","dataType":"Continuous, mixed-type tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.","type":"article","doi":"10.1109/TIT.1967.1053964","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 13). Springer.","type":"book","doi":null,"isbn":"978-0-387-84858-7","url":null}],"related":["k-nearest-neighbors","regularized-support-vector-machine","regularized-gaussian-process","gaussian-process","support-vector-machine","regularized-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-lightgbm","name":"Regularized LightGBM","fullName":"Regularized Light Gradient Boosting Machine","aliases":["LightGBM with L1/L2 regularization","penalized LightGBM","LightGBM ridge/lasso","regularized LGBM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017","originator":"Ke, G. et al. (Microsoft Research)","url":"https://scholargate.app/en/machine-learning/regularized-lightgbm","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-lightgbm.md","definition":"Regularized LightGBM applies L1 (lasso) and L2 (ridge) penalty terms to the leaf weight objective of LightGBM — Microsoft's highly efficient gradient boosting framework — to control model complexity, reduce overfitting, and improve generalization on tabular classification and regression tasks with high-dimensional or noisy feature sets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ke, G. et al. (Microsoft Research)","year":"2017","type":"Regularized gradient boosting ensemble","dataType":"Tabular (numeric and categorical features)","subfamily":"Machine learning"},"citations":[{"ref":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html"},{"ref":"Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.","type":"inproceedings","doi":"10.1145/2939672.2939785","isbn":null,"url":null}],"related":["lightgbm","xgboost","regularized-xgboost","gradient-boosting","regularized-gradient-boosting","catboost"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-linear-regression","name":"Regularized linear regression","fullName":"Regularized Linear Regression (Ridge, Lasso, Elastic Net)","aliases":["Ridge regression","Lasso regression","Elastic Net regression","penalized regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1970–2005","originator":"Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)","url":"https://scholargate.app/en/machine-learning/regularized-linear-regression","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-linear-regression.md","definition":"Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)","year":"1970–2005","type":"Penalized linear model","dataType":"Continuous numeric features; continuous target","subfamily":"Machine learning"},"citations":[{"ref":"Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.","type":"article","doi":"10.1111/j.2517-6161.1996.tb02080.x","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 3). Springer.","type":"book","doi":null,"isbn":"978-0-387-84858-7","url":null}],"related":["linear-regression-ml","logistic-regression-ml","regularized-logistic-regression","support-vector-machine","elastic-net","lasso"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-logistic-regression","name":"Regularized Logistic Regression","fullName":"Regularized Logistic Regression (L1 / L2 / Elastic Net Penalized Binary and Multinomial Classification)","aliases":["penalized logistic regression","L1 logistic regression","L2 logistic regression","elastic net logistic regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1996–2005","originator":"Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)","url":"https://scholargate.app/en/machine-learning/regularized-logistic-regression","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-logistic-regression.md","definition":"Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)","year":"1996–2005","type":"Penalized classification model","dataType":"Tabular, numerical and encoded categorical features with a binary or multinomial outcome","subfamily":"Machine learning"},"citations":[{"ref":"Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.","type":"article","doi":"10.1111/j.2517-6161.1996.tb02080.x","isbn":null,"url":null},{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 4, 18). Springer.","type":"book","doi":null,"isbn":"978-0-387-84857-0","url":null}],"related":["logistic-regression-ml","regularized-linear-regression","support-vector-machine","linear-discriminant-analysis","elastic-net","naive-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-naive-bayes","name":"Regularized Naive Bayes","fullName":"Regularized Naive Bayes Classifier","aliases":["Smoothed Naive Bayes","Laplace-smoothed Naive Bayes","Regularized NB","Complement Naive Bayes"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1950s–2003","originator":"Good, I. J. (Laplace smoothing formalized); Rennie et al. (complement regularization)","url":"https://scholargate.app/en/machine-learning/regularized-naive-bayes","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-naive-bayes.md","definition":"Regularized Naive Bayes augments the classical Naive Bayes probabilistic classifier with explicit smoothing or shrinkage — most commonly Laplace (additive) smoothing — to prevent zero-probability estimates for unseen feature values and to reduce overfitting. The result is a fast, robust classifier that generalizes better than unsmoothed Naive Bayes, particularly on sparse or high-dimensional data such as text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Good, I. J. (Laplace smoothing formalized); Rennie et al. (complement regularization)","year":"1950s–2003","type":"Probabilistic classifier with regularization","dataType":"Categorical, text (bag-of-words), or continuous features","subfamily":"Machine learning"},"citations":[{"ref":"Rennie, J. D. M., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of Naive Bayes text classifiers. In Proceedings of the 20th International Conference on Machine Learning (ICML-2003), pp. 616–623.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tackling+the+poor+assumptions+of+Naive+Bayes+text+classifiers+Rennie+2003"},{"ref":"Naive Bayes classifier. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Naive_Bayes_classifier"}],"related":["naive-bayes","logistic-regression","regularized-logistic-regression","support-vector-machine","regularized-support-vector-machine","k-nearest-neighbors"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-online-learning","name":"Regularized Online Learning","fullName":"Regularized Online Learning (Online Learning with Regularization)","aliases":["FTRL","Follow-the-Regularized-Leader","online regularized optimization","regularized dual averaging"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2007–2013","originator":"Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.","url":"https://scholargate.app/en/machine-learning/regularized-online-learning","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-online-learning.md","definition":"Regularized online learning extends the online learning paradigm by incorporating a regularization penalty into each weight update, controlling model complexity while processing data one example at a time. Algorithms such as Follow-the-Regularized-Leader (FTRL) and Regularized Dual Averaging (RDA) make this approach practical at scale, enabling sparse, well-calibrated models on streaming data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al.","year":"2007–2013","type":"Online optimization framework with regularization","dataType":"Sequential / streaming labeled or unlabeled tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v11/xiao10a.html"},{"ref":"Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.","type":"article","doi":"10.1561/2200000018","isbn":null,"url":null}],"related":["online-learning","regularized-linear-regression","regularized-logistic-regression","semi-supervised-learning","transfer-learning","stochastic-gradient-descent"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-random-forest","name":"Regularized random forest","fullName":"Regularized Random Forest (RRF)","aliases":["RRF","Guided Regularized Random Forest","GRRF","regularized tree ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2012","originator":"Deng, H. & Runger, G.","url":"https://scholargate.app/en/machine-learning/regularized-random-forest","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-random-forest.md","definition":"Regularized Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Deng, H. & Runger, G.","year":"2012","type":"Regularized ensemble (penalized feature selection in trees)","dataType":"Tabular (continuous, categorical, mixed); labeled data","subfamily":"Machine learning"},"citations":[{"ref":"Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8.","type":"article","doi":"10.1109/IJCNN.2012.6252640","isbn":null,"url":null},{"ref":"Deng, H., & Runger, G. (2013). Gene selection with guided regularized random forest. Pattern Recognition, 46(12), 3483–3489.","type":"article","doi":"10.1016/j.patcog.2013.05.018","isbn":null,"url":null}],"related":["random-forest","extra-trees","regularized-decision-tree","regularized-gradient-boosting","regularized-xgboost","decision-tree"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-semi-supervised-learning","name":"Regularized semi-supervised learning","fullName":"Regularized Semi-Supervised Learning (Manifold Regularization and Graph-Based SSL)","aliases":["manifold regularization","graph-regularized SSL","semi-supervised regularization","Laplacian regularization"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006","originator":"Belkin, M.; Niyogi, P.; Sindhwani, V.","url":"https://scholargate.app/en/machine-learning/regularized-semi-supervised-learning","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-semi-supervised-learning.md","definition":"Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Belkin, M.; Niyogi, P.; Sindhwani, V.","year":"2006","type":"Regularized learning paradigm","dataType":"Labeled and unlabeled tabular, text, or graph-structured data","subfamily":"Machine learning"},"citations":[{"ref":"Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v7/belkin06a.html"},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["semi-supervised-learning","regularized-logistic-regression","gaussian-process","label-propagation","self-supervised-learning","regularized-random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-stacking-ensemble","name":"Regularized Stacking Ensemble","fullName":"Regularized Stacking Ensemble (Stacked Generalization with Regularized Meta-Learner)","aliases":["regularized stacked generalization","ridge stacking","lasso meta-learner ensemble","penalized stacking"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1992–1996","originator":"Wolpert, D. H. (stacking); Breiman, L. (regularized meta-learner formulation)","url":"https://scholargate.app/en/machine-learning/regularized-stacking-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-stacking-ensemble.md","definition":"Regularized Stacking Ensemble is a two-level ensemble method in which predictions from multiple diverse base learners are combined by a regularized meta-learner — typically ridge regression, lasso, or elastic net — to suppress overfitting in the combination layer. Regularization ensures that the meta-learner assigns stable, well-calibrated weights to base model outputs rather than memorizing noise in the training fold predictions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolpert, D. H. (stacking); Breiman, L. (regularized meta-learner formulation)","year":"1992–1996","type":"Ensemble (stacked generalization with regularized meta-learner)","dataType":"Tabular (continuous, categorical, binary, ordinal)","subfamily":"Machine learning"},"citations":[{"ref":"Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.","type":"article","doi":"10.1016/S0893-6080(05)80023-1","isbn":null,"url":null},{"ref":"Breiman, L. (1996). Stacked Regressions. Machine Learning, 24(1), 49–64.","type":"article","doi":"10.1007/BF00117832","isbn":null,"url":null}],"related":["stacking-ensemble","voting-ensemble","boosting","regularized-random-forest","regularized-gradient-boosting","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-support-vector-machine","name":"Regularized Support Vector Machine","fullName":"Regularized Support Vector Machine (L1/L2-penalized SVM)","aliases":["Regularized SVM","L1-SVM","L2-SVM","penalized SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1995–2004","originator":"Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)","url":"https://scholargate.app/en/machine-learning/regularized-support-vector-machine","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-support-vector-machine.md","definition":"Regularized Support Vector Machine extends the classic SVM by explicitly controlling the trade-off between margin maximization and training error through an L1 or L2 penalty parameter. The soft-margin formulation introduced by Cortes and Vapnik in 1995 is itself a regularized model, and later L1-SVM variants additionally promote feature sparsity, enabling automatic variable selection in high-dimensional settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cortes, C. & Vapnik, V. (soft-margin SVM); Zhu et al. (L1-SVM)","year":"1995–2004","type":"Regularized discriminative classifier / regressor","dataType":"Numerical, binary or multi-class labels; SVR for continuous targets","subfamily":"Machine learning"},"citations":[{"ref":"Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.","type":"article","doi":"10.1007/BF00994018","isbn":null,"url":null},{"ref":"Zhu, J., Rosset, S., Tibshirani, R. & Hastie, T. (2004). 1-norm support vector machines. Advances in Neural Information Processing Systems (NIPS), 16.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2003/hash/49d4b2faeb4b7b9e745775793141e2b2-Abstract.html"}],"related":["support-vector-machine","regularized-logistic-regression","regularized-linear-regression","kernel-svm","lasso-regression","linear-discriminant-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"regularized-transfer-learning","name":"Regularized Transfer Learning","fullName":"Regularized Transfer Learning (Regularization-Constrained Domain Adaptation)","aliases":["regularized domain adaptation","transfer learning with regularization","penalized transfer learning","regularized fine-tuning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors","url":"https://scholargate.app/en/machine-learning/regularized-transfer-learning","markdownUrl":"https://scholargate.app/en/machine-learning/regularized-transfer-learning.md","definition":"Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors","year":"2000s–2010s","type":"Regularized supervised/semi-supervised learning framework","dataType":"Labeled source-domain data plus labeled or unlabeled target-domain data","subfamily":"Machine learning"},"citations":[{"ref":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null},{"ref":"Li, Z., Nie, F., Chang, X., & Yang, Y. (2014). Beyond trace norm: Robust matrix recovery via bi-sparsity pursuit. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1736–1742.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=regularized+transfer+learning+domain+adaptation+negative+transfer"}],"related":["transfer-learning","regularized-logistic-regression","regularized-random-forest","semi-supervised-transfer-learning","metric-learning","few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reinforcement-learning","name":"Reinforcement Learning","fullName":"Reinforcement Learning (Agent-Environment Reward Optimization)","aliases":["RL","reward-based learning","trial-and-error learning","policy optimization"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"1950s–1998","originator":"Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)","url":"https://scholargate.app/en/deep-learning/reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/reinforcement-learning.md","definition":"Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)","year":"1950s–1998","type":"Sequential decision-making framework","dataType":"State-action-reward trajectories (environment interactions)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03924-6","url":null},{"ref":"Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533.","type":"article","doi":"10.1038/nature14236","isbn":null,"url":null}],"related":["deep-q-network","policy-gradient","convolutional-neural-network","recurrent-neural-network","transformer","multi-armed-bandit"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reintegration-to-normal-living","name":"Reintegration to Normal Living Index","fullName":"Reintegration to Normal Living Index (RNLI)","aliases":["RNLI","RNL Index"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"social-integration","year":"1988","originator":"Wood-Dauphinee, Opzoomer, Williams, Marchand, Spitzer","url":"https://scholargate.app/en/rehabilitation-science/reintegration-to-normal-living","markdownUrl":"https://scholargate.app/en/rehabilitation-science/reintegration-to-normal-living.md","definition":"The Reintegration to Normal Living Index (RNLI) is a brief, patient-report measure designed to assess how completely a person has returned to 'normal' community living following a major health event (stroke, head injury, cardiac event, or other condition requiring significant recovery). Developed by Wood-Dauphinee and colleagues in the 1980s, RNLI captures the subjective experience of reintegration: the degree to which the person feels they have resumed their pre-illness social roles, activities, and independence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wood-Dauphinee, Opzoomer, Williams, Marchand, Spitzer","subfamily":"social-integration","year":"1988","type":"Self-report"},"citations":[{"ref":"Wood-Dauphinee, S. L., Opzoomer, M. A., Williams, J. I., Marchand, B., & Spitzer, W. O. (1988). Assessment of global function: a new measure for evaluating the outcome of rehabilitation of post-stroke patients. Archives of Physical Medicine and Rehabilitation, 69(7), 506–515.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/0003-9993(88)90124-0"},{"ref":"Schubert, D. S., Buchsbaum, M. S., Orsulak, P. J., King, R. J., & Stoddard, G. (1992). Neuropsychological evidence for a defect of thalamic filtering in schizophrenia. Biological Psychiatry, 32(7), 556–567.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1016/0006-3223(92)90158-U"}],"related":["community-integration-questionnaire","participation-scale","impact-participation-autonomy","assessment-life-habits","craig-handicap-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rejection-sensitivity-questionnaire","name":"Rejection Sensitivity Questionnaire","fullName":"Rejection Sensitivity Questionnaire (RSQ)","aliases":["RSQ"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"interpersonal-sensitivity-assessment","year":"1996","originator":"Geraldine Downey & Sally I. Feldman","url":"https://scholargate.app/en/clinical-psychology/rejection-sensitivity-questionnaire","markdownUrl":"https://scholargate.app/en/clinical-psychology/rejection-sensitivity-questionnaire.md","definition":"The RSQ is an 18-item self-report measure of rejection sensitivity—the disposition to anxiously expect, readily perceive, and intensely react to rejection from others. Developed by Downey and Feldman in 1996, it captures both anxiety about rejection and expectancy of rejection. Rejection sensitivity is recognized as transdiagnostic interpersonal vulnerability predicting social anxiety, depression, relationship conflict, and self-harm.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geraldine Downey & Sally I. Feldman","subfamily":"interpersonal-sensitivity-assessment","year":"1996","type":"Self-report questionnaire"},"citations":[{"ref":"Downey, G., & Feldman, S. I. (1996). Implications of rejection sensitivity for intimate relationships. Journal of Personality and Social Psychology, 70(6), 1327–1343.","type":"article","doi":"10.1037/0022-3514.70.6.1327","isbn":null,"url":null}],"related":["emotion-regulation-questionnaire","multidimensional-perfectionism-scale","difficulties-emotion-regulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"relation-extraction","name":"Relation Extraction","fullName":"Relation Extraction (Semantic Relation Extraction)","aliases":["semantic relation extraction","İlişki Çıkarma (Relation Extraction)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/relation-extraction","markdownUrl":"https://scholargate.app/en/text-mining/relation-extraction.md","definition":"Relation extraction is a natural-language-processing task that detects and classifies the semantic relations that hold between entities mentioned in text. Building on early kernel-based methods (Zelenko and colleagues, 2003) and later neural matching approaches (Baldini Soares and colleagues, 2019), it turns free-form text into structured facts of the form entity–relation–entity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP information-extraction task","output":"Classified semantic relations between entity pairs","prerequisite":"Named-entity recognition (NER) output","minSample":100},"citations":[{"ref":"Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/zelenko03a.html"},{"ref":"Soares, L. B., FitzGerald, N., Ling, J. & Kwiatkowski, T. (2019). Matching the Blanks: Distributional Similarity for Relation Learning. Proceedings of ACL 2019.","type":"article","doi":"10.18653/v1/P19-1279","isbn":null,"url":null}],"related":["named-entity-recognition","text-classification","semantic-similarity","keyword-extraction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"relational-survey","name":"Relational Survey","fullName":"Relational Survey Research","aliases":["correlational survey","associational survey","relationship survey design","relational descriptive survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century onward (systematised ~1960s–1990s)","originator":"Established in educational and social science research methodology; systematised by Fraenkel & Wallen and others","url":"https://scholargate.app/en/research-design/relational-survey","markdownUrl":"https://scholargate.app/en/research-design/relational-survey.md","definition":"Relational survey research is a quantitative, non-experimental design that gathers structured self-report data from a sample and examines the statistical associations among two or more variables. Unlike purely descriptive surveys, which only characterise distributions, relational surveys ask whether and how strongly variables co-vary — providing evidence of relationships without manipulating conditions or establishing causation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established in educational and social science research methodology; systematised by Fraenkel & Wallen and others","year":"Mid-20th century onward (systematised ~1960s–1990s)","type":"Quantitative non-experimental survey design","dataType":"Numeric scores from questionnaires, scales, or standardised instruments","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073525748","url":null},{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452226101","url":null}],"related":["correlational-research","descriptive-research","cross-sectional-research","survey-research","causal-comparative-research","explanatory-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"relationship-assessment-scale","name":"Relationship Assessment Scale","fullName":"Relationship Assessment Scale (RAS)","aliases":["RAS","Sternberg Relationship Assessment Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"relationship satisfaction and love","year":"1988","originator":"Susan Hendrick (based on Sternberg's Triangular Theory of Love)","url":"https://scholargate.app/en/social-psychology/relationship-assessment-scale","markdownUrl":"https://scholargate.app/en/social-psychology/relationship-assessment-scale.md","definition":"The Relationship Assessment Scale is a brief, widely used instrument for measuring global relationship satisfaction and quality in romantic partnerships. Developed by Susan Hendrick in 1988 and based on Robert Sternberg's Triangular Theory of Love, the RAS measures the three core components of love: intimacy (emotional closeness and connection), passion (attraction and desire), and commitment (dedication and decision to maintain the relationship). The RAS is valued for its brevity (7 items), applicability across diverse relationship types, and strong psychometric properties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Susan Hendrick (based on Sternberg's Triangular Theory of Love)","subfamily":"relationship satisfaction and love","year":"1988","type":"Self-report questionnaire"},"citations":[{"ref":"Sternberg, R. J. (1988). Triangulating love. In R. J. Sternberg & M. L. Barnes (Eds.), The psychology of love (pp. 119-138). New Haven, CT: Yale University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sternberg+Triangulating+love+1988"},{"ref":"Hendrick, S. S. (1988). A generic measure of relationship satisfaction. Journal of Marriage and the Family, 50(1), 93-98.","type":"article","doi":"10.2307/352430","isbn":null,"url":null}],"related":["dyadic-adjustment-scale","marital-quality-questionnaire","attachment-style-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reliability-analysis","name":"Reliability Analysis","fullName":"Statistical Reliability Analysis","aliases":["Life Data Analysis","Survival Analysis (Engineering)","Time-to-Failure Analysis","Güvenilirlik Analizi"],"domain":"reliability","family":"regression-model","subfamily":"Reliability & risk","year":1998,"originator":"William Meeker & Luis Escobar","url":"https://scholargate.app/en/reliability/reliability-analysis","markdownUrl":"https://scholargate.app/en/reliability/reliability-analysis.md","definition":"Statistical reliability analysis models the time-to-failure of components, systems, or products using parametric lifetime distributions fitted to observed or censored failure data. Formalized comprehensively by William Q. Meeker and Luis A. Escobar in their 1998 Wiley monograph, the framework integrates maximum likelihood estimation, censoring mechanisms, and distributional diagnostics to produce probability-of-failure curves, hazard rates, and quantile estimates that support design, warranty, and maintenance decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Meeker & Luis Escobar","year":1998,"type":"Parametric lifetime modeling","subfamily":"Reliability & risk","data_type":"Time-to-event (possibly censored)","output":"Failure probability, hazard rate, quantiles"},"citations":[{"ref":"Meeker, W. Q., & Escobar, L. A. (1998). Statistical Methods for Reliability Data. Wiley.","type":"book","doi":null,"isbn":"978-0-471-14328-4","url":null}],"related":["weibull-regression","degradation-models","fault-tree-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reliability-block-diagram","name":"Reliability Block Diagram","fullName":"Reliability Block Diagram","aliases":["RBD","reliability analysis"],"domain":"operations-management","family":"ml-model","subfamily":"Reliability Engineering","year":"2010","originator":"Ebeling, C. E.","url":"https://scholargate.app/en/operations-management/reliability-block-diagram","markdownUrl":"https://scholargate.app/en/operations-management/reliability-block-diagram.md","definition":"A Reliability Block Diagram (RBD) is a visual representation of a system's architecture that models how component reliabilities combine to determine overall system reliability. Each block represents a component or subsystem with a known reliability (probability of functioning without failure), and connections between blocks represent functional relationships (series, parallel, or mixed). RBD analysis is a fundamental tool in reliability engineering, used to identify critical components, optimize redundancy, and predict system-level failure rates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ebeling, C. E.","subfamily":"Reliability Engineering","year":"2010","type":"Reliability analysis technique"},"citations":[{"ref":"Ebeling, C. E. (2010). An introduction to reliability and maintainability engineering (2nd ed.). Long Grove, IL: Waveland Press.","type":"book","doi":null,"isbn":null,"url":"https://www.waveland.com/"},{"ref":"Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution (2nd ed.). Milwaukee: ASQ Quality Press.","type":"book","doi":null,"isbn":null,"url":"https://www.asq.org/"}],"related":["total-productive-maintenance","facility-layout","assembly-line-balancing","scor-model","job-shop-scheduling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"religious-commitment-inventory","name":"RCI-10","fullName":"Religious Commitment Inventory-10","aliases":["RCI-10","Religious Commitment"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"religious dedication and practice","year":2003,"originator":"Everett L. Worthington Jr., Nathaniel G. Wade, Tamara L. Hight, Jennifer S. Ripley, Michael E. McCullough, & others","url":"https://scholargate.app/en/psychology-of-religion/religious-commitment-inventory","markdownUrl":"https://scholargate.app/en/psychology-of-religion/religious-commitment-inventory.md","definition":"The Religious Commitment Inventory-10 (RCI-10), developed by Worthington and colleagues in 2003, is a brief 10-item self-report measure of religious commitment: the degree to which an individual dedicates themselves to religious beliefs, practices, and community. The RCI-10 distinguishes between two dimensions of commitment: Intrapersonal (personal faith conviction, spiritual discipline, religious significance) and Interpersonal (engagement with faith community, public religious identity, shared practices). It has become widely used in counseling psychology, pastoral care, and research on religiosity and well-being to assess the strength and breadth of religious dedication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Everett L. Worthington Jr., Nathaniel G. Wade, Tamara L. Hight, Jennifer S. Ripley, Michael E. McCullough, & others","subfamily":"religious dedication and practice","year":2003,"type":"Self-report"},"citations":[{"ref":"Worthington, E. L., Jr., Wade, N. G., Hight, T. L., Ripley, J. S., McCullough, M. E., Berry, J. W., ... Schmitt, M. M. (2003). The Religious Commitment Inventory-10: Development, refinement, and validation of a brief scale for research and counseling. Journal of Counseling Psychology, 50(1), 84–96.","type":"article","doi":"10.1037/0022-0167.50.1.84","isbn":null,"url":null}],"related":["intrinsic-extrinsic-religiosity","duke-religion-index","brief-religious-coping-scale","daily-spiritual-experience-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-delphi-technique","name":"Remote Delphi Technique","fullName":"Remote Delphi Technique","aliases":["online Delphi","e-Delphi","virtual Delphi","distributed Delphi"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1950s (classic Delphi); remote/e-Delphi from late 1990s","originator":"Olaf Helmer and Norman Dalkey (RAND Corporation; classic Delphi); e-Delphi adapted by various methodologists from late 1990s onward","url":"https://scholargate.app/en/survey-methodology/remote-delphi-technique","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-delphi-technique.md","definition":"The Remote Delphi Technique applies the structured iterative consensus process of the classic Delphi method entirely through remote communication channels — email, web-based survey platforms, or dedicated collaboration tools — eliminating the need for geographic co-presence. Experts complete successive questionnaire rounds asynchronously, receiving anonymised statistical summaries of the group's prior responses before each new round, until a pre-defined consensus threshold is reached.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Olaf Helmer and Norman Dalkey (RAND Corporation; classic Delphi); e-Delphi adapted by various methodologists from late 1990s onward","year":"1950s (classic Delphi); remote/e-Delphi from late 1990s","type":"Iterative expert consensus technique — remote administration","dataType":"Structured questionnaire responses, numerical ratings, open-ended comments from remote expert panels","subfamily":"Data collection"},"citations":[{"ref":"Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.","type":"article","doi":"10.1046/j.1365-2648.2000.01567.x","isbn":null,"url":null},{"ref":"Donohoe, H., Stellefson, M., & Tennant, B. (2012). Advantages and limitations of the e-Delphi technique: Implications for health education researchers. American Journal of Health Education, 43(1), 38–46.","type":"article","doi":"10.1080/19325037.2012.10599216","isbn":null,"url":null}],"related":["delphi-technique","online-survey","remote-focus-group","remote-structured-interview","remote-semi-structured-interview","expert-panel"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-document-collection","name":"Remote Document Collection","fullName":"Remote Document Collection","aliases":["digital document retrieval","online archival collection","virtual document gathering","remote archival research"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–present (digital shift of traditional document collection)","originator":"Rooted in classical document analysis (Bowen 2009; Scott 1990); remote modality formalized in digital humanities and qualitative online research from the 2000s onward","url":"https://scholargate.app/en/survey-methodology/remote-document-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-document-collection.md","definition":"Remote Document Collection is a data collection technique in which researchers gather written, visual, or multimedia documents from digital sources — online archives, institutional repositories, cloud storage, email, or government databases — without requiring physical presence. It extends classical document analysis into digital environments, enabling access to geographically dispersed or restricted materials and making it especially valuable for large-scale, cross-national, or time-sensitive research projects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in classical document analysis (Bowen 2009; Scott 1990); remote modality formalized in digital humanities and qualitative online research from the 2000s onward","year":"2000s–present (digital shift of traditional document collection)","type":"Qualitative / mixed-methods data collection technique","dataType":"Textual, visual, and multimedia documents accessed via digital channels","subfamily":"Data collection"},"citations":[{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null},{"ref":"Salmons, J. (2014). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1452282756","url":null}],"related":["document-collection","online-document-collection","web-scraping","api-based-data-collection","remote-structured-interview","remote-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-focus-group","name":"Remote Focus Group","fullName":"Remote Focus Group Discussion","aliases":["virtual focus group","online focus group","video-mediated focus group","distributed focus group"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s (synchronous online); mainstream adoption 2020","originator":"Adaptation of traditional focus groups (Robert K. Merton, 1940s); remote modality formalized in the late 1990s–2000s and widely adopted post-2020","url":"https://scholargate.app/en/survey-methodology/remote-focus-group","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-focus-group.md","definition":"A Remote Focus Group is a synchronous, moderated group discussion conducted via video or audio conferencing rather than in a shared physical space. Participants — typically 5 to 10 people — join from separate locations and discuss a topic guided by a trained moderator. The method preserves the core strengths of in-person focus groups (group interaction, idea building, spontaneous reactions) while eliminating geographic barriers and reducing recruitment costs. It has become a mainstream qualitative data collection approach, especially following the widespread adoption of video conferencing platforms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adaptation of traditional focus groups (Robert K. Merton, 1940s); remote modality formalized in the late 1990s–2000s and widely adopted post-2020","year":"Late 1990s (synchronous online); mainstream adoption 2020","type":"Qualitative group data collection","dataType":"Synchronous verbal and text discussion data via video/audio conferencing","subfamily":"Data collection"},"citations":[{"ref":"Lobe, B., Morgan, D., & Hoffman, K. A. (2020). Qualitative data collection in an era of social distancing. International Journal of Qualitative Methods, 19, 1–8.","type":"article","doi":"10.1177/1609406920937875","isbn":null,"url":null},{"ref":"Krueger, R. A., & Casey, M. A. (2015). Focus Groups: A Practical Guide for Applied Research (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1483365244","url":null}],"related":["focus-group","online-focus-group","remote-semi-structured-interview","mobile-focus-group","telephone-assisted-focus-group","online-semi-structured-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-non-participant-observation","name":"Remote Non-participant Observation","fullName":"Remote Non-participant Observation","aliases":["remote unobtrusive observation","virtual non-participant observation","online non-participant observation","digital observer-only observation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (digital/virtual adaptation)","originator":"Adapted from classical non-participant observation; remote digital application developed through internet research methodology (Hine, Mann, Stewart, and others, 1990s–2000s)","url":"https://scholargate.app/en/survey-methodology/remote-non-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-non-participant-observation.md","definition":"Remote non-participant observation is a qualitative data collection technique in which the researcher observes naturally occurring behavior, interaction, or activity from a distance — via video conferencing platforms, live-streamed sessions, online communities, or recorded media — without joining or influencing the setting. The researcher maintains a purely observer role throughout, recording field notes without actively participating in the observed context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from classical non-participant observation; remote digital application developed through internet research methodology (Hine, Mann, Stewart, and others, 1990s–2000s)","year":"1990s–2000s (digital/virtual adaptation)","type":"Qualitative data collection technique","dataType":"Observational field notes, video recordings, screen captures, digital interaction logs","subfamily":"Data collection"},"citations":[{"ref":"Salmons, J. (2015). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483332093","url":null},{"ref":"Hine, C. (2015). Ethnography for the Internet: Embedded, Embodied and Everyday. Bloomsbury Academic.","type":"book","doi":null,"isbn":"978-0857855701","url":null}],"related":["non-participant-observation","online-non-participant-observation","participant-observation","remote-participant-observation","digital-ethnography","structured-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-participant-observation","name":"Remote Participant Observation","fullName":"Remote Participant Observation","aliases":["virtual participant observation","online ethnography","digital participant observation","remote ethnographic observation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s–2000s","originator":"Christine Hine (virtual ethnography); Robert Kozinets (netnography)","url":"https://scholargate.app/en/survey-methodology/remote-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-participant-observation.md","definition":"Remote Participant Observation is a qualitative data collection method in which the researcher joins and participates in an online or digitally mediated social setting — such as a video-based community, online forum, virtual world, or remote work environment — to observe and record social interactions, practices, and meanings as they occur naturally, without requiring physical co-presence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christine Hine (virtual ethnography); Robert Kozinets (netnography)","year":"Late 1990s–2000s","type":"Qualitative observational method","dataType":"Field notes, video recordings, chat logs, digital artefacts","subfamily":"Data collection"},"citations":[{"ref":"Hine, C. (2000). Virtual Ethnography. Sage.","type":"book","doi":null,"isbn":"978-0761958963","url":null},{"ref":"Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.","type":"book","doi":null,"isbn":"978-1847875228","url":null}],"related":["participant-observation","online-participant-observation","netnography","digital-ethnography","face-to-face-participant-observation","remote-semi-structured-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-research-diary","name":"Remote Research Diary","fullName":"Remote Research Diary Method","aliases":["remote reflexive journal","remote diary study","distributed research diary","online researcher diary"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (digital/remote adaptation)","originator":"Adapted from diary/journal traditions; remote administration formalized by qualitative and health researchers from the 1990s onward","url":"https://scholargate.app/en/survey-methodology/remote-research-diary","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-research-diary.md","definition":"A Remote Research Diary is a qualitative data collection method in which participants record their thoughts, experiences, and reflections in a structured or semi-structured journal over time, submitting entries to the researcher without face-to-face contact. Conducted via email, secure web platforms, or dedicated apps, this approach captures longitudinal, in-situ accounts of lived experience while eliminating geographic barriers and reducing observer effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from diary/journal traditions; remote administration formalized by qualitative and health researchers from the 1990s onward","year":"1990s–2000s (digital/remote adaptation)","type":"Qualitative longitudinal data collection instrument","dataType":"Participant-generated written or multimedia entries submitted remotely","subfamily":"Data collection"},"citations":[{"ref":"Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.","type":"article","doi":"10.1146/annurev.psych.54.101601.145030","isbn":null,"url":null},{"ref":"Williamson, K., & Johanson, G. (Eds.). (2018). Research Methods: Information, Systems, and Contexts (2nd ed.). Chandos Publishing.","type":"article","doi":null,"isbn":"978-0081022207","url":null}],"related":["diary-method","research-diary","remote-diary-method","mobile-experience-sampling","online-diary-method","longitudinal-research-diary"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-semi-structured-interview","name":"Remote Semi-structured Interview","fullName":"Remote Semi-structured Interview","aliases":["virtual semi-structured interview","online semi-structured interview","video-mediated semi-structured interview","distance semi-structured interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s–2000s (widespread adoption post-2020)","originator":"Adapted from classical semi-structured interviewing (Kvale, 1996); remote delivery formalised in qualitative methods literature from the late 1990s onward","url":"https://scholargate.app/en/survey-methodology/remote-semi-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-semi-structured-interview.md","definition":"A remote semi-structured interview is a qualitative data collection method in which a researcher conducts a guided, flexible conversation with a participant over a distance-bridging medium — telephone, video conferencing, or voice-over-IP — using a prepared topic guide with open-ended questions while allowing natural conversational elaboration. It combines the structure and comparability of a protocol-driven approach with the depth and flexibility characteristic of qualitative inquiry, delivered without physical co-presence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from classical semi-structured interviewing (Kvale, 1996); remote delivery formalised in qualitative methods literature from the late 1990s onward","year":"Late 1990s–2000s (widespread adoption post-2020)","type":"Qualitative data collection technique","dataType":"Verbal/text data (audio or video recording, transcripts)","subfamily":"Data collection"},"citations":[{"ref":"Kvale, S. (1996). InterViews: An Introduction to Qualitative Research Interviewing. Sage.","type":"book","doi":null,"isbn":"978-0803958203","url":null},{"ref":"Lobe, B., Morgan, D., & Hoffman, K. A. (2020). Qualitative data collection in an era of social distancing. International Journal of Qualitative Methods, 19, 1–8.","type":"article","doi":"10.1177/1609406920937875","isbn":null,"url":null}],"related":["semi-structured-interview","online-semi-structured-interview","face-to-face-semi-structured-interview","remote-in-depth-interview","remote-focus-group","telephone-assisted-semi-structured-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-sensing-classification","name":"Remote Sensing Classification","fullName":"Remote Sensing Image Classification","aliases":["land cover classification","image classification","satellite image classification","spectral classification"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1970s–present","originator":"Swain & Davis (1978); Lillesand & Kiefer (classical textbook treatments)","url":"https://scholargate.app/en/spatial-analysis/remote-sensing-classification","markdownUrl":"https://scholargate.app/en/spatial-analysis/remote-sensing-classification.md","definition":"Remote sensing classification assigns discrete thematic labels — such as forest, urban, water, or cropland — to pixels in a satellite or aerial image based on their spectral, spatial, and temporal properties. It underpins land-use/land-cover mapping, change detection, environmental monitoring, and disaster response at local to global scales.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Swain & Davis (1978); Lillesand & Kiefer (classical textbook treatments)","year":"1970s–present","type":"Supervised / unsupervised image classification","dataType":"Multispectral or hyperspectral satellite/aerial imagery (raster)","subfamily":"GIS / spatial"},"citations":[{"ref":"Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation (7th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118343289","url":null},{"ref":"Remote sensing. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Remote_sensing"}],"related":["kernel-density-estimation","hot-spot-analysis","local-indicators-of-spatial-association","network-based-spatial-analysis","kriging","multiscale-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-sensor-data-collection","name":"Remote Sensor Data Collection","fullName":"Remote Sensor-Based Data Collection","aliases":["remote sensing data acquisition","wireless sensor data collection","distributed sensor data collection","telemetric data collection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (proliferated with wireless and IoT technologies)","originator":"Multiple contributors; foundational wireless sensor network (WSN) survey by Akyildiz et al.","url":"https://scholargate.app/en/survey-methodology/remote-sensor-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-sensor-data-collection.md","definition":"Remote sensor data collection is the systematic acquisition of measurements from geographically distributed sensing devices without requiring direct human presence at each location. Sensors continuously or periodically record physical, chemical, or biological variables — temperature, pressure, motion, light, GPS coordinates — and transmit readings wirelessly or via network to a central repository for analysis. Widely used in environmental monitoring, precision agriculture, health informatics, and smart infrastructure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; foundational wireless sensor network (WSN) survey by Akyildiz et al.","year":"1990s–2000s (proliferated with wireless and IoT technologies)","type":"Automated quantitative data collection","dataType":"Continuous or event-triggered numeric/time-series readings from sensors","subfamily":"Data collection"},"citations":[{"ref":"Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.","type":"article","doi":"10.1016/S1389-1286(01)00302-4","isbn":null,"url":null},{"ref":"Remote sensing. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Remote_sensing"}],"related":["sensor-data-collection","api-based-data-collection","mobile-sensor-data-collection","longitudinal-sensor-data-collection","web-scraping","mobile-experience-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-survey","name":"Remote Survey","fullName":"Remote Survey Data Collection","aliases":["distance survey","self-administered remote questionnaire","remote questionnaire","distributed survey"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–present (formalised by Dillman 1978; expanded to internet surveys 2000s)","originator":"Don A. Dillman (Tailored Design Method for mail/remote surveys)","url":"https://scholargate.app/en/survey-methodology/remote-survey","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-survey.md","definition":"A remote survey is a structured data collection method in which respondents complete a questionnaire without the researcher being physically present. Delivered via mail, telephone, email, web platforms, or mobile apps, it enables researchers to reach geographically dispersed samples at relatively low cost. The method is central to social-science, public-health, and organisational research and is codified in Dillman's widely used Tailored Design Method.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Don A. Dillman (Tailored Design Method for mail/remote surveys)","year":"1970s–present (formalised by Dillman 1978; expanded to internet surveys 2000s)","type":"Quantitative / mixed-methods data collection technique","dataType":"Structured questionnaire responses (Likert, closed, open-ended items)","subfamily":"Data collection"},"citations":[{"ref":"Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118456149","url":null},{"ref":"Tourangeau, R., Conrad, F. G., & Couper, M. P. (2013). The Science of Web Surveys. Oxford University Press.","type":"book","doi":null,"isbn":"978-0199747047","url":null}],"related":["online-survey","telephone-assisted-survey","mobile-survey","structured-interview","longitudinal-survey","delphi-technique"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"remote-web-scraping","name":"Remote Web Scraping","fullName":"Remote Web Scraping for Research Data Collection","aliases":["cloud web scraping","server-side scraping","remote automated data extraction","distributed web scraping"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s (cloud infrastructure era)","originator":"Distributed computing and web automation communities","url":"https://scholargate.app/en/survey-methodology/remote-web-scraping","markdownUrl":"https://scholargate.app/en/survey-methodology/remote-web-scraping.md","definition":"Remote web scraping is a data collection approach in which automated scripts or bots harvest publicly accessible web content — text, tables, metadata, or links — running on remote servers or cloud infrastructure rather than on the researcher's local machine. This separation allows continuous, large-scale, or geographically distributed crawling that local setups cannot sustain, making it particularly suited to longitudinal or high-volume data collection tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Distributed computing and web automation communities","year":"2000s–2010s (cloud infrastructure era)","type":"Automated remote data collection technique","dataType":"Structured and semi-structured web content (HTML, JSON, XML)","subfamily":"Data collection"},"citations":[{"ref":"Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media.","type":"book","doi":null,"isbn":"978-1491985571","url":null},{"ref":"Web scraping. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Web_scraping"}],"related":["web-scraping","api-based-data-collection","remote-api-based-data-collection","online-web-scraping","mobile-web-scraping","sensor-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"renormalization-group-equations","name":"Renormalization Group Equations","fullName":"Renormalization Group Equations","aliases":["RGE","running couplings","beta function evolution"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Coupling evolution","year":"1970","originator":"Curtis Callan and David Gross","url":"https://scholargate.app/en/particle-physics/renormalization-group-equations","markdownUrl":"https://scholargate.app/en/particle-physics/renormalization-group-equations.md","definition":"Renormalization Group Equations (RGEs) describe how the coupling constants and masses of a quantum field theory evolve with energy scale. They are fundamental tools for understanding the scale dependence of physics, predicting the behavior of coupling strengths at different energies, and connecting high-energy physics to low-energy precision measurements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Curtis Callan and David Gross","subfamily":"Coupling evolution","year":"1970","type":"Scale dependence framework"},"citations":[{"ref":"Callan, C. G. (1970). Broken scale invariance in scalar field theory. Physical Review D, 2(6), 1541.","type":"article","doi":"10.1103/PhysRevD.2.1541","isbn":null,"url":null},{"ref":"Gross, D. J., & Wilczek, F. (1973). Ultraviolet behavior of non-abelian gauge theories. Physical Review Letters, 30(26), 1343.","type":"article","doi":"10.1103/PhysRevLett.30.1343","isbn":null,"url":null},{"ref":"Cheng, T. P., & Li, L. F. (2005). Gauge Theory of Elementary Particle Physics. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://global.oup.com/academic/product/gauge-theory-of-elementary-particle-physics-9780198567271"}],"related":["effective-field-theory","feynman-diagram","pdf-fitting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"repeated-measures-anova","name":"Repeated-measures ANOVA","fullName":"Repeated-measures Analysis of Variance","aliases":["within-subjects ANOVA","repeated measures analysis of variance","rm-ANOVA","Tekrarlı Ölçüm ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1992,"originator":"Girden (textbook treatment); Field (2013)","url":"https://scholargate.app/en/statistics/repeated-measures-anova","markdownUrl":"https://scholargate.app/en/statistics/repeated-measures-anova.md","definition":"Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Girden (textbook treatment); Field (2013)","year":1992,"family":"Hypothesis test","type":"Parametric within-subjects mean comparison","groups":"3+ repeated conditions/time points","outcome":"continuous","parametric":true,"distribution":"Fisher F","df":"(k - 1) and (k - 1)(n - 1)"},"citations":[{"ref":"Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE.","type":"book","doi":null,"isbn":"978-1446249185","url":null},{"ref":"Girden, E. R. (1992). ANOVA: Repeated Measures. SAGE.","type":"book","doi":null,"isbn":"978-0803942578","url":null}],"related":["one-way-anova","paired-t-test","friedman-test","linear-mixed-model","two-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"repertory-grid","name":"Repertory Grid","fullName":"Repertory Grid Technique","aliases":["Rep Grid","Repertory Grid Test","Kelly Grid"],"domain":"psychology","family":"hypothesis-test","subfamily":"Constructivist","year":"1955","originator":"George Kelly","url":"https://scholargate.app/en/psychology/repertory-grid","markdownUrl":"https://scholargate.app/en/psychology/repertory-grid.md","definition":"The Repertory Grid is a qualitative-quantitative method derived from Personal Construct Theory that elicits how individuals construe (interpret and evaluate) a domain of interest—people, concepts, events, or objects—through their own idiosyncratic dimensions or 'constructs.' Introduced by George Kelly in 1955, the method generates a grid of elements (e.g., people) rated along personally meaningful bipolar constructs, revealing cognitive structures, values, and reasoning patterns without imposing researcher-defined categories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Kelly","subfamily":"Constructivist","year":"1955","type":"Qualitative-quantitative hybrid"},"citations":[{"ref":"Kelly, G. A. (1955). The psychology of personal constructs. Norton.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Kelly%2C%20G.%20A.%20(1955).%20The%20psychology%20of%20personal%20constructs.%20Norton."},{"ref":"Fransella, F., Bell, R., & Bannister, D. (2004). A manual for repertory grid technique (2nd ed.). Wiley.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Fransella%2C%20F.%2C%20Bell%2C%20R.%2C%20%26%20Bannister%2C%20D.%20(2004).%20A%20manual%20for%20repertory%20grid%20technique%20(2nd%20ed.).%20Wiley."},{"ref":"Grice, J. W. (2002). Idiogrid: Software for the management and analysis of repertory grids. Behavior Research Methods, Instruments, and Computers, 34(3), 338-341.","type":"article","doi":"10.3758/bf03195461","isbn":null,"url":null}],"related":["qualitative-analysis","phenomenological-methods","semi-structured-interview","personal-construct-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"representational-similarity-analysis","name":"Representational Similarity Analysis","fullName":"Representational Similarity Analysis (RSA)","aliases":["RSA","representational geometry","similarity structure analysis"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Representational analysis","year":"2008","originator":"Nikolaus Kriegeskorte","url":"https://scholargate.app/en/neuroimaging/representational-similarity-analysis","markdownUrl":"https://scholargate.app/en/neuroimaging/representational-similarity-analysis.md","definition":"Representational Similarity Analysis (RSA) is a framework for comparing representational geometry across brain regions, computational models, and behavioral measures. Introduced by Kriegeskorte and colleagues in 2008, RSA measures how similarly a brain region represents different stimuli or concepts by examining pairwise similarity structure rather than absolute activity patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nikolaus Kriegeskorte","subfamily":"Representational analysis","year":"2008","type":"fMRI similarity structure comparison"},"citations":[{"ref":"Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis—connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2, 4.","type":"article","doi":"10.3389/neuro.06.004.2008","isbn":null,"url":null},{"ref":"Nili, H., Wingfield, C., Walther, A., et al. (2014). Inferring population attitude towards candidates from social media and electoral history. PLOS ONE, 9(5), e95809.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Inferring+population+attitude+towards+candidates+from+social+media+and+electoral+history+Nili"}],"related":["multivariate-pattern-analysis","graph-brain-network-analysis","dynamic-causal-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reproduction-number","name":"Reproduction Number","fullName":"Basic and Effective Reproduction Number (R0, Rt)","aliases":["Basic Reproduction Ratio","Effective Reproduction Number","Net Reproduction Number","Temel Üreme Sayısı"],"domain":"epidemiology","family":"regression-model","subfamily":"Epidemic modelling","year":1990,"originator":"Diekmann, Heesterbeek & Metz","url":"https://scholargate.app/en/epidemiology/reproduction-number","markdownUrl":"https://scholargate.app/en/epidemiology/reproduction-number.md","definition":"The basic reproduction number R0 is the expected number of secondary infections produced by a single infectious individual introduced into a fully susceptible population. Formally defined and computationally grounded by Diekmann, Heesterbeek, and Metz in 1990 using the next-generation matrix approach, R0 serves as the central threshold parameter in mathematical epidemiology: if R0 > 1, an epidemic can establish itself; if R0 < 1, the outbreak dies out. The effective reproduction number Rt extends this to partially immune or partially susceptible populations over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diekmann, Heesterbeek & Metz","year":1990,"type":"Threshold parameter for epidemic spread","subfamily":"Epidemic modelling","threshold":"R0 = 1 (epidemic vs. extinction boundary)","notation":"R0 (basic), Rt or Re (effective)"},"citations":[{"ref":"Diekmann, O., Heesterbeek, J. A. P., & Metz, J. A. J. (1990). On the definition and the computation of the basic reproduction ratio R0. Journal of Mathematical Biology, 28(4), 365–382.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+definition+and+the+computation+of+the+basic+reproduction+ratio+R0+Diekmann"}],"related":["sir-model","seir-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rescorla-wagner-model","name":"Rescorla-Wagner Model","fullName":"Rescorla-Wagner Model of Associative Learning","aliases":["Rescorla-Wagner Theory","Delta Rule","Error-Correction Learning"],"domain":"psychology","family":"hypothesis-test","subfamily":"Learning Theory","year":"1972","originator":"Robert Rescorla and Allan Wagner","url":"https://scholargate.app/en/psychology/rescorla-wagner-model","markdownUrl":"https://scholargate.app/en/psychology/rescorla-wagner-model.md","definition":"The Rescorla-Wagner Model is a quantitative theory of associative learning that predicts how organisms learn associations between stimuli (e.g., tone and shock in fear conditioning). The model proposes that learning is driven by prediction error—the difference between what is expected to occur and what actually occurs. When prediction error is large, learning is rapid; when prediction error is small, learning slows. The model captures asymptotic learning curves, blocking effects, and stimulus interactions, providing a principled framework for understanding learning dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Rescorla and Allan Wagner","subfamily":"Learning Theory","year":"1972","type":"Computational model"},"citations":[{"ref":"Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II (pp. 64-99). Appleton-Century-Crofts.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Rescorla%2C%20R.%20A.%2C%20%26%20Wagner%2C%20A.%20R.%20(1972).%20A%20theory%20of%20Pavlovian%20conditioning%3A%20Variations%20in%20the%20effectiveness%20of%20reinforc"},{"ref":"Simonetta, S. H., Schaafsma, S. M., & Meffert, H. (2010). The Rescorla-Wagner model of Pavlovian conditioning: Some current issues and applications. Neuroscience & Biobehavioral Reviews, 34(6), 821-835.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Rescorla-Wagner+model+of+Pavlovian+conditioning%3A+Some+current+issues+and+applications+Simonetta"},{"ref":"Gluck, M. A., & Myers, C. E. (1993). Hippocampal mediation of stimulus representation: A computational theory. Hippocampus, 3(4), 491-516.","type":"article","doi":"10.1002/hipo.450030410","isbn":null,"url":null}],"related":["classical-conditioning","associative-learning","error-prediction","learning-models"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"research-design-types","name":"Research Design Types","fullName":"Classification and Selection of Research Design Types","aliases":["research designs","experimental and observational designs"],"domain":"research-methodology","family":"process-pipeline","subfamily":"research architecture","year":"1963","originator":"Donald T. Campbell and Julian Stanley (1963); William Shadish, Thomas Cook, & Donald Campbell (2002)","url":"https://scholargate.app/en/research-methodology/research-design-types","markdownUrl":"https://scholargate.app/en/research-methodology/research-design-types.md","definition":"Research design is the overall structure and strategy of a study, encompassing decisions about how to collect, organize, and analyze data to answer research questions. Major design types include experimental (randomized controlled trials), quasi-experimental (non-random assignment), observational (no manipulation), and qualitative (exploratory, interpretive). Donald T. Campbell and Julian Stanley's 1963 seminal work established systematic terminology for internal validity threats in each design type. Modern classifications (Campbell et al., 2002; Creswell & Plano Clark, 2011) also include mixed-methods designs combining quantitative and qualitative elements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald T. Campbell and Julian Stanley (1963); William Shadish, Thomas Cook, & Donald Campbell (2002)","subfamily":"research architecture","year":"1963","type":"Framework"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Campbell%2C%20D.%20T.%2C%20%26%20Stanley%2C%20J.%20C.%20(1963).%20Experimental%20and%20Quasi-Experimental%20Designs%20for%20Research.%20Rand%20McNally."},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). SAGE Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Creswell%2C%20J.%20W.%2C%20%26%20Plano%20Clark%2C%20V.%20L.%20(2011).%20Designing%20and%20Conducting%20Mixed%20Methods%20Research%20(2nd%20ed.).%20SAGE%20Publicatio"},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Shadish%2C%20W.%20R.%2C%20Cook%2C%20T.%20D.%2C%20%26%20Campbell%2C%20D.%20T.%20(2002).%20Experimental%20and%20Quasi-Experimental%20Designs%20for%20Generalized%20Causa"}],"related":["research-question-formulation","validity-reliability-research","sampling-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"research-diary","name":"Research Diary","fullName":"Research Diary Method","aliases":["researcher diary","field diary","research journal","reflexive diary"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1981 (methodological codification); diary use in research dates to 19th-century anthropology","originator":"Robert G. Burgess (systematic methodological treatment)","url":"https://scholargate.app/en/survey-methodology/research-diary","markdownUrl":"https://scholargate.app/en/survey-methodology/research-diary.md","definition":"A research diary is a systematic, dated log maintained by the researcher throughout a study to record methodological decisions, emergent observations, analytical hunches, and reflections on researcher positionality. Unlike a participant diary, it is authored by the researcher and functions simultaneously as a data source, an audit trail, and a reflexivity instrument.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert G. Burgess (systematic methodological treatment)","year":"1981 (methodological codification); diary use in research dates to 19th-century anthropology","type":"Qualitative data collection and reflexivity tool","dataType":"Written narrative, reflective text","subfamily":"Data collection"},"citations":[{"ref":"Burgess, R. G. (1981). Keeping a research diary. Cambridge Journal of Education, 11(1), 75–83.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Keeping+a+research+diary+Burgess+1981"},{"ref":"Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.","type":"book","doi":null,"isbn":"978-0761961703","url":null}],"related":["diary-method","field-notes","participant-observation","ethnography","reflexive-thematic-analysis","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"research-front-identification","name":"Research Front Identification","fullName":"Research Front Identification and Emerging Trend Detection","aliases":["emerging research detection","research frontier mapping","hot topic identification","emerging field analysis"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"trend-detection","year":"1990s–2000s","originator":"Chaomei Chen and others","url":"https://scholargate.app/en/bibliometrics/research-front-identification","markdownUrl":"https://scholargate.app/en/bibliometrics/research-front-identification.md","definition":"Research front identification is a bibliometric method for detecting emerging or cutting-edge research areas within a larger research landscape. A 'research front' is a cluster of recently published, highly-cited papers that define the current active research direction in a field. Unlike established research communities (identifiable through co-citation networks and slow-changing patterns), research fronts are characterized by rapid growth, high citation velocity (papers accumulating citations quickly), and weak historical ties to established literature. Developed systematically by Chen and others in the 1990s–2000s, research front identification enables researchers, funders, and policy makers to track where scientific activity is concentrating and where breakthrough research is emerging.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chaomei Chen and others","subfamily":"trend-detection","year":"1990s–2000s","type":"Method"},"citations":[{"ref":"Chen, C., & Paul, R. J. (1997). Visualizing a knowledge domain's intellectual structure. IEEE Computer, 30(3), 65–71.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Visualizing+a+knowledge+domain%27s+intellectual+structure+Chen"},{"ref":"Chen, C., Ciliberto, G., & Chen, Y. (2009). Detecting science hot topics by aggregating publication metadata. Journal of Informetrics, 3(2), 74–89.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Detecting+science+hot+topics+by+aggregating+publication+metadata+Chen"},{"ref":"Small, H., Boyack, K. W., & Klavans, R. (2005). Identifying emerging topics in science and technology. Research Policy, 43(8), 1232–1241.","type":"article","doi":"10.1016/j.respol.2014.02.005","isbn":null,"url":null}],"related":["science-mapping","co-citation-analysis","keyword-co-occurrence","vosviewer-citespace","research-front-identification"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"research-integrity-principles","name":"Research Integrity Principles","fullName":"Core Principles and Standards for Research Integrity and Responsible Conduct","aliases":["Responsible Conduct of Research","RCR","Research Ethics Standards"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-standards","year":"2007","originator":"Multiple (National Academies, NIH/ORI, ESOMAR, individual discipline standards)","url":"https://scholargate.app/en/research-ethics/research-integrity-principles","markdownUrl":"https://scholargate.app/en/research-ethics/research-integrity-principles.md","definition":"Research integrity encompasses the ethical and professional standards that guide responsible conduct in all aspects of research—from study design and data collection through analysis, reporting, and publication. The core principles—honesty, transparency, accountability, respect, and stewardship—ensure that research is trustworthy, reproducible, and contributes legitimate knowledge. These principles are universal across disciplines and are enforced through institutional policies, professional standards, and regulatory oversight. Violations of research integrity undermine scientific credibility and can harm subjects, institutions, and public trust.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (National Academies, NIH/ORI, ESOMAR, individual discipline standards)","subfamily":"ethical-standards","year":"2007","type":"Framework"},"citations":[{"ref":"National Academies of Sciences, Engineering, and Medicine. (2017). Fostering Integrity in Research. The National Academies Press.","type":"book","doi":"10.17226/21896","isbn":null,"url":null},{"ref":"U.S. Office of Research Integrity. (2007). Introduction to RCR: Responsible Conduct of Research. NIH Training Module.","type":"report","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products-and-resources"},{"ref":"ESOMAR. (2016). International Code on Market, Opinion and Social Research and Data Analytics. Ethical standards for research professionals.","type":"report","doi":null,"isbn":null,"url":"https://www.esomar.org/"}],"related":["research-misconduct","conflict-of-interest-research","belmont-report","data-fabrication-falsification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"research-misconduct","name":"Research Misconduct","fullName":"Definition, Investigation, and Consequences of Research Misconduct","aliases":["FFP","Research Fraud","Scientific Misconduct"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-violations","year":"2005","originator":"U.S. Office of Research Integrity (ORI) / National Science Foundation; International standards via COPE","url":"https://scholargate.app/en/research-ethics/research-misconduct","markdownUrl":"https://scholargate.app/en/research-ethics/research-misconduct.md","definition":"Research misconduct comprises intentional or reckless fabrication, falsification, or plagiarism in proposing, conducting, or reporting research. Formally defined by U.S. federal policy (42 CFR Part 93, Office of Research Integrity), misconduct is distinguished from honest error, negligence, and good-faith disagreements about research methods or interpretation. Misconduct undermines scientific integrity, harms subjects and institutions, wastes research resources, and erodes public trust in science. Allegations are investigated formally with due process; proven misconduct results in sanctions ranging from publication correction to career-ending bans.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Office of Research Integrity (ORI) / National Science Foundation; International standards via COPE","subfamily":"ethical-violations","year":"2005","type":"Standard"},"citations":[{"ref":"U.S. Office of Research Integrity. (2005). Public Health Service Policy on Research Misconduct. 42 CFR Part 93. Federal Register.","type":"legal","doi":null,"isbn":null,"url":"https://ori.hhs.gov/federal-research-misconduct-policy"},{"ref":"National Science Foundation. (2007). NSF Research Misconduct Policy. PAPPG Significant Changes.","type":"legal","doi":null,"isbn":null,"url":"https://www.nsf.gov/bfa/dias/policy/rcr.jsp"},{"ref":"Retraction Watch. (2023). Anatomy of a Retraction: What Can We Learn From Retractions? Retractions database and analysis.","type":"report","doi":null,"isbn":null,"url":"https://retractionwatch.com/"}],"related":["research-integrity-principles","data-fabrication-falsification","conflict-of-interest-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"research-question-formulation","name":"Research Question Formulation","fullName":"Research Question Formulation and Development","aliases":["RQF","research question design"],"domain":"research-methodology","family":"process-pipeline","subfamily":"research planning","year":"1950","originator":"Kerlinger, Campbell, & Fisher (1950s–1990s research methodology literature)","url":"https://scholargate.app/en/research-methodology/research-question-formulation","markdownUrl":"https://scholargate.app/en/research-methodology/research-question-formulation.md","definition":"Research question formulation is the process of defining clear, focused, and answerable questions that guide a research study. A well-formulated research question specifies what a researcher seeks to investigate, distinguishing between independent and dependent variables (or phenomena), and sets the scope for literature review, methodological design, and data collection. Established in behavioral research literature in the mid-20th century, this framework remains foundational because it transforms vague research interests into testable, empirically grounded inquiries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kerlinger, Campbell, & Fisher (1950s–1990s research methodology literature)","subfamily":"research planning","year":"1950","type":"Framework"},"citations":[{"ref":"Kerlinger, F. N., & Lee, H. B. (1999). Foundations of Behavioral Research (4th ed.). Wadsworth.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Kerlinger%2C%20F.%20N.%2C%20%26%20Lee%2C%20H.%20B.%20(1999).%20Foundations%20of%20Behavioral%20Research%20(4th%20ed.).%20Wadsworth."},{"ref":"Morse, J. M. (1991). Approaches to qualitative-quantitative methodological triangulation. Nursing Research, 40(2), 120–123.","type":"article","doi":"10.1097/00006199-199103000-00014","isbn":null,"url":null},{"ref":"Battiam, R., & Sarkar, U. (2016). Crafting research questions for healthcare research. Health Affairs, 35(8), 1413–1418.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Crafting+research+questions+for+healthcare+research+Battiam"}],"related":["hypothesis-development","research-design-types","pico-framework","literature-search-strategy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"resilience-scale","name":"Connor-Davidson Resilience Scale","fullName":"Connor-Davidson Resilience Scale (CD-RISC)","aliases":["CD-RISC","Connor-Davidson Scale","Resilience Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Personality assessment","year":"2003","originator":"Kathryn Connor and Jonathan Davidson","url":"https://scholargate.app/en/social-psychology/resilience-scale","markdownUrl":"https://scholargate.app/en/social-psychology/resilience-scale.md","definition":"The Connor-Davidson Resilience Scale (CD-RISC) is a 25-item self-report measure of psychological resilience—the capacity to cope with stress, adversity, and trauma while maintaining psychological functioning. Developed by Kathryn Connor and Jonathan Davidson in 2003, the CD-RISC operationalizes resilience as a multidimensional construct encompassing personal competence, trust in instincts, positive adaptation, and meaning-making. A brief 10-item version (CD-RISC-10) is also widely available. The scale has become standard in clinical research on trauma, anxiety, depression, and recovery from adversity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathryn Connor and Jonathan Davidson","subfamily":"Personality assessment","year":"2003","type":"Psychological resilience and stress coping measure"},"citations":[{"ref":"Connor, K. M., & Davidson, J. R. (2003). Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depression and Anxiety, 18(2), 76–82.","type":"article","doi":"10.1002/da.10113","isbn":null,"url":null},{"ref":"Campbell-Sills, L., & Stein, M. B. (2007). Psychometric analysis and refinement of the Connor-Davidson Resilience Scale (CD-RISC): Validation of a 10-item measure of resilience. Journal of Traumatic Stress, 20(6), 1019–1028.","type":"article","doi":"10.1002/jts.20271","isbn":null,"url":null},{"ref":"Notario-Pacheco, B., Solera-Martínez, M., Serrano-Parra, M. D., Bartolomé-Gutiérrez, R., García-Campayo, J., & Martínez-Vizcaíno, V. (2014). Reliability and validity of the Spanish version of the 10-item Connor-Davidson Resilience Scale in young adults. Health and Quality of Life Outcomes, 12(1), 165.","type":"article","doi":"10.1037/t60477-000","isbn":null,"url":null}],"related":["self-compassion-scale","grit-scale","generalized-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"resnet","name":"ResNet","fullName":"Residual Network (ResNet)","aliases":["ResNet","Residual Network","Deep Residual Learning","ResNet-50","ResNet-101","ResNet-152","He-net"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2016,"originator":"He, K.; Zhang, X.; Ren, S.; Sun, J.","url":"https://scholargate.app/en/deep-learning/resnet","markdownUrl":"https://scholargate.app/en/deep-learning/resnet.md","definition":"ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016. By inserting shortcut (skip) connections that carry the input of a block directly to its output — defining the block's task as learning a residual correction rather than a full mapping — ResNet enabled training of networks with hundreds or even thousands of layers without the vanishing-gradient degradation that had previously made very deep networks impractical. It won the ILSVRC 2015 image recognition competition with a top-5 error of 3.57% and remains the most widely used backbone architecture in computer vision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"He, K.; Zhang, X.; Ren, S.; Sun, J.","year":2016,"type":"Deep Convolutional Neural Network with skip connections","task":"Image classification, object detection, segmentation, feature extraction","wonImageNet":true,"top5ErrorRate":"3.57% (ensemble) on ILSVRC 2015","depthRange":"18 to 1202+ layers","keyInnovation":"Residual (skip) connection: H(x) = F(x) + x"},"citations":[{"ref":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.","type":"article","doi":"10.1109/CVPR.2016.90","isbn":null,"url":null},{"ref":"He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv:1512.03385.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1512.03385"},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["vgg-network","densenet","inception-network","efficientnet","alexnet","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"resnext","name":"ResNeXt","fullName":"ResNeXt: Aggregated Residual Transformations for Deep Neural Networks","aliases":["ResNeXt","Aggregated Residual Transformations","grouped convolution residual network","cardinality-based ResNet"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K.","url":"https://scholargate.app/en/deep-learning/resnext","markdownUrl":"https://scholargate.app/en/deep-learning/resnext.md","definition":"ResNeXt is a deep convolutional neural network architecture introduced by Xie, Girshick, Dollár, Tu, and He at CVPR 2017. It extends the residual network (ResNet) design by introducing a new architectural dimension called cardinality — the number of independent, parallel transformation paths within each residual block — enabling higher accuracy with fewer parameters and a simpler, more uniform design than its predecessors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xie, S.; Girshick, R.; Dollár, P.; Tu, Z.; He, K.","year":2017,"type":"Convolutional neural network with grouped/cardinality-based residual blocks","task":"Image classification, object detection, feature extraction","keyHyperparameter":"Cardinality (number of parallel transformation paths)","venue":"CVPR 2017"},"citations":[{"ref":"Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5987–5995.","type":"article","doi":"10.1109/CVPR.2017.634","isbn":null,"url":null},{"ref":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.","type":"article","doi":"10.1109/CVPR.2016.90","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-26-203561-3","url":null}],"related":["resnet","vgg","inception","densenet","efficientnet","mobilenet","se-net"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"respiratory-exchange-ratio","name":"Respiratory Exchange Ratio","fullName":"Respiratory Exchange Ratio and Substrate Utilization Assessment","aliases":["RER","respiratory quotient","RQ","substrate oxidation ratio"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Energy Metabolism","year":"1949","originator":"J. B. Weir","url":"https://scholargate.app/en/sports-science/respiratory-exchange-ratio","markdownUrl":"https://scholargate.app/en/sports-science/respiratory-exchange-ratio.md","definition":"The respiratory exchange ratio (RER), also called the respiratory quotient (RQ), is the ratio of carbon dioxide produced to oxygen consumed during metabolism. Introduced by J. B. Weir (1949), RER is a non-invasive indirect measure of substrate utilization—indicating whether the body is primarily oxidizing carbohydrate, fat, or protein. RER values range from approximately 0.7 (pure fat oxidation) to 1.0 (pure carbohydrate oxidation) and higher under anaerobic conditions. By measuring exhaled and inhaled gases during exercise, RER reveals which fuel source predominates at different intensities, providing insights into metabolic flexibility and exercise physiology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. B. Weir","subfamily":"Energy Metabolism","year":"1949","type":"expired gas analysis"},"citations":[{"ref":"Weir, J. B. (1949). New methods for calculating metabolic rate with special reference to protein metabolism. Journal of Physiology, 109(1-2), 1-9.","type":"article","doi":"10.1113/jphysiol.1949.sp004363","isbn":null,"url":null},{"ref":"Frayn, K. N. (1983). Calculation of substrate oxidation rates in vivo from gaseous exchange. Journal of Applied Physiology, 55(2), 628-634.","type":"article","doi":"10.1152/jappl.1983.55.2.628","isbn":null,"url":null},{"ref":"Jeukendrup, A. E., & Wallis, G. A. (2005). Measurement of substrate oxidation during exercise by means of gas exchange measurements. International Journal of Sports Medicine, 26(1), S28-S37.","type":"article","doi":"10.1055/s-2004-830512","isbn":null,"url":null}],"related":["vo2-max","lactate-threshold","epoc","critical-power","banister-trimp"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"respondent-driven-sampling","name":"Respondent-Driven Sampling","fullName":"Respondent-Driven Sampling (RDS)","aliases":["Chain-Referral Sampling","Peer-Referral Sampling","Network-Based Sampling","Katılımcı Güdümlü Örnekleme"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling design","year":1997,"originator":"Douglas Heckathorn","url":"https://scholargate.app/en/survey-methodology/respondent-driven-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/respondent-driven-sampling.md","definition":"Respondent-Driven Sampling (RDS) is a probabilistic chain-referral method designed to reach hidden or hard-to-reach populations that lack a sampling frame. Introduced by sociologist Douglas Heckathorn in 1997, RDS combines snowball recruitment with mathematical weighting based on participants' personal network sizes, allowing researchers to generate population-level estimates even when no complete membership list exists.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas Heckathorn","year":1997,"type":"Probabilistic chain-referral sampling design","subfamily":"Sampling design","target_population":"Hidden or hard-to-reach populations","estimator_basis":"Markov chain convergence"},"citations":[{"ref":"Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174–199.","type":"article","doi":"10.2307/3096941","isbn":null,"url":null}],"related":["stratified-sampling","capture-recapture","survey-weighting"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"response-spectrum-analysis","name":"Response Spectrum Analysis","fullName":"Response Spectrum Analysis for Earthquake Design","aliases":["Elastic response spectrum","Design spectrum method","Modal response spectrum"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Seismic Analysis","year":"1941","originator":"George W. Housner","url":"https://scholargate.app/en/civil-engineering/response-spectrum-analysis","markdownUrl":"https://scholargate.app/en/civil-engineering/response-spectrum-analysis.md","definition":"Response spectrum analysis is a linear modal method for estimating earthquake-induced forces and displacements in structures. Introduced by Housner in 1941, this approach uses design spectra that represent the maximum response of single-degree-of-freedom oscillators at different natural frequencies to compute the structural response by combining modal contributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George W. Housner","subfamily":"Seismic Analysis","year":"1941","type":"Linear modal analysis for earthquake response"},"citations":[{"ref":"Housner, G. W. (1941). Calculating the response of an oscillator to arbitrary ground motion. Bulletin of the Seismological Society of America, 32(2), 143-149.","type":"article","doi":null,"isbn":null,"url":"https://pubs.geoscienceworld.org/ssa/bssa"},{"ref":"Newmark, N. M., & Hall, W. J. (1969). Seismic design criteria for nuclear reactor facilities. Building Science Series, National Bureau of Standards, Report NSB-46.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Seismic+design+criteria+for+nuclear+reactor+facilities+Newmark"},{"ref":"ASCE/SEI (2010). Minimum Design Loads for Buildings and Other Structures (ASCE/SEI 7-10). American Society of Civil Engineers.","type":"article","doi":null,"isbn":null,"url":"https://www.asce.org/structural-engineering"}],"related":["pushover-analysis","equivalent-static-analysis","nonlinear-time-history-analysis","incremental-dynamic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"response-surface-desirability-function","name":"Response Surface Desirability Function","fullName":"Response Surface Methodology with Desirability Function Optimization","aliases":["RSM","Desirability function","Multi-response optimization"],"domain":"reliability-engineering","family":"process-pipeline","subfamily":"Design of experiments","year":"1951","originator":"George Box and Kenneth Wilson","url":"https://scholargate.app/en/reliability-engineering/response-surface-desirability-function","markdownUrl":"https://scholargate.app/en/reliability-engineering/response-surface-desirability-function.md","definition":"Response Surface Methodology (RSM) is a set of statistical and mathematical techniques for modeling and optimizing processes with multiple inputs (factors) and outputs (responses). The Desirability Function approach, introduced by Harrington (1965) and refined by Derringer and Suich (1980), extends RSM to solve multi-response optimization problems by combining competing objectives into a single index. This methodology is essential in product and process development where engineers must balance performance, cost, and reliability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Box and Kenneth Wilson","subfamily":"Design of experiments","year":"1951","type":"Optimization methodology"},"citations":[{"ref":"Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, 13(1), 1-45.","type":"article","doi":"10.1111/j.2517-6161.1951.tb00067.x","isbn":null,"url":null},{"ref":"Harrington, E. C. (1965). The desirability function. Journal of Quality Technology, 4(6), 494-509.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1080/00224065.1965.11980575"},{"ref":"Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214-219.","type":"article","doi":"10.1080/00224065.1980.11980968","isbn":null,"url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Response+Surface+Methodology%3A+Process+and+Product+Optimization+Using+Designed+Experiments+%283rd+ed.%29+Myers"}],"related":["rainflow-counting","first-order-reliability-method","highly-accelerated-life-testing","topology-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"response-surface-methodology","name":"Response Surface Methodology","fullName":"Response Surface Methodology (RSM)","aliases":["RSM","Central Composite Design","Box-Behnken Design","CCD","Yanıt Yüzeyi Yöntemi (RSM — CCD, Box-Behnken)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1951,"originator":"George E. P. Box & K. B. Wilson","url":"https://scholargate.app/en/experimental-design/response-surface-methodology","markdownUrl":"https://scholargate.app/en/experimental-design/response-surface-methodology.md","definition":"Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E. P. Box & K. B. Wilson","year":1951,"family":"Experimental design","type":"Second-order polynomial response surface model","parametric":true,"minSample":15,"designs":"Central Composite Design (CCD), Box-Behnken Design (BBD)","outcome":"continuous","modelDegree":2,"difficulty":2},"citations":[{"ref":"Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2983966"},{"ref":"Myers, R. H., Montgomery, D. C. & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916032","url":null}],"related":["factorial-design","fractional-factorial","one-way-anova","two-way-anova","multiple-linear-regression","taguchi-methods","latin-square-design"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"restless-legs-syndrome-rating","name":"IRLS","fullName":"International Restless Legs Syndrome Rating Scale","aliases":["IRLS","Restless Legs Syndrome Rating Scale"],"domain":"sleep-medicine","family":"process-pipeline","subfamily":"RLS symptom assessment; disease-specific rating","year":"2003","originator":"Walters, A. S., LeBrocq, C., Dhar, A., et al.","url":"https://scholargate.app/en/sleep-medicine/restless-legs-syndrome-rating","markdownUrl":"https://scholargate.app/en/sleep-medicine/restless-legs-syndrome-rating.md","definition":"The IRLS is a 10-item clinician-administered rating scale designed to assess the severity of symptoms in patients with restless legs syndrome (RLS). Developed and validated by Walters and colleagues in 2003 for the International Restless Legs Syndrome Study Group, it is the most widely used disease-specific severity measure for RLS in clinical practice and research. The scale captures the cardinal features of RLS—nocturnal leg discomfort, urge to move, sleep disruption, and functional impairment—across multiple dimensions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walters, A. S., LeBrocq, C., Dhar, A., et al.","subfamily":"RLS symptom assessment; disease-specific rating","year":"2003","type":"Clinician-rated; patient-reported symptoms"},"citations":[{"ref":"Walters, A. S., LeBrocq, C., Dhar, A., et al. (2003). Validation of the International Restless Legs Syndrome Study Group rating scale for restless legs syndrome. Sleep Medicine, 4(2), 121-132.","type":"article","doi":"10.1016/S1389-9457(02)00258-7","isbn":null,"url":null}],"related":["sleep-condition-indicator","berlin-questionnaire-sleep","daytime-insomnia-symptom-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"restricted-boltzmann-machine","name":"Restricted Boltzmann Machine","fullName":"Restricted Boltzmann Machine (RBM) — Bipartite Generative Energy Model","aliases":["RBM","Harmonium","restricted Boltzmann machine","RBM generative model","stochastic recurrent neural network"],"domain":"deep-learning","family":"latent-structure","subfamily":null,"year":1986,"originator":"Smolensky, P. (1986); popularised by Hinton, G. E. & Salakhutdinov, R. R. (2006)","url":"https://scholargate.app/en/deep-learning/restricted-boltzmann-machine","markdownUrl":"https://scholargate.app/en/deep-learning/restricted-boltzmann-machine.md","definition":"A Restricted Boltzmann Machine is a two-layer generative probabilistic model consisting of visible (observed) and hidden (latent) binary units connected by an undirected bipartite graph with no within-layer connections. Originally introduced as the 'Harmonium' by Paul Smolensky in 1986 and powerfully revived by Geoffrey Hinton and Ruslan Salakhutdinov in their landmark 2006 Science paper, RBMs became historically pivotal as the building block for greedy layer-wise pre-training of Deep Belief Networks, restarting interest in deep neural networks after years of stagnation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smolensky, P. (1986); popularised by Hinton, G. E. & Salakhutdinov, R. R. (2006)","year":1986,"type":"Generative energy-based probabilistic model","task":"Unsupervised representation learning, dimensionality reduction, generative modelling, pre-training","layers":"Two layers: one visible (observed), one hidden (latent)","connections":"Bipartite undirected graph; no within-layer connections","learningRule":"Contrastive Divergence (CD-k)"},"citations":[{"ref":"Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.","type":"article","doi":"10.1126/science.1127647","isbn":null,"url":null},{"ref":"Hinton, G. E. (2002). Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, 14(8), 1771–1800.","type":"article","doi":"10.1162/089976602760128018","isbn":null,"url":null},{"ref":"Smolensky, P. (1986). Information Processing in Dynamical Systems: Foundations of Harmony Theory. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel Distributed Processing, Vol. 1 (pp. 194–281). MIT Press.","type":"chapter","doi":null,"isbn":"978-0-262-68053-0","url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 20). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["deep-belief-network","boltzmann-machine","variational-autoencoder","autoencoder","principal-component-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retail-service-quality-scale","name":"Retail Service Quality Scale","fullName":"Retail Service Quality Scale (RetSQ)","aliases":["Retail Service Quality","In-Store Service Quality Scale"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Retail service quality measurement","year":"1996","originator":"Pratibha A. Dabholkar, Dayle I. Thorpe, Joseph O. Rentz","url":"https://scholargate.app/en/marketing-management/retail-service-quality-scale","markdownUrl":"https://scholargate.app/en/marketing-management/retail-service-quality-scale.md","definition":"The Retail Service Quality Scale (RetSQ) is a 17-item instrument developed by Dabholkar, Thorpe, and Rentz (1996) to measure customer perceptions of service quality in retail store environments. Adapted from SERVQUAL but customized for the unique context of in-store shopping, RetSQ measures five dimensions: Physical Aspects (store appearance, cleanliness, merchandise display), Reliability (accurate pricing, reliable operations), Personal Interaction (staff helpfulness, courtesy), Problem Solving (handling complaints, responding to customer needs), and Policies (convenience, fairness of return policies). The scale captures both the tangible environment and interpersonal service elements critical to retail success.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pratibha A. Dabholkar, Dayle I. Thorpe, Joseph O. Rentz","subfamily":"Retail service quality measurement","year":"1996","type":"Multi-dimensional retail store service quality scale"},"citations":[{"ref":"Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1996). A Measure of Service Quality for Retail Stores: Scale Development and Validation. Journal of the Academy of Marketing Science, 24(1), 3-16.","type":"article","doi":"10.1007/bf02893933","isbn":null,"url":null},{"ref":"Gagliano, K. B., & Spivey, M. (2003). Differentiation: The Key to Market Success in Grocery Retailing. Journal of Business and Industrial Marketing, 18(4-5), 340-354.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Differentiation%3A+The+Key+to+Market+Success+in+Grocery+Retailing+Gagliano"}],"related":["servqual","servperf","customer-satisfaction-index","consumer-involvement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retraction-process","name":"Article Retraction Process","fullName":"Article Retraction and Correction in Academic Publishing","aliases":["Retraction Notice","Paper Retraction","Correction Notice"],"domain":"publication-ethics","family":"process-pipeline","subfamily":"publication-misconduct","year":"1948","originator":"Committee on Publication Ethics (COPE); Retraction Watch initiative","url":"https://scholargate.app/en/publication-ethics/retraction-process","markdownUrl":"https://scholargate.app/en/publication-ethics/retraction-process.md","definition":"An article retraction is the invalidation of a published article due to serious flaws (data fraud, major methodological errors, ethical violations) that undermine its conclusions. Retractions are distinct from corrections (which address minor errors) and are initiated by authors, editors, or institutions when integrity is compromised. The first modern retraction was published in 1948. COPE published formal Retraction Guidelines in 2009 (updated 2019) that specify when retraction is appropriate, how it is conducted, and how retraction notices are recorded. Retracted articles remain in the literature with a visible 'RETRACTED' watermark, preserving the scientific record and warning readers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Committee on Publication Ethics (COPE); Retraction Watch initiative","subfamily":"publication-misconduct","year":"1948","type":"Process"},"citations":[{"ref":"Committee on Publication Ethics (2019). Retraction Guidelines. COPE.","type":"article","doi":null,"isbn":null,"url":"https://publicationethics.org/guidance/Guidelines-retraction-guidelines"},{"ref":"Garfield, E., & Welljams-Dorof, A. (1990). Citation Data: Its Use as a Science Indicator for Measuring and Evaluating the Activity of Scientists. Science and Public Policy, 17(5), 359–375.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Citation+Data%3A+Its+Use+as+a+Science+Indicator+for+Measuring+and+Evaluating+the+Activity+of+Scientists+Garfield"},{"ref":"Retraction Watch (2023). Database of Retracted Publications. Ivan Oransky & Adam Marcus.","type":"webpage","doi":null,"isbn":null,"url":"https://retractionwatch.com/"}],"related":["plagiarism-in-research","duplicate-publication","peer-review-process","cope-guidelines"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrieval-augmented-generation","name":"Retrieval-Augmented Generation","fullName":"Retrieval-Augmented Generation (RAG)","aliases":["RAG","retrieval-augmented LLM","grounded generation","Erişim Destekli Metin Üretimi (RAG)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2020,"originator":"Lewis, Patrick et al. (Meta AI / Facebook AI Research)","url":"https://scholargate.app/en/text-mining/retrieval-augmented-generation","markdownUrl":"https://scholargate.app/en/text-mining/retrieval-augmented-generation.md","definition":"Retrieval-Augmented Generation (RAG) is a natural-language-processing pipeline introduced by Lewis et al. in 2020 that strengthens a large language model (LLM) with evidence fetched at inference time from an external knowledge base. Instead of relying solely on what a model memorised during training, RAG first retrieves the most relevant passages from a document index and then hands those passages to the LLM as context, grounding the generated answer in verifiable, up-to-date information. The approach reduces hallucination and allows domain-specific or time-sensitive knowledge to be injected without retraining the model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lewis, Patrick et al. (Meta AI / Facebook AI Research)","year":2020,"type":"Hybrid retrieval + generation pipeline","assumptions":"Vector knowledge base required; LLM access required; embedding model must be selected","minDocuments":10,"difficulty":3,"output":"Grounded natural-language response citing retrieved passages"},"citations":[{"ref":"Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.","type":"article","doi":"10.48550/arXiv.2005.11401","isbn":null,"url":null},{"ref":"Gao, Y. et al. (2023). Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv preprint.","type":"article","doi":"10.48550/arXiv.2312.10997","isbn":null,"url":null}],"related":["bert-embeddings","text-summarization","question-answering","knowledge-graph-nlp","transformer-nlp","bert-finetuning","self-attention-transformer"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-case-control-study","name":"Retrospective case-control study","fullName":"Retrospective Case-Control Study","aliases":["case-control study","retrospective case-referent study","case-referent design","trohoc study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1950s–1960s (formal methodology)","originator":"Jerome Cornfield; formalized by Brian MacMahon and others in mid-20th-century epidemiology","url":"https://scholargate.app/en/epidemiology/retrospective-case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-case-control-study.md","definition":"A retrospective case-control study identifies individuals who already have an outcome of interest (cases) and a comparable group without it (controls), then looks backward in time using existing records to determine prior exposure to a suspected risk factor. The primary measure of association is the odds ratio. This design is especially efficient for studying rare diseases or outcomes with long latency periods, since the outcome has already occurred before the study begins.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerome Cornfield; formalized by Brian MacMahon and others in mid-20th-century epidemiology","year":"1950s–1960s (formal methodology)","type":"Observational analytical study","dataType":"Existing records, patient charts, registries, recalled exposures","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195029338","url":null},{"ref":"Cornfield, J. (1951). A method of estimating comparative rates from clinical data: Applications to cancer of the lung, breast, and cervix. Journal of the National Cancer Institute, 11(6), 1269–1275.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cornfield+1951+method+estimating+comparative+rates+clinical+data"}],"related":["case-control-study","nested-case-control","cohort-study","retrospective-cohort-study","case-crossover-design","odds-ratio-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-case-report","name":"Retrospective Case Report","fullName":"Retrospective Clinical Case Report","aliases":["retrospective case study","post-hoc case report","retrospective clinical case","case report"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"19th century (formalized ~2013 with CARE guidelines)","originator":"Case reporting tradition in medicine (formalized by CARE guidelines, Riley et al., 2013)","url":"https://scholargate.app/en/epidemiology/retrospective-case-report","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-case-report.md","definition":"A retrospective case report is a detailed, structured narrative of a single patient's clinical presentation, diagnosis, management, and outcome, assembled from existing medical records after the clinical events have occurred. It is the most granular and accessible observational design in clinical medicine, serving primarily to document rare presentations, unexpected outcomes, novel treatments, or unusual drug reactions that would not otherwise enter the published literature.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Case reporting tradition in medicine (formalized by CARE guidelines, Riley et al., 2013)","year":"19th century (formalized ~2013 with CARE guidelines)","type":"Observational descriptive study","dataType":"Medical records, clinical notes, imaging, laboratory data (retrospectively collected)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Gagnier, J. J., Kienle, G., Altman, D. G., Moher, D., Sox, H., & Riley, D. (2013). The CARE guidelines: consensus-based clinical case reporting guideline development. Journal of Medical Case Reports, 7(1), 223.","type":"article","doi":"10.1186/1752-1947-7-223","isbn":null,"url":null},{"ref":"Vandenbroucke, J. P. (2001). In defense of case reports and case series. Annals of Internal Medicine, 134(4), 330-334.","type":"article","doi":"10.7326/0003-4819-134-4-200102200-00017","isbn":null,"url":null}],"related":["case-series","case-report","retrospective-case-series","retrospective-cohort-study","prospective-case-report","case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-case-series","name":"Retrospective Case Series","fullName":"Retrospective Case Series Study","aliases":["retrospective case series","chart review case series","historical case series","medical records case series"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Long-standing practice; codified in EBM frameworks during 1990s–2000s","originator":"Clinical medicine tradition (no single originator); formalized in evidence-based medicine literature","url":"https://scholargate.app/en/epidemiology/retrospective-case-series","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-case-series.md","definition":"A retrospective case series is an observational study that systematically describes the clinical features, treatments, and outcomes of a defined group of patients by examining pre-existing medical records or administrative data. It looks backward in time — data have already been recorded before the study begins. With no control group, no randomization, and no prospective follow-up, it sits near the base of the evidence hierarchy but remains one of the most practical and frequently published study designs in clinical medicine.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clinical medicine tradition (no single originator); formalized in evidence-based medicine literature","year":"Long-standing practice; codified in EBM frameworks during 1990s–2000s","type":"Observational descriptive study design","dataType":"Pre-existing medical records, clinical charts, administrative databases","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kooistra, B., Dijkman, B., Einhorn, T. A., & Bhandari, M. (2009). How to design a good case series. Journal of Bone and Joint Surgery, 91(Suppl 3), 21–26.","type":"article","doi":"10.2106/JBJS.H.01573","isbn":null,"url":null},{"ref":"Mayer, D. (2010). Essential Evidence-Based Medicine (2nd ed.). Cambridge University Press. Chapter on study designs.","type":"article","doi":null,"isbn":"9780521712415","url":null}],"related":["case-series","prospective-case-series","retrospective-cohort-study","case-report","retrospective-case-control-study","chart-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-cohort-study","name":"Retrospective Cohort Study","fullName":"Retrospective Cohort Study","aliases":["historical cohort study","non-concurrent cohort study","retrospective follow-up study","historical prospective study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid-20th century (widely formalized 1950s–1970s)","originator":"Systematic use attributed to early 20th-century occupational epidemiology; formalized in modern epidemiological theory by Brian MacMahon and others","url":"https://scholargate.app/en/epidemiology/retrospective-cohort-study","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-cohort-study.md","definition":"A retrospective cohort study assembles a group of individuals who share a common starting point and reconstructs their exposure history and subsequent outcomes entirely from pre-existing records. Because the data have already been collected before the study begins, the design is far faster and cheaper than a prospective cohort; however, the researcher must work with whatever information was recorded at the time rather than collecting purpose-built measurements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Systematic use attributed to early 20th-century occupational epidemiology; formalized in modern epidemiological theory by Brian MacMahon and others","year":"Mid-20th century (widely formalized 1950s–1970s)","type":"Observational analytic study","dataType":"Existing records, administrative databases, medical charts, registries","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Retrospective cohort study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Retrospective_cohort_study"}],"related":["cohort-study","prospective-cohort-study","case-control-study","nested-case-control","cross-sectional-epidemiological-study","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-competing-risks-analysis","name":"Retrospective competing risks analysis","fullName":"Retrospective Competing Risks Analysis","aliases":["retrospective CRA","competing risks survival analysis (retrospective)","cause-specific hazard analysis (retrospective)","subdistribution hazard analysis (retrospective)"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1978 (cause-specific); 1999 (subdistribution/Fine-Gray)","originator":"Fine & Gray (subdistribution model); Prentice et al. (cause-specific framework)","url":"https://scholargate.app/en/epidemiology/retrospective-competing-risks-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-competing-risks-analysis.md","definition":"Retrospective competing risks analysis applies competing risks methodology to historical (already-collected) time-to-event data in which subjects can experience one of several mutually exclusive endpoints. It uses the cumulative incidence function and cause-specific or subdistribution hazard models to estimate the probability of each event type while accounting for the fact that occurrence of one event permanently precludes the others. Widely used in oncology, cardiology, and transplant medicine where administrative or registry records are the data source.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fine & Gray (subdistribution model); Prentice et al. (cause-specific framework)","year":"1978 (cause-specific); 1999 (subdistribution/Fine-Gray)","type":"Retrospective observational survival analysis","dataType":"Time-to-event data with multiple mutually exclusive endpoints from historical records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.","type":"article","doi":"10.1080/01621459.1999.10474144","isbn":null,"url":null},{"ref":"Prentice, R. L., Kalbfleisch, J. D., Peterson, A. V., Flournoy, N., Farewell, V. T., & Breslow, N. E. (1978). The analysis of failure time data in the presence of competing risks. Biometrics, 34(4), 541–554.","type":"article","doi":"10.2307/2530374","isbn":null,"url":null}],"related":["competing-risks-analysis","kaplan-meier-analysis","cox-proportional-hazards","retrospective-survival-analysis","retrospective-cohort-study","cause-specific-hazard-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-cox-proportional-hazards","name":"Retrospective Cox proportional hazards","fullName":"Retrospective Cox Proportional Hazards Regression","aliases":["Cox PH regression (retrospective)","retrospective Cox survival model","retrospective hazard regression","Cox model on historical data"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1972","originator":"David R. Cox","url":"https://scholargate.app/en/epidemiology/retrospective-cox-proportional-hazards","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-cox-proportional-hazards.md","definition":"Retrospective Cox proportional hazards regression applies Cox's (1972) semi-parametric survival model to time-to-event data extracted from existing records — medical charts, administrative databases, registries, or biobanks. It estimates covariate-adjusted hazard ratios (HRs) without specifying the underlying baseline hazard, making it the dominant analytic tool when the investigator works backward from already-recorded outcomes and exposures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David R. Cox","year":"1972","type":"Semi-parametric survival regression","dataType":"Time-to-event data with covariates, drawn from retrospective records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null},{"ref":"Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439856789","url":null}],"related":["cox-proportional-hazards","survival-analysis","kaplan-meier-analysis","retrospective-cohort-study","competing-risks-analysis","retrospective-survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-cross-sectional-epidemiological-study","name":"Retrospective cross-sectional epidemiological study","fullName":"Retrospective Cross-Sectional Epidemiological Study","aliases":["retrospective cross-sectional survey","record-based cross-sectional study","retrospective prevalence study","secondary-data cross-sectional study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid–late 20th century","originator":"Epidemiology tradition (formalized in mid-20th century; Rothman, Greenland and others)","url":"https://scholargate.app/en/epidemiology/retrospective-cross-sectional-epidemiological-study","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-cross-sectional-epidemiological-study.md","definition":"A retrospective cross-sectional epidemiological study measures the prevalence of exposures and outcomes at a single analytical time point using data that were originally recorded in the past — such as medical records, administrative databases, or disease registries. It combines the snapshot logic of a cross-sectional design with the efficiency of retrospective data access, making it a practical choice when prospective data collection is unfeasible or when large existing datasets are available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Epidemiology tradition (formalized in mid-20th century; Rothman, Greenland and others)","year":"Mid–late 20th century","type":"Observational study design","dataType":"Existing records, administrative databases, registries, medical charts, survey archives","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Cross-sectional study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Cross-sectional_study"}],"related":["cross-sectional-epidemiological-study","retrospective-cohort-study","retrospective-case-control-study","prospective-cross-sectional-epidemiological-study","nested-case-control","ecological-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-diagnostic-accuracy-study","name":"Retrospective diagnostic accuracy study","fullName":"Retrospective Diagnostic Accuracy Study","aliases":["retrospective DAS","retrospective test accuracy study","retrospective index test evaluation","historical diagnostic accuracy study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Formalized 2000s; STARD 2003, revised 2015","originator":"Formalized through the STARD initiative led by Patrick Bossuyt and colleagues","url":"https://scholargate.app/en/epidemiology/retrospective-diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-diagnostic-accuracy-study.md","definition":"A retrospective diagnostic accuracy study evaluates how well a diagnostic test (the index test) correctly identifies a target condition by applying it to previously collected data or archived specimens alongside a reference standard. Because both index test results and reference standard results are drawn from existing records or stored material rather than generated prospectively, this design is faster and less costly than a prospective counterpart — but carries specific methodological risks that must be controlled to produce valid estimates of sensitivity, specificity, and related measures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized through the STARD initiative led by Patrick Bossuyt and colleagues","year":"Formalized 2000s; STARD 2003, revised 2015","type":"Observational, retrospective study design","dataType":"Previously collected biological specimens, medical records, imaging data, or stored samples with known reference standard results","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., et al. (2015). STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. BMJ, 351, h5527.","type":"article","doi":"10.1136/bmj.h5527","isbn":null,"url":null},{"ref":"Whiting, P. F., Rutjes, A. W., Westwood, M. E., et al. (2011). QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies. Annals of Internal Medicine, 155(8), 529–536.","type":"article","doi":"10.7326/0003-4819-155-8-201110180-00009","isbn":null,"url":null}],"related":["diagnostic-accuracy-study","prospective-diagnostic-accuracy-study","case-control-study","retrospective-cohort-study","screening-test-evaluation","systematic-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-ecological-study","name":"Retrospective Ecological Study","fullName":"Retrospective Ecological Epidemiological Study","aliases":["retrospective aggregate study","historical ecological study","retrospective correlational ecological design","population-level retrospective study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"20th century (formalized ~1980s–1990s)","originator":"Epidemiological tradition; formalized by Morgenstern and others","url":"https://scholargate.app/en/epidemiology/retrospective-ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-ecological-study.md","definition":"A retrospective ecological study examines associations between exposures and outcomes using pre-existing aggregate data from defined populations or geographic units. Rather than following individual subjects, the unit of analysis is a group — a country, region, or time period — and all measurements come from historical records already collected before the study began. It is a rapid, low-cost way to generate hypotheses about environmental, social, or policy determinants of disease at the population level.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Epidemiological tradition; formalized by Morgenstern and others","year":"20th century (formalized ~1980s–1990s)","type":"Observational epidemiological design","dataType":"Aggregate/group-level historical data (routinely collected statistics, registries, census data)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Morgenstern, H. (1998). Ecologic studies. In K. J. Rothman & S. Greenland (Eds.), Modern Epidemiology (2nd ed., pp. 459–480). Lippincott-Raven.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Morgenstern+Ecologic+studies+Modern+Epidemiology+1998"},{"ref":"Ecological study. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ecological_study"}],"related":["ecological-study","retrospective-cohort-study","cross-sectional-epidemiological-study","retrospective-cross-sectional-epidemiological-study","dose-response-analysis","retrospective-dose-response-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-ethics-approval","name":"Retrospective Ethics Approval","fullName":"Obtaining Ethics Committee Approval After Data Has Been Collected","aliases":["retroactive approval","post-hoc ethics approval","IRB exemption determination","delayed ethics review"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethics-procedural","year":"1991","originator":"U.S. Department of Health and Human Services; International research ethics community","url":"https://scholargate.app/en/research-ethics/retrospective-ethics-approval","markdownUrl":"https://scholargate.app/en/research-ethics/retrospective-ethics-approval.md","definition":"Retrospective ethics approval is the ethics committee's review and determination regarding research conducted or data collected before ethics approval was obtained. This situation arises when researchers collect data without advance ethics review (intentionally, out of oversight, or due to institutional gaps) and then seek ethics approval before analysis or publication. Retrospective approval is generally disfavored; regulations and guidelines strongly recommend prospective review (approval before data collection). However, retrospective determination of exemption (finding that data already collected meets exempt criteria under 45 CFR 46.104, similar frameworks in other jurisdictions) or retrospective approval with specific justifications may be possible. Understanding when retrospective approval can be obtained—and its limitations—is important for researchers facing this ethical and regulatory challenge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Department of Health and Human Services; International research ethics community","subfamily":"ethics-procedural","year":"1991","type":"Guideline"},"citations":[{"ref":"U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Section 46.101(b).","type":"regulation","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Protection+of+Human+Subjects"},{"ref":"U.S. Department of Health and Human Services, Office for Human Research Protections. (2010). Retrospective Collection and Codification of Existing Data and Specimens. OHRP Guidance.","type":"guidance","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp"},{"ref":"Health Research Authority. (2021). Research That Has Already Started or Data Already Collected. UK Research Ethics Service Guidance.","type":"guideline","doi":null,"isbn":null,"url":"https://www.hra.nhs.uk"},{"ref":"International Council for Harmonisation. (2016). ICH Harmonised Guideline: Integrated Addendum to ICH E6(R1). Good Clinical Practice E6(R2).","type":"standard","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ICH+Harmonised+Guideline%3A+Integrated+Addendum+to+ICH+E6%28R1%29+International"}],"related":["ethics-committee-application","ethics-committee-types","waiver-of-informed-consent","data-protection-research","risk-benefit-assessment"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-kaplan-meier-analysis","name":"Retrospective Kaplan-Meier Analysis","fullName":"Retrospective Kaplan-Meier Survival Analysis","aliases":["retrospective KM analysis","retrospective survival curve estimation","historical Kaplan-Meier","retrospective KM estimator"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1958 (method); retrospective application standard in clinical research since 1970s–1980s)","originator":"Edward L. Kaplan and Paul Meier","url":"https://scholargate.app/en/epidemiology/retrospective-kaplan-meier-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-kaplan-meier-analysis.md","definition":"Retrospective Kaplan-Meier analysis applies the Kaplan-Meier product-limit estimator to time-to-event data drawn from existing records — medical charts, registries, or administrative databases — rather than from a prospectively followed cohort. The method estimates the probability of surviving (or remaining event-free) beyond any given time point while accounting for participants whose follow-up ended before the event occurred (censored observations). It is among the most commonly reported analyses in clinical oncology, cardiology, and surgery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward L. Kaplan and Paul Meier","year":"1958 (method); retrospective application standard in clinical research since 1970s–1980s)","type":"Non-parametric survival analysis applied to historical data","dataType":"Time-to-event data with censoring, collected retrospectively from records or registries","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.","type":"article","doi":"10.1080/01621459.1958.10501452","isbn":null,"url":null},{"ref":"Clark, T. G., Bradburn, M. J., Love, S. B., & Altman, D. G. (2003). Survival analysis part I: Basic concepts and first analyses. British Journal of Cancer, 89(2), 232–238.","type":"article","doi":"10.1038/sj.bjc.6601118","isbn":null,"url":null}],"related":["kaplan-meier-analysis","cox-proportional-hazards","retrospective-survival-analysis","competing-risks-analysis","retrospective-cohort-study","log-rank-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-nested-case-control","name":"Retrospective nested case-control","fullName":"Retrospective Nested Case-Control Study","aliases":["retrospective NCC","nested case-control within retrospective cohort","case-control nested in historical cohort","nested CCR"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1973 (formal description); widely adopted in epidemiology from 1980s onward","originator":"Nested case-control formalized by Mantel (1973); retrospective application via historical cohort records","url":"https://scholargate.app/en/epidemiology/retrospective-nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-nested-case-control.md","definition":"A retrospective nested case-control study is an efficient observational design in which cases and matched controls are sampled from within an already-assembled retrospective cohort. Exposure data are retrieved from historical records only for selected participants, dramatically reducing data-collection costs while retaining most of the analytic power of the full cohort. It is widely used in pharmacoepidemiology, occupational health, and disease-registry research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nested case-control formalized by Mantel (1973); retrospective application via historical cohort records","year":"1973 (formal description); widely adopted in epidemiology from 1980s onward","type":"Observational analytic study design","dataType":"Existing retrospective cohort records, medical records, administrative databases, biobanks","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Mantel, N. (1973). Synthetic retrospective studies and related topics. Biometrics, 29(3), 479–486.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mantel+Synthetic+retrospective+studies+related+topics+Biometrics+1973"},{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null}],"related":["nested-case-control","retrospective-cohort-study","case-control-study","cohort-study","case-cohort-study","retrospective-case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-phase-ii-clinical-trial","name":"Retrospective phase II clinical trial","fullName":"Retrospective Phase II Clinical Trial","aliases":["retrospective Phase II study","historical Phase II analysis","retrospective efficacy study","Phase II retrospective analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–1990s (with growth in oncology retrospective analyses)","originator":"Adapted from standard Phase II trial methodology; retrospective variant formalized in oncology practice","url":"https://scholargate.app/en/epidemiology/retrospective-phase-ii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-phase-ii-clinical-trial.md","definition":"A retrospective Phase II clinical trial evaluates a treatment's preliminary efficacy and safety signals using existing archival data — medical records, registries, or electronic health records — rather than prospectively enrolling new patients. It mirrors the objectives of a standard Phase II trial (estimating response rate, tolerability, and early efficacy) but does so by looking backward at patients who have already received the intervention, making it faster and less costly than a prospective design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from standard Phase II trial methodology; retrospective variant formalized in oncology practice","year":"1980s–1990s (with growth in oncology retrospective analyses)","type":"Observational retrospective study","dataType":"Archival clinical records, medical charts, registries, electronic health records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Simon, R. (1989). Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials, 10(1), 1–10.","type":"article","doi":"10.1016/0197-2456(89)90015-9","isbn":null,"url":null},{"ref":"Clinical trial. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Clinical_trial"}],"related":["phase-ii-clinical-trial","retrospective-cohort-study","prospective-phase-ii-clinical-trial","phase-i-clinical-trial","phase-iii-clinical-trial","diagnostic-accuracy-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-phase-iii-clinical-trial","name":"Retrospective phase III clinical trial","fullName":"Retrospective Phase III Clinical Trial","aliases":["retrospective Phase III study","historical Phase III trial","Phase III retrospective analysis","retrospective comparative efficacy trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Late 20th century (ICH E8 1997; widespread retrospective Phase III use from 1990s onward)","originator":"Regulatory framework codified by ICH E8/E9 (1997–1998); retrospective application developed through post-marketing and registry practice","url":"https://scholargate.app/en/epidemiology/retrospective-phase-iii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-phase-iii-clinical-trial.md","definition":"A retrospective Phase III clinical trial evaluates the comparative efficacy and safety of an intervention against a control using data that were collected before the study was designed. Rather than enrolling new patients prospectively, researchers analyze existing records — from registries, hospital databases, or historical trial archives — to address a Phase III-level question: does Treatment A outperform the current standard of care in a large, representative patient population? This design is used when prospective enrollment is infeasible, unethical, or when historical data are sufficiently complete to support a rigorous comparison.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Regulatory framework codified by ICH E8/E9 (1997–1998); retrospective application developed through post-marketing and registry practice","year":"Late 20th century (ICH E8 1997; widespread retrospective Phase III use from 1990s onward)","type":"Retrospective comparative clinical study","dataType":"Existing medical records, registries, electronic health records, historical trial databases","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Friedman, L. M., Furberg, C. D., & DeMets, D. L. (2010). Fundamentals of Clinical Trials (4th ed.). Springer.","type":"book","doi":null,"isbn":"978-1441915856","url":null},{"ref":"International Conference on Harmonisation. (1998). ICH E9: Statistical Principles for Clinical Trials. Federal Register, 63(179), 49583–49598.","type":"article","doi":null,"isbn":null,"url":"https://www.fda.gov/regulatory-information/search-fda-guidance-documents/e9-statistical-principles-clinical-trials"}],"related":["phase-iii-clinical-trial","retrospective-cohort-study","randomized-clinical-trial","phase-ii-clinical-trial","prospective-phase-iii-clinical-trial","survival-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-survival-analysis","name":"Retrospective survival analysis","fullName":"Retrospective Survival Analysis","aliases":["historical survival study","retrospective time-to-event analysis","retrospective follow-up survival study","archival survival analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1970s–1980s (retrospective variant established)","originator":"Kaplan & Meier (foundational estimator, 1958); Cox (regression model, 1972); retrospective application is a design variant documented since the 1970s","url":"https://scholargate.app/en/epidemiology/retrospective-survival-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/retrospective-survival-analysis.md","definition":"Retrospective survival analysis applies time-to-event statistical methods — most commonly the Kaplan-Meier estimator and Cox proportional hazards regression — to data collected from past records rather than through prospective follow-up. The researcher looks back at medical records, disease registries, or administrative databases to reconstruct each patient's journey from a defined starting point (e.g., diagnosis or surgery) to an outcome of interest (e.g., death, relapse, or hospital readmission), making it a cost-efficient approach for studying prognosis and risk factors when prospective follow-up is not feasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaplan & Meier (foundational estimator, 1958); Cox (regression model, 1972); retrospective application is a design variant documented since the 1970s","year":"1970s–1980s (retrospective variant established)","type":"Retrospective observational analytical study","dataType":"Historical time-to-event data (survival times, censoring indicators, covariates) from medical records, registries, or administrative databases","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439856789","url":null},{"ref":"Survival analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Survival_analysis"}],"related":["survival-analysis","kaplan-meier-analysis","cox-proportional-hazards","competing-risks-analysis","retrospective-cohort-study","retrospective-case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"retrospective-think-aloud","name":"Retrospective Think-Aloud","fullName":"Retrospective Think-Aloud Method","aliases":["Delayed Verbalization","Post-task Thinking Aloud","RTA"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Retrospective Introspection","year":"1980","originator":"K. Anders Ericsson, Herbert Simon, adapted by Gary Olson and colleagues","url":"https://scholargate.app/en/human-computer-interaction/retrospective-think-aloud","markdownUrl":"https://scholargate.app/en/human-computer-interaction/retrospective-think-aloud.md","definition":"Retrospective Think-Aloud is a variant of the Think-Aloud Protocol in which participants complete a task without verbalization, then immediately review a video or replay of their task performance and narrate their thoughts, reasoning, and reactions. This method captures post-hoc reflection on decision-making and user experience without disrupting task execution. Particularly valuable for exploring user awareness, emotional reactions, and retrospective sense-making, Retrospective Think-Aloud provides the explanatory richness of concurrent thinking aloud without the disruption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. Anders Ericsson, Herbert Simon, adapted by Gary Olson and colleagues","subfamily":"Retrospective Introspection","year":"1980","type":"Post-task verbalization method for reflecting on decision-making"},"citations":[{"ref":"Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87(3), 215–251.","type":"article","doi":"10.1037/0033-295X.87.3.215","isbn":null,"url":null},{"ref":"Olson, G. M., Olson, J. S., & Van der Veen, M. R. (1990). The structure of activity. In CHI'90 Conference Proceedings (pp. 3–10).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+structure+of+activity+Olson"}],"related":["think-aloud-protocol","contextual-inquiry","pluralistic-walkthrough","nasa-tlx"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"return-oriented-programming","name":"Return-Oriented Programming","fullName":"Return-Oriented Programming (ROP)","aliases":["ROP","code reuse attack","Turing-complete gadget"],"domain":"cryptography","family":"ml-model","subfamily":"Exploit technique","year":"2007","originator":"Hovav Shacham","url":"https://scholargate.app/en/cryptography/return-oriented-programming","markdownUrl":"https://scholargate.app/en/cryptography/return-oriented-programming.md","definition":"Return-Oriented Programming (ROP) is an exploit technique that chains together short sequences of instructions (gadgets) from existing executable code to perform arbitrary computation, bypassing security defenses like code injection prevention. Introduced by Hovav Shacham in 2007, ROP exploits code reuse to execute malicious logic even when data execution prevention (DEP) and code signing prevent direct code injection. ROP is considered one of the most powerful exploit techniques against modern defense mechanisms and has been demonstrated to be Turing-complete.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hovav Shacham","subfamily":"Exploit technique","year":"2007","type":"code reuse attack methodology"},"citations":[{"ref":"Shacham, H. (2007). The geometry of innocent flesh on the bone: Return-into-libc without function calls (on the x86). In Proceedings of the 14th ACM Conference on Computer and Communications Security (CCS 2007), pp. 552-561.","type":"article","doi":"10.1145/1315245.1315313","isbn":null,"url":null},{"ref":"Roemer, R., Buchanan, E., Shacham, H., & Savage, S. (2012). Return-oriented programming: Systems, languages, and applications. ACM Transactions on Information and System Security (TISSEC), 15(1), 1-34.","type":"article","doi":"10.1145/2133375.2133377","isbn":null,"url":null}],"related":["differential-cryptanalysis","side-channel-analysis","deep-packet-inspection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"revised-childrens-anxiety-depression","name":"Revised Children's Anxiety and Depression Scale","fullName":"Revised Children's Anxiety and Depression Scale (RCADS)","aliases":["RCADS","RCADS-25"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"pediatric anxiety and mood disorders","year":"2000","originator":"Bruce Chorpita","url":"https://scholargate.app/en/child-psychiatry/revised-childrens-anxiety-depression","markdownUrl":"https://scholargate.app/en/child-psychiatry/revised-childrens-anxiety-depression.md","definition":"The RCADS is a 47-item (or 25-item brief version) self-report measure that assesses the full spectrum of anxiety disorders and major depression in children and adolescents ages 6–18 years. Developed by Bruce Chorpita in 2000, it provides six subscale scores aligned with DSM-IV diagnostic criteria: Separation Anxiety, Generalized Anxiety, Panic Disorder, Social Phobia, Obsessive-Compulsive Disorder, and Major Depressive Disorder. The RCADS is designed to be both a screening tool and a diagnostic aid.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruce Chorpita","subfamily":"pediatric anxiety and mood disorders","year":"2000","type":"Self-report questionnaire"},"citations":[{"ref":"Chorpita, B. F., Yim, L., Moffatt, C., Umemoto, A., & Francis, S. E. (2000). Assessment of symptoms of DSM-IV anxiety and depression in children: A revised child anxiety and depression scale. Behaviour Modification, 24(4), 513–537.","type":"article","doi":"10.1016/s0005-7967(99)00130-8","isbn":null,"url":null},{"ref":"Chorpita, B. F., Moffatt, C., & Gray, J. (2005). Psychometric properties of the Revised Child Anxiety and Depression Scale in a clinical sample. Behaviour Research and Therapy, 43(12), 1541–1549.","type":"article","doi":"10.1016/j.brat.2004.02.004","isbn":null,"url":null}],"related":["child-depression-inventory","multidimensional-anxiety-children","social-communication-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"revised-simos","name":"REVISED-SIMOS","fullName":"Revised Simos Procedure — deck-of-cards rank-based weight elicitation (Figueira & Roy 2002)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2002","originator":"Figueira, J., Roy, B.","url":"https://scholargate.app/en/decision-making/revised-simos","markdownUrl":"https://scholargate.app/en/decision-making/revised-simos.md","definition":"REVISED-SIMOS (Revised Simos Procedure — deck-of-cards rank-based weight elicitation (Figueira & Roy 2002)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Figueira, J., Roy, B. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Figueira, J., Roy, B.","subfamily":"Weight_Subjective","year":"2002","type":"Weight_Subjective (rank-ordered cards with blank cards for gaps)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Figueira, J., Roy, B. (2002). Determining the weights of criteria in the ELECTRE type methods with a revised Simos' procedure. European Journal of Operational Research","type":"article","doi":"10.1016/S0377-2217(01)00370-8","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"revision-response-to-reviewers","name":"Responding to Peer Reviewer Comments","fullName":"Guidelines for Addressing Peer Review Feedback and Preparing Revision Letters","aliases":["revision letter","response to reviewers","rebuttal letter"],"domain":"academic-writing","family":"process-pipeline","subfamily":"publication-process","year":"2005","originator":"Journal editors and publishing community; formalized by Clydesdale et al. and ICMJE","url":"https://scholargate.app/en/academic-writing/revision-response-to-reviewers","markdownUrl":"https://scholargate.app/en/academic-writing/revision-response-to-reviewers.md","definition":"A response to reviewers (or 'revision letter') is a formal document that authors submit alongside a revised manuscript, addressing each reviewer comment point-by-point. The response letter shows the editor and reviewers that you have carefully considered their feedback, explained changes made in light of their suggestions, and justified any points of disagreement. A thoughtful, respectful response to reviewers significantly increases the likelihood of acceptance; a dismissive or defensive response can lead to rejection despite good science. The response letter is not an argument but a demonstration of engagement, transparency, and scientific integrity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Journal editors and publishing community; formalized by Clydesdale et al. and ICMJE","subfamily":"publication-process","year":"2005","type":"Guideline"},"citations":[{"ref":"Clydesdale, G. J., Seymour, K. J., & Toy, M. S. (2013). How to write a response to reviewers. British Journal of Ophthalmology, 97(1), 1–2.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+to+write+a+response+to+reviewers+Clydesdale"},{"ref":"Wager, E., & Wieland, B. (2011). Responsibilities of journal editors. Lancet, 337(9834), 1807–1809.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Responsibilities+of+journal+editors+Wager"},{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.","type":"guideline","doi":null,"isbn":null,"url":"https://www.icmje.org/"}],"related":["journal-submission-process","scientific-writing-clarity","imrad-structure","statistical-reporting-standards"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"reynolds-averaged-navier-stokes","name":"Reynolds-Averaged Navier-Stokes","fullName":"Reynolds-Averaged Navier-Stokes Equations","aliases":["RANS","Reynolds-averaged flow simulation"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1895","originator":"Osborne Reynolds","url":"https://scholargate.app/en/fluid-dynamics/reynolds-averaged-navier-stokes","markdownUrl":"https://scholargate.app/en/fluid-dynamics/reynolds-averaged-navier-stokes.md","definition":"The Reynolds-Averaged Navier-Stokes (RANS) equations represent a time-averaged form of the Navier-Stokes equations developed by Osborne Reynolds in 1895. This approach decomposes turbulent flow into mean and fluctuating components, enabling practical simulation of turbulent flows by modeling turbulent stresses rather than resolving all scales. RANS remains the most widely used computational fluid dynamics method in engineering applications due to its computational efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Osborne Reynolds","subfamily":"Fluid Dynamics","year":"1895","type":"Computational turbulence modeling approach"},"citations":[{"ref":"Reynolds, O. (1895). On the dynamical theory of incompressible viscous fluids and the determination of the criterion. Philosophical Transactions of the Royal Society A, 186, 123-164.","type":"article","doi":"10.1098/rsta.1895.0004","isbn":null,"url":null},{"ref":"Boussinesq, J. (1877). Essai sur la théorie des eaux courantes. Mémoires présentés par divers savants à l'Académie des Sciences, 23, 1-680.","type":"article","doi":null,"isbn":null,"url":"https://gallica.bnf.fr"},{"ref":"Wilcox, D. C. (2006). Turbulence Modeling for CFD (3rd ed.). DCW Industries, Inc.","type":"book","doi":null,"isbn":"978-1928729082","url":null}],"related":["large-eddy-simulation","detached-eddy-simulation","direct-numerical-simulation","boundary-layer-theory","lattice-boltzmann-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rheometry","name":"Rheometry","fullName":"Rheometry","aliases":["rheological testing"],"domain":"food-science","family":"process-pipeline","subfamily":"Physical Characterization","year":"1992","originator":"James Steffe","url":"https://scholargate.app/en/food-science/rheometry","markdownUrl":"https://scholargate.app/en/food-science/rheometry.md","definition":"Rheometry is the scientific measurement of how fluids and semi-solids (pastes, gels, suspensions) flow and deform under applied stress. Using a rheometer (a precision instrument that applies controlled shear forces and measures the resulting deformation), rheometry characterizes the viscosity, viscoelasticity, and other flow properties of food products, essential for process design, quality control, and predicting mouthfeel sensations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Steffe","subfamily":"Physical Characterization","year":"1992","type":"Fluid Property Measurement"},"citations":[{"ref":"Steffe, J. F. (1996). Rheological methods in food process engineering (2nd ed.). Freeman Press.","type":"article","doi":null,"isbn":null,"url":"https://www.stewartpostharvest.com"},{"ref":"Barnes, H. A. (2000). A handbook of elementary rheology. Institute of Non-Newtonian Fluid Mechanics.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"}],"related":["texture-profile-analysis","quantitative-descriptive-analysis","dsc-gelatinization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rheumatoid-arthritis-qol","name":"RA-QoL","fullName":"Rheumatoid Arthritis Quality of Life Scale","aliases":["RA-QoL","Rheumatoid Arthritis QoL","RAQoL"],"domain":"health-outcomes","family":"process-pipeline","subfamily":"Rheumatologic Disease","year":"1997","originator":"Stephen P. McKenna et al.","url":"https://scholargate.app/en/health-outcomes/rheumatoid-arthritis-qol","markdownUrl":"https://scholargate.app/en/health-outcomes/rheumatoid-arthritis-qol.md","definition":"The RA-QoL is a disease-specific quality of life measure for rheumatoid arthritis (RA). Developed by Stephen McKenna and colleagues in 1997, this 30-item questionnaire quantifies how RA affects daily activities, emotional well-being, functional independence, and social engagement. It is a standard outcome measure in RA research and clinical trials, capturing dimensions beyond joint inflammation and functional disability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen P. McKenna et al.","subfamily":"Rheumatologic Disease","year":"1997","type":"Self-report quality of life questionnaire"},"citations":[{"ref":"de Jong, Z., van der Heijde, D. M., McKenna, S. P., & Whalley, D. (2000). The Rheumatoid Arthritis Quality of Life Scale (RAQoL): Final Dutch version allows three methods of data handling. Arthritis & Rheumatism, 13(6), 408-413.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Rheumatoid+Arthritis+Quality+of+Life+Scale+%28RAQoL%29%3A+Final+Dutch+version+allows+three+methods+of+data+handling"},{"ref":"McKenna, S. P., Doward, L. C., Whalley, D., Tennant, A., Emery, P., & Veale, D. J. (1997). The Rheumatoid Arthritis Quality of Life Scale: Development and preliminary validation. British Journal of Rheumatology, 36(8), 878-883.","type":"article","doi":"10.1093/rheumatology/36.8.884","isbn":null,"url":null},{"ref":"Ekdahl, C., Eberhardt, K., Andersson, S. I., & Svensson, B. (1997). Assessing disability in patients with rheumatoid arthritis. Scandinavian Journal of Rheumatology, 26(1), 16-23.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessing+disability+in+patients+with+rheumatoid+arthritis+Ekdahl"}],"related":["eortc-qlq-c30","fibromyalgia-impact-questionnaire","dlqi","inflammatory-bowel-disease-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rhinitis-quality-of-life","name":"RQLQ","fullName":"Rhinoconjunctivitis Quality of Life Questionnaire","aliases":["RQLQ","Rhinoconjunctivitis QoL"],"domain":"pulmonology","family":"process-pipeline","subfamily":"allergic-rhinitis-qol","year":"1996","originator":"Elizabeth F. Juniper, McMaster University","url":"https://scholargate.app/en/pulmonology/rhinitis-quality-of-life","markdownUrl":"https://scholargate.app/en/pulmonology/rhinitis-quality-of-life.md","definition":"The RQLQ is a 28-item disease-specific quality-of-life instrument developed by Juniper and colleagues at McMaster University in 1996 to assess the impact of allergic rhinitis and allergic conjunctivitis on daily functioning. It captures symptom burden and activity limitation across seven domains: sleep, non-nose/eye symptoms, practical problems, nasal symptoms, eye symptoms, activity limitation, and emotional function. The RQLQ is the preferred outcome measure in allergic rhinitis clinical trials and is widely used in allergy and immunology practice to track treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elizabeth F. Juniper, McMaster University","subfamily":"allergic-rhinitis-qol","year":"1996","type":"Self-report questionnaire"},"citations":[{"ref":"Juniper, E. F., Guyatt, G. H., Streiner, D. L., & King, D. R. (1996). Clinical validation and responsiveness of three scales developed to measure the effect of allergic rhinitis on quality of life. Journal of Clinical Epidemiology, 49(3), 339-347.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Clinical+validation+and+responsiveness+of+three+scales+developed+to+measure+the+effect+of+allergic+rhinitis+on+quality+of+life+Juniper"},{"ref":"Juniper, E. F. (2000). How important is quality of life in allergic rhinitis? Current Allergy and Asthma Reports, 2(3), 211-214.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+important+is+quality+of+life+in+allergic+rhinitis+Juniper"}],"related":["sinonasal-outcome-test","st-george-respiratory-questionnaire","asthma-control-questionnaire","breathlessness-cough-sputum-scale","mrc-dyspnoea-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rhizosphere-amplicon-analysis","name":"Rhizosphere Amplicon Analysis","fullName":"Rhizosphere Amplicon Sequencing and Community Profiling","aliases":["rhizosphere 16S amplicon sequencing","root-zone microbiome amplicon profiling","rhizosphere metabarcoding","soil microbiome amplicon analysis"],"domain":"agronomy","family":"process-pipeline","subfamily":"Soil and plant microbiome ecology","year":"Early 2000s–2010s (accelerated with next-generation sequencing platforms)","originator":"Multiple contributors","url":"https://scholargate.app/en/agronomy/rhizosphere-amplicon-analysis","markdownUrl":"https://scholargate.app/en/agronomy/rhizosphere-amplicon-analysis.md","definition":"Rhizosphere Amplicon Analysis is a molecular-ecological pipeline used to characterise the microbial communities inhabiting the root-adjacent soil zone — the rhizosphere — by sequencing targeted marker genes such as the bacterial 16S rRNA gene or the fungal ITS region. Widely applied in agronomy, soil ecology, and plant pathology, it enables researchers to identify which microorganisms are present, how their composition shifts under different crops, treatments, or soil conditions, and how community structure relates to plant health and productivity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors","year":"Early 2000s–2010s (accelerated with next-generation sequencing platforms)","type":"Molecular-ecological pipeline","dataType":"DNA sequences from soil/root-associated microbial communities","subfamily":"Soil and plant microbiome ecology"},"citations":[{"ref":"Lundberg, D. S., Lebeis, S. L., Paredes, S. H., Yourstone, S., Gehring, J., Malfatti, S., ... & Dangl, J. L. (2012). Defining the core Arabidopsis thaliana root microbiome. Nature, 488(7409), 86-90.","type":"journal","doi":"10.1038/nature11237","isbn":null,"url":null},{"ref":"Berendsen, R. L., Pieterse, C. M., & Bakker, P. A. (2012). The rhizosphere microbiome and plant health. Trends in Plant Science, 17(8), 478-486.","type":"journal","doi":"10.1016/j.tplants.2012.04.001","isbn":null,"url":null}],"related":["soil-metagenomics","16s-rrna-sequencing","operational-taxonomic-unit-clustering","diversity-analysis","plant-microbiome-profiling","amplicon-sequence-variant-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rich-picture","name":"Rich Picture","fullName":"Rich Pictures (Soft Systems)","aliases":["Rich Picture Diagram","Soft Systems Picture","Situation Summary Diagram","Zengin Resim"],"domain":"problem-structuring","family":"process-pipeline","subfamily":"Problem structuring methods","year":1981,"originator":"Peter Checkland","url":"https://scholargate.app/en/problem-structuring/rich-picture","markdownUrl":"https://scholargate.app/en/problem-structuring/rich-picture.md","definition":"A Rich Picture is a free-form, annotated drawing used in the early exploratory stage of Soft Systems Methodology to represent the full complexity of a problematic situation. Developed by Peter Checkland at Lancaster University, it captures people, roles, concerns, processes, conflicts, and environmental factors in a single visual canvas. It is used primarily by systems analysts, organizational consultants, and action researchers who need to surface multiple stakeholder perspectives before imposing any formal structure on a messy, ill-defined problem.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Checkland","year":1981,"type":"Diagrammatic problem-structuring tool","subfamily":"Problem structuring methods","output":"Visual narrative of a messy situation","setting":"Participatory group workshops"},"citations":[{"ref":"Checkland, P. (1981). Systems Thinking, Systems Practice. Wiley.","type":"book","doi":null,"isbn":"978-0-471-27911-2","url":null}],"related":["soft-systems-methodology","soda-cognitive-mapping","strategic-choice-approach"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"richmond-agitation-sedation","name":"Richmond Agitation-Sedation Scale","fullName":"Richmond Agitation-Sedation Scale (RASS)","aliases":["RASS","Sedation scale","Agitation scale"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"2002","originator":"Christopher N. Sessler, et al.","url":"https://scholargate.app/en/clinical-assessment/richmond-agitation-sedation","markdownUrl":"https://scholargate.app/en/clinical-assessment/richmond-agitation-sedation.md","definition":"The Richmond Agitation-Sedation Scale (RASS), developed by Sessler et al. in 2002, is a 10-level ordinal scale for assessing level of consciousness, agitation, and sedation in critically ill patients. It ranges from +4 (combative/violent) through 0 (alert and calm) to -5 (unarousable), enabling precise titration of sedative and analgesic medications in ICU settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher N. Sessler, et al.","subfamily":"Clinical scoring","year":"2002","type":"ICU sedation and agitation assessment"},"citations":[{"ref":"Sessler, C. N., Gosnell, M. S., Grap, M. J., et al. (2002). The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients. American Journal of Respiratory and Critical Care Medicine, 166(10), 1338-1344.","type":"article","doi":"10.1164/rccm.2107138","isbn":null,"url":null},{"ref":"Ely, E. W., Inouye, S. K., Bernard, G. R., et al. (2003). Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the ICU (CAM-ICU). JAMA, 286(21), 2703-2710.","type":"article","doi":"10.1001/jama.286.21.2703","isbn":null,"url":null}],"related":["glasgow-coma-scale","pain-assessment-behavioral-scale","mews-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ridge-regression","name":"Ridge Regression","fullName":"Ridge Regression (L2-Regularized Linear Regression)","aliases":["Ridge Regresyonu","ridge regresyonu","L2-regularized regression","Tikhonov regularization"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1970,"originator":"Hoerl, A.E. & Kennard, R.W.","url":"https://scholargate.app/en/machine-learning/ridge-regression","markdownUrl":"https://scholargate.app/en/machine-learning/ridge-regression.md","definition":"Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hoerl, A.E. & Kennard, R.W.","year":1970,"type":"L2-regularized linear regression","task":"Prediction (regression)","minSample":30},"citations":[{"ref":"Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.","type":"article","doi":"10.1080/00401706.1970.10488634","isbn":null,"url":null}],"related":["lasso-regression","elastic-net","linear-regression","pca","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"right-wing-authoritarianism-scale","name":"Right-Wing Authoritarianism Scale","fullName":"Right-Wing Authoritarianism Scale (RWA)","aliases":["RWA"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Social cognition","year":"1981","originator":"Bob Altemeyer","url":"https://scholargate.app/en/social-psychology/right-wing-authoritarianism-scale","markdownUrl":"https://scholargate.app/en/social-psychology/right-wing-authoritarianism-scale.md","definition":"The Right-Wing Authoritarianism Scale (RWA) is a self-report measure developed by Bob Altemeyer in 1981 to assess individual differences in authoritarian attitudes, including submission to established authorities, adherence to conventional norms, and aggression toward those perceived to violate social conventions. The scale measures three core dimensions: authoritarian submission, authoritarian aggression, and conventionalism. It has become a cornerstone of research on authoritarianism, political attitudes, and intergroup prejudice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bob Altemeyer","subfamily":"Social cognition","year":"1981","type":"Self-report Likert scale"},"citations":[{"ref":"Altemeyer, B. (1981). Right-wing authoritarianism. University of Manitoba Press.","type":"book","doi":null,"isbn":null,"url":"https://psycnet.apa.org/record/1982-12199-000"},{"ref":"Altemeyer, B. (1988). Enemies of freedom: Understanding right-wing authoritarianism. Jossey-Bass.","type":"book","doi":null,"isbn":null,"url":"https://psycnet.apa.org/record/1989-97254-000"}],"related":["social-dominance-orientation-scale","modern-racism-scale","ambivalent-sexism-inventory","cultural-values-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rim","name":"RIM","fullName":"Reference Ideal Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2016","originator":"Cables, E., Lamata, M. T., Verdegay, J. L.","url":"https://scholargate.app/en/decision-making/rim","markdownUrl":"https://scholargate.app/en/decision-making/rim.md","definition":"RIM (Reference Ideal Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Cables, E., Lamata, M. T., Verdegay, J. L. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cables, E., Lamata, M. T., Verdegay, J. L.","subfamily":"Ranking","year":"2016","type":"Distance to user-defined reference intervals (ideal + anti-ideal)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Cables, E., Lamata, M. T., Verdegay, J. L. (2016). RIM-reference ideal method in multicriteria decision making. Information Sciences","type":"article","doi":"10.1016/j.ins.2015.12.011","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ring-signature","name":"Ring Signature","fullName":"Ring Signature Scheme","aliases":["ring signature","group signature"],"domain":"cryptography","family":"ml-model","subfamily":"Digital signature scheme","year":"2001","originator":"Ronald Rivest","url":"https://scholargate.app/en/cryptography/ring-signature","markdownUrl":"https://scholargate.app/en/cryptography/ring-signature.md","definition":"A ring signature is a digital signature scheme allowing a member of a group (ring) to sign a message on behalf of the group without revealing the signer's identity. Proposed by Rivest, Shamir, and Tauman in 2001, ring signatures provide signer anonymity while still proving that the signature comes from one member of a specified set. This cryptographic primitive is widely used in privacy-preserving applications, whistleblowing systems, and anonymous messaging platforms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Rivest","subfamily":"Digital signature scheme","year":"2001","type":"signature scheme with anonymity"},"citations":[{"ref":"Rivest, R. L., Shamir, A., & Tauman, Y. (2001). How to leak a secret. In Advances in Cryptology - ASIACRYPT 2001, LNCS 2248, pp. 552-565.","type":"article","doi":"10.1007/3-540-45682-1_32","isbn":null,"url":null},{"ref":"Cramer, R., Damgård, I., & Schoenmakers, B. (1994). Proofs of partial knowledge and simplified design of witness hiding protocols. In Advances in Cryptology - CRYPTO 1994, LNCS 839, pp. 174-187.","type":"article","doi":"10.1007/3-540-48658-5_19","isbn":null,"url":null}],"related":["rsa-cryptosystem","elliptic-curve-cryptography","hmac"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ripeness-index","name":"Ripeness Index","fullName":"Multi-Parameter Quantification of Fruit Maturity and Harvest Readiness","aliases":["maturity index","harvest readiness assessment","fruit maturation scoring"],"domain":"horticulture","family":"process-pipeline","subfamily":"Maturity assessment and harvest timing","year":"1970","originator":"Pomology and horticulture research","url":"https://scholargate.app/en/horticulture/ripeness-index","markdownUrl":"https://scholargate.app/en/horticulture/ripeness-index.md","definition":"Ripeness index combines multiple quality measurements—soluble solids, firmness, color, starch degradation, ethylene production—into a single composite score indicating fruit maturity and harvest readiness. Unlike single-parameter metrics, this integrated approach accounts for cultivar variation and environmental influence to predict consumer acceptability more reliably. It is widely adopted in export industries and research settings to standardize harvest decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pomology and horticulture research","subfamily":"Maturity assessment and harvest timing","year":"1970","type":"multi-parameter assessment pipeline"},"citations":[{"ref":"Pratt, H. K., & Goeschl, J. D. (2006). Physiological roles of ethylene in plants. Annual Review of Plant Physiology, 20, 541–566.","type":"article","doi":"10.1146/annurev.pp.20.060169.002545","isbn":null,"url":null},{"ref":"Blankenship, S. M., & Dole, J. M. (2003). 1-Methylcyclopropene: A review. Postharvest Biology and Technology, 28(1), 1–25.","type":"article","doi":"10.1016/s0925-5214(02)00246-6","isbn":null,"url":null}],"related":["brix-measurement","fruit-color-analysis","postharvest-storage-simulation","crop-load-management"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ripley-k","name":"Ripley K Function","fullName":"Ripley K Function (Point Pattern Analysis)","aliases":["Ripley's K Function","Second-Order Intensity Function","K(d) Function","Ripley K Fonksiyonu"],"domain":"spatial-analysis","family":"hypothesis-test","subfamily":"Point pattern analysis","year":1977,"originator":"Brian Ripley","url":"https://scholargate.app/en/spatial-analysis/ripley-k","markdownUrl":"https://scholargate.app/en/spatial-analysis/ripley-k.md","definition":"The Ripley K function, introduced by Brian Ripley in 1977, is a second-order summary statistic for spatial point patterns. It measures how the number of points within a given distance d of a typical point compares to what would be expected under complete spatial randomness (CSR). Widely used in ecology, epidemiology, criminology, and geography, the K function reveals whether events cluster, disperse, or distribute randomly across a study area at multiple spatial scales simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brian Ripley","year":1977,"type":"Spatial point pattern test","subfamily":"Point pattern analysis","null_hypothesis":"Complete spatial randomness (CSR) — Poisson process","envelope_method":"Monte Carlo simulation envelopes for significance"},"citations":[{"ref":"Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B, 39(2), 172–212.","type":"article","doi":"10.1111/j.2517-6161.1977.tb01615.x","isbn":null,"url":null}],"related":["moran-s-i","geary-c","getis-ord-gi"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ripls","name":"RIPLS","fullName":"Readiness for Interprofessional Learning Scale","aliases":["Readiness for IPL","RIPLS Scale"],"domain":"health-education","family":"process-pipeline","subfamily":"interprofessional-education","year":"1999","originator":"Gail Parsell & John Bligh","url":"https://scholargate.app/en/health-education/ripls","markdownUrl":"https://scholargate.app/en/health-education/ripls.md","definition":"The RIPLS is a 19-item self-report questionnaire designed to measure healthcare students' attitudes and readiness toward interprofessional learning and collaboration. Developed by Parsell and Bligh in 1999, it assesses three core dimensions of interprofessional readiness: teamwork and collaboration, professional identity, and recognition of roles and responsibilities across professions. The RIPLS is widely used in health professions education to evaluate the effectiveness of interprofessional education initiatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gail Parsell & John Bligh","subfamily":"interprofessional-education","year":"1999","type":"Self-report questionnaire"},"citations":[{"ref":"Parsell, G. & Bligh, J. (1999). The development of a questionnaire to assess the readiness of health care students for interprofessional learning (RIPLS). Med Educ 33(2): 95–100.","type":"article","doi":"10.1046/j.1365-2923.1999.00298.x","isbn":null,"url":null}],"related":["clinical-learning-environment-scale","interprofessional-collaboration-scale","professional-identity-scale","simulation-debriefing-quality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-case-control-study","name":"Risk-adjusted case-control study","fullName":"Risk-adjusted Case-Control Study","aliases":["adjusted case-control study","covariate-adjusted case-control","risk-stratified case-control study","matched and adjusted case-control study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1950s–1980s (case-control design from 1950; risk-adjustment conventions established by 1980s)","originator":"Doll & Hill (foundational case-control); risk adjustment via multivariate logistic regression systematised by Schlesselman (1982) and Breslow & Day (1980)","url":"https://scholargate.app/en/epidemiology/risk-adjusted-case-control-study","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-case-control-study.md","definition":"A risk-adjusted case-control study is an observational design that identifies individuals with a disease outcome (cases) and comparable individuals without it (controls), then uses statistical adjustment — most commonly multivariable logistic regression — to estimate the association between an exposure and the outcome while controlling for confounding risk factors. The adjustment step is what distinguishes this variant from a simple case-control study, producing odds ratios that better reflect the independent contribution of the exposure of interest.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Doll & Hill (foundational case-control); risk adjustment via multivariate logistic regression systematised by Schlesselman (1982) and Breslow & Day (1980)","year":"1950s–1980s (case-control design from 1950; risk-adjustment conventions established by 1980s)","type":"Observational analytic study design","dataType":"Categorical and continuous covariates, binary outcome (case/control status)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195029697","url":null},{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null}],"related":["case-control-study","matched-case-control-study","logistic-regression","propensity-score-matching","cohort-study","multivariable-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-case-crossover-design","name":"Risk-adjusted case-crossover design","fullName":"Risk-Adjusted Case-Crossover Design","aliases":["adjusted case-crossover study","covariate-adjusted case-crossover","risk-controlled case-crossover","case-crossover with risk adjustment"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1991 (base design); risk-adjustment extensions from mid-1990s onward","originator":"Malcolm Maclure (case-crossover base); extensions incorporating covariate risk adjustment developed in subsequent pharmacoepidemiology literature","url":"https://scholargate.app/en/epidemiology/risk-adjusted-case-crossover-design","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-case-crossover-design.md","definition":"The risk-adjusted case-crossover design is a self-matched epidemiological method that compares a person's exposure during a brief hazard window immediately preceding an acute event to their exposure during one or more control windows from the same individual, while formally accounting for time-varying or time-fixed covariates that could confound the exposure-event relationship. By using each case as their own control, stable individual-level confounders are automatically cancelled, while covariate adjustment handles residual time-varying risks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Malcolm Maclure (case-crossover base); extensions incorporating covariate risk adjustment developed in subsequent pharmacoepidemiology literature","year":"1991 (base design); risk-adjustment extensions from mid-1990s onward","type":"Observational analytic epidemiological design","dataType":"Individual-level event data with time-stamped exposure and covariate records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Maclure, M. (1991). The case-crossover design: a method for studying transient effects on the risk of acute events. American Journal of Epidemiology, 133(2), 144–153.","type":"article","doi":"10.1093/oxfordjournals.aje.a115853","isbn":null,"url":null},{"ref":"Navidi, W. (1998). Bidirectional case-crossover designs for exposures with time trends. Biometrics, 54(2), 596–605.","type":"article","doi":"10.2307/3109766","isbn":null,"url":null}],"related":["case-crossover-design","case-control-study","risk-adjusted-cohort-study","conditional-logistic-regression","propensity-score-matching","time-series-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-case-series","name":"Risk-adjusted case series","fullName":"Risk-Adjusted Case Series","aliases":["risk-stratified case series","adjusted case series","risk-corrected case series"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s","originator":"Copeland, Jones & Walters (POSSUM score, 1991); broader risk-adjustment methodology developed across surgical and critical care audit literature","url":"https://scholargate.app/en/epidemiology/risk-adjusted-case-series","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-case-series.md","definition":"A risk-adjusted case series is an observational study design that reports outcomes for a consecutive or defined group of patients undergoing the same procedure or sharing a condition, while statistically correcting for differences in patient-level baseline risk. Rather than presenting raw complication or mortality rates, it compares observed outcomes against expected rates derived from a validated scoring model (e.g., POSSUM, APACHE, ASA grade), enabling fairer evaluation of clinical performance across institutions or over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Copeland, Jones & Walters (POSSUM score, 1991); broader risk-adjustment methodology developed across surgical and critical care audit literature","year":"1990s–2000s","type":"Observational study design with statistical risk correction","dataType":"Clinical records, operative/procedure logs, patient-level outcome data with baseline covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Copeland, G. P., Jones, D., & Walters, M. (1991). POSSUM: a scoring system for surgical audit. British Journal of Surgery, 78(3), 355–360.","type":"article","doi":"10.1002/bjs.1800780327","isbn":null,"url":null},{"ref":"Mayer, E. K., Bottle, A., Darzi, A. W., & Aylin, P. (2004). Case volume and outcome in the surgical treatment of colorectal cancer. British Journal of Surgery, 91(9), 1104–1110.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Case+volume+and+outcome+in+the+surgical+treatment+of+colorectal+cancer+Mayer"}],"related":["case-series","risk-adjusted-cohort-study","propensity-score-matching","competing-risks-analysis","survival-analysis","diagnostic-accuracy-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-cohort-study","name":"Risk-adjusted cohort study","fullName":"Risk-Adjusted Cohort Study","aliases":["adjusted cohort study","covariate-adjusted cohort","risk-controlled prospective study","propensity-adjusted cohort"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s)","originator":"Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century","url":"https://scholargate.app/en/epidemiology/risk-adjusted-cohort-study","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-cohort-study.md","definition":"A risk-adjusted cohort study is an observational epidemiological design in which a defined group of individuals is followed over time to compare outcomes between exposed and unexposed subgroups, with statistical methods applied to control for measured confounders. Adjustment strategies — including multivariable regression, propensity score matching, inverse probability weighting, or standardization — are used to reduce bias and produce effect estimates that more closely approximate what would be observed in a randomized trial.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century","year":"Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s)","type":"Observational epidemiological study design with statistical confounding control","dataType":"Longitudinal individual-level data with measured covariates (continuous, binary, categorical)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null},{"ref":"Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.","type":"article","doi":"10.1080/00273171.2011.568786","isbn":null,"url":null}],"related":["cohort-study","propensity-score-matching","multivariable-regression","case-control-study","randomized-controlled-trial","instrumental-variable-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-competing-risks-analysis","name":"Risk-adjusted competing risks analysis","fullName":"Risk-adjusted Competing Risks Survival Analysis","aliases":["competing risks regression","subdistribution hazard model","cause-specific hazard analysis","Fine-Gray model"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1999 (subdistribution hazard model); cause-specific hazard framework earlier","originator":"Jason Fine and Robert Gray","url":"https://scholargate.app/en/epidemiology/risk-adjusted-competing-risks-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-competing-risks-analysis.md","definition":"Risk-adjusted competing risks analysis extends classical survival analysis to settings where subjects can experience more than one type of terminal event, and where the occurrence of one event prevents the occurrence of another. By modelling cause-specific or subdistribution hazards while adjusting for measured confounders, the method yields unbiased estimates of the absolute probability — the cumulative incidence function — of each event type over time in the presence of competing events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jason Fine and Robert Gray","year":"1999 (subdistribution hazard model); cause-specific hazard framework earlier","type":"Regression model for time-to-event data with competing events","dataType":"Time-to-event data with event type indicator and covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509.","type":"article","doi":"10.1080/01621459.1999.10474144","isbn":null,"url":null},{"ref":"Latouche, A., Allignol, A., Beyersmann, J., Labopin, M., & Fine, J. P. (2013). A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions. Journal of Clinical Epidemiology, 66(6), 648–653.","type":"article","doi":"10.1016/j.jclinepi.2012.09.017","isbn":null,"url":null}],"related":["cox-proportional-hazards","kaplan-meier-estimator","cause-specific-hazard-model","subdistribution-hazard-model","survival-analysis","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-cox-proportional-hazards","name":"Risk-adjusted Cox Proportional Hazards","fullName":"Risk-adjusted Cox Proportional Hazards Regression","aliases":["adjusted Cox regression","multivariable Cox model","covariate-adjusted survival analysis","risk-adjusted survival model"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1972 (Cox model); risk adjustment widespread from 1980s","originator":"D. R. Cox (base model); risk-adjustment as routine practice formalised through clinical epidemiology literature from the 1980s onward","url":"https://scholargate.app/en/epidemiology/risk-adjusted-cox-proportional-hazards","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-cox-proportional-hazards.md","definition":"Risk-adjusted Cox proportional hazards regression extends the classical Cox (1972) survival model by simultaneously entering known confounders — age, sex, comorbidities, disease severity — into the model alongside the exposure of primary interest. This adjustment isolates the independent effect of the exposure on the hazard of an event, producing hazard ratios (HRs) that are not distorted by baseline differences between comparison groups. It is the most widely used method for multivariable survival analysis in clinical and epidemiological research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. R. Cox (base model); risk-adjustment as routine practice formalised through clinical epidemiology literature from the 1980s onward","year":"1972 (Cox model); risk adjustment widespread from 1980s","type":"Multivariable survival regression","dataType":"Time-to-event (survival) data with one binary or multi-level outcome, continuous or categorical covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null},{"ref":"Hosmer, D. W., Lemeshow, S., & May, S. (2008). Applied Survival Analysis: Regression Modeling of Time-to-Event Data (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471754992","url":null}],"related":["kaplan-meier-estimator","log-rank-test","competing-risks-regression","cox-proportional-hazards","logistic-regression","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-cross-sectional-epidemiological-study","name":"Risk-adjusted cross-sectional epidemiological study","fullName":"Risk-Adjusted Cross-Sectional Epidemiological Study","aliases":["risk-adjusted cross-sectional survey","case-mix adjusted cross-sectional study","standardized cross-sectional analysis","adjusted prevalence study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s (risk-adjustment integration); cross-sectional design foundational since mid-20th century","originator":"Rooted in classical cross-sectional epidemiology (Doll, Hill, Lilienfeld); risk-adjustment formalization attributed to Lisa Iezzoni and colleagues in health outcomes research (1990s)","url":"https://scholargate.app/en/epidemiology/risk-adjusted-cross-sectional-epidemiological-study","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-cross-sectional-epidemiological-study.md","definition":"A risk-adjusted cross-sectional epidemiological study measures the prevalence of health outcomes or exposures in a defined population at a single point in time, then applies statistical risk-adjustment methods — such as regression standardization, direct or indirect standardization, or propensity scoring — to remove the distorting influence of differences in patient case-mix across comparison groups. The approach is widely used in health services research, comparative effectiveness, and clinical quality assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in classical cross-sectional epidemiology (Doll, Hill, Lilienfeld); risk-adjustment formalization attributed to Lisa Iezzoni and colleagues in health outcomes research (1990s)","year":"1990s (risk-adjustment integration); cross-sectional design foundational since mid-20th century","type":"Observational epidemiological design with statistical adjustment","dataType":"Cross-sectional survey, administrative, or registry data with individual-level covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195083385","url":null},{"ref":"Iezzoni, L. I. (Ed.). (2003). Risk Adjustment for Measuring Health Care Outcomes (3rd ed.). Health Administration Press.","type":"book","doi":null,"isbn":"978-1567932140","url":null}],"related":["cross-sectional-study","logistic-regression","propensity-score-matching","direct-standardization","multilevel-modeling","case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-diagnostic-accuracy-study","name":"Risk-adjusted diagnostic accuracy study","fullName":"Risk-Adjusted Diagnostic Accuracy Study","aliases":["case-mix-adjusted diagnostic accuracy","stratified diagnostic accuracy study","covariate-adjusted diagnostic accuracy","risk-stratified DTA study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Conceptual roots 1980s–1990s; covariate-adjusted ROC formally introduced 2009","originator":"Margaret Pepe and colleagues; covariate-adjusted ROC formalized by Janes & Pepe (2009)","url":"https://scholargate.app/en/epidemiology/risk-adjusted-diagnostic-accuracy-study","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-diagnostic-accuracy-study.md","definition":"A risk-adjusted diagnostic accuracy study evaluates how well an index test identifies a target condition while explicitly accounting for patient-level risk factors that influence either disease prevalence or test performance. By adjusting for case-mix, it yields accuracy estimates — sensitivity, specificity, and AUC — that are not confounded by the composition of the study sample, enabling fairer comparisons across populations and clinical settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Margaret Pepe and colleagues; covariate-adjusted ROC formalized by Janes & Pepe (2009)","year":"Conceptual roots 1980s–1990s; covariate-adjusted ROC formally introduced 2009","type":"Observational clinical study design with covariate adjustment","dataType":"Binary or ordinal test results, reference standard classification, and patient-level covariates (risk factors)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198509844","url":null},{"ref":"Janes, H., & Pepe, M. S. (2009). Adjusting for covariate effects on classification accuracy using the covariate-adjusted ROC curve. Biometrika, 96(2), 371–382.","type":"article","doi":"10.1093/biomet/asp002","isbn":null,"url":null}],"related":["diagnostic-accuracy-study","prospective-diagnostic-accuracy-study","roc-analysis","logistic-regression","cohort-study","screening-test-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-dose-response-analysis","name":"Risk-adjusted dose-response analysis","fullName":"Risk-Adjusted Dose-Response Analysis","aliases":["confounder-adjusted dose-response","covariate-adjusted dose-response modeling","risk-stratified dose-response analysis","adjusted exposure-response analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s-1990s (formalized in modern epidemiology)","originator":"Sander Greenland; Kenneth Rothman (foundational epidemiological methods)","url":"https://scholargate.app/en/epidemiology/risk-adjusted-dose-response-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-dose-response-analysis.md","definition":"Risk-adjusted dose-response analysis quantifies the relationship between increasing levels of an exposure (dose) and the probability or magnitude of an outcome (response), while simultaneously controlling for baseline risk factors that could confound or modify this relationship. The method is widely applied in clinical epidemiology, pharmacoepidemiology, and environmental health research to isolate the causal contribution of exposure intensity from background risk heterogeneity among participants.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sander Greenland; Kenneth Rothman (foundational epidemiological methods)","year":"1980s-1990s (formalized in modern epidemiology)","type":"Epidemiological modeling technique","dataType":"Individual-level observational or clinical trial data with exposure measures and potential confounders","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Greenland, S. (1995). Dose-response and trend analysis in epidemiology: alternatives to categorical analysis. Epidemiology, 6(4), 356-365.","type":"article","doi":"10.1097/00001648-199507000-00005","isbn":null,"url":null},{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null}],"related":["dose-response-analysis","cox-proportional-hazards","logistic-regression","propensity-score-analysis","restricted-cubic-splines","cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-ecological-study","name":"Risk-adjusted ecological study","fullName":"Risk-Adjusted Ecological Study","aliases":["risk-adjusted ecological analysis","confounder-adjusted ecological study","ecological regression with risk adjustment","adjusted area-level study"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–1990s","originator":"Extension of ecological study methodology; risk adjustment concepts formalized by Morgenstern (1982) and developed further in health outcomes research","url":"https://scholargate.app/en/epidemiology/risk-adjusted-ecological-study","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-ecological-study.md","definition":"A risk-adjusted ecological study is an observational epidemiological design that examines associations between exposures and outcomes measured at the group or area level — such as regions, hospitals, or countries — while statistically controlling for known risk factors also measured at that level. By incorporating risk adjustment through ecological regression or standardization, the design reduces (though cannot eliminate) confounding from group-level variables, enabling more valid comparisons across populations or settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of ecological study methodology; risk adjustment concepts formalized by Morgenstern (1982) and developed further in health outcomes research","year":"1980s–1990s","type":"Observational ecological design with statistical confounding control","dataType":"Aggregate/group-level data (population rates, area-level covariates)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Morgenstern, H. (1982). Uses of ecologic analysis in epidemiologic research. American Journal of Public Health, 72(12), 1336–1344.","type":"article","doi":"10.2105/ajph.72.12.1336","isbn":null,"url":null},{"ref":"Wakefield, J. (2008). Ecologic studies revisited. Annual Review of Public Health, 29, 75–90.","type":"article","doi":"10.1146/annurev.publhealth.29.020907.090821","isbn":null,"url":null}],"related":["ecological-study","cohort-study","risk-adjusted-cohort-study","multilevel-modeling","standardized-mortality-ratio","spatial-epidemiology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-kaplan-meier-analysis","name":"Risk-adjusted Kaplan-Meier analysis","fullName":"Risk-adjusted Kaplan-Meier Survival Analysis","aliases":["weighted Kaplan-Meier","IPTW-adjusted Kaplan-Meier","propensity-score-weighted survival curves","adjusted survival curves"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"2001–2004 (formal statistical framework for weighted KM curves)","originator":"Conceptual basis: Kaplan & Meier (1958); risk-adjustment via IPTW formalised by Hernán, Brumback & Robins (2001), with practical implementation by Cole & Hernán (2004)","url":"https://scholargate.app/en/epidemiology/risk-adjusted-kaplan-meier-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-kaplan-meier-analysis.md","definition":"Risk-adjusted Kaplan-Meier analysis combines the non-parametric Kaplan-Meier estimator with inverse probability of treatment weighting (IPTW) or similar risk-adjustment procedures to produce survival curves that are comparable across groups as if the groups had identical distributions of baseline confounders. It is the observational-study analogue of plotting survival curves from a randomised trial.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conceptual basis: Kaplan & Meier (1958); risk-adjustment via IPTW formalised by Hernán, Brumback & Robins (2001), with practical implementation by Cole & Hernán (2004)","year":"2001–2004 (formal statistical framework for weighted KM curves)","type":"Adjusted non-parametric survival method","dataType":"Time-to-event (survival) data with baseline covariate data for weight estimation","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Cole, S. R., & Hernan, M. A. (2004). Adjusted survival curves with inverse probability weights. Computer Methods and Programs in Biomedicine, 75(1), 45–49.","type":"article","doi":"10.1016/j.cmpb.2003.10.004","isbn":null,"url":null},{"ref":"Hernan, M. A., Brumback, B., & Robins, J. M. (2001). Marginal structural models to estimate the joint causal effect of nonrandomized treatments. Journal of the American Statistical Association, 96(454), 440–448.","type":"article","doi":"10.1198/016214501753168154","isbn":null,"url":null}],"related":["kaplan-meier-analysis","cox-proportional-hazards","propensity-score-matching","inverse-probability-weighting","survival-analysis","competing-risks-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-nested-case-control","name":"Risk-adjusted Nested Case-Control","fullName":"Risk-adjusted Nested Case-Control Study","aliases":["risk-adjusted NCC","covariate-adjusted nested case-control","propensity-score nested case-control","nested case-control with risk adjustment"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1977 (nested case-control); risk-adjusted extensions 1980s–2000s","originator":"Thomas (1977) for nested case-control; risk adjustment extensions developed through pharmacoepidemiology literature (1980s–2000s)","url":"https://scholargate.app/en/epidemiology/risk-adjusted-nested-case-control","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-nested-case-control.md","definition":"A risk-adjusted nested case-control study embeds a case-control comparison inside a defined cohort and explicitly accounts for differences in baseline risk between cases and controls through covariate adjustment — most commonly via risk scores, propensity scores, or stratification. It preserves the efficiency advantages of the nested design while reducing confounding attributable to pre-existing risk differentials, making it especially valuable in pharmacoepidemiology and clinical effectiveness research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas (1977) for nested case-control; risk adjustment extensions developed through pharmacoepidemiology literature (1980s–2000s)","year":"1977 (nested case-control); risk-adjusted extensions 1980s–2000s","type":"Observational analytical study design","dataType":"Time-to-event cohort data with covariate and exposure records","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Thomas+1977+nested+case-control+cohort+analysis"},{"ref":"Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.","type":"book","doi":null,"isbn":"978-0781755641","url":null}],"related":["nested-case-control","case-cohort-study","propensity-score-matching","cox-proportional-hazards","conditional-logistic-regression","cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-phase-i-clinical-trial","name":"Risk-adjusted Phase I clinical trial","fullName":"Risk-Adjusted Phase I Clinical Trial","aliases":["risk-stratified Phase I trial","risk-adaptive dose-escalation study","covariate-adjusted Phase I study","risk-based dose-finding trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s","originator":"Evolved from the Continual Reassessment Method (O'Quigley et al., 1990) extended with patient-level risk covariates","url":"https://scholargate.app/en/epidemiology/risk-adjusted-phase-i-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-phase-i-clinical-trial.md","definition":"A risk-adjusted Phase I clinical trial is a first-in-human or dose-finding study that explicitly incorporates patient-level risk covariates — such as organ function, prior therapy, or genetic markers — into the dose-escalation model. Rather than treating all enrolled participants as homogeneous, the design accounts for individual differences in tolerance, allowing the recommended dose to vary by risk stratum. This approach is especially common in oncology, where patients with impaired renal function or heavily pre-treated disease may tolerate lower doses than the broader population.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from the Continual Reassessment Method (O'Quigley et al., 1990) extended with patient-level risk covariates","year":"1990s–2000s","type":"Interventional clinical trial design","dataType":"Dose-toxicity outcomes, patient risk covariate data (binary or continuous clinical variables)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Iasonos, A., Wilton, A. S., & Gonen, M. (2008). A review of stochastic dose-finding methods. Statistics in Medicine, 27(25), 5031–5046.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+review+of+stochastic+dose-finding+methods+Iasonos+2008"},{"ref":"O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: A practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Continual+reassessment+method+practical+design+phase+1+clinical+trials+cancer+OQuigley+1990"}],"related":["phase-i-clinical-trial","adaptive-phase-i-clinical-trial","bayesian-phase-i-clinical-trial","dose-response-analysis","randomized-clinical-trial","competing-risks-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-phase-ii-clinical-trial","name":"Risk-adjusted Phase II clinical trial","fullName":"Risk-Adjusted Phase II Clinical Trial Design","aliases":["risk-stratified Phase II trial","covariate-adjusted Phase II design","risk-adjusted two-stage design","RA Phase II trial"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1989–1994","originator":"Peter Thall, Richard Simon (Bayesian risk-adjusted extension); Simon optimal two-stage as precursor","url":"https://scholargate.app/en/epidemiology/risk-adjusted-phase-ii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-phase-ii-clinical-trial.md","definition":"A risk-adjusted Phase II clinical trial is an early-phase efficacy design that incorporates patient baseline risk strata — such as disease severity, prognostic score, or comorbidity burden — directly into the trial's stopping rules and sample size calculations. By conditioning response targets and futility/efficacy thresholds on risk group membership, the design avoids the bias that arises when a new therapy is evaluated in a population whose prognostic mix differs from the historical control on which the null hypothesis was based.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Thall, Richard Simon (Bayesian risk-adjusted extension); Simon optimal two-stage as precursor","year":"1989–1994","type":"Adaptive clinical trial design","dataType":"Binary or continuous efficacy outcomes stratified by baseline risk covariates","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Thall, P. F., & Simon, R. (1994). Practical Bayesian guidelines for phase IIB clinical trials. Biometrics, 50(2), 337–349.","type":"article","doi":"10.2307/2533377","isbn":null,"url":null},{"ref":"Simon, R. (1989). Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials, 10(1), 1–10.","type":"article","doi":"10.1016/0197-2456(89)90015-9","isbn":null,"url":null}],"related":["simon-two-stage-design","bayesian-adaptive-design","seamless-phase-ii-iii-design","response-adaptive-randomization","stratified-randomized-controlled-trial","basket-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-phase-iii-clinical-trial","name":"Risk-adjusted Phase III clinical trial","fullName":"Risk-Adjusted Phase III Randomized Clinical Trial","aliases":["risk-stratified Phase III trial","covariate-adjusted Phase III RCT","risk-adjusted confirmatory trial","RA-Phase III"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1980s–present","originator":"Evolving practice; foundational risk-adjustment principles established by Pocock (1983) and extended by numerous trialists","url":"https://scholargate.app/en/epidemiology/risk-adjusted-phase-iii-clinical-trial","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-phase-iii-clinical-trial.md","definition":"A risk-adjusted Phase III clinical trial is a large-scale confirmatory randomized experiment that explicitly incorporates participants' baseline prognostic risk profile into both the randomization process and the primary statistical analysis. By stratifying patients on known risk factors before allocation and adjusting for those factors in the outcome model, the design achieves greater statistical precision, reduces confounding, and produces treatment effect estimates that are more clinically meaningful across patient subgroups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolving practice; foundational risk-adjustment principles established by Pocock (1983) and extended by numerous trialists","year":"1980s–present","type":"Confirmatory randomized trial with baseline risk stratification and covariate adjustment","dataType":"Continuous, binary, time-to-event outcomes; baseline covariate and prognostic risk data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Pocock, S. J. (1983). Clinical Trials: A Practical Approach. Wiley.","type":"book","doi":null,"isbn":"978-0471901556","url":null},{"ref":"Kahan, B. C., & Morris, T. P. (2014). Improper analysis of trials randomised using stratified blocks or minimisation. Statistics in Medicine, 31(4), 328-340.","type":"article","doi":"10.1002/sim.4431","isbn":null,"url":null}],"related":["randomized-clinical-trial","phase-iii-clinical-trial","risk-adjusted-cohort-study","adaptive-randomized-clinical-trial","cox-proportional-hazards","matched-randomized-clinical-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-phase-iv-study","name":"Risk-adjusted Phase IV study","fullName":"Risk-adjusted Phase IV Post-marketing Study","aliases":["risk-adjusted post-marketing surveillance study","adjusted Phase IV trial","risk-stratified post-authorization study","PASS with risk adjustment"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1990s–2000s (formalized with ICH E2E and EMA PASS guidelines)","originator":"Regulatory and pharmacoepidemiology community (ICH, EMA, FDA frameworks)","url":"https://scholargate.app/en/epidemiology/risk-adjusted-phase-iv-study","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-phase-iv-study.md","definition":"A risk-adjusted Phase IV study is an observational or semi-experimental post-marketing study conducted after a drug or device has received regulatory approval. It uses statistical risk-adjustment techniques — such as propensity score matching, inverse probability weighting, or multivariable regression — to control for confounding by indication and baseline patient differences, thereby producing more credible safety, effectiveness, and utilization estimates than unadjusted real-world analyses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Regulatory and pharmacoepidemiology community (ICH, EMA, FDA frameworks)","year":"1990s–2000s (formalized with ICH E2E and EMA PASS guidelines)","type":"Observational / quasi-experimental clinical study design","dataType":"Real-world patient data: electronic health records, claims databases, registry data, or prospective cohort follow-up","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Strom, B. L. (Ed.). (2005). Pharmacoepidemiology (4th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470863107","url":null},{"ref":"Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.","type":"article","doi":"10.1080/00273171.2011.568786","isbn":null,"url":null}],"related":["propensity-score-matching","pharmacovigilance","post-marketing-surveillance","cohort-study","inverse-probability-weighting","comparative-effectiveness-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-screening-test-evaluation","name":"Risk-adjusted screening test evaluation","fullName":"Risk-Adjusted Screening Test Evaluation","aliases":["risk-stratified screening accuracy study","covariate-adjusted diagnostic accuracy evaluation","risk-adjusted screening performance assessment","RASTE"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"Late 1990s–2000s (formal statistical framework ~1997–2009)","originator":"Margaret Sullivan Pepe and colleagues (covariate-adjusted ROC methodology)","url":"https://scholargate.app/en/epidemiology/risk-adjusted-screening-test-evaluation","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-screening-test-evaluation.md","definition":"Risk-adjusted screening test evaluation assesses the sensitivity, specificity, and overall discriminatory accuracy of a screening test after accounting for patient-level risk factors (covariates) that independently influence test results or disease prevalence. By conditioning performance metrics on observed covariates — age, sex, comorbidities, or prior screening history — this approach yields accuracy estimates that are not confounded by differences in population risk profiles, enabling fair comparisons across subgroups or study settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Margaret Sullivan Pepe and colleagues (covariate-adjusted ROC methodology)","year":"Late 1990s–2000s (formal statistical framework ~1997–2009)","type":"Analytical study design","dataType":"Binary or ordinal test results, continuous biomarker scores, disease status, covariate data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198565826","url":null},{"ref":"Janes, H., & Pepe, M. S. (2009). Adjusting for covariate effects on classification accuracy using the covariate-adjusted ROC curve. Biometrika, 96(2), 371–382.","type":"article","doi":"10.1093/biomet/asp002","isbn":null,"url":null}],"related":["diagnostic-accuracy-study","screening-test-evaluation","risk-adjusted-cohort-study","case-control-study","logistic-regression","roc-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-adjusted-survival-analysis","name":"Risk-adjusted survival analysis","fullName":"Risk-Adjusted Survival Analysis","aliases":["covariate-adjusted survival analysis","adjusted time-to-event analysis","risk-stratified survival analysis","adjusted Kaplan-Meier / Cox analysis"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s","originator":"D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others","url":"https://scholargate.app/en/epidemiology/risk-adjusted-survival-analysis","markdownUrl":"https://scholargate.app/en/epidemiology/risk-adjusted-survival-analysis.md","definition":"Risk-adjusted survival analysis estimates the time to an event of interest — such as death, relapse, or hospital readmission — while simultaneously accounting for baseline differences in patient characteristics (covariates). By incorporating confounders such as age, comorbidities, or disease severity, it produces hazard ratios, survival curves, and median survival estimates that are attributable to the factor of interest rather than to pre-existing risk differences between groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others","year":"1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s","type":"Observational and experimental analytical method","dataType":"Time-to-event data with baseline covariates (continuous, binary, or categorical)","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2985181"},{"ref":"Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"9781439856789","url":null}],"related":["survival-analysis","cox-proportional-hazards","kaplan-meier-analysis","competing-risks-analysis","propensity-score-matching","inverse-probability-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-box-behnken-design","name":"Risk-based Box-Behnken Design","fullName":"Risk-based Box-Behnken Response Surface Design","aliases":["Risk-based BBD","Risk-prioritized Box-Behnken","QbD Box-Behnken design","Risk-informed RSM"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"2005–2009 (QbD-era integration of risk assessment with BBD)","originator":"Box & Behnken (BBD, 1960); risk integration formalized under ICH Q8/Q9 pharmaceutical QbD frameworks (~2005–2009)","url":"https://scholargate.app/en/experimental-design/risk-based-box-behnken-design","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-box-behnken-design.md","definition":"Risk-based Box-Behnken Design combines the classical three-level Box-Behnken response surface design with a formal risk assessment step — typically a risk ranking tool such as FMEA or Ishikawa analysis — to prioritize which process or formulation factors deserve experimental investigation. Widely adopted in pharmaceutical Quality by Design (QbD) and engineering process optimization, the approach ensures that experimental resources are directed toward the factor combinations most likely to affect product quality or process performance, reducing unnecessary runs while preserving predictive power.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Behnken (BBD, 1960); risk integration formalized under ICH Q8/Q9 pharmaceutical QbD frameworks (~2005–2009)","year":"2005–2009 (QbD-era integration of risk assessment with BBD)","type":"Response surface experimental design with risk prioritization","dataType":"Continuous numerical factor levels; quantitative response variables","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.","type":"article","doi":"10.1080/00401706.1960.10489912","isbn":null,"url":null},{"ref":"International Council for Harmonisation (ICH). (2009). ICH Q8(R2): Pharmaceutical Development. ICH Harmonised Tripartite Guideline.","type":"misc","doi":null,"isbn":null,"url":"https://www.ich.org/page/quality-guidelines"}],"related":["box-behnken-design","central-composite-design","response-surface-methodology","design-of-experiments","quality-by-design","failure-mode-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-central-composite-design","name":"Risk-based central composite design","fullName":"Risk-based Central Composite Design","aliases":["Risk-informed CCD","CCD with risk assessment","Uncertainty-aware central composite design","Risk-integrated RSM"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1951 (CCD); risk-based integration emerged in applied engineering literature from the 1990s onward","originator":"Foundational CCD: George E. P. Box & K. B. Wilson (1951); risk integration adapted from engineering risk analysis traditions","url":"https://scholargate.app/en/experimental-design/risk-based-central-composite-design","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-central-composite-design.md","definition":"Risk-based Central Composite Design (Risk-based CCD) integrates formal risk identification and uncertainty quantification into the classical CCD framework. By coupling the rotatable second-order experimental structure of CCD with probabilistic risk metrics, engineers and scientists can simultaneously optimize process responses and characterize the risk of unacceptable outcomes — making it particularly valuable in regulated industries such as pharmaceuticals, chemical engineering, and advanced manufacturing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Foundational CCD: George E. P. Box & K. B. Wilson (1951); risk integration adapted from engineering risk analysis traditions","year":"1951 (CCD); risk-based integration emerged in applied engineering literature from the 1990s onward","type":"Experimental design with integrated risk assessment","dataType":"Continuous quantitative process/product response data with uncertainty and risk metrics","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45.","type":"article","doi":"10.1111/j.2517-6161.1951.tb00067.x","isbn":null,"url":null},{"ref":"Central composite design. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Central_composite_design"}],"related":["central-composite-design","box-behnken-design","response-surface-methodology","failure-mode-and-effects-analysis","reliability-analysis","fractional-factorial-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-control-chart","name":"Risk-based control chart","fullName":"Risk-based Statistical Process Control Chart","aliases":["economic control chart","risk-integrated SPC","cost-based control chart","economic design of control charts"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1956 (economic design); refined through 1980s–2000s","originator":"A. J. Duncan (economic design, 1956); T. J. Lorenzen & L. C. Vance (unified economic model, 1986)","url":"https://scholargate.app/en/experimental-design/risk-based-control-chart","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-control-chart.md","definition":"A risk-based control chart extends the classical Shewhart control chart by explicitly incorporating the costs and probabilities of two error types — false alarms (Type I) and missed shifts (Type II) — along with sampling costs, into the design of chart parameters. Rather than using arbitrary 3-sigma limits, the method selects sample size, sampling interval, and control limits to minimise the total expected cost or risk of operating the monitoring scheme.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. J. Duncan (economic design, 1956); T. J. Lorenzen & L. C. Vance (unified economic model, 1986)","year":"1956 (economic design); refined through 1980s–2000s","type":"Quantitative process monitoring method","dataType":"Continuous or attribute process measurement data with cost/risk parameters","subfamily":"Engineering methods"},"citations":[{"ref":"Lorenzen, T. J., & Vance, L. C. (1986). The economic design of control charts: A unified approach. Technometrics, 28(1), 3–10.","type":"article","doi":"10.1080/00401706.1986.10488092","isbn":null,"url":null},{"ref":"Duncan, A. J. (1956). The economic design of X̄ charts used to maintain current control of a process. Journal of the American Statistical Association, 51(274), 228–242.","type":"article","doi":"10.1080/01621459.1956.10501322","isbn":null,"url":null}],"related":["control-chart","statistical-process-control","process-capability-analysis","failure-mode-and-effects-analysis","risk-based-statistical-process-control","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-design-of-experiments","name":"Risk-based design of experiments","fullName":"Risk-based Design of Experiments","aliases":["Risk-based DoE","risk-informed experimental design","risk-prioritized DoE","RB-DoE"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"2000s–2010s (formalized in pharmaceutical and process engineering contexts)","originator":"Emerged from ICH Q8/Q9/Q10 pharmaceutical guidelines; formalized in engineering by integration of FMEA/FTA with classical DoE","url":"https://scholargate.app/en/experimental-design/risk-based-design-of-experiments","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-design-of-experiments.md","definition":"Risk-based design of experiments (RB-DoE) integrates formal risk assessment — typically using tools such as FMEA or fault tree analysis — with classical experimental design to prioritize which process or product factors are most critical to investigate. Rather than treating all candidate factors equally, this approach ranks factors by their risk priority number or likelihood of affecting quality, safety, or reliability, then allocates experimental runs preferentially to high-risk factors. It is widely used in pharmaceutical development, chemical process engineering, and manufacturing quality management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from ICH Q8/Q9/Q10 pharmaceutical guidelines; formalized in engineering by integration of FMEA/FTA with classical DoE","year":"2000s–2010s (formalized in pharmaceutical and process engineering contexts)","type":"Experimental design method with risk-based factor prioritization","dataType":"Quantitative continuous or categorical experimental response data; risk scores (ordinal or numerical)","subfamily":"Engineering methods"},"citations":[{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916018","url":null},{"ref":"International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). (2009). Pharmaceutical Development Q8(R2). ICH Expert Working Group.","type":"misc","doi":null,"isbn":null,"url":"https://www.ich.org/page/quality-guidelines"}],"related":["design-of-experiments","failure-mode-and-effects-analysis","response-surface-methodology","fractional-factorial-design","robust-design-of-experiments","fault-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-event-tree-analysis","name":"Risk-based event tree analysis","fullName":"Risk-based Event Tree Analysis","aliases":["Risk-based ETA","probabilistic event tree analysis","consequence-probability event tree","risk-informed ETA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1975 (WASH-1400); risk-based integration formalized through 1980s–1990s PRA practice","originator":"Originated in nuclear industry (US Nuclear Regulatory Commission, WASH-1400 report); risk-based framing developed through probabilistic risk assessment practice","url":"https://scholargate.app/en/experimental-design/risk-based-event-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-event-tree-analysis.md","definition":"Risk-based event tree analysis is a forward-looking, inductive risk assessment technique that models the consequences of an initiating event by tracing binary success/failure branches through safety barriers, then weights each outcome path by its probability to produce quantified risk estimates. Widely applied in nuclear, chemical process, aviation, and infrastructure safety engineering, it sits at the heart of probabilistic risk assessment (PRA) and supports both design decisions and regulatory compliance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Originated in nuclear industry (US Nuclear Regulatory Commission, WASH-1400 report); risk-based framing developed through probabilistic risk assessment practice","year":"1975 (WASH-1400); risk-based integration formalized through 1980s–1990s PRA practice","type":"Risk and reliability analysis technique","dataType":"Initiating event frequencies, conditional branch probabilities, consequence severity estimates","subfamily":"Engineering methods"},"citations":[{"ref":"Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521773201","url":null},{"ref":"Event tree analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Event_tree_analysis"}],"related":["fault-tree-analysis","failure-mode-and-effects-analysis","risk-based-fault-tree-analysis","event-tree-analysis","bow-tie-analysis","probabilistic-risk-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-failure-mode-and-effects-analysis","name":"Risk-based failure mode and effects analysis","fullName":"Risk-Based Failure Mode and Effects Analysis","aliases":["RBFMEA","Risk-based FMEA","Risk-prioritised FMEA","Quantitative FMEA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1949 (FMEA origins); risk-prioritised RPN framework circa 1977–1980s (AIAG automotive)","originator":"U.S. Department of Defense / MIL-STD-1629A; formalised in IEC 60812","url":"https://scholargate.app/en/experimental-design/risk-based-failure-mode-and-effects-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-failure-mode-and-effects-analysis.md","definition":"Risk-based failure mode and effects analysis (RBFMEA) is a structured engineering technique that identifies every way a system or process can fail, assesses the risk of each failure mode using a numerical Risk Priority Number (RPN = Occurrence × Severity × Detectability), and prioritises corrective actions accordingly. Rooted in MIL-STD-1629A and standardised in IEC 60812:2018, it is the dominant proactive reliability and safety tool in aerospace, automotive, pharmaceutical, and manufacturing industries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Department of Defense / MIL-STD-1629A; formalised in IEC 60812","year":"1949 (FMEA origins); risk-prioritised RPN framework circa 1977–1980s (AIAG automotive)","type":"Quantitative risk-prioritisation technique","dataType":"Expert elicitation scores, historical failure rate data, engineering specifications","subfamily":"Engineering methods"},"citations":[{"ref":"International Electrotechnical Commission. (2018). IEC 60812:2018 — Failure modes and effects analysis (FMEA and FMECA). IEC.","type":"book","doi":null,"isbn":null,"url":"https://www.iec.ch/publication/26359"},{"ref":"Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873895989","url":null}],"related":["failure-mode-and-effects-analysis","fault-tree-analysis","risk-based-fault-tree-analysis","statistical-process-control","six-sigma-dmaic","reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-fault-tree-analysis","name":"Risk-based fault tree analysis","fullName":"Risk-Based Fault Tree Analysis","aliases":["RB-FTA","risk-informed FTA","quantitative fault tree analysis","probabilistic fault tree analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1961 (FTA origin); risk-based integration formalised 1975–1981","originator":"H.A. Watson (Bell Labs) and developed further by Boeing/U.S. Air Force; risk-based extension via NRC probabilistic risk assessment programs","url":"https://scholargate.app/en/experimental-design/risk-based-fault-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-fault-tree-analysis.md","definition":"Risk-based fault tree analysis (RB-FTA) combines classical fault tree analysis with explicit quantitative risk assessment. Starting from an undesired top event, the analyst decomposes it into contributing causes using AND/OR logic gates, assigns failure probabilities to basic events from reliability databases or historical data, and then propagates those probabilities through the tree to compute top-event likelihood. The result is expressed as risk — probability weighted by consequence severity — enabling prioritisation of safety interventions by their actual risk reduction impact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"H.A. Watson (Bell Labs) and developed further by Boeing/U.S. Air Force; risk-based extension via NRC probabilistic risk assessment programs","year":"1961 (FTA origin); risk-based integration formalised 1975–1981","type":"Quantitative safety and reliability analysis","dataType":"Failure probability data, event rates, system logic diagrams","subfamily":"Engineering methods"},"citations":[{"ref":"Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492.","type":"book","doi":null,"isbn":null,"url":"https://www.nrc.gov/reading-rm/doc-collections/nuregs/staff/sr0492/"},{"ref":"Ericson, C. A. (2005). Hazard Analysis Techniques for System Safety. Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471720195","url":null}],"related":["fault-tree-analysis","failure-mode-and-effects-analysis","event-tree-analysis","risk-based-reliability-analysis","bayesian-fault-tree-analysis","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-full-factorial-design","name":"Risk-based full factorial design","fullName":"Risk-Based Full Factorial Design of Experiments","aliases":["risk-informed full factorial design","RB-FFD","risk-prioritized factorial experiment","risk-based FFD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"2000s (formal integration with risk frameworks circa 2005–2009)","originator":"Developed at the intersection of classical factorial experimentation (Fisher, 1935) and formal risk analysis frameworks (ICH Q8/Q9, 2005–2009)","url":"https://scholargate.app/en/experimental-design/risk-based-full-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-full-factorial-design.md","definition":"Risk-based full factorial design integrates formal risk analysis — typically Failure Mode and Effects Analysis (FMEA) or a comparable risk-ranking tool — with a full factorial experiment to ensure that factors posing the greatest quality or safety risk receive exhaustive experimental coverage. All combinations of selected factor levels are run, but the selection of which factors to include and the range of their levels is explicitly guided by prior risk scores rather than purely by engineering intuition or resource availability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed at the intersection of classical factorial experimentation (Fisher, 1935) and formal risk analysis frameworks (ICH Q8/Q9, 2005–2009)","year":"2000s (formal integration with risk frameworks circa 2005–2009)","type":"Structured experimental design with risk-informed factor prioritization","dataType":"Continuous or categorical experimental response data; prior risk scores or FMEA severity/occurrence ratings","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null},{"ref":"International Council for Harmonisation. (2009). ICH Q8(R2): Pharmaceutical Development — Quality by Design and Risk-Based Experimental Approaches. ICH Secretariat.","type":"misc","doi":null,"isbn":null,"url":"https://database.ich.org/sites/default/files/Q8_R2_Guideline.pdf"}],"related":["full-factorial-design","fractional-factorial-design","risk-based-fractional-factorial-design","failure-mode-and-effects-analysis","robust-full-factorial-design","design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-process-capability-analysis","name":"Risk-based Process Capability Analysis","fullName":"Risk-based Process Capability Analysis","aliases":["RBPCA","risk-adjusted capability analysis","capability-risk integration","risk-informed SPC"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (formal integration with risk analysis)","originator":"Evolved from classical capability indices (Juran, Kane) integrated with risk frameworks (FMEA, ISO 9001)","url":"https://scholargate.app/en/experimental-design/risk-based-process-capability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-process-capability-analysis.md","definition":"Risk-based Process Capability Analysis (RBPCA) combines classical process capability indices (Cp, Cpk, Pp, Ppk) with structured risk assessment tools — such as FMEA risk priority numbers — to prioritise improvement actions not merely by how capable a process is, but by the potential harm its failures can cause. The approach is widely used in automotive, aerospace, medical device, and pharmaceutical manufacturing to align quality engineering decisions with risk management requirements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from classical capability indices (Juran, Kane) integrated with risk frameworks (FMEA, ISO 9001)","year":"1990s–2000s (formal integration with risk analysis)","type":"Quantitative quality engineering method","dataType":"Continuous process measurement data, specification limits, risk scores","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119399308","url":null},{"ref":"Breyfogle, F. W. (2003). Implementing Six Sigma: Smarter Solutions Using Statistical Methods (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471265726","url":null}],"related":["process-capability-analysis","statistical-process-control","failure-mode-and-effects-analysis","six-sigma","fault-tree-analysis","control-charts"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-quality-function-deployment","name":"Risk-based quality function deployment","fullName":"Risk-Based Quality Function Deployment","aliases":["Risk-based QFD","QFD with risk analysis","FMEA-integrated QFD","risk-integrated House of Quality"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (QFD: 1966–1972; risk-based extensions: ~1995–2010)","originator":"Yoji Akao (QFD foundation); risk integration developed by multiple authors in quality engineering literature from the 1990s onward","url":"https://scholargate.app/en/experimental-design/risk-based-quality-function-deployment","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-quality-function-deployment.md","definition":"Risk-based quality function deployment (Risk-based QFD) integrates formal risk analysis — most commonly Failure Mode and Effects Analysis (FMEA) or risk matrices — into the classic QFD House of Quality framework. By weighting customer requirements and engineering characteristics against their associated failure risks, teams prioritise design and process decisions not only by customer importance but also by potential harm, regulatory exposure, or reliability impact. It is widely used in automotive, aerospace, medical device, and industrial product development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yoji Akao (QFD foundation); risk integration developed by multiple authors in quality engineering literature from the 1990s onward","year":"1990s–2000s (QFD: 1966–1972; risk-based extensions: ~1995–2010)","type":"Structured quality planning method with integrated risk assessment","dataType":"Customer voice data, engineering specifications, risk severity/occurrence/detectability ratings","subfamily":"Engineering methods"},"citations":[{"ref":"Akao, Y. (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press, Cambridge, MA.","type":"book","doi":null,"isbn":"978-0915299416","url":null},{"ref":"Carnevalli, J. A., & Miguel, P. C. (2008). Review, analysis and classification of the literature on QFD — Types of research, difficulties and benefits. International Journal of Production Economics, 114(2), 737–754.","type":"article","doi":"10.1016/j.ijpe.2008.03.006","isbn":null,"url":null}],"related":["quality-function-deployment","failure-mode-and-effects-analysis","risk-based-failure-mode-and-effects-analysis","robust-quality-function-deployment","fault-tree-analysis","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-reliability-analysis","name":"Risk-based reliability analysis","fullName":"Risk-Based Reliability Analysis","aliases":["RBRA","risk-informed reliability analysis","risk-based dependability analysis","probabilistic risk and reliability assessment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960s–1990s (risk-informed frameworks codified ~1980s–1990s)","originator":"Multiple contributors; formalized in reliability engineering literature from the 1960s onward (MIL-HDBK-217, IEC 60300 series)","url":"https://scholargate.app/en/experimental-design/risk-based-reliability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-reliability-analysis.md","definition":"Risk-based reliability analysis (RBRA) is an engineering methodology that combines classical reliability analysis — quantifying failure rates, component lifetimes, and system dependability — with risk assessment frameworks that weigh the severity and consequences of each failure mode. By ranking failures according to both their likelihood and their impact, RBRA guides engineers in allocating inspection, maintenance, and redesign resources where they matter most, rather than treating all potential failures as equally important.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; formalized in reliability engineering literature from the 1960s onward (MIL-HDBK-217, IEC 60300 series)","year":"1960s–1990s (risk-informed frameworks codified ~1980s–1990s)","type":"Quantitative / semi-quantitative engineering analysis","dataType":"Failure rate data, component reliability data, risk priority numbers, probability distributions","subfamily":"Engineering methods"},"citations":[{"ref":"Modarres, M., Kaminskiy, M., & Krivtsov, V. (2006). Reliability Engineering and Risk Analysis: A Practical Guide (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-0849392016","url":null},{"ref":"Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873895989","url":null}],"related":["failure-mode-and-effects-analysis","fault-tree-analysis","reliability-analysis","risk-based-fault-tree-analysis","risk-based-failure-mode-and-effects-analysis","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-response-surface-methodology","name":"Risk-based Response Surface Methodology","fullName":"Risk-based Response Surface Methodology","aliases":["Risk-based RSM","reliability-based RSM","probabilistic RSM","risk-integrated response surface methodology"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (risk-based extensions)","originator":"Builds on Box & Wilson (1951) RSM; risk integration formalized in engineering reliability literature from the 1990s onward","url":"https://scholargate.app/en/experimental-design/risk-based-response-surface-methodology","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-response-surface-methodology.md","definition":"Risk-based Response Surface Methodology (Risk-based RSM) extends classical RSM by embedding probabilistic risk or reliability constraints into the experimental optimization process. Rather than seeking a single optimal point under deterministic conditions, it identifies factor settings that achieve performance goals while keeping the probability of failure or unacceptable outcomes below a specified threshold — making it especially valuable in safety-critical and high-variability engineering contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Builds on Box & Wilson (1951) RSM; risk integration formalized in engineering reliability literature from the 1990s onward","year":"1990s–2000s (risk-based extensions)","type":"Experimental optimization with probabilistic risk constraints","dataType":"Continuous numerical responses; probability-of-failure or risk metrics","subfamily":"Engineering methods"},"citations":[{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470174463","url":null},{"ref":"Khuri, A. I., & Fallah, R. (2017). Response surface methodology with stochastic constraints for expensive simulation. Journal of Applied Statistics, 44(3), 518–535.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Response+surface+methodology+with+stochastic+constraints+for+expensive+simulation"}],"related":["response-surface-methodology","robust-response-surface-methodology","central-composite-design","bayesian-response-surface-methodology","failure-mode-and-effects-analysis","reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-root-cause-analysis","name":"Risk-based Root Cause Analysis","fullName":"Risk-based Root Cause Analysis","aliases":["Risk-based RCA","RBRCA","Risk-weighted root cause analysis","Risk-informed failure investigation"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (risk-informed extension of classical RCA)","originator":"Developed within safety and quality engineering communities; risk integration formalized through CCPS and ISO 31000 frameworks","url":"https://scholargate.app/en/experimental-design/risk-based-root-cause-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-root-cause-analysis.md","definition":"Risk-based Root Cause Analysis (RBRCA) integrates classical root cause investigation with quantitative or semi-quantitative risk assessment to ensure that corrective actions are directed first at the causes that carry the highest probability and consequence of recurrence. Unlike standard RCA, which identifies root causes without systematically ranking their hazard potential, RBRCA assigns risk scores to each identified cause, allowing organizations to allocate limited remediation resources where they can reduce overall risk most efficiently.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within safety and quality engineering communities; risk integration formalized through CCPS and ISO 31000 frameworks","year":"1990s–2000s (risk-informed extension of classical RCA)","type":"Hybrid risk-analytic investigation method","dataType":"Incident reports, process data, failure records, risk registers, expert judgment","subfamily":"Engineering methods"},"citations":[{"ref":"Latino, R. J., & Latino, K. C. (2006). Root Cause Analysis: Improving Performance for Bottom-Line Results (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-0849380815","url":null},{"ref":"Center for Chemical Process Safety (2003). Guidelines for Investigating Chemical Process Incidents (2nd ed.). American Institute of Chemical Engineers / Wiley-AIChE.","type":"book","doi":null,"isbn":"978-0816908929","url":null}],"related":["root-cause-analysis","failure-mode-and-effects-analysis","fault-tree-analysis","risk-based-failure-mode-and-effects-analysis","statistical-process-control","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-six-sigma-dmaic","name":"Risk-based Six Sigma DMAIC","fullName":"Risk-based Six Sigma Define-Measure-Analyze-Improve-Control","aliases":["Risk-integrated DMAIC","DMAIC with risk analysis","Risk-aware Six Sigma","RB-DMAIC"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s","originator":"Motorola (Six Sigma, 1986); risk integration formalized in quality engineering literature from the 1990s onward","url":"https://scholargate.app/en/experimental-design/risk-based-six-sigma-dmaic","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-six-sigma-dmaic.md","definition":"Risk-based Six Sigma DMAIC embeds structured risk assessment — typically failure mode and effects analysis (FMEA), risk priority numbers (RPN), or probabilistic risk tools — at each stage of the standard DMAIC cycle. The goal is not only to reduce defects and variation but to prioritize improvement actions by their risk consequence, ensuring that critical failure modes are addressed before less impactful ones. It is widely applied in manufacturing, healthcare, aerospace, and process industries where both quality and safety are at stake.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Motorola (Six Sigma, 1986); risk integration formalized in quality engineering literature from the 1990s onward","year":"1990s–2000s","type":"Process improvement methodology with embedded risk assessment","dataType":"Process performance metrics, defect counts, risk scores (RPN), control chart data","subfamily":"Engineering methods"},"citations":[{"ref":"De Mast, J., & Lokkerbol, J. (2012). An analysis of the Six Sigma DMAIC method from the perspective of problem solving. International Journal of Production Economics, 139(2), 604–614.","type":"article","doi":"10.1016/j.ijpe.2012.05.035","isbn":null,"url":null},{"ref":"Six Sigma. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Six_Sigma"}],"related":["six-sigma-dmaic","failure-mode-and-effects-analysis","risk-based-failure-mode-and-effects-analysis","statistical-process-control","risk-based-statistical-process-control","fault-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-statistical-process-control","name":"Risk-based statistical process control","fullName":"Risk-Based Statistical Process Control","aliases":["Risk-based SPC","RBSPC","risk-prioritized SPC","risk-informed process monitoring"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1920s (SPC foundations); risk-based integration formalized in 2000s–2010s","originator":"Integrated from SPC (Shewhart, 1920s; Deming, 1950s) and risk analysis frameworks (FDA ICH Q10, ISO 31000)","url":"https://scholargate.app/en/experimental-design/risk-based-statistical-process-control","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-statistical-process-control.md","definition":"Risk-based statistical process control (Risk-based SPC) is an engineering quality method that integrates formal risk analysis — typically FMEA or a risk matrix — with statistical process monitoring to focus control chart resources on the process parameters that pose the greatest risk to product quality or system safety. Rather than applying control charts uniformly across all variables, risk-based SPC directs tighter monitoring toward high-risk, high-impact process characteristics identified through structured hazard prioritization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Integrated from SPC (Shewhart, 1920s; Deming, 1950s) and risk analysis frameworks (FDA ICH Q10, ISO 31000)","year":"1920s (SPC foundations); risk-based integration formalized in 2000s–2010s","type":"Hybrid quality-risk engineering method","dataType":"Continuous or attribute process measurement data, risk scores (FMEA RPN or risk matrices)","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119399308","url":null},{"ref":"Statistical process control. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Statistical_process_control"}],"related":["statistical-process-control","control-chart","failure-mode-and-effects-analysis","risk-based-failure-mode-and-effects-analysis","process-capability-analysis","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-based-taguchi-method","name":"Risk-based Taguchi method","fullName":"Risk-based Taguchi Method for Robust Parameter Design","aliases":["Risk-integrated Taguchi","RBTM","Taguchi risk optimization","robust design with risk analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1950s–1980s (Taguchi); risk extensions from 1990s onward","originator":"Genichi Taguchi (Taguchi method); risk integration developed by multiple engineering researchers","url":"https://scholargate.app/en/experimental-design/risk-based-taguchi-method","markdownUrl":"https://scholargate.app/en/experimental-design/risk-based-taguchi-method.md","definition":"The risk-based Taguchi method combines Genichi Taguchi's robust parameter design framework with explicit risk identification and quantification. By overlaying a risk assessment layer — typically drawing on failure mode analysis or probabilistic criteria — onto the standard signal-to-noise ratio optimization process, the approach selects factor settings that simultaneously maximize performance robustness and minimize the probability or severity of process/product failure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Genichi Taguchi (Taguchi method); risk integration developed by multiple engineering researchers","year":"1950s–1980s (Taguchi); risk extensions from 1990s onward","type":"Experimental optimization with risk overlay","dataType":"Continuous process/product response data, orthogonal array experiments","subfamily":"Engineering methods"},"citations":[{"ref":"Taguchi, G. (1986). Introduction to Quality Engineering: Designing Quality into Products and Processes. Asian Productivity Organization.","type":"book","doi":null,"isbn":"978-9283310846","url":null},{"ref":"Nair, V. N. (1992). Taguchi's parameter design: A panel discussion. Technometrics, 34(2), 127–161.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Taguchi+parameter+design+panel+discussion+Technometrics+1992"}],"related":["taguchi-method","failure-mode-effects-analysis","response-surface-methodology","robust-parameter-design","monte-carlo-simulation","design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-benefit-assessment","name":"Risk-Benefit Assessment in Research Protocols","fullName":"Systematic Evaluation of Research Risks and Benefits for Ethics Review","aliases":["risk-benefit analysis","risk-benefit calculation","risk-benefit justification","harm-benefit ratio"],"domain":"research-ethics","family":"process-pipeline","subfamily":"ethical-evaluation","year":"1979","originator":"U.S. Department of Health and Human Services; International research ethics community","url":"https://scholargate.app/en/research-ethics/risk-benefit-assessment","markdownUrl":"https://scholargate.app/en/research-ethics/risk-benefit-assessment.md","definition":"A risk-benefit assessment is a systematic evaluation of the potential harms and benefits of a proposed research study, documented in ethics committee applications. The Belmont Report (1979) established the principle of beneficence—maximizing benefits while minimizing harm—as a cornerstone of research ethics. Regulatory frameworks (45 CFR 46.111 in the U.S., equivalent in other jurisdictions) require ethics committees to determine that risks are reasonable in relation to anticipated benefits before approving research. This assessment is not a simple calculation (risks + benefits) but a qualitative judgment incorporating probability, magnitude, and distribution of harms and benefits.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Department of Health and Human Services; International research ethics community","subfamily":"ethical-evaluation","year":"1979","type":"Framework"},"citations":[{"ref":"The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research.","type":"report","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html"},{"ref":"U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Section 46.102(a).","type":"regulation","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Protection+of+Human+Subjects"},{"ref":"International Council for Harmonisation. (2016). ICH Harmonised Guideline: Integrated Addendum to ICH E6(R1). Good Clinical Practice E6(R2).","type":"standard","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ICH+Harmonised+Guideline%3A+Integrated+Addendum+to+ICH+E6%28R1%29+International"},{"ref":"U.S. Department of Health and Human Services, Office for Human Research Protections. (2019). Risk and Benefit Assessment in Research. National Institutes of Health.","type":"guidance","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp/regulations-and-policy/informed-consent/index.html"}],"related":["ethics-committee-application","ethics-committee-types","vulnerable-populations-research","waiver-of-informed-consent","clinical-trial-registration"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-neutral-valuation","name":"Risk-Neutral Valuation","fullName":"Risk-Neutral Probability Derivative Valuation","aliases":["Risk-Neutral Measure","Q-Measure"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Valuation Theory","year":"1979","originator":"John Harrison and David Kreps","url":"https://scholargate.app/en/quantitative-finance/risk-neutral-valuation","markdownUrl":"https://scholargate.app/en/quantitative-finance/risk-neutral-valuation.md","definition":"Risk-neutral valuation (1979) is the fundamental principle that derivative prices equal the expected payoff discounted at the risk-free rate, computed under a risk-neutral probability measure (Q-measure). This principle, formalized by Harrison and Kreps, eliminates the need to estimate risk premia and is the foundation of modern derivatives pricing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Harrison and David Kreps","subfamily":"Valuation Theory","year":"1979","type":"Fundamental Principle"},"citations":[{"ref":"Harrison, J. M., & Kreps, D. M. (1979). Martingales and arbitrage in multiperiod securities markets. Journal of Economic Theory, 20(3), 381-408.","type":"article","doi":"10.1016/0022-0531(79)90043-7","isbn":null,"url":null},{"ref":"Breeden, D. T., & Litzenberger, R. H. (1978). Prices of state-contingent claims implicit in option prices. Journal of Business, 51(4), 621-651.","type":"article","doi":"10.1086/296025","isbn":null,"url":null}],"related":["sabr-model","bates-model","libor-market-model","change-of-numeraire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-parity-model","name":"Risk Parity Portfolio","fullName":"Risk Parity (Equal Risk Contribution) Portfolio Model","aliases":["equal risk contribution","ERC portfolio","risk budgeting","All Weather strategy","Risk Paritesi Portföy Modeli"],"domain":"finance","family":"regression-model","subfamily":null,"year":2010,"originator":"Maillard, Roncalli & Teïletche (2010); popularised by Qian (2005) and Bridgewater All Weather","url":"https://scholargate.app/en/finance/risk-parity-model","markdownUrl":"https://scholargate.app/en/finance/risk-parity-model.md","definition":"Risk parity is a portfolio weighting model, formalised by Maillard, Roncalli and Teïletche (2010), in which every asset contributes an equal share of the total portfolio risk. It needs only the covariance (risk) structure of the assets and no forecast of expected returns, and it underpins Bridgewater's All Weather strategy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maillard, Roncalli & Teïletche (2010); popularised by Qian (2005) and Bridgewater All Weather","year":2010,"type":"Portfolio weighting model (risk budgeting)","estimator":"Equal risk contribution via constrained numerical optimisation","inputs":"Asset return covariance matrix (no expected-return forecast)","minSample":60},"citations":[{"ref":"Maillard, S., Roncalli, T. & Teïletche, J. (2010). The Properties of Equally Weighted Risk Contribution Portfolios. Journal of Portfolio Management, 36(4), 60–70.","type":"article","doi":"10.3905/jpm.2010.36.4.060","isbn":null,"url":null},{"ref":"Qian, E. (2005). Risk Parity Portfolios: Efficient Portfolios Through True Diversification. PanAgora Asset Management.","type":"report","doi":null,"isbn":null,"url":"https://www.panagora.com/insights/risk-parity-portfolios-efficient-portfolios-through-true-diversification/"}],"related":["mean-variance-optimization","minimum-variance-portfolio","black-litterman-model","tail-risk-measures","value-at-risk"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"risk-terrain-modeling","name":"Risk Terrain Modeling","fullName":"Risk Terrain Modeling for Crime Prediction and Prevention","aliases":["environmental criminology","RTM analysis","crime risk mapping"],"domain":"forensics","family":"process-pipeline","subfamily":"Geospatial analysis","year":"2011","originator":"Joel Caplan","url":"https://scholargate.app/en/forensics/risk-terrain-modeling","markdownUrl":"https://scholargate.app/en/forensics/risk-terrain-modeling.md","definition":"Risk Terrain Modeling (RTM) is a geospatial crime prediction method that identifies high-risk locations by analyzing environmental and geographic features that attract or facilitate crime. Developed by Joel Caplan, Lichen Kennedy, and James Miller in 2011, RTM bridges environmental criminology theory with geographic information systems (GIS) to create predictive risk maps. Unlike methods that predict offender location (e.g., geographic profiling), RTM predicts where crimes are likely to occur based on terrain characteristics, infrastructure, and social environmental factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joel Caplan","subfamily":"Geospatial analysis","year":"2011","type":"Geographic information systems and crime science method"},"citations":[{"ref":"Caplan, J. M., Kennedy, L. W., & Miller, J. (2011). Risk terrain modeling: Brokering criminological theory and GIS methods for crime forecasting. Journal of Research and Practice in Criminal Justice, 17(1), 56-69.","type":"article","doi":null,"isbn":null,"url":"https://www.semanticscholar.org/paper/Risk-Terrain-Modeling%3A-Brokering-Criminological-for/9a8f4b0c78da9e1f0e5b8e0d8e3c2a1b"},{"ref":"Kennedy, L. W. (2008). Crime and Environment. Routledge.","type":"book","doi":null,"isbn":null,"url":"https://www.routledge.com/Crime-and-Environment/Kennedy/p/book/9780415420128"},{"ref":"Brantingham, P. J., & Brantingham, P. L. (1991). Environmental criminology. Sage Publications.","type":"article","doi":null,"isbn":null,"url":"https://us.sagepub.com/en-us/nam/environmental-criminology/book8747"}],"related":["geographic-profiling","network-analysis-of-case-law","crime-linkage-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rivermead-mobility-index","name":"RMI","fullName":"Rivermead Mobility Index","aliases":["Rivermead Mobility Index"],"domain":"neurology","family":"process-pipeline","subfamily":"Stroke and neurological mobility assessment","year":"1991","originator":"Frank Collen, Derick Wade, and Rivermead Rehabilitation Centre","url":"https://scholargate.app/en/neurology/rivermead-mobility-index","markdownUrl":"https://scholargate.app/en/neurology/rivermead-mobility-index.md","definition":"The Rivermead Mobility Index (RMI) is a brief, clinician-observed performance test of basic mobility abilities developed for assessing stroke and neurological rehabilitation outcomes. Published in 1991 by Frank Collen and colleagues at Rivermead Rehabilitation Centre (Oxford, UK), the 15-item index measures bed mobility, sitting/standing balance, transfers, and ambulation. The RMI is widely used in stroke units and rehabilitation settings to track functional recovery and predict discharge outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Collen, Derick Wade, and Rivermead Rehabilitation Centre","subfamily":"Stroke and neurological mobility assessment","year":"1991","type":"Clinician-observed performance test"},"citations":[{"ref":"Collen, F. M., Wade, D. T., Robb, G. F., Bradshaw, C. M. (1991). The Rivermead Mobility Index: A further development of the Rivermead Motor Assessment. International Disability Studies, 13(2), 50-54.","type":"article","doi":"10.3109/03790799109166684","isbn":null,"url":null}],"related":["edss-multiple-sclerosis","nihss","msfc","ischemic-stroke-functional-outcome","updrs"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rna-seq-differential-expression","name":"RNA-seq Differential Expression","fullName":"RNA Sequencing Differential Expression Analysis","aliases":["RNA-seq DE analysis","transcriptomic differential expression","bulk RNA-seq DE","DEA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2008–2010 (RNA-seq DE methodology established)","originator":"Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)","url":"https://scholargate.app/en/bioinformatics/rna-seq-differential-expression","markdownUrl":"https://scholargate.app/en/bioinformatics/rna-seq-differential-expression.md","definition":"RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)","year":"2008–2010 (RNA-seq DE methodology established)","type":"Quantitative genomics pipeline","dataType":"Raw RNA sequencing read counts (bulk tissue or cell-population level)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.","type":"article","doi":"10.1186/s13059-014-0550-8","isbn":null,"url":null},{"ref":"Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140.","type":"article","doi":"10.1093/bioinformatics/btp616","isbn":null,"url":null}],"related":["single-cell-rna-seq-analysis","gene-set-enrichment-analysis","pathway-enrichment-analysis","sequence-alignment","variant-calling","chip-seq-peak-calling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rna-velocity","name":"RNA Velocity","fullName":"RNA Velocity for Single-Cell Trajectory Inference","aliases":["Velocity analysis","Transcriptomic velocity","Cell fate prediction"],"domain":"genetics","family":"process-pipeline","subfamily":"Single-cell genomics","year":"2018","originator":"Gioele La Manno & Pavel Soldatov","url":"https://scholargate.app/en/genetics/rna-velocity","markdownUrl":"https://scholargate.app/en/genetics/rna-velocity.md","definition":"RNA velocity is a computational method that infers the future developmental state of individual cells from single-cell RNA-sequencing data. Developed by La Manno and colleagues in 2018, RNA velocity analysis measures the direction and pace of cell state transitions by analyzing the ratio of unspliced to spliced mRNA transcripts within individual cells. This enables prediction of cell trajectories and differentiation pathways without requiring temporal sampling or manipulation, providing unique insights into cell fate decisions during development and disease.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gioele La Manno & Pavel Soldatov","subfamily":"Single-cell genomics","year":"2018","type":"Trajectory inference method"},"citations":[{"ref":"La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., & Merad, M. (2018). RNA velocity of single cells. Nature, 560(7737), 494–498.","type":"article","doi":"10.1038/s41586-018-0414-6","isbn":null,"url":null},{"ref":"Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Raj, B. (2020). Generalizing RNA velocity to transient cell states through smoothed differentiation of expected counts. Nature Biotechnology, 38(12), 1408–1417.","type":"article","doi":"10.1038/s41587-020-0591-3","isbn":null,"url":null},{"ref":"Chen, H., & Albergante, L. (2022). scVelo: RNA velocity at single-cell resolution. bioRxiv.","type":"article","doi":null,"isbn":null,"url":"https://github.com/theislab/scvelo"}],"related":["hi-c-analysis","atac-seq-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"roberta-based-classification","name":"RoBERTa-based Classification","fullName":"RoBERTa-based Text Classification (Robustly Optimized BERT Pretraining Approach)","aliases":["RoBERTa classifier","RoBERTa text classification","Robustly Optimized BERT Classification","RoBERTa fine-tuning for classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Liu, Y. et al. (Facebook AI Research / University of Washington)","url":"https://scholargate.app/en/deep-learning/roberta-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/roberta-based-classification.md","definition":"RoBERTa-based Classification applies the RoBERTa pre-trained transformer — trained more robustly than BERT with dynamic masking and larger batches — to text categorisation tasks by adding a lightweight classification head on top of the [CLS] token representation and fine-tuning the entire model on labelled examples. It consistently matches or outperforms BERT on standard NLP benchmarks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, Y. et al. (Facebook AI Research / University of Washington)","year":"2019","type":"Pre-trained transformer fine-tuned for sequence classification","dataType":"Text (sequences of tokens)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.11692"},{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null}],"related":["bert-based-classification","transformer","sentence-embeddings","long-short-term-memory","fine-tuned-roberta-based-classification","gated-recurrent-unit"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-active-learning","name":"Robust Active Learning","fullName":"Robust Active Learning (Noise-Tolerant Query-Based Learning)","aliases":["RAL","noise-tolerant active learning","robust query learning","adversarially robust active learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006","originator":"Balcan, M.-F.; Beygelzimer, A.; Langford, J.","url":"https://scholargate.app/en/machine-learning/robust-active-learning","markdownUrl":"https://scholargate.app/en/machine-learning/robust-active-learning.md","definition":"Robust Active Learning extends the standard active learning framework to handle noisy labels, adversarial perturbations, and unreliable oracles. Rather than assuming perfect labeling, it incorporates statistical or adversarial robustness guarantees into the query selection process, maintaining sample efficiency while tolerating corruption in the annotation process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Balcan, M.-F.; Beygelzimer, A.; Langford, J.","year":"2006","type":"Active learning with robustness guarantees","dataType":"Labeled and unlabeled tabular or structured data, potentially with label noise","subfamily":"Machine learning"},"citations":[{"ref":"Balcan, M.-F., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 65–72. ACM.","type":"inproceedings","doi":"10.1145/1143844.1143853","isbn":null,"url":null},{"ref":"Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.","type":"article","doi":null,"isbn":null,"url":"https://burrsettles.com/pub/settles.activelearning.pdf"}],"related":["active-learning","semi-supervised-learning","online-learning","robust-random-forest","robust-support-vector-machine","few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-adf-unit-root-test","name":"Robust ADF Unit Root Test","fullName":"Robust Augmented Dickey-Fuller Unit Root Test","aliases":["robust ADF test","HAC-corrected ADF","heteroscedasticity-robust unit root test","GLS-detrended ADF"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1996-2001","originator":"Ng and Perron (2001); Elliott, Rothenberg, and Stock (1996)","url":"https://scholargate.app/en/econometrics/robust-adf-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/robust-adf-unit-root-test.md","definition":"The Robust ADF unit root test extends the classical ADF procedure with improvements that correct for size distortions arising from heteroscedastic or serially correlated errors, and from poor lag-length selection. Drawing on GLS detrending (Elliott, Rothenberg, and Stock 1996) and modified information criteria (Ng and Perron 2001), it delivers reliable size and power in the presence of non-standard error processes common in macroeconomic and financial time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ng and Perron (2001); Elliott, Rothenberg, and Stock (1996)","year":"1996-2001","type":"Unit root / stationarity test","dataType":"Univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Ng, S., and Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), 1519-1554.","type":"article","doi":"10.1111/1468-0262.00256","isbn":null,"url":null},{"ref":"Elliott, G., Rothenberg, T. J., and Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813-836.","type":"article","doi":"10.2307/2171846","isbn":null,"url":null}],"related":["augmented-dickey-fuller-unit-root-test","phillips-perron-unit-root-test","kpss-test","zivot-andrews-structural-break-test","panel-adf-unit-root-test","nonlinear-adf-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-agent-based-modeling","name":"Robust Agent-Based Modeling","fullName":"Robust Agent-Based Modeling — Uncertainty and Sensitivity Analysis for Agent-Based Simulations","aliases":["Robust ABM","ABM Robustness Analysis","Uncertainty-Aware ABM","Robust Multi-Agent Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s","originator":"Ligmann-Zielinska, A.; Railsback, S. F.; Grimm, V.","url":"https://scholargate.app/en/simulation/robust-agent-based-modeling","markdownUrl":"https://scholargate.app/en/simulation/robust-agent-based-modeling.md","definition":"Robust Agent-Based Modeling (Robust ABM) integrates systematic uncertainty quantification and sensitivity analysis into agent-based simulation workflows. Rather than relying on a single parameter configuration, it explores the full parameter space to identify which inputs drive model outcomes, ensuring that conclusions hold across plausible input ranges and model structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ligmann-Zielinska, A.; Railsback, S. F.; Grimm, V.","year":"2000s","type":"Simulation robustness framework","dataType":"Agent rules, parameter distributions, simulation outputs","subfamily":"Simulation / optimization"},"citations":[{"ref":"Ligmann-Zielinska, A., Cheetham, W. (2006). Spatially-explicit sensitivity analysis of an agent-based model of land use change. International Journal of Geographical Information Science, 20(12), 1355-1377.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spatially-explicit+sensitivity+analysis+agent-based+model+land+use+change+Ligmann-Zielinska+2006"},{"ref":"Railsback, S. F., Grimm, V. (2011). Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press.","type":"book","doi":null,"isbn":"9780691136745","url":null}],"related":["agent-based-modeling","monte-carlo-simulation","sensitivity-analysis","stochastic-agent-based-modeling","robust-sensitivity-analysis","robust-scenario-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-ancova","name":"Robust ANCOVA","fullName":"Robust Analysis of Covariance","aliases":["robust ANCOVA","heteroscedastic ANCOVA","trimmed-mean ANCOVA","resistant ANCOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1990s–2000s","originator":"Rand R. Wilcox and colleagues","url":"https://scholargate.app/en/statistics/robust-ancova","markdownUrl":"https://scholargate.app/en/statistics/robust-ancova.md","definition":"Robust ANCOVA is a covariate-adjusted group comparison that replaces classical ANCOVA's ordinary least squares estimation with resistant methods — typically trimmed means or M-estimators — so that the test retains valid Type I error control and reasonable power when data contain outliers, heavy-tailed distributions, or heteroscedastic errors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand R. Wilcox and colleagues","year":"1990s–2000s","type":"Robust parametric covariate-adjusted comparison","dataType":"Continuous outcome, continuous or categorical covariate(s), categorical grouping factor","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Keselman, H. J., Wilcox, R. R., Algina, J., Fradette, K., & Othman, A. R. (2008). A comparative study of robust tests for spread: Asymmetric trimming strategies. British Journal of Mathematical and Statistical Psychology, 61(2), 235–253.","type":"article","doi":"10.1348/000711008x299742","isbn":null,"url":null}],"related":["ancova","robust-one-way-anova","robust-mancova","robust-independent-samples-t-test","robust-repeated-measures-anova","bootstrap-ancova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-anova","name":"Robust ANOVA","fullName":"Robust Analysis of Variance (Welch & Trimmed Mean)","aliases":["Welch ANOVA","trimmed-mean ANOVA","heteroscedastic one-way ANOVA","Robust ANOVA (Welch & Trimmed Mean)","robust ANOVA"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1951,"originator":"Welch (1951); robust trimmed-mean approach popularised by Wilcox","url":"https://scholargate.app/en/statistics/robust-anova","markdownUrl":"https://scholargate.app/en/statistics/robust-anova.md","definition":"Robust ANOVA compares the central tendency of three or more groups when the classical assumptions of normality and equal variances fail. It combines Welch's heteroscedasticity-adjusted statistic, introduced by Welch in 1951, with trimmed-mean tests advanced by Wilcox, giving reliable comparisons in the presence of outliers and unequal group spreads.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Welch (1951); robust trimmed-mean approach popularised by Wilcox","year":1951,"type":"Robust one-way analysis of variance","estimator":"Welch heteroscedastic statistic; trimmed-mean (e.g. 20%) test","outcome":"continuous","comparison":"group means / trimmed means","minSample":20},"citations":[{"ref":"Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336.","type":"article","doi":"10.1093/biomet/38.3-4.330","isbn":null,"url":null},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["ols-regression","permutation-test","bootstrap-inference","winsorized-estimation","theil-sen-estimator"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-ant-colony-optimization","name":"Robust Ant Colony Optimization","fullName":"Robust Ant Colony Optimization — ACO metaheuristic with explicit uncertainty and worst-case robustness handling","aliases":["Robust ACO","Uncertainty-aware ACO","Min-max ACO","Robust ACO Metaheuristic"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1992 (ACO); robust variants from ~2005","originator":"Dorigo, M. (ACO); robust extensions by multiple authors in 2000s–2010s","url":"https://scholargate.app/en/simulation/robust-ant-colony-optimization","markdownUrl":"https://scholargate.app/en/simulation/robust-ant-colony-optimization.md","definition":"Robust Ant Colony Optimization (Robust ACO) extends the classic ant colony metaheuristic by explicitly incorporating parameter uncertainty and worst-case or expected-case robustness criteria into the solution search. Rather than optimizing for a single nominal scenario, it seeks solutions that perform well across a range of plausible problem realizations, making it suitable for real-world combinatorial problems where input data (costs, demands, travel times) are uncertain or variable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorigo, M. (ACO); robust extensions by multiple authors in 2000s–2010s","year":"1992 (ACO); robust variants from ~2005","type":"Metaheuristic with robustness wrapper","dataType":"Combinatorial / discrete optimization data with uncertain parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Dorigo, M. (1992). Optimization, learning and natural algorithms. PhD Thesis, Politecnico di Milano, Italy.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Optimization+learning+and+natural+algorithms+Dorigo+1992"},{"ref":"Gutjahr, W. J., & Pflug, G. C. (2010). Simulated annealing for noisy cost functions. Journal of Global Optimization, 12(2), 123–147. (For robust stochastic metaheuristics including ACO under uncertainty.)","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=stochastic+ant+colony+optimization+robust+uncertainty"}],"related":["ant-colony-optimization","robust-genetic-algorithm","robust-simulated-annealing","robust-particle-swarm-optimization","stochastic-ant-colony-optimization","multi-objective-ant-colony-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-approximate-bayesian-computation","name":"Robust Approximate Bayesian Computation","fullName":"Robust Approximate Bayesian Computation","aliases":["Robust ABC","robust ABC inference","outlier-robust ABC","robust likelihood-free inference"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2016","originator":"Ruli, Sartori & Ventura; Frazier, Drovandi & Nott (2016–2020)","url":"https://scholargate.app/en/bayesian/robust-approximate-bayesian-computation","markdownUrl":"https://scholargate.app/en/bayesian/robust-approximate-bayesian-computation.md","definition":"Robust ABC extends standard Approximate Bayesian Computation to handle outliers, model misspecification, and sensitivity to summary statistic choice. By replacing conventional distance measures with robust alternatives — such as composite scores, trimmed statistics, or synthetic likelihoods — it protects posterior inference from being distorted by atypical observations or an imperfect simulator.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ruli, Sartori & Ventura; Frazier, Drovandi & Nott (2016–2020)","year":"2016","type":"likelihood-free inference","dataType":"continuous, count, or complex simulator output","subfamily":"Bayesian / computational"},"citations":[{"ref":"Ruli, E., Sartori, N. & Ventura, L. (2016). Approximate Bayesian computation with composite score functions. Statistics and Computing, 26(3), 679–692.","type":"article","doi":"10.1007/s11222-015-9551-z","isbn":null,"url":null},{"ref":"Frazier, D. T., Drovandi, C. & Nott, D. J. (2020). Robust Approximate Bayesian Inference with Synthetic Likelihood. Journal of Computational and Graphical Statistics, 30(4), 958–976.","type":"article","doi":"10.1080/10618600.2021.1875839","isbn":null,"url":null}],"related":["approximate-bayesian-computation","sequential-monte-carlo","bayesian-inference-with-measurement-error","robust-bayesian-inference","particle-filter","robust-variational-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-ar-model","name":"Robust AR model","fullName":"Robust Autoregressive Model","aliases":["robust autoregression","outlier-robust AR","M-estimator AR","heavy-tail AR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1986","originator":"Martin & Yohai (influential early work); broader robust time series literature","url":"https://scholargate.app/en/econometrics/robust-ar-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-ar-model.md","definition":"The robust AR model fits an autoregressive time series specification using estimation methods — typically M-estimators or bounded-influence estimators — that resist distortion from outliers and heavy-tailed error distributions. Unlike OLS-based AR estimation, robust variants down-weight extreme observations so that a small number of contaminated data points cannot dominate the fitted dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin & Yohai (influential early work); broader robust time series literature","year":"1986","type":"Robust time series model","dataType":"Univariate time series, continuous, possibly with outliers or heavy tails","subfamily":"Econometrics / time series"},"citations":[{"ref":"Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. Annals of Statistics, 14(3), 781–818.","type":"article","doi":"10.1214/aos/1176350027","isbn":null,"url":null},{"ref":"Francq, C., & Zakoian, J.-M. (2010). GARCH Models: Structure, Statistical Inference and Financial Applications. Wiley.","type":"book","doi":null,"isbn":"978-0470683910","url":null}],"related":["autoregressive-model","arma-model","arima-model","robust-ols","robust-gls","robust-vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-arch-model","name":"Robust ARCH model","fullName":"Robust Autoregressive Conditional Heteroscedasticity Model","aliases":["robust ARCH","outlier-robust ARCH","heavy-tailed ARCH","robust conditional volatility model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002–2008","originator":"Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s","url":"https://scholargate.app/en/econometrics/robust-arch-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-arch-model.md","definition":"The Robust ARCH model extends the classical Autoregressive Conditional Heteroscedasticity framework by replacing the standard maximum-likelihood estimator with robust alternatives that downweight or eliminate the influence of outliers. This makes volatility estimates resistant to extreme observations that frequently contaminate financial and macroeconomic time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s","year":"2002–2008","type":"Volatility / conditional heteroscedasticity model","dataType":"Financial time series, returns, macroeconomic series with outliers","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null},{"ref":"Iqbal, F. (2013). Robust estimation for the ARCH models. Revista Colombiana de Estadística, 36(1), 41–56.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+estimation+for+the+ARCH+models+Iqbal+2013"}],"related":["arch-model","garch-model","egarch-model","robust-regression","quantile-regression","stochastic-volatility-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-ardl-bounds-test","name":"Robust ARDL bounds test","fullName":"Robust Autoregressive Distributed Lag Bounds Test","aliases":["Robust ARDL","Robust bounds testing approach","Sam-McNown-Goh bounds test","Bootstrap ARDL bounds test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2019","originator":"Sam, McNown & Goh","url":"https://scholargate.app/en/econometrics/robust-ardl-bounds-test","markdownUrl":"https://scholargate.app/en/econometrics/robust-ardl-bounds-test.md","definition":"The Robust ARDL bounds test is an augmented version of the Pesaran-Shin-Smith (2001) ARDL bounds testing approach that resolves its two key weaknesses: size distortion under mixed integration orders and the degenerate-case problem. It introduces three separate test statistics — an overall F-test and two new Wald statistics for the dependent and independent variables — evaluated against bootstrap-generated critical values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sam, McNown & Goh","year":"2019","type":"Cointegration test","dataType":"Time series (I(0), I(1), or mixed integration orders)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sam, C. Y., McNown, R., & Goh, S. K. (2019). An augmented autoregressive distributed lag bounds test for cointegration. Economic Modelling, 80, 130-141.","type":"article","doi":"10.1016/j.econmod.2018.11.001","isbn":null,"url":null},{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null}],"related":["ardl-bounds-test","engle-granger-cointegration","johansen-cointegration","error-correction-model","vecm","nonlinear-ardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-arellano-bond-gmm","name":"Robust Arellano-Bond GMM","fullName":"Robust Arellano-Bond Generalized Method of Moments Estimator","aliases":["Robust Difference GMM","AB-GMM with robust standard errors","Robust first-difference GMM","Arellano-Bond robust estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991","originator":"Arellano & Bond (1991); robust inference extensions by Windmeijer (2005)","url":"https://scholargate.app/en/econometrics/robust-arellano-bond-gmm","markdownUrl":"https://scholargate.app/en/econometrics/robust-arellano-bond-gmm.md","definition":"The Robust Arellano-Bond GMM estimator applies the Arellano-Bond first-difference GMM approach to dynamic panel data while computing heteroscedasticity- and autocorrelation-consistent (robust) standard errors. This combination handles the Nickell bias from lagged dependent variables and simultaneously yields reliable inference when error variances differ across units or periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bond (1991); robust inference extensions by Windmeijer (2005)","year":"1991","type":"Dynamic panel GMM estimator with robust inference","dataType":"Balanced or unbalanced panel data with a lagged dependent variable","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86-136.","type":"article","doi":"10.1177/1536867X0900900106","isbn":null,"url":null}],"related":["arellano-bond-gmm-estimator","panel-system-gmm","dynamic-panel-data-model","panel-fixed-effects-model","difference-gmm","panel-arellano-bond-gmm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-arima-model","name":"Robust ARIMA model","fullName":"Robust Autoregressive Integrated Moving Average Model","aliases":["robust ARIMA","outlier-resistant ARIMA","robust time series estimation","ARIMA with outlier detection"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1986–1993","originator":"Tsay (1986); Chen & Liu (1993)","url":"https://scholargate.app/en/econometrics/robust-arima-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-arima-model.md","definition":"Robust ARIMA extends the classical ARIMA framework to detect and correct the influence of outliers and structural breaks during estimation. By jointly identifying anomalous observations and re-estimating model parameters, it produces coefficient estimates and forecasts that are far less distorted by isolated shocks or data errors than standard ARIMA.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tsay (1986); Chen & Liu (1993)","year":"1986–1993","type":"Robust time series model","dataType":"Univariate time series, possibly with outliers or structural breaks","subfamily":"Econometrics / time series"},"citations":[{"ref":"Tsay, R. S. (1986). Time series model specification in the presence of outliers. Journal of the American Statistical Association, 81(393), 132–141.","type":"article","doi":"10.1080/01621459.1986.10478250","isbn":null,"url":null},{"ref":"Chen, C., & Liu, L.-M. (1993). Joint estimation of model parameters and outlier effects in time series. Journal of the American Statistical Association, 88(421), 284–297.","type":"article","doi":"10.2307/2290724","isbn":null,"url":null}],"related":["arima-model","sarima-model","arimax-model","exponential-smoothing","state-space-model","quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-arma-model","name":"Robust ARMA Model","fullName":"Robust Autoregressive Moving Average Model","aliases":["robust ARMA","outlier-robust ARMA","M-estimator ARMA","resistant ARMA estimation"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1986","originator":"Martin & Yohai (1986); broader robust time series literature","url":"https://scholargate.app/en/econometrics/robust-arma-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-arma-model.md","definition":"The Robust ARMA model extends the classical Autoregressive Moving Average framework by replacing the sensitive least-squares loss with outlier-resistant estimation methods — typically M-estimators or median-based approaches. This protects coefficient estimates and forecasts from being distorted by additive outliers, level shifts, or innovational outliers that are common in economic and financial time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin & Yohai (1986); broader robust time series literature","year":"1986","type":"Robust time series model","dataType":"Univariate time series (continuous, stationary)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Franses, P. H., & Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15(1), 1-9.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Franses+Ghijsels+Additive+outliers+GARCH+forecasting+volatility+1999"},{"ref":"Martin, R. D., & Yohai, V. J. (1986). Influence functionals for time series. The Annals of Statistics, 14(3), 781-818.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Martin+Yohai+influence+functionals+time+series+1986+Annals+Statistics"}],"related":["arma-model","arima-model","robust-ar-model","robust-ols","structural-break-arma-model","robust-ma-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-autoencoder-anomaly-detection","name":"Robust Autoencoder anomaly detection","fullName":"Robust Autoencoder-Based Anomaly Detection","aliases":["Robust Deep Autoencoder","Robust AE Anomaly Detection","RDAE","Robust Reconstruction-Based Anomaly Detection"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017","originator":"Zhou, C. & Paffenroth, R. C.","url":"https://scholargate.app/en/machine-learning/robust-autoencoder-anomaly-detection","markdownUrl":"https://scholargate.app/en/machine-learning/robust-autoencoder-anomaly-detection.md","definition":"Robust Autoencoder Anomaly Detection extends the standard autoencoder framework with robustness mechanisms — such as sparse decomposition, robust loss functions, or adversarial regularisation — so that the model learns a compact representation of normal behaviour while remaining resistant to the corrupting influence of anomalies embedded in the training data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, C. & Paffenroth, R. C.","year":"2017","type":"Unsupervised anomaly detection (robust deep learning)","dataType":"Continuous, mixed, or high-dimensional tabular data; image data","subfamily":"Machine learning"},"citations":[{"ref":"Zhou, C., & Paffenroth, R. C. (2017). Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 665–674). ACM.","type":"inproceedings","doi":"10.1145/3097983.3098052","isbn":null,"url":null},{"ref":"Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1901.03407"}],"related":["autoencoder-anomaly-detection","one-class-svm","isolation-forest","gaussian-mixture-model","robust-one-class-svm","robust-isolation-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-bagging","name":"Robust Bagging","fullName":"Robust Bagging (Bootstrap Aggregating with Robust Base Learners)","aliases":["robust bootstrap aggregating","robust ensemble bagging","outlier-resistant bagging","robust BAGGing"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1996–2000s","originator":"Breiman, L. (bagging); robust variants developed by various authors in 2000s","url":"https://scholargate.app/en/machine-learning/robust-bagging","markdownUrl":"https://scholargate.app/en/machine-learning/robust-bagging.md","definition":"Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiman, L. (bagging); robust variants developed by various authors in 2000s","year":"1996–2000s","type":"Ensemble (robust bootstrap aggregating)","dataType":"Tabular (continuous, categorical, mixed); tolerates outliers and label noise","subfamily":"Machine learning"},"citations":[{"ref":"Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.","type":"article","doi":"10.1007/BF00058655","isbn":null,"url":null},{"ref":"Chen, C., Liaw, A., & Breiman, L. (2004). Using Random Forest to Learn Imbalanced Data. University of California, Berkeley, Technical Report 666.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Using+Random+Forest+to+Learn+Imbalanced+Data"}],"related":["bagging","random-forest","robust-random-forest","boosting","robust-boosting","voting-ensemble"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-bayesian-inference","name":"Robust Bayesian Inference","fullName":"Robust Bayesian Inference","aliases":["Bayesian sensitivity analysis","prior robustness","epsilon-contamination Bayesian analysis","robust Bayes"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1984–1990","originator":"James O. Berger","url":"https://scholargate.app/en/bayesian/robust-bayesian-inference","markdownUrl":"https://scholargate.app/en/bayesian/robust-bayesian-inference.md","definition":"Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether findings are stable or critically dependent on prior assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James O. Berger","year":"1984–1990","type":"Bayesian sensitivity / robustness framework","dataType":"any (continuous, count, categorical)","subfamily":"Bayesian / computational"},"citations":[{"ref":"Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328.","type":"article","doi":"10.1016/0378-3758(90)90079-A","isbn":null,"url":null},{"ref":"Insua, D. R. & Ruggeri, F. (Eds.) (2000). Robust Bayesian Analysis. Springer.","type":"book","doi":null,"isbn":"978-0387988665","url":null}],"related":["bayesian-regression","hierarchical-bayesian-inference","bayesian-model-averaging","markov-chain-monte-carlo","approximate-bayesian-computation","variational-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-bayesian-model-averaging","name":"Robust Bayesian Model Averaging","fullName":"Robust Bayesian Model Averaging","aliases":["robust BMA","outlier-robust BMA","robust model averaging","heavy-tailed BMA"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1999–2012","originator":"Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others","url":"https://scholargate.app/en/bayesian/robust-bayesian-model-averaging","markdownUrl":"https://scholargate.app/en/bayesian/robust-bayesian-model-averaging.md","definition":"Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observations, or when the prior on model parameters would otherwise dominate the results.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others","year":"1999–2012","type":"Bayesian model selection and averaging","dataType":"continuous, count, or binary outcomes; regression settings","subfamily":"Bayesian / computational"},"citations":[{"ref":"Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+model+averaging%3A+A+tutorial+Hoeting"},{"ref":"Ley, E., & Steel, M. F. J. (2012). Mixtures of g-priors for Bayesian model averaging with economic applications. Journal of Econometrics, 171(2), 251–266.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mixtures+of+g-priors+for+Bayesian+model+averaging+with+economic+applications+Ley"}],"related":["bayesian-model-averaging","bayesian-regression","robust-bayesian-inference","hierarchical-bayesian-inference","variational-inference","mcmc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-bayesian-network","name":"Robust Bayesian Network","fullName":"Robust Bayesian Network","aliases":["RBN","credal network","imprecise Bayesian network","sensitivity analysis in Bayesian networks"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1991-2000","originator":"Fabio Cozman (credal networks); Peter Walley (imprecise probabilities)","url":"https://scholargate.app/en/bayesian/robust-bayesian-network","markdownUrl":"https://scholargate.app/en/bayesian/robust-bayesian-network.md","definition":"A Robust Bayesian Network extends a classical Bayesian network by replacing each precise conditional probability table with a set of allowable probability distributions — called a credal set. Instead of a single probability for each query, inference returns a range of probabilities, honestly reflecting uncertainty about the model's numeric parameters while preserving the interpretable directed-acyclic-graph structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fabio Cozman (credal networks); Peter Walley (imprecise probabilities)","year":"1991-2000","type":"probabilistic graphical model with set-valued probabilities","dataType":"categorical, mixed, or continuous data with uncertain or imprecisely specified probabilities","subfamily":"Bayesian / computational"},"citations":[{"ref":"Cozman, F. G. (2000). Credal networks. Artificial Intelligence, 120(2), 199-233.","type":"article","doi":"10.1016/S0004-3702(00)00029-1","isbn":null,"url":null},{"ref":"Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, London.","type":"book","doi":null,"isbn":"978-0412286605","url":null}],"related":["bayesian-network","credal-network","hierarchical-bayesian-inference","approximate-bayesian-computation","robust-bayesian-inference","bayesian-model-averaging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-boosting","name":"Robust Boosting","fullName":"Robust Boosting (Boosting with Robust Loss Functions)","aliases":["noise-tolerant boosting","robust AdaBoost","boosting with robust losses","outlier-resistant boosting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1999–2001","originator":"Freund, Y.; Mason, L. et al.","url":"https://scholargate.app/en/machine-learning/robust-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/robust-boosting.md","definition":"Robust Boosting modifies standard boosting algorithms — such as AdaBoost or gradient boosting — by replacing the default exponential or squared loss with robust loss functions (e.g., Huber, logistic, or truncated losses) or by incorporating noise-tolerance mechanisms, so that the ensemble remains accurate even when training data contain outliers, label noise, or heavy-tailed errors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Freund, Y.; Mason, L. et al.","year":"1999–2001","type":"Ensemble (robust sequential boosting)","dataType":"Tabular (numeric, binary, or mixed features)","subfamily":"Machine learning"},"citations":[{"ref":"Freund, Y. (2001). An adaptive version of the boost by majority algorithm. Machine Learning, 43(3), 293–318.","type":"article","doi":"10.1023/A:1010852229904","isbn":null,"url":null},{"ref":"Mason, L., Baxter, J., Bartlett, P., & Frean, M. (2000). Boosting Algorithms as Gradient Descent. Advances in Neural Information Processing Systems (NIPS), 12, 512–518.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/1999/hash/4eff0720836a198b6174eecf02cbfdbf-Abstract.html"}],"related":["boosting","gradient-boosting","xgboost","robust-random-forest","robust-gradient-boosting","regularized-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-box-behnken-design","name":"Robust Box-Behnken Design","fullName":"Robust Box-Behnken Design for Parameter Optimization","aliases":["Robust BBD","BBD robust parameter design","robust response surface BBD","noise-robust Box-Behnken"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960 (BBD); robust integration practice emerged 1990s–2000s","originator":"Box & Behnken (BBD foundation); robust integration drawing on Taguchi (1986) and Myers et al.","url":"https://scholargate.app/en/experimental-design/robust-box-behnken-design","markdownUrl":"https://scholargate.app/en/experimental-design/robust-box-behnken-design.md","definition":"Robust Box-Behnken design combines the efficiency of the Box-Behnken design (BBD) — a three-level response surface design requiring no corner runs — with robust parameter design principles to identify factor settings that optimize the mean response while simultaneously minimizing sensitivity to uncontrollable noise factors. It is widely applied in manufacturing, chemical engineering, and product development when both performance and consistency under real-world variation matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Behnken (BBD foundation); robust integration drawing on Taguchi (1986) and Myers et al.","year":"1960 (BBD); robust integration practice emerged 1990s–2000s","type":"Experimental design with robustness optimization","dataType":"Continuous factor levels, numeric response variables (quality characteristics)","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.","type":"article","doi":"10.1080/00401706.1960.10489912","isbn":null,"url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916032","url":null}],"related":["box-behnken-design","taguchi-method","central-composite-design","robust-full-factorial-design","robust-taguchi-method","response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-canonical-correlation-analysis","name":"Robust Canonical Correlation Analysis","fullName":"Robust Canonical Correlation Analysis","aliases":["Robust CCA","RCCA","robust CCA","outlier-resistant canonical correlation"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2003","originator":"Croux & Dehon (building on Hotelling's CCA framework)","url":"https://scholargate.app/en/statistics/robust-canonical-correlation-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-canonical-correlation-analysis.md","definition":"Robust canonical correlation analysis extends classical CCA by replacing the standard sample covariance matrix with a robust estimator — such as the Minimum Covariance Determinant (MCD) or S-estimator — so that outlying observations do not distort the estimated canonical correlations and canonical variates between two sets of variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Croux & Dehon (building on Hotelling's CCA framework)","year":"2003","type":"Robust multivariate association","dataType":"Continuous multivariate (two sets of variables)","subfamily":"Multivariate analysis"},"citations":[{"ref":"Croux, C. & Dehon, C. (2003). Robust estimation of the canonical correlations. Computational Statistics, 18(3), 555–569.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+estimation+of+the+canonical+correlations+Croux"},{"ref":"Canonical correlation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Canonical_correlation"}],"related":["canonical-correlation-analysis","robust-principal-component-analysis","robust-exploratory-factor-analysis","robust-discriminant-analysis","robust-multidimensional-scaling","principal-component-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-causal-impact-analysis","name":"Robust Causal Impact Analysis","fullName":"Robust Causal Impact Analysis with Sensitivity and Placebo Checks","aliases":["robust CausalImpact","sensitivity-augmented causal impact","causal impact with robustness checks","robust BSTS causal inference"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015","originator":"Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)","url":"https://scholargate.app/en/causal-inference/robust-causal-impact-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/robust-causal-impact-analysis.md","definition":"Robust Causal Impact Analysis extends the Bayesian structural time-series CausalImpact framework (Brodersen et al., 2015) by embedding systematic robustness checks — in-time placebo tests, in-space placebo controls, covariate sensitivity analysis, and prior sensitivity assessments — to verify that a detected intervention effect is genuine and not an artifact of model choices or coincidental data patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)","year":"2015","type":"Bayesian causal inference with robustness validation","dataType":"Univariate or multivariate time-series with control covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.","type":"article","doi":"10.1214/14-AOAS788","isbn":null,"url":null},{"ref":"Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press.","type":"book","doi":null,"isbn":"978-0300251685","url":null}],"related":["causal-impact-analysis","synthetic-control-method","bayesian-causal-impact-analysis","placebo-test","sensitivity-analysis-for-causality","interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-central-composite-design","name":"Robust Central Composite Design","fullName":"Robust Central Composite Design for Response Surface Methodology","aliases":["Robust CCD","CCD with robust optimization","robust RSM with CCD","robust response surface CCD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1951 (CCD); robust integration from 1980s–1990s","originator":"George E. P. Box & K. B. Wilson (CCD foundation); robust extension via Taguchi and Myers–Montgomery tradition","url":"https://scholargate.app/en/experimental-design/robust-central-composite-design","markdownUrl":"https://scholargate.app/en/experimental-design/robust-central-composite-design.md","definition":"Robust Central Composite Design (Robust CCD) combines the efficient quadratic fitting capability of the central composite design with robust optimization principles to find factor settings that simultaneously achieve a target mean response and minimize the effect of uncontrollable noise factors on response variability. It is widely applied in manufacturing, chemical engineering, and product development when both performance and consistency under real-world variation are critical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George E. P. Box & K. B. Wilson (CCD foundation); robust extension via Taguchi and Myers–Montgomery tradition","year":"1951 (CCD); robust integration from 1980s–1990s","type":"Experimental design with robust optimization","dataType":"Continuous quantitative response variables; noise factor measurements","subfamily":"Engineering methods"},"citations":[{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470174463","url":null},{"ref":"Khuri, A. I., & Mukhopadhyay, S. (2010). Response surface methodology. WIREs Computational Statistics, 2(2), 128–149.","type":"article","doi":"10.1002/wics.73","isbn":null,"url":null}],"related":["central-composite-design","response-surface-methodology","taguchi-method","box-behnken-design","robust-design-of-experiments","robust-response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-chi-square-test","name":"Robust chi-square test","fullName":"Robust Chi-Square Test of Independence / Goodness-of-Fit","aliases":["robust chi-squared test","Cressie-Read power divergence test","adjusted chi-square test","robust contingency test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1984 (power divergence); 1900 (Pearson baseline)","originator":"Cressie & Read (power divergence framework); Pearson chi-square extended by multiple authors","url":"https://scholargate.app/en/statistics/robust-chi-square-test","markdownUrl":"https://scholargate.app/en/statistics/robust-chi-square-test.md","definition":"The robust chi-square test extends the classic Pearson chi-square framework to remain reliable when standard assumptions — especially the minimum expected-cell-count rule — are violated. Using power divergence statistics (Cressie & Read, 1984) or resampling-based corrections, it produces valid inferences for sparse contingency tables, small samples, and unbalanced categorical data where the ordinary chi-square approximation breaks down.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cressie & Read (power divergence framework); Pearson chi-square extended by multiple authors","year":"1984 (power divergence); 1900 (Pearson baseline)","type":"Robust categorical association / goodness-of-fit test","dataType":"Categorical (nominal or ordinal) frequency counts","subfamily":"Classical statistics"},"citations":[{"ref":"Cressie, N., & Read, T. R. C. (1984). Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society: Series B, 46(3), 440–464.","type":"article","doi":"10.1111/j.2517-6161.1984.tb01318.x","isbn":null,"url":null},{"ref":"Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471360933","url":null}],"related":["chi-square-test","fishers-exact-test","likelihood-ratio-test","robust-fishers-exact-test","bootstrap-chi-square-test","permutation-chi-square-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-cluster-analysis","name":"Robust Cluster Analysis","fullName":"Trimmed Robust Cluster Analysis (TCLUST)","aliases":["TCLUST","trimmed clustering","robust clustering","Robust Küme Analizi (TCLUST)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2008,"originator":"García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST)","url":"https://scholargate.app/en/statistics/robust-cluster-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-cluster-analysis.md","definition":"Robust Cluster Analysis is a trimmed model-based clustering method, introduced by García-Escudero and colleagues in 2008, that partitions continuous multivariate data into clusters while resisting the influence of outliers and noise. By setting aside a fraction of the most discordant observations, it keeps the recovered cluster structure from being contaminated by stray points.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST)","year":2008,"type":"Robust model-based clustering","estimator":"Trimmed maximum likelihood (impartial trimming)","outcome":"cluster labels for continuous multivariate data","minSample":50},"citations":[{"ref":"García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A General Trimming Approach to Robust Cluster Analysis. The Annals of Statistics, 36(3), 1324-1345.","type":"article","doi":"10.1214/07-AOS515","isbn":null,"url":null},{"ref":"Riani, M., Cerioli, A., Atkinson, A. C., & Perrotta, D. (2014). Monitoring Robust Regression / Robust Clustering. Statistics and Computing.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/journal/11222"}],"related":["robust-pca","robust-discriminant-analysis","w-estimator","mm-estimator","cluster-robust-se"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-co-kriging","name":"Robust Co-Kriging","fullName":"Robust Co-Kriging Spatial Interpolation","aliases":["robust cokriging","outlier-resistant co-kriging","robust multivariate kriging","RCK"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1993-1998","originator":"Cressie, N. A. C.; Genton, M. G.","url":"https://scholargate.app/en/spatial-analysis/robust-co-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/robust-co-kriging.md","definition":"Robust Co-Kriging is a multivariate geostatistical interpolation method that jointly estimates values at unsampled locations using two or more spatially correlated variables, while applying robust estimators for the variogram and cross-variogram to limit the distorting influence of spatial outliers or non-Gaussian measurement errors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cressie, N. A. C.; Genton, M. G.","year":"1993-1998","type":"Robust spatial interpolation","dataType":"Multivariate georeferenced continuous data with potential outliers","subfamily":"GIS / spatial"},"citations":[{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). John Wiley & Sons. Chapter 3 covers robust variogram estimation and co-kriging.","type":"book","doi":null,"isbn":"978-0471002550","url":null},{"ref":"Genton, M. G., & Rousseeuw, P. J. (1995). The Median Absolute Deviation of Spatial Data. Computational Statistics and Data Analysis, 20(4), 385-400.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+variogram+estimation+spatial+data+Genton+Rousseeuw"}],"related":["co-kriging","ordinary-kriging","robust-ordinary-kriging","robust-kriging","robust-spatial-regression","local-co-kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-confirmatory-factor-analysis","name":"Robust Confirmatory Factor Analysis","fullName":"Robust Confirmatory Factor Analysis","aliases":["Robust CFA","CFA with robust standard errors","Satorra-Bentler CFA","non-normal CFA"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1984–1994","originator":"Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)","url":"https://scholargate.app/en/statistics/robust-confirmatory-factor-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-confirmatory-factor-analysis.md","definition":"Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)","year":"1984–1994","type":"Confirmatory latent variable model with robust estimation","dataType":"Continuous or ordinal indicators that violate multivariate normality","subfamily":"Multivariate analysis"},"citations":[{"ref":"Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Satorra+Bentler+1994+corrections+test+statistics+standard+errors+covariance+structure"},{"ref":"Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37(1), 62–83.","type":"article","doi":"10.1111/j.2044-8317.1984.tb00789.x","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","structural-equation-modeling","robust-structural-equation-modeling","exploratory-factor-analysis","robust-exploratory-factor-analysis","multilevel-confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-conjoint-analysis","name":"Robust Conjoint Analysis","fullName":"Robust Conjoint Analysis","aliases":["robust CA","outlier-resistant conjoint analysis","robust stated preference analysis"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1990s–2000s","originator":"Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework","url":"https://scholargate.app/en/statistics/robust-conjoint-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-conjoint-analysis.md","definition":"Robust conjoint analysis decomposes respondent preferences for multi-attribute products or services into part-worth utilities while guarding against the distorting influence of outlying ratings or unusual respondents. It adapts classical conjoint estimation with robust regression or robust aggregation techniques so that conclusions about attribute importance remain trustworthy even when a minority of evaluations deviate markedly from the majority.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework","year":"1990s–2000s","type":"Preference decomposition / stated preference","dataType":"Ranked or rated profiles of attribute combinations","subfamily":"Multivariate analysis"},"citations":[{"ref":"Croux, C., Filzmoser, P., & Oliveira, M. R. (2007). Algorithms for Projection-Pursuit Robust Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems, 87(2), 218–225.","type":"article","doi":"10.1016/j.chemolab.2007.01.004","isbn":null,"url":null},{"ref":"Green, P. E., & Srinivasan, V. (1978). Conjoint Analysis in Consumer Research: Issues and Outlook. Journal of Consumer Research, 5(2), 103–123.","type":"book","doi":"10.1086/208721","isbn":null,"url":null}],"related":["conjoint-analysis","robust-discriminant-analysis","robust-canonical-correlation-analysis","principal-component-analysis","robust-principal-component-analysis","mixture-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-content-validity","name":"Robust Content Validity","fullName":"Robust Content Validity Assessment","aliases":["robust CVR","outlier-resistant content validity","robust content validity index","robust expert-panel validation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1975 (base); 2000s–2010s (robust extensions)","originator":"Grounded in Lawshe (1975) CVR framework; robust extensions draw on Huber, Wilcox, and robust statistics tradition","url":"https://scholargate.app/en/psychometrics/robust-content-validity","markdownUrl":"https://scholargate.app/en/psychometrics/robust-content-validity.md","definition":"Robust content validity assessment applies outlier-resistant statistical methods to the aggregation of expert panel ratings in content validation studies. By detecting and down-weighting idiosyncratic or extreme rater judgements, it yields Content Validity Ratio (CVR) and Content Validity Index (CVI) estimates that reflect the consensus of the panel more accurately than standard averaging when one or a few raters deviate sharply from the group.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grounded in Lawshe (1975) CVR framework; robust extensions draw on Huber, Wilcox, and robust statistics tradition","year":"1975 (base); 2000s–2010s (robust extensions)","type":"Validity evidence / expert judgement procedure with outlier-resistant aggregation","dataType":"Expert panel ratings (ordinal / categorical), potentially with divergent or extreme raters","subfamily":"Scale / measurement"},"citations":[{"ref":"Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+quantitative+approach+to+content+validity+Lawshe"},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["content-validity","construct-validity","convergent-validity","discriminant-validity","scale-development","robust-item-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-control-chart","name":"Robust Control Chart","fullName":"Robust Control Chart for Statistical Process Monitoring","aliases":["robust Shewhart chart","outlier-resistant control chart","robust SPC chart","distribution-free control chart"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1989–1997 (foundational period)","originator":"David M. Rocke; L. G. Tatum (key contributors)","url":"https://scholargate.app/en/experimental-design/robust-control-chart","markdownUrl":"https://scholargate.app/en/experimental-design/robust-control-chart.md","definition":"A robust control chart replaces the classical mean and standard deviation estimators in a Shewhart-style chart with resistant alternatives — such as the median and median absolute deviation (MAD) — so that a small fraction of outliers or non-normal process data cannot distort the control limits. The approach preserves the real-time monitoring logic of standard control charts while protecting against inflated or deflated limits caused by contaminated Phase I reference data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David M. Rocke; L. G. Tatum (key contributors)","year":"1989–1997 (foundational period)","type":"Statistical process monitoring technique","dataType":"Continuous measurement data, potentially non-normal or contaminated with outliers","subfamily":"Engineering methods"},"citations":[{"ref":"Tatum, L. G. (1997). Robust estimation of the process standard deviation for control charts. Technometrics, 39(2), 127–141.","type":"article","doi":"10.1080/00401706.1997.10485078","isbn":null,"url":null},{"ref":"Rocke, D. M. (1989). Robust control charts. Technometrics, 31(2), 173–184.","type":"article","doi":"10.1080/00401706.1989.10488511","isbn":null,"url":null}],"related":["control-chart","statistical-process-control","robust-statistical-process-control","process-capability-analysis","robust-process-capability-analysis","failure-mode-and-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-correlation","name":"Robust Correlation","fullName":"Robust Correlation (Spearman, Kendall, and Biweight)","aliases":["Spearman correlation","Kendall tau","biweight midcorrelation","rank correlation","Robust Korelasyon (Spearman & Kendall & Biweight)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2012,"originator":"Spearman rank, Kendall tau; biweight from Wilcox / Shevlyakov & Oja robust statistics tradition","url":"https://scholargate.app/en/statistics/robust-correlation","markdownUrl":"https://scholargate.app/en/statistics/robust-correlation.md","definition":"Robust Correlation is a family of association measures that resist outliers, covering Spearman's rank correlation, Kendall's tau, and the biweight midcorrelation. Drawing on the robust-statistics tradition described by Wilcox (2012) and Shevlyakov & Oja (2016), it measures how strongly two variables move together without being distorted by a few extreme points.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Spearman rank, Kendall tau; biweight from Wilcox / Shevlyakov & Oja robust statistics tradition","year":2012,"type":"Robust correlation measures","estimator":"Rank-based (Spearman, Kendall) and biweight midcorrelation","outcome":"association strength","minSample":20,"requiresNormality":false},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing. Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Shevlyakov, G. & Oja, H. (2016). Robust Correlation: Theory and Applications. Wiley.","type":"book","doi":null,"isbn":"978-1118493458","url":null}],"related":["pearson-correlation","ols-regression","quantile-regression","spearman-correlation","kendall-tau"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-correspondence-analysis","name":"Robust Correspondence Analysis","fullName":"Robust Correspondence Analysis","aliases":["RCA","outlier-resistant correspondence analysis","robust CA"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2000s (robust extensions of CA developed since the early 2000s)","originator":"Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues","url":"https://scholargate.app/en/statistics/robust-correspondence-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-correspondence-analysis.md","definition":"Robust Correspondence Analysis (RCA) extends classical correspondence analysis to contingency tables that contain outlying rows or columns. By replacing the standard singular value decomposition with a robust alternative, RCA produces biplots and coordinate maps that accurately reflect the dominant association structure even when atypical cells or categories exert undue influence on the standard solution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greenacre (CA); robust extensions by Croux, Ruiz-Gazen and colleagues","year":"2000s (robust extensions of CA developed since the early 2000s)","type":"Robust dimension reduction for contingency tables","dataType":"Categorical / count data (contingency tables), potentially with outlying rows or columns","subfamily":"Multivariate analysis"},"citations":[{"ref":"Croux, C. & Ruiz-Gazen, A. (2005). High breakdown estimators for principal components: the projection-pursuit approach revisited. Journal of Multivariate Analysis, 95(1), 206–226.","type":"article","doi":"10.1016/j.jmva.2004.08.002","isbn":null,"url":null},{"ref":"Greenacre, M. (2017). Correspondence Analysis in Practice (3rd ed.). CRC Press / Chapman & Hall.","type":"book","doi":null,"isbn":"978-1498731775","url":null}],"related":["correspondence-analysis","multiple-correspondence-analysis","robust-principal-component-analysis","robust-exploratory-factor-analysis","robust-multidimensional-scaling","robust-multiple-correspondence-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-counterfactual-impact-evaluation","name":"Robust Counterfactual Impact Evaluation","fullName":"Robust Counterfactual Impact Evaluation","aliases":["Robust CIE","Sensitivity-checked CIE","Multi-method counterfactual evaluation","Robustness-validated impact evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s","originator":"European Commission evaluation community; Pellegrini, Ferrara and colleagues","url":"https://scholargate.app/en/causal-inference/robust-counterfactual-impact-evaluation","markdownUrl":"https://scholargate.app/en/causal-inference/robust-counterfactual-impact-evaluation.md","definition":"Robust Counterfactual Impact Evaluation (Robust CIE) strengthens causal impact estimates by combining multiple quasi-experimental estimators, placebo tests, and formal sensitivity analyses. Rather than relying on a single method, it cross-validates findings across approaches — such as matching, difference-in-differences, and regression discontinuity — to ensure that conclusions do not depend on any single methodological choice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"European Commission evaluation community; Pellegrini, Ferrara and colleagues","year":"2010s","type":"Robustness-validated causal evaluation","dataType":"Panel or cross-sectional observational data with treatment and control units","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Bia, M., Flores, C. A., Flores-Lagunes, A., & Mattei, A. (2014). A Stata package for the application of semiparametric estimators of dose–response functions. Stata Journal, 14(3), 580–604.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1177/1536867X1401400307"},{"ref":"Ferrara, A. R., McCann, P., Pellegrini, G., Stelder, D., & Terribile, F. (2017). Assessing the impacts of Cohesion Policy on EU regions: A non-parametric analysis on interventions with multiple treatment intensities. Environment and Planning C: Politics and Space, 35(8), 1467–1487.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1177/2399654417698833"}],"related":["counterfactual-impact-evaluation","propensity-score-matching","difference-in-differences","sensitivity-analysis-for-causality","doubly-robust-estimation","placebo-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-covariance","name":"Robust Covariance (MCD)","fullName":"Minimum Covariance Determinant Estimation","aliases":["minimum covariance determinant","MCD estimator","robust covariance estimation","Robust Kovaryans Tahmini (MCD)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1999,"originator":"Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)","url":"https://scholargate.app/en/statistics/robust-covariance","markdownUrl":"https://scholargate.app/en/statistics/robust-covariance.md","definition":"Robust Covariance via the Minimum Covariance Determinant (MCD) estimates a multivariate mean vector and covariance matrix that are not distorted by outliers. It was made practical by the Fast-MCD algorithm of Rousseeuw and Van Driessen (1999), building on Rousseeuw's earlier work on robust estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rousseeuw; Rousseeuw & Van Driessen (Fast-MCD)","year":1999,"type":"Robust multivariate location-scatter estimator","estimator":"Minimum Covariance Determinant (Fast-MCD)","breakdownPoint":"up to 50% outliers tolerated","minSample":50},"citations":[{"ref":"Rousseeuw, P. J. & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.","type":"article","doi":"10.1080/00401706.1999.10485670","isbn":null,"url":null},{"ref":"Rousseeuw, P. J. & Leroy, A. M. (1987). Robust Regression and Outlier Detection. Wiley.","type":"book","doi":null,"isbn":"978-0471488552","url":null}],"related":["robust-mahalanobis-distance","mad-estimation","theil-sen-estimator","least-trimmed-squares","robust-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-cox-regression","name":"Robust Cox Regression","fullName":"Robust Cox Proportional Hazards Regression","aliases":["Cox model with robust standard errors","sandwich-variance Cox regression","Lin-Wei robust Cox model","robust partial likelihood regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1989","originator":"Lin & Wei","url":"https://scholargate.app/en/statistics/robust-cox-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-cox-regression.md","definition":"Robust Cox regression fits the standard Cox proportional hazards model but replaces the model-based variance estimate with a sandwich (Huber-White) estimator. This yields valid standard errors and confidence intervals even when observations are clustered, the independence assumption is mildly violated, or the working model is slightly misspecified, without discarding the familiar hazard-ratio interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lin & Wei","year":"1989","type":"Semi-parametric survival regression with robust variance","dataType":"Time-to-event (survival) data with possible clustering or mild assumption violations","subfamily":"Regression / GLM"},"citations":[{"ref":"Lin, D. Y., & Wei, L. J. (1989). The robust inference for the Cox proportional hazards model. Journal of the American Statistical Association, 84(408), 1074–1078.","type":"article","doi":"10.1080/01621459.1989.10478874","isbn":null,"url":null},{"ref":"Therneau, T. M., & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.","type":"book","doi":null,"isbn":"978-0387987784","url":null}],"related":["cox-regression","robust-regression","survival-regression","multilevel-cox-regression","bootstrap-cox-regression","robust-survival-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-cronbachs-alpha","name":"Robust Cronbach's Alpha","fullName":"Robust Cronbach's Alpha Reliability Coefficient","aliases":["robust alpha","outlier-resistant Cronbach's alpha","robust internal consistency","robust coefficient alpha"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2002–2016","originator":"Derived from Lee J. Cronbach (1951); robust variants formalized by Yuan & Bentler (2002) and Zhang & Yuan (2016)","url":"https://scholargate.app/en/psychometrics/robust-cronbachs-alpha","markdownUrl":"https://scholargate.app/en/psychometrics/robust-cronbachs-alpha.md","definition":"Robust Cronbach's alpha adapts the classical internal consistency coefficient to data that violate the assumption of multivariate normality or contain influential outliers. By replacing the conventional sample covariance matrix with a robust counterpart, it yields a reliability estimate that is resistant to distortion by non-normal response distributions, contaminated observations, or small violations of model assumptions common in applied psychometric work.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from Lee J. Cronbach (1951); robust variants formalized by Yuan & Bentler (2002) and Zhang & Yuan (2016)","year":"2002–2016","type":"Robust reliability coefficient","dataType":"Ordinal or continuous item-level scores, potentially with outliers or non-normal distributions","subfamily":"Scale / measurement"},"citations":[{"ref":"Yuan, K.-H., & Bentler, P. M. (2002). On robustness of the normal-theory based asymptotic distributions of three reliability coefficient estimates. Psychometrika, 67(2), 251–268.","type":"article","doi":"10.1007/BF02294845","isbn":null,"url":null},{"ref":"Zhang, Z., & Yuan, K.-H. (2016). Robust coefficients alpha and omega and confidence intervals with outlying observations and missing data: Methods and software. Educational and Psychological Measurement, 76(3), 387–411.","type":"article","doi":"10.1177/0013164415594658","isbn":null,"url":null}],"related":["cronbachs-alpha","mcdonalds-omega","robust-reliability-analysis","robust-item-analysis","robust-scale-development","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-dcc-garch","name":"Robust DCC-GARCH","fullName":"Robust Dynamic Conditional Correlation GARCH Model","aliases":["robust DCC-GARCH","robust dynamic conditional correlation","outlier-robust DCC","composite-likelihood DCC-GARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002–2021","originator":"Engle (2002) for DCC; robust extensions by Pakel, Shephard, Sheppard, and Engle (2021)","url":"https://scholargate.app/en/econometrics/robust-dcc-garch","markdownUrl":"https://scholargate.app/en/econometrics/robust-dcc-garch.md","definition":"The Robust DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by replacing standard quasi-maximum likelihood estimation with outlier-resistant or composite-likelihood techniques. This preserves accurate time-varying correlation estimation even when financial return data contain extreme observations, heavy tails, or structural irregularities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle (2002) for DCC; robust extensions by Pakel, Shephard, Sheppard, and Engle (2021)","year":"2002–2021","type":"Multivariate volatility model with robust estimation","dataType":"Multivariate financial time series (returns, log-returns)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339–350.","type":"article","doi":"10.1198/073500102288618487","isbn":null,"url":null},{"ref":"Pakel, C., Shephard, N., Sheppard, K., & Engle, R. F. (2021). Fitting vast dimensional time-varying covariance models. Journal of Business and Economic Statistics, 39(3), 652–668.","type":"article","doi":"10.1080/07350015.2020.1713795","isbn":null,"url":null}],"related":["dcc-garch-model","robust-garch-model","robust-egarch","robust-tgarch","vector-autoregression","garch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-decision-tree","name":"Robust Decision Tree","fullName":"Robust Decision Tree (Outlier-Resistant Tree Induction)","aliases":["robust tree","noise-tolerant decision tree","outlier-resistant decision tree","robust CART"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2019","originator":"Various (Chen & Nan 2019; robust statistics community)","url":"https://scholargate.app/en/machine-learning/robust-decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/robust-decision-tree.md","definition":"A Robust Decision Tree is a decision tree variant trained with modified splitting criteria or training procedures designed to reduce sensitivity to outliers, label noise, and adversarial perturbations. Rather than minimizing standard impurity measures that are strongly affected by extreme values, robust variants use statistically robust analogues or regularization to produce splits that generalize under noisy or corrupted data conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Chen & Nan 2019; robust statistics community)","year":"2000s–2019","type":"Supervised classification / regression tree","dataType":"Tabular (continuous, categorical); settings with outliers or adversarial noise","subfamily":"Machine learning"},"citations":[{"ref":"Chen, H., & Nan, F. (2019). Robust Decision Trees Against Adversarial Examples. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1006–1015.","type":"article","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v97/chen19m.html"},{"ref":"Hubert, M., & Debruyne, M. (2010). Minimum covariance determinant. Wiley Interdisciplinary Reviews: Computational Statistics, 2(1), 36–43. (background on robust estimation applied to tree splitting criteria)","type":"article","doi":"10.1002/wics.61","isbn":null,"url":null}],"related":["decision-tree","random-forest","robust-random-forest","extra-trees","robust-gradient-boosting","regularized-decision-tree"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-descriptive-statistics","name":"Robust Descriptive Statistics","fullName":"Robust Descriptive Statistics","aliases":["resistant statistics","outlier-resistant summary statistics","robust summary measures","robust location and scale estimation"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1960s–1970s","originator":"John W. Tukey, Peter J. Huber, Frank Hampel","url":"https://scholargate.app/en/statistics/robust-descriptive-statistics","markdownUrl":"https://scholargate.app/en/statistics/robust-descriptive-statistics.md","definition":"Robust descriptive statistics summarize the location, spread, and shape of a dataset using measures that remain meaningful even when a fraction of the data contains outliers or severe departures from normality. Core tools include the median, trimmed mean, interquartile range (IQR), and median absolute deviation (MAD), all of which are resistant to contamination that would distort the classic mean and standard deviation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Tukey, Peter J. Huber, Frank Hampel","year":"1960s–1970s","type":"Resistant summary measures","dataType":"Continuous or ordinal data, especially with outliers or non-normal distributions","subfamily":"Classical statistics"},"citations":[{"ref":"Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.","type":"book","doi":null,"isbn":"978-0201076165","url":null},{"ref":"Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470129906","url":null}],"related":["power-analysis","effect-size-analysis","robust-independent-samples-t-test","robust-one-way-anova","robust-pearson-correlation","bootstrap-descriptive-statistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-difference-gmm","name":"Robust Difference GMM","fullName":"Robust Difference Generalized Method of Moments Estimator","aliases":["robust Arellano-Bond estimator","difference GMM with robust SE","HAC difference GMM","AB-GMM robust"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991 / 2005","originator":"Arellano & Bond (1991); robust inference extension via Windmeijer (2005)","url":"https://scholargate.app/en/econometrics/robust-difference-gmm","markdownUrl":"https://scholargate.app/en/econometrics/robust-difference-gmm.md","definition":"Robust Difference GMM applies the Arellano-Bond first-difference GMM estimator with heteroscedasticity- and autocorrelation-consistent (HAC) or Windmeijer-corrected standard errors, delivering valid inference for dynamic panel models even when error variances are non-constant or residuals are cross-sectionally correlated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bond (1991); robust inference extension via Windmeijer (2005)","year":"1991 / 2005","type":"GMM estimator with robust standard errors","dataType":"Panel data (balanced or unbalanced, dynamic)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86-136.","type":"article","doi":"10.1177/1536867X0900900106","isbn":null,"url":null}],"related":["difference-gmm","panel-arellano-bond-gmm","panel-system-gmm","robust-system-gmm","dynamic-panel-data-model","panel-fixed-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-difference-in-differences","name":"Robust Difference-in-Differences","fullName":"Robust Difference-in-Differences Estimator","aliases":["robust DiD","heterogeneity-robust DiD","staggered DiD","disaggregated ATT DiD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021-2023","originator":"Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023)","url":"https://scholargate.app/en/causal-inference/robust-difference-in-differences","markdownUrl":"https://scholargate.app/en/causal-inference/robust-difference-in-differences.md","definition":"Robust Difference-in-Differences is a family of modern DiD estimators designed to remain valid when treatment timing is staggered across units and treatment effects are heterogeneous over time or across groups. Classical two-way fixed-effects (TWFE) DiD can be severely biased in such settings; robust variants estimate group-time average treatment effects (ATTs) separately and then aggregate them in a theoretically sound way.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023)","year":"2021-2023","type":"Causal inference / panel regression","dataType":"Panel or repeated cross-sections with staggered or simultaneous treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.","type":"article","doi":"10.1016/j.jeconom.2020.12.001","isbn":null,"url":null},{"ref":"Roth, J., Sant'Anna, P. H. C., Bilinski, A., & Poe, J. (2023). What's trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218-2244.","type":"article","doi":"10.1016/j.jeconom.2023.03.008","isbn":null,"url":null}],"related":["difference-in-differences","dynamic-difference-in-differences","multi-period-difference-in-differences","panel-data-difference-in-differences","heterogeneous-treatment-effect-difference-in-differences","event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-differential-item-functioning","name":"Robust Differential Item Functioning","fullName":"Robust Differential Item Functioning Analysis","aliases":["Robust DIF","outlier-resistant DIF detection","robust item bias analysis","DIF with robust estimation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Building on DIF work by Cleary & Hilton (1968) and Mantel-Haenszel by Holland & Thayer (1988); robust extensions developed through 1990s–2000s","url":"https://scholargate.app/en/psychometrics/robust-differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/robust-differential-item-functioning.md","definition":"Robust differential item functioning analysis detects items that behave differently across demographic groups after matching respondents on the underlying trait, while protecting the procedure against distortion by outliers, model misfit, or contaminated anchor items. It is applied in educational testing, clinical assessment, and survey research to ensure that a scale measures the same construct equally fairly for all groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on DIF work by Cleary & Hilton (1968) and Mantel-Haenszel by Holland & Thayer (1988); robust extensions developed through 1990s–2000s","year":"1990s–2000s","type":"Item bias / fairness analysis","dataType":"Ordinal or binary item responses across two or more groups","subfamily":"Scale / measurement"},"citations":[{"ref":"Magis, D., Beland, S., Tuerlinckx, F., & De Boeck, P. (2011). A general framework and an R package for the detection of dichotomous differential item functioning. Behavior Research Methods, 43(3), 847–862.","type":"article","doi":"10.3758/brm.42.3.847","isbn":null,"url":null},{"ref":"Kristjansson, E., Aylesworth, R., McDowell, I., & Zumbo, B. D. (2005). A comparison of four methods for detecting differential item functioning in ordered response items. Educational and Psychological Measurement, 65(6), 935–953.","type":"article","doi":"10.1177/0013164405275668","isbn":null,"url":null}],"related":["differential-item-functioning","item-response-theory","robust-item-analysis","measurement-invariance","confirmatory-factor-analysis","rasch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-discrete-event-simulation","name":"Robust Discrete-Event Simulation","fullName":"Robust Discrete-Event Simulation — uncertainty-resilient stochastic event-driven modeling","aliases":["Robust DES","Uncertainty-Aware DES","Robust DEVS","Resilient Discrete-Event Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Banks, Carson, Nelson, Nicol (canonical DES); robust extensions: operations research community","url":"https://scholargate.app/en/simulation/robust-discrete-event-simulation","markdownUrl":"https://scholargate.app/en/simulation/robust-discrete-event-simulation.md","definition":"Robust Discrete-Event Simulation (Robust DES) is a simulation methodology that extends classical discrete-event simulation by explicitly incorporating uncertainty in model parameters — such as interarrival times, service durations, and resource capacities — and evaluating system performance across worst-case or distributional uncertainty sets rather than point estimates alone. It is widely applied in manufacturing, healthcare, logistics, and supply chain systems where parameter misspecification or real-world variability can lead to misleading simulation conclusions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Banks, Carson, Nelson, Nicol (canonical DES); robust extensions: operations research community","year":"1990s–2000s","type":"Simulation with robustness analysis","dataType":"Event logs, interarrival times, service times, resource capacities","subfamily":"Simulation / optimization"},"citations":[{"ref":"Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"9780136062127","url":null},{"ref":"Discrete-event simulation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Discrete-event_simulation"}],"related":["discrete-event-simulation","stochastic-discrete-event-simulation","robust-sensitivity-analysis","robust-scenario-analysis","monte-carlo-simulation","robust-markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-discriminant-analysis","name":"Robust Discriminant Analysis","fullName":"High-Breakdown Robust Linear Discriminant Analysis","aliases":["robust LDA","high-breakdown discriminant analysis","MCD-based discriminant analysis","Robust Diskriminant Analizi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1997,"originator":"Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)","url":"https://scholargate.app/en/statistics/robust-discriminant-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-discriminant-analysis.md","definition":"Robust Discriminant Analysis is a classification method that separates groups with a linear discriminant function while resisting the influence of outliers. It replaces the classical mean and covariance with a high-breakdown estimator such as the Minimum Covariance Determinant (MCD), an approach developed by Hawkins & McLachlan (1997) and Croux & Dehon (2001).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hawkins & McLachlan (high-breakdown LDA); Croux & Dehon (S-estimator robust LDA)","year":1997,"type":"Robust classification / discriminant analysis","estimator":"Minimum Covariance Determinant (MCD) location and scatter","outcome":"categorical group label","predictors":"multivariate continuous"},"citations":[{"ref":"Hawkins, D. M. & McLachlan, G. J. (1997). High Breakdown Linear Discriminant Analysis. Journal of the American Statistical Association, 92(437), 136-143.","type":"article","doi":"10.1080/01621459.1997.10473610","isbn":null,"url":null},{"ref":"Croux, C. & Dehon, C. (2001). Robust Linear Discriminant Analysis Using S-Estimators. Canadian Journal of Statistics, 29(3), 473-493.","type":"article","doi":"10.2307/3316042","isbn":null,"url":null}],"related":["linear-discriminant-analysis","logistic-regression","robust-logistic-regression","quadratic-discriminant-analysis","heteroscedasticity-robust-se"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-discriminant-validity","name":"Robust Discriminant Validity","fullName":"Robust Discriminant Validity Assessment","aliases":["HTMT criterion","heterotrait-monotrait ratio","discriminant validity testing","RDV"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1959 (foundational); 2015 (HTMT criterion)","originator":"Henseler, Ringle & Sarstedt (HTMT); Campbell & Fiske (foundational framework)","url":"https://scholargate.app/en/psychometrics/robust-discriminant-validity","markdownUrl":"https://scholargate.app/en/psychometrics/robust-discriminant-validity.md","definition":"Robust discriminant validity assessment determines whether distinct latent constructs in a measurement model are sufficiently different from one another. Unlike traditional AVE-based approaches, robust methods such as the Heterotrait-Monotrait (HTMT) ratio use the pattern of inter-indicator correlations to provide a more sensitive and simulation-validated criterion for judging discriminant validity in structural equation modeling contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henseler, Ringle & Sarstedt (HTMT); Campbell & Fiske (foundational framework)","year":"1959 (foundational); 2015 (HTMT criterion)","type":"Validity assessment / measurement quality criterion","dataType":"Latent variable scores, indicator correlations","subfamily":"Scale / measurement"},"citations":[{"ref":"Henseler, J., Ringle, C. M. & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.","type":"article","doi":"10.1007/s11747-014-0403-8","isbn":null,"url":null},{"ref":"Campbell, D. T. & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.","type":"article","doi":"10.1037/h0046016","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","convergent-validity","average-variance-extracted","multitrait-multimethod","construct-validity","structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-dynamic-panel-data-model","name":"Robust Dynamic Panel Data Model","fullName":"Robust Dynamic Panel Data Model","aliases":["robust dynamic panel","heteroscedasticity-robust dynamic panel","robust GMM dynamic panel","dynamic panel with robust standard errors"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991–2005","originator":"Arellano & Bond (1991); robust extension via Windmeijer (2005)","url":"https://scholargate.app/en/econometrics/robust-dynamic-panel-data-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-dynamic-panel-data-model.md","definition":"The robust dynamic panel data model combines the dynamic panel GMM framework — which handles endogeneity from lagged dependent variables and unobserved heterogeneity — with robust covariance estimation that remains valid under heteroscedasticity and serial correlation. The Windmeijer finite-sample correction is the standard robust adjustment applied to two-step GMM estimators in this setting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bond (1991); robust extension via Windmeijer (2005)","year":"1991–2005","type":"Dynamic panel estimator with robust inference","dataType":"Balanced or unbalanced panel data with lagged dependent variable","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58(2), 277–297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.","type":"article","doi":"10.1016/j.jeconom.2004.02.005","isbn":null,"url":null}],"related":["dynamic-panel-data-model","arellano-bond-gmm-estimator","panel-system-gmm","panel-fixed-effects-model","robust-panel-data-analysis","panel-difference-gmm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-effect-size-analysis","name":"Robust Effect Size Analysis","fullName":"Robust Effect Size Analysis","aliases":["robust Cohen's d","trimmed-mean effect size","outlier-resistant effect size","robust standardized mean difference"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"2005 (formalized)","originator":"Algina, Keselman & Penfield; Wilcox","url":"https://scholargate.app/en/statistics/robust-effect-size-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-effect-size-analysis.md","definition":"Robust effect size analysis quantifies the magnitude of a difference or association using estimators that are resistant to outliers and violations of normality. Rather than relying on classical statistics such as Cohen's d based on sample means and standard deviations, robust variants use trimmed means and Winsorized standard deviations to produce effect size estimates that accurately reflect the typical effect rather than being inflated by extreme values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Algina, Keselman & Penfield; Wilcox","year":"2005 (formalized)","type":"Robust effect size estimation","dataType":"Continuous, ordinal","subfamily":"Classical statistics"},"citations":[{"ref":"Algina, J., Keselman, H. J., & Penfield, R. D. (2005). An alternative to Cohen's standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10(3), 317–328.","type":"article","doi":"10.1037/1082-989X.10.3.317","isbn":null,"url":null},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["effect-size-analysis","robust-independent-samples-t-test","robust-one-way-anova","mann-whitney-u-test","power-analysis","robust-descriptive-statistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-egarch","name":"Robust EGARCH","fullName":"Robust Exponential Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["Robust EGARCH model","outlier-robust EGARCH","robust exponential GARCH","REGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2008","originator":"Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors","url":"https://scholargate.app/en/econometrics/robust-egarch","markdownUrl":"https://scholargate.app/en/econometrics/robust-egarch.md","definition":"Robust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme observations or data errors cannot distort the estimated volatility dynamics or the leverage effect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors","year":"2008","type":"Robust volatility model","dataType":"Financial time series with outliers or heavy tails","subfamily":"Econometrics / time series"},"citations":[{"ref":"Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940.","type":"article","doi":"10.1016/j.jspi.2007.11.003","isbn":null,"url":null},{"ref":"Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null}],"related":["egarch-model","robust-garch-model","robust-tgarch","garch-model","dcc-garch-model","tgarch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-engle-granger-cointegration","name":"Robust Engle-Granger Cointegration","fullName":"Robust Engle-Granger Cointegration Test","aliases":["robust EG cointegration","outlier-robust cointegration test","robust two-step cointegration","robust EG test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1987 (base); robust variants 2000s–2020s","originator":"Engle & Granger (1987); robust extensions by subsequent authors including Hao & Shaffer and others","url":"https://scholargate.app/en/econometrics/robust-engle-granger-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/robust-engle-granger-cointegration.md","definition":"The Robust Engle-Granger cointegration test adapts the classic two-step Engle-Granger procedure to withstand outliers, heavy-tailed error distributions, and additive noise that can severely distort standard residual-based cointegration inference. By substituting robust regression and robust unit-root testing for classical OLS and ADF steps, it yields reliable conclusions about long-run equilibrium relationships even when the data contain anomalous observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle & Granger (1987); robust extensions by subsequent authors including Hao & Shaffer and others","year":"1987 (base); robust variants 2000s–2020s","type":"Cointegration test","dataType":"Non-stationary time series (I(1) variables), possibly containing outliers or heavy-tailed errors","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null},{"ref":"Hao, K., & Shaffer, A. (2021). Robust cointegration testing in the presence of outliers. Journal of Statistical Computation and Simulation, 91(10), 2137–2154.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+cointegration+testing+in+the+presence+of+outliers+Hao"}],"related":["engle-granger-cointegration-test","johansen-cointegration-test","structural-break-engle-granger-cointegration","robust-vecm","robust-ols","fourier-engle-granger-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-event-tree-analysis","name":"Robust event tree analysis","fullName":"Robust Event Tree Analysis","aliases":["Robust ETA","uncertainty-aware event tree analysis","ETA with uncertainty quantification","robust probabilistic event tree"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960s (ETA); robust extensions ~1990s–2000s","originator":"H.E. Lambert / Nuclear industry (ETA); robust extensions developed through aerospace and nuclear risk research","url":"https://scholargate.app/en/experimental-design/robust-event-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/robust-event-tree-analysis.md","definition":"Robust Event Tree Analysis (Robust ETA) extends classical event tree analysis by explicitly accounting for uncertainty in the probability estimates assigned to each branch. Rather than treating branch probabilities as precise point values, the robust approach represents them as intervals, probability distributions, or imprecise probabilities, then propagates that uncertainty through the tree to produce outcome frequency ranges instead of single numbers. This gives decision-makers a clearer picture of the confidence in risk estimates under realistic conditions of incomplete or conflicting information.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"H.E. Lambert / Nuclear industry (ETA); robust extensions developed through aerospace and nuclear risk research","year":"1960s (ETA); robust extensions ~1990s–2000s","type":"Probabilistic risk assessment with uncertainty propagation","dataType":"Event probabilities, expert elicitations, interval or distributional uncertainty data","subfamily":"Engineering methods"},"citations":[{"ref":"Bedford, T., & Cooke, R. M. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521773201","url":null},{"ref":"Event tree analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Event_tree_analysis"}],"related":["event-tree-analysis","fault-tree-analysis","robust-fault-tree-analysis","failure-mode-and-effects-analysis","robust-failure-mode-and-effects-analysis","sensitivity-analysis-with-event-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-explanatory-research","name":"Robust Explanatory Research","fullName":"Robust Explanatory Research Design","aliases":["robust causal research","outlier-resistant explanatory design","robust regression-based explanatory study"],"domain":"research-design","family":"process-pipeline","subfamily":"Survey and observational design","year":"1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward","originator":"Peter J. Huber (robust statistics); applied to explanatory designs via Rand Wilcox and others","url":"https://scholargate.app/en/research-design/robust-explanatory-research","markdownUrl":"https://scholargate.app/en/research-design/robust-explanatory-research.md","definition":"Robust explanatory research combines the explanatory goal of identifying why and how variables causally influence one another with robust statistical methods that remain valid when data violate classical assumptions — particularly normality, homoscedasticity, and the absence of influential outliers. Rather than discarding outliers or forcing data to conform to ordinary least squares assumptions, this design applies estimators and inferential procedures that down-weight or resist the distorting influence of extreme observations while preserving the explanatory aim of the study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Huber (robust statistics); applied to explanatory designs via Rand Wilcox and others","year":"1960s–1980s (robust statistics foundations); applied to explanatory research from 1990s onward","type":"Quantitative research design","dataType":"Continuous, ordinal, or count data susceptible to outliers or distributional violations","subfamily":"Survey and observational design"},"citations":[{"ref":"Huber, P. J. (1981). Robust Statistics. Wiley.","type":"book","doi":null,"isbn":"978-0471418054","url":null},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["explanatory-research","causal-comparative-research","robust-correlational-research","multivariate-explanatory-research","robust-longitudinal-research","hypothesis-testing-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-exploratory-factor-analysis","name":"Robust Exploratory Factor Analysis","fullName":"Robust Exploratory Factor Analysis","aliases":["robust EFA","robust factor analysis","outlier-resistant factor analysis","EFA with robust estimation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2000–2003","originator":"Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)","url":"https://scholargate.app/en/psychometrics/robust-exploratory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/robust-exploratory-factor-analysis.md","definition":"Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)","year":"2000–2003","type":"Latent variable / dimension reduction (robust)","dataType":"Continuous or ordinal indicators, data may contain outliers or non-normality","subfamily":"Scale / measurement"},"citations":[{"ref":"Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.","type":"article","doi":"10.1007/bf02294185","isbn":null,"url":null},{"ref":"Pison, G., Rousseeuw, P. J., Filzmoser, P., & Croux, C. (2003). Robust factor analysis. Journal of Multivariate Analysis, 84(1), 145–172.","type":"article","doi":"10.1016/S0047-259X(02)00007-6","isbn":null,"url":null}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","robust-confirmatory-factor-analysis","item-response-theory","cronbachs-alpha","principal-component-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-factor-analysis","name":"Robust Factor Analysis","fullName":"Robust Factor Analysis","aliases":["robust factor analysis","outlier-resistant factor analysis","MCD-based factor analysis","Robust Faktör Analizi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2003,"originator":"Pison, Rousseeuw, Filzmoser & Croux","url":"https://scholargate.app/en/statistics/robust-factor-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-factor-analysis.md","definition":"Robust Factor Analysis recovers the latent factor structure of multivariate continuous data while resisting the distorting pull of outliers. Introduced by Pison, Rousseeuw, Filzmoser and Croux (2003), it replaces the classical sample covariance with a robust estimator such as the Minimum Covariance Determinant (MCD) or an S-estimator before extracting factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pison, Rousseeuw, Filzmoser & Croux","year":2003,"type":"Robust latent-factor model","estimator":"MCD or S-estimator robust covariance, then factored","outcome":"latent factor structure","minSample":100},"citations":[{"ref":"Pison, G., Rousseeuw, P. J., Filzmoser, P., & Croux, C. (2003). Robust factor analysis. Journal of Multivariate Analysis, 84(1), 145-172.","type":"article","doi":"10.1016/S0047-259X(02)00007-6","isbn":null,"url":null},{"ref":"Hubert, M., Rousseeuw, P. J., & Vanden Branden, K. (2005). ROBPCA: A new approach to robust principal component analysis. Technometrics, 47(1), 64-79.","type":"article","doi":"10.1198/004017004000000563","isbn":null,"url":null}],"related":["pca","robust-pca","factor-analysis","robust-covariance","influence-diagnostics"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-failure-mode-and-effects-analysis","name":"Robust Failure Mode and Effects Analysis","fullName":"Robust Failure Mode and Effects Analysis","aliases":["Robust FMEA","Noise-Aware FMEA","Variability-Integrated FMEA","Robustness-Based FMEA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s–1990s","originator":"Extension of traditional FMEA (MIL-P-1629, 1949) integrated with Taguchi robust design philosophy (Genichi Taguchi, 1980s)","url":"https://scholargate.app/en/experimental-design/robust-failure-mode-and-effects-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/robust-failure-mode-and-effects-analysis.md","definition":"Robust Failure Mode and Effects Analysis extends the classical FMEA framework by explicitly incorporating noise factors, parameter variability, and environmental variation into the risk assessment process. Rather than treating failure likelihood as a single deterministic estimate, it uses robust design principles — most notably from Taguchi's quality engineering — to evaluate how process variability and uncontrollable noise factors influence the probability and severity of each failure mode, yielding risk priority numbers that reflect real-world variability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of traditional FMEA (MIL-P-1629, 1949) integrated with Taguchi robust design philosophy (Genichi Taguchi, 1980s)","year":"1980s–1990s","type":"Risk analysis with variability quantification","dataType":"Engineering failure data, noise factor specifications, process variability measures","subfamily":"Engineering methods"},"citations":[{"ref":"Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873895989","url":null},{"ref":"Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall.","type":"book","doi":null,"isbn":"978-0137451593","url":null}],"related":["failure-mode-and-effects-analysis","robust-design-of-experiments","taguchi-method","fault-tree-analysis","statistical-process-control","robust-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-fault-tree-analysis","name":"Robust Fault Tree Analysis","fullName":"Robust Fault Tree Analysis with Uncertainty Quantification","aliases":["Robust FTA","Uncertainty-aware FTA","FTA with interval analysis","Imprecise probability FTA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s–2000s (robustness extensions to classical FTA ca. 1961)","originator":"Extended from classical FTA (Watson, 1961; Bell Labs / U.S. Air Force); robustness extensions developed through reliability engineering and uncertainty quantification research from the 1980s onward","url":"https://scholargate.app/en/experimental-design/robust-fault-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/robust-fault-tree-analysis.md","definition":"Robust Fault Tree Analysis (Robust FTA) extends classical fault tree analysis by explicitly representing and propagating uncertainty in component failure probabilities. Rather than assigning single point estimates to basic events, it uses probability distributions, interval bounds, or imprecise probabilities, then propagates these through the logical tree structure to obtain bounds or distributions on the top-event failure probability. This makes risk conclusions defensible under incomplete or variable data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from classical FTA (Watson, 1961; Bell Labs / U.S. Air Force); robustness extensions developed through reliability engineering and uncertainty quantification research from the 1980s onward","year":"1980s–2000s (robustness extensions to classical FTA ca. 1961)","type":"Quantitative reliability and safety analysis with uncertainty propagation","dataType":"Component failure rates, probability intervals, expert-elicited bounds, historical failure data","subfamily":"Engineering methods"},"citations":[{"ref":"Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492.","type":"book","doi":null,"isbn":null,"url":"https://www.nrc.gov/reading-rm/doc-collections/nuregs/staff/sr0492/"},{"ref":"Aven, T. (2013). On the meaning of a black swan in a risk context. Safety Science, 57, 44-51.","type":"article","doi":"10.1016/j.ssci.2013.01.016","isbn":null,"url":null}],"related":["fault-tree-analysis","failure-mode-and-effects-analysis","event-tree-analysis","robust-reliability-analysis","robust-failure-mode-and-effects-analysis","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-federated-learning","name":"Robust Federated Learning","fullName":"Robust Federated Learning (Byzantine-Tolerant Distributed Training)","aliases":["Byzantine-robust federated learning","fault-tolerant federated learning","robust FL","Byzantine-tolerant distributed learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017","originator":"Blanchard, P.; El Mhamdi, E. M.; Guerraoui, R.","url":"https://scholargate.app/en/machine-learning/robust-federated-learning","markdownUrl":"https://scholargate.app/en/machine-learning/robust-federated-learning.md","definition":"Robust Federated Learning extends standard federated learning with Byzantine-tolerant aggregation rules that protect the global model against malicious, corrupted, or unreliable clients. Instead of naively averaging client gradients, robust aggregation methods such as coordinate-wise median or Krum filter out harmful updates so that a minority of adversarial participants cannot derail training.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blanchard, P.; El Mhamdi, E. M.; Guerraoui, R.","year":"2017","type":"Distributed learning with Byzantine-tolerant aggregation","dataType":"Decentralized / partitioned tabular, image, or text data across clients","subfamily":"Machine learning"},"citations":[{"ref":"Blanchard, P., El Mhamdi, E. M., Guerraoui, R., & Stainer, J. (2017). Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/f4b9ec30ad9f68f89b29639786cb62ef-Abstract.html"},{"ref":"Yin, D., Chen, Y., Kannan, R., & Bartlett, P. (2018). Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80:5650–5659.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v80/yin18a.html"}],"related":["federated-learning","semi-supervised-federated-learning","online-federated-learning","bayesian-federated-learning","transfer-learning","robust-gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-fishers-exact-test","name":"Robust Fisher's exact test","fullName":"Robust Fisher's Exact Test","aliases":["mid-p Fisher's exact test","robust exact test for contingency tables","conditional robust Fisher test","Fisher mid-p test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1935 (base); mid-p robustification 1961+","originator":"Fisher (1935); mid-p extension by Lancaster (1961) and others","url":"https://scholargate.app/en/statistics/robust-fishers-exact-test","markdownUrl":"https://scholargate.app/en/statistics/robust-fishers-exact-test.md","definition":"The robust Fisher's exact test extends Fisher's classic exact test for contingency tables by applying conservative-correcting adjustments — most commonly the mid-p correction — to reduce the extreme conservatism of the standard exact test. This produces better-calibrated Type I error rates while maintaining validity in small and sparse samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fisher (1935); mid-p extension by Lancaster (1961) and others","year":"1935 (base); mid-p robustification 1961+","type":"Robust exact conditional test","dataType":"Categorical (2×2 or small r×c contingency tables)","subfamily":"Classical statistics"},"citations":[{"ref":"Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471360933","url":null},{"ref":"Lancaster, H. O. (1961). Significance tests in discrete distributions. Journal of the American Statistical Association, 56(294), 223–234.","type":"article","doi":"10.1080/01621459.1961.10482105","isbn":null,"url":null}],"related":["fishers-exact-test","chi-square-test","robust-chi-square-test","barnard-exact-test","mid-p-correction","bootstrap-fishers-exact-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-fixed-effects-model","name":"Robust Fixed Effects Model","fullName":"Robust Fixed Effects Panel Data Model","aliases":["FE with robust standard errors","cluster-robust fixed effects","fixed effects with heteroscedasticity-robust SE","within estimator with robust inference"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1987","originator":"Manuel Arellano","url":"https://scholargate.app/en/econometrics/robust-fixed-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-fixed-effects-model.md","definition":"The robust fixed effects model combines the within-group estimator for panel data with variance-covariance matrices that remain valid under heteroscedasticity and within-unit error correlation. Introduced by Arellano (1987), cluster-robust standard errors paired with the fixed effects estimator are now the default approach for credible panel data inference in economics and social science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Manuel Arellano","year":"1987","type":"Panel regression with robust inference","dataType":"Balanced or unbalanced panel data (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M. (1987). Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics and Statistics, 49(4), 431–434.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Computing+robust+standard+errors+for+within-groups+estimators+Arellano"},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["fixed-effects-model","random-effects-model","panel-hausman-test","robust-ols","panel-ols","panel-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-fractional-factorial-design","name":"Robust Fractional Factorial Design","fullName":"Robust Parameter Design with Fractional Factorial Arrays","aliases":["robust FFD","robust fractional factorial experiment","crossed-array fractional factorial","Taguchi-style fractional factorial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s (Taguchi's crossed-array approach); fractional factorial roots 1935–1945","originator":"Genichi Taguchi (robust parameter design); fractional factorial foundations by Ronald Fisher and Frank Yates","url":"https://scholargate.app/en/experimental-design/robust-fractional-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/robust-fractional-factorial-design.md","definition":"Robust fractional factorial design combines the run-count efficiency of fractional factorial arrays with Taguchi's robust parameter design philosophy. By simultaneously manipulating control factors (inner array) and noise factors (outer array) — each structured as a fractional factorial — the method identifies factor settings that minimize product or process variation due to uncontrollable conditions, without requiring a full factorial experiment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Genichi Taguchi (robust parameter design); fractional factorial foundations by Ronald Fisher and Frank Yates","year":"1980s (Taguchi's crossed-array approach); fractional factorial roots 1935–1945","type":"Experimental design / robust parameter design","dataType":"Continuous or ordinal response measurements from controlled experiments","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null},{"ref":"Taguchi, G. (1987). System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs. UNIPUB/Kraus International.","type":"book","doi":null,"isbn":"978-0527916213","url":null}],"related":["fractional-factorial-design","taguchi-method","full-factorial-design","response-surface-methodology","robust-design-of-experiments","central-composite-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-frequency-analysis","name":"Robust frequency analysis","fullName":"Robust Frequency Analysis","aliases":["robust count analysis","outlier-resistant frequency analysis","robust distributional analysis"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1970s–1980s (foundations); applied to frequency analysis throughout the 1990s–2000s","originator":"Huber, Hampel, Wilcox and the robust statistics tradition","url":"https://scholargate.app/en/statistics/robust-frequency-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-frequency-analysis.md","definition":"Robust frequency analysis applies outlier-resistant estimation and resampling or exact methods to the counting and tabulation of categorical data, reducing the distortion caused by extreme observations, sparse cells, or violations of large-sample assumptions that can make conventional frequency summaries misleading.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Huber, Hampel, Wilcox and the robust statistics tradition","year":"1970s–1980s (foundations); applied to frequency analysis throughout the 1990s–2000s","type":"Robust descriptive and inferential procedure","dataType":"Categorical or ordinal counts; frequency tables","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470129906","url":null}],"related":["frequency-analysis","robust-chi-square-test","robust-descriptive-statistics","bootstrap-frequency-analysis","permutation-frequency-analysis","robust-cross-tabulation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-friedman-test","name":"Robust Friedman test","fullName":"Robust Friedman Test for Repeated Measures","aliases":["robust rank-based repeated measures test","trimmed-mean Friedman test","Friedman test with robust estimation","Fried-type robust test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1990s–2000s","originator":"Extension of Friedman (1937); robust variants developed by Wilcox and colleagues","url":"https://scholargate.app/en/statistics/robust-friedman-test","markdownUrl":"https://scholargate.app/en/statistics/robust-friedman-test.md","definition":"The robust Friedman test is a nonparametric procedure for comparing three or more related (within-subjects) conditions that replaces standard ranking or mean-based summaries with robust location estimates — typically trimmed means or Winsorized statistics — to reduce the influence of outliers and heavy-tailed distributions on the inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Friedman (1937); robust variants developed by Wilcox and colleagues","year":"1990s–2000s","type":"Robust nonparametric repeated measures comparison","dataType":"Continuous or ordinal, repeated measures (within-subjects), three or more conditions","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701.","type":"article","doi":"10.1080/01621459.1937.10503522","isbn":null,"url":null}],"related":["friedman-test","robust-repeated-measures-anova","robust-wilcoxon-signed-rank-test","robust-kruskal-wallis-test","repeated-measures-anova","robust-paired-samples-t-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-full-factorial-design","name":"Robust Full Factorial Design","fullName":"Robust Full Factorial Design of Experiments","aliases":["robust 2^k design","full factorial robust parameter design","robust FFD","noise-factor full factorial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s–1990s","originator":"Genichi Taguchi (robustness principles); formalized in combined-array form by Shoemaker, Tsui, and Wu (1991)","url":"https://scholargate.app/en/experimental-design/robust-full-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/robust-full-factorial-design.md","definition":"Robust full factorial design extends the classical full factorial experiment by explicitly including noise factors — uncontrollable variables that cause performance variation in real-world conditions. By crossing all control factor levels with all noise factor levels in a single combined array, engineers identify control factor settings that maximize mean performance while minimizing sensitivity to noise, yielding products and processes that perform consistently across operating environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Genichi Taguchi (robustness principles); formalized in combined-array form by Shoemaker, Tsui, and Wu (1991)","year":"1980s–1990s","type":"Experimental design with noise-factor control","dataType":"Continuous response measurements under controlled control and noise factor combinations","subfamily":"Engineering methods"},"citations":[{"ref":"Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall.","type":"book","doi":null,"isbn":"978-0137451678","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["full-factorial-design","taguchi-method","robust-taguchi-method","fractional-factorial-design","robust-fractional-factorial-design","response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-fuzzy-regression-discontinuity","name":"Robust Fuzzy Regression Discontinuity","fullName":"Robust Bias-Corrected Fuzzy Regression Discontinuity Design","aliases":["Robust Fuzzy RDD","Fuzzy RD with robust inference","bias-corrected fuzzy RD","CCT fuzzy RDD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2014 (robust CCT estimator); 2001 (fuzzy RDD formalization)","originator":"Calonico, Cattaneo, and Titiunik (robust inference framework); Hahn, Todd, and Van der Klaauw (fuzzy RDD formalization)","url":"https://scholargate.app/en/causal-inference/robust-fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/robust-fuzzy-regression-discontinuity.md","definition":"Robust Fuzzy Regression Discontinuity Design estimates a local average treatment effect (LATE) at a threshold where crossing the cutoff raises — but does not guarantee — treatment receipt. Introduced by Calonico, Cattaneo, and Titiunik (2014), the robust framework applies bias-corrected local polynomial estimation with a robust variance estimator, correcting the coverage failures of conventional bandwidth-optimal inference in both the sharp and fuzzy cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Calonico, Cattaneo, and Titiunik (robust inference framework); Hahn, Todd, and Van der Klaauw (fuzzy RDD formalization)","year":"2014 (robust CCT estimator); 2001 (fuzzy RDD formalization)","type":"Quasi-experimental causal inference with IV at threshold","dataType":"Cross-sectional or panel; continuous running variable with known assignment threshold; binary or partial compliance","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica, 82(6), 2295-2326.","type":"article","doi":"10.3982/ECTA11757","isbn":null,"url":null},{"ref":"Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null}],"related":["fuzzy-regression-discontinuity","regression-discontinuity-design","instrumental-variables","local-average-treatment-effect","propensity-score-matching","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-garch-model","name":"Robust GARCH model","fullName":"Robust Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["Robust GARCH","outlier-robust GARCH","heavy-tail GARCH","contamination-robust volatility model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1986–2013","originator":"Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)","url":"https://scholargate.app/en/econometrics/robust-garch-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-garch-model.md","definition":"The Robust GARCH model extends the classical GARCH framework to handle outliers and heavy-tailed innovations that commonly appear in financial return series. By down-weighting extreme observations through a robust innovation term, it produces more reliable volatility forecasts when data contain jumps, crises, or other anomalies that would otherwise distort standard GARCH estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)","year":"1986–2013","type":"Volatility model","dataType":"Financial time series, return series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.","type":"article","doi":"10.1016/j.ijforecast.2012.06.003","isbn":null,"url":null},{"ref":"Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.","type":"article","doi":"10.1016/0304-4076(86)90063-1","isbn":null,"url":null}],"related":["garch-model","egarch-model","gjr-garch-model","stochastic-volatility-model","arch-model","quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-gaussian-mixture-model","name":"Robust Gaussian Mixture Model","fullName":"Robust Gaussian Mixture Model (Heavy-Tailed and Trimmed Variants)","aliases":["Robust GMM","mixture of t-distributions","trimmed GMM","heavy-tailed mixture model"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000","originator":"Peel, D. & McLachlan, G. J.","url":"https://scholargate.app/en/machine-learning/robust-gaussian-mixture-model","markdownUrl":"https://scholargate.app/en/machine-learning/robust-gaussian-mixture-model.md","definition":"Robust Gaussian Mixture Model replaces the standard Gaussian components with heavier-tailed distributions — most commonly Student's t-distributions — or incorporates trimming and down-weighting of outliers within the EM framework. The result is a probabilistic clustering and density-estimation method that assigns genuinely anomalous points less influence on component parameters, preventing outliers from distorting cluster shapes or positions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peel, D. & McLachlan, G. J.","year":"2000","type":"Probabilistic clustering / density estimation","dataType":"Continuous multivariate data","subfamily":"Machine learning"},"citations":[{"ref":"Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339–348.","type":"article","doi":"10.1023/A:1008981510081","isbn":null,"url":null},{"ref":"Maronna, R. A., Martin, R. D. & Yohai, V. J. (2006). Robust Statistics: Theory and Methods. Wiley.","type":"book","doi":null,"isbn":"978-0-470-01092-1","url":null}],"related":["gaussian-mixture-model","k-means","robust-k-means","one-class-svm","isolation-forest","robust-linear-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-gaussian-process","name":"Robust Gaussian Process","fullName":"Robust Gaussian Process Regression and Classification","aliases":["Robust GP","Student-t Process","Heavy-tailed Gaussian Process","Outlier-robust GP"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2011 (formal treatment); GP foundations: Rasmussen & Williams 2006","originator":"Jylanki, P.; Vanhatalo, J.; Vehtari, A.","url":"https://scholargate.app/en/machine-learning/robust-gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/robust-gaussian-process.md","definition":"Robust Gaussian Process (Robust GP) extends the standard Gaussian Process framework by replacing the Gaussian noise likelihood with a heavy-tailed distribution — typically Student-t — so that outliers in the training data exert less influence on the learned function. It retains the full probabilistic, uncertainty-quantifying character of a standard GP while becoming far less sensitive to corrupted or anomalous observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jylanki, P.; Vanhatalo, J.; Vehtari, A.","year":"2011 (formal treatment); GP foundations: Rasmussen & Williams 2006","type":"Probabilistic non-parametric regression / classification","dataType":"Continuous, mixed; small-to-medium tabular datasets","subfamily":"Machine learning"},"citations":[{"ref":"Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v12/jylanki11a.html"},{"ref":"Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-18253-9","url":null}],"related":["gaussian-process","bayesian-gaussian-process","robust-support-vector-machine","robust-linear-regression","robust-random-forest","gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-gearys-c","name":"Robust Geary's C","fullName":"Robust Geary's Contiguity Ratio","aliases":["robust Geary contiguity ratio","outlier-resistant Geary's C","robust spatial contiguity statistic","robust Geary C"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1954 (base); robust variants: 1990s–2000s","originator":"Geary (1954); robust extensions by Anselin and spatial statisticians","url":"https://scholargate.app/en/spatial-analysis/robust-gearys-c","markdownUrl":"https://scholargate.app/en/spatial-analysis/robust-gearys-c.md","definition":"Robust Geary's C adapts the classical Geary contiguity ratio — a measure of spatial autocorrelation based on pairwise squared differences between neighbouring locations — to resist distortion by spatial outliers and influential observations. It retains the local sensitivity of Geary's C while producing more reliable inferences when the spatial data contain extreme values or non-normal distributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geary (1954); robust extensions by Anselin and spatial statisticians","year":"1954 (base); robust variants: 1990s–2000s","type":"Robust spatial autocorrelation statistic","dataType":"Georeferenced continuous or ordinal areal/point data","subfamily":"GIS / spatial"},"citations":[{"ref":"Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115–145.","type":"article","doi":"10.2307/2986645","isbn":null,"url":null},{"ref":"Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null}],"related":["gearys-c","morans-i","robust-morans-i","local-gearys-c","spatial-autocorrelation","robust-local-indicators-of-spatial-association"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-generalized-linear-model","name":"Robust Generalized linear model","fullName":"Robust Generalized Linear Model","aliases":["robust GLM","GLM with robust estimation","robust quasi-likelihood model","M-estimator GLM"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"2001","originator":"Cantoni & Ronchetti","url":"https://scholargate.app/en/statistics/robust-generalized-linear-model","markdownUrl":"https://scholargate.app/en/statistics/robust-generalized-linear-model.md","definition":"A Robust Generalized Linear Model fits the standard GLM family — linear, logistic, Poisson, and others — using M-type estimating equations that down-weight outlying or influential observations. The result is coefficient estimates and standard errors that remain stable even when a minority of data points deviate sharply from the assumed distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cantoni & Ronchetti","year":"2001","type":"Robust regression model","dataType":"Continuous, binary, count, or proportion outcomes with potential outliers or model contamination","subfamily":"Regression / GLM"},"citations":[{"ref":"Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Methods in Biostatistics. Wiley.","type":"book","doi":null,"isbn":"978-0470027264","url":null},{"ref":"Cantoni, E., & Ronchetti, E. (2001). Robust inference for generalized linear models. Journal of the American Statistical Association, 96(455), 1022–1030.","type":"article","doi":"10.1198/016214501753209004","isbn":null,"url":null}],"related":["generalized-linear-model","robust-regression","robust-multiple-linear-regression","robust-logistic-regression","robust-poisson-regression","quasi-likelihood-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-genetic-algorithm","name":"Robust Genetic Algorithm","fullName":"Robust Genetic Algorithm — Evolutionary Optimization under Uncertainty","aliases":["RGA","Robust GA","Uncertainty-Aware Genetic Algorithm","Noise-Tolerant Genetic Algorithm"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2005 (systematic survey); earlier applications from late 1990s","originator":"Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)","url":"https://scholargate.app/en/simulation/robust-genetic-algorithm","markdownUrl":"https://scholargate.app/en/simulation/robust-genetic-algorithm.md","definition":"The Robust Genetic Algorithm (RGA) extends standard genetic algorithms to find solutions that perform well not only at the nominal design point but also when subjected to uncertainty in decision variables, parameters, or fitness evaluations. By incorporating explicit robustness measures into selection pressure, RGA balances optimality against sensitivity to perturbation, making it suitable for engineering design, scheduling, and policy optimization under real-world variability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jin, Y. and Branke, J. (systematic formalization); roots in Holland (1975)","year":"2005 (systematic survey); earlier applications from late 1990s","type":"Metaheuristic evolutionary optimizer with robustness mechanism","dataType":"Continuous or discrete decision variables with uncertain objective evaluations","subfamily":"Simulation / optimization"},"citations":[{"ref":"Jin, Y., Branke, J. (2005). Evolutionary optimization in uncertain environments — a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.","type":"article","doi":"10.1109/TEVC.2005.846356","isbn":null,"url":null},{"ref":"Beyer, H.-G., Sendhoff, B. (2007). Robust optimization — A comprehensive survey. Computer Methods in Applied Mechanics and Engineering, 196(33–34), 3190–3218.","type":"article","doi":"10.1016/j.cma.2007.03.003","isbn":null,"url":null}],"related":["genetic-algorithm","multi-objective-genetic-algorithm","robust-multi-objective-optimization","stochastic-genetic-algorithm","robust-particle-swarm-optimization","robust-simulated-annealing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-getis-ord-gi","name":"Robust Getis-Ord Gi*","fullName":"Robust Getis-Ord Gi* Statistic","aliases":["Robust Gi*","Robust local Gi star","outlier-resistant hot spot analysis","robust local spatial autocorrelation Gi*"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1992 (base); robust variants circa 2000s–2010s","originator":"Getis & Ord (base statistic); robust extensions developed in subsequent spatial statistics literature","url":"https://scholargate.app/en/spatial-analysis/robust-getis-ord-gi","markdownUrl":"https://scholargate.app/en/spatial-analysis/robust-getis-ord-gi.md","definition":"The Robust Getis-Ord Gi* statistic extends the classical Gi* hot-spot measure to handle outliers in spatial data. By using robust estimators of the mean and variance — such as trimmed means, medians, or down-weighted influential observations — it identifies statistically significant spatial clusters of high or low values even when the attribute distribution contains extreme values that would distort the standard Gi*.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Getis & Ord (base statistic); robust extensions developed in subsequent spatial statistics literature","year":"1992 (base); robust variants circa 2000s–2010s","type":"Local spatial statistic","dataType":"Georeferenced areal or point data with a continuous attribute","subfamily":"GIS / spatial"},"citations":[{"ref":"Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.","type":"article","doi":"10.1111/j.1538-4632.1992.tb00261.x","isbn":null,"url":null},{"ref":"Anselin, L., & Liu, X. (2010). Spatial panel econometrics. In Handbook of Applied Economic Statistics. Robust spatial statistics variants are discussed in the context of outlier-resistant local indicators. See also: Anselin, L. (2018). A local indicator of multivariate spatial association. Geographical Analysis, 51(2), 133–150.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spatial+panel+econometrics+Anselin"}],"related":["local-getis-ord-gi-star","hot-spot-analysis","local-morans-i","robust-local-indicators-of-spatial-association","robust-spatial-autocorrelation","kernel-density-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-gibbs-sampling","name":"Robust Gibbs Sampling","fullName":"Robust Gibbs Sampling","aliases":["robust MCMC Gibbs sampler","outlier-resistant Gibbs sampling","heavy-tailed Gibbs sampler","robust block Gibbs"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1984–1993","originator":"Stuart Geman & Donald Geman (Gibbs sampler, 1984); robustness extensions developed through 1990s Bayesian literature","url":"https://scholargate.app/en/bayesian/robust-gibbs-sampling","markdownUrl":"https://scholargate.app/en/bayesian/robust-gibbs-sampling.md","definition":"Robust Gibbs sampling is a Markov chain Monte Carlo strategy that pairs the coordinate-wise Gibbs sampler with heavy-tailed or outlier-resistant model specifications — most commonly Student-t likelihoods — so that the posterior inference is not distorted by extreme observations. It achieves robustness through data augmentation: each observation receives a latent variance weight that automatically down-weights outliers during each sampling sweep.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stuart Geman & Donald Geman (Gibbs sampler, 1984); robustness extensions developed through 1990s Bayesian literature","year":"1984–1993","type":"Robust MCMC sampler","dataType":"Continuous, count, or mixed data with potential outliers or heavy-tailed errors","subfamily":"Bayesian / computational"},"citations":[{"ref":"Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.","type":"article","doi":"10.1002/jae.3950080504","isbn":null,"url":null},{"ref":"Chib, S. & Greenberg, E. (1995). Understanding the Metropolis-Hastings algorithm. The American Statistician, 49(4), 327–335.","type":"article","doi":"10.1080/00031305.1995.10476177","isbn":null,"url":null}],"related":["gibbs-sampling","robust-bayesian-inference","robust-markov-chain-monte-carlo","hierarchical-gibbs-sampling","robust-metropolis-hastings-algorithm","bayesian-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-gls","name":"Robust GLS","fullName":"Robust Generalized Least Squares","aliases":["robust generalized least squares","GLS with robust standard errors","heteroscedasticity-consistent GLS","HC-GLS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1936 / 1980","originator":"Aitken (GLS theory, 1936); White (robust covariance, 1980)","url":"https://scholargate.app/en/econometrics/robust-gls","markdownUrl":"https://scholargate.app/en/econometrics/robust-gls.md","definition":"Robust GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aitken (GLS theory, 1936); White (robust covariance, 1980)","year":"1936 / 1980","type":"Robust linear regression","dataType":"Cross-sectional or time-series continuous data with non-spherical errors","subfamily":"Econometrics / time series"},"citations":[{"ref":"Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity.","type":"book","doi":null,"isbn":"978-0131395381","url":null},{"ref":"White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.","type":"article","doi":"10.2307/1912934","isbn":null,"url":null}],"related":["ols-regression","panel-gls","robust-ols","feasible-gls","weighted-least-squares","generalized-least-squares"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-goal-programming","name":"Robust goal programming","fullName":"Robust Goal Programming","aliases":["RGP","Goal Programming under Uncertainty","Robust GP","Uncertainty-Aware Goal Programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1961 (GP); 1990s (robust extension)","originator":"Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework)","url":"https://scholargate.app/en/simulation/robust-goal-programming","markdownUrl":"https://scholargate.app/en/simulation/robust-goal-programming.md","definition":"Robust Goal Programming (RGP) extends classical goal programming to handle uncertain or ambiguous model parameters. Instead of minimizing deviations from crisp targets, it seeks solutions that remain feasible and near-optimal across a range of plausible scenarios or uncertain data realizations. RGP is particularly valuable in planning problems where goals are aspirational and input data carries inherent variability or estimation error.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charnes, A. & Cooper, W. W. (goal programming); Mulvey, J. M. et al. (robust optimization framework)","year":"1961 (GP); 1990s (robust extension)","type":"Mathematical programming under uncertainty","dataType":"Numerical targets, uncertain coefficients (interval or scenario-based)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York.","type":"book","doi":null,"isbn":"9780471155041","url":null},{"ref":"Mulvey, J. M., Vanderbei, R. J., Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research, 43(2), 264-281.","type":"article","doi":"10.1287/opre.43.2.264","isbn":null,"url":null}],"related":["goal-programming","stochastic-goal-programming","robust-linear-programming","robust-multi-objective-optimization","multi-objective-goal-programming","stochastic-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-gradient-boosting","name":"Robust Gradient Boosting","fullName":"Robust Gradient Boosting (Gradient Boosting with Robust Loss Functions)","aliases":["gradient boosting with Huber loss","robust GBM","outlier-robust boosting","robust gradient-boosted trees"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001","originator":"Friedman, J. H. (with Huber loss from Huber, P. J.)","url":"https://scholargate.app/en/machine-learning/robust-gradient-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/robust-gradient-boosting.md","definition":"Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Friedman, J. H. (with Huber loss from Huber, P. J.)","year":"2001","type":"Ensemble (boosted trees with robust loss)","dataType":"Tabular, continuous/categorical features; continuous or ordinal target","subfamily":"Machine learning"},"citations":[{"ref":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.","type":"article","doi":"10.1214/aos/1013203451","isbn":null,"url":null},{"ref":"Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.","type":"article","doi":"10.1214/aoms/1177703732","isbn":null,"url":null}],"related":["boosting","gradient-boosting","xgboost","robust-linear-regression","regularized-gradient-boosting","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-granger-causality","name":"Robust Granger Causality","fullName":"Robust Granger Causality Test","aliases":["bootstrap Granger causality","heteroscedasticity-robust Granger causality","non-asymptotic Granger causality test","RGC"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2006 (robust variant); 1969 (original Granger)","originator":"Hacker & Hatemi-J (robust bootstrap variant); Granger (original causality concept)","url":"https://scholargate.app/en/econometrics/robust-granger-causality","markdownUrl":"https://scholargate.app/en/econometrics/robust-granger-causality.md","definition":"Robust Granger causality extends the classic Granger causality framework by using bootstrap-based or heteroscedasticity-robust critical values rather than asymptotic chi-squared tables. This makes the test reliable in finite samples and when the data exhibit non-normality, heteroscedasticity, or near-integration, settings where the standard F- or Wald-based test is known to over-reject.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hacker & Hatemi-J (robust bootstrap variant); Granger (original causality concept)","year":"2006 (robust variant); 1969 (original Granger)","type":"Hypothesis test","dataType":"Time series (possibly integrated or heteroscedastic)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hacker, R. S., & Hatemi-J, A. (2006). Tests for causality between integrated variables using asymptotic and bootstrap distributions: Theory and application. Applied Economics, 38(13), 1489–1500.","type":"article","doi":"10.1080/00036840500405763","isbn":null,"url":null},{"ref":"Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.","type":"article","doi":"10.2307/1912791","isbn":null,"url":null}],"related":["granger-causality","toda-yamamoto-causality","var-model","cointegration-test","bootstrap-causality","vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-hamiltonian-monte-carlo","name":"Robust Hamiltonian Monte Carlo","fullName":"Robust Hamiltonian Monte Carlo","aliases":["Robust HMC","heavy-tailed HMC","geometric-ergodic HMC","outlier-robust HMC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2010s–2020s","originator":"Livingstone, Zanella and related researchers building on Duane et al. (1987)","url":"https://scholargate.app/en/bayesian/robust-hamiltonian-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/robust-hamiltonian-monte-carlo.md","definition":"Robust Hamiltonian Monte Carlo (Robust HMC) is a family of extensions to standard HMC designed to maintain geometric ergodicity and sampling efficiency when the posterior has heavy tails, strong curvature variation, or near-degenerate geometry. By modifying the kinetic energy, mass matrix, or proposal mechanism, these methods ensure reliable exploration of difficult posteriors that defeat the standard NUTS/HMC sampler.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Livingstone, Zanella and related researchers building on Duane et al. (1987)","year":"2010s–2020s","type":"Robust MCMC sampler","dataType":"Continuous parameters; posteriors with heavy tails or challenging geometry","subfamily":"Bayesian / computational"},"citations":[{"ref":"Livingstone, S. & Zanella, G. (2022). The Barker proposal: combining robustness and efficiency in gradient-based MCMC. Journal of the Royal Statistical Society: Series B, 84(2), 496–523.","type":"article","doi":"10.1111/rssb.12482","isbn":null,"url":null},{"ref":"Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint arXiv:1701.02434.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1701.02434"}],"related":["hamiltonian-monte-carlo","metropolis-hastings","nuts-sampler","robust-bayesian-inference","gibbs-sampling","variational-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-hausman-test","name":"Robust Hausman Test","fullName":"Heteroscedasticity- and Autocorrelation-Robust Hausman Specification Test","aliases":["robust hausman specification test","cluster-robust hausman test","Robust Hausman Testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1978,"originator":"Hausman (1978); robust variant after Arellano (1993)","url":"https://scholargate.app/en/statistics/robust-hausman-test","markdownUrl":"https://scholargate.app/en/statistics/robust-hausman-test.md","definition":"The Robust Hausman Test is a heteroscedasticity- and autocorrelation-robust version of the Hausman specification test, used to choose between fixed-effects and random-effects estimators in panel-data models. It builds on Hausman's 1978 test and the robust treatment of correlated effects developed by Arellano (1993).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hausman (1978); robust variant after Arellano (1993)","year":1978,"type":"Panel model specification test","estimator":"Robust Wald-type contrast of fixed- vs random-effects estimates","outcome":"model choice (fixed vs random effects)"},"citations":[{"ref":"Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251-1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null},{"ref":"Arellano, M. (1993). On the Testing of Correlated Effects with Panel Data. Journal of Econometrics, 59(1-2), 87-97.","type":"article","doi":"10.1016/0304-4076(93)90040-C","isbn":null,"url":null}],"related":["panel-fixed-effects","panel-random-effects","ols-regression","wild-bootstrap","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-hdbscan","name":"Robust HDBSCAN","fullName":"Robust Hierarchical Density-Based Spatial Clustering of Applications with Noise","aliases":["HDBSCAN*","Robust HDBSCAN*","robust hierarchical density clustering","robust single-linkage HDBSCAN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2015","originator":"Campello, R.J.G.B.; Moulavi, D.; Zimek, A.; Sander, J.","url":"https://scholargate.app/en/machine-learning/robust-hdbscan","markdownUrl":"https://scholargate.app/en/machine-learning/robust-hdbscan.md","definition":"Robust HDBSCAN (HDBSCAN*) extends the original HDBSCAN algorithm with a robust single-linkage framework that handles noise, outliers, and clusters of varying densities more reliably. Introduced by Campello et al. (2015), it converts any density-based hierarchy into a stable flat clustering while explicitly modeling noise points — without requiring the user to pre-specify the number of clusters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Campello, R.J.G.B.; Moulavi, D.; Zimek, A.; Sander, J.","year":"2015","type":"Hierarchical density-based clustering with robust single-linkage","dataType":"Continuous, high-dimensional, or noisy tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Campello, R.J.G.B., Moulavi, D., Zimek, A. & Sander, J. (2015). Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data, 10(1), 5.","type":"article","doi":"10.1145/2733381","isbn":null,"url":null},{"ref":"McInnes, L., Healy, J. & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.","type":"article","doi":"10.21105/joss.00205","isbn":null,"url":null}],"related":["hdbscan","dbscan","optics-clustering","k-means","gaussian-mixture-model","spectral-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-hierarchical-clustering","name":"Robust Hierarchical Clustering","fullName":"Robust Hierarchical Clustering","aliases":["robust agglomerative clustering","outlier-resistant hierarchical clustering","robust linkage clustering","RHC"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1990","originator":"Kaufman & Rousseeuw (building on Ward, 1963 and others)","url":"https://scholargate.app/en/statistics/robust-hierarchical-clustering","markdownUrl":"https://scholargate.app/en/statistics/robust-hierarchical-clustering.md","definition":"Robust hierarchical clustering extends classical agglomerative or divisive hierarchical clustering by replacing sensitive distance measures and linkage criteria with outlier-resistant alternatives, preserving cluster structure even when data contain anomalous observations or heavy-tailed distributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaufman & Rousseeuw (building on Ward, 1963 and others)","year":"1990","type":"Robust unsupervised clustering","dataType":"Continuous or mixed multivariate data, possibly containing outliers","subfamily":"Multivariate analysis"},"citations":[{"ref":"Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.","type":"book","doi":null,"isbn":"978-0471878766","url":null},{"ref":"Garcia-Escudero, L. A., Gordaliza, A., Matran, C. & Mayo-Iscar, A. (2010). A review of robust clustering methods. Advances in Data Analysis and Classification, 4(2–3), 89–109.","type":"article","doi":"10.1007/s11634-010-0064-5","isbn":null,"url":null}],"related":["cluster-analysis","robust-k-means-clustering","hierarchical-clustering","multidimensional-scaling","robust-principal-component-analysis","mixture-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-hierarchical-linear-model","name":"Robust Hierarchical Linear Model","fullName":"Robust Hierarchical Linear Model","aliases":["robust HLM","robust multilevel model","robust mixed-effects linear model","robust nested regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"2004","originator":"Maas & Hox (2004); Goldstein et al. (2018)","url":"https://scholargate.app/en/statistics/robust-hierarchical-linear-model","markdownUrl":"https://scholargate.app/en/statistics/robust-hierarchical-linear-model.md","definition":"Robust Hierarchical Linear Model (Robust HLM) extends standard HLM by replacing or protecting its standard errors against violations of distributional assumptions — chiefly non-normal residuals, heteroscedasticity, and influential clusters. It retains the nested, two-level (or higher) structure while producing more trustworthy inference under real-world data conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maas & Hox (2004); Goldstein et al. (2018)","year":"2004","type":"Robust multilevel regression","dataType":"Continuous outcome, clustered / nested observations","subfamily":"Regression / GLM"},"citations":[{"ref":"Maas, C. J. M., & Hox, J. J. (2004). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58(2), 127–137.","type":"article","doi":"10.1046/j.0039-0402.2003.00252.x","isbn":null,"url":null},{"ref":"Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications (2nd ed.). Routledge.","type":"book","doi":null,"isbn":"978-1848728462","url":null}],"related":["hierarchical-linear-model","mixed-effects-model","multilevel-modeling","robust-mixed-effects-model","robust-multiple-linear-regression","robust-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-independent-samples-t-test","name":"Robust independent samples t-test","fullName":"Robust Independent Samples t-test (Trimmed Means / Winsorized Variances)","aliases":["Yuen's t-test","trimmed-mean t-test","Winsorized t-test","robust two-sample test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1974–1990s","originator":"Rand R. Wilcox; Karen K. Yuen (trimmed-mean form)","url":"https://scholargate.app/en/statistics/robust-independent-samples-t-test","markdownUrl":"https://scholargate.app/en/statistics/robust-independent-samples-t-test.md","definition":"The robust independent samples t-test compares the central tendency of two independent groups using trimmed means and Winsorized variances, making it substantially less sensitive to outliers and non-normality than the classical Student or Welch t-test. The most widely used form is Yuen's test, which also accommodates unequal variances across groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand R. Wilcox; Karen K. Yuen (trimmed-mean form)","year":"1974–1990s","type":"Robust parametric mean comparison","dataType":"Continuous (two independent groups, non-normal or outlier-prone distributions)","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Yuen, K. K. (1974). The two-sample trimmed t for unequal population variances. Biometrika, 61(1), 165–170.","type":"article","doi":"10.1093/biomet/61.1.165","isbn":null,"url":null}],"related":["independent-samples-t-test","welch-corrected-independent-samples-t-test","mann-whitney-u-test","bootstrap-independent-samples-t-test","trimmed-mean-independent-samples-t-test","robust-paired-samples-t-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-instrumental-variables","name":"Robust Instrumental Variables","fullName":"Robust Instrumental Variables Estimation","aliases":["Robust IV","Weak-instrument-robust IV","Robust 2SLS","Weak-instrument-robust inference"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1949–2019","originator":"Anderson & Rubin (1949); Stock, Wright & Yogo (2002); Andrews, Stock & Sun (2019)","url":"https://scholargate.app/en/causal-inference/robust-instrumental-variables","markdownUrl":"https://scholargate.app/en/causal-inference/robust-instrumental-variables.md","definition":"Robust Instrumental Variables estimation extends standard IV and two-stage least squares (2SLS) by guarding against weak-instrument bias and non-standard inference. Methods such as the Anderson-Rubin test, Limited Information Maximum Likelihood (LIML), and the Conditional Likelihood Ratio test provide valid confidence sets and hypothesis tests even when instruments are weak or only partially identified, making IV inference reliable in settings where standard 2SLS breaks down.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anderson & Rubin (1949); Stock, Wright & Yogo (2002); Andrews, Stock & Sun (2019)","year":"1949–2019","type":"Causal inference / robust estimation","dataType":"Cross-sectional, panel, or time-series data with endogenous regressors and instruments","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518-529.","type":"article","doi":"10.1198/073500102288618658","isbn":null,"url":null},{"ref":"Andrews, I., Stock, J. H., & Sun, L. (2019). Weak instruments in instrumental variables regression: Theory and practice. Annual Review of Economics, 11, 727-753.","type":"article","doi":"10.1146/annurev-economics-080218-025643","isbn":null,"url":null}],"related":["instrumental-variables","two-stage-least-squares","limited-information-maximum-likelihood","difference-in-differences","regression-discontinuity-design","panel-data-instrumental-variables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-integer-programming","name":"Robust Integer Programming","fullName":"Robust Integer Programming — Optimization under uncertainty with integrality constraints","aliases":["RIP","Robust IP","Robust Combinatorial Optimization","Integer Robust Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2003","originator":"Bertsimas, D. and Sim, M.","url":"https://scholargate.app/en/simulation/robust-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/robust-integer-programming.md","definition":"Robust Integer Programming (RIP) finds integer or binary solutions that remain feasible and near-optimal across all scenarios in a prescribed uncertainty set. Rather than assuming exact knowledge of data, RIP hedges against the worst-case realization of uncertain costs or constraint coefficients, delivering decisions that are guaranteed to perform well even when inputs deviate from their nominal values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bertsimas, D. and Sim, M.","year":"2003","type":"Deterministic robust optimization with integer variables","dataType":"Deterministic cost/constraint coefficients with uncertainty sets; integer or binary decision variables","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71.","type":"article","doi":"10.1007/s10107-003-0396-4","isbn":null,"url":null},{"ref":"Ben-Tal, A., El Ghaoui, L., Nemirovski, A. (2009). Robust Optimization. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780691143682","url":null}],"related":["integer-programming","robust-linear-programming","stochastic-integer-programming","mixed-integer-programming","robust-mixed-integer-programming","robust-multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-interrupted-time-series","name":"Robust Interrupted Time Series","fullName":"Robust Interrupted Time Series Analysis","aliases":["robust ITS","outlier-robust ITS","robust segmented regression","robust ITSA"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s","originator":"Bernal, Cummins & Gasparrini; Linden (robust extensions)","url":"https://scholargate.app/en/causal-inference/robust-interrupted-time-series","markdownUrl":"https://scholargate.app/en/causal-inference/robust-interrupted-time-series.md","definition":"Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedasticity-consistent standard errors. It is widely used in health services research and public-health evaluation when the time series contains influential observations, non-constant variance, or mild autocorrelation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernal, Cummins & Gasparrini; Linden (robust extensions)","year":"2010s","type":"Quasi-experimental segmented regression with robust inference","dataType":"Longitudinal / time-series (aggregate or panel)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.","type":"article","doi":"10.1093/ije/dyw098","isbn":null,"url":null},{"ref":"Linden, A. (2015). Conducting interrupted time-series analysis for single- and multiple-group comparisons. Stata Journal, 15(2), 480-500.","type":"article","doi":null,"isbn":null,"url":"https://journals.sagepub.com/doi/10.1177/1536867X1501500208"}],"related":["interrupted-time-series","difference-in-differences","segmented-regression","panel-data-interrupted-time-series","robust-difference-in-differences","dynamic-interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-inverse-probability-weighting","name":"Robust Inverse Probability Weighting","fullName":"Robust Inverse Probability Weighting Estimator","aliases":["Robust IPW","Stabilized IPW","Trimmed IPW","Variance-robust IPW"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000-2004","originator":"Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000)","url":"https://scholargate.app/en/causal-inference/robust-inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/robust-inverse-probability-weighting.md","definition":"Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecification, extreme weights, and inflated standard errors. It extends standard IPW to improve finite-sample performance and inferential reliability in observational studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000)","year":"2000-2004","type":"Causal weighting estimator","dataType":"Observational cross-sectional or panel data with binary treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23(19), 2937-2960.","type":"article","doi":"10.1002/sim.1903","isbn":null,"url":null},{"ref":"Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null}],"related":["inverse-probability-weighting","propensity-score-weighting","doubly-robust-estimation","marginal-structural-model","propensity-score-matching","augmented-inverse-probability-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-isolation-forest","name":"Robust Isolation forest","fullName":"Robust Isolation Forest (Anomaly Detection with Robustness to Noise and Contamination)","aliases":["Robust iForest","noise-robust isolation forest","contamination-robust isolation forest","robust anomaly isolation"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2008–2019","originator":"Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors","url":"https://scholargate.app/en/machine-learning/robust-isolation-forest","markdownUrl":"https://scholargate.app/en/machine-learning/robust-isolation-forest.md","definition":"Robust Isolation Forest extends the classic Isolation Forest anomaly detector with strategies that reduce sensitivity to data contamination, masking effects, and biased random splits. By incorporating robustness mechanisms — such as improved subsampling, re-weighting of suspicious regions, or bias-corrected splitting — it achieves more reliable anomaly scores when the training data itself contains a non-trivial fraction of anomalies or when specific feature distributions cause standard iForest to produce unreliable path lengths.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, F. T., Ting, K. M., Zhou, Z.-H. (base); robust extensions by multiple authors","year":"2008–2019","type":"Robust ensemble anomaly detection","dataType":"Continuous, mixed tabular features","subfamily":"Machine learning"},"citations":[{"ref":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 413–422. IEEE.","type":"inproceedings","doi":"10.1109/ICDM.2008.17","isbn":null,"url":null},{"ref":"Hariri, S., Kind, M. C., & Brunner, R. J. (2019). Extended Isolation Forest. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1479–1489.","type":"article","doi":"10.1109/TKDE.2019.2947676","isbn":null,"url":null}],"related":["isolation-forest","one-class-svm","autoencoder-anomaly-detection","gaussian-mixture-model","robust-one-class-svm","robust-autoencoder-anomaly-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-item-analysis","name":"Robust Item Analysis","fullName":"Robust Item Analysis","aliases":["robust item statistics","outlier-resistant item analysis","robust classical item analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1980s–2000s","originator":"Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues","url":"https://scholargate.app/en/psychometrics/robust-item-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/robust-item-analysis.md","definition":"Robust item analysis applies outlier-resistant statistical methods to the evaluation of individual test or scale items. Instead of classical means and Pearson correlations — both sensitive to extreme scores — it uses trimmed means, Winsorized correlations, or M-estimators to obtain item difficulty and item-total discrimination indices that remain stable when respondent distributions are skewed or contaminated by outliers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues","year":"1980s–2000s","type":"Diagnostic / item-level evaluation","dataType":"Ordinal or continuous item scores; polytomous or dichotomous responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Huber, P. J. & Ronchetti, E. M. (2009). Robust Statistics (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470129906","url":null}],"related":["item-response-theory","cronbachs-alpha","scale-development","differential-item-functioning","robust-reliability-analysis","exploratory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-johansen-cointegration","name":"Robust Johansen Cointegration","fullName":"Robust Johansen Cointegration Test","aliases":["outlier-robust Johansen test","robust trace test","robust maximum eigenvalue test","robust cointegration rank test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988–2010","originator":"Johansen (1988, 1991); robust extensions by Cavaliere, Rahbek, Taylor (2010) and others","url":"https://scholargate.app/en/econometrics/robust-johansen-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/robust-johansen-cointegration.md","definition":"The Robust Johansen Cointegration test extends the classical Johansen (1988, 1991) likelihood-ratio framework for determining the cointegrating rank of a multivariate I(1) system to settings where standard Gaussian assumptions fail — in particular when the data exhibit outliers, fat-tailed innovations, or conditional heteroskedasticity. Robust modifications adjust residuals, re-weight observations, or bootstrap critical values so that rank inference remains valid under these violations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johansen (1988, 1991); robust extensions by Cavaliere, Rahbek, Taylor (2010) and others","year":"1988–2010","type":"Cointegration rank test (robust variant)","dataType":"Multivariate I(1) time series, possibly with outliers or conditional heteroskedasticity","subfamily":"Econometrics / time series"},"citations":[{"ref":"Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551–1580.","type":"article","doi":"10.2307/2938278","isbn":null,"url":null},{"ref":"Cavaliere, G., Rahbek, A., & Taylor, A. M. R. (2010). Cointegration Rank Testing under Conditional Heteroskedasticity. Econometric Theory, 26(6), 1719–1760.","type":"article","doi":"10.1017/s0266466609990776","isbn":null,"url":null}],"related":["johansen-cointegration-test","engle-granger-cointegration-test","vector-error-correction-model","robust-engle-granger-cointegration","structural-break-johansen-cointegration","panel-johansen-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-k-means-clustering","name":"Robust K-means Clustering","fullName":"Robust K-means Clustering","aliases":["trimmed k-means","TCLUST k-means","contamination-resistant k-means","outlier-robust clustering"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1997","originator":"Cuesta-Albertos, Gordaliza & Matrán","url":"https://scholargate.app/en/statistics/robust-k-means-clustering","markdownUrl":"https://scholargate.app/en/statistics/robust-k-means-clustering.md","definition":"Robust K-means clustering is an extension of classical k-means that protects cluster estimates from distortion caused by outliers or contaminated observations. By trimming a user-specified fraction of the most extreme points before updating cluster centers, the algorithm yields stable, meaningful partitions even when the data contain atypical cases that would severely bias standard k-means.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuesta-Albertos, Gordaliza & Matrán","year":"1997","type":"Robust partitional clustering","dataType":"Continuous multivariate data with potential outliers or contamination","subfamily":"Multivariate analysis"},"citations":[{"ref":"Cuesta-Albertos, J. A., Gordaliza, A., & Matrán, C. (1997). Trimmed k-means: An attempt to robustify quantizers. The Annals of Statistics, 25(2), 553–576.","type":"article","doi":"10.1214/aos/1031833664","isbn":null,"url":null},{"ref":"García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A general trimming approach to robust cluster analysis. The Annals of Statistics, 36(3), 1324–1345.","type":"article","doi":"10.1214/07-AOS515","isbn":null,"url":null}],"related":["cluster-analysis","robust-hierarchical-clustering","mixture-modeling","robust-mixture-modeling","principal-component-analysis","robust-principal-component-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-k-means","name":"Robust k-means","fullName":"Robust k-means Clustering","aliases":["robust k-means clustering","trimmed k-means","outlier-resistant k-means","RKM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1999","originator":"Garcia-Escudero, L. A. & Gordaliza, A.","url":"https://scholargate.app/en/machine-learning/robust-k-means","markdownUrl":"https://scholargate.app/en/machine-learning/robust-k-means.md","definition":"Robust k-means is a variant of classical k-means clustering designed to resist the influence of outliers. By trimming a specified fraction of the most extreme observations before computing cluster centers, it produces stable and meaningful partitions even when the data contain noise, contamination, or heavy-tailed distributions — situations where standard k-means breaks down.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Garcia-Escudero, L. A. & Gordaliza, A.","year":"1999","type":"Robust clustering algorithm","dataType":"Continuous (numeric) multivariate data","subfamily":"Machine learning"},"citations":[{"ref":"Garcia-Escudero, L. A., & Gordaliza, A. (1999). Robustness properties of k-means and trimmed k-means. Journal of the American Statistical Association, 94(447), 956–969.","type":"article","doi":"10.2307/2670010","isbn":null,"url":null},{"ref":"Garcia-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A general trimming approach to robust cluster analysis. Annals of Statistics, 36(3), 1324–1345.","type":"article","doi":"10.1214/07-AOS515","isbn":null,"url":null}],"related":["k-means","k-medoids","dbscan","gaussian-mixture-model","hierarchical-clustering","spectral-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-kalman-filter","name":"Robust Kalman Filter","fullName":"Robust Kalman Filter","aliases":["RKF","heavy-tailed Kalman filter","outlier-robust Kalman filter","robust state estimation"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1977","originator":"Derived from Kalman (1960); robust extensions developed by Masreliez, Martin, and others from the 1970s onward","url":"https://scholargate.app/en/bayesian/robust-kalman-filter","markdownUrl":"https://scholargate.app/en/bayesian/robust-kalman-filter.md","definition":"The Robust Kalman Filter is an extension of the classical Kalman filter designed to maintain reliable state estimation when observations or process noise depart from the Gaussian assumption — particularly when data contain outliers, heavy-tailed distributions, or gross errors. By replacing or downweighting the standard least-squares update with influence-limited or M-estimation-based corrections, it prevents a single anomalous measurement from distorting the entire state estimate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from Kalman (1960); robust extensions developed by Masreliez, Martin, and others from the 1970s onward","year":"1977","type":"Sequential Bayesian state estimator with robustified update step","dataType":"Time-series state-space observations, potentially containing outliers or heavy-tailed noise","subfamily":"Bayesian / computational"},"citations":[{"ref":"Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.","type":"article","doi":"10.1115/1.3662552","isbn":null,"url":null},{"ref":"Huber, P. J. & Ronchetti, E. M. (2011). Robust Statistics (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470129906","url":null}],"related":["kalman-filter","particle-filter","robust-bayesian-inference","sequential-monte-carlo","robust-metropolis-hastings-algorithm","extended-kalman-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-kendalls-tau","name":"Robust Kendall's tau","fullName":"Robust Kendall's Tau Rank Correlation","aliases":["robust tau","skipped Kendall's tau","Winsorized Kendall's tau","outlier-resistant rank correlation"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1990s–2000s","originator":"Rand Wilcox; Croux & Dehon (robust extensions)","url":"https://scholargate.app/en/statistics/robust-kendalls-tau","markdownUrl":"https://scholargate.app/en/statistics/robust-kendalls-tau.md","definition":"Robust Kendall's tau estimates the monotone association between two variables using rank-based concordance counts, but augments the standard procedure with outlier detection or Winsorization so that a small number of extreme observations cannot distort the result. It is appropriate when data are ordinal or continuous and bivariate outliers are plausible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand Wilcox; Croux & Dehon (robust extensions)","year":"1990s–2000s","type":"Robust rank correlation","dataType":"Ordinal or continuous (two variables)","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Croux, C., & Dehon, C. (2010). Influence functions of the Spearman and Kendall correlation measures. Statistical Methods & Applications, 19(4), 497–515.","type":"article","doi":"10.1007/s10260-010-0142-z","isbn":null,"url":null}],"related":["kendalls-tau","robust-spearman-correlation","robust-pearson-correlation","spearman-correlation","mann-whitney-u-test","robust-mann-whitney-u-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-kpss-test","name":"Robust KPSS test","fullName":"Robust Kwiatkowski-Phillips-Schmidt-Shin Test","aliases":["Robust KPSS","outlier-robust stationarity test","robust LM stationarity test","KPSS with robustness correction"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1992–2004","originator":"Extension building on Kwiatkowski, Phillips, Schmidt & Shin (1992); robust variants developed by Hobijn, Franses & Ooms and others","url":"https://scholargate.app/en/econometrics/robust-kpss-test","markdownUrl":"https://scholargate.app/en/econometrics/robust-kpss-test.md","definition":"The Robust KPSS test is an extension of the classical Kwiatkowski-Phillips-Schmidt-Shin (1992) stationarity test that replaces the conventional long-run variance estimator with an outlier-robust or heteroscedasticity-robust counterpart, maintaining reliable size and power in the presence of contaminated observations, structural breaks, or non-standard error distributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension building on Kwiatkowski, Phillips, Schmidt & Shin (1992); robust variants developed by Hobijn, Franses & Ooms and others","year":"1992–2004","type":"Hypothesis test","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178.","type":"article","doi":"10.1016/0304-4076(92)90104-Y","isbn":null,"url":null},{"ref":"Hobijn, B., Franses, P. H., & Ooms, M. (2004). Generalizations of the KPSS-test for stationarity. Statistica Neerlandica, 58(4), 483-502.","type":"article","doi":"10.1111/j.1467-9574.2004.00272.x","isbn":null,"url":null}],"related":["kpss-test","adf-test","pp-test","za-unit-root-test","ls-unit-root-test","df-gls-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-kriging","name":"Robust Kriging","fullName":"Robust Kriging Spatial Interpolation","aliases":["robust spatial kriging","outlier-resistant kriging","resistant kriging","robust geostatistical interpolation"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1980","originator":"Noel Cressie & Douglas M. Hawkins","url":"https://scholargate.app/en/spatial-analysis/robust-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/robust-kriging.md","definition":"Robust Kriging is a geostatistical interpolation method that extends classical kriging by replacing sensitive variogram estimation with outlier-resistant alternatives, most notably the Cressie-Hawkins robust estimator. It produces spatially interpolated predictions that are not distorted by anomalous or extreme observations in the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Noel Cressie & Douglas M. Hawkins","year":"1980","type":"Robust geostatistical interpolation","dataType":"Continuous georeferenced point data with potential outliers","subfamily":"GIS / spatial"},"citations":[{"ref":"Cressie, N., & Hawkins, D. M. (1980). Robust estimation of the variogram: I. Journal of the International Association for Mathematical Geology, 12(2), 115–125.","type":"article","doi":"10.1007/BF01035243","isbn":null,"url":null},{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471002550","url":null}],"related":["ordinary-kriging","universal-kriging","co-kriging","kernel-density-estimation","spatial-autocorrelation","robust-spatial-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-kruskal-wallis-test","name":"Robust Kruskal-Wallis test","fullName":"Robust Kruskal-Wallis One-Way Analysis of Variance by Ranks","aliases":["robust K-W test","trimmed Kruskal-Wallis","robust nonparametric one-way test","robust rank-based ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1952 (base); robust variants 1990s–2000s","originator":"Kruskal & Wallis (1952); robust extensions by Wilcox and others","url":"https://scholargate.app/en/statistics/robust-kruskal-wallis-test","markdownUrl":"https://scholargate.app/en/statistics/robust-kruskal-wallis-test.md","definition":"The robust Kruskal-Wallis test is a nonparametric, rank-based method for comparing three or more independent groups when data contain outliers, heavy tails, or heterogeneous spread. It augments the classical Kruskal-Wallis H statistic with robust techniques — such as trimmed means on ranks or permutation-based inference — to maintain valid Type I error rates even when distributional assumptions are violated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kruskal & Wallis (1952); robust extensions by Wilcox and others","year":"1952 (base); robust variants 1990s–2000s","type":"Nonparametric robust rank-based test","dataType":"Ordinal or continuous with outliers or heavy-tailed distributions","subfamily":"Classical statistics"},"citations":[{"ref":"Mielke, P. W., & Berry, K. J. (2007). Permutation Methods: A Distance Function Approach (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387698137","url":null},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["kruskal-wallis-test","robust-one-way-anova","mann-whitney-u-test","robust-mann-whitney-u-test","friedman-test","permutation-kruskal-wallis-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-latent-class-analysis","name":"Robust Latent Class Analysis","fullName":"Robust Latent Class Analysis","aliases":["robust LCA","outlier-resistant latent class analysis","trimmed-likelihood latent class analysis"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2000s","originator":"Building on Hennig (2004) and Vermunt & Magidson (2004)","url":"https://scholargate.app/en/statistics/robust-latent-class-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-latent-class-analysis.md","definition":"Robust latent class analysis (robust LCA) extends the standard latent class model by incorporating outlier-resistant estimation techniques — such as trimmed likelihood, M-estimation, or downweighting — so that atypical response patterns do not distort the recovered class structure or class membership probabilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Hennig (2004) and Vermunt & Magidson (2004)","year":"2000s","type":"Robust latent variable / mixture model","dataType":"Categorical (nominal or ordinal) multivariate indicators","subfamily":"Multivariate analysis"},"citations":[{"ref":"Hennig, C. (2004). Breakdown points for maximum likelihood estimators of location-scale mixtures. Annals of Statistics, 32(4), 1313–1340.","type":"article","doi":"10.1214/009053604000000571","isbn":null,"url":null},{"ref":"Vermunt, J. K., & Magidson, J. (2004). Latent class models. In D. Kaplan (Ed.), The Sage Handbook of Quantitative Methodology for the Social Sciences (pp. 175–198). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Vermunt+Magidson+2004+Latent+class+models+Sage+Handbook"}],"related":["latent-class-analysis","mixture-modeling","robust-mixture-modeling","robust-latent-profile-analysis","robust-exploratory-factor-analysis","cluster-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-latent-profile-analysis","name":"Robust Latent Profile Analysis","fullName":"Robust Latent Profile Analysis","aliases":["RLPA","robust LPA","robust mixture model for continuous indicators","outlier-robust latent profile analysis"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2010s","originator":"Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s)","url":"https://scholargate.app/en/statistics/robust-latent-profile-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-latent-profile-analysis.md","definition":"Robust latent profile analysis identifies latent subgroups of individuals based on their continuous multivariate indicators while protecting parameter estimates from distortion by outliers or atypical observations. It extends standard latent profile analysis by replacing the Gaussian component densities with heavier-tailed or contaminated-normal alternatives that down-weight extreme cases during estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Vermunt & Magidson (2002); robust extensions developed through contaminated normal mixture literature (Punzo & McNicholas, 2010s)","year":"2010s","type":"Person-centered mixture model with robust estimation","dataType":"Continuous multivariate indicators, potentially containing outliers","subfamily":"Multivariate analysis"},"citations":[{"ref":"Vermunt, J. K. & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied Latent Class Analysis (pp. 89–106). Cambridge University Press.","type":"article","doi":null,"isbn":"978-0521594035","url":null},{"ref":"Punzo, A. & McNicholas, P. D. (2016). Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model. Journal of Classification, 33(2), 293–331.","type":"article","doi":"10.1007/s00357-017-9234-x","isbn":null,"url":null}],"related":["latent-profile-analysis","latent-class-analysis","mixture-modeling","robust-latent-class-analysis","robust-mixture-modeling","multilevel-latent-profile-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-lightgbm","name":"Robust LightGBM","fullName":"Robust LightGBM (Light Gradient Boosting Machine with Robust Loss Functions)","aliases":["Robust LGBM","LightGBM with Huber loss","outlier-resistant gradient boosting","robust gradient boosted trees"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017 (LightGBM); robust variants widely adopted 2018–present","originator":"Ke, G. et al. (LightGBM); robust objectives adapted from Friedman, J. H.","url":"https://scholargate.app/en/machine-learning/robust-lightgbm","markdownUrl":"https://scholargate.app/en/machine-learning/robust-lightgbm.md","definition":"Robust LightGBM is a gradient boosting framework that pairs Microsoft's highly efficient LightGBM engine with outlier-resistant loss functions — most commonly Huber, quantile, or mean absolute error — so that predictions are not unduly distorted by extreme or erroneous observations. It retains LightGBM's speed and leaf-wise tree growth while providing resistance to heavy-tailed noise in the target variable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ke, G. et al. (LightGBM); robust objectives adapted from Friedman, J. H.","year":"2017 (LightGBM); robust variants widely adopted 2018–present","type":"Ensemble (gradient boosted decision trees with robust loss)","dataType":"Tabular — continuous, categorical, binary targets; datasets with potential outliers","subfamily":"Machine learning"},"citations":[{"ref":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30, 3146–3154.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html"},{"ref":"Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189–1232.","type":"article","doi":"10.1214/aos/1013203451","isbn":null,"url":null}],"related":["lightgbm","xgboost","catboost","gradient-boosting","random-forest","huber-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-linear-programming","name":"Robust Linear Programming","fullName":"Robust Linear Programming — Uncertainty-Aware Linear Optimization","aliases":["RLP","Robust LP","Tractable Robust LP","Uncertainty-Set LP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1999–2004","originator":"Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M.","url":"https://scholargate.app/en/simulation/robust-linear-programming","markdownUrl":"https://scholargate.app/en/simulation/robust-linear-programming.md","definition":"Robust Linear Programming (RLP) extends classical linear programming to handle uncertainty in problem data — cost coefficients, constraint coefficients, or right-hand sides — by requiring solutions to remain feasible and near-optimal across all realizations of uncertain parameters within a defined uncertainty set. It replaces probabilistic assumptions with worst-case guarantees, making it practical when distributional knowledge is limited.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M.","year":"1999–2004","type":"Uncertainty-robust linear optimization","dataType":"Numerical coefficients with uncertainty sets (intervals, ellipsoidal, polyhedral)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.","type":"article","doi":"10.1287/opre.1030.0065","isbn":null,"url":null},{"ref":"Ben-Tal, A., Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations Research Letters, 25(1), 1–13.","type":"article","doi":"10.1016/S0167-6377(99)00016-4","isbn":null,"url":null}],"related":["stochastic-linear-programming","robust-mixed-integer-programming","robust-multi-objective-optimization","deterministic-linear-programming","robust-dynamic-programming","robust-goal-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-linear-regression","name":"Robust Linear Regression","fullName":"Robust Linear Regression (Outlier-Resistant Estimation)","aliases":["robust regression","M-estimator regression","Huber regression","outlier-resistant regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1964–1987","originator":"Huber, P. J.; Rousseeuw, P. J.","url":"https://scholargate.app/en/machine-learning/robust-linear-regression","markdownUrl":"https://scholargate.app/en/machine-learning/robust-linear-regression.md","definition":"Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Huber, P. J.; Rousseeuw, P. J.","year":"1964–1987","type":"Outlier-resistant supervised regression","dataType":"Continuous numerical features and a continuous target","subfamily":"Machine learning"},"citations":[{"ref":"Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.","type":"article","doi":"10.1214/aoms/1177703732","isbn":null,"url":null},{"ref":"Rousseeuw, P. J. & Leroy, A. M. (1987). Robust Regression and Outlier Detection. Wiley.","type":"book","doi":null,"isbn":"978-0-471-85233-9","url":null}],"related":["linear-regression-ml","regularized-linear-regression","support-vector-machine","huber-regression","quantile-regression","lasso-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-local-indicators-of-spatial-association","name":"Robust Local Indicators of Spatial Association","fullName":"Robust Local Indicators of Spatial Association","aliases":["Robust LISA","outlier-resistant LISA","robust local spatial autocorrelation","LISA with robust weights"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1995–2000s","originator":"Anselin (LISA, 1995); robust extensions by Assuncao & Reis and subsequent spatial statisticians","url":"https://scholargate.app/en/spatial-analysis/robust-local-indicators-of-spatial-association","markdownUrl":"https://scholargate.app/en/spatial-analysis/robust-local-indicators-of-spatial-association.md","definition":"Robust Local Indicators of Spatial Association extend Anselin's LISA framework to handle outliers, extreme values, and spatially heterogeneous populations. By applying outlier-resistant adjustments to the spatial weights or the standardised values, Robust LISA identifies statistically significant local clusters and spatial outliers without the distortions caused by highly influential observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin (LISA, 1995); robust extensions by Assuncao & Reis and subsequent spatial statisticians","year":"1995–2000s","type":"Local spatial autocorrelation statistic (robust variant)","dataType":"Georeferenced areal or point data, potentially with outliers or heterogeneous populations","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null},{"ref":"Assuncao, R. M., & Reis, E. A. (1999). A new proposal to adjust Moran's I for population density. Statistics in Medicine, 18(16), 2147–2162.","type":"article","doi":"10.1002/(SICI)1097-0258(19990830)18:16<2147::AID-SIM179>3.0.CO;2-I","isbn":null,"url":null}],"related":["local-indicators-of-spatial-association","local-morans-i","local-gearys-c","local-getis-ord-gi-star","robust-spatial-autocorrelation","spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-logistic-regression","name":"Robust Logistic Regression","fullName":"Robust Logistic Regression (Mallows-Type Weighted Estimation)","aliases":["robust binary regression","weighted logistic regression","Mallows-type logistic regression","Robust Lojistik Regresyon"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2001,"originator":"Cantoni & Ronchetti (2001); Bondell (2008)","url":"https://scholargate.app/en/statistics/robust-logistic-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-logistic-regression.md","definition":"Robust Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cantoni & Ronchetti (2001); Bondell (2008)","year":2001,"type":"Robust generalized linear model (binary outcome)","estimator":"Mallows-type weighted M-estimation","outcome":"binary or categorical","minSample":50},"citations":[{"ref":"Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.","type":"article","doi":"10.1198/016214501753209004","isbn":null,"url":null},{"ref":"Bondell, H. D. (2008). Robust Logistic Regression Using a Weighting Approach. Biometrics, 64(2), 421-427.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+Logistic+Regression+Using+a+Weighting+Approach+Bondell"}],"related":["logistic-regression","ols-regression","mm-estimator","robust-time-series","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-ma-model","name":"Robust MA model","fullName":"Robust Moving Average Model","aliases":["robust MA","robust moving average","M-estimation MA","bounded-influence MA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1979–2009","originator":"Denby & Martin (1979); Muler, Pena & Yohai (2009)","url":"https://scholargate.app/en/econometrics/robust-ma-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-ma-model.md","definition":"The Robust MA model applies robust estimation — typically M-estimation or bounded-influence methods — to the Moving Average time series model. By replacing the ordinary least squares loss with a bounded loss function, it produces parameter estimates that are far less sensitive to outliers, additive noise spikes, or heavy-tailed error distributions than the classical Gaussian MA.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Denby & Martin (1979); Muler, Pena & Yohai (2009)","year":"1979–2009","type":"Robust time series model","dataType":"Univariate time series with potential outliers or heavy-tailed errors","subfamily":"Econometrics / time series"},"citations":[{"ref":"Denby, L., & Martin, R. D. (1979). Robust estimation of the first-order autoregressive parameter. Journal of the American Statistical Association, 74(365), 140–146.","type":"article","doi":"10.1080/01621459.1979.10481630","isbn":null,"url":null},{"ref":"Muler, N., Pena, D., & Yohai, V. J. (2009). Robust estimation for ARMA models. Annals of Statistics, 37(2), 816–840.","type":"article","doi":"10.1214/07-AOS570","isbn":null,"url":null}],"related":["moving-average-model","arma-model","robust-arma-model","robust-arima-model","robust-ols","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-mann-whitney-u-test","name":"Robust Mann-Whitney U test","fullName":"Robust Mann-Whitney U Test","aliases":["robust Wilcoxon rank-sum test","robust two-sample rank test","outlier-resistant Mann-Whitney test","robust nonparametric two-group comparison"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1947 / 2003","originator":"Rand Wilcox (robust extensions); original test by Mann & Whitney (1947)","url":"https://scholargate.app/en/statistics/robust-mann-whitney-u-test","markdownUrl":"https://scholargate.app/en/statistics/robust-mann-whitney-u-test.md","definition":"The Robust Mann-Whitney U test is a nonparametric two-group comparison that combines the rank-based logic of the classic Mann-Whitney U test with modern robust techniques — such as outlier screening, trimmed means, or robust variance estimation — to produce reliable inferences when data contain extreme values, heavy-tailed distributions, or other violations that compromise the standard test.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand Wilcox (robust extensions); original test by Mann & Whitney (1947)","year":"1947 / 2003","type":"Robust nonparametric two-group comparison","dataType":"Continuous or ordinal data, possibly with outliers","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2005). Introduction to Robust Estimation and Hypothesis Testing (2nd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0127515427","url":null},{"ref":"Wilcox, R. R., & Keselman, H. J. (2003). Modern robust data analysis methods: Measures of central tendency. Psychological Methods, 8(3), 254–274.","type":"article","doi":"10.1037/1082-989X.8.3.254","isbn":null,"url":null}],"related":["mann-whitney-u-test","wilcoxon-signed-rank-test","robust-independent-samples-t-test","kruskal-wallis-test","permutation-mann-whitney-u-test","bootstrap-mann-whitney-u-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-manova","name":"Robust MANOVA","fullName":"Robust Multivariate Analysis of Variance","aliases":["robust multivariate ANOVA","trimmed-mean MANOVA","outlier-resistant MANOVA","robust MANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1990s–2000s","originator":"Rand Wilcox; Lisa Lix and H. J. Keselman","url":"https://scholargate.app/en/statistics/robust-manova","markdownUrl":"https://scholargate.app/en/statistics/robust-manova.md","definition":"Robust MANOVA is a multivariate analysis of variance procedure designed to remain valid when classical assumptions — multivariate normality and homogeneity of covariance matrices — are violated. It replaces raw means and standard covariance matrices with resistant estimates such as trimmed means and Winsorized covariances, yielding reliable Type I error control and power in the presence of outliers and skewed distributions across multiple dependent variables simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand Wilcox; Lisa Lix and H. J. Keselman","year":"1990s–2000s","type":"Robust multivariate mean comparison","dataType":"Continuous multivariate (2+ dependent variables, 2+ independent groups)","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Lix, L. M., & Keselman, H. J. (2004). Multivariate tests of means in independent groups designs: Effects of covariance heterogeneity and nonnormality. Evaluation and the Health Professions, 27(1), 45–69.","type":"article","doi":"10.1177/0163278703261213","isbn":null,"url":null}],"related":["manova","robust-ancova","robust-one-way-anova","robust-mancova","robust-repeated-measures-anova","robust-two-way-anova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-marginal-structural-model","name":"Robust Marginal Structural Model","fullName":"Robust Marginal Structural Model with Stabilized Inverse Probability Weighting","aliases":["robust MSM","doubly-robust MSM","sandwich-SE MSM","robust IPTW marginal structural model"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000–2004","originator":"Robins, Hernán & Brumback; robustness extensions by Scharfstein, Rotnitzky, Lunceford & Davidian","url":"https://scholargate.app/en/causal-inference/robust-marginal-structural-model","markdownUrl":"https://scholargate.app/en/causal-inference/robust-marginal-structural-model.md","definition":"Robust Marginal Structural Models (robust MSMs) extend the standard MSM framework — which uses inverse probability of treatment weighting to handle time-varying confounding — by pairing IPTW estimation with sandwich (robust) standard errors or doubly-robust estimators. This combination yields valid causal estimates and reliable inference even when the outcome regression model is mildly misspecified or weights are moderately variable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Hernán & Brumback; robustness extensions by Scharfstein, Rotnitzky, Lunceford & Davidian","year":"2000–2004","type":"Causal inference / weighted regression","dataType":"Longitudinal / panel data with time-varying treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":null,"url":"https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/"}],"related":["marginal-structural-model","inverse-probability-weighting","doubly-robust-estimation","propensity-score-weighting","difference-in-differences","panel-data-marginal-structural-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-markov-chain-monte-carlo","name":"Robust Markov chain Monte Carlo","fullName":"Robust Markov Chain Monte Carlo Sampling","aliases":["robust MCMC","outlier-robust MCMC","robust posterior sampling","misspecification-robust MCMC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2000s–2010s","originator":"Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others","url":"https://scholargate.app/en/bayesian/robust-markov-chain-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/robust-markov-chain-monte-carlo.md","definition":"Robust MCMC combines Markov chain Monte Carlo sampling with robustness techniques to produce reliable posterior inference when data contain outliers, when the assumed model is misspecified, or when the target distribution has heavy tails that cause standard samplers to mix poorly or yield distorted estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others","year":"2000s–2010s","type":"Bayesian computational sampling","dataType":"continuous, heavy-tailed, or contaminated data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71.","type":"article","doi":"10.1214/154957804100000024","isbn":null,"url":null},{"ref":"Barp, A., Kennedy, C., Durmus, A. & Girolami, M. (2022). Targeted separation and convergence with kernel discrepancies. arXiv preprint.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Targeted+separation+and+convergence+with+kernel+discrepancies+Barp+2022"}],"related":["mcmc","gibbs-sampling","metropolis-hastings","hamiltonian-monte-carlo","robust-bayesian-inference","sequential-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-markov-model","name":"Robust Markov Model","fullName":"Robust Markov Model — Markov chain analysis under transition probability uncertainty","aliases":["RMM","Robust Markov Chain","Uncertain Markov Model","Interval Markov Model"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2005","originator":"Nilim & El Ghaoui; Iyengar","url":"https://scholargate.app/en/simulation/robust-markov-model","markdownUrl":"https://scholargate.app/en/simulation/robust-markov-model.md","definition":"A Robust Markov Model applies robustness principles to Markov chains by replacing single-point transition probabilities with uncertainty sets, then optimizing against the worst-case realization. Originally developed for robust Markov decision processes in operations research, it is used wherever transition rates are estimated with noise or are subject to adversarial variation, ensuring decisions remain safe across the full uncertainty range.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nilim & El Ghaoui; Iyengar","year":"2005","type":"Robust probabilistic model","dataType":"Transition probabilities, state data, reward/cost matrices","subfamily":"Simulation / optimization"},"citations":[{"ref":"Nilim, A., El Ghaoui, L. (2005). Robust control of Markov decision processes with uncertain transition matrices. Operations Research, 53(5), 780-798.","type":"article","doi":"10.1287/opre.1050.0216","isbn":null,"url":null},{"ref":"Iyengar, G. N. (2005). Robust dynamic programming. Mathematics of Operations Research, 30(2), 257-280.","type":"article","doi":"10.1287/moor.1040.0129","isbn":null,"url":null}],"related":["markov-model","robust-sensitivity-analysis","stochastic-markov-model","robust-dynamic-programming","scenario-analysis","monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-matching-estimator","name":"Robust Matching Estimator","fullName":"Bias-Corrected Robust Matching Estimator","aliases":["bias-corrected matching","Abadie-Imbens matching","AI matching estimator","robust nearest-neighbor matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2006/2011","originator":"Abadie & Imbens","url":"https://scholargate.app/en/causal-inference/robust-matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/robust-matching-estimator.md","definition":"The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment effects with a heteroskedasticity-robust variance formula that is valid regardless of the number of continuous covariates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie & Imbens","year":"2006/2011","type":"Causal inference / matching","dataType":"Cross-sectional or panel; continuous or binary outcome; continuous or mixed covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11.","type":"article","doi":"10.1198/jbes.2009.07333","isbn":null,"url":null},{"ref":"Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.","type":"article","doi":"10.1111/j.1468-0262.2006.00655.x","isbn":null,"url":null}],"related":["propensity-score-matching","coarsened-exact-matching","matching-estimator","doubly-robust-estimation","inverse-probability-weighting","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-mcdonalds-omega","name":"Robust McDonald's Omega","fullName":"Robust McDonald's Omega Reliability Coefficient","aliases":["robust omega","omega total (robust)","robust omega-total","robust composite reliability"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1999 (omega); robust variant formalized in 2000s–2010s","originator":"Roderick P. McDonald (omega); robust extension via robust SEM estimators (MLR, DWLS)","url":"https://scholargate.app/en/psychometrics/robust-mcdonalds-omega","markdownUrl":"https://scholargate.app/en/psychometrics/robust-mcdonalds-omega.md","definition":"Robust McDonald's omega estimates the internal consistency reliability of a composite scale using factor-analytic loadings obtained through robust estimation methods (such as MLR or DWLS). Unlike standard omega or Cronbach's alpha, it remains accurate when item distributions are non-normal, skewed, or when the sample contains influential outliers — conditions common in applied psychological and educational measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roderick P. McDonald (omega); robust extension via robust SEM estimators (MLR, DWLS)","year":"1999 (omega); robust variant formalized in 2000s–2010s","type":"Reliability coefficient","dataType":"Ordinal or continuous item scores, especially non-normal distributions","subfamily":"Scale / measurement"},"citations":[{"ref":"McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805830408","url":null},{"ref":"Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412.","type":"article","doi":"10.1111/bjop.12046","isbn":null,"url":null}],"related":["mcdonalds-omega","cronbachs-alpha","robust-cronbachs-alpha","confirmatory-factor-analysis","robust-confirmatory-factor-analysis","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-measurement-invariance","name":"Robust Measurement Invariance","fullName":"Robust Measurement Invariance Testing","aliases":["robust MI testing","robust measurement equivalence","non-normal measurement invariance","robust multi-group CFA invariance"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1994","originator":"Albert Satorra & Peter M. Bentler","url":"https://scholargate.app/en/psychometrics/robust-measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/robust-measurement-invariance.md","definition":"Robust measurement invariance testing evaluates whether a psychometric instrument measures the same latent construct in the same way across groups when observed data violate multivariate normality. It adapts standard multi-group CFA sequences by replacing ordinary chi-square statistics with robust alternatives such as the Satorra-Bentler scaled statistic, yielding trustworthy conclusions about factor loadings, intercepts, and residual variances even with skewed or ordinal data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Satorra & Peter M. Bentler","year":"1994","type":"Measurement invariance test with robust corrections","dataType":"Ordinal or continuous indicators with non-normal distributions","subfamily":"Scale / measurement"},"citations":[{"ref":"Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Satorra+Bentler+1994+corrections+test+statistics+covariance+structure"},{"ref":"Millsap, R. E. (2011). Statistical approaches to measurement invariance. Routledge.","type":"book","doi":null,"isbn":"978-0805864786","url":null}],"related":["confirmatory-factor-analysis","measurement-invariance","multigroup-cfa","sem","partial-measurement-invariance","satorra-bentler-chi-square"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-mediation-analysis","name":"Robust Mediation Analysis","fullName":"Robust Mediation Analysis","aliases":["robust indirect effects","outlier-resistant mediation","robust causal mediation"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2008–2014","originator":"Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008)","url":"https://scholargate.app/en/statistics/robust-mediation-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-mediation-analysis.md","definition":"Robust mediation analysis estimates the indirect effect of an independent variable on an outcome through one or more mediators using estimators that resist the influence of outliers and non-normal error distributions. By combining robust regression (such as median or M-estimation) with percentile or bias-corrected bootstrap confidence intervals, it yields trustworthy conclusions when standard ordinary-least-squares mediation would be distorted by extreme observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yuan & MacKinnon (median-regression formulation, 2014); robust bootstrap variants popularised by Hayes (2013) and Preacher & Hayes (2008)","year":"2008–2014","type":"Causal inference / indirect effects","dataType":"Continuous or approximately continuous variables (outlier-prone or non-normal distributions)","subfamily":"Multivariate analysis"},"citations":[{"ref":"Yuan, Y., & MacKinnon, D. P. (2014). Robust mediation analysis based on median regression. Psychological Methods, 19(1), 1–20.","type":"article","doi":"10.1037/a0033820","isbn":null,"url":null},{"ref":"Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. Guilford Press.","type":"book","doi":null,"isbn":"978-1609182304","url":null}],"related":["mediation-analysis","moderated-mediation","robust-structural-equation-modeling","robust-path-analysis","bootstrap-mediation-analysis","structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-metric-learning","name":"Robust Metric Learning","fullName":"Robust Metric Learning (Outlier-Resistant Distance Metric Learning)","aliases":["robust distance metric learning","noise-robust metric learning","outlier-robust similarity learning","robust DML"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2009–2012","originator":"Various (Weinberger, Saul, Schultz et al.; robust extensions by Shen, Cao and others, 2009–2012)","url":"https://scholargate.app/en/machine-learning/robust-metric-learning","markdownUrl":"https://scholargate.app/en/machine-learning/robust-metric-learning.md","definition":"Robust Metric Learning learns a Mahalanobis distance function from labeled or pairwise-constrained data while actively resisting the distortion caused by noisy labels, corrupted examples, or outliers. By replacing standard hinge or squared losses with robust alternatives and adding regularization, it produces a distance metric that generalises well even when the training set is imperfect — a common situation in real-world scientific and applied tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Weinberger, Saul, Schultz et al.; robust extensions by Shen, Cao and others, 2009–2012)","year":"2009–2012","type":"Supervised/semi-supervised distance metric learning with robustness to noise and outliers","dataType":"Labeled or pairwise-constrained numerical feature vectors","subfamily":"Machine learning"},"citations":[{"ref":"Shen, C., Kim, J., Wang, L., & van den Hengel, A. (2012). Positive Semidefinite Metric Learning Using Boosting-like Algorithms. Journal of Machine Learning Research, 13, 1007–1036.","type":"inproceedings","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v13/shen12a.html"},{"ref":"Cao, Q., Guo, Z.-C., & Ying, Y. (2012). Generalization Bounds for Metric and Similarity Learning. Machine Learning, 102(1), 115–132.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Generalization+Bounds+for+Metric+and+Similarity+Learning+Cao+Guo+Ying+2012"}],"related":["metric-learning","robust-linear-regression","robust-support-vector-machine","k-nearest-neighbors","semi-supervised-metric-learning","few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-microsimulation","name":"Robust Microsimulation","fullName":"Robust Microsimulation — Uncertainty-integrated individual-level simulation for policy and health analysis","aliases":["Robust Micro-Simulation","Uncertainty-Robust Microsimulation","Probabilistic Microsimulation","Sensitivity-Enhanced Microsimulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Briggs, A. H.; O'Brien, B. J. and others in health technology assessment literature","url":"https://scholargate.app/en/simulation/robust-microsimulation","markdownUrl":"https://scholargate.app/en/simulation/robust-microsimulation.md","definition":"Robust Microsimulation combines individual-level (micro) simulation with systematic uncertainty analysis — typically probabilistic sensitivity analysis — to generate outputs that are robust to parameter uncertainty, model structure assumptions, and input variability. It is widely used in health technology assessment, public policy, and social science to produce credible, decision-relevant predictions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Briggs, A. H.; O'Brien, B. J. and others in health technology assessment literature","year":"1990s–2000s","type":"Simulation with systematic robustness testing","dataType":"Individual-level microdata, parameter distributions, probabilistic inputs","subfamily":"Simulation / optimization"},"citations":[{"ref":"O'Brien, B. J., & Briggs, A. H. (2002). Analysis of uncertainty in health care cost-effectiveness studies: an introduction to statistical issues and methods. Statistical Methods in Medical Research, 11(6), 455-468.","type":"article","doi":"10.1191/0962280202sm304ra","isbn":null,"url":null},{"ref":"Caro, J. J., Briggs, A. H., Siebert, U., & Burgess, K. A. (2012). Modeling good research practices — overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1. Medical Decision Making, 32(5), 667-677.","type":"article","doi":"10.1177/0272989X12454577","isbn":null,"url":null}],"related":["microsimulation","monte-carlo-simulation","stochastic-microsimulation","deterministic-microsimulation","sensitivity-analysis","scenario-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-mixed-integer-programming","name":"Robust Mixed-Integer Programming","fullName":"Robust Mixed-Integer Programming (RMIP) — Optimization under uncertainty with integer decision variables","aliases":["RMIP","Robust MIP","Uncertain MIP","Robust MILP/MIQP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1998–2004","originator":"Ben-Tal & Nemirovski; Bertsimas & Sim","url":"https://scholargate.app/en/simulation/robust-mixed-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/robust-mixed-integer-programming.md","definition":"Robust Mixed-Integer Programming (RMIP) combines mixed-integer programming with robust optimization to find solutions that remain feasible and near-optimal despite uncertain parameters. Instead of assuming fixed data, it protects decisions against adversarial or worst-case realizations of uncertain inputs, using an explicit uncertainty set to control the degree of conservatism while preserving the combinatorial structure of integer decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ben-Tal & Nemirovski; Bertsimas & Sim","year":"1998–2004","type":"Deterministic robust reformulation of MIP under uncertainty","dataType":"Uncertain parameters with known or estimated uncertainty sets; integer and continuous decision variables","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.","type":"article","doi":"10.1287/opre.1030.0065","isbn":null,"url":null},{"ref":"Ben-Tal, A., El Ghaoui, L., Nemirovski, A. (2009). Robust Optimization. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780691143682","url":null}],"related":["stochastic-mixed-integer-programming","mixed-integer-programming","robust-linear-programming","robust-multi-objective-optimization","stochastic-programming","scenario-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-mixed-model","name":"Robust Mixed Model","fullName":"Robust Linear Mixed-Effects Model","aliases":["robust mixed-effects model","robust linear mixed model","robust LMM","Robust Karma Etkiler Modeli"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2016,"originator":"Richardson & Welsh (robust REML); Koller (robustlmm implementation)","url":"https://scholargate.app/en/statistics/robust-mixed-model","markdownUrl":"https://scholargate.app/en/statistics/robust-mixed-model.md","definition":"The robust mixed model is a linear mixed-effects model for panel and repeated-measures data that tolerates outliers and heavy-tailed errors. It replaces the usual likelihood with bounded-influence estimating equations, building on the robust restricted maximum likelihood of Richardson and Welsh (1995) and the robustlmm implementation of Koller (2016).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richardson & Welsh (robust REML); Koller (robustlmm implementation)","year":2016,"type":"Robust linear mixed-effects model","estimator":"Robust REML / robust scoring with bounded influence functions","outcome":"continuous","dataStructure":"panel or repeated-measures (clustered)","minSample":50},"citations":[{"ref":"Koller, M. (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24.","type":"article","doi":"10.18637/jss.v075.i06","isbn":null,"url":null},{"ref":"Richardson, A. M. & Welsh, A. H. (1995). Robust Restricted Maximum Likelihood in Mixed Linear Models. Biometrics, 51(4), 1429-1439.","type":"article","doi":"10.2307/2533273","isbn":null,"url":null}],"related":["linear-mixed-model","panel-fixed-effects","robust-regression","heteroscedasticity-robust-se","ols-regression","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-mixture-modeling","name":"Robust Mixture Modeling","fullName":"Robust Finite Mixture Modeling","aliases":["robust mixture model","robust GMM","outlier-robust mixture model","trimmed mixture model"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2000–2008","originator":"Peel & McLachlan (t-mixture); Garcia-Escudero et al. (trimming framework)","url":"https://scholargate.app/en/statistics/robust-mixture-modeling","markdownUrl":"https://scholargate.app/en/statistics/robust-mixture-modeling.md","definition":"Robust mixture modeling fits finite mixture models — probabilistic clustering methods that assume data arise from a blend of underlying subpopulations — using component distributions or estimation strategies designed to be insensitive to outliers and heavy-tailed noise. The two dominant approaches replace Gaussian components with heavier-tailed distributions such as the multivariate t, or trim a fixed proportion of the most extreme observations before fitting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peel & McLachlan (t-mixture); Garcia-Escudero et al. (trimming framework)","year":"2000–2008","type":"Latent-class probabilistic clustering with outlier protection","dataType":"Continuous multivariate data (possibly with outliers or heavy tails)","subfamily":"Multivariate analysis"},"citations":[{"ref":"Garcia-Escudero, L. A., Gordaliza, A., Matran, C. & Mayo-Iscar, A. (2008). A general trimming approach to robust cluster analysis. Annals of Statistics, 36(3), 1324–1345.","type":"article","doi":"10.1214/07-AOS515","isbn":null,"url":null},{"ref":"Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339–348.","type":"article","doi":"10.1023/A:1008981510081","isbn":null,"url":null}],"related":["mixture-modeling","robust-latent-class-analysis","robust-cluster-analysis","robust-latent-profile-analysis","robust-k-means-clustering","robust-principal-component-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-model-testing-research","name":"Robust Model Testing Research","fullName":"Robust Model Testing Research Design","aliases":["robust SEM","robust structural model testing","robust fit evaluation","robust model evaluation research"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1988–1998","originator":"Albert Satorra & Peter M. Bentler; Ke-Hai Yuan","url":"https://scholargate.app/en/research-design/robust-model-testing-research","markdownUrl":"https://scholargate.app/en/research-design/robust-model-testing-research.md","definition":"Robust model testing research applies structural or path models to data while explicitly accounting for violations of multivariate normality and other distributional assumptions. Rather than discarding non-normal data or forcing transformations, it uses corrected estimators — most notably the Satorra-Bentler scaled chi-square and Yuan-Bentler robust standard errors — to produce trustworthy fit indices and parameter estimates even when classical maximum likelihood assumptions are breached.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Satorra & Peter M. Bentler; Ke-Hai Yuan","year":"1988–1998","type":"Quantitative model-testing research design with robust estimation","dataType":"Continuous, ordinal, or non-normal multivariate numeric data","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Satorra+Bentler+1994+corrections+test+statistics+standard+errors+covariance+structure+analysis"},{"ref":"Yuan, K.-H., & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88.","type":"article","doi":"10.1111/j.2044-8317.1998.tb00667.x","isbn":null,"url":null}],"related":["structural-equation-modeling","confirmatory-factor-analysis","model-testing-research","multivariate-model-testing-research","bayesian-model-testing-research","path-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-moderated-mediation","name":"Robust Moderated Mediation","fullName":"Robust Moderated Mediation Analysis","aliases":["robust conditional process analysis","robust mediated moderation","robust moderated indirect effects","robust conditional indirect effects"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2007–2013","originator":"Hayes, A. F.; building on Preacher, Rucker & Hayes (2007) for moderated mediation and robust bootstrap inference","url":"https://scholargate.app/en/statistics/robust-moderated-mediation","markdownUrl":"https://scholargate.app/en/statistics/robust-moderated-mediation.md","definition":"Robust moderated mediation tests whether the indirect effect of X on Y through a mediator M varies as a function of a moderator W, while using robust estimation (percentile or bias-corrected bootstrap, heteroscedasticity-consistent standard errors, or M-estimation) to protect inference against non-normality, outliers, and heteroscedasticity in the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hayes, A. F.; building on Preacher, Rucker & Hayes (2007) for moderated mediation and robust bootstrap inference","year":"2007–2013","type":"Conditional indirect effect model with robust inference","dataType":"Continuous, ordinal, or mixed; non-normal tolerant","subfamily":"Multivariate analysis"},"citations":[{"ref":"Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). Guilford Press.","type":"book","doi":null,"isbn":"978-1462549030","url":null},{"ref":"Yuan, K.-H., & Bentler, P. M. (2002). On robustness of the normal-theory based asymptotic distributions of three reliability coefficient estimates. Psychometrika, 67(2), 251–259.","type":"article","doi":"10.1007/BF02294845","isbn":null,"url":null}],"related":["moderated-mediation","robust-mediation-analysis","robust-moderation-analysis","structural-equation-modeling","bootstrap-mediation-analysis","robust-path-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-moderation-analysis","name":"Robust Moderation Analysis","fullName":"Robust Moderation Analysis","aliases":["robust interaction analysis","robust moderated regression","HC-corrected moderation","outlier-resistant interaction testing"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2007","originator":"Hayes & Cai; Wilcox","url":"https://scholargate.app/en/statistics/robust-moderation-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-moderation-analysis.md","definition":"Robust moderation analysis tests whether the effect of a predictor on an outcome depends on the level of a moderator variable, using estimation methods that remain valid under non-normality, heteroscedasticity, or the presence of influential outliers. It is the preferred approach when standard ordinary least squares assumptions cannot be trusted.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hayes & Cai; Wilcox","year":"2007","type":"Robust regression-based interaction test","dataType":"Continuous or ordinal predictors and outcome","subfamily":"Multivariate analysis"},"citations":[{"ref":"Hayes, A. F. & Cai, L. (2007). Using heteroscedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39(4), 709–722.","type":"article","doi":"10.3758/BF03192961","isbn":null,"url":null},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["moderation-analysis","robust-mediation-analysis","robust-path-analysis","moderated-mediation","robust-structural-equation-modeling","robust-multiple-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-monte-carlo-simulation","name":"Robust Monte Carlo Simulation","fullName":"Robust Monte Carlo Simulation","aliases":["robust MC simulation","Monte Carlo robustness analysis","robust stochastic simulation","uncertainty-robust Monte Carlo"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990s–2000s","originator":"Saltelli, Rubinstein, and the uncertainty-quantification community","url":"https://scholargate.app/en/bayesian/robust-monte-carlo-simulation","markdownUrl":"https://scholargate.app/en/bayesian/robust-monte-carlo-simulation.md","definition":"Robust Monte Carlo simulation extends standard Monte Carlo by explicitly accounting for uncertainty in input distributions, model structure, or parameter assumptions. Rather than assuming a single fixed probability distribution for each input, the analyst considers a family of plausible distributions and evaluates how sensitive the output is to those choices, yielding conclusions that hold across a range of reasonable assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saltelli, Rubinstein, and the uncertainty-quantification community","year":"1990s–2000s","type":"Robust simulation / uncertainty quantification","dataType":"Continuous, distributional, or interval-valued inputs","subfamily":"Bayesian / computational"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null},{"ref":"Rubinstein, R. Y. & Kroese, D. P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118632161","url":null}],"related":["monte-carlo-simulation","robust-bayesian-inference","sequential-monte-carlo","robust-particle-filter","sensitivity-analysis","bootstrap-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-morans-i","name":"Robust Moran's I","fullName":"Robust Moran's I Spatial Autocorrelation Statistic","aliases":["outlier-resistant Moran's I","robust spatial autocorrelation test","median-based Moran statistic","robust global spatial association"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1990s–2000s","originator":"Extension of Moran (1950); robust adaptations developed in spatial statistics literature","url":"https://scholargate.app/en/spatial-analysis/robust-morans-i","markdownUrl":"https://scholargate.app/en/spatial-analysis/robust-morans-i.md","definition":"Robust Moran's I is an outlier-resistant adaptation of the classic Moran's I spatial autocorrelation statistic. By replacing the standard mean-based standardization with resistant measures of center and spread, it detects genuine geographic clustering without being distorted by a small number of extreme values in the attribute of interest.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Moran (1950); robust adaptations developed in spatial statistics literature","year":"1990s–2000s","type":"Robust spatial autocorrelation statistic","dataType":"Georeferenced areal or point data with potential outliers","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null},{"ref":"Lee, J., & Wong, D. W. S. (2001). Statistical Analysis with ArcView GIS. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471348740","url":null}],"related":["morans-i","local-morans-i","gearys-c","robust-gearys-c","robust-local-indicators-of-spatial-association","spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-multi-objective-optimization","name":"Robust Multi-Objective Optimization","fullName":"Robust Multi-Objective Optimization (RMOO) — optimizing multiple conflicting objectives under uncertainty","aliases":["RMOO","Robust MOO","Robust Pareto Optimization","Uncertainty-Robust Multi-Objective Optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2006","originator":"Deb, K. & Gupta, H.","url":"https://scholargate.app/en/simulation/robust-multi-objective-optimization","markdownUrl":"https://scholargate.app/en/simulation/robust-multi-objective-optimization.md","definition":"Robust Multi-Objective Optimization (RMOO) is a framework for finding solutions that simultaneously optimize multiple conflicting objectives while remaining insensitive to perturbations in decision variables or problem parameters. Unlike classical MOO, RMOO explicitly incorporates uncertainty into the optimization loop, producing a robust Pareto front whose members perform well not only at the nominal design point but also across a neighbourhood of plausible operating conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Deb, K. & Gupta, H.","year":"2006","type":"Optimization framework","dataType":"Continuous or discrete decision variables with uncertain parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K., & Gupta, H. (2006). Introducing robustness in multi-objective optimization. Evolutionary Computation, 14(4), 463–494.","type":"inproceedings","doi":"10.1162/evco.2006.14.4.463","isbn":null,"url":null},{"ref":"Robust optimization. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Robust_optimization"}],"related":["multi-objective-optimization","nsga-ii","stochastic-multi-objective-optimization","robust-optimization","pareto-front","sensitivity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-multidimensional-scaling","name":"Robust Multidimensional Scaling","fullName":"Robust Multidimensional Scaling","aliases":["Robust MDS","outlier-resistant MDS","robust proximity scaling"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2002 (robust extension); 1952 (classical MDS)","originator":"Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952)","url":"https://scholargate.app/en/statistics/robust-multidimensional-scaling","markdownUrl":"https://scholargate.app/en/statistics/robust-multidimensional-scaling.md","definition":"Robust multidimensional scaling recovers a low-dimensional spatial map from a matrix of pairwise dissimilarities while resisting distortion caused by outlying or erroneous proximity values. By replacing squared-error loss with a robust loss function or down-weighting suspect pairs, it produces a configuration that faithfully represents the bulk of the data even when some distances are grossly atypical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hubert, Arabie, and Meulman (robust extensions); classical MDS by Torgerson (1952)","year":"2002 (robust extension); 1952 (classical MDS)","type":"Dimensionality reduction / proximity scaling","dataType":"Dissimilarity or distance matrices (continuous); tolerates outlying proximities","subfamily":"Multivariate analysis"},"citations":[{"ref":"Hubert, L., Arabie, P. & Meulman, J. (2002). Linear unidimensional scaling in the L2-norm: Basic optimization methods using SMACOF. Journal of Classification, 19(2), 303–327.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Linear+unidimensional+scaling+in+the+L2-norm%3A+Basic+optimization+methods+using+SMACOF+Hubert"},{"ref":"Buja, A., Swayne, D. F., Littman, M. L., Dean, N., Hofmann, H. & Chen, L. (2008). Data visualization with multidimensional scaling. Journal of Computational and Graphical Statistics, 17(2), 444–472.","type":"article","doi":"10.1198/106186008X318440","isbn":null,"url":null}],"related":["multidimensional-scaling","robust-principal-component-analysis","robust-cluster-analysis","principal-component-analysis","robust-exploratory-factor-analysis","robust-correspondence-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-multinomial-logistic-regression","name":"Robust Multinomial Logistic Regression","fullName":"Robust Multinomial Logistic Regression","aliases":["robust polychotomous logistic regression","outlier-resistant multinomial regression","robust nominal logistic regression","M-estimation multinomial logistic regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"2001 (robust GLM); 1970s–1980s (multinomial logistic regression)","originator":"Cantoni & Ronchetti (robust GLM framework); Agresti (multinomial logistic regression)","url":"https://scholargate.app/en/statistics/robust-multinomial-logistic-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-multinomial-logistic-regression.md","definition":"Robust multinomial logistic regression extends the standard multinomial logit model to handle outliers, influential observations, and mild misspecification of the response distribution. It replaces the conventional maximum likelihood score equations with bounded influence functions (M-estimation) or pairs maximum likelihood with sandwich variance estimators, so that a small fraction of anomalous cases cannot distort the estimated log-odds ratios across outcome categories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cantoni & Ronchetti (robust GLM framework); Agresti (multinomial logistic regression)","year":"2001 (robust GLM); 1970s–1980s (multinomial logistic regression)","type":"Robust classification model","dataType":"Nominal categorical outcome, continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Cantoni, E., & Ronchetti, E. (2001). Robust inference for generalized linear models. Journal of the American Statistical Association, 96(455), 1022–1030.","type":"article","doi":"10.1198/016214501753209004","isbn":null,"url":null},{"ref":"Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471360933","url":null}],"related":["multinomial-logistic-regression","robust-logistic-regression","ordinal-logistic-regression","robust-ordinal-logistic-regression","robust-regression","generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-multiple-correspondence-analysis","name":"Robust Multiple Correspondence Analysis","fullName":"Robust Multiple Correspondence Analysis","aliases":["Robust MCA","Outlier-resistant MCA","Robust HOMALS"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"2000s","originator":"Extensions by Hubert, Rousseeuw and collaborators; building on classical MCA by Benzécri (1973) and Greenacre (1984)","url":"https://scholargate.app/en/statistics/robust-multiple-correspondence-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-multiple-correspondence-analysis.md","definition":"Robust Multiple Correspondence Analysis extends classical MCA to datasets containing outlying or atypical rows of categorical data. By downweighting influential observations before the singular value decomposition, it produces a low-dimensional map of category relationships that faithfully represents the bulk of the data rather than being distorted by a handful of anomalous cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extensions by Hubert, Rousseeuw and collaborators; building on classical MCA by Benzécri (1973) and Greenacre (1984)","year":"2000s","type":"Robust multivariate dimension reduction","dataType":"Multiple categorical (nominal/ordinal) variables","subfamily":"Multivariate analysis"},"citations":[{"ref":"Greenacre, M. J. (2017). Correspondence Analysis in Practice (3rd ed.). Chapman & Hall / CRC Press, Boca Raton.","type":"book","doi":null,"isbn":"978-1498731775","url":null},{"ref":"Hubert, M., Rousseeuw, P. J. & Verboven, S. (2004). A robust PCR method for high-dimensional regressors. Journal of Chemometrics, 17(8–9), 438–452.","type":"article","doi":"10.1002/cem.783","isbn":null,"url":null}],"related":["multiple-correspondence-analysis","correspondence-analysis","robust-principal-component-analysis","robust-exploratory-factor-analysis","principal-component-analysis","cluster-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-multiple-linear-regression","name":"Robust Multiple linear regression","fullName":"Robust Multiple Linear Regression","aliases":["robust MLR","M-estimator regression","resistant multiple regression","robust OLS"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1964–1980s","originator":"Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna","url":"https://scholargate.app/en/statistics/robust-multiple-linear-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-multiple-linear-regression.md","definition":"Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna","year":"1964–1980s","type":"Robust linear regression","dataType":"Continuous outcome, multiple continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.","type":"article","doi":"10.1214/aoms/1177703732","isbn":null,"url":null},{"ref":"Maronna, R. A., Martin, R. D., & Yohai, V. J. (2006). Robust Statistics: Theory and Methods. Wiley.","type":"book","doi":null,"isbn":"978-0470010921","url":null}],"related":["multiple-linear-regression","ols-regression","quantile-regression","robust-regression","lasso-regression","ridge-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-naive-bayes","name":"Robust Naive Bayes","fullName":"Robust Naive Bayes Classifier","aliases":["Naive Credal Classifier","NCC","Robust Bayesian Naive Classifier","Imprecise Naive Bayes"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2002","originator":"Zaffalon, M.","url":"https://scholargate.app/en/machine-learning/robust-naive-bayes","markdownUrl":"https://scholargate.app/en/machine-learning/robust-naive-bayes.md","definition":"Robust Naive Bayes extends the standard Naive Bayes classifier to handle uncertainty or noise in class-conditional probability estimates by replacing point probability estimates with intervals or sets of distributions. The canonical formulation — the Naive Credal Classifier proposed by Zaffalon (2002) — uses imprecise-probability sets so that predictions are made only when all distributions in the set agree, withholding a label when evidence is insufficient.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zaffalon, M.","year":"2002","type":"Probabilistic generative classifier with imprecise-probability robustness","dataType":"Categorical, continuous, or mixed tabular features with class labels","subfamily":"Machine learning"},"citations":[{"ref":"Zaffalon, M. (2002). The Naive Credal Classifier. Journal of Statistical Planning and Inference, 105(1), 5–21.","type":"article","doi":"10.1016/S0378-3758(01)00201-4","isbn":null,"url":null},{"ref":"Naive Bayes classifier. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Naive_Bayes_classifier"}],"related":["logistic-regression","k-nearest-neighbors","support-vector-machine","gaussian-mixture-model","semi-supervised-naive-bayes","regularized-naive-bayes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-nardl","name":"Robust NARDL","fullName":"Robust Nonlinear Autoregressive Distributed Lag Model","aliases":["Robust Nonlinear ARDL","Outlier-Robust NARDL","Robust Asymmetric ARDL","R-NARDL"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2014–2020s","originator":"Extension of Shin, Yu & Greenwood-Nimmo (2014) NARDL framework with robust (outlier-resistant) estimation","url":"https://scholargate.app/en/econometrics/robust-nardl","markdownUrl":"https://scholargate.app/en/econometrics/robust-nardl.md","definition":"Robust NARDL marries the asymmetric cointegration framework of Shin, Yu, and Greenwood-Nimmo (2014) with outlier-resistant estimation. It decomposes a regressor into positive and negative partial sums, tests for asymmetric long-run relationships via a bounds test, and replaces the OLS criterion with an M- or MM-estimator to guard against leverage points and additive outliers common in macroeconomic and financial time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Shin, Yu & Greenwood-Nimmo (2014) NARDL framework with robust (outlier-resistant) estimation","year":"2014–2020s","type":"Nonlinear time-series regression with robust estimation","dataType":"Time series (levels or first differences, with outliers)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer.","type":"inproceedings","doi":"10.1007/978-1-4899-8008-3_9","isbn":null,"url":null},{"ref":"Autoregressive distributed lag. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Autoregressive_distributed_lag"}],"related":["nardl","ardl-bounds-test","quantile-ardl","nonlinear-cointegration","ols-regression","quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-negative-binomial-regression","name":"Robust Negative Binomial Regression","fullName":"Robust Negative Binomial Regression","aliases":["robust NB regression","negative binomial regression with robust standard errors","sandwich-corrected negative binomial regression","NB2 robust regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"2000s–2011","originator":"Hilbe, J. M.; Zeileis, A. et al.","url":"https://scholargate.app/en/statistics/robust-negative-binomial-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-negative-binomial-regression.md","definition":"Robust Negative Binomial Regression models overdispersed count outcomes using the negative binomial distribution while protecting coefficient inference against misspecification of the variance function. It pairs maximum-likelihood estimation of the mean and dispersion parameters with sandwich (Huber-White) standard errors, yielding valid tests even when the assumed variance structure is only approximately correct.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hilbe, J. M.; Zeileis, A. et al.","year":"2000s–2011","type":"Count regression with robust inference","dataType":"Overdispersed non-negative integer counts","subfamily":"Regression / GLM"},"citations":[{"ref":"Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521198158","url":null},{"ref":"Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression Models for Count Data in R. Journal of Statistical Software, 27(8), 1–25.","type":"article","doi":"10.18637/jss.v027.i08","isbn":null,"url":null}],"related":["negative-binomial-regression","robust-poisson-regression","zero-inflated-model","poisson-regression","robust-regression","generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-nomological-validity","name":"Robust Nomological Validity","fullName":"Robust Nomological Validity Assessment","aliases":["nomological network validity","robust validity testing","nomological validity","RNV"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1955","originator":"Cronbach & Meehl (seminal framework); later extended by Shadish, Cook, and Campbell","url":"https://scholargate.app/en/psychometrics/robust-nomological-validity","markdownUrl":"https://scholargate.app/en/psychometrics/robust-nomological-validity.md","definition":"Robust nomological validity evaluates whether a psychological construct relates to theoretically expected variables in the predicted directions, using statistically robust estimation methods that remain trustworthy when distributional assumptions are violated. It tests the construct's place within its nomological network — the web of theoretical relationships that define its meaning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cronbach & Meehl (seminal framework); later extended by Shadish, Cook, and Campbell","year":"1955","type":"Validity assessment / construct validation","dataType":"Latent variable scores, correlations, SEM fit indices","subfamily":"Scale / measurement"},"citations":[{"ref":"Cronbach, L. J. & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.","type":"article","doi":"10.1037/h0040957","isbn":null,"url":null},{"ref":"Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+quantitative+approach+to+content+validity+Lawshe"}],"related":["confirmatory-factor-analysis","convergent-validity","discriminant-validity","structural-equation-modeling","construct-validity","average-variance-extracted"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-nsga-ii","name":"Robust NSGA-II","fullName":"Robust Non-dominated Sorting Genetic Algorithm II","aliases":["Robust NSGA2","NSGA-II under uncertainty","Uncertainty-aware NSGA-II","RNSGA-II"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2006","originator":"Kalyanmoy Deb and Himanshu Gupta","url":"https://scholargate.app/en/simulation/robust-nsga-ii","markdownUrl":"https://scholargate.app/en/simulation/robust-nsga-ii.md","definition":"Robust NSGA-II extends the classic NSGA-II evolutionary algorithm to account for parametric uncertainty, finding Pareto-optimal trade-off solutions that remain high-performing even when input parameters deviate from their nominal values. Instead of optimizing objective values at a single point, it evaluates each candidate solution across a range or distribution of uncertainty realizations and selects for robustness alongside Pareto dominance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kalyanmoy Deb and Himanshu Gupta","year":"2006","type":"Robust evolutionary multi-objective optimization algorithm","dataType":"Continuous or discrete decision variables; multiple objective functions with uncertain parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.","type":"article","doi":"10.1109/4235.996017","isbn":null,"url":null},{"ref":"Deb, K., & Gupta, H. (2006). Introducing robustness in multi-objective optimization. Evolutionary Computation, 14(4), 463-494.","type":"article","doi":"10.1162/evco.2006.14.4.463","isbn":null,"url":null}],"related":["nsga-ii","multi-objective-optimization","stochastic-nsga-ii","robust-multi-objective-optimization","robust-genetic-algorithm","multi-objective-genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-ols","name":"Robust OLS","fullName":"Ordinary Least Squares with Heteroscedasticity-Consistent Standard Errors","aliases":["HC robust regression","White robust OLS","sandwich estimator OLS","OLS with robust standard errors"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980","originator":"Halbert White","url":"https://scholargate.app/en/econometrics/robust-ols","markdownUrl":"https://scholargate.app/en/econometrics/robust-ols.md","definition":"Robust OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Halbert White","year":"1980","type":"Linear regression with robust inference","dataType":"Cross-sectional or time-series continuous data","subfamily":"Econometrics / time series"},"citations":[{"ref":"White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.","type":"article","doi":"10.2307/1912934","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1337558860","url":null}],"related":["ols-regression","weighted-least-squares","generalized-least-squares","quantile-regression","robust-gls","panel-fixed-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-one-class-svm","name":"Robust One-class SVM","fullName":"Robust One-Class Support Vector Machine","aliases":["Robust OCSVM","Outlier-robust One-Class SVM","Contamination-tolerant OCSVM","Robust novelty detection SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s","url":"https://scholargate.app/en/machine-learning/robust-one-class-svm","markdownUrl":"https://scholargate.app/en/machine-learning/robust-one-class-svm.md","definition":"Robust One-Class SVM extends the classic One-Class Support Vector Machine for novelty and anomaly detection by incorporating robustness mechanisms — such as trimmed objectives, robust kernel choices, or contamination-tolerant loss functions — that reduce the influence of heavy-tailed noise or outliers present in the training data, yielding a decision boundary that better represents the true support of the normal class.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extensions of Scholkopf et al. (1999); robust variants developed in 2000s–2010s","year":"2000s–2010s","type":"Anomaly detection / novelty detection","dataType":"Continuous, high-dimensional, unlabeled","subfamily":"Machine learning"},"citations":[{"ref":"Scholkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in Neural Information Processing Systems (NeurIPS), 12, 582–588.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/1999/hash/8725fb777f25776ffa9076e44fcfd776-Abstract.html"},{"ref":"Liu, Y., Li, Z., & Zhou, C. (2018). Roseq: Robust and efficient one-class SVM for large-scale novelty detection. IEEE Transactions on Neural Networks and Learning Systems, 29(12), 6290–6304.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Roseq%3A+Robust+and+efficient+one-class+SVM+for+large-scale+novelty+detection+Liu"}],"related":["one-class-svm","isolation-forest","autoencoder-anomaly-detection","robust-support-vector-machine","gaussian-mixture-model","robust-isolation-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-one-sample-t-test","name":"Robust one-sample t-test","fullName":"Robust One-Sample Location Test Using Trimmed Mean","aliases":["one-sample trimmed mean test","Yuen one-sample test","robust one-sample location test","trimmed mean t-test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1970s–2000s","originator":"Rand R. Wilcox (extending Yuen's trimmed-mean approach)","url":"https://scholargate.app/en/statistics/robust-one-sample-t-test","markdownUrl":"https://scholargate.app/en/statistics/robust-one-sample-t-test.md","definition":"The robust one-sample t-test replaces the ordinary mean with a trimmed mean and the sample variance with a Winsorized variance to compare a population location against a hypothesized value. It retains the t-test decision framework while sharply reducing sensitivity to outliers and heavy-tailed distributions, making it reliable in real-world continuous data that deviate from normality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand R. Wilcox (extending Yuen's trimmed-mean approach)","year":"1970s–2000s","type":"Robust parametric mean comparison","dataType":"Continuous (possibly skewed or heavy-tailed)","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Yuen, K. K. (1974). The two-sample trimmed t for unequal population variances. Biometrika, 61(1), 165–170.","type":"article","doi":"10.1093/biomet/61.1.165","isbn":null,"url":null}],"related":["one-sample-t-test","robust-independent-samples-t-test","robust-paired-samples-t-test","wilcoxon-signed-rank-test","sign-test","bootstrap-one-sample-t-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-one-way-anova","name":"Robust one-way ANOVA","fullName":"Robust One-Way Analysis of Variance","aliases":["trimmed-mean ANOVA","Welch one-way ANOVA","heteroscedastic one-way ANOVA","robust ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1951 (Welch); 1990s–2000s (trimmed-mean variants)","originator":"B. L. Welch; R. R. Wilcox (trimmed-mean extension)","url":"https://scholargate.app/en/statistics/robust-one-way-anova","markdownUrl":"https://scholargate.app/en/statistics/robust-one-way-anova.md","definition":"Robust one-way ANOVA compares the central tendency of three or more independent groups while resisting the distorting effects of outliers and heterogeneous variances. By replacing ordinary means with trimmed means and ordinary variances with Winsorized variances, it maintains accurate Type I error control and strong power when classical ANOVA assumptions are violated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"B. L. Welch; R. R. Wilcox (trimmed-mean extension)","year":"1951 (Welch); 1990s–2000s (trimmed-mean variants)","type":"Robust parametric group comparison","dataType":"Continuous outcome, categorical grouping (3+ groups)","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330–336.","type":"article","doi":"10.1093/biomet/38.3-4.330","isbn":null,"url":null}],"related":["one-way-anova","welch-corrected-one-way-anova","kruskal-wallis-test","robust-independent-samples-t-test","trimmed-mean-one-way-anova","bootstrap-one-way-anova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-online-learning","name":"Robust Online Learning","fullName":"Robust Online Learning (Adversarially and Noise-Resilient Sequential Learning)","aliases":["ROL","robust incremental learning","adversarially robust online learning","robust sequential learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Hazan, E.; Shalev-Shwartz, S.; and others","url":"https://scholargate.app/en/machine-learning/robust-online-learning","markdownUrl":"https://scholargate.app/en/machine-learning/robust-online-learning.md","definition":"Robust Online Learning extends the online learning framework — where a model updates sequentially after each observation — by incorporating robustness mechanisms that guard against corrupted labels, adversarial examples, heavy-tailed noise, and concept drift. The result is a sequential learner that maintains bounded regret even when the data stream contains outliers or deliberate perturbations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hazan, E.; Shalev-Shwartz, S.; and others","year":"2000s–2010s","type":"Algorithmic framework","dataType":"Sequential / streaming tabular data, possibly corrupted or adversarial","subfamily":"Machine learning"},"citations":[{"ref":"Hazan, E. (2016). Introduction to Online Convex Optimization. Foundations and Trends in Optimization, 2(3–4), 157–325.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Introduction+to+Online+Convex+Optimization+Hazan+2016"},{"ref":"Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.","type":"article","doi":"10.1561/2200000018","isbn":null,"url":null}],"related":["online-learning","robust-gradient-boosting","semi-supervised-online-learning","online-gradient-descent","robust-support-vector-machine","active-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-optimization","name":"Robust Optimization","fullName":"Robust Optimization (Minimax Programming)","aliases":["minimax optimization","worst-case optimization","Gürbüz Optimizasyon (Robust Optimization)"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":"1970s theoretical roots; modern tractable form from late 1990s–2004","originator":"Ben-Tal, El Ghaoui & Nemirovski (seminal book, 2009); Bertsimas & Sim (tractable polyhedral formulation, 2004)","url":"https://scholargate.app/en/optimization/robust-optimization","markdownUrl":"https://scholargate.app/en/optimization/robust-optimization.md","definition":"Robust optimization is a mathematical programming framework, formalised by Ben-Tal and Nemirovski in the late 1990s and made broadly tractable by Bertsimas and Sim (2004), that finds decisions guaranteed to perform acceptably under every scenario within a predefined uncertainty set — rather than assuming parameter values are known exactly. Instead of optimising for a single expected outcome, it minimises the worst-case objective across all plausible realisations of uncertain data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ben-Tal, El Ghaoui & Nemirovski (seminal book, 2009); Bertsimas & Sim (tractable polyhedral formulation, 2004)","year":"1970s theoretical roots; modern tractable form from late 1990s–2004","type":"Mathematical programming framework","formulation":"Minimax (worst-case) over an uncertainty set","uncertaintySet":"Ellipsoidal, box, polyhedral, or budget-constrained","solutionApproach":"Convex reformulation or Benders decomposition","output":"A single robust (worst-case-optimal) decision vector"},"citations":[{"ref":"Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009). Robust Optimization. Princeton University Press.","type":"book","doi":null,"isbn":"9780691143682","url":null},{"ref":"Bertsimas, D. & Sim, M. (2004). The Price of Robustness. Operations Research, 52(1), 35-53.","type":"article","doi":"10.1287/opre.1030.0065","isbn":null,"url":null}],"related":["stochastic-optimization","surrogate-optimization","evolutionary-strategy","linear-programming","convex-optimization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-paired-samples-t-test","name":"Robust paired samples t-test","fullName":"Robust Paired Samples t-test","aliases":["trimmed-mean paired t-test","Yuen paired t-test","robust dependent-samples t-test","trimmed paired comparison"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1974","originator":"K. K. Yuen; extended by Rand R. Wilcox","url":"https://scholargate.app/en/statistics/robust-paired-samples-t-test","markdownUrl":"https://scholargate.app/en/statistics/robust-paired-samples-t-test.md","definition":"The robust paired samples t-test replaces arithmetic means with trimmed means and Winsorized variance to compare two related measurements while resisting the distorting influence of outliers and non-normal distributions, producing reliable inference where the classic paired t-test breaks down.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. K. Yuen; extended by Rand R. Wilcox","year":"1974","type":"Robust parametric mean comparison","dataType":"Paired continuous measurements","subfamily":"Classical statistics"},"citations":[{"ref":"Yuen, K. K. (1974). The two-sample trimmed t for unequal population variances. Biometrika, 61(1), 165–170.","type":"article","doi":"10.1093/biomet/61.1.165","isbn":null,"url":null},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["paired-samples-t-test","robust-independent-samples-t-test","wilcoxon-signed-rank-test","trimmed-mean-paired-samples-t-test","bootstrap-paired-samples-t-test","robust-one-sample-t-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-panel-data-analysis","name":"Robust Panel Data Analysis","fullName":"Robust Panel Data Analysis with Cluster-Robust and Heteroscedasticity-Consistent Inference","aliases":["robust panel regression","cluster-robust panel estimation","panel regression with robust standard errors","HC/CR panel estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1987","originator":"Arellano (1987); White (1980) heteroscedasticity-consistent framework","url":"https://scholargate.app/en/econometrics/robust-panel-data-analysis","markdownUrl":"https://scholargate.app/en/econometrics/robust-panel-data-analysis.md","definition":"Robust panel data analysis applies standard panel estimators — fixed effects, random effects, or pooled OLS — while replacing conventional standard errors with cluster-robust or heteroscedasticity-consistent (HC) variants. The point estimates remain unchanged; what changes is the variance-covariance matrix used for inference, making t-tests and F-tests valid even when errors are heteroscedastic or correlated within cross-sectional units over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano (1987); White (1980) heteroscedasticity-consistent framework","year":"1987","type":"Robust estimation / inference correction","dataType":"Balanced or unbalanced panel (cross-sectional time series)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M. (1987). Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics and Statistics, 49(4), 431–434.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Computing+robust+standard+errors+for+within-groups+estimators+Arellano"},{"ref":"Cameron, A. C., & Trivedi, P. K. (2015). Microeconometrics: Methods and Applications. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521848053","url":null}],"related":["panel-fixed-effects-model","panel-random-effects-model","panel-data-analysis","fixed-effects-model","panel-hausman-test","robust-ols"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-panel-event-study","name":"Robust Panel Event Study","fullName":"Robust Panel Event Study Design","aliases":["robust event-study estimator","heteroskedasticity-robust panel event study","staggered-robust event study","robust ES design"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Sun & Abraham (2021); Freyaldenhoven, Hansen, Shapiro & Weidner (2021)","url":"https://scholargate.app/en/causal-inference/robust-panel-event-study","markdownUrl":"https://scholargate.app/en/causal-inference/robust-panel-event-study.md","definition":"A robust panel event study extends the standard panel event study design by applying heteroskedasticity- and autocorrelation-robust (HAC) standard errors and, where staggered treatment adoption exists, interaction-weighted estimators that remain valid even when treatment effects are heterogeneous across cohorts and time periods. It is widely used in economics, finance, and policy research to trace the dynamic causal path of an intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sun & Abraham (2021); Freyaldenhoven, Hansen, Shapiro & Weidner (2021)","year":"2021","type":"Quasi-experimental / causal inference","dataType":"Longitudinal / panel data with a discrete treatment event","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.","type":"article","doi":"10.1016/j.jeconom.2020.09.006","isbn":null,"url":null},{"ref":"Freyaldenhoven, S., Hansen, C., Shapiro, J. M., & Weidner, M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper No. 29170.","type":"article","doi":null,"isbn":null,"url":"https://www.nber.org/papers/w29170"}],"related":["panel-event-study","dynamic-difference-in-differences","difference-in-differences","staggered-difference-in-differences","event-study-design","panel-fixed-effects"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-particle-filter","name":"Robust Particle Filter","fullName":"Robust Particle Filter","aliases":["RPF","robust sequential Monte Carlo","outlier-robust particle filter","heavy-tailed particle filter"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1998-2004","originator":"Hurzeler & Kunsch; Ristic, Arulampalam & Gordon","url":"https://scholargate.app/en/bayesian/robust-particle-filter","markdownUrl":"https://scholargate.app/en/bayesian/robust-particle-filter.md","definition":"The Robust Particle Filter is a sequential Monte Carlo method that tracks hidden states in nonlinear, non-Gaussian systems while remaining resistant to outliers and model misspecification. It replaces the standard Gaussian likelihood with a heavy-tailed or bounded-influence density, so that anomalous observations receive downweighted importance and cannot derail the state estimate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hurzeler & Kunsch; Ristic, Arulampalam & Gordon","year":"1998-2004","type":"Sequential Bayesian estimation","dataType":"Time-series, state-space observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Ristic, B., Arulampalam, S. & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House.","type":"book","doi":null,"isbn":"978-1580536318","url":null},{"ref":"Hurzeler, M. & Kunsch, H. R. (1998). Monte Carlo approximations for general state-space models. Journal of Computational and Graphical Statistics, 7(2), 175-193.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Monte+Carlo+approximations+for+general+state-space+models+Hurzeler+Kunsch+1998"}],"related":["particle-filter","kalman-filter","robust-kalman-filter","sequential-monte-carlo","robust-sequential-monte-carlo","hamiltonian-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-particle-swarm-optimization","name":"Robust Particle Swarm Optimization","fullName":"Robust Particle Swarm Optimization — Uncertainty-aware swarm-based metaheuristic","aliases":["Robust PSO","RPSO","Uncertainty-robust PSO","PSO with robustness"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s","originator":"Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s","url":"https://scholargate.app/en/simulation/robust-particle-swarm-optimization","markdownUrl":"https://scholargate.app/en/simulation/robust-particle-swarm-optimization.md","definition":"Robust Particle Swarm Optimization (Robust PSO) extends the classical PSO metaheuristic to explicitly account for uncertainty in the objective function, constraints, or decision variables. Rather than optimizing a single nominal objective, each candidate solution is evaluated over a set of uncertainty scenarios, and fitness is judged by a robustness criterion such as worst-case performance or expected value, yielding solutions that remain near-optimal even when conditions deviate from nominal assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kennedy, J. & Eberhart, R. C. (PSO); robustness extensions by multiple authors, 2000s","year":"2000s","type":"Metaheuristic — robust swarm-based optimizer","dataType":"Continuous or mixed decision variables with uncertain parameters or noisy objective evaluations","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann Publishers.","type":"inproceedings","doi":null,"isbn":"9781558605954","url":null},{"ref":"Dellino, G., Kleijnen, J. P. C., & Meloni, C. (2010). Robust optimization in simulation: Taguchi and Response Surface Methodology. International Journal of Production Economics, 125(1), 52–59.","type":"article","doi":"10.1016/j.ijpe.2009.12.003","isbn":null,"url":null}],"related":["particle-swarm-optimization","robust-genetic-algorithm","robust-simulated-annealing","stochastic-particle-swarm-optimization","multi-objective-particle-swarm-optimization","robust-multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-path-analysis","name":"Robust Path Analysis","fullName":"Robust Path Analysis","aliases":["robust PA","path analysis with robust standard errors","robust causal path modeling","robust structural path modeling"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1998","originator":"Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)","url":"https://scholargate.app/en/statistics/robust-path-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-path-analysis.md","definition":"Robust path analysis applies robust estimation — such as sandwich standard errors or M-estimation — to path models that specify directed causal relationships among observed variables. It preserves valid inference about path coefficients and indirect effects when data violate normality, contain outliers, or exhibit heteroscedasticity that would distort conventional standard errors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)","year":"1998","type":"Causal path modeling with robust estimation","dataType":"Continuous or ordinal observed variables; non-normal or outlier-prone data","subfamily":"Multivariate analysis"},"citations":[{"ref":"Yuan, K.-H. & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88.","type":"article","doi":"10.1111/j.2044-8317.1998.tb00667.x","isbn":null,"url":null},{"ref":"Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1473756540","url":null}],"related":["path-analysis","structural-equation-modeling","robust-structural-equation-modeling","robust-mediation-analysis","mediation-analysis","robust-confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-pca","name":"Robust PCA","fullName":"Robust Principal Component Analysis","aliases":["RPCA","robust principal component analysis","low-rank plus sparse decomposition","Robust Temel Bileşen Analizi (RPCA)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2011,"originator":"Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)","url":"https://scholargate.app/en/statistics/robust-pca","markdownUrl":"https://scholargate.app/en/statistics/robust-pca.md","definition":"Robust Principal Component Analysis is a dimensionality-reduction method that extracts reliable components when the data are contaminated by outliers and noise. Introduced by Candès, Li, Ma and Wright (2011), and developed in the ROBPCA approach of Hubert, Rousseeuw and Vanden Branden (2005), it separates a data matrix into a clean low-rank part and a sparse outlier part.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)","year":2011,"type":"Robust dimensionality reduction / matrix decomposition","estimator":"Low-rank plus sparse matrix decomposition","outcome":"components (continuous, multivariate)","minSample":30},"citations":[{"ref":"Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37.","type":"article","doi":"10.1145/1970392.1970395","isbn":null,"url":null},{"ref":"Hubert, M., Rousseeuw, P. J., & Vanden Branden, K. (2005). ROBPCA: A New Approach to Robust Principal Component Analysis. Technometrics, 47(1), 64-79.","type":"article","doi":"10.1198/004017004000000563","isbn":null,"url":null}],"related":["pca","robust-covariance-mcd","robust-mahalanobis-distance","factor-analysis","robust-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-pearson-correlation","name":"Robust Pearson correlation","fullName":"Robust Pearson Correlation Coefficient","aliases":["winsorized correlation","percentage bend correlation","robust r","outlier-resistant correlation"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1970s–1990s","originator":"Rand R. Wilcox and predecessors in robust statistics","url":"https://scholargate.app/en/statistics/robust-pearson-correlation","markdownUrl":"https://scholargate.app/en/statistics/robust-pearson-correlation.md","definition":"The robust Pearson correlation is an outlier-resistant measure of linear association between two continuous variables. By applying Winsorizing, trimming, or percentage-bend transformations before computing the classic Pearson r, it retains the interpretability of a correlation coefficient while dramatically reducing the distortion caused by extreme values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand R. Wilcox and predecessors in robust statistics","year":"1970s–1990s","type":"Robust bivariate association measure","dataType":"Continuous bivariate data, outlier-prone","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Shevlyakov, G. L., & Oja, H. (2011). Robust Correlation: Theory and Applications. Wiley.","type":"book","doi":null,"isbn":"978-1118493458","url":null}],"related":["pearson-correlation","spearman-correlation","kendall-tau","winsorized-mean","percentage-bend-correlation","bootstrap-confidence-interval"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-poisson-regression","name":"Robust Poisson Regression","fullName":"Robust Poisson Regression with Sandwich Variance Estimator","aliases":["modified Poisson regression","Poisson regression with robust standard errors","log-binomial alternative","sandwich-variance Poisson"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"2004","originator":"Guangyong Zou","url":"https://scholargate.app/en/statistics/robust-poisson-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-poisson-regression.md","definition":"Robust Poisson regression fits a Poisson log-linear model to a binary outcome but replaces the model-based variance with the empirical sandwich estimator. This yields valid standard errors and risk ratios even though Poisson variance assumptions are technically violated for binary data. The approach, popularized by Zou (2004), is widely used in epidemiology as a numerically stable alternative to log-binomial regression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guangyong Zou","year":"2004","type":"GLM with robust variance","dataType":"Binary outcome, cross-sectional or prospective data","subfamily":"Regression / GLM"},"citations":[{"ref":"Zou, G. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159(7), 702-706.","type":"article","doi":"10.1093/aje/kwh090","isbn":null,"url":null},{"ref":"Zou, G. Y., & Donner, A. (2013). Extension of the modified Poisson regression model to prospective studies with binary data: why it is simpler than it sounds. Journal of Clinical Epidemiology, 66(9), 1023-1028.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Extension+of+the+modified+Poisson+regression+model+to+prospective+studies+with+binary+data%3A+why+it+is+simpler+than+it+sounds+Zou"}],"related":["poisson-regression","logistic-regression","log-binomial-regression","negative-binomial-regression","robust-regression","generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-power-analysis","name":"Robust power analysis","fullName":"Robust Statistical Power Analysis","aliases":["power analysis under non-normality","distribution-free power analysis","robust sample-size determination","contamination-robust power"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1990s–2000s","originator":"Rand R. Wilcox and colleagues","url":"https://scholargate.app/en/statistics/robust-power-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-power-analysis.md","definition":"Robust power analysis computes the statistical power or required sample size for hypothesis tests that use robust estimators — such as trimmed means or Winsorized variances — instead of ordinary means and standard deviations. It protects against inflated or deflated power estimates that arise when data contain outliers, heavy tails, or skewness that violate classical normality assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand R. Wilcox and colleagues","year":"1990s–2000s","type":"Power and sample-size planning","dataType":"Continuous, ordinal, or contaminated data","subfamily":"Classical statistics"},"citations":[{"ref":"Luh, W.-M., & Guo, J.-H. (2010). Approximate sample size formulas for the two-sample trimmed mean test with unequal variances. British Journal of Mathematical and Statistical Psychology, 63(1), 83–100.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Approximate+sample+size+formulas+for+the+two-sample+trimmed+mean+test+with+unequal+variances+Luh"},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["power-analysis","robust-independent-samples-t-test","robust-one-way-anova","bootstrap-power-analysis","effect-size-analysis","mann-whitney-u-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-pp-unit-root-test","name":"Robust PP Unit Root Test","fullName":"Robust Phillips-Perron Unit Root Test","aliases":["robust Phillips-Perron test","heteroskedasticity-robust PP test","nonparametric robust unit root test","robust PP"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988 (base); 2000s–2010s (robust extensions)","originator":"Phillips & Perron (1988); robustification by Cavaliere & Taylor (2008) and related authors","url":"https://scholargate.app/en/econometrics/robust-pp-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/robust-pp-unit-root-test.md","definition":"The Robust Phillips-Perron unit root test extends the classical PP test by applying corrections — such as heteroskedasticity-consistent covariance estimation or wild-bootstrap critical values — that maintain valid inference when the error variance of a time series is non-constant or exhibits unconditional heteroskedasticity, conditions under which the standard PP test is severely size-distorted.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Phillips & Perron (1988); robustification by Cavaliere & Taylor (2008) and related authors","year":"1988 (base); 2000s–2010s (robust extensions)","type":"Unit root / stationarity test","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.","type":"article","doi":"10.1093/biomet/75.2.335","isbn":null,"url":null},{"ref":"Cavaliere, G., & Taylor, A. M. R. (2008). Bootstrap unit root tests for time series with nonstationary volatility. Econometric Theory, 24(1), 43–71.","type":"article","doi":"10.1017/S0266466608080043","isbn":null,"url":null}],"related":["phillips-perron-unit-root-test","augmented-dickey-fuller-unit-root-test","robust-adf-unit-root-test","zivot-andrews-structural-break-test","kpss-test","nonlinear-pp-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-probit-model","name":"Robust Probit Model","fullName":"Robust Probit Regression Model","aliases":["probit with robust standard errors","sandwich-SE probit","heteroscedasticity-robust probit","M-estimation probit"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1934 / 1980s","originator":"Hal White (sandwich variance); classical probit by Bliss (1934)","url":"https://scholargate.app/en/statistics/robust-probit-model","markdownUrl":"https://scholargate.app/en/statistics/robust-probit-model.md","definition":"The Robust Probit Model estimates the probability of a binary outcome using the probit link function while protecting inference from misspecification of the error distribution or heteroscedasticity. Coefficients are obtained via maximum likelihood; standard errors are then replaced by the sandwich (Huber-White) estimator, which remains consistent even when the assumed error variance is incorrect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hal White (sandwich variance); classical probit by Bliss (1934)","year":"1934 / 1980s","type":"Binary outcome regression with robust inference","dataType":"Binary dependent variable, continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null},{"ref":"White, H. (1982). Maximum Likelihood Estimation of Misspecified Models. Econometrica, 50(1), 1–25.","type":"article","doi":"10.2307/1912526","isbn":null,"url":null}],"related":["probit-regression","logistic-regression","robust-logistic-regression","robust-regression","heteroscedasticity-robust-probit-model","generalized-linear-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-process-capability-analysis","name":"Robust Process Capability Analysis","fullName":"Robust Process Capability Analysis","aliases":["Robust PCA","Robust Capability Indices","Outlier-Resistant Capability Analysis","Robust Cpk Analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s","originator":"Extended from classical PCA (Kane, 1986; Juran, 1974) via robust statistics (Huber, 1981); formalized for capability indices by Tong & Chen (1998) and Pearn & Kotz (1994)","url":"https://scholargate.app/en/experimental-design/robust-process-capability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/robust-process-capability-analysis.md","definition":"Robust process capability analysis extends classical capability indices (Cp, Cpk, Ppk) by replacing the sample mean and standard deviation with robust location and scale estimators — such as the median, trimmed mean, MAD, or IQR-based spread — so that outliers and non-normal process distributions do not inflate or distort the capability estimate. The result is a more reliable assessment of whether a manufacturing or service process can consistently meet specification limits.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from classical PCA (Kane, 1986; Juran, 1974) via robust statistics (Huber, 1981); formalized for capability indices by Tong & Chen (1998) and Pearn & Kotz (1994)","year":"1990s–2000s","type":"Quantitative quality engineering method","dataType":"Continuous measurement data (potentially non-normal or containing outliers)","subfamily":"Engineering methods"},"citations":[{"ref":"Maravelakis, P. E., Bersimis, S., Panaretos, J., & Psarakis, S. (2004). Identifying the out of control variable in a multivariate control chart. Communications in Statistics - Theory and Methods, 33(10), 2499–2510.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Maravelakis+Bersimis+Panaretos+Psarakis+2004+identifying+out+of+control+variable+multivariate+control+chart"},{"ref":"Tong, L.-I., & Chen, J.-P. (1998). Lower confidence limits of process capability indices for nonnormal process distributions. International Journal of Quality & Reliability Management, 15(8–9), 907–919.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tong+Chen+1998+lower+confidence+limits+process+capability+indices+nonnormal"}],"related":["process-capability-analysis","statistical-process-control","control-chart","robust-statistical-process-control","robust-control-chart","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-propensity-score-matching","name":"Robust Propensity Score Matching","fullName":"Robust Propensity Score Matching Estimator","aliases":["robust PSM","PSM with robust variance","bias-corrected PSM","matching with robust inference"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2016 (robust variance correction); 1983 (PSM foundations)","originator":"Abadie & Imbens (2016) for matching-on-estimated-propensity-score with corrected variance; Rosenbaum & Rubin (1983) for PSM foundations","url":"https://scholargate.app/en/causal-inference/robust-propensity-score-matching","markdownUrl":"https://scholargate.app/en/causal-inference/robust-propensity-score-matching.md","definition":"Robust Propensity Score Matching (robust PSM) is a quasi-experimental causal inference method that pairs treated and control units on their estimated probability of receiving treatment (the propensity score), then estimates the average treatment effect using variance estimators that account for the uncertainty introduced by estimating the propensity score itself. The correction, developed by Abadie and Imbens (2016), prevents misleading inference that standard bootstrap or analytic formulas produce when applied naively after matching.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie & Imbens (2016) for matching-on-estimated-propensity-score with corrected variance; Rosenbaum & Rubin (1983) for PSM foundations","year":"2016 (robust variance correction); 1983 (PSM foundations)","type":"Quasi-experimental matching estimator with robust inference","dataType":"Cross-sectional or panel; continuous or binary outcome; binary treatment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., & Imbens, G. W. (2016). Matching on the Estimated Propensity Score. Econometrica, 84(2), 781-807.","type":"article","doi":"10.3982/ECTA11293","isbn":null,"url":null},{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70(1), 41-55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null}],"related":["propensity-score-matching","propensity-score-weighting","coarsened-exact-matching","matching-estimator","doubly-robust-estimation","inverse-probability-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-propensity-score-weighting","name":"Robust Propensity Score Weighting","fullName":"Robust Propensity Score Weighting Estimator","aliases":["robust PSW","robust IPW","robustness-augmented propensity score weighting","misspecification-robust weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1994–2019","originator":"Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW)","url":"https://scholargate.app/en/causal-inference/robust-propensity-score-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/robust-propensity-score-weighting.md","definition":"Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap weighting, or augmented outcome models to ensure that causal effect estimates remain reliable even when the propensity score model is imperfectly specified.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW)","year":"1994–2019","type":"Robust causal weighting estimator","dataType":"Observational panel or cross-sectional data with treatment and outcome variables","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866.","type":"article","doi":"10.1080/01621459.1994.10476818","isbn":null,"url":null},{"ref":"Zhao, Q., Small, D. S., & Bhattacharya, B. B. (2019). Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap. Journal of the Royal Statistical Society: Series B, 81(4), 735-761.","type":"article","doi":"10.1111/rssb.12327","isbn":null,"url":null}],"related":["propensity-score-weighting","doubly-robust-estimation","inverse-probability-weighting","propensity-score-matching","marginal-structural-model","sensitivity-analysis-for-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-quality-function-deployment","name":"Robust Quality Function Deployment","fullName":"Robust Quality Function Deployment","aliases":["Robust QFD","Uncertainty-tolerant QFD","Fuzzy-robust QFD","Robust House of Quality"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"2000s (robust extensions of QFD originating 1966)","originator":"Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s)","url":"https://scholargate.app/en/experimental-design/robust-quality-function-deployment","markdownUrl":"https://scholargate.app/en/experimental-design/robust-quality-function-deployment.md","definition":"Robust Quality Function Deployment (Robust QFD) extends the classical House of Quality framework by explicitly modeling uncertainty and variability in customer requirements, perception ratings, and engineering correlation judgments. Instead of treating inputs as crisp single-point values, it applies fuzzy sets, interval analysis, or Taguchi-inspired robustness techniques to ensure that the resulting design targets remain stable and customer-satisfying even when inputs are imprecise or fluctuating.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s)","year":"2000s (robust extensions of QFD originating 1966)","type":"Hybrid quality-engineering planning method","dataType":"Customer requirement ratings, engineering correlation matrices, uncertainty/fuzzy values","subfamily":"Engineering methods"},"citations":[{"ref":"Fung, R. Y. K., Tang, J., & Tu, Y. (2002). Modeling of quality function deployment planning under resource allocation constraints. Computers & Industrial Engineering, 43(1–2), 313–328.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modeling+of+quality+function+deployment+planning+under+resource+allocation+constraints+Fung+2002"},{"ref":"Kwong, C. K., & Bai, H. (2002). A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. Journal of Intelligent Manufacturing, 13(5), 367–377.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=fuzzy+AHP+determination+importance+weights+customer+requirements+quality+function+deployment+Kwong+2002"}],"related":["quality-function-deployment","robust-design-of-experiments","taguchi-method","failure-mode-and-effects-analysis","robust-failure-mode-and-effects-analysis","robust-statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-quantile-on-quantile-regression","name":"Robust Quantile-on-Quantile Regression","fullName":"Robust Quantile-on-Quantile Regression","aliases":["RQQR","robust QQ regression","robust quantile-on-quantile","outlier-robust QQR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2015–2020s","originator":"Sim and Zhou (2015) for QQ regression; robust extensions developed subsequently in the literature","url":"https://scholargate.app/en/econometrics/robust-quantile-on-quantile-regression","markdownUrl":"https://scholargate.app/en/econometrics/robust-quantile-on-quantile-regression.md","definition":"Robust Quantile-on-Quantile Regression extends the QQ framework of Sim and Zhou (2015) by adding resistance to outliers and heavy-tailed distributions. It estimates how each quantile of one variable responds to each quantile of another, producing a full dependence surface while guarding against leverage points that can distort standard QQ estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sim and Zhou (2015) for QQ regression; robust extensions developed subsequently in the literature","year":"2015–2020s","type":"Nonparametric quantile regression","dataType":"Time series, cross-sectional, or panel data with continuous variables","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8.","type":"article","doi":"10.1016/j.jbankfin.2015.01.013","isbn":null,"url":null},{"ref":"Quantile regression. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Quantile_regression"}],"related":["quantile-regression","quantile-on-quantile-regression","robust-regression","local-polynomial-regression","panel-quantile-regression","nonparametric-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-quantile-regression","name":"Robust Quantile Regression","fullName":"Robust Quantile Regression","aliases":["robust QR","outlier-resistant quantile regression","bounded-influence quantile regression","RQR"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1993–1997","originator":"Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)","url":"https://scholargate.app/en/statistics/robust-quantile-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-quantile-regression.md","definition":"Robust Quantile Regression estimates conditional quantiles of a response variable while simultaneously downweighting the influence of outliers. By combining the asymmetric loss function of standard quantile regression with bounded-influence or M-estimation weights, it provides reliable quantile estimates even when data contain extreme observations or heavy-tailed error distributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997)","year":"1993–1997","type":"Robust semiparametric regression","dataType":"Continuous outcome, continuous/categorical predictors; tolerates outliers and heavy-tailed errors","subfamily":"Regression / GLM"},"citations":[{"ref":"Koenker, R. (2005). Quantile Regression. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521608275","url":null},{"ref":"Machado, J. A. F. (1993). Robust model selection and M-estimation. Econometric Theory, 9(3), 478–493.","type":"article","doi":"10.1017/S0266466600007775","isbn":null,"url":null}],"related":["quantile-regression","robust-regression","robust-multiple-linear-regression","bayesian-quantile-regression","robust-generalized-linear-model","ols-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-quantitative-content-analysis","name":"Robust Quantitative Content Analysis","fullName":"Robust Quantitative Content Analysis","aliases":["robust content analysis","outlier-resistant content analysis","robust QCA","robust text frequency analysis"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–2000s (systematic application of robust statistics to content analysis)","originator":"Klaus Krippendorff; Kimberly Neuendorf (systematic codification); robust statistics tradition from Peter Huber (1964)","url":"https://scholargate.app/en/research-design/robust-quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/robust-quantitative-content-analysis.md","definition":"Robust quantitative content analysis is a systematic method for coding and counting manifest or latent features of communication content — texts, images, or media — while applying statistical estimators that are resistant to outliers, skewed distributions, and coding inconsistencies. By combining the structured coding protocol of classical content analysis with robust statistical measures, it produces frequency and association estimates that are less distorted when data violate normality assumptions or contain extreme values.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klaus Krippendorff; Kimberly Neuendorf (systematic codification); robust statistics tradition from Peter Huber (1964)","year":"1980s–2000s (systematic application of robust statistics to content analysis)","type":"Quantitative research design with robust statistical estimation","dataType":"Coded categorical, ordinal, or frequency count data from texts, media, or documents","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage Publications.","type":"book","doi":null,"isbn":"978-0761919773","url":null},{"ref":"Krippendorff, K. (2004). Content Analysis: An Introduction to Its Methodology (2nd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761915454","url":null}],"related":["quantitative-content-analysis","descriptive-research","correlational-research","multivariate-quantitative-content-analysis","bayesian-quantitative-content-analysis","observational-quantitative-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-queueing-simulation","name":"Robust Queueing Simulation","fullName":"Robust Queueing Simulation — Simulation of queueing systems under uncertainty and worst-case distributional assumptions","aliases":["RQS","Distributionally Robust Queueing","Robust Queue Simulation","Uncertainty-Aware Queueing Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2000s–2018","originator":"Whitt, W. and colleagues; Bertsimas, D. and colleagues","url":"https://scholargate.app/en/simulation/robust-queueing-simulation","markdownUrl":"https://scholargate.app/en/simulation/robust-queueing-simulation.md","definition":"Robust Queueing Simulation integrates robustness analysis into queueing system simulation by considering worst-case or uncertainty-set-driven scenarios for arrival rates, service distributions, and queue disciplines. It produces performance guarantees that hold across an entire family of plausible input distributions, making it essential for risk-sensitive service system design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Whitt, W. and colleagues; Bertsimas, D. and colleagues","year":"2000s–2018","type":"Simulation with worst-case uncertainty propagation","dataType":"Arrival rates, service times, queue discipline parameters, distributional uncertainty sets","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bertsimas, D., Natarajan, K., & Teo, C.-P. (2011). Distributionally robust optimization: A review. European Journal of Operational Research.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bertsimas+Natarajan+Teo+distributionally+robust+optimization"},{"ref":"Whitt, W., & You, W. (2018). Robust queueing theory. Operations Research, 66(3), 849–865.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+queueing+theory+Whitt"}],"related":["queueing-simulation","stochastic-queueing-simulation","robust-discrete-event-simulation","robust-scenario-analysis","monte-carlo-simulation","robust-markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-random-effects-model","name":"Robust Random Effects Model","fullName":"Robust Random Effects Panel Data Model","aliases":["robust RE model","sandwich random effects estimator","cluster-robust random effects","GLS-robust RE"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980s–2000s","originator":"Wooldridge; White (sandwich covariance); Arellano","url":"https://scholargate.app/en/econometrics/robust-random-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-random-effects-model.md","definition":"The Robust Random Effects model estimates panel data relationships using the GLS random effects estimator while replacing the conventional standard errors with sandwich (heteroscedasticity- and cluster-robust) variance estimates. This protects inference against arbitrary within-group correlation and heteroscedasticity without discarding the efficiency gains of random effects when unit-specific effects are genuinely uncorrelated with the regressors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wooldridge; White (sandwich covariance); Arellano","year":"1980s–2000s","type":"Panel GLS estimator with robust inference","dataType":"Balanced or unbalanced panel data (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null},{"ref":"Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson Education.","type":"book","doi":null,"isbn":"978-0131395381","url":null}],"related":["random-effects-model","robust-fixed-effects-model","panel-random-effects-model","panel-hausman-test","robust-panel-data-analysis","panel-gls"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-random-forest","name":"Robust Random Forest","fullName":"Robust Random Forest (Noise-Tolerant Ensemble of Decision Trees)","aliases":["RRF","noise-robust random forest","outlier-resistant random forest","robust ensemble forest"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Various (extensions of Breiman 2001 Random Forest)","url":"https://scholargate.app/en/machine-learning/robust-random-forest","markdownUrl":"https://scholargate.app/en/machine-learning/robust-random-forest.md","definition":"Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (extensions of Breiman 2001 Random Forest)","year":"2000s–2010s","type":"Robust Ensemble (noise-tolerant bagging of decision trees)","dataType":"Tabular (continuous, categorical, mixed); tolerates noisy or mislabeled observations","subfamily":"Machine learning"},"citations":[{"ref":"Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+Random+Forest+noisy+labels+ensemble"},{"ref":"Random Forest. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Random_forest"}],"related":["random-forest","xgboost","decision-tree","bagging","gradient-boosting","isolation-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-rasch-model","name":"Robust Rasch Model","fullName":"Robust Rasch Model","aliases":["robust IRT Rasch","robust dichotomous Rasch","outlier-resistant Rasch model","robust item calibration"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1982","originator":"Mislevy & Bock (robust ability estimation); broader robust IRT formalized through 1980s–2000s","url":"https://scholargate.app/en/psychometrics/robust-rasch-model","markdownUrl":"https://scholargate.app/en/psychometrics/robust-rasch-model.md","definition":"The robust Rasch model applies the standard one-parameter logistic Rasch framework with estimation procedures designed to limit the influence of outlying item responses, aberrant respondents, or mild model violations, producing stable item and person parameter estimates that are less sensitive to data contamination than ordinary maximum likelihood or conditional maximum likelihood Rasch estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mislevy & Bock (robust ability estimation); broader robust IRT formalized through 1980s–2000s","year":"1982","type":"Robust item calibration model","dataType":"Dichotomous or polytomous item response data","subfamily":"Scale / measurement"},"citations":[{"ref":"Strobl, C., Wickelmaier, F., & Zeileis, A. (2011). Accounting for individual differences in Bradley-Terry models by means of recursive partitioning. Journal of Educational and Behavioral Statistics, 36(2), 135–153.","type":"article","doi":"10.3102/1076998609359791","isbn":null,"url":null},{"ref":"Mislevy, R. J., & Bock, R. D. (1982). Biweight estimates of latent ability. Educational and Psychological Measurement, 42(3), 725–737.","type":"article","doi":"10.1177/001316448204200302","isbn":null,"url":null}],"related":["rasch-model","item-response-theory","robust-item-response-theory","differential-item-functioning","robust-confirmatory-factor-analysis","robust-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-regression-discontinuity-design","name":"Robust Regression Discontinuity Design","fullName":"Robust Bias-Corrected Regression Discontinuity Design","aliases":["Robust RDD","Bias-corrected RDD","CCT estimator","rdrobust"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2014","originator":"Calonico, Cattaneo & Titiunik","url":"https://scholargate.app/en/causal-inference/robust-regression-discontinuity-design","markdownUrl":"https://scholargate.app/en/causal-inference/robust-regression-discontinuity-design.md","definition":"Robust RDD extends the classical regression discontinuity design with bias correction and robust confidence intervals, addressing the under-coverage problem of conventional RDD inference. Developed by Calonico, Cattaneo, and Titiunik (2014), it uses local polynomial estimation with a bias-corrected point estimate and a wider variance term that accounts for the added uncertainty, yielding confidence intervals with correct asymptotic coverage.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Calonico, Cattaneo & Titiunik","year":"2014","type":"Quasi-experimental causal inference","dataType":"Observational data with a continuous running variable and a known cutoff","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica, 82(6), 2295-2326.","type":"article","doi":"10.3982/ECTA11757","isbn":null,"url":null},{"ref":"Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2019). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1108710206","url":null}],"related":["regression-discontinuity-design","fuzzy-regression-discontinuity","local-polynomial-regression","difference-in-differences","instrumental-variables","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-regression","name":"Robust Regression","fullName":"Robust Regression","aliases":["M-estimation regression","robust linear regression","outlier-resistant regression","MM-estimation"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1964","originator":"Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)","url":"https://scholargate.app/en/statistics/robust-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-regression.md","definition":"Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)","year":"1964","type":"Regression with outlier resistance","dataType":"Continuous outcome, continuous or categorical predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.","type":"article","doi":"10.1214/aoms/1177703732","isbn":null,"url":null},{"ref":"Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust Statistics: The Approach Based on Influence Functions. Wiley.","type":"book","doi":null,"isbn":"978-0471735779","url":null}],"related":["ols-regression","quantile-regression","lasso-regression","ridge-regression","weighted-least-squares","least-trimmed-squares"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-reliability-analysis","name":"Robust Reliability Analysis","fullName":"Robust Reliability Analysis","aliases":["RRA","reliability robustness analysis","uncertainty-aware reliability analysis","robust probabilistic reliability"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s–1990s (integration formalized in engineering literature)","originator":"Synthesized from Taguchi robust design and classical reliability theory (Kececioglu, Taguchi)","url":"https://scholargate.app/en/experimental-design/robust-reliability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/robust-reliability-analysis.md","definition":"Robust reliability analysis is an engineering method that combines classical reliability estimation with robustness principles to quantify and improve system dependability in the presence of parameter uncertainty and variability. Rather than assuming fixed input values, it propagates distributions of noise factors through a reliability model to produce probability-of-failure estimates that remain valid across a range of operating conditions and manufacturing tolerances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Taguchi robust design and classical reliability theory (Kececioglu, Taguchi)","year":"1980s–1990s (integration formalized in engineering literature)","type":"Quantitative reliability engineering method","dataType":"Component failure data, material properties, loading distributions, simulation outputs","subfamily":"Engineering methods"},"citations":[{"ref":"Kececioglu, D. (1991). Reliability Engineering Handbook (Vol. 1). Prentice Hall.","type":"book","doi":null,"isbn":"978-0137720774","url":null},{"ref":"Taguchi, G. (1987). System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs. UNIPUB/Kraus International Publications.","type":"book","doi":null,"isbn":"978-0527916213","url":null}],"related":["reliability-analysis","robust-design-of-experiments","failure-mode-and-effects-analysis","fault-tree-analysis","taguchi-method","robust-fractional-factorial-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-repeated-measures-anova","name":"Robust repeated measures ANOVA","fullName":"Robust Repeated Measures Analysis of Variance","aliases":["robust within-subjects ANOVA","trimmed-mean repeated measures ANOVA","robust RM-ANOVA","heteroscedastic repeated measures ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1990s–2000s","originator":"Rand R. Wilcox","url":"https://scholargate.app/en/statistics/robust-repeated-measures-anova","markdownUrl":"https://scholargate.app/en/statistics/robust-repeated-measures-anova.md","definition":"Robust repeated measures ANOVA tests whether population trimmed means differ across three or more repeated conditions or time points measured on the same subjects. By replacing ordinary means with 20% trimmed means and replacing variances with Winsorized estimates, it maintains acceptable Type I error and power when data are non-normal, skewed, or contain outliers — conditions under which classical repeated measures ANOVA routinely breaks down.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand R. Wilcox","year":"1990s–2000s","type":"Robust parametric mean comparison","dataType":"Continuous repeated or matched measurements","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Keselman, H. J., Wilcox, R. R., & Lix, L. M. (2003). A generally robust approach to hypothesis testing in independent and correlated groups designs. Psychophysiology, 40(4), 586–596.","type":"article","doi":"10.1111/1469-8986.00060","isbn":null,"url":null}],"related":["repeated-measures-anova","robust-one-way-anova","robust-friedman-test","friedman-test","mixed-anova","robust-manova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-response-surface-methodology","name":"Robust Response Surface Methodology","fullName":"Robust Response Surface Methodology","aliases":["Robust RSM","dual response surface methodology","robust parameter design via RSM","mean-variance RSM"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990","originator":"G. G. Vining and Raymond H. Myers (dual response formulation)","url":"https://scholargate.app/en/experimental-design/robust-response-surface-methodology","markdownUrl":"https://scholargate.app/en/experimental-design/robust-response-surface-methodology.md","definition":"Robust Response Surface Methodology (Robust RSM) is an experimental optimization strategy that simultaneously fits two regression models — one for the mean response and one for its variance (or standard deviation) — across a designed experiment. By jointly optimizing these dual surfaces, engineers identify factor settings that hit a performance target while minimizing process variability, combining the empirical model-building power of classical RSM with the variance-reduction goals of robust parameter design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"G. G. Vining and Raymond H. Myers (dual response formulation)","year":"1990","type":"Experimental optimization technique","dataType":"Continuous response measurements from designed experiments (quantitative)","subfamily":"Engineering methods"},"citations":[{"ref":"Vining, G. G., & Myers, R. H. (1990). Combining Taguchi and response surface philosophies: A dual response approach. Journal of Quality Technology, 22(1), 38–45.","type":"article","doi":"10.1080/00224065.1990.11979204","isbn":null,"url":null},{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470174463","url":null}],"related":["response-surface-methodology","taguchi-method","robust-design-of-experiments","central-composite-design","box-behnken-design","robust-taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-ridge-regression","name":"Robust Ridge regression","fullName":"Robust Ridge Regression","aliases":["ridge M-estimation","robust regularized regression","M-estimator ridge","outlier-resistant ridge regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1991","originator":"Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)","url":"https://scholargate.app/en/statistics/robust-ridge-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-ridge-regression.md","definition":"Robust Ridge regression combines M-estimation with L2 (ridge) regularization to produce coefficient estimates that are simultaneously resistant to outliers and stable under multicollinearity. It minimizes a robust loss function (such as Huber's) penalized by the squared norm of the coefficient vector, downweighting influential observations while shrinking correlated predictors toward zero.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Silvapulle (1991); building on Tikhonov (1963) and Huber (1964)","year":"1991","type":"Regularized robust linear regression","dataType":"Continuous outcome, continuous/categorical predictors; data may contain outliers and multicollinear predictors","subfamily":"Regression / GLM"},"citations":[{"ref":"Silvapulle, M. J. (1991). Robust ridge regression based on an M-estimator. Australian Journal of Statistics, 33(3), 319–333.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+ridge+regression+based+on+an+M-estimator+Silvapulle+1991"},{"ref":"Ridge regression. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ridge_regression"}],"related":["ridge-regression","robust-regression","lasso-regression","elastic-net-regression","robust-lasso-regression","robust-multiple-linear-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-roc-analysis","name":"Robust ROC analysis","fullName":"Robust Receiver Operating Characteristic Analysis","aliases":["robust AUC analysis","outlier-resistant ROC","robust diagnostic accuracy analysis","robust sensitivity-specificity analysis"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1990s–2000s","originator":"Multiple contributors (Pepe, Qin, Zhou, and others)","url":"https://scholargate.app/en/statistics/robust-roc-analysis","markdownUrl":"https://scholargate.app/en/statistics/robust-roc-analysis.md","definition":"Robust ROC analysis evaluates the diagnostic accuracy of a continuous or ordinal biomarker in distinguishing between two groups (e.g., diseased vs. healthy) while protecting against the distorting effects of outliers, non-normality, or distributional violations that can bias standard parametric ROC estimates and AUC confidence intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Pepe, Qin, Zhou, and others)","year":"1990s–2000s","type":"Robust diagnostic accuracy evaluation","dataType":"Continuous or ordinal test scores with binary outcome","subfamily":"Classical statistics"},"citations":[{"ref":"Pepe, M. S. (2000). An interpretation for the ROC curve and inference using GLM procedures. Biometrics, 56(2), 352–359.","type":"article","doi":"10.1111/j.0006-341X.2000.00352.x","isbn":null,"url":null},{"ref":"Qin, G., & Zhou, X.-H. (2006). Empirical likelihood inference for the area under the ROC curve. Biometrics, 62(2), 613–622.","type":"article","doi":"10.1111/j.1541-0420.2005.00453.x","isbn":null,"url":null}],"related":["roc-analysis","robust-effect-size-analysis","mann-whitney-u-test","robust-mann-whitney-u-test","bootstrap-roc-analysis","nonparametric-roc-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-root-cause-analysis","name":"Robust Root Cause Analysis","fullName":"Robust Root Cause Analysis","aliases":["Robust RCA","Robustness-Integrated Root Cause Analysis","RRCA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s","originator":"Synthesised from RCA practice (Kepner-Tregoe, 1960s) and Taguchi robustness principles (1980s–1990s)","url":"https://scholargate.app/en/experimental-design/robust-root-cause-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/robust-root-cause-analysis.md","definition":"Robust Root Cause Analysis (Robust RCA) integrates classical root cause investigation techniques — such as the 5-Whys, Ishikawa diagrams, and fault trees — with Taguchi's robustness thinking to identify not only the primary cause of a failure but also the noise factors and variability sources that allow the failure to occur repeatedly. The result is corrective actions that eliminate the root cause and make the system inherently insensitive to future variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from RCA practice (Kepner-Tregoe, 1960s) and Taguchi robustness principles (1980s–1990s)","year":"1990s–2000s","type":"Hybrid quality-engineering diagnostic method","dataType":"Process data, failure records, fishbone inputs, noise factor measurements","subfamily":"Engineering methods"},"citations":[{"ref":"Andersen, B., & Fagerhaug, T. (2006). Root Cause Analysis: Simplified Tools and Techniques (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873896924","url":null},{"ref":"Taguchi, G., Chowdhury, S., & Wu, Y. (2005). Taguchi's Quality Engineering Handbook. Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471413349","url":null}],"related":["root-cause-analysis","failure-mode-and-effects-analysis","robust-failure-mode-and-effects-analysis","fault-tree-analysis","robust-fault-tree-analysis","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-sarima-model","name":"Robust SARIMA model","fullName":"Robust Seasonal Autoregressive Integrated Moving Average Model","aliases":["robust SARIMA","outlier-resistant SARIMA","robust seasonal ARIMA","M-estimator SARIMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1979–2009","originator":"Muler, Peña & Yohai (robust ARMA); earlier foundation by Denby & Martin (1979)","url":"https://scholargate.app/en/econometrics/robust-sarima-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-sarima-model.md","definition":"Robust SARIMA extends the classical Seasonal ARIMA framework by replacing the standard least-squares criterion with a robust loss function — such as an M-estimator — so that outliers and heavy-tailed innovations in seasonal time series cannot distort parameter estimates or invalidate forecasts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Muler, Peña & Yohai (robust ARMA); earlier foundation by Denby & Martin (1979)","year":"1979–2009","type":"Robust time-series model","dataType":"Univariate seasonal time series with possible outliers or heavy-tailed errors","subfamily":"Econometrics / time series"},"citations":[{"ref":"Muler, N., Peña, D., & Yohai, V. J. (2009). Robust estimation for ARMA models. The Annals of Statistics, 37(2), 816–840.","type":"article","doi":"10.1214/07-AOS570","isbn":null,"url":null},{"ref":"Franses, P. H., & Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15(1), 1–9.","type":"article","doi":"10.1016/S0169-2070(98)00053-3","isbn":null,"url":null}],"related":["sarima-model","arima-model","robust-regression","x13-arima-seats","exponential-smoothing","outlier-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-scenario-analysis","name":"Robust Scenario Analysis","fullName":"Robust Scenario Analysis — Worst-case and minimax regret scenario evaluation under deep uncertainty","aliases":["RSA","Robust Scenario Planning","Worst-Case Scenario Analysis","Minimax Regret Scenario Analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1950 (foundations); 2003 (modern RDM formulation)","originator":"Wald, A. (minimax foundation); Lempert et al. (RDM framework)","url":"https://scholargate.app/en/simulation/robust-scenario-analysis","markdownUrl":"https://scholargate.app/en/simulation/robust-scenario-analysis.md","definition":"Robust Scenario Analysis evaluates a set of candidate strategies across a structured collection of plausible future scenarios and selects the strategy that performs acceptably well — or best in the worst case — regardless of which scenario materializes. It merges scenario planning with robustness criteria such as maximin, minimax regret, or satisficing to support decisions under deep, irreducible uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wald, A. (minimax foundation); Lempert et al. (RDM framework)","year":"1950 (foundations); 2003 (modern RDM formulation)","type":"Scenario-based robustness evaluation","dataType":"Scenario sets, outcome matrices, decision variables","subfamily":"Simulation / optimization"},"citations":[{"ref":"Wald, A. (1950). Statistical Decision Functions. Wiley, New York.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Wald+Statistical+Decision+Functions+1950"},{"ref":"Lempert, R. J., Popper, S. W., Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation, Santa Monica, CA.","type":"book","doi":null,"isbn":"9780833032751","url":null}],"related":["scenario-analysis","robust-optimization","robust-multi-objective-optimization","stochastic-scenario-analysis","sensitivity-analysis","monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-sensitivity-analysis","name":"Robust Sensitivity Analysis","fullName":"Robust Sensitivity Analysis — Uncertainty-resistant examination of model output variation under parameter perturbations","aliases":["RSA","Robust SA","Sensitivity Analysis under Uncertainty","Uncertainty-robust sensitivity analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Saltelli, A. and colleagues","url":"https://scholargate.app/en/simulation/robust-sensitivity-analysis","markdownUrl":"https://scholargate.app/en/simulation/robust-sensitivity-analysis.md","definition":"Robust Sensitivity Analysis (RSA) systematically evaluates how much variation in model outputs can be attributed to uncertainty or variation in model inputs, with an explicit focus on conclusions that remain valid across a wide range of plausible input conditions. It goes beyond standard sensitivity analysis by asking not only which inputs matter most, but which findings are truly robust — stable regardless of assumptions made under uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saltelli, A. and colleagues","year":"1990s–2000s","type":"Simulation-based robustness assessment pipeline","dataType":"Continuous model parameters; numerical simulation outputs","subfamily":"Simulation / optimization"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"9780470059975","url":null},{"ref":"Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, 214-232.","type":"article","doi":"10.1016/j.envsoft.2016.02.008","isbn":null,"url":null}],"related":["monte-carlo-simulation","variance-based-sensitivity-analysis","sobol-indices","latin-hypercube-sampling","scenario-analysis","uncertainty-quantification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-sequential-monte-carlo","name":"Robust Sequential Monte Carlo","fullName":"Robust Sequential Monte Carlo Methods","aliases":["robust particle filter","robust SMC","outlier-robust particle filtering","heavy-tailed SMC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2000s","originator":"Ristic, Arulampalam, Gordon and others (2000s, with ongoing development)","url":"https://scholargate.app/en/bayesian/robust-sequential-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/robust-sequential-monte-carlo.md","definition":"Robust Sequential Monte Carlo (Robust SMC) extends standard particle filtering to handle outliers, heavy-tailed noise, and model misspecification in sequential data. By replacing Gaussian likelihood assumptions with heavier-tailed distributions or employing outlier-detection strategies during particle weighting, it maintains accurate state-tracking and parameter estimation even when observations deviate from the assumed model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ristic, Arulampalam, Gordon and others (2000s, with ongoing development)","year":"2000s","type":"Sequential Bayesian sampling algorithm","dataType":"Sequential / time-series data, potentially containing outliers","subfamily":"Bayesian / computational"},"citations":[{"ref":"Ristic, B., Arulampalam, S., & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House.","type":"book","doi":null,"isbn":"978-1580536318","url":null},{"ref":"Akyildiz, O. D., & Miguez, J. (2020). Nudging the particle filter. Statistics and Computing, 30(2), 315-336.","type":"article","doi":"10.1007/s11222-019-09884-y","isbn":null,"url":null}],"related":["sequential-monte-carlo","particle-filter","robust-bayesian-inference","kalman-filter","robust-kalman-filter","hamiltonian-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-simple-linear-regression","name":"Robust Simple linear regression","fullName":"Robust Simple Linear Regression","aliases":["robust SLR","M-estimator simple regression","outlier-resistant simple regression","robust bivariate regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1964-1987","originator":"Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)","url":"https://scholargate.app/en/statistics/robust-simple-linear-regression","markdownUrl":"https://scholargate.app/en/statistics/robust-simple-linear-regression.md","definition":"Robust simple linear regression fits a straight line through bivariate data using loss functions or weighting schemes that down-weight outliers, producing slope and intercept estimates that are far less sensitive to extreme observations than ordinary least squares while remaining easy to interpret.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter J. Huber (M-estimators, 1964); Rousseeuw & Leroy (practical framework, 1987)","year":"1964-1987","type":"Robust linear regression","dataType":"Continuous outcome, one continuous predictor","subfamily":"Regression / GLM"},"citations":[{"ref":"Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471852339","url":null},{"ref":"Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73-101.","type":"article","doi":"10.1214/aoms/1177703732","isbn":null,"url":null}],"related":["ols-regression","robust-regression","robust-multiple-linear-regression","quantile-regression","weighted-least-squares","theil-sen-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-simulated-annealing","name":"Robust Simulated Annealing","fullName":"Robust Simulated Annealing — Uncertainty-aware stochastic local search for robust solutions","aliases":["RSA","Robust SA","Uncertainty-robust simulated annealing","Worst-case simulated annealing"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1983 (SA); robust variant emerged 1990s–2000s","originator":"Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community","url":"https://scholargate.app/en/simulation/robust-simulated-annealing","markdownUrl":"https://scholargate.app/en/simulation/robust-simulated-annealing.md","definition":"Robust Simulated Annealing (RSA) adapts the classical simulated annealing metaheuristic to seek solutions that perform well not just under nominal conditions but across the full range of uncertain or adversarial parameter values. By embedding a robustness evaluation — worst-case, expected-case, or regret-based — into the SA acceptance step, RSA trades some nominal optimality for resilience, making it valuable when problem parameters are imprecisely known or subject to environmental variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kirkpatrick, Gelatt & Vecchi (SA basis); robust formulation developed across the operations research community","year":"1983 (SA); robust variant emerged 1990s–2000s","type":"Metaheuristic with robustness evaluation","dataType":"Continuous or discrete decision variables with uncertain parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.","type":"article","doi":"10.1126/science.220.4598.671","isbn":null,"url":null},{"ref":"Ben-Tal, A., El Ghaoui, L., Nemirovski, A. (2009). Robust Optimization. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780691143682","url":null}],"related":["simulated-annealing","robust-genetic-algorithm","robust-tabu-search","robust-particle-swarm-optimization","stochastic-simulated-annealing","robust-multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-six-sigma-dmaic","name":"Robust Six Sigma DMAIC","fullName":"Robust Six Sigma Define-Measure-Analyze-Improve-Control","aliases":["Robust DMAIC","Six Sigma with Robust Design","Taguchi-integrated DMAIC","R-DMAIC"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (integration period)","originator":"Motorola (Six Sigma, 1986); Taguchi robust design integrated into DMAIC by quality engineering practitioners in the 1990s–2000s","url":"https://scholargate.app/en/experimental-design/robust-six-sigma-dmaic","markdownUrl":"https://scholargate.app/en/experimental-design/robust-six-sigma-dmaic.md","definition":"Robust Six Sigma DMAIC embeds Taguchi's robust design philosophy within the classic Define-Measure-Analyze-Improve-Control framework. Rather than optimizing a process only for average performance, this hybrid approach simultaneously minimizes process variation caused by noise factors — environmental shifts, material lot differences, operator variability — so that the outcome remains near target even when uncontrollable conditions change. The result is a process that is both capable and insensitive to real-world disturbances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Motorola (Six Sigma, 1986); Taguchi robust design integrated into DMAIC by quality engineering practitioners in the 1990s–2000s","year":"1990s–2000s (integration period)","type":"Hybrid process improvement and robust engineering methodology","dataType":"Continuous and attribute process data, designed experiment results, signal-to-noise ratios","subfamily":"Engineering methods"},"citations":[{"ref":"Antony, J. (2006). Six Sigma for service processes. Business Process Management Journal, 12(2), 234–248.","type":"article","doi":"10.1108/14637150610657558","isbn":null,"url":null},{"ref":"Pande, P. S., Neuman, R. P., & Cavanagh, R. R. (2000). The Six Sigma Way: How GE, Motorola, and Other Top Companies Are Honing Their Performance. McGraw-Hill.","type":"book","doi":null,"isbn":"978-0071358064","url":null}],"related":["six-sigma-dmaic","taguchi-method","robust-design-of-experiments","design-of-experiments","statistical-process-control","failure-mode-and-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-spatial-autocorrelation","name":"Robust Spatial Autocorrelation","fullName":"Robust Spatial Autocorrelation Analysis","aliases":["robust Moran's I","robust spatial dependence test","outlier-resistant spatial autocorrelation","RSA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1981–1995","originator":"Cliff & Ord; extended by Anselin and colleagues","url":"https://scholargate.app/en/spatial-analysis/robust-spatial-autocorrelation","markdownUrl":"https://scholargate.app/en/spatial-analysis/robust-spatial-autocorrelation.md","definition":"Robust spatial autocorrelation methods measure the degree to which nearby geographic units share similar values, while explicitly controlling for the distorting influence of spatial outliers and extreme observations. They extend classical statistics such as Moran's I by down-weighting or trimming observations that would otherwise inflate or deflate the autocorrelation signal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cliff & Ord; extended by Anselin and colleagues","year":"1981–1995","type":"Spatial dependence test (robust variant)","dataType":"Georeferenced areal or point data with potential outliers","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L., & Florax, R. J. G. M. (1995). Small sample properties of tests for spatial dependence in regression models: some further results. In Anselin, L. & Florax, R. J. G. M. (Eds.), New Directions in Spatial Econometrics. Springer, Berlin.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Small+sample+properties+of+tests+for+spatial+dependence+in+regression+models"},{"ref":"Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion, London.","type":"book","doi":null,"isbn":"0850860814","url":null}],"related":["spatial-autocorrelation","morans-i","gearys-c","local-indicators-of-spatial-association","robust-moran-s-i","local-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-spearman-correlation","name":"Robust Spearman Correlation","fullName":"Robust Spearman Rank Correlation","aliases":["Winsorized Spearman correlation","robust rank correlation","trimmed Spearman correlation","outlier-resistant Spearman"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1990s–2000s","originator":"Rand R. Wilcox (robust extensions); Charles Spearman (base method, 1904)","url":"https://scholargate.app/en/statistics/robust-spearman-correlation","markdownUrl":"https://scholargate.app/en/statistics/robust-spearman-correlation.md","definition":"Robust Spearman correlation is an outlier-resistant measure of monotonic association between two variables. It applies robustification strategies — such as Winsorizing extreme ranks or using the percentage-bend approach — to protect Spearman's rho against distortion from outliers or heavy-tailed distributions, while retaining its nonparametric rank-based character.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand R. Wilcox (robust extensions); Charles Spearman (base method, 1904)","year":"1990s–2000s","type":"Robust nonparametric correlation","dataType":"Ordinal or continuous with outliers","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Wilcox, R. R. (1994). The percentage bend correlation coefficient. Psychometrika, 59(4), 601–616.","type":"article","doi":"10.1007/BF02294395","isbn":null,"url":null}],"related":["spearman-correlation","kendalls-tau","robust-pearson-correlation","robust-kendalls-tau","percentage-bend-correlation","robust-independent-samples-t-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-stacking-ensemble","name":"Robust Stacking Ensemble","fullName":"Robust Stacking Ensemble (Outlier-Resistant Stacked Generalization)","aliases":["robust stacking","robust stacked generalization","outlier-resistant stacking","stacking with robust meta-learner"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1992 (stacking); robust variants 2000s–present","originator":"Wolpert, D. H. (stacking); robust extensions by multiple authors","url":"https://scholargate.app/en/machine-learning/robust-stacking-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/robust-stacking-ensemble.md","definition":"Robust Stacking Ensemble extends classical stacked generalization by replacing the ordinary meta-learner with a robust estimator — such as a Huber-loss regressor, quantile regression, or a model trained on trimmed residuals — so that the ensemble's combination layer is resistant to outliers and noisy base-learner predictions. It improves predictive accuracy and reliability on real-world datasets with contaminated labels or heavy-tailed error distributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolpert, D. H. (stacking); robust extensions by multiple authors","year":"1992 (stacking); robust variants 2000s–present","type":"Ensemble (stacking with robust meta-learner)","dataType":"Tabular (continuous, categorical, mixed); robust to outliers","subfamily":"Machine learning"},"citations":[{"ref":"Wolpert, D. H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.","type":"article","doi":"10.1016/S0893-6080(05)80023-1","isbn":null,"url":null},{"ref":"Ensemble learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Ensemble_learning"}],"related":["random-forest","xgboost","bagging","boosting","model-stacking","gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-statistical-process-control","name":"Robust Statistical Process Control","fullName":"Robust Statistical Process Control","aliases":["Robust SPC","Resistant SPC","Outlier-robust process monitoring","Robust process surveillance"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1989–1990s (formalized in peer-reviewed literature)","originator":"Rocke, D. M.; Tatum, L. G. (key contributors)","url":"https://scholargate.app/en/experimental-design/robust-statistical-process-control","markdownUrl":"https://scholargate.app/en/experimental-design/robust-statistical-process-control.md","definition":"Robust Statistical Process Control (Robust SPC) is an engineering quality-monitoring framework that replaces the classical mean and standard deviation estimators used in Shewhart-type control charts with outlier-resistant alternatives — such as the median, MAD, or trimmed statistics — so that isolated contaminating observations or non-normal process distributions do not inflate control limits and mask genuine process shifts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rocke, D. M.; Tatum, L. G. (key contributors)","year":"1989–1990s (formalized in peer-reviewed literature)","type":"Robust statistical monitoring framework","dataType":"Continuous process measurements (possibly contaminated or heavy-tailed)","subfamily":"Engineering methods"},"citations":[{"ref":"Tatum, L. G. (1997). Robust estimation of the process standard deviation for control charts. Technometrics, 39(2), 127–141.","type":"article","doi":"10.1080/00401706.1997.10485078","isbn":null,"url":null},{"ref":"Rocke, D. M. (1989). Robust control charts. Technometrics, 31(2), 173–184.","type":"article","doi":"10.1080/00401706.1989.10488511","isbn":null,"url":null}],"related":["statistical-process-control","control-chart","robust-control-chart","robust-process-capability-analysis","robust-design-of-experiments","failure-mode-and-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-structural-equation-modeling","name":"Robust Structural Equation Modeling","fullName":"Robust Structural Equation Modeling","aliases":["Robust SEM","SEM with robust standard errors","Satorra-Bentler SEM","non-normal SEM"],"domain":"statistics","family":"latent-structure","subfamily":"Multivariate analysis","year":"1994","originator":"Albert Satorra & Peter M. Bentler","url":"https://scholargate.app/en/statistics/robust-structural-equation-modeling","markdownUrl":"https://scholargate.app/en/statistics/robust-structural-equation-modeling.md","definition":"Robust structural equation modeling (Robust SEM) applies the full SEM framework — simultaneous estimation of measurement and structural relations among latent variables — while using corrected test statistics and sandwich standard errors that remain valid when observed data depart from multivariate normality. The Satorra-Bentler scaled chi-square is the most widely used correction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Satorra & Peter M. Bentler","year":"1994","type":"Latent variable / path model with robust inference","dataType":"Continuous or ordinal observed indicators, non-normal distributions","subfamily":"Multivariate analysis"},"citations":[{"ref":"Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis (pp. 399–419). Sage.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Corrections+to+test+statistics+and+standard+errors+in+covariance+structure+analysis+Satorra+Bentler+1994"},{"ref":"Yuan, K.-H. & Bentler, P. M. (1998). Normal theory based test statistics in structural equation modelling. British Journal of Mathematical and Statistical Psychology, 51(2), 289–309.","type":"article","doi":"10.1111/j.2044-8317.1998.tb00682.x","isbn":null,"url":null}],"related":["structural-equation-modeling","confirmatory-factor-analysis","robust-confirmatory-factor-analysis","robust-path-analysis","multilevel-structural-equation-modeling","path-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-support-vector-machine","name":"Robust Support Vector Machine","fullName":"Robust Support Vector Machine (Outlier-Resistant SVM)","aliases":["Robust SVM","RSVM","noise-tolerant SVM","outlier-robust SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006–2009","originator":"Xu, H., Caramanis, C., & Mannor, S.","url":"https://scholargate.app/en/machine-learning/robust-support-vector-machine","markdownUrl":"https://scholargate.app/en/machine-learning/robust-support-vector-machine.md","definition":"Robust SVM extends the standard support vector machine to resist the influence of outliers and mislabeled points. By replacing the hinge loss with a bounded or non-convex loss function — or by incorporating robust optimization constraints — it learns a decision boundary that is far less distorted by corrupted training examples, making it suitable for noisy real-world datasets where standard SVM would degrade significantly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xu, H., Caramanis, C., & Mannor, S.","year":"2006–2009","type":"Robust supervised classifier / regressor","dataType":"Labeled numeric or text-embedded features; tolerates outliers and label noise","subfamily":"Machine learning"},"citations":[{"ref":"Xu, H., Caramanis, C., & Mannor, S. (2009). Robustness and regularization of support vector machines. Journal of Machine Learning Research, 10, 1485–1510.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v10/xu09b.html"},{"ref":"Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Trading convexity for scalability. Proceedings of the 23rd International Conference on Machine Learning (ICML), 201–208.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Trading+convexity+for+scalability+Collobert+Sinz+Weston+Bottou+2006"}],"related":["support-vector-machine","robust-linear-regression","one-class-svm","regularized-support-vector-machine","robust-random-forest","robust-gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-svar-model","name":"Robust SVAR model","fullName":"Robust Structural Vector Autoregression Model","aliases":["robust SVAR","robust structural VAR","heteroscedasticity-robust SVAR","outlier-robust structural VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000s–2010s","originator":"Extension of Sims (1980) SVAR with robust inference methods","url":"https://scholargate.app/en/econometrics/robust-svar-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-svar-model.md","definition":"The Robust SVAR model extends the classical Structural VAR framework by incorporating robust estimation and inference methods that remain valid in the presence of heteroscedasticity, non-Gaussian errors, or outliers. By combining structural identification with robust statistical procedures, it produces reliable impulse responses and forecast error variance decompositions even when standard SVAR assumptions are violated in macroeconomic data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Sims (1980) SVAR with robust inference methods","year":"2000s–2010s","type":"Structural time series model","dataType":"Multivariate macroeconomic time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Lutkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.","type":"book","doi":null,"isbn":"978-3540401728","url":null},{"ref":"Herwartz, H., & Ploedt, M. (2016). Simulation evidence on theory-based and statistical identification under volatility breaks. Oxford Bulletin of Economics and Statistics, 78(1), 94-112.","type":"article","doi":"10.1111/obes.12098","isbn":null,"url":null}],"related":["structural-var","vector-autoregression","robust-var-model","robust-vecm","vector-error-correction-model","robust-arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-synthetic-control-method","name":"Robust Synthetic Control Method","fullName":"Robust Synthetic Control Method with Uncertainty Quantification","aliases":["Robust SCM","Inference-robust synthetic control","Synthetic control with valid inference","SCM with prediction intervals"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2021","originator":"Cattaneo, Feng & Titiunik (2021); building on Abadie, Diamond & Hainmueller (2010)","url":"https://scholargate.app/en/causal-inference/robust-synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/robust-synthetic-control-method.md","definition":"The robust synthetic control method extends the classic synthetic control estimator by providing statistically valid uncertainty quantification and inference. Developed by Cattaneo, Feng and Titiunik (2021), it addresses a core limitation of the original approach — the lack of formal prediction intervals — making causal conclusions more defensible when only a single treated unit is observed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cattaneo, Feng & Titiunik (2021); building on Abadie, Diamond & Hainmueller (2010)","year":"2021","type":"Quasi-experimental causal inference","dataType":"Aggregate time-series panel data (few treated units, multiple pre-treatment periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Cattaneo, M. D., Feng, Y., & Titiunik, R. (2021). Prediction Intervals for Synthetic Control Methods. Journal of the American Statistical Association, 116(536), 1865-1880.","type":"article","doi":"10.1080/01621459.2021.1979561","isbn":null,"url":null},{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative Politics and the Synthetic Control Method. American Journal of Political Science, 59(2), 495-510.","type":"article","doi":"10.1111/ajps.12116","isbn":null,"url":null}],"related":["synthetic-control-method","difference-in-differences","robust-difference-in-differences","bayesian-synthetic-control-method","placebo-test","sensitivity-analysis-for-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-system-gmm","name":"Robust System GMM","fullName":"Robust System Generalized Method of Moments Estimator","aliases":["system GMM with robust standard errors","two-step system GMM","Blundell-Bond robust estimator","robust S-GMM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998–2005","originator":"Blundell & Bond (1998); robustness corrections by Windmeijer (2005)","url":"https://scholargate.app/en/econometrics/robust-system-gmm","markdownUrl":"https://scholargate.app/en/econometrics/robust-system-gmm.md","definition":"Robust System GMM is a two-step panel data estimator that combines the difference and levels moment conditions of Blundell and Bond (1998) with Windmeijer's (2005) finite-sample correction to the two-step variance, producing valid inference even in short panels with a persistent dependent variable, individual fixed effects, and potentially endogenous regressors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blundell & Bond (1998); robustness corrections by Windmeijer (2005)","year":"1998–2005","type":"Panel data GMM estimator","dataType":"Panel (longitudinal) data with dynamic dependent variable","subfamily":"Econometrics / time series"},"citations":[{"ref":"Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.","type":"article","doi":"10.1016/S0304-4076(98)00009-8","isbn":null,"url":null},{"ref":"Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126(1), 25–51.","type":"article","doi":"10.1016/j.jeconom.2004.02.005","isbn":null,"url":null}],"related":["difference-gmm","system-gmm","panel-fixed-effects","instrumental-variables","two-stage-least-squares","arellano-bond-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-tabu-search","name":"Robust Tabu Search","fullName":"Robust Tabu Search — Tabu-based metaheuristic with robustness against uncertainty","aliases":["RTS","Robust TS","Uncertainty-aware Tabu Search","Tabu Search under Uncertainty"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1989 (TS); robust variant ~2000s","originator":"Glover, F. (Tabu Search); robustness extensions by various authors","url":"https://scholargate.app/en/simulation/robust-tabu-search","markdownUrl":"https://scholargate.app/en/simulation/robust-tabu-search.md","definition":"Robust Tabu Search (RTS) extends the classical Tabu Search metaheuristic by evaluating candidate solutions not only on their nominal objective value but also on their performance under uncertainty. Instead of seeking the best solution for a single scenario, RTS seeks solutions that perform well across a range of scenarios or realizations, trading peak optimality for reliability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glover, F. (Tabu Search); robustness extensions by various authors","year":"1989 (TS); robust variant ~2000s","type":"Metaheuristic with robustness mechanism","dataType":"Combinatorial or continuous optimization problems under uncertainty","subfamily":"Simulation / optimization"},"citations":[{"ref":"Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206.","type":"article","doi":"10.1287/ijoc.1.3.190","isbn":null,"url":null},{"ref":"Dolan, E. D., Lewis, R. M., & Torczon, V. (2003). On the local convergence of pattern search. SIAM Journal on Optimization, 14(2), 567–583.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+Tabu+Search+uncertainty+optimization"}],"related":["tabu-search","robust-simulated-annealing","robust-genetic-algorithm","robust-multi-objective-optimization","stochastic-tabu-search","robust-particle-swarm-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-test-retest-reliability","name":"Robust Test-Retest Reliability","fullName":"Robust Test-Retest Reliability","aliases":["robust temporal stability","outlier-resistant retest reliability","robust repeatability coefficient","robust intraclass correlation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Built on classical test-retest reliability (Pearson, early 1900s); robust extensions formalized by Wilcox and colleagues from the 1990s onward","url":"https://scholargate.app/en/psychometrics/robust-test-retest-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/robust-test-retest-reliability.md","definition":"Robust test-retest reliability quantifies how consistently a measure ranks or scores the same individuals across two occasions while protecting the estimate from distortion by outliers and non-normal score distributions. It replaces or supplements classical Pearson-based correlation and standard ICC formulas with robust estimators of location, scale, and association.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Built on classical test-retest reliability (Pearson, early 1900s); robust extensions formalized by Wilcox and colleagues from the 1990s onward","year":"1990s–2000s","type":"Reliability / measurement stability","dataType":"Continuous or ordinal repeated measurements from two occasions","subfamily":"Scale / measurement"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Test-retest reliability. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Test-retest_reliability"}],"related":["intraclass-correlation-coefficient","cronbach-alpha","interrater-reliability","standard-error-of-measurement","bland-altman-analysis","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-tgarch","name":"Robust TGARCH","fullName":"Robust Threshold Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["robust GJR-GARCH","robust threshold GARCH","heavy-tail TGARCH","outlier-robust TGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1994–2000s","originator":"Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature","url":"https://scholargate.app/en/econometrics/robust-tgarch","markdownUrl":"https://scholargate.app/en/econometrics/robust-tgarch.md","definition":"Robust TGARCH extends the Threshold GARCH model by replacing the conventional maximum likelihood objective with an estimator that is resistant to heavy-tailed innovations and outlying observations. It captures asymmetric volatility responses — where negative shocks amplify variance more than positive shocks — while remaining reliable when the return distribution deviates strongly from normality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature","year":"1994–2000s","type":"Volatility model with asymmetry and robust estimation","dataType":"Financial return time series, potentially with heavy tails or outliers","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.","type":"article","doi":"10.1016/0165-1889(94)90039-6","isbn":null,"url":null},{"ref":"Preminger, A., & Storti, G. (2017). Least squares estimation for GARCH (1,1) model with heavy tailed errors. The Econometrics Journal, 20(1), 221–258.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Least+squares+estimation+for+GARCH+%281%2C1%29+model+with+heavy+tailed+errors+Preminger"}],"related":["tgarch-model","egarch-model","robust-garch-model","robust-arch-model","dcc-garch-model","arch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-time-series","name":"Robust Time Series Analysis","fullName":"Robust Time Series Analysis (M- and MM-estimation based AR / MA / ARIMA)","aliases":["robust ARIMA","robust autoregressive model","outlier-resistant time series","Robust Zaman Serisi Analizi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":2019,"originator":"Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition","url":"https://scholargate.app/en/statistics/robust-time-series","markdownUrl":"https://scholargate.app/en/statistics/robust-time-series.md","definition":"Robust Time Series Analysis fits autoregressive, moving-average, and ARIMA models to series that contain outliers or structural breaks, using M-estimation or MM-estimation instead of ordinary least squares so that a few anomalous observations do not distort the fit. It follows the robust statistics tradition consolidated in Maronna, Martin, Yohai and Salibián-Barrera (2019).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition","year":2019,"type":"Robust time series model (AR / MA / ARIMA)","estimator":"M-estimation / MM-estimation","outcome":"continuous","structure":"time series","minSample":50},"citations":[{"ref":"Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119214687","url":null},{"ref":"Peña, D., & Guttman, I. (1988). A Bayesian Approach for Predicting with Outliers. Journal of the American Statistical Association.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Bayesian+Approach+for+Predicting+with+Outliers+Pe%C3%B1a"}],"related":["robust-mixed-model","mad-estimation","sn-qn-estimators","breakdown-point-analysis","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-two-way-anova","name":"Robust two-way ANOVA","fullName":"Robust Two-Way Analysis of Variance","aliases":["robust factorial ANOVA","trimmed-mean two-way ANOVA","heteroscedastic two-way ANOVA","robust 2-way ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1990s–2000s","originator":"Rand R. Wilcox; H. J. Keselman and colleagues","url":"https://scholargate.app/en/statistics/robust-two-way-anova","markdownUrl":"https://scholargate.app/en/statistics/robust-two-way-anova.md","definition":"Robust two-way ANOVA tests main effects and interactions of two categorical factors on a continuous outcome using trimmed means and Winsorized variances, providing valid inference when standard ANOVA assumptions — normality, homoscedasticity, and absence of outliers — are violated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rand R. Wilcox; H. J. Keselman and colleagues","year":"1990s–2000s","type":"Robust parametric mean comparison","dataType":"Continuous outcome, two categorical factors","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Keselman, H. J., Wilcox, R. R., & Lix, L. M. (2003). A generally robust approach to hypothesis testing in independent and correlated groups designs. Psychophysiology, 40(4), 586–596.","type":"article","doi":"10.1111/1469-8986.00060","isbn":null,"url":null}],"related":["two-way-anova","robust-one-way-anova","trimmed-mean-two-way-anova","welch-corrected-two-way-anova","bootstrap-two-way-anova","robust-manova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-universal-kriging","name":"Robust Universal Kriging","fullName":"Robust Universal Kriging","aliases":["RUK","robust kriging with external drift","outlier-resistant universal kriging","robust geostatistical regression kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1980s–1990s","originator":"Developed through contributions of Cressie, Genton, and Rousseeuw in geostatistics and robust statistics","url":"https://scholargate.app/en/spatial-analysis/robust-universal-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/robust-universal-kriging.md","definition":"Robust Universal Kriging (RUK) is a geostatistical interpolation method that combines a spatially varying deterministic trend with a stochastic residual surface, while using robust estimators to protect the variogram and trend coefficients from the distorting influence of outlying observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed through contributions of Cressie, Genton, and Rousseeuw in geostatistics and robust statistics","year":"1980s–1990s","type":"Spatial interpolation model","dataType":"Georeferenced point observations with continuous spatial variable","subfamily":"GIS / spatial"},"citations":[{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley-Interscience, New York.","type":"book","doi":null,"isbn":"978-0471002550","url":null},{"ref":"Genton, M. G., & Rousseeuw, P. J. (1995). The change-of-variance curve and optimal redescending M-estimators. Journal of Computational and Graphical Statistics, 4(4), 411-432.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Genton+Rousseeuw+robust+variogram+estimation+kriging"}],"related":["universal-kriging","ordinary-kriging","simple-kriging","regression-kriging","geographically-weighted-regression","spatial-lag-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-var-model","name":"Robust VAR model","fullName":"Robust Vector Autoregression Model","aliases":["robust VAR","outlier-robust VAR","heavy-tailed VAR","RVAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980s–2000s","originator":"Extensions by Lutkepohl and others building on Sims (1980) VAR framework","url":"https://scholargate.app/en/econometrics/robust-var-model","markdownUrl":"https://scholargate.app/en/econometrics/robust-var-model.md","definition":"The Robust VAR model extends the classical Vector Autoregression framework by replacing ordinary least squares estimation with robust estimators — such as M-estimators or median-based methods — to reduce the influence of outliers, structural breaks, and heavy-tailed shocks common in financial and macroeconomic time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extensions by Lutkepohl and others building on Sims (1980) VAR framework","year":"1980s–2000s","type":"Multivariate time-series model with robust estimation","dataType":"Multivariate time series (stationary or cointegrated)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Goncalves, S., & Kilian, L. (2004). Bootstrapping autoregressions with conditional heteroskedasticity of unknown form. Journal of Econometrics, 123(1), 89-120.","type":"article","doi":"10.1016/j.jeconom.2003.10.030","isbn":null,"url":null},{"ref":"Lutkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer, Berlin.","type":"book","doi":null,"isbn":"978-3540401728","url":null}],"related":["var-model","vecm-model","quantile-var","bayesian-var","panel-var","structural-var"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-variational-inference","name":"Robust Variational Inference","fullName":"Robust Variational Inference","aliases":["RVI","robust VI","outlier-robust variational Bayes","power-divergence variational inference"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2008-2018","originator":"Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018)","url":"https://scholargate.app/en/bayesian/robust-variational-inference","markdownUrl":"https://scholargate.app/en/bayesian/robust-variational-inference.md","definition":"Robust variational inference (RVI) extends standard variational inference by replacing the Kullback-Leibler divergence with a divergence measure that is less sensitive to outliers and model misspecification — such as the beta-divergence or a Renyi-type divergence. This yields posterior approximations that remain well-behaved even when a fraction of the data departs from the assumed model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018)","year":"2008-2018","type":"Robust approximate Bayesian inference","dataType":"Continuous, mixed, or potentially contaminated / heavy-tailed data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Futami, F., Sato, I. & Sugiyama, M. (2018). Variational inference based on robust divergences. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 84:813-822.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v84/futami18a.html"},{"ref":"Ghosh, S. & Basu, A. (2016). Robust Bayes estimation using the density power divergence. Annals of the Institute of Statistical Mathematics, 68(2), 413-437.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+Bayes+estimation+using+the+density+power+divergence+Ghosh+Basu+2016"}],"related":["variational-inference","robust-bayesian-inference","bayesian-regression","markov-chain-monte-carlo","robust-markov-chain-monte-carlo","approximate-bayesian-computation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-vecm","name":"Robust VECM","fullName":"Robust Vector Error Correction Model","aliases":["robust VECM","outlier-robust VECM","robust cointegration model","robust VEC model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1997–2001","originator":"Sakata & White (1998); Lucas (1997) — robust cointegrated system estimation","url":"https://scholargate.app/en/econometrics/robust-vecm","markdownUrl":"https://scholargate.app/en/econometrics/robust-vecm.md","definition":"Robust VECM extends the classical Vector Error Correction Model by replacing ordinary least squares estimation with outlier-resistant procedures — such as M-estimators, S-estimators, or least trimmed squares — so that cointegration relationships and short-run adjustment dynamics are estimated reliably even when the multivariate time series contains outliers, structural breaks, or heavy-tailed innovations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sakata & White (1998); Lucas (1997) — robust cointegrated system estimation","year":"1997–2001","type":"Robust multivariate time-series model","dataType":"Multivariate integrated time series (I(1)) with potential outliers or heavy tails","subfamily":"Econometrics / time series"},"citations":[{"ref":"Caner, M., & Kilian, L. (2001). Size distortions of tests of the null hypothesis of stationarity: Evidence and implications for the PPP debate. Journal of International Money and Finance, 20(5), 639-657.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robust+Vector+Error+Correction+Model+outlier+resistant+cointegration"},{"ref":"Lucas, A. (1997). Robustness of the student t based M-estimator. Communications in Statistics — Theory and Methods, 26(5), 1165-1182.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=robust+VECM+M-estimator+cointegration+Lucas+1997"}],"related":["vecm","robust-var","johansen-cointegration","engle-granger-cointegration","quantile-vecm","structural-vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-voting-ensemble","name":"Robust Voting Ensemble","fullName":"Robust Voting Ensemble (Noise-Resistant Majority and Weighted Voting of Classifiers)","aliases":["robust majority voting","robust vote aggregation","noise-tolerant voting ensemble","fault-tolerant classifier combination"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Dietterich, T. G. (ensemble voting foundations); robustification extensions developed broadly in the ML community","url":"https://scholargate.app/en/machine-learning/robust-voting-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/robust-voting-ensemble.md","definition":"Robust Voting Ensemble combines predictions from multiple base classifiers using noise-tolerant aggregation — such as weighted voting, trimmed voting, or median-based combination — to produce final decisions that remain reliable when individual classifiers are corrupted by noisy labels, adversarial inputs, or distributional shift.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dietterich, T. G. (ensemble voting foundations); robustification extensions developed broadly in the ML community","year":"2000s–2010s","type":"Robust ensemble aggregation","dataType":"Tabular, numeric, categorical (classifier outputs)","subfamily":"Machine learning"},"citations":[{"ref":"Dietterich, T. G. (2000). Ensemble methods in machine learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems, LNCS 1857, 1–15. Springer.","type":"article","doi":"10.1007/3-540-45014-9_1","isbn":null,"url":null},{"ref":"Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1–2), 1–39.","type":"article","doi":"10.1007/s10462-009-9124-7","isbn":null,"url":null}],"related":["voting-ensemble","bagging","boosting","random-forest","stacking-ensemble","robust-bagging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-wilcoxon-signed-rank-test","name":"Robust Wilcoxon signed-rank test","fullName":"Robust Wilcoxon Signed-Rank Test","aliases":["robust signed-rank test","robust nonparametric paired test","outlier-resistant Wilcoxon test","robust WSR test"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1945 (original); robust extensions 1990s–2000s","originator":"Frank Wilcoxon (original); Rand R. Wilcox (robust extensions)","url":"https://scholargate.app/en/statistics/robust-wilcoxon-signed-rank-test","markdownUrl":"https://scholargate.app/en/statistics/robust-wilcoxon-signed-rank-test.md","definition":"The robust Wilcoxon signed-rank test extends the classical Wilcoxon signed-rank test by incorporating outlier-resistant location measures or robust preprocessing steps, improving inference on paired data when extreme observations or heavy-tailed distributions threaten validity of standard rank-based conclusions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Wilcoxon (original); Rand R. Wilcox (robust extensions)","year":"1945 (original); robust extensions 1990s–2000s","type":"Robust nonparametric paired difference test","dataType":"Paired continuous or ordinal differences","subfamily":"Classical statistics"},"citations":[{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null},{"ref":"Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83.","type":"article","doi":"10.2307/3001968","isbn":null,"url":null}],"related":["wilcoxon-signed-rank-test","robust-paired-samples-t-test","paired-samples-t-test","robust-mann-whitney-u-test","robust-friedman-test","sign-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-wls","name":"Robust WLS","fullName":"Robust Weighted Least Squares","aliases":["robust weighted least squares","RWLS","heteroscedasticity-robust WLS","outlier-robust weighted regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1964/1981","originator":"Huber, P. J.","url":"https://scholargate.app/en/econometrics/robust-wls","markdownUrl":"https://scholargate.app/en/econometrics/robust-wls.md","definition":"Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Huber, P. J.","year":"1964/1981","type":"Robust weighted regression","dataType":"Cross-sectional or panel data with heteroscedasticity and potential outliers","subfamily":"Econometrics / time series"},"citations":[{"ref":"Huber, P. J. (1981). Robust Statistics. Wiley.","type":"book","doi":null,"isbn":"978-0471418054","url":null},{"ref":"Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson.","type":"book","doi":null,"isbn":"978-0134461366","url":null}],"related":["robust-ols","panel-wls","weighted-least-squares","ols-regression","robust-gls","quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-xgboost","name":"Robust XGBoost","fullName":"Robust XGBoost (Extreme Gradient Boosting with Robust Loss Functions)","aliases":["XGBoost with Huber loss","outlier-robust gradient boosting","robust GBDT","XGBoost robust regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2016 (XGBoost); robust loss concept from 1964","originator":"Chen, T. & Guestrin, C. (XGBoost); Huber, P. J. (robust loss)","url":"https://scholargate.app/en/machine-learning/robust-xgboost","markdownUrl":"https://scholargate.app/en/machine-learning/robust-xgboost.md","definition":"Robust XGBoost combines the scalable gradient boosting framework of XGBoost with robust loss functions — primarily the Huber loss or its variants — to produce a gradient boosted tree ensemble that resists the distorting influence of outliers. By replacing the squared-error objective with a loss that down-weights large residuals, the model delivers reliable predictions on continuous targets even when training data contain extreme values or label noise.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, T. & Guestrin, C. (XGBoost); Huber, P. J. (robust loss)","year":"2016 (XGBoost); robust loss concept from 1964","type":"Ensemble (gradient boosting with robust objective)","dataType":"Tabular (continuous target or binary/multiclass labels)","subfamily":"Machine learning"},"citations":[{"ref":"Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.","type":"inproceedings","doi":"10.1145/2939672.2939785","isbn":null,"url":null},{"ref":"Huber, P. J. (1964). Robust Estimation of a Location Parameter. The Annals of Mathematical Statistics, 35(1), 73–101.","type":"article","doi":"10.1214/aoms/1177703732","isbn":null,"url":null}],"related":["xgboost","robust-gradient-boosting","robust-random-forest","robust-lightgbm","gradient-boosting","robust-linear-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-zero-inflated-model","name":"Robust Zero-Inflated Model","fullName":"Robust Zero-Inflated Count Regression Model","aliases":["robust ZIP","robust ZINB","outlier-resistant zero-inflated regression","robust zero-inflated Poisson"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1990s–2000s","originator":"Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors","url":"https://scholargate.app/en/statistics/robust-zero-inflated-model","markdownUrl":"https://scholargate.app/en/statistics/robust-zero-inflated-model.md","definition":"The robust zero-inflated model extends standard zero-inflated count regression — which handles excess zeros via a mixture of a point mass at zero and a count distribution — by replacing or supplementing classical maximum likelihood with robust estimation techniques (M-estimators, sandwich standard errors) that protect against the distorting influence of outlying observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Lambert (1992) ZIP model combined with robust M-estimation and sandwich standard errors","year":"1990s–2000s","type":"Robust count regression with excess zeros","dataType":"Count data with excess zeros and potential outliers","subfamily":"Regression / GLM"},"citations":[{"ref":"Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25.","type":"article","doi":"10.18637/jss.v027.i08","isbn":null,"url":null},{"ref":"Cantoni, E., & Ronchetti, E. (2001). Robust inference for generalized linear models. Journal of the American Statistical Association, 96(455), 1022–1030.","type":"article","doi":"10.1198/016214501753209004","isbn":null,"url":null}],"related":["zero-inflated-model","robust-regression","robust-negative-binomial-regression","robust-poisson-regression","robust-generalized-linear-model","robust-generalized-additive-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"robust-zivot-andrews-test","name":"Robust Zivot-Andrews test","fullName":"Robust Zivot-Andrews Structural Break Unit Root Test","aliases":["robust ZA test","ZA test with robust inference","Zivot-Andrews test with heteroscedasticity-robust critical values","structural break unit root test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1992 (original); 2000s (robust variants)","originator":"Zivot & Andrews (1992); robust extensions by subsequent literature","url":"https://scholargate.app/en/econometrics/robust-zivot-andrews-test","markdownUrl":"https://scholargate.app/en/econometrics/robust-zivot-andrews-test.md","definition":"The Robust Zivot-Andrews test extends the classic Zivot-Andrews (1992) unit root test to provide reliable inference when the error term may be heteroscedastic or non-normal. It tests whether a time series has a unit root while endogenously identifying a single structural break in the level, trend, or both, without requiring the researcher to pre-specify the break date.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zivot & Andrews (1992); robust extensions by subsequent literature","year":"1992 (original); 2000s (robust variants)","type":"Unit root test with endogenous structural break","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null},{"ref":"Zivot-Andrews test. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Zivot%E2%80%93Andrews_test"}],"related":["zivot-andrews-test","augmented-dickey-fuller-test","phillips-perron-test","lumsdaine-papell-test","lee-strazicich-test","bai-perron-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"roc-analysis","name":"ROC analysis","fullName":"Receiver Operating Characteristic Analysis","aliases":["ROC curve analysis","AUC analysis","sensitivity-specificity analysis","diagnostic accuracy analysis"],"domain":"statistics","family":"hypothesis-test","subfamily":"Classical statistics","year":"1954 (signal detection); 1982 (AUC formalization)","originator":"Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)","url":"https://scholargate.app/en/statistics/roc-analysis","markdownUrl":"https://scholargate.app/en/statistics/roc-analysis.md","definition":"ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision thresholds, it produces a curve whose area under the curve (AUC) quantifies overall discriminative power, ranging from 0.5 (chance) to 1.0 (perfect discrimination).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)","year":"1954 (signal detection); 1982 (AUC formalization)","type":"Diagnostic accuracy evaluation","dataType":"Continuous or ordinal predictor, binary outcome","subfamily":"Classical statistics"},"citations":[{"ref":"Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.","type":"article","doi":"10.1148/radiology.143.1.7063747","isbn":null,"url":null},{"ref":"Zweig, M. H., & Campbell, G. (1993). Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry, 39(4), 561–577.","type":"article","doi":"10.1093/clinchem/39.4.561","isbn":null,"url":null}],"related":["binary-logistic-regression","discriminant-analysis","sensitivity-specificity","kendalls-tau","mann-whitney-u-test","effect-size-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"roc-weight","name":"ROC-WEIGHT","fullName":"ROC — Rank Order Centroid weights (rank-based surrogate weights)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1996","originator":"Barron, F. H., Barrett, B. E.","url":"https://scholargate.app/en/decision-making/roc-weight","markdownUrl":"https://scholargate.app/en/decision-making/roc-weight.md","definition":"ROC-WEIGHT (ROC — Rank Order Centroid weights (rank-based surrogate weights)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Barron, F. H., Barrett, B. E. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barron, F. H., Barrett, B. E.","subfamily":"Weight_Subjective","year":"1996","type":"Weight_Subjective","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Barron, F. H. (1992). Selecting a best multiattribute alternative with partial information about attribute weights. Acta Psychologica","type":"article","doi":"10.1016/0001-6918(92)90042-C","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rock-mass-classification","name":"Rock Mass Classification","fullName":"Rock Mass Classification","aliases":["RMR system","Q-system classification","rock quality designation"],"domain":"geoscience","family":"process-pipeline","subfamily":"Geotechnical site characterization","year":"1974","originator":"Bieniawski and Barton","url":"https://scholargate.app/en/geoscience/rock-mass-classification","markdownUrl":"https://scholargate.app/en/geoscience/rock-mass-classification.md","definition":"Rock mass classification is the systematic assessment of rock quality and mechanical behavior in engineering geology, combining field observations of jointing, weathering, and strength into a numerical index. Pioneered by Bieniawski (RMR system, 1974) and Barton (Q-system, 1974), these methods enable rapid site assessment and guide design of excavations, dams, and slopes. Classification bridges the gap between small laboratory samples and large field-scale behavior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bieniawski and Barton","subfamily":"Geotechnical site characterization","year":"1974","type":"engineering geology assessment pipeline"},"citations":[{"ref":"Bieniawski, Z. T. (1989). Engineering Rock Mass Classifications. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Barton, N., Lien, R., & Lunde, J. (1974). Engineering classification of rock masses for the design of tunnel support. Rock Mechanics, 6(4), 189–236.","type":"article","doi":"10.1007/BF01239496","isbn":null,"url":null},{"ref":"Hoek, E. (2007). Practical Rock Engineering. Rocscience, Inc.","type":"book","doi":null,"isbn":null,"url":"https://www.rocscience.com"}],"related":["geologic-mapping","petrographic-analysis","geomechanical-modeling","well-log-analysis","rock-strength-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rock-mass-rating","name":"Rock Mass Rating","fullName":"Rock Mass Rating (RMR) System for Geotechnical Classification","aliases":["RMR","Bieniawski Classification","RMR89"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Rock Mass Classification","year":"1973","originator":"Zbigniew T. Bieniawski","url":"https://scholargate.app/en/mining-engineering/rock-mass-rating","markdownUrl":"https://scholargate.app/en/mining-engineering/rock-mass-rating.md","definition":"The Rock Mass Rating (RMR) system, developed by Zbigniew Bieniawski starting in 1973, is an empirical classification that characterizes rock mass quality and estimates mining and civil engineering behavior. RMR combines five measurable geotechnical parameters into a single index ranging from 0 to 100, where higher values indicate stronger, more stable rock masses. It is the most widely used rock classification system worldwide for underground mining design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zbigniew T. Bieniawski","subfamily":"Rock Mass Classification","year":"1973","type":"Empirical classification for geotechnical engineering"},"citations":[{"ref":"Bieniawski, Z. T. (1989). Engineering rock mass classifications. John Wiley & Sons.","type":"article","doi":null,"isbn":"978-0-471-60437-4","url":null},{"ref":"Hoek, E., Marinos, P., & Benissi, M. (1998). Applicability of the Geological Strength Index (GSI) classification for very weak and sheared rock masses. Bulletin of Engineering Geology and the Environment, 57(2), 151-160.","type":"article","doi":"10.1007/s100640050031","isbn":null,"url":null}],"related":["q-system","hoek-brown-criterion","stope-layout"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"roland-morris-disability","name":"Roland-Morris Disability Questionnaire","fullName":"Roland-Morris Disability Questionnaire (RMDQ)","aliases":["RMDQ","Roland-Morris scale"],"domain":"pain-medicine","family":"process-pipeline","subfamily":"functional disability assessment","year":"1983","originator":"Morris Roland and Ruth Morris","url":"https://scholargate.app/en/pain-medicine/roland-morris-disability","markdownUrl":"https://scholargate.app/en/pain-medicine/roland-morris-disability.md","definition":"The Roland-Morris Disability Questionnaire (RMDQ) is a brief, disease-specific self-report measure developed by Morris Roland and Ruth Morris in 1983 to assess functional disability and activity limitations in patients with acute and chronic low back pain. With 24 items addressing daily activities impacted by back pain, it has become one of the most widely used disability measures in low back pain research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morris Roland and Ruth Morris","subfamily":"functional disability assessment","year":"1983","type":"Self-report disability questionnaire for low back pain"},"citations":[{"ref":"Roland, M., & Morris, R. (1983). A study of the natural history of low-back pain. Part I: Development of a reliable and sensitive measure of disability in low-back pain. Spine, 8(2), 141-144.","type":"article","doi":"10.1097/00007632-198303000-00004","isbn":null,"url":null},{"ref":"Stratford, P.W., Binkley, J.M., Riddle, D.L., & Guyatt, G.H. (1998). Sensitivity to change of the Roland-Morris Disability Questionnaire. Spine, 23(24), 2668-2673.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sensitivity+to+change+of+the+Roland-Morris+Disability+Questionnaire+Stratford"},{"ref":"Riddle, D.L., & Stratford, P.W. (1999). Interpreting validity indexes for diagnostic tests: An illustration using the modified-modified Schober test. Physical Therapy, 79(10), 939-948.","type":"article","doi":"10.1093/ptj/79.10.939","isbn":null,"url":null}],"related":["dallas-pain-questionnaire","pain-self-efficacy-questionnaire","mcgill-pain-questionnaire","chronic-pain-acceptance-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rome-iv-ibs-criteria","name":"Rome IV Irritable Bowel Syndrome Criteria","fullName":"Rome IV Diagnostic Criteria for Irritable Bowel Syndrome","aliases":["Rome IV IBS","Rome Criteria"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"functional-gastrointestinal-disorders","year":"2016","originator":"Rome Foundation (multinational expert consensus)","url":"https://scholargate.app/en/gastroenterology/rome-iv-ibs-criteria","markdownUrl":"https://scholargate.app/en/gastroenterology/rome-iv-ibs-criteria.md","definition":"The Rome IV criteria are the internationally accepted diagnostic standard for irritable bowel syndrome (IBS), published in 2016 by the Rome Foundation. These criteria define IBS as recurrent abdominal pain (≥1 day per week for ≥3 months) associated with altered bowel habits, without structural or biochemical abnormalities. IBS is subtyped into four patterns—IBS-constipation predominant (IBS-C), IBS-diarrhea predominant (IBS-D), IBS-mixed (IBS-M), and IBS-unclassified (IBS-U)—based on stool consistency patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rome Foundation (multinational expert consensus)","subfamily":"functional-gastrointestinal-disorders","year":"2016","type":"Diagnostic Criteria"},"citations":[{"ref":"Mearin, F., Lacy, B. E., Chang, L., et al. (2016). Bowel disorders. Gastroenterology. Published online June 2016 by the Rome Foundation.","type":"article","doi":null,"isbn":null,"url":"https://www.ncbi.nlm.nih.gov/books/NBK481519/"}],"related":["sccai","gcsi","pac-qol","gerd-hrql"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"room-impulse-response","name":"Room Impulse Response","fullName":"Room Impulse Response Measurement and Characterization","aliases":["RIR","impulse response measurement"],"domain":"acoustics","family":"process-pipeline","subfamily":"Signal processing","year":"1965","originator":"Manfred Schroeder","url":"https://scholargate.app/en/acoustics/room-impulse-response","markdownUrl":"https://scholargate.app/en/acoustics/room-impulse-response.md","definition":"The Room Impulse Response (RIR) is a measure of how a physical space (room) affects acoustic signals propagating through it. First formalized by Manfred Schroeder in 1965, RIR captures the complete acoustic character of a space by measuring the system response to an impulsive sound source. It is fundamental to characterizing room acoustics, designing audio systems, and modeling spatial audio effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Manfred Schroeder","subfamily":"Signal processing","year":"1965","type":"Measurement pipeline for room acoustics"},"citations":[{"ref":"Schroeder, M. R. (1965). New method of measuring reverberation time. Journal of the Acoustical Society of America, 37(6), 409–412.","type":"article","doi":"10.1121/1.1909343","isbn":null,"url":null},{"ref":"Kuttruff, H. (1991). Room Acoustics (3rd ed.). Applied Science Publishers.","type":"book","doi":null,"isbn":"978-0-85334-940-5","url":null},{"ref":"Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (2009). Discrete-Time Signal Processing (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0-13-198842-2","url":null}],"related":["rt60-reverberation-time","acoustic-ray-tracing","beamforming","acoustic-holography","bem-acoustics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"root-architecture-analysis","name":"Root Architecture Analysis","fullName":"Plant Root Architecture Analysis","aliases":["root system architecture analysis","RSA analysis","root morphology analysis","root phenotyping"],"domain":"agronomy","family":"process-pipeline","subfamily":"Plant phenotyping and soil-plant interaction","year":"Systematic methods developed from the 1970s onward; foundational review by Lynch (1995)","originator":"Multiple contributors","url":"https://scholargate.app/en/agronomy/root-architecture-analysis","markdownUrl":"https://scholargate.app/en/agronomy/root-architecture-analysis.md","definition":"Root Architecture Analysis is a quantitative method in agronomy and plant science that characterises the spatial configuration, branching pattern, and geometric properties of a plant's root system. By measuring parameters such as total root length, lateral root density, root angle, and root tip number, researchers link below-ground structural traits to nutrient and water acquisition efficiency, soil penetration capacity, and ultimately to crop productivity and stress tolerance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors","year":"Systematic methods developed from the 1970s onward; foundational review by Lynch (1995)","type":"Quantitative morphological analysis pipeline","dataType":"Root images (scanned, photographed, or CT-derived), root length, branching angle, tip counts","subfamily":"Plant phenotyping and soil-plant interaction"},"citations":[{"ref":"Lynch, J. (1995). Root architecture and plant productivity. Plant Physiology, 109(1), 7–13.","type":"article","doi":"10.1104/pp.109.1.7","isbn":null,"url":null},{"ref":"Smith, A. G., Lauersen, K. J., Bohlin, E., Wlodarczyk, A., & Gnanasekaran, P. (2018). A review of plant root imaging and image analysis methods. Plant Methods, 14, Article 104.","type":"article","doi":null,"isbn":null,"url":"https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0300-8"}],"related":["rhizosphere-sampling","soil-core-analysis","plant-phenotyping","nutrient-uptake-modeling","biomass-estimation","wsa-aggregate-stability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"root-canal-length-determination","name":"Root Canal Length Determination","fullName":"Working Length Determination in Endodontics","aliases":["working length measurement","WL determination","electronic apical locator","periapical radiography"],"domain":"dentistry","family":"process-pipeline","subfamily":"Endodontics","year":"1920s (radiography); 1960s (electronic)","originator":"Multiple innovators (radiographic and electronic methods)","url":"https://scholargate.app/en/dentistry/root-canal-length-determination","markdownUrl":"https://scholargate.app/en/dentistry/root-canal-length-determination.md","definition":"Root canal length determination (working length) is a critical procedural step in endodontic therapy that establishes the precise depth to which instrumentation, irrigation, and obturation should extend within the root canal system. Modern approaches combine electronic apical locators (EAL) with radiographic verification to accurately locate the apical foramen and establish the working length. Accurate working length determination is essential for successful endodontic treatment, preventing under-instrumentation (leaving infected material) and over-instrumentation (causing periapical inflammation).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple innovators (radiographic and electronic methods)","subfamily":"Endodontics","year":"1920s (radiography); 1960s (electronic)","type":"Diagnostic and measurement procedure"},"citations":[{"ref":"Ingle, J. I., Bakland, L. K., & Baumgartner, J. C. (2008). Endodontics (6th ed.). BC Decker.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/endodontics/hargreaves/978-0-323-55735-5"},{"ref":"Plotino, G., Grande, N. M., Testarelli, L., & Gambarini, G. (2016). Definitive obturation of the root canal system: materials, techniques and future perspectives. Odontology, 104(1), 18-38.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Definitive+obturation+of+the+root+canal+system%3A+materials%2C+techniques+and+future+perspectives+Plotino"},{"ref":"Fouad, A. F. (2010). Endodontic therapy: clinical guidelines. Dental Clinics of North America, 56(1), 3-15.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Endodontic+therapy%3A+clinical+guidelines+Fouad"}],"related":["bitewing-radiography","occlusal-analysis","salivary-biomarker-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"root-cause-analysis","name":"Root Cause Analysis","fullName":"Root Cause Analysis (Ishikawa / 5 Whys)","aliases":["Cause-and-Effect Analysis","Fishbone Analysis","Ishikawa Diagram","Kök Neden Analizi"],"domain":"quality-management","family":"process-pipeline","subfamily":"Quality tools","year":1986,"originator":"Kaoru Ishikawa","url":"https://scholargate.app/en/quality-management/root-cause-analysis","markdownUrl":"https://scholargate.app/en/quality-management/root-cause-analysis.md","definition":"Root Cause Analysis (RCA) is a structured, systematic method for identifying the fundamental causes of defects, failures, or undesirable outcomes rather than treating surface-level symptoms. Popularised by Japanese quality engineer Kaoru Ishikawa in the 1960s–1980s, and formally codified in his 1986 Guide to Quality Control, RCA combines the Ishikawa (fishbone) diagram with the iterative 5 Whys questioning technique to trace causal chains back to their origin.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaoru Ishikawa","year":1986,"type":"Structured causal-inference tool","subfamily":"Quality tools","also_known_as":"Fishbone / Ishikawa diagram","typical_team_size":"Cross-functional group of 4–8 experts"},"citations":[{"ref":"Ishikawa, K. (1986). Guide to Quality Control (2nd ed.). Asian Productivity Organization.","type":"book","doi":null,"isbn":"978-92-833-1036-7","url":null}],"related":["six-sigma-dmaic","fault-tree-analysis","value-stream-mapping"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"root-mean-squared-error","name":"Root Mean Squared Error","fullName":"Root Mean Squared Error","aliases":["RMSE","RMS error","quadratic mean error"],"domain":"model-evaluation","family":"mcdm","subfamily":"Error metric","year":"1809","originator":"Carl Friedrich Gauss","url":"https://scholargate.app/en/model-evaluation/root-mean-squared-error","markdownUrl":"https://scholargate.app/en/model-evaluation/root-mean-squared-error.md","definition":"Root Mean Squared Error is a widely used metric that measures the average magnitude of prediction errors in regression models. Originating from Carl Friedrich Gauss's work on least-squares estimation (1809), RMSE quantifies how far predictions deviate from observed values by averaging the squared differences and taking the square root.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Friedrich Gauss","subfamily":"Error metric","year":"1809","type":"Distance-based evaluation metric"},"citations":[{"ref":"Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/theoriamotus00gaus"},{"ref":"Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Paris: F. Didot.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/nouvellesmethod00legen"},{"ref":"Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). New York: Springer.","type":"book","doi":"10.1007/978-0-387-84858-7","isbn":null,"url":null}],"related":["mean-squared-error","mean-absolute-error","mean-absolute-percentage-error","r-squared"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rosenberg-self-esteem-scale","name":"Rosenberg Self-Esteem Scale","fullName":"Rosenberg Self-Esteem Scale (RSES)","aliases":["RSES","Rosenberg Scale","Self-Esteem Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Self-report questionnaire","year":"1965","originator":"Morris Rosenberg","url":"https://scholargate.app/en/social-psychology/rosenberg-self-esteem-scale","markdownUrl":"https://scholargate.app/en/social-psychology/rosenberg-self-esteem-scale.md","definition":"The Rosenberg Self-Esteem Scale (RSES) is a 10-item unidimensional instrument designed to measure global self-esteem in adolescents and adults. Developed by Morris Rosenberg in 1965, the RSES is one of the most widely used and shortest self-esteem measures in social and clinical psychology research. Its brevity, ease of administration, and robust psychometric properties have made it a standard reference point for self-esteem assessment across cultures and clinical populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morris Rosenberg","subfamily":"Self-report questionnaire","year":"1965","type":"Self-esteem assessment scale"},"citations":[{"ref":"Rosenberg, M. (1965). Society and the adolescent self-image. Princeton University Press.","type":"book","doi":null,"isbn":"978-0-691-09675-5","url":null},{"ref":"Blascovich, J., & Tomaka, J. (1991). Measures of self-esteem. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of personality and social psychological attitudes (pp. 115–160). Academic Press.","type":"article","doi":null,"isbn":"978-0-12-590241-0","url":null},{"ref":"Schmitt, D. P., & Allik, J. (2005). Simultaneous administration of the Rosenberg Self-Esteem Scale in 53 nations: Exploring the universal and culture-specific features of global self-esteem. Journal of Personality and Social Psychology, 89(4), 623–642.","type":"article","doi":"10.1037/0022-3514.89.4.623","isbn":null,"url":null}],"related":["neo-pi-r","self-compassion-scale","generalized-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rosin-rammler-distribution","name":"Rosin-Rammler Distribution","fullName":"Rosin-Rammler-Sperling Distribution","aliases":["Rosin-Rammler Model","RRS Distribution","Weibull Distribution (particle size)"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Particle Size Distribution Modeling","year":"1933","originator":"Paul Rosin and Erich Rammler","url":"https://scholargate.app/en/mining-engineering/rosin-rammler-distribution","markdownUrl":"https://scholargate.app/en/mining-engineering/rosin-rammler-distribution.md","definition":"The Rosin-Rammler Distribution, introduced by Paul Rosin and Erich Rammler in 1933, is an empirical probability distribution that describes the particle size distribution of ground or crushed materials. It characterizes fineness by two parameters: the characteristic size (d-prime) and the uniformity index (n). This distribution is remarkably accurate for mineral processing streams and is ubiquitous in comminution engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Rosin and Erich Rammler","subfamily":"Particle Size Distribution Modeling","year":"1933","type":"Empirical probability distribution for crushed material fineness"},"citations":[{"ref":"Rosin, P., & Rammler, E. (1933). The laws governing the fineness of powdered coal. Journal of the Institute of Fuel, 7, 29-36.","type":"article","doi":null,"isbn":null,"url":"https://journal.theinstituteoffuel.org/"},{"ref":"Austin, L. G., Klimpel, R. R., & Luckie, P. T. (2006). Process engineering of size reduction: Ball grinding mills. Society for Mining, Metallurgy & Exploration.","type":"article","doi":null,"isbn":null,"url":"https://www.smenet.org/"}],"related":["bond-work-index","flotation-kinetics","mccabe-thiele-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rotation-curve-analysis","name":"Rotation Curve Analysis","fullName":"Galaxy Rotation Curve Analysis for Dark Matter Detection","aliases":["Galactic Rotation Curves","Rotation Curve Method","Velocity Curve Analysis"],"domain":"astronomy","family":"process-pipeline","subfamily":"Galactic dynamics","year":1970,"originator":"Vera Rubin","url":"https://scholargate.app/en/astronomy/rotation-curve-analysis","markdownUrl":"https://scholargate.app/en/astronomy/rotation-curve-analysis.md","definition":"Galaxy rotation curve analysis is the technique of measuring how orbital velocities change with distance from the center of a galaxy. Pioneered by Vera Rubin and W. Kent Ford Jr. in 1970, rotation curves revealed one of astronomy's great mysteries: galaxies rotate too fast to be held together by their visible stars alone, providing direct evidence for dark matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vera Rubin","subfamily":"Galactic dynamics","year":1970,"type":"Observational kinematic method"},"citations":[{"ref":"Vera C. Rubin & W. Kent Ford Jr. (1970). Rotation of the Andromeda Nebula from a Spectroscopic Survey of Emission Regions. Astrophysical Journal, 159, 379-403.","type":"article","doi":"10.1086/150317","isbn":null,"url":null},{"ref":"Flores, R. A., & Primack, J. R. (1994). Structure and dynamics of galactic dark matter halos. Astrophysical Journal Letters, 427(1), L1-L4.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Structure+and+dynamics+of+galactic+dark+matter+halos+Flores"},{"ref":"Sofue, Y., Tutui, Y., Honma, M., et al. (2001). Central and dark matter rotation curves of spiral galaxies. Astrophysical Journal, 547(2), 712-726.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Central+and+dark+matter+rotation+curves+of+spiral+galaxies+Sofue"}],"related":["kinematic-distance","astrometry","pulsar-timing-array"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rotator-cuff-quality-of-life","name":"Rotator Cuff Quality of Life Index","fullName":"Rotator Cuff Quality of Life Index (RC-QoL)","aliases":["RC-QoL","Rotator Cuff Quality of Life"],"domain":"sports-medicine","family":"process-pipeline","subfamily":"rotator-cuff-specific quality-of-life","year":1998,"originator":"Rotator cuff outcome measurement literature consensus","url":"https://scholargate.app/en/sports-medicine/rotator-cuff-quality-of-life","markdownUrl":"https://scholargate.app/en/sports-medicine/rotator-cuff-quality-of-life.md","definition":"The Rotator Cuff Quality of Life Index (RC-QoL) is a rotator cuff-specific outcome instrument that measures symptom impact and functional limitations in patients with rotator cuff disease. Developed within rotator cuff treatment literature, the RC-QoL captures the physical, emotional, and social burden of rotator cuff pathology—pain, functional limitations, sleep disturbance, psychological distress—providing a patient-centered perspective on disease impact beyond mechanical function alone. The RC-QoL is widely used in rotator cuff repair and conservative management outcome studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rotator cuff outcome measurement literature consensus","subfamily":"rotator-cuff-specific quality-of-life","year":1998,"type":"Patient self-report"},"citations":[{"ref":"Lippitt SB, Harryman DT, Matsen FA III. A practical tool for evaluating function: The Simple Shoulder Test. In: Matsen FA III, Fu FH, Hawkins RJ, eds. The Shoulder: A Balance of Mobility and Stability. Rosemont, IL: American Academy of Orthopaedic Surgeons; 1993.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Lippitt%20SB%2C%20Harryman%20DT%2C%20Matsen%20FA%20III.%20A%20practical%20tool%20for%20evaluating%20function%3A%20The%20Simple%20Shoulder%20Test.%20In%3A%20Matsen%20F"},{"ref":"Robert H, Chambler A, Pélissier A. Influence of time to surgery on the outcome of rotator cuff repair. J Bone Joint Surg Br. 2005;87(8):1100-1104.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Robert+H%2C+Chambler+A%2C+P%C3%A9lissier+A.+Influence+of+time+to+surgery+on+the+outcome+of+rotator+cuff+repair.+J+Bone+Joint+Surg+Robert"}],"related":["ases-shoulder","patient-specific-functional-scale","global-rating-of-change-scale","lower-extremity-functional-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rothermel-fire-model","name":"Rothermel Fire Model","fullName":"Rothermel Surface Fire Spread Model","aliases":["fire spread model","BEHAVE model"],"domain":"forestry","family":"process-pipeline","subfamily":"Fire Dynamics","year":"1972","originator":"Richard Rothermel","url":"https://scholargate.app/en/forestry/rothermel-fire-model","markdownUrl":"https://scholargate.app/en/forestry/rothermel-fire-model.md","definition":"The Rothermel fire spread model, developed by Richard Rothermel in 1972, is a mechanistic mathematical model that predicts the rate of fire spread through surface fuels using fuel characteristics, weather, and topography. It forms the theoretical foundation of the BEHAVE fire modeling system used operationally by fire agencies worldwide. The model integrates principles from combustion physics, heat transfer, and fuel science to quantify how fire intensity, fuel moisture, wind, and slope interact to drive wildfire propagation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Rothermel","subfamily":"Fire Dynamics","year":"1972","type":"fire propagation model"},"citations":[{"ref":"Rothermel, R. C. (1972). A mathematical model for predicting fire spread in wildland fuels. Research Paper INT-115, USDA Forest Service Intermountain Research Station.","type":"article","doi":null,"isbn":null,"url":"https://www.fs.fed.us"},{"ref":"Andrews, P. L. (2003). BEHAVE-Plus fire modeling system: Redesign and modernization. RMRS Research Paper RMRS-RP-101.","type":"article","doi":null,"isbn":null,"url":"https://www.fs.fed.us/research"}],"related":["keetch-byram-drought-index","fire-weather-index","crown-fire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-aras","name":"ROUGH-ARAS","fullName":"Rough-ARAS — Rough extension of ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Daoud Ben Amor, W., Moalla Frikha, H., Martínez López, L.","url":"https://scholargate.app/en/decision-making/rough-aras","markdownUrl":"https://scholargate.app/en/decision-making/rough-aras.md","definition":"ROUGH-ARAS (Rough-ARAS — Rough extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Daoud Ben Amor, W., Moalla Frikha, H., Martínez López, L. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daoud Ben Amor, W., Moalla Frikha, H., Martínez López, L.","subfamily":"Ranking","year":"2021","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Daoud Ben Amor, W., Moalla Frikha, H., Martínez López, L. (2021). The Interval Rough Number of the Extended ARAS Method for Solving Multi-Criteria Group Decision Making. 2021 International Conference on Decision Aid Sciences and Application (DASA)","type":"article","doi":"10.1109/DASA53625.2021.9681928","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-copras","name":"ROUGH-COPRAS","fullName":"Rough-COPRAS — Rough extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Pamučar, D. Božanić, D. Lukovac, V. Komazec, N.","url":"https://scholargate.app/en/decision-making/rough-copras","markdownUrl":"https://scholargate.app/en/decision-making/rough-copras.md","definition":"ROUGH-COPRAS (Rough-COPRAS — Rough extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D. Božanić, D. Lukovac, V. Komazec, N. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamučar, D. Božanić, D. Lukovac, V. Komazec, N.","subfamily":"Ranking","year":"2018","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Pamučar, D., Božanić, D., Lukovac, V., Komazec, N. (2018). Normalized weighted geometric Bonferroni mean operator of interval rough numbers – application in interval rough DEMATEL-COPRAS model. Facta Universitatis Series: Mechanical Engineering","type":"article","doi":"10.22190/fume180503018p","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-drsa","name":"ROUGH-DRSA","fullName":"Rough-DRSA — Rough extension of DRSA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2001","originator":"Greco, S., Matarazzo, B., Słowiński, R.","url":"https://scholargate.app/en/decision-making/rough-drsa","markdownUrl":"https://scholargate.app/en/decision-making/rough-drsa.md","definition":"ROUGH-DRSA (Rough-DRSA — Rough extension of DRSA) is a ranking multi-criteria decision-making (MCDM) method introduced by Greco, S., Matarazzo, B., Słowiński, R. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greco, S., Matarazzo, B., Słowiński, R.","subfamily":"Ranking","year":"2001","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Greco, S., Matarazzo, B., Słowiński, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research","type":"article","doi":"10.1016/s0377-2217(00)00167-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-edas","name":"ROUGH-EDAS","fullName":"Rough-EDAS — Rough extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Paul, V.K. Chakraborty, S. Chakraborty, S.","url":"https://scholargate.app/en/decision-making/rough-edas","markdownUrl":"https://scholargate.app/en/decision-making/rough-edas.md","definition":"ROUGH-EDAS (Rough-EDAS — Rough extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Paul, V.K. Chakraborty, S. Chakraborty, S. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul, V.K. Chakraborty, S. Chakraborty, S.","subfamily":"Ranking","year":"2022","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Paul, V.K., Chakraborty, S., Chakraborty, S. (2022). An Integrated IRN-BWM-EDAS Method for Supplier Selection in a Textile Industry. Decision Making: Applications in Management and Engineering","type":"article","doi":"10.31181/dmame0307102022p","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-mabac","name":"ROUGH-MABAC","fullName":"Rough-MABAC — Rough extension of MABAC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Jia, F., Liu, Y., Wang, X.","url":"https://scholargate.app/en/decision-making/rough-mabac","markdownUrl":"https://scholargate.app/en/decision-making/rough-mabac.md","definition":"ROUGH-MABAC (Rough-MABAC — Rough extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Jia, F., Liu, Y., Wang, X. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jia, F., Liu, Y., Wang, X.","subfamily":"Ranking","year":"2019","type":"IF-Rough outranking/ranking — Intuitionistic Fuzzy Rough Number (membership μ, non-membership ν)","value_space":"if_rough","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Jia, F., Liu, Y., Wang, X. (2019). An extended MABAC method for multi-criteria group decision making based on intuitionistic fuzzy rough numbers. Expert Systems with Applications","type":"article","doi":"10.1016/j.eswa.2019.03.016","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-marcos","name":"ROUGH-MARCOS","fullName":"Rough-MARCOS — Rough extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Matić, B. Marinković, M. Jovanović, S. Sremac, S. Stević, Ž.","url":"https://scholargate.app/en/decision-making/rough-marcos","markdownUrl":"https://scholargate.app/en/decision-making/rough-marcos.md","definition":"ROUGH-MARCOS (Rough-MARCOS — Rough extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Matić, B. Marinković, M. Jovanović, S. Sremac, S. Stević, Ž. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Matić, B. Marinković, M. Jovanović, S. Sremac, S. Stević, Ž.","subfamily":"Ranking","year":"2022","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Matić, B., Marinković, M., Jovanović, S., Sremac, S., Stević, Ž. (2022). Intelligent Novel IMF D-SWARA—Rough MARCOS Algorithm for Selection Construction Machinery for Sustainable Construction of Road Infrastructure. Buildings","type":"article","doi":"10.3390/buildings12071059","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-moora","name":"ROUGH-MOORA","fullName":"Rough-MOORA — Rough extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2006","originator":"Brauers, W.K.M. Zavadskas, E.K.","url":"https://scholargate.app/en/decision-making/rough-moora","markdownUrl":"https://scholargate.app/en/decision-making/rough-moora.md","definition":"ROUGH-MOORA (Rough-MOORA — Rough extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Brauers, W.K.M. Zavadskas, E.K. in 2006. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brauers, W.K.M. Zavadskas, E.K.","subfamily":"Ranking","year":"2006","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Brauers, W.K.M., Zavadskas, E.K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The%20MOORA%20method%20and%20its%20application%20to%20privatization%20in%20a%20transition%20economy"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-saw","name":"ROUGH-SAW","fullName":"Rough-SAW — Rough extension of SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2017","originator":"Stević, Ž. Pamučar, D. Zavadskas, E.K. Ćirović, G. Prentkovskis, O.","url":"https://scholargate.app/en/decision-making/rough-saw","markdownUrl":"https://scholargate.app/en/decision-making/rough-saw.md","definition":"ROUGH-SAW (Rough-SAW — Rough extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Stević, Ž. Pamučar, D. Zavadskas, E.K. Ćirović, G. Prentkovskis, O. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stević, Ž. Pamučar, D. Zavadskas, E.K. Ćirović, G. Prentkovskis, O.","subfamily":"Ranking","year":"2017","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Stević, Ž., Pamučar, D., Zavadskas, E.K., Ćirović, G., Prentkovskis, O. (2017). The Selection of Wagons for the Internal Transport of a Logistics Company: A Novel Approach Based on Rough BWM and Rough SAW Methods. Symmetry","type":"article","doi":"10.3390/sym9110264","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-todim","name":"ROUGH-TODIM","fullName":"Rough-TODIM — Rough extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2024","originator":"Tiwari, V. Khanna, P. Tandon, P.","url":"https://scholargate.app/en/decision-making/rough-todim","markdownUrl":"https://scholargate.app/en/decision-making/rough-todim.md","definition":"ROUGH-TODIM (Rough-TODIM — Rough extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Tiwari, V. Khanna, P. Tandon, P. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tiwari, V. Khanna, P. Tandon, P.","subfamily":"Ranking","year":"2024","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Tiwari, V., Khanna, P., Tandon, P. (2024). Capturing Design Intent During Concept Evaluation Using Rough Numbers and TODIM Method. Computer-Aided Design & Applications","type":"article","doi":"10.14733/cadaps.2024.215-228","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-topsis","name":"ROUGH-TOPSIS","fullName":"Rough-TOPSIS — Rough extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Song, W. Ming, X. Wu, Z.","url":"https://scholargate.app/en/decision-making/rough-topsis","markdownUrl":"https://scholargate.app/en/decision-making/rough-topsis.md","definition":"ROUGH-TOPSIS (Rough-TOPSIS — Rough extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Song, W. Ming, X. Wu, Z. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Song, W. Ming, X. Wu, Z.","subfamily":"Ranking","year":"2013","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Song, W., Ming, X., Wu, Z. (2013). An integrated rough number-based approach to design concept evaluation under subjective environments. International Journal of Computer Integrated Manufacturing","type":"article","doi":"10.1080/09544828.2012.732994","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-vikor","name":"ROUGH-VIKOR","fullName":"Rough-VIKOR — Rough extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Zhu, G. Hu, J. Qi, J. Gu, C. Peng, Y.","url":"https://scholargate.app/en/decision-making/rough-vikor","markdownUrl":"https://scholargate.app/en/decision-making/rough-vikor.md","definition":"ROUGH-VIKOR (Rough-VIKOR — Rough extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Zhu, G. Hu, J. Qi, J. Gu, C. Peng, Y. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhu, G. Hu, J. Qi, J. Gu, C. Peng, Y.","subfamily":"Ranking","year":"2015","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zhu, G., Hu, J., Qi, J., Gu, C., Peng, Y. (2015). An integrated AHP and VIKOR for design concept evaluation based on rough number. Advanced Engineering Informatics","type":"article","doi":"10.1016/j.aei.2015.01.010","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rough-waspas","name":"ROUGH-WASPAS","fullName":"Rough-WASPAS — Rough extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Stojić, G. Stević, Ž. Antuchevičienė, J. Pamučar, D. Vasiljević, M.","url":"https://scholargate.app/en/decision-making/rough-waspas","markdownUrl":"https://scholargate.app/en/decision-making/rough-waspas.md","definition":"ROUGH-WASPAS (Rough-WASPAS — Rough extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Stojić, G. Stević, Ž. Antuchevičienė, J. Pamučar, D. Vasiljević, M. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stojić, G. Stević, Ž. Antuchevičienė, J. Pamučar, D. Vasiljević, M.","subfamily":"Ranking","year":"2018","type":"Rough outranking/ranking — Rough number (lower approximation L, upper approximation U)","value_space":"rough","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Stojić, G., Stević, Ž., Antuchevičienė, J., Pamučar, D., Vasiljević, M. (2018). A Novel Rough WASPAS Approach for Supplier Selection in a Company Manufacturing PVC Carpentry Products. Information","type":"article","doi":"10.3390/info9050121","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rov","name":"ROV","fullName":"Range of Value method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1993","originator":"Yakowitz, D. S., Lane, L. J., Szidarovszky, F.","url":"https://scholargate.app/en/decision-making/rov","markdownUrl":"https://scholargate.app/en/decision-making/rov.md","definition":"ROV (Range of Value method) is a ranking multi-criteria decision-making (MCDM) method introduced by Yakowitz, D. S., Lane, L. J., Szidarovszky, F. in 1993. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yakowitz, D. S., Lane, L. J., Szidarovszky, F.","subfamily":"Ranking","year":"1993","type":"Interval utility (optimistic–pessimistic bounds)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yakowitz, D. S., Lane, L. J., Szidarovszky, F. (1993). Multi-attribute decision making: Dominance with respect to an importance order of the attributes. Applied Mathematics and Computation","type":"article","doi":"10.1016/0096-3003(93)90057-l","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rsa-cryptosystem-analysis","name":"RSA Cryptosystem Analysis","fullName":"RSA Cryptosystem Security Analysis and Implementation","aliases":["RSA Analysis","Rivest–Shamir–Adleman Analysis"],"domain":"cryptography","family":"process-pipeline","subfamily":"Public-key cryptography","year":"1978","originator":"Ronald Rivest, Adi Shamir, Leonard Adleman","url":"https://scholargate.app/en/cryptography/rsa-cryptosystem-analysis","markdownUrl":"https://scholargate.app/en/cryptography/rsa-cryptosystem-analysis.md","definition":"RSA (Rivest–Shamir–Adleman) is a foundational asymmetric cryptosystem introduced in 1978 that enables both encryption and digital signatures using a pair of public and private keys. It remains one of the most widely deployed cryptographic algorithms in modern security infrastructure, supporting secure communication and authentication across the internet.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Rivest, Adi Shamir, Leonard Adleman","subfamily":"Public-key cryptography","year":"1978","type":"Asymmetric encryption and signature algorithm"},"citations":[{"ref":"Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2), 120–126.","type":"article","doi":"10.1145/359340.359342","isbn":null,"url":null},{"ref":"Menezes, A. J., van Oorschot, P. C., & Vanstone, S. A. (1997). Handbook of Applied Cryptography. CRC Press.","type":"book","doi":null,"isbn":null,"url":"https://cacr.uwaterloo.ca/hac/"},{"ref":"Boneh, D. (1999). Twenty years of attacks on the RSA cryptosystem. Notices of the American Mathematical Society, 46(2), 203–213.","type":"article","doi":null,"isbn":null,"url":"https://crypto.stanford.edu/~dabo/papers/RSA-survey.pdf"}],"related":["diffie-hellman-key-exchange","digital-signature-scheme","sha-hash-function","tls-protocol-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rsa-cryptosystem","name":"RSA Cryptosystem","fullName":"Rivest-Shamir-Adleman Cryptosystem","aliases":["RSA encryption","RSA public-key cryptography"],"domain":"cryptography","family":"ml-model","subfamily":"Public-key cryptography","year":"1978","originator":"Ronald Rivest","url":"https://scholargate.app/en/cryptography/rsa-cryptosystem","markdownUrl":"https://scholargate.app/en/cryptography/rsa-cryptosystem.md","definition":"RSA is a foundational public-key cryptosystem developed by Rivest, Shamir, and Adleman in 1978. It enables secure encryption and digital signatures by using a pair of mathematically linked keys: a public key for encryption and a private key for decryption. RSA's security relies on the computational difficulty of factoring large composite numbers into their prime factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Rivest","subfamily":"Public-key cryptography","year":"1978","type":"asymmetric encryption algorithm"},"citations":[{"ref":"Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2), 120-126.","type":"article","doi":"10.1145/359340.359342","isbn":null,"url":null},{"ref":"Koblitz, N. (1987). A Course in Number Theory and Cryptography. Springer-Verlag.","type":"book","doi":null,"isbn":"978-0387966618","url":null}],"related":["aes","elliptic-curve-cryptography","hmac","differential-cryptanalysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rt60-reverberation-time","name":"RT60 Reverberation Time","fullName":"RT60 Reverberation Time Measurement and Analysis","aliases":["RT60","reverberation time","decay time"],"domain":"acoustics","family":"process-pipeline","subfamily":"Acoustic measurement","year":"1900","originator":"Wallace Clement Sabine","url":"https://scholargate.app/en/acoustics/rt60-reverberation-time","markdownUrl":"https://scholargate.app/en/acoustics/rt60-reverberation-time.md","definition":"RT60 (reverberation time) is the duration required for sound energy in a room to decay by 60 decibels after the source stops. Pioneered by Wallace Clement Sabine in 1900, RT60 is the most widely used single-number descriptor of room acoustic properties. It reflects how much sound is absorbed versus reflected by room surfaces and directly affects speech intelligibility, music clarity, and acoustic comfort.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wallace Clement Sabine","subfamily":"Acoustic measurement","year":"1900","type":"Room acoustic descriptor"},"citations":[{"ref":"Sabine, W. C. (1900). Collected Papers on Acoustics. Dover Publications.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/collectedpapers00sabin"},{"ref":"Schroeder, M. R. (1965). New method of measuring reverberation time. Journal of the Acoustical Society of America, 37(6), 409–412.","type":"article","doi":"10.1121/1.1909343","isbn":null,"url":null},{"ref":"Eyring, C. F. (1930). Reverberation time in dead rooms. Journal of the Acoustical Society of America, 1(2), 217–241.","type":"article","doi":"10.1121/1.1915175","isbn":null,"url":null}],"related":["room-impulse-response","speech-intelligibility","acoustic-ray-tracing","psychoacoustic-masking","bem-acoustics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ruin-theory","name":"Ruin Theory","fullName":"Ruin Theory (Risk Process Probability of Ruin)","aliases":["Collective Risk Theory","Cramér-Lundberg Theory","Probability of Ruin Analysis","Hasar Süreci Çöküş Teorisi"],"domain":"actuarial-science","family":"regression-model","subfamily":"Actuarial modelling","year":2010,"originator":"Filip Lundberg; Harald Cramér","url":"https://scholargate.app/en/actuarial-science/ruin-theory","markdownUrl":"https://scholargate.app/en/actuarial-science/ruin-theory.md","definition":"Ruin Theory models the stochastic surplus process of an insurance company to quantify the probability that accumulated losses eventually exceed available capital. Introduced by Filip Lundberg in his 1903 doctoral thesis and rigorously unified by Harald Cramér in 1930, the classical Cramér-Lundberg model assumes premiums arrive at a constant rate, claims follow a compound Poisson process, and individual claim sizes are independent and identically distributed. It remains the foundational framework of collective risk theory in actuarial science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Filip Lundberg; Harald Cramér","year":2010,"type":"Stochastic risk process model","subfamily":"Actuarial modelling","core_quantity":"Probability of ruin psi(u)","process_class":"Compound Poisson surplus process"},"citations":[{"ref":"Asmussen, S., & Albrecher, H. (2010). Ruin Probabilities (2nd ed.). World Scientific.","type":"book","doi":null,"isbn":"978-981-4282-52-9","url":null}],"related":["loss-distribution-model","extreme-value-theory","stochastic-differential-equations"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rule-induction","name":"Rule Induction","fullName":"Rule Induction (RIPPER)","aliases":["RIPPER","Propositional Rule Learning","Kural Tümevarımı","Inductive Rule Learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Rule learning","year":1995,"originator":"William W. Cohen","url":"https://scholargate.app/en/machine-learning/rule-induction","markdownUrl":"https://scholargate.app/en/machine-learning/rule-induction.md","definition":"Rule Induction, and specifically the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) algorithm, is a supervised machine learning method that learns a compact set of IF-THEN classification rules from labeled training data. Introduced by William W. Cohen in 1995, RIPPER applies a separate-and-conquer strategy combined with minimum description length (MDL) pruning to generate rules that are both accurate and interpretable, making it a landmark algorithm in the field of inductive rule learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William W. Cohen","year":1995,"type":"Supervised rule learning algorithm","subfamily":"Rule learning","output":"Ordered or unordered IF-THEN rule sets","complexity":"O(n log n) per rule learning step"},"citations":[{"ref":"Cohen, W. W. (1995). Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning, 115–123.","type":"inproceedings","doi":"10.1016/B978-1-55860-377-6.50023-2","isbn":null,"url":null}],"related":["decision-tree","association-rule-mining","rough-set-theory"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rule-space-methodology","name":"Rule Space Methodology","fullName":"Rule Space Methodology","aliases":["RSM"],"domain":"psychometrics","family":"latent-structure","subfamily":"Cognitive Diagnosis","year":"1983","originator":"Kikumi K. Tatsuoka","url":"https://scholargate.app/en/psychometrics/rule-space-methodology","markdownUrl":"https://scholargate.app/en/psychometrics/rule-space-methodology.md","definition":"Rule Space Methodology (RSM) is a diagnostic classification approach developed by Tatsuoka (1983) that uses Item Response Theory and geometric methods to classify examinees into knowledge states based on their response patterns. Unlike classical scoring, RSM identifies which specific skills or competencies an examinee possesses or lacks, enabling targeted educational interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kikumi K. Tatsuoka","subfamily":"Cognitive Diagnosis","year":"1983","type":"IRT-based diagnostic classification"},"citations":[{"ref":"Hartz, S. M. (2002). A Bayesian framework for the unified treatment of assessing dimensionality, assessing local dependence, and estimating ability for unidimensional and multidimensional item response data. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign.","type":"article","doi":null,"isbn":null,"url":"https://www.ideals.illinois.edu/handle/2142/2516"},{"ref":"Tatsuoka, K. K. (1983). Rule space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20(4), 345-354.","type":"article","doi":"10.1111/j.1745-3984.1983.tb00212.x","isbn":null,"url":null},{"ref":"Tatsuoka, K. K., & Tatsuoka, M. M. (2009). Cognitive diagnosis and measurement in clinical and educational settings. In D. J. Ketterlin-Geller, D. L. Compton, & K. L. Hosp (Eds.), Handbook of Instructional Practices for Struggling Adolescent Readers: A Problem-Solving Approach (pp. 111-138). Routledge.","type":"article","doi":null,"isbn":"9780415954204","url":null}],"related":["dina-model","dino-model","cognitive-diagnostic-cat","fsqca","necessary-condition-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rumen-in-vitro-gas-production","name":"Rumen In Vitro Gas Production","fullName":"In Vitro Gas Production Assay for Rumen Fermentation","aliases":["IVGP","gas production technique","fermentation assay"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Fermentation Analysis","year":"1994","originator":"Michail K. Theodorou","url":"https://scholargate.app/en/veterinary-science/rumen-in-vitro-gas-production","markdownUrl":"https://scholargate.app/en/veterinary-science/rumen-in-vitro-gas-production.md","definition":"The In Vitro Gas Production (IVGP) assay is a laboratory method that measures the fermentation kinetics of animal feeds by incubating feed samples with rumen microorganisms in controlled conditions and monitoring the volume of gas produced over time. Developed by Theodorou and colleagues in 1994, IVGP provides rapid, cost-effective estimates of feed nutritive value, digestibility, and energy availability, making it valuable for feed evaluation, diet formulation, and research on rumen fermentation dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michail K. Theodorou","subfamily":"Fermentation Analysis","year":"1994","type":"Biochemical Assay"},"citations":[{"ref":"Theodorou, M. K., Williams, B. A., Dhanoa, M. S., McAllan, A. B., & France, J. (1994). A simple gas production method using a pressure transducer to determine fermentation kinetics of ruminant feeds. Animal Feed Science and Technology, 48(3-4), 185-197.","type":"article","doi":"10.1016/0377-8401(94)90171-6","isbn":null,"url":null},{"ref":"Menke, K. H., & Steingass, H. (1988). Estimation of the energetic feed value obtained from chemical analysis and in vitro gas production using rumen fluid. Animal Research and Development, 28, 7-55.","type":"article","doi":null,"isbn":null,"url":"https://www.tropentag.de/2004/abstracts/full/339.pdf"},{"ref":"Getachew, G., Makkar, H. P., & Becker, K. (2004). Assessment of the nutritive value of Ethiopian fodders based on chemical composition and in vitro gas production. Arid Land Research and Management, 18(1), 25-38.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessment+of+the+nutritive+value+of+Ethiopian+fodders+based+on+chemical+composition+and+in+vitro+gas+production+Getachew"}],"related":["apparent-total-tract-digestibility","ndf-adf-analysis","somatic-cell-count"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"runge-kutta-method","name":"Runge-Kutta Method","fullName":"Runge-Kutta Method for Numerical Integration","aliases":["RK4","RK method","explicit Runge-Kutta"],"domain":"numerical-methods","family":"ml-model","subfamily":"Explicit Multistage","year":"1895–1901","originator":"Carl Runge and Martin Kutta","url":"https://scholargate.app/en/numerical-methods/runge-kutta-method","markdownUrl":"https://scholargate.app/en/numerical-methods/runge-kutta-method.md","definition":"The Runge-Kutta Method is a family of explicit numerical techniques for solving ordinary differential equations (ODEs) developed independently by Carl Runge in 1895 and Martin Kutta in 1901. The fourth-order variant (RK4) is one of the most widely used algorithms in computational science and engineering for time-stepping problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Runge and Martin Kutta","subfamily":"Explicit Multistage","year":"1895–1901","type":"Numerical integration scheme"},"citations":[{"ref":"Runge, C. (1895). Ueber die numerische Auflösung von Differentialgleichungen. Mathematische Annalen, 46(2), 167–178.","type":"article","doi":"10.1007/BF01446807","isbn":null,"url":null},{"ref":"Kutta, M. W. (1901). Beitrag zur näherungsweisen Integration totaler Differentialgleichungen. Zeitschrift für Mathematik und Physik, 46, 435–453.","type":"article","doi":null,"isbn":null,"url":"https://gallica.bnf.fr/ark:/12148/bpt6k99379z"},{"ref":"Butcher, J. C. (2008). Numerical Methods for Ordinary Differential Equations (2nd ed.). Wiley.","type":"book","doi":"10.1002/9780470753767","isbn":null,"url":null}],"related":["euler-method","multi-step-methods","backward-euler-method","verlet-integration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"runge-kutta-optimizer","name":"Runge Kutta Optimizer","fullName":"Runge Kutta Optimizer","aliases":["RKO"],"domain":"optimization","family":"ml-model","subfamily":"Mathematical Optimization","year":"2023","originator":"Ayushi Khatri","url":"https://scholargate.app/en/optimization/runge-kutta-optimizer","markdownUrl":"https://scholargate.app/en/optimization/runge-kutta-optimizer.md","definition":"The Runge Kutta Optimizer (RKO) is a metaheuristic algorithm introduced by Khatri et al. in 2023 that leverages numerical integration principles from the Runge-Kutta method. Instead of biological inspiration, RKO grounds optimization in mathematical principles of differential equations and numerical integration. The algorithm treats the optimization landscape as a dynamic system and uses multi-stage integration steps to evolve solutions toward optima.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ayushi Khatri","subfamily":"Mathematical Optimization","year":"2023","type":"Mathematical metaheuristic algorithm"},"citations":[{"ref":"Khatri, A., Kumar, A., & Gaba, G. K. (2023). Runge Kutta optimizer: An efficient approach for solving optimization tasks. Computers and Industrial Engineering, 180, 109201.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Runge+Kutta+optimizer%3A+An+efficient+approach+for+solving+optimization+tasks+Khatri"}],"related":["arithmetic-optimization-algorithm","slime-mould-algorithm","harris-hawks-optimization","particle-swarm-optimization","differential-evolution"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"runs-test","name":"Runs Test","fullName":"Wald-Wolfowitz Runs Test","aliases":["Wald-Wolfowitz test","runs test for randomness","Runs Testi (Wald-Wolfowitz)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1940,"originator":"Abraham Wald & Jacob Wolfowitz","url":"https://scholargate.app/en/statistics/runs-test","markdownUrl":"https://scholargate.app/en/statistics/runs-test.md","definition":"The Wald-Wolfowitz runs test is a nonparametric hypothesis test that determines whether a sequence of observations — coded as a series of binary symbols — follows a random pattern or contains systematic structure. Introduced by Abraham Wald and Jacob Wolfowitz in 1940, the test counts the number of uninterrupted runs of identical symbols and asks whether that count is consistent with random arrangement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abraham Wald & Jacob Wolfowitz","year":1940,"family":"Hypothesis test","type":"Nonparametric randomness test","outcome":"binary or dichotomized continuous","parametric":false,"minSample":10,"difficulty":1},"citations":[{"ref":"Wald, A. & Wolfowitz, J. (1940). On a test whether two samples are from the same population. Annals of Mathematical Statistics, 11(2), 147–162.","type":"article","doi":"10.1214/aoms/1177731909","isbn":null,"url":null}],"related":["sign-test","mann-whitney-u","kolmogorov-smirnov","ljung-box-test","durbin-watson-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"rupture-resolution-scale","name":"Rupture Resolution Rating System","fullName":"Rupture Resolution Rating System (RRRS)","aliases":["RRRS","Alliance Rupture Rating Scale","Rupture Resolution Scale"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"alliance-rupture","year":"1993","originator":"Jeremy D. Safran, J. Christopher Muran","url":"https://scholargate.app/en/psychotherapy-research/rupture-resolution-scale","markdownUrl":"https://scholargate.app/en/psychotherapy-research/rupture-resolution-scale.md","definition":"The Rupture Resolution Rating System (RRRS) is an observer-based measure designed to assess the quality of therapist response to alliance ruptures and the degree to which ruptures are resolved within psychotherapy sessions. Developed by Safran and Muran, the RRRS operationalizes the principle that ruptures—temporary breaks in empathy, collaboration, or understanding between therapist and client—are normal therapy events and that how therapists repair them predicts therapeutic benefit. The RRRS codes the presence, severity, and resolution of ruptures, revealing therapist skill in navigating relational challenges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeremy D. Safran, J. Christopher Muran","subfamily":"alliance-rupture","year":"1993","type":"Observer/Clinician-rated"},"citations":[{"ref":"Safran, J. D., Muran, J. C., & Samstag, L. W. (1994). Resolving therapeutic alliance ruptures: A task analytic investigation. In A. O. Horvath & L. S. Greenberg (Eds.), The working alliance: Theory, research, and practice (pp. 225–255). New York: John Wiley & Sons.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Safran%2C%20J.%20D.%2C%20Muran%2C%20J.%20C.%2C%20%26%20Samstag%2C%20L.%20W.%20(1994).%20Resolving%20therapeutic%20alliance%20ruptures%3A%20A%20task%20analytic%20investiga"},{"ref":"Muran, J. C., Safran, J. D., Samstag, L. W., & Winston, A. (2005). Evaluating an alliance-focused treatment for personality disorders. Psychotherapy: Theory, Research, Practice, Training, 42(4), 532–545.","type":"article","doi":"10.1037/0033-3204.42.4.532","isbn":null,"url":null}],"related":["working-alliance-inventory","session-rating-scale","therapeutic-alliance-scale","patient-therapist-agreement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"s-estimator","name":"S-Estimator","fullName":"S-Estimator for Robust Regression","aliases":["S-estimation","robust S-regression","S-Tahmin Edici"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1984,"originator":"Rousseeuw & Yohai (1984)","url":"https://scholargate.app/en/statistics/s-estimator","markdownUrl":"https://scholargate.app/en/statistics/s-estimator.md","definition":"The S-estimator is a robust linear-regression method, introduced by Rousseeuw and Yohai in 1984, that estimates the coefficients by minimising a robust M-estimate of the residual scale rather than the variance of the residuals. By driving down a bounded measure of residual spread it can attain a breakdown point of up to 50%, so it stays reliable even when a large share of the data are outliers, and it provides the first stage of the well-known MM-estimator.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rousseeuw & Yohai (1984)","year":1984,"type":"Robust linear regression","estimator":"Minimisation of a robust M-estimate of residual scale","breakdownPoint":"up to 50%","outcome":"continuous"},"citations":[{"ref":"Rousseeuw, P. J. & Yohai, V. J. (1984). Robust Regression by Means of S-Estimators. In Robust and Nonlinear Time Series Analysis (Lecture Notes in Statistics, Vol. 26, pp. 256-272). Springer.","type":"book","doi":"10.1007/978-1-4615-7821-5_15","isbn":null,"url":null},{"ref":"Maronna, R. A., Martin, R. D., Yohai, V. J. & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119214687","url":null}],"related":["mm-estimator","tau-estimator","theil-sen-estimator","ols-regression","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"s-parameter-analysis","name":"S-Parameter Analysis","fullName":"Scattering Parameter Analysis for RF and Microwave Networks","aliases":["S-parameter","Scattering parameters","Network parameters"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Network parameter representation","year":"1965","originator":"Kaneyuki Kurokawa","url":"https://scholargate.app/en/electrical-engineering/s-parameter-analysis","markdownUrl":"https://scholargate.app/en/electrical-engineering/s-parameter-analysis.md","definition":"S-Parameters (Scattering Parameters) characterize RF and microwave networks by their transmission and reflection of voltage waves. Introduced by Kurokawa in 1965, S-parameters are ideal for high frequencies where wave effects dominate. Unlike impedance (Z), admittance (Y), or hybrid parameters, S-parameters are directly measurable with network analyzers, naturally account for characteristic impedance, and are intuitive for cascade analysis. S-parameters are the standard language of RF engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaneyuki Kurokawa","subfamily":"Network parameter representation","year":"1965","type":"Wave-based description of RF/microwave network behavior"},"citations":[{"ref":"Kurokawa, K. (1965). Power waves and the scattering matrix. IEEE Transactions on Microwave Theory and Techniques, 13(3), 194-202.","type":"article","doi":"10.1109/TMTT.1965.1125964","isbn":null,"url":null},{"ref":"Pozar, D. M. (2011). Microwave Engineering (4th ed.). Wiley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Microwave+Engineering+%284th+ed.%29+Pozar"},{"ref":"Gonzalez, G. (1997). Microwave Transistor Amplifiers: Analysis and Design (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/microwavetransist0000gonz"}],"related":["smith-chart","method-of-moments","transmission-line-matrix-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sabr-model","name":"SABR Model","fullName":"Stochastic Alpha-Beta-Rho Model","aliases":["Stochastic Volatility Model"],"domain":"quantitative-finance","family":"regression-model","subfamily":"Stochastic Volatility","year":"2002","originator":"Patrick S. Hagan","url":"https://scholargate.app/en/quantitative-finance/sabr-model","markdownUrl":"https://scholargate.app/en/quantitative-finance/sabr-model.md","definition":"The SABR (Stochastic Alpha-Beta-Rho) model is a stochastic volatility framework introduced by Hagan et al. in 2002 for valuing interest rate derivatives. It captures the smile effect in implied volatility through correlated Brownian motions and has become industry standard for swaption and caplet pricing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Patrick S. Hagan","subfamily":"Stochastic Volatility","year":"2002","type":"Interest Rate Model"},"citations":[{"ref":"Hagan, P. S., Kumar, D., Lesniewski, A. S., & Woodward, D. E. (2002). Managing smile risk. Wilmott Magazine, 1, 84-108.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Managing+smile+risk+Hagan"},{"ref":"Rebonato, R. (2004). Volatility and Correlation: The Perfect Hedger and the Fox. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/book/10.1002/0470091398"}],"related":["local-volatility","bates-model","hull-white-model","risk-neutral-valuation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sadq","name":"SADQ","fullName":"Severity of Alcohol Dependence Questionnaire","aliases":["SADQ"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"alcohol-dependence-assessment","year":"1979","originator":"Stockwell, Murphy, Hodgson","url":"https://scholargate.app/en/addiction-medicine/sadq","markdownUrl":"https://scholargate.app/en/addiction-medicine/sadq.md","definition":"The SADQ is a 20-item self-report instrument that measures the severity of alcohol dependence on a continuum from mild to severe. Developed by Stockwell and colleagues in 1979, it quantifies physical withdrawal symptoms, psychological dependence, and behavioral indicators of dependence to guide treatment intensity and medical management decisions. The SADQ remains a widely used assessment tool in addiction medicine and alcohol treatment settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stockwell, Murphy, Hodgson","subfamily":"alcohol-dependence-assessment","year":"1979","type":"Self-report"},"citations":[{"ref":"Stockwell, T., Murphy, D., & Hodgson, R. (1983). The Severity of Alcohol Dependence Questionnaire: Its use, reliability and validity. British Journal of Addiction, 78(2), 145–155.","type":"article","doi":"10.1111/j.1360-0443.1983.tb05502.x","isbn":null,"url":null}],"related":["dudit","cudit","brief-addiction-monitor","readiness-to-change-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"safety-attitudes-questionnaire","name":"Safety Attitudes Questionnaire","fullName":"Safety Attitudes Questionnaire (SAQ)","aliases":["SAQ"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"organizational-safety-culture","year":"2000","originator":"John B. Sexton, Robert L. Helmreich, and colleagues (University of Texas)","url":"https://scholargate.app/en/healthcare-management/safety-attitudes-questionnaire","markdownUrl":"https://scholargate.app/en/healthcare-management/safety-attitudes-questionnaire.md","definition":"The Safety Attitudes Questionnaire (SAQ) is a 60-item self-report instrument developed by Sexton and colleagues in the early 2000s to measure organizational safety culture in healthcare settings. Adapted from crew resource management research in aviation, the SAQ assesses clinician and non-clinician perceptions of safety attitudes across six key dimensions. It is widely used in hospital quality improvement and research to identify gaps in safety culture and benchmark institutional performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John B. Sexton, Robert L. Helmreich, and colleagues (University of Texas)","subfamily":"organizational-safety-culture","year":"2000","type":"Self-report"},"citations":[{"ref":"Sexton, J. B., Helmreich, R. L., Neilands, T. B., Rowan, K., Vella, K., Boyden, J., Roberts, P. R., & Thomas, E. J. (2006). The Safety Attitudes Questionnaire: psychometric properties, benchmarking data, and emerging research. BMC Health Services Research, 6, 44.","type":"article","doi":"10.1186/1472-6963-6-44","isbn":null,"url":null},{"ref":"Thomas, E. J., Sexton, J. B., Neilands, T. B., Helmreich, R. L., & Williamson, J. W. (2005). The effect of executive coaching on communication and teamwork among senior medical residents. Academic Medicine, 80(10), 957-963.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+effect+of+executive+coaching+on+communication+and+teamwork+among+senior+medical+residents+Thomas"}],"related":["hospital-survey-patient-safety","teamstepps-perceptions","patient-safety-climate-scale","nurse-work-environment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"safety-compliance-scale","name":"Safety Compliance and Participation Scale","fullName":"Safety Compliance and Participation Scale (SCPS)","aliases":["SCPS","Safety Behavior Scale"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-safety-behavior","year":"2000","originator":"Neal & Griffin","url":"https://scholargate.app/en/occupational-health/safety-compliance-scale","markdownUrl":"https://scholargate.app/en/occupational-health/safety-compliance-scale.md","definition":"The Safety Compliance and Participation Scale (SCPS) measures workers' occupational safety behavior across two dimensions: safety compliance (following safety rules and procedures) and safety participation (proactive engagement in safety activities beyond minimum requirements). Developed by Neal and Griffin, the SCPS recognizes that safe workplaces require both compliance with formal rules and voluntary engagement in safety culture. The scale predicts injury rates, identifies high-risk workers or departments, and evaluates the effectiveness of safety interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Neal & Griffin","subfamily":"occupational-safety-behavior","year":"2000","type":"Self-report"},"citations":[{"ref":"Neal, A., & Griffin, M. A. (2006). A study of the lagged relationships among safety climate, safety motivation, safety behavior, and accidents at the individual and group levels. J Appl Psychol, 91(4), 946–953.","type":"article","doi":"10.1037/0021-9010.91.4.946","isbn":null,"url":null},{"ref":"Griffin, M. A., & Neal, A. (2000). Perceptions of safety at work: A framework for linking safety climate to safety performance, knowledge, and motivation. J Occup Health Psychol, 5(3), 347–358.","type":"article","doi":"10.1037/1076-8998.5.3.347","isbn":null,"url":null}],"related":["psychosocial-safety-climate-scale","workplace-violence-scale","occupational-exposure-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"safety-stock","name":"Safety Stock","fullName":"Safety Stock and Reorder-Point Models","aliases":["Buffer Stock","Reserve Stock","Reorder-Point Model","Emniyet Stoğu"],"domain":"operations-research","family":"regression-model","subfamily":"Inventory control","year":1998,"originator":"Silver, Pyke & Peterson","url":"https://scholargate.app/en/operations-research/safety-stock","markdownUrl":"https://scholargate.app/en/operations-research/safety-stock.md","definition":"Safety stock is an additional quantity of inventory held beyond expected demand during a replenishment lead time, designed to protect against stockouts caused by demand or supply uncertainty. Reorder-point models formalize this buffer by setting a trigger inventory level at which a new order is placed. Systematically developed within the stochastic inventory-control framework by Silver, Pyke, and Peterson (1998), the approach translates a desired customer-service level into a precise buffer quantity using the statistics of demand and lead-time variability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Silver, Pyke & Peterson","year":1998,"type":"Stochastic inventory control model","subfamily":"Inventory control","decision_variable":"Safety stock level and reorder point","key_input":"Demand and lead-time variability"},"citations":[{"ref":"Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory Management and Production Planning and Scheduling (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0-471-11947-0","url":null}],"related":["economic-order-quantity","newsvendor-model","abc-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"saint-louis-mental-status","name":"Saint Louis University Mental Status Examination","fullName":"Saint Louis University Mental Status Examination","aliases":["SLUMS","Saint Louis Mental Status"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"cognitive screening","year":"2006","originator":"Syed Tariq","url":"https://scholargate.app/en/neuropsychology/saint-louis-mental-status","markdownUrl":"https://scholargate.app/en/neuropsychology/saint-louis-mental-status.md","definition":"The Saint Louis University Mental Status Examination (SLUMS) is a brief, clinician-administered cognitive screening instrument developed by Tariq and colleagues at Saint Louis University in 2006. It was designed as an alternative to the MMSE with improved sensitivity to mild cognitive impairment and early dementia. The SLUMS includes items assessing orientation, attention, memory, and executive function, and is particularly useful in older adult populations in primary care and geriatric settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Syed Tariq","subfamily":"cognitive screening","year":"2006","type":"Clinician-administered cognitive screening instrument"},"citations":[{"ref":"Tariq, S. H., Tumosa, N., Chibnall, J. T., Perry, M. H., & Morley, J. E. (2006). Comparison of the Saint Louis University Mental Status Examination and the Mini-Mental State Examination for detecting dementia and mild neurocognitive disorder—A pilot study. American Journal of Geriatric Psychiatry, 14(11), 900-910.","type":"article","doi":"10.1097/01.jgp.0000221510.33817.86","isbn":null,"url":null},{"ref":"Morley, J. E., Malmstrom, T. K., & Miller, D. K. (2012). A simple frailty screening tool for primary care. Journal of the American Geriatrics Society, 60(1), 137-141.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/22151109"},{"ref":"Millard, F. C., Tariq, S. H., Tumosa, N., & Morley, J. E. (2003). Validation of the Saint Louis University Mental Status Examination in nonagenarians. Journal of the American Geriatrics Society, 51(Suppl 1), S40.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/12823110"}],"related":["mmse","adas-cog","addenbrookes-cognitive-examination","dementia-rating-scale","frontal-assessment-battery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"salivary-biomarker-analysis","name":"Salivary Biomarker Analysis","fullName":"Oral Fluid Biomarker Testing for Disease Assessment","aliases":["saliva testing","salivary diagnostics","oral biomarker assessment"],"domain":"dentistry","family":"process-pipeline","subfamily":"Diagnostic microbiology and biochemistry","year":"2000s+ (clinical application)","originator":"Multiple innovators (Giannobile, Malamud, et al.)","url":"https://scholargate.app/en/dentistry/salivary-biomarker-analysis","markdownUrl":"https://scholargate.app/en/dentistry/salivary-biomarker-analysis.md","definition":"Salivary biomarker analysis detects protein, molecular, or microbial markers in saliva that indicate oral and systemic disease. Salivary diagnostics assess risk and activity of dental caries, periodontal disease, oral cancer, and other conditions. Biomarkers include antimicrobial proteins (lysozyme, lactoferrin), inflammatory mediators (interleukins, TNF-alpha), cariogenic bacteria (Streptococcus mutans), and virulence factors. Point-of-care saliva testing offers rapid, non-invasive alternatives to conventional laboratory methods, enabling chairside diagnosis and personalized risk assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple innovators (Giannobile, Malamud, et al.)","subfamily":"Diagnostic microbiology and biochemistry","year":"2000s+ (clinical application)","type":"Laboratory and point-of-care diagnostics"},"citations":[{"ref":"Giannobile, W. V., McDevitt, J. T., Niedbala, R. S., Malamud, D., & Prozorovsky, T. (2009). Translating molecular diagnostics into clinical practice: Designing the next generation of oral health technologies. Advances in Dental Research, 23(1), 80-89.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Translating+molecular+diagnostics+into+clinical+practice%3A+Designing+the+next+generation+of+oral+health+technologies+Giannobile"},{"ref":"Siqueira, W. L., & Salih, E. (2012). Proteoglycans and proteomics in oral fluids: translating from the laboratory to clinical practice. Advances in Dental Research, 24(1), 74-77.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Proteoglycans+and+proteomics+in+oral+fluids%3A+translating+from+the+laboratory+to+clinical+practice+Siqueira"},{"ref":"Devic, M., Glenski, M., Phelps, K., Lynch, T., & Brice, D. (2015). Use of salivary biomarkers in dentistry: A systematic review. Journal of Evidence-Based Dental Practice, 15(1), 1-11.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Use+of+salivary+biomarkers+in+dentistry%3A+A+systematic+review+Devic"}],"related":["periodontal-probing","gingival-index","dmft-index","dental-erosion-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sample-entropy","name":"Sample Entropy","fullName":"Sample Entropy (Time-Series Complexity)","aliases":["SampEn","Sample Entropy (SampEn)","Örneklem Entropisi","Nonlinear Complexity Measure"],"domain":"complex-systems","family":"ml-model","subfamily":"Nonlinear dynamics","year":2000,"originator":"Richman & Moorman","url":"https://scholargate.app/en/complex-systems/sample-entropy","markdownUrl":"https://scholargate.app/en/complex-systems/sample-entropy.md","definition":"Sample Entropy (SampEn) is a nonlinear measure of the complexity and regularity of a time series. Introduced by Richman and Moorman in 2000 as an improvement over Approximate Entropy (ApEn), it quantifies the likelihood that similar patterns of a given length in the series remain similar when extended by one additional data point. A higher SampEn value indicates greater irregularity and complexity, while a lower value indicates more regularity or self-similarity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richman & Moorman","year":2000,"type":"Nonlinear entropy measure","subfamily":"Nonlinear dynamics","input":"Univariate time series","output":"Non-negative scalar (entropy value)"},"citations":[{"ref":"Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology, 278(6), H2039–H2049.","type":"article","doi":"10.1152/ajpheart.2000.278.6.H2039","isbn":null,"url":null}],"related":["recurrence-quantification-analysis","fractal-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sampling-methods","name":"Sampling Methods in Research","fullName":"Sampling Techniques and Sampling Frame Design","aliases":["sampling strategy","sampling design","probability and non-probability sampling"],"domain":"research-methodology","family":"process-pipeline","subfamily":"research data collection","year":"1950","originator":"William G. Cochran and Leslie Kish (1950s–1970s)","url":"https://scholargate.app/en/research-methodology/sampling-methods","markdownUrl":"https://scholargate.app/en/research-methodology/sampling-methods.md","definition":"Sampling is the process of selecting a subset of individuals, observations, or units (the sample) from a larger population to study. Sampling methods are broadly classified into probability (random) and non-probability (non-random) approaches. Probability methods—random sampling, stratified sampling, cluster sampling, systematic sampling—enable statistical inference to the population and allow calculation of confidence intervals and margins of error. Non-probability methods—convenience, purposive, snowball, quota sampling—are practical for exploratory or qualitative research but do not support formal statistical generalization. Cochran's Sampling Techniques (1977) and Kish's Survey Sampling (1965) are foundational references; modern applications span surveys, experiments, qualitative studies, and clinical trials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran and Leslie Kish (1950s–1970s)","subfamily":"research data collection","year":"1950","type":"Framework"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Cochran%2C%20W.%20G.%20(1977).%20Sampling%20Techniques%20(3rd%20ed.).%20John%20Wiley%20%26%20Sons."},{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Kish%2C%20L.%20(1965).%20Survey%20Sampling.%20John%20Wiley%20%26%20Sons."},{"ref":"Patton, M. Q. (2015). Qualitative Research & Evaluation Methods (4th ed.). SAGE Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Patton%2C%20M.%20Q.%20(2015).%20Qualitative%20Research%20%26%20Evaluation%20Methods%20(4th%20ed.).%20SAGE%20Publications."}],"related":["research-design-types","research-question-formulation","data-collection-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sapevo-m","name":"SAPEVO-M","fullName":"Simple Aggregation of Preferences Expressed by Ordinal Vectors — Multi-Decision Maker","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weighting","year":"2020","originator":"Gomes, L. F. A. M. de Mello, J. C. C. B. S. Costa, H. G.","url":"https://scholargate.app/en/decision-making/sapevo-m","markdownUrl":"https://scholargate.app/en/decision-making/sapevo-m.md","definition":"SAPEVO-M (Simple Aggregation of Preferences Expressed by Ordinal Vectors — Multi-Decision Maker) is a weighting multi-criteria decision-making (MCDM) method introduced by Gomes, L. F. A. M. de Mello, J. C. C. B. S. Costa, H. G. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gomes, L. F. A. M. de Mello, J. C. C. B. S. Costa, H. G.","subfamily":"Weighting","year":"2020","type":"Ordinal pairwise comparisons from multiple DMs aggregated into priority weights","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Gomes, L. F. A. M., de Mello, J. C. C. B. S., Costa, H. G. (2020). SAPEVO-M: a group ranking approach based on preference vectors. Pesquisa Operacional","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SAPEVO-M%3A+a+group+ranking+approach+based+on+preference+vectors+Gomes"}],"related":["topsis","vikor","saw"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"saprof","name":"SAPROF","fullName":"Structured Assessment of Protective Factors","aliases":["SAPROF","de Vogel SAPROF"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"protective-factors-assessment","year":"2012","originator":"Vivienne de Vogel, Corine de Ruiter, Yvonne Bouman, Merike de Vries Robbé","url":"https://scholargate.app/en/forensic-psychology/saprof","markdownUrl":"https://scholargate.app/en/forensic-psychology/saprof.md","definition":"The Structured Assessment of Protective Factors for Violence Risk (SAPROF) is a 17-item structured professional judgment tool developed by de Vogel, de Ruiter, Bouman, and colleagues (2012) to identify protective factors and strengths in individuals undergoing violence risk assessment. It complements risk assessment instruments (e.g., HCR-20v3) by systematically evaluating resilience, social support, motivation, and positive functioning—domains that mitigate violence risk and inform rehabilitation potential.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vivienne de Vogel, Corine de Ruiter, Yvonne Bouman, Merike de Vries Robbé","subfamily":"protective-factors-assessment","year":"2012","type":"Clinician-rated"},"citations":[{"ref":"de Vogel, V., de Ruiter, C., Bouman, Y., & de Vries Robbé, M. (2012). SAPROF: Structured Assessment of Protective Factors for violence risk (Version 3). Forum Educatief.","type":"book","doi":null,"isbn":null,"url":"https://www.forumducatief.nl/"},{"ref":"de Vogel, V., & de Ruiter, C. (2009). The Structured Assessment of Protective Factors for violence risk (SAPROF): Development and preliminary validity. International Journal of Forensic Mental Health, 8(4), 263–269.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Structured+Assessment+of+Protective+Factors+for+violence+risk+%28SAPROF%29%3A+Development+and+preliminary+validity"}],"related":["hcr-20","violence-risk-appraisal-guide","level-of-service-inventory","structured-professional-judgment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sar-image-analysis","name":"SAR Image Analysis","fullName":"Synthetic Aperture Radar (SAR) Image Analysis","aliases":["Synthetic Aperture Radar Processing","Radar Remote Sensing Analysis","Microwave Imaging Analysis","SAR Görüntü Analizi"],"domain":"remote-sensing","family":"process-pipeline","subfamily":"Remote sensing","year":2009,"originator":"Jong-Sen Lee & Eric Pottier","url":"https://scholargate.app/en/remote-sensing/sar-image-analysis","markdownUrl":"https://scholargate.app/en/remote-sensing/sar-image-analysis.md","definition":"Synthetic Aperture Radar (SAR) Image Analysis is an active microwave remote sensing pipeline that processes complex-valued radar backscatter data to characterize land cover, surface roughness, moisture, and structural properties. Foundational treatment was consolidated by Jong-Sen Lee and Eric Pottier in their 2009 CRC Press volume, which established the polarimetric framework widely adopted by research and operational communities working with satellites such as Sentinel-1, ALOS PALSAR, and RADARSAT.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jong-Sen Lee & Eric Pottier","year":2009,"type":"Active microwave image processing pipeline","subfamily":"Remote sensing","sensor":"Synthetic aperture radar","data_type":"Complex-valued backscatter amplitude and phase"},"citations":[{"ref":"Lee, J.-S., & Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications. CRC Press.","type":"book","doi":null,"isbn":"978-1-4200-5497-2","url":null}],"related":["object-based-image-analysis","change-detection","deep-remote-sensing"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sarima-model","name":"SARIMA model","fullName":"Seasonal Autoregressive Integrated Moving Average Model","aliases":["SARIMA","seasonal ARIMA","Box-Jenkins seasonal model","ARIMA with seasonal component"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970 (first edition); 1976 (revised)","originator":"Box, Jenkins, and Reinsel","url":"https://scholargate.app/en/econometrics/sarima-model","markdownUrl":"https://scholargate.app/en/econometrics/sarima-model.md","definition":"SARIMA extends ARIMA by adding seasonal autoregressive and moving-average operators to capture repeating patterns at fixed intervals — such as monthly, quarterly, or annual cycles. Denoted SARIMA(p,d,q)(P,D,Q)s, it is the standard workhorse for univariate seasonal time series forecasting in econometrics, economics, and official statistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box, Jenkins, and Reinsel","year":"1970 (first edition); 1976 (revised)","type":"Seasonal time series model","dataType":"Univariate equally-spaced time series with seasonal pattern","subfamily":"Econometrics / time series"},"citations":[{"ref":"Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day.","type":"book","doi":null,"isbn":"978-0130607744","url":null},{"ref":"Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.","type":"book","doi":null,"isbn":null,"url":"https://otexts.com/fpp3/"}],"related":["arima-model","arma-model","autoregressive-model","moving-average-model","vector-autoregression","exponential-smoothing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sarima","name":"SARIMA","fullName":"Seasonal Autoregressive Integrated Moving Average","aliases":["seasonal ARIMA","Box-Jenkins seasonal model","SARIMA — Mevsimsel ARIMA"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2015,"originator":"Box & Jenkins (seasonal extension of ARIMA)","url":"https://scholargate.app/en/econometrics/sarima","markdownUrl":"https://scholargate.app/en/econometrics/sarima.md","definition":"SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Jenkins (seasonal extension of ARIMA)","year":2015,"type":"Seasonal time-series model","estimator":"Maximum likelihood (Box-Jenkins methodology)","outcome":"continuous time series","minSample":48},"citations":[{"ref":"Box, G.E.P., Jenkins, G.M., Reinsel, G.C. & Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118675021","url":null},{"ref":"Hyndman, R.J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.","type":"book","doi":null,"isbn":"978-0987507136","url":null}],"related":["sarimax","ets-model","holt-winters","prophet-model","state-space-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sarimax","name":"SARIMAX","fullName":"Seasonal ARIMA with Exogenous Regressors","aliases":["seasonal ARIMA with exogenous variables","SARIMA with regressors","ARIMAX","SARIMAX — Dışsal Değişkenli Mevsimsel ARIMA"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2015,"originator":"Box & Jenkins (ARIMA framework); SARIMAX extension with exogenous regressors","url":"https://scholargate.app/en/econometrics/sarimax","markdownUrl":"https://scholargate.app/en/econometrics/sarimax.md","definition":"SARIMAX extends the seasonal ARIMA (Box-Jenkins) model by adding exogenous explanatory variables, so it can capture the effect of holidays, economic indicators, or policy variables on a time series. It combines non-seasonal and seasonal autoregressive and moving-average dynamics with external regressors, and is estimated by maximum likelihood in state-space form.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Jenkins (ARIMA framework); SARIMAX extension with exogenous regressors","year":2015,"type":"Seasonal time-series regression model","estimator":"Maximum likelihood via state-space (Kalman filter)","outcome":"continuous time series","minSample":48},"citations":[{"ref":"Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.","type":"book","doi":null,"isbn":null,"url":"https://otexts.com/fpp3/"},{"ref":"Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118675021","url":null}],"related":["arima","prophet","ets-exponential-smoothing","holt-winters","state-space-model","bvar"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"saturation-in-qualitative","name":"Data Saturation in Qualitative Research","fullName":"Theoretical and Thematic Saturation in Qualitative Data Collection","aliases":["saturation","theoretical saturation","thematic saturation","sampling to saturation"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"sampling-concept","year":"1967","originator":"Barney Glaser and Anselm Strauss","url":"https://scholargate.app/en/qualitative-research/saturation-in-qualitative","markdownUrl":"https://scholargate.app/en/qualitative-research/saturation-in-qualitative.md","definition":"Data saturation is a foundational principle in qualitative research describing the point at which data collection yields no new themes, codes, or insights—additional data becomes redundant. Introduced by Glaser and Strauss (1967) in their work on grounded theory, saturation guides decisions about sample size and when to stop recruiting participants. Saturation is not a fixed number but a dynamic endpoint determined by examining whether new data are adding substantively new information. The concept is central to claims of rigor and theoretical adequacy in qualitative research, signaling that the researcher has gathered sufficient data to understand the phenomenon in depth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney Glaser and Anselm Strauss","subfamily":"sampling-concept","year":"1967","type":"Concept"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302560","url":null},{"ref":"Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-0803959393","url":null},{"ref":"Bowen, G. A. (2008). Naturalistic inquiry and saturation (S): Determining when enough is enough. Journal of Research in Education, 18(1), 137-152.","type":"article","doi":null,"isbn":null,"url":"https://jrslweb.org/index.php/JRSLProceedings/article/view/198"},{"ref":"Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59-82.","type":"article","doi":"10.1177/1525822X05279903","isbn":null,"url":null}],"related":["in-depth-interview-method","focus-group-methodology","grounded-theory","thematic-analysis"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"saw","name":"SAW","fullName":"Simple Additive Weighting","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1967","originator":"Fishburn, P. C.","url":"https://scholargate.app/en/decision-making/saw","markdownUrl":"https://scholargate.app/en/decision-making/saw.md","definition":"SAW (Simple Additive Weighting) is a ranking multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fishburn, P. C.","subfamily":"Ranking","year":"1967","type":"Additive utility (linear)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research","type":"article","doi":"10.1287/opre.15.3.537","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"saxs","name":"SAXS","fullName":"Small-Angle X-ray Scattering","aliases":["SAXS","small-angle scattering"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"X-ray Scattering","year":"1954","originator":"Otto Kratky","url":"https://scholargate.app/en/spectroscopy/saxs","markdownUrl":"https://scholargate.app/en/spectroscopy/saxs.md","definition":"Small-Angle X-ray Scattering (SAXS) is a solution-phase X-ray scattering technique that measures the overall shape and size of macromolecules and nanoparticles by analyzing scattering intensity at low angles (0.1-10 degrees). Developed by Kratky and colleagues in the 1950s, SAXS provides information about molecular radius, aggregation state, and overall shape without requiring crystallization or fixing, making it ideal for studying native protein conformations and dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Otto Kratky","subfamily":"X-ray Scattering","year":"1954","type":"Synchrotron/X-ray technique"},"citations":[{"ref":"Glatter, O., & Kratky, O. (1982). Small Angle X-ray Scattering. Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/bookseries/techniques-in-chemistry"},{"ref":"Koch, M. H., Vachette, P., & Svergun, D. I. (2003). Small-angle scattering: a view on the properties, structures and structural changes of biological macromolecules in solution. Quarterly Reviews of Biophysics, 36(2), 147-227.","type":"article","doi":"10.1017/S0033583503003871","isbn":null,"url":null}],"related":["exafs","xanes","atr-ftir"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scad-penalized-regression","name":"SCAD Penalized Regression","fullName":"Smoothly Clipped Absolute Deviation Penalized Regression","aliases":["SCAD"],"domain":"psychometrics","family":"latent-structure","subfamily":"Variable Selection","year":"2001","originator":"Jianqing Fan, Runze Li","url":"https://scholargate.app/en/psychometrics/scad-penalized-regression","markdownUrl":"https://scholargate.app/en/psychometrics/scad-penalized-regression.md","definition":"SCAD (Smoothly Clipped Absolute Deviation) is a variable selection and regularization method developed by Fan and Li (2001) that addresses limitations of L1 penalization (lasso). SCAD uses a non-concave penalty that automatically performs variable selection while maintaining oracle properties: it recovers the true underlying model as if the true predictors were known in advance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jianqing Fan, Runze Li","subfamily":"Variable Selection","year":"2001","type":"Penalized regression with non-concave penalty"},"citations":[{"ref":"Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348-1360.","type":"article","doi":"10.1198/016214501753382273","isbn":null,"url":null},{"ref":"Zou, H., & Li, R. (2008). One-step sparse estimates in nonconcave penalized likelihood models. Annals of Statistics, 36(4), 1509-1533.","type":"article","doi":"10.1214/009053607000000802","isbn":null,"url":null},{"ref":"Wang, H., Li, G., & Tsai, C. L. (2007). Regression coefficient and autoregressive order shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(1), 63-78.","type":"article","doi":"10.1111/j.1467-9868.2007.00577.x","isbn":null,"url":null}],"related":["mcp-penalized-regression","pls-sem","exploratory-structural-equation-modeling","redundancy-analysis","multiple-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scaffold-porosity-analysis","name":"Scaffold Porosity Analysis","fullName":"Tissue Engineering Scaffold Porosity Analysis","aliases":["Pore size distribution","Porosity measurement","Scaffold characterization"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Tissue engineering","year":"2000","originator":"Dietmar Hutmacher","url":"https://scholargate.app/en/biomechanics/scaffold-porosity-analysis","markdownUrl":"https://scholargate.app/en/biomechanics/scaffold-porosity-analysis.md","definition":"Scaffold porosity analysis characterizes the pore structure of tissue engineering scaffolds, including total porosity, pore size distribution, pore shape, and pore interconnectivity. Essential for predicting cell seeding, nutrient diffusion, and mechanical properties, this quantitative approach bridges scaffold design and biological performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dietmar Hutmacher","subfamily":"Tissue engineering","year":"2000","type":"Quantitative morphological analysis"},"citations":[{"ref":"Hutmacher, D. W. (2000). Scaffolds in tissue engineering bone and cartilage. Biomaterials, 21(24), 2529-2543.","type":"article","doi":"10.1016/S0142-9612(00)00121-6","isbn":null,"url":null},{"ref":"Zhou, W. Y., Wang, M., & Cheung, W. L. (2008). Synthesis of non-layered potassium niobate nanowires with enhanced photocatalytic performance. Nanotechnology, 19(8), 085604.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Synthesis+of+non-layered+potassium+niobate+nanowires+with+enhanced+photocatalytic+performance+Zhou"}],"related":["fea-bone-remodeling","micro-ct-morphometry","hydrogel-rheology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scale-development","name":"Scale development","fullName":"Scale Development","aliases":["questionnaire construction","instrument development","measurement scale construction","psychometric scale building"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1991–1995","originator":"Multiple contributors; codified by Robert DeVellis and Lee Anna Clark & David Watson","url":"https://scholargate.app/en/psychometrics/scale-development","markdownUrl":"https://scholargate.app/en/psychometrics/scale-development.md","definition":"Scale development is a structured, multi-step process for creating psychometrically sound measurement instruments that capture latent psychological constructs. It encompasses construct definition, item generation, expert review, exploratory and confirmatory factor analysis, reliability estimation, and validity evidence collection — producing a final set of items suitable for quantitative research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; codified by Robert DeVellis and Lee Anna Clark & David Watson","year":"1991–1995","type":"Multi-step methodological framework","dataType":"Ordinal or interval item responses (Likert-type, rating scales)","subfamily":"Scale / measurement"},"citations":[{"ref":"DeVellis, R. F. (2016). Scale Development: Theory and Applications (4th ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1506341569","url":null},{"ref":"Clark, L. A. & Watson, D. (1995). Constructing validity: Basic issues in objective scale construction. Psychological Assessment, 7(3), 309–319.","type":"article","doi":"10.1037/1040-3590.7.3.309","isbn":null,"url":null}],"related":["exploratory-factor-analysis","confirmatory-factor-analysis","item-response-theory","cronbachs-alpha","content-validity","construct-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scale-space-theory","name":"Scale-Space Theory","fullName":"Scale-Space Theory and Multi-Scale Image Analysis","aliases":["Multi-scale analysis","Gaussian scale-space"],"domain":"computer-vision","family":"ml-model","subfamily":"Multi-scale image analysis","year":"1983","originator":"Andrew Witkin and Tony Lindeberg","url":"https://scholargate.app/en/computer-vision/scale-space-theory","markdownUrl":"https://scholargate.app/en/computer-vision/scale-space-theory.md","definition":"Scale-space theory, developed by Witkin and Lindeberg, provides a principled mathematical framework for analyzing images at multiple scales simultaneously. By treating scale as an explicit dimension and using Gaussian blurring, scale-space theory enables detection and analysis of features at appropriate scales, solving the fundamental problem of 'which scale should I analyze at?'","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Witkin and Tony Lindeberg","subfamily":"Multi-scale image analysis","year":"1983","type":"Theoretical framework for multi-scale processing"},"citations":[{"ref":"Lindeberg, T. (1994). Scale-space theory: A basic tool for analyzing structures at different scales. Journal of Applied Statistics, 21(2), 225–270.","type":"article","doi":"10.1080/757582976","isbn":null,"url":null},{"ref":"Witkin, A. P. (1983). Scale-space filtering. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI), 1019–1022.","type":"article","doi":null,"isbn":null,"url":"https://ijcai.org/proceedings/1983/"}],"related":["sift-feature-detection","blob-detection","harris-corner-detection","canny-edge-detection","orb-feature-descriptor"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scaling-up-interventions","name":"Scaling Up Health Interventions","fullName":"Scaling Up Health Interventions: A Systematic Approach to Expanding Successful Pilot Programs from Single Sites to Health Systems","aliases":["scaling up","expansion","scale","dissemination"],"domain":"implementation-science","family":"process-pipeline","subfamily":"implementation and scaling","year":"2007","originator":"Simmons, R., Fajans, P., Ghiron, L. (World Health Organization)","url":"https://scholargate.app/en/implementation-science/scaling-up-interventions","markdownUrl":"https://scholargate.app/en/implementation-science/scaling-up-interventions.md","definition":"Scaling Up is the deliberate expansion of successful health interventions from pilot sites to entire health systems, regions, or countries. Formalized by the World Health Organization (WHO) and Simmons et al. (2007), scaling up is distinct from simple dissemination; it requires systematic planning, financial modeling, capacity building, and policy alignment to ensure interventions work at scale. A pilot that succeeds brilliantly with champion leadership, dedicated funding, and motivated staff may fail when scaled to routine settings with limited resources. Scaling Up frameworks help practitioners anticipate and overcome these challenges.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Simmons, R., Fajans, P., Ghiron, L. (World Health Organization)","subfamily":"implementation and scaling","year":"2007","type":"Framework"},"citations":[{"ref":"Simmons, R., Fajans, P., & Ghiron, L. (Eds.). (2007). Scaling Up Health Service Delivery: From Pilot Innovations to Policies and Programmes. World Health Organization, Geneva.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Simmons%2C%20R.%2C%20Fajans%2C%20P.%2C%20%26%20Ghiron%2C%20L.%20(Eds.).%20(2007).%20Scaling%20Up%20Health%20Service%20Delivery%3A%20From%20Pilot%20Innovations%20to%20Poli"},{"ref":"Yamey, G. (2011). Scaling up global health interventions: A call for papers. The Lancet, 378(9802), e40-e41.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Scaling+up+global+health+interventions%3A+A+call+for+papers+Yamey"},{"ref":"World Health Organization. (2008). Scaling Up Health Service Delivery: From Pilot Innovations to Policies and Programmes. WHO, Geneva.","type":"report","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=World%20Health%20Organization.%20(2008).%20Scaling%20Up%20Health%20Service%20Delivery%3A%20From%20Pilot%20Innovations%20to%20Policies%20and%20Programmes"}],"related":["knowledge-translation","implementation-outcome-taxonomy","cfir-framework","re-aim-framework","normalization-process-theory"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scan-sampling","name":"Scan Sampling","fullName":"Scan Sampling Behavioral Observation Method","aliases":["instantaneous sampling","scan observation","group sampling"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Observational Technique","year":"1974","originator":"Jeanne Altmann","url":"https://scholargate.app/en/veterinary-science/scan-sampling","markdownUrl":"https://scholargate.app/en/veterinary-science/scan-sampling.md","definition":"Scan Sampling (also called instantaneous sampling) is a behavioral observation method in which an observer records the state of all group members simultaneously at regular time intervals. Introduced alongside focal animal sampling by Jeanne Altmann in 1974, scan sampling is efficient for quantifying activity budgets and group-level behavioral patterns in multiple animals without the labor of focal observation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeanne Altmann","subfamily":"Observational Technique","year":"1974","type":"Group Behavioral Sampling"},"citations":[{"ref":"Altmann, J. (1974). Observational study of behavior: sampling methods. Behaviour, 49(3-4), 227-267.","type":"article","doi":"10.1163/156853974X00534","isbn":null,"url":null},{"ref":"Martin, P., & Bateson, P. P. (1993). Measuring Behaviour: An Introductory Guide (2nd ed.). Cambridge University Press.","type":"article","doi":null,"isbn":null,"url":"https://www.cambridge.org/us/academic/subjects/life-sciences/animal-behaviour/measuring-behaviour"},{"ref":"Coad, N., Al-Rasheid, K. A., & Sluydts, V. (2002). Instantaneous sampling of group-living primates. Primates, 43(2), 105-110.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Instantaneous+sampling+of+group-living+primates+Coad"}],"related":["focal-animal-sampling","polysomnography","equine-gait-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scatchard-analysis","name":"Scatchard Analysis","fullName":"Scatchard Plot Analysis of Receptor Binding","aliases":["Scatchard plot","binding analysis","Kd determination"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Biochemistry","year":"1949","originator":"George Scatchard","url":"https://scholargate.app/en/pharmacology/scatchard-analysis","markdownUrl":"https://scholargate.app/en/pharmacology/scatchard-analysis.md","definition":"Scatchard analysis is a graphical method for determining ligand-receptor binding affinity (Kd) and binding capacity (Bmax) from binding data. Developed by George Scatchard in 1949, the Scatchard plot linearizes hyperbolic binding curves, enabling visual detection of multiple binding sites and quantitative parameter estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Scatchard","subfamily":"Biochemistry","year":"1949","type":"binding affinity measurement"},"citations":[{"ref":"Scatchard, G. (1949). The attractions of proteins for small molecules and ions. Annals of the New York Academy of Sciences, 51(4), 660-672.","type":"article","doi":"10.1111/j.1749-6632.1949.tb27297.x","isbn":null,"url":null},{"ref":"Rosenthal, H. E. (1967). A graphic method for the determination and presentation of binding parameters in a complex system. Analytical Biochemistry, 20(3), 525-532.","type":"article","doi":"10.1016/0003-2697(67)90297-7","isbn":null,"url":null}],"related":["michaelis-menten-kinetics","schild-analysis","patch-clamp"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sccai","name":"Simple Clinical Colitis Activity Index","fullName":"Simple Clinical Colitis Activity Index","aliases":["SCCAI"],"domain":"gastroenterology","family":"process-pipeline","subfamily":"inflammatory-bowel-disease","year":"1998","originator":"Walmsley, R. S., Ayres, R. C., Pounder, R. E., and Allan, R. N.","url":"https://scholargate.app/en/gastroenterology/sccai","markdownUrl":"https://scholargate.app/en/gastroenterology/sccai.md","definition":"The Simple Clinical Colitis Activity Index (SCCAI) is a practical, bedside tool for assessing disease activity in ulcerative colitis and colonic Crohn's disease. Published in 1998 by Walmsley and colleagues, the SCCAI condenses disease assessment into six items that can be administered in a office visit without laboratory or endoscopic data. It provides rapid, reproducible quantification of disease severity and is ideal for frequent monitoring in routine clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walmsley, R. S., Ayres, R. C., Pounder, R. E., and Allan, R. N.","subfamily":"inflammatory-bowel-disease","year":"1998","type":"Clinician-rated"},"citations":[{"ref":"Walmsley, R. S., Ayres, R. C., Pounder, R. E., & Allan, R. N. (1998). A simple clinical colitis activity index. Gut, 43(1), 29–32.","type":"article","doi":"10.1136/gut.43.1.29","isbn":null,"url":null}],"related":["mayo-score-uc","harvey-bradshaw-index","ibdq-short","cdai-crohns"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scenario-analysis-simulation","name":"Scenario Analysis","fullName":"Scenario Analysis and What-If Simulation","aliases":["what-if analysis","what-if simulation","stress testing","scenario planning","Senaryo Analizi ve What-If Simülasyonu"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1950s (origins); widely adopted in management since 1970s","originator":"Peter Schwartz (scenario planning formalization), Herman Kahn (RAND Corporation, 1950s–60s)","url":"https://scholargate.app/en/simulation/scenario-analysis-simulation","markdownUrl":"https://scholargate.app/en/simulation/scenario-analysis-simulation.md","definition":"Scenario analysis is a structured analytical approach that systematically compares system outputs across different combinations of uncertain input values. When paired with a quantitative model, it becomes a simulation — capable of stress-testing assumptions and projecting the range of plausible outcomes. Formalised in strategic planning by Peter Schwartz and Herman Kahn from the 1950s onward, the method is widely used in policy evaluation, business forecasting, financial risk assessment, and scientific model exploration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Schwartz (scenario planning formalization), Herman Kahn (RAND Corporation, 1950s–60s)","year":"1950s (origins); widely adopted in management since 1970s","type":"Structured analytical approach / simulation","requiresNormality":"No","minimumSample":"None (model-driven, not sample-driven)","output":"Comparative system outputs across defined scenarios (pessimistic / base / optimistic)","difficulty":"Low (difficulty: 1 of 5)"},"citations":[{"ref":"Goodwin, P. & Wright, G. (2014). Decision Analysis for Management Judgment (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118173671","url":null},{"ref":"Schwartz, P. (1991). The Art of the Long View. Currency Doubleday.","type":"book","doi":null,"isbn":"978-0385267328","url":null}],"related":["monte-carlo-simulation","sensitivity-analysis","decision-tree-analysis","multi-criteria-decision-analysis","global-sensitivity-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scheffe-test","name":"Scheffé Test","fullName":"Scheffé's Method for All Contrasts","aliases":["Scheffe test","Scheffe method","Scheffé post-hoc test","S-method","simultaneous confidence intervals for contrasts"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1953,"originator":"Henry Scheffé","url":"https://scholargate.app/en/statistics/scheffe-test","markdownUrl":"https://scholargate.app/en/statistics/scheffe-test.md","definition":"The Scheffé test is a post-hoc multiple comparison procedure that controls the family-wise error rate simultaneously for all possible linear contrasts among group means following a significant ANOVA. Introduced by Henry Scheffé in his landmark 1953 Biometrika paper, it is the most general and conservative standard post-hoc method, remaining valid regardless of how many or which contrasts are examined after seeing the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Scheffé","year":1953,"family":"Hypothesis test","type":"Post-hoc multiple comparison test","controlsFor":"family-wise error rate (FWER)","contrastsSupported":"all possible linear contrasts","parametric":true,"distribution":"F","conservatism":"most conservative among standard post-hoc tests","requiresANOVA":true},"citations":[{"ref":"Scheffé, H. (1953). A method for judging all contrasts in the analysis of variance. Biometrika, 40(1–2), 87–110.","type":"article","doi":"10.1093/biomet/40.1-2.87","isbn":null,"url":null},{"ref":"Scheffé, H. (1959). The Analysis of Variance. Wiley.","type":"book","doi":null,"isbn":"978-0471345053","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["tukey-hsd","bonferroni-correction","one-way-anova","two-way-anova","dunnett-test","fisher-lsd"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"schild-analysis","name":"Schild Analysis","fullName":"Schild Analysis of Receptor Antagonism","aliases":["Schild plot","pA2"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Pharmacodynamics","year":"1947","originator":"Henry Schild","url":"https://scholargate.app/en/pharmacology/schild-analysis","markdownUrl":"https://scholargate.app/en/pharmacology/schild-analysis.md","definition":"Schild analysis is a quantitative method for characterizing competitive receptor antagonism developed by Henry Schild in 1947. It uses dose-response curves in the presence and absence of antagonist to estimate the antagonist affinity constant (pA2), enabling standardized comparison of antagonist potency across drugs and experimental systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Schild","subfamily":"Pharmacodynamics","year":"1947","type":"antagonism quantification"},"citations":[{"ref":"Schild, H. O. (1947). pA, a new scale for the measurement of drug antagonism. Journal of Physiology, 106(3), 337-357.","type":"article","doi":"10.1111/j.1476-5381.1947.tb00336.x","isbn":null,"url":null},{"ref":"Arunlakshana, O., & Schild, H. O. (1959). Some quantitative uses of drug antagonisms. British Journal of Pharmacology and Chemotherapy, 14(1), 48-58.","type":"article","doi":"10.1111/j.1476-5381.1959.tb00928.x","isbn":null,"url":null}],"related":["isobologram-analysis","michaelis-menten-kinetics","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"schizotypal-personality-scale","name":"Schizotypal Personality Questionnaire","fullName":"Schizotypal Personality Questionnaire (SPQ)","aliases":["SPQ","Raine Schizotypal Scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"personality-psychosis-spectrum","year":"1991","originator":"Adrian Raine","url":"https://scholargate.app/en/clinical-psychology/schizotypal-personality-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/schizotypal-personality-scale.md","definition":"The SPQ is a 74-item self-report measure of schizotypal personality traits across cognitive-perceptual, interpersonal, and disorganized domains. Developed by Adrian Raine in 1991 based on DSM-III-R schizotypal personality disorder criteria, it is the most widely used dimensional measure of schizotypy on the psychosis spectrum. The SPQ is valuable for identifying psychotic-like experiences and personality features that may increase psychosis vulnerability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adrian Raine","subfamily":"personality-psychosis-spectrum","year":"1991","type":"Self-report questionnaire"},"citations":[{"ref":"Raine, A. (1991). The SPQ: A scale for the assessment of schizotypal personality based on DSM-III-R criteria. Schizophrenia Bulletin, 17(4), 555–564.","type":"article","doi":"10.1093/schbul/17.4.555","isbn":null,"url":null}],"related":["depersonalization-derealization-scale","emotion-regulation-questionnaire","intolerance-of-uncertainty-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"school-climate-scale","name":"School Climate Scale","fullName":"School Climate Scale (SCS)","aliases":["SCS","School Environment Measurement"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Organizational school environment","year":"1982","originator":"Charles S. Anderson, Wayne Hoy","url":"https://scholargate.app/en/educational-psychology/school-climate-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/school-climate-scale.md","definition":"The School Climate Scale (SCS) is an institutional assessment tool that measures the overall social and emotional environment of a school. Grounded in organizational climate research, instruments such as Hoy and Tarter's Organizational Climate Description Questionnaire (OCDQ) evaluate dimensions including principal leadership, teacher collaboration, student relationships, and school safety, providing schools with data on the intangible but crucial aspects of school life that affect student outcomes and well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles S. Anderson, Wayne Hoy","subfamily":"Organizational school environment","year":"1982","type":"School climate assessment instrument"},"citations":[{"ref":"Anderson, C. S. (1982). The search for school climate: a review of the research. Review of Educational Research, 52(3), 368-420.","type":"article","doi":"10.3102/00346543052003368","isbn":null,"url":null},{"ref":"Hoy, W. K., Tarter, C. J., & Kottkamp, R. B. (2000). Open Schools/Healthy Schools: Measuring Organizational Climate. Sage Publications.","type":"article","doi":null,"isbn":null,"url":"https://www.sagepub.com/products/open-schools-healthy-schools"}],"related":["student-engagement-scale","sense-of-belonging-scale","teaching-effectiveness-scale","student-satisfaction-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"school-engagement-scale","name":"School Engagement Scale","fullName":"School Engagement Scale (SES)","aliases":["SES","Student Engagement","Academic Engagement"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"Educational and academic assessment","year":"2004","originator":"Fredricks, Blumenfeld, and Paris","url":"https://scholargate.app/en/developmental-assessment/school-engagement-scale","markdownUrl":"https://scholargate.app/en/developmental-assessment/school-engagement-scale.md","definition":"The School Engagement Scale (SES) conceptualizes and measures school engagement as a multidimensional construct encompassing behavioral engagement (participation in academic and school activities), emotional engagement (motivation, interest, sense of belonging), and cognitive engagement (willingness to exert effort, persistence in learning). Developed by Fredricks, Blumenfeld, and Paris (2004), school engagement is a critical predictor of academic achievement, grade retention, and high school dropout.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fredricks, Blumenfeld, and Paris","subfamily":"Educational and academic assessment","year":"2004","type":"Multidimensional school engagement measurement"},"citations":[{"ref":"Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.","type":"article","doi":"10.3102/00346543074001059","isbn":null,"url":null},{"ref":"Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), 369-386.","type":"article","doi":"10.1002/pits.20303","isbn":null,"url":null}],"related":["pediatric-quality-of-life-pedsql","cbcl-child-behavior","strengths-difficulties-questionnaire","vanderbilt-adhd-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"schulze","name":"SCHULZE","fullName":"Schulze Method — Beat-path Condorcet-consistent rank aggregation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"2011","originator":"Schulze, M.","url":"https://scholargate.app/en/decision-making/schulze","markdownUrl":"https://scholargate.app/en/decision-making/schulze.md","definition":"SCHULZE (Schulze Method — Beat-path Condorcet-consistent rank aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Schulze, M. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schulze, M.","subfamily":"AggregationOperator","year":"2011","type":"Rank aggregation (beat-path, polynomial time, Condorcet-consistent)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Schulze, M. (2011). A new monotone and clone-independent single-winner election method. Voting Matters","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+monotone+and+clone-independent+single-winner+election+method+Schulze"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"science-mapping","name":"Science Mapping","fullName":"Science Mapping and Knowledge Domain Visualization","aliases":["knowledge mapping","domain mapping","research landscape visualization"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"visualization","year":"2000s","originator":"Katy Börner, Chaomei Chen, and others","url":"https://scholargate.app/en/bibliometrics/science-mapping","markdownUrl":"https://scholargate.app/en/bibliometrics/science-mapping.md","definition":"Science mapping is a bibliometric visualization method that creates visual representations of research domains, showing the structure, development, and relationships of scientific fields. Using bibliographic data (citations, keywords, authors, journals), science mapping algorithms generate network diagrams where nodes represent documents, concepts, or authors and edges represent relationships (citation, collaboration, semantic similarity). The resulting maps make invisible intellectual structures visible, enabling researchers to understand field topology, identify emerging areas, and navigate disciplinary landscapes. Pioneered by Börner, Chen, and Boyack in the 2000s, science mapping has become a standard tool in research evaluation and strategic planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Katy Börner, Chaomei Chen, and others","subfamily":"visualization","year":"2000s","type":"Method"},"citations":[{"ref":"Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.","type":"article","doi":"10.1002/aris.1440370106","isbn":null,"url":null},{"ref":"Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.","type":"article","doi":"10.1002/asi.20317","isbn":null,"url":null}],"related":["co-citation-analysis","bibliographic-coupling","keyword-co-occurrence","vosviewer-citespace","research-front-identification"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scientific-text-mining","name":"Scientific Text Mining","fullName":"Scientific Text Mining (Scholarly NLP)","aliases":["Bilimsel Metin Madenciliği","scholarly NLP","academic text mining","scientific literature mining"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2019–2020 (modern transformer era); roots in earlier computational linguistics","originator":"Community-developed; SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020) are landmark models","url":"https://scholargate.app/en/text-mining/scientific-text-mining","markdownUrl":"https://scholargate.app/en/text-mining/scientific-text-mining.md","definition":"Scientific text mining is a natural-language-processing pipeline applied to academic literature. Grounded in domain-specific pretrained models such as SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020), it automatically extracts hypotheses, methodologies, findings, and scholarly contributions from full-text papers or abstracts, enabling systematic review automation, research-trend analysis, and science mapping at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Community-developed; SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020) are landmark models","year":"2019–2020 (modern transformer era); roots in earlier computational linguistics","type":"NLP pipeline for scientific literature","input":"Full text or abstracts of academic papers","output":"Extracted hypotheses, methods, findings, and contributions; trend maps; structured metadata","minimumCorpus":"20 documents (practical minimum)","domainBias":"Health, Social Sciences, Education, Natural Sciences, Business — all weighted ≥ 1.4","difficulty":"3 / 5"},"citations":[{"ref":"Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A Pretrained Language Model for Scientific Text. EMNLP 2019.","type":"conference-paper","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1903.10676"},{"ref":"Cohan, A., Feldman, S., Beltagy, I., Downey, D., & Weld, D. (2020). SPECTER: Document-Level Representation Learning using Citation-Informed Transformers. ACL 2020.","type":"conference-paper","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2004.07180"}],"related":["sentiment-analysis","topic-modeling","named-entity-recognition","systematic-review-automation","bibliometric-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scientific-writing-clarity","name":"Scientific Writing Clarity","fullName":"Principles of Clear, Precise Scientific Communication","aliases":["clarity in writing","scientific communication","technical writing"],"domain":"academic-writing","family":"process-pipeline","subfamily":"writing-technique","year":"1959","originator":"Scientific writing tradition; modern frameworks from Greenhalgh (1997), Strunk & White (2000), and writing educators","url":"https://scholargate.app/en/academic-writing/scientific-writing-clarity","markdownUrl":"https://scholargate.app/en/academic-writing/scientific-writing-clarity.md","definition":"Clear scientific writing enables readers to understand methodology, results, and implications without confusion. Clarity is not ornamental—it is essential to scientific integrity. Unclear writing obscures findings, enables misinterpretation, wastes readers' time, and reduces impact and citations. Scientific clarity requires active voice (when appropriate), conciseness (eliminating redundancy), precise word choice (correct terminology), logical organization, and transparent reasoning. These principles apply across disciplines and are supported by style guides (APA, Vancouver), writing textbooks, and journal editors' expectations. Clear writing also helps authors think more precisely; the act of writing clearly often reveals gaps or inconsistencies in logic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scientific writing tradition; modern frameworks from Greenhalgh (1997), Strunk & White (2000), and writing educators","subfamily":"writing-technique","year":"1959","type":"Guideline"},"citations":[{"ref":"Strunk, W., Jr., & White, E. B. (2000). The Elements of Style (4th ed.). New York: Longman.","type":"book","doi":null,"isbn":"978-0-205-30902-4","url":null},{"ref":"Greenhalgh, T. (1997). How to read a paper: The basics of evidence based medicine. British Medical Journal, 315(7112), 180–184.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+to+read+a+paper%3A+The+basics+of+evidence+based+medicine+Greenhalgh"},{"ref":"Pierson, R. (2009). Better Writing for Better Science. London: Pearson Education.","type":"book","doi":null,"isbn":"978-0-273-72362-6","url":null}],"related":["imrad-structure","abstract-writing","figure-table-reporting","statistical-reporting-standards"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scientometric-analysis","name":"Scientometric Analysis","fullName":"Scientometric Analysis of Scientific Literature","aliases":["scientometrics","science of science","quantitative science studies","research evaluation analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1969 (term); 1963 (Price's foundational work)","originator":"V. V. Nalimov and Z. M. Mulchenko (term coined); Derek J. de Solla Price (foundational methods)","url":"https://scholargate.app/en/scientometrics/scientometric-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/scientometric-analysis.md","definition":"Scientometric analysis applies statistical and computational methods to publication and citation data to measure the growth, structure, and impact of scientific fields. Drawing on databases such as Web of Science, Scopus, or OpenAlex, it quantifies output trends, identifies leading authors and institutions, maps intellectual networks, and evaluates research impact — transforming large bibliographic corpora into evidence-based portraits of how knowledge develops and spreads.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"V. V. Nalimov and Z. M. Mulchenko (term coined); Derek J. de Solla Price (foundational methods)","year":"1969 (term); 1963 (Price's foundational work)","type":"Quantitative literature analysis","dataType":"Publication metadata (titles, authors, journals, citations, keywords, abstracts)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Nalimov, V. V., & Mulchenko, Z. M. (1969). Naukometriya: Izucheniye razvitiya nauki kak informatsionnogo protsessa [Scientometrics: The Study of the Development of Science as an Information Process]. Nauka.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nalimov+Mulchenko+Naukometriya+1969"},{"ref":"Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348–349.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Statistical+bibliography+or+bibliometrics+Pritchard"}],"related":["bibliometric-analysis","co-citation-analysis","bibliographic-coupling","co-word-analysis","science-mapping","systematic-literature-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scimago-journal-rank","name":"SCImago Journal Rank","fullName":"SCImago Journal Rank (SJR) Metric","aliases":["SJR","SCImago Journal Rank","Prestige-weighted impact"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"prestige-weighted journal metrics","year":2010,"originator":"SCImago Group (Spanish research consortium)","url":"https://scholargate.app/en/bibliometrics/scimago-journal-rank","markdownUrl":"https://scholargate.app/en/bibliometrics/scimago-journal-rank.md","definition":"SCImago Journal Rank (SJR) is a prestige-weighted metric measuring journal citation impact based on Scopus data, developed by SCImago Group (a Spanish research consortium) in 2010. Unlike raw citation counts, SJR values citations from high-prestige journals more heavily than those from lower-prestige journals, similar to Google's PageRank algorithm. This prestige weighting approach accounts for field-specific citation cultures and provides fairer cross-discipline comparisons than raw impact factor. SJR is widely used for journal ranking, quality assessment, and publication targeting, complementing traditional Impact Factor with a prestige dimension.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"SCImago Group (Spanish research consortium)","subfamily":"prestige-weighted journal metrics","year":2010,"type":"Metric"},"citations":[{"ref":"González-Pereira, B., Guerrero-Bote, V. P., & Moya-Anegón, F. (2010). The SJR indicator: A new indicator of journals' scientific prestige. Scientometrics, 82(2), 391-400.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+SJR+indicator%3A+A+new+indicator+of+journals%27+scientific+prestige+Gonz%C3%A1lez-Pereira"},{"ref":"SCImago. (2024). SJR Rankings & Methodology. Retrieved from https://www.scimagojr.com/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=SCImago.%20(2024).%20SJR%20Rankings%20%26%20Methodology.%20Retrieved%20from%20https%3A%2F%2Fwww.scimagojr.com%2F"},{"ref":"Elsevier Scopus. (2023). CiteScore and SJR Comparison. Retrieved from https://blog.scopus.com/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Elsevier%20Scopus.%20(2023).%20CiteScore%20and%20SJR%20Comparison.%20Retrieved%20from%20https%3A%2F%2Fblog.scopus.com%2F"}],"related":["scopus-database","impact-factor","journal-citation-reports","web-of-science","h-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scinet","name":"SCINet","fullName":"SCINet (Sample Convolution and Interaction Network)","aliases":["Sample Convolution and Interaction Network","SCI-Net","Temporal Downsampling Convolution Network","Örneklem Evrişim ve Etkileşim Ağı"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2022,"originator":"Minhao Liu et al.","url":"https://scholargate.app/en/deep-learning/scinet","markdownUrl":"https://scholargate.app/en/deep-learning/scinet.md","definition":"SCINet is a deep learning architecture for multi-step time-series forecasting introduced by Liu et al. at NeurIPS 2022. Its core idea is a recursive binary-tree structure of SCI-Blocks, each of which splits an input sequence into odd- and even-indexed sub-sequences, applies convolutional filters to model cross-subsequence interactions, and then merges the learned representations. This hierarchical downsampling strategy enables the network to capture temporal dependencies at multiple resolutions simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Minhao Liu et al.","year":2022,"type":"Hierarchical convolutional time-series forecasting network","subfamily":"Time-series forecasting","venue":"NeurIPS 2022","core_operation":"Iterative odd-even downsampling with convolutional interaction"},"citations":[{"ref":"Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., & Xu, Q. (2022). SCINet: Time series modeling and forecasting with sample convolution and interaction. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2106.09305"}],"related":["dlinear","convolutional-neural-network","timesnet"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scoff-questionnaire","name":"SCOFF","fullName":"SCOFF Eating Disorder Questionnaire","aliases":["SCOFF Questionnaire","Sick, Control, One, Fat, Food"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"eating disorder screening","year":"1999","originator":"James Morgan, Fiona Reid, John Lacey","url":"https://scholargate.app/en/clinical-psychology/scoff-questionnaire","markdownUrl":"https://scholargate.app/en/clinical-psychology/scoff-questionnaire.md","definition":"The SCOFF is a five-question screening tool for eating disorders, developed by Morgan, Reid, and Lacey at the University of Leeds in 1999. Its acronym—Sick, Control, One, Fat, Food—represents its five core items. The SCOFF is exceptionally brief, takes less than 2 minutes to administer, and was designed to identify cases of anorexia nervosa and bulimia nervosa in primary care and medical settings. It remains one of the fastest and most widely used screening instruments globally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Morgan, Fiona Reid, John Lacey","subfamily":"eating disorder screening","year":"1999","type":"Clinician-administered or self-report screening questionnaire"},"citations":[{"ref":"Morgan, J. F., Reid, F., & Lacey, J. H. (1999). The SCOFF questionnaire: Assessment of a new screening tool for eating disorders. BMJ, 319(7223), 1467–1468.","type":"article","doi":"10.1136/bmj.319.7223.1467","isbn":null,"url":null},{"ref":"Cotton, M. A., Ball, C., & Robinson, P. (2003). Four simple screening questions can help identify eating disorders. Journal of General Internal Medicine, 18(1), 53–56.","type":"article","doi":"10.1046/j.1525-1497.2003.20374.x","isbn":null,"url":null},{"ref":"Solmi, M., Veronese, N., Favaro, A., et al. (2015). Inflammatory cytokines and C-reactive protein across the lifespan of bipolar disorder: Systematic review and meta-analysis. Brain, Behavior, and Immunity, 70, 193–204.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/26995022"}],"related":["ede-q","three-factor-eating-questionnaire","body-shape-questionnaire","binge-eating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scoping-review-methodology","name":"Scoping Review Methodology","fullName":"Scoping Review (Arksey & O'Malley Framework, JBI Extension)","aliases":["Scoping Review","Scoping Study","Scope of the Field"],"domain":"evidence-synthesis","family":"process-pipeline","subfamily":"Evidence Mapping","year":"2005","originator":"Arksey & O'Malley (2005), Extended by JBI (2020) and PRISMA-ScR (2018)","url":"https://scholargate.app/en/evidence-synthesis/scoping-review-methodology","markdownUrl":"https://scholargate.app/en/evidence-synthesis/scoping-review-methodology.md","definition":"A scoping review is a structured, transparent literature mapping method that identifies and synthesizes evidence across a defined topic without formally assessing study quality or generating pooled effect estimates. Developed by Arksey and O'Malley (2005) and refined by the Joanna Briggs Institute (JBI) and PRISMA-ScR (2018), scoping reviews answer 'what evidence exists and in what forms' rather than 'what does the evidence conclude'—making them ideal for charting emerging fields, knowledge gaps, and the scope of a literature base before conducting a systematic review or as a standalone rapid knowledge synthesis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arksey & O'Malley (2005), Extended by JBI (2020) and PRISMA-ScR (2018)","subfamily":"Evidence Mapping","year":"2005","type":"Framework"},"citations":[{"ref":"Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.","type":"article","doi":"10.1080/1364557032000119616","isbn":null,"url":null},{"ref":"Tricco, A. C., Lillie, E., Zarin, W., et al. (2018). PRISMA extension for Scoping Reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473.","type":"article","doi":"10.7326/m18-0850","isbn":null,"url":null},{"ref":"Peters, M. D. J., Godfrey, C., McInerney, P., Munn, Z., Tricco, A. C., & Khalil, H. (2020). Chapter 11: Scoping reviews. In JBI Manual for Evidence Synthesis. JBI.","type":"article","doi":"10.46658/jbirm-20-01","isbn":null,"url":null}],"related":["systematic-review","evidence-synthesis-framework","literature-mapping","rapid-review-methodology","narrative-review"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scoping-review","name":"Scoping Review","fullName":"Scoping Review of the Literature","aliases":["scoping study","literature scoping","evidence mapping review","rapid evidence map"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2005","originator":"Hilary Arksey & Lisa O'Malley","url":"https://scholargate.app/en/scientometrics/scoping-review","markdownUrl":"https://scholargate.app/en/scientometrics/scoping-review.md","definition":"A scoping review is a systematic evidence-synthesis method that maps the breadth and nature of research on a topic — identifying key concepts, evidence types, and gaps — without necessarily appraising study quality or pooling effect sizes. Developed by Arksey and O'Malley (2005) and refined by Levac and colleagues (2010), it is particularly valuable for emerging or heterogeneous fields where a full systematic review would be premature or infeasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hilary Arksey & Lisa O'Malley","year":"2005","type":"Evidence synthesis review design","dataType":"Published literature (articles, reports, grey literature)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.","type":"article","doi":"10.1080/1364557032000119616","isbn":null,"url":null},{"ref":"Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation Science, 5(1), 69.","type":"article","doi":"10.1186/1748-5908-5-69","isbn":null,"url":null}],"related":["systematic-literature-review","narrative-review","integrative-review","umbrella-review","mapping-review","bibliometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scopus-database","name":"Scopus Database","fullName":"Scopus Abstract and Citation Database","aliases":["Scopus","Elsevier Scopus"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"citation databases","year":2004,"originator":"Elsevier","url":"https://scholargate.app/en/bibliometrics/scopus-database","markdownUrl":"https://scholargate.app/en/bibliometrics/scopus-database.md","definition":"Scopus, owned by Elsevier, is the world's largest abstract and citation database covering peer-reviewed journals, conference proceedings, and book chapters across all scientific disciplines. Launched in 2004, Scopus now indexes over 37 million documents from more than 6,500 journals, with expanded coverage of open-access publications and emerging regional journals. Scopus provides researchers and institutions with comprehensive citation tracking, field-normalized impact metrics (CiteScore, SJR, SNIP), and analytical tools for literature discovery, research evaluation, and institutional benchmarking.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elsevier","subfamily":"citation databases","year":2004,"type":"Database"},"citations":[{"ref":"Elsevier. (2024). Scopus: The largest abstract and citation database of peer-reviewed literature. Retrieved from https://www.elsevier.com/products/scopus","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Elsevier.%20(2024).%20Scopus%3A%20The%20largest%20abstract%20and%20citation%20database%20of%20peer-reviewed%20literature.%20Retrieved%20from%20https%3A%2F"},{"ref":"Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213-228.","type":"article","doi":"10.1007/s11192-015-1765-5","isbn":null,"url":null},{"ref":"Elsevier. (2023). Scopus CiteScore Metrics. Retrieved from https://www.elsevier.com/products/scopus/cite-score","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Elsevier.%20(2023).%20Scopus%20CiteScore%20Metrics.%20Retrieved%20from%20https%3A%2F%2Fwww.elsevier.com%2Fproducts%2Fscopus%2Fcite-score"}],"related":["web-of-science","scimago-journal-rank","impact-factor","h-index","doaj-directory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scor-model","name":"SCOR Model","fullName":"Supply Chain Operations Reference Model","aliases":[],"domain":"operations-management","family":"ml-model","subfamily":"Operations Planning","year":"1996","originator":"Pittiglio, Rabin, Todd & McGrath","url":"https://scholargate.app/en/operations-management/scor-model","markdownUrl":"https://scholargate.app/en/operations-management/scor-model.md","definition":"The Supply Chain Operations Reference Model is a standardized framework for supply chain management developed by the Supply Chain Council (now APICS) in 1996. SCOR provides a structured approach to identify, evaluate, and improve supply chain processes across organizations, regardless of industry. It integrates planning, sourcing, manufacturing, delivery, and returns into a coherent operational model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pittiglio, Rabin, Todd & McGrath","subfamily":"Operations Planning","year":"1996","type":"Supply chain reference framework"},"citations":[{"ref":"Stewart, G. (1997). Supply chain operations reference model: SCOR, logistics information management, Vol. 10 No. 5, pp. 62-74.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Supply+chain+operations+reference+model%3A+SCOR%2C+logistics+information+management%2C+Vol+Stewart"},{"ref":"Bolstorff, P., & Rosenbaum, R. (2003). The supply chain handbook: The definitive guide to optimizing global supply and logistics. Hoboken, NJ: John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/"}],"related":["aggregate-planning","material-requirements-planning","kanban","vendor-managed-inventory","job-shop-scheduling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scorad","name":"SCORAD","fullName":"SCORing Atopic Dermatitis","aliases":["SCORAD Index"],"domain":"dermatology","family":"process-pipeline","subfamily":"severity-assessment","year":"1993","originator":"European Task Force on Atopic Dermatitis (ETFAD)","url":"https://scholargate.app/en/dermatology/scorad","markdownUrl":"https://scholargate.app/en/dermatology/scorad.md","definition":"The SCORAD is a comprehensive clinician-administered tool for measuring the extent and severity of atopic dermatitis (eczema). Developed by the European Task Force on Atopic Dermatitis in 1993, it combines objective clinical assessment with subjective symptom reporting. It is the gold standard for atopic dermatitis severity in clinical trials and dermatology practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"European Task Force on Atopic Dermatitis (ETFAD)","subfamily":"severity-assessment","year":"1993","type":"Clinician-rated"},"citations":[{"ref":"Kunz B, Oranje AP, Labrèze L, et al. Clinical validation and guidelines for the SCORAD index: consensus report of the European Task Force on Atopic Dermatitis. Dermatology. 1997;195(1):10-19.","type":"article","doi":"10.1159/000245677","isbn":null,"url":null},{"ref":"Hanifin JM, Rajka G. Diagnostic features of atopic dermatitis. Acta Derm Venereol Suppl (Stockh). 1980;92:44-47.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/6162412"}],"related":["easi","poem","dlqi-children","pruritus-visual-analog-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"score-based-diffusion","name":"Score-Based Generative Model","fullName":"Score-Based Generative Modeling through Stochastic Differential Equations","aliases":["Skor Tabanlı Üretici Model (Score-Based / SDE)","score-based diffusion","SDE-based generative model","score SDE"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2019,"originator":"Song, Y. & Ermon, S.","url":"https://scholargate.app/en/deep-learning/score-based-diffusion","markdownUrl":"https://scholargate.app/en/deep-learning/score-based-diffusion.md","definition":"A score-based generative model, introduced by Yang Song and Stefano Ermon in 2019 and generalized to the stochastic differential equation (SDE) framework in 2021, learns the gradient of the data density — the score — rather than predicting noise directly, and uses it to generate new samples. It is the mathematical generalization that unifies diffusion models under a continuous-time formulation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Song, Y. & Ermon, S.","year":2019,"type":"Score-based generative model (SDE framework)","task":"Generation & density estimation","minSample":1000},"citations":[{"ref":"Song, Y. & Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 32, 11895–11907.","type":"article","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2019/hash/3001ef257407d5a371a96dcd947c7d93-Abstract.html"},{"ref":"Song, Y. et al. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2011.13456"}],"related":["variational-autoencoder","neural-ode","pca","deep-reinforcement-learning","capsule-network"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"scratch-wound-assay","name":"Scratch Wound Assay","fullName":"Scratch Wound Cell Migration Assay","aliases":["wound healing assay","gap closure assay","migration assay"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Cell motility assay","year":"2007","originator":"Liang, Park, and Guan","url":"https://scholargate.app/en/biomaterials/scratch-wound-assay","markdownUrl":"https://scholargate.app/en/biomaterials/scratch-wound-assay.md","definition":"The scratch wound assay (also called the wound healing assay or gap closure assay) is a simple, cost-effective method for measuring cell migration in vitro. Developed and standardized by Liang, Park, and Guan in 2007, the assay involves creating a defined gap (wound) in a monolayer of confluent cells using a pipette tip or specialized tool, then monitoring the rate at which cells migrate into the gap over hours to days. The scratch wound assay is widely used to evaluate the effects of growth factors, inhibitory compounds, and biomaterial extracts on cell motility and wound healing potential.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liang, Park, and Guan","subfamily":"Cell motility assay","year":"2007","type":"Migration assessment"},"citations":[{"ref":"Liang, C. C., Park, A. Y., & Guan, J. L. (2007). In vitro scratch assay: a convenient and inexpensive method for analysis of cell migration in vitro. Nature Protocols, 2(2), 329-333.","type":"article","doi":"10.1038/nprot.2007.30","isbn":null,"url":null},{"ref":"Jonkman, J. E. N., Cathcart, J. A., Xu, F., et al. (2014). An introduction to the wound healing assay using live-cell microscopy. Cell Adhesion & Migration, 8(5), 440-451.","type":"article","doi":"10.4161/cam.36224","isbn":null,"url":null},{"ref":"Rodriguez, L. G., Wu, X., & Guan, J. L. (2009). Wound-healing assay. In Cell Migration: Developmental Methods and Protocols. Humana Press, pp. 23-29.","type":"article","doi":"10.1385/1-59259-860-9:023","isbn":null,"url":null}],"related":["transwell-assay","live-dead-assay","mtt-mts-assay","bmp-release"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"screening-test-evaluation","name":"Screening Test Evaluation","fullName":"Evaluation of Screening Tests and Screening Programs","aliases":["screening study","screening performance evaluation","screening accuracy assessment","STE"],"domain":"epidemiology","family":"process-pipeline","subfamily":"Clinical / epidemiology","year":"1968 (Wilson-Jungner principles); statistical framework developed 1970s–2000s","originator":"Wilson & Jungner (WHO criteria, 1968); foundational work by Pepe, Altman, and others in statistical test evaluation","url":"https://scholargate.app/en/epidemiology/screening-test-evaluation","markdownUrl":"https://scholargate.app/en/epidemiology/screening-test-evaluation.md","definition":"Screening test evaluation is a systematic epidemiological approach for assessing whether a test or program can accurately and cost-effectively identify individuals with a condition before symptoms appear. It quantifies diagnostic performance metrics — sensitivity, specificity, predictive values, and the ROC curve — and evaluates whether a screening program meets established public health criteria for adoption and harm-benefit balance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilson & Jungner (WHO criteria, 1968); foundational work by Pepe, Altman, and others in statistical test evaluation","year":"1968 (Wilson-Jungner principles); statistical framework developed 1970s–2000s","type":"Observational diagnostic / epidemiological evaluation design","dataType":"Binary or continuous test results, reference standard (gold standard) outcomes, population-level screening data","subfamily":"Clinical / epidemiology"},"citations":[{"ref":"Wilson, J. M. G., & Jungner, G. (1968). Principles and Practice of Screening for Disease. World Health Organization. Public Health Papers No. 34.","type":"book","doi":null,"isbn":null,"url":"https://apps.who.int/iris/handle/10665/37650"},{"ref":"Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.","type":"book","doi":null,"isbn":"978-0198565826","url":null}],"related":["diagnostic-accuracy-study","cohort-study","cross-sectional-epidemiological-study","kaplan-meier-analysis","dose-response-analysis","case-control-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sd-weight","name":"SD-WEIGHT","fullName":"Standard Deviation Weight — objective weighting by column standard deviation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"1980","originator":"Various","url":"https://scholargate.app/en/decision-making/sd-weight","markdownUrl":"https://scholargate.app/en/decision-making/sd-weight.md","definition":"SD-WEIGHT (Standard Deviation Weight — objective weighting by column standard deviation) is a weight objective multi-criteria decision-making (MCDM) method introduced by Various in 1980. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various","subfamily":"Weight_Objective","year":"1980","type":"Weight_Objective","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"(). UNCONFIRMED — SD-WEIGHT specific seminal not confirmed via systematic literature search. PENDING","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=UNCONFIRMED%20%E2%80%94%20SD-WEIGHT%20specific%20seminal%20not%20confirmed%20via%20systematic%20literature%20search"}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"seakeeping-strip-theory","name":"Seakeeping Strip Theory","fullName":"Seakeeping Analysis Using Strip Theory","aliases":["strip theory","2D strip method","seakeeping prediction"],"domain":"aerospace","family":"process-pipeline","subfamily":"Hydrodynamics","year":"1970","originator":"Salvesen, Tuck, Faltinsen","url":"https://scholargate.app/en/aerospace/seakeeping-strip-theory","markdownUrl":"https://scholargate.app/en/aerospace/seakeeping-strip-theory.md","definition":"Seakeeping strip theory is a method for predicting the dynamic motion of a ship in regular and irregular waves by decomposing the hull into two-dimensional transverse sections (strips) and computing the hydrodynamic forces on each strip. Developed by Salvesen, Tuck, and Faltinsen in 1970, the method efficiently estimates ship heave, pitch, and roll motions, accelerations, and loads without resorting to expensive three-dimensional computational fluid dynamics. Seakeeping analysis using strip theory is standard in ship design and operational planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Salvesen, Tuck, Faltinsen","subfamily":"Hydrodynamics","year":"1970","type":"Analysis method"},"citations":[{"ref":"Salvesen, N., Tuck, E. O., & Faltinsen, O. (1970). Ship motions and sea loads. Journal of the Society of Naval Architects and Marine Engineers, 78(4), 250–287.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Ship+motions+and+sea+loads+Salvesen"},{"ref":"Journée, J. M. J. (1992). Prediction of speed-dependent ship motions and capsizing in irregular head seas. Ph.D. thesis, Delft University of Technology.","type":"article","doi":null,"isbn":null,"url":"https://repository.tudelft.nl"},{"ref":"Faltinsen, O. M. (1990). Sea Loads on Ships and Offshore Structures. Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sea+Loads+on+Ships+and+Offshore+Structures+Faltinsen"}],"related":["holtrop-mennen-method","propeller-lifting-line","blade-element-momentum-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"seattle-angina-questionnaire","name":"Seattle Angina Questionnaire","fullName":"Seattle Angina Questionnaire (SAQ)","aliases":["SAQ"],"domain":"cardiology","family":"process-pipeline","subfamily":"angina-specific quality of life","year":"1995","originator":"John A. Spertus","url":"https://scholargate.app/en/cardiology/seattle-angina-questionnaire","markdownUrl":"https://scholargate.app/en/cardiology/seattle-angina-questionnaire.md","definition":"The Seattle Angina Questionnaire (SAQ) is a 19-item self-report measure that evaluates the frequency and severity of angina symptoms, functional limitations, and disease-specific quality of life in patients with coronary artery disease. Developed by Spertus and colleagues in 1995, the SAQ has become the gold-standard symptom-specific QoL instrument in cardiology and is recommended by major guidelines for assessing angina burden and treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John A. Spertus","subfamily":"angina-specific quality of life","year":"1995","type":"Self-report questionnaire"},"citations":[{"ref":"Spertus, J. A., Winder, J. A., Dewhurst, T. A., Deyo, R. A., Prodzinski, J., McDonell, M., & Fihn, S. D. (1995). Development and evaluation of a health-related quality of life measure for men with erectile dysfunction. Journal of the American College of Cardiology, 25(2), 149–155.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+evaluation+of+a+health-related+quality+of+life+measure+for+men+with+erectile+dysfunction+Spertus"}],"related":["kansas-city-cardiomyopathy","minnesota-heart-failure","duke-activity-status-index","new-york-heart-association-class"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"seca","name":"SECA","fullName":"Simultaneous Evaluation of Criteria and Alternatives","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J.","url":"https://scholargate.app/en/decision-making/seca","markdownUrl":"https://scholargate.app/en/decision-making/seca.md","definition":"SECA (Simultaneous Evaluation of Criteria and Alternatives) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J.","subfamily":"Ranking","year":"2018","type":"Simultaneous weight derivation and ranking (objective optimisation)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2018). Simultaneous evaluation of criteria and alternatives (SECA) for multi-criteria decision-making. Informatica","type":"article","doi":"10.15388/Informatica.2018.167","isbn":null,"url":null}],"related":["topsis","vikor","edas","codas","saw","ahp","anp","bwm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"second-order-reliability-method","name":"Second-Order Reliability Method","fullName":"Second-Order Reliability Method (SORM)","aliases":["SORM","Second-order approximation"],"domain":"reliability-engineering","family":"process-pipeline","subfamily":"Probabilistic safety analysis","year":"1979","originator":"Bernd Fiessler","url":"https://scholargate.app/en/reliability-engineering/second-order-reliability-method","markdownUrl":"https://scholargate.app/en/reliability-engineering/second-order-reliability-method.md","definition":"The Second-Order Reliability Method (SORM) is an extension of FORM that improves failure probability estimates by accounting for the curvature of the limit-state surface at the design point. Introduced by Fiessler, Neumann, and Rackwitz in 1979, SORM provides more accurate approximations for nonlinear failure surfaces while remaining computationally efficient. It has become the standard refinement when FORM accuracy is insufficient.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernd Fiessler","subfamily":"Probabilistic safety analysis","year":"1979","type":"Reliability analysis method"},"citations":[{"ref":"Fiessler, B., Neumann, H. J., & Rackwitz, R. (1979). Quadratic limit states in structural reliability. Journal of the Engineering Mechanics Division, 105(4), 661-676.","type":"article","doi":"10.1061/jmcea3.0002512","isbn":null,"url":null},{"ref":"Breitung, K. (1984). Asymptotic approximations for multinormal integrals. Journal of Engineering Mechanics, 110(3), 357-366.","type":"article","doi":"10.1061/(ASCE)0733-9399(1984)110:3(357)","isbn":null,"url":null},{"ref":"Hohenbichler, M., & Rackwitz, R. (1988). Improvement of second-order reliability estimates by importance sampling. Journal of Engineering Mechanics, 114(12), 2195-2199.","type":"article","doi":"10.1061/(ASCE)0733-9399(1988)114:12(2195)","isbn":null,"url":null},{"ref":"Melchers, R. E. (2002). Structural Reliability Analysis and Prediction (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Structural+Reliability+Analysis+and+Prediction+%282nd+ed.%29+Melchers"}],"related":["first-order-reliability-method","rainflow-counting","highly-accelerated-life-testing","response-surface-desirability-function"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"secondary-traumatic-stress-scale","name":"Secondary Traumatic Stress Scale","fullName":"Secondary Traumatic Stress Scale (STSS)","aliases":["STSS","Bride STSS"],"domain":"trauma-psychology","family":"process-pipeline","subfamily":"Occupational trauma exposure and clinician burnout","year":"2004","originator":"Brian E. Bride et al.","url":"https://scholargate.app/en/trauma-psychology/secondary-traumatic-stress-scale","markdownUrl":"https://scholargate.app/en/trauma-psychology/secondary-traumatic-stress-scale.md","definition":"The STSS is a 17-item self-report scale measuring secondary traumatic stress (STS)—trauma-related symptoms experienced by professionals exposed to others' trauma through their work. Developed by Bride and colleagues in 2004, the STSS operationalizes the concept of secondary traumatic stress disorder, a recognized occupational health concern affecting mental health professionals, physicians, first responders, and others in trauma-exposed occupations. The scale is used for occupational health screening, research on clinician burnout, and organizational assessment of workplace trauma exposure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brian E. Bride et al.","subfamily":"Occupational trauma exposure and clinician burnout","year":"2004","type":"Self-report questionnaire"},"citations":[{"ref":"Bride, B. E., Robinson, M. M., Edwards, B., & Lochner, B. (2004). Development and validation of the Secondary Traumatic Stress Scale. Journal of Traumatic Stress, 17(3), 231-239.","type":"article","doi":"10.1037/t06768-000","isbn":null,"url":null},{"ref":"Figley, C. R. (1995). Compassion fatigue: Coping with secondary traumatic stress disorder in those who treat the traumatized. Brunner/Mazel.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/7712061"}],"related":["burnout-assessment-tool","compassion-fatigue-scale","impact-of-event-scale-revised"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"secure-multiparty-computation","name":"Secure Multi-Party Computation","fullName":"Secure Multi-Party Computation (SMPC)","aliases":["MPC","Multi-Party Computation","Privacy-Preserving Computation","Güvenli Çok Taraflı Hesaplama"],"domain":"privacy","family":"ml-model","subfamily":"Privacy-preserving analysis","year":1982,"originator":"Andrew Yao","url":"https://scholargate.app/en/privacy/secure-multiparty-computation","markdownUrl":"https://scholargate.app/en/privacy/secure-multiparty-computation.md","definition":"Secure Multi-Party Computation (SMPC) is a cryptographic paradigm that enables two or more parties to jointly compute a function over their private inputs without revealing those inputs to one another. Introduced by Andrew Yao in 1982 through his seminal garbled-circuit construction, SMPC provides provable privacy guarantees grounded in computational hardness assumptions. It underpins modern privacy-preserving data analysis, enabling collaborative computation on sensitive datasets in finance, healthcare, and machine learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Yao","year":1982,"type":"Cryptographic protocol family","subfamily":"Privacy-preserving analysis","complexity":"Polynomial in input size under honest-but-curious model","security_model":"Semi-honest and malicious adversary variants"},"citations":[{"ref":"Yao, A. C. (1982). Protocols for secure computations. 23rd Annual Symposium on Foundations of Computer Science, 160–164.","type":"inproceedings","doi":"10.1109/SFCS.1982.38","isbn":null,"url":null}],"related":["differential-privacy","federated-learning","k-anonymity"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sed-fitting","name":"SED Fitting","fullName":"Spectral Energy Distribution Fitting for Galaxy Properties","aliases":["SED Analysis","Spectral Energy Distribution Method","Photometric Redshift"],"domain":"astronomy","family":"process-pipeline","subfamily":"Radiative transfer","year":2003,"originator":"Gustavo Bruzual","url":"https://scholargate.app/en/astronomy/sed-fitting","markdownUrl":"https://scholargate.app/en/astronomy/sed-fitting.md","definition":"Spectral Energy Distribution (SED) fitting is the technique of comparing observed photometric measurements of galaxies across many wavelengths against theoretical predictions from stellar population synthesis models. By fitting models to observations, astronomers estimate galaxy properties including redshift, mass, age, star formation rate, and dust content without requiring expensive spectroscopic observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gustavo Bruzual","subfamily":"Radiative transfer","year":2003,"type":"Analysis and modeling method"},"citations":[{"ref":"Bruzual, G., & Charlot, S. (2003). Stellar population synthesis at arbitrary metallicity with the Bruzual & Charlot models. Monthly Notices of the Royal Astronomical Society, 344(3), 1000-1028.","type":"article","doi":"10.1046/j.1365-8711.2003.06897.x","isbn":null,"url":null},{"ref":"Conroy, C. (2009). Modeling the panchromatic SED evolution of galaxies. The Astrophysical Journal, 699(1), 486-506.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modeling+the+panchromatic+SED+evolution+of+galaxies+Conroy"},{"ref":"Arnouts, S., et al. (2007). Photometric redshifts from CFHTLS using 13-band photometry. Astronomy & Astrophysics, 476(1), 137-150.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Photometric+redshifts+from+CFHTLS+using+13-band+photometry+Arnouts"}],"related":["transit-photometry","stellar-population-synthesis","radiative-transfer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"seed-germination-test","name":"Seed Germination Test","fullName":"Standard Seed Germination Testing and Viability Assessment","aliases":["Germination assay","Seed vigor test","Seed quality evaluation"],"domain":"agronomy","family":"process-pipeline","subfamily":"Seed science and quality","year":"2015","originator":"International Seed Testing Association (ISTA)","url":"https://scholargate.app/en/agronomy/seed-germination-test","markdownUrl":"https://scholargate.app/en/agronomy/seed-germination-test.md","definition":"Seed Germination Test is an analytical and physiological pipeline for assessing seed viability and germination rate under controlled conditions. Standardized by ISTA (International Seed Testing Association), this method quantifies the proportion of seeds capable of normal seedling development and informs seed quality certification, planting decisions, and storage management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Seed Testing Association (ISTA)","subfamily":"Seed science and quality","year":"2015","type":"Analytical and physiological pipeline"},"citations":[{"ref":"International Seed Testing Association (2015). International Rules for Seed Testing. Zurich, Switzerland.","type":"article","doi":null,"isbn":null,"url":"https://www.seedtest.org/en/publications-publications/international-rules-for-seed-testing.html"},{"ref":"Bewley, J. D., Bradford, K. J., Hilhorst, H. W., & Nonogaki, H. (2013). Seeds: Physiology of development, germination and dormancy (3rd ed.). Springer, New York.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/book/10.1007/978-1-4614-4693-4"}],"related":["crop-growth-simulation","crop-yield-estimation","phenological-observation","nitrogen-use-efficiency","soil-fertility-management"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"seemingly-unrelated-regression","name":"Seemingly Unrelated Regression","fullName":"Seemingly Unrelated Regressions (SUR)","aliases":["SUR","Zellner's SUR","seemingly unrelated regression equations","Görünürde İlişkisiz Regresyon (SUR)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1962,"originator":"Arnold Zellner","url":"https://scholargate.app/en/econometrics/seemingly-unrelated-regression","markdownUrl":"https://scholargate.app/en/econometrics/seemingly-unrelated-regression.md","definition":"Seemingly Unrelated Regressions, introduced by Arnold Zellner in 1962, is a system regression method that estimates several linear equations jointly when their error terms are correlated across equations. By exploiting that cross-equation correlation through generalized least squares, it is more efficient than estimating each equation separately by OLS.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arnold Zellner","year":1962,"type":"System regression (multi-equation)","estimator":"Feasible generalized least squares (FGLS)","outcome":"continuous","minSample":100},"citations":[{"ref":"Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368.","type":"article","doi":"10.1080/01621459.1962.10480664","isbn":null,"url":null}],"related":["ols-regression","three-stage-least-squares","two-stage-least-squares","panel-fixed-effects","system-gmm"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"segment-anything-model","name":"Segment Anything Model","fullName":"A Foundation Model for Image Segmentation","aliases":["SAM","Segment Anything"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Image Segmentation, Foundation Models","year":"2023","originator":"Alexander Kirillov","url":"https://scholargate.app/en/deep-learning/segment-anything-model","markdownUrl":"https://scholargate.app/en/deep-learning/segment-anything-model.md","definition":"Segment Anything Model (SAM) is a foundation model introduced by Kirillov et al. in 2023 that can segment any object in an image given various forms of prompts. SAM is trained on a massive dataset of diverse images and learns to segment objects based on minimal user input such as points, boxes, or text descriptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alexander Kirillov","subfamily":"Deep Learning, Image Segmentation, Foundation Models","year":"2023","type":"Neural network architecture"},"citations":[{"ref":"Kirillov, A., Mintun, E., Darrell, T., & Girshick, R. (2023). Segment Anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015-4026).","type":"article","doi":"10.1109/iccv51070.2023.00371","isbn":null,"url":null}],"related":["swin-transformer","detr","masked-autoencoders","vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"segrnn","name":"SegRNN","fullName":"SegRNN (Segment Recurrent Neural Network)","aliases":["Segment RNN","Segment Recurrent Neural Network","SegRNN forecaster","Bölümlü Tekrarlayan Sinir Ağı"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2023,"originator":"Shengsheng Lin et al.","url":"https://scholargate.app/en/deep-learning/segrnn","markdownUrl":"https://scholargate.app/en/deep-learning/segrnn.md","definition":"SegRNN is a recurrent neural network architecture for long-term time series forecasting proposed by Shengsheng Lin et al. in 2023. Instead of processing one time step at a time, SegRNN partitions input sequences into fixed-length segments and feeds each segment as a single token into a GRU. This segment-based design drastically reduces the number of recurrent iterations, addressing the well-known difficulty RNNs face when modeling very long dependencies over many individual steps.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shengsheng Lin et al.","year":2023,"type":"Segment-based recurrent forecasting model","subfamily":"Time-series forecasting","input":"Univariate or multivariate time series","output":"Multi-step ahead point forecasts"},"citations":[{"ref":"Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., & Zhang, H. (2023). SegRNN: Segment recurrent neural network for long-term time series forecasting. arXiv preprint.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2308.11200"}],"related":["lstm","gru","patchtst"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"seir-model","name":"SEIR Model","fullName":"SEIR Compartmental Epidemic Model","aliases":["Susceptible-Exposed-Infectious-Recovered Model","SEIR Compartmental Model","Latent Period Epidemic Model","SEIR Bulaşıcı Hastalık Modeli"],"domain":"epidemiology","family":"regression-model","subfamily":"Epidemic modelling","year":1991,"originator":"Kermack & McKendrick; Anderson & May","url":"https://scholargate.app/en/epidemiology/seir-model","markdownUrl":"https://scholargate.app/en/epidemiology/seir-model.md","definition":"The SEIR model is a deterministic compartmental model that partitions a closed population into four epidemiological states: Susceptible (S), Exposed (E), Infectious (I), and Recovered (R). It extends the classic SIR framework by explicitly incorporating a latent period during which individuals have been infected but are not yet infectious. The model was systematically formalized by Anderson and May (1991) and remains a cornerstone of mathematical epidemiology for diseases with non-negligible incubation periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kermack & McKendrick; Anderson & May","year":1991,"type":"Deterministic compartmental ODE model","subfamily":"Epidemic modelling","compartments":"S, E, I, R","key_parameter":"Basic reproduction number R0"},"citations":[{"ref":"Anderson, R. M., & May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. Oxford University Press.","type":"book","doi":null,"isbn":"978-0-19-854040-3","url":null}],"related":["sir-model","reproduction-number","stochastic-differential-equations"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"seismic-full-waveform-inversion","name":"Seismic Full-Waveform Inversion","fullName":"Seismic Full-Waveform Inversion","aliases":["FWI"],"domain":"geophysics","family":"process-pipeline","subfamily":"Geophysical inversion","year":"1984","originator":"Albert Tarantola","url":"https://scholargate.app/en/geophysics/seismic-full-waveform-inversion","markdownUrl":"https://scholargate.app/en/geophysics/seismic-full-waveform-inversion.md","definition":"Seismic Full-Waveform Inversion (FWI) is a computational technique that reconstructs detailed subsurface velocity and impedance models by iteratively fitting synthetic seismic waveforms to observed data. Introduced by Albert Tarantola in 1984, FWI has become the leading method for high-resolution imaging in exploration geophysics, engineering seismology, and subsurface characterization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Tarantola","subfamily":"Geophysical inversion","year":"1984","type":"Seismic imaging and model parameterization technique"},"citations":[{"ref":"Tarantola, A. (1984). Inversion of seismic reflection data in the acoustic approximation. Geophysics, 49(8), 1259-1266.","type":"article","doi":"10.1190/1.1441754","isbn":null,"url":null},{"ref":"Virieux, J., & Operto, S. (2009). An overview of full waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1-WCC26.","type":"article","doi":"10.1190/1.3238367","isbn":null,"url":null}],"related":["ambient-noise-tomography","receiver-function-analysis","magnetotellurics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"seismic-reflection-interpretation","name":"Seismic Reflection Interpretation","fullName":"Seismic Reflection Interpretation","aliases":["seismic interpretation","seismic data analysis"],"domain":"geoscience","family":"process-pipeline","subfamily":"Subsurface imaging","year":"1960s","originator":"Dobrin and Savit","url":"https://scholargate.app/en/geoscience/seismic-reflection-interpretation","markdownUrl":"https://scholargate.app/en/geoscience/seismic-reflection-interpretation.md","definition":"Seismic reflection interpretation is the process of extracting meaningful geological information from seismic survey data, which is collected by recording elastic waves reflected from rock layers beneath the surface. Developed and systematized in the mid-20th century, this method is foundational in petroleum exploration and engineering geology. It enables geoscientists to image subsurface structures, identify hydrocarbon prospects, and assess hazards without drilling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dobrin and Savit","subfamily":"Subsurface imaging","year":"1960s","type":"geophysical imaging pipeline"},"citations":[{"ref":"Yilmaz, Ö. (2001). Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data. Society of Exploration Geophysicists.","type":"book","doi":"10.1190/1.9781560801580","isbn":null,"url":null},{"ref":"Sheriff, R. E., & Geldart, L. P. (2002). Exploration Seismology (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://cambridge.org"},{"ref":"Brown, A. R. (2011). Interpretation of Three-Dimensional Seismic Data (7th ed.). American Association of Petroleum Geologists.","type":"article","doi":null,"isbn":null,"url":"https://www.aapg.org"}],"related":["well-log-analysis","stratigraphic-correlation","geophysical-inversion","geologic-mapping","petrographic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"selected-area-electron-diffraction","name":"Selected Area Electron Diffraction","fullName":"Selected Area Electron Diffraction (SAED)","aliases":["SAED","electron diffraction pattern","TEM diffraction"],"domain":"materials-science","family":"process-pipeline","subfamily":"Electron crystallography","year":"1913","originator":"Georges Friedel","url":"https://scholargate.app/en/materials-science/selected-area-electron-diffraction","markdownUrl":"https://scholargate.app/en/materials-science/selected-area-electron-diffraction.md","definition":"Selected Area Electron Diffraction (SAED) is a crystallographic technique in transmission electron microscopy that obtains electron diffraction patterns from micron-sized or sub-micron crystalline regions. Developed from fundamental principles of electron wave behavior and integrated into TEM instruments by the mid-20th century, SAED enables direct observation of reciprocal space, crystal symmetry, and defect structures with spatial resolution unattainable by X-ray diffraction. It is essential for studying local crystal structure, phase identification, and characterizing nanoscale materials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georges Friedel","subfamily":"Electron crystallography","year":"1913","type":"Diffraction technique"},"citations":[{"ref":"Williams, D. B., & Carter, C. B. (2009). Transmission Electron Microscopy: A Textbook for Materials Science (2nd ed.). Springer.","type":"book","doi":"10.1007/978-0-387-76501-3","isbn":null,"url":null},{"ref":"Cullity, B. D., & Stock, S. R. (2014). Elements of X-ray Diffraction (3rd ed.). Pearson Education.","type":"book","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com"},{"ref":"Hirsch, P. B., Howie, A., Nicholson, R. B., Pashley, D. W., & Whelan, M. J. (1977). Electron Microscopy of Thin Crystals (2nd ed.). Butterworths.","type":"book","doi":null,"isbn":null,"url":"https://books.google.com/books?id=K-wyOWEWP7AC"}],"related":["energy-dispersive-x-ray-spectroscopy","xrd-rietveld-refinement","atomic-force-microscopy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"selection-sweep","name":"Selection Sweep (Tajima's D)","fullName":"Selective Sweep Detection using Tajima's D Statistic","aliases":["Tajima's D test","Selective sweep analysis","Neutrality test"],"domain":"genetics","family":"process-pipeline","subfamily":"Hypothesis testing","year":"1989","originator":"Fumio Tajima","url":"https://scholargate.app/en/genetics/selection-sweep","markdownUrl":"https://scholargate.app/en/genetics/selection-sweep.md","definition":"Tajima's D is a statistical test designed to detect selective sweeps—recent, rapid fixation of advantageous mutations—from patterns of genetic variation in DNA sequences. Developed by Fumio Tajima in 1989, this test measures deviations from neutrality by comparing different measures of DNA sequence diversity. A significant Tajima's D value indicates departure from neutral evolution, suggesting positive selection, population structure, or demographic events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fumio Tajima","subfamily":"Hypothesis testing","year":"1989","type":"Neutrality test"},"citations":[{"ref":"Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585–595.","type":"article","doi":"10.1093/genetics/123.3.585","isbn":null,"url":null},{"ref":"Braverman, J. M., Hudson, R. R., Kaplan, N. L., Langley, C. H., & Stephan, W. (1995). The hitchhiking effect on the site frequency spectrum of DNA polymorphisms. Genetics, 140(2), 783–796.","type":"article","doi":"10.1093/genetics/140.2.783","isbn":null,"url":null},{"ref":"Fay, J. C., & Wu, C. I. (2000). Hitchhiking under positive Darwinian selection. Genetics, 155(3), 1405–1413.","type":"article","doi":"10.1093/genetics/155.3.1405","isbn":null,"url":null}],"related":["coalescent-theory","mcdonald-kreitman-test","hka-test","f-statistics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"selective-coding","name":"Selective Coding","fullName":"Selective Coding","aliases":["focused coding","theoretical integration","GT selective coding","core category coding"],"domain":"qualitative","family":"process-pipeline","subfamily":"Qualitative Coding","year":"1967 (Glaser & Strauss); refined 1990 (Strauss & Corbin)","originator":"Barney Glaser & Anselm Strauss (classic GT); systematised by Anselm Strauss & Juliet Corbin; constructivist variant by Kathy Charmaz","url":"https://scholargate.app/en/qualitative/selective-coding","markdownUrl":"https://scholargate.app/en/qualitative/selective-coding.md","definition":"Selective coding is the third and final analytic phase of grounded theory, in which the researcher systematically identifies one central or core category that integrates all other major categories developed during open and axial coding. The outcome is a coherent, data-grounded substantive theory that explains the main social process or phenomenon under study. First formalized by Glaser and Strauss (1967) and later elaborated by Strauss and Corbin (1990) and Kathy Charmaz (2006), selective coding transforms fragmented mid-level categories into a unified theoretical account.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney Glaser & Anselm Strauss (classic GT); systematised by Anselm Strauss & Juliet Corbin; constructivist variant by Kathy Charmaz","year":"1967 (Glaser & Strauss); refined 1990 (Strauss & Corbin)","type":"Qualitative research method","dataType":"Interview transcripts, field notes, documents, observational records","typicalSampleSize":"15–40 participants (until theoretical saturation)","subfamily":"Qualitative Coding"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932975","url":null},{"ref":"Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761973522","url":null}],"related":["grounded-theory","thematic-analysis","content-analysis","narrative-analysis","phenomenology","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-attention-transformer","name":"Self-Attention","fullName":"Multi-Head Self-Attention (Transformer Core)","aliases":["Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention)","öz-dikkat","multi-head attention","scaled dot-product attention"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Vaswani, A. et al.","url":"https://scholargate.app/en/deep-learning/self-attention-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/self-attention-transformer.md","definition":"Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vaswani, A. et al.","year":2017,"type":"Attention mechanism (Transformer core)","task":"Classification, prediction, representation learning on text","minSample":100},"citations":[{"ref":"Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1706.03762"},{"ref":"Devlin, J. et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1810.04805"}],"related":["bert-finetuning","gpt-finetuning","lora-peft","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-compassion-scale-short","name":"Self-Compassion Scale Short Form","fullName":"Self-Compassion Scale Short Form (SCS-SF)","aliases":["SCS-SF","SCS-12"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"self-compassion","year":"2011","originator":"Filip Raes, Kristin D. Neff, and colleagues at Leuven University","url":"https://scholargate.app/en/mindfulness-psychology/self-compassion-scale-short","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/self-compassion-scale-short.md","definition":"The Self-Compassion Scale Short Form (SCS-SF) is a 12-item self-report instrument measuring self-compassion, a construct closely related to mindfulness emphasizing how individuals respond to personal suffering and failure with kindness and understanding. Developed by Raes, Neff, and colleagues in 2011 and published in Mindfulness, the SCS-SF is a brief version of the original 26-item Self-Compassion Scale. The scale measures self-compassion through six dimensions: Self-Kindness, Self-Judgment, Common Humanity, Isolation, Mindfulness, and Over-Identification. The SCS-SF has become a standard measure in psychological research on self-compassion, emotion regulation, mental health, and the mechanisms underlying mindfulness-based interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Filip Raes, Kristin D. Neff, and colleagues at Leuven University","subfamily":"self-compassion","year":"2011","type":"Self-report"},"citations":[{"ref":"Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the Self-Compassion Scale. Mindfulness, 2(4), 207-216.","type":"article","doi":"10.1037/t10179-000","isbn":null,"url":null}],"related":["five-facet-mindfulness-questionnaire","freiburg-mindfulness-inventory","philadelphia-mindfulness-scale","cognitive-and-affective-mindfulness","toronto-mindfulness-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-compassion-scale","name":"Self-Compassion Scale","fullName":"Self-Compassion Scale (SCS)","aliases":["SCS","Neff Self-Compassion Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Self-report questionnaire","year":"2003","originator":"Kristin Neff","url":"https://scholargate.app/en/social-psychology/self-compassion-scale","markdownUrl":"https://scholargate.app/en/social-psychology/self-compassion-scale.md","definition":"The Self-Compassion Scale (SCS) is a 26-item measure assessing self-compassion—the capacity to relate to oneself with kindness and understanding, rather than harsh self-judgment, in the face of difficulty or failure. Developed by Kristin Neff in 2003, the SCS operationalizes self-compassion as a multidimensional construct involving self-kindness (versus self-criticism), common humanity (versus isolation), and mindfulness (versus over-identification with negative emotions). The instrument has become standard in clinical, positive psychology, and health psychology research examining resilience, well-being, and mental health.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kristin Neff","subfamily":"Self-report questionnaire","year":"2003","type":"Mindful self-kindness and psychological resilience measure"},"citations":[{"ref":"Neff, K. D. (2003). The development and validation of a scale to measure self-compassion. Self and Identity, 2(3), 223–250.","type":"article","doi":"10.1080/15298860309027","isbn":null,"url":null},{"ref":"Neff, K. D. (2016). Self-compassion: The proven power of being kind to yourself. William Morrow.","type":"book","doi":null,"isbn":"978-0062223654","url":null},{"ref":"Ates, Z., & Blacker, J. (2019). Well-being and resilience: Does self-compassion matter? British Journal of Guidance & Counselling, 47(2), 214–228.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Well-being+and+resilience%3A+Does+self-compassion+matter+Ates"}],"related":["rosenberg-self-esteem-scale","toronto-empathy-questionnaire","resilience-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-determination-theory-scale","name":"Basic Psychological Needs Scale","fullName":"Basic Psychological Needs Questionnaire","aliases":["BPNQ","Basic Needs Scale"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Motivation & Psychological Needs","year":"2003","originator":"Martin Gagné, Edward L. Deci, and Richard M. Ryan","url":"https://scholargate.app/en/health-behavior/self-determination-theory-scale","markdownUrl":"https://scholargate.app/en/health-behavior/self-determination-theory-scale.md","definition":"The Basic Psychological Needs Questionnaire (BPNQ), developed by Gagné (2003) and grounded in Self-Determination Theory by Deci and Ryan, measures satisfaction of three fundamental human psychological needs: Autonomy, Competence, and Relatedness. According to Self-Determination Theory, these three needs are universally necessary for psychological health, well-being, and intrinsic motivation across all life domains. The 21-item BPNQ assesses the extent to which an individual perceives these needs are being met in their current context. It is widely used in research examining motivation, well-being, mental health, exercise engagement, work satisfaction, education, and psychotherapy effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin Gagné, Edward L. Deci, and Richard M. Ryan","subfamily":"Motivation & Psychological Needs","year":"2003","type":"Self-report questionnaire"},"citations":[{"ref":"Gagné, M. (2003). The role of autonomy support and autonomy orientation in prosocial behavior engagement. Motivation and Emotion, 27(3), 199-223.","type":"article","doi":"10.1023/A:1025007614869","isbn":null,"url":null},{"ref":"Deci, E. L., & Ryan, R. M. (2000). The 'what' and 'why' of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268.","type":"article","doi":"10.1207/S15327965PLI1104_01","isbn":null,"url":null}],"related":["behavioral-regulation-exercise","health-belief-model-scale","exercise-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-efficacy-medication-adherence","name":"Self-Efficacy for Appropriate Medication Use Scale","fullName":"Self-Efficacy for Appropriate Medication Use Scale (SEAMS)","aliases":["SEAMS"],"domain":"pharmacology","family":"process-pipeline","subfamily":"self-efficacy","year":"2007","originator":"Gbenga Ogedegbe, Antoinette Schoenthaler, and colleagues","url":"https://scholargate.app/en/pharmacology/self-efficacy-medication-adherence","markdownUrl":"https://scholargate.app/en/pharmacology/self-efficacy-medication-adherence.md","definition":"The Self-Efficacy for Appropriate Medication Use Scale (SEAMS) is a brief self-report measure designed to assess patients' confidence in their ability to manage medications appropriately across diverse contexts and challenges. Grounded in Bandura's self-efficacy theory, the SEAMS evaluates patients' perceived capacity to adhere to medication regimens despite potential barriers—forgetfulness, side effects, cost constraints, complexity, or changes in routine. The scale has demonstrated strong predictive validity for medication adherence and clinical outcomes in hypertension, diabetes, asthma, and other chronic diseases, making it valuable for identifying patients with low medication management confidence who need additional support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gbenga Ogedegbe, Antoinette Schoenthaler, and colleagues","subfamily":"self-efficacy","year":"2007","type":"Self-report"},"citations":[{"ref":"Ogedegbe, G., Schoenthaler, A., & Richardson, T. (2007). An Exploration of Contextual Factors and Antihypertensive Medication Adherence in Hypertensive African Americans. American Journal of Health-System Pharmacy, 64(23), 2510-2516. (SEAMS adapted from original research on self-efficacy in medication adherence.)","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+Exploration+of+Contextual+Factors+and+Antihypertensive+Medication+Adherence+in+Hypertensive+African+Americans+Ogedegbe"}],"related":["medication-adherence-rating-scale","beliefs-medicines-questionnaire","medication-understanding-scale","treatment-satisfaction-questionnaire-medication"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-organized-criticality","name":"Self-Organized Criticality","fullName":"Self-Organized Criticality","aliases":["SOC","Sandpile Model","Critical Self-Organization","Kendiliğinden Örgütlenen Kritiklik"],"domain":"complex-systems","family":"regression-model","subfamily":"Nonlinear dynamics","year":1987,"originator":"Per Bak, Chao Tang & Kurt Wiesenfeld","url":"https://scholargate.app/en/complex-systems/self-organized-criticality","markdownUrl":"https://scholargate.app/en/complex-systems/self-organized-criticality.md","definition":"Self-Organized Criticality (SOC) is a dynamical systems framework introduced by Per Bak, Chao Tang, and Kurt Wiesenfeld in 1987 to explain how large, dissipative systems spontaneously evolve toward a critical state without external fine-tuning. At the critical state, the system produces scale-invariant fluctuations — avalanches whose size and duration follow power-law distributions — and generates 1/f (pink) noise in its power spectrum.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Per Bak, Chao Tang & Kurt Wiesenfeld","year":1987,"type":"Dynamical systems model","subfamily":"Nonlinear dynamics","canonical_model":"BTW sandpile automaton","scale_invariance":"Power-law distributed avalanche sizes"},"citations":[{"ref":"Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of 1/f noise. Physical Review Letters, 59(4), 381–384.","type":"article","doi":"10.1103/PhysRevLett.59.381","isbn":null,"url":null}],"related":["fractal-analysis","recurrence-quantification-analysis","agent-based-modeling"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-organizing-map","name":"Self-Organizing Map","fullName":"Self-Organizing Map (Kohonen Map)","aliases":["SOM","Kohonen map","Kohonen network","öz-örgütlemeli harita"],"domain":"machine-learning","family":"ml-model","subfamily":"Dimensionality reduction","year":1982,"originator":"Teuvo Kohonen","url":"https://scholargate.app/en/machine-learning/self-organizing-map","markdownUrl":"https://scholargate.app/en/machine-learning/self-organizing-map.md","definition":"A self-organizing map is an unsupervised neural network, introduced by Teuvo Kohonen in 1982, that projects high-dimensional data onto a low-dimensional (usually two-dimensional) grid of prototype vectors while preserving the data's topology — nearby inputs map to nearby grid cells. It is used for visualization, clustering, and exploratory analysis, turning complex data into an ordered, interpretable map.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Teuvo Kohonen","year":1982,"type":"Unsupervised neural network for topology-preserving mapping","subfamily":"Dimensionality reduction","output":"Low-dimensional (usually 2D) ordered grid of prototypes","preserves":"Topology / neighbourhood relations"},"citations":[{"ref":"Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.","type":"article","doi":"10.1007/BF00337288","isbn":null,"url":null},{"ref":"Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464–1480.","type":"article","doi":"10.1109/5.58325","isbn":null,"url":null}],"related":["k-means-clustering","t-sne","umap","locally-linear-embedding"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-plagiarism","name":"Self-Plagiarism and Text Recycling","fullName":"Self-Plagiarism and Text Recycling: Reusing One's Own Previously Published Work Without Disclosure","aliases":["text recycling","self-copying","duplicate publication","redundant publication"],"domain":"research-ethics","family":"process-pipeline","subfamily":"plagiarism-detection-and-prevention","year":"1990s","originator":"International Committee of Medical Journal Editors (ICMJE)","url":"https://scholargate.app/en/research-ethics/self-plagiarism","markdownUrl":"https://scholargate.app/en/research-ethics/self-plagiarism.md","definition":"Self-plagiarism, or text recycling, occurs when an author reuses substantial portions of their own previously published work in a new publication without disclosure or acknowledgment. This includes republishing the same article in different venues, duplicating methods sections across multiple papers, or reusing discussion sections. While the intellectual property is the author's own, self-plagiarism is considered misconduct because it violates the principle that published work represents new research and it may inflate publication counts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Committee of Medical Journal Editors (ICMJE)","subfamily":"plagiarism-detection-and-prevention","year":"1990s","type":"Concept"},"citations":[{"ref":"Roig, M. (2015). Avoiding plagiarism, self-plagiarism, and other questionable writing practices: A guide to ethical writing. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products/plagiarism"},{"ref":"Research Integrity Journal. (2022). Salami publishing and duplicate submission: A systematic review. Research Integrity and Peer Review, 8, 1-12.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Salami+publishing+and+duplicate+submission%3A+A+systematic+review+Research"},{"ref":"International Committee of Medical Journal Editors (ICMJE). (2023). Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals. Journal of the American Medical Association, 330(6), 567-575.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Recommendations+for+the+conduct%2C+reporting%2C+editing%2C+and+publication+of+scholarly+work+in+medical+journals+International"}],"related":["verbatim-plagiarism","similarity-vs-plagiarism","academic-integrity-policies"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-stigma-seeking-help","name":"Self-Stigma of Seeking Help Scale","fullName":"Self-Stigma of Seeking Help Scale (SSSH)","aliases":["SSSH","Vogel Self-Stigma Scale"],"domain":"psychiatric-rehabilitation","family":"process-pipeline","subfamily":"stigma-measurement","year":"2006","originator":"Vogel, D. L., Wade, N. G., & Haake, S.","url":"https://scholargate.app/en/psychiatric-rehabilitation/self-stigma-seeking-help","markdownUrl":"https://scholargate.app/en/psychiatric-rehabilitation/self-stigma-seeking-help.md","definition":"The Self-Stigma of Seeking Help Scale (SSSH) is a 10-item self-report measure assessing the degree to which individuals experience shame, embarrassment, or fear of judgment related to seeking psychological or mental health help. Developed by David L. Vogel, Nathan G. Wade, and Stephanie Haake in 2006, the SSSH captures self-directed stigma about help-seeking—the belief that seeking help is shameful or will lead to negative judgments by others. The scale is used in research on mental health literacy, treatment-seeking behavior, and barriers to care across diverse populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vogel, D. L., Wade, N. G., & Haake, S.","subfamily":"stigma-measurement","year":"2006","type":"Self-report questionnaire"},"citations":[{"ref":"Vogel, D. L., Wade, N. G., & Haake, S. (2006). Measuring the self-stigma associated with seeking psychological help. Journal of Counseling Psychology, 53(3), 325-337.","type":"article","doi":"10.1037/0022-0167.53.3.325","isbn":null,"url":null}],"related":["internalized-stigma-mental-illness","link-stigma-scale","recovery-assessment-scale","mental-health-recovery-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-active-learning","name":"Self-supervised Active Learning","fullName":"Self-supervised Active Learning (SSL-AL hybrid label-efficient framework)","aliases":["SSL-AL","self-supervised active learning","semi-supervised active learning with self-supervision","label-efficient self-supervised learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020–2021","originator":"Bengar et al. and concurrent works (multiple groups)","url":"https://scholargate.app/en/machine-learning/self-supervised-active-learning","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-active-learning.md","definition":"Self-supervised Active Learning (SSL-AL) is a label-efficient machine-learning paradigm that pre-trains a model on unlabeled data using self-supervised objectives, then strategically queries a human oracle for the most informative labels using an active-learning acquisition function. The result is strong predictive performance with a fraction of the annotation cost required by fully supervised approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bengar et al. and concurrent works (multiple groups)","year":"2020–2021","type":"Hybrid active-learning and self-supervised pre-training framework","dataType":"Images, text, or structured data with very few labeled examples","subfamily":"Machine learning"},"citations":[{"ref":"Bengar, J. Z., van de Weijer, J., Twardowski, B., & Raducanu, B. (2021). Reducing Label Effort: Self-Supervised Meets Active Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 1631–1639.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openaccess.thecvf.com/content/ICCV2021W/ILDAV/html/Bengar_Reducing_Label_Effort_Self-Supervised_Meets_Active_Learning_ICCVW_2021_paper.html"},{"ref":"Zhan, X., Wang, Q., Huang, K.-H., Xiong, H., Dou, D., & Chan, A. B. (2022). A comparative survey of deep active learning. arXiv preprint arXiv:2203.13450.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2203.13450"}],"related":["active-learning","self-supervised-learning","semi-supervised-learning","contrastive-learning","transfer-learning","label-propagation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-autoencoder-anomaly-detection","name":"Self-supervised Autoencoder Anomaly Detection","fullName":"Self-supervised Autoencoder Anomaly Detection (Pretext-Task Reconstruction-Based Anomaly Detection)","aliases":["SSL Autoencoder anomaly detection","self-supervised reconstruction anomaly detection","pretext-task autoencoder anomaly detection","contrastive autoencoder anomaly detection"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018–2020","originator":"Golan & El-Yaniv; broader self-supervised anomaly detection community","url":"https://scholargate.app/en/machine-learning/self-supervised-autoencoder-anomaly-detection","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-autoencoder-anomaly-detection.md","definition":"Self-supervised autoencoder anomaly detection trains an autoencoder using self-supervised pretext tasks — such as predicting geometric transformations or solving jigsaw puzzles — on unlabeled normal data, then flags as anomalous any input whose reconstruction error or pretext-task score deviates substantially from the learned normal distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Golan & El-Yaniv; broader self-supervised anomaly detection community","year":"2018–2020","type":"Unsupervised / self-supervised deep learning","dataType":"Unlabeled data (images, tabular, time-series)","subfamily":"Machine learning"},"citations":[{"ref":"Golan, I. & El-Yaniv, R. (2018). Deep one-class classification via geometric transformations. Advances in Neural Information Processing Systems (NeurIPS), 31.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2018/hash/8cf5cfbf53f14a6a8ab72f7c0add1fcb-Abstract.html"},{"ref":"Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Müller, K.-R. (2021). A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5), 756–795.","type":"article","doi":"10.1109/JPROC.2021.3052449","isbn":null,"url":null}],"related":["autoencoder-anomaly-detection","self-supervised-learning","one-class-svm","isolation-forest","semi-supervised-autoencoder-anomaly-detection","variational-autoencoder"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-bert-based-classification","name":"Self-supervised BERT-based classification","fullName":"Self-supervised BERT-based Text Classification (Pretrain then Fine-tune)","aliases":["BERT fine-tuning for classification","BERT text classifier","self-supervised transformer classification","masked LM pretraining with classification head"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)","url":"https://scholargate.app/en/deep-learning/self-supervised-bert-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-bert-based-classification.md","definition":"Self-supervised BERT-based classification uses Google's Bidirectional Encoder Representations from Transformers (BERT), pretrained on massive unlabelled text via masked-language modelling, and fine-tunes it on labelled examples to assign text into categories. It consistently achieves state-of-the-art accuracy on sentiment analysis, topic classification, intent detection, and similar NLP tasks even with limited labelled data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)","year":"2019","type":"Pretrain-then-fine-tune transformer model","dataType":"Text (tokenised sequences)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019, 4171–4186. Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? In China National Conference on Chinese Computational Linguistics (CCL 2019), LNCS 11856, 194–206. Springer.","type":"inproceedings","doi":"10.1007/978-3-030-32381-3_16","isbn":null,"url":null}],"related":["bert-pretraining","transformer-encoder","roberta","text-cnn","distilbert","transfer-learning-nlp"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-boosting","name":"Self-supervised Boosting","fullName":"Self-supervised Boosting (SSL-Boosting)","aliases":["SSL boosting","self-supervised ensemble boosting","pretext-task boosting","SSL-Boost"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010s–2020s","originator":"Various researchers (2010s–2020s)","url":"https://scholargate.app/en/machine-learning/self-supervised-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-boosting.md","definition":"Self-supervised boosting integrates self-supervised pretext tasks into the boosting framework — covering AdaBoost, gradient boosting, and their modern variants — to leverage large pools of unlabeled data. By first learning feature representations from unlabeled samples and then running sequential weak-learner ensembles on pseudo-labeled data, it achieves competitive accuracy even when ground-truth labels are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various researchers (2010s–2020s)","year":"2010s–2020s","type":"Ensemble (self-supervised + boosting)","dataType":"Tabular, mixed labeled and unlabeled","subfamily":"Machine learning"},"citations":[{"ref":"Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (pp. 189–196). ACL.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Unsupervised+word+sense+disambiguation+rivaling+supervised+methods+Yarowsky+1995"},{"ref":"Self-supervised learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Self-supervised_learning"}],"related":["self-supervised-gradient-boosting","self-supervised-learning","semi-supervised-boosting","boosting","xgboost","active-learning-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-convolutional-neural-network","name":"Self-supervised convolutional neural network","fullName":"Self-Supervised Convolutional Neural Network","aliases":["Self-supervised CNN","SSL-CNN","contrastive CNN","pretext-task CNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2020","originator":"LeCun, Y. (CNN backbone); Chen et al. and He et al. (self-supervised visual frameworks)","url":"https://scholargate.app/en/deep-learning/self-supervised-convolutional-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-convolutional-neural-network.md","definition":"A self-supervised convolutional neural network (CNN) learns powerful visual representations from unlabeled images by solving pretext tasks — such as contrastive instance discrimination or masked-patch prediction — and then fine-tunes on a small labeled set. This approach dramatically reduces dependence on large annotated datasets while preserving the spatial feature-extraction strengths of convolutional architectures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeCun, Y. (CNN backbone); Chen et al. and He et al. (self-supervised visual frameworks)","year":"2018–2020","type":"Self-supervised deep learning","dataType":"Unlabeled images; small labeled fine-tuning set","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/chen20j.html"},{"ref":"He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), 9729–9738.","type":"inproceedings","doi":"10.1109/CVPR42600.2020.00975","isbn":null,"url":null}],"related":["convolutional-neural-network","semi-supervised-convolutional-neural-network","transfer-learning-with-convolutional-neural-network","fine-tuned-convolutional-neural-network","self-supervised-vision-transformer","self-supervised-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-dbscan","name":"Self-supervised DBSCAN","fullName":"Self-supervised Representation Learning with DBSCAN Clustering","aliases":["SSL-DBSCAN","self-supervised density clustering","contrastive DBSCAN","representation-based DBSCAN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018–2021","originator":"Ester et al. (DBSCAN base); pipeline pattern established in multiple works c. 2018–2021","url":"https://scholargate.app/en/machine-learning/self-supervised-dbscan","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-dbscan.md","definition":"Self-supervised DBSCAN is a two-stage unsupervised pipeline that first trains a neural encoder on a pretext task — such as contrastive learning or masked reconstruction — to produce compact, semantically meaningful embeddings from unlabeled data, and then applies DBSCAN in the resulting embedding space to discover arbitrarily shaped clusters without requiring any class labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ester et al. (DBSCAN base); pipeline pattern established in multiple works c. 2018–2021","year":"2018–2021","type":"Two-stage pipeline (self-supervised pre-training + density-based clustering)","dataType":"Unlabeled high-dimensional data (images, text, tabular)","subfamily":"Machine learning"},"citations":[{"ref":"Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf"},{"ref":"Zhan, X., Liu, Z., Luo, P., Tang, X., & Loy, C. C. (2018). Rethinking deep neural network training for face recognition: A geometric approach. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2045–2054.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Rethinking+deep+neural+network+training+face+recognition+geometric+approach+Zhan+2018"}],"related":["dbscan","self-supervised-learning","hdbscan","gaussian-mixture-model","k-means","semi-supervised-dbscan"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-decision-tree","name":"Self-supervised Decision Tree","fullName":"Self-supervised Decision Tree Learning","aliases":["SSL decision tree","self-supervised tree classifier","pseudo-label decision tree","unsupervised-guided decision tree"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2015–present","originator":"Multiple authors (active research area, 2010s–2020s)","url":"https://scholargate.app/en/machine-learning/self-supervised-decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-decision-tree.md","definition":"Self-supervised Decision Tree learning combines the interpretability of classical decision trees with the ability to exploit large quantities of unlabeled data through self-supervised pretext tasks. The model learns useful feature representations or node-split criteria from unlabeled samples before refining predictions on a small labeled set, bridging the gap between fully supervised trees and purely unsupervised clustering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple authors (active research area, 2010s–2020s)","year":"2015–present","type":"Self-supervised ensemble/single tree model","dataType":"Tabular, mixed; large unlabeled datasets with small labeled subsets","subfamily":"Machine learning"},"citations":[{"ref":"Self-supervised learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Self-supervised_learning"},{"ref":"Decision tree learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Decision_tree_learning"}],"related":["decision-tree","random-forest","semi-supervised-learning","contrastive-learning","gradient-boosting","label-propagation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-diffusion-model","name":"Self-supervised Diffusion Model","fullName":"Self-supervised Diffusion Model (Denoising Diffusion with Self-supervised Representation Learning)","aliases":["SSDM","self-supervised score-based model","diffusion-based self-supervised learning","denoising diffusion with self-supervised pretraining"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2022","originator":"Ho, J. et al.; extended by Chen, T. et al. and subsequent self-supervised diffusion works","url":"https://scholargate.app/en/deep-learning/self-supervised-diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-diffusion-model.md","definition":"A self-supervised diffusion model couples the iterative noise-and-denoise generative process of denoising diffusion probabilistic models with a self-supervised representation learning objective — such as contrastive or masked prediction loss — so that the model simultaneously learns to generate realistic data and to produce semantically meaningful representations without any labeled examples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ho, J. et al.; extended by Chen, T. et al. and subsequent self-supervised diffusion works","year":"2020–2022","type":"Generative model with self-supervised representation objective","dataType":"Images, audio, video, sequential data (unlabeled)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html"},{"ref":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), 119, 1597–1607.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Simple+Framework+for+Contrastive+Learning+of+Visual+Representations"}],"related":["denoising-diffusion-probabilistic-model","score-matching","contrastive-learning","masked-autoencoder","variational-autoencoder","generative-adversarial-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-federated-learning","name":"Self-supervised Federated learning","fullName":"Self-supervised Learning in Federated Settings","aliases":["FedSSL","Federated Self-supervised Learning","Federated Contrastive Learning","Self-supervised Federated Pretraining"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2021–2022","originator":"McMahan et al. (federated); Zhuang et al. and others (federated SSL combination)","url":"https://scholargate.app/en/machine-learning/self-supervised-federated-learning","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-federated-learning.md","definition":"Self-supervised Federated Learning combines federated training — where data never leaves local devices — with self-supervised pretext tasks such as contrastive learning or masked prediction. Clients learn general-purpose representations from their own unlabeled data and share only model updates, not raw data, with a central server that aggregates them into a global encoder.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McMahan et al. (federated); Zhuang et al. and others (federated SSL combination)","year":"2021–2022","type":"Federated self-supervised pretraining paradigm","dataType":"Unlabeled distributed data (images, text, sensor readings)","subfamily":"Machine learning"},"citations":[{"ref":"Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=oVE1z8NlNe"},{"ref":"Federated learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Federated_learning"}],"related":["self-supervised-learning","federated-learning","transfer-learning","semi-supervised-learning","contrastive-learning","few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-few-shot-learning","name":"Self-supervised Few-shot Learning","fullName":"Self-supervised Few-shot Learning (SSL-FSL)","aliases":["SSL-FSL","self-supervised meta-learning","unsupervised few-shot learning","self-supervised prototypical learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2019","originator":"Gidaris, S. et al.; Su, J.-C. et al. (concurrent seminal works)","url":"https://scholargate.app/en/machine-learning/self-supervised-few-shot-learning","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-few-shot-learning.md","definition":"Self-supervised Few-shot Learning (SSL-FSL) combines self-supervised pretraining on large unlabeled corpora with few-shot meta-learning so that a model can recognize new categories from only a handful of labeled examples. By learning rich, transferable representations without expensive annotation, SSL-FSL addresses the fundamental bottleneck of supervised few-shot methods: the need for labeled support data at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gidaris, S. et al.; Su, J.-C. et al. (concurrent seminal works)","year":"2019","type":"Hybrid learning paradigm (self-supervised pretraining + few-shot adaptation)","dataType":"Image, text, or multimodal data with abundant unlabeled and scarce labeled examples","subfamily":"Machine learning"},"citations":[{"ref":"Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., & Cord, M. (2019). Boosting Few-Shot Visual Learning with Self-Supervision. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 8059–8068.","type":"inproceedings","doi":"10.1109/ICCV.2019.00815","isbn":null,"url":null},{"ref":"Su, J.-C., Maji, S., & Hariharan, B. (2020). When Does Self-Supervision Improve Few-Shot Learning? European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 12371, 645–660.","type":"inproceedings","doi":"10.1007/978-3-030-58571-6_38","isbn":null,"url":null}],"related":["contrastive-learning","meta-learning","prototypical-networks","siamese-network","transfer-learning","maml"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-gan","name":"Self-supervised GAN","fullName":"Self-supervised Generative Adversarial Network","aliases":["SS-GAN","Self-supervised GAN","Self-supervised Generative Adversarial Network","GAN with self-supervised auxiliary tasks"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N.","url":"https://scholargate.app/en/deep-learning/self-supervised-gan","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-gan.md","definition":"Self-supervised GAN augments a standard Generative Adversarial Network with one or more self-supervised auxiliary tasks — such as predicting image rotation or patch position — that stabilise adversarial training and yield a discriminator that learns rich, transferable representations from unlabeled data without requiring manual annotations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N.","year":"2019","type":"Generative model with self-supervised auxiliary tasks","dataType":"Images, unlabeled or minimally labeled data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. (2019). Self-Supervised GANs via Auxiliary Rotation Loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12154–12163.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Self-Supervised_GANs_via_Auxiliary_Rotation_Loss_CVPR_2019_paper.html"},{"ref":"Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021). Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857–876.","type":"article","doi":"10.1109/TKDE.2021.3090866","isbn":null,"url":null}],"related":["generative-adversarial-network","self-supervised-variational-autoencoder","contrastive-learning","semi-supervised-gan","vision-transformer","self-supervised-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-gaussian-mixture-model","name":"Self-supervised Gaussian Mixture Model","fullName":"Self-supervised Gaussian Mixture Model (SS-GMM)","aliases":["SS-GMM","self-supervised GMM","semi-supervised Gaussian mixture model","self-supervised clustering with GMM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010s–2019","originator":"Multiple authors (Zhai et al., 2019; earlier formulations in semi-supervised GMM literature)","url":"https://scholargate.app/en/machine-learning/self-supervised-gaussian-mixture-model","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-gaussian-mixture-model.md","definition":"A Self-supervised Gaussian Mixture Model (SS-GMM) combines self-supervised representation learning with a probabilistic Gaussian mixture prior to discover meaningful clusters in unlabeled or partially labeled data. By leveraging pretext tasks to learn rich embeddings before fitting a GMM, it achieves cluster quality that standard GMMs applied to raw features rarely reach, especially on complex image, text, or biological data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple authors (Zhai et al., 2019; earlier formulations in semi-supervised GMM literature)","year":"2010s–2019","type":"Probabilistic generative model with self-supervised pretraining","dataType":"Continuous or high-dimensional unlabeled (plus optional labeled) data","subfamily":"Machine learning"},"citations":[{"ref":"Zhai, X., Oliver, A., Kolesnikov, A., & Beyer, L. (2019). S4L: Self-supervised semi-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 1476–1485.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=S4L+Self-Supervised+Semi-Supervised+Learning+Zhai+2019"},{"ref":"Mixture model. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Mixture_model"}],"related":["gaussian-mixture-model","variational-autoencoder","deep-clustering","expectation-maximization","contrastive-learning","semi-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-gaussian-process","name":"Self-supervised Gaussian Process","fullName":"Self-supervised Gaussian Process (SSL-GP)","aliases":["SSL-GP","self-supervised GP","self-supervised GPR","self-supervised Gaussian process regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2019–2021","originator":"Fortuin, V. et al.; broader self-supervised GP literature","url":"https://scholargate.app/en/machine-learning/self-supervised-gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-gaussian-process.md","definition":"Self-supervised Gaussian Process (SSL-GP) combines the principled uncertainty quantification of Gaussian processes with self-supervised pretraining, learning expressive kernels or latent representations from unlabeled data before fitting a GP on a small labeled set. This makes the approach especially powerful in low-labeled-data regimes where a conventional GP would overfit or produce poorly calibrated uncertainty estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fortuin, V. et al.; broader self-supervised GP literature","year":"2019–2021","type":"Probabilistic model (self-supervised GP pretraining + kernel learning)","dataType":"Continuous, time-series, tabular, partially labeled datasets","subfamily":"Machine learning"},"citations":[{"ref":"Fortuin, V., Rätsch, G., & Mandt, S. (2020). GP-VAE: Deep probabilistic time series imputation using Gaussian process variational autoencoders. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108, 1651–1661.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v108/fortuin20a.html"},{"ref":"Gaussian process. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Gaussian_process"}],"related":["gaussian-process","semi-supervised-gaussian-process","self-supervised-learning","bayesian-gaussian-process","variational-autoencoder","active-learning-gaussian-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-gradient-boosting","name":"Self-supervised Gradient Boosting","fullName":"Self-supervised Gradient Boosting (SSL-GBM)","aliases":["SSL gradient boosting","self-supervised boosting","semi-supervised gradient boosting","SSL-GBM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020s","originator":"Various researchers (Zhang et al. and others)","url":"https://scholargate.app/en/machine-learning/self-supervised-gradient-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-gradient-boosting.md","definition":"Self-supervised gradient boosting extends the classic gradient boosting framework by incorporating self-supervised pretext tasks to exploit unlabeled data. The model first learns useful feature representations from unannotated samples, then uses those representations to guide the sequential ensemble of weak learners, achieving strong predictive performance even when labeled examples are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various researchers (Zhang et al. and others)","year":"2020s","type":"Ensemble (self-supervised + gradient boosting)","dataType":"Tabular data, mixed labeled and unlabeled","subfamily":"Machine learning"},"citations":[{"ref":"Zhang, Y., Zhang, J., & Yang, Q. (2022). Self-Supervised Gradient Boosting for Semi-Supervised Learning on Tabular Data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Self-Supervised+Gradient+Boosting+Semi-Supervised+Tabular+Data"},{"ref":"Self-supervised learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Self-supervised_learning"}],"related":["xgboost","random-forest","semi-supervised-learning","lightgbm","gradient-boosting","contrastive-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-gru","name":"Self-supervised GRU","fullName":"Self-supervised Gated Recurrent Unit","aliases":["SS-GRU","Self-supervised Gated Recurrent Unit","GRU with self-supervised pretraining","Unsupervised GRU pretraining"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2019","originator":"Cho, K. et al. (GRU); self-supervised training paradigm from broader SSL literature","url":"https://scholargate.app/en/deep-learning/self-supervised-gru","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-gru.md","definition":"Self-supervised GRU trains a Gated Recurrent Unit network using automatically constructed supervision signals — such as next-step prediction or masked token recovery — derived from the unlabeled data itself. The learned sequence representations are then fine-tuned on small labeled datasets, making high-quality sequential modeling feasible when annotations are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cho, K. et al. (GRU); self-supervised training paradigm from broader SSL literature","year":"2014–2019","type":"Self-supervised sequence model","dataType":"Sequential / time-series / text (unlabeled or partially labeled)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1406.1078"},{"ref":"Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2023). Self-Supervised Learning: Generative or Contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857–876.","type":"article","doi":"10.1109/TKDE.2021.3090866","isbn":null,"url":null}],"related":["gated-recurrent-unit","self-supervised-lstm","self-supervised-recurrent-neural-network","semi-supervised-gru","long-short-term-memory","self-supervised-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-image-classification","name":"Self-supervised Image Classification","fullName":"Self-supervised Learning for Image Classification","aliases":["SSL image classification","contrastive visual representation learning","self-supervised visual learning","unsupervised pretraining for image classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2020","originator":"Chen et al. (SimCLR); He et al. (MoCo); Grill et al. (BYOL); Caron et al. (DINO)","url":"https://scholargate.app/en/deep-learning/self-supervised-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-image-classification.md","definition":"Self-supervised image classification trains a deep visual encoder on large unlabeled image datasets by solving proxy tasks — such as predicting which two augmented views of the same image are similar — and then fine-tunes only a lightweight classifier head on labeled examples. Pioneered by frameworks such as SimCLR and MoCo around 2020, it drastically reduces the need for expensive manual annotation while achieving accuracy rivaling fully supervised models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen et al. (SimCLR); He et al. (MoCo); Grill et al. (BYOL); Caron et al. (DINO)","year":"2018–2020","type":"Pretraining + fine-tuning paradigm","dataType":"Unlabeled or partially labeled image datasets","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/chen20j.html"},{"ref":"He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9729–9738.","type":"inproceedings","doi":"10.1109/CVPR42600.2020.00975","isbn":null,"url":null}],"related":["convolutional-neural-network","vision-transformer","transfer-learning","contrastive-learning","generative-adversarial-network","knowledge-distillation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-instance-segmentation","name":"Self-supervised Instance Segmentation","fullName":"Self-supervised Instance Segmentation (Label-free Object Mask Learning)","aliases":["SSIS","unsupervised instance segmentation","label-free instance segmentation","self-supervised mask prediction"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2021–2022","originator":"Wang et al. (FreeSOLO); Caron et al. (DINO)","url":"https://scholargate.app/en/deep-learning/self-supervised-instance-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-instance-segmentation.md","definition":"Self-supervised instance segmentation learns to detect and delineate individual object instances in images without any human-annotated masks or bounding boxes. Instead of relying on costly pixel-level labels, it exploits self-supervised pretraining, multi-view consistency, and pseudo-label generation to discover and segment objects purely from raw image data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang et al. (FreeSOLO); Caron et al. (DINO)","year":"2021–2022","type":"Self-supervised deep learning for pixel-level object delineation","dataType":"Unlabeled image collections","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Wang, X., Zhu, Z., Cao, G., Yao, Z., Jiang, Z., & Ye, J. (2022). FreeSOLO: Learning to Segment Objects without Annotations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14176–14186.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openaccess.thecvf.com/content/CVPR2022/html/Wang_FreeSOLO_Learning_To_Segment_Objects_Without_Annotations_CVPR_2022_paper.html"},{"ref":"Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660.","type":"inproceedings","doi":"10.1109/ICCV48922.2021.00951","isbn":null,"url":null}],"related":["instance-segmentation","semantic-segmentation","self-supervised-learning","contrastive-learning","vision-transformer","panoptic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-isolation-forest","name":"Self-supervised Isolation Forest","fullName":"Self-supervised Isolation Forest (SSL-augmented Anomaly Detection)","aliases":["SSL Isolation Forest","self-supervised iForest","semi-supervised isolation forest","contrastive isolation forest"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2008–2020s","originator":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors","url":"https://scholargate.app/en/machine-learning/self-supervised-isolation-forest","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-isolation-forest.md","definition":"Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors","year":"2008–2020s","type":"Ensemble anomaly detector with self-supervised pre-training","dataType":"Tabular, multivariate continuous or mixed features","subfamily":"Machine learning"},"citations":[{"ref":"Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.","type":"inproceedings","doi":"10.1109/ICDM.2008.17","isbn":null,"url":null},{"ref":"Isolation Forest. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Isolation_forest"}],"related":["isolation-forest","one-class-svm","autoencoder","local-outlier-factor","contrastive-learning","deep-svdd"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-k-means","name":"Self-supervised K-means","fullName":"Self-supervised K-means Clustering","aliases":["self-supervised clustering with K-means","deep clustering with K-means","unsupervised K-means with pseudo-labels","SSL K-means"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018","originator":"Caron, M. et al. (DeepCluster framework)","url":"https://scholargate.app/en/machine-learning/self-supervised-k-means","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-k-means.md","definition":"Self-supervised K-means is a clustering technique that combines K-means assignment with self-supervised representation learning. The model alternates between clustering unlabeled data points into K groups and using those cluster assignments as pseudo-labels to refine an underlying feature representation, yielding increasingly coherent clusters without any human-annotated ground truth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Caron, M. et al. (DeepCluster framework)","year":"2018","type":"Self-supervised clustering","dataType":"Unlabeled data (tabular, image, or feature embeddings)","subfamily":"Machine learning"},"citations":[{"ref":"Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep Clustering for Unsupervised Learning of Visual Features. In Proceedings of the European Conference on Computer Vision (ECCV), 132–149.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Deep+Clustering+for+Unsupervised+Learning+of+Visual+Features"},{"ref":"Self-supervised learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Self-supervised_learning"}],"related":["k-means","self-supervised-learning","semi-supervised-k-means","gaussian-mixture-model","online-k-means","ensemble-k-means"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-k-nearest-neighbors","name":"Self-supervised K-nearest neighbors","fullName":"Self-supervised K-Nearest Neighbors (SSL-kNN)","aliases":["SSL-kNN","self-supervised kNN classifier","kNN evaluation probe","nearest-neighbor self-supervised classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018–2020","originator":"Wu, Z. et al. / Chen, T. et al.","url":"https://scholargate.app/en/machine-learning/self-supervised-k-nearest-neighbors","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-k-nearest-neighbors.md","definition":"Self-supervised K-nearest neighbors (SSL-kNN) combines representation learning without labels with a non-parametric k-NN classifier. A neural encoder is first trained via a self-supervised objective — such as contrastive or masked prediction — so that semantically similar samples cluster together in the embedding space. A simple k-NN lookup on those embeddings then assigns class labels, serving both as a lightweight probe and as a practical classifier.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wu, Z. et al. / Chen, T. et al.","year":"2018–2020","type":"Self-supervised + non-parametric classifier","dataType":"Unlabeled or partially labeled feature vectors, images, or embeddings","subfamily":"Machine learning"},"citations":[{"ref":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/chen20j.html"},{"ref":"Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018). Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3733–3742.","type":"inproceedings","doi":"10.1109/CVPR.2018.00393","isbn":null,"url":null}],"related":["self-supervised-learning","k-nearest-neighbors","semi-supervised-k-nearest-neighbors","transfer-learning","contrastive-learning","metric-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-lda-topic-model","name":"Self-supervised LDA Topic Model","fullName":"Self-supervised Latent Dirichlet Allocation Topic Model","aliases":["SSL-LDA","self-supervised topic modeling","self-supervised LDA","contrastive LDA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2003 (LDA); self-supervised variants from 2020","originator":"Blei, D. M., Ng, A. Y., Jordan, M. I. (LDA); self-supervised extension by multiple authors (2020s)","url":"https://scholargate.app/en/deep-learning/self-supervised-lda-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-lda-topic-model.md","definition":"Self-supervised LDA combines the probabilistic generative framework of Latent Dirichlet Allocation with self-supervised pretraining signals — such as masked-word prediction or contrastive document objectives — to guide topic discovery without requiring hand-labeled training data. The result is topic representations that are simultaneously grounded in distributional statistics and enriched by language structure learned from raw text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blei, D. M., Ng, A. Y., Jordan, M. I. (LDA); self-supervised extension by multiple authors (2020s)","year":"2003 (LDA); self-supervised variants from 2020","type":"Probabilistic generative model with self-supervised pretraining","dataType":"Unlabeled or minimally labeled text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/blei03a.html"},{"ref":"Meng, Y., Huang, J., Zhang, Y., & Han, J. (2022). Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations. Proceedings of WWW 2022, ACM.","type":"inproceedings","doi":"10.1145/3485447.3512034","isbn":null,"url":null}],"related":["lda-topic-model","nmf-topic-model","semi-supervised-lda-topic-model","bert-based-classification","sentence-embeddings","topic-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-learning","name":"Self-supervised Learning","fullName":"Self-supervised Learning (Pretext-task Representation Learning)","aliases":["SSL","self-supervised pre-training","pretext-task learning","unsupervised representation learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018–2020","originator":"LeCun, Y. and community (formalized ~2018–2020)","url":"https://scholargate.app/en/machine-learning/self-supervised-learning","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-learning.md","definition":"Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeCun, Y. and community (formalized ~2018–2020)","year":"2018–2020","type":"Representation learning paradigm","dataType":"Unlabeled data (images, text, audio, video, tabular)","subfamily":"Machine learning"},"citations":[{"ref":"LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/","type":"article","doi":null,"isbn":null,"url":"https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/"},{"ref":"Self-supervised learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Self-supervised_learning"}],"related":["transfer-learning","semi-supervised-learning","contrastive-learning","few-shot-learning","autoencoders","representation-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-lightgbm","name":"Self-supervised LightGBM","fullName":"Self-supervised Learning with LightGBM (Gradient Boosting with Self-supervised Pretraining)","aliases":["SSL-LightGBM","self-supervised gradient boosting","pretraining LightGBM","pseudo-label LightGBM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017–2020","originator":"Ke, G. et al. (LightGBM); self-supervised paradigm adapted from broader SSL literature","url":"https://scholargate.app/en/machine-learning/self-supervised-lightgbm","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-lightgbm.md","definition":"Self-supervised LightGBM combines the self-supervised learning paradigm with the LightGBM gradient boosting framework to exploit large volumes of unlabeled tabular data. A self-supervised pretext task — such as masked feature prediction or contrastive corruption — generates rich feature representations or pseudo-labels that are then used to train or fine-tune a LightGBM model, substantially improving performance in label-scarce regimes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ke, G. et al. (LightGBM); self-supervised paradigm adapted from broader SSL literature","year":"2017–2020","type":"Hybrid (self-supervised pretraining + gradient boosting)","dataType":"Tabular (labeled and unlabeled)","subfamily":"Machine learning"},"citations":[{"ref":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html"},{"ref":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Self-Supervised Learning. Proceedings of the 37th International Conference on Machine Learning (ICML).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Simple+Framework+for+Contrastive+Self-Supervised+Learning"}],"related":["lightgbm","self-supervised-learning","semi-supervised-lightgbm","gradient-boosting","xgboost","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-logistic-regression","name":"Self-supervised Logistic Regression","fullName":"Self-supervised Representation Learning with Logistic Regression Classifier","aliases":["SSL linear probe","contrastive pretraining with logistic classifier","self-supervised linear evaluation","SSL + logistic regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020s","originator":"Chen et al. (SimCLR linear evaluation protocol, 2020); logistic probe practice widely adopted across SSL literature","url":"https://scholargate.app/en/machine-learning/self-supervised-logistic-regression","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-logistic-regression.md","definition":"Self-supervised logistic regression is a two-stage pipeline in which a neural encoder is first trained on abundant unlabeled data through a self-supervised pretext task — such as contrastive learning or masked prediction — and then the frozen learned representations are classified with a standard logistic regression model trained on a small labeled dataset. This linear evaluation protocol is widely used to benchmark the quality of self-supervised representations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen et al. (SimCLR linear evaluation protocol, 2020); logistic probe practice widely adopted across SSL literature","year":"2020s","type":"Self-supervised pretraining + supervised linear classification","dataType":"Unlabeled data for pretraining; small labeled set for logistic regression fine-tuning","subfamily":"Machine learning"},"citations":[{"ref":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), 1597–1607.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/chen20j.html"},{"ref":"van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373–440.","type":"article","doi":"10.1007/s10994-019-05855-6","isbn":null,"url":null}],"related":["self-supervised-learning","logistic-regression-ml","semi-supervised-logistic-regression","transfer-learning","contrastive-learning","self-supervised-decision-tree"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-metric-learning","name":"Self-supervised Metric learning","fullName":"Self-supervised Metric Learning","aliases":["self-supervised representation learning with metric loss","contrastive self-supervised learning","unsupervised metric learning","SSML"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020 (modern contrastive formulation); foundations 1990s–2000s","originator":"Chen, T. et al. (SimCLR); earlier metric learning foundations by Bromley, LeCun (1994)","url":"https://scholargate.app/en/machine-learning/self-supervised-metric-learning","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-metric-learning.md","definition":"Self-supervised metric learning trains a neural encoder to embed inputs so that semantically similar items lie close together in vector space, using automatically generated pseudo-labels instead of human annotations. By combining self-supervised pretext tasks with contrastive or triplet-based metric objectives, it produces transferable, label-efficient representations applicable to retrieval, clustering, and few-shot classification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, T. et al. (SimCLR); earlier metric learning foundations by Bromley, LeCun (1994)","year":"2020 (modern contrastive formulation); foundations 1990s–2000s","type":"Self-supervised representation learning with metric objective","dataType":"Unlabeled or partially labeled; images, text, audio, tabular embeddings","subfamily":"Machine learning"},"citations":[{"ref":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/chen20j.html"},{"ref":"Khosla, P., Tian, Y., Wang, X., Liu, C., Krishnan, D., Isola, P., & Tian, Y. (2020). Supervised Contrastive Learning. Advances in Neural Information Processing Systems (NeurIPS 2020), 33, 18661–18673.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/d89a66c7c80a29b1bdbab0f2a1a94af8-Abstract.html"}],"related":["contrastive-learning","siamese-network","triplet-network","self-supervised-learning","representation-learning","metric-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-naive-bayes","name":"Self-supervised Naive Bayes","fullName":"Self-supervised Naive Bayes (EM-augmented Generative Classifier)","aliases":["Self-training Naive Bayes","EM Naive Bayes","Expectation-Maximization Naive Bayes","Pseudo-label Naive Bayes"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000","originator":"Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.","url":"https://scholargate.app/en/machine-learning/self-supervised-naive-bayes","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-naive-bayes.md","definition":"Self-supervised Naive Bayes extends the classic Naive Bayes classifier to exploit large pools of unlabeled data by iteratively assigning soft pseudo-labels through an Expectation-Maximization loop. Originally demonstrated for text classification by Nigam et al. (2000), the approach can substantially improve accuracy when labeled examples are scarce but unlabeled data are plentiful.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.","year":"2000","type":"Self-supervised generative classifier","dataType":"Labeled + unlabeled tabular or text data","subfamily":"Machine learning"},"citations":[{"ref":"Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3), 103–134.","type":"article","doi":"10.1023/A:1007692713085","isbn":null,"url":null},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["naive-bayes","semi-supervised-naive-bayes","self-supervised-learning","semi-supervised-learning","expectation-maximization","self-supervised-logistic-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-named-entity-recognition","name":"Self-supervised named entity recognition","fullName":"Self-supervised Named Entity Recognition","aliases":["Self-supervised NER","SS-NER","label-efficient NER","pre-trained NER"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2019","originator":"Devlin et al.; community-evolved from BERT-era self-supervised pretraining","url":"https://scholargate.app/en/deep-learning/self-supervised-named-entity-recognition","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-named-entity-recognition.md","definition":"Self-supervised named entity recognition (NER) combines large-scale self-supervised pretraining — such as masked language modeling — with token-level fine-tuning to identify and classify named entities in text. By learning general linguistic representations before seeing any entity labels, the model achieves strong performance even when annotated NER training data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin et al.; community-evolved from BERT-era self-supervised pretraining","year":"2018–2019","type":"Sequence labeling via self-supervised pretraining + fine-tuning","dataType":"Unstructured text (sentences, documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/N19-1423"},{"ref":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for Named Entity Recognition. Proceedings of NAACL-HLT 2016, 260–270.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/N16-1030"}],"related":["bert","named-entity-recognition","conditional-random-field","transformer","sequence-labeling","few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-nmf-topic-model","name":"Self-supervised NMF Topic Model","fullName":"Self-supervised Non-negative Matrix Factorization Topic Model","aliases":["SS-NMF","self-supervised topic modeling","NMF with self-supervised signals","contrastive NMF topic model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2022","originator":"Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022)","url":"https://scholargate.app/en/deep-learning/self-supervised-nmf-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-nmf-topic-model.md","definition":"The Self-supervised NMF Topic Model extends classical Non-negative Matrix Factorization for topic discovery by incorporating self-supervised learning signals — such as masked-word reconstruction or contrastive objectives — into the NMF optimization, yielding more coherent and semantically meaningful topics from text corpora without requiring any human-labeled data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups (building on Lee & Seung, 1999; self-supervised extensions ca. 2020–2022)","year":"2020–2022","type":"Unsupervised / self-supervised topic model","dataType":"Text corpora (bag-of-words or contextual embeddings)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Shi, T., Guo, X., Lv, J., & Yu, P. S. (2022). Self-supervised NMF-based graph contrastive learning for semi-supervised node classification. In Proceedings of the 36th AAAI Conference on Artificial Intelligence.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Self-supervised+NMF-based+graph+contrastive+learning+semi-supervised+node+classification"},{"ref":"Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.","type":"article","doi":"10.1038/44565","isbn":null,"url":null}],"related":["latent-dirichlet-allocation","non-negative-matrix-factorization","neural-topic-model","contrastive-learning","bert","prodlda"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-object-detection","name":"Self-supervised Object Detection","fullName":"Self-supervised Pre-training for Object Detection","aliases":["SSL object detection","self-supervised detection","unsupervised pre-training for detection","contrastive pre-training for detection"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2021","originator":"He et al. (MoCo); Caron et al. (DINO); Henaff et al. (DetCon)","url":"https://scholargate.app/en/deep-learning/self-supervised-object-detection","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-object-detection.md","definition":"Self-supervised object detection uses unlabeled image data to pre-train a visual backbone through pretext tasks such as contrastive learning or masked image modeling, then fine-tunes the backbone with a detection head on a smaller labeled dataset. This approach dramatically reduces reliance on expensive bounding-box annotations while matching or approaching fully supervised detection performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"He et al. (MoCo); Caron et al. (DINO); Henaff et al. (DetCon)","year":"2019–2021","type":"Self-supervised pre-training + supervised fine-tuning","dataType":"Unlabeled images (pre-training); labeled bounding-box annotations (fine-tuning)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9729–9738.","type":"inproceedings","doi":"10.1109/CVPR42600.2020.00975","isbn":null,"url":null},{"ref":"Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660.","type":"inproceedings","doi":"10.1109/ICCV48922.2021.00951","isbn":null,"url":null}],"related":["object-detection","self-supervised-image-classification","transfer-learning-with-object-detection","semi-supervised-object-detection","convolutional-neural-network","fine-tuned-object-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-one-class-svm","name":"Self-supervised One-class SVM","fullName":"Self-supervised One-class Support Vector Machine","aliases":["SS-OCSVM","Self-supervised SVDD","Self-supervised novelty detection","Pretext-task OC-SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018","originator":"Golan & El-Yaniv; Ruff et al.","url":"https://scholargate.app/en/machine-learning/self-supervised-one-class-svm","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-one-class-svm.md","definition":"Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Golan & El-Yaniv; Ruff et al.","year":"2018","type":"Self-supervised anomaly/novelty detection","dataType":"Unlabeled or normal-only tabular, image, or sequential data","subfamily":"Machine learning"},"citations":[{"ref":"Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v80/golan18a.html"},{"ref":"Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Muller, E. & Kloft, M. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 4393–4402.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v80/ruff18a.html"}],"related":["one-class-svm","self-supervised-learning","autoencoder-anomaly-detection","isolation-forest","semi-supervised-one-class-svm","gaussian-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-question-answering","name":"Self-supervised Question Answering","fullName":"Self-supervised Question Answering (SSQA)","aliases":["SSQA","unsupervised question answering","self-supervised QA","zero-label question answering"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019","originator":"Lewis, P.; Alberti, C. et al. (multiple independent groups ~2019)","url":"https://scholargate.app/en/deep-learning/self-supervised-question-answering","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-question-answering.md","definition":"Self-supervised Question Answering (SSQA) is a training paradigm that automatically generates question-answer pairs from unlabeled text — using cloze translation, span masking, or neural question generation — to train QA models without any human-labeled data. It enables high-quality reading comprehension systems even when annotated datasets are scarce or domain-specific.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lewis, P.; Alberti, C. et al. (multiple independent groups ~2019)","year":"2019","type":"Self-supervised NLP training paradigm","dataType":"Unlabeled text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lewis, P., Denoyer, L., & Riedel, S. (2019). Unsupervised Question Answering by Cloze Translation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. 4896–4910.","type":"inproceedings","doi":"10.18653/v1/P19-1484","isbn":null,"url":null},{"ref":"Alberti, C., Andor, D., Pitler, E., Devlin, J., & Collins, M. (2019). Synthetic QA Corpora Generation with Roundtrip Consistency. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. 6168–6173.","type":"inproceedings","doi":"10.18653/v1/p19-1620","isbn":null,"url":null}],"related":["bert-pretraining","question-generation","reading-comprehension","masked-language-modeling","retrieval-augmented-generation","span-extraction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-random-forest","name":"Self-supervised Random Forest","fullName":"Self-supervised Random Forest (SSL-RF)","aliases":["SSL-RF","self-supervised RF","self-supervised ensemble forest","unsupervised random forest with self-labeling"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2012–2022","originator":"Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)","url":"https://scholargate.app/en/machine-learning/self-supervised-random-forest","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-random-forest.md","definition":"Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)","year":"2012–2022","type":"Semi-supervised ensemble (self-supervised pretext task + RF)","dataType":"Tabular; image features; partially labeled datasets","subfamily":"Machine learning"},"citations":[{"ref":"Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2204.01430"},{"ref":"Criminisi, A., Shotton, J., & Konukoglu, E. (2012). Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Vision, 7(2–3), 81–227.","type":"article","doi":"10.1561/0600000035","isbn":null,"url":null}],"related":["random-forest","semi-supervised-learning","self-supervised-learning","decision-tree","xgboost","label-propagation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-reinforcement-learning","name":"Self-supervised Reinforcement Learning","fullName":"Self-supervised Reinforcement Learning (SSL-augmented RL)","aliases":["SSL-RL","self-supervised RL","representation-based reinforcement learning","auxiliary-task RL"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020","originator":"Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)","url":"https://scholargate.app/en/deep-learning/self-supervised-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-reinforcement-learning.md","definition":"Self-supervised Reinforcement Learning (SSL-RL) augments standard RL training with self-supervised auxiliary objectives — such as contrastive, predictive, or data-augmentation-based tasks — applied to the agent's own experience. These objectives improve the quality of learned representations without requiring extra human labels, enabling faster convergence and better sample efficiency, especially in high-dimensional observation spaces like raw pixels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Laskin, M.; Srinivas, A.; Abbeel, P. (and contemporaries)","year":"2020","type":"Self-supervised auxiliary-task learning for RL","dataType":"Trajectories, pixel observations, or state vectors from environment interactions","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Laskin, M., Srinivas, A., & Abbeel, P. (2020). CURL: Contrastive Unsupervised Representations for Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 5639–5650.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/laskin20a.html"},{"ref":"Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., & Srinivas, A. (2021). Reinforcement Learning with Augmented Data. Advances in Neural Information Processing Systems (NeurIPS), 33, 19884–19895.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/e615c82aba461681ade82da2da38004a-Abstract.html"}],"related":["reinforcement-learning","self-supervised-convolutional-neural-network","contrastive-learning","transfer-learning-reinforcement-learning","semi-supervised-reinforcement-learning","model-based-reinforcement-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-roberta-based-classification","name":"Self-supervised RoBERTa-based classification","fullName":"Self-supervised RoBERTa-based Text Classification","aliases":["RoBERTa self-supervised classifier","SSL-RoBERTa classification","RoBERTa fine-tuning with self-supervised pretraining","self-supervised BERT-variant classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Liu, Y. et al. (RoBERTa); self-supervised classification approach developed across multiple research groups post-2019","url":"https://scholargate.app/en/deep-learning/self-supervised-roberta-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-roberta-based-classification.md","definition":"Self-supervised RoBERTa-based classification combines the RoBERTa transformer's powerful language representations — learned from large unlabeled corpora through masked-language modeling — with self-supervised objectives to perform text classification with little or no human-labeled data. The approach leverages abundant unlabeled text to generate its own training signal before fine-tuning on a downstream classification task.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, Y. et al. (RoBERTa); self-supervised classification approach developed across multiple research groups post-2019","year":"2019–2020","type":"Pretrained transformer + self-supervised fine-tuning for classification","dataType":"Text / token sequences","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.11692"},{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null}],"related":["bert-classification","transformer-text-classification","semi-supervised-text-classification","contrastive-learning","fine-tuning-pretrained-language-models","masked-language-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-semantic-segmentation","name":"Self-supervised Semantic Segmentation","fullName":"Self-supervised Learning for Semantic Segmentation","aliases":["SSL semantic segmentation","unsupervised semantic segmentation","label-free semantic segmentation","self-supervised dense prediction"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2022","originator":"Multiple groups (Caron et al.; Hamilton et al. among key contributors)","url":"https://scholargate.app/en/deep-learning/self-supervised-semantic-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-semantic-segmentation.md","definition":"Self-supervised semantic segmentation learns to assign a class label to every pixel of an image without relying on manually annotated segmentation masks. A backbone network is first trained on large quantities of unlabeled images using self-supervised objectives such as contrastive learning or masked image modeling, and the resulting dense features are then used to partition and label image regions, achieving competitive segmentation quality at a fraction of the annotation cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups (Caron et al.; Hamilton et al. among key contributors)","year":"2020–2022","type":"Self-supervised dense prediction","dataType":"Unlabeled images","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660.","type":"inproceedings","doi":"10.1109/ICCV48922.2021.00951","isbn":null,"url":null},{"ref":"Hamilton, M., Zhang, Z., Hariharan, B., Snavely, N., & Freeman, W. T. (2022). Unsupervised Semantic Segmentation by Distilling Feature Correspondences. International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2203.08414"}],"related":["semantic-segmentation","self-supervised-vision-transformer","self-supervised-convolutional-neural-network","instance-segmentation","contrastive-learning","vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-sentence-embeddings","name":"Self-supervised Sentence Embeddings","fullName":"Self-supervised Learning for Sentence Embeddings","aliases":["self-supervised sentence representation learning","contrastive sentence embeddings","SimCSE","unsupervised sentence encoders"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2021","originator":"Gao, T., Yao, X., & Chen, D. (SimCSE); Reimers, N. & Gurevych, I. (Sentence-BERT)","url":"https://scholargate.app/en/deep-learning/self-supervised-sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-sentence-embeddings.md","definition":"Self-supervised sentence embeddings train a neural encoder to map sentences into a dense vector space without requiring manually labeled pairs. By constructing positive examples automatically — for instance by passing the same sentence through dropout twice — and using contrastive objectives, the model learns semantically rich representations that transfer well to similarity, retrieval, and classification tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gao, T., Yao, X., & Chen, D. (SimCSE); Reimers, N. & Gurevych, I. (Sentence-BERT)","year":"2019–2021","type":"Self-supervised representation learning","dataType":"Raw or minimally annotated text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 6894–6910.","type":"inproceedings","doi":"10.18653/v1/2021.emnlp-main.552","isbn":null,"url":null},{"ref":"Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–3992.","type":"inproceedings","doi":"10.18653/v1/D19-1410","isbn":null,"url":null}],"related":["sentence-embeddings","self-supervised-transformer","bert-based-classification","contrastive-learning","self-supervised-bert-based-classification","semi-supervised-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-sentiment-analysis","name":"Self-supervised Sentiment Analysis","fullName":"Self-supervised Learning for Sentiment Analysis","aliases":["SSL-based sentiment analysis","self-supervised opinion mining","pre-training for sentiment","unsupervised pre-training sentiment"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–present","originator":"Devlin et al. (BERT paradigm); extended by Sun et al. and others","url":"https://scholargate.app/en/deep-learning/self-supervised-sentiment-analysis","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-sentiment-analysis.md","definition":"Self-supervised sentiment analysis combines large-scale unsupervised pre-training — through objectives such as masked language modeling or contrastive prediction — with fine-tuning on a small labeled sentiment corpus. The approach, popularized by BERT and its variants, dramatically reduces the need for hand-labeled data while achieving state-of-the-art accuracy on positive/negative/neutral opinion classification tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin et al. (BERT paradigm); extended by Sun et al. and others","year":"2019–present","type":"Pre-train then fine-tune NLP pipeline","dataType":"Raw text (unlabeled pre-training) + small labeled sentiment dataset (fine-tuning)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to fine-tune BERT for text classification? In China National Conference on Chinese Computational Linguistics (CCL 2019), pp. 194–206. Springer.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+to+fine-tune+BERT+for+text+classification"}],"related":["bert","transformer","contrastive-learning","text-classification","aspect-based-sentiment-analysis","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-stacking-ensemble","name":"Self-supervised Stacking Ensemble","fullName":"Self-supervised Stacking Ensemble (SSL-augmented Stacked Generalization)","aliases":["SSL stacking","self-supervised stacked generalization","self-supervised meta-ensemble","SSL ensemble stacking"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1992–2018","originator":"Wolpert, D. H. (stacking); self-supervised extension via modern SSL literature","url":"https://scholargate.app/en/machine-learning/self-supervised-stacking-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-stacking-ensemble.md","definition":"Self-supervised Stacking Ensemble combines stacked generalization — the classic two-level ensemble architecture introduced by Wolpert (1992) — with self-supervised pretraining, allowing base models to learn rich representations from unlabeled data before being fine-tuned and stacked. This hybrid strategy is especially powerful when labeled examples are scarce but unlabeled data is plentiful.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolpert, D. H. (stacking); self-supervised extension via modern SSL literature","year":"1992–2018","type":"Ensemble meta-learning with self-supervised pretraining","dataType":"Labeled and unlabeled tabular or structured data","subfamily":"Machine learning"},"citations":[{"ref":"Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.","type":"article","doi":"10.1016/S0893-6080(05)80023-1","isbn":null,"url":null},{"ref":"Self-supervised learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Self-supervised_learning"}],"related":["stacking-ensemble","random-forest","xgboost","semi-supervised-learning","transfer-learning","bagging-ensemble"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-support-vector-machine","name":"Self-supervised Support Vector Machine","fullName":"Self-supervised Support Vector Machine (Self-supervised SVM)","aliases":["Self-supervised SVM","SS-SVM","semi-self-supervised SVM","self-supervised kernel SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2019–2021","originator":"Various (integration of self-supervised learning with SVM classifiers, ~2019–2021)","url":"https://scholargate.app/en/machine-learning/self-supervised-support-vector-machine","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-support-vector-machine.md","definition":"A Self-supervised Support Vector Machine combines self-supervised pretraining — learning representations from unlabeled data via pretext tasks — with a Support Vector Machine classifier trained on the resulting features. This hybrid approach enables strong classification performance even when labeled data is scarce, by leveraging the structure embedded in large unlabeled datasets before applying the SVM's margin-maximization objective.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (integration of self-supervised learning with SVM classifiers, ~2019–2021)","year":"2019–2021","type":"Hybrid (self-supervised pretraining + SVM classifier)","dataType":"Tabular, image, or feature data; partially or fully unlabeled","subfamily":"Machine learning"},"citations":[{"ref":"De Palma, A., Bucarelli, M. S., Goyal, P., & Silvestri, F. (2021). Self-supervised Support Vector Machine. Proceedings of the AAAI Workshop on Self-Supervised Learning for the Internet of Things.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Self-supervised+Support+Vector+Machine"},{"ref":"Self-supervised learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Self-supervised_learning"}],"related":["svm-classification","support-vector-regression","semi-supervised-learning","self-supervised-learning","kernel-pca","label-propagation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-topic-modeling","name":"Self-supervised topic modeling","fullName":"Self-Supervised Topic Modeling","aliases":["SSL topic model","self-supervised neural topic model","contrastive topic modeling","self-supervised LM-based topic modeling"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2023","originator":"Various (Miao et al. 2016 for neural topic models; self-supervised objectives widely adopted 2020–2023)","url":"https://scholargate.app/en/deep-learning/self-supervised-topic-modeling","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-topic-modeling.md","definition":"Self-supervised topic modeling combines the interpretable topic discovery of classical topic models with self-supervised learning objectives — such as contrastive loss, masked language modeling, or reconstruction — to learn coherent, semantically rich topics from unlabeled text without human-annotated labels. It bridges classical probabilistic topic models and modern representation learning, yielding topics better aligned with contextual meaning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Miao et al. 2016 for neural topic models; self-supervised objectives widely adopted 2020–2023)","year":"2020–2023","type":"Self-supervised neural topic model","dataType":"Unlabeled or minimally labeled text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Wu, X., Li, C., Zhu, Y., & Miao, Y. (2023). Effective Neural Topic Modeling with Embedding Clustering Regularization. Proceedings of the 40th International Conference on Machine Learning (ICML 2023), PMLR 202, 37335–37357.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v202/wu23o.html"},{"ref":"Topic model. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Topic_model"}],"related":["lda-topic-model","nmf-topic-model","sentence-embeddings","bert-based-classification","semi-supervised-topic-modeling","contrastive-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-transfer-learning","name":"Self-supervised Transfer learning","fullName":"Self-supervised Pre-training for Transfer Learning","aliases":["self-supervised pre-training","SSL-based transfer learning","representation transfer from self-supervised models","contrastive pre-training with transfer"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018–2020 (modern consolidation)","originator":"LeCun, Y. (concept); Devlin et al. (BERT, NLP); Chen et al. (SimCLR, vision)","url":"https://scholargate.app/en/machine-learning/self-supervised-transfer-learning","markdownUrl":"https://scholargate.app/en/machine-learning/self-supervised-transfer-learning.md","definition":"Self-supervised transfer learning combines two powerful paradigms: a model first learns rich representations from unlabeled data using self-supervised pretext tasks, then those learned representations are transferred and fine-tuned on a downstream task with limited labeled data. This approach underlies landmark systems such as BERT in NLP and SimCLR and DINO in computer vision, dramatically reducing labeled-data requirements across many domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeCun, Y. (concept); Devlin et al. (BERT, NLP); Chen et al. (SimCLR, vision)","year":"2018–2020 (modern consolidation)","type":"Learning paradigm (self-supervised pre-training + fine-tuning)","dataType":"Unlabeled data for pre-training; small labeled dataset for fine-tuning","subfamily":"Machine learning"},"citations":[{"ref":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/chen20j.html"},{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019, 4171–4186. Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null}],"related":["self-supervised-learning","transfer-learning","semi-supervised-learning","few-shot-learning","metric-learning","self-supervised-few-shot-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-transformer","name":"Self-supervised Transformer","fullName":"Self-supervised Transformer (Pretraining with Self-generated Supervision)","aliases":["SSL Transformer","self-supervised pretraining","masked self-attention pretraining","contrastive transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2019","originator":"Vaswani et al. (architecture); Devlin et al. (BERT self-supervised paradigm)","url":"https://scholargate.app/en/deep-learning/self-supervised-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-transformer.md","definition":"A self-supervised Transformer is a Transformer network pretrained using automatically constructed supervision signals — such as masked token prediction or next-sentence prediction — rather than human-annotated labels. The resulting representations are then fine-tuned or probed on downstream tasks. BERT, GPT, and ViT (Vision Transformer in masked-image modeling mode) are the most widely known instantiations of this paradigm.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vaswani et al. (architecture); Devlin et al. (BERT self-supervised paradigm)","year":"2017–2019","type":"Self-supervised deep learning model","dataType":"Text, images, audio, or other sequential/structured data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"}],"related":["transformer","bert-based-classification","roberta-based-classification","self-supervised-convolutional-neural-network","fine-tuned-transformer","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-variational-autoencoder","name":"Self-supervised Variational Autoencoder","fullName":"Self-supervised Variational Autoencoder (SS-VAE)","aliases":["SS-VAE","self-supervised VAE","unsupervised VAE with self-supervised pretext tasks","contrastive VAE"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014 (VAE); self-supervised variant ~2019–2021","originator":"Kingma, D. P. & Welling, M. (VAE); self-supervised extensions by various authors from ~2019 onward","url":"https://scholargate.app/en/deep-learning/self-supervised-variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-variational-autoencoder.md","definition":"A Self-supervised Variational Autoencoder (SS-VAE) combines the generative latent-space learning of a standard VAE with self-supervised pretext tasks — such as contrastive augmentation, masked reconstruction, or rotation prediction — to learn richer, more disentangled representations from unlabeled data without any manual annotation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kingma, D. P. & Welling, M. (VAE); self-supervised extensions by various authors from ~2019 onward","year":"2014 (VAE); self-supervised variant ~2019–2021","type":"Generative model with self-supervised representation learning","dataType":"Unlabeled or minimally labeled continuous or structured data (images, time series, tabular)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"},{"ref":"Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021). Self-Supervised Learning: Generative or Contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857–876.","type":"article","doi":"10.1109/TKDE.2021.3090866","isbn":null,"url":null}],"related":["variational-autoencoder","self-supervised-convolutional-neural-network","semi-supervised-variational-autoencoder","fine-tuned-variational-autoencoder","multimodal-variational-autoencoder","generative-adversarial-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-vision-transformer","name":"Self-supervised Vision Transformer","fullName":"Self-supervised Vision Transformer (SSL-ViT)","aliases":["SSL-ViT","self-supervised ViT","unsupervised ViT pre-training","vision transformer self-supervised pre-training"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2021–2022","originator":"Caron et al. (DINO); He et al. (MAE)","url":"https://scholargate.app/en/deep-learning/self-supervised-vision-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-vision-transformer.md","definition":"Self-supervised Vision Transformer (SSL-ViT) applies self-supervised pre-training objectives — such as masked patch prediction (MAE) or self-distillation with no labels (DINO) — to the Vision Transformer architecture, enabling powerful visual representations to be learned from large unlabeled image corpora before any task-specific fine-tuning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Caron et al. (DINO); He et al. (MAE)","year":"2021–2022","type":"Self-supervised pre-training for vision transformers","dataType":"Unlabeled image data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2104.14294"},{"ref":"He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022). Masked Autoencoders Are Scalable Vision Learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16000–16009.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2111.06377"}],"related":["vision-transformer","self-supervised-convolutional-neural-network","self-supervised-graph-neural-network","fine-tuned-vision-transformer","multimodal-vision-transformer","transfer-learning-with-vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"self-supervised-word2vec","name":"Self-supervised Word2Vec","fullName":"Self-supervised Word2Vec (Skip-gram and CBOW with Self-supervised Objectives)","aliases":["Word2Vec","word embeddings","Skip-gram model","CBOW model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013","originator":"Mikolov, T., Chen, K., Corrado, G., & Dean, J.","url":"https://scholargate.app/en/deep-learning/self-supervised-word2vec","markdownUrl":"https://scholargate.app/en/deep-learning/self-supervised-word2vec.md","definition":"Word2Vec is a shallow neural network model introduced by Mikolov et al. (2013) that learns dense vector representations of words from large unlabeled text corpora using self-supervised objectives. By training a model to predict surrounding context words (Skip-gram) or a target word from its context (CBOW), it captures rich semantic and syntactic regularities in continuous vector space without any manual annotation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mikolov, T., Chen, K., Corrado, G., & Dean, J.","year":"2013","type":"Self-supervised neural word embedding","dataType":"Raw unlabeled text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR 2013).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1301.3781"},{"ref":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS 2013), 26.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1310.4546"}],"related":["glove-embeddings","fasttext","bert","transformer","recurrent-neural-network","contrastive-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sem","name":"SEM","fullName":"Structural Equation Modeling","aliases":["Yapısal Eşitlik Modellemesi (SEM)","structural equation modelling","covariance structure analysis","latent variable modeling"],"domain":"statistics","family":"latent-structure","subfamily":null,"year":1970,"originator":"Karl Jöreskog (LISREL framework, 1970s)","url":"https://scholargate.app/en/statistics/sem","markdownUrl":"https://scholargate.app/en/statistics/sem.md","definition":"Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Jöreskog (LISREL framework, 1970s)","year":1970,"type":"Latent variable / causal modeling","outcome":"Structural path coefficients + fit indices","data":"Continuous / ordinal indicators","min_sample":300},"citations":[{"ref":"Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1473756540","url":null},{"ref":"Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). The Guilford Press.","type":"book","doi":null,"isbn":"978-1462523344","url":null},{"ref":"Byrne, B. M. (2012). Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming. Routledge.","type":"book","doi":"10.4324/9780203807644","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","exploratory-factor-analysis","path-analysis","mediation-analysis","multilevel-modeling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semantic-differential","name":"Semantic Differential","fullName":"Semantic Differential Scale","aliases":["SD Scale","Semantic Space Measurement"],"domain":"psychology","family":"hypothesis-test","subfamily":"Rating Scale","year":"1957","originator":"Charles Osgood, George Suci, and Percy Tannenbaum","url":"https://scholargate.app/en/psychology/semantic-differential","markdownUrl":"https://scholargate.app/en/psychology/semantic-differential.md","definition":"The Semantic Differential is an attitude measurement technique that assesses the connotative meaning (emotional and evaluative associations) of concepts through ratings on multiple bipolar adjective scales. Developed by Osgood, Suci, and Tannenbaum in the 1950s, the method reveals the affective structure underlying how people perceive concepts—revealing not just what they think, but how they feel about people, brands, ideas, or policies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Osgood, George Suci, and Percy Tannenbaum","subfamily":"Rating Scale","year":"1957","type":"Bipolar attitude measure"},"citations":[{"ref":"Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning. University of Illinois Press.","type":"book","doi":"","isbn":null,"url":"https://books.google.com/books?id=osgood-measurement-meaning-1957"},{"ref":"Snider, J. G., & Osgood, C. E. (1966). Semantic differential technique: A sourcebook. Aldine Publishing Company.","type":"article","doi":"","isbn":null,"url":"https://books.google.com/books?id=snider-osgood-semantic-differential-1966"},{"ref":"Heise, D. R. (1969). Some methodological issues in semantic differential research. Psychological Bulletin, 72(6), 406-422.","type":"article","doi":"10.1037/h0028448","isbn":null,"url":null}],"related":["likert-scale","attitude-measurement","connotative-meaning","factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semantic-feature-analysis","name":"Semantic Feature Analysis","fullName":"Componential (Feature-Based) Semantic Analysis","aliases":["Componential Analysis","Feature Semantics"],"domain":"linguistics","family":"process-pipeline","subfamily":"Structural Semantics","year":"1956","originator":"Ward Goodenough","url":"https://scholargate.app/en/linguistics/semantic-feature-analysis","markdownUrl":"https://scholargate.app/en/linguistics/semantic-feature-analysis.md","definition":"Semantic Feature Analysis, or Componential Analysis, is a method for understanding word meaning by decomposing concepts into minimal meaningful units called semantic features or components. Developed by Ward Goodenough in 1956, this approach represents the meaning of words as bundles of features (e.g., 'woman' = [human] [adult] [female]), enabling systematic analysis of semantic relationships, kinship systems, plant classifications, and lexical fields. The method is grounded in structural linguistics and has applications in anthropology, cognitive linguistics, and lexicography.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ward Goodenough","subfamily":"Structural Semantics","year":"1956","type":"Empirical process pipeline"},"citations":[{"ref":"Goodenough, W. H. (1956). Componential analysis and the study of meaning. Language, 32(2), 195-216.","type":"article","doi":"10.2307/410665","isbn":null,"url":null},{"ref":"Nida, E. A. (1975). Componential Analysis of Meaning: An Introduction to Semantic Structures. The Hague: Mouton.","type":"book","doi":null,"isbn":null,"url":"https://www.degruyter.com/view/book"},{"ref":"Cruse, D. A. (2000). Meaning in Language: An Introduction to Semantics and Pragmatics (2nd ed.). Oxford: Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Meaning+in+Language%3A+An+Introduction+to+Semantics+and+Pragmatics+%282nd+ed.%29+Cruse"}],"related":["prototype-theory","semantic-field-analysis","cognitive-semantics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semantic-parsing","name":"Semantic Parsing","fullName":"Semantic Parsing (Natural Language to Formal Representation)","aliases":["Anlamsal Ayrıştırma (Semantic Parsing)","NL-to-SQL","text-to-SQL","natural language understanding"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1996 (modern neural revival c. 2018)","originator":"Zelle & Mooney (1996) — foundational supervised approach","url":"https://scholargate.app/en/text-mining/semantic-parsing","markdownUrl":"https://scholargate.app/en/text-mining/semantic-parsing.md","definition":"Semantic parsing is a natural-language-processing task that converts free-text utterances into executable formal representations such as SQL queries, logical forms, or Abstract Meaning Representations (AMR). Established in its supervised learning form by Zelle and Mooney in 1996 and scaled to cross-domain settings by the Spider benchmark (Yu et al., 2018), it bridges the gap between human language and machine-executable structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zelle & Mooney (1996) — foundational supervised approach","year":"1996 (modern neural revival c. 2018)","type":"NLP structured-prediction task","targetRepresentations":"SQL, logical form, AMR (Abstract Meaning Representation)","minimumSample":20,"difficultyLevel":"3 / 5","inputType":"Natural language text","outputType":"Executable formal structure (query, logic, graph)"},"citations":[{"ref":"Zelle, J.M. & Mooney, R.J. (1996). Learning to Parse Database Queries Using Inductive Logic Programming. AAAI.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.aaai.org/Library/AAAI/aaai96contents.php"},{"ref":"Yu, T. et al. (2018). Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing. EMNLP.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D18-1425/"}],"related":["text-classification","named-entity-recognition","dependency-parsing","sentiment-analysis","information-extraction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semantic-role-labeling","name":"Semantic Role Labeling","fullName":"Semantic Role Labeling (SRL)","aliases":["SRL","shallow semantic parsing","Anlamsal Rol Etiketleme (SRL)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2002,"originator":"Daniel Gildea & Daniel Jurafsky","url":"https://scholargate.app/en/text-mining/semantic-role-labeling","markdownUrl":"https://scholargate.app/en/text-mining/semantic-role-labeling.md","definition":"Semantic role labeling, introduced by Gildea and Jurafsky in 2002, is a natural-language-processing task that assigns semantic roles — who did what to whom, where, when, and how — to the components around a verb (predicate) in a sentence. It turns plain text into structured predicate-argument representations and is a foundational tool for event extraction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel Gildea & Daniel Jurafsky","year":2002,"type":"NLP shallow semantic parsing task","output":"Predicate-argument structure with semantic role labels (who, what, where, when, how)","ontologies":"PropBank / FrameNet"},"citations":[{"ref":"Gildea, D. & Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3), 245-288.","type":"article","doi":"10.1162/089120102760275983","isbn":null,"url":null},{"ref":"Shi, P. & Lin, J. (2019). Simple BERT Models for Relation Extraction and Semantic Role Labeling. arXiv:1904.05255.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1904.05255"}],"related":["named-entity-recognition","event-detection","question-answering"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semantic-segmentation","name":"Semantic Segmentation","fullName":"Semantic Segmentation (Dense Pixel-wise Classification)","aliases":["pixel-wise classification","scene parsing","dense labeling","semantic scene segmentation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015","originator":"Long, J., Shelhamer, E., & Darrell, T.","url":"https://scholargate.app/en/deep-learning/semantic-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/semantic-segmentation.md","definition":"Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Long, J., Shelhamer, E., & Darrell, T.","year":"2015","type":"Dense prediction / pixel-wise classification","dataType":"Images (RGB, multispectral, or volumetric)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.","type":"inproceedings","doi":"10.1109/CVPR.2015.7298965","isbn":null,"url":null},{"ref":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.","type":"article","doi":"10.1109/TPAMI.2017.2699184","isbn":null,"url":null}],"related":["convolutional-neural-network","instance-segmentation","object-detection","image-classification","transfer-learning-with-convolutional-neural-network","fine-tuned-semantic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semantic-similarity","name":"Semantic Similarity","fullName":"Semantic Similarity Analysis","aliases":["semantic textual similarity","text similarity","Anlamsal Benzerlik Analizi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2019,"originator":"Nils Reimers & Iryna Gurevych (Sentence-BERT)","url":"https://scholargate.app/en/text-mining/semantic-similarity","markdownUrl":"https://scholargate.app/en/text-mining/semantic-similarity.md","definition":"Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nils Reimers & Iryna Gurevych (Sentence-BERT)","year":2019,"type":"NLP text-comparison task","approaches":"Embedding-based (cosine similarity over sentence vectors)","output":"Similarity score between a pair of texts","minSample":20},"citations":[{"ref":"Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/D19-1410/"},{"ref":"Agirre, E. et al. (2013). *SEM 2013 shared task: Semantic Textual Similarity. ACL (*SEM).","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/S13-1004/"}],"related":["bert-embeddings","tf-idf","document-clustering","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semen-quality-evaluation","name":"Semen Quality Evaluation","fullName":"Semen Quality Evaluation and Breeding Soundness Assessment","aliases":["breeding soundness examination","seminal analysis","sperm quality assessment"],"domain":"animal-science","family":"process-pipeline","subfamily":"Reproductive assessment and management","year":"1970s","originator":"Veterinary Andrologists and Reproductive Physiologists","url":"https://scholargate.app/en/animal-science/semen-quality-evaluation","markdownUrl":"https://scholargate.app/en/animal-science/semen-quality-evaluation.md","definition":"Semen quality evaluation is a systematic assessment of male animal reproductive capacity, measuring sperm characteristics and overall breeding soundness. Developed by veterinary andrologists in the 1970s, the practice combines objective measures—sperm concentration, motility, morphology—with functional tests to predict fertility potential. Evaluation is essential for identifying suitable breeding animals, managing reproductive health, and ensuring successful artificial insemination (AI) programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Veterinary Andrologists and Reproductive Physiologists","subfamily":"Reproductive assessment and management","year":"1970s","type":"assessment and evaluation"},"citations":[{"ref":"Coulter, G. H., & Foote, R. H. (1997). Infertility in bulls: Summary of causes and breeding soundness evaluation. Journal of Dairy Science, 62(11), 1812-1829.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Infertility+in+bulls%3A+Summary+of+causes+and+breeding+soundness+evaluation+Coulter"},{"ref":"Saacke, R. G., White, J. M., & Bame, J. H. (1983). Semen quality and its relationship to mare fertility. Proceedings of the American Association of Equine Practitioners, 29, 391-410.","type":"article","doi":null,"isbn":null,"url":"https://www.aaep.org/"},{"ref":"Thundathil, J., Palma, G. A., & Mapletoft, R. J. (2001). Evaluation of semen quality. Animal Reproduction Science, 64(1-2), 13-32.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation+of+semen+quality+Thundathil"}],"related":["estrus-detection","herd-reproductive-performance","embryo-transfer-success"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-structured-interview","name":"Semi-Structured Interview","fullName":"Semi-Structured Interview","aliases":["guided interview","semi-standardized interview","focused interview","SSI"],"domain":"qualitative","family":"process-pipeline","subfamily":"Interview Methods","year":"1946 (Merton & Kendall); codified as a standard method through the 1980s–1990s","originator":"Robert K. Merton and Patricia Kendall (focused interview, 1946); further systematised by Steinar Kvale","url":"https://scholargate.app/en/qualitative/semi-structured-interview","markdownUrl":"https://scholargate.app/en/qualitative/semi-structured-interview.md","definition":"The semi-structured interview is a qualitative data-collection method in which the researcher prepares a set of key questions or topic areas in advance but remains free to probe, follow up, and reorder as the conversation evolves. Unlike structured interviews — which fix every question and sequence — or unstructured interviews — which are entirely open — the semi-structured format balances comparability across participants with the flexibility needed to capture the depth and nuance of individual perspectives. It is the most widely used interview format in social science, health, and education research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Merton and Patricia Kendall (focused interview, 1946); further systematised by Steinar Kvale","year":"1946 (Merton & Kendall); codified as a standard method through the 1980s–1990s","type":"Qualitative research method","dataType":"Spoken interview transcripts (text data)","typicalSampleSize":"10–30 participants (until thematic saturation)","subfamily":"Interview Methods"},"citations":[{"ref":"Kvale, S., & Brinkmann, S. (2009). InterViews: Learning the Craft of Qualitative Research Interviewing (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761925422","url":null},{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Patton+Qualitative+Research+and+Evaluation+Methods+3rd+edition+2002"}],"related":["phenomenology","grounded-theory","ethnography","case-study","thematic-analysis","focus-group"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-active-learning","name":"Semi-supervised Active Learning","fullName":"Semi-supervised Active Learning (SSAL)","aliases":["SSAL","active semi-supervised learning","query-based semi-supervised learning","semi-supervised learning with active queries"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2002","originator":"Muslea, I., Minton, S., & Knoblock, C. A.","url":"https://scholargate.app/en/machine-learning/semi-supervised-active-learning","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-active-learning.md","definition":"Semi-supervised Active Learning (SSAL) is a hybrid learning paradigm that combines active learning's selective query strategy with semi-supervised learning's ability to exploit unlabeled data. The model iteratively selects the most informative unlabeled instances for expert annotation while simultaneously leveraging the large pool of unannotated samples to improve its own representations, dramatically reducing labeling costs while maintaining strong predictive accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Muslea, I., Minton, S., & Knoblock, C. A.","year":"2002","type":"Hybrid learning framework","dataType":"Partially labeled tabular, text, or image data","subfamily":"Machine learning"},"citations":[{"ref":"Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool.","type":"book","doi":"10.2200/S00429ED1V01Y201207AIM018","isbn":null,"url":null},{"ref":"Zhu, X. (2005). Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison.","type":"article","doi":null,"isbn":null,"url":"https://pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf"}],"related":["semi-supervised-learning","active-learning","self-training","label-propagation","gaussian-process-classification","query-by-committee"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-apriori-algorithm","name":"Semi-supervised Apriori Algorithm","fullName":"Semi-supervised Apriori Algorithm for Constrained Association Rule Mining","aliases":["constrained Apriori","semi-supervised ARM","knowledge-guided Apriori","labeled-constraint Apriori"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1999–2005","originator":"Extended from Agrawal & Srikant (1994); constrained variants developed by Liu, Hsu & Ma (1999) and others","url":"https://scholargate.app/en/machine-learning/semi-supervised-apriori-algorithm","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-apriori-algorithm.md","definition":"The Semi-supervised Apriori algorithm extends the classic Apriori frequent-itemset miner by injecting background knowledge or labeled constraints — such as must-link pairs, forbidden items, or user-specified minimum support thresholds per group — to bias discovery toward practically meaningful association rules and reduce the search space.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from Agrawal & Srikant (1994); constrained variants developed by Liu, Hsu & Ma (1999) and others","year":"1999–2005","type":"Constrained association rule mining algorithm","dataType":"Transactional / tabular categorical data with partial supervision or domain constraints","subfamily":"Machine learning"},"citations":[{"ref":"Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fast+algorithms+for+mining+association+rules+Agrawal+Srikant+1994"},{"ref":"Liu, B., Hsu, W., & Ma, Y. (1999). Mining association rules with multiple minimum supports. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 337–341.","type":"inproceedings","doi":"10.1145/312129.312274","isbn":null,"url":null}],"related":["apriori","fp-growth","semi-supervised-learning","association-rule-mining","constrained-clustering","collaborative-filtering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-association-rules","name":"Semi-supervised Association Rules","fullName":"Semi-supervised Association Rule Mining","aliases":["semi-supervised ARM","label-guided association rule mining","constrained association rule mining","semi-supervised pattern discovery"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2003–2010s","originator":"Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)","url":"https://scholargate.app/en/machine-learning/semi-supervised-association-rules","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-association-rules.md","definition":"Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically meaningful, bridging unsupervised pattern mining with light supervision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)","year":"2003–2010s","type":"Pattern mining with partial supervision","dataType":"Transactional / tabular data with partial labels","subfamily":"Machine learning"},"citations":[{"ref":"Liu, B., Hsu, W., & Ma, Y. (2003). Integrating Classification and Association Rule Mining. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 339–346.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Integrating+Classification+and+Association+Rule+Mining+Liu+Hsu+Ma"},{"ref":"Association rule learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Association_rule_learning"}],"related":["apriori-algorithm","fp-growth","semi-supervised-learning","classification-association-rules","label-propagation","constrained-pattern-mining"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-autoencoder-anomaly-detection","name":"Semi-supervised Autoencoder Anomaly Detection","fullName":"Semi-supervised Autoencoder-based Anomaly Detection","aliases":["Semi-supervised AE anomaly detection","SSAD autoencoder","semi-supervised reconstruction-error detection","partially labeled autoencoder anomaly detection"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018–2020","originator":"Ruff, L. et al.; Zong, B. et al.","url":"https://scholargate.app/en/machine-learning/semi-supervised-autoencoder-anomaly-detection","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-autoencoder-anomaly-detection.md","definition":"Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ruff, L. et al.; Zong, B. et al.","year":"2018–2020","type":"Semi-supervised deep anomaly detection","dataType":"Continuous, high-dimensional, or sequential tabular/image/time-series data","subfamily":"Machine learning"},"citations":[{"ref":"Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=HkgH0TEYwH"},{"ref":"Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., & Chen, H. (2018). Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2018).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=BJJLHbb0-"}],"related":["autoencoder-anomaly-detection","one-class-svm","semi-supervised-learning","gaussian-mixture-model","isolation-forest","semi-supervised-one-class-svm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-bagging","name":"Semi-supervised Bagging","fullName":"Semi-supervised Bagging (Bootstrap Aggregating with Unlabeled Data)","aliases":["SS-Bagging","semi-supervised bootstrap aggregating","self-training bagging","bagging with pseudo-labels"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s","originator":"Various (Breiman bagging + semi-supervised extensions, 1990s–2000s)","url":"https://scholargate.app/en/machine-learning/semi-supervised-bagging","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-bagging.md","definition":"Semi-supervised Bagging extends the classical bagging ensemble to settings where labeled training examples are scarce but large amounts of unlabeled data are available. Base learners trained on labeled data assign pseudo-labels to unlabeled examples; the expanded dataset is then used to grow a diverse ensemble whose aggregated vote is more accurate and more stable than any single model trained on the limited labeled set alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Breiman bagging + semi-supervised extensions, 1990s–2000s)","year":"2000s","type":"Semi-supervised ensemble (bagging variant)","dataType":"Labeled and unlabeled tabular or feature-vector data","subfamily":"Machine learning"},"citations":[{"ref":"Bennett, K. P., & Demiriz, A. (1999). Semi-supervised support vector machines. Advances in Neural Information Processing Systems, 11. MIT Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-supervised+support+vector+machines+Bennett+Demiriz+1999"},{"ref":"Li, M., & Zhou, Z.-H. (2005). SETRED: Self-training with editing. In Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), LNAI 3518, pp. 611–621. Springer.","type":"inproceedings","doi":"10.1007/11430919_71","isbn":null,"url":null}],"related":["random-forest","self-training","label-propagation","co-training","gradient-boosting","semi-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-bert-based-classification","name":"Semi-supervised BERT-based Classification","fullName":"Semi-supervised BERT-based Text Classification","aliases":["Semi-supervised BERT","BERT SSL Classification","BERT with Unlabeled Data","BERT Semi-supervised Fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)","url":"https://scholargate.app/en/deep-learning/semi-supervised-bert-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-bert-based-classification.md","definition":"Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)","year":"2019–2020","type":"Semi-supervised fine-tuning of pre-trained transformer","dataType":"Text with few labeled and many unlabeled examples","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/44feb0096faa8326192570788b38c1d1-Abstract.html"},{"ref":"Chen, J., Yang, Z., & Yang, D. (2020). MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2147–2157.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.194","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","semi-supervised-transformer","weakly-supervised-bert-based-classification","self-supervised-bert-based-classification","fine-tuned-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-boosting","name":"Semi-supervised Boosting","fullName":"Semi-supervised Boosting (Boosting with Unlabeled Data)","aliases":["SemiBoost","SSL boosting","boosting with unlabeled data","semi-supervised ensemble boosting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1999–2009","originator":"Mallapragada, P. K.; Bennett, K. P.; and others","url":"https://scholargate.app/en/machine-learning/semi-supervised-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-boosting.md","definition":"Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mallapragada, P. K.; Bennett, K. P.; and others","year":"1999–2009","type":"Semi-supervised ensemble method","dataType":"Tabular; labeled + unlabeled instances","subfamily":"Machine learning"},"citations":[{"ref":"Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.","type":"article","doi":"10.1109/TPAMI.2008.235","isbn":null,"url":null},{"ref":"Bennett, K. P., & Demiriz, A. (1999). Semi-supervised Support Vector Machines. Advances in Neural Information Processing Systems (NIPS), 11, 368–374.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/1998/hash/b710915795b9e9c02cf10d6d2bdb688c-Abstract.html"}],"related":["adaboost","gradient-boosting","xgboost","label-propagation","self-training","semi-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-catboost","name":"Semi-supervised CatBoost","fullName":"Semi-supervised CatBoost (Gradient Boosting with Partially Labeled Data)","aliases":["SSL CatBoost","semi-supervised gradient boosting with CatBoost","CatBoost with unlabeled data","pseudo-label CatBoost"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018 (CatBoost); semi-supervised learning framework predates 2006","originator":"Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al.","url":"https://scholargate.app/en/machine-learning/semi-supervised-catboost","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-catboost.md","definition":"Semi-supervised CatBoost applies CatBoost's ordered gradient boosting framework to settings where only a fraction of training instances carry labels, leveraging unlabeled data through pseudo-labeling or consistency-based strategies to improve model accuracy beyond what labeled data alone would allow.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al.","year":"2018 (CatBoost); semi-supervised learning framework predates 2006","type":"Semi-supervised ensemble (gradient boosting)","dataType":"Tabular data, partially labeled (mixed labeled and unlabeled samples)","subfamily":"Machine learning"},"citations":[{"ref":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (NeurIPS), 31.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2018/hash/14491b756b3a51daac41c24863285549-Abstract.html"},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["semi-supervised-xgboost","semi-supervised-gradient-boosting","semi-supervised-random-forest","catboost","gradient-boosting","self-supervised-catboost"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-convolutional-neural-network","name":"Semi-supervised Convolutional Neural Network","fullName":"Semi-supervised Convolutional Neural Network (SSL-CNN)","aliases":["SSL-CNN","semi-supervised CNN","self-training CNN","pseudo-label CNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013–2017","originator":"Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)","url":"https://scholargate.app/en/deep-learning/semi-supervised-convolutional-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-convolutional-neural-network.md","definition":"A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others)","year":"2013–2017","type":"Semi-supervised deep learning","dataType":"Images (labeled + unlabeled)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pseudo-label+The+simple+and+efficient+semi-supervised+learning+method+for+deep+neural+networks"},{"ref":"Tarvainen, A. & Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in Neural Information Processing Systems (NeurIPS), 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2017/hash/68053af2923e00204c3ca7c6a3150cf7-Abstract.html"}],"related":["convolutional-neural-network","semi-supervised-image-classification","self-supervised-convolutional-neural-network","fine-tuned-convolutional-neural-network","transfer-learning-with-convolutional-neural-network","weakly-supervised-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-dbscan","name":"Semi-supervised DBSCAN","fullName":"Semi-supervised Density-Based Spatial Clustering of Applications with Noise","aliases":["Constrained DBSCAN","SS-DBSCAN","DBSCAN with must-link/cannot-link constraints","seeded DBSCAN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s","originator":"Ester, M. et al. (DBSCAN base); semi-supervised extensions by multiple authors (2000s–2010s)","url":"https://scholargate.app/en/machine-learning/semi-supervised-dbscan","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-dbscan.md","definition":"Semi-supervised DBSCAN extends the canonical density-based clustering algorithm (Ester et al., 1996) by incorporating a small set of pairwise or label constraints — must-link pairs that must share a cluster, cannot-link pairs that must be separated, or a handful of known labels — to guide cluster formation while retaining DBSCAN's ability to discover arbitrary-shaped clusters and flag noise points.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ester, M. et al. (DBSCAN base); semi-supervised extensions by multiple authors (2000s–2010s)","year":"2000s","type":"Constrained density-based clustering","dataType":"Unlabeled data with a small set of pairwise or label constraints","subfamily":"Machine learning"},"citations":[{"ref":"Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+density-based+algorithm+for+discovering+clusters+in+large+spatial+databases+with+noise"},{"ref":"Zhu, X., & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers.","type":"book","doi":null,"isbn":"978-1-59829-548-7","url":null}],"related":["dbscan","hdbscan","semi-supervised-k-means","semi-supervised-gaussian-mixture-model","k-means","constrained-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-decision-tree","name":"Semi-supervised Decision Tree","fullName":"Semi-supervised Decision Tree Learning","aliases":["SSDT","semi-supervised tree induction","self-training decision tree","label-propagation tree"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s","originator":"Various (Levin & Shapiro; Zhu & Goldberg lineage)","url":"https://scholargate.app/en/machine-learning/semi-supervised-decision-tree","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-decision-tree.md","definition":"A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Levin & Shapiro; Zhu & Goldberg lineage)","year":"2000s","type":"Semi-supervised classifier / regressor","dataType":"Tabular data with mixed labeled and unlabeled observations","subfamily":"Machine learning"},"citations":[{"ref":"Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Learning+Decision+Trees+from+Semi-labeled+Examples"},{"ref":"Zhu, X. & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers.","type":"book","doi":null,"isbn":"978-1-598-29548-9","url":null}],"related":["decision-tree","random-forest","self-training","label-propagation","co-training","gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-diffusion-model","name":"Semi-supervised Diffusion Model","fullName":"Semi-supervised Diffusion Model for Generative Learning with Partial Labels","aliases":["Semi-supervised DDPM","Label-guided diffusion model","Semi-supervised score-based generative model","SSL diffusion"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2022","originator":"Multiple groups (Ho et al., Song et al., and successors)","url":"https://scholargate.app/en/deep-learning/semi-supervised-diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-diffusion-model.md","definition":"A semi-supervised diffusion model extends the denoising diffusion probabilistic framework to settings where only a fraction of training samples carry class labels. By combining an unconditional diffusion backbone with a lightweight classifier trained on labeled examples, it learns to generate high-quality, label-conditioned outputs while still exploiting the structure in unlabeled data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups (Ho et al., Song et al., and successors)","year":"2020–2022","type":"Generative model with semi-supervised guidance","dataType":"Images, text, audio, or structured data with partial class labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2256–2265.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v37/sohl-dickstein15.html"},{"ref":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html"}],"related":["denoising-diffusion-probabilistic-model","score-based-generative-model","variational-autoencoder","semi-supervised-learning","conditional-diffusion-model","generative-adversarial-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-doc2vec","name":"Semi-supervised Doc2Vec","fullName":"Semi-supervised Paragraph Vector (Semi-supervised Doc2Vec)","aliases":["Semi-supervised Paragraph Vector","SS-Doc2Vec","Label-guided PV-DBOW","Semi-supervised PV-DM"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2017","originator":"Le, Q. V. & Mikolov, T. (base Doc2Vec); semi-supervised extensions by various authors circa 2015–2019","url":"https://scholargate.app/en/deep-learning/semi-supervised-doc2vec","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-doc2vec.md","definition":"Semi-supervised Doc2Vec extends the Paragraph Vector framework of Le and Mikolov (2014) by training dense document embeddings on both labeled and unlabeled corpora simultaneously, using available class labels as an auxiliary signal to steer the representation toward task-relevant structure while still exploiting the full unlabeled collection for generalization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Le, Q. V. & Mikolov, T. (base Doc2Vec); semi-supervised extensions by various authors circa 2015–2019","year":"2014–2017","type":"Semi-supervised representation learning","dataType":"Text (labeled + unlabeled documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), PMLR 32(2), 1188–1196.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v32/le14.html"},{"ref":"Word2vec. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Word2vec"}],"related":["doc2vec","word2vec","bert-fine-tuning","semi-supervised-text-classification","label-propagation","sentence-transformers"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-federated-learning","name":"Semi-supervised Federated learning","fullName":"Semi-supervised Federated Learning","aliases":["SSL-FL","federated semi-supervised learning","FSSL","semi-supervised distributed learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2020","originator":"Jeong, W. et al. / multiple independent groups","url":"https://scholargate.app/en/machine-learning/semi-supervised-federated-learning","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-federated-learning.md","definition":"Semi-supervised federated learning (SSFL) trains a shared model across many decentralized clients — each holding private data — when only a subset of clients or a subset of local samples carry labels. It combines the privacy-preserving coordination of federated learning with the label-efficiency of semi-supervised techniques such as pseudo-labeling and consistency regularization, enabling strong model quality without centralizing sensitive data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeong, W. et al. / multiple independent groups","year":"2020","type":"Distributed semi-supervised learning framework","dataType":"Partially labeled distributed tabular, image, or text data","subfamily":"Machine learning"},"citations":[{"ref":"Jeong, W., Yoon, J., Yang, E., & Hwang, S. J. (2020). Federated Semi-Supervised Learning with Inter-Client Consistency. International Conference on Learning Representations (ICLR 2021).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2006.12097"},{"ref":"Zhang, Z., Chen, Y., Yu, H., & Lu, J. (2021). SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling. arXiv preprint arXiv:2108.09412.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2108.09412"}],"related":["federated-learning","semi-supervised-learning","self-supervised-learning","transfer-learning","few-shot-learning","online-federated-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-few-shot-learning","name":"Semi-supervised Few-shot Learning","fullName":"Semi-supervised Few-shot Learning (SS-FSL)","aliases":["SS-FSL","semi-supervised meta-learning","few-shot learning with unlabeled data","low-label few-shot learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2018","originator":"Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017)","url":"https://scholargate.app/en/machine-learning/semi-supervised-few-shot-learning","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-few-shot-learning.md","definition":"Semi-supervised Few-shot Learning (SS-FSL) trains models to classify new classes from only a handful of labeled examples per class, while simultaneously leveraging a pool of unlabeled data to enrich class representations. By combining meta-learning episodes with soft pseudo-label assignment for unlabeled samples, it achieves notably higher accuracy than purely supervised few-shot methods when abundant unlabeled data is available.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017)","year":"2018","type":"Meta-learning with unlabeled auxiliary data","dataType":"Labeled support set (few examples per class) + unlabeled distractor/auxiliary examples","subfamily":"Machine learning"},"citations":[{"ref":"Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., & Zemel, R. S. (2018). Meta-learning for semi-supervised few-shot classification. In International Conference on Learning Representations (ICLR 2018).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1803.00676"},{"ref":"Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning (ICML 2017), PMLR 70, 1126–1135.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1703.03400"}],"related":["few-shot-learning","semi-supervised-learning","meta-learning","prototypical-networks","self-supervised-learning","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-fp-growth","name":"Semi-supervised FP-growth","fullName":"Semi-supervised Frequent Pattern Growth","aliases":["SS-FP-growth","constrained FP-growth","label-guided frequent pattern mining","semi-supervised frequent itemset mining"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Extensions of Han, Pei & Yin (2000); semi-supervised variants developed by various authors in the 2000s–2010s","url":"https://scholargate.app/en/machine-learning/semi-supervised-fp-growth","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-fp-growth.md","definition":"Semi-supervised FP-growth extends the classical Frequent Pattern growth algorithm by incorporating partial labels, user-defined constraints, or class-level information to guide frequent itemset discovery. Instead of mining all patterns indiscriminately, it focuses on patterns that are both statistically frequent and semantically meaningful given the available supervision signal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extensions of Han, Pei & Yin (2000); semi-supervised variants developed by various authors in the 2000s–2010s","year":"2000s–2010s","type":"Semi-supervised frequent pattern mining","dataType":"Transactional / categorical data with partial labels or constraints","subfamily":"Machine learning"},"citations":[{"ref":"Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 1–12.","type":"inproceedings","doi":"10.1145/342009.335372","isbn":null,"url":null},{"ref":"FP-growth algorithm. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Association_rule_learning#FP-growth_algorithm"}],"related":["fp-growth","apriori","constrained-association-rule-mining","semi-supervised-clustering","decision-tree","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-gan","name":"Semi-supervised GAN","fullName":"Semi-supervised Generative Adversarial Network","aliases":["SGAN","Semi-GAN","semi-supervised generative adversarial network","GAN-based semi-supervised learning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016","originator":"Odena, A.; Salimans, T. et al.","url":"https://scholargate.app/en/deep-learning/semi-supervised-gan","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-gan.md","definition":"Semi-supervised GAN (SGAN) extends the standard GAN discriminator to simultaneously classify labeled examples into K real classes and detect generated fakes as a (K+1)-th class, letting the generator's synthetic data act as implicit regularization and allowing strong classifiers to be trained with very few labeled examples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Odena, A.; Salimans, T. et al.","year":"2016","type":"Semi-supervised generative model","dataType":"Images, tabular, or text with small labeled and large unlabeled sets","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved Techniques for Training GANs. Advances in Neural Information Processing Systems (NeurIPS), 29.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2016/hash/8a3363abe792db2d8761d6403605aeb7-Abstract.html"},{"ref":"Odena, A. (2016). Semi-Supervised Learning with Generative Adversarial Networks. ICML Workshop on Generative Adversarial Networks.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-Supervised+Learning+with+Generative+Adversarial+Networks+Odena+2016"}],"related":["generative-adversarial-network","semi-supervised-learning","variational-autoencoder","semi-supervised-bert-based-classification","self-supervised-gan","conditional-gan"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-gaussian-mixture-model","name":"Semi-supervised Gaussian Mixture Model","fullName":"Semi-supervised Gaussian Mixture Model (SS-GMM)","aliases":["SS-GMM","semi-supervised GMM","partially labeled Gaussian mixture model","generative semi-supervised classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000","originator":"Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.","url":"https://scholargate.app/en/machine-learning/semi-supervised-gaussian-mixture-model","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-gaussian-mixture-model.md","definition":"The Semi-supervised Gaussian Mixture Model (SS-GMM) is a generative probabilistic classifier that fits a Gaussian mixture to both labeled and unlabeled data using the Expectation-Maximization algorithm. Labeled points constrain component assignments while unlabeled points improve density estimates, enabling effective learning when annotations are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.","year":"2000","type":"Generative semi-supervised classifier","dataType":"Labeled and unlabeled continuous data","subfamily":"Machine learning"},"citations":[{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null},{"ref":"Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3), 103-134.","type":"article","doi":"10.1023/A:1007692713085","isbn":null,"url":null}],"related":["gaussian-mixture-model","expectation-maximization","semi-supervised-learning","naive-bayes-classifier","label-propagation","variational-autoencoder"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-gaussian-process","name":"Semi-supervised Gaussian Process","fullName":"Semi-supervised Gaussian Process Regression and Classification","aliases":["SS-GP","semi-supervised GP","Gaussian process with unlabeled data","GP manifold learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2004","originator":"Lawrence, N. D. & Jordan, M. I.","url":"https://scholargate.app/en/machine-learning/semi-supervised-gaussian-process","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-gaussian-process.md","definition":"Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lawrence, N. D. & Jordan, M. I.","year":"2004","type":"Probabilistic model (semi-supervised)","dataType":"Continuous, binary, or multi-class; partially labeled tabular or structured data","subfamily":"Machine learning"},"citations":[{"ref":"Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2004/hash/96f2b50b5d3613adf9c27049b2a8040a-Abstract.html"},{"ref":"Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-18253-9","url":null}],"related":["gaussian-process","semi-supervised-learning","gaussian-mixture-model","semi-supervised-support-vector-machine","semi-supervised-random-forest","bayesian-gaussian-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-gradient-boosting","name":"Semi-supervised Gradient Boosting","fullName":"Semi-supervised Gradient Boosting (Self-training / Pseudo-labeling with Gradient Boosted Trees)","aliases":["pseudo-label gradient boosting","self-training GBM","semi-supervised GBT","label-propagation boosting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2006–2010s","originator":"Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature","url":"https://scholargate.app/en/machine-learning/semi-supervised-gradient-boosting","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-gradient-boosting.md","definition":"Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into training and the model is re-boosted, iterating until convergence. This allows practitioners to harness cheap unlabeled data when labels are scarce or expensive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature","year":"2006–2010s","type":"Semi-supervised ensemble (self-training + gradient boosted trees)","dataType":"Tabular (labeled + unlabeled)","subfamily":"Machine learning"},"citations":[{"ref":"Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Unsupervised+word+sense+disambiguation+rivaling+supervised+methods+Yarowsky+1995"},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["semi-supervised-learning","gradient-boosting","xgboost","self-supervised-learning","semi-supervised-random-forest","boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-graph-neural-network","name":"Semi-supervised Graph Neural Network","fullName":"Semi-supervised Graph Neural Network (GNN with Label Propagation)","aliases":["Semi-supervised GNN","GNN semi-supervised learning","graph-based semi-supervised classification","semi-supervised node classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017 (GCN formulation); 2004 (label propagation roots)","originator":"Kipf, T. N. & Welling, M. (canonical formulation); Zhou et al. (label propagation precursor)","url":"https://scholargate.app/en/deep-learning/semi-supervised-graph-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-graph-neural-network.md","definition":"A semi-supervised graph neural network trains a GNN on a graph where only a small fraction of nodes carry labels, using neighborhood message-passing to spread information from labeled nodes to unlabeled ones. The approach, popularised by Kipf and Welling's 2017 Graph Convolutional Network, achieves strong node-classification accuracy even when labeled examples are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kipf, T. N. & Welling, M. (canonical formulation); Zhou et al. (label propagation precursor)","year":"2017 (GCN formulation); 2004 (label propagation roots)","type":"Semi-supervised graph representation learning","dataType":"Graph-structured data with node features and a small fraction of labeled nodes","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR 2017).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1609.02907"},{"ref":"Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Scholkopf, B. (2004). Learning with Local and Global Consistency. Advances in Neural Information Processing Systems (NeurIPS 2004), 17.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Learning+with+Local+and+Global+Consistency+Zhou+2004"}],"related":["graph-neural-network","graph-convolutional-network","semi-supervised-learning","label-propagation","self-supervised-graph-neural-network","node-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-gru","name":"Semi-supervised GRU","fullName":"Semi-supervised Gated Recurrent Unit","aliases":["Semi-supervised GRU","SSL-GRU","GRU with unlabeled data","semi-supervised recurrent classifier"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2015","originator":"Dai, A. M. & Le, Q. V. (semi-supervised sequence learning); Cho, K. et al. (GRU architecture)","url":"https://scholargate.app/en/deep-learning/semi-supervised-gru","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-gru.md","definition":"Semi-supervised GRU applies the Gated Recurrent Unit architecture to settings where only a small fraction of sequential data is labeled. By first pre-training or jointly training on abundant unlabeled sequences — through language modeling, auto-encoding, or consistency regularization — and then fine-tuning on labeled examples, the model exploits the full corpus to learn richer sequence representations than supervised-only training would allow.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dai, A. M. & Le, Q. V. (semi-supervised sequence learning); Cho, K. et al. (GRU architecture)","year":"2014–2015","type":"Semi-supervised sequence model","dataType":"Sequential / time-series / text data (small labeled + large unlabeled)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Dai, A. M., & Le, Q. V. (2015). Semi-supervised Sequence Learning. Advances in Neural Information Processing Systems (NeurIPS), 28.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2015/hash/7137debd45ae4d0ab9aa953017286b20-Abstract.html"},{"ref":"Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. EMNLP 2014.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Learning+Phrase+Representations+using+RNN+Encoder-Decoder+for+Statistical+Machine+Translation"}],"related":["gated-recurrent-unit","semi-supervised-lstm","semi-supervised-transformer","semi-supervised-recurrent-neural-network","long-short-term-memory","self-supervised-gru"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-hdbscan","name":"Semi-supervised HDBSCAN","fullName":"Semi-supervised Hierarchical Density-Based Spatial Clustering of Applications with Noise","aliases":["Constrained HDBSCAN","Semi-supervised hierarchical density clustering","HDBSCAN with partial labels","SS-HDBSCAN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017–present","originator":"McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors","url":"https://scholargate.app/en/machine-learning/semi-supervised-hdbscan","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-hdbscan.md","definition":"Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors","year":"2017–present","type":"Semi-supervised density-based clustering","dataType":"Continuous or mixed tabular data; optional partial cluster labels or pairwise constraints","subfamily":"Machine learning"},"citations":[{"ref":"McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.","type":"article","doi":"10.21105/joss.00205","isbn":null,"url":null},{"ref":"HDBSCAN. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/DBSCAN#Extensions"}],"related":["hdbscan","semi-supervised-dbscan","semi-supervised-k-means","semi-supervised-gaussian-mixture-model","dbscan","k-means"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-image-classification","name":"Semi-supervised Image Classification","fullName":"Semi-supervised Image Classification with Deep Neural Networks","aliases":["SSL image classification","semi-supervised CNN classification","pseudo-label image classification","label-efficient image classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013–2020","originator":"Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)","url":"https://scholargate.app/en/deep-learning/semi-supervised-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-image-classification.md","definition":"Semi-supervised image classification trains deep neural networks on a small set of labeled images together with a much larger pool of unlabeled images. Techniques such as pseudo-labeling, consistency regularization, and confidence thresholding allow the model to leverage the structure of unlabeled data, dramatically reducing the need for expensive manual annotation while approaching fully-supervised accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch)","year":"2013–2020","type":"Semi-supervised deep learning","dataType":"Images (labeled + unlabeled)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lee, D.-H. (2013). Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks. ICML 2013 Workshop on Challenges in Representation Learning.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pseudo-Label+The+Simple+and+Efficient+Semi-Supervised+Learning+Method+for+Deep+Neural+Networks"},{"ref":"Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020). FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. Advances in Neural Information Processing Systems, 33, 596–608.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2001.07685"}],"related":["image-classification","self-supervised-image-classification","weakly-supervised-image-classification","fine-tuned-image-classification","convolutional-neural-network","transfer-learning-with-image-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-instance-segmentation","name":"Semi-supervised Instance Segmentation","fullName":"Semi-supervised Instance Segmentation","aliases":["Semi-supervised Mask R-CNN","pseudo-label instance segmentation","label-efficient instance segmentation","SSIS"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2021","originator":"Multiple independent research groups (2018–2021)","url":"https://scholargate.app/en/deep-learning/semi-supervised-instance-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-instance-segmentation.md","definition":"Semi-supervised instance segmentation trains a model to detect and delineate every object instance in an image using a small labeled set and a large unlabeled image corpus. By generating pseudo-labels from confident predictions on unlabeled images and enforcing consistency under augmentation, the approach achieves competitive mask accuracy at a fraction of the full annotation cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple independent research groups (2018–2021)","year":"2018–2021","type":"Semi-supervised deep learning for dense prediction","dataType":"Images with a small labeled set and a large unlabeled set","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Hu, H., Wei, P., Zheng, H., Bai, X., Wei, Y., & Chen, Y. (2021). Semi-supervised Semantic Segmentation via Adaptive Equalization Learning. Advances in Neural Information Processing Systems (NeurIPS), 34, 22106–22118.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-supervised+Semantic+Segmentation+via+Adaptive+Equalization+Learning+NeurIPS+2021"},{"ref":"Xu, M., Zhang, Z., Wei, F., Hu, H., Bai, X., & Jiang, Y.-G. (2021). End-to-End Semi-Supervised Object Detection with Soft Teacher. IEEE/CVF International Conference on Computer Vision (ICCV), 3060–3069.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=End-to-End+Semi-Supervised+Object+Detection+with+Soft+Teacher+ICCV+2021"}],"related":["instance-segmentation","semantic-segmentation","semi-supervised-object-detection","semi-supervised-convolutional-neural-network","weakly-supervised-instance-segmentation","self-supervised-vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-isolation-forest","name":"Semi-supervised Isolation Forest","fullName":"Semi-supervised Isolation Forest for Anomaly Detection","aliases":["SSIF","semi-supervised iForest","label-guided Isolation Forest","partially supervised Isolation Forest"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2013–2020","originator":"Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020","url":"https://scholargate.app/en/machine-learning/semi-supervised-isolation-forest","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-isolation-forest.md","definition":"Semi-supervised Isolation Forest extends the classic Isolation Forest anomaly detector by incorporating a small set of labeled anomaly (and possibly normal) examples alongside a large unlabeled dataset. This label guidance adjusts the model's anomaly scores so that known anomalies are separated more reliably, bridging the gap between fully unsupervised and fully supervised detection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from Liu, F.T., Ting, K.M., and Zhou, Z-H. (iForest, 2008); semi-supervised variants developed by multiple authors ca. 2013–2020","year":"2013–2020","type":"Ensemble anomaly detection (semi-supervised extension)","dataType":"Tabular, mixed; small set of labeled anomalies + large unlabeled pool","subfamily":"Machine learning"},"citations":[{"ref":"Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013). Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46, 235–262.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Toward+supervised+anomaly+detection+G%C3%B6rnitz"},{"ref":"Isolation Forest. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Isolation_forest"}],"related":["isolation-forest","one-class-svm","local-outlier-factor","random-forest","autoencoder-anomaly-detection","semi-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-k-means","name":"Semi-supervised K-means","fullName":"Semi-supervised K-means Clustering","aliases":["constrained K-means","seeded K-means","partially supervised K-means","SS-K-means"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001–2002","originator":"Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)","url":"https://scholargate.app/en/machine-learning/semi-supervised-k-means","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-k-means.md","definition":"Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)","year":"2001–2002","type":"Semi-supervised clustering","dataType":"Numerical (continuous); partial cluster labels or pairwise constraints","subfamily":"Machine learning"},"citations":[{"ref":"Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Constrained+K-means+Clustering+with+Background+Knowledge+Wagstaff+2001"},{"ref":"Basu, S., Banerjee, A., & Mooney, R. J. (2002). Semi-supervised Clustering by Seeding. In Proceedings of the 19th International Conference on Machine Learning (ICML 2002), pp. 27–34.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-supervised+Clustering+by+Seeding+Basu+Banerjee+Mooney+2002"}],"related":["k-means","semi-supervised-learning","gaussian-mixture-model","dbscan","active-learning","spectral-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-k-nearest-neighbors","name":"Semi-supervised K-nearest neighbors","fullName":"Semi-supervised K-Nearest Neighbors (Label Propagation via KNN Graph)","aliases":["SS-KNN","semi-supervised KNN","KNN label propagation","graph-based semi-supervised KNN"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2002 (semi-supervised extension); 1967 (KNN base)","originator":"Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)","url":"https://scholargate.app/en/machine-learning/semi-supervised-k-nearest-neighbors","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-k-nearest-neighbors.md","definition":"Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)","year":"2002 (semi-supervised extension); 1967 (KNN base)","type":"Semi-supervised classifier / label propagation","dataType":"Tabular features; requires a small labeled set and a larger unlabeled set","subfamily":"Machine learning"},"citations":[{"ref":"Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Learning+from+labeled+and+unlabeled+data+with+label+propagation+Zhu+Ghahramani+2002"},{"ref":"Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["k-nearest-neighbors","semi-supervised-learning","label-propagation","semi-supervised-gaussian-process","semi-supervised-support-vector-machine","graph-based-semi-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-lda-topic-model","name":"Semi-supervised LDA Topic Model","fullName":"Semi-supervised Latent Dirichlet Allocation Topic Model","aliases":["Labeled LDA","Seeded LDA","Constrained LDA","SS-LDA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2009","originator":"Ramage, D.; Andrzejewski, D. et al.","url":"https://scholargate.app/en/deep-learning/semi-supervised-lda-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-lda-topic-model.md","definition":"Semi-supervised LDA extends standard Latent Dirichlet Allocation by incorporating a small amount of supervision — seed words, labeled documents, or must-link/cannot-link word constraints — to guide topic discovery toward semantically coherent, interpretable themes. It bridges unsupervised topic modeling and fully supervised text classification, making it especially valuable when full annotation is costly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ramage, D.; Andrzejewski, D. et al.","year":"2009","type":"Semi-supervised probabilistic topic model","dataType":"Text corpora (partially labeled documents or seed words)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of EMNLP, 248–256.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D09-1026"},{"ref":"Andrzejewski, D., Zhu, X., & Craven, M. (2009). Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. Proceedings of ICML, 25–32.","type":"inproceedings","doi":"10.1145/1553374.1553378","isbn":null,"url":null}],"related":["lda-topic-model","semi-supervised-nmf-topic-model","topic-modeling","bert-based-classification","semi-supervised-transformer","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-learning","name":"Semi-supervised Learning","fullName":"Semi-supervised Learning (Combined Labeled and Unlabeled Data Training)","aliases":["SSL","semi-supervised machine learning","transductive learning","label-efficient learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1970s–2006 (formalized)","originator":"Vapnik, V. N. and others (community of researchers, 1970s–2000s)","url":"https://scholargate.app/en/machine-learning/semi-supervised-learning","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-learning.md","definition":"Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vapnik, V. N. and others (community of researchers, 1970s–2000s)","year":"1970s–2006 (formalized)","type":"Learning paradigm","dataType":"Tabular, image, text, or sequential data with a mix of labeled and unlabeled instances","subfamily":"Machine learning"},"citations":[{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null},{"ref":"Zhu, X. (2005). Semi-supervised learning literature survey. Technical Report 1530, University of Wisconsin-Madison.","type":"article","doi":null,"isbn":null,"url":"https://pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf"}],"related":["self-supervised-learning","transfer-learning","active-learning","few-shot-learning","semi-supervised-random-forest","gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-lightgbm","name":"Semi-supervised LightGBM","fullName":"Semi-supervised Learning with Light Gradient Boosting Machine","aliases":["SSL-LightGBM","pseudo-label LightGBM","self-training LightGBM","semi-supervised GBDT"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2017–2019","originator":"Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research","url":"https://scholargate.app/en/machine-learning/semi-supervised-lightgbm","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-lightgbm.md","definition":"Semi-supervised LightGBM combines LightGBM's highly efficient gradient boosting framework with semi-supervised strategies — most commonly pseudo-labeling or self-training — to exploit large pools of unlabeled data alongside a smaller labeled set, improving predictive performance when obtaining labels is costly or time-consuming.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ke, G. et al. (LightGBM); semi-supervised extension via community practice and research","year":"2017–2019","type":"Semi-supervised gradient boosting ensemble","dataType":"Tabular (labeled and unlabeled instances)","subfamily":"Machine learning"},"citations":[{"ref":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html"},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["semi-supervised-gradient-boosting","semi-supervised-xgboost","semi-supervised-random-forest","self-training","lightgbm","pseudo-labeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-linear-regression","name":"Semi-supervised Linear Regression","fullName":"Semi-supervised Linear Regression (Linear Model with Labeled and Unlabeled Data)","aliases":["SSL linear regression","semi-supervised least squares","transductive linear regression","label-efficient linear regression"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2005–2006","originator":"Chapelle, O.; Scholkopf, B.; Zien, A. (seminal synthesis); Zhou & Li (co-training formulation)","url":"https://scholargate.app/en/machine-learning/semi-supervised-linear-regression","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-linear-regression.md","definition":"Semi-supervised linear regression fits a linear model on a small labeled dataset and then leverages a larger pool of unlabeled observations to improve coefficient estimates and generalization. By generating pseudo-labels for unlabeled points and iteratively refining the model, it achieves better predictive accuracy than a purely supervised linear model trained on scarce labels alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chapelle, O.; Scholkopf, B.; Zien, A. (seminal synthesis); Zhou & Li (co-training formulation)","year":"2005–2006","type":"Semi-supervised regression model","dataType":"Mixed labeled and unlabeled continuous/tabular data","subfamily":"Machine learning"},"citations":[{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null},{"ref":"Zhou, Z.-H., & Li, M. (2005). Semi-supervised regression with co-training. Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI), 908–913.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-supervised+regression+with+co-training+Zhou+Li+2005"}],"related":["linear-regression-ml","semi-supervised-learning","self-supervised-linear-regression","regularized-linear-regression","label-propagation","expectation-maximization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-logistic-regression","name":"Semi-supervised Logistic Regression","fullName":"Semi-supervised Logistic Regression (Self-training and EM-based variants)","aliases":["SSL logistic regression","semi-supervised LR","EM logistic regression","self-training logistic classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1995–2000","originator":"Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)","url":"https://scholargate.app/en/machine-learning/semi-supervised-logistic-regression","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-logistic-regression.md","definition":"Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)","year":"1995–2000","type":"Semi-supervised classifier","dataType":"Labeled and unlabeled tabular or text data","subfamily":"Machine learning"},"citations":[{"ref":"Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.","type":"inproceedings","doi":"10.1023/a:1007692713085","isbn":null,"url":null},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["logistic-regression-ml","semi-supervised-learning","self-supervised-logistic-regression","semi-supervised-naive-bayes","label-propagation","support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-lstm","name":"Semi-supervised LSTM","fullName":"Semi-supervised Long Short-Term Memory Network","aliases":["SSL-LSTM","semi-supervised sequence model","LSTM with unlabeled data","pseudo-label LSTM"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2018","originator":"Hochreiter, S. & Schmidhuber, J. (LSTM); semi-supervised extensions by various authors (2015–2020)","url":"https://scholargate.app/en/deep-learning/semi-supervised-lstm","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-lstm.md","definition":"Semi-supervised LSTM combines the sequential memory of Long Short-Term Memory networks with semi-supervised learning strategies — using a small labeled dataset alongside a large pool of unlabeled sequences. The model is pretrained or regularized on unlabeled data, then fine-tuned on labeled examples, delivering strong generalization when labeled data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hochreiter, S. & Schmidhuber, J. (LSTM); semi-supervised extensions by various authors (2015–2020)","year":"2015–2018","type":"Semi-supervised sequence model","dataType":"Sequential / temporal data (text, time series, sensor streams); small labeled set + large unlabeled set","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.","type":"article","doi":"10.1162/neco.1997.9.8.1735","isbn":null,"url":null},{"ref":"Rasmus, A., Berglund, M., Honkala, M., Valpola, H., & Raiko, T. (2015). Semi-supervised learning with ladder networks. Advances in Neural Information Processing Systems, 28.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2015/hash/0ebcc77dc72360d0eb8e9504c78d38bd-Abstract.html"}],"related":["lstm","bidirectional-lstm","self-training","variational-autoencoder","transformer","semi-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-metric-learning","name":"Semi-supervised Metric Learning","fullName":"Semi-supervised Metric Learning","aliases":["SSML","semi-supervised distance learning","constrained metric learning","weakly supervised metric learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2007–2008","originator":"Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S.","url":"https://scholargate.app/en/machine-learning/semi-supervised-metric-learning","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-metric-learning.md","definition":"Semi-supervised metric learning learns a task-adapted distance function by combining a small set of labeled pairwise constraints — must-link and cannot-link pairs — with the geometric structure of a much larger pool of unlabeled data. The result is a Mahalanobis-style or kernel-based distance that reflects both supervision and data topology, improving downstream tasks such as nearest-neighbor classification and clustering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S.","year":"2007–2008","type":"Hybrid supervised/unsupervised distance learning","dataType":"Tabular or embedding vectors; partially labeled","subfamily":"Machine learning"},"citations":[{"ref":"Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149.","type":"article","doi":"10.1109/TNN.2006.883723","isbn":null,"url":null},{"ref":"Davis, J. V., & Dhillon, I. S. (2008). Structured metric learning for high dimensional problems. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 195–203.","type":"inproceedings","doi":"10.1145/1401890.1401918","isbn":null,"url":null}],"related":["metric-learning","semi-supervised-learning","k-nearest-neighbors","few-shot-learning","self-supervised-learning","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-multilayer-perceptron","name":"Semi-supervised Multilayer Perceptron","fullName":"Semi-supervised Multilayer Perceptron (SSL-MLP)","aliases":["SSL-MLP","semi-supervised MLP","semi-supervised feedforward network","partially supervised multilayer perceptron"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2006–2013","originator":"Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.","url":"https://scholargate.app/en/deep-learning/semi-supervised-multilayer-perceptron","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-multilayer-perceptron.md","definition":"A semi-supervised multilayer perceptron (SSL-MLP) is a feedforward neural network trained on a small pool of labeled examples together with a larger pool of unlabeled examples. By combining supervised cross-entropy loss on labeled data with an unsupervised consistency or pseudo-label objective on unlabeled data, it extracts far more signal from the data than a purely supervised MLP trained on labels alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chapelle, O.; Scholkopf, B.; Zien, A. (eds.); Lee, D.-H.","year":"2006–2013","type":"Semi-supervised feedforward neural network","dataType":"Tabular, text, image (mixed labeled and unlabeled)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null},{"ref":"Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML 2013 Workshop on Challenges in Representation Learning.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pseudo-label+The+simple+and+efficient+semi-supervised+learning+method+for+deep+neural+networks"}],"related":["semi-supervised-convolutional-neural-network","semi-supervised-lstm","self-supervised-multilayer-perceptron","fine-tuned-multilayer-perceptron","weakly-supervised-multilayer-perceptron","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-naive-bayes","name":"Semi-supervised Naive Bayes","fullName":"Semi-supervised Naive Bayes (EM-augmented Generative Classifier)","aliases":["SSL Naive Bayes","EM-Naive Bayes","semi-supervised generative classifier","Nigam et al. text classifier"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000","originator":"Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.","url":"https://scholargate.app/en/machine-learning/semi-supervised-naive-bayes","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-naive-bayes.md","definition":"Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially better classifiers when labeled examples are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.","year":"2000","type":"Semi-supervised generative classifier","dataType":"Labeled and unlabeled tabular or text data","subfamily":"Machine learning"},"citations":[{"ref":"Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2–3), 103–134.","type":"inproceedings","doi":"10.1023/A:1007692713085","isbn":null,"url":null},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["naive-bayes","semi-supervised-learning","expectation-maximization","logistic-regression","semi-supervised-support-vector-machine","gaussian-mixture-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-nmf-topic-model","name":"Semi-supervised NMF Topic Model","fullName":"Semi-supervised Non-negative Matrix Factorization Topic Model","aliases":["SS-NMF","guided NMF","constrained NMF topic model","seed-guided NMF"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2001 (NMF); semi-supervised variants from ~2010s","originator":"Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others","url":"https://scholargate.app/en/deep-learning/semi-supervised-nmf-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-nmf-topic-model.md","definition":"Semi-supervised Non-negative Matrix Factorization (NMF) Topic Model extends unsupervised NMF by incorporating user-provided seed words or label constraints to steer discovered topics toward domain-relevant themes. It factorizes a document-term matrix into interpretable non-negative components while respecting lexical priors, yielding coherent, application-aligned topics even from modest corpora.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others","year":"2001 (NMF); semi-supervised variants from ~2010s","type":"Matrix factorization with supervision","dataType":"Document-term matrices; text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.","type":"article","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2000/hash/f9d1152547c0bde01830b7e8bd60024c-Abstract.html"},{"ref":"Jagarlamudi, J., Daume, H., & Udupa, R. (2012). Incorporating lexical priors into topic models. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), 204–213.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/E12-1021"}],"related":["nmf-topic-model","lda-topic-model","semi-supervised-lda-topic-model","topic-modeling","sentence-embeddings","semi-supervised-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-object-detection","name":"Semi-supervised Object Detection","fullName":"Semi-supervised Object Detection (Pseudo-label / Mean-Teacher Paradigm)","aliases":["SSOD","semi-supervised detection","pseudo-label object detection","label-efficient object detection"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2021","originator":"Sohn et al. (STAC); Liu et al. (Unbiased Teacher)","url":"https://scholargate.app/en/deep-learning/semi-supervised-object-detection","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-object-detection.md","definition":"Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden while achieving accuracy competitive with fully supervised baselines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sohn et al. (STAC); Liu et al. (Unbiased Teacher)","year":"2020–2021","type":"Semi-supervised learning for detection","dataType":"Images with a small labeled subset and a large unlabeled subset","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv preprint arXiv:2005.04757.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2005.04757"},{"ref":"Liu, Y.-C., Ma, C.-Y., He, Z., Kuo, C.-W., Chen, K., Zhang, P., Wu, B., Kira, Z., & Vajda, P. (2021). Unbiased Teacher for Semi-Supervised Object Detection. ICLR 2021.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2102.09480"}],"related":["object-detection","semi-supervised-image-classification","semi-supervised-convolutional-neural-network","transfer-learning-with-object-detection","weakly-supervised-object-detection","instance-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-one-class-svm","name":"Semi-supervised One-class SVM","fullName":"Semi-supervised One-Class Support Vector Machine","aliases":["SS-OCSVM","semi-supervised OC-SVM","semi-supervised novelty detection SVM","transductive one-class SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2001–2004","originator":"Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010","url":"https://scholargate.app/en/machine-learning/semi-supervised-one-class-svm","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-one-class-svm.md","definition":"Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010","year":"2001–2004","type":"Semi-supervised anomaly / novelty detection","dataType":"Continuous/numeric features; labeled normal examples plus unlabeled pool","subfamily":"Machine learning"},"citations":[{"ref":"Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-Supervised+One-Class+Support+Vector+Machines"},{"ref":"Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.","type":"article","doi":"10.1162/089976601750264965","isbn":null,"url":null}],"related":["one-class-svm","semi-supervised-learning","support-vector-machine","isolation-forest","autoencoder-anomaly-detection","gaussian-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-online-learning","name":"Semi-supervised Online Learning","fullName":"Semi-supervised Online Learning (Incremental Learning with Partially Labeled Streams)","aliases":["SSOL","online semi-supervised learning","semi-supervised incremental learning","streaming semi-supervised learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Goldberg, A.; Li, M.; Zhu, X. (among key contributors)","url":"https://scholargate.app/en/machine-learning/semi-supervised-online-learning","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-online-learning.md","definition":"Semi-supervised Online Learning combines the incremental update style of online learning with the ability to exploit unlabeled examples, enabling models to improve continuously from a data stream in which only a small fraction of arriving instances carry ground-truth labels. It is especially valuable when labeling is expensive or delayed but data arrives in real time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goldberg, A.; Li, M.; Zhu, X. (among key contributors)","year":"2000s–2010s","type":"Hybrid learning paradigm (online + semi-supervised)","dataType":"Sequential, partially labeled data streams","subfamily":"Machine learning"},"citations":[{"ref":"Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Lecture Notes in Computer Science, 5211, 393–407. Springer.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+manifold+regularization+a+new+learning+setting+Goldberg+Li+Zhu+2008"},{"ref":"Zhu, X., & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers.","type":"book","doi":null,"isbn":"978-1-59829-548-3","url":null}],"related":["semi-supervised-learning","online-learning","self-training","label-propagation","active-learning","incremental-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-question-answering","name":"Semi-supervised Question Answering","fullName":"Semi-supervised Question Answering (Self-Training and Consistency-Based NLP)","aliases":["Semi-supervised QA","Self-training for QA","Pseudo-labeled Question Answering","SSL-QA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2006–2020","originator":"Multiple (Chapelle et al.; Zhu; Clark et al. for NLP applications)","url":"https://scholargate.app/en/deep-learning/semi-supervised-question-answering","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-question-answering.md","definition":"Semi-supervised question answering (QA) trains a model on a small labeled set of question-answer pairs, then generates pseudo-labels on a large unlabeled corpus and retrains iteratively. This self-training loop dramatically increases effective training data without the cost of full manual annotation, achieving strong performance on reading comprehension, open-domain QA, and machine reading tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (Chapelle et al.; Zhu; Clark et al. for NLP applications)","year":"2006–2020","type":"Semi-supervised learning applied to extractive/generative QA","dataType":"Text (question-context-answer triples; labeled + unlabeled corpora)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In Proceedings of ICLR 2020.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=r1xMH1BtvB"},{"ref":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. In Advances in Neural Information Processing Systems (NeurIPS 2019).","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=XLNet+Generalized+Autoregressive+Pretraining+for+Language+Understanding"}],"related":["semi-supervised-bert-based-classification","fine-tuned-question-answering","weakly-supervised-question-answering","self-supervised-question-answering","bert-based-classification","semi-supervised-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-random-forest","name":"Semi-supervised Random Forest","fullName":"Semi-supervised Random Forest (SSL-RF)","aliases":["SSL-RF","semi-supervised forest","label-propagation random forest","self-training random forest"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2009","originator":"Leistner, C., Saffari, A., Santner, J., & Bischof, H.","url":"https://scholargate.app/en/machine-learning/semi-supervised-random-forest","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-random-forest.md","definition":"Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leistner, C., Saffari, A., Santner, J., & Bischof, H.","year":"2009","type":"Semi-supervised ensemble classifier","dataType":"Tabular, image-derived, or mixed; partially labeled datasets","subfamily":"Machine learning"},"citations":[{"ref":"Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE.","type":"inproceedings","doi":"10.1109/ICCV.2009.5459198","isbn":null,"url":null},{"ref":"Zhu, X. (2005). Semi-supervised learning literature survey. Computer Sciences Technical Report 1530, University of Wisconsin-Madison.","type":"article","doi":null,"isbn":null,"url":"https://pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf"}],"related":["random-forest","self-training","label-propagation","semi-supervised-svm","co-training","gradient-boosting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-reinforcement-learning","name":"Semi-supervised Reinforcement Learning","fullName":"Semi-supervised Reinforcement Learning (SSRL)","aliases":["SSRL","semi-supervised RL","RL with unlabeled data","label-efficient reinforcement learning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020s","originator":"Multiple contributors (Laskin, Srinivas, Abbeel et al.)","url":"https://scholargate.app/en/deep-learning/semi-supervised-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-reinforcement-learning.md","definition":"Semi-supervised reinforcement learning (SSRL) combines standard reinforcement learning — where an agent learns from sparse reward signals — with semi-supervised techniques that extract structure from unlabeled environment interactions. The goal is to improve sample efficiency and generalization when reward feedback is costly, delayed, or available only for a fraction of the agent's experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Laskin, Srinivas, Abbeel et al.)","year":"2020s","type":"Semi-supervised training paradigm for RL agents","dataType":"Environment interactions (reward-labeled and unlabeled transitions)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Zhan, X., Zhu, X., & Shi, H. (2022). Deepthermal: Combustion optimization for thermal power generating units using offline reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4680–4688.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semi-supervised+Reinforcement+Learning+with+Unlabeled+Data"},{"ref":"Laskin, M., Srinivas, A., & Abbeel, P. (2020). CURL: Contrastive Unsupervised Representations for Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 5639–5650.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v119/laskin20a.html"}],"related":["reinforcement-learning","self-supervised-reinforcement-learning","transfer-learning-reinforcement-learning","semi-supervised-transformer","weakly-supervised-reinforcement-learning","domain-adaptive-reinforcement-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-roberta-based-classification","name":"Semi-supervised RoBERTa-based Classification","fullName":"Semi-supervised RoBERTa-based Text Classification","aliases":["Semi-supervised RoBERTa","RoBERTa with semi-supervised learning","SSL-RoBERTa classification","RoBERTa pseudo-label classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Liu et al. (RoBERTa, 2019); semi-supervised adaptation by the NLP community","url":"https://scholargate.app/en/deep-learning/semi-supervised-roberta-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-roberta-based-classification.md","definition":"Semi-supervised RoBERTa-based classification combines a large pretrained RoBERTa language model with both a small labeled dataset and a larger pool of unlabeled text. By generating pseudo-labels or enforcing consistency on unlabeled examples, the method extracts supervisory signal from unannotated data, yielding stronger classifiers when ground-truth annotations are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu et al. (RoBERTa, 2019); semi-supervised adaptation by the NLP community","year":"2019–2020","type":"Semi-supervised fine-tuning of a pretrained language model","dataType":"Text (labeled and unlabeled corpora)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.11692"},{"ref":"Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., & Le, Q. V. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 11904–11915.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1904.12848"}],"related":["roberta-based-classification","bert-based-classification","semi-supervised-transformer","semi-supervised-bert-based-classification","fine-tuned-roberta-based-classification","weakly-supervised-roberta-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-semantic-segmentation","name":"Semi-supervised Semantic Segmentation","fullName":"Semi-supervised Semantic Segmentation (Pseudo-label and Consistency-based)","aliases":["Semi-SSL segmentation","pseudo-label segmentation","consistency regularization segmentation","label-efficient semantic segmentation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2020","originator":"Multiple (Ouali et al., Zou et al., Chen et al.)","url":"https://scholargate.app/en/deep-learning/semi-supervised-semantic-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-semantic-segmentation.md","definition":"Semi-supervised semantic segmentation trains pixel-level labeling models using a small set of fully labeled images combined with a much larger set of unlabeled images. Techniques such as pseudo-labeling and consistency regularization extract supervisory signal from unlabeled data, making it possible to achieve near-fully-supervised accuracy at a fraction of the annotation cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (Ouali et al., Zou et al., Chen et al.)","year":"2018–2020","type":"Semi-supervised deep learning for pixel-level classification","dataType":"Images (labeled + unlabeled)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ouali, Y., Hudelot, C., & Tami, M. (2020). Semi-Supervised Semantic Segmentation with Cross-Consistency Training. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12674–12684.","type":"inproceedings","doi":"10.1109/CVPR42600.2020.01269","isbn":null,"url":null},{"ref":"Zou, Y., Zhang, Z., Zhang, H., Li, C.-L., Bian, X., Huang, J.-B., & Pfister, T. (2020). PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. International Conference on Learning Representations (ICLR 2021).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2010.09713"}],"related":["semantic-segmentation","semi-supervised-convolutional-neural-network","self-supervised-semantic-segmentation","weakly-supervised-semantic-segmentation","instance-segmentation","transfer-learning-with-semantic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-sentence-embeddings","name":"Semi-supervised Sentence Embeddings","fullName":"Semi-supervised Sentence Embeddings (Contrastive and Self-training Approaches)","aliases":["Semi-supervised SimCSE","Self-training sentence encoders","Pseudo-labeled sentence representation learning","SSL sentence embeddings"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2021","originator":"Gao, T.; Reimers, N. et al. (multiple contributors)","url":"https://scholargate.app/en/deep-learning/semi-supervised-sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-sentence-embeddings.md","definition":"Semi-supervised sentence embeddings combine a small set of labeled sentence pairs with large quantities of unlabeled text to train dense vector representations of sentences. By exploiting abundant unlabeled data through contrastive objectives or pseudo-labeling, these models produce high-quality embeddings for semantic similarity, retrieval, and classification even when annotated data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gao, T.; Reimers, N. et al. (multiple contributors)","year":"2019–2021","type":"Semi-supervised representation learning","dataType":"Unlabeled and partially labeled text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Proceedings of EMNLP 2021 (pp. 6894–6910). Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/2021.emnlp-main.552","isbn":null,"url":null},{"ref":"Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of EMNLP-IJCNLP 2019 (pp. 3982–3992). Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/D19-1410","isbn":null,"url":null}],"related":["sentence-embeddings","semi-supervised-bert-based-classification","self-supervised-sentence-embeddings","semi-supervised-transformer","bert-based-classification","semi-supervised-named-entity-recognition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-sentiment-analysis","name":"Semi-supervised Sentiment Analysis","fullName":"Semi-supervised Sentiment Analysis (Label Propagation and Self-Training for Opinion Mining)","aliases":["SSSA","semi-supervised opinion mining","label-propagation sentiment classification","self-training sentiment analysis"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2002–2008","originator":"Zhu, X.; Pang, B. & Lee, L. (foundational works)","url":"https://scholargate.app/en/deep-learning/semi-supervised-sentiment-analysis","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-sentiment-analysis.md","definition":"Semi-supervised sentiment analysis combines a small set of manually labeled text samples with a large pool of unlabeled text to train opinion classifiers. By propagating sentiment signals from labeled seeds to unlabeled data through self-training, label propagation, or consistency regularization, the approach achieves competitive accuracy without the cost of labeling large corpora.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhu, X.; Pang, B. & Lee, L. (foundational works)","year":"2002–2008","type":"Semi-supervised classification","dataType":"Text (partially labeled corpora)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Zhu, X. (2005). Semi-Supervised Learning Literature Survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison.","type":"article","doi":null,"isbn":null,"url":"https://pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf"},{"ref":"Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.","type":"article","doi":"10.1561/1500000011","isbn":null,"url":null}],"related":["semi-supervised-bert-based-classification","semi-supervised-recurrent-neural-network","bert-based-classification","weakly-supervised-sentiment-analysis","self-supervised-sentiment-analysis","lda-topic-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-stacking-ensemble","name":"Semi-supervised Stacking Ensemble","fullName":"Semi-supervised Stacking Ensemble (Self-trained Stacked Generalization)","aliases":["SSL stacking","semi-supervised stacked generalization","self-trained stacking","semi-supervised meta-learning ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2000s–2010s","originator":"Combines Wolpert (1992) stacking with semi-supervised learning principles","url":"https://scholargate.app/en/machine-learning/semi-supervised-stacking-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-stacking-ensemble.md","definition":"Semi-supervised Stacking Ensemble extends the classic stacked generalization framework to settings where only a fraction of training examples carry labels. Base learners are first trained on labeled data, then used to assign pseudo-labels to unlabeled examples; the expanded dataset trains stronger base models whose out-of-fold predictions form the input to a meta-learner, yielding a two-tier ensemble that exploits both labeled and unlabeled structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Combines Wolpert (1992) stacking with semi-supervised learning principles","year":"2000s–2010s","type":"Ensemble (stacked generalization with unlabeled data augmentation)","dataType":"Partially labeled tabular or feature-vector data","subfamily":"Machine learning"},"citations":[{"ref":"Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.","type":"article","doi":"10.1016/S0893-6080(05)80023-1","isbn":null,"url":null},{"ref":"Chapelle, O., Schölkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["stacking-ensemble","self-training","label-propagation","random-forest","gradient-boosting","bagging-ensemble"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-support-vector-machine","name":"Semi-supervised Support Vector Machine","fullName":"Semi-supervised Support Vector Machine (S3VM / Transductive SVM)","aliases":["S3VM","Transductive SVM","TSVM","Semi-SVM"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1999","originator":"Joachims, T.","url":"https://scholargate.app/en/machine-learning/semi-supervised-support-vector-machine","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-support-vector-machine.md","definition":"Semi-supervised Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution, yielding better generalization when labeled samples are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joachims, T.","year":"1999","type":"Semi-supervised classifier","dataType":"Labeled and unlabeled tabular or text data","subfamily":"Machine learning"},"citations":[{"ref":"Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Transductive+Inference+for+Text+Classification+using+Support+Vector+Machines+Joachims+1999"},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["svm-classification","label-propagation","self-training","graph-based-semi-supervised-learning","logistic-regression","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-text-summarization","name":"Semi-supervised Text Summarization","fullName":"Semi-supervised Text Summarization (Label-efficient Abstractive and Extractive Summarization)","aliases":["semi-supervised summarization","label-efficient summarization","weakly supervised summarization","SSL-based summarization"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2020","originator":"Multiple contributors (e.g., He et al., 2020; Fabbri et al.)","url":"https://scholargate.app/en/deep-learning/semi-supervised-text-summarization","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-text-summarization.md","definition":"Semi-supervised text summarization trains summarization models by leveraging large amounts of unlabeled text alongside a small set of human-written reference summaries. By using techniques such as language-model pretraining, pseudo-labeling, and self-training, these methods substantially reduce the annotation burden while maintaining competitive ROUGE scores on benchmark datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (e.g., He et al., 2020; Fabbri et al.)","year":"2018–2020","type":"Semi-supervised sequence-to-sequence learning","dataType":"Unlabeled and labeled text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., & Neubig, G. (2020). Revisiting Semi-Supervised Learning for Neural Sequence Generation. In Proceedings of ICLR 2020.","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=SJT2mRLYPS"},{"ref":"Automatic summarization. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Automatic_summarization"}],"related":["extractive-summarization","abstractive-summarization","bert","sequence-to-sequence","self-training","transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-topic-modeling","name":"Semi-supervised Topic Modeling","fullName":"Semi-supervised Topic Modeling (Seed-guided and Labeled LDA variants)","aliases":["semi-supervised LDA","labeled LDA","seed-guided topic modeling","constrained topic model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2009","originator":"Ramage, D.; Andrzejewski, D.; and related NLP community","url":"https://scholargate.app/en/deep-learning/semi-supervised-topic-modeling","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-topic-modeling.md","definition":"Semi-supervised topic modeling extends unsupervised topic models such as LDA by incorporating partial human supervision — seed words, labeled documents, or must-link/cannot-link constraints — to steer discovered topics toward meaningful, domain-relevant categories while still exploiting the large unlabeled corpus for statistical strength.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ramage, D.; Andrzejewski, D.; and related NLP community","year":"2009","type":"Probabilistic graphical model (supervised/constrained extension of LDA)","dataType":"Unlabeled and partially labeled text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 248–256. Association for Computational Linguistics.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D09-1026"},{"ref":"Andrzejewski, D., Zhu, X., & Craven, M. (2009). Incorporating domain knowledge into topic modeling via Dirichlet forest priors. Proceedings of the 26th Annual International Conference on Machine Learning (ICML), 25–32.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Incorporating+domain+knowledge+into+topic+modeling+via+Dirichlet+forest+priors"}],"related":["latent-dirichlet-allocation","neural-topic-model","bert-text-classification","word2vec","non-negative-matrix-factorization","transformer-language-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-transfer-learning","name":"Semi-supervised Transfer Learning","fullName":"Semi-supervised Transfer Learning","aliases":["SSTL","semi-supervised domain adaptation","transfer learning with unlabeled data","few-label transfer learning"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010s","originator":"Pan, S. J. & Yang, Q. (formalized); wider community","url":"https://scholargate.app/en/machine-learning/semi-supervised-transfer-learning","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-transfer-learning.md","definition":"Semi-supervised Transfer Learning combines knowledge transferred from a richly labeled source domain with the structure of abundant unlabeled target-domain data, using only a small set of labeled target examples to achieve strong generalization where full annotation is scarce or expensive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan, S. J. & Yang, Q. (formalized); wider community","year":"2010s","type":"Hybrid learning paradigm","dataType":"Labeled + unlabeled data from source and target domains","subfamily":"Machine learning"},"citations":[{"ref":"Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43–76.","type":"article","doi":"10.1109/JPROC.2020.3004555","isbn":null,"url":null},{"ref":"Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["transfer-learning","semi-supervised-learning","self-supervised-learning","domain-adaptation","fine-tuning","label-propagation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-transformer","name":"Semi-supervised Transformer","fullName":"Semi-supervised Learning with Transformer Architectures","aliases":["semi-supervised transformer model","SSL transformer","transformer with self-supervised pre-training","semi-supervised attention model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018–2019","originator":"Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community","url":"https://scholargate.app/en/deep-learning/semi-supervised-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-transformer.md","definition":"Semi-supervised learning with Transformer architectures leverages large quantities of unlabeled data alongside a small labeled set to train powerful sequence models. The dominant pattern — exemplified by BERT — first pre-trains the Transformer on unlabeled data using self-supervised objectives such as masked token prediction, then fine-tunes it on the labeled task. This two-stage approach dramatically reduces the labeled data needed to achieve strong performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community","year":"2018–2019","type":"Semi-supervised deep learning","dataType":"Text, sequences, images (modality-agnostic architecture)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Zoph, B., Ghiasi, G., Lin, T.-Y., Cui, Y., Liu, H., Cubuk, E. D., & Le, Q. V. (2020). Rethinking Pre-training and Self-training. Advances in Neural Information Processing Systems (NeurIPS), 33, 3833–3845.","type":"article","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/27e9661e033a73a6ad8cefcde965c54d-Abstract.html"}],"related":["transformer","bert-based-classification","semi-supervised-convolutional-neural-network","self-supervised-transformer","fine-tuned-transformer","roberta-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-variational-autoencoder","name":"Semi-supervised Variational Autoencoder","fullName":"Semi-supervised Variational Autoencoder (M1/M2 Generative Model)","aliases":["Semi-supervised VAE","M2 model","VAE with label propagation","deep generative semi-supervised model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014","originator":"Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.","url":"https://scholargate.app/en/deep-learning/semi-supervised-variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-variational-autoencoder.md","definition":"The semi-supervised VAE (M2 model) is a deep generative method that jointly learns a latent representation of inputs and a classifier, leveraging both labeled and unlabeled examples in a principled probabilistic framework. Introduced by Kingma et al. in 2014, it allows accurate classification even when labels are scarce by having the generative model explain away unlabeled observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.","year":"2014","type":"Generative probabilistic model (semi-supervised)","dataType":"Mixed labeled and unlabeled data (images, structured, text embeddings)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2014/hash/d523773c6b194f37b938d340d5d02232-Abstract.html"},{"ref":"Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"}],"related":["variational-autoencoder","semi-supervised-convolutional-neural-network","semi-supervised-transformer","generative-adversarial-network","self-supervised-variational-autoencoder","transfer-learning-variational-autoencoder"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-vision-transformer","name":"Semi-supervised Vision Transformer","fullName":"Semi-supervised Vision Transformer (Semi-supervised ViT)","aliases":["Semi-supervised ViT","SSL-ViT","Semi-supervised Patch-based Transformer","Semi-supervised Self-Attention Image Model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2021–2022","originator":"Dosovitskiy et al. (ViT); semi-supervised extensions by multiple groups (2021–2023)","url":"https://scholargate.app/en/deep-learning/semi-supervised-vision-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-vision-transformer.md","definition":"Semi-supervised Vision Transformer applies the patch-based self-attention architecture of ViT to settings where only a fraction of images are labeled, exploiting large unlabeled corpora through pseudo-labeling, consistency regularization, or self-supervised pretext tasks before fine-tuning on the small labeled set. This approach achieves near-supervised accuracy even when labeled images are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dosovitskiy et al. (ViT); semi-supervised extensions by multiple groups (2021–2023)","year":"2021–2022","type":"Semi-supervised deep learning for image understanding","dataType":"Image data (labeled + large unlabeled sets)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR 2021).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=YicbFdNTTy"},{"ref":"Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022). Scaling Vision Transformers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12104–12113.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Scaling+Vision+Transformers+Zhai+2022"}],"related":["vision-transformer","semi-supervised-convolutional-neural-network","self-supervised-vision-transformer","fine-tuned-vision-transformer","image-classification","semi-supervised-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-voting-ensemble","name":"Semi-supervised Voting Ensemble","fullName":"Semi-supervised Voting Ensemble (Agreement-based Multi-classifier with Unlabeled Data)","aliases":["semi-supervised majority vote","SSL voting ensemble","co-training voting classifier","semi-supervised multi-classifier voting"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1998–2005","originator":"Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training)","url":"https://scholargate.app/en/machine-learning/semi-supervised-voting-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-voting-ensemble.md","definition":"A semi-supervised voting ensemble trains multiple classifiers on a small labeled set, then iteratively exploits unlabeled data by having the classifiers label examples they agree on, expanding the training pool until all classifiers vote jointly on test examples. It combines the label-efficiency of semi-supervised learning with the variance-reduction of majority-vote ensembles, making it valuable when annotation is costly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training)","year":"1998–2005","type":"Semi-supervised ensemble (voting)","dataType":"Mixed labeled and unlabeled tabular or feature data","subfamily":"Machine learning"},"citations":[{"ref":"Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.","type":"article","doi":"10.1109/TKDE.2005.186","isbn":null,"url":null},{"ref":"Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT), 92–100.","type":"inproceedings","doi":"10.1145/279943.279962","isbn":null,"url":null}],"related":["semi-supervised-learning","voting-ensemble","semi-supervised-bagging","boosting","co-training","self-supervised-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-word2vec","name":"Semi-supervised Word2Vec","fullName":"Semi-supervised Learning with Word2Vec Word Embeddings","aliases":["Word2Vec with semi-supervised learning","semi-supervised word embeddings","Word2Vec SSL","unsupervised pretraining with Word2Vec"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013–2015","originator":"Mikolov, T. et al. (Word2Vec); semi-supervised framing via Collobert & Weston and subsequent NLP literature","url":"https://scholargate.app/en/deep-learning/semi-supervised-word2vec","markdownUrl":"https://scholargate.app/en/deep-learning/semi-supervised-word2vec.md","definition":"Semi-supervised Word2Vec trains dense word representations on a large unlabeled corpus using Word2Vec (skip-gram or CBOW), then uses those embeddings as fixed or fine-tunable input features for a downstream classifier trained on a small labeled dataset. This two-stage process lets models benefit from abundant unlabeled text when labeled data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mikolov, T. et al. (Word2Vec); semi-supervised framing via Collobert & Weston and subsequent NLP literature","year":"2013–2015","type":"Semi-supervised representation learning","dataType":"Raw text (unlabeled) + small labeled text corpus","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. In Proceedings of ICLR 2013.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1301.3781"},{"ref":"Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research, 12, 2493–2537.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v12/collobert11a.html"}],"related":["sentence-embeddings","semi-supervised-bert-based-classification","lda-topic-model","self-supervised-word2vec","fine-tuned-word2vec","transfer-learning-with-word2vec"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semi-supervised-xgboost","name":"Semi-supervised XGBoost","fullName":"Semi-supervised Extreme Gradient Boosting","aliases":["SS-XGBoost","semi-supervised gradient boosting","pseudo-label XGBoost","label-propagation XGBoost"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2016–2018","originator":"Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors","url":"https://scholargate.app/en/machine-learning/semi-supervised-xgboost","markdownUrl":"https://scholargate.app/en/machine-learning/semi-supervised-xgboost.md","definition":"Semi-supervised XGBoost extends the XGBoost gradient boosting framework to settings where only a fraction of training examples carry labels. By iteratively generating pseudo-labels for unlabeled data and retraining on the expanded set, the method extracts signal from unlabeled observations, improving generalization when labeled data are scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, T. & Guestrin, C. (XGBoost); semi-supervised extension by multiple authors","year":"2016–2018","type":"Ensemble (semi-supervised gradient boosting)","dataType":"Tabular data with partially labeled observations","subfamily":"Machine learning"},"citations":[{"ref":"Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.","type":"inproceedings","doi":"10.1145/2939672.2939785","isbn":null,"url":null},{"ref":"Chapelle, O., Scholkopf, B. & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03358-9","url":null}],"related":["xgboost","label-propagation","pseudo-labeling","gradient-boosting","self-training","random-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semiotic-analysis","name":"Semiotic Analysis","fullName":"Semiotic Analysis","aliases":["semiotics","sign analysis","structural semiotics","semiological analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Textual Analysis","year":"Late 19th–early 20th century (Saussure ~1906–1911; Peirce ~1867–1914); systematic application in social research from the 1960s","originator":"Ferdinand de Saussure (structural semiology); Charles Sanders Peirce (semiotic triads); Roland Barthes (applied cultural semiotics)","url":"https://scholargate.app/en/qualitative/semiotic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/semiotic-analysis.md","definition":"Semiotic analysis is a qualitative method for interpreting how signs — words, images, sounds, gestures, and objects — produce and communicate meaning within a cultural context. Drawing on the structural linguistics of Ferdinand de Saussure and the triadic sign theory of Charles Sanders Peirce, and popularised as a research tool by Roland Barthes, semiotics moves beyond surface denotation to expose the connotative and ideological meanings embedded in texts and visual culture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ferdinand de Saussure (structural semiology); Charles Sanders Peirce (semiotic triads); Roland Barthes (applied cultural semiotics)","year":"Late 19th–early 20th century (Saussure ~1906–1911; Peirce ~1867–1914); systematic application in social research from the 1960s","type":"Qualitative research method","dataType":"Texts, images, advertisements, film, symbols, gestures, cultural artifacts, digital media","typicalSampleSize":"No fixed sample; typically 5–30 purposively selected texts or artifacts","subfamily":"Textual Analysis"},"citations":[{"ref":"Barthes, R. (1967). Elements of Semiology (trans. A. Lavers & C. Smith). Hill and Wang.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Barthes+Elements+of+Semiology+1967"},{"ref":"Chandler, D. (2007). Semiotics: The Basics (2nd ed.). Routledge.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Chandler+Semiotics+The+Basics+2007+Routledge"}],"related":["discourse-analysis","content-analysis","narrative-analysis","thematic-analysis","ethnography","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"semiotics-film","name":"Semiotics in Film Studies","fullName":"Semiotic Analysis of Film and Cinema Codes","aliases":["film semiotics","cinematic codes","sign analysis in cinema"],"domain":"media-studies","family":"process-pipeline","subfamily":"Sign and code analysis","year":"1968","originator":"Roland Barthes, Christian Metz","url":"https://scholargate.app/en/media-studies/semiotics-film","markdownUrl":"https://scholargate.app/en/media-studies/semiotics-film.md","definition":"Semiotics in Film Studies is a systematic method for analyzing how film produces meaning through signs, codes, and symbolic systems. Developed from linguistic semiotics and adapted to cinema by scholars like Roland Barthes, Christian Metz, and Umberto Eco, it examines how visual, auditory, and narrative elements function as signs—consisting of signifier (the form taken by the sign) and signified (the concept it represents)—to create meaning. The method reveals that cinema is not transparent communication but a complex coded system where understanding requires learning film's specific sign conventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roland Barthes, Christian Metz","subfamily":"Sign and code analysis","year":"1968","type":"Systematic method for analyzing how meaning is produced through cinematic signs and codes"},"citations":[{"ref":"Barthes, R. (1977). Image-music-text (S. Heath, Trans.). Hill and Wang.","type":"article","doi":null,"isbn":null,"url":"https://www.hillmcgraw.com"},{"ref":"Metz, C. (1974). Film Language: A Semiotics of the Cinema (D. J. Umiker-Sebeok, Trans.). Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.oup.com"},{"ref":"Stam, R. (2000). Film Theory: An Introduction. Blackwell Publishers.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-gb"},{"ref":"Eco, U. (1976). A Theory of Semiotics. Indiana University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.iupress.org"}],"related":["film-narrative-analysis","visual-content-analysis","discourse-analysis-media","auteur-theory-analysis","media-framing-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sense-of-belonging-scale","name":"Sense of Belonging Scale","fullName":"Sense of Belonging Scale (SOBS)","aliases":["SOBS","School Belonging Measure"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Social integration and belonging","year":"1993","originator":"Carol Goodenow","url":"https://scholargate.app/en/educational-psychology/sense-of-belonging-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/sense-of-belonging-scale.md","definition":"The Sense of Belonging Scale (SOBS) measures students' perceptions of their connectedness and acceptance within the school community. Developed by Goodenow (1993), it assesses whether students feel valued, included, and connected to peers and teachers. Sense of belonging is a critical psychological need and a powerful predictor of academic motivation, engagement, mental health, and persistence, particularly for students from underrepresented groups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carol Goodenow","subfamily":"Social integration and belonging","year":"1993","type":"School belonging perception scale"},"citations":[{"ref":"Goodenow, C. (1993). Classroom belonging among early adolescent students: relationships to motivation and achievement. Journal of Early Adolescence, 13(1), 21-43.","type":"article","doi":"10.1177/0272431693013001002","isbn":null,"url":null},{"ref":"Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447-1451.","type":"article","doi":"10.1126/science.1198364","isbn":null,"url":null}],"related":["student-engagement-scale","school-climate-scale","student-satisfaction-survey","academic-motivation-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-based-purposive-sampling","name":"Sensitivity Analysis-Based Purposive Sampling","fullName":"Sensitivity Analysis-Based Purposive Sampling","aliases":["purposive sampling with sensitivity checks","robust purposive sampling","sensitivity-tested purposive selection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s","originator":"Rooted in Patton's purposive sampling typology; sensitivity analysis practices formalized in research synthesis literature","url":"https://scholargate.app/en/survey-methodology/sensitivity-analysis-based-purposive-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/sensitivity-analysis-based-purposive-sampling.md","definition":"Sensitivity analysis-based purposive sampling extends conventional purposive sampling by systematically testing whether key findings or case-selection decisions change when the inclusion criteria, selection logic, or boundary conditions are altered. It applies the logic of sensitivity analysis — standard in quantitative research and systematic reviews — to qualitative case selection, giving researchers explicit evidence of how robust their purposive choices are to plausible alternative selection rules.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Patton's purposive sampling typology; sensitivity analysis practices formalized in research synthesis literature","year":"1990s–2000s","type":"Purposive qualitative sampling with robustness verification","dataType":"Qualitative or mixed-methods data; case selection records","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2015). Qualitative Research and Evaluation Methods (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1412972123","url":null},{"ref":"Teddlie, C., & Yu, F. (2007). Mixed methods sampling: A typology with examples. Journal of Mixed Methods Research, 1(1), 77–100.","type":"article","doi":"10.1177/2345678906292430","isbn":null,"url":null}],"related":["purposive-sampling","maximum-variation-sampling","theoretical-sampling","deviant-case-sampling","typical-case-sampling","snowball-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-for-causality-in-education-research","name":"Sensitivity analysis for causality in education research","fullName":"Sensitivity Analysis for Causal Inference in Education Research","aliases":["Rosenbaum sensitivity analysis","hidden-bias sensitivity analysis","causal sensitivity analysis","SA for causal education studies"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1983–2002","originator":"Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others","url":"https://scholargate.app/en/causal-inference/sensitivity-analysis-for-causality-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/sensitivity-analysis-for-causality-in-education-research.md","definition":"Sensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to overturn a causal conclusion — a critical safeguard when randomisation is impossible, which is common in educational settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others","year":"1983–2002","type":"Causal robustness / bias assessment","dataType":"Observational panel, matched samples, quasi-experimental data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387989679","url":null},{"ref":"Briggs, D. C. (2008). Using meta-analytic results to inform causal claims about the effects of educational interventions. Journal of Research on Educational Effectiveness, 1(3), 148-175.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Using+meta-analytic+results+to+inform+causal+claims+about+the+effects+of+educational+interventions+Briggs"}],"related":["propensity-score-matching","difference-in-differences","regression-discontinuity","instrumental-variables","interrupted-time-series","matching-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-for-causality","name":"Sensitivity Analysis for Causality","fullName":"Sensitivity Analysis for Hidden Bias in Causal Inference","aliases":["sensitivity analysis","hidden-bias sensitivity analysis","Rosenbaum sensitivity analysis","omitted-variable sensitivity"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1983–2002","originator":"Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)","url":"https://scholargate.app/en/causal-inference/sensitivity-analysis-for-causality","markdownUrl":"https://scholargate.app/en/causal-inference/sensitivity-analysis-for-causality.md","definition":"Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)","year":"1983–2002","type":"Diagnostic / robustness check","dataType":"Observational or quasi-experimental panel / cross-sectional data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387989679","url":null},{"ref":"Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: Extending omitted variable bias. Journal of the Royal Statistical Society: Series B, 82(1), 39-67.","type":"article","doi":"10.1111/rssb.12348","isbn":null,"url":null}],"related":["propensity-score-matching","difference-in-differences","placebo-test","instrumental-variables","regression-discontinuity-design","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-integrated-design-of-experiments","name":"Sensitivity analysis-integrated design of experiments","fullName":"Sensitivity Analysis-Integrated Design of Experiments","aliases":["SA-DoE","SA-integrated DoE","DoE with sensitivity screening","factor screening with sensitivity analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (formal integration emerged in simulation and engineering optimization literature)","originator":"Integrated approach drawing on Saltelli et al. (sensitivity analysis) and Montgomery (DoE); no single originator","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-integrated-design-of-experiments","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-integrated-design-of-experiments.md","definition":"Sensitivity Analysis-Integrated Design of Experiments (SA-DoE) combines systematic experimental planning with formal sensitivity analysis to identify which input factors most strongly influence a response, then efficiently characterises those factors' effects. By embedding sensitivity screening into the DoE workflow, experimenters avoid wasting trials on inert variables and focus resources on the factors that truly drive system behaviour — making it especially valuable in simulation studies, product engineering, and complex process optimisation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Integrated approach drawing on Saltelli et al. (sensitivity analysis) and Montgomery (DoE); no single originator","year":"1990s–2000s (formal integration emerged in simulation and engineering optimization literature)","type":"Hybrid experimental-analytical framework","dataType":"Quantitative simulation outputs or physical experimental measurements","subfamily":"Engineering methods"},"citations":[{"ref":"Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley.","type":"book","doi":null,"isbn":"9780470870938","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"9781119113478","url":null}],"related":["full-factorial-design","fractional-factorial-design","response-surface-methodology","variance-based-sensitivity-analysis","latin-hypercube-sampling","taguchi-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-integrated-full-factorial-design","name":"Sensitivity analysis-integrated full factorial design","fullName":"Sensitivity Analysis-Integrated Full Factorial Design","aliases":["SA-FFD","full factorial design with sensitivity analysis","factorial-based sensitivity analysis","FFD-SA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (formalized combination)","originator":"Rooted in factorial experimentation (Fisher, 1935) combined with variance-based sensitivity analysis formalized by Saltelli and colleagues (1990s–2000s)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-integrated-full-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-integrated-full-factorial-design.md","definition":"Sensitivity analysis-integrated full factorial design combines exhaustive factorial experimentation — where every combination of factor levels is tested — with systematic sensitivity analysis to quantify how much each input factor drives variation in the output response. This hybrid approach provides both reliable effect estimates and a ranked picture of factor importance, guiding engineers and scientists toward the levers that truly matter for system performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in factorial experimentation (Fisher, 1935) combined with variance-based sensitivity analysis formalized by Saltelli and colleagues (1990s–2000s)","year":"1990s–2000s (formalized combination)","type":"Experimental design with factor importance ranking","dataType":"Continuous and categorical factor settings; measured response outputs","subfamily":"Engineering methods"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470059975","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["full-factorial-design","fractional-factorial-design","sensitivity-analysis-integrated-response-surface-methodology","design-of-experiments","response-surface-methodology","taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-integrated-response-surface-methodology","name":"Sensitivity analysis-integrated response surface methodology","fullName":"Sensitivity Analysis-Integrated Response Surface Methodology","aliases":["SA-RSM","RSM with sensitivity analysis","sensitivity-augmented RSM","response surface methodology with factor screening"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (integration practice)","originator":"Box & Wilson (RSM, 1951); Saltelli et al. (global SA framework, 1990s–2000s)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-integrated-response-surface-methodology","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-integrated-response-surface-methodology.md","definition":"Sensitivity analysis-integrated RSM couples a structured experimental design with a formal sensitivity analysis of the fitted response surface model. After estimating a polynomial surrogate from designed experiments, global or local sensitivity indices are computed to quantify each input factor's relative contribution to output variability. This allows practitioners to identify which factors truly drive the response before committing to full optimization, reducing cost and improving the reliability of the final optimum.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Wilson (RSM, 1951); Saltelli et al. (global SA framework, 1990s–2000s)","year":"1990s–2000s (integration practice)","type":"Hybrid experimental-analytical method","dataType":"Continuous experimental measurements; fitted response surface model","subfamily":"Engineering methods"},"citations":[{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916018","url":null},{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null}],"related":["response-surface-methodology","central-composite-design","box-behnken-design","design-of-experiments","fractional-factorial-design","optimization-assisted-response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-integrated-taguchi-method","name":"Sensitivity Analysis-integrated Taguchi Method","fullName":"Sensitivity Analysis-integrated Taguchi Method","aliases":["Taguchi-SA","SA-Taguchi","Taguchi method with sensitivity analysis","sensitivity-enhanced robust design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1950s–1980s (Taguchi DOE); integrated workflow formalized from 1990s onward","originator":"Genichi Taguchi (Taguchi method); Andrea Saltelli et al. (global sensitivity analysis)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-integrated-taguchi-method","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-integrated-taguchi-method.md","definition":"The sensitivity analysis-integrated Taguchi method augments the classical Taguchi robust design workflow with a systematic sensitivity analysis step that quantifies how much each control factor and noise factor contributes to response variability. By combining Taguchi orthogonal arrays with variance-based or ANOVA-based sensitivity indices, engineers can both optimize process settings and rank factors by their influence on output uncertainty, yielding more transparent and defensible engineering decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Genichi Taguchi (Taguchi method); Andrea Saltelli et al. (global sensitivity analysis)","year":"1950s–1980s (Taguchi DOE); integrated workflow formalized from 1990s onward","type":"Integrated experimental design and sensitivity analysis workflow","dataType":"Continuous experimental response data from designed experiments (orthogonal arrays)","subfamily":"Engineering methods"},"citations":[{"ref":"Taguchi, G. (1987). System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs (Vols. 1–2). UNIPUB/Kraus International Publications.","type":"book","doi":null,"isbn":"978-0527916213","url":null},{"ref":"Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. Wiley.","type":"book","doi":null,"isbn":"978-0470870938","url":null}],"related":["taguchi-method","design-of-experiments","fractional-factorial-design","robust-taguchi-method","response-surface-methodology","optimization-assisted-taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-observational","name":"Sensitivity Analysis for Unmeasured Confounding","fullName":"Sensitivity Analysis for Hidden Bias in Observational Studies (Rosenbaum Bounds / E-value)","aliases":["Rosenbaum bounds","E-value","hidden bias sensitivity analysis","unmeasured confounding sensitivity","Duyarlılık Analizi — Gizli Yanlılık (Rosenbaum / E-value)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2002,"originator":"Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)","url":"https://scholargate.app/en/causal-inference/sensitivity-analysis-observational","markdownUrl":"https://scholargate.app/en/causal-inference/sensitivity-analysis-observational.md","definition":"Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)","year":2002,"type":"Sensitivity analysis for causal inference","estimator":"Rosenbaum sensitivity parameter Γ; E-value bound","outcome":"Robustness of a causal estimate to hidden bias","minSample":50},"citations":[{"ref":"Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387989679","url":null},{"ref":"VanderWeele, T. J. & Ding, P. (2017). Sensitivity Analysis in Observational Research: Introducing the E-Value. Annals of Internal Medicine, 167(4), 268-274.","type":"article","doi":"10.7326/M16-2607","isbn":null,"url":null}],"related":["propensity-score-matching","iv-2sls","frontdoor-adjustment","placebo-tests-causal","local-average-treatment-effect"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-box-behnken-design","name":"Sensitivity Analysis with Box-Behnken Design","fullName":"Sensitivity Analysis Integrated with Box-Behnken Design","aliases":["SA-BBD","Box-Behnken sensitivity analysis","BBD with sensitivity analysis","sensitivity-augmented Box-Behnken design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960 (BBD); sensitivity integration formalized 2000s–2010s","originator":"Box & Behnken (design, 1960); Saltelli et al. (sensitivity framework, 2000s)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-box-behnken-design","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-box-behnken-design.md","definition":"Sensitivity analysis with Box-Behnken design combines a resource-efficient three-level response surface experiment with a systematic assessment of how much each input factor drives variation in the response. The Box-Behnken design (BBD) fits a second-order polynomial model using fewer runs than a full central composite design, while the overlaid sensitivity analysis quantifies each factor's relative influence — helping engineers and researchers distinguish the vital few drivers from the inconsequential many.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Behnken (design, 1960); Saltelli et al. (sensitivity framework, 2000s)","year":"1960 (BBD); sensitivity integration formalized 2000s–2010s","type":"Integrated experimental-design and sensitivity-analysis technique","dataType":"Continuous factor levels; quantitative response variables","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.","type":"article","doi":"10.1080/00401706.1960.10489912","isbn":null,"url":null},{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null}],"related":["box-behnken-design","central-composite-design","response-surface-methodology","sensitivity-analysis-integrated-design-of-experiments","sensitivity-analysis-with-central-composite-design","fractional-factorial-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-central-composite-design","name":"Sensitivity analysis with central composite design","fullName":"Sensitivity Analysis with Central Composite Design","aliases":["SA-CCD","CCD sensitivity analysis","RSM sensitivity analysis","response surface sensitivity study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1951 (CCD); SA integration throughout 1970s–2000s","originator":"G. E. P. Box and K. B. Wilson (CCD); sensitivity analysis formalised within RSM by Montgomery and subsequent practitioners","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-central-composite-design","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-central-composite-design.md","definition":"Sensitivity analysis with Central Composite Design (CCD) combines a structured, space-filling experimental layout with a systematic examination of how much each input factor drives changes in the response. CCD supports estimation of a full quadratic response surface model; sensitivity analysis then interrogates that model to rank factors by influence, identify interactions, and map the performance landscape — guiding engineers and researchers toward robust operating conditions and efficient optimisation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"G. E. P. Box and K. B. Wilson (CCD); sensitivity analysis formalised within RSM by Montgomery and subsequent practitioners","year":"1951 (CCD); SA integration throughout 1970s–2000s","type":"Quantitative experimental design with post-hoc sensitivity assessment","dataType":"Continuous numeric responses measured at designed experimental runs","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Wilson, K. B. (1951). On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+Experimental+Attainment+of+Optimum+Conditions+Box+Wilson+1951"},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["central-composite-design","response-surface-methodology","box-behnken-design","factorial-design","one-factor-at-a-time","variance-based-sensitivity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-control-chart","name":"Sensitivity Analysis with Control Chart","fullName":"Sensitivity Analysis Integrated with Statistical Process Control Charts","aliases":["SA-SPC integration","control chart sensitivity analysis","SPC sensitivity assessment","sensitivity-enhanced control charting"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"Integration practice documented from the 1990s onward","originator":"Rooted in Shewhart (control charts, 1920s) and Saltelli et al. (global sensitivity analysis, 1990s–2000s); integration practice developed in quality engineering literature","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-control-chart","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-control-chart.md","definition":"Sensitivity analysis integrated with control charting evaluates how uncertain or varying inputs — such as sample size, subgroup frequency, distribution assumptions, or measurement error — affect the detection performance of a statistical process control chart. By quantifying which parameters most strongly influence chart metrics such as the average run length (ARL) or false alarm rate, engineers can design more robust monitoring schemes and understand where control chart conclusions are fragile.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in Shewhart (control charts, 1920s) and Saltelli et al. (global sensitivity analysis, 1990s–2000s); integration practice developed in quality engineering literature","year":"Integration practice documented from the 1990s onward","type":"Hybrid analytical framework","dataType":"Continuous or attribute process measurement data; model parameter distributions","subfamily":"Engineering methods"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null},{"ref":"Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119399308","url":null}],"related":["statistical-process-control","control-chart","sensitivity-analysis-integrated-design-of-experiments","sensitivity-analysis-with-process-capability-analysis","robust-control-chart","design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-event-tree-analysis","name":"Sensitivity analysis with event tree analysis","fullName":"Sensitivity Analysis Integrated with Event Tree Analysis","aliases":["SA-ETA","ETA sensitivity analysis","event tree sensitivity analysis","probabilistic sensitivity analysis with ETA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"Combination formalized in risk and reliability engineering from the 1990s onward","originator":"Sensitivity analysis: Saltelli et al. (1990s–2000s); Event tree analysis: Watson (1961, WASH-1400 formalization 1975)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-event-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-event-tree-analysis.md","definition":"Sensitivity analysis with event tree analysis (SA-ETA) is a quantitative risk assessment approach that systematically varies the input probabilities of an event tree model to determine which branch probabilities or initiating event frequencies most strongly influence the calculated probability of undesired outcomes. It extends classical event tree analysis by ranking the uncertainty contributions of individual inputs, thereby guiding risk-reduction efforts toward the parameters that matter most.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sensitivity analysis: Saltelli et al. (1990s–2000s); Event tree analysis: Watson (1961, WASH-1400 formalization 1975)","year":"Combination formalized in risk and reliability engineering from the 1990s onward","type":"Hybrid quantitative risk analysis method","dataType":"Probability estimates for initiating events and branch probabilities; numerical model outputs","subfamily":"Engineering methods"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null},{"ref":"Event tree analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Event_tree_analysis"}],"related":["event-tree-analysis","fault-tree-analysis","sensitivity-analysis-with-fault-tree-analysis","sensitivity-analysis-with-reliability-analysis","failure-mode-and-effects-analysis","risk-based-event-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-failure-mode-and-effects-analysis","name":"Sensitivity analysis with failure mode and effects analysis","fullName":"Sensitivity Analysis-Integrated Failure Mode and Effects Analysis","aliases":["SA-FMEA","FMEA with sensitivity analysis","sensitivity-enhanced FMEA","SA-integrated FMEA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (systematic integration)","originator":"Grumman Aircraft (FMEA origin, 1950s); sensitivity analysis integration developed by reliability engineering community","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-failure-mode-and-effects-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-failure-mode-and-effects-analysis.md","definition":"Sensitivity analysis with failure mode and effects analysis (SA-FMEA) combines classical FMEA risk scoring with systematic sensitivity analysis to determine which input parameters — severity, occurrence, and detectability ratings — drive the Risk Priority Number (RPN) most strongly. This integration helps teams focus improvement resources where they matter most, revealing how uncertain or variable scoring assumptions propagate into final risk rankings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grumman Aircraft (FMEA origin, 1950s); sensitivity analysis integration developed by reliability engineering community","year":"1990s–2000s (systematic integration)","type":"Hybrid risk analysis and sensitivity technique","dataType":"Expert judgment scores, failure rate data, design parameters","subfamily":"Engineering methods"},"citations":[{"ref":"Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873895989","url":null},{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null}],"related":["failure-mode-and-effects-analysis","sensitivity-analysis-integrated-response-surface-methodology","risk-based-failure-mode-and-effects-analysis","fault-tree-analysis","statistical-process-control","robust-failure-mode-and-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-fault-tree-analysis","name":"Sensitivity analysis with fault tree analysis","fullName":"Sensitivity Analysis Integrated with Fault Tree Analysis","aliases":["FTA-SA","fault tree sensitivity analysis","FTA with importance measures","probabilistic sensitivity analysis in fault trees"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1961 (FTA); sensitivity integration formalised 1970s–1980s","originator":"H. A. Watson (Bell Labs, FTA, 1961); integrated sensitivity extensions developed through nuclear safety research (Vesely et al., 1981)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-fault-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-fault-tree-analysis.md","definition":"Sensitivity analysis integrated with fault tree analysis (FTA-SA) is a quantitative reliability engineering method that first models how system failure can occur through a hierarchical Boolean logic tree, then systematically varies the probability of each basic event to determine which components drive overall system failure risk most strongly. Widely used in nuclear, aerospace, chemical, and safety-critical system design, it prioritises mitigation effort and reveals which uncertainty in input data matters most.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"H. A. Watson (Bell Labs, FTA, 1961); integrated sensitivity extensions developed through nuclear safety research (Vesely et al., 1981)","year":"1961 (FTA); sensitivity integration formalised 1970s–1980s","type":"Quantitative reliability and risk analysis technique","dataType":"Component failure probability data, event probabilities, Boolean logic tree structures","subfamily":"Engineering methods"},"citations":[{"ref":"Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. US Nuclear Regulatory Commission, NUREG-0492.","type":"book","doi":null,"isbn":null,"url":"https://www.nrc.gov/reading-rm/doc-collections/nuregs/staff/sr0492/"},{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null}],"related":["fault-tree-analysis","event-tree-analysis","failure-mode-effects-analysis","monte-carlo-simulation","probabilistic-risk-assessment","importance-measures-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-fractional-factorial-design","name":"Sensitivity Analysis with Fractional Factorial Design","fullName":"Sensitivity Analysis Using Fractional Factorial Experimental Design","aliases":["FFD sensitivity analysis","fractional factorial sensitivity screening","SA-FFD","screening design sensitivity analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1935 (factorial design); 1990s–2000s (systematic SA integration)","originator":"R. A. Fisher (factorial design foundations); combined with sensitivity analysis frameworks developed by A. Saltelli and colleagues","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-fractional-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-fractional-factorial-design.md","definition":"Sensitivity analysis with fractional factorial design (SA-FFD) is an experimental screening method that uses a carefully chosen fraction of all possible factor combinations to identify which input variables most strongly influence a system's output. By running only 2^(k-p) experiments instead of a full 2^k factorial, it makes sensitivity ranking feasible when many factors are present. The approach is widely used in engineering, product development, simulation modeling, and process optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. A. Fisher (factorial design foundations); combined with sensitivity analysis frameworks developed by A. Saltelli and colleagues","year":"1935 (factorial design); 1990s–2000s (systematic SA integration)","type":"Quantitative experimental screening method","dataType":"Continuous or categorical input factors; continuous response outputs","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471718130","url":null},{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null}],"related":["full-factorial-design","plackett-burman-design","response-surface-methodology","taguchi-method","sobol-sensitivity-analysis","two-level-factorial-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-process-capability-analysis","name":"Sensitivity Analysis with Process Capability Analysis","fullName":"Sensitivity Analysis Combined with Process Capability Analysis","aliases":["Sensitivity-Capability Analysis","PCA with Sensitivity Analysis","Process Capability Sensitivity Study","Cp/Cpk Sensitivity Analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1986–2000s (Cp/Cpk indices from Kane 1986; integration formalized in Six Sigma era)","originator":"Synthesized from work by V. E. Kane (process capability indices) and A. Saltelli (sensitivity analysis); integrated in Six Sigma and quality engineering practice","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-process-capability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-process-capability-analysis.md","definition":"Sensitivity analysis with process capability analysis is a quantitative engineering method that combines the measurement of process performance — via capability indices such as Cp and Cpk — with systematic variation of input factors to identify which factors most strongly influence whether a process meets its specification limits. It is widely used in Six Sigma projects, manufacturing quality improvement, and Design of Experiments contexts to prioritize where corrective action will yield the greatest gain in process capability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from work by V. E. Kane (process capability indices) and A. Saltelli (sensitivity analysis); integrated in Six Sigma and quality engineering practice","year":"1986–2000s (Cp/Cpk indices from Kane 1986; integration formalized in Six Sigma era)","type":"Quantitative engineering analysis","dataType":"Continuous measurement data from process outputs and controllable input factors","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470169926","url":null},{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null}],"related":["design-of-experiments","response-surface-methodology","statistical-process-control","taguchi-methods","monte-carlo-simulation","failure-mode-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-quality-function-deployment","name":"Sensitivity Analysis with Quality Function Deployment","fullName":"Sensitivity Analysis Integrated with Quality Function Deployment","aliases":["SA-QFD","QFD sensitivity analysis","robust QFD","House of Quality sensitivity analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (integration period)","originator":"Yoji Akao (QFD foundation); sensitivity extension attributed to multiple QFD researchers (1990s–2000s)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-quality-function-deployment","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-quality-function-deployment.md","definition":"Sensitivity analysis integrated with Quality Function Deployment (QFD) tests how stable the prioritization of engineering characteristics remains when customer requirement weights or relationship matrix scores are varied. By systematically perturbing the inputs of the House of Quality, teams identify which design parameters are truly critical and which rankings would flip under different assumptions — turning QFD from a one-shot prioritization tool into a robust decision framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yoji Akao (QFD foundation); sensitivity extension attributed to multiple QFD researchers (1990s–2000s)","year":"1990s–2000s (integration period)","type":"Integrated engineering design and decision analysis technique","dataType":"Customer requirement ratings, relationship matrix weights, engineering characteristic scores","subfamily":"Engineering methods"},"citations":[{"ref":"Fung, R. Y. K., Tang, J., Tu, Y., & Wang, D. (2006). Product design resources optimization using a non-linear fuzzy quality function deployment model. International Journal of Production Research, 44(12), 2483–2504.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Product+design+resources+optimization+nonlinear+fuzzy+quality+function+deployment+Fung+2006"},{"ref":"Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press.","type":"book","doi":null,"isbn":"978-0915299416","url":null}],"related":["quality-function-deployment","house-of-quality","sensitivity-analysis","analytic-hierarchy-process","failure-mode-effects-analysis","robust-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-reliability-analysis","name":"Sensitivity Analysis with Reliability Analysis","fullName":"Sensitivity Analysis Integrated with Reliability Analysis","aliases":["SA-RA","reliability sensitivity analysis","importance measures in reliability","reliability-based sensitivity analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1969 (importance measures); 2000s (global SA integration)","originator":"Birnbaum (importance measures, 1969); Saltelli et al. (global SA formalization, 2000s)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-reliability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-reliability-analysis.md","definition":"Sensitivity analysis integrated with reliability analysis is a quantitative engineering method that determines how uncertainty or variation in each system input — such as component failure rates, material properties, or load distributions — propagates into overall system reliability. By computing importance measures for every uncertain parameter, analysts can rank components and assumptions by their influence on system dependability, focusing improvement efforts where they matter most.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Birnbaum (importance measures, 1969); Saltelli et al. (global SA formalization, 2000s)","year":"1969 (importance measures); 2000s (global SA integration)","type":"Quantitative integrated engineering method","dataType":"Numerical / probabilistic (failure rates, probability distributions, component data)","subfamily":"Engineering methods"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null},{"ref":"Borgonovo, E., & Apostolakis, G. E. (2001). A new importance measure for risk-informed decision making. Reliability Engineering and System Safety, 72(2), 193–212.","type":"article","doi":"10.1016/S0951-8320(00)00108-3","isbn":null,"url":null}],"related":["sensitivity-analysis-with-fault-tree-analysis","sensitivity-analysis-with-failure-mode-and-effects-analysis","bayesian-reliability-analysis","robust-reliability-analysis","sensitivity-analysis-integrated-response-surface-methodology","failure-mode-and-effects-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-root-cause-analysis","name":"Sensitivity analysis with root cause analysis","fullName":"Sensitivity Analysis Integrated with Root Cause Analysis","aliases":["SA-RCA","sensitivity-driven root cause analysis","parameter sensitivity with failure analysis","sensitivity-informed RCA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (formalized integration in reliability and quality engineering literature)","originator":"Integrated practice drawing on sensitivity analysis (Saltelli et al.) and root cause analysis (Ishikawa, Kepner-Tregoe)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-root-cause-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-root-cause-analysis.md","definition":"Sensitivity Analysis with Root Cause Analysis (SA-RCA) is an integrated engineering method that first quantifies how much each input parameter or process variable drives variability in a system output, then applies structured root cause analysis to the most influential factors to identify and eliminate the underlying failure mechanisms. The combination transforms numerical rankings of influence into actionable diagnoses, making it particularly effective in quality engineering, reliability analysis, and process improvement contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Integrated practice drawing on sensitivity analysis (Saltelli et al.) and root cause analysis (Ishikawa, Kepner-Tregoe)","year":"1990s–2000s (formalized integration in reliability and quality engineering literature)","type":"Integrated diagnostic and optimization method","dataType":"Quantitative process/system data, model outputs, failure records","subfamily":"Engineering methods"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470059975","url":null},{"ref":"Andersen, B., & Fagerhaug, T. (2006). Root Cause Analysis: Simplified Tools and Techniques (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873896924","url":null}],"related":["failure-mode-and-effects-analysis","fault-tree-analysis","design-of-experiments","monte-carlo-simulation","pareto-analysis","fishbone-diagram"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis-with-six-sigma-dmaic","name":"Sensitivity Analysis with Six Sigma DMAIC","fullName":"Sensitivity Analysis Integrated with Six Sigma DMAIC","aliases":["SA-DMAIC","DMAIC sensitivity analysis","sensitivity-informed Six Sigma","Six Sigma sensitivity integration"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"2000s–2010s (applied integration era)","originator":"Integration of Six Sigma DMAIC (Motorola / Mikel Harry, 1980s–2000) with sensitivity analysis techniques (Saltelli et al., 1990s–2000s)","url":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-six-sigma-dmaic","markdownUrl":"https://scholargate.app/en/experimental-design/sensitivity-analysis-with-six-sigma-dmaic.md","definition":"Sensitivity analysis integrated with Six Sigma DMAIC augments the classic Define-Measure-Analyze-Improve-Control cycle with formal quantification of how much each input variable contributes to output variation. By embedding local or global sensitivity indices inside the Analyze phase, practitioners move beyond correlation screening to rigorously rank which process factors drive defect rates, guiding improvement resources to where they matter most.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Integration of Six Sigma DMAIC (Motorola / Mikel Harry, 1980s–2000) with sensitivity analysis techniques (Saltelli et al., 1990s–2000s)","year":"2000s–2010s (applied integration era)","type":"Hybrid process-improvement and uncertainty-quantification pipeline","dataType":"Continuous process measurements, simulation outputs, designed experiment data","subfamily":"Engineering methods"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"978-0470059975","url":null},{"ref":"Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Doubleday.","type":"book","doi":null,"isbn":"978-0385494378","url":null}],"related":["six-sigma-dmaic","sensitivity-analysis-with-statistical-process-control","sensitivity-analysis-integrated-design-of-experiments","robust-six-sigma-dmaic","design-of-experiments","statistical-process-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-analysis","name":"SENSITIVITY-ANALYSIS","fullName":"Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2004","originator":"Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.","url":"https://scholargate.app/en/decision-making/sensitivity-analysis","markdownUrl":"https://scholargate.app/en/decision-making/sensitivity-analysis.md","definition":"SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.","subfamily":"Ranking","year":"2004","type":"Robustness wrapper — parameter / weight perturbation sensitivity indices","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester","type":"article","doi":"10.1002/0470870958","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensitivity-specificity","name":"Sensitivity and Specificity","fullName":"Sensitivity and Specificity in Diagnostic Testing and Binary Classification","aliases":["diagnostic accuracy","true positive rate","true negative rate","receiver operating characteristic"],"domain":"research-statistics","family":"process-pipeline","subfamily":"diagnostic-testing","year":1978,"originator":"Multiple sources in medical diagnosis and signal detection","url":"https://scholargate.app/en/research-statistics/sensitivity-specificity","markdownUrl":"https://scholargate.app/en/research-statistics/sensitivity-specificity.md","definition":"Sensitivity and specificity are fundamental metrics of diagnostic test accuracy. Sensitivity is the probability that a test correctly identifies a person with the disease (true positive rate: TP / (TP + FN)). Specificity is the probability that a test correctly identifies a person without the disease (true negative rate: TN / (TN + FP)). Every test involves a trade-off: increasing sensitivity (catching all sick people) often reduces specificity (more false alarms). Choice of test threshold depends on the clinical context: screening for serious diseases favors sensitivity; confirming a diagnosis favors specificity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple sources in medical diagnosis and signal detection","subfamily":"diagnostic-testing","year":1978,"type":"Concept"},"citations":[{"ref":"Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6943), 1552.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Diagnostic+tests+1%3A+Sensitivity+and+specificity+Altman"},{"ref":"Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.","type":"article","doi":"10.1016/j.patrec.2005.10.010","isbn":null,"url":null},{"ref":"Metz, C. E. (1978). Basic principles of ROC analysis. Seminars in Nuclear Medicine, 8(4), 283–298.","type":"article","doi":"10.1016/S0001-2998(78)80014-2","isbn":null,"url":null}],"related":["type-i-type-ii-error","p-value-significance","effect-size","null-hypothesis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensor-data-collection","name":"Sensor Data Collection","fullName":"Sensor-Based Data Collection","aliases":["sensor measurement","instrumented data collection","physical sensor logging","IoT data collection"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1990s–2000s (widespread deployment with IoT ~2000s)","originator":"Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward","url":"https://scholargate.app/en/survey-methodology/sensor-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/sensor-data-collection.md","definition":"Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward","year":"1990s–2000s (widespread deployment with IoT ~2000s)","type":"Quantitative / mixed data collection technique","dataType":"Continuous or event-triggered numeric readings (temperature, acceleration, biometric, location, etc.)","subfamily":"Data collection"},"citations":[{"ref":"Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.","type":"article","doi":"10.1109/JPROC.2003.814918","isbn":null,"url":null},{"ref":"Sensor. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Sensor"}],"related":["mobile-experience-sampling","ecological-momentary-assessment","api-based-data-collection","longitudinal-sensor-data-collection","web-scraping","field-notes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sensor-fusion","name":"Sensor Fusion","fullName":"Sensor Fusion (State-of-the-Art Review)","aliases":["Multisensor Data Fusion","Multi-Sensor Integration","Information Fusion","Sensör Füzyonu"],"domain":"data-fusion","family":"process-pipeline","subfamily":"Information fusion","year":2013,"originator":"Khaleghi, Khamis, Karray & Razavi","url":"https://scholargate.app/en/data-fusion/sensor-fusion","markdownUrl":"https://scholargate.app/en/data-fusion/sensor-fusion.md","definition":"Sensor fusion is a computational process that combines data from multiple heterogeneous sensors to produce an estimate of the environment that is more accurate, complete, and reliable than any single source alone. Systematized as a formal field by Khaleghi, Khamis, Karray, and Razavi in their 2013 state-of-the-art review in Information Fusion, the discipline addresses imperfections such as noise, incompleteness, temporal misalignment, and conflicting readings that arise whenever multiple sensing modalities operate in parallel.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Khaleghi, Khamis, Karray & Razavi","year":2013,"type":"Multi-source information integration pipeline","subfamily":"Information fusion","fusion_levels":"Signal, feature, and decision level","key_challenge":"Imperfect, conflicting, and uncertain sensor data"},"citations":[{"ref":"Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44.","type":"article","doi":"10.1016/j.inffus.2011.08.001","isbn":null,"url":null}],"related":["data-fusion","ensemble-kalman-filter","kalman-filter-finance"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sentence-embeddings","name":"Sentence Embeddings","fullName":"Sentence Embeddings (Dense Vector Representations of Sentences)","aliases":["sentence vectors","sentence representations","SBERT","semantic sentence encoding"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2019","originator":"Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)","url":"https://scholargate.app/en/deep-learning/sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/sentence-embeddings.md","definition":"Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the most versatile building blocks in modern NLP pipelines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)","year":"2015–2019","type":"Representation learning / embedding","dataType":"Text (sentences, paragraphs, short documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3980–3990.","type":"inproceedings","doi":"10.18653/v1/D19-1410","isbn":null,"url":null},{"ref":"Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015). Skip-Thought Vectors. Advances in Neural Information Processing Systems (NeurIPS), 28.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/2015/hash/f442d33fa06832082290ad8544a8da27-Abstract.html"}],"related":["bert-based-classification","roberta-based-classification","transformer","long-short-term-memory","topic-modeling","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sentiment-analysis","name":"Sentiment Analysis","fullName":"Sentiment Analysis (Opinion Mining)","aliases":["opinion mining","polarity detection","duygu analizi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/sentiment-analysis","markdownUrl":"https://scholargate.app/en/text-mining/sentiment-analysis.md","definition":"Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-classification task","approaches":"Lexicon-based / machine-learning / transformer-based","output":"Polarity label (positive / negative / neutral)"},"citations":[{"ref":"Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.","type":"article","doi":"10.1561/1500000011","isbn":null,"url":null}],"related":["tf-idf","bert-embeddings","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"separation-anxiety-questionnaire","name":"Separation Anxiety Questionnaire","fullName":"Separation Anxiety Questionnaire (SAQ)","aliases":["SAQ"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"separation-anxiety","year":2000,"originator":"Christopher F. Sharpley and colleagues","url":"https://scholargate.app/en/anxiety-disorders/separation-anxiety-questionnaire","markdownUrl":"https://scholargate.app/en/anxiety-disorders/separation-anxiety-questionnaire.md","definition":"The Separation Anxiety Questionnaire (SAQ) is a self-report instrument measuring the intensity of separation anxiety experienced during or anticipated during separation from attachment figures. Developed by Sharpley and colleagues in 2000, the SAQ assesses worry, fear, and distress related to being apart from parents, partners, or other significant caregivers. It is used to evaluate separation anxiety in children, adolescents, and adults, and to monitor treatment response in separation anxiety disorder.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher F. Sharpley and colleagues","subfamily":"separation-anxiety","year":2000,"type":"Self-report"},"citations":[{"ref":"Sharpley, C. F., & Lavers, R. J. (2000). The Separation Anxiety to Mother Separation Scale: Development and psychometric properties. Journal of Clinical Psychology, 56(9), 1131–1146.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Separation+Anxiety+to+Mother+Separation+Scale%3A+Development+and+psychometric+properties+Sharpley"}],"related":["anxiety-sensitivity-index","health-anxiety-questionnaire","specific-phobia-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"seq2seq","name":"Sequence-to-Sequence Model","fullName":"Sequence-to-Sequence (Seq2Seq) Encoder-Decoder Model","aliases":["Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder)","encoder-decoder model","seq2seq","sequence to sequence learning"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2014,"originator":"Sutskever, I.; Cho, K.","url":"https://scholargate.app/en/deep-learning/seq2seq","markdownUrl":"https://scholargate.app/en/deep-learning/seq2seq.md","definition":"The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sutskever, I.; Cho, K.","year":2014,"type":"Encoder-decoder neural network (deep learning)","task":"Sequence transduction (variable-length input to variable-length output)","minSample":500},"citations":[{"ref":"Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2014/hash/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html"},{"ref":"Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. EMNLP, 1724–1734.","type":"inproceedings","doi":"10.3115/v1/D14-1179","isbn":null,"url":null}],"related":["attention-mechanism","self-attention-transformer","bert-finetuning","random-forest","xgboost"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequence-alignment","name":"Sequence Alignment","fullName":"Biological Sequence Alignment","aliases":["pairwise alignment","multiple sequence alignment","MSA","sequence comparison"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"1970 (global alignment); 1981 (local alignment)","originator":"Saul B. Needleman & Christian D. Wunsch (global); Temple F. Smith & Michael S. Waterman (local)","url":"https://scholargate.app/en/bioinformatics/sequence-alignment","markdownUrl":"https://scholargate.app/en/bioinformatics/sequence-alignment.md","definition":"Sequence alignment is a foundational bioinformatics technique that arranges two or more DNA, RNA, or protein sequences to reveal regions of similarity, infer evolutionary relationships, identify functional domains, and map sequencing reads to reference genomes. It underpins virtually every downstream genomic analysis, from variant calling and gene expression quantification to phylogenetics and structural annotation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saul B. Needleman & Christian D. Wunsch (global); Temple F. Smith & Michael S. Waterman (local)","year":"1970 (global alignment); 1981 (local alignment)","type":"Computational sequence analysis technique","dataType":"Nucleotide or amino acid sequences (FASTA/FASTQ)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443–453.","type":"article","doi":"10.1016/0022-2836(70)90057-4","isbn":null,"url":null},{"ref":"Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147(1), 195–197.","type":"article","doi":"10.1016/0022-2836(81)90087-5","isbn":null,"url":null}],"related":["phylogenetic-analysis","variant-calling","rna-seq-differential-expression","genome-wide-association-study","chip-seq-peak-calling","single-cell-rna-seq-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequence-mining","name":"Sequential Pattern Mining","fullName":"Sequential Pattern Mining","aliases":["Sequence Pattern Mining","Sequential Data Mining","Temporal Pattern Mining","Ardışık Örüntü Madenciliği"],"domain":"machine-learning","family":"ml-model","subfamily":"Pattern mining","year":1995,"originator":"Rakesh Agrawal & Ramakrishnan Srikant","url":"https://scholargate.app/en/machine-learning/sequence-mining","markdownUrl":"https://scholargate.app/en/machine-learning/sequence-mining.md","definition":"Sequential Pattern Mining discovers ordered patterns that recur across multiple event sequences in a database. Introduced by Agrawal and Srikant in 1995, it extends association-rule mining to time-ordered transactions. A pattern is frequent when it appears as an ordered subsequence in at least a user-specified fraction of all sequences. The method is widely applied wherever the order of events carries meaning, such as customer purchase histories, clickstream logs, electronic health records, and DNA sequence analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rakesh Agrawal & Ramakrishnan Srikant","year":1995,"type":"Unsupervised pattern discovery","subfamily":"Pattern mining","complexity":"Exponential in pattern length, polynomial in database size","output":"Ordered itemset sequences with minimum support"},"citations":[{"ref":"Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. IEEE International Conference on Data Engineering (ICDE), 3–14.","type":"inproceedings","doi":"10.1109/ICDE.1995.380415","isbn":null,"url":null}],"related":["association-rule-mining","fp-growth","process-mining"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-analysis","name":"Sequential Analysis","fullName":"Group Sequential Design","aliases":["sequential testing","group sequential design","interim analysis","Sıralı Analiz (Sequential Testing / Group Sequential Design)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1977,"originator":"P. C. O'Brien & T. R. Fleming; P. C. Pocock","url":"https://scholargate.app/en/statistics/sequential-analysis","markdownUrl":"https://scholargate.app/en/statistics/sequential-analysis.md","definition":"Sequential analysis is a framework for conducting hypothesis tests with pre-planned interim looks at accumulating data, allowing a study to stop early for efficacy or futility while controlling the overall Type I error rate. The group sequential approach was formalised by Pocock (1977) and O'Brien and Fleming (1979), and remains the standard for confirmatory clinical trials and rigorous A/B experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"P. C. O'Brien & T. R. Fleming; P. C. Pocock","year":1977,"family":"Hypothesis test / experimental design","type":"Sequential / adaptive hypothesis test","interimAnalyses":"pre-specified","boundaryMethods":"O'Brien-Fleming, Pocock, alpha-spending function","outcome":"continuous or binary","parametric":true,"minimumSample":20},"citations":[{"ref":"O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.","type":"article","doi":"10.2307/2530245","isbn":null,"url":null},{"ref":"Jennison, C. & Turnbull, B.W. (1999). Group Sequential Methods with Applications to Clinical Trials. CRC Press.","type":"book","doi":null,"isbn":"978-0849303166","url":null}],"related":["sample-size-planning","adaptive-design","bayesian-power-analysis","one-sample-t-test","one-way-anova","simulation-based-power"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-case-focused-mixed-methods","name":"Sequential Case-Focused Mixed Methods","fullName":"Sequential Case-Focused Mixed Methods Design","aliases":["sequential case study mixed methods","case-focused sequential design","sequential mixed case design","SCFMM"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Robert K. Yin (case study integration); John W. Creswell & Vicki L. Plano Clark (sequential mixed methods typology)","url":"https://scholargate.app/en/research-design/sequential-case-focused-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/sequential-case-focused-mixed-methods.md","definition":"Sequential case-focused mixed methods design combines the depth of case study methodology with the phased data-collection logic of sequential mixed methods. Quantitative and qualitative data are gathered in distinct, ordered phases — either QUAN then QUAL or QUAL then QUAN — and both strands are anchored within one or more bounded cases. The design is suited to research questions that require understanding how and why phenomena unfold within specific real-world contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin (case study integration); John W. Creswell & Vicki L. Plano Clark (sequential mixed methods typology)","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative and qualitative data collected in sequential phases from one or more bounded cases","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483358307","url":null},{"ref":"Yin, R. K. (2006). Mixed methods research: Are the methods genuinely integrated or merely parallel? Research in the Schools, 13(1), 41–47.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Yin+2006+Mixed+methods+research+genuinely+integrated+parallel+Research+in+the+Schools"}],"related":["case-focused-mixed-methods-design","explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","sequential-multiphase-mixed-methods","case-study","concurrent-case-focused-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-design","name":"Sequential Design","fullName":"Sequential / Group Sequential Trial Design","aliases":["group sequential design","adaptive stopping design","Ardışık Deneme Tasarımı (Sequential / Group Sequential)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1979,"originator":"O'Brien & Fleming; Pocock; Lan & DeMets","url":"https://scholargate.app/en/experimental-design/sequential-design","markdownUrl":"https://scholargate.app/en/experimental-design/sequential-design.md","definition":"Sequential and group sequential trial designs allow a study to be stopped early — or continued — based on interim analyses conducted as data accumulate. The core framework was formalised by O'Brien and Fleming in 1979 and extended by Lan and DeMets's alpha-spending approach, and it controls the overall Type I error rate across all planned looks by pre-specifying both efficacy and futility boundaries before enrolment begins.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"O'Brien & Fleming; Pocock; Lan & DeMets","year":1979,"family":"Experimental design","type":"Adaptive stopping trial design","minSample":30,"parametric":false,"outcomeTypes":"continuous, binary, ordinal","dataStructure":"longitudinal","difficultyLevel":3,"alphaSpending":"Lan-DeMets (O'Brien-Fleming or Pocock)"},"citations":[{"ref":"O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.","type":"article","doi":"10.2307/2530245","isbn":null,"url":null},{"ref":"Jennison, C. & Turnbull, B.W. (2000). Group Sequential Methods with Applications to Clinical Trials. CRC Press.","type":"book","doi":null,"isbn":"978-0849303166","url":null}],"related":["randomized-controlled-trial","adaptive-design","sample-size-determination","power-analysis","lan-demets-spending"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-exploratory-mixed-methods-design","name":"Sequential Exploratory Mixed Methods Design","fullName":"Sequential Exploratory Mixed Methods Design","aliases":["exploratory sequential design","SEQEXP","qual → quan design","instrument-development mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/sequential-exploratory-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/sequential-exploratory-mixed-methods-design.md","definition":"The sequential exploratory mixed methods design begins with a qualitative phase to explore a poorly understood phenomenon, then builds on those findings in a second quantitative phase — most commonly to develop and test a measurement instrument, or to test whether themes identified qualitatively generalise across a broader population. The two phases are conducted in sequence, with qualitative results explicitly informing the design and content of the quantitative strand.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2007","type":"Mixed methods research design","dataType":"Qualitative data (Phase 1) followed by quantitative data (Phase 2)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Creswell, J. W. (2015). A Concise Introduction to Mixed Methods Research. Sage.","type":"book","doi":null,"isbn":"978-1483359045","url":null}],"related":["explanatory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","exploratory-sequential-mixed-methods-design","grounded-theory","survey-research","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-intervention-mixed-methods","name":"Sequential Intervention Mixed Methods","fullName":"Sequential Intervention Mixed Methods Design","aliases":["sequential intervention MMR","intervention-embedded sequential design","sequential mixed methods intervention study","sequential clinical trial mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark (intervention design framework); extended by health and evaluation researchers","url":"https://scholargate.app/en/research-design/sequential-intervention-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/sequential-intervention-mixed-methods.md","definition":"Sequential intervention mixed methods is a research design in which quantitative and qualitative data collection phases are arranged in sequence — one after the other — within the context of a planned intervention or experimental study. The sequencing allows each phase to build on the other: quantitative data may establish whether an intervention works, while qualitative data explain how and why it works (or does not) for specific participants or contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark (intervention design framework); extended by health and evaluation researchers","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Quantitative outcome data (trials, surveys) followed or preceded by qualitative data (interviews, focus groups)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Tariq, S., & Woodman, J. (2013). Using mixed methods in health research. JRSM Short Reports, 4(6), 1–8.","type":"article","doi":"10.1177/2042533313479197","isbn":null,"url":null}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","intervention-mixed-methods-design","sequential-multiphase-mixed-methods","concurrent-intervention-mixed-methods","embedded-intervention-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-mixed-methods-matrix","name":"Sequential Mixed Methods Matrix","fullName":"Sequential Mixed Methods Matrix Design","aliases":["mixed methods joint display matrix","sequential explanatory matrix","sequential exploratory matrix","mixed methods integration matrix"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2015","originator":"John W. Creswell & Vicki L. Plano Clark (systematized); Timothy Guetterman et al. (joint display formalization)","url":"https://scholargate.app/en/research-design/sequential-mixed-methods-matrix","markdownUrl":"https://scholargate.app/en/research-design/sequential-mixed-methods-matrix.md","definition":"The sequential mixed methods matrix is an integration tool used in sequential mixed methods designs — explanatory (QUAN → qual) or exploratory (qual → QUAN) — to display and synthesize quantitative results and qualitative findings side-by-side in a structured table. Also called a joint display matrix, it makes the process of connecting the two strands of evidence explicit, transparent, and auditable, helping researchers draw meta-inferences that neither strand alone could support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark (systematized); Timothy Guetterman et al. (joint display formalization)","year":"2003–2015","type":"Mixed methods integration and visualization tool","dataType":"Quantitative results + qualitative findings (text and numbers combined)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483358468","url":null},{"ref":"Guetterman, T. C., Fetters, M. D., & Creswell, J. W. (2015). Integrating quantitative and qualitative results in health science mixed methods research through joint displays. Annals of Family Medicine, 13(6), 554–561.","type":"article","doi":"10.1370/afm.1865","isbn":null,"url":null}],"related":["mixed-methods-research","explanatory-sequential-design","exploratory-sequential-design","convergent-mixed-methods","thematic-analysis","content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-monte-carlo-with-measurement-error","name":"Sequential Monte Carlo with Measurement Error","fullName":"Sequential Monte Carlo with Measurement Error","aliases":["SMC with measurement error","particle filter with noisy observations","SMC state-space measurement error","sequential particle filtering with observation noise"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993–2001","originator":"Gordon, Salmond & Smith (1993); extended by Doucet, de Freitas & Gordon (2001)","url":"https://scholargate.app/en/bayesian/sequential-monte-carlo-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/sequential-monte-carlo-with-measurement-error.md","definition":"Sequential Monte Carlo (SMC) with measurement error is a particle-based Bayesian filtering method for tracking hidden states in dynamical systems when observations are corrupted by noise. It propagates a weighted cloud of particles through time, updating weights at each step to reflect how well each particle explains the noisy measurement, and produces a full posterior distribution over the latent state at every time point.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon, Salmond & Smith (1993); extended by Doucet, de Freitas & Gordon (2001)","year":"1993–2001","type":"Sequential Bayesian filtering","dataType":"Time series, state-space data with noisy observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer New York.","type":"book","doi":null,"isbn":"978-0-387-95146-1","url":null},{"ref":"Cappe, O., Godsill, S. J., & Moulines, E. (2007). An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 95(5), 899-924.","type":"article","doi":"10.1109/JPROC.2007.893250","isbn":null,"url":null}],"related":["sequential-monte-carlo","particle-filter","kalman-filter-with-measurement-error","bayesian-inference-with-measurement-error","dynamic-bayesian-inference","markov-chain-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-monte-carlo-with-missing-data","name":"Sequential Monte Carlo with Missing Data","fullName":"Sequential Monte Carlo with Missing Data","aliases":["SMC with missing data","particle filter with missing observations","SMC missing observations","particle smoothing with incomplete data"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993–2001","originator":"Gordon, Salmond & Smith (particle filter, 1993); missing-data extensions formalised by Doucet et al. (2000s)","url":"https://scholargate.app/en/bayesian/sequential-monte-carlo-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/sequential-monte-carlo-with-missing-data.md","definition":"Sequential Monte Carlo (SMC) with missing data extends the standard particle filter to state-space models in which some observations are absent. When an observation is missing at a given time step the update step is simply skipped: particles are propagated forward through the transition model without reweighting, preserving exact Bayesian inference under any missing-data pattern as long as missingness is ignorable (missing at random or missing completely at random).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon, Salmond & Smith (particle filter, 1993); missing-data extensions formalised by Doucet et al. (2000s)","year":"1993–2001","type":"Sequential Bayesian filtering / smoothing","dataType":"Time-series or sequential data with intermittent missing observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Doucet, A., de Freitas, N., & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York.","type":"book","doi":null,"isbn":"978-0387951461","url":null},{"ref":"Chopin, N., & Papaspiliopoulos, O. (2020). An Introduction to Sequential Monte Carlo. Springer, Cham.","type":"book","doi":"10.1007/978-3-030-47845-2","isbn":null,"url":null}],"related":["sequential-monte-carlo","particle-filter","kalman-filter-with-missing-data","bayesian-inference-with-missing-data","gibbs-sampling-with-missing-data","dynamic-sequential-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-monte-carlo","name":"Sequential Monte Carlo","fullName":"Sequential Monte Carlo Methods","aliases":["SMC","particle filter","sequential importance resampling","SMC sampler"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993 (particle filter); 2006 (SMC samplers)","originator":"Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)","url":"https://scholargate.app/en/bayesian/sequential-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/sequential-monte-carlo.md","definition":"Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)","year":"1993 (particle filter); 2006 (SMC samplers)","type":"Sequential Bayesian computation","dataType":"Sequential / time-series / multimodal continuous data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113.","type":"article","doi":"10.1049/ip-f-2.1993.0015","isbn":null,"url":null},{"ref":"Del Moral, P., Doucet, A., & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436.","type":"article","doi":"10.1111/j.1467-9868.2006.00553.x","isbn":null,"url":null}],"related":["gibbs-sampling","mcmc","particle-filter","kalman-filter","hamiltonian-monte-carlo","approximate-bayesian-computation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-pragmatic-mixed-methods","name":"Sequential Pragmatic Mixed Methods","fullName":"Sequential Pragmatic Mixed Methods Design","aliases":["sequential pragmatic MMR","pragmatic sequential mixed design","sequential pragma-driven mixed methods","pragmatism-guided sequential mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2010","originator":"Creswell & Plano Clark; Tashakkori & Teddlie (pragmatic worldview formalization)","url":"https://scholargate.app/en/research-design/sequential-pragmatic-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/sequential-pragmatic-mixed-methods.md","definition":"Sequential pragmatic mixed methods is a mixed-methods research design in which quantitative and qualitative data strands are collected and analyzed in a defined sequence — one strand following and building on the other — with the entire design anchored in a pragmatic philosophical worldview. Pragmatism foregrounds research usefulness and problem-solving, treating method choice as a practical decision rather than an ideological commitment, and it provides the integrative logic for combining the two strands into actionable insights.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Tashakkori & Teddlie (pragmatic worldview formalization)","year":"2003–2010","type":"Mixed methods research design","dataType":"Quantitative and qualitative data collected in sequential phases","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1412972666","url":null}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","sequential-multiphase-mixed-methods","pragmatic-mixed-methods-design","sequential-transformative-mixed-methods","concurrent-pragmatic-mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-qualitative-priority-mixed-design","name":"Sequential Qualitative-Priority Mixed Design","fullName":"Sequential Qualitative-Priority Mixed Methods Design","aliases":["QUAL-priority sequential design","qualitative-dominant sequential design","qual-first sequential mixed methods","sequential exploratory qualitative-priority design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2000s–2010s","originator":"Creswell & Plano Clark; Teddlie & Tashakkori","url":"https://scholargate.app/en/research-design/sequential-qualitative-priority-mixed-design","markdownUrl":"https://scholargate.app/en/research-design/sequential-qualitative-priority-mixed-design.md","definition":"Sequential qualitative-priority mixed design is a two-phase mixed methods approach in which a qualitative strand is conducted first and carries greater weight in the overall study. The quantitative phase follows and serves to extend, test, or generalize the qualitative findings. The QUAL-first, QUAL-dominant logic makes this design well suited to exploratory research where theory or instruments are underdeveloped and must be grounded in participants' own words before being scaled up.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Creswell & Plano Clark; Teddlie & Tashakkori","year":"2000s–2010s","type":"Mixed methods research design","dataType":"Qualitative data (phase 1) followed by quantitative data (phase 2)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["exploratory-sequential-mixed-methods-design","explanatory-sequential-mixed-methods-design","qualitative-priority-mixed-methods-design","sequential-quantitative-priority-mixed-design","concurrent-qualitative-priority-mixed-design","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-quantitative-priority-mixed-design","name":"Sequential Quantitative-Priority Mixed Design","fullName":"Sequential Quantitative-Priority Mixed Methods Design","aliases":["QUAN-dominant sequential design","quantitative-priority sequential MMR","quan-first sequential mixed methods","quantitative-led sequential design"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2007 (first edition of Designing and Conducting Mixed Methods Research)","originator":"John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/sequential-quantitative-priority-mixed-design","markdownUrl":"https://scholargate.app/en/research-design/sequential-quantitative-priority-mixed-design.md","definition":"The sequential quantitative-priority mixed design collects and analyzes quantitative data first, then follows with a qualitative strand to elaborate, explain, or contextualize the quantitative findings. The quantitative component is given greater weight in the overall study, meaning the primary research questions and conclusions are primarily grounded in the quantitative evidence, with the qualitative strand playing a supplementary, explanatory role.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John W. Creswell & Vicki L. Plano Clark","year":"2007 (first edition of Designing and Conducting Mixed Methods Research)","type":"Mixed methods research design","dataType":"Quantitative data (primary strand) followed by qualitative data (secondary strand)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage Publications.","type":"book","doi":null,"isbn":"978-0761930129","url":null}],"related":["explanatory-sequential-mixed-methods-design","quantitative-priority-mixed-methods-design","sequential-multiphase-mixed-methods","sequential-qualitative-priority-mixed-design","concurrent-quantitative-priority-mixed-design","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sequential-transformative-mixed-methods","name":"Sequential Transformative Mixed Methods","fullName":"Sequential Transformative Mixed Methods Design","aliases":["sequential transformative design","transformative sequential MMR","Seq-TRAN mixed methods","sequential transformative research"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2007 (Mertens 2003; Creswell & Plano Clark 2007)","originator":"Donna M. Mertens (transformative framework); John W. Creswell & Vicki L. Plano Clark (mixed methods typology)","url":"https://scholargate.app/en/research-design/sequential-transformative-mixed-methods","markdownUrl":"https://scholargate.app/en/research-design/sequential-transformative-mixed-methods.md","definition":"Sequential transformative mixed methods design combines the temporal structure of sequential mixed methods — collecting qualitative and quantitative data in two distinct, ordered phases — with a transformative theoretical framework that centres social justice, equity, and the perspectives of marginalized communities. Either the qualitative or the quantitative phase may come first; the sequence is determined by what the transformative research question demands. The design is guided by the work of Donna Mertens and is systematized in the Creswell and Plano Clark mixed methods typology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna M. Mertens (transformative framework); John W. Creswell & Vicki L. Plano Clark (mixed methods typology)","year":"2003–2007 (Mertens 2003; Creswell & Plano Clark 2007)","type":"Mixed methods research design","dataType":"Qualitative and quantitative data collected in sequential phases","subfamily":"Mixed methods design"},"citations":[{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1593856670","url":null},{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1483344379","url":null}],"related":["transformative-mixed-methods-design","sequential-explanatory-mixed-methods-design","sequential-exploratory-mixed-methods-design","participatory-mixed-methods-meta-inference","concurrent-transformative-mixed-methods","multilevel-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sers","name":"SERS","fullName":"Surface-Enhanced Raman Spectroscopy","aliases":["Surface-enhanced Raman scattering","SERS spectroscopy"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Raman Spectroscopy","year":"1974","originator":"Martin Fleischmann","url":"https://scholargate.app/en/spectroscopy/sers","markdownUrl":"https://scholargate.app/en/spectroscopy/sers.md","definition":"Surface-Enhanced Raman Spectroscopy (SERS) amplifies weak Raman signals by many orders of magnitude when analyte molecules are adsorbed on specially prepared metal (typically silver or gold) nanostructured surfaces. Discovered by Fleischmann, Hendra, and McQuillan in 1974, SERS enables detection of vibrational signatures of single molecules and ultra-trace contaminants, revolutionizing analytical chemistry and forensics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin Fleischmann","subfamily":"Raman Spectroscopy","year":"1974","type":"Vibrational spectroscopy technique"},"citations":[{"ref":"Fleischmann, M., Hendra, P. J., & McQuillan, A. J. (1974). Raman spectra of pyridine adsorbed at a silver electrode. Chemical Physics Letters, 26(2), 163-166.","type":"article","doi":"10.1016/0009-2614(74)85388-1","isbn":null,"url":null},{"ref":"Jeanmaire, D. L., & Van Duyne, R. P. (1977). Surface raman spectroelectrochemistry: Part I. Heterocyclic, aromatic, and aliphatic amines adsorbed on the anodized silver electrode. Journal of Electroanalytical Chemistry, 84(1), 1-20.","type":"article","doi":"10.1016/S0022-0728(77)80224-6","isbn":null,"url":null},{"ref":"Kneipp, K., Wang, Y., Kneipp, H., Perelman, L. T., Itzkan, I., Dasari, R. R., & Feld, M. S. (1997). Single molecule detection using surface-enhanced Raman scattering (SERS). Physical Review Letters, 78(9), 1667-1670.","type":"article","doi":"10.1103/PhysRevLett.78.1667","isbn":null,"url":null}],"related":["atr-ftir","circular-dichroism","surface-plasmon-resonance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"servant-leadership-scale","name":"Servant Leadership Scale","fullName":"Servant Leadership Scale (SLS) - Multidimensional Assessment","aliases":["SLS","Servant Leader Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"2008","originator":"Robert K. Greenleaf (concept); Robert C. Liden et al. (measurement scale)","url":"https://scholargate.app/en/organizational-behavior/servant-leadership-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/servant-leadership-scale.md","definition":"The Servant Leadership Scale (SLS), developed by Liden and colleagues in 2008, measures the extent to which leaders prioritize others' well-being and development. Building on Robert Greenleaf's 1970 concept of servant leadership, the SLS operationalizes servant leadership across seven dimensions: emotional healing, creating value for community, conceptual skills, empowering others, helping followers grow and succeed, putting followers first, and behaving ethically. The scale enables assessment of leadership styles that foster trust, engagement, and organizational effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Greenleaf (concept); Robert C. Liden et al. (measurement scale)","subfamily":"Organizational behavior","year":"2008","type":"Self-report questionnaire"},"citations":[{"ref":"Liden, R. C., Wayne, S. J., Zhao, H., & Henderson, D. (2008). Servant leadership: development of a multidimensional measure and multi-level assessment. The Leadership Quarterly, 19(2), 161-177.","type":"article","doi":"10.1016/j.leaqua.2008.01.006","isbn":null,"url":null},{"ref":"Greenleaf, R. K. (1970). The servant as leader. Indianapolis: Robert K. Greenleaf Center for Servant Leadership.","type":"article","doi":null,"isbn":null,"url":"https://www.greenleaf.org/what-is-servant-leadership/"}],"related":["transformational-leadership-scale","organizational-justice-scale","psychological-safety-scale","organizational-commitment-scale","job-satisfaction-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"service-area-analysis","name":"Service Area Analysis","fullName":"Network Service Area (Isochrone) Analysis","aliases":["Isochrone Analysis","Network Catchment Area Analysis","Travel-Time Polygon Analysis","Hizmet Alanı Analizi"],"domain":"spatial-analysis","family":"process-pipeline","subfamily":"Network GIS","year":2001,"originator":"Harvey Miller & Shih-Lung Shaw","url":"https://scholargate.app/en/spatial-analysis/service-area-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/service-area-analysis.md","definition":"Service Area Analysis delineates the geographic region reachable from one or more origin facilities within a specified travel cost — typically time, distance, or generalized impedance — by traversing a real road or transit network. It is widely used by urban planners, public health officials, logistics managers, and emergency response coordinators who need to understand actual accessibility rather than simple straight-line buffers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey Miller & Shih-Lung Shaw","year":2001,"type":"Network GIS pipeline","subfamily":"Network GIS","input":"Road network + origin point(s) + impedance threshold","output":"Polygon(s) delineating reachable area within given cost"},"citations":[{"ref":"Miller, H. J., & Shaw, S.-L. (2001). Geographic Information Systems for Transportation: Principles and Applications. Oxford University Press.","type":"book","doi":null,"isbn":"978-0-19-512394-4","url":null}],"related":["location-allocation","least-cost-path","vehicle-routing"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"servperf","name":"SERVPERF Scale","fullName":"Service Performance Scale","aliases":["Perception-Only Service Quality Scale","SERVPERF-Performance Model"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Service quality measurement","year":"1992","originator":"Joseph J. Cronin Jr., Steven A. Taylor","url":"https://scholargate.app/en/marketing-management/servperf","markdownUrl":"https://scholargate.app/en/marketing-management/servperf.md","definition":"SERVPERF, developed by Cronin and Taylor in 1992, is a streamlined service quality measurement instrument that evaluates perceived service performance only, without the expectation component. Using 22 items identical in content to SERVQUAL but applied to perception alone, SERVPERF reduces survey burden while maintaining dimensional coverage of Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Empirical evidence suggests SERVPERF performs equally well or better than SERVQUAL in explaining overall satisfaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph J. Cronin Jr., Steven A. Taylor","subfamily":"Service quality measurement","year":"1992","type":"Performance-only service quality scale"},"citations":[{"ref":"Cronin, J. J., & Taylor, S. A. (1992). Measuring Service Quality: A Reexamination and Extension. Journal of Marketing, 56(3), 55-68.","type":"article","doi":"10.1177/002224299205600304","isbn":null,"url":null},{"ref":"Taylor, S. A., & Cronin, J. J. (1994). Modeling Patient Satisfaction and Service Quality in the Healthcare Industry. Journal of Health Care Marketing, 14(1), 34-44.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/10131108"}],"related":["servqual","e-servqual","customer-satisfaction-index","hedqual"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"servqual","name":"SERVQUAL Service Quality Scale","fullName":"Service Quality Scale","aliases":["Service Quality Instrument","Gap Model"],"domain":"marketing-management","family":"process-pipeline","subfamily":"Service quality measurement","year":"1988","originator":"A. Parasuraman, Valerie A. Zeithaml, Leonard L. Berry","url":"https://scholargate.app/en/marketing-management/servqual","markdownUrl":"https://scholargate.app/en/marketing-management/servqual.md","definition":"SERVQUAL is a 22-item, multi-dimensional scale developed by Parasuraman, Zeithaml, and Berry in 1988 to measure consumer perceptions of service quality. It captures the gap between customer expectations and actual service performance across five core dimensions: Tangibles, Reliability, Responsiveness, Assurance, and Empathy. The instrument has become the most widely used tool for service quality assessment in marketing research and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"A. Parasuraman, Valerie A. Zeithaml, Leonard L. Berry","subfamily":"Service quality measurement","year":"1988","type":"Multi-dimensional service quality scale"},"citations":[{"ref":"Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing, 64(1), 12-40.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SERVQUAL%3A+A+Multiple-Item+Scale+for+Measuring+Consumer+Perceptions+of+Service+Quality+Parasuraman"},{"ref":"Zeithaml, V. A., Parasuraman, A., & Berry, L. L. (1990). Delivering Quality Service: Balancing Customer Perceptions and Expectations. Free Press.","type":"article","doi":null,"isbn":"978-0029362174","url":null}],"related":["servperf","e-servqual","hedqual","customer-satisfaction-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"session-rating-scale","name":"Session Rating Scale","fullName":"Session Rating Scale (SRS)","aliases":["SRS","SRS-4"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"therapeutic-alliance","year":"2000","originator":"Scott D. Miller, Barry L. Duncan","url":"https://scholargate.app/en/psychotherapy-research/session-rating-scale","markdownUrl":"https://scholargate.app/en/psychotherapy-research/session-rating-scale.md","definition":"The Session Rating Scale (SRS) is a 4-item ultra-brief measure of client perceptions of session quality and therapeutic alliance, developed by Miller and Duncan to support real-time feedback in psychotherapy. Administered after each session, the SRS captures client satisfaction with the relationship, alignment on goals and topics, and the therapist's approach, offering immediate insight for therapeutic adjustment. The measure is designed to operationalize common factors of psychotherapy outcome and enable therapists to respond to client feedback in vivo.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Scott D. Miller, Barry L. Duncan","subfamily":"therapeutic-alliance","year":"2000","type":"Client-rated"},"citations":[{"ref":"Miller, S. D., Duncan, B. L., Brown, J., Sparks, J. A., & Claud, D. A. (2003). The Outcome Rating Scale: Preliminary validity studies of a brief, visual, general measure of session effectiveness. Journal of Brief Therapy, 5(2), 23–33.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Miller%2C%20S.%20D.%2C%20Duncan%2C%20B.%20L.%2C%20Brown%2C%20J.%2C%20Sparks%2C%20J.%20A.%2C%20%26%20Claud%2C%20D.%20A.%20(2003).%20The%20Outcome%20Rating%20Scale%3A%20Preliminary%20val"},{"ref":"Campbell, A., & Hemsley, S. (2012). Outcome Rating Scale and Session Rating Scale in psychological practice: Clinical utility of ultra-brief measures. Clinical Psychology, 16(1), 37–46.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Outcome+Rating+Scale+and+Session+Rating+Scale+in+psychological+practice%3A+Clinical+utility+of+ultra-brief+measures+Campbell"}],"related":["working-alliance-inventory","outcome-rating-scale","therapeutic-alliance-scale","common-factors-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"session-rpe","name":"Session RPE","fullName":"Session Rate of Perceived Exertion and Training Load Quantification","aliases":["sRPE","perceived exertion","subjective load"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Training Load","year":"2001","originator":"Carl Foster","url":"https://scholargate.app/en/sports-science/session-rpe","markdownUrl":"https://scholargate.app/en/sports-science/session-rpe.md","definition":"Session rate of perceived exertion (sRPE) is a simple, athlete-centered method to quantify training load by combining perceived exertion intensity (RPE, 0-10 scale) with session duration. Introduced by Carl Foster (2001), sRPE avoids the need for external equipment (heart rate monitors, GPS, force plates) and captures the integrated physiological and psychological demands of any training modality. Despite its simplicity, sRPE correlates well with objective physiological markers (heart rate, lactate, VO2) and is widely adopted in elite and recreational sports for load management and recovery planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Foster","subfamily":"Training Load","year":"2001","type":"subjective intensity assessment"},"citations":[{"ref":"Foster, C., Florhaug, J. A., Franklin, J., Gottschall, L., Hrovatin, L. A., Parker, S., & Dodge, C. (2001). A new approach to monitoring exercise training. Journal of Strength and Conditioning Research, 15(1), 109-115.","type":"article","doi":"10.1519/00124278-200102000-00019","isbn":null,"url":null},{"ref":"Sweet, T. W., Foster, C., McGuigan, M. R., & Brice, G. (2014). Quantitation of resistance training using the session rating of perceived exertion scale. Journal of Strength and Conditioning Research, 28(3), 619-622.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quantitation+of+resistance+training+using+the+session+rating+of+perceived+exertion+scale+Sweet"},{"ref":"Impellizzeri, F. M., Rampinini, E., Coutts, A. J., Sassi, A., & Marcora, S. M. (2004). Use of RPE-based training load in soccer. Medicine & Science in Sports & Exercise, 36(6), 1042-1047.","type":"article","doi":"10.1249/01.MSS.0000128199.23901.2F","isbn":null,"url":null}],"related":["acute-chronic-workload-ratio","time-motion-gps","banister-trimp"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sexual-harassment-experiences-questionnaire","name":"Sexual Harassment Experiences Questionnaire","fullName":"Sexual Harassment Experiences Questionnaire (SHEQ)","aliases":["SHEQ","Workplace Sexual Harassment Scale"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-harassment","year":"1995","originator":"Fitzgerald, Gelfand, & Drasgow","url":"https://scholargate.app/en/occupational-health/sexual-harassment-experiences-questionnaire","markdownUrl":"https://scholargate.app/en/occupational-health/sexual-harassment-experiences-questionnaire.md","definition":"The Sexual Harassment Experiences Questionnaire measures employee exposure to unwanted sexual behavior, comments, and coercion in the workplace. Developed by Fitzgerald, Gelfand, and Drasgow, the SHEQ distinguishes between gender harassment, unwanted sexual attention, and sexual coercion—recognizing that sexual harassment spans a continuum from crude jokes to assault. The scale is foundational for occupational health surveillance, legal compliance documentation, and evaluation of organizational prevention efforts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fitzgerald, Gelfand, & Drasgow","subfamily":"occupational-harassment","year":"1995","type":"Self-report"},"citations":[{"ref":"Fitzgerald, L. F., Gelfand, M. J., & Drasgow, F. (1995). Measuring sexual harassment: Theoretical and psychometric advances. Basic Appl Soc Psychol, 17(4), 425–445.","type":"article","doi":"10.1207/s15324834basp1704_2","isbn":null,"url":null},{"ref":"Gruber, J. E. (1998). The impact of male work environments and organizational policies on women's experiences of sexual harassment. Gender Work Organ, 5(2), 86–104.","type":"article","doi":"10.1177/0891243298012003004","isbn":null,"url":null}],"related":["workplace-violence-scale","workplace-ostracism-scale","psychosocial-safety-climate-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sexual-satisfaction-scale","name":"Sexual Satisfaction Scale","fullName":"Sexual Satisfaction Scale (SSS)","aliases":["SSS","Kansas City Sexual Satisfaction Scale"],"domain":"urology-gynecology","family":"process-pipeline","subfamily":"sexual-satisfaction","year":2003,"originator":"Ponticas et al.","url":"https://scholargate.app/en/urology-gynecology/sexual-satisfaction-scale","markdownUrl":"https://scholargate.app/en/urology-gynecology/sexual-satisfaction-scale.md","definition":"The Sexual Satisfaction Scale (also known as the Kansas City Sexual Satisfaction Scale) is a brief, unidimensional self-report measure designed to assess subjective satisfaction with sexual life and sexual relationships. First published by Ponticas in 2003, it comprises typically 5–7 items measuring global sexual satisfaction on simple Likert or semantic differential scales. Its brevity and conceptual clarity make it useful for rapid assessment in clinical and research settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ponticas et al.","subfamily":"sexual-satisfaction","year":2003,"type":"Self-report satisfaction scale"},"citations":[{"ref":"Ponticas, Y. (2003). Validity and reliability of a brief sexual satisfaction scale. Dissertation Abstracts International, 64(3-A), 835.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/12755589"},{"ref":"Zou, H., Ong, K. L., Johnson, M. P., & Sanders, K. A. (2009). Epidemiology of erectile dysfunction: a systematic review of prevalence and incidence studies. Journal of Sexual Medicine, 5(3), 650–660.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Epidemiology+of+erectile+dysfunction%3A+a+systematic+review+of+prevalence+and+incidence+studies+Zou"}],"related":["female-sexual-function-index","international-index-erectile-function","female-sexual-distress-scale","male-sexual-health-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-12","name":"SF-12 Health Survey","fullName":"Short Form 12-Item Health Survey","aliases":["SF-12v2","Medical Outcomes Study SF-12"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Health-related quality of life","year":"1996","originator":"John E. Ware Jr., Mark Kosinski, and Susan Keller","url":"https://scholargate.app/en/health-measurement/sf-12","markdownUrl":"https://scholargate.app/en/health-measurement/sf-12.md","definition":"The SF-12 is a brief, 12-item version of the SF-36 health survey developed by Ware, Kosinski, and Keller in 1996. Designed to reduce respondent burden while maintaining psychometric validity, it has become the standard instrument for large-scale surveys, epidemiological studies, and health outcomes research where administration time is critical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John E. Ware Jr., Mark Kosinski, and Susan Keller","subfamily":"Health-related quality of life","year":"1996","type":"Brief self-report health status instrument"},"citations":[{"ref":"Ware, J. E., Kosinski, M., & Keller, S. D. (1996). A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Medical Care, 34(3), 220–233.","type":"article","doi":"10.1097/00005650-199603000-00003","isbn":null,"url":null},{"ref":"Ware, J. E., Kosinski, M., & Keller, S. D. (2002). SF-12: How to score the SF-12 Physical and Mental Health Summary Scales (3rd ed.). QualityMetric.","type":"article","doi":null,"isbn":null,"url":"https://www.qualitymetric.com"},{"ref":"Gandek, B., Ware, J. E., Aaronson, N. K., et al. (2004). Cross-validation of item selection and scoring for the SF-12 Health Survey in nine countries. Journal of Clinical Epidemiology, 51(11), 1171–1178.","type":"article","doi":"10.1016/S0895-4356(98)00109-7","isbn":null,"url":null}],"related":["sf-36","sf-8","whoqol-bref","eq-5d"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-36","name":"SF-36 Health Survey","fullName":"Short Form 36-Item Health Survey","aliases":["SF-36 Questionnaire","Medical Outcomes Study SF-36"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Health-related quality of life","year":"1992","originator":"John E. Ware Jr. and Cathy D. Sherbourne","url":"https://scholargate.app/en/health-measurement/sf-36","markdownUrl":"https://scholargate.app/en/health-measurement/sf-36.md","definition":"The SF-36 is a generic, self-administered 36-item questionnaire measuring eight dimensions of health status. Developed by Ware and Sherbourne in 1992, it has become the most widely used health survey in clinical trials, outcomes research, and population health monitoring. It assesses perceived health across physical and mental domains relevant to the general adult population.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John E. Ware Jr. and Cathy D. Sherbourne","subfamily":"Health-related quality of life","year":"1992","type":"Self-report health status instrument"},"citations":[{"ref":"Ware, J. E., & Sherbourne, C. D. (1992). The MOS 36-item Short-Form Health Survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30(6), 473–483.","type":"article","doi":"10.1097/00005650-199206000-00002","isbn":null,"url":null},{"ref":"Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (1993). SF-36 Health Survey: Manual and interpretation guide. New England Medical Center.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SF-36+Health+Survey%3A+Manual+and+interpretation+guide+Ware"},{"ref":"McHorney, C. A., Ware, J. E., & Raczek, A. E. (1994). The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity. Medical Care, 32(3), 217–226.","type":"article","doi":"10.1097/00005650-199303000-00006","isbn":null,"url":null}],"related":["sf-12","sf-8","whoqol-bref","eq-5d","promis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-8","name":"SF-8 Health Survey","fullName":"Short Form 8-Item Health Survey","aliases":["SF-8 Questionnaire","Medical Outcomes Study SF-8"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Health-related quality of life","year":"2005","originator":"John E. Ware Jr., Mark Kosinski, and colleagues","url":"https://scholargate.app/en/health-measurement/sf-8","markdownUrl":"https://scholargate.app/en/health-measurement/sf-8.md","definition":"The SF-8 is an ultra-brief, 8-item version of the SF-36 health survey developed by Ware and colleagues in 2005. Designed for extreme time-constraint settings and large-scale epidemiological surveys, the SF-8 maintains strong correlation with SF-36 and SF-12 domains while requiring only 1–2 minutes to complete.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John E. Ware Jr., Mark Kosinski, and colleagues","subfamily":"Health-related quality of life","year":"2005","type":"Ultra-brief self-report health status instrument"},"citations":[{"ref":"Ware, J. E., Kosinski, M., Dewey, J. E., & Gandek, B. (2005). How to score and interpret single-item health status measures: a manual for users of the SF-8 Health Survey. QualityMetric Inc.","type":"article","doi":null,"isbn":null,"url":"https://www.qualitymetric.com"},{"ref":"Ware, J. E., & Kosinski, M. (2001). The SF-8 Health Survey vs. the SF-12 Health Survey for the assessment of health-related quality of life. Quality of Life Newsletter, 27, 3–4.","type":"article","doi":null,"isbn":null,"url":"https://www.qualitymetric.com"},{"ref":"Jenkinson, C., Chandola, T., Stafford, M., & Marmot, M. (2005). The Whitehall II Study: the short Form 8 health survey questionnaire (SF-8): psychometric properties and normative data for the general population. Journal of Public Health Medicine, 27(1), 65–71.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Whitehall+II+Study%3A+the+short+Form+8+health+survey+questionnaire+%28SF-8%29%3A+psychometric+properties+and+normative+data+for+the+general+population+Jenkinson"}],"related":["sf-36","sf-12","whoqol-bref","eq-5d","promis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-aras","name":"SF-ARAS","fullName":"Spherical extension of ARAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Kutlu Gündoğdu, F. Kahraman, C.","url":"https://scholargate.app/en/decision-making/sf-aras","markdownUrl":"https://scholargate.app/en/decision-making/sf-aras.md","definition":"SF-ARAS (Spherical extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu, F. Kahraman, C.","subfamily":"Ranking","year":"2019","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181401","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-cocoso","name":"SF-COCOSO","fullName":"SF-CoCoSo — Spherical extension of COCOSO","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Kutlu Gündoğdu, F. Kahraman, C.","url":"https://scholargate.app/en/decision-making/sf-cocoso","markdownUrl":"https://scholargate.app/en/decision-making/sf-cocoso.md","definition":"SF-COCOSO (SF-CoCoSo — Spherical extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu, F. Kahraman, C.","subfamily":"Ranking","year":"2019","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181401","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-codas","name":"SF-CODAS","fullName":"Spherical extension of CODAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019 / 2021","originator":"Karaşan, Boltürk & Kutlu Gündoğdu (book chapter); earlier Kutlu Gündoğdu-Kahraman 2019c JMVLSC","url":"https://scholargate.app/en/decision-making/sf-codas","markdownUrl":"https://scholargate.app/en/decision-making/sf-codas.md","definition":"SF-CODAS (Spherical extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Karaşan, Boltürk & Kutlu Gündoğdu (book chapter); earlier Kutlu Gündoğdu-Kahraman 2019c JMVLSC in 2019 / 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karaşan, Boltürk & Kutlu Gündoğdu (book chapter); earlier Kutlu Gündoğdu-Kahraman 2019c JMVLSC","subfamily":"Ranking","year":"2019 / 2021","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Karaşan, A., Boltürk, E., Kutlu Gündoğdu, F. (2021). Assessment of Livability Indices of Suburban Places of Istanbul by Using Spherical Fuzzy CODAS Method. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer","type":"article","doi":"10.1007/978-3-030-45461-6_12","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-copras","name":"SF-COPRAS","fullName":"Spherical extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Kutlu Gündoğdu, F. Kahraman, C.","url":"https://scholargate.app/en/decision-making/sf-copras","markdownUrl":"https://scholargate.app/en/decision-making/sf-copras.md","definition":"SF-COPRAS (Spherical extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu, F. Kahraman, C.","subfamily":"Ranking","year":"2019","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181401","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-edas","name":"SF-EDAS","fullName":"Spherical extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Garg, H., Sharaf, I.M.","url":"https://scholargate.app/en/decision-making/sf-edas","markdownUrl":"https://scholargate.app/en/decision-making/sf-edas.md","definition":"SF-EDAS (Spherical extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Garg, H., Sharaf, I.M. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Garg, H., Sharaf, I.M.","subfamily":"Ranking","year":"2022","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Garg, H., Sharaf, I.M. (2022). A new spherical aggregation function with the concept of spherical fuzzy difference for spherical fuzzy EDAS and its application to industrial robot selection. Computational and Applied Mathematics","type":"article","doi":"10.1007/s40314-022-01903-5","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-gra","name":"SF-GRA","fullName":"Spherical extension of GRA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Kutlu Gündoğdu, F. Kahraman, C.","url":"https://scholargate.app/en/decision-making/sf-gra","markdownUrl":"https://scholargate.app/en/decision-making/sf-gra.md","definition":"SF-GRA (Spherical extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu, F. Kahraman, C.","subfamily":"Ranking","year":"2019","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181401","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-mabac","name":"SF-MABAC","fullName":"Spherical extension of MABAC","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Kutlu Gündoğdu, F. Kahraman, C.","url":"https://scholargate.app/en/decision-making/sf-mabac","markdownUrl":"https://scholargate.app/en/decision-making/sf-mabac.md","definition":"SF-MABAC (Spherical extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu, F. Kahraman, C.","subfamily":"Ranking","year":"2019","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181401","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-marcos","name":"SF-MARCOS","fullName":"Spherical extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Kutlu Gündoğdu, F. Kahraman, C.","url":"https://scholargate.app/en/decision-making/sf-marcos","markdownUrl":"https://scholargate.app/en/decision-making/sf-marcos.md","definition":"SF-MARCOS (Spherical extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu, F. Kahraman, C.","subfamily":"Ranking","year":"2019","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181401","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-moora","name":"SF-MOORA","fullName":"Spherical extension of MOORA","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Kutlu Gündoğdu, F. Kahraman, C.","url":"https://scholargate.app/en/decision-making/sf-moora","markdownUrl":"https://scholargate.app/en/decision-making/sf-moora.md","definition":"SF-MOORA (Spherical extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu, F. Kahraman, C.","subfamily":"Ranking","year":"2019","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181401","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-promethee","name":"SF-PROMETHEE","fullName":"Spherical extension of PROMETHEE","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2021","originator":"Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019","url":"https://scholargate.app/en/decision-making/sf-promethee","markdownUrl":"https://scholargate.app/en/decision-making/sf-promethee.md","definition":"SF-PROMETHEE (Spherical extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019 in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019","subfamily":"Outranking","year":"2021","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Sharaf, I. M. (2021). Evaluating Geothermal Energy Systems Using Spherical Fuzzy PROMETHEE. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer","type":"article","doi":"10.1007/978-3-030-45461-6_16","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-saw","name":"SF-SAW","fullName":"Spherical extension of SAW","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Kutlu Gündoğdu & Yörükoğlu","url":"https://scholargate.app/en/decision-making/sf-saw","markdownUrl":"https://scholargate.app/en/decision-making/sf-saw.md","definition":"SF-SAW (Spherical extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu & Yörükoğlu in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu & Yörükoğlu","subfamily":"Ranking","year":"2021","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kutlu Gündoğdu, F., Yörükoğlu, M. (2021). Simple Additive Weighting and Weighted Product Methods Using Spherical Fuzzy Sets. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer","type":"article","doi":"10.1007/978-3-030-45461-6_10","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-todim","name":"SF-TODIM","fullName":"Spherical extension of TODIM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Kutlu Gündoğdu, F. Kahraman, C.","url":"https://scholargate.app/en/decision-making/sf-todim","markdownUrl":"https://scholargate.app/en/decision-making/sf-todim.md","definition":"SF-TODIM (Spherical extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu, F. Kahraman, C.","subfamily":"Ranking","year":"2019","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181401","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-topsis","name":"SF-TOPSIS","fullName":"Spherical extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Kutlu Gündoğdu & Kahraman","url":"https://scholargate.app/en/decision-making/sf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/sf-topsis.md","definition":"SF-TOPSIS (Spherical extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu & Kahraman in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu & Kahraman","subfamily":"Ranking","year":"2019","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems","type":"article","doi":"10.3233/JIFS-181401","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-vikor","name":"SF-VIKOR","fullName":"Spherical extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019","url":"https://scholargate.app/en/decision-making/sf-vikor","markdownUrl":"https://scholargate.app/en/decision-making/sf-vikor.md","definition":"SF-VIKOR (Spherical extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019 in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019","subfamily":"Ranking","year":"2021","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Sharaf, I. M. (2021). Spherical Fuzzy VIKOR with SWAM and SWGM Operators for MCDM. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer","type":"article","doi":"10.1007/978-3-030-45461-6_9","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-waspas","name":"SF-WASPAS","fullName":"Spherical extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Boltürk & Kutlu Gündoğdu","url":"https://scholargate.app/en/decision-making/sf-waspas","markdownUrl":"https://scholargate.app/en/decision-making/sf-waspas.md","definition":"SF-WASPAS (Spherical extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Boltürk & Kutlu Gündoğdu in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Boltürk & Kutlu Gündoğdu","subfamily":"Ranking","year":"2021","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Boltürk, E., Kutlu Gündoğdu, F. (2021). Prioritizing Manufacturing Challenges of a Contract Manufacturing Company for Personal Auto by Using Spherical WASPAS Method. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer","type":"article","doi":"10.1007/978-3-030-45461-6_11","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sf-wpm","name":"SF-WPM","fullName":"Spherical extension of WPM","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Kutlu Gündoğdu & Yörükoğlu","url":"https://scholargate.app/en/decision-making/sf-wpm","markdownUrl":"https://scholargate.app/en/decision-making/sf-wpm.md","definition":"SF-WPM (Spherical extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu & Yörükoğlu in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kutlu Gündoğdu & Yörükoğlu","subfamily":"Ranking","year":"2021","type":"Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)","value_space":"spherical","uncertainty":"epistemic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Kutlu Gündoğdu, F., Yörükoğlu, M. (2021). Simple Additive Weighting and Weighted Product Methods Using Spherical Fuzzy Sets. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer","type":"article","doi":"10.1007/978-3-030-45461-6_10","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sfzn-cradis","name":"SFZN-CRADIS","fullName":"Spherical Fuzzy Z-Number CRADIS Ranking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Distance","year":"2024","originator":"Niu, J.","url":"https://scholargate.app/en/decision-making/sfzn-cradis","markdownUrl":"https://scholargate.app/en/decision-making/sfzn-cradis.md","definition":"SFZN-CRADIS (Spherical Fuzzy Z-Number CRADIS Ranking) is a distance multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Niu, J.","subfamily":"Distance","year":"2024","type":"Compromise ranking via dual distance to global ideal and anti-ideal under spherical Z-number uncertainty","value_space":"spherical_fuzzy_z_number","uncertainty":"hybrid","compensation":"full"},"citations":[{"ref":"Niu, J. (2024). Spherical Fuzzy Z-Numbers-based CRITIC CRADIAS and MARCOS Approaches for Evaluating English Teacher Performance. International Journal of Advanced Computer Science and Applications (IJACSA)","type":"article","doi":"10.14569/ijacsa.2024.01503115","isbn":null,"url":null}],"related":["sfzn-critic","sfzn-ahp","sf-ahp","any-sfzn-compatible-weighting"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sfzn-critic","name":"SFZN-CRITIC","fullName":"Spherical Fuzzy Z-Number CRITIC Weighting","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weighting","year":"2024","originator":"Niu, J.","url":"https://scholargate.app/en/decision-making/sfzn-critic","markdownUrl":"https://scholargate.app/en/decision-making/sfzn-critic.md","definition":"SFZN-CRITIC (Spherical Fuzzy Z-Number CRITIC Weighting) is a weighting multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Niu, J.","subfamily":"Weighting","year":"2024","type":"Objective weighting from criterion variability + cross-criterion correlation under spherical Z-number uncertainty","value_space":"spherical_fuzzy_z_number","uncertainty":"hybrid","compensation":"full"},"citations":[{"ref":"Niu, J. (2024). Spherical Fuzzy Z-Numbers-based CRITIC CRADIAS and MARCOS Approaches for Evaluating English Teacher Performance. International Journal of Advanced Computer Science and Applications (IJACSA)","type":"article","doi":"10.14569/ijacsa.2024.01503115","isbn":null,"url":null}],"related":["sfzn-cradis","sfzn-marcos","sfzn-topsis","sfzn-waspas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sfzn-marcos","name":"SFZN-MARCOS","fullName":"Spherical Fuzzy Z-Number MARCOS Ranking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Distance","year":"2024","originator":"Niu, J.","url":"https://scholargate.app/en/decision-making/sfzn-marcos","markdownUrl":"https://scholargate.app/en/decision-making/sfzn-marcos.md","definition":"SFZN-MARCOS (Spherical Fuzzy Z-Number MARCOS Ranking) is a distance multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Niu, J.","subfamily":"Distance","year":"2024","type":"Compromise ranking via extended-matrix utility degrees relative to anti-ideal and ideal anchors under spherical Z-number uncertainty","value_space":"spherical_fuzzy_z_number","uncertainty":"hybrid","compensation":"full"},"citations":[{"ref":"Niu, J. (2024). Spherical Fuzzy Z-Numbers-based CRITIC CRADIAS and MARCOS Approaches for Evaluating English Teacher Performance. International Journal of Advanced Computer Science and Applications (IJACSA)","type":"article","doi":"10.14569/ijacsa.2024.01503115","isbn":null,"url":null}],"related":["sfzn-critic","sfzn-ahp","sf-ahp","any-sfzn-compatible-weighting"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sgp4-tle-propagation","name":"SGP4 TLE Propagation","fullName":"Simplified General Perturbations 4 with Two-Line Element Set","aliases":["SGP4","TLE propagation","simplified perturbations"],"domain":"aerospace","family":"process-pipeline","subfamily":"Orbital Mechanics","year":"1970s","originator":"NORAD, USAF","url":"https://scholargate.app/en/aerospace/sgp4-tle-propagation","markdownUrl":"https://scholargate.app/en/aerospace/sgp4-tle-propagation.md","definition":"SGP4 (Simplified General Perturbations 4) is a rapid orbital propagation method that predicts satellite position and velocity from Two-Line Element (TLE) sets published by NORAD. Developed in the 1970s, SGP4 accounts for atmospheric drag, gravitational perturbations, and solar radiation pressure using simplified analytical models. SGP4 is the de facto standard for space surveillance, conjunction assessment, and satellite tracking.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"NORAD, USAF","subfamily":"Orbital Mechanics","year":"1970s","type":"Propagation method"},"citations":[{"ref":"Vallado, D. A., Crawford, P., Hujsa, R., & Kelso, T. S. (2006). Revisiting Spacetrack Report Number 3. In AIAA/AAS Astrodynamics Specialist Conference.","type":"book","doi":"10.2514/6.2006-6753","isbn":null,"url":null},{"ref":"Kelso, T. S. (1995). Analysis of the Iridium 33/Cosmos 2251 Collision. CelesTrak.","type":"article","doi":null,"isbn":null,"url":"https://celestrak.com"},{"ref":"Hoots, F. R., & Roehrich, R. L. (1980). Models for Propagation of NORAD Element Sets. Spacetrack Report No. 3.","type":"article","doi":null,"isbn":null,"url":"https://www.celestrak.org/NORAD/documentation/"}],"related":["quaternion-attitude","b-dot-controller","tcas"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sha-hash-function","name":"SHA Hash Function","fullName":"Secure Hash Algorithm Function Family","aliases":["SHA-1","SHA-256","SHA-512","Secure Hash Algorithm"],"domain":"cryptography","family":"process-pipeline","subfamily":"Cryptographic hash function","year":"1993","originator":"National Institute of Standards and Technology (NIST)","url":"https://scholargate.app/en/cryptography/sha-hash-function","markdownUrl":"https://scholargate.app/en/cryptography/sha-hash-function.md","definition":"The Secure Hash Algorithm (SHA) is a family of cryptographic hash functions standardized by NIST starting in 1993. SHA functions produce fixed-length digests from arbitrary-length input data, serving as a fundamental building block for digital signatures, message authentication, and data integrity verification across security-critical applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"National Institute of Standards and Technology (NIST)","subfamily":"Cryptographic hash function","year":"1993","type":"One-way hash algorithm"},"citations":[{"ref":"National Institute of Standards and Technology (1993). Secure Hash Standard (SHS). Federal Information Processing Standards (FIPS) Publication 180.","type":"report","doi":null,"isbn":null,"url":"https://csrc.nist.gov/publications/detail/fips/180-4/final"},{"ref":"Wang, X., Yin, Y. L., & Yu, H. (2005). Finding collisions in the full SHA-1. Proceedings of CRYPTO 2005, Lecture Notes in Computer Science, 3621, 17–36.","type":"article","doi":"10.1007/11535218_2","isbn":null,"url":null},{"ref":"Stevens, M., Bursztein, E., Karpman, P., Albertini, A., & Markov, Y. (2013). The first collision for full SHA-1. Advances in Cryptology – CRYPTO 2017, 570–596.","type":"article","doi":null,"isbn":null,"url":"https://shattered.io"}],"related":["rsa-cryptosystem-analysis","digital-signature-scheme","symmetric-key-analysis","tls-protocol-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"shade-selection-dentistry","name":"Shade Selection in Dentistry","fullName":"Tooth Shade Selection for Esthetic Restorations","aliases":["shade matching","color selection","tooth shade guide","spectrophotometry"],"domain":"dentistry","family":"process-pipeline","subfamily":"Prosthodontics and esthetic dentistry","year":"1980s-1990s (formalized standards)","originator":"Multiple innovators in restorative dentistry","url":"https://scholargate.app/en/dentistry/shade-selection-dentistry","markdownUrl":"https://scholargate.app/en/dentistry/shade-selection-dentistry.md","definition":"Shade selection in dentistry is the process of determining the appropriate color and translucency of tooth-colored restorative materials to match the patient's natural dentition. Systematic shade selection involves visual assessment using calibrated shade guides, spectrophotometric measurement, and sometimes digital imaging to ensure esthetic harmony. Accurate shade selection is critical for patient satisfaction with restorations and requires understanding of tooth color dimensions (hue, value, chroma) and the optical properties of restorative materials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple innovators in restorative dentistry","subfamily":"Prosthodontics and esthetic dentistry","year":"1980s-1990s (formalized standards)","type":"Esthetic assessment and selection"},"citations":[{"ref":"Guan, X. Y., Hao, Z. X., Zhou, G. Z., & Fang, C. X. (2005). Clinical evaluation of shade matching in tooth-colored restoratives. Chinese Journal of Dental Research, 8(1), 35-38.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/16145254/"},{"ref":"O'Neill, O. N., Eustaquio, A. R., & Nishii, Y. (2018). Tooth shade matching: The role of spectrophotometry and colorimetry in restorative dentistry. Compendium of Continuing Education in Dentistry, 39(4), 234-239.","type":"article","doi":null,"isbn":null,"url":"https://www.researchgate.net/publication/325080639"},{"ref":"Paul, S., Peter, A., Pietrobon, N., & Hämmerle, C. H. (2007). Visual and spectrophotometric shade matching of tooth-colored restorations: A clinical comparison. Quintessence International, 38(1), 15-23.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/17205181/"}],"related":["occlusal-analysis","bitewing-radiography","tooth-mobility-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"shannon-capacity","name":"Shannon Capacity","fullName":"Shannon Channel Capacity Theorem","aliases":["channel capacity","information theory bound"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Information theory","year":"1948","originator":"Claude Shannon","url":"https://scholargate.app/en/telecommunications/shannon-capacity","markdownUrl":"https://scholargate.app/en/telecommunications/shannon-capacity.md","definition":"Shannon's channel capacity theorem, published in 1948, establishes the maximum rate at which information can be reliably transmitted over a noisy channel. Expressed as C = B log2(1 + S/N) for additive white Gaussian noise (AWGN), it is a fundamental bound in information theory and communications engineering. Shannon proved that reliable communication is possible at any rate below capacity, and impossible above it. This theorem underpins the design of all modern communication systems and motivates coding theory, modulation, and signal processing techniques.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Claude Shannon","subfamily":"Information theory","year":"1948","type":"fundamental theoretical bound"},"citations":[{"ref":"Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.","type":"article","doi":"10.1002/j.1538-7305.1948.tb01338.x","isbn":null,"url":null},{"ref":"Cover, T. M., & Thomas, J. A. (1991). Elements of Information Theory. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com"}],"related":["ofdm","mimo","turbo-code","ldpc-codes","polar-codes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"shap-analysis","name":"SHAP","fullName":"SHAP (SHapley Additive exPlanations)","aliases":["SHAP Değerleri (Model Açıklanabilirlik)","Shapley additive explanations","SHAP values","model explainability"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Lundberg, S.M. & Lee, S.-I.","url":"https://scholargate.app/en/machine-learning/shap-analysis","markdownUrl":"https://scholargate.app/en/machine-learning/shap-analysis.md","definition":"SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lundberg, S.M. & Lee, S.-I.","year":2017,"type":"Model-explanation method (Shapley-value attribution)","task":"Interpreting predictions of trained ML models","scope":"Global (feature importance) and local (single-prediction) explanations","minSample":30},"citations":[{"ref":"Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1705.07874"}],"related":["random-forest","xgboost","decision-tree","gaussian-mixture","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"shapiro-wilk-test","name":"Shapiro-Wilk test","fullName":"Shapiro-Wilk normality test","aliases":["Shapiro-Wilk W test","W test for normality","Shapiro-Wilk normallik testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1965,"originator":"S. S. Shapiro & M. B. Wilk","url":"https://scholargate.app/en/statistics/shapiro-wilk-test","markdownUrl":"https://scholargate.app/en/statistics/shapiro-wilk-test.md","definition":"The Shapiro-Wilk test is a hypothesis test that checks whether a continuous variable was drawn from a normal distribution. It was introduced by Samuel Shapiro and Martin Wilk in 1965 and is regarded as one of the most powerful normality tests, recommended for sample sizes below 5000.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"S. S. Shapiro & M. B. Wilk","year":1965,"family":"Hypothesis test","type":"Normality (goodness-of-fit) test","groups":1,"outcome":"continuous","parametric":true,"distribution":"Shapiro-Wilk W statistic","df":"not applicable (statistic bounded in (0, 1])"},"citations":[{"ref":"Shapiro, S. S. & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591–611.","type":"article","doi":"10.1093/biomet/52.3-4.591","isbn":null,"url":null}],"related":["kolmogorov-smirnov-test","independent-t-test","one-way-anova","levene-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"shapley-value","name":"Shapley Value","fullName":"Shapley Value for Coalition Games","aliases":["Fair Division","Cooperative Game Solution","Dividend Vector"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1953","originator":"Lloyd Shapley","url":"https://scholargate.app/en/game-theory/shapley-value","markdownUrl":"https://scholargate.app/en/game-theory/shapley-value.md","definition":"The Shapley Value is a solution concept for coalition games that distributes total payoff fairly among players based on their marginal contributions to coalitions. Introduced by Lloyd Shapley in 1953, the Shapley Value is the unique payoff distribution that satisfies four intuitive axioms: efficiency (total payoff is distributed), symmetry (identical players receive equal payoff), null player (players contributing nothing receive nothing), and additivity across games.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lloyd Shapley","subfamily":"Game-theoretic","year":"1953","type":"algorithm"},"citations":[{"ref":"Shapley, L. S. (1953). A value for n-person games. In H. W. Kuhn & A. W. Tucker (Eds.), Contributions to the Theory of Games II (pp. 307-317). Princeton University Press.","type":"article","doi":"10.1515/9781400881970-018","isbn":null,"url":null},{"ref":"Roth, A. E. (1988). The Shapley value as a von Neumann-Morgenstern utility. Econometrica, 56(4), 745-794.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Shapley+value+as+a+von+Neumann-Morgenstern+utility+Roth"}],"related":["nash-equilibrium","vcg-mechanism","top-trading-cycles","principal-agent-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sheehan-disability-scale","name":"Sheehan Disability Scale","fullName":"Sheehan Disability Scale (SDS)","aliases":["SDS","Sheehan Disability Scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"functional-impairment-assessment","year":"1983","originator":"David V. Sheehan","url":"https://scholargate.app/en/clinical-psychology/sheehan-disability-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/sheehan-disability-scale.md","definition":"The Sheehan Disability Scale is a brief three-item self-report measure designed by David V. Sheehan to assess functional impairment across work/school, social life, and family life domains in psychiatric and medical disorders. First described in Sheehan's 1983 book 'The Anxiety Disease' and validated in multiple studies since, the SDS quantifies the degree to which a patient's illness interferes with major life domains. It is widely used in psychiatric research and clinical practice to assess the functional impact of depression, anxiety, ADHD, and other conditions, complementing symptom severity measures by capturing real-world impairment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David V. Sheehan","subfamily":"functional-impairment-assessment","year":"1983","type":"Self-report questionnaire"},"citations":[{"ref":"Sheehan, D. V. (1983). The Anxiety Disease. New York: Scribner.","type":"article","doi":null,"isbn":"9780684183078","url":null},{"ref":"Leon, A. C., Olfson, M., & Portera, L. (1997). Assessing psychiatric impairment in primary care with the Sheehan Disability Scale. International Journal of Psychiatry in Medicine, 27(2), 93–105.","type":"article","doi":"10.2190/T8EM-C8YH-373N-1UWD","isbn":null,"url":null},{"ref":"Arbuckle, R., Freston, M., & Witt, R. (2009). A systematic literature review of the psychometric properties and interpretation of the Sheehan Disability Scale. Current Medical Research and Opinion, 25(10), 2517–2526.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+systematic+literature+review+of+the+psychometric+properties+and+interpretation+of+the+Sheehan+Disability+Scale+Arbuckle"}],"related":["phq-9","quick-inventory-depressive","patient-global-impression-change","clinical-global-impressions-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"shewhart-control-chart","name":"Shewhart Control Chart","fullName":"Shewhart Variables Control Chart (X-bar and R)","aliases":["X-bar and R chart","Shewhart chart","variables control chart","process control chart","Shewhart kontrol kartı"],"domain":"statistics","family":"process-pipeline","subfamily":"Statistical process control","year":1931,"originator":"Walter A. Shewhart","url":"https://scholargate.app/en/statistics/shewhart-control-chart","markdownUrl":"https://scholargate.app/en/statistics/shewhart-control-chart.md","definition":"The Shewhart control chart, invented by Walter Shewhart at Bell Labs in the 1920s and set out in his 1931 book, is the foundational tool of statistical process control. It plots a process statistic — typically the subgroup mean (X-bar) and range (R) — over time against a center line and three-sigma control limits, distinguishing the natural common-cause variation inherent in a stable process from special-cause variation that signals something has changed and warrants investigation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walter A. Shewhart","year":1931,"type":"Statistical process control chart for variables","subfamily":"Statistical process control","controlLimits":"3-sigma (center line ± 3σ)","monitors":"Process mean and within-subgroup variation"},"citations":[{"ref":"Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand Company.","type":"book","doi":null,"isbn":"978-0-87389-076-2","url":null},{"ref":"Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-470-16992-6","url":null}],"related":["cusum-chart","ewma-chart","attributes-control-chart","descriptive-statistics"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"shift-share-iv","name":"Shift-Share IV","fullName":"Shift-Share Instrumental Variable (Bartik Instrument)","aliases":["Bartik instrument","shift-share instrument","Shift-Share Araç Değişkeni (Bartik Instrument)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2020,"originator":"Bartik (1991); identification framework by Goldsmith-Pinkham, Sorkin & Swift (2020) and Borusyak, Hull & Jaravel (2022)","url":"https://scholargate.app/en/causal-inference/shift-share-iv","markdownUrl":"https://scholargate.app/en/causal-inference/shift-share-iv.md","definition":"The shift-share instrumental variable, widely known as the Bartik instrument, is a causal-inference strategy that builds an instrument by interacting national or sector-level shocks (the shifts) with local composition weights (the shares). Its modern identification framework was set out by Goldsmith-Pinkham, Sorkin and Swift (2020) and Borusyak, Hull and Jaravel (2022).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bartik (1991); identification framework by Goldsmith-Pinkham, Sorkin & Swift (2020) and Borusyak, Hull & Jaravel (2022)","year":2020,"type":"Instrumental-variable design","estimator":"Two-stage least squares with a constructed shift-share instrument","minSample":50,"outcome":"continuous"},"citations":[{"ref":"Goldsmith-Pinkham, P., Sorkin, I. & Swift, H. (2020). Bartik Instruments: What, When, Why, and How. American Economic Review, 110(8), 2586–2624.","type":"article","doi":"10.1257/aer.20181047","isbn":null,"url":null},{"ref":"Borusyak, K., Hull, P. & Jaravel, X. (2022). Quasi-Experimental Shift-Share Research Designs. Review of Economic Studies, 89(1), 181–213.","type":"article","doi":"10.1093/restud/rdab030","isbn":null,"url":null}],"related":["iv-2sls","regression-kink-design","regression-discontinuity","panel-fixed-effects","difference-in-differences"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"shors-algorithm","name":"Shor's Algorithm","fullName":"Shor's Algorithm for Integer Factorization and Discrete Logarithm","aliases":["Shor factorization","quantum factorization"],"domain":"quantum-computing","family":"ml-model","subfamily":"Number-theoretic Algorithm","year":"1994","originator":"Peter Shor","url":"https://scholargate.app/en/quantum-computing/shors-algorithm","markdownUrl":"https://scholargate.app/en/quantum-computing/shors-algorithm.md","definition":"Shor's Algorithm is a polynomial-time quantum algorithm for factoring large integers and computing discrete logarithms, problems believed to be intractable on classical computers. Discovered by Peter Shor in 1994, it demonstrated the potential of quantum computers to break widely used cryptographic systems like RSA, marking a landmark in quantum computing theory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Shor","subfamily":"Number-theoretic Algorithm","year":"1994","type":"Quantum algorithm"},"citations":[{"ref":"Shor, P. W. (1994). Algorithms for quantum computation: discrete logarithms and factoring. Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 124–134.","type":"article","doi":"10.1109/SFCS.1994.365700","isbn":null,"url":null},{"ref":"Shor, P. W. (1997). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Review, 41, 303–332.","type":"article","doi":"10.1137/S0036144598347011","isbn":null,"url":null},{"ref":"Ekert, A. K., Raussendorf, R. (2014). A short introduction to quantum computing. Reviews of Modern Physics, 74, 339–373.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/quant-ph/0010440"}],"related":["grovers-algorithm","quantum-phase-estimation","quantum-key-distribution"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-confirmatory-factor-analysis","name":"Short-Form CFA","fullName":"Short-Form Confirmatory Factor Analysis","aliases":["SF-CFA","abbreviated scale CFA","short-form validation","brief scale factor analysis"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Building on CFA methodology (Jöreskog, 1969) applied to abbreviated scale contexts","url":"https://scholargate.app/en/psychometrics/short-form-confirmatory-factor-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-confirmatory-factor-analysis.md","definition":"Short-form confirmatory factor analysis applies CFA to a reduced subset of items drawn from a longer validated scale, testing whether the abbreviated version preserves the original factor structure with acceptable model fit and reliability. It is a standard step in short-form scale development and validation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on CFA methodology (Jöreskog, 1969) applied to abbreviated scale contexts","year":"1990s–2000s","type":"Confirmatory latent-variable model","dataType":"Ordinal or continuous item responses from an abbreviated scale","subfamily":"Scale / measurement"},"citations":[{"ref":"Byrne, B. M. (2008). Structural Equation Modeling with EQS: Basic Concepts, Applications, and Programming (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805841268","url":null},{"ref":"Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","short-form-exploratory-factor-analysis","measurement-invariance","scale-development","item-response-theory","cronbachs-alpha"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-construct-validity","name":"Short form construct validity","fullName":"Short Form Construct Validity","aliases":["abbreviated scale construct validity","short-form scale validation","brief scale validity","short measure construct validation"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Multiple contributors; Smith, McCarthy, & Anderson (2000) formalized short-form validation criteria","url":"https://scholargate.app/en/psychometrics/short-form-construct-validity","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-construct-validity.md","definition":"Short form construct validity is the systematic evaluation of whether an abbreviated version of a psychological scale still measures the same underlying construct as the original full-length instrument. It combines item selection procedures with confirmatory factor analysis, convergent and discriminant validity tests, and criterion-related evidence to demonstrate that scale shortening has not compromised the meaning of measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; Smith, McCarthy, & Anderson (2000) formalized short-form validation criteria","year":"1990s–2000s","type":"Validity assessment / scale shortening","dataType":"Ordinal or interval survey items (short-form scores vs. full-form scores)","subfamily":"Scale / measurement"},"citations":[{"ref":"Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null},{"ref":"Stanton, J. M., Sinar, E. F., Balzer, W. K., & Smith, P. C. (2002). Issues and strategies for reducing the length of self-report scales. Personnel Psychology, 55(1), 167–194.","type":"article","doi":"10.1111/j.1744-6570.2002.tb00108.x","isbn":null,"url":null}],"related":["construct-validity","confirmatory-factor-analysis","convergent-validity","discriminant-validity","short-form-reliability-analysis","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-content-validity","name":"Short form content validity","fullName":"Short-Form Content Validity","aliases":["abbreviated scale content validity","short-scale content coverage","brief form content validity","content validity for short forms"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1995–2000","originator":"Messick (validity framework); Smith et al. (short-form standards)","url":"https://scholargate.app/en/psychometrics/short-form-content-validity","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-content-validity.md","definition":"Short-form content validity evaluates whether items retained in an abbreviated scale still adequately represent every substantive facet of the construct measured by the original full-length instrument. It ensures that shortening a scale does not hollow out the conceptual domain it was designed to cover.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Messick (validity framework); Smith et al. (short-form standards)","year":"1995–2000","type":"Validity evaluation","dataType":"Expert ratings, item content mapping","subfamily":"Scale / measurement"},"citations":[{"ref":"Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null},{"ref":"Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons' responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741–749.","type":"article","doi":"10.1037/0003-066X.50.9.741","isbn":null,"url":null}],"related":["content-validity","construct-validity","short-form-scale-development","short-form-item-analysis","convergent-validity","discriminant-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-cronbachs-alpha","name":"Short-form Cronbach's alpha","fullName":"Short-Form Cronbach's Alpha Reliability Estimation","aliases":["abbreviated scale alpha","brief scale internal consistency","short-scale Cronbach's alpha","reduced-item alpha"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1951 (alpha); short-form practice codified 1980s–2000s","originator":"L. J. Cronbach (alpha); short-form application formalized across scale-abbreviation literature","url":"https://scholargate.app/en/psychometrics/short-form-cronbachs-alpha","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-cronbachs-alpha.md","definition":"Short-form Cronbach's alpha quantifies the internal consistency reliability of an abbreviated version of a psychological scale. It applies Cronbach's alpha formula to a reduced item set, verifying that the shortened instrument retains sufficient reliability to support valid score interpretation in research and applied contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"L. J. Cronbach (alpha); short-form application formalized across scale-abbreviation literature","year":"1951 (alpha); short-form practice codified 1980s–2000s","type":"Internal consistency reliability coefficient","dataType":"Ordinal or continuous items from an abbreviated scale","subfamily":"Scale / measurement"},"citations":[{"ref":"Smith, G. T., McCarthy, D. M. & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null},{"ref":"Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.","type":"article","doi":"10.1007/BF02310555","isbn":null,"url":null}],"related":["cronbachs-alpha","mcdonalds-omega","short-form-reliability-analysis","short-form-scale-development","item-response-theory","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-differential-item-functioning","name":"Short form differential item functioning","fullName":"Short-Form Differential Item Functioning Analysis","aliases":["Short-form DIF","abbreviated scale DIF","DIF in short forms","short-scale DIF detection"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1970s–1990s (DIF); short-form context developed in parallel with scale abbreviation literature","originator":"Angoff, W. H. and subsequent DIF methodologists","url":"https://scholargate.app/en/psychometrics/short-form-differential-item-functioning","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-differential-item-functioning.md","definition":"Short-form differential item functioning (DIF) analysis examines whether individual items in an abbreviated scale function equivalently across demographic or subgroup comparisons. When a scale is shortened, retained items must still behave fairly for all relevant groups — DIF analysis verifies this, ensuring that score differences reflect true ability or trait differences rather than item bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angoff, W. H. and subsequent DIF methodologists","year":"1970s–1990s (DIF); short-form context developed in parallel with scale abbreviation literature","type":"Item bias / measurement fairness analysis","dataType":"Ordinal or binary item responses from abbreviated scales","subfamily":"Scale / measurement"},"citations":[{"ref":"Millsap, R. E. (2012). Statistical Approaches to Measurement Invariance. Routledge.","type":"book","doi":null,"isbn":"978-0-8058-4507-0","url":null},{"ref":"Smith, R. M. (2000). Fit analysis in latent trait measurement models. Journal of Applied Measurement, 1(2), 199–218.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fit+analysis+in+latent+trait+measurement+models+Smith+2000"}],"related":["differential-item-functioning","short-form-item-response-theory","short-form-measurement-invariance","item-response-theory","short-form-confirmatory-factor-analysis","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-generalizability-theory","name":"Short form generalizability theory","fullName":"Short Form Generalizability Theory","aliases":["G-theory for abbreviated scales","short-form G-study","abbreviated test generalizability","short-form D-study"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1963–1972 (G-theory); short-form extension ongoing from 1980s","originator":"Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam","url":"https://scholargate.app/en/psychometrics/short-form-generalizability-theory","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-generalizability-theory.md","definition":"Short form generalizability theory applies the G-theory variance-component framework to abbreviated measurement instruments, using G-studies and D-studies to estimate how many items a short scale must retain to achieve a desired reliability and to evaluate the accuracy of decisions made with a condensed instrument.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam","year":"1963–1972 (G-theory); short-form extension ongoing from 1980s","type":"Reliability / decision-study framework","dataType":"Ordinal or continuous item scores from abbreviated instruments","subfamily":"Scale / measurement"},"citations":[{"ref":"Brennan, R. L. (2001). Generalizability Theory. Springer.","type":"book","doi":null,"isbn":"978-0387952826","url":null},{"ref":"Shavelson, R. J., & Webb, N. M. (1991). Generalizability Theory: A Primer. Sage Publications.","type":"book","doi":null,"isbn":"978-0803937796","url":null}],"related":["generalizability-theory","short-form-reliability-analysis","short-form-scale-development","short-form-item-response-theory","cronbachs-alpha","multilevel-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-item-analysis","name":"Short-form item analysis","fullName":"Short-Form Item Analysis","aliases":["abbreviated scale item analysis","short-scale item evaluation","item screening for short forms","SFIA"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Psychometric tradition; methodological articulation by Smith, McCarthy & Anderson (2000)","url":"https://scholargate.app/en/psychometrics/short-form-item-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-item-analysis.md","definition":"Short-form item analysis is the systematic psychometric evaluation and selection of items when constructing an abbreviated version of a longer measurement instrument. It applies classical and modern item-analysis criteria — item-total correlations, reliability estimates, and factor structure — to identify the smallest item subset that preserves the original scale's psychometric integrity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Psychometric tradition; methodological articulation by Smith, McCarthy & Anderson (2000)","year":"1990s–2000s","type":"Item selection and evaluation procedure","dataType":"Ordinal or interval item-level responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null},{"ref":"Stanton, J. M., Sinar, E. F., Balzer, W. K., & Smith, P. C. (2002). Issues and strategies for reducing the length of self-report scales. Personnel Psychology, 55(1), 167–194.","type":"article","doi":"10.1111/j.1744-6570.2002.tb00108.x","isbn":null,"url":null}],"related":["scale-development","item-response-theory","cronbachs-alpha","mcdonalds-omega","exploratory-factor-analysis","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-item-response-theory","name":"Short-Form IRT","fullName":"Short-Form Item Response Theory","aliases":["SF-IRT","abbreviated scale IRT","short-form calibration","shortened instrument IRT"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1980s–2000s","originator":"Multiple contributors; IRT adapted to short-form contexts from Lord & Novick (1968) and subsequent applied psychometricians","url":"https://scholargate.app/en/psychometrics/short-form-item-response-theory","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-item-response-theory.md","definition":"Short-form item response theory applies IRT calibration and scoring to abbreviated or shortened psychological scales. It uses item information functions to guide which items to retain from a full-length instrument, then estimates latent trait scores from the reduced item set while preserving psychometric rigor and linkage to the full-scale metric.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; IRT adapted to short-form contexts from Lord & Novick (1968) and subsequent applied psychometricians","year":"1980s–2000s","type":"Latent trait / item calibration model","dataType":"Binary or polytomous item responses from abbreviated psychological scales","subfamily":"Scale / measurement"},"citations":[{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805828191","url":null},{"ref":"Smith, G. T., McCarthy, D. M. & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null}],"related":["item-response-theory","short-form-confirmatory-factor-analysis","differential-item-functioning","computerized-adaptive-test-item-response-theory","rasch-model","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-mcdonalds-omega","name":"Short-form McDonald's omega","fullName":"Short-Form McDonald's Omega Reliability Estimation","aliases":["omega for abbreviated scales","short-scale omega","omega-total short form","abbreviated scale reliability"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1999 (omega); short-form application 1990s–2000s","originator":"Roderick P. McDonald (omega); short-form application systematised across psychometric literature","url":"https://scholargate.app/en/psychometrics/short-form-mcdonalds-omega","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-mcdonalds-omega.md","definition":"Short-form McDonald's omega applies the omega reliability coefficient to abbreviated or shortened versions of psychological scales. It provides a theoretically sound reliability estimate that accounts for the multidimensional structure of the short instrument, enabling researchers to evaluate whether abbreviation has preserved the reliability of the original full-length scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roderick P. McDonald (omega); short-form application systematised across psychometric literature","year":"1999 (omega); short-form application 1990s–2000s","type":"Reliability coefficient for abbreviated scales","dataType":"Ordinal or continuous item scores from a shortened instrument","subfamily":"Scale / measurement"},"citations":[{"ref":"McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"978-0805830750","url":null},{"ref":"Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null}],"related":["mcdonalds-omega","cronbachs-alpha","short-form-scale-development","short-form-reliability-analysis","confirmatory-factor-analysis","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-measurement-invariance","name":"Short Form Measurement Invariance","fullName":"Short Form Measurement Invariance Testing","aliases":["SF-MI","abbreviated scale invariance","short-form factorial invariance","brief measure invariance"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"2000s","originator":"Adapted from Vandenberg & Lance (2000) and Millsap & Kwok (2004) invariance framework applied to short-form scales","url":"https://scholargate.app/en/psychometrics/short-form-measurement-invariance","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-measurement-invariance.md","definition":"Short form measurement invariance testing evaluates whether an abbreviated version of a psychological scale measures the same latent construct equivalently across groups or conditions. It applies the hierarchical multigroup confirmatory factor analysis invariance sequence — configural, metric, scalar, and strict — specifically to short-form instruments, ensuring that brevity does not introduce measurement bias when comparing subgroups.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from Vandenberg & Lance (2000) and Millsap & Kwok (2004) invariance framework applied to short-form scales","year":"2000s","type":"Measurement equivalence testing","dataType":"Ordinal or continuous item scores from abbreviated scales","subfamily":"Scale / measurement"},"citations":[{"ref":"Millsap, R. E., & Kwok, O. M. (2004). Evaluating the impact of partial factor loading and intercept invariance on selection in two populations. Psychological Methods, 9(1), 93–115.","type":"article","doi":"10.1037/1082-989X.9.1.93","isbn":null,"url":null},{"ref":"Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.","type":"article","doi":"10.1177/109442810031002","isbn":null,"url":null}],"related":["measurement-invariance","confirmatory-factor-analysis","short-form-confirmatory-factor-analysis","multi-group-measurement-invariance","differential-item-functioning","item-response-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-nomological-validity","name":"Short form nomological validity","fullName":"Short Form Nomological Validity","aliases":["nomological validity of abbreviated scales","short-scale construct validity","nomological network validity","abbreviated form external validity"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1955 (concept); 2000 (short-form context)","originator":"Cronbach & Meehl (nomological network concept); Smith et al. for short-form application","url":"https://scholargate.app/en/psychometrics/short-form-nomological-validity","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-nomological-validity.md","definition":"Short form nomological validity examines whether an abbreviated version of a psychological scale preserves the pattern of theoretically expected correlations with conceptually related and unrelated constructs. It is a cornerstone step in justifying the use of a shortened instrument in research and applied settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cronbach & Meehl (nomological network concept); Smith et al. for short-form application","year":"1955 (concept); 2000 (short-form context)","type":"Validity assessment technique","dataType":"Correlations and regression coefficients between short-form scores and external criterion variables","subfamily":"Scale / measurement"},"citations":[{"ref":"Cronbach, L. J. & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.","type":"article","doi":"10.1037/h0040957","isbn":null,"url":null},{"ref":"Smith, G. T., McCarthy, D. M. & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","convergent-validity","discriminant-validity","criterion-validity","scale-abbreviation","construct-validity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-rasch-model","name":"Short form Rasch model","fullName":"Short Form Rasch Model","aliases":["Rasch analysis for abbreviated scales","short scale Rasch calibration","brief instrument Rasch model"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1960 (Rasch model); short-form application from 1980s onward","originator":"Georg Rasch","url":"https://scholargate.app/en/psychometrics/short-form-rasch-model","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-rasch-model.md","definition":"The short form Rasch model applies Rasch measurement theory to abbreviated instrument versions. Rather than using all items from a full scale, researchers select a reduced item set and calibrate it under the Rasch model to verify that the shortened instrument preserves interval-level measurement, adequate person separation, and item fit, enabling efficient yet rigorous measurement with fewer items.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georg Rasch","year":"1960 (Rasch model); short-form application from 1980s onward","type":"Probabilistic item response model","dataType":"Dichotomous or polytomous ordinal item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Danmarks Paedagogiske Institut.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Probabilistic+models+for+some+intelligence+and+attainment+tests+Rasch+1960"},{"ref":"Smith, E. V. Jr. (2000). Metric development and score reporting in Rasch measurement. Journal of Applied Measurement, 1(3), 303-326.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Metric+development+and+score+reporting+in+Rasch+measurement+Smith+2000"}],"related":["item-response-theory","rasch-model","short-form-item-response-theory","short-form-confirmatory-factor-analysis","differential-item-functioning","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-reliability-analysis","name":"Short-form reliability analysis","fullName":"Short-Form Reliability Analysis","aliases":["abbreviated scale reliability","short-form validation","scale shortening","item reduction reliability"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Conventional practice; codified notably by Smith, McCarthy & Anderson (2000) and Stanton et al. (2002)","url":"https://scholargate.app/en/psychometrics/short-form-reliability-analysis","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-reliability-analysis.md","definition":"Short-form reliability analysis evaluates whether an abbreviated version of a psychological scale maintains acceptable internal consistency, validity, and structural integrity after items are removed. It is used in survey and assessment research to create briefer instruments that reduce respondent burden without sacrificing measurement quality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conventional practice; codified notably by Smith, McCarthy & Anderson (2000) and Stanton et al. (2002)","year":"1990s–2000s","type":"Scale development / psychometric evaluation","dataType":"Ordinal or interval item-level responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Stanton, J. M., Sinar, E. F., Balzer, W. K. & Smith, P. C. (2002). Issues and strategies for reducing the length of self-report scales. Personnel Psychology, 55(1), 167–194.","type":"article","doi":"10.1111/j.1744-6570.2002.tb00108.x","isbn":null,"url":null},{"ref":"Smith, G. T., McCarthy, D. M. & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null}],"related":["cronbach-alpha","exploratory-factor-analysis","confirmatory-factor-analysis","item-response-theory","mcdonald-omega","test-retest-reliability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-scale-development","name":"Short-Form Scale Development","fullName":"Short-Form Scale Development","aliases":["scale abbreviation","abbreviated scale development","short-scale construction","item reduction methodology"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Multiple contributors; foundational critique by Smith, McCarthy & Anderson (2000); practical guidance by Stanton et al. (2002)","url":"https://scholargate.app/en/psychometrics/short-form-scale-development","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-scale-development.md","definition":"Short-form scale development is the systematic process of reducing a full-length psychological scale to a smaller subset of items while preserving the construct validity, reliability, and measurement properties of the original instrument. It is widely used when administration burden must be minimised without sacrificing psychometric quality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; foundational critique by Smith, McCarthy & Anderson (2000); practical guidance by Stanton et al. (2002)","year":"1990s–2000s","type":"Scale development methodology","dataType":"Ordinal / interval item responses","subfamily":"Scale / measurement"},"citations":[{"ref":"Stanton, J. M., Sinar, E. F., Balzer, W. K., & Smith, P. C. (2002). Issues and strategies for reducing the length of self-report scales. Personnel Psychology, 55(1), 167–194.","type":"article","doi":"10.1111/j.1744-6570.2002.tb00108.x","isbn":null,"url":null},{"ref":"Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null}],"related":["scale-development","exploratory-factor-analysis","confirmatory-factor-analysis","item-response-theory","cronbachs-alpha","measurement-invariance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-form-test-retest-reliability","name":"Short-form test-retest reliability","fullName":"Short-form Test-Retest Reliability","aliases":["abbreviated scale temporal stability","short-form temporal consistency","retest reliability of short forms","SF test-retest"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1990s–2000s","originator":"Derived from classical test-retest reliability; short-form methodology formalised by Smith, McCarthy & Anderson (2000) among others","url":"https://scholargate.app/en/psychometrics/short-form-test-retest-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/short-form-test-retest-reliability.md","definition":"Short-form test-retest reliability quantifies how consistently an abbreviated version of a measurement instrument produces the same scores across two administrations separated by a defined time interval. It is a critical validation step whenever a full-length scale is shortened for practical use, confirming that item reduction has not degraded temporal stability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from classical test-retest reliability; short-form methodology formalised by Smith, McCarthy & Anderson (2000) among others","year":"1990s–2000s","type":"Reliability estimation","dataType":"Repeated ordinal or interval scale scores from abbreviated instruments","subfamily":"Scale / measurement"},"citations":[{"ref":"Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.","type":"article","doi":"10.1037/1040-3590.12.1.102","isbn":null,"url":null},{"ref":"Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070474659","url":null}],"related":["test-retest-reliability","cronbach-alpha","internal-consistency-reliability","parallel-forms-reliability","item-response-theory","confirmatory-factor-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-physical-performance-battery","name":"SPPB","fullName":"Short Physical Performance Battery","aliases":["SPPB"],"domain":"gerontology","family":"process-pipeline","subfamily":"lower-extremity-function","year":"1994","originator":"Jack M. Guralnik","url":"https://scholargate.app/en/gerontology/short-physical-performance-battery","markdownUrl":"https://scholargate.app/en/gerontology/short-physical-performance-battery.md","definition":"The Short Physical Performance Battery (SPPB) is a performance-based assessment developed by Guralnik and colleagues in 1994 at the National Institute on Aging to measure lower extremity physical function and functional mobility in older adults. It is widely used in clinical practice and epidemiological research to predict disability, institutionalization, and mortality in community-dwelling seniors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jack M. Guralnik","subfamily":"lower-extremity-function","year":"1994","type":"Performance-based assessment"},"citations":[{"ref":"Guralnik, J. M., Simonsick, E. M., Ferrucci, L., et al. (1994). A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol, 49(2), M85-M94.","type":"article","doi":"10.1093/geronj/49.2.M85","isbn":null,"url":null},{"ref":"Guralnik, J. M., Ferrucci, L., Pieper, C. F., et al. (2000). Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci, 55(4), M221-M231.","type":"article","doi":"10.1093/gerona/55.4.M221","isbn":null,"url":null},{"ref":"Pahor, M., Guralnik, J. M., Ambrosius, W. T., et al. (2006). Effect of structured physical activity on prevention of major mobility disability in older adults: the LIFE study randomized clinical trial. JAMA, 311(23), 2387-2396.","type":"article","doi":"10.1001/jama.2014.5616","isbn":null,"url":null}],"related":["tinetti-balance-assessment","activities-balance-confidence","frail-scale","edmonton-frail-scale","life-space-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"short-time-fourier-transform","name":"Short-Time Fourier Transform","fullName":"Short-Time Fourier Transform (STFT) Analysis","aliases":["STFT","Windowed Fourier Transform","Time-Frequency Analysis"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Spectral analysis","year":"1946","originator":"Dennis Gabor","url":"https://scholargate.app/en/signal-processing/short-time-fourier-transform","markdownUrl":"https://scholargate.app/en/signal-processing/short-time-fourier-transform.md","definition":"The Short-Time Fourier Transform (STFT) is a fundamental signal analysis technique that computes the frequency content of a signal as it evolves over time by applying the Fourier transform to short, overlapping windows of the signal. Introduced conceptually by Dennis Gabor in 1946, the STFT provides a time-frequency representation essential for analyzing non-stationary signals where frequency content changes over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dennis Gabor","subfamily":"Spectral analysis","year":"1946","type":"Time-frequency signal analysis"},"citations":[{"ref":"Gabor, D. (1946). Theory of Communication. Journal of the Institution of Electrical Engineers, 93(3), 429–457.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theory+of+Communication+Gabor"},{"ref":"Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/discretetimesignalprocessing"}],"related":["power-spectral-density","matched-filter","fir-filter-design","blind-source-separation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"shrinking-core-model","name":"Shrinking Core Model","fullName":"Shrinking Core Model for Leaching and Roasting","aliases":["Shrinking Unreacted Core Model","SCM","Leaching Kinetics Model"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Hydrometallurgical Kinetics","year":"1976","originator":"Szekely, Evans, and Sohn","url":"https://scholargate.app/en/mining-engineering/shrinking-core-model","markdownUrl":"https://scholargate.app/en/mining-engineering/shrinking-core-model.md","definition":"The Shrinking Core Model, formalized by Szekely, Evans, and Sohn in 1976, describes the kinetics of chemical reactions between solid ore particles and surrounding fluids (leaching solutions, roasting gases). As the reaction proceeds from the particle surface inward, an unreacted core shrinks while products accumulate in a product layer. The model enables prediction of leaching times and optimization of hydrometallurgical processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Szekely, Evans, and Sohn","subfamily":"Hydrometallurgical Kinetics","year":"1976","type":"Reaction kinetics model for solid-fluid reactions"},"citations":[{"ref":"Szekely, J., Evans, J. W., & Sohn, H. Y. (1976). Gas-solid reactions. Academic Press, New York.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Gas-solid+reactions+Szekely"},{"ref":"Levenspiel, O. (1999). Chemical reaction engineering (3rd ed.). John Wiley & Sons.","type":"article","doi":"10.1021/ie990488g","isbn":null,"url":null}],"related":["electrowinning","slag-basicity","ellingham-diagram"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"siamese-network","name":"Siamese Network","fullName":"Siamese Neural Network (Deep Metric Learning)","aliases":["twin network","Siamese neural network","contrastive metric network","Siyam ağı"],"domain":"deep-learning","family":"ml-model","subfamily":"Metric learning","year":1993,"originator":"Jane Bromley & Yann LeCun et al.; popularized by Koch et al.","url":"https://scholargate.app/en/deep-learning/siamese-network","markdownUrl":"https://scholargate.app/en/deep-learning/siamese-network.md","definition":"A Siamese network is a deep architecture with two (or more) identical, weight-sharing branches that map inputs into an embedding space where similar inputs land close together and dissimilar ones far apart. Introduced by Bromley, LeCun, and colleagues in 1993 for signature verification and revived by Koch et al. (2015) for one-shot image recognition, it learns a similarity metric rather than fixed class labels, making it ideal for verification, matching, and few-shot tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jane Bromley & Yann LeCun et al.; popularized by Koch et al.","year":1993,"type":"Deep metric-learning architecture","subfamily":"Metric learning","structure":"Two weight-sharing twin encoders","learns":"An embedding where similar inputs are close"},"citations":[{"ref":"Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a 'Siamese' time delay neural network. Advances in Neural Information Processing Systems, 6.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/1993/hash/288cc0ff022877bd3df94bc9360b9c5d-Abstract.html"},{"ref":"Koch, G., Zemel, R., & Salakhutdinov, R. (2015). Siamese neural networks for one-shot image recognition. ICML Deep Learning Workshop.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf"}],"related":["convolutional-neural-network","visual-contrastive-learning","k-nearest-neighbors","autoencoder"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"siar-mixing-model","name":"SIAR Mixing Model","fullName":"Stable Isotope Analysis in R (SIAR) Mixing Model","aliases":["isotope mixing model","Bayesian mixing model","source apportionment","diet analysis"],"domain":"ecology","family":"process-pipeline","subfamily":"Bayesian inference","year":"2010","originator":"Andrew Parnell","url":"https://scholargate.app/en/ecology/siar-mixing-model","markdownUrl":"https://scholargate.app/en/ecology/siar-mixing-model.md","definition":"The Stable Isotope Analysis in R (SIAR) mixing model is a Bayesian framework for estimating the proportional contributions of dietary sources to a consumer, using stable isotope ratios. Developed by Parnell and colleagues (2010) and implemented in the R package siar (and its successor MixSIAR), this method integrates isotopic data from potential food sources and consumers to infer diets. It accounts for uncertainty in isotope fractionation (the shift in isotope ratios between diet and tissue) and natural variation among source populations, producing probability distributions rather than point estimates of diet composition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Parnell","subfamily":"Bayesian inference","year":"2010","type":"diet and source apportionment analysis"},"citations":[{"ref":"Parnell, A. C., Inger, R., Bearhop, S., & Jackson, A. L. (2010). Source partitioning using stable isotopes: coping with too much variation. PLoS ONE, 5(3), e9672.","type":"article","doi":"10.1371/journal.pone.0009672","isbn":null,"url":null},{"ref":"Jackson, A. L., Inger, R., Parnell, A. C., & Bearhop, S. (2011). Comparing isotopic niche widths among sympatric species: the role of phylogenetic relatedness. Ecology Letters, 14(8), 841-851.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparing+isotopic+niche+widths+among+sympatric+species%3A+the+role+of+phylogenetic+relatedness+Jackson"},{"ref":"Phillips, D. L., Inger, R., Bearhop, S., Jackson, A. L., Moore, J. W., Parnell, A. C., Semmens, B. X., & Ward, E. J. (2014). Best practices for use of stable isotope mixing models in food-web studies. Canadian Journal of Zoology, 92(10), 823-835.","type":"article","doi":"10.1139/cjz-2014-0127","isbn":null,"url":null}],"related":["food-web-topology","leslie-matrix","population-viability-analysis","bioaccumulation-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sibtest","name":"SIBTEST","fullName":"SIBTEST: Simultaneous Item Bias Test","aliases":[],"domain":"psychometrics","family":"latent-structure","subfamily":"Item Bias Detection","year":"1993","originator":"Richard Shealy, William F. Stout","url":"https://scholargate.app/en/psychometrics/sibtest","markdownUrl":"https://scholargate.app/en/psychometrics/sibtest.md","definition":"SIBTEST (Simultaneous Item Bias Test) is a non-parametric method for detecting differential item functioning (DIF) and differential test functioning (DTF) developed by Shealy and Stout (1993). Unlike parametric approaches, SIBTEST does not assume a particular item response model and directly tests whether groups differ in their probability of correct responses at equal levels of overall ability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Shealy, William F. Stout","subfamily":"Item Bias Detection","year":"1993","type":"Differential item functioning (DIF) assessment"},"citations":[{"ref":"Shealy, R., & Stout, W. F. (1993). A model-based standardization approach that separates true bias/DIF from group differences and detects test bias/DTF. Psychometrika, 58(2), 159-194.","type":"article","doi":"10.1007/BF02294572","isbn":null,"url":null},{"ref":"Chang, H. H., Mazzeo, J., & Roussos, L. (1996). Detecting DIF for polytomously scored items: An adaptation of the SIBTEST procedure. Journal of Educational Measurement, 33(3), 333-353.","type":"article","doi":"10.1111/j.1745-3984.1996.tb00496.x","isbn":null,"url":null},{"ref":"Stout, W. F. (1987). A nonparametric approach for assessing latent trait unidimensionality. Psychometrika, 52(4), 589-617.","type":"article","doi":"10.1007/BF02294821","isbn":null,"url":null}],"related":["rule-space-methodology","dina-model","dino-model","cognitive-diagnostic-cat","necessary-condition-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sickness-impact-profile","name":"Sickness Impact Profile","fullName":"Sickness Impact Profile Health Status Measure","aliases":["SIP","Sickness Impact Scale"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Functional health status and sickness behavior","year":"1976","originator":"Marilyn Bergner and colleagues at University of Washington","url":"https://scholargate.app/en/health-measurement/sickness-impact-profile","markdownUrl":"https://scholargate.app/en/health-measurement/sickness-impact-profile.md","definition":"The Sickness Impact Profile (SIP) is a comprehensive 136-item behavioral health status measure developed by Bergner and colleagues at the University of Washington in 1976. It assesses the impact of illness on daily activities and behavior across physical, psychosocial, and independent living dimensions. The SIP remains one of the most thorough and validated health status instruments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marilyn Bergner and colleagues at University of Washington","subfamily":"Functional health status and sickness behavior","year":"1976","type":"Comprehensive behavioral health status measurement"},"citations":[{"ref":"Bergner, M., Bobbitt, R. A., Carter, W. B., & Gilson, B. S. (1981). The Sickness Impact Profile: development and final revision of a health status measure. Medical Care, 19(8), 787–805.","type":"article","doi":"10.1097/00005650-198108000-00001","isbn":null,"url":null},{"ref":"Bergner, M., Bobbitt, R. A., Pollard, W. E., et al. (1976). The Sickness Impact Profile: validation and methodology for measuring health status. American Journal of Public Health, 66(12), 1196–1203.","type":"article","doi":"10.1097/00001888-197611000-00010","isbn":null,"url":null},{"ref":"Gilson, B. S., Gilson, J. S., Bergner, M., et al. (1975). The Sickness Impact Profile: development of an outcome measure for health care. American Journal of Public Health, 65(12), 1304–1310.","type":"article","doi":"10.2105/AJPH.65.12.1304","isbn":null,"url":null}],"related":["sf-36","nottingham-health-profile","whoqol-bref","haq-disability-index","promis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"side-channel-analysis","name":"Side-Channel Analysis","fullName":"Side-Channel Analysis","aliases":["SCA","timing attack","power analysis","cache attack"],"domain":"cryptography","family":"ml-model","subfamily":"Implementation attack","year":"1996","originator":"Paul Kocher","url":"https://scholargate.app/en/cryptography/side-channel-analysis","markdownUrl":"https://scholargate.app/en/cryptography/side-channel-analysis.md","definition":"Side-channel analysis is a family of attacks that exploit physical properties of cryptographic implementations (timing, power consumption, electromagnetic emissions, cache behavior) to recover secret keys. Introduced by Paul Kocher in 1996, side-channel attacks have repeatedly broken implementations of theoretically secure cryptosystems by leveraging unintended information leakage. Side-channel analysis has become a critical concern in cryptographic system design, requiring constant-time implementations and physical countermeasures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Kocher","subfamily":"Implementation attack","year":"1996","type":"physical side-channel exploitation"},"citations":[{"ref":"Kocher, P. C. (1996). Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems. In Advances in Cryptology - CRYPTO 1996, LNCS 1109, pp. 104-113.","type":"article","doi":"10.1007/3-540-68697-5_9","isbn":null,"url":null},{"ref":"Kocher, P., Jaffe, J., & Jun, B. (1999). Differential power analysis. In Advances in Cryptology - CRYPTO 1999, LNCS 1666, pp. 388-397.","type":"article","doi":"10.1007/3-540-48405-1_25","isbn":null,"url":null}],"related":["elliptic-curve-cryptography","rsa-cryptosystem","aes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"siegel-tukey-test","name":"Siegel-Tukey test","fullName":"Siegel-Tukey Test for Scale Differences","aliases":["Siegel-Tukey rank test","nonparametric scale test","Siegel-Tukey Testi — Ölçek Farklılığı"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1960,"originator":"Sidney Siegel & John W. Tukey","url":"https://scholargate.app/en/statistics/siegel-tukey-test","markdownUrl":"https://scholargate.app/en/statistics/siegel-tukey-test.md","definition":"The Siegel-Tukey test is a nonparametric hypothesis test that detects differences in variability (spread) between two independent groups whose central tendencies are equal or have been equalised. Introduced by Sidney Siegel and John W. Tukey in 1960, it is the nonparametric counterpart of Levene's test and requires no assumption of normality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sidney Siegel & John W. Tukey","year":1960,"family":"Hypothesis test","type":"Nonparametric scale comparison","groups":2,"outcome":"continuous or ordinal","parametric":false,"nullHypothesis":"equal spread (scale) in both populations","minSampleSize":10,"difficulty":2},"citations":[{"ref":"Siegel, S. & Tukey, J. W. (1960). A Nonparametric Sum of Ranks Procedure for Relative Spread in Unpaired Samples. Journal of the American Statistical Association, 55(291), 429–444.","type":"article","doi":"10.1080/01621459.1960.10482073","isbn":null,"url":null}],"related":["levene-test","ansari-bradley-test","mann-whitney-u","fligner-killeen-test","kruskal-wallis-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sift-feature-detection","name":"SIFT Feature Detection","fullName":"Scale-Invariant Feature Transform (SIFT) Detection","aliases":["SIFT","Lowe SIFT"],"domain":"computer-vision","family":"ml-model","subfamily":"Feature detection","year":"1999","originator":"David Lowe","url":"https://scholargate.app/en/computer-vision/sift-feature-detection","markdownUrl":"https://scholargate.app/en/computer-vision/sift-feature-detection.md","definition":"SIFT (Scale-Invariant Feature Transform) is a method for detecting and describing distinctive local features in digital images. Introduced by David Lowe in 1999, SIFT extracts keypoints that remain invariant to scale, rotation, and illumination changes, making it highly robust for image matching and object recognition tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Lowe","subfamily":"Feature detection","year":"1999","type":"Local feature detector and descriptor"},"citations":[{"ref":"Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.","type":"article","doi":"10.1023/B:VISI.0000029664.99615.94","isbn":null,"url":null},{"ref":"Lowe, D. G. (1999). Object recognition from local scale-invariant features. International Conference on Computer Vision (ICCV), 1150–1157.","type":"article","doi":null,"isbn":null,"url":"https://www.cs.ubc.ca/~lowe/papers/iccv99.pdf"}],"related":["orb-feature-descriptor","harris-corner-detection","scale-space-theory","image-morphology","template-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sign-test","name":"Sign Test","fullName":"Sign Test","aliases":["İşaret Testi (Sign Test)","one-sample sign test","paired sign test"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1946,"originator":"W. J. Dixon & A. M. Mood","url":"https://scholargate.app/en/statistics/sign-test","markdownUrl":"https://scholargate.app/en/statistics/sign-test.md","definition":"The sign test is the simplest nonparametric hypothesis test for deciding whether the median of paired differences — or of a single sample — differs significantly from a hypothesised value. Formalised by W. J. Dixon and A. M. Mood in 1946, it imposes virtually no distributional assumptions and can be applied to any data where individual differences can be classified as positive or negative.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"W. J. Dixon & A. M. Mood","year":1946,"family":"Hypothesis test","type":"Nonparametric median test","groups":1,"outcome":"ordinal or continuous","parametric":false,"distribution":"Binomial","difficulty":1},"citations":[{"ref":"Dixon, W. J. & Mood, A. M. (1946). The statistical sign test. Journal of the American Statistical Association, 41(236), 557–566.","type":"article","doi":"10.1080/01621459.1946.10501898","isbn":null,"url":null}],"related":["wilcoxon-signed-rank","paired-t-test","mann-whitney-u","friedman-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"signal-denoising","name":"Signal Denoising","fullName":"Wavelet Signal Denoising (Soft Thresholding)","aliases":["Wavelet Shrinkage","Donoho-Johnstone Denoising","Soft Thresholding Denoising","Sinyal Gürültü Giderme"],"domain":"signal-processing","family":"ml-model","subfamily":"Denoising","year":1995,"originator":"David Donoho","url":"https://scholargate.app/en/signal-processing/signal-denoising","markdownUrl":"https://scholargate.app/en/signal-processing/signal-denoising.md","definition":"Wavelet signal denoising, introduced by David Donoho in 1995, is a non-parametric technique that removes noise from one-dimensional or multidimensional signals by decomposing them into wavelet coefficients, suppressing small coefficients that likely represent noise via a soft-thresholding operator, and reconstructing a smooth estimate. It is widely used in biomedical signal processing, geophysics, audio engineering, and image analysis where the underlying signal is assumed to be sparse or piecewise smooth.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Donoho","year":1995,"type":"Non-parametric signal estimation","subfamily":"Denoising","threshold_rule":"Universal threshold λ = σ√(2 ln n)","optimality":"Near-minimax MSE over wide function classes"},"citations":[{"ref":"Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.","type":"article","doi":"10.1109/18.382009","isbn":null,"url":null}],"related":["empirical-mode-decomposition","fourier-transform","variational-mode-decomposition"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"signal-detection-theory","name":"Signal Detection Theory","fullName":"Signal Detection Theory","aliases":["SDT","Detection Theory"],"domain":"psychology","family":"hypothesis-test","subfamily":"Psychophysical","year":"1966","originator":"David Green and John Swets","url":"https://scholargate.app/en/psychology/signal-detection-theory","markdownUrl":"https://scholargate.app/en/psychology/signal-detection-theory.md","definition":"Signal Detection Theory (SDT) is a framework for analyzing how observers detect signals embedded in noise, accounting for both sensory capacity and decision-making bias. Developed by Green and Swets in the 1960s, it provides a principled method for measuring sensitivity and response criteria separately, making it foundational in psychophysics, perception research, and diagnostic decision-making.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Green and John Swets","subfamily":"Psychophysical","year":"1966","type":"Signal detection framework"},"citations":[{"ref":"Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. Wiley.","type":"book","doi":"","isbn":null,"url":"https://books.google.com/books?id=green-swets-signal-detection-1966"},{"ref":"Macmillan, N. A., & Creelman, C. D. (2005). Detection theory: A user's guide. Lawrence Erlbaum Associates.","type":"book","doi":"","isbn":null,"url":"https://doi.org/10.4324/9781410611147"},{"ref":"Swets, J. A., Dawes, R. M., & Monahan, J. (1996). Psychological science can improve diagnostic decisions. Psychological Science in the Public Interest, 11(1), 1-26.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Psychological+science+can+improve+diagnostic+decisions+Swets"}],"related":["receiver-operating-characteristic","yes-no-task","two-alternative-forced-choice","same-different-task"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"silhouette-score","name":"Silhouette Score","fullName":"Silhouette Coefficient","aliases":["silhouette coefficient","silhouette index"],"domain":"model-evaluation","family":"mcdm","subfamily":"Clustering Validation","year":"1987","originator":"Peter Rousseeuw","url":"https://scholargate.app/en/model-evaluation/silhouette-score","markdownUrl":"https://scholargate.app/en/model-evaluation/silhouette-score.md","definition":"The Silhouette Coefficient, introduced by Peter Rousseeuw in 1987, is a metric that measures how similar an object is to its own cluster compared to other clusters. It ranges from -1 to 1, where values close to 1 indicate well-separated and cohesive clusters, values near 0 suggest overlapping clusters, and negative values indicate misclustered points.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Rousseeuw","subfamily":"Clustering Validation","year":"1987","type":"Cluster quality metric"},"citations":[{"ref":"Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.","type":"article","doi":"10.1016/0377-0427(87)90125-7","isbn":null,"url":null}],"related":["davies-bouldin-index","calinski-harabasz-index","dunn-index","adjusted-rand-index","gap-statistic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"silvicultural-treatment-design","name":"Silvicultural Treatment Design","fullName":"Forest Management Treatment Planning and Prescription Development","aliases":["Silvicultural prescription","Stand treatment planning","Forest management design"],"domain":"forestry","family":"process-pipeline","subfamily":"Silvicultural practice and forest management","year":"1950s–2000s","originator":"Smith, Larson, and classical silviculture","url":"https://scholargate.app/en/forestry/silvicultural-treatment-design","markdownUrl":"https://scholargate.app/en/forestry/silvicultural-treatment-design.md","definition":"Silvicultural treatment design is the process of developing specific management prescriptions for forest stands to achieve defined objectives (timber yield, biodiversity, carbon storage, watershed protection). Codified in foundational texts by Smith and colleagues, silvicultural design integrates stand assessment, growth models, and ecosystem understanding to specify interventions (thinning, shelterwood, clear-cut, rotation-age modification) that steer forest development toward intended outcomes while respecting ecological constraints.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smith, Larson, and classical silviculture","subfamily":"Silvicultural practice and forest management","year":"1950s–2000s","type":"Planning and decision pipeline"},"citations":[{"ref":"Smith, D. M., Larson, B. C., Kelty, M. J., & Ashton, P. M. S. (1997). The Practice of Silviculture: Applied Forest Ecology (9th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/practiceofsilvi"},{"ref":"Nyland, R. D. (2002). Silviculture: Concepts and Applications (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/silviculturconc"},{"ref":"Seely, B., Welham, C., & Kimmins, J. P. (2015). Carbon Sequestration in the Boreal Forest: Natural Disturbance and Human Management. Climatic Change, 67(2-3), 385–400.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Carbon+Sequestration+in+the+Boreal+Forest%3A+Natural+Disturbance+and+Human+Management+Seely"},{"ref":"Pommerening, A., & Muszta, A. (2015). Methods of Evaluating Ungulate Browsing Damage on Forest Vegetation. Forestry, 78(2), 143–156.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Methods+of+Evaluating+Ungulate+Browsing+Damage+on+Forest+Vegetation+Pommerening"}],"related":["forest-inventory-sampling","stand-basal-area-measurement","biodiversity-index-forest","carbon-stock-estimation-forest"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simclr","name":"SimCLR","fullName":"A Simple Framework for Contrastive Learning of Visual Representations","aliases":["Simple contrastive learning","SimCLR framework"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Self-Supervised Learning, Contrastive Learning","year":"2020","originator":"Ting Chen","url":"https://scholargate.app/en/deep-learning/simclr","markdownUrl":"https://scholargate.app/en/deep-learning/simclr.md","definition":"SimCLR is a self-supervised learning framework introduced by Chen et al. in 2020 that learns visual representations by contrasting similar and dissimilar views of images. The method applies strong data augmentations to create different views of the same image, then trains an encoder to bring similar views close in representation space while pushing dissimilar views apart.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ting Chen","subfamily":"Deep Learning, Self-Supervised Learning, Contrastive Learning","year":"2020","type":"Neural network architecture"},"citations":[{"ref":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In International conference on machine learning (pp. 1597-1607). PMLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2002.05709"}],"related":["masked-autoencoders","vision-transformer","swin-transformer","few-shot-object-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simheuristics","name":"Simheuristics","fullName":"Simheuristics (Simulation + Metaheuristics)","aliases":["Simulation-based Metaheuristics","Stochastic Metaheuristics with Simulation","Hybrid Simulation-Optimization","Simülistik Sezgiseller"],"domain":"optimization","family":"process-pipeline","subfamily":"Metaheuristics","year":2015,"originator":"Juan et al.","url":"https://scholargate.app/en/optimization/simheuristics","markdownUrl":"https://scholargate.app/en/optimization/simheuristics.md","definition":"Simheuristics is a hybrid algorithmic framework that integrates Monte Carlo or discrete-event simulation into metaheuristic search procedures to solve stochastic combinatorial optimization problems. Introduced by Juan et al. in 2015, it addresses settings where objective function evaluations involve random variables, providing near-optimal solutions with probabilistic quality guarantees. The approach is especially suited for real-world logistics, transportation, and scheduling problems where uncertainty is inherent and classical deterministic solvers fail to capture variability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Juan et al.","year":2015,"type":"Hybrid simulation-optimization framework","subfamily":"Metaheuristics","complexity":"Problem-dependent; typically O(n·k·r) per iteration where r is simulation replications","output":"Near-optimal solution with probabilistic performance guarantees"},"citations":[{"ref":"Juan, A. A., et al. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72.","type":"article","doi":"10.1016/j.orp.2015.03.001","isbn":null,"url":null}],"related":["matheuristics","discrete-event-simulation","stochastic-optimization"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"similarity-vs-plagiarism","name":"Similarity vs Plagiarism: Understanding the Distinction","fullName":"Similarity Index vs Plagiarism: Why Similarity Percentages Do Not Constitute Plagiarism Verdicts","aliases":["similarity index","turnitin score","similarity percentage"],"domain":"research-ethics","family":"process-pipeline","subfamily":"plagiarism-detection-and-prevention","year":"2000s","originator":"Academic integrity frameworks and plagiarism detection software companies","url":"https://scholargate.app/en/research-ethics/similarity-vs-plagiarism","markdownUrl":"https://scholargate.app/en/research-ethics/similarity-vs-plagiarism.md","definition":"A critical distinction exists between similarity percentages generated by plagiarism detection software (Turnitin, iThenticate) and an actual plagiarism verdict. A similarity index is a red flag requiring review; it is not a plagiarism determination. High similarity can result from legitimate quotations, references, shared technical language, or common knowledge. Conversely, low similarity does not guarantee absence of plagiarism. Human expert judgment is essential—similarity detection software provides data, not judgment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Academic integrity frameworks and plagiarism detection software companies","subfamily":"plagiarism-detection-and-prevention","year":"2000s","type":"Concept"},"citations":[{"ref":"Hirsch, L. R. (2013). Recognizing plagiarism: A guide for academic professionals. Teaching Professor Blog.","type":"article","doi":null,"isbn":null,"url":"https://www.teachingprofessor.com"},{"ref":"Declerck, K., Decock, P., Macq, B., & Vandenbossche, J. (2021). Similarity index in plagiarism detection: A critical perspective. Research Integrity and Peer Review, 6, 1-8.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Similarity+index+in+plagiarism+detection%3A+A+critical+perspective+Declerck"},{"ref":"Steneck, N. H. (2007). Introduction to the responsible conduct of research. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products"}],"related":["verbatim-plagiarism","paraphrasing-plagiarism","mosaic-plagiarism","turnitin-ithenticate"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simple-exponential-smoothing","name":"Exponential Smoothing","fullName":"Simple and Double Exponential Smoothing (SES / Holt)","aliases":["SES","Holt's linear trend method","exponential smoothing forecasting","Basit ve Çift Üstel Düzleştirme (SES / Holt)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1957,"originator":"Robert G. Brown (SES); Charles C. Holt (linear trend)","url":"https://scholargate.app/en/econometrics/simple-exponential-smoothing","markdownUrl":"https://scholargate.app/en/econometrics/simple-exponential-smoothing.md","definition":"Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert G. Brown (SES); Charles C. Holt (linear trend)","year":1957,"type":"Exponential smoothing forecasting model","estimator":"Recursive weighting via smoothing parameters α (level) and β (trend)","outcome":"continuous","structure":"univariate time series","minSample":10},"citations":[{"ref":"Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://search.worldcat.org/title/1156903"},{"ref":"Holt, C. C. (1957). Forecasting Trends and Seasonals by Exponentially Weighted Averages. Office of Naval Research Memorandum 52, Carnegie Institute of Technology.","type":"report","doi":null,"isbn":null,"url":"https://doi.org/10.1016/j.ijforecast.2003.09.015"}],"related":["holt-winters-seasonal","arima","state-space-model","structural-time-series","moving-average-smoothing"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simple-linear-regression","name":"Simple Linear Regression","fullName":"Simple Linear Regression (OLS)","aliases":["SLR","ordinary least squares regression","OLS regression","bivariate regression","least squares regression"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1805,"originator":"Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)","url":"https://scholargate.app/en/statistics/simple-linear-regression","markdownUrl":"https://scholargate.app/en/statistics/simple-linear-regression.md","definition":"Simple linear regression is the foundational parametric method for modelling a straight-line relationship between one continuous predictor and one continuous outcome, estimating the slope and intercept by ordinary least squares (OLS). The least squares principle was first published by Adrien-Marie Legendre in 1805, and Francis Galton introduced the concept of regression to the mean in 1886, coining the term that names the entire family of methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)","year":1805,"family":"Regression model","type":"Parametric bivariate regression","predictors":1,"outcome":"continuous","parametric":true,"estimator":"Ordinary least squares (OLS)","df_residual":"n - 2"},"citations":[{"ref":"Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la méthode des moindres quarrés, pp. 72–80]","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/bub_gb_S-Aw43kRv-IC"},{"ref":"Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.","type":"article","doi":"10.2307/2841583","isbn":null,"url":null},{"ref":"Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to Linear Regression Analysis (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119578727","url":null}],"related":["multiple-linear-regression","pearson-correlation","polynomial-regression","logistic-regression","independent-t-test","one-way-anova","ridge-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simple-random-sampling","name":"Simple random sampling","fullName":"Simple Random Sampling","aliases":["SRS","unrestricted random sampling","equal-probability sampling","EPSEM"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Early 20th century; systematized by Cochran 1953/1977","originator":"William Gosset, Jerzy Neyman, and formalized by William Cochran","url":"https://scholargate.app/en/survey-methodology/simple-random-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/simple-random-sampling.md","definition":"Simple random sampling (SRS) is the foundational probability sampling method in which every unit in the population has an equal and independent chance of being selected. Because selection is governed purely by chance, SRS eliminates systematic bias, supports unbiased estimation of population parameters, and provides the statistical baseline against which all more complex probability designs are evaluated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Gosset, Jerzy Neyman, and formalized by William Cochran","year":"Early 20th century; systematized by Cochran 1953/1977","type":"Probability sampling design","dataType":"Any population with a complete, enumerable sampling frame","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Lohr, S. L. (2009). Sampling: Design and Analysis (2nd ed.). Brooks/Cole.","type":"book","doi":null,"isbn":"978-0495105275","url":null}],"related":["stratified-sampling","cluster-sampling","systematic-sampling","multistage-sampling","purposive-sampling","quota-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simplex-method","name":"Simplex Method","fullName":"The Simplex Method for Linear Programming","aliases":["simplex algorithm"],"domain":"operations-research","family":"ml-model","subfamily":"Optimization","year":"1947","originator":"George Dantzig","url":"https://scholargate.app/en/operations-research/simplex-method","markdownUrl":"https://scholargate.app/en/operations-research/simplex-method.md","definition":"The Simplex Method, developed by George Dantzig in 1947, is a foundational algorithm for solving linear programming problems. It systematically explores vertices of the feasible region to find the optimal solution where the objective function is maximized or minimized subject to linear constraints.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Dantzig","subfamily":"Optimization","year":"1947","type":"algorithm"},"citations":[{"ref":"Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press.","type":"book","doi":"10.1515/9781400884179","isbn":null,"url":null},{"ref":"Vanderbei, R. J. (2014). Linear Programming: Foundations and Extensions (4th ed.). Springer.","type":"book","doi":"10.1007/978-1-4614-7630-6","isbn":null,"url":null}],"related":["column-generation","benders-decomposition","augmented-lagrangian-method","dijkstra-algorithm","interior-point-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulated-annealing","name":"Simulated Annealing","fullName":"Simulated Annealing","aliases":["Benzetimli Tavlama (Simulated Annealing)","SA","probabilistic local search"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":1983,"originator":null,"url":"https://scholargate.app/en/optimization/simulated-annealing","markdownUrl":"https://scholargate.app/en/optimization/simulated-annealing.md","definition":"Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Kirkpatrick, Gelatt & Vecchi","year":1983,"type":"Probabilistic metaheuristic / local search","inspiration":"Physical annealing process in metallurgy","keyParameter":"Temperature (cooling schedule)","convergence":"Theoretically guaranteed with sufficiently slow cooling","sampleSizeRequired":"None (no training data needed)","difficultyLevel":"Intermediate (2/5)"},"citations":[{"ref":"Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.","type":"article","doi":"10.1126/science.220.4598.671","isbn":null,"url":null},{"ref":"van Laarhoven, P.J.M. & Aarts, E.H.L. (1987). Simulated Annealing: Theory and Applications. Springer.","type":"book","doi":null,"isbn":"9789027725431","url":null}],"related":["genetic-algorithm","particle-swarm-optimization","tabu-search","differential-evolution","ant-colony-optimization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-box-behnken-design","name":"Simulation-assisted Box-Behnken design","fullName":"Simulation-Assisted Box-Behnken Design of Experiments","aliases":["SA-BBD","computer-aided Box-Behnken design","simulation-based BBD","virtual Box-Behnken design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1960 (base design); simulation-assisted application developed from the 1990s onward","originator":"Box-Behnken (1960) for the base design; simulation integration emerged from computer experiment methodology in the 1980s-2000s","url":"https://scholargate.app/en/experimental-design/simulation-assisted-box-behnken-design","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-box-behnken-design.md","definition":"Simulation-assisted Box-Behnken design couples the three-level, near-spherical Box-Behnken experimental matrix with computer simulation models — such as finite-element analysis, computational fluid dynamics, or discrete-event simulation — to map how multiple controllable factors jointly affect one or more output responses, while eliminating the need for costly or hazardous physical prototype fabrication at every design point.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box-Behnken (1960) for the base design; simulation integration emerged from computer experiment methodology in the 1980s-2000s","year":"1960 (base design); simulation-assisted application developed from the 1990s onward","type":"Simulation-integrated response surface design","dataType":"Computer simulation output (numerical); optionally augmented with physical experimental data","subfamily":"Engineering methods"},"citations":[{"ref":"Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455-475.","type":"article","doi":"10.1080/00401706.1960.10489912","isbn":null,"url":null},{"ref":"Fang, K. T., Li, R., & Sudjianto, A. (2006). Design and Modeling for Computer Experiments. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584885467","url":null}],"related":["box-behnken-design","simulation-assisted-central-composite-design","response-surface-methodology","simulation-assisted-full-factorial-design","computer-experiment-design","latin-hypercube-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-causal-comparative-research","name":"Simulation-assisted causal-comparative research","fullName":"Simulation-Assisted Causal-Comparative Research Design","aliases":["simulation-augmented causal-comparative design","ex post facto simulation design","SA-CCR","causal-comparative with simulation validation"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Late 20th–early 21st century (hybrid approach formalized ~1990s–2000s)","originator":"Synthesized from causal-comparative tradition (Donald T. Campbell; Julian Stanley) and simulation methodology","url":"https://scholargate.app/en/research-design/simulation-assisted-causal-comparative-research","markdownUrl":"https://scholargate.app/en/research-design/simulation-assisted-causal-comparative-research.md","definition":"Simulation-assisted causal-comparative research is a hybrid observational design that combines the ex post facto logic of causal-comparative studies — comparing groups that differ on a naturally occurring variable — with computational simulation to strengthen causal inference, test counterfactuals, and assess the robustness of observed group differences. By augmenting real-world comparisons with simulated scenarios, researchers can explore causal mechanisms that cannot be manipulated experimentally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from causal-comparative tradition (Donald T. Campbell; Julian Stanley) and simulation methodology","year":"Late 20th–early 21st century (hybrid approach formalized ~1990s–2000s)","type":"Hybrid observational-simulation design","dataType":"Existing group data (observational) plus synthetic/simulated data","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-1260087352","url":null},{"ref":"Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0136062127","url":null}],"related":["causal-comparative-research","ex-post-facto-research","monte-carlo-simulation","quasi-experimental-design","propensity-score-matching","agent-based-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-confirmatory-research","name":"Simulation-assisted confirmatory research","fullName":"Simulation-Assisted Confirmatory Research","aliases":["simulation-based confirmatory design","Monte Carlo confirmatory research","computational confirmatory study","simulation-enhanced hypothesis testing"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–2000s (widespread integration in behavioral and social sciences)","originator":"No single originator; tradition formalized through Monte Carlo methods (Metropolis & Ulam, 1949) applied to confirmatory designs","url":"https://scholargate.app/en/research-design/simulation-assisted-confirmatory-research","markdownUrl":"https://scholargate.app/en/research-design/simulation-assisted-confirmatory-research.md","definition":"Simulation-assisted confirmatory research integrates computational simulation — most commonly Monte Carlo methods — into a hypothesis-driven, confirmatory study design. Before or alongside empirical data collection, the researcher runs simulated data under specified model assumptions to establish expected parameter distributions, verify statistical power, and anticipate the behavior of the chosen analysis. The empirical findings are then evaluated against those simulation-derived benchmarks, strengthening the evidential value of confirmatory conclusions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"No single originator; tradition formalized through Monte Carlo methods (Metropolis & Ulam, 1949) applied to confirmatory designs","year":"1980s–2000s (widespread integration in behavioral and social sciences)","type":"Quantitative hybrid design","dataType":"Simulated data, empirical quantitative data, parametric assumptions","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Morey, R. D., Chambers, C. D., Aitken, M. R. F., Harris, C. R., Hoekstra, R., Lakens, D., Lewandowsky, S., Morey, C. C., Newman, D. P., Schonbrodt, F. D., Vanpaemel, W., Wagenmakers, E. J., & Zwaan, R. A. (2022). The Peer Reviewers' Openness Initiative: Incentivising open research practices through peer review. Royal Society Open Science, 3(1), 150547.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Simulation+assisted+confirmatory+research+Monte+Carlo+power+analysis"},{"ref":"Morris, T. P., White, I. R., & Crowther, M. J. (2019). Using simulation studies to evaluate statistical methods. Statistics in Medicine, 38(11), 2074–2102.","type":"article","doi":"10.1002/sim.8086","isbn":null,"url":null}],"related":["confirmatory-factor-analysis","power-analysis","monte-carlo-simulation","randomized-controlled-trial","preregistered-study","structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-control-chart","name":"Simulation-assisted control chart","fullName":"Simulation-Assisted Statistical Process Control Chart","aliases":["simulation-based SPC","Monte Carlo control chart design","simulation-enhanced SPC","virtual control chart"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1920s (control charts); simulation integration from 1980s–1990s","originator":"Walter A. Shewhart (control charts); simulation integration developed through work of W.H. Woodall, D.C. Montgomery and collaborators","url":"https://scholargate.app/en/experimental-design/simulation-assisted-control-chart","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-control-chart.md","definition":"Simulation-assisted control chart integrates Monte Carlo or discrete-event simulation with traditional Shewhart-type control charting to design, validate, and optimize chart parameters before deployment on a real process. Rather than relying solely on assumed distributional forms, the practitioner builds a simulation model of the process, generates virtual data under in-control and out-of-control scenarios, and uses these runs to calibrate control limits, estimate average run length (ARL), and stress-test chart sensitivity — all without interrupting production.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walter A. Shewhart (control charts); simulation integration developed through work of W.H. Woodall, D.C. Montgomery and collaborators","year":"1920s (control charts); simulation integration from 1980s–1990s","type":"Hybrid quality monitoring method","dataType":"Continuous or attribute process measurement data; simulation output data","subfamily":"Engineering methods"},"citations":[{"ref":"Woodall, W. H., & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology, 31(4), 376–386.","type":"article","doi":"10.1080/00224065.1999.11979944","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2009). Statistical Quality Control: A Modern Introduction (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470169926","url":null}],"related":["control-chart","statistical-process-control","simulation-assisted-statistical-process-control","monte-carlo-simulation","process-capability-analysis","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-cross-sectional-research","name":"Simulation-assisted cross-sectional research","fullName":"Simulation-Assisted Cross-Sectional Research Design","aliases":["simulation-enhanced cross-sectional study","hybrid simulation cross-sectional design","cross-sectional simulation study","SACSR"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"2000s–2010s (consolidated as a named hybrid approach)","originator":"Emerged from epidemiology and systems science (no single originator; synthesises Pearce-type cross-sectional designs with simulation modelling traditions from Sterman and colleagues)","url":"https://scholargate.app/en/research-design/simulation-assisted-cross-sectional-research","markdownUrl":"https://scholargate.app/en/research-design/simulation-assisted-cross-sectional-research.md","definition":"Simulation-assisted cross-sectional research combines the one-time, population-wide snapshot of a classic cross-sectional survey with computational simulation — such as agent-based modelling or Monte Carlo methods — to extend what can be inferred from data collected at a single point in time. Empirical cross-sectional data calibrate the simulation, which then explores counterfactuals, rare subgroups, or dynamic processes that the survey alone cannot reveal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerged from epidemiology and systems science (no single originator; synthesises Pearce-type cross-sectional designs with simulation modelling traditions from Sterman and colleagues)","year":"2000s–2010s (consolidated as a named hybrid approach)","type":"Quantitative hybrid research design","dataType":"Cross-sectional survey or observational data combined with computational simulation output","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Pearce, N. (2012). Classification of epidemiological study designs. International Journal of Epidemiology, 41(2), 393–397.","type":"article","doi":"10.1093/ije/dys049","isbn":null,"url":null},{"ref":"Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill.","type":"book","doi":null,"isbn":"978-0072389159","url":null}],"related":["cross-sectional-study","agent-based-modeling","monte-carlo-simulation","mixed-methods-research","longitudinal-study","survey-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-design-of-experiments","name":"Simulation-assisted design of experiments","fullName":"Simulation-Assisted Design of Experiments","aliases":["Simulation-based DoE","Virtual DoE","Computer-aided DoE","SA-DoE"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1970s–1990s (formalized with computer experimentation growth)","originator":"Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.","url":"https://scholargate.app/en/experimental-design/simulation-assisted-design-of-experiments","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-design-of-experiments.md","definition":"Simulation-assisted design of experiments (SA-DoE) integrates computational simulation tools — such as finite element analysis (FEA), computational fluid dynamics (CFD), or discrete-event simulation — with classical DoE principles to systematically explore the factor space of a system. Rather than running costly or hazardous physical trials, researchers execute a structured set of virtual experiments across selected factor combinations, then fit a surrogate model to the simulation outputs to understand main effects, interactions, and optimal settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al.","year":"1970s–1990s (formalized with computer experimentation growth)","type":"Hybrid experimental-computational method","dataType":"Computer simulation outputs (continuous numerical responses)","subfamily":"Engineering methods"},"citations":[{"ref":"Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer.","type":"book","doi":null,"isbn":"978-0387954202","url":null},{"ref":"Kleijnen, J. P. C. (2015). Design and Analysis of Simulation Experiments (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-3319185668","url":null}],"related":["design-of-experiments","response-surface-methodology","central-composite-design","fractional-factorial-design","latin-hypercube-sampling","sensitivity-analysis-integrated-design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-event-tree-analysis","name":"Simulation-assisted event tree analysis","fullName":"Simulation-Assisted Event Tree Analysis","aliases":["Monte Carlo ETA","stochastic event tree analysis","simulation-enhanced ETA","probabilistic event tree simulation"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1970s–1990s (formalized in probabilistic risk assessment practice)","originator":"H.A. Watson (Bell Telephone Laboratories, ETA origins ~1961); Monte Carlo integration of ETA developed in nuclear/aerospace PRA community 1970s–1990s","url":"https://scholargate.app/en/experimental-design/simulation-assisted-event-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-event-tree-analysis.md","definition":"Simulation-assisted event tree analysis (ETA) extends classical event tree analysis by replacing fixed point-estimate branch probabilities with Monte Carlo or discrete-event simulation. This allows analysts to propagate uncertainty through every branch of the tree and obtain full probability distributions over accident sequences and system outcomes, yielding far richer risk insights than deterministic ETA alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"H.A. Watson (Bell Telephone Laboratories, ETA origins ~1961); Monte Carlo integration of ETA developed in nuclear/aerospace PRA community 1970s–1990s","year":"1970s–1990s (formalized in probabilistic risk assessment practice)","type":"Probabilistic risk and reliability assessment method","dataType":"Component failure rates, conditional branch probabilities, simulation-generated samples","subfamily":"Engineering methods"},"citations":[{"ref":"Zio, E. (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering and System Safety, 94(2), 125–141.","type":"article","doi":"10.1016/j.ress.2008.06.002","isbn":null,"url":null},{"ref":"Event tree analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Event_tree_analysis"}],"related":["event-tree-analysis","fault-tree-analysis","failure-mode-and-effects-analysis","simulation-assisted-fault-tree-analysis","bayesian-event-tree-analysis","risk-based-event-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-ex-post-facto-design","name":"Simulation-assisted ex post facto design","fullName":"Simulation-Assisted Ex Post Facto Research Design","aliases":["simulation-enhanced causal-comparative design","ex post facto with simulation","retrospective simulation design","SAEPF design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Ex post facto: 1964; simulation-assisted hybrid: 1990s–2000s","originator":"Kerlinger, F. N. (ex post facto basis); simulation integration drawn from computational social science (Axelrod, Epstein, 1990s)","url":"https://scholargate.app/en/research-design/simulation-assisted-ex-post-facto-design","markdownUrl":"https://scholargate.app/en/research-design/simulation-assisted-ex-post-facto-design.md","definition":"Simulation-assisted ex post facto design is a non-experimental observational approach in which the researcher examines already-occurred events or conditions using existing records and then supplements the empirical analysis with computational simulation to approximate counterfactual scenarios that cannot be observed in reality. The design retains the retrospective, naturalistic character of classic ex post facto research while leveraging agent-based, Monte Carlo, or system-dynamics simulation to address the inherent confound limitations of purely archival work.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kerlinger, F. N. (ex post facto basis); simulation integration drawn from computational social science (Axelrod, Epstein, 1990s)","year":"Ex post facto: 1964; simulation-assisted hybrid: 1990s–2000s","type":"Non-experimental observational design with computational augmentation","dataType":"Existing records, archival data, administrative datasets, supplemented by simulation-generated counterfactual data","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Foundations+of+Behavioral+Research+Kerlinger+1964"},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["ex-post-facto-design","causal-comparative-design","quasi-experimental-design","agent-based-modeling","monte-carlo-simulation","retrospective-cohort-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-failure-mode-and-effects-analysis","name":"Simulation-assisted failure mode and effects analysis","fullName":"Simulation-Assisted Failure Mode and Effects Analysis","aliases":["Simulation-FMEA","Monte Carlo FMEA","Simulation-based FMEA","SA-FMEA"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1949 (FMEA); simulation-assisted variant: 1980s–1990s","originator":"FMEA originates from US MIL-P-1629 (1949); simulation integration developed in reliability engineering from the 1980s–1990s","url":"https://scholargate.app/en/experimental-design/simulation-assisted-failure-mode-and-effects-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-failure-mode-and-effects-analysis.md","definition":"Simulation-assisted FMEA enhances the classical Failure Mode and Effects Analysis by replacing point-estimate occurrence ratings with probabilistic simulation — typically Monte Carlo — to quantify failure probability distributions across a system's components. This yields statistically grounded Risk Priority Numbers (RPNs) rather than expert guesses, enabling more rigorous identification and prioritization of critical failure modes in complex engineering systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"FMEA originates from US MIL-P-1629 (1949); simulation integration developed in reliability engineering from the 1980s–1990s","year":"1949 (FMEA); simulation-assisted variant: 1980s–1990s","type":"Reliability and risk analysis method","dataType":"Failure rate data, probability distributions, engineering system models","subfamily":"Engineering methods"},"citations":[{"ref":"Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.","type":"book","doi":null,"isbn":"978-0873895989","url":null},{"ref":"Failure mode and effects analysis. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Failure_mode_and_effects_analysis"}],"related":["failure-mode-and-effects-analysis","fault-tree-analysis","event-tree-analysis","reliability-analysis","monte-carlo-simulation","simulation-assisted-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-fault-tree-analysis","name":"Simulation-assisted fault tree analysis","fullName":"Simulation-Assisted Fault Tree Analysis","aliases":["SA-FTA","Monte Carlo FTA","simulation-based FTA","stochastic fault tree analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1970s–1980s (widespread adoption in nuclear and aerospace industries)","originator":"Fault tree analysis: H. A. Watson (Bell Labs, 1961); Monte Carlo integration in reliability: Herman Kahn / Stanislaw Ulam (RAND, late 1940s); combination formalized in reliability engineering literature from the 1970s onward","url":"https://scholargate.app/en/experimental-design/simulation-assisted-fault-tree-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-fault-tree-analysis.md","definition":"Simulation-assisted fault tree analysis (SA-FTA) combines the logical structure of classical fault tree analysis with Monte Carlo or discrete-event simulation to estimate the probability and timing of an undesired top event when component failures follow complex, non-exponential, or correlated probability distributions. The approach overcomes the analytical limitations of Boolean algebra-based FTA and is widely used in nuclear, aerospace, chemical process, and manufacturing reliability engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fault tree analysis: H. A. Watson (Bell Labs, 1961); Monte Carlo integration in reliability: Herman Kahn / Stanislaw Ulam (RAND, late 1940s); combination formalized in reliability engineering literature from the 1970s onward","year":"1970s–1980s (widespread adoption in nuclear and aerospace industries)","type":"Quantitative reliability and risk analysis technique","dataType":"Component failure rate data, probability distributions, logic gate structures","subfamily":"Engineering methods"},"citations":[{"ref":"Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. US Nuclear Regulatory Commission, NUREG-0492.","type":"book","doi":null,"isbn":null,"url":"https://www.nrc.gov/reading-rm/doc-collections/nuregs/staff/sr0492/"},{"ref":"Zio, E. (2013). The Monte Carlo Simulation Method for System Reliability and Risk Analysis. Springer.","type":"book","doi":null,"isbn":"978-1447145882","url":null}],"related":["fault-tree-analysis","failure-mode-and-effects-analysis","event-tree-analysis","reliability-analysis","monte-carlo-simulation","risk-based-fault-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-fractional-factorial-design","name":"Simulation-assisted fractional factorial design","fullName":"Simulation-Assisted Fractional Factorial Design","aliases":["SA-FFD","virtual fractional factorial design","computer-aided fractional factorial design","simulation-based FFD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"FFD: 1950s; simulation integration: 1980s–2000s","originator":"Box, Hunter & Hunter (FFD basis); Kleijnen and others (simulation integration)","url":"https://scholargate.app/en/experimental-design/simulation-assisted-fractional-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-fractional-factorial-design.md","definition":"Simulation-assisted fractional factorial design (SA-FFD) combines the statistical efficiency of fractional factorial experimentation with computerized simulation models to screen and estimate factor effects when physical experiments are too costly, hazardous, or time-consuming. A carefully chosen subset of factor-level combinations — the fractional factorial array — is executed inside a validated simulation model instead of (or alongside) a real process, dramatically reducing resource requirements while preserving the ability to identify main effects and low-order interactions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box, Hunter & Hunter (FFD basis); Kleijnen and others (simulation integration)","year":"FFD: 1950s; simulation integration: 1980s–2000s","type":"Experimental design with computational augmentation","dataType":"Simulated output responses (continuous or discrete); factor settings from fractional factorial array","subfamily":"Engineering methods"},"citations":[{"ref":"Kleijnen, J. P. C. (2008). Design and Analysis of Simulation Experiments. Springer.","type":"book","doi":null,"isbn":"978-0387718125","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["fractional-factorial-design","full-factorial-design","central-composite-design","response-surface-methodology","design-of-experiments","simulation-assisted-design-of-experiments"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-full-factorial-design","name":"Simulation-assisted full factorial design","fullName":"Simulation-Assisted Full Factorial Design of Experiments","aliases":["SA-FFD","computer simulation full factorial","virtual full factorial design","simulation-based full factorial DOE"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (simulation-DOE integration formalized)","originator":"Montgomery (DOE foundations); Kleijnen (simulation DOE formalization)","url":"https://scholargate.app/en/experimental-design/simulation-assisted-full-factorial-design","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-full-factorial-design.md","definition":"Simulation-assisted full factorial design integrates full factorial design of experiments (DOE) with computer simulation models — such as discrete-event simulation, finite element analysis, or Monte Carlo methods — to systematically explore every combination of factor levels and quantify their effects on system responses. It enables comprehensive experimentation in contexts where physical trials would be costly, dangerous, or infeasible.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Montgomery (DOE foundations); Kleijnen (simulation DOE formalization)","year":"1990s–2000s (simulation-DOE integration formalized)","type":"Experimental design with computer simulation","dataType":"Simulation output (continuous or discrete numerical responses)","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null},{"ref":"Kleijnen, J. P. C. (2015). Design and Analysis of Simulation Experiments (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-3319185668","url":null}],"related":["full-factorial-design","fractional-factorial-design","simulation-assisted-response-surface-methodology","simulation-assisted-taguchi-method","design-of-experiments","central-composite-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-hypothesis-testing-research","name":"Simulation-assisted hypothesis testing research","fullName":"Simulation-Assisted Hypothesis Testing Research","aliases":["simulation-based hypothesis testing","Monte Carlo hypothesis testing","computational hypothesis testing","simulation-assisted inference"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1980s–1990s (bootstrap: 1979; permutation inference: mid-20th century; unified simulation-assisted framing: 1990s–2000s)","originator":"Bradley Efron (bootstrap framework); Phillip Good (permutation tests); Monte Carlo tradition traced to Stanislaw Ulam and John von Neumann","url":"https://scholargate.app/en/research-design/simulation-assisted-hypothesis-testing-research","markdownUrl":"https://scholargate.app/en/research-design/simulation-assisted-hypothesis-testing-research.md","definition":"Simulation-assisted hypothesis testing research replaces or supplements analytical probability theory with computational simulation — resampling, permutation, or Monte Carlo methods — to construct null distributions and evaluate hypotheses. Rather than assuming a parametric distribution and consulting a table, the researcher generates thousands of simulated datasets from the observed data or a specified model, building an empirical null distribution against which the observed test statistic is compared. The approach is especially valuable when analytic assumptions (normality, large samples) cannot be met.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bradley Efron (bootstrap framework); Phillip Good (permutation tests); Monte Carlo tradition traced to Stanislaw Ulam and John von Neumann","year":"1980s–1990s (bootstrap: 1979; permutation inference: mid-20th century; unified simulation-assisted framing: 1990s–2000s)","type":"Quantitative research design integrating computational simulation with classical hypothesis testing","dataType":"Numeric (continuous, ordinal, count, or categorical data suitable for resampling or stochastic simulation)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Efron, B., & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC.","type":"book","doi":null,"isbn":"978-0412042317","url":null},{"ref":"Good, P. I. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387988641","url":null}],"related":["monte-carlo-simulation","bootstrap-resampling","permutation-test","power-analysis","randomization-test","bayesian-hypothesis-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-process-capability-analysis","name":"Simulation-assisted process capability analysis","fullName":"Simulation-Assisted Process Capability Analysis","aliases":["Monte Carlo process capability","simulation-based Cpk analysis","stochastic capability analysis","virtual process capability study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s–1990s (mature practice by mid-1990s)","originator":"Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues)","url":"https://scholargate.app/en/experimental-design/simulation-assisted-process-capability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-process-capability-analysis.md","definition":"Simulation-assisted process capability analysis combines Monte Carlo simulation with classical capability indices (Cp, Cpk, Cpm) to evaluate whether a process can consistently meet specification limits when direct measurement is costly, dangerous, or impractical. By propagating input distributions through a process model, the analyst obtains a simulated output distribution and derives capability metrics without waiting for physical production runs. The approach is especially valuable during product design, process scale-up, and tolerance stack-up studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed through integration of Monte Carlo simulation with classical capability indices (Juran, Kane, Kotz and colleagues)","year":"1980s–1990s (mature practice by mid-1990s)","type":"Quantitative engineering quality method","dataType":"Simulated continuous process output distributions; specification limits","subfamily":"Engineering methods"},"citations":[{"ref":"Kotz, S., & Lovelace, C. R. (1998). Process Capability Indices in Theory and Practice. Arnold.","type":"book","doi":null,"isbn":"978-0340691281","url":null},{"ref":"Rubinstein, R. Y., & Kroese, D. P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118632161","url":null}],"related":["process-capability-analysis","statistical-process-control","monte-carlo-simulation","design-of-experiments","robust-process-capability-analysis","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-quality-function-deployment","name":"Simulation-assisted quality function deployment","fullName":"Simulation-Assisted Quality Function Deployment","aliases":["SA-QFD","simulation-integrated QFD","simulation-driven house of quality","QFD with simulation"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (QFD: 1966; simulation integration: ~1995–2005)","originator":"Yoji Akao (QFD foundation); simulation integration developed by engineering researchers in 1990s–2000s","url":"https://scholargate.app/en/experimental-design/simulation-assisted-quality-function-deployment","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-quality-function-deployment.md","definition":"Simulation-assisted quality function deployment (SA-QFD) integrates computational simulation into the classic QFD framework to replace or supplement costly physical prototypes when evaluating how engineering design decisions satisfy customer requirements. By embedding simulation models — such as finite element analysis, discrete-event simulation, or system dynamics — within the House of Quality matrix, engineers can rapidly quantify the impact of technical characteristics on customer satisfaction and iteratively refine design priorities before committing to production.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yoji Akao (QFD foundation); simulation integration developed by engineering researchers in 1990s–2000s","year":"1990s–2000s (QFD: 1966; simulation integration: ~1995–2005)","type":"Hybrid engineering design and quality planning method","dataType":"Customer requirement ratings, engineering characteristic data, simulation output metrics","subfamily":"Engineering methods"},"citations":[{"ref":"Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press.","type":"book","doi":null,"isbn":"978-0915299416","url":null},{"ref":"Park, T., & Kim, K. J. (2003). Determination of an optimal set of design requirements using house of quality. Journal of Operations Management, 21(2), 133–146.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Determination+of+an+optimal+set+of+design+requirements+using+house+of+quality"}],"related":["quality-function-deployment","simulation-assisted-design-of-experiments","design-of-experiments","robust-quality-function-deployment","failure-mode-and-effects-analysis","response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-quantitative-content-analysis","name":"Simulation-assisted quantitative content analysis","fullName":"Simulation-Assisted Quantitative Content Analysis","aliases":["SA-QCA","simulation-augmented content analysis","Monte Carlo content analysis","computational content analysis with simulation"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"2000s–2010s","originator":"Extension of Neuendorf (2002) and Krippendorff (2018) quantitative content analysis traditions, with simulation augmentation developed within computational social science","url":"https://scholargate.app/en/research-design/simulation-assisted-quantitative-content-analysis","markdownUrl":"https://scholargate.app/en/research-design/simulation-assisted-quantitative-content-analysis.md","definition":"Simulation-assisted quantitative content analysis (SA-QCA) extends classical quantitative content analysis by integrating computational simulation — typically Monte Carlo methods or agent-based models — to validate coding schemes, estimate coder reliability under controlled conditions, test category distinctiveness, and assess the robustness of frequency-based conclusions before or alongside the analysis of real text corpora. The method preserves the systematic, replicable counting logic of quantitative content analysis while adding a simulation layer that strengthens methodological rigour.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Neuendorf (2002) and Krippendorff (2018) quantitative content analysis traditions, with simulation augmentation developed within computational social science","year":"2000s–2010s","type":"Quantitative / computational research method","dataType":"Text corpora (media, documents, social media), plus simulated synthetic text samples","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage Publications.","type":"book","doi":null,"isbn":"978-0761919964","url":null},{"ref":"Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1506395661","url":null}],"related":["quantitative-content-analysis","computational-text-analysis","monte-carlo-simulation","intercoder-reliability","automated-content-analysis","mixed-methods-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-reliability-analysis","name":"Simulation-assisted reliability analysis","fullName":"Simulation-Assisted Reliability Analysis","aliases":["SARA","Monte Carlo reliability analysis","simulation-based reliability assessment","virtual reliability testing"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1940s–1980s (Monte Carlo foundations ~1940s; simulation-reliability integration ~1970s–1980s)","originator":"Enrico Fermi, John von Neumann, Stanislaw Ulam (Monte Carlo foundations); Freudenthal (structural reliability); Melchers (simulation integration)","url":"https://scholargate.app/en/experimental-design/simulation-assisted-reliability-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-reliability-analysis.md","definition":"Simulation-assisted reliability analysis combines probabilistic reliability theory with computational simulation — most commonly Monte Carlo methods or finite-element models — to estimate the probability that a system, component, or structure will perform its intended function under uncertain operating conditions. Rather than relying solely on closed-form analytical solutions, it propagates uncertainty through high-fidelity numerical models to quantify failure risk across complex, nonlinear, or multi-failure-mode systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Enrico Fermi, John von Neumann, Stanislaw Ulam (Monte Carlo foundations); Freudenthal (structural reliability); Melchers (simulation integration)","year":"1940s–1980s (Monte Carlo foundations ~1940s; simulation-reliability integration ~1970s–1980s)","type":"Quantitative probabilistic engineering method","dataType":"Numerical data, probability distributions, physics-based or empirical failure models","subfamily":"Engineering methods"},"citations":[{"ref":"Melchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119266075","url":null},{"ref":"Zio, E. (2013). The Monte Carlo Simulation Method for System Reliability and Risk Analysis. Springer.","type":"book","doi":null,"isbn":"978-1447145882","url":null}],"related":["monte-carlo-simulation","failure-mode-and-effects-analysis","fault-tree-analysis","reliability-analysis","sensitivity-analysis-with-reliability-analysis","robust-reliability-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-response-surface-methodology","name":"Simulation-assisted response surface methodology","fullName":"Simulation-Assisted Response Surface Methodology","aliases":["SA-RSM","simulation-based RSM","computer simulation RSM","metamodel-assisted RSM"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1951 (RSM); simulation integration widely adopted from 1980s onward","originator":"Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions","url":"https://scholargate.app/en/experimental-design/simulation-assisted-response-surface-methodology","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-response-surface-methodology.md","definition":"Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead of running physical experiments, the researcher executes simulation runs at design points prescribed by an RSM design, fits a polynomial metamodel (surrogate) to the simulation outputs, and uses that metamodel to locate optimal factor settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions","year":"1951 (RSM); simulation integration widely adopted from 1980s onward","type":"Experimental optimization method","dataType":"Computer simulation output (continuous numerical responses)","subfamily":"Engineering methods"},"citations":[{"ref":"Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118916025","url":null},{"ref":"Kleijnen, J. P. C. (2008). Design and Analysis of Simulation Experiments. Springer.","type":"book","doi":null,"isbn":"978-0387718125","url":null}],"related":["response-surface-methodology","central-composite-design","box-behnken-design","design-of-experiments","optimization-assisted-response-surface-methodology","robust-response-surface-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-root-cause-analysis","name":"Simulation-assisted root cause analysis","fullName":"Simulation-Assisted Root Cause Analysis","aliases":["Sim-RCA","simulation-based RCA","virtual root cause analysis","computational root cause analysis"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1990s–2000s (widespread adoption in engineering reliability contexts)","originator":"Evolved from root cause analysis practice (Kepner & Tregoe, 1960s) integrated with simulation methods (1990s–2000s in reliability engineering)","url":"https://scholargate.app/en/experimental-design/simulation-assisted-root-cause-analysis","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-root-cause-analysis.md","definition":"Simulation-assisted root cause analysis (Sim-RCA) integrates computational simulation — such as discrete-event simulation, Monte Carlo methods, or finite-element analysis — into the structured root cause analysis process to diagnose the underlying causes of complex failures or defects. By running virtual experiments on a system model, investigators can test hypothetical causal pathways safely, rapidly, and at scale, without disrupting live operations or waiting for rare failure events to recur.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Evolved from root cause analysis practice (Kepner & Tregoe, 1960s) integrated with simulation methods (1990s–2000s in reliability engineering)","year":"1990s–2000s (widespread adoption in engineering reliability contexts)","type":"Analytical / diagnostic engineering method","dataType":"Process data, failure records, system parameters, simulation model outputs","subfamily":"Engineering methods"},"citations":[{"ref":"Latino, R. J., & Latino, K. C. (2006). Root Cause Analysis: Improving Performance for Bottom-Line Results (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-0849338267","url":null},{"ref":"Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0136062127","url":null}],"related":["root-cause-analysis","failure-mode-and-effects-analysis","fault-tree-analysis","statistical-process-control","simulation-assisted-failure-mode-and-effects-analysis","simulation-assisted-fault-tree-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-six-sigma-dmaic","name":"Simulation-assisted Six Sigma DMAIC","fullName":"Simulation-Assisted Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control)","aliases":["Sim-DMAIC","Simulation-integrated DMAIC","Six Sigma with simulation","DMAIC simulation modeling"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"2000s–present (systematic integration of simulation with DMAIC)","originator":"Integration practice emerged from industrial engineering and operations research communities; DMAIC framework originates with Motorola/GE Six Sigma (1980s–1990s)","url":"https://scholargate.app/en/experimental-design/simulation-assisted-six-sigma-dmaic","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-six-sigma-dmaic.md","definition":"Simulation-assisted Six Sigma DMAIC embeds discrete-event or Monte Carlo simulation models inside the classic DMAIC cycle (Define, Measure, Analyze, Improve, Control) to test process changes virtually before committing to physical implementation. By running thousands of simulated scenarios, teams quantify variation, identify bottlenecks, and verify improvement hypotheses at low cost and with minimal disruption to live operations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Integration practice emerged from industrial engineering and operations research communities; DMAIC framework originates with Motorola/GE Six Sigma (1980s–1990s)","year":"2000s–present (systematic integration of simulation with DMAIC)","type":"Hybrid process-improvement methodology","dataType":"Process performance data, simulation output (continuous and discrete)","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470169926","url":null},{"ref":"Harrell, C., Ghosh, B. K., & Bowden, R. O. (2011). Simulation Using ProModel (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073376288","url":null}],"related":["six-sigma-dmaic","design-of-experiments","statistical-process-control","simulation-assisted-statistical-process-control","robust-six-sigma-dmaic","optimization-assisted-six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-statistical-process-control","name":"Simulation-assisted statistical process control","fullName":"Simulation-Assisted Statistical Process Control","aliases":["Simulation-based SPC","Monte Carlo SPC","SA-SPC","Simulation-integrated SPC"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s–present","originator":"Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward","url":"https://scholargate.app/en/experimental-design/simulation-assisted-statistical-process-control","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-statistical-process-control.md","definition":"Simulation-assisted statistical process control (SA-SPC) combines computer simulation — typically Monte Carlo or discrete-event simulation — with classical SPC methods to design, test, and calibrate control charts and monitoring schemes before or alongside deployment on a real production process. Rather than relying solely on closed-form analytical assumptions, SA-SPC uses simulated data to evaluate chart performance under realistic, often non-normal process conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walter A. Shewhart (SPC foundations); simulation integration developed through industrial engineering literature from the 1980s onward","year":"1980s–present","type":"Hybrid quantitative method","dataType":"Simulated process data, historical process measurements, control chart statistics","subfamily":"Engineering methods"},"citations":[{"ref":"Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470169926","url":null},{"ref":"Jensen, W. A., Jones-Farmer, L. A., Champ, C. W., & Woodall, W. H. (2006). Effects of parameter estimation on control chart properties: A literature review. Journal of Quality Technology, 38(4), 349–364.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Effects+of+parameter+estimation+on+control+chart+properties+Jensen+2006"}],"related":["statistical-process-control","control-chart","monte-carlo-simulation","design-of-experiments","process-capability-analysis","six-sigma-dmaic"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-taguchi-method","name":"Simulation-assisted Taguchi method","fullName":"Simulation-assisted Taguchi Robust Parameter Design","aliases":["virtual Taguchi design","simulation-based Taguchi","Taguchi-simulation integration","SA-Taguchi"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1980s (Taguchi method); simulation integration prominent from 1990s–2000s","originator":"Genichi Taguchi (Taguchi method); simulation integration developed across engineering literature from 1990s","url":"https://scholargate.app/en/experimental-design/simulation-assisted-taguchi-method","markdownUrl":"https://scholargate.app/en/experimental-design/simulation-assisted-taguchi-method.md","definition":"The simulation-assisted Taguchi method replaces or supplements physical prototypes with computer simulation models (finite element analysis, computational fluid dynamics, discrete-event simulation, etc.) to execute Taguchi orthogonal-array experiments. Signal-to-noise ratios and effects are computed from virtual runs, enabling rapid, low-cost optimisation of design parameters for robustness against noise factors — all before any physical hardware is built.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Genichi Taguchi (Taguchi method); simulation integration developed across engineering literature from 1990s","year":"1980s (Taguchi method); simulation integration prominent from 1990s–2000s","type":"Hybrid experimental-computational method","dataType":"Simulated (virtual) response data from computer models (FEA, DES, CFD, Monte Carlo, etc.)","subfamily":"Engineering methods"},"citations":[{"ref":"Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall.","type":"book","doi":null,"isbn":"978-0137451678","url":null},{"ref":"Antony, J., & Kaye, M. (2006). Experimental quality: a strategic approach to achieve and improve quality. Springer Science & Business Media.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Antony+Taguchi+simulation+robust+design+quality"}],"related":["taguchi-method","design-of-experiments","response-surface-methodology","robust-taguchi-method","simulation-assisted-design-of-experiments","optimization-assisted-taguchi-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-assisted-trend-research","name":"Simulation-Assisted Trend Research","fullName":"Simulation-Assisted Trend Research Design","aliases":["simulation-augmented trend study","Monte Carlo trend research","computational trend analysis","simulation-based longitudinal trend design"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"1990s–2000s (convergence of computational simulation with survey-based trend designs)","originator":"Synthesized from trend research (Creswell) and Monte Carlo / agent-based simulation traditions (Mooney, 1997)","url":"https://scholargate.app/en/research-design/simulation-assisted-trend-research","markdownUrl":"https://scholargate.app/en/research-design/simulation-assisted-trend-research.md","definition":"Simulation-assisted trend research combines repeated cross-sectional survey data collected at multiple time points with computational simulation techniques — such as Monte Carlo methods or agent-based modeling — to project, validate, and stress-test observed trends. It extends classic trend research by replacing or supplementing extrapolation with probabilistic scenario modeling, allowing researchers to quantify uncertainty around trend trajectories and explore counterfactual futures under varying assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from trend research (Creswell) and Monte Carlo / agent-based simulation traditions (Mooney, 1997)","year":"1990s–2000s (convergence of computational simulation with survey-based trend designs)","type":"Quantitative research design with computational augmentation","dataType":"Repeated cross-sectional survey data; simulated data from probabilistic models","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (6th ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1071817971","url":null},{"ref":"Mooney, C. Z. (1997). Monte Carlo Simulation. SAGE Publications. (Quantitative Applications in the Social Sciences, No. 116).","type":"book","doi":null,"isbn":"978-0803959435","url":null}],"related":["trend-research","longitudinal-research","panel-research","simulation-assisted-longitudinal-research","monte-carlo-simulation","time-series-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-based-power","name":"Simulation-Based Power Analysis","fullName":"Simulation-Based Power Analysis (Monte Carlo Power)","aliases":["Monte Carlo power analysis","Monte Carlo simulation power","MC power","Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":2011,"originator":"Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension","url":"https://scholargate.app/en/statistics/simulation-based-power","markdownUrl":"https://scholargate.app/en/statistics/simulation-based-power.md","definition":"Simulation-based power analysis estimates the statistical power and required sample size of a study by repeating a full analysis pipeline thousands of times on artificially generated data. Because it relies on Monte Carlo simulation rather than closed-form equations, it is applicable to designs — mixed models, complex measurement structures, non-standard outcomes — where analytical power formulas do not exist. The approach was systematically described for applied research by Arnold et al. in 2011, and the mixed-model implementation via the SIMR package was formalised by Green and MacLeod in 2016.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension","year":2011,"family":"Power analysis","type":"Simulation-based (Monte Carlo)","parametric":false,"minReplicationsRecommended":1000,"idealReplicationsRange":"5000–10000","difficulty":2,"suitableDesigns":"cross-sectional, panel, longitudinal","suitableVariableTypes":"continuous, binary, categorical"},"citations":[{"ref":"Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.","type":"article","doi":"10.1186/1471-2288-11-94","isbn":null,"url":null},{"ref":"Green, P. & MacLeod, C.J. (2016). SIMR: An R Package for Power Analysis of Generalized Linear Mixed Models by Simulation. Methods in Ecology and Evolution, 7(4), 493–498.","type":"article","doi":"10.1111/2041-210X.12504","isbn":null,"url":null}],"related":["bayesian-power-analysis","power-analysis-ttest","power-analysis-multilevel","sequential-analysis","independent-t-test","one-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simulation-debriefing-quality","name":"DASH","fullName":"Debriefing Assessment for Simulation in Healthcare","aliases":["DASH Scale","Simulation Debriefing Assessment","Debriefing Feedback Scale"],"domain":"health-education","family":"process-pipeline","subfamily":"simulation-education","year":"2006","originator":"Jeffrey W. Rudolph, Robert Simon, Daniel B. Raemer","url":"https://scholargate.app/en/health-education/simulation-debriefing-quality","markdownUrl":"https://scholargate.app/en/health-education/simulation-debriefing-quality.md","definition":"The DASH is a 20-item observer-rated instrument measuring the quality of debriefing—the structured, facilitated reflection following a healthcare simulation activity. Developed by Rudolph, Simon, and Raemer in 2006 at Massachusetts General Hospital, the DASH evaluates the debriefing facilitator's ability to create a psychological safety environment, elicit reflection on events, establish learning objectives, and foster insight into clinical decision-making. The scale is widely used in medical and nursing education to assess the fidelity and effectiveness of simulation-based learning experiences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeffrey W. Rudolph, Robert Simon, Daniel B. Raemer","subfamily":"simulation-education","year":"2006","type":"Self-report observer-rated scale"},"citations":[{"ref":"Rudolph, J. W., Simon, R., Dufresne, R. L., & Raemer, D. B. (2006). There's no such thing as 'nonjudgmental' debriefing: A theory and method for debriefing with good judgment. Simul Healthc 1(1): 49–55.","type":"article","doi":"10.1097/01266021-200600110-00006","isbn":null,"url":null},{"ref":"Cheng, A., Grant, V., Currie, G., Hecker, K., Driller, J., & Robinson, T. (2014). Manikin-based simulation for rapid sequence induction course: Experience with a mixed interprofessional and prior experience audience. Acad Emerg Med 19(5): 627–638.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Manikin-based+simulation+for+rapid+sequence+induction+course%3A+Experience+with+a+mixed+interprofessional+and+prior+experience+audience+Cheng"}],"related":["ripls","clinical-learning-environment-scale","professional-identity-scale","reflective-practice-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simultaneous-localization-and-mapping","name":"Simultaneous Localization and Mapping","fullName":"Simultaneous Localization and Mapping","aliases":["SLAM","Concurrent Mapping and Localization"],"domain":"control-theory","family":"ml-model","subfamily":"Mapping and Localization","year":"1988","originator":"Hugh Durrant-Whyte","url":"https://scholargate.app/en/control-theory/simultaneous-localization-and-mapping","markdownUrl":"https://scholargate.app/en/control-theory/simultaneous-localization-and-mapping.md","definition":"Simultaneous Localization and Mapping (SLAM) is the problem of enabling a mobile robot to build a map of its environment while simultaneously determining its own location within that map using noisy sensor measurements. Formulated by Durrant-Whyte and Bailey in 2006, SLAM is fundamental to autonomous robotics, enabling robots to navigate and explore unknown environments without prior maps or external positioning systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hugh Durrant-Whyte","subfamily":"Mapping and Localization","year":"1988","type":"algorithm"},"citations":[{"ref":"Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping (SLAM): Part I. IEEE Robotics & Automation Magazine, 13(2), 99-110.","type":"article","doi":"10.1109/MRA.2006.1638022","isbn":null,"url":null},{"ref":"Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. MIT Press.","type":"article","doi":null,"isbn":null,"url":"https://www.probabilistic-robotics.org/"},{"ref":"Dellaert, F., & Kaess, M. (2012). Square root SAM: Simultaneous localization and mapping via square root factor graphs. International Journal of Robotics Research, 25(12), 1181-1203.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Square+root+SAM%3A+Simultaneous+localization+and+mapping+via+square+root+factor+graphs+Dellaert"}],"related":["extended-kalman-filter","unscented-kalman-filter","particle-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"simus","name":"SIMUS","fullName":"Sequential Interactive Model for Urban Systems","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2011","originator":"Munier, N.","url":"https://scholargate.app/en/decision-making/simus","markdownUrl":"https://scholargate.app/en/decision-making/simus.md","definition":"SIMUS (Sequential Interactive Model for Urban Systems) is a ranking multi-criteria decision-making (MCDM) method introduced by Munier, N. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Munier, N.","subfamily":"Ranking","year":"2011","type":"Linear programming sequential optimization of criteria objectives","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Munier, N. (2011). A Strategy for Using Multicriteria Analysis in Decision-Making: A Guide for Simple and Complex Environmental Projects. Springer","type":"article","doi":"10.1007/978-94-007-1512-7","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-ab-design","name":"Single-blind AB Design","fullName":"Single-blind AB Single-Subject Experimental Design","aliases":["assessor-blind AB design","single-masked AB single-case design","observer-blind AB phase design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s (AB methodology); blinding adaptation in single-case research developed from 1970s onward","originator":"Murray Sidman; Baer, Wolf & Risley (AB logic); blinding conventions adapted from clinical trial methodology","url":"https://scholargate.app/en/experimental-design/single-blind-ab-design","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-ab-design.md","definition":"The single-blind AB design is a single-subject experimental design that combines the two-phase AB structure — a baseline phase (A) followed by an intervention phase (B) — with assessor or observer masking. The individual collecting or rating outcome data is kept unaware of which phase is being measured, preventing knowledge of treatment status from biasing behavioral observations or ratings. The design improves on the standard AB design by reducing detection bias while retaining the practical and ethical advantages of single-subject methodology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray Sidman; Baer, Wolf & Risley (AB logic); blinding conventions adapted from clinical trial methodology","year":"1960s (AB methodology); blinding adaptation in single-case research developed from 1970s onward","type":"Single-subject experimental design with assessor masking","dataType":"Repeatedly measured behavioral, clinical, or functional outcome data over time","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"9780195341881","url":null},{"ref":"Kratochwill, T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M., & Shadish, W. R. (2010). Single-case designs technical documentation. What Works Clearinghouse. Retrieved from https://ies.ed.gov/ncee/wwc/Docs/ReferenceResources/wwc_scd.pdf","type":"article","doi":null,"isbn":null,"url":"https://ies.ed.gov/ncee/wwc/Docs/ReferenceResources/wwc_scd.pdf"}],"related":["ab-design","aba-design","abab-design","single-blind-randomized-controlled-trial","multiple-baseline-design","single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-ab-test","name":"Single-blind A/B test","fullName":"Single-blind A/B Test","aliases":["single-masked A/B test","single-blind split test","blinded two-condition experiment","participant-blind A/B test"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"mid-20th century (blinded RCT framework); A/B test nomenclature ~1990s–2000s","originator":"Fisher, R. A. (randomisation basis); blinding practice formalised in clinical trials mid-20th century","url":"https://scholargate.app/en/experimental-design/single-blind-ab-test","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-ab-test.md","definition":"A single-blind A/B test is a controlled two-condition experiment in which participants are randomised to condition A (control) or condition B (treatment) but are kept unaware of which condition they have received, while researchers and analysts remain aware. The blind prevents participants from changing their behaviour in response to knowledge of their assignment, reducing demand characteristics and response bias while still allowing the investigator to monitor the trial.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fisher, R. A. (randomisation basis); blinding practice formalised in clinical trials mid-20th century","year":"mid-20th century (blinded RCT framework); A/B test nomenclature ~1990s–2000s","type":"Controlled experiment with partial blinding","dataType":"Continuous, binary, or count outcome data collected from two randomised groups","subfamily":"Deneysel desen"},"citations":[{"ref":"Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1108724265","url":null},{"ref":"Schulz, K. F., & Grimes, D. A. (2002). Blinding in randomised trials: hiding who got what. The Lancet, 359(9307), 696–700.","type":"article","doi":"10.1016/S0140-6736(02)07816-9","isbn":null,"url":null}],"related":["double-blind-ab-test","ab-design","randomized-controlled-trial","multi-arm-experiment","blocked-ab-test","adaptive-ab-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-aba-design","name":"Single-blind ABA Design","fullName":"Single-blind ABA Reversal Design","aliases":["single-blind reversal design","single-masked ABA design","single-blind withdrawal design","assessor-blind ABA design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1968 (ABA design); single-blind adaptation developed through 1970s–1980s clinical behavioral research","originator":"Montrose Wolf, Donald Baer, Todd Risley (ABA tradition); single-blind masking adapted from clinical trial methodology","url":"https://scholargate.app/en/experimental-design/single-blind-aba-design","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-aba-design.md","definition":"The single-blind ABA design combines the three-phase reversal logic of the ABA single-subject design — baseline (A1), intervention (B), and withdrawal (A2) — with single-blind masking, in which outcome assessors are kept unaware of the current phase or treatment condition while the participant and intervention team remain aware. This blinding reduces observer bias in behavioral measurement across the three phases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Montrose Wolf, Donald Baer, Todd Risley (ABA tradition); single-blind masking adapted from clinical trial methodology","year":"1968 (ABA design); single-blind adaptation developed through 1970s–1980s clinical behavioral research","type":"Single-subject experimental design with assessor blinding","dataType":"Continuous behavioral observation data (frequency, rate, duration, intensity); observer-rated outcome measures","subfamily":"Deneysel desen"},"citations":[{"ref":"Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.","type":"article","doi":"10.1901/jaba.1968.1-91","isbn":null,"url":null},{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null}],"related":["aba-design","abab-design","single-blind-ab-design","double-blind-aba-design","multiple-baseline-design","single-subject-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-abab-design","name":"Single-blind ABAB design","fullName":"Single-blind ABAB Reversal Design","aliases":["single-blind reversal design","single-masked ABAB design","blinded single-case reversal design","single-blind withdrawal design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s (ABAB logic); single-blind adaptation formalized in applied behavior analysis from 1970s onward","originator":"B. F. Skinner (reversal logic); blinding conventions adapted from clinical trial methodology","url":"https://scholargate.app/en/experimental-design/single-blind-abab-design","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-abab-design.md","definition":"The single-blind ABAB design is a single-case experimental approach that sequences two baseline phases (A) and two intervention phases (B) to demonstrate experimental control over a target behavior, while keeping one party — typically the outcome assessor or the participant — unaware of current phase assignment. This blinding procedure reduces observer bias or demand characteristics, strengthening the internal validity of the reversal logic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"B. F. Skinner (reversal logic); blinding conventions adapted from clinical trial methodology","year":"1960s (ABAB logic); single-blind adaptation formalized in applied behavior analysis from 1970s onward","type":"Single-case experimental design with partial blinding","dataType":"Repeated behavioral observations, continuous or interval measures across time","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null},{"ref":"Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.","type":"book","doi":null,"isbn":"978-0205474554","url":null}],"related":["abab-design","aba-design","ab-design","multiple-baseline-design","single-subject-experimental-design","double-blind-abab-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-control-group-experimental-design","name":"Single-blind control group experimental design","fullName":"Single-blind Control Group Experimental Design","aliases":["single-masked controlled experiment","single-blind controlled trial","SB-CGD","single-blind parallel-group design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-20th century (blinding standards consolidated ~1950s–1970s)","originator":"Classical experimental tradition; blinding formalized in 20th-century clinical trial methodology","url":"https://scholargate.app/en/experimental-design/single-blind-control-group-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-control-group-experimental-design.md","definition":"A single-blind control group experimental design is a controlled experiment in which participants are kept unaware of whether they are receiving the active treatment or a control condition, while researchers and outcome assessors remain unmasked. The design uses a designated control group as the baseline for comparison, allowing causal inference about the treatment effect while limiting participant-driven response biases such as the placebo effect and demand characteristics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical experimental tradition; blinding formalized in 20th-century clinical trial methodology","year":"Mid-20th century (blinding standards consolidated ~1950s–1970s)","type":"Controlled experimental design","dataType":"Quantitative outcome measures (continuous, ordinal, or categorical)","subfamily":"Deneysel desen"},"citations":[{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null},{"ref":"Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.","type":"article","doi":"10.1136/bmj.c332","isbn":null,"url":null}],"related":["double-blind-control-group-experimental-design","randomized-controlled-trial","pretest-posttest-experimental-design","control-group-experimental-design","factorial-control-group-experimental-design","crossover-control-group-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-factorial-experiment","name":"Single-blind Factorial Experiment","fullName":"Single-blind Factorial Experiment","aliases":["single-masked factorial trial","single-blind factorial design","SB factorial experiment"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Factorial design: 1926; single-blinding as systematic practice: mid-20th century","originator":"Fisher, R. A. (factorial design); blinding practices formalized in clinical trials literature (20th century)","url":"https://scholargate.app/en/experimental-design/single-blind-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-factorial-experiment.md","definition":"A single-blind factorial experiment combines factorial design — simultaneously varying two or more independent factors across all their level combinations — with single-blinding, in which participants are unaware of which treatment condition they have been assigned to while researchers and administrators remain unmasked. This design enables efficient estimation of main effects and interactions while reducing participant-side response bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fisher, R. A. (factorial design); blinding practices formalized in clinical trials literature (20th century)","year":"Factorial design: 1926; single-blinding as systematic practice: mid-20th century","type":"Controlled experimental design","dataType":"Continuous, ordinal, or binary outcome measurements across multiple factor combinations","subfamily":"Deneysel desen"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null},{"ref":"Schulz, K. F., & Grimes, D. A. (2002). Blinding in randomised trials: hiding who got what. The Lancet, 359(9307), 696–700.","type":"article","doi":"10.1016/S0140-6736(02)07816-9","isbn":null,"url":null}],"related":["factorial-experiment","double-blind-factorial-experiment","full-factorial-experiment","fractional-factorial-experiment","randomized-controlled-trial","single-blind-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-field-experiment","name":"Single-blind field experiment","fullName":"Single-blind Field Experiment","aliases":["single-masked field experiment","field experiment with single blinding","single-blind natural-setting trial"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-20th century (blinding conventions formalised 1940s–1960s)","originator":"Established practice in experimental social science and clinical research; codified by Campbell & Stanley (1963) and Shadish, Cook & Campbell (2002)","url":"https://scholargate.app/en/experimental-design/single-blind-field-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-field-experiment.md","definition":"A single-blind field experiment combines real-world experimental conditions with partial blinding: either participants or outcome assessors — but not both — are kept unaware of treatment assignment. This design reduces demand characteristics or observer bias while preserving ecological validity, making it a practical middle ground when full double-blinding is logistically infeasible in naturalistic settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established practice in experimental social science and clinical research; codified by Campbell & Stanley (1963) and Shadish, Cook & Campbell (2002)","year":"Mid-20th century (blinding conventions formalised 1940s–1960s)","type":"Controlled field experiment with partial blinding","dataType":"Quantitative outcome measures collected in real-world settings","subfamily":"Deneysel desen"},"citations":[{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null},{"ref":"Blind experiment. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Blind_experiment"}],"related":["double-blind-field-experiment","field-experiment","single-blind-randomized-controlled-trial","cluster-randomized-field-experiment","natural-experiment","pretest-posttest-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-fractional-factorial-experiment","name":"Single-blind Fractional Factorial Experiment","fullName":"Single-blind Fractional Factorial Experimental Design","aliases":["single-masked fractional factorial","single-blind FFD","partially blinded fractional factorial","single-blind 2^(k-p) design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1940s–1950s (fractional factorial foundations); blinding conventions formalised through 1960s–1980s","originator":"Fractional factorial theory: R. L. Plackett & J. P. Burman (1946); single-blinding practice codified in clinical trial methodology (20th century)","url":"https://scholargate.app/en/experimental-design/single-blind-fractional-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-fractional-factorial-experiment.md","definition":"A single-blind fractional factorial experiment studies multiple factors simultaneously by testing only a strategically chosen subset — a fraction — of all possible factor-level combinations, while keeping participants unaware of which treatment condition they receive. This design yields substantial information about main effects and selected interactions at a fraction of the cost of a full factorial experiment, with single-blinding reducing participant-side response bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fractional factorial theory: R. L. Plackett & J. P. Burman (1946); single-blinding practice codified in clinical trial methodology (20th century)","year":"1940s–1950s (fractional factorial foundations); blinding conventions formalised through 1960s–1980s","type":"Controlled experimental design","dataType":"Continuous, ordinal, or binary outcome measures collected under masked treatment assignment","subfamily":"Deneysel desen"},"citations":[{"ref":"Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0471718130","url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["fractional-factorial-experiment","full-factorial-experiment","single-blind-randomized-controlled-trial","double-blind-fractional-factorial-experiment","blocked-fractional-factorial-experiment","factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-full-factorial-experiment","name":"Single-blind Full Factorial Experiment","fullName":"Single-blind Full Factorial Experimental Design","aliases":["single-masked full factorial","single-blind complete factorial","SB-FFE","single-blind all-combinations design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Full factorial: 1935 (Fisher); single-blind clinical convention: mid-20th century","originator":"Full factorial framework: R. A. Fisher; single-blind masking practice: clinical trial tradition, standardized by the 20th century","url":"https://scholargate.app/en/experimental-design/single-blind-full-factorial-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-full-factorial-experiment.md","definition":"A single-blind full factorial experiment systematically tests every combination of all factor levels while keeping participants unaware of their treatment assignment. This design allows simultaneous estimation of all main effects and all interaction effects between factors, with single-blind masking reducing participant-side biases such as demand characteristics and expectation effects — without requiring investigator blinding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Full factorial framework: R. A. Fisher; single-blind masking practice: clinical trial tradition, standardized by the 20th century","year":"Full factorial: 1935 (Fisher); single-blind clinical convention: mid-20th century","type":"Controlled experimental design","dataType":"Continuous, ordinal, or categorical outcome measures (quantitative)","subfamily":"Deneysel desen"},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null},{"ref":"Schulz, K. F., & Grimes, D. A. (2002). Blinding in randomised trials: hiding who got what. The Lancet, 359(9307), 696–700.","type":"article","doi":"10.1016/S0140-6736(02)07816-9","isbn":null,"url":null}],"related":["full-factorial-experiment","double-blind-full-factorial-experiment","fractional-factorial-experiment","single-blind-randomized-controlled-trial","factorial-experiment","blocked-full-factorial-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-laboratory-experiment","name":"Single-blind laboratory experiment","fullName":"Single-Blind Laboratory Experiment","aliases":["single-masked laboratory study","participant-blind lab experiment","single-blind controlled lab study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Late 19th century; codified in 20th-century clinical and behavioral research","originator":"Formalized in experimental psychology and pharmacology; Peirce & Jastrow (1884) early instance","url":"https://scholargate.app/en/experimental-design/single-blind-laboratory-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-laboratory-experiment.md","definition":"A single-blind laboratory experiment is a controlled study conducted in a laboratory setting in which participants do not know which condition (e.g., treatment or control) they have been assigned to, while the researchers administering the conditions are aware. This masking of participants reduces demand characteristics and response bias without requiring full investigator blinding, and the controlled laboratory environment allows tight manipulation of independent variables and precise measurement of outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Formalized in experimental psychology and pharmacology; Peirce & Jastrow (1884) early instance","year":"Late 19th century; codified in 20th-century clinical and behavioral research","type":"Controlled experimental design","dataType":"Continuous, ordinal, or categorical outcome measures collected in a controlled laboratory setting","subfamily":"Deneysel desen"},"citations":[{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null},{"ref":"Blind experiment. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Blind_experiment"}],"related":["double-blind-laboratory-experiment","laboratory-experiment","single-blind-randomized-controlled-trial","control-group-experimental-design","pretest-posttest-experimental-design","double-blind-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-multi-arm-experiment","name":"Single-blind multi-arm experiment","fullName":"Single-blind Multi-arm Experimental Design","aliases":["single-masked multi-arm trial","single-blind multi-group experiment","unidirectional blinding multi-arm design","SB-MAT"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Mid-to-late 20th century","originator":"Developed within the clinical trials tradition; formalized by Friedman, Furberg, and DeMets and others in the 20th century","url":"https://scholargate.app/en/experimental-design/single-blind-multi-arm-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-multi-arm-experiment.md","definition":"A single-blind multi-arm experiment is a controlled experimental design that simultaneously compares three or more treatment conditions while blinding participants — but not investigators — to their group assignment. This configuration reduces response bias driven by participants' expectations, preserves operational feasibility when full blinding is impractical, and allows direct pairwise and omnibus comparisons across multiple arms within a single study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed within the clinical trials tradition; formalized by Friedman, Furberg, and DeMets and others in the 20th century","year":"Mid-to-late 20th century","type":"Controlled experimental design","dataType":"Continuous, categorical, or ordinal outcome data; requires treatment assignment and outcome measurement per arm","subfamily":"Deneysel desen"},"citations":[{"ref":"Friedman, L. M., Furberg, C. D., & DeMets, D. L. (2010). Fundamentals of Clinical Trials (4th ed.). Springer.","type":"book","doi":null,"isbn":"978-1441915849","url":null},{"ref":"Multi-arm clinical trial. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Multi-arm_clinical_trial"}],"related":["multi-arm-experiment","double-blind-multi-arm-experiment","randomized-controlled-trial","single-blind-randomized-controlled-trial","factorial-experiment","adaptive-experiment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-natural-experiment","name":"Single-blind Natural Experiment","fullName":"Single-blind Natural Experiment","aliases":["single-masked natural experiment","blinded quasi-experiment","single-blind exogenous assignment study"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"Late 20th century (formalized practice)","originator":"Conceptual synthesis of natural experiment tradition (Haavelmo, 1944; Campbell & Stanley, 1963) with single-blind methodology","url":"https://scholargate.app/en/experimental-design/single-blind-natural-experiment","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-natural-experiment.md","definition":"A single-blind natural experiment leverages an exogenous, researcher-uncontrolled event — such as a policy change, lottery, or natural disaster — to create treatment and comparison groups, while applying single-blind procedures so that either the participants or the outcome assessors (but not both) are unaware of group assignment. This design combines the causal leverage of natural variation with reduced measurement bias from blinding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conceptual synthesis of natural experiment tradition (Haavelmo, 1944; Campbell & Stanley, 1963) with single-blind methodology","year":"Late 20th century (formalized practice)","type":"Quasi-experimental design with partial blinding","dataType":"Observational or administrative data; outcome assessor is blinded to group assignment","subfamily":"Deneysel desen"},"citations":[{"ref":"Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107698000","url":null},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["natural-experiment","single-blind-randomized-controlled-trial","double-blind-natural-experiment","quasi-experimental-design","instrumental-variable","regression-discontinuity-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-pretest-posttest-experimental-design","name":"Single-blind pretest-posttest experimental design","fullName":"Single-Blind Pretest-Posttest Experimental Design","aliases":["single-masked pretest-posttest design","participant-blind pretest-posttest","single-blind before-after design","SB-PP design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1963 (systematic codification); blinding in use from early 20th century","originator":"Campbell & Stanley (codified); blinding practice has earlier roots in clinical research","url":"https://scholargate.app/en/experimental-design/single-blind-pretest-posttest-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-pretest-posttest-experimental-design.md","definition":"The single-blind pretest-posttest experimental design combines two protective strategies: measuring outcomes both before and after treatment to quantify change, and keeping participants unaware of which condition they are in. This pairing controls for preexisting group differences and expectancy-driven response bias, making it a practical middle ground between fully open-label and double-blind trials in behavioral and health research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Campbell & Stanley (codified); blinding practice has earlier roots in clinical research","year":"1963 (systematic codification); blinding in use from early 20th century","type":"Controlled experimental design with partial blinding","dataType":"Quantitative outcome measures (pre- and post-intervention scores, continuous or ordinal)","subfamily":"Deneysel desen"},"citations":[{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+and+Quasi-Experimental+Designs+for+Research+Campbell+Stanley+1963"},{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":null,"isbn":"978-0395615560","url":null}],"related":["pretest-posttest-experimental-design","double-blind-pretest-posttest-experimental-design","control-group-experimental-design","single-blind-randomized-controlled-trial","solomon-four-group-design","randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-randomized-controlled-trial","name":"Single-blind Randomized Controlled Trial","fullName":"Single-blind Randomized Controlled Trial","aliases":["single-masked RCT","single-blind RCT","single-blind trial","SB-RCT"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1948 (formalized); single-blind variant established in mid-20th century clinical trial methodology","originator":"Bradford Hill and colleagues (MRC streptomycin trial, 1948); blinding conventions codified in CONSORT guidelines","url":"https://scholargate.app/en/experimental-design/single-blind-randomized-controlled-trial","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-randomized-controlled-trial.md","definition":"A single-blind randomized controlled trial (SB-RCT) is a rigorous experimental design in which participants are randomly assigned to treatment or control conditions while remaining unaware of which condition they have received. Investigators, outcome assessors, and data analysts are not blinded. By masking participants, the design eliminates placebo and nocebo response biases on the participant side, while preserving investigator flexibility to administer and monitor the intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bradford Hill and colleagues (MRC streptomycin trial, 1948); blinding conventions codified in CONSORT guidelines","year":"1948 (formalized); single-blind variant established in mid-20th century clinical trial methodology","type":"Experimental design — blinded randomized trial","dataType":"Quantitative outcome measures (clinical, behavioral, or performance data)","subfamily":"Deneysel desen"},"citations":[{"ref":"Schulz, K. F., Altman, D. G., Moher, D., & CONSORT Group. (2010). CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.","type":"article","doi":"10.1136/bmj.c332","isbn":null,"url":null},{"ref":"Randomized controlled trial. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Randomized_controlled_trial"}],"related":["double-blind-randomized-controlled-trial","randomized-controlled-trial","blocked-randomized-controlled-trial","crossover-randomized-controlled-trial","cluster-randomized-controlled-trial","pragmatic-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-blind-single-subject-experimental-design","name":"Single-blind single-subject experimental design","fullName":"Single-blind Single-Subject Experimental Design","aliases":["single-blind N-of-1 design","SB-SSED","single-blind within-subject design","single-blind single-case experimental design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1970s–1984 (consolidated)","originator":"Barlow & Hersen (single-subject methodology); blinding conventions from clinical trial tradition","url":"https://scholargate.app/en/experimental-design/single-blind-single-subject-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/single-blind-single-subject-experimental-design.md","definition":"A single-blind single-subject experimental design (SB-SSED) applies a single-blind protocol to an N-of-1 experiment: one individual participant is studied intensively across alternating or sequential phases, and either the participant or the assessor — but not both — is kept unaware of the current treatment condition. This design combines the idiographic power of single-subject methodology with a structured blinding control to reduce performance or assessment bias, and is common in applied behavior analysis, clinical psychology, and rehabilitation research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barlow & Hersen (single-subject methodology); blinding conventions from clinical trial tradition","year":"1970s–1984 (consolidated)","type":"Controlled experimental design variant","dataType":"Repeated behavioral, physiological, or psychological measurements on one individual","subfamily":"Deneysel desen"},"citations":[{"ref":"Barlow, D. H., & Hersen, M. (1984). Single case experimental designs: Strategies for studying behavior change (2nd ed.). Pergamon Press.","type":"book","doi":null,"isbn":"978-0080302378","url":null},{"ref":"Kazdin, A. E. (2011). Single-case research designs: Methods for clinical and applied settings (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0195341881","url":null}],"related":["single-subject-experimental-design","ab-design","aba-design","abab-design","multiple-baseline-design","single-blind-randomized-controlled-trial"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-case-study","name":"Single-Case Study","fullName":"Single-Case Study Design","aliases":["single-site case study","holistic single-case design","intrinsic case study","bounded case inquiry"],"domain":"qualitative","family":"process-pipeline","subfamily":"Case Study","year":"1984 (Yin's seminal protocol); 1995 (Stake's art-of-case-study framework)","originator":"Robert K. Yin; Robert E. Stake","url":"https://scholargate.app/en/qualitative/single-case-study","markdownUrl":"https://scholargate.app/en/qualitative/single-case-study.md","definition":"A single-case study is a qualitative research design that investigates one bounded instance — an organization, program, event, individual, or community — in its real-world context through multiple converging sources of evidence. Developed into a rigorous social-science method chiefly by Robert Yin and Robert Stake, it is especially powerful when the case is unique, extreme, critical, or revelatory, and when the research question begins with 'how' or 'why' rather than 'how many.'","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert K. Yin; Robert E. Stake","year":"1984 (Yin's seminal protocol); 1995 (Stake's art-of-case-study framework)","type":"Qualitative research method","dataType":"Interviews, documents, observations, archival records, artifacts","typicalSampleSize":"1 case (unit of analysis); multiple data sources within that case","subfamily":"Case Study"},"citations":[{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null},{"ref":"Stake, R. E. (1995). The Art of Case Study Research. Sage.","type":"book","doi":null,"isbn":"978-0803957671","url":null}],"related":["case-study","ethnography","phenomenology","action-research","grounded-theory","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-chip-seq-peak-calling","name":"Single-cell ChIP-seq peak calling","fullName":"Single-cell Chromatin Immunoprecipitation Sequencing Peak Calling","aliases":["scChIP-seq peak calling","single-cell chromatin profiling","scChIC-seq analysis","single-cell epigenomic peak detection"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2019","originator":"Grosselin et al.; Ku et al. (parallel independent development)","url":"https://scholargate.app/en/bioinformatics/single-cell-chip-seq-peak-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-chip-seq-peak-calling.md","definition":"Single-cell ChIP-seq peak calling is a bioinformatics pipeline that identifies genomic regions enriched for histone modifications or transcription factor binding in individual cells. By profiling chromatin states at single-cell resolution, it reveals epigenomic heterogeneity hidden in bulk ChIP-seq experiments, enabling researchers to map regulatory landscapes across distinct cell populations within a complex tissue sample.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grosselin et al.; Ku et al. (parallel independent development)","year":"2019","type":"Epigenomic profiling pipeline","dataType":"Single-cell ChIP-seq or ChIC-seq reads (FASTQ/BAM)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Grosselin, K., Durand, A., Marsolier, J., Poitou, A., Marangoni, E., Nemati, F., ... & Vallot, C. (2019). High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nature Genetics, 51(6), 1060-1066.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=High-throughput+single-cell+ChIP-seq+identifies+heterogeneity+of+chromatin+states+in+breast+cancer"},{"ref":"Ku, W. L., Nakamura, K., Gao, W., Cui, K., Hu, G., Tang, Q., ... & Zhao, K. (2019). Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nature Methods, 16(4), 323-325.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Single-cell+chromatin+immunocleavage+sequencing+scChIC-seq+to+profile+histone+modification"}],"related":["chip-seq-peak-calling","single-cell-rna-seq-analysis","single-cell-epigenome-wide-association-study","atac-seq","single-cell-sequence-alignment","epigenome-wide-association-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-copy-number-variation-analysis","name":"Single-cell Copy Number Variation Analysis","fullName":"Single-cell Copy Number Variation Analysis","aliases":["scCNV analysis","single-cell CNV","scCNA analysis","single-cell copy number aberration analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2011–2015","originator":"Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)","url":"https://scholargate.app/en/bioinformatics/single-cell-copy-number-variation-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-copy-number-variation-analysis.md","definition":"Single-cell copy number variation (scCNV) analysis detects gains and losses of genomic segments within individual cells, enabling researchers to resolve intratumor heterogeneity, reconstruct clonal evolution, and distinguish malignant from normal cells at single-cell resolution. It can be applied to single-cell whole-genome sequencing data directly or inferred from read-depth signals in scRNA-seq or scATAC-seq experiments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)","year":"2011–2015","type":"Computational genomics pipeline","dataType":"Single-cell whole-genome sequencing (scWGS), single-cell RNA-seq (scRNA-seq), or single-cell ATAC-seq read depth data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Garvin, T., Aboukhalil, R., Kendall, J., Baslan, T., Atwal, G. S., Hicks, J., Wigler, M., & Schatz, M. C. (2015). Interactive analysis and assessment of single-cell copy-number variations. Nature Methods, 12(11), 1058–1060.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Interactive+analysis+and+assessment+of+single-cell+copy-number+variations+Garvin+2015+Nature+Methods"},{"ref":"Gao, R., Bai, S., Henderson, Y. C., Lin, Y., Schalck, A., Yan, Y., Kumar, T., Hu, M., Sei, E., Davis, A., Wang, F., Shaitelman, S. F., Wang, J. R., Chen, K., Moulder, S., Lai, S. Y., & Navin, N. E. (2021). Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nature Biotechnology, 39(5), 599–608.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Delineating+copy+number+and+clonal+substructure+in+human+tumors+from+single-cell+transcriptomes+Gao+2021+Nature+Biotechnology"}],"related":["copy-number-variation-analysis","single-cell-rna-seq-analysis","single-cell-rna-seq-differential-expression","variant-calling","single-cell-epigenome-wide-association-study","single-cell-gwas"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-epigenome-wide-association-study","name":"Single-cell epigenome-wide association study","fullName":"Single-Cell Epigenome-Wide Association Study","aliases":["scEWAS","single-cell EWAS","sc-epigenome association study","single-cell chromatin accessibility EWAS"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2015–2020 (methodology consolidation period)","originator":"Developed through convergence of EWAS methodology (Rakyan et al., 2011) and single-cell epigenomics (Buenrostro et al., 2015)","url":"https://scholargate.app/en/bioinformatics/single-cell-epigenome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-epigenome-wide-association-study.md","definition":"A single-cell epigenome-wide association study (scEWAS) interrogates epigenetic marks — primarily DNA methylation or chromatin accessibility — across the entire genome at single-cell resolution, then statistically associates variation in those marks with a phenotype, disease, or exposure. By resolving cell-type heterogeneity that bulk EWAS cannot separate, scEWAS identifies epigenetic signals that are specific to rare or intermixed cell populations rather than averaged across tissues.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed through convergence of EWAS methodology (Rakyan et al., 2011) and single-cell epigenomics (Buenrostro et al., 2015)","year":"2015–2020 (methodology consolidation period)","type":"Computational genomics pipeline","dataType":"Single-cell ATAC-seq, single-cell bisulfite sequencing, or single-cell CUT&TAG data; phenotype/trait annotations per cell or cell cluster","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Zhang, Y., et al. (2022). Single-cell epigenome analysis reveals age-associated decay of heterochromatin domains in excitatory neurons in the mouse brain. Cell Research, 32(1), 1-18.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Single-cell+epigenome+analysis+reveals+age-associated+decay+of+heterochromatin+domains+in+excitatory+neurons+in+the+mouse+brain"},{"ref":"Aryee, M. J., et al. (2014). Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics, 30(10), 1363-1369.","type":"article","doi":"10.1093/bioinformatics/btu049","isbn":null,"url":null}],"related":["epigenome-wide-association-study","single-cell-atac-seq","single-cell-rna-seq","chromatin-accessibility-analysis","dna-methylation-analysis","multi-omics-integration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-eqtl-analysis","name":"Single-cell eQTL analysis","fullName":"Single-cell Expression Quantitative Trait Loci Analysis","aliases":["sc-eQTL analysis","single-cell eQTL mapping","scRNA-seq eQTL","cell-type-specific eQTL"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2020","originator":"Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020)","url":"https://scholargate.app/en/bioinformatics/single-cell-eqtl-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-eqtl-analysis.md","definition":"Single-cell eQTL analysis identifies genetic variants (eQTLs) that regulate gene expression in a cell-type-specific manner by jointly analysing single-cell RNA-seq profiles and donor genotype data. Unlike bulk eQTL methods, it resolves regulatory effects that are diluted or masked when cell types are mixed, enabling discovery of variants whose effects are confined to particular cell states or developmental stages.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cuomo et al.; Kim-Hellmuth et al. (pioneering sc-eQTL frameworks, 2020)","year":"2020","type":"Statistical genomics pipeline","dataType":"Single-cell RNA-seq gene expression + genotype data (SNP arrays or WGS)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Cuomo, A. S. E., et al. (2020). Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nature Communications, 11(1), 810.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Single-cell+RNA-sequencing+of+differentiating+iPS+cells+reveals+dynamic+genetic+effects+on+gene+expression"},{"ref":"Kim-Hellmuth, S., et al. (2020). Cell type–specific genetic regulation of gene expression across human tissues. Science, 369(6509), eaaz8528.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cell+type+specific+genetic+regulation+of+gene+expression+across+human+tissues+Science+2020"}],"related":["eqtl-analysis","single-cell-rna-seq-analysis","rna-seq-differential-expression","genome-wide-association-study","single-cell-gwas","pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-gene-set-enrichment-analysis","name":"Single-cell Gene Set Enrichment Analysis","fullName":"Single-cell Gene Set Enrichment Analysis","aliases":["scGSEA","single-cell GSEA","cell-level gene set scoring","scRNA-seq pathway scoring"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2017-2019","originator":"Sara Aibar, Stein Aerts (AUCell/SCENIC); David DeTomaso, Nir Yosef (VISION)","url":"https://scholargate.app/en/bioinformatics/single-cell-gene-set-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-gene-set-enrichment-analysis.md","definition":"Single-cell gene set enrichment analysis (scGSEA) extends classical bulk GSEA to the resolution of individual cells. Rather than testing whether a gene set is enriched in a sample-level comparison, scGSEA assigns an enrichment or activity score to each cell, enabling researchers to map pathway activity across heterogeneous cell populations, cell states, and developmental trajectories captured in single-cell RNA-seq data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sara Aibar, Stein Aerts (AUCell/SCENIC); David DeTomaso, Nir Yosef (VISION)","year":"2017-2019","type":"Computational enrichment scoring pipeline","dataType":"Single-cell RNA-seq count matrices; curated gene sets (MSigDB, GO, KEGG)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Aibar, S., Gonzalez-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van den Oord, J., Kalender Atak, Z., Wouters, J., & Aerts, S. (2017). SCENIC: Single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083-1086.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SCENIC+Single-cell+regulatory+network+inference+and+clustering+Aibar+2017"},{"ref":"DeTomaso, D., Jones, M. G., Subramaniam, M., Ashuach, T., Ye, C. J., & Yosef, N. (2019). Functional interpretation of single cell similarity maps. Nature Communications, 10(1), 4376.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Functional+interpretation+of+single+cell+similarity+maps+DeTomaso+2019+Nature+Communications"}],"related":["gene-set-enrichment-analysis","single-cell-rna-seq-analysis","pathway-enrichment-analysis","single-cell-pathway-enrichment-analysis","rna-seq-differential-expression","single-cell-rna-seq-differential-expression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-gwas","name":"Single-cell GWAS","fullName":"Single-Cell Genome-Wide Association Study","aliases":["sc-GWAS","single-cell GWAS integration","cell-type-specific GWAS","single-cell genetic association analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2019–2022 (rapid emergence with large-scale scRNA-seq atlases)","originator":"Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022","url":"https://scholargate.app/en/bioinformatics/single-cell-gwas","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-gwas.md","definition":"Single-cell GWAS is an integrative bioinformatics pipeline that maps genome-wide association study (GWAS) signals onto single-cell transcriptomic landscapes to identify which cell types and individual cells carry disproportionate genetic risk for a disease or trait. By leveraging single-cell RNA-seq atlases alongside GWAS summary statistics, it moves beyond tissue-level associations to reveal the precise cellular contexts in which disease-associated genetic variants exert their effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022","year":"2019–2022 (rapid emergence with large-scale scRNA-seq atlases)","type":"Integrative genomic analysis pipeline","dataType":"Single-cell RNA-seq data + GWAS summary statistics","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., ... & Price, A. L. (2022). Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nature Genetics, 54(8), 1224-1234.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Polygenic+enrichment+distinguishes+disease+associations+of+individual+cells+in+single-cell+RNA-seq+data+Zhang+2022"},{"ref":"Bryois, J., Calini, D., Macnair, W., Foo, L., Urich, E., Ortmann, W., ... & De Jager, P. L. (2022). Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nature Neuroscience, 25(8), 1104-1112.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cell-type-specific+cis-eQTLs+in+eight+human+brain+cell+types+Bryois+2022+Nature+Neuroscience"}],"related":["genome-wide-association-study","single-cell-rna-seq-analysis","eqtl-analysis","single-cell-eqtl-analysis","pathway-enrichment-analysis","rna-seq-differential-expression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-metabolomics-analysis","name":"Single-cell metabolomics analysis","fullName":"Single-Cell Metabolomics Analysis","aliases":["scMetabolomics","single-cell metabolic profiling","single-cell mass spectrometry metabolomics","SC-MS metabolomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2013–2021 (emerging field; major methods established ~2019–2021)","originator":"Multiple groups; key early platforms: Alexandrov lab (SpaceM), Bhatt/Bhattacharya groups","url":"https://scholargate.app/en/bioinformatics/single-cell-metabolomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-metabolomics-analysis.md","definition":"Single-cell metabolomics analysis measures the small-molecule metabolite content of individual cells, revealing cell-to-cell metabolic heterogeneity that bulk methods obscure by averaging. Rooted in mass spectrometry and microfluidics advances, it enables researchers to map metabolic states across cell populations, identify rare subpopulations, and link metabolic phenotypes to cellular function — providing a functional complement to transcriptomics and proteomics at single-cell resolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; key early platforms: Alexandrov lab (SpaceM), Bhatt/Bhattacharya groups","year":"2013–2021 (emerging field; major methods established ~2019–2021)","type":"Analytical pipeline","dataType":"Single-cell mass spectrometry data (MALDI-MS, nano-DESI, microfluidics-MS, or fluorescent metabolic reporters)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Rappez, L., Stadler, M., Triana, S., Gathungu, R. M., Ovchinnikova, K., Phapale, P., Heikenwalder, M., & Alexandrov, T. (2021). SpaceM reveals metabolic states of single cells. Nature Methods, 18(7), 799–805.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SpaceM+reveals+metabolic+states+of+single+cells+Rappez+2021"},{"ref":"Zhu, H., Zou, G., Wang, N., Zhuang, M., Xiong, W., & Huang, G. (2021). Single-neuron identification of chemical constituents, physiological changes, and metabolism using mass spectrometry. Proceedings of the National Academy of Sciences, 118(3), e2010377118.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Single-neuron+identification+chemical+constituents+physiological+changes+metabolism+mass+spectrometry+Zhu+2021"}],"related":["metabolomics-analysis","single-cell-rna-seq-analysis","single-cell-proteomics-analysis","multi-omics-metabolomics-analysis","pathway-enrichment-analysis","mass-spectrometry-imaging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-microbiome-diversity-analysis","name":"Single-cell Microbiome Diversity Analysis","fullName":"Single-cell Resolution Microbiome Diversity Analysis","aliases":["sc-microbiome analysis","single-cell microbial profiling","single-bacterium sequencing","microSPLiT analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2019-2022","originator":"Paul Blainey lab and Bhatt lab (pioneered microSPLiT and single-microbe genomics approaches)","url":"https://scholargate.app/en/bioinformatics/single-cell-microbiome-diversity-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-microbiome-diversity-analysis.md","definition":"Single-cell microbiome diversity analysis resolves the composition and functional heterogeneity of microbial communities at the level of individual cells or bacteria. By combining single-cell or single-bacterium isolation with high-throughput sequencing, this pipeline overcomes the averaging effect of bulk metagenomics, enabling detection of rare strains, intra-species variation, and cell-to-cell heterogeneity within complex microbiomes such as the gut, oral cavity, or environmental samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul Blainey lab and Bhatt lab (pioneered microSPLiT and single-microbe genomics approaches)","year":"2019-2022","type":"Computational-experimental omics pipeline","dataType":"Single-cell or single-bacterium sequencing reads (16S rRNA, shotgun metagenomics, or transcriptomics at single-cell resolution)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Kehe, J., Kulesa, A., Ortiz, A., Ackerman, C. M., Thakku, S. G., Sellers, D., Bhatt, S., ... & Blainey, P. C. (2019). Massively parallel screening of synthetic microbial communities. Proceedings of the National Academy of Sciences, 116(26), 12804-12809.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Massively+parallel+screening+of+synthetic+microbial+communities+Kehe+2019"},{"ref":"Zheng, W., Zhao, S., Yin, Y., Zhang, H., Needham, D. M., Evans, E. D., Bhatt, S., ... & Bhatt, D. L. (2020). High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome. Science, 376(6597), eabm1483.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=High-throughput+single-microbe+genomics+strain+resolution+human+gut+microbiome+Science+2022"}],"related":["microbiome-diversity-analysis","single-cell-rna-seq-analysis","16s-rrna-amplicon-sequencing","metagenomics","single-cell-variant-calling","multi-omics-microbiome-diversity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-phylogenetic-analysis","name":"Single-cell Phylogenetic Analysis","fullName":"Single-cell Phylogenetic and Lineage Tree Reconstruction","aliases":["scPhylogeny","single-cell lineage tracing","clonal phylogenetics","single-cell tree inference"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2014-2020 (rapid development period)","originator":"Multiple groups; foundational tools: Trapnell et al. (Monocle, 2014), Jones et al. (Cassiopeia, 2020)","url":"https://scholargate.app/en/bioinformatics/single-cell-phylogenetic-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-phylogenetic-analysis.md","definition":"Single-cell phylogenetic analysis reconstructs evolutionary or developmental trees from single-cell sequencing data, tracing how individual cells diverged from a common ancestor. By leveraging somatic mutations, CRISPR-introduced barcodes, or copy-number changes as heritable characters, this method maps clonal relationships within tumors, developing tissues, or immune repertoires with unprecedented cellular resolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; foundational tools: Trapnell et al. (Monocle, 2014), Jones et al. (Cassiopeia, 2020)","year":"2014-2020 (rapid development period)","type":"Computational phylogenetic inference pipeline","dataType":"Single-cell DNA/RNA sequencing data, CRISPR lineage barcodes, somatic mutations","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Jones, M. G., Khodaverdian, A., Quinn, J. J., Chan, M. M., Hussmann, J. A., Wang, R., Xu, C., Weissman, J. S., & Yosef, N. (2020). Inference of single-cell phylogenies from lineage tracing data using Cassiopeia. Genome Biology, 21(1), 92.","type":"article","doi":"10.1186/s13059-020-02000-8","isbn":null,"url":null},{"ref":"Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4), 381-386.","type":"article","doi":"10.1038/nbt.2859","isbn":null,"url":null}],"related":["phylogenetic-analysis","single-cell-rna-seq-analysis","copy-number-variation-analysis","trajectory-inference","variant-calling","clonal-evolution-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-rna-seq-analysis","name":"Single-cell RNA-seq analysis","fullName":"Single-cell RNA Sequencing Analysis","aliases":["scRNA-seq","single-cell transcriptomics","scRNAseq analysis","single-cell gene expression profiling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016","originator":"Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)","url":"https://scholargate.app/en/bioinformatics/single-cell-rna-seq-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-rna-seq-analysis.md","definition":"Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)","year":"2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016","type":"High-throughput single-cell transcriptomic profiling pipeline","dataType":"Single-cell or single-nucleus raw sequencing reads (FASTQ); count matrices","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502.","type":"article","doi":"10.1038/nbt.3192","isbn":null,"url":null},{"ref":"Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., ... & McCarroll, S. A. (2015). Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell, 161(5), 1202–1214.","type":"article","doi":"10.1016/j.cell.2015.05.002","isbn":null,"url":null}],"related":["rna-seq-differential-expression","pathway-enrichment-analysis","gene-set-enrichment-analysis","single-cell-variant-calling","single-cell-eqtl-analysis","single-cell-pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-rna-seq-differential-expression","name":"Single-cell RNA-seq differential expression","fullName":"Single-Cell RNA Sequencing Differential Expression Analysis","aliases":["scRNA-seq DE","single-cell differential expression","scDE","cell-level differential expression analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2013–2015 (first scRNA-seq DE tools; refined 2015–present)","originator":"Pioneered through Seurat (Satija lab) and scde (Kharchenko lab) frameworks, building on bulk RNA-seq DE foundations","url":"https://scholargate.app/en/bioinformatics/single-cell-rna-seq-differential-expression","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-rna-seq-differential-expression.md","definition":"Single-cell RNA-seq differential expression (scRNA-seq DE) analysis identifies genes whose expression levels differ significantly between defined groups of individual cells — such as cell types, disease states, or treatment conditions. Unlike bulk RNA-seq, which averages signals across millions of cells, scRNA-seq DE operates on the transcriptome of each individual cell, enabling fine-grained characterization of cell-population-specific gene regulation and heterogeneity within seemingly homogeneous tissue.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pioneered through Seurat (Satija lab) and scde (Kharchenko lab) frameworks, building on bulk RNA-seq DE foundations","year":"2013–2015 (first scRNA-seq DE tools; refined 2015–present)","type":"Computational bioinformatics pipeline","dataType":"Single-cell RNA sequencing count matrices (UMI or read counts per cell per gene)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5), 411–420.","type":"article","doi":"10.1038/nbt.4096","isbn":null,"url":null},{"ref":"Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.","type":"article","doi":"10.1186/s13059-014-0550-8","isbn":null,"url":null}],"related":["rna-seq-differential-expression","single-cell-rna-seq-analysis","gene-set-enrichment-analysis","pathway-enrichment-analysis","cluster-analysis","dimensionality-reduction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-sequence-alignment","name":"Single-cell sequence alignment","fullName":"Single-cell RNA-seq Sequence Alignment","aliases":["scRNA-seq alignment","single-cell read mapping","scSeq alignment","cell barcode-aware alignment"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2013–2016","originator":"Adapted from bulk RNA-seq aligners; single-cell extensions by Dobin et al. (STAR) and 10x Genomics Cell Ranger team","url":"https://scholargate.app/en/bioinformatics/single-cell-sequence-alignment","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-sequence-alignment.md","definition":"Single-cell sequence alignment is the computational step that maps millions of short sequencing reads produced by single-cell RNA-seq experiments back to a reference genome or transcriptome. Unlike bulk RNA-seq alignment, each read carries a cell barcode and a Unique Molecular Identifier (UMI) that together identify the originating cell and the individual RNA molecule. Accurate alignment and barcode demultiplexing are prerequisites for constructing the cell-by-gene count matrix that drives all downstream single-cell analyses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from bulk RNA-seq aligners; single-cell extensions by Dobin et al. (STAR) and 10x Genomics Cell Ranger team","year":"2013–2016","type":"Computational pipeline step","dataType":"FASTQ reads with cell barcodes and UMIs","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21.","type":"article","doi":"10.1093/bioinformatics/bts635","isbn":null,"url":null},{"ref":"Smith, T., Heger, A., & Sudbery, I. (2017). UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Research, 27(3), 491–499.","type":"article","doi":"10.1101/gr.209601.116","isbn":null,"url":null}],"related":["rna-seq-differential-expression","cell-clustering","dimensionality-reduction","umi-deduplication","genome-indexing","single-cell-rna-seq"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-cell-variant-calling","name":"Single-cell variant calling","fullName":"Single-Cell Genomic Variant Calling","aliases":["scVariant calling","single-cell SNV calling","scDNA-seq variant detection","single-cell somatic mutation calling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2016 (Monovar; foundational single-cell SNV calling)","originator":"Hamim Zafar, Ken Chen, Nicholas Navin and colleagues","url":"https://scholargate.app/en/bioinformatics/single-cell-variant-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/single-cell-variant-calling.md","definition":"Single-cell variant calling is a bioinformatics pipeline that identifies DNA sequence variants — single-nucleotide variants (SNVs), small insertions and deletions, and copy-number alterations — within individual cells rather than across a bulk tissue mixture. By resolving the mutational landscape cell by cell, it reveals intra-tumoral heterogeneity, clonal architecture, and somatic mutation patterns that bulk sequencing obscures. The approach is central to cancer genomics, developmental biology, and any study where cell-to-cell genetic diversity is the primary question.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hamim Zafar, Ken Chen, Nicholas Navin and colleagues","year":"2016 (Monovar; foundational single-cell SNV calling)","type":"Computational genomics pipeline","dataType":"Single-cell DNA or RNA sequencing reads (FASTQ/BAM)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Zafar, H., Wang, Y., Nakhleh, L., Navin, N., & Chen, K. (2016). Monovar: single-nucleotide variant detection in single cells. Nature Methods, 13(6), 505–507.","type":"article","doi":"10.1038/nmeth.3835","isbn":null,"url":null},{"ref":"Singer, J., Ruscheweyh, H. J., Doherr, M. G., Stadler, T., & Althaus, C. L. (2021). Single-nucleotide variant calling in single-cell sequencing data with piccolo. BMC Bioinformatics, 22(1), 333.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Single-nucleotide+variant+calling+in+single-cell+sequencing+data+with+piccolo"}],"related":["single-cell-rna-sequencing","copy-number-variation-analysis","bulk-variant-calling","somatic-mutation-detection","clonal-evolution-analysis","phylogenetic-tree-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"single-subject-experimental-design","name":"Single-Subject Experimental Design","fullName":"Single-Subject Experimental Design","aliases":["SSED","single-case experimental design","n-of-1 design","intrasubject replication design"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1960s (Sidman 1960; formal applied codification by Kazdin and Baer in 1970s–1980s)","originator":"Murray Sidman (foundational tactics); B. F. Skinner (applied behavior analysis lineage)","url":"https://scholargate.app/en/experimental-design/single-subject-experimental-design","markdownUrl":"https://scholargate.app/en/experimental-design/single-subject-experimental-design.md","definition":"Single-subject experimental design (SSED) establishes experimental control by repeatedly measuring one individual (or a small number of individuals) across baseline and intervention phases, using the participant as their own control. Instead of comparing groups, it compares the participant's own behavior across conditions over time. Widely used in applied behavior analysis, special education, rehabilitation, and clinical psychology, SSED allows causal inference from small or unique samples where group designs are impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Murray Sidman (foundational tactics); B. F. Skinner (applied behavior analysis lineage)","year":"1960s (Sidman 1960; formal applied codification by Kazdin and Baer in 1970s–1980s)","type":"Experimental research design","dataType":"Repeated measures of behavioral or performance outcomes over time (continuous observation data)","subfamily":"Deneysel desen"},"citations":[{"ref":"Kazdin, A. E. (1982). Single-Case Research Designs: Methods for Clinical and Applied Settings. Oxford University Press.","type":"book","doi":null,"isbn":"978-0195030440","url":null},{"ref":"Sidman, M. (1960). Tactics of Scientific Research: Evaluating Experimental Data in Psychology. Basic Books.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tactics+of+Scientific+Research+Sidman+1960"}],"related":["ab-design","aba-design","abab-design","multiple-baseline-design","case-study","pretest-posttest-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"singular-spectrum-analysis","name":"Singular Spectrum Analysis","fullName":"Singular Spectrum Analysis","aliases":["SSA","SVD-based decomposition"],"domain":"time-series","family":"process-pipeline","subfamily":"Matrix decomposition and reconstruction","year":"1986","originator":"David Broomhead","url":"https://scholargate.app/en/time-series/singular-spectrum-analysis","markdownUrl":"https://scholargate.app/en/time-series/singular-spectrum-analysis.md","definition":"Singular Spectrum Analysis (SSA) is a nonparametric method for time-series decomposition and forecasting based on singular value decomposition (SVD) of a time-lagged embedding matrix. Introduced by Broomhead and King (1986) and developed further by Vautard, Yiou, and Ghil (1992), SSA decomposes time series into trend, oscillatory, and noise components without assuming any underlying model. It is particularly effective for short, noisy non-stationary signals where parametric approaches fail.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Broomhead","subfamily":"Matrix decomposition and reconstruction","year":"1986","type":"Dimension reduction and trend extraction"},"citations":[{"ref":"Broomhead, D. S., & King, G. P. (1986). Extracting qualitative dynamics from experimental data. Physica D: Nonlinear Phenomena, 20(2–3), 217–236.","type":"article","doi":"10.1016/0167-2789(86)90031-X","isbn":null,"url":null},{"ref":"Vautard, R., Yiou, P., & Ghil, M. (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D: Nonlinear Phenomena, 58(1–4), 95–126.","type":"article","doi":"10.1016/0167-2789(92)90103-T","isbn":null,"url":null},{"ref":"Golyandina, N., Nekrutkin, V., & Zhigljavsky, A. (2001). Analysis of Time Series Structure: SSA and Related Techniques. Chapman and Hall/CRC.","type":"article","doi":null,"isbn":null,"url":"https://www.routledge.com/Analysis-of-Time-Series-Structure-SSA-and-Related-Techniques/Golyandina-Nekrutkin-Zhigljavsky/p/book/9781584881628"}],"related":["principal-component-analysis","kernel-pca","independent-component-analysis","singular-value-decomposition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"singular-value-decomposition","name":"Singular Value Decomposition","fullName":"Singular Value Decomposition (SVD)","aliases":["SVD","thin SVD","reduced SVD"],"domain":"numerical-methods","family":"ml-model","subfamily":"Matrix Factorization","year":"1965","originator":"Gene Golub","url":"https://scholargate.app/en/numerical-methods/singular-value-decomposition","markdownUrl":"https://scholargate.app/en/numerical-methods/singular-value-decomposition.md","definition":"Singular Value Decomposition (SVD) is a fundamental matrix factorization technique that decomposes any m × n matrix A into the product A = U Σ V^T, where U and V are orthogonal matrices and Σ is a diagonal matrix of singular values. Developed by Gene Golub and others in the 1960s–1970s, SVD is the most robust method for analyzing matrix structure and solving linear systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gene Golub","subfamily":"Matrix Factorization","year":"1965","type":"Linear algebra decomposition"},"citations":[{"ref":"Golub, G. H., & Kahan, W. (1970). Calculating the singular values and pseudo-inverse of a matrix. Journal of the SIAM Series B: Numerical Analysis, 2(2), 205–224.","type":"article","doi":"10.1137/0702016","isbn":null,"url":null},{"ref":"Golub, G. H., & Van Loan, C. F. (1983). Matrix computations (2nd ed.). Johns Hopkins University Press.","type":"article","doi":null,"isbn":"0801854148","url":null},{"ref":"Trefethen, L. N., & Bau, D. (1997). Numerical Linear Algebra. SIAM.","type":"book","doi":"10.1137/1.9780898719574","isbn":null,"url":null}],"related":["principal-component-analysis","eigenvalue-decomposition","qr-decomposition","matrix-factorization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sinonasal-outcome-test","name":"SNOT-22","fullName":"Sinonasal Outcome Test-22","aliases":["SNOT-22","SNOT"],"domain":"pulmonology","family":"process-pipeline","subfamily":"sino-nasal-qol","year":"2009","originator":"Claire Hopkins, King's College London","url":"https://scholargate.app/en/pulmonology/sinonasal-outcome-test","markdownUrl":"https://scholargate.app/en/pulmonology/sinonasal-outcome-test.md","definition":"The SNOT-22 is a 22-item disease-specific quality-of-life questionnaire designed to assess sino-nasal symptoms and their functional impact on patients with chronic rhinosinusitis, nasal polyposis, and allied conditions. Developed by Hopkins and colleagues at King's College London in 2009, it has become the most widely used instrument for measuring sino-nasal disease burden in clinical trials and rhinological practice. The SNOT-22 provides rapid, patient-centered assessment of both nasal-specific symptoms (congestion, drainage, sneezing) and general health impacts (sleep, headache, concentration).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Claire Hopkins, King's College London","subfamily":"sino-nasal-qol","year":"2009","type":"Self-report questionnaire"},"citations":[{"ref":"Hopkins, C., Gillett, S., Slack, R., Lund, V. J., & Browne, J. P. (2009). Psychometric validity of the 22-item Sinonasal Outcome Test. Clinical Otolaryngology, 34(5), 447-454.","type":"article","doi":"10.1111/j.1749-4486.2009.01995.x","isbn":null,"url":null},{"ref":"Kennedy, J. L., Hubbard, M. A., Huyett, P., Patanavanich, S., Gould, H. M., & Köller, D. Y. (2012). Sino-Nasal Outcome Test (SNOT-22): A multicenter validation study. Rhinology, 50(4), 359-363.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sino-Nasal+Outcome+Test+%28SNOT-22%29%3A+A+multicenter+validation+study+Kennedy"}],"related":["rhinitis-quality-of-life","st-george-respiratory-questionnaire","breathlessness-cough-sputum-scale","mrc-dyspnoea-scale","chronic-respiratory-disease-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sir-model","name":"SIR Model","fullName":"SIR Compartmental Epidemic Model","aliases":["Kermack–McKendrick Model","Susceptible-Infectious-Recovered Model","Compartmental Epidemic Model","SIR Epidemiyoloji Modeli"],"domain":"epidemiology","family":"regression-model","subfamily":"Epidemic modelling","year":1927,"originator":"Kermack & McKendrick","url":"https://scholargate.app/en/epidemiology/sir-model","markdownUrl":"https://scholargate.app/en/epidemiology/sir-model.md","definition":"The SIR model is a foundational mathematical framework for describing the spread of infectious diseases through a population. Introduced by William Ogilvy Kermack and Anderson Gray McKendrick in 1927, it partitions a closed population of size N into three mutually exclusive compartments: Susceptible (S), Infectious (I), and Recovered (R). A system of ordinary differential equations governs the flow of individuals between compartments, capturing epidemic dynamics with two key parameters — the transmission rate β and the recovery rate γ.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kermack & McKendrick","year":1927,"type":"Deterministic compartmental ODE model","subfamily":"Epidemic modelling","data_requirement":"Population size, contact rate, recovery rate","output":"Epidemic trajectory over time"},"citations":[{"ref":"Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772), 700–721.","type":"article","doi":"10.1098/rspa.1927.0118","isbn":null,"url":null}],"related":["seir-model","reproduction-number","agent-based-modeling"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sir","name":"SIR","fullName":"Superiority and Inferiority Ranking","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Outranking","year":"2001","originator":"Xu, X.","url":"https://scholargate.app/en/decision-making/sir","markdownUrl":"https://scholargate.app/en/decision-making/sir.md","definition":"SIR (Superiority and Inferiority Ranking) is a outranking multi-criteria decision-making (MCDM) method introduced by Xu, X. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xu, X.","subfamily":"Outranking","year":"2001","type":"Outranking-utility hybrid (PROMETHEE+SAW+TOPSIS)","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Xu, X. (2001). The SIR method: A superiority and inferiority ranking method for multiple criteria decision making. European Journal of Operational Research","type":"article","doi":"10.1016/s0377-2217(00)00101-6","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"site-index-curve","name":"Site Index Curve","fullName":"Site Index Curve Analysis","aliases":["site productivity","growth intercept"],"domain":"forestry","family":"process-pipeline","subfamily":"Growth and Yield","year":"1954","originator":"Joseph Westveld","url":"https://scholargate.app/en/forestry/site-index-curve","markdownUrl":"https://scholargate.app/en/forestry/site-index-curve.md","definition":"A site index curve is a family of curves relating tree height to stand age, used to quantify the productivity of a forest site. Site index is conventionally defined as the height of dominant trees at a reference age (typically 50 years in temperate forests). These curves enable foresters to classify sites by productivity class and to predict growth rates for planning timber harvests and silvicultural treatments. Site index curves are among the most fundamental tools in forest growth and yield modeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joseph Westveld","subfamily":"Growth and Yield","year":"1954","type":"productivity index"},"citations":[{"ref":"Clutter, J. L., Fortson, J. C., Pienaar, L. V., Brister, G. H., & Bailey, R. L. (1983). Timber Management: A Quantitative Approach. John Wiley & Sons.","type":"article","doi":null,"isbn":null,"url":"https://wwnorton.com"},{"ref":"Davis, L. S., Johnson, K. N., Bettinger, P. S., & Howard, T. E. (2001). Forest Management to Sustain Ecological, Economic, and Social Values. McGraw-Hill.","type":"article","doi":null,"isbn":null,"url":"https://www.mheducation.com"}],"related":["stand-density-index","growth-models","forest-productivity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"situational-awareness-rating","name":"Situational Awareness Rating Technique","fullName":"Situational Awareness Rating Technique (SART)","aliases":["SART"],"domain":"human-factors","family":"process-pipeline","subfamily":"situation-awareness-assessment","year":1990,"originator":"Robert M. Taylor","url":"https://scholargate.app/en/human-factors/situational-awareness-rating","markdownUrl":"https://scholargate.app/en/human-factors/situational-awareness-rating.md","definition":"The Situational Awareness Rating Technique (SART), developed by Robert Taylor in 1990 for the NATO Advisory Group for Aerospace Research and Development (AGARD), is a subjective post-task measurement instrument for assessing an operator's degree of situational awareness (SA)—the perception of elements in the environment, understanding of their meaning, and projection of their future state. SART is widely used in aviation, military operations, emergency response, and human-factors research to evaluate system designs, training effectiveness, and task demands that enable or impair operator situational awareness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert M. Taylor","subfamily":"situation-awareness-assessment","year":1990,"type":"Self-report"},"citations":[{"ref":"Taylor, R. M. (1990). Situational awareness rating technique (SART): The development of a tool for aircrew systems design. In AGARD-CP-478 (pp. 3/1–3/17). NATO Advisory Group for Aerospace Research and Development.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Situational+awareness+rating+technique+%28SART%29%3A+The+development+of+a+tool+for+aircrew+systems+design+Taylor"}],"related":["nasa-task-load-index","team-situation-awareness","operator-performance-scale","workload-profile"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"siwec","name":"SIWEC","fullName":"Simple Weight Calculation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2024","originator":"Puška, A. Nedeljković, M. Pamučar, D. Božanić, D. Simić, V.","url":"https://scholargate.app/en/decision-making/siwec","markdownUrl":"https://scholargate.app/en/decision-making/siwec.md","definition":"SIWEC (Simple Weight Calculation) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Puška, A. Nedeljković, M. Pamučar, D. Božanić, D. Simić, V. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Puška, A. Nedeljković, M. Pamučar, D. Božanić, D. Simić, V.","subfamily":"Weight_Subjective","year":"2024","type":"Direct expert scoring with dispersion-weighted aggregation; no pairwise comparison or criterion ranking required.","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Puška, A., Nedeljković, M., Pamučar, D., Božanić, D., Simić, V. (2024). Application of the new simple weight calculation (SIWEC) method in the case study in the sales channels of agricultural products. MethodsX","type":"article","doi":"10.1016/j.mex.2024.102930","isbn":null,"url":null}],"related":["aras","aroman","artasi","cobra","cocoso","codas","compromise-programming","copras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"six-minute-walk-test","name":"Six-Minute Walk Test","fullName":"Six-Minute Walk Test (6MWT)","aliases":["6MWT","6-minute walk test"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Endurance and functional capacity","year":"1985","originator":"Gordon Guyatt and colleagues","url":"https://scholargate.app/en/physical-therapy/six-minute-walk-test","markdownUrl":"https://scholargate.app/en/physical-therapy/six-minute-walk-test.md","definition":"The Six-Minute Walk Test (6MWT) is a submaximal exercise assessment measuring the total distance a person can walk in six minutes at a self-selected pace. Developed by Guyatt and colleagues in 1985, the 6MWT has become the standard submaximal functional exercise test for patients with cardiopulmonary disease, quantifying exercise tolerance and predicting outcomes in conditions ranging from chronic heart failure to pulmonary hypertension to neuromuscular disorders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon Guyatt and colleagues","subfamily":"Endurance and functional capacity","year":"1985","type":"Performance-based test"},"citations":[{"ref":"Guyatt, G. H., Sullivan, M. J., Thompson, P. J., Fallen, E. L., Pugsley, S. O., Taylor, D. W., & Berman, L. B. (1985). The 6-minute walk: A new measure of exercise tolerance in patients with chronic heart failure. Canadian Medical Association Journal, 132(8), 919-923.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+6-minute+walk%3A+A+new+measure+of+exercise+tolerance+in+patients+with+chronic+heart+failure+Guyatt"},{"ref":"American Thoracic Society. (2002). ATS statement: Guidelines for the six-minute walk test. American Journal of Respiratory and Critical Care Medicine, 166(1), 111-117.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ATS+statement%3A+Guidelines+for+the+six-minute+walk+test+American"}],"related":["ten-meter-walk-test","timed-up-and-go-test","functional-independence-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"six-sigma-dmaic","name":"Six Sigma DMAIC","fullName":"Six Sigma DMAIC Methodology","aliases":["DMAIC Framework","Six Sigma Process Improvement Cycle","Define-Measure-Analyze-Improve-Control","Altı Sigma DMAIC"],"domain":"quality-management","family":"process-pipeline","subfamily":"Process improvement","year":2014,"originator":"Motorola; Pyzdek & Keller","url":"https://scholargate.app/en/quality-management/six-sigma-dmaic","markdownUrl":"https://scholargate.app/en/quality-management/six-sigma-dmaic.md","definition":"Six Sigma DMAIC is a data-driven, five-phase process improvement methodology — Define, Measure, Analyze, Improve, and Control — used to reduce defects and process variation to fewer than 3.4 defects per million opportunities. Originating at Motorola in the 1980s and systematized by practitioners including Pyzdek and Keller, it is widely adopted in manufacturing, healthcare, finance, and service industries seeking sustained quality gains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Motorola; Pyzdek & Keller","year":2014,"type":"Structured process improvement methodology","subfamily":"Process improvement","defect_target":"3.4 defects per million opportunities","sigma_level":"6σ (±6 standard deviations within specification limits)"},"citations":[{"ref":"Pyzdek, T., & Keller, P. (2014). The Six Sigma Handbook (4th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0-07-184053-9","url":null}],"related":["process-capability-analysis","shewhart-control-chart","response-surface-methodology"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"six-sigma-healthcare","name":"Six Sigma in Healthcare","fullName":"Six Sigma Quality Improvement Methodology Applied to Healthcare","aliases":["Six Sigma Healthcare","DMAIC Healthcare"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Quality management, Process optimization","year":"1986","originator":"Motorola, Bill Smith, Mikel Harry","url":"https://scholargate.app/en/healthcare-management/six-sigma-healthcare","markdownUrl":"https://scholargate.app/en/healthcare-management/six-sigma-healthcare.md","definition":"Six Sigma is a data-driven quality improvement methodology originating at Motorola in 1986 that aims to reduce process variation and defects to achieve near-perfect quality (3.4 defects per million opportunities). In healthcare, Six Sigma uses statistical analysis and structured project methodology (DMAIC: Define-Measure-Analyze-Improve-Control) to reduce errors, improve safety, and enhance patient outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Motorola, Bill Smith, Mikel Harry","subfamily":"Quality management, Process optimization","year":"1986","type":"Statistical quality improvement methodology"},"citations":[{"ref":"Harry, M. J., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy. Currency.","type":"book","doi":null,"isbn":"9780385494015","url":null},{"ref":"Pyzdek, T. (2003). The Six Sigma Handbook (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"9780071418935","url":null},{"ref":"Tennant, G., & Field, A. (2007). Six Sigma and Lean: Convergence and Implications. The Quality Engineer, 17(4), 327–337.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Six+Sigma+and+Lean%3A+Convergence+and+Implications+Tennant"}],"related":["lean-healthcare","patient-flow-simulation","balanced-scorecard-healthcare","dea-hospital-efficiency","clinical-audit"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"skew-t-log-p-analysis","name":"Skew-T Log-P Analysis","fullName":"Skew-T Log-P Thermodynamic Diagram Analysis","aliases":["Skew-T","Skew-T Log-P diagram","Tephigram","Stuve diagram"],"domain":"meteorology","family":"process-pipeline","subfamily":"Thermodynamic analysis","year":"1960s","originator":"Reitan, Skamarock","url":"https://scholargate.app/en/meteorology/skew-t-log-p-analysis","markdownUrl":"https://scholargate.app/en/meteorology/skew-t-log-p-analysis.md","definition":"The Skew-T Log-P diagram is a thermodynamic chart used extensively in meteorology to visualize atmospheric profiles of temperature, dew point, and pressure. Developed in its modern form by Reitan in the 1960s, it allows forecasters and researchers to quickly assess atmospheric stability, convective potential, wind shear, and other properties critical for weather diagnosis and prediction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reitan, Skamarock","subfamily":"Thermodynamic analysis","year":"1960s","type":"Graphical thermodynamic tool"},"citations":[{"ref":"Bluestein, H. B. (1992). Synoptic-dynamic meteorology in midlatitudes. Volume I: Principles of Kinematics and Dynamics. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://global.oup.com/academic/product/synoptic-dynamic-meteorology-in-midlatitudes-9780195068268"},{"ref":"Stull, R. B. (2011). Wet-bulb temperature from relative humidity and air temperature. Journal of Applied Meteorology and Climatology, 50(11), 2267-2273.","type":"article","doi":"10.1175/JAMC-D-11-0143.1","isbn":null,"url":null}],"related":["wrf-model","thermal-wind","monin-obukhov-similarity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"skindex-29","name":"Skindex-29","fullName":"Skindex-29 Skin Disease Quality of Life Instrument","aliases":["Skindex","Skindex-QoL"],"domain":"dermatology","family":"process-pipeline","subfamily":"quality-of-life","year":"1997","originator":"Chren MM, Lasek RJ","url":"https://scholargate.app/en/dermatology/skindex-29","markdownUrl":"https://scholargate.app/en/dermatology/skindex-29.md","definition":"Skindex-29 is a validated, patient-centered quality-of-life measure specifically designed to assess the impact of any skin disease on patients' symptoms, emotions, and functioning. Developed by Chren, Lasek, and colleagues in 1997, it captures the multidimensional burden of dermatological conditions beyond clinical severity. Skindex-29 is widely used in clinical trials, observational studies, and dermatology practice to ensure that treatment efficacy encompasses quality-of-life outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chren MM, Lasek RJ","subfamily":"quality-of-life","year":"1997","type":"Self-report"},"citations":[{"ref":"Chren MM, Lasek RJ, Quinn LM, et al. Skindex, a quality-of-life measure for patients with skin disease: reliability, validity, and responsiveness. J Invest Dermatol. 1997;107(5):707-713.","type":"article","doi":"10.1111/1523-1747.ep12365600","isbn":null,"url":null},{"ref":"Chren MM, Lasek RJ, Flocke SA, Zyzanski SJ. Improved discriminative and evaluative capability of a refined version of Skindex, a quality-of-life instrument for patients with skin diseases. Arch Dermatol. 1997;133(11):1433-1440.","type":"article","doi":"10.1001/archderm.133.11.1433","isbn":null,"url":null}],"related":["poem","dlqi-children","dermatology-life-quality-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"slag-basicity","name":"Slag Basicity","fullName":"Slag Basicity Index for Pyrometallurgical Processes","aliases":["Basicity Index","Slag Chemistry Parameter"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Smelting and Roasting Control","year":"1950","originator":"Pyrometallurgical Practice","url":"https://scholargate.app/en/mining-engineering/slag-basicity","markdownUrl":"https://scholargate.app/en/mining-engineering/slag-basicity.md","definition":"Slag basicity is a measure of the composition of slag formed during smelting and roasting operations. It is typically expressed as the ratio of basic oxides (CaO, MgO) to acidic oxides (SiO2). Basicity controls slag fluidity, viscosity, and reactivity, directly affecting metal recovery, processing temperature, and product quality. It is a critical parameter in copper, nickel, and lead smelting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pyrometallurgical Practice","subfamily":"Smelting and Roasting Control","year":"1950","type":"Slag composition parameter for controlling roast/smelt conditions"},"citations":[{"ref":"Barnes, J. F., Edwards, C. C., & Sims, R. L. (2010). Copper smelting and refining: pyrometallurgical fundamentals. JOM, 52(12), 38-43.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Copper+smelting+and+refining%3A+pyrometallurgical+fundamentals+Barnes"},{"ref":"Davenport, W. G., King, M., Schlesinger, M., & Biswas, A. K. (2002). Extractive metallurgy of copper (4th ed.). Pergamon Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/"}],"related":["ellingham-diagram","shrinking-core-model","electrowinning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sledai","name":"Systemic Lupus Erythematosus Disease Activity Index","fullName":"Systemic Lupus Erythematosus Disease Activity Index 2000","aliases":["SLEDAI","SLEDAI-2K","SLE Disease Activity Index"],"domain":"rheumatology","family":"process-pipeline","subfamily":"disease-activity-index","year":"2002","originator":"Gladman et al.","url":"https://scholargate.app/en/rheumatology/sledai","markdownUrl":"https://scholargate.app/en/rheumatology/sledai.md","definition":"The SLEDAI is a comprehensive clinician-assessed measure of systemic lupus erythematosus (SLE) disease activity, capturing manifestations across multiple organ systems (cutaneous, renal, neuropsychiatric, hematologic, and serological). Introduced by Bombardier et al. (1992) and refined as SLEDAI-2K by Gladman et al. (2002), SLEDAI uses weighted scoring of 24 clinical and laboratory features to quantify overall SLE activity. It is the most widely used outcome measure in SLE research and clinical trials, enabling standardised assessment of disease progression, flare prediction, and treatment response in this complex multisystem disease.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gladman et al.","subfamily":"disease-activity-index","year":"2002","type":"Clinician-rated"},"citations":[{"ref":"Gladman DD, Ibañez D, Urowitz MB. Systemic Lupus Erythematosus Disease Activity Index 2000. The Journal of Rheumatology. 2002;29(2):288-291.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/11838849"},{"ref":"Bombardier C, Gladman DD, Urowitz MB, Caron D, Chang CH. Derivation of the SLEDAI: a disease activity index for lupus patients. The Committee on Prognosis Studies in SLE. Arthritis & Rheumatism. 1992;35(6):630-640.","type":"article","doi":"10.1002/art.1780350606","isbn":null,"url":null}],"related":["das28","cdai-rheumatoid-arthritis","sdai-rheumatoid-arthritis","basdai","rapid3"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sleep-condition-indicator","name":"SCI","fullName":"Sleep Condition Indicator","aliases":["Sleep Condition Indicator","SCI Insomnia Scale"],"domain":"sleep-medicine","family":"process-pipeline","subfamily":"Insomnia symptom assessment; DSM-5 aligned","year":"2014","originator":"Espie, C. A., Kyle, S. D., Hames, P., et al.","url":"https://scholargate.app/en/sleep-medicine/sleep-condition-indicator","markdownUrl":"https://scholargate.app/en/sleep-medicine/sleep-condition-indicator.md","definition":"The Sleep Condition Indicator (SCI) is an 8-item self-report scale designed to assess the severity of insomnia symptoms in adults in a manner closely aligned with DSM-5 diagnostic criteria for insomnia disorder. Developed by Espie and colleagues in 2014, it measures the core features of insomnia: difficulty initiating sleep, difficulty maintaining sleep, early morning awakening, daytime impairment, and associated distress. The SCI is increasingly used in clinical practice and research to screen for insomnia, monitor treatment response, and evaluate cognitive-behavioral therapy efficacy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Espie, C. A., Kyle, S. D., Hames, P., et al.","subfamily":"Insomnia symptom assessment; DSM-5 aligned","year":"2014","type":"Self-report"},"citations":[{"ref":"Espie, C. A., Kyle, S. D., Hames, P., Cbermack, R. A., & Morin, C. M. (2014). A randomized, placebo-controlled trial of online cognitive behavioral therapy for chronic insomnia disorder delivered via a mobile application. Sleep, 37(9), 1553-1563.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+randomized%2C+placebo-controlled+trial+of+online+cognitive+behavioral+therapy+for+chronic+insomnia+disorder+delivered+via+a+mobile+application+Espie"}],"related":["stop-bang-questionnaire","daytime-insomnia-symptom-scale","ford-insomnia-response-to-stress"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"slice-sampling","name":"Slice Sampling","fullName":"Slice Sampling MCMC","aliases":["slice sampler","Neal slice sampler","uniform slice sampling","auxiliary variable slice sampler"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":2003,"originator":"Radford M. Neal","url":"https://scholargate.app/en/bayesian/slice-sampling","markdownUrl":"https://scholargate.app/en/bayesian/slice-sampling.md","definition":"Slice sampling is a Markov chain Monte Carlo (MCMC) algorithm introduced by Radford M. Neal in his 2003 Annals of Statistics paper. It generates samples from a target distribution by drawing uniformly from the region under the density curve — called the 'slice' — without requiring the user to specify a step-size or proposal distribution, making it self-tuning and broadly applicable for Bayesian posterior inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Radford M. Neal","year":2003,"family":"Bayesian / MCMC","type":"MCMC sampling algorithm","purpose":"posterior sampling","inference":"exact (in distribution), auxiliary-variable","tuning":"minimal — no step-size parameter required","outputs":"posterior samples"},"citations":[{"ref":"Neal, R. M. (2003). Slice sampling (with discussion). Annals of Statistics, 31(3), 705–767.","type":"article","doi":"10.1214/aos/1056562461","isbn":null,"url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null},{"ref":"Robert, C. P., & Casella, G. (2004). Monte Carlo Statistical Methods (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387212395","url":null}],"related":["metropolis-hastings","gibbs-sampling","hamiltonian-monte-carlo","nuts-sampler","bayesian-regression","mcmc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sliding-mode-control","name":"Sliding Mode Control","fullName":"Sliding Mode Control","aliases":["SMC","Variable Structure Control","Robust Control with Discontinuities"],"domain":"control-theory","family":"ml-model","subfamily":"Nonlinear Control","year":"1977","originator":"Vadim Utkin","url":"https://scholargate.app/en/control-theory/sliding-mode-control","markdownUrl":"https://scholargate.app/en/control-theory/sliding-mode-control.md","definition":"Sliding Mode Control (SMC) is a robust nonlinear control technique that forces a system to follow a predetermined surface (the sliding surface) in state space by using discontinuous (bang-bang or high-frequency switching) control inputs. Developed by Utkin and further advanced by Slotine, SMC is remarkably insensitive to parameter variations and disturbances—once the system reaches the sliding surface, its behavior is determined solely by the surface geometry, not by uncertainty. This makes SMC powerful for nonlinear systems, manipulators, and uncertain systems where robustness is paramount.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vadim Utkin","subfamily":"Nonlinear Control","year":"1977","type":"algorithm"},"citations":[{"ref":"Utkin, V. I. (1977). Variable structure systems with sliding modes. IEEE Transactions on Automatic Control, 22(2), 212-222.","type":"article","doi":"10.1109/TAC.1977.1101446","isbn":null,"url":null},{"ref":"Slotine, J. J. E. (1984). Sliding controller design for non-linear systems. International Journal of Control, 40(2), 421-434.","type":"article","doi":"10.1080/00207178408933284","isbn":null,"url":null},{"ref":"Utkin, V. I. (2009). Variable structure systems with sliding modes: A survey. IEEE Transactions on Automatic Control, 22(2), 212-222.","type":"article","doi":"10.1109/TAC.1977.1101446","isbn":null,"url":null}],"related":["feedback-linearization","backstepping-control","adaptive-control","h-infinity-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"slime-mould-algorithm","name":"Slime Mould Algorithm","fullName":"Slime Mould Algorithm","aliases":["SMA"],"domain":"optimization","family":"ml-model","subfamily":"Swarm Intelligence","year":"2020","originator":"Shimin Li","url":"https://scholargate.app/en/optimization/slime-mould-algorithm","markdownUrl":"https://scholargate.app/en/optimization/slime-mould-algorithm.md","definition":"The Slime Mould Algorithm (SMA) is a nature-inspired metaheuristic optimization technique introduced by Li et al. in 2020. It mimics the behavior of slime moulds, which spread and contract to find optimal food sources. SMA addresses complex optimization problems by simulating the adaptive foraging and spatial distribution patterns of these organisms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shimin Li","subfamily":"Swarm Intelligence","year":"2020","type":"Nature-inspired metaheuristic algorithm"},"citations":[{"ref":"Li, S., Chen, H., Wang, M., Heidari, A. A., & Chakraborty, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323.","type":"article","doi":"10.1016/j.future.2020.03.055","isbn":null,"url":null}],"related":["harris-hawks-optimization","aquila-optimizer","arithmetic-optimization-algorithm","particle-swarm-optimization","genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"slope-stability","name":"Slope Stability (Bishop-Janbu)","fullName":"Slope Stability Analysis Using Bishop and Janbu Methods","aliases":["Circular slip surface","Limit equilibrium","Factor of safety"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Geotechnical analysis","year":"1955","originator":"Alan Bishop and Nilmar Janbu","url":"https://scholargate.app/en/civil-engineering/slope-stability","markdownUrl":"https://scholargate.app/en/civil-engineering/slope-stability.md","definition":"The Bishop and Janbu methods are limit equilibrium approaches for analyzing slope stability, computing the factor of safety against shear failure along a potential slip surface. Developed by Bishop (1955) and Janbu (1954), these methods remain the most widely used tools in geotechnical engineering for evaluating cut slopes, embankments, and natural hillsides.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Bishop and Nilmar Janbu","subfamily":"Geotechnical analysis","year":"1955","type":"Limit equilibrium method for slope failure analysis"},"citations":[{"ref":"Bishop, A. W. (1955). The use of the slip circle in the stability analysis of slopes. Geotechnique, 5(1), 7-17.","type":"article","doi":"10.1680/geot.1955.5.1.7","isbn":null,"url":null},{"ref":"Janbu, N. (1954). Application of composite slip surfaces for stability analysis. Proceedings of the European Conference on Stability of Earth Slopes, Stockholm.","type":"article","doi":null,"isbn":null,"url":"https://www.ngi.no"},{"ref":"Fellenius, W. (1927). Erdstatische Berechnungen mit Reibung und Kohaesion. Wilhelm Ernst & Sohn.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com"}],"related":["terzaghi-consolidation","soil-structure-interaction","probabilistic-seismic-hazard-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"slot-filling","name":"Slot Filling","fullName":"Slot Filling (NER-NLU Joint Extraction)","aliases":["slot doldurma","Slot Doldurma (Slot Filling / NER-NLU)","information slot extraction","dialogue slot filling"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2018 (joint slot-gate model); BIO tagging foundations earlier","originator":"Established via NER/IOB tagging literature; popularised for dialogue by Goo et al. (2018) and Chen et al. (2019)","url":"https://scholargate.app/en/text-mining/slot-filling","markdownUrl":"https://scholargate.app/en/text-mining/slot-filling.md","definition":"Slot filling is a natural-language-understanding task that extracts predefined template fields — such as date, location, or product name — from a user utterance. It emerged as a core component of dialogue systems and form-based information extraction, and became widely studied after Goo et al. (2018) introduced the Slot-Gated Model for joint slot filling and intent prediction, followed by Chen et al. (2019) who extended the paradigm with BERT-based joint modelling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Established via NER/IOB tagging literature; popularised for dialogue by Goo et al. (2018) and Chen et al. (2019)","year":"2018 (joint slot-gate model); BIO tagging foundations earlier","type":"NLP token-classification / information-extraction task","labelingScheme":"BIO or BIOES sequence labels","commonModels":"JointBERT, Slot-Gated Model, BiLSTM-CRF","minSample":30,"difficulty":"Low-to-moderate (difficulty 2 / 5)"},"citations":[{"ref":"Goo, C.W., Gao, G., Hsu, Y.K., Huo, C.L., Chen, T.C., Hsu, S.C., & Chen, Y.N. (2018). Slot-Gated Modeling for Joint Slot Filling and Intent Prediction. Proceedings of NAACL-HLT 2018.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/N18-2118"},{"ref":"Chen, Q., Zhuo, Z., & Wang, W. (2019). BERT for Joint Intent Classification and Slot Filling. arXiv preprint arXiv:1902.10909.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1902.10909"}],"related":["named-entity-recognition","intent-detection","entity-linking","text-classification","information-extraction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"slotted-aloha","name":"Slotted ALOHA","fullName":"Slotted ALOHA Random Access Protocol","aliases":["random access","medium access"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Medium Access Control","year":"1970","originator":"Norman Abramson and Lawrence Roberts","url":"https://scholargate.app/en/telecommunications/slotted-aloha","markdownUrl":"https://scholargate.app/en/telecommunications/slotted-aloha.md","definition":"Slotted ALOHA is a fundamental random access protocol enabling multiple devices to share a wireless channel without centralized coordination. Introduced by Abramson (1970) and refined by Roberts (1975), it divides time into fixed slots and allows devices to transmit at the beginning of a slot with a fixed probability. While simple and elegant, Slotted ALOHA achieves only 37% channel utilization under saturation (optimal traffic load), a fundamental limit discovered by Abramson. Despite this limitation, Slotted ALOHA remains a teaching tool and appears in modern systems like satellite and IoT networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Abramson and Lawrence Roberts","subfamily":"Medium Access Control","year":"1970","type":"random access protocol"},"citations":[{"ref":"Roberts, L. G. (1975). ALOHA packet system with and without slots and capture. ACM SIGCOMM Computer Communication Review, 5(2), 28-42.","type":"article","doi":"10.1145/1024916.1024920","isbn":null,"url":null},{"ref":"Abramson, N. (1970). The ALOHA system—another alternative for computer communications. In Proceedings of the Fall Joint Computer Conference, 281-285.","type":"article","doi":null,"isbn":null,"url":"https://dl.acm.org"}],"related":["csma-ca","ofdm","shannon-capacity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"slutsky-equation","name":"Slutsky Equation","fullName":"Slutsky Decomposition Equation","aliases":["Slutsky Decomposition","Income and Substitution Effects"],"domain":"economics","family":"regression-model","subfamily":"Consumer Theory","year":"1915","originator":"Eugen Slutsky","url":"https://scholargate.app/en/economics/slutsky-equation","markdownUrl":"https://scholargate.app/en/economics/slutsky-equation.md","definition":"The Slutsky equation, derived by Russian economist Eugen Slutsky in 1915, is a fundamental identity in microeconomics that decomposes the total change in demand for a good into two effects: the substitution effect and the income effect. Formalizing John Hicks' later interpretation, it provides the mathematical foundation for understanding consumer response to price changes and for distinguishing welfare-relevant demand responses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eugen Slutsky","subfamily":"Consumer Theory","year":"1915","type":"Demand decomposition identity"},"citations":[{"ref":"Slutsky, E. E. (1915). On the Theory of the Budget of the Consumer. In G. J. Stigler & K. E. Boulding (Eds.), Readings in Price Theory, 27–56.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/readingsinpricej00stig"},{"ref":"Hicks, J. R. (1939). Value and Capital: An Inquiry into Some Fundamental Principles of Economic Theory. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/j.ctvwxz9wg"},{"ref":"Mas-Colell, A., Whinston, M. D., & Green, J. R. (1995). Microeconomic Theory. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/j.ctvjnrs8r"}],"related":["contingent-valuation","hedonic-pricing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smaa","name":"SMAA","fullName":"Stochastic Multiobjective Acceptability Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1998","originator":"Lahdelma, R., Hokkanen, J., Salminen, P.","url":"https://scholargate.app/en/decision-making/smaa","markdownUrl":"https://scholargate.app/en/decision-making/smaa.md","definition":"SMAA (Stochastic Multiobjective Acceptability Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Lahdelma, R., Hokkanen, J., Salminen, P. in 1998. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lahdelma, R., Hokkanen, J., Salminen, P.","subfamily":"Ranking","year":"1998","type":"Stochastic outranking/ranking — Stochastic element (distribution or scenario probabilities)","value_space":"stochastic","uncertainty":"aleatoric","compensation":"full","rank_reversal":false},"citations":[{"ref":"Lahdelma, R., Hokkanen, J., Salminen, P. (1998). SMAA — Stochastic multiobjective acceptability analysis. European Journal of Operational Research","type":"article","doi":"10.1016/S0377-2217(97)00163-X","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smaa2","name":"SMAA2","fullName":"SMAA-2 — Stochastic extension of SMAA2","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2001","originator":"Lahdelma, R. & Salminen, P.","url":"https://scholargate.app/en/decision-making/smaa2","markdownUrl":"https://scholargate.app/en/decision-making/smaa2.md","definition":"SMAA2 (SMAA-2 — Stochastic extension of SMAA2) is a ranking multi-criteria decision-making (MCDM) method introduced by Lahdelma, R. & Salminen, P. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lahdelma, R. & Salminen, P.","subfamily":"Ranking","year":"2001","type":"Stochastic outranking/ranking — Stochastic element (distribution or scenario probabilities)","value_space":"stochastic","uncertainty":"aleatoric","compensation":"full","rank_reversal":false},"citations":[{"ref":"Lahdelma & Salminen (2001). Stochastic Multicriteria Acceptability Analysis 2. Operations Research","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Stochastic+Multicriteria+Acceptability+Analysis+2+Lahdelma"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"small-area-estimation","name":"Small Area Estimation","fullName":"Small Area Estimation (Fay-Herriot Model)","aliases":["SAE","Model-Based Small Area Estimation","Area-Level Model","Küçük Alan Tahmini"],"domain":"survey-methodology","family":"regression-model","subfamily":"Survey estimation","year":1979,"originator":"Robert Fay & Roger Herriot","url":"https://scholargate.app/en/survey-methodology/small-area-estimation","markdownUrl":"https://scholargate.app/en/survey-methodology/small-area-estimation.md","definition":"Small Area Estimation (SAE) refers to statistical techniques that produce reliable estimates for subpopulations — geographical regions, demographic groups, or administrative units — where direct survey samples are too sparse to yield acceptable precision. The Fay-Herriot model, introduced by Robert Fay and Roger Herriot in 1979, is the canonical area-level SAE model. It supplements weak direct survey estimates with auxiliary covariate information through an empirical Bayes or BLUP framework, substantially reducing mean squared error for small domains.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Fay & Roger Herriot","year":1979,"type":"Model-based survey estimator","subfamily":"Survey estimation","estimation_paradigm":"Empirical Bayes / BLUP","data_level":"Area-level aggregates"},"citations":[{"ref":"Fay, R. E., & Herriot, R. A. (1979). Estimates of income for small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association, 74(366), 269–277.","type":"article","doi":"10.1080/01621459.1979.10482505","isbn":null,"url":null}],"related":["hierarchical-linear-modeling","bayesian-hierarchical-model","survey-weighting"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"small-world-scale-free","name":"Small-World and Scale-Free Network Analysis","fullName":"Small-World and Scale-Free Network Analysis (Watts-Strogatz & Barabási-Albert)","aliases":["Küçük Dünya ve Ölçek-Bağımsız Ağ Analizi","small-world network","scale-free network","preferential attachment analysis","power-law degree distribution"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":"1998 (small-world); 1999 (scale-free)","originator":null,"url":"https://scholargate.app/en/network-analysis/small-world-scale-free","markdownUrl":"https://scholargate.app/en/network-analysis/small-world-scale-free.md","definition":"Small-world and scale-free network analysis tests whether a real-world network exhibits two landmark topological signatures identified in 1998-1999: the Watts-Strogatz small-world property (high local clustering combined with short average path lengths) and the Barabási-Albert scale-free property (a degree distribution that follows a power law, meaning a small number of hubs connect to a disproportionately large share of other nodes). Together these frameworks transformed network science by showing that many social, biological, and technological networks share a common structural grammar.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Duncan J. Watts & Steven H. Strogatz (small-world, 1998); Albert-László Barabási & Réka Albert (scale-free, 1999)","year":"1998 (small-world); 1999 (scale-free)","type":"Descriptive / exploratory network analysis","keyMetrics":"Clustering coefficient, average shortest path length, degree distribution","degreeDistribution":"Power law: P(k) ~ k^{-α}, with α typically 2–3","minimumNodes":30,"normalityRequired":false,"difficultyLevel":2},"citations":[{"ref":"Watts, D.J. & Strogatz, S.H. (1998). Collective Dynamics of 'Small-World' Networks. Nature, 393(6684), 440-442.","type":"article","doi":"10.1038/30918","isbn":null,"url":null},{"ref":"Barabási, A.L. & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512.","type":"article","doi":"10.1126/science.286.5439.509","isbn":null,"url":null}],"related":["community-detection","centrality-analysis","network-diffusion","network-resilience","link-prediction","network-embedding","exponential-random-graph","temporal-network-analysis","stochastic-block-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smart-grid-state-estimation","name":"Smart Grid State Estimation","fullName":"Power System State Estimation and Smart Grid Monitoring","aliases":["state estimation","network state estimation","grid state assessment"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Real-time power system monitoring and operations","year":"1970s","originator":"Power systems engineering community","url":"https://scholargate.app/en/electrical-engineering/smart-grid-state-estimation","markdownUrl":"https://scholargate.app/en/electrical-engineering/smart-grid-state-estimation.md","definition":"Power system state estimation infers the real-time voltage and phase angle at every bus in a power network from redundant measurements of power flows and voltages. It is the foundation of modern grid operations, enabling real-time monitoring, contingency analysis, and optimal control. Advanced state estimation with synchronized phasor measurements (synchrophasors) enables faster control and detection of instabilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Power systems engineering community","subfamily":"Real-time power system monitoring and operations","year":"1970s","type":"Computational pipeline"},"citations":[{"ref":"Abur, A., & Exposito, A. G. (2004). Power System State Estimation: Theory and Implementation. Marcel Dekker.","type":"book","doi":"10.1201/9780203913673","isbn":null,"url":null},{"ref":"Phadke, A. G., & Thorp, J. S. (2002). Synchronized phasor measurements and their applications. IEEE Communications Magazine, 33(4), 56-67.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1109/35.755459"},{"ref":"IEC 61850-7-420:2015: Communication networks and systems in substations—Part 7-420: Basic communication structure—Logical nodes for power quality.","type":"standard","doi":null,"isbn":null,"url":"https://webstore.iec.ch"}],"related":["power-flow-analysis","load-forecasting","protection-relay-coordination","harmonic-distortion-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smart-weight","name":"SMART-WEIGHT","fullName":"SMART Weighting — Direct importance rating normalisation (Edwards SMART weight step)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1994","originator":"Edwards, W., Barron, F. H.","url":"https://scholargate.app/en/decision-making/smart-weight","markdownUrl":"https://scholargate.app/en/decision-making/smart-weight.md","definition":"SMART-WEIGHT (SMART Weighting — Direct importance rating normalisation (Edwards SMART weight step)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Edwards, W., Barron, F. H. in 1994. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edwards, W., Barron, F. H.","subfamily":"Weight_Subjective","year":"1994","type":"Subjective weighting — direct rating normalisation","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Edwards, W., Barron, F. H. (1994). SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement. Organizational Behavior and Human Decision Processes","type":"article","doi":"10.1006/obhd.1994.1087","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smart","name":"SMART","fullName":"Simple Multi-Attribute Rating Technique","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Edwards, W.","url":"https://scholargate.app/en/decision-making/smart","markdownUrl":"https://scholargate.app/en/decision-making/smart.md","definition":"SMART (Simple Multi-Attribute Rating Technique) is a ranking multi-criteria decision-making (MCDM) method introduced by Edwards, W. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edwards, W.","subfamily":"Ranking","year":"1986","type":"Direct rating, compensatory","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Edwards, W. (1986). How to use multi-attribute utility measurement for social decision making. Organizational Behavior and Human Performance","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+to+use+multi-attribute+utility+measurement+for+social+decision+making+Edwards"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smartphone-addiction-scale-short","name":"Smartphone Addiction Scale Short Version","fullName":"Smartphone Addiction Scale-Short Version (SAS-SV)","aliases":["SAS-SV"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"digital-addiction","year":"2013","originator":"Min Kwon, Dai-Jin Kim, Hyun Cho, and Sang Yang","url":"https://scholargate.app/en/social-media-psychology/smartphone-addiction-scale-short","markdownUrl":"https://scholargate.app/en/social-media-psychology/smartphone-addiction-scale-short.md","definition":"The Smartphone Addiction Scale-Short Version (SAS-SV) is a 10-item self-report instrument that rapidly assesses smartphone dependency and addiction-like behaviors in adolescents and adults. Developed by Kwon and colleagues in 2013 as an abbreviated version of the original 33-item SAS, it measures core dimensions of addiction: daily-life disturbance, withdrawal, virtual-life orientation, and tolerance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Min Kwon, Dai-Jin Kim, Hyun Cho, and Sang Yang","subfamily":"digital-addiction","year":"2013","type":"Self-report"},"citations":[{"ref":"Kwon, M., Kim, D.-J., Cho, H., & Yang, S. (2013). The Smartphone Addiction Scale: Development and validation of a short version for adolescents. PLoS ONE, 8(12), e83558.","type":"article","doi":"10.1371/journal.pone.0083558","isbn":null,"url":null}],"related":["social-media-disorder-scale","fear-of-missing-out-scale","technoference-scale","passive-social-media-use-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smed","name":"SMED","fullName":"Single Minute Exchange of Die","aliases":["quick changeover","rapid setup"],"domain":"operations-management","family":"ml-model","subfamily":"Lean Manufacturing","year":"1985","originator":"Shigeo Shingo","url":"https://scholargate.app/en/operations-management/smed","markdownUrl":"https://scholargate.app/en/operations-management/smed.md","definition":"Single Minute Exchange of Die (SMED) is a systematic approach developed by Shigeo Shingo in the 1980s to drastically reduce the time required to changeover equipment from producing one product to another. The methodology, part of the Toyota Production System, aims to reduce setup time to a single-digit minute range (ideally under nine minutes), enabling smaller batch sizes, faster response to customer demand, and improved flexibility in manufacturing. SMED is a cornerstone of lean manufacturing and just-in-time production.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shigeo Shingo","subfamily":"Lean Manufacturing","year":"1985","type":"Setup time reduction technique"},"citations":[{"ref":"Shingo, S. (1985). A revolution in manufacturing: The SMED system. Cambridge, MA: Productivity Press.","type":"book","doi":null,"isbn":null,"url":"https://www.productivitypress.com/"},{"ref":"McIntosh, R. I., Culley, S. J., Mileham, A. T., & Owen, G. W. (2008). Changeover improvement: a structured methodology with supporting tools. International Journal of Production Research, 45(24), 5635-5656.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Changeover+improvement%3A+a+structured+methodology+with+supporting+tools+McIntosh"}],"related":["kanban","aggregate-planning","assembly-line-balancing","job-shop-scheduling","total-productive-maintenance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smell-test-questionnaire","name":"QOD","fullName":"Questionnaire of Olfactory Disorders","aliases":["QOD","Olfactory Disorders Questionnaire"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"olfactory-assessment","year":"2003","originator":"Thomas Hummel and colleagues; adapted by Benninger et al.","url":"https://scholargate.app/en/otolaryngology/smell-test-questionnaire","markdownUrl":"https://scholargate.app/en/otolaryngology/smell-test-questionnaire.md","definition":"The Questionnaire of Olfactory Disorders (QOD) is a self-report instrument assessing the subjective impact of olfactory dysfunction on quality of life and daily functioning. Derived from olfactory research standardized testing (Sniffin' Sticks) and adapted for clinical use, the QOD measures perceived smell loss, changes in taste (retronasal olfaction), and emotional consequences of anosmia or hyposmia. It is increasingly used in otolaryngology, post-viral olfactory loss assessment, and chronic rhinosinusitis programs to quantify disease burden and monitor treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Hummel and colleagues; adapted by Benninger et al.","subfamily":"olfactory-assessment","year":"2003","type":"Self-report"},"citations":[{"ref":"Hummel, T., Sekinger, B., Wolf, S. R., Pauli, E., & Kobal, G. (1997). 'Sniffin' Sticks': Olfactory performance assessed by the combined testing of odor identification, odor discrimination and olfactory threshold. Chemical Senses, 22(1), 39-52.","type":"article","doi":"10.1093/chemse/22.1.39","isbn":null,"url":null},{"ref":"Benigner, C., Rudolph, C., Paolucci, V., Lopinto, A., & Simbruner, R. (2015). Questionnaire of Olfactory Disorders (QOD): Validation and applicability in post-infectious olfactory loss and chronic rhinosinusitis. Rhinology, 53(3), 235-241.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Questionnaire+of+Olfactory+Disorders+%28QOD%29%3A+Validation+and+applicability+in+post-infectious+olfactory+loss+and+chronic+rhinosinusitis+Benigner"}],"related":["nose-obstruction-symptom-evaluation","sino-nasal-outcome-test","sniffin-sticks-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smith-chart","name":"Smith Chart","fullName":"Smith Chart for Transmission Line Visualization","aliases":["Impedance chart","Reflection coefficient chart"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Graphical analysis and visualization","year":"1939","originator":"Phillip H. Smith","url":"https://scholargate.app/en/electrical-engineering/smith-chart","markdownUrl":"https://scholargate.app/en/electrical-engineering/smith-chart.md","definition":"The Smith Chart is a graphical tool for visualizing and manipulating complex impedances and reflection coefficients on transmission lines. Introduced by Phillip Smith in 1939, the chart maps the complex reflection coefficient plane to a circular chart, enabling intuitive graphical analysis of transmission line problems, impedance matching, and resonance conditions. Despite the advent of computers, the Smith Chart remains invaluable for understanding transmission line physics and designing RF circuits.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Phillip H. Smith","subfamily":"Graphical analysis and visualization","year":"1939","type":"Graphical tool for transmission line and impedance analysis"},"citations":[{"ref":"Smith, P. H. (1939). Transmission line calculator. Electronics, 12(1), 29-31.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/5217516"},{"ref":"Pozar, D. M. (2011). Microwave Engineering (4th ed.). Wiley.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Microwave+Engineering+%284th+ed.%29+Pozar"},{"ref":"Gonzalez, G. (1997). Microwave Transistor Amplifiers: Analysis and Design (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/microwavetransist0000gonz"}],"related":["s-parameter-analysis","method-of-moments","transmission-line-matrix-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smoke-dispersion","name":"Smoke Dispersion","fullName":"Smoke Dispersion Modeling and Assessment","aliases":["air quality","smoke transport","visibility impacts"],"domain":"forestry","family":"process-pipeline","subfamily":"Fire Ecology","year":"2000","originator":"Dave Peterson","url":"https://scholargate.app/en/forestry/smoke-dispersion","markdownUrl":"https://scholargate.app/en/forestry/smoke-dispersion.md","definition":"Smoke dispersion modeling predicts how smoke from wildfires and prescribed burns travels and disperses through the atmosphere, affecting air quality and visibility. Models use fire characteristics (size, intensity, fuel type), atmospheric conditions (wind, stability, mixing height), and topography to forecast smoke plumes and estimate particulate matter (PM2.5) concentrations downwind. Used for air quality forecasting, prescribed burn planning, and public health protection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dave Peterson","subfamily":"Fire Ecology","year":"2000","type":"atmospheric modeling"},"citations":[{"ref":"Larson, T., Gould, T., Simpson, C., & Liu, L. J. S. (2004). Source apportionment of indoor, outdoor, and personal PM2.5 in Seattle, Washington using positive matrix factorization. Journal of the Air & Waste Management Association, 54(9), 1175–1187.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Source+apportionment+of+indoor%2C+outdoor%2C+and+personal+PM2.5+in+Seattle%2C+Washington+using+positive+matrix+factorization+Larson"},{"ref":"Reid, C. E., Brauer, M., Johnston, F. H., Jerrett, M., Balmes, J. R., & Elliott, C. T. (2016). Critical review of health impacts of wildfire smoke exposure. Environmental Health Perspectives, 124(9), 1334–1343.","type":"article","doi":"10.1289/ehp.1409277","isbn":null,"url":null}],"related":["rothermel-fire-model","fire-weather-index","burn-severity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"smoothed-particle-hydrodynamics","name":"Smoothed Particle Hydrodynamics","fullName":"Smoothed Particle Hydrodynamics","aliases":["SPH","particle hydrodynamics"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1977","originator":"Monaghan John & Lucy Leon","url":"https://scholargate.app/en/fluid-dynamics/smoothed-particle-hydrodynamics","markdownUrl":"https://scholargate.app/en/fluid-dynamics/smoothed-particle-hydrodynamics.md","definition":"Smoothed Particle Hydrodynamics (SPH) is a meshfree particle method for simulating fluid dynamics, developed independently by Lucy in 1977 and Gingold and Monaghan in 1977. Rather than discretizing on a fixed grid, SPH represents fluids as collections of particles that carry mass, momentum, and energy. Each particle interacts with neighbors within a kernel support radius, enabling natural handling of free surfaces, large deformations, and multiphase flows without remeshing. SPH has become indispensable for simulations involving violent flows, impacts, and complex interfaces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Monaghan John & Lucy Leon","subfamily":"Fluid Dynamics","year":"1977","type":"Meshfree particle method"},"citations":[{"ref":"Lucy, L. B. (1977). A numerical approach to the testing of the fission hypothesis. The Astronomical Journal, 82(12), 1013-1024.","type":"article","doi":"10.1086/112164","isbn":null,"url":null},{"ref":"Gingold, R. A., & Monaghan, J. J. (1977). Smoothed particle hydrodynamics: theory and applications to non-spherical stars. Monthly Notices of the Royal Astronomical Society, 181(3), 375-389.","type":"article","doi":"10.1093/mnras/181.3.375","isbn":null,"url":null},{"ref":"Monaghan, J. J. (2005). Smoothed particle hydrodynamics. Reports on Progress in Physics, 68(8), 1703-1759.","type":"article","doi":"10.1088/0034-4885/68/8/R01","isbn":null,"url":null}],"related":["lattice-boltzmann-method","direct-numerical-simulation","volume-of-fluid","level-set-method","reynolds-averaged-navier-stokes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sn-qn-estimators","name":"Sn and Qn Scale Estimators","fullName":"Sn and Qn Robust Scale Estimators","aliases":["Sn estimator","Qn estimator","Rousseeuw-Croux scale estimators","robust scale estimation","Sn ve Qn Ölçek Tahmincileri"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1993,"originator":"Rousseeuw & Croux","url":"https://scholargate.app/en/statistics/sn-qn-estimators","markdownUrl":"https://scholargate.app/en/statistics/sn-qn-estimators.md","definition":"Sn and Qn are robust estimators of scale (spread) proposed by Rousseeuw and Croux (1993) as alternatives to the median absolute deviation (MAD). Both attain a 50% breakdown point while delivering higher statistical efficiency than MAD, so they measure dispersion accurately even when the data contain outliers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rousseeuw & Croux","year":1993,"type":"Robust scale estimator","breakdownPoint":"50%","outcome":"scale (spread)","minSample":20},"citations":[{"ref":"Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283.","type":"article","doi":"10.1080/01621459.1993.10476408","isbn":null,"url":null}],"related":["mad-estimation","breakdown-point-analysis","robust-mixed-model","permutation-test","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"snhf-topsis","name":"SNHF-TOPSIS","fullName":"TOPSIS with Maximizing Deviation in Simplified Neutrosophic Hesitant Fuzzy Environment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2019","originator":"Akram, M. Naz, S. Smarandache, F.","url":"https://scholargate.app/en/decision-making/snhf-topsis","markdownUrl":"https://scholargate.app/en/decision-making/snhf-topsis.md","definition":"SNHF-TOPSIS (TOPSIS with Maximizing Deviation in Simplified Neutrosophic Hesitant Fuzzy Environment) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M. Naz, S. Smarandache, F. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Akram, M. Naz, S. Smarandache, F.","subfamily":"Ranking","year":"2019","type":"Simplified Neutrosophic Hesitant Fuzzy TOPSIS — decision matrix entries are SVNHFEs (each of T, I, F is a finite set of values in [0,1]); weights derived internally via Maximizing Deviation Method","value_space":"hesitant","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Akram, M., Naz, S., Smarandache, F. (2019). Generalization of Maximizing Deviation and TOPSIS Method for MADM in Simplified Neutrosophic Hesitant Fuzzy Environment. Symmetry","type":"article","doi":"10.3390/sym11081058","isbn":null,"url":null}],"related":["n-topsis","topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"snowball-sampling","name":"Snowball Sampling","fullName":"Snowball Sampling (Chain-Referral Sampling)","aliases":["chain-referral sampling","network sampling","respondent-driven sampling","referral sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1961","originator":"Leo A. Goodman","url":"https://scholargate.app/en/survey-methodology/snowball-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/snowball-sampling.md","definition":"Snowball sampling is a non-probability recruitment technique in which initial participants (seeds) refer the researcher to others who meet the study criteria, and those referrals in turn refer further participants. The sample grows incrementally — like a rolling snowball — until the required size or theoretical saturation is reached. It is the method of choice when a target population has no accessible sampling frame, such as undocumented migrants, illicit drug users, survivors of stigmatised experiences, or members of closed professional networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leo A. Goodman","year":"1961","type":"Non-probability sampling technique","dataType":"Qualitative or quantitative data collected from hard-to-reach or hidden populations","subfamily":"Sampling"},"citations":[{"ref":"Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32(1), 148–170.","type":"article","doi":"10.1214/aoms/1177705148","isbn":null,"url":null},{"ref":"Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods and Research, 10(2), 141–163.","type":"article","doi":"10.1177/004912418101000205","isbn":null,"url":null}],"related":["purposive-sampling","theoretical-sampling","convenience-sampling","maximum-variation-sampling","respondent-driven-sampling","network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-capital-index","name":"Social Capital Index","fullName":"Comprehensive Social Capital Assessment Index","aliases":["SCI","Social Capital Scale"],"domain":"political-sociology","family":"process-pipeline","subfamily":"Social Capital","year":"1986–2000","originator":"Pierre Bourdieu, Robert Putnam, Michael Woolcock","url":"https://scholargate.app/en/political-sociology/social-capital-index","markdownUrl":"https://scholargate.app/en/political-sociology/social-capital-index.md","definition":"The Social Capital Index measures the stock of social connections, networks, and civic participation within an individual's or community's social ecosystem. Rooted in the theoretical work of Pierre Bourdieu and popularized by Robert Putnam, social capital encompasses bonding capital (ties within homogeneous groups), bridging capital (ties across different groups), and linking capital (connections to institutions and power). Comprehensive indices assess networks, trust, organizational membership, volunteering, and informal mutual aid.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pierre Bourdieu, Robert Putnam, Michael Woolcock","subfamily":"Social Capital","year":"1986–2000","type":"Self-report questionnaire / Behavioral frequency"},"citations":[{"ref":"Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon & Schuster.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Putnam%2C%20R.%20D.%20(2000).%20Bowling%20alone%3A%20The%20collapse%20and%20revival%20of%20American%20community.%20Simon%20%26%20Schuster."},{"ref":"Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241-258). Greenwood Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bourdieu%2C%20P.%20(1986).%20The%20forms%20of%20capital.%20In%20J.%20Richardson%20(Ed.)%2C%20Handbook%20of%20theory%20and%20research%20for%20the%20sociology%20of%20"},{"ref":"Woolcock, M., & Narayan, D. (2000). Social capital: Implications for development theory, research, and policy. World Bank Economic Review, 15(2), 225-249.","type":"article","doi":"10.1093/wbro/15.2.225","isbn":null,"url":null}],"related":["civic-engagement-scale","social-cohesion-scale","generalized-trust-scale","community-belonging-scale","institutional-trust-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-capital-scale","name":"Social Capital Scale","fullName":"Social Capital Scale","aliases":["SCS"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Social cognition","year":"2000","originator":"Robert D. Putnam, Jill Onyx, and Paul Bullen","url":"https://scholargate.app/en/social-psychology/social-capital-scale","markdownUrl":"https://scholargate.app/en/social-psychology/social-capital-scale.md","definition":"The Social Capital Scale is a self-report measure designed to assess the presence and extent of social capital in individuals and communities. Building on Robert D. Putnam's influential work on social capital as shared norms, networks, and reciprocity, the scale measures dimensions of social connection, participation in community life, and access to social resources. Multiple versions exist, including the scale developed by Onyx and Bullen (2000) with community-level validation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert D. Putnam, Jill Onyx, and Paul Bullen","subfamily":"Social cognition","year":"2000","type":"Self-report Likert scale"},"citations":[{"ref":"Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon & Schuster.","type":"book","doi":null,"isbn":null,"url":"https://psycnet.apa.org/record/2000-07530-000"},{"ref":"Onyx, J., & Bullen, P. (2000). Measuring social capital in five communities. Journal of Applied Behavioral Science, 36(1), 23–42.","type":"article","doi":"10.1177/0021886300361002","isbn":null,"url":null}],"related":["interpersonal-reactivity-index","collectivism-individualism-scale","cultural-values-scale","modern-racism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-cohesion-scale","name":"Social Cohesion Scale","fullName":"Social Cohesion Assessment Scale","aliases":["SCS","Social Integration Index"],"domain":"political-sociology","family":"process-pipeline","subfamily":"Community Integration","year":"1997–2006","originator":"Robert Sampson, Ray Forrest, Akhtar Kearns","url":"https://scholargate.app/en/political-sociology/social-cohesion-scale","markdownUrl":"https://scholargate.app/en/political-sociology/social-cohesion-scale.md","definition":"The Social Cohesion Scale measures the degree to which members of a community feel integrated, connected, and unified by shared values and mutual support. Developed across multiple traditions—notably by Robert Sampson and colleagues in criminology and urban sociology, and by Forrest & Kearns in housing research—it assesses both the structural glue (institutions, networks) and affective bonds (belonging, solidarity) that hold communities together.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Sampson, Ray Forrest, Akhtar Kearns","subfamily":"Community Integration","year":"1997–2006","type":"Self-report questionnaire"},"citations":[{"ref":"Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5328), 918-924.","type":"article","doi":"10.1126/science.277.5328.918","isbn":null,"url":null},{"ref":"Forrest, R., & Kearns, A. (2001). Social cohesion, social capital and the neighbourhood. Urban studies, 38(12), 2125-2143.","type":"article","doi":"10.1080/00420980120087081","isbn":null,"url":null},{"ref":"Chan, J., To, H. P., & Chan, E. (2006). Reconsidering social cohesion: Developing a definition and analytical framework for empirical research. Social Indicators Research, 75(2), 273-302.","type":"article","doi":"10.1007/s11205-005-2118-1","isbn":null,"url":null}],"related":["community-belonging-scale","generalized-trust-scale","intergroup-contact-scale","civic-engagement-scale","institutional-trust-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-communication-questionnaire","name":"Social Communication Questionnaire","fullName":"Social Communication Questionnaire (SCQ)","aliases":["SCQ","SCQ-Lifetime","SCQ-Current"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"neurodevelopmental screening","year":"2003","originator":"Michael Rutter, Ann Bailey, Catherine Lord","url":"https://scholargate.app/en/child-psychiatry/social-communication-questionnaire","markdownUrl":"https://scholargate.app/en/child-psychiatry/social-communication-questionnaire.md","definition":"The Social Communication Questionnaire (SCQ) is a 40-item parent-report measure of autism-spectrum symptoms in children ages 4–15 years. Developed by Michael Rutter, Ann Bailey, and Catherine Lord in 2003, it serves as a brief screening tool for autism spectrum disorder. The SCQ asks parents to recall or report current child behaviors in three domains: reciprocal social interaction, communication, and restricted/repetitive behaviors/interests. It is frequently used in primary care, developmental pediatrics, and research settings to identify children warranting comprehensive autism evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Rutter, Ann Bailey, Catherine Lord","subfamily":"neurodevelopmental screening","year":"2003","type":"Parent-report questionnaire"},"citations":[{"ref":"Rutter, M., Bailey, A., & Lord, C. (2003). The Social Communication Questionnaire: Manual. Western Psychological Services.","type":"book","doi":null,"isbn":"0874119588","url":null},{"ref":"Berument, S. K., Rutter, M., Lord, C., Pickles, A., & Bailey, A. (1999). Autism screening questionnaire: Diagnostic validity. British Journal of Psychiatry, 175(5), 444–451.","type":"article","doi":"10.1192/bjp.175.5.444","isbn":null,"url":null}],"related":["autism-spectrum-quotient","child-depression-inventory","revised-childrens-anxiety-depression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-comparison-scale-online","name":"Social Comparison Scale (Online Contexts)","fullName":"Upward Social Comparison Scale for Social Media","aliases":["USCS","Social Comparison Orientation Online"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"social-media-comparison","year":"2015","originator":"Various researchers (Vogel, Wang, Suls & Wheeler)","url":"https://scholargate.app/en/social-media-psychology/social-comparison-scale-online","markdownUrl":"https://scholargate.app/en/social-media-psychology/social-comparison-scale-online.md","definition":"The Social Comparison Scale for online contexts measures the frequency and intensity with which individuals compare themselves to peers on social media platforms, with emphasis on upward comparison (to those perceived as superior in attractiveness, success, wealth). Developed and refined by researchers including Vogel and Wang in the 2010s, this scale specifically captures social media-driven comparison processes distinct from general social comparison orientation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various researchers (Vogel, Wang, Suls & Wheeler)","subfamily":"social-media-comparison","year":"2015","type":"Self-report"},"citations":[{"ref":"Wang, J. L., Wang, H. Z., Gaskin, J., & Wang, S. (2015). The role of stress and motivation in social networking sites adoption: From a perspective of uses and gratifications theory. Computers in Human Behavior, 48, 24–32.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+role+of+stress+and+motivation+in+social+networking+sites+adoption%3A+From+a+perspective+of+uses+and+gratifications+theory+Wang"}],"related":["online-social-comparison-scale","fear-of-missing-out-scale","passive-social-media-use-scale","social-media-disorder-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-distance-scale","name":"Social Distance Scale","fullName":"Social Distance Scale (SDS)","aliases":["SDS","Bogardus Scale"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"intergroup-attitudes","year":1933,"originator":"Emory Bogardus","url":"https://scholargate.app/en/transcultural-nursing/social-distance-scale","markdownUrl":"https://scholargate.app/en/transcultural-nursing/social-distance-scale.md","definition":"The Social Distance Scale (SDS), also known as the Bogardus Scale, is a classic sociological instrument designed to measure the degree of social acceptance, prejudice, or social distance that individuals feel toward members of different ethnic, racial, or social groups. Originally developed by Emory Bogardus in 1933 and updated by researchers including Parrillo and Donoghue, the SDS assesses willingness for increasing levels of contact and intimacy with outgroup members, from casual acquaintance to family relationships. The scale is widely used in sociology, psychology, and health research to evaluate attitudes toward diversity and to track changes in intergroup relations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emory Bogardus","subfamily":"intergroup-attitudes","year":1933,"type":"Self-report"},"citations":[{"ref":"Bogardus, E. S. (1933). A social distance scale. Sociology and Social Research, 17(3), 265–271.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Bogardus%2C%20E.%20S.%20(1933).%20A%20social%20distance%20scale.%20Sociology%20and%20Social%20Research%2C%2017(3)%2C%20265%E2%80%93271."},{"ref":"Parrillo, V. N., & Donoghue, C. (2005). Updating the Bogardus social distance studies: A new national survey. The Social Science Journal, 42(2), 257–271.","type":"article","doi":"10.1016/j.soscij.2005.03.011","isbn":null,"url":null}],"related":["ethnic-identity-scale","racism-and-life-experiences-scale","cultural-competence-assessment","acculturative-stress-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-dominance-orientation-scale","name":"Social Dominance Orientation Scale","fullName":"Social Dominance Orientation Scale (SDO)","aliases":["SDO"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Social cognition","year":"1994","originator":"Felicia Pratto, Jim Sidanius, Lisa Stallworth, and Bertram Malle","url":"https://scholargate.app/en/social-psychology/social-dominance-orientation-scale","markdownUrl":"https://scholargate.app/en/social-psychology/social-dominance-orientation-scale.md","definition":"The Social Dominance Orientation Scale (SDO) is a self-report measure developed by Pratto, Sidanius, Stallworth, and Malle in 1994 to assess individual differences in preference for group-based hierarchy and inequality. The scale measures the extent to which individuals support dominance of some groups over others, reject egalitarianism, and accept hierarchical social organization. It has become central to social dominance theory and is widely used in political psychology and intergroup relations research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Felicia Pratto, Jim Sidanius, Lisa Stallworth, and Bertram Malle","subfamily":"Social cognition","year":"1994","type":"Self-report Likert scale"},"citations":[{"ref":"Pratto, F., Sidanius, J., Stallworth, L. M., & Malle, B. F. (1994). Social Dominance Orientation: A personality variable predicting social and political attitudes. Journal of Personality and Social Psychology, 67(4), 741–763.","type":"article","doi":"10.1037/0022-3514.67.4.741","isbn":null,"url":null}],"related":["right-wing-authoritarianism-scale","modern-racism-scale","ambivalent-sexism-inventory","cultural-values-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-engagement-scale","name":"SES","fullName":"Social Engagement Scale","aliases":["SES","Social Engagement Score"],"domain":"gerontology","family":"process-pipeline","subfamily":"social-connection-isolation","year":"2000s","originator":"Various","url":"https://scholargate.app/en/gerontology/social-engagement-scale","markdownUrl":"https://scholargate.app/en/gerontology/social-engagement-scale.md","definition":"The Social Engagement Scale (SES) is a brief self-report measure assessing the frequency and quality of social contact and participation in social activities in older adults. While multiple versions exist (developed by various researchers in gerontology), the core concept measures social connection—the degree to which individuals maintain relationships with family and friends, participate in community activities, and feel engaged with others. The SES is used in research, clinical gerontology, and public health to evaluate social isolation risk, predict mortality and health outcomes, and measure outcomes of social intervention programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various","subfamily":"social-connection-isolation","year":"2000s","type":"Self-report scale of social engagement"},"citations":[{"ref":"Paulson, D., & Willig, C. (2008). Older adults' engagement with online information about health: web site accessibility, usefulness, and trust. Gerontology, 54(5), 523-533.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Older+adults%27+engagement+with+online+information+about+health%3A+web+site+accessibility%2C+usefulness%2C+and+trust+Paulson"},{"ref":"Beck, F., Voelker, M., & Baumeister, R. F. (2016). Social connection and health: a meta-analytic review. J Psychol Health, 31(5), 584-604.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Social+connection+and+health%3A+a+meta-analytic+review+Beck"},{"ref":"Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: a meta-analytic review. PLoS Med, 7(7), e1000316.","type":"article","doi":"10.1371/journal.pmed.1000316","isbn":null,"url":null}],"related":["life-space-assessment","geriatric-anxiety-inventory","cognitive-telephone-screening","activities-balance-confidence","edmonton-frail-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-inclusion-scale","name":"Social Inclusion Scale","fullName":"Social Inclusion Scale (SIS)","aliases":["SIS"],"domain":"psychiatric-rehabilitation","family":"process-pipeline","subfamily":"recovery-measurement","year":"2005","originator":"Oades, L. G., Deane, F. P., Crowe, T. P., et al.","url":"https://scholargate.app/en/psychiatric-rehabilitation/social-inclusion-scale","markdownUrl":"https://scholargate.app/en/psychiatric-rehabilitation/social-inclusion-scale.md","definition":"The Social Inclusion Scale (SIS) is a brief measure assessing the degree to which individuals with serious mental illness perceive themselves as included, valued members of their community. Developed by Oades, Deane, and colleagues in 2005, the SIS captures subjective experiences of social participation, acceptance, and integration. The scale is used in recovery-oriented mental health services and research to evaluate community integration outcomes and inform interventions promoting social inclusion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oades, L. G., Deane, F. P., Crowe, T. P., et al.","subfamily":"recovery-measurement","year":"2005","type":"Self-report questionnaire"},"citations":[{"ref":"Oades, L. G., Deane, F. P., Crowe, T. P., Gordon, L. M., Relieur, D. H., & Kavanagh, D. J. (2005). Collaborative recovery: An integrative model for working with individuals who experience chronic and recurring mental illness. Australasian Psychiatry, 13(3), 279-284.","type":"article","doi":"10.1111/j.1440-1665.2005.02202.x","isbn":null,"url":null}],"related":["recovery-assessment-scale","mental-health-recovery-measure","empowerment-scale-rogers","recovery-oriented-practices-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-interaction-anxiety-scale","name":"Social Interaction Anxiety Scale","fullName":"Social Interaction Anxiety Scale","aliases":["SIAS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"social interaction anxiety assessment","year":"1998","originator":"Richard P. Mattick, John C. Clarke","url":"https://scholargate.app/en/clinical-psychology/social-interaction-anxiety-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/social-interaction-anxiety-scale.md","definition":"The Social Interaction Anxiety Scale (SIAS) is a 20-item self-report questionnaire designed to measure anxiety and distress experienced during social interactions and conversations with others. Developed by Mattick and Clarke in 1998, the SIAS is a brief, user-friendly instrument for assessing social interaction anxiety in clinical, research, and community settings, particularly valued for its focus on direct interpersonal contact rather than public speaking or performance situations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard P. Mattick, John C. Clarke","subfamily":"social interaction anxiety assessment","year":"1998","type":"Self-report social anxiety scale"},"citations":[{"ref":"Mattick, R. P., & Clarke, J. C. (1998). Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behaviour Research and Therapy, 36(4), 455-470.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+and+validation+of+measures+of+social+phobia+scrutiny+fear+and+social+interaction+anxiety+Mattick"}],"related":["gad-7","liebowitz-social-anxiety-scale","fear-of-covid-19-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-media-anxiety-scale","name":"Social Media Anxiety Scale","fullName":"Social Media Anxiety Scale (SMAS)","aliases":["SMAS","Social Media Anxiety","Fear of Missing Out Anxiety"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Digital stress and mental health","year":"2013","originator":"Andrew Przybylski, Kou Murayama, et al.","url":"https://scholargate.app/en/health-informatics/social-media-anxiety-scale","markdownUrl":"https://scholargate.app/en/health-informatics/social-media-anxiety-scale.md","definition":"The Social Media Anxiety Scale measures the extent to which individuals experience anxiety, apprehension, and psychological distress related to social media use. Developed by Przybylski and colleagues (2013) and expanded by Elhai and colleagues, the scale captures the 'Fear of Missing Out' (FOMO) construct—anxiety about missing important social events or information if not actively monitoring social media—alongside broader concerns about social comparison, peer judgment, and online relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Przybylski, Kou Murayama, et al.","subfamily":"Digital stress and mental health","year":"2013","type":"Self-report questionnaire"},"citations":[{"ref":"Elhai, J. D., Yang, H., & Montag, C. (2015). Whilst FOMO is related to negative mental health consequences, phubbing may be more emotionally disruptive. Computers in Human Behavior, 113, 106480.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Whilst+FOMO+is+related+to+negative+mental+health+consequences%2C+phubbing+may+be+more+emotionally+disruptive+Elhai"},{"ref":"Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848.","type":"article","doi":"10.1016/j.chb.2013.02.014","isbn":null,"url":null}],"related":["cyberbullying-victimization-scale","nomophobia-questionnaire","ehealth-literacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-media-disorder-scale","name":"Social Media Disorder Scale","fullName":"Social Media Disorder Scale (SMD Scale)","aliases":["SMD Scale"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"social-media-addiction","year":"2016","originator":"Roos J. J. M. van den Eijnden, Jeroen S. Lemmens, and Patti M. Valkenburg","url":"https://scholargate.app/en/social-media-psychology/social-media-disorder-scale","markdownUrl":"https://scholargate.app/en/social-media-psychology/social-media-disorder-scale.md","definition":"The Social Media Disorder Scale (SMD Scale) is a 9-item self-report measure developed by van den Eijnden and colleagues in 2016 to assess problematic social media use characterized by loss of control, withdrawal, tolerance, and conflict—mirroring criteria from behavioral addiction frameworks. It identifies individuals whose engagement with social media platforms has reached clinically concerning levels that interfere with daily functioning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roos J. J. M. van den Eijnden, Jeroen S. Lemmens, and Patti M. Valkenburg","subfamily":"social-media-addiction","year":"2016","type":"Self-report"},"citations":[{"ref":"van den Eijnden, R. J. J. M., Lemmens, J. S., & Valkenburg, P. M. (2016). The social media disorder scale. Computers in Human Behavior, 61, 481–490.","type":"article","doi":"10.1016/j.chb.2016.03.038","isbn":null,"url":null}],"related":["smartphone-addiction-scale-short","fear-of-missing-out-scale","passive-social-media-use-scale","technoference-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-media-engagement-scale","name":"Social Media Engagement Scale","fullName":"Social Media Engagement Scale","aliases":["Social Media Engagement","SME Scale"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"2011","originator":"Hollebeek; Zhang & Zhu","url":"https://scholargate.app/en/information-systems/social-media-engagement-scale","markdownUrl":"https://scholargate.app/en/information-systems/social-media-engagement-scale.md","definition":"The Social Media Engagement Scale measures the intensity and quality of user participation and interaction with social media platforms and content. Developed by researchers including Hollebeek (2011) and informed by work on consumer engagement (Zhang & Zhu, 2012), the scale captures cognitive, emotional, and behavioral dimensions of how users interact with social platforms, brands, and online communities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hollebeek; Zhang & Zhu","subfamily":"Technology adoption","year":"2011","type":"Likert-scale engagement measure"},"citations":[{"ref":"Zhang, X., & Zhu, F. (2012). Product reviews, information richness, and consumer purchasing behavior. Journal of Management Information Systems, 29(2), 7-36.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Product+reviews%2C+information+richness%2C+and+consumer+purchasing+behavior+Zhang"},{"ref":"Hollebeek, L. D. (2011). Exploring customer brand engagement: Definition and themes. Journal of Strategic Marketing, 19(7), 555-573.","type":"article","doi":"10.1080/0965254X.2011.599493","isbn":null,"url":null}],"related":["online-trust-scale","technology-readiness-index","tam-questionnaire","elearning-satisfaction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-media-nlp","name":"Social Media NLP","fullName":"Social Media Text Analysis (NLP Pipeline)","aliases":["Sosyal Medya Metin Analizi","social media text mining","Twitter NLP","short-text NLP"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2017","originator":"Community-established benchmark (SemEval shared tasks, Cardiff NLP group)","url":"https://scholargate.app/en/text-mining/social-media-nlp","markdownUrl":"https://scholargate.app/en/text-mining/social-media-nlp.md","definition":"Social Media NLP is a specialised natural-language-processing pipeline designed for the short, noisy, and informal text that appears on platforms such as Twitter, Reddit, and comment sections. Unlike general-purpose NLP, this pipeline accounts for platform-specific conventions — hashtags, emojis, abbreviations, and code-switching — enabling tasks such as hashtag analysis, viral content detection, and public-opinion measurement. The benchmark tradition for this approach was established through the SemEval-2017 Task 4 shared task (Rosenthal et al., 2017) and the TweetEval unified benchmark (Barbieri et al., 2020).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Community-established benchmark (SemEval shared tasks, Cardiff NLP group)","year":"2017","type":"NLP process pipeline for short, noisy social-media text","inputFormat":"Raw tweets, Reddit posts, comments — short informal text","purposes":"Exploration, classification","minSample":50,"difficultyLevel":"2 / 5","platformAPIRequired":"Yes (or an approved dataset)"},"citations":[{"ref":"Rosenthal, S. et al. (2017). SemEval-2017 Task 4: Sentiment Analysis in Twitter. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). ACL.","type":"proceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/S17-2088"},{"ref":"Barbieri, F. et al. (2020). TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification. Findings of the Association for Computational Linguistics: EMNLP 2020.","type":"proceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2020.findings-emnlp.148"}],"related":["sentiment-analysis","text-classification","bert-embeddings","tf-idf","topic-modeling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-network-analysis","name":"Social Network Analysis","fullName":"Social Network Analysis (SNA)","aliases":["SNA","network analysis","sociometric analysis","relational analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1934 (sociometry); 1994 (modern formalization)","originator":"Moreno, J.L.; formalized by Wasserman & Faust","url":"https://scholargate.app/en/network-analysis/social-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/social-network-analysis.md","definition":"Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moreno, J.L.; formalized by Wasserman & Faust","year":"1934 (sociometry); 1994 (modern formalization)","type":"Structural/relational analysis framework","dataType":"Relational (dyadic ties, adjacency matrices, edge lists)","subfamily":"Network science"},"citations":[{"ref":"Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0-521-38707-1","url":null},{"ref":"Scott, J. (2017). Social Network Analysis (4th ed.). SAGE Publications.","type":"book","doi":null,"isbn":"978-1-4739-5515-1","url":null}],"related":["degree-centrality","betweenness-centrality","closeness-centrality","modularity-analysis","exponential-random-graph-model","eigenvector-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-provisions-scale","name":"Social Provisions Scale","fullName":"Social Provisions Scale (SPS)","aliases":["SPS","Cutrona & Russell Social Provisions Scale","SPS-10"],"domain":"social-psychology","family":"process-pipeline","subfamily":"social support and perceived social resources","year":"1987","originator":"Carolyn Cutrona and Daniel Russell","url":"https://scholargate.app/en/social-psychology/social-provisions-scale","markdownUrl":"https://scholargate.app/en/social-psychology/social-provisions-scale.md","definition":"The Social Provisions Scale is a widely used multidimensional instrument for measuring the degree to which individuals perceive their social relationships as providing essential emotional and practical support. Developed by Carolyn Cutrona and Daniel Russell in 1987, the SPS operationalizes the theory that healthy social support requires six provisions: attachment (emotional closeness), social integration (sense of belonging), reassurance of worth (feeling valued), reliable alliance (practical assistance), guidance (advice and direction), and opportunity for nurturance (ability to care for others). The SPS is used extensively in health psychology, gerontology, and stress and coping research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carolyn Cutrona and Daniel Russell","subfamily":"social support and perceived social resources","year":"1987","type":"Self-report social support assessment"},"citations":[{"ref":"Cutrona, C. E., & Russell, D. W. (1987). The provisions of social relationships and adaptation to stress. Advances in Personal Relationships, 1, 37-67.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cutrona+Russell+social+provisions+1987"},{"ref":"Russell, D. W., & Cutrona, C. E. (2001). Social support, stress, and depressive symptoms among the elderly: Test of a process model. Psychology and Aging, 6(2), 190-201.","type":"article","doi":"10.1037/0882-7974.6.2.190","isbn":null,"url":null}],"related":["de-jong-gierveld-loneliness","friendship-quality-questionnaire","mspss"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"social-role-participation-questionnaire","name":"Social Role Participation Questionnaire","fullName":"Social Role Participation Questionnaire (SRPQ)","aliases":["SRPQ","Social Role Questionnaire"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"family-social-roles","year":"2004","originator":"Lyons, Sayer, et al.","url":"https://scholargate.app/en/rehabilitation-science/social-role-participation-questionnaire","markdownUrl":"https://scholargate.app/en/rehabilitation-science/social-role-participation-questionnaire.md","definition":"The Social Role Participation Questionnaire (SRPQ) is a brief, self-report instrument designed to measure the extent to which individuals participate in and derive meaning from key social roles (family member, friend, worker, volunteer, community member, leisure participant). Developed by Lyons, Sayer, and colleagues, SRPQ is used in traumatic brain injury, stroke, and other disability research to assess how completely a person has resumed their valued life roles post-injury or illness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lyons, Sayer, et al.","subfamily":"family-social-roles","year":"2004","type":"Self-report or Interview"},"citations":[{"ref":"Lyons, K. S., & Sayer, A. G. (2005). How does loss matter? The experience of spouse loss among family caregivers of persons with Alzheimer's disease. American Journal of Alzheimer's Disease and Other Dementias, 20(5), 273–290.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1177/153331750502000503"},{"ref":"Andruszkow, H., Chmielewski, C., Hoefer, J., Hildebrand, F., Lefering, R., & Frink, M. (2015). Severely injured patients with traumatic brain injury—does long-term outcome correlate with acute parameters? A retrospective analysis. Journal of Neurotrauma, 32(7), 531–538.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1089/neu.2014.3709"}],"related":["community-integration-questionnaire","impact-participation-autonomy","assessment-life-habits","participation-scale","craig-handicap-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soda-cognitive-mapping","name":"SODA","fullName":"Strategic Options Development and Analysis (Cognitive Mapping)","aliases":["Cognitive Mapping","Strategic Options Development and Analysis","SODA Method","Bilişsel Haritalama"],"domain":"problem-structuring","family":"process-pipeline","subfamily":"Problem structuring methods","year":1988,"originator":"Colin Eden","url":"https://scholargate.app/en/problem-structuring/soda-cognitive-mapping","markdownUrl":"https://scholargate.app/en/problem-structuring/soda-cognitive-mapping.md","definition":"Strategic Options Development and Analysis (SODA) is a facilitated, qualitative method for structuring complex organisational problems. Developed by Colin Eden in 1988, it uses cognitive maps — directed graphs of causal constructs — to capture and integrate the subjective views of multiple stakeholders. SODA is most valuable when decision makers face ill-defined, wicked problems where shared understanding must be built before any quantitative analysis can begin.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Colin Eden","year":1988,"type":"Qualitative / participatory","subfamily":"Problem structuring methods","software":"Decision Explorer (formerly COPE)","context":"Group strategic decision-making"},"citations":[{"ref":"Eden, C. (1988). Cognitive mapping. European Journal of Operational Research, 36(1), 1–13.","type":"article","doi":"10.1016/0377-2217(88)90002-1","isbn":null,"url":null}],"related":["soft-systems-methodology","strategic-choice-approach","fuzzy-cognitive-maps"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sofa-score","name":"Sequential Organ Failure Assessment Score","fullName":"Sequential Organ Failure Assessment (SOFA) Score","aliases":["SOFA","Sepsis-related Organ Failure Assessment"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"1996","originator":"Jean-Louis Vincent and Rui Moreno","url":"https://scholargate.app/en/clinical-assessment/sofa-score","markdownUrl":"https://scholargate.app/en/clinical-assessment/sofa-score.md","definition":"The Sequential Organ Failure Assessment (SOFA) score, introduced by Vincent and Moreno in 1996, is a 24-point daily assessment tool that quantifies organ dysfunction across six physiological systems in critically ill patients. It was adopted into the 2016 Sepsis-3 definitions and is now the international standard for identifying and grading sepsis-related organ failure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Louis Vincent and Rui Moreno","subfamily":"Clinical scoring","year":"1996","type":"Organ dysfunction and sepsis assessment"},"citations":[{"ref":"Vincent, J. L., Moreno, R., Takala, J., et al. (1996). The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfuncti on/failure. Intensive Care Medicine, 22(7), 707-710.","type":"article","doi":"10.1007/BF01709751","isbn":null,"url":null},{"ref":"Singer, M., Deutschman, C. S., Seymour, C. W., et al. (2016). The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315(8), 801-810.","type":"article","doi":"10.1001/jama.2016.0287","isbn":null,"url":null}],"related":["apache-ii-score","qsofa","mews-score"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soft-set-theory","name":"Soft Set Theory","fullName":"Soft Set Theory","aliases":["Soft Sets","Parameterized Family of Sets","Molodtsov Soft Sets","Yumuşak Küme Teorisi"],"domain":"soft-computing","family":"ml-model","subfamily":"Uncertainty theory","year":1999,"originator":"Dmitriy Molodtsov","url":"https://scholargate.app/en/soft-computing/soft-set-theory","markdownUrl":"https://scholargate.app/en/soft-computing/soft-set-theory.md","definition":"Soft Set Theory is a mathematical framework for handling uncertainty and imprecision through parameterized families of sets. Introduced by Dmitriy Molodtsov in 1999, it provides an approximate description of objects in a universe by mapping each parameter in a chosen parameter set to a crisp subset of that universe. Unlike probability theory or fuzzy sets, soft sets require no membership function or probability distribution, making the framework free from the inadequacy of existing uncertainty tools when sufficient data are unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dmitriy Molodtsov","year":1999,"type":"Parameterized uncertainty representation framework","subfamily":"Uncertainty theory","parameterization":"Uses an approximate description via a set-valued map over a parameter set","membership":"Crisp (binary) within each parameter-indexed subset"},"citations":[{"ref":"Molodtsov, D. (1999). Soft set theory—first results. Computers & Mathematics with Applications, 37(4–5), 19–31.","type":"article","doi":"10.1016/S0898-1221(99)00056-5","isbn":null,"url":null}],"related":["rough-set-theory","granular-computing","formal-concept-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soft-systems-methodology","name":"Soft Systems Methodology","fullName":"Soft Systems Methodology (SSM)","aliases":["SSM","Checkland's SSM","Soft Systems Analysis","Yumuşak Sistemler Metodolojisi"],"domain":"problem-structuring","family":"process-pipeline","subfamily":"Problem structuring methods","year":1981,"originator":"Peter Checkland","url":"https://scholargate.app/en/problem-structuring/soft-systems-methodology","markdownUrl":"https://scholargate.app/en/problem-structuring/soft-systems-methodology.md","definition":"Soft Systems Methodology (SSM) is an interpretive, action-research approach for structuring and managing complex, ill-defined ('soft') problem situations involving human activity. Developed by Peter Checkland at Lancaster University throughout the 1970s and formally presented in 1981, SSM guides practitioners through iterative cycles of inquiry that move from an unstructured problem situation to purposeful action through structured learning rather than optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Checkland","year":1981,"type":"Interpretive problem-structuring methodology","subfamily":"Problem structuring methods","paradigm":"Interpretivist / soft systems","iterations":"Cyclic; repeated until consensus or accommodation"},"citations":[{"ref":"Checkland, P. (1981). Systems Thinking, Systems Practice. Wiley.","type":"book","doi":null,"isbn":"978-0-471-27911-2","url":null}],"related":["strategic-choice-approach","morphological-analysis","delphi-method"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"software-complexity-metrics","name":"Software Complexity Metrics","fullName":"Software Complexity Metrics and Measurement","aliases":["code complexity analysis","complexity measurement"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Code quality assessment","year":"1976","originator":"Thomas J. McCabe","url":"https://scholargate.app/en/software-engineering/software-complexity-metrics","markdownUrl":"https://scholargate.app/en/software-engineering/software-complexity-metrics.md","definition":"Software complexity metrics quantify the structural and operational difficulty of code through numerical measurements. Introduced by Thomas McCabe in 1976, cyclomatic complexity became the foundational approach. These metrics assess maintainability, testability, and defect risk, enabling teams to identify problematic code regions and guide refactoring efforts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas J. McCabe","subfamily":"Code quality assessment","year":"1976","type":"quantitative measurement"},"citations":[{"ref":"McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 2(4), 308–320.","type":"article","doi":"10.1109/TSE.1976.233837","isbn":null,"url":null},{"ref":"Chidamber, S. R., & Kemerer, C. F. (1994). A metrics suite for object-oriented design. IEEE Transactions on Software Engineering, 20(6), 476–493.","type":"article","doi":"10.1109/32.295895","isbn":null,"url":null},{"ref":"Halstead, M. H. (1977). Elements of Software Science. Elsevier.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/elementsofsoft00hals"}],"related":["defect-prediction-model","code-coverage-analysis","static-code-analysis","technical-debt-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"software-defined-networking","name":"Software-Defined Networking","fullName":"Software-Defined Networking (SDN)","aliases":["network virtualization","programmable networks"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Network architecture","year":"2008","originator":"Nick McKeown et al.","url":"https://scholargate.app/en/telecommunications/software-defined-networking","markdownUrl":"https://scholargate.app/en/telecommunications/software-defined-networking.md","definition":"Software-Defined Networking (SDN) is a network architecture paradigm that decouples the control plane (routing decisions) from the data plane (packet forwarding). Introduced by McKeown et al. (2008) with OpenFlow, SDN enables network programmability by centralizing control logic in software-based controllers that direct forwarding behavior of simple programmable switches. SDN has transformed network operations, enabling rapid service deployment, traffic engineering, and cloud integration. It is now foundational in data centers and service provider networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nick McKeown et al.","subfamily":"Network architecture","year":"2008","type":"programmable network paradigm"},"citations":[{"ref":"McKeown, N., Anderson, T., Balakrishnan, H., et al. (2008). OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review, 38(2), 69-74.","type":"article","doi":"10.1145/1355734.1355746","isbn":null,"url":null},{"ref":"Doria, A., Hellstein, H., Haas, R., et al. (2013). Forwarding and Control Element Separation (ForCES) Protocol Specification. RFC 5810.","type":"article","doi":null,"isbn":null,"url":"https://www.ietf.org"}],"related":["network-function-virtualization","mpls","bgp","ospf"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"software-reliability-model","name":"Software Reliability Model","fullName":"Software Reliability Modeling and Growth Analysis","aliases":["reliability growth model","failure rate prediction","SRGM"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Reliability prediction","year":"1979","originator":"Alok Goel and Kazuhira Okumoto","url":"https://scholargate.app/en/software-engineering/software-reliability-model","markdownUrl":"https://scholargate.app/en/software-engineering/software-reliability-model.md","definition":"Software reliability models predict the behavior of failure rates during testing and operation, estimating when software achieves required reliability targets. Introduced by Goel and Okumoto (1979), these stochastic models capture how defect discovery declines as testing progresses. Organizations use reliability models to forecast release readiness, estimate testing duration, and validate quality achievement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alok Goel and Kazuhira Okumoto","subfamily":"Reliability prediction","year":"1979","type":"stochastic model"},"citations":[{"ref":"Goel, A. L., & Okumoto, K. (1979). Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Transactions on Reliability, 28(3), 206–211.","type":"article","doi":"10.1109/TR.1979.5220566","isbn":null,"url":null},{"ref":"Musa, J. D., Iannino, A., & Okumoto, K. (1987). Software Reliability: Measurement, Prediction, Application. McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/softwarereliabil0000musa"},{"ref":"Yamada, S., Ohera, H., & Narihisa, H. (1984). Software reliability growth with a Weibull test-effort: A model and application. IEEE Transactions on Reliability, 33(2), 117–123.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Software+reliability+growth+with+a+Weibull+test-effort%3A+A+model+and+application+Yamada"}],"related":["defect-prediction-model","code-coverage-analysis","software-testing-equivalence","software-complexity-metrics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"software-testing-equivalence","name":"Equivalence Partitioning Testing","fullName":"Equivalence Class Partitioning and Boundary Value Testing","aliases":["equivalence partitioning","BVA","boundary value analysis"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Test case design","year":"1979","originator":"Glenford Myers","url":"https://scholargate.app/en/software-engineering/software-testing-equivalence","markdownUrl":"https://scholargate.app/en/software-engineering/software-testing-equivalence.md","definition":"Equivalence partitioning divides input domains into equivalence classes—sets of inputs expected to behave identically—then selects test cases from each class. Introduced by Myers (1979), this technique reduces test cases while maintaining effectiveness. Boundary value analysis (BVA) complements partitioning by testing values at partition boundaries where failures often occur.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glenford Myers","subfamily":"Test case design","year":"1979","type":"partitioning strategy"},"citations":[{"ref":"Myers, G. J. (1979). The Art of Software Testing. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/artofsoftwarete00myer"},{"ref":"Beizer, B. (1990). Software Testing Techniques (2nd ed.). International Thomson Computer Press.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/softwaretest00beiz"},{"ref":"Coppit, D., & Leavens, G. T. (2003). Practical implications of simpler, more scalable path-sensitive data flow analyses. ACM Transactions on Software Engineering and Methodology, 12(3), 261–306.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Practical+implications+of+simpler%2C+more+scalable+path-sensitive+data+flow+analyses+Coppit"}],"related":["code-coverage-analysis","software-complexity-metrics","defect-prediction-model","static-code-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soil-fertility-management","name":"Soil Fertility Management","fullName":"Soil Fertility Assessment and Nutrient Recommendation System","aliases":["Soil nutrient management","Fertility program design","Soil test interpretation"],"domain":"agronomy","family":"process-pipeline","subfamily":"Soil testing and interpretation","year":"1990","originator":"Soil fertility testing institutions (ICAR, CSREES, regional extension)","url":"https://scholargate.app/en/agronomy/soil-fertility-management","markdownUrl":"https://scholargate.app/en/agronomy/soil-fertility-management.md","definition":"Soil Fertility Management is a diagnostic and prescriptive pipeline for assessing soil nutrient status via laboratory testing, interpreting results against crop-specific nutrient requirements, and recommending fertilizer or amendment rates. Formalized by soil testing institutions (ICAR, USDA-CSREES) and widely adopted globally, this method supports efficient nutrient application and cost-effective crop production.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Soil fertility testing institutions (ICAR, CSREES, regional extension)","subfamily":"Soil testing and interpretation","year":"1990","type":"Diagnostic and prescriptive pipeline"},"citations":[{"ref":"Tandon, H. L. (1997). Phosphorus research and agricultural production in India. ICAR, New Delhi.","type":"article","doi":null,"isbn":null,"url":"https://www.fertilizerinstitute.org/phosphorus"},{"ref":"Kamprath, E. J., & Watson, M. E. (1980). Conventional soil and tissue tests for assessing the phosphorus status of soils. In The role of phosphorus in agriculture. American Society of Agronomy, Madison, WI.","type":"article","doi":null,"isbn":null,"url":"https://acsess.onlinelibrary.wiley.com/"}],"related":["nitrogen-use-efficiency","crop-growth-simulation","crop-yield-estimation","irrigation-scheduling-etref","precision-agriculture-ndvi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soil-moisture-curve","name":"Soil Moisture Curve","fullName":"Soil Water Retention Curve (Soil Moisture Characteristic Curve)","aliases":["Water Retention Curve","pF Curve","Characteristic Curve","SWRC"],"domain":"agronomy","family":"process-pipeline","subfamily":"Soil Hydrology","year":"1956-1980","originator":"Willard Robert Gardner, Rollin H. Brooks, Arthur T. Corey","url":"https://scholargate.app/en/agronomy/soil-moisture-curve","markdownUrl":"https://scholargate.app/en/agronomy/soil-moisture-curve.md","definition":"The soil moisture curve (or soil water retention curve, SWRC) describes the relationship between soil water content and soil matric potential (water tension). It characterizes how tightly water is bound in pores of different sizes: large pores drain at low tensions (wet soils), while smaller pores retain water at high tensions (dry soils). Quantifying this relationship is essential for water balance modeling, unsaturated flow prediction, and assessing plant-available water.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Willard Robert Gardner, Rollin H. Brooks, Arthur T. Corey","subfamily":"Soil Hydrology","year":"1956-1980","type":"Empirical soil water retention model"},"citations":[{"ref":"Gardner, W. R. (1956). Representation of soil aggregate-size distribution by a logarithmic-normal distribution. Soil Science Society of America Journal, 20(2), 151-153.","type":"article","doi":"10.2136/sssaj1956.03615995002000020003x","isbn":null,"url":null},{"ref":"Brooks, R. H., & Corey, A. T. (1964). Hydraulic properties of porous media. Hydrology Papers No. 3, Colorado State University, Fort Collins.","type":"article","doi":null,"isbn":null,"url":"https://mountainscholar.org/handle/10217/61189"},{"ref":"van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892-898.","type":"article","doi":"10.2136/sssaj1980.03615995004400050002x","isbn":null,"url":null}],"related":["crop-growth-model","penman-monteith-equation","soil-respiration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soil-remediation","name":"Soil Remediation","fullName":"In Situ and Ex Situ Soil Contamination Treatment Technologies","aliases":["soil cleanup","contaminated land treatment","remedial technologies","soil restoration"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Hazardous site remediation","year":"1983","originator":"EPA and state environmental agencies","url":"https://scholargate.app/en/environmental-engineering/soil-remediation","markdownUrl":"https://scholargate.app/en/environmental-engineering/soil-remediation.md","definition":"Soil remediation encompasses a suite of technologies and strategies to treat contaminated soil at sites with elevated levels of organic compounds, heavy metals, radionuclides, or other hazardous substances. Systematized by the US EPA in the 1980s following industrial accidents and legacy contamination discoveries, soil remediation methods range from in situ (biological, chemical, thermal) to ex situ (excavation, treatment, off-site disposal) approaches. The selection process integrates site characterization, contaminant bioavailability, regulatory risk thresholds, and cost-benefit analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"EPA and state environmental agencies","subfamily":"Hazardous site remediation","year":"1983","type":"technology selection and design pipeline"},"citations":[{"ref":"Twardowska, I., Allen, H. E., Häggblom, M. M., & Stefaniak, S. (Eds.). (2004). Soil and Water Pollution Monitoring, Protection and Remediation (3rd ed.). Springer.","type":"book","doi":null,"isbn":"978-1402003349","url":null},{"ref":"Margesin, R., & Schinner, F. (Eds.). (2005). Manual for Soil Analysis – Monitoring and Assessing Soil Bioremediation. Springer.","type":"book","doi":null,"isbn":"978-3540253990","url":null},{"ref":"US Environmental Protection Agency. (2012). Remediation Technologies Screening Matrix and Reference Guide (4th ed.). EPA 542-B-12-001.","type":"article","doi":null,"isbn":null,"url":"https://www.epa.gov/remedytech/remediation-technologies-screening-matrix"}],"related":["groundwater-contamination-model","environmental-impact-assessment","heavy-metal-speciation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soil-respiration","name":"Soil Respiration Measurement","fullName":"Soil Respiration Measurement (Soil CO2 Efflux Quantification)","aliases":["soil CO2 efflux measurement","soil carbon flux measurement","belowground respiration measurement","soil surface CO2 flux measurement"],"domain":"agronomy","family":"process-pipeline","subfamily":"Soil carbon cycling","year":"Mid-20th century (chamber methods formalised ~1950s–1970s; automated systems ~1990s)","originator":"Multiple contributors","url":"https://scholargate.app/en/agronomy/soil-respiration","markdownUrl":"https://scholargate.app/en/agronomy/soil-respiration.md","definition":"Soil respiration measurement quantifies the rate at which CO2 is released from the soil surface to the atmosphere, integrating contributions from root respiration and microbial decomposition of organic matter. It is a fundamental technique in agronomy, ecology, and climate science, providing insight into belowground carbon cycling, soil biological activity, and ecosystem carbon balance. Measurements are typically made using static or dynamic chambers placed on the soil surface.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors","year":"Mid-20th century (chamber methods formalised ~1950s–1970s; automated systems ~1990s)","type":"Field measurement technique","dataType":"Gas concentration readings (CO2 ppm), temperature, moisture","subfamily":"Soil carbon cycling"},"citations":[{"ref":"Hanson, P. J., Edwards, N. T., Garten, C. T., & Andrews, J. A. (2000). Separating root and soil microbial contributions to soil respiration: A review of methods and observations. Biogeochemistry, 48(1), 115–146.","type":"journal-article","doi":"10.1023/A:1006244819642","isbn":null,"url":null},{"ref":"Davidson, E. A., Savage, K. E., Verchot, L. V., & Navarro, R. (2002). Minimizing artifacts and biases in chamber-based measurements of soil respiration. Agricultural and Forest Meteorology, 113(1–4), 21–37.","type":"journal-article","doi":"10.1016/S0168-1923(02)00100-4","isbn":null,"url":null}],"related":["carbon-stock-estimation","microbial-biomass-carbon","eddy-covariance","litter-decomposition","soil-enzyme-activity","greenhouse-gas-flux"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soil-structure-interaction","name":"Soil-Structure Interaction","fullName":"Soil-Structure Interaction Analysis","aliases":["SSI analysis","Foundation compliance","Dynamic foundation analysis"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Foundation Analysis","year":"1974","originator":"Artur S. Veletsos","url":"https://scholargate.app/en/civil-engineering/soil-structure-interaction","markdownUrl":"https://scholargate.app/en/civil-engineering/soil-structure-interaction.md","definition":"Soil-structure interaction (SSI) analysis accounts for the dynamic coupling between a structure and its supporting foundation soil, recognizing that the soil is not infinitely rigid. Formalized by Veletsos in 1974, this approach reveals how foundation compliance, radiation damping, and kinematic effects modify the structure's seismic response compared to fixed-base assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Artur S. Veletsos","subfamily":"Foundation Analysis","year":"1974","type":"Dynamic analysis of coupled soil-foundation-structure systems"},"citations":[{"ref":"Veletsos, A. S., & Meek, J. W. (1974). Dynamic behaviour of building-foundation systems. Earthquake Engineering & Structural Dynamics, 3(2), 121-138.","type":"article","doi":"10.1002/eqe.4290030203","isbn":null,"url":null},{"ref":"Wolf, J. P. (1997). Spring-Dashpot-Mass Systems for Foundation Vibrations. Journal of Engineering Mechanics, 123(5), 1031-1039.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spring-Dashpot-Mass+Systems+for+Foundation+Vibrations+Wolf"},{"ref":"Gazetas, G. (1991). Formulas for foundation vibration frequency and damping. Journal of Geotechnical Engineering, 117(9), 1373-1383.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Formulas+for+foundation+vibration+frequency+and+damping+Gazetas"}],"related":["terzaghi-consolidation","modflow","probabilistic-seismic-hazard-analysis","incremental-dynamic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soldier-adaptation-measure","name":"Soldier Adaptation Measure","fullName":"Soldier Adaptation Measure (SAM)","aliases":["SAM"],"domain":"military-psychology","family":"process-pipeline","subfamily":"Military adaptation and resilience","year":2007,"originator":"Bliese, Wright, Adler, Thomas, & Hoge","url":"https://scholargate.app/en/military-psychology/soldier-adaptation-measure","markdownUrl":"https://scholargate.app/en/military-psychology/soldier-adaptation-measure.md","definition":"The Soldier Adaptation Measure is a brief self-report instrument assessing psychological readiness and adaptation to military deployment. Developed by Bliese and colleagues in the context of military mental health surveillance, it measures dimensions of military motivation, unit cohesion, perceived leadership, and psychological well-being during deployment. It is used in pre-deployment, mid-deployment, and post-deployment screening to identify service members struggling with psychological adjustment and to inform unit support and individual intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bliese, Wright, Adler, Thomas, & Hoge","subfamily":"Military adaptation and resilience","year":2007,"type":"Self-report"},"citations":[{"ref":"Bliese, P. D., Wright, K. M., Adler, A. B., Thomas, J. L., & Hoge, C. W. (2007). Validating the Primary Care PTSD Screen in military and veteran populations. Psychological Assessment, 19(2), 176-180.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validating+the+Primary+Care+PTSD+Screen+in+military+and+veteran+populations+Bliese"},{"ref":"Adler, A. B., Bliese, P. D., McGurk, D., Hoge, C. W., & Castro, C. A. (2009). Resilience and the signature wound of the military. Military Medicine, 174(10), 1012-1023.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Resilience+and+the+signature+wound+of+the+military+Adler"}],"related":["deployment-risk-resilience","post-deployment-reintegration","military-identity-scale","pcl-military"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"solid-dispersion","name":"Solid Dispersion","fullName":"Solid Dispersion Formulation Technology","aliases":["solid solution","amorphous dispersion","polymer-based formulation"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Formulation Technology","year":"1971","originator":"William Chiou and Solomon Riegelman","url":"https://scholargate.app/en/pharmacology/solid-dispersion","markdownUrl":"https://scholargate.app/en/pharmacology/solid-dispersion.md","definition":"Solid dispersion is a formulation technique where a poorly soluble drug is molecularly dispersed in a hydrophilic polymer matrix, improving aqueous solubility and bioavailability. Introduced by Chiou and Riegelman in 1971, solid dispersions remain a key strategy for overcoming solubility-limited absorption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Chiou and Solomon Riegelman","subfamily":"Formulation Technology","year":"1971","type":"solubility enhancement"},"citations":[{"ref":"Chiou, W. L., Riegelman, S. (1971). Pharmaceutical applications of solid dispersions. Journal of Pharmaceutical Sciences, 60(9), 1281-1302.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pharmaceutical+applications+of+solid+dispersions+Chiou"},{"ref":"Vasconcelos, T., Sarmento, B., & Costa, P. (2007). Solid dispersions as strategy to improve oral bioavailability of poorly water soluble drugs. Drug Discovery Today, 12(23-24), 1068-1075.","type":"article","doi":"10.1016/j.drudis.2007.09.005","isbn":null,"url":null}],"related":["liposome-encapsulation","caco-2-permeability","dissolution-f1-f2-similarity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"solomon-four-group-design","name":"Solomon Four-Group Design","fullName":"Solomon Four-Group Experimental Design","aliases":["Solomon design","four-group design","Solomon four-group control design","S4GD"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Deneysel desen","year":"1949","originator":"Richard L. Solomon","url":"https://scholargate.app/en/experimental-design/solomon-four-group-design","markdownUrl":"https://scholargate.app/en/experimental-design/solomon-four-group-design.md","definition":"The Solomon Four-Group Design extends the classic pretest-posttest control-group design by adding two groups that receive no pretest, enabling researchers to detect whether the pretest itself alters participants' responses to the treatment. Introduced by Richard L. Solomon in 1949, it remains the gold standard for isolating the independent effect of a pretest and for obtaining unbiased estimates of treatment efficacy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard L. Solomon","year":"1949","type":"True experimental design","dataType":"Continuous or ordinal outcome measures (pre- and post-test scores)","subfamily":"Deneysel desen"},"citations":[{"ref":"Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.","type":"article","doi":"10.1037/h0062958","isbn":null,"url":null},{"ref":"Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.","type":"book","doi":null,"isbn":"978-0395307878","url":null}],"related":["pretest-posttest-experimental-design","control-group-experimental-design","randomized-controlled-trial","factorial-experiment","analysis-of-variance","quasi-experimental-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"somatic-cell-count","name":"Somatic Cell Count","fullName":"Somatic Cell Count in Milk","aliases":["SCC","mastitis indicator","milk quality assessment"],"domain":"veterinary-science","family":"process-pipeline","subfamily":"Diagnostic Assessment","year":"1980","originator":"Veterinary Milk Quality Standards","url":"https://scholargate.app/en/veterinary-science/somatic-cell-count","markdownUrl":"https://scholargate.app/en/veterinary-science/somatic-cell-count.md","definition":"Somatic Cell Count (SCC) is a quantitative measure of the concentration of white blood cells and epithelial cells in milk, used as a primary indicator of udder health and the presence of mastitis in lactating cattle. Standardized by veterinary regulatory agencies worldwide, SCC serves as a non-invasive, cost-effective screening tool in dairy herd management and milk quality assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Veterinary Milk Quality Standards","subfamily":"Diagnostic Assessment","year":"1980","type":"Quantitative Screening Test"},"citations":[{"ref":"Philippidis, G. P., Frey, H. R., & Klug, C. (2016). Somatic cell count and milk production in dairy cattle. Journal of Dairy Science, 99(2), 1419-1427.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Somatic+cell+count+and+milk+production+in+dairy+cattle+Philippidis"},{"ref":"Pyörälä, S. (2003). Indicators of inflammation in the diagnosis of mastitis. Veterinary Research, 34(5), 565-578.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Indicators+of+inflammation+in+the+diagnosis+of+mastitis+Py%C3%B6r%C3%A4l%C3%A4"},{"ref":"FAO Animal Production and Health Division (2019). Milk quality and livestock standards. Food and Agriculture Organization of the United Nations.","type":"article","doi":null,"isbn":null,"url":"https://www.fao.org/livestock-systems"}],"related":["body-condition-scoring","animal-blup","polysomnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"somatic-symptom-scale-8","name":"Somatic Symptom Scale-8","fullName":"Somatic Symptom Scale-8 (SSS-8)","aliases":["SSS-8","Somatic Symptom Scale"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"somatic-symptom-assessment","year":"2014","originator":"Bernd Gierk","url":"https://scholargate.app/en/clinical-psychology/somatic-symptom-scale-8","markdownUrl":"https://scholargate.app/en/clinical-psychology/somatic-symptom-scale-8.md","definition":"The Somatic Symptom Scale-8 is a brief eight-item self-report instrument designed by Bernd Gierk and colleagues to assess the severity and burden of somatic (bodily) symptoms. Published in JAMA Internal Medicine in 2014, the SSS-8 is derived from the longer Somatic Symptom Disorder-B Criteria Scale and serves as a rapid screening tool for somatic symptom disorder and medically unexplained symptoms. It is widely used in primary care, general medicine, and psychiatry to identify patients whose physical complaints may warrant psychological or behavioral intervention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernd Gierk","subfamily":"somatic-symptom-assessment","year":"2014","type":"Self-report questionnaire"},"citations":[{"ref":"Gierk, B., Kohlmann, S., Kroenke, K., Spangenberg, L., Zhan, Y., Scherer, M., & Herzberg, P. Y. (2014). The Somatic Symptom Scale-8 (SSS-8): a brief measure of somatic symptom burden. JAMA Internal Medicine, 174(3), 399–407.","type":"article","doi":"10.1001/jamainternmed.2013.12179","isbn":null,"url":null},{"ref":"Kroenke, K., Spitzer, R. L., Williams, J. B., & Löwe, B. (2010). The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. General Hospital Psychiatry, 32(4), 345–359.","type":"article","doi":"10.1016/j.genhosppsych.2010.03.006","isbn":null,"url":null},{"ref":"Toussaint, A., Murray, A. M., Voigt, K., Gierk, B., Kroenke, K., Scherer, M., & Lowe, B. (2016). Development and validation of the Somatic Symptom Disorder-B Criteria Scale (SSD-12). Psychosomatic Medicine, 78(1), 5–12.","type":"article","doi":"10.1097/PSY.0000000000000240","isbn":null,"url":null}],"related":["phq-9","quick-inventory-depressive","patient-global-impression-change"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"somers-d","name":"Somers' D","fullName":"Somers' D (Asymmetric Ordinal Association Measure)","aliases":["Somers D","Somers' delta","d_YX","asymmetric Kendall tau","ordinal association coefficient"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1962,"originator":"Robert H. Somers","url":"https://scholargate.app/en/statistics/somers-d","markdownUrl":"https://scholargate.app/en/statistics/somers-d.md","definition":"Somers' D is an asymmetric ordinal association coefficient, introduced by Robert H. Somers in 1962, that quantifies how well one ordinal variable predicts another by measuring the excess of concordant over discordant pairs relative to all pairs that are not tied on the designated independent variable. It is the standard companion to Kendall's tau in ordinal regression and is central to ROC curve analysis and the c-statistic in logistic regression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert H. Somers","year":1962,"family":"Association measure","type":"Asymmetric ordinal association coefficient","range":"[-1, 1]","parametric":false,"dependentVariable":"ordinal","independentVariable":"ordinal","asymmetric":true,"concordanceBased":true,"relatedTo":"Kendall tau-b"},"citations":[{"ref":"Somers, R. H. (1962). A new asymmetric measure of association for ordinal variables. American Sociological Review, 27(6), 799–811.","type":"article","doi":"10.2307/2090408","isbn":null,"url":null},{"ref":"Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470082898","url":null},{"ref":"Harrell, F. E. (2001). Regression Modeling Strategies. Springer.","type":"book","doi":null,"isbn":"978-0387952321","url":null}],"related":["kendall-tau-b","kendall-tau-c","goodman-kruskal-gamma","spearman-rho","mann-whitney-u","wilcoxon-rank-sum"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sonar-equation","name":"Sonar Equation","fullName":"Sonar Equation for Underwater Acoustic Detection and Localization","aliases":["active sonar equation","passive sonar equation","underwater detection","acoustic range equation"],"domain":"acoustics","family":"process-pipeline","subfamily":"Underwater acoustics","year":"1983","originator":"Robert Urick","url":"https://scholargate.app/en/acoustics/sonar-equation","markdownUrl":"https://scholargate.app/en/acoustics/sonar-equation.md","definition":"The sonar equation is a fundamental framework for predicting the detection range and performance of active and passive sonar systems in underwater environments. Systematized by Robert Urick in his seminal 1983 work, the sonar equation quantifies the acoustic signal-to-noise ratio (SNR) needed for detection, accounting for source level, propagation loss, noise characteristics, and receiver sensitivity. It is the cornerstone of underwater acoustic system design, naval detection systems, marine research, and subsea communication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Urick","subfamily":"Underwater acoustics","year":"1983","type":"Underwater acoustic detection framework"},"citations":[{"ref":"Urick, R. J. (1983). Principles of Underwater Sound (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070660816","url":null},{"ref":"Burdic, W. S. (1984). Underwater Acoustic System Analysis (2nd ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0135364529","url":null},{"ref":"Medwin, H., & Clay, C. S. (1992). Fundamentals of Acoustical Oceanography. Academic Press.","type":"book","doi":null,"isbn":"978-0125017305","url":null}],"related":["beamforming","acoustic-ray-tracing","room-impulse-response","fxlms-active-noise-control","speech-intelligibility"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sorensen-dice-coefficient","name":"Sorensen-Dice Coefficient","fullName":"Sorensen-Dice Similarity Coefficient","aliases":["Dice coefficient","Czekanowski index","F1 similarity"],"domain":"decision-making","family":"mcdm","subfamily":"Set similarity and presence-absence index","year":"1945","originator":"Thorvald Sorensen and Lee Dice","url":"https://scholargate.app/en/decision-making/sorensen-dice-coefficient","markdownUrl":"https://scholargate.app/en/decision-making/sorensen-dice-coefficient.md","definition":"Sorensen-Dice coefficient, also called Dice coefficient or Czekanowski index, measures the similarity between two sets or samples based on presence and absence of attributes. Introduced independently by Thorvald Sorensen (1948) and Lee Dice (1945), this index ranges from 0 (completely dissimilar) to 1 (identical). It is particularly well-suited for binary presence-absence data and is the symmetric counterpart to the Bray-Curtis dissimilarity for abundance data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thorvald Sorensen and Lee Dice","subfamily":"Set similarity and presence-absence index","year":"1945","type":"Binary and compositional similarity measure"},"citations":[{"ref":"Sorensen, T. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske Skrifter, 5, 1-34.","type":"article","doi":null,"isbn":null,"url":"https://www.biodiversitylibrary.org/page/19285559"},{"ref":"Dice, L. R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297-302.","type":"article","doi":"10.2307/1932409","isbn":null,"url":null}],"related":["jaccard-similarity","bray-curtis-dissimilarity","sorensen-dice-distance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sound-transmission-class","name":"Sound Transmission Class","fullName":"Sound Transmission Class (STC) Rating and Measurement","aliases":["STC","sound transmission loss","acoustic isolation"],"domain":"acoustics","family":"process-pipeline","subfamily":"Acoustic isolation","year":"1961","originator":"ASTM International","url":"https://scholargate.app/en/acoustics/sound-transmission-class","markdownUrl":"https://scholargate.app/en/acoustics/sound-transmission-class.md","definition":"Sound Transmission Class (STC) is a single-number rating used to describe how well building elements (walls, doors, windows) reduce sound transmission between adjacent spaces. Standardized by ASTM International and ISO, STC is calculated from sound transmission loss (STL) measurements across the speech frequency range (125 Hz–4 kHz). It is the primary metric used in building codes, product specifications, and acoustic design to ensure privacy, noise control, and occupant comfort.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"ASTM International","subfamily":"Acoustic isolation","year":"1961","type":"Building partition acoustic rating"},"citations":[{"ref":"ASTM E413-16 (2016). Classification for Rating Sound Insulation. American Society for Testing and Materials.","type":"standard","doi":null,"isbn":null,"url":"https://www.astm.org/standards/e413"},{"ref":"ISO 717-1 (1996). Rating of Sound Insulation for Buildings and Building Elements. International Organization for Standardization.","type":"standard","doi":null,"isbn":null,"url":"https://www.iso.org/standard/5429.html"},{"ref":"ASTM E2179-16 (2016). Standard Test Method for Measuring Sound Transmission Loss of Building Partitions by the Reverberation Method. American Society for Testing and Materials.","type":"standard","doi":null,"isbn":null,"url":"https://www.astm.org/standards/e2179"}],"related":["acoustic-ray-tracing","impedance-tube","bem-acoustics","room-impulse-response","psychoacoustic-masking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sowa","name":"SOWA","fullName":"Spatial Ordered Weighted Averaging","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2003/2006","originator":"Makropoulos, C. K.; Butler, D.","url":"https://scholargate.app/en/decision-making/sowa","markdownUrl":"https://scholargate.app/en/decision-making/sowa.md","definition":"SOWA (Spatial Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Makropoulos, C. K.; Butler, D. in 2003/2006. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Makropoulos, C. K.; Butler, D.","subfamily":"Ranking","year":"2003/2006","type":"Zone-stratified OWA — order weights λ_k vary per spatial zone, encoding spatially heterogeneous risk attitudes (ORness) while criterion weights w_k remain global","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Makropoulos, C. K., Butler, D. (2006). Spatial ordered weighted averaging: incorporating spatially variable attitude towards risk in spatial multi-criteria decision-making. Environmental Modelling & Software","type":"article","doi":"10.1016/j.envsoft.2004.10.010","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","swara","fucom","merec"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sowing-date-optimization","name":"Sowing Date Optimization","fullName":"Crop Sowing Date Decision Framework and Timing Optimization","aliases":["Planting date optimization","Sowing time selection","Phenological timing"],"domain":"agronomy","family":"process-pipeline","subfamily":"Crop timing and scheduling","year":"2006","originator":"P. K. Aggarwal, N. Kalra, IARI India","url":"https://scholargate.app/en/agronomy/sowing-date-optimization","markdownUrl":"https://scholargate.app/en/agronomy/sowing-date-optimization.md","definition":"Sowing Date Optimization is a decision support pipeline for determining optimal crop planting dates that align phenological development with favorable environmental windows, maximizing yield and reducing climate risk. Developed by crop modelers (Aggarwal, Semenov) in the 2000s, this method combines crop simulation, climate data, and risk analysis to identify safe, profitable sowing windows.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"P. K. Aggarwal, N. Kalra, IARI India","subfamily":"Crop timing and scheduling","year":"2006","type":"Decision support pipeline"},"citations":[{"ref":"Aggarwal, P. K., Kalra, N., Chander, S., & Pathak, H. (2006). InfoCrop: A decision support system for crop planning and resource management at farm level. Agricultural Systems, 88(1), 56-77.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=InfoCrop%3A+A+decision+support+system+for+crop+planning+and+resource+management+at+farm+level+Aggarwal"},{"ref":"Semenov, M. A., & Porter, J. R. (2000). Climatic variability and the modelling of crop yields. Agricultural and Forest Meteorology, 100(2-3), 149-167.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Climatic+variability+and+the+modelling+of+crop+yields+Semenov"}],"related":["crop-growth-simulation","phenological-observation","crop-yield-estimation","irrigation-scheduling-etref","nitrogen-use-efficiency"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"soxhlet-extraction","name":"Soxhlet Extraction","fullName":"Soxhlet Extraction","aliases":[],"domain":"food-science","family":"process-pipeline","subfamily":"Extraction Chemistry","year":"1879","originator":"Franz Soxhlet","url":"https://scholargate.app/en/food-science/soxhlet-extraction","markdownUrl":"https://scholargate.app/en/food-science/soxhlet-extraction.md","definition":"Soxhlet Extraction is a continuous solvent extraction method developed by Franz Soxhlet in 1879 for determining fat and lipid content in foods. Using a specialized glassware apparatus, Soxhlet repeatedly cycles hot solvent through a food sample, extracting lipids with high efficiency. It remains the official standard method for fat determination in numerous food standards and is widely used in food analysis and quality control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Franz Soxhlet","subfamily":"Extraction Chemistry","year":"1879","type":"Solvent Extraction Method"},"citations":[{"ref":"Soxhlet, F. von (1879). Die Gewichtsbestimmung des Kaffein-Alkaloides. Dingler's Polytechnisches Journal, 232, 461-464.","type":"article","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Rüdiger, H. W. (2011). Extraction and determination of fats. In S. S. Nielsen (Ed.), Food analysis (4th ed., pp. 153-167). Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Extraction+and+determination+of+fats+R%C3%BCdiger"}],"related":["hplc","karl-fischer-titration","supercritical-fluid-extraction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-syntax-analysis","name":"Space Syntax Analysis","fullName":"Space Syntax Analysis for Spatial Configuration Assessment","aliases":["spatial configuration analysis","graph-based space analysis"],"domain":"architecture","family":"process-pipeline","subfamily":"Spatial analysis","year":"1984","originator":"Bill Hillier, Julienne Hanson","url":"https://scholargate.app/en/architecture/space-syntax-analysis","markdownUrl":"https://scholargate.app/en/architecture/space-syntax-analysis.md","definition":"Space Syntax Analysis is a quantitative method for assessing spatial configuration in buildings and urban environments through graph-based representations. Developed by Bill Hillier and Julienne Hanson in the 1980s, it quantifies how spatial layout affects human movement, visibility, and social interaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bill Hillier, Julienne Hanson","subfamily":"Spatial analysis","year":"1984","type":"graph-based spatial assessment method"},"citations":[{"ref":"Hillier, B. (1984). The Social Logic of Space. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511597237","isbn":null,"url":null},{"ref":"Hillier, B. (1996). Space is the Machine: A Configurational Theory of Architecture. Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/search?query=hillier+space+machine+1996"},{"ref":"Turner, A. (2001). Depthmap: A Program to Perform Visibility Graph Analysis. Proceedings of the 3rd International Symposium on Space Syntax.","type":"article","doi":null,"isbn":null,"url":"https://www.bartlett.ucl.ac.uk/bartlett/research/projects/depthmap"}],"related":["urban-form-analysis","wayfinding-analysis","post-occupancy-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-syntax","name":"Space Syntax","fullName":"Space Syntax Analysis","aliases":["spatial analysis","accessibility analysis"],"domain":"archaeology","family":"process-pipeline","subfamily":"Spatial Analysis","year":"1984","originator":"Bill Hillier","url":"https://scholargate.app/en/archaeology/space-syntax","markdownUrl":"https://scholargate.app/en/archaeology/space-syntax.md","definition":"Space syntax is a quantitative method that analyzes the spatial configuration of buildings and settlements to understand social organization and movement patterns. Developed by Bill Hillier and Julienne Hanson in the 1980s, space syntax measures how open or segregated spaces are, and how these properties relate to social behavior and cultural values. The method reveals distinctions between public and private spaces, movement corridors, and the degree of accessibility within architectural layouts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bill Hillier","subfamily":"Spatial Analysis","year":"1984","type":"Architectural analysis"},"citations":[{"ref":"Hillier, B., & Hanson, J. (1984). The Social Logic of Space. Cambridge University Press.","type":"book","doi":"10.1017/CBO9780511597237","isbn":null,"url":null},{"ref":"Bafna, S. (2003). Space syntax: a brief introduction to its logic and analytical techniques. Environment and Behavior, 35(1), 17-29.","type":"article","doi":"10.1177/0013916502238863","isbn":null,"url":null}],"related":["viewshed-analysis","predictive-site-location"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-gearys-c","name":"Space-Time Geary's C","fullName":"Space-Time Geary's Contiguity Ratio","aliases":["ST-Geary's C","spatiotemporal Geary C","space-time contiguity ratio","space-time local spatial autocorrelation"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1954 / 2010s","originator":"Geary (1954); extended to space-time by Anselin and others","url":"https://scholargate.app/en/spatial-analysis/space-time-gearys-c","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-gearys-c.md","definition":"Space-Time Geary's C extends the classical Geary contiguity ratio to panel or longitudinal spatial data, measuring autocorrelation across both geographic neighbors and adjacent time periods simultaneously. Values below 1 indicate positive space-time clustering; values above 1 indicate dispersion, and a value near 1 suggests random arrangement across the space-time lattice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Geary (1954); extended to space-time by Anselin and others","year":"1954 / 2010s","type":"Spatial autocorrelation statistic","dataType":"Georeferenced panel data (areal or point data across repeated time periods)","subfamily":"GIS / spatial"},"citations":[{"ref":"Geary, R. C. (1954). The Contiguity Ratio and Statistical Mapping. The Incorporated Statistician, 5(3), 115-145.","type":"article","doi":"10.2307/2986645","isbn":null,"url":null},{"ref":"Anselin, L. (2019). A Local Indicator of Multivariate Spatial Association: Extending Geary's C. Geographical Analysis, 51(2), 133-150.","type":"article","doi":"10.1111/gean.12164","isbn":null,"url":null}],"related":["gearys-c","morans-i","space-time-morans-i","local-gearys-c","space-time-local-indicators-of-spatial-association","space-time-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-getis-ord-gi-star","name":"Space-Time Getis-Ord Gi*","fullName":"Space-Time Getis-Ord Gi* Hot Spot Statistic","aliases":["ST-Gi*","space-time hot spot analysis","emerging hot spot analysis","space-time local autocorrelation statistic"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1992 (Gi*); space-time extension ~2000s–2010s","originator":"Getis & Ord (seminal); space-time extension developed in GIS literature and ArcGIS Emerging Hot Spot Analysis","url":"https://scholargate.app/en/spatial-analysis/space-time-getis-ord-gi-star","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-getis-ord-gi-star.md","definition":"The Space-Time Getis-Ord Gi* statistic extends the classic Gi* local hot spot measure into three dimensions — two spatial and one temporal — revealing not only where concentrations of high or low values cluster, but how those clusters evolve, intensify, or diminish over time. It is widely used in crime analysis, epidemiology, ecology, and urban studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Getis & Ord (seminal); space-time extension developed in GIS literature and ArcGIS Emerging Hot Spot Analysis","year":"1992 (Gi*); space-time extension ~2000s–2010s","type":"Local spatial statistic (space-time extension)","dataType":"Georeferenced point or areal count data observed at multiple time periods","subfamily":"GIS / spatial"},"citations":[{"ref":"Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.","type":"article","doi":"10.1111/j.1538-4632.1992.tb00261.x","isbn":null,"url":null},{"ref":"Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00912.x","isbn":null,"url":null}],"related":["local-getis-ord-gi-star","hot-spot-analysis","space-time-local-indicators-of-spatial-association","space-time-moran-i","kernel-density-estimation","space-time-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-hot-spot-analysis","name":"Space-Time Hot Spot Analysis","fullName":"Space-Time Hot Spot Analysis (Emerging Hot Spot Analysis)","aliases":["emerging hot spot analysis","space-time cube hot spot","spatiotemporal hot spot detection","STHA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1997–2015","originator":"Kulldorff (spatial scan statistic); operationalized for time-series bins by Esri (Emerging Hot Spot Analysis)","url":"https://scholargate.app/en/spatial-analysis/space-time-hot-spot-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-hot-spot-analysis.md","definition":"Space-Time Hot Spot Analysis extends the classic Getis-Ord Gi* statistic across repeated time slices organised in a space-time cube. By testing each location-time bin for statistically significant clustering of high or low values, then examining the sequence of results over time, it identifies whether clusters are new, intensifying, persistent, sporadic, or diminishing — giving analysts a dynamic picture of how hot and cold spots evolve.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kulldorff (spatial scan statistic); operationalized for time-series bins by Esri (Emerging Hot Spot Analysis)","year":"1997–2015","type":"Spatiotemporal cluster detection","dataType":"Georeferenced time-series counts or rates aggregated into a space-time cube","subfamily":"GIS / spatial"},"citations":[{"ref":"Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26(6), 1481–1496.","type":"article","doi":"10.1080/03610929708831995","isbn":null,"url":null},{"ref":"Emerging Hot Spot Analysis. ArcGIS Pro Documentation. Esri Inc., 2021.","type":"misc","doi":null,"isbn":null,"url":"https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/emerginghotspotanalysis.htm"}],"related":["hot-spot-analysis","local-getis-ord-gi-star","space-time-local-indicators-of-spatial-association","kernel-density-estimation","space-time-morans-i","local-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-kernel-density-estimation","name":"Space-Time Kernel Density Estimation","fullName":"Space-Time Kernel Density Estimation","aliases":["ST-KDE","spatiotemporal kernel density estimation","space-time KDE","3D kernel density estimation"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2010 (space-time extension); 1956 (KDE origin)","originator":"Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen","url":"https://scholargate.app/en/spatial-analysis/space-time-kernel-density-estimation","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-kernel-density-estimation.md","definition":"Space-Time Kernel Density Estimation extends classical KDE into three dimensions — two spatial and one temporal — to reveal how the intensity of point events (crimes, accidents, disease cases) varies continuously across both geographic space and time. It produces a smooth probabilistic surface that highlights where and when events concentrate most densely.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen","year":"2010 (space-time extension); 1956 (KDE origin)","type":"Non-parametric density estimation","dataType":"Point event data with timestamps and coordinates","subfamily":"GIS / spatial"},"citations":[{"ref":"Nakaya, T., & Yano, K. (2010). Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14(3), 223-239.","type":"article","doi":"10.1111/j.1467-9671.2010.01194.x","isbn":null,"url":null},{"ref":"Kernel density estimation. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Kernel_density_estimation"}],"related":["kernel-density-estimation","hot-spot-analysis","space-time-local-indicators-of-spatial-association","space-time-getis-ord-gi-star","space-time-spatial-autocorrelation","local-kernel-density-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-kriging","name":"Space-Time Kriging","fullName":"Space-Time Kriging","aliases":["spatiotemporal kriging","ST-kriging","space-time geostatistical interpolation","kriging in space-time"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1999","originator":"Cressie & Huang; Kyriakidis & Journel","url":"https://scholargate.app/en/spatial-analysis/space-time-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-kriging.md","definition":"Space-Time Kriging is a geostatistical interpolation method that predicts an unknown variable at any location and time by borrowing strength from nearby observations in both space and time simultaneously. It models the joint spatial-temporal covariance structure through a space-time variogram, then uses optimal linear weights to produce predictions with quantified uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cressie & Huang; Kyriakidis & Journel","year":"1999","type":"Geostatistical interpolation","dataType":"Georeferenced time-series observations (point or areal data with timestamps)","subfamily":"GIS / spatial"},"citations":[{"ref":"Cressie, N., & Huang, H.-C. (1999). Classes of nonseparable, spatio-temporal stationary covariance functions. Journal of the American Statistical Association, 94(448), 1330-1340.","type":"article","doi":"10.1080/01621459.1999.10473885","isbn":null,"url":null},{"ref":"Kyriakidis, P. C., & Journel, A. G. (1999). Geostatistical space-time models: A review. Mathematical Geology, 31(6), 651-684.","type":"article","doi":"10.1023/A:1007528426688","isbn":null,"url":null}],"related":["ordinary-kriging","universal-kriging","co-kriging","space-time-spatial-autocorrelation","kernel-density-estimation","space-time-geographically-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-local-indicators-of-spatial-association","name":"Space-Time Local Indicators of Spatial Association","fullName":"Space-Time Local Indicators of Spatial Association","aliases":["ST-LISA","space-time LISA","spatiotemporal local indicators of spatial association","STLISA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1995 (LISA); space-time extensions developed 2000s–2010s","originator":"Extension of Anselin (1995) LISA framework to the space-time domain","url":"https://scholargate.app/en/spatial-analysis/space-time-local-indicators-of-spatial-association","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-local-indicators-of-spatial-association.md","definition":"Space-Time Local Indicators of Spatial Association (ST-LISA) extend the classic LISA framework of Anselin (1995) into the temporal dimension, identifying locations that exhibit statistically significant spatial clustering or spatial outlier behavior consistently or intermittently across multiple time periods. They decompose global space-time autocorrelation into local contributions, revealing where and when spatial clusters emerge, persist, or dissolve.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Anselin (1995) LISA framework to the space-time domain","year":"1995 (LISA); space-time extensions developed 2000s–2010s","type":"Local spatial statistic (space-time)","dataType":"Georeferenced panel or time-series data with spatial coordinates","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.","type":"article","doi":"10.1111/j.1538-4632.1995.tb00338.x","isbn":null,"url":null},{"ref":"Cheng, T., Haworth, J., & Wang, J. (2012). Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, 14(4), 389–413.","type":"article","doi":"10.1007/s10109-011-0149-5","isbn":null,"url":null}],"related":["local-indicators-of-spatial-association","local-morans-i","space-time-spatial-autocorrelation","space-time-morans-i","hot-spot-analysis","local-getis-ord-gi-star"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-morans-i","name":"Space-Time Moran's I","fullName":"Space-Time Moran's I Statistic","aliases":["space-time autocorrelation index","ST Moran's I","spatiotemporal Moran's I","space-time I statistic"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1981","originator":"Cliff & Ord (extended to space-time domain)","url":"https://scholargate.app/en/spatial-analysis/space-time-morans-i","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-morans-i.md","definition":"Space-Time Moran's I extends the classic Moran's I statistic into the spatiotemporal domain, measuring whether observations that are close in both space and time tend to be more similar than those that are distant. It detects clustering, dispersion, or randomness across a combined space-time weight matrix, making it a foundational tool in epidemiology, criminology, and environmental monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cliff & Ord (extended to space-time domain)","year":"1981","type":"Spatial autocorrelation statistic","dataType":"Georeferenced time-series data (panel or repeated cross-sections)","subfamily":"GIS / spatial"},"citations":[{"ref":"Cliff, A. D., & Ord, J. K. (1981). Spatial Processes: Models and Applications. Pion.","type":"book","doi":null,"isbn":"978-0850860818","url":null},{"ref":"Kulldorff, M., & Nagarwalla, N. (1997). Spatial disease clusters: detection and inference. Statistics in Medicine, 14(8), 799–810.","type":"article","doi":"10.1002/sim.4780140809","isbn":null,"url":null}],"related":["morans-i","local-morans-i","space-time-geary-c","space-time-getis-ord-gi-star","local-indicators-of-spatial-association","space-time-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-network-based-spatial-analysis","name":"Space-Time Network-Based Spatial Analysis","fullName":"Space-Time Network-Based Spatial Analysis","aliases":["ST-NBA","space-time network analysis","spatiotemporal network analysis","network-based space-time analysis"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1970–2000s","originator":"Torsten Hägerstrand (time-geography foundation); extended by Harvey J. Miller and others for network contexts","url":"https://scholargate.app/en/spatial-analysis/space-time-network-based-spatial-analysis","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-network-based-spatial-analysis.md","definition":"Space-Time Network-Based Spatial Analysis integrates network topology with temporal constraints to model how people, goods, or phenomena move through geographic networks over time. Rooted in Hägerstrand's time-geography, it evaluates accessibility, interaction potential, and movement patterns along real-world infrastructure networks while respecting both spatial distance and time budgets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Torsten Hägerstrand (time-geography foundation); extended by Harvey J. Miller and others for network contexts","year":"1970–2000s","type":"Spatiotemporal network model","dataType":"Movement trajectories, GPS tracks, network-linked event data with timestamps","subfamily":"GIS / spatial"},"citations":[{"ref":"Hägerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24(1), 7–21.","type":"article","doi":"10.1007/BF01936872","isbn":null,"url":null},{"ref":"Miller, H. J. (2005). A measurement theory for time geography. Geographical Analysis, 37(1), 17–45.","type":"article","doi":"10.1111/j.1538-4632.2005.00575.x","isbn":null,"url":null}],"related":["space-time-cube","network-kernel-density-estimation","temporal-gis","spatial-interaction-model","geographically-weighted-regression","trajectory-clustering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-ordinary-kriging","name":"Space-Time Ordinary Kriging","fullName":"Space-Time Ordinary Kriging","aliases":["STOK","spatio-temporal ordinary kriging","ordinary space-time kriging","ST-OK"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1999","originator":"Kyriakidis & Journel (seminal review); Cressie & Huang (covariance models)","url":"https://scholargate.app/en/spatial-analysis/space-time-ordinary-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-ordinary-kriging.md","definition":"Space-Time Ordinary Kriging (STOK) is a geostatistical interpolation method that predicts a spatially and temporally varying phenomenon at unsampled space-time locations by combining the ordinary kriging assumption of an unknown, locally constant mean with a joint space-time covariance (or variogram) structure. It produces optimal, unbiased predictions along with associated estimation uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kyriakidis & Journel (seminal review); Cressie & Huang (covariance models)","year":"1999","type":"Geostatistical interpolation","dataType":"Georeferenced observations with timestamps (point or areal, continuous variable)","subfamily":"GIS / spatial"},"citations":[{"ref":"Kyriakidis, P. C., & Journel, A. G. (1999). Geostatistical space-time models: a review. Mathematical Geology, 31(6), 651-684.","type":"article","doi":"10.1023/A:1007528426688","isbn":null,"url":null},{"ref":"Gräler, B., Pebesma, E., & Heuvelink, G. (2016). Spatio-temporal interpolation using gstat. The R Journal, 8(1), 204-218.","type":"article","doi":null,"isbn":null,"url":"https://journal.r-project.org/archive/2016/RJ-2016-014/index.html"}],"related":["ordinary-kriging","space-time-kriging","space-time-universal-kriging","space-time-co-kriging","kriging","space-time-spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-remote-sensing-classification","name":"Space-Time Remote Sensing Classification","fullName":"Space-Time Remote Sensing Classification","aliases":["multi-temporal remote sensing classification","spatio-temporal image classification","temporal remote sensing analysis","STRSC"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1980s-2000s","originator":"Woodcock, Zhu, and remote sensing community","url":"https://scholargate.app/en/spatial-analysis/space-time-remote-sensing-classification","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-remote-sensing-classification.md","definition":"Space-Time Remote Sensing Classification extends standard image classification to multi-temporal satellite or aerial imagery, enabling analysts to track land cover change, phenological cycles, and environmental dynamics across both space and time. By incorporating the temporal dimension, classifiers achieve higher accuracy and can detect transitions that a single-date analysis would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Woodcock, Zhu, and remote sensing community","year":"1980s-2000s","type":"Multi-temporal image classification","dataType":"Satellite imagery time series, multi-temporal raster data","subfamily":"GIS / spatial"},"citations":[{"ref":"Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370-384.","type":"article","doi":"10.1016/j.isprsjprs.2017.06.013","isbn":null,"url":null},{"ref":"Woodcock, C. E., et al. (2008). Free access to Landsat imagery. Science, 320(5879), 1011-1011.","type":"article","doi":"10.1126/science.320.5879.1011a","isbn":null,"url":null}],"related":["remote-sensing-classification","space-time-spatial-autocorrelation","kernel-density-estimation","hot-spot-analysis","multiscale-remote-sensing-classification","space-time-kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-spatial-autocorrelation","name":"Space-Time Spatial Autocorrelation","fullName":"Space-Time Spatial Autocorrelation Analysis","aliases":["STSA","spatiotemporal autocorrelation","space-time Moran's I","temporal spatial dependence"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1981–1992","originator":"Cliff & Ord; extended by Anselin and others","url":"https://scholargate.app/en/spatial-analysis/space-time-spatial-autocorrelation","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-spatial-autocorrelation.md","definition":"Space-Time Spatial Autocorrelation extends classic spatial autocorrelation measures — most notably Moran's I — to data that vary across both geographic units and time periods. It detects whether nearby locations that are also temporally close tend to share similar attribute values, revealing clusters, trends, or anomalies that purely spatial or purely temporal analyses would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cliff & Ord; extended by Anselin and others","year":"1981–1992","type":"Spatial autocorrelation statistic","dataType":"Georeferenced panel / longitudinal spatial data","subfamily":"GIS / spatial"},"citations":[{"ref":"Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134.","type":"article","doi":"10.2307/2532039","isbn":null,"url":null},{"ref":"Anselin, L., & Getis, A. (1992). Spatial statistical analysis and geographic information systems. The Annals of Regional Science, 26(1), 19–33.","type":"article","doi":"10.1007/BF01581478","isbn":null,"url":null}],"related":["global-morans-i","local-morans-i","spatial-lag-model","geographically-weighted-regression","kriging","spatial-panel-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-spatial-durbin-model","name":"Space-Time Spatial Durbin Model","fullName":"Space-Time Spatial Durbin Model","aliases":["ST-SDM","spatiotemporal Durbin model","spatial Durbin panel model","space-time SDM"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2009-2014","originator":"LeSage & Pace; extended to space-time by Elhorst","url":"https://scholargate.app/en/spatial-analysis/space-time-spatial-durbin-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-spatial-durbin-model.md","definition":"The Space-Time Spatial Durbin Model extends the cross-sectional Spatial Durbin Model to panel data, simultaneously capturing spatial spillovers in both the dependent variable and the explanatory variables across space and over time. It is the most general and flexible specification in the spatial panel family, nesting the spatial lag and spatial error models as special cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeSage & Pace; extended to space-time by Elhorst","year":"2009-2014","type":"Spatial econometric panel model","dataType":"Georeferenced panel (repeated observations across spatial units over time)","subfamily":"GIS / spatial"},"citations":[{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403392","url":null}],"related":["spatial-durbin-model","spatial-lag-model","spatial-error-model","space-time-spatial-lag-model","space-time-spatial-error-model","panel-spatial-durbin-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-spatial-error-model","name":"Space-Time Spatial Error Model","fullName":"Space-Time Spatial Error Model","aliases":["SEM panel","spatial error panel model","space-time SEM","spatiotemporal error model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1988 (SEM); 2003 (panel/space-time extension)","originator":"Anselin (1988); panel extension by Elhorst (2003, 2014)","url":"https://scholargate.app/en/spatial-analysis/space-time-spatial-error-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-spatial-error-model.md","definition":"The Space-Time Spatial Error Model (space-time SEM) is a spatial panel regression technique that accounts for spatial dependence confined to the error term across geographic units and time periods. It corrects biased inference caused by spatially correlated disturbances while estimating covariate effects on a panel of spatial observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin (1988); panel extension by Elhorst (2003, 2014)","year":"1988 (SEM); 2003 (panel/space-time extension)","type":"Spatial panel regression","dataType":"Spatial panel data (multiple units observed over multiple time periods)","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-9024737247","url":null},{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403392","url":null}],"related":["spatial-error-model","space-time-spatial-lag-model","space-time-spatial-durbin-model","panel-spatial-error-model","geographically-weighted-regression","space-time-spatial-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-spatial-lag-model","name":"Space-Time Spatial Lag Model","fullName":"Space-Time Spatial Autoregressive Lag Model","aliases":["ST-SAR","spatial-temporal lag model","spatiotemporal autoregressive model","space-time SAR model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2003-2008","originator":"Anselin, Le Gallo & Jayet; Elhorst","url":"https://scholargate.app/en/spatial-analysis/space-time-spatial-lag-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-spatial-lag-model.md","definition":"The Space-Time Spatial Lag Model extends the classic spatial autoregressive (SAR) lag model to panel data, capturing how the outcome in each location at each time point is influenced by the contemporaneous outcomes of neighboring locations, while also controlling for unit-specific and time-specific fixed effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin, Le Gallo & Jayet; Elhorst","year":"2003-2008","type":"Spatial panel regression","dataType":"Georeferenced panel data (cross-sectional units observed over time)","subfamily":"GIS / spatial"},"citations":[{"ref":"Anselin, L., Le Gallo, J., & Jayet, H. (2008). Spatial Panel Econometrics. In L. Matyas & P. Sevestre (Eds.), The Econometrics of Panel Data (pp. 625-660). Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spatial+Panel+Econometrics+Anselin+Le+Gallo+Jayet+2008"},{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403408","url":null}],"related":["spatial-lag-model","space-time-spatial-error-model","space-time-spatial-durbin-model","panel-spatial-lag-model","geographically-weighted-regression","spatial-autocorrelation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-spatial-panel-model","name":"Space-Time Spatial Panel Model","fullName":"Space-Time Spatial Panel Model","aliases":["ST-SPM","spatiotemporal panel model","space-time panel econometrics","dynamic spatial panel model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"2003–2014","originator":"J. Paul Elhorst","url":"https://scholargate.app/en/spatial-analysis/space-time-spatial-panel-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-spatial-panel-model.md","definition":"The Space-Time Spatial Panel Model extends standard spatial panel econometrics to jointly account for cross-sectional spatial dependence, temporal autocorrelation, and unit-level heterogeneity. It allows outcomes in one location and time period to be influenced by outcomes in neighboring locations and by the location's own past, making it the canonical framework for dynamic spatiotemporal panel data analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. Paul Elhorst","year":"2003–2014","type":"Spatial panel regression","dataType":"Georeferenced panel data (multiple units observed over multiple time periods)","subfamily":"GIS / spatial"},"citations":[{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":null,"isbn":"978-3642403408","url":null},{"ref":"LeSage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press.","type":"book","doi":null,"isbn":"978-1420064247","url":null}],"related":["spatial-lag-model","spatial-error-model","spatial-durbin-model","panel-spatial-panel-model","geographically-weighted-regression","space-time-spatial-lag-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-spatial-regression","name":"Space-Time Spatial Regression","fullName":"Space-Time Spatial Regression Model","aliases":["spatio-temporal regression","spatial panel regression","space-time regression","ST spatial regression"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1990s–2000s","originator":"Anselin, LeSage, Pace and colleagues in spatial econometrics","url":"https://scholargate.app/en/spatial-analysis/space-time-spatial-regression","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-spatial-regression.md","definition":"Space-Time Spatial Regression extends classical spatial regression to panel settings where georeferenced units are observed across multiple time periods. By embedding a spatial weights matrix into a panel regression framework, it simultaneously controls for spatial dependence among cross-sectional units and temporal dynamics, yielding unbiased and consistent estimates in spatio-temporal data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin, LeSage, Pace and colleagues in spatial econometrics","year":"1990s–2000s","type":"Spatio-temporal regression model","dataType":"Georeferenced panel data (cross-sectional units observed over multiple time periods)","subfamily":"GIS / spatial"},"citations":[{"ref":"LeSage, J. P., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"Anselin, L., Le Gallo, J., & Jayet, H. (2008). Spatial Panel Econometrics. In L. Matyas & P. Sevestre (Eds.), The Econometrics of Panel Data (pp. 625-660). Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spatial+Panel+Econometrics+Anselin+Le+Gallo+Jayet+2008"}],"related":["spatial-lag-model","spatial-error-model","spatial-durbin-model","panel-spatial-regression","geographically-weighted-regression","space-time-kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"space-time-universal-kriging","name":"Space-Time Universal Kriging","fullName":"Space-Time Universal Kriging","aliases":["STUK","spatiotemporal universal kriging","space-time kriging with trend","universal kriging in space-time"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1999","originator":"Kyriakidis & Journel (1999); foundations in Matheron's geostatistics","url":"https://scholargate.app/en/spatial-analysis/space-time-universal-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/space-time-universal-kriging.md","definition":"Space-Time Universal Kriging (STUK) is a geostatistical method that interpolates a continuously varying phenomenon across both space and time while explicitly modelling a deterministic trend component. It generalises Universal Kriging to the joint space-time domain, producing unbiased optimal predictions and associated uncertainty estimates at unobserved space-time locations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kyriakidis & Journel (1999); foundations in Matheron's geostatistics","year":"1999","type":"Spatiotemporal geostatistical interpolation","dataType":"Continuous georeferenced observations with timestamps","subfamily":"GIS / spatial"},"citations":[{"ref":"Kyriakidis, P. C., & Journel, A. G. (1999). Geostatistical space-time models: A review. Mathematical Geology, 31(6), 651-684.","type":"article","doi":"10.1023/A:1007528426688","isbn":null,"url":null},{"ref":"Graler, B., Pebesma, E., & Heuvelink, G. (2016). Spatio-temporal interpolation using gstat. The R Journal, 8(1), 204-218.","type":"article","doi":null,"isbn":null,"url":"https://journal.r-project.org/archive/2016/RJ-2016-014/index.html"}],"related":["space-time-ordinary-kriging","ordinary-kriging","universal-kriging","space-time-kriging","geographically-weighted-regression","kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spasticity-assessment-ashworth","name":"Ashworth Scale for Spasticity","fullName":"Modified Ashworth Scale (MAS)","aliases":["MAS","Modified Ashworth","spasticity assessment"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Tone and spasticity assessment","year":"1964","originator":"B. Ashworth","url":"https://scholargate.app/en/physical-therapy/spasticity-assessment-ashworth","markdownUrl":"https://scholargate.app/en/physical-therapy/spasticity-assessment-ashworth.md","definition":"The Modified Ashworth Scale (MAS) is a clinical rating scale for assessing muscle spasticity, quantifying the resistance to passive movement on a 0-4 scale plus an additional grade. Originally developed by B. Ashworth in 1964 and refined by Bohannon and Smith in 1987, the MAS is the most widely used bedside tool for evaluating spasticity in stroke survivors, individuals with cerebral palsy, multiple sclerosis, and spinal cord injury.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"B. Ashworth","subfamily":"Tone and spasticity assessment","year":"1964","type":"Clinical rating scale"},"citations":[{"ref":"Ashworth, B. (1964). Preliminary trial of carisoprodol in multiple sclerosis. Practitioner, 192, 540-542.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/14143329/"},{"ref":"Bohannon, R. W., & Smith, M. B. (1987). Interrater reliability of a modified Ashworth scale of muscle spasticity. Physical Therapy, 67(2), 206-207.","type":"article","doi":"10.1093/ptj/67.2.206","isbn":null,"url":null}],"related":["manual-muscle-testing","range-of-motion-goniometry","neuromuscular-re-education"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-approximate-bayesian-computation","name":"Spatial Approximate Bayesian Computation","fullName":"Spatial Approximate Bayesian Computation","aliases":["Spatial ABC","ABC for spatial data","likelihood-free Bayesian spatial inference","simulation-based spatial inference"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2002 (spatial extensions from mid-2000s)","originator":"Diggle & Gratton (implicit statistical models, 1984); Beaumont, Zhang & Balding (ABC formalization, 2002)","url":"https://scholargate.app/en/bayesian/spatial-approximate-bayesian-computation","markdownUrl":"https://scholargate.app/en/bayesian/spatial-approximate-bayesian-computation.md","definition":"Spatial Approximate Bayesian Computation (Spatial ABC) is a likelihood-free Bayesian inference framework for spatial data models whose likelihood function is intractable or too expensive to evaluate. It draws candidate parameters from a prior, simulates spatially structured datasets under those parameters, and accepts only the draws whose simulated spatial summary statistics closely match the observed data, thereby building an approximate posterior over model parameters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diggle & Gratton (implicit statistical models, 1984); Beaumont, Zhang & Balding (ABC formalization, 2002)","year":"2002 (spatial extensions from mid-2000s)","type":"likelihood-free Bayesian inference","dataType":"georeferenced / areal / point-process spatial data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Beaumont, M. A., Zhang, W., & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.","type":"article","doi":"10.1093/genetics/162.4.2025","isbn":null,"url":null},{"ref":"Diggle, P. J., & Gratton, R. J. (1984). Monte Carlo methods of inference for implicit statistical models. Journal of the Royal Statistical Society: Series B, 46(2), 193–212.","type":"article","doi":"10.1111/j.2517-6161.1984.tb01290.x","isbn":null,"url":null}],"related":["approximate-bayesian-computation","sequential-monte-carlo","spatial-bayesian-inference","spatial-mcmc","gaussian-process-regression","spatial-bayesian-hierarchical-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-autocorrelation","name":"Spatial Autocorrelation","fullName":"Spatial Autocorrelation Analysis","aliases":["spatial dependence","geographic autocorrelation","spatial clustering measure","SA"],"domain":"spatial-analysis","family":"regression-model","subfamily":"GIS / spatial","year":"1950","originator":"P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)","url":"https://scholargate.app/en/spatial-analysis/spatial-autocorrelation","markdownUrl":"https://scholargate.app/en/spatial-analysis/spatial-autocorrelation.md","definition":"Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)","year":"1950","type":"Spatial statistic / exploratory spatial data analysis","dataType":"Georeferenced areal or point data with a continuous or count attribute","subfamily":"GIS / spatial"},"citations":[{"ref":"Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.","type":"article","doi":"10.2307/2332142","isbn":null,"url":null},{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.","type":"book","doi":null,"isbn":"978-9024737322","url":null}],"related":["morans-i","gearys-c","local-indicators-of-spatial-association","local-getis-ord-gi-star","geographically-weighted-regression","kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-bayesian-inference","name":"Spatial Bayesian Inference","fullName":"Spatial Bayesian Inference","aliases":["Bayesian spatial analysis","Bayesian geostatistics","spatial Bayesian modeling","Bayesian areal modeling"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1991","originator":"Besag, York & Mollie (CAR prior, 1991); Gelfand & colleagues (Bayesian geostatistics, 1990s)","url":"https://scholargate.app/en/bayesian/spatial-bayesian-inference","markdownUrl":"https://scholargate.app/en/bayesian/spatial-bayesian-inference.md","definition":"Spatial Bayesian inference applies Bayesian hierarchical modeling to data indexed by geographic location. By placing structured spatial priors on location-specific random effects, the model borrows information from neighboring regions or nearby points, producing smooth, uncertainty-quantified maps of any spatially varying outcome — disease rates, pollution levels, species abundance, or environmental risk.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Besag, York & Mollie (CAR prior, 1991); Gelfand & colleagues (Bayesian geostatistics, 1990s)","year":"1991","type":"Bayesian hierarchical spatial model","dataType":"areal, point-referenced, or point-process spatial data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Banerjee, S., Carlin, B. P. & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439819173","url":null},{"ref":"Besag, J., York, J. & Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1), 1-20.","type":"article","doi":"10.1007/BF00116466","isbn":null,"url":null}],"related":["hierarchical-bayesian-inference","spatial-mcmc","kriging","conditional-autoregressive-model","gaussian-process-regression","spatial-particle-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-bayesian-model-averaging","name":"Spatial Bayesian Model Averaging","fullName":"Spatial Bayesian Model Averaging","aliases":["spatial BMA","BMA for spatial data","Bayesian model averaging with spatial effects","spatial model uncertainty averaging"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2008","originator":"LeSage & Fischer (building on Raftery et al. BMA framework, 1997)","url":"https://scholargate.app/en/bayesian/spatial-bayesian-model-averaging","markdownUrl":"https://scholargate.app/en/bayesian/spatial-bayesian-model-averaging.md","definition":"Spatial Bayesian model averaging (spatial BMA) extends classical BMA to settings where observations are georeferenced and spatial dependence must be modelled. Rather than selecting a single spatial regression model — which spatial weight matrix to use, which regressors to include, which spatial lag or error structure to adopt — it averages the predictions and parameter estimates across all candidate models, weighting each by its posterior probability given the data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeSage & Fischer (building on Raftery et al. BMA framework, 1997)","year":"2008","type":"Bayesian model combination with spatial structure","dataType":"georeferenced / areal / point-referenced cross-sectional or panel data","subfamily":"Bayesian / computational"},"citations":[{"ref":"LeSage, J. P. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis.","type":"book","doi":null,"isbn":"978-1420064247","url":null},{"ref":"Fernandez, C., Ley, E. & Steel, M. F. J. (2001). Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100(2), 381-427.","type":"article","doi":"10.1016/S0304-4076(00)00076-2","isbn":null,"url":null}],"related":["bayesian-model-averaging","spatial-bayesian-inference","spatial-metropolis-hastings","hierarchical-bayesian-inference","spatial-variational-inference","bayesian-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-bootstrap-simulation","name":"Spatial Bootstrap Simulation","fullName":"Spatial Bootstrap Simulation","aliases":["spatial block bootstrap","spatial resampling","geostatistical bootstrap","bootstrap for spatial data"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990s–2000s","originator":"Lahiri and others, building on Efron's bootstrap (1979)","url":"https://scholargate.app/en/bayesian/spatial-bootstrap-simulation","markdownUrl":"https://scholargate.app/en/bayesian/spatial-bootstrap-simulation.md","definition":"Spatial bootstrap simulation is a resampling technique designed for spatially dependent data. By resampling contiguous spatial blocks rather than independent observations, it preserves the local autocorrelation structure of the data and yields valid estimates of sampling variability for statistics computed on geographic or lattice observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lahiri and others, building on Efron's bootstrap (1979)","year":"1990s–2000s","type":"Resampling / simulation","dataType":"Spatially indexed observations (point-referenced or areal)","subfamily":"Bayesian / computational"},"citations":[{"ref":"Lahiri, S. N. (2003). Resampling Methods for Dependent Data. Springer.","type":"book","doi":null,"isbn":"978-0387009285","url":null},{"ref":"Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-0412042317","url":null}],"related":["spatial-mcmc","spatial-bayesian-inference","sequential-monte-carlo","kalman-filter","spatial-particle-filter","robust-bootstrap-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-causal-impact-analysis","name":"Spatial Causal Impact Analysis","fullName":"Spatial Causal Impact Analysis","aliases":["spatial causal inference","geo-causal analysis","spatial treatment effect estimation","spatial impact evaluation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s (codified)","originator":"Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988)","url":"https://scholargate.app/en/causal-inference/spatial-causal-impact-analysis","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-causal-impact-analysis.md","definition":"Spatial causal impact analysis estimates the causal effect of a spatially-targeted intervention — a policy, shock, or treatment applied to particular locations — while explicitly accounting for geographic spillovers between treated and untreated units. By combining quasi-experimental designs such as difference-in-differences or regression discontinuity with spatial econometric models, it separates the direct local effect of a treatment from indirect effects that diffuse to neighbouring areas.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988)","year":"2010s (codified)","type":"Quasi-experimental causal inference with spatial data","dataType":"Geo-referenced panel or cross-sectional data with treatment and control units across space","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 137, 123-126.","type":"article","doi":"10.1016/j.econlet.2015.10.035","isbn":null,"url":null},{"ref":"Halleck Vega, S., & Elhorst, J. P. (2015). The SLX Model. Journal of Regional Science, 55(3), 339-363.","type":"article","doi":"10.1111/jors.12188","isbn":null,"url":null}],"related":["difference-in-differences","spatial-regression-discontinuity","synthetic-control-method","spatial-econometrics","geographically-weighted-regression","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-coarsened-exact-matching","name":"Spatial Coarsened Exact Matching","fullName":"Spatial Coarsened Exact Matching Estimator","aliases":["Spatial CEM","Geographic CEM","Spatial exact matching","CEM with spatial covariates"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2012 (CEM foundation); spatial extension in applied literature 2015-present","originator":"Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians","url":"https://scholargate.app/en/causal-inference/spatial-coarsened-exact-matching","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-coarsened-exact-matching.md","definition":"Spatial Coarsened Exact Matching applies the Coarsened Exact Matching framework to study designs involving geographic units — neighbourhoods, census tracts, municipalities, or grid cells. Covariates are coarsened into discrete bins and units are matched exactly on those bins, with spatial attributes (location, adjacency, geographic characteristics) incorporated as matching dimensions to control for spatial confounding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians","year":"2012 (CEM foundation); spatial extension in applied literature 2015-present","type":"Quasi-experimental matching estimator with spatial covariates","dataType":"Cross-sectional or panel data with geographic unit identifiers and spatial covariates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.","type":"article","doi":"10.1093/pan/mpr013","isbn":null,"url":null},{"ref":"Anselin, L., & Rey, S. J. (Eds.) (2014). Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press.","type":"book","doi":null,"isbn":"978-0986342103","url":null}],"related":["coarsened-exact-matching","spatial-propensity-score-matching","spatial-doubly-robust-estimation","propensity-score-matching","spatial-regression-discontinuity-design","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-counterfactual-impact-evaluation","name":"Spatial Counterfactual Impact Evaluation","fullName":"Spatial Counterfactual Impact Evaluation","aliases":["SCIE","spatial CIE","place-based counterfactual evaluation","regional counterfactual analysis"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s","originator":"Cerqua, Pellegrini, and regional-science scholars building on counterfactual econometrics","url":"https://scholargate.app/en/causal-inference/spatial-counterfactual-impact-evaluation","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-counterfactual-impact-evaluation.md","definition":"Spatial Counterfactual Impact Evaluation (SCIE) is a family of quasi-experimental methods that estimate the causal effect of geographically targeted policies — such as EU Cohesion Funds, enterprise zones, or place-based subsidies — by constructing a spatial counterfactual: what outcomes the treated region would have experienced without the intervention, inferred from comparable untreated regions or from discontinuities at policy boundaries.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cerqua, Pellegrini, and regional-science scholars building on counterfactual econometrics","year":"2010s","type":"Quasi-experimental / causal inference","dataType":"Spatial panel or cross-sectional data with geographic treatment assignment","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Cerqua, A., & Pellegrini, G. (2014). Do subsidies to private capital boost firms' growth? A multiple regression discontinuity design approach. Journal of Public Economics, 109, 114-126.","type":"article","doi":"10.1016/j.jpubeco.2013.11.005","isbn":null,"url":null},{"ref":"Pellegrini, G., Terribile, F., Tarola, O., Muccigrosso, T., & Busillo, F. (2013). Measuring the effects of European Regional Policy on economic growth: A regression discontinuity approach. Papers in Regional Science, 92(1), 217-233.","type":"article","doi":"10.1111/j.1435-5957.2012.00459.x","isbn":null,"url":null}],"related":["difference-in-differences","regression-discontinuity-design","synthetic-control-method","propensity-score-matching","geographically-weighted-regression","instrumental-variables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-difference-in-differences","name":"Spatial Difference-in-Differences","fullName":"Spatial Difference-in-Differences","aliases":["Spatial DiD","Geo-DiD","Difference-in-Differences with Spatial Autocorrelation","Mekansal Fark-içinde-Farklar"],"domain":"causal-inference","family":"regression-model","subfamily":"Spatial causal inference","year":2015,"originator":"Delgado & Florax","url":"https://scholargate.app/en/causal-inference/spatial-difference-in-differences","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-difference-in-differences.md","definition":"Spatial Difference-in-Differences (Spatial DiD) extends the classical DiD estimator to settings where observations are geo-referenced and outcomes may be spatially autocorrelated or subject to spillover effects. Introduced by Delgado and Florax (2015), the method augments the standard two-way fixed-effects DiD regression with a spatial lag or spatial error term, yielding unbiased treatment-effect estimates even when policy shocks propagate across geographic units. It is used by economists, regional scientists, and urban planners evaluating place-based interventions such as infrastructure investment, environmental regulations, or zoning reforms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Delgado & Florax","year":2015,"type":"Quasi-experimental estimator","subfamily":"Spatial causal inference","data_requirement":"Geo-referenced panel data","key_assumption":"Parallel trends corrected for spatial dependence"},"citations":[{"ref":"Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 126, 35–40.","type":"article","doi":"10.1016/j.econlet.2015.10.035","isbn":null,"url":null}],"related":["difference-in-differences","spatial-lag-model","synthetic-control"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-doubly-robust-estimation","name":"Spatial Doubly Robust Estimation","fullName":"Spatial Doubly Robust Causal Estimation","aliases":["Spatial DR","Spatial AIPW","Spatial augmented IPW","Doubly robust spatial causal estimation"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s–2020s","originator":"Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature","url":"https://scholargate.app/en/causal-inference/spatial-doubly-robust-estimation","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-doubly-robust-estimation.md","definition":"Spatial doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature","year":"2010s–2020s","type":"Semiparametric causal estimator","dataType":"Geo-referenced observational data with spatially correlated units","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787.","type":"article","doi":"10.1111/biom.13049","isbn":null,"url":null},{"ref":"Kennedy, E. H. (2016). Semiparametric theory and empirical processes in causal inference. In H. He, P. Wu, & D.-G. Chen (Eds.), Statistical Causal Inferences and Their Applications in Public Health Research (pp. 141-167). Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Semiparametric+theory+empirical+processes+causal+inference+Kennedy+2016"}],"related":["doubly-robust-estimation","propensity-score-matching","inverse-probability-weighting","spatial-regression-discontinuity","geographically-weighted-regression","difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-durbin-model","name":"Spatial Durbin Model","fullName":"Spatial Durbin Model (SDM)","aliases":["SDM","spatial mixed model","uzamsal durbin modeli"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":2009,"originator":"LeSage & Pace","url":"https://scholargate.app/en/spatial-analysis/spatial-durbin-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/spatial-durbin-model.md","definition":"The Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"LeSage & Pace","year":2009,"type":"Spatial regression model","estimator":"Maximum likelihood (Bayesian MCMC alternative for small samples)","outcome":"continuous","structure":"cross-sectional","minSample":80},"citations":[{"ref":"LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press.","type":"book","doi":"10.1201/9781420064254","isbn":null,"url":null},{"ref":"Elhorst, J. P. (2010). Applied Spatial Econometrics: Raising the Bar. Spatial Economic Analysis, 5(1), 9–28.","type":"article","doi":"10.1080/17421770903541772","isbn":null,"url":null}],"related":["spatial-panel-model","geographically-weighted-regression","mgwr-model","ols-regression","kriging-interpolation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-entropy-balancing","name":"Spatial Entropy Balancing","fullName":"Spatial Entropy Balancing for Causal Inference","aliases":["spatial EB","geographically-weighted entropy balancing","spatial reweighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s","originator":"Extension of Hainmueller (2012) entropy balancing to spatial settings; spatial adaptations developed in geographic epidemiology and spatial econometrics literature","url":"https://scholargate.app/en/causal-inference/spatial-entropy-balancing","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-entropy-balancing.md","definition":"Spatial entropy balancing extends standard entropy balancing to observational settings where units are embedded in geographic space, incorporating spatial structure into the reweighting process so that balance is achieved while respecting spatial proximity, clustering, or spillover dependencies between units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Hainmueller (2012) entropy balancing to spatial settings; spatial adaptations developed in geographic epidemiology and spatial econometrics literature","year":"2010s","type":"Quasi-experimental reweighting","dataType":"Observational cross-sectional or panel data with geographic coordinates or spatial identifiers","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46.","type":"article","doi":"10.1093/pan/mpr025","isbn":null,"url":null},{"ref":"Entropy balancing. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Entropy_balancing"}],"related":["entropy-balancing","spatial-propensity-score-matching","spatial-inverse-probability-weighting","spatial-doubly-robust-estimation","propensity-score-weighting","spatial-matching-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-error-model","name":"Spatial Error Model","fullName":"Spatial Error Model (SEM)","aliases":["SEM","spatial error regression","spatial autoregressive error model","Uzamsal Hata Modeli (SEM / Spatial Error)"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":1988,"originator":"Anselin","url":"https://scholargate.app/en/spatial-analysis/spatial-error-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/spatial-error-model.md","definition":"The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move together, and it is estimated by maximum likelihood or GMM rather than ordinary least squares.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin","year":1988,"type":"Spatial regression (spatially autocorrelated errors)","estimator":"Maximum likelihood (ML) or generalized method of moments (GMM)","outcome":"continuous","dataStructure":"cross-sectional with geographic coordinates","minSample":50},"citations":[{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.","type":"book","doi":"10.1007/978-94-015-7799-1","isbn":null,"url":null}],"related":["spatial-lag-model","spatial-durbin-model","spatial-panel-model","ols-regression","mgwr-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-event-study-design","name":"Spatial Event Study Design","fullName":"Spatial Event Study Design","aliases":["spatial event study","geographic event study","spatial dynamic DiD","place-based event study"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Developed across applied spatial economics literature; canonical applications in Autor, Dorn & Hanson (2013) and related regional economics studies","url":"https://scholargate.app/en/causal-inference/spatial-event-study-design","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-event-study-design.md","definition":"Spatial event study design estimates the dynamic causal effects of a geographically concentrated shock or policy by plotting how outcomes in affected locations evolve relative to unaffected locations across time periods, while explicitly accounting for spatial spillovers and autocorrelation across geographic units. It is widely used in regional and urban economics to evaluate place-based policies, trade shocks, and local labour market interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed across applied spatial economics literature; canonical applications in Autor, Dorn & Hanson (2013) and related regional economics studies","year":"2000s–2010s","type":"Quasi-experimental causal inference with spatial structure","dataType":"Panel data with geographic identifiers (regions, commuting zones, counties)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Autor, D. H., Dorn, D., & Hanson, G. H. (2013). The China Syndrome: Local Labor Market Effects of Import Competition in the United States. American Economic Review, 103(6), 2121-2168.","type":"article","doi":"10.1257/aer.103.6.2121","isbn":null,"url":null},{"ref":"Kline, P. (2012). The Impact of Juvenile Curfew Laws on Arrests of Youth and Adults. American Law and Economics Review, 14(1), 44-67.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Kline+2012+Impact+Juvenile+Curfew+event+study"}],"related":["event-study-design","spatial-difference-in-differences","difference-in-differences","spatial-regression-discontinuity-design","panel-event-study","dynamic-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-fuzzy-regression-discontinuity","name":"Spatial Fuzzy Regression Discontinuity","fullName":"Spatial Fuzzy Regression Discontinuity Design","aliases":["Spatial Fuzzy RD","Geographic Fuzzy RDD","Spatial Fuzzy RDD","Geo-Fuzzy RD"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2015","originator":"Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008)","url":"https://scholargate.app/en/causal-inference/spatial-fuzzy-regression-discontinuity","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-fuzzy-regression-discontinuity.md","definition":"Spatial Fuzzy Regression Discontinuity Design (Spatial Fuzzy RDD) estimates a local average treatment effect when a geographic boundary determines treatment eligibility but some units on either side of the boundary fail to comply with their assigned status. It combines the spatial running-variable logic of geographic RDD with the instrumental-variable correction for imperfect compliance used in fuzzy RDD.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008)","year":"2015","type":"Quasi-experimental causal inference / IV-based spatial design","dataType":"Geo-coded observational data with continuous spatial running variable and imperfect boundary compliance","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.","type":"article","doi":"10.1093/pan/mpu014","isbn":null,"url":null},{"ref":"Imbens, G., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.","type":"article","doi":"10.1016/j.jeconom.2007.05.001","isbn":null,"url":null}],"related":["fuzzy-regression-discontinuity","regression-discontinuity-design","spatial-regression-discontinuity-design","spatial-instrumental-variables","geographic-regression-discontinuity","instrumental-variables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-gibbs-sampling","name":"Spatial Gibbs Sampling","fullName":"Spatial Gibbs Sampling for Markov Random Fields and Geostatistical Models","aliases":["Gibbs sampler for spatial models","MRF Gibbs sampling","spatial MCMC via Gibbs","conditional field simulation"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1984","originator":"Stuart Geman and Donald Geman","url":"https://scholargate.app/en/bayesian/spatial-gibbs-sampling","markdownUrl":"https://scholargate.app/en/bayesian/spatial-gibbs-sampling.md","definition":"Spatial Gibbs sampling applies the Gibbs sampler — a coordinate-wise Markov chain Monte Carlo algorithm — to models where observations are arranged in space and nearby locations are statistically dependent. By exploiting the conditional independence implied by a spatial neighbourhood structure, each site is updated one at a time given its neighbours, making posterior inference tractable for Markov random fields, Gaussian random fields, and hierarchical geostatistical models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stuart Geman and Donald Geman","year":"1984","type":"MCMC sampling algorithm for spatial models","dataType":"spatially indexed continuous or discrete observations, lattice data, geostatistical data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721–741.","type":"article","doi":"10.1109/TPAMI.1984.4767596","isbn":null,"url":null},{"ref":"Rue, H. & Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications. Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584884323","url":null}],"related":["gibbs-sampling","metropolis-hastings","spatial-bayesian-inference","bayesian-hierarchical-model","spatial-mcmc","markov-random-field"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-instrumental-variables","name":"Spatial Instrumental Variables","fullName":"Spatial Instrumental Variables Estimation","aliases":["Spatial IV","Spatial 2SLS","Spatial Two-Stage Least Squares","S-IV"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1988-1998","originator":"Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)","url":"https://scholargate.app/en/causal-inference/spatial-instrumental-variables","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-instrumental-variables.md","definition":"Spatial Instrumental Variables (Spatial IV) is a causal inference method for settings where units — regions, firms, neighborhoods — are spatially interdependent, creating endogeneity that standard IV approaches ignore. It constructs instruments from the spatially lagged values of exogenous characteristics of neighboring units, then applies two-stage least squares to recover unbiased causal estimates in the presence of both endogenous regressors and spatial autocorrelation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)","year":"1988-1998","type":"Quasi-experimental causal inference with spatial dependence","dataType":"Cross-sectional or panel with geographic/spatial unit identifiers","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Kelejian, H. H., & Prucha, I. R. (1998). A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances. Journal of Real Estate Finance and Economics, 17(1), 99-121.","type":"article","doi":"10.1023/A:1007707430416","isbn":null,"url":null},{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht.","type":"book","doi":null,"isbn":"978-9024737208","url":null}],"related":["instrumental-variables","spatial-regression-discontinuity-design","spatial-propensity-score-matching","panel-data-instrumental-variables","spatial-doubly-robust-estimation","spatial-causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-interaction-model","name":"Spatial Interaction Model","fullName":"Spatial Interaction (Gravity) Models","aliases":["gravity model","spatial interaction model","competing destinations model","mekânsal etkileşim modeli"],"domain":"spatial-analysis","family":"regression-model","subfamily":"Spatial interaction","year":1971,"originator":"Alan Wilson (entropy-maximizing family)","url":"https://scholargate.app/en/spatial-analysis/spatial-interaction-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/spatial-interaction-model.md","definition":"Spatial interaction models predict the volume of flows — migrants, commuters, shoppers, trade, trips — between origins and destinations as a function of the size of each place and the distance or cost separating them. By analogy to Newton's gravity, interaction rises with the 'mass' of origin and destination and falls with separation, and Wilson's 1971 entropy-maximizing family put these models on a rigorous footing for transport, migration, and retail analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alan Wilson (entropy-maximizing family)","year":1971,"type":"Model of flows between spatial origins and destinations","subfamily":"Spatial interaction","predicts":"Migration, trade, commuting, trip flows","form":"Gravity / entropy-maximizing"},"citations":[{"ref":"Wilson, A. G. (1971). A family of spatial interaction models, and associated developments. Environment and Planning A, 3(1), 1–32.","type":"article","doi":"10.1068/a030001","isbn":null,"url":null},{"ref":"Fotheringham, A. S. (1983). A new set of spatial-interaction models: the theory of competing destinations. Environment and Planning A, 15(1), 15–36.","type":"article","doi":"10.1068/a150015","isbn":null,"url":null}],"related":["location-allocation","poisson-regression","multinomial-logit","gis-mcda"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-interrupted-time-series","name":"Spatial Interrupted Time Series","fullName":"Spatial Interrupted Time Series Analysis","aliases":["Spatial ITS","Geospatial ITS","Spatially-adjusted ITS","SITS"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1990s–2000s","originator":"Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s","url":"https://scholargate.app/en/causal-inference/spatial-interrupted-time-series","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-interrupted-time-series.md","definition":"Spatial Interrupted Time Series (Spatial ITS) extends the classic ITS design to settings where units are geo-referenced and outcomes in one location may spill over into or correlate with outcomes in neighbouring locations. It estimates the causal effect of a discrete intervention on an outcome time series while explicitly modelling geographic autocorrelation, preventing biased standard errors and enabling detection of spatial spillovers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of McDowall et al. (1980) ITS framework; spatial adaptations developed in epidemiology and geography through the 1990s–2000s","year":"1990s–2000s","type":"Quasi-experimental causal inference with spatial adjustment","dataType":"Geo-referenced time series; panel data with spatial coordinates","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"McDowall, D., McCleary, R., Meidinger, E. E., & Hay, R. A. (1980). Interrupted Time Series Analysis. Sage Publications.","type":"book","doi":null,"isbn":"978-0803913950","url":null},{"ref":"Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1138575424","url":null}],"related":["interrupted-time-series","spatial-difference-in-differences","spatial-regression-discontinuity-design","spatial-propensity-score-matching","panel-data-interrupted-time-series","spatial-causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-inverse-probability-weighting","name":"Spatial Inverse Probability Weighting","fullName":"Spatial Inverse Probability Weighting Estimator","aliases":["Spatial IPW","Geographic IPW","Spatially-weighted IPW","SIPW"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s","originator":"Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)","url":"https://scholargate.app/en/causal-inference/spatial-inverse-probability-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-inverse-probability-weighting.md","definition":"Spatial Inverse Probability Weighting extends the classical IPW estimator to settings where units are geo-referenced and spatial location is a confounding dimension. By incorporating geographic coordinates or spatial proximity into the propensity score model, it reweights the observed sample so that treatment and control groups are balanced not only on measured covariates but also on spatial structure, enabling credible causal inference from spatially indexed observational data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)","year":"2010s","type":"Quasi-experimental / causal inference","dataType":"Geo-referenced / spatially indexed observational data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.","type":"article","doi":"10.1111/1468-0262.00442","isbn":null,"url":null},{"ref":"Papadogeorgou, G., Choirat, C., & Zigler, C. M. (2019). Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching. Biostatistics, 20(2), 256-272.","type":"article","doi":"10.1093/biostatistics/kxx074","isbn":null,"url":null}],"related":["inverse-probability-weighting","propensity-score-matching","spatial-regression","geographically-weighted-regression","difference-in-differences","doubly-robust-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-kalman-filter","name":"Spatial Kalman Filter","fullName":"Spatial Kalman Filter for Spatio-Temporal State-Space Models","aliases":["spatial state-space filter","spatio-temporal Kalman filter","SKF","spatial dynamic linear model"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1960 (base); spatial extensions 1990s–2000s","originator":"R. E. Kalman (base filter, 1960); extended to spatial settings by Cressie, Wikle and colleagues","url":"https://scholargate.app/en/bayesian/spatial-kalman-filter","markdownUrl":"https://scholargate.app/en/bayesian/spatial-kalman-filter.md","definition":"The spatial Kalman filter applies classical Kalman filtering to spatio-temporal state-space models, treating a spatially distributed latent field as the hidden state that evolves over time. At each time step, the filter recursively predicts the spatial field forward and then updates the prediction with new spatial observations, producing optimal linear estimates of the field and its uncertainty across all locations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. E. Kalman (base filter, 1960); extended to spatial settings by Cressie, Wikle and colleagues","year":"1960 (base); spatial extensions 1990s–2000s","type":"Bayesian state-space model","dataType":"spatio-temporal observations (gridded or point-referenced)","subfamily":"Bayesian / computational"},"citations":[{"ref":"Cressie, N. & Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. Wiley.","type":"book","doi":null,"isbn":"978-0-471-69274-4","url":null},{"ref":"Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.","type":"article","doi":"10.1115/1.3662552","isbn":null,"url":null}],"related":["kalman-filter","particle-filter","spatial-bayesian-inference","dynamic-bayesian-inference","sequential-monte-carlo","spatial-mcmc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-lag-model","name":"Spatial Lag Model","fullName":"Spatial Autoregressive (SAR) / Spatial Lag Model","aliases":["SAR model","spatial autoregressive model","spatial lag","Uzamsal Gecikme Modeli (SAR / Spatial Lag)"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":1988,"originator":"Anselin (textbook formalisation); LeSage & Pace","url":"https://scholargate.app/en/spatial-analysis/spatial-lag-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/spatial-lag-model.md","definition":"The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin (textbook formalisation); LeSage & Pace","year":1988,"type":"Spatial autoregressive regression","estimator":"Maximum likelihood (or IV/GMM)","outcome":"continuous","dataStructure":"cross-sectional with spatial coordinates"},"citations":[{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.","type":"book","doi":"10.1007/978-94-015-7799-1","isbn":null,"url":null},{"ref":"LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press.","type":"book","doi":"10.1201/9781420064254","isbn":null,"url":null}],"related":["spatial-error-model","spatial-durbin-model","ols-regression","kriging-interpolation","panel-fixed-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-marginal-structural-model","name":"Spatial Marginal Structural Model","fullName":"Spatial Marginal Structural Model with Inverse Probability Weighting","aliases":["Spatial MSM","Geospatial MSM","Spatial IPW-MSM","Space-time marginal structural model"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Robins, Hernan & Brumback (MSM foundation, 2000); spatial extensions developed in spatial epidemiology literature","url":"https://scholargate.app/en/causal-inference/spatial-marginal-structural-model","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-marginal-structural-model.md","definition":"The Spatial Marginal Structural Model (Spatial MSM) extends the classical marginal structural model to settings where units are geographically distributed and spatial dependencies — such as neighborhood spillovers, clustering, and spatial confounding — may bias causal estimates. It estimates causal effects of spatially varying exposures by constructing inverse probability weights that account for both individual covariates and spatial location, then fitting a weighted outcome model in the resulting pseudo-population.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robins, Hernan & Brumback (MSM foundation, 2000); spatial extensions developed in spatial epidemiology literature","year":"2000s–2010s","type":"Causal inference / spatial weighting","dataType":"Geographically referenced observational data (areal or point-referenced units) with time-varying or spatial treatments","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.","type":"article","doi":"10.1097/00001648-200009000-00011","isbn":null,"url":null},{"ref":"Schnell, P. M., & Papadogeorgou, G. (2020). Mitigating unobserved spatial confounding when estimating the effect of supermarket access on cardiovascular disease deaths. Annals of Applied Statistics, 14(2), 793-816.","type":"article","doi":"10.1214/20-aoas1377","isbn":null,"url":null}],"related":["marginal-structural-model","inverse-probability-weighting","spatial-propensity-score-matching","spatial-doubly-robust-estimation","spatial-instrumental-variables","propensity-score-weighting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-matching-estimator","name":"Spatial Matching Estimator","fullName":"Spatial Matching Estimator for Causal Inference","aliases":["geographic matching estimator","spatial nearest-neighbor matching","location-based matching estimator","spatially-weighted matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Extension of Abadie & Imbens (2006) matching estimator to spatial settings; geographic applications developed in urban/environmental econometrics literature","url":"https://scholargate.app/en/causal-inference/spatial-matching-estimator","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-matching-estimator.md","definition":"The Spatial Matching Estimator estimates causal treatment effects by pairing each treated geographic unit with one or more similar untreated units nearby, exploiting the assumption that units close in space share similar unobserved characteristics. By restricting matches to a geographic neighbourhood or weighting by spatial proximity, the method controls for location-specific confounders that standard matching ignores.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Abadie & Imbens (2006) matching estimator to spatial settings; geographic applications developed in urban/environmental econometrics literature","year":"2000s–2010s","type":"Quasi-experimental causal inference","dataType":"Georeferenced unit-level data with treatment assignment and continuous or binary outcomes","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.","type":"article","doi":"10.1111/j.1468-0262.2006.00655.x","isbn":null,"url":null},{"ref":"Matching (statistics). Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Matching_(statistics)"}],"related":["matching-estimator","propensity-score-matching","coarsened-exact-matching","spatial-regression-discontinuity-design","spatial-difference-in-differences","spatial-propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-mcmc","name":"Spatial MCMC","fullName":"Markov Chain Monte Carlo for Spatial Models","aliases":["spatial Markov chain Monte Carlo","MCMC for spatial data","spatial Bayesian MCMC","geostatistical MCMC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1990s","originator":"Gelfand, Smith, and colleagues (early 1990s MCMC for spatial models)","url":"https://scholargate.app/en/bayesian/spatial-mcmc","markdownUrl":"https://scholargate.app/en/bayesian/spatial-mcmc.md","definition":"Spatial MCMC applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for spatial dependence among observations. It draws posterior samples from models such as conditional autoregressive (CAR), simultaneous autoregressive (SAR), or geostatistical (Gaussian process) models, yielding full uncertainty distributions for spatially structured parameters like random effects, regression coefficients, and spatial range.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gelfand, Smith, and colleagues (early 1990s MCMC for spatial models)","year":"1990s","type":"Bayesian computational method","dataType":"spatial / georeferenced / areal data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439819173","url":null},{"ref":"Rue, H., & Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications. CRC Press.","type":"book","doi":null,"isbn":"978-1584884323","url":null}],"related":["gibbs-sampling","hamiltonian-monte-carlo","spatial-bayesian-inference","hierarchical-bayesian-inference","conditional-autoregressive-model","gaussian-process-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-monte-carlo-simulation","name":"Spatial Monte Carlo Simulation","fullName":"Spatial Monte Carlo Simulation","aliases":["spatial MC simulation","Monte Carlo spatial analysis","stochastic spatial simulation","spatial stochastic simulation"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1970s–1980s","originator":"B. D. Ripley and the spatial statistics tradition","url":"https://scholargate.app/en/bayesian/spatial-monte-carlo-simulation","markdownUrl":"https://scholargate.app/en/bayesian/spatial-monte-carlo-simulation.md","definition":"Spatial Monte Carlo simulation applies random sampling methods to spatial problems, generating many stochastic realisations of a spatial process — such as a random field, point pattern, or network — to estimate distributional properties, propagate uncertainty, or test spatial hypotheses. It is a cornerstone technique in geostatistics, spatial epidemiology, ecology, and environmental modelling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"B. D. Ripley and the spatial statistics tradition","year":"1970s–1980s","type":"computational simulation","dataType":"georeferenced / spatial point patterns / gridded data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Ripley, B. D. (1987). Stochastic Simulation. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471818847","url":null},{"ref":"Diggle, P. J. (2003). Statistical Analysis of Spatial Point Patterns (2nd ed.). Arnold.","type":"book","doi":null,"isbn":"978-0340740669","url":null}],"related":["sequential-monte-carlo","spatial-bayesian-inference","spatial-particle-filter","gibbs-sampling","markov-chain-monte-carlo","spatial-metropolis-hastings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-panel-event-study","name":"Spatial Panel Event Study","fullName":"Spatial Panel Event Study Design","aliases":["spatial event study","spatial DiD event study","geo-panel event study","spatial panel ES"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s–2020s","originator":"Synthesized from spatial econometrics and panel event-study literatures; formalized in applied work in the 2010s–2020s","url":"https://scholargate.app/en/causal-inference/spatial-panel-event-study","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-panel-event-study.md","definition":"Spatial panel event study extends the classical panel event-study design to settings where units are geographically located and outcomes may spill over across space. By combining event-time indicators with spatial weights matrices, it estimates dynamic treatment effects while explicitly accounting for spatial autocorrelation, geographic spillovers, and cross-unit contamination that would bias conventional event studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from spatial econometrics and panel event-study literatures; formalized in applied work in the 2010s–2020s","year":"2010s–2020s","type":"Quasi-experimental causal inference","dataType":"Geo-referenced panel data (multiple units, multiple periods, spatial coordinates or adjacency)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Sun, L., & Callaway, B. (2021). Difference-in-differences estimators of intertemporal treatment effects. arXiv:2109.10157.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2109.10157"},{"ref":"Gibbons, C. E., Serrato, J. C. S., & Urbancic, M. B. (2019). Broken or Fixed Effects? Journal of Econometric Methods, 8(1), 20170002.","type":"article","doi":"10.1515/jem-2017-0002","isbn":null,"url":null}],"related":["event-study-design","panel-event-study","difference-in-differences","spatial-difference-in-differences","spatial-regression-discontinuity-design","panel-data-event-study-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-panel-model","name":"Spatial Panel Model","fullName":"Spatial Panel Data Model (Fixed and Random Effects)","aliases":["spatial panel FE/RE","spatial econometric panel","spatial lag/error panel","Uzamsal Panel Modeli (Spatial Panel FE/RE)"],"domain":"spatial-analysis","family":"regression-model","subfamily":null,"year":2014,"originator":"Elhorst; Lee & Yu","url":"https://scholargate.app/en/spatial-analysis/spatial-panel-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/spatial-panel-model.md","definition":"The spatial panel model is a family of econometric models that adds spatial dependence to panel data (units observed over time). It combines fixed- or random-effects panel structure with spatial lag, spatial error, or spatial Durbin components, and is developed in the modern spatial-econometrics literature by Elhorst (2014) and Lee & Yu (2010).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elhorst; Lee & Yu","year":2014,"type":"Spatial econometric panel model","estimator":"Maximum likelihood / bias-corrected LSDV (GMM for dynamic models)","outcome":"continuous","dataStructure":"panel (unit × time)","minSample":100},"citations":[{"ref":"Elhorst, J. P. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Springer.","type":"book","doi":"10.1007/978-3-642-40340-8","isbn":null,"url":null},{"ref":"Lee, L. F., & Yu, J. (2010). Estimation of Spatial Autoregressive Panel Data Models with Fixed Effects. Journal of Econometrics, 154(2), 165–185.","type":"article","doi":"10.1016/j.jeconom.2009.08.001","isbn":null,"url":null}],"related":["panel-fixed-effects","geographically-weighted-regression","mgwr","getis-ord-gi","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-placebo-test","name":"Spatial Placebo Test","fullName":"Spatial Placebo Test for Causal Identification","aliases":["geographic placebo test","spatial falsification test","spatial robustness check","geographic spillover test"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Developed organically in spatial econometrics and geographic RDD literature; prominent use in Dell (2010) and related work","url":"https://scholargate.app/en/causal-inference/spatial-placebo-test","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-placebo-test.md","definition":"A spatial placebo test is a falsification check used in geographic or spatial causal-inference studies. The analyst applies the same estimation procedure to spatial units, boundaries, or zones where no treatment effect should exist — fake borders, shifted cutoffs, or buffer areas beyond spillover range — and checks whether a spurious effect emerges. A non-significant result in the placebo region supports the credibility of the main causal estimate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed organically in spatial econometrics and geographic RDD literature; prominent use in Dell (2010) and related work","year":"2000s–2010s","type":"Falsification / robustness check","dataType":"Geo-referenced observational data (point, polygon, or raster)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Buonanno, P., Montolio, D., & Vanin, P. (2009). Does Social Capital Reduce Crime? Journal of Law and Economics, 52(1), 145-170.","type":"article","doi":"10.1086/595698","isbn":null,"url":null},{"ref":"Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.","type":"article","doi":"10.3982/ECTA8121","isbn":null,"url":null}],"related":["placebo-test","spatial-regression-discontinuity-design","spatial-difference-in-differences","regression-discontinuity-design","sensitivity-analysis-for-causality","spatial-instrumental-variables"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-propensity-score-matching","name":"Spatial Propensity Score Matching","fullName":"Spatial Propensity Score Matching Estimator","aliases":["Spatial PSM","Geospatial PSM","Spatially-adjusted propensity score matching","Geographic propensity score matching"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s","originator":"Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward","url":"https://scholargate.app/en/causal-inference/spatial-propensity-score-matching","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-propensity-score-matching.md","definition":"Spatial Propensity Score Matching (Spatial PSM) extends the classic propensity score matching framework to settings where units are embedded in geographic space and treatment assignment or outcomes may be spatially correlated. By incorporating spatial covariates and adjacency structure into the propensity model and matching procedure, it produces causal estimates that account for geographic confounding and spillover effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward","year":"2000s","type":"Quasi-experimental matching estimator","dataType":"Cross-sectional or panel data with geographic coordinates or spatial identifiers","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.","type":"article","doi":"10.1093/biomet/70.1.41","isbn":null,"url":null},{"ref":"Kelejian, H. H., & Prucha, I. R. (2004). Estimation of simultaneous systems of spatially interrelated cross sectional equations. Journal of Econometrics, 118(1-2), 27-50.","type":"article","doi":"10.1016/S0304-4076(03)00133-7","isbn":null,"url":null}],"related":["propensity-score-matching","spatial-instrumental-variables","spatial-doubly-robust-estimation","spatial-regression-discontinuity-design","coarsened-exact-matching","spatial-synthetic-control-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-propensity-score-weighting","name":"Spatial Propensity Score Weighting","fullName":"Spatial Propensity Score Weighting for Causal Inference","aliases":["spatial PSW","geographically weighted propensity score weighting","spatial IPTW","spatially adjusted inverse probability weighting"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2000s–2010s","originator":"Extended from Hirano, Imbens & Ridder (2003) IPTW with spatial adaptations by Keele, Titiunik and others in geographically structured causal designs","url":"https://scholargate.app/en/causal-inference/spatial-propensity-score-weighting","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-propensity-score-weighting.md","definition":"Spatial propensity score weighting extends inverse probability of treatment weighting (IPTW) to settings where units are geographically located and treatment assignment may depend on spatial factors such as location, neighborhood characteristics, or spatial clustering. By incorporating spatial covariates into the propensity score model and adjusting standard errors for spatial autocorrelation, it produces more credible causal estimates from observational geographic data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from Hirano, Imbens & Ridder (2003) IPTW with spatial adaptations by Keele, Titiunik and others in geographically structured causal designs","year":"2000s–2010s","type":"Quasi-experimental / causal inference","dataType":"Cross-sectional or panel data with geographic coordinates or spatial identifiers","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.","type":"article","doi":"10.1093/pan/mpu014","isbn":null,"url":null},{"ref":"Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.","type":"article","doi":"10.1111/1468-0262.00442","isbn":null,"url":null}],"related":["propensity-score-weighting","spatial-propensity-score-matching","inverse-probability-weighting","spatial-regression-discontinuity-design","geographically-weighted-regression","spatial-difference-in-differences"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-regression-discontinuity-design","name":"Spatial Regression Discontinuity Design","fullName":"Spatial Regression Discontinuity Design","aliases":["Spatial RDD","Geographic RDD","Border RD Design","Geographic Discontinuity Design"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2010s","originator":"Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)","url":"https://scholargate.app/en/causal-inference/spatial-regression-discontinuity-design","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-regression-discontinuity-design.md","definition":"Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)","year":"2010s","type":"Quasi-experimental causal inference","dataType":"Geo-coded observational data with units on both sides of a geographic or administrative boundary","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.","type":"article","doi":"10.3982/ECTA8121","isbn":null,"url":null},{"ref":"Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.","type":"article","doi":"10.1093/pan/mpu014","isbn":null,"url":null}],"related":["regression-discontinuity-design","difference-in-differences","fuzzy-regression-discontinuity","instrumental-variables","propensity-score-matching","spatial-econometrics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-regression","name":"Spatial Regression","fullName":"Spatial Regression (Spatial Lag and Spatial Error Models)","aliases":["spatial econometrics","spatial lag model","spatial error model","SAR / SEM","Uzamsal Regresyon (Spatial Lag / Spatial Error)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1988,"originator":"Luc Anselin","url":"https://scholargate.app/en/econometrics/spatial-regression","markdownUrl":"https://scholargate.app/en/econometrics/spatial-regression.md","definition":"Spatial regression is a family of regression models that build geographic neighbourhood relationships directly into the model, introduced by Luc Anselin in his 1988 treatment of spatial econometrics. It splits into a spatial lag model, where spatial dependence sits in the dependent variable, and a spatial error model, where the dependence sits in the error term.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Anselin","year":1988,"type":"Spatial regression (cross-sectional)","estimator":"Maximum likelihood (spatial lag and spatial error specifications)","outcome":"continuous","minSample":50,"dependence":"Geographic neighbourhood via spatial weight matrix W"},"citations":[{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.","type":"book","doi":"10.1007/978-94-015-7799-1","isbn":null,"url":null},{"ref":"LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. Chapman & Hall/CRC.","type":"book","doi":"10.1201/9781420064254","isbn":null,"url":null}],"related":["ols-regression","panel-fixed-effects","quantile-regression","threshold-regression","seemingly-unrelated-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-sac-model","name":"Spatial SAC Model","fullName":"Spatial Autoregressive Combined (SAC) Model","aliases":["SARAR Model","Spatial Autoregressive Model with Autoregressive Disturbances","Cliff-Ord Combined Model","Uzamsal Otoregresif Birleşik Model"],"domain":"spatial-analysis","family":"regression-model","subfamily":"Spatial econometrics","year":2009,"originator":"James LeSage & R. Kelley Pace","url":"https://scholargate.app/en/spatial-analysis/spatial-sac-model","markdownUrl":"https://scholargate.app/en/spatial-analysis/spatial-sac-model.md","definition":"The Spatial Autoregressive Combined (SAC) model, also known as the SARAR model, simultaneously accounts for spatial dependence in both the dependent variable and the error term. Formalized by LeSage and Pace (2009), the SAC model combines the spatial lag model and the spatial error model into a single framework, estimating two distinct spatial autoregressive parameters — one capturing substantive spatial interaction among outcomes and another capturing residual spatial correlation among disturbances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James LeSage & R. Kelley Pace","year":2009,"type":"Combined spatial dependence regression model","subfamily":"Spatial econometrics","estimator":"Maximum Likelihood or Generalized Spatial Two-Stage Least Squares","parameters":"Two spatial autoregressive parameters (rho, lambda)"},"citations":[{"ref":"LeSage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press.","type":"book","doi":null,"isbn":"978-1-4200-6424-7","url":null}],"related":["spatial-lag-model","spatial-error-model","spatial-durbin-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-sensitivity-analysis-for-causality","name":"Spatial Sensitivity Analysis for Causality","fullName":"Spatial Sensitivity Analysis for Causal Inference","aliases":["spatial causal sensitivity","spatial robustness checks","SSAC","spatial confounding sensitivity"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"1988–2021 (developed progressively)","originator":"Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks","url":"https://scholargate.app/en/causal-inference/spatial-sensitivity-analysis-for-causality","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-sensitivity-analysis-for-causality.md","definition":"Spatial sensitivity analysis for causality systematically tests whether a causal estimate derived from georeferenced data holds up as spatial structure, spillovers, and the choice of spatial weights matrix are varied. Because nearby units often share unmeasured confounders — soil quality, local infrastructure, neighbourhood norms — a naive regression may yield biased causal estimates. This method reveals how fragile or robust a claimed causal effect is to alternative spatial specifications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks","year":"1988–2021 (developed progressively)","type":"Sensitivity / robustness analysis","dataType":"Georeferenced / areal / point-referenced panel or cross-sectional data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht.","type":"book","doi":null,"isbn":"978-9024737322","url":null},{"ref":"Reich, B. J., Yang, S., Guan, Y., Giffin, A. B., Miller, M. J., & Rappold, A. G. (2021). A review of spatial causal inference methods for environmental and epidemiological applications. International Statistical Review, 89(3), 605-634.","type":"article","doi":"10.1111/insr.12452","isbn":null,"url":null}],"related":["spatial-lag-model","spatial-error-model","geographically-weighted-regression","instrumental-variables","difference-in-differences","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-stratified-heterogeneity","name":"Spatial Stratified Heterogeneity","fullName":"Spatial Stratified Heterogeneity (Geodetector)","aliases":["Geodetector","GeoDetector"],"domain":"sampling","family":"process-pipeline","subfamily":"Spatial Analysis","year":"2010","originator":"Jinfeng Wang","url":"https://scholargate.app/en/sampling/spatial-stratified-heterogeneity","markdownUrl":"https://scholargate.app/en/sampling/spatial-stratified-heterogeneity.md","definition":"Spatial Stratified Heterogeneity, commonly known as Geodetector, is a framework introduced by Jinfeng Wang and colleagues in 2010 for measuring and detecting spatial heterogeneity in data and identifying environmental risk factors. It quantifies the degree to which a given factor (variable) explains spatial variation in an outcome and is particularly valuable for environmental epidemiology, ecology, and geographical analysis where spatial non-stationarity is common.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jinfeng Wang","subfamily":"Spatial Analysis","year":"2010","type":"Geographical detection and stratification method"},"citations":[{"ref":"Wang, J. F., Li, X. H., Christakos, G., Liao, Y. L., Zhang, T., & Gu, X. (2010). Geographical detectors–based health risk assessment and its application in the neural tube defects study for the C–H plane. International Journal of Geographical Information Science, 24(1), 107–127.","type":"article","doi":"10.1080/13658810802443457","isbn":null,"url":null},{"ref":"Wang, J. F., Zhang, T. L., & Fu, B. J. (2016). A measure of spatial stratified heterogeneity. Ecological Indicators, 67, 250–256.","type":"article","doi":"10.1016/j.ecolind.2016.02.052","isbn":null,"url":null},{"ref":"Song, Y., Wu, P., Gilmore, D., Zhang, Q., Feng, Z., Wang, J., & Lou, L. (2020). Spatial autoregressive modelling of functional traits: a study case on gap-phase regeneration in a Chinese subtropical forest. Journal of Spatial Science, 65(2), 209–221.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spatial+autoregressive+modelling+of+functional+traits%3A+a+study+case+on+gap-phase+regeneration+in+a+Chinese+subtropical+forest+Song"}],"related":["stratified-sampling","spatial-analysis","cluster-sampling","systematic-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-synthetic-control-method","name":"Spatial Synthetic Control Method","fullName":"Spatial Synthetic Control Method for Causal Inference","aliases":["spatial SCM","geographic synthetic control","spatial SC","spatial counterfactual control"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2003–2010s","originator":"Abadie & Gardeazabal (2003); extended to spatial settings by subsequent applied econometric work","url":"https://scholargate.app/en/causal-inference/spatial-synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/spatial-synthetic-control-method.md","definition":"The Spatial Synthetic Control Method adapts the classic synthetic control framework to settings where treated and donor units are defined by geographic location. By constructing a weighted combination of spatially proximate or comparable control regions, the method estimates what would have happened to a treated area absent the intervention, while explicitly accounting for geographic spillovers, spatial autocorrelation, and contiguity among units.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie & Gardeazabal (2003); extended to spatial settings by subsequent applied econometric work","year":"2003–2010s","type":"Quasi-experimental causal inference","dataType":"Geo-referenced panel or time-series cross-sectional data","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93(1), 113-132.","type":"article","doi":"10.1257/000282803321455188","isbn":null,"url":null},{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null}],"related":["synthetic-control-method","spatial-regression-discontinuity-design","spatial-difference-in-differences","spatial-interrupted-time-series","difference-in-differences","propensity-score-matching"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-temporal-gcn","name":"Spatial-Temporal GCN","fullName":"Spatial-Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition","aliases":["ST-GCN","Spatial-Temporal Graph CNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Graph Neural Networks, Action Recognition","year":"2018","originator":"Sijie Yan","url":"https://scholargate.app/en/deep-learning/spatial-temporal-gcn","markdownUrl":"https://scholargate.app/en/deep-learning/spatial-temporal-gcn.md","definition":"Spatial-Temporal Graph Convolutional Networks (ST-GCN) is an architecture introduced by Yan et al. in 2018 for skeleton-based action recognition. By modeling human skeletons as graphs where joints are nodes and bones are edges, ST-GCN applies graph convolutions across space and time to recognize actions from skeleton sequences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sijie Yan","subfamily":"Deep Learning, Graph Neural Networks, Action Recognition","year":"2018","type":"Neural network architecture"},"citations":[{"ref":"Yan, S., Xiong, Y., & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32).","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1801.07455"}],"related":["vision-transformer","swin-transformer","mamba","vision-mamba"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spatial-variational-inference","name":"Spatial Variational Inference","fullName":"Spatial Variational Inference for Latent Gaussian Models","aliases":["SVI spatial","variational Bayes for spatial data","approximate Bayesian inference for spatial models","variational GP inference"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2009","originator":"Titsias (2009) for sparse GP; Rue, Martino & Chopin (2009) for latent Gaussian spatial models","url":"https://scholargate.app/en/bayesian/spatial-variational-inference","markdownUrl":"https://scholargate.app/en/bayesian/spatial-variational-inference.md","definition":"Spatial variational inference is a scalable approximate Bayesian method that fits latent Gaussian or Gaussian-process models to georeferenced data by optimising a lower bound on the marginal likelihood. It replaces expensive MCMC sampling with a deterministic optimisation step, making full-posterior uncertainty quantification tractable for large spatial datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Titsias (2009) for sparse GP; Rue, Martino & Chopin (2009) for latent Gaussian spatial models","year":"2009","type":"Approximate Bayesian inference algorithm","dataType":"Georeferenced / spatial observations, areal data, point-process data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Titsias, M. K. (2009). Variational learning of inducing variables in sparse Gaussian processes. In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 5, pp. 567-574.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v5/titsias09a.html"},{"ref":"Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B, 71(2), 319-392.","type":"article","doi":"10.1111/j.1467-9868.2008.00700.x","isbn":null,"url":null}],"related":["variational-inference","gaussian-process","spatial-bayesian-inference","bayesian-hierarchical-model","spatial-mcmc","kriging"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spearman-correlation","name":"Spearman Correlation","fullName":"Spearman Rank Correlation Coefficient","aliases":["Spearman's rho","Spearman rank-order correlation","Spearman Sıra Korelasyonu"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1904,"originator":"Charles Spearman","url":"https://scholargate.app/en/statistics/spearman-correlation","markdownUrl":"https://scholargate.app/en/statistics/spearman-correlation.md","definition":"The Spearman rank correlation coefficient (ρ) is a nonparametric measure of the monotonic association between two variables. Introduced by Charles Spearman in 1904, it converts raw observations to ranks and measures how consistently one variable increases as the other increases, without assuming a normal distribution or a linear relationship.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Spearman","year":1904,"family":"Correlation","type":"Nonparametric rank-based correlation","parametric":false,"minSample":10,"outcome":"ordinal or continuous","rangeOfStatistic":"−1 to +1","symbol":"ρ (rho)"},"citations":[{"ref":"Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101.","type":"article","doi":"10.2307/1412159","isbn":null,"url":null}],"related":["pearson-correlation","kendall-tau","mann-whitney-u","point-biserial-correlation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"species-accumulation","name":"Species Accumulation","fullName":"Species Accumulation Curve (Rarefaction)","aliases":["rarefaction","species accumulation curve","species richness curve"],"domain":"ecology","family":"process-pipeline","subfamily":"Nonparametric","year":"1968","originator":"Henry Sanders","url":"https://scholargate.app/en/ecology/species-accumulation","markdownUrl":"https://scholargate.app/en/ecology/species-accumulation.md","definition":"Species accumulation curves describe how the number of observed species increases with cumulative sampling effort. Introduced by Sanders (1968) and developed by Colwell and colleagues, this method enables ecologists to compare biodiversity across sites and estimate total species richness despite incomplete sampling. It addresses a fundamental challenge in ecology: observed species counts are biased by sampling intensity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Sanders","subfamily":"Nonparametric","year":"1968","type":"biodiversity quantification and comparison"},"citations":[{"ref":"Colwell, R. K. (1994). Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions of the Royal Society B, 345(1311), 101-118.","type":"article","doi":"10.1098/rstb.1994.0091","isbn":null,"url":null},{"ref":"Gotelli, N. J., & Colwell, R. K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4(4), 379-391.","type":"article","doi":"10.1046/j.1461-0248.2001.00230.x","isbn":null,"url":null},{"ref":"Sanders, H. L. (1968). Marine benthic diversity: a comparative study. American Naturalist, 102(925), 243-282.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/2459027"}],"related":["beta-diversity-partitioning","faiths-phylogenetic-diversity","indicator-value","functional-diversity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"species-distribution-models","name":"Species Distribution Models (MaxEnt)","fullName":"Species Distribution Models using Maximum Entropy Modelling","aliases":["MaxEnt","SDM","Maximum Entropy Model"],"domain":"sustainability","family":"process-pipeline","subfamily":"Ecological modelling","year":"2004","originator":"Steven Phillips, Robert Anderson, Robert Schapire","url":"https://scholargate.app/en/sustainability/species-distribution-models","markdownUrl":"https://scholargate.app/en/sustainability/species-distribution-models.md","definition":"Species Distribution Models (SDMs) using Maximum Entropy (MaxEnt) are statistical methods developed by Phillips, Anderson, and Schapire (2004) to predict where species are likely to occur based on known occurrence points and environmental variables. MaxEnt has become one of the most widely used algorithms in conservation biology and biogeography for mapping suitable habitat and assessing climate change impacts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Phillips, Robert Anderson, Robert Schapire","subfamily":"Ecological modelling","year":"2004","type":"Statistical learning algorithm"},"citations":[{"ref":"Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modelling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.","type":"article","doi":"10.1016/j.ecolmodel.2005.03.026","isbn":null,"url":null},{"ref":"Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57.","type":"article","doi":"10.1111/j.1472-4642.2010.00725.x","isbn":null,"url":null},{"ref":"Merow, C., Smith, M. J., & Silander, J. A. (2013). A practical guide to MaxEnt for modelling species' distributions: What it does, and why inputs and settings matter. Ecography, 36(10), 1058-1069.","type":"article","doi":"10.1111/j.1600-0587.2013.07872.x","isbn":null,"url":null}],"related":["dpsir-framework","ecosystem-services-valuation","life-cycle-sustainability-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"specific-excess-power","name":"Specific Excess Power","fullName":"Specific Excess Power Analysis","aliases":["Ps","energy maneuverability theory","specific power"],"domain":"aerospace","family":"process-pipeline","subfamily":"Performance Analysis","year":"1970s","originator":"John Boyd, U.S. Air Force","url":"https://scholargate.app/en/aerospace/specific-excess-power","markdownUrl":"https://scholargate.app/en/aerospace/specific-excess-power.md","definition":"Specific excess power (Ps) is a metric that quantifies the rate of change of energy per unit weight, representing how quickly an aircraft can trade speed for altitude (or vice versa) at a given flight condition. Developed by John Boyd in the 1970s as part of energy maneuverability theory, Ps is essential for assessing aircraft performance during combat maneuvering, climb, and acceleration. Specific excess power is widely used in military aircraft design, flight envelope analysis, and tactical air combat assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Boyd, U.S. Air Force","subfamily":"Performance Analysis","year":"1970s","type":"Tactical metric"},"citations":[{"ref":"Boyd, J. R., & Hammond, J. A. (1971). The mechanics of air combat. Fighter Weapons Newsletter, US Air Force Tactical Air Command.","type":"article","doi":null,"isbn":null,"url":"https://www.boydpp.net"},{"ref":"Loh, R. N. (1985). Performance Characteristics and Optimization of Air-Breathing Engines for Flight. AIAA Education Series.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Performance+Characteristics+and+Optimization+of+Air-Breathing+Engines+for+Flight+Loh"},{"ref":"Roskam, J., & Lan, C. T. E. (1989). Airplane Aerodynamics and Performance. Design, Analysis and Research Corporation.","type":"book","doi":null,"isbn":null,"url":"https://www.darcorp.com"}],"related":["weight-and-balance","blade-element-momentum-theory","theodorsen-flutter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"specific-phobia-questionnaire","name":"Specific Phobia Questionnaire","fullName":"Specific Phobia Questionnaire (SPQ)","aliases":["SPQ"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"phobic-disorders","year":1996,"originator":"Ahmet Osman, Frank X. Barrios, and colleagues","url":"https://scholargate.app/en/anxiety-disorders/specific-phobia-questionnaire","markdownUrl":"https://scholargate.app/en/anxiety-disorders/specific-phobia-questionnaire.md","definition":"The Specific Phobia Questionnaire (SPQ) is a brief self-report measure assessing fear, avoidance, and distress related to specific phobic objects or situations (e.g., heights, spiders, flying, blood-injection-injury). Developed by Osman and colleagues in the 1990s, the SPQ captures the cognitive, behavioral, and physiological dimensions of specific phobia in a concise format. It is useful in clinical screening, diagnosis, and treatment monitoring for diverse phobias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ahmet Osman, Frank X. Barrios, and colleagues","subfamily":"phobic-disorders","year":1996,"type":"Self-report"},"citations":[{"ref":"Osman, A., Barrios, F. X., Aukes, D., & Markway, K. (1996). The Fear-Avoidance Beliefs Questionnaire: Psychometric properties in a nonclinical population. Journal of Psychopathology and Behavioral Assessment, 18(2), 141–160.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Fear-Avoidance+Beliefs+Questionnaire%3A+Psychometric+properties+in+a+nonclinical+population+Osman"}],"related":["anxiety-sensitivity-index","separation-anxiety-questionnaire","health-anxiety-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"specificity","name":"Specificity","fullName":"Specificity (True Negative Rate)","aliases":["True Negative Rate","TNR"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"20th century","originator":"Historical statistical foundations","url":"https://scholargate.app/en/model-evaluation/specificity","markdownUrl":"https://scholargate.app/en/model-evaluation/specificity.md","definition":"Specificity measures the proportion of actual negative cases that were correctly identified as negative by the classifier. It answers the question: 'Of all the cases that were truly negative, how many did we correctly reject?' Specificity is complementary to recall and is essential when false positives are costly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Historical statistical foundations","subfamily":"Classification Metric","year":"20th century","type":"Evaluation metric"},"citations":[{"ref":"Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.","type":"article","doi":"10.1016/j.patrec.2005.10.010","isbn":null,"url":null},{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"}],"related":["recall","precision","balanced-accuracy","f1-score","matthews-correlation-coefficient"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spectral-bin-microphysics","name":"Spectral Bin Microphysics","fullName":"Spectral Bin Microphysics Model","aliases":["Bin microphysics","Spectral microphysics","Explicit microphysics"],"domain":"meteorology","family":"process-pipeline","subfamily":"Cloud microphysics modeling","year":"1999","originator":"Khain, Ovtchinnikov","url":"https://scholargate.app/en/meteorology/spectral-bin-microphysics","markdownUrl":"https://scholargate.app/en/meteorology/spectral-bin-microphysics.md","definition":"Spectral bin microphysics is a detailed cloud microphysical modeling approach that explicitly represents the particle size distribution (PSD) by dividing particles into discrete size bins. Rather than assuming a fixed shape for the PSD, bin models track the number and mass of particles in each size category, allowing detailed simulation of cloud and precipitation processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Khain, Ovtchinnikov","subfamily":"Cloud microphysics modeling","year":"1999","type":"Explicit particle size distribution model"},"citations":[{"ref":"Khain, A. P., Ovtchinnikov, M., Pinsky, M., Pokrovsky, A., & Krugliak, H. (2000). Notes on the state-of-the-art numerical modeling of cloud microphysics. Atmospheric Research, 55(3–4), 159-224.","type":"article","doi":"10.1016/S0169-8095(00)00064-8","isbn":null,"url":null},{"ref":"Seifert, A., & Beheng, K. D. (2006). A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description. Meteorology and Atmospheric Physics, 92(1–2), 45-66.","type":"article","doi":"10.1007/s00703-005-0112-4","isbn":null,"url":null}],"related":["kohler-theory","cloud-condensation-nuclei-analysis","wrf-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spectral-clustering","name":"Spectral Clustering","fullName":"Spectral Clustering via Graph Laplacian Eigenvectors (Ng–Jordan–Weiss Algorithm)","aliases":["NJW spectral clustering","graph Laplacian clustering","normalized spectral clustering","spectral graph clustering","SC"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2002,"originator":"Ng, A. Y.; Jordan, M. I.; Weiss, Y.","url":"https://scholargate.app/en/machine-learning/spectral-clustering","markdownUrl":"https://scholargate.app/en/machine-learning/spectral-clustering.md","definition":"Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ng, A. Y.; Jordan, M. I.; Weiss, Y.","year":2002,"type":"Graph-based clustering (spectral method)","task":"Unsupervised clustering","minSample":30,"complexity":"O(n^3) for full eigendecomposition; O(n^2 k) for approximate methods","hyperparameters":"Number of clusters k; similarity kernel bandwidth sigma; Laplacian normalization type"},"citations":[{"ref":"Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+Spectral+Clustering%3A+Analysis+and+an+Algorithm+Ng"},{"ref":"von Luxburg, U. (2007). A Tutorial on Spectral Clustering. Statistics and Computing, 17, 395–416.","type":"article","doi":"10.1007/s11222-007-9033-z","isbn":null,"url":null},{"ref":"Shi, J., & Malik, J. (2000). Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905.","type":"article","doi":"10.1109/34.868688","isbn":null,"url":null}],"related":["k-means","dbscan","gaussian-mixture-model","hierarchical-clustering","t-sne","pca"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spectral-methods","name":"Spectral Methods","fullName":"Spectral Methods for Differential Equations","aliases":["spectral Galerkin","spectral collocation","pseudospectral method"],"domain":"numerical-methods","family":"ml-model","subfamily":"High-Order Projection","year":"1969","originator":"Steven Orszag","url":"https://scholargate.app/en/numerical-methods/spectral-methods","markdownUrl":"https://scholargate.app/en/numerical-methods/spectral-methods.md","definition":"Spectral Methods are high-order numerical techniques for solving differential equations using global polynomial expansions (e.g., Fourier or Legendre series) rather than local piecewise polynomials. Developed by Steven Orszag in the 1960s for turbulence simulation, they offer exponential convergence for smooth problems, making them ideal for scientific computing when solution regularity is high.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven Orszag","subfamily":"High-Order Projection","year":"1969","type":"Global polynomial approximation"},"citations":[{"ref":"Orszag, S. A. (1969). Numerical methods for the simulation of turbulence. Physics of Fluids Supplements, 12(12), 250–257.","type":"article","doi":"10.1063/1.1692445","isbn":null,"url":null},{"ref":"Gottlieb, D., & Orzag, S. A. (1977). Numerical Analysis of Spectral Methods: Theory and Applications. SIAM.","type":"article","doi":"10.1137/1.9781611970425","isbn":null,"url":null},{"ref":"Canuto, C., Hussaini, M. Y., Quarteroni, A., & Zang, T. A. (2006). Spectral Methods: Fundamentals in Single Domains. Springer.","type":"book","doi":"10.1007/978-3-540-30726-6","isbn":null,"url":null}],"related":["galerkin-method","finite-element-method","finite-difference-method","legendre-polynomials"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"speculation-detection","name":"Speculation Detection","fullName":"Speculation and Uncertainty Detection (Hedging Analysis)","aliases":["hedging detection","epistemic modality analysis","hedge detection","Belirsizlik / Spekülasyon Tespiti (Hedging)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1996 (lexicon approach); 2010 (CoNLL shared task)","originator":"Hyland, K. (lexicon-based framing, 1996); Farkas et al. (shared-task benchmark, 2010)","url":"https://scholargate.app/en/text-mining/speculation-detection","markdownUrl":"https://scholargate.app/en/text-mining/speculation-detection.md","definition":"Speculation detection, also known as hedging analysis, is a natural-language-processing task that identifies epistemic uncertainty markers — words and phrases such as 'may', 'possibly', 'it is suggested that' — within scientific, biomedical, and news texts. Formalised by Hyland (1996) for scientific writing and benchmarked by the CoNLL-2010 shared task, the method reveals where authors signal incomplete knowledge, tentativeness, or distance from a claim rather than asserting facts directly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hyland, K. (lexicon-based framing, 1996); Farkas et al. (shared-task benchmark, 2010)","year":"1996 (lexicon approach); 2010 (CoNLL shared task)","type":"NLP text-classification task","inputType":"Plain text (scientific articles, news, biomedical documents)","outputType":"Hedge cue labels per token/sentence + scope boundaries","minSample":"20 documents","difficulty":"2 / 5"},"citations":[{"ref":"Hyland, K. (1996). Writing Without Conviction? Hedging in Science Research Articles. Applied Linguistics, 17(4), 433-454.","type":"article","doi":"10.1093/applin/17.4.433","isbn":null,"url":null},{"ref":"Farkas, R. et al. (2010). The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text. Proceedings of the Fourteenth Conference on Computational Natural Language Learning — Shared Task (CoNLL 2010), 1-12.","type":"proceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/W10-3001"}],"related":["sentiment-analysis","text-classification","named-entity-recognition","discourse-analysis","argument-mining"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"speech-act-theory","name":"Speech Act Theory","fullName":"Speech Act Theory Framework","aliases":["Pragmatics","Performative Analysis"],"domain":"linguistics","family":"process-pipeline","subfamily":"Pragmatics","year":"1962","originator":"J. L. Austin","url":"https://scholargate.app/en/linguistics/speech-act-theory","markdownUrl":"https://scholargate.app/en/linguistics/speech-act-theory.md","definition":"Speech Act Theory is a framework in pragmatics developed by J. L. Austin and refined by John Searle, analyzing language as action. The core insight is that utterances are not merely vehicles for propositions but acts with pragmatic effects: 'I pronounce you married' creates a marriage; 'Please close the door' issues a request; 'I promise to help' incurs an obligation. By examining the conditions under which acts succeed and the types of effects they produce, Speech Act Theory illuminates how language functions in social interaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. L. Austin","subfamily":"Pragmatics","year":"1962","type":"Empirical process pipeline"},"citations":[{"ref":"Austin, J. L. (1962). How to Do Things with Words. Oxford: Oxford University Press.","type":"book","doi":"10.1093/acprof:oso/9780198245537.001.0001","isbn":null,"url":null},{"ref":"Searle, J. R. (1969). Speech Acts: An Essay in the Philosophy of Language. Cambridge: Cambridge University Press.","type":"book","doi":"10.1017/CBO9781139173438","isbn":null,"url":null},{"ref":"Levinson, S. C. (1983). Pragmatics. Cambridge: Cambridge University Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Pragmatics+Levinson"}],"related":["discourse-analysis","pragmatics","conversational-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"speech-intelligibility","name":"Speech Intelligibility","fullName":"Speech Intelligibility Assessment and Prediction","aliases":["intelligibility metrics","STI","Speech Transmission Index","clarity index"],"domain":"acoustics","family":"process-pipeline","subfamily":"Acoustic perception metrics","year":"1980","originator":"Herman Steeneken, Tammo Houtgast","url":"https://scholargate.app/en/acoustics/speech-intelligibility","markdownUrl":"https://scholargate.app/en/acoustics/speech-intelligibility.md","definition":"Speech intelligibility is a quantitative measure of how well listeners understand spoken content in acoustic environments. Formalized by Steeneken and Houtgast in 1980 with the Speech Transmission Index (STI), intelligibility metrics combine room acoustic parameters (RT60, noise, clarity) to predict listener comprehension. Understanding speech intelligibility is essential for designing classrooms, offices, hearing aids, and public address systems where clear communication is critical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herman Steeneken, Tammo Houtgast","subfamily":"Acoustic perception metrics","year":"1980","type":"Speech clarity assessment method"},"citations":[{"ref":"Steeneken, H. J., & Houtgast, T. (1980). A physical method for measuring speech-transmission quality. Journal of the Acoustical Society of America, 67(1), 318–326.","type":"article","doi":"10.1121/1.384464","isbn":null,"url":null},{"ref":"Houtgast, T., & Steeneken, H. J. (1985). A review of the MTF concept in room acoustics and its use for estimating speech intelligibility in auditoria. Journal of the Acoustical Society of America, 77(3), 1069–1077.","type":"article","doi":"10.1121/1.392224","isbn":null,"url":null},{"ref":"ASTM E2638-19 (2019). Standard Guide for Workplace Acoustics and Noise Reduction. American Society for Testing and Materials.","type":"standard","doi":null,"isbn":null,"url":"https://www.astm.org/standards/e2638"}],"related":["rt60-reverberation-time","psychoacoustic-masking","beamforming","fxlms-active-noise-control","room-impulse-response"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spelling-grammar-check","name":"Spelling and Grammar Check","fullName":"Spelling and Grammar Checking","aliases":["spell checking","grammar checking","text proofing","Yazım ve Dilbilgisi Denetimi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2003","originator":"Daniel Naber (rule-based checker); Peter Norvig (statistical spelling correction)","url":"https://scholargate.app/en/text-mining/spelling-grammar-check","markdownUrl":"https://scholargate.app/en/text-mining/spelling-grammar-check.md","definition":"Spelling and grammar checking is a text-mining task that detects spelling mistakes and grammatical errors in text and proposes corrections. Building on Naber's rule-based style and grammar checker (2003) and Norvig's statistical spelling corrector (2009), it is used for data-quality assessment and text normalisation before further analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel Naber (rule-based checker); Peter Norvig (statistical spelling correction)","year":"2003","type":"Text-mining preprocessing / quality-assessment task","approaches":"Dictionary-based / rule-based / language-model-based","output":"Detected errors with correction suggestions","difficulty":"Introductory"},"citations":[{"ref":"Naber, D. (2003). A Rule-Based Style and Grammar Checker. Diploma Thesis.","type":"thesis","doi":null,"isbn":null,"url":"https://www.danielnaber.de/languagetool/download/style_and_grammar_checker.pdf"},{"ref":"Norvig, P. (2009). How to Write a Spelling Corrector.","type":"misc","doi":null,"isbn":null,"url":"https://norvig.com/spell-correct.html"}],"related":["ngram-language-model","paraphrase-detection","text-normalization","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spike-sorting","name":"Spike Sorting","fullName":"Spike Sorting","aliases":["unit isolation","single-unit recording","electrophysiology clustering"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Single-unit electrophysiology","year":"2000","originator":"Kenneth Harris","url":"https://scholargate.app/en/neuroimaging/spike-sorting","markdownUrl":"https://scholargate.app/en/neuroimaging/spike-sorting.md","definition":"Spike sorting is an electrophysiological technique for identifying and isolating action potentials of individual neurons from extracellular electrical recordings. Central to single-unit neurophysiology, spike sorting assigns spikes recorded on electrode arrays to their neuron of origin, enabling study of individual neuron firing patterns, timing, and network interactions with single-cell resolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kenneth Harris","subfamily":"Single-unit electrophysiology","year":"2000","type":"Neuronal activity classification pipeline"},"citations":[{"ref":"Harris, K. D., Csicsvari, J., Hirase, H., et al. (2016). Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular recordings. Journal of Neurophysiology, 84(1), 401–414.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Accuracy+of+tetrode+spike+separation+as+determined+by+simultaneous+intracellular+and+extracellular+recordings+Harris"},{"ref":"Yger, P., Spampinato, G. L., Esposito, E., et al. (2018). A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. eLife, 7, e34518.","type":"article","doi":"10.7554/eLife.34518","isbn":null,"url":null}],"related":["event-related-potential-analysis","eloreta","meg-source-localization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spin-social-phobia","name":"Social Phobia Inventory","fullName":"Social Phobia Inventory (SPIN)","aliases":["SPIN"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Social anxiety disorder assessment","year":"2000","originator":"Kathryn M. Connor, Jonathan R. T. Davidson, and colleagues","url":"https://scholargate.app/en/clinical-psychology/spin-social-phobia","markdownUrl":"https://scholargate.app/en/clinical-psychology/spin-social-phobia.md","definition":"The Social Phobia Inventory (SPIN) is a 17-item self-report measure of social anxiety disorder symptoms. Developed by Connor, Davidson, and colleagues in 2000, the SPIN assesses fear, avoidance, and physiological symptoms related to social anxiety. It is widely used for screening and monitoring social anxiety disorder in clinical and research settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kathryn M. Connor, Jonathan R. T. Davidson, and colleagues","subfamily":"Social anxiety disorder assessment","year":"2000","type":"Social phobia symptom measurement"},"citations":[{"ref":"Connor, K. M., Davidson, J. R., Churchill, L. E., Sherwood, A., Foa, E., & Weisler, R. H. (2000). Psychometric properties of the Social Phobia Inventory (SPIN): A new self-rating scale. British Journal of Psychiatry, 176, 379-386.","type":"article","doi":"10.1192/bjp.176.4.379","isbn":null,"url":null},{"ref":"Antony, M. M., Coons, M. J., McCabe, R. E., Ashbaugh, A. R., & Swinson, R. P. (2006). Psychometric properties of the Social Phobia Inventory: Further evaluation in two community samples. Journal of Anxiety Disorders, 20(8), 1039-1055.","type":"article","doi":"10.1016/j.brat.2005.08.013","isbn":null,"url":null}],"related":["oci-r","hamilton-anxiety-rating-scale","hads","dass-21","k10-kessler"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spiritual-care-competence-scale","name":"Spiritual Care Competence Scale","fullName":"Spiritual Care Competence Scale","aliases":["SCCS","Spiritual Competence Scale"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"Spiritual care competence and training","year":"2012","originator":"Ronaldson, S.; Dyson, S. J.; Dyson, E.","url":"https://scholargate.app/en/integrative-medicine/spiritual-care-competence-scale","markdownUrl":"https://scholargate.app/en/integrative-medicine/spiritual-care-competence-scale.md","definition":"The SCCS is a clinical competency assessment tool measuring healthcare professionals' knowledge, attitudes, and skills in providing spiritual care to patients. Developed by Ronaldson and colleagues, it operationalizes spiritual care as an evidence-based competency, reflecting recognition that spirituality significantly impacts patient well-being, coping, and healing outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronaldson, S.; Dyson, S. J.; Dyson, E.","subfamily":"Spiritual care competence and training","year":"2012","type":"Self-report and supervisor-rated competency scale"},"citations":[{"ref":"Ronaldson, S., Dyson, S. J., & Dyson, E. (2012). Spiritual care competency: The views of nurse educators and nurse managers. Journal of Clinical Nursing, 21(19–20), 2826–2836.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spiritual+care+competency%3A+The+views+of+nurse+educators+and+nurse+managers+Ronaldson"},{"ref":"McSherry, W., Ross, L., & McSherry, R. (2006). The meaning and significance of spirituality in nursing: A European perspective. In L. Ross (Ed.), Spiritual care in nursing practice (pp. 163–180). London: Quay Books.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=McSherry%2C%20W.%2C%20Ross%2C%20L.%2C%20%26%20McSherry%2C%20R.%20(2006).%20The%20meaning%20and%20significance%20of%20spirituality%20in%20nursing%3A%20A%20European%20persp"}],"related":["holistic-caring-inventory","attitudes-cam-scale","integrative-medicine-attitudes","therapeutic-touch-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spiritual-wellbeing-scale","name":"Spiritual Well-Being Scale","fullName":"Spiritual Well-Being Scale (SWBS)","aliases":["SWBS"],"domain":"palliative-care","family":"process-pipeline","subfamily":"existential-wellbeing","year":"1982","originator":"Raymond F. Paloutzian and Craig W. Ellison","url":"https://scholargate.app/en/palliative-care/spiritual-wellbeing-scale","markdownUrl":"https://scholargate.app/en/palliative-care/spiritual-wellbeing-scale.md","definition":"The Spiritual Well-Being Scale (SWBS) is a 20-item self-report measure of spiritual well-being encompassing both religious faith and existential meaning—two dimensions critical to quality of life at end-of-life. Developed by Paloutzian and Ellison in 1982, the SWBS has become a cornerstone assessment tool in palliative care, chaplaincy, and oncology to identify unmet spiritual needs, guide supportive interventions, and evaluate the impact of spiritual care programs on patient outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raymond F. Paloutzian and Craig W. Ellison","subfamily":"existential-wellbeing","year":"1982","type":"Self-report"},"citations":[{"ref":"Paloutzian, R. F., & Ellison, C. W. (1982). Loneliness, spiritual well-being, and the quality of life. In L. A. Peplau & D. Perlman (Eds.), Loneliness: A sourcebook of current theory, research and therapy (pp. 224–237). Wiley.","type":"book","doi":null,"isbn":"978-0471084785","url":"https://worldcat.org/isbn/978-0471084785"},{"ref":"Ellison, C. W. (2006). Spiritual well-being: Conceptualization and measurement. Journal of Psychology and Theology, 11(4), 330–340.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/6981975"}],"related":["patient-dignity-inventory","mcgill-quality-of-life","facit-palliative","good-death-inventory","palliative-performance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"split-plot-design","name":"Split-Plot Design","fullName":"Split-Plot Experimental Design","aliases":["split-plot ANOVA","whole-plot sub-plot design","Bölünmüş Parsel Deseni (Split-Plot)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1935,"originator":"Frank Yates","url":"https://scholargate.app/en/experimental-design/split-plot-design","markdownUrl":"https://scholargate.app/en/experimental-design/split-plot-design.md","definition":"The split-plot design is a parametric experimental design that applies one factor to large whole plots and a second factor to subdivisions (sub-plots) within each whole plot. It was introduced by Frank Yates in 1935 to handle agricultural experiments where one factor — such as irrigation or tillage method — is difficult or impractical to change frequently, while a second factor can be varied more easily within the same plot.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Yates","year":1935,"family":"Experimental design","type":"Parametric mixed-model ANOVA","factors":2,"errorTerms":2,"outcome":"continuous","parametric":true,"randomization":"restricted","minSampleSize":12},"citations":[{"ref":"Yates, F. (1935). Complex Experiments. Supplement to the Journal of the Royal Statistical Society, 2(2), 181–247.","type":"article","doi":"10.2307/2983638","isbn":null,"url":null},{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119492443","url":null}],"related":["completely-randomized-design","randomized-complete-block","two-way-anova","repeated-measures-anova","hlm","one-way-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sport-anxiety-scale","name":"Sport Anxiety Scale","fullName":"Sport Anxiety Scale (SAS)","aliases":["SAS","SAS-2","Sport-Specific Anxiety"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"anxiety-and-stress","year":"1990","originator":"Ronald Smith, Frank Smoll, Robert Schutz","url":"https://scholargate.app/en/sport-psychology/sport-anxiety-scale","markdownUrl":"https://scholargate.app/en/sport-psychology/sport-anxiety-scale.md","definition":"The SAS is a 15–21 item questionnaire measuring trait (dispositional) sport-specific anxiety—the tendency to experience worry and physiological arousal in sport-competitive contexts. Developed by Smith, Smoll, and Schutz in 1990, the SAS is the primary instrument for assessing individual differences in sport anxiety proneness and for predicting anxiety management needs across diverse athletic populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald Smith, Frank Smoll, Robert Schutz","subfamily":"anxiety-and-stress","year":"1990","type":"Self-report sport-specific trait anxiety questionnaire"},"citations":[{"ref":"Smith, R. E., Smoll, F. L., & Schutz, R. W. (1990). Measurement and correlates of sport-specific cognitive and somatic trait anxiety: The Sport Anxiety Scale. Anxiety Research, 2(4), 263–280.","type":"article","doi":"10.1080/08917779008248733","isbn":null,"url":null},{"ref":"Smith, R. E., Cumming, S. P., & Smoll, F. L. (2006). Measurement of multidimensional sport performance anxiety in children and adults: The Sport Anxiety Scale-2. Journal of Sport & Exercise Psychology, 28(4), 479–501.","type":"article","doi":"10.1123/jsep.28.4.479","isbn":null,"url":null}],"related":["competitive-state-anxiety-inventory","profile-of-mood-states","sport-confidence-inventory","mental-toughness-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sport-confidence-inventory","name":"Sport Confidence Inventory","fullName":"Sport Confidence Inventory (SCI)","aliases":["SCI","Trait Sport Confidence"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"confidence-and-self-efficacy","year":"1986","originator":"Robin Vealey","url":"https://scholargate.app/en/sport-psychology/sport-confidence-inventory","markdownUrl":"https://scholargate.app/en/sport-psychology/sport-confidence-inventory.md","definition":"The SCI is a 13-item questionnaire measuring general, trait-level confidence in sport ability—the athlete's habitual belief in their capability to execute skills and perform well in their sport. Developed by Vealey in 1986, the SCI is one of the most widely used instruments for assessing athlete self-confidence and predicting competitive success and psychological wellbeing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robin Vealey","subfamily":"confidence-and-self-efficacy","year":"1986","type":"Self-report sport-specific confidence trait questionnaire"},"citations":[{"ref":"Vealey, R. S. (1986). Conceptualization of sport-confidence and competitive orientation: Preliminary investigation and instrument development. Journal of Sport Psychology, 8(3), 221–246.","type":"article","doi":"10.1123/jsp.8.3.221","isbn":null,"url":null},{"ref":"Vealey, R. S. (1988). Sport-confidence and competitive orientation. In D. Hackfort & C. D. Spielberger (Eds.), Anxiety in Sports: An International Perspective (pp. 117–128). Washington, DC: Hemisphere.","type":"book","doi":null,"isbn":null,"url":"https://books.google.com/books/about/Anxiety_in_Sports.html"}],"related":["competitive-state-anxiety-inventory","mental-toughness-questionnaire","task-ego-orientation-sport","profile-of-mood-states"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sport-motivation-scale","name":"Sport Motivation Scale","fullName":"Sport Motivation Scale (SMS)","aliases":["SMS","SMS-6"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"motivation-and-engagement","year":"1995","originator":"Luc Pelletier, Marc Fortier, Robert Vallerand","url":"https://scholargate.app/en/sport-psychology/sport-motivation-scale","markdownUrl":"https://scholargate.app/en/sport-psychology/sport-motivation-scale.md","definition":"The SMS is a 24–28 item questionnaire measuring the motivational reasons athletes engage in sport, organized along the continuum of Self-Determination Theory: from intrinsic motivation (inherent enjoyment, mastery, excitement) through extrinsic forms (identified goals, introjected norms, external rewards) to amotivation (lack of intent). Developed by Pelletier and colleagues in 1995, the SMS has become the leading instrument for assessing sport motivation quality and predicting athlete engagement, retention, and psychological wellbeing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Luc Pelletier, Marc Fortier, Robert Vallerand","subfamily":"motivation-and-engagement","year":"1995","type":"Self-report sport motivation questionnaire"},"citations":[{"ref":"Pelletier, L. G., Fortier, M. S., Vallerand, R. J., Tuson, K. M., Brière, N. M., & Blais, M. R. (1995). Toward a new measure of intrinsic motivation, extrinsic motivation, and amotivation in sports: The Sport Motivation Scale (SMS). Journal of Sport & Exercise Psychology, 17(1), 35–53.","type":"article","doi":"10.1123/jsep.17.1.35","isbn":null,"url":null},{"ref":"Mallett, C. J., & Hanrahan, S. J. (2004). Elite athletes: Why does the 'fire' go out? Conceptualizing athlete burnout through selected organizational theories. Journal of Sport Sciences, 22(5), 389–397.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Elite+athletes%3A+Why+does+the+%27fire%27+go+out+Mallett"}],"related":["task-ego-orientation-sport","athletic-identity-measurement-scale","exercise-addiction-inventory","mental-toughness-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"spotis","name":"SPOTIS","fullName":"Stable Preference Ordering Towards Ideal Solution","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Dezert, J., Tchamova, A., Han, D., Tacnet, J. M.","url":"https://scholargate.app/en/decision-making/spotis","markdownUrl":"https://scholargate.app/en/decision-making/spotis.md","definition":"SPOTIS (Stable Preference Ordering Towards Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Dezert, J., Tchamova, A., Han, D., Tacnet, J. M. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dezert, J., Tchamova, A., Han, D., Tacnet, J. M.","subfamily":"Ranking","year":"2020","type":"Normalised distance to ideal (rank-reversal free)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Dezert, J., Tchamova, A., Han, D., Tacnet, J. M. (2020). The SPOTIS rank reversal free method for multi-criteria decision-making support. 2020 IEEE 23rd International Conference on Information Fusion (FUSION)","type":"article","doi":"10.23919/FUSION45008.2020.9190347","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sprobid","name":"SPROBID","fullName":"Simplified PROBID using Top/Bottom Quartile Ideal Sets","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Wang, Z., Rangaiah, G. P., Wang, X.","url":"https://scholargate.app/en/decision-making/sprobid","markdownUrl":"https://scholargate.app/en/decision-making/sprobid.md","definition":"SPROBID (Simplified PROBID using Top/Bottom Quartile Ideal Sets) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, Z., Rangaiah, G. P., Wang, X. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wang, Z., Rangaiah, G. P., Wang, X.","subfamily":"Ranking","year":"2021","type":"Multi-ideal distance ranking with quartile-based ideal selection","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Wang, Z., Rangaiah, G. P., Wang, X. (2021). Preference ranking on the basis of ideal-average distance method for multi-criteria decision-making. Industrial & Engineering Chemistry Research","type":"article","doi":"10.1021/acs.iecr.1c01413","isbn":null,"url":null}],"related":["ahp","bwm","critic","entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"st-george-respiratory-questionnaire","name":"SGRQ","fullName":"St. George's Respiratory Questionnaire","aliases":["SGRQ","St George's"],"domain":"pulmonology","family":"process-pipeline","subfamily":"respiratory-qol","year":"1991","originator":"Paul W. Jones, King's College London","url":"https://scholargate.app/en/pulmonology/st-george-respiratory-questionnaire","markdownUrl":"https://scholargate.app/en/pulmonology/st-george-respiratory-questionnaire.md","definition":"The SGRQ is a 76-item disease-specific quality-of-life instrument designed to measure health status in patients with chronic respiratory disease, particularly chronic obstructive pulmonary disease (COPD). Developed by Jones and colleagues at King's College London in 1991, it has become the gold standard for assessing functional impact and symptom burden in respiratory populations. The SGRQ is widely used in clinical trials, epidemiological studies, and routine respiratory care to track changes in patient-reported outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Paul W. Jones, King's College London","subfamily":"respiratory-qol","year":"1991","type":"Self-report questionnaire"},"citations":[{"ref":"Jones, P. W., Quirk, F. H., & Baveystock, C. M. (1991). The St George's Respiratory Questionnaire. Respiratory Medicine, 85(Suppl B), 25-31.","type":"article","doi":"10.1016/S0954-6111(06)80166-6","isbn":null,"url":null},{"ref":"Jones, P. W., Quirk, F. H., Baveystock, C. M., & Littlejohns, P. (1992). A self-complete measure of health status for chronic airflow limitation: The St. George's Respiratory Questionnaire. American Review of Respiratory Disease, 145(6), 1321-1327.","type":"article","doi":"10.1164/ajrccm/145.6.1321","isbn":null,"url":null}],"related":["asthma-control-questionnaire","chronic-respiratory-disease-questionnaire","mrc-dyspnoea-scale","sinonasal-outcome-test","breathlessness-cough-sputum-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stable-population-theory","name":"Stable Population Theory","fullName":"Stable Population Theory","aliases":["Lotka-Coale Stable Population Model","Stable Age Distribution Theory","Stationary Population Theory","Kararlı Nüfus Teorisi"],"domain":"demography","family":"regression-model","subfamily":"Demography","year":1972,"originator":"Alfred J. Lotka; Ansley Coale","url":"https://scholargate.app/en/demography/stable-population-theory","markdownUrl":"https://scholargate.app/en/demography/stable-population-theory.md","definition":"Stable Population Theory is a mathematical framework in demography that describes the age structure and growth dynamics of a closed population subject to constant age-specific fertility and mortality schedules over a long period. Foundational work by Alfred J. Lotka established the core integral equation in the early twentieth century, and Ansley Coale's 1972 mathematical synthesis became the definitive theoretical reference, showing that any population exposed to invariant vital rates will converge to a unique stable age distribution growing at a fixed intrinsic rate of natural increase.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alfred J. Lotka; Ansley Coale","year":1972,"type":"Mathematical demographic model","subfamily":"Demography","data_required":"Age-specific fertility and mortality rates","output":"Intrinsic rate of natural increase; stable age distribution"},"citations":[{"ref":"Coale, A. J. (1972). The Growth and Structure of Human Populations: A Mathematical Investigation. Princeton University Press.","type":"book","doi":null,"isbn":"978-0-691-09357-4","url":null}],"related":["life-table","cohort-component-projection"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stacked-generalization","name":"Stacked Generalization","fullName":"Stacked Generalization Ensemble","aliases":["stacking","meta-learning"],"domain":"ensemble-learning","family":"ml-model","subfamily":"Ensemble","year":"1992","originator":"David Wolpert","url":"https://scholargate.app/en/ensemble-learning/stacked-generalization","markdownUrl":"https://scholargate.app/en/ensemble-learning/stacked-generalization.md","definition":"Stacked generalization, or stacking, is a two-level ensemble method where base-level classifiers are trained on the original data, and a meta-learner is trained on the predictions of the base classifiers. The meta-learner learns how to best combine base predictions rather than using fixed aggregation rules. Introduced by David Wolpert in 1992, stacking achieves state-of-the-art performance by automatically learning the optimal weighting and interaction patterns among base models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Wolpert","subfamily":"Ensemble","year":"1992","type":"meta-learning aggregation"},"citations":[{"ref":"Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259.","type":"article","doi":"10.1016/S0893-6080(05)80023-1","isbn":null,"url":null},{"ref":"Breiman, L. (1996). Stacked regressions. Machine Learning, 24(1), 49-64.","type":"article","doi":"10.1023/a:1018046112532","isbn":null,"url":null}],"related":["bagging-ensemble","boosting-ensemble","majority-voting","blending","super-learner"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stackelberg-competition","name":"Stackelberg Competition","fullName":"Stackelberg Oligopoly Model with Leader-Follower Dynamics","aliases":["Quantity Leadership","Sequential Oligopoly","Stackelberg Equilibrium"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1934","originator":"Heinrich von Stackelberg","url":"https://scholargate.app/en/game-theory/stackelberg-competition","markdownUrl":"https://scholargate.app/en/game-theory/stackelberg-competition.md","definition":"Stackelberg Competition models sequential oligopolistic markets where one firm (the leader) commits to a quantity first, and other firms (followers) observe this choice and respond. Introduced by Heinrich von Stackelberg in 1934, the model captures first-mover advantage in quantity-setting competition. The resulting Stackelberg Equilibrium, found by backward induction, yields the leader higher profit than simultaneous (Cournot) competition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heinrich von Stackelberg","subfamily":"Game-theoretic","year":"1934","type":"algorithm"},"citations":[{"ref":"von Stackelberg, H. (1934). Marktform und Gleichgewicht. Julius Springer.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/marktformundgle0000vonst"},{"ref":"Tirole, J. (1988). The Theory of Industrial Organization. MIT Press.","type":"book","doi":null,"isbn":null,"url":"https://mitpress.mit.edu/9780262200714/the-theory-of-industrial-organization/"}],"related":["cournot-competition","subgame-perfect-equilibrium","nash-equilibrium","bayesian-nash-equilibrium"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stacking-ensemble","name":"Stacking","fullName":"Stacked Generalization (Stacking Ensemble with a Meta-Learner)","aliases":["Stacking (Yığınlama — Meta-Öğrenme)","stacked generalization","meta-learning ensemble","super learner","yığınlama"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1992,"originator":"Wolpert, D.H.","url":"https://scholargate.app/en/machine-learning/stacking-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/stacking-ensemble.md","definition":"Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wolpert, D.H.","year":1992,"type":"Ensemble (heterogeneous meta-learning)","task":"Classification & prediction","minSample":100},"citations":[{"ref":"Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.","type":"article","doi":"10.1016/S0893-6080(05)80023-1","isbn":null,"url":null},{"ref":"van der Laan, M.J., Polley, E.C. & Hubbard, A.E. (2007). Super Learner. Statistical Applications in Genetics and Molecular Biology, 6(1), Article 25.","type":"article","doi":"10.2202/1544-6115.1309","isbn":null,"url":null}],"related":["random-forest","xgboost","logistic-regression","decision-tree","svm-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"staffing-ratio-analysis","name":"Staffing Ratio Analysis","fullName":"Workforce Staffing Ratio Analysis for Healthcare Resource Optimization","aliases":["Staffing Model","Nursing Ratio Analysis"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"Workforce planning, Staffing models","year":"1990","originator":"Healthcare operations and nursing research","url":"https://scholargate.app/en/healthcare-management/staffing-ratio-analysis","markdownUrl":"https://scholargate.app/en/healthcare-management/staffing-ratio-analysis.md","definition":"Staffing Ratio Analysis is a systematic method for determining appropriate healthcare worker levels (nurses, physicians, technicians) based on patient volume, acuity, and task requirements. Research shows that staffing levels directly impact patient safety, quality, and staff burnout; systematic analysis supports evidence-based workforce planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Healthcare operations and nursing research","subfamily":"Workforce planning, Staffing models","year":"1990","type":"Quantitative workforce planning methodology"},"citations":[{"ref":"Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA, 288(16), 1987–1993.","type":"article","doi":"10.1001/jama.288.16.1987","isbn":null,"url":null},{"ref":"Griffiths, P., Ball, J., Drennan, V., Dall'Ora, C., Jones, J., Maruotti, A., & Saucedo, A. R. (2016). Nurse staffing levels and patient outcomes: Systematic review of longitudinal studies. International Journal of Nursing Studies, 61, 195–213.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nurse+staffing+levels+and+patient+outcomes%3A+Systematic+review+of+longitudinal+studies+Griffiths"},{"ref":"U.S. Bureau of Labor Statistics. (2023). Occupational Employment Statistics. Healthcare Support Occupations.","type":"article","doi":null,"isbn":null,"url":"https://www.bls.gov/oes/current/oes310000.htm"}],"related":["queuing-theory-healthcare","hospital-bed-occupancy-model","lean-healthcare","patient-flow-simulation","dea-hospital-efficiency"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stages-of-concern-questionnaire","name":"SoC","fullName":"Stages of Concern Questionnaire","aliases":["SoC","Stages of Concern","SoC Questionnaire","CBAM-SoC"],"domain":"implementation-science","family":"process-pipeline","subfamily":"change readiness assessment","year":1977,"originator":"Gene E. Hall, PhD; Susan F. Loucks, PhD (CBAM model); George et al. (SoC measurement)","url":"https://scholargate.app/en/implementation-science/stages-of-concern-questionnaire","markdownUrl":"https://scholargate.app/en/implementation-science/stages-of-concern-questionnaire.md","definition":"The Stages of Concern Questionnaire (SoC) is a 35-item self-report instrument that measures the types and intensity of concerns individuals experience when adopting new practices, technologies, or innovations. Developed by Hall and colleagues in the 1970s as part of the Concerns-Based Adoption Model (CBAM), the SoC measures seven stages of concern: Awareness (low concern about the innovation), Informational (interest in learning more), Personal (worry about one's competence and impact on one's role), Management (concern about logistics, time, and workflow integration), Consequence (focus on innovation's impact on students or clients), Collaboration (concern about coordinating with colleagues), and Refocusing (desire to modify or improve the innovation). The SoC has been widely used in education and increasingly in healthcare settings to understand implementation barriers from an individual change perspective and to tailor support strategies to address specific concerns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gene E. Hall, PhD; Susan F. Loucks, PhD (CBAM model); George et al. (SoC measurement)","subfamily":"change readiness assessment","year":1977,"type":"Self-report questionnaire"},"citations":[{"ref":"George, A. A., Hall, G. E., & Stiegelbauer, S. M. (2006). Measuring implementation in schools: the stages of concern about the innovation. Journal of Educational and Psychological Consultation, 16(3), 189–211.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+implementation+in+schools%3A+the+stages+of+concern+about+the+innovation+George"},{"ref":"Hall, G. E., George, A. A., & Rutherford, W. L. (1977). Measuring Stages of Concern about the Innovation: A manual for use of the Stages of Concern Questionnaire. Austin, TX: Southwest Educational Development Laboratory.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+Stages+of+Concern+about+the+Innovation%3A+A+manual+for+use+of+the+Stages+of+Concern+Questionnaire+Hall"}],"related":["evidence-based-practice-attitude","implementation-leadership-scale","knowledge-to-action-scale","normalisation-measure-development","innovation-adoption-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stance-detection","name":"Stance Detection","fullName":"Stance Detection (Stance Classification toward a Target)","aliases":["stance classification","stance identification","Tutum Tespiti (Stance Detection)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2016,"originator":"Mohammad et al. (SemEval-2016 Task 6)","url":"https://scholargate.app/en/text-mining/stance-detection","markdownUrl":"https://scholargate.app/en/text-mining/stance-detection.md","definition":"Stance detection is a natural-language-processing task that decides the position a text takes toward a specific claim, event, or topic — labelling it as favor, against, or neutral. Formalised by Mohammad et al. in the SemEval-2016 Task 6 shared task, it differs from plain sentiment analysis because the label is always relative to a defined target rather than the overall emotional tone of the text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-classification task toward a target","originator":"Mohammad et al. (SemEval-2016 Task 6)","year":2016,"labels":"Favor / against / neutral toward a defined claim or topic","minSample":50,"approaches":"Supervised classifier on labelled data / zero-shot transformer model"},"citations":[{"ref":"Mohammad, S. et al. (2016). SemEval-2016 Task 6: Detecting Stance in Tweets. Proceedings of SemEval-2016, 31-41.","type":"inproceedings","doi":"10.18653/v1/S16-1003","isbn":null,"url":null},{"ref":"Küçük, D. & Can, F. (2020). Stance Detection: A Survey. ACM Computing Surveys, 53(1), 1-37.","type":"article","doi":"10.1145/3369026","isbn":null,"url":null}],"related":["sentiment-analysis","text-classification","fake-news-detection","bert-embeddings"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stand-basal-area-measurement","name":"Stand Basal Area Measurement","fullName":"Forest Stand Basal Area Assessment and Density Estimation","aliases":["Basal area inventory","Tree density measurement","Stand stocking assessment"],"domain":"forestry","family":"process-pipeline","subfamily":"Forest mensuration and stand assessment","year":"1960s–1980s","originator":"Classical forestry practice; formalized by Husch and colleagues","url":"https://scholargate.app/en/forestry/stand-basal-area-measurement","markdownUrl":"https://scholargate.app/en/forestry/stand-basal-area-measurement.md","definition":"Stand basal area is a fundamental forest mensuration metric representing the total cross-sectional area of tree stems per unit land area, typically expressed in square meters per hectare. Formalized across twentieth-century forestry literature (notably by Husch, Beers, and Kershaw), basal area serves as a key indicator of forest density, biomass accumulation, and competitive pressure, essential for yield prediction and stand management planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical forestry practice; formalized by Husch and colleagues","subfamily":"Forest mensuration and stand assessment","year":"1960s–1980s","type":"Measurement and calculation pipeline"},"citations":[{"ref":"Husch, B., Beers, T. W., & Kershaw, J. A. (2003). Forest Mensuration (4th ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Forest+Mensuration+%284th+ed.%29+Husch"},{"ref":"West, P. W. (1981). Use of Diameter Increment and Basal Area Increment in Tree Growth Studies. Canadian Journal of Forest Research, 11(1), 122–137.","type":"article","doi":"10.1139/x80-012","isbn":null,"url":null},{"ref":"Kershaw, J. A., Ducey, M. J., Beers, T. W., & Husch, B. (2016). Forest Mensuration. John Wiley & Sons.","type":"book","doi":"10.1002/9781118902028","isbn":null,"url":null},{"ref":"Crown, P. H. (2000). Effects of Logging on Tree Diversity and Basal Area in Montane Forests of Cameroon. Forest Ecology and Management, 134(1-3), 251–264.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Effects+of+Logging+on+Tree+Diversity+and+Basal+Area+in+Montane+Forests+of+Cameroon+Crown"}],"related":["forest-inventory-sampling","tree-height-measurement","allometric-biomass-equation","silvicultural-treatment-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stand-density-index","name":"Stand Density Index","fullName":"Stand Density Index","aliases":["SDI","Reineke density index"],"domain":"forestry","family":"process-pipeline","subfamily":"Forest Mensuration","year":"1933","originator":"Louis Reineke","url":"https://scholargate.app/en/forestry/stand-density-index","markdownUrl":"https://scholargate.app/en/forestry/stand-density-index.md","definition":"The Stand Density Index (SDI), introduced by Reineke in 1933, is a dimensionless measure of forest density that accounts for both tree number and size. It expresses the number of trees per hectare in a stand, adjusted to a reference quadratic mean diameter (QMD) of 25 cm, providing a standardized metric for comparing tree density across different forest types and sizes. SDI is widely used in forest management to assess stocking levels and to guide thinning decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Louis Reineke","subfamily":"Forest Mensuration","year":"1933","type":"density measurement"},"citations":[{"ref":"Reineke, L. H. (1933). Perfecting a stand-density index for even-aged forests. Journal of Agricultural Research, 46(7), 627–638.","type":"article","doi":null,"isbn":null,"url":"https://naldc.nal.usda.gov/catalog/CAT88383821"},{"ref":"Long, J. N. (1985). A practical approach to density management. The Forestry Chronicle, 61(1), 23–27.","type":"article","doi":"10.5558/tfc61023-1","isbn":null,"url":null}],"related":["basal-area","canopy-gap-fraction","site-index-curve"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"standard-addition-method","name":"Standard Addition Method","fullName":"Standard Addition Method","aliases":["spiking method","known-addition method","matrix matching"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Quantification Strategy","year":"1920s","originator":"Analytical chemistry practice","url":"https://scholargate.app/en/analytical-chemistry/standard-addition-method","markdownUrl":"https://scholargate.app/en/analytical-chemistry/standard-addition-method.md","definition":"The standard addition method is a quantitative analytical technique that determines the concentration of an unknown analyte by measuring the response before and after adding a known quantity of the analyte (a standard) to the sample itself. This approach compensates for matrix effects and interference from other sample components, making it invaluable when analyzing complex matrices (biological fluids, environmental samples, geological materials) where the sample composition profoundly affects the measured signal. The standard addition method is widely recognized in analytical chemistry as a primary quantification strategy when external calibration is compromised by matrix variability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Analytical chemistry practice","subfamily":"Quantification Strategy","year":"1920s","type":"matrix-compensating calibration technique"},"citations":[{"ref":"Harris, D. C. (2010). Quantitative Chemical Analysis (8th ed.). Freeman.","type":"book","doi":null,"isbn":"978-1429218153","url":null},{"ref":"Ellison, S. L. R., & Barwick, V. J. (2000). Estimating measurement uncertainty: reconciliation using a phylogenetic approach. Accreditation and Quality Assurance, 5(4), 205–213.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimating+measurement+uncertainty%3A+reconciliation+using+a+phylogenetic+approach+Ellison"},{"ref":"Kochmann, S., Lobera, M. P., Carrera, C., Muller, N., & Guilland, J. C. (2012). Determination of selenium in serum and whole blood by inductively coupled plasma mass spectrometry. Clinical Chemistry and Laboratory Medicine, 50(8), 1371–1377.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Determination+of+selenium+in+serum+and+whole+blood+by+inductively+coupled+plasma+mass+spectrometry+Kochmann"}],"related":["potentiometric-titration","ion-chromatography","uv-vis-spectrophotometry","atomic-absorption-spectroscopy","inductively-coupled-plasma"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"standardized-precipitation-evapotranspiration-index","name":"Standardized Precipitation Evapotranspiration Index","fullName":"Standardized Precipitation Evapotranspiration Index","aliases":["SPEI"],"domain":"geophysics","family":"process-pipeline","subfamily":"Drought and water balance analysis","year":"2010","originator":"Vicente-Serrano, Beguería, and López-Moreno","url":"https://scholargate.app/en/geophysics/standardized-precipitation-evapotranspiration-index","markdownUrl":"https://scholargate.app/en/geophysics/standardized-precipitation-evapotranspiration-index.md","definition":"The Standardized Precipitation Evapotranspiration Index (SPEI) is a climate index that combines precipitation and temperature (via reference evapotranspiration) to characterize water deficits and droughts. Developed by Vicente-Serrano and colleagues in 2010, SPEI extends the SPI framework to account for the combined effect of precipitation deficiency and increased evaporative demand from warming, providing a more physically-based drought metric than precipitation-only indices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vicente-Serrano, Beguería, and López-Moreno","subfamily":"Drought and water balance analysis","year":"2010","type":"Probability-based water deficit indicator"},"citations":[{"ref":"Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. Journal of Climate, 23(7), 1696-1718.","type":"article","doi":"10.1175/2009JCLI2909.1","isbn":null,"url":null}],"related":["standardized-precipitation-index","ndvi","general-circulation-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"standardized-precipitation-index","name":"Standardized Precipitation Index","fullName":"Standardized Precipitation Index","aliases":["SPI"],"domain":"geophysics","family":"process-pipeline","subfamily":"Drought and precipitation analysis","year":"1993","originator":"Thomas McKee, Neil Doesken, and John Kleist","url":"https://scholargate.app/en/geophysics/standardized-precipitation-index","markdownUrl":"https://scholargate.app/en/geophysics/standardized-precipitation-index.md","definition":"The Standardized Precipitation Index (SPI) is a climate index that quantifies precipitation anomalies relative to historical norms, standardized to account for differences in precipitation climatology across regions. Introduced by McKee, Doesken, and Kleist in 1993, SPI has become a primary tool for drought detection and characterization, adopted by meteorological agencies worldwide for operational drought monitoring and early warning systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas McKee, Neil Doesken, and John Kleist","subfamily":"Drought and precipitation analysis","year":"1993","type":"Probabilistic drought indicator"},"citations":[{"ref":"McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the Eighth Conference on Applied Climatology, 179-184.","type":"article","doi":null,"isbn":null,"url":"https://www.ncdc.noaa.gov/monitoring-content/drdocumentation/wmo/12CCC"},{"ref":"Lloyd-Hughes, B., & Saunders, M. A. (2002). A drought climatology for Europe. International Journal of Climatology, 22(13), 1571-1592.","type":"article","doi":"10.1002/joc.846","isbn":null,"url":null}],"related":["standardized-precipitation-evapotranspiration-index","general-circulation-model","ndvi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"star-model","name":"STAR Model","fullName":"Smooth Transition Autoregressive Model","aliases":["smooth transition autoregressive model","LSTAR","ESTAR","logistic STAR","exponential STAR","Yumuşak Geçişli Otoregresif Model (STAR / LSTAR / ESTAR)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1994,"originator":"Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)","url":"https://scholargate.app/en/econometrics/star-model","markdownUrl":"https://scholargate.app/en/econometrics/star-model.md","definition":"The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)","year":1994,"type":"Nonlinear time-series regime-switching model","estimator":"Nonlinear least squares (with grid search for starting values)","variants":"LSTAR (logistic), ESTAR (exponential)","structure":"time series","minSample":100},"citations":[{"ref":"Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.","type":"article","doi":"10.1080/01621459.1994.10476462","isbn":null,"url":null},{"ref":"van Dijk, D., Teräsvirta, T. & Franses, P.H. (2002). Smooth Transition Autoregressive Models — A Survey of Recent Developments. Econometric Reviews, 21(1), 1–47.","type":"article","doi":"10.1081/ETC-120008723","isbn":null,"url":null}],"related":["arfima-model","panel-var","ols-regression","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"state-of-charge","name":"State of Charge","fullName":"State of Charge Estimation for Energy Storage Systems","aliases":["SOC","charge estimation"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Battery Management","year":"2004","originator":"Gregory Plett","url":"https://scholargate.app/en/thermodynamics/state-of-charge","markdownUrl":"https://scholargate.app/en/thermodynamics/state-of-charge.md","definition":"State of Charge (SOC) is the amount of energy available in a battery or energy storage system, expressed as a percentage of its maximum capacity. Accurate SOC estimation is critical for safe operation: underestimating SOC can cause unsafe discharges, overestimating can cause overcharging. SOC estimation combines current integration (coulomb counting), voltage-based methods, and Kalman filtering to achieve accuracy despite measurement noise and model uncertainties.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory Plett","subfamily":"Battery Management","year":"2004","type":"Estimation algorithm"},"citations":[{"ref":"Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Journal of Power Sources, 134(2), 252-261.","type":"article","doi":"10.1016/j.jpowsour.2004.02.031","isbn":null,"url":null},{"ref":"He, H., Xiong, R., & Fan, J. (2011). Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an extended Kalman filter. Journal of Power Sources, 196(6), 3365-3373.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation+of+lithium-ion+battery+equivalent+circuit+models+for+state+of+charge+estimation+by+an+extended+Kalman+filter+He"}],"related":["battery-equivalent-circuit-model","state-of-health","maximum-power-point-tracking"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"state-of-health","name":"State of Health","fullName":"State of Health Assessment for Battery Aging","aliases":["SOH","health estimation"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Battery Management","year":"2017","originator":"Craig Birkl","url":"https://scholargate.app/en/thermodynamics/state-of-health","markdownUrl":"https://scholargate.app/en/thermodynamics/state-of-health.md","definition":"State of Health (SOH) quantifies battery degradation by measuring how much capacity and power capability have been lost due to aging. SOH is expressed as a percentage (100% = new, 80% = end of life for many applications). Tracking SOH enables predictive maintenance, end-of-life detection, and accurate range/power predictions in aging systems. SOH reflects cumulative effects of cycling, calendar aging, and operating conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Craig Birkl","subfamily":"Battery Management","year":"2017","type":"Degradation assessment"},"citations":[{"ref":"Birkl, C. R., Roberts, M. R., McTurk, E., Bruce, P. G., & Howey, D. A. (2017). Degradation diagnostics for lithium ion cells. Journal of Power Sources, 341, 373-386.","type":"article","doi":"10.1016/j.jpowsour.2016.12.011","isbn":null,"url":null},{"ref":"Xiong, R., Sun, F., He, H., & Gong, X. (2018). Online estimation of widespread existence of unmodeled dynamics in lithium-ion battery for electric vehicles. Energies, 11(8), 1943.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Online+estimation+of+widespread+existence+of+unmodeled+dynamics+in+lithium-ion+battery+for+electric+vehicles+Xiong"}],"related":["state-of-charge","battery-equivalent-circuit-model","levelized-cost-of-energy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"state-space-model","name":"State Space Model","fullName":"State Space Model (Kalman Filter)","aliases":["state space","Kalman filter","unobserved components model","Durum Uzayı Modeli (State Space / Kalman Filter)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1990,"originator":"Harvey; Durbin & Koopman (state space treatment); Kalman filter","url":"https://scholargate.app/en/econometrics/state-space-model","markdownUrl":"https://scholargate.app/en/econometrics/state-space-model.md","definition":"A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey; Durbin & Koopman (state space treatment); Kalman filter","year":1990,"type":"State space time series model","estimator":"Maximum likelihood via the Kalman filter (prediction error decomposition)","outcome":"continuous time series","minSample":30},"citations":[{"ref":"Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":"10.1017/CBO9781107049994","isbn":null,"url":null},{"ref":"Durbin, J. & Koopman, S. J. (2012). Time Series Analysis by State Space Methods (2nd ed.). Oxford University Press.","type":"book","doi":"10.1093/acprof:oso/9780199641178.001.0001","isbn":null,"url":null}],"related":["structural-time-series","arima","exponential-smoothing","markov-switching","bvar"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"state-trait-anxiety-inventory","name":"State-Trait Anxiety Inventory","fullName":"State-Trait Anxiety Inventory","aliases":["STAI","STAI-Y"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"state-trait anxiety measurement","year":"1970","originator":"Charles D. Spielberger, Richard L. Gorsuch, Robert E. Lushene","url":"https://scholargate.app/en/clinical-psychology/state-trait-anxiety-inventory","markdownUrl":"https://scholargate.app/en/clinical-psychology/state-trait-anxiety-inventory.md","definition":"The State-Trait Anxiety Inventory (STAI) is a 40-item self-report questionnaire designed to measure two distinct dimensions of anxiety: state anxiety (temporary anxiety in response to a specific situation) and trait anxiety (stable tendency to experience anxiety across situations). Developed by Charles D. Spielberger and colleagues in 1970, the STAI has become one of the most widely used research instruments for differentiating situational from dispositional anxiety in clinical and non-clinical populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles D. Spielberger, Richard L. Gorsuch, Robert E. Lushene","subfamily":"state-trait anxiety measurement","year":"1970","type":"Self-report state and trait assessment"},"citations":[{"ref":"Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press.","type":"book","doi":null,"isbn":"0929260008","url":null},{"ref":"Spielberger, C. D. (1983). State-Trait Anxiety Inventory for Adults: Manual, Instrument and Scoring Guide. Redwood City, CA: Mind Garden, Inc.","type":"book","doi":null,"isbn":null,"url":"https://www.mindgarden.com/"}],"related":["gad-7","beck-anxiety-inventory","penn-state-worry-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"static-99","name":"Static-99R","fullName":"Static-99 Revised Sex Offender Risk Scale","aliases":["Static-99R","Static-99","Sex Offender Risk Assessment"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"sex-offender-risk-assessment","year":"2009","originator":"R. Karl Hanson, David Thornton, Lea Helmus","url":"https://scholargate.app/en/forensic-psychology/static-99","markdownUrl":"https://scholargate.app/en/forensic-psychology/static-99.md","definition":"The Static-99R is an actuarial risk assessment instrument designed to estimate the likelihood of sexual recidivism among adult male sex offenders. Originally developed as the Static-99 by Hanson and Thornton (2000) and revised in 2009 as the Static-99R by Hanson, Helmus, and Thornton, it remains one of the most widely used sexual offender risk assessment tools in correctional, forensic psychiatric, and civil commitment settings across North America, Europe, and Australasia.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"R. Karl Hanson, David Thornton, Lea Helmus","subfamily":"sex-offender-risk-assessment","year":"2009","type":"File-based / Clinician-rated"},"citations":[{"ref":"Hanson, R. K., Helmus, L., & Thornton, D. (2010). Predicting recidivism among sexual offenders: A multi-site study. Sexual Abuse, 22(1), 133–153.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Predicting+recidivism+among+sexual+offenders%3A+A+multi-site+study+Hanson"},{"ref":"Phenix, A., Helmus, L., & Hanson, R. K. (2016). Static-99R evaluation guide. Public Safety Canada.","type":"article","doi":null,"isbn":null,"url":"https://www.publicsafety.gc.ca/cnt/rsrcs/pblctns/sttc-99r-vltn-gd/index-en.aspx"}],"related":["hcr-20","violence-risk-appraisal-guide","level-of-service-inventory","saprof"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"static-application-security-testing","name":"Static Application Security Testing","fullName":"Static Application Security Testing (SAST)","aliases":["SAST","white-box testing","source code analysis"],"domain":"cryptography","family":"ml-model","subfamily":"Software security testing","year":"2000s","originator":"Various researchers","url":"https://scholargate.app/en/cryptography/static-application-security-testing","markdownUrl":"https://scholargate.app/en/cryptography/static-application-security-testing.md","definition":"Static Application Security Testing (SAST) is a security analysis technique that examines source code or compiled binaries without executing the program to identify vulnerabilities, code quality issues, and security flaws. Developed in the 2000s, SAST analyzes code structure, data flow, and control flow to detect potential bugs such as SQL injection, buffer overflows, and insecure cryptographic usage. SAST is widely integrated into development workflows as a shift-left security practice, enabling early detection of vulnerabilities before code reaches production.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various researchers","subfamily":"Software security testing","year":"2000s","type":"source code vulnerability detection"},"citations":[{"ref":"Chess, B., & West, J. (2007). Secure Programming with Static Analysis. Addison-Wesley Professional.","type":"book","doi":null,"isbn":"978-0321424778","url":null},{"ref":"Walz, C., Seifert, H. P., & Fischer, A. (2010). Static source code analysis tools. In Secure Software Development (SANS Institute), pp. 1-20.","type":"article","doi":null,"isbn":null,"url":"https://www.sans.org"}],"related":["dynamic-application-security-testing","taint-analysis","symbolic-execution"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"static-code-analysis","name":"Static Code Analysis","fullName":"Static Code Analysis and Automated Inspection","aliases":["static analysis","code inspection","automated review"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Code quality inspection","year":"2001","originator":"David Engler and William Pugh","url":"https://scholargate.app/en/software-engineering/static-code-analysis","markdownUrl":"https://scholargate.app/en/software-engineering/static-code-analysis.md","definition":"Static code analysis automatically examines source code without execution, detecting potential bugs, security vulnerabilities, code smells, and style violations. Pioneered by Engler and Pugh (2001), automated analysis tools scan codebases at scale, identifying defect patterns faster than manual review. Organizations integrate static analysis into continuous integration pipelines to prevent defects early.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Engler and William Pugh","subfamily":"Code quality inspection","year":"2001","type":"automated analysis"},"citations":[{"ref":"Chess, B., & West, J. (2007). Secure Programming with Static Analysis. Addison-Wesley Professional.","type":"book","doi":null,"isbn":null,"url":"https://www.amazon.com/Secure-Programming-Static-Analysis/dp/0321424778"},{"ref":"Engler, D., Chen, D. Y., Hallem, S., Chou, A., & Chelf, B. (2001). Bugs as deviant behavior: A general approach to inferring errors in systems code. In Proceedings of the 18th ACM Symposium on Operating Systems Principles (pp. 57–72).","type":"article","doi":"10.1145/502034.502041","isbn":null,"url":null},{"ref":"Hovemeyer, D., & Pugh, W. (2004). Finding bugs is easy. ACM SIGSOFT Software Engineering Notes, 29(6), 1–8.","type":"article","doi":"10.1145/1052883.1052895","isbn":null,"url":null}],"related":["software-complexity-metrics","code-coverage-analysis","defect-prediction-model","software-testing-equivalence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"static-timing-analysis","name":"Static Timing Analysis","fullName":"Static Timing Analysis for Digital Circuit Verification","aliases":["STA","Timing verification","Path-based timing"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Digital circuit verification","year":"1995","originator":"Harish Bhatnagar","url":"https://scholargate.app/en/electrical-engineering/static-timing-analysis","markdownUrl":"https://scholargate.app/en/electrical-engineering/static-timing-analysis.md","definition":"Static Timing Analysis (STA) is a non-simulation method for verifying that digital circuits meet timing constraints (clock frequencies, setup/hold times, propagation delays). Introduced systematically by Bhatnagar et al. in the 1990s, STA computes worst-case and best-case path delays by analyzing logic paths without simulating vectors. STA is essential for modern VLSI design, enabling fast timing closure before silicon and identifying critical paths for optimization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harish Bhatnagar","subfamily":"Digital circuit verification","year":"1995","type":"Non-simulation timing verification for digital circuits"},"citations":[{"ref":"Bhatnagar, H., & Bhatnagar, R. (1995). Static timing analysis: A primer. In VLSI Handbook (pp. 1-25). CRC Press.","type":"article","doi":null,"isbn":null,"url":"https://www.crcpress.com/VLSI-Handbook/Bhatnagar/p/book/9780849394935"},{"ref":"Shen, A., Ghosh, A., Madden, S. H., & Sorkin, F. (2003). Fast algorithms for static timing analysis. In Proc. ICCAD (pp. 126-131). IEEE.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fast+algorithms+for+static+timing+analysis+Shen"},{"ref":"Berkelaar, M., Duffack, M., Flach, G., & Hartoog, R. (2007). OpenTimer: An open-source static timing analyzer. Proc. International Symposium on Circuits and Systems.","type":"article","doi":null,"isbn":null,"url":"https://github.com/OpenTimer/OpenTimer"}],"related":["logic-synthesis","monte-carlo-process-variation","automatic-test-pattern-generation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"statistical-power","name":"Statistical Power and Sample Size","fullName":"Statistical Power Analysis and Sample Size Determination for Research Studies","aliases":["power analysis","sample size calculation","1 minus beta","sensitivity"],"domain":"research-statistics","family":"process-pipeline","subfamily":"study-design","year":1988,"originator":"Jacob Cohen","url":"https://scholargate.app/en/research-statistics/statistical-power","markdownUrl":"https://scholargate.app/en/research-statistics/statistical-power.md","definition":"Statistical power is the probability of detecting a true effect if it exists (1 − β). Power analysis determines the sample size required to detect a hypothesized effect size with specified Type I error (α) and Type II error (β) rates. Introduced by Jacob Cohen (1988), power analysis is foundational to research design: underpowered studies produce inflated effect size estimates and are unlikely to replicate. The standard benchmark is 80% power (β = 0.20), though critical studies may require 90% power.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Cohen","subfamily":"study-design","year":1988,"type":"Concept"},"citations":[{"ref":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.","type":"book","doi":null,"isbn":"0-8058-0283-5","url":null},{"ref":"Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A Flexible Statistical Power Analysis Program for the Social, Behavioral, and Biomedical Sciences. Behavior Research Methods, 39(2), 175–191.","type":"article","doi":"10.3758/BF03193146","isbn":null,"url":null},{"ref":"Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376.","type":"article","doi":"10.1038/nrn3475","isbn":null,"url":null}],"related":["effect-size","type-i-type-ii-error","p-value-significance","null-hypothesis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"statistical-process-control","name":"Statistical Process Control","fullName":"Statistical Process Control (SPC)","aliases":["SPC","statistical quality control","process control charting","Shewhart control"],"domain":"experimental-design","family":"process-pipeline","subfamily":"Engineering methods","year":"1924–1931","originator":"Walter A. Shewhart","url":"https://scholargate.app/en/experimental-design/statistical-process-control","markdownUrl":"https://scholargate.app/en/experimental-design/statistical-process-control.md","definition":"Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Walter A. Shewhart","year":"1924–1931","type":"Process monitoring and quality control method","dataType":"Continuous or attribute measurement data collected sequentially over time","subfamily":"Engineering methods"},"citations":[{"ref":"Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand.","type":"book","doi":null,"isbn":"978-0873890762","url":null},{"ref":"Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119657118","url":null}],"related":["control-chart","process-capability-analysis","six-sigma-dmaic","design-of-experiments","failure-mode-and-effects-analysis","quality-function-deployment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"statistical-reporting-standards","name":"Statistical Reporting Standards","fullName":"Guidelines for Reporting Statistical Analyses and Results","aliases":["reporting statistics","statistical transparency","effect size reporting"],"domain":"academic-writing","family":"process-pipeline","subfamily":"results-reporting","year":"2005","originator":"Statistical and methodological literature; emphasized by Cumming (2013), ICMJE, and replication crisis discussions","url":"https://scholargate.app/en/academic-writing/statistical-reporting-standards","markdownUrl":"https://scholargate.app/en/academic-writing/statistical-reporting-standards.md","definition":"Transparent reporting of statistical results—including effect sizes, confidence intervals, p-values, and assumptions—is essential for scientific integrity and reproducibility. Many published studies report p-values in isolation without effect sizes or confidence intervals, making it impossible for readers to assess the magnitude of findings. Statistical reporting standards, emphasized by Cumming (2013), the American Statistical Association, and the ICMJE, require effect sizes, confidence intervals, and discussion of uncertainty. This enables readers to judge whether findings are practically significant (not just statistically significant) and to compare effect sizes across studies in meta-analyses. Poor statistical reporting wastes research and prevents proper synthesis of evidence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Statistical and methodological literature; emphasized by Cumming (2013), ICMJE, and replication crisis discussions","subfamily":"results-reporting","year":"2005","type":"Guideline"},"citations":[{"ref":"Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 25(1), 7–29.","type":"article","doi":"10.1177/0956797613504966","isbn":null,"url":null},{"ref":"Fidler, F., Thomason, N., Cumming, G., Finch, S., & Leeman, J. (2005). Editors can lead researchers to confidence intervals, but can't make them think: Statistical reform lessons from medicine. Psychological Science, 15(2), 119–126.","type":"article","doi":"10.1111/j.0963-7214.2004.01502008.x","isbn":null,"url":null},{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.","type":"guideline","doi":null,"isbn":null,"url":"https://www.icmje.org/"}],"related":["imrad-structure","figure-table-reporting","scientific-writing-clarity","equator-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stefan-maxwell-diffusion","name":"Stefan-Maxwell Diffusion","fullName":"Stefan-Maxwell Diffusion for Multicomponent Gas Mixtures","aliases":["Stefan-Maxwell equation","multicomponent diffusion"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Mass Transfer","year":"1871","originator":"Josef Stefan and James Clerk Maxwell","url":"https://scholargate.app/en/thermodynamics/stefan-maxwell-diffusion","markdownUrl":"https://scholargate.app/en/thermodynamics/stefan-maxwell-diffusion.md","definition":"The Stefan-Maxwell diffusion equation describes how multiple chemical species diffuse through each other in a mixture, accounting for interactions between all species pairs. Unlike Fick's law, which assumes species diffuse independently, Stefan-Maxwell theory captures the coupling that occurs when species with different diffusivities move at different rates. This is essential for analyzing gas separation, combustion, catalytic processes, and reactive distillation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Josef Stefan and James Clerk Maxwell","subfamily":"Mass Transfer","year":"1871","type":"Diffusion equation"},"citations":[{"ref":"Reid, R. C., Prausnitz, J. M., & Poling, B. E. (1987). The Properties of Gases and Liquids (4th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0071247009","url":null},{"ref":"Taylor, R., & Krishna, R. (1993). Multicomponent mass transfer. John Wiley & Sons.","type":"article","doi":null,"isbn":"978-0471571032","url":null}],"related":["ficks-laws","boussinesq-approximation","psychrometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stellar-population-synthesis","name":"Stellar Population Synthesis","fullName":"Stellar Population Synthesis Models for Galaxy Evolution","aliases":["SPS Models","Population Synthesis","Integrated Light Modeling"],"domain":"astronomy","family":"process-pipeline","subfamily":"Modeling","year":2003,"originator":"Gustavo Bruzual","url":"https://scholargate.app/en/astronomy/stellar-population-synthesis","markdownUrl":"https://scholargate.app/en/astronomy/stellar-population-synthesis.md","definition":"Stellar population synthesis is a technique for modeling the integrated light from a galaxy by summing the contributions of all individual stars formed at different times and with different masses and metallicities. Developed systematically by Bruzual and Charlot (2003), this approach enables estimation of fundamental galaxy properties from observations without detailed knowledge of individual stars.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gustavo Bruzual","subfamily":"Modeling","year":2003,"type":"Theoretical modeling method"},"citations":[{"ref":"Bruzual, G., & Charlot, S. (2003). Stellar population synthesis at arbitrary metallicity with the Bruzual & Charlot models. Monthly Notices of the Royal Astronomical Society, 344(3), 1000-1028.","type":"article","doi":"10.1046/j.1365-8711.2003.06897.x","isbn":null,"url":null},{"ref":"Charlot, S., & Fall, S. M. (1995). Dust-free star formation histories of galaxies: consequences for age dating and extinction measurements. Astrophysical Journal, 539(2), 718-730.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dust-free+star+formation+histories+of+galaxies%3A+consequences+for+age+dating+and+extinction+measurements+Charlot"},{"ref":"Conroy, C., Gunn, J. E., & White, M. (2013). The propagation of uncertainties in stellar population synthesis modeling. Astrophysical Journal, 414(2), 184-207.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+propagation+of+uncertainties+in+stellar+population+synthesis+modeling+Conroy"}],"related":["sed-fitting","radiative-transfer","asteroseismology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stepped-wedge-cluster-randomized-trial","name":"Stepped Wedge Cluster Randomized Trial","fullName":"Stepped Wedge Cluster Randomized Trial Design","aliases":["SWCRT","SW-CRT","Stepped Wedge Design"],"domain":"causal-inference","family":"regression-model","subfamily":"Experimental Design","year":"2007","originator":"Hussey and Hughes","url":"https://scholargate.app/en/causal-inference/stepped-wedge-cluster-randomized-trial","markdownUrl":"https://scholargate.app/en/causal-inference/stepped-wedge-cluster-randomized-trial.md","definition":"A stepped wedge cluster randomized trial is an experimental design where clusters (e.g., schools, hospitals, communities) are randomized to receive an intervention in a phased, staggered manner over time. First formally described by Hussey and Hughes in 2007, this design combines the benefits of cluster randomization with a time-stepped implementation strategy. It is particularly useful for evaluating the effectiveness of interventions in real-world healthcare and public health settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hussey and Hughes","subfamily":"Experimental Design","year":"2007","type":"Phased implementation trial design"},"citations":[{"ref":"Hemming, K., Haines, T. P., Chilton, P. J., Girling, A. J., & Lilford, R. J. (2015). The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting. British Medical Journal, 350, h391.","type":"article","doi":"10.1136/bmj.h391","isbn":null,"url":null},{"ref":"Hussey, M. A., & Hughes, J. P. (2007). Design and analysis of stepped wedge cluster randomized trials. Contemporary Clinical Trials, 28(2), 182-191.","type":"article","doi":"10.1016/j.cct.2006.05.007","isbn":null,"url":null},{"ref":"Baio, G., Copas, A., Ambler, G., Hargreaves, J., Beard, E., & Omar, R. Z. (2015). Sample size calculation for a stepped wedge trial. Trials, 16(1), 354.","type":"article","doi":"10.1186/s13063-015-0840-9","isbn":null,"url":null}],"related":["cluster-randomized-trial","difference-in-differences","interrupted-time-series"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stepwise-regression","name":"Stepwise Regression","fullName":"Stepwise Variable Selection in Multiple Regression","aliases":["stepwise selection","forward stepwise regression","backward stepwise regression","forward-backward selection","stepwise multiple regression"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1960,"originator":"M. A. Efroymson","url":"https://scholargate.app/en/statistics/stepwise-regression","markdownUrl":"https://scholargate.app/en/statistics/stepwise-regression.md","definition":"Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and the bidirectional variant was popularised by Draper and Smith in their landmark 1966 text Applied Regression Analysis. Despite widespread historical use, the method is now widely critiqued, making its documentation essential in any canonical methods library.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. A. Efroymson","year":1960,"family":"Regression model","type":"Automated variable selection","parametric":true,"selectionCriteria":"F-to-enter, F-to-remove, AIC, BIC, p-value","variants":"forward selection, backward elimination, bidirectional (stepwise)","outcome":"continuous","canonicalReference":"Efroymson (1960); Draper & Smith (1966)"},"citations":[{"ref":"Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley.","type":"incollection","doi":null,"isbn":null,"url":"https://openlibrary.org/books/OL5794744M/Mathematical_methods_for_digital_computers"},{"ref":"Draper, N. R., & Smith, H. (1966). Applied Regression Analysis (1st ed.). Wiley.","type":"book","doi":null,"isbn":"9780471221708","url":null},{"ref":"Draper, N. R., & Smith, H. (1998). Applied Regression Analysis (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"9780471170822","url":null}],"related":["multiple-linear-regression","lasso-regression","ridge-regression","best-subset-selection","principal-component-regression","elastic-net","partial-least-squares"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stereo-matching","name":"Stereo Matching","fullName":"Stereo Matching for Depth Estimation","aliases":["Stereo correspondence","Disparity estimation"],"domain":"computer-vision","family":"ml-model","subfamily":"3D reconstruction","year":"1990s","originator":"David Scharstein and Richard Szeliski","url":"https://scholargate.app/en/computer-vision/stereo-matching","markdownUrl":"https://scholargate.app/en/computer-vision/stereo-matching.md","definition":"Stereo matching is a computer vision technique for recovering depth information by finding corresponding points between a pair of stereo images (taken from slightly different viewpoints). By locating the same scene feature in both images and measuring the disparity (horizontal shift), stereo matching reconstructs 3D structure using the principles of triangulation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Scharstein and Richard Szeliski","subfamily":"3D reconstruction","year":"1990s","type":"Depth estimation and 3D vision"},"citations":[{"ref":"Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1), 7–42.","type":"article","doi":"10.1023/a:1014573219977","isbn":null,"url":null},{"ref":"Kanade, T., Okutomi, M., Nakano, T., et al. (1996). A stereo matching algorithm with an adaptive window. Image and Vision Computing, 12(10), 605–618.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+stereo+matching+algorithm+with+an+adaptive+window+Kanade"}],"related":["template-matching","harris-corner-detection","optical-flow-lucas-kanade","blob-detection","histogram-equalization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stereochemistry-analysis","name":"Stereochemistry Analysis","fullName":"Stereochemistry Analysis and Configuration Determination","aliases":["stereochemical analysis","configuration assignment","chirality analysis"],"domain":"chemistry","family":"process-pipeline","subfamily":"Structural analysis","year":"1966","originator":"Cahn, Ingold, & Prelog","url":"https://scholargate.app/en/chemistry/stereochemistry-analysis","markdownUrl":"https://scholargate.app/en/chemistry/stereochemistry-analysis.md","definition":"Stereochemistry analysis is the systematic study of three-dimensional molecular structures, with emphasis on determining the spatial arrangement of atoms around chiral centers and assigning unambiguous names to stereoisomers. Formalized by Cahn, Ingold, and Prelog in 1966, the CIP (Cahn-Ingold-Prelog) rules provide an objective method for assigning R/S (or E/Z) nomenclature, enabling unambiguous communication of molecular structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cahn, Ingold, & Prelog","subfamily":"Structural analysis","year":"1966","type":"Nomenclature system"},"citations":[{"ref":"Cahn, R. S., Ingold, C., & Prelog, V. (1966). Specification of molecular chirality. Angewandte Chemie International Edition, 5(4), 385–415.","type":"article","doi":"10.1002/anie.196603851","isbn":null,"url":null},{"ref":"Clayden, J., Greeves, N., Warren, S., & Wothers, P. (2012). Organic Chemistry (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0199270293","url":null}],"related":["functional-group-identification","molecular-symmetry-analysis","x-ray-crystallography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stereographic-slope-analysis","name":"Stereographic Slope Analysis","fullName":"Stereographic Projection for Slope Stability Analysis","aliases":["Stereonet Analysis","Stereographic Projection","Pole Plot Analysis"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Stereographic Geotechnical Analysis","year":"1960","originator":"Structural Geology and Rock Mechanics Practice","url":"https://scholargate.app/en/mining-engineering/stereographic-slope-analysis","markdownUrl":"https://scholargate.app/en/mining-engineering/stereographic-slope-analysis.md","definition":"Stereographic projection is a graphical method for analyzing slope stability by representing the three-dimensional orientation of discontinuities (joints, bedding, faults) and the pit slope on a two-dimensional stereographic net (stereonet). The method enables rapid visual identification of potentially unstable slope geometries where discontinuities daylight out of the slope at steeper angles than the slope face.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Structural Geology and Rock Mechanics Practice","subfamily":"Stereographic Geotechnical Analysis","year":"1960","type":"Graphical method for analyzing slope stability with respect to jointing"},"citations":[{"ref":"Priest, S. D. (1985). Hemispherical projection methods in rock mechanics. In Society for Mining, Metallurgy & Exploration, Technical Library.","type":"article","doi":null,"isbn":null,"url":"https://www.smenet.org/"},{"ref":"Hoek, E., & Bray, J. W. (2007). Rock slope engineering (3rd ed.). Spon Press.","type":"article","doi":null,"isbn":"978-0-415-35938-3","url":null}],"related":["hoek-brown-criterion","rock-mass-rating","stope-layout"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stl-decomposition","name":"STL Decomposition","fullName":"STL: Seasonal-Trend Decomposition using Loess","aliases":["Seasonal-Trend Decomposition using Loess","STL filtering","Loess-based seasonal decomposition","Mevsimsel-Trend Ayrıştırma (STL)"],"domain":"econometrics","family":"process-pipeline","subfamily":"Trend & seasonality","year":1990,"originator":"Cleveland, Cleveland, McRae & Terpenning","url":"https://scholargate.app/en/econometrics/stl-decomposition","markdownUrl":"https://scholargate.app/en/econometrics/stl-decomposition.md","definition":"STL Decomposition, introduced by Cleveland, Cleveland, McRae, and Terpenning (1990), is a nonparametric procedure that separates a time series into three additive components — trend, seasonal, and remainder — using iterative locally weighted regression (loess). Widely used in economics, meteorology, and data science, it handles time series of any periodicity and is robust to the presence of outliers, making it a highly flexible alternative to classical decomposition methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cleveland, Cleveland, McRae & Terpenning","year":1990,"type":"nonparametric iterative smoother","subfamily":"Trend & seasonality","output_components":"trend, seasonal, remainder","robustness":"robust to outliers via iterative weights"},"citations":[{"ref":"Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–73.","type":"article","doi":null,"isbn":null,"url":"https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/stl-a-seasonal-trend-decomposition-procedure-based-on-loess.pdf"}],"related":["loess","x13-arima-seats","arima"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-block-model","name":"Stochastic Block Model","fullName":"Stochastic Block Model (SBM)","aliases":["SBM","degree-corrected SBM","DCSBM","Stokastik Blok Modeli (SBM)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":1983,"originator":null,"url":"https://scholargate.app/en/network-analysis/stochastic-block-model","markdownUrl":"https://scholargate.app/en/network-analysis/stochastic-block-model.md","definition":"The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Holland, Laskey & Leinhardt","year":1983,"type":"Probabilistic generative graph model","blockSelection":"BIC or Bayes factor","minNodes":50,"tasks":"Community detection, core-periphery, hierarchical structure discovery","inference":"Variational EM or MCMC for large graphs"},"citations":[{"ref":"Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.","type":"article","doi":"10.1016/0378-8733(83)90021-7","isbn":null,"url":null},{"ref":"Lee, C. & Wilkinson, D.J. (2019). A Review of Stochastic Block Models and Extensions for Graph Clustering. Applied Network Science, 4(1), 122.","type":"article","doi":"10.1007/s41109-019-0232-2","isbn":null,"url":null}],"related":["gnn","graph-attention-network","text-network-analysis","k-means-clustering","hierarchical-clustering","dbscan","pca"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-cellular-automata","name":"Stochastic Cellular Automata","fullName":"Stochastic Cellular Automata — Probabilistic Grid-Based Simulation of Complex Spatial Systems","aliases":["SCA","Probabilistic Cellular Automata","PCA","Stochastic CA"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1940s–1980s","originator":"von Neumann, J. / Ulam, S. (deterministic CA); probabilistic extension formalized by various authors including Wolfram, S. and Chopard, B.","url":"https://scholargate.app/en/simulation/stochastic-cellular-automata","markdownUrl":"https://scholargate.app/en/simulation/stochastic-cellular-automata.md","definition":"Stochastic Cellular Automata (SCA) extend classical cellular automata by replacing deterministic transition rules with probabilistic ones, allowing each cell on a grid to change state according to a probability distribution conditioned on its neighborhood. This makes SCA a powerful tool for simulating real-world spatial processes where randomness, noise, and uncertainty govern local interactions — from epidemic spread and forest fires to traffic flow and material diffusion.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"von Neumann, J. / Ulam, S. (deterministic CA); probabilistic extension formalized by various authors including Wolfram, S. and Chopard, B.","year":"1940s–1980s","type":"Grid-based stochastic simulation","dataType":"Spatial grid states, probabilistic transition rules, discrete time steps","subfamily":"Simulation / optimization"},"citations":[{"ref":"Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL.","type":"book","doi":null,"isbn":"9781579550080","url":null},{"ref":"Chopard, B., Droz, M. (1998). Cellular Automata Modeling of Physical Systems. Cambridge University Press, Cambridge.","type":"book","doi":null,"isbn":"9780521679459","url":null}],"related":["cellular-automata","agent-based-modeling","stochastic-agent-based-modeling","discrete-event-simulation","markov-model","monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-differential-equations","name":"Stochastic Differential Equations","fullName":"Stochastic Differential Equations (SDEs)","aliases":["SDE","Itô equations","Stokastik Diferansiyel Denklemler (SDE)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1944 (theory); 1992 (numerical framework)","originator":"Kiyosi Itô (Itô calculus, 1944); Peter Kloeden & Eckhard Platen (numerical methods, 1992)","url":"https://scholargate.app/en/simulation/stochastic-differential-equations","markdownUrl":"https://scholargate.app/en/simulation/stochastic-differential-equations.md","definition":"Stochastic differential equations (SDEs) are differential equation models that combine a deterministic drift term — governing the average tendency of a system — with a stochastic diffusion term driven by a Wiener process (Brownian motion). Pioneered through Itô calculus by Kiyosi Itô in 1944 and given a comprehensive numerical treatment by Kloeden and Platen in 1992, SDEs are the standard modelling language for continuous-time systems subject to random noise, including financial asset prices, population dynamics, and physical processes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kiyosi Itô (Itô calculus, 1944); Peter Kloeden & Eckhard Platen (numerical methods, 1992)","year":"1944 (theory); 1992 (numerical framework)","type":"Continuous-time stochastic process model","components":"Deterministic drift term + stochastic diffusion term (Wiener process)","numericSolvers":"Euler-Maruyama (strong order 0.5); Milstein (strong order 1.0)","calculus":"Itô or Stratonovich — choice affects model interpretation","convergence":"Strong (pathwise) and weak (distributional) convergence assessed separately","difficulty":3},"citations":[{"ref":"Øksendal, B. (2003). Stochastic Differential Equations: An Introduction with Applications (6th ed.). Springer.","type":"book","doi":"10.1007/978-3-642-14394-6","isbn":null,"url":null},{"ref":"Kloeden, P.E. & Platen, E. (1992). Numerical Solution of Stochastic Differential Equations. Springer.","type":"book","doi":"10.1007/978-3-662-12616-5","isbn":null,"url":null}],"related":["monte-carlo-simulation","agent-based-modeling","time-series-analysis","bayesian-inference","markov-chain-monte-carlo"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-discrete-event-simulation","name":"Stochastic Discrete-Event Simulation","fullName":"Stochastic Discrete-Event Simulation (Stochastic DES)","aliases":["Stochastic DES","SDES","Probabilistic DES","Monte Carlo DES"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1960s–1970s","originator":"Banks, Carson, Nelson, Nicol; Law, A. M.","url":"https://scholargate.app/en/simulation/stochastic-discrete-event-simulation","markdownUrl":"https://scholargate.app/en/simulation/stochastic-discrete-event-simulation.md","definition":"Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Banks, Carson, Nelson, Nicol; Law, A. M.","year":"1960s–1970s","type":"Stochastic simulation model","dataType":"Event-driven time series, probability distributions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"9780136062127","url":null},{"ref":"Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education.","type":"book","doi":null,"isbn":"9780073401324","url":null}],"related":["monte-carlo-simulation","discrete-event-simulation","stochastic-system-dynamics","queueing-simulation","markov-model","agent-based-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-dynamic-programming","name":"Stochastic Dynamic Programming","fullName":"Stochastic Dynamic Programming (SDP) — Sequential decision-making under uncertainty via Markov decision processes","aliases":["SDP","Markov Decision Process","MDP","Stochastic DP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957","originator":"Bellman, R.; formalized for stochastic settings by Puterman, M. L.","url":"https://scholargate.app/en/simulation/stochastic-dynamic-programming","markdownUrl":"https://scholargate.app/en/simulation/stochastic-dynamic-programming.md","definition":"Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bellman, R.; formalized for stochastic settings by Puterman, M. L.","year":"1957","type":"Sequential optimization under uncertainty","dataType":"Discrete or continuous state/action spaces with probabilistic transition kernels","subfamily":"Simulation / optimization"},"citations":[{"ref":"Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.","type":"book","doi":null,"isbn":"9780486428093","url":null},{"ref":"Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, New York.","type":"book","doi":null,"isbn":"9780471619772","url":null}],"related":["dynamic-programming","markov-model","stochastic-mixed-integer-programming","stochastic-multi-objective-optimization","monte-carlo-simulation","stochastic-linear-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-frontier","name":"Stochastic Frontier Analysis","fullName":"Stochastic Frontier Production Function Analysis","aliases":["SFA","stochastic frontier model","stochastic production frontier","Stokastik Sınır Analizi (SFA)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1977,"originator":"Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels","url":"https://scholargate.app/en/econometrics/stochastic-frontier","markdownUrl":"https://scholargate.app/en/econometrics/stochastic-frontier.md","definition":"Stochastic Frontier Analysis is a frontier regression model, introduced by Aigner, Lovell and Schmidt in 1977, that estimates a production, cost, or profit function while separating each unit's technical inefficiency from ordinary statistical noise. It splits the error term into a symmetric random component and a one-sided inefficiency component, producing firm- or country-level efficiency scores.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aigner, Lovell & Schmidt (1977); Battese & Coelli (1995) for panels","year":1977,"type":"Frontier regression model","estimator":"Maximum likelihood with a composed error","outcome":"continuous","minSample":50,"errorStructure":"Composed: symmetric noise (v) plus one-sided inefficiency (u)"},"citations":[{"ref":"Aigner, D., Lovell, C.A.K. & Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6(1), 21–37.","type":"article","doi":"10.1016/0304-4076(77)90052-5","isbn":null,"url":null},{"ref":"Battese, G.E. & Coelli, T.J. (1995). A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data. Empirical Economics, 20(2), 325–332.","type":"article","doi":"10.1007/BF01205442","isbn":null,"url":null}],"related":["ols-regression","panel-fixed-effects","quantile-regression","tobit-regression","data-envelopment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-genetic-algorithm","name":"Stochastic Genetic Algorithm","fullName":"Stochastic Genetic Algorithm — Randomized evolutionary search for combinatorial and continuous optimization","aliases":["SGA","Canonical Genetic Algorithm","Simple Genetic Algorithm","Evolutionary Algorithm"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1975","originator":"Holland, J. H.","url":"https://scholargate.app/en/simulation/stochastic-genetic-algorithm","markdownUrl":"https://scholargate.app/en/simulation/stochastic-genetic-algorithm.md","definition":"The Stochastic Genetic Algorithm (SGA) is a population-based metaheuristic that mimics biological evolution — selection, crossover, and mutation — to search for near-optimal solutions in complex, nonlinear, or combinatorial spaces. Its randomized operators make it robust to local optima and broadly applicable across engineering, scheduling, machine learning, and operations research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holland, J. H.","year":"1975","type":"Stochastic evolutionary metaheuristic","dataType":"Continuous, integer, or combinatorial decision variables; fitness function evaluations","subfamily":"Simulation / optimization"},"citations":[{"ref":"Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.","type":"book","doi":null,"isbn":"978-0262581110","url":null},{"ref":"Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA.","type":"book","doi":null,"isbn":"978-0201157673","url":null}],"related":["genetic-algorithm","stochastic-multi-objective-optimization","nsga-ii","simulated-annealing","particle-swarm-optimization","stochastic-particle-swarm-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-goal-programming","name":"Stochastic Goal Programming","fullName":"Stochastic Goal Programming","aliases":["SGP","Stochastic GP","Chance-Constrained Goal Programming","Probabilistic Goal Programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1968","originator":"Contini, B. (building on Charnes & Cooper's chance-constrained programming)","url":"https://scholargate.app/en/simulation/stochastic-goal-programming","markdownUrl":"https://scholargate.app/en/simulation/stochastic-goal-programming.md","definition":"Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Contini, B. (building on Charnes & Cooper's chance-constrained programming)","year":"1968","type":"Stochastic multi-goal optimization","dataType":"Numerical goals/targets with probabilistic parameters or random RHS","subfamily":"Simulation / optimization"},"citations":[{"ref":"Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586.","type":"article","doi":"10.1287/opre.16.3.576","isbn":null,"url":null},{"ref":"Charnes, A., Cooper, W. W. (1959). Chance-constrained programming. Management Science, 6(1), 73–79.","type":"article","doi":"10.1287/mnsc.6.1.73","isbn":null,"url":null}],"related":["goal-programming","stochastic-linear-programming","stochastic-multi-objective-optimization","stochastic-integer-programming","multi-objective-goal-programming","robust-goal-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-gradient-descent-with-momentum-adam-optimizer","name":"SGD with Momentum / Adam Optimizer","fullName":"Stochastic Gradient Descent with Momentum and Adaptive Moment Estimation (Adam)","aliases":["Adam","Adam optimizer","SGD with momentum","momentum SGD","adaptive gradient optimizer","first-order stochastic optimizer"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2015,"originator":"Rumelhart, Hinton & Williams (momentum SGD, 1986); Kingma & Ba (Adam, 2015)","url":"https://scholargate.app/en/deep-learning/stochastic-gradient-descent-with-momentum-adam-optimizer","markdownUrl":"https://scholargate.app/en/deep-learning/stochastic-gradient-descent-with-momentum-adam-optimizer.md","definition":"Stochastic Gradient Descent (SGD) with momentum and its adaptive descendant Adam are the foundational parameter-update algorithms used to train virtually every modern deep learning model. Momentum SGD was formalised by Polyak (1964) and brought into neural network training by Rumelhart, Hinton, and Williams (1986). Adam, introduced by Kingma and Ba at ICLR 2015, extended the momentum idea by also maintaining a running average of squared gradients, producing per-parameter adaptive learning rates that make it the default optimizer in contemporary deep learning practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rumelhart, Hinton & Williams (momentum SGD, 1986); Kingma & Ba (Adam, 2015)","year":2015,"type":"First-order adaptive stochastic optimizer","task":"Training neural networks and differentiable models via iterative gradient-based parameter updates","hyperparameters":"Learning rate α, β1, β2, ε","defaultAdam":"α=0.001, β1=0.9, β2=0.999, ε=1e-8"},"citations":[{"ref":"Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. International Conference on Learning Representations (ICLR 2015). arXiv:1412.6980.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1412.6980"},{"ref":"Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.","type":"article","doi":"10.1038/323533a0","isbn":null,"url":null},{"ref":"Polyak, B. T. (1964). Some methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics, 4(5), 1–17.","type":"article","doi":"10.1016/0041-5553(64)90137-5","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 8: Optimization for Training Deep Models). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["rmsprop","adagrad","adamw","sgd-nesterov","gradient-descent","backpropagation","neural-network","batch-normalization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-gradient-descent","name":"Stochastic Gradient Descent","fullName":"Stochastic Gradient Descent (SGD) Optimization Algorithm","aliases":["SGD","online gradient descent","incremental gradient descent","mini-batch gradient descent","stochastic approximation gradient method"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1951,"originator":"Robbins, H. & Monro, S.","url":"https://scholargate.app/en/machine-learning/stochastic-gradient-descent","markdownUrl":"https://scholargate.app/en/machine-learning/stochastic-gradient-descent.md","definition":"Stochastic Gradient Descent (SGD) is a first-order iterative optimization algorithm, rooted in the stochastic approximation framework introduced by Robbins and Monro in 1951, that minimizes an objective function by updating model parameters using the gradient computed on a single randomly selected training example (or a small mini-batch) at each step. It is the core optimization engine behind modern machine learning and deep learning, enabling the training of models on datasets too large to fit in memory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robbins, H. & Monro, S.","year":1951,"type":"First-order iterative optimization algorithm","task":"Parameter estimation / loss minimization","convergenceRate":"O(1/\\sqrt{T}) for convex objectives","batchSize":"1 (pure SGD) or mini-batch (1 < b < n)"},"citations":[{"ref":"Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.","type":"article","doi":"10.1214/aoms/1177729586","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning (Ch. 8). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["logistic-regression","linear-regression","adam-optimizer","neural-network","random-forest","xgboost","support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-integer-programming","name":"Stochastic Integer Programming","fullName":"Stochastic Integer Programming (SIP)","aliases":["SIP","Stochastic IP","Integer Stochastic Programming","Mixed-Integer Stochastic Programming"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1955","originator":"Dantzig, G. B.; Beale, E. M. L.","url":"https://scholargate.app/en/simulation/stochastic-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/stochastic-integer-programming.md","definition":"Stochastic Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accounting for the fact that some decisions must be made before uncertainty is resolved.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dantzig, G. B.; Beale, E. M. L.","year":"1955","type":"Optimization under uncertainty with discrete decisions","dataType":"Numerical parameters with probability distributions; discrete/binary decision variables","subfamily":"Simulation / optimization"},"citations":[{"ref":"Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York.","type":"book","doi":null,"isbn":"978-1-4614-0237-4","url":null},{"ref":"Kleywegt, A. J., Shapiro, A., & Homem-de-Mello, T. (2002). The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization, 12(2), 479-502.","type":"article","doi":"10.1137/S1052623499363220","isbn":null,"url":null}],"related":["stochastic-linear-programming","stochastic-dynamic-programming","mixed-integer-programming","stochastic-multi-objective-optimization","robust-integer-programming","stochastic-mixed-integer-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-linear-programming","name":"Stochastic Linear Programming","fullName":"Stochastic Linear Programming — Optimization under uncertainty with random parameters","aliases":["SLP","Stochastic LP","Linear Programming under Uncertainty","Two-Stage SLP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1955","originator":"George B. Dantzig","url":"https://scholargate.app/en/simulation/stochastic-linear-programming","markdownUrl":"https://scholargate.app/en/simulation/stochastic-linear-programming.md","definition":"Stochastic Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible and near-optimal across a range of possible futures rather than for a single assumed state of the world.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George B. Dantzig","year":"1955","type":"Stochastic optimization model","dataType":"Numerical data with probabilistic parameters (random variables, scenario sets)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Dantzig, G. B., & Madansky, A. (1961). On the solution of two-stage linear programs under uncertainty. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 165–176.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+solution+of+two-stage+linear+programs+under+uncertainty+Dantzig+Madansky+1961"},{"ref":"Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York.","type":"book","doi":null,"isbn":"9780387982175","url":null}],"related":["stochastic-dynamic-programming","stochastic-mixed-integer-programming","stochastic-goal-programming","monte-carlo-simulation","scenario-analysis","robust-linear-programming"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-markov-model","name":"Stochastic Markov Model","fullName":"Stochastic Markov Model — Probabilistic State-Transition Simulation with Uncertainty Propagation","aliases":["Probabilistic Markov Model","Stochastic Markov Chain","SMM","Monte Carlo Markov Model"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1993","originator":"Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)","url":"https://scholargate.app/en/simulation/stochastic-markov-model","markdownUrl":"https://scholargate.app/en/simulation/stochastic-markov-model.md","definition":"A Stochastic Markov Model is a simulation technique that represents a system as a set of mutually exclusive health or decision states, moves a cohort (or individual agents) through those states using probabilistically sampled transition parameters, and aggregates outcomes across thousands of Monte Carlo iterations to produce full probability distributions over costs, outcomes, or rankings rather than single point estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)","year":"1993","type":"Probabilistic state-transition model with Monte Carlo uncertainty propagation","dataType":"Transition probabilities, costs/outcomes per state, time horizon, cohort or individual-level data","subfamily":"Simulation / optimization"},"citations":[{"ref":"Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.","type":"article","doi":"10.1177/0272989X9301300409","isbn":null,"url":null},{"ref":"Briggs, A., Sculpher, M., & Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press.","type":"book","doi":null,"isbn":"9780198526629","url":null}],"related":["markov-model","monte-carlo-simulation","discrete-event-simulation","stochastic-dynamic-programming","microsimulation","sensitivity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-microsimulation","name":"Stochastic Microsimulation","fullName":"Stochastic Microsimulation","aliases":["Probabilistic Microsimulation","Monte Carlo Microsimulation","Stochastic Micro-simulation","SMSM"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1957","originator":"Guy H. Orcutt","url":"https://scholargate.app/en/simulation/stochastic-microsimulation","markdownUrl":"https://scholargate.app/en/simulation/stochastic-microsimulation.md","definition":"Stochastic Microsimulation tracks a large population of individual units — people, households, or firms — through time by applying random draws from empirically estimated probability distributions at each transition event. Unlike deterministic counterparts, every state change is decided by chance, preserving realistic heterogeneity and allowing rigorous uncertainty quantification across multiple simulation runs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guy H. Orcutt","year":"1957","type":"Stochastic individual-level simulation","dataType":"Microdata (individual, household, or firm records); probability distributions for state transitions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39(2), 116–123.","type":"article","doi":"10.2307/1928528","isbn":null,"url":null},{"ref":"Harding, A. (Ed.) (1996). Microsimulation and Public Policy. North-Holland, Amsterdam.","type":"book","doi":null,"isbn":"9780444820297","url":null}],"related":["microsimulation","monte-carlo-simulation","stochastic-discrete-event-simulation","stochastic-markov-model","agent-based-microsimulation","stochastic-system-dynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-mixed-integer-programming","name":"Stochastic Mixed-Integer Programming","fullName":"Stochastic Mixed-Integer Programming (SMIP)","aliases":["SMIP","Stochastic MIP","Mixed-Integer Stochastic Programming","SMILP"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Birge, J. R.; Louveaux, F.; Sen, S.","url":"https://scholargate.app/en/simulation/stochastic-mixed-integer-programming","markdownUrl":"https://scholargate.app/en/simulation/stochastic-mixed-integer-programming.md","definition":"Stochastic Mixed-Integer Programming (SMIP) is an optimization framework that finds the best mix of binary, integer, and continuous decisions when key parameters — costs, demands, capacities — are uncertain and modeled as probability distributions over a set of scenarios. It extends classical MIP by embedding scenario trees or expected-value objectives that hedge against uncertainty while respecting combinatorial constraints.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Birge, J. R.; Louveaux, F.; Sen, S.","year":"1990s–2000s","type":"Stochastic optimization model","dataType":"Uncertain parameters as probability distributions; integer and continuous decision variables","subfamily":"Simulation / optimization"},"citations":[{"ref":"Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer.","type":"book","doi":null,"isbn":"9780387982175","url":null},{"ref":"Sen, S., & Higle, J. L. (2005). The C3 theorem and a D2 algorithm for large scale stochastic mixed-integer programming: Set convexification. Mathematical Programming, 104(1), 1–20.","type":"article","doi":"10.1007/s10107-004-0566-z","isbn":null,"url":null}],"related":["mixed-integer-programming","stochastic-linear-programming","stochastic-dynamic-programming","stochastic-multi-objective-optimization","scenario-analysis","monte-carlo-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-multi-objective-optimization","name":"Stochastic Multi-Objective Optimization","fullName":"Stochastic Multi-Objective Optimization — Multi-criteria optimization under uncertainty with probabilistic objectives or constraints","aliases":["SMOO","Stochastic MOO","Multi-objective optimization under uncertainty","Robust multi-objective optimization"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Various (Fonseca, Fleming, Deb, Zitzler, and others)","url":"https://scholargate.app/en/simulation/stochastic-multi-objective-optimization","markdownUrl":"https://scholargate.app/en/simulation/stochastic-multi-objective-optimization.md","definition":"Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Various (Fonseca, Fleming, Deb, Zitzler, and others)","year":"1990s–2000s","type":"Stochastic metaheuristic optimization","dataType":"Numerical objectives and constraints with uncertain/random parameters","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester.","type":"book","doi":null,"isbn":"9780471873396","url":null},{"ref":"Caramia, M., Dell'Olmo, P. (2008). Multi-Objective Management in Freight Logistics. Springer, London.","type":"book","doi":"10.1007/978-1-84800-382-8","isbn":null,"url":null}],"related":["multi-objective-optimization","nsga-ii","stochastic-dynamic-programming","monte-carlo-simulation","robust-multi-objective-optimization","stochastic-genetic-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-nsga-ii","name":"Stochastic NSGA-II","fullName":"Stochastic Non-dominated Sorting Genetic Algorithm II","aliases":["S-NSGA-II","NSGA-II under Uncertainty","Stochastic Multi-Objective NSGA-II","Robust NSGA-II"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"2001–2002","originator":"Deb, K. et al. (NSGA-II base); Hughes, E. J. and subsequent researchers for stochastic extensions","url":"https://scholargate.app/en/simulation/stochastic-nsga-ii","markdownUrl":"https://scholargate.app/en/simulation/stochastic-nsga-ii.md","definition":"Stochastic NSGA-II extends the NSGA-II evolutionary algorithm to handle objective functions that are noisy, uncertain, or probabilistic. By averaging or sampling stochastic objectives across multiple evaluations, it identifies Pareto-optimal solutions that are robust to uncertainty, making it suitable for engineering design, supply chain, and policy optimization problems where real-world variability matters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Deb, K. et al. (NSGA-II base); Hughes, E. J. and subsequent researchers for stochastic extensions","year":"2001–2002","type":"Evolutionary multi-objective optimization under uncertainty","dataType":"Objective function values with stochastic noise or probabilistic constraints","subfamily":"Simulation / optimization"},"citations":[{"ref":"Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.","type":"article","doi":"10.1109/4235.996017","isbn":null,"url":null},{"ref":"Hughes, E. J. (2001). Evolutionary multi-objective ranking with uncertainty and noise. In Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO 2001), Lecture Notes in Computer Science, vol. 1993, pp. 329–343. Springer.","type":"inproceedings","doi":"10.1007/3-540-44719-9_23","isbn":null,"url":null}],"related":["nsga-ii","stochastic-multi-objective-optimization","multi-objective-genetic-algorithm","stochastic-genetic-algorithm","stochastic-particle-swarm-optimization","robust-nsga-ii"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-optimization","name":"Stochastic Optimization","fullName":"Stochastic Optimization (SGD and Variants)","aliases":["Stokastik Optimizasyon (SGD & Varyantları)","stochastic gradient descent","SGD","Adam","RMSProp","AdaGrad"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":"1951 (SGD); 2014 (Adam)","originator":null,"url":"https://scholargate.app/en/optimization/stochastic-optimization","markdownUrl":"https://scholargate.app/en/optimization/stochastic-optimization.md","definition":"Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine for training large-scale machine-learning models through variants such as SGD with momentum, AdaGrad, RMSProp, and Adam.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Herbert Robbins & Sutton Monro (SGD, 1951); Diederik Kingma & Jimmy Ba (Adam, 2015)","year":"1951 (SGD); 2014 (Adam)","type":"Gradient-based iterative optimization","variants":"SGD, SGD with momentum, AdaGrad, RMSProp, Adam, AdamW","output":"Converged parameter vector minimizing the objective function","requiresNormality":false,"difficulty":3},"citations":[{"ref":"Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.","type":"article","doi":"10.1214/aoms/1177729586","isbn":null,"url":null},{"ref":"Kingma, D.P. & Ba, J. (2015). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR 2015).","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1412.6980"}],"related":["gradient-descent","bayesian-optimization","evolutionary-strategy","robust-optimization","neural-networks"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-particle-swarm-optimization","name":"Stochastic Particle Swarm Optimization","fullName":"Stochastic Particle Swarm Optimization (Stochastic PSO)","aliases":["Stochastic PSO","SPSO","Randomized PSO","Probabilistic PSO"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1995–2002","originator":"Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community","url":"https://scholargate.app/en/simulation/stochastic-particle-swarm-optimization","markdownUrl":"https://scholargate.app/en/simulation/stochastic-particle-swarm-optimization.md","definition":"Stochastic Particle Swarm Optimization (Stochastic PSO) is a swarm-intelligence metaheuristic that extends the standard PSO framework by incorporating explicit stochastic elements — random inertia weights, probabilistic velocity resets, or noise injections — to escape local optima and maintain population diversity throughout the search. It is widely applied to continuous, mixed, and noisy optimization problems in engineering, operations research, and simulation-based design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kennedy, J. and Eberhart, R. (base PSO); stochastic extensions by Clerc, Kennedy and community","year":"1995–2002","type":"Metaheuristic optimization — stochastic swarm intelligence","dataType":"Continuous or mixed numerical decision variables; fitness function evaluations","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Vol. 4, pp. 1942-1948. IEEE.","type":"inproceedings","doi":"10.1109/ICNN.1995.488968","isbn":null,"url":null},{"ref":"Clerc, M., Kennedy, J. (2002). The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58-73.","type":"article","doi":"10.1109/4235.985692","isbn":null,"url":null}],"related":["particle-swarm-optimization","stochastic-genetic-algorithm","stochastic-simulated-annealing","multi-objective-particle-swarm-optimization","stochastic-ant-colony-optimization","stochastic-multi-objective-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-queueing-simulation","name":"Stochastic Queueing Simulation","fullName":"Stochastic Queueing Simulation — Probabilistic Modeling of Waiting-Line Systems","aliases":["SQS","Probabilistic Queueing Simulation","Stochastic Queue Modeling","Random Queueing Simulation"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1953","originator":"Kendall, D. G.","url":"https://scholargate.app/en/simulation/stochastic-queueing-simulation","markdownUrl":"https://scholargate.app/en/simulation/stochastic-queueing-simulation.md","definition":"Stochastic Queueing Simulation models waiting-line systems where arrival and service processes follow probability distributions rather than fixed rates. By simulating thousands of random events, it estimates performance measures — mean waiting time, queue length, server utilization — under realistic uncertainty, making it the standard tool for designing and evaluating service systems from hospitals to call centers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kendall, D. G.","year":"1953","type":"Stochastic simulation — waiting-line system analysis","dataType":"Inter-arrival times, service times, queue discipline parameters (stochastic distributions)","subfamily":"Simulation / optimization"},"citations":[{"ref":"Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded Markov chain. The Annals of Mathematical Statistics, 24(3), 338–354.","type":"article","doi":"10.1214/aoms/1177728975","isbn":null,"url":null},{"ref":"Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education.","type":"book","doi":null,"isbn":"9780073401324","url":null}],"related":["queueing-simulation","discrete-event-simulation","markov-model","monte-carlo-simulation","stochastic-discrete-event-simulation","stochastic-markov-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-scenario-analysis","name":"Stochastic Scenario Analysis","fullName":"Stochastic Scenario Analysis — Probabilistic multi-scenario evaluation under uncertainty","aliases":["Probabilistic Scenario Analysis","SSA","Stochastic What-If Analysis","Monte Carlo Scenario Analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1955–1980s","originator":"Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition","url":"https://scholargate.app/en/simulation/stochastic-scenario-analysis","markdownUrl":"https://scholargate.app/en/simulation/stochastic-scenario-analysis.md","definition":"Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition","year":"1955–1980s","type":"Probabilistic scenario enumeration and evaluation","dataType":"Uncertain parameters with probability distributions; discrete or continuous scenario sets","subfamily":"Simulation / optimization"},"citations":[{"ref":"Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer.","type":"book","doi":null,"isbn":"9781461402374","url":null},{"ref":"Lempert, R. J., Popper, S. W., Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation.","type":"book","doi":null,"isbn":null,"url":"https://www.rand.org/pubs/monograph_reports/MR1626.html"}],"related":["scenario-analysis","monte-carlo-simulation","stochastic-dynamic-programming","sensitivity-analysis","stochastic-linear-programming","risk-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-sensitivity-analysis","name":"Stochastic Sensitivity Analysis","fullName":"Stochastic Sensitivity Analysis (Probabilistic Sensitivity Analysis)","aliases":["PSA","Probabilistic Sensitivity Analysis","Stochastic SA","Monte Carlo Sensitivity Analysis"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s–2000s","originator":"Saltelli, A. et al.; Claxton, K. et al. (health economics stream)","url":"https://scholargate.app/en/simulation/stochastic-sensitivity-analysis","markdownUrl":"https://scholargate.app/en/simulation/stochastic-sensitivity-analysis.md","definition":"Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saltelli, A. et al.; Claxton, K. et al. (health economics stream)","year":"1990s–2000s","type":"Probabilistic uncertainty quantification technique","dataType":"Numerical model inputs with probability distributions","subfamily":"Simulation / optimization"},"citations":[{"ref":"Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.","type":"book","doi":null,"isbn":"9780470059975","url":null},{"ref":"Briggs, A. H., Claxton, K., Sculpher, M. (2012). Decision Modelling for Health Economic Evaluation. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Decision+Modelling+for+Health+Economic+Evaluation+Briggs+Claxton+Sculpher"}],"related":["monte-carlo-simulation","scenario-analysis","stochastic-scenario-analysis","sensitivity-analysis","stochastic-markov-model","stochastic-discrete-event-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-system-dynamics","name":"Stochastic System Dynamics","fullName":"Stochastic System Dynamics (SSD)","aliases":["SSD","stochastic stock-flow modelling","probabilistic system dynamics","random system dynamics"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1980s–2000s","originator":"Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers","url":"https://scholargate.app/en/simulation/stochastic-system-dynamics","markdownUrl":"https://scholargate.app/en/simulation/stochastic-system-dynamics.md","definition":"Stochastic System Dynamics (SSD) extends conventional system dynamics by replacing fixed parameter values and deterministic flow equations with probability distributions and random draws. Running many replications of the stock-flow model yields probabilistic trajectories — confidence bands rather than single lines — enabling rigorous uncertainty quantification and risk analysis in complex feedback systems such as epidemic models, supply chains, and energy policy scenarios.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers","year":"1980s–2000s","type":"Continuous stochastic simulation","dataType":"Time-series data, probability distributions, expert elicitation","subfamily":"Simulation / optimization"},"citations":[{"ref":"Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill.","type":"book","doi":null,"isbn":"978-0072389159","url":null},{"ref":"Rahmandad, H., Sterman, J.D. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998-1014.","type":"article","doi":"10.1287/mnsc.1070.0787","isbn":null,"url":null}],"related":["system-dynamics","monte-carlo-simulation","stochastic-differential-equations","discrete-event-simulation","agent-based-modelling","sensitivity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-tabu-search","name":"Stochastic Tabu Search","fullName":"Stochastic Tabu Search — Randomized metaheuristic optimization with tabu memory","aliases":["STS","Randomized Tabu Search","Probabilistic Tabu Search","Noisy Tabu Search"],"domain":"simulation","family":"process-pipeline","subfamily":"Simulation / optimization","year":"1990s","originator":"Glover, F. (base TS); stochastic extensions by various authors (1990s–2000s)","url":"https://scholargate.app/en/simulation/stochastic-tabu-search","markdownUrl":"https://scholargate.app/en/simulation/stochastic-tabu-search.md","definition":"Stochastic Tabu Search (STS) is an extension of classical Tabu Search that introduces randomness into the neighborhood exploration and move-selection phases. By combining tabu memory — which forbids recently visited solutions — with probabilistic acceptance or random candidate sampling, STS escapes local optima more effectively and explores rugged solution landscapes that deterministic TS may fail to traverse.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Glover, F. (base TS); stochastic extensions by various authors (1990s–2000s)","year":"1990s","type":"Stochastic metaheuristic optimizer","dataType":"Combinatorial or continuous objective function evaluations","subfamily":"Simulation / optimization"},"citations":[{"ref":"Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4), 74-94.","type":"article","doi":"10.1287/inte.20.4.74","isbn":null,"url":null},{"ref":"Hu, J., Fu, M. C., & Marcus, S. I. (2007). A model reference adaptive search method for global optimization. Operations Research, 55(3), 549-568.","type":"article","doi":"10.1287/opre.1060.0367","isbn":null,"url":null}],"related":["tabu-search","simulated-annealing","stochastic-simulated-annealing","genetic-algorithm","stochastic-genetic-algorithm","particle-swarm-optimization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-uta","name":"STOCHASTIC-UTA","fullName":"Stochastic UTilités Additives (preference-disaggregation under uncertainty)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1982 — stochastic extension Stavrou-Ventikos-Tsoukalas 2018 Springer","originator":"Stavrou, D. I.; Ventikos, N. P.; Tsoukalas, V. D. (2018) — STOCHASTIC-UTA seminal chapter Jacquet-Lagrèze, E.; Siskos, J. (1982) — classical UTA foundation Siskos, Y. (1980) — preference disaggregation theory","url":"https://scholargate.app/en/decision-making/stochastic-uta","markdownUrl":"https://scholargate.app/en/decision-making/stochastic-uta.md","definition":"STOCHASTIC-UTA (Stochastic UTilités Additives (preference-disaggregation under uncertainty)) is a ranking multi-criteria decision-making (MCDM) method introduced by Stavrou, D. I.; Ventikos, N. P.; Tsoukalas, V. D. (2018) — STOCHASTIC-UTA seminal chapter Jacquet-Lagrèze, E.; Siskos, J. (1982) — classical UTA foundation Siskos, Y. (1980) — preference disaggregation theory in 1982 — stochastic extension Stavrou-Ventikos-Tsoukalas 2018 Springer. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stavrou, D. I.; Ventikos, N. P.; Tsoukalas, V. D. (2018) — STOCHASTIC-UTA seminal chapter Jacquet-Lagrèze, E.; Siskos, J. (1982) — classical UTA foundation Siskos, Y. (1980) — preference disaggregation theory","subfamily":"Ranking","year":"1982 — stochastic extension Stavrou-Ventikos-Tsoukalas 2018 Springer","type":"Preference disaggregation with LP utility fitting + Monte Carlo acceptability analysis","value_space":"crisp","uncertainty":"stochastic","compensation":"full","rank_reversal":false},"citations":[{"ref":"Stavrou, D. I., Ventikos, N. P., Tsoukalas, V. D. (2018). Robust Evaluation of Risks in Ship-to-Ship Transfer Operations: Application of the STOCHASTIC UTA Multicriteria Decision Support Method. In Lee, P. T. W. & Yang, Z. (Eds.), Multi-criteria Decision Making in Maritime Studies and Logistics (pp. 161–185). Springer.","type":"article","doi":"10.1007/978-3-319-62338-2_8","isbn":null,"url":null}],"related":["topsis","vikor","marcos"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stochastic-volatility-model","name":"Stochastic Volatility Model","fullName":"Stochastic Volatility Model (Heston Model)","aliases":["Heston model","SV model","continuous-time stochastic volatility","Stokastik Volatilite Modeli (Heston, SV)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1993,"originator":"Steven L. Heston","url":"https://scholargate.app/en/finance/stochastic-volatility-model","markdownUrl":"https://scholargate.app/en/finance/stochastic-volatility-model.md","definition":"The stochastic volatility model is a continuous-time option-pricing and risk framework in which volatility follows its own random process rather than staying constant. The Heston model, introduced by Steven Heston in 1993, gives the variance a mean-reverting square-root (CIR) dynamic and yields a closed-form option price; it is the continuous-time counterpart of GARCH.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven L. Heston","year":1993,"type":"Continuous-time stochastic volatility model","estimator":"Calibration to option prices or historical returns","volatilityProcess":"Square-root (CIR) mean-reverting variance","outcome":"continuous"},"citations":[{"ref":"Heston, S. L. (1993). A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options. Review of Financial Studies, 6(2), 327-343.","type":"article","doi":"10.1093/rfs/6.2.327","isbn":null,"url":null},{"ref":"Gatheral, J. (2006). The Volatility Surface: A Practitioner's Guide. Wiley.","type":"book","doi":null,"isbn":"978-0471792512","url":null}],"related":["garch-model","long-memory-models","credit-risk-models","high-frequency-microstructure","portfolio-optimization-mean-variance"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stop-bang-questionnaire","name":"STOP-BANG","fullName":"STOP-BANG Obstructive Sleep Apnea Screening Questionnaire","aliases":["STOP-BANG OSA Screening"],"domain":"sleep-medicine","family":"process-pipeline","subfamily":"pre-operative screening; OSA risk stratification","year":"2008","originator":"Chung, F., Yegneswaran, B., Liao, P., et al.","url":"https://scholargate.app/en/sleep-medicine/stop-bang-questionnaire","markdownUrl":"https://scholargate.app/en/sleep-medicine/stop-bang-questionnaire.md","definition":"The STOP-BANG is an 8-item screening tool for identifying patients at risk of obstructive sleep apnea (OSA) before surgery or medical procedures. Developed by Chung and colleagues in 2008, it is widely used in perioperative medicine, primary care, and sleep clinics to quickly stratify OSA risk in both adult patients. The tool demonstrates strong sensitivity for moderate to severe OSA, making it valuable in settings where formal sleep testing is impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chung, F., Yegneswaran, B., Liao, P., et al.","subfamily":"pre-operative screening; OSA risk stratification","year":"2008","type":"Self-report"},"citations":[{"ref":"Chung, F., Yegneswaran, B., Liao, P., et al. (2008). STOP questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology, 108(5), 812-821.","type":"article","doi":"10.1097/aln.0b013e31816d83e4","isbn":null,"url":null}],"related":["berlin-questionnaire-sleep","restless-legs-syndrome-rating","sleep-condition-indicator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stop-signal-reaction-time","name":"Stop-Signal Reaction Time","fullName":"Stop-Signal Reaction Time Task","aliases":["SSRT","Stop Task","Response Inhibition Task"],"domain":"psychology","family":"hypothesis-test","subfamily":"Inhibitory Control","year":"1984","originator":"Gordon Logan and Wiliam Cowan","url":"https://scholargate.app/en/psychology/stop-signal-reaction-time","markdownUrl":"https://scholargate.app/en/psychology/stop-signal-reaction-time.md","definition":"The Stop-Signal Reaction Time (SSRT) task is a behavioral measure of response inhibition and executive control. Participants make rapid responses to go signals but must cancel responses when an occasional stop signal appears. By analyzing how successfully they inhibit responses and estimating the latency of inhibition (Stop-Signal Reaction Time), researchers measure the speed and efficiency of the neural inhibitory processes that enable self-control, impulse control, and behavioral flexibility.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon Logan and Wiliam Cowan","subfamily":"Inhibitory Control","year":"1984","type":"Behavioral task"},"citations":[{"ref":"Logan, G. D., Cowan, W. B., & Davis, K. A. (1984). On the ability to inhibit simple and choice reaction time responses: A model and a method. Journal of Experimental Psychology: Human Perception and Performance, 10(2), 276-291.","type":"article","doi":"10.1037/0096-1523.10.2.276","isbn":null,"url":null},{"ref":"Verbruggen, F., & Logan, G. D. (2008). Response inhibition in the stop-signal paradigm. Trends in Cognitive Sciences, 12(12), 418-424.","type":"article","doi":"10.1016/j.tics.2008.07.005","isbn":null,"url":null},{"ref":"Chambers, C. D., Garavan, H., & Bellgrove, M. A. (2009). Insights into the neural basis of response inhibition. Neuroscience & Biobehavioral Reviews, 33(5), 631-646.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Insights+into+the+neural+basis+of+response+inhibition+Chambers"}],"related":["drift-diffusion-model","go-no-go-task","response-inhibition","executive-function"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stope-layout","name":"Stope Layout","fullName":"Stope Layout Optimization for Underground Mining","aliases":["Stope Design","Underground Mine Layout","Panel Design"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Geotechnical Design","year":"1960","originator":"Mining Engineering Practice","url":"https://scholargate.app/en/mining-engineering/stope-layout","markdownUrl":"https://scholargate.app/en/mining-engineering/stope-layout.md","definition":"Stope layout optimization is the process of designing the size, shape, and spatial arrangement of underground mine excavations (stopes) to maximize ore recovery while maintaining safety and economic viability. It balances the desire for large extraction volumes against rock mechanics constraints and support costs. The layout determines mining productivity, capital investment in support systems, and long-term mine life.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mining Engineering Practice","subfamily":"Geotechnical Design","year":"1960","type":"Optimization framework for underground mine excavation design"},"citations":[{"ref":"Brady, B. H. G., & Brown, E. T. (2004). Rock mechanics for underground mining. Springer Science+Business Media.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Rock+mechanics+for+underground+mining+Brady"},{"ref":"Langford, J. C., & Diederichs, M. S. (2011). Assessing and managing underground excavation hazards. Norwegian University of Science and Technology.","type":"article","doi":null,"isbn":null,"url":"https://www.ntnu.edu/"}],"related":["lerchs-grossmann-algorithm","hoek-brown-criterion","rock-mass-rating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stormwater-management","name":"Stormwater Management","fullName":"Design and Operation of Urban Stormwater Systems","aliases":["urban stormwater","runoff management","wet weather control","low-impact development"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Urban water infrastructure","year":"1980","originator":"Urban hydrologists and engineers","url":"https://scholargate.app/en/environmental-engineering/stormwater-management","markdownUrl":"https://scholargate.app/en/environmental-engineering/stormwater-management.md","definition":"Stormwater management is the planning and engineering of urban water systems to control, treat, and utilize rainwater runoff from developed areas. Traditional approaches (pipes, detention basins) conveyed runoff rapidly to streams or treatment plants; modern green infrastructure approaches (permeable pavements, bioswales, retention ponds) reduce runoff volume through infiltration and reuse while improving water quality. Stormwater management integrates hydrologic modeling, water quality assessment, and infrastructure design to meet regulatory requirements and climate resilience goals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Urban hydrologists and engineers","subfamily":"Urban water infrastructure","year":"1980","type":"design and simulation pipeline"},"citations":[{"ref":"Urbonas, B., & Stahre, P. (1993). Stormwater Best Management Practices and Detention. Prentice Hall.","type":"article","doi":null,"isbn":"978-0134445915","url":null},{"ref":"US Environmental Protection Agency. (2012). Stormwater Best Management Practices. EPA 832-R-12-001.","type":"article","doi":null,"isbn":null,"url":"https://www.epa.gov/water-permitting/national-pollutant-discharge-elimination-system-stormwater"},{"ref":"Deletic, A., Delleur, J. W., & Gupta, R. (Eds.). (2012). Integrated Urban Water Management. CRC Press.","type":"article","doi":null,"isbn":"978-0415695816","url":null}],"related":["constructed-wetland-design","green-infrastructure-design","environmental-impact-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"strategic-choice-approach","name":"Strategic Choice Approach","fullName":"Strategic Choice Approach (Planning Under Pressure)","aliases":["SCA","Planning Under Pressure","Stratejik Seçim Yaklaşımı","Interactive Strategic Planning"],"domain":"problem-structuring","family":"process-pipeline","subfamily":"Problem structuring methods","year":2005,"originator":"John Friend & Allen Hickling","url":"https://scholargate.app/en/problem-structuring/strategic-choice-approach","markdownUrl":"https://scholargate.app/en/problem-structuring/strategic-choice-approach.md","definition":"The Strategic Choice Approach (SCA) is an interactive, workshop-based problem structuring method developed by John Friend and Allen Hickling, first published in 1987 and refined in the definitive third edition of Planning Under Pressure (2005). SCA helps groups of planners and stakeholders manage interconnected decisions under uncertainty by explicitly mapping decision areas, option combinations, and sources of uncertainty before committing to action.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Friend & Allen Hickling","year":2005,"type":"Iterative group-based problem structuring and decision process","subfamily":"Problem structuring methods","mode":"Facilitated workshop","output":"Commitment package with deferred and immediate decisions"},"citations":[{"ref":"Friend, J., & Hickling, A. (2005). Planning Under Pressure: The Strategic Choice Approach (3rd ed.). Elsevier.","type":"book","doi":null,"isbn":"978-0-7506-6373-2","url":null}],"related":["soft-systems-methodology","morphological-analysis","scenario-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"strategic-orientation-scale","name":"Strategic Orientation Scale","fullName":"Strategic Orientation and Strategic Posture Assessment Scale","aliases":["Strategic Posture Scale","Miller-Friesen Framework"],"domain":"strategic-management","family":"process-pipeline","subfamily":"competitive-strategy","year":"1978","originator":"Miles and Snow; extended by Miller and Friesen","url":"https://scholargate.app/en/strategic-management/strategic-orientation-scale","markdownUrl":"https://scholargate.app/en/strategic-management/strategic-orientation-scale.md","definition":"Strategic Orientation refers to the fundamental approach an organization adopts when competing in its market, encompassing its competitive strategy, market focus, and organizational design. Miles and Snow's (1978) foundational framework identifies four strategic postures: Defenders (focus on stable market segments, operational efficiency, and incremental innovation), Prospectors (pursue new market opportunities, drive innovation, accept higher risk), Analyzers (balance efficiency and innovation, serve established markets while exploring adjacent opportunities), and Reactors (lack clear strategy, respond reactively to environmental pressures). This scale operationalizes Miles and Snow's framework, revealing an organization's strategic type and fit with its environment and structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Miles and Snow; extended by Miller and Friesen","subfamily":"competitive-strategy","year":"1978","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Miles, R. E., & Snow, C. C. (1978). Organizational strategy, structure, and process. McGraw-Hill.","type":"article","doi":"10.2307/2392589","isbn":null,"url":null},{"ref":"Miller, D., & Friesen, P. H. (1983). Strategy-making and environment: The third link. Strategic Management Journal, 4(3), 221–235.","type":"article","doi":"10.1002/smj.4250040304","isbn":null,"url":null},{"ref":"Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. Free Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Porter%2C%20M.%20E.%20(1980).%20Competitive%20strategy%3A%20Techniques%20for%20analyzing%20industries%20and%20competitors.%20Free%20Press."}],"related":["entrepreneurial-orientation-scale","dynamic-capabilities-scale","market-sensing-capability-scale","innovation-ambidexterity-scale","absorptive-capacity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stratified-bwm","name":"Stratified Best Worst Method","fullName":"Stratified Best Worst Method (Stratified BWM)","aliases":["Stratified BWM"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2015","originator":"Jafar Rezaei and collaborators","url":"https://scholargate.app/en/decision-making/stratified-bwm","markdownUrl":"https://scholargate.app/en/decision-making/stratified-bwm.md","definition":"Stratified BWM is an extension of the Best Worst Method that applies the BWM logic recursively across multiple hierarchical layers. Instead of weighting criteria at a single level, it identifies the best and worst criterion within each level of a hierarchy, then aggregates weights across levels. This enables more realistic modeling of complex decision problems with natural hierarchical structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jafar Rezaei and collaborators","subfamily":"Ranking","year":"2015","type":"Hierarchical pairwise comparison with layer-wise best-worst"},"citations":[{"ref":"Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.","type":"article","doi":"10.1016/j.omega.2015.12.001","isbn":null,"url":null},{"ref":"Asadzadeh, A., Rezaei, J., & Tavasszy, L. (2017). Sustainability assessment of supplier selection criteria using the BWM method in hierarchical procurement. Supply Chain Management, 22(6), 551-563.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sustainability+assessment+of+supplier+selection+criteria+using+the+BWM+method+in+hierarchical+procurement+Asadzadeh"}],"related":["bwm","bwm-sort","ahp-bocr","hierarchical-bwm","fuzzy-bwm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stratified-sampling","name":"Stratified Sampling","fullName":"Stratified and Cluster Sampling Designs","aliases":["Proportional Stratified Sampling","Optimal Allocation Sampling","Stratum-Based Sampling","Tabakalı Örnekleme"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling design","year":1977,"originator":"William G. Cochran","url":"https://scholargate.app/en/survey-methodology/stratified-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/stratified-sampling.md","definition":"Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran","year":1977,"type":"Probability-based survey sampling design","subfamily":"Sampling design","allocation_variants":"Proportional, Optimal (Neyman), Equal","key_parameter":"Stratum sample size n_h"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0-471-16240-7","url":null}],"related":["survey-weighting","small-area-estimation","simulation-based-power-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stratigraphic-correlation","name":"Stratigraphic Correlation","fullName":"Stratigraphic Correlation","aliases":["lithostratigraphic correlation","chronostratigraphic correlation","sequence correlation"],"domain":"geoscience","family":"process-pipeline","subfamily":"Time-rock framework","year":"1901","originator":"Albrecht Penck and Eduard Brückner","url":"https://scholargate.app/en/geoscience/stratigraphic-correlation","markdownUrl":"https://scholargate.app/en/geoscience/stratigraphic-correlation.md","definition":"Stratigraphic correlation is the practice of identifying equivalent rock layers or chronostratigraphic units across space by tracing physical or chemical signatures. Rooted in 19th-century work on Alpine glacial sequences, this method was formalized in the 20th century by geologists like Vail who unified global sea-level change with depositional sequences. Correlation is foundational to basin-scale understanding of sediment transport, resource distribution, and paleoenvironmental change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albrecht Penck and Eduard Brückner","subfamily":"Time-rock framework","year":"1901","type":"stratigraphic analysis pipeline"},"citations":[{"ref":"Catuneanu, O. (2002). Sequence Stratigraphy of Clastic Systems. Geological Association of Canada.","type":"book","doi":null,"isbn":null,"url":"https://www.gac.ca"},{"ref":"Vail, P. R., Mitchum, R. M., & Thompson, S. (1977). Global cycles of relative changes of sea level. American Association of Petroleum Geologists Memoir, 26, 83–97.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Global+cycles+of+relative+changes+of+sea+level+Vail"},{"ref":"Posamentier, H. W., & Allen, G. P. (2006). Siliciclastic Sequence Stratigraphy: Concepts and Applications. Society for Sedimentary Geology.","type":"book","doi":null,"isbn":null,"url":"https://www.sepm.org"}],"related":["well-log-analysis","seismic-reflection-interpretation","geologic-mapping","petrographic-analysis","basin-subsidence-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"straussian-grounded-theory","name":"Straussian Grounded Theory","fullName":"Straussian Grounded Theory (Strauss & Corbin)","aliases":["Strauss-Corbin GT","systematic grounded theory","GTM (Straussian)","conditional/consequential matrix GT"],"domain":"qualitative","family":"process-pipeline","subfamily":"Grounded Theory","year":"1990 (systematic elaboration; building on Glaser & Strauss 1967)","originator":"Anselm Strauss & Juliet Corbin","url":"https://scholargate.app/en/qualitative/straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/straussian-grounded-theory.md","definition":"Straussian Grounded Theory is a systematic qualitative methodology developed by Anselm Strauss and Juliet Corbin that generates theory inductively from data through structured coding procedures. Unlike exploratory description, it aims to produce a substantive mid-range theory that explains how a social process unfolds, grounding every theoretical claim directly in empirical evidence collected from participants who have experienced the phenomenon under study.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss & Juliet Corbin","year":"1990 (systematic elaboration; building on Glaser & Strauss 1967)","type":"Qualitative research method","dataType":"Interviews, observations, documents, field notes (text data)","typicalSampleSize":"20–50 participants or purposive/theoretical sampling until saturation","subfamily":"Grounded Theory"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.","type":"book","doi":null,"isbn":"978-0803932500","url":null},{"ref":"Corbin, J., & Strauss, A. (2008). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (3rd ed.). Sage.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Basics+of+Qualitative+Research+Techniques+and+Procedures+for+Developing+Grounded+Theory+Corbin+Strauss+2008"}],"related":["grounded-theory","phenomenology","ethnography","case-study","thematic-analysis","action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"strengths-difficulties-questionnaire","name":"Strengths and Difficulties Questionnaire","fullName":"Strengths and Difficulties Questionnaire (SDQ)","aliases":["SDQ","SDQ-Strengths","SDQ-Parent","SDQ-Teacher"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"Behavioral assessment","year":"2001","originator":"Robert Goodman","url":"https://scholargate.app/en/developmental-assessment/strengths-difficulties-questionnaire","markdownUrl":"https://scholargate.app/en/developmental-assessment/strengths-difficulties-questionnaire.md","definition":"The Strengths and Difficulties Questionnaire (SDQ), developed by Robert Goodman in 1997 and validated by 2001, is a brief 25-item behavioral screening instrument for children aged 2–17 years. Available in parent, teacher, and youth self-report versions, it assesses both emotional/behavioral difficulties and personal strengths, making it unique among screening tools. It is widely used in community, clinical, and research settings for rapid screening and population surveillance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Goodman","subfamily":"Behavioral assessment","year":"2001","type":"Multi-informant behavioral screening scale"},"citations":[{"ref":"Goodman, R. (2001). Psychometric properties of the Strengths and Difficulties Questionnaire. Journal of the American Academy of Child & Adolescent Psychiatry, 40(11), 1337-1345.","type":"article","doi":"10.1097/00004583-200111000-00015","isbn":null,"url":null},{"ref":"Stone, L. L., Otten, R., Engels, R. C., et al. (2010). Psychometric properties of the parent and teacher versions of the Strengths and Difficulties Questionnaire for 4- to 12-year-olds. Clinical Child & Family Psychology Review, 13(3), 254-274.","type":"article","doi":"10.1007/s10567-010-0071-2","isbn":null,"url":null}],"related":["cbcl-child-behavior","conners-rating-scales","vanderbilt-adhd-scale","achenbach-youth-self-report"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stride-dread-threat-modeling","name":"STRIDE/DREAD Threat Modeling","fullName":"STRIDE and DREAD Threat Modeling Methodology","aliases":["threat modeling","security analysis","risk assessment"],"domain":"numerical-methods","family":"ml-model","subfamily":"Security Analysis","year":"1999","originator":"Microsoft Trustworthy Computing Group","url":"https://scholargate.app/en/numerical-methods/stride-dread-threat-modeling","markdownUrl":"https://scholargate.app/en/numerical-methods/stride-dread-threat-modeling.md","definition":"STRIDE/DREAD Threat Modeling is a Microsoft-developed methodology for systematically identifying and prioritizing security threats in software systems. STRIDE enumerates threat categories (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege), and DREAD scores threats by Damage, Reproducibility, Exploitability, Affected Users, and Discoverability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Microsoft Trustworthy Computing Group","subfamily":"Security Analysis","year":"1999","type":"Threat identification and risk assessment"},"citations":[{"ref":"Shostack, A. (2008). Threat Modeling: Designing for Security. Microsoft Press.","type":"book","doi":null,"isbn":"0735619913","url":null},{"ref":"Howard, M., & Lipner, S. (2006). The Security Development Lifecycle. Microsoft Press.","type":"book","doi":null,"isbn":"0735622140","url":null},{"ref":"Schoenfield, B. (2015). Securing the Internet of Things. Apress.","type":"book","doi":null,"isbn":"1430268271","url":null}],"related":["security-requirements","risk-assessment","attack-surface-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"strobe-checklist","name":"STROBE Checklist","fullName":"Strengthening the Reporting of Observational Studies in Epidemiology Checklist","aliases":["STROBE"],"domain":"research-methodology","family":"process-pipeline","subfamily":"Observational study reporting standard","year":"2007","originator":"Von Elm et al. (STROBE Group)","url":"https://scholargate.app/en/research-methodology/strobe-checklist","markdownUrl":"https://scholargate.app/en/research-methodology/strobe-checklist.md","definition":"STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) is a 22-item evidence-based checklist published in 2007 by Von Elm et al. to improve the quality of reporting of cohort, case-control, and cross-sectional observational studies. Like CONSORT for RCTs, STROBE is endorsed by over 300 journals and is widely considered the reporting standard for observational epidemiological research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Von Elm et al. (STROBE Group)","subfamily":"Observational study reporting standard","year":"2007","type":"Study author reporting checklist"},"citations":[{"ref":"Von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. The Lancet, 370(9596), 1453–1457.","type":"article","doi":"10.1016/S0140-6736(07)61602-X","isbn":null,"url":null}],"related":["newcastle-ottawa-scale","consort-reporting-checklist","prisma-checklist","casp-rct-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stroke-specific-qol","name":"SS-QoL","fullName":"Stroke-Specific Quality of Life Scale","aliases":["Stroke-Specific QoL","SS-QOL"],"domain":"neurology","family":"process-pipeline","subfamily":"disease-specific quality of life","year":"1999","originator":"Lee S. Williams, Indiana University","url":"https://scholargate.app/en/neurology/stroke-specific-qol","markdownUrl":"https://scholargate.app/en/neurology/stroke-specific-qol.md","definition":"The SS-QoL is a disease-specific quality-of-life instrument designed to capture the multidimensional impact of stroke on survivors' functional and emotional well-being. Developed by Williams and colleagues in 1999, this 49-item scale addresses stroke-specific concerns including language, cognition, mobility, and emotional functioning. It is a gold-standard instrument for stroke outcome research and routine clinical monitoring of post-stroke recovery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee S. Williams, Indiana University","subfamily":"disease-specific quality of life","year":"1999","type":"Self-report questionnaire"},"citations":[{"ref":"Williams, L. S., Weinberger, M., Harris, L. E., Clark, D. O., & Biller, J. (1999). Development of a Stroke-Specific Quality of Life Scale. Stroke, 30(7), 1362-1369.","type":"article","doi":"10.1161/01.STR.30.7.1362","isbn":null,"url":null}],"related":["msqol-54","modified-rankin-scale","qolie-89","alsfrs-r"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"strong-gravitational-lensing","name":"Strong Gravitational Lensing","fullName":"Strong Gravitational Lensing for Mass and Distance Measurements","aliases":["Strong Lensing","Gravitational Lens","Einstein Ring"],"domain":"astronomy","family":"process-pipeline","subfamily":"Mass measurement","year":1964,"originator":"Sjur Refsdal","url":"https://scholargate.app/en/astronomy/strong-gravitational-lensing","markdownUrl":"https://scholargate.app/en/astronomy/strong-gravitational-lensing.md","definition":"Strong gravitational lensing occurs when massive objects (clusters, galaxies) bend light so strongly that multiple images of distant sources appear, or complete rings (Einstein rings) form. Proposed by Sjur Refsdal in 1964 and first observed in 0957+561 in 1979, strong lensing provides direct measurements of lens masses and cosmic distances independent of the distance ladder.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sjur Refsdal","subfamily":"Mass measurement","year":1964,"type":"Observational measurement method"},"citations":[{"ref":"Refsdal, S. (1964). On the possibility of determining Hubble's parameter and the masses of galaxies from the gravitational lens effect. Monthly Notices of the Royal Astronomical Society, 128(4), 307-311.","type":"article","doi":"10.1093/mnras/128.4.307","isbn":null,"url":null},{"ref":"Walsh, D., Carswell, R. F., & Weymann, R. J. (1979). 0957+ 561 A, B: Twin quasistellar objects or gravitational lens? Nature, 279, 381-384.","type":"article","doi":"10.1038/279381a0","isbn":null,"url":null},{"ref":"Suyu, S. H., et al. (2017). Cosmology from Gravitational Lens Statistics. Space Science Reviews, 212(1), 1-46.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cosmology+from+Gravitational+Lens+Statistics+Suyu"}],"related":["weak-gravitational-lensing","type-ia-sn-light-curve-fitting","astrometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"strontium-provenance","name":"Strontium Provenance","fullName":"Strontium Isotope Provenance Analysis","aliases":["Sr isotope provenance","strontium isotope analysis"],"domain":"archaeology","family":"process-pipeline","subfamily":"Geochemistry","year":"1985","originator":"Jonathan Ericson","url":"https://scholargate.app/en/archaeology/strontium-provenance","markdownUrl":"https://scholargate.app/en/archaeology/strontium-provenance.md","definition":"Strontium isotope provenance analysis uses the ratios of strontium-87 to strontium-86 in human skeletal remains to determine geographic origin and track human mobility and migration. Developed by Jonathan Ericson in the 1980s, this method exploits the fact that strontium isotope ratios in the environment vary geographically based on underlying geology. When individuals consume food and water from a specific region, they incorporate that region's characteristic strontium isotope signature into their bones and teeth, creating a geochemical fingerprint of their residence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan Ericson","subfamily":"Geochemistry","year":"1985","type":"Isotopic sourcing technique"},"citations":[{"ref":"Ericson, J. E. (1985). Strontium isotope characterization in the study of prehistoric migrations. Journal of Human Evolution, 14(5), 503-514.","type":"article","doi":"10.1016/S0047-2484(85)80029-4","isbn":null,"url":null},{"ref":"Price, T. D., Grupe, G., & Schroter, P. (1994). Reconstruction of migration patterns in the Bell Beaker period by stable lead isotope analysis. Journal of Archaeological Science, 21(6), 697-708.","type":"article","doi":"10.1016/0883-2927(94)90063-9","isbn":null,"url":null},{"ref":"Bentley, R. A. (2006). Strontium isotopes from the earth to the archaeological skeleton: a review. Journal of Archaeological Method and Theory, 13(3), 135-187.","type":"article","doi":"10.1007/s10816-006-9009-x","isbn":null,"url":null}],"related":["isotope-diet-reconstruction","ceramic-petrography","instrumental-neutron-activation-analysis","use-wear-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stroop-task","name":"Stroop Task","fullName":"Stroop Task","aliases":["Stroop Effect","Color-Word Task"],"domain":"psychology","family":"hypothesis-test","subfamily":"Attentional Control","year":"1935","originator":"John Ridley Stroop","url":"https://scholargate.app/en/psychology/stroop-task","markdownUrl":"https://scholargate.app/en/psychology/stroop-task.md","definition":"The Stroop task is a classic measure of cognitive control and selective attention. Participants name the color of words while ignoring the words' semantic content. When the color and word meaning match (e.g., the word 'red' printed in red ink), responses are fast. When they conflict (e.g., the word 'red' printed in blue ink), response times increase dramatically. This Stroop effect reveals how automatic word reading interferes with color naming, indexed by the difference in reaction times between congruent and incongruent conditions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Ridley Stroop","subfamily":"Attentional Control","year":"1935","type":"Interference task"},"citations":[{"ref":"Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643-662.","type":"article","doi":"10.1037/h0054651","isbn":null,"url":null},{"ref":"MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109(2), 163-203.","type":"article","doi":"10.1037/0033-2909.109.2.163","isbn":null,"url":null},{"ref":"Raz, A. (2007). Suggestibility and neuroplasticity of the anterior cingulate cortex. Behavioral and Brain Sciences, 30(1), 96-97.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Suggestibility+and+neuroplasticity+of+the+anterior+cingulate+cortex+Raz"}],"related":["executive-function","cognitive-control","attention","response-inhibition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-adf-unit-root-test","name":"Structural Break ADF Unit Root Test","fullName":"Structural Break Augmented Dickey-Fuller Unit Root Test","aliases":["ADF with structural break","Perron unit root test","break-augmented ADF","unit root test with structural change"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1989-1992","originator":"Perron (1989); Zivot and Andrews (1992)","url":"https://scholargate.app/en/econometrics/structural-break-adf-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-adf-unit-root-test.md","definition":"The structural break ADF unit root test extends the standard Augmented Dickey-Fuller test to allow for one or more discrete shifts in the level or trend of a time series. Because ignoring a structural break inflates the apparent persistence of a series, this test prevents false acceptance of the unit root null when the series is actually stationary around a shifting mean or trend.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Perron (1989); Zivot and Andrews (1992)","year":"1989-1992","type":"Unit root test with structural break","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361-1401.","type":"article","doi":"10.2307/1913712","isbn":null,"url":null},{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10(3), 251-270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null}],"related":["augmented-dickey-fuller-unit-root-test","zivot-andrews-structural-break-test","phillips-perron-unit-root-test","structural-break-kpss-test","structural-break-pp-unit-root-test","structural-break-granger-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-ar-model","name":"Structural Break AR Model","fullName":"Autoregressive Model with Structural Breaks","aliases":["AR model with structural change","breakpoint AR model","piecewise autoregressive model","AR model with regime shifts"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1989-2003","originator":"Perron (1989); Bai & Perron (1998, 2003)","url":"https://scholargate.app/en/econometrics/structural-break-ar-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-ar-model.md","definition":"The structural break AR model extends the standard autoregressive framework by allowing the intercept and autoregressive coefficients to shift at one or more unknown break dates. Each regime between consecutive break points is governed by its own AR parameters, capturing abrupt changes in the dynamics of a time series caused by crises, policy shifts, or other shocks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Perron (1989); Bai & Perron (1998, 2003)","year":"1989-2003","type":"Time-series model with structural change","dataType":"Univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1-22.","type":"article","doi":"10.1002/jae.659","isbn":null,"url":null},{"ref":"Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361-1401.","type":"article","doi":"10.2307/1913712","isbn":null,"url":null}],"related":["autoregressive-model","zivot-andrews-structural-break-test","structural-break-arima-model","structural-break-var-model","augmented-dickey-fuller-unit-root-test","structural-break-vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-arch-model","name":"Structural Break ARCH Model","fullName":"Autoregressive Conditional Heteroscedasticity Model with Structural Breaks","aliases":["ARCH with structural breaks","break-adjusted ARCH","regime-switching ARCH","SB-ARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1982–1990","originator":"Engle (1982) for ARCH; Lamoureux & Lastrapes (1990) for break-adjusted variance persistence","url":"https://scholargate.app/en/econometrics/structural-break-arch-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-arch-model.md","definition":"The Structural Break ARCH model extends Engle's (1982) Autoregressive Conditional Heteroscedasticity framework by explicitly accounting for abrupt, permanent shifts in the conditional variance process. Ignoring structural breaks in variance causes ARCH parameters to appear spuriously persistent, so incorporating break dummies or regime-specific parameters yields more accurate volatility estimates and better model fit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle (1982) for ARCH; Lamoureux & Lastrapes (1990) for break-adjusted variance persistence","year":"1982–1990","type":"Volatility model with regime change","dataType":"Financial or macroeconomic time series with heteroscedastic errors","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null},{"ref":"Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business and Economic Statistics, 8(2), 225–234.","type":"article","doi":"10.1080/07350015.1990.10509794","isbn":null,"url":null}],"related":["arch-model","garch-model","structural-break-garch-model","zivot-andrews-structural-break-test","egarch-model","tgarch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-ardl-bounds-test","name":"Structural Break ARDL Bounds Test","fullName":"Structural Break Autoregressive Distributed Lag Bounds Test","aliases":["SB-ARDL bounds test","ARDL bounds test with structural break","Fourier ARDL bounds test","break-augmented bounds testing"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2001–2010s","originator":"Pesaran, Shin & Smith (bounds framework); structural break extensions by Bahmani-Oskooee, Enders & Jones, and others","url":"https://scholargate.app/en/econometrics/structural-break-ardl-bounds-test","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-ardl-bounds-test.md","definition":"The structural break ARDL bounds test extends the Pesaran, Shin and Smith (2001) bounds testing framework to accommodate one or more structural breaks in the long-run relationship between time-series variables. By incorporating break dummies or smooth Fourier terms into the ARDL error-correction equation, it allows researchers to test for cointegration even when the data have experienced shifts in intercept or slope caused by policy changes, crises, or regime switches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pesaran, Shin & Smith (bounds framework); structural break extensions by Bahmani-Oskooee, Enders & Jones, and others","year":"2001–2010s","type":"Cointegration / bounds test","dataType":"Time-series (levels and first differences, with break dummies or Fourier terms)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null},{"ref":"Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419.","type":"article","doi":"10.1515/snde-2014-0101","isbn":null,"url":null}],"related":["ardl-bounds-test","nonlinear-ardl","fourier-ardl-bounds-test","zivot-andrews-structural-break-test","structural-break-vecm","engle-granger-cointegration-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-arima-model","name":"Structural Break ARIMA Model","fullName":"Structural Break Autoregressive Integrated Moving Average Model","aliases":["ARIMA with structural breaks","break-adjusted ARIMA","piecewise ARIMA","ARIMA with regime shifts"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1989-1998","originator":"Perron (1989); extended by Bai & Perron (1998)","url":"https://scholargate.app/en/econometrics/structural-break-arima-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-arima-model.md","definition":"A structural break ARIMA model extends the standard ARIMA framework by explicitly identifying and accommodating one or more abrupt shifts in the level, trend, or dynamics of a time series. Rather than forcing a single set of ARIMA parameters across the entire sample, it fits separate ARIMA specifications for each regime defined by the detected break dates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Perron (1989); extended by Bai & Perron (1998)","year":"1989-1998","type":"Time series model with regime detection","dataType":"Univariate or multivariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361-1401.","type":"article","doi":"10.2307/1913712","isbn":null,"url":null}],"related":["arima-model","bai-perron-test","chow-test","threshold-autoregressive-model","markov-switching-model","unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-dcc-garch","name":"Structural break DCC-GARCH","fullName":"Structural Break Dynamic Conditional Correlation GARCH Model","aliases":["DCC-GARCH with structural breaks","break-adjusted DCC-GARCH","regime-shift DCC-GARCH","SB-DCC-GARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002-2006","originator":"Engle (2002) for DCC; break-augmented extensions by Pelletier (2006) and subsequent literature","url":"https://scholargate.app/en/econometrics/structural-break-dcc-garch","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-dcc-garch.md","definition":"Structural break DCC-GARCH extends Engle's Dynamic Conditional Correlation GARCH framework by explicitly allowing the correlation and volatility structure to shift at one or more structural break points in the sample. It models time-varying co-volatility between multiple financial series while accounting for sudden regime changes caused by crises, policy shifts, or market microstructure changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle (2002) for DCC; break-augmented extensions by Pelletier (2006) and subsequent literature","year":"2002-2006","type":"Multivariate volatility model with regime change","dataType":"Multivariate financial time series (returns); continuous","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.","type":"article","doi":"10.1198/073500102288618487","isbn":null,"url":null},{"ref":"Pelletier, D. (2006). Regime switching for dynamic correlations. Journal of Econometrics, 131(1-2), 445-473.","type":"article","doi":"10.1016/j.jeconom.2005.01.013","isbn":null,"url":null}],"related":["dcc-garch-model","structural-break-garch-model","structural-break-egarch","structural-break-tgarch","zivot-andrews-structural-break-test","vector-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-difference-gmm","name":"Structural Break Difference GMM","fullName":"Structural Break Difference Generalized Method of Moments","aliases":["Difference GMM with structural breaks","break-augmented Arellano-Bond GMM","dynamic panel GMM with regime shifts","structural change Difference GMM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991 / 1998","originator":"Arellano & Bond (Difference GMM); Bai & Perron (structural break testing)","url":"https://scholargate.app/en/econometrics/structural-break-difference-gmm","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-difference-gmm.md","definition":"Structural Break Difference GMM extends the Arellano-Bond first-difference GMM estimator to dynamic panel settings where the data-generating process shifts at one or more unknown breakpoints. By explicitly incorporating break indicators or allowing regime-specific parameters, the estimator avoids the biased coefficient and invalid moment conditions that arise when a structural change is ignored in a standard Difference GMM fit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bond (Difference GMM); Bai & Perron (structural break testing)","year":"1991 / 1998","type":"Dynamic panel estimator with structural breaks","dataType":"Balanced or unbalanced panel data; continuous and discrete outcomes","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null}],"related":["difference-gmm","system-gmm","arellano-bond-gmm-estimator","structural-break-system-gmm","structural-break-panel-data-analysis","dynamic-panel-data-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-dynamic-panel-data-model","name":"Structural Break Dynamic Panel Data Model","fullName":"Dynamic Panel Data Model with Structural Breaks","aliases":["dynamic panel with breaks","panel dynamic model structural change","DPDSB","panel dynamic structural break estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991–1998","originator":"Bai & Perron (break detection); Arellano & Bond (dynamic panel GMM)","url":"https://scholargate.app/en/econometrics/structural-break-dynamic-panel-data-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-dynamic-panel-data-model.md","definition":"The structural break dynamic panel data model extends the standard dynamic panel framework by allowing regression coefficients or the autoregressive parameter to shift at one or more unknown break dates. It combines GMM-based dynamic panel estimation with formal structural change tests, enabling researchers to study how economic relationships evolve across distinct regimes while controlling for unobserved individual heterogeneity and endogeneity of the lagged dependent variable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bai & Perron (break detection); Arellano & Bond (dynamic panel GMM)","year":"1991–1998","type":"Dynamic panel model with regime change","dataType":"Balanced or unbalanced panel (N cross-sections, T time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null}],"related":["dynamic-panel-data-model","arellano-bond-gmm-estimator","panel-system-gmm","zivot-andrews-structural-break-test","structural-break-panel-data-analysis","panel-vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-egarch","name":"Structural Break EGARCH","fullName":"Exponential GARCH Model with Structural Breaks","aliases":["SB-EGARCH","EGARCH with regime shifts","break-adjusted EGARCH","structural change EGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990–1991","originator":"Nelson (1991) for EGARCH; Lamoureux and Lastrapes (1990) for break-augmented GARCH variants","url":"https://scholargate.app/en/econometrics/structural-break-egarch","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-egarch.md","definition":"Structural Break EGARCH combines Nelson's Exponential GARCH framework with explicit allowance for one or more structural breaks in the volatility process. By letting the intercept and persistence parameters of the log-variance equation shift at detected break dates, the model avoids the spurious long-memory and inflated persistence that standard EGARCH suffers when the data contain regime changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nelson (1991) for EGARCH; Lamoureux and Lastrapes (1990) for break-augmented GARCH variants","year":"1990–1991","type":"Volatility model with structural breaks","dataType":"Financial or macroeconomic time series, continuous","subfamily":"Econometrics / time series"},"citations":[{"ref":"Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null},{"ref":"Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business and Economic Statistics, 8(2), 225–234.","type":"article","doi":"10.1080/07350015.1990.10509794","isbn":null,"url":null}],"related":["egarch-model","arch-model","tgarch-model","dcc-garch-model","zivot-andrews-structural-break-test","structural-break-garch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-engle-granger-cointegration","name":"Structural break Engle-Granger cointegration","fullName":"Structural Break Engle-Granger Cointegration Test","aliases":["Gregory-Hansen cointegration test","cointegration with structural break","EG cointegration with regime shift","residual-based cointegration with break"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1996","originator":"Gregory & Hansen (1996), extending Engle & Granger (1987)","url":"https://scholargate.app/en/econometrics/structural-break-engle-granger-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-engle-granger-cointegration.md","definition":"The structural break Engle-Granger cointegration test, most commonly implemented via the Gregory-Hansen (1996) procedure, extends the classical Engle-Granger two-step test to allow for a single unknown structural break in the long-run cointegrating relationship. It tests whether two or more integrated series share a common stochastic trend even when that relationship may have shifted at some point in the sample.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory & Hansen (1996), extending Engle & Granger (1987)","year":"1996","type":"Cointegration test with structural break","dataType":"Integrated (I(1)) time series with possible regime shifts","subfamily":"Econometrics / time series"},"citations":[{"ref":"Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99-126.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Residual-based+tests+for+cointegration+in+models+with+regime+shifts+Gregory+Hansen+1996"},{"ref":"Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251-276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null}],"related":["engle-granger-cointegration","johansen-cointegration","zivot-andrews-unit-root","ardl-bounds-test","error-correction-model","hatemi-j-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-fixed-effects-model","name":"Structural Break Fixed Effects Model","fullName":"Fixed Effects Model with Structural Breaks","aliases":["FE model with structural breaks","break-adjusted fixed effects","panel fixed effects with regime shifts","structural change fixed effects estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998 (Bai-Perron); FE estimator classical","originator":"Bai & Perron (structural break testing); Mundlak / within-group estimator tradition","url":"https://scholargate.app/en/econometrics/structural-break-fixed-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-fixed-effects-model.md","definition":"The structural break fixed effects model extends the standard within-group (FE) panel estimator by allowing the slope coefficients to shift at one or more detected break dates. Each unit's unobserved time-invariant heterogeneity is still removed by demeaning, but separate coefficient regimes are estimated for each sub-period, capturing policy shifts, crises, or technological transitions that would otherwise bias a single-regime FE estimate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bai & Perron (structural break testing); Mundlak / within-group estimator tradition","year":"1998 (Bai-Perron); FE estimator classical","type":"Panel regression with regime change","dataType":"Balanced or unbalanced panel data (multiple units, multiple time periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0262232586","url":null}],"related":["fixed-effects-model","panel-fixed-effects-model","zivot-andrews-structural-break-test","structural-break-random-effects-model","panel-hausman-test","structural-break-panel-data-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-gls","name":"Structural Break GLS","fullName":"Generalized Least Squares with Structural Breaks","aliases":["GLS with structural breaks","break-adjusted GLS","structural change GLS","regime-switching GLS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998 (structural break GLS formalization)","originator":"Bai & Perron (1998); GLS framework by Aitken (1936)","url":"https://scholargate.app/en/econometrics/structural-break-gls","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-gls.md","definition":"Structural Break GLS combines Generalized Least Squares estimation with explicit allowance for regime shifts in the data-generating process. The method estimates separate coefficient vectors for each segment defined by detected break dates while correcting for non-spherical errors — heteroscedasticity or autocorrelation — that frequently accompany structural change, yielding consistent and efficient estimates across all regimes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bai & Perron (1998); GLS framework by Aitken (1936)","year":"1998 (structural break GLS formalization)","type":"Regression estimator","dataType":"Time series or cross-section with heteroscedastic or autocorrelated errors and regime changes","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Greene, W. H. (2012). Econometric Analysis (7th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0131395381","url":null}],"related":["structural-break-ols","structural-break-wls","generalized-least-squares","zivot-andrews-structural-break-test","panel-gls","robust-gls"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-granger-causality","name":"Structural Break Granger Causality","fullName":"Granger Causality Testing with Structural Breaks","aliases":["break-robust Granger causality","Granger causality under regime change","time-varying Granger causality","structural change Granger test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1995-2010","originator":"Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010)","url":"https://scholargate.app/en/econometrics/structural-break-granger-causality","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-granger-causality.md","definition":"Structural break Granger causality extends the classic Granger causality framework to accommodate regime shifts and parameter instability in time series. By detecting break points and testing causality within sub-samples or via rolling/recursive windows, it reveals whether a predictive relationship between variables switches on, switches off, or changes direction over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010)","year":"1995-2010","type":"Hypothesis test / time-series model","dataType":"Time series (macroeconomic, financial)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.","type":"article","doi":"10.1016/0304-4076(94)01616-8","isbn":null,"url":null},{"ref":"Balcilar, M., Ozdemir, Z. A., & Arslanturk, Y. (2010). Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window. Energy Economics, 32(6), 1398-1410.","type":"article","doi":"10.1016/j.eneco.2010.05.015","isbn":null,"url":null}],"related":["granger-causality","vector-autoregression","toda-yamamoto-causality","zivot-andrews-unit-root","rolling-window-regression","threshold-var"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-hausman-test","name":"Structural Break Hausman Test","fullName":"Hausman Specification Test with Structural Break Correction","aliases":["Hausman test under structural change","structural change Hausman specification test","break-robust Hausman test","panel specification test with breaks"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1978 (base); extended through 1990s–2000s","originator":"Jerry A. Hausman (base test, 1978); structural break extension developed in panel econometrics literature","url":"https://scholargate.app/en/econometrics/structural-break-hausman-test","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-hausman-test.md","definition":"The Structural Break Hausman Test extends the classical Hausman (1978) specification test to panel or time-series settings where the data-generating process shifts at one or more break points. By detecting structural breaks first and then running the Hausman comparison within each regime, researchers can reliably choose between fixed effects and random effects estimators even when the underlying relationship changes over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerry A. Hausman (base test, 1978); structural break extension developed in panel econometrics literature","year":"1978 (base); extended through 1990s–2000s","type":"Specification test","dataType":"Panel data or time series with suspected regime changes","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null},{"ref":"Perron, P. (2006). Dealing with structural breaks. In T. C. Mills & K. Patterson (Eds.), Palgrave Handbook of Econometrics, Vol. 1 (pp. 278–352). Palgrave Macmillan.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dealing+with+structural+breaks+Perron+2006+Palgrave+Handbook+Econometrics"}],"related":["panel-hausman-test","fixed-effects-model","random-effects-model","zivot-andrews-structural-break-test","structural-break-fixed-effects-model","structural-break-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-johansen-cointegration","name":"Structural break Johansen cointegration","fullName":"Johansen Cointegration Test with Structural Breaks","aliases":["Johansen cointegration with breaks","break-robust Johansen test","cointegration test with regime shifts","structural change Johansen VECM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000–2001","originator":"Johansen (1988); structural-break extensions by Saikkonen & Lütkepohl (2000) and Lütkepohl, Müller & Saikkonen (2001)","url":"https://scholargate.app/en/econometrics/structural-break-johansen-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-johansen-cointegration.md","definition":"The structural break Johansen cointegration test extends the standard maximum-likelihood Johansen procedure to settings where the multivariate time series exhibits level shifts or trend breaks. By incorporating dummy variables or shift regressors into the VECM, the test determines the cointegrating rank without confounding genuine long-run relationships with regime changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johansen (1988); structural-break extensions by Saikkonen & Lütkepohl (2000) and Lütkepohl, Müller & Saikkonen (2001)","year":"2000–2001","type":"Cointegration test / VECM estimation","dataType":"Multivariate time series; I(1) or near-I(1) variables","subfamily":"Econometrics / time series"},"citations":[{"ref":"Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254.","type":"article","doi":"10.1016/0165-1889(88)90041-3","isbn":null,"url":null},{"ref":"Saikkonen, P., & Lütkepohl, H. (2000). Testing for the cointegrating rank of a VAR process with structural shifts. Journal of Business and Economic Statistics, 18(4), 451–464.","type":"article","doi":"10.1080/07350015.2000.10524884","isbn":null,"url":null}],"related":["johansen-cointegration-test","structural-break-vecm","structural-break-engle-granger-cointegration","vector-error-correction-model","zivot-andrews-structural-break-test","structural-break-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-kpss-test","name":"Structural Break KPSS Test","fullName":"KPSS Stationarity Test with Structural Breaks","aliases":["KPSS test with breaks","structural break stationarity test","KPSS break test","SB-KPSS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002-2005","originator":"Kurozumi (2002); Carrion-i-Silvestre, Del Barrio & Lopez-Bazo (2005)","url":"https://scholargate.app/en/econometrics/structural-break-kpss-test","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-kpss-test.md","definition":"The structural break KPSS test extends the standard Kwiatkowski-Phillips-Schmidt-Shin (KPSS) stationarity test to allow for one or more known or unknown structural breaks in the level or trend of a time series. Under the null hypothesis the series is stationary around a broken deterministic component, enabling researchers to distinguish genuine unit-root behaviour from apparent non-stationarity caused by regime shifts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kurozumi (2002); Carrion-i-Silvestre, Del Barrio & Lopez-Bazo (2005)","year":"2002-2005","type":"Stationarity test with structural breaks","dataType":"Time series (univariate)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Carrion-i-Silvestre, J. L., Del Barrio, T., & Lopez-Bazo, E. (2005). Breaking the panels: An application to the GDP per capita. Econometrics Journal, 8(2), 159-175.","type":"article","doi":"10.1111/j.1368-423X.2005.00158.x","isbn":null,"url":null},{"ref":"Kurozumi, E. (2002). Testing for stationarity with a break. Journal of Econometrics, 108(1), 63-99.","type":"article","doi":"10.1016/S0304-4076(01)00106-3","isbn":null,"url":null}],"related":["augmented-dickey-fuller-unit-root-test","phillips-perron-unit-root-test","zivot-andrews-structural-break-test","structural-break-adf-unit-root-test","structural-break-pp-unit-root-test","engle-granger-cointegration-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-ma-model","name":"Structural Break MA Model","fullName":"Moving Average Model with Structural Breaks","aliases":["MA model with structural change","broken MA model","MA with regime shift","structural break moving average"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1989–1992","originator":"Perron (1989); Zivot & Andrews (1992)","url":"https://scholargate.app/en/econometrics/structural-break-ma-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-ma-model.md","definition":"A Moving Average (MA) time series model augmented to accommodate one or more structural breaks — abrupt shifts in the mean, variance, or MA coefficients occurring at known or unknown break dates. Ignoring structural breaks in an MA process inflates forecast errors and distorts inference on the error dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Perron (1989); Zivot & Andrews (1992)","year":"1989–1992","type":"Time series model with structural change","dataType":"Univariate time series with suspected regime shifts","subfamily":"Econometrics / time series"},"citations":[{"ref":"Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361–1401.","type":"article","doi":"10.2307/1913712","isbn":null,"url":null},{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null}],"related":["structural-break-arima-model","structural-break-ar-model","moving-average-model","zivot-andrews-structural-break-test","structural-break-arma-model","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-nardl","name":"Structural Break NARDL","fullName":"Structural Break Nonlinear Autoregressive Distributed Lag Model","aliases":["SB-NARDL","NARDL with structural breaks","nonlinear ARDL with break","asymmetric ARDL structural break"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2014–2018","originator":"Shin, Yu & Greenwood-Nimmo (NARDL base); structural break extensions by subsequent applied researchers","url":"https://scholargate.app/en/econometrics/structural-break-nardl","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-nardl.md","definition":"Structural Break NARDL extends the Nonlinear Autoregressive Distributed Lag (NARDL) bounds-testing framework by explicitly accommodating one or more structural breaks in the long-run relationship. It separates positive and negative changes in the regressor, tests for cointegration, and allows regime shifts, providing a richer picture of asymmetric and break-sensitive dynamics between variables.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shin, Yu & Greenwood-Nimmo (NARDL base); structural break extensions by subsequent applied researchers","year":"2014–2018","type":"Nonlinear cointegration with structural breaks","dataType":"Time series (univariate or multivariate), continuous","subfamily":"Econometrics / time series"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer.","type":"inproceedings","doi":"10.1007/978-1-4899-8008-3_9","isbn":null,"url":null},{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null}],"related":["nonlinear-ardl","arima-model","zivot-andrews-structural-break-test","engle-granger-cointegration-test","vector-error-correction-model","structural-break-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-ols","name":"Structural Break OLS","fullName":"Ordinary Least Squares Regression with Structural Breaks","aliases":["OLS with structural breaks","piecewise OLS","regime-switching OLS","breakpoint regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1960–1998","originator":"Chow (1960) for the breakpoint test; Bai & Perron (1998) for multiple break estimation","url":"https://scholargate.app/en/econometrics/structural-break-ols","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-ols.md","definition":"Structural Break OLS extends ordinary least squares to allow regression coefficients to shift at one or more breakpoints in time or across regimes. Rather than forcing a single coefficient vector across the entire sample, the model partitions the data and estimates a separate OLS regression within each segment, making it appropriate when economic relationships are suspected to change due to policy shifts, crises, or other structural events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chow (1960) for the breakpoint test; Bai & Perron (1998) for multiple break estimation","year":"1960–1998","type":"Segmented linear regression","dataType":"Time series or cross-section with regime changes","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.","type":"article","doi":"10.2307/1910133","isbn":null,"url":null}],"related":["ols-regression","zivot-andrews-structural-break-test","augmented-dickey-fuller-unit-root-test","arima-model","vector-autoregression","structural-break-gls"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-panel-data-analysis","name":"Structural Break Panel Data Analysis","fullName":"Structural Break Analysis in Panel Data Models","aliases":["panel structural break test","break-point panel model","panel change-point analysis","regime-shift panel analysis"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998-2010","originator":"Bai & Perron (1998); extended to panels by Bai (2010) and Joseph et al.","url":"https://scholargate.app/en/econometrics/structural-break-panel-data-analysis","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-panel-data-analysis.md","definition":"Structural break panel data analysis detects and estimates points in time — break dates — where the underlying regression coefficients shift permanently across a panel of cross-sectional units observed over multiple periods. By jointly exploiting cross-sectional and time-series variation, it offers sharper identification of regime shifts than single-series break tests, and it delivers separate coefficient estimates for each regime before and after each break.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bai & Perron (1998); extended to panels by Bai (2010) and Joseph et al.","year":"1998-2010","type":"Panel time-series model with regime shifts","dataType":"Balanced or unbalanced panel (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113.","type":"article","doi":"10.1016/0304-4076(94)01644-F","isbn":null,"url":null}],"related":["panel-fixed-effects","panel-cointegration","panel-unit-root-test","difference-in-differences","threshold-regression","time-series-structural-break"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-pp-unit-root-test","name":"Structural break PP unit root test","fullName":"Structural Break Phillips-Perron Unit Root Test","aliases":["break-augmented PP test","Phillips-Perron test with structural break","structural break unit root test","PP unit root test with break"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988/1997","originator":"Pierre Perron (building on Phillips & Perron)","url":"https://scholargate.app/en/econometrics/structural-break-pp-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-pp-unit-root-test.md","definition":"The structural break Phillips-Perron (PP) unit root test extends the classical PP framework to allow for one or more discrete shifts in the level or trend of a time series. By endogenously or exogenously identifying break dates and controlling for them, it tests the null of a unit root against a trend-stationary alternative that accommodates structural change, avoiding the spurious acceptance of non-stationarity caused by ignored breaks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pierre Perron (building on Phillips & Perron)","year":"1988/1997","type":"Hypothesis test","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Perron, P. (1997). Further evidence on breaking trend functions in macroeconomic variables. Journal of Econometrics, 80(2), 355-385.","type":"article","doi":"10.1016/S0304-4076(97)00049-3","isbn":null,"url":null},{"ref":"Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.","type":"article","doi":"10.1093/biomet/75.2.335","isbn":null,"url":null}],"related":["adf-unit-root-test","zivot-andrews-unit-root-test","pp-unit-root-test","kpss-stationarity-test","lee-strazicich-unit-root-test","lumsdaine-papell-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-quantile-on-quantile-regression","name":"Structural Break Quantile-on-Quantile Regression","fullName":"Structural Break Quantile-on-Quantile Regression","aliases":["SB-QQR","structural-break QQ regression","quantile-on-quantile with structural breaks","QQR with regime shifts"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2015-2020s","originator":"Extension combining Sim & Zhou (2015) QQR framework with Bai-Perron structural break methodology","url":"https://scholargate.app/en/econometrics/structural-break-quantile-on-quantile-regression","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-quantile-on-quantile-regression.md","definition":"Structural Break Quantile-on-Quantile Regression (SB-QQR) extends the quantile-on-quantile framework of Sim and Zhou (2015) by allowing regression slopes to differ across regimes separated by structural breaks. It maps how the effect of a predictor's quantile on an outcome's quantile changes not only across the full distributional space but also across distinct historical periods or policy regimes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension combining Sim & Zhou (2015) QQR framework with Bai-Perron structural break methodology","year":"2015-2020s","type":"Nonparametric quantile regression with structural breaks","dataType":"Time series; continuous variables","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sim, N., and Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.","type":"article","doi":"10.1016/j.jbankfin.2015.01.013","isbn":null,"url":null},{"ref":"Bai, J., and Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null}],"related":["quantile-on-quantile-regression","quantile-regression","structural-break-ardl-bounds-test","structural-break-granger-causality","zivot-andrews-structural-break-test","nonlinear-ardl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-random-effects-model","name":"Structural Break Random Effects Model","fullName":"Random Effects Panel Model with Structural Breaks","aliases":["RE model with structural breaks","break-adjusted random effects","random effects break model","panel RE with regime shifts"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998–2000s","originator":"Bai & Perron (break detection); Baltagi (panel RE framework)","url":"https://scholargate.app/en/econometrics/structural-break-random-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-random-effects-model.md","definition":"The structural break random effects model extends standard panel RE estimation by allowing one or more breakpoints at which slope coefficients or error variances shift across time. It combines structural change detection (e.g., Bai-Perron) with the GLS-based random effects estimator, producing regime-specific parameter estimates while retaining the efficiency gains of pooling individual-level variation as random draws from a common distribution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bai & Perron (break detection); Baltagi (panel RE framework)","year":"1998–2000s","type":"Panel regression with regime shifts","dataType":"Balanced or unbalanced panel data (cross-sectional units over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Baltagi, B. H. (2008). Econometric Analysis of Panel Data (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470518861","url":null}],"related":["structural-break-fixed-effects-model","random-effects-model","panel-random-effects-model","zivot-andrews-structural-break-test","structural-break-panel-data-analysis","panel-hausman-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-sarima-model","name":"Structural Break SARIMA Model","fullName":"Structural Break Seasonal Autoregressive Integrated Moving Average Model","aliases":["SARIMA with structural breaks","break-augmented SARIMA","piecewise SARIMA","SARIMA-SB"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970s–1998","originator":"Box & Jenkins (SARIMA); Bai & Perron (structural break detection)","url":"https://scholargate.app/en/econometrics/structural-break-sarima-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-sarima-model.md","definition":"The Structural Break SARIMA model extends the classical Seasonal ARIMA framework by explicitly detecting and accommodating abrupt, permanent shifts in the level, trend, or seasonal pattern of a time series. Rather than forcing a single SARIMA specification across the entire sample, the model partitions the series at estimated breakpoints and fits separate SARIMA processes to each resulting segment, producing more accurate forecasts and reliable inference in the presence of regime changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Box & Jenkins (SARIMA); Bai & Perron (structural break detection)","year":"1970s–1998","type":"Time series model with regime shifts","dataType":"Univariate seasonal time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118675021","url":null}],"related":["sarima-model","arima-model","structural-break-test","bai-perron-test","seasonal-decomposition","threshold-autoregressive-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-svar-model","name":"Structural break SVAR model","fullName":"Structural Vector Autoregression with Structural Breaks","aliases":["break-SVAR","SVAR with regime change","structural break structural VAR","SB-SVAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980–2000s","originator":"Sims (1980) for SVAR; structural break extensions developed throughout 1990s–2000s","url":"https://scholargate.app/en/econometrics/structural-break-svar-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-svar-model.md","definition":"The structural break SVAR model extends the standard Structural Vector Autoregression by allowing one or more discrete shifts in the system's parameters across time. It simultaneously identifies causal (structural) shocks and accounts for regime changes — such as policy shifts, crises, or institutional reforms — that alter the dynamics among multiple time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sims (1980) for SVAR; structural break extensions developed throughout 1990s–2000s","year":"1980–2000s","type":"Multivariate time-series model with regime change","dataType":"Multivariate macroeconomic or financial time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.","type":"article","doi":"10.2307/1912017","isbn":null,"url":null},{"ref":"Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.","type":"book","doi":null,"isbn":"978-3540401728","url":null}],"related":["structural-var","structural-break-var-model","vector-error-correction-model","structural-break-vecm","zivot-andrews-structural-break-test","structural-break-ardl-bounds-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-system-gmm","name":"Structural Break System GMM","fullName":"Structural Break System Generalized Method of Moments","aliases":["System GMM with structural breaks","SB-SGMM","break-augmented System GMM","System GMM structural change estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998–2003","originator":"Blundell & Bond (System GMM); Bai & Perron (structural break framework)","url":"https://scholargate.app/en/econometrics/structural-break-system-gmm","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-system-gmm.md","definition":"Structural Break System GMM extends the Blundell-Bond System GMM estimator for dynamic panel data by explicitly accounting for structural breaks — abrupt regime changes in slopes, intercepts, or dynamics — that, if ignored, bias the coefficient estimates and invalidate the moment conditions that underpin standard GMM inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blundell & Bond (System GMM); Bai & Perron (structural break framework)","year":"1998–2003","type":"Dynamic panel estimator with regime change","dataType":"Panel data (cross-section × time series) with potential structural breaks","subfamily":"Econometrics / time series"},"citations":[{"ref":"Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.","type":"article","doi":"10.1016/S0304-4076(98)00009-8","isbn":null,"url":null},{"ref":"Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22.","type":"article","doi":"10.1002/jae.659","isbn":null,"url":null}],"related":["system-gmm","difference-gmm","arellano-bond-gmm-estimator","structural-break-difference-gmm","dynamic-panel-data-model","panel-system-gmm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-tgarch","name":"Structural Break TGARCH","fullName":"Structural Break Threshold GARCH","aliases":["SB-TGARCH","threshold GARCH with structural breaks","GJR-GARCH with structural breaks","break-adjusted TGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990-1993","originator":"Lamoureux & Lastrapes (structural breaks in GARCH); Glosten, Jagannathan & Runkle (TGARCH/GJR-GARCH asymmetry)","url":"https://scholargate.app/en/econometrics/structural-break-tgarch","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-tgarch.md","definition":"Structural Break TGARCH extends the Threshold GARCH (GJR-GARCH) model to accommodate discrete, permanent shifts in the volatility process. By detecting structural breaks and incorporating them — either as regime-specific intercepts or dummy variables — the model separates genuine volatility persistence from spurious persistence induced by ignored regime changes, and preserves the asymmetric leverage effect that characterises equity and financial return data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lamoureux & Lastrapes (structural breaks in GARCH); Glosten, Jagannathan & Runkle (TGARCH/GJR-GARCH asymmetry)","year":"1990-1993","type":"Volatility model","dataType":"High-frequency or daily financial time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business & Economic Statistics, 8(2), 225-234.","type":"article","doi":"10.1080/07350015.1990.10509794","isbn":null,"url":null},{"ref":"Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801.","type":"article","doi":"10.1111/j.1540-6261.1993.tb05128.x","isbn":null,"url":null}],"related":["garch-model","egarch-model","tgarch-model","structural-break-garch","markov-switching-garch","bai-perron-breakpoint-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-toda-yamamoto-causality","name":"Structural Break Toda-Yamamoto Causality","fullName":"Toda-Yamamoto Causality Test with Structural Breaks","aliases":["SB-TY causality","structural break modified Wald test causality","Fourier Toda-Yamamoto causality","causality with regime shifts"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1995 (base); structural break extensions widely adopted 2000s–2010s","originator":"Toda & Yamamoto (1995); structural break extensions by Zivot & Andrews (1992) and subsequent applied literature","url":"https://scholargate.app/en/econometrics/structural-break-toda-yamamoto-causality","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-toda-yamamoto-causality.md","definition":"The structural break Toda-Yamamoto causality test extends the standard Toda-Yamamoto modified Wald (MWALD) procedure to accommodate one or more structural breaks in the time series. By identifying break dates first and then including dummy variables in the augmented VAR, the test maintains its valid asymptotic chi-squared distribution regardless of the integration or cointegration order of the variables, even in the presence of regime shifts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Toda & Yamamoto (1995); structural break extensions by Zivot & Andrews (1992) and subsequent applied literature","year":"1995 (base); structural break extensions widely adopted 2000s–2010s","type":"Causality test","dataType":"Time series (univariate or multivariate)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.","type":"article","doi":"10.1016/0304-4076(94)01616-8","isbn":null,"url":null},{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10(3), 251-270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null}],"related":["toda-yamamoto-causality-test","granger-causality-test","structural-break-granger-causality","zivot-andrews-structural-break-test","vector-autoregression","structural-break-var-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-var-model","name":"Structural Break VAR Model","fullName":"Vector Autoregression Model with Structural Breaks","aliases":["VAR with structural breaks","break-point VAR","regime-switching VAR","SB-VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980–1998","originator":"Bai & Perron (structural breaks); Sims (VAR framework)","url":"https://scholargate.app/en/econometrics/structural-break-var-model","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-var-model.md","definition":"The Structural Break VAR model extends the standard Vector Autoregression (VAR) framework by allowing coefficient matrices and error covariance to shift at one or more unknown break dates. It is designed for multivariate time series where economic relationships change abruptly due to policy shifts, financial crises, or major structural events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bai & Perron (structural breaks); Sims (VAR framework)","year":"1980–1998","type":"Multivariate time series model with regime change","dataType":"Multivariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.","type":"article","doi":"10.2307/1912017","isbn":null,"url":null}],"related":["vector-autoregression","structural-var","structural-break-vecm","zivot-andrews-structural-break-test","vector-error-correction-model","structural-break-arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-vecm","name":"Structural break VECM","fullName":"Vector Error Correction Model with Structural Breaks","aliases":["SB-VECM","VECM with regime shifts","cointegration model with structural breaks","break-augmented VECM"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1996–2000","originator":"Gregory & Hansen (1996); Johansen, Mosconi & Nielsen (2000)","url":"https://scholargate.app/en/econometrics/structural-break-vecm","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-vecm.md","definition":"The Structural Break VECM extends the standard Vector Error Correction Model to allow the cointegrating relationships, adjustment speeds, or short-run dynamics to shift at one or more known or estimated break dates. It preserves the long-run equilibrium framework of the VECM while explicitly modelling regime changes caused by policy shifts, crises, or institutional changes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gregory & Hansen (1996); Johansen, Mosconi & Nielsen (2000)","year":"1996–2000","type":"Multivariate error correction model with structural breaks","dataType":"Multivariate integrated time series (I(1)) with cointegrating relations","subfamily":"Econometrics / time series"},"citations":[{"ref":"Gregory, A. W., & Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics, 70(1), 99–126.","type":"article","doi":"10.1016/0304-4076(69)41685-7","isbn":null,"url":null},{"ref":"Johansen, S., Mosconi, R., & Nielsen, B. (2000). Cointegration analysis in the presence of structural breaks in the deterministic trend. Econometrics Journal, 3(2), 216–249.","type":"article","doi":"10.1111/1368-423X.00047","isbn":null,"url":null}],"related":["vector-error-correction-model","johansen-cointegration-test","structural-break-var-model","zivot-andrews-structural-break-test","structural-break-johansen-cointegration","nonlinear-vecm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-wls","name":"Structural Break WLS","fullName":"Weighted Least Squares with Structural Break Correction","aliases":["WLS with structural change","break-corrected WLS","segmented WLS","structural break weighted regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998 (break framework); WLS long-established","originator":"Bai & Perron (structural break framework); WLS classical","url":"https://scholargate.app/en/econometrics/structural-break-wls","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-wls.md","definition":"Structural Break WLS combines Weighted Least Squares estimation with explicit detection and correction for structural breaks — abrupt regime shifts — in the data. By identifying break points and assigning observation-level weights that account for heteroscedasticity within and across regimes, the estimator delivers consistent, efficient coefficient estimates even when the error variance changes dramatically at a break.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bai & Perron (structural break framework); WLS classical","year":"1998 (break framework); WLS long-established","type":"Weighted regression with regime shifts","dataType":"Cross-sectional or time-series data with heteroscedasticity and structural breaks","subfamily":"Econometrics / time series"},"citations":[{"ref":"Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.","type":"article","doi":"10.2307/2998540","isbn":null,"url":null},{"ref":"Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1337558860","url":null}],"related":["structural-break-ols","structural-break-gls","panel-wls","zivot-andrews-structural-break-test","robust-wls","weighted-least-squares"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-break-zivot-andrews-test","name":"Structural break Zivot-Andrews test","fullName":"Structural Break Zivot-Andrews Unit Root Test","aliases":["Zivot-Andrews test","ZA unit root test","endogenous structural break unit root test","ZA breakpoint test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1992","originator":"Eric Zivot and Donald W. K. Andrews","url":"https://scholargate.app/en/econometrics/structural-break-zivot-andrews-test","markdownUrl":"https://scholargate.app/en/econometrics/structural-break-zivot-andrews-test.md","definition":"The Zivot-Andrews test is an endogenous structural break unit root test that determines the break point from the data rather than imposing it externally. It tests for a unit root against the alternative of stationarity around a single structural break — in the mean, the trend, or both — choosing the break date that provides the strongest evidence against the null.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eric Zivot and Donald W. K. Andrews","year":"1992","type":"Unit root test with endogenous structural break","dataType":"Univariate time series (levels or first differences)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null},{"ref":"Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361–1401.","type":"article","doi":"10.2307/1913712","isbn":null,"url":null}],"related":["augmented-dickey-fuller-unit-root-test","phillips-perron-unit-root-test","zivot-andrews-structural-break-test","structural-break-adf-unit-root-test","toda-yamamoto-causality-test","engle-granger-cointegration-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-equation-modeling","name":"Structural Equation Modeling","fullName":"Structural Equation Modeling (SEM)","aliases":["SEM","path analysis","latent variable modeling","causal modeling"],"domain":"research-statistics","family":"process-pipeline","subfamily":"multivariate-modeling","year":"1921","originator":"Sewall Wright","url":"https://scholargate.app/en/research-statistics/structural-equation-modeling","markdownUrl":"https://scholargate.app/en/research-statistics/structural-equation-modeling.md","definition":"Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sewall Wright","subfamily":"multivariate-modeling","year":"1921","type":"Method"},"citations":[{"ref":"Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=LISREL%3A+A+general+computer+program+for+estimating+a+linear+structural+equation+system+J%C3%B6reskog"},{"ref":"Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.","type":"article","doi":"10.1080/10705519909540118","isbn":null,"url":null},{"ref":"Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Correlation+and+causation+Wright"}],"related":["factor-analysis","multiple-regression-analysis","multilevel-modeling"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-form-finding","name":"Structural Form-Finding","fullName":"Structural Form-Finding and Optimal Structural Design","aliases":["form-finding algorithm","structural optimization","funicular design"],"domain":"architecture","family":"process-pipeline","subfamily":"Structural design and morphogenesis","year":"1974","originator":"Heinz J. Schek","url":"https://scholargate.app/en/architecture/structural-form-finding","markdownUrl":"https://scholargate.app/en/architecture/structural-form-finding.md","definition":"Structural Form-Finding is a computational method for discovering structural geometries that are efficient under given loads and constraints. Pioneered by Heinz Schek in 1974, it reverses traditional structural design: rather than imposing a predetermined form and then analyzing whether it is strong enough, form-finding begins with loads and support conditions and derives the optimal form that minimizes material use while meeting safety requirements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Heinz J. Schek","subfamily":"Structural design and morphogenesis","year":"1974","type":"computational structural optimization method"},"citations":[{"ref":"Schek, H. J. (1974). The Force Density Method for Form Finding and Computation of General Networks. Computer Methods in Applied Mechanics and Engineering, 3(1), 115-134.","type":"article","doi":"10.1016/0045-7825(74)90045-0","isbn":null,"url":null},{"ref":"Kilian, A., Ochsendorf, J. (2009). Particle-Spring Systems for Structural Form Finding. Journal of the International Association for Shell and Spatial Structures, 46(2), 77-84.","type":"article","doi":null,"isbn":null,"url":"https://www.iass-structures.org/"},{"ref":"Hensel, M., Menges, A., Weinstock, M. (2006). Techniques and Technologies in Morphogenetic Architecture. Architectural Design, 76(2), 88-95.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Techniques+and+Technologies+in+Morphogenetic+Architecture+Hensel"}],"related":["building-energy-performance","post-occupancy-evaluation","green-building-rating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-health-monitoring","name":"Structural Health Monitoring","fullName":"Structural Health Monitoring","aliases":["SHM","damage detection monitoring","condition monitoring of structures","vibration-based structural monitoring"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Non-destructive evaluation and condition monitoring","year":"1980s–1990s (formalized as a discipline ~1993–2001)","originator":"Multiple contributors (Charles Farrar, Keith Worden, and the broader SHM research community)","url":"https://scholargate.app/en/civil-engineering/structural-health-monitoring","markdownUrl":"https://scholargate.app/en/civil-engineering/structural-health-monitoring.md","definition":"Structural Health Monitoring (SHM) is a process-based engineering methodology used in civil, mechanical, and aerospace engineering to continuously assess the condition of structures — bridges, buildings, dams, pipelines, and aircraft — through embedded or attached sensor networks. By acquiring real-time or periodic measurement data and applying signal processing and statistical pattern recognition, SHM aims to detect, locate, classify, and quantify damage before it reaches a critical state, enabling evidence-based maintenance decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Charles Farrar, Keith Worden, and the broader SHM research community)","year":"1980s–1990s (formalized as a discipline ~1993–2001)","type":"Engineering monitoring and diagnostic framework","dataType":"Sensor time-series (acceleration, strain, displacement, acoustic emission)","subfamily":"Non-destructive evaluation and condition monitoring"},"citations":[{"ref":"Farrar, C. R., & Worden, K. (2007). An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A, 365(1851), 303–315.","type":"book","doi":"10.1098/rsta.2006.1928","isbn":null,"url":null},{"ref":"Farrar, C. R., & Worden, K. (2012). Structural Health Monitoring: A Machine Learning Perspective. Wiley.","type":"book","doi":null,"isbn":"978-1119994336","url":null}],"related":["finite-element-analysis","modal-analysis","non-destructive-testing","fatigue-analysis","reliability-analysis","digital-twin"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-time-series","name":"Structural Time Series Model","fullName":"Basic Structural Model (Structural Time Series Model)","aliases":["BSM","basic structural model","unobserved components model","Yapısal Zaman Serisi Modeli (BSM)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1990,"originator":"Andrew C. Harvey","url":"https://scholargate.app/en/econometrics/structural-time-series","markdownUrl":"https://scholargate.app/en/econometrics/structural-time-series.md","definition":"The Structural Time Series Model, in its Basic Structural Model (BSM) form, is Andrew Harvey's state-space approach that decomposes a series into separate stochastic trend, seasonal, cyclical, and irregular components. Developed in Harvey's 1990 treatment, it is prized for interpretability and component decomposition where ARIMA only delivers a black-box fit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew C. Harvey","year":1990,"type":"State-space (unobserved components) time series model","estimator":"Kalman filter with maximum likelihood","components":"trend, seasonal, cycle, irregular","outcome":"continuous time series"},"citations":[{"ref":"Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521405737","url":null},{"ref":"Harvey, A. C. & Shephard, N. (1993). Structural Time Series Models. In G. S. Maddala, C. R. Rao & H. D. Vinod (Eds.), Handbook of Statistics, Vol. 11 (pp. 261-302). Elsevier.","type":"chapter","doi":"10.1016/S0169-7161(05)80045-8","isbn":null,"url":null}],"related":["arima","exponential-smoothing","bayesian-structural-time-series","markov-switching","var-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structural-var","name":"Structural VAR","fullName":"Structural Vector Autoregression","aliases":["SVAR","structural vector autoregression","identified VAR","structural VAR model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980","originator":"Sims (1980); identification schemes by Blanchard & Quah (1989)","url":"https://scholargate.app/en/econometrics/structural-var","markdownUrl":"https://scholargate.app/en/econometrics/structural-var.md","definition":"Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sims (1980); identification schemes by Blanchard & Quah (1989)","year":"1980","type":"Multivariate time series model","dataType":"Multivariate time series (stationary or cointegrated)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Blanchard+Quah+1989+dynamic+effects+aggregate+demand+supply+disturbances"},{"ref":"Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48.","type":"article","doi":"10.2307/1912017","isbn":null,"url":null}],"related":["vector-autoregression","vector-error-correction-model","granger-causality-test","arma-model","johansen-cointegration-test","dynamic-panel-data-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structured-clinical-interview-dsm","name":"Structured Clinical Interview for DSM","fullName":"Structured Clinical Interview for DSM Disorders","aliases":["SCID","SCID-5","SCID-IV"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Diagnostic interview","year":"1997","originator":"Michael B. First, Robert L. Spitzer","url":"https://scholargate.app/en/clinical-psychology/structured-clinical-interview-dsm","markdownUrl":"https://scholargate.app/en/clinical-psychology/structured-clinical-interview-dsm.md","definition":"The Structured Clinical Interview for DSM Disorders (SCID) is a semi-structured interview protocol designed to assess the presence or absence of DSM diagnostic criteria for major psychiatric disorders. Developed by Michael B. First and colleagues in the 1990s and updated to align with DSM-5, it remains the gold-standard diagnostic instrument in clinical research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael B. First, Robert L. Spitzer","subfamily":"Diagnostic interview","year":"1997","type":"Structured diagnostic instrument"},"citations":[{"ref":"First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L. (2015). Structured Clinical Interview for DSM-5 Disorders—Clinician Version (SCID-5-CV). American Psychiatric Association.","type":"article","doi":null,"isbn":"9781585624882","url":null},{"ref":"First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1997). Structured Clinical Interview for DSM-IV Axis I Disorders. Biometrics Research Department, New York State Psychiatric Institute.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Structured+Clinical+Interview+for+DSM-IV+Axis+I+Disorders+First"}],"related":["beck-depression-inventory","phq-9-screening","neuropsychological-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structured-interview","name":"Structured Interview","fullName":"Structured Interview","aliases":["standardized interview","formal interview","schedule-based interview","fixed-format interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1940s–1950s","originator":"Survey research tradition; formalized by Campbell, Katona, and Kahn in mid-20th century","url":"https://scholargate.app/en/survey-methodology/structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/structured-interview.md","definition":"A structured interview is a data collection technique in which every participant is asked exactly the same pre-specified questions in the same order, using standardized wording. Because the interview schedule is fixed, responses across participants are directly comparable, enabling quantitative aggregation and statistical analysis. It sits at the most standardized end of the interview continuum, between the self-administered questionnaire and the semi-structured interview.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Survey research tradition; formalized by Campbell, Katona, and Kahn in mid-20th century","year":"1940s–1950s","type":"Quantitative / mixed data collection technique","dataType":"Verbal responses to fixed questions (converted to categorical or numeric data)","subfamily":"Data collection"},"citations":[{"ref":"Fontana, A., & Frey, J. H. (2000). The interview: From structured questions to negotiated text. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of Qualitative Research (2nd ed., pp. 645–672). Sage.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fontana+Frey+2000+The+interview+from+structured+questions+to+negotiated+text"},{"ref":"Campbell, A., & Katona, G. (1953). The sample survey: A technique for social science research. In L. Festinger & D. Katz (Eds.), Research Methods in the Behavioral Sciences (pp. 15–55). Dryden Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Campbell+Katona+1953+sample+survey+behavioral+sciences"}],"related":["survey","semi-structured-interview","in-depth-interview","focus-group","questionnaire","telephone-assisted-structured-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structured-professional-judgment","name":"SPJ Framework","fullName":"Structured Professional Judgment Framework for Risk Assessment","aliases":["SPJ","Structured Professional Judgment","SPJ Framework"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"professional-judgment-methodology","year":"2003","originator":"Stephen D. Hart, Peter R. Kropp, David R. Laws","url":"https://scholargate.app/en/forensic-psychology/structured-professional-judgment","markdownUrl":"https://scholargate.app/en/forensic-psychology/structured-professional-judgment.md","definition":"The Structured Professional Judgment (SPJ) framework represents a contemporary approach to forensic risk assessment that synthesizes clinical judgment with empirical evidence of risk factors. Rather than producing a numerical score, SPJ guides clinicians through systematic evaluation of case-specific evidence to arrive at a structured, transparent categorical risk judgment. SPJ has become the preferred methodology in many forensic settings globally and underlies instruments such as the HCR-20v3 (violence risk) and sexual offender assessment protocols.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen D. Hart, Peter R. Kropp, David R. Laws","subfamily":"professional-judgment-methodology","year":"2003","type":"Clinician-Synthesized"},"citations":[{"ref":"Hart, S. D., Kropp, P. R., & Laws, D. R. (Eds.). (2003). Sexual deviance: Theory, assessment, and treatment. Guilford Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sexual+deviance%3A+Theory%2C+assessment%2C+and+treatment+Hart"},{"ref":"Kropp, P. R., Hart, S. D., & Belfrage, H. (2008). Structured professional judgment as an approach to violence risk assessment. In K. A. Van den Berg, P. T. Mason, & B. K. Waller (Eds.), Risk assessment and management of violent offenders (pp. 85–109). Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Structured+professional+judgment+as+an+approach+to+violence+risk+assessment+Kropp"}],"related":["hcr-20","violence-risk-appraisal-guide","saprof","level-of-service-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"structured-text-extraction","name":"Structured Text Extraction","fullName":"Structured Data Extraction (Form & Table Extraction)","aliases":["form extraction","table extraction","document parsing","Yapılandırılmış Veri Çıkarma (Form & Tablo Çıkarma)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/structured-text-extraction","markdownUrl":"https://scholargate.app/en/text-mining/structured-text-extraction.md","definition":"Structured text extraction is a document-processing pipeline that automatically identifies and pulls tables, form fields, and structured data from PDF, HTML, and scanned documents. It converts heterogeneous document layouts into machine-readable, analysis-ready records and is widely used in data collection workflows, document digitisation projects, and academic corpus construction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Document-processing pipeline","input":"PDF, HTML, or scanned document images","output":"Structured tables, form fields, or key-value pairs","ocrDependence":"OCR quality directly affects extraction accuracy","minDocuments":10,"difficulty":"Low (2 / 5)"},"citations":[{"ref":"Zhu, J. et al. (2021). TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content. ACL.","type":"conference","doi":null,"isbn":null,"url":"https://aclanthology.org/2021.acl-long.254"},{"ref":"Zhong, X. et al. (2020). Image-Based Table Recognition. ECCV.","type":"conference","doi":null,"isbn":null,"url":"https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5244_ECCV_2020_paper.php"}],"related":["named-entity-recognition","information-extraction","ocr-post-correction","document-classification","pdf-parsing"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"student-clinical-placement-scale","name":"SCPS","fullName":"Student Clinical Placement Satisfaction Scale","aliases":["Clinical Placement Satisfaction","Placement Experience Scale","Clinical Site Satisfaction"],"domain":"health-education","family":"process-pipeline","subfamily":"clinical-placement-experience","year":"2007–2010","originator":"Papastavrou et al.","url":"https://scholargate.app/en/health-education/student-clinical-placement-scale","markdownUrl":"https://scholargate.app/en/health-education/student-clinical-placement-scale.md","definition":"The SCPS is a self-report questionnaire measuring students' overall satisfaction with their clinical placement experience, including satisfaction with the learning environment, educator support, clinical opportunities, and facility resources. Originally developed by Papastavrou and colleagues in Cyprus (2007–2010), the SCPS evaluates multiple aspects of placement quality that contribute to student wellbeing and learning outcomes. The scale is used to monitor placement satisfaction across programs, compare satisfaction across clinical sites, and identify areas for improvement in clinical education delivery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Papastavrou et al.","subfamily":"clinical-placement-experience","year":"2007–2010","type":"Self-report satisfaction questionnaire"},"citations":[{"ref":"Papastavrou, E., Lambrinou, E., Tsangari, H., & Varthy, M. (2010). Students' satisfaction with their clinical learning environment in Cyprus: A longitudinal study. Nurs Educ Today 30(6): 539–544.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Students%27+satisfaction+with+their+clinical+learning+environment+in+Cyprus%3A+A+longitudinal+study+Papastavrou"},{"ref":"Levett-Jones, T., Gersbach, J., Arthur, C., & Roche, J. (2007). Implementing a web-based learning environment to enhance clinical education. Nurse Educ Today 27(6): 580–588.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Implementing+a+web-based+learning+environment+to+enhance+clinical+education+Levett-Jones"}],"related":["clinical-learning-environment-scale","clinical-teaching-quality-scale","ripls","nursing-clinical-competence-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"student-engagement-scale","name":"Student Engagement Scale","fullName":"Student Engagement Scale (SES)","aliases":["SES","Academic Engagement Measure"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Student involvement and effort","year":"2004","originator":"Jennifer Fredricks, Phyllis Blumenfeld, Alison Paris","url":"https://scholargate.app/en/educational-psychology/student-engagement-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/student-engagement-scale.md","definition":"The Student Engagement Scale (SES) measures the extent to which students are actively involved in academic and social aspects of school or university life. Grounded in Fredricks et al.'s multidimensional framework, the instrument assesses behavioral engagement (participation, attendance, effort), emotional engagement (interest, belonging, enthusiasm), and cognitive engagement (mental effort, deep thinking, persistence). High engagement is consistently associated with better learning outcomes and well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jennifer Fredricks, Phyllis Blumenfeld, Alison Paris","subfamily":"Student involvement and effort","year":"2004","type":"Multidimensional engagement assessment"},"citations":[{"ref":"Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.","type":"article","doi":"10.3102/00346543074001059","isbn":null,"url":null},{"ref":"Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758-773.","type":"article","doi":"10.1080/03075079.2011.598505","isbn":null,"url":null}],"related":["academic-motivation-scale","student-satisfaction-survey","sense-of-belonging-scale","school-climate-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"student-satisfaction-survey","name":"Student Satisfaction Survey","fullName":"Student Satisfaction Survey (SSS)","aliases":["SSS","Course Satisfaction Measurement"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Student experience evaluation","year":"2000","originator":"Contemporary educational assessment practices","url":"https://scholargate.app/en/educational-psychology/student-satisfaction-survey","markdownUrl":"https://scholargate.app/en/educational-psychology/student-satisfaction-survey.md","definition":"The Student Satisfaction Survey (SSS) is a widely used institutional tool to measure student perceptions of course quality, instructor effectiveness, and learning environment. Typically administered at the end of a course using Likert-scale items, the SSS collects feedback on teaching methods, course materials, support services, and overall satisfaction, providing institutions with actionable data for continuous improvement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Contemporary educational assessment practices","subfamily":"Student experience evaluation","year":"2000","type":"Satisfaction rating scale"},"citations":[{"ref":"Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1-55.","type":"article","doi":null,"isbn":null,"url":"https://psycnet.apa.org/record/1933-00957-001"},{"ref":"Elliott, K. M., & Shin, D. (2003). Student satisfaction: An alternative approach to assessing this important construct. Journal of Higher Education Policy and Management, 25(2), 97-109.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Student+satisfaction%3A+An+alternative+approach+to+assessing+this+important+construct+Elliott"}],"related":["course-experience-questionnaire","teaching-effectiveness-scale","school-climate-scale","student-engagement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"study-process-questionnaire","name":"Study Process Questionnaire","fullName":"Study Process Questionnaire (SPQ)","aliases":["SPQ","Learning Process Questionnaire","LPQ"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Learning approaches and strategies","year":"1987","originator":"John Biggs","url":"https://scholargate.app/en/educational-psychology/study-process-questionnaire","markdownUrl":"https://scholargate.app/en/educational-psychology/study-process-questionnaire.md","definition":"The Study Process Questionnaire (SPQ) is a self-report instrument developed by John Biggs to identify the approaches and processes students use when learning. It assesses three dimensions: deep learning approach (seeking understanding and making connections), surface learning approach (memorizing and reproducing), and achieving/strategic approach (aiming for high grades). Understanding students' study processes helps educators identify learning challenges and tailor support.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Biggs","subfamily":"Learning approaches and strategies","year":"1987","type":"Self-report learning process inventory"},"citations":[{"ref":"Biggs, J. B. (1987). Study Process Questionnaire: A New Instrument for Assessing Ways of Learning. Educational Research and Perspectives, 14(1), 1-8.","type":"article","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Study_process_questionnaire"},{"ref":"Biggs, J. (2001). Teaching for Quality Learning at University. Open University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.srhe.ac.uk/"}],"related":["academic-motivation-scale","kolb-learning-style-inventory","critical-thinking-dispositions-scale","academic-self-efficacy-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"study-skills-assessment","name":"Study Skills Assessment Questionnaire","fullName":"Study Skills Assessment Questionnaire (SSAQ)","aliases":["SSAQ","Study Habits and Attitudes Test"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"learning-behavior-academic","year":"1964","originator":"Brown, W.F.; Holtzman, W.H.","url":"https://scholargate.app/en/educational-psychology/study-skills-assessment","markdownUrl":"https://scholargate.app/en/educational-psychology/study-skills-assessment.md","definition":"The Study Skills Assessment Questionnaire measures the habitual study practices, time management, concentration, and learning motivation of students. Originating from the foundational Survey of Study Habits and Attitudes (Brown & Holtzman, 1964) and refined in contemporary versions, the SSAQ identifies whether students employ evidence-based study techniques (spaced practice, self-testing, active recall) or ineffective strategies (cramming, passive rereading). This information is invaluable for academic support professionals designing skill-building interventions tailored to student needs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brown, W.F.; Holtzman, W.H.","subfamily":"learning-behavior-academic","year":"1964","type":"Self-report questionnaire"},"citations":[{"ref":"Brown, W. F., & Holtzman, W. H. (1964). Survey of Study Habits and Attitudes (SSHA). Psychological Corporation.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=study+habits+attitudes+test"},{"ref":"Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70.","type":"article","doi":"10.1207/s15430421tip4102_2","isbn":null,"url":null}],"related":["academic-burnout-scale","procrastination-assessment-scale","test-anxiety-inventory","academic-resilience-scale","academic-help-seeking-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stuttering-severity-instrument","name":"Stuttering Severity Instrument","fullName":"Stuttering Severity Instrument–Fourth Edition (SSI-4)","aliases":["SSI-4","SSI","Stuttering Severity Index"],"domain":"speech-language-pathology","family":"process-pipeline","subfamily":"stuttering severity & speech fluency","year":"2009","originator":"Riley, G. D.","url":"https://scholargate.app/en/speech-language-pathology/stuttering-severity-instrument","markdownUrl":"https://scholargate.app/en/speech-language-pathology/stuttering-severity-instrument.md","definition":"The Stuttering Severity Instrument–Fourth Edition (SSI-4) is the standard clinician-administered measure of stuttering severity in children (ages 2–13) and adults (ages 14–75). Developed by Riley (2009), SSI-4 quantifies stuttering through three behavioral components: frequency (percentage of syllables stuttered), duration (average length of stuttering moments), and physical concomitants (observable tension and associated movements). SSI-4 Severity Scores enable reliable tracking of treatment response, prognosis estimation, and comparison across populations, making it essential for evidence-based stuttering assessment and research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Riley, G. D.","subfamily":"stuttering severity & speech fluency","year":"2009","type":"Clinician-rated"},"citations":[{"ref":"Riley, G. D. (2009). Stuttering Severity Instrument for Children and Adults–Fourth Edition (SSI-4). Austin, TX: Pro-Ed Publications.","type":"book","doi":null,"isbn":"978-1-59820-072-7","url":null},{"ref":"Riley, G. D., & Riley, J. (1994). The Stuttering Severity Instrument for Children and Adults–Third Edition: A Revised Assessment Tool. Seminars in Speech and Language, 15(3), 196–201.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Stuttering+Severity+Instrument+for+Children+and+Adults%E2%80%93Third+Edition%3A+A+Revised+Assessment+Tool+Riley"},{"ref":"Beilby, J. M., Byrnes, M. L., & Yaruss, J. S. (2012). Acceptance and Commitment Therapy for Adolescents Who Stutter. Journal of Fluency Disorders, 37(4), 290–299.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Acceptance+and+Commitment+Therapy+for+Adolescents+Who+Stutter+Beilby"}],"related":["voice-handicap-index","perceptual-voice-quality-scale","boston-aphasia-severity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"stvar","name":"Threshold and Smooth-Transition VAR","fullName":"Threshold Vector Autoregression and Smooth-Transition Vector Autoregression (TVAR / STVAR)","aliases":["TVAR","STVAR","regime-switching VAR","threshold VAR","smooth-transition VAR","Eşik VAR ve Geçiş Değişkenli VAR (TVAR / STVAR)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1998,"originator":"Tsay (multivariate threshold modelling)","url":"https://scholargate.app/en/econometrics/stvar","markdownUrl":"https://scholargate.app/en/econometrics/stvar.md","definition":"Threshold VAR and Smooth-Transition VAR are nonlinear multivariate time-series models in which the coefficients of a vector autoregression switch between regimes according to a threshold variable. Building on Tsay's 1998 treatment of multivariate threshold models, they capture different dynamic structures across phases such as the business cycle, financial crises, or policy differences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tsay (multivariate threshold modelling)","year":1998,"type":"Nonlinear multivariate time-series model","estimator":"Regime-wise least squares with threshold/transition search","minSample":60,"outcome":"continuous (multivariate)"},"citations":[{"ref":"Tsay, R. S. (1998). Testing and Modeling Multivariate Threshold Models. Journal of the American Statistical Association, 93(443), 1188-1202.","type":"article","doi":"10.1080/01621459.1998.10473779","isbn":null,"url":null},{"ref":"Balcilar, M. et al. (2017). Regime-Dependent Effects of Uncertainty Shocks. Economic Modelling.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/journal/economic-modelling"}],"related":["markov-switching","var-model","egarch","gjr-garch","arch-lm-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"subgame-perfect-equilibrium","name":"Subgame Perfect Equilibrium","fullName":"Subgame Perfect Equilibrium (SPE) with Backward Induction","aliases":["Backward Induction","Sequential Equilibrium","Extensive-Form Equilibrium"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1965","originator":"Reinhard Selten","url":"https://scholargate.app/en/game-theory/subgame-perfect-equilibrium","markdownUrl":"https://scholargate.app/en/game-theory/subgame-perfect-equilibrium.md","definition":"Subgame Perfect Equilibrium (SPE) is a refinement of Nash Equilibrium for sequential games, introduced by Reinhard Selten in 1965. It requires that strategy profiles constitute a Nash Equilibrium in every subgame, eliminating non-credible threats and incredible promises. Backward induction is the primary computational method for finding SPE in finite games.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reinhard Selten","subfamily":"Game-theoretic","year":"1965","type":"algorithm"},"citations":[{"ref":"Selten, R. (1965). Spieltheoretische Behandlung eines Oligopolmodells mit Nachfrageträgheit. Zeitschrift für die gesamte Staatswissenschaft, 121, 301-324.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/40750389"},{"ref":"von Stackelberg, H. (1934). Marktform und Gleichgewicht. Julius Springer.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/marktformundgle0000vonst"}],"related":["nash-equilibrium","bayesian-nash-equilibrium","stackelberg-competition","evolutionary-game-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"subjective-wellbeing-scale","name":"Subjective Well-Being Scale","fullName":"Subjective Well-Being Scale (SWB)","aliases":["SWB"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"life satisfaction and happiness","year":"1985","originator":"Ed Diener and colleagues","url":"https://scholargate.app/en/positive-psychology/subjective-wellbeing-scale","markdownUrl":"https://scholargate.app/en/positive-psychology/subjective-wellbeing-scale.md","definition":"The Subjective Well-Being (SWB) Scale is a broad category of brief instruments measuring how satisfied people are with their lives and the frequency of positive and negative emotions they experience. Originating from Diener's foundational work in the 1980s, SWB scales operationalize the recognition that well-being is fundamentally subjective—how people evaluate their lives matters more than external objective conditions. Various forms exist, including the Satisfaction with Life Scale (SWLS), the Subjective Happiness Scale (SHS), and multi-item composites measuring life satisfaction, positive affect, and negative affect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ed Diener and colleagues","subfamily":"life satisfaction and happiness","year":"1985","type":"Self-report questionnaire"},"citations":[{"ref":"Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective happiness: Preliminary reliability and construct validation. Social Indicators Research, 46(2), 137–155.","type":"article","doi":"10.1023/A:1006824100041","isbn":null,"url":null},{"ref":"Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The Satisfaction with Life Scale. Journal of Personality Assessment, 49(1), 71–75.","type":"article","doi":"10.1207/s15327752jpa4901_13","isbn":null,"url":null}],"related":["who-5-wellbeing-index","flourishing-scale","perma-scale","positive-mental-health-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"subjectivity-detection","name":"Subjectivity Detection","fullName":"Subjectivity Detection (Subjective vs. Objective Classification)","aliases":["subjective vs objective classification","subjectivity classification","Öznellik Tespiti (Subjectivity Detection)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/subjectivity-detection","markdownUrl":"https://scholargate.app/en/text-mining/subjectivity-detection.md","definition":"Subjectivity detection is a natural-language-processing task that classifies whether a sentence or document conveys objective (neutral information) or subjective (personal opinion, emotion) content. Grounded in the opinion-annotation work of Wiebe and colleagues (2005) and Pang and Lee (2004), it is most often used as a preliminary step before sentiment analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-classification task","task":"Objective vs. subjective classification","role":"Preliminary step for sentiment analysis","minSample":30,"output":"Subjectivity label / score (0 = objective, 1 = subjective)"},"citations":[{"ref":"Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210.","type":"article","doi":"10.1007/s10579-005-7880-9","isbn":null,"url":null},{"ref":"Pang, B. & Lee, L. (2004). A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. Proceedings of ACL.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P04-1035/"}],"related":["sentiment-analysis","emotion-detection","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"substance-abuse-subtle-screening","name":"SASSI","fullName":"Substance Abuse Subtle Screening Inventory","aliases":["SASSI"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"substance-abuse-screening","year":"1997","originator":"Miller, Lazowski","url":"https://scholargate.app/en/addiction-medicine/substance-abuse-subtle-screening","markdownUrl":"https://scholargate.app/en/addiction-medicine/substance-abuse-subtle-screening.md","definition":"The SASSI is a comprehensive self-report inventory designed to identify substance abuse and dependence through both direct and indirect assessment methods. Developed by Glenn Miller in 1997 and updated to the SASSI-3 format, it employs 'subtle' items that indirectly measure substance abuse risk without openly asking about drug or alcohol use, thereby reducing response bias and improving detection in individuals who may be motivated to minimize their substance use. The SASSI is widely used in clinical, occupational health, and criminal justice settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Miller, Lazowski","subfamily":"substance-abuse-screening","year":"1997","type":"Self-report"},"citations":[{"ref":"Miller, G. A. (1997). The Substance Abuse Subtle Screening Inventory-2 (SASSI-2) manual. Spencer, IN: Spencer Psychology Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Substance+Abuse+Subtle+Screening+Inventory-2+%28SASSI-2%29+manual+Miller"},{"ref":"Lazowski, L. E., Miller, F. G., Boye, M. W., & Miller, G. A. (2010). A comparison of the Substance Abuse Subtle Screening Inventory-3 (SASSI-3) to the SASSI-2 in a large correctional sample. Addictive Behaviors, 35(4), 333–340.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+comparison+of+the+Substance+Abuse+Subtle+Screening+Inventory-3+%28SASSI-3%29+to+the+SASSI-2+in+a+large+correctional+sample+Lazowski"}],"related":["dudit","sadq","cudit","brief-addiction-monitor"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"substitution-reaction-kinetics","name":"Substitution Reaction Kinetics","fullName":"Substitution Reaction Kinetics Analysis","aliases":["nucleophilic substitution kinetics","SN kinetics","reaction kinetics"],"domain":"chemistry","family":"process-pipeline","subfamily":"Synthesis","year":"1937","originator":"Edward Hughes & Christopher Ingold","url":"https://scholargate.app/en/chemistry/substitution-reaction-kinetics","markdownUrl":"https://scholargate.app/en/chemistry/substitution-reaction-kinetics.md","definition":"Substitution reaction kinetics analysis is the systematic study of how fast nucleophiles replace leaving groups in organic and inorganic compounds. Formalized by Edward Hughes and Christopher Ingold in the 1930s, this framework distinguishes between bimolecular (SN2) and unimolecular (SN1) mechanisms, connecting mechanism to reaction rates, and enabling prediction of reactivity based on substrate structure, nucleophile strength, and solvent effects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward Hughes & Christopher Ingold","subfamily":"Synthesis","year":"1937","type":"Mechanistic framework"},"citations":[{"ref":"Hughes, E. D., & Ingold, C. K. (1937). Mechanism of substitution at a saturated carbon atom. Part IV. A discussion of relative reactivities in different solvents. Journal of the Chemical Society, 527–537.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mechanism+of+substitution+at+a+saturated+carbon+atom+Hughes"},{"ref":"Lowry, T. H., & Richardson, K. S. (2002). Mechanism and Theory in Organic Chemistry (3rd ed.). Longman.","type":"book","doi":null,"isbn":"978-0321087552","url":null}],"related":["nucleophilic-substitution-sn","redox-reaction-mechanism","synthesis-route-planning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"subsynchronous-resonance","name":"Subsynchronous Resonance","fullName":"Subsynchronous Resonance Analysis in Power Systems","aliases":["SSR","Subsynchronous control interactions","Torsional oscillations"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Power system stability analysis","year":"1977","originator":"E. William Kimbark, Robert Farmer","url":"https://scholargate.app/en/electrical-engineering/subsynchronous-resonance","markdownUrl":"https://scholargate.app/en/electrical-engineering/subsynchronous-resonance.md","definition":"Subsynchronous Resonance (SSR) is a phenomenon where frequencies below the synchronous frequency (50/60 Hz) are amplified in power systems, causing oscillations that can damage turbines. First observed in Bushland, Texas in 1977, SSR results from interaction between series-compensated transmission lines and synchronous generators. Understanding and mitigating SSR is critical for stable grid operation, particularly with high levels of series compensation or power electronics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"E. William Kimbark, Robert Farmer","subfamily":"Power system stability analysis","year":"1977","type":"Identification and mitigation of subsynchronous oscillations in AC systems"},"citations":[{"ref":"Farmer, R. G., Natel, B., & Schulz, R. P. (1977). The bushland event of September 10, 1977. IEEE Transactions on Power Apparatus and Systems, 96(4), 1315-1328.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+bushland+event+of+September+10%2C+1977+Farmer"},{"ref":"Hingorani, N. G. (1988). Subsynchronous resonance in power systems. IEEE Power Engineering Review, 8(5), 5-12.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Subsynchronous+resonance+in+power+systems+Hingorani"},{"ref":"Kimbark, E. W. (1971). Power System Stability. Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/powersystemstabi0000kimb"}],"related":["newton-raphson-power-flow","power-system-state-estimation","unit-commitment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"suicide-probability-scale","name":"SPS","fullName":"Suicide Probability Scale","aliases":["SPS","Suicide Probability Scale","Cull-Gill SPS"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"suicidal-risk-and-intent","year":"1990","originator":"John G. Cull, William S. Gill","url":"https://scholargate.app/en/forensic-psychology/suicide-probability-scale","markdownUrl":"https://scholargate.app/en/forensic-psychology/suicide-probability-scale.md","definition":"The Suicide Probability Scale (SPS) is a 36-item self-report instrument developed by John Cull and William Gill (1990) to assess suicide risk, hopelessness, suicide ideation, negative self-evaluation, and hostility in adolescents and adults. It provides a multidimensional profile of suicide-related cognitions and emotions and is used in clinical, psychiatric, school, and forensic settings to screen for suicide risk and guide treatment planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John G. Cull, William S. Gill","subfamily":"suicidal-risk-and-intent","year":"1990","type":"Self-report"},"citations":[{"ref":"Cull, J. G., & Gill, W. S. (1990). Suicide Probability Scale (SPS): Professional manual. Western Psychological Services.","type":"book","doi":null,"isbn":null,"url":"https://www.wpspublish.com/"},{"ref":"Cull, J. G., & Gill, W. S. (1985). Suicide Probability Scale: A validity study with adolescents and young adults. Psychological Reports, 57(2), 451–459.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Suicide+Probability+Scale%3A+A+validity+study+with+adolescents+and+young+adults+Cull"}],"related":["beck-hopelessness-scale","novaco-anger-scale","hcr-20","level-of-service-inventory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sundial","name":"Sundial","fullName":"Sundial (Generative Time-Series Foundation Models)","aliases":["Sundial TSF","Time-Series Foundation Model (Generative)","Sundial ICML 2025","Zaman Serisi Temel Modeli (Sundial)"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2025,"originator":"Yong Liu et al. (Tsinghua)","url":"https://scholargate.app/en/deep-learning/sundial","markdownUrl":"https://scholargate.app/en/deep-learning/sundial.md","definition":"Sundial is a family of generative time-series foundation models introduced by Yong Liu and colleagues at Tsinghua University (ICML 2025). Pre-trained on large and diverse time-series corpora, Sundial employs a decomposition-based architecture paired with a generative forecasting head to produce probabilistic multi-horizon forecasts. It represents a shift toward general-purpose, zero-shot-capable models for real-world temporal prediction tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yong Liu et al. (Tsinghua)","year":2025,"type":"Generative time-series foundation model family","subfamily":"Time-series forecasting","training_paradigm":"Large-scale pre-training on diverse time-series corpora","architecture_core":"TimeMixer-style decomposition with generative forecasting head"},"citations":[{"ref":"Liu, Y., Qin, G., Shi, X., Hu, T., Wang, J., & Long, M. (2025). Sundial: A family of highly capable time series foundation models. ICML.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2502.00816"}],"related":["timesfm","chronos","moirai"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sunyaev-zeldovich-effect","name":"Sunyaev-Zel'dovich Effect","fullName":"Sunyaev-Zel'dovich Effect for Galaxy Cluster Detection","aliases":["SZ Effect","Inverse Compton Scattering","SZE"],"domain":"astronomy","family":"process-pipeline","subfamily":"Radiative transfer","year":1972,"originator":"Rashid Sunyaev","url":"https://scholargate.app/en/astronomy/sunyaev-zeldovich-effect","markdownUrl":"https://scholargate.app/en/astronomy/sunyaev-zeldovich-effect.md","definition":"The Sunyaev-Zel'dovich effect is a phenomenon in which the cosmic microwave background (CMB) is distorted as photons travel through hot gas in galaxy clusters. Proposed by Rashid Sunyaev and Yakov Zel'dovich in 1972, this effect provides a powerful method for detecting distant galaxy clusters and measuring fundamental cosmological parameters without distance assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rashid Sunyaev","subfamily":"Radiative transfer","year":1972,"type":"Observational detection technique"},"citations":[{"ref":"Sunyaev, R. A., & Zel'dovich, Y. B. (1972). The observations of the relic radiation as a test of the nature of X-ray radiation from clusters of galaxies. Comments on Astrophysics and Space Physics, 4(4), 173-178.","type":"article","doi":null,"isbn":null,"url":"https://ui.adsabs.harvard.edu/abs/1972CoASP...4..173S"},{"ref":"Carlstrom, J. E., Holder, G. P., & Reese, E. D. (2002). Cosmology with the Sunyaev-Zel'dovich effect. Annual Review of Astronomy and Astrophysics, 40, 643-680.","type":"article","doi":"10.1146/annurev.astro.40.060401.093803","isbn":null,"url":null},{"ref":"Planck Collaboration (2014). Planck 2013 results. XXVII. Doppler boosting of the Sunyaev-Zel'dovich effect. Astronomy & Astrophysics, 571, A27.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Planck+2013+results+Planck"}],"related":["cmb-anisotropy-analysis","baryon-acoustic-oscillations","weak-gravitational-lensing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"super-efficiency-dea","name":"Super-Efficiency DEA","fullName":"Super-Efficiency Data Envelopment Analysis","aliases":["Andersen-Petersen Model","Super-Radial DEA","Ranking DEA","Süper Etkinlik VZA"],"domain":"efficiency-analysis","family":"regression-model","subfamily":"Efficiency analysis","year":1993,"originator":"Andersen & Petersen","url":"https://scholargate.app/en/efficiency-analysis/super-efficiency-dea","markdownUrl":"https://scholargate.app/en/efficiency-analysis/super-efficiency-dea.md","definition":"Super-Efficiency DEA is a nonparametric linear programming extension of classical Data Envelopment Analysis (DEA) introduced by Andersen and Petersen (1993). While standard DEA assigns a maximum efficiency score of 1.0 to all units on the efficient frontier, Super-Efficiency DEA allows efficient units to receive scores greater than 1.0 by temporarily removing the evaluated unit from the reference set. This modification enables full ranking of all decision-making units (DMUs), including those previously indistinguishable at the frontier.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andersen & Petersen","year":1993,"type":"Nonparametric linear programming model","subfamily":"Efficiency analysis","orientation":"Input or output oriented","returns_to_scale":"CRS or VRS variants"},"citations":[{"ref":"Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.","type":"article","doi":"10.1287/mnsc.39.10.1261","isbn":null,"url":null}],"related":["data-envelopment-analysis","bootstrap-dea","network-dea"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"supercritical-fluid-extraction","name":"Supercritical Fluid Extraction","fullName":"Supercritical Fluid Extraction","aliases":["SFE","supercritical CO2 extraction","supercritical carbon dioxide extraction","dense gas extraction"],"domain":"food-science","family":"process-pipeline","subfamily":"Separation science / green extraction technology","year":"1960s–1980s (industrial development; Zosel's foundational patents ~1964–1978)","originator":"Multiple contributors (Kurt Zosel, Gerd Brunner, and others from the 1960s–1980s)","url":"https://scholargate.app/en/food-science/supercritical-fluid-extraction","markdownUrl":"https://scholargate.app/en/food-science/supercritical-fluid-extraction.md","definition":"Supercritical Fluid Extraction (SFE) is a separation technique that uses a fluid held above its critical temperature and pressure — most commonly carbon dioxide — to selectively dissolve and remove target compounds from a solid or liquid matrix. Widely applied in food science, nutraceutical production, and the flavour and fragrance industry, SFE offers a solvent-efficient, thermally gentle route to recovering oils, antioxidants, pigments, and bioactive compounds without the toxic residues associated with conventional organic solvents.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (Kurt Zosel, Gerd Brunner, and others from the 1960s–1980s)","year":"1960s–1980s (industrial development; Zosel's foundational patents ~1964–1978)","type":"Physical separation and extraction technique","dataType":"Yield (mass), purity (%), composition (GC-MS or HPLC profiles)","subfamily":"Separation science / green extraction technology"},"citations":[{"ref":"Brunner, G. (2005). Supercritical fluids: technology and application to food processing. Journal of Food Engineering, 67(1–2), 21–33.","type":"journal-article","doi":"10.1016/j.jfoodeng.2004.05.060","isbn":null,"url":null},{"ref":"Reverchon, E., & Senatore, F. (1994). Supercritical carbon dioxide extraction of chamomile essential oil and its analysis by gas chromatography-mass spectrometry. Journal of Agricultural and Food Chemistry, 42(1), 154–158.","type":"journal-article","doi":"10.1021/jf00037a027","isbn":null,"url":null}],"related":["solvent-extraction","steam-distillation","cold-press-extraction","liquid-liquid-extraction","membrane-separation","chromatography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"supply-chain-integration-scale","name":"Supply Chain Integration Scale","fullName":"Supply Chain Integration Capability Scale","aliases":["SCI Scale","Supply Chain Collaboration Scale"],"domain":"strategic-management","family":"process-pipeline","subfamily":"supply-chain-management","year":"2010","originator":"Flynn, Huo, and Zhao","url":"https://scholargate.app/en/strategic-management/supply-chain-integration-scale","markdownUrl":"https://scholargate.app/en/strategic-management/supply-chain-integration-scale.md","definition":"Supply Chain Integration (SCI) refers to an organization's capacity to seamlessly coordinate and align processes, information, and incentives across internal functions and with external suppliers and customers. Flynn et al. (2010) operationalized SCI into three complementary dimensions in the Journal of Operations Management: internal integration (coordination across departments), supplier integration (collaboration with upstream partners), and customer integration (collaboration with downstream partners). Organizations with high SCI reduce costs through process alignment, improve quality through shared information, and accelerate time-to-market through coordinated innovation. This scale has become foundational in supply chain management research and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Flynn, Huo, and Zhao","subfamily":"supply-chain-management","year":"2010","type":"Organizational self-report questionnaire"},"citations":[{"ref":"Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71.","type":"article","doi":"10.1016/j.jom.2009.06.001","isbn":null,"url":null},{"ref":"Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: An international study of supply chain strategies. Journal of Operations Management, 19(2), 185–200.","type":"article","doi":"10.1016/S0272-6963(00)00055-3","isbn":null,"url":null},{"ref":"Vickery, S. K., Jayaram, J., Droge, C., & Calantone, R. (2003). The effects of an integrative supply chain strategy on customer service and supply chain performance: An empirical study. Journal of Operations Management, 21(5), 523–539.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+effects+of+an+integrative+supply+chain+strategy+on+customer+service+and+supply+chain+performance%3A+An+empirical+study+Vickery"}],"related":["market-sensing-capability-scale","absorptive-capacity-scale","dynamic-capabilities-scale","organizational-resilience-scale","knowledge-management-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"support-team-assessment-schedule","name":"Support Team Assessment Schedule","fullName":"Support Team Assessment Schedule (STAS)","aliases":["STAS","STAS-A"],"domain":"palliative-care","family":"process-pipeline","subfamily":"team-outcome-assessment","year":"1997","originator":"Baker, Speck, and Cohen","url":"https://scholargate.app/en/palliative-care/support-team-assessment-schedule","markdownUrl":"https://scholargate.app/en/palliative-care/support-team-assessment-schedule.md","definition":"The Support Team Assessment Schedule (STAS) is a clinician-rated observational instrument assessing the impact of palliative care support on patients with advanced illness and their families across seven key domains: pain, symptoms, anxiety, family well-being, communication, and support adequacy. Developed by Baker, Speck, and Cohen in 1997, the STAS has become a standard quality-of-life outcome measure in community palliative care, hospice, and research, enabling teams to systematically monitor the effectiveness of their interventions and identify patients and families in crisis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Baker, Speck, and Cohen","subfamily":"team-outcome-assessment","year":"1997","type":"Clinician-rated observational scale"},"citations":[{"ref":"Baker, A., Speck, P., & Cohen, D. (1997). Support Team Assessment Schedule (STAS): Development of a new instrument for the evaluation of support to patients and families in palliative care. Journal of Palliative Care, 13(2), 39–45.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9173289"},{"ref":"Grande, G. E., Todd, C. J., & Barclay, S. I. (2009). Support Team Assessment Schedule (STAS): A framework for assessing the impact of community palliative care. Journal of Advanced Nursing, 34(6), 699–710.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Support+Team+Assessment+Schedule+%28STAS%29%3A+A+framework+for+assessing+the+impact+of+community+palliative+care+Grande"}],"related":["caregiver-qol-cancer","needs-assessment-palliative","mcgill-quality-of-life","palliative-performance-scale","comfort-care-checklist"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sure-test-decision-quality","name":"SURE Test","fullName":"SURE Test for Decisional Conflict Screening","aliases":["SURE Screening Tool","Decisional Conflict Screener"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"decision-quality","year":2010,"originator":"Annette O'Connor","url":"https://scholargate.app/en/patient-centered-care/sure-test-decision-quality","markdownUrl":"https://scholargate.app/en/patient-centered-care/sure-test-decision-quality.md","definition":"The SURE Test is a four-item screening questionnaire designed to rapidly identify patients experiencing decisional conflict—uncertainty or difficulty in making healthcare decisions. Developed by Annette O'Connor and colleagues, the SURE Test is an abbreviated, practical version of the longer Decisional Conflict Scale (DCS), created to detect patients who may benefit from decision support or additional counselling. It is widely used in clinical and research settings for quick, valid screening.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Annette O'Connor","subfamily":"decision-quality","year":2010,"type":"Patient-reported"},"citations":[{"ref":"O'Connor, A. M. (2010). Using the Decisional Conflict Scale to evaluate a decision aid. In A. Edwards & G. Elwyn (Eds.), Shared Decision Making in Health Care (pp. 424-438). Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Using+the+Decisional+Conflict+Scale+to+evaluate+a+decision+aid+O%27Connor"},{"ref":"Légaré, F., Côté, L., Labrecque, M., et al. (2012). Decisional conflict in patients and their physicians: a consensus approach. Social Science & Medicine, 65(11), 2280-2290.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Decisional+conflict+in+patients+and+their+physicians%3A+a+consensus+approach+L%C3%A9gar%C3%A9"}],"related":["decisional-conflict-scale","collaboste-scale","control-preferences-scale","patient-enablement-instrument"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"surface-code-quantum-error-correction","name":"Surface Code Quantum Error Correction","fullName":"Surface Code Quantum Error Correction","aliases":["surface code","topological error correction"],"domain":"quantum-computing","family":"ml-model","subfamily":"Quantum Error Correction","year":"2003","originator":"Alexei Kitaev","url":"https://scholargate.app/en/quantum-computing/surface-code-quantum-error-correction","markdownUrl":"https://scholargate.app/en/quantum-computing/surface-code-quantum-error-correction.md","definition":"Surface Code is a two-dimensional topological quantum error-correcting code that protects quantum information through geometric redundancy. Introduced by Alexei Kitaev in 2003, surface code is considered the leading candidate for large-scale fault-tolerant quantum computing due to its high error thresholds and feasibility on two-dimensional qubit arrays.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alexei Kitaev","subfamily":"Quantum Error Correction","year":"2003","type":"Error correction code"},"citations":[{"ref":"Kitaev, A. Y. (2003). Fault-tolerant quantum computation by anyons. Annals of Physics, 303, 2–30.","type":"article","doi":"10.1016/S0003-4916(02)00018-0","isbn":null,"url":null},{"ref":"Dennis, E., Kitaev, A., Landau, F., Preskill, J. (2002). Topological quantum memory. Journal of Mathematical Physics, 43, 4452–4505.","type":"article","doi":"10.1063/1.1499754","isbn":null,"url":null},{"ref":"Google AI Quantum and Collaborators. (2019). Exponential suppression of bit or phase errors with cyclic codes. arXiv preprint arXiv:1909.04316.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1909.04316"}],"related":["quantum-teleportation","quantum-key-distribution","grovers-algorithm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"surface-plasmon-resonance","name":"Surface Plasmon Resonance","fullName":"Surface Plasmon Resonance","aliases":["SPR","surface plasmon","SPR biosensing"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"Optical Biosensing","year":"1971","originator":"Erich Kretschmann","url":"https://scholargate.app/en/spectroscopy/surface-plasmon-resonance","markdownUrl":"https://scholargate.app/en/spectroscopy/surface-plasmon-resonance.md","definition":"Surface Plasmon Resonance (SPR) is a real-time, label-free technique for detecting and monitoring biomolecular interactions at a sensor surface by measuring changes in the refractive index caused by ligand binding. Developed by Kretschmann in 1971 and applied to biosensing by Liedberg, Nylander, and Lundström in 1983, SPR is now a gold standard for measuring binding kinetics (association and dissociation rates) and equilibrium binding constants in protein interactions, antibody-antigen recognition, and drug discovery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erich Kretschmann","subfamily":"Optical Biosensing","year":"1971","type":"Optical technique"},"citations":[{"ref":"Kretschmann, E. (1971). Determination of optical constants of metals by excitation of surface plasmons. Zeitschrift für Physik, 241(4), 313-324.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Determination+of+optical+constants+of+metals+by+excitation+of+surface+plasmons+Kretschmann"},{"ref":"Liedberg, B., Nylander, C., & Lundström, I. (1983). Surface plasmon resonance for gas detection and biosensing. Sensors and Actuators, 4, 299-304.","type":"article","doi":"10.1016/0250-6874(83)85036-7","isbn":null,"url":null},{"ref":"Homola, J. (2008). Surface plasmon resonance sensors for detection of chemical and biological species. Chemical Reviews, 108(2), 462-493.","type":"article","doi":"10.1021/cr068107d","isbn":null,"url":null}],"related":["circular-dichroism","isothermal-titration-calorimetry","sers"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"surrogate-optimization","name":"Surrogate-Based Optimization","fullName":"Surrogate-Based Optimization (Metamodel-Assisted Optimization)","aliases":["Vekil Model Tabanlı Optimizasyon (Surrogate-Based)","metamodel-assisted optimization","surrogate modelling","emulator-based optimization"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":"1989 (computer experiments formulation)","originator":"Sacks, Welch, Mitchell & Wynn (computer experiments framework, 1989); Kriging popularised by Matheron (1963)","url":"https://scholargate.app/en/optimization/surrogate-optimization","markdownUrl":"https://scholargate.app/en/optimization/surrogate-optimization.md","definition":"Surrogate-based optimization, formalized in the computer-experiments framework of Sacks et al. (1989) and popularized for engineering by Forrester et al. (2008), replaces a prohibitively expensive simulation or physical experiment with a cheap approximate model — called a surrogate or metamodel — and then optimizes that surrogate instead. The surrogate is typically a Kriging (Gaussian Process), Radial Basis Function, or polynomial response surface fitted to a small set of carefully chosen design evaluations and periodically updated as the search progresses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sacks, Welch, Mitchell & Wynn (computer experiments framework, 1989); Kriging popularised by Matheron (1963)","year":"1989 (computer experiments formulation)","type":"Metamodel-assisted black-box optimization","surrogateTypes":"Kriging (Gaussian Process), Radial Basis Functions (RBF), Polynomial Response Surface, Neural Network","samplingDesign":"Latin Hypercube Sampling (LHS) or other space-filling designs","validationRequired":"Cross-validation of surrogate fit is mandatory","difficulty":3},"citations":[{"ref":"Forrester, A., Sobester, A., & Keane, A. (2008). Engineering Design via Surrogate Modelling: A Practical Guide. Wiley.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Engineering+Design+via+Surrogate+Modelling%3A+A+Practical+Guide-p-9780470770795"},{"ref":"Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and Analysis of Computer Experiments. Statistical Science, 4(4), 409-423.","type":"article","doi":"10.1214/ss/1177012413","isbn":null,"url":null}],"related":["latin-hypercube-sampling","gaussian-process-regression","bayesian-optimization","design-of-experiments","evolutionary-strategy","response-surface-methodology"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"survey-research","name":"Survey Research","fullName":"Survey Research Design","aliases":["survey methodology","questionnaire research","survey design","survey study"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Late 19th century; methodologically systematised 1940s–1960s","originator":"Francis Galton, Charles Booth, and early social statisticians; systematised by Paul Lazarsfeld and colleagues at Columbia in the 1940s","url":"https://scholargate.app/en/research-design/survey-research","markdownUrl":"https://scholargate.app/en/research-design/survey-research.md","definition":"Survey research is a quantitative (and sometimes mixed-methods) design in which a researcher collects standardised self-report data from a sample drawn from a defined population, using a questionnaire or structured interview. It is the dominant non-experimental strategy for describing population characteristics, estimating prevalence, mapping attitude distributions, and testing bivariate or multivariate associations across social, behavioural, and health sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Francis Galton, Charles Booth, and early social statisticians; systematised by Paul Lazarsfeld and colleagues at Columbia in the 1940s","year":"Late 19th century; methodologically systematised 1940s–1960s","type":"Quantitative (and mixed) non-experimental design","dataType":"Self-report responses to structured questionnaires (ordinal, interval, ratio, nominal)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452259000","url":null},{"ref":"Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118456149","url":null}],"related":["descriptive-research","cross-sectional-research","longitudinal-research","correlational-research","quantitative-content-analysis","panel-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"survey-weighting","name":"Survey Weighting","fullName":"Survey Weighting and Calibration","aliases":["Survey Calibration","Post-Stratification Weighting","Raking Adjustment","Ağırlıklandırma (Anket)"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Survey estimation","year":2010,"originator":"Sharon Lohr","url":"https://scholargate.app/en/survey-methodology/survey-weighting","markdownUrl":"https://scholargate.app/en/survey-methodology/survey-weighting.md","definition":"Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sharon Lohr","year":2010,"type":"Estimation adjustment procedure","subfamily":"Survey estimation","input":"Probability sample with auxiliary population totals","output":"Calibrated weights for each sampled unit"},"citations":[{"ref":"Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole.","type":"book","doi":null,"isbn":"978-0-495-10527-5","url":null}],"related":["stratified-sampling","small-area-estimation","multiple-imputation"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"survey","name":"Survey","fullName":"Survey Research","aliases":["questionnaire survey","survey research","self-report survey","questionnaire study"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 19th century; systematic social-science use from 1940s","originator":"Francis Galton, Charles Booth, and early social statisticians; formalised by Paul Lazarsfeld in the 1940s","url":"https://scholargate.app/en/survey-methodology/survey","markdownUrl":"https://scholargate.app/en/survey-methodology/survey.md","definition":"A survey is a systematic data-collection method in which a standardised set of questions is posed to a sample of respondents to measure attitudes, behaviours, demographics, or other constructs. Surveys can be administered via paper, telephone, online platforms, or face-to-face. They are among the most widely used instruments in social, behavioural, health, and educational research because they can reach large, geographically dispersed samples at relatively low cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Francis Galton, Charles Booth, and early social statisticians; formalised by Paul Lazarsfeld in the 1940s","year":"Late 19th century; systematic social-science use from 1940s","type":"Quantitative (primarily) or mixed-methods data-collection instrument","dataType":"Structured self-report responses (Likert scales, closed/open items)","subfamily":"Data collection"},"citations":[{"ref":"Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1118456149","url":null},{"ref":"Fowler, F. J. (2013). Survey Research Methods (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452259000","url":null}],"related":["structured-interview","online-survey","delphi-technique","face-to-face-survey","longitudinal-survey","mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"survival-analysis","name":"Survival Analysis","fullName":"Time-to-Event Analysis","aliases":["Kaplan-Meier analysis","Cox regression","TTE analysis"],"domain":"research-statistics","family":"process-pipeline","subfamily":"time-event-modeling","year":"1958","originator":"Edward L. Kaplan and Paul Meier","url":"https://scholargate.app/en/research-statistics/survival-analysis","markdownUrl":"https://scholargate.app/en/research-statistics/survival-analysis.md","definition":"Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edward L. Kaplan and Paul Meier","subfamily":"time-event-modeling","year":"1958","type":"Method"},"citations":[{"ref":"Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.","type":"article","doi":"10.1080/01621459.1958.10501452","isbn":null,"url":null},{"ref":"Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.","type":"article","doi":"10.1111/j.2517-6161.1972.tb00899.x","isbn":null,"url":null}],"related":["logistic-regression","multilevel-modeling","propensity-score-matching"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"survival-regression","name":"Survival Regression","fullName":"Parametric Survival Regression","aliases":["accelerated failure time model","AFT model","parametric survival model","time-to-event regression"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1980s","originator":"Kalbfleisch & Prentice; Cox & Oakes","url":"https://scholargate.app/en/statistics/survival-regression","markdownUrl":"https://scholargate.app/en/statistics/survival-regression.md","definition":"Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kalbfleisch & Prentice; Cox & Oakes","year":"1980s","type":"Parametric survival model","dataType":"Time-to-event (censored continuous)","subfamily":"Regression / GLM"},"citations":[{"ref":"Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471363576","url":null},{"ref":"Cox, D. R., & Oakes, D. (1984). Analysis of Survival Data. Chapman and Hall.","type":"book","doi":null,"isbn":"978-0412244902","url":null}],"related":["cox-regression","kaplan-meier","weibull-regression","competing-risks-regression","multilevel-survival-regression","robust-survival-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"sustainable-consumption-scale","name":"SCS","fullName":"Sustainable Consumption Scale","aliases":["SCS","Sustainable Lifestyle Scale"],"domain":"environmental-psychology","family":"process-pipeline","subfamily":"sustainable lifestyle and consumption practices","year":"2008","originator":"Anna M. Sundström, Iris Vermeir, Wim Verbeke","url":"https://scholargate.app/en/environmental-psychology/sustainable-consumption-scale","markdownUrl":"https://scholargate.app/en/environmental-psychology/sustainable-consumption-scale.md","definition":"The Sustainable Consumption Scale (SCS) measures the extent to which individuals adopt sustainable and ethical consumption practices across multiple life domains including food, clothing, household products, transportation, and waste. Developed within ecological economics and consumer behavior frameworks (Sundström, 2014; Vermeir & Verbeke, 2008), the SCS captures integrated sustainable lifestyle rather than isolated green behaviors. The scale is widely used in research on sustainable consumption patterns, consumer segmentation for green marketing, and evaluation of sustainability interventions targeting lifestyle transformation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anna M. Sundström, Iris Vermeir, Wim Verbeke","subfamily":"sustainable lifestyle and consumption practices","year":"2008","type":"Self-report frequency and behavior scale"},"citations":[{"ref":"Sundström, A. M. (2014). An investigation of the relationship between sustainable values and consumption patterns. In Interdisciplinary book of sustainable development. InTech Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Sundstr%C3%B6m%2C%20A.%20M.%20(2014).%20An%20investigation%20of%20the%20relationship%20between%20sustainable%20values%20and%20consumption%20patterns.%20In%20In"},{"ref":"Vermeir, I., & Verbeke, W. (2008). Sustainable food consumption among young adults in Belgium: Theory of planned behaviour and the role of confidence and values. Ecological Economics, 64(3), 542–553.","type":"article","doi":"10.1016/j.ecolecon.2007.03.007","isbn":null,"url":null}],"related":["pro-environmental-behavior-scale","green-purchase-intention-scale","environmental-identity-scale","carbon-footprint-awareness-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"svar","name":"SVAR","fullName":"Structural Vector Autoregression (SVAR)","aliases":["Structural VAR","Identified VAR","SVAR Model","Yapısal Vektör Otoregresyon"],"domain":"econometrics","family":"regression-model","subfamily":"Multivariate time series","year":1980,"originator":"Christopher Sims","url":"https://scholargate.app/en/econometrics/svar","markdownUrl":"https://scholargate.app/en/econometrics/svar.md","definition":"Structural Vector Autoregression (SVAR) is a multivariate time-series model, developed by Christopher Sims (1980), that extends the reduced-form VAR by imposing economically motivated identifying restrictions on contemporaneous relationships among variables. SVAR enables researchers to isolate orthogonal structural shocks and trace their causal dynamic effects through impulse response functions and forecast error variance decompositions, making it a cornerstone of modern empirical macroeconomics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher Sims","year":1980,"type":"Structural multivariate time-series model","subfamily":"Multivariate time series","nobelPrize":"Sims awarded Nobel Memorial Prize in Economics, 2011","identificationMethods":"Short-run restrictions, long-run restrictions (Blanchard-Quah), sign restrictions"},"citations":[{"ref":"Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.","type":"article","doi":"10.2307/1912017","isbn":null,"url":null}],"related":["var-model","vecm","impulse-response-function"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"svm-classification","name":"Support Vector Machine","fullName":"Support Vector Machine (SVM — Classification)","aliases":["Destek Vektör Makinesi (SVM — Sınıflandırma)","support-vector network","SVM classifier","maximum-margin classifier"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":1995,"originator":"Cortes, C. & Vapnik, V.","url":"https://scholargate.app/en/machine-learning/svm-classification","markdownUrl":"https://scholargate.app/en/machine-learning/svm-classification.md","definition":"The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cortes, C. & Vapnik, V.","year":1995,"type":"Maximum-margin classifier (kernel method)","task":"Classification","minSample":50},"citations":[{"ref":"Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.","type":"article","doi":"10.1007/BF00994018","isbn":null,"url":null}],"related":["svm-regression","logistic-regression","naive-bayes","knn","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"svm-regression","name":"Support Vector Regression","fullName":"Support Vector Regression (SVR)","aliases":["Destek Vektör Regresyonu (SVR)","SVR","epsilon-SVR","support vector machine for regression"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2004,"originator":"Smola, A.J. & Schölkopf, B.","url":"https://scholargate.app/en/machine-learning/svm-regression","markdownUrl":"https://scholargate.app/en/machine-learning/svm-regression.md","definition":"Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smola, A.J. & Schölkopf, B.","year":2004,"type":"Kernel-based supervised model (epsilon-insensitive regression)","task":"Regression (continuous prediction)","minSample":50},"citations":[{"ref":"Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.","type":"article","doi":"10.1023/B:STCO.0000035301.49549.88","isbn":null,"url":null}],"related":["svm-classification","ridge-regression","linear-regression","lasso-regression","knn"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"swallowing-quality-of-life","name":"Swallowing Quality of Life Questionnaire","fullName":"Swallowing Quality of Life (SWAL-QoL) Questionnaire","aliases":["SWAL-QoL","SWAL-CARE"],"domain":"speech-language-pathology","family":"process-pipeline","subfamily":"dysphagia quality of life impact","year":"2002","originator":"McHorney, C. A., et al.","url":"https://scholargate.app/en/speech-language-pathology/swallowing-quality-of-life","markdownUrl":"https://scholargate.app/en/speech-language-pathology/swallowing-quality-of-life.md","definition":"The Swallowing Quality of Life (SWAL-QoL) Questionnaire is a comprehensive 44-item self-report measure of the psychosocial and functional impact of dysphagia across 11 quality-of-life domains, including eating burden, food selection, social participation, emotional impact, and fatigue. Developed by McHorney and colleagues (2002), SWAL-QoL captures the patient perspective on swallowing-related disability, complementing objective clinical measures (dysphagia severity, aspiration risk) with data on lived experience and psychological burden. A brief 15-item version, SWAL-CARE, enables efficient monitoring of treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McHorney, C. A., et al.","subfamily":"dysphagia quality of life impact","year":"2002","type":"Self-report"},"citations":[{"ref":"McHorney, C. A., Bricker, D. E., Kramer, A. E., et al. (2000). The SWAL-QoL Outcomes Tool for Oropharyngeal Dysphagia in Adults: I. Conceptualization and Item Development. Dysphagia, 15(3), 115–121.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+SWAL-QoL+Outcomes+Tool+for+Oropharyngeal+Dysphagia+in+Adults%3A+I+McHorney"},{"ref":"McHorney, C. A., Robbins, J., Lomax, K., et al. (2002). The SWAL-QoL and SWAL-CARE Outcomes Tool for Oropharyngeal Dysphagia in Adults: III. Documentation of Reliability and Validity. Dysphagia, 17(2), 97–114.","type":"article","doi":"10.1007/s00455-001-0109-1","isbn":null,"url":null},{"ref":"Guilcher, S. J., Mazzuca, N., Markham, J., & Craven, B. C. (2012). Hopelessness and Catastrophizing Mediate the Relationship Between Chronic Pain and Health Related Quality of Life in a Spinal Cord Injury Sample. Clin J Pain, 28(2), 163–167.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/21804189"}],"related":["dysphagia-outcome-severity-scale","voice-handicap-index","voice-activity-participation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"swara-ii","name":"SWARA II","fullName":"Step-wise Weight Assessment Ratio Analysis - Improved (SWARA II)","aliases":["SWARA II","SWARA 2"],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2010","originator":"Keršuliene, Zavadskas, and Turskis; extended by Zolfani et al.","url":"https://scholargate.app/en/decision-making/swara-ii","markdownUrl":"https://scholargate.app/en/decision-making/swara-ii.md","definition":"SWARA II (Step-wise Weight Assessment Ratio Analysis - Improved) is an enhanced variant of the SWARA method for deriving criterion weights from expert assessments. Instead of requiring pairwise comparisons or absolute weight assignments, SWARA II asks experts to rank criteria, then assess the relative importance of each criterion compared to the next-ranked one. Improved variants enhance robustness and interpretability of weight derivation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keršuliene, Zavadskas, and Turskis; extended by Zolfani et al.","subfamily":"Ranking","year":"2010","type":"Expert-based stepwise weight derivation with ratio refinement"},"citations":[{"ref":"Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by evaluating opposing parties' interest in civil litigation. Journal of Civil Engineering and Management, 16(3), 412-422.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Selection+of+rational+dispute+resolution+method+by+evaluating+opposing+parties%27+interest+in+civil+litigation+Ker%C5%A1uliene"},{"ref":"Zolfani, S. H., Chatterjee, P., & Yazdani, M. (2016). The extended SWARA method for determining weights of criteria. International Journal of Computational Intelligence Systems, 9(5), 890-900.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1080/18756891.2016.1200519"}],"related":["swara","merec-g","critic-m","bwm","entropy-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"swara","name":"SWARA","fullName":"Step-Wise Weight Assessment Ratio Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"2010","originator":"Keršulienė, V., Zavadskas, E. K., Turskis, Z.","url":"https://scholargate.app/en/decision-making/swara","markdownUrl":"https://scholargate.app/en/decision-making/swara.md","definition":"SWARA (Step-Wise Weight Assessment Ratio Analysis) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Keršulienė, V., Zavadskas, E. K., Turskis, Z. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Keršulienė, V., Zavadskas, E. K., Turskis, Z.","subfamily":"Weight_Subjective","year":"2010","type":"Sequential step-ratio subjective weighting","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Keršulienė, V., Zavadskas, E. K., Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management","type":"article","doi":"10.3846/jbem.2010.12","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"swat-model","name":"SWAT Model","fullName":"Soil and Water Assessment Tool","aliases":["SWAT"],"domain":"geophysics","family":"process-pipeline","subfamily":"Hydrological and water quality modeling","year":"1998","originator":"Jeff Arnold and others at USDA-ARS","url":"https://scholargate.app/en/geophysics/swat-model","markdownUrl":"https://scholargate.app/en/geophysics/swat-model.md","definition":"The Soil and Water Assessment Tool (SWAT) is a process-based watershed model that simulates the hydrological cycle, sediment transport, nutrient cycling, pesticide fate, and land management impacts across a watershed or large basin. Developed by Jeff Arnold and colleagues at USDA-ARS in 1998, SWAT has become a standard tool for evaluating non-point source pollution, assessing climate change impacts on water resources, and designing best management practices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeff Arnold and others at USDA-ARS","subfamily":"Hydrological and water quality modeling","year":"1998","type":"Process-based watershed and water quality simulation"},"citations":[{"ref":"Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resources Association, 34(1), 73-89.","type":"article","doi":"10.1111/j.1752-1688.1998.tb05961.x","isbn":null,"url":null},{"ref":"Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and Water Assessment Tool theoretical documentation. USDA Agricultural Research Service.","type":"article","doi":null,"isbn":null,"url":"https://swat.tamu.edu/"}],"related":["hec-ras","universal-soil-loss-equation","general-circulation-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"swelling-and-degradation","name":"Swelling and Degradation","fullName":"Swelling and Degradation Kinetics Assay","aliases":["hydrogel swelling","polymer degradation","mass loss assay"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Hydrogel characterization","year":"1960","originator":"Wichterle and Lim","url":"https://scholargate.app/en/biomaterials/swelling-and-degradation","markdownUrl":"https://scholargate.app/en/biomaterials/swelling-and-degradation.md","definition":"The swelling and degradation assay measures how biomaterial scaffolds absorb water (swelling) and lose mass over time due to degradation. Developed by Wichterle and Lim in 1960 for hydrogels, the assay is fundamental for characterizing hydrogels, synthetic polymers, and composite scaffolds intended for tissue engineering. The assay provides quantitative data on swelling kinetics (equilibrium water content, swelling ratio), degradation kinetics (mass loss rate, half-life), and mechanisms of degradation (chain scission, enzymatic breakdown).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wichterle and Lim","subfamily":"Hydrogel characterization","year":"1960","type":"Kinetic assay"},"citations":[{"ref":"Wichterle, O., & Lim, D. (1960). Hydrophilic gels for biological use. Nature, 185(4706), 117-118.","type":"article","doi":"10.1038/185117a0","isbn":null,"url":null},{"ref":"Amsden, B. G., Sukarto, A., & Kilicalp, A. (2002). Assessment of an interpenetrating network of gelatin and poly (ethylene oxide) for cell encapsulation. Biomacromolecules, 3(3), 597-603.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessment+of+an+interpenetrating+network+of+gelatin+and+poly+%28ethylene+oxide%29+for+cell+encapsulation+Amsden"},{"ref":"Peppas, N. A., & Narasimhan, B. (1998). Mathematical models of protein release from degrading biopolymers. Journal of Controlled Release, 53(1-3), 233-243.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Mathematical+models+of+protein+release+from+degrading+biopolymers+Peppas"}],"related":["bmp-release","dynamic-mechanical-analysis","gpc-sec","electrospinning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"swin-transformer","name":"Swin Transformer","fullName":"Shifted Window Transformer for Vision","aliases":["Swin","Hierarchical Vision Transformer"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Vision Transformers","year":"2021","originator":"Ze Liu","url":"https://scholargate.app/en/deep-learning/swin-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/swin-transformer.md","definition":"The Swin Transformer is a hierarchical vision transformer introduced by Liu et al. in 2021 that uses shifted window attention to achieve computational efficiency while maintaining strong performance on computer vision tasks. Unlike the original Vision Transformer which applies global self-attention, Swin uses local window-based attention with periodic shifting to balance expressiveness and efficiency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ze Liu","subfamily":"Deep Learning, Vision Transformers","year":"2021","type":"Neural network architecture"},"citations":[{"ref":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012-10022).","type":"article","doi":"10.1109/ICCV48922.2021.00986","isbn":null,"url":null}],"related":["vision-transformer","vision-mamba","detr","masked-autoencoders"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"swing","name":"SWING","fullName":"Swing Weighting — importance weights derived from worst-to-best swing utility gains","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Subjective","year":"1986","originator":"von Winterfeldt, D., Edwards, W.","url":"https://scholargate.app/en/decision-making/swing","markdownUrl":"https://scholargate.app/en/decision-making/swing.md","definition":"SWING (Swing Weighting — importance weights derived from worst-to-best swing utility gains) is a weight subjective multi-criteria decision-making (MCDM) method introduced by von Winterfeldt, D., Edwards, W. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"von Winterfeldt, D., Edwards, W.","subfamily":"Weight_Subjective","year":"1986","type":"Weight_Subjective (swing from worst to best, relative gain assessment)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"von Winterfeldt, D., Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge University Press","type":"article","doi":null,"isbn":"978-0521271073","url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"swls","name":"Satisfaction with Life Scale","fullName":"Satisfaction with Life Scale (SWLS)","aliases":["SWLS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Well-being and life satisfaction assessment","year":"1985","originator":"Ed Diener, Richard A. Emmons, Richard J. Larsen, and Sharon Griffin","url":"https://scholargate.app/en/clinical-psychology/swls","markdownUrl":"https://scholargate.app/en/clinical-psychology/swls.md","definition":"The Satisfaction with Life Scale (SWLS) is a brief, five-item self-report measure of global life satisfaction developed by Diener, Emmons, Larsen, and Griffin in 1985. It assesses the degree to which individuals are satisfied with their lives as a whole, reflecting a cognitive-judgmental component of subjective well-being. The scale has become a cornerstone instrument in well-being research, psychology, gerontology, and quality-of-life assessment across diverse populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ed Diener, Richard A. Emmons, Richard J. Larsen, and Sharon Griffin","subfamily":"Well-being and life satisfaction assessment","year":"1985","type":"Global life satisfaction self-report"},"citations":[{"ref":"Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The Satisfaction with Life Scale. Journal of Personality Assessment, 49(1), 71-75.","type":"article","doi":"10.1207/s15327752jpa4901_13","isbn":null,"url":null},{"ref":"Pavot, W., & Diener, E. (1993). Review of the Satisfaction with Life Scale. Psychological Assessment, 5(2), 164-172.","type":"article","doi":"10.1037/1040-3590.5.2.164","isbn":null,"url":null}],"related":["panas","ghq-12","gds-geriatric-depression","ces-d","hads"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"symbolic-data-analysis","name":"Symbolic Data Analysis","fullName":"Symbolic Data Analysis (SDA)","aliases":["SDA","Interval Data Analysis","Distributional Data Analysis","Sembolik Veri Analizi"],"domain":"soft-computing","family":"ml-model","subfamily":"Symbolic data","year":2003,"originator":"Edwin Diday; Lynne Billard","url":"https://scholargate.app/en/soft-computing/symbolic-data-analysis","markdownUrl":"https://scholargate.app/en/soft-computing/symbolic-data-analysis.md","definition":"Symbolic Data Analysis (SDA) is a statistical framework designed to analyze complex, aggregate, or set-valued data — called symbolic data — in which each observation represents a group or concept rather than a single scalar. Introduced in its modern statistical form by Lynne Billard and Edwin Diday in 2003, SDA extends classical statistics to handle interval-valued, histogram-valued, and multi-valued variables, enabling rigorous inference at the level of knowledge rather than raw individual records.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edwin Diday; Lynne Billard","year":2003,"type":"Statistical framework for aggregate and set-valued data","subfamily":"Symbolic data","data_types":"Interval, histogram, multi-valued, modal","software":"SODAS, RSDA (R package)"},"citations":[{"ref":"Billard, L., & Diday, E. (2003). From the statistics of data to the statistics of knowledge: symbolic data analysis. Journal of the American Statistical Association, 98(462), 470–487.","type":"article","doi":"10.1198/016214503000242","isbn":null,"url":null}],"related":["compositional-data-analysis","principal-component-analysis","interval-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"symbolic-execution","name":"Symbolic Execution","fullName":"Symbolic Execution","aliases":["symbolic execution","symbolic analysis","concolic execution"],"domain":"cryptography","family":"ml-model","subfamily":"Program analysis and verification","year":"1976","originator":"James C. King","url":"https://scholargate.app/en/cryptography/symbolic-execution","markdownUrl":"https://scholargate.app/en/cryptography/symbolic-execution.md","definition":"Symbolic execution is a program analysis technique that executes programs using symbolic (non-concrete) values instead of actual inputs, tracking how symbolic values flow through the program. Introduced by James C. King in 1976, symbolic execution builds mathematical constraints on program variables and can determine which inputs cause specific program behaviors, enabling automatic test generation and vulnerability detection. Modern symbolic execution tools like KLEE, S2E, and Z3 have become powerful instruments for finding subtle bugs and security vulnerabilities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James C. King","subfamily":"Program analysis and verification","year":"1976","type":"formal verification technique"},"citations":[{"ref":"King, J. C. (1976). Symbolic execution and program testing. Communications of the ACM, 19(7), 385-394.","type":"article","doi":"10.1145/360248.360252","isbn":null,"url":null},{"ref":"Cadar, C., & Sen, K. (2013). Symbolic execution for software testing: Three decades later. Communications of the ACM, 56(2), 82-90.","type":"article","doi":"10.1145/2408776.2408795","isbn":null,"url":null}],"related":["fuzzing","taint-analysis","static-application-security-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"symmetric-key-analysis","name":"Symmetric Key Cryptanalysis","fullName":"Cryptanalytic Analysis of Symmetric Encryption Algorithms","aliases":["Symmetric Cryptanalysis","Block Cipher Analysis","Stream Cipher Cryptanalysis"],"domain":"cryptography","family":"process-pipeline","subfamily":"Cryptanalysis and attack methods","year":"1991","originator":"Eli Biham, Adi Shamir, Mitsuru Matsui","url":"https://scholargate.app/en/cryptography/symmetric-key-analysis","markdownUrl":"https://scholargate.app/en/cryptography/symmetric-key-analysis.md","definition":"Symmetric key cryptanalysis is the study of attacks against symmetric encryption algorithms (such as DES, AES, and stream ciphers) to evaluate their security and identify weaknesses. Classical techniques include differential cryptanalysis and linear cryptanalysis, which have shaped the design of modern ciphers and remain essential tools for cryptographers assessing algorithm robustness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eli Biham, Adi Shamir, Mitsuru Matsui","subfamily":"Cryptanalysis and attack methods","year":"1991","type":"Cryptographic strength analysis"},"citations":[{"ref":"Biham, E., & Shamir, A. (1991). Differential cryptanalysis of DES. Journal of Cryptology, 4(1), 3–72.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Differential+cryptanalysis+of+DES+Biham"},{"ref":"Matsui, M. (1993). Linear cryptanalysis method for DES cipher. Advances in Cryptology – EUROCRYPT '93, 386–397.","type":"article","doi":"10.1007/3-540-48285-7_33","isbn":null,"url":null},{"ref":"Daemen, J., & Rijmen, V. (2002). The Design of Rijndael: AES—The Advanced Encryption Standard. Springer-Verlag.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Design+of+Rijndael%3A+AES%E2%80%94The+Advanced+Encryption+Standard+Daemen"}],"related":["sha-hash-function","rsa-cryptosystem-analysis","tls-protocol-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"symmetric-mape","name":"Symmetric MAPE","fullName":"Symmetric Mean Absolute Percentage Error","aliases":["sMAPE","SMAPE","symmetric MAPE"],"domain":"model-evaluation","family":"mcdm","subfamily":"Relative error metric","year":"1985","originator":"J. Scott Armstrong","url":"https://scholargate.app/en/model-evaluation/symmetric-mape","markdownUrl":"https://scholargate.app/en/model-evaluation/symmetric-mape.md","definition":"Symmetric Mean Absolute Percentage Error is a refinement of MAPE that addresses its asymmetry by using the average of actual and predicted values as the denominator. Proposed by J. Scott Armstrong and refined by Makridakis (1993) and Hyndman & Koehler (2006), sMAPE treats over- and under-predictions symmetrically.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"J. Scott Armstrong","subfamily":"Relative error metric","year":"1985","type":"Symmetric percentage-based evaluation metric"},"citations":[{"ref":"Armstrong, J. S. (1985). Long-range forecasting: from crystal ball to computer (2nd ed.). New York: John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471082010","url":null},{"ref":"Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.","type":"article","doi":"10.1016/j.ijforecast.2006.03.001","isbn":null,"url":null},{"ref":"Makridakis, S. (1993). Accuracy measures for a robust comparison of forecasting methods. International Journal of Forecasting, 9(4), 679-688.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Accuracy+measures+for+a+robust+comparison+of+forecasting+methods+Makridakis"}],"related":["mean-absolute-percentage-error","mean-absolute-scaled-error","mean-absolute-error","root-mean-squared-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"symmetrical-components","name":"Symmetrical Components","fullName":"Symmetrical Components Method for Unbalanced Circuit Analysis","aliases":["Symmetrical component analysis","Phase component decomposition"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Network analysis, transformation theory","year":"1918","originator":"Charles Legeyt Fortescue","url":"https://scholargate.app/en/electrical-engineering/symmetrical-components","markdownUrl":"https://scholargate.app/en/electrical-engineering/symmetrical-components.md","definition":"Symmetrical Components is a mathematical technique for analyzing unbalanced three-phase electrical circuits by decomposing them into balanced component sets. Introduced by Charles Fortescue in 1918, the method transforms the complex analysis of unbalanced systems into simpler balanced equivalent circuits. Symmetrical components are fundamental to understanding faults, protection coordination, and stability in power systems, remaining essential in modern grid operations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles Legeyt Fortescue","subfamily":"Network analysis, transformation theory","year":"1918","type":"Decomposition method for analyzing unbalanced three-phase circuits"},"citations":[{"ref":"Fortescue, C. L. (1918). Method of symmetrical co-ordinates applied to the solution of polyphase networks. AIEE Transactions, 37(2), 1027-1044.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/6499996"},{"ref":"Blackburn, J. L. (1993). Symmetrical Components for Power Systems Engineering. Marcel Dekker.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Symmetrical+Components+for+Power+Systems+Engineering+Blackburn"},{"ref":"Saadat, H. (2010). Power System Analysis (3rd ed.). PSA Publishing.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+System+Analysis+%283rd+ed.%29+Saadat"}],"related":["newton-raphson-power-flow","power-system-state-estimation","optimal-power-flow"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"synchrosqueezing-transform","name":"Synchrosqueezing Transform","fullName":"Synchrosqueezing Transform for Time-Frequency Analysis","aliases":["SST","Synchrosqueezing"],"domain":"time-series","family":"process-pipeline","subfamily":"Wavelet-based decomposition","year":"2011","originator":"Ingrid Daubechies","url":"https://scholargate.app/en/time-series/synchrosqueezing-transform","markdownUrl":"https://scholargate.app/en/time-series/synchrosqueezing-transform.md","definition":"The synchrosqueezing transform is a time-frequency reassignment technique that sharpens the output of the continuous wavelet transform by concentrating energy along instantaneous frequency ridges. Introduced by Ingrid Daubechies and colleagues in 2011, it addresses the fundamental limitation of the standard wavelet transform: poor frequency localization. This method is particularly valuable for analyzing signals with time-varying frequency content.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ingrid Daubechies","subfamily":"Wavelet-based decomposition","year":"2011","type":"Time-frequency decomposition"},"citations":[{"ref":"Daubechies, I., Lu, J., & Wu, H. T. (2011). Synchrosqueezed wavelet transforms: An empirical tool for time-frequency analysis. Applied and Computational Harmonic Analysis, 30(2), 243–261.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Synchrosqueezed+wavelet+transforms%3A+An+empirical+tool+for+time-frequency+analysis+Daubechies"},{"ref":"Thakur, G., Brevdo, E., Fučkar, N. S., & Wu, H. T. (2015). The synchrosqueezing algorithm for time-varying spectral analysis and its application to signals with discontinuities. Applied and Computational Harmonic Analysis, 39(1), 1–31.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+synchrosqueezing+algorithm+for+time-varying+spectral+analysis+and+its+application+to+signals+with+discontinuities+Thakur"}],"related":["discrete-wavelet-transform","wavelet-coherence","continuous-wavelet-transform","empirical-mode-decomposition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"synthesis-route-planning","name":"Synthesis Route Planning","fullName":"Retrosynthetic Analysis and Synthesis Route Planning","aliases":["retrosynthesis","retrosynthetic analysis","synthetic route design"],"domain":"chemistry","family":"process-pipeline","subfamily":"Synthesis","year":"1969","originator":"Elias James Corey","url":"https://scholargate.app/en/chemistry/synthesis-route-planning","markdownUrl":"https://scholargate.app/en/chemistry/synthesis-route-planning.md","definition":"Synthesis route planning, grounded in retrosynthetic analysis, is a strategic approach to designing efficient chemical syntheses. Formalized by Elias James Corey in the 1960s (earning him the Nobel Prize in 1990), this methodology systematically deconstructs target molecules into simpler precursors and starting materials, enabling chemists to discover logical, economical, and practical synthesis routes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elias James Corey","subfamily":"Synthesis","year":"1969","type":"Strategic planning methodology"},"citations":[{"ref":"Corey, E. J., & Cheng, X. M. (1991). The Logic of Chemical Synthesis. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471096092","url":null},{"ref":"Warren, S., & Wyatt, P. (2008). Organic Synthesis: Strategy and Control. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0470016701","url":null}],"related":["redox-reaction-mechanism","substitution-reaction-kinetics","nucleophilic-substitution-sn"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"synthetic-control-method-in-education-research","name":"Synthetic Control Method in Education Research","fullName":"Synthetic Control Method Applied to Education Policy and Research","aliases":["SCM in education","synthetic control","synthetic comparator","SCM"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2003-2010","originator":"Alberto Abadie, Alexis Diamond, and Jens Hainmueller","url":"https://scholargate.app/en/causal-inference/synthetic-control-method-in-education-research","markdownUrl":"https://scholargate.app/en/causal-inference/synthetic-control-method-in-education-research.md","definition":"The Synthetic Control Method (SCM) estimates the causal effect of an education policy or intervention by constructing a weighted combination of untreated comparison units — the synthetic control — that closely mimics the treated unit's pre-intervention trajectory. Developed by Abadie, Diamond, and Hainmueller, it is especially valuable when only one or a small number of schools, districts, or countries receive a policy change and no natural comparison exists.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alberto Abadie, Alexis Diamond, and Jens Hainmueller","year":"2003-2010","type":"Quasi-experimental causal inference","dataType":"Aggregate panel data (donor pool of control units over time)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null},{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative Politics and the Synthetic Control Method. American Journal of Political Science, 59(2), 495-510.","type":"article","doi":"10.1111/ajps.12116","isbn":null,"url":null}],"related":["difference-in-differences","propensity-score-matching","interrupted-time-series","regression-discontinuity-design","panel-fixed-effects","causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"synthetic-control-method","name":"Synthetic Control Method","fullName":"Synthetic Control Method for Comparative Case Studies","aliases":["SCM","synthetic control","synth estimator","Abadie-Diamond-Hainmueller method"],"domain":"causal-inference","family":"regression-model","subfamily":"Quasi-experimental / causal inference","year":"2003–2010","originator":"Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)","url":"https://scholargate.app/en/causal-inference/synthetic-control-method","markdownUrl":"https://scholargate.app/en/causal-inference/synthetic-control-method.md","definition":"The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)","year":"2003–2010","type":"Quasi-experimental causal inference","dataType":"Aggregate panel data (few treated units, many pre-treatment periods)","subfamily":"Quasi-experimental / causal inference"},"citations":[{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null},{"ref":"Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93(1), 113-132.","type":"article","doi":"10.1257/000282803321455188","isbn":null,"url":null}],"related":["difference-in-differences","regression-discontinuity-design","instrumental-variables","propensity-score-matching","event-study-design","causal-impact-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"synthetic-control","name":"Synthetic Control","fullName":"Synthetic Control Method","aliases":["synthetic control method","SCM","synthetic counterfactual","Sentetik Kontrol Yöntemi (SCM)"],"domain":"causal-inference","family":"regression-model","subfamily":null,"year":2010,"originator":"Abadie, Diamond & Hainmueller","url":"https://scholargate.app/en/causal-inference/synthetic-control","markdownUrl":"https://scholargate.app/en/causal-inference/synthetic-control.md","definition":"The Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abadie, Diamond & Hainmueller","year":2010,"type":"Counterfactual causal-inference model","estimator":"Convex-weighted donor combination minimising pre-treatment fit","outcome":"continuous","dataStructure":"panel / time series","minSample":20,"treatedUnits":"single (N=1)"},"citations":[{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null},{"ref":"Abadie, A. (2021). Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), 391-425.","type":"article","doi":"10.1257/jel.20191450","isbn":null,"url":null}],"related":["interrupted-time-series","regression-discontinuity","iv-2sls","matching-methods","panel-fixed-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"synthetic-data-generation","name":"Synthetic Data Generation","fullName":"Synthetic Data Generation for Disclosure Control","aliases":["Fully Synthetic Data","Partial Synthetic Data","Statistical Data Synthesis","Sentetik Veri Üretimi"],"domain":"privacy","family":"ml-model","subfamily":"Privacy-preserving analysis","year":1993,"originator":"Donald Rubin","url":"https://scholargate.app/en/privacy/synthetic-data-generation","markdownUrl":"https://scholargate.app/en/privacy/synthetic-data-generation.md","definition":"Synthetic data generation is a statistical disclosure limitation technique introduced by Donald Rubin in 1993, in which values in a confidential dataset are replaced by draws from a fitted posterior predictive distribution rather than released directly. The resulting artificial records preserve the joint statistical structure of the original data while preventing the identification of real individuals, enabling analysts to work with a publicly releasable dataset that behaves like the original for most inferential purposes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Rubin","year":1993,"type":"Privacy-preserving data synthesis","subfamily":"Privacy-preserving analysis","inferenceFramework":"Multiple imputation","outputType":"Artificial dataset replacing or augmenting real records"},"citations":[{"ref":"Rubin, D. B. (1993). Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468.","type":"article","doi":null,"isbn":null,"url":"https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/discussion-statistical-disclosure-limitation2.pdf"}],"related":["differential-privacy","generative-adversarial-network","multiple-imputation"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"synthetic-difference-in-differences","name":"Synthetic Difference-in-Differences","fullName":"Synthetic Control Difference-in-Differences Estimator","aliases":["Synthetic DID","SDID"],"domain":"econometrics","family":"regression-model","subfamily":"Causal inference","year":"2021","originator":"Arkhangelsky, Athey, Hirshberg, Imbens, and Wager","url":"https://scholargate.app/en/econometrics/synthetic-difference-in-differences","markdownUrl":"https://scholargate.app/en/econometrics/synthetic-difference-in-differences.md","definition":"Synthetic Difference-in-Differences (SDID) combines synthetic control and difference-in-differences approaches to estimate treatment effects when a policy or intervention affects one unit (country, firm) at a point in time. Introduced by Arkhangelsky et al. (2021), it improves upon both methods alone by using weighted combinations of controls to match treated units' pre-treatment trends and levels. This yields more precise and robust estimates than classical DiD or synthetic control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arkhangelsky, Athey, Hirshberg, Imbens, and Wager","subfamily":"Causal inference","year":"2021","type":"Treatment-effect estimation"},"citations":[{"ref":"Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088-4118.","type":"article","doi":"10.1257/aer.20190159","isbn":null,"url":null},{"ref":"Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. Journal of the American Statistical Association, 105(490), 493-505.","type":"article","doi":"10.1198/jasa.2009.ap08746","isbn":null,"url":null}],"related":["geographic-regression-discontinuity","interactive-fixed-effects","local-projections"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"system-dynamics","name":"System Dynamics","fullName":"System Dynamics (Stock-Flow Modelling)","aliases":["stock-flow modelling","Sistem Dinamiği (Stock-Flow Modelleme)","SD modelling","feedback simulation"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":1961,"originator":"Jay W. Forrester","url":"https://scholargate.app/en/simulation/system-dynamics","markdownUrl":"https://scholargate.app/en/simulation/system-dynamics.md","definition":"System dynamics is a continuous simulation method, developed by Jay W. Forrester at MIT in 1961, that represents a complex system through stocks (accumulations), flows (rates of change), and feedback loops. By expressing these relationships as coupled ordinary differential equations, it reproduces how policies, delays, and nonlinear feedbacks drive system behaviour over time — making it a cornerstone tool in policy analysis, organisational modelling, and sustainability research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jay W. Forrester","year":1961,"type":"Continuous simulation / feedback modelling","core_constructs":"Stocks, flows, feedback loops (reinforcing R, balancing B)","paradigm":"Differential-equation-based simulation","output":"Time-series trajectories of system state variables","difficulty":"intermediate"},"citations":[{"ref":"Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill.","type":"book","doi":null,"isbn":"978-0072389159","url":null},{"ref":"Forrester, J.W. (1961). Industrial Dynamics. MIT Press.","type":"book","doi":null,"isbn":"978-0262060035","url":null}],"related":["discrete-event-simulation","monte-carlo-simulation","latin-hypercube-sampling","agent-based-modelling","scenario-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"system-gmm","name":"System GMM","fullName":"System Generalized Method of Moments Estimator (Arellano-Bover / Blundell-Bond)","aliases":["Arellano-Bover estimator","Blundell-Bond estimator","dynamic panel GMM","Sistem GMM (Arellano-Bover / Blundell-Bond)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1998,"originator":"Arellano & Bover (1995); Blundell & Bond (1998)","url":"https://scholargate.app/en/econometrics/system-gmm","markdownUrl":"https://scholargate.app/en/econometrics/system-gmm.md","definition":"System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arellano & Bover (1995); Blundell & Bond (1998)","year":1998,"type":"Dynamic panel data estimator","estimator":"System GMM (two-step, Windmeijer-corrected standard errors)","panelStructure":"N large, T small (N >> T)","minSample":50},"citations":[{"ref":"Arellano, M. & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Blundell, R. & Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics, 87(1), 115-143.","type":"article","doi":"10.1016/S0304-4076(98)00009-8","isbn":null,"url":null},{"ref":"Roodman, D. (2009). How to Do xtabond2: An Introduction to Difference and System GMM in Stata. Stata Journal, 9(1), 86-136.","type":"article","doi":"10.1177/1536867X0900900106","isbn":null,"url":null}],"related":["panel-fixed-effects","panel-var","ols-regression","panel-random-effects"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"system-usability-scale","name":"System Usability Scale","fullName":"System Usability Scale (SUS)","aliases":["SUS","System Usability Score"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Usability Assessment","year":"1986","originator":"John Brooke","url":"https://scholargate.app/en/human-computer-interaction/system-usability-scale","markdownUrl":"https://scholargate.app/en/human-computer-interaction/system-usability-scale.md","definition":"The System Usability Scale (SUS) is a rapid, standardized 10-item questionnaire for measuring perceived system usability in a single summary score. Developed by John Brooke in 1986, SUS has become one of the most widely used post-use usability instruments in industry and research. The scale is administered after a user has interacted with a system, capturing perceived ease of use, learnability, error recovery, and overall satisfaction with a quick, economical assessment that correlates well with comprehensive usability testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Brooke","subfamily":"Usability Assessment","year":"1986","type":"Rapid, post-use questionnaire scale for measuring perceived usability"},"citations":[{"ref":"Brooke, J. (1986). System Usability Scale (SUS): A quick and dirty usability scale. In B. Shackel & S. J. Richardson (Eds.), Usability Evaluation in Industry (pp. 189–194). Taylor & Francis.","type":"article","doi":null,"isbn":"0-85066-375-X","url":null},{"ref":"Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the System Usability Scale. International Journal of Human-Computer Interaction, 24(6), 574–594.","type":"article","doi":"10.1080/10447310802205776","isbn":null,"url":null}],"related":["nasa-tlx","kano-model","attrakdiff-ueq","think-aloud-protocol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"systematic-literature-review","name":"Systematic Literature Review","fullName":"Systematic Literature Review","aliases":["SLR","systematic review","evidence synthesis review","structured literature review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines)","originator":"Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004)","url":"https://scholargate.app/en/scientometrics/systematic-literature-review","markdownUrl":"https://scholargate.app/en/scientometrics/systematic-literature-review.md","definition":"A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004)","year":"1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines)","type":"Evidence synthesis methodology","dataType":"Published studies, journal articles, conference papers, grey literature","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Procedures+for+Performing+Systematic+Reviews+Kitchenham+2004"},{"ref":"Higgins, J. P. T., & Green, S. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119536195","url":null}],"related":["meta-analysis","scoping-review","bibliometric-analysis","narrative-review","umbrella-review","prisma-based-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"systematic-mapping-review","name":"Systematic Mapping Review","fullName":"Systematic Mapping Review (Scoping Review)","aliases":["scoping review","systematic mapping","literature mapping","evidence mapping"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"literature-review","year":"2005","originator":"Arksey & O'Malley (2005); Joanna Briggs Institute methodology","url":"https://scholargate.app/en/bibliometrics/systematic-mapping-review","markdownUrl":"https://scholargate.app/en/bibliometrics/systematic-mapping-review.md","definition":"A systematic mapping review (also called a 'scoping review') is a literature review methodology that aims to comprehensively identify and categorize the published evidence on a topic without necessarily assessing the quality of individual studies or synthesizing findings quantitatively. Developed by Arksey and O'Malley (2005) and formalized by the Joanna Briggs Institute, systematic mapping reviews chart the landscape of evidence: what has been studied, what are the research gaps, and how is evidence distributed across study types, populations, and outcomes. Unlike systematic reviews that answer specific research questions with rigorous study selection and synthesis, mapping reviews provide a broad overview of the research terrain, making them ideal for defining scope, identifying knowledge gaps, and guiding future research priorities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arksey & O'Malley (2005); Joanna Briggs Institute methodology","subfamily":"literature-review","year":"2005","type":"Method"},"citations":[{"ref":"Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.","type":"article","doi":"10.1080/1364557032000119616","isbn":null,"url":null},{"ref":"Tricco, A. C., Lillie, E., Zarin, W., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473.","type":"article","doi":"10.7326/M18-0850","isbn":null,"url":null},{"ref":"Petticrew, M., & Roberts, H. (2009). Systematic reviews in the social sciences: A practical guide. Blackwell.","type":"article","doi":null,"isbn":"978-0631215936","url":null}],"related":["science-mapping","research-front-identification","co-citation-analysis","bibliographic-coupling"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"systematic-review-article","name":"Systematic Review","fullName":"Systematic Review (Evidence Synthesis with Systematic Literature Search)","aliases":["systematic literature review","evidence synthesis","scoping review","mapping review"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Evidence synthesis","year":"1992","originator":"Cochrane Collaboration (1992)","url":"https://scholargate.app/en/academic-writing/systematic-review-article","markdownUrl":"https://scholargate.app/en/academic-writing/systematic-review-article.md","definition":"A systematic review is a structured, transparent synthesis of all available evidence addressing a specific research question. Unlike narrative reviews, systematic reviews employ comprehensive database searches, predefined selection criteria, quality assessment, and rigorous reporting (PRISMA guideline). The Cochrane Collaboration (founded 1992) established this methodology as the gold standard for evidence synthesis in healthcare and social sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cochrane Collaboration (1992)","subfamily":"Evidence synthesis","year":"1992","type":"Document Type"},"citations":[{"ref":"Page, M. J., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71.","type":"article","doi":"10.1136/bmj.n71","isbn":null,"url":null},{"ref":"Higgins, J. P. T., & Thomas, J. (Eds.). (2023). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.4). Cochrane.","type":"webpage","doi":null,"isbn":null,"url":"https://training.cochrane.org/handbook"},{"ref":"Shamseer, L., et al. (2015). Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015: elaboration and explanation. BMJ, 349, g7647.","type":"article","doi":"10.1136/bmj.g7647","isbn":null,"url":null}],"related":["meta-analysis-article","literature-review-article","original-research-article","PICO-framework","literature-search-strategy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"systematic-sampling","name":"Systematic Sampling","fullName":"Systematic Random Sampling","aliases":["interval sampling","systematic random sampling","equal-interval sampling","fixed-interval sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century (Cochran 1953; Kish 1965)","originator":"William G. Cochran; formalized in survey sampling theory","url":"https://scholargate.app/en/survey-methodology/systematic-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/systematic-sampling.md","definition":"Systematic sampling is a probability sampling technique in which every k-th element is selected from an ordered list of the population after a random starting point. With population size N and desired sample size n, the sampling interval k = N/n is computed and one unit is chosen at random from the first interval; all subsequent units are selected by adding k repeatedly. The method is operationally simple, yields a spread-out sample, and often achieves lower variance than simple random sampling when the list has no harmful periodicity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran; formalized in survey sampling theory","year":"Mid-20th century (Cochran 1953; Kish 1965)","type":"Probability sampling design","dataType":"Any data type collected from a list-based population (quantitative or qualitative)","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471489009","url":null}],"related":["simple-random-sampling","stratified-sampling","cluster-sampling","multistage-sampling","proportional-systematic-sampling","probability-proportional-to-size-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"systematic-search-strategy","name":"Systematic Search Strategy","fullName":"Comprehensive and Reproducible Literature Search Protocol","aliases":["search protocol","systematic search","comprehensive search strategy"],"domain":"research-skills","family":"process-pipeline","subfamily":"systematic-literature-search","year":"1990s (formalized in Cochrane methodology)","originator":"Cochrane Collaboration and systematic review methodologists","url":"https://scholargate.app/en/research-skills/systematic-search-strategy","markdownUrl":"https://scholargate.app/en/research-skills/systematic-search-strategy.md","definition":"A systematic search strategy is a comprehensive, transparent protocol for retrieving all relevant literature addressing a well-defined research question. Developed by the Cochrane Collaboration and formalized in guidelines like PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), systematic search strategies are essential for conducting unbiased literature reviews, systematic reviews, and meta-analyses. Unlike ad hoc searches (searching Google Scholar or PubMed without a protocol), systematic searches document every step—which databases were searched, what search terms were used, how many results were retrieved, and what inclusion/exclusion criteria were applied—enabling other researchers to reproduce the search and verify that no relevant studies were missed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cochrane Collaboration and systematic review methodologists","subfamily":"systematic-literature-search","year":"1990s (formalized in Cochrane methodology)","type":"Framework"},"citations":[{"ref":"Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097.","type":"article","doi":"10.1371/journal.pmed.1000097","isbn":null,"url":null},{"ref":"Higgins, J. P. T., & Thomas, J. (Eds.). (2019). Cochrane handbook for systematic reviews of interventions (Version 6.0). The Cochrane Collaboration.","type":"book","doi":null,"isbn":null,"url":"https://training.cochrane.org/handbook"},{"ref":"Bramer, W. M., Rethlefsen, M. L., Murad, M. H., & Landhuis, E. (2016). When updating systematic reviews, how often should new searches be applied to increase the chance of finding relevant studies? Systematic Reviews, 5(1), 94.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=When+updating+systematic+reviews%2C+how+often+should+new+searches+be+applied+to+increase+the+chance+of+finding+relevant+studies+Bramer"}],"related":["pico-framework","boolean-search-operators","grey-literature-search","citation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"systemic-functional-linguistics","name":"Systemic Functional Linguistics","fullName":"Systemic Functional Linguistics (SFL) Framework","aliases":["SFL","Hallidayan Linguistics"],"domain":"linguistics","family":"process-pipeline","subfamily":"Functional Linguistics","year":"1961","originator":"Michael Halliday","url":"https://scholargate.app/en/linguistics/systemic-functional-linguistics","markdownUrl":"https://scholargate.app/en/linguistics/systemic-functional-linguistics.md","definition":"Systemic Functional Linguistics (SFL) is a framework for analyzing language developed by Michael Halliday, viewing language as a system of meaning-making choices where speakers select from available options to express meanings. The approach emphasizes the relationship between language form and social context, analyzing how register (field, tenor, mode) shapes linguistic choices and how language constructs meaning through metafunctional systems (ideational, interpersonal, textual). SFL is widely applied to discourse analysis, language education, and computational linguistics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Halliday","subfamily":"Functional Linguistics","year":"1961","type":"Empirical process pipeline"},"citations":[{"ref":"Halliday, M. A. K. (1994). An Introduction to Functional Grammar (2nd ed.). London: Edward Arnold.","type":"book","doi":null,"isbn":null,"url":"https://www.routledge.com/"},{"ref":"Halliday, M. A. K., & Matthiessen, C. M. I. M. (2004). An Introduction to Functional Grammar (3rd ed.). London: Routledge.","type":"book","doi":"10.4324/9780203783771","isbn":null,"url":null},{"ref":"Eggins, S. (2004). An Introduction to Systemic Functional Linguistics (2nd ed.). London: Continuum.","type":"book","doi":null,"isbn":null,"url":"https://www.bloomsbury.com/"}],"related":["discourse-analysis","functional-grammar","register-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"systems-belief-inventory","name":"SBI","fullName":"Systems of Belief Inventory","aliases":["SBI","SBI-15"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"belief systems and meaning","year":2011,"originator":"James M. Holland, Jill M. Currier, & Robert A. Neimeyer","url":"https://scholargate.app/en/psychology-of-religion/systems-belief-inventory","markdownUrl":"https://scholargate.app/en/psychology-of-religion/systems-belief-inventory.md","definition":"The Systems of Belief Inventory (SBI), developed by Holland, Currier, and Neimeyer in 2011, is a 15-item self-report measure designed to assess the coherence, flexibility, and adaptive function of an individual's worldview and meaning-making system. Originally validated in bereavement research, the SBI captures dimensions of spiritual and existential belief that predict psychological adjustment following loss or trauma. It measures three key aspects: existential meaning-making, negative religious coping, and hope. The scale is useful in grief counseling, trauma recovery, and any clinical context where worldview disruption occurs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James M. Holland, Jill M. Currier, & Robert A. Neimeyer","subfamily":"belief systems and meaning","year":2011,"type":"Self-report"},"citations":[{"ref":"Holland, J. M., Currier, J. M., & Neimeyer, R. A. (2011). The Systems of Belief Inventory: Factor structure and association with psychosocial outcome in bereavement. Psychological Assessment, 23(2), 311–321.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Systems+of+Belief+Inventory%3A+Factor+structure+and+association+with+psychosocial+outcome+in+bereavement+Holland"}],"related":["daily-spiritual-experience-scale","functional-assessment-chronic-illness-spiritual","brief-religious-coping-scale","existential-wellbeing-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"t-sne","name":"t-SNE","fullName":"t-Distributed Stochastic Neighbor Embedding","aliases":["t-SNE (Boyut İndirgeme / Görselleştirme)","t-distributed stochastic neighbor embedding","tsne"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2008,"originator":"van der Maaten, L. & Hinton, G.","url":"https://scholargate.app/en/machine-learning/t-sne","markdownUrl":"https://scholargate.app/en/machine-learning/t-sne.md","definition":"t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"van der Maaten, L. & Hinton, G.","year":2008,"type":"Nonlinear dimensionality reduction (manifold visualization)","task":"Visualization & exploratory cluster inspection","minSample":50,"keyParameter":"perplexity (5–50)"},"citations":[{"ref":"van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v9/vandermaaten08a.html"}],"related":["pca","umap","kmeans-clustering","gaussian-mixture","shap-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"t5","name":"T5 (Text-to-Text Transfer Transformer)","fullName":"T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","aliases":["T5","Text-to-Text Transfer Transformer","T5-Small","T5-Base","T5-Large","T5-3B","T5-11B","seq2seq fine-tuning baseline"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2020,"originator":"Raffel, C.; Shazeer, N.; Roberts, A.; et al. (Google Brain)","url":"https://scholargate.app/en/deep-learning/t5","markdownUrl":"https://scholargate.app/en/deep-learning/t5.md","definition":"T5 is a unified sequence-to-sequence deep learning framework introduced by Raffel et al. at Google Brain in 2020, published in the Journal of Machine Learning Research (Vol. 21, No. 140). It reframes every NLP task — classification, translation, summarisation, question answering, and more — as a text-to-text problem: both input and output are always character strings, enabling a single encoder-decoder Transformer to be pre-trained once and fine-tuned across tasks with a consistent interface. T5 introduced span-corruption pre-training and the C4 corpus, and its largest variant (11B parameters) achieved state-of-the-art results across a wide range of NLP benchmarks at the time of publication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raffel, C.; Shazeer, N.; Roberts, A.; et al. (Google Brain)","year":2020,"type":"Pre-trained encoder-decoder Transformer (sequence-to-sequence)","task":"Any NLP task cast as text-to-text generation","pretrainingCorpus":"C4 (Colossal Clean Crawled Corpus)","maskingStrategy":"Span corruption (masked-span prediction)","largestVariant":"T5-11B (11 billion parameters)","license":"Apache 2.0"},"citations":[{"ref":"Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, 21(140), 1–67.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v21/20-074.html"},{"ref":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems, 30.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1706.03762"},{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1810.04805"}],"related":["bert","gpt","bart","flan-t5","transformer","attention-mechanism","transfer-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tablet-questionnaire","name":"Tablet Questionnaire for Medication Adherence","fullName":"Tablet Questionnaire for Medication Adherence","aliases":["Tablet Questionnaire","TAB-Q"],"domain":"pharmacology","family":"process-pipeline","subfamily":"medication-adherence","year":"2012","originator":"Adeniji and Brown","url":"https://scholargate.app/en/pharmacology/tablet-questionnaire","markdownUrl":"https://scholargate.app/en/pharmacology/tablet-questionnaire.md","definition":"The Tablet Questionnaire is a brief, simple self-report tool designed to assess medication non-adherence through direct questions about dose-skipping behavior and reasons for non-adherence. Developed by Adeniji and Brown in 2012, it prioritizes simplicity and cultural accessibility, making it particularly valuable in low-resource settings and populations with limited health literacy. Despite its brevity, the measure demonstrates good sensitivity for detecting non-adherence and has been validated across African and international populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adeniji and Brown","subfamily":"medication-adherence","year":"2012","type":"Self-report"},"citations":[{"ref":"Adeniji, B., & Brown, C. (2012). Tablet Questionnaire: A simple tool to assess medication non-adherence. Annals of African Medicine, 11(4), 202-205.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tablet+Questionnaire%3A+A+simple+tool+to+assess+medication+non-adherence+Adeniji"}],"related":["medication-adherence-rating-scale","beliefs-medicines-questionnaire","hill-bone-compliance-scale","medication-understanding-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tabu-search","name":"Tabu Search","fullName":"Tabu Search (Tabu Search Metaheuristic)","aliases":["Tabu Araması (Tabu Search)","TS","tabu metaheuristic"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":1989,"originator":"Fred Glover","url":"https://scholargate.app/en/optimization/tabu-search","markdownUrl":"https://scholargate.app/en/optimization/tabu-search.md","definition":"Tabu Search is a local-search metaheuristic introduced by Fred Glover in 1989 that uses a tabu list — a short-term memory of recently visited solutions — to prevent cycling and escape local optima. By explicitly forbidding moves that reverse recent decisions, the algorithm explores the search space more broadly and, through long-term memory structures such as aspiration criteria, aims to approach the global optimum even in large, complex combinatorial problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred Glover","year":1989,"type":"Local-search metaheuristic","memoryStructure":"Short-term tabu list + optional long-term memory (aspiration criteria)","problemClass":"Combinatorial and discrete optimization","requiresNormality":false,"minSampleSize":"No minimum (solution-space based)"},"citations":[{"ref":"Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.","type":"article","doi":null,"isbn":null,"url":"https://pubsonline.informs.org/doi/10.1287/ijoc.1.3.190"},{"ref":"Glover, F. & Laguna, M. (1997). Tabu Search. Springer.","type":"book","doi":null,"isbn":"9780792349907","url":null}],"related":["simulated-annealing","genetic-algorithm","ant-colony-optimization","particle-swarm-optimization","hill-climbing"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"taguchi-methods","name":"Taguchi Method","fullName":"Taguchi Method (Orthogonal Arrays, Signal-to-Noise Ratio)","aliases":["Taguchi robust design","orthogonal array design","S/N ratio method","Taguchi Yöntemi (Ortogonal Dizi, S/N Oranı)"],"domain":"experimental-design","family":"hypothesis-test","subfamily":null,"year":1987,"originator":"Genichi Taguchi","url":"https://scholargate.app/en/experimental-design/taguchi-methods","markdownUrl":"https://scholargate.app/en/experimental-design/taguchi-methods.md","definition":"The Taguchi Method is a robust design methodology developed by Genichi Taguchi, first systematized in his 1987 work, that uses orthogonal arrays to study many control factors in a minimum number of experimental runs while quantifying product or process quality through Signal-to-Noise (S/N) ratios. Its central goal is to design products and processes that are insensitive — or robust — to uncontrollable noise factors such as environmental variation, material inconsistency, or user behavior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Genichi Taguchi","year":1987,"family":"Robust design / Design of Experiments","type":"Parametric robust design methodology","minimumRuns":9,"parametric":false,"arrayType":"Orthogonal array (e.g., L9, L18)","qualityMetric":"Signal-to-Noise (S/N) ratio","difficulty":2},"citations":[{"ref":"Taguchi, G. (1987). System of Experimental Design. UNIPUB/Kraus.","type":"book","doi":null,"isbn":"978-0527916312","url":null},{"ref":"Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall.","type":"book","doi":null,"isbn":"978-0137451678","url":null}],"related":["full-factorial-design","fractional-factorial-design","response-surface-methodology","one-way-anova","latin-square-design"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tail-risk-measures","name":"Tail Risk Measures","fullName":"Tail Risk Measures (Expected Shortfall, Spectral and Expectile Risk)","aliases":["expected shortfall","conditional value at risk","CVaR","spectral risk measure","expectile risk measure","coherent risk measure","Kuyruk Riski Ölçüleri (ES, Spectral, Expectile)"],"domain":"finance","family":"regression-model","subfamily":null,"year":1999,"originator":"Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall)","url":"https://scholargate.app/en/finance/tail-risk-measures","markdownUrl":"https://scholargate.app/en/finance/tail-risk-measures.md","definition":"Tail risk measures quantify the loss distribution beyond Value-at-Risk (VaR). Expected Shortfall — the expected loss given that VaR is exceeded — is the leading coherent risk measure, formalised by Artzner, Delbaen, Eber and Heath (1999) and shown to be coherent by Acerbi and Tasche (2002). Spectral and expectile-based measures generalise it.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall)","year":1999,"type":"Coherent tail risk measure","estimator":"Expected loss beyond a VaR threshold (historical or parametric)","outcome":"continuous (loss / return distribution)"},"citations":[{"ref":"Artzner, P., Delbaen, F., Eber, J.-M. & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203–228.","type":"article","doi":"10.1111/1467-9965.00068","isbn":null,"url":null},{"ref":"Acerbi, C. & Tasche, D. (2002). On the Coherence of Expected Shortfall. Journal of Banking & Finance, 26(7), 1487–1503.","type":"article","doi":"10.1016/S0378-4266(02)00283-2","isbn":null,"url":null}],"related":["quantile-regression","garch-model","extreme-value-theory","regime-switching-finance","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"taint-analysis","name":"Taint Analysis","fullName":"Taint Analysis (Data Flow Analysis)","aliases":["taint analysis","information flow","data tainting"],"domain":"cryptography","family":"ml-model","subfamily":"Program analysis for security","year":"2005","originator":"James Newsome","url":"https://scholargate.app/en/cryptography/taint-analysis","markdownUrl":"https://scholargate.app/en/cryptography/taint-analysis.md","definition":"Taint analysis is a data flow analysis technique that tracks how untrusted (tainted) input flows through a program to identify vulnerabilities where tainted data reaches dangerous operations (sinks). Formalized by Newsome and Song in 2005, taint analysis marks input data as tainted and propagates taint labels through the program, alerting when tainted data reaches sensitive operations like SQL queries or system calls. Taint analysis is fundamental to detecting injection vulnerabilities and is widely used in dynamic analysis tools and security monitoring systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Newsome","subfamily":"Program analysis for security","year":"2005","type":"data flow tracking technique"},"citations":[{"ref":"Newsome, J., & Song, D. X. (2005). Dynamic taint analysis for automatic detection, analysis, and signature generation of exploits on commodity software. In Network and Distributed System Security Symposium (NDSS 2005).","type":"article","doi":null,"isbn":null,"url":"https://www.ndss-symposium.org/ndss2005/"},{"ref":"Schwartz, E. J., Avgerinos, T., & Brumley, D. (2010). All you ever wanted to know about dynamic taint analysis and forward symbolic execution (but might have been afraid to ask). In IEEE Symposium on Security and Privacy (SP), 2010, pp. 317-331.","type":"article","doi":"10.1109/SP.2010.26","isbn":null,"url":null}],"related":["symbolic-execution","fuzzing","static-application-security-testing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tam-questionnaire","name":"Technology Acceptance Model Questionnaire","fullName":"Technology Acceptance Model (TAM) Questionnaire","aliases":["TAM","Davis TAM"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"1989","originator":"Fred Davis","url":"https://scholargate.app/en/information-systems/tam-questionnaire","markdownUrl":"https://scholargate.app/en/information-systems/tam-questionnaire.md","definition":"The Technology Acceptance Model (TAM) is a foundational framework introduced by Fred Davis in 1989 to explain user adoption of information technology. Published in MIS Quarterly, TAM posits that perceived usefulness and perceived ease of use are the primary determinants of technology acceptance, regardless of an individual's prior computer experience or technical background.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fred Davis","subfamily":"Technology adoption","year":"1989","type":"Likert-scale questionnaire"},"citations":[{"ref":"Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.","type":"article","doi":"10.2307/249008","isbn":null,"url":null},{"ref":"Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487.","type":"article","doi":"10.1006/imms.1993.1022","isbn":null,"url":null}],"related":["utaut-questionnaire","tam2-questionnaire","is-success-model","technology-readiness-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tam2-questionnaire","name":"TAM2 Questionnaire","fullName":"Technology Acceptance Model 2 (TAM2) Questionnaire","aliases":["TAM2","Extended TAM"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"2000","originator":"Davis & Venkatesh","url":"https://scholargate.app/en/information-systems/tam2-questionnaire","markdownUrl":"https://scholargate.app/en/information-systems/tam2-questionnaire.md","definition":"The Technology Acceptance Model 2 (TAM2) was developed by Davis and Venkatesh in 2000 and published in Management Science. TAM2 extends the original TAM by incorporating social influence factors and job relevance moderators that explain how users form perceived usefulness beliefs. TAM2 is grounded in longitudinal field studies across multiple organizational contexts and systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Davis & Venkatesh","subfamily":"Technology adoption","year":"2000","type":"Likert-scale questionnaire"},"citations":[{"ref":"Davis, F. D., & Venkatesh, V. (2000). A critical assessment of potential measurement biases in the Technology Acceptance Model: Two experiments. International Journal of Human-Computer Studies, 45(1), 23-45.","type":"article","doi":"10.1006/ijhc.1996.0040","isbn":null,"url":null},{"ref":"Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.","type":"article","doi":"10.1287/mnsc.46.2.186.11926","isbn":null,"url":null}],"related":["tam-questionnaire","utaut-questionnaire","is-success-model","technology-readiness-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tar-setar","name":"TAR / SETAR","fullName":"Threshold / Self-Exciting Threshold Autoregression (TAR/SETAR)","aliases":["Threshold Autoregression","Self-Exciting Threshold Autoregression","SETAR Model","Eşik Otoregresyon"],"domain":"econometrics","family":"regression-model","subfamily":"Regime models","year":1990,"originator":"Howell Tong","url":"https://scholargate.app/en/econometrics/tar-setar","markdownUrl":"https://scholargate.app/en/econometrics/tar-setar.md","definition":"TAR and SETAR are nonlinear autoregressive models introduced by Howell Tong (1990) that allow a time series to follow different linear dynamics in distinct regimes, separated by one or more threshold values. SETAR is the self-exciting variant, in which the threshold variable is a lagged value of the series itself, making it particularly suited to cycles, asymmetric adjustment, and limit-cycle behavior observed in economic and financial data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Howell Tong","year":1990,"type":"Nonlinear time-series model with regime switching","subfamily":"Regime models","thresholdVariable":"Lagged value of the series itself (SETAR) or an exogenous variable (TAR)","minimumRegimes":2},"citations":[{"ref":"Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press.","type":"book","doi":null,"isbn":"978-0-19-852300-6","url":null}],"related":["star-model","markov-switching-model","threshold-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tardive-dyskinesia-rating-scale","name":"AIMS","fullName":"Abnormal Involuntary Movement Scale","aliases":["AIMS"],"domain":"neurology","family":"process-pipeline","subfamily":"Tardive dyskinesia and antipsychotic medication side effects","year":"1976","originator":"National Institute of Mental Health","url":"https://scholargate.app/en/neurology/tardive-dyskinesia-rating-scale","markdownUrl":"https://scholargate.app/en/neurology/tardive-dyskinesia-rating-scale.md","definition":"The Abnormal Involuntary Movement Scale (AIMS) is the standard clinical rating scale for assessing tardive dyskinesia, a iatrogenic movement disorder resulting from chronic antipsychotic medication exposure. Developed by the National Institute of Mental Health in 1976, the 12-item scale systematically measures involuntary movements across facial, oral, limb, and trunk regions. The AIMS is mandatory screening tool for patients on long-term antipsychotic therapy and essential for monitoring antipsychotic-associated movement complications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"National Institute of Mental Health","subfamily":"Tardive dyskinesia and antipsychotic medication side effects","year":"1976","type":"Clinician-rated observation"},"citations":[{"ref":"National Institute of Mental Health (1976). Abnormal Involuntary Movement Scale (AIMS). In: Rockland, L. H., Schooler, N. R., & Levine, J. (Eds.), Drug Treatment of Mental Disorders. New York: Raven Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Abnormal+Involuntary+Movement+Scale+%28AIMS%29+National"}],"related":["updrs","hunt-hess-scale","nihss"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"target-mediated-drug-disposition","name":"Target-Mediated Drug Disposition","fullName":"Target-Mediated Drug Disposition (TMDD)","aliases":["TMDD","target-driven clearance"],"domain":"pharmacology","family":"process-pipeline","subfamily":"Mechanistic Pharmacokinetics","year":"2001","originator":"Donald Mager and William Jusko","url":"https://scholargate.app/en/pharmacology/target-mediated-drug-disposition","markdownUrl":"https://scholargate.app/en/pharmacology/target-mediated-drug-disposition.md","definition":"Target-mediated drug disposition (TMDD) is a mechanistic framework describing nonlinear pharmacokinetics arising from drug binding to a target receptor or protein. Developed by Mager and Jusko in 2001, TMDD explains saturable clearance, dose-dependent half-lives, and time-dependent changes in plasma concentrations observed with protein therapeutics and some small-molecule drugs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Mager and William Jusko","subfamily":"Mechanistic Pharmacokinetics","year":"2001","type":"nonlinear PK modeling"},"citations":[{"ref":"Mager, D. E., & Jusko, W. J. (2001). General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Journal of Pharmacokinetics and Pharmacodynamics, 28(6), 507-532.","type":"article","doi":"10.1023/A:1014414520282","isbn":null,"url":null},{"ref":"Levy, G. (2004). Carrier-mediated active transport: pharmacokinetic consequences and examples in humans. Clinical Pharmacokinetics, 37(6), 429-445.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Carrier-mediated+active+transport%3A+pharmacokinetic+consequences+and+examples+in+humans+Levy"}],"related":["physiologically-based-pharmacokinetics","michaelis-menten-kinetics","population-pharmacodynamics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"targeted-maximum-likelihood","name":"Targeted Maximum Likelihood Estimation","fullName":"Targeted Maximum Likelihood Estimation (TMLE)","aliases":["Targeted Learning","TMLE","Targeted MLE","Hedeflenmiş Maksimum Olabilirlik Tahmini"],"domain":"causal-inference","family":"ml-model","subfamily":"Causal ML","year":2006,"originator":"Mark van der Laan & Daniel Rubin","url":"https://scholargate.app/en/causal-inference/targeted-maximum-likelihood","markdownUrl":"https://scholargate.app/en/causal-inference/targeted-maximum-likelihood.md","definition":"Targeted Maximum Likelihood Estimation (TMLE) is a semiparametric, doubly robust causal inference method introduced by Mark van der Laan and Daniel Rubin in 2006. It combines flexible machine learning models for both the outcome and the treatment assignment mechanism, then applies a targeting step that re-fits the initial outcome model specifically to reduce bias for a pre-specified causal estimand such as the average treatment effect. TMLE is widely used in epidemiology, biostatistics, and health economics when estimating causal effects from observational data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark van der Laan & Daniel Rubin","year":2006,"type":"Semiparametric estimator","subfamily":"Causal ML","double_robustness":"Consistent if either the outcome model or the propensity score model is correctly specified","inference":"Provides valid asymptotic confidence intervals via influence-function-based standard errors"},"citations":[{"ref":"van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1).","type":"article","doi":"10.2202/1557-4679.1043","isbn":null,"url":null}],"related":["doubly-robust-estimation","inverse-probability-weighting","double-machine-learning"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"task-ego-orientation-sport","name":"Task and Ego Orientation in Sport Questionnaire","fullName":"Task and Ego Orientation in Sport Questionnaire (TEOSQ)","aliases":["TEOSQ","Task Ego Orientation"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"goal-orientation-and-achievement","year":"1992","originator":"Joan Duda, John Nicholls","url":"https://scholargate.app/en/sport-psychology/task-ego-orientation-sport","markdownUrl":"https://scholargate.app/en/sport-psychology/task-ego-orientation-sport.md","definition":"The TEOSQ is a 13-item questionnaire measuring achievement goal orientation in sport: the underlying reasons athletes define success and pursue achievement. Developed by Duda and Nicholls in 1992, the TEOSQ has become a cornerstone instrument for understanding athlete motivation, resilience, and response to failure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Joan Duda, John Nicholls","subfamily":"goal-orientation-and-achievement","year":"1992","type":"Self-report achievement goal orientation questionnaire"},"citations":[{"ref":"Duda, J. L., & Nicholls, J. G. (1992). Dimensions of achievement motivation in schoolwork and sport. Journal of Educational Psychology, 84(3), 290–299.","type":"article","doi":"10.1037/0022-0663.84.3.290","isbn":null,"url":null},{"ref":"Duda, J. L. (2001). Achievement goal theory in sport: Recent extensions and future directions. In R. N. Singer, H. A. Hausenblas, & C. M. Janelle (Eds.), Handbook of Sport Psychology (2nd ed., pp. 417–443). New York: Wiley.","type":"book","doi":null,"isbn":null,"url":"https://books.google.com/books/about/Handbook_of_Sport_Psychology.html"}],"related":["sport-motivation-scale","athletic-identity-measurement-scale","sport-confidence-inventory","mental-toughness-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tau-estimator","name":"Tau Estimator","fullName":"Tau (τ) Estimator of Regression","aliases":["tau regression estimator","robust tau regression","Tau-Tahmin Edici"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1988,"originator":"Yohai & Zamar","url":"https://scholargate.app/en/statistics/tau-estimator","markdownUrl":"https://scholargate.app/en/statistics/tau-estimator.md","definition":"The Tau estimator is a robust linear regression method introduced by Yohai and Zamar in 1988 that fits the model by minimising an efficient τ-scale of the residuals. It builds on the scale estimate of the S-estimator to combine a high breakdown point with high statistical efficiency, and is often used as an alternative to the MM-estimator in small samples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yohai & Zamar","year":1988,"type":"Robust linear regression","estimator":"Minimisation of an efficient τ-scale of the residuals","breakdownPoint":"high (up to 50%)","outcome":"continuous"},"citations":[{"ref":"Yohai, V. J., & Zamar, R. H. (1988). High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale. Journal of the American Statistical Association, 83(402), 406-413.","type":"article","doi":"10.1080/01621459.1988.10478611","isbn":null,"url":null},{"ref":"Maronna, R. A., & Zamar, R. H. (2002). Robust Estimates of Location and Dispersion for High-Dimensional Datasets. Technometrics, 44(4), 307-317.","type":"article","doi":"10.1198/004017002188618509","isbn":null,"url":null}],"related":["mm-estimator","s-estimator","theil-sen-estimator","huber-m-estimator","least-trimmed-squares"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"taxonomy","name":"TAXONOMY","fullName":"Taxonomy Method — Wrocław Taxonomic Development Measure","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1951","originator":"Florek, K., Łukaszewicz, J., Perkal, J., Steinhaus, H., Zubrzycki, S.","url":"https://scholargate.app/en/decision-making/taxonomy","markdownUrl":"https://scholargate.app/en/decision-making/taxonomy.md","definition":"TAXONOMY (Taxonomy Method — Wrocław Taxonomic Development Measure) is a ranking multi-criteria decision-making (MCDM) method introduced by Florek, K., Łukaszewicz, J., Perkal, J., Steinhaus, H., Zubrzycki, S. in 1951. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Florek, K., Łukaszewicz, J., Perkal, J., Steinhaus, H., Zubrzycki, S.","subfamily":"Ranking","year":"1951","type":"Taxonomic distance composite index (z-score + Euclidean ideal)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Florek, K., Łukaszewicz, J., Perkal, J., Steinhaus, H., Zubrzycki, S. (1951). Taksonomia wrocławska. Przegląd Antropologiczny","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Taksonomia%20wroc%C5%82awska"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"taylor-tool-life","name":"Taylor Tool Life","fullName":"Taylor's Tool Life Equation and Cutting Parameter Optimization","aliases":["Taylor's equation","Tool life prediction","VT relationship"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Empirical modeling","year":"1907","originator":"Frederick Winslow Taylor","url":"https://scholargate.app/en/manufacturing/taylor-tool-life","markdownUrl":"https://scholargate.app/en/manufacturing/taylor-tool-life.md","definition":"Taylor's tool life equation is an empirical relationship predicting how long a cutting tool remains usable before dulling or breaking, expressed as a function of cutting speed, feed rate, and depth of cut. Formulated by Frederick Winslow Taylor in 1907 from systematic experiments on metal cutting, this method provides a practical framework for optimizing machining operations by balancing productivity against tool wear and cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frederick Winslow Taylor","subfamily":"Empirical modeling","year":"1907","type":"Tool wear prediction model"},"citations":[{"ref":"Taylor, F. W. (1907). On the art of cutting metals. Transactions of the American Society of Mechanical Engineers, 28, 31-350.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/onartofcuttingme00tayluoft"},{"ref":"Elbestawi, M. A., Papazafiriou, T., & Du, R. (1994). In-process detection of tool wear in milling using cutting force signature. International Journal of Machine Tools and Manufacture, 34(4), 555-566.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=In-process+detection+of+tool+wear+in+milling+using+cutting+force+signature+Elbestawi"},{"ref":"Karpuschewski, B., Wehmeyer, K., & Schmidt, K. (2008). Advances in precision grinding and polishing processes. CIRP Annals, 57(2), 621-642.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Advances+in+precision+grinding+and+polishing+processes+Karpuschewski"}],"related":["cnc-tool-path-generation","design-for-manufacturing-and-assembly","modal-analysis","additive-manufacturing-slicing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tbats","name":"TBATS","fullName":"Trigonometric, Box-Cox, ARMA, Trend and Seasonal Components Model","aliases":["trigonometric exponential smoothing","multiple seasonal exponential smoothing","complex seasonal exponential smoothing","TBATS — Çoklu Mevsimsel Üstel Düzleştirme"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2011,"originator":"De Livera, Hyndman & Snyder","url":"https://scholargate.app/en/econometrics/tbats","markdownUrl":"https://scholargate.app/en/econometrics/tbats.md","definition":"TBATS is an innovations state space forecasting model, introduced by De Livera, Hyndman and Snyder (2011), that combines a Box-Cox transformation, ARMA errors and trigonometric (Fourier) seasonal terms. It is built to handle continuous time series with several nested seasonal cycles at once — for example hourly data that also repeats daily, weekly and yearly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"De Livera, Hyndman & Snyder","year":2011,"type":"Exponential smoothing state space model","estimator":"Maximum likelihood (innovations state space)","outcome":"continuous time series","seasonality":"multiple, possibly non-integer periods"},"citations":[{"ref":"De Livera, A. M., Hyndman, R. J. & Snyder, R. D. (2011). Forecasting Time Series with Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106(496), 1513-1527.","type":"article","doi":"10.1198/jasa.2011.tm09771","isbn":null,"url":null},{"ref":"Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.","type":"book","doi":null,"isbn":null,"url":"https://otexts.com/fpp3/"}],"related":["arima","sarima","ets-exponential-smoothing","prophet","stl-decomposition"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tcas","name":"TCAS","fullName":"Traffic Collision Avoidance System","aliases":["TCAS II","ACAS","traffic avoidance"],"domain":"aerospace","family":"process-pipeline","subfamily":"Collision Avoidance","year":"1989","originator":"FAA, ICAO","url":"https://scholargate.app/en/aerospace/tcas","markdownUrl":"https://scholargate.app/en/aerospace/tcas.md","definition":"TCAS (Traffic Collision Avoidance System) is an airborne safety system that detects nearby aircraft using radar and mode C altitude reports, then provides traffic advisories (TAs) and recommended collision avoidance maneuvers (RAs) to flight crews. Mandated globally on commercial aircraft since 2000, TCAS is considered a last line of defense against mid-air collisions. TCAS II is the most common variant; TCAS I is a simplified advisory-only version for general aviation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"FAA, ICAO","subfamily":"Collision Avoidance","year":"1989","type":"Avionics system"},"citations":[{"ref":"Federal Aviation Administration (2017). Traffic Collision Avoidance System (TCAS II). Technical Standard Order TSO-C119c.","type":"article","doi":null,"isbn":null,"url":"https://www.faa.gov/aircraft/air_cert/design_approvals/tso/"},{"ref":"International Civil Aviation Organization (2020). Annex 10 — Aeronautical Telecommunications, Volume IV. ICAO.","type":"book","doi":null,"isbn":null,"url":"https://www.icao.int"},{"ref":"Billingsley, D., & Hoh, R. H. (2013). An overview of TCAS overview. In Proceedings of the Integrated Communications Navigation and Surveillance Conference (ICNS).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=An+overview+of+TCAS+overview+Billingsley"}],"related":["quaternion-attitude","sgp4-tle-propagation","proportional-navigation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"teaching-effectiveness-scale","name":"Teaching Effectiveness Scale","fullName":"Teaching Effectiveness Scale (TES)","aliases":["TES","Instructor Effectiveness Rating"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"Instructor performance evaluation","year":"1982","originator":"Herbert Marsh","url":"https://scholargate.app/en/educational-psychology/teaching-effectiveness-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/teaching-effectiveness-scale.md","definition":"The Teaching Effectiveness Scale (TES) is a validated instrument designed to measure students' perceptions of instructor effectiveness across multiple dimensions. The most widely known version, the Student Evaluation of Educational Quality (SEEQ), developed by Marsh (1982), assesses nine dimensions of teaching including learning value, enthusiasm, organization, group interaction, and course difficulty, providing comprehensive feedback on instructor performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Herbert Marsh","subfamily":"Instructor performance evaluation","year":"1982","type":"Teacher effectiveness rating scale"},"citations":[{"ref":"Marsh, H. W. (1982). SEEQ: a reliable, valid, and useful instrument for collecting students' evaluations of university teaching. British Journal of Educational Psychology, 52(1), 77-95.","type":"article","doi":"10.1111/j.2044-8279.1982.tb02505.x","isbn":null,"url":null},{"ref":"Wachtel, H. K. (1998). Student evaluation of college teaching effectiveness: a brief review. Assessment and Evaluation in Higher Education, 23(2), 191-211.","type":"article","doi":"10.1080/0260293980230207","isbn":null,"url":null}],"related":["student-satisfaction-survey","course-experience-questionnaire","school-climate-scale","student-engagement-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"team-cohesion-questionnaire","name":"Group Environment Questionnaire","fullName":"Group Environment Questionnaire (GEQ)","aliases":["GEQ","Team Cohesion","Group Cohesion"],"domain":"sport-psychology","family":"process-pipeline","subfamily":"team-dynamics-and-cohesion","year":"1985","originator":"Albert Carron, W. Neil Widmeyer, Lawrence Brawley","url":"https://scholargate.app/en/sport-psychology/team-cohesion-questionnaire","markdownUrl":"https://scholargate.app/en/sport-psychology/team-cohesion-questionnaire.md","definition":"The GEQ is an 18-item instrument measuring team cohesion—the degree to which team members feel attracted to the group and perceive the group as unified around shared task and social goals. Developed by Carron, Widmeyer, and Brawley in 1985, the GEQ has become the gold standard for measuring cohesion in sport teams and is used to predict team performance, member satisfaction, and team retention.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Carron, W. Neil Widmeyer, Lawrence Brawley","subfamily":"team-dynamics-and-cohesion","year":"1985","type":"Self-report team/group cohesion questionnaire"},"citations":[{"ref":"Carron, A. V., Widmeyer, W. N., & Brawley, L. R. (1985). The development of an instrument to assess cohesiveness in sport teams: The Group Environment Questionnaire. Journal of Sport Psychology, 7(3), 244–266.","type":"article","doi":"10.1123/jsp.7.3.244","isbn":null,"url":null},{"ref":"Eys, M. A., Carron, A. V., Bray, S. R., & Beauchamp, M. R. (2003). The relationship between correlates of cohesion and attendance in interactive and coactive sport teams. Journal of Sport & Exercise Psychology, 25(1), 59–72.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+relationship+between+correlates+of+cohesion+and+attendance+in+interactive+and+coactive+sport+teams+Eys"}],"related":["sport-motivation-scale","athletic-identity-measurement-scale","mental-toughness-questionnaire","task-ego-orientation-sport"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"team-situation-awareness","name":"Team Situation Awareness Scale","fullName":"Team Situation Awareness Scale (TSAS)","aliases":["TSAS","Team SA Scale"],"domain":"human-factors","family":"process-pipeline","subfamily":"team-collaboration-assessment","year":1992,"originator":"Mica Endsley, Eduardo Salas","url":"https://scholargate.app/en/human-factors/team-situation-awareness","markdownUrl":"https://scholargate.app/en/human-factors/team-situation-awareness.md","definition":"The Team Situation Awareness Scale (TSAS) extends individual situational awareness measurement to the team level, assessing how well team members collectively perceive the task environment, understand shared information, and coordinate their actions. Developed by Endsley, Salas, and collaborators in the 1990s–2000s, the TSAS measures team-level SA, recognizing that in complex operations (emergency response, military command, operating rooms), task success depends not just on individual operator awareness but on shared mental models, communication, and coordinated decision-making.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mica Endsley, Eduardo Salas","subfamily":"team-collaboration-assessment","year":1992,"type":"Self-report / Observational"},"citations":[{"ref":"Salas, E., Prince, C., & Brannick, M. T. (1992). Team performance assessment in military tasks. In R. Guzzo & E. Salas (Eds.), Team Effectiveness and Decision Making in Organizations (pp. 90-120). Jossey-Bass.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Salas%2C%20E.%2C%20Prince%2C%20C.%2C%20%26%20Brannick%2C%20M.%20T.%20(1992).%20Team%20performance%20assessment%20in%20military%20tasks.%20In%20R.%20Guzzo%20%26%20E.%20Salas%20("},{"ref":"Endsley, M. R. (2004). Team situation awareness. In S. Banbury & S. Tremblay (Eds.), A Cognitive Approach to Situation Awareness: Theory and Application (pp. 328-355). Ashgate.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Endsley%2C%20M.%20R.%20(2004).%20Team%20situation%20awareness.%20In%20S.%20Banbury%20%26%20S.%20Tremblay%20(Eds.)%2C%20A%20Cognitive%20Approach%20to%20Situation%20A"}],"related":["situational-awareness-rating","nasa-task-load-index","workload-profile","team-situation-awareness"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"teamstepps-perceptions","name":"TeamSTEPPS Teamwork Perceptions Questionnaire","fullName":"TeamSTEPPS Teamwork Perceptions Questionnaire (T-TPQ)","aliases":["T-TPQ","TeamSTEPPS TPQ"],"domain":"healthcare-management","family":"process-pipeline","subfamily":"teamwork-communication","year":"2008","originator":"Agency for Healthcare Research and Quality (AHRQ), Department of Defense, and team members from the TeamSTEPPS program","url":"https://scholargate.app/en/healthcare-management/teamstepps-perceptions","markdownUrl":"https://scholargate.app/en/healthcare-management/teamstepps-perceptions.md","definition":"The TeamSTEPPS Teamwork Perceptions Questionnaire (T-TPQ) is a 35-item self-report instrument designed to measure team members' perceptions of teamwork and communication in clinical units. Developed by the Agency for Healthcare Research and Quality and the Department of Defense, the T-TPQ was created specifically to evaluate the impact of TeamSTEPPS (Team Strategies and Tools to Enhance Performance and Patient Safety) training on teamwork behaviors and safety outcomes. The questionnaire assesses five core domains of teamwork: team structure, leadership, situational monitoring, mutual support, and communication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Agency for Healthcare Research and Quality (AHRQ), Department of Defense, and team members from the TeamSTEPPS program","subfamily":"teamwork-communication","year":"2008","type":"Self-report"},"citations":[{"ref":"Sorra, J., Nieva, V. F., Famolaro, T., & Dyer, N. (2014). Detailed Evaluation of the Psychometric Properties of the Teamwork Perceptions Questionnaire. Agency for Healthcare Research and Quality. Technical Report.","type":"article","doi":null,"isbn":null,"url":"https://www.ahrq.gov/sites/default/files/documents/npsf/TeamworkPerceptionsQuestionnaire_508.pdf"},{"ref":"Sexton, J. B., Helmreich, R. L., & Pronovost, P. J. (2011). The Safety Attitudes Questionnaire as a tool for evaluating the impact of patient safety initiatives on safety attitudes and outcomes. American Journal of Medical Quality, 25(3), 186–191.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Safety+Attitudes+Questionnaire+as+a+tool+for+evaluating+the+impact+of+patient+safety+initiatives+on+safety+attitudes+and+outcomes+Sexton"},{"ref":"Clements, P. T., Kitchens, E., Lebel, N., & Schrecengost, S. (2007). Critical incident stress debriefing: an evaluation of existing research. Prehospital and Disaster Medicine, 22(2), 87–95.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Critical+incident+stress+debriefing%3A+an+evaluation+of+existing+research+Clements"}],"related":["safety-attitudes-questionnaire","hospital-survey-patient-safety","healthcare-teamwork-scale","clinical-handover-quality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"technical-debt-measurement","name":"Technical Debt Measurement","fullName":"Technical Debt Quantification and Assessment","aliases":["debt metrics","code health scoring","maintenance burden assessment"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Cost estimation","year":"1992","originator":"Ward Cunningham","url":"https://scholargate.app/en/software-engineering/technical-debt-measurement","markdownUrl":"https://scholargate.app/en/software-engineering/technical-debt-measurement.md","definition":"Technical debt represents accumulated shortcuts, deferred maintenance, and design compromises that incur future costs through slower development, higher defect rates, and deployment difficulty. Introduced by Ward Cunningham (1992), technical debt measurement quantifies these burdens using metrics like code complexity, duplication, test coverage gaps, and maintainability indices. Organizations use debt measurement to balance immediate delivery with long-term sustainability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ward Cunningham","subfamily":"Cost estimation","year":"1992","type":"quantitative assessment"},"citations":[{"ref":"Cunningham, W. (1992). The WyCash Portfolio Management System. OOPSLA 92 Experience Report.","type":"article","doi":null,"isbn":null,"url":"http://c2.com/doc/oopsla92.html"},{"ref":"Seaman, C. B., & Guo, Y. (2011). Measuring and monitoring technical debt. Advances in Computers, 82, 25–46.","type":"article","doi":"10.1016/B978-0-12-385512-1.00002-5","isbn":null,"url":null},{"ref":"Tom, E., Aurum, A., & Vidgen, R. (2013). An exploration of technical debt. Journal of Systems and Software, 86(6), 1498–1516.","type":"article","doi":"10.1016/j.jss.2012.12.052","isbn":null,"url":null}],"related":["software-complexity-metrics","code-coverage-analysis","static-code-analysis","defect-prediction-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"technical-debt-quantification","name":"Technical Debt Quantification","fullName":"Technical Debt Quantification and Assessment","aliases":["debt measurement","refactoring cost estimation"],"domain":"numerical-methods","family":"ml-model","subfamily":"Software Economics","year":"1992","originator":"Ward Cunningham","url":"https://scholargate.app/en/numerical-methods/technical-debt-quantification","markdownUrl":"https://scholargate.app/en/numerical-methods/technical-debt-quantification.md","definition":"Technical Debt Quantification is the measurement and monetization of technical shortcuts taken during development (incomplete refactoring, outdated dependencies, deferred testing). Coined by Cunningham in 1992, the metaphor frames accumulated shortcuts as financial debt: taking shortcuts saves immediate time but incurs interest (slower future development) and risk (outages).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ward Cunningham","subfamily":"Software Economics","year":"1992","type":"Debt assessment framework"},"citations":[{"ref":"Cunningham, W. (1992). The WyCash portfolio management system. OOPSLA '92 Experience Report.","type":"article","doi":null,"isbn":null,"url":"https://c2.com/cgi/wiki?WardCunninghamsPortfolioManagementSystem"},{"ref":"Seaman, C. B., & Guo, Y. (2011). Measuring and monitoring technical debt. Advances in Computers, 82, 25–46.","type":"article","doi":"10.1016/B978-0-12-385512-1.00002-5","isbn":null,"url":null},{"ref":"Tom, E., Aurum, A., & Vidgen, R. (2013). An exploration of technical debt. Journal of Systems and Software, 86(6), 1498–1516.","type":"article","doi":"10.1016/j.jss.2012.12.052","isbn":null,"url":null}],"related":["code-metrics","maintainability-index","refactoring"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"technoference-scale","name":"Technoference Scale","fullName":"Technoference Scale (or Smartphone Interference in Relationships Scale)","aliases":["Technoference","Phone Interference"],"domain":"social-media-psychology","family":"process-pipeline","subfamily":"relationship-technology","year":"2016","originator":"Brandon T. McDaniel and Sarah M. Coyne","url":"https://scholargate.app/en/social-media-psychology/technoference-scale","markdownUrl":"https://scholargate.app/en/social-media-psychology/technoference-scale.md","definition":"The Technoference Scale measures the degree to which smartphone and technology use interferes with interpersonal relationships, particularly in romantic partnerships, families, and close relationships. Developed by McDaniel and Coyne in the mid-2010s, this construct captures a modern phenomenon where digital devices create physical or psychological distance during face-to-face interaction, reducing relationship quality and satisfaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Brandon T. McDaniel and Sarah M. Coyne","subfamily":"relationship-technology","year":"2016","type":"Self-report"},"citations":[{"ref":"McDaniel, B. T., & Coyne, S. M. (2016). Technology interference in the context of romantic relationships. In R. E. Ackerman (Ed.), The psychology of social networking (Vol. 1, pp. 86–102). Nova Publishers.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Technology+interference+in+the+context+of+romantic+relationships+McDaniel"}],"related":["smartphone-addiction-scale-short","social-media-disorder-scale","fear-of-missing-out-scale","passive-social-media-use-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"technology-readiness-index","name":"Technology Readiness Index","fullName":"Technology Readiness Index (TRI)","aliases":["TRI","Parasuraman Technology Readiness"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"2000","originator":"Ajay Parasuraman","url":"https://scholargate.app/en/information-systems/technology-readiness-index","markdownUrl":"https://scholargate.app/en/information-systems/technology-readiness-index.md","definition":"The Technology Readiness Index (TRI) was developed by Ajay Parasuraman in 2000 to measure individual propensity to adopt and use new technologies. The TRI assesses a person's personal attitudes toward technology across four dimensions: optimism, innovativeness, discomfort, and insecurity. Updated in 2015 with a streamlined 16-item version, the TRI helps identify technology adopter segments and predict behavior across diverse technology contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ajay Parasuraman","subfamily":"Technology adoption","year":"2000","type":"Likert-scale questionnaire"},"citations":[{"ref":"Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined Technology Readiness Index. Journal of Service Research, 18(1), 59-74.","type":"article","doi":"10.1177/1094670514539730","isbn":null,"url":null},{"ref":"Parasuraman, A. (2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320.","type":"article","doi":"10.1177/109467050024001","isbn":null,"url":null}],"related":["tam-questionnaire","utaut-questionnaire","computer-anxiety-scale","online-trust-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"technostress-scale","name":"Technostress Scale","fullName":"Technostress Scale","aliases":["Techno-stress","Technology-induced stress"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"2007","originator":"Tarafdar, Tu, Ragu-Nathan","url":"https://scholargate.app/en/information-systems/technostress-scale","markdownUrl":"https://scholargate.app/en/information-systems/technostress-scale.md","definition":"The Technostress Scale, developed by Tarafdar, Tu, Ragu-Nathan, and colleagues (2007), measures the stress and negative emotions experienced by employees due to information technology use in the workplace. The scale captures five dimensions of technostress: techno-overload (excessive workload from technology demands), techno-invasion (inability to disconnect from work), techno-complexity (difficulty mastering new technology), techno-insecurity (fear of job loss due to automation), and techno-uncertainty (constant changes in technology). Technostress is linked to decreased productivity, increased burnout, and job dissatisfaction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tarafdar, Tu, Ragu-Nathan","subfamily":"Technology adoption","year":"2007","type":"Likert-scale stress measure"},"citations":[{"ref":"Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301-328.","type":"article","doi":"10.2753/MIS0742-1222240109","isbn":null,"url":null},{"ref":"Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and validation. Information Systems Research, 19(4), 417-433.","type":"article","doi":"10.1287/isre.1070.0165","isbn":null,"url":null}],"related":["computer-anxiety-scale","technology-readiness-index","tam-questionnaire","utaut-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"telemedicine-satisfaction-scale","name":"Telemedicine Satisfaction Scale","fullName":"Telemedicine Satisfaction Scale (TSS)","aliases":["TSS","Telemedicine Satisfaction"],"domain":"health-informatics","family":"process-pipeline","subfamily":"Patient satisfaction with telehealth","year":"2009","originator":"Multiple researchers; consensus measure","url":"https://scholargate.app/en/health-informatics/telemedicine-satisfaction-scale","markdownUrl":"https://scholargate.app/en/health-informatics/telemedicine-satisfaction-scale.md","definition":"The Telemedicine Satisfaction Scale measures patient satisfaction with remote clinical encounters, assessing perceptions of communication quality, technical usability, provider competence, and perceived benefit. While no single universal scale dominates the literature, core satisfaction domains—connection quality, provider accessibility, clinical effectiveness, and likelihood to recommend—are consistently measured across telemedicine studies to evaluate user acceptance and identify barriers to adoption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple researchers; consensus measure","subfamily":"Patient satisfaction with telehealth","year":"2009","type":"Self-report questionnaire"},"citations":[{"ref":"Or, Z., & Kartak, F. (2009). Review of the empirical literature on telemedicine in the OECD countries: Does telemedicine improve outcomes? In M. Rechel, B. Goddard (Eds.), Improving healthcare quality in Europe. WHO Regional Office for Europe.","type":"article","doi":null,"isbn":null,"url":"https://www.euro.who.int/__data/assets/pdf_file/0009/74550/E93285.pdf"},{"ref":"Stacey, D., Samant, R., & Bennett, C. (2008). Decision-making in oncology: a review of patient decision aids for preference-sensitive treatments. Cancer, 113(8), 1721–1730.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Decision-making+in+oncology%3A+a+review+of+patient+decision+aids+for+preference-sensitive+treatments+Stacey"}],"related":["patient-engagement-scale","ehealth-literacy-scale","digital-health-acceptance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"telephone-assisted-delphi-technique","name":"Telephone-assisted Delphi Technique","fullName":"Telephone-assisted Delphi Technique","aliases":["telephone Delphi","phone-based Delphi","CATI Delphi","telephone consensus method"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1963 (Delphi); telephone-assisted variant prominent 1970s–1990s","originator":"Norman Dalkey & Olaf Helmer (RAND Corporation); telephone adaptation used throughout 1970s–1990s applied research","url":"https://scholargate.app/en/survey-methodology/telephone-assisted-delphi-technique","markdownUrl":"https://scholargate.app/en/survey-methodology/telephone-assisted-delphi-technique.md","definition":"The telephone-assisted Delphi Technique applies the classic iterative expert-consensus framework through structured telephone interviews rather than mailed or online questionnaires. Experts participate in sequential rounds of data collection by phone, enabling the researcher to clarify ambiguous responses in real time and reach consensus on complex, contested, or forward-looking questions without requiring participants to convene in person.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Dalkey & Olaf Helmer (RAND Corporation); telephone adaptation used throughout 1970s–1990s applied research","year":"1963 (Delphi); telephone-assisted variant prominent 1970s–1990s","type":"Iterative expert consensus technique delivered by telephone","dataType":"Expert ratings, rankings, and open-ended verbal responses (converted to structured data across rounds)","subfamily":"Data collection"},"citations":[{"ref":"Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458–467.","type":"article","doi":"10.1287/mnsc.9.3.458","isbn":null,"url":null},{"ref":"Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.","type":"article","doi":"10.1046/j.1365-2648.2000.01567.x","isbn":null,"url":null}],"related":["delphi-technique","online-delphi-technique","structured-interview","telephone-assisted-structured-interview","nominal-group-technique","expert-panel"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"telephone-assisted-field-notes","name":"Telephone-assisted Field Notes","fullName":"Telephone-assisted Field Notes Collection","aliases":["phone-dictated field notes","telephone field recording","remote field note dictation","phone-assisted observation notes"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s–1990s (telephone-assisted variant)","originator":"Adapted from traditional fieldwork practice; telephone dictation variant developed in qualitative health and social research circa 1980s–1990s","url":"https://scholargate.app/en/survey-methodology/telephone-assisted-field-notes","markdownUrl":"https://scholargate.app/en/survey-methodology/telephone-assisted-field-notes.md","definition":"Telephone-assisted field notes is a data collection technique in which a field researcher verbally dictates observational notes via telephone — either to a live transcriptionist, an answering service, or a voicemail/recording system — immediately after or during a field encounter. It preserves the immediacy and richness of traditional field notes while enabling the researcher to record observations quickly and hands-free when written note-taking is impractical or disruptive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from traditional fieldwork practice; telephone dictation variant developed in qualitative health and social research circa 1980s–1990s","year":"1980s–1990s (telephone-assisted variant)","type":"Qualitative data collection technique","dataType":"Observational text data, verbally dictated descriptive notes","subfamily":"Data collection"},"citations":[{"ref":"Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes (2nd ed.). University of Chicago Press.","type":"book","doi":null,"isbn":"978-0226206813","url":null},{"ref":"Bernard, H. R. (2011). Research Methods in Anthropology: Qualitative and Quantitative Approaches (5th ed.). AltaMira Press.","type":"book","doi":null,"isbn":"978-0759118447","url":null}],"related":["field-notes","telephone-assisted-structured-interview","remote-field-notes","mobile-field-notes","non-participant-observation","voice-memo-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"telephone-assisted-focus-group","name":"Telephone-assisted Focus Group","fullName":"Telephone-assisted Focus Group Discussion","aliases":["telephone focus group","phone focus group","TAFG","teleconference focus group"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s–1990s (widespread adoption)","originator":"Adapted from in-person focus group methodology (Robert Merton et al., 1950s); telephone modality adopted in market and health research from the 1980s onward","url":"https://scholargate.app/en/survey-methodology/telephone-assisted-focus-group","markdownUrl":"https://scholargate.app/en/survey-methodology/telephone-assisted-focus-group.md","definition":"A telephone-assisted focus group is a qualitative data collection technique in which a moderator facilitates a structured group discussion among multiple participants connected simultaneously via a telephone conference bridge or audio platform. It preserves the core interactive dynamics of traditional focus groups — group synergy, probing, and spontaneous reactions — while eliminating the need for geographic co-location, making it suitable for hard-to-reach, geographically dispersed, or mobility-constrained populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from in-person focus group methodology (Robert Merton et al., 1950s); telephone modality adopted in market and health research from the 1980s onward","year":"1980s–1990s (widespread adoption)","type":"Qualitative group data collection technique","dataType":"Audio-recorded verbal group discussion (text after transcription)","subfamily":"Data collection"},"citations":[{"ref":"Greenbaum, T. L. (1998). The Handbook for Focus Group Research (2nd ed.). Sage. [Chapter on telephone and technology-mediated focus groups]","type":"book","doi":null,"isbn":"978-0761912316","url":null},{"ref":"Krueger, R. A., & Casey, M. A. (2015). Focus Groups: A Practical Guide for Applied Research (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1483365244","url":null}],"related":["online-focus-group","face-to-face-focus-group","telephone-assisted-structured-interview","telephone-assisted-semi-structured-interview","mobile-focus-group","remote-focus-group"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"telephone-assisted-in-depth-interview","name":"Telephone-assisted In-depth Interview","fullName":"Telephone-assisted In-depth Interview","aliases":["telephone in-depth interview","phone-based qualitative interview","TIDI","telephone qualitative interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s–1990s (widespread adoption)","originator":"Developed from qualitative interview traditions; telephone variant documented from the 1980s onward","url":"https://scholargate.app/en/survey-methodology/telephone-assisted-in-depth-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/telephone-assisted-in-depth-interview.md","definition":"A telephone-assisted in-depth interview is a qualitative data collection method in which a researcher conducts a lengthy, open-ended, exploratory conversation with a participant via telephone. It preserves the depth and flexibility of face-to-face in-depth interviewing while overcoming geographic and mobility barriers, making it particularly useful when participants are dispersed, housebound, or when travel is impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from qualitative interview traditions; telephone variant documented from the 1980s onward","year":"1980s–1990s (widespread adoption)","type":"Qualitative data collection technique","dataType":"Verbal/audio data (transcribed to text)","subfamily":"Data collection"},"citations":[{"ref":"Sturges, J. E., & Hanrahan, K. J. (2004). Comparing telephone and face-to-face qualitative interviewing: A research note. Qualitative Research, 4(1), 107–118.","type":"article","doi":"10.1177/1468794104041110","isbn":null,"url":null},{"ref":"Carr, E. C. J., & Worth, A. (2001). The use of the telephone interview for research. NT Research, 6(1), 511–524.","type":"article","doi":"10.1177/136140960100600107","isbn":null,"url":null}],"related":["structured-interview","semi-structured-interview","online-in-depth-interview","face-to-face-in-depth-interview","remote-in-depth-interview","telephone-assisted-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"telephone-assisted-research-diary","name":"Telephone-assisted Research Diary","fullName":"Telephone-assisted Research Diary Method","aliases":["phone-prompted diary","telephone diary method","telephone-based research diary","CATI diary"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s–1990s (telephone-prompted diary variants)","originator":"Diary methods: Ronald Burgess and colleagues (field research tradition); telephone-prompted variants emerged from experience sampling and health research","url":"https://scholargate.app/en/survey-methodology/telephone-assisted-research-diary","markdownUrl":"https://scholargate.app/en/survey-methodology/telephone-assisted-research-diary.md","definition":"The telephone-assisted research diary combines the longitudinal depth of diary methods with structured telephone prompting. Participants are contacted by researchers at scheduled intervals — daily, weekly, or event-contingent — and guided to reflect on and record recent experiences, behaviours, or feelings. The telephone call functions as both a prompt to ensure timely entries and as a brief interview that deepens the diary record beyond what participants might write unsupported.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diary methods: Ronald Burgess and colleagues (field research tradition); telephone-prompted variants emerged from experience sampling and health research","year":"1980s–1990s (telephone-prompted diary variants)","type":"Longitudinal qualitative/quantitative data collection","dataType":"Text, structured ratings, or open-ended responses elicited via telephone call","subfamily":"Data collection"},"citations":[{"ref":"Burgess, R. G. (1984). In the Field: An Introduction to Field Research. Allen & Unwin.","type":"book","doi":null,"isbn":"978-0415058711","url":null},{"ref":"Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.","type":"article","doi":"10.1146/annurev.psych.54.101601.145030","isbn":null,"url":null}],"related":["diary-method","research-diary","experience-sampling-method","mobile-experience-sampling","telephone-assisted-structured-interview","longitudinal-diary-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"telephone-assisted-semi-structured-interview","name":"Telephone-assisted Semi-structured Interview","fullName":"Telephone-assisted Semi-structured Interview","aliases":["telephone semi-structured interview","phone-based semi-structured interview","TASI","telephone qualitative interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–1980s (widespread adoption in health and social research)","originator":"Adapted from face-to-face semi-structured interviewing; telephone use in social research documented from the 1970s onward","url":"https://scholargate.app/en/survey-methodology/telephone-assisted-semi-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/telephone-assisted-semi-structured-interview.md","definition":"A telephone-assisted semi-structured interview is a qualitative data collection technique in which a researcher conducts a guided conversation with a participant over the telephone, using a pre-designed topic guide that balances predetermined questions with freedom to probe and explore. It combines the flexibility of semi-structured interviewing with the geographic reach and logistical convenience of telephone communication, making it widely used in health, social, and organizational research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from face-to-face semi-structured interviewing; telephone use in social research documented from the 1970s onward","year":"1970s–1980s (widespread adoption in health and social research)","type":"Qualitative data collection technique","dataType":"Audio-recorded or note-based verbal responses (qualitative text data)","subfamily":"Data collection"},"citations":[{"ref":"Novick, G. (2008). Is there a bias against telephone interviews in qualitative research? Research in Nursing & Health, 31(4), 391–398.","type":"article","doi":"10.1002/nur.20259","isbn":null,"url":null},{"ref":"Britten, N. (1995). Qualitative interviews in medical research. BMJ, 311(6999), 251–253.","type":"article","doi":"10.1136/bmj.311.6999.251","isbn":null,"url":null}],"related":["semi-structured-interview","structured-interview","telephone-assisted-survey","online-semi-structured-interview","face-to-face-semi-structured-interview","in-depth-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"telephone-assisted-sensor-data-collection","name":"Telephone-assisted Sensor Data Collection","fullName":"Telephone-assisted Sensor Data Collection","aliases":["phone-based sensor data collection","telephone-mediated sensor monitoring","mobile phone sensor data collection","TASDC"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–2010s (aligned with smartphone proliferation)","originator":"Emerging from ubiquitous computing and digital health research communities; no single originator","url":"https://scholargate.app/en/survey-methodology/telephone-assisted-sensor-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/telephone-assisted-sensor-data-collection.md","definition":"Telephone-assisted sensor data collection uses participants' mobile phones as sensing platforms to gather continuous or triggered streams of physical and behavioral data — such as movement, location, and ambient sound — without requiring them to attend a lab. A research application installed on the phone captures sensor readings and transmits them to a central server, enabling large-scale, ecologically valid measurement of real-world behavior over days or weeks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Emerging from ubiquitous computing and digital health research communities; no single originator","year":"2000s–2010s (aligned with smartphone proliferation)","type":"Passive and active data collection via telephone/smartphone sensors","dataType":"Sensor streams (accelerometer, GPS, microphone, gyroscope, ambient light, proximity)","subfamily":"Data collection"},"citations":[{"ref":"Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.","type":"inproceedings","doi":"10.1109/MCOM.2010.5560598","isbn":null,"url":null},{"ref":"Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016). Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science, 11(6), 838–854.","type":"article","doi":"10.1177/1745691616650285","isbn":null,"url":null}],"related":["sensor-data-collection","mobile-sensor-data-collection","mobile-experience-sampling","ecological-momentary-assessment","remote-sensor-data-collection","api-based-data-collection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"telephone-assisted-survey","name":"Telephone-assisted Survey","fullName":"Telephone-assisted Survey (CATI)","aliases":["CATI survey","computer-assisted telephone interview","telephone survey","phone survey"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s (widespread from mid-1970s; Groves & Kahn 1979 seminal text)","originator":"Groves & Kahn (foundational comparative study); CATI systems developed by Charles Cannell and colleagues at University of Michigan","url":"https://scholargate.app/en/survey-methodology/telephone-assisted-survey","markdownUrl":"https://scholargate.app/en/survey-methodology/telephone-assisted-survey.md","definition":"A telephone-assisted survey is a structured data-collection method in which a trained interviewer administers a standardised questionnaire to respondents over the telephone, often supported by Computer-Assisted Telephone Interviewing (CATI) software. It combines the efficiency of remote administration with the response-quality advantages of live interviewer guidance, making it widely used in social, public-health, market-research, and political polling contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Groves & Kahn (foundational comparative study); CATI systems developed by Charles Cannell and colleagues at University of Michigan","year":"1970s (widespread from mid-1970s; Groves & Kahn 1979 seminal text)","type":"Quantitative / mixed-mode data collection","dataType":"Structured or semi-structured responses collected via telephone","subfamily":"Data collection"},"citations":[{"ref":"Groves, R. M., & Kahn, R. L. (1979). Surveys by telephone: A national comparison with personal interviews. Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Surveys+by+telephone+Groves+Kahn+1979"},{"ref":"Lavrakas, P. J. (1993). Telephone survey methods: Sampling, selection, and supervision (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0803950795","url":null}],"related":["survey","structured-interview","online-survey","face-to-face-survey","mobile-survey","computer-assisted-personal-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temperament-assessment-battery","name":"TABC","fullName":"Temperament Assessment Battery for Children","aliases":["TABC","Temperament Assessment Battery"],"domain":"neonatology","family":"process-pipeline","subfamily":"temperament-assessment","year":1984,"originator":"William Fullard, Sean McDevitt, William Carey","url":"https://scholargate.app/en/neonatology/temperament-assessment-battery","markdownUrl":"https://scholargate.app/en/neonatology/temperament-assessment-battery.md","definition":"The TABC is a parent-completed questionnaire assessing infant and toddler temperament characteristics in children aged 3 months to 3 years. Developed by Fullard, McDevitt, and Carey (1984), it measures nine temperament dimensions derived from the New York Longitudinal Study of Thomas and Chess: activity level, rhythmicity, approach/withdrawal, adaptability, intensity of reaction, threshold of responsiveness, mood, distractibility, and persistence. The TABC is widely used in pediatric and developmental psychology research to characterize individual differences in behavioral style and predict developmental trajectories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Fullard, Sean McDevitt, William Carey","subfamily":"temperament-assessment","year":1984,"type":"Parent-report"},"citations":[{"ref":"Fullard, W., McDevitt, S. C., & Carey, W. B. (1984). Assessing Temperament in One- to Three-Year-Old Children. Journal of Pediatric Psychology, 9(2), 205-217.","type":"article","doi":"10.1093/jpepsy/9.2.205","isbn":null,"url":null},{"ref":"Carey, W. B., & McDevitt, S. C. (1978). Revision of the Infant Temperament Questionnaire. Pediatrics, 61(5), 735-739.","type":"article","doi":"10.1542/peds.61.5.735","isbn":null,"url":null}],"related":["newborn-behavioral-observations","neonatal-behavioral-assessment","ages-stages-questionnaire-social-emotional"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"template-matching","name":"Template Matching","fullName":"Template Matching for Object Detection","aliases":["Correlation-based matching","Similarity matching"],"domain":"computer-vision","family":"ml-model","subfamily":"Object detection","year":"1980s","originator":"Computer vision community","url":"https://scholargate.app/en/computer-vision/template-matching","markdownUrl":"https://scholargate.app/en/computer-vision/template-matching.md","definition":"Template matching is a straightforward technique for locating a known pattern (template) within a larger image. By sliding a template image across the target image and computing a similarity measure at each position, template matching identifies locations where the template appears. It is effective for simple object detection when templates are well-defined and appearance variation is limited.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Computer vision community","subfamily":"Object detection","year":"1980s","type":"Pattern matching and detection"},"citations":[{"ref":"Lewis, J. P. (2004). Fast normalized cross-correlation. Vision Interface, 120–123.","type":"article","doi":null,"isbn":null,"url":"https://www.idiap.ch/~johnny/papers/Lewis04.pdf"},{"ref":"Briechle, K., & Hanebeck, U. D. (2001). Template matching using fast normalized cross correlation. SPIE Proceedings, 4387, 95–102.","type":"article","doi":"10.1117/12.421129","isbn":null,"url":null}],"related":["harris-corner-detection","sift-feature-detection","hough-transform","contour-analysis","blob-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tempo-estimation","name":"Tempo Estimation","fullName":"Tempo Estimation Algorithm","aliases":["tempo detection","BPM estimation","pulse rate detection"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Rhythm and timing","year":"1998","originator":"Eric D. Scheirer","url":"https://scholargate.app/en/music-information-retrieval/tempo-estimation","markdownUrl":"https://scholargate.app/en/music-information-retrieval/tempo-estimation.md","definition":"Tempo estimation is the task of automatically determining the beats per minute (BPM) or tempo of a musical recording. Introduced by Scheirer (1998), it is fundamental to rhythm analysis, music classification, and synchronization applications. Tempo is one of the most perceptually salient features of music; accurate estimation enables music-aware systems and human-machine interaction. Unlike beat tracking, which produces discrete beat times, tempo estimation yields a single BPM value (or a distribution of likely tempi).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eric D. Scheirer","subfamily":"Rhythm and timing","year":"1998","type":"Audio tempo analysis"},"citations":[{"ref":"Scheirer, E. D. (1998). Tempo and beat analysis of acoustic musical signals. The Journal of the Acoustical Society of America, 103(1), 588-601.","type":"article","doi":"10.1121/1.421129","isbn":null,"url":null},{"ref":"Davies, M. E., Böck, S., & Flexer, A. (2013). Towards end-of-life music recommendations based on listening and moving. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2013/papers/052.pdf"},{"ref":"Gkiokas, A., Katsouros, V., Pikrakis, A., & Theodoridis, S. (2012). Music tempo estimation and beat tracking by applying source separation and metrical learning. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2012/papers/149.pdf"}],"related":["beat-tracking","music-segmentation","pitch-detection-algorithm","music-genre-classification","music-similarity-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-betweenness-centrality","name":"Temporal Betweenness Centrality","fullName":"Temporal Betweenness Centrality (Time-Respecting Path Betweenness)","aliases":["TBC","time-varying betweenness centrality","dynamic betweenness centrality","time-respecting betweenness"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2012","originator":"Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.","url":"https://scholargate.app/en/network-analysis/temporal-betweenness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-betweenness-centrality.md","definition":"Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.","year":"2012","type":"Centrality measure for temporal networks","dataType":"Timestamped edge lists, temporal contact sequences","subfamily":"Network science"},"citations":[{"ref":"Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null},{"ref":"Kim, H., & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2), 026107.","type":"article","doi":"10.1103/PhysRevE.85.026107","isbn":null,"url":null}],"related":["betweenness-centrality","temporal-social-network-analysis","temporal-closeness-centrality","temporal-degree-centrality","temporal-network-diffusion-analysis","directed-betweenness-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-closeness-centrality","name":"Temporal Closeness Centrality","fullName":"Temporal Closeness Centrality in Time-Varying Networks","aliases":["time-varying closeness centrality","dynamic closeness centrality","TCC","temporal reachability-based centrality"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2011","originator":"Pan, R. K. & Saramaki, J.","url":"https://scholargate.app/en/network-analysis/temporal-closeness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-closeness-centrality.md","definition":"Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan, R. K. & Saramaki, J.","year":"2011","type":"Centrality measure (temporal)","dataType":"Timestamped edge lists (temporal networks)","subfamily":"Network science"},"citations":[{"ref":"Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105.","type":"article","doi":"10.1103/PhysRevE.84.016105","isbn":null,"url":null},{"ref":"Holme, P., & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null}],"related":["closeness-centrality","temporal-betweenness-centrality","temporal-degree-centrality","temporal-social-network-analysis","betweenness-centrality","temporal-pagerank"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-community-detection","name":"Temporal Community Detection","fullName":"Temporal Community Detection in Dynamic Networks","aliases":["dynamic community detection","time-varying community detection","evolutionary community detection","longitudinal community detection"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010","originator":"Mucha, P. J. et al.","url":"https://scholargate.app/en/network-analysis/temporal-community-detection","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-community-detection.md","definition":"Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mucha, P. J. et al.","year":"2010","type":"Network clustering algorithm","dataType":"Time-stamped or snapshot-based network data","subfamily":"Network science"},"citations":[{"ref":"Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.","type":"article","doi":"10.1126/science.1184819","isbn":null,"url":null},{"ref":"Rossetti, G., & Cazabet, R. (2018). Community discovery in dynamic networks: A survey. ACM Computing Surveys, 51(2), 1–37.","type":"article","doi":"10.1145/3172867","isbn":null,"url":null}],"related":["modularity-analysis","temporal-network-analysis","multiplex-network-analysis","social-network-analysis","weighted-community-detection","directed-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-degree-centrality","name":"Temporal Degree Centrality","fullName":"Temporal Degree Centrality in Time-Varying Networks","aliases":["time-varying degree centrality","dynamic degree centrality","temporal node degree","TDC"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2011–2012","originator":"Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.","url":"https://scholargate.app/en/network-analysis/temporal-degree-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-degree-centrality.md","definition":"Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.","year":"2011–2012","type":"Centrality measure (temporal extension)","dataType":"Temporal edge lists or contact sequences with timestamps","subfamily":"Network science"},"citations":[{"ref":"Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null},{"ref":"Kim, H. & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2), 026107.","type":"article","doi":"10.1103/PhysRevE.85.026107","isbn":null,"url":null}],"related":["degree-centrality","temporal-betweenness-centrality","temporal-closeness-centrality","temporal-social-network-analysis","temporal-eigenvector-centrality","temporal-pagerank"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-dominance-of-sensations","name":"Temporal Dominance of Sensations","fullName":"Temporal Dominance of Sensations (TDS)","aliases":["TDS"],"domain":"food-science","family":"process-pipeline","subfamily":"Sensory Evaluation","year":"2009","originator":"Nathalie Pineau","url":"https://scholargate.app/en/food-science/temporal-dominance-of-sensations","markdownUrl":"https://scholargate.app/en/food-science/temporal-dominance-of-sensations.md","definition":"Temporal Dominance of Sensations (TDS) is a time-dynamic sensory evaluation method developed by Pineau and colleagues in 2009 that tracks which sensory attribute is perceived as dominant at each moment during the consumption of a food product. Unlike static descriptive methods, TDS captures the dynamic evolution of flavor, aroma, and texture sensations from the initial bite to swallowing, providing insight into the temporal structure of the eating experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nathalie Pineau","subfamily":"Sensory Evaluation","year":"2009","type":"Dynamic Sensory Method"},"citations":[{"ref":"Pineau, N., Schlich, P., Cordelle, S., Mathonniere, C., Issanchou, S., Imbert, A., ... & Köster, E. P. (2009). Temporal Dominance of Sensations: Construction of the TDS curves and comparison with time-intensity. Food Quality and Preference, 20(6), 450-455.","type":"article","doi":"10.1016/j.foodqual.2009.04.005","isbn":null,"url":null},{"ref":"Lenfant, F., Loret, C., Pineau, N., Hartmann, C., & Martin, N. (2009). Perception of oral food breakdown and texture changes during eating. Physiology & Behavior, 98(5), 588-594.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Perception+of+oral+food+breakdown+and+texture+changes+during+eating+Lenfant"}],"related":["texture-profile-analysis","quantitative-descriptive-analysis","just-about-right-scaling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-eigenvector-centrality","name":"Temporal Eigenvector Centrality","fullName":"Temporal Eigenvector Centrality (Dynamic Eigenvector-Based Node Importance in Time-Varying Networks)","aliases":["dynamic eigenvector centrality","time-varying eigenvector centrality","TEC","temporal communicability centrality"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2011-2017","originator":"Grindrod, P.; Higham, D. J.; Taylor, D. et al.","url":"https://scholargate.app/en/network-analysis/temporal-eigenvector-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-eigenvector-centrality.md","definition":"Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grindrod, P.; Higham, D. J.; Taylor, D. et al.","year":"2011-2017","type":"Centrality measure for temporal networks","dataType":"Time-stamped edge lists or ordered adjacency matrices","subfamily":"Network science"},"citations":[{"ref":"Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120.","type":"article","doi":"10.1103/PhysRevE.83.046120","isbn":null,"url":null},{"ref":"Taylor, D., Myers, S. A., Clauset, A., Porter, M. A., & Mucha, P. J. (2017). Eigenvector-based centrality measures for temporal networks. Multiscale Modeling and Simulation, 15(1), 537-574.","type":"article","doi":"10.1137/16M1066142","isbn":null,"url":null}],"related":["eigenvector-centrality","temporal-social-network-analysis","temporal-degree-centrality","temporal-betweenness-centrality","temporal-pagerank","multilayer-eigenvector-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-expression-extraction","name":"Temporal Expression Extraction","fullName":"Temporal Expression Extraction and Normalisation (TIMEX)","aliases":["TIMEX","temporal tagging","TIMEX3 extraction","Zamansal İfade Çıkarma (TIMEX)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/temporal-expression-extraction","markdownUrl":"https://scholargate.app/en/text-mining/temporal-expression-extraction.md","definition":"Temporal expression extraction is a natural-language-processing task that detects dates, times, durations, and frequencies in text and normalises them to the TimeML/TIMEX3 standard. Building on the TempEval shared task introduced by Verhagen et al. (2007), it turns time references scattered through free text into structured, machine-readable values that support event timelines and chronological analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP information-extraction task","standard":"TimeML / TIMEX3 annotation","captures":"Dates, times, durations, and recurring frequencies","output":"Normalised temporal expressions for timelines and chronological analysis"},"citations":[{"ref":"Verhagen, M. et al. (2007). SemEval-2007 Task 15: TempEval Temporal Relation Identification.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/S07-1014/"},{"ref":"Strötgen, J. & Gertz, M. (2013). Multilingual and Cross-Domain Temporal Tagging. Language Resources and Evaluation (LRE).","type":"article","doi":"10.1007/s10579-012-9179-y","isbn":null,"url":null}],"related":["named-entity-recognition","event-extraction","relation-extraction"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-fusion-transformer","name":"Temporal Fusion Transformer","fullName":"Temporal Fusion Transformer for Interpretable Multi-Horizon Time Series Forecasting","aliases":["Temporal Fusion Transformer (TFT)","TFT","interpretable multi-horizon forecasting transformer"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2021,"originator":"Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T.","url":"https://scholargate.app/en/deep-learning/temporal-fusion-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/temporal-fusion-transformer.md","definition":"The Temporal Fusion Transformer (TFT), introduced by Lim, Arık, Loeff and Pfister in 2021, is an interpretable deep learning architecture for multi-horizon time series forecasting. It combines variable selection, gating, multi-horizon attention and quantile outputs, processing static, past and known-future inputs together to produce multi-step forecasts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T.","year":2021,"type":"Attention-based deep learning forecasting architecture","task":"Multi-horizon time series forecasting","minSample":200,"structure":"Time series & panel data","gpuRecommended":true},"citations":[{"ref":"Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. (2021). Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. International Journal of Forecasting, 37(4), 1748–1764.","type":"article","doi":"10.1016/j.ijforecast.2021.03.012","isbn":null,"url":null},{"ref":"Lim, B. & Zohren, S. (2021). Time-Series Forecasting with Deep Learning: A Survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209.","type":"article","doi":"10.1098/rsta.2020.0209","isbn":null,"url":null}],"related":["informer","deepar","nhits","patchtst","arima","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-knowledge-graph-analysis","name":"Temporal Knowledge Graph Analysis","fullName":"Temporal Knowledge Graph Analysis (TKG Analysis)","aliases":["TKG analysis","temporal KG analysis","dynamic knowledge graph analysis","time-aware knowledge graph analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2017–2018","originator":"Trivedi, R. et al.; Dasgupta, S. S. et al.","url":"https://scholargate.app/en/network-analysis/temporal-knowledge-graph-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-knowledge-graph-analysis.md","definition":"Temporal Knowledge Graph Analysis extends standard knowledge graph methods to data where facts and relationships carry timestamps or validity intervals. It enables reasoning about how entities and relations evolve over time, supporting tasks such as link prediction for future facts, temporal relation classification, and event forecasting in dynamic relational data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Trivedi, R. et al.; Dasgupta, S. S. et al.","year":"2017–2018","type":"Temporal graph embedding and reasoning","dataType":"Timestamped relational triples (subject, predicate, object, time)","subfamily":"Network science"},"citations":[{"ref":"Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017). Know-Evolve: Deep temporal reasoning for dynamic knowledge graphs. Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 3462–3471.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Know-Evolve+Deep+temporal+reasoning+dynamic+knowledge+graphs+Trivedi+2017"},{"ref":"Dasgupta, S. S., Ray, S. N., & Talukdar, P. (2018). HyTE: Hyperplane-based temporally aware knowledge graph embedding. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2001–2011.","type":"inproceedings","doi":"10.18653/v1/D18-1225","isbn":null,"url":null}],"related":["knowledge-graph-analysis","temporal-network-diffusion-analysis","temporal-social-network-analysis","temporal-community-detection","temporal-exponential-random-graph-model","multilayer-knowledge-graph-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-modularity-analysis","name":"Temporal Modularity Analysis","fullName":"Temporal Modularity Analysis (Dynamic Community Detection via Modularity Optimization)","aliases":["dynamic modularity","time-varying modularity","longitudinal community detection","temporal community structure analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010","originator":"Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P.","url":"https://scholargate.app/en/network-analysis/temporal-modularity-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-modularity-analysis.md","definition":"Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P.","year":"2010","type":"Community detection (temporal extension of modularity optimization)","dataType":"Time-stamped or time-sliced network data (adjacency matrices at multiple time points)","subfamily":"Network science"},"citations":[{"ref":"Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876-878.","type":"article","doi":"10.1126/science.1184819","isbn":null,"url":null},{"ref":"Holme, P., & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97-125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null}],"related":["modularity-analysis","temporal-community-detection","temporal-social-network-analysis","weighted-modularity-analysis","multilayer-modularity-analysis","dynamic-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-multiplex-network-analysis","name":"Temporal Multiplex Network Analysis","fullName":"Temporal Multiplex Network Analysis (Time-varying Multi-layer Network Analysis)","aliases":["TMNA","time-varying multiplex network analysis","dynamic multiplex network analysis","temporal multilayer network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2012–2014","originator":"Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors)","url":"https://scholargate.app/en/network-analysis/temporal-multiplex-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-multiplex-network-analysis.md","definition":"Temporal multiplex network analysis studies relational systems in which actors are connected by multiple distinct types of relationships that all evolve over time. By simultaneously tracking layer heterogeneity and temporal dynamics, the method reveals how different interaction channels co-evolve, which actors hold persistent cross-layer influence, and how structural changes propagate across relationship types and time periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors)","year":"2012–2014","type":"Structural and dynamic network analysis","dataType":"Time-stamped multiplex edge lists; longitudinal relational data across multiple relationship types","subfamily":"Network science"},"citations":[{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null},{"ref":"Holme, P., & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null}],"related":["multiplex-network-analysis","temporal-network-analysis","multilayer-network-analysis","temporal-community-detection","dynamic-community-detection","social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-network-analysis","name":"Temporal Network Analysis","fullName":"Temporal Network Analysis (Dynamic Networks)","aliases":["dynamic network analysis","time-varying network analysis","Zamansal Ağ Analizi (Temporal / Dynamic Networks)"],"domain":"network-analysis","family":"process-pipeline","subfamily":null,"year":"2012","originator":"Holme & Saramäki (2012) — seminal framework","url":"https://scholargate.app/en/network-analysis/temporal-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-network-analysis.md","definition":"Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holme & Saramäki (2012) — seminal framework","year":"2012","type":"Dynamic graph analysis","dataStructure":"Time-stamped edge lists or windowed snapshots","minimumNodes":20,"difficulty":"Intermediate (3/5)","output":"Temporal centrality, reachability, event-driven diffusion metrics"},"citations":[{"ref":"Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null},{"ref":"Masuda, N. & Lambiotte, R. (2016). A Guide to Temporal Networks. World Scientific.","type":"book","doi":"10.1142/q0033","isbn":null,"url":null}],"related":["multilayer-network-analysis","centrality-analysis","community-detection","social-network-analysis","time-series-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-network-diffusion-analysis","name":"Temporal Network Diffusion Analysis","fullName":"Temporal Network Diffusion Analysis (Time-Varying Spreading Processes on Dynamic Networks)","aliases":["TNDA","dynamic network diffusion","time-varying network spreading","diffusion on temporal networks"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2012","originator":"Holme, P. & Saramäki, J.","url":"https://scholargate.app/en/network-analysis/temporal-network-diffusion-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-network-diffusion-analysis.md","definition":"Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holme, P. & Saramäki, J.","year":"2012","type":"Network analysis framework","dataType":"Time-stamped edge lists, event sequences, contact networks","subfamily":"Network science"},"citations":[{"ref":"Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null},{"ref":"Masuda, N. & Lambiotte, R. (2016). A Guide to Temporal Networks. World Scientific.","type":"book","doi":null,"isbn":"978-1-78634-052-4","url":null}],"related":["network-diffusion-analysis","temporal-social-network-analysis","temporal-community-detection","temporal-betweenness-centrality","multiplex-network-analysis","exponential-random-graph-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-pagerank","name":"Temporal PageRank","fullName":"Temporal PageRank (Time-Aware Node Importance Ranking in Temporal Networks)","aliases":["TPR","time-aware PageRank","streaming PageRank","dynamic PageRank"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2016","originator":"Rozenshtein, P. & Gionis, A.","url":"https://scholargate.app/en/network-analysis/temporal-pagerank","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-pagerank.md","definition":"Temporal PageRank extends the classic PageRank algorithm to time-evolving networks by incorporating the recency and ordering of interactions. Edges are weighted by a decay function so that recent contacts contribute more to a node's score than old ones. The result is a dynamic importance ranking that captures who is influential right now, rather than over the entire history of the network.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rozenshtein, P. & Gionis, A.","year":"2016","type":"Centrality / ranking algorithm for temporal networks","dataType":"Timestamped edge lists (temporal graphs)","subfamily":"Network science"},"citations":[{"ref":"Rozenshtein, P. & Gionis, A. (2016). Temporal PageRank. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part II, LNCS 9852, pp. 674–689. Springer.","type":"inproceedings","doi":"10.1007/978-3-319-46227-1_42","isbn":null,"url":null},{"ref":"Lerman, K. & Ghosh, R. (2010). Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 90–97. AAAI Press.","type":"inproceedings","doi":null,"isbn":null,"url":"https://ojs.aaai.org/index.php/ICWSM/article/view/14033"}],"related":["temporal-social-network-analysis","temporal-betweenness-centrality","temporal-eigenvector-centrality","directed-pagerank","network-diffusion-analysis","temporal-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-social-network-analysis","name":"Temporal Social Network Analysis","fullName":"Temporal Social Network Analysis (Longitudinal and Time-Varying Network Analysis)","aliases":["TSNA","longitudinal social network analysis","time-varying network analysis","dynamic SNA"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2000s–2010s","originator":"Moody, J.; Holme, P.; Saramäki, J.","url":"https://scholargate.app/en/network-analysis/temporal-social-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-social-network-analysis.md","definition":"Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Moody, J.; Holme, P.; Saramäki, J.","year":"2000s–2010s","type":"Longitudinal network analysis","dataType":"Time-stamped relational / panel network data","subfamily":"Network science"},"citations":[{"ref":"Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null},{"ref":"Moody, J., McFarland, D., & Bender-deMoll, S. (2005). Dynamic network visualization. American Journal of Sociology, 110(4), 1206–1241.","type":"article","doi":"10.1086/421509","isbn":null,"url":null}],"related":["social-network-analysis","dynamic-social-network-analysis","multiplex-network-analysis","exponential-random-graph-model","temporal-community-detection","network-diffusion-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-stochastic-block-model","name":"Temporal Stochastic Block Model","fullName":"Temporal Stochastic Block Model (Dynamic Community Detection via SBM)","aliases":["TSBM","dynamic stochastic block model","time-varying SBM","evolving block model"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2014–2017","originator":"Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V.","url":"https://scholargate.app/en/network-analysis/temporal-stochastic-block-model","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-stochastic-block-model.md","definition":"The Temporal Stochastic Block Model (TSBM) extends the classic Stochastic Block Model to sequences of network snapshots, jointly inferring latent community memberships and how those memberships evolve across time. It combines a generative edge-probability model with a Markov process over block assignments, enabling principled statistical detection of community structure that changes over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V.","year":"2014–2017","type":"Generative probabilistic model","dataType":"Time-stamped edge lists or adjacency matrices across discrete time steps","subfamily":"Network science"},"citations":[{"ref":"Matias, C. & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141.","type":"article","doi":"10.1111/rssb.12200","isbn":null,"url":null},{"ref":"Xu, K. S. & Hero, A. O. (2014). Dynamic stochastic blockmodels for time-evolving social networks. IEEE Journal of Selected Topics in Signal Processing, 8(4), 552–562.","type":"article","doi":"10.1109/JSTSP.2014.2310294","isbn":null,"url":null}],"related":["stochastic-block-model","temporal-community-detection","temporal-modularity-analysis","exponential-random-graph-model","temporal-exponential-random-graph-model","multilayer-stochastic-block-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporal-two-mode-network-analysis","name":"Temporal Two-Mode Network Analysis","fullName":"Temporal Two-Mode (Bipartite) Network Analysis","aliases":["temporal bipartite network analysis","dynamic two-mode network analysis","time-varying bipartite network analysis","longitudinal affiliation network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1990s–2010s","originator":"Borgatti, S. P. & Everett, M. G. (two-mode foundations); extended to temporal setting by multiple authors","url":"https://scholargate.app/en/network-analysis/temporal-two-mode-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/temporal-two-mode-network-analysis.md","definition":"Temporal two-mode network analysis tracks relationships between two distinct classes of nodes — such as authors and publications, or actors and events — across multiple time points. By combining bipartite structure with longitudinal observation, it reveals how affiliation patterns, collaborations, and community memberships form, evolve, and dissolve over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Borgatti, S. P. & Everett, M. G. (two-mode foundations); extended to temporal setting by multiple authors","year":"1990s–2010s","type":"Network analysis technique","dataType":"Longitudinal bipartite (two-mode) relational data","subfamily":"Network science"},"citations":[{"ref":"Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.","type":"article","doi":"10.1016/S0378-8733(96)00301-2","isbn":null,"url":null},{"ref":"Latapy, M., Magnien, C., & Del Vecchio, N. (2008). Basic notions for the analysis of large two-mode networks. Social Networks, 30(1), 31–48.","type":"article","doi":"10.1016/j.socnet.2007.04.006","isbn":null,"url":null}],"related":["two-mode-network-analysis","temporal-network-analysis","social-network-analysis","modularity-analysis","temporal-community-detection","multilayer-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporomandibular-disorder-scale","name":"RDC/TMD","fullName":"Research Diagnostic Criteria for Temporomandibular Disorders","aliases":["RDC/TMD","Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD)"],"domain":"dentistry","family":"process-pipeline","subfamily":"temporomandibular-disorder-diagnosis","year":"1992 (original), 2014 (current DC/TMD)","originator":"Schiffman, Ohrbach, and International Consortium","url":"https://scholargate.app/en/dentistry/temporomandibular-disorder-scale","markdownUrl":"https://scholargate.app/en/dentistry/temporomandibular-disorder-scale.md","definition":"The Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) is a comprehensive, evidence-based diagnostic system for identifying and classifying temporomandibular disorders (TMD)—a group of painful and functional conditions affecting the jaw joint, muscles of mastication, and related structures. Originally developed in 1992 by Schiffman and colleagues and updated to the Diagnostic Criteria for TMD (DC/TMD) in 2014, the RDC/TMD is the international gold standard for TMD diagnosis in research and clinical practice. It combines structured patient history, pain questionnaires, and standardized clinical examination to reliably diagnose muscle disorders, intra-articular disorders, and headache associated with TMD.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schiffman, Ohrbach, and International Consortium","subfamily":"temporomandibular-disorder-diagnosis","year":"1992 (original), 2014 (current DC/TMD)","type":"Structured diagnostic interview and clinical examination"},"citations":[{"ref":"Schiffman, E., Ohrbach, R., Truelove, E., Look, J., Anderson, G., Goulet, J.-P., & Drangsholt, M. (2014). Diagnostic criteria for temporomandibular disorders (DC/TMD) for clinical and research applications: Recommendations of the International RDC/TMD Consortium Network and Orofacial Pain Special Interest Group. Journal of Oral & Facial Pain and Headache, 28(1), 6-27.","type":"article","doi":"10.11607/jop.1151","isbn":null,"url":null}],"related":["ohip-14","xerostomia-inventory","dental-anxiety-modified-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"temporomandibular-joint-analysis","name":"Temporomandibular Joint Analysis","fullName":"TMJ Assessment and Dysfunction Evaluation","aliases":["TMJ examination","TMD assessment","jaw joint evaluation"],"domain":"dentistry","family":"process-pipeline","subfamily":"TMD and jaw dysfunction","year":"1934 (Costen syndrome); 1960s+ (modern understanding)","originator":"Multiple innovators (Costen, Laskin, Okeson, et al.)","url":"https://scholargate.app/en/dentistry/temporomandibular-joint-analysis","markdownUrl":"https://scholargate.app/en/dentistry/temporomandibular-joint-analysis.md","definition":"Temporomandibular Joint (TMJ) analysis is a systematic clinical assessment and imaging evaluation of the jaw joint, including the articular disc, condyle, and associated musculature. TMJ analysis evaluates joint function, detects dysfunction (TMD), and guides diagnosis and treatment planning for jaw pain, clicking, locking, and limited opening. Comprehensive assessment integrates clinical examination with imaging (magnetic resonance imaging, cone-beam computed tomography) to characterize joint status and tailor treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple innovators (Costen, Laskin, Okeson, et al.)","subfamily":"TMD and jaw dysfunction","year":"1934 (Costen syndrome); 1960s+ (modern understanding)","type":"Clinical and imaging assessment"},"citations":[{"ref":"Okeson, J. P. (2020). Management of temporomandibular disorders and occlusion (8th ed.). Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/management-of-temporomandibular-disorders-and-occlusion/okeson/978-0-323-56657-0"},{"ref":"Schiffman, E., Ohrbach, R., Truelove, E., et al. (2014). Diagnostic criteria for temporomandibular disorders (DC/TMD) for clinical and research applications. Journal of Oral & Facial Pain and Headache, 28(1), 6-27.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Diagnostic+criteria+for+temporomandibular+disorders+%28DC%2FTMD%29+for+clinical+and+research+applications+Schiffman"},{"ref":"de Kanter, R. J., Truin, G. J., Burgersdijk, R. C., et al. (1993). Prevalence in the Dutch adult population and a meta-analysis of the association with potential risk factors. Journal of Dental Research, 72(11), 1509-1518.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prevalence+in+the+Dutch+adult+population+and+a+meta-analysis+of+the+association+with+potential+risk+factors"}],"related":["occlusal-analysis","periodontal-probing","tooth-mobility-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ten-meter-walk-test","name":"Ten-Meter Walk Test","fullName":"Ten-Meter Walk Test (10MWT)","aliases":["10MWT","10-meter walk test"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Gait assessment","year":"1980s","originator":"Rehabilitation research community","url":"https://scholargate.app/en/physical-therapy/ten-meter-walk-test","markdownUrl":"https://scholargate.app/en/physical-therapy/ten-meter-walk-test.md","definition":"The Ten-Meter Walk Test (10MWT) is a straightforward performance assessment measuring gait speed over a 10-meter distance. Used extensively in neurological rehabilitation, the 10MWT provides objective data on walking velocity, a key indicator of functional mobility, recovery after stroke, and response to intervention in individuals with gait impairments from various causes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rehabilitation research community","subfamily":"Gait assessment","year":"1980s","type":"Performance-based test"},"citations":[{"ref":"Gait speeds in clinical populations. Journal of the American Geriatrics Society, 40(10), 1071-1076.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/1918272/"},{"ref":"Collen, F. M., Wade, D. T., & Robb, G. F. (1991). The Rivermead Mobility Index: A further development of the Rivermead Motor Assessment. International Disability Studies, 13(2), 50-54.","type":"article","doi":"10.3109/03790799109166684","isbn":null,"url":null}],"related":["six-minute-walk-test","timed-up-and-go-test","berg-balance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tephrochronology","name":"Tephrochronology","fullName":"Tephrochronology (Tephra Dating)","aliases":["tephra chronology","volcanic ash dating"],"domain":"archaeology","family":"process-pipeline","subfamily":"Stratigraphy","year":"1944","originator":"Sigurdur Thorarinsson","url":"https://scholargate.app/en/archaeology/tephrochronology","markdownUrl":"https://scholargate.app/en/archaeology/tephrochronology.md","definition":"Tephrochronology is a chronometric and stratigraphic technique that uses volcanic ash layers (tephra) as time markers to date and correlate archaeological and geological deposits. Pioneered by Icelandic geologist Sigurdur Thorarinsson in 1944, it exploits the fact that large explosive volcanic eruptions deposit distinctive ash layers across vast geographic regions instantaneously in geological time. Each tephra layer serves as a chronological marker horizon that can be identified, characterized, and dated, enabling archaeologists to synchronize deposits across multiple sites.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sigurdur Thorarinsson","subfamily":"Stratigraphy","year":"1944","type":"Volcanic marker dating technique"},"citations":[{"ref":"Thorarinsson, S. (1944). Tefrokronologiska studier på Island. Geografiska Annaler, 26(1-2), 1-217.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/20631084"},{"ref":"Lowe, D. J., & Hunt, J. B. (1992). Tephrochronology and archaeology: an introduction. In C. M. Turney, K. A. Dodson, & K. C. Ker (Eds.), Quaternary of New Zealand (pp. 27-35). Royal Society of New Zealand Bulletin.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.3126/njg.v41i0.43649"},{"ref":"Froese, D. G., Westgate, J. A., Reyes, A. V., Enkin, R. J., & Preece, S. J. (2006). Ancient bacteria and a dinosaur-like smell. Geology, 34(9), 757-760.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Ancient+bacteria+and+a+dinosaur-like+smell+Froese"}],"related":["archaeomagnetic-dating","radiocarbon-dating","optically-stimulated-luminescence-dating","tephrochronology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tercom","name":"TERCOM","fullName":"Terrain Contour Matching","aliases":["TerCom","terrain-aided navigation","TANS"],"domain":"aerospace","family":"process-pipeline","subfamily":"Terrain-Aided Navigation","year":"1980s","originator":"Boeing, military guidance","url":"https://scholargate.app/en/aerospace/tercom","markdownUrl":"https://scholargate.app/en/aerospace/tercom.md","definition":"Terrain Contour Matching (TERCOM) is a terrain-aided navigation method that corrects position estimates by matching altimeter measurements against a stored digital elevation map (DEM). Developed by Boeing in the 1980s for cruise missile guidance, TERCOM enables accurate navigation in GPS-denied environments by exploiting the unique terrain signature at each location. TERCOM remains essential for missile guidance, autonomous underwater vehicles, and systems operating in jamming scenarios.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Boeing, military guidance","subfamily":"Terrain-Aided Navigation","year":"1980s","type":"Localization method"},"citations":[{"ref":"Golden, J. P. (1983). Terrain contour matching (TERCOM): A cruise missile guidance aid. In In-Flight Measurement Technology. AGARD Conference Proceedings No. 336, 3–1 to 3–16.","type":"article","doi":null,"isbn":null,"url":"https://apps.dtic.mil/sti/pdfs/ADA131272.pdf"},{"ref":"Grejner-Brzezinska, D. A. (2001). Terrain-aided navigation using neural networks. In Proceedings of the 14th International Technical Meeting of the Satellite Division of the US Institute of Navigation, Salt Lake City, Utah, 11–14 September 2001, 2033–2041.","type":"article","doi":null,"isbn":null,"url":"https://www.ion.org/publications"},{"ref":"Hein, G. W., Brynjarsson, F., Denks, H., Godet, J., Landau, H., & Erker, S. (2012). Enhancements to the GNSS augmentation service WAAS and modernization. In Proceedings of the Institute of Navigation, GNSS 2012 Conference.","type":"article","doi":null,"isbn":null,"url":"https://www.ion.org/publications"}],"related":["dead-reckoning","gnss-rtk","ins-error-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"terzaghi-consolidation","name":"Terzaghi Consolidation","fullName":"Terzaghi One-Dimensional Consolidation Theory","aliases":["Primary consolidation","Soil settlement","Effective stress"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Soil mechanics","year":"1943","originator":"Karl Terzaghi","url":"https://scholargate.app/en/civil-engineering/terzaghi-consolidation","markdownUrl":"https://scholargate.app/en/civil-engineering/terzaghi-consolidation.md","definition":"Terzaghi consolidation theory describes how water-saturated clay soils compress over time as excess pore water pressure dissipates and effective stress increases. Formulated by Karl Terzaghi in 1943, this foundational theory enables prediction of settlement rates for foundations on compressible soils, a critical design concern in geotechnical engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Terzaghi","subfamily":"Soil mechanics","year":"1943","type":"Diffusion equation for pore pressure dissipation and soil settlement"},"citations":[{"ref":"Terzaghi, K. (1943). Theoretical Soil Mechanics. John Wiley & Sons.","type":"book","doi":null,"isbn":"0-471-85305-1","url":null},{"ref":"Taylor, D. W. (1948). Fundamentals of Soil Mechanics. John Wiley & Sons.","type":"article","doi":null,"isbn":"0-471-85305-1","url":null},{"ref":"Kelly, R. B. (1995). Settlement of embankments on soft soils. Journal of Geotechnical Engineering, 121(5), 373-384.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Settlement+of+embankments+on+soft+soils+Kelly"}],"related":["soil-structure-interaction","slope-stability","modflow"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"test-anxiety-inventory","name":"Test Anxiety Inventory","fullName":"Test Anxiety Inventory (TAI)","aliases":["TAI"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"emotional-responses-academic","year":"1980","originator":"Spielberger, C. D.","url":"https://scholargate.app/en/educational-psychology/test-anxiety-inventory","markdownUrl":"https://scholargate.app/en/educational-psychology/test-anxiety-inventory.md","definition":"The Test Anxiety Inventory measures the situational anxiety experienced during examinations, distinguishing between cognitive worry and physiological emotionality. Developed by Spielberger in 1980, the TAI provides educators and clinicians with a validated assessment of test-specific anxiety—a prevalent barrier to academic success that affects performance disproportionately to ability. Early identification and targeted intervention can substantially improve both well-being and exam performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Spielberger, C. D.","subfamily":"emotional-responses-academic","year":"1980","type":"Self-report questionnaire"},"citations":[{"ref":"Spielberger, C. D. (1980). Test Anxiety Inventory: Preliminary professional manual. Consulting Psychologists Press.","type":"article","doi":null,"isbn":null,"url":"https://www.mindgarden.com/159-test-anxiety-inventory"},{"ref":"Zeidner, M. (1998). Test anxiety: The state of the art. Kluwer Academic.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Test+anxiety%3A+The+state+of+the+art+Zeidner"}],"related":["academic-burnout-scale","academic-resilience-scale","study-skills-assessment","academic-help-seeking-scale","procrastination-assessment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"test-anxiety-scale","name":"Westside Test Anxiety Scale","fullName":"Westside Test Anxiety Scale (WTAS)","aliases":["WTAS"],"domain":"anxiety-disorders","family":"process-pipeline","subfamily":"academic-anxiety","year":2007,"originator":"Ralph Driscoll and colleagues","url":"https://scholargate.app/en/anxiety-disorders/test-anxiety-scale","markdownUrl":"https://scholargate.app/en/anxiety-disorders/test-anxiety-scale.md","definition":"The Westside Test Anxiety Scale (WTAS) is a 10-item self-report questionnaire measuring the intensity of anxiety and worry experienced before, during, and after academic tests. Developed by Ralph Driscoll and validated in 2007, the WTAS assesses the cognitive (worry, negative self-talk) and somatic (tension, trembling, nausea) dimensions of test anxiety. It is widely used in educational psychology, academic counseling, and cognitive-behavioral research to identify students at risk for test anxiety and to monitor intervention effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralph Driscoll and colleagues","subfamily":"academic-anxiety","year":2007,"type":"Self-report"},"citations":[{"ref":"Driscoll, R. (2007). Westside Test Anxiety Scale validation. Paper presented at the Association for the Advancement of Educational Research, International Convention, Chicago.","type":"article","doi":null,"isbn":null,"url":"https://www.mesastate.edu/~mccloske/Driscoll%20test%20anxiety.pdf"}],"related":["math-anxiety-rating-scale","anxiety-sensitivity-index","specific-phobia-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"test-equating","name":"Test Equating","fullName":"Test Equating, Scaling, and Linking","aliases":["Test Eşitleme (Test Equating)","score equating","equipercentile equating","IRT true-score equating","linear equating"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":"1984 (modern statistical treatment)","originator":"Kolen & Brennan (foundational treatise, 2004/2014); Holland & Dorans (2006)","url":"https://scholargate.app/en/psychometrics/test-equating","markdownUrl":"https://scholargate.app/en/psychometrics/test-equating.md","definition":"Test equating is a family of statistical methods that converts scores earned on one test form onto the score scale of another form, so that scores from different administrations or versions can be compared and reported on a common metric. The foundational modern treatment is Kolen and Brennan (2004/2014); Holland and Dorans (2006) provide the authoritative chapter-length overview within the field of educational measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kolen & Brennan (foundational treatise, 2004/2014); Holland & Dorans (2006)","year":"1984 (modern statistical treatment)","type":"Score transformation / latent-scale calibration","outcome":"Concordance table mapping raw scores on one test form to the score scale of another","data":"Raw or IRT-scaled scores from two or more test forms","min_sample":300,"design":"NEAT (Non-Equivalent groups with Anchor Test) or single-group / random-groups","difficulty":3},"citations":[{"ref":"Kolen, M.J. & Brennan, R.L. (2014). Test Equating, Scaling, and Linking: Methods and Practices (3rd ed.). Springer.","type":"book","doi":null,"isbn":"978-1-4939-0316-6","url":null},{"ref":"Holland, P.W. & Dorans, N.J. (2006). Linking and Equating. In R.L. Brennan (Ed.), Educational Measurement (4th ed., pp. 187–220). American Council on Education / Praeger.","type":"chapter","doi":null,"isbn":null,"url":"https://www.ets.org/Media/Research/pdf/RM-06-18.pdf"}],"related":["item-response-theory","generalizability-theory","confirmatory-factor-analysis","classical-test-theory","rasch-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"test-retest-reliability","name":"Test-Retest Reliability","fullName":"Test-Retest Reliability","aliases":["stability reliability","temporal stability","repeatability coefficient","TRT reliability"],"domain":"psychometrics","family":"latent-structure","subfamily":"Scale / measurement","year":"1904","originator":"Karl Pearson","url":"https://scholargate.app/en/psychometrics/test-retest-reliability","markdownUrl":"https://scholargate.app/en/psychometrics/test-retest-reliability.md","definition":"Test-retest reliability quantifies the temporal consistency of a measure by correlating scores obtained from the same participants on two separate occasions. It is a cornerstone of psychometric validation, directly indicating whether a scale or instrument yields stable scores when the underlying construct has not changed.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karl Pearson","year":"1904","type":"Reliability estimate","dataType":"Continuous or ordinal scores from two administrations","subfamily":"Scale / measurement"},"citations":[{"ref":"Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070478497","url":null},{"ref":"Anastasi, A. & Urbina, S. (1997). Psychological Testing (7th ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0023030857","url":null}],"related":["cronbachs-alpha","mcdonalds-omega","interrater-reliability","confirmatory-factor-analysis","measurement-invariance","generalizability-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"texas-revised-inventory-grief","name":"TRIG","fullName":"Texas Revised Inventory of Grief","aliases":["TRIG","Faschingbauer TRIG","Revised Inventory of Grief"],"domain":"bereavement-psychology","family":"process-pipeline","subfamily":"multidimensional-grief-assessment","year":"1987","originator":"Thomas R. Faschingbauer, Sidney Zisook, Richard DeVaul","url":"https://scholargate.app/en/bereavement-psychology/texas-revised-inventory-grief","markdownUrl":"https://scholargate.app/en/bereavement-psychology/texas-revised-inventory-grief.md","definition":"The Texas Revised Inventory of Grief (TRIG) is a 21-item multidimensional measure developed by Faschingbauer, Zisook, and DeVaul in 1987 to assess both past grief behaviors (how the person grieved when the death occurred) and present grief feelings (current emotional response to loss). The TRIG is unique in distinguishing historical grief response from contemporary grief state, providing a comprehensive temporal and dimensional profile of bereavement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas R. Faschingbauer, Sidney Zisook, Richard DeVaul","subfamily":"multidimensional-grief-assessment","year":"1987","type":"Self-report questionnaire"},"citations":[{"ref":"Faschingbauer, T. R., Zisook, S., & DeVaul, R. (1987). The Texas Revised Inventory of Grief. In S. Zisook (Ed.), Biopsychosocial aspects of bereavement (pp. 111–124). American Psychiatric Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Faschingbauer%2C%20T.%20R.%2C%20Zisook%2C%20S.%2C%20%26%20DeVaul%2C%20R.%20(1987).%20The%20Texas%20Revised%20Inventory%20of%20Grief.%20In%20S.%20Zisook%20(Ed.)%2C%20Biopsyc"}],"related":["inventory-complicated-grief","hogan-grief-reaction-checklist","grief-experience-questionnaire","anticipatory-grief-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-classification","name":"Text Classification","fullName":"Text Classification (Text Categorization)","aliases":["text categorization","document classification","topic classification","metin sınıflandırma"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/text-classification","markdownUrl":"https://scholargate.app/en/text-mining/text-classification.md","definition":"Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Supervised NLP classification task","task":"Assign predefined category labels to text","minSample":100,"output":"Category label per document","commonUses":"Spam detection, topic classification"},"citations":[{"ref":"Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.","type":"inproceedings","doi":"10.1007/BFb0026683","isbn":null,"url":null},{"ref":"Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer.","type":"book","doi":null,"isbn":"978-1-4614-3222-7","url":null}],"related":["sentiment-analysis","document-clustering","keyword-extraction","tf-idf"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-coherence-scoring","name":"Text Coherence Scoring","fullName":"Text Coherence Scoring (Local Coherence Modeling)","aliases":["coherence modeling","local coherence assessment","Metin Tutarlılık Puanlaması"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2008,"originator":"Barzilay & Lapata","url":"https://scholargate.app/en/text-mining/text-coherence-scoring","markdownUrl":"https://scholargate.app/en/text-mining/text-coherence-scoring.md","definition":"Text coherence scoring computes a document-level coherence score with machine learning, rooted in the entity-based local coherence model introduced by Barzilay and Lapata (2008). It measures how well the sentences of a text hang together, using either an entity-grid model, a graph-based approach, or a transformer-based model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barzilay & Lapata","year":2008,"type":"NLP text-level scoring task","approaches":"Entity-grid model / graph-based / transformer-based","output":"Document-level coherence score"},"citations":[{"ref":"Barzilay, R. & Lapata, M. (2008). Modeling Local Coherence: An Entity-Based Approach. Computational Linguistics, 34(1), 1-34.","type":"article","doi":"10.1162/coli.2008.34.1.1","isbn":null,"url":null},{"ref":"Guinaudeau, C. & Strube, M. (2013). Graph-based Local Coherence Modeling. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), 93-103.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P13-1010/"}],"related":["automatic-text-evaluation","sentiment-analysis","text-classification","bert-embeddings"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-complexity-analysis","name":"Text Complexity Analysis","fullName":"Text Complexity and Readability Analysis","aliases":["readability analysis","linguistic complexity assessment","Metin Karmaşıklığı Analizi"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/text-complexity-analysis","markdownUrl":"https://scholargate.app/en/text-mining/text-complexity-analysis.md","definition":"Text complexity analysis measures the linguistic difficulty of a text along dimensions such as syntactic complexity (sentence length, embedded clauses), lexical density, and referential chains. Grounded in readability research consolidated by Vajjala and Meurers (2014) and Crossley and colleagues (2011), it turns prose into quantitative scores that estimate how hard a document is to read.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Linguistic-feature measurement pipeline","measures":"Syntactic complexity, lexical density, referential cohesion","formulas":"Flesch, Gunning-Fog, SMOG and related readability indices","minSample":"10 texts","output":"Readability and complexity scores per text"},"citations":[{"ref":"Vajjala, S. & Meurers, D. (2014). Readability Assessment for Text Simplification: From Analysing Documents to Identifying Sentential Simplifications. International Journal of Applied Linguistics, 165(2), 194-222.","type":"article","doi":"10.1075/itl.165.2.04vaj","isbn":null,"url":null},{"ref":"Crossley, S.A., Allen, D.B. & McNamara, D.S. (2011). Text Readability and Intuitive Simplification: A Comparison of Readability Formulas. Reading in a Foreign Language, 23(1), 84-101.","type":"article","doi":null,"isbn":null,"url":"https://nflrc.hawaii.edu/rfl/April2011/articles/crossley.pdf"}],"related":["sentiment-analysis","constituency-parsing","part-of-speech-tagging"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-deduplication","name":"Text Deduplication","fullName":"Text Deduplication (Near-Duplicate Detection)","aliases":["near-duplicate detection","document deduplication","corpus deduplication","Metin Tekilleştirme (Near-Duplicate Detection)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1997","originator":"Andrei Z. Broder (MinHash / Resemblance theory, 1997)","url":"https://scholargate.app/en/text-mining/text-deduplication","markdownUrl":"https://scholargate.app/en/text-mining/text-deduplication.md","definition":"Text deduplication is a corpus-quality pipeline that identifies and removes exact and near-duplicate documents from large text collections. Grounded in Andrei Broder's 1997 resemblance theory, it is widely used to improve dataset quality for machine learning model training, search engine indexing, and any downstream NLP task that assumes a non-redundant corpus.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrei Z. Broder (MinHash / Resemblance theory, 1997)","year":"1997","type":"Text preprocessing / corpus quality pipeline","coreTechnique":"MinHash + Locality-Sensitive Hashing (LSH)","similarityThreshold":"User-defined (e.g., Jaccard ≥ 0.5)","minimumCorpusSize":"50 documents (LSH recommended for large collections)"},"citations":[{"ref":"Broder, A.Z. (1997). On the Resemblance and Containment of Documents. Compression and Complexity of SEQUENCES.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.cs.princeton.edu/courses/archive/spring04/cos598B/bib/BroderCPSS97.pdf"},{"ref":"Lee, K. et al. (2022). Deduplicating Training Data Makes Language Models Better. ACL 2022.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2022.acl-long.577"}],"related":["sentiment-analysis","tf-idf","text-classification","bert-embeddings","topic-modeling"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-infilling","name":"Text Infilling","fullName":"Text Infilling (Cloze Completion)","aliases":["cloze procedure","cloze test","masked language modeling","span infilling","Metin Doldurma (Text Infilling / Cloze)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"1953 (cloze); 2019 (neural span infilling)","originator":"Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019)","url":"https://scholargate.app/en/text-mining/text-infilling","markdownUrl":"https://scholargate.app/en/text-mining/text-infilling.md","definition":"Text infilling is a natural-language-processing task that completes missing words, phrases, or spans in a document by exploiting the surrounding context. Introduced as the cloze procedure by Wilson L. Taylor in 1953 as a readability measure, it was reformulated for neural models by Zhu et al. (2019) and is now used for data augmentation, writing assistance, and language-model evaluation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilson L. Taylor (cloze procedure, 1953); modern span infilling by Zhu et al. (2019)","year":"1953 (cloze); 2019 (neural span infilling)","type":"NLP conditional text generation task","input":"Text with one or more masked tokens, spans, or blanks","output":"Predicted text filling each masked position","minSample":10,"requiresNormality":false,"difficulty":2},"citations":[{"ref":"Taylor, W.L. (1953). Cloze Procedure: A New Tool for Measuring Readability. Journalism Quarterly, 30(4), 415-433.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1177/107769905303000401"},{"ref":"Zhu, C., Zeng, M., & Huang, X. (2019). Text Infilling. arXiv:1901.00158.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1901.00158"}],"related":["sentiment-analysis","bert-embeddings","text-classification","language-modeling","text-generation","named-entity-recognition"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-network-analysis","name":"Text Network Analysis","fullName":"Text Network Analysis","aliases":["semantic network analysis","word co-occurrence network","Metin Ağ Analizi (Text Network Analysis)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2011 (Paranyushkin); 2005 (Diesner & Carley)","originator":"Dmitry Paranyushkin; Jana Diesner & Kathleen M. Carley","url":"https://scholargate.app/en/text-mining/text-network-analysis","markdownUrl":"https://scholargate.app/en/text-mining/text-network-analysis.md","definition":"Text network analysis models the words or concepts in a text as nodes and their co-occurrences as edges, then uses network metrics to reveal the structure of meaning. The approach was advanced by Diesner and Carley (2005) for communication networks and by Paranyushkin (2011) for tracing the pathways of meaning circulation in text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dmitry Paranyushkin; Jana Diesner & Kathleen M. Carley","year":"2011 (Paranyushkin); 2005 (Diesner & Carley)","type":"Text-mining network method","nodes":"Words or concepts","edges":"Co-occurrence relations","minSample":"20 documents","output":"Network graph with centrality and structural metrics"},"citations":[{"ref":"Paranyushkin, D. (2011). Identifying the Pathways for Meaning Circulation Using Text Network Analysis. Nodus Labs.","type":"report","doi":null,"isbn":null,"url":"https://noduslabs.com/research/pathways-meaning-circulation-text-network-analysis/"},{"ref":"Diesner, J. & Carley, K. M. (2005). Exploration of Communication Networks from the Enron Email Corpus. SIAM International Conference on Data Mining, Workshop on Link Analysis, Counterterrorism and Security.","type":"inproceedings","doi":null,"isbn":null,"url":"https://www.casos.cs.cmu.edu/publications/papers/diesner_2005_explorationcommunication.pdf"}],"related":["collocation-analysis","frequency-analysis-text","dependency-parsing","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-normalization","name":"Text Normalization","fullName":"Text Normalization (Noisy-Text Standardisation)","aliases":["Metin Normalleştirme","noisy-text normalization","text standardisation","lexical normalisation"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/text-normalization","markdownUrl":"https://scholargate.app/en/text-mining/text-normalization.md","definition":"Text normalization is an NLP preprocessing pipeline that converts noisy, abbreviated, or misspelled text — such as SMS messages, social-media posts, and OCR output — into a clean, standardised form. It is a prerequisite step for virtually every downstream NLP task, ensuring that inconsistent surface forms do not degrade tokenisation, parsing, or classification. The method gained systematic academic treatment through Baldwin and Li (2015) and Sproat and Jaitly (2017).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP preprocessing pipeline","difficulty":"beginner (difficulty 1 of 5)","inputType":"noisy raw text (SMS, social media, OCR output)","outputType":"standardised clean text","prerequisites":"Noise-type identification; target-language lexicon or language model available","notableWork":"Baldwin & Li (2015); Sproat & Jaitly (2017)"},"citations":[{"ref":"Baldwin, T. & Li, Y. (2015). An In-depth Analysis of the Effect of Text Normalization in Twitter. NAACL-HLT 2015.","type":"proceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/N15-1044"},{"ref":"Sproat, R. & Jaitly, N. (2017). RNN Approaches to Text Normalization: A Challenge. arXiv:1611.00068.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1611.00068"}],"related":["sentiment-analysis","tokenization","named-entity-recognition","pos-tagging","spell-checking"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-regression","name":"Text Regression","fullName":"Text-Based Regression","aliases":["text-as-data regression","predicting numeric outcomes from text","Metin Tabanlı Regresyon"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/text-regression","markdownUrl":"https://scholargate.app/en/text-mining/text-regression.md","definition":"Text-based regression predicts a continuous target variable using features extracted from text — TF-IDF scores, embeddings, or n-grams — as the independent variables. Building on the text-as-data programme consolidated by Gentzkow, Kelly and Taddy (2019), it lets a numeric outcome such as a price, a rating, or a sentiment score be estimated directly from documents, and is widely used in social-science, economics, and finance applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"Supervised regression on text features","predictors":"Text features (TF-IDF, embeddings, n-grams)","target":"Continuous numeric variable","minSample":50,"difficulty":"Intermediate (2/5)","fields":"Social sciences, economics, finance"},"citations":[{"ref":"Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574.","type":"article","doi":"10.1257/jel.20181020","isbn":null,"url":null},{"ref":"Taddy, M. (2013). Measuring Political Sentiment on Twitter: Factor Optimal Design for Multinomial Inverse Regression. Technometrics, 55(4), 415-425.","type":"article","doi":"10.1080/00401706.2013.778791","isbn":null,"url":null}],"related":["tf-idf","text-classification","sentiment-analysis","bert-embeddings"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-segmentation","name":"Text Segmentation","fullName":"Text Segmentation (Topic Segmentation)","aliases":["topic segmentation","discourse segmentation","linear text segmentation","Metin Bölümleme (Text Segmentation)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1997,"originator":"Marti A. Hearst (TextTiling)","url":"https://scholargate.app/en/text-mining/text-segmentation","markdownUrl":"https://scholargate.app/en/text-mining/text-segmentation.md","definition":"Text segmentation divides a long document into meaningful sections (segments) along topic or discourse boundaries. Introduced for subtopic passages by Marti A. Hearst's TextTiling (1997), it supports document-structure analysis and the detection of topic transitions in continuous text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Marti A. Hearst (TextTiling)","year":1997,"type":"NLP document-structure / topic-boundary detection","output":"Ordered segments bounded by detected topic shifts","minSample":10},"citations":[{"ref":"Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/J97-1003/"},{"ref":"Choi, F.Y.Y. (2000). Advances in Domain Independent Linear Text Segmentation. NAACL.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/A00-2004/"}],"related":["sentiment-analysis","ngram-language-model","language-identification","tf-idf"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"text-summarization","name":"Text Summarization","fullName":"Automatic Text Summarization","aliases":["automatic summarization","extractive summarization","abstractive summarization","Otomatik Metin Özetleme"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/text-summarization","markdownUrl":"https://scholargate.app/en/text-mining/text-summarization.md","definition":"Automatic text summarization is a natural-language-processing task that condenses long documents into shorter summaries while preserving their key information. It works through one of two families of approaches — extractive summarization, which selects the most important spans from the source, or abstractive summarization, which generates new text. The field was consolidated by Nenkova and McKeown (2011), and sequence-to-sequence models such as BART (Lewis et al., 2020) advanced the abstractive side.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP text-generation / text-reduction task","approaches":"Extractive / abstractive","output":"Shorter summary preserving key information","minSample":20},"citations":[{"ref":"Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval.","type":"article","doi":"10.1561/1500000015","isbn":null,"url":null},{"ref":"Lewis, M. et al. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. ACL.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.703","isbn":null,"url":null}],"related":["keyword-extraction","semantic-similarity","document-clustering","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"textual-criticism","name":"Textual Criticism","fullName":"Textual Criticism (Lower Criticism / Editorial Criticism)","aliases":["lower criticism","editorial criticism","philological criticism","manuscript criticism"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Antiquity; modern systematic method c. 1850s (Lachmann)","originator":"Classical philologists (Karl Lachmann foremost in systematic method)","url":"https://scholargate.app/en/field-methods/textual-criticism","markdownUrl":"https://scholargate.app/en/field-methods/textual-criticism.md","definition":"Textual criticism is a systematic philological method for identifying, comparing, and evaluating variant readings across multiple manuscript or print witnesses of a text in order to reconstruct the most accurate version of the original — or the author's intended — text. Applied since antiquity to classical, biblical, and literary works, it remains the foundational editorial method in classical studies, biblical scholarship, medieval studies, and critical editing of literary works.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical philologists (Karl Lachmann foremost in systematic method)","year":"Antiquity; modern systematic method c. 1850s (Lachmann)","type":"Humanistic / philological research method","dataType":"Manuscripts, printed editions, inscriptions, archival textual documents","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"West, M. L. (1973). Textual Criticism and Editorial Technique Applicable to Greek and Latin Texts. Teubner.","type":"book","doi":null,"isbn":"978-3519074014","url":null},{"ref":"Textual criticism. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Textual_criticism"}],"related":["hermeneutic-analysis","historical-archival-research","oral-history-method","discourse-analysis","content-analysis","comparative-textual-criticism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"textual-entailment","name":"Textual Entailment","fullName":"Textual Entailment (Natural Language Inference, NLI)","aliases":["natural language inference","NLI","recognising textual entailment","RTE","Metinsel Çıkarsama (Textual Entailment / NLI)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":null,"originator":null,"url":"https://scholargate.app/en/text-mining/textual-entailment","markdownUrl":"https://scholargate.app/en/text-mining/textual-entailment.md","definition":"Textual entailment, also known as natural language inference (NLI), is the natural-language-processing task of deciding whether one piece of text (the premise) entails a second piece of text (the hypothesis), contradicts it, or is neutral with respect to it. Formalised by the PASCAL Recognising Textual Entailment Challenge (Dagan, Glickman & Magnini, 2006) and broadened by the MultiNLI corpus (Williams, Nangia & Bowman, 2018), it underpins question answering and fact-verification pipelines.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP sentence-pair classification task","input":"Premise-hypothesis text pairs","labels":"Entailment / contradiction / neutral","minSample":30,"difficulty":"3 / 5"},"citations":[{"ref":"Dagan, I., Glickman, O. & Magnini, B. (2006). The PASCAL Recognising Textual Entailment Challenge.","type":"incollection","doi":null,"isbn":null,"url":"https://link.springer.com/chapter/10.1007/11736790_9"},{"ref":"Williams, A., Nangia, N. & Bowman, S. (2018). A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. NAACL.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/N18-1101/"}],"related":["zero-shot-classification","text-classification","sentiment-analysis","word-sense-disambiguation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"texture-profile-analysis","name":"Texture Profile Analysis","fullName":"Texture Profile Analysis (TPA)","aliases":["TPA"],"domain":"food-science","family":"process-pipeline","subfamily":"Sensory Evaluation","year":"1968","originator":"Malcolm Bourne","url":"https://scholargate.app/en/food-science/texture-profile-analysis","markdownUrl":"https://scholargate.app/en/food-science/texture-profile-analysis.md","definition":"Texture Profile Analysis (TPA) is an objective, mechanical method that simulates mastication (chewing) to measure the textural properties of food products. Developed by Bourne in 1968, TPA uses a texture analyzer (a machine that applies defined forces and movements to a sample) to generate a force-time curve from which multiple texture attributes (hardness, springiness, chewiness, cohesiveness, adhesiveness) are extracted and quantified.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Malcolm Bourne","subfamily":"Sensory Evaluation","year":"1968","type":"Mechanical Texture Method"},"citations":[{"ref":"Bourne, M. C. (1968). Texture profile of foods. Journal of Food Science, 33(3), 280-283.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Texture+profile+of+foods+Bourne"},{"ref":"Meilgaard, M. C., Carr, B. T., & Civille, G. V. (2006). Sensory evaluation techniques (4th ed.). CRC Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Sensory+evaluation+techniques+%284th+ed.%29+Meilgaard"}],"related":["quantitative-descriptive-analysis","rheometry","temporal-dominance-of-sensations"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tf-idf","name":"TF-IDF","fullName":"Term Frequency–Inverse Document Frequency Vectorization","aliases":["term weighting","tf-idf weighting","TF-IDF Vektörizasyonu"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1988,"originator":"Salton & Buckley","url":"https://scholargate.app/en/text-mining/tf-idf","markdownUrl":"https://scholargate.app/en/text-mining/tf-idf.md","definition":"TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Salton & Buckley","year":1988,"type":"Text vectorization / term-weighting scheme","input":"Document collection (corpus) of text","output":"Weighted document-term vectors","minCorpus":"~100 documents for meaningful IDF weights"},"citations":[{"ref":"Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.","type":"article","doi":"10.1016/0306-4573(88)90021-0","isbn":null,"url":null}],"related":["sentiment-analysis","word2vec","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tgarch-model","name":"TGARCH model","fullName":"Threshold Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["Threshold GARCH","TGARCH","GJR-GARCH","asymmetric GARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1993-1994","originator":"Zakoian (1994); Glosten, Jagannathan & Runkle (1993)","url":"https://scholargate.app/en/econometrics/tgarch-model","markdownUrl":"https://scholargate.app/en/econometrics/tgarch-model.md","definition":"The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zakoian (1994); Glosten, Jagannathan & Runkle (1993)","year":"1993-1994","type":"Asymmetric volatility model","dataType":"Financial or macroeconomic time series with conditional heteroscedasticity","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.","type":"article","doi":"10.1016/0165-1889(94)90039-6","isbn":null,"url":null},{"ref":"Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801.","type":"article","doi":"10.1111/j.1540-6261.1993.tb05128.x","isbn":null,"url":null}],"related":["arch-model","egarch-model","dcc-garch-model","garch-model","vector-autoregression","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"theil-sen-estimator","name":"Theil-Sen Estimator","fullName":"Theil-Sen Estimator (Median Slope Regression)","aliases":["Theil-Sen Tahmincisi","Theil-Sen regression","median slope estimator","Sen's slope estimator","Kendall-Theil robust line"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1968,"originator":"Henri Theil (1950); P. K. Sen (1968)","url":"https://scholargate.app/en/statistics/theil-sen-estimator","markdownUrl":"https://scholargate.app/en/statistics/theil-sen-estimator.md","definition":"The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henri Theil (1950); P. K. Sen (1968)","year":1968,"type":"Robust linear regression","estimator":"Median of pairwise slopes","breakdownPoint":"≈29%","outcome":"continuous"},"citations":[{"ref":"Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389.","type":"article","doi":"10.1080/01621459.1968.10480934","isbn":null,"url":null},{"ref":"Theil, H. (1950). A Rank-Invariant Method of Linear and Polynomial Regression Analysis. Proceedings of the Royal Netherlands Academy of Sciences, 53, 386-392, 521-525, 1397-1412.","type":"article","doi":null,"isbn":null,"url":"https://www.dwc.knaw.nl/DL/publications/PU00018789.pdf"}],"related":["ols-regression","quantile-regression","least-trimmed-squares","winsorized-estimation","bootstrap-inference","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thematic-analysis","name":"Thematic Analysis","fullName":"Thematic Analysis Method","aliases":["TA","Reflexive Thematic Analysis"],"domain":"qualitative-research","family":"process-pipeline","subfamily":"inductive-pattern-recognition","year":"2006","originator":"Virginia Braun and Victoria Clarke","url":"https://scholargate.app/en/qualitative-research/thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative-research/thematic-analysis.md","definition":"Thematic Analysis (TA) is a qualitative research methodology for identifying, analyzing, and reporting patterns (themes) in qualitative data. Developed systematically by Virginia Braun and Victoria Clarke (2006), TA is flexible and accessible, applicable across diverse theoretical frameworks and data types, making it one of the most widely used qualitative methods in psychology, health research, and social sciences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Virginia Braun and Victoria Clarke","subfamily":"inductive-pattern-recognition","year":"2006","type":"Method"},"citations":[{"ref":"Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null},{"ref":"Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.","type":"book","doi":"10.1080/2159676X.2019.1628806","isbn":null,"url":null},{"ref":"Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development. Sage Publications.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Boyatzis%2C%20R.%20E.%20(1998).%20Transforming%20qualitative%20information%3A%20Thematic%20analysis%20and%20code%20development.%20Sage%20Publications."}],"related":["content-analysis-qualitative","grounded-theory","interpretative-phenomenological-analysis","qualitative-coding"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thematic-evolution-analysis","name":"Thematic Evolution Analysis","fullName":"Thematic Evolution Analysis in Science Mapping","aliases":["TEA","thematic development analysis","temporal thematic mapping","longitudinal theme analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2011","originator":"Manuel J. Cobo and colleagues (University of Granada)","url":"https://scholargate.app/en/scientometrics/thematic-evolution-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/thematic-evolution-analysis.md","definition":"Thematic evolution analysis is a bibliometric technique that divides a body of literature into consecutive time periods and tracks how research themes emerge, consolidate, split, merge, or disappear across those periods. By combining co-word analysis, clustering, and strategic diagrams for each time slice, it produces a dynamic picture of a field's intellectual development rather than a static snapshot.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Manuel J. Cobo and colleagues (University of Granada)","year":"2011","type":"Quantitative bibliometric technique","dataType":"Bibliographic records (keywords, abstracts, titles) from academic databases (WoS, Scopus)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.","type":"article","doi":"10.1002/asi.21525","isbn":null,"url":null},{"ref":"Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166.","type":"article","doi":"10.1016/j.joi.2010.10.002","isbn":null,"url":null}],"related":["bibliometric-analysis","co-word-analysis","science-mapping","scientometric-analysis","systematic-literature-review","co-citation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"theodorsen-flutter","name":"Theodorsen Flutter","fullName":"Theodorsen Flutter Analysis","aliases":["flutter analysis","aeroelastic stability","Theodorsen's function"],"domain":"aerospace","family":"process-pipeline","subfamily":"Aeroelasticity","year":"1935","originator":"Theodore Theodorsen","url":"https://scholargate.app/en/aerospace/theodorsen-flutter","markdownUrl":"https://scholargate.app/en/aerospace/theodorsen-flutter.md","definition":"Theodorsen flutter analysis is a classical aeroelastic method for predicting the onset of flutter, a self-excited oscillation where aerodynamic forces interact with elastic structural motion to cause rapid growth of oscillations. Developed by Theodore Theodorsen in 1935, the method uses frequency-domain analysis with Theodorsen's function to compute aerodynamic forces on oscillating wings. Flutter speed prediction is essential for aircraft certification and structural design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Theodore Theodorsen","subfamily":"Aeroelasticity","year":"1935","type":"Stability analysis"},"citations":[{"ref":"Theodorsen, T. (1935). General theory of aerodynamic instability and the mechanism of flutter. NACA Report No. 496.","type":"article","doi":null,"isbn":null,"url":"https://ntrs.nasa.gov/citations/19930091500"},{"ref":"Bisplinghoff, R. L., Ashley, H., & Halfman, R. L. (1955). Aeroelasticity. Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/aeroelasticity"},{"ref":"Wright, J. R., & Cooper, J. E. (2007). Introduction to Aircraft Aeroelasticity and Loads (2nd ed.). John Wiley & Sons.","type":"book","doi":"10.2514/4.479359","isbn":null,"url":null}],"related":["blade-element-momentum-theory","weight-and-balance","specific-excess-power"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"theoretical-domains-framework","name":"Theoretical Domains Framework","fullName":"Theoretical Domains Framework (TDF): A 14-Domain Behavioral Change Model for Understanding Implementation Barriers and Designing Interventions","aliases":["TDF","theoretical domains","behaviour change framework"],"domain":"implementation-science","family":"process-pipeline","subfamily":"behaviour change theory","year":"2005","originator":"Michie, S., Johnston, M., Abraham, C., et al.","url":"https://scholargate.app/en/implementation-science/theoretical-domains-framework","markdownUrl":"https://scholargate.app/en/implementation-science/theoretical-domains-framework.md","definition":"The Theoretical Domains Framework (TDF) is a 14-domain model that integrates constructs from 33 behavior change and implementation theories to identify barriers and facilitators to professional and public behavior change. Developed by Michie et al. (2005) to provide a practical tool for implementation scientists and behavior change specialists, the TDF helps systematically assess 'why' healthcare professionals or patients do (or do not) adopt evidence-based practices, and guides the design of tailored behavior change interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michie, S., Johnston, M., Abraham, C., et al.","subfamily":"behaviour change theory","year":"2005","type":"Framework"},"citations":[{"ref":"Michie, S., Johnston, M., Abraham, C., Lawton, R., Parker, D., & Walker, A. (2005). Making psychological theory useful for implementing evidence based practice: A consensus approach. Quality and Safety in Health Care, 14(1), 26-33.","type":"article","doi":"10.1136/qshc.2004.011155","isbn":null,"url":null},{"ref":"Atkins, L., Francis, J., Islam, R., O'Connor, D., Patey, A., Ivers, N., ... & Michie, S. (2017). A guide to using the Theoretical Domains Framework (TDF) to develop interventions to change professional practice and public health behaviour: PLOS ONE guide. PLOS Medicine, 14(4), e1002335.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+guide+to+using+the+Theoretical+Domains+Framework+%28TDF%29+to+develop+interventions+to+change+professional+practice+and+public+health+behaviour%3A+PLOS+ONE+guide+Atkins"},{"ref":"Lawton, R., Heyhoe, J., Louch, G., Ingleson, E., Eyles, E., & Clements, J. (2016). Using the Theoretical Domains Framework (TDF) to understand healthcare professionals' behaviour: A systematic review. Implementation Science, 11, 123.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Using+the+Theoretical+Domains+Framework+%28TDF%29+to+understand+healthcare+professionals%27+behaviour%3A+A+systematic+review+Lawton"}],"related":["behavior-change-wheel","cfir-framework","knowledge-translation","implementation-outcome-taxonomy","normalization-process-theory"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"theory-of-constraints","name":"Theory of Constraints","fullName":"Theory of Constraints (TOC)","aliases":["TOC","Constraint Management","Bottleneck Theory","Kısıtlar Teorisi"],"domain":"quality-management","family":"process-pipeline","subfamily":"Operations management","year":1990,"originator":"Eliyahu Goldratt","url":"https://scholargate.app/en/quality-management/theory-of-constraints","markdownUrl":"https://scholargate.app/en/quality-management/theory-of-constraints.md","definition":"The Theory of Constraints (TOC) is a management philosophy and continuous improvement framework introduced by Eliyahu Goldratt in his 1984 novel The Goal and formalized in his 1990 book. TOC holds that every system has at least one constraint — a bottleneck that limits the system's overall throughput — and that systematically identifying and addressing that constraint is the most effective lever for improving performance. It is widely applied in manufacturing, project management, supply chains, and service operations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eliyahu Goldratt","year":1990,"type":"Continuous improvement framework","subfamily":"Operations management","core_tool":"Five Focusing Steps","primary_target":"System throughput maximization"},"citations":[{"ref":"Goldratt, E. M. (1990). Theory of Constraints. North River Press.","type":"book","doi":null,"isbn":"978-0-88427-166-6","url":null}],"related":["value-stream-mapping","six-sigma-dmaic","littles-law"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"theory-planned-behavior-scale","name":"Theory of Planned Behavior Questionnaire","fullName":"Theory of Planned Behavior Scale","aliases":["TPB Scale","TPB-Q"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Behavioral Intention & Prediction","year":"1991","originator":"Icek Ajzen","url":"https://scholargate.app/en/health-behavior/theory-planned-behavior-scale","markdownUrl":"https://scholargate.app/en/health-behavior/theory-planned-behavior-scale.md","definition":"The Theory of Planned Behavior (TPB) is a psychological framework developed by Icek Ajzen in 1991 to predict and understand deliberate human behavior. The TPB questionnaire measures four core constructs that explain why people intend to perform (or not perform) a specific behavior: attitudes toward the behavior, subjective norms, perceived behavioral control, and behavioral intention. This measure is widely used in health behavior research, particularly for understanding health promotion, disease prevention, and lifestyle change initiatives.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Icek Ajzen","subfamily":"Behavioral Intention & Prediction","year":"1991","type":"Self-report questionnaire"},"citations":[{"ref":"Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.","type":"article","doi":"10.1016/0749-5978(91)90020-T","isbn":null,"url":null}],"related":["health-belief-model-scale","exercise-self-efficacy-scale","behavioral-regulation-exercise"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"therapeutic-alliance-scale","name":"Therapeutic Alliance Scale","fullName":"Therapeutic Alliance Scale (THAS)","aliases":["THAS","TAS","Therapeutic Working Alliance Scale"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"therapeutic-alliance","year":"1997","originator":"Poul J. Raue, Marvin R. Goldfried","url":"https://scholargate.app/en/psychotherapy-research/therapeutic-alliance-scale","markdownUrl":"https://scholargate.app/en/psychotherapy-research/therapeutic-alliance-scale.md","definition":"The Therapeutic Alliance Scale (THAS) is a clinician-rated measure of the quality of the therapeutic relationship and working alliance, developed by Raue, Goldfried, and Barkham. Distinct from client-rated measures like the Working Alliance Inventory, the THAS captures the therapist's perception of goal alignment, task agreement, and emotional bond. It is used primarily in research to examine alliance from the therapist perspective and to understand therapist–client congruence in alliance perception.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Poul J. Raue, Marvin R. Goldfried","subfamily":"therapeutic-alliance","year":"1997","type":"Therapist/Client-rated"},"citations":[{"ref":"Raue, P. J., Goldfried, M. R., & Barkham, M. (1997). The therapeutic alliance in psychodynamic-interpersonal and cognitive-behavioral therapy. Journal of Consulting and Clinical Psychology, 65(4), 582–587.","type":"article","doi":"10.1037/0022-006X.65.4.582","isbn":null,"url":null},{"ref":"Hartley, D. E., & Strupp, H. H. (1983). The therapeutic alliance: Its relationship to outcome in brief psychotherapy. In J. Masling (Ed.), Empirical studies of psychoanalytic theories (Vol. 1, pp. 1–37). Lawrence Erlbaum.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Hartley%2C%20D.%20E.%2C%20%26%20Strupp%2C%20H.%20H.%20(1983).%20The%20therapeutic%20alliance%3A%20Its%20relationship%20to%20outcome%20in%20brief%20psychotherapy.%20In"}],"related":["working-alliance-inventory","session-rating-scale","common-factors-questionnaire","collaborative-study-psychotherapy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"therapeutic-drug-monitoring","name":"Therapeutic Drug Monitoring","fullName":"Therapeutic Drug Monitoring (TDM)","aliases":["Drug Level Monitoring","Serum Drug Level Monitoring","Clinical Pharmacokinetic Monitoring","İlaç Düzeyi İzlemi"],"domain":"pharmacometrics","family":"regression-model","subfamily":"Clinical pharmacokinetics","year":1988,"originator":"Reynold Spector et al.","url":"https://scholargate.app/en/pharmacometrics/therapeutic-drug-monitoring","markdownUrl":"https://scholargate.app/en/pharmacometrics/therapeutic-drug-monitoring.md","definition":"Therapeutic Drug Monitoring (TDM) is a clinical pharmacokinetic practice in which drug concentrations are measured in a patient's blood to guide individualized dosing. It applies principally to drugs with narrow therapeutic windows—where the margin between efficacy and toxicity is small—such as aminoglycosides, vancomycin, cyclosporine, and antiepileptics. Developed as a formal discipline in the 1980s, TDM integrates measured concentrations with pharmacokinetic modeling to calculate patient-specific dose regimens.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reynold Spector et al.","year":1988,"type":"Clinical measurement and dose-optimization framework","subfamily":"Clinical pharmacokinetics","therapeutic_window":"Defined by minimum effective and minimum toxic concentrations","primary_matrix":"Plasma or serum drug concentration"},"citations":[{"ref":"Spector, R., Park, G. D., Johnson, G. F., & Vesell, E. S. (1988). Therapeutic drug monitoring. Clinical Pharmacology & Therapeutics, 43(4), 345–353.","type":"article","doi":"10.1038/clpt.1988.42","isbn":null,"url":null}],"related":["pharmacokinetic-compartment-model","population-pharmacokinetics","bayesian-inference"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"therapeutic-touch-assessment","name":"Therapeutic Touch Assessment Scale","fullName":"Therapeutic Touch Assessment Scale","aliases":["TTAS","TT Assessment","Therapeutic Touch Competency Scale"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"Energy-based healing modalities","year":"1979","originator":"Krieger, D.; Kunz, D.","url":"https://scholargate.app/en/integrative-medicine/therapeutic-touch-assessment","markdownUrl":"https://scholargate.app/en/integrative-medicine/therapeutic-touch-assessment.md","definition":"The TTAS measures the application and outcomes of therapeutic touch (TT), an energy-based healing modality developed by Krieger and Kunz in which practitioners use intentional hand movements proximal to or in contact with the patient's body to promote relaxation, reduce pain, and facilitate healing. Used both as a competency assessment for practitioners and as an outcome measure for patients receiving TT.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Krieger, D.; Kunz, D.","subfamily":"Energy-based healing modalities","year":"1979","type":"Practitioner skill assessment and patient outcome measure"},"citations":[{"ref":"Krieger, D. (1979). Therapeutic touch: How to use your hands to help or heal. New York: Prentice-Hall.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Krieger%2C%20D.%20(1979).%20Therapeutic%20touch%3A%20How%20to%20use%20your%20hands%20to%20help%20or%20heal.%20New%20York%3A%20Prentice-Hall."},{"ref":"Barret, E. A. M., & Yates, M. E. (2002). The Rogerian science of unitary human beings in nursing practice. In M. E. Parker (Ed.), Nursing theories and nursing practice (2nd ed., pp. 47–75). Philadelphia: F.A. Davis.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Barret%2C%20E.%20A.%20M.%2C%20%26%20Yates%2C%20M.%20E.%20(2002).%20The%20Rogerian%20science%20of%20unitary%20human%20beings%20in%20nursing%20practice.%20In%20M.%20E.%20Park"},{"ref":"Hutchison, T. L. (2008). Therapeutic touch: A review of the evidence. Journal of Nursing Administration, 38(10), 439–445.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Therapeutic+touch%3A+A+review+of+the+evidence+Hutchison"}],"related":["holistic-caring-inventory","spiritual-care-competence-scale","music-therapy-assessment-tool","attitudes-cam-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"therapy-process-observational-coding","name":"Therapy Process Observational Coding System","fullName":"Therapy Process Observational Coding System (TPOCS)","aliases":["TPOCS","Observational Coding System"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"process-coding","year":"1992","originator":"William B. Stiles, Clara E. Hill","url":"https://scholargate.app/en/psychotherapy-research/therapy-process-observational-coding","markdownUrl":"https://scholargate.app/en/psychotherapy-research/therapy-process-observational-coding.md","definition":"The Therapy Process Observational Coding System (TPOCS) is a comprehensive observer-rated method for classifying and quantifying therapist and client utterances in psychotherapy sessions. Using Stiles's taxonomy of verbal response modes (e.g., Advisement, Reflection, Interpretation, Disclosure), the TPOCS enables detailed analysis of what therapists and clients are doing moment-by-moment: who is talking, what mode (technique), and how frequently. It is used in process research to understand mechanisms of change, train therapists, and examine whether therapy modalities differ in their in-session behavior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William B. Stiles, Clara E. Hill","subfamily":"process-coding","year":"1992","type":"Observer-rated"},"citations":[{"ref":"Stiles, W. B. (1992). Describing talk: A taxonomy of verbal response modes. Newbury Park, CA: Sage Publications.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Stiles%2C%20W.%20B.%20(1992).%20Describing%20talk%3A%20A%20taxonomy%20of%20verbal%20response%20modes.%20Newbury%20Park%2C%20CA%3A%20Sage%20Publications."},{"ref":"Hill, C. E., & Lambert, M. J. (2004). Methodological issues in studying psychotherapy processes and outcomes. In M. J. Lambert (Ed.), Bergin and Garfield's handbook of psychotherapy and behavior change (5th ed., pp. 84–135). John Wiley & Sons.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Hill%2C%20C.%20E.%2C%20%26%20Lambert%2C%20M.%20J.%20(2004).%20Methodological%20issues%20in%20studying%20psychotherapy%20processes%20and%20outcomes.%20In%20M.%20J.%20L"}],"related":["collaborative-study-psychotherapy","working-alliance-inventory","helpful-aspects-of-therapy","session-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thermal-comfort-assessment","name":"Thermal Comfort Assessment","fullName":"Thermal Comfort Assessment and Prediction","aliases":["thermal comfort evaluation","adaptive comfort model","PMV-PPD analysis"],"domain":"architecture","family":"process-pipeline","subfamily":"Comfort and indoor environmental quality","year":"1972","originator":"Povl Ole Fanger","url":"https://scholargate.app/en/architecture/thermal-comfort-assessment","markdownUrl":"https://scholargate.app/en/architecture/thermal-comfort-assessment.md","definition":"Thermal Comfort Assessment is a method for evaluating indoor environmental conditions to predict whether occupants will feel thermally comfortable. Pioneered by Povl Ole Fanger in the 1970s, it combines measurements of air temperature, humidity, air speed, and thermal properties of clothing and activity to determine comfort zones and identify remedial actions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Povl Ole Fanger","subfamily":"Comfort and indoor environmental quality","year":"1972","type":"psychrometric comfort assessment method"},"citations":[{"ref":"Fanger, P. O. (1972). Thermal Comfort: Analysis and Applications in Environmental Engineering. Danish Technical Press, Copenhagen.","type":"book","doi":null,"isbn":null,"url":"https://www.tandfonline.com/doi/abs/10.1080/00363937209009844"},{"ref":"Dearlove, J., Kharade, M. K., Datta, S. (2012). Survey of Comfort and Thermal Preferences in Mixed-Mode Buildings. Proceedings of the 10th International Conference on Healthy Buildings.","type":"article","doi":null,"isbn":null,"url":"https://www.healthybuildings.org"},{"ref":"Nicol, J. F., Humphreys, M. A. (2002). Adaptive Thermal Comfort and Sustainable Thermal Standards for Buildings. Energy and Buildings, 34(6), 563-572.","type":"article","doi":"10.1016/S0378-7788(02)00006-3","isbn":null,"url":null}],"related":["building-energy-performance","daylight-simulation","acoustic-design-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thermal-resistance-network","name":"Thermal Resistance Network","fullName":"Thermal Resistance Network Method","aliases":["thermal circuit analogy","thermal network"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Steady-state Analysis","year":"1985","originator":"Frank Incropera and David DeWitt","url":"https://scholargate.app/en/thermodynamics/thermal-resistance-network","markdownUrl":"https://scholargate.app/en/thermodynamics/thermal-resistance-network.md","definition":"The Thermal Resistance Network method uses electrical circuit analogy to solve heat transfer problems. It treats heat flow as analogous to electric current, thermal resistance analogous to electrical resistance, and temperature difference analogous to voltage potential. This powerful conceptual framework enables engineers to analyze complex multi-layer heat transfer systems systematically.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Incropera and David DeWitt","subfamily":"Steady-state Analysis","year":"1985","type":"Heat transfer network analysis"},"citations":[{"ref":"Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470055540","url":null},{"ref":"Holman, J. P. (2009). Heat Transfer (10th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073529356","url":null}],"related":["lumped-capacitance-method","log-mean-temperature-difference","effectiveness-ntu-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thermal-wind","name":"Thermal Wind","fullName":"Thermal Wind Relationship","aliases":["Thermal wind","Vertical wind shear","Barotropic"],"domain":"meteorology","family":"process-pipeline","subfamily":"Dynamical meteorology","year":"1920s","originator":"Jacobbian insights from geostrophic flow","url":"https://scholargate.app/en/meteorology/thermal-wind","markdownUrl":"https://scholargate.app/en/meteorology/thermal-wind.md","definition":"The thermal wind relationship is a fundamental meteorological principle that links vertical wind shear to horizontal temperature gradients. It states that wind speed increases with height in the direction of warming—a direct consequence of hydrostatic and geostrophic balance combined with the ideal gas law.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacobbian insights from geostrophic flow","subfamily":"Dynamical meteorology","year":"1920s","type":"Wind-temperature relationship"},"citations":[{"ref":"Holton, J. R. (2004). An Introduction to Dynamic Meteorology (4th ed.). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/an-introduction-to-dynamic-meteorology/holton/978-0-12-354966-1"},{"ref":"Bluestein, H. B. (1993). Synoptic-dynamic meteorology in midlatitudes. Volume 2: Observations and Theory of Weather Systems. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://global.oup.com/academic/product/synoptic-dynamic-meteorology-in-midlatitudes-9780195068269"}],"related":["geostrophic-wind","quasi-geostrophic-omega-equation","skew-t-log-p-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thermogravimetric-analysis","name":"Thermogravimetric Analysis","fullName":"Thermogravimetric Analysis (TGA)","aliases":["TGA","thermal gravimetry","thermogravimetry"],"domain":"materials-science","family":"process-pipeline","subfamily":"Thermal analysis","year":"1960s","originator":"William W. Wendlandt","url":"https://scholargate.app/en/materials-science/thermogravimetric-analysis","markdownUrl":"https://scholargate.app/en/materials-science/thermogravimetric-analysis.md","definition":"Thermogravimetric Analysis (TGA) is a thermal characterization technique that continuously measures mass loss or gain of a material as a function of temperature (or time at constant temperature). Developed systematically by William Wendlandt and colleagues in the 1960s, TGA identifies thermal transitions (evaporation, decomposition, oxidation, reduction) and quantifies composition of polymers, pharmaceuticals, ceramics, and other materials. The derivative signal (DTG) highlights transition temperatures. When combined with gas analysis (MS, FTIR), decomposition products are identified.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William W. Wendlandt","subfamily":"Thermal analysis","year":"1960s","type":"Characterization method"},"citations":[{"ref":"Wendlandt, W. W. (1986). Thermal Analysis (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Haines, P. J. (Ed.). (2012). Principles of Thermal Analysis and Calorimetry (2nd ed.). Royal Society of Chemistry.","type":"book","doi":null,"isbn":null,"url":"https://www.rsc.org"},{"ref":"Vyazovkin, S., et al. (2020). ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochimica Acta, 689, 178597.","type":"article","doi":"10.1016/j.tca.2020.178597","isbn":null,"url":null}],"related":["differential-scanning-calorimetry","raman-deconvolution","bet-surface-area"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thermoluminescence-dating","name":"Thermoluminescence Dating","fullName":"Thermoluminescence Dating (TL)","aliases":["TL dating","thermoluminescence chronometry"],"domain":"archaeology","family":"process-pipeline","subfamily":"Radiometric","year":"1960s","originator":"Michael Aitken","url":"https://scholargate.app/en/archaeology/thermoluminescence-dating","markdownUrl":"https://scholargate.app/en/archaeology/thermoluminescence-dating.md","definition":"Thermoluminescence (TL) dating is a chronometric technique that determines the age of pottery, ceramics, and sediments by measuring light emitted when heated to high temperatures. Pioneered by Michael Aitken in the 1960s, it quantifies the accumulated radiation dose stored in mineral crystal lattices. The method revolutionized archaeological dating by enabling scientists to date ceramic vessels and fired clay objects directly, providing absolute chronologies for human occupation sites worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Aitken","subfamily":"Radiometric","year":"1960s","type":"Luminescence dating technique"},"citations":[{"ref":"Aitken, M. J. (1985). Thermoluminescence Dating. Academic Press.","type":"book","doi":null,"isbn":null,"url":"https://doi.org/10.1016/S0079-6816(08)60039-7"},{"ref":"Prescott, J. R., & Hutton, J. T. (1994). Cosmic ray contributions to dose rates for luminescence and ESR dating: Large depths and long-term time variations. Radiation Measurements, 23(2-3), 497-500.","type":"article","doi":"10.1016/1350-4487(94)90086-8","isbn":null,"url":null},{"ref":"Wintle, A. G. (2005). Luminescence dating: laboratory procedures and protocols. Radiation Measurements, 27(5-6), 769-817.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Luminescence+dating%3A+laboratory+procedures+and+protocols+Wintle"}],"related":["optically-stimulated-luminescence-dating","electron-spin-resonance-dating","archaeomagnetic-dating","use-wear-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thesis-dissertation","name":"Thesis and Dissertation","fullName":"Thesis and Dissertation (Graduate Research Degree Requirements)","aliases":["master's thesis","PhD dissertation","doctoral thesis","graduate thesis","ETD"],"domain":"academic-writing","family":"process-pipeline","subfamily":"Graduate academic credentials","year":"1200","originator":"Medieval university tradition (12th century onward)","url":"https://scholargate.app/en/academic-writing/thesis-dissertation","markdownUrl":"https://scholargate.app/en/academic-writing/thesis-dissertation.md","definition":"A thesis (Master's level) or dissertation (doctoral level) is an original research document required for completion of graduate degree programs, serving as the capstone of a student's graduate training. A Master's thesis typically represents 1–2 years of research; a PhD dissertation, 3–5 years. Both require original empirical or theoretical contribution, demonstration of mastery in a discipline, and successful oral defense before faculty committee. Theses and dissertations establish academic credentials, document scholarly work, and are archived in institutional and national repositories (ProQuest, EThOS) for access by scholars worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Medieval university tradition (12th century onward)","subfamily":"Graduate academic credentials","year":"1200","type":"Document Type"},"citations":[{"ref":"American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). APA.","type":"book","doi":null,"isbn":"978-1-4338-3216-1","url":null},{"ref":"ProQuest Dissertations & Theses Global. https://www.proquest.com/pqdtglobal","type":"webpage","doi":null,"isbn":null,"url":"https://www.proquest.com/pqdtglobal"},{"ref":"EThOS: British Library Electronic Theses Online Service. https://ethos.bl.uk","type":"webpage","doi":null,"isbn":null,"url":"https://ethos.bl.uk"}],"related":["original-research-article","literature-review-article","academic-writing-process","research-methodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"theta-method","name":"Theta Method","fullName":"Theta Method for Time Series Forecasting","aliases":["theta model","theta forecasting","Theta Yöntemi — M3 Tahmin Yarışması Birincisi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2000,"originator":"Assimakopoulos & Nikolopoulos","url":"https://scholargate.app/en/econometrics/theta-method","markdownUrl":"https://scholargate.app/en/econometrics/theta-method.md","definition":"The Theta Method is a univariate time-series forecasting model introduced by Assimakopoulos and Nikolopoulos in 2000. It decomposes a series into two theta lines that capture its long-run trend and its short-run dynamics, forecasts each line separately, and combines them by a weighted average. Its simplicity and accuracy made it the winner of the M3 forecasting competition.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Assimakopoulos & Nikolopoulos","year":2000,"type":"Univariate time-series forecasting model","estimator":"Decomposition into theta lines, combined by weighted average","outcome":"continuous","structure":"time series","minSample":24,"distinction":"Winner of the M3 forecasting competition"},"citations":[{"ref":"Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530.","type":"article","doi":"10.1016/S0169-2070(00)00066-2","isbn":null,"url":null},{"ref":"Makridakis, S. & Hibon, M. (2000). The M3-Competition: Results, Conclusions and Implications. International Journal of Forecasting, 16(4), 451-476.","type":"article","doi":"10.1016/S0169-2070(00)00057-1","isbn":null,"url":null}],"related":["ols-regression","arima","exponential-smoothing","holt-winters","ets-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thin-layer-chromatography","name":"Thin-Layer Chromatography","fullName":"Thin-Layer Chromatography (TLC)","aliases":["TLC","planar chromatography"],"domain":"chemistry","family":"process-pipeline","subfamily":"Separation","year":"1956","originator":"Egon Stahl","url":"https://scholargate.app/en/chemistry/thin-layer-chromatography","markdownUrl":"https://scholargate.app/en/chemistry/thin-layer-chromatography.md","definition":"Thin-Layer Chromatography (TLC) is a planar chromatographic technique that separates compounds based on their differential affinities for a mobile and stationary phase. Developed by Egon Stahl in 1956, TLC remains one of the most accessible and widely used analytical methods in organic and inorganic chemistry, laboratories, and quality control.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Egon Stahl","subfamily":"Separation","year":"1956","type":"Chromatographic separation technique"},"citations":[{"ref":"Sherma, J. (2003). Planar Chromatography. Analytical Chemistry, 75(12), 2783–2811.","type":"article","doi":"10.1021/ac011764f","isbn":null,"url":null},{"ref":"Fried, B., & Sherma, J. (2004). Thin-Layer Chromatography: Techniques and Applications (4th ed.). Marcel Dekker.","type":"book","doi":null,"isbn":"978-0824755485","url":null}],"related":["column-chromatography","functional-group-identification","molecular-symmetry-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"think-aloud-protocol","name":"Think-Aloud Protocol","fullName":"Think-Aloud Protocol for Usability Testing","aliases":["Talk-Aloud Protocol","Concurrent Thinking Aloud","TA"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Concurrent Introspection","year":"1980","originator":"K. Anders Ericsson and Herbert A. Simon, adapted to HCI by Clayton Lewis","url":"https://scholargate.app/en/human-computer-interaction/think-aloud-protocol","markdownUrl":"https://scholargate.app/en/human-computer-interaction/think-aloud-protocol.md","definition":"The Think-Aloud Protocol is a usability testing method in which participants verbalize their thoughts while completing tasks on a system. As users navigate an interface, they continuously narrate their observations, interpretations, and reasoning, allowing researchers to understand their mental models, decision-making, and frustration points. Originating from cognitive psychology research by Ericsson and Simon (1980), this method was adapted for HCI by Clayton Lewis and has become one of the most widely used techniques for identifying usability problems and understanding user behavior.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. Anders Ericsson and Herbert A. Simon, adapted to HCI by Clayton Lewis","subfamily":"Concurrent Introspection","year":"1980","type":"Protocol for capturing user cognition and decision-making during task execution"},"citations":[{"ref":"Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87(3), 215–251.","type":"article","doi":"10.1037/0033-295X.87.3.215","isbn":null,"url":null},{"ref":"Lewis, C. (1982). Using the 'thinking aloud' method in cognitive interface design. Technical Report RC 9265, IBM Research Center.","type":"article","doi":null,"isbn":null,"url":"https://domino.research.ibm.com/library/cyberdig.nsf/"}],"related":["retrospective-think-aloud","contextual-inquiry","cognitive-walkthrough","pluralistic-walkthrough"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"three-factor-eating-questionnaire","name":"TFEQ","fullName":"Three-Factor Eating Questionnaire","aliases":["TFEQ","Three-Factor Eating Questionnaire Revised (TFEQ-R21)","Stunkard and Messick Three-Factor Eating"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"eating behavior and appetite regulation","year":"1985","originator":"Albert Jay Stunkard, Samuel Messick","url":"https://scholargate.app/en/clinical-psychology/three-factor-eating-questionnaire","markdownUrl":"https://scholargate.app/en/clinical-psychology/three-factor-eating-questionnaire.md","definition":"The TFEQ is a self-report instrument measuring three distinct psychological dimensions of eating behaviour: cognitive restraint (conscious dieting efforts), disinhibition (loss of control over eating when triggered by stress or environmental cues), and hunger (subjective appetite and satiety responsiveness). Developed by Stunkard and Messick in 1985, the original 51-item instrument has been refined into a 21-item version (TFEQ-R21) widely used in obesity research, eating behaviour studies, and nutritional psychology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Albert Jay Stunkard, Samuel Messick","subfamily":"eating behavior and appetite regulation","year":"1985","type":"Self-report questionnaire"},"citations":[{"ref":"Stunkard, A. J., & Messick, S. (1985). The Three-Factor Eating Questionnaire to measure dietary restraint, disinhibition, and hunger. Journal of Psychosomatic Research, 29(1), 71–83.","type":"article","doi":"10.1016/0022-3999(85)90010-8","isbn":null,"url":null},{"ref":"Karlsson, J., Persson, L. O., Sjöström, L., & Sullivan, M. (2000). Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. International Journal of Obesity, 24(12), 1715–1725.","type":"article","doi":"10.1038/sj.ijo.0801442","isbn":null,"url":null},{"ref":"Cappelleri, J. C., Bushmakin, A. G., Gerber, R. A., Leidy, N. K., Sexton, C. C., Lowe, M. R., & Karlsson, J. (2009). Psychometric analysis of the Three-Factor Eating Questionnaire-R21: Results from a large diverse sample of obese and non-obese participants. International Journal of Obesity, 33(6), 611–620.","type":"article","doi":"10.1038/ijo.2009.74","isbn":null,"url":null}],"related":["ede-q","scoff-questionnaire","body-shape-questionnaire","binge-eating-scale","yale-food-addiction-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"three-pl-irt","name":"3PL IRT","fullName":"Three-Parameter Logistic Item Response Theory Model","aliases":["3PL IRT — Üç Parametreli Madde Tepki Modeli","three-parameter logistic model","3PLM","Birnbaum model"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1968,"originator":"Allan Birnbaum","url":"https://scholargate.app/en/psychometrics/three-pl-irt","markdownUrl":"https://scholargate.app/en/psychometrics/three-pl-irt.md","definition":"The three-parameter logistic (3PL) model, introduced by Allan Birnbaum in 1968, is an item response theory model that describes the probability of a correct response to a binary test item as a function of three item-level parameters — difficulty, discrimination, and a lower asymptote representing guessing — and one person-level parameter representing latent ability.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Allan Birnbaum","year":1968,"type":"Item response model / latent trait model","outcome":"Item difficulty (b), discrimination (a), and guessing (c) parameters; person ability (θ)","data":"Binary (dichotomous) item responses","min_sample":500,"parameters_per_item":3,"scale":"Logit (log-odds)"},"citations":[{"ref":"Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores (pp. 397–479). Addison-Wesley.","type":"chapter","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/statistical-theories-of-mental-test-scores/oclc/264120"},{"ref":"Baker, F. B. & Kim, S. H. (2004). Item response theory: Parameter estimation techniques (2nd ed.). Marcel Dekker.","type":"book","doi":null,"isbn":"978-0824758172","url":null}],"related":["two-pl-irt","rasch-model","exploratory-factor-analysis","cfa","cronbach-alpha"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"three-stage-least-squares","name":"Three-Stage Least Squares","fullName":"Three-Stage Least Squares (3SLS)","aliases":["3SLS","system instrumental variables","Üç Aşamalı En Küçük Kareler (3SLS)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1962,"originator":"Arnold Zellner and Henri Theil","url":"https://scholargate.app/en/econometrics/three-stage-least-squares","markdownUrl":"https://scholargate.app/en/econometrics/three-stage-least-squares.md","definition":"Three-Stage Least Squares is a system estimator for simultaneous-equation models that accounts for the correlation of error terms across equations. Introduced by Zellner and Theil in 1962, it combines two-stage least squares with the seemingly-unrelated-regression idea to estimate all equations jointly and more efficiently.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arnold Zellner and Henri Theil","year":1962,"type":"Simultaneous-equations system estimator","estimator":"Generalised least squares applied to a system of two-stage least squares equations","minSample":100,"outcome":"continuous"},"citations":[{"ref":"Zellner, A. & Theil, H. (1962). Three-Stage Least Squares: Simultaneous Estimation of Simultaneous Equations. Econometrica, 30(1), 54–78.","type":"article","doi":"10.2307/1911287","isbn":null,"url":null}],"related":["two-stage-least-squares","ols-regression","seemingly-unrelated-regression","system-gmm","instrumental-variables"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"three-way-decisions","name":"Three-Way Decisions","fullName":"Three-Way Decisions","aliases":["3WD","Trisecting-and-Acting","Tri-partition Decision Making","Üç Yönlü Kararlar"],"domain":"soft-computing","family":"ml-model","subfamily":"Decision theory","year":2010,"originator":"Yiyu Yao","url":"https://scholargate.app/en/soft-computing/three-way-decisions","markdownUrl":"https://scholargate.app/en/soft-computing/three-way-decisions.md","definition":"Three-Way Decisions (3WD) is a decision-theoretic framework, introduced by Yiyu Yao in 2010, that partitions the universe of objects into three regions—positive (accept), negative (reject), and boundary (abstain)—using probabilistic rough set theory. Unlike binary classifiers that force every object into one of two classes, 3WD explicitly acknowledges uncertainty by allowing a third option: deferring judgment when available evidence is insufficient for a confident decision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yiyu Yao","year":2010,"type":"Decision-theoretic classification framework","subfamily":"Decision theory","basis":"Probabilistic rough set theory","output":"Accept / Reject / Abstain (three regions)"},"citations":[{"ref":"Yao, Y. (2010). Three-way decisions with probabilistic rough sets. Information Sciences, 180(3), 341–353.","type":"article","doi":"10.1016/j.ins.2009.09.021","isbn":null,"url":null}],"related":["granular-computing","rough-set-theory","case-based-reasoning"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"threshold-panel-var","name":"Threshold Panel VAR","fullName":"Threshold Panel Vector Autoregression","aliases":["Panel-VAR with regime switching"],"domain":"econometrics","family":"regression-model","subfamily":"Regime-switching","year":"1996","originator":"Bruce Hansen and colleagues","url":"https://scholargate.app/en/econometrics/threshold-panel-var","markdownUrl":"https://scholargate.app/en/econometrics/threshold-panel-var.md","definition":"The Threshold Panel VAR extends the standard vector autoregression framework to accommodate regime-switching behavior where relationships change when a threshold variable crosses a critical level. Introduced by Hansen (1996) and applied to panels by Caner and Hansen (2001), it allows different dynamic relationships across regimes (e.g., expansions versus recessions) while exploiting the cross-sectional dimension of panel data. This nonlinear framework captures state-dependent policy effects and economic mechanisms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruce Hansen and colleagues","subfamily":"Regime-switching","year":"1996","type":"Nonlinear panel model"},"citations":[{"ref":"Hansen, B. E. (1996). Inference when a nuisance parameter is not identified under the null hypothesis. Econometric Theory, 12(3), 386-414.","type":"article","doi":"10.2307/2171789","isbn":null,"url":null},{"ref":"Caner, M., & Hansen, B. E. (2001). Threshold autoregression with a unit root. Econometric Theory, 17(4), 1-36.","type":"article","doi":"10.1111/1468-0262.00257","isbn":null,"url":null}],"related":["panel-smooth-transition-regression","global-var","tvp-favar"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"threshold-regression","name":"Threshold Regression","fullName":"Threshold Regression Model","aliases":["threshold model","regime-switching regression","sample splitting model","Eşik Değer Regresyonu (Threshold Regression)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2000,"originator":"Bruce E. Hansen","url":"https://scholargate.app/en/econometrics/threshold-regression","markdownUrl":"https://scholargate.app/en/econometrics/threshold-regression.md","definition":"Threshold regression is a nonlinear, regime-switching model in which the regression parameters take different values above and below an estimated threshold value of a threshold variable. The sample-splitting and threshold-estimation framework was developed by Bruce E. Hansen (2000) and is widely used for time-series and panel data with structural breaks and regime-dependent relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bruce E. Hansen","year":2000,"type":"Nonlinear regime-switching regression","estimator":"Conditional least squares with grid search over the threshold","outcome":"continuous","inference":"Bootstrap confidence interval for the threshold (Hansen, 2000)","minSample":100},"citations":[{"ref":"Hansen, B. E. (2000). Sample Splitting and Threshold Estimation. Econometrica, 68(3), 575-603.","type":"article","doi":"10.1111/1468-0262.00124","isbn":null,"url":null}],"related":["ols-regression","panel-fixed-effects","quantile-regression","ardl-bounds-test","nardl-model"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thurstone-scaling","name":"Thurstone Scaling","fullName":"Thurstone Scaling (Law of Comparative Judgment)","aliases":["Law of Comparative Judgment","Thurstone's Method of Equal-Appearing Intervals","Case V Scaling","Thurstone Ölçekleme"],"domain":"statistics","family":"latent-structure","subfamily":"Psychological scaling","year":1927,"originator":"Louis Leon Thurstone","url":"https://scholargate.app/en/statistics/thurstone-scaling","markdownUrl":"https://scholargate.app/en/statistics/thurstone-scaling.md","definition":"Thurstone Scaling, formally the Law of Comparative Judgment, is a psychometric model introduced by Louis Leon Thurstone in 1927 for deriving interval-level scale values from pairwise comparison data. By assuming that each stimulus evokes a normally distributed discriminal process on a psychological continuum, the method converts proportions of preference judgments into z-scores and recovers the latent positions of stimuli, enabling rigorous attitude and preference measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Louis Leon Thurstone","year":1927,"type":"Psychological measurement and attitude scaling model","subfamily":"Psychological scaling","input":"Pairwise comparison frequencies","output":"Interval-level scale values on a psychological continuum"},"citations":[{"ref":"Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34(4), 273–286.","type":"article","doi":"10.1037/h0070288","isbn":null,"url":null}],"related":["bradley-terry-model","correspondence-analysis","2pl-irt"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thyroid-eye-disease-questionnaire","name":"TED-QoL","fullName":"Thyroid Eye Disease Quality of Life Questionnaire","aliases":["GO-QoL","Graves Ophthalmopathy Quality of Life"],"domain":"endocrinology","family":"process-pipeline","subfamily":"Graves' ophthalmopathy-specific quality of life","year":2001,"originator":"Caroline Terwee, Markus Gerding, Frank Dekker","url":"https://scholargate.app/en/endocrinology/thyroid-eye-disease-questionnaire","markdownUrl":"https://scholargate.app/en/endocrinology/thyroid-eye-disease-questionnaire.md","definition":"The TED-QoL (also known as GO-QoL, Graves' Ophthalmopathy Quality of Life questionnaire) is a 16-item disease-specific instrument assessing quality of life impacts in patients with thyroid eye disease (TED), the ophthalmologic manifestation of Graves' disease. Developed by Terwee, Gerding, and colleagues in 2001, it captures both functional vision limitations and psychological distress related to the characteristic eye changes (exophthalmos, lid retraction, diplopia, appearance concerns). It is the gold-standard outcome measure for TED quality of life assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Caroline Terwee, Markus Gerding, Frank Dekker","subfamily":"Graves' ophthalmopathy-specific quality of life","year":2001,"type":"Patient self-report questionnaire"},"citations":[{"ref":"Terwee, C. B., Gerding, M. N., Dekker, F. W., et al. (2001). Development of a disease-specific questionnaire for patients with Graves' ophthalmopathy: The GO-QoL. Br J Ophthalmol, 82(7), 773-779.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Development+of+a+disease-specific+questionnaire+for+patients+with+Graves%27+ophthalmopathy%3A+The+GO-QoL+Terwee"},{"ref":"Gerding, M. N., Terwee, C. B., Dekker, F. W., et al. (2003). Quality of life in patients with Graves' ophthalmopathy is markedly impaired: Measurement by the Medical Outcomes Study instrument. Thyroid, 7(11), 885-889.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Quality+of+life+in+patients+with+Graves%27+ophthalmopathy+is+markedly+impaired%3A+Measurement+by+the+Medical+Outcomes+Study+instrument+Gerding"}],"related":["thyroid-patient-reported-outcomes","hyperthyroidism-symptoms-checklist","adrenal-insufficiency-qol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"thyroid-patient-reported-outcomes","name":"ThyPRO","fullName":"Thyroid-Related Patient Reported Outcomes Scale","aliases":["ThyPRO-85","ThyPRO-39"],"domain":"endocrinology","family":"process-pipeline","subfamily":"Endocrine-specific quality of life","year":2009,"originator":"Torquil Watt, Jens Bjørner, Marianne Groenvold","url":"https://scholargate.app/en/endocrinology/thyroid-patient-reported-outcomes","markdownUrl":"https://scholargate.app/en/endocrinology/thyroid-patient-reported-outcomes.md","definition":"ThyPRO is a comprehensive patient-reported outcome measure assessing the quality of life impact of thyroid disease and its treatment across 13 dimensions. Developed by Watt and colleagues in 2009, it is the most extensively validated thyroid-specific instrument, covering both physical and psychological domains relevant to patients with hyperthyroidism, hypothyroidism, and thyroid cancer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Torquil Watt, Jens Bjørner, Marianne Groenvold","subfamily":"Endocrine-specific quality of life","year":2009,"type":"Patient self-report questionnaire"},"citations":[{"ref":"Watt, T., Bjorner, J. B., Groenvold, M., et al. (2009). Establishing construct validity for the thyroid-related patient reported outcomes (ThyPRO): An initial examination. J Clin Endocrinol Metab, 94(9), 3572-3580.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Establishing+construct+validity+for+the+thyroid-related+patient+reported+outcomes+%28ThyPRO%29%3A+An+initial+examination+Watt"},{"ref":"Watt, T., Groenvold, M., Bjorner, J. B., et al. (2012). Validity and responsiveness of the Danish version of ThyPRO and comparison with the Perceived Deficits Questionnaire. Value Health, 15(3), 528-534.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validity+and+responsiveness+of+the+Danish+version+of+ThyPRO+and+comparison+with+the+Perceived+Deficits+Questionnaire+Watt"}],"related":["growth-hormone-deficiency-scale","diabetes-symptom-checklist","adrenal-insufficiency-qol"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tidal-harmonic-analysis","name":"Tidal Harmonic Analysis","fullName":"Tidal Harmonic Analysis","aliases":["Tidal Constituents","Harmonic Tidal Prediction"],"domain":"oceanography","family":"process-pipeline","subfamily":"Tidal Dynamics","year":"1867","originator":"William Thomson","url":"https://scholargate.app/en/oceanography/tidal-harmonic-analysis","markdownUrl":"https://scholargate.app/en/oceanography/tidal-harmonic-analysis.md","definition":"Tidal harmonic analysis is a mathematical method that decomposes observed sea level or current time series into a sum of sinusoidal components with specific frequencies, amplitudes, and phases corresponding to astronomical tidal constituents. Developed by William Thomson (Lord Kelvin) in 1867, harmonic analysis enables prediction of tides and understanding of tidal dynamics in coastal regions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Thomson","subfamily":"Tidal Dynamics","year":"1867","type":"fourier-analysis"},"citations":[{"ref":"Godin, G. (1972). The Analysis of Tides. University of Toronto Press.","type":"article","doi":null,"isbn":null,"url":"https://www.utpress.utoronto.ca/"},{"ref":"Pugh, D. T., & Woodworth, P. L. (2014). Sea-Level Science: Understanding Tides, Surges, Tsunamis and Mean Sea-Level Changes. Cambridge University Press.","type":"article","doi":"10.1017/CBO9781139235778","isbn":null,"url":null}],"related":["acoustic-doppler-current-profiler","ctd-profiling","geostrophic-velocity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tide","name":"TiDE","fullName":"TiDE (Time-series Dense Encoder)","aliases":["Time-series Dense Encoder","TiDE model","Dense Encoder for Long-term Forecasting","Yoğun Kodlayıcı Zaman Serisi Modeli"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2023,"originator":"Abhimanyu Das et al.","url":"https://scholargate.app/en/deep-learning/tide","markdownUrl":"https://scholargate.app/en/deep-learning/tide.md","definition":"TiDE (Time-series Dense Encoder) is an MLP-based encoder-decoder architecture for long-term multivariate time-series forecasting, introduced by Abhimanyu Das and colleagues at Google Research in 2023. The model encodes past time-series observations together with static and dynamic covariates through stacked dense (MLP) layers, then decodes a latent representation into future forecasts. TiDE demonstrates that simple linear and dense architectures can match or outperform Transformer-based models on standard long-term forecasting benchmarks while being significantly faster.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abhimanyu Das et al.","year":2023,"type":"MLP-based encoder-decoder for long-term time-series forecasting","subfamily":"Time-series forecasting","architecture":"Dense MLP encoder-decoder with covariate projection","input":"Lookback window with optional static and dynamic covariates"},"citations":[{"ref":"Das, A., Kong, W., Leach, A., Mathur, S., Sen, R., & Yu, R. (2023). Long-term forecasting with TiDE: Time-series dense encoder. Transactions on Machine Learning Research.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2304.08424"}],"related":["dlinear","multilayer-perceptron","tsmixer"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tifn-codas","name":"TIFN-CODAS","fullName":"Triangular Intuitionistic Fuzzy Number CODAS (Daami Remadi & Frikha 2023)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1986","originator":"Atanassov, K. T.","url":"https://scholargate.app/en/decision-making/tifn-codas","markdownUrl":"https://scholargate.app/en/decision-making/tifn-codas.md","definition":"TIFN-CODAS (Triangular Intuitionistic Fuzzy Number CODAS (Daami Remadi & Frikha 2023)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Atanassov, K. T.","subfamily":"Ranking","year":"1986","type":"Combinative Distance-based Assessment under Triangular Intuitionistic Fuzzy uncertainty (TIFN: {(a1,a2,a3); (a'1,a2,a'3)}; a'1 ≤ a1 ≤ a2 ≤ a3 ≤ a'3) — MCGDM with linguistic-to-TIFN translation","value_space":"triangular_intuitionistic","uncertainty":"epistemic","compensation":"full","rank_reversal":true},"citations":[{"ref":"Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems","type":"article","doi":"10.1016/S0165-0114(86)80034-3","isbn":null,"url":null}],"related":["ahp","anp","bwm","critic","entropy","if-entropy","merec","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tight-binding-model","name":"Tight-Binding Model","fullName":"Tight-Binding Model","aliases":["TB model","hopping model"],"domain":"quantum-computing","family":"ml-model","subfamily":"Band Structure Method","year":"1954","originator":"John Slater and George Koster","url":"https://scholargate.app/en/quantum-computing/tight-binding-model","markdownUrl":"https://scholargate.app/en/quantum-computing/tight-binding-model.md","definition":"The Tight-Binding (TB) model is a simplified semi-empirical approach for computing electronic band structures and properties of solids. Formulated by Slater and Koster in 1954, TB treats electron hopping between atomic sites as the dominant interaction, enabling efficient calculations of band dispersion for a wide variety of materials.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Slater and George Koster","subfamily":"Band Structure Method","year":"1954","type":"Simplified electronic structure model"},"citations":[{"ref":"Slater, J. C., Koster, G. F. (1954). Simplified LCAO method for the periodic potential problem. Physical Review, 94, 1498–1524.","type":"article","doi":"10.1103/PhysRev.94.1498","isbn":null,"url":null},{"ref":"Ashcroft, N. W., Mermin, N. D. (1976). Solid State Physics. Holt, Rinehart and Winston.","type":"article","doi":null,"isbn":null,"url":"https://www.holtscience.com/books/solid-state-physics"},{"ref":"Mahan, G. D. (2000). Many-Particle Physics (3rd ed.). Kluwer Academic/Plenum Publishers.","type":"article","doi":null,"isbn":null,"url":"https://www.springer.com/gp/book/9780306463389"}],"related":["density-functional-theory","hartree-fock-method","kkr-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tillage-erosion-model","name":"Tillage Erosion Model","fullName":"Soil Movement and Erosion Assessment from Tillage Operations","aliases":["Tillage soil loss","Soil redistribution model","Erosion prediction"],"domain":"agronomy","family":"process-pipeline","subfamily":"Soil conservation and erosion control","year":"1992","originator":"M. J. Lindstrom, W. W. Nelson, T. E. Schumacher","url":"https://scholargate.app/en/agronomy/tillage-erosion-model","markdownUrl":"https://scholargate.app/en/agronomy/tillage-erosion-model.md","definition":"Tillage Erosion Model is a physical transport and modeling pipeline for predicting soil movement and redistribution caused by tillage operations on sloping land. Developed by soil scientists (Lindstrom, Nelson, Lobb) in the 1990s–2000s, this method quantifies how plowing, disking, and other soil-disturbing implements physically move soil downslope, leading to long-term productivity loss on upper slopes and soil accumulation in valleys.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. J. Lindstrom, W. W. Nelson, T. E. Schumacher","subfamily":"Soil conservation and erosion control","year":"1992","type":"Physical transport and modeling pipeline"},"citations":[{"ref":"Lindstrom, M. J., Nelson, W. W., & Schumacher, T. E. (1992). Soil movement by tillage as affected by slope. Soil Science Society of America Journal, 56(4), 1104-1108.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Soil+movement+by+tillage+as+affected+by+slope+Lindstrom"},{"ref":"Lobb, D. A., Kachanoski, R. G., & Miller, M. H. (2003). Tillage translocation and tillage erosion as processes of soil redistribution on agricultural hillslopes. In Soil Erosion Research for the 21st Century: Proceedings of the International Symposium (pp. 15-17).","type":"article","doi":null,"isbn":null,"url":"https://www.asabe.org/"}],"related":["soil-fertility-management","crop-growth-simulation","nitrogen-use-efficiency","irrigation-scheduling-etref","precision-agriculture-ndvi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"timber-harvest-scheduling","name":"Timber Harvest Scheduling","fullName":"Timber Harvest Scheduling Optimization","aliases":["harvest scheduling","timber rotation","forest planning"],"domain":"forestry","family":"process-pipeline","subfamily":"Forest Management","year":"1977","originator":"K. Norman Johnson","url":"https://scholargate.app/en/forestry/timber-harvest-scheduling","markdownUrl":"https://scholargate.app/en/forestry/timber-harvest-scheduling.md","definition":"Timber harvest scheduling is an optimization method that determines which forest stands should be harvested and when, to achieve management objectives (economic return, sustained yield, biodiversity, wildlife habitat) while respecting constraints (minimum harvest age, ending inventory level, adjacent-stand restrictions). It integrates growth models, economic data, and spatial forest inventory to generate long-term management plans spanning decades. Harvest scheduling is essential for operational forest management and landscape-level planning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. Norman Johnson","subfamily":"Forest Management","year":"1977","type":"optimization algorithm"},"citations":[{"ref":"Johnson, K. N., & Scheurman, H. L. (1977). Techniques for prescribing optimal timber harvest and investment under different objectives. Forest Science Monograph 18.","type":"article","doi":null,"isbn":null,"url":"https://academic.oup.com/forestscience"},{"ref":"Bettinger, P., Boston, K., Siry, J. P., & Grebner, D. L. (2009). Forest Management and Planning. Academic Press, 2nd edition.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Forest+Management+and+Planning+Bettinger"}],"related":["site-index-curve","stand-density-index","forest-productivity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"timbre-analysis","name":"Timbre Analysis","fullName":"Timbre Analysis and Characterization Algorithm","aliases":["tone color analysis","spectral characterization","timbre descriptor extraction"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Spectral and acoustic characterization","year":"1977","originator":"John M. Grey","url":"https://scholargate.app/en/music-information-retrieval/timbre-analysis","markdownUrl":"https://scholargate.app/en/music-information-retrieval/timbre-analysis.md","definition":"Timbre analysis is the computational characterization and modeling of tone color—the perceived quality that distinguishes one instrument from another even at the same pitch and loudness. Pioneered by Grey (1977), timbre analysis extracts acoustic descriptors that characterize spectral shape, temporal dynamics, and harmonic content. It underlies instrument identification, music similarity assessment, and audio retrieval. Unlike melody and rhythm, timbre is high-dimensional and context-dependent, making it one of the most challenging aspects of music analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John M. Grey","subfamily":"Spectral and acoustic characterization","year":"1977","type":"Acoustic feature extraction and analysis"},"citations":[{"ref":"Grey, J. M. (1977). Multidimensional perceptual scaling of musical timbres. The Journal of the Acoustical Society of America, 61(5), 1270-1277.","type":"article","doi":"10.1121/1.381428","isbn":null,"url":null},{"ref":"Peeters, G., Giordano, B. L., Susini, P., Misdariis, N., & McAdams, S. (2011). The Timbre Toolbox: Extracting audio descriptors from musical signals. Journal of the Acoustical Society of America, 130(5), 2902-2916.","type":"article","doi":"10.1121/1.3642604","isbn":null,"url":null},{"ref":"Seetharaman, P., Wlodarczyk, B., & Wichern, G. (2017). A categorical query-by-timbre system for musical audio. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2017/papers/064.pdf"}],"related":["instrument-recognition","music-genre-classification","music-similarity-measure","pitch-detection-algorithm","vocal-separation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-dependent-cox","name":"Time-Dependent Cox Regression","fullName":"Cox Regression with Time-Varying Covariates","aliases":["time-varying covariate Cox model","extended Cox model","Zamana Bağlı Kovaryatlı Cox Regresyonu"],"domain":"survival","family":"survival","subfamily":null,"year":1972,"originator":"Cox, D. R. (extended formulation by Therneau & Grambsch)","url":"https://scholargate.app/en/survival/time-dependent-cox","markdownUrl":"https://scholargate.app/en/survival/time-dependent-cox.md","definition":"Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the method of choice whenever a covariate — such as a laboratory measurement, a medication dose, or a disease severity score — changes over time rather than remaining fixed from study entry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cox, D. R. (extended formulation by Therneau & Grambsch)","year":1972,"type":"Semi-parametric hazard regression model","dataStructure":"Long-format (start–stop intervals)","handles":"Right-censoring, time-varying covariates","minimumSample":50},"citations":[{"ref":"Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.","type":"book","doi":"10.1007/978-1-4757-3294-8","isbn":null,"url":null}],"related":["cox-ph","kaplan-meier","joint-model-survival","frailty-model","fine-gray-model","flexible-parametric-survival"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-dependent-dft","name":"Time-Dependent DFT","fullName":"Time-Dependent Density Functional Theory (TDDFT)","aliases":["TDDFT","TDDFT/DFT"],"domain":"quantum-computing","family":"ml-model","subfamily":"Electronic Structure Theory","year":"1984","originator":"Erich Runge and Eberhard Gross","url":"https://scholargate.app/en/quantum-computing/time-dependent-dft","markdownUrl":"https://scholargate.app/en/quantum-computing/time-dependent-dft.md","definition":"Time-Dependent Density Functional Theory (TDDFT) extends DFT to excited states and time-dependent phenomena. Formulated by Runge and Gross in 1984, TDDFT enables calculation of excitation energies, optical spectra, and charge-transfer processes with moderate computational cost, making it invaluable for photochemistry and materials science.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erich Runge and Eberhard Gross","subfamily":"Electronic Structure Theory","year":"1984","type":"Excited state method"},"citations":[{"ref":"Runge, E., Gross, E. K. (1984). Density-functional theory for time-dependent systems. Physical Review Letters, 52, 997–1000.","type":"article","doi":"10.1103/PhysRevLett.52.997","isbn":null,"url":null},{"ref":"Casida, M. E. (1995). Time-dependent density-functional response theory for molecules. In Recent Advances in Density Functional Methods. World Scientific.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?cluster=1652482341814831088"},{"ref":"Huix-Rotllant, M., et al. (2020). Assessment of time-dependent density functional theory for excited states. In Handbook of Excited State Spectroscopy. World Scientific.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1142/10711"}],"related":["density-functional-theory","hartree-fock-method","coupled-cluster-ccsd"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-moe","name":"Time-MoE","fullName":"Time-MoE (Mixture-of-Experts Time-Series Foundation Model)","aliases":["Time Mixture-of-Experts","Time-MoE Foundation Model","Sparse Time-Series Transformer","Zaman Karışık Uzmanlar Modeli"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2024,"originator":"Xiaoming Shi et al.","url":"https://scholargate.app/en/deep-learning/time-moe","markdownUrl":"https://scholargate.app/en/deep-learning/time-moe.md","definition":"Time-MoE is a billion-scale autoregressive foundation model for universal time-series forecasting, introduced by Shi et al. in 2024 and accepted at ICLR 2025. It combines a decoder-only transformer architecture with sparse Mixture-of-Experts (MoE) feed-forward layers, enabling the model to scale to billions of parameters while activating only a small subset of expert networks per token—dramatically increasing capacity without proportional compute cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xiaoming Shi et al.","year":2024,"type":"Sparse mixture-of-experts autoregressive foundation model","subfamily":"Time-series forecasting","parameters":"Up to 2.4 billion (sparse activation)","training_data":"Time-300B benchmark corpus"},"citations":[{"ref":"Shi, X., Wang, S., Nie, Y., Li, D., Ye, Z., Wen, Q., & Jin, M. (2024). Time-MoE: Billion-scale time series foundation models with mixture of experts. ICLR 2025.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2409.16040"}],"related":["mixture-of-experts","timesfm","chronos"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-motion-gps","name":"Time-Motion GPS","fullName":"Time-Motion Analysis and GPS Movement Tracking","aliases":["GPS analysis","movement tracking","workload quantification","physical demands"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Sports Biomechanics","year":"2010","originator":"Osgnach & Di Prampero","url":"https://scholargate.app/en/sports-science/time-motion-gps","markdownUrl":"https://scholargate.app/en/sports-science/time-motion-gps.md","definition":"Time-motion analysis with GPS and micro-sensor technology quantifies the movement patterns, workload, and physical demands during training or match play in team sports. Pioneered by Osgnach and colleagues (2010), modern GPS units track athletes' positions in real-time, calculating distance covered, velocity profiles, and acceleration/deceleration frequencies. Combined with heart rate and other sensor data, GPS analysis provides comprehensive workload quantification enabling coaching staff to monitor player fatigue, balance training intensity, and prevent injury. GPS is now standard in elite soccer, rugby, Australian Rules football, and other intermittent sports.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Osgnach & Di Prampero","subfamily":"Sports Biomechanics","year":"2010","type":"GPS tracking"},"citations":[{"ref":"Gregory, P., & Drust, B. (2007). Physical demands of rugby union: quantification of accelerations and movements patterns in play. Journal of Strength and Conditioning Research, 21(2), 309-314.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Physical+demands+of+rugby+union%3A+quantification+of+accelerations+and+movements+patterns+in+play+Gregory"},{"ref":"Osgnach, C., Poser, S., Bernardini, R., Rinaldo, R., & di Prampero, P. E. (2010). Energy cost and metabolic power in elite soccer: a new match analysis approach. Medicine and Science in Sports and Exercise, 42(1), 170-178.","type":"article","doi":"10.1249/mss.0b013e3181ae5cfd","isbn":null,"url":null},{"ref":"Cummins, C., Orr, R., O'Connor, H., & West, C. (2013). Global positioning systems (GPS) and microtechnology sensors in team sports: A systematic review. Sports Medicine, 43(10), 1025-1042.","type":"article","doi":"10.1007/s40279-013-0069-2","isbn":null,"url":null}],"related":["acute-chronic-workload-ratio","banister-trimp","session-rpe","heart-rate-recovery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-of-flight-pid","name":"Time-of-Flight PID","fullName":"Time-of-Flight Particle Identification","aliases":["ToF","flight time measurement","velocity measurement"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Particle identification","year":"1970","originator":"Classical measurement technique","url":"https://scholargate.app/en/particle-physics/time-of-flight-pid","markdownUrl":"https://scholargate.app/en/particle-physics/time-of-flight-pid.md","definition":"Time-of-Flight (ToF) particle identification measures the time taken for a particle to travel a known distance, enabling determination of the particle's velocity and mass. This complementary technique to Cherenkov and ionization energy loss provides robust particle separation across wide momentum ranges in modern detectors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Classical measurement technique","subfamily":"Particle identification","year":"1970","type":"Timing-based method"},"citations":[{"ref":"Heilbronn, L. H., & Zeitlin, C. (2010). Measurement of particle identification efficiencies. Nuclear Instruments and Methods in Physics Research Section B, 268(23-24), 3577–3583.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measurement+of+particle+identification+efficiencies+Heilbronn"},{"ref":"Adinolfi, M., et al. (2013). Performance of the LHCb VELO detector and vertex reconstruction. Journal of Instrumentation, 8(12), P12008.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Performance+of+the+LHCb+VELO+detector+and+vertex+reconstruction+Adinolfi"},{"ref":"Stelzer, B., et al. (2015). Time-of-flight measurements in particle detection. Reviews of Scientific Instruments, 86(1), 013301.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Time-of-flight+measurements+in+particle+detection+Stelzer"}],"related":["cherenkov-detection","hep-track-reconstruction","calorimeter-calibration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-approximate-bayesian-computation","name":"Time series approximate Bayesian computation","fullName":"Time Series Approximate Bayesian Computation","aliases":["TS-ABC","time series ABC","likelihood-free inference for time series","ABC for dynamical systems"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2009","originator":"Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension","url":"https://scholargate.app/en/bayesian/time-series-approximate-bayesian-computation","markdownUrl":"https://scholargate.app/en/bayesian/time-series-approximate-bayesian-computation.md","definition":"Time series ABC is a likelihood-free Bayesian inference method that estimates the posterior distribution of model parameters for dynamical or time-indexed systems by comparing summary statistics of simulated trajectories to those of the observed series, bypassing the need to evaluate an analytic likelihood. It is particularly valuable for complex mechanistic or stochastic models whose likelihoods are intractable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension","year":"2009","type":"likelihood-free Bayesian inference","dataType":"sequential / time series observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Toni, T., Welch, D., Strelkowa, N., Ipsen, A. & Stumpf, M. P. H. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6(31), 187–202.","type":"article","doi":"10.1098/rsif.2008.0172","isbn":null,"url":null},{"ref":"Sisson, S. A., Fan, Y. & Beaumont, M. A. (Eds.) (2018). Handbook of Approximate Bayesian Computation. CRC Press.","type":"book","doi":null,"isbn":"978-1439881507","url":null}],"related":["approximate-bayesian-computation","sequential-monte-carlo","particle-filter","dynamic-bayesian-inference","kalman-filter","time-series-bayesian-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-bayesian-hierarchical-model","name":"Time series Bayesian hierarchical model","fullName":"Time Series Bayesian Hierarchical Model","aliases":["TSBHM","Bayesian hierarchical time series","hierarchical dynamic Bayesian model","multilevel Bayesian time series"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1989–1997","originator":"West & Harrison (dynamic models); Gelman et al. (hierarchical Bayesian framework)","url":"https://scholargate.app/en/bayesian/time-series-bayesian-hierarchical-model","markdownUrl":"https://scholargate.app/en/bayesian/time-series-bayesian-hierarchical-model.md","definition":"A time series Bayesian hierarchical model combines the hierarchical (multilevel) Bayesian framework with a dynamic state-space structure to analyse temporal data collected on multiple units or groups. Priors encode beliefs about both within-unit dynamics and cross-unit variation, and the posterior is obtained via MCMC or sequential Monte Carlo, yielding full probabilistic forecasts with calibrated uncertainty.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"West & Harrison (dynamic models); Gelman et al. (hierarchical Bayesian framework)","year":"1989–1997","type":"Bayesian hierarchical model for time series","dataType":"longitudinal, time series, panel data","subfamily":"Bayesian / computational"},"citations":[{"ref":"West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387947259","url":null},{"ref":"Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.","type":"book","doi":null,"isbn":"978-1439840955","url":null}],"related":["hierarchical-bayesian-inference","dynamic-bayesian-network","kalman-filter","bayesian-regression","time-series-mcmc","multilevel-bayesian-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-bayesian-inference","name":"Time series Bayesian inference","fullName":"Bayesian Inference for Time Series Models","aliases":["Bayesian time series analysis","Bayesian state-space modeling","probabilistic time series inference","BSTS"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1989","originator":"Mike West and Jeff Harrison","url":"https://scholargate.app/en/bayesian/time-series-bayesian-inference","markdownUrl":"https://scholargate.app/en/bayesian/time-series-bayesian-inference.md","definition":"Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mike West and Jeff Harrison","year":"1989","type":"Bayesian probabilistic model","dataType":"Sequential / time-ordered observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387947259","url":null},{"ref":"Prado, R. & West, M. (2010). Time Series: Modeling, Computation, and Inference. CRC Press.","type":"book","doi":null,"isbn":"978-1420093360","url":null}],"related":["kalman-filter","particle-filter","dynamic-bayesian-network","bayesian-regression","sequential-monte-carlo","hierarchical-bayesian-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-bayesian-model-averaging","name":"Time series Bayesian model averaging","fullName":"Time Series Bayesian Model Averaging","aliases":["TS-BMA","Bayesian model averaging for time series","BMA forecasting","time series BMA"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1999–2010","originator":"Hoeting, Madigan, Raftery, Volinsky (BMA); Raftery et al. for dynamic/time-series extensions","url":"https://scholargate.app/en/bayesian/time-series-bayesian-model-averaging","markdownUrl":"https://scholargate.app/en/bayesian/time-series-bayesian-model-averaging.md","definition":"Time series Bayesian model averaging (TS-BMA) combines forecasts from an ensemble of time series models — such as AR, VAR, or state-space specifications — by weighting each model by its posterior probability given observed data. Rather than selecting one model and discarding uncertainty about which model is best, TS-BMA integrates over model uncertainty, producing forecasts that are more robust and better calibrated than any single model alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hoeting, Madigan, Raftery, Volinsky (BMA); Raftery et al. for dynamic/time-series extensions","year":"1999–2010","type":"Bayesian ensemble / model combination","dataType":"time series (univariate or multivariate, continuous)","subfamily":"Bayesian / computational"},"citations":[{"ref":"Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bayesian+model+averaging%3A+A+tutorial+Hoeting"},{"ref":"Raftery, A. E., Kárný, M., & Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics, 52(1), 52–66.","type":"article","doi":"10.1198/TECH.2009.08104","isbn":null,"url":null}],"related":["bayesian-model-averaging","dynamic-model-averaging","bayesian-regression","time-series-bayesian-inference","kalman-filter","sequential-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-chip-seq-peak-calling","name":"Time-series ChIP-seq peak calling","fullName":"Time-series Chromatin Immunoprecipitation Sequencing Peak Calling","aliases":["longitudinal ChIP-seq analysis","dynamic ChIP-seq peak calling","time-course ChIP-seq","temporal chromatin profiling"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2008–2012 (ChIP-seq); time-series extensions ~2015–2020","originator":"ENCODE Consortium; extended by Haiminen et al. and broader epigenomics community","url":"https://scholargate.app/en/bioinformatics/time-series-chip-seq-peak-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-chip-seq-peak-calling.md","definition":"Time-series ChIP-seq peak calling extends standard chromatin immunoprecipitation sequencing analysis to samples collected at multiple time points. By identifying and comparing protein-DNA binding peaks across a temporal dimension, the method reveals how transcription factor occupancy, histone modifications, or chromatin remodeler binding evolve during biological processes such as differentiation, circadian cycles, or stimulus response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"ENCODE Consortium; extended by Haiminen et al. and broader epigenomics community","year":"2008–2012 (ChIP-seq); time-series extensions ~2015–2020","type":"Computational epigenomics pipeline","dataType":"ChIP-seq read alignments (BAM/BED) at multiple time points; matched input controls","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., ... & Snyder, M. (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research, 22(9), 1813–1831.","type":"article","doi":"10.1101/gr.136184.111","isbn":null,"url":null},{"ref":"Haiminen, N., Karlebach, G., Kharchenko, P. V., & Lähdesmäki, H. (2018). TimeChIP: time-series peak calling for ChIP-seq data. Bioinformatics, 34(24), 4161–4167.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=TimeChIP+time-series+peak+calling+ChIP-seq+Haiminen+2018"}],"related":["chip-seq-peak-calling","atac-seq-analysis","rna-seq-differential-expression","time-series-rna-seq-differential-expression","epigenome-wide-association-study","chromatin-accessibility-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-copy-number-variation-analysis","name":"Time-series copy number variation analysis","fullName":"Time-Series Copy Number Variation Analysis","aliases":["longitudinal CNV analysis","temporal copy number analysis","time-series CNV profiling","serial CNV analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s–present","originator":"Developed from foundational CNV methods (Olshen et al. 2004; Ding et al. 2010) extended to longitudinal tumor genomics frameworks","url":"https://scholargate.app/en/bioinformatics/time-series-copy-number-variation-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-copy-number-variation-analysis.md","definition":"Time-series copy number variation (CNV) analysis is a computational genomics pipeline that characterizes chromosomal gains and losses across multiple sequential samples from the same individual or tumor. By comparing copy number profiles at successive time points — such as diagnosis, mid-treatment, relapse — it reconstructs the clonal dynamics and evolutionary trajectories driving genome instability, enabling researchers to track how sub-populations expand, contract, or acquire new aberrations over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from foundational CNV methods (Olshen et al. 2004; Ding et al. 2010) extended to longitudinal tumor genomics frameworks","year":"2010s–present","type":"Computational genomics pipeline","dataType":"Paired-end whole-genome or whole-exome sequencing data from multiple time points per sample","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Dentro, S. C., et al. (2021). Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell, 184(8), 2239-2254.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Characterizing+genetic+intra-tumor+heterogeneity+across+2658+human+cancer+genomes"},{"ref":"Zaccaria, S., & Raphael, B. J. (2020). Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. Nature Communications, 11(1), 4301.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Accurate+quantification+of+copy-number+aberrations+and+whole-genome+duplications+in+multi-sample+tumor+sequencing+data"}],"related":["copy-number-variation-analysis","variant-calling","single-cell-copy-number-variation-analysis","rna-seq-differential-expression","genome-wide-association-study","multi-omics-copy-number-variation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-epigenome-wide-association-study","name":"Time-series Epigenome-wide Association Study","fullName":"Longitudinal Epigenome-wide Association Study","aliases":["time-series EWAS","longitudinal EWAS","repeated-measures EWAS","dynamic methylation association study"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s","originator":"Extended from EWAS (Rakyan et al., 2011); longitudinal designs formalised by multiple groups ~2010s","url":"https://scholargate.app/en/bioinformatics/time-series-epigenome-wide-association-study","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-epigenome-wide-association-study.md","definition":"A time-series epigenome-wide association study (time-series EWAS) extends the classic cross-sectional EWAS design to longitudinal settings, measuring DNA methylation across the entire epigenome at multiple time points within the same subjects. The goal is to identify CpG sites whose methylation levels change systematically over time, or to characterise how epigenetic associations with an exposure or phenotype evolve across developmental stages, treatment periods, or disease trajectories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extended from EWAS (Rakyan et al., 2011); longitudinal designs formalised by multiple groups ~2010s","year":"2010s","type":"Longitudinal epigenomic association pipeline","dataType":"Genome-wide DNA methylation arrays or bisulfite sequencing at multiple time points per subject","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., ... & Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1), 208.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Critical+evaluation+of+the+Illumina+MethylationEPIC+BeadChip+microarray+for+whole-genome+DNA+methylation+profiling"},{"ref":"Waterland, R. A., Kellermayer, R., Laritsky, E., Rayco-Solon, P., Harris, R. A., Travisano, M., ... & Prentice, A. M. (2010). Season of conception in rural Gambia affects DNA methylation at putative human metastable epialleles. PLoS Genetics, 6(12), e1001252.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Season+of+conception+in+rural+Gambia+affects+DNA+methylation+at+putative+human+metastable+epialleles"}],"related":["epigenome-wide-association-study","time-series-gwas","longitudinal-cohort-study","dna-methylation-analysis","rna-seq-differential-expression","time-series-single-cell-rna-seq-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-eqtl-analysis","name":"Time-series eQTL analysis","fullName":"Time-series Expression Quantitative Trait Loci Analysis","aliases":["dynamic eQTL analysis","longitudinal eQTL mapping","ts-eQTL","temporal eQTL"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s–2019 (concept established earlier; dynamic framework formalized ~2019)","originator":"Multiple groups; formalized by Strober et al. and others in the context of cellular differentiation (2019)","url":"https://scholargate.app/en/bioinformatics/time-series-eqtl-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-eqtl-analysis.md","definition":"Time-series eQTL analysis identifies genetic variants (eQTLs) whose effect on gene expression changes over time or across developmental stages. By combining longitudinal RNA-seq data with individual genotypes, the method captures how the same SNP can activate, silence, or reshape gene regulation at different time points — revealing the temporal architecture of the genome's regulatory program in processes such as differentiation, disease progression, and environmental response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; formalized by Strober et al. and others in the context of cellular differentiation (2019)","year":"2010s–2019 (concept established earlier; dynamic framework formalized ~2019)","type":"Genetic mapping method","dataType":"Time-stamped RNA-seq gene expression data paired with genotype (SNP array or WGS)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Fair, B. J., et al. (2020). Gene expression variability in human and chimpanzee populations share common determinants. eLife, 9, e59929.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Gene+expression+variability+in+human+and+chimpanzee+populations+share+common+determinants+eLife+2020"},{"ref":"Strober, B. J., et al. (2019). Dynamic genetic regulation of gene expression during cellular differentiation. Science, 364(6447), 1287–1290.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1126/science.aaw0040"}],"related":["eqtl-mapping","differential-expression-analysis","genome-wide-association-study","co-expression-network-analysis","chromatin-accessibility-analysis","mediation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-gene-set-enrichment-analysis","name":"Time-series gene set enrichment analysis","fullName":"Time-Series Gene Set Enrichment Analysis","aliases":["longitudinal GSEA","dynamic GSEA","time-course GSEA","TS-GSEA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2005 (GSEA foundation); time-series adaptations 2007–2014","originator":"Extension of GSEA (Subramanian et al., 2005); time-series adaptations developed through maSigPro (Conesa lab) and related tools","url":"https://scholargate.app/en/bioinformatics/time-series-gene-set-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-gene-set-enrichment-analysis.md","definition":"Time-series gene set enrichment analysis (TS-GSEA) extends the classical GSEA framework to detect biologically coordinated gene sets — pathways, gene ontology terms, or curated signatures — whose collective expression changes meaningfully over time. Rather than comparing two snapshots, it models the full temporal trajectory of gene expression to identify which functional programs are activated, suppressed, or dynamically remodelled during a biological process such as development, treatment response, or disease progression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of GSEA (Subramanian et al., 2005); time-series adaptations developed through maSigPro (Conesa lab) and related tools","year":"2005 (GSEA foundation); time-series adaptations 2007–2014","type":"Gene set enrichment method for longitudinal omics data","dataType":"Time-course RNA-seq or microarray expression matrices with multiple ordered time points","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.","type":"article","doi":"10.1073/pnas.0506580102","isbn":null,"url":null},{"ref":"Nueda, M. J., Tarazona, S., & Conesa, A. (2014). Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics, 30(18), 2598–2602.","type":"article","doi":"10.1093/bioinformatics/btu333","isbn":null,"url":null}],"related":["gene-set-enrichment-analysis","rna-seq-differential-expression","time-series-rna-seq-differential-expression","pathway-enrichment-analysis","single-cell-rna-seq-analysis","multi-omics-gene-set-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-kalman-filter","name":"Time Series Kalman Filter","fullName":"Kalman Filter for Time Series State-Space Models","aliases":["state-space Kalman filter","structural time series filter","Kalman smoother for time series","time series state-space filter"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1960; time series state-space framework formalised 1980s–1990s","originator":"Rudolf E. Kalman; systematised for time series by Andrew Harvey and Søren Johansen","url":"https://scholargate.app/en/bayesian/time-series-kalman-filter","markdownUrl":"https://scholargate.app/en/bayesian/time-series-kalman-filter.md","definition":"The time series Kalman filter applies the Kalman filtering and smoothing algorithm within a state-space representation of time series models. It recursively extracts unobserved components — trend, seasonality, cycles, and irregular noise — from observed data, providing optimal filtered and smoothed state estimates together with their uncertainty, and enabling exact likelihood evaluation for parameter estimation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf E. Kalman; systematised for time series by Andrew Harvey and Søren Johansen","year":"1960; time series state-space framework formalised 1980s–1990s","type":"recursive Bayesian filter / state-space smoother","dataType":"univariate or multivariate time series, equally or irregularly spaced","subfamily":"Bayesian / computational"},"citations":[{"ref":"Durbin, J. & Koopman, S. J. (2012). Time Series Analysis by State Space Methods (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"978-0199641178","url":null},{"ref":"Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521321969","url":null}],"related":["kalman-filter","sequential-monte-carlo","particle-filter","dynamic-bayesian-network","bayesian-regression","time-series-bayesian-inference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-mcmc","name":"Time series MCMC","fullName":"Markov Chain Monte Carlo for Time Series Models","aliases":["MCMC time series","Bayesian time series MCMC","time series posterior sampling","sequential Bayesian MCMC"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1994–1997","originator":"Carter & Kohn; West & Harrison","url":"https://scholargate.app/en/bayesian/time-series-mcmc","markdownUrl":"https://scholargate.app/en/bayesian/time-series-mcmc.md","definition":"Time series MCMC applies Markov chain Monte Carlo methods to Bayesian inference over time-ordered data. Rather than optimising a single parameter estimate, it draws samples from the full joint posterior of parameters and latent states, yielding probability distributions that honestly reflect uncertainty about dynamics, trends, and seasonal patterns across every time point.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carter & Kohn; West & Harrison","year":"1994–1997","type":"Bayesian posterior sampling for time-ordered data","dataType":"time series (univariate or multivariate, continuous or count)","subfamily":"Bayesian / computational"},"citations":[{"ref":"Carter, C. K. & Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3), 541–553.","type":"article","doi":"10.1093/biomet/81.3.541","isbn":null,"url":null},{"ref":"West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0387947259","url":null}],"related":["gibbs-sampling","sequential-monte-carlo","kalman-filter","particle-filter","dynamic-bayesian-inference","hamiltonian-monte-carlo"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-metabolomics-analysis","name":"Time-series metabolomics analysis","fullName":"Time-Series Metabolomics Analysis","aliases":["longitudinal metabolomics","dynamic metabolomics","temporal metabolome profiling","kinetic metabolomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2000s–2010s","originator":"Developed from general metabolomics workflows; longitudinal extensions pioneered by A. K. Smilde, R. Bino, and colleagues","url":"https://scholargate.app/en/bioinformatics/time-series-metabolomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-metabolomics-analysis.md","definition":"Time-series metabolomics analysis profiles small-molecule metabolites from biological samples collected at multiple, ordered time points, enabling researchers to capture the dynamic flux of metabolic pathways in response to stimuli, disease progression, drug treatment, or developmental change. By integrating longitudinal statistical models with standard metabolomics preprocessing, the approach goes beyond a static metabolic snapshot to reveal how, when, and in what sequence metabolic responses unfold.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from general metabolomics workflows; longitudinal extensions pioneered by A. K. Smilde, R. Bino, and colleagues","year":"2000s–2010s","type":"Quantitative longitudinal omics pipeline","dataType":"Mass spectrometry (LC-MS, GC-MS) or NMR spectral data collected at multiple time points from the same or matched biological subjects","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Smilde, A. K., van der Werf, M. J., Bijlsma, S., van der Werff-van der Vat, B. J. C., & Jellema, R. H. (2005). Fusion of mass spectrometry-based metabolomics data. Analytical Chemistry, 77(20), 6729–6736.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fusion+of+mass+spectrometry-based+metabolomics+data+Smilde+2005"},{"ref":"Redestig, H., & Costa, I. G. (2011). Detection and interpretation of metabolite–transcript coresponses using combined profiling data. Bioinformatics, 27(13), i357–i365.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Detection+and+interpretation+of+metabolite+transcript+coresponses+Redestig+Costa+2011+Bioinformatics"}],"related":["metabolomics-analysis","multi-omics-metabolomics-analysis","time-series-rna-seq-differential-expression","pathway-enrichment-analysis","machine-learning-assisted-metabolomics-analysis","single-cell-metabolomics-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-microbiome-diversity-analysis","name":"Time-series microbiome diversity analysis","fullName":"Longitudinal Microbiome Diversity Analysis","aliases":["longitudinal microbiome diversity analysis","temporal microbiome analysis","repeated-measures microbiome diversity","time-course microbiome analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020)","originator":"Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleagues","url":"https://scholargate.app/en/bioinformatics/time-series-microbiome-diversity-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-microbiome-diversity-analysis.md","definition":"Time-series microbiome diversity analysis tracks how the richness, evenness, and community composition of microbial communities change across multiple time points within the same subjects. By combining standard diversity metrics with longitudinal statistical models, it separates true temporal dynamics from inter-individual variation, identifying when and how perturbations such as diet changes, antibiotic treatment, or disease onset reshape the microbiome.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleagues","year":"2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020)","type":"Longitudinal observational / bioinformatics pipeline","dataType":"16S rRNA amplicon counts, shotgun metagenomics read tables, OTU/ASV tables across multiple time points","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583.","type":"article","doi":"10.1038/nmeth.3869","isbn":null,"url":null},{"ref":"Chen, Y., Lun, A. T. L., & Smyth, G. K. (2023). Differential abundance testing on single-cell data using quasi-likelihood methods. Genome Biology, 24(1), 188.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Differential+abundance+testing+single-cell+data+quasi-likelihood+methods+Chen+2023"}],"related":["microbiome-diversity-analysis","rna-seq-differential-expression","time-series-rna-seq-differential-expression","pathway-enrichment-analysis","single-cell-rna-seq-analysis","multi-omics-microbiome-diversity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-particle-filter","name":"Time series particle filter","fullName":"Time Series Particle Filter (Sequential Monte Carlo for State-Space Models)","aliases":["SMC for time series","bootstrap particle filter","sequential importance resampling","particle filtering"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993","originator":"Gordon, Salmond & Smith","url":"https://scholargate.app/en/bayesian/time-series-particle-filter","markdownUrl":"https://scholargate.app/en/bayesian/time-series-particle-filter.md","definition":"The time series particle filter is a Sequential Monte Carlo method that tracks the hidden state of a nonlinear, non-Gaussian state-space model as new observations arrive one at a time. It represents the evolving posterior distribution over the latent state as a weighted cloud of random samples (particles), updating them at each time step through propagation, likelihood weighting, and resampling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon, Salmond & Smith","year":"1993","type":"Sequential Bayesian filtering","dataType":"Sequential / time series observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107-113.","type":"article","doi":"10.1049/ip-f-2.1993.0015","isbn":null,"url":null},{"ref":"Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer.","type":"book","doi":null,"isbn":"978-0387951461","url":null}],"related":["kalman-filter","sequential-monte-carlo","time-series-bayesian-inference","particle-filter","time-series-kalman-filter","dynamic-bayesian-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-pathway-enrichment-analysis","name":"Time-series pathway enrichment analysis","fullName":"Time-Series Pathway Enrichment Analysis","aliases":["temporal pathway analysis","longitudinal pathway enrichment","dynamic pathway analysis","TPEA"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2005–2014","originator":"Bar-Joseph and colleagues (temporal gene expression); extended by Cheng, Bhatt et al. for pathway-level time-series inference","url":"https://scholargate.app/en/bioinformatics/time-series-pathway-enrichment-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-pathway-enrichment-analysis.md","definition":"Time-series pathway enrichment analysis identifies biological pathways whose coordinated gene activity changes significantly across ordered time points. Rather than treating each time point independently, the method models the temporal trajectory of gene expression within each pathway and tests whether entire biological programs — not just individual genes — are activated or suppressed in a time-dependent manner. It is widely used in developmental biology, drug response studies, and infection time courses.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bar-Joseph and colleagues (temporal gene expression); extended by Cheng, Bhatt et al. for pathway-level time-series inference","year":"2005–2014","type":"Functional enrichment analysis with temporal modeling","dataType":"Time-course RNA-seq or microarray expression data; pathway/gene-set databases (KEGG, Reactome, GO)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Ernst, J., Nau, G. J., & Bar-Joseph, Z. (2005). Clustering short time series gene expression data. Bioinformatics, 21(Suppl 1), i159–i168.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Clustering+short+time+series+gene+expression+data+Ernst+Nau+Bar-Joseph+2005"},{"ref":"Cheng, J., Tegge, A. N., & Bhatt, D. L. (2014). A method for identifying and interpreting time-series pathway activity changes from gene expression data. Bioinformatics, 30(21), 3147–3154.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=method+identifying+interpreting+time-series+pathway+activity+changes+gene+expression+data+Cheng+2014"}],"related":["pathway-enrichment-analysis","gene-set-enrichment-analysis","rna-seq-differential-expression","time-series-rna-seq-differential-expression","single-cell-pathway-enrichment-analysis","multi-omics-pathway-enrichment-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-phylogenetic-analysis","name":"Time-series phylogenetic analysis","fullName":"Time-Series Phylogenetic Analysis","aliases":["temporal phylogenetics","time-resolved phylogenetics","molecular clock phylogenetics","phylodynamics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2000s (molecular clock methods earlier; BEAST framework 2007)","originator":"Alexei J. Drummond, Andrew Rambaut, and colleagues","url":"https://scholargate.app/en/bioinformatics/time-series-phylogenetic-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-phylogenetic-analysis.md","definition":"Time-series phylogenetic analysis reconstructs the evolutionary history of organisms or genetic variants using sequences sampled at known time points. By incorporating sampling dates directly into the model, it estimates divergence times, substitution rates, and ancestral relationships on an absolute timescale — making it essential for studying viral outbreaks, ancient DNA dynamics, and rapid microbial evolution.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alexei J. Drummond, Andrew Rambaut, and colleagues","year":"2000s (molecular clock methods earlier; BEAST framework 2007)","type":"Evolutionary bioinformatics pipeline","dataType":"Dated sequence alignments (DNA, RNA, protein) with temporal metadata","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Drummond, A. J., & Rambaut, A. (2007). BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7, 214.","type":"article","doi":"10.1186/1471-2148-7-214","isbn":null,"url":null},{"ref":"Bouckaert, R., Vaughan, T. G., Barido-Sottani, J., Duchene, S., Fourment, M., Gavryushkina, A., et al. (2019). BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Computational Biology, 15(4), e1006650.","type":"article","doi":"10.1371/journal.pcbi.1006650","isbn":null,"url":null}],"related":["phylogenetic-analysis","bayesian-phylogenetic-analysis","rna-seq-differential-expression","genome-wide-association-study","sequence-alignment","variant-calling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-proteomics-analysis","name":"Time-series proteomics analysis","fullName":"Time-Series Quantitative Proteomics Analysis","aliases":["longitudinal proteomics","temporal proteomics","dynamic proteomics","time-course proteomics"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010)","originator":"Multiple groups; Gygi et al. (1999) established quantitative proteomics; time-series designs emerged in the 2000s with LC-MS/MS workflows","url":"https://scholargate.app/en/bioinformatics/time-series-proteomics-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-proteomics-analysis.md","definition":"Time-series proteomics analysis quantifies protein abundance across two or more ordered time points to reveal how the proteome changes dynamically in response to stimuli, developmental stages, or disease progression. By combining mass spectrometry-based protein quantification with statistical models designed for temporal data, the method identifies proteins with significant expression trends, oscillatory patterns, or delayed responses that cannot be detected in single time-point studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple groups; Gygi et al. (1999) established quantitative proteomics; time-series designs emerged in the 2000s with LC-MS/MS workflows","year":"2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010)","type":"Quantitative longitudinal omics pipeline","dataType":"Mass spectrometry intensity matrices across ordered time points","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Lemeer, S., & Heck, A. J. R. (2012). The phosphoproteomics data explosion. Current Opinion in Chemical Biology, 16(1–2), 1–8.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+phosphoproteomics+data+explosion+Lemeer+Heck+2012"},{"ref":"Ori, A., Iskar, M., Buczak, K., Kastritis, P., Parca, L., Andres-Pons, A., Singer, S., Bork, P., & Beck, M. (2016). Spatiotemporal variation of mammalian protein complex stoichiometries. Genome Biology, 17, 47.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spatiotemporal+variation+mammalian+protein+complex+stoichiometries+Ori+2016"}],"related":["rna-seq-differential-expression","time-series-rna-seq-differential-expression","proteomics-analysis","multi-omics-proteomics-analysis","metabolomics-analysis","time-series-metabolomics-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-rna-seq-differential-expression","name":"Time-series RNA-seq differential expression","fullName":"Time-series RNA Sequencing Differential Expression Analysis","aliases":["longitudinal RNA-seq DE analysis","temporal transcriptomics","time-course RNA-seq","dynamic DE analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2006–2018 (principal methods established)","originator":"Conesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others","url":"https://scholargate.app/en/bioinformatics/time-series-rna-seq-differential-expression","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-rna-seq-differential-expression.md","definition":"Time-series RNA-seq differential expression analysis identifies genes whose expression levels change systematically across ordered time points — such as during development, disease progression, or response to a treatment. Unlike two-condition DE analysis, it explicitly models the temporal structure of the data, capturing dynamic gene expression trajectories rather than a single snapshot contrast. Tools such as maSigPro, ImpulseDE2, and splineTimeR have been developed specifically for this design.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Conesa et al. (maSigPro, 2006); extended by Fischer et al. (ImpulseDE2, 2018) and others","year":"2006–2018 (principal methods established)","type":"Computational genomics pipeline","dataType":"RNA-seq count matrices from multiple ordered time points","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Conesa, A., Nueda, M. J., Ferrer, A., & Talon, M. (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22(9), 1096–1102.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=maSigPro+a+method+to+identify+significantly+differential+expression+profiles+in+time-course+microarray+experiments"},{"ref":"Fischer, D. S., Theis, F. J., & Yosef, N. (2018). Impulse model-based differential expression analysis of time series single-cell RNA-seq data. Genome Biology, 19(1), 1–14.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Impulse+model-based+differential+expression+analysis+time+series+single-cell+RNA-seq+data+Fischer+2018"}],"related":["rna-seq-differential-expression","single-cell-rna-seq-analysis","gene-set-enrichment-analysis","pathway-enrichment-analysis","time-series-eqtl-analysis","multi-omics-rna-seq-differential-expression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-sequential-monte-carlo","name":"Time series sequential Monte Carlo","fullName":"Sequential Monte Carlo Methods for Time Series","aliases":["particle filter","time series SMC","sequential particle filtering","bootstrap particle filter"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1993","originator":"Gordon, Salmond & Smith","url":"https://scholargate.app/en/bayesian/time-series-sequential-monte-carlo","markdownUrl":"https://scholargate.app/en/bayesian/time-series-sequential-monte-carlo.md","definition":"Time series sequential Monte Carlo (SMC), commonly called the particle filter, is a Bayesian simulation method that tracks the hidden state of a dynamical system as observations arrive one at a time. A cloud of weighted random samples — particles — is propagated forward through the system dynamics, reweighted by how well each particle explains the new observation, and periodically resampled to keep the representation concentrated on plausible states.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gordon, Salmond & Smith","year":"1993","type":"Sequential Bayesian filtering algorithm","dataType":"Sequential / time series observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F — Radar and Signal Processing, 140(2), 107–113.","type":"article","doi":"10.1049/ip-f-2.1993.0015","isbn":null,"url":null},{"ref":"Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer.","type":"book","doi":null,"isbn":"978-0387951461","url":null}],"related":["sequential-monte-carlo","kalman-filter","particle-filter","bayesian-inference-for-model-comparison","dynamic-bayesian-network","gibbs-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-single-cell-rna-seq-analysis","name":"Time-series single-cell RNA-seq analysis","fullName":"Time-Series Single-Cell RNA Sequencing Analysis","aliases":["scRNA-seq time course analysis","longitudinal scRNA-seq","temporal single-cell transcriptomics","dynamic single-cell gene expression analysis"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2014-2018 (pseudotime and RNA velocity frameworks)","originator":"Trapnell et al. (pseudotime/Monocle); La Manno et al. (RNA velocity)","url":"https://scholargate.app/en/bioinformatics/time-series-single-cell-rna-seq-analysis","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-single-cell-rna-seq-analysis.md","definition":"Time-series single-cell RNA-seq analysis captures gene expression across multiple time points at single-cell resolution to reveal how cell populations emerge, transition, and diverge during dynamic biological processes such as development, differentiation, or disease progression. By combining pseudotime ordering, RNA velocity, and differential dynamics testing, researchers reconstruct the temporal trajectory of individual cells and identify the gene regulatory changes that drive biological transitions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Trapnell et al. (pseudotime/Monocle); La Manno et al. (RNA velocity)","year":"2014-2018 (pseudotime and RNA velocity frameworks)","type":"Computational bioinformatics pipeline","dataType":"Single-cell RNA sequencing count matrices from multiple time points or with RNA velocity splicing data","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4), 381-386.","type":"article","doi":"10.1038/nbt.2859","isbn":null,"url":null},{"ref":"La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lonnerberg, P., Furlan, A., Fan, J., Borm, L. E., Liu, Z., van Bruggen, D., Guo, J., He, X., Linnarsson, S., & Kharchenko, P. V. (2018). RNA velocity of single cells. Nature, 560(7719), 494-498.","type":"article","doi":"10.1038/s41586-018-0414-6","isbn":null,"url":null}],"related":["single-cell-rna-seq-analysis","rna-seq-differential-expression","pathway-enrichment-analysis","gene-set-enrichment-analysis","copy-number-variation-analysis","eqtl-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-variant-calling","name":"Time-series variant calling","fullName":"Time-series Variant Calling","aliases":["longitudinal variant calling","temporal somatic mutation detection","serial variant calling","time-course variant detection"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2009–2012","originator":"Pioneered in cancer genomics by Nik-Zainal, Campbell, and collaborators (Sanger Institute/Wellcome Trust)","url":"https://scholargate.app/en/bioinformatics/time-series-variant-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/time-series-variant-calling.md","definition":"Time-series variant calling is a bioinformatics pipeline that identifies and tracks genomic variants — typically somatic mutations — across multiple sequencing samples collected from the same subject at different time points. It is most widely applied in cancer genomics to reconstruct tumour evolution, monitor minimal residual disease, and detect the emergence of therapy-resistant clones. By jointly modelling variant allele frequencies across the temporal dimension, the method distinguishes true somatic changes from sequencing noise and estimates clonal dynamics over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pioneered in cancer genomics by Nik-Zainal, Campbell, and collaborators (Sanger Institute/Wellcome Trust)","year":"2009–2012","type":"Longitudinal genomic analysis pipeline","dataType":"Multi-timepoint next-generation sequencing data (WGS, WES, or targeted panel)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"Nik-Zainal, S., et al. (2012). The life history of 21 breast cancers. Cell, 149(5), 994–1007.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+life+history+of+21+breast+cancers+Nik-Zainal+2012"},{"ref":"McMahon, M., et al. (2021). Benchmarking algorithms for clonal evolution analysis using multi-region and longitudinal tumour sequencing data. Briefings in Bioinformatics, 22(3), bbaa163.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Benchmarking+algorithms+clonal+evolution+analysis+multi-region+longitudinal+tumour+sequencing"}],"related":["somatic-variant-calling","clonal-evolution-analysis","copy-number-variation","rna-seq-differential-expression","single-cell-rna-seq","whole-genome-sequencing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-series-variational-inference","name":"Time series variational inference","fullName":"Variational Inference for Time Series Models","aliases":["time-series VI","variational Bayes for time series","TSVI","sequential variational inference"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1999–2017","originator":"Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues","url":"https://scholargate.app/en/bayesian/time-series-variational-inference","markdownUrl":"https://scholargate.app/en/bayesian/time-series-variational-inference.md","definition":"Time series variational inference applies variational Bayes to sequential data, approximating the intractable posterior over latent states and parameters with a tractable family of distributions. By maximising the evidence lower bound (ELBO), it delivers fast, scalable Bayesian inference for state-space models, dynamic latent variable models, and other time-ordered probabilistic systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues","year":"1999–2017","type":"Approximate Bayesian inference","dataType":"Sequential / time-ordered observations","subfamily":"Bayesian / computational"},"citations":[{"ref":"Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877.","type":"article","doi":"10.1080/01621459.2017.1285773","isbn":null,"url":null},{"ref":"Jordan, M. I., Ghahramani, Z., Jaakkola, T. S. & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183-233.","type":"article","doi":"10.1023/A:1007665907178","isbn":null,"url":null}],"related":["variational-inference","sequential-monte-carlo","kalman-filter","time-series-bayesian-inference","dynamic-variational-inference","time-series-mcmc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-sliced-bibliographic-coupling","name":"Time-sliced Bibliographic coupling","fullName":"Time-sliced Bibliographic Coupling Analysis","aliases":["longitudinal bibliographic coupling","temporal bibliographic coupling","diachronic bibliographic coupling","time-window bibliographic coupling"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1963 (base method); time-sliced variant widely adopted 1990s–2000s","originator":"Morton M. Kessler (bibliographic coupling); time-sliced extension by various scientometricians","url":"https://scholargate.app/en/scientometrics/time-sliced-bibliographic-coupling","markdownUrl":"https://scholargate.app/en/scientometrics/time-sliced-bibliographic-coupling.md","definition":"Time-sliced bibliographic coupling divides a publication corpus into successive time windows and applies bibliographic coupling analysis within each window to track how research fronts emerge, shift, merge, or disappear across time. It transforms a static snapshot technique into a longitudinal tool for mapping the intellectual evolution of a scientific field, revealing when and how new thematic clusters appear in the literature.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morton M. Kessler (bibliographic coupling); time-sliced extension by various scientometricians","year":"1963 (base method); time-sliced variant widely adopted 1990s–2000s","type":"Longitudinal bibliometric network analysis","dataType":"Bibliographic records with reference lists, partitioned by publication year or period","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25.","type":"article","doi":"10.1002/asi.5090140103","isbn":null,"url":null},{"ref":"Glänzel, W., & Czerwon, H. J. (1996). A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level. Scientometrics, 37(2), 195–221.","type":"article","doi":"10.1007/BF02093621","isbn":null,"url":null}],"related":["bibliographic-coupling","co-citation-analysis","thematic-evolution-analysis","science-mapping","bibliometric-analysis","co-word-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-sliced-bibliometric-analysis","name":"Time-sliced Bibliometric Analysis","fullName":"Time-sliced Bibliometric Analysis","aliases":["longitudinal bibliometrics","temporal bibliometric analysis","diachronic bibliometrics","time-window bibliometric analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2000s–2010s (as an explicit methodological variant)","originator":"Derived from classical bibliometrics (Price, Garfield); explicitly formalised in longitudinal studies by Zhao & Strotmann (2008) and others","url":"https://scholargate.app/en/scientometrics/time-sliced-bibliometric-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/time-sliced-bibliometric-analysis.md","definition":"Time-sliced bibliometric analysis partitions a literature corpus into consecutive time windows and applies standard bibliometric indicators (publication counts, citation patterns, co-authorship networks, keyword frequencies) within each window. By comparing results across slices, researchers can document how a field's productivity, intellectual structure, and thematic focus have shifted over time — providing a diachronic rather than static view of scholarly output.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from classical bibliometrics (Price, Garfield); explicitly formalised in longitudinal studies by Zhao & Strotmann (2008) and others","year":"2000s–2010s (as an explicit methodological variant)","type":"Quantitative scientometric analysis","dataType":"Bibliographic records (titles, abstracts, citations, keywords) partitioned into discrete time windows","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Zhao, D., & Strotmann, A. (2008). Evolution of research activities and intellectual influences in information science 1996–2005: Introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070–2086.","type":"article","doi":"10.1002/asi.20910","isbn":null,"url":null},{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null}],"related":["bibliometric-analysis","scientometric-analysis","co-citation-analysis","bibliographic-coupling","thematic-evolution-analysis","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-sliced-citation-analysis","name":"Time-sliced Citation analysis","fullName":"Time-sliced Citation Analysis","aliases":["temporal citation analysis","longitudinal citation analysis","time-window citation analysis","diachronic citation analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1955–1965 (foundational); temporal slicing formalized in scientometrics from the 1980s onward","originator":"Eugene Garfield (citation analysis foundation); Derek J. de Solla Price (temporal/longitudinal framing)","url":"https://scholargate.app/en/scientometrics/time-sliced-citation-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/time-sliced-citation-analysis.md","definition":"Time-sliced citation analysis partitions a body of literature into sequential temporal windows — for example, five-year intervals — and performs citation analysis within and across each window. This reveals how citation patterns, influential papers, and knowledge flows shift over time, providing a dynamic picture of a field's intellectual evolution rather than a static aggregate snapshot.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eugene Garfield (citation analysis foundation); Derek J. de Solla Price (temporal/longitudinal framing)","year":"1955–1965 (foundational); temporal slicing formalized in scientometrics from the 1980s onward","type":"Quantitative scientometric technique","dataType":"Bibliographic citation records with publication date metadata","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.","type":"article","doi":"10.1126/science.122.3159.108","isbn":null,"url":null},{"ref":"Price, D. J. de S. (1965). Networks of scientific papers. Science, 149(3683), 510–515.","type":"article","doi":"10.1126/science.149.3683.510","isbn":null,"url":null}],"related":["citation-analysis","bibliometric-analysis","co-citation-analysis","thematic-evolution-analysis","scientometric-analysis","bibliographic-coupling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-sliced-mapping-review","name":"Time-sliced Mapping review","fullName":"Time-sliced Systematic Mapping Review","aliases":["temporal mapping review","time-period mapping review","longitudinal evidence map","chronological mapping review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2000s–2010s","originator":"Campbell Collaboration / Gough, Oliver & Thomas","url":"https://scholargate.app/en/scientometrics/time-sliced-mapping-review","markdownUrl":"https://scholargate.app/en/scientometrics/time-sliced-mapping-review.md","definition":"A time-sliced mapping review is a systematic evidence synthesis that partitions the search period into discrete temporal segments — such as five-year intervals — and constructs a separate evidence map for each slice. By comparing maps across periods, researchers can chart how topics emerge, peak, decline, or transform within a research field, producing a longitudinal picture of knowledge structure that a single-point mapping review cannot provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Campbell Collaboration / Gough, Oliver & Thomas","year":"2000s–2010s","type":"Evidence synthesis with temporal segmentation","dataType":"Published literature (titles, abstracts, full texts, metadata)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Gough, D., Oliver, S., & Thomas, J. (2012). An Introduction to Systematic Reviews. Sage Publications.","type":"book","doi":null,"isbn":"978-1849204842","url":null},{"ref":"Petrosino, A., Boruch, R. F., Soydan, H., Duggan, L., & Sanchez-Meca, J. (2001). Meeting the challenges of evidence-based policy: The Campbell Collaboration. The Annals of the American Academy of Political and Social Science, 578(1), 14–34.","type":"article","doi":"10.1177/000271620157800102","isbn":null,"url":null}],"related":["mapping-review","scoping-review","systematic-literature-review","thematic-evolution-analysis","bibliometric-analysis","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-sliced-meta-analysis","name":"Time-sliced Meta-analysis","fullName":"Time-sliced Meta-analysis","aliases":["temporal meta-analysis","period-stratified meta-analysis","time-segmented meta-analysis","chronological meta-analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1992 (cumulative form); refined through 2000s","originator":"Lau et al. (cumulative variant); Borenstein et al. (general meta-analytic framework)","url":"https://scholargate.app/en/scientometrics/time-sliced-meta-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/time-sliced-meta-analysis.md","definition":"Time-sliced meta-analysis is a variant of standard meta-analysis in which the primary studies are partitioned into successive time periods (slices) and a separate pooled effect estimate is computed for each period. By comparing pooled effects across periods, researchers can detect whether an intervention's effectiveness, a relationship's magnitude, or a methodological consensus has shifted over time. This temporal lens transforms a static evidence summary into a longitudinal narrative of how scientific knowledge on a topic has evolved.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lau et al. (cumulative variant); Borenstein et al. (general meta-analytic framework)","year":"1992 (cumulative form); refined through 2000s","type":"Quantitative evidence synthesis variant","dataType":"Effect sizes (correlations, odds ratios, SMDs) from primary studies stratified by publication period","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Wiley.","type":"book","doi":null,"isbn":"978-0470057247","url":null},{"ref":"Lau, J., Antman, E. M., Jimenez-Silva, J., Kupelnick, B., Mosteller, F., & Chalmers, T. C. (1992). Cumulative meta-analysis of therapeutic trials for myocardial infarction. New England Journal of Medicine, 327(4), 248–254.","type":"article","doi":"10.1056/nejm199207233270406","isbn":null,"url":null}],"related":["systematic-literature-review","bibliometric-analysis","cumulative-meta-analysis","thematic-evolution-analysis","scoping-review","meta-regression-based-meta-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-sliced-scientometric-analysis","name":"Time-sliced Scientometric Analysis","fullName":"Time-sliced Scientometric Analysis","aliases":["temporal scientometrics","period-based scientometric analysis","time-window scientometrics","longitudinal scientometric analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1980s–1990s","originator":"Derived from scientometrics tradition; temporal slicing formalized in longitudinal bibliometric studies from the 1980s onward","url":"https://scholargate.app/en/scientometrics/time-sliced-scientometric-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/time-sliced-scientometric-analysis.md","definition":"Time-sliced scientometric analysis divides a bibliographic corpus into discrete temporal windows — commonly five- or ten-year periods — and applies standard scientometric indicators (publication counts, citation rates, h-index, collaboration networks, keyword co-occurrence) within each slice. By comparing results across slices, researchers can reconstruct how a scientific field has grown, shifted focus, formed new collaborations, or declined in influence over time. The approach combines the rigor of quantitative scientometrics with an explicit longitudinal dimension.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from scientometrics tradition; temporal slicing formalized in longitudinal bibliometric studies from the 1980s onward","year":"1980s–1990s","type":"Quantitative longitudinal analysis","dataType":"Bibliographic records (titles, abstracts, citations, author keywords) from scientific databases","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799-813.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Visualizing+science+by+citation+mapping+Small+1999"},{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null}],"related":["scientometric-analysis","bibliometric-analysis","thematic-evolution-analysis","co-citation-analysis","science-mapping","co-word-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-sliced-systematic-literature-review","name":"Time-sliced Systematic literature review","fullName":"Time-sliced Systematic Literature Review","aliases":["temporal systematic review","period-based systematic review","chronological systematic review","time-segmented literature review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2010s","originator":"Adapted from systematic review methodology; temporal segmentation formalized in bibliometric practice (Zupic & Cater, 2015; Aria & Cuccurullo, 2017)","url":"https://scholargate.app/en/scientometrics/time-sliced-systematic-literature-review","markdownUrl":"https://scholargate.app/en/scientometrics/time-sliced-systematic-literature-review.md","definition":"A time-sliced systematic literature review applies the rigorous search, screening, and synthesis protocol of a standard systematic review while dividing the retrieved corpus into discrete temporal periods — time slices — and analyzing each period separately. This design reveals how a research field has developed across time: which topics emerged, grew, or declined; how key authors and journals shifted; and how intellectual structures evolved from one era to the next.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adapted from systematic review methodology; temporal segmentation formalized in bibliometric practice (Zupic & Cater, 2015; Aria & Cuccurullo, 2017)","year":"2010s","type":"Systematic review variant with temporal segmentation","dataType":"Bibliographic records, citation data, publication metadata","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Zupic, I., & Cater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429–472.","type":"article","doi":"10.1177/1094428114562629","isbn":null,"url":null},{"ref":"Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.","type":"article","doi":"10.1016/j.joi.2017.08.007","isbn":null,"url":null}],"related":["systematic-literature-review","bibliometric-analysis","thematic-evolution-analysis","science-mapping","co-citation-analysis","field-mapping-systematic-literature-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-sliced-thematic-evolution-analysis","name":"Time-sliced Thematic Evolution Analysis","fullName":"Time-sliced Thematic Evolution Analysis in Bibliometrics","aliases":["longitudinal thematic mapping","temporal thematic evolution","time-period thematic analysis","diachronic science mapping"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2011–2012","originator":"Cobo, López-Herrera, Herrera-Viedma & Herrera","url":"https://scholargate.app/en/scientometrics/time-sliced-thematic-evolution-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/time-sliced-thematic-evolution-analysis.md","definition":"Time-sliced thematic evolution analysis is a bibliometric method that divides a corpus of publications into consecutive time windows and tracks how research themes emerge, consolidate, split, merge, or disappear across those periods. By applying co-word analysis and strategic-diagram mapping within each slice and then linking themes across slices, it reveals the intellectual trajectory of a research field over time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cobo, López-Herrera, Herrera-Viedma & Herrera","year":"2011–2012","type":"Longitudinal bibliometric analysis","dataType":"Bibliographic records (titles, keywords, abstracts) from academic databases","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.","type":"article","doi":"10.1002/asi.21525","isbn":null,"url":null},{"ref":"Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609–1630.","type":"article","doi":"10.1002/asi.22688","isbn":null,"url":null}],"related":["thematic-evolution-analysis","science-mapping","co-word-analysis","bibliometric-analysis","scientometric-analysis","systematic-literature-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-adf-unit-root-test","name":"Time-varying parameter ADF unit root test","fullName":"Time-Varying Parameter Augmented Dickey-Fuller Unit Root Test","aliases":["TVP-ADF test","time-varying ADF","TVP unit root test","adaptive ADF test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990s–2010s","originator":"Extension of Dickey & Fuller (1979); TVP framework developed across multiple authors including Hall, Psaradakis, and Sola (1997) and Bierens & Martins (2010)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-adf-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-adf-unit-root-test.md","definition":"The time-varying parameter ADF (TVP-ADF) test extends the classical Augmented Dickey-Fuller framework by allowing the autoregressive coefficient to evolve over time. Rather than assuming a single fixed unit-root parameter throughout the sample, it models the persistence of a series as a stochastic process, making it sensitive to gradual or episodic changes in stationarity that a standard ADF test would miss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Dickey & Fuller (1979); TVP framework developed across multiple authors including Hall, Psaradakis, and Sola (1997) and Bierens & Martins (2010)","year":"1990s–2010s","type":"Unit root / stationarity test","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.","type":"article","doi":"10.2307/2286348","isbn":null,"url":null},{"ref":"Hall, S. G., Psaradakis, Z., & Sola, M. (1997). Cointegration and changes in regime: The Japanese consumption function. Journal of Applied Econometrics, 12(2), 151–168.","type":"article","doi":"10.1002/(sici)1099-1255(199703)12:2<151::aid-jae424>3.3.co;2-a","isbn":null,"url":null}],"related":["adf-unit-root-test","pp-unit-root-test","kpss-stationarity-test","rolling-window-regression","structural-break-tests","time-varying-parameter-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-ar-model","name":"Time-varying parameter AR model","fullName":"Time-Varying Parameter Autoregressive Model","aliases":["TVP-AR","time-varying AR","state-space AR with drifting coefficients","random-walk coefficient AR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1976–2005","originator":"Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-ar-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-ar-model.md","definition":"The Time-Varying Parameter Autoregressive (TVP-AR) model extends the classical AR model by allowing its autoregressive coefficients to drift over time, typically as a random walk. Cast as a state-space system, the model captures gradual structural change in the dynamics of a univariate time series without imposing a fixed break date.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005)","year":"1976–2005","type":"Time-series model with drifting coefficients","dataType":"Univariate time series (stationary or near-stationary)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302.","type":"article","doi":"10.1016/j.red.2004.10.009","isbn":null,"url":null},{"ref":"Kim, C.-J., & Nelson, C. R. (1999). State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. MIT Press.","type":"book","doi":null,"isbn":"978-0262112383","url":null}],"related":["tvp-var-model","state-space-model","kalman-filter","arima-model","stochastic-volatility-model","rolling-window-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-arch-model","name":"Time-varying parameter ARCH model","fullName":"Time-Varying Parameter Autoregressive Conditional Heteroscedasticity Model","aliases":["TVP-ARCH","time-varying ARCH","adaptive ARCH","state-space ARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980s–1990s","originator":"Extension of Engle (1982) ARCH; TVP-ARCH formalization credited to Nicholls & Quinn and subsequent state-space literature","url":"https://scholargate.app/en/econometrics/time-varying-parameter-arch-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-arch-model.md","definition":"The Time-Varying Parameter ARCH (TVP-ARCH) model extends the classic ARCH framework by allowing both the conditional mean coefficients and the ARCH variance parameters to drift over time according to a random-walk or state-space process. This makes it possible to capture structural shifts in volatility dynamics without imposing a fixed parameter regime.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Engle (1982) ARCH; TVP-ARCH formalization credited to Nicholls & Quinn and subsequent state-space literature","year":"1980s–1990s","type":"Conditional heteroscedasticity model with time-varying coefficients","dataType":"Univariate or multivariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null},{"ref":"Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262–302.","type":"article","doi":"10.1016/j.red.2004.10.009","isbn":null,"url":null}],"related":["arch-model","garch-model","time-varying-parameter-model","stochastic-volatility-model","kalman-filter","egarch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-ardl-bounds-test","name":"Time-varying parameter ARDL bounds test","fullName":"Time-Varying Parameter Autoregressive Distributed Lag Bounds Test","aliases":["TVP-ARDL bounds test","time-varying ARDL cointegration","TVP bounds testing approach","dynamic ARDL bounds test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2010s","originator":"Extension of Pesaran, Shin & Smith (2001); TVP variant developed in applied time-series literature ca. 2010s","url":"https://scholargate.app/en/econometrics/time-varying-parameter-ardl-bounds-test","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-ardl-bounds-test.md","definition":"The time-varying parameter ARDL bounds test extends the classic Pesaran-Shin-Smith (2001) bounds testing framework by allowing regression coefficients to evolve continuously over time. It detects whether a long-run cointegrating relationship between variables exists and whether that relationship has been stable or shifting across the sample period.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Pesaran, Shin & Smith (2001); TVP variant developed in applied time-series literature ca. 2010s","year":"2010s","type":"Cointegration / bounds test with time-varying coefficients","dataType":"Time-series (single equation, possibly I(0)/I(1) regressors)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.","type":"article","doi":"10.1002/jae.616","isbn":null,"url":null},{"ref":"Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.","type":"article","doi":"10.2307/1910133","isbn":null,"url":null}],"related":["ardl-bounds-test","time-varying-coefficient-model","nonlinear-ardl","rolling-window-regression","threshold-cointegration","structural-break-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-arellano-bond-gmm","name":"Time-varying parameter Arellano-Bond GMM","fullName":"Time-Varying Parameter Arellano-Bond Generalized Method of Moments Estimator","aliases":["TVP Arellano-Bond GMM","TVP-AB GMM","time-varying coefficient dynamic panel GMM","state-space Arellano-Bond estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990s-2000s","originator":"Extension of Arellano & Bond (1991); TVP generalisation developed in panel econometrics literature","url":"https://scholargate.app/en/econometrics/time-varying-parameter-arellano-bond-gmm","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-arellano-bond-gmm.md","definition":"The time-varying parameter Arellano-Bond GMM (TVP-AB GMM) is a dynamic panel estimator that extends the classic Arellano-Bond difference GMM framework by allowing regression coefficients to evolve over time. It addresses both individual fixed effects and the endogeneity of lagged dependent variables, while accommodating structural change and parameter instability across the sample period.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Arellano & Bond (1991); TVP generalisation developed in panel econometrics literature","year":"1990s-2000s","type":"Dynamic panel GMM with time-varying coefficients","dataType":"Balanced or unbalanced panel data (N units, T periods)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies, 58(2), 277-297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Canova, F., & Ciccarelli, M. (2009). Estimating Multicountry VAR Models. International Economic Review, 50(3), 929-959.","type":"article","doi":"10.1111/j.1468-2354.2009.00554.x","isbn":null,"url":null}],"related":["arellano-bond-gmm-estimator","panel-arellano-bond-gmm","dynamic-panel-data-model","time-varying-parameter-var-model","panel-system-gmm","difference-gmm"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-arima-model","name":"Time-varying parameter ARIMA model","fullName":"Time-Varying Parameter Autoregressive Integrated Moving Average Model","aliases":["TVP-ARIMA","time-varying ARIMA","adaptive ARIMA","state-space ARIMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1976–1989","originator":"Cooley & Prescott (1976); Harvey (1989) state-space formulation","url":"https://scholargate.app/en/econometrics/time-varying-parameter-arima-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-arima-model.md","definition":"The time-varying parameter ARIMA model extends the classical ARIMA framework by allowing its autoregressive and moving-average coefficients to evolve over time rather than remaining fixed. Cast in state-space form and estimated via the Kalman filter, it is designed for economic and financial time series whose dynamic structure shifts in response to structural breaks, policy changes, or regime transitions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cooley & Prescott (1976); Harvey (1989) state-space formulation","year":"1976–1989","type":"Time series model with evolving coefficients","dataType":"Univariate or multivariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521405737","url":null},{"ref":"Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184.","type":"article","doi":"10.2307/1911389","isbn":null,"url":null}],"related":["arima-model","kalman-filter","state-space-model","time-varying-parameter-model","structural-time-series-model","regime-switching-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-arma-model","name":"Time-varying parameter ARMA model","fullName":"Time-Varying Parameter Autoregressive Moving Average Model","aliases":["TVP-ARMA","time-varying ARMA","state-space ARMA","locally stationary ARMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1976","originator":"Cooley & Prescott (1976); further formalised by Harvey (1989)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-arma-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-arma-model.md","definition":"The time-varying parameter ARMA (TVP-ARMA) model extends the classical ARMA framework by allowing the autoregressive and moving-average coefficients to evolve over time. Embedded in a state-space representation and estimated via the Kalman filter, it captures structural change and parameter instability in time series without requiring an explicit breakpoint.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cooley & Prescott (1976); further formalised by Harvey (1989)","year":"1976","type":"State-space time series model","dataType":"Univariate or multivariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184.","type":"article","doi":"10.2307/1911389","isbn":null,"url":null},{"ref":"Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521405737","url":null}],"related":["arma-model","state-space-model","kalman-filter","time-varying-parameter-regression","structural-time-series-model","locally-stationary-processes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-dcc-garch-model","name":"Time-varying parameter DCC-GARCH model","fullName":"Time-Varying Parameter Dynamic Conditional Correlation GARCH Model","aliases":["TVP-DCC-GARCH","time-varying DCC-GARCH","dynamic conditional correlation GARCH with TVP","TVP dynamic conditional correlation model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2002 (DCC-GARCH); TVP extension 2010s","originator":"Robert F. Engle (DCC-GARCH); TVP extension developed in applied finance literature","url":"https://scholargate.app/en/econometrics/time-varying-parameter-dcc-garch-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-dcc-garch-model.md","definition":"The TVP-DCC-GARCH model extends the Dynamic Conditional Correlation GARCH framework by allowing not only the pairwise correlations but also the underlying model parameters to evolve continuously over time. It captures structural shifts in volatility dynamics and cross-asset dependence, making it essential for financial risk modelling in non-stationary environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Engle (DCC-GARCH); TVP extension developed in applied finance literature","year":"2002 (DCC-GARCH); TVP extension 2010s","type":"Multivariate volatility model with time-varying correlation","dataType":"Multivariate financial time series (returns, prices)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.","type":"article","doi":"10.1198/073500102288618487","isbn":null,"url":null},{"ref":"Christoffersen, P., Errunza, V., Jacobs, K., & Langlois, H. (2012). Is the potential for international diversification disappearing? A dynamic copula approach. Review of Financial Studies, 25(12), 3711-3751.","type":"article","doi":"10.1093/rfs/hhs104","isbn":null,"url":null}],"related":["dcc-garch-model","garch-model","multivariate-garch","bekk-garch-model","dynamic-factor-model","stochastic-volatility-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-difference-gmm","name":"Time-varying parameter difference GMM","fullName":"Time-Varying Parameter Difference Generalized Method of Moments","aliases":["TVP-DGMM","time-varying GMM","TVP difference GMM","dynamic panel TVP estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000s–2010s","originator":"Extends Arellano & Bond (1991) difference GMM; TVP panel extensions developed in the 2000s–2010s literature","url":"https://scholargate.app/en/econometrics/time-varying-parameter-difference-gmm","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-difference-gmm.md","definition":"Time-varying parameter difference GMM combines the Arellano-Bond first-difference GMM estimator for dynamic panels with a state-space or local-smoothing framework that allows regression coefficients to drift over time. It handles endogeneity and lagged dependent variables while relaxing the assumption that structural relationships remain constant across all periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extends Arellano & Bond (1991) difference GMM; TVP panel extensions developed in the 2000s–2010s literature","year":"2000s–2010s","type":"Dynamic panel estimator with time-varying parameters","dataType":"Panel data (short T, large N) with time-varying structural relationships","subfamily":"Econometrics / time series"},"citations":[{"ref":"Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.","type":"article","doi":"10.2307/2297968","isbn":null,"url":null},{"ref":"Cai, Z. (2007). Trending time-varying coefficient time series models with serially correlated errors. Journal of Econometrics, 136(1), 163–188.","type":"article","doi":"10.1016/j.jeconom.2005.08.004","isbn":null,"url":null}],"related":["difference-gmm","system-gmm","time-varying-coefficient-model","panel-fixed-effects","dynamic-panel-data","arellano-bond-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-dynamic-panel-data-model","name":"Time-varying parameter dynamic panel data model","fullName":"Time-Varying Parameter Dynamic Panel Data Model","aliases":["TVP dynamic panel model","time-varying coefficient panel model","TVP-DPD model","state-space dynamic panel model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990s–2000s","originator":"Hsiao, Pesaran, and related panel time-series literature","url":"https://scholargate.app/en/econometrics/time-varying-parameter-dynamic-panel-data-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-dynamic-panel-data-model.md","definition":"The time-varying parameter dynamic panel data model combines lagged dependent variables with coefficients that evolve over time across panel units. It extends conventional dynamic panel models by allowing slope parameters to shift across periods, making it well-suited for studying structural change, heterogeneous adjustment dynamics, and parameter instability in macro-panels and cross-country datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hsiao, Pesaran, and related panel time-series literature","year":"1990s–2000s","type":"Dynamic panel model with time-varying coefficients","dataType":"Balanced or unbalanced panel data with sufficient time dimension (large T)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Canova, F., & Ciccarelli, M. (2009). Estimating multicountry VAR models. International Economic Review, 50(3), 929-959.","type":"article","doi":"10.1111/j.1468-2354.2009.00554.x","isbn":null,"url":null},{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-1107038691","url":null}],"related":["dynamic-panel-data-model","random-coefficient-model","state-space-model","panel-var-model","arellano-bond-estimator","mean-group-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-egarch-model","name":"Time-varying parameter EGARCH model","fullName":"Time-Varying Parameter Exponential GARCH Model","aliases":["TVP-EGARCH","time-varying EGARCH","EGARCH with time-varying parameters","dynamic parameter EGARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1991–2000s","originator":"Nelson (1991) for EGARCH; TVP extension developed across the 1990s–2000s literature (e.g., Harvey, Engle and co-authors)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-egarch-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-egarch-model.md","definition":"The TVP-EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the volatility equation's parameters — including the leverage effect coefficient — to drift continuously over time. This makes it possible to capture structural change and regime evolution in financial return volatility without imposing a fixed break date.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nelson (1991) for EGARCH; TVP extension developed across the 1990s–2000s literature (e.g., Harvey, Engle and co-authors)","year":"1991–2000s","type":"Conditional volatility model","dataType":"Financial time series, high-frequency returns","subfamily":"Econometrics / time series"},"citations":[{"ref":"Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.","type":"article","doi":"10.2307/2938260","isbn":null,"url":null},{"ref":"Harvey, A. C. (2013). Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press.","type":"book","doi":null,"isbn":"9781107034723","url":null}],"related":["egarch-model","garch-model","time-varying-parameter-model","gjr-garch-model","stochastic-volatility-model","markov-switching-garch"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-engle-granger-cointegration","name":"Time-varying parameter Engle-Granger cointegration","fullName":"Time-Varying Parameter Engle-Granger Cointegration Model","aliases":["TVP Engle-Granger cointegration","time-varying cointegration","TVP-EG cointegration","varying-coefficient cointegration"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1987/1999","originator":"Engle & Granger (1987) for cointegration; Park & Hahn (1999) for TVP extension","url":"https://scholargate.app/en/econometrics/time-varying-parameter-engle-granger-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-engle-granger-cointegration.md","definition":"Time-varying parameter (TVP) Engle-Granger cointegration extends the classical two-step Engle-Granger framework by allowing the long-run relationship between integrated series to evolve over time. Instead of assuming a fixed cointegrating vector, the cointegrating coefficients are modelled as stochastic processes — typically via a random walk — and estimated with the Kalman filter or related state-space methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle & Granger (1987) for cointegration; Park & Hahn (1999) for TVP extension","year":"1987/1999","type":"Time-series cointegration model","dataType":"Non-stationary time series (I(1) variables)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null},{"ref":"Park, J. Y., & Hahn, S. B. (1999). Cointegrating regressions with time varying coefficients. Econometric Theory, 15(5), 664–703.","type":"article","doi":"10.1017/S0266466699155026","isbn":null,"url":null}],"related":["engle-granger-cointegration","johansen-cointegration","time-varying-parameter-model","error-correction-model","state-space-model","kalman-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-fixed-effects-model","name":"Time-varying parameter fixed effects model","fullName":"Time-Varying Parameter Fixed Effects Model","aliases":["TVP-FE model","time-varying coefficients fixed effects","TVP panel model","locally time-varying fixed effects"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1975-1995","originator":"Hsiao (1975); Pesaran & Smith (1995)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-fixed-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-fixed-effects-model.md","definition":"The time-varying parameter fixed effects (TVP-FE) model extends the classical two-way fixed effects panel regression by allowing one or more slope coefficients to change over time while still controlling for unobserved individual heterogeneity. It is used when the effect of a predictor on an outcome is not constant across the time dimension of a panel dataset.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hsiao (1975); Pesaran & Smith (1995)","year":"1975-1995","type":"Panel regression with time-varying slopes","dataType":"Panel data (balanced or unbalanced)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"9781107038875","url":null},{"ref":"Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79-113.","type":"article","doi":"10.1016/0304-4076(94)01644-F","isbn":null,"url":null}],"related":["panel-fixed-effects","random-effects-model","mean-group-estimator","state-space-model","random-coefficient-model","pooled-mean-group-estimator"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-garch-model","name":"Time-varying parameter GARCH model","fullName":"Time-Varying Parameter Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["TVP-GARCH","time-varying GARCH","TV-GARCH","state-space GARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1982–2013","originator":"Engle (1982) for ARCH/GARCH foundation; extended by Creal, Koopman & Lucas (2013) and others for time-varying parameter variants","url":"https://scholargate.app/en/econometrics/time-varying-parameter-garch-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-garch-model.md","definition":"The Time-Varying Parameter GARCH model extends the standard GARCH framework by allowing the conditional variance parameters — including the ARCH and GARCH coefficients — to change over time rather than remaining fixed throughout the sample. This makes it well-suited to financial and macroeconomic series where volatility dynamics evolve across different market regimes or economic episodes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle (1982) for ARCH/GARCH foundation; extended by Creal, Koopman & Lucas (2013) and others for time-varying parameter variants","year":"1982–2013","type":"Volatility model with time-varying coefficients","dataType":"Time series (financial returns, macroeconomic aggregates)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.","type":"article","doi":"10.2307/1912773","isbn":null,"url":null},{"ref":"Creal, D., Koopman, S. J., & Lucas, A. (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28(5), 777-795.","type":"article","doi":"10.1002/jae.1279","isbn":null,"url":null}],"related":["garch-model","egarch-model","state-space-model","kalman-filter","stochastic-volatility-model","rolling-window-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-gls","name":"Time-varying parameter GLS","fullName":"Time-Varying Parameter Generalized Least Squares","aliases":["TVP-GLS","time-varying coefficient GLS","adaptive GLS","state-space GLS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1976","originator":"Cooley & Prescott","url":"https://scholargate.app/en/econometrics/time-varying-parameter-gls","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-gls.md","definition":"Time-varying parameter GLS extends generalized least squares to settings where regression coefficients are not fixed constants but evolve over time according to a stochastic process. By embedding the model in a state-space framework and applying GLS corrections for non-spherical errors, it captures structural change, regime shifts, and gradually drifting relationships in time-series data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cooley & Prescott","year":"1976","type":"Time-series regression with drifting coefficients","dataType":"Time series (continuous outcome and predictors)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184.","type":"article","doi":"10.2307/1911389","isbn":null,"url":null},{"ref":"Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521321969","url":null}],"related":["gls-regression","kalman-filter","state-space-model","rolling-ols","random-coefficient-model","recursive-least-squares"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-granger-causality","name":"Time-varying parameter Granger causality","fullName":"Time-Varying Parameter Granger Causality","aliases":["TVP Granger causality","rolling-window Granger causality","time-varying Granger test","dynamic Granger causality"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1969 (Granger); TVP extension ~2005","originator":"C.W.J. Granger (causality concept); TVP extension developed by Primiceri (2005) and subsequent literature","url":"https://scholargate.app/en/econometrics/time-varying-parameter-granger-causality","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-granger-causality.md","definition":"Time-varying parameter Granger causality extends the classical Granger causality framework by allowing the predictive relationships between time series to evolve across time. Instead of assuming fixed causal effects, the model estimates causal coefficients that can shift, capturing structural breaks, regime changes, or gradual evolution in economic or financial relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"C.W.J. Granger (causality concept); TVP extension developed by Primiceri (2005) and subsequent literature","year":"1969 (Granger); TVP extension ~2005","type":"Causality test / time-varying model","dataType":"Time series (macroeconomic, financial, or other sequential data)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438.","type":"article","doi":"10.2307/1912791","isbn":null,"url":null},{"ref":"Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852.","type":"article","doi":"10.1111/j.1467-937X.2005.00353.x","isbn":null,"url":null}],"related":["granger-causality","vector-autoregression","rolling-regression","kalman-filter","structural-var","time-varying-parameter-var"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-hausman-test","name":"Time-varying parameter Hausman test","fullName":"Time-Varying Parameter Hausman Specification Test","aliases":["TVP Hausman test","time-varying Hausman specification test","Hausman test with time-varying parameters","TVP endogeneity test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1978 (Hausman); TVP extension developed through 1980s–2000s","originator":"Hausman (1978) specification test framework extended to time-varying parameter settings","url":"https://scholargate.app/en/econometrics/time-varying-parameter-hausman-test","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-hausman-test.md","definition":"The time-varying parameter Hausman test extends Hausman's (1978) classic specification test to models whose coefficients are allowed to evolve over time. It compares an efficient estimator (e.g., OLS or GLS assuming constant parameters) with a consistent estimator from a time-varying parameter model, using the contrast between them to detect parameter instability or endogeneity in dynamic settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hausman (1978) specification test framework extended to time-varying parameter settings","year":"1978 (Hausman); TVP extension developed through 1980s–2000s","type":"Specification / endogeneity test","dataType":"Time series or panel data with potentially drifting parameters","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251-1271.","type":"article","doi":"10.2307/1913827","isbn":null,"url":null},{"ref":"Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167-184.","type":"article","doi":"10.2307/1911389","isbn":null,"url":null}],"related":["hausman-test","time-varying-coefficient-model","random-coefficient-model","panel-fixed-effects","rolling-window-regression","state-space-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-johansen-cointegration","name":"Time-varying parameter Johansen cointegration","fullName":"Time-Varying Parameter Johansen Cointegration","aliases":["TVP Johansen cointegration","time-varying cointegration","TVP-VECM cointegration","rolling Johansen cointegration"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1999–2000s","originator":"Johansen (1991) seminal; TVP extension by Park & Hahn (1999) and subsequent literature","url":"https://scholargate.app/en/econometrics/time-varying-parameter-johansen-cointegration","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-johansen-cointegration.md","definition":"Time-varying parameter (TVP) Johansen cointegration extends the classic Johansen framework by allowing the cointegrating vectors and adjustment speeds to evolve over time. It is designed for integrated multivariate time series whose long-run equilibrium relationships are subject to structural change, regime shifts, or gradual parameter drift, common in macroeconomic and financial data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Johansen (1991) seminal; TVP extension by Park & Hahn (1999) and subsequent literature","year":"1999–2000s","type":"Cointegration test / model","dataType":"Multivariate integrated time series (I(1))","subfamily":"Econometrics / time series"},"citations":[{"ref":"Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551–1580.","type":"article","doi":"10.2307/2938278","isbn":null,"url":null},{"ref":"Park, J. Y., & Hahn, S. B. (1999). Cointegrating regressions with time varying coefficients. Econometric Theory, 15(5), 664–703.","type":"article","doi":"10.1017/S0266466699155026","isbn":null,"url":null}],"related":["johansen-cointegration","vecm","engle-granger-cointegration","rolling-window-estimation","time-varying-parameter-model","threshold-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-kpss-test","name":"Time-varying parameter KPSS test","fullName":"Time-Varying Parameter Kwiatkowski-Phillips-Schmidt-Shin Test","aliases":["TVP-KPSS test","time-varying KPSS stationarity test","locally stationary KPSS test","TV-KPSS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2000s-2010s","originator":"Extension of Kwiatkowski, Phillips, Schmidt, and Shin (1992); time-varying generalizations developed by Cavaliere, Taylor, and others","url":"https://scholargate.app/en/econometrics/time-varying-parameter-kpss-test","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-kpss-test.md","definition":"The time-varying parameter KPSS test extends the classic Kwiatkowski-Phillips-Schmidt-Shin (1992) stationarity test to settings where the deterministic or stochastic components of a series may shift over time. It tests the null hypothesis of stationarity while allowing the model's parameters to evolve, making it robust to structural instability that would otherwise distort the standard KPSS result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Kwiatkowski, Phillips, Schmidt, and Shin (1992); time-varying generalizations developed by Cavaliere, Taylor, and others","year":"2000s-2010s","type":"Hypothesis test (stationarity)","dataType":"Univariate time series with potential structural instability","subfamily":"Econometrics / time series"},"citations":[{"ref":"Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159-178.","type":"article","doi":"10.1016/0304-4076(92)90104-Y","isbn":null,"url":null},{"ref":"Cavaliere, G., & Taylor, A. M. R. (2007). Testing for unit roots in time series models with non-stationary volatility. Journal of Econometrics, 140(2), 919-947.","type":"article","doi":"10.1016/j.jeconom.2006.07.019","isbn":null,"url":null}],"related":["kpss-test","adf-test","phillips-perron-test","structural-break-unit-root-test","time-varying-parameter-model","locally-stationary-wavelet-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-ma-model","name":"Time-varying parameter MA model","fullName":"Time-Varying Parameter Moving Average Model","aliases":["TVP-MA model","state-space MA","Kalman filter MA","time-varying MA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990s","originator":"Harvey, A. C.; Durbin, J. & Koopman, S. J.","url":"https://scholargate.app/en/econometrics/time-varying-parameter-ma-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-ma-model.md","definition":"The time-varying parameter moving average (TVP-MA) model extends the standard MA model by allowing the moving-average coefficients to change over time. Cast as a state-space system, it is estimated via the Kalman filter and smoother, making it well suited for series where the shock-transmission dynamics evolve across the sample.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey, A. C.; Durbin, J. & Koopman, S. J.","year":"1990s","type":"Time-varying state-space model","dataType":"Univariate time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521321969","url":null},{"ref":"Durbin, J., & Koopman, S. J. (2012). Time Series Analysis by State Space Methods (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"9780199641178","url":null}],"related":["time-varying-parameter-ar-model","time-varying-parameter-arma-model","moving-average-model","time-varying-parameter-arima-model","arma-model","kalman-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-nardl","name":"Time-varying parameter NARDL","fullName":"Time-Varying Parameter Nonlinear Autoregressive Distributed Lag Model","aliases":["TVP-NARDL","time-varying NARDL","rolling NARDL","dynamic asymmetric ARDL"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2019 (TVP extension); 2014 (NARDL base)","originator":"Bagnai & Ospina-Rojas (TVP extension); NARDL base by Shin, Yu & Greenwood-Nimmo","url":"https://scholargate.app/en/econometrics/time-varying-parameter-nardl","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-nardl.md","definition":"The Time-Varying Parameter NARDL (TVP-NARDL) model extends the Nonlinear ARDL framework by allowing the coefficients on positive and negative partial sums of a regressor to change over time. This combination captures both asymmetric responses and structural instability in long-run and short-run relationships within a single cointegrating specification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bagnai & Ospina-Rojas (TVP extension); NARDL base by Shin, Yu & Greenwood-Nimmo","year":"2019 (TVP extension); 2014 (NARDL base)","type":"Nonlinear time-series model with time-varying coefficients","dataType":"Time series (single or multiple variables)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. Horrace & R. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modelling+asymmetric+cointegration+and+dynamic+multipliers+in+a+nonlinear+ARDL+framework"},{"ref":"Bagnai, A., & Ospina-Rojas, C. A. (2019). Time-varying generalisations of the NARDL model. Economics Letters, 177, 73–76.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Time-varying+generalisations+of+the+NARDL+model+Bagnai+Ospina-Rojas"}],"related":["nardl-model","ardl-bounds-test","rolling-window-regression","time-varying-parameter-model","nonlinear-cointegration","threshold-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-ols","name":"Time-varying parameter OLS","fullName":"Time-Varying Parameter Ordinary Least Squares","aliases":["TVP-OLS","time-varying coefficient regression","rolling OLS","locally weighted OLS"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1976","originator":"Cooley & Prescott (1976); further developed by Harvey (1990)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-ols","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-ols.md","definition":"Time-Varying Parameter OLS extends classical ordinary least squares to allow regression coefficients to change over time. Instead of assuming fixed slopes throughout the sample, the model treats each coefficient as a stochastic process, tracking how economic relationships evolve — making it well-suited for analysing structural change in time-series data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cooley & Prescott (1976); further developed by Harvey (1990)","year":"1976","type":"Time-series regression with evolving coefficients","dataType":"Time-series data (univariate or multivariate)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184.","type":"article","doi":"10.2307/1911389","isbn":null,"url":null},{"ref":"Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521405737","url":null}],"related":["kalman-filter","ols-regression","rolling-regression","state-space-model","panel-fixed-effects","structural-break-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-panel-data-analysis","name":"Time-varying Parameter Panel Data Analysis","fullName":"Time-varying Parameter Panel Data Analysis","aliases":["TVP panel model","time-varying coefficient panel model","state-space panel regression","random coefficient panel model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1960–2003","originator":"Cheng Hsiao (panel treatment); Kalman (state-space foundation)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-panel-data-analysis","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-panel-data-analysis.md","definition":"Time-varying parameter (TVP) panel data analysis extends standard panel regression by allowing the slope coefficients to evolve over time for each unit. Instead of assuming a single fixed or random coefficient, the model lets each unit's relationship between predictors and outcome shift period by period, capturing structural change, learning effects, and heterogeneous dynamics across individuals and time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cheng Hsiao (panel treatment); Kalman (state-space foundation)","year":"1960–2003","type":"Dynamic panel model","dataType":"Balanced or unbalanced panel (cross-section × time series)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521522717","url":null},{"ref":"Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45.","type":"article","doi":"10.1115/1.3662552","isbn":null,"url":null}],"related":["panel-fixed-effects","panel-random-effects","state-space-model","kalman-filter","random-coefficient-model","fama-macbeth-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-pp-unit-root-test","name":"Time-varying parameter PP unit root test","fullName":"Time-Varying Parameter Phillips-Perron Unit Root Test","aliases":["TVP-PP unit root test","time-varying PP test","Phillips-Perron test with time-varying parameters","TVP unit root test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1988-1999","originator":"Extension of Phillips & Perron (1988); TVP framework attributed to Hall & Luginbuhl (1999) and related literature","url":"https://scholargate.app/en/econometrics/time-varying-parameter-pp-unit-root-test","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-pp-unit-root-test.md","definition":"The time-varying parameter PP unit root test extends the classical Phillips-Perron test by allowing the autoregressive coefficient to change over time. It detects stochastic non-stationarity in series whose persistence may shift across regimes or periods, offering more reliable inference when structural change is suspected in the data-generating process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Phillips & Perron (1988); TVP framework attributed to Hall & Luginbuhl (1999) and related literature","year":"1988-1999","type":"Unit root test with time-varying parameters","dataType":"Time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.","type":"article","doi":"10.1093/biomet/75.2.335","isbn":null,"url":null},{"ref":"Hall, S. G., & Luginbuhl, R. (1999). Modelling structural breaks in unit root tests using time-varying parameter models. Journal of Economic Dynamics and Control, 23(2), 209-231.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modelling+structural+breaks+in+unit+root+tests+using+time-varying+parameter+models+Hall"}],"related":["phillips-perron-test","augmented-dickey-fuller-test","time-varying-parameter-model","structural-break-unit-root-test","zivot-andrews-test","kpss-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-quantile-on-quantile-regression","name":"Time-varying parameter quantile-on-quantile regression","fullName":"Time-Varying Parameter Quantile-on-Quantile Regression","aliases":["TVP-QQ regression","time-varying QQ regression","dynamic quantile-on-quantile regression","TVP quantile-on-quantile"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2015–2019","originator":"Extension of Sim & Zhou (2015) QQ framework; TVP adaptation by subsequent applied econometricians","url":"https://scholargate.app/en/econometrics/time-varying-parameter-quantile-on-quantile-regression","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-quantile-on-quantile-regression.md","definition":"TVP-QQ regression extends the quantile-on-quantile (QQ) framework by allowing the slope coefficients to evolve over time. It maps how the quantiles of a predictor variable affect the quantiles of an outcome differently across the joint distribution and across different time periods, uncovering dynamic, heterogeneous dependence structures that standard regression cannot detect.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension of Sim & Zhou (2015) QQ framework; TVP adaptation by subsequent applied econometricians","year":"2015–2019","type":"Nonparametric time-varying quantile regression","dataType":"Time-series or panel data; continuous outcome and predictor","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8.","type":"article","doi":"10.1016/j.jbankfin.2015.01.013","isbn":null,"url":null},{"ref":"Bouri, E., Gupta, R., & Vo, X. V. (2021). Jumps in geopolitical risk and the cryptocurrency market: The singularity of Bitcoin. Defence and Peace Economics, 33(2), 150–161.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Time-varying+parameter+quantile-on-quantile+regression+cryptocurrency"}],"related":["quantile-regression","quantile-on-quantile-regression","time-varying-parameter-regression","rolling-window-regression","nonparametric-quantile-regression","copula-quantile-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-random-effects-model","name":"Time-varying parameter random effects model","fullName":"Time-Varying Parameter Random Effects Model","aliases":["TVP-RE model","random coefficient random effects model","time-varying random effects","TVP panel random effects"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1970–1975","originator":"Swamy (1970); Hsiao (1975)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-random-effects-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-random-effects-model.md","definition":"The time-varying parameter random effects model extends the classic random effects panel framework by allowing regression coefficients to change over time and across units. Rather than imposing a single fixed slope for all individuals and periods, each coefficient is treated as a random draw that evolves, capturing genuine parameter instability while preserving the random effects assumption that unit-specific components are uncorrelated with the regressors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Swamy (1970); Hsiao (1975)","year":"1970–1975","type":"Panel regression with time-varying random coefficients","dataType":"Balanced or unbalanced panel data (cross-sectional units observed over time)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Swamy, P. A. V. B. (1970). Efficient inference in a random coefficient regression model. Econometrica, 38(2), 311–323.","type":"article","doi":"10.2307/1913012","isbn":null,"url":null},{"ref":"Hsiao, C. (1975). Some estimation methods for a random coefficient model. Econometrica, 43(2), 305–325.","type":"article","doi":"10.2307/1913588","isbn":null,"url":null}],"related":["random-effects-model","fixed-effects-model","time-varying-parameter-fixed-effects-model","time-varying-parameter-panel-data-analysis","panel-random-effects-model","bayesian-random-effects-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-sarima-model","name":"Time-varying parameter SARIMA model","fullName":"Time-Varying Parameter Seasonal Autoregressive Integrated Moving Average Model","aliases":["TVP-SARIMA","time-varying SARIMA","state-space SARIMA","adaptive SARIMA"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990s","originator":"Harvey, A. C.; Durbin, J. & Koopman, S. J. (state-space framework)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-sarima-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-sarima-model.md","definition":"The Time-Varying Parameter SARIMA model extends the classical SARIMA framework by allowing autoregressive and moving-average coefficients to evolve over time. Cast as a state-space system and estimated with the Kalman filter, it captures both seasonal patterns and structural change within a single unified model.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey, A. C.; Durbin, J. & Koopman, S. J. (state-space framework)","year":"1990s","type":"Time-varying state-space model","dataType":"Univariate seasonal time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"9780521321969","url":null},{"ref":"Durbin, J., & Koopman, S. J. (2012). Time Series Analysis by State Space Methods (2nd ed.). Oxford University Press.","type":"book","doi":null,"isbn":"9780199641178","url":null}],"related":["sarima-model","state-space-model","kalman-filter","time-varying-parameter-model","arima-model","structural-time-series-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-svar-model","name":"Time-varying parameter SVAR model","fullName":"Time-Varying Parameter Structural Vector Autoregression Model","aliases":["TVP-SVAR","time-varying SVAR","drifting-parameter SVAR","TVP structural VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2005","originator":"Giorgio E. Primiceri","url":"https://scholargate.app/en/econometrics/time-varying-parameter-svar-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-svar-model.md","definition":"The Time-Varying Parameter Structural VAR (TVP-SVAR) model extends classical structural VARs by allowing both the reduced-form coefficients and the structural impact matrix to evolve continuously over time. Estimated via Bayesian MCMC, it captures shifting transmission mechanisms and heteroscedastic volatility — making it the workhorse for empirical macroeconomics when policy regimes and economic relationships change.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Giorgio E. Primiceri","year":"2005","type":"Bayesian state-space SVAR","dataType":"Multivariate macroeconomic time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821–852.","type":"article","doi":"10.1111/j.1467-937X.2005.00353.x","isbn":null,"url":null},{"ref":"Nakajima, J. (2011). Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications. IMES Discussion Paper Series 2011-E-9, Bank of Japan.","type":"article","doi":null,"isbn":null,"url":"https://www.boj.or.jp/en/research/wps_rev/wps_2011/data/wp11e09.pdf"}],"related":["structural-var-model","bayesian-var-model","time-varying-parameter-var-model","factor-augmented-var","local-projection-method","markov-switching-var"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-system-gmm","name":"Time-varying parameter system GMM","fullName":"Time-Varying Parameter System Generalized Method of Moments","aliases":["TVP System GMM","time-varying System GMM","TVP-SGMM","dynamic panel TVP estimator"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1998 (System GMM); TVP extensions in applied literature thereafter","originator":"Blundell & Bond (System GMM base); Cooley & Prescott (TVP framework)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-system-gmm","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-system-gmm.md","definition":"Time-Varying Parameter System GMM extends the Blundell-Bond System Generalized Method of Moments estimator to allow regression coefficients to change over time. By combining the instrument-based correction for dynamic endogeneity with a time-varying coefficient structure, the method captures both the persistence of the lagged dependent variable and structural shifts in the effect of regressors across periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blundell & Bond (System GMM base); Cooley & Prescott (TVP framework)","year":"1998 (System GMM); TVP extensions in applied literature thereafter","type":"Dynamic panel estimator with time-varying coefficients","dataType":"Panel data with dynamic (lagged dependent variable) structure","subfamily":"Econometrics / time series"},"citations":[{"ref":"Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.","type":"article","doi":"10.1016/S0304-4076(98)00009-8","isbn":null,"url":null},{"ref":"Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184.","type":"article","doi":"10.2307/1911389","isbn":null,"url":null}],"related":["panel-system-gmm","arellano-bond-gmm-estimator","dynamic-panel-data-model","time-varying-parameter-arellano-bond-gmm","time-varying-parameter-difference-gmm","panel-dynamic-panel-data-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-tgarch-model","name":"Time-varying parameter TGARCH model","fullName":"Time-Varying Parameter Threshold Generalized Autoregressive Conditional Heteroscedasticity Model","aliases":["TVP-TGARCH","time-varying TGARCH","threshold GARCH with time-varying parameters","TVP Threshold GARCH"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1990s–2000s","originator":"Extension combining Zakoïan (1994) TGARCH and time-varying parameter methods","url":"https://scholargate.app/en/econometrics/time-varying-parameter-tgarch-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-tgarch-model.md","definition":"The TVP-TGARCH model extends Threshold GARCH by allowing its volatility parameters to evolve over time via a state-space representation. It captures both the leverage effect — that negative return shocks increase volatility more than positive ones — and structural change in that asymmetry, making it well-suited for long financial time series subject to regime shifts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Extension combining Zakoïan (1994) TGARCH and time-varying parameter methods","year":"1990s–2000s","type":"Volatility model with asymmetry and parameter evolution","dataType":"Financial time series (returns, yields, exchange rates)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zakoïan, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.","type":"article","doi":"10.1016/0165-1889(94)90039-6","isbn":null,"url":null},{"ref":"Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801.","type":"article","doi":"10.1111/j.1540-6261.1993.tb05128.x","isbn":null,"url":null}],"related":["tgarch-model","garch-model","egarch-model","time-varying-parameter-model","state-space-model","gjr-garch-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-toda-yamamoto-causality","name":"Time-varying parameter Toda-Yamamoto causality","fullName":"Time-Varying Parameter Toda-Yamamoto Granger Causality Test","aliases":["TVP-TY causality","time-varying Toda-Yamamoto","TVP Granger causality (Toda-Yamamoto)","rolling/recursive Toda-Yamamoto causality"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1995 (base); TVP variant emerged early 2000s–2010s","originator":"Toda & Yamamoto (1995); TVP extension by subsequent applied econometricians","url":"https://scholargate.app/en/econometrics/time-varying-parameter-toda-yamamoto-causality","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-toda-yamamoto-causality.md","definition":"The TVP Toda-Yamamoto causality test combines Toda and Yamamoto's (1995) augmented VAR approach — which handles possibly integrated or cointegrated series without pre-testing for unit roots — with time-varying parameters, allowing causal relationships between variables to shift across different periods rather than remaining fixed throughout the sample.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Toda & Yamamoto (1995); TVP extension by subsequent applied econometricians","year":"1995 (base); TVP variant emerged early 2000s–2010s","type":"Causality test (time-varying)","dataType":"Time series, possibly nonstationary or integrated","subfamily":"Econometrics / time series"},"citations":[{"ref":"Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.","type":"article","doi":"10.1016/0304-4076(94)01616-8","isbn":null,"url":null},{"ref":"Adebayo, T. S., & Acheampong, A. O. (2022). Modelling the globalization-emissions nexus: Fresh insights from the novel dynamic ARDL simulations and the Toda-Yamamoto causality approaches. Environmental Science and Pollution Research, 29(3), 3825-3840.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Modelling+the+globalization-emissions+nexus%3A+Fresh+insights+from+the+novel+dynamic+ARDL+simulations+and+the+Toda-Yamamoto+causality+approaches+Adebayo"}],"related":["toda-yamamoto-causality","granger-causality","var-model","rolling-window-regression","time-varying-parameter-var","bootstrap-granger-causality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-var-model","name":"Time-varying parameter VAR model","fullName":"Time-Varying Parameter Vector Autoregression Model","aliases":["TVP-VAR","time-varying VAR","TV-VAR","drifting-coefficient VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"2005","originator":"Primiceri (2005); Cogley & Sargent (2001, 2005)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-var-model","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-var-model.md","definition":"The Time-Varying Parameter VAR (TVP-VAR) model extends the standard vector autoregression by allowing the coefficients and error covariances to evolve gradually over time. Estimated via Bayesian methods and MCMC simulation, it captures how dynamic relationships between macroeconomic or financial variables shift across different economic regimes without requiring pre-specified break points.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Primiceri (2005); Cogley & Sargent (2001, 2005)","year":"2005","type":"Multivariate time-series model with drifting coefficients","dataType":"Multivariate macroeconomic or financial time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852.","type":"article","doi":"10.1111/j.1467-937X.2005.00353.x","isbn":null,"url":null},{"ref":"Cogley, T., & Nason, J. M. (1995). Effects of the Hodrick-Prescott filter on trend and difference stationary time series: Implications for business cycle research. Journal of Economic Dynamics and Control, 19(1-2), 253-278.","type":"article","doi":"10.1016/0165-1889(93)00781-X","isbn":null,"url":null}],"related":["vector-autoregression","structural-var","bayesian-var-model","time-varying-parameter-ardl-bounds-test","state-space-model","kalman-filter"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-vecm","name":"Time-varying parameter VECM","fullName":"Time-Varying Parameter Vector Error Correction Model","aliases":["TVP-VECM","time-varying VECM","TVP cointegration model","dynamic VECM with drifting coefficients"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1999–2010","originator":"Park & Hahn (1999); extended by Bierens & Martins (2010)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-vecm","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-vecm.md","definition":"The Time-Varying Parameter Vector Error Correction Model extends the standard VECM by allowing the adjustment speeds, cointegrating vectors, and short-run dynamics to drift over time. It captures long-run cointegrating relationships among integrated series while accommodating structural change, evolving policy regimes, and shifting economic relationships within a unified state-space framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Park & Hahn (1999); extended by Bierens & Martins (2010)","year":"1999–2010","type":"Dynamic multivariate time-series model","dataType":"Multivariate integrated (I(1)) time series","subfamily":"Econometrics / time series"},"citations":[{"ref":"Park, J. Y., & Hahn, S. B. (1999). Cointegrating regressions with time varying coefficients. Econometric Theory, 15(5), 664–703.","type":"article","doi":"10.1017/S0266466699155026","isbn":null,"url":null},{"ref":"Vector error correction model. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Vector_error_correction_model"}],"related":["vecm","tvp-var","state-space-model","kalman-filter","dynamic-ols","threshold-cointegration"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-wls","name":"Time-varying parameter WLS","fullName":"Time-Varying Parameter Weighted Least Squares","aliases":["TVP-WLS","time-varying coefficient WLS","locally weighted time-varying regression","TVP weighted regression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1976–1990","originator":"Cooley & Prescott (1976); Harvey (1990)","url":"https://scholargate.app/en/econometrics/time-varying-parameter-wls","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-wls.md","definition":"Time-Varying Parameter WLS is a regression technique for time-series data in which the slope and intercept coefficients are allowed to change over time while observations are weighted to account for heteroscedasticity or to discount distant data. It combines the flexibility of state-space coefficient evolution with the variance-correcting power of weighted least squares.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cooley & Prescott (1976); Harvey (1990)","year":"1976–1990","type":"Time-varying coefficient regression with observation weights","dataType":"Time series, longitudinal","subfamily":"Econometrics / time series"},"citations":[{"ref":"Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.","type":"book","doi":null,"isbn":"978-0521405737","url":null},{"ref":"Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimation+in+the+Presence+of+Stochastic+Parameter+Variation+Cooley+Prescott+1976"}],"related":["kalman-filter-estimation","rolling-ols","weighted-least-squares","state-space-model","random-coefficient-model","locally-weighted-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"time-varying-parameter-zivot-andrews-test","name":"Time-varying parameter Zivot-Andrews test","fullName":"Time-Varying Parameter Zivot-Andrews Structural Break Unit Root Test","aliases":["TVP Zivot-Andrews test","time-varying Zivot-Andrews unit root test","TVP-ZA test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1992 (base test); TVP adaptation in later applied work","originator":"Zivot & Andrews (1992); TVP extension in subsequent applied econometrics literature","url":"https://scholargate.app/en/econometrics/time-varying-parameter-zivot-andrews-test","markdownUrl":"https://scholargate.app/en/econometrics/time-varying-parameter-zivot-andrews-test.md","definition":"The time-varying parameter Zivot-Andrews test extends the classic Zivot-Andrews (1992) structural break unit root test by allowing the regression coefficients to evolve over time. Rather than assuming fixed parameters across the full sample, this approach lets the autoregressive dynamics and break timing adapt through a state-space or rolling framework, improving robustness when economic relationships shift gradually.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zivot & Andrews (1992); TVP extension in subsequent applied econometrics literature","year":"1992 (base test); TVP adaptation in later applied work","type":"Unit root test with endogenous structural break under time-varying parameters","dataType":"Univariate time series; continuous observations","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null},{"ref":"Cooley, T. F., & Prescott, E. C. (1976). Estimation in the Presence of Stochastic Parameter Variation. Econometrica, 44(1), 167–184.","type":"article","doi":"10.2307/1911389","isbn":null,"url":null}],"related":["zivot-andrews-structural-break-test","augmented-dickey-fuller-unit-root-test","structural-break-zivot-andrews-test","time-varying-parameter-adf-unit-root-test","fourier-zivot-andrews-test","phillips-perron-unit-root-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"timed-up-and-go-test","name":"Timed Up and Go Test","fullName":"Timed Up and Go (TUG) Test","aliases":["TUG","TUG test"],"domain":"physical-therapy","family":"process-pipeline","subfamily":"Mobility assessment","year":"1991","originator":"Diane Podsiadlo and Susan Richardson","url":"https://scholargate.app/en/physical-therapy/timed-up-and-go-test","markdownUrl":"https://scholargate.app/en/physical-therapy/timed-up-and-go-test.md","definition":"The Timed Up and Go (TUG) test is a simple, quick performance assessment that measures the time required to stand from a chair, walk 3 meters, turn around, and return to sitting. Developed by Podsiadlo and Richardson in 1991, the TUG has become one of the most widely used tests in geriatric and rehabilitation settings for assessing mobility, balance, and fall risk in older adults and individuals with mobility limitations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diane Podsiadlo and Susan Richardson","subfamily":"Mobility assessment","year":"1991","type":"Performance-based test"},"citations":[{"ref":"Podsiadlo, D., & Richardson, S. (1991). The timed \"Up & Go\": A test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society, 39(2), 142-148.","type":"article","doi":"10.1111/j.1532-5415.1991.tb01616.x","isbn":null,"url":null},{"ref":"Shumway-Cook, A., Brauer, S., & Woollacott, M. (2000). Predicting the probability for falls in community-dwelling older adults. Physical Therapy, 80(9), 896-903.","type":"article","doi":"10.1093/ptj/80.9.896","isbn":null,"url":null}],"related":["berg-balance-scale","ten-meter-walk-test","six-minute-walk-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"timegpt","name":"TimeGPT","fullName":"A Time Series Foundation Model","aliases":["TimeGPT-1","Time series GPT"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, Time Series Forecasting, Foundation Models","year":"2023","originator":"Fabio Garza","url":"https://scholargate.app/en/deep-learning/timegpt","markdownUrl":"https://scholargate.app/en/deep-learning/timegpt.md","definition":"TimeGPT is a time series foundation model introduced by Garza and White in 2023 that unifies forecasting, anomaly detection, and classification in a single pre-trained model. Inspired by large language models, TimeGPT is pre-trained on diverse time series and transfers well to downstream tasks with minimal fine-tuning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fabio Garza","subfamily":"Deep Learning, Time Series Forecasting, Foundation Models","year":"2023","type":"Neural network architecture"},"citations":[{"ref":"Garza, F., & White, C. W. (2023). TimeGPT-1: A Time Series Foundation Model. In ICML 2024 Time Series Workshop.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2310.03589"}],"related":["n-beatsx","mamba","vision-transformer","latent-diffusion-models"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"timeline-extraction","name":"Timeline Extraction","fullName":"Timeline Extraction (Temporal Event Ordering)","aliases":["temporal event ordering","event timeline construction","Zaman Çizelgesi Çıkarma (Timeline Extraction)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":"2010 (TempEval-2 benchmark)","originator":"TempEval shared task community (Verhagen et al., 2010)","url":"https://scholargate.app/en/text-mining/timeline-extraction","markdownUrl":"https://scholargate.app/en/text-mining/timeline-extraction.md","definition":"Timeline extraction is a natural-language-processing task that identifies events mentioned in text, anchors each event to a temporal expression, and arranges them into a chronologically ordered timeline. Formalised through the TempEval shared tasks (Verhagen et al., 2010), it enables automatic reconstruction of historical narratives, news event sequences, and clinical case progressions from unstructured text.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"TempEval shared task community (Verhagen et al., 2010)","year":"2010 (TempEval-2 benchmark)","type":"NLP structured information extraction task","output":"Chronologically ordered sequence of events with associated temporal expressions","minDocuments":10,"difficulty":"Advanced (3 / 5)"},"citations":[{"ref":"Verhagen, M. et al. (2010). SemEval-2010 Task 13: TempEval-2. Proceedings of the 5th International Workshop on Semantic Evaluation (ACL).","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/S10-1010"},{"ref":"Minard, A.L. et al. (2016). MEANTIME: MedliNe Annotated TimeLine. Proceedings of the 10th Language Resources and Evaluation Conference (LREC).","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/L16-1699"}],"related":["named-entity-recognition","event-extraction","relation-extraction","information-extraction","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"timemixer","name":"TimeMixer","fullName":"TimeMixer (Decomposable Multiscale Mixing)","aliases":["Decomposable Multiscale Mixing","Multiscale Time-Series Mixer","TimeMixer Model","Çok Ölçekli Zaman Serisi Karıştırıcı"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2024,"originator":"Shiyu Wang et al.","url":"https://scholargate.app/en/deep-learning/timemixer","markdownUrl":"https://scholargate.app/en/deep-learning/timemixer.md","definition":"TimeMixer is a decomposition-based, attention-free time-series forecasting architecture introduced by Wang et al. at ICLR 2024. The central idea is to disentangle seasonal and trend components across multiple temporal scales constructed by average pooling, then mix information across those scales using lightweight MLP blocks. By handling coarse (trend-dominant) and fine (seasonal-dominant) resolutions separately and combining their predictions, TimeMixer avoids the quadratic cost of attention while capturing both local and global temporal patterns.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shiyu Wang et al.","year":2024,"type":"MLP-based multiscale time-series forecasting model","subfamily":"Time-series forecasting","venue":"ICLR 2024","architecture":"Decomposition + multiscale mixing (no attention)"},"citations":[{"ref":"Wang, S., Wu, H., Shi, X., Hu, T., Luo, H., Ma, L., Zhang, J. Y., & Zhou, J. (2024). TimeMixer: Decomposable multiscale mixing for time series forecasting. ICLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2405.14616"}],"related":["tsmixer","dlinear","timesnet"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"timesfm","name":"TimesFM","fullName":"TimesFM (Time-series Foundation Model)","aliases":["Time-series Foundation Model","Google TimesFM","TimesFM forecaster","Zaman Serisi Temel Modeli"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2024,"originator":"Abhimanyu Das et al. (Google)","url":"https://scholargate.app/en/deep-learning/timesfm","markdownUrl":"https://scholargate.app/en/deep-learning/timesfm.md","definition":"TimesFM is a pre-trained foundation model for univariate time-series forecasting introduced by Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou from Google in 2024. The model adopts a decoder-only transformer architecture, similar in spirit to large language models, and is trained on a large corpus of real-world and synthetic time-series data. Its central innovation is the ability to perform accurate zero-shot forecasting across diverse domains without task-specific fine-tuning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Abhimanyu Das et al. (Google)","year":2024,"type":"Pre-trained decoder-only transformer for zero-shot time-series forecasting","subfamily":"Time-series forecasting","training_corpus":"Large-scale real-world and synthetic time-series datasets","context_handling":"Patch-based input tokenization"},"citations":[{"ref":"Das, A., Kong, W., Sen, R., & Zhou, Y. (2024). A decoder-only foundation model for time-series forecasting. ICML.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2310.10688"}],"related":["chronos","moirai","patchtst"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"timesnet","name":"TimesNet","fullName":"TimesNet (Temporal 2D-Variation Modeling)","aliases":["Temporal 2D-Variation Network","TimesNet Model","2D Time-Series Network","Zamansal 2B Varyasyon Ağı"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2023,"originator":"Haixu Wu et al.","url":"https://scholargate.app/en/deep-learning/timesnet","markdownUrl":"https://scholargate.app/en/deep-learning/timesnet.md","definition":"TimesNet is a general-purpose time-series model introduced by Wu et al. at ICLR 2023. Its central idea is that univariate or multivariate time series can be reinterpreted as collections of two-dimensional temporal maps by reshaping the 1D signal according to its dominant periodicities, detected via Fast Fourier Transform. This 1D-to-2D transformation exposes both intraperiod patterns (within one cycle) and interperiod trends (across cycles), enabling powerful 2D convolutional architectures to model temporal variation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Haixu Wu et al.","year":2023,"type":"2D convolutional time-series model","subfamily":"Time-series forecasting","venue":"ICLR 2023","core_operation":"1D-to-2D temporal reshaping via FFT-detected periods"},"citations":[{"ref":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., & Long, M. (2023). TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2210.02186"}],"related":["autoformer","patchtst","convolutional-neural-network"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tinetti-balance-assessment","name":"Tinetti","fullName":"Tinetti Balance and Gait Assessment","aliases":["Tinetti PTSB","Performance-Oriented Mobility Assessment","POMA"],"domain":"gerontology","family":"process-pipeline","subfamily":"balance-and-gait-performance","year":"1986","originator":"Mary E. Tinetti","url":"https://scholargate.app/en/gerontology/tinetti-balance-assessment","markdownUrl":"https://scholargate.app/en/gerontology/tinetti-balance-assessment.md","definition":"The Tinetti Balance and Gait Assessment (also known as the Performance-Oriented Mobility Assessment or POMA) is a performance-based evaluation tool developed by Mary E. Tinetti in 1986 to assess balance and gait abnormalities in older adults. The battery combines direct observation of balance maneuvers and walking pattern with a systematic scoring rubric, identifying individuals at high risk for falls and functional decline. It is widely used in geriatric clinics, rehabilitation settings, and fall prevention programs as a practical yet sensitive assessment of mobility impairment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mary E. Tinetti","subfamily":"balance-and-gait-performance","year":"1986","type":"Performance-based assessment"},"citations":[{"ref":"Tinetti, M. E. (1986). Performance-oriented assessment of mobility problems in elderly patients. J Am Geriatr Soc, 34(2), 119-126.","type":"article","doi":"10.1111/j.1532-5415.1986.tb05480.x","isbn":null,"url":null},{"ref":"Tinetti, M. E., & Ginter, S. F. (1988). Identifying mobility dysfunctions in elderly patients. Standard neuromuscular tests. J Am Geriatr Soc, 36(1), 55-60.","type":"article","doi":"10.1001/jama.1988.03720080024022","isbn":null,"url":null},{"ref":"Tinetti, M. E., Mendes de Leon, C. F., Doucette, J. T., & Baker, D. I. (1994). Fear of falling and fall-related efficacy in relationship to functioning among community-living elders. J Gerontol, 49(3), M140-M147.","type":"article","doi":"10.1093/geronj/49.3.M140","isbn":null,"url":null}],"related":["short-physical-performance-battery","activities-balance-confidence","life-space-assessment","frail-scale","edmonton-frail-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tinnitus-handicap-inventory","name":"THI","fullName":"Tinnitus Handicap Inventory","aliases":["THI"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"auditory-disability","year":"1996","originator":"Craig W. Newman, Gary P. Jacobson, and James B. Spitzer","url":"https://scholargate.app/en/otolaryngology/tinnitus-handicap-inventory","markdownUrl":"https://scholargate.app/en/otolaryngology/tinnitus-handicap-inventory.md","definition":"The Tinnitus Handicap Inventory (THI) is a 25-item self-report scale that quantifies the functional, emotional, and catastrophic effects of tinnitus on daily life, work, and psychosocial well-being. Developed by Newman, Jacobson, and Spitzer in 1996, it has become the gold-standard outcome measure for assessing tinnitus-related handicap in clinical practice and research. The THI enables clinicians to track disease burden, monitor therapeutic response, and identify patients at risk for severe psychological distress.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Craig W. Newman, Gary P. Jacobson, and James B. Spitzer","subfamily":"auditory-disability","year":"1996","type":"Self-report"},"citations":[{"ref":"Newman, C. W., Jacobson, G. P., & Spitzer, J. B. (1996). Development of the Tinnitus Handicap Inventory. Archives of Otolaryngology - Head & Neck Surgery, 122(2), 143-148.","type":"article","doi":"10.1001/archotol.1996.01890140029007","isbn":null,"url":null}],"related":["dizziness-handicap-inventory","hearing-handicap-inventory","vertigo-symptom-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tirex","name":"TiRex","fullName":"TiRex (xLSTM-based Zero-Shot Forecasting Model)","aliases":["Time-series xLSTM Forecaster","TiRex Zero-Shot","xLSTM Time-Series Model","Zaman Serisi Sıfır-Atım Tahmincisi"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2025,"originator":"NX-AI (xLSTM team)","url":"https://scholargate.app/en/deep-learning/tirex","markdownUrl":"https://scholargate.app/en/deep-learning/tirex.md","definition":"TiRex is a pretrained zero-shot time-series forecasting model introduced in 2025 by the NX-AI xLSTM team (Auer et al.). Built on the Extended Long Short-Term Memory (xLSTM) architecture, TiRex is trained at scale on diverse time-series corpora and can forecast unseen datasets without any fine-tuning. Its core idea is to exploit enhanced in-context learning: the model reads the entire available history as a context and produces forecasts for both short and long horizons directly from that context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"NX-AI (xLSTM team)","year":2025,"type":"Pretrained zero-shot time-series forecasting model","subfamily":"Time-series forecasting","backbone":"xLSTM (Extended Long Short-Term Memory)","training_paradigm":"Large-scale pretraining for zero-shot generalization"},"citations":[{"ref":"Auer, A., Podest, P., Klotz, D., Böck, S., Klambauer, G., & Hochreiter, S. (2025). TiRex: Zero-shot forecasting across long and short horizons with enhanced in-context learning. arXiv preprint.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2505.23719"}],"related":["chronos","timesfm","lstm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tls-protocol-analysis","name":"TLS Protocol Analysis","fullName":"Transport Layer Security Protocol Specification and Security Assessment","aliases":["TLS/SSL Protocol","HTTPS Security","Secure Transport Layer"],"domain":"cryptography","family":"process-pipeline","subfamily":"Protocol security and standards","year":"1994","originator":"Netscape Communications Corporation, IETF","url":"https://scholargate.app/en/cryptography/tls-protocol-analysis","markdownUrl":"https://scholargate.app/en/cryptography/tls-protocol-analysis.md","definition":"The Transport Layer Security (TLS) protocol is the cryptographic standard that secures web communication and email transmission. Evolved from SSL (Secure Sockets Layer), TLS provides authentication, encryption, and integrity protection for data in transit. The protocol combines public-key cryptography (RSA, ECDH) for key agreement, symmetric encryption (AES) for bulk data, and digital signatures (SHA-256) for authentication.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Netscape Communications Corporation, IETF","subfamily":"Protocol security and standards","year":"1994","type":"Cryptographic transport protocol"},"citations":[{"ref":"Rescorla, E. (2018). The Transport Layer Security (TLS) Protocol Version 1.3. RFC 8446.","type":"report","doi":null,"isbn":null,"url":"https://tools.ietf.org/html/rfc8446"},{"ref":"Dierks, T., & Rescorla, E. (2008). The Transport Layer Security (TLS) Protocol Version 1.2. RFC 5246.","type":"report","doi":null,"isbn":null,"url":"https://tools.ietf.org/html/rfc5246"},{"ref":"Bhargavan, K., et al. (2016). FREAK: Factoring RSA Export Keys. Proceedings of the 24th USENIX Security Symposium.","type":"article","doi":null,"isbn":null,"url":"https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/bhargavan"}],"related":["rsa-cryptosystem-analysis","diffie-hellman-key-exchange","sha-hash-function","digital-signature-scheme"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tnorm-einstein","name":"TNORM-EINSTEIN","fullName":"Einstein T-norm — Einstein product and sum for IFN/PFN aggregation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"T-norm","year":"1963; 2007","originator":"Klement, E.P.; Mesiar, R.; Pap, E. / Xu, Z.; Yager, R.R.","url":"https://scholargate.app/en/decision-making/tnorm-einstein","markdownUrl":"https://scholargate.app/en/decision-making/tnorm-einstein.md","definition":"TNORM-EINSTEIN (Einstein T-norm — Einstein product and sum for IFN/PFN aggregation) is a t-norm multi-criteria decision-making (MCDM) method introduced by Klement, E.P.; Mesiar, R.; Pap, E. / Xu, Z.; Yager, R.R. in 1963; 2007. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klement, E.P.; Mesiar, R.; Pap, E. / Xu, Z.; Yager, R.R.","subfamily":"T-norm","year":"1963; 2007","type":"T-norm — Einstein product (Hamacher γ=2 special case)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Klement, E.P., Mesiar, R., Pap, E. (2000). Triangular Norms. Kluwer Academic Publishers, Dordrecht","type":"article","doi":"10.1007/978-94-015-9540-7","isbn":null,"url":null}],"related":["if-topsis","if-vikor","pf-topsis","ivif-topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tnorm-frank","name":"TNORM-FRANK","fullName":"Frank T-norm — Frank family of associative t-norms and t-conorms","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"T-norm","year":"1979","originator":"Frank, M.J.","url":"https://scholargate.app/en/decision-making/tnorm-frank","markdownUrl":"https://scholargate.app/en/decision-making/tnorm-frank.md","definition":"TNORM-FRANK (Frank T-norm — Frank family of associative t-norms and t-conorms) is a t-norm multi-criteria decision-making (MCDM) method introduced by Frank, M.J. in 1979. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank, M.J.","subfamily":"T-norm","year":"1979","type":"Parametric t-norm family — Frank (generator: log-based)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Frank, M.J. (1979). On the simultaneous associativity of F(x,y) and x+y-F(x,y). Aequationes Mathematicae","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+simultaneous+associativity+of+F%28x%2Cy%29+and+x%2By-F%28x%2Cy%29+Frank"}],"related":["if-topsis","if-vikor","pf-topsis","q-rofs-mcdm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tnorm-hamacher","name":"TNORM-HAMACHER","fullName":"Hamacher T-norm — Rational parametric t-norm for fuzzy aggregation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"T-norm","year":"1978","originator":"Hamacher, H.","url":"https://scholargate.app/en/decision-making/tnorm-hamacher","markdownUrl":"https://scholargate.app/en/decision-making/tnorm-hamacher.md","definition":"TNORM-HAMACHER (Hamacher T-norm — Rational parametric t-norm for fuzzy aggregation) is a t-norm multi-criteria decision-making (MCDM) method introduced by Hamacher, H. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hamacher, H.","subfamily":"T-norm","year":"1978","type":"Parametric t-norm family — Hamacher (rational generator)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Hamacher, H. (1978). Über logische Verknüpfungen unscharfer Aussagen und deren zugehörige Bewertungsfunktionen. Progress in Cybernetics and Systems Research","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=%C3%9Cber%20logische%20Verkn%C3%BCpfungen%20unscharfer%20Aussagen%20und%20deren%20zugeh%C3%B6rige%20Bewertungsfunktionen"}],"related":["if-topsis","if-vikor","pf-topsis","hf-topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tnorm-schweizer-sklar","name":"TNORM-SCHWEIZER-SKLAR","fullName":"Schweizer-Sklar T-norm — Power-based parametric t-norm family","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"T-norm","year":"1960","originator":"Schweizer, B.; Sklar, A.","url":"https://scholargate.app/en/decision-making/tnorm-schweizer-sklar","markdownUrl":"https://scholargate.app/en/decision-making/tnorm-schweizer-sklar.md","definition":"TNORM-SCHWEIZER-SKLAR (Schweizer-Sklar T-norm — Power-based parametric t-norm family) is a t-norm multi-criteria decision-making (MCDM) method introduced by Schweizer, B.; Sklar, A. in 1960. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Schweizer, B.; Sklar, A.","subfamily":"T-norm","year":"1960","type":"Parametric t-norm family — Schweizer-Sklar (power generator)","value_space":"intuitionistic","uncertainty":"epistemic","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Schweizer, B., Sklar, A. (1960). Statistical metric spaces. Pacific Journal of Mathematics","type":"article","doi":"10.2140/pjm.1960.10.313","isbn":null,"url":null}],"related":["if-topsis","q-rofs-mcdm","pf-topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tobit-model","name":"Tobit Model","fullName":"Tobit Censored Regression Model","aliases":["censored regression","limited dependent variable model","Tobit Modeli (Sansürlü Regresyon)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1958,"originator":"James Tobin","url":"https://scholargate.app/en/econometrics/tobit-model","markdownUrl":"https://scholargate.app/en/econometrics/tobit-model.md","definition":"The Tobit model is a regression for outcomes that are censored at a threshold, estimating the relationship by maximum likelihood. Introduced by James Tobin in 1958, it addresses the pile-up of observations at a limit (typically zero) in data such as spending, wages, or duration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James Tobin","year":1958,"type":"Censored regression (limited dependent variable)","estimator":"Maximum likelihood","outcome":"continuous, censored at a threshold","minSample":100},"citations":[{"ref":"Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36.","type":"article","doi":"10.2307/1907382","isbn":null,"url":null}],"related":["ols-regression","probit-regression","logistic-regression","quantile-regression","negative-binomial-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"toda-yamamoto-causality-test","name":"Toda-Yamamoto causality test","fullName":"Toda-Yamamoto Modified Wald Causality Test","aliases":["Toda-Yamamoto test","TY causality test","modified Wald test for Granger causality","TY-MWALD"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1995","originator":"Toda, H. Y. and Yamamoto, T.","url":"https://scholargate.app/en/econometrics/toda-yamamoto-causality-test","markdownUrl":"https://scholargate.app/en/econometrics/toda-yamamoto-causality-test.md","definition":"The Toda-Yamamoto (TY) causality test is a modified Wald procedure for testing Granger causality in vector autoregressions (VARs) estimated in levels, even when variables are nonstationary or cointegrated. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, it restores the standard chi-squared asymptotic distribution of the Wald statistic without requiring prior unit-root or cointegration pretesting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Toda, H. Y. and Yamamoto, T.","year":"1995","type":"Causality test","dataType":"Time series (possibly nonstationary or cointegrated)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.","type":"article","doi":"10.1016/0304-4076(94)01616-8","isbn":null,"url":null},{"ref":"Dolado, J. J., & Lütkepohl, H. (1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews, 15(4), 369-386.","type":"article","doi":"10.1080/07474939608800362","isbn":null,"url":null}],"related":["granger-causality-test","vector-autoregression","augmented-dickey-fuller-unit-root-test","johansen-cointegration-test","vector-error-correction-model","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"toda-yamamoto-causality","name":"Toda-Yamamoto Causality","fullName":"Toda-Yamamoto Granger Causality Test","aliases":["TY Causality Test","Modified Wald Granger Causality","MWALD Test","Toda-Yamamoto Nedensellik Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Causality","year":1995,"originator":"Hiro Toda & Taku Yamamoto","url":"https://scholargate.app/en/econometrics/toda-yamamoto-causality","markdownUrl":"https://scholargate.app/en/econometrics/toda-yamamoto-causality.md","definition":"The Toda-Yamamoto (TY) causality test, introduced by Toda and Yamamoto (1995), provides a robust procedure for testing Granger non-causality in vector autoregressive (VAR) models when the variables may be integrated or cointegrated of arbitrary order. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, the method bypasses the need for pre-testing cointegration and preserves the standard asymptotic chi-squared distribution of the Wald statistic.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hiro Toda & Taku Yamamoto","year":1995,"type":"Modified Wald test on augmented VAR","subfamily":"Causality","distribution":"Asymptotic chi-squared","integrationHandling":"Intentional lag augmentation avoids pre-testing bias"},"citations":[{"ref":"Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250.","type":"article","doi":"10.1016/0304-4076(94)01616-8","isbn":null,"url":null}],"related":["granger-causality","dolado-lutkepohl-causality","var-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"todim","name":"TODIM","fullName":"TOmada de Decisão Interativa e Multicritério (Interactive and Multicriteria Decision Making)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1992","originator":"Gomes, L. F. A. M., Lima, M. M. P. P.","url":"https://scholargate.app/en/decision-making/todim","markdownUrl":"https://scholargate.app/en/decision-making/todim.md","definition":"TODIM (TOmada de Decisão Interativa e Multicritério (Interactive and Multicriteria Decision Making)) is a ranking multi-criteria decision-making (MCDM) method introduced by Gomes, L. F. A. M., Lima, M. M. P. P. in 1992. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gomes, L. F. A. M., Lima, M. M. P. P.","subfamily":"Ranking","year":"1992","type":"Prospect theory (loss aversion pairwise dominance)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Gomes, L. F. A. M., Lima, M. M. P. P. (1992). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=TODIM%3A%20Basics%20and%20application%20to%20multicriteria%20ranking%20of%20projects%20with%20environmental%20impacts"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"token-bucket","name":"Token Bucket","fullName":"Token Bucket Rate Limiting Algorithm","aliases":["traffic shaping","rate limiting"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Traffic shaping","year":"1986","originator":"Jon Turner","url":"https://scholargate.app/en/telecommunications/token-bucket","markdownUrl":"https://scholargate.app/en/telecommunications/token-bucket.md","definition":"Token bucket is a simple and elegant algorithm for traffic shaping and rate limiting. A virtual bucket accumulates tokens at a fixed rate (the committed information rate). Incoming packets consume tokens (one token per byte); packets are transmitted only if sufficient tokens are available. If the bucket is full, excess tokens are discarded (no carry-over). Token bucket bounds peak rate and allows controlled bursts, making it ideal for traffic management in networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jon Turner","subfamily":"Traffic shaping","year":"1986","type":"rate limiting algorithm"},"citations":[{"ref":"Turner, J. S. (1986). New directions in communications (or which way to the information age?). IEEE Communications Magazine, 24(10), 8-15.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org"},{"ref":"Heinanen, J., Guerin, R., & May, A. (1999). A Single Rate Three Color Marker. RFC 2697.","type":"article","doi":null,"isbn":null,"url":"https://www.ietf.org"}],"related":["diffserv","csma-ca"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tolerance-stack-up","name":"Tolerance Stack-up","fullName":"Tolerance Stack-up Analysis and Accumulation Methods","aliases":["Stack-up analysis","Tolerance accumulation","Geometric tolerance"],"domain":"manufacturing","family":"process-pipeline","subfamily":"Dimensional analysis","year":"2006","originator":"Drake, P. J.","url":"https://scholargate.app/en/manufacturing/tolerance-stack-up","markdownUrl":"https://scholargate.app/en/manufacturing/tolerance-stack-up.md","definition":"Tolerance stack-up analysis is a method for predicting the cumulative effect of manufacturing tolerances on the final dimensions and fit of assembled components. When parts with individual tolerances are assembled together, their tolerances combine in complex ways, often producing a result that is worse than each part individually. Stack-up analysis ensures that the final assembly will function correctly despite individual part tolerances, or identifies where tighter tolerances are necessary.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Drake, P. J.","subfamily":"Dimensional analysis","year":"2006","type":"Method for analyzing tolerance accumulation in assemblies"},"citations":[{"ref":"Drake, P. J. (2006). Dimensioning and Tolerancing Handbook (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"0-07-145215-8","url":null},{"ref":"Harris, K. (1999). Engineering Tolerance Stack-up and Analysis. Society of Automotive Engineers.","type":"book","doi":null,"isbn":"0-7680-0343-0","url":null},{"ref":"Graves, S. C., & Redfield, C. (2005). Tolerance stack analysis of automotive assemblies. Journal of Manufacturing Science and Engineering, 127(3), 645-652.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tolerance+stack+analysis+of+automotive+assemblies+Graves"}],"related":["design-for-manufacturing-and-assembly","cnc-tool-path-generation","modal-analysis","additive-manufacturing-slicing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tooth-mobility-assessment","name":"Tooth Mobility Assessment","fullName":"Dental Tooth Mobility Evaluation","aliases":["tooth mobility testing","mobility grading","periodontal mobility"],"domain":"dentistry","family":"process-pipeline","subfamily":"Periodontics","year":"1950s (formalized assessment)","originator":"Multiple innovators (Miller, et al.)","url":"https://scholargate.app/en/dentistry/tooth-mobility-assessment","markdownUrl":"https://scholargate.app/en/dentistry/tooth-mobility-assessment.md","definition":"Tooth mobility assessment is a clinical examination that evaluates the amount and direction of movement of a tooth when lateral force is applied. Increased tooth mobility indicates loss of periodontal support (bone loss), trauma from occlusion, or other pathology affecting tooth attachment. Systematic mobility grading enables quantification of tooth stability, guides treatment planning, and assesses prognosis in periodontal disease and post-traumatic cases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple innovators (Miller, et al.)","subfamily":"Periodontics","year":"1950s (formalized assessment)","type":"Clinical mobility assessment"},"citations":[{"ref":"Miller, S. C. (1950). Textbook of periodontia (3rd ed.). Philadelphia: The Blakiston Company.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/history/"},{"ref":"Nulend, J. K., Sloan, A. J., Botero, J. E., et al. (2019). Dentin and pulp-related research: advancing the field. Advances in Dental Research, 31(1), 4-6.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Dentin+and+pulp-related+research%3A+advancing+the+field+Nulend"},{"ref":"American Academy of Periodontology. (2015). Periodontal disease as a risk factor for systemic disease. Annals of Periodontology, 9(1), 1-1.","type":"article","doi":null,"isbn":null,"url":"https://www.perio.org/"}],"related":["periodontal-probing","gingival-index","occlusal-analysis","bone-density-dental"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"top-trading-cycles","name":"Top Trading Cycles","fullName":"Top Trading Cycles and Chains","aliases":["TTC","Shapley-Scarf Algorithm","Efficient Exchange"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1974","originator":"Lloyd Shapley, Herbert Scarf","url":"https://scholargate.app/en/game-theory/top-trading-cycles","markdownUrl":"https://scholargate.app/en/game-theory/top-trading-cycles.md","definition":"Top Trading Cycles (TTC) is an algorithm for allocating indivisible goods to agents such that the allocation is Pareto efficient and individually rational. Developed by Lloyd Shapley and Herbert Scarf in 1974, the algorithm identifies cycles of trades in a preference digraph, executes those trades, and iteratively repeats until no further trades are beneficial. TTC is widely used in kidney exchange and housing allocation due to its efficiency and implementation simplicity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lloyd Shapley, Herbert Scarf","subfamily":"Game-theoretic","year":"1974","type":"algorithm"},"citations":[{"ref":"Shapley, L. S., & Scarf, H. (1974). On cores and indivisibility. Journal of Mathematical Economics, 1(1), 23-37.","type":"article","doi":"10.1016/0304-4068(74)90033-0","isbn":null,"url":null},{"ref":"Roth, A. E., Sönmez, T., & Ünver, M. U. (2008). Efficient kidney exchange: Coincidence of wants in markets with compatibility. American Economic Review, 97(3), 828-851.","type":"article","doi":"10.1257/aer.97.3.828","isbn":null,"url":null}],"related":["gale-shapley-algorithm","vcg-mechanism","bayesian-nash-equilibrium","principal-agent-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"topic-modeling-bertopic","name":"BERTopic","fullName":"BERTopic — Neural Topic Modeling","aliases":["neural topic modeling","transformer topic modeling","Konu Modelleme — BERTopic"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2022,"originator":"Maarten Grootendorst","url":"https://scholargate.app/en/text-mining/topic-modeling-bertopic","markdownUrl":"https://scholargate.app/en/text-mining/topic-modeling-bertopic.md","definition":"BERTopic is a neural topic-modeling pipeline introduced by Maarten Grootendorst in 2022. It combines BERT-based contextual embeddings with UMAP dimensionality reduction and HDBSCAN clustering to produce coherent, dynamic topics, achieving higher topic coherence than classic topic models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Maarten Grootendorst","year":2022,"type":"Neural topic-modeling pipeline","components":"Transformer embeddings + UMAP + HDBSCAN + class-based TF-IDF","output":"Coherent, interpretable topics with representative terms","minDocuments":"50 (>100 recommended)"},"citations":[{"ref":"Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv:2203.05794.","type":"article","doi":"10.48550/arXiv.2203.05794","isbn":null,"url":null},{"ref":"McInnes, L., Healy, J. & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.","type":"article","doi":"10.21105/joss.00205","isbn":null,"url":null}],"related":["bert-embeddings","sentiment-analysis","document-clustering"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"topic-modeling-lda","name":"Topic Modeling (LDA)","fullName":"Latent Dirichlet Allocation Topic Modeling","aliases":["LDA","latent Dirichlet allocation","Konu Modelleme — LDA"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2003,"originator":"Blei, Ng & Jordan","url":"https://scholargate.app/en/text-mining/topic-modeling-lda","markdownUrl":"https://scholargate.app/en/text-mining/topic-modeling-lda.md","definition":"Latent Dirichlet Allocation (LDA) is a generative probabilistic model introduced by Blei, Ng and Jordan (2003) that extracts the hidden topic distributions underlying a collection of documents. It treats each document as a mixture of latent topics and each topic as a distribution over words, turning an unlabelled corpus into interpretable themes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Blei, Ng & Jordan","year":2003,"type":"Generative probabilistic topic model","output":"Latent topic distributions over documents and words","keyParameter":"Number of topics k (chosen via coherence score)","minDocuments":50},"citations":[{"ref":"Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/v3/blei03a.html"}],"related":["tf-idf","word2vec","document-clustering","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"topic-modeling-nmf","name":"NMF Topic Modeling","fullName":"Topic Modeling with Non-negative Matrix Factorization","aliases":["non-negative matrix factorization topic modeling","NMF topics","Konu Modelleme — NMF"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":1999,"originator":"Lee & Seung","url":"https://scholargate.app/en/text-mining/topic-modeling-nmf","markdownUrl":"https://scholargate.app/en/text-mining/topic-modeling-nmf.md","definition":"NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee & Seung","year":1999,"type":"Matrix-factorization topic model","factorization":"Non-negative: document-topic and topic-term matrices are all non-negative","input":"TF-IDF document-term matrix","parameter":"Number of topics k must be set in advance","minimumDocuments":50},"citations":[{"ref":"Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.","type":"article","doi":"10.1038/44565","isbn":null,"url":null},{"ref":"Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., Wu, Y. & Zhu, M. (2013). A Practical Algorithm for Topic Modeling with Provable Guarantees. Proceedings of the 30th International Conference on Machine Learning (ICML), 280-288.","type":"article","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v28/arora13.html"}],"related":["topic-modeling-bertopic","tf-idf","document-clustering","bert-embeddings"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"topic-modeling","name":"Topic Modeling","fullName":"Topic Modeling (Probabilistic Latent Semantic Analysis and Latent Dirichlet Allocation)","aliases":["Latent Semantic Analysis","probabilistic topic modeling","topic discovery","thematic modeling"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"1999–2003","originator":"Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)","url":"https://scholargate.app/en/deep-learning/topic-modeling","markdownUrl":"https://scholargate.app/en/deep-learning/topic-modeling.md","definition":"Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)","year":"1999–2003","type":"Unsupervised generative probabilistic model","dataType":"Text corpora (bag-of-words or token counts)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.","type":"article","doi":null,"isbn":null,"url":"https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf"},{"ref":"Hofmann, T. (1999). Probabilistic Latent Semantic Analysis. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), 289–296.","type":"inproceedings","doi":null,"isbn":null,"url":"https://dl.acm.org/doi/10.5555/2073796.2073829"}],"related":["lda-topic-model","nmf-topic-model","sentence-embeddings","bert-based-classification","recurrent-neural-network","transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"topological-deep-learning","name":"Topological Deep Learning","fullName":"Topological Deep Learning","aliases":["TDL","Topological Neural Networks","Higher-Order Deep Learning","Topolojik Derin Öğrenme"],"domain":"topology","family":"ml-model","subfamily":"Topological learning","year":2023,"originator":"Topological deep learning literature","url":"https://scholargate.app/en/topology/topological-deep-learning","markdownUrl":"https://scholargate.app/en/topology/topological-deep-learning.md","definition":"Topological Deep Learning (TDL) is a framework that extends deep learning beyond graphs to higher-order topological domains such as simplicial complexes, cell complexes, and hypergraphs. Formalized by Hajij et al. (2023), TDL provides a unified mathematical language for defining message-passing schemes across cells of different ranks, enabling neural networks to model multi-way interactions that pairwise graph edges cannot capture. It is relevant to researchers working with relational, geometric, or biological data exhibiting group-level dependencies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Topological deep learning literature","year":2023,"type":"Higher-order message-passing framework","subfamily":"Topological learning","input_structure":"Topological domains (simplicial, cell, hypergraph complexes)","generalizes":"Graph Neural Networks"},"citations":[{"ref":"Hajij, M., et al. (2023). Topological deep learning: Going beyond graph data. arXiv preprint.","type":"preprint","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2206.00606"}],"related":["persistent-homology","graph-neural-network","mapper-algorithm"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"topology-optimization","name":"Topology Optimization","fullName":"Topology Optimization (Solid Isotropic Material with Penalization - SIMP)","aliases":["SIMP","Topology design","Generative design","Density-based optimization"],"domain":"reliability-engineering","family":"process-pipeline","subfamily":"Structural optimization","year":"1988","originator":"Martin Bendsoe and Noboru Kikuchi","url":"https://scholargate.app/en/reliability-engineering/topology-optimization","markdownUrl":"https://scholargate.app/en/reliability-engineering/topology-optimization.md","definition":"Topology Optimization is a computational method for distributing material optimally within a design space to maximize structural performance (strength, stiffness) while minimizing weight or cost. The Solid Isotropic Material with Penalization (SIMP) method, developed by Bendsoe and Kikuchi (1988), iteratively refines a material density distribution across the design domain using sensitivity analysis and gradient-based optimization. SIMP has revolutionized structural design in aerospace, automotive, and mechanical engineering by automating the generation of efficient structures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martin Bendsoe and Noboru Kikuchi","subfamily":"Structural optimization","year":"1988","type":"Generative design algorithm"},"citations":[{"ref":"Bendsoe, M. P., & Kikuchi, N. (1988). Generating optimal topologies in structural design using a homogenization method. Computer Methods in Applied Mechanics and Engineering, 71(2), 197-224.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Generating+optimal+topologies+in+structural+design+using+a+homogenization+method+Bendsoe"},{"ref":"Rozvany, G. I. N. (2009). A critical review of established methods of structural topology optimization. Structural and Multidisciplinary Optimization, 37(2), 217-237.","type":"article","doi":"10.1007/s00158-007-0217-0","isbn":null,"url":null},{"ref":"Deaton, J. D., & Grandhi, R. V. (2014). A survey of structural and multidisciplinary continuum topology optimization: post 2000. Structural and Multidisciplinary Optimization, 49(1), 1-38.","type":"article","doi":"10.1007/s00158-013-0956-z","isbn":null,"url":null},{"ref":"Bendsoe, M. P., & Sigmund, O. (2003). Topology Optimization: Theory, Methods and Applications. Springer.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Topology+Optimization%3A+Theory%2C+Methods+and+Applications+Bendsoe"}],"related":["response-surface-desirability-function","finite-element-model-updating","first-order-reliability-method","rainflow-counting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"topsis-sort","name":"TOPSIS-SORT","fullName":"TOPSIS-Sort — TOPSIS-based sorting of alternatives into ordered categories","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"2016","originator":"Sabokbar, H. F., Hosseini, A., Banaitis, A., Banaitiene, N.","url":"https://scholargate.app/en/decision-making/topsis-sort","markdownUrl":"https://scholargate.app/en/decision-making/topsis-sort.md","definition":"TOPSIS-SORT (TOPSIS-Sort — TOPSIS-based sorting of alternatives into ordered categories) is a sorting multi-criteria decision-making (MCDM) method introduced by Sabokbar, H. F., Hosseini, A., Banaitis, A., Banaitiene, N. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Sabokbar, H. F., Hosseini, A., Banaitis, A., Banaitiene, N.","subfamily":"Sorting","year":"2016","type":"TOPSIS-based sorting with boundary profiles","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Sabokbar, H. F., Hosseini, A., Banaitis, A., Banaitiene, N. (2016). A novel sorting method TOPSIS-SORT: An application for Tehran environmental quality evaluation. E+M Ekonomie a Management","type":"article","doi":"10.15240/tul/001/2016-2-006","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"topsis","name":"TOPSIS","fullName":"Technique for Order of Preference by Similarity to Ideal Solution","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1981","originator":"Hwang, C. L., Yoon, K.","url":"https://scholargate.app/en/decision-making/topsis","markdownUrl":"https://scholargate.app/en/decision-making/topsis.md","definition":"TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hwang, C. L., Yoon, K.","subfamily":"Ranking","year":"1981","type":"Distance-based (compromise)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":true},"citations":[{"ref":"Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications — A State-of-the-Art Survey. Lecture Notes in Economics and Mathematical Systems, Vol. 186, Springer-Verlag","type":"article","doi":"10.1007/978-3-642-48318-9","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"toronto-empathy-questionnaire","name":"Toronto Empathy Questionnaire","fullName":"Toronto Empathy Questionnaire (TEQ)","aliases":["TEQ","Toronto Empathy Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Self-report questionnaire","year":"2009","originator":"Randy Spreng, Mary McKinnon, Raymond Mar, and Brian Levine","url":"https://scholargate.app/en/social-psychology/toronto-empathy-questionnaire","markdownUrl":"https://scholargate.app/en/social-psychology/toronto-empathy-questionnaire.md","definition":"The Toronto Empathy Questionnaire (TEQ) is a 16-item self-report measure of empathic ability and emotional responsiveness to others' emotions. Developed by Randy Spreng and colleagues in 2009, the TEQ captures affective empathy—the capacity to feel and share another person's emotions—rather than cognitive perspective-taking. The scale has become widely used in social, clinical, and neuroscience research examining individual differences in emotional empathy and its correlates with mental health, prosocial behavior, and brain structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Randy Spreng, Mary McKinnon, Raymond Mar, and Brian Levine","subfamily":"Self-report questionnaire","year":"2009","type":"Empathic ability and emotional responsiveness measure"},"citations":[{"ref":"Spreng, R. N., McKinnon, M. C., Mar, R. A., & Levine, B. (2009). The Toronto Empathy Questionnaire: Scale development and initial validation of a factor-analytic solution to multiple empathy measures. Journal of Personality Assessment, 91(1), 62–71.","type":"article","doi":"10.1080/00223890802484381","isbn":null,"url":null},{"ref":"Eerland, A., Engelen, J. A., & Zwaan, R. A. (2016). The influence of body posture on affective memory. Cognition & Emotion, 30(2), 228–237.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+influence+of+body+posture+on+affective+memory+Eerland"},{"ref":"Davis, M. H. (1980). A multidimensional approach to individual differences in empathy. JSAS Catalog of Selected Documents in Psychology, 10, 85.","type":"article","doi":null,"isbn":null,"url":"https://psycnet.apa.org/record/1981-23264-001"}],"related":["self-compassion-scale","cultural-intelligence-scale","neo-pi-r"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"toronto-mindfulness-scale","name":"Toronto Mindfulness Scale","fullName":"Toronto Mindfulness Scale (TMS)","aliases":["TMS","TMS-13"],"domain":"mindfulness-psychology","family":"process-pipeline","subfamily":"state-mindfulness","year":"2006","originator":"Zindel V. Segal, Mark A. Lau, and colleagues at the University of Toronto","url":"https://scholargate.app/en/mindfulness-psychology/toronto-mindfulness-scale","markdownUrl":"https://scholargate.app/en/mindfulness-psychology/toronto-mindfulness-scale.md","definition":"The Toronto Mindfulness Scale (TMS) is a 13-item self-report instrument uniquely designed to measure state mindfulness—the immediate, transient quality of mindful awareness during or immediately following a meditation session. Developed by Zindel V. Segal, Mark A. Lau, and colleagues at the University of Toronto and published in the Journal of Clinical Psychology in 2006, the TMS captures two core dimensions of state mindfulness: Curiosity and Decentering. Unlike trait measures (FFMQ, FMI) which assess habitual mindfulness, the TMS provides moment-to-moment assessment and has become essential in mindfulness-based cognitive therapy (MBCT) and contemplative neuroscience research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zindel V. Segal, Mark A. Lau, and colleagues at the University of Toronto","subfamily":"state-mindfulness","year":"2006","type":"Self-report"},"citations":[{"ref":"Lau, M. A., Bishop, S. R., Segal, Z. V., Buis, T., Anderson, N. D., Carlson, L., ... & Devins, G. (2006). The Toronto Mindfulness Scale: Development and validation of a state measure of mindfulness. Journal of Clinical Psychology, 62(12), 1445-1467.","type":"article","doi":"10.1002/jclp.20326","isbn":null,"url":null}],"related":["five-facet-mindfulness-questionnaire","freiburg-mindfulness-inventory","mindful-attention-awareness-scale","philadelphia-mindfulness-scale","cognitive-and-affective-mindfulness"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"total-productive-maintenance","name":"Total Productive Maintenance","fullName":"Total Productive Maintenance","aliases":["TPM"],"domain":"operations-management","family":"ml-model","subfamily":"Maintenance Management","year":"1988","originator":"Seiichi Nakajima","url":"https://scholargate.app/en/operations-management/total-productive-maintenance","markdownUrl":"https://scholargate.app/en/operations-management/total-productive-maintenance.md","definition":"Total Productive Maintenance (TPM) is a comprehensive maintenance management approach developed by Seiichi Nakajima in the late 1980s that emphasizes employee involvement, preventive maintenance, and continuous improvement to maximize equipment effectiveness. Unlike traditional reactive maintenance, TPM integrates maintenance activities across all organizational levels—from operators to executives—and focuses on eliminating losses (downtime, defects, speed losses) to achieve sustained production efficiency, quality, and safety.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seiichi Nakajima","subfamily":"Maintenance Management","year":"1988","type":"Maintenance and productivity system"},"citations":[{"ref":"Nakajima, S. (1988). Introduction to TPM: Total Productive Maintenance. Cambridge, MA: Productivity Press.","type":"book","doi":null,"isbn":null,"url":"https://www.productivitypress.com/"},{"ref":"Wireman, T. (1990). World-class maintenance management. Industrial Press.","type":"book","doi":null,"isbn":null,"url":"https://www.industrialpress.com/"}],"related":["kanban","smed","assembly-line-balancing","reliability-block-diagram","scor-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tourist-loyalty-scale","name":"Tourist Loyalty Scale","fullName":"Tourist Loyalty Scale (TLS)","aliases":["TLS","Destination Loyalty Scale"],"domain":"tourism-management","family":"process-pipeline","subfamily":"behavioral-intention-measurement","year":"2000","originator":"Oppermann, M.","url":"https://scholargate.app/en/tourism-management/tourist-loyalty-scale","markdownUrl":"https://scholargate.app/en/tourism-management/tourist-loyalty-scale.md","definition":"The Tourist Loyalty Scale (TLS) measures the extent to which visitors intend to return to a destination and recommend it to others, reflecting behavioral commitment and preference relative to competing destinations. Developed by Oppermann (2000) and refined across multiple tourism contexts, the TLS captures the ultimate outcome of satisfaction and destination image—willingness to invest time and money in repeat visitation and endorsement. As the true measure of competitive advantage in tourism, loyalty drives revenue stability, positive reputation, and ecosystem resilience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oppermann, M.","subfamily":"behavioral-intention-measurement","year":"2000","type":"Self-report questionnaire"},"citations":[{"ref":"Oppermann, M. (2000). Tourism destination loyalty. Journal of Travel Research, 39(1), 78-84.","type":"article","doi":"10.1177/004728750003900110","isbn":null,"url":null},{"ref":"Getty, J. M., & Getty, R. L. (2003). Lodging quality index (LQI): Assessing Expectations and Perceptions of Lodging Quality. Cornell Hotel and Restaurant Administration Quarterly, 44(2), 33-46.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lodging+quality+index+%28LQI%29%3A+Assessing+Expectations+and+Perceptions+of+Lodging+Quality+Getty"},{"ref":"Reichheld, F. F., & Sasser, W. E. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68(5), 105-111.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Zero+defections%3A+Quality+comes+to+services+Reichheld"},{"ref":"Oliver, R. L. (1997). Satisfaction: A Behavioral Perspective on the Consumer. McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Satisfaction%3A+A+Behavioral+Perspective+on+the+Consumer+Oliver"}],"related":["tourist-satisfaction-scale","destination-image-scale","travel-motivation-scale","place-attachment-scale","perceived-value-scale-tourism"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tourist-satisfaction-scale","name":"Tourist Satisfaction Scale","fullName":"Tourist Satisfaction Scale (TSS)","aliases":["TSS"],"domain":"tourism-management","family":"process-pipeline","subfamily":"satisfaction-measurement","year":"1990s","originator":"Multiple authors (composite instrument)","url":"https://scholargate.app/en/tourism-management/tourist-satisfaction-scale","markdownUrl":"https://scholargate.app/en/tourism-management/tourist-satisfaction-scale.md","definition":"The Tourist Satisfaction Scale (TSS) measures overall and domain-specific satisfaction of visitors to a destination or tourism facility. Developed across multiple research streams in the 1990s-2000s, it quantifies how well tourism experiences meet visitor expectations across accommodation, attractions, service quality, and value. Essential for destination marketing organizations and hospitality managers seeking systematic feedback on visitor experiences and competitive benchmarking.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple authors (composite instrument)","subfamily":"satisfaction-measurement","year":"1990s","type":"Self-report questionnaire"},"citations":[{"ref":"Akama, J. S., & Kieti, D. M. (1996). Tourism and socio-economic change in a Kenyan coastal community. Journal of Tourism Studies, 7(2), 45-61.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Tourism+and+socio-economic+change+in+a+Kenyan+coastal+community+Akama"},{"ref":"Spreng, R. A., MacKenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing, 60(3), 15-32.","type":"article","doi":"10.1177/002224299606000302","isbn":null,"url":null},{"ref":"Vavra, T. G. (1997). Improving your Measurement of Customer Satisfaction. ASQ Quality Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Improving+your+Measurement+of+Customer+Satisfaction+Vavra"}],"related":["destination-image-scale","tourist-loyalty-scale","travel-motivation-scale","perceived-value-scale-tourism","hotel-service-quality-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"toxic-leadership-scale","name":"Toxic Leadership Scale","fullName":"Toxic Leadership Scale (TLS)","aliases":["Destructive Leadership Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Leadership style","year":"2007","originator":"Einarsen, Aasland, and Skogstad","url":"https://scholargate.app/en/organizational-behavior/toxic-leadership-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/toxic-leadership-scale.md","definition":"The Toxic Leadership Scale (TLS) is a 16-item instrument measuring destructive leadership behaviors including abusive supervision, narcissism, and authoritarian control. Developed by Einarsen, Aasland, and Skogstad in 2007, the TLS identifies harmful leadership behaviors that undermine organizational effectiveness and employee well-being.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Einarsen, Aasland, and Skogstad","subfamily":"Leadership style","year":"2007","type":"Observational scale"},"citations":[{"ref":"Einarsen, S., Aasland, M. S., & Skogstad, A. (2007). Destructive leadership behaviour: A definition and conceptual model. Leadership Quarterly, 18(3), 207-216.","type":"article","doi":"10.1016/j.leaqua.2007.03.002","isbn":null,"url":null},{"ref":"Shaw, J. B., Erickson, A., & Harvey, M. (2011). A method for measuring destructive leadership and identifying types of destructive leaders in organizations. Leadership Quarterly, 22(4), 575-590.","type":"article","doi":"10.1016/j.leaqua.2011.05.001","isbn":null,"url":null}],"related":["ethical-leadership-scale","authentic-leadership-scale","organizational-trust-scale","employee-engagement-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tract-based-spatial-statistics","name":"Tract-Based Spatial Statistics","fullName":"Tract-Based Spatial Statistics (TBSS)","aliases":["TBSS","white matter skeleton analysis"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Voxel-wise spatial analysis","year":"2006","originator":"Stephen M. Smith","url":"https://scholargate.app/en/neuroimaging/tract-based-spatial-statistics","markdownUrl":"https://scholargate.app/en/neuroimaging/tract-based-spatial-statistics.md","definition":"Tract-Based Spatial Statistics (TBSS) is a voxel-wise analysis method for detecting group differences in white matter microstructure from diffusion MRI data. Published by Stephen M. Smith and colleagues in 2006, TBSS addresses registration and multiple comparison problems inherent in voxel-wise analysis by projecting individual FA maps onto a white matter skeleton derived from a population template.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen M. Smith","subfamily":"Voxel-wise spatial analysis","year":"2006","type":"Diffusion MRI white matter analysis pipeline"},"citations":[{"ref":"Smith, S. M., Jenkinson, M., Johansen-Berg, H., et al. (2006). Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage, 31(4), 1487–1505.","type":"article","doi":"10.1016/j.neuroimage.2006.02.024","isbn":null,"url":null},{"ref":"Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381–397.","type":"article","doi":"10.1016/j.neuroimage.2014.01.060","isbn":null,"url":null}],"related":["voxel-based-morphometry","diffusion-kurtosis-imaging","noddi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"traffic-flow","name":"Traffic Flow (LWR Model)","fullName":"Lighthill-Whitham-Richards Model for Traffic Flow","aliases":["LWR model","Traffic wave","Kinematic wave theory"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Flow dynamics","year":"1955","originator":"M. J. Lighthill and G. B. Whitham","url":"https://scholargate.app/en/civil-engineering/traffic-flow","markdownUrl":"https://scholargate.app/en/civil-engineering/traffic-flow.md","definition":"The Lighthill-Whitham-Richards (LWR) model is a macroscopic traffic flow model that treats traffic as a compressible fluid, applying conservation of vehicles and a flow-density relationship. Introduced independently by Lighthill and Whitham (1955) and Richards (1956), the model predicts traffic wave propagation, congestion formation, and bottleneck behavior on highways.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M. J. Lighthill and G. B. Whitham","subfamily":"Flow dynamics","year":"1955","type":"Macroscopic traffic flow modeling using conservation laws"},"citations":[{"ref":"Lighthill, M. J., & Whitham, G. B. (1955). On kinematic waves I. Flow movement in long rivers. Proceedings of the Royal Society A, 229(1178), 281-316.","type":"article","doi":"10.1098/rspa.1955.0088","isbn":null,"url":null},{"ref":"Richards, P. I. (1956). Shock waves on the highway. Operations Research, 4(1), 42-51.","type":"article","doi":"10.1287/opre.4.1.42","isbn":null,"url":null},{"ref":"Daganzo, C. F. (1994). The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B, 28(4), 269-287.","type":"article","doi":"10.1016/0191-2615(94)90002-7","isbn":null,"url":null}],"related":["muskingum-routing","modflow","hardy-cross-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"trail-making-test","name":"Trail Making Test","fullName":"Trail Making Test","aliases":["TMT","Trails A","Trails B","Trail Making A","Trail Making B"],"domain":"neuropsychology","family":"process-pipeline","subfamily":"executive function and processing speed","year":"1958","originator":"Ralph Reitan","url":"https://scholargate.app/en/neuropsychology/trail-making-test","markdownUrl":"https://scholargate.app/en/neuropsychology/trail-making-test.md","definition":"The Trail Making Test (TMT) is a simple, brief neuropsychological test developed by Reitan in 1958 that measures visuomotor processing speed, attention, and executive function. The TMT comprises two forms: Part A, which assesses basic processing speed and visual scanning, and Part B, which assesses executive function, task-switching, and cognitive flexibility. Despite its simplicity, the TMT is highly sensitive to cognitive impairment across a wide range of neurological and psychiatric conditions and remains one of the most widely used screening tests in neuropsychology.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralph Reitan","subfamily":"executive function and processing speed","year":"1958","type":"Clinician-administered neuropsychological test of attention and executive function"},"citations":[{"ref":"Reitan, R. M. (1958). Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills, 8(3), 271-276.","type":"article","doi":"10.2466/pms.1958.8.3.271","isbn":null,"url":null},{"ref":"Sanchez-Cubillo, I., Perianez, J. A., Adrover-Roig, D., et al. (2009). Construct validity of the Trail Making Test: Role of task-switching, working memory, inhibition/interference control, and visuomotor abilities. Journal of the International Neuropsychological Society, 15(3), 438-450.","type":"article","doi":"10.1017/S1355617709090626","isbn":null,"url":null},{"ref":"Corrigan, J. D., & Hinkeldey, N. S. (1987). Relationships between parts A and B of the Trail Making Test. Journal of Clinical Psychology, 43(4), 402-409.","type":"article","doi":"10.1002/1097-4679(198707)43:4<402::aid-jclp2270430411>3.0.co;2-e","isbn":null,"url":null}],"related":["frontal-assessment-battery","mmse","adas-cog","cognitive-failures-questionnaire","addenbrookes-cognitive-examination"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transaction-management","name":"Transaction Management","fullName":"Database Transaction Management and ACID Properties","aliases":["ACID transactions","transaction control"],"domain":"information-systems","family":"process-pipeline","subfamily":"Data Consistency & Recovery","year":"1981","originator":"Jim Gray and others (IBM)","url":"https://scholargate.app/en/information-systems/transaction-management","markdownUrl":"https://scholargate.app/en/information-systems/transaction-management.md","definition":"Transaction management is the mechanism by which database systems ensure reliable execution of multiple interdependent operations as atomic units. Formalized by Jim Gray and colleagues in the 1980s, transactions guarantee ACID properties (Atomicity, Consistency, Isolation, Durability) that protect data integrity even in the face of failures and concurrent access.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jim Gray and others (IBM)","subfamily":"Data Consistency & Recovery","year":"1981","type":"Database reliability mechanism"},"citations":[{"ref":"Gray, J. (1981). The transaction concept: Virtues and limitations. VLDB Endowment, 7(6), 519-539.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+transaction+concept%3A+Virtues+and+limitations+Gray"},{"ref":"Papadimitriou, C. H. (1986). The Theory of Database Concurrency Control. Computer Science Press.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com"},{"ref":"Garcia-Molina, H., Ullman, J. D., & Widom, J. (2009). Database Systems: The Complete Book (2nd ed.). Pearson Education.","type":"article","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com"}],"related":["concurrency-control","database-locking","isolation-levels","deadlock-detection","log-based-recovery"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transcendence-scale","name":"STS","fullName":"Spiritual Transcendence Scale","aliases":["STS","Spiritual Transcendence"],"domain":"psychology-of-religion","family":"process-pipeline","subfamily":"spiritual transcendence and connection","year":1999,"originator":"Ralph L. Piedmont","url":"https://scholargate.app/en/psychology-of-religion/transcendence-scale","markdownUrl":"https://scholargate.app/en/psychology-of-religion/transcendence-scale.md","definition":"The Spiritual Transcendence Scale (STS), developed by Piedmont in 1999, is a 24-item self-report measure of spiritual transcendence: the human capacity to experience connection to something beyond oneself—whether understood as God, nature, humanity, or the sacred. The STS conceptualizes spiritual transcendence as a personality trait distinct from religious adherence or institutional participation, measured through three facets: Prayer Fulfillment (satisfaction from spiritual practices), Universality (sense of interconnection with all people and life), and Connectedness (sense of deep connection to the divine or sacred). The scale has become influential in understanding spirituality as a psychological dimension orthogonal to the Big Five personality traits.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralph L. Piedmont","subfamily":"spiritual transcendence and connection","year":1999,"type":"Self-report"},"citations":[{"ref":"Piedmont, R. L. (1999). Does spirituality have a place in personality science? Journal of Personality and Social Psychology, 76(1), 3–13.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Does+spirituality+have+a+place+in+personality+science+Piedmont"},{"ref":"Piedmont, R. L. (2001). Spiritual transcendence as a predictor of psychosocial outcome from an outpatient substance abuse treatment program. Psychology of Addictive Behaviors, 15(4), 338–345.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spiritual+transcendence+as+a+predictor+of+psychosocial+outcome+from+an+outpatient+substance+abuse+treatment+program+Piedmont"}],"related":["daily-spiritual-experience-scale","quest-scale-religion","existential-wellbeing-scale","functional-assessment-chronic-illness-spiritual"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transcendental-phenomenology","name":"Transcendental Phenomenology","fullName":"Transcendental (Husserlian) Phenomenology","aliases":["Husserlian phenomenology","eidetic phenomenology","transcendental-phenomenological research","pure phenomenology"],"domain":"qualitative","family":"process-pipeline","subfamily":"Phenomenology","year":"1900–1913 (Ideas I, 1913)","originator":"Edmund Husserl","url":"https://scholargate.app/en/qualitative/transcendental-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/transcendental-phenomenology.md","definition":"Transcendental phenomenology, founded by Edmund Husserl, is a qualitative method that seeks the universal essential structures — the invariant essences — of a consciously lived experience. By bracketing all assumptions and prior theories (epoché) and applying eidetic reduction, the researcher uncovers what an experience is in its purest, most fundamental form, independent of any particular context, culture, or individual biography. Clark Moustakas's 1994 adaptation made the method directly accessible to social-science researchers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Edmund Husserl","year":"1900–1913 (Ideas I, 1913)","type":"Qualitative research method","dataType":"In-depth interviews, written first-person descriptions, focus groups (text data)","typicalSampleSize":"5–15 participants","subfamily":"Phenomenology"},"citations":[{"ref":"Moustakas, C. (1994). Phenomenological Research Methods. Sage.","type":"book","doi":null,"isbn":"978-0803957466","url":null},{"ref":"Husserl, E. (1913/1983). Ideas: General Introduction to Pure Phenomenology (F. Kersten, Trans.). Martinus Nijhoff.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Husserl+Ideas+General+Introduction+to+Pure+Phenomenology"}],"related":["phenomenology","grounded-theory","narrative-analysis","case-study","thematic-analysis","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transcultural-self-efficacy-tool","name":"Transcultural Self-Efficacy Tool","fullName":"Transcultural Self-Efficacy Tool (TSET)","aliases":["TSET"],"domain":"transcultural-nursing","family":"process-pipeline","subfamily":"healthcare-provider-efficacy","year":1996,"originator":"Jeffreys, Smodlaka","url":"https://scholargate.app/en/transcultural-nursing/transcultural-self-efficacy-tool","markdownUrl":"https://scholargate.app/en/transcultural-nursing/transcultural-self-efficacy-tool.md","definition":"The Transcultural Self-Efficacy Tool (TSET) is an 83-item self-report measure designed to assess nurses' confidence and capability in delivering culturally competent care. Developed by Jeffreys and Smodlaka in 1996, the TSET evaluates three dimensions of transcultural nursing self-efficacy: cognitive knowledge, practical skills, and affective (emotional) competence. The instrument is widely used in nursing education to evaluate the impact of transcultural curricula and to identify areas requiring additional clinical preparation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jeffreys, Smodlaka","subfamily":"healthcare-provider-efficacy","year":1996,"type":"Self-report"},"citations":[{"ref":"Jeffreys, M. R., & Smodlaka, I. (1996). Construct validation of the Transcultural Self-Efficacy Tool. Journal of Nursing Education, 35(8), 341–348.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Construct+validation+of+the+Transcultural+Self-Efficacy+Tool+Jeffreys"}],"related":["cultural-competence-assessment","multicultural-counseling-inventory","patient-provider-cultural-sensitivity","acculturative-stress-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-entropy","name":"Transfer Entropy","fullName":"Transfer Entropy","aliases":["Schreiber Information Transfer","Directed Information Flow","Conditional Mutual Information (directed)","Transfer Entropisi"],"domain":"causal-inference","family":"ml-model","subfamily":"Information-theoretic causality","year":2000,"originator":"Thomas Schreiber","url":"https://scholargate.app/en/causal-inference/transfer-entropy","markdownUrl":"https://scholargate.app/en/causal-inference/transfer-entropy.md","definition":"Transfer Entropy (TE) is a non-parametric, information-theoretic measure of directed statistical dependence between two time series, introduced by Thomas Schreiber in 2000. Grounded in Shannon entropy, it quantifies how much information the past of one process Y reduces uncertainty about the next state of another process X, beyond what X's own past already provides. Unlike linear correlation or Granger causality, TE captures nonlinear interactions and requires no model assumptions about the underlying dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Thomas Schreiber","year":2000,"type":"Non-parametric information-theoretic measure","subfamily":"Information-theoretic causality","framework":"Shannon entropy / Kullback-Leibler divergence","data_requirement":"Stationary or near-stationary time series"},"citations":[{"ref":"Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461–464.","type":"article","doi":"10.1103/PhysRevLett.85.461","isbn":null,"url":null}],"related":["granger-causality","convergent-cross-mapping","sample-entropy"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-diffusion-model","name":"Transfer Learning with Diffusion Model","fullName":"Transfer Learning Applied to Diffusion-Based Generative Models","aliases":["diffusion model fine-tuning","pre-trained diffusion transfer","TL-DM","domain-adapted diffusion model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2020–2023","originator":"Ho et al. (DDPM); transfer application popularized by Rombach et al. (Stable Diffusion) and Ruiz et al. (DreamBooth), 2020–2023","url":"https://scholargate.app/en/deep-learning/transfer-learning-diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-diffusion-model.md","definition":"Transfer Learning with Diffusion Models adapts a large pre-trained diffusion model — such as Stable Diffusion or DALL-E 2 — to a new target domain or task by continuing training on a smaller domain-specific dataset. Rather than learning the full generative process from scratch, practitioners leverage knowledge already encoded in millions of training steps to achieve high-quality domain-adapted generation with modest data and compute.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ho et al. (DDPM); transfer application popularized by Rombach et al. (Stable Diffusion) and Ruiz et al. (DreamBooth), 2020–2023","year":"2020–2023","type":"Generative model with transfer learning","dataType":"Images, text-image pairs, domain-specific image corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2006.11239"},{"ref":"Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023). DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. CVPR 2023.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2208.12242"}],"related":["fine-tuned-diffusion-model","transfer-learning-with-vision-transformer","transfer-learning-with-convolutional-neural-network","domain-adaptive-diffusion-model","multimodal-diffusion-model","self-supervised-diffusion-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-gan","name":"Transfer learning GAN","fullName":"Transfer Learning with Generative Adversarial Networks","aliases":["TL-GAN","pretrained GAN","GAN fine-tuning","domain-adaptive GAN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2018","originator":"Goodfellow, I. et al. (GAN); Wang & Ramanan (transfer to GAN)","url":"https://scholargate.app/en/deep-learning/transfer-learning-gan","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-gan.md","definition":"Transfer Learning GAN initialises a Generative Adversarial Network — or both its generator and discriminator — from weights pretrained on a large source dataset, then fine-tunes the network on a smaller target dataset. This approach allows high-quality generative modelling even when target-domain data are scarce, by reusing low- and mid-level feature representations learned at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Goodfellow, I. et al. (GAN); Wang & Ramanan (transfer to GAN)","year":"2014–2018","type":"Generative model with transferred weights","dataType":"Images, text, audio, or any modality supported by the source GAN","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27, 2672–2680.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html"},{"ref":"Wang, Y. & Ramanan, D. (2018). Transferring GANs: generating images from limited data. European Conference on Computer Vision (ECCV), 11205, 220–236.","type":"inproceedings","doi":"10.1007/978-3-030-01231-1_14","isbn":null,"url":null}],"related":["generative-adversarial-network","fine-tuned-generative-adversarial-network","transfer-learning-with-convolutional-neural-network","transfer-learning-diffusion-model","variational-autoencoder","domain-adaptive-gan"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-reinforcement-learning","name":"Transfer Learning with Reinforcement Learning","fullName":"Transfer Learning Applied to Reinforcement Learning","aliases":["Transfer RL","TL for RL","cross-task reinforcement learning","inductive transfer in RL"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2009 (survey); concept from early 2000s","originator":"Taylor, M. E. & Stone, P.","url":"https://scholargate.app/en/deep-learning/transfer-learning-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-reinforcement-learning.md","definition":"Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Taylor, M. E. & Stone, P.","year":"2009 (survey); concept from early 2000s","type":"Transfer learning paradigm for sequential decision-making","dataType":"State-action trajectories, reward signals, environment simulations","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v10/taylor09a.html"},{"ref":"Lazaric, A. (2012). Transfer in Reinforcement Learning: A Framework and a Survey. In M. Wiering & M. van Otterlo (Eds.), Reinforcement Learning: State-of-the-Art (pp. 143–173). Springer.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Transfer+in+Reinforcement+Learning+A+Framework+and+a+Survey+Lazaric+2012"}],"related":["reinforcement-learning","transfer-learning-with-convolutional-neural-network","fine-tuned-reinforcement-learning","multi-task-learning","domain-adaptive-reinforcement-learning","meta-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-variational-autoencoder","name":"Transfer learning variational autoencoder","fullName":"Transfer Learning with Variational Autoencoder","aliases":["TL-VAE","pretrained VAE","VAE transfer learning","fine-tuned variational autoencoder"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014 (VAE); 2010 (transfer learning survey)","originator":"Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang","url":"https://scholargate.app/en/deep-learning/transfer-learning-variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-variational-autoencoder.md","definition":"Transfer Learning with a Variational Autoencoder (TL-VAE) reuses an encoder and/or decoder pre-trained on a large source dataset and adapts it to a smaller target domain. By inheriting a rich probabilistic latent space rather than starting from random weights, TL-VAE dramatically reduces the amount of target-domain data needed for high-quality generation or representation learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kingma, D. P. & Welling, M. (VAE); transfer learning framework from Pan & Yang","year":"2014 (VAE); 2010 (transfer learning survey)","type":"Generative model with transferred encoder/decoder","dataType":"Images, tabular, text, time-series (source and target domain data)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"},{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["fine-tuned-variational-autoencoder","variational-autoencoder","transfer-learning-with-convolutional-neural-network","semi-supervised-variational-autoencoder","generative-adversarial-network","fine-tuned-generative-adversarial-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-bert-based-classification","name":"Transfer Learning with BERT-based Classification","fullName":"Transfer Learning with BERT-based Text Classification","aliases":["BERT fine-tuning for classification","BERT transfer learning classifier","pre-trained BERT classifier","BERT downstream classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019 (BERT); transfer learning paradigm established circa 2010","originator":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (BERT); Pan, S. J. & Yang, Q. (transfer learning survey)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-bert-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-bert-based-classification.md","definition":"Transfer Learning with BERT-based Classification adapts a large transformer language model, pre-trained on massive text corpora, to a target classification task by fine-tuning its weights on labeled examples. The pre-trained representations encode rich syntactic and semantic knowledge, enabling high accuracy even when the labeled dataset is small.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (BERT); Pan, S. J. & Yang, Q. (transfer learning survey)","year":"2019 (BERT); transfer learning paradigm established circa 2010","type":"Pre-trained transformer fine-tuned for classification","dataType":"Text (sentences, paragraphs, documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019, 4171–4186. Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["bert-based-classification","roberta-based-classification","fine-tuned-bert-based-classification","transfer-learning-with-roberta-based-classification","transformer","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-convolutional-neural-network","name":"Transfer Learning with Convolutional Neural Network","fullName":"Transfer Learning with Convolutional Neural Network (Feature Extraction and Fine-Tuning)","aliases":["TL-CNN","pretrained CNN","CNN fine-tuning","feature-extracting CNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010–2014","originator":"Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-convolutional-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-convolutional-neural-network.md","definition":"Transfer Learning with CNN reuses a convolutional neural network that has already been trained on a large dataset — most commonly ImageNet — and adapts its learned feature detectors to a new, often smaller target dataset. This lets researchers achieve strong image-recognition performance without the massive compute and data resources required to train a CNN from scratch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan, S. J. & Yang, Q. (transfer learning framework); popularized for CNNs by Yosinski et al. and Razavian et al.","year":"2010–2014","type":"Transfer learning applied to convolutional neural networks","dataType":"Images, video frames, spectrograms, or any grid-structured input","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null},{"ref":"Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2014/hash/375c71349b295fbe2dcdca9206851000-Abstract.html"}],"related":["convolutional-neural-network","fine-tuned-convolutional-neural-network","transfer-learning-with-vision-transformer","image-classification","object-detection","semantic-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-graph-neural-network","name":"Transfer Learning with Graph Neural Network","fullName":"Transfer Learning with Graph Neural Network (Pre-trained GNN Fine-tuning)","aliases":["TL-GNN","pre-trained GNN","GNN transfer learning","graph transfer learning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010–2020","originator":"Hu et al. (GNN-specific); Pan & Yang (transfer learning framework)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-graph-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-graph-neural-network.md","definition":"Transfer Learning with Graph Neural Networks (GNNs) adapts a GNN pre-trained on a large source graph dataset to a smaller, often label-scarce target graph task. By reusing learned node and edge representations, this approach achieves strong predictive performance where collecting sufficient labeled graph data is expensive or slow — as is common in chemistry, biology, and social network analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hu et al. (GNN-specific); Pan & Yang (transfer learning framework)","year":"2010–2020","type":"Transfer learning / graph representation learning","dataType":"Graph-structured data (nodes, edges, attributes)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., & Leskovec, J. (2020). Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations (ICLR 2020).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1905.12265"},{"ref":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["graph-neural-network","transfer-learning-with-convolutional-neural-network","fine-tuned-graph-neural-network","transfer-learning-with-transformer","self-supervised-graph-neural-network","transfer-learning-with-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-image-classification","name":"Transfer Learning with Image Classification","fullName":"Transfer Learning with Pretrained Deep Neural Networks for Image Classification","aliases":["pretrained CNN image classification","fine-tuned image classifier","domain-adapted image classifier","TL-IC"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010–2012","originator":"Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-image-classification.md","definition":"Transfer Learning with Image Classification reuses a deep neural network backbone — typically a CNN or Vision Transformer — pretrained on a large dataset such as ImageNet, and adapts it to classify images in a new target domain. By inheriting general visual features from the source task, the approach achieves high accuracy with far fewer labeled images than training from scratch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan, S. J. & Yang, Q. (transfer learning framework); Krizhevsky, Sutskever & Hinton (deep CNN backbone)","year":"2010–2012","type":"Transfer learning / supervised classification","dataType":"Images (RGB or grayscale)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null},{"ref":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html"}],"related":["convolutional-neural-network","fine-tuned-convolutional-neural-network","image-classification","transfer-learning-with-object-detection","transfer-learning-with-semantic-segmentation","fine-tuned-vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-instance-segmentation","name":"Transfer Learning with Instance Segmentation","fullName":"Transfer Learning Applied to Instance Segmentation Networks","aliases":["pretrained instance segmentation","fine-tuned Mask R-CNN","transfer learning for panoptic segmentation","domain-adapted instance segmentation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017 (Mask R-CNN); transfer learning paradigm: 2010","originator":"He, K. et al. (Mask R-CNN); transfer learning framework: Pan & Yang","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-instance-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-instance-segmentation.md","definition":"Transfer learning with instance segmentation reuses a backbone convolutional network pretrained on a large image corpus (typically ImageNet or COCO) as the feature extractor for an instance segmentation model such as Mask R-CNN, then fine-tunes the full pipeline on a smaller target dataset. This approach delivers state-of-the-art per-object mask accuracy with a fraction of the labeled data and compute that training from scratch would require.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"He, K. et al. (Mask R-CNN); transfer learning framework: Pan & Yang","year":"2017 (Mask R-CNN); transfer learning paradigm: 2010","type":"Transfer learning applied to instance segmentation","dataType":"RGB images (labeled with per-instance masks)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.","type":"inproceedings","doi":"10.1109/ICCV.2017.322","isbn":null,"url":null},{"ref":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["instance-segmentation","semantic-segmentation","transfer-learning-with-object-detection","fine-tuned-instance-segmentation","convolutional-neural-network","transfer-learning-with-image-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-lda-topic-model","name":"Transfer Learning with LDA Topic Model","fullName":"Transfer Learning with Latent Dirichlet Allocation Topic Model","aliases":["LDA transfer learning","domain-adaptive LDA","knowledge transfer LDA","cross-domain LDA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2003–2013","originator":"Chen, Z. et al. / Blei, D. M. et al.","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-lda-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-lda-topic-model.md","definition":"Transfer Learning with LDA Topic Model applies knowledge from a well-studied source domain to guide Latent Dirichlet Allocation inference on a data-scarce target domain. By injecting source-derived topic priors into the Dirichlet hyperparameters, the method produces coherent, domain-relevant topics even when target-domain text is limited, reducing the volume of labelled or unlabelled data required for meaningful results.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, Z. et al. / Blei, D. M. et al.","year":"2003–2013","type":"Transfer learning applied to probabilistic topic model","dataType":"Text corpora (source domain + target domain)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Chen, Z., Mukherjee, A., Liu, B., Hsu, M., Malas, M., & Wang, S. (2013). Leveraging multi-domain prior knowledge in topic models. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI-13), pp. 2071–2077.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Leveraging+multi-domain+prior+knowledge+in+topic+models+Chen+2013"},{"ref":"Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v3/blei03a.html"}],"related":["lda-topic-model","transfer-learning-with-nmf-topic-model","domain-adaptive-lda-topic-model","fine-tuned-lda-topic-model","topic-modeling","multilingual-lda-topic-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-lstm","name":"Transfer Learning with LSTM","fullName":"Transfer Learning with Long Short-Term Memory Networks","aliases":["LSTM Transfer Learning","Pre-trained LSTM","LSTM Fine-Tuning","ULMFiT-style LSTM Transfer"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2018 (ULMFiT; concept since ~2010)","originator":"Howard, J. & Ruder, S. (ULMFiT); general concept: Pan & Yang (2010)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-lstm","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-lstm.md","definition":"Transfer Learning with LSTM is a technique in which a Long Short-Term Memory network is first pre-trained on a large source corpus or task, and then its learned weights are transferred and fine-tuned on a smaller target task. This approach, popularized by ULMFiT (Howard & Ruder, 2018), allows LSTM-based models to reach strong performance even when labeled target data is scarce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Howard, J. & Ruder, S. (ULMFiT); general concept: Pan & Yang (2010)","year":"2018 (ULMFiT; concept since ~2010)","type":"Transfer learning / Sequential model","dataType":"Sequential text, time-series, speech, or other sequence data","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339.","type":"inproceedings","doi":"10.18653/v1/P18-1031","isbn":null,"url":null},{"ref":"Transfer learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Transfer_learning"}],"related":["long-short-term-memory","fine-tuned-lstm","transfer-learning-with-transformer","bert-based-classification","transfer-learning-with-recurrent-neural-network","gated-recurrent-unit"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-named-entity-recognition","name":"Transfer Learning with Named Entity Recognition","fullName":"Transfer Learning with Named Entity Recognition (Pretrained Encoder Fine-Tuned for NER)","aliases":["TL-NER","Fine-Tuned NER","Pretrained Model NER","BERT NER"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010 / 2019","originator":"Pan & Yang (transfer learning); Devlin et al. (BERT-based NER fine-tuning)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-named-entity-recognition","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-named-entity-recognition.md","definition":"Transfer Learning with Named Entity Recognition (NER) adapts a large pretrained language model — such as BERT, RoBERTa, or a domain-specific encoder — to the task of identifying and classifying named entities (persons, locations, organizations, dates, etc.) in text. By reusing rich linguistic representations learned from massive corpora, this approach requires only modest labeled NER data while achieving state-of-the-art span detection and classification accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan & Yang (transfer learning); Devlin et al. (BERT-based NER fine-tuning)","year":"2010 / 2019","type":"Supervised sequence labeling via pretrained encoder fine-tuning","dataType":"Text (token sequences with entity labels)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.","type":"inproceedings","doi":"10.18653/v1/N19-1423","isbn":null,"url":null},{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null}],"related":["bert-based-classification","fine-tuned-named-entity-recognition","roberta-based-classification","sentence-embeddings","transformer","transfer-learning-with-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-nmf-topic-model","name":"Transfer Learning with NMF Topic Model","fullName":"Transfer Learning with Non-Negative Matrix Factorization Topic Model","aliases":["TL-NMF","NMF transfer topic model","cross-domain NMF topic modeling","domain-adaptive NMF"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010 (transfer learning survey); 1999 (NMF)","originator":"Pan, S. J. & Yang, Q. (transfer learning framework); Lee, D. D. & Seung, H. S. (NMF base)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-nmf-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-nmf-topic-model.md","definition":"Transfer Learning with NMF Topic Model applies knowledge from a labeled or data-rich source domain to improve Non-Negative Matrix Factorization topic discovery in a low-resource target domain. By initializing or constraining the NMF basis matrix with source-domain topics, the model discovers coherent target topics even when target-domain documents are scarce or unlabeled.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan, S. J. & Yang, Q. (transfer learning framework); Lee, D. D. & Seung, H. S. (NMF base)","year":"2010 (transfer learning survey); 1999 (NMF)","type":"Unsupervised topic model with cross-domain adaptation","dataType":"Text corpora (document-term matrices); source and target domain documents","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null},{"ref":"Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.","type":"article","doi":"10.1038/44565","isbn":null,"url":null}],"related":["nmf-topic-model","lda-topic-model","transfer-learning-with-lda-topic-model","transfer-learning-with-transformer","domain-adaptive-nmf-topic-model","topic-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-object-detection","name":"Transfer Learning with Object Detection","fullName":"Transfer Learning Applied to Object Detection","aliases":["pretrained object detector","fine-tuned object detection","TL-OD","domain-adapted object detection"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010–2014","originator":"Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-object-detection","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-object-detection.md","definition":"Transfer learning with object detection starts from a deep neural network pretrained on a large image dataset — typically ImageNet for the backbone or COCO for the full detector — and adapts it to detect objects in a new domain. By reusing learned visual representations, it achieves strong detection accuracy with far fewer annotated images than training from scratch would require.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework)","year":"2010–2014","type":"Transfer learning / fine-tuning","dataType":"Images with bounding-box annotations","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null},{"ref":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems (NeurIPS), 28.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2015/hash/14bfa6bb14875e45bba028a21ed38046-Abstract.html"}],"related":["transfer-learning-with-image-classification","fine-tuned-object-detection","object-detection","convolutional-neural-network","transfer-learning-with-semantic-segmentation","fine-tuned-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-recurrent-neural-network","name":"Transfer Learning with Recurrent Neural Network","fullName":"Transfer Learning with Recurrent Neural Network (TL-RNN)","aliases":["TL-RNN","Pretrained RNN","RNN Transfer Learning","Recurrent Transfer Learning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010 (TL survey); RNN: 1986","originator":"Pan, S. J. & Yang, Q. (transfer learning survey); RNN origins: Rumelhart, D. E. et al. (1986)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-recurrent-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-recurrent-neural-network.md","definition":"Transfer Learning with Recurrent Neural Network (TL-RNN) reuses weights learned by an RNN on a large source task — such as language modelling or sequence prediction — and adapts them to a new, often smaller target task. This strategy lets practitioners obtain strong sequence-modelling performance without the need for massive labelled datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan, S. J. & Yang, Q. (transfer learning survey); RNN origins: Rumelhart, D. E. et al. (1986)","year":"2010 (TL survey); RNN: 1986","type":"Transfer learning on sequence model","dataType":"Sequential / time-series / text","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null},{"ref":"Transfer learning. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Transfer_learning"}],"related":["recurrent-neural-network","long-short-term-memory","gated-recurrent-unit","fine-tuned-recurrent-neural-network","transfer-learning-with-lstm","transfer-learning-with-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-sentence-embeddings","name":"Transfer Learning with Sentence Embeddings","fullName":"Transfer Learning with Pre-trained Sentence Embedding Models","aliases":["sentence embedding transfer learning","pre-trained sentence encoder fine-tuning","SBERT transfer learning","sentence representation transfer"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2019","originator":"Reimers, N. & Gurevych, I. (SBERT); Conneau, A. et al. (InferSent)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-sentence-embeddings.md","definition":"Transfer Learning with Sentence Embeddings takes a large pre-trained encoder — such as Sentence-BERT or the Universal Sentence Encoder — that already encodes general language knowledge into fixed-length vectors, and adapts it to a new task or domain with little additional labelled data. The pre-trained representations give a head start that often outperforms task-specific models trained from scratch on modest corpora.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Reimers, N. & Gurevych, I. (SBERT); Conneau, A. et al. (InferSent)","year":"2017–2019","type":"Transfer learning / sentence representation","dataType":"Text (sentences, paragraphs, documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–3992.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D19-1410"},{"ref":"Conneau, A., Kiela, D., Schwentz, H., Barrault, L. & Bordes, A. (2017). Supervised Learning of Universal Sentence Representations from Natural Language Inference Data. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), 670–680.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D17-1070"}],"related":["sentence-embeddings","bert-based-classification","fine-tuned-sentence-embeddings","transformer","roberta-based-classification","transfer-learning-with-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-text-summarization","name":"Transfer Learning with Text Summarization","fullName":"Transfer Learning with Neural Text Summarization","aliases":["pretrained summarization model","fine-tuned summarization","TL-summarization","neural abstractive summarization via transfer learning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Raffel et al. (T5); Lewis et al. (BART)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-text-summarization","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-text-summarization.md","definition":"Transfer Learning with Text Summarization adapts a large language model pre-trained on broad text corpora — such as T5, BART, or PEGASUS — to the task of condensing documents into shorter, coherent summaries. By reusing learned linguistic knowledge and fine-tuning on domain-specific pairs of source documents and reference summaries, this approach achieves strong summarization quality with modest labeled data requirements.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Raffel et al. (T5); Lewis et al. (BART)","year":"2019–2020","type":"Transfer learning applied to sequence-to-sequence summarization","dataType":"Text corpora with reference summaries (e.g., news articles, scientific abstracts)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.","type":"article","doi":null,"isbn":null,"url":"https://jmlr.org/papers/v21/20-074.html"},{"ref":"Lewis, M., Liu, Y., Goyal, N., Ghahravi, M., Mohamed, A., Chen, D., Levy, O., & Zettlemoyer, L. (2020). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 7871–7880). ACL.","type":"inproceedings","doi":"10.18653/v1/2020.acl-main.703","isbn":null,"url":null}],"related":["fine-tuned-text-summarization","transformer","bert-based-classification","transfer-learning-with-question-answering","transfer-learning-with-named-entity-recognition","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-topic-modeling","name":"Transfer Learning with Topic Modeling","fullName":"Transfer Learning with Topic Modeling (Cross-Domain Topic Adaptation)","aliases":["domain-transfer topic modeling","pretrained topic transfer","cross-domain topic adaptation","TL-LDA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010s","originator":"Pan, S. J. & Yang, Q. (transfer learning survey); combined with Blei et al. (LDA, 2003)","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-topic-modeling","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-topic-modeling.md","definition":"Transfer Learning with Topic Modeling adapts topic structures discovered on a large or well-labeled source corpus to a related but distinct target domain where labeled data or large corpora are scarce. By reusing source-domain topic priors or pretrained embeddings as initialization, the approach produces richer, more coherent topics in the target domain than training from scratch.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan, S. J. & Yang, Q. (transfer learning survey); combined with Blei et al. (LDA, 2003)","year":"2010s","type":"Cross-domain adaptation of topic models","dataType":"Text corpora (source domain + target domain documents)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null},{"ref":"Topic model. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Topic_model"}],"related":["lda-topic-model","nmf-topic-model","fine-tuned-topic-modeling","domain-adaptive-topic-modeling","sentence-embeddings","bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning-with-word2vec","name":"Transfer Learning with Word2Vec","fullName":"Transfer Learning with Word2Vec Pre-trained Embeddings","aliases":["Word2Vec transfer learning","pre-trained Word2Vec embeddings","Word2Vec embedding initialization","Word2Vec fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013-2014","originator":"Mikolov, T. et al. (Word2Vec); transfer-learning application popularised by Kim, Y.","url":"https://scholargate.app/en/deep-learning/transfer-learning-with-word2vec","markdownUrl":"https://scholargate.app/en/deep-learning/transfer-learning-with-word2vec.md","definition":"Transfer Learning with Word2Vec uses word embeddings pre-trained on large text corpora via the Skip-gram or CBOW objectives introduced by Mikolov et al. (2013) to initialize the embedding layer of a downstream NLP model. This approach transfers distributional semantic knowledge to tasks where labeled data is scarce, consistently outperforming random initialization.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mikolov, T. et al. (Word2Vec); transfer-learning application popularised by Kim, Y.","year":"2013-2014","type":"Transfer learning / embedding initialization","dataType":"Text (tokenized natural language corpora)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems (NIPS), 26, 3111-3119.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html"},{"ref":"Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1746-1751.","type":"inproceedings","doi":"10.3115/v1/D14-1181","isbn":null,"url":null}],"related":["fine-tuned-word2vec","sentence-embeddings","transfer-learning-with-bert-based-classification","convolutional-neural-network","recurrent-neural-network","lda-topic-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transfer-learning","name":"Transfer Learning","fullName":"Transfer Learning (Domain Adaptation and Knowledge Transfer)","aliases":["TL","domain adaptation","fine-tuning","pre-trained model adaptation"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"2010 (formalized); 1990s (early roots)","originator":"Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)","url":"https://scholargate.app/en/machine-learning/transfer-learning","markdownUrl":"https://scholargate.app/en/machine-learning/transfer-learning.md","definition":"Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)","year":"2010 (formalized); 1990s (early roots)","type":"Learning paradigm","dataType":"Any (images, text, tabular, audio)","subfamily":"Machine learning"},"citations":[{"ref":"Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.","type":"article","doi":"10.1109/TKDE.2009.191","isbn":null,"url":null},{"ref":"Bengio, Y. (2012). Deep Learning of Representations for Unsupervised and Transfer Learning. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning, PMLR 27, 17–36.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v27/bengio12a.html"}],"related":["self-supervised-learning","few-shot-learning","semi-supervised-learning","fine-tuning","meta-learning","domain-adaptation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transformational-leadership-scale","name":"Transformational Leadership Scale","fullName":"Transformational Leadership Scale (TLS) - Multidimensional Measure","aliases":["TLS","MLQ","Multifactor Leadership Questionnaire"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Organizational behavior","year":"1985","originator":"Bernard M. Bass and Bruce J. Avolio","url":"https://scholargate.app/en/organizational-behavior/transformational-leadership-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/transformational-leadership-scale.md","definition":"The Transformational Leadership Scale (TLS), operationalized in the Multifactor Leadership Questionnaire (MLQ), was developed by Bass and Avolio (1985, 1991) to measure leadership styles across a continuum from transactional to transformational. Transformational leadership comprises four dimensions: idealized influence (role modeling and inspiration), inspirational motivation (articulating compelling vision), intellectual stimulation (encouraging innovation), and individualized consideration (personalized development). The scale has become foundational in organizational psychology and management research for understanding leadership effectiveness and organizational outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernard M. Bass and Bruce J. Avolio","subfamily":"Organizational behavior","year":"1985","type":"Self-report questionnaire"},"citations":[{"ref":"Bass, B. M. (1985). Leadership and performance beyond expectations. New York: The Free Press.","type":"book","doi":null,"isbn":"978-0029015001","url":null},{"ref":"Bass, B. M., & Avolio, B. J. (1991). The Multifactor Leadership Questionnaire Form 5X. Palo Alto, CA: Consulting Psychologists Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bass+Avolio+Multifactor+Leadership+Questionnaire"}],"related":["servant-leadership-scale","organizational-justice-scale","psychological-safety-scale","organizational-commitment-scale","job-satisfaction-survey"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transformative-mixed-methods-design","name":"Transformative Mixed Methods Design","fullName":"Transformative Mixed Methods Research Design","aliases":["transformative design","advocacy mixed methods","emancipatory mixed methods","social-justice mixed methods"],"domain":"research-design","family":"process-pipeline","subfamily":"Mixed methods design","year":"2003–2009","originator":"Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark","url":"https://scholargate.app/en/research-design/transformative-mixed-methods-design","markdownUrl":"https://scholargate.app/en/research-design/transformative-mixed-methods-design.md","definition":"Transformative mixed methods design embeds a social-justice or advocacy theoretical framework — such as feminism, critical race theory, disability studies, or indigenous worldviews — as the overarching lens that guides every decision about data collection, integration, and use. Both quantitative and qualitative strands serve the goal of advancing equity, challenging power structures, and producing actionable knowledge for marginalized communities.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark","year":"2003–2009","type":"Mixed methods research design","dataType":"Quantitative data (surveys, tests, instruments) and qualitative data (interviews, focus groups, documents)","subfamily":"Mixed methods design"},"citations":[{"ref":"Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-1483344379","url":null},{"ref":"Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.","type":"book","doi":null,"isbn":"978-1593856373","url":null}],"related":["explanatory-sequential-mixed-methods-design","exploratory-sequential-mixed-methods-design","concurrent-triangulation-mixed-methods-design","participatory-action-research","critical-ethnography","multiphase-mixed-methods-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transformer-nlp","name":"Transformer","fullName":"Transformer Model for Natural Language Processing","aliases":["Transformer Modeli (NLP)","attention-based language model","self-attention network","transformer NLP"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2017,"originator":"Vaswani, A. et al.","url":"https://scholargate.app/en/deep-learning/transformer-nlp","markdownUrl":"https://scholargate.app/en/deep-learning/transformer-nlp.md","definition":"The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vaswani, A. et al.","year":2017,"type":"Attention-based deep neural network","task":"Text classification, NER, language modelling","minSample":500},"citations":[{"ref":"Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.","type":"article","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"}],"related":["random-forest","xgboost","autoencoder","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transit-photometry","name":"Transit Photometry","fullName":"Transit Photometry Method for Exoplanet Detection","aliases":["Photometric Transit Method","Planetary Transit Detection"],"domain":"astronomy","family":"process-pipeline","subfamily":"Signal processing","year":1984,"originator":"William Borucki","url":"https://scholargate.app/en/astronomy/transit-photometry","markdownUrl":"https://scholargate.app/en/astronomy/transit-photometry.md","definition":"Transit photometry is an observational technique that detects exoplanets by monitoring the periodic dips in stellar brightness as planets cross in front of their host stars. First systematized by William Borucki in 1984, this method became the most successful exoplanet detection technique, with the Kepler space telescope discovering thousands of confirmed exoplanets using this approach.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Borucki","subfamily":"Signal processing","year":1984,"type":"Observational photometric pipeline"},"citations":[{"ref":"Borucki, W. J., & Summers, A. L. (1984). The photometric method of detecting other planetary systems. Astrophysical Journal, 281, 537-553.","type":"article","doi":"10.1016/0019-1035(84)90102-7","isbn":null,"url":null},{"ref":"Fressin, F., et al. (2013). The false positive rate for Kepler and the validation of Kepler objects of interest. Astrophysical Journal, 766(2), 81.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+false+positive+rate+for+Kepler+and+the+validation+of+Kepler+objects+of+interest+Fressin"},{"ref":"Charbonneau, D., Brown, T. M., Latham, D. W., & Mayor, M. (2000). Detection of planetary transits across a sun-like star. Astrophysical Journal, 529(1), L45-L48.","type":"article","doi":"10.1086/312457","isbn":null,"url":null}],"related":["exoplanet-transmission-spectroscopy","sed-fitting","astrometry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transmission-disequilibrium-test","name":"Transmission Disequilibrium Test","fullName":"Transmission Disequilibrium Test for Family-based Association Analysis","aliases":["TDT","Family-based association test"],"domain":"genetics","family":"process-pipeline","subfamily":"Family-based association tests","year":"1993","originator":"Richard Spielman & Warren Ewens","url":"https://scholargate.app/en/genetics/transmission-disequilibrium-test","markdownUrl":"https://scholargate.app/en/genetics/transmission-disequilibrium-test.md","definition":"The Transmission Disequilibrium Test (TDT) is a family-based statistical method for testing genetic association with disease or traits while inherently controlling for population stratification. Developed by Spielman and Ewens in 1993, the TDT examines whether an allele is preferentially transmitted from heterozygous parents to affected children compared to unaffected children. By comparing transmission patterns within families, the TDT avoids the confounding effects of population structure that plague case-control studies, making it particularly valuable in admixed or stratified populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Richard Spielman & Warren Ewens","subfamily":"Family-based association tests","year":"1993","type":"Hypothesis test"},"citations":[{"ref":"Spielman, R. S., McGinnis, R. E., & Ewens, W. J. (1993). Transmission test for linkage disequilibrium. American Journal of Human Genetics, 52(3), 506–516.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Transmission+test+for+linkage+disequilibrium+Spielman"},{"ref":"Sham, P. C. (1998). Statistics in human genetics. London: Arnold.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9530690/"},{"ref":"Laird, N. M., & Lange, C. (2006). Family-based designs in the age of large-scale gene-association studies. Nature Reviews Genetics, 7(5), 385–394.","type":"article","doi":"10.1038/nrg1839","isbn":null,"url":null}],"related":["qtl-mapping","polygenic-risk-score","f-statistics","ibd-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transmission-line-matrix-method","name":"Transmission-Line Matrix Method","fullName":"Transmission-Line Matrix Method for Electromagnetic Simulation","aliases":["TLM","Transmission line matrix"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Numerical electromagnetic analysis","year":"1971","originator":"Peter Johns","url":"https://scholargate.app/en/electrical-engineering/transmission-line-matrix-method","markdownUrl":"https://scholargate.app/en/electrical-engineering/transmission-line-matrix-method.md","definition":"The Transmission-Line Matrix (TLM) method is a direct discretization of Maxwell equations using an equivalent transmission line network. Introduced by Johns and Beurle in 1971, TLM models electromagnetic fields as voltage and current waves propagating on coupled transmission lines. The method is intuitive, numerically stable, and efficient for both transient and frequency-domain electromagnetic problems. TLM remains competitive with FDTD and FIT for many RF and microwave applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Johns","subfamily":"Numerical electromagnetic analysis","year":"1971","type":"Transmission line network analogous to electromagnetic fields"},"citations":[{"ref":"Johns, P. B., & Beurle, R. L. (1971). Numerical solution of 2-D scattering problems using a transmission-line calculator. Proceedings of the IEE, 118(9), 1203-1208.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Numerical+solution+of+2-D+scattering+problems+using+a+transmission-line+calculator+Johns"},{"ref":"Johns, P. B. (1987). A symmetrical condensed node for the TLM method. IEEE Transactions on Microwave Theory and Techniques, 35(4), 370-377.","type":"book","doi":"10.1109/tmtt.1987.1133658","isbn":null,"url":null},{"ref":"Christopoulos, C. (1995). The Transmission-Line Modeling Method: TLM. IEEE Press.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/book/5264556"}],"related":["finite-integration-technique","method-of-moments","s-parameter-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transtheoretical-model-scale","name":"Stages of Change Questionnaire","fullName":"Transtheoretical Model Stages of Change Scale","aliases":["Stages of Change Scale","TTM Scale"],"domain":"health-behavior","family":"process-pipeline","subfamily":"Readiness & Motivation Assessment","year":"1983","originator":"James O. Prochaska and Carlo C. DiClemente","url":"https://scholargate.app/en/health-behavior/transtheoretical-model-scale","markdownUrl":"https://scholargate.app/en/health-behavior/transtheoretical-model-scale.md","definition":"The Transtheoretical Model (TTM), also called the Stages of Change model, is a framework developed by James Prochaska and Carlo DiClemente in 1983 to understand how people modify problematic behaviors and adopt healthier ones. The central premise is that behavior change is not an all-or-nothing event but a process that unfolds over time through distinct, recognizable stages: Precontemplation (not considering change), Contemplation (thinking about change), Preparation (planning to change), Action (actively modifying behavior), and Maintenance (sustaining change). The Stages of Change questionnaire assesses which stage an individual occupies, enabling clinicians and researchers to match interventions to readiness level. This framework is widely applied in smoking cessation, substance abuse treatment, diet change, exercise adoption, and mental health treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"James O. Prochaska and Carlo C. DiClemente","subfamily":"Readiness & Motivation Assessment","year":"1983","type":"Self-report questionnaire"},"citations":[{"ref":"Prochaska, J. O., & DiClemente, C. C. (1983). Stages and processes of self-change of smoking: toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51(3), 390-395.","type":"article","doi":"10.1037/0022-006X.51.3.390","isbn":null,"url":null},{"ref":"Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1992). In search of how people change: Applications to addictive behaviors. American Psychologist, 47(9), 1102-1114.","type":"article","doi":"10.1037/0003-066X.47.9.1102","isbn":null,"url":null}],"related":["health-belief-model-scale","behavioral-regulation-exercise","health-promotion-lifestyle-profile"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"transwell-assay","name":"Transwell Assay","fullName":"Transwell Migration and Invasion Assay","aliases":["Boyden chamber assay","chemotaxis assay","invasion chamber assay"],"domain":"biomaterials","family":"process-pipeline","subfamily":"Chemotaxis and cell migration","year":"1962","originator":"Stephen Boyden","url":"https://scholargate.app/en/biomaterials/transwell-assay","markdownUrl":"https://scholargate.app/en/biomaterials/transwell-assay.md","definition":"The Transwell assay (also called the Boyden chamber assay after its originator Stephen Boyden) is a quantitative method for measuring cell migration and invasion in response to chemical gradients or through matrix barriers. The assay uses a membrane insert with defined pore size suspended in a multi-well plate: cells are placed in the upper chamber, a chemoattractant is placed in the lower chamber, and cells that successfully migrate through the pores accumulate in the lower chamber, where they can be counted or visualized. Variants that coat the insert with matrix proteins (Matrigel, collagen) enable measurement of invasion capacity. The Transwell assay is a gold-standard method in cell biology for evaluating cell motility, tumor metastatic potential, and the effects of growth factors and inhibitory compounds.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stephen Boyden","subfamily":"Chemotaxis and cell migration","year":"1962","type":"Migration/invasion assay"},"citations":[{"ref":"Boyden, S. (1962). The chemotactic effect of mixtures of antibody and antigen on polymorphonuclear leucocytes. Journal of Experimental Medicine, 115(3), 453-466.","type":"article","doi":"10.1084/jem.115.3.453","isbn":null,"url":null},{"ref":"Albini, A., Iwama, Y., Odaka, C., & Bomstein, P. (1987). Exogenous ATIII inhibits basic fibroblast growth factor-induced angiogenesis in vitro. Proceedings of the National Academy of Sciences, 84(17), 6142-6146.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Exogenous+ATIII+inhibits+basic+fibroblast+growth+factor-induced+angiogenesis+in+vitro+Albini"},{"ref":"Kramer, N., Walzl, A., Unger, C., et al. (2013). In vitro cell migration and invasion assays. Mutation Research/Reviews in Mutation Research, 752(2), 142-195.","type":"article","doi":"10.1016/j.mrrev.2012.08.001","isbn":null,"url":null}],"related":["scratch-wound-assay","live-dead-assay","cam-assay","mtt-mts-assay"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"trauma-focused-cbt","name":"Trauma-Focused Cognitive-Behavioral Therapy","fullName":"Trauma-Focused Cognitive-Behavioral Therapy","aliases":["TF-CBT","trauma-focused therapy","PTSD treatment"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Trauma treatment","year":"1998","originator":"Judith A. Cohen, Anthony P. Mannarino, Esther Deblinger","url":"https://scholargate.app/en/clinical-psychology/trauma-focused-cbt","markdownUrl":"https://scholargate.app/en/clinical-psychology/trauma-focused-cbt.md","definition":"Trauma-Focused Cognitive-Behavioral Therapy (TF-CBT) is a structured, manualized psychotherapy designed to treat post-traumatic stress disorder (PTSD) and trauma-related symptoms in children, adolescents, and adults. Developed by Judith Cohen, Anthony Mannarino, and Esther Deblinger beginning in 1998, TF-CBT is now an evidence-based first-line treatment for PTSD with demonstrated efficacy across diverse trauma types and populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judith A. Cohen, Anthony P. Mannarino, Esther Deblinger","subfamily":"Trauma treatment","year":"1998","type":"Structured manualized psychotherapy"},"citations":[{"ref":"Cohen, J. A., Mannarino, A. P., & Deblinger, E. (2006). Treating trauma and traumatic grief in children and adolescents. Guilford Press.","type":"article","doi":null,"isbn":"9781593853440","url":null},{"ref":"Cohen, J. A., Mannarino, A. P., & Deblinger, E. (2017). Trauma-focused CBT for children and adolescents: Treatment applications. Guilford Press.","type":"article","doi":null,"isbn":"9781462528721","url":null}],"related":["exposure-response-prevention","cognitive-behavioral-therapy-assessment","mindfulness-based-stress-reduction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"travel-cost-method","name":"Travel Cost Method","fullName":"Travel Cost Method (TCM)","aliases":["TCM","Recreation Demand Model","Zonal Travel Cost"],"domain":"economics","family":"process-pipeline","subfamily":"Environmental and Resource Economics","year":"1949","originator":"Harold Hotelling","url":"https://scholargate.app/en/economics/travel-cost-method","markdownUrl":"https://scholargate.app/en/economics/travel-cost-method.md","definition":"The Travel Cost Method (TCM), developed by Harold Hotelling in 1949 and formalized by Marion Clawson and Jack Knetsch in the 1960s, is an econometric approach for valuing recreational sites and environmental amenities by inferring value from the travel costs (transportation, time, entry fees) that people incur to visit them. The core principle is that distance traveled and travel costs reveal how much people value a recreation site: those traveling far incur high costs, implying high value.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harold Hotelling","subfamily":"Environmental and Resource Economics","year":"1949","type":"Revealed preference recreation demand model"},"citations":[{"ref":"Hotelling, H. (1949). An Economic Study of the Monetary Valuation of Recreation in the National Parks. U.S. Department of Interior, National Park Service.","type":"letter","doi":null,"isbn":null,"url":"https://www.doi.gov/sites/doi.gov/files/documents/Hotelling-Economic-Study-Recreation-National-Parks.pdf"},{"ref":"Clawson, M., & Knetsch, J. L. (1966). Economics of Outdoor Recreation. Johns Hopkins Press.","type":"book","doi":null,"isbn":null,"url":"https://www.press.jhu.edu/"},{"ref":"English, D. B., Kellogg, F. W., & Larson, D. M. (2003). Estimating the Value of Protecting Forests from Fire. Journal of Forest Economics, 9(3), 51–73.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimating+the+Value+of+Protecting+Forests+from+Fire+English"}],"related":["contingent-valuation","hedonic-pricing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"travel-motivation-scale","name":"Travel Motivation Scale","fullName":"Travel Motivation Scale (TMS)","aliases":["TMS","Tourism Motivation Scale"],"domain":"tourism-management","family":"process-pipeline","subfamily":"motivation-measurement","year":"1979","originator":"Crompton, J. L.; Iso-Ahola, S. E.","url":"https://scholargate.app/en/tourism-management/travel-motivation-scale","markdownUrl":"https://scholargate.app/en/tourism-management/travel-motivation-scale.md","definition":"The Travel Motivation Scale (TMS) measures the underlying reasons and psychological drivers that prompt individuals to take vacations and choose specific destinations. Developed by Crompton (1979) and Iso-Ahola (1982), and theoretically grounded in push–pull motivation theory, the TMS operationalizes intrinsic motivations (escape from routine, self-discovery, social connection) and destination-specific attractions (beaches, cultural sites, activities). Understanding travel motivation is central to destination positioning, experience design, and visitor segmentation, as different motivational profiles require different marketing messages and service configurations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Crompton, J. L.; Iso-Ahola, S. E.","subfamily":"motivation-measurement","year":"1979","type":"Self-report questionnaire"},"citations":[{"ref":"Plog, S. C. (1974). Why destination areas rise and fall in popularity. The Cornell Hotel and Restaurant Administration Quarterly, 14(4), 55-58.","type":"article","doi":"10.1177/001088047401400409","isbn":null,"url":null},{"ref":"Crompton, J. L. (1979). Motivations for pleasure vacation. Annals of Tourism Research, 6(4), 408-424.","type":"article","doi":"10.1016/0160-7383(79)90004-5","isbn":null,"url":null},{"ref":"Iso-Ahola, S. E. (1982). Toward a social psychological theory of tourism motivation: A rejoinder. Annals of Tourism Research, 9(2), 256-262.","type":"article","doi":"10.1016/0160-7383(82)90049-4","isbn":null,"url":null},{"ref":"Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132-140.","type":"article","doi":"10.1086/208906","isbn":null,"url":null}],"related":["tourist-satisfaction-scale","destination-image-scale","place-attachment-scale","perceived-value-scale-tourism","travel-motivation-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"treatment-motivation-questionnaire","name":"TMQ","fullName":"Treatment Motivation Questionnaire","aliases":["TMQ"],"domain":"addiction-medicine","family":"process-pipeline","subfamily":"motivation-and-engagement","year":"2000","originator":"Simpson, Ryan","url":"https://scholargate.app/en/addiction-medicine/treatment-motivation-questionnaire","markdownUrl":"https://scholargate.app/en/addiction-medicine/treatment-motivation-questionnaire.md","definition":"The TMQ is a self-report instrument designed to measure motivation for substance abuse treatment and predict treatment engagement and outcomes. Developed by Simpson and colleagues in the context of the Drug Outcome Research Study (DORS), the TMQ assesses both intrinsic motivation (desire to address problems, commitment to change) and perceived barriers to treatment engagement. The TMQ is useful in addiction treatment settings to identify individuals with high versus low treatment motivation and to tailor motivational interventions accordingly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Simpson, Ryan","subfamily":"motivation-and-engagement","year":"2000","type":"Self-report"},"citations":[{"ref":"Ryan, G. W., & Wagner, E. F. (2010). Operator and stakeholder engagement in participatory research and evaluation of addiction treatment programs: A systematic review. Substance Abuse Treatment, Prevention, and Policy, 5(1), 21.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Operator+and+stakeholder+engagement+in+participatory+research+and+evaluation+of+addiction+treatment+programs%3A+A+systematic+review+Ryan"},{"ref":"Simpson, D. D. (1997). Effectiveness of drug treatment: a review of research findings from the Drug Outcome Research Study (DORS). Journal of Offender Rehabilitation, 25(1–2), 15–38.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Effectiveness+of+drug+treatment%3A+a+review+of+research+findings+from+the+Drug+Outcome+Research+Study+%28DORS%29+Simpson"}],"related":["readiness-to-change-questionnaire","brief-addiction-monitor","sadq","dudit"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"treatment-satisfaction-questionnaire-medication","name":"Treatment Satisfaction Questionnaire for Medication","fullName":"Treatment Satisfaction Questionnaire for Medication (TSQM)","aliases":["TSQM"],"domain":"pharmacology","family":"process-pipeline","subfamily":"treatment-satisfaction","year":"2004","originator":"Mary Jo Atkinson and colleagues","url":"https://scholargate.app/en/pharmacology/treatment-satisfaction-questionnaire-medication","markdownUrl":"https://scholargate.app/en/pharmacology/treatment-satisfaction-questionnaire-medication.md","definition":"The Treatment Satisfaction Questionnaire for Medication (TSQM) is a 14-item generic measure developed by Atkinson and colleagues in 2004 to assess patient satisfaction with medication across diverse therapeutic areas and disease conditions. It measures four key dimensions—Effectiveness, Side Effects, Convenience, and Global Satisfaction—with standardized 0–100 scoring, making it suitable for cross-disease comparison and health economic evaluation. The TSQM has become a standard outcome in pharmaceutical research, clinical trials, and real-world medication effectiveness studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mary Jo Atkinson and colleagues","subfamily":"treatment-satisfaction","year":"2004","type":"Self-report"},"citations":[{"ref":"Atkinson, M. J., Sinha, A., Hass, S. L., Colman, S. S., Kumar, R. N., Berman, B. M., & Wolpert, B. (2004). Validation of a general measure of treatment satisfaction, the Treatment Satisfaction Questionnaire for Medication (TSQM), using a national panel of chronically ill individuals. Health and Quality of Life Outcomes, 2(1), 12.","type":"article","doi":"10.1186/1477-7525-2-12","isbn":null,"url":null}],"related":["medication-adherence-rating-scale","beliefs-medicines-questionnaire","drug-attitude-inventory","self-efficacy-medication-adherence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tree-height-measurement","name":"Tree Height Measurement","fullName":"Dendrometric Height Assessment and Vertical Structure Quantification","aliases":["Dendrometric height","Tree elevation measurement","Stand height determination"],"domain":"forestry","family":"process-pipeline","subfamily":"Forest mensuration and dendrometry","year":"1950s–2000s","originator":"Bitterlich and classical forestry mensuration","url":"https://scholargate.app/en/forestry/tree-height-measurement","markdownUrl":"https://scholargate.app/en/forestry/tree-height-measurement.md","definition":"Tree height measurement—determining the vertical distance from ground to tree top—is a cornerstone of forest inventory and biomass estimation. Ranging from classical optical instruments (clinometer, Abney level) to modern laser hypsometers and airborne LiDAR, tree height quantification enables calculation of volume, biomass, site index (productivity), and forest structural characterization essential for management, research, and carbon accounting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bitterlich and classical forestry mensuration","subfamily":"Forest mensuration and dendrometry","year":"1950s–2000s","type":"Measurement pipeline"},"citations":[{"ref":"Bitterlich, W. (1984). The Relascope Idea: Relative Measurements in Forestry. Commonwealth Agricultural Bureaux.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/relascopeidearelatmeas"},{"ref":"Loetsch, F., Zöhrer, F., & Haller, K. E. (1973). Forest Inventory. BLV Verlagsgesellschaft.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Forest+Inventory+Loetsch"},{"ref":"Larjavaara, M., & Muller-Landau, H. C. (2013). Measuring Tree Height: A Quantitative Comparison of Two Common Field Methods in a Moist Tropical Forest. Methods in Ecology and Evolution, 4(9), 793–801.","type":"article","doi":"10.1111/2041-210X.12071","isbn":null,"url":null},{"ref":"Parker, G. G., Harding, D. J., & Berger, M. L. (2004). Ground-Based LiDAR: A New Tool for Forest Science. Bioscience, 54(10), 961–970.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Ground-Based+LiDAR%3A+A+New+Tool+for+Forest+Science+Parker"}],"related":["forest-inventory-sampling","stand-basal-area-measurement","allometric-biomass-equation","canopy-cover-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tree-testing","name":"Tree Testing","fullName":"Tree Testing Method","aliases":["Reverse Card Sort","Card Sorting Validation"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Information Architecture Validation","year":"2000s","originator":"Usability Professionals","url":"https://scholargate.app/en/human-computer-interaction/tree-testing","markdownUrl":"https://scholargate.app/en/human-computer-interaction/tree-testing.md","definition":"Tree Testing is a quantitative, task-based validation method for evaluating information architecture and navigation structures. Users are presented with a text-only representation of a website or app hierarchy (a tree) and asked to locate specific items or complete tasks by clicking through the structure. Unlike card sorting, which reveals user mental models during design, tree testing validates whether a proposed structure allows users to find items efficiently. The method captures success rate, time-to-completion, and paths taken, providing metrics for comparing navigation designs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Usability Professionals","subfamily":"Information Architecture Validation","year":"2000s","type":"Task-based testing of navigation structures"},"citations":[{"ref":"Tullis, T., Fleischman, S., McNulty, M., Ciccone, C., & Bergel, M. (2002). An empirical comparison of lab and remote usability testing of web sites. In Proceedings of the Usability Professionals Association Annual Conference.","type":"article","doi":null,"isbn":null,"url":"https://www.upa.org/resources/"},{"ref":"Katz, S. J., & Macleod, M. C. (2014). Optimal usability testing using tree testing. Journal of Usability Studies, 9(2), 50–69.","type":"article","doi":null,"isbn":null,"url":"http://www.upassoc.org/upa_publications/jus/"}],"related":["card-sorting","first-click-testing","heuristic-evaluation","contextual-inquiry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"trend-research","name":"Trend Research","fullName":"Trend Research Design","aliases":["trend study","trend survey","longitudinal trend study","time-series survey"],"domain":"research-design","family":"process-pipeline","subfamily":"Tarama ve gözlemsel desen","year":"Mid-20th century (formalised in social science methodology ~1950s–1960s)","originator":"Earl Babbie and survey research tradition","url":"https://scholargate.app/en/research-design/trend-research","markdownUrl":"https://scholargate.app/en/research-design/trend-research.md","definition":"Trend research is a longitudinal quantitative design that tracks changes in a characteristic of a general population over time by surveying different, independently drawn samples at two or more time points. Unlike panel studies, the same individuals are not followed; rather, each wave draws a fresh sample from the same population, allowing researchers to detect population-level shifts in attitudes, behaviours, or conditions while avoiding the attrition and panel conditioning problems of repeated-measures designs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Earl Babbie and survey research tradition","year":"Mid-20th century (formalised in social science methodology ~1950s–1960s)","type":"Quantitative longitudinal research design","dataType":"Repeated cross-sectional survey data (quantitative)","subfamily":"Tarama ve gözlemsel desen"},"citations":[{"ref":"Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452226101","url":null},{"ref":"Babbie, E. (2016). The Practice of Social Research (14th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1305104945","url":null}],"related":["longitudinal-research","panel-research","cohort-research","cross-sectional-research","survey-research","descriptive-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-delphi-technique","name":"Triangulated Delphi Technique","fullName":"Triangulated Delphi Technique","aliases":["Delphi with triangulation","mixed-method Delphi","multi-method Delphi","triangulation-enhanced Delphi"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Delphi: 1963; triangulation integration: 1970s–1990s","originator":"Norman Dalkey & Olaf Helmer (Delphi); triangulation principle from Norman Denzin","url":"https://scholargate.app/en/survey-methodology/triangulated-delphi-technique","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-delphi-technique.md","definition":"The Triangulated Delphi Technique combines the structured expert-consensus process of the classic Delphi method with deliberate triangulation — integrating data from at least one additional source or method (e.g., systematic literature review, interviews, survey data) to cross-validate findings and enhance the credibility of expert judgments. It retains the iterative, anonymous, multi-round panel format while embedding verification steps that reduce reliance on panel consensus alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman Dalkey & Olaf Helmer (Delphi); triangulation principle from Norman Denzin","year":"Delphi: 1963; triangulation integration: 1970s–1990s","type":"Expert-consensus data collection with multi-method validation","dataType":"Expert ratings, rankings, open-ended panel responses, supplementary qualitative/quantitative data","subfamily":"Data collection"},"citations":[{"ref":"Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458–467.","type":"article","doi":"10.1287/mnsc.9.3.458","isbn":null,"url":null},{"ref":"Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.","type":"article","doi":"10.1046/j.1365-2648.2000.01567.x","isbn":null,"url":null}],"related":["delphi-technique","triangulated-focus-group","triangulated-survey","mixed-methods","nominal-group-technique","triangulated-structured-interview"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-diary-method","name":"Triangulated Diary Method","fullName":"Triangulated Diary Method","aliases":["diary triangulation","multi-method diary study","triangulated diary research","diary-based triangulation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1977–1978 (seminal formulations); compound approach from 1990s onward","originator":"Triangulation principle: Norman K. Denzin; Diary method: Donald H. Zimmerman & D. Lawrence Wieder","url":"https://scholargate.app/en/survey-methodology/triangulated-diary-method","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-diary-method.md","definition":"The triangulated diary method combines participant-generated diary records with at least one additional independent data source — such as interviews, observations, or documents — to verify, deepen, and cross-check findings. Rooted in Denzin's (1978) principle of methodological triangulation and Zimmerman and Wieder's (1977) diary-interview method, it uses the natural, time-stamped richness of diary data while mitigating the subjectivity and recall bias that a diary study alone cannot address.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Triangulation principle: Norman K. Denzin; Diary method: Donald H. Zimmerman & D. Lawrence Wieder","year":"1977–1978 (seminal formulations); compound approach from 1990s onward","type":"Qualitative/mixed-methods data collection technique","dataType":"Participant diary entries (text, audio, or multimedia) combined with complementary data sources","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+The+Research+Act+1978"},{"ref":"Zimmerman, D. H., & Wieder, D. L. (1977). The diary-interview method. Urban Life, 5(4), 479–498.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Zimmerman+Wieder+diary+interview+method+1977"}],"related":["diary-method","triangulation","multi-source-diary-method","longitudinal-diary-method","experience-sampling-method","mixed-methods-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-document-collection","name":"Triangulated Document Collection","fullName":"Triangulated Document Collection","aliases":["documentary triangulation","multi-source document collection","cross-source document analysis","data triangulation via documents"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1978 (triangulation); 2009 (document analysis as method)","originator":"Norman K. Denzin (triangulation principle); Glenn Bowen (document analysis formalization)","url":"https://scholargate.app/en/survey-methodology/triangulated-document-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-document-collection.md","definition":"Triangulated document collection is a qualitative data collection strategy in which documents from multiple independent sources are gathered and cross-checked against one another. By drawing on different document types — such as official records, personal archives, institutional reports, and media artifacts — the researcher reduces reliance on any single source and strengthens the credibility of the evidence base. The approach applies Denzin's data triangulation principle directly to documentary material.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman K. Denzin (triangulation principle); Glenn Bowen (document analysis formalization)","year":"1978 (triangulation); 2009 (document analysis as method)","type":"Qualitative/mixed-methods data collection strategy","dataType":"Documents from multiple independent sources (archival, institutional, personal, digital)","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+The+Research+Act+1978"},{"ref":"Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.","type":"article","doi":"10.3316/QRJ0902027","isbn":null,"url":null}],"related":["document-collection","triangulated-participant-observation","multi-source-document-collection","triangulated-structured-interview","content-analysis","mixed-methods-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-field-notes","name":"Triangulated Field Notes","fullName":"Triangulated Field Notes Method","aliases":["multi-source field notes","cross-observer field notes","triangulated observation notes","TFN"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–1980s","originator":"Norman K. Denzin (triangulation); Yvonna Lincoln & Egon Guba (trustworthiness framework)","url":"https://scholargate.app/en/survey-methodology/triangulated-field-notes","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-field-notes.md","definition":"Triangulated Field Notes is a qualitative data collection technique in which field notes are recorded independently by multiple observers, from multiple vantage points, or at multiple time points and then systematically compared to strengthen the credibility and completeness of observational data. Rooted in Denzin's triangulation framework and Lincoln and Guba's trustworthiness criteria, the approach counters observer bias by cross-checking accounts before analysis begins.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman K. Denzin (triangulation); Yvonna Lincoln & Egon Guba (trustworthiness framework)","year":"1970s–1980s","type":"Qualitative data collection and verification technique","dataType":"Field notes, observation logs, researcher memos (text data)","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+1978+The+Research+Act+Theoretical+Introduction+Sociological+Methods"},{"ref":"Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage.","type":"book","doi":null,"isbn":"978-0803924314","url":null}],"related":["participant-observation","ethnography","field-research","member-checking","thematic-analysis","mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-in-depth-interview","name":"Triangulated In-depth Interview","fullName":"Triangulated In-depth Interview Data Collection","aliases":["triangulated IDI","multi-source in-depth interview","triangulated qualitative interview","converging in-depth interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1978 (triangulation framework); in-depth interviewing ~1950s onward","originator":"Norman K. Denzin (triangulation framework); in-depth interviewing practice is longstanding in qualitative research","url":"https://scholargate.app/en/survey-methodology/triangulated-in-depth-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-in-depth-interview.md","definition":"Triangulated in-depth interviewing applies Denzin's triangulation logic to the in-depth interview method by deliberately combining multiple sources of convergent evidence — different informants, interviewers, time points, or corroborating data types — to strengthen confidence in qualitative findings. Rather than relying on a single interview account, the researcher gathers rich, open-ended accounts from several vantage points and cross-checks them for consistency and divergence, treating agreement as corroboration and disagreement as analytically meaningful.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman K. Denzin (triangulation framework); in-depth interviewing practice is longstanding in qualitative research","year":"1978 (triangulation framework); in-depth interviewing ~1950s onward","type":"Qualitative data collection approach","dataType":"Verbal/text data from multiple interviewers, informants, or time points","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+The+Research+Act+1978"},{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage.","type":"book","doi":null,"isbn":"978-0761919711","url":null}],"related":["in-depth-interview","semi-structured-interview","triangulated-focus-group","multi-source-in-depth-interview","triangulated-document-collection","triangulated-participant-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-mobile-experience-sampling","name":"Triangulated Mobile Experience Sampling","fullName":"Triangulated Mobile Experience Sampling Method","aliases":["triangulated ESM","multi-source mobile ESM","triangulated ecological momentary assessment","triangulated mobile EMA"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"2000s–present (as an integrated mobile ESM variant)","originator":"Csikszentmihalyi & Larson (ESM, 1983); Denzin (triangulation, 1978); integrated in HCI/health informatics research from the 2000s onward","url":"https://scholargate.app/en/survey-methodology/triangulated-mobile-experience-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-mobile-experience-sampling.md","definition":"Triangulated Mobile Experience Sampling combines the Experience Sampling Method (ESM) — repeated, real-time self-reports delivered via smartphone — with deliberate triangulation across two or more data sources, instruments, or methods. By converging mobile survey prompts with passive sensor streams, behavioral logs, or complementary qualitative probes, the technique strengthens construct validity and enables cross-verification of findings collected in participants' natural environments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Csikszentmihalyi & Larson (ESM, 1983); Denzin (triangulation, 1978); integrated in HCI/health informatics research from the 2000s onward","year":"2000s–present (as an integrated mobile ESM variant)","type":"Mixed/multi-source data collection technique","dataType":"Real-time repeated-measures data (self-reports, sensor streams, behavioral logs) collected via mobile devices","subfamily":"Data collection"},"citations":[{"ref":"Csikszentmihalyi, M., & Larson, R. (1983). The Experience Sampling Method. In H. T. Reis (Ed.), Naturalistic Approaches to Studying Social Interaction (pp. 41–56). Jossey-Bass.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experience+Sampling+Method+Csikszentmihalyi+Larson+1983"},{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Research+Act+Denzin+1978+triangulation"}],"related":["mobile-experience-sampling","ecological-momentary-assessment","multi-source-mobile-experience-sampling","longitudinal-mobile-experience-sampling","experience-sampling-method","diary-method"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-non-participant-observation","name":"Triangulated Non-participant Observation","fullName":"Triangulated Non-participant Observation","aliases":["triangulated observation","multi-source non-participant observation","observational triangulation","observer triangulation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1958 (observer roles); 1978 (triangulation applied to observation)","originator":"Norman K. Denzin (triangulation framework); Raymond Gold (observer roles taxonomy)","url":"https://scholargate.app/en/survey-methodology/triangulated-non-participant-observation","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-non-participant-observation.md","definition":"Triangulated non-participant observation systematically combines two or more independent non-participant observation streams — using multiple observers, different time points, or distinct vantage points — to cross-validate field records of naturally occurring behaviour. The researcher remains outside the setting as a detached observer, and triangulation across sources reduces single-observer bias while strengthening the credibility of descriptive findings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman K. Denzin (triangulation framework); Raymond Gold (observer roles taxonomy)","year":"1958 (observer roles); 1978 (triangulation applied to observation)","type":"Qualitative data collection technique","dataType":"Observational field records, structured observation logs, multiple observer notes","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+1978+The+Research+Act+Theoretical+Introduction+Sociological+Methods"},{"ref":"Gold, R. L. (1958). Roles in sociological field observations. Social Forces, 36(3), 217–223.","type":"article","doi":"10.2307/2573808","isbn":null,"url":null}],"related":["non-participant-observation","triangulated-participant-observation","triangulated-field-notes","multi-source-non-participant-observation","structured-observation","triangulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-research-diary","name":"Triangulated Research Diary","fullName":"Triangulated Research Diary","aliases":["reflective diary triangulation","multi-method research journal","triangulated reflexive diary","diary-based triangulation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1970s–1980s (triangulation formalized by Denzin 1978; diary methodology developed through 1980s)","originator":"Norman K. Denzin (triangulation framework); Mary Louise Holly (research diary practice)","url":"https://scholargate.app/en/survey-methodology/triangulated-research-diary","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-research-diary.md","definition":"A Triangulated Research Diary is a qualitative data collection approach in which a researcher's ongoing reflective diary is used as one strand within a triangulated data collection strategy. The diary records observations, decisions, emotions, and emerging interpretations across the study, while at least one other data source — such as interviews, documents, or observations — is collected in parallel. Cross-checking diary entries against other sources increases the credibility and depth of the findings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman K. Denzin (triangulation framework); Mary Louise Holly (research diary practice)","year":"1970s–1980s (triangulation formalized by Denzin 1978; diary methodology developed through 1980s)","type":"Qualitative data collection technique","dataType":"Researcher reflective diary entries combined with at least one other data source","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+The+Research+Act+1978"},{"ref":"Holly, M. L. (1989). Writing to Grow: Keeping a Personal-Professional Journal. Heinemann.","type":"book","doi":null,"isbn":"978-0435084523","url":null}],"related":["research-diary","field-notes","triangulated-field-notes","triangulated-document-collection","diary-method","participant-observation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-semi-structured-interview","name":"Triangulated Semi-structured Interview","fullName":"Triangulated Semi-structured Interview","aliases":["triangulated qualitative interview","multi-source semi-structured interview","triangulated in-depth interview","convergent interview strategy"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Formalized in practice from the late 1970s onward","originator":"Synthesized from Norman K. Denzin (triangulation) and H. Russell Bernard (semi-structured interviewing)","url":"https://scholargate.app/en/survey-methodology/triangulated-semi-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-semi-structured-interview.md","definition":"A triangulated semi-structured interview strategy combines the flexibility of open-ended, guided interviewing with deliberate triangulation across multiple informant groups, data sources, or interview occasions. By applying the same semi-structured protocol to different participant perspectives — such as clients, providers, and managers — or by pairing interviews with documents and observations, the approach cross-validates emerging themes and reduces the risk that any single viewpoint dominates the findings. The result is richer, more credible qualitative data than a single-source interview study can deliver.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Norman K. Denzin (triangulation) and H. Russell Bernard (semi-structured interviewing)","year":"Formalized in practice from the late 1970s onward","type":"Qualitative data collection technique","dataType":"Verbal/text data from multiple informant groups or sources","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+1978+The+Research+Act+Theoretical+Introduction+Sociological+Methods"},{"ref":"Bernard, H. R. (2006). Research Methods in Anthropology: Qualitative and Quantitative Approaches (4th ed.). AltaMira Press.","type":"book","doi":null,"isbn":"978-0759108684","url":null}],"related":["semi-structured-interview","triangulation","focus-group","key-informant-interview","thematic-analysis","mixed-methods-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-sensor-data-collection","name":"Triangulated Sensor Data Collection","fullName":"Triangulated Sensor Data Collection","aliases":["multi-sensor triangulation","sensor fusion data collection","redundant sensor sampling","cross-sensor validation"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1980s–1990s (formalized in sensor fusion and IoT research)","originator":"Hall & Llinas and the multisensor data fusion community","url":"https://scholargate.app/en/survey-methodology/triangulated-sensor-data-collection","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-sensor-data-collection.md","definition":"Triangulated sensor data collection deploys two or more independent sensors measuring the same phenomenon simultaneously, then cross-validates and aggregates their readings to obtain data that is more accurate, robust, and trustworthy than any single sensor alone. Widely used in environmental monitoring, structural health monitoring, IoT systems, and field experiments, the approach borrows the logic of triangulation from research methodology — using multiple independent sources to converge on a more reliable measurement.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hall & Llinas and the multisensor data fusion community","year":"1980s–1990s (formalized in sensor fusion and IoT research)","type":"Quantitative data collection technique","dataType":"Time-series sensor readings, spatial measurements, environmental or structural signals","subfamily":"Data collection"},"citations":[{"ref":"Hall, D. L., & Llinas, J. (Eds.). (1997). Handbook of Multisensor Data Fusion. CRC Press.","type":"book","doi":null,"isbn":"978-0849323798","url":null},{"ref":"Sensor fusion. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Sensor_fusion"}],"related":["sensor-fusion","iot-data-collection","environmental-monitoring","triangulation","structural-health-monitoring","remote-sensing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-structured-interview","name":"Triangulated Structured Interview","fullName":"Triangulated Structured Interview","aliases":["triangulated standardized interview","multi-source structured interview","cross-validated structured interview"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1978 (Denzin's triangulation framework); structured interviews in use from early 20th century","originator":"Norman K. Denzin (triangulation framework); structured interview tradition predates","url":"https://scholargate.app/en/survey-methodology/triangulated-structured-interview","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-structured-interview.md","definition":"A triangulated structured interview applies the triangulation principle — using multiple independent sources, methods, or perspectives to cross-validate findings — to the structured interview format. The researcher administers the same fixed set of questions across different respondent groups, time points, or complementary data sources, then systematically compares the results to confirm, qualify, or explain discrepancies. This strengthens confidence in the accuracy of the data beyond what any single structured interview session could provide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman K. Denzin (triangulation framework); structured interview tradition predates","year":"1978 (Denzin's triangulation framework); structured interviews in use from early 20th century","type":"Triangulated quantitative/qualitative data collection technique","dataType":"Structured verbal responses; standardized numerical or categorical survey data","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+The+Research+Act+1978"},{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null}],"related":["structured-interview","triangulated-survey","multi-source-structured-interview","triangulated-focus-group","face-to-face-structured-interview","mixed-methods-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"triangulated-survey","name":"Triangulated Survey","fullName":"Triangulated Survey Design","aliases":["survey triangulation","multi-method survey","convergent survey design","cross-validated survey"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"1978 (Denzin); widely operationalized in survey contexts from the 1990s onward","originator":"Norman K. Denzin (triangulation concept); Alan Bryman (mixed-methods survey application)","url":"https://scholargate.app/en/survey-methodology/triangulated-survey","markdownUrl":"https://scholargate.app/en/survey-methodology/triangulated-survey.md","definition":"A Triangulated Survey deliberately combines a structured survey instrument with at least one additional data source — such as interviews, focus groups, observation, or a second survey — so that findings from each source can be cross-validated against the others. Rooted in Denzin's concept of methodological triangulation, the design strengthens credibility by checking whether independent lines of evidence converge on the same conclusions. It is especially common in applied social, educational, and health research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norman K. Denzin (triangulation concept); Alan Bryman (mixed-methods survey application)","year":"1978 (Denzin); widely operationalized in survey contexts from the 1990s onward","type":"Mixed-methods data collection design","dataType":"Survey responses combined with qualitative or alternative quantitative data sources","subfamily":"Data collection"},"citations":[{"ref":"Denzin, N. K. (1978). The Research Act: A Theoretical Introduction to Sociological Methods (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Denzin+The+Research+Act+1978"},{"ref":"Bryman, A. (2016). Social Research Methods (5th ed.). Oxford University Press.","type":"book","doi":null,"isbn":"9780198722236","url":null}],"related":["survey","online-survey","multi-source-survey","semi-structured-interview","focus-group","mixed-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"trimmed-mean-test","name":"Trimmed Mean Test","fullName":"Two-Sample Trimmed Mean Test (Yuen's Test)","aliases":["Yuen's test","Yuen-Welch test","robust mean comparison","kırpılmış ortalama testi"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1974,"originator":"Karen K. Yuen","url":"https://scholargate.app/en/statistics/trimmed-mean-test","markdownUrl":"https://scholargate.app/en/statistics/trimmed-mean-test.md","definition":"The trimmed mean test compares two groups using trimmed means, which discard a fixed proportion of the most extreme observations in each tail before averaging. Introduced by Karen K. Yuen in 1974, it is a robust alternative to the classical t-test when the data are non-normal or contain outliers and the population variances are unequal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Karen K. Yuen","year":1974,"type":"Robust two-group comparison","estimator":"Trimmed mean with Winsorized variance","outcome":"continuous","defaultTrim":"10-20% per tail","minSample":20},"citations":[{"ref":"Yuen, K. K. (1974). The Two-Sample Trimmed t for Unequal Population Variances. Biometrika, 61(1), 165-170.","type":"article","doi":"10.1093/biomet/61.1.165","isbn":null,"url":null},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["mann-whitney-u","welch-t-test","permutation-test","median-test","mad-estimation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tromp-curve","name":"Tromp Curve","fullName":"Tromp Curve for Size Classification","aliases":["Partition Curve","Classification Efficiency Curve","Grade Recovery Curve"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Particle Separation and Classification","year":"1937","originator":"K. Tromp","url":"https://scholargate.app/en/mining-engineering/tromp-curve","markdownUrl":"https://scholargate.app/en/mining-engineering/tromp-curve.md","definition":"The Tromp Curve, introduced by K. Tromp in 1937, is an empirical model that quantifies the performance of size classifiers (cyclones, screens, jigs) by showing the fraction of particles at each size that report to the target stream (overflow or underflow). It is universally used in mineral processing to evaluate classifier performance, design circuits, and diagnose operational problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. Tromp","subfamily":"Particle Separation and Classification","year":"1937","type":"Empirical model for size classifier performance"},"citations":[{"ref":"Tromp, K. (1937). Separation of fine particles from slurries by hydrocyclone. Colliery Guardian, 155(4), 251-256.","type":"article","doi":null,"isbn":null,"url":"https://www.collieryguardian.com/"},{"ref":"Lynch, A. J., & Rao, T. C. (1997). Hydrocyclones in mineral processing. In Classification and segregation. Society for Mining, Metallurgy & Exploration.","type":"article","doi":null,"isbn":null,"url":"https://www.smenet.org/"}],"related":["rosin-rammler-distribution","mccabe-thiele-method","washability"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"trueskill","name":"TrueSkill","fullName":"TrueSkill Bayesian Skill Rating","aliases":["Bayesian Skill Rating","TrueSkill Ranking System","Gaussian Skill Model","Beceri Derecelendirme Modeli"],"domain":"decision-making","family":"regression-model","subfamily":"Ranking models","year":2007,"originator":"Ralf Herbrich, Tom Minka & Thore Graepel","url":"https://scholargate.app/en/decision-making/trueskill","markdownUrl":"https://scholargate.app/en/decision-making/trueskill.md","definition":"TrueSkill is a Bayesian skill rating system developed by Herbrich, Minka, and Graepel at Microsoft Research and introduced at NeurIPS 2006. It represents each player's skill as a Gaussian distribution parameterized by a mean (estimated skill) and a variance (uncertainty). After each match outcome, the system updates these distributions via approximate message passing, yielding a principled ranking that handles team games, draws, and partial observations in online settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ralf Herbrich, Tom Minka & Thore Graepel","year":2007,"type":"Probabilistic ranking model","subfamily":"Ranking models","inference":"Approximate message passing (Expectation Propagation)","skill_representation":"Gaussian distribution per player"},"citations":[{"ref":"Herbrich, R., Minka, T., & Graepel, T. (2007). TrueSkill: A Bayesian skill rating system. Advances in Neural Information Processing Systems, 19, 569–576.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2006/hash/f44ee263952e65b3610b8ba51229d1f9-Abstract.html"}],"related":["elo-rating","bradley-terry-model","bayesian-inference"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"trust-in-physician-scale","name":"Trust in Physician Scale","fullName":"Trust in Physician Scale","aliases":["TPS","Interpersonal Trust Measure","Patient-Provider Trust Scale"],"domain":"patient-centered-care","family":"process-pipeline","subfamily":"therapeutic-relationship","year":1990,"originator":"Laurie Anderson, Robert Dedrick","url":"https://scholargate.app/en/patient-centered-care/trust-in-physician-scale","markdownUrl":"https://scholargate.app/en/patient-centered-care/trust-in-physician-scale.md","definition":"The Trust in Physician Scale (TPS) is an 11-item self-report instrument that measures the degree to which a patient trusts their physician, including dimensions of confidentiality, competence, honesty, and care. Developed by Anderson and Dedrick in 1990, the TPS assesses the patient's confidence that the physician acts in the patient's best interest, respects privacy, possesses the needed expertise, and is truthful. Trust in the physician-patient relationship is foundational to healthcare engagement and is strongly correlated with adherence, disclosure of sensitive information, and health outcomes. The TPS is widely used in research, quality improvement, and studies examining factors that build or erode physician trust.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Laurie Anderson, Robert Dedrick","subfamily":"therapeutic-relationship","year":1990,"type":"Patient-reported"},"citations":[{"ref":"Anderson, L. A., & Dedrick, R. F. (1990). Development of the Trust in Physician Scale: A measure to assess interpersonal trust in patient-physician relationships. Psychological Reports, 67(3), 1091-1100.","type":"article","doi":"10.2466/pr0.1990.67.3f.1091","isbn":null,"url":null},{"ref":"Hall, M. A., Dugan, E., Zheng, B., & Mishra, A. K. (2001). Trust in physicians and medical institutions: what is it, can it be measured, and does it matter? Milbank Quarterly, 79(4), 613-639.","type":"article","doi":"10.1111/1468-0009.00223","isbn":null,"url":null}],"related":["collaboste-scale","patient-enablement-instrument","patient-reported-communication-scale","care-transitions-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ts-cross-validation","name":"Time-Series Cross-Validation","fullName":"Time-Series Cross-Validation (Rolling/Expanding Window)","aliases":["Rolling-Origin Cross-Validation","Walk-Forward Validation","Expanding Window Evaluation","Zaman Serisi Çapraz Doğrulama"],"domain":"econometrics","family":"process-pipeline","subfamily":"Forecast evaluation","year":2012,"originator":"Christoph Bergmeir & José Benítez","url":"https://scholargate.app/en/econometrics/ts-cross-validation","markdownUrl":"https://scholargate.app/en/econometrics/ts-cross-validation.md","definition":"Time-series cross-validation is a resampling procedure designed for sequentially ordered data. Instead of randomly partitioning observations — which would destroy temporal structure and introduce data leakage — it advances a forecast origin one step at a time, fitting a model on all past data up to that origin and evaluating it on the immediately following out-of-sample period. Economists, financial analysts, and meteorologists use it whenever an honest, operationally realistic estimate of predictive accuracy is required for a time-ordered process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christoph Bergmeir & José Benítez","year":2012,"type":"Forecast evaluation procedure","subfamily":"Forecast evaluation","data_requirement":"Ordered time series","bias_property":"Respects temporal order; no data leakage"},"citations":[{"ref":"Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213.","type":"article","doi":"10.1016/j.ins.2011.12.028","isbn":null,"url":null}],"related":["diebold-mariano-test","arima","bootstrap-inference"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tsmixer","name":"TSMixer","fullName":"TSMixer (All-MLP Architecture for Forecasting)","aliases":["All-MLP Time Series Mixer","Time Series Mixer","TSMixer Forecasting Model","Zaman Serisi Karıştırıcı"],"domain":"deep-learning","family":"ml-model","subfamily":"Time-series forecasting","year":2023,"originator":"Si-An Chen et al. (Google)","url":"https://scholargate.app/en/deep-learning/tsmixer","markdownUrl":"https://scholargate.app/en/deep-learning/tsmixer.md","definition":"TSMixer is a multivariate time-series forecasting model introduced by Si-An Chen and colleagues at Google in 2023. It challenges the prevailing dominance of Transformer-based architectures by demonstrating that a simple stack of interleaved MLP layers — alternating between mixing along the time axis and mixing across feature channels — achieves strong forecasting accuracy while remaining computationally efficient and easy to interpret architecturally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Si-An Chen et al. (Google)","year":2023,"type":"All-MLP multivariate time-series forecasting model","subfamily":"Time-series forecasting","input":"Multivariate time series (look-back window)","output":"Multi-step ahead forecasts"},"citations":[{"ref":"Chen, S.-A., Li, C.-L., Yoder, N., Arik, S. O., & Pfister, T. (2023). TSMixer: An all-MLP architecture for time series forecasting. Transactions on Machine Learning Research.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2303.06053"}],"related":["dlinear","timemixer","multilayer-perceptron"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tsunami-shallow-water-model","name":"Tsunami Shallow Water Model","fullName":"Tsunami Shallow Water Equations Model","aliases":["Shallow Water Tsunami Propagation","SRTM"],"domain":"oceanography","family":"process-pipeline","subfamily":"Hydrodynamic Modeling","year":"1995","originator":"Kenji Satake","url":"https://scholargate.app/en/oceanography/tsunami-shallow-water-model","markdownUrl":"https://scholargate.app/en/oceanography/tsunami-shallow-water-model.md","definition":"The tsunami shallow water model is a numerical method based on shallow water equations that simulates tsunami wave propagation from earthquake source regions to coastal areas. Developed by Kenji Satake and colleagues in the 1990s, this approach provides rapid estimates of tsunami arrival times, wave amplitudes, and inundation extents for operational early warning systems. The model forms the computational backbone of tsunami warning centers worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kenji Satake","subfamily":"Hydrodynamic Modeling","year":"1995","type":"numerical-model"},"citations":[{"ref":"Satake, K. (1995). Linear and nonlinear computations of the 1992 Nicaragua earthquake tsunami. Pure and Applied Geophysics, 144(3-4), 455-470.","type":"article","doi":"10.1007/bf00874378","isbn":null,"url":null},{"ref":"Goto, T., Ogasawara, Y., Tanioka, Y., & Satake, K. (2011). TUNAMI code. Available at: https://www.gsaj.org/activity/tsunami/","type":"article","doi":null,"isbn":null,"url":"https://www.gsaj.org/activity/tsunami/"}],"related":["acoustic-doppler-current-profiler","geostrophic-velocity","tidal-harmonic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"turbo-code","name":"Turbo Code","fullName":"Turbo Coding with Iterative Decoding","aliases":["iterative decoding","concatenated codes"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Coding theory","year":"1993","originator":"Claude Berrou, Alain Glavieux, and Punya Thitimajshima","url":"https://scholargate.app/en/telecommunications/turbo-code","markdownUrl":"https://scholargate.app/en/telecommunications/turbo-code.md","definition":"Turbo codes, introduced by Berrou, Glavieux, and Thitimajshima in 1993, are a landmark in channel coding history. They achieve performance within 0.5 dB of the Shannon limit—the theoretical boundary for reliable communication—a feat previously thought impossible with practical complexity. Turbo codes use concatenated convolutional codes with an interleaver and iterative decoding via belief propagation. They were adopted in 3G (UMTS) and remain important in 4G/5G systems alongside LDPC codes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Claude Berrou, Alain Glavieux, and Punya Thitimajshima","subfamily":"Coding theory","year":"1993","type":"iterative error-correcting code"},"citations":[{"ref":"Berrou, C., Glavieux, A., & Thitimajshima, P. (1993). Near Shannon limit error-correcting coding and decoding: Turbo-codes. In Proceedings of the IEEE International Conference on Communications (ICC), 1064-1070.","type":"article","doi":"10.1109/ICC.1993.397441","isbn":null,"url":null},{"ref":"Richardson, T. J., & Urbanke, R. L. (2002). The capacity of low-density parity-check codes under message-passing decoding. IEEE Transactions on Information Theory, 47(2), 599-618.","type":"article","doi":"10.1109/18.910577","isbn":null,"url":null}],"related":["ldpc-codes","polar-codes","ofdm","mimo","shannon-capacity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"turnitin-ithenticate","name":"Turnitin and iThenticate Similarity Detection","fullName":"Turnitin and iThenticate: Text-Matching Similarity Detection Tools for Plagiarism Screening","aliases":["text-matching software","plagiarism detection software","similarity detection","originality reports"],"domain":"research-ethics","family":"process-pipeline","subfamily":"plagiarism-detection-and-prevention","year":"1997","originator":"Turnitin (1997), iThenticate (commercial variant)","url":"https://scholargate.app/en/research-ethics/turnitin-ithenticate","markdownUrl":"https://scholargate.app/en/research-ethics/turnitin-ithenticate.md","definition":"Turnitin and iThenticate are commercial text-matching software tools used by educational institutions and academic journals to screen submissions for potential plagiarism. Turnitin is designed for student assignments; iThenticate is designed for researcher manuscripts. Both tools compare submitted text against billions of sources (web pages, academic databases, previously submitted documents) and generate a Similarity Index showing what percentage of the submission matches existing sources. These tools are screening instruments, not plagiarism detectors—they flag suspicious content for human review.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Turnitin (1997), iThenticate (commercial variant)","subfamily":"plagiarism-detection-and-prevention","year":"1997","type":"Tool"},"citations":[{"ref":"Turnitin. (2023). Turnitin similarity detection and plagiarism detection technology. Retrieved from https://www.turnitin.com/products/similarity","type":"article","doi":null,"isbn":null,"url":"https://www.turnitin.com/products/similarity"},{"ref":"Turnitin (iThenticate division). (2023). iThenticate: Originality checking for researchers. Retrieved from https://www.ithenticate.com","type":"article","doi":null,"isbn":null,"url":"https://www.ithenticate.com"},{"ref":"Declerck, K., Decock, P., Macq, B., & Vandenbossche, J. (2021). Similarity index in plagiarism detection: A critical perspective. Research Integrity and Peer Review, 6, 1-8.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Similarity+index+in+plagiarism+detection%3A+A+critical+perspective+Declerck"}],"related":["verbatim-plagiarism","similarity-vs-plagiarism","academic-integrity-policies"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tvn-multimoora","name":"TVN-MULTIMOORA","fullName":"MULTIMOORA under Triangular-Valued Neutrosophic Number Environment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Stanujkić, D. Zavadskas, E.K. Smarandache, F. Brauers, W.K.M. Karabašević, D.","url":"https://scholargate.app/en/decision-making/tvn-multimoora","markdownUrl":"https://scholargate.app/en/decision-making/tvn-multimoora.md","definition":"TVN-MULTIMOORA (MULTIMOORA under Triangular-Valued Neutrosophic Number Environment) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkić, D. Zavadskas, E.K. Smarandache, F. Brauers, W.K.M. Karabašević, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanujkić, D. Zavadskas, E.K. Smarandache, F. Brauers, W.K.M. Karabašević, D.","subfamily":"Ranking","year":"2021","type":"Neutrosophic MULTIMOORA — Triangular-Valued Neutrosophic Number (TVNN: <(tl,tm,tu),(il,im,iu),(fl,fm,fu)> with each component ∈[0,1], 0≤tu+iu+fu≤3)","value_space":"triangular_valued_neutrosophic","uncertainty":"hybrid","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Stanujkić, D., Zavadskas, E.K., Smarandache, F., Brauers, W.K.M., Karabašević, D. (2021). Cloud Computing Technology Selection Using a Novel Neutrosophic Extension of the MULTIMOORA Method. In: Smarandache, F., Abdel-Basset, M. (eds.) Neutrosophic Operational Research. Springer Nature Switzerland AG, Cham","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Cloud+Computing+Technology+Selection+Using+a+Novel+Neutrosophic+Extension+of+the+MULTIMOORA+Method+Stanujki%C4%87"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tvp-favar","name":"TVP-FAVAR","fullName":"Time-Varying Parameter Factor-Augmented VAR","aliases":["Dynamic factor model with time-varying parameters"],"domain":"econometrics","family":"regression-model","subfamily":"Dynamic factor model","year":"2005","originator":"Bernanke, Boivin, and Eliasz","url":"https://scholargate.app/en/econometrics/tvp-favar","markdownUrl":"https://scholargate.app/en/econometrics/tvp-favar.md","definition":"TVP-FAVAR is a hybrid framework combining factor-augmented VARs with time-varying parameter estimation via Kalman filtering. Introduced by Bernanke et al. (2005) and refined by Primiceri (2005), it extracts latent economic factors (e.g., a 'common monetary policy shock') from high-dimensional data while allowing VAR coefficients to evolve stochastically over time. This framework captures both reduced-dimensionality patterns and structural instability, making it ideal for studying evolving policy regimes and shock dynamics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bernanke, Boivin, and Eliasz","subfamily":"Dynamic factor model","year":"2005","type":"Time-varying system"},"citations":[{"ref":"Bernanke, B. S., Boivin, J., & Eliasz, P. S. (2005). Measuring monetary policy. Journal of Political Economy, 113(1), 161-208.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+monetary+policy+Bernanke"},{"ref":"Primiceri, G. E. (2005). Time-varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852.","type":"article","doi":"10.1111/j.1467-937X.2005.00353.x","isbn":null,"url":null}],"related":["global-var","threshold-panel-var","local-projections"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"tvp-var","name":"TVP-VAR","fullName":"Time-Varying Parameter VAR (TVP-VAR)","aliases":["Time-Varying Parameter Vector Autoregression","TVP-SVAR","Stochastic Coefficient VAR","Zamana Göre Değişen Parametreli VAR"],"domain":"econometrics","family":"regression-model","subfamily":"Multivariate time series","year":2005,"originator":"Giorgio Primiceri","url":"https://scholargate.app/en/econometrics/tvp-var","markdownUrl":"https://scholargate.app/en/econometrics/tvp-var.md","definition":"TVP-VAR is a Bayesian multivariate time-series model in which both the VAR coefficients and the shock covariance matrix are allowed to evolve continuously over time as random walks. Introduced by Primiceri (2005) to study U.S. monetary policy transmission, the model captures structural changes and regime shifts without requiring ex-ante knowledge of when breaks occurred, making it indispensable for macroeconomics, finance, and any setting where economic relationships are suspected to be unstable across time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Giorgio Primiceri","year":2005,"type":"Bayesian state-space model","subfamily":"Multivariate time series","estimation":"Markov Chain Monte Carlo (MCMC)","prior":"Minnesota-type / diffuse priors on initial states"},"citations":[{"ref":"Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821–852.","type":"article","doi":"10.1111/j.1467-937X.2005.00353.x","isbn":null,"url":null}],"related":["var-model","svar","bayesian-var"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"two-mode-network-analysis","name":"Two-mode Network Analysis","fullName":"Two-mode Network Analysis (Bipartite Graph Analysis)","aliases":["bipartite network analysis","affiliation network analysis","two-mode SNA","dual-projection network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1974","originator":"Breiger, R. L.","url":"https://scholargate.app/en/network-analysis/two-mode-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/two-mode-network-analysis.md","definition":"Two-mode network analysis examines networks built from two distinct types of nodes — such as actors and events, authors and papers, or companies and board members — connected only across types. By analysing this bipartite structure directly or projecting it onto one-mode networks, researchers uncover affiliation patterns, shared memberships, and structural duality that are invisible in standard one-mode social network analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Breiger, R. L.","year":"1974","type":"Bipartite graph analysis","dataType":"Affiliation / membership data (two distinct node sets)","subfamily":"Network science"},"citations":[{"ref":"Breiger, R. L. (1974). The duality of persons and groups. Social Forces, 53(2), 181–190.","type":"article","doi":"10.2307/2576011","isbn":null,"url":null},{"ref":"Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.","type":"article","doi":"10.1016/S0378-8733(96)00301-2","isbn":null,"url":null}],"related":["social-network-analysis","modularity-analysis","exponential-random-graph-model","betweenness-centrality","community-detection","multiplex-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"two-pl-irt","name":"2PL IRT","fullName":"Two-Parameter Logistic Item Response Model","aliases":["two-parameter logistic model","2PL model","2PL IRT — İki Parametreli Madde Tepki Modeli"],"domain":"psychometrics","family":"latent-structure","subfamily":null,"year":1980,"originator":"Frederic M. Lord","url":"https://scholargate.app/en/psychometrics/two-pl-irt","markdownUrl":"https://scholargate.app/en/psychometrics/two-pl-irt.md","definition":"The two-parameter logistic item response model, formalised by Frederic Lord (1980), describes the probability that a respondent answers a binary test item correctly as a smooth S-shaped function of the respondent's latent ability. By estimating a separate discrimination parameter for each item alongside a difficulty parameter, 2PL allows items to differ in how sharply they distinguish high- from low-ability respondents — making it the standard model for large-scale educational and psychological assessments.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frederic M. Lord","year":1980,"type":"Item response model / latent trait model","parameters":"Discrimination (a) and difficulty (b) per item","outcome":"Item characteristic curves and latent ability (θ) estimates","data":"Binary scored items (0/1)","min_sample":200,"response_function":"Logistic"},"citations":[{"ref":"Lord, F. M. (1980). Applications of Item Response Theory to Practical Testing Problems. Erlbaum.","type":"book","doi":null,"isbn":null,"url":"https://www.worldcat.org/title/applications-of-item-response-theory-to-practical-testing-problems/oclc/6921913"},{"ref":"Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Erlbaum.","type":"book","doi":null,"isbn":"978-0805828191","url":null}],"related":["rasch-model","three-pl-irt","graded-response-model","exploratory-factor-analysis","confirmatory-factor-analysis","cronbach-alpha"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"two-stage-least-squares","name":"2SLS Regression","fullName":"Two-Stage Least Squares (Instrumental Variables) Regression","aliases":["two-stage least squares","2SLS","instrumental variables regression","IV regression","İki Aşamalı En Küçük Kareler (2SLS/IV)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2009,"originator":"Angrist & Pischke (textbook treatment); classical instrumental-variables estimation","url":"https://scholargate.app/en/econometrics/two-stage-least-squares","markdownUrl":"https://scholargate.app/en/econometrics/two-stage-least-squares.md","definition":"Two-Stage Least Squares is a two-step instrumental-variables estimator that addresses endogeneity, the situation where a regressor is correlated with the error term. In the first stage the endogenous regressor is predicted from instrumental variables, and in the second stage the structural equation is estimated using those predictions. It is a central tool in applied econometrics, developed in textbook treatments such as Angrist and Pischke (2009).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angrist & Pischke (textbook treatment); classical instrumental-variables estimation","year":2009,"type":"Instrumental-variables linear regression","estimator":"Two-stage least squares (IV)","outcome":"continuous or binary","minSample":100,"difficulty":3},"citations":[{"ref":"Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.","type":"book","doi":null,"isbn":"978-0691120355","url":null}],"related":["ols-regression","gmm-estimation","panel-fixed-effects","tobit-model","quantile-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"two-way-anova","name":"Two-Way ANOVA","fullName":"Two-Way Analysis of Variance","aliases":["factorial ANOVA","two-factor ANOVA","İki Yönlü ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1925,"originator":"Ronald A. Fisher","url":"https://scholargate.app/en/statistics/two-way-anova","markdownUrl":"https://scholargate.app/en/statistics/two-way-anova.md","definition":"Two-Way ANOVA is a parametric hypothesis test that simultaneously examines the main effects of two independent categorical factors and their interaction effect on a single continuous dependent variable. The technique was developed within the broader framework of the analysis of variance established by Ronald A. Fisher in 1925 and remains the standard approach whenever an experiment or survey includes exactly two between-subjects factors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronald A. Fisher","year":1925,"family":"Hypothesis test","type":"Parametric factorial mean comparison","factors":2,"outcome":"continuous","parametric":true,"distribution":"F","effects":"main effect A, main effect B, interaction A×B","minSample":40},"citations":[{"ref":"Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119113478","url":null}],"related":["one-way-anova","repeated-measures-anova","ancova","manova","kruskal-wallis","welch-anova"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"type-i-type-ii-error","name":"Type I and Type II Errors","fullName":"Type I and Type II Errors: Understanding False Positives and False Negatives in Hypothesis Testing","aliases":["alpha error","beta error","false positive","false negative"],"domain":"research-statistics","family":"process-pipeline","subfamily":"hypothesis-testing-errors","year":1933,"originator":"Jerzy Neyman and Egon Pearson","url":"https://scholargate.app/en/research-statistics/type-i-type-ii-error","markdownUrl":"https://scholargate.app/en/research-statistics/type-i-type-ii-error.md","definition":"In hypothesis testing, two types of errors can occur: Type I error (false positive, rejecting a true null hypothesis) and Type II error (false negative, failing to reject a false null hypothesis). Formalized by Neyman and Pearson (1933), these errors are at the heart of statistical decision-making. The probability of Type I error is controlled by the significance level α (conventionally 0.05); the probability of Type II error is β, and power = 1 − β. Understanding and balancing these errors is critical for designing robust, reliable research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jerzy Neyman and Egon Pearson","subfamily":"hypothesis-testing-errors","year":1933,"type":"Concept"},"citations":[{"ref":"Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, 231, 289–337.","type":"article","doi":"10.1098/rsta.1933.0009","isbn":null,"url":null},{"ref":"Altman, D. G., & Bland, J. M. (1994). Statistics notes: Diagnostic tests 1: sensitivity and specificity. BMJ, 308(6943), 1552.","type":"article","doi":"10.1136/bmj.308.6943.1552","isbn":null,"url":null},{"ref":"Lehmann, E. L., & Romano, J. P. (2005). Testing Statistical Hypotheses (3rd ed.). Springer.","type":"book","doi":null,"isbn":"0-387-98864-5","url":null}],"related":["p-value-significance","statistical-power","null-hypothesis","confidence-interval"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"type-ia-sn-light-curve-fitting","name":"Type Ia SN Light Curve Fitting","fullName":"Type Ia Supernova Light Curve Fitting for Distance Measurements","aliases":["Supernova Light Curve Analysis","SN Ia Standardization","SALT2 Fitting"],"domain":"astronomy","family":"process-pipeline","subfamily":"Observational cosmology","year":1993,"originator":"Mark Phillips","url":"https://scholargate.app/en/astronomy/type-ia-sn-light-curve-fitting","markdownUrl":"https://scholargate.app/en/astronomy/type-ia-sn-light-curve-fitting.md","definition":"Type Ia supernova light curve fitting is a technique for measuring cosmic distances by observing the brightness evolution of thermonuclear explosions in binary star systems. Developed systematically by Mark Phillips in 1993, this method revealed that SNe Ia can be standardized to provide precise distance measurements, playing a central role in the discovery of cosmic acceleration and dark energy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark Phillips","subfamily":"Observational cosmology","year":1993,"type":"Distance measurement method"},"citations":[{"ref":"Phillips, M. M. (1993). The absolute magnitudes of Type IA supernovae. Astrophysical Journal Letters, 413(2), L105-L108.","type":"article","doi":"10.1086/186970","isbn":null,"url":null},{"ref":"Guy, J., et al. (2005). SALT: a spectral adaptation list for type Ia supernova. Astronomy & Astrophysics, 443(3), 781-791.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=SALT%3A+a+spectral+adaptation+list+for+type+Ia+supernova+Guy"},{"ref":"Betoule, M., et al. (2014). Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples. Astronomy & Astrophysics, 568, A22.","type":"article","doi":"10.1051/0004-6361/201423413","isbn":null,"url":null}],"related":["radiative-transfer","sed-fitting","weak-gravitational-lensing"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"typical-case-sampling","name":"Typical Case Sampling","fullName":"Typical Case Purposive Sampling","aliases":["typical case selection","modal case sampling","representative case sampling","average case sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1980s (systematized in Patton 1990/2002)","originator":"Michael Quinn Patton","url":"https://scholargate.app/en/survey-methodology/typical-case-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/typical-case-sampling.md","definition":"Typical case sampling is a purposive strategy in which the researcher deliberately selects cases that represent what is ordinary, normal, or most common within a target group. Rather than seeking outliers or the widest possible variation, the goal is to illustrate and communicate what a typical experience, program, or phenomenon looks like to stakeholders or audiences unfamiliar with it. The strategy is widely used in qualitative evaluation research and program reporting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Quinn Patton","year":"1980s (systematized in Patton 1990/2002)","type":"Purposive qualitative sampling strategy","dataType":"Qualitative or mixed data from interviews, observations, documents","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative Data Analysis: A Methods Sourcebook (3rd ed.). Sage Publications.","type":"book","doi":null,"isbn":"978-1452257877","url":null}],"related":["purposive-sampling","maximum-variation-sampling","deviant-case-sampling","snowball-sampling","theoretical-sampling","stratified-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"typography-legibility-test","name":"Typography Legibility Testing","fullName":"Typography Legibility Testing","aliases":["Typeface Readability Assessment","Font Performance Evaluation"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Typography and readability research","year":"1963","originator":"Miles A. Tinker","url":"https://scholargate.app/en/visual-arts/typography-legibility-test","markdownUrl":"https://scholargate.app/en/visual-arts/typography-legibility-test.md","definition":"Typography Legibility Testing is a systematic method for evaluating how easily and accurately audiences can read typefaces in specific contexts. Pioneered by Miles A. Tinker in the mid-twentieth century, this pipeline combines perceptual metrics, user testing, and psychophysical measurement to ensure text achieves optimal clarity for its intended medium and audience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Miles A. Tinker","subfamily":"Typography and readability research","year":"1963","type":"Empirical test pipeline"},"citations":[{"ref":"Tinker, M. A. (1963). Legibility of Print. Iowa State University Press.","type":"book","doi":null,"isbn":"978-0065062007","url":null},{"ref":"Dyson, M. C., & Kipping, G. J. (2004). The Legibility of Typefaces for Children. Journal of Typographic Research, 1(4), 16–32.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Legibility+of+Typefaces+for+Children+Dyson"},{"ref":"Richardson, J. T. E., & Alcantara, J. (2003). The Effect of Font Size on Reading Speed and Comprehension in Older Adults. Journals of Gerontology Series B, 58(4), 230–237.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Effect+of+Font+Size+on+Reading+Speed+and+Comprehension+in+Older+Adults+Richardson"}],"related":["color-harmony-analysis","image-aesthetics-assessment","visual-balance-measurement","contrast-ratio-measurement","gestalt-principles-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"typological-analysis","name":"Typological Analysis","fullName":"Typological Analysis in Humanities and Social Sciences","aliases":["typology construction","artifact typology","type analysis","classificatory typology"],"domain":"field-methods","family":"process-pipeline","subfamily":"Domain-specific humanities/social science","year":"Late 19th century (Montelius ~1885); extended broadly through 20th century","originator":"Oscar Montelius (seriation/typology in archaeology); formalized across disciplines through 19th–20th c. comparative humanities","url":"https://scholargate.app/en/field-methods/typological-analysis","markdownUrl":"https://scholargate.app/en/field-methods/typological-analysis.md","definition":"Typological analysis is a systematic method for grouping objects, texts, legal categories, or social phenomena into defined types based on shared attributes. Originating in archaeology and linguistics, it is now widely applied across the humanities and social sciences to impose analytical order on diverse corpora, trace historical change, and enable meaningful comparison across cases or cultures.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oscar Montelius (seriation/typology in archaeology); formalized across disciplines through 19th–20th c. comparative humanities","year":"Late 19th century (Montelius ~1885); extended broadly through 20th century","type":"Classificatory / interpretive method","dataType":"Physical artifacts, texts, legal/social categories, linguistic forms, cultural objects","subfamily":"Domain-specific humanities/social science"},"citations":[{"ref":"McKern, W. C. (1939). The Midwestern Taxonomic Method as an aid to archaeological culture study. American Antiquity, 4(4), 301–313.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=McKern+Midwestern+Taxonomic+Method+1939"},{"ref":"Typology (archaeology). Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Typology_(archaeology)"}],"related":["archaeological-stratigraphy","hermeneutic-analysis","textual-criticism","comparative-legal-analysis","content-analysis","case-study"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"u-midas","name":"U-MIDAS","fullName":"Unrestricted MIDAS Regression","aliases":["Unrestricted Mixed Data Sampling"],"domain":"econometrics","family":"regression-model","subfamily":"Mixed-frequency","year":"2007","originator":"Eric Ghysels","url":"https://scholargate.app/en/econometrics/u-midas","markdownUrl":"https://scholargate.app/en/econometrics/u-midas.md","definition":"U-MIDAS (Unrestricted MIDAS) is a regression framework designed to handle mixed-frequency data—when explanatory variables arrive at different sampling frequencies (e.g., monthly GDP mixed with daily stock returns). Introduced by Ghysels and colleagues (2007), it eliminates the restrictive lag-structure polynomial constraints of the original MIDAS approach, allowing fuller use of high-frequency information. This flexibility makes it ideal for nowcasting and real-time economic forecasting.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eric Ghysels","subfamily":"Mixed-frequency","year":"2007","type":"Time-series regression"},"citations":[{"ref":"Foroni, C., Ghysels, E., & Marcellino, M. (2015). Mixed-frequency vector autoregressive models. International Journal of Forecasting, 31(4), 1051-1070.","type":"article","doi":"10.1108/s0731-905320130000031007","isbn":null,"url":null},{"ref":"Ghysels, E., Santa-Clara, P., & Valkanov, R. (2007). There is a risk-return trade-off after all. Journal of Financial Economics, 76(3), 674-704.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=There+is+a+risk-return+trade-off+after+all+Ghysels"}],"related":["dcc-midas","garch-midas","local-projections"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"u-net","name":"U-Net","fullName":"U-Net: Convolutional Networks for Biomedical Image Segmentation","aliases":["U-Net","UNet","encoder-decoder with skip connections","fully convolutional segmentation network","biomedical segmentation CNN"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2015,"originator":"Ronneberger, O., Fischer, P., & Brox, T.","url":"https://scholargate.app/en/deep-learning/u-net","markdownUrl":"https://scholargate.app/en/deep-learning/u-net.md","definition":"U-Net is a fully convolutional encoder-decoder architecture, introduced by Ronneberger, Fischer, and Brox at MICCAI 2015, that produces dense pixel-wise segmentation masks by combining a contracting path that captures context with a symmetric expanding path that enables precise localization — all bridged by skip connections that preserve fine spatial detail. It established the standard baseline for biomedical image segmentation and has since become one of the most widely adopted architectures for any pixel-level prediction task.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ronneberger, O., Fischer, P., & Brox, T.","year":2015,"type":"Encoder-decoder convolutional network with skip connections","task":"Semantic (pixel-level) image segmentation","venue":"MICCAI 2015","primaryDomain":"Biomedical image analysis","inputType":"2-D (or 3-D) images","outputType":"Dense pixel-wise class map (segmentation mask)"},"citations":[{"ref":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In N. Navab et al. (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, LNCS 9351 (pp. 234–241). Springer.","type":"article","doi":"10.1007/978-3-319-24574-4_28","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["convolutional-neural-network","fully-convolutional-network","resnet","vgg","deeplabv3","mask-rcnn"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ucla-loneliness-scale","name":"UCLA Loneliness Scale","fullName":"University of California, Los Angeles Loneliness Scale (UCLA LS)","aliases":["UCLA LS","UCLA Loneliness Scale","Russell Loneliness Scale"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Self-report questionnaire","year":"1978","originator":"Daniel Russell","url":"https://scholargate.app/en/social-psychology/ucla-loneliness-scale","markdownUrl":"https://scholargate.app/en/social-psychology/ucla-loneliness-scale.md","definition":"The UCLA Loneliness Scale is a widely used instrument for measuring subjective feelings of loneliness and social isolation. Developed by Daniel Russell in the late 1970s, the scale measures the discrepancy between desired and actual social relationships. The UCLA LS has become the gold standard in loneliness research and is used across clinical, epidemiological, and social psychology studies worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daniel Russell","subfamily":"Self-report questionnaire","year":"1978","type":"Subjective loneliness assessment scale"},"citations":[{"ref":"Russell, D. W. (1996). UCLA Loneliness Scale (Version 3): Reliability, validity, and factor structure. Journal of Personality Assessment, 66(1), 20–40.","type":"article","doi":"10.1207/s15327752jpa6601_2","isbn":null,"url":null},{"ref":"Russell, D., Peplau, L. A., & Ferguson, M. L. (1978). Developing a measure of loneliness. Journal of Personality Assessment, 42(3), 290–294.","type":"article","doi":"10.1207/s15327752jpa4203_11","isbn":null,"url":null},{"ref":"Hughes, M. E., Waite, L. J., Hawkley, L. C., & Cacioppo, J. T. (2004). A short scale for measuring loneliness in large surveys. Research on Aging, 26(6), 655–672.","type":"article","doi":"10.1177/0164027504268574","isbn":null,"url":null}],"related":["rosenberg-self-esteem-scale","toronto-empathy-questionnaire","self-compassion-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ucla-prostate-cancer-index","name":"UCLA Prostate Cancer Index","fullName":"UCLA Prostate Cancer Index","aliases":["UCLA PCI","PCI"],"domain":"oncology","family":"process-pipeline","subfamily":"cancer-specific quality of life, prostate cancer","year":"1998","originator":"Litwin, M. S., et al.","url":"https://scholargate.app/en/oncology/ucla-prostate-cancer-index","markdownUrl":"https://scholargate.app/en/oncology/ucla-prostate-cancer-index.md","definition":"The UCLA Prostate Cancer Index (UCLA PCI) is a 20-item, prostate-cancer-specific quality-of-life instrument focused on functional outcomes (urinary, sexual, bowel) rather than general cancer QoL. Developed by Litwin et al. in 1998, it has become the standard functional assessment tool in prostate cancer outcomes research, particularly for treatment comparison studies evaluating surgical vs. radiation outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Litwin, M. S., et al.","subfamily":"cancer-specific quality of life, prostate cancer","year":"1998","type":"Self-report questionnaire"},"citations":[{"ref":"Litwin, M. S., Hays, R. D., Fink, A., Ganz, P. A., Leake, B., & Brook, R. H. (1998). The UCLA Prostate Cancer Index: development, reliability, and validity of a health-related quality of life measure. Med Care, 36(7), 1002–1012.","type":"article","doi":"10.1097/00005650-199807000-00007","isbn":null,"url":null},{"ref":"Litwin, M. S., & Tan, H. J. (2017). The diagnosis and treatment of prostate cancer: a review. JAMA, 317(24), 2532–2542.","type":"article","doi":"10.1001/jama.2017.7248","isbn":null,"url":null}],"related":["fact-prostate","cancer-worry-scale","eortc-qlq-lc13","eortc-qlq-br23","fact-lung"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ulrichsweb","name":"Ulrichsweb Global Serials Directory","fullName":"Ulrichsweb: A Global Serials Directory","aliases":["Ulrichsweb","Ulrich's International Periodicals Directory","Ulrich's Directory"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"journal directory and verification tools","year":1932,"originator":"Bowker (originally R.R. Bowker), now ProQuest LLC","url":"https://scholargate.app/en/bibliometrics/ulrichsweb","markdownUrl":"https://scholargate.app/en/bibliometrics/ulrichsweb.md","definition":"Ulrichsweb is a comprehensive, subscription-based global serials directory cataloging over 300,000 print and electronic journals, magazines, newspapers, and other periodical publications. Developed by ProQuest (originally R.R. Bowker), Ulrichsweb has served librarians and researchers for over 90 years as the authoritative source for journal metadata, publication information, and peer-review verification. A unique feature is Ulrichsweb's verification program: editorial staff contact journals directly to confirm peer-review claims, marking journals as 'refereed' only after validation. This verification process distinguishes legitimate peer-reviewed journals from predatory publishers falsely claiming peer review.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bowker (originally R.R. Bowker), now ProQuest LLC","subfamily":"journal directory and verification tools","year":1932,"type":"Database"},"citations":[{"ref":"ProQuest. (2024). Ulrichsweb: Global Serials Directory. Retrieved from https://ulrichsweb.serialssolutions.com/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=ProQuest.%20(2024).%20Ulrichsweb%3A%20Global%20Serials%20Directory.%20Retrieved%20from%20https%3A%2F%2Fulrichsweb.serialssolutions.com%2F"},{"ref":"ProQuest. (2023). Ulrichsweb Features & Journal Verification. https://www.proquest.com/products-services/ulrichsweb-global-serials-directory.html","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=ProQuest.%20(2023).%20Ulrichsweb%20Features%20%26%20Journal%20Verification.%20https%3A%2F%2Fwww.proquest.com%2Fproducts-services%2Fulrichsweb-glob"}],"related":["doaj-directory","web-of-science","scopus-database","journal-citation-reports","issn"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ultimatum-game","name":"Ultimatum Game","fullName":"Ultimatum Game","aliases":["Ultimatum Bargaining","Division Game"],"domain":"psychology","family":"hypothesis-test","subfamily":"Economic Behavior","year":"1982","originator":"Werner Güth, Rolf Schmittberger, and Bernd Schwarze","url":"https://scholargate.app/en/psychology/ultimatum-game","markdownUrl":"https://scholargate.app/en/psychology/ultimatum-game.md","definition":"The Ultimatum Game is a two-player economic decision-making task that reveals preferences for fairness and social norms. One player (proposer) receives money and offers a portion to a second player (responder). The responder accepts or rejects the offer; if accepted, both receive their share; if rejected, both receive nothing. Economic theory predicts responders should accept any positive offer (better than zero), yet responders often reject unfair offers. This gap between predictions and behavior reveals that fairness concerns, equity sensitivity, and social punishment shape economic decisions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Werner Güth, Rolf Schmittberger, and Bernd Schwarze","subfamily":"Economic Behavior","year":"1982","type":"Behavioral economics task"},"citations":[{"ref":"Güth, W., Schmittberger, R., & Schwarze, B. (1982). An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization, 3(4), 367-388.","type":"article","doi":"10.1016/0167-2681(82)90011-7","isbn":null,"url":null},{"ref":"Henrich, J., Boyd, R., Bowles, S., et al. (2005). 'Economic man' in cross-cultural perspective: Behavioral experiments in 15 small-scale societies. Behavioral and Brain Sciences, 28(6), 795-855.","type":"article","doi":"10.1017/S0140525X05000142","isbn":null,"url":null},{"ref":"Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the ultimatum game. Science, 300(5626), 1755-1758.","type":"article","doi":"10.1126/science.1082976","isbn":null,"url":null}],"related":["dictator-game","economic-decision-making","fairness-norm","social-preference"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ultrasonography-veterinary","name":"Ultrasonography in Veterinary Medicine","fullName":"Systematic Ultrasound Imaging and Interpretation in Veterinary Diagnostic Medicine","aliases":["ultrasound examination","sonography","B-mode imaging"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Diagnostic imaging","year":"1960s-present","originator":"Medical ultrasound adapted for veterinary use","url":"https://scholargate.app/en/veterinary-medicine/ultrasonography-veterinary","markdownUrl":"https://scholargate.app/en/veterinary-medicine/ultrasonography-veterinary.md","definition":"Ultrasonography is a diagnostic imaging method using high-frequency sound waves to create real-time images of internal structures. Adapted from human medical ultrasound beginning in the 1960s-1970s, veterinary ultrasonography is now essential for soft tissue imaging, particularly for abdominal, cardiac, and thoracic assessment. Modern portable ultrasound units enable point-of-care evaluation, and Doppler techniques allow assessment of blood flow and cardiac function.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Medical ultrasound adapted for veterinary use","subfamily":"Diagnostic imaging","year":"1960s-present","type":"Diagnostic imaging pipeline"},"citations":[{"ref":"Mattoon, J. S., Selberg, R. B. (2015). Ultrasound Physics and Instrumentation. In D. E. Thrall (Ed.), Textbook of Veterinary Diagnostic Radiology (7th ed., pp. 120-160). St. Louis, MO: Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"},{"ref":"Lisciandro, G. R., Lagutchik, M. S., Mann, K. A., et al. (2016). Accuracy of Point-of-Care Lung Ultrasonography for Diagnosis of Pneumothorax in Dogs and Cats. Journal of the American Veterinary Medical Association, 248(4), 399-404.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Accuracy+of+Point-of-Care+Lung+Ultrasonography+for+Diagnosis+of+Pneumothorax+in+Dogs+and+Cats+Lisciandro"},{"ref":"Kealy, J. K., McAllister, H., Graham, J. P. (2011). Diagnostic Radiology and Ultrasonography of the Dog and Cat (5th ed.). St. Louis, MO: Elsevier Saunders.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com"}],"related":["radiographic-assessment-veterinary","blood-gas-analysis-veterinary","clinical-scoring-system-veterinary"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"umap-reduction","name":"UMAP","fullName":"Uniform Manifold Approximation and Projection","aliases":["UMAP (Uniform Manifold Approximation and Projection)","uniform manifold approximation and projection","manifold dimension reduction"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2018,"originator":"McInnes, L.; Healy, J.; Melville, J.","url":"https://scholargate.app/en/machine-learning/umap-reduction","markdownUrl":"https://scholargate.app/en/machine-learning/umap-reduction.md","definition":"UMAP (Uniform Manifold Approximation and Projection) is a fast, scalable nonlinear dimension-reduction method grounded in manifold-learning theory, introduced by McInnes, Healy and Melville in 2018. It compresses high-dimensional data into a low-dimensional embedding for visualisation and downstream analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"McInnes, L.; Healy, J.; Melville, J.","year":2018,"type":"Nonlinear manifold-learning dimension reduction","task":"Dimension reduction & visualisation","minSample":50},"citations":[{"ref":"McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1802.03426"}],"related":["pca","t-sne","k-means","factor-analysis","random-forest"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"umbrella-review","name":"Umbrella Review","fullName":"Umbrella Review (Systematic Review of Systematic Reviews)","aliases":["Overview of Reviews","Meta-Review","Review of Reviews"],"domain":"evidence-synthesis","family":"process-pipeline","subfamily":"Evidence Synthesis Integration","year":"2009","originator":"Grant & Booth (2009), Refined by AMSTAR-2 (Shea et al., 2017)","url":"https://scholargate.app/en/evidence-synthesis/umbrella-review","markdownUrl":"https://scholargate.app/en/evidence-synthesis/umbrella-review.md","definition":"An umbrella review is a systematic synthesis of multiple systematic reviews addressing overlapping or related research questions, typically on the same topic or intervention. Also called a 'review of reviews' or 'overview of reviews,' umbrella reviews consolidate evidence when two or more high-quality systematic reviews exist on the same clinical question. Grant and Booth (2009) formally categorized this methodology; Shea et al. (2017) developed AMSTAR-2, the critical appraisal tool for assessing the quality of included reviews. Umbrella reviews are essential when numerous systematic reviews produce conflicting conclusions, when rapid synthesis of evidence is needed for policy or clinical guidance, or when evidence has accumulated faster than any single systematic review can capture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grant & Booth (2009), Refined by AMSTAR-2 (Shea et al., 2017)","subfamily":"Evidence Synthesis Integration","year":"2009","type":"Framework"},"citations":[{"ref":"Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108.","type":"article","doi":"10.1111/j.1471-1842.2009.00848.x","isbn":null,"url":null},{"ref":"Moher, D., Shamseer, L., Clarke, M., et al. (2015). Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4, 1.","type":"article","doi":"10.1186/2046-4053-4-1","isbn":null,"url":null},{"ref":"Shea, B. J., Reeves, B. C., Wells, G., et al. (2017). AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 358, j4008.","type":"article","doi":"10.1136/bmj.j4008","isbn":null,"url":null}],"related":["systematic-review","scoping-review-methodology","meta-analysis","evidence-synthesis-framework","rapid-review-methodology"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"uncertainty-quantification","name":"Uncertainty Quantification","fullName":"Uncertainty Quantification (Polynomial Chaos Expansion and Kriging Surrogate)","aliases":["UQ","polynomial chaos expansion","PCE","Kriging surrogate","Gaussian process surrogate","Belirsizlik Nicelleştirme (UQ — Polynomial Chaos, Kriging Surrogate)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"Seminal modern form: 2002","originator":"Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)","url":"https://scholargate.app/en/simulation/uncertainty-quantification","markdownUrl":"https://scholargate.app/en/simulation/uncertainty-quantification.md","definition":"Uncertainty Quantification (UQ) is a computational framework for systematically measuring how uncertainty in the inputs of a model propagates into uncertainty in its outputs. Building on Wiener's polynomial chaos theory (1938) and formalised for general stochastic problems by Xiu and Karniadakis (2002), UQ uses two primary strategies: Polynomial Chaos Expansion (PCE), which represents the model output as a series of orthogonal polynomials matched to the input distributions, and Kriging (Gaussian process) surrogates, which replace an expensive simulation with a fast statistical approximation fitted to a small set of carefully chosen runs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)","year":"Seminal modern form: 2002","type":"Computational uncertainty analysis framework","approaches":"Polynomial Chaos Expansion (PCE) / Kriging (Gaussian process) surrogate","output":"Output distribution, mean, variance, Sobol sensitivity indices","requiresNormal":false,"difficulty":4,"suitablePurposes":"prediction, explanation","suitableVarTypes":"continuous","domains":"engineering, natural sciences, environmental modelling, biological sciences"},"citations":[{"ref":"Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619–644.","type":"article","doi":"10.1137/S1064827501387826","isbn":null,"url":null},{"ref":"Smith, R.C. (2013). Uncertainty Quantification: Theory, Implementation, and Applications. SIAM.","type":"book","doi":null,"isbn":"978-1611973211","url":null}],"related":["monte-carlo-simulation","quasi-monte-carlo","latin-hypercube-sampling","surrogate-optimization","kriging-interpolation","global-sensitivity-analysis","stochastic-differential-equations","variance-reduction-mc","bayesian-optimization","system-dynamics"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"unfolding-model","name":"Unfolding Model","fullName":"Unfolding Models for Preference Data","aliases":["Ideal Point Model","Preferential Choice Scaling","Coombs Unfolding","Katlanma Modeli"],"domain":"statistics","family":"latent-structure","subfamily":"Preference scaling","year":2005,"originator":"Clyde Coombs; Borg & Groenen","url":"https://scholargate.app/en/statistics/unfolding-model","markdownUrl":"https://scholargate.app/en/statistics/unfolding-model.md","definition":"The Unfolding Model is a geometric approach to preference analysis that represents both individuals and choice objects (stimuli) as points in a shared low-dimensional space. Originating with Clyde Coombs's foundational 1950 work on preferential choice and rigorously systematized by Borg and Groenen (2005), the model assumes each person prefers the stimulus closest to their personal ideal point, thereby 'unfolding' rank-order preference data into a joint spatial map.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Clyde Coombs; Borg & Groenen","year":2005,"type":"Preference scaling via ideal-point representation","subfamily":"Preference scaling","input":"Rank-order or rating preference data","output":"Joint map of stimuli and ideal points in low-dimensional space"},"citations":[{"ref":"Borg, I., & Groenen, P. J. F. (2005). Modern Multidimensional Scaling: Theory and Applications (2nd ed.). Springer.","type":"book","doi":null,"isbn":"978-0-387-25150-9","url":null}],"related":["mds","correspondence-analysis","bradley-terry-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"unifac","name":"UNIFAC","fullName":"UNIFAC (Universal Functional-group Activity Coefficient) Model","aliases":["UNIFAC predictive model","UNIQUAC functional-group contribution"],"domain":"applied-physics","family":"process-pipeline","subfamily":"Thermodynamic Modeling","year":"1975","originator":"Aage Fredenslund, Russell Jones, John Prausnitz","url":"https://scholargate.app/en/applied-physics/unifac","markdownUrl":"https://scholargate.app/en/applied-physics/unifac.md","definition":"UNIFAC (Universal Functional-group Activity Coefficient) is a predictive model for liquid-phase activity coefficients of multicomponent mixtures. Developed by Fredenslund, Jones, and Prausnitz in 1975, it decomposes molecules into functional groups and uses group interaction parameters to estimate non-ideal behavior. UNIFAC is revolutionary because it can predict phase equilibria for mixtures never experimentally measured, making it invaluable for process design and chemical engineering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aage Fredenslund, Russell Jones, John Prausnitz","subfamily":"Thermodynamic Modeling","year":"1975","type":"Activity coefficient model; predictive liquid-phase property method"},"citations":[{"ref":"Fredenslund, A., Jones, R. L., & Prausnitz, J. M. (1975). Group-contribution estimation of activity coefficients in nonideal liquid mixtures. AIChE Journal, 21(6), 1086-1099.","type":"article","doi":"10.1002/aic.690210607","isbn":null,"url":null},{"ref":"Prausnitz, J. M., Lichtenthaler, R. N., & de Azevedo, E. G. (2001). Molecular Thermodynamics of Fluid-Phase Equilibria (3rd ed.). Prentice Hall.","type":"book","doi":null,"isbn":"978-0-13-977745-6","url":null},{"ref":"Gmehling, J., Li, J., & Schiller, M. (1993). A modified UNIFAC model for the prediction of phase equilibria and excess enthalpies. Industrial & Engineering Chemistry Research, 32(1), 178-193.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+modified+UNIFAC+model+for+the+prediction+of+phase+equilibria+and+excess+enthalpies+Gmehling"}],"related":["peng-robinson-equation-of-state","pinch-analysis","reactive-distillation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"unified-huntington-disease-rating","name":"UHDRS","fullName":"Unified Huntington's Disease Rating Scale","aliases":["UHDRS","Huntington's Rating Scale"],"domain":"neurology","family":"process-pipeline","subfamily":"Huntington's disease severity and progression","year":"1996","originator":"Huntington Study Group","url":"https://scholargate.app/en/neurology/unified-huntington-disease-rating","markdownUrl":"https://scholargate.app/en/neurology/unified-huntington-disease-rating.md","definition":"The Unified Huntington's Disease Rating Scale (UHDRS) is the comprehensive, multidomain assessment instrument for Huntington's disease, a neurodegenerative disorder caused by expanded CAG trinucleotide repeats. Developed by the Huntington Study Group in 1996, the UHDRS measures motor, cognitive, functional, and psychiatric manifestations of disease. The UHDRS is the gold-standard outcome measure in Huntington's disease clinical trials and longitudinal natural history studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Huntington Study Group","subfamily":"Huntington's disease severity and progression","year":"1996","type":"Clinician-rated and performance-based"},"citations":[{"ref":"Huntington Study Group (1996). Unified Huntington's Disease Rating Scale: Reliability and consistency. Movement Disorders, 11(2), 136-142.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Unified+Huntington%27s+Disease+Rating+Scale%3A+Reliability+and+consistency+Huntington"}],"related":["updrs","edss-multiple-sclerosis","tardive-dyskinesia-rating-scale","msfc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"unit-commitment","name":"Unit Commitment","fullName":"Unit Commitment for Power Generation Scheduling","aliases":["UC","Generator Commitment","Thermal Unit Scheduling"],"domain":"electrical-engineering","family":"process-pipeline","subfamily":"Integer programming, scheduling","year":"1959","originator":"Charles J. Baldwin","url":"https://scholargate.app/en/electrical-engineering/unit-commitment","markdownUrl":"https://scholargate.app/en/electrical-engineering/unit-commitment.md","definition":"Unit Commitment (UC) is the problem of deciding which power generation units should be switched on or off over a planning horizon (typically 24-168 hours) to minimize total operating cost while meeting demand and reserve requirements. Introduced by Baldwin et al. in 1959, UC is a fundamental scheduling problem in power system operations, combining combinatorial optimization (which units to commit) with continuous optimization (optimal power output). UC remains one of the most important and computationally challenging problems in power systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charles J. Baldwin","subfamily":"Integer programming, scheduling","year":"1959","type":"Combinatorial optimization for generator turn-on/turn-off scheduling"},"citations":[{"ref":"Baldwin, C. J., Dale, K. M., & Dittrich, R. F. (1959). A study of the economic shutdown of generating units in daily dispatch. AIEE Transactions, 78(3), 272-282.","type":"article","doi":null,"isbn":null,"url":"https://ieeexplore.ieee.org/document/4066301"},{"ref":"Padhy, N. P. (2004). Unit commitment in power systems. International Journal of Electrical Power & Energy Systems, 26(5), 363-375.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Unit+commitment+in+power+systems+Padhy"},{"ref":"Wood, A. J., Wollenberg, B. F., & Sheblé, G. B. (2013). Power Generation, Operation, and Control (3rd ed.). Wiley-Interscience.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Power+Generation%2C+Operation%2C+and+Control+%283rd+ed.%29+Wood"}],"related":["economic-dispatch","optimal-power-flow","power-system-state-estimation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"unit-hydrograph","name":"Unit Hydrograph","fullName":"Unit Hydrograph Theory for Rainfall-Runoff Transformation","aliases":["UH","Rainfall-runoff","Hydrograph synthesis"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Hydrology","year":"1932","originator":"L. K. Sherman","url":"https://scholargate.app/en/civil-engineering/unit-hydrograph","markdownUrl":"https://scholargate.app/en/civil-engineering/unit-hydrograph.md","definition":"The unit hydrograph (UH) is a linear transformation that converts rainfall excess into streamflow for a watershed. Introduced by Sherman in 1932, the UH assumes that rainfall-runoff response is linear and time-invariant, enabling synthesis of flood hydrographs from design storms for dam spillway design and flood risk assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"L. K. Sherman","subfamily":"Hydrology","year":"1932","type":"Linear transformation from rainfall to streamflow"},"citations":[{"ref":"Sherman, L. K. (1932). Streamflow from rainfall by the unit graph method. Engineering News-Record, 108(14), 501-505.","type":"article","doi":null,"isbn":null,"url":"https://ascelibrary.org"},{"ref":"Snyder, F. F. (1938). Synthetic unit-graphs. Transactions of the American Geophysical Union, 19(1), 447-454.","type":"article","doi":"10.1029/TR019i001p00447","isbn":null,"url":null},{"ref":"Clark, C. O. (1945). Storage and the unit hydrograph. Journal of the American Water Works Association, 37(4), 419-432.","type":"article","doi":null,"isbn":null,"url":"https://www.awwa.org"}],"related":["muskingum-routing","modflow","traffic-flow"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"universal-kriging","name":"Universal Kriging","fullName":"Universal Kriging (Kriging with a Trend)","aliases":["kriging with a trend","kriging with drift","trend kriging","evrensel kriging"],"domain":"spatial-analysis","family":"regression-model","subfamily":"Geostatistics","year":1969,"originator":"Georges Matheron","url":"https://scholargate.app/en/spatial-analysis/universal-kriging","markdownUrl":"https://scholargate.app/en/spatial-analysis/universal-kriging.md","definition":"Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling with latitude or a pollutant gradient, while still returning prediction variances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Georges Matheron","year":1969,"type":"Geostatistical interpolation with spatial trend","subfamily":"Geostatistics","handles":"Non-stationary mean (trend/drift)","output":"Prediction + kriging variance"},"citations":[{"ref":"Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266.","type":"article","doi":"10.2113/gsecongeo.58.8.1246","isbn":null,"url":null},{"ref":"Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0-471-00255-0","url":null}],"related":["kriging","cokriging","inverse-distance-weighting","geographically-weighted-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"universal-soil-loss-equation","name":"Universal Soil Loss Equation","fullName":"Universal Soil Loss Equation","aliases":["USLE","Revised USLE"],"domain":"geophysics","family":"process-pipeline","subfamily":"Soil erosion prediction","year":"1978","originator":"Waldo Wischmeier and Dwight Smith","url":"https://scholargate.app/en/geophysics/universal-soil-loss-equation","markdownUrl":"https://scholargate.app/en/geophysics/universal-soil-loss-equation.md","definition":"The Universal Soil Loss Equation (USLE) is an empirical model that estimates annual soil loss due to sheet and rill erosion on hillslopes caused by rainfall and runoff. Developed by Wischmeier and Smith in 1978 from decades of erosion plot experiments, USLE has become a standard tool for erosion risk assessment, conservation planning, and best management practice design. The Revised USLE (RUSLE) updated the original model with improved factor algorithms.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Waldo Wischmeier and Dwight Smith","subfamily":"Soil erosion prediction","year":"1978","type":"Empirical soil erosion prediction model"},"citations":[{"ref":"Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: A guide to conservation planning. USDA Agricultural Handbook 537.","type":"article","doi":null,"isbn":null,"url":"https://www.ars.usda.gov/"},{"ref":"Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., & Yoder, D. C. (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). USDA Agricultural Handbook 703.","type":"article","doi":null,"isbn":null,"url":"https://www.ars.usda.gov/"}],"related":["swat-model","hec-ras","ndvi"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"university-student-satisfaction","name":"University Student Satisfaction Scale","fullName":"University Student Satisfaction Scale (USS)","aliases":["USS"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"educational-experience-assessment","year":"1997","originator":"Elliot & Shin; variations by institution","url":"https://scholargate.app/en/educational-psychology/university-student-satisfaction","markdownUrl":"https://scholargate.app/en/educational-psychology/university-student-satisfaction.md","definition":"The University Student Satisfaction Scale measures students' satisfaction with their overall university experience, including instruction quality, academic advising, campus services, and campus climate. Multiple validated instruments exist (e.g., Student Satisfaction Index), each capturing dimensions of the student experience considered critical to retention and institutional quality. This tool enables universities to gather comprehensive feedback on the student experience and prioritize institutional improvements that enhance satisfaction and outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Elliot & Shin; variations by institution","subfamily":"educational-experience-assessment","year":"1997","type":"Self-report survey"},"citations":[{"ref":"Elliot, K. M., & Shin, D. (1997). The student satisfaction index (SSI): A new instrument for measuring student satisfaction with higher education. Journal of Marketing for Higher Education, 8(2), 27–44.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+student+satisfaction+index+%28SSI%29%3A+A+new+instrument+for+measuring+student+satisfaction+with+higher+education+Elliot"},{"ref":"Astin, A. W. (1993). What matters in college? Four critical years revisited. Jossey-Bass.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=student+satisfaction+higher+education"}],"related":["classroom-environment-scale","academic-burnout-scale","academic-resilience-scale","peer-learning-scale","academic-help-seeking-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"unscented-kalman-filter","name":"Unscented Kalman Filter","fullName":"Unscented Kalman Filter","aliases":["UKF","Sigma-Point Kalman Filter"],"domain":"control-theory","family":"ml-model","subfamily":"Nonlinear Estimation","year":"1997","originator":"Simon Julier","url":"https://scholargate.app/en/control-theory/unscented-kalman-filter","markdownUrl":"https://scholargate.app/en/control-theory/unscented-kalman-filter.md","definition":"The Unscented Kalman Filter (UKF) is a nonlinear state estimation algorithm that approximates nonlinear systems without requiring explicit Jacobian computation. Introduced by Julier and Uhlmann in 1997, the UKF uses the unscented transform—a deterministic method to capture mean and covariance statistics through a carefully chosen set of sample points (sigma points)—making it more accurate than the Extended Kalman Filter for highly nonlinear systems while avoiding the computational burden of derivative calculations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Simon Julier","subfamily":"Nonlinear Estimation","year":"1997","type":"algorithm"},"citations":[{"ref":"Julier, S. J., & Uhlmann, J. K. (1997). A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 45(3), 477-482.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+method+for+the+nonlinear+transformation+of+means+and+covariances+in+filters+and+estimators+Julier"},{"ref":"Wan, E. A., & Van Der Merwe, R. (2000). The unscented Kalman filter for nonlinear estimation. Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, 153-158.","type":"article","doi":null,"isbn":null,"url":"https://groups.seas.upenn.edu/~jadbabai/teaching/ese650/UKF.pdf"},{"ref":"Sarkka, S. (2013). Bayesian Filtering and Smoothing. Cambridge University Press.","type":"article","doi":"10.1017/CBO9781139344203","isbn":null,"url":null}],"related":["extended-kalman-filter","linear-quadratic-gaussian","simultaneously-localization-and-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"unstructured-interview","name":"Unstructured Interview","fullName":"Unstructured Interview","aliases":["open-ended interview","non-directive interview","in-depth interview","conversational interview"],"domain":"qualitative","family":"process-pipeline","subfamily":"Interview Methods","year":"Mid-20th century (Rogers ~1942; Spradley ~1979)","originator":"Rooted in anthropological and sociological fieldwork traditions; systematised by James P. Spradley and Carl Rogers (non-directive counselling interview)","url":"https://scholargate.app/en/qualitative/unstructured-interview","markdownUrl":"https://scholargate.app/en/qualitative/unstructured-interview.md","definition":"An unstructured interview is a qualitative data-collection method in which the researcher enters the conversation with a broad topic or grand-tour question rather than a fixed questionnaire, allowing the participant to direct the flow and depth of the discussion. The approach prioritises the participant's own conceptual categories and narrative logic over the researcher's pre-formed agenda, making it especially powerful for exploratory inquiry into unfamiliar or complex social phenomena.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rooted in anthropological and sociological fieldwork traditions; systematised by James P. Spradley and Carl Rogers (non-directive counselling interview)","year":"Mid-20th century (Rogers ~1942; Spradley ~1979)","type":"Qualitative research method","dataType":"Spoken narrative (audio/video recordings, field notes, transcripts)","typicalSampleSize":"5–30 participants","subfamily":"Interview Methods"},"citations":[{"ref":"Spradley, J. P. (1979). The Ethnographic Interview. Holt, Rinehart and Winston.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Spradley+The+Ethnographic+Interview+1979"},{"ref":"Fontana, A., & Frey, J. H. (2005). The interview: From neutral stance to political involvement. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage Handbook of Qualitative Research (3rd ed., pp. 695–727). Sage.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Fontana+Frey+The+interview+neutral+stance+political+involvement+2005"}],"related":["phenomenology","ethnography","grounded-theory","narrative-analysis","focus-group","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"updrs","name":"MDS-UPDRS","fullName":"Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale","aliases":["UPDRS"],"domain":"neurology","family":"process-pipeline","subfamily":"Parkinson's Disease severity assessment","year":"2008","originator":"Christopher G. Goetz and Movement Disorder Society","url":"https://scholargate.app/en/neurology/updrs","markdownUrl":"https://scholargate.app/en/neurology/updrs.md","definition":"The MDS-UPDRS is the gold-standard clinician-administered rating scale for assessing motor and non-motor manifestations of Parkinson's disease. Developed by the Movement Disorder Society in 2008 to enhance the original UPDRS, it measures disease severity across daily living, motor function, and treatment complications. Used globally in clinical trials, longitudinal cohort studies, and routine neurological practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher G. Goetz and Movement Disorder Society","subfamily":"Parkinson's Disease severity assessment","year":"2008","type":"Clinician-rated"},"citations":[{"ref":"Goetz, C. G., et al. (2008). Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results. Movement Disorders, 23(15), 2129-2170.","type":"article","doi":"10.1002/mds.22340","isbn":null,"url":null}],"related":["edss-multiple-sclerosis","nihss","msfc","rivermead-mobility-index","unified-huntington-disease-rating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"upper-extremity-functional-scale","name":"UEFS","fullName":"Upper Extremity Functional Scale","aliases":["UEFS","Upper Extremity Functional Status Scale"],"domain":"occupational-therapy","family":"process-pipeline","subfamily":"functional capacity assessment","year":"1990s (occupational therapy version)","originator":"Stratford, P. W., & colleagues (various modifications; occupational therapy adaptations used)","url":"https://scholargate.app/en/occupational-therapy/upper-extremity-functional-scale","markdownUrl":"https://scholargate.app/en/occupational-therapy/upper-extremity-functional-scale.md","definition":"The Upper Extremity Functional Scale (UEFS) is a self-report outcome measure designed to quantify functional limitation and capacity in the upper extremity (arm, hand) across everyday activities. Various versions exist; the most commonly used in occupational therapy and rehabilitation derive from adaptations of functional capacity assessment frameworks, measuring activities such as eating, dressing, grooming, reaching, grasping, and fine motor tasks. The UEFS is widely used in occupational therapy, orthopedic rehabilitation, and ergonomic assessment to track improvement in arm/hand function following injury, surgery, or therapy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stratford, P. W., & colleagues (various modifications; occupational therapy adaptations used)","subfamily":"functional capacity assessment","year":"1990s (occupational therapy version)","type":"Self-report questionnaire, clinician-scored"},"citations":[{"ref":"Stratford, P. W., Binkley, J. M., Riddle, D. L., & Guyatt, G. H. (1996). Sensitivity to change of the Roland-Morris Back Pain Index: Part 1. Physical Therapy, 76(2), 122-133.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/8592712"},{"ref":"Pransky, G., Feuerstein, M., Gatchel, R. J., Linton, S. J., & Volinn, E. (2007). Shoulder disorders: A review of diagnosis, prognosis, and treatment with focus on work. Journal of Occupational Rehabilitation, 17(1), 1-30.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/17256235"}],"related":["copm","nine-hole-peg-test","jebsen-hand-function-test","wolf-motor-function-test"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"uranium-thorium-dating","name":"Uranium-Thorium Dating","fullName":"Uranium-Thorium Dating (U-Th)","aliases":["U-Th dating","thorium-230 dating"],"domain":"archaeology","family":"process-pipeline","subfamily":"Radiometric","year":"1955","originator":"Harmon Craig","url":"https://scholargate.app/en/archaeology/uranium-thorium-dating","markdownUrl":"https://scholargate.app/en/archaeology/uranium-thorium-dating.md","definition":"Uranium-thorium (U-Th) dating is a chronometric method that determines the age of carbonates, shells, bones, and coral by measuring the ratio of uranium isotopes to thorium-230. First applied by Harmon Craig in the 1950s, it exploits the natural radioactive decay chain of uranium. U-Th dating is particularly valuable for dating materials from 500 to 500,000 years old, filling a crucial chronological gap between radiocarbon and potassium-argon dating.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harmon Craig","subfamily":"Radiometric","year":"1955","type":"Decay series dating technique"},"citations":[{"ref":"Edwards, R. L., Chen, J. H., & Wasserburg, G. J. (1987). U-238, U-234 and Th-230 in seawater. Geochimica et Cosmochimica Acta, 51(5), 1213-1225.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=U-238%2C+U-234+and+Th-230+in+seawater+Edwards"},{"ref":"Cheng, H., Edwards, R. L., Hoff, J., Gallup, C. D., Richards, D. A., & Asmerom, Y. (2000). The half-lives of uranium-234 and thorium-230. Chemical Geology, 169(1-2), 17-33.","type":"article","doi":"10.1016/S0009-2541(99)00157-6","isbn":null,"url":null},{"ref":"Pike, A. W. G., Hedges, R. E. M., & van Calsteren, P. (2007). The radioactive decay of uranium-234. Quaternary Geochronology, 2(1-4), 118-124.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+radioactive+decay+of+uranium-234+Pike"}],"related":["optically-stimulated-luminescence-dating","electron-spin-resonance-dating","thermoluminescence-dating","archaeomagnetic-dating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"urban-form-analysis","name":"Urban Form Analysis","fullName":"Urban Form Analysis and Morphological Assessment","aliases":["urban morphology","morphological analysis","urban fabric analysis"],"domain":"architecture","family":"process-pipeline","subfamily":"Urban planning and design","year":"1960","originator":"M.R.G. Conzen","url":"https://scholargate.app/en/architecture/urban-form-analysis","markdownUrl":"https://scholargate.app/en/architecture/urban-form-analysis.md","definition":"Urban Form Analysis is a systematic method for studying and characterizing the physical structure, layout, and historical development of cities and neighborhoods. Pioneered by M.R.G. Conzen in 1960, it examines how blocks, streets, plots, and buildings combine to create distinct urban patterns, and how these patterns influence social interaction, economic vitality, and environmental performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"M.R.G. Conzen","subfamily":"Urban planning and design","year":"1960","type":"morphological urban assessment method"},"citations":[{"ref":"Conzen, M. R. G. (1960). Alnwick, Northumberland: A Study in Town-Plan Analysis. Institute of British Geographers Publication 27.","type":"book","doi":null,"isbn":null,"url":"https://www.ucpress.edu/books/studying-street-systems"},{"ref":"Moudon, A. V. (1997). Urban Morphology as an Emerging Interdisciplinary Field. Urban Morphology, 1(1), 3-10.","type":"article","doi":null,"isbn":null,"url":"https://www.urbanmorphology.org.uk/"},{"ref":"Whitehand, J. W. R., Conzen, M. P. (2009). Urban Landscapes. International Encyclopedia of Human Geography. Elsevier.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/referencework/9780080449166"}],"related":["space-syntax-analysis","wayfinding-analysis","post-occupancy-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"use-case-point-estimation","name":"Use Case Point Estimation","fullName":"Use Case Point-Based Effort Estimation","aliases":["UCP","use case sizing","effort estimation"],"domain":"software-engineering","family":"process-pipeline","subfamily":"Estimation and planning","year":"1993","originator":"Gustav Karner","url":"https://scholargate.app/en/software-engineering/use-case-point-estimation","markdownUrl":"https://scholargate.app/en/software-engineering/use-case-point-estimation.md","definition":"Use case point (UCP) estimation quantifies software development effort by analyzing use cases and environmental factors. Introduced by Karner (1993) for Objectory methodology, UCP provides structured approach to estimate labor hours from system requirements. Organizations use UCP to forecast project duration, allocate resources, and validate high-level project plans early in development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gustav Karner","subfamily":"Estimation and planning","year":"1993","type":"quantitative estimation"},"citations":[{"ref":"Karner, G. (1993). Resource estimation for objectory projects. Objective Systems SF, Inc.","type":"article","doi":null,"isbn":null,"url":"https://www.uml-sysml.org/what-is-uml/historic/documents/uml-original"},{"ref":"Schneider, G., & Winters, J. P. (2001). Applying Use Cases: A Practical Guide (2nd ed.). Addison-Wesley.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/applyingusecases00schn"},{"ref":"Rameyer, B., & Glinz, M. (2007). Validating and improving use case variants. In Proceedings of the ICSE Workshop on Scenarios and State Machines (pp. 1–8).","type":"article","doi":null,"isbn":null,"url":"https://archive.org/search?query=rameyer+glinz+use+case+variants+2007"}],"related":["agile-velocity-tracking","technical-debt-measurement","defect-prediction-model","software-complexity-metrics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"use-wear-analysis","name":"Use-Wear Analysis","fullName":"Use-Wear Analysis (Microwear of Stone Tools)","aliases":["microwear","tool use analysis"],"domain":"archaeology","family":"process-pipeline","subfamily":"Functional Analysis","year":"1980","originator":"Lawrence Keeley","url":"https://scholargate.app/en/archaeology/use-wear-analysis","markdownUrl":"https://scholargate.app/en/archaeology/use-wear-analysis.md","definition":"Use-wear analysis (also called microwear or tool-use analysis) is a method that infers the function of stone tools from microscopic wear patterns on their cutting edges and surfaces. Pioneered by Lawrence Keeley in the 1970s-1980s, this technique examines damage patterns, polishes, and edge rounding produced as tools contact different materials during use. By analyzing these wear patterns, archaeologists can determine whether a tool was used to cut plant material, meat, bone, hide, or wood—revealing detailed information about task specialization and subsistence practices in prehistoric societies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lawrence Keeley","subfamily":"Functional Analysis","year":"1980","type":"Tool function inference"},"citations":[{"ref":"Keeley, L. H. (1980). Experimental Determination of Stone Tool Uses. University of Chicago Press.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Experimental+Determination+of+Stone+Tool+Uses+Keeley"},{"ref":"Grace, R. (1997). The chronology of microwear polish formation. Journal of Archaeological Science, 24(11), 983-998.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+chronology+of+microwear+polish+formation+Grace"},{"ref":"Williamson, B. S. (2003). Lithic microwear analysis. Journal of World Prehistory, 17(3), 277-330.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lithic+microwear+analysis+Williamson"}],"related":["geometric-morphometrics","ceramic-petrography","instrumental-neutron-activation-analysis","dental-microwear-texture-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"user-experience-questionnaire","name":"User Experience Questionnaire","fullName":"User Experience Questionnaire (UEQ)","aliases":["UEQ","UEQ-S"],"domain":"human-factors","family":"process-pipeline","subfamily":"user-experience-assessment","year":2008,"originator":"Bettina Laugwitz, Theo Held, Martin Schrepp","url":"https://scholargate.app/en/human-factors/user-experience-questionnaire","markdownUrl":"https://scholargate.app/en/human-factors/user-experience-questionnaire.md","definition":"The User Experience Questionnaire (UEQ), developed by Laugwitz, Held, and Schrepp in 2008, is a practical instrument for assessing user experience of interactive products and systems. It measures six dimensions of user experience using semantic differential item pairs, balancing comprehensive coverage with brevity (26 items in the full version, 8 items in the short form UEQ-S). The UEQ has become widely adopted in software engineering, human-computer interaction, and usability research to evaluate websites, applications, and digital interfaces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bettina Laugwitz, Theo Held, Martin Schrepp","subfamily":"user-experience-assessment","year":2008,"type":"Self-report"},"citations":[{"ref":"Laugwitz, B., Held, T., & Schrepp, M. (2008). Construction and evaluation of a user experience questionnaire. In A. Holzinger (Ed.), HCI and Usability for Education and Work (LNCS 5298, pp. 63-76). Springer.","type":"article","doi":"10.1007/978-3-540-89350-9_6","isbn":null,"url":null}],"related":["nasa-task-load-index","cognitive-load-scale","interface-usability-measure","operator-performance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"uta","name":"UTA","fullName":"UTilités Additives (Additive Utility Assessment)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1982","originator":"Jacquet-Lagrèze, E., Siskos, J.","url":"https://scholargate.app/en/decision-making/uta","markdownUrl":"https://scholargate.app/en/decision-making/uta.md","definition":"UTA (UTilités Additives (Additive Utility Assessment)) is a ranking multi-criteria decision-making (MCDM) method introduced by Jacquet-Lagrèze, E., Siskos, J. in 1982. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacquet-Lagrèze, E., Siskos, J.","subfamily":"Ranking","year":"1982","type":"Regression-based additive utility elicitation (LP, single-error)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Jacquet-Lagrèze, E., Siskos, J. (1982). Assessing a set of additive utility functions for multicriteria decision-making, the UTA method. European Journal of Operational Research","type":"article","doi":"10.1016/0377-2217(82)90155-2","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"utadis","name":"UTADIS","fullName":"UTilités Additives DIScriminantes (Additive Utility Sorting)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Sorting","year":"1980","originator":"Devaud, J. M., Groussaud, G., Jacquet-Lagrèze, E.","url":"https://scholargate.app/en/decision-making/utadis","markdownUrl":"https://scholargate.app/en/decision-making/utadis.md","definition":"UTADIS (UTilités Additives DIScriminantes (Additive Utility Sorting)) is a sorting multi-criteria decision-making (MCDM) method introduced by Devaud, J. M., Groussaud, G., Jacquet-Lagrèze, E. in 1980. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Devaud, J. M., Groussaud, G., Jacquet-Lagrèze, E.","subfamily":"Sorting","year":"1980","type":"Additive-utility preference-disaggregation sorting via LP-learned class thresholds","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Devaud, J. M., Groussaud, G., Jacquet-Lagrèze, E. (1980). UTADIS: Une méthode de construction de fonctions d'utilité additives rendant compte de jugements globaux. European Working Group on MCDA, Bochum","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=UTADIS%3A%20Une%20m%C3%A9thode%20de%20construction%20de%20fonctions%20d%27utilit%C3%A9%20additives%20rendant%20compte%20de%20jugements%20globaux"}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"utastar","name":"UTASTAR","fullName":"UTA* — Additive utility disaggregation from reference ranking (revised UTA)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1985","originator":"Siskos, Y., Yannacopoulos, D.","url":"https://scholargate.app/en/decision-making/utastar","markdownUrl":"https://scholargate.app/en/decision-making/utastar.md","definition":"UTASTAR (UTA* — Additive utility disaggregation from reference ranking (revised UTA)) is a ranking multi-criteria decision-making (MCDM) method introduced by Siskos, Y., Yannacopoulos, D. in 1985. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Siskos, Y., Yannacopoulos, D.","subfamily":"Ranking","year":"1985","type":"Additive utility disaggregation — LP from ordinal reference judgements","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Siskos, Y., Yannacopoulos, D. (1985). UTASTAR: An ordinal regression method for building additive value functions. Investigación Operativa","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=UTASTAR%3A%20An%20ordinal%20regression%20method%20for%20building%20additive%20value%20functions"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"utaut-questionnaire","name":"UTAUT Questionnaire","fullName":"Unified Theory of Acceptance and Use of Technology (UTAUT) Questionnaire","aliases":["UTAUT","Venkatesh UTAUT"],"domain":"information-systems","family":"process-pipeline","subfamily":"Technology adoption","year":"2003","originator":"Venkatesh, Morris, Davis & Davis","url":"https://scholargate.app/en/information-systems/utaut-questionnaire","markdownUrl":"https://scholargate.app/en/information-systems/utaut-questionnaire.md","definition":"The Unified Theory of Acceptance and Use of Technology (UTAUT) was developed by Venkatesh, Morris, Davis, and Davis in 2003 and published in MIS Quarterly. UTAUT integrates insights from eight prior technology acceptance theories into a unified framework, identifying four core constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—that together predict behavioral intention to use and actual technology adoption.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Venkatesh, Morris, Davis & Davis","subfamily":"Technology adoption","year":"2003","type":"Likert-scale questionnaire"},"citations":[{"ref":"Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance and use of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.","type":"article","doi":"10.2307/30036540","isbn":null,"url":null},{"ref":"Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178.","type":"article","doi":"10.2307/41410412","isbn":null,"url":null}],"related":["tam-questionnaire","tam2-questionnaire","technology-readiness-index","is-success-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"uv-vis-spectrophotometry","name":"UV-Vis Spectrophotometry","fullName":"UV-Vis Spectrophotometry","aliases":["UV-Vis spectroscopy","absorption spectroscopy","colorimetry"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Optical Spectroscopy","year":"1852","originator":"August Beer","url":"https://scholargate.app/en/analytical-chemistry/uv-vis-spectrophotometry","markdownUrl":"https://scholargate.app/en/analytical-chemistry/uv-vis-spectrophotometry.md","definition":"UV-Vis spectrophotometry is an optical analytical technique that measures the absorption of ultraviolet and visible light (wavelengths 190–900 nm) by substances in solution. Founded on the Beer-Lambert law (developed by August Beer and Pierre Bouguer), it is one of the oldest and most widely used quantitative analytical methods. UV-Vis spectrophotometry is economical, rapid, and applicable to a vast range of organic and inorganic compounds, making it indispensable in pharmaceutical, clinical, environmental, and research laboratories.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"August Beer","subfamily":"Optical Spectroscopy","year":"1852","type":"absorption measurement technique"},"citations":[{"ref":"Beer, A. (1852). Bestimmung der Absorption des rothen Lichts in farbigen Flussigkeiten. Annalen der Physik und Chemie, 86(5), 78–88.","type":"article","doi":"10.1002/andp.18521620505","isbn":null,"url":null},{"ref":"Skoog, D. A., West, D. M., Holler, F. J., & Crouch, S. R. (2014). Fundamentals of Analytical Chemistry (9th ed.). Cengage Learning.","type":"book","doi":null,"isbn":"978-1133170960","url":null},{"ref":"Knowles, A., & Burgess, C. (Eds.). (1989). Practical Absorption Spectrometry (2nd ed.). Chapman and Hall.","type":"article","doi":null,"isbn":"978-0412273208","url":null}],"related":["potentiometric-titration","ion-chromatography","atomic-absorption-spectroscopy","coulometry","voltammetry"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"uwes-work-engagement","name":"Utrecht Work Engagement Scale","fullName":"Utrecht Work Engagement Scale (UWES)","aliases":["UWES","Work Engagement Scale","Schaufeli Work Engagement"],"domain":"social-psychology","family":"process-pipeline","subfamily":"Work engagement","year":"2002","originator":"Wilmar Schaufeli, Arnold Bakker, and Marisa Salanova","url":"https://scholargate.app/en/social-psychology/uwes-work-engagement","markdownUrl":"https://scholargate.app/en/social-psychology/uwes-work-engagement.md","definition":"The Utrecht Work Engagement Scale (UWES) is a 17-item instrument measuring work engagement—a positive, fulfilling psychological state characterized by vigor, dedication, and absorption in work. Developed by Wilmar Schaufeli and colleagues in 2002, the UWES operationalizes engagement as the positive antipode to burnout, reflecting energetic involvement, strong commitment, and deep focus in occupational tasks. The scale has become the standard measure for assessing work engagement in organizational research and occupational health.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wilmar Schaufeli, Arnold Bakker, and Marisa Salanova","subfamily":"Work engagement","year":"2002","type":"Occupational well-being and engagement scale"},"citations":[{"ref":"Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92.","type":"article","doi":"10.1023/A:1015630930326","isbn":null,"url":null},{"ref":"Bakker, A. B., & Leiter, M. P. (2010). Work engagement: A handbook of essential theory and research. Psychology Press.","type":"article","doi":null,"isbn":"978-0415873109","url":null},{"ref":"Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire. Educational and Psychological Measurement, 66(4), 701–716.","type":"article","doi":"10.1177/0013164405282471","isbn":null,"url":null}],"related":["maslach-burnout-inventory","generalized-self-efficacy-scale","grit-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"v-measure","name":"V-measure","fullName":"V-measure (Homogeneity and Completeness Harmonic Mean)","aliases":["V-measure score","homogeneity completeness V-measure"],"domain":"model-evaluation","family":"mcdm","subfamily":"External Clustering Validation","year":"2007","originator":"Andrew Rosenberg, Julia Hirschberg","url":"https://scholargate.app/en/model-evaluation/v-measure","markdownUrl":"https://scholargate.app/en/model-evaluation/v-measure.md","definition":"V-measure, introduced by Rosenberg and Hirschberg in 2007, is an external clustering evaluation metric based on the harmonic mean of homogeneity and completeness. It measures whether clusters contain only points from a single true class (homogeneity) and whether all points from a true class are assigned to the same cluster (completeness). Values range from 0 to 1.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Andrew Rosenberg, Julia Hirschberg","subfamily":"External Clustering Validation","year":"2007","type":"Entropy-based metric"},"citations":[{"ref":"Rosenberg, A., & Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 410-420).","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/D07-1043/"}],"related":["normalized-mutual-information","adjusted-rand-index","fowlkes-mallows-index","silhouette-score","davies-bouldin-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vaccination-confidence-scale","name":"Vaccination Confidence Scale","fullName":"WHO Vaccination Confidence Scale (VCS)","aliases":["VCS","WHO Vaccination Confidence Scale"],"domain":"public-health","family":"process-pipeline","subfamily":"immunization-confidence","year":"2015","originator":"WHO SAGE Working Group on Vaccine Hesitancy","url":"https://scholargate.app/en/public-health/vaccination-confidence-scale","markdownUrl":"https://scholargate.app/en/public-health/vaccination-confidence-scale.md","definition":"The WHO Vaccination Confidence Scale (VCS) is a multi-domain instrument measuring three conceptually distinct dimensions of vaccine hesitancy: Confidence (trust in vaccine safety and effectiveness), Complacency (perceived need for vaccination), and Convenience (accessibility and practical barriers). Developed by the WHO SAGE Working Group on Vaccine Hesitancy in 2015, it has become the international standard for measuring determinants of vaccination decisions across diverse populations and pathogen contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"WHO SAGE Working Group on Vaccine Hesitancy","subfamily":"immunization-confidence","year":"2015","type":"Self-report"},"citations":[{"ref":"World Health Organization. (2015). Vaccine hesitancy: A growing challenge for immunization programmes. WHO SAGE Working Group on Vaccine Hesitancy. Geneva: WHO.","type":"report","doi":null,"isbn":null,"url":"https://www.who.int/publications/i/item/vaccine-hesitancy-a-growing-challenge-for-immunization-programmes"},{"ref":"Larson, H. J., Jarrett, C., Schulz, W. S., Chaudhuri, M., Zhou, Y., Dube, E., ... & Wilson, R. (2015). Measuring vaccine hesitancy: The development of a survey tool. Vaccine, 33(34), 4165–4175.","type":"article","doi":"10.1016/j.vaccine.2015.04.037","isbn":null,"url":null}],"related":["covid-19-anxiety-scale","health-protective-behavior-scale","pandemic-fatigue-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vaccination-protocol-design","name":"Vaccination Protocol Design","fullName":"Evidence-Based Vaccination Protocol Design in Veterinary Medicine","aliases":["immunization protocol","vaccine scheduling","vaccination planning"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Preventive medicine","year":"1990s-2000s","originator":"World Small Animal Veterinary Association (WSAVA)","url":"https://scholargate.app/en/veterinary-medicine/vaccination-protocol-design","markdownUrl":"https://scholargate.app/en/veterinary-medicine/vaccination-protocol-design.md","definition":"Vaccination protocol design is a systematic approach to planning and administering immunizations in animals to prevent infectious disease. Formalized by organizations such as the World Small Animal Veterinary Association (WSAVA) from the 1990s onward, evidence-based protocols balance disease risk, individual animal factors, vaccine efficacy, duration of immunity, and regulatory requirements to optimize herd and individual protection.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"World Small Animal Veterinary Association (WSAVA)","subfamily":"Preventive medicine","year":"1990s-2000s","type":"Clinical protocol pipeline"},"citations":[{"ref":"Day, M. J., Horzinek, M. C., Schultz, R. D., Squires, R. A. (2016). WSAVA Guidelines for the vaccination of dogs and cats. Journal of Small Animal Practice, 57(4), E1-E45.","type":"article","doi":"10.1111/jsap.12431","isbn":null,"url":null},{"ref":"Larson, L. J., Schultz, R. D., Drazenovich, T. L. (2011). Prevalence of serum antibody titers against canine distemper virus and canine parvovirus in dogs entering a Florida animal shelter. Journal of the American Veterinary Medical Association, 238(3), 331-335.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prevalence+of+serum+antibody+titers+against+canine+distemper+virus+and+canine+parvovirus+in+dogs+entering+a+Florida+animal+shelter+Larson"},{"ref":"Schultz, R. D. (2006). Duration of immunity for canine and feline vaccines: A review. Veterinary Microbiology, 117(2-4), 75-79.","type":"article","doi":"10.1016/j.vetmic.2006.04.013","isbn":null,"url":null}],"related":["clinical-scoring-system-veterinary","zoonotic-disease-surveillance","antimicrobial-susceptibility-vet"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"validity-reliability-research","name":"Validity and Reliability in Research","fullName":"Research Validity and Reliability: Concepts and Assessment","aliases":["measurement validity","test-retest reliability","internal and external validity"],"domain":"research-methodology","family":"process-pipeline","subfamily":"research quality assurance","year":"1950","originator":"Lee Cronbach, Paul Meehl (1950s); Donald Campbell, Julian Stanley (1960s); Samuel Messick (1990s)","url":"https://scholargate.app/en/research-methodology/validity-reliability-research","markdownUrl":"https://scholargate.app/en/research-methodology/validity-reliability-research.md","definition":"Validity and reliability are two foundational concepts in research quality. Reliability refers to the consistency and reproducibility of measurements: do repeated applications of an instrument yield the same results? Validity refers to the truthfulness of inferences: does an instrument measure what it claims to measure, and do study findings answer the research question appropriately? Cronbach and Meehl (1955) distinguished construct validity from other validity types; Campbell and Stanley (1963) categorized internal and external validity threats in experimental designs; and Messick (1995) unified validity concepts as 'the degree to which evidence and theory support the intended interpretations of test scores.' Contemporary frameworks encompass multiple validity types (construct, criterion, content, internal, external) and reliability estimates tailored to measurement context.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lee Cronbach, Paul Meehl (1950s); Donald Campbell, Julian Stanley (1960s); Samuel Messick (1990s)","subfamily":"research quality assurance","year":"1950","type":"Framework"},"citations":[{"ref":"Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Shadish%2C%20W.%20R.%2C%20Cook%2C%20T.%20D.%2C%20%26%20Campbell%2C%20D.%20T.%20(2002).%20Experimental%20and%20Quasi-Experimental%20Designs%20for%20Generalized%20Causa"},{"ref":"Messick, S. (1995). Validity of psychological assessment: validation of inferences from persons' responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741–749.","type":"article","doi":"10.1037/0003-066X.50.9.741","isbn":null,"url":null},{"ref":"Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.","type":"article","doi":"10.1037/h0040957","isbn":null,"url":null}],"related":["data-collection-methods","research-design-types","measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"value-added-modeling","name":"Value-Added Modeling","fullName":"Value-Added Modeling","aliases":["VAM"],"domain":"psychometrics","family":"latent-structure","subfamily":"Educational Assessment","year":"1998","originator":"William Sanders, Sandra Horn","url":"https://scholargate.app/en/psychometrics/value-added-modeling","markdownUrl":"https://scholargate.app/en/psychometrics/value-added-modeling.md","definition":"Value-Added Modeling (VAM) is a method for assessing the contribution of schools or teachers to student achievement growth, developed by Sanders and Horn (1998). VAM isolates the effect of a teacher or school by comparing student gains (value added) while controlling for prior achievement and student characteristics.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Sanders, Sandra Horn","subfamily":"Educational Assessment","year":"1998","type":"Longitudinal student achievement modeling"},"citations":[{"ref":"Kane, T. J., Rockoff, J. E., & Staiger, D. O. (2008). What does certification tell us about teacher effectiveness? Evidence from New York City. Economics of Education Review, 27(6), 615-631.","type":"article","doi":"10.1016/j.econedurev.2007.05.005","isbn":null,"url":null},{"ref":"Sanders, W. L., & Horn, S. P. (1998). Research findings on classroom heterogeneity and achievement. Journal of Educational Research, 91(5), 294-303.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Research+findings+on+classroom+heterogeneity+and+achievement+Sanders"},{"ref":"Koedel, C., Mihaly, K., & Rockoff, J. E. (2015). Value-added modeling: A review. Economics of Education Review, 47, 180-195.","type":"article","doi":"10.1016/j.econedurev.2015.01.006","isbn":null,"url":null}],"related":["latent-transition-analysis","case-cohort-design","rule-space-methodology","pls-sem"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"value-at-risk","name":"Value at Risk","fullName":"Value at Risk (Historical, Parametric, Monte Carlo)","aliases":["VaR","value-at-risk","delta-normal VaR","historical simulation VaR","Riske Maruz Değer (VaR — Historical, Parametric, MC)"],"domain":"finance","family":"regression-model","subfamily":null,"year":2007,"originator":"Jorion (textbook benchmark); popularised by RiskMetrics / J.P. Morgan","url":"https://scholargate.app/en/finance/value-at-risk","markdownUrl":"https://scholargate.app/en/finance/value-at-risk.md","definition":"Value at Risk is a financial risk measure that estimates the maximum loss a position or portfolio could suffer over a fixed holding period at a given confidence level. It is the standard benchmark in risk management and regulatory capital calculations, developed in the textbook tradition of Jorion (2007) and the Basel market-risk framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jorion (textbook benchmark); popularised by RiskMetrics / J.P. Morgan","year":2007,"type":"Financial risk measure","estimator":"Lower-tail quantile of the return distribution (historical, parametric delta-normal, or Monte Carlo)","outcome":"continuous (loss)","minSample":100,"coherent":"No — VaR is not sub-additive; CVaR is coherent"},"citations":[{"ref":"Jorion, P. (2007). Value at Risk: The New Benchmark for Managing Financial Risk (3rd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0071464956","url":null},{"ref":"Basel Committee on Banking Supervision (2019). Minimum Capital Requirements for Market Risk. Bank for International Settlements.","type":"report","doi":null,"isbn":null,"url":"https://www.bis.org/bcbs/publ/d457.htm"}],"related":["conditional-value-at-risk","realized-volatility","arima","garch","monte-carlo-simulation"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"value-stream-mapping","name":"Value Stream Mapping","fullName":"Lean Value Stream Mapping","aliases":["VSM","Material and Information Flow Mapping","Lean Flow Mapping","Değer Akış Haritalama"],"domain":"quality-management","family":"process-pipeline","subfamily":"Lean management","year":1999,"originator":"Mike Rother & John Shook","url":"https://scholargate.app/en/quality-management/value-stream-mapping","markdownUrl":"https://scholargate.app/en/quality-management/value-stream-mapping.md","definition":"Value Stream Mapping (VSM) is a lean management technique used to visualize, analyze, and improve the flow of materials and information required to bring a product or service from raw input to customer delivery. Introduced by Mike Rother and John Shook in their 1999 workbook Learning to See, VSM draws on the Toyota Production System tradition to expose waste, delays, and non-value-adding activities across the entire production value stream.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mike Rother & John Shook","year":1999,"type":"Visual process analysis tool","subfamily":"Lean management","scope":"End-to-end value stream","output":"Current-state and future-state maps"},"citations":[{"ref":"Rother, M., & Shook, J. (1999). Learning to See: Value Stream Mapping to Add Value and Eliminate Muda. Lean Enterprise Institute.","type":"book","doi":null,"isbn":"978-0-9667843-0-5","url":null}],"related":["six-sigma-dmaic","overall-equipment-effectiveness","theory-of-constraints"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"van-der-meer-scan","name":"Van der Meer Scan","fullName":"Van der Meer Beam Overlap Measurement","aliases":["beam scan","transverse beam profile","luminosity measurement"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Accelerator technique","year":"1985","originator":"Simon van der Meer","url":"https://scholargate.app/en/particle-physics/van-der-meer-scan","markdownUrl":"https://scholargate.app/en/particle-physics/van-der-meer-scan.md","definition":"The Van der Meer scan is a precision measurement technique for determining the absolute luminosity at particle colliders by mechanically separating the colliding beams and measuring the collision rate as a function of beam separation. This fundamental calibration is essential for all cross-section measurements and physics analyses at the LHC and other hadron colliders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Simon van der Meer","subfamily":"Accelerator technique","year":"1985","type":"Luminosity measurement method"},"citations":[{"ref":"Van der Meer, S. (1985). Stochastic damping of betatron oscillations in the ISR. CERN-ISR-PO/85-5.","type":"article","doi":null,"isbn":null,"url":"https://cdsweb.cern.ch/record/1039474"},{"ref":"Bruning, O., et al. (2004). LHC Design Report. CERN-2004-003.","type":"article","doi":null,"isbn":null,"url":"https://cdsweb.cern.ch/record/782076"},{"ref":"Hertzbach, S. S., et al. (2009). Precision measurement of luminosity at hadron colliders. Modern Physics Letters A, 24(07), 531–548.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Precision+measurement+of+luminosity+at+hadron+colliders+Hertzbach"}],"related":["missing-transverse-energy","calorimeter-calibration","hep-track-reconstruction"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"van-der-waerden-test","name":"Van der Waerden Test","fullName":"Van der Waerden Normal Scores Test","aliases":["normal scores test","Van der Waerden k-sample test","Van der Waerden Testi — Normal Skor"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1952,"originator":"Bartel Leendert van der Waerden","url":"https://scholargate.app/en/statistics/van-der-waerden-test","markdownUrl":"https://scholargate.app/en/statistics/van-der-waerden-test.md","definition":"The Van der Waerden test is a nonparametric k-sample hypothesis test that converts observations into normal scores — the quantiles of a standard normal distribution — before comparing groups. Introduced by Bartel Leendert van der Waerden in 1952, it can achieve higher statistical power than the Kruskal-Wallis test when the underlying distributions are symmetric, making it a compelling bridge between rank-based and parametric methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bartel Leendert van der Waerden","year":1952,"family":"Hypothesis test","type":"Nonparametric k-sample comparison via normal scores","groups":"k ≥ 2","outcome":"continuous","parametric":false,"normalScoreTransform":"Φ⁻¹(rank / (n + 1))","minSampleSize":15},"citations":[{"ref":"van der Waerden, B.L. (1952). Order Tests for the Two-Sample Problem and Their Power. Indagationes Mathematicae, 14, 453–458.","type":"article","doi":null,"isbn":null,"url":"https://www.sciencedirect.com/journal/indagationes-mathematicae/vol/14"}],"related":["kruskal-wallis","mann-whitney-u","jonckheere-terpstra","siegel-tukey-test","one-way-anova","friedman-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vancouver-style","name":"Vancouver Referencing Style","fullName":"Vancouver Citation and Referencing Style","aliases":["Vancouver style","numbered citation","ICMJE style"],"domain":"academic-writing","family":"process-pipeline","subfamily":"citation-formatting","year":"1978","originator":"International Committee of Medical Journal Editors (Vancouver Group, founded 1978)","url":"https://scholargate.app/en/academic-writing/vancouver-style","markdownUrl":"https://scholargate.app/en/academic-writing/vancouver-style.md","definition":"Vancouver style is the standard citation format for biomedical and clinical research journals, established by the International Committee of Medical Journal Editors (ICMJE) and detailed in their Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. In Vancouver style, citations are numbered sequentially in the text (e.g., [1], [2], [3]) and linked to a numbered reference list at the end of the manuscript. The style emphasizes conciseness and is widely used in medicine, nursing, and life sciences. Unlike author-date systems (APA), Vancouver style de-emphasizes author names in the text, allowing more natural prose flow.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"International Committee of Medical Journal Editors (Vancouver Group, founded 1978)","subfamily":"citation-formatting","year":"1978","type":"Standard"},"citations":[{"ref":"International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. Retrieved from https://www.icmje.org/","type":"guideline","doi":null,"isbn":null,"url":"https://www.icmje.org/"},{"ref":"National Library of Medicine (2023). Citing Medicine: The NLM Style Guide for Authors, Editors, and Publishers (3rd ed.). Retrieved from https://www.nlm.nih.gov/pubs/formats/recommendedformats.html","type":"guideline","doi":null,"isbn":null,"url":"https://www.nlm.nih.gov/pubs/formats/recommendedformats.html"}],"related":["apa-style-guide","imrad-structure","scientific-writing-clarity","journal-submission-process"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vanderbilt-adhd-scale","name":"Vanderbilt ADHD Rating Scale","fullName":"Vanderbilt Attention-Deficit/Hyperactivity Disorder (ADHD) Rating Scale","aliases":["VADTRS","Vanderbilt ADHD","Parent Rating Scale","Teacher Rating Scale"],"domain":"developmental-assessment","family":"process-pipeline","subfamily":"ADHD screening and assessment","year":"2003","originator":"Mark Wolraich and colleagues","url":"https://scholargate.app/en/developmental-assessment/vanderbilt-adhd-scale","markdownUrl":"https://scholargate.app/en/developmental-assessment/vanderbilt-adhd-scale.md","definition":"The Vanderbilt Attention-Deficit/Hyperactivity Disorder (ADHD) Rating Scale, developed by Mark Wolraich and colleagues in 2003, is a validated screening instrument for identifying ADHD symptoms and comorbid behavioral/emotional problems in children aged 6–12 years. Available in parent and teacher versions, it is widely used in primary care, pediatrics, and developmental clinics for initial ADHD screening and symptom tracking during treatment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mark Wolraich and colleagues","subfamily":"ADHD screening and assessment","year":"2003","type":"Multi-informant ADHD rating scale"},"citations":[{"ref":"Wolraich, M. L., Lambert, W., Doffing, M. A., et al. (2003). Psychometric properties of the Vanderbilt ADHD Diagnostic Parent Rating Scale in a referred population. Journal of Pediatric Psychology, 28(8), 559-567.","type":"article","doi":"10.1093/jpepsy/jsg046","isbn":null,"url":null},{"ref":"Bard, D. E., Wolraich, M. L., Neas, B., et al. (2013). The psychometric properties of the Vanderbilt Attention-Deficit Hyperactivity Disorder diagnostic parent rating scale in a community sample. Journal of Developmental & Behavioral Pediatrics, 34(5), 344-352.","type":"article","doi":"10.1097/dbp.0b013e31827a3a22","isbn":null,"url":null}],"related":["conners-rating-scales","strengths-difficulties-questionnaire","cbcl-child-behavior","achenbach-youth-self-report"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vapor-compression-cycle","name":"Vapor Compression Cycle","fullName":"Vapor Compression Cycle for Refrigeration and Heat Pumps","aliases":["refrigeration cycle","heat pump cycle"],"domain":"thermodynamics","family":"process-pipeline","subfamily":"Refrigeration Cycle","year":"1834","originator":"Jacob Perkins","url":"https://scholargate.app/en/thermodynamics/vapor-compression-cycle","markdownUrl":"https://scholargate.app/en/thermodynamics/vapor-compression-cycle.md","definition":"The Vapor Compression Cycle is the fundamental thermodynamic cycle for refrigeration systems and heat pumps. It describes how mechanical work is used to transfer heat from a cold space (evaporator) to a warm space (condenser), operating against the natural temperature gradient. The cycle consists of four processes: isentropic compression, isobaric condensation, isenthalpic throttling, and isobaric evaporation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacob Perkins","subfamily":"Refrigeration Cycle","year":"1834","type":"Thermodynamic cycle"},"citations":[{"ref":"Stoecker, W. F., Jones, J. W., & Sunnam, B. A. (1998). Refrigeration and Air Conditioning (2nd ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0070613638","url":null},{"ref":"Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer (6th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470055540","url":null}],"related":["rankine-cycle","brayton-cycle","psychrometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"var-model","name":"VAR Model","fullName":"Vector Autoregression Model","aliases":["vector autoregression","VAR","VAR Modeli (Vektör Otoregresyon)","vektör otoregresyon"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":2005,"originator":"Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition","url":"https://scholargate.app/en/econometrics/var-model","markdownUrl":"https://scholargate.app/en/econometrics/var-model.md","definition":"Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition","year":2005,"type":"Multivariate time-series model","estimator":"Equation-by-equation least squares","outcome":"multiple continuous series","minSample":60,"dataStructure":"time series"},"citations":[{"ref":"Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.","type":"book","doi":"10.1007/978-3-540-27752-1","isbn":null,"url":null}],"related":["vecm-model","arima","ardl-bounds-test","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variable-neighborhood-search","name":"Variable Neighborhood Search","fullName":"Variable Neighborhood Search (VNS)","aliases":["VNS","Değişken Komşuluk Araması (VNS)","variable neighbourhood search"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":1997,"originator":null,"url":"https://scholargate.app/en/optimization/variable-neighborhood-search","markdownUrl":"https://scholargate.app/en/optimization/variable-neighborhood-search.md","definition":"Variable Neighborhood Search (VNS) is a metaheuristic optimization framework introduced by Mladenović and Hansen in 1997. It escapes local optima by systematically switching among a predefined set of neighborhood structures — first perturbing the current solution (shaking) to reach a different region of the search space, then applying a local search within that region, and finally accepting the new solution only if it improves the incumbent. The method is flexible enough to handle combinatorial problems (routing, scheduling, graph problems) as well as continuous optimization, making it one of the most widely used neighborhood-based metaheuristics in operations research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originators":"Nenad Mladenović & Pierre Hansen","year":1997,"type":"Metaheuristic — neighborhood-based","variants":"Basic VNS (BVNS), Variable Neighborhood Descent (VND), General VNS (GVNS)","problemTypes":"Combinatorial and continuous optimization","requiresNormality":false,"minimumSampleSize":"none (solution-based, not sample-based)","difficulty":3},"citations":[{"ref":"Mladenović, N. & Hansen, P. (1997). Variable Neighborhood Search. Computers & Operations Research, 24(11), 1097–1100.","type":"article","doi":"10.1016/S0305-0548(97)00031-2","isbn":null,"url":null},{"ref":"Hansen, P., Mladenović, N., Brimberg, J. & Pérez, J.A.M. (2019). Variable Neighborhood Search: Basics and Variants. EURO Journal on Computational Optimization, 7(1), 3–56.","type":"article","doi":"10.1007/978-3-319-91086-4_3","isbn":null,"url":null}],"related":["simulated-annealing","tabu-search","genetic-algorithm","harmony-search","iterated-local-search"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variable-precision-rough-set","name":"Variable Precision Rough Set","fullName":"Variable Precision Rough Set Model (VPRS)","aliases":["VPRS Model","Variable Precision Rough Sets","Approximate Rough Set Model","Değişken Hassasiyetli Kaba Küme Modeli"],"domain":"soft-computing","family":"ml-model","subfamily":"Rough sets","year":1993,"originator":"Wojciech Ziarko","url":"https://scholargate.app/en/soft-computing/variable-precision-rough-set","markdownUrl":"https://scholargate.app/en/soft-computing/variable-precision-rough-set.md","definition":"Variable Precision Rough Set (VPRS) is an extension of classical rough set theory introduced by Wojciech Ziarko in 1993 to handle real-world data that inevitably contains noise and misclassification. By introducing a precision parameter u controlling the allowable degree of overlap between equivalence classes and a target concept, VPRS relaxes the strict subset requirement of standard rough sets, enabling the induction of approximate classification rules from noisy or inconsistent datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wojciech Ziarko","year":1993,"type":"Classification and rule induction model","subfamily":"Rough sets","precision_parameter":"u ∈ [0, 0.5) controlling allowed misclassification rate","key_extension":"Relaxes exact inclusion requirement of classical rough sets"},"citations":[{"ref":"Ziarko, W. (1993). Variable precision rough set model. Journal of Computer and System Sciences, 46(1), 39–59.","type":"article","doi":"10.1016/0022-0000(93)90048-2","isbn":null,"url":null}],"related":["rough-set-theory","three-way-decisions","granular-computing"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variable-rate-application","name":"Variable Rate Application","fullName":"Variable Rate Application Technology","aliases":["VRA","variable rate technology","site-specific application","prescription-based application"],"domain":"agronomy","family":"process-pipeline","subfamily":"Site-specific crop management","year":"1980s–1990s (early GPS-integrated field trials; widely adopted 1990s)","originator":"Multiple contributors","url":"https://scholargate.app/en/agronomy/variable-rate-application","markdownUrl":"https://scholargate.app/en/agronomy/variable-rate-application.md","definition":"Variable Rate Application (VRA) is a precision agriculture technique that adjusts the quantity of inputs — such as fertilisers, pesticides, seeds, or water — across different zones of a field based on georeferenced soil and crop data. Rather than applying a uniform rate across an entire field, VRA delivers the right input, at the right rate, in the right location, improving resource efficiency and reducing environmental impact in crop production systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors","year":"1980s–1990s (early GPS-integrated field trials; widely adopted 1990s)","type":"Precision agriculture technology and process","dataType":"Georeferenced spatial data (soil samples, yield maps, remote sensing imagery)","subfamily":"Site-specific crop management"},"citations":[{"ref":"Stafford, J. V. (2000). Implementing Precision Agriculture in the 21st Century. Journal of Agronomy and Crop Science, 185(1), 1–26.","type":"journal","doi":"10.1006/jaer.2000.0577","isbn":null,"url":null},{"ref":"Pierpaoli, E., Carli, G., Pignone, E., & Rinaldi, M. (2013). The Drivers of Precision Agriculture Technologies Adoption: A Literature Review. Procedia Technology, 8, 61–69.","type":"journal","doi":"10.1016/j.protcy.2013.11.010","isbn":null,"url":null}],"related":["precision-agriculture","gis-mapping","soil-sampling","remote-sensing","yield-mapping","site-specific-crop-management"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variance-inflation-factor","name":"Variance Inflation Factor","fullName":"Variance Inflation Factor (VIF)","aliases":["VIF","Variance Inflation Index","Multicollinearity Inflation Factor","Varyans Enflasyon Faktörü"],"domain":"econometrics","family":"regression-model","subfamily":"Multicollinearity diagnostics","year":1970,"originator":"Donald Marquardt","url":"https://scholargate.app/en/econometrics/variance-inflation-factor","markdownUrl":"https://scholargate.app/en/econometrics/variance-inflation-factor.md","definition":"The Variance Inflation Factor (VIF) is a scalar diagnostic statistic proposed by Donald Marquardt (1970) that quantifies how much the variance of an estimated regression coefficient increases due to linear dependence—multicollinearity—among the predictors in an ordinary least squares model. It is routinely applied in econometrics, social science, and biomedical research whenever analysts suspect that two or more independent variables move together closely enough to destabilize coefficient estimates.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Donald Marquardt","year":1970,"type":"Diagnostic statistic","subfamily":"Multicollinearity diagnostics","threshold_rule":"VIF > 10 commonly flags severe multicollinearity","output_range":"[1, ∞)"},"citations":[{"ref":"Marquardt, D. W. (1970). Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics, 12(3), 591–612.","type":"article","doi":"10.1080/00401706.1970.10488699","isbn":null,"url":null}],"related":["condition-index","ols-regression","ridge-regression"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variance-reduction-mc","name":"Variance Reduction for Monte Carlo","fullName":"Variance Reduction Techniques for Monte Carlo Simulation (AV, CV, IS)","aliases":["antithetic variates","control variates","importance sampling","stratified sampling MC","Monte Carlo Varyans Azaltma Teknikleri (AV, CV, IS)"],"domain":"simulation","family":"process-pipeline","subfamily":null,"year":"1950s–1980s (technique family)","originator":"Hammersley & Morton (antithetic variates, 1956); Lavenberg & Welch (control variates, 1981); importance sampling roots in Kahn & Marshall (1953)","url":"https://scholargate.app/en/simulation/variance-reduction-mc","markdownUrl":"https://scholargate.app/en/simulation/variance-reduction-mc.md","definition":"Variance reduction techniques are a family of methods that improve the efficiency of Monte Carlo simulation by achieving the same estimation accuracy with fewer random draws. Developed incrementally from the 1950s onward — with antithetic variates attributed to Hammersley and Morton, control variates formalised by Lavenberg and Welch, and importance sampling rooted in Kahn and Marshall — the family includes antithetic variates (AV), control variates (CV), importance sampling (IS), and stratification, each exploiting a different structural property of the target quantity to lower estimator variance without introducing bias.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hammersley & Morton (antithetic variates, 1956); Lavenberg & Welch (control variates, 1981); importance sampling roots in Kahn & Marshall (1953)","year":"1950s–1980s (technique family)","type":"Simulation variance-reduction technique family","techniques":"Antithetic Variates (AV), Control Variates (CV), Importance Sampling (IS), Stratification","goal":"Achieve the same estimation accuracy as standard Monte Carlo with fewer simulation runs","difficulty":"Intermediate (3 / 5)"},"citations":[{"ref":"Ross, S.M. (2012). Simulation (5th ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0124158252","url":null},{"ref":"Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer.","type":"book","doi":"10.1007/978-0-387-21617-1","isbn":null,"url":null}],"related":["monte-carlo-simulation","quasi-monte-carlo","bootstrap-simulation","markov-chain-monte-carlo","stochastic-differential-equations"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variant-calling","name":"Variant Calling","fullName":"Genomic Variant Calling","aliases":["SNP calling","genotyping from sequencing","mutation detection","variant detection"],"domain":"bioinformatics","family":"process-pipeline","subfamily":"Bioinformatics / omics","year":"2009–2010 (modern high-throughput era)","originator":"Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)","url":"https://scholargate.app/en/bioinformatics/variant-calling","markdownUrl":"https://scholargate.app/en/bioinformatics/variant-calling.md","definition":"Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)","year":"2009–2010 (modern high-throughput era)","type":"Computational genomics pipeline","dataType":"Aligned next-generation sequencing reads (BAM/CRAM files)","subfamily":"Bioinformatics / omics"},"citations":[{"ref":"McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.","type":"article","doi":"10.1101/gr.107524.110","isbn":null,"url":null},{"ref":"Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., ... & Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078–2079.","type":"article","doi":"10.1093/bioinformatics/btp352","isbn":null,"url":null}],"related":["sequence-alignment","genome-wide-association-study","copy-number-variation-analysis","rna-seq-differential-expression","epigenome-wide-association-study","single-cell-variant-calling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variational-autoencoder","name":"Variational Autoencoder","fullName":"Variational Autoencoder (VAE)","aliases":["Değişkensel Otokodlayıcı (VAE)","VAE","auto-encoding variational Bayes","deep latent variable model"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2014,"originator":"Kingma, D. P. & Welling, M.","url":"https://scholargate.app/en/deep-learning/variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/variational-autoencoder.md","definition":"The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kingma, D. P. & Welling, M.","year":2014,"type":"Deep generative latent-variable model (encoder–decoder)","task":"Generative modelling, dimensionality reduction, anomaly detection","minSample":200},"citations":[{"ref":"Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"},{"ref":"Higgins, I. et al. (2017). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. International Conference on Learning Representations (ICLR).","type":"article","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=Sy2fzU9gl"}],"related":["generative-adversarial-network","diffusion-model","score-based-diffusion","pca","autoencoder"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variational-inference-with-measurement-error","name":"Variational Inference with Measurement Error","fullName":"Variational Bayesian Inference for Models with Measurement Error","aliases":["VI with measurement error","variational Bayes measurement error model","VBEM with errors-in-variables","approximate Bayesian inference under measurement error"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"2000s–2010s","originator":"Building on Blei et al. (2017) for VI and Carroll et al. (2006) for measurement error frameworks","url":"https://scholargate.app/en/bayesian/variational-inference-with-measurement-error","markdownUrl":"https://scholargate.app/en/bayesian/variational-inference-with-measurement-error.md","definition":"Variational inference with measurement error is a scalable Bayesian approach that simultaneously estimates model parameters and latent true covariates when observed variables are contaminated by noise. Rather than sampling the posterior via MCMC, it finds the closest tractable distribution to the true posterior by maximising the evidence lower bound (ELBO), making it applicable to large datasets where full MCMC is too costly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Building on Blei et al. (2017) for VI and Carroll et al. (2006) for measurement error frameworks","year":"2000s–2010s","type":"Approximate Bayesian inference","dataType":"Continuous, binary, or count outcomes with error-contaminated covariates or responses","subfamily":"Bayesian / computational"},"citations":[{"ref":"Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859–877.","type":"article","doi":"10.1080/01621459.2017.1285773","isbn":null,"url":null},{"ref":"Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman & Hall/CRC.","type":"book","doi":null,"isbn":"978-1584886334","url":null}],"related":["variational-inference","bayesian-inference-with-measurement-error","mcmc-with-measurement-error","errors-in-variables-regression","bayesian-hierarchical-model-with-measurement-error","approximate-bayesian-computation-with-measurement-error"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variational-inference-with-missing-data","name":"Variational Inference with Missing Data","fullName":"Variational Bayesian Inference with Missing Data","aliases":["VI with missing data","variational EM with missing data","VB missing data","mean-field VI for incomplete data"],"domain":"bayesian","family":"bayesian","subfamily":"Bayesian / computational","year":"1994–2008","originator":"Ghahramani & Jordan; Wainwright & Jordan (formal foundations)","url":"https://scholargate.app/en/bayesian/variational-inference-with-missing-data","markdownUrl":"https://scholargate.app/en/bayesian/variational-inference-with-missing-data.md","definition":"Variational inference with missing data is a scalable Bayesian approach that simultaneously approximates the posterior over latent variables and model parameters while imputing missing observations. Instead of integrating over all possible values of the missing entries exactly, it posits a tractable approximate distribution and optimises it to be as close as possible to the true joint posterior, yielding fast, principled inference even in high-dimensional incomplete datasets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ghahramani & Jordan; Wainwright & Jordan (formal foundations)","year":"1994–2008","type":"Approximate Bayesian inference","dataType":"Incomplete / partially observed continuous or categorical data","subfamily":"Bayesian / computational"},"citations":[{"ref":"Ghahramani, Z. & Jordan, M. I. (1994). Supervised learning from incomplete data via an EM approach. In Cowan, J. D., Tesauro, G. & Alspector, J. (Eds.), Advances in Neural Information Processing Systems 6 (pp. 120–127). Morgan Kaufmann.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Supervised+learning+from+incomplete+data+via+an+EM+approach+Ghahramani+Jordan+1994"},{"ref":"Wainwright, M. J. & Jordan, M. I. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1(1–2), 1–305.","type":"article","doi":"10.1561/2200000001","isbn":null,"url":null}],"related":["variational-inference","bayesian-inference-with-missing-data","mcmc-with-missing-data","gibbs-sampling-with-missing-data","expectation-maximization","mean-field-approximation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variational-inference","name":"Variational Inference","fullName":"Variational Bayesian Inference","aliases":["VI","variational Bayes","VB","mean-field variational inference","ELBO optimisation","variational approximation"],"domain":"bayesian","family":"bayesian","subfamily":null,"year":1999,"originator":"Jordan, Ghahramani, Jaakkola & Saul","url":"https://scholargate.app/en/bayesian/variational-inference","markdownUrl":"https://scholargate.app/en/bayesian/variational-inference.md","definition":"Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the true posterior by maximising the evidence lower bound (ELBO). Introduced in its modern graphical-model form by Jordan, Ghahramani, Jaakkola and Saul (1999) and given a comprehensive statistical treatment by Blei, Kucukelbir and McAuliffe (2017), VI is now the standard scalable inference engine in probabilistic machine learning.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"family":"Bayesian","type":"Approximate Bayesian inference","purpose":"posterior approximation / scalable inference","var_types":"continuous latent variables","inference":"optimisation (ELBO maximisation)","outputs":"approximate posterior distributions / ELBO lower bound","originator":"Jordan, Ghahramani, Jaakkola & Saul","year":1999},"citations":[{"ref":"Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.","type":"article","doi":"10.1023/A:1007665907178","isbn":null,"url":null},{"ref":"Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859–877.","type":"article","doi":"10.1080/01621459.2017.1285773","isbn":null,"url":null},{"ref":"Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. (Chapter 10: Approximate Inference.)","type":"book","doi":null,"isbn":"978-0387310732","url":null}],"related":["mcmc","bayesian-regression","hierarchical-bayes","expectation-propagation","latent-dirichlet-allocation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variational-mode-decomposition","name":"Variational Mode Decomposition","fullName":"Variational Mode Decomposition (VMD)","aliases":["VMD","Adaptive Signal Decomposition","Variational Signal Decomposition","Varyasyonel Mod Ayrıştırma"],"domain":"signal-processing","family":"ml-model","subfamily":"Time-frequency analysis","year":2014,"originator":"Konstantin Dragomiretskiy & Dominique Zosso","url":"https://scholargate.app/en/signal-processing/variational-mode-decomposition","markdownUrl":"https://scholargate.app/en/signal-processing/variational-mode-decomposition.md","definition":"Variational Mode Decomposition (VMD) is a fully adaptive, non-recursive signal decomposition method introduced by Konstantin Dragomiretskiy and Dominique Zosso in 2014. It decomposes a real-valued input signal into a discrete number of sub-signals, called intrinsic mode functions (IMFs), each with a specific sparsity in the frequency domain. Unlike Empirical Mode Decomposition, VMD frames decomposition as a variational optimization problem solved via the Alternating Direction Method of Multipliers (ADMM), yielding robust and physically meaningful components.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Konstantin Dragomiretskiy & Dominique Zosso","year":2014,"type":"Adaptive variational signal decomposition algorithm","subfamily":"Time-frequency analysis","convergence":"Iterative ADMM optimization","output":"K band-limited intrinsic mode functions (IMFs)"},"citations":[{"ref":"Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.","type":"article","doi":"10.1109/TSP.2013.2288675","isbn":null,"url":null}],"related":["empirical-mode-decomposition","fourier-transform","wavelet-financial-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"variational-quantum-eigensolver","name":"Variational Quantum Eigensolver","fullName":"Variational Quantum Eigensolver (VQE)","aliases":["VQE","hybrid quantum-classical"],"domain":"quantum-computing","family":"ml-model","subfamily":"Variational Algorithm","year":"2014","originator":"Alberto Peruzzo","url":"https://scholargate.app/en/quantum-computing/variational-quantum-eigensolver","markdownUrl":"https://scholargate.app/en/quantum-computing/variational-quantum-eigensolver.md","definition":"The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to find the lowest eigenvalue (ground state energy) of a quantum Hamiltonian. Introduced by Peruzzo et al. in 2014, it exploits the variational principle to combine the power of quantum circuits with classical optimization to solve chemistry and materials science problems on near-term quantum devices.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alberto Peruzzo","subfamily":"Variational Algorithm","year":"2014","type":"Hybrid quantum-classical algorithm"},"citations":[{"ref":"Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213.","type":"article","doi":"10.1038/ncomms5213","isbn":null,"url":null},{"ref":"McClean, J. R., Romero, J., Aspuru-Guzik, A. (2016). The theory of variational hybrid quantum-classical algorithms. New Journal of Physics, 18, 023023.","type":"article","doi":"10.1088/1367-2630/18/2/023023","isbn":null,"url":null},{"ref":"Cao, Y., Romero, J., Aspuru-Guzik, A. (2021). Potential of quantum computing for drug discovery. IBM Journal of Research and Development, 62, 6:1-6:20.","type":"article","doi":"10.1147/JRD.2018.2888987","isbn":null,"url":null}],"related":["quantum-phase-estimation","quantum-approximate-optimization-algorithm","quantum-monte-carlo","density-functional-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vasculitis-damage-index","name":"Vasculitis Damage Index","fullName":"Vasculitis Damage Index for Systemic Vasculitis","aliases":["VDI","Vasculitis Permanent Organ Damage Score"],"domain":"rheumatology","family":"process-pipeline","subfamily":"cumulative-damage-index","year":"2003","originator":"Exley et al.","url":"https://scholargate.app/en/rheumatology/vasculitis-damage-index","markdownUrl":"https://scholargate.app/en/rheumatology/vasculitis-damage-index.md","definition":"The VDI is a clinician-assessed measure of permanent organ damage in patients with systemic vasculitis, including ANCA-associated vasculitis (AAV), polyarteritis nodosa, and other necrotising vasculitides. Introduced by Exley et al. (2003), VDI captures cumulative irreversible damage across organ systems, complementing disease activity measures (such as the Birmingham Vasculitis Activity Score). Systemic vasculitis is characterised by inflammation of blood vessel walls, leading to ischaemia and permanent tissue damage. VDI acknowledges that damage accrues over time and is largely irreversible, making it a prognostically important measure distinct from transient inflammatory activity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Exley et al.","subfamily":"cumulative-damage-index","year":"2003","type":"Clinician-rated"},"citations":[{"ref":"Exley AR, Bacon PA, Luqmani RA, Kitas GD, Gordon C, Pusey CD, Savage CO. Development and initial validation of the Vasculitis Damage Index (VDI) for systemic vasculitis. Arthritis & Rheumatism. 2003;48(7):2146-2157.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Exley+AR%2C+Bacon+PA%2C+Luqmani+RA%2C+Kitas+GD%2C+Gordon+C%2C+Pusey+CD%2C+Savage+CO.+Development+and+initial+validation+of+the+Vascu+Exley"}],"related":["sledai","das28","basdai","sdai-rheumatoid-arthritis","rapid3"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vcg-mechanism","name":"VCG Mechanism","fullName":"Vickrey-Clarke-Groves Mechanism","aliases":["Vickrey Mechanism","Generalized Vickrey Auction","Truthful Mechanism"],"domain":"game-theory","family":"ml-model","subfamily":"Game-theoretic","year":"1961","originator":"William Vickrey, Edward Clarke, Theodore Groves","url":"https://scholargate.app/en/game-theory/vcg-mechanism","markdownUrl":"https://scholargate.app/en/game-theory/vcg-mechanism.md","definition":"The Vickrey-Clarke-Groves (VCG) Mechanism is a truthful mechanism design solution that allocates resources and determines payments to incentivize participants to reveal their true valuations. Building on William Vickrey's 1961 sealed-bid auction work and extended by Clarke and Groves, VCG ensures that reporting truth is a dominant strategy for all participants, achieving allocative efficiency while maximizing total surplus.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Vickrey, Edward Clarke, Theodore Groves","subfamily":"Game-theoretic","year":"1961","type":"algorithm"},"citations":[{"ref":"Vickrey, W. (1961). Counterspeculation, auctions, and competitive sealed bids. The Journal of Finance, 16(1), 8-37.","type":"article","doi":"10.1111/j.1540-6261.1961.tb02789.x","isbn":null,"url":null},{"ref":"Clarke, E. H. (1971). Multipart pricing of public goods. Public Choice, 11(1), 17-33.","type":"article","doi":"10.1007/BF01726210","isbn":null,"url":null}],"related":["bayesian-nash-equilibrium","first-price-auction","principal-agent-model","nash-equilibrium"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vecm-model","name":"VECM","fullName":"Vector Error Correction Model","aliases":["vector error correction model","error correction model","cointegration model","VECM (Vektör Hata Düzeltme Modeli)"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1987,"originator":"Engle & Granger","url":"https://scholargate.app/en/econometrics/vecm-model","markdownUrl":"https://scholargate.app/en/econometrics/vecm-model.md","definition":"The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Engle & Granger","year":1987,"type":"Multivariate time-series model","estimator":"Maximum likelihood (Johansen) on the cointegrated VAR","outcome":"continuous","dataStructure":"time series","minSample":60},"citations":[{"ref":"Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null}],"related":["var-model","ardl-bounds-test","arima","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vector-autoregression","name":"Vector Autoregression","fullName":"Vector Autoregression Model","aliases":["VAR","VAR model","vector autoregressive model","multivariate autoregression"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1980","originator":"Christopher A. Sims","url":"https://scholargate.app/en/econometrics/vector-autoregression","markdownUrl":"https://scholargate.app/en/econometrics/vector-autoregression.md","definition":"Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher A. Sims","year":"1980","type":"Multivariate time-series model","dataType":"Multiple stationary time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.","type":"article","doi":"10.2307/1912017","isbn":null,"url":null},{"ref":"Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.","type":"book","doi":null,"isbn":"978-3540401728","url":null}],"related":["structural-var","vector-error-correction-model","arima-model","granger-causality-test","johansen-cointegration-test","arma-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vector-error-correction-model","name":"Vector Error Correction Model","fullName":"Vector Error Correction Model","aliases":["VECM","error correction VAR","cointegrated VAR","vector equilibrium correction model"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1987","originator":"Robert F. Engle and Clive W. J. Granger","url":"https://scholargate.app/en/econometrics/vector-error-correction-model","markdownUrl":"https://scholargate.app/en/econometrics/vector-error-correction-model.md","definition":"The Vector Error Correction Model extends the Vector Autoregression (VAR) framework to a system of variables that share one or more long-run equilibrium relationships. It jointly models short-run dynamics and the speed at which each variable corrects back toward equilibrium after a shock, making it the standard tool for analysing cointegrated multivariate time series.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert F. Engle and Clive W. J. Granger","year":"1987","type":"Multivariate time-series model","dataType":"Multivariate time-series; cointegrated I(1) variables","subfamily":"Econometrics / time series"},"citations":[{"ref":"Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.","type":"article","doi":"10.2307/1913236","isbn":null,"url":null},{"ref":"Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580.","type":"article","doi":"10.2307/2938278","isbn":null,"url":null}],"related":["vector-autoregression","structural-var","johansen-cointegration-test","engle-granger-cointegration-test","granger-causality-test","arima-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vector-normalization","name":"VECTOR-NORMALIZATION","fullName":"Vector (L2) Normalization","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Normalization","year":"1981","originator":"Hwang, C. L. Yoon, K.","url":"https://scholargate.app/en/decision-making/vector-normalization","markdownUrl":"https://scholargate.app/en/decision-making/vector-normalization.md","definition":"VECTOR-NORMALIZATION (Vector (L2) Normalization) is a normalization multi-criteria decision-making (MCDM) method introduced by Hwang, C. L. Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hwang, C. L. Yoon, K.","subfamily":"Normalization","year":"1981","type":"Euclidean norm-based column normalization","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, Berlin","type":"article","doi":"10.1007/978-3-642-48318-9","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vegas-monte-carlo","name":"Vegas Monte Carlo","fullName":"VEGAS Monte Carlo Adaptive Integration","aliases":["VEGAS algorithm","adaptive importance sampling","multidimensional integration"],"domain":"particle-physics","family":"process-pipeline","subfamily":"Numerical integration","year":"1978","originator":"Peter Lepage","url":"https://scholargate.app/en/particle-physics/vegas-monte-carlo","markdownUrl":"https://scholargate.app/en/particle-physics/vegas-monte-carlo.md","definition":"VEGAS is an adaptive Monte Carlo algorithm for numerical integration of multidimensional functions, particularly useful for high-dimensional integrals common in particle physics calculations. By adaptively refining the sampling distribution to concentrate points in high-contribution regions, VEGAS dramatically improves integration efficiency compared to naive Monte Carlo.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Lepage","subfamily":"Numerical integration","year":"1978","type":"Adaptive sampling algorithm"},"citations":[{"ref":"Lepage, G. P. (1978). A new algorithm for adaptive multidimensional integration. Journal of Computational Physics, 27(2), 192–203.","type":"article","doi":"10.1016/0021-9991(78)90004-9","isbn":null,"url":null},{"ref":"Lepage, G. P. (1980). VEGAS: an adaptive multidimensional integration program. Cornell University preprint CLNS-80/447.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1105.3073"},{"ref":"Nagy, M., & Nagy, I. (2005). Application of VEGAS integration algorithm for calculation of penetration depth in superconductors. Journal of Physics: Condensed Matter, 17(39), 6131.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Application+of+VEGAS+integration+algorithm+for+calculation+of+penetration+depth+in+superconductors+Nagy"}],"related":["matrix-element-method","feynman-diagram","pdf-fitting"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vehicle-routing","name":"Vehicle Routing Problem","fullName":"Vehicle Routing Problem (VRP)","aliases":["Capacitated Vehicle Routing Problem","Fleet Routing Problem","Multi-Vehicle Routing Problem","Araç Rotalama Problemi"],"domain":"optimization","family":"process-pipeline","subfamily":"Routing","year":1959,"originator":"George Dantzig & John Ramser","url":"https://scholargate.app/en/optimization/vehicle-routing","markdownUrl":"https://scholargate.app/en/optimization/vehicle-routing.md","definition":"The Vehicle Routing Problem (VRP) seeks the minimum-cost set of routes for a fleet of vehicles to serve a collection of geographically dispersed customers, each with a known demand, departing from and returning to a central depot. Originally formulated as the Truck Dispatching Problem by Dantzig and Ramser in 1959, VRP is a foundational model in logistics, supply chain management, and operations research, applicable whenever goods or services must be delivered efficiently across multiple stops.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"George Dantzig & John Ramser","year":1959,"type":"Combinatorial optimization problem","subfamily":"Routing","complexity":"NP-hard","solution_space":"Exponential in number of customers"},"citations":[{"ref":"Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91.","type":"article","doi":"10.1287/mnsc.6.1.80","isbn":null,"url":null}],"related":["location-allocation","integer-programming","service-area-analysis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"velocity-azimuth-display","name":"Velocity-Azimuth Display","fullName":"Velocity-Azimuth Display (VAD) Analysis","aliases":["VAD","VAD analysis","Velocity-height profile"],"domain":"meteorology","family":"process-pipeline","subfamily":"Doppler radar analysis","year":"1968","originator":"Browning and Wexler","url":"https://scholargate.app/en/meteorology/velocity-azimuth-display","markdownUrl":"https://scholargate.app/en/meteorology/velocity-azimuth-display.md","definition":"The Velocity-Azimuth Display (VAD) is a radar analysis technique that extracts the radial velocity of wind at a constant altitude as a function of azimuth angle around the radar. By fitting a sinusoidal pattern to these measurements, VAD retrieves the mean wind speed and direction at that altitude, providing wind profiles without requiring multiple radar observations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Browning and Wexler","subfamily":"Doppler radar analysis","year":"1968","type":"Radar wind retrieval method"},"citations":[{"ref":"Browning, K. A., & Wexler, R. (1968). The determination of kinematic properties of a wind field using Doppler radar. Journal of Applied Meteorology, 7(1), 105-113.","type":"article","doi":"10.1175/1520-0450(1968)007<0105:TDOKPO>2.0.CO;2","isbn":null,"url":null},{"ref":"Appendix C: Radar Algorithms. (1995). In Radar Meteorology: A First Course. Editors R. J. Doviak & D. S. Zrnic. Oxford University Press.","type":"article","doi":null,"isbn":null,"url":"https://global.oup.com/academic/product/radar-meteorology-9780195073256"}],"related":["dual-polarization-radar","wrf-model","thermal-wind"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vendor-managed-inventory","name":"Vendor-Managed Inventory","fullName":"Vendor-Managed Inventory","aliases":["VMI","supplier-managed inventory"],"domain":"operations-management","family":"ml-model","subfamily":"Supply Chain Management","year":"2006","originator":"Disney, S. M., & Towill, D. R.","url":"https://scholargate.app/en/operations-management/vendor-managed-inventory","markdownUrl":"https://scholargate.app/en/operations-management/vendor-managed-inventory.md","definition":"Vendor-Managed Inventory (VMI) is a supply chain arrangement in which the supplier (vendor) has visibility into the customer's inventory levels and assumes responsibility for replenishing inventory to pre-agreed levels. Rather than customers placing orders based on internal forecasts, the supplier monitors actual consumption and triggers replenishment shipments automatically. VMI reduces administrative burden, minimizes stock-outs, improves cash flow (by reducing inventory in the supply chain), and fosters collaboration between supplier and customer.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Disney, S. M., & Towill, D. R.","subfamily":"Supply Chain Management","year":"2006","type":"Business and inventory model"},"citations":[{"ref":"Disney, S. M., & Towill, D. R. (2006). Vendor-managed inventory: A taxonomy of approaches and implications. International Journal of Production Economics, 106(2), 440-456.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Vendor-managed+inventory%3A+A+taxonomy+of+approaches+and+implications+Disney"},{"ref":"Smaros, J., Holström, J., Kärkkäinen, M., & Ala-Risku, T. (2003). Collaborative forecasting and planning in grocery supply chains. International Journal of Operations & Production Management, 23(9), 998-1020.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Collaborative+forecasting+and+planning+in+grocery+supply+chains+Smaros"}],"related":["inventory-routing","bullwhip-effect","aggregate-planning","kanban","scor-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"verbatim-plagiarism","name":"Verbatim Plagiarism","fullName":"Verbatim Plagiarism: Direct Word-for-Word Copying Without Attribution","aliases":["direct plagiarism","copy-and-paste plagiarism","literal copying"],"domain":"research-ethics","family":"process-pipeline","subfamily":"plagiarism-detection-and-prevention","year":"1950s","originator":"Academic integrity framework (modern definition)","url":"https://scholargate.app/en/research-ethics/verbatim-plagiarism","markdownUrl":"https://scholargate.app/en/research-ethics/verbatim-plagiarism.md","definition":"Verbatim plagiarism is the most straightforward and recognizable form of academic misconduct: copying text word-for-word from a source without quotation marks, citation, or attribution. It is the most easily detected form of plagiarism and carries severe institutional and career consequences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Academic integrity framework (modern definition)","subfamily":"plagiarism-detection-and-prevention","year":"1950s","type":"Concept"},"citations":[{"ref":"Council of Canadian Academies (2019). The state of science and technology in Canada. Ottawa: Council of Canadian Academies.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Council%20of%20Canadian%20Academies%20(2019).%20The%20state%20of%20science%20and%20technology%20in%20Canada.%20Ottawa%3A%20Council%20of%20Canadian%20Academi"},{"ref":"Roig, M. (2015). Avoiding plagiarism, self-plagiarism, and other questionable writing practices: A guide to ethical writing. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products/plagiarism"},{"ref":"Steneck, N. H. (2007). Introduction to the responsible conduct of research. U.S. Department of Health and Human Services Office of Research Integrity.","type":"article","doi":null,"isbn":null,"url":"https://ori.hhs.gov/education/products"}],"related":["paraphrasing-plagiarism","mosaic-plagiarism","similarity-vs-plagiarism","turnitin-ithenticate"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vertigo-symptom-scale","name":"VSS","fullName":"Vertigo Symptom Scale","aliases":["VSS"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"vestibular-symptom-severity","year":"1992","originator":"Lucy Yardley and colleagues","url":"https://scholargate.app/en/otolaryngology/vertigo-symptom-scale","markdownUrl":"https://scholargate.app/en/otolaryngology/vertigo-symptom-scale.md","definition":"The Vertigo Symptom Scale (VSS) is a self-report questionnaire assessing the frequency and severity of vertigo and associated symptoms (nausea, lightheadedness, visual disturbance, head motion intolerance). Developed by Yardley et al. in 1992, the VSS measures symptom burden rather than handicap, making it distinct from disability-focused measures. The VSS is valuable for characterizing symptom clusters, monitoring symptom progression, and evaluating treatment response in vestibular and central dizziness disorders.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lucy Yardley and colleagues","subfamily":"vestibular-symptom-severity","year":"1992","type":"Self-report"},"citations":[{"ref":"Yardley, L., Masson, E., Verschuur, C., Haacke, N., & Luxon, L. (1992). Symptoms, anxiety and handicap in balance-dizzy patients: A replication study using the Vertigo Symptom Scale. Journal of Psychosomatic Research, 36(8), 731-740.","type":"article","doi":"10.1037/t12521-000","isbn":null,"url":null}],"related":["dizziness-handicap-inventory","vestibular-activities-participation","anxiety-sensitivity-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vestibular-activities-participation","name":"VAP","fullName":"Vestibular Activities and Participation","aliases":["VAP"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"vestibular-participation","year":"2005","originator":"Vestibular Research Community (Hallberg et al., adapted and expanded)","url":"https://scholargate.app/en/otolaryngology/vestibular-activities-participation","markdownUrl":"https://scholargate.app/en/otolaryngology/vestibular-activities-participation.md","definition":"The Vestibular Activities and Participation (VAP) scale is a patient-reported measure designed to assess limitations in activities of daily living and restrictions in social participation resulting from vestibular dysfunction. Developed within the International Classification of Functioning, Disability and Health (ICF) framework, the VAP evaluates the 'activity' and 'participation' domains rather than impairment alone. It complements clinical vestibular testing and handicap measures (e.g., Dizziness Handicap Inventory) by capturing real-world functional outcomes and quality-of-life limitations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Vestibular Research Community (Hallberg et al., adapted and expanded)","subfamily":"vestibular-participation","year":"2005","type":"Self-report"},"citations":[{"ref":"Hallberg, L. R., Möller, C., Dahlström, Å., & Widén, L. (2005). The Hearing Impairment Impact Profile (HIIP): Development and validation of a scale for assessing hearing-related quality of life. Swedish version. Journal of Audiological Medicine, 13(1), 12-20.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Hearing+Impairment+Impact+Profile+%28HIIP%29%3A+Development+and+validation+of+a+scale+for+assessing+hearing-related+quality+of+life+Hallberg"},{"ref":"Collin, S. M., Baguley, D. M., & Earnshaw, J. (2020). Vestibular Activities and Participation: Validity and reliability of the VAP outcome measure in vestibular disorder populations. Otology & Neurotology, 41(7), 897-905.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Vestibular+Activities+and+Participation%3A+Validity+and+reliability+of+the+VAP+outcome+measure+in+vestibular+disorder+populations+Collin"}],"related":["dizziness-handicap-inventory","vertigo-symptom-scale","activities-specific-balance-confidence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vggnet","name":"VGGNet","fullName":"Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)","aliases":["VGG","VGG-16","VGG-19","Very Deep ConvNet","Oxford Net"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2014,"originator":"Simonyan, K. & Zisserman, A. (Visual Geometry Group, Oxford)","url":"https://scholargate.app/en/deep-learning/vggnet","markdownUrl":"https://scholargate.app/en/deep-learning/vggnet.md","definition":"VGGNet is a deep convolutional neural network architecture introduced by Karen Simonyan and Andrew Zisserman at the Visual Geometry Group, Oxford, in 2014 (published at ICLR 2015). It demonstrated that network depth — achieved exclusively through stacking small 3x3 convolutional filters — is the single most critical factor for high image-classification accuracy, and its two canonical variants (VGG-16 and VGG-19) became the dominant benchmark architectures for CNN design throughout the mid-2010s.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Simonyan, K. & Zisserman, A. (Visual Geometry Group, Oxford)","year":2014,"type":"Deep Convolutional Neural Network (image classification)","task":"Image classification, feature extraction, transfer learning","canonicalVariants":"VGG-16 (138M parameters), VGG-19 (144M parameters)","filterSize":"3x3 (all convolutional layers)","depth":"16–19 weight layers","activation":"ReLU","pooling":"Max-pooling (5 layers)","topDataset":"ImageNet ILSVRC-2014 (1st in localisation, 2nd in classification)"},"citations":[{"ref":"Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs.CV]. Published at ICLR 2015.","type":"article","doi":"10.48550/arXiv.1409.1556","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["alexnet","resnet","googlenet-inception","densenet","mobilenet","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"via-character-strengths","name":"VIA Inventory of Strengths","fullName":"Values in Action Inventory of Strengths","aliases":["VIA Character Strengths","VIA-IS"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"character assessment","year":"2004","originator":"Christopher Peterson and Martin Seligman","url":"https://scholargate.app/en/positive-psychology/via-character-strengths","markdownUrl":"https://scholargate.app/en/positive-psychology/via-character-strengths.md","definition":"The Values in Action (VIA) Inventory of Strengths, developed by Peterson and Seligman in 2004, is a comprehensive instrument designed to identify and measure 24 core character strengths organized under six virtues. Grounded in ancient philosophy and contemporary psychology, the VIA shifts the focus from deficits and pathology to human strengths and positive potential. It exists in multiple versions (full 240-item VIA-240, brief 96-item VIA-96, and mobile-optimized formats), making it accessible for research, clinical practice, coaching, and organizational development.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher Peterson and Martin Seligman","subfamily":"character assessment","year":"2004","type":"Self-report questionnaire"},"citations":[{"ref":"Peterson, C., & Seligman, M. E. P. (2004). Character strengths and virtues: A handbook and classification. Oxford University Press.","type":"book","doi":null,"isbn":null,"url":"https://www.oup.com/academic/product/character-strengths-and-virtues-9780195167015"},{"ref":"McGrath, R. E. (2015). Integrating psychological and cultural perspectives on virtue: The hierarchical structure of character strengths. The Journal of Positive Psychology, 10(3), 208–214.","type":"article","doi":"10.1080/17439760.2014.994222","isbn":null,"url":null}],"related":["flourishing-scale","perma-scale","hope-scale","positive-mental-health-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vickers-hardness","name":"Vickers Hardness","fullName":"Vickers Hardness Testing","aliases":["Vickers hardness test","Vickers microhardness","HV"],"domain":"materials-science","family":"process-pipeline","subfamily":"Mechanical testing","year":"1922","originator":"Smith and Sandland","url":"https://scholargate.app/en/materials-science/vickers-hardness","markdownUrl":"https://scholargate.app/en/materials-science/vickers-hardness.md","definition":"Vickers Hardness testing is a mechanical characterization technique for determining material hardness by pressing a diamond pyramid indenter into a material surface under controlled load and measuring the resulting indent dimensions. Invented by Smith and Sandland in 1922, Vickers hardness is applicable across an enormous hardness range (1-2000 HV) using the same indenter geometry at different loads. It is the most versatile hardness test, widely used in materials science, metallurgy, and quality control for assessing material strength and comparing alloy performance.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Smith and Sandland","subfamily":"Mechanical testing","year":"1922","type":"Hardness test"},"citations":[{"ref":"Smith, E., & Sandland, G. E. (1922). An accurate method of determining the hardness of metals with particular reference to high-hardness alloys. The Institution of Steel Engineers, 8, 623-641.","type":"article","doi":null,"isbn":null,"url":"https://www.worldscientific.com"},{"ref":"ASTM E92-17: Standard test methods for Vickers hardness and Knoop hardness of metallic materials. ASTM International.","type":"standard","doi":null,"isbn":null,"url":"https://www.astm.org"},{"ref":"Torrance, A. A., & Horne, A. (2014). The application of surface topography measurement techniques to microhardness testing. Tribology and Interface Engineering, 22, 213-226.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+application+of+surface+topography+measurement+techniques+to+microhardness+testing+Torrance"}],"related":["nanoindentation","atomic-force-microscopy","finite-element-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"viewshed-analysis","name":"Viewshed Analysis","fullName":"Viewshed Analysis","aliases":["visibility analysis","landscape archaeology"],"domain":"archaeology","family":"process-pipeline","subfamily":"GIS Analysis","year":"1995","originator":"David Wheatley","url":"https://scholargate.app/en/archaeology/viewshed-analysis","markdownUrl":"https://scholargate.app/en/archaeology/viewshed-analysis.md","definition":"Viewshed analysis examines what is visible from specific locations or within a defined area using digital elevation models (DEMs) and geographic information systems (GIS). Pioneered by David Wheatley in the 1990s, the method reveals how landscape features (hilltops, valleys, water sources) controlled visibility and movement. Archaeologists use viewshed analysis to understand settlement placement, ritual monument visibility, and territorial organization in prehistoric and historic landscapes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"David Wheatley","subfamily":"GIS Analysis","year":"1995","type":"Landscape-scale analysis"},"citations":[{"ref":"Wheatley, D. (1995). Cumulative viewshed analysis: a GIS-based method for investigating intervisibility, and its archaeological application. In G. R. Lock & Z. Stancic (Eds.), Archaeology and GIS (pp. 171-185).","type":"article","doi":null,"isbn":null,"url":"https://www.routledge.com/Archaeology-and-GIS-Mapping-our-Pasts/Lock-Stancic/p/book/9780415124683"},{"ref":"Llobera, M. (2003). Extending GIS-based visual analysis: the concept of visualscapes. International Journal of Geographical Information Science, 17(1), 25-48.","type":"article","doi":"10.1080/713811741","isbn":null,"url":null}],"related":["space-syntax","predictive-site-location"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vikor-smaa","name":"VIKOR-SMAA","fullName":"VIKOR with Stochastic Multicriteria Acceptability Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2009","originator":"Tervonen, T. Figueira, J. R. Lahdelma, R. Dias, J. A. Salminen, P.","url":"https://scholargate.app/en/decision-making/vikor-smaa","markdownUrl":"https://scholargate.app/en/decision-making/vikor-smaa.md","definition":"VIKOR-SMAA (VIKOR with Stochastic Multicriteria Acceptability Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Tervonen, T. Figueira, J. R. Lahdelma, R. Dias, J. A. Salminen, P. in 2009. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tervonen, T. Figueira, J. R. Lahdelma, R. Dias, J. A. Salminen, P.","subfamily":"Ranking","year":"2009","type":"Monte Carlo acceptability indices from VIKOR under weight uncertainty","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Tervonen, T., Figueira, J. R., Lahdelma, R., Dias, J. A., Salminen, P. (2009). A stochastic method for robustness analysis in sorting problems. European Journal of Operational Research","type":"article","doi":"10.1016/j.ejor.2007.09.008","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vikor","name":"VIKOR","fullName":"VlseKriterijumska Optimizacija I Kompromisno Resenje (Multicriteria Optimisation and Compromise Solution)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1998","originator":"Opricovic, S.","url":"https://scholargate.app/en/decision-making/vikor","markdownUrl":"https://scholargate.app/en/decision-making/vikor.md","definition":"VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje (Multicriteria Optimisation and Compromise Solution)) is a ranking multi-criteria decision-making (MCDM) method introduced by Opricovic, S. in 1998. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Opricovic, S.","subfamily":"Ranking","year":"1998","type":"Compromise / aggregation-function based","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":true},"citations":[{"ref":"Opricovic, S. (1998). Multicriteria Optimization of Civil Engineering Systems. PhD Dissertation, Faculty of Civil Engineering, University of Belgrade","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multicriteria+Optimization+of+Civil+Engineering+Systems+Opricovic"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"violence-risk-appraisal-guide","name":"VRAG","fullName":"Violence Risk Appraisal Guide","aliases":["VRAG","Harris-Rice-Quinsey VRAG"],"domain":"forensic-psychology","family":"process-pipeline","subfamily":"actuarial-violence-prediction","year":"1993","originator":"Grant T. Harris, Marnie E. Rice, Vernon L. Quinsey","url":"https://scholargate.app/en/forensic-psychology/violence-risk-appraisal-guide","markdownUrl":"https://scholargate.app/en/forensic-psychology/violence-risk-appraisal-guide.md","definition":"The Violence Risk Appraisal Guide (VRAG) is an actuarial instrument developed by Harris, Rice, and Quinsey (1993) to estimate the probability of violent recidivism among adult male offenders released from forensic psychiatric hospitals. It represents one of the earliest empirically validated violence prediction tools and remains widely used in correctional, forensic psychiatric, and civil commitment settings. The VRAG combines static historical variables with dynamic factors to generate a probability-based risk assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Grant T. Harris, Marnie E. Rice, Vernon L. Quinsey","subfamily":"actuarial-violence-prediction","year":"1993","type":"Clinician-rated / File-based"},"citations":[{"ref":"Harris, G. T., Rice, M. E., & Quinsey, V. L. (1993). Violent recidivism of mentally disordered offenders: The development of a statistical prediction instrument. Neuropsychiatry Neuropsychology and Behavioral Neurology, 6(4), 269–279.","type":"article","doi":"10.1177/0093854893020004001","isbn":null,"url":null},{"ref":"Harris, G. T., Rice, M. E., Camilleri, J. A., & Boer, D. P. (2015). Prospective, multi-site study of adolescent violent recidivism: The Violent Offense Risk Assessment Guide. Canadian Journal of Criminology and Criminal Justice, 57(4), 486–524.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Prospective%2C+multi-site+study+of+adolescent+violent+recidivism%3A+The+Violent+Offense+Risk+Assessment+Guide+Harris"}],"related":["hcr-20","static-99","level-of-service-inventory","saprof"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vision-mamba","name":"Vision Mamba","fullName":"Vision Mamba: Efficient State Space Models for Image Understanding","aliases":["ViM","Mamba for Vision"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep Learning, State Space Models","year":"2024","originator":"Li Zhu","url":"https://scholargate.app/en/deep-learning/vision-mamba","markdownUrl":"https://scholargate.app/en/deep-learning/vision-mamba.md","definition":"Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Li Zhu","subfamily":"Deep Learning, State Space Models","year":"2024","type":"Neural network architecture"},"citations":[{"ref":"Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2401.09417"}],"related":["swin-transformer","mamba","vision-transformer","spatial-temporal-gcn"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vision-related-adl-scale","name":"Vision-ADL","fullName":"Vision-Related Activities of Daily Living Scale","aliases":["Vision-ADL","Vision ADL","Vision-Related ADL"],"domain":"ophthalmology","family":"process-pipeline","subfamily":"vision-specific activities of daily living","year":"2000","originator":"Massof RW, Stelmack J et al.","url":"https://scholargate.app/en/ophthalmology/vision-related-adl-scale","markdownUrl":"https://scholargate.app/en/ophthalmology/vision-related-adl-scale.md","definition":"The Vision-Related Activities of Daily Living (Vision-ADL) Scale is a comprehensive instrument measuring self-reported difficulty with vision-dependent daily activities across a wide spectrum of vision loss severities. Developed by Massof, Stelmack, and colleagues at the Johns Hopkins Low Vision Clinic and VA Low Vision Service, the Vision-ADL employs adaptive item administration—presenting only vision-dependent activities relevant to the patient's functional level—to maximize responsiveness and precision across mild to profound vision loss.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Massof RW, Stelmack J et al.","subfamily":"vision-specific activities of daily living","year":"2000","type":"Self-report"},"citations":[{"ref":"Massof, R. W., Ahmadian, L., Grover, L. L., et al. (2007). The Activity Inventory: an adaptive visual function questionnaire. Optom Vis Sci, 84(8), 763-774.","type":"article","doi":"10.1097/opx.0b013e3181339efd","isbn":null,"url":null},{"ref":"Stelmack, J., Tang, X. C., Wei, Y., & Massof, R. W. (2005). The Veterans Affairs Low-Vision Visual Functioning Questionnaire: relationship to neural measures of contrast sensitivity. Optom Vis Sci, 82(2), 87-105.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Veterans+Affairs+Low-Vision+Visual+Functioning+Questionnaire%3A+relationship+to+neural+measures+of+contrast+sensitivity+Stelmack"}],"related":["nei-vfq-25","low-vision-quality-of-life","impact-vision-impairment","visual-function-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vision-transformer","name":"Vision Transformer","fullName":"Vision Transformer (ViT)","aliases":["Görsel Transformer (ViT)","görsel transformer","ViT","patch transformer for images"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2021,"originator":"Dosovitskiy, A. et al.","url":"https://scholargate.app/en/deep-learning/vision-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/vision-transformer.md","definition":"The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dosovitskiy, A. et al.","year":2021,"type":"Transformer architecture for images (self-attention over patches)","task":"Image classification & prediction","minSample":1000,"requiresGpu":true},"citations":[{"ref":"Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2010.11929"},{"ref":"Touvron, H. et al. (2021). Training Data-Efficient Image Transformers. ICML.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2012.12877"}],"related":["variational-autoencoder","generative-adversarial-network","diffusion-model","random-forest","svm-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-analog-scale-pain","name":"Visual Analog Scale for Pain","fullName":"Visual Analog Scale (VAS) for Pain Intensity Assessment","aliases":["VAS","Pain VAS","Visual Rating Scale"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"1974","originator":"E. Carl Huskisson","url":"https://scholargate.app/en/clinical-assessment/visual-analog-scale-pain","markdownUrl":"https://scholargate.app/en/clinical-assessment/visual-analog-scale-pain.md","definition":"The Visual Analog Scale (VAS) is a 10-centimeter line for measuring pain intensity, developed by Huskisson in 1974. Patients mark their current pain level along the continuum from no pain to worst pain imaginable. It remains one of the most widely used single-item pain measures in clinical practice and research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"E. Carl Huskisson","subfamily":"Clinical scoring","year":"1974","type":"Pain intensity measurement"},"citations":[{"ref":"Huskisson, E. C. (1974). Measurement of pain. Lancet, 2(7889), 1127-1131.","type":"article","doi":"10.1016/s0140-6736(74)90884-8","isbn":null,"url":null},{"ref":"Price, D. D., McGrath, P. A., Rafii, A., & Buckingham, B. (1983). The validation of visual analogue scales as ratio scale measures for chronic and experimental pain. Pain, 17(1), 45-56.","type":"article","doi":"10.1016/0304-3959(83)90126-4","isbn":null,"url":null}],"related":["glasgow-coma-scale","numeric-rating-scale-pain","faces-pain-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-analysis","name":"Visual analysis","fullName":"Visual Analysis in Qualitative Research","aliases":["visual research methods","image analysis","visual inquiry","visual data analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Formalized in social sciences from the 1980s–2000s","originator":"Roots in art history and semiotics (Panofsky, Barthes); social science applications developed by Gillian Rose and Marcus Banks","url":"https://scholargate.app/en/qualitative/visual-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-analysis.md","definition":"Visual analysis is a qualitative research approach that systematically examines visual materials — such as photographs, films, artworks, advertisements, and diagrams — to understand how meaning is produced, communicated, and interpreted. Drawing on traditions from art history, semiotics, and social science, it treats visual objects as data that carry social, cultural, and ideological significance. Multiple frameworks exist, from formal compositional analysis to discourse-based and audience-reception approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roots in art history and semiotics (Panofsky, Barthes); social science applications developed by Gillian Rose and Marcus Banks","year":"Formalized in social sciences from the 1980s–2000s","type":"Qualitative research approach","dataType":"Images, photographs, film, video, artwork, diagrams, visual media","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473943056","url":null},{"ref":"Banks, M. (2007). Using Visual Data in Qualitative Research. Sage.","type":"book","doi":null,"isbn":"978-0761943754","url":null}],"related":["semiotic-analysis","discourse-analysis","content-analysis","ethnography","document-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-balance-measurement","name":"Visual Balance Measurement","fullName":"Visual Balance Measurement","aliases":["Composition Equilibrium Analysis","Weight Distribution Assessment"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Composition and spatial analysis","year":"1974","originator":"Rudolf Arnheim","url":"https://scholargate.app/en/visual-arts/visual-balance-measurement","markdownUrl":"https://scholargate.app/en/visual-arts/visual-balance-measurement.md","definition":"Visual Balance Measurement is a computational method for assessing compositional equilibrium in images and designs. Drawing from art theory and perceptual psychology, this pipeline quantifies how visual weight is distributed across a composition, determining whether elements are harmoniously balanced or weighted unevenly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf Arnheim","subfamily":"Composition and spatial analysis","year":"1974","type":"Analytical pipeline"},"citations":[{"ref":"Balakrishnan, M., & Itti, L. (2008). Computational Assessment of Compositional Balance in Visual Art. ACM Multimedia Conference Proceedings, 381–390.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Computational+Assessment+of+Compositional+Balance+in+Visual+Art+Balakrishnan"},{"ref":"Zhang, L., Zhang, B., & He, H. (2015). Contiguous Motion Segmentation using Center-Biased Feature Subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(5), 1074–1086.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Contiguous+Motion+Segmentation+using+Center-Biased+Feature+Subspaces+Zhang"},{"ref":"Arnheim, R. (1974). Art and Visual Perception: A Psychology of the Creative Eye. University of California Press.","type":"book","doi":null,"isbn":"978-0520243835","url":null}],"related":["visual-complexity-measure","gestalt-principles-analysis","color-harmony-analysis","image-aesthetics-assessment","visual-saliency-map"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-complexity-measure","name":"Visual Complexity Measure","fullName":"Visual Complexity Measure","aliases":["Aesthetic Complexity Assessment","Visual Information Density Metric"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Computational aesthetics and perception","year":"2011","originator":"Adrian Forsythe","url":"https://scholargate.app/en/visual-arts/visual-complexity-measure","markdownUrl":"https://scholargate.app/en/visual-arts/visual-complexity-measure.md","definition":"Visual Complexity Measure is a computational pipeline for quantifying the informational density and structural intricacy of visual compositions. Drawing from cognitive psychology and computational aesthetics research, this method provides objective metrics for how much visual processing demand a design, image, or artwork places on viewers.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adrian Forsythe","subfamily":"Computational aesthetics and perception","year":"2011","type":"Analytical pipeline"},"citations":[{"ref":"Forsythe, A., Nadal, M., Shackelford, N., & Cela-Conde, C. J. (2011). Predicting Beauty: Fractal Dimension and Visual Complexity in Art. Biology Letters, 7(2), 203–205.","type":"article","doi":"10.1348/000712610x498958","isbn":null,"url":null},{"ref":"Reid, B., D'Mello, S., & Hussain, M. S. (2015). Complexity and Aesthetic Preference. Cognitive Science, 39(5), 1174–1203.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Complexity+and+Aesthetic+Preference+Reid"},{"ref":"Reber, R., Schwarz, N., & Winkielman, P. (1994). Processing Fluency and Aesthetic Pleasure: Is Beauty in the Processing? Personality and Social Psychology Review, 8(4), 364–382.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Processing+Fluency+and+Aesthetic+Pleasure%3A+Is+Beauty+in+the+Processing+Reber"}],"related":["color-harmony-analysis","image-aesthetics-assessment","visual-saliency-map","gestalt-principles-analysis","visual-balance-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-content-analysis","name":"Visual Content Analysis","fullName":"Systematic Analysis of Visual and Pictorial Media","aliases":["visual analysis","image analysis","iconographic analysis"],"domain":"media-studies","family":"process-pipeline","subfamily":"Qualitative visual and cultural analysis","year":"1955","originator":"Erwin Panofsky, Gillian Rose","url":"https://scholargate.app/en/media-studies/visual-content-analysis","markdownUrl":"https://scholargate.app/en/media-studies/visual-content-analysis.md","definition":"Visual Content Analysis is a systematic qualitative method for interpreting images, photographs, films, and other visual media to understand their meanings, social contexts, and cultural significance. Developed from art history, semiotics, and cultural studies—particularly Erwin Panofsky's iconographic method and contemporary approaches by Gillian Rose and Kress and Van Leeuwen—it decodes how images communicate through composition, color, symbol, and cultural convention. The method recognizes that images are not transparent representations but complex texts that require careful interpretive work to reveal embedded meanings and ideological assumptions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Erwin Panofsky, Gillian Rose","subfamily":"Qualitative visual and cultural analysis","year":"1955","type":"Multi-layered analytical method for interpreting images and visual meaning"},"citations":[{"ref":"Panofsky, E. (1955). Meaning in the Visual Arts. Doubleday.","type":"book","doi":null,"isbn":null,"url":"https://www.doubleday.com"},{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Images (4th ed.). SAGE Publications.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Visual+Methodologies%3A+An+Introduction+to+Researching+with+Images+%284th+ed.%29+Rose"},{"ref":"Kress, G., & Van Leeuwen, T. (2006). Reading Images: The Grammar of Visual Design (2nd ed.). Routledge.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Reading+Images%3A+The+Grammar+of+Visual+Design+%282nd+ed.%29+Kress"},{"ref":"Hall, S. (Ed.). (1997). Representation: Cultural Representations and Signifying Practices. SAGE Publications.","type":"book","doi":null,"isbn":null,"url":"https://www.sagepub.com"}],"related":["semiotics-film","media-framing-analysis","discourse-analysis-media","film-narrative-analysis","genre-analysis-film"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-autoethnography","name":"Visual Elicitation Autoethnography","fullName":"Visual Elicitation Autoethnography","aliases":["VEA","photo-elicitation autoethnography","visual autoethnography","image-elicited autoethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Synthesised from Douglas Harper (photo elicitation, 2002) and Heewon Chang (autoethnography as method, 2008); popularised in education and health humanities research in the 2010s","url":"https://scholargate.app/en/qualitative/visual-elicitation-autoethnography","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-autoethnography.md","definition":"Visual elicitation autoethnography (VEA) is a qualitative self-study method that combines the personal narrative orientation of autoethnography with the stimulus power of visual artefacts — photographs, drawings, or found images — to prompt and deepen autobiographical reflection. The researcher produces or selects images from their own life, then uses those images as elicitation tools to generate rich written or spoken narratives about a cultural phenomenon they have lived through, positioning the self as both researcher and research subject.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Douglas Harper (photo elicitation, 2002) and Heewon Chang (autoethnography as method, 2008); popularised in education and health humanities research in the 2010s","year":"2000s–2010s","type":"Qualitative self-study design","dataType":"Researcher-produced visual artefacts (photographs, drawings, collages) combined with autobiographical written narrative","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Chang, H. (2008). Autoethnography as Method. Left Coast Press.","type":"book","doi":null,"isbn":"978-1598741230","url":null},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["autoethnography","photo-elicitation","narrative-inquiry","arts-based-research","reflexive-thematic-analysis","duoethnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-biographical-research","name":"Visual Elicitation Biographical Research","fullName":"Visual Elicitation Biographical Research","aliases":["photo-elicitation biography","visual biographical method","image-based life history research","VEBR"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (synthesis of older traditions)","originator":"Douglas Harper (photo elicitation); Ken Plummer, Daniel Bertaux (biographical tradition); integrated by visual qualitative researchers in the 1990s–2000s","url":"https://scholargate.app/en/qualitative/visual-elicitation-biographical-research","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-biographical-research.md","definition":"Visual elicitation biographical research combines the life-history interview tradition with image-based elicitation techniques. Participants bring or choose photographs, drawings, personal objects, or other visual artefacts that represent moments and meanings in their lives. These visuals serve as prompts in extended biographical interviews, releasing richer, more emotionally grounded narratives than verbal questioning alone typically achieves. The method is used in education, health, migration studies, and other fields where lived experience over time is central.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas Harper (photo elicitation); Ken Plummer, Daniel Bertaux (biographical tradition); integrated by visual qualitative researchers in the 1990s–2000s","year":"1990s–2000s (synthesis of older traditions)","type":"Qualitative research design","dataType":"Photographs, personal images, objects, and biographical interview narratives (text and visual)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.","type":"book","doi":null,"isbn":"978-0761952985","url":null}],"related":["biographical-research","narrative-analysis","photo-elicitation","life-history-research","arts-based-research","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-case-study","name":"Visual Elicitation Case Study","fullName":"Visual Elicitation Case Study","aliases":["photo elicitation case study","image-based case study","visual methods case study","elicitation-based case study"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2002 (photo elicitation formalised); integrated approach emerged 2000s–2010s","originator":"Douglas Harper (photo elicitation); Robert K. Yin (case study framework)","url":"https://scholargate.app/en/qualitative/visual-elicitation-case-study","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-case-study.md","definition":"Visual elicitation case study is a qualitative design that embeds photo or image elicitation within a case study framework. Participants respond to photographs, drawings, or other visual materials during in-depth interviews, generating richer and often unexpected data than verbal questioning alone. The case study structure then situates these image-prompted accounts within a bounded real-world context — an individual, organization, community, or event — enabling a holistic, detailed understanding of the case.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas Harper (photo elicitation); Robert K. Yin (case study framework)","year":"2002 (photo elicitation formalised); integrated approach emerged 2000s–2010s","type":"Qualitative research design","dataType":"Visual materials (photographs, drawings, maps, video stills) combined with interview transcripts and case documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["case-study","photo-elicitation","ethnography","narrative-analysis","phenomenology","participatory-action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-classic-grounded-theory","name":"Visual elicitation classic grounded theory","fullName":"Visual Elicitation Classic Grounded Theory","aliases":["photo-elicitation CGT","image-based classic grounded theory","visual data classic GT","Glaserian grounded theory with visual elicitation"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1967 (classic GT); visual elicitation integration from 1990s–2000s","originator":"Barney Glaser & Anselm Strauss (classic GT, 1967); Douglas Harper (photo elicitation, 2002)","url":"https://scholargate.app/en/qualitative/visual-elicitation-classic-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-classic-grounded-theory.md","definition":"Visual elicitation classic grounded theory combines Glaser and Strauss's original discovery-oriented grounded theory with visual elicitation interviewing, in which photographs, drawings, or other images serve as prompts that stimulate participant talk. The approach retains classic GT's commitment to emergent, inductive theory building — following the data without imposing a priori conceptual frameworks — while using visual materials to deepen and enrich participants' verbal accounts of their experiences.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barney Glaser & Anselm Strauss (classic GT, 1967); Douglas Harper (photo elicitation, 2002)","year":"1967 (classic GT); visual elicitation integration from 1990s–2000s","type":"Qualitative research design","dataType":"Photographs, drawings, maps, or other visual materials combined with interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.","type":"book","doi":null,"isbn":"978-0202302607","url":null},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["classic-grounded-theory","visual-elicitation-grounded-theory","visual-elicitation-constructivist-grounded-theory","photo-elicitation","visual-analysis","grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-content-analysis","name":"Visual elicitation content analysis","fullName":"Visual Elicitation Content Analysis","aliases":["photo elicitation content analysis","image-elicited content analysis","visual stimulus content analysis","VECA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2002 (synthesis of photo elicitation with systematic content analysis)","originator":"Douglas Harper (photo elicitation); Klaus Krippendorff (content analysis framework)","url":"https://scholargate.app/en/qualitative/visual-elicitation-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-content-analysis.md","definition":"Visual elicitation content analysis combines the photograph or image-based interview technique known as photo elicitation with the systematic coding procedures of content analysis. Participants are shown selected visual stimuli — photographs, drawings, video stills, or researcher-produced images — and invited to respond verbally. Those verbal responses are then subjected to structured content analysis to identify recurring themes, categories, and patterns across participants, bridging the depth of elicited meaning with the rigor of systematic coding.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas Harper (photo elicitation); Klaus Krippendorff (content analysis framework)","year":"2002 (synthesis of photo elicitation with systematic content analysis)","type":"Qualitative–interpretive hybrid method","dataType":"Visual materials (photographs, images, video stills) combined with interview or verbal response data","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506395661","url":null}],"related":["photo-elicitation","content-analysis","thematic-analysis","visual-ethnography","multimodal-discourse-analysis","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-conversation-analysis","name":"Visual Elicitation Conversation Analysis","fullName":"Visual Elicitation Conversation Analysis","aliases":["VECA","image-elicited conversation analysis","photo elicitation CA","visual-aided CA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Hybrid approach emerging 1990s–2000s; constituent methods established 1960s–1970s","originator":"Synthesised from Douglas Harper (visual elicitation) and Harvey Sacks, Emanuel Schegloff, Gail Jefferson (conversation analysis)","url":"https://scholargate.app/en/qualitative/visual-elicitation-conversation-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-conversation-analysis.md","definition":"Visual elicitation conversation analysis (VECA) is a qualitative hybrid method that uses photographs, drawings, maps, or other visual stimuli to prompt and structure participant talk, and then subjects the resulting interaction to systematic conversation analysis (CA). The approach leverages the evocative power of images to generate richer, more embodied accounts of experience while applying CA's rigorous sequential analysis of turn-taking, repair, and action formation to reveal how meaning is collaboratively constructed in situ.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Douglas Harper (visual elicitation) and Harvey Sacks, Emanuel Schegloff, Gail Jefferson (conversation analysis)","year":"Hybrid approach emerging 1990s–2000s; constituent methods established 1960s–1970s","type":"Qualitative hybrid method","dataType":"Audio/video recordings of talk prompted by visual stimuli (photographs, drawings, maps, objects)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.","type":"article","doi":"10.2307/412243","isbn":null,"url":null}],"related":["conversation-analysis","photo-elicitation","multimodal-discourse-analysis","focus-group-research","narrative-analysis","ethnomethodology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-critical-discourse-analysis","name":"Visual Elicitation Critical Discourse Analysis","fullName":"Visual Elicitation Critical Discourse Analysis","aliases":["VECDA","photo-elicitation CDA","visual-elicitation CDA","image-elicitation critical discourse analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (integration of established traditions)","originator":"Synthesized from Douglas Harper (photo elicitation) and Norman Fairclough (CDA); integrated approach developed across visual and critical discourse scholarship","url":"https://scholargate.app/en/qualitative/visual-elicitation-critical-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-critical-discourse-analysis.md","definition":"Visual Elicitation Critical Discourse Analysis (VECDA) is a qualitative methodology that uses photographs, drawings, or other visual materials as prompts to elicit participant talk, then subjects both the visual artifacts and the resulting discourse to critical discourse analysis (CDA). The approach uncovers how power, ideology, and social structures are reproduced or contested through the interplay of image and language, making it particularly powerful for social justice, health, education, and media research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Douglas Harper (photo elicitation) and Norman Fairclough (CDA); integrated approach developed across visual and critical discourse scholarship","year":"2000s–2010s (integration of established traditions)","type":"Qualitative research design combining visual elicitation and discourse analysis","dataType":"Visual materials (photographs, images, diagrams) and participant-generated or interview discourse","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Fairclough, N. (1995). Critical Discourse Analysis: The Critical Study of Language. Longman.","type":"book","doi":null,"isbn":"978-0582219847","url":null}],"related":["critical-discourse-analysis","photo-elicitation","multimodal-discourse-analysis","visual-ethnography","thematic-analysis","narrative-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-digital-ethnography","name":"Visual Elicitation Digital Ethnography","fullName":"Visual Elicitation Digital Ethnography","aliases":["VEDE","digital photo elicitation ethnography","visual digital ethnography","image-based digital ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Synthesized from Douglas Harper (photo elicitation, 2002) and Sarah Pink (visual ethnography, 2001/2007)","url":"https://scholargate.app/en/qualitative/visual-elicitation-digital-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-digital-ethnography.md","definition":"Visual elicitation digital ethnography is a qualitative research design that embeds visual elicitation techniques — using photographs, videos, or digital images as interview stimuli — within digital ethnographic fieldwork conducted in online or digitally mediated environments. Participants produce or select visual materials from their digital lives, which are then used to elicit in-depth talk about meanings, identities, and practices that verbal questioning alone often fails to surface.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesized from Douglas Harper (photo elicitation, 2002) and Sarah Pink (visual ethnography, 2001/2007)","year":"2000s–2010s","type":"Qualitative research design","dataType":"Digital images, video, screenshots, social media content, interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Pink, S. (2007). Doing Visual Ethnography: Images, Media and Representation in Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412929523","url":null},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["digital-ethnography","photo-elicitation","virtual-ethnography","participatory-action-research","multimodal-analysis","netnography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-discourse-analysis","name":"Visual Elicitation Discourse Analysis","fullName":"Visual Elicitation Discourse Analysis","aliases":["VEDA","photo-elicitation discourse analysis","image-elicitation discourse analysis","visual elicitation interview analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Late 1990s–2000s (consolidation as a combined approach)","originator":"Synthesised from photo-elicitation (Clark, 1969; Harper, 2002) and discourse analysis (Foucault; Fairclough)","url":"https://scholargate.app/en/qualitative/visual-elicitation-discourse-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-discourse-analysis.md","definition":"Visual Elicitation Discourse Analysis (VEDA) is a qualitative method that uses photographs or other images as interview stimuli to generate participant talk, which is then subjected to systematic discourse analysis. By anchoring conversation in concrete visual materials, VEDA accesses meanings, ideologies, and subject positions that purely verbal questioning often fails to surface. The approach combines the depth of elicitation interviewing with the critical, language-focused rigour of discourse analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from photo-elicitation (Clark, 1969; Harper, 2002) and discourse analysis (Foucault; Fairclough)","year":"Late 1990s–2000s (consolidation as a combined approach)","type":"Qualitative combined method","dataType":"Visual materials (photographs, images, video stills) plus interview or focus-group transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473942028","url":null}],"related":["discourse-analysis","photo-elicitation","thematic-analysis","multimodal-discourse-analysis","narrative-analysis","critical-discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-document-analysis","name":"Visual Elicitation Document Analysis","fullName":"Visual Elicitation Document Analysis","aliases":["VEDA","visual document elicitation","photo-elicitation document analysis","image-assisted document analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1980s–2000s (consolidated in social science by 2000s)","originator":"Developed from convergence of visual sociology (Harper, Banks) and document analysis traditions","url":"https://scholargate.app/en/qualitative/visual-elicitation-document-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-document-analysis.md","definition":"Visual elicitation document analysis is a qualitative method that uses visual materials — photographs, drawings, institutional images, maps, or archival visuals — embedded within or alongside documents to prompt deeper participant reflection and to enrich the interpretive reading of those documents. By treating visuals as primary analytic objects rather than mere illustrations, the approach bridges visual elicitation (provoking meaning-making through images) and systematic document analysis (examining records for evidence of social, institutional, or cultural processes).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed from convergence of visual sociology (Harper, Banks) and document analysis traditions","year":"1980s–2000s (consolidated in social science by 2000s)","type":"Qualitative analytic approach","dataType":"Visual documents (photographs, drawings, maps, archival images, institutional visuals) combined with textual/contextual document data","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Banks, M. (2007). Using Visual Data in Qualitative Research. Sage.","type":"book","doi":null,"isbn":"978-0761943402","url":null},{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473942028","url":null}],"related":["document-analysis","photo-elicitation","content-analysis","multimodal-analysis","narrative-analysis","thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-ethnography","name":"Visual elicitation ethnography","fullName":"Visual Elicitation Ethnography","aliases":["photo elicitation ethnography","visual methods ethnography","image-based ethnography","VEE"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (photo elicitation roots to Collier 1957; consolidated by Pink 2001)","originator":"Douglas Harper (photo elicitation); Sarah Pink (visual ethnography synthesis)","url":"https://scholargate.app/en/qualitative/visual-elicitation-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-ethnography.md","definition":"Visual elicitation ethnography is a qualitative research design that integrates sustained ethnographic fieldwork with the systematic use of visual stimuli — photographs, video clips, drawings, or participant-produced images — to prompt deeper, more reflexive accounts from community members. By combining prolonged immersion in a social setting with image-mediated interviews, researchers gain access to tacit knowledge and cultural meanings that verbal questioning alone rarely surfaces.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas Harper (photo elicitation); Sarah Pink (visual ethnography synthesis)","year":"1990s–2000s (photo elicitation roots to Collier 1957; consolidated by Pink 2001)","type":"Qualitative visual-participatory research design","dataType":"Photographs, video, drawings, objects, field notes, ethnographic interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Pink, S. (2007). Doing Visual Ethnography: Images, Media and Representation in Research (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1412929417","url":null},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["ethnography","visual-analysis","participatory-ethnography","digital-ethnography","narrative-inquiry","photo-voice"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-grounded-theory","name":"Visual Elicitation Grounded Theory","fullName":"Visual Elicitation Grounded Theory","aliases":["photo-elicitation grounded theory","visual GT","image-based grounded theory","VE-GT"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (formalized integration)","originator":"Synthesis of photo-elicitation (John Collier Jr., 1957) and grounded theory (Glaser & Strauss, 1967); integrated application developed across 1990s–2000s visual sociology","url":"https://scholargate.app/en/qualitative/visual-elicitation-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-grounded-theory.md","definition":"Visual Elicitation Grounded Theory (VE-GT) is a qualitative design that augments classical grounded theory with visual elicitation techniques — photographs, drawings, video stills, or participant-produced images — as the primary stimulus for data collection. Instead of relying solely on verbal prompts, the researcher uses images to help participants articulate meanings, memories, and social processes that are difficult to express in words alone. The resulting interview data are then analysed using the full grounded theory analytic cycle of open coding, axial coding, and theoretical sampling to generate a substantive theory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesis of photo-elicitation (John Collier Jr., 1957) and grounded theory (Glaser & Strauss, 1967); integrated application developed across 1990s–2000s visual sociology","year":"1990s–2000s (formalized integration)","type":"Qualitative research design","dataType":"Visual materials (photos, drawings, video stills) combined with interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Clark, A. (2006). Anonymising research participants: Assumptions, ethics and practicalities. Social Research Update, 36, 1–4. (For broader context see also: Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.)","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Anonymising+research+participants%3A+Assumptions%2C+ethics+and+practicalities+Clark"},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["grounded-theory","photo-elicitation","visual-ethnography","constructivist-grounded-theory","narrative-analysis","phenomenology"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-hermeneutic-phenomenology","name":"Visual elicitation hermeneutic phenomenology","fullName":"Visual Elicitation Hermeneutic Phenomenology","aliases":["photo-elicitation hermeneutic phenomenology","visual-method hermeneutic phenomenology","image-based hermeneutic phenomenology","VEHP"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (integration emerged in qualitative health and education research)","originator":"Synthesised from van Manen's hermeneutic phenomenology and Harper's photo-elicitation tradition","url":"https://scholargate.app/en/qualitative/visual-elicitation-hermeneutic-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-hermeneutic-phenomenology.md","definition":"Visual elicitation hermeneutic phenomenology is a qualitative design that combines the image-based interview technique of visual elicitation with the interpretive, context-sensitive tradition of hermeneutic phenomenology. Participants produce or select photographs, drawings, or other images related to a lived experience; those images then anchor an in-depth interview in which meaning is co-constructed between researcher and participant. The approach draws on van Manen's hermeneutic phenomenology and Harper's photo-elicitation method to access layers of experiential meaning that words alone often cannot reach.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from van Manen's hermeneutic phenomenology and Harper's photo-elicitation tradition","year":"1990s–2000s (integration emerged in qualitative health and education research)","type":"Qualitative research design","dataType":"Participant-produced or researcher-selected images combined with in-depth interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.","type":"book","doi":null,"isbn":"978-0791404645","url":null}],"related":["hermeneutic-phenomenology","phenomenology","visual-analysis","photo-elicitation","interpretive-phenomenology","participatory-visual-methods"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-institutional-ethnography","name":"Visual Elicitation Institutional Ethnography","fullName":"Visual Elicitation Institutional Ethnography","aliases":["photo elicitation IE","visual IE","image-based institutional ethnography","visual data institutional ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (integration period; IE roots ~1987, photo elicitation ~1967)","originator":"Dorothy E. Smith (IE); Douglas Harper (photo elicitation); integration developed by feminist and critical ethnographers in the 2000s–2010s","url":"https://scholargate.app/en/qualitative/visual-elicitation-institutional-ethnography","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-institutional-ethnography.md","definition":"Visual elicitation institutional ethnography (IE) integrates photo or image elicitation techniques into Dorothy Smith's institutional ethnography framework. Participants produce or select photographs and other visual materials that represent their everyday experience within an institution; these images then anchor in-depth interviews that surface the ruling relations — texts, policies, and organizational discourses — that coordinate people's work and lives from outside their immediate standpoint.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dorothy E. Smith (IE); Douglas Harper (photo elicitation); integration developed by feminist and critical ethnographers in the 2000s–2010s","year":"2000s–2010s (integration period; IE roots ~1987, photo elicitation ~1967)","type":"Qualitative multimodal research design","dataType":"Photographs, participant-produced images, interview transcripts, institutional texts and documents","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.","type":"book","doi":null,"isbn":"978-0759105010","url":null},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["institutional-ethnography","photo-elicitation","ethnography","participatory-action-research","narrative-analysis","discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-interpretive-phenomenological-analysis","name":"Visual Elicitation Interpretive Phenomenological Analysis","fullName":"Visual Elicitation Interpretive Phenomenological Analysis","aliases":["VE-IPA","photo-elicitation IPA","image-based IPA","visual-method IPA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (IPA ~1996; VE-IPA integration from ~2005 onward)","originator":"Jonathan A. Smith (IPA); integrated with photo-elicitation tradition from Douglas Harper and others","url":"https://scholargate.app/en/qualitative/visual-elicitation-interpretive-phenomenological-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-interpretive-phenomenological-analysis.md","definition":"Visual Elicitation Interpretive Phenomenological Analysis (VE-IPA) combines the idiographic, sense-making framework of Interpretive Phenomenological Analysis with visual elicitation techniques — photographs, participant-produced drawings, or other images — to deepen access to lived experience. Visuals serve as concrete anchors that help participants articulate feelings and meanings that are difficult to express in words alone, making the approach especially productive for embodied, emotional, or tacit dimensions of experience.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan A. Smith (IPA); integrated with photo-elicitation tradition from Douglas Harper and others","year":"2000s–2010s (IPA ~1996; VE-IPA integration from ~2005 onward)","type":"Qualitative interpretive design","dataType":"In-depth interviews with visual stimuli (photographs, drawings, participant-produced images, artifacts)","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretive Phenomenological Analysis: Theory, Method and Research. Sage.","type":"book","doi":null,"isbn":"978-1412908344","url":null},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["interpretive-phenomenological-analysis","phenomenology","photo-elicitation","narrative-analysis","thematic-analysis","arts-based-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-life-history-research","name":"Visual Elicitation Life History Research","fullName":"Visual Elicitation Life History Research","aliases":["photo-elicitation life history","visual life history interview","image-based life history method","VELHR"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (synthesis codified)","originator":"Convergence of photo-elicitation (John Collier Jr., 1957) and life history traditions (Thomas & Znaniecki, 1918; Plummer, 1983)","url":"https://scholargate.app/en/qualitative/visual-elicitation-life-history-research","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-life-history-research.md","definition":"Visual elicitation life history research is a qualitative method that combines the biographical depth of life history interviewing with the evocative power of photographs, personal objects, or other visual materials. Participants select or bring images that are meaningful to their life story; these visuals then serve as prompts during in-depth interviews, unlocking memories and meanings that words alone might not surface. The result is a richly layered biographical narrative grounded in concrete, participant-chosen artefacts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Convergence of photo-elicitation (John Collier Jr., 1957) and life history traditions (Thomas & Znaniecki, 1918; Plummer, 1983)","year":"1990s–2000s (synthesis codified)","type":"Qualitative research design","dataType":"Photographs, personal objects, drawings, and in-depth biographical interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Clark, C. D. (1999). The autodriven interview: A photographic viewfinder into children's experience. Visual Sociology, 14(1), 39–50.","type":"article","doi":"10.1080/14725869908583801","isbn":null,"url":null},{"ref":"Plummer, K. (2001). Documents of Life 2: An Invitation to a Critical Humanism. Sage.","type":"book","doi":null,"isbn":"978-0761952770","url":null}],"related":["photo-elicitation","life-history-research","narrative-inquiry","participatory-action-research","visual-ethnography","oral-history"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-metaphor-analysis","name":"Visual Elicitation Metaphor Analysis","fullName":"Visual Elicitation Metaphor Analysis (VEMA)","aliases":["VEMA","ZMET","Zaltman Metaphor Elicitation Technique","visual metaphor elicitation"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1995","originator":"Gerald Zaltman (Harvard Business School)","url":"https://scholargate.app/en/qualitative/visual-elicitation-metaphor-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-metaphor-analysis.md","definition":"Visual Elicitation Metaphor Analysis (VEMA) is a qualitative technique in which participants select or create images that represent their thoughts, feelings, or experiences about a topic, and then articulate the metaphors embedded in those images during a guided interview. Originally formalised as the Zaltman Metaphor Elicitation Technique (ZMET) by Gerald Zaltman in 1995, the approach rests on the premise that most human thought is nonverbal and structured through metaphor, making images a more direct gateway to deep mental models than verbal questioning alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gerald Zaltman (Harvard Business School)","year":"1995","type":"Visual-projective qualitative technique","dataType":"Participant-selected images, metaphorical narratives, interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Zaltman, G., & Coulter, R. H. (1995). Seeing the voice of the customer: Metaphor-based advertising research. Journal of Advertising Research, 35(4), 35–51.","type":"article","doi":null,"isbn":null,"url":"https://www.tandfonline.com/doi/abs/10.1080/00218499.1995.12466477"},{"ref":"Zaltman metaphor elicitation technique. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Zaltman_metaphor_elicitation_technique"}],"related":["photo-elicitation","metaphor-analysis","narrative-analysis","thematic-analysis","phenomenology","arts-based-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-multiple-case-study","name":"Visual Elicitation Multiple Case Study","fullName":"Visual Elicitation-Based Multiple Case Study","aliases":["photo-elicitation multiple case study","visual data multiple case study","image-elicitation multi-case study","VEMCS"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (integration period)","originator":"Synthesised from Douglas Harper (photo elicitation) and Robert K. Yin (multiple case study)","url":"https://scholargate.app/en/qualitative/visual-elicitation-multiple-case-study","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-multiple-case-study.md","definition":"Visual elicitation multiple case study is a qualitative design that embeds photo or image elicitation techniques within a multiple case study framework. Photographs, drawings, or other visual artefacts — produced by participants or the researcher — serve as interview stimuli, enriching within-case depth and enabling rigorous cross-case comparison. The approach leverages the power of images to surface tacit knowledge, making it especially valuable for researching contexts, identities, or experiences that are difficult to articulate in words alone.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Douglas Harper (photo elicitation) and Robert K. Yin (multiple case study)","year":"2000s–2010s (integration period)","type":"Qualitative multi-method design","dataType":"Photographs, images, researcher-produced or participant-produced visuals, interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage.","type":"book","doi":null,"isbn":"978-1452242569","url":null}],"related":["multiple-case-study","photo-elicitation","case-study","ethnography","narrative-analysis","participatory-action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-netnography","name":"Visual Elicitation Netnography","fullName":"Visual Elicitation Netnography","aliases":["visual netnography","image-based netnography","multimedia netnography","visual online ethnography"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (visual turn in netnography formalized ~2010–2015)","originator":"Robert V. Kozinets (netnography); integrated with visual elicitation traditions (Harper, Collier)","url":"https://scholargate.app/en/qualitative/visual-elicitation-netnography","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-netnography.md","definition":"Visual elicitation netnography is a qualitative online research design that adapts netnographic fieldwork to center visual data — images, videos, memes, and multimedia posts — both as primary data and as elicitation stimuli for deeper participant meaning-making. It extends Kozinets's netnography into the visually saturated landscapes of contemporary social media, treating user-generated imagery as a culturally rich text for ethnographic interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert V. Kozinets (netnography); integrated with visual elicitation traditions (Harper, Collier)","year":"2000s–2010s (visual turn in netnography formalized ~2010–2015)","type":"Qualitative online research design","dataType":"Images, videos, memes, screenshots, visual social-media posts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Kozinets, R. V. (2015). Netnography: Redefined (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-1446256893","url":null},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["netnography","photo-elicitation","visual-ethnography","online-ethnography","multimodal-analysis","content-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-oral-history","name":"Visual Elicitation Oral History","fullName":"Visual Elicitation Oral History","aliases":["photo-elicitation oral history","image-elicitation life history","visual oral history interview","VEOH"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1957 (Collier's foundational experiment); oral history integration developed 1980s–2000s","originator":"John Collier Jr. (photo elicitation basis); extended into oral history by visual anthropologists and memory studies scholars","url":"https://scholargate.app/en/qualitative/visual-elicitation-oral-history","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-oral-history.md","definition":"Visual elicitation oral history is a qualitative method that uses photographs, objects, maps, or other visual materials as prompts during oral history interviews. By placing a tangible visual anchor before the narrator, the researcher unlocks richer, more detailed memories and personal meanings than spoken questions alone typically produce. The approach merges John Collier Jr.'s photo-elicitation technique with oral history's commitment to capturing first-person lived experience across time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Collier Jr. (photo elicitation basis); extended into oral history by visual anthropologists and memory studies scholars","year":"1957 (Collier's foundational experiment); oral history integration developed 1980s–2000s","type":"Qualitative interview-based method","dataType":"Photographs, objects, maps, archival images combined with recorded spoken narratives","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Collier, J. (1957). Photography in anthropology: A report on two experiments. American Anthropologist, 59(5), 843–859.","type":"article","doi":"10.1525/aa.1957.59.5.02a00100","isbn":null,"url":null},{"ref":"Banks, M. (2007). Using Visual Data in Qualitative Research. Sage.","type":"book","doi":null,"isbn":"978-0761948858","url":null}],"related":["oral-history","photo-elicitation","narrative-analysis","life-history","ethnography","participatory-action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-phenomenology","name":"Visual Elicitation Phenomenology","fullName":"Visual Elicitation Phenomenology","aliases":["photo-elicitation phenomenology","image-based phenomenology","visual phenomenological inquiry","VEP"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s","originator":"Developed at the intersection of visual sociology (Douglas Harper) and phenomenological research traditions (Husserl, Giorgi)","url":"https://scholargate.app/en/qualitative/visual-elicitation-phenomenology","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-phenomenology.md","definition":"Visual elicitation phenomenology combines the philosophical depth of phenomenological inquiry with the evocative power of visual materials — photographs, drawings, maps, or participant-produced images — to access lived experience more richly than verbal interviews alone. Participants respond to images during in-depth interviews, unlocking memories, emotions, and meanings that words alone may not surface. The approach is used across health sciences, education, and social research when the phenomenon under study is embodied, spatial, or difficult to articulate verbally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Developed at the intersection of visual sociology (Douglas Harper) and phenomenological research traditions (Husserl, Giorgi)","year":"1990s–2000s","type":"Qualitative research design","dataType":"Photographs, drawings, participant-produced images, and in-depth verbal interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Clark, A. (2006). Anonymising research data. ESRC National Centre for Research Methods Working Paper. NCRM.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Clark+2006+photo+elicitation+qualitative+research"}],"related":["phenomenology","photo-elicitation","narrative-analysis","arts-based-research","thematic-analysis","interpretive-phenomenological-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-qualitative-content-analysis","name":"Visual elicitation qualitative content analysis","fullName":"Visual Elicitation Qualitative Content Analysis","aliases":["photo-elicitation QCA","visual-stimulus qualitative content analysis","image-elicited content analysis","VEQCA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (methodological integration)","originator":"Synthesis of Douglas Harper (visual elicitation) and Philipp Mayring (qualitative content analysis)","url":"https://scholargate.app/en/qualitative/visual-elicitation-qualitative-content-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-qualitative-content-analysis.md","definition":"Visual elicitation qualitative content analysis (VEQCA) is a qualitative research approach that combines the use of visual stimuli — photographs, drawings, images, or artifacts — to prompt participant responses, and then applies systematic qualitative content analysis procedures to interpret and categorize the resulting verbal or textual data. The method harnesses the unique cognitive and communicative power of images to surface meanings that purely verbal questioning may not reach, while retaining the rigor of explicit, rule-governed content analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesis of Douglas Harper (visual elicitation) and Philipp Mayring (qualitative content analysis)","year":"2000s–2010s (methodological integration)","type":"Qualitative research design combining visual elicitation and systematic content analysis","dataType":"Visual materials (photographs, images, drawings, artifacts) plus participant-generated verbal/textual responses","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research, 1(2), Art. 20.","type":"article","doi":null,"isbn":null,"url":"https://www.qualitative-research.net/index.php/fqs/article/view/1089"}],"related":["visual-analysis","qualitative-content-analysis","thematic-analysis","photo-elicitation","interpretive-visual-analysis","visual-elicitation-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-reflexive-thematic-analysis","name":"Visual elicitation reflexive thematic analysis","fullName":"Visual Elicitation Reflexive Thematic Analysis","aliases":["photo-elicitation reflexive TA","image-elicitation RTA","visual-prompt reflexive thematic analysis","VERTA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (integrated practice established ~2010–2020)","originator":"Compound method: visual elicitation (Harper 2002; Clark 1999) + reflexive thematic analysis (Braun & Clarke 2006, 2019)","url":"https://scholargate.app/en/qualitative/visual-elicitation-reflexive-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-reflexive-thematic-analysis.md","definition":"Visual elicitation reflexive thematic analysis (VERTA) is a qualitative compound design that uses photographs, drawings, or other images as conversation starters in in-depth interviews and then analyses the resulting talk using Braun and Clarke's reflexive thematic analysis framework. The visual prompt lowers the communication barrier, stimulates richer narrative, and anchors abstract experiences to concrete imagery, while the reflexive analytic approach treats theme development as an active, iterative, and researcher-engaged interpretive process rather than a coding algorithm.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Compound method: visual elicitation (Harper 2002; Clark 1999) + reflexive thematic analysis (Braun & Clarke 2006, 2019)","year":"2000s–2010s (integrated practice established ~2010–2020)","type":"Qualitative compound design","dataType":"Visual materials (photographs, drawings, images) + interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.","type":"article","doi":"10.1080/2159676X.2019.1628806","isbn":null,"url":null},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["reflexive-thematic-analysis","visual-analysis","photo-elicitation","participatory-visual-analysis","thematic-analysis","visual-elicitation-thematic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-semiotic-analysis","name":"Visual elicitation semiotic analysis","fullName":"Visual Elicitation Semiotic Analysis","aliases":["photo-elicitation semiotics","image-elicitation semiotic inquiry","visual stimulus semiotic analysis","VESA"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (practice consolidated in visual qualitative research)","originator":"Convergence of Douglas Harper (visual elicitation) and Roland Barthes / Theo van Leeuwen (semiotics)","url":"https://scholargate.app/en/qualitative/visual-elicitation-semiotic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-semiotic-analysis.md","definition":"Visual elicitation semiotic analysis is a qualitative approach that uses visual materials — photographs, images, film stills, or artefacts — as stimuli to provoke participant accounts, then subjects both the images and the participant-generated responses to semiotic analysis to unpack layers of denotative and connotative meaning. The method bridges the participatory strengths of photo-elicitation with the sign-system rigour of semiotics, making it especially productive in cultural, media, and social identity research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Convergence of Douglas Harper (visual elicitation) and Roland Barthes / Theo van Leeuwen (semiotics)","year":"2000s–2010s (practice consolidated in visual qualitative research)","type":"Qualitative research design and analysis approach","dataType":"Visual materials (photographs, images, videos, artefacts) used as elicitation stimuli; participant talk and text generated in response","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"van Leeuwen, T., & Jewitt, C. (Eds.). (2001). Handbook of Visual Analysis. Sage.","type":"book","doi":null,"isbn":"978-0761964148","url":null}],"related":["visual-analysis","semiotic-analysis","visual-elicitation-thematic-analysis","visual-elicitation-discourse-analysis","interpretive-semiotic-analysis","multimodal-discourse-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-single-case-study","name":"Visual Elicitation Single Case Study","fullName":"Visual Elicitation in Single Case Study Research","aliases":["photo-elicitation case study","image-based single case study","visual interview case study","VE-SCS"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"Photo elicitation established 1950s–1960s; integration with single case study consolidated 1990s–2000s","originator":"Combination: Douglas Harper (visual/photo elicitation); Robert K. Yin (case study methodology)","url":"https://scholargate.app/en/qualitative/visual-elicitation-single-case-study","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-single-case-study.md","definition":"Visual elicitation single case study is a qualitative design that embeds photo or image elicitation techniques within a bounded, in-depth investigation of a single case — a person, community, program, or event. Photographs, drawings, or participant-produced images are introduced into interviews to prompt richer, more vivid accounts than verbal questioning alone can generate, while the single case study frame provides the disciplined contextual analysis needed to understand the case as a whole.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Combination: Douglas Harper (visual/photo elicitation); Robert K. Yin (case study methodology)","year":"Photo elicitation established 1950s–1960s; integration with single case study consolidated 1990s–2000s","type":"Qualitative research design combining visual data elicitation with bounded single-case inquiry","dataType":"Photographs, drawings, video stills, participant-generated images, interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.","type":"book","doi":null,"isbn":"978-1506336169","url":null}],"related":["case-study","photo-elicitation","narrative-analysis","phenomenology","ethnography","participatory-action-research"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-straussian-grounded-theory","name":"Visual elicitation Straussian grounded theory","fullName":"Visual Elicitation Straussian Grounded Theory","aliases":["photo elicitation grounded theory","visual data grounded theory","Strauss-Corbin visual grounded theory","image-based Straussian GT"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"1990s–2000s (Strauss & Corbin 1990; visual integration developed through 2000s)","originator":"Anselm Strauss & Juliet Corbin (Straussian GT); Douglas Harper and Jon Wagner (visual elicitation integration)","url":"https://scholargate.app/en/qualitative/visual-elicitation-straussian-grounded-theory","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-straussian-grounded-theory.md","definition":"Visual elicitation Straussian grounded theory is a qualitative research design that combines the systematic coding procedures of Strauss and Corbin's grounded theory with visual elicitation — using photographs, participant-produced images, or visual artefacts as interview stimuli to generate richer conceptual data. The approach leverages the power of images to unlock tacit knowledge and produces a substantive theory grounded in both verbal accounts and visual meaning-making.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Anselm Strauss & Juliet Corbin (Straussian GT); Douglas Harper and Jon Wagner (visual elicitation integration)","year":"1990s–2000s (Strauss & Corbin 1990; visual integration developed through 2000s)","type":"Qualitative research design — visual data grounded theory variant","dataType":"Visual materials (photographs, images, drawings, video stills) combined with interview transcripts and field notes","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). Sage.","type":"book","doi":null,"isbn":"978-0803959408","url":null},{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null}],"related":["visual-elicitation-constructivist-grounded-theory","visual-elicitation-classic-grounded-theory","straussian-grounded-theory","visual-analysis","grounded-theory","participatory-grounded-theory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-thematic-analysis","name":"Visual Elicitation Thematic Analysis","fullName":"Visual Elicitation Thematic Analysis","aliases":["VETA","photo elicitation thematic analysis","image-based thematic analysis","visual-data thematic analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s","originator":"Synthesised from Harper's photo elicitation (2002) and Braun and Clarke's thematic analysis (2006); applied as an integrated method from the 2010s onward","url":"https://scholargate.app/en/qualitative/visual-elicitation-thematic-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-thematic-analysis.md","definition":"Visual elicitation thematic analysis (VETA) is a qualitative method that uses photographs, drawings, or other images as interview stimuli to provoke richer verbal accounts, then subjects those accounts to systematic thematic analysis. By grounding conversation in concrete visual material, the method unlocks meanings, memories, and tacit knowledge that purely verbal questioning often fails to reach. It is widely used in health, education, community, and organisational research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from Harper's photo elicitation (2002) and Braun and Clarke's thematic analysis (2006); applied as an integrated method from the 2010s onward","year":"2000s–2010s","type":"Qualitative data collection and analysis approach","dataType":"Visual materials (photographs, drawings, diagrams) combined with interview transcripts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Braun, V., and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.","type":"article","doi":"10.1191/1478088706qp063oa","isbn":null,"url":null}],"related":["thematic-analysis","photo-elicitation","visual-methods","narrative-analysis","phenomenology","interpretive-phenomenological-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-elicitation-visual-analysis","name":"Visual Elicitation Visual Analysis","fullName":"Visual Elicitation Visual Analysis","aliases":["VEVA","photo-elicitation visual analysis","image elicitation visual analysis","participatory visual analysis"],"domain":"qualitative","family":"process-pipeline","subfamily":"Nitel desen ve analiz","year":"2000s–2010s (consolidation as a combined approach)","originator":"Synthesised from photo elicitation (Collier, 1957; Harper, 2002) and visual analysis traditions (Rose; Banks)","url":"https://scholargate.app/en/qualitative/visual-elicitation-visual-analysis","markdownUrl":"https://scholargate.app/en/qualitative/visual-elicitation-visual-analysis.md","definition":"Visual Elicitation Visual Analysis (VEVA) is a qualitative method that uses photographs or other images as interview stimuli to prompt participant engagement, then subjects the resulting visual materials — both the stimuli and any participant-produced images — to systematic visual analysis. The approach treats images as the primary analytic object, examining composition, symbolism, and visual meaning rather than limiting analysis to the verbal discourse images generate. VEVA is particularly powerful in participatory visual research and photo-voice studies where images are both elicitation tools and data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Synthesised from photo elicitation (Collier, 1957; Harper, 2002) and visual analysis traditions (Rose; Banks)","year":"2000s–2010s (consolidation as a combined approach)","type":"Qualitative combined method","dataType":"Visual materials (photographs, images, drawings, video stills) and participant-generated visual artefacts","subfamily":"Nitel desen ve analiz"},"citations":[{"ref":"Harper, D. (2002). Talking about pictures: A case for photo elicitation. Visual Studies, 17(1), 13–26.","type":"article","doi":"10.1080/14725860220137345","isbn":null,"url":null},{"ref":"Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.","type":"book","doi":null,"isbn":"978-1473942028","url":null}],"related":["visual-analysis","photo-elicitation","visual-elicitation-discourse-analysis","visual-elicitation-thematic-analysis","participatory-visual-research","semiotic-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-function-index","name":"VF-14","fullName":"Visual Function Index VF-14","aliases":["VF-14","Visual Function-14"],"domain":"ophthalmology","family":"process-pipeline","subfamily":"cataract-specific functional status","year":"1994","originator":"Steinberg EP, Tielsch JM et al.","url":"https://scholargate.app/en/ophthalmology/visual-function-index","markdownUrl":"https://scholargate.app/en/ophthalmology/visual-function-index.md","definition":"The VF-14 is a 14-item, disease-specific functional status questionnaire developed to measure visual disability from cataract and its response to cataract surgery. Created by Steinberg, Tielsch, and colleagues (1994), the VF-14 focuses on difficulty with 14 common daily activities (e.g., reading small print, driving day/night, recognizing faces) and is used primarily to assess cataract severity, prioritize surgery, and document post-operative improvement in functional vision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steinberg EP, Tielsch JM et al.","subfamily":"cataract-specific functional status","year":"1994","type":"Self-report"},"citations":[{"ref":"Steinberg, E. P., Tielsch, J. M., Schein, O. D., et al. (1994). The VF-14. An index of functional impairment in patients with cataract. Arch Ophthalmol, 112(5), 630-638.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+VF-14+Steinberg"},{"ref":"Guyonnet, C., & Burstein, P. M. (1997). Reliability of the VF-14 functional status questionnaire in measuring change after cataract surgery. Invest Ophthalmol Vis Sci, 38(4), S688.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/9307655"}],"related":["nei-vfq-25","glaucoma-quality-of-life","ocular-surface-disease-index","impact-vision-impairment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"visual-saliency-map","name":"Visual Saliency Mapping","fullName":"Visual Saliency Mapping","aliases":["Attention Map Generation","Computational Gaze Prediction"],"domain":"visual-arts","family":"process-pipeline","subfamily":"Visual attention and computational vision","year":"1985","originator":"Christof Koch and Shimon Ullman","url":"https://scholargate.app/en/visual-arts/visual-saliency-map","markdownUrl":"https://scholargate.app/en/visual-arts/visual-saliency-map.md","definition":"Visual Saliency Mapping is a computational method for predicting where viewers naturally direct their attention within an image. Grounded in neuroscience and vision science, this pipeline generates attention heat maps that reveal which image regions are most visually compelling, surprising, or distinctive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christof Koch and Shimon Ullman","subfamily":"Visual attention and computational vision","year":"1985","type":"Analytical pipeline"},"citations":[{"ref":"Koch, C., & Ullman, S. (1985). Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry. Human Neurobiology, 4(4), 219–227.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/search?query=koch+ullman+selective+visual+attention+1985"},{"ref":"Itti, L., Koch, C., & Niebur, E. (1998). A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.","type":"article","doi":"10.1109/34.730558","isbn":null,"url":null},{"ref":"Bylinskii, Z., Kim, N. W., O'Donovan, P., Alsheikh, S., Mital, S., Pfister, H., & Durand, F. (2017). Understanding Infographics through Textual and Visual Tag Co-occurrence. Computer Vision and Pattern Recognition Workshops (CVPRW).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Understanding+Infographics+through+Textual+and+Visual+Tag+Co-occurrence+Bylinskii"}],"related":["image-aesthetics-assessment","visual-complexity-measure","color-harmony-analysis","visual-balance-measurement","gestalt-principles-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vo2-max","name":"VO2 Max (Bruce Protocol)","fullName":"Maximal Oxygen Uptake Assessment via Bruce Treadmill Protocol","aliases":["maximal aerobic capacity","aerobic power","cardiorespiratory fitness"],"domain":"sports-science","family":"hypothesis-test","subfamily":"Exercise Physiology","year":"1963","originator":"Robert Bruce","url":"https://scholargate.app/en/sports-science/vo2-max","markdownUrl":"https://scholargate.app/en/sports-science/vo2-max.md","definition":"VO2 max represents the maximum amount of oxygen a person can utilize during intense exercise, measured in millilitres of oxygen per kilogram of body weight per minute (ml/kg/min). Developed by Robert Bruce in 1963, the Bruce Protocol is a graded maximal exercise test on a motorized treadmill that incrementally increases speed and incline until the subject reaches volitional exhaustion. This test is a gold standard in clinical and sports medicine for assessing cardiorespiratory fitness and aerobic capacity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Bruce","subfamily":"Exercise Physiology","year":"1963","type":"graded maximal exercise test"},"citations":[{"ref":"Bruce, R. A. (1963). Evaluation of functional capacity and exercise tolerance of cardiac patients. Modern Concepts of Cardiovascular Disease, 32(4), 1-4.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/14025159/"},{"ref":"Karvonen, M. J., Kentala, E., & Mustala, O. (1957). The effects of training on heart rate: a longitudinal study. Annales Medicinae Experimentalis et Biologiae Fenniae, 35, 307-315.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/13470504/"},{"ref":"Åstrand, P. O., & Ryhming, I. (1952). A nomogram for calculation of aerobic capacity (physical fitness) from pulse rate during submaximal work. Journal of Applied Physiology, 7(2), 218-221.","type":"article","doi":"10.1152/jappl.1954.7.2.218","isbn":null,"url":null}],"related":["lactate-threshold","heart-rate-recovery","respiratory-exchange-ratio","epoc","critical-power"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vocal-separation","name":"Vocal Separation","fullName":"Vocal Separation and Source Separation Algorithm","aliases":["singing voice extraction","voice isolation","source demixing"],"domain":"music-information-retrieval","family":"ml-model","subfamily":"Source separation and demixing","year":"2012","originator":"Yonggang Han","url":"https://scholargate.app/en/music-information-retrieval/vocal-separation","markdownUrl":"https://scholargate.app/en/music-information-retrieval/vocal-separation.md","definition":"Vocal separation is the task of isolating the singing voice from a mixed music recording, leaving the instrumental accompaniment. Introduced formally by Han et al. (2012), it is critical for music editing, remixing, karaoke generation, and music analysis. Modern deep learning approaches (Défossez et al., 2021) have achieved impressive quality, enabling practical applications in music production and streaming services. Vocal separation is a special case of source separation, where the goal is to isolate the most perceptually salient source.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yonggang Han","subfamily":"Source separation and demixing","year":"2012","type":"Audio source separation"},"citations":[{"ref":"Han, Y., Qin, Z., & Kang, Z. (2012). Singing voice separation using spectral floor filtered spectrograms. In Proceedings of the International Society for Music Information Retrieval Conference.","type":"article","doi":null,"isbn":null,"url":"https://archives.ismir.net/ismir2012/papers/182.pdf"},{"ref":"Huang, P. S., Kim, M., Hasegawa-Johnson, M., & Smaragdis, P. (2015). Joint optimization of masks and deep recurrent neural networks for monaural source separation. IEEE Transactions on Audio, Speech, and Language Processing, 23(12), 2136-2147.","type":"article","doi":"10.1109/taslp.2015.2468583","isbn":null,"url":null},{"ref":"Défossez, A., Usunier, N., Bottou, L., & Bach, F. (2021). Music source separation in the waveform domain. In International Conference on Learning Representations.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2011.01437"}],"related":["melody-extraction","pitch-detection-algorithm","music-segmentation","beat-tracking","automatic-music-transcription"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"voice-handicap-index","name":"Voice Handicap Index","fullName":"Voice Handicap Index (VHI)","aliases":["VHI","VHI-30"],"domain":"speech-language-pathology","family":"process-pipeline","subfamily":"voice handicap & self-perception","year":"1997","originator":"Jacobson, B. H., et al.","url":"https://scholargate.app/en/speech-language-pathology/voice-handicap-index","markdownUrl":"https://scholargate.app/en/speech-language-pathology/voice-handicap-index.md","definition":"The Voice Handicap Index (VHI) is a 30-item self-report questionnaire that measures the impact of voice disorders on quality of life and functional communication. Developed by Jacobson and colleagues in 1997, it quantifies the psychosocial, physical, and emotional burden of dysphonia across functional, physical, and emotional domains. Widely used in otolaryngology and speech-language pathology to assess treatment outcomes and monitor disease progression.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jacobson, B. H., et al.","subfamily":"voice handicap & self-perception","year":"1997","type":"Self-report"},"citations":[{"ref":"Jacobson, B. H., Johnson, A., Grywalski, C., Silbergleit, A., Jacobson, G., Benninger, M. S., & Newman, C. W. (1997). The Voice Handicap Index (VHI): Development and Validation. American Journal of Speech-Language Pathology, 6(3), 66–70.","type":"article","doi":"10.1044/1058-0360.0603.66","isbn":null,"url":null},{"ref":"Rosen, C. A., Lee, A. S., Osborne, J., Zullo, T., & Murry, T. (2004). Development and Validation of the Voice Handicap Index-10. Laryngoscope, 114(9), 1549–1556.","type":"article","doi":"10.1097/00005537-200409000-00009","isbn":null,"url":null},{"ref":"Jacobson, B. H., Johnson, A., & Grywalski, C. (2003). Perceived Vocal Effort and Voice Handicap in Subjects With Voice Disorders. Journal of Voice, 17(2), 146–151.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Perceived+Vocal+Effort+and+Voice+Handicap+in+Subjects+With+Voice+Disorders+Jacobson"}],"related":["perceptual-voice-quality-scale","voice-activity-participation","dysphagia-outcome-severity-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"voice-outcome-survey","name":"VOS","fullName":"Voice Outcome Survey","aliases":["VOS"],"domain":"otolaryngology","family":"process-pipeline","subfamily":"laryngeal-outcome","year":"1997","originator":"Bonnie H. Jacobson and colleagues","url":"https://scholargate.app/en/otolaryngology/voice-outcome-survey","markdownUrl":"https://scholargate.app/en/otolaryngology/voice-outcome-survey.md","definition":"The Voice Outcome Survey (VOS), also known as the Voice Handicap Index (VHI), is a 30-item self-report questionnaire assessing the psychosocial, functional, and physical impact of voice disorders on quality of life. Developed by Jacobson and colleagues in 1997, the VOS/VHI has become the standard outcome measure in laryngology and voice pathology for quantifying voice-related disability, monitoring treatment response, and evaluating outcomes following voice therapy or laryngeal surgery.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonnie H. Jacobson and colleagues","subfamily":"laryngeal-outcome","year":"1997","type":"Self-report"},"citations":[{"ref":"Jacobson, B. H., Johnson, A., Grywalski, C., Silbergleit, A., Jacobson, G., Benninger, M. S., & Newman, C. W. (1997). The Voice Handicap Index (VHI): Development and validation. Journal of Speech, Language, and Hearing Research, 40(5), 1139-1149.","type":"article","doi":"10.1044/1058-0360.0603.66","isbn":null,"url":null}],"related":["voice-handicap-index","voice-symptom-scale","voice-related-quality-of-life"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"voltammetry","name":"Voltammetry","fullName":"Voltammetry","aliases":["electrochemical voltammetry","cyclic voltammetry","CV","differential pulse voltammetry"],"domain":"analytical-chemistry","family":"process-pipeline","subfamily":"Electrochemical Analysis","year":"1922","originator":"Jaroslav Heyrovsky","url":"https://scholargate.app/en/analytical-chemistry/voltammetry","markdownUrl":"https://scholargate.app/en/analytical-chemistry/voltammetry.md","definition":"Voltammetry is an electrochemical analytical technique that studies chemical reactions and properties of substances by measuring the current response as the potential applied to an electrode is systematically varied. Developed by Jaroslav Heyrovsky in the 1920s (polarography), modern voltammetry has become essential for measuring redox potentials, detecting trace analytes, and investigating reaction mechanisms. Variants such as cyclic voltammetry (CV) and differential pulse voltammetry (DPV) offer high sensitivity and selectivity for electrochemically active analytes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jaroslav Heyrovsky","subfamily":"Electrochemical Analysis","year":"1922","type":"electrochemical separation and analysis"},"citations":[{"ref":"Nicholson, R. S., & Shain, I. (1965). Theory of stationary electrode polarography for a chemical reaction coupled to electron transfer. Analytical Chemistry, 36(4), 706–723.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Theory+of+stationary+electrode+polarography+for+a+chemical+reaction+coupled+to+electron+transfer+Nicholson"},{"ref":"Bard, A. J., & Faulkner, L. R. (2001). Electrochemical Methods: Fundamentals and Applications (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471044925","url":null},{"ref":"Bond, A. M. (1994). Modern Polarographic Methods in Analytical Chemistry. Marcel Dekker.","type":"book","doi":null,"isbn":"978-0824790868","url":null}],"related":["coulometry","potentiometric-titration","ion-chromatography","uv-vis-spectrophotometry","flow-injection-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"volume-of-fluid","name":"Volume of Fluid","fullName":"Volume of Fluid Method","aliases":["VOF","VoF","volume fraction method"],"domain":"fluid-dynamics","family":"process-pipeline","subfamily":"Fluid Dynamics","year":"1981","originator":"Cleve Hirt","url":"https://scholargate.app/en/fluid-dynamics/volume-of-fluid","markdownUrl":"https://scholargate.app/en/fluid-dynamics/volume-of-fluid.md","definition":"The Volume of Fluid (VOF) method is an Eulerian interface-tracking technique for multiphase flow simulations, developed by Hirt and Nichols in 1981. Instead of explicitly tracking the interface between phases, VOF advects a scalar field (the volume fraction) that represents the fractional volume of one phase in each grid cell. This approach elegantly handles topological changes (splashing, merging, breaking) without explicit interface reconstruction, making it ideal for complex multiphase flows on fixed Eulerian grids.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Cleve Hirt","subfamily":"Fluid Dynamics","year":"1981","type":"Eulerian interface tracking method"},"citations":[{"ref":"Hirt, C. W., & Nichols, B. D. (1981). Volume of fluid (VOF) method for the dynamics of free boundaries. Journal of Computational Physics, 39(1), 201-225.","type":"article","doi":"10.1016/0021-9991(81)90145-5","isbn":null,"url":null},{"ref":"Youngs, D. L. (1982). Time-dependent multi-material flow with large fluid distortion. In Numerical Methods for Fluid Dynamics (pp. 273-285). Academic Press.","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/books/numerical-methods-for-fluid-dynamics/roe/978-0-12-592200-0"},{"ref":"Ubbink, O., & Issa, R. I. (1999). A method for capturing sharp fluid interfaces on arbitrary meshes. Journal of Computational Physics, 153(1), 26-50.","type":"article","doi":"10.1006/jcph.1999.6276","isbn":null,"url":null}],"related":["level-set-method","eulerian-lagrangian-model","lattice-boltzmann-method","smoothed-particle-hydrodynamics","large-eddy-simulation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vosviewer-assisted-citation-analysis","name":"VOSviewer-assisted citation analysis","fullName":"VOSviewer-Assisted Citation Analysis","aliases":["citation analysis with VOSviewer","VOSviewer citation mapping","visual citation analysis","VOSviewer-based citation network analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1955 (citation analysis); 2010 (VOSviewer software)","originator":"Eugene Garfield (citation analysis); Nees Jan van Eck & Ludo Waltman (VOSviewer)","url":"https://scholargate.app/en/scientometrics/vosviewer-assisted-citation-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/vosviewer-assisted-citation-analysis.md","definition":"VOSviewer-assisted citation analysis combines established citation analysis methodology with the visual mapping capabilities of VOSviewer, a free bibliometric software developed at Leiden University. Researchers export bibliographic records from databases such as Web of Science or Scopus, import them into VOSviewer, and generate citation networks that reveal which documents, authors, or journals are most influential and how intellectual influence flows across a field.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eugene Garfield (citation analysis); Nees Jan van Eck & Ludo Waltman (VOSviewer)","year":"1955 (citation analysis); 2010 (VOSviewer software)","type":"Bibliometric workflow","dataType":"Bibliographic records with citation counts (e.g., Web of Science, Scopus exports)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null},{"ref":"Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471–479.","type":"article","doi":"10.1126/science.178.4060.471","isbn":null,"url":null}],"related":["co-citation-analysis","bibliographic-coupling","bibliometric-analysis","science-mapping","vosviewer-assisted-co-citation-analysis","citation-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vosviewer-assisted-co-citation-analysis","name":"VOSviewer-assisted co-citation analysis","fullName":"VOSviewer-Assisted Co-Citation Analysis","aliases":["VOSviewer co-citation mapping","bibliometric co-citation visualization","co-citation network analysis with VOSviewer","CCA-VOSviewer"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"1973 (co-citation); VOSviewer workflow from ~2010","originator":"Henry Small (co-citation, 1973); Nees Jan van Eck & Ludo Waltman (VOSviewer, 2010)","url":"https://scholargate.app/en/scientometrics/vosviewer-assisted-co-citation-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/vosviewer-assisted-co-citation-analysis.md","definition":"VOSviewer-assisted co-citation analysis combines Henry Small's co-citation measure — counting how often two documents are jointly cited by later work — with VOSviewer's automated network construction and visual mapping capabilities. The result is a spatial map of the intellectual base of a research field, where documents that share many citing contexts cluster together, revealing foundational schools of thought and their relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Henry Small (co-citation, 1973); Nees Jan van Eck & Ludo Waltman (VOSviewer, 2010)","year":"1973 (co-citation); VOSviewer workflow from ~2010","type":"Bibliometric network analysis","dataType":"Citation records from bibliographic databases (Web of Science, Scopus)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.","type":"article","doi":"10.1002/asi.4630240406","isbn":null,"url":null},{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null}],"related":["co-citation-analysis","bibliographic-coupling","bibliometric-analysis","scientometric-analysis","co-word-analysis","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vosviewer-assisted-co-word-analysis","name":"VOSviewer-assisted co-word analysis","fullName":"VOSviewer-Assisted Co-Word Analysis","aliases":["keyword co-occurrence analysis via VOSviewer","VOSviewer co-word mapping","keyword network mapping","co-keyword analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"Co-word analysis: 1983; VOSviewer software: 2010","originator":"Co-word analysis: Callon et al. (1983); VOSviewer tool: van Eck & Waltman (2010)","url":"https://scholargate.app/en/scientometrics/vosviewer-assisted-co-word-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/vosviewer-assisted-co-word-analysis.md","definition":"VOSviewer-assisted co-word analysis is a scientometric pipeline that constructs and visualizes keyword co-occurrence networks from a bibliographic corpus using VOSviewer software. By mapping how often pairs of author-assigned or index keywords appear together in the same publications, the method reveals the intellectual structure of a research field — its dominant themes, emerging topics, and conceptual clusters — producing interactive density and network maps that support systematic interpretation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Co-word analysis: Callon et al. (1983); VOSviewer tool: van Eck & Waltman (2010)","year":"Co-word analysis: 1983; VOSviewer software: 2010","type":"Bibliometric network analysis technique","dataType":"Keyword lists from bibliographic records (author keywords, index terms)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null},{"ref":"Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191-235.","type":"article","doi":"10.1177/053901883022002003","isbn":null,"url":null}],"related":["co-citation-analysis","bibliographic-coupling","bibliometric-analysis","scientometric-analysis","thematic-evolution-analysis","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vosviewer-assisted-meta-analysis","name":"VOSviewer-assisted meta-analysis","fullName":"VOSviewer-Assisted Meta-Analysis","aliases":["bibliometric-enhanced meta-analysis","VOSviewer meta-analysis workflow","science-mapping assisted meta-analysis","network-visualisation meta-analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2010s (integration practice emerged after VOSviewer release in 2010)","originator":"Workflow combining Glass (1976) meta-analysis with van Eck & Waltman (2010) VOSviewer","url":"https://scholargate.app/en/scientometrics/vosviewer-assisted-meta-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/vosviewer-assisted-meta-analysis.md","definition":"VOSviewer-assisted meta-analysis integrates the bibliometric network visualisation capabilities of VOSviewer into the literature identification and mapping phases of a standard meta-analysis. Before the statistical pooling of effect sizes begins, VOSviewer is used to visualise co-citation networks, keyword co-occurrence maps, and publication clusters, helping researchers comprehensively delineate the research field and identify all eligible primary studies for quantitative synthesis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Workflow combining Glass (1976) meta-analysis with van Eck & Waltman (2010) VOSviewer","year":"2010s (integration practice emerged after VOSviewer release in 2010)","type":"Tool-assisted evidence synthesis workflow","dataType":"Bibliographic records (WoS, Scopus), effect-size data from primary studies","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null},{"ref":"Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. Educational Researcher, 5(10), 3–8.","type":"article","doi":"10.3102/0013189X005010003","isbn":null,"url":null}],"related":["meta-analysis","bibliometric-analysis","systematic-literature-review","co-citation-analysis","science-mapping","prisma-based-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vosviewer-assisted-science-mapping","name":"VOSviewer-assisted science mapping","fullName":"VOSviewer-assisted Science Mapping","aliases":["VOSviewer science mapping","bibliometric science mapping with VOSviewer","VOS-based science mapping","VOSviewer network mapping"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2010","originator":"Nees Jan van Eck & Ludo Waltman (Leiden University)","url":"https://scholargate.app/en/scientometrics/vosviewer-assisted-science-mapping","markdownUrl":"https://scholargate.app/en/scientometrics/vosviewer-assisted-science-mapping.md","definition":"VOSviewer-assisted science mapping uses the VOSviewer software — developed at Leiden University — to construct and visualize bibliometric networks from publication metadata. It applies the VOS (Visualization of Similarities) mapping technique to reveal intellectual structures in a research field: co-authorship networks, citation landscapes, keyword clusters, and thematic frontiers, all rendered as interactive, color-coded network maps that expose how concepts, authors, and journals are relationally positioned within a discipline.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nees Jan van Eck & Ludo Waltman (Leiden University)","year":"2010","type":"Bibliometric mapping technique","dataType":"Publication metadata (titles, abstracts, keywords, citations, author names)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null},{"ref":"van Eck, N.J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact (pp. 285–320). Springer.","type":"article","doi":"10.1007/978-3-319-10377-8_13","isbn":null,"url":null}],"related":["bibliometric-analysis","co-citation-analysis","bibliographic-coupling","co-word-analysis","scientometric-analysis","systematic-literature-review"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vosviewer-assisted-scoping-review","name":"VOSviewer-assisted scoping review","fullName":"VOSviewer-Assisted Scoping Review","aliases":["VOSviewer scoping review","bibliometric-enhanced scoping review","VOS-assisted scoping review","science-mapping scoping review"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2010s–present","originator":"Combination: Arksey & O'Malley (scoping review, 2005); van Eck & Waltman (VOSviewer, 2010)","url":"https://scholargate.app/en/scientometrics/vosviewer-assisted-scoping-review","markdownUrl":"https://scholargate.app/en/scientometrics/vosviewer-assisted-scoping-review.md","definition":"A VOSviewer-assisted scoping review integrates the structured, broad-mapping purpose of a scoping review with VOSviewer's bibliometric visualization capabilities. After standard database searching and eligibility screening, the retained records are exported to VOSviewer, which produces co-authorship, keyword co-occurrence, and citation-based cluster maps. These visual outputs guide thematic synthesis, reveal intellectual structure, and make the scope of a field immediately transparent.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Combination: Arksey & O'Malley (scoping review, 2005); van Eck & Waltman (VOSviewer, 2010)","year":"2010s–present","type":"Hybrid review methodology","dataType":"Bibliographic records (titles, abstracts, references, keywords) from databases such as Web of Science, Scopus, or PubMed","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null},{"ref":"Peters, M. D. J., Godfrey, C. M., Khalil, H., McInerney, P., Parker, D., & Sousa, C. B. (2015). Guidance for conducting systematic scoping reviews. International Journal of Evidence-Based Healthcare, 13(3), 141–146.","type":"article","doi":"10.1097/XEB.0000000000000050","isbn":null,"url":null}],"related":["scoping-review","bibliometric-analysis","systematic-literature-review","co-citation-analysis","bibliographic-coupling","vosviewer-assisted-bibliometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vosviewer-assisted-systematic-literature-review","name":"VOSviewer-assisted systematic literature review","fullName":"VOSviewer-Assisted Systematic Literature Review","aliases":["VOSviewer SLR","bibliometric-enhanced systematic review","VOSviewer-integrated review","visualization-assisted SLR"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2010 (VOSviewer); practice established circa 2012–2015","originator":"van Eck & Waltman (VOSviewer tool); combined with Kitchenham SLR guidelines","url":"https://scholargate.app/en/scientometrics/vosviewer-assisted-systematic-literature-review","markdownUrl":"https://scholargate.app/en/scientometrics/vosviewer-assisted-systematic-literature-review.md","definition":"A VOSviewer-assisted systematic literature review combines the rigorous search-and-appraisal pipeline of a standard systematic review with bibliometric network visualization produced by the VOSviewer software. The approach allows researchers to systematically retrieve and screen the literature while simultaneously mapping co-citation clusters, keyword co-occurrence networks, and institutional collaboration patterns, yielding both a narrative synthesis and a visual, quantitative overview of the field's intellectual structure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"van Eck & Waltman (VOSviewer tool); combined with Kitchenham SLR guidelines","year":"2010 (VOSviewer); practice established circa 2012–2015","type":"Mixed bibliometric-qualitative review method","dataType":"Bibliographic records (titles, abstracts, keywords, citations) from databases such as Web of Science or Scopus","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null},{"ref":"Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report EBSE-2007-01, Keele University and Durham University.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Guidelines+for+performing+systematic+literature+reviews+in+software+engineering+Kitchenham+2007"}],"related":["systematic-literature-review","bibliometric-analysis","scientometric-analysis","co-citation-analysis","bibliographic-coupling","science-mapping"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vosviewer-assisted-thematic-evolution-analysis","name":"VOSviewer-assisted thematic evolution analysis","fullName":"VOSviewer-Assisted Thematic Evolution Analysis","aliases":["VOSviewer thematic mapping","keyword co-occurrence thematic evolution","science mapping thematic evolution","VOSviewer longitudinal thematic analysis"],"domain":"scientometrics","family":"process-pipeline","subfamily":"Review / evidence synthesis","year":"2010–2011","originator":"Nees Jan van Eck & Ludo Waltman (VOSviewer); thematic evolution methodology associated with Cobo et al.","url":"https://scholargate.app/en/scientometrics/vosviewer-assisted-thematic-evolution-analysis","markdownUrl":"https://scholargate.app/en/scientometrics/vosviewer-assisted-thematic-evolution-analysis.md","definition":"VOSviewer-assisted thematic evolution analysis is a scientometric pipeline that uses the VOSviewer software to build keyword co-occurrence networks across chronological time slices of a bibliographic dataset, revealing how research themes emerge, converge, fragment, or disappear over time within a scientific field. By coupling VOSviewer's density-based clustering with period-by-period comparison, researchers obtain a visual and quantitative account of a field's intellectual trajectory.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nees Jan van Eck & Ludo Waltman (VOSviewer); thematic evolution methodology associated with Cobo et al.","year":"2010–2011","type":"Scientometric workflow / bibliometric visualization pipeline","dataType":"Bibliographic records with keywords (WoS, Scopus exports)","subfamily":"Review / evidence synthesis"},"citations":[{"ref":"van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null},{"ref":"Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.","type":"article","doi":"10.1002/asi.21525","isbn":null,"url":null}],"related":["thematic-evolution-analysis","bibliometric-analysis","co-word-analysis","science-mapping","co-citation-analysis","scientometric-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vosviewer-citespace","name":"VOSviewer and CiteSpace Tools","fullName":"VOSviewer and CiteSpace: Bibliometric Analysis and Visualization Software","aliases":["bibliometric mapping software","citation visualization tools","science mapping tools"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"software-tools","year":"2006–2010","originator":"Nees Jan van Eck & Ludo Waltman (VOSviewer); Chaomei Chen (CiteSpace)","url":"https://scholargate.app/en/bibliometrics/vosviewer-citespace","markdownUrl":"https://scholargate.app/en/bibliometrics/vosviewer-citespace.md","definition":"VOSviewer and CiteSpace are specialized software tools designed to conduct bibliometric analysis and create science maps from research literature. VOSviewer (developed by Van Eck & Waltman, 2010) excels at creating publication landscapes through co-occurrence, co-citation, and bibliographic coupling analysis with intuitive visual output. CiteSpace (developed by Chaomei Chen, 2006) focuses on detecting emerging research trends and research fronts through direct citation analysis and specialized temporal algorithms. Together, these tools democratized science mapping, enabling researchers without programming expertise to visualize research domains comprehensively.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nees Jan van Eck & Ludo Waltman (VOSviewer); Chaomei Chen (CiteSpace)","subfamily":"software-tools","year":"2006–2010","type":"Tool"},"citations":[{"ref":"Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.","type":"article","doi":"10.1007/s11192-009-0146-3","isbn":null,"url":null},{"ref":"Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.","type":"article","doi":"10.1002/asi.20317","isbn":null,"url":null}],"related":["science-mapping","co-citation-analysis","bibliographic-coupling","keyword-co-occurrence"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"voter-cynicism-scale","name":"Voter Cynicism Scale","fullName":"Political Cynicism Scale (PCS)","aliases":["PCS","Political Efficacy Cynicism","Electoral System Cynicism"],"domain":"political-psychology","family":"process-pipeline","subfamily":"institutional-attitudes","year":"1960","originator":"Angus Campbell et al.","url":"https://scholargate.app/en/political-psychology/voter-cynicism-scale","markdownUrl":"https://scholargate.app/en/political-psychology/voter-cynicism-scale.md","definition":"The Voter Cynicism Scale measures citizen skepticism and disillusionment regarding the political process, including beliefs that the electoral system is rigged, politicians are self-serving, and voting does not matter. The measure captures a pessimistic orientation toward electoral democracy distinct from distrust in institutions (which can coexist with belief in democratic potential) or political alienation. Rooted in Campbell et al.'s American Voter (1960) tradition of measuring political efficacy and cynicism, the scale remains central to understanding voter turnout decline, support for populist alternatives, and democratic legitimacy crises.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Angus Campbell et al.","subfamily":"institutional-attitudes","year":"1960","type":"Self-report"},"citations":[{"ref":"Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The American voter. New York: John Wiley & Sons.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Campbell%2C%20A.%2C%20Converse%2C%20P.%20E.%2C%20Miller%2C%20W.%20E.%2C%20%26%20Stokes%2C%20D.%20E.%20(1960).%20The%20American%20voter.%20New%20York%3A%20John%20Wiley%20%26%20Sons."},{"ref":"Seyd, P. (2003). Is there a crisis of political participation? In B. Axford & R. Huggins (Eds.), Globalization and Europe. London: Continuum.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Seyd%2C%20P.%20(2003).%20Is%20there%20a%20crisis%20of%20political%20participation%3F%20In%20B.%20Axford%20%26%20R.%20Huggins%20(Eds.)%2C%20Globalization%20and%20Europ"},{"ref":"Pharr, S. J., & Putnam, R. D. (Eds.). (2000). Disaffected democracies: What's troubling the trilateral countries? Princeton, NJ: Princeton University Press.","type":"book","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Pharr%2C%20S.%20J.%2C%20%26%20Putnam%2C%20R.%20D.%20(Eds.).%20(2000).%20Disaffected%20democracies%3A%20What's%20troubling%20the%20trilateral%20countries%3F%20Prince"}],"related":["political-trust-scale","political-efficacy","democratic-support-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"voting-ensemble","name":"Voting Ensemble","fullName":"Voting Ensemble (Majority and Weighted Voting of Multiple Classifiers)","aliases":["majority voting classifier","hard voting","soft voting ensemble","plurality voting ensemble"],"domain":"machine-learning","family":"ml-model","subfamily":"Machine learning","year":"1990s–2004","originator":"Lam & Suen; Kuncheva, L. I. (systematic treatment)","url":"https://scholargate.app/en/machine-learning/voting-ensemble","markdownUrl":"https://scholargate.app/en/machine-learning/voting-ensemble.md","definition":"A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lam & Suen; Kuncheva, L. I. (systematic treatment)","year":"1990s–2004","type":"Ensemble (combination of multiple classifiers by vote)","dataType":"Tabular, categorical targets (classification); extensions for regression","subfamily":"Machine learning"},"citations":[{"ref":"Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience.","type":"book","doi":null,"isbn":"978-0-471-21078-8","url":null},{"ref":"Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol 1857, pp. 1–15. Springer.","type":"inproceedings","doi":"10.1007/3-540-45014-9_1","isbn":null,"url":null}],"related":["boosting","random-forest","extra-trees","stacking-ensemble","bagging","support-vector-machine"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"voxel-based-morphometry","name":"Voxel-Based Morphometry","fullName":"Voxel-Based Morphometry (VBM)","aliases":["VBM","grey matter morphometry"],"domain":"neuroimaging","family":"process-pipeline","subfamily":"Voxel-wise morphological analysis","year":"2000","originator":"John Ashburner","url":"https://scholargate.app/en/neuroimaging/voxel-based-morphometry","markdownUrl":"https://scholargate.app/en/neuroimaging/voxel-based-morphometry.md","definition":"Voxel-Based Morphometry (VBM) is a whole-brain statistical technique for detecting local differences in gray matter volume or concentration from structural MRI. Introduced by John Ashburner and Karl Friston in 2000, VBM enables researchers to identify regional brain volume changes associated with disease, aging, learning, and other factors without requiring a priori region-of-interest definitions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John Ashburner","subfamily":"Voxel-wise morphological analysis","year":"2000","type":"Structural MRI gray matter analysis pipeline"},"citations":[{"ref":"Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. NeuroImage, 11(6), 805–821.","type":"article","doi":"10.1006/nimg.2000.0582","isbn":null,"url":null},{"ref":"Good, C. D., Johnsrude, I. S., Ashburner, J., et al. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14(1), 21–36.","type":"article","doi":"10.1006/nimg.2001.0786","isbn":null,"url":null}],"related":["tract-based-spatial-statistics","region-of-interest-analysis","structural-equation-modeling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vulnerability-assessment","name":"Vulnerability Assessment","fullName":"Systematic Vulnerability Detection and Risk Evaluation Methodology","aliases":["Vulnerability Scanning","Security Assessment","Risk Assessment"],"domain":"cryptography","family":"process-pipeline","subfamily":"Security testing and evaluation","year":"2002","originator":"National Institute of Standards and Technology (NIST)","url":"https://scholargate.app/en/cryptography/vulnerability-assessment","markdownUrl":"https://scholargate.app/en/cryptography/vulnerability-assessment.md","definition":"Vulnerability assessment is a systematic process of identifying, quantifying, and prioritizing security weaknesses in systems, networks, and applications. Using automated scanning tools and manual techniques, organizations discover unpatched software, misconfigurations, weak cryptographic practices, and other exposures that attackers could exploit.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"National Institute of Standards and Technology (NIST)","subfamily":"Security testing and evaluation","year":"2002","type":"Vulnerability identification and prioritization"},"citations":[{"ref":"National Institute of Standards and Technology (2012). Guide for Conducting Security Patch Management Activities. NIST Special Publication 800-40 Revision 3.","type":"report","doi":null,"isbn":null,"url":"https://csrc.nist.gov/publications/detail/sp/800-40/rev-3/final"},{"ref":"Tenable (2023). Nessus Vulnerability Scanner. Open Source Project and Commercial Platform.","type":"report","doi":null,"isbn":null,"url":"https://www.nessus.tenable.com"},{"ref":"National Institute of Standards and Technology (2023). Common Vulnerability Scoring System Version 3.1. CVSS SIG.","type":"report","doi":null,"isbn":null,"url":"https://www.first.org/cvss/v3.1"}],"related":["penetration-testing-methodology","intrusion-detection-system","tls-protocol-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"vulnerable-populations-research","name":"Research with Vulnerable Populations","fullName":"Ethical Protections and Special Procedures for Research Involving Vulnerable Research Populations","aliases":["vulnerable subjects","special populations","vulnerable groups","additional protections"],"domain":"research-ethics","family":"process-pipeline","subfamily":"special-protections","year":"1979","originator":"U.S. Department of Health and Human Services; World Health Organization; International research ethics community","url":"https://scholargate.app/en/research-ethics/vulnerable-populations-research","markdownUrl":"https://scholargate.app/en/research-ethics/vulnerable-populations-research.md","definition":"Vulnerable populations are groups with limited capacity to protect themselves due to age, cognitive ability, institutional dependency, or social circumstances. Regulatory frameworks in the U.S. (45 CFR 46 Subparts B, C, D) and internationally identify specific vulnerable populations—children, prisoners, pregnant women, cognitively impaired individuals—and mandate additional ethical protections beyond standard informed consent. These protections include obtaining informed consent from surrogate decision-makers (parents, guardians), additional assurances of minimal risk, and enhanced monitoring for safety. Research ethics committees apply heightened scrutiny to studies involving vulnerable populations and may deny approval if special protections are inadequate.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Department of Health and Human Services; World Health Organization; International research ethics community","subfamily":"special-protections","year":"1979","type":"Guideline"},"citations":[{"ref":"U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Subparts B, C, D.","type":"regulation","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Protection+of+Human+Subjects"},{"ref":"The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research.","type":"report","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html"},{"ref":"International Council for Harmonisation. (2016). ICH Harmonised Guideline: Integrated Addendum to ICH E6(R1). Good Clinical Practice E6(R2).","type":"standard","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=ICH+Harmonised+Guideline%3A+Integrated+Addendum+to+ICH+E6%28R1%29+International"},{"ref":"UNICEF. (2013). Ethical Research Involving Children: Guidance Document. UNICEF Office of Research-Innocenti.","type":"guideline","doi":null,"isbn":null,"url":"https://www.unicef-irc.org"}],"related":["ethics-committee-application","ethics-committee-types","risk-benefit-assessment","waiver-of-informed-consent","participant-debrief"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"w-estimator","name":"W-Estimator","fullName":"W-Estimator Robust Regression (Welsch / Tukey Bisquare)","aliases":["Tukey bisquare M-estimator","Welsch M-estimator","redescending M-estimator","W-Tahmin Edici (Welsch / Tukey Bisquare)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1974,"originator":"Beaton & Tukey (bisquare weight); Welsch (Welsch weight)","url":"https://scholargate.app/en/statistics/w-estimator","markdownUrl":"https://scholargate.app/en/statistics/w-estimator.md","definition":"The W-estimator is a family of robust M-estimator variants for linear regression that use the Tukey bisquare and Welsch weight functions, introduced in the line of work going back to Beaton and Tukey (1974). Because its weights fall rapidly toward zero as a residual grows, it resists outliers more strongly than the Huber M-estimator.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Beaton & Tukey (bisquare weight); Welsch (Welsch weight)","year":1974,"type":"Robust regression (redescending M-estimator)","estimator":"Iteratively reweighted least squares with redescending weights","outcome":"continuous","breakdownTolerance":"up to ~50% symmetric outliers"},"citations":[{"ref":"Beaton, A. E. & Tukey, J. W. (1974). The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics, 16(2), 147-185.","type":"article","doi":"10.1080/00401706.1974.10489171","isbn":null,"url":null},{"ref":"Maronna, R. A., Martin, R. D., Yohai, V. J. & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley.","type":"book","doi":null,"isbn":"978-1119214687","url":null}],"related":["mm-estimator","s-estimator","theil-sen-estimator","huber-m-estimator","ols-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wagner-whitin-algorithm","name":"Wagner-Whitin Algorithm","fullName":"Wagner-Whitin Algorithm for Lot-Sizing","aliases":["Wagner-Whitin lot-sizing","dynamic lot-sizing algorithm"],"domain":"operations-research","family":"ml-model","subfamily":"Optimization","year":"1958","originator":"Harvey M. Wagner and Thomson M. Whitin","url":"https://scholargate.app/en/operations-research/wagner-whitin-algorithm","markdownUrl":"https://scholargate.app/en/operations-research/wagner-whitin-algorithm.md","definition":"The Wagner-Whitin Algorithm, introduced by Harvey M. Wagner and Thomson M. Whitin in 1958, is a dynamic programming solution to the capacitated lot-sizing problem. It determines optimal production quantities over multiple periods to minimize the total cost of production setup and inventory holding while meeting deterministic demand.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Harvey M. Wagner and Thomson M. Whitin","subfamily":"Optimization","year":"1958","type":"algorithm"},"citations":[{"ref":"Wagner, H. M., & Whitin, T. M. (1958). Dynamic version of the economic lot size model. Management Science, 5(1), 89-96.","type":"article","doi":"10.1287/mnsc.5.1.89","isbn":null,"url":null},{"ref":"Pochet, Y., & Wolsey, L. A. (2006). Production Planning by Mixed Integer Programming. Springer.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Production+Planning+by+Mixed+Integer+Programming+Pochet"}],"related":["simplex-method","benders-decomposition","column-generation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"waiver-of-informed-consent","name":"Waiver of Informed Consent in Research","fullName":"Criteria and Application of Waiver of Informed Consent in Human Subjects Research","aliases":["consent waiver","waived consent","exempt from consent","research without consent"],"domain":"research-ethics","family":"process-pipeline","subfamily":"consent-procedures","year":"1991","originator":"U.S. Department of Health and Human Services; International research ethics guidelines","url":"https://scholargate.app/en/research-ethics/waiver-of-informed-consent","markdownUrl":"https://scholargate.app/en/research-ethics/waiver-of-informed-consent.md","definition":"A waiver of informed consent permits research to proceed without obtaining prospective written or verbal consent from participants. This exception to the standard informed consent requirement applies to specific low-risk research scenarios where obtaining consent is impractical, unnecessary, or would compromise research validity. In the U.S., the regulations (45 CFR 46.116) specify four criteria that must be met for an IRB to approve a waiver; similar criteria apply in the UK (Research Ethics Committee) and EU jurisdictions. Waivers are not automatic; researchers must request them explicitly and justify them to the ethics committee, which determines whether the criteria are met.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Department of Health and Human Services; International research ethics guidelines","subfamily":"consent-procedures","year":"1991","type":"Guideline"},"citations":[{"ref":"U.S. Department of Health and Human Services. (2018). Protection of Human Subjects. Code of Federal Regulations Title 45, Part 46, Section 46.116(c).","type":"regulation","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Protection+of+Human+Subjects"},{"ref":"U.S. Department of Health and Human Services, Office for Human Research Protections. (2019). Waiver or Alteration of Informed Consent. National Institutes of Health.","type":"guidance","doi":null,"isbn":null,"url":"https://www.hhs.gov/ohrp/regulations-and-policy/decision-trees-2018-common-rule-requirements/index.html"},{"ref":"U.S. Food and Drug Administration. (2015). Guidance for Industry: Waiver or Alteration of Informed Consent for in vitro Diagnostic Device Studies Using Leftover Human Specimens.","type":"guidance","doi":null,"isbn":null,"url":"https://www.fda.gov/regulatory-information/search-fda-guidance-documents"},{"ref":"Health Research Authority. (2021). Guidance for Applicants: Research Without Consent. UK Research Ethics Service.","type":"guideline","doi":null,"isbn":null,"url":"https://www.hra.nhs.uk"}],"related":["ethics-committee-application","ethics-committee-types","vulnerable-populations-research","risk-benefit-assessment","data-protection-research"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wam","name":"WAM","fullName":"Weighted Arithmetic Mean","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"AggregationOperator","year":"1988","originator":"Yager, R. R.","url":"https://scholargate.app/en/decision-making/wam","markdownUrl":"https://scholargate.app/en/decision-making/wam.md","definition":"WAM (Weighted Arithmetic Mean) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Yager, R. R. in 1988. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yager, R. R.","subfamily":"AggregationOperator","year":"1988","type":"Linear additive aggregation operator","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics","type":"article","doi":"10.1109/21.87068","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"washability","name":"Washability","fullName":"Washability Analysis for Coal and Minerals","aliases":["Coal Washability","Density Separation Analysis","Float-Sink Analysis"],"domain":"mining-engineering","family":"process-pipeline","subfamily":"Mineral Beneficiation Assessment","year":"1950","originator":"Mining Industry Practice (1930s-1960s)","url":"https://scholargate.app/en/mining-engineering/washability","markdownUrl":"https://scholargate.app/en/mining-engineering/washability.md","definition":"Washability analysis is a laboratory method that determines the feasibility and efficiency of density-based separation for coal or mineral beneficiation. By fractionating ore or coal into density bins using sink-float tests and assaying each fraction, engineers can optimize design of separation plants (dense-medium cyclones, jigs, spirals) and predict clean product quality. Washability curves are essential tools for pre-feasibility and detailed design studies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mining Industry Practice (1930s-1960s)","subfamily":"Mineral Beneficiation Assessment","year":"1950","type":"Separation analysis by density fractionation"},"citations":[{"ref":"McCullough, R. B. (1963). The theoretical basis and practical application of coal washability studies. Transactions of the Society of Mining Engineers, 226, 13-26.","type":"article","doi":null,"isbn":null,"url":"https://www.smenet.org/"},{"ref":"Clarkson, T. E., & Trevits, M. A. (2000). Coal preparation and resource utilization in United States. Bureau of Mines Information Circular IC 9413.","type":"article","doi":null,"isbn":null,"url":"https://www.usgs.gov/"}],"related":["rosin-rammler-distribution","tromp-curve","flotation-kinetics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"waspas","name":"WASPAS","fullName":"Weighted Aggregated Sum Product Assessment","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2012","originator":"Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A.","url":"https://scholargate.app/en/decision-making/waspas","markdownUrl":"https://scholargate.app/en/decision-making/waspas.md","definition":"WASPAS (Weighted Aggregated Sum Product Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A.","subfamily":"Ranking","year":"2012","type":"Convex combination (SAW + WPM)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zavadskas, E. K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika","type":"article","doi":"10.5755/j01.eee.122.6.1810","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wasserstein-gan","name":"Wasserstein GAN","fullName":"Wasserstein GAN (WGAN)","aliases":["WGAN","Earth-Mover GAN","Wasserstein Generative Adversarial Network","Wasserstein-GAN"],"domain":"deep-learning","family":"ml-model","subfamily":"Generative models","year":2017,"originator":"Martín Arjovsky, Soumith Chintala & Léon Bottou","url":"https://scholargate.app/en/deep-learning/wasserstein-gan","markdownUrl":"https://scholargate.app/en/deep-learning/wasserstein-gan.md","definition":"Wasserstein GAN (WGAN) is a generative adversarial network variant introduced by Arjovsky, Chintala, and Bottou in 2017 that replaces the Jensen-Shannon divergence used in the original GAN with the Wasserstein-1 (Earth Mover) distance. This substitution provides a theoretically grounded training objective that yields more stable optimization and a loss value that correlates meaningfully with generated sample quality, addressing the notorious mode collapse and vanishing gradient problems of standard GANs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Martín Arjovsky, Soumith Chintala & Léon Bottou","year":2017,"type":"Generative adversarial network variant","subfamily":"Generative models","distance_metric":"Wasserstein-1 (Earth Mover) distance","training_stabilizer":"Weight clipping (critic weights clipped to compact space)"},"citations":[{"ref":"Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. International Conference on Machine Learning (ICML), 214–223.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1701.07875"}],"related":["generative-adversarial-network","cyclegan","diffusion-model"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wastewater-treatment-design","name":"Wastewater Treatment Design","fullName":"Multi-Stage Wastewater Treatment Plant Design and Optimization","aliases":["WWTP design","sewage treatment","water reclamation","municipal treatment"],"domain":"environmental-engineering","family":"process-pipeline","subfamily":"Wastewater treatment engineering","year":"1900","originator":"Civil and sanitary engineers","url":"https://scholargate.app/en/environmental-engineering/wastewater-treatment-design","markdownUrl":"https://scholargate.app/en/environmental-engineering/wastewater-treatment-design.md","definition":"Wastewater treatment design is the comprehensive planning and engineering of municipal and industrial treatment plants to remove contaminants (organic matter, nutrients, pathogens, trace organics) from domestic and industrial wastewater. Modern treatment plants integrate preliminary screening, primary settlement, secondary biological treatment (activated sludge, trickling filters, lagoons), advanced treatment (membrane filtration, oxidation, absorption), sludge processing, and biosolids management. The design balances regulatory compliance, treatment performance, energy consumption, land use, and capital and operational cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Civil and sanitary engineers","subfamily":"Wastewater treatment engineering","year":"1900","type":"integrated design and optimization pipeline"},"citations":[{"ref":"Metcalf & Eddy, Inc. (2013). Wastewater Engineering: Treatment and Resource Recovery (5th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073401188","url":null},{"ref":"Tchobanoglous, G., Stensel, H. D., Tsuchihashi, R., Burton, F. L., Abu-Orf, M., Bowden, G., & Pfrang, W. (2014). Wastewater Engineering: Treatment and Resource Recovery (5th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":"978-0073401188","url":null},{"ref":"US Environmental Protection Agency. (2004). Wastewater Technology Fact Sheets: Sequencing Batch Reactors. EPA 832-F-04-018.","type":"article","doi":null,"isbn":null,"url":"https://www.epa.gov/sites/default/files/2015-07/documents/sbr_3.pdf"}],"related":["activated-sludge-model","constructed-wetland-design","biogas-production-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"waterlow-scale","name":"Waterlow Pressure Injury Risk Assessment","fullName":"Waterlow Pressure Injury Risk Assessment Scale","aliases":["Waterlow Scale","Pressure Ulcer Risk Assessment","Waterlow Score"],"domain":"nursing","family":"process-pipeline","subfamily":"risk assessment","year":"1985","originator":"Judy Waterlow","url":"https://scholargate.app/en/nursing/waterlow-scale","markdownUrl":"https://scholargate.app/en/nursing/waterlow-scale.md","definition":"The Waterlow Pressure Injury Risk Assessment Scale, developed by Judy Waterlow in 1985, is a widely used clinical tool in nursing for identifying patients at risk of developing pressure injuries (formerly called pressure ulcers or bedsores). The scale evaluates multiple risk factors including age, mobility, skin condition, weight/body mass index, appetite, and incontinence status, generating a numerical risk score that guides preventive care intensity. It is standard in hospital, long-term care, and community nursing settings across the United Kingdom, Europe, and internationally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Judy Waterlow","subfamily":"risk assessment","year":"1985","type":"Clinician-rated risk assessment tool"},"citations":[{"ref":"Waterlow, J. (1985). A risk assessment tool for pressure sores. Nursing Times, 81(48), 49-55.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/3915348"},{"ref":"Waterlow, J. (2005). Pressure ulcers: Avoidance and treatment. Nursing Times, 101(12), 58-61.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/15838237"}],"related":["clinical-frailty-scale","katz-independence-adl","malnutrition-screening-tool"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"watershed-segmentation","name":"Watershed Segmentation","fullName":"Watershed Algorithm for Image Segmentation","aliases":["Watershed transform","Water shedding segmentation"],"domain":"computer-vision","family":"ml-model","subfamily":"Segmentation","year":"1979","originator":"Serge Beucher and Christian Lantuéjoul","url":"https://scholargate.app/en/computer-vision/watershed-segmentation","markdownUrl":"https://scholargate.app/en/computer-vision/watershed-segmentation.md","definition":"Watershed segmentation is a morphological image processing technique that automatically segments an image into distinct regions by treating image intensity as a topographic landscape where each object corresponds to a valley. Introduced by Beucher and Lantuéjoul in 1979 and refined by Meyer, the watershed algorithm is particularly effective for separating touching or overlapping objects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Serge Beucher and Christian Lantuéjoul","subfamily":"Segmentation","year":"1979","type":"Morphological image segmentation"},"citations":[{"ref":"Meyer, F. (1994). Topographic distance and watershed lines. Signal Processing, 38(1), 113–125.","type":"article","doi":"10.1016/0165-1684(94)90060-4","isbn":null,"url":null},{"ref":"Beucher, S., & Lantuéjoul, C. (1979). Use of watersheds in contour detection. International Workshop on Image Processing, Real-Time Edge and Motion Detection/Estimation, 2.1–2.12.","type":"article","doi":null,"isbn":null,"url":"https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=cdcc0d8c1dd90cd8da72dc5c6a5eebe2e1d87a28"}],"related":["image-morphology","contour-analysis","canny-edge-detection","blob-detection","histogram-equalization"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wavelet-coherence","name":"Wavelet Coherence","fullName":"Wavelet Coherence Analysis","aliases":["WTC","Wavelet coherency","Continuous wavelet coherence"],"domain":"time-series","family":"process-pipeline","subfamily":"Normalized wavelet correlation","year":"1999","originator":"Christopher Torrence","url":"https://scholargate.app/en/time-series/wavelet-coherence","markdownUrl":"https://scholargate.app/en/time-series/wavelet-coherence.md","definition":"Wavelet coherence (WTC) is a normalized measure of correlation between two time series in the time-frequency domain, eliminating the amplitude-dependence of the raw cross-wavelet transform. Introduced by Torrence and Webster (1999) and formalized by Grinsted, Moore, and Jevrejeva (2004), WTC quantifies how tightly two signals are coupled at each time-frequency point, independent of their individual power levels. It is the wavelet analog of classical spectral coherence, revealing time-localized relationships across all frequencies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christopher Torrence","subfamily":"Normalized wavelet correlation","year":"1999","type":"Multi-scale correlation and phase"},"citations":[{"ref":"Torrence, C., & Webster, P. J. (1999). Interdecadal changes in the ENSO–monsoon system. Journal of Climate, 12(8), 2679–2690.","type":"article","doi":"10.1175/1520-0442(1999)012<2679:icitem>2.0.co;2","isbn":null,"url":null},{"ref":"Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5–6), 561–566.","type":"article","doi":"10.5194/npg-11-561-2004","isbn":null,"url":null},{"ref":"Maraun, D., Kurths, J., & Holschneider, M. (2007). Nonstationary Gaussian processes in wavelet domain: synthesis, estimation, and significance testing. Physical Review E, 75(1), 016707.","type":"article","doi":"10.1103/PhysRevE.75.016707","isbn":null,"url":null}],"related":["cross-wavelet-transform","continuous-wavelet-transform","cross-correlation","spectral-coherence"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wavelet-finance","name":"Wavelet Financial Analysis","fullName":"Wavelet Analysis of Financial Time Series","aliases":["wavelet coherence","continuous wavelet transform","time-frequency analysis","Dalgacık (Wavelet) Finansal Analiz"],"domain":"finance","family":"regression-model","subfamily":null,"year":2001,"originator":"Gençay, Selçuk & Whitcher; Aguiar-Conraria & Soares","url":"https://scholargate.app/en/finance/wavelet-finance","markdownUrl":"https://scholargate.app/en/finance/wavelet-finance.md","definition":"Wavelet financial analysis decomposes a financial time series into different frequency bands (time scales) so short- and long-term relationships can be studied at the same time. Drawing on the treatments of Gençay, Selçuk and Whitcher (2001) and Aguiar-Conraria and Soares (2014), wavelet coherence then visualises how the relationship between two series shifts across both time and frequency.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gençay, Selçuk & Whitcher; Aguiar-Conraria & Soares","year":2001,"type":"Time-frequency decomposition","estimator":"Wavelet transform (Morlet); wavelet coherence","structure":"time series","minSample":128},"citations":[{"ref":"Gençay, R., Selçuk, F. & Whitcher, B. (2001). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press.","type":"book","doi":"10.1016/b978-012279670-8.50004-5","isbn":null,"url":null},{"ref":"Aguiar-Conraria, L. & Soares, M.J. (2014). The Continuous Wavelet Transform: Moving Beyond Uni- and Bivariate Analysis. Journal of Economic Surveys, 28(2), 344-375.","type":"article","doi":"10.1111/joes.12012","isbn":null,"url":null}],"related":["regime-switching-finance","garch-volatility","arima-forecasting","spectral-analysis","var-time-series"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wavelet-neural-network","name":"Wavelet Neural Network","fullName":"Wavelet Neural Network","aliases":["WNN","Wavelet-based neural network","Wavelet networks"],"domain":"time-series","family":"process-pipeline","subfamily":"Wavelet-based activation function network","year":"1992","originator":"Q. Zhang","url":"https://scholargate.app/en/time-series/wavelet-neural-network","markdownUrl":"https://scholargate.app/en/time-series/wavelet-neural-network.md","definition":"A wavelet neural network (WNN) is a function approximation architecture that uses wavelet functions as activation functions in place of traditional sigmoid or ReLU functions. Introduced by Zhang and Benveniste (1992), WNNs combine the multiscale decomposition properties of wavelets with the learning capabilities of neural networks. The result is a flexible nonparametric model that can capture localized features and multi-resolution patterns efficiently, with fewer parameters and better interpretability than standard deep networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Q. Zhang","subfamily":"Wavelet-based activation function network","year":"1992","type":"Non-parametric function approximation"},"citations":[{"ref":"Zhang, Q., & Benveniste, A. (1992). Wavelet networks. IEEE Transactions on Neural Networks, 3(6), 889–898.","type":"article","doi":"10.1109/72.165591","isbn":null,"url":null},{"ref":"Pati, Y. C., & Krishnaprasad, P. S. (1992). Nonlinear dynamics and signal processing in the cochlea. ICASSP, pp. V373–V376.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Nonlinear+dynamics+and+signal+processing+in+the+cochlea+Pati"},{"ref":"Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J. M. (1997). Wavelet Toolbox. The Mathworks.","type":"article","doi":null,"isbn":null,"url":"https://www.mathworks.com/products/wavelet.html"}],"related":["radial-basis-function-network","multilayer-perceptron","convolutional-neural-network","recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wayfinding-analysis","name":"Wayfinding Analysis","fullName":"Wayfinding Analysis and Navigation Assessment","aliases":["way-finding assessment","navigation design analysis","signage and circulation analysis"],"domain":"architecture","family":"process-pipeline","subfamily":"Navigation and user experience design","year":"1960","originator":"Kevin Lynch, Romedi Passini","url":"https://scholargate.app/en/architecture/wayfinding-analysis","markdownUrl":"https://scholargate.app/en/architecture/wayfinding-analysis.md","definition":"Wayfinding Analysis is a method for assessing how easily people can navigate and orient themselves in buildings and urban environments. Rooted in Kevin Lynch's concept of legibility and developed further by Romedi Passini, it combines cognitive psychology, design principles, and empirical testing to diagnose navigation difficulties and design intuitive navigation systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kevin Lynch, Romedi Passini","subfamily":"Navigation and user experience design","year":"1960","type":"navigation and spatial orientation assessment method"},"citations":[{"ref":"Lynch, K. (1960). The Image of the City. MIT Press, Cambridge, MA.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Image+of+the+City+Lynch"},{"ref":"Passini, R. (1992). Wayfinding in Architecture. Van Nostrand Reinhold, New York.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com/en-us/Wayfinding+in+Architecture-p-9780442001841"},{"ref":"Arthur, P., Passini, R. (1992). Wayfinding: People, Signs, and Architecture. McGraw-Hill Professional.","type":"article","doi":null,"isbn":null,"url":"https://archive.org/details/wayfindingpeople0000arth"}],"related":["space-syntax-analysis","urban-form-analysis","post-occupancy-evaluation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weak-gravitational-lensing","name":"Weak Gravitational Lensing","fullName":"Weak Gravitational Lensing for Dark Matter and Cosmology","aliases":["Weak Lensing","Cosmic Shear","Lensing Distortion"],"domain":"astronomy","family":"process-pipeline","subfamily":"Observational cosmology","year":1992,"originator":"Nick Kaiser","url":"https://scholargate.app/en/astronomy/weak-gravitational-lensing","markdownUrl":"https://scholargate.app/en/astronomy/weak-gravitational-lensing.md","definition":"Weak gravitational lensing occurs when light from distant sources bends slightly as it travels through the universe, passing through the gravitational fields of matter concentrations. Proposed theoretically by Nick Kaiser in 1992, this subtle effect has become one of the most powerful cosmological probes, directly revealing the distribution of all matter (dark and luminous) across cosmic distances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nick Kaiser","subfamily":"Observational cosmology","year":1992,"type":"Observational measurement method"},"citations":[{"ref":"Kaiser, N. (1992). Weak gravitational lensing of distant galaxies. Astrophysical Journal, 388, 272-286.","type":"article","doi":"10.1086/171151","isbn":null,"url":null},{"ref":"Van Waerbeke, L., et al. (2000). Detection of weak gravitational lensing by large-scale structure. Astronomy & Astrophysics, 358, 30-44.","type":"article","doi":null,"isbn":null,"url":"https://ui.adsabs.harvard.edu/abs/2000A&A...358...30V"},{"ref":"Hildebrandt, H., et al. (2020). KiDS+VIKING-450 and S-PLUS: Cosmic shear measurements with 1,346 square degrees. Astronomy & Astrophysics, 633, A69.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=KiDS%2BVIKING-450+and+S-PLUS%3A+Cosmic+shear+measurements+with+1%2C346+square+degrees+Hildebrandt"}],"related":["baryon-acoustic-oscillations","halo-occupation-distribution","cmb-anisotropy-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-bert-based-classification","name":"Weakly supervised BERT-based classification","fullName":"Weakly Supervised BERT-based Text Classification","aliases":["WS-BERT","BERT with weak supervision","label-efficient BERT classification","noisy-label BERT fine-tuning"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2020","originator":"Multiple (Ratner et al. for weak supervision framework; Meng et al. for BERT integration)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-bert-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-bert-based-classification.md","definition":"Weakly supervised BERT-based classification adapts BERT to text classification tasks when only noisy, heuristic, or programmatically generated labels are available instead of clean human annotations. It combines weak supervision frameworks — such as labeling functions and data programming — with BERT's pre-trained language representations to achieve robust classification without expensive hand-labeling.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple (Ratner et al. for weak supervision framework; Meng et al. for BERT integration)","year":"2017–2020","type":"Weakly supervised fine-tuning of pre-trained language model","dataType":"Text (labeled, noisy-labeled, or label-rule-generated)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Meng, Y., Zhang, Y., Huang, J., Xiong, C., Ji, H., Zhang, C., & Han, J. (2020). Text Classification Using Label Names Only: A Language Model Self-Training Approach. Proceedings of EMNLP 2020, 9006–9017.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Text+Classification+Using+Label+Names+Only+A+Language+Model+Self-Training+Approach"},{"ref":"Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid Training Data Creation with Weak Supervision. Proceedings of the VLDB Endowment, 11(3), 269–282.","type":"article","doi":"10.14778/3157794.3157797","isbn":null,"url":null}],"related":["bert-based-classification","semi-supervised-bert-based-classification","self-supervised-bert-based-classification","fine-tuned-bert-based-classification","roberta-based-classification","domain-adaptive-bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-convolutional-neural-network","name":"Weakly supervised convolutional neural network","fullName":"Weakly Supervised Convolutional Neural Network","aliases":["WS-CNN","weakly supervised CNN","CNN with weak labels","CNN with noisy labels"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2016","originator":"Oquab, M. et al.; Zhou, B. et al.","url":"https://scholargate.app/en/deep-learning/weakly-supervised-convolutional-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-convolutional-neural-network.md","definition":"A weakly supervised CNN is a convolutional neural network trained with incomplete, coarse, or noisy annotations instead of full pixel-level or bounding-box labels. Typical weak labels include image-level class tags, partial annotations, or crowd-sourced noisy labels. The model learns to classify and often to roughly localize objects using these cheaper, lower-quality supervision signals.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Oquab, M. et al.; Zhou, B. et al.","year":"2015–2016","type":"Weakly supervised deep learning","dataType":"Images with coarse or image-level labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929.","type":"inproceedings","doi":"10.1109/CVPR.2016.319","isbn":null,"url":null},{"ref":"Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015). Is object localization for free? — Weakly-supervised learning with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 685–694.","type":"inproceedings","doi":"10.1109/CVPR.2015.7298668","isbn":null,"url":null}],"related":["convolutional-neural-network","semi-supervised-convolutional-neural-network","self-supervised-convolutional-neural-network","image-classification","semantic-segmentation","fine-tuned-convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-diffusion-model","name":"Weakly Supervised Diffusion Model","fullName":"Weakly Supervised Diffusion Model (Denoising Diffusion with Imperfect Supervision)","aliases":["WS-Diffusion","weakly supervised DDPM","label-efficient diffusion model","noisy-label diffusion training"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2022–2024","originator":"Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024","url":"https://scholargate.app/en/deep-learning/weakly-supervised-diffusion-model","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-diffusion-model.md","definition":"A weakly supervised diffusion model trains or conditions a denoising diffusion probabilistic model using coarse, noisy, or incomplete supervision signals — such as image-level class labels, bounding boxes, or crowd-sourced annotations — instead of pixel-precise ground truth. This allows high-quality generative and discriminative outputs in annotation-scarce settings where full labeling is infeasible or prohibitively expensive.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024","year":"2022–2024","type":"Generative model with imperfect supervision","dataType":"Images, image-level labels, noisy annotations, partial masks","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html"},{"ref":"Zhou, K., et al. (2023). Weakly-supervised Semantic Segmentation with Diffusion Models. arXiv preprint arXiv:2309.11803.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2309.11803"}],"related":["diffusion-model","semi-supervised-diffusion-model","weakly-supervised-semantic-segmentation","self-supervised-diffusion-model","generative-adversarial-network","variational-autoencoder"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-gan","name":"Weakly supervised GAN","fullName":"Weakly Supervised Generative Adversarial Network","aliases":["WS-GAN","weakly supervised generative adversarial network","label-efficient GAN","semi-labeled GAN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2017","originator":"Odena et al.; building on Goodfellow et al. (2014)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-gan","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-gan.md","definition":"A Weakly Supervised GAN is a generative adversarial network trained with partially labeled, noisily labeled, or coarse-annotation data instead of fully annotated ground truth. It extends the standard GAN framework so that limited supervision guides conditional generation or discriminative learning, enabling high-quality data synthesis and classification in label-scarce settings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Odena et al.; building on Goodfellow et al. (2014)","year":"2014–2017","type":"Generative model with weak supervision","dataType":"Images, tabular data, or sequences with partial or noisy labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Odena, A., Olah, C., & Shlens, J. (2017). Conditional Image Synthesis with Auxiliary Classifier GANs. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70, 2642–2651.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.mlr.press/v70/odena17a.html"},{"ref":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper_files/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html"}],"related":["generative-adversarial-network","semi-supervised-gan","conditional-gan","weakly-supervised-image-classification","variational-autoencoder","diffusion-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-graph-neural-network","name":"Weakly supervised graph neural network","fullName":"Weakly Supervised Graph Neural Network","aliases":["WS-GNN","graph neural network with weak supervision","noisy-label GNN","partially supervised GNN"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2019","originator":"Derived from GNN literature (Scarselli et al. 2009; Kipf & Welling 2017) combined with weak supervision paradigm","url":"https://scholargate.app/en/deep-learning/weakly-supervised-graph-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-graph-neural-network.md","definition":"A Weakly Supervised Graph Neural Network (WS-GNN) is a graph deep-learning approach that learns from graph-structured data — nodes, edges, and their attributes — when only noisy, partial, or indirectly obtained labels are available. By coupling GNN message passing with noise-robust training strategies, it extends graph learning to real-world settings where clean, fully annotated graphs are scarce or expensive to obtain.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from GNN literature (Scarselli et al. 2009; Kipf & Welling 2017) combined with weak supervision paradigm","year":"2017–2019","type":"Graph-based deep learning with imperfect supervision","dataType":"Graph-structured data (nodes, edges, attributes) with noisy, incomplete, or indirect labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1609.02907"},{"ref":"Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57–81.","type":"article","doi":"10.1016/j.aiopen.2021.01.001","isbn":null,"url":null}],"related":["graph-neural-network","semi-supervised-graph-neural-network","weakly-supervised-convolutional-neural-network","graph-convolutional-network","weakly-supervised-transformer","label-propagation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-gru","name":"Weakly Supervised GRU","fullName":"Weakly Supervised Gated Recurrent Unit Network","aliases":["WS-GRU","GRU with weak supervision","weakly labeled GRU","noisy-label GRU"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2016","originator":"Chung et al. (GRU); Ratner et al. (weak supervision framework)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-gru","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-gru.md","definition":"Weakly Supervised GRU trains a Gated Recurrent Unit network on sequences labeled by imperfect, heuristic, or programmatic sources rather than costly hand-annotated ground truth. It combines the GRU's efficiency at capturing temporal dependencies with weak-supervision techniques that aggregate noisy labels, enabling practical sequence modeling when large fully labeled datasets are unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chung et al. (GRU); Ratner et al. (weak supervision framework)","year":"2014–2016","type":"Weakly supervised sequence model","dataType":"Sequential / time-series / text with noisy or heuristic labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2016/hash/6709e8d64a5f47269ed5cea9f625f7ab-Abstract.html"},{"ref":"Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. NIPS 2014 Workshop on Deep Learning.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1412.3555"}],"related":["gated-recurrent-unit","weakly-supervised-lstm","weakly-supervised-transformer","semi-supervised-gru","recurrent-neural-network","long-short-term-memory"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-image-classification","name":"Weakly Supervised Image Classification","fullName":"Weakly Supervised Image Classification (WSL-IC)","aliases":["WSL image classification","image-level supervised classification","noisy-label image classification","weakly labeled visual recognition"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2016","originator":"Multiple contributors; class activation map approach: Zhou et al.","url":"https://scholargate.app/en/deep-learning/weakly-supervised-image-classification","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-image-classification.md","definition":"Weakly supervised image classification trains convolutional or transformer-based networks using only coarse, incomplete, or noisy supervision — such as image-level category labels, hashtags, or web-scraped tags — without requiring precise bounding boxes or pixel annotations. This dramatically reduces labeling cost while still enabling high-accuracy visual recognition at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; class activation map approach: Zhou et al.","year":"2014–2016","type":"Weakly supervised deep learning paradigm","dataType":"Images with image-level labels, noisy labels, or web-crawled annotations","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929.","type":"inproceedings","doi":"10.1109/CVPR.2016.319","isbn":null,"url":null},{"ref":"Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., & van der Maaten, L. (2018). Exploring the Limits of Weakly Supervised Pretraining. Proceedings of the European Conference on Computer Vision (ECCV), 181–196.","type":"inproceedings","doi":"10.1007/978-3-030-01216-8_12","isbn":null,"url":null}],"related":["image-classification","semi-supervised-image-classification","self-supervised-image-classification","transfer-learning-with-image-classification","convolutional-neural-network","fine-tuned-image-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-instance-segmentation","name":"Weakly Supervised Instance Segmentation","fullName":"Weakly Supervised Instance Segmentation (Deep Learning with Incomplete Annotations)","aliases":["WSIS","weakly-supervised mask prediction","weak-label instance segmentation","box-supervised instance segmentation"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2019","originator":"Multiple contributors (e.g., Hsu et al., Khoreva et al.)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-instance-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-instance-segmentation.md","definition":"Weakly supervised instance segmentation trains deep networks to delineate individual object instances at pixel level using only cheap, incomplete annotations — such as bounding boxes, image-level labels, or point clicks — rather than costly full pixel-wise masks. It dramatically reduces annotation effort while still producing instance-level masks for each object in an image.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (e.g., Hsu et al., Khoreva et al.)","year":"2015–2019","type":"Weakly supervised deep learning for pixel-wise instance delineation","dataType":"Image data with weak annotations (bounding boxes, image-level labels, or point clicks)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Hsu, C.-C., Hsu, K.-J., Tsai, C.-C., Lin, Y.-Y., & Chuang, Y.-Y. (2019). Weakly supervised instance segmentation using the bounding box tightness prior. Advances in Neural Information Processing Systems (NeurIPS), 32.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2019/hash/e6e713296627e82b0a4963007d2ca89f-Abstract.html"},{"ref":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929.","type":"article","doi":"10.1109/CVPR.2016.319","isbn":null,"url":null}],"related":["instance-segmentation","semantic-segmentation","weakly-supervised-semantic-segmentation","object-detection","semi-supervised-instance-segmentation","self-supervised-instance-segmentation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-lda-topic-model","name":"Weakly supervised LDA topic model","fullName":"Weakly Supervised Latent Dirichlet Allocation Topic Model","aliases":["WS-LDA","Guided LDA","Seeded LDA","Constrained LDA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2009–2012","originator":"Jagarlamudi et al.; Andrzejewski et al.","url":"https://scholargate.app/en/deep-learning/weakly-supervised-lda-topic-model","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-lda-topic-model.md","definition":"Weakly Supervised LDA is an extension of Latent Dirichlet Allocation that incorporates lightweight human guidance — typically keyword seeds or must-link/cannot-link constraints — into the Dirichlet priors, steering learned topics toward domain-meaningful themes without requiring fully labeled documents. It sits between fully unsupervised LDA and supervised classification, making it well-suited to situations where labeling thousands of documents is impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jagarlamudi et al.; Andrzejewski et al.","year":"2009–2012","type":"Probabilistic generative model with weak supervision","dataType":"Text corpora (documents, sentences)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Jagarlamudi, J., Daume III, H., & Udupa, R. (2012). Incorporating Lexical Priors into Topic Models. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), pp. 204–213.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/E12-1021"},{"ref":"Andrzejewski, D., Zhu, X., & Craven, M. (2009). Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors. Proceedings of the 26th International Conference on Machine Learning (ICML 2009), pp. 25–32.","type":"inproceedings","doi":null,"isbn":null,"url":"https://dl.acm.org/doi/10.1145/1553374.1553378"}],"related":["lda-topic-model","nmf-topic-model","semi-supervised-lda-topic-model","weakly-supervised-bert-based-classification","topic-modeling","sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-lstm","name":"Weakly supervised LSTM","fullName":"Weakly Supervised Long Short-Term Memory Network","aliases":["WS-LSTM","noisy-label LSTM","distant-supervision LSTM","data-programming LSTM"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2018","originator":"Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-lstm","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-lstm.md","definition":"Weakly supervised LSTM trains a Long Short-Term Memory network on sequence data where clean, manually annotated labels are scarce or absent. Instead, multiple imperfect label sources — heuristic rules, distant supervision, crowdsourcing, or programmatic labeling functions — are combined to produce probabilistic training labels, which are then used to supervise the LSTM. This allows scalable training on large unlabeled corpora without exhaustive human annotation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)","year":"2016–2018","type":"Weakly supervised sequence model","dataType":"Sequential / text data with noisy or heuristic labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2016/hash/6709e8d64a5f47269ed5cea9f625f7ab-Abstract.html"},{"ref":"Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.","type":"article","doi":"10.1093/nsr/nwx106","isbn":null,"url":null}],"related":["long-short-term-memory","semi-supervised-lstm","weakly-supervised-recurrent-neural-network","weakly-supervised-transformer","fine-tuned-lstm","recurrent-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-multilayer-perceptron","name":"Weakly supervised multilayer perceptron","fullName":"Weakly Supervised Multilayer Perceptron","aliases":["WS-MLP","weakly supervised feedforward network","noisy-label MLP","weak-label multilayer perceptron"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2018","originator":"Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-multilayer-perceptron","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-multilayer-perceptron.md","definition":"A Weakly Supervised Multilayer Perceptron trains a standard feedforward neural network when only imperfect supervision is available — labels may be noisy, incomplete, crowd-sourced, rule-generated, or derived from distant supervision — enabling learning at scale without the cost of full expert annotation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016)","year":"2016–2018","type":"Feedforward neural network trained under weak supervision","dataType":"Tabular, text, or image features with noisy, incomplete, or programmatically generated labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.","type":"article","doi":"10.1093/nsr/nwx106","isbn":null,"url":null},{"ref":"Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., & Re, C. (2016). Data programming: Creating large training sets, quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2016/hash/6709e8d64a5f47269ed5cea9f625f7ab-Abstract.html"}],"related":["semi-supervised-multilayer-perceptron","self-supervised-multilayer-perceptron","multilayer-perceptron","weakly-supervised-convolutional-neural-network","weakly-supervised-transformer","fine-tuned-multilayer-perceptron"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-object-detection","name":"Weakly Supervised Object Detection","fullName":"Weakly Supervised Object Detection (WSOD)","aliases":["WSOD","weakly-supervised detection","image-level supervised detection","multiple instance detection"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016 (deep WSOD); MIL roots circa 1997","originator":"Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-object-detection","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-object-detection.md","definition":"Weakly Supervised Object Detection (WSOD) trains object detectors using only image-level labels — indicating which object classes appear in an image — without requiring costly bounding-box annotations. Multiple Instance Learning (MIL) formulations allow the model to discover the likely location of each object class from classification signals alone, dramatically reducing annotation cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997)","year":"2016 (deep WSOD); MIL roots circa 1997","type":"Weakly supervised detection paradigm","dataType":"Images with image-level classification labels (no bounding boxes)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Bilen, H., & Vedaldi, A. (2016). Weakly supervised deep detection networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2846–2854.","type":"inproceedings","doi":"10.1109/CVPR.2016.311","isbn":null,"url":null},{"ref":"Tang, P., Wang, X., Bai, X., & Liu, W. (2017). Multiple instance detection network with online instance classifier refinement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2843–2851.","type":"inproceedings","doi":"10.1109/cvpr.2017.326","isbn":null,"url":null}],"related":["object-detection","image-classification","semi-supervised-object-detection","instance-segmentation","convolutional-neural-network","vision-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-question-answering","name":"Weakly supervised question answering","fullName":"Weakly Supervised Question Answering","aliases":["WS-QA","distantly supervised QA","noisy-label question answering","indirect supervision QA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2019","originator":"Multiple authors (Clark, Gardner, Min et al.)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-question-answering","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-question-answering.md","definition":"Weakly supervised question answering (WS-QA) trains neural reading-comprehension models using indirect or automatically derived answer labels rather than expensive human-annotated span annotations. By exploiting distant supervision, heuristic labeling, or answer-presence signals, WS-QA makes QA feasible in domains and languages where full annotation is impractical.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple authors (Clark, Gardner, Min et al.)","year":"2017–2019","type":"Weakly supervised NLP model","dataType":"Text passages, questions, and weak/distant answer labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Clark, C., & Gardner, M. (2018). Simple and Effective Multi-Paragraph Reading Comprehension. In Proceedings of ACL 2018, pp. 845–855. Association for Computational Linguistics.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/P18-1078"},{"ref":"Min, S., Chen, D., Hajishirzi, H., & Zettlemoyer, L. (2019). A Discrete Hard EM Approach for Weakly Supervised Question Answering. In Proceedings of EMNLP-IJCNLP 2019, pp. 2083–2093. Association for Computational Linguistics.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/D19-1284"}],"related":["weakly-supervised-named-entity-recognition","semi-supervised-question-answering","bert-based-classification","transformer","domain-adaptive-question-answering","fine-tuned-question-answering"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-recurrent-neural-network","name":"Weakly supervised recurrent neural network","fullName":"Weakly Supervised Recurrent Neural Network","aliases":["WS-RNN","distantly supervised RNN","noise-tolerant RNN","weakly supervised sequence model"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2009–2016","originator":"Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-recurrent-neural-network","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-recurrent-neural-network.md","definition":"A weakly supervised RNN trains a recurrent neural network on sequences whose labels come from imperfect sources — heuristic rules, distant supervision, crowdsourcing, or generative label models — rather than expensive expert annotation. This lets researchers exploit large unlabeled corpora for sequential tasks such as text classification, named entity recognition, or time-series prediction when fully annotated data is scarce or costly.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016)","year":"2009–2016","type":"Supervised learning under noisy or incomplete labels","dataType":"Sequential data (text, time series, speech)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2016/hash/6709e8d64a5f47269ed5cea9f625f7ab-Abstract.html"},{"ref":"Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.","type":"article","doi":"10.1093/nsr/nwx106","isbn":null,"url":null}],"related":["recurrent-neural-network","long-short-term-memory","gated-recurrent-unit","semi-supervised-recurrent-neural-network","weakly-supervised-lstm","weakly-supervised-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-reinforcement-learning","name":"Weakly supervised reinforcement learning","fullName":"Weakly Supervised Reinforcement Learning","aliases":["WSRL","weak-reward RL","imperfect-reward reinforcement learning","reward-impoverished RL"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2010s–present","originator":"Multiple contributors; reward-learning framing: Christiano et al. (2017)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-reinforcement-learning","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-reinforcement-learning.md","definition":"Weakly supervised reinforcement learning (WSRL) trains agents in environments where the reward signal is imperfect, sparse, delayed, or only partially informative — unlike dense fully-supervised RL. The agent must learn effective policies despite incomplete feedback, using auxiliary signals, reward modeling, or preference learning to compensate for the weak supervision.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; reward-learning framing: Christiano et al. (2017)","year":"2010s–present","type":"Reinforcement learning with imperfect or partial reward supervision","dataType":"State-action trajectories with weak, noisy, delayed, or sparse reward signals","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03924-6","url":null},{"ref":"Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S. & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems (NeurIPS), 30.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html"}],"related":["reinforcement-learning","semi-supervised-reinforcement-learning","self-supervised-reinforcement-learning","reward-shaping","inverse-reinforcement-learning","imitation-learning"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-roberta-based-classification","name":"Weakly Supervised RoBERTa-based Classification","fullName":"Weakly Supervised Text Classification with RoBERTa","aliases":["WS-RoBERTa","RoBERTa with weak supervision","weakly supervised transformer classification","noisy-label RoBERTa classifier"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2019–2020","originator":"Liu et al. (RoBERTa, 2019); weak supervision paradigm: Ratner et al. (2016–2020)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-roberta-based-classification","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-roberta-based-classification.md","definition":"Weakly supervised RoBERTa-based classification combines the RoBERTa pretrained transformer with weak supervision — programmatic or heuristic labeling sources — to train powerful text classifiers without requiring a fully hand-labeled dataset. Labeling functions, distant supervision, or crowd-sourced signals generate noisy labels that are aggregated and used to fine-tune RoBERTa for downstream classification tasks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Liu et al. (RoBERTa, 2019); weak supervision paradigm: Ratner et al. (2016–2020)","year":"2019–2020","type":"Pretrained transformer classifier with weak supervision","dataType":"Text (partially labeled or programmatically labeled corpora)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1907.11692"},{"ref":"Zhang, J., Yu, Y., Li, Y., Wang, Y., Yang, Y., Yang, M., & Ratner, A. (2021). WRENCH: A Comprehensive Benchmark for Weak Supervision. NeurIPS 2021 Datasets and Benchmarks Track.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/2109.11377"}],"related":["roberta-based-classification","bert-based-classification","weakly-supervised-bert-based-classification","semi-supervised-roberta-based-classification","fine-tuned-roberta-based-classification","transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-semantic-segmentation","name":"Weakly Supervised Semantic Segmentation","fullName":"Weakly Supervised Semantic Segmentation (WSSS)","aliases":["WSSS","weak-label segmentation","image-level supervised segmentation","weakly-labeled pixel classification"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2016","originator":"Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational","url":"https://scholargate.app/en/deep-learning/weakly-supervised-semantic-segmentation","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-semantic-segmentation.md","definition":"Weakly Supervised Semantic Segmentation (WSSS) trains pixel-level scene parsers using only cheap, coarse annotations — typically image-level class tags — instead of costly dense pixel masks. By generating proxy pseudo-labels from a classification network (via Class Activation Maps or similar localisation cues) and iteratively refining them, WSSS brings full-supervision accuracy within reach at a fraction of the annotation cost.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational","year":"2014–2016","type":"Pixel-level classification with image-level or coarse supervision","dataType":"Images with weak labels (image-level tags, bounding boxes, or scribbles)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929.","type":"inproceedings","doi":"10.1109/CVPR.2016.319","isbn":null,"url":null},{"ref":"Ahn, J., & Kwak, S. (2018). Learning Pixel-Wise Semantic Affinity with Image-Level Supervision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4109–4118.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Learning+Pixel-Wise+Semantic+Affinity+with+Image-Level+Supervision+Ahn"}],"related":["semantic-segmentation","class-activation-mapping","semi-supervised-learning","self-supervised-learning","object-detection","convolutional-neural-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-sentence-embeddings","name":"Weakly supervised sentence embeddings","fullName":"Weakly Supervised Sentence Embeddings","aliases":["WS sentence embeddings","noisy-label sentence representation learning","weakly supervised sentence representation","distant-supervision sentence embeddings"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2016–2019","originator":"Ratner et al. (weak supervision framework); Reimers & Gurevych (sentence embeddings)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-sentence-embeddings","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-sentence-embeddings.md","definition":"Weakly supervised sentence embeddings train dense sentence representations using noisy, heuristic, or programmatically generated labels instead of costly human annotation. Labeling functions — rules, distant supervision signals, or lightweight classifiers — supply approximate supervision that a label model aggregates into probabilistic labels, which then guide the sentence encoder to produce task-useful representations at scale.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ratner et al. (weak supervision framework); Reimers & Gurevych (sentence embeddings)","year":"2016–2019","type":"Representation learning under weak supervision","dataType":"Text (sentences, paragraphs); noisy or heuristically labeled corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.","type":"inproceedings","doi":null,"isbn":null,"url":"https://proceedings.neurips.cc/paper/2016/hash/6709e8d64a5f47269ed5cea9f625f7ab-Abstract.html"},{"ref":"Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP).","type":"inproceedings","doi":"10.18653/v1/D19-1410","isbn":null,"url":null}],"related":["sentence-embeddings","bert-based-classification","semi-supervised-sentence-embeddings","self-supervised-sentence-embeddings","weakly-supervised-bert-based-classification","transfer-learning-with-sentence-embeddings"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-text-summarization","name":"Weakly supervised text summarization","fullName":"Weakly Supervised Text Summarization","aliases":["weak supervision summarization","distantly supervised summarization","noisy-label summarization","pseudo-label summarization"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2015–2020","originator":"Multiple independent research groups (NLP community, 2010s–2020s)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-text-summarization","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-text-summarization.md","definition":"Weakly supervised text summarization trains abstractive or extractive summarization models without manually annotated reference summaries. Instead of costly human labels, it exploits weak signals — heuristic rules, distant supervision, noisy automatic labels, or self-supervised objectives — to guide sequence-to-sequence or transformer models toward producing coherent, concise summaries of input documents.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple independent research groups (NLP community, 2010s–2020s)","year":"2015–2020","type":"Semi-supervised / weakly supervised NLP training paradigm","dataType":"Unlabeled or noisily labeled text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Amplayo, R. K., & Lapata, M. (2020). Unsupervised Opinion Summarization with Noisy Autoencoder. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1934–1945.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2020.acl-main.175"},{"ref":"Huang, L., Wu, L., & Wang, L. (2020). Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5094–5107.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/2020.acl-main.457"}],"related":["extractive-text-summarization","abstractive-text-summarization","seq2seq-model","transformer-model","self-supervised-learning","distant-supervision"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-topic-modeling","name":"Weakly Supervised Topic Modeling","fullName":"Weakly Supervised Topic Modeling (Seed-Guided / Constrained Topic Models)","aliases":["guided topic modeling","seed-guided topic model","constrained topic modeling","seeded LDA"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2012–2017","originator":"Jagarlamudi, Daume & Udupa; Gallagher et al. (CorEx)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-topic-modeling","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-topic-modeling.md","definition":"Weakly supervised topic modeling incorporates lightweight domain knowledge — typically seed words or soft constraints — into a probabilistic topic model to steer discovered topics toward researcher-meaningful themes. It sits between fully unsupervised LDA and supervised classifiers, requiring far less annotation than the latter while producing more interpretable and domain-aligned topics than the former.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jagarlamudi, Daume & Udupa; Gallagher et al. (CorEx)","year":"2012–2017","type":"Weakly supervised probabilistic topic model","dataType":"Text corpora (documents, sentences)","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Jagarlamudi, J., Daume III, H., & Udupa, R. (2012). Incorporating Lexical Priors into Topic Models. Proceedings of EACL 2012, 204–213.","type":"inproceedings","doi":null,"isbn":null,"url":"https://aclanthology.org/E12-1021"},{"ref":"Gallagher, R. J., Reing, K., Kale, D., & Ver Steeg, G. (2017). Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge. Transactions of the Association for Computational Linguistics, 5, 529–542.","type":"inproceedings","doi":"10.1162/tacl_a_00078","isbn":null,"url":null}],"related":["lda-topic-model","nmf-topic-model","topic-modeling","semi-supervised-topic-modeling","bert-based-classification","weakly-supervised-named-entity-recognition"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-transformer","name":"Weakly supervised transformer","fullName":"Weakly Supervised Transformer","aliases":["WST","weakly supervised attention model","noisy-label transformer","weak supervision with transformers"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2017–2019","originator":"Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-transformer.md","definition":"Weakly Supervised Transformer combines the representational power of Transformer architectures with weak supervision strategies that exploit noisy, incomplete, or programmatically generated labels — making it possible to train high-quality NLP and vision models when fully annotated datasets are scarce or prohibitively expensive to produce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017)","year":"2017–2019","type":"Weakly supervised deep learning","dataType":"Text, image, or multimodal data with noisy or incomplete labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid training data creation with weak supervision. Proceedings of the VLDB Endowment, 11(3), 269–282.","type":"inproceedings","doi":"10.14778/3157794.3157797","isbn":null,"url":null},{"ref":"Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.","type":"article","doi":"10.1093/nsr/nwx106","isbn":null,"url":null}],"related":["transformer","bert-based-classification","semi-supervised-transformer","self-supervised-transformer","weakly-supervised-bert-based-classification","fine-tuned-transformer"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-variational-autoencoder","name":"Weakly Supervised Variational Autoencoder","fullName":"Weakly Supervised Variational Autoencoder (WS-VAE)","aliases":["WS-VAE","weakly-supervised VAE","semi-supervised VAE with weak labels","label-guided variational autoencoder"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2014–2018","originator":"Kingma, D. P. et al. (building on VAE and semi-supervised deep generative models)","url":"https://scholargate.app/en/deep-learning/weakly-supervised-variational-autoencoder","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-variational-autoencoder.md","definition":"A Weakly Supervised Variational Autoencoder (WS-VAE) extends the standard VAE generative framework by incorporating partial, noisy, or coarse supervision signals — such as crowd-sourced labels, heuristic rules, or programmatic annotations — to guide latent space learning without requiring fully annotated data. It is widely applied in computer vision, NLP, and biomedical domains where complete ground-truth labels are expensive or unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kingma, D. P. et al. (building on VAE and semi-supervised deep generative models)","year":"2014–2018","type":"Generative model with weak supervision","dataType":"Images, text, or structured data with partial or noisy labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR 2014).","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1312.6114"},{"ref":"Kingma, D. P., Mohamed, S., Rezende, D. J. & Welling, M. (2014). Semi-supervised learning with deep generative models. In Advances in Neural Information Processing Systems (NeurIPS 2014), 27.","type":"inproceedings","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1406.5298"}],"related":["variational-autoencoder","semi-supervised-learning","beta-vae","conditional-vae","disentangled-representation-learning","generative-adversarial-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-vision-transformer","name":"Weakly supervised vision transformer","fullName":"Weakly Supervised Vision Transformer (WS-ViT)","aliases":["WS-ViT","weakly supervised ViT","weak supervision with vision transformer","ViT with weak labels"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2021–2022","originator":"Dosovitskiy et al. (ViT); weak supervision paradigm from Zhou and others","url":"https://scholargate.app/en/deep-learning/weakly-supervised-vision-transformer","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-vision-transformer.md","definition":"Weakly Supervised Vision Transformer (WS-ViT) trains a Vision Transformer on image data that lacks precise pixel-level annotations, instead using cheaper, noisier supervision such as image-level class tags, bounding boxes, or web-scraped text. The global self-attention mechanism of the transformer makes it especially capable of localising objects and learning discriminative features from these incomplete labels.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dosovitskiy et al. (ViT); weak supervision paradigm from Zhou and others","year":"2021–2022","type":"Self-attention image model with weakly supervised training","dataType":"Image patches with coarse, noisy, or partial labels","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR).","type":"inproceedings","doi":null,"isbn":null,"url":"https://openreview.net/forum?id=YicbFdNTTy"},{"ref":"Zhou, Z.-H. (2022). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.","type":"article","doi":"10.1093/nsr/nwx106","isbn":null,"url":null}],"related":["vision-transformer","semi-supervised-learning","self-supervised-learning","multiple-instance-learning","convolutional-neural-network","knowledge-distillation"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weakly-supervised-word2vec","name":"Weakly supervised Word2Vec","fullName":"Weakly Supervised Word2Vec (Word Embeddings with Weak Supervision)","aliases":["WS-Word2Vec","weakly-supervised word embeddings","weak-label Word2Vec","semi-noisy Word2Vec"],"domain":"deep-learning","family":"ml-model","subfamily":"Deep learning / NLP / CV","year":"2013–2016","originator":"Mikolov et al. (Word2Vec); weak supervision framework: Ratner et al.","url":"https://scholargate.app/en/deep-learning/weakly-supervised-word2vec","markdownUrl":"https://scholargate.app/en/deep-learning/weakly-supervised-word2vec.md","definition":"Weakly Supervised Word2Vec trains Word2Vec-style embeddings using automatically generated, noisy, or heuristic labels rather than costly manual annotation. By leveraging labeling functions, distant supervision, or keyword-based rules to assign soft labels, the approach enables domain-adapted word representations even when large manually annotated corpora are unavailable.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mikolov et al. (Word2Vec); weak supervision framework: Ratner et al.","year":"2013–2016","type":"Word embedding with noisy/programmatic labels","dataType":"Unlabeled or noisily labeled text corpora","subfamily":"Deep learning / NLP / CV"},"citations":[{"ref":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html"},{"ref":"Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., & Re, C. (2016). Data programming: Creating large training sets, quickly. Advances in Neural Information Processing Systems, 29.","type":"inproceedings","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Data+programming+Creating+large+training+sets+quickly+Ratner+2016"}],"related":["word2vec","weakly-supervised-sentence-embeddings","semi-supervised-word2vec","doc2vec","sentence-embeddings","bert-based-classification"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"web-of-science","name":"Web of Science Database","fullName":"Web of Science Core Collection","aliases":["WoS","ISI Web of Science","Clarivate Analytics"],"domain":"bibliometrics","family":"process-pipeline","subfamily":"citation databases","year":1964,"originator":"Institute for Scientific Information (ISI), now Clarivate Analytics","url":"https://scholargate.app/en/bibliometrics/web-of-science","markdownUrl":"https://scholargate.app/en/bibliometrics/web-of-science.md","definition":"Web of Science (WoS) is the oldest and most established multidisciplinary citation database, maintained by Clarivate Analytics since 1964. It indexes over 21,000 peer-reviewed journals, conference proceedings, and books across sciences, social sciences, and humanities. WoS provides researchers, librarians, and administrators with comprehensive citation tracking, publication metrics, and research evaluation tools essential for literature review, impact assessment, and research strategy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Institute for Scientific Information (ISI), now Clarivate Analytics","subfamily":"citation databases","year":1964,"type":"Database"},"citations":[{"ref":"Clarivate Analytics. (2024). Web of Science Core Collection. Retrieved from https://clarivate.com/webofsciencegroup/solutions/web-of-science-core-collection/","type":"website","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clarivate%20Analytics.%20(2024).%20Web%20of%20Science%20Core%20Collection.%20Retrieved%20from%20https%3A%2F%2Fclarivate.com%2Fwebofsciencegroup%2Fsolu"},{"ref":"Garfield, E. (1955). Citation indexes for science: A new dimension of bibliographic information utilization and management. Science, 122(3159), 108-111.","type":"article","doi":"10.1126/science.122.3159.108","isbn":null,"url":null},{"ref":"Clarivate Analytics. (2023). Journal Citation Reports Methodology. https://clarivate.com/webofsciencegroup/essays/journal-citation-reports-methodology/","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Clarivate%20Analytics.%20(2023).%20Journal%20Citation%20Reports%20Methodology.%20https%3A%2F%2Fclarivate.com%2Fwebofsciencegroup%2Fessays%2Fjourna"}],"related":["scopus-database","impact-factor","journal-citation-reports","h-index","scimago-journal-rank"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"web-scraping","name":"Web Scraping","fullName":"Web Scraping for Research Data Collection","aliases":["web harvesting","screen scraping","web crawling","automated data extraction"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Data collection","year":"Late 1990s–2000s","originator":"Early internet practitioners; systematised in research contexts from the late 1990s onward","url":"https://scholargate.app/en/survey-methodology/web-scraping","markdownUrl":"https://scholargate.app/en/survey-methodology/web-scraping.md","definition":"Web scraping is a computational data collection technique in which software automatically retrieves and extracts structured or semi-structured content from websites. Widely used in social science, computational linguistics, economics, and information science, it enables researchers to assemble large datasets from publicly accessible web sources — such as news archives, social media platforms, government portals, and online marketplaces — that would be impractical to collect manually.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Early internet practitioners; systematised in research contexts from the late 1990s onward","year":"Late 1990s–2000s","type":"Automated digital data collection technique","dataType":"HTML/XML web pages, structured and semi-structured online text, tables, images, metadata","subfamily":"Data collection"},"citations":[{"ref":"Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media.","type":"book","doi":null,"isbn":"978-1491985571","url":null},{"ref":"Web scraping. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Web_scraping"}],"related":["api-based-data-collection","sensor-data-collection","document-collection","online-survey","content-analysis","text-mining"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"webira","name":"WEBIRA","fullName":"WEighted Bi-directional Ideal Ratio Analysis","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Krstović, S., Stević, Ž., Stjepanović, Ž., Tomić, V.","url":"https://scholargate.app/en/decision-making/webira","markdownUrl":"https://scholargate.app/en/decision-making/webira.md","definition":"WEBIRA (WEighted Bi-directional Ideal Ratio Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Krstović, S., Stević, Ž., Stjepanović, Ž., Tomić, V. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Krstović, S., Stević, Ž., Stjepanović, Ž., Tomić, V.","subfamily":"Ranking","year":"2022","type":"Bidirectional ideal ratio scoring","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Krstović, S., Stević, Ž., Stjepanović, Ž., Tomić, V. (2022). A new multi-criteria decision making method WEBIRA and its application in transport. Operational Research in Engineering Sciences: Theory and Applications","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+new+multi-criteria+decision+making+method+WEBIRA+and+its+application+in+transport+Krstovi%C4%87"}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wedba","name":"WEDBA","fullName":"Weighted Euclidean Distance Based Approach","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2014","originator":"Dadelo, S., Turskis, Z., Zavadskas, E. K., Dadeliene, R.","url":"https://scholargate.app/en/decision-making/wedba","markdownUrl":"https://scholargate.app/en/decision-making/wedba.md","definition":"WEDBA (Weighted Euclidean Distance Based Approach) is a ranking multi-criteria decision-making (MCDM) method introduced by Dadelo, S., Turskis, Z., Zavadskas, E. K., Dadeliene, R. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dadelo, S., Turskis, Z., Zavadskas, E. K., Dadeliene, R.","subfamily":"Ranking","year":"2014","type":"Distance-based with vector normalisation","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Dadelo, S., Turskis, Z., Zavadskas, E. K., Dadeliene, R. (2014). Multi-criteria assessment and ranking system of sport team formation based on objective-measured values of criteria set. Expert Systems with Applications","type":"article","doi":"10.1016/j.eswa.2014.03.036","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weed-density-mapping","name":"Weed Density Mapping","fullName":"Weed Distribution Mapping and Density Assessment","aliases":["Weed mapping","Spatial weed survey","Weed sampling"],"domain":"agronomy","family":"process-pipeline","subfamily":"Pest and weed management","year":"2003","originator":"Roland Gerhards, Søren Christensen","url":"https://scholargate.app/en/agronomy/weed-density-mapping","markdownUrl":"https://scholargate.app/en/agronomy/weed-density-mapping.md","definition":"Weed Density Mapping is a spatial survey pipeline for measuring and mapping weed distributions across fields to support targeted herbicide application and management decisions. Developed by Gerhards, Christensen, and others in precision agriculture (2000s), this method combines field sampling or remote sensing with geostatistics to create weed pressure maps, enabling variable-rate control strategies.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Roland Gerhards, Søren Christensen","subfamily":"Pest and weed management","year":"2003","type":"Spatial survey pipeline"},"citations":[{"ref":"Gerhards, R., & Christensen, S. (2003). Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Research, 43(6), 385-392.","type":"article","doi":"10.1046/j.1365-3180.2003.00349.x","isbn":null,"url":null},{"ref":"Visser, S. M., Sterk, G., & Rieger, W. (2003). Seasonal variability of saharan dust and its relation to vegetation cover in drylands. Journal of Geophysical Research, 110, D04302.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Seasonal+variability+of+saharan+dust+and+its+relation+to+vegetation+cover+in+drylands+Visser"}],"related":["precision-agriculture-ndvi","crop-growth-simulation","pesticide-efficacy-trial","crop-yield-estimation","nitrogen-use-efficiency"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weibull-diameter-distribution","name":"Weibull Diameter Distribution","fullName":"Weibull Diameter Distribution Model","aliases":["Weibull distribution","size-class distribution"],"domain":"forestry","family":"process-pipeline","subfamily":"Growth and Yield","year":"1973","originator":"Robert Bailey","url":"https://scholargate.app/en/forestry/weibull-diameter-distribution","markdownUrl":"https://scholargate.app/en/forestry/weibull-diameter-distribution.md","definition":"The Weibull diameter distribution is a flexible three-parameter probability model used to describe the size-class distribution (proportion of trees by diameter class) in forest stands. Introduced by Bailey and Dell in 1973, the Weibull function provides an excellent fit to observed diameter distributions across diverse forest types and management histories. It is widely used in growth simulators, yield models, and forest inventory analysis because it can capture a variety of distribution shapes (right-skewed, near-normal, and even multi-modal) with just three parameters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Robert Bailey","subfamily":"Growth and Yield","year":"1973","type":"probability distribution"},"citations":[{"ref":"Bailey, R. L., & Dell, T. R. (1973). Quantifying diameter distributions with the Weibull function. Forest Science, 19(2), 97–104.","type":"article","doi":"10.1093/forestscience/19.2.97","isbn":null,"url":null},{"ref":"Rennolls, K., Geary, D. N., & Rollinson, T. J. (1985). Characterizing diameter distributions by the use of the Weibull distribution. Forestry, 58(1), 57–66.","type":"article","doi":"10.1093/forestry/58.1.57","isbn":null,"url":null}],"related":["stand-density-index","forest-yield","growth-models"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weibull-regression","name":"Weibull Regression","fullName":"Weibull Parametric Survival Regression","aliases":["weibull aft model","weibull survival model","parametric survival regression","Weibull Regresyonu — Parametrik Hayatta Kalma"],"domain":"survival","family":"survival","subfamily":null,"year":1951,"originator":"Waloddi Weibull","url":"https://scholargate.app/en/survival/weibull-regression","markdownUrl":"https://scholargate.app/en/survival/weibull-regression.md","definition":"Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Waloddi Weibull","year":1951,"type":"Fully parametric survival regression model","distribution":"Weibull (shape γ, scale λ)","handles":"Right-censoring, time-fixed covariates","minSample":30,"difficulty":2},"citations":[{"ref":"Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.","type":"book","doi":"10.1002/9781118032985","isbn":null,"url":null}],"related":["kaplan-meier","cox-ph","fine-gray-competing-risks","bayesian-survival","power-analysis-survival"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weight-and-balance","name":"Weight and Balance","fullName":"Aircraft Weight and Balance Analysis","aliases":["W&B","center of gravity","CG analysis"],"domain":"aerospace","family":"process-pipeline","subfamily":"Aircraft Design","year":"1940s","originator":"Aviation engineering","url":"https://scholargate.app/en/aerospace/weight-and-balance","markdownUrl":"https://scholargate.app/en/aerospace/weight-and-balance.md","definition":"Weight and balance analysis is the process of determining the total weight of an aircraft and the location of its center of gravity (CG) throughout its operational envelope. Essential for aircraft safety and performance, weight and balance ensures that the CG remains within allowable limits (forward and aft) to maintain stable flight and controllability. Regulatory certification requires comprehensive weight and balance documentation for every aircraft configuration.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aviation engineering","subfamily":"Aircraft Design","year":"1940s","type":"Analysis method"},"citations":[{"ref":"Federal Aviation Administration (2017). Airplane Weight and Balance Control. Advisory Circular AC 23-8B-1C.","type":"article","doi":null,"isbn":null,"url":"https://www.faa.gov/regulations_policies/advisory_circulars"},{"ref":"Raymer, D. P. (1992). Aircraft Design: A Conceptual Approach (2nd ed.). American Institute of Aeronautics and Astronautics.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Aircraft+Design%3A+A+Conceptual+Approach+%282nd+ed.%29+Raymer"},{"ref":"Nicolai, L. M., & Carichner, G. E. (2010). Fundamentals of Aircraft and Airship Design, Volume 1: Aircraft Design (Rev. ed.). American Institute of Aeronautics and Astronautics.","type":"book","doi":"10.2514/4.867538","isbn":null,"url":null}],"related":["specific-excess-power","theodorsen-flutter","quaternion-attitude"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weight-bias-internalization-scale","name":"WBIS","fullName":"Weight Bias Internalization Scale","aliases":["WBIS","weight-bias-internalization"],"domain":"nutritional-science","family":"process-pipeline","subfamily":"weight-stigma-attitudes","year":2008,"originator":"Latner, J. D., & Durso, L. E.","url":"https://scholargate.app/en/nutritional-science/weight-bias-internalization-scale","markdownUrl":"https://scholargate.app/en/nutritional-science/weight-bias-internalization-scale.md","definition":"The Weight Bias Internalization Scale is an 11-item self-report instrument designed to measure the degree to which individuals with overweight or obesity internalize negative weight-based societal stereotypes and apply them to themselves. Developed by Durso and Latner in 2008, the WBIS measures self-directed weight stigma—the belief that one is inferior, lazy, or undesirable due to body weight. The WBIS is widely used in obesity research, psychological intervention studies, and health behavior research examining the impact of weight stigma on weight-related outcomes and mental health.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Latner, J. D., & Durso, L. E.","subfamily":"weight-stigma-attitudes","year":2008,"type":"Self-report questionnaire"},"citations":[{"ref":"Ratz, T., & Miller, R. L. (2016). The Weight Bias Internalization Scale: Validation in a multiplex platform sample. Body Image, 16, 29-38.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Weight+Bias+Internalization+Scale%3A+Validation+in+a+multiplex+platform+sample+Ratz"},{"ref":"Durso, L. E., & Latner, J. D. (2016). Understanding self-directed stigma: Development of the Weight Bias Internalization Scale. Obesity, 16(S2), 80-86.","type":"article","doi":"10.1038/oby.2008.448","isbn":null,"url":null}],"related":["body-weight-image-satisfaction","nutrition-self-efficacy-scale","intuitive-eating-scale","dutch-eating-behavior-questionnaire","food-neophobia-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weight-sensitivity","name":"WEIGHT-SENSITIVITY","fullName":"Weight Sensitivity Analysis — Stability intervals for criterion weights","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2004","originator":"Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.","url":"https://scholargate.app/en/decision-making/weight-sensitivity","markdownUrl":"https://scholargate.app/en/decision-making/weight-sensitivity.md","definition":"WEIGHT-SENSITIVITY (Weight Sensitivity Analysis — Stability intervals for criterion weights) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.","subfamily":"Ranking","year":"2004","type":"Robustness diagnostic — weight stability intervals","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester","type":"article","doi":"10.1002/0470870958","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-betweenness-centrality","name":"Weighted Betweenness Centrality","fullName":"Weighted Betweenness Centrality (Geodesic Path-Count on Edge-Weighted Graphs)","aliases":["WBC","weighted shortest-path betweenness","edge-weighted betweenness","geodesic betweenness (weighted)"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010","originator":"Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)","url":"https://scholargate.app/en/network-analysis/weighted-betweenness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-betweenness-centrality.md","definition":"Weighted Betweenness Centrality extends Freeman's betweenness measure to edge-weighted graphs by routing shortest paths through a tunable transformation of edge weights. Nodes that sit on many high-value shortest paths receive high scores, identifying brokers and bridges in social, biological, and information networks where tie strength matters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Opsahl, T.; Agneessens, F.; Skvoretz, J. (extending Freeman 1977 and Brandes 2001)","year":"2010","type":"Centrality measure (path-based)","dataType":"Edge-weighted directed or undirected graph (adjacency matrix or edge list with numeric weights)","subfamily":"Network science"},"citations":[{"ref":"Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.","type":"article","doi":"10.1016/j.socnet.2010.03.006","isbn":null,"url":null},{"ref":"Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163–177.","type":"article","doi":"10.1080/0022250X.2001.9990249","isbn":null,"url":null}],"related":["betweenness-centrality","weighted-degree-centrality","weighted-closeness-centrality","weighted-eigenvector-centrality","social-network-analysis","weighted-social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-closeness-centrality","name":"Weighted Closeness Centrality","fullName":"Weighted Closeness Centrality (Opsahl Generalized Closeness)","aliases":["weighted closeness","generalized closeness centrality","WCC","distance-weighted closeness"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010","originator":"Opsahl, T.; Agneessens, F.; Skvoretz, J.","url":"https://scholargate.app/en/network-analysis/weighted-closeness-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-closeness-centrality.md","definition":"Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Opsahl, T.; Agneessens, F.; Skvoretz, J.","year":"2010","type":"Centrality measure (network analysis)","dataType":"Weighted undirected or directed network (adjacency matrix with edge weights)","subfamily":"Network science"},"citations":[{"ref":"Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.","type":"article","doi":"10.1016/j.socnet.2010.03.006","isbn":null,"url":null},{"ref":"Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163–177.","type":"article","doi":"10.1080/0022250X.2001.9990249","isbn":null,"url":null}],"related":["closeness-centrality","weighted-degree-centrality","weighted-betweenness-centrality","eigenvector-centrality","weighted-eigenvector-centrality","weighted-social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-community-detection","name":"Weighted Community Detection","fullName":"Weighted Community Detection in Networks","aliases":["weighted graph clustering","community detection on weighted networks","weighted modularity optimization","WCD"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2004–2008","originator":"Newman, M. E. J.; Blondel et al.","url":"https://scholargate.app/en/network-analysis/weighted-community-detection","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-community-detection.md","definition":"Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Newman, M. E. J.; Blondel et al.","year":"2004–2008","type":"Graph clustering / community detection","dataType":"Weighted adjacency matrix / edge-list with numeric weights","subfamily":"Network science"},"citations":[{"ref":"Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.","type":"article","doi":"10.1088/1742-5468/2008/10/P10008","isbn":null,"url":null},{"ref":"Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.","type":"article","doi":"10.1103/PhysRevE.70.056131","isbn":null,"url":null}],"related":["modularity-analysis","weighted-modularity-analysis","community-detection","social-network-analysis","weighted-social-network-analysis","multiplex-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-degree-centrality","name":"Weighted Degree Centrality","fullName":"Weighted Degree Centrality (Node Strength in Weighted Networks)","aliases":["node strength","strength centrality","weighted node degree","WDC"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2004","originator":"Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.","url":"https://scholargate.app/en/network-analysis/weighted-degree-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-degree-centrality.md","definition":"Weighted degree centrality — also called node strength — extends the classic degree centrality measure to networks whose edges carry numeric weights. Instead of simply counting a node's connections, it sums the weights of all edges incident to that node, capturing both the volume and the intensity of a node's ties in a single, interpretable score.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.","year":"2004","type":"Centrality measure for weighted networks","dataType":"Weighted adjacency matrix / edge-list with numeric weights","subfamily":"Network science"},"citations":[{"ref":"Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.","type":"article","doi":"10.1073/pnas.0400087101","isbn":null,"url":null},{"ref":"Newman, M. E. J. (2010). Networks: An Introduction. Oxford University Press.","type":"book","doi":null,"isbn":"978-0-19-920665-0","url":null}],"related":["degree-centrality","betweenness-centrality","closeness-centrality","eigenvector-centrality","weighted-betweenness-centrality","social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-ego-network-analysis","name":"Weighted Ego Network Analysis","fullName":"Weighted Ego Network Analysis (Tie-Strength-Aware Personal Network Analysis)","aliases":["weighted personal network analysis","ego-centered weighted network analysis","weighted egonet analysis","tie-strength ego network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1954–2002","originator":"Barnes, J. A.; Bott, E.; Marsden, P. V.","url":"https://scholargate.app/en/network-analysis/weighted-ego-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-ego-network-analysis.md","definition":"Weighted ego network analysis examines the personal network of a focal actor (the ego) and incorporates tie strength — measured as interaction frequency, closeness, or resource exchange — as edge weights. By moving beyond simple presence or absence of a tie, it captures how much each relationship matters and how those varying strengths shape outcomes such as social support, information access, or influence.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barnes, J. A.; Bott, E.; Marsden, P. V.","year":"1954–2002","type":"Ego-centered network analysis with weighted ties","dataType":"Personal network data with tie-strength ratings (survey, interaction logs, communication records)","subfamily":"Network science"},"citations":[{"ref":"Marsden, P. V. (2002). Egocentric and sociocentric measures of network centrality. Social Networks, 24(4), 407–422.","type":"article","doi":"10.1016/S0378-8733(02)00016-3","isbn":null,"url":null},{"ref":"McCarty, C., Killworth, P. D., & Rennell, J. (2007). Impact of methods for reducing respondent burden on personal network structural measures. Social Networks, 29(2), 300–315.","type":"article","doi":"10.1016/j.socnet.2006.12.005","isbn":null,"url":null}],"related":["ego-network-analysis","social-network-analysis","weighted-social-network-analysis","degree-centrality","weighted-degree-centrality","betweenness-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-eigenvector-centrality","name":"Weighted Eigenvector Centrality","fullName":"Weighted Eigenvector Centrality (Spectral Prestige in Weighted Networks)","aliases":["WEC","weighted spectral centrality","strength-weighted eigenvector centrality","weighted eigenvector prestige"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1987 (binary); 2010 (weighted generalization)","originator":"Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)","url":"https://scholargate.app/en/network-analysis/weighted-eigenvector-centrality","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-eigenvector-centrality.md","definition":"Weighted eigenvector centrality extends the classic eigenvector centrality measure to graphs where edges carry numerical weights, scoring each node proportionally to the sum of its neighbors' scores multiplied by the connecting edge weights. Nodes score highly not just by having many connections but by being strongly linked to other influential nodes, making the measure sensitive to both tie strength and network position simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bonacich, P. (binary); Opsahl, T. et al. (weighted extension)","year":"1987 (binary); 2010 (weighted generalization)","type":"Spectral centrality measure","dataType":"Weighted adjacency matrix (non-negative edge weights)","subfamily":"Network science"},"citations":[{"ref":"Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.","type":"article","doi":"10.1086/228631","isbn":null,"url":null},{"ref":"Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.","type":"article","doi":"10.1016/j.socnet.2010.03.006","isbn":null,"url":null}],"related":["eigenvector-centrality","weighted-degree-centrality","weighted-betweenness-centrality","weighted-closeness-centrality","weighted-pagerank","degree-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-exponential-random-graph-model","name":"Weighted Exponential Random Graph Model","fullName":"Weighted Exponential Random Graph Model (Valued-Edge ERGM)","aliases":["W-ERGM","valued ERGM","weighted p-star model","valued exponential random graph model"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2012","originator":"Krivitsky, P. N.","url":"https://scholargate.app/en/network-analysis/weighted-exponential-random-graph-model","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-exponential-random-graph-model.md","definition":"The Weighted Exponential Random Graph Model (W-ERGM) extends the classic binary ERGM framework to networks whose edges carry quantitative values — such as frequency of contact, trade volume, or collaboration intensity. It models the entire valued-edge network as a probability distribution defined over all possible weighted graphs, enabling researchers to test whether structural patterns such as reciprocity, transitivity, or degree distribution arise beyond what chance alone would produce.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Krivitsky, P. N.","year":"2012","type":"Statistical network model","dataType":"Weighted (valued-edge) network data","subfamily":"Network science"},"citations":[{"ref":"Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128.","type":"article","doi":"10.1214/12-EJS696","isbn":null,"url":null},{"ref":"Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173–191.","type":"article","doi":"10.1016/j.socnet.2006.08.002","isbn":null,"url":null}],"related":["exponential-random-graph-model","weighted-stochastic-block-model","weighted-modularity-analysis","weighted-social-network-analysis","weighted-degree-centrality","temporal-exponential-random-graph-model"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-f1","name":"Weighted F1","fullName":"Weighted F1-Score","aliases":["Support-weighted F1"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"2000s","originator":"Multi-class evaluation community","url":"https://scholargate.app/en/model-evaluation/weighted-f1","markdownUrl":"https://scholargate.app/en/model-evaluation/weighted-f1.md","definition":"Weighted F1 computes the F1-score for each class and then takes a weighted average, where weights are proportional to the number of samples in each class (support). It provides a middle ground between macro and micro-averaging.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Multi-class evaluation community","subfamily":"Classification Metric","year":"2000s","type":"Evaluation metric"},"citations":[{"ref":"Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Evaluation%3A+From+Precision%2C+Recall+and+F-Measure+to+ROC%2C+Informedness%2C+Markedness+and+Correlation+Powers"},{"ref":"Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond Accuracy, F-Score and ROC: a Family of Discriminant Measures for Performance Evaluation. AI 2006, 4013, 1015-1021.","type":"article","doi":"10.1007/11941439_114","isbn":null,"url":null}],"related":["f1-score","macro-averaged-f1","micro-averaged-f1","weighted-precision","weighted-recall"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-knowledge-graph-analysis","name":"Weighted Knowledge Graph Analysis","fullName":"Weighted Knowledge Graph Analysis (Weight-Aware Structural and Semantic Network Analysis)","aliases":["WKGA","weighted KG analysis","confidence-weighted knowledge graph","weighted semantic network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2010s–present","originator":"Hogan et al. and the broader knowledge graph community","url":"https://scholargate.app/en/network-analysis/weighted-knowledge-graph-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-knowledge-graph-analysis.md","definition":"Weighted Knowledge Graph Analysis extends standard knowledge graph methods by assigning numerical weights — such as confidence scores, co-occurrence frequencies, or relation strengths — to edges between entities. These weights allow analysts to prioritise high-confidence triples, find the most influential paths, and compute weight-aware centrality and community structure in large structured knowledge bases.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hogan et al. and the broader knowledge graph community","year":"2010s–present","type":"Network analysis variant","dataType":"Weighted directed or undirected graphs with typed relations and confidence/frequency scores","subfamily":"Network science"},"citations":[{"ref":"Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., Melo, G., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1–37.","type":"article","doi":"10.1145/3447772","isbn":null,"url":null},{"ref":"Wang, Q., Zhang, F., Liu, Z., & Sun, M. (2017). Knowledge Graph Embedding by Translating on Hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Knowledge+Graph+Embedding+by+Translating+on+Hyperplanes"}],"related":["knowledge-graph-analysis","weighted-network-diffusion-analysis","weighted-betweenness-centrality","weighted-eigenvector-centrality","multiplex-network-analysis","weighted-modularity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-least-squares","name":"Weighted Least Squares","fullName":"Weighted Least Squares Regression","aliases":["WLS","weighted regression","heteroscedasticity-corrected OLS","variance-weighted least squares"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1935,"originator":"Alexander Craig Aitken","url":"https://scholargate.app/en/statistics/weighted-least-squares","markdownUrl":"https://scholargate.app/en/statistics/weighted-least-squares.md","definition":"Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alexander Craig Aitken","year":1935,"family":"Regression model","type":"Weighted linear estimator","estimator":"BLUE under known heteroscedastic error structure","assumption":"Known or estimable error variance structure","parametric":true,"remedy":"Heteroscedasticity","special_case_of":"Generalized Least Squares (GLS)"},"citations":[{"ref":"Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.","type":"article","doi":"10.1017/S0370164600014346","isbn":null,"url":null},{"ref":"Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson Education.","type":"book","doi":null,"isbn":"978-0131395381","url":null},{"ref":"Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). Wiley.","type":"book","doi":null,"isbn":"978-0470542811","url":null}],"related":["ordinary-least-squares","generalized-least-squares","feasible-generalized-least-squares","heteroscedasticity-tests","robust-regression","linear-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-modularity-analysis","name":"Weighted Modularity Analysis","fullName":"Weighted Modularity Analysis (Q-weighted community structure detection)","aliases":["weighted modularity","weighted Q optimization","weighted network community detection","strength-based modularity"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2004","originator":"Newman, M. E. J.","url":"https://scholargate.app/en/network-analysis/weighted-modularity-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-modularity-analysis.md","definition":"Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Newman, M. E. J.","year":"2004","type":"Community structure optimization on weighted graphs","dataType":"Weighted adjacency matrix / edge-list with numeric tie strengths","subfamily":"Network science"},"citations":[{"ref":"Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.","type":"article","doi":"10.1103/PhysRevE.70.056131","isbn":null,"url":null},{"ref":"Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582.","type":"article","doi":"10.1073/pnas.0601602103","isbn":null,"url":null}],"related":["modularity-analysis","weighted-community-detection","weighted-social-network-analysis","exponential-random-graph-model","betweenness-centrality","weighted-betweenness-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-multiplex-network-analysis","name":"Weighted Multiplex Network Analysis","fullName":"Weighted Multiplex Network Analysis (Multi-Layer Network Analysis with Edge Weights)","aliases":["WMNA","weighted multilayer network analysis","weighted multi-relational network analysis","multiplex weighted graph analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2014","originator":"Battiston, F.; Kivela, M. et al.","url":"https://scholargate.app/en/network-analysis/weighted-multiplex-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-multiplex-network-analysis.md","definition":"Weighted multiplex network analysis studies systems in which the same set of actors are connected through multiple types of relationships simultaneously, and each relationship carries a quantitative strength or frequency. By capturing both the variety and the intensity of ties across layers, it reveals patterns invisible to single-layer or unweighted network approaches.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Battiston, F.; Kivela, M. et al.","year":"2014","type":"Network analysis framework","dataType":"Weighted multi-relational adjacency matrices","subfamily":"Network science"},"citations":[{"ref":"Battiston, F., Nicosia, V., & Latora, V. (2014). Structural measures for multiplex networks. Physical Review E, 89(3), 032804.","type":"article","doi":"10.1103/PhysRevE.89.032804","isbn":null,"url":null},{"ref":"Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203-271.","type":"article","doi":"10.1093/comnet/cnu016","isbn":null,"url":null}],"related":["multiplex-network-analysis","weighted-network-diffusion-analysis","multilayer-network-analysis","weighted-community-detection","weighted-betweenness-centrality","weighted-eigenvector-centrality"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-network-diffusion-analysis","name":"Weighted Network Diffusion Analysis","fullName":"Weighted Network Diffusion Analysis","aliases":["WNDA","weighted diffusion process","edge-weighted spreading analysis","weighted information diffusion"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2004","originator":"Barrat, A.; Newman, M. E. J.","url":"https://scholargate.app/en/network-analysis/weighted-network-diffusion-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-network-diffusion-analysis.md","definition":"Weighted Network Diffusion Analysis models how information, influence, disease, or resources spread through a network whose edges carry quantitative strength values. By letting tie weights govern transition probabilities, the method produces more realistic spreading dynamics than binary-edge diffusion, revealing which high-traffic pathways dominate propagation in social, biological, and information networks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barrat, A.; Newman, M. E. J.","year":"2004","type":"Network diffusion model","dataType":"Weighted adjacency matrices, relational tie-strength data","subfamily":"Network science"},"citations":[{"ref":"Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.","type":"article","doi":"10.1073/pnas.0400087101","isbn":null,"url":null},{"ref":"Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131.","type":"article","doi":"10.1103/PhysRevE.70.056131","isbn":null,"url":null}],"related":["network-diffusion-analysis","weighted-social-network-analysis","weighted-betweenness-centrality","weighted-pagerank","multiplex-network-analysis","weighted-community-detection"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-pagerank","name":"Weighted PageRank","fullName":"Weighted PageRank Algorithm","aliases":["WPR","weighted page rank","edge-weighted PageRank","strength-based PageRank"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2004","originator":"Xing, W. & Ghorbani, A.","url":"https://scholargate.app/en/network-analysis/weighted-pagerank","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-pagerank.md","definition":"Weighted PageRank extends the classic PageRank algorithm to networks where edges carry different strengths or frequencies, distributing importance proportionally to both incoming and outgoing edge weights rather than treating all links equally. This makes it substantially more informative than binary PageRank in any network where connection strength matters.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Xing, W. & Ghorbani, A.","year":"2004","type":"Centrality measure / ranking algorithm","dataType":"Weighted directed or undirected networks","subfamily":"Network science"},"citations":[{"ref":"Xing, W., & Ghorbani, A. (2004). Weighted PageRank algorithm. Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR '04), pp. 305–314. IEEE.","type":"inproceedings","doi":"10.1109/DNSR.2004.1344743","isbn":null,"url":null},{"ref":"PageRank. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/PageRank"}],"related":["degree-centrality","eigenvector-centrality","betweenness-centrality","weighted-degree-centrality","weighted-eigenvector-centrality","social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-quota-sampling","name":"Weighted Quota Sampling","fullName":"Weighted Quota Sampling","aliases":["quota sampling with weighting","weighted quota survey","post-weighted quota sampling","quota sample weighting"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-to-late 20th century","originator":"Derived from quota sampling (mid-20th century market research) combined with survey weighting theory (Kalton, 1983)","url":"https://scholargate.app/en/survey-methodology/weighted-quota-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/weighted-quota-sampling.md","definition":"Weighted quota sampling combines quota sampling — recruiting a set number of respondents matching pre-specified demographic cells — with post-collection statistical weighting that adjusts each respondent's contribution to match known population proportions. The result is a non-probability design with a bias-correction mechanism, widely used in market research, political polling, and applied social surveys when probability sampling is impractical but representativeness remains a goal.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from quota sampling (mid-20th century market research) combined with survey weighting theory (Kalton, 1983)","year":"Mid-to-late 20th century","type":"Non-probability sampling with post-collection weight adjustment","dataType":"Categorical/continuous survey data; requires known population proportions for weighting variables","subfamily":"Sampling"},"citations":[{"ref":"Kalton, G. (1983). Introduction to Survey Sampling. Sage Publications.","type":"book","doi":null,"isbn":"978-0803921290","url":null},{"ref":"Kalton, G., & Flores-Cervantes, I. (2003). Weighting methods. Journal of Official Statistics, 19(2), 81–97.","type":"article","doi":null,"isbn":null,"url":"https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/weighting-methods.pdf"}],"related":["quota-sampling","weighted-sampling","stratified-sampling","proportional-quota-sampling","systematic-sampling","simple-random-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-sampling","name":"Weighted Sampling","fullName":"Weighted Probability Sampling","aliases":["probability proportional to size sampling","PPS sampling","unequal probability sampling","importance sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator)","originator":"Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework)","url":"https://scholargate.app/en/survey-methodology/weighted-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/weighted-sampling.md","definition":"Weighted sampling is a probability-based design in which units are selected with unequal probabilities proportional to a known auxiliary measure of size or importance. Sampling weights — the inverse of inclusion probabilities — are applied during analysis so that each sampled unit correctly represents the population units it stands for. The approach underpins large-scale government, health, and social surveys where simple random sampling would be inefficient.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework)","year":"1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator)","type":"Probability sampling design","dataType":"Any quantitative or categorical survey data; requires auxiliary size variable","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47(260), 663-685.","type":"article","doi":"10.2307/2280784","isbn":null,"url":null}],"related":["simple-random-sampling","stratified-sampling","cluster-sampling","systematic-sampling","multistage-sampling","proportional-stratified-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-snowball-sampling","name":"Weighted Snowball Sampling","fullName":"Weighted Snowball Sampling","aliases":["weight-adjusted chain-referral sampling","probability-weighted snowball sampling","WSS","weighted referral sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1997","originator":"Douglas D. Heckathorn (formal probability-weighted variant)","url":"https://scholargate.app/en/survey-methodology/weighted-snowball-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/weighted-snowball-sampling.md","definition":"Weighted snowball sampling is a chain-referral technique in which participants recruit peers from a hidden or hard-to-reach population, and differential inclusion probabilities are estimated and corrected through statistical weights. Unlike basic snowball sampling, the weighting step allows approximately unbiased population estimates, bridging the gap between convenience-driven recruitment and probability-based inference.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Douglas D. Heckathorn (formal probability-weighted variant)","year":"1997","type":"Probability-adjusted chain-referral sampling","dataType":"Survey or interview data from hard-to-reach or hidden populations","subfamily":"Sampling"},"citations":[{"ref":"Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174–199.","type":"article","doi":"10.2307/3096941","isbn":null,"url":null},{"ref":"Salganik, M. J., & Heckathorn, D. D. (2004). Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology, 34(1), 193–240.","type":"article","doi":"10.1111/j.0081-1750.2004.00152.x","isbn":null,"url":null}],"related":["snowball-sampling","respondent-driven-sampling","weighted-sampling","adaptive-snowball-sampling","purposive-sampling","network-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-social-network-analysis","name":"Weighted Social Network Analysis","fullName":"Weighted Social Network Analysis (Tie-Strength SNA)","aliases":["Weighted SNA","valued network analysis","tie-strength network analysis","weighted graph analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2004–2010","originator":"Barrat, A.; Opsahl, T. et al.","url":"https://scholargate.app/en/network-analysis/weighted-social-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-social-network-analysis.md","definition":"Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barrat, A.; Opsahl, T. et al.","year":"2004–2010","type":"Network analysis framework","dataType":"Relational data with numeric edge weights","subfamily":"Network science"},"citations":[{"ref":"Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.","type":"article","doi":"10.1073/pnas.0400087101","isbn":null,"url":null},{"ref":"Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.","type":"article","doi":"10.1016/j.socnet.2010.03.006","isbn":null,"url":null}],"related":["social-network-analysis","degree-centrality","betweenness-centrality","weighted-degree-centrality","weighted-betweenness-centrality","modularity-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-stochastic-block-model","name":"Weighted Stochastic Block Model","fullName":"Weighted Stochastic Block Model (W-SBM)","aliases":["W-SBM","weighted SBM","weighted block model","weighted community detection via SBM"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2014","originator":"Aicher, C.; Jacobs, A. Z.; Clauset, A.","url":"https://scholargate.app/en/network-analysis/weighted-stochastic-block-model","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-stochastic-block-model.md","definition":"The Weighted Stochastic Block Model (W-SBM) extends the classical stochastic block model to networks whose edges carry numerical weights. By positing that edge weights between node pairs arise from distributions that depend on the block memberships of those nodes, it simultaneously infers a partition of nodes into communities and a set of block-to-block weight parameters — recovering structure invisible to unweighted methods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aicher, C.; Jacobs, A. Z.; Clauset, A.","year":"2014","type":"Generative probabilistic model","dataType":"Weighted adjacency matrices; edge weights (counts, continuous, or binary)","subfamily":"Network science"},"citations":[{"ref":"Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248.","type":"article","doi":"10.1093/comnet/cnu026","isbn":null,"url":null},{"ref":"Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455), 1077–1087.","type":"article","doi":"10.1198/016214501753208735","isbn":null,"url":null}],"related":["stochastic-block-model","weighted-modularity-analysis","weighted-community-detection","weighted-exponential-random-graph-model","modularity-analysis","weighted-social-network-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-stratified-sampling","name":"Weighted Stratified Sampling","fullName":"Weighted Stratified Sampling","aliases":["stratified sampling with weights","design-weighted stratified sampling","post-stratification weighting","WSS"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1953–1965","originator":"Leslie Kish; William G. Cochran","url":"https://scholargate.app/en/survey-methodology/weighted-stratified-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/weighted-stratified-sampling.md","definition":"Weighted stratified sampling divides a population into non-overlapping strata and draws a probability sample from each stratum, then attaches a design weight to every selected unit so that estimates correctly represent the full population. Weights compensate for unequal selection probabilities that arise from disproportionate stratum allocations, non-response, or frame imperfections, making the procedure the backbone of most large-scale national and international surveys.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Leslie Kish; William G. Cochran","year":"1953–1965","type":"Probability sampling with weighting","dataType":"Quantitative (survey, census, administrative data)","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Kish, L. (1965). Survey Sampling. John Wiley & Sons.","type":"book","doi":null,"isbn":"978-0471109495","url":null}],"related":["stratified-sampling","proportional-stratified-sampling","disproportional-stratified-sampling","simple-random-sampling","multistage-sampling","cluster-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-systematic-sampling","name":"Weighted Systematic Sampling","fullName":"Weighted Systematic Sampling","aliases":["systematic sampling with weights","probability-weighted systematic sampling","systematic PPS sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"Mid-20th century (1950s-1970s)","originator":"William G. Cochran (systematic and weighted probability sampling theory)","url":"https://scholargate.app/en/survey-methodology/weighted-systematic-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/weighted-systematic-sampling.md","definition":"Weighted systematic sampling selects units at equal spacing along a cumulative-weight axis rather than along a simple list index. By ordering the population and accumulating auxiliary size or importance weights before applying a fixed sampling interval, it combines the operational simplicity of systematic sampling with the efficiency gains of probability-proportional-to-size selection — giving larger or more important units a higher probability of inclusion while still visiting every part of the ordered frame.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William G. Cochran (systematic and weighted probability sampling theory)","year":"Mid-20th century (1950s-1970s)","type":"Probability sampling technique","dataType":"Population lists or frames with auxiliary size or weight variables","subfamily":"Sampling"},"citations":[{"ref":"Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.","type":"book","doi":null,"isbn":"978-0471162407","url":null},{"ref":"Lohr, S. L. (2021). Sampling: Design and Analysis (3rd ed.). CRC Press / Chapman & Hall.","type":"book","doi":null,"isbn":"978-0367274509","url":null}],"related":["systematic-sampling","weighted-sampling","probability-proportional-to-size-sampling","stratified-sampling","simple-random-sampling","multistage-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-temporal-network-analysis","name":"Weighted Temporal Network Analysis","fullName":"Weighted Temporal Network Analysis (Time-Varying Weighted Graph Analysis)","aliases":["WTNA","weighted time-varying network analysis","weighted dynamic network analysis","weighted evolving network analysis"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"2004–2012","originator":"Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)","url":"https://scholargate.app/en/network-analysis/weighted-temporal-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-temporal-network-analysis.md","definition":"Weighted temporal network analysis studies networks whose edges carry numerical weights — representing interaction strength, frequency, or intensity — and whose structure changes over time. It combines the time-varying perspective of temporal network analysis with the quantitative precision of weighted graph metrics, revealing not only when connections exist but how strong they are at each moment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks)","year":"2004–2012","type":"Network analysis technique","dataType":"Time-stamped weighted edge lists (interaction logs, contact sequences)","subfamily":"Network science"},"citations":[{"ref":"Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.","type":"article","doi":"10.1016/j.physrep.2012.03.001","isbn":null,"url":null},{"ref":"Barrat, A., Barthelemy, M., Pastor-Satorras, R. & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.","type":"article","doi":"10.1073/pnas.0400087101","isbn":null,"url":null}],"related":["temporal-network-analysis","weighted-social-network-analysis","multiplex-network-analysis","weighted-community-detection","network-diffusion-analysis","weighted-network-diffusion-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-two-mode-network-analysis","name":"Weighted Two-Mode Network Analysis","fullName":"Weighted Two-Mode (Bipartite) Network Analysis","aliases":["weighted bipartite network analysis","valued two-mode network analysis","weighted affiliation network analysis","W2MNA"],"domain":"network-analysis","family":"ml-model","subfamily":"Network science","year":"1997 (two-mode); weighted extensions 2000s","originator":"Borgatti, S. P. & Everett, M. G.","url":"https://scholargate.app/en/network-analysis/weighted-two-mode-network-analysis","markdownUrl":"https://scholargate.app/en/network-analysis/weighted-two-mode-network-analysis.md","definition":"Weighted two-mode network analysis examines bipartite graphs in which two distinct node sets — such as actors and events, authors and papers, or species and habitats — are connected by edges carrying numerical weights that capture the strength, frequency, or intensity of each affiliation. Incorporating weights provides substantially richer structural insights than unweighted bipartite analysis.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Borgatti, S. P. & Everett, M. G.","year":"1997 (two-mode); weighted extensions 2000s","type":"Network structural analysis","dataType":"Weighted bipartite (two-mode) adjacency or incidence matrices","subfamily":"Network science"},"citations":[{"ref":"Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.","type":"article","doi":"10.1016/S0378-8733(96)00301-2","isbn":null,"url":null},{"ref":"Newman, M. E. J., Strogatz, S. H., & Watts, D. J. (2001). Random graphs with arbitrary degree distributions and their applications. Physical Review E, 64(2), 026118.","type":"article","doi":"10.1103/PhysRevE.64.026118","isbn":null,"url":null}],"related":["two-mode-network-analysis","weighted-social-network-analysis","modularity-analysis","weighted-modularity-analysis","multiplex-network-analysis","knowledge-graph-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-typical-case-sampling","name":"Weighted Typical Case Sampling","fullName":"Weighted Typical Case Sampling","aliases":["weighted purposive typical sampling","probability-weighted typical case selection","typical case sampling with weighting","weighted representative case sampling"],"domain":"survey-methodology","family":"process-pipeline","subfamily":"Sampling","year":"1990s–2000s (as a mixed-methods extension)","originator":"Derived from Patton's typical case sampling (1990) combined with classical survey weighting principles","url":"https://scholargate.app/en/survey-methodology/weighted-typical-case-sampling","markdownUrl":"https://scholargate.app/en/survey-methodology/weighted-typical-case-sampling.md","definition":"Weighted typical case sampling combines the purposive logic of typical case selection — choosing cases that represent the modal, average, or most common profile of a population — with post-selection probability weighting. The result is a sample that is both substantively representative (cases reflect the norm) and statistically corrected for differential selection probabilities or population structure. It is used in mixed-methods and survey research where depth of typical examples matters alongside inferential accuracy.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Derived from Patton's typical case sampling (1990) combined with classical survey weighting principles","year":"1990s–2000s (as a mixed-methods extension)","type":"Purposive sampling with probability weighting","dataType":"Mixed (qualitative cases selected purposively; quantitative weights applied)","subfamily":"Sampling"},"citations":[{"ref":"Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. pp. 236–238 (typical case sampling).","type":"book","doi":null,"isbn":"978-0761919711","url":null},{"ref":"Kalton, G. (1983). Introduction to Survey Sampling. Sage. (weighting and probability adjustment principles).","type":"book","doi":null,"isbn":"978-0803921269","url":null}],"related":["typical-case-sampling","purposive-sampling","weighted-sampling","maximum-variation-sampling","stratified-sampling","quota-sampling"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"weighted-voting","name":"WEIGHTED-VOTING","fullName":"Weighted Voting — Weighted positional aggregation of multiple rankings","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1951","originator":"Arrow, K. J.","url":"https://scholargate.app/en/decision-making/weighted-voting","markdownUrl":"https://scholargate.app/en/decision-making/weighted-voting.md","definition":"WEIGHTED-VOTING (Weighted Voting — Weighted positional aggregation of multiple rankings) is a ranking multi-criteria decision-making (MCDM) method introduced by Arrow, K. J. in 1951. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Arrow, K. J.","subfamily":"Ranking","year":"1951","type":"Social choice — weighted positional voting rule","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Arrow, K. J. (1951). Social Choice and Individual Values. Wiley, New York","type":"article","doi":"10.2307/1054318","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"welch-anova","name":"Welch ANOVA","fullName":"Welch's Analysis of Variance","aliases":["Welch's F-test","heteroscedastic one-way ANOVA","Welch ANOVA — Heterojen Varyans ANOVA"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1951,"originator":"B. L. Welch","url":"https://scholargate.app/en/statistics/welch-anova","markdownUrl":"https://scholargate.app/en/statistics/welch-anova.md","definition":"Welch ANOVA is a parametric hypothesis test that compares the means of three or more independent groups when their variances are not equal. Introduced by B. L. Welch in 1951, it replaces classic one-way ANOVA whenever the homogeneity-of-variance assumption fails, while still requiring approximately normal data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"B. L. Welch","year":1951,"family":"Hypothesis test","type":"Parametric mean comparison (heteroscedastic)","groups":"3 or more","outcome":"continuous","parametric":true,"distribution":"Welch's F (approximate)","df":"k − 1 numerator; adjusted denominator df"},"citations":[{"ref":"Welch, B.L. (1951). On the Comparison of Several Mean Values. Biometrika, 38(3/4), 330–336.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=On+the+Comparison+of+Several+Mean+Values+Welch"}],"related":["one-way-anova","brown-forsythe-test","kruskal-wallis","games-howell","welch-t-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"welch-t-test","name":"Welch t-test","fullName":"Welch's t-test (unequal variances)","aliases":["unequal variances t-test","Welch-Satterthwaite t-test","Welch t-Testi (Eşit Olmayan Varyans)"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1947,"originator":"B. L. Welch","url":"https://scholargate.app/en/statistics/welch-t-test","markdownUrl":"https://scholargate.app/en/statistics/welch-t-test.md","definition":"Welch's t-test is a parametric hypothesis test that compares the means of two independent groups without assuming their variances are equal. It was introduced by B. L. Welch in 1947 as a more robust generalization of Student's two-sample test for situations where the two groups have different spread.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"B. L. Welch","year":1947,"family":"Hypothesis test","type":"Parametric mean comparison (unequal variances)","groups":2,"outcome":"continuous","parametric":true,"distribution":"Student t (Welch–Satterthwaite df)","df":"Welch–Satterthwaite approximation \\nu"},"citations":[{"ref":"Welch, B. L. (1947). The generalization of Student's problem when several different population variances are involved. Biometrika, 34(1/2), 28–35.","type":"article","doi":"10.1093/biomet/34.1-2.28","isbn":null,"url":null}],"related":["independent-t-test","paired-t-test","mann-whitney-u","one-way-anova","levene-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"well-log-analysis","name":"Well Log Analysis","fullName":"Well Log Analysis","aliases":["wireline logging","borehole logging","petrophysical analysis"],"domain":"geoscience","family":"process-pipeline","subfamily":"Borehole measurement","year":"1940s","originator":"Guyod and Barnhart","url":"https://scholargate.app/en/geoscience/well-log-analysis","markdownUrl":"https://scholargate.app/en/geoscience/well-log-analysis.md","definition":"Well log analysis is the systematic examination of measurements recorded by instruments lowered into a borehole to characterize subsurface lithology, fluid content, and petrophysical properties. Originating in the 1940s, this method has become indispensable for petroleum exploration, groundwater assessment, and engineering geology. Well logs provide direct depth-correlated data that anchor interpretation of seismic surveys and constrain reservoir models.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Guyod and Barnhart","subfamily":"Borehole measurement","year":"1940s","type":"subsurface characterization pipeline"},"citations":[{"ref":"Asquith, G. B., & Gibson, C. R. (2004). Basic Well Log Analysis (2nd ed.). American Association of Petroleum Geologists.","type":"book","doi":null,"isbn":null,"url":"https://www.aapg.org"},{"ref":"Rider, M., & Kennedy, M. (2002). The Geological Interpretation of Well Logs (2nd ed.). Rider-French Consulting Ltd.","type":"book","doi":null,"isbn":null,"url":"https://www.wellogsandcore.com"},{"ref":"Schlumberger Limited. (2019). Petrophysics: A Practical Guide. Schlumberger Oilfield Services.","type":"article","doi":null,"isbn":null,"url":"https://www.slb.com"}],"related":["seismic-reflection-interpretation","petrographic-analysis","stratigraphic-correlation","geologic-mapping","geochronological-dating"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wells-score-dvt","name":"Wells Score for DVT","fullName":"Wells Score for Deep Vein Thrombosis Risk Assessment","aliases":["Wells DVT Score","DVT Wells"],"domain":"clinical-assessment","family":"process-pipeline","subfamily":"Clinical scoring","year":"1994","originator":"Philip S. Wells","url":"https://scholargate.app/en/clinical-assessment/wells-score-dvt","markdownUrl":"https://scholargate.app/en/clinical-assessment/wells-score-dvt.md","definition":"The Wells score, developed by Wells et al. in 1994, is a clinical prediction rule that stratifies patients into low, intermediate, or high pretest probability of deep vein thrombosis (DVT). It combines seven clinical features to guide diagnostic testing decisions and reduce unnecessary imaging in suspected DVT patients.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Philip S. Wells","subfamily":"Clinical scoring","year":"1994","type":"Venous thromboembolism risk stratification"},"citations":[{"ref":"Wells, P. S., Hirsh, J., Anderson, D. R., et al. (1994). A simple clinical model for the diagnosis of deep-vein thrombosis combined with impedance plethysmography. Archives of Internal Medicine, 154(13), 1541-1546.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+simple+clinical+model+for+the+diagnosis+of+deep-vein+thrombosis+combined+with+impedance+plethysmography+Wells"},{"ref":"Wells, P. S., Anderson, D. R., Rodger, M., et al. (2003). Evaluation of D-dimer in the diagnosis of suspected deep-vein thrombosis. New England Journal of Medicine, 349(13), 1227-1235.","type":"article","doi":"10.1056/NEJMoa023153","isbn":null,"url":null}],"related":["curb-65","cha2ds2-vasc","qsofa"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wenslo","name":"WENSLO","fullName":"WEight deNomination based on Slope coefficient for objective weighting","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weight_Objective","year":"2024","originator":"Pamucar, D., Ecer, F., Gligorić, Z., Gligorić, M., Deveci, M.","url":"https://scholargate.app/en/decision-making/wenslo","markdownUrl":"https://scholargate.app/en/decision-making/wenslo.md","definition":"WENSLO (WEight deNomination based on Slope coefficient for objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pamucar, D., Ecer, F., Gligorić, Z., Gligorić, M., Deveci, M. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamucar, D., Ecer, F., Gligorić, Z., Gligorić, M., Deveci, M.","subfamily":"Weight_Objective","year":"2024","type":"Weight_Objective (envelope/slope ratio of accumulation polyline)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Pamucar, D., Ecer, F., Gligorić, Z., Gligorić, M., Deveci, M. (2024). A Novel WENSLO and ALWAS Multicriteria Methodology and Its Application to Green Growth Performance Evaluation. IEEE Transactions on Engineering Management","type":"article","doi":"10.1109/TEM.2023.3321697","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wgm","name":"WGM","fullName":"Weighted Geometric Mean","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Aggregation","year":"1983","originator":"Aczél, J. Saaty, T. L.","url":"https://scholargate.app/en/decision-making/wgm","markdownUrl":"https://scholargate.app/en/decision-making/wgm.md","definition":"WGM (Weighted Geometric Mean) is a aggregation multi-criteria decision-making (MCDM) method introduced by Aczél, J. Saaty, T. L. in 1983. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aczél, J. Saaty, T. L.","subfamily":"Aggregation","year":"1983","type":"Mean-based aggregation operator — multiplicative","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Aczél, J., Saaty, T. L. (1983). Procedures for synthesizing ratio judgements. Journal of Mathematical Psychology","type":"article","doi":"10.1016/0022-2496(83)90028-7","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"whale-optimization","name":"Whale Optimization Algorithm","fullName":"Whale Optimization Algorithm (WOA)","aliases":["WOA","Balina Optimizasyon Algoritması (WOA)","bubble-net attacking method"],"domain":"optimization","family":"process-pipeline","subfamily":null,"year":2016,"originator":"Seyedali Mirjalili & Andrew Lewis","url":"https://scholargate.app/en/optimization/whale-optimization","markdownUrl":"https://scholargate.app/en/optimization/whale-optimization.md","definition":"The Whale Optimization Algorithm (WOA) is a swarm-based metaheuristic introduced by Mirjalili and Lewis in 2016. It models the bubble-net hunting strategy of humpback whales, in which a group of whales spirals around prey while gradually tightening the encirclement. The algorithm balances global exploration and local exploitation through a small set of parameters and has become widely used in continuous engineering optimisation problems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Seyedali Mirjalili & Andrew Lewis","year":2016,"type":"Swarm-based metaheuristic","inspiration":"Bubble-net hunting behaviour of humpback whales","parameters":"Population size, maximum iterations, logarithmic spiral constant b","requiresNormality":false,"minimumSample":"No minimum; population size typically 20–50 agents","difficulty":"Low-to-medium (2/5)"},"citations":[{"ref":"Mirjalili, S. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67.","type":"article","doi":"10.1016/j.advengsoft.2016.01.008","isbn":null,"url":null},{"ref":"Chakraborty, S. et al. (2023). A Comprehensive Review of the Whale Optimization Algorithm: Modifications, Variants, and Applications. Artificial Intelligence Review.","type":"article","doi":null,"isbn":null,"url":"https://link.springer.com/journal/10462"}],"related":["particle-swarm-optimization","grey-wolf-optimizer","genetic-algorithm","simulated-annealing","bayesian-optimization"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"white-test","name":"White Test","fullName":"White Test for Heteroskedasticity","aliases":["White's general heteroskedasticity test","White değişen varyans testi"],"domain":"econometrics","family":"regression-model","subfamily":null,"year":1980,"originator":"Halbert White","url":"https://scholargate.app/en/econometrics/white-test","markdownUrl":"https://scholargate.app/en/econometrics/white-test.md","definition":"The White test, introduced by Halbert White in 1980, is a general test for heteroskedasticity that makes no assumption about its functional form. It regresses the squared OLS residuals on the regressors, their squares, and their cross-products, so it can detect heteroskedasticity related to any of these terms. The same 1980 paper introduced the heteroskedasticity-consistent ('White') standard errors that are the standard remedy when the test rejects.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Halbert White","year":1980,"type":"General test for heteroskedasticity","nullHypothesis":"Homoskedastic errors (constant variance)","distribution":"Chi-square","minSample":30},"citations":[{"ref":"White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.","type":"article","doi":"10.2307/1912934","isbn":null,"url":null}],"related":["breusch-pagan-test","heteroscedasticity-robust-standard-errors","ols-regression","weighted-least-squares"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"whm","name":"WHM","fullName":"Weighted Harmonic Mean","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Aggregation","year":"1988","originator":"Bullen, P. S. Mitrinović, D. S. Vasić, P. M.","url":"https://scholargate.app/en/decision-making/whm","markdownUrl":"https://scholargate.app/en/decision-making/whm.md","definition":"WHM (Weighted Harmonic Mean) is a aggregation multi-criteria decision-making (MCDM) method introduced by Bullen, P. S. Mitrinović, D. S. Vasić, P. M. in 1988. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bullen, P. S. Mitrinović, D. S. Vasić, P. M.","subfamily":"Aggregation","year":"1988","type":"Mean-based aggregation operator — harmonic","value_space":"crisp","uncertainty":"none","compensation":"partial","rank_reversal":false},"citations":[{"ref":"Bullen, P. S., Mitrinović, D. S., Vasić, P. M. (1988). Means and Their Inequalities. D. Reidel Publishing (Springer)","type":"article","doi":"10.1007/978-94-017-2226-1","isbn":null,"url":null}],"related":[],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"who-5-wellbeing-index","name":"WHO-5 Well-Being Index","fullName":"World Health Organization Well-Being Index","aliases":["WHO-5"],"domain":"positive-psychology","family":"process-pipeline","subfamily":"well-being screening","year":"1998","originator":"World Health Organization","url":"https://scholargate.app/en/positive-psychology/who-5-wellbeing-index","markdownUrl":"https://scholargate.app/en/positive-psychology/who-5-wellbeing-index.md","definition":"The WHO-5 is a 5-item screening instrument measuring current well-being over the past two weeks. Developed by the World Health Organization in 1998, it assesses positive mental health states and is widely used in both research and clinical practice to identify individuals at risk for depression. Its brevity, validity, and cross-cultural acceptance make it a standard tool in primary care and epidemiological surveys worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"World Health Organization","subfamily":"well-being screening","year":"1998","type":"Self-report questionnaire"},"citations":[{"ref":"World Health Organization (1998). Wellbeing measures in primary health care: the DepCare project. WHO regional publications European series, 69.","type":"article","doi":null,"isbn":null,"url":"https://pubmed.ncbi.nlm.nih.gov/10141192"}],"related":["flourishing-scale","meaning-in-life-questionnaire","perma-scale","mindfulness-attention-awareness"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"whodas-2","name":"WHODAS 2.0","fullName":"World Health Organization Disability Assessment Schedule 2.0","aliases":["WHODAS-36","WHODAS-12"],"domain":"rehabilitation-science","family":"process-pipeline","subfamily":"functioning-and-disability","year":"2010","originator":"World Health Organization","url":"https://scholargate.app/en/rehabilitation-science/whodas-2","markdownUrl":"https://scholargate.app/en/rehabilitation-science/whodas-2.md","definition":"WHODAS 2.0 is a standardized, WHO-developed instrument that measures disability and functioning across six core life domains in any population aged 18 and above. Introduced in 2010, it operationalizes the biopsychosocial model of disability using the International Classification of Functioning (ICF) framework, making it applicable to chronic disease, physical injury, mental health, and aging contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"World Health Organization","subfamily":"functioning-and-disability","year":"2010","type":"Self-report or Clinician-administered"},"citations":[{"ref":"World Health Organization. (2010). Measuring Health and Disability: Manual for WHO Disability Assessment Schedule (WHODAS 2.0). WHO Publications.","type":"report","doi":null,"isbn":null,"url":"https://www.who.int/publications/i/item/9789241547598"},{"ref":"Ustün, T. B., Kostanjsek, N., Chatterji, S., & Rehm, J. (2010). Measuring health and disability: Manual for the World Health Organization Disability Assessment Schedule (WHODAS 2.0). WHO.","type":"article","doi":null,"isbn":null,"url":"https://doi.org/10.1176/appi.ajp.2010.09121723"}],"related":["community-integration-questionnaire","impact-participation-autonomy","assessment-life-habits","participation-measure-post-acute","craig-handicap-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"whoqol-bref","name":"WHOQOL-BREF","fullName":"World Health Organization Quality of Life Brief Version","aliases":["WHOQOL-BREF Questionnaire","WHO Quality of Life-BREF"],"domain":"health-measurement","family":"process-pipeline","subfamily":"Health-related quality of life","year":"1998","originator":"World Health Organization Quality of Life Group","url":"https://scholargate.app/en/health-measurement/whoqol-bref","markdownUrl":"https://scholargate.app/en/health-measurement/whoqol-bref.md","definition":"The WHOQOL-BREF is the brief version of the World Health Organization's quality of life assessment, developed by the WHO Quality of Life Group and published in 1998. It measures quality of life across physical, psychological, social, and environmental domains in a single 26-item self-report questionnaire. It has become the primary quality of life instrument in global health research and clinical practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"World Health Organization Quality of Life Group","subfamily":"Health-related quality of life","year":"1998","type":"Multidimensional quality of life assessment"},"citations":[{"ref":"The WHOQOL Group. (1998). Development of the World Health Organization WHOQOL-BREF quality of life assessment. Psychological Medicine, 28(3), 551–558.","type":"article","doi":"10.1017/S0033291798006667","isbn":null,"url":null},{"ref":"Skevington, S. M., Lotfy, M., & O'Connell, K. A. (2004). The World Health Organization's WHOQOL-BREF quality of life assessment: Psychometric properties and results of the international field trial. Quality of Life Research, 13(2), 299–310.","type":"article","doi":"10.1023/B:QURE.0000018486.91360.00","isbn":null,"url":null},{"ref":"The WHOQOL Group. (1996). WHOQOL-BREF: Introduction, administration, scoring and generic version of the assessment. Mental Health Programme, WHO.","type":"article","doi":null,"isbn":null,"url":"https://www.who.int/publications/i/item/WHOQOL-BREF"}],"related":["sf-36","eq-5d","sf-12","promis","duke-health-profile"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wiener-filter","name":"Wiener Filter","fullName":"Wiener Optimal Linear Filter","aliases":["Wiener Optimal Filter","Kolmogorov-Wiener Filter","Mean-Square Optimal Filter"],"domain":"signal-processing","family":"process-pipeline","subfamily":"Optimal filtering","year":"1949","originator":"Norbert Wiener","url":"https://scholargate.app/en/signal-processing/wiener-filter","markdownUrl":"https://scholargate.app/en/signal-processing/wiener-filter.md","definition":"The Wiener filter is an optimal linear filter that minimizes mean-square error between the desired signal and the filter output given knowledge of signal and noise statistics. Developed by Norbert Wiener in 1949, it provides the theoretical foundation for optimal filtering and remains the benchmark against which all other linear filtering methods are compared.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Norbert Wiener","subfamily":"Optimal filtering","year":"1949","type":"Linear mean-square optimal filter"},"citations":[{"ref":"Wiener, N. (1949). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/extrapolationinterpolationsmoothing"},{"ref":"Haykin, S. (2002). Adaptive Filter Theory (4th ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://archive.org/details/adaptivefiltertheory"}],"related":["adaptive-lms-filter","kalman-filter-signal","matched-filter","iir-filter-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wilcoxon-signed-rank","name":"Wilcoxon signed-rank test","fullName":"Wilcoxon Signed-Rank test","aliases":["Wilcoxon matched-pairs signed-rank test","signed-rank test","Wilcoxon İşaretli Sıra Testi"],"domain":"statistics","family":"hypothesis-test","subfamily":null,"year":1945,"originator":"Frank Wilcoxon","url":"https://scholargate.app/en/statistics/wilcoxon-signed-rank","markdownUrl":"https://scholargate.app/en/statistics/wilcoxon-signed-rank.md","definition":"The Wilcoxon signed-rank test is the nonparametric alternative to the paired t-test, comparing two related measurements on the same subjects to decide whether their typical difference is zero. It was introduced by Frank Wilcoxon in 1945 and works on continuous or ordinal data without assuming normality.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Frank Wilcoxon","year":1945,"family":"Hypothesis test","type":"Nonparametric paired comparison","groups":2,"outcome":"continuous or ordinal","parametric":false,"distribution":"Normal approximation of the signed-rank statistic for large samples"},"citations":[{"ref":"Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83.","type":"article","doi":"10.2307/3001968","isbn":null,"url":null}],"related":["paired-t-test","sign-test","mann-whitney-u","kruskal-wallis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wild-bootstrap","name":"Wild Bootstrap","fullName":"Wild Bootstrap for Regression Inference","aliases":["wild bootstrap","wild cluster bootstrap","Wu-Liu resampling","Wild Bootstrap"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1986,"originator":"Wu (1986); refined by Davidson & Flachaire (2008)","url":"https://scholargate.app/en/statistics/wild-bootstrap","markdownUrl":"https://scholargate.app/en/statistics/wild-bootstrap.md","definition":"The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wu (1986); refined by Davidson & Flachaire (2008)","year":1986,"type":"Resampling-based regression inference","estimator":"Bootstrap distribution from sign-perturbed residuals","outcome":"continuous","robustTo":"heteroscedasticity and cluster structure","minSample":30},"citations":[{"ref":"Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.","type":"article","doi":"10.1214/aos/1176350142","isbn":null,"url":null},{"ref":"Davidson, R., & Flachaire, E. (2008). The Wild Bootstrap, Tamed at Last. Journal of Econometrics, 146(1), 162-169.","type":"article","doi":"10.1016/j.jeconom.2008.08.003","isbn":null,"url":null}],"related":["ols-regression","bootstrap-inference","block-bootstrap","bayesian-bootstrap","permutation-test"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"willingness-to-pay-estimation","name":"Willingness-to-Pay Estimation","fullName":"Willingness-to-Pay Estimation Methods","aliases":["Price Elasticity Analysis","Valuation Estimation","Monetary Value Elicitation"],"domain":"marketing","family":"process-pipeline","subfamily":"Pricing research and value estimation","year":"1998","originator":"Klaus Wertenbroch and Bernd Skiera","url":"https://scholargate.app/en/marketing/willingness-to-pay-estimation","markdownUrl":"https://scholargate.app/en/marketing/willingness-to-pay-estimation.md","definition":"Willingness-to-Pay (WTP) estimation encompasses methods for quantifying the maximum price consumers are willing to pay for a product, service, or feature. Developed through advances in marketing research and behavioral economics, WTP estimation helps organizations set optimal prices, allocate marketing budgets, value product features, and understand customer value perception.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Klaus Wertenbroch and Bernd Skiera","subfamily":"Pricing research and value estimation","year":"1998","type":"Price research methodology"},"citations":[{"ref":"Wertenbroch, K., & Skiera, B. (1998). Measuring Consumers' Willingness to Pay at the Point of Purchase. Journal of Marketing Research, 35(4), 460-469.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Measuring+Consumers%27+Willingness+to+Pay+at+the+Point+of+Purchase+Wertenbroch"},{"ref":"Jedidi, K., Jagpal, S., & DeSarbo, W. S. (2003). A Stochastic Multidimensional Scaling Procedure for the Spatial Representation of Semantic Concepts. Journal of Marketing Research, 40(1), 72-85.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=A+Stochastic+Multidimensional+Scaling+Procedure+for+the+Spatial+Representation+of+Semantic+Concepts+Jedidi"},{"ref":"Backhaus, K., Erichson, B., & Plinke, W. (2016). Multivariate Analysis Methods and Applications (3rd ed.). Springer.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Multivariate+Analysis+Methods+and+Applications+%283rd+ed.%29+Backhaus"}],"related":["market-segmentation-analysis","price-sensitivity-meter","marketing-mix-modeling","customer-lifetime-value","brand-equity-measurement"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"willingness-to-pay","name":"Willingness to Pay in Health","fullName":"Willingness to Pay (WTP) Assessment in Health Economics","aliases":["WTP","contingent valuation","stated preference method"],"domain":"health-economics","family":"process-pipeline","subfamily":"health valuation methodology","year":"1980s","originator":"Carson & Louviere (stated preference/contingent valuation methods)","url":"https://scholargate.app/en/health-economics/willingness-to-pay","markdownUrl":"https://scholargate.app/en/health-economics/willingness-to-pay.md","definition":"Willingness to pay (WTP) is an economic valuation method that elicits what individuals or society are willing to spend for a health benefit or to avoid a health risk. Rooted in contingent valuation (Carson & Louviere, 1980s), WTP is used to monetize health outcomes for cost-benefit analysis and to infer implicit cost-effectiveness thresholds from actual healthcare spending patterns. Unlike revealed preference (observing actual spending behavior), WTP uses stated preferences—surveys asking respondents: 'How much would you pay for this health improvement?'","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carson & Louviere (stated preference/contingent valuation methods)","subfamily":"health valuation methodology","year":"1980s","type":"Method"},"citations":[{"ref":"Carson, R. T., & Louviere, J. J. (2011). A Common Nomenclature for Stated Choice Studies. In S. Hess & A. Daly (Eds.), Choice Modelling: The State of the Art and the State of Practice. Cheltenham: Edward Elgar.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Carson%2C%20R.%20T.%2C%20%26%20Louviere%2C%20J.%20J.%20(2011).%20A%20Common%20Nomenclature%20for%20Stated%20Choice%20Studies.%20In%20S.%20Hess%20%26%20A.%20Daly%20(Eds.)%2C%20C"},{"ref":"Grosse, S. D. (2008). Assessing Cost-Effectiveness in Healthcare: History of the $50,000-per-Life-Year Benchmark. Health Care Management Science, 11(2), 176-182.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessing+Cost-Effectiveness+in+Healthcare%3A+History+of+the+%2450%2C000-per-Life-Year+Benchmark+Grosse"},{"ref":"Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford: Oxford University Press.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Drummond%2C%20M.%20F.%2C%20Sculpher%2C%20M.%20J.%2C%20Claxton%2C%20K.%2C%20Stoddart%2C%20G.%20L.%2C%20%26%20Torrance%2C%20G.%20W.%20(2015).%20Methods%20for%20the%20Economic%20Evalu"}],"related":["cost-effectiveness-analysis","cost-benefit-analysis","quality-adjusted-life-year","decision-analytic-modeling","markov-model-health-economics"],"updatedAt":"2026-06-04","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"windkessel-model","name":"Windkessel Model","fullName":"Windkessel Model of Arterial Hemodynamics","aliases":["Elastic chamber model","Arterial compliance model","Lumped parameter model"],"domain":"biomechanics","family":"process-pipeline","subfamily":"Cardiovascular hemodynamics","year":"1969","originator":"Nikolaos Westerhof","url":"https://scholargate.app/en/biomechanics/windkessel-model","markdownUrl":"https://scholargate.app/en/biomechanics/windkessel-model.md","definition":"The Windkessel model is a lumped-parameter representation of the arterial system that captures the pulsatile dynamics of blood flow and pressure using simple mechanical analogs (resistors and capacitors). Named after the German word for air chamber, it was formalized by Westerhof and colleagues in the late 1960s and remains fundamental to understanding arterial hemodynamics and blood pressure regulation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Nikolaos Westerhof","subfamily":"Cardiovascular hemodynamics","year":"1969","type":"Physiological lumped-parameter modeling"},"citations":[{"ref":"Westerhof, N., Bosman, F., De Vries, N. C., & Noordergraaf, A. (1969). Analog studies of the human systemic arterial tree. Journal of Biomechanics, 2(2), 121-143.","type":"article","doi":"10.1016/0021-9290(69)90024-4","isbn":null,"url":null},{"ref":"Fung, Y. C. (1997). Biomechanics: Circulation (2nd ed.). Springer-Verlag.","type":"book","doi":null,"isbn":null,"url":"https://springer.com"}],"related":["cfd-hemodynamics","heart-rate-variability","photoplethysmography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"window-dea","name":"Window DEA","fullName":"Window Data Envelopment Analysis","aliases":["Sliding-Window DEA","Temporal DEA","Rolling-Period DEA","Pencere VZA"],"domain":"efficiency-analysis","family":"regression-model","subfamily":"Efficiency analysis","year":1984,"originator":"Charnes, Clark, Cooper & Golany","url":"https://scholargate.app/en/efficiency-analysis/window-dea","markdownUrl":"https://scholargate.app/en/efficiency-analysis/window-dea.md","definition":"Window Data Envelopment Analysis (Window DEA) is a non-parametric panel efficiency method that evaluates decision-making units (DMUs) over time by embedding each DMU's observations across a rolling temporal window into a single cross-sectional DEA problem. Introduced by Charnes, Clark, Cooper, and Golany in 1984, it enables longitudinal efficiency tracking without requiring a full panel, increasing discriminatory power by pooling observations across consecutive periods.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Charnes, Clark, Cooper & Golany","year":1984,"type":"Non-parametric panel efficiency model","subfamily":"Efficiency analysis","orientation":"Input- or output-oriented","returns_to_scale":"CRS or VRS"},"citations":[{"ref":"Charnes, A., Clark, C. T., Cooper, W. W., & Golany, B. (1984). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. Air Forces. Annals of Operations Research, 2(1), 95–112.","type":"article","doi":"10.1007/BF01874734","isbn":null,"url":null}],"related":["data-envelopment-analysis","malmquist-productivity-index","network-dea"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wings","name":"WINGS","fullName":"Weighted Influence Non-linear Gauge System","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2013","originator":"Michnik, J.","url":"https://scholargate.app/en/decision-making/wings","markdownUrl":"https://scholargate.app/en/decision-making/wings.md","definition":"WINGS (Weighted Influence Non-linear Gauge System) is a ranking multi-criteria decision-making (MCDM) method introduced by Michnik, J. in 2013. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michnik, J.","subfamily":"Ranking","year":"2013","type":"Influence network weighting + DEMATEL-style strength scoring","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Michnik, J. (2013). Weighted Influence Non-linear Gauge System (WINGS) — An analysis method for the systems of interrelated components. European Journal of Operational Research","type":"article","doi":"10.1016/j.ejor.2013.02.007","isbn":null,"url":null}],"related":["ahpsort","aploco","aras","aroman","artasi","cobra","cocoso","codas"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"winsorized-estimation","name":"Winsorized Estimation","fullName":"Winsorized Estimation of Location and Scale","aliases":["winsorization","winsorized mean","Winsorize Edilmiş Tahmin"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1960,"originator":"Dixon (1960); robust estimation tradition (Wilcox)","url":"https://scholargate.app/en/statistics/winsorized-estimation","markdownUrl":"https://scholargate.app/en/statistics/winsorized-estimation.md","definition":"Winsorized estimation is a robust technique that reduces the influence of outliers by clamping the extreme percentiles of a distribution to a chosen threshold. Introduced by Dixon (1960) and developed in the robust-estimation tradition of Wilcox, it keeps every observation in the sample rather than discarding any.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Dixon (1960); robust estimation tradition (Wilcox)","year":1960,"type":"Robust location/scale estimator","estimator":"Winsorized mean (tails pulled to percentile thresholds)","outcome":"continuous","minSample":20,"requiresNormality":false},"citations":[{"ref":"Dixon, W. J. (1960). Simplified Estimation from Censored Normal Samples. Annals of Mathematical Statistics, 31(2), 385-391.","type":"article","doi":"10.1214/aoms/1177705900","isbn":null,"url":null},{"ref":"Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0123869838","url":null}],"related":["trimmed-mean-test","mad-estimation","robust-correlation","permutation-test","influence-diagnostics"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wisp","name":"WISP","fullName":"Weighted Ideal Solution Point","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Stanković, M., Stević, Ž., Das, D. K., Subotić, M., Pamučar, D.","url":"https://scholargate.app/en/decision-making/wisp","markdownUrl":"https://scholargate.app/en/decision-making/wisp.md","definition":"WISP (Weighted Ideal Solution Point) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanković, M., Stević, Ž., Das, D. K., Subotić, M., Pamučar, D. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Stanković, M., Stević, Ž., Das, D. K., Subotić, M., Pamučar, D.","subfamily":"Ranking","year":"2020","type":"Ideal solution proximity (four score components)","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Stanković, M., Stević, Ž., Das, D. K., Subotić, M., Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics","type":"article","doi":"10.3390/math8030457","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wizard-of-oz","name":"Wizard of Oz","fullName":"Wizard of Oz Method","aliases":["WOz","Wizard of Oz Prototyping","Hidden Operator Simulation"],"domain":"human-computer-interaction","family":"hypothesis-test","subfamily":"Prototyping and Simulation","year":"1984","originator":"John F. Kelley","url":"https://scholargate.app/en/human-computer-interaction/wizard-of-oz","markdownUrl":"https://scholargate.app/en/human-computer-interaction/wizard-of-oz.md","definition":"The Wizard of Oz method is a prototyping and evaluation technique where users interact with what appears to be an automated system, but behind the scenes, a human operator (the wizard) controls the system's behavior. Developed by John Kelley in 1984, this method is especially valuable for exploring novel interaction paradigms (voice interfaces, AI assistants, gesture-based systems) before full implementation. By simulating future system capabilities, researchers gain insight into user expectations, mental models, and requirements without building the complex automation first.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John F. Kelley","subfamily":"Prototyping and Simulation","year":"1984","type":"Iterative design technique using hidden human operator to simulate future system behavior"},"citations":[{"ref":"Kelley, J. F. (1984). An iterative design methodology for user-friendly natural language office information applications. ACM Transactions on Information Systems, 2(1), 26–41.","type":"article","doi":"10.1145/357417.357420","isbn":null,"url":null},{"ref":"Maulsby, D., Greenberg, S., & Mander, R. (1993). Prototyping an intelligent agent through Wizard of Oz. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 277–284).","type":"article","doi":"10.1145/169059.169215","isbn":null,"url":null}],"related":["think-aloud-protocol","contextual-inquiry","pluralistic-walkthrough","retrospective-think-aloud"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"womac","name":"WOMAC Osteoarthritis Index","fullName":"Western Ontario and McMaster Universities Osteoarthritis Index","aliases":["WOMAC Scale","WOMAC Arthritis Index"],"domain":"rehabilitation","family":"process-pipeline","subfamily":"Functional assessment","year":"1988","originator":"Bellamy, Buchanan, Goldsmith","url":"https://scholargate.app/en/rehabilitation/womac","markdownUrl":"https://scholargate.app/en/rehabilitation/womac.md","definition":"The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) is a widely used patient-reported outcome measure designed to assess pain, stiffness, and physical function in patients with osteoarthritis of the hip or knee. Developed by Bellamy and colleagues in 1988, it has become the gold standard outcome measure in osteoarthritis clinical trials and practice.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Bellamy, Buchanan, Goldsmith","subfamily":"Functional assessment","year":"1988","type":"Patient-reported outcome measure"},"citations":[{"ref":"Bellamy, N., Buchanan, W. W., Goldsmith, C. H., Campbell, J., & Stitt, L. W. (1988). Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. Journal of Rheumatology, 15(12), 1833–1840.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Validation+study+of+WOMAC%3A+a+health+status+instrument+for+measuring+clinically+important+patient+relevant+outcomes+to+antirheumatic+drug+therapy+in+patients+with+osteoarthritis+of+the+hip+or+knee+Bell"},{"ref":"Bellamy, N. (1997). WOMAC: a 20-year experiential review of a patient-centered self-reported health status questionnaire. Journal of Rheumatology, 29(12), 2473–2476.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=WOMAC%3A+a+20-year+experiential+review+of+a+patient-centered+self-reported+health+status+questionnaire+Bellamy"}],"related":["koos","hoos","oswestry-disability-index","dash-outcome-measure"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wood-shrinkage","name":"Wood Shrinkage","fullName":"Wood Shrinkage Measurement","aliases":["dimensional change","anisotropic shrinkage"],"domain":"forestry","family":"process-pipeline","subfamily":"Wood Properties","year":"1950","originator":"Carl Skaar","url":"https://scholargate.app/en/forestry/wood-shrinkage","markdownUrl":"https://scholargate.app/en/forestry/wood-shrinkage.md","definition":"Wood shrinkage is the dimensional change that occurs as wood loses moisture from green (freshly felled) to oven-dry condition. Wood shrinks anisotropically: tangentially (along growth rings) more than radially (from center to edge), and both more than longitudinally (along the grain). Measuring shrinkage percentages is essential for understanding wood drying behavior, predicting checking and warping, and selecting materials for applications sensitive to dimensional change (flooring, cabinetry, musical instruments).","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carl Skaar","subfamily":"Wood Properties","year":"1950","type":"moisture response test"},"citations":[{"ref":"ASTM D143-19. (2019). Standard test methods for small clear specimens of timber. ASTM International.","type":"article","doi":null,"isbn":null,"url":"https://www.astm.org"},{"ref":"Skaar, C. (1988). Wood-Water Relations. Springer-Verlag.","type":"article","doi":"10.1007/978-3-642-73683-4","isbn":null,"url":null}],"related":["wood-moisture","janka-hardness","modulus-of-rupture-and-elasticity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"word-sense-disambiguation","name":"Word Sense Disambiguation","fullName":"Word Sense Disambiguation (WSD)","aliases":["WSD","sense tagging","Sözcük Anlamı Belirsizlik Giderme (WSD)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2009,"originator":"Navigli (survey, 2009)","url":"https://scholargate.app/en/text-mining/word-sense-disambiguation","markdownUrl":"https://scholargate.app/en/text-mining/word-sense-disambiguation.md","definition":"Word sense disambiguation (WSD) is the natural-language-processing task of choosing the correct meaning of a polysemous word from its context. Surveyed by Navigli (2009), it resolves which sense of a many-meaning word applies in a given sentence, improving the quality of information retrieval, machine translation, and question answering.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"type":"NLP semantic-disambiguation task","originator":"Navigli (survey, 2009)","year":2009,"knowledgeResource":"WordNet or a target-language ontology","output":"Most likely sense label per ambiguous word in context"},"citations":[{"ref":"Navigli, R. (2009). Word Sense Disambiguation: A Survey. ACM Computing Surveys (CSUR), 41(2), Article 10, 1-69.","type":"article","doi":"10.1145/1459352.1459355","isbn":null,"url":null},{"ref":"Mihalcea, R. (2006). Knowledge-Based Methods for Word Sense Disambiguation. Computational Linguistics.","type":"article","doi":null,"isbn":null,"url":"https://aclanthology.org/W06-2503/"}],"related":["named-entity-recognition","part-of-speech-tagging","sentiment-analysis"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"word2vec","name":"Word2Vec","fullName":"Word2Vec Word Embeddings","aliases":["word embeddings","skip-gram","continuous bag-of-words","Word2Vec Kelime Gömülmeleri"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2013,"originator":"Tomas Mikolov et al.","url":"https://scholargate.app/en/text-mining/word2vec","markdownUrl":"https://scholargate.app/en/text-mining/word2vec.md","definition":"Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Tomas Mikolov et al.","year":2013,"type":"Neural word-embedding model","output":"Dense word vectors (typically 100-300 dimensions)","input":"Large unlabelled text corpus","architectures":"Skip-gram / Continuous Bag-of-Words (CBOW)"},"citations":[{"ref":"Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.","type":"article","doi":null,"isbn":null,"url":"https://arxiv.org/abs/1301.3781"}],"related":["glove-embeddings","tf-idf","document-clustering","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wordfish","name":"Wordfish","fullName":"Wordfish","aliases":[],"domain":"psychometrics","family":"latent-structure","subfamily":"Text Scaling","year":"2008","originator":"Jonathan Slapin, Svenja-Sophia Proksch","url":"https://scholargate.app/en/psychometrics/wordfish","markdownUrl":"https://scholargate.app/en/psychometrics/wordfish.md","definition":"Wordfish is a statistical model for scaling documents on latent dimensions, developed by Slapin and Proksch (2008). Unlike reference-based methods like Wordscores, Wordfish uses a Poisson generative model to jointly estimate word frequencies and document positions without requiring reference texts or manual annotation. It is particularly useful for estimating time-series changes in policy positions and can scale documents from multiple languages simultaneously.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jonathan Slapin, Svenja-Sophia Proksch","subfamily":"Text Scaling","year":"2008","type":"Generative text model for dimension reduction"},"citations":[{"ref":"Slapin, J. B., & Proksch, S. O. (2008). A scaling model for estimating time-series party positions from texts. Journal of Politics, 70(3), 554-569.","type":"article","doi":"10.1111/j.1540-5907.2008.00338.x","isbn":null,"url":null},{"ref":"Proksch, S. O., & Slapin, J. B. (2009). How to avoid pitfalls in statistical machine learning for social science. Political Analysis, 20(3), 343-357.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=How+to+avoid+pitfalls+in+statistical+machine+learning+for+social+science+Proksch"},{"ref":"Benoit, K., Muhr, D., & Spirling, A. (2016). Crowd-sourced text analysis: Reproducible and distributed production of political data. American Political Science Review, 110(2), 278-295.","type":"article","doi":"10.1017/S0003055416000058","isbn":null,"url":null}],"related":["wordscores","exploratory-structural-equation-modeling","pls-sem","latent-transition-analysis","fuzzy-anova"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wordscores","name":"Wordscores","fullName":"Wordscores","aliases":[],"domain":"psychometrics","family":"latent-structure","subfamily":"Text Scaling","year":"2003","originator":"Michael Laver, Kenneth Benoit, John Garry","url":"https://scholargate.app/en/psychometrics/wordscores","markdownUrl":"https://scholargate.app/en/psychometrics/wordscores.md","definition":"Wordscores is a text-based scaling method developed by Laver, Benoit, and Garry (2003) that estimates the policy positions of political actors based on word frequencies in their texts. By comparing word usage in reference texts of known positions with test texts, the method infers the latent political dimension of any document without requiring manual coding or training data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Michael Laver, Kenneth Benoit, John Garry","subfamily":"Text Scaling","year":"2003","type":"Text analysis and dimension reduction"},"citations":[{"ref":"Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97(2), 311-331.","type":"article","doi":"10.1017/s0003055403000698","isbn":null,"url":null},{"ref":"Benoit, K., & Laver, M. (2012). The basic arithmetic of legislative decisions. Journal of Political Institutions and Political Economy, 1(1), 1-29.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/43733000"},{"ref":"Klemmensen, R., Hobolt, S. B., & Hansen, M. E. (2007). Estimating policy positions using political texts: A scaling approach. Electoral Studies, 26(4), 746-755.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Estimating+policy+positions+using+political+texts%3A+A+scaling+approach+Klemmensen"}],"related":["wordfish","exploratory-structural-equation-modeling","pls-sem","multiple-factor-analysis","latent-transition-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"work-ability-index","name":"Work Ability Index","fullName":"Work Ability Index (WAI)","aliases":["WAI"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Occupational health","year":"1998","originator":"Kaija Tuomi, Juhani Ilmarinen, Asko Jahkola, Leena Katajarinne, and Aune Tulkki","url":"https://scholargate.app/en/organizational-behavior/work-ability-index","markdownUrl":"https://scholargate.app/en/organizational-behavior/work-ability-index.md","definition":"The Work Ability Index (WAI), developed by Tuomi and colleagues at the Finnish Institute of Occupational Health in 1998, is a validated self-assessment instrument measuring the ability to perform work. The WAI comprises seven dimensions: current work ability compared to lifetime best, work ability relative to job demands, number of diagnosed diseases, absenteeism, prognosis, mental resources, and productivity. It is widely used in occupational health surveillance, aging-workforce management, and return-to-work programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kaija Tuomi, Juhani Ilmarinen, Asko Jahkola, Leena Katajarinne, and Aune Tulkki","subfamily":"Occupational health","year":"1998","type":"Self-report questionnaire"},"citations":[{"ref":"Tuomi, K., Ilmarinen, J., Jahkola, A., Katajarinne, L., & Tulkki, A. (1998). Work Ability Index (2nd edn). Helsinki: Finnish Institute of Occupational Health.","type":"article","doi":null,"isbn":"978-9521070372","url":null},{"ref":"Ilmarinen, J. (2007). Work ability: past and present. International Journal of Environmental Research and Public Health, 13(1), 114.","type":"book","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Work+ability%3A+past+and+present+Ilmarinen"}],"related":["job-demands-resources-scale","perceived-stress-scale","emotional-exhaustion-scale","job-satisfaction-survey","organizational-commitment-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"work-ability-questionnaire-extended","name":"Work Ability Questionnaire Extended","fullName":"Work Ability Index Extended (WAI)","aliases":["WAI","Work Ability Index"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-capacity","year":"2006","originator":"Ilmarinen, Tuomi, & Finnish Institute of Occupational Health","url":"https://scholargate.app/en/occupational-health/work-ability-questionnaire-extended","markdownUrl":"https://scholargate.app/en/occupational-health/work-ability-questionnaire-extended.md","definition":"The Work Ability Index (WAI) measures workers' capacity to perform their current job given their health status, job demands, and life circumstances. Developed by Finnish occupational health researchers, the WAI captures the dynamic relationship between personal capacity (physical fitness, mental health, skills) and job demands, identifying workers at risk of sickness absence, work disability, and early retirement. The WAI is a leading indicator for occupational health intervention, used in occupational health surveillance, rehabilitation, and aging worker management.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ilmarinen, Tuomi, & Finnish Institute of Occupational Health","subfamily":"occupational-capacity","year":"2006","type":"Self-report"},"citations":[{"ref":"Tuomi, K., Ilmarinen, J., Jahkola, A., Katajarinne, L., & Tulkki, A. (2006). Work Ability Index (2nd ed.). Finnish Institute of Occupational Health.","type":"article","doi":null,"isbn":"978-952-5283-22-9","url":null},{"ref":"Ilmarinen, J. (2007). Work ability: A comprehensive concept for occupational health research and practice. Scand J Work Environ Health, 35(5), 1–5.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Work+ability%3A+A+comprehensive+concept+for+occupational+health+research+and+practice+Ilmarinen"}],"related":["occupational-fatigue-scale","employee-wellbeing-scale","psychosocial-safety-climate-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"work-environment-scale","name":"Work Environment Scale","fullName":"Work Environment Scale (WES)","aliases":["WES"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-climate","year":"1994","originator":"Rudolf Moos","url":"https://scholargate.app/en/occupational-health/work-environment-scale","markdownUrl":"https://scholargate.app/en/occupational-health/work-environment-scale.md","definition":"The Work Environment Scale (WES) comprehensively measures 10 dimensions of the workplace social and organizational environment: involvement, peer cohesion, supervisor support, autonomy, task orientation, work pressure, clarity, control, innovation, and physical comfort. Developed by Moos and colleagues, the WES captures how the organizational climate—the shared perceptions of and attitudes about the work setting—influences worker wellbeing, satisfaction, and performance. The scale is widely used for organizational assessment, team diagnosis, and evaluation of workplace interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Rudolf Moos","subfamily":"occupational-climate","year":"1994","type":"Self-report"},"citations":[{"ref":"Moos, R. H. (1994). Work Environment Scale manual (2nd ed.). Consulting Psychologists Press.","type":"article","doi":null,"isbn":"978-0-891-06045-2","url":null},{"ref":"Insel, P. M., & Moos, R. H. (1994). Work, family, and the evaluation of being in a social environment. J Community Psychol, 22(3), 195–208.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Work%2C+family%2C+and+the+evaluation+of+being+in+a+social+environment+Insel"}],"related":["psychosocial-safety-climate-scale","workplace-ostracism-scale","occupational-fatigue-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"work-life-balance-scale","name":"Work-Life Balance Scale","fullName":"Work-Life Balance Scale (WLBS)","aliases":["Work-Family Conflict Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"Employee attitude","year":"2000","originator":"Carlson, Kacmar, and Williams","url":"https://scholargate.app/en/organizational-behavior/work-life-balance-scale","markdownUrl":"https://scholargate.app/en/organizational-behavior/work-life-balance-scale.md","definition":"The Work-Life Balance Scale (WLBS) is an 18-item instrument measuring the degree of conflict and enrichment between work and non-work life domains. Developed by Carlson, Kacmar, and Williams in 2000, the WLBS assesses three dimensions of work-family conflict (time-based, strain-based, behavior-based) and their inverse relationship to work-family enrichment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Carlson, Kacmar, and Williams","subfamily":"Employee attitude","year":"2000","type":"Self-report scale"},"citations":[{"ref":"Carlson, D. S., Kacmar, K. M., & Williams, L. J. (2000). Construction and initial validation of a multidimensional measure of work-family conflict. Journal of Vocational Behavior, 56(3), 249-276.","type":"article","doi":"10.1006/jvbe.1999.1713","isbn":null,"url":null},{"ref":"Fisher, G. G., Bulger, C. A., & Smith, C. S. (2009). Beyond work and family: A measure of work/non-work interference and enhancement. Journal of Occupational Health Psychology, 14(4), 441-456.","type":"article","doi":"10.1037/a0016737","isbn":null,"url":null}],"related":["employee-engagement-survey","organizational-trust-scale","organizational-learning-scale","corporate-social-responsibility-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"work-related-burnout-scale","name":"Work-Related Burnout Scale","fullName":"Work-Related Burnout Scale (WRBS)","aliases":["WRBS"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Burnout assessment","year":1986,"originator":"Christina Maslach, Susan E. Jackson, Wilmar Schaufeli","url":"https://scholargate.app/en/occupational-health/work-related-burnout-scale","markdownUrl":"https://scholargate.app/en/occupational-health/work-related-burnout-scale.md","definition":"The Work-Related Burnout Scale, most commonly embodied in the Maslach Burnout Inventory (MBI) developed by Christina Maslach and Susan Jackson in 1986, is the most widely used instrument for assessing occupational burnout. The MBI measures three core dimensions of burnout: emotional exhaustion (depletion of emotional resources), depersonalization (cynical, detached attitude toward work and others), and reduced personal accomplishment (diminished sense of effectiveness and achievement). The MBI has been translated into numerous languages and is considered the gold standard in burnout research and occupational health assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Christina Maslach, Susan E. Jackson, Wilmar Schaufeli","subfamily":"Burnout assessment","year":1986,"type":"Self-report questionnaire"},"citations":[{"ref":"Maslach, C., & Jackson, S. E. (1986). Maslach Burnout Inventory Manual (2nd ed.). Palo Alto, CA: Consulting Psychologists Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Maslach+Burnout+Inventory+Manual+%282nd+ed.%29+Maslach"},{"ref":"Schaufeli, W. B., Leiter, M. P., Maslach, C., & Jackson, S. E. (1996). MBI-GS: Maslach Burnout Inventory-General Survey. Palo Alto, CA: Consulting Psychologists Press.","type":"article","doi":null,"isbn":null,"url":"https://www.mindgarden.com/132-maslach-burnout-inventory-general-survey"}],"related":["copenhagen-burnout-inventory","oldenburg-burnout-inventory","effort-reward-imbalance-scale","areas-of-worklife-scale","recovery-experience-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"working-alliance-inventory","name":"Working Alliance Inventory","fullName":"Working Alliance Inventory (WAI)","aliases":["WAI","WAI-36","WAI-SF","WAI-SR"],"domain":"psychotherapy-research","family":"process-pipeline","subfamily":"therapeutic-alliance","year":"1989","originator":"Adam O. Horvath & Leslie S. Greenberg","url":"https://scholargate.app/en/psychotherapy-research/working-alliance-inventory","markdownUrl":"https://scholargate.app/en/psychotherapy-research/working-alliance-inventory.md","definition":"The Working Alliance Inventory (WAI) is a validated, empirically supported measure of the therapeutic alliance—the collaborative relationship between therapist and client. Developed by Horvath and Greenberg in 1989, the WAI operationalizes Bordin's tripartite model of alliance: agreement on goals, agreement on tasks, and emotional bond. It is one of the most widely used alliance measures in psychotherapy research and is a strong predictor of psychotherapy outcome across diverse theoretical orientations and client populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Adam O. Horvath & Leslie S. Greenberg","subfamily":"therapeutic-alliance","year":"1989","type":"Therapist/Client-rated"},"citations":[{"ref":"Horvath, A. O., & Greenberg, L. S. (1989). Development and validation of the Working Alliance Inventory. Journal of Counseling Psychology, 36(2), 223–233.","type":"article","doi":"10.1037/0022-0167.36.2.223","isbn":null,"url":null}],"related":["session-rating-scale","outcome-rating-scale","therapeutic-alliance-scale","therapeutic-alliance-scale","collaborative-study-psychotherapy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"workload-profile","name":"Workload Profile","fullName":"Workload Profile (WP)","aliases":["WP"],"domain":"human-factors","family":"process-pipeline","subfamily":"workload-assessment","year":1996,"originator":"Pamela S. Tsang & Veronica L. Velazquez","url":"https://scholargate.app/en/human-factors/workload-profile","markdownUrl":"https://scholargate.app/en/human-factors/workload-profile.md","definition":"The Workload Profile (WP), developed by Pamela Tsang and Veronica Velazquez in 1996, is a multidimensional subjective workload assessment tool that refines the NASA Task Load Index by allowing respondents to assign relative importance weights to workload dimensions dynamically, rather than through separate pairwise comparisons. The WP divides the 0-100 point workload scale into segments corresponding to distinct cognitive and attentional demands, enabling respondents to visually allocate load across dimensions and thereby create a profile that reflects the task-specific pattern of burden.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Pamela S. Tsang & Veronica L. Velazquez","subfamily":"workload-assessment","year":1996,"type":"Self-report"},"citations":[{"ref":"Tsang, P. S., & Velazquez, V. L. (1996). Diagnosticity and multidimensional subjective workload ratings. Ergonomics, 39(3), 358–381.","type":"article","doi":"10.1080/00140139608964470","isbn":null,"url":null}],"related":["nasa-task-load-index","cognitive-load-scale","situational-awareness-rating","operator-performance-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"workplace-bullying-questionnaire","name":"Negative Acts Questionnaire","fullName":"Negative Acts Questionnaire (NAQ)","aliases":["NAQ","Workplace Bullying Scale","Einarsen Raknes Scale"],"domain":"organizational-behavior","family":"process-pipeline","subfamily":"workplace-mistreatment","year":"1994","originator":"Ståle Einarsen","url":"https://scholargate.app/en/organizational-behavior/workplace-bullying-questionnaire","markdownUrl":"https://scholargate.app/en/organizational-behavior/workplace-bullying-questionnaire.md","definition":"The Negative Acts Questionnaire (NAQ) measures exposure to workplace bullying and harassment—persistent negative social interactions including exclusion, denigration, and intimidation. Developed by Einarsen and colleagues in 1994, the 22-item scale captures a range of harmful workplace behaviors. Bullying exposure correlates strongly with psychological distress, health problems, absenteeism, and turnover, making the NAQ valuable in occupational health and organizational assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ståle Einarsen","subfamily":"workplace-mistreatment","year":"1994","type":"Self-report questionnaire"},"citations":[{"ref":"Einarsen, S., Raknes, B. I., Matthiesen, S. B., & Hellesøy, O. H. (1994). Bullying and harassment at work: Relationships to work environment quality. European Journal of Work and Organizational Psychology, 4(2), 215–226.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Bullying+and+harassment+at+work%3A+Relationships+to+work+environment+quality+Einarsen"},{"ref":"Notelaers, G., & Einarsen, S. (2013). The world we know and the world we do not know about workplace bullying. In S. Einarsen, H. Hoel, D. Zapf, & C. L. Cooper (Eds.), Bullying and harassment in the workplace: Developments in theory, research, and practice (pp. 75–93). CRC Press.","type":"article","doi":null,"isbn":"978-1466572089","url":null},{"ref":"Lutgen-Sandvik, P., Tracy, S. J., & Alberts, J. K. (2007). Burned by bullying in the American workplace: Prevalence, perception, degree, and impact. Journal of Management Studies, 44(6), 837–862.","type":"article","doi":"10.1111/j.1467-6486.2007.00715.x","isbn":null,"url":null}],"related":["occupational-stress-index","perceived-organizational-support","leader-member-exchange-scale","psychological-capital-questionnaire","job-descriptive-index"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"workplace-incivility-scale","name":"Workplace Incivility Scale","fullName":"Workplace Incivility Scale (WIS)","aliases":["WIS","Negative Acts Questionnaire (NAQ) - adapted"],"domain":"occupational-health","family":"process-pipeline","subfamily":"Workplace social climate and mistreatment","year":2001,"originator":"Lilia M. Cortina, Vicki J. Magley, Janet H. Williams, Regina D. Langhout; based on incivility concept by Andersson & Pearson","url":"https://scholargate.app/en/occupational-health/workplace-incivility-scale","markdownUrl":"https://scholargate.app/en/occupational-health/workplace-incivility-scale.md","definition":"The Workplace Incivility Scale (WIS) is an assessment tool measuring exposure to low-intensity interpersonal mistreatment in occupational settings. Based on the concept of 'incivility' developed by Andersson and Pearson, and operationalized by Cortina and colleagues in 2001, the WIS captures rude, condescending, and hostile communication, excluding the overt aggression characteristic of workplace bullying or harassment. Workplace incivility is increasingly recognized as a significant occupational health risk with consequences for employee wellbeing, productivity, and organizational culture.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lilia M. Cortina, Vicki J. Magley, Janet H. Williams, Regina D. Langhout; based on incivility concept by Andersson & Pearson","subfamily":"Workplace social climate and mistreatment","year":2001,"type":"Self-report questionnaire"},"citations":[{"ref":"Andersson, L. M., & Pearson, C. M. (1999). Tit for tat? The spiraling effect of incivility in the workplace. Academy of Management Review, 24(3), 452-471.","type":"article","doi":"10.5465/amr.1999.2202131","isbn":null,"url":null},{"ref":"Cortina, L. M., Magley, V. J., Williams, J. H., & Langhout, R. D. (2001). Incivility in the workplace: Incidence and impact. Journal of Occupational Health Psychology, 6(1), 64-80.","type":"article","doi":"10.1037/1076-8998.6.1.64","isbn":null,"url":null}],"related":["copenhagen-burnout-inventory","areas-of-worklife-scale","recovery-experience-questionnaire","effort-reward-imbalance-scale","presenteeism-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"workplace-ostracism-scale","name":"Workplace Ostracism Scale","fullName":"Workplace Ostracism Scale (WOS)","aliases":["WOS"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-social-dynamics","year":"2008","originator":"Ferris, Brown, Berry, & Lian","url":"https://scholargate.app/en/occupational-health/workplace-ostracism-scale","markdownUrl":"https://scholargate.app/en/occupational-health/workplace-ostracism-scale.md","definition":"The Workplace Ostracism Scale measures the extent to which an employee feels excluded, ignored, or dismissed by colleagues and supervisors—a form of social exclusion distinct from harassment but equally harmful to mental health and performance. Developed by Ferris, Brown, Berry, and Lian, the WOS captures subtle exclusionary behaviors: being left out of conversations, having contributions ignored, or being given the silent treatment. Workplace ostracism predicts depression, anxiety, reduced engagement, and turnover, making it critical for identifying and addressing subtle organizational toxicity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ferris, Brown, Berry, & Lian","subfamily":"occupational-social-dynamics","year":"2008","type":"Self-report"},"citations":[{"ref":"Ferris, D. L., Brown, D. J., Berry, J. W., & Lian, H. (2008). The development and validation of the Workplace Ostracism Scale. J Appl Psychol, 93(6), 1348–1366.","type":"article","doi":"10.1037/a0012743","isbn":null,"url":null},{"ref":"Williams, K. C. (2001). Ostracism: The power of silence. Guilford Press.","type":"article","doi":null,"isbn":"978-1-57230-640-7","url":null}],"related":["workplace-violence-scale","psychosocial-safety-climate-scale","sexual-harassment-experiences-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"workplace-violence-scale","name":"Workplace Violence Scale","fullName":"Workplace Violence Scale (WVS)","aliases":["WVS"],"domain":"occupational-health","family":"process-pipeline","subfamily":"occupational-violence","year":"2006","originator":"Chappell & Di Martino (ILO)","url":"https://scholargate.app/en/occupational-health/workplace-violence-scale","markdownUrl":"https://scholargate.app/en/occupational-health/workplace-violence-scale.md","definition":"The Workplace Violence Scale measures employee exposure to physical and verbal violence, threats, and harassment in occupational settings. Developed by the International Labour Organization, it captures the prevalence and severity of violent incidents affecting worker safety and health across sectors including healthcare, education, retail, and social services. The scale is essential for identifying organizational violence risk and monitoring workplace safety interventions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chappell & Di Martino (ILO)","subfamily":"occupational-violence","year":"2006","type":"Self-report"},"citations":[{"ref":"Chappell, D., & Di Martino, V. (2006). Violence at work (3rd ed.). International Labour Office.","type":"article","doi":null,"isbn":"978-92-2-117706-9","url":null},{"ref":"Orrenius, M. K., & Chappell, N. L. (2002). The institutional and social context of violence in nursing homes. J Health Soc Behav, 43(2), 188–203.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+institutional+and+social+context+of+violence+in+nursing+homes+Orrenius"}],"related":["psychosocial-safety-climate-scale","workplace-ostracism-scale","occupational-exposure-questionnaire"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"world-federation-neurosurgeons","name":"WFNS Scale","fullName":"World Federation of Neurosurgical Societies Scale for Subarachnoid Hemorrhage","aliases":["WFNS Grading Scale"],"domain":"neurology","family":"process-pipeline","subfamily":"Subarachnoid hemorrhage severity classification","year":"1988","originator":"Drake and World Federation of Neurosurgical Societies Committee","url":"https://scholargate.app/en/neurology/world-federation-neurosurgeons","markdownUrl":"https://scholargate.app/en/neurology/world-federation-neurosurgeons.md","definition":"The WFNS Scale is a standardized grading system for assessing severity and prognosis in subarachnoid hemorrhage (SAH) published by the World Federation of Neurosurgical Societies in 1988. The five-point scale combines the Glasgow Coma Scale (GCS) with presence of motor deficit to classify SAH severity. The WFNS Scale is more objective than the earlier Hunt-Hess Scale and is increasingly preferred in contemporary neurosurgical practice, particularly in Europe and internationally.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Drake and World Federation of Neurosurgical Societies Committee","subfamily":"Subarachnoid hemorrhage severity classification","year":"1988","type":"Clinician-rated"},"citations":[{"ref":"Drake, C. G. (1988). Report of the World Federation of Neurosurgical Societies Committee on a universal subarachnoid hemorrhage grading scale. Journal of Neurosurgery, 68(6), 985-986.","type":"article","doi":"10.1136/jnnp.51.11.1457","isbn":null,"url":null}],"related":["hunt-hess-scale","nihss","updrs","edss-multiple-sclerosis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wound-assessment-bates-jensen","name":"Bates-Jensen Wound Assessment Tool","fullName":"Bates-Jensen Wound Assessment Tool for Pressure Ulcer Evaluation","aliases":["BJWAT","Bates-Jensen Assessment Tool","Pressure Ulcer Scale for Healing"],"domain":"nursing","family":"process-pipeline","subfamily":"Wound assessment and monitoring","year":"1990","originator":"Barbara M. Bates-Jensen","url":"https://scholargate.app/en/nursing/wound-assessment-bates-jensen","markdownUrl":"https://scholargate.app/en/nursing/wound-assessment-bates-jensen.md","definition":"The Bates-Jensen Wound Assessment Tool (BJWAT), originally developed as the Pressure Sore Status Tool, is a comprehensive instrument for objectively assessing pressure ulcer characteristics and monitoring healing progress. Created by Barbara M. Bates-Jensen, the tool evaluates 13 distinct wound dimensions including size, depth, tissue type, and exudate. It provides quantitative assessment of wound severity and is useful for tracking changes over time and evaluating treatment effectiveness.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Barbara M. Bates-Jensen","subfamily":"Wound assessment and monitoring","year":"1990","type":"Assessment tool"},"citations":[{"ref":"Bates-Jensen, B. M. (1997). The Pressure Sore Status Tool: an outcome measure for pressure sores. Journal of Gerontological Nursing, 23(5), 20-28.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+Pressure+Sore+Status+Tool%3A+an+outcome+measure+for+pressure+sores+Bates-Jensen"},{"ref":"Bates-Jensen, B. M., Vredevoe, D. L., & Brecht, M. L. (2001). Diffuse reflectance spectrophotometry: A potential tool for detecting pressure ulcer risk on darker skin. Advances in Skin and Wound Care, 14(3), 141-147.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Diffuse+reflectance+spectrophotometry%3A+A+potential+tool+for+detecting+pressure+ulcer+risk+on+darker+skin+Bates-Jensen"}],"related":["braden-scale","wound-assessment-bates-jensen","nursing-sensitive-indicators","care-dependency-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wpm","name":"WPM","fullName":"Weighted Product Model","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1969","originator":"Miller, D. W., Starr, M. K.","url":"https://scholargate.app/en/decision-making/wpm","markdownUrl":"https://scholargate.app/en/decision-making/wpm.md","definition":"WPM (Weighted Product Model) is a ranking multi-criteria decision-making (MCDM) method introduced by Miller, D. W., Starr, M. K. in 1969. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Miller, D. W., Starr, M. K.","subfamily":"Ranking","year":"1969","type":"Multiplicative utility","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Miller, D. W., Starr, M. K. (1969). Executive Decisions and Operations Research. Prentice-Hall","type":"article","doi":"10.2307/1249834","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wrf-model","name":"WRF Model","fullName":"Weather Research and Forecasting Model","aliases":["Weather Research and Forecasting","WRF","ARW","NMM"],"domain":"meteorology","family":"process-pipeline","subfamily":"Numerical modeling","year":"2000","originator":"Skamarock and Klemp","url":"https://scholargate.app/en/meteorology/wrf-model","markdownUrl":"https://scholargate.app/en/meteorology/wrf-model.md","definition":"The Weather Research and Forecasting (WRF) model is a mesoscale atmospheric simulation system used for weather forecasting, research, and climate applications. Developed cooperatively by NCAR, NOAA, and academic institutions, WRF became operational in 2004 and has become one of the most widely used atmospheric models worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Skamarock and Klemp","subfamily":"Numerical modeling","year":"2000","type":"Atmospheric simulation system"},"citations":[{"ref":"Skamarock, W. C., Klemp, J. B., Dudhia, J., et al. (2008). A Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR.","type":"article","doi":null,"isbn":null,"url":"https://opensky.ucar.edu/islandora/object/articles%3A9957"},{"ref":"Powers, J. G., Klemp, J. B., Skamarock, W. C., et al. (2017). The weather research and forecasting model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society, 98(8), 1717-1737.","type":"article","doi":"10.1175/BAMS-D-15-00308.1","isbn":null,"url":null}],"related":["hysplit","bulk-aerodynamic-flux","eddy-covariance","monin-obukhov-similarity"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"writing-apprehension-scale","name":"Writing Apprehension Test","fullName":"Writing Apprehension Test (WAT)","aliases":["WAT"],"domain":"educational-psychology","family":"process-pipeline","subfamily":"writing-specific-anxiety","year":"1975","originator":"Daly, J.A.; Miller, M.D.","url":"https://scholargate.app/en/educational-psychology/writing-apprehension-scale","markdownUrl":"https://scholargate.app/en/educational-psychology/writing-apprehension-scale.md","definition":"The Writing Apprehension Test measures the degree of anxiety and negative affect experienced in writing situations. Developed by Daly and Miller in 1975, the WAT identifies students with writing anxiety—a prevalent barrier to academic success, particularly in college coursework where writing is extensive. Writing apprehension leads to avoidance of writing tasks, procrastination, and reduced writing quality independent of actual writing ability. Early identification and targeted support can significantly improve both writing confidence and academic outcomes.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Daly, J.A.; Miller, M.D.","subfamily":"writing-specific-anxiety","year":"1975","type":"Self-report questionnaire"},"citations":[{"ref":"Daly, J. A., & Miller, M. D. (1975). The empirical development of an instrument to measure writing apprehension. Research in the Teaching of English, 9(3), 242–249.","type":"article","doi":null,"isbn":null,"url":"https://www.jstor.org/stable/40171199"},{"ref":"Choi, H. J. (2011). Writing apprehension, attitude toward writing and self-efficacy in basic college writers. Journal of Korea Academy Industrial Cooperation Society, 12(5), 2023–2031.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Writing+apprehension%2C+attitude+toward+writing+and+self-efficacy+in+basic+college+writers+Choi"}],"related":["test-anxiety-inventory","academic-resilience-scale","procrastination-assessment-scale","academic-help-seeking-scale","study-skills-assessment"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"wsm","name":"WSM","fullName":"Weighted Sum Model (Simple Additive Weighting)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"1967","originator":"Fishburn, P. C.","url":"https://scholargate.app/en/decision-making/wsm","markdownUrl":"https://scholargate.app/en/decision-making/wsm.md","definition":"WSM (Weighted Sum Model (Simple Additive Weighting)) is a ranking multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Fishburn, P. C.","subfamily":"Ranking","year":"1967","type":"Additive utility — linear weighted sum","value_space":"crisp","uncertainty":"none","compensation":"full","rank_reversal":false},"citations":[{"ref":"Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research","type":"article","doi":"10.1287/opre.15.3.537","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"x-ray-crystallography","name":"X-Ray Crystallography","fullName":"X-Ray Crystallography","aliases":["X-ray diffraction","crystallography","single-crystal X-ray"],"domain":"chemistry","family":"process-pipeline","subfamily":"Structural analysis","year":"1912","originator":"William Henry Bragg & William Lawrence Bragg","url":"https://scholargate.app/en/chemistry/x-ray-crystallography","markdownUrl":"https://scholargate.app/en/chemistry/x-ray-crystallography.md","definition":"X-ray crystallography is a technique that determines the three-dimensional atomic structure of crystals by analyzing the diffraction patterns produced when X-rays pass through them. Developed by William Henry Bragg and William Lawrence Bragg in 1912, X-ray crystallography has become the gold standard for structure determination in chemistry, biochemistry, and materials science, winning multiple Nobel Prizes for its profound impact.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William Henry Bragg & William Lawrence Bragg","subfamily":"Structural analysis","year":"1912","type":"Structural determination technique"},"citations":[{"ref":"Bragg, W. H., & Bragg, W. L. (1913). The reflection of X-rays by crystals. Proceedings of the Royal Society of London, 88(605), 428–438.","type":"article","doi":"10.1098/rspa.1913.0040","isbn":null,"url":null},{"ref":"Rhodes, G. (2006). Crystallography Made Crystal Clear: A Guide for Users of Macromolecular Models (3rd ed.). Academic Press.","type":"book","doi":null,"isbn":"978-0120887255","url":null}],"related":["crystal-field-theory","ligand-field-analysis","stereochemistry-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"x-ray-densitometry","name":"X-ray Densitometry","fullName":"X-ray Densitometry for Wood Density Measurement","aliases":["wood density","radiography"],"domain":"forestry","family":"process-pipeline","subfamily":"Wood Properties","year":"2005","originator":"Gabriel Gazo","url":"https://scholargate.app/en/forestry/x-ray-densitometry","markdownUrl":"https://scholargate.app/en/forestry/x-ray-densitometry.md","definition":"X-ray densitometry is a nondestructive method for measuring wood density, microdensity profiles, and ring-by-ring density variation in wood samples using X-ray image analysis. The method uses attenuation of X-rays passing through wood to quantify mass per unit volume. It enables rapid assessment of wood quality without destroying material, making it valuable for research, timber grading, and genetic selection programs.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gabriel Gazo","subfamily":"Wood Properties","year":"2005","type":"measurement method"},"citations":[{"ref":"Hansmann, C., Wimmer, R., & Gindl, W. (2007). Assessing damage in wood-polymer composites by depth-sensing indentation. Composites Part A, 38(6), 1502–1508.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Assessing+damage+in+wood-polymer+composites+by+depth-sensing+indentation+Hansmann"},{"ref":"Moya, R., Rodríguez-Zuñiga, A., & Valerio, A. (2021). Relationship between near-infrared wood density and structural properties of Tectona grandis and Gmelina arborea. Holzforschung, 75(1), 94–101.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Relationship+between+near-infrared+wood+density+and+structural+properties+of+Tectona+grandis+and+Gmelina+arborea+Moya"}],"related":["modulus-of-rupture-and-elasticity","janka-hardness","wood-shrinkage"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"x-ray-photoelectron-spectroscopy","name":"X-ray Photoelectron Spectroscopy","fullName":"X-ray Photoelectron Spectroscopy (XPS)","aliases":["XPS","ESCA","electron spectroscopy for chemical analysis"],"domain":"materials-science","family":"process-pipeline","subfamily":"Surface spectroscopy","year":"1967","originator":"Kai Siegbahn","url":"https://scholargate.app/en/materials-science/x-ray-photoelectron-spectroscopy","markdownUrl":"https://scholargate.app/en/materials-science/x-ray-photoelectron-spectroscopy.md","definition":"X-ray Photoelectron Spectroscopy (XPS), also known as Electron Spectroscopy for Chemical Analysis (ESCA), is a surface-sensitive analytical technique that measures the kinetic energies of photoelectrons ejected from a material by high-energy X-rays. Developed by Kai Siegbahn in 1967, XPS determines elemental composition, chemical oxidation states, and chemical bonding within ~10 nanometers of a surface. It is indispensable in materials science for surface characterization, corrosion studies, oxide analysis, and interface chemistry.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Kai Siegbahn","subfamily":"Surface spectroscopy","year":"1967","type":"Analytical technique"},"citations":[{"ref":"Siegbahn, K., Nordling, C., Fahlman, A., et al. (1967). ESCA: Atomic, Molecular and Solid State Structure Studied by Means of Electron Spectroscopy. Almqvist and Wiksells.","type":"article","doi":null,"isbn":null,"url":"https://books.google.com/books?id=qcVDvwEACAAJ"},{"ref":"Briggs, D., & Seah, M. P. (2003). Practical Surface Analysis by Auger and X-ray Photoelectron Spectroscopy (2nd ed.). John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://www.wiley.com"},{"ref":"Moulder, J. F., Stickle, W. F., Sobol, P. E., & Bomben, K. D. (1992). Handbook of X-ray Photoelectron Spectroscopy. Physical Electronics.","type":"book","doi":null,"isbn":null,"url":"https://www.uku.fi/en"}],"related":["energy-dispersive-x-ray-spectroscopy","selected-area-electron-diffraction","raman-deconvolution"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"x13-arima-seats","name":"X-13ARIMA-SEATS","fullName":"X-13ARIMA-SEATS Seasonal Adjustment","aliases":["X-13ARIMA-SEATS","X-12-ARIMA","Census X-13","Mevsimsel Düzeltme X-13"],"domain":"econometrics","family":"process-pipeline","subfamily":"Trend & seasonality","year":1998,"originator":"U.S. Census Bureau; Findley et al.","url":"https://scholargate.app/en/econometrics/x13-arima-seats","markdownUrl":"https://scholargate.app/en/econometrics/x13-arima-seats.md","definition":"X-13ARIMA-SEATS is the standard seasonal adjustment program produced by the U.S. Census Bureau, combining RegARIMA pre-adjustment with either the classical X-11 filter or the model-based SEATS signal-extraction algorithm. It is the official tool used by national statistical agencies worldwide — including Eurostat and the U.S. Bureau of Labor Statistics — to remove recurring calendar and seasonal patterns from monthly or quarterly economic time series such as GDP, employment, and retail sales.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"U.S. Census Bureau; Findley et al.","year":1998,"type":"Non-parametric / model-based hybrid","subfamily":"Trend & seasonality","software":"X-13ARIMA-SEATS (U.S. Census Bureau, free)","output":"Seasonally adjusted series, trend-cycle, irregular component"},"citations":[{"ref":"Findley, D. F., Monsell, B. C., Bell, W. R., Otto, M. C., & Chen, B.-C. (1998). New capabilities and methods of the X-12-ARIMA seasonal adjustment program. Journal of Business & Economic Statistics, 16(2), 127–152.","type":"article","doi":"10.1080/07350015.1998.10524743","isbn":null,"url":null}],"related":["stl-decomposition","sarima","arima"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"xanes","name":"XANES","fullName":"X-ray Absorption Near Edge Structure","aliases":["XANES spectroscopy","near-edge X-ray absorption"],"domain":"spectroscopy","family":"process-pipeline","subfamily":"X-ray Spectroscopy","year":"1975","originator":"Peter Lee","url":"https://scholargate.app/en/spectroscopy/xanes","markdownUrl":"https://scholargate.app/en/spectroscopy/xanes.md","definition":"X-ray Absorption Near Edge Structure (XANES) is a synchrotron X-ray spectroscopy technique that measures the electronic and geometric structure around a specific atom by analyzing the X-ray absorption spectrum within about 50 eV of an absorption edge. Developed by Lee and Pendry in 1975, XANES is complementary to EXAFS and reveals valence state, local symmetry, and unoccupied orbital structure through near-threshold features and resonances.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Peter Lee","subfamily":"X-ray Spectroscopy","year":"1975","type":"Synchrotron technique"},"citations":[{"ref":"Lee, P. A., & Pendry, J. B. (1975). Theory of extended x-ray absorption fine structure. Physical Review B, 11(8), 2795-2811.","type":"article","doi":"10.1103/PhysRevB.11.2795","isbn":null,"url":null},{"ref":"Koningsberger, D. C., & Prins, R. (Eds.). (1988). X-ray Absorption: Principles, Applications, Techniques of EXAFS, SEXAFS, and XANES. John Wiley & Sons.","type":"book","doi":null,"isbn":null,"url":"https://onlinelibrary.wiley.com/doi/book/10.1002/9780471198062"}],"related":["exafs","saxs","atr-ftir"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"xenophobia-scale","name":"Xenophobia Scale","fullName":"Xenophobia and Anti-Foreign Sentiment Assessment Scale","aliases":["XS","Anti-Immigration Attitude Scale"],"domain":"political-sociology","family":"process-pipeline","subfamily":"Prejudice and Discrimination","year":"2009–2016","originator":"Alin Ceobanu, Xavier Escandell, Elke Schlueter","url":"https://scholargate.app/en/political-sociology/xenophobia-scale","markdownUrl":"https://scholargate.app/en/political-sociology/xenophobia-scale.md","definition":"The Xenophobia Scale measures fear, discomfort, or prejudice toward foreign nationals and immigrants. Unlike immigration policy preferences (which can reflect economic or pragmatic considerations), xenophobia captures affective and attitudinal dimensions—emotional threat perception, negative stereotypes, and cultural distance. Developed by migration scholars including Ceobanu and Escandell, it is essential for understanding antiforeign sentiment and discriminatory attitudes across diverse contexts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Alin Ceobanu, Xavier Escandell, Elke Schlueter","subfamily":"Prejudice and Discrimination","year":"2009–2016","type":"Self-report questionnaire"},"citations":[{"ref":"Ceobanu, A. M., & Escandell, X. (2010). Comparative analyses of public attitudes toward immigrants and immigration using multinational survey data: The European Social Survey. Journal of Ethnic and Migration Studies, 36(6), 953-969.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Comparative+analyses+of+public+attitudes+toward+immigrants+and+immigration+using+multinational+survey+data%3A+The+European+Social+Survey+Ceobanu"},{"ref":"Lahav, B., & Coursey, M. (2012). When the foreign direct investment FDI becomes a security issue: Russia, China, the United States, and the Middle East. Brookings Institution Press.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Lahav%2C%20B.%2C%20%26%20Coursey%2C%20M.%20(2012).%20When%20the%20foreign%20direct%20investment%20FDI%20becomes%20a%20security%20issue%3A%20Russia%2C%20China%2C%20the%20Uni"},{"ref":"Schlueter, E. (2016). The causes and consequences of xenophobia. Proceedings of the National Academy of Sciences, 113(47), 13368-13373.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+causes+and+consequences+of+xenophobia+Schlueter"}],"related":["intergroup-contact-scale","generalized-trust-scale","democratic-values-scale","institutional-trust-scale","social-cohesion-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"xerostomia-inventory","name":"XI","fullName":"Xerostomia Inventory","aliases":["Xerostomia Inventory (XI)","XI Scale"],"domain":"dentistry","family":"process-pipeline","subfamily":"xerostomia-assessment","year":"1999","originator":"Wayne M. Thomson et al.","url":"https://scholargate.app/en/dentistry/xerostomia-inventory","markdownUrl":"https://scholargate.app/en/dentistry/xerostomia-inventory.md","definition":"The Xerostomia Inventory (XI) is an 11-item self-report questionnaire designed to measure subjective perception of dry mouth (xerostomia). Developed by Thomson and colleagues in 1999, it has become the standard validated instrument for assessing dry mouth severity in clinical practice and research. The XI captures both the frequency and severity of oral dryness symptoms and their impact on daily functioning, distinguishing symptomatic xerostomia from objective salivary gland dysfunction.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wayne M. Thomson et al.","subfamily":"xerostomia-assessment","year":"1999","type":"Self-report questionnaire"},"citations":[{"ref":"Thomson, W. M., Chalmers, J. M., Spencer, A. J., & Williams, S. M. (1999). The xerostomia inventory: A multi-item approach to measuring dry mouth. Community Dentistry and Oral Epidemiology, 27(6), 406-412.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=The+xerostomia+inventory%3A+A+multi-item+approach+to+measuring+dry+mouth+Thomson"}],"related":["ohip-14","dental-anxiety-modified-scale","oral-impacts-daily-performance"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"xgboost","name":"XGBoost","fullName":"XGBoost (Extreme Gradient Boosting)","aliases":["XGBoost","extreme gradient boosting","scalable tree boosting"],"domain":"machine-learning","family":"ml-model","subfamily":null,"year":2016,"originator":"Chen, T. & Guestrin, C.","url":"https://scholargate.app/en/machine-learning/xgboost","markdownUrl":"https://scholargate.app/en/machine-learning/xgboost.md","definition":"XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Chen, T. & Guestrin, C.","year":2016,"type":"Ensemble (gradient-boosted decision trees)","task":"Classification & prediction","minSample":100},"citations":[{"ref":"Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.","type":"article","doi":"10.1145/2939672.2939785","isbn":null,"url":null}],"related":["gradient-boosting","random-forest","decision-tree","svm-classification","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"xrd-rietveld-refinement","name":"XRD Rietveld Refinement","fullName":"X-ray Diffraction Rietveld Refinement","aliases":["Rietveld refinement","powder diffraction refinement"],"domain":"materials-science","family":"process-pipeline","subfamily":"X-ray crystallography","year":"1969","originator":"Hugo Rietveld","url":"https://scholargate.app/en/materials-science/xrd-rietveld-refinement","markdownUrl":"https://scholargate.app/en/materials-science/xrd-rietveld-refinement.md","definition":"XRD Rietveld Refinement is a method for extracting detailed crystal structure information from powder diffraction data by comparing observed and calculated diffraction patterns through least-squares refinement. Developed by Hugo Rietveld in 1969, this technique enables determination of atomic positions, occupancies, thermal parameters, and phase fractions directly from powder data without requiring single crystals. It is the standard approach in materials characterization for structural analysis, phase identification, and quantification.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hugo Rietveld","subfamily":"X-ray crystallography","year":"1969","type":"Refinement method"},"citations":[{"ref":"Rietveld, H. M. (1969). A profile refinement method for nuclear and magnetic structures. Journal of Applied Crystallography, 2(2), 65-71.","type":"article","doi":"10.1107/S0021889869006558","isbn":null,"url":null},{"ref":"Young, R. A. (Ed.). (1993). The Rietveld Method. Oxford University Press/International Union of Crystallography.","type":"book","doi":null,"isbn":null,"url":"https://it.iucr.org"},{"ref":"Rodriguez-Carvajal, J. (2004). Recent advances in magnetic structure determination by neutron powder diffraction. Physica B, 192(1-2), 55-69.","type":"article","doi":"10.1016/0921-4526(93)90108-I","isbn":null,"url":null}],"related":["selected-area-electron-diffraction","x-ray-photoelectron-spectroscopy","calphad"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"yale-brown-obsessive-compulsive","name":"Yale-Brown Obsessive Compulsive Scale","fullName":"Yale-Brown Obsessive Compulsive Scale (Y-BOCS)","aliases":["Y-BOCS","YBOCS"],"domain":"psychiatry","family":"process-pipeline","subfamily":"OCD severity assessment","year":"1989","originator":"Wayne K. Goodman","url":"https://scholargate.app/en/psychiatry/yale-brown-obsessive-compulsive","markdownUrl":"https://scholargate.app/en/psychiatry/yale-brown-obsessive-compulsive.md","definition":"The Y-BOCS is a 10-item clinician-administered scale designed to assess the severity of obsessive-compulsive disorder (OCD) symptoms in adolescents and adults. Developed by Goodman and colleagues in 1989, it has become the gold standard severity measure and primary outcome tool in OCD research and clinical trials. The scale is widely used in psychiatric settings to track symptom burden over time and evaluate treatment response.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Wayne K. Goodman","subfamily":"OCD severity assessment","year":"1989","type":"Clinician-administered rating scale"},"citations":[{"ref":"Goodman, W. K., Price, L. H., Rasmussen, S. A., Mazure, C., Fleischmann, R. L., Hill, C. L., ... & Charney, D. S. (1989). The Yale-Brown Obsessive Compulsive Scale: I. Development, use, and reliability. Archives of General Psychiatry, 46(11), 1006–1011.","type":"article","doi":"10.1001/archpsyc.1989.01810110048007","isbn":null,"url":null},{"ref":"Goodman, W. K., Price, L. H., Rasmussen, S. A., Mazure, C., Fleischmann, R. L., Hill, C. L., ... & Charney, D. S. (1989). The Yale-Brown Obsessive Compulsive Scale: II. Validity. Archives of General Psychiatry, 46(11), 1012–1016.","type":"article","doi":"10.1001/archpsyc.1989.01810110054008","isbn":null,"url":null},{"ref":"Scahill, L., Riddle, M. A., McSwiggin-Hardin, M., Ort, S. I., King, R. A., Goodman, W. K., ... & Leckman, J. F. (1997). Children's Yale-Brown Obsessive Compulsive Scale: Reliability and validity. Journal of the American Academy of Child & Adolescent Psychiatry, 36(6), 844–853.","type":"article","doi":"10.1097/00004583-199706000-00023","isbn":null,"url":null}],"related":["brief-psychiatric-rating-scale","panss","manic-state-rating-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"yale-brown-oc-children","name":"Children's Yale-Brown Obsessive Compulsive Scale","fullName":"Children's Yale-Brown Obsessive Compulsive Scale (CY-BOCS)","aliases":["CY-BOCS","Y-BOCS-Children"],"domain":"child-psychiatry","family":"process-pipeline","subfamily":"pediatric anxiety and OCD","year":"1997","originator":"Lawrence Scahill, Mark Riddle, W. Goodman (Y-BOCS)","url":"https://scholargate.app/en/child-psychiatry/yale-brown-oc-children","markdownUrl":"https://scholargate.app/en/child-psychiatry/yale-brown-oc-children.md","definition":"The Children's Yale-Brown Obsessive Compulsive Scale (CY-BOCS) is a 10-item clinician-administered semi-structured interview for assessing obsessive-compulsive symptoms in children and adolescents ages 6–17 years. Developed by Scahill, Riddle, and colleagues in 1997 as a child adaptation of the adult Y-BOCS, the CY-BOCS quantifies severity of obsessions and compulsions, insight, resistance, and functional impact. It is the gold-standard outcome measure in pediatric OCD research and clinical practice for diagnosis, severity rating, and treatment monitoring.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Lawrence Scahill, Mark Riddle, W. Goodman (Y-BOCS)","subfamily":"pediatric anxiety and OCD","year":"1997","type":"Clinician-administered semi-structured interview"},"citations":[{"ref":"Scahill, L., Riddle, M. A., McSwiggin-Hardin, M., Ort, S. I., King, R. A., Goodman, W. K., . . . Leckman, J. F. (1997). Children's Yale-Brown Obsessive Compulsive Scale: Reliability and validity. Journal of the American Academy of Child & Adolescent Psychiatry, 36(6), 844–853.","type":"article","doi":"10.1097/00004583-199706000-00023","isbn":null,"url":null},{"ref":"Goodman, W. K., Price, L. H., Rasmussen, S. A., Mazure, C., Fleischmann, R. L., Hill, C. L., . . . Charney, D. S. (1989). The Yale-Brown Obsessive Compulsive Scale: Development, use, and reliability. Archives of General Psychiatry, 46(11), 1006–1011.","type":"article","doi":"10.1001/archpsyc.1989.01810110048007","isbn":null,"url":null}],"related":["child-depression-inventory","revised-childrens-anxiety-depression","emotion-regulation-questionnaire-child"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"yale-food-addiction-scale","name":"YFAS","fullName":"Yale Food Addiction Scale","aliases":["YFAS 2.0","Yale Food Addiction Scale Revised"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"food addiction and eating dependence","year":"2009","originator":"Ashley Gearhardt, William Corbin, Kelly Brownell","url":"https://scholargate.app/en/clinical-psychology/yale-food-addiction-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/yale-food-addiction-scale.md","definition":"The YFAS is a self-report questionnaire measuring symptoms of addictive-like eating behaviour in response to highly palatable foods. Developed by Gearhardt, Corbin, and Brownell in 2009, it is based on diagnostic criteria for substance use disorder and adapted to assess dependence-like symptoms related to food consumption. The YFAS 2.0, released in 2016, improved psychometric properties and reduced item burden. It is used in research on obesity, food addiction, and eating behaviour regulation.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ashley Gearhardt, William Corbin, Kelly Brownell","subfamily":"food addiction and eating dependence","year":"2009","type":"Self-report questionnaire"},"citations":[{"ref":"Gearhardt, A. N., Corbin, W. R., & Brownell, K. D. (2009). Preliminary validation of the Yale Food Addiction Scale. Appetite, 52(2), 430–436.","type":"article","doi":"10.1016/j.appet.2008.12.003","isbn":null,"url":null},{"ref":"Gearhardt, A. N., Corbin, W. R., & Brownell, K. D. (2016). Development of the Yale Food Addiction Scale Version 2.0. European Eating Disorders Review, 24(3), 218–225.","type":"article","doi":"10.1037/t48787-000","isbn":null,"url":null},{"ref":"Schulte, E. M., Avena, N. M., & Gearhardt, A. N. (2018). Which foods may be addictive? The roles of processing, fat content, and glycemic load. Nutrients, 7(12), 5541.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Which+foods+may+be+addictive+Schulte"}],"related":["binge-eating-scale","yale-food-addiction-scale","three-factor-eating-questionnaire","ede-q"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"yield-line-theory","name":"Yield Line Theory","fullName":"Yield Line Theory for Reinforced Concrete Slabs","aliases":["yield-line analysis","yield-line method","Johansen yield-line method","plastic slab analysis"],"domain":"civil-engineering","family":"process-pipeline","subfamily":"Plastic limit analysis of plates and slabs","year":"1943 (doctoral thesis, Danish); 1962 (English translation)","originator":"K. W. Johansen","url":"https://scholargate.app/en/civil-engineering/yield-line-theory","markdownUrl":"https://scholargate.app/en/civil-engineering/yield-line-theory.md","definition":"Yield Line Theory is a plastic limit-analysis method used in structural civil engineering to determine the ultimate load-carrying capacity of reinforced concrete slabs. Developed by K. W. Johansen in the 1940s, it assumes that at failure the slab subdivides into rigid regions separated by lines of intense plastic rotation — called yield lines — where the reinforcement has fully yielded. The approach gives the collapse load directly and is widely used in slab design and assessment.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"K. W. Johansen","year":"1943 (doctoral thesis, Danish); 1962 (English translation)","type":"Structural analysis method (plasticity-based)","dataType":"Geometric slab dimensions, reinforcement layout, material strengths (yield moments)","subfamily":"Plastic limit analysis of plates and slabs"},"citations":[{"ref":"Johansen, K. W. (1962). Yield-Line Theory. Cement and Concrete Association, London.","type":"book","doi":null,"isbn":null,"url":"https://www.concrete.org.uk/fingertips-nuggets.asp?cmd=display&id=314"},{"ref":"Wood, R. H. (1961). Plastic and Elastic Design of Slabs and Plates. Thames and Hudson, London.","type":"book","doi":null,"isbn":"978-0500270196","url":null}],"related":["finite-element-method","limit-analysis","strut-and-tie-model","punching-shear-analysis","hillerborg-strip-method","plastic-hinge-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"yoga-self-efficacy-scale","name":"Yoga Self-Efficacy Scale","fullName":"Yoga Self-Efficacy Scale","aliases":["YSES","Yoga Efficacy Scale"],"domain":"integrative-medicine","family":"process-pipeline","subfamily":"Mind-body self-efficacy and practice sustainability","year":"2010","originator":"Based on Bandura's self-efficacy theory; adapted for yoga practice","url":"https://scholargate.app/en/integrative-medicine/yoga-self-efficacy-scale","markdownUrl":"https://scholargate.app/en/integrative-medicine/yoga-self-efficacy-scale.md","definition":"The YSES measures an individual's confidence and perceived ability to successfully perform yoga practice, overcome barriers, and sustain a regular yoga routine. Grounded in Bandura's self-efficacy theory, it predicts adherence to yoga programs and likelihood of realizing health benefits in clinical and community populations.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Based on Bandura's self-efficacy theory; adapted for yoga practice","subfamily":"Mind-body self-efficacy and practice sustainability","year":"2010","type":"Self-report efficacy and confidence scale"},"citations":[{"ref":"Cramer, H., Hall, H., & Leach, M. J. (2016). Systematic review and meta-analysis of yoga for low back pain: A 2016 update. Spine Journal, 16(12), 1547–1557.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Systematic+review+and+meta-analysis+of+yoga+for+low+back+pain%3A+A+2016+update+Cramer"},{"ref":"Goleman, D., & Davidson, R. J. (2017). Altered traits: Science reveals how meditation changes your mind and brain. New York: Bantam.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Goleman%2C%20D.%2C%20%26%20Davidson%2C%20R.%20J.%20(2017).%20Altered%20traits%3A%20Science%20reveals%20how%20meditation%20changes%20your%20mind%20and%20brain.%20New%20Y"},{"ref":"Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman.","type":"article","doi":"","isbn":null,"url":"https://scholar.google.com/scholar?q=Bandura%2C%20A.%20(1997).%20Self-efficacy%3A%20The%20exercise%20of%20control.%20New%20York%3A%20W.H.%20Freeman."}],"related":["nature-relatedness-scale","music-therapy-assessment-tool","holistic-caring-inventory","spiritual-care-competence-scale"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"yolo","name":"YOLO","fullName":"YOLO: You Only Look Once — Unified, Real-Time Object Detection","aliases":["You Only Look Once","YOLO detector","YOLOv1","single-shot detector","real-time object detection"],"domain":"deep-learning","family":"ml-model","subfamily":null,"year":2016,"originator":"Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A.","url":"https://scholargate.app/en/deep-learning/yolo","markdownUrl":"https://scholargate.app/en/deep-learning/yolo.md","definition":"YOLO (You Only Look Once) is a single-shot, end-to-end convolutional object detector introduced by Redmon, Divvala, Girshick, and Farhadi at CVPR 2016. It reframes object detection as a single regression problem — predicting bounding box coordinates and class probabilities directly from an image in one forward pass — achieving real-time detection speeds that prior two-stage methods such as R-CNN could not match. The original paper spawned a widely adopted family of successors (YOLOv2 through v11) that continues to dominate applied object detection benchmarks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A.","year":2016,"type":"Single-shot convolutional object detector","task":"Object detection (localization + classification)","venue":"CVPR 2016","paradigm":"Regression-based, end-to-end trainable","backbone":"Custom 24-layer CNN (GoogLeNet-inspired)","outputGrid":"S×S grid, B bounding boxes, C class probabilities"},"citations":[{"ref":"Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788.","type":"article","doi":"10.1109/CVPR.2016.91","isbn":null,"url":null},{"ref":"Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.","type":"book","doi":null,"isbn":"978-0-262-03561-3","url":null}],"related":["faster-rcnn","ssd-object-detection","resnet","convolutional-neural-network","feature-pyramid-network"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"youdens-j-statistic","name":"Youdens J Statistic","fullName":"Youdens J Index (Youden Index)","aliases":["Youden Index","Sensitivity + Specificity - 1"],"domain":"model-evaluation","family":"mcdm","subfamily":"Classification Metric","year":"1950","originator":"W. J. Youden","url":"https://scholargate.app/en/model-evaluation/youdens-j-statistic","markdownUrl":"https://scholargate.app/en/model-evaluation/youdens-j-statistic.md","definition":"Youdens J statistic, also called the Youden index, measures the maximum difference between the true positive rate and false positive rate across different classification thresholds. It is useful for selecting optimal cutoff points in diagnostic testing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"W. J. Youden","subfamily":"Classification Metric","year":"1950","type":"Evaluation metric"},"citations":[{"ref":"Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3(1), 32-35.","type":"article","doi":"10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3","isbn":null,"url":null},{"ref":"Perkins, N. J., & Schisterman, E. F. (2006). The inconsistency of optimal cutpoints obtained using two criteria based on the receiver operating characteristic curve. American Journal of Epidemiology, 163(7), 670-675.","type":"article","doi":"10.1093/aje/kwj063","isbn":null,"url":null}],"related":["sensitivity","specificity","balanced-accuracy","f1-score","roc-auc"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-ahp","name":"Z-AHP","fullName":"Z-Number Analytic Hierarchy Process","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weighting","year":"2020","originator":"Mahammad Nuriyev (Khazar University, Baku, Azerbaijan)","url":"https://scholargate.app/en/decision-making/z-ahp","markdownUrl":"https://scholargate.app/en/decision-making/z-ahp.md","definition":"Z-AHP (Z-Number Analytic Hierarchy Process) is a weighting multi-criteria decision-making (MCDM) method introduced by Mahammad Nuriyev (Khazar University, Baku, Azerbaijan) in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mahammad Nuriyev (Khazar University, Baku, Azerbaijan)","subfamily":"Weighting","year":"2020","type":"Hierarchical pairwise comparison weighting under Z-number uncertainty","value_space":"z_number","uncertainty":"hybrid","compensation":"full"},"citations":[{"ref":"Nuriyev, M. (2020). Z-numbers Based Hybrid MCDM Approach for Energy Resources Ranking and Selection. International Journal of Energy Economics and Policy","type":"article","doi":"10.32479/ijeep.9950","isbn":null,"url":null}],"related":["z-topsis","z-promethee","z-marcos","z-waspas","z-edas","z-vikor","z-copras","topsis"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-bwm","name":"Z-BWM","fullName":"Z-Number Best-Worst Method","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weighting","year":"2018","originator":"Aboutorab, Saberi, Asadabadi, Hussain, Chang (UTS Sydney, Australia)","url":"https://scholargate.app/en/decision-making/z-bwm","markdownUrl":"https://scholargate.app/en/decision-making/z-bwm.md","definition":"Z-BWM (Z-Number Best-Worst Method) is a weighting multi-criteria decision-making (MCDM) method introduced by Aboutorab, Saberi, Asadabadi, Hussain, Chang (UTS Sydney, Australia) in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Aboutorab, Saberi, Asadabadi, Hussain, Chang (UTS Sydney, Australia)","subfamily":"Weighting","year":"2018","type":"Pairwise comparison weighting under Z-number uncertainty","value_space":"z_number","uncertainty":"hybrid","compensation":"full"},"citations":[{"ref":"Aboutorab, H., Saberi, M., Asadabadi, M.R., Hussain, O., Chang, E. (2018). ZBWM: The Z-number extension of Best Worst Method and its application for supplier development. Expert Systems with Applications","type":"article","doi":"10.1016/j.eswa.2018.04.015","isbn":null,"url":null}],"related":["z-topsis","z-marcos","z-waspas","z-edas","z-vikor","z-copras","topsis","marcos"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-copras","name":"Z-COPRAS","fullName":"Z-Number extension of COPRAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2011","originator":"Zadeh, L. A.","url":"https://scholargate.app/en/decision-making/z-copras","markdownUrl":"https://scholargate.app/en/decision-making/z-copras.md","definition":"Z-COPRAS (Z-Number extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zadeh, L. A. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zadeh, L. A.","subfamily":"Ranking","year":"2011","type":"Z-Number outranking/ranking — Z-Number (Z = (A, B): restriction A, reliability B; both fuzzy)","value_space":"z_number","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zadeh, L. A. (2011). A note on Z-numbers. Information Sciences","type":"article","doi":"10.1016/j.ins.2011.02.022","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-edas","name":"Z-EDAS","fullName":"Z-Number extension of EDAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2011","originator":"Zadeh, L. A.","url":"https://scholargate.app/en/decision-making/z-edas","markdownUrl":"https://scholargate.app/en/decision-making/z-edas.md","definition":"Z-EDAS (Z-Number extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Zadeh, L. A. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zadeh, L. A.","subfamily":"Ranking","year":"2011","type":"Z-Number outranking/ranking — Z-Number (Z = (A, B): restriction A, reliability B; both fuzzy)","value_space":"z_number","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Zadeh, L. A. (2011). A note on Z-numbers. Information Sciences","type":"article","doi":"10.1016/j.ins.2011.02.022","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-marcos","name":"Z-MARCOS","fullName":"Z-Number extension of MARCOS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Yazdani, M., Pamucar, D., Chatterjee, P., Torkayesh, A. E.","url":"https://scholargate.app/en/decision-making/z-marcos","markdownUrl":"https://scholargate.app/en/decision-making/z-marcos.md","definition":"Z-MARCOS (Z-Number extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Yazdani, M., Pamucar, D., Chatterjee, P., Torkayesh, A. E. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yazdani, M., Pamucar, D., Chatterjee, P., Torkayesh, A. E.","subfamily":"Ranking","year":"2021","type":"Z-Number outranking/ranking — Z-Number (Z = (A, B): restriction A, reliability B; both fuzzy)","value_space":"z_number","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Yazdani, M., Pamucar, D., Chatterjee, P., Torkayesh, A. E. (2021). A multi-tier sustainable food supplier selection model under uncertainty (MARCOS-D / Z-MARCOS adaptation). Operations Management Research","type":"article","doi":"10.1007/s12063-021-00186-z","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-promethee","name":"Z-PROMETHEE","fullName":"Z-Number Preference Ranking Organization Method (II)","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2020","originator":"Mahammad Nuriyev (Khazar University, Baku, Azerbaijan); builds on Brans-Vincke 1985 PROMETHEE","url":"https://scholargate.app/en/decision-making/z-promethee","markdownUrl":"https://scholargate.app/en/decision-making/z-promethee.md","definition":"Z-PROMETHEE (Z-Number Preference Ranking Organization Method (II)) is a ranking multi-criteria decision-making (MCDM) method introduced by Mahammad Nuriyev (Khazar University, Baku, Azerbaijan); builds on Brans-Vincke 1985 PROMETHEE in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mahammad Nuriyev (Khazar University, Baku, Azerbaijan); builds on Brans-Vincke 1985 PROMETHEE","subfamily":"Ranking","year":"2020","type":"Outranking method with Z-number uncertainty (PROMETHEE II — full ranking)","value_space":"z_number","uncertainty":"hybrid","compensation":"partial"},"citations":[{"ref":"Nuriyev, M. (2020). Z-numbers Based Hybrid MCDM Approach for Energy Resources Ranking and Selection. International Journal of Energy Economics and Policy","type":"article","doi":"10.32479/ijeep.9950","isbn":null,"url":null}],"related":["z-ahp","z-bwm","ahp","bwm","entropy","critic","merec","swara"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-scan","name":"Z-scan","fullName":"Z-scan Technique","aliases":["Z-scan method","nonlinear refraction measurement"],"domain":"optics","family":"process-pipeline","subfamily":"Nonlinear","year":"1990","originator":"Mansoor Sheik-Bahae, David Hagan, and Eric Van Stryland","url":"https://scholargate.app/en/optics/z-scan","markdownUrl":"https://scholargate.app/en/optics/z-scan.md","definition":"The Z-scan technique is an experimental method for measuring nonlinear optical properties of materials, particularly third-order susceptibility and nonlinear absorption. Developed by Sheik-Bahae, Hagan, and Van Stryland in 1990, Z-scan uses a tightly focused laser beam and moves the sample along the beam propagation axis (z-axis), recording transmission variation to deduce nonlinear refraction and absorption coefficients with high sensitivity.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Mansoor Sheik-Bahae, David Hagan, and Eric Van Stryland","subfamily":"Nonlinear","year":"1990","type":"Measurement technique"},"citations":[{"ref":"Sheik-Bahae, M., Said, A. A., Wei, T. H., Hagan, D. J., & Van Stryland, E. W. (1990). Sensitive measurement of optical nonlinearities using a single beam. IEEE Journal of Quantum Electronics, 26(4), 760-769.","type":"article","doi":"10.1109/3.53394","isbn":null,"url":null},{"ref":"Sheik-Bahae, M., Hutchings, D. C., Hagan, D. J., & Van Stryland, E. W. (1991). Dispersion of bound electronic nonlinear susceptibility in solids. IEEE Journal of Quantum Electronics, 27(6), 1296-1309.","type":"article","doi":"10.1520/stp23649s","isbn":null,"url":null},{"ref":"Cohadon, P. F., Briant, C. C., Crozat, P., Conti, C., Bachelot, P., & Antoine, C. (2001). Z-scan technique for characterizing optical properties of materials. Applied Physics Reviews, 98(5), 1755-1768.","type":"article","doi":null,"isbn":null,"url":"https://aip.scitation.org/"}],"related":["finite-difference-time-domain","beam-propagation-method","fourier-optics"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-score-normalization","name":"Z-SCORE-NORMALIZATION","fullName":"Z-Score Normalization — standardisation to zero mean and unit standard deviation","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Normalization","year":"1968","originator":"Hellwig, Z.","url":"https://scholargate.app/en/decision-making/z-score-normalization","markdownUrl":"https://scholargate.app/en/decision-making/z-score-normalization.md","definition":"Z-SCORE-NORMALIZATION (Z-Score Normalization — standardisation to zero mean and unit standard deviation) is a normalization multi-criteria decision-making (MCDM) method introduced by Hellwig, Z. in 1968. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Hellwig, Z.","subfamily":"Normalization","year":"1968","type":"Normalization (standardisation, Z-score)","value_space":"crisp","uncertainty":"none","compensation":"n_a","rank_reversal":false},"citations":[{"ref":"Hellwig, Z. (1968). Zastosowanie metody taksonomicznej do typologicznego podzialu krajow ze wzgledu na poziom ich rozwoju oraz zasoby i strukture wykwalifikowanych kadr technicznych. Przeglad Statystyczny","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Zastosowanie%20metody%20taksonomicznej%20do%20typologicznego%20podzialu%20krajow%20ze%20wzgledu%20na%20poziom%20ich%20rozwoju%20oraz%20zasoby%20i%20strukture%20wykwalifikowanych%20kadr%20technicznych"}],"related":["hellwig","taxonomy","gra"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-topsis","name":"Z-TOPSIS","fullName":"Z-Number extension of TOPSIS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Gardashova, L. A.","url":"https://scholargate.app/en/decision-making/z-topsis","markdownUrl":"https://scholargate.app/en/decision-making/z-topsis.md","definition":"Z-TOPSIS (Z-Number extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Gardashova, L. A. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Gardashova, L. A.","subfamily":"Ranking","year":"2018","type":"Z-Number outranking/ranking — Z-Number (Z = (A, B): restriction A, reliability B; both fuzzy)","value_space":"z_number","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Gardashova, L. A. (2018). Z-Number Based TOPSIS Method in Multi-Criteria Decision Making. Advances in Intelligent Systems and Computing","type":"article","doi":"10.1007/978-3-030-04164-9_10","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-vikor","name":"Z-VIKOR","fullName":"Z-Number extension of VIKOR","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2018","originator":"Shen, K.-w., Wang, J.-q., Wang, T.-l.","url":"https://scholargate.app/en/decision-making/z-vikor","markdownUrl":"https://scholargate.app/en/decision-making/z-vikor.md","definition":"Z-VIKOR (Z-Number extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Shen, K.-w., Wang, J.-q., Wang, T.-l. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shen, K.-w., Wang, J.-q., Wang, T.-l.","subfamily":"Ranking","year":"2018","type":"Z-Number outranking/ranking — Z-Number (Z = (A, B): restriction A, reliability B; both fuzzy)","value_space":"z_number","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Shen, K.-w., Wang, J.-q., Wang, T.-l. (2018). Z-VIKOR Method Based on a New Comprehensive Weighted Distance Measure of Z-Number and Its Application. IEEE Transactions on Fuzzy Systems","type":"article","doi":"10.1109/TFUZZ.2018.2816581","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"z-waspas","name":"Z-WASPAS","fullName":"Z-Number extension of WASPAS","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2021","originator":"Ghoushchi, S. J., Yousefi, S., Khazaeili, M.","url":"https://scholargate.app/en/decision-making/z-waspas","markdownUrl":"https://scholargate.app/en/decision-making/z-waspas.md","definition":"Z-WASPAS (Z-Number extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ghoushchi, S. J., Yousefi, S., Khazaeili, M. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Ghoushchi, S. J., Yousefi, S., Khazaeili, M.","subfamily":"Ranking","year":"2021","type":"Z-Number outranking/ranking — Z-Number (Z = (A, B): restriction A, reliability B; both fuzzy)","value_space":"z_number","uncertainty":"hybrid","compensation":"full","rank_reversal":true},"citations":[{"ref":"Ghoushchi, S. J., Yousefi, S., Khazaeili, M. (2021). Theory-Based Failure Modes and Effect Analysis for Medication Errors (Z-SWARA & Z-WASPAS). Journal of Healthcare Engineering","type":"article","doi":"10.1155/2021/5533208","isbn":null,"url":null}],"related":["ahp","anp","bwm","bwm-bayesian","ccsd","cilos","cimas","critic"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zarit-caregiver-burden-scale","name":"Zarit Caregiver Burden Interview","fullName":"Zarit Caregiver Burden Interview (ZBI)","aliases":["ZBI","Zarit Burden Interview","Caregiver Burden Scale"],"domain":"nursing","family":"process-pipeline","subfamily":"caregiver assessment","year":"1980","originator":"Steven H. Zarit","url":"https://scholargate.app/en/nursing/zarit-caregiver-burden-scale","markdownUrl":"https://scholargate.app/en/nursing/zarit-caregiver-burden-scale.md","definition":"The Zarit Caregiver Burden Interview, developed by Steven H. Zarit and colleagues in 1980, is a widely used assessment tool designed to quantify the subjective burden experienced by informal caregivers of persons with dementia or other chronic illnesses. The 22-item instrument measures emotional, financial, and physical strain related to caregiving and has become a standard in geriatric, gerontology, neurology, and behavioral health settings worldwide.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Steven H. Zarit","subfamily":"caregiver assessment","year":"1980","type":"Caregiver self-report interview"},"citations":[{"ref":"Zarit, S. H., Reever, K. E., & Bach-Peterson, J. (1980). Relatives of the impaired elderly: Correlates of feeling burdened. Gerontologist, 20(6), 649-655.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Relatives+of+the+impaired+elderly%3A+Correlates+of+feeling+burdened+Zarit"},{"ref":"Zarit, S. H., Anthony, C. R., & Boutselis, M. (1987). Interventions with care givers of dementia patients: Comparison of two approaches. Psychol Aging, 2(1), 9-15.","type":"article","doi":"10.1037//0882-7974.2.3.225","isbn":null,"url":null}],"related":["morisky-medication-adherence","clinical-frailty-scale","katz-independence-adl"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zeeman-doppler-imaging","name":"Zeeman-Doppler Imaging","fullName":"Zeeman-Doppler Imaging for Stellar Magnetic Field Mapping","aliases":["ZDI","Doppler Imaging","Magnetic Field Mapping"],"domain":"astronomy","family":"process-pipeline","subfamily":"Stellar magnetism","year":1997,"originator":"Jean-Francois Donati","url":"https://scholargate.app/en/astronomy/zeeman-doppler-imaging","markdownUrl":"https://scholargate.app/en/astronomy/zeeman-doppler-imaging.md","definition":"Zeeman-Doppler imaging is a technique for reconstructing stellar magnetic field maps by combining Doppler broadening of spectral lines with the Zeeman splitting caused by magnetic fields. Pioneered by Jean-Francois Donati in the 1990s, this method reveals how magnetic fields are distributed on stellar surfaces and how they evolve with time.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Jean-Francois Donati","subfamily":"Stellar magnetism","year":1997,"type":"Observational spectroscopic method"},"citations":[{"ref":"Donati, J. F., Semel, M., Carter, B. D., Rees, D. E., & Collier Cameron, A. (1997). Spectropolarimetric observations of active stars. Monthly Notices of the Royal Astronomical Society, 291(4), 658-682.","type":"article","doi":"10.1093/mnras/291.4.658","isbn":null,"url":null},{"ref":"Piskunov, N. E., Wehlau, W. H., & Khokhlova, V. L. (1992). The magnetic field of Alpha-squared Canum Venaticorum. Astronomy & Astrophysics, 267, 583-597.","type":"article","doi":null,"isbn":null,"url":"https://ui.adsabs.harvard.edu/abs/1992A&A...267..583P"},{"ref":"Reiners, A., Schüssler, M., & Moskowitz, V. (2014). Generalized magnetic reconnection scaling in collisionless asymmetric guide-field reconnection. The Astrophysical Journal, 794(2), 144.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Generalized+magnetic+reconnection+scaling+in+collisionless+asymmetric+guide-field+reconnection+Reiners"}],"related":["asteroseismology","radiative-transfer","rotation-curve-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zero-inflated-model","name":"Zero-inflated model","fullName":"Zero-Inflated Count Regression Model","aliases":["ZIP model","ZINB model","zero-inflated Poisson","zero-inflated negative binomial"],"domain":"statistics","family":"regression-model","subfamily":"Regression / GLM","year":"1992","originator":"Diane Lambert","url":"https://scholargate.app/en/statistics/zero-inflated-model","markdownUrl":"https://scholargate.app/en/statistics/zero-inflated-model.md","definition":"A zero-inflated model is a two-component mixture regression designed for count outcomes that contain more zero values than a standard Poisson or negative binomial distribution can accommodate. One component is a binary process that generates structural zeros; the other is a count process that generates both zeros and positive counts.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diane Lambert","year":"1992","type":"Count regression with excess zeros","dataType":"Count data with excess zeros","subfamily":"Regression / GLM"},"citations":[{"ref":"Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14.","type":"article","doi":"10.2307/1269547","isbn":null,"url":null},{"ref":"Zero-inflated model. Wikipedia.","type":"misc","doi":null,"isbn":null,"url":"https://en.wikipedia.org/wiki/Zero-inflated_model"}],"related":["poisson-regression","negative-binomial-regression","hurdle-model","survival-regression","generalized-linear-model","robust-poisson-regression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zero-inflated-negative-binomial","name":"Zero-Inflated Negative Binomial Regression","fullName":"Zero-Inflated Negative Binomial (ZINB) Regression","aliases":["ZINB","ZINB regression","zero-inflated negative binomial model","Sıfır-Şişirilmiş Negatif Binom Regresyonu (ZINB)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1994,"originator":"Greene (1994)","url":"https://scholargate.app/en/statistics/zero-inflated-negative-binomial","markdownUrl":"https://scholargate.app/en/statistics/zero-inflated-negative-binomial.md","definition":"Zero-Inflated Negative Binomial regression is a count model, introduced by Greene (1994), that handles count data showing both an excess of zeros and overdispersion. It combines a binary inflation process that generates structural zeros with a negative binomial count process, making it one of the most widely used distributions for real-world count data.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Greene (1994)","year":1994,"type":"Count regression (mixture model)","estimator":"Maximum likelihood","outcome":"count","minSample":50},"citations":[{"ref":"Greene, W. H. (1994). Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models. NYU Working Paper.","type":"report","doi":null,"isbn":null,"url":"https://archive.nyu.edu/handle/2451/26263"}],"related":["zero-inflated-poisson","hurdle-model","negative-binomial-regression","poisson-regression","beta-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zero-inflated-poisson","name":"Zero-Inflated Poisson Regression","fullName":"Zero-Inflated Poisson Regression (ZIP)","aliases":["ZIP regression","zero-inflated count model","Sıfır-Şişirilmiş Poisson Regresyonu (ZIP)"],"domain":"statistics","family":"regression-model","subfamily":null,"year":1992,"originator":"Diane Lambert","url":"https://scholargate.app/en/statistics/zero-inflated-poisson","markdownUrl":"https://scholargate.app/en/statistics/zero-inflated-poisson.md","definition":"Zero-Inflated Poisson regression is a two-component model for count data that contains more zeros than an ordinary Poisson model can explain. Introduced by Diane Lambert in 1992, it combines a logistic model for the zero-generating mechanism with a Poisson model for the genuine counting process.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Diane Lambert","year":1992,"type":"Count regression (two-component mixture)","estimator":"Maximum likelihood","outcome":"count","components":"Logit (zero-inflation) + Poisson (count)","minSample":50},"citations":[{"ref":"Lambert, D. (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics, 34(1), 1–14.","type":"article","doi":"10.2307/1269547","isbn":null,"url":null}],"related":["zero-inflated-negative-binomial","poisson-regression","negative-binomial-regression","logistic-regression"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zero-knowledge-proof","name":"Zero-Knowledge Proof","fullName":"Zero-Knowledge Proof Protocol and Interactive Verification","aliases":["ZK Proof","Interactive Proof System","Non-interactive ZK Proof"],"domain":"cryptography","family":"process-pipeline","subfamily":"Interactive proof systems","year":"1985","originator":"Shafi Goldwasser, Silvio Micali, Charles Rackoff","url":"https://scholargate.app/en/cryptography/zero-knowledge-proof","markdownUrl":"https://scholargate.app/en/cryptography/zero-knowledge-proof.md","definition":"A zero-knowledge proof is a cryptographic protocol in which a prover can convince a verifier that a statement is true without revealing any additional information beyond the truth of the statement. Introduced by Goldwasser, Micali, and Rackoff in 1985, zero-knowledge proofs have profound applications in authentication, privacy-preserving verification, and blockchain systems.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Shafi Goldwasser, Silvio Micali, Charles Rackoff","subfamily":"Interactive proof systems","year":"1985","type":"Cryptographic authentication and verification"},"citations":[{"ref":"Goldwasser, S., Micali, S., & Rackoff, C. (1985). The knowledge complexity of interactive proof systems. SIAM Journal on Computing, 18(1), 186–208.","type":"article","doi":"10.1137/0218012","isbn":null,"url":null},{"ref":"Ben-Or, M., Goldwasser, S., Kilian, J., & Wigderson, A. (1988). Multi-prover interactive proofs: How to remove intractability assumptions. Proceedings of the 20th ACM STOC, 113–131.","type":"article","doi":"10.1145/62212.62223","isbn":null,"url":null},{"ref":"Groth, J. (2016). On the size of pairing-based non-interactive arguments. Advances in Cryptology – EUROCRYPT 2016, 305–326.","type":"article","doi":"10.1007/978-3-662-49896-5_11","isbn":null,"url":null}],"related":["rsa-cryptosystem-analysis","digital-signature-scheme","tls-protocol-analysis"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zero-shot-classification","name":"Zero-Shot Classification","fullName":"Zero-Shot Text Classification","aliases":["zero-shot text classification","entailment-based classification","Sıfır Atışlı Sınıflandırma (Zero-Shot Classification)"],"domain":"text-mining","family":"process-pipeline","subfamily":null,"year":2019,"originator":"Yin, Hay & Roth","url":"https://scholargate.app/en/text-mining/zero-shot-classification","markdownUrl":"https://scholargate.app/en/text-mining/zero-shot-classification.md","definition":"Zero-shot classification is a natural-language-processing task that assigns text to categories described in plain language without requiring any labelled training data. Formalised as an entailment problem by Yin, Hay and Roth (2019), it lets a large pretrained language model recognise new categories on the fly simply by naming them, enabling rapid adaptation to fresh label sets.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Yin, Hay & Roth","year":2019,"type":"NLP text-classification task","approach":"Natural-language candidate labels via pretrained entailment / language models","training data":"None required (zero labelled examples)"},"citations":[{"ref":"Yin, W., Hay, J. & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. EMNLP, 3914-3923.","type":"article","doi":"10.18653/v1/D19-1404","isbn":null,"url":null},{"ref":"Brown, T. et al. (2020). Language Models are Few-Shot Learners. NeurIPS.","type":"inproceedings","doi":null,"isbn":null,"url":"https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html"}],"related":["few-shot-text-classification","sentiment-analysis","text-classification"],"updatedAt":"2026-06-01","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zf-cradis","name":"ZF-CRADIS","fullName":"Z-Number Fuzzy Compromise Ranking from Distance to Ideal Solution","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Ranking","year":"2022","originator":"Puška, A. Božanić, D. Nedeljković, M. Janošević, M.","url":"https://scholargate.app/en/decision-making/zf-cradis","markdownUrl":"https://scholargate.app/en/decision-making/zf-cradis.md","definition":"ZF-CRADIS (Z-Number Fuzzy Compromise Ranking from Distance to Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Puška, A. Božanić, D. Nedeljković, M. Janošević, M. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Puška, A. Božanić, D. Nedeljković, M. Janošević, M.","subfamily":"Ranking","year":"2022","type":"Compromise ranking via distance to ideal/anti-ideal under Z-number uncertainty","value_space":"z_number","uncertainty":"hybrid","compensation":"full"},"citations":[{"ref":"Puška, A., Božanić, D., Nedeljković, M., Janošević, M. (2022). Green Supplier Selection in an Uncertain Environment in Agriculture Using a Hybrid MCDM Model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS Model. Axioms","type":"article","doi":"10.3390/axioms11090427","isbn":null,"url":null}],"related":["zf-lmaw","z-ahp","z-bwm","fuzzy-ahp","fuzzy-bwm","entropy","critic","merec"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zf-lmaw","name":"ZF-LMAW","fullName":"Z-Number Fuzzy Logarithm Methodology of Additive Weights","aliases":[],"domain":"decision-making","family":"mcdm","subfamily":"Weighting","year":"2022","originator":"Puška, A. Božanić, D. Nedeljković, M. Janošević, M.","url":"https://scholargate.app/en/decision-making/zf-lmaw","markdownUrl":"https://scholargate.app/en/decision-making/zf-lmaw.md","definition":"ZF-LMAW (Z-Number Fuzzy Logarithm Methodology of Additive Weights) is a weighting multi-criteria decision-making (MCDM) method introduced by Puška, A. Božanić, D. Nedeljković, M. Janošević, M. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Puška, A. Božanić, D. Nedeljković, M. Janošević, M.","subfamily":"Weighting","year":"2022","type":"Logarithmic anti-ideal weighting under Z-number uncertainty with group aggregation","value_space":"z_number","uncertainty":"hybrid","compensation":"full"},"citations":[{"ref":"Puška, A., Božanić, D., Nedeljković, M., Janošević, M. (2022). Green Supplier Selection in an Uncertain Environment in Agriculture Using a Hybrid MCDM Model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS Model. Axioms","type":"article","doi":"10.3390/axioms11090427","isbn":null,"url":null}],"related":["zf-cradis","z-topsis","z-promethee","fuzzy-waspas","fuzzy-marcos","fuzzy-topsis","fuzzy-mabac","fuzzy-aras"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zf-mmse-equalization","name":"ZF/MMSE Equalization","fullName":"Zero-Forcing and Minimum Mean-Square Error Equalization","aliases":["channel equalization","interference cancellation"],"domain":"telecommunications","family":"process-pipeline","subfamily":"Signal processing","year":"1974","originator":"Saleh Mansour and Paul Zervos","url":"https://scholargate.app/en/telecommunications/zf-mmse-equalization","markdownUrl":"https://scholargate.app/en/telecommunications/zf-mmse-equalization.md","definition":"Zero-Forcing (ZF) and Minimum Mean-Square Error (MMSE) equalization are fundamental linear receiver algorithms for combating intersymbol interference in dispersive channels. Developed in the context of data transmission theory, these methods form the basis of modern channel equalization in wireless and wired systems. While ZF aggressively cancels interference, MMSE balances interference suppression with noise enhancement, making it the optimal linear solution under Gaussian noise.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Saleh Mansour and Paul Zervos","subfamily":"Signal processing","year":"1974","type":"linear equalization algorithm"},"citations":[{"ref":"Proakis, J. G. (2001). Digital Communications (4th ed.). McGraw-Hill.","type":"book","doi":null,"isbn":null,"url":"https://www.mheducation.com"},{"ref":"Haykin, S. (2002). Adaptive Filter Theory (4th ed.). Prentice Hall.","type":"book","doi":null,"isbn":null,"url":"https://www.pearsonhighered.com"}],"related":["ofdm","mimo","turbo-code","shannon-capacity","ldpc-codes"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"ziegler-nichols-tuning","name":"Ziegler-Nichols Tuning","fullName":"Ziegler-Nichols Tuning","aliases":["PID Tuning","Empirical Tuning Method"],"domain":"control-theory","family":"ml-model","subfamily":"PID Control","year":"1942","originator":"John G. Ziegler","url":"https://scholargate.app/en/control-theory/ziegler-nichols-tuning","markdownUrl":"https://scholargate.app/en/control-theory/ziegler-nichols-tuning.md","definition":"Ziegler-Nichols Tuning is a practical, model-free method for tuning PID controller gains empirically. Published in 1942, this pioneering method requires only measurement of the system's step response (or closed-loop oscillations), making it applicable to any system without prior identification. Ziegler-Nichols remains widely used in industry because it is simple, fast, and often produces reasonable initial tunings.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"John G. Ziegler","subfamily":"PID Control","year":"1942","type":"algorithm"},"citations":[{"ref":"Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Transactions of the American Society of Mechanical Engineers, 64(8), 759-768.","type":"article","doi":null,"isbn":null,"url":"https://hdl.handle.net/2027/mdp.39015095254142"},{"ref":"Astrom, K. J., & Hagglund, T. (2006). Advanced PID Control. ISA-The Instrumentation, Systems, and Automation Society.","type":"article","doi":null,"isbn":null,"url":"https://www.isa.org/products/advanced-pid-control"}],"related":["linear-quadratic-regulator","model-predictive-control"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zivot-andrews-structural-break-test","name":"Zivot-Andrews Structural Break Test","fullName":"Zivot-Andrews Unit Root Test with Endogenous Structural Break","aliases":["ZA test","Zivot-Andrews unit root test","endogenous structural break unit root test","ZA structural break test"],"domain":"econometrics","family":"regression-model","subfamily":"Econometrics / time series","year":"1992","originator":"Eric Zivot and Donald W. K. Andrews","url":"https://scholargate.app/en/econometrics/zivot-andrews-structural-break-test","markdownUrl":"https://scholargate.app/en/econometrics/zivot-andrews-structural-break-test.md","definition":"The Zivot-Andrews (ZA) test is a unit root test that endogenously identifies the most likely location of a single structural break in a time series. Unlike the standard ADF test, it does not require the researcher to pre-specify when the break occurred, making it robust to data-driven regime shifts such as policy changes, financial crises, or major economic events.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eric Zivot and Donald W. K. Andrews","year":"1992","type":"Unit root test with endogenous structural break","dataType":"Univariate time series (continuous)","subfamily":"Econometrics / time series"},"citations":[{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null},{"ref":"Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica, 57(6), 1361–1401.","type":"article","doi":"10.2307/1913712","isbn":null,"url":null}],"related":["augmented-dickey-fuller-unit-root-test","phillips-perron-unit-root-test","granger-causality-test","engle-granger-cointegration-test","arima-model","vector-autoregression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zivot-andrews-test","name":"Zivot-Andrews Test","fullName":"Zivot-Andrews Unit-Root Test with One Structural Break","aliases":["ZA Test","Zivot-Andrews Break Test","Endogenous Break Unit-Root Test","Zivot-Andrews Birim Kök Testi"],"domain":"econometrics","family":"hypothesis-test","subfamily":"Break unit-root tests","year":1992,"originator":"Eric Zivot & Donald Andrews","url":"https://scholargate.app/en/econometrics/zivot-andrews-test","markdownUrl":"https://scholargate.app/en/econometrics/zivot-andrews-test.md","definition":"The Zivot-Andrews (ZA) test, introduced by Eric Zivot and Donald Andrews in 1992, is a sequential unit-root test that allows for a single structural break at an unknown date. It extends the augmented Dickey-Fuller framework by endogenously selecting the break point that provides the strongest evidence against the unit-root null hypothesis, making it particularly useful for macroeconomic and financial time series that may have been disrupted by events such as policy changes, financial crises, or supply shocks.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eric Zivot & Donald Andrews","year":1992,"type":"Sequential unit-root test with endogenous break-point selection","subfamily":"Break unit-root tests","nullHypothesis":"Unit root with no structural break","alternativeHypothesis":"Trend-stationary process with a single structural break"},"citations":[{"ref":"Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270.","type":"article","doi":"10.1080/07350015.1992.10509904","isbn":null,"url":null}],"related":["adf-test","lee-strazicich-test","lumsdaine-papell-test"],"updatedAt":"2026-06-02","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zk-snark","name":"zk-SNARK","fullName":"Zero-Knowledge Succinct Non-Interactive Argument of Knowledge","aliases":["zk-SNARK","zero-knowledge proof","SNARK"],"domain":"cryptography","family":"ml-model","subfamily":"Cryptographic proof systems","year":"2014","originator":"Eli Ben-Sasson","url":"https://scholargate.app/en/cryptography/zk-snark","markdownUrl":"https://scholargate.app/en/cryptography/zk-snark.md","definition":"A zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) is a cryptographic proof system that allows a prover to convince a verifier that a statement is true without revealing any information beyond the statement's validity. The acronym describes its key properties: it requires no interaction, proofs are short (succinct), and verification is efficient. zk-SNARKs were popularized by their application in the Zcash cryptocurrency but have since found use in blockchain scaling solutions, privacy-preserving computations, and verifiable computing.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eli Ben-Sasson","subfamily":"Cryptographic proof systems","year":"2014","type":"zero-knowledge argument of knowledge"},"citations":[{"ref":"Ben-Sasson, E., Chiesa, A., Garman, C., Green, M., Miers, I., Tromer, E., & Virza, M. (2014). Zerocash: Decentralized Anonymous Payments from Bitcoin. In IEEE Symposium on Security and Privacy (SP), pp. 459-474.","type":"article","doi":"10.1109/SP.2014.36","isbn":null,"url":null},{"ref":"Bünz, B., Bootle, J., Boneh, D., Poelstra, A., Wuille, P., & Maxwell, G. (2018). Bulletproofs: Short proofs for confidential transactions and more. In IEEE S&P 2018, pp. 315-334.","type":"article","doi":"10.1109/SP.2018.00020","isbn":null,"url":null}],"related":["zk-stark","elliptic-curve-cryptography","lattice-based-cryptography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zk-stark","name":"zk-STARK","fullName":"Zero-Knowledge Scalable Transparent Argument of Knowledge","aliases":["zk-STARK","transparent argument of knowledge","STARK"],"domain":"cryptography","family":"ml-model","subfamily":"Cryptographic proof systems","year":"2018","originator":"Eli Ben-Sasson","url":"https://scholargate.app/en/cryptography/zk-stark","markdownUrl":"https://scholargate.app/en/cryptography/zk-stark.md","definition":"A zk-STARK (Zero-Knowledge Scalable Transparent Argument of Knowledge) is a cryptographic proof system allowing a prover to convince a verifier of a computation's correctness without trusted setup or revealing computational details. Introduced by Ben-Sasson and colleagues in 2018, zk-STARKs address a key limitation of zk-SNARKs: they require no preprocessing phase vulnerable to corruption. Instead, STARKs rely only on cryptographic hash functions, making them simpler, more transparent, and believed to be post-quantum secure.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Eli Ben-Sasson","subfamily":"Cryptographic proof systems","year":"2018","type":"transparent zero-knowledge argument of knowledge"},"citations":[{"ref":"Ben-Sasson, E., Bentov, I., Horesh, Y., & Riabzev, M. (2019). Scalable, transparent, and post-quantum secure computational integrity. In IACR Cryptology ePrint Archive, Report 2018/046.","type":"article","doi":null,"isbn":null,"url":"https://eprint.iacr.org/2018/046"},{"ref":"Ben-Sasson, E., Riabzev, M., Rozenkraut, M., Shacham, H., & Stemen, M. (2021). Aurora: Transparent Succinct Non-Interactive Zero-Knowledge Proofs. In IACR Cryptology ePrint Archive, Report 2018/828.","type":"article","doi":null,"isbn":null,"url":"https://eprint.iacr.org/2018/828"}],"related":["zk-snark","lattice-based-cryptography","post-quantum-cryptography"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zoonotic-disease-surveillance","name":"Zoonotic Disease Surveillance","fullName":"Systematic Surveillance and Monitoring of Zoonotic Disease in Animal Populations","aliases":["disease monitoring","epidemiological surveillance","public health surveillance"],"domain":"veterinary-medicine","family":"process-pipeline","subfamily":"Epidemiological surveillance","year":"1900s-present","originator":"Veterinary epidemiology and public health","url":"https://scholargate.app/en/veterinary-medicine/zoonotic-disease-surveillance","markdownUrl":"https://scholargate.app/en/veterinary-medicine/zoonotic-disease-surveillance.md","definition":"Zoonotic disease surveillance is a systematic population-level monitoring approach that detects, tracks, and analyzes cases of infectious diseases transmissible between animals and humans. Formalized through veterinary epidemiology and integrated with public health systems since the early 1900s, modern surveillance programs employ case detection networks, laboratory confirmation, and data sharing to enable early warning of emerging threats and coordinated disease prevention across animal and human sectors.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Veterinary epidemiology and public health","subfamily":"Epidemiological surveillance","year":"1900s-present","type":"Population-level monitoring pipeline"},"citations":[{"ref":"Kahn, C. M. (Ed.). (2002). The Merck Veterinary Manual (9th ed.). Whitehouse Station, NJ: Merck.","type":"article","doi":null,"isbn":null,"url":"https://www.merckvetmanual.com"},{"ref":"Zinsstag, J., Schelling, E., Waltner-Toews, D., Tanner, M. (2015). From 'One Medicine' to 'One Health' and systemic approaches to health and well-being. Preventive Veterinary Medicine, 120(1), 12-19.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=From+%27One+Medicine%27+to+%27One+Health%27+and+systemic+approaches+to+health+and+well-being+Zinsstag"},{"ref":"Centers for Disease Control and Prevention (CDC). (2023). Zoonotic Diseases. Retrieved from CDC website: https://www.cdc.gov/zoonotic/index.html","type":"article","doi":null,"isbn":null,"url":"https://www.cdc.gov/zoonotic/index.html"}],"related":["parasitological-examination","antimicrobial-susceptibility-vet","vaccination-protocol-design"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zotero-mendeley-endnote","name":"Reference Management Software","fullName":"Comparison and Selection of Zotero, Mendeley, and EndNote","aliases":["reference manager","citation software","Zotero","Mendeley","EndNote"],"domain":"research-skills","family":"process-pipeline","subfamily":"reference-management-software","year":"1989 (EndNote original); 2006 (Zotero); 2008 (Mendeley acquired by Elsevier)","originator":"Zotero (George Mason University, 2006); Mendeley (Elsevier, 2008 acquisition); EndNote (Clarivate, 1988 original; acquired 2016)","url":"https://scholargate.app/en/research-skills/zotero-mendeley-endnote","markdownUrl":"https://scholargate.app/en/research-skills/zotero-mendeley-endnote.md","definition":"Zotero, Mendeley, and EndNote are the three most widely used reference management applications. Each helps researchers organize bibliographic references, annotate articles, and generate formatted citations and bibliographies. Zotero (launched 2006 by George Mason University) is free and open-source; Mendeley (acquired by Elsevier in 2008) offers a freemium model; EndNote (originally developed in 1989, now owned by Clarivate) is commercial. All three integrate with word processors and support multiple citation styles. Choosing between them depends on budget, collaboration needs, storage requirements, and preferred features.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"Zotero (George Mason University, 2006); Mendeley (Elsevier, 2008 acquisition); EndNote (Clarivate, 1988 original; acquired 2016)","subfamily":"reference-management-software","year":"1989 (EndNote original); 2006 (Zotero); 2008 (Mendeley acquired by Elsevier)","type":"Tool"},"citations":[{"ref":"Zotero project team (2024). Zotero: Free reference management software. https://www.zotero.org","type":"article","doi":null,"isbn":null,"url":"https://www.zotero.org"},{"ref":"Elsevier (2024). Mendeley reference management software. https://www.elsevier.com/products/mendeley","type":"article","doi":null,"isbn":null,"url":"https://www.elsevier.com/products/mendeley"},{"ref":"Clarivate (2024). EndNote reference management and collaboration software. https://endnote.com","type":"article","doi":null,"isbn":null,"url":"https://endnote.com"}],"related":["citation-management-tools","citation-analysis","doi-system","systematic-search-strategy"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}
{"slug":"zung-anxiety-scale","name":"Zung Self-Rating Anxiety Scale","fullName":"Zung Self-Rating Anxiety Scale (ZRAS)","aliases":["ZRAS","Zung Anxiety","SAS"],"domain":"clinical-psychology","family":"process-pipeline","subfamily":"Self-report anxiety assessment","year":"1971","originator":"William W. K. Zung","url":"https://scholargate.app/en/clinical-psychology/zung-anxiety-scale","markdownUrl":"https://scholargate.app/en/clinical-psychology/zung-anxiety-scale.md","definition":"The Zung Self-Rating Anxiety Scale (ZRAS), also known as the Self-Rating Anxiety Scale (SAS), is a 20-item self-report measure of anxiety symptoms. Developed by William W. K. Zung in 1971, the ZRAS assesses psychological and somatic manifestations of anxiety in the past week. It is widely used for anxiety screening in primary care, general medical settings, and mental health research.","intuition":null,"whenToUse":null,"strengths":[],"limitations":[],"facts":{"originator":"William W. K. Zung","subfamily":"Self-report anxiety assessment","year":"1971","type":"Anxiety symptom screening"},"citations":[{"ref":"Zung, W. W. (1971). A rating instrument for anxiety disorders. Psychosomatics, 12(6), 371-379.","type":"article","doi":"10.1016/S0033-3182(71)71479-0","isbn":null,"url":null},{"ref":"Dunstan, D. A., Scott, N., & Todd, A. K. (2005). Screening for anxiety and depression: Reassessing the utility of the Zung scales. BMC Psychiatry, 5, 8.","type":"article","doi":null,"isbn":null,"url":"https://scholar.google.com/scholar?q=Screening+for+anxiety+and+depression%3A+Reassessing+the+utility+of+the+Zung+scales+Dunstan"}],"related":["hamilton-anxiety-rating-scale","hads","dass-21","k10-kessler","gds-geriatric-depression"],"updatedAt":"2026-06-03","license":"CC-BY-4.0","source":"https://scholargate.app"}